[
  {
    "path": ".gitignore",
    "content": "commit.bat\r\n*.pyc\r\n"
  },
  {
    "path": "20news.bat",
    "content": "python topicExp.py -s 20news train\r\npython topicExp.py -i 20news-train-11314-sep281-em150-best.topic.vec 20news train,test\r\npython classEval.py 20news topicprop\r\npython classEval.py 20news topic-wvavg\r\n"
  },
  {
    "path": "README.md",
    "content": "# TopicVec\nTopicVec is the source code for \"Generative Topic Embedding: a Continuous Representation of Documents\" (ACL 2016).\n\nPSDVec (in folder 'psdvec') is the source code for \"A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution\" (EMNLP 2015).\n\n#### Update v0.7: \nThe topic inference is now 6 times faster.\n\n#### Update v0.6:\n##### Algorithm update: \ntopicvecDir.py: uses exact inference instead a second-order approximation in the M-step.\n\n#### Update v0.5:\n##### Main algorithm: \ntopicvecDir.py: uses a Dirichlet prior for topic mixting proportions.\n\n####Required files on Dropbox:\nhttps://www.dropbox.com/sh/lqbk3iioobegbp8/AACc8Kfr1KZIkKl9bGaIrOjfa?dl=0\n\n1. Pretrained 180000 embeddings (25000 cores) in 3 archives. For faster loading into Python, 25000-180000-500-BLK-8.0.vec.npy can be used;\n2. Unigram files top1grams-wiki.txt & top1grams-reuters.txt;\n3. RCV1 cleansed corpus ( before downloading, please apply for permission from NIST according to: http://trec.nist.gov/data/reuters/reuters.html ).\n\nIf you are in China, you can also download the above files from baidu netdisk without the hassle of \"climbing over the wall\":\nhttps://pan.baidu.com/s/1gVmRhK1HA2XwVWZbZHHLZQ#list/path=%2F\n"
  },
  {
    "path": "anatest.py",
    "content": "import numpy as np\r\nfrom utils import *\r\n\r\nembedding_arrays = np.load(\"25000-180000-500-BLK-8.0.vec.npy\")\r\nV, vocab, word2ID, skippedWords_whatever = embedding_arrays\r\nmodel = VecModel(V, vocab, word2ID, vecNormalize=True)\r\nw1, w2 = predict_ana(model, \"fish\", \"water\", \"plant\", \"soil\")\r\nprint w1, w2\r\nw1, w2 = predict_ana(model, \"player\", \"team\", \"student\", \"classroom\")\r\nprint w1, w2\r\n"
  },
  {
    "path": "classEval.py",
    "content": "from sklearn import svm, metrics\r\nfrom sklearn.datasets import load_svmlight_file\r\nimport sys\r\n\r\ndef getScores( true_classes, pred_classes, average):\r\n    precision = metrics.precision_score( true_classes, pred_classes, average=average )\r\n    recall = metrics.recall_score( true_classes, pred_classes, average=average )\r\n    f1 = metrics.f1_score( true_classes, pred_classes, average=average )\r\n    accuracy = metrics.accuracy_score( true_classes, pred_classes )\r\n    return precision, recall, f1, accuracy\r\n    \r\n\"\"\"\r\nTopicProp_ITER = int(sys.argv[1])\r\ntopicNum = int(sys.argv[2])\r\ntrain_file = \"20news-train-11314-sep%d-em40-i%d.topic.prop\" %(topicNum, TopicProp_ITER)\r\ntest_file = \"20news-test-7532-sep%d-em40-i%d.topic.prop\" %(topicNum, TopicProp_ITER)\r\n\r\ntrain_features, train_docs_cat_name = load_matrix_from_text( train_file, \"training proportion\", \"\\t\" )\r\ntest_features, test_docs_cat_name = load_matrix_from_text( test_file, \"test proportion\", \"\\t\" )\r\n\r\ntrue_train_classes = []\r\ntrue_test_classes = []\r\n\r\nfor train_cat_name in train_docs_cat_name[0]:\r\n    true_train_classes.append( int(train_cat_name) )\r\nfor test_cat_name in test_docs_cat_name[0]:\r\n    true_test_classes.append( int(test_cat_name) )\r\n\"\"\"\r\n\r\ncorpus = sys.argv[1]\r\nfiletype = sys.argv[2]\r\n# selected feature dimensions can be specified in the last argument as:\r\n# 1-400 (starting from 1)\r\nif len(sys.argv) > 3:\r\n    dims = sys.argv[3].split(\"-\")\r\n    dims[0] = int(dims[0]) - 1\r\n    dims[1] = int(dims[1])\r\nelse:\r\n    dims = None\r\n        \r\nif corpus == '20news':\r\n    train_file = \"20news-train-11314.svm-%s.txt\" %filetype\r\n    test_file = \"20news-test-7532.svm-%s.txt\" %filetype\r\nelse:\r\n    train_file = \"reuters-train-5770.svm-%s.txt\" %filetype\r\n    test_file = \"reuters-test-2255.svm-%s.txt\" %filetype\r\n\r\ntrain_features_sparse, true_train_classes = load_svmlight_file(train_file)\r\ntest_features_sparse, true_test_classes = load_svmlight_file(test_file)\r\n#nonzeroColIDs = np.union1d( train_features_sparse.nonzero()[1], test_features_sparse.nonzero()[1] )\r\n#train_features = train_features_sparse[:, nonzeroColIDs].toarray()\r\n#test_features = test_features_sparse[:, nonzeroColIDs].toarray()\r\n    \r\n#pdb.set_trace()\r\n#print \"%dx%d sparse feature matrices reduced to %dx%d\" %( tuple(train_features_sparse.shape) +\r\n#                                                tuple(train_features.shape) )\r\n\r\ntrain_features = train_features_sparse.toarray()\r\ntest_features = test_features_sparse.toarray()\r\n\r\nprint \"Train: %dx%d. Test: %dx%d\" %( tuple( train_features.shape + test_features.shape ) )\r\n\r\nif dims:\r\n    train_features = train_features[ :, dims[0]:dims[1] ]\r\n    test_features = test_features[ :, dims[0]:dims[1] ]\r\n    print \"Choose only features %d-%d\" %( dims[0]+1, dims[1] )\r\nelse:\r\n    train_features = train_features[ :, : ]\r\n    test_features = test_features[ :, : ]\r\n        \r\nmodel = svm.LinearSVC(penalty='l1', dual=False)\r\n\r\nprint \"Training...\",\r\nmodel.fit( train_features, true_train_classes )\r\nprint \"Done.\"\r\n\r\npred_train_classes = model.predict( train_features )\r\npred_test_classes = model.predict( test_features )\r\n\r\nprint metrics.classification_report(true_train_classes, pred_train_classes, digits=3)\r\nprint metrics.classification_report(true_test_classes, pred_test_classes, digits=3)\r\n\r\nfor average in ['micro', 'macro']:\r\n    train_precision, train_recall, train_f1, train_acc = getScores( true_train_classes, pred_train_classes, average )\r\n    print \"Train Prec (%s average): %.3f, recall: %.3f, F1: %.3f, Acc: %.3f\" %( average, \r\n                        train_precision, train_recall, train_f1, train_acc )\r\n    \r\n    test_precision, test_recall, test_f1, test_acc = getScores( true_test_classes, pred_test_classes, average )\r\n    print \"Test Prec (%s average): %.3f, recall: %.3f, F1: %.3f, Acc: %.3f\" %(  average, \r\n                        test_precision, test_recall, test_f1, test_acc )\r\n"
  },
  {
    "path": "corpusLoader.py",
    "content": "# -*- coding=GBK -*-\r\n\r\nfrom sklearn.datasets import fetch_20newsgroups\r\nfrom nltk.corpus import reuters\r\nimport HTMLParser\r\nimport os\r\nimport pdb\r\nfrom utils import *\r\n\r\ndef extractSentenceWords(doc, remove_url=True, remove_punc=\"utf-8\", min_length=1):\r\n    if remove_punc:\r\n        # ensure doc_u is in unicode\r\n        if not isinstance(doc, unicode):\r\n            encoding = remove_punc\r\n            doc_u = doc.decode(encoding)\r\n        else:\r\n            doc_u = doc\r\n        # remove unicode punctuation marks, keep ascii punctuation marks\r\n        doc_u = doc_u.translate(unicode_punc_tbl)\r\n        if not isinstance(doc, unicode):\r\n            doc = doc_u.encode(encoding)\r\n        else:\r\n            doc = doc_u\r\n            \r\n    if remove_url:\r\n        re_url = r\"(https?:\\/\\/)?(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0-9@:%_\\+.~#?&//=]*)\"\r\n        doc = re.sub( re_url, \"\", doc )\r\n            \r\n    sentences = re.split( r\"\\s*[,;:`\\\"()?!{}]\\s*|--+|\\s*-\\s+|''|\\.\\s|\\.$|\\.\\.+||\", doc ) #\"\r\n    wc = 0\r\n    wordsInSentences = []\r\n    \r\n    for sentence in sentences:\r\n        if sentence == \"\":\r\n            continue\r\n\r\n        if not re.search( \"[A-Za-z0-9]\", sentence ):\r\n            continue\r\n\r\n        words = re.split( r\"\\s+\\+|^\\+|\\+?[\\-*\\/&%=<>\\[\\]~\\|\\@\\$]+\\+?|\\'\\s+|\\'s\\s+|\\'s$|\\s+\\'|^\\'|\\'$|\\$|\\\\|\\s+\", sentence )\r\n\r\n        words = filter( lambda w: w, words )\r\n\r\n        if len(words) >= min_length:\r\n            wordsInSentences.append(words)\r\n            wc += len(words)\r\n\r\n    #print \"%d words extracted\" %wc\r\n    return wordsInSentences, wc\r\n    \r\ndef load_20news(setName):\r\n    newsgroups_subset = fetch_20newsgroups(subset=setName, remove=('headers', 'footers')) #, 'quotes'\r\n    totalLineNum = 0\r\n    readDocNum = 0\r\n    print \"Loading 20 newsgroup %s data...\" %setName\r\n        \r\n    setDocNum = len(newsgroups_subset.data)\r\n    orig_docs_name = []\r\n    orig_docs_cat = []\r\n    orig_docs_words = []\r\n    \r\n    catNum = len(newsgroups_subset.target_names)\r\n    cats_docsWords = [ [] for i in xrange(catNum) ]\r\n    cats_docNames = [ [] for i in xrange(catNum) ]\r\n    \r\n    emptyFileNum = 0\r\n    \r\n    for d, text in enumerate(newsgroups_subset.data):\r\n        if d % 50 == 49 or d == setDocNum - 1:\r\n            print \"\\r%d %d\\r\" %( d + 1, totalLineNum ),\r\n        text = text.encode(\"utf-8\")\r\n        lines = text.split(\"\\n\")\r\n        if len(text) == 0 or len(lines) == 0:\r\n            emptyFileNum += 1\r\n            continue\r\n    \r\n        readDocNum += 1\r\n        totalLineNum += len(lines)\r\n    \r\n        catID = newsgroups_subset.target[d]\r\n        category = newsgroups_subset.target_names[catID]\r\n    \r\n        text = \" \".join(lines)\r\n    \r\n        wordsInSentences, wc = extractSentenceWords(text)\r\n        filename = newsgroups_subset.filenames[d]\r\n        filename = os.path.basename(filename)\r\n        orig_docs_words.append( wordsInSentences )\r\n        orig_docs_name.append(filename)\r\n        orig_docs_cat.append(catID)\r\n        cats_docsWords[catID].append(wordsInSentences)\r\n        cats_docNames[catID].append(filename)\r\n    \r\n    print \"Done. %d docs read, %d empty docs skipped. Totally %d lines\" %(readDocNum, emptyFileNum, totalLineNum)\r\n    return setDocNum, orig_docs_words, orig_docs_name, orig_docs_cat, \\\r\n                cats_docsWords, cats_docNames, newsgroups_subset.target_names\r\n    \r\ndef load_reuters(setName):\r\n    html = HTMLParser.HTMLParser()\r\n    doc_ids = reuters.fileids()\r\n    cat2all_ids = {}\r\n    cat2train_ids = {}\r\n    cat2test_ids = {}\r\n    cat2all_num = {}\r\n    cand_docNum = 0\r\n    \r\n    for doc_id in doc_ids:\r\n        # only choose docs belonging in one category\r\n        if len( reuters.categories(doc_id) ) == 1:\r\n            cat = reuters.categories(doc_id)[0]\r\n            cand_docNum += 1\r\n            \r\n            if doc_id.startswith(\"train\"):\r\n                cat2set_ids = cat2train_ids\r\n            else:\r\n                cat2set_ids = cat2test_ids\r\n                \r\n            if cat in cat2set_ids:\r\n                cat2set_ids[cat].append(doc_id)\r\n            else:\r\n                cat2set_ids[cat] = [ doc_id ]\r\n            \r\n            # both train and test doc_ids are put in cat2all_ids\r\n            if cat in cat2all_ids:\r\n                cat2all_ids[cat].append(doc_id)\r\n            else:\r\n                cat2all_ids[cat] = [ doc_id ]\r\n            if cat in cat2all_num:\r\n                cat2all_num[cat] += 1\r\n            else:\r\n                cat2all_num[cat] = 1\r\n            \r\n    print \"Totally %d docs, %d single-category docs in %d categories\" %( len(doc_ids), \r\n                    cand_docNum, len(cat2train_ids) )\r\n                    \r\n    sorted_cats = sorted( cat2all_num.keys(), key=lambda cat: cat2all_num[cat],\r\n                            reverse=True )\r\n                            \r\n    catNum = 10\r\n    cats_docsWords = [ [] for i in xrange(catNum) ]\r\n    cats_docNames = [ [] for i in xrange(catNum) ]\r\n                            \r\n    topN_cats = sorted_cats[:catNum]\r\n    print \"Top 10 categories:\"\r\n    keptAllDocNum = 0\r\n    keptTrainDocNum = 0\r\n    keptTestDocNum = 0\r\n    \r\n    for cat in topN_cats:\r\n        print \"%s: %d/%d\" %( cat, len(cat2train_ids[cat]), len(cat2test_ids[cat]) )\r\n        keptTrainDocNum += len(cat2train_ids[cat])\r\n        keptTestDocNum += len(cat2test_ids[cat])\r\n        keptAllDocNum += len(cat2train_ids[cat]) + len(cat2test_ids[cat])\r\n        \r\n    print \"Totally %d docs kept, %d in train, %d in test\" %( keptAllDocNum, \r\n                        keptTrainDocNum, keptTestDocNum )    \r\n    \r\n    if setName == \"train\":\r\n        cat2set_ids = cat2train_ids\r\n        setDocNum = keptTrainDocNum\r\n    elif setName == \"test\":\r\n        cat2set_ids = cat2test_ids\r\n        setDocNum = keptTestDocNum\r\n    elif setName == \"all\":\r\n        cat2set_ids = cat2all_ids\r\n        setDocNum = keptAllDocNum\r\n    else:\r\n        raise Exception(\"Unknown set name %s\" %setName)\r\n            \r\n    orig_docs_name = []\r\n    orig_docs_cat = []\r\n    orig_docs_words = []\r\n    readDocNum = 0\r\n    totalLineNum = 0\r\n    emptyFileNum = 0\r\n    \r\n    for cat_id, cat in enumerate(topN_cats):\r\n        for doc_id in cat2set_ids[cat]:\r\n            if readDocNum % 50 == 49 or readDocNum == setDocNum - 1:\r\n                print \"\\r%d %d\\r\" %( readDocNum + 1, totalLineNum ),\r\n            text = html.unescape( reuters.raw(doc_id) )\r\n            text = text.encode(\"utf-8\")\r\n            lines = text.split(\"\\n\")\r\n            if len(text) == 0 or len(lines) == 0:\r\n                emptyFileNum += 1\r\n                continue\r\n        \r\n            readDocNum += 1\r\n            totalLineNum += len(lines)\r\n        \r\n            text = \" \".join(lines)\r\n            wordsInSentences, wc = extractSentenceWords(text)\r\n            \r\n            filename = doc_id\r\n            orig_docs_words.append( wordsInSentences )\r\n            orig_docs_name.append(filename)\r\n            orig_docs_cat.append(cat_id)\r\n            cats_docsWords[cat_id].append(wordsInSentences)\r\n            cats_docNames[cat_id].append(filename)\r\n            \r\n    print \"Done. %d docs read, %d empty docs skipped. Totally %d lines\" %(readDocNum, emptyFileNum, totalLineNum)\r\n    return setDocNum, orig_docs_words, orig_docs_name, orig_docs_cat, \\\r\n                cats_docsWords, cats_docNames, topN_cats\r\n    "
  },
  {
    "path": "csv2topic.py",
    "content": "import numpy as np\r\nimport getopt\r\nimport sys\r\nimport pdb\r\nimport os\r\nimport csv\r\nfrom topicvecDir import topicvecDir\r\nfrom utils import *\r\n\r\ncustomStopwords = \"based via using approach learning multi algorithm algorithms\"\r\n\r\nconfig = dict(  csv_filenames = None,\r\n                short_name = None,\r\n                unigramFilename = \"top1grams-wiki.txt\",\r\n                word_vec_file = \"25000-180000-500-BLK-8.0.vec\",\r\n                K = 20,\r\n                N0 = 500,\r\n                max_l = 5,\r\n                init_l = 1,\r\n                max_grad_norm = 0,\r\n                # cap the sum of Em when updating topic embeddings\r\n                # to avoid too big gradients\r\n                grad_scale_Em_base = 2500,\r\n                topW = 30,\r\n                topTopicMassFracPrintThres = 0.1,\r\n                alpha0 = 0.1,\r\n                alpha1 = 0.1,\r\n                iniDelta = 0.1,\r\n                MAX_EM_ITERS = 100,\r\n                topicDiff_tolerance = 2e-3,\r\n                printTopics_iterNum = 10,\r\n                zero_topic0 = True,\r\n                useDrdtApprox = False,\r\n                customStopwords = customStopwords,\r\n                remove_stop = True,\r\n                normalize_vecs = False,\r\n                # shift all embeddings in a document, so that their average is 0\r\n                rebase_vecs = True,\r\n                rebase_norm_thres = 0.2,\r\n                evalKmeans = False,\r\n                verbose = 1,\r\n                seed = 0\r\n            )\r\n\r\ndef usage():\r\n    print \"\"\"topicvecDir.py [ -v vec_file -a alpha ... ] csv_file\r\nOptions:\r\n  -k:  Number of topic embeddings to extract. Default: 20\r\n  -v:  Existing embedding file of all words.\r\n  -r:  Existing residual file of core words.\r\n  -a:  Hyperparameter alpha. Default: 0.1.\r\n  -i:  Number of iterations of the EM procedure. Default: 100\r\n  -u:  Unigram file, to obtain unigram probs.\r\n  -l:  Magnitude of topic embeddings.\r\n  -A:  Append to the old log file.\r\n  -s:  Seed the random number generator to x. Used to repeat experiments\r\n  -n:  Nickname (short name) for the csv_file\r\n\"\"\"\r\n\r\ndef getOptions():\r\n    global config\r\n\r\n    try:\r\n        opts, args = getopt.getopt(sys.argv[1:],\"k:v:i:u:l:s:n:Ah\")\r\n        if len(args) < 1:\r\n            raise getopt.GetoptError(\"\")\r\n        config['csv_filenames'] = args\r\n            \r\n        for opt, arg in opts:\r\n            if opt == '-k':\r\n                config['K'] = int(arg)\r\n            if opt == '-v':\r\n                config['vec_file'] = arg\r\n            if opt == '-a':\r\n                config['alpha1'] = float(opt)\r\n            if opt == '-i':\r\n                config['MAX_EM_ITERS'] = int(arg)\r\n            if opt == '-u':\r\n                config['unigramFilename'] = arg\r\n            if opt == '-l':\r\n                config['max_l'] = int(arg)\r\n            if opt == '-s':\r\n                config['seed'] = int(arg)\r\n            if opt == '-A':\r\n                config['appendLogfile'] = True\r\n            if opt == '-n':\r\n                config['short_name'] = arg\r\n            if opt == '-r':\r\n                config['useDrdtApprox'] = True\r\n            if opt == '-h':\r\n                usage()\r\n                sys.exit(0)\r\n\r\n        basename = os.path.basename(args[0])\r\n        if config['short_name']:\r\n            config['logfilename'] = config['short_name']\r\n        elif len(args) > 1:\r\n            config['logfilename'] = \"(%d)%s\" %( len(args), basename )\r\n        else:\r\n            config['logfilename'] = basename\r\n\r\n    except getopt.GetoptError:\r\n        usage()\r\n        sys.exit(2)\r\n\r\n    return config\r\n\r\ndef main():\r\n    config = getOptions()\r\n\r\n    docwords = []\r\n    csvfiles_filecount = 0\r\n    csvfiles_wc = 0\r\n    csvfiles_rowcount = 0\r\n    file_rownames = []\r\n    for csv_filename in config['csv_filenames']:\r\n        csvfile_wc = 0\r\n        csvfile_rowcount = 0\r\n        with open(csv_filename) as DOC:\r\n            docreader = csv.reader(DOC)\r\n            for row in docreader:\r\n                doc = row[0] \r\n                wordsInSentences, wc = extractSentenceWords(doc, min_length=2)\r\n                csvfile_wc += wc\r\n                csvfile_rowcount += 1\r\n                docwords.append(wordsInSentences)\r\n                file_rownames.append( \"%s-row%d\" %(csv_filename, csvfile_rowcount) )\r\n        csvfile_avgwc = csvfile_wc * 1.0 / csvfile_rowcount\r\n        print \"%d words extracted from %d rows in '%s'. Avg %.1f words each row\" %( csvfile_wc, \r\n                    csvfile_rowcount, csv_filename, csvfile_avgwc )\r\n                    \r\n        csvfiles_wc += csvfile_wc\r\n        csvfiles_rowcount += csvfile_rowcount\r\n        csvfiles_filecount += 1\r\n    csvfiles_avgwc = csvfiles_wc * 1.0 / csvfiles_rowcount\r\n    if csvfiles_filecount > 1:\r\n        print \"%d words extracted from %d rows in %d csv files. Avg %.1f words each row\" %(csvfiles_wc, \r\n                    csvfiles_rowcount, csvfiles_filecount, csvfiles_avgwc)\r\n    \r\n    topicvec = topicvecDir(**config)\r\n    topicvec.setDocs( docwords, file_rownames )\r\n    \r\n    if 'evalKmeans' in config and config['evalKmeans']:\r\n        topicvec.kmeans()\r\n        topicvec.printTopWordsInTopic(None, True)\r\n        exit(0)\r\n        \r\n    best_last_Ts, Em, docs_Em, Pi = topicvec.inference()\r\n\r\n    basename = os.path.basename(config['logfilename'])\r\n    basetrunk = os.path.splitext(basename)[0]\r\n\r\n    best_it, best_T, best_loglike = best_last_Ts[0]\r\n    save_matrix_as_text( basetrunk + \"-em%d-best.topic.vec\" %best_it, \"topic\", best_T  )\r\n\r\n    if best_last_Ts[1]:\r\n        last_it, last_T, last_loglike = best_last_Ts[1]\r\n        save_matrix_as_text( basetrunk + \"-em%d-last.topic.vec\" %last_it, \"topic\", last_T  )\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n"
  },
  {
    "path": "file2topic.py",
    "content": "import numpy as np\r\nimport getopt\r\nimport sys\r\nimport pdb\r\nimport os\r\nfrom topicvecDir import topicvecDir\r\nfrom utils import *\r\n\r\ncustomStopwords = \"based via using approach learning multi algorithm algorithms\"\r\n\r\nconfig = dict(  doc_filenames = None,\r\n                short_name = None,\r\n                unigramFilename = \"top1grams-wiki.txt\",\r\n                word_vec_file = \"25000-180000-500-BLK-8.0.vec\",\r\n                K = 20,\r\n                N0 = 500,\r\n                max_l = 5,\r\n                init_l = 1,\r\n                max_grad_norm = 0,\r\n                # cap the sum of Em when updating topic embeddings\r\n                # to avoid too big gradients\r\n                grad_scale_Em_base = 2500,\r\n                topW = 30,\r\n                topTopicMassFracPrintThres = 0.1,\r\n                alpha0 = 0.1,\r\n                alpha1 = 0.1,\r\n                iniDelta = 0.1,\r\n                MAX_EM_ITERS = 100,\r\n                topicDiff_tolerance = 2e-3,\r\n                printTopics_iterNum = 10,\r\n                zero_topic0 = True,\r\n                useDrdtApprox = False,\r\n                customStopwords = customStopwords,\r\n                remove_stop = True,\r\n                normalize_vecs = False,\r\n                # shift all embeddings in a document, so that their average is 0\r\n                rebase_vecs = True,\r\n                rebase_norm_thres = 0.2,\r\n                evalKmeans = False,\r\n                verbose = 1,\r\n                seed = 0\r\n            )\r\n\r\ndef usage():\r\n    print \"\"\"topicvecDir.py [ -v vec_file -a alpha ... ] doc_file\r\nOptions:\r\n  -k:  Number of topic embeddings to extract. Default: 20\r\n  -v:  Existing embedding file of all words.\r\n  -r:  Existing residual file of core words.\r\n  -a:  Hyperparameter alpha. Default: 0.1.\r\n  -i:  Number of iterations of the EM procedure. Default: 100\r\n  -u:  Unigram file, to obtain unigram probs.\r\n  -l:  Magnitude of topic embeddings.\r\n  -A:  Append to the old log file.\r\n  -s:  Seed the random number generator to x. Used to repeat experiments\r\n  -n:  Nickname (short name) for the doc_file\r\n\"\"\"\r\n\r\ndef getOptions():\r\n    global config\r\n\r\n    try:\r\n        opts, args = getopt.getopt(sys.argv[1:],\"k:v:i:u:l:s:n:Ah\")\r\n        if len(args) < 1:\r\n            raise getopt.GetoptError(\"\")\r\n        config['doc_filenames'] = args\r\n            \r\n        for opt, arg in opts:\r\n            if opt == '-k':\r\n                config['K'] = int(arg)\r\n            if opt == '-v':\r\n                config['vec_file'] = arg\r\n            if opt == '-a':\r\n                config['alpha1'] = float(opt)\r\n            if opt == '-i':\r\n                config['MAX_EM_ITERS'] = int(arg)\r\n            if opt == '-u':\r\n                config['unigramFilename'] = arg\r\n            if opt == '-l':\r\n                config['max_l'] = int(arg)\r\n            if opt == '-s':\r\n                config['seed'] = int(arg)\r\n            if opt == '-A':\r\n                config['appendLogfile'] = True\r\n            if opt == '-n':\r\n                config['short_name'] = arg\r\n            if opt == '-r':\r\n                config['useDrdtApprox'] = True\r\n            if opt == '-h':\r\n                usage()\r\n                sys.exit(0)\r\n\r\n        if config['short_name']:\r\n            config['logfilename'] = config['short_name']\r\n        elif len(args) > 1:\r\n            config['logfilename'] = \"(%d)%s\" %( len(args), args[0] )\r\n        else:\r\n            config['logfilename'] = args[0]\r\n\r\n    except getopt.GetoptError:\r\n        usage()\r\n        sys.exit(2)\r\n\r\n    return config\r\n\r\ndef main():\r\n    config = getOptions()\r\n\r\n    docwords = []\r\n    for doc_filename in config['doc_filenames']:\r\n        with open(doc_filename) as DOC:\r\n            doc = DOC.readlines()\r\n            doc = \"\".join(doc)\r\n    \r\n        wordsInSentences, wc = extractSentenceWords(doc, 2)\r\n        print \"%d words extracted from '%s'\" %(wc, doc_filename)\r\n        docwords.append(wordsInSentences)\r\n\r\n    topicvec = topicvecDir(**config)\r\n    topicvec.setDocs( docwords, config['doc_filenames'] )\r\n    \r\n    if 'evalKmeans' in config and config['evalKmeans']:\r\n        topicvec.kmeans()\r\n        topicvec.printTopWordsInTopic(None, True)\r\n        exit(0)\r\n        \r\n    best_last_Ts, Em, docs_Em, Pi = topicvec.inference()\r\n\r\n    basename = os.path.basename(config['logfilename'])\r\n    basetrunk = os.path.splitext(basename)[0]\r\n\r\n    best_it, best_T, best_loglike = best_last_Ts[0]\r\n    save_matrix_as_text( basetrunk + \"-em%d-best.topic.vec\" %best_it, \"topic\", best_T  )\r\n\r\n    if best_last_Ts[1]:\r\n        last_it, last_T, last_loglike = best_last_Ts[1]\r\n        save_matrix_as_text( basetrunk + \"-em%d-last.topic.vec\" %last_it, \"topic\", last_T  )\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n"
  },
  {
    "path": "psdvec/README.md",
    "content": "# PSDVec\r\nPSDVec is the source code for \"A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution\" (EMNLP 2015).\r\n\r\nSee \"PSDVec.pdf\" for a manual (```PSDVec: a Toolbox for Incremental and Scalable Word Embedding```, accepted by Neurocomputing, 2016).\r\n\r\n#### Update v0.42: Tikhonov Regularization (=Spherical Gaussian Prior) to embeddings in block-wise factorization:\r\n1. Obtain 25000 core embeddings using Weighted PSD Approximation, into _25000-500-EM.vec_:\r\n    * ```python factorize.py -w 25000 top2grams-wiki.txt```  \r\n2. Obtain 45000 noncore embeddings using Weighted Least Squares, totaling 80000 (25000 cores + 55000 noncores), into _25000-80000-500-BLK-2.0.vec_:\r\n    * ```python factorize.py -v 25000-500-EM.vec -o 55000 -t2 top2grams-wiki.txt```\r\n3. Incrementally learn other 50000 noncore embeddings (based on 25000 cores), into _25000-130000-500-BLK-4.0.vec_:\r\n    * ```python factorize.py -v 25000-80000-500-BLK-2.0.vec -b 25000 -o 50000 -t4 top2grams-wiki.txt```\r\n4. Repeat 3 again, with Tikhonov coeff = 8 to get more embeddings of rarer words, into _25000-180000-500-BLK-8.0.vec_:\r\n    * ```python factorize.py -v 25000-130000-500-BLK-4.0.vec -b 25000 -o 50000 -t8 top2grams-wiki.txt```\r\n\r\nPretrained 180,000 embeddings and evaluation results are uploaded. Now the performance is systematically better than other methods.\r\n\r\n#### Update v0.41: Gradient Descent (GD) solution:\r\n* ```python factorize.py -G 500 -w 120000 top2grams-wiki.txt```\r\n* GD is fast and scalable, but the performance is much worse (~10% lower on the testsets). It's not recommended, unless initialized using unweighted Eigendecomposition (which is still not scalable).\r\n\r\n#### Update v0.4: Online Block-wise Factorization\r\n\r\nTestsets are by courtesy of Omer Levy (https://bitbucket.org/omerlevy/hyperwords/src).\r\n\r\nThe Gradient Descent algorithm was based on the suggestion of Peilin Zhao (not included as a part of the papers).\r\n"
  },
  {
    "path": "psdvec/addheader.py",
    "content": "#!/usr/bin/python\nimport os\nimport sys\nimport re\n\noldVecFilename = sys.argv[1]\nnewVecFilename = sys.argv[2]\n\nstream = os.popen( \"wc %s\" %oldVecFilename )\noutput = stream.read()\noutput = output.strip()\nlinecount, wordcount, charcount, filename = re.split(\" +\", output)\nlinecount = int(linecount)\nwordcount = int(wordcount)\n\nif wordcount % linecount != 0:\n    print \"Error: line count %d does not divide word count %d\" %(linecount, wordcount)\n    sys.exit(1)\n\nveclen = wordcount / linecount - 1\nprint \"%d %d\" %(linecount, veclen)\nVEC = open(newVecFilename, \"w\")\nVEC.write( \"%d %d\\n\" %(linecount, veclen) )\nVEC.close()\nos.popen( \"cat %s >> %s\" %(oldVecFilename, newVecFilename) )\n\nstream = os.popen( \"ls -l %s\" %oldVecFilename )\nprint stream.read().strip()\nstream = os.popen( \"ls -l %s\" %newVecFilename )\nprint stream.read().strip()\n"
  },
  {
    "path": "psdvec/analogy.py",
    "content": "import sys\r\nimport re\r\nfrom utils import *\r\n\r\ndef pred_ana( model, a, a2, b, maxcands = 10 ):\r\n    questWordIndices = [ model.word2id[x] for x in (a,a2,b) ]\r\n    # b2 is effectively iterating through the vocab. The row is all the cosine values\r\n    b2a2 = model.sim_row(a2)\r\n    b2a  = model.sim_row(a)\r\n    b2b  = model.sim_row(b)\r\n\r\n    mulsims = ( b2a2 + 1 ) * ( b2b + 1 ) / ( b2a + 1.001 )\r\n    mulsims[questWordIndices] = -10000\r\n    b2s = []\r\n    for i in xrange(maxcands):\r\n        imul = np.nanargmax(mulsims)\r\n        b2mul  = model.vocab[imul]\r\n        b2s.append( [ b2mul, mulsims[imul] ] ) \r\n        mulsims[imul] = -10000\r\n        \r\n    return b2s\r\n    \r\nembedding_npyfile = \"25000-180000-500-BLK-8.0.vec.npy\"\r\nembedding_arrays = np.load(embedding_npyfile)\r\nV, vocab, word2ID, skippedWords_whatever = embedding_arrays\r\nprint \"%d words loaded from '%s'\" %(len(vocab), embedding_npyfile)\r\nmodel = VecModel(V, vocab, word2ID, vecNormalize=True)\r\nprint \"Model initialized. Ready for input:\"\r\n\r\nwhile True:\r\n    line = raw_input()\r\n    line = line.strip()\r\n    words = re.split(\"\\s+\", line)\r\n    if len(words) != 3:\r\n        print \"Only 3 words are allowed\"\r\n        continue\r\n    \r\n    oov = 0\r\n    for w in words:\r\n        if w not in model:\r\n            print \"'%s' not in vocab\" %w\r\n            oov += 1\r\n    if oov > 0:\r\n        continue\r\n        \r\n    a, a2, b = words\r\n    b2s = pred_ana( model, a, a2, b )\r\n    for word, sim in b2s:\r\n        print word, sim\r\n    print\r\n        \r\n"
  },
  {
    "path": "psdvec/bench.sh",
    "content": "#!/bin/sh\nexport ROOT=/home/shaohua/D\n#export CORPUS=$ROOT/corpus/cleanwiki.txt\n#export DIM=500\n#export MINCOUNT=100\n#export SUFFIX=wiki\nexport CORPUS=$ROOT/corpus/rcv1clean.txt\nexport DIM=50\nexport MINCOUNT=50\nexport SUFFIX=rcv1\n\ncd $ROOT/corpus/\necho PSD:\n./fact-$SUFFIX.sh\ncd $ROOT/word2vec\necho word2vec:\ntime ./word2vec -train $CORPUS -output $ROOT/corpus/word2vec-$SUFFIX.vec -size $DIM -window 5 -sample 1e-4 -negative 15 -min-count $MINCOUNT\ncd $ROOT/corpus/glove/\necho glove:\ntime ./$SUFFIX.sh\ncd $ROOT/corpus/singular/\necho singular:\ntime ./singular --corpus $CORPUS --output ./$SUFFIX --rare $MINCOUNT --window 3 --dim $DIM\necho PPM and SVD:\ncd $ROOT/corpus/hyperwords\n./train-$SUFFIX.sh\necho Sparse:\ntail -n+2 $ROOT/corpus/word2vec-$SUFFIX.vec >  $ROOT/corpus/word2vec-$SUFFIX-headless.vec\ncd $ROOT/corpus/sparse/\ntime ./sparse ../word2vec-$SUFFIX-headless.vec 5 0.5 1e-5 4 sparse-$SUFFIX.vec\n"
  },
  {
    "path": "psdvec/benchspeed.py",
    "content": "import numpy as np\r\nimport time\r\n\r\nclass Timer(object):\r\n    def __init__(self, name=None):\r\n        self.name = name\r\n        self.tstart = time.time()\r\n        self.tlast = self.tstart\r\n        self.firstCall = True\r\n\r\n    def getElapseTime(self, isStr=True):\r\n        totalElapsed = time.time() - self.tstart\r\n        # elapsed time since last call\r\n        interElapsed = time.time() - self.tlast\r\n        self.tlast = time.time()\r\n\r\n        firstCall = self.firstCall\r\n        self.firstCall = False\r\n\r\n        if isStr:\r\n            if self.name:\r\n                if firstCall:\r\n                    return '%s elapsed: %.2f' % ( self.name, totalElapsed )\r\n                return '%s elapsed: %.2f/%.2f' % ( self.name, totalElapsed, interElapsed )\r\n            else:\r\n                if firstCall:\r\n                    return 'Elapsed: %.2f' % ( totalElapsed )\r\n                return 'Elapsed: %.2f/%.2f' % ( totalElapsed, interElapsed )\r\n        else:\r\n            return totalElapsed, interElapsed\r\n\r\n    def printElapseTime(self):\r\n        print self.getElapseTime()\r\n\r\ndef timeToStr(timeNum, fmt=\"%H:%M:%S\"):\r\n    timeStr = time.strftime(fmt, time.localtime(timeNum))\r\n    return timeStr\r\n\r\ndef block_factorize( core_size, noncore_size, N0, tikhonovCoeff ):\r\n    # new WGsum: noncore_size * core_size\r\n    WGsum = np.random.random((noncore_size,core_size))\r\n    Wsum = np.random.random((noncore_size,core_size))\r\n    Wsum[ np.isclose(Wsum,0) ] = 0.001\r\n    Gwmean = WGsum\r\n\r\n    V1 = np.random.random((core_size,N0))\r\n    # embeddings of noncore words\r\n    # new V2: noncore_size * N0\r\n    V2 = np.zeros( ( noncore_size, N0 ), dtype=np.float32 )\r\n    Tikhonov = np.identity(N0) * tikhonovCoeff\r\n\r\n    timer = Timer()\r\n\r\n    print \"Begin finding embeddings of non-core words\"\r\n\r\n    # Find each noncore word's embedding\r\n    for i in xrange(noncore_size):\r\n        # core_size\r\n        wi = Wsum[i]\r\n        # new VW: N0 * core_size\r\n        VW = V1.T * wi\r\n        # new VWV: N0 * N0\r\n        VWV = VW.dot(V1)\r\n        if False:\r\n            VWV_Tik = VWV + Tikhonov\r\n            V2[i] = np.linalg.inv(VWV_Tik).dot( VW.dot(Gwmean[i]) )\r\n        if i >= 0 and i % 100 == 99:\r\n            print \"\\r%d / %d.\" %(i+1,noncore_size),\r\n            print timer.getElapseTime(), \"\\r\",\r\n\r\n    print\r\n\r\nblock_factorize(15000, 1000, 500, 2)\r\n#block_factorize(15000, 10000, 50, 2)\r\n"
  },
  {
    "path": "psdvec/catbench.py",
    "content": "import os\r\n\r\ntestsetNames = [ \"ap\", \"battig\", \"esslli\" ]\r\ntestsetCatNums = [ 13, 10, 6 ]\r\nalgNames = [ \"PSDVec\", \"word2vec\", \"CCA\" ]\r\nCLmethods = [ \"rbr\", \"direct\", \"graph\" ]\r\nvclusterPath = \"D:\\\\cluto-2.1.2\\\\MSWIN-x86_64-openmp\\\\vcluster.exe\"\r\ntestsetDir = \"./concept categorization\"\r\n\r\nfor CLmethod in CLmethods:\r\n    for i, testsetName in enumerate(testsetNames):\r\n        for algName in algNames:\r\n            vecFilename = testsetDir + \"/\" + testsetName + \"-\" + algName + \".vec\"\r\n            labelFilename = testsetDir + \"/\" + testsetName + \"-\" + algName + \".label\"\r\n            catNum = testsetCatNums[i]\r\n            print \"%s on %s using %s:\" %( algName, testsetName, CLmethod )\r\n            stream = os.popen( '%s -rclassfile=\"%s\" -clmethod=%s \"%s\" %d' %( vclusterPath, \r\n                               labelFilename, CLmethod, vecFilename, catNum ) )\r\n            output = stream.read()\r\n            lines = output.split(\"\\n\")\r\n            for line in lines:\r\n                if line.find(\"way clustering\") >= 0:\r\n                    print line\r\n    print\r\n    "
  },
  {
    "path": "psdvec/cleancorpus.py",
    "content": "import sys\r\nimport os\r\nimport gensim.corpora.wikicorpus\r\n\r\n# check and process input arguments\r\nif len(sys.argv) < 3:\r\n    print \"Usage: cleancorpus.py infile_name outfile_name\"\r\n    sys.exit(1)\r\n    \r\ninfilename, outfilename = sys.argv[1:3]\r\n\r\nif os.path.isfile(outfilename):\r\n    print \"Output file %s exists. Change the file name and try again.\" %outfilename\r\n    sys.exit(1)\r\n    \r\nlinecount = 0\r\nbytecount = 0\r\nwordcount = 0\r\n\r\noutput = open(outfilename, 'w')     \r\nIN = open(infilename)\r\nfor line in IN:\r\n    tokens = gensim.corpora.wikicorpus.tokenize(line)\r\n    output.write( \"%s\\n\" %(\" \".join(tokens)) )\r\n    linecount += 1\r\n    bytecount += len(line)\r\n    wordcount += len(tokens)\r\n    if linecount % 500 == 0:\r\n        print \"\\r%d    %d    %d    \\r\" %(linecount, bytecount/1024/1024, wordcount),\r\n        "
  },
  {
    "path": "psdvec/competitors/glove/demo.sh",
    "content": "#!/bin/bash\n\n# Makes programs, downloads sample data, trains a GloVe model, and then evaluates it.\n# One optional argument can specify the language used for eval script: matlab, octave or [default] python\n\nCORPUS=../rcv1clean.txt\nVOCAB_FILE=vocab.txt\nCOOCCURRENCE_FILE=cooccurrence.bin\nCOOCCURRENCE_SHUF_FILE=cooccurrence.shuf.bin\nBUILDDIR=build\nSAVE_FILE=glove-rcv1.vec\nVERBOSE=2\nMEMORY=16.0\nVOCAB_MIN_COUNT=50\nVECTOR_SIZE=50\nMAX_ITER=15\nWINDOW_SIZE=3\nBINARY=0\nNUM_THREADS=8\nX_MAX=10\n\n$BUILDDIR/vocab_count -min-count $VOCAB_MIN_COUNT -verbose $VERBOSE < $CORPUS > $VOCAB_FILE\nif [[ $? -eq 0 ]]\n  then\n  $BUILDDIR/cooccur -memory $MEMORY -vocab-file $VOCAB_FILE -verbose $VERBOSE -window-size $WINDOW_SIZE < $CORPUS > $COOCCURRENCE_FILE\n  if [[ $? -eq 0 ]]\n  then\n    $BUILDDIR/shuffle -memory $MEMORY -verbose $VERBOSE < $COOCCURRENCE_FILE > $COOCCURRENCE_SHUF_FILE\n    if [[ $? -eq 0 ]]\n    then\n       $BUILDDIR/glove -save-file $SAVE_FILE -threads $NUM_THREADS -input-file $COOCCURRENCE_SHUF_FILE -x-max $X_MAX -iter $MAX_ITER -vector-size $VECTOR_SIZE -binary $BINARY -vocab-file $VOCAB_FILE -verbose $VERBOSE\n       if [[ $? -eq 0 ]]\n       then\n           if [ \"$1\" = 'matlab' ]; then\n               matlab -nodisplay -nodesktop -nojvm -nosplash < ./eval/matlab/read_and_evaluate.m 1>&2 \n           elif [ \"$1\" = 'octave' ]; then\n               octave < ./eval/octave/read_and_evaluate_octave.m 1>&2 \n           else\n               python eval/python/evaluate.py\n           fi\n       fi\n    fi\n  fi\nfi\n"
  },
  {
    "path": "psdvec/competitors/glove/rcv1.sh",
    "content": "#!/bin/bash\n\n# Makes programs, downloads sample data, trains a GloVe model, and then evaluates it.\n# One optional argument can specify the language used for eval script: matlab, octave or [default] python\n\nCORPUS=../rcv1clean.txt\nVOCAB_FILE=vocab-rcv1.txt\nCOOCCURRENCE_FILE=cooccurrence-rcv1.bin\nCOOCCURRENCE_SHUF_FILE=cooccurrence.shuf-rcv1.bin\nBUILDDIR=build\nSAVE_FILE=glove-rcv1.vec\nVERBOSE=2\nMEMORY=16.0\nVOCAB_MIN_COUNT=50\nVECTOR_SIZE=50\nMAX_ITER=15\nWINDOW_SIZE=3\nBINARY=0\nNUM_THREADS=8\nX_MAX=10\n\n$BUILDDIR/vocab_count -min-count $VOCAB_MIN_COUNT -verbose $VERBOSE < $CORPUS > $VOCAB_FILE\nif [[ $? -eq 0 ]]\n  then\n  $BUILDDIR/cooccur -memory $MEMORY -vocab-file $VOCAB_FILE -verbose $VERBOSE -window-size $WINDOW_SIZE < $CORPUS > $COOCCURRENCE_FILE\n  if [[ $? -eq 0 ]]\n  then\n    $BUILDDIR/shuffle -memory $MEMORY -verbose $VERBOSE < $COOCCURRENCE_FILE > $COOCCURRENCE_SHUF_FILE\n    if [[ $? -eq 0 ]]\n    then\n       $BUILDDIR/glove -save-file $SAVE_FILE -threads $NUM_THREADS -input-file $COOCCURRENCE_SHUF_FILE -x-max $X_MAX -iter $MAX_ITER -vector-size $VECTOR_SIZE -binary $BINARY -vocab-file $VOCAB_FILE -verbose $VERBOSE\n       if [[ $? -eq 0 ]]\n       then\n           if [ \"$1\" = 'matlab' ]; then\n               matlab -nodisplay -nodesktop -nojvm -nosplash < ./eval/matlab/read_and_evaluate.m 1>&2 \n           elif [ \"$1\" = 'octave' ]; then\n               octave < ./eval/octave/read_and_evaluate_octave.m 1>&2 \n           else\n               python eval/python/evaluate.py\n           fi\n       fi\n    fi\n  fi\nfi\n"
  },
  {
    "path": "psdvec/competitors/glove/vocab-rcv1.txt",
    "content": "the 10027055\nof 4587086\nto 4566012\nin 3906588\nand 3414395\nsaid 2511393\non 2155635\nfor 1664451\nat 1281038\nwas 1160891\nthat 1143953\nis 1086192\nit 1066127\nby 982213\nwith 956024\nfrom 934724\npercent 857975\nbe 806244\nas 762359\nmillion 726400\nhe 710222\nyear 673622\nits 668590\nwill 653452\nhas 598752\nbut 596463\nwere 578481\nan 569515\nwould 560721\nnot 553982\nare 549324\nhave 537104\nwhich 517309\nhad 503678\nmarket 456507\nthis 452646\nup 439784\nnew 435347\nwe 408281\nafter 401950\nbank 375841\none 370881\nthey 365045\ncompany 357352\nlast 332567\ngovernment 331941\nbeen 328076\nalso 307456\ntwo 306608\nbillion 306571\nhis 300674\ntheir 297054\nfirst 292165\nor 291005\nshares 281766\nmore 275569\nover 274325\nabout 273657\nnewsroom 260182\nthere 252838\nweek 250915\nshare 246184\nwho 245482\nthan 241103\nsome 241006\nmay 235280\nper 228328\nno 226528\ntuesday 222208\nthree 221393\npct 219909\nall 217783\nexpected 215475\nwednesday 215426\nnet 214706\nthursday 214393\nother 212406\ngroup 208866\nmonday 208049\nfriday 207227\nprices 203402\nstate 200172\ncould 198120\nsales 196189\ntold 192334\nout 191802\ntrade 191650\nif 188392\nprice 187255\ndown 186904\nagainst 185665\nday 182673\ninto 180960\nmonth 177786\npresident 176637\nminister 174169\nstock 170782\nwhen 170491\nrate 166959\nvs 163452\ninterest 160771\ntime 160459\noil 157649\nmonths 156241\nforeign 153462\nyears 153373\nincome 153233\nnext 153170\njune 150248\nbetween 150137\npoints 149863\nbefore 147982\nend 147897\nyen 147802\npeople 147643\nhigh 145524\neuropean 145339\nwhile 145013\nindex 144768\ninc 144077\nquarter 141119\nunder 140046\ntotal 139395\nmarch 138258\ninternational 137839\nonly 135913\nsince 135001\ndollar 133575\ntax 131527\nmost 131064\nrates 130714\ncentral 130426\ncorp 130013\nreuters 129633\ndue 129092\noff 128698\nadded 128229\nnow 127957\njuly 127750\nbusiness 126807\nworld 126123\nprofit 125646\ncan 125265\nfive 125155\ntrading 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112\nbastille 112\nbeilin 112\nbewildered 112\nbfg 112\nbibi 112\nbiosystems 112\nbloodletting 112\nbookmaker 112\nboosters 112\nboulogne 112\nbunting 112\ncalifornians 112\ncantons 112\ncantor 112\ncapral 112\ncasbah 112\nchafee 112\nchaplin 112\ncharting 112\nchili 112\nciumara 112\ncogema 112\ncomprehensively 112\ncraxi 112\ndamazin 112\ndeflatoq 112\ndemoskop 112\ndermagraft 112\ndiagonal 112\ndisinflation 112\ndorothy 112\neac 112\neddy 112\nelaboration 112\nenzymes 112\nequipments 112\nerhard 112\ness 112\nevictions 112\nexpropriated 112\nflopped 112\nfmb 112\nframing 112\ngignoux 112\ngimelstob 112\nglue 112\nhalpern 112\nheidrun 112\nherbal 112\nholomisa 112\nhorticultural 112\nhovers 112\nhrbr 112\nhtml 112\ninicjatyw 112\ninstituting 112\ninvoking 112\njournals 112\nkaramanlis 112\nkatzin 112\nkhalili 112\nkingmaker 112\nkolinska 112\nkomitek 112\nkristin 112\nkrylya 112\nlegalisation 112\nleonean 112\nlipinski 112\nlobo 112\nmagdeburg 112\nmangled 112\nmaquiladora 112\nmcneill 112\nmeasles 112\nmelnhof 112\nmerson 112\nmonomer 112\nmontella 112\nmoulinier 112\nmoyne 112\nmuggings 112\nmunir 112\nmurasoli 112\nnafin 112\nnag 112\nnagel 112\nnanga 112\nnegate 112\nnoone 112\nnutrients 112\nosteen 112\noverheat 112\npicketing 112\npropensity 112\npseb 112\nquickening 112\nramqvist 112\nrcn 112\nreconciling 112\nreconfirmed 112\nrefuel 112\nregrouping 112\nreimer 112\nrpm 112\nrtbf 112\nsalon 112\nsampath 112\nsequestration 112\nshying 112\nslimmer 112\nslotted 112\nsom 112\nsorrell 112\nspectacularly 112\nspokane 112\nsprinted 112\ntamilnadu 112\nteledyne 112\nthorniest 112\ntjx 112\ntossing 112\nudmr 112\nujung 112\nunexplored 112\nunprotected 112\nvacate 112\nvenezolana 112\nvynosovy 112\nwedneday 112\nwella 112\nwirth 112\nwoodard 112\nwreak 112\nwrested 112\nyasay 112\nyordan 112\nzetor 112\nzhenhai 112\naeci 111\naframaxes 111\nagnew 111\narr 111\nashraf 111\naugmented 111\naum 111\naunt 111\naw 111\naydin 111\nbalu 111\nbataan 111\nbaytown 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110\nbeja 110\nbhar 110\nblakeslee 110\nboosey 110\nbooty 110\nbottomline 110\nboudreau 110\nbroadside 110\nbrodsky 110\nbyzantine 110\ncabotage 110\ncanfield 110\ncarryout 110\ncatherwood 110\ncatwalk 110\ncharming 110\nchennai 110\nchicks 110\nchilectra 110\nchoir 110\ncineplex 110\nclassifications 110\ncoherence 110\nconakry 110\nconquering 110\ncontraceptives 110\ncoverings 110\ncrutchings 110\ndayan 110\ndeans 110\ndecoration 110\ndeniz 110\ndiscontinuing 110\nducked 110\nece 110\nefes 110\nefp 110\nemanating 110\nemissary 110\nentangled 110\nenticing 110\nertl 110\neureko 110\nexplorations 110\nfiddle 110\nfilippini 110\nfiumicino 110\nflouting 110\ngalaxies 110\ngam 110\ngd 110\nglassfibre 110\ngopal 110\ngora 110\ngothic 110\ngovpx 110\nhackman 110\nhadi 110\nhebert 110\nhejailan 110\nherschelle 110\nhohenwutzen 110\nillustration 110\ninhumane 110\ninsights 110\njakubowska 110\njaroslav 110\njuin 110\nkash 110\nkety 110\nkingsley 110\nkrajina 110\nlabuhan 110\nliew 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109\nkearsarge 109\nkispest 109\nkitty 109\nlaffan 109\nlash 109\nlautenberg 109\nlesbians 109\nmaeda 109\nmains 109\nmania 109\nmanipur 109\nmauss 109\nmedaphis 109\nmeiji 109\nmilitiaman 109\nminali 109\nmnyandu 109\nmod 109\nmufamadi 109\nmultiplying 109\nnagarjuna 109\nnanagas 109\nneedless 109\nopatov 109\northopedic 109\npacing 109\nperilya 109\npetrolite 109\nplano 109\npolly 109\npostpones 109\npotassium 109\nprecariously 109\npristine 109\nqatargas 109\nrealign 109\nrecessed 109\nrelics 109\nrevitalize 109\nroadster 109\nromp 109\nrudd 109\nsawing 109\nschiffer 109\nseafield 109\nserna 109\nsidewalks 109\nsifting 109\nsincan 109\nsmits 109\nsomeday 109\nsonoma 109\nsphinx 109\nspoils 109\nstandout 109\nsuperjumbo 109\nsymbolism 109\ntakeda 109\ntelefon 109\ntelerate 109\ntmp 109\ntracor 109\ntransposed 109\ntrekked 109\nturkestan 109\nubiquitous 109\nulcc 109\nunhedged 109\nunsourced 109\nupr 109\nuralmash 109\nvalecom 109\nvance 109\nvetted 109\nwiduri 109\nwilma 109\nww 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50\ntemperamental 50\ntenderers 50\nterusuke 50\ntestore 50\ntexfi 50\ntft 50\ntheratx 50\nthereto 50\nthika 50\nthousandth 50\nthrasher 50\nthrun 50\nthrusting 50\nthuram 50\ntikholova 50\ntimarui 50\ntizi 50\ntlcom 50\ntls 50\ntoldo 50\ntorches 50\ntorrents 50\ntova 50\ntoyama 50\ntracer 50\ntransferee 50\ntranspower 50\ntrfrs 50\ntricolour 50\ntruths 50\ntunica 50\nturbans 50\nturkmens 50\nujun 50\nukav 50\nuniceb 50\nuniformity 50\nuninformed 50\nunmet 50\nunyielding 50\nuptake 50\nurdinola 50\nursus 50\nusb 50\nusi 50\nvai 50\nvasparr 50\nvelox 50\nvossen 50\nwagoner 50\nwarms 50\nwarmup 50\nwarts 50\nwatchman 50\nwattyl 50\nweatherby 50\nwel 50\nwhittman 50\nwidness 50\nwildes 50\nwindowless 50\nwpl 50\nwreaking 50\nwrinkles 50\nxiv 50\nxvii 50\nyedinaya 50\nyme 50\nyoshio 50\nzeeland 50\nzenden 50\nzesa 50\n"
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    "path": "psdvec/competitors/glove/vocab-wiki.txt",
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1948439\namerican 1942549\nout 1826242\ncan 1821879\nthree 1821068\nsome 1793689\nwould 1784221\nbetween 1778360\nteam 1769460\nwhere 1766542\nlater 1762012\nabout 1741549\nused 1728924\nst 1707873\nsouth 1702725\nhim 1692048\nfilm 1687794\nunder 1662234\nstates 1658924\nsuch 1651187\nthen 1646834\nborn 1622633\npart 1618973\nseason 1610513\nmade 1610146\nwar 1607873\nknown 1595735\nmany 1586859\nsecond 1582040\nnorth 1555070\njohn 1552217\nwhile 1543360\nhistory 1538630\nseries 1530879\nhigh 1506291\nthan 1487792\nsee 1455866\nthese 1451913\nname 1434527\nmusic 1430219\nthrough 1417822\nbeing 1403300\nwell 1391546\nbecame 1383984\nincluding 1374977\nmarch 1368789\nbefore 1361207\ncounty 1353230\nboth 1352925\ngroup 1346964\nlife 1321436\nalbum 1300856\npeople 1296567\nleague 1284761\narea 1283924\nearly 1280768\nseptember 1280705\noctober 1273775\nhowever 1265714\nbest 1264163\nwill 1254766\nthem 1252749\njanuary 1247086\nhouse 1243355\nagainst 1239650\njune 1235044\nsince 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1951\nwatling 1951\nchristen 1950\nclot 1950\ndials 1950\ngettin 1950\nhelmuth 1950\niti 1950\nkároly 1950\nkenta 1950\nmaldon 1950\nmontebello 1950\nnoda 1950\noup 1950\nserjeant 1950\nskrull 1950\nverónica 1950\nbeaker 1949\nbelvoir 1949\nbitcoin 1949\nchristendom 1949\nfloris 1949\nheung 1949\nkaroo 1949\nmessing 1949\npicts 1949\nprodigious 1949\nquirk 1949\nbharti 1948\ncricketing 1948\nfeaturette 1948\nkovacs 1948\nkym 1948\nlull 1948\noverground 1948\nplumber 1948\nvcd 1948\nyousef 1948\ncrossword 1947\ndiscworld 1947\ndownriver 1947\nfarkas 1947\ngigabit 1947\nimpeded 1947\nisolde 1947\nkorg 1947\nmucosa 1947\nsoweto 1947\nstalling 1947\nstuccoed 1947\nsuncorp 1947\nvdc 1947\nverein 1947\nwisely 1947\ncartels 1946\ninflected 1946\nipsos 1946\nmalton 1946\nmccloud 1946\nmilli 1946\nokamoto 1946\nstorrs 1946\ntcs 1946\nulu 1946\nbelén 1945\nbonsai 1945\ndepressing 1945\nerasure 1945\njoost 1945\nminstrels 1945\nmockery 1945\npuppetry 1945\nroadmap 1945\nshanty 1945\nstipulates 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1939\nvindication 1939\nxenophon 1939\nyusuke 1939\nbačka 1938\nbikaner 1938\nbsk 1938\ncarpentier 1938\ndeviant 1938\netheridge 1938\ngrierson 1938\nironi 1938\nkemble 1938\nkher 1938\nmaculata 1938\normonde 1938\nproverb 1938\nroberson 1938\nsnelling 1938\nsnippets 1938\nsophomores 1938\ncaptions 1937\ndedicating 1937\nfalsetto 1937\ngarson 1937\ngazi 1937\nlý 1937\nlora 1937\noban 1937\nradii 1937\nrcs 1937\ntrincomalee 1937\ncustomarily 1936\nczęstochowa 1936\nflume 1936\ngulshan 1936\nikaw 1936\nlaxmikant 1936\nmöller 1936\nmonomer 1936\nmullin 1936\nsèvres 1936\nwonderfully 1936\nchromatin 1935\ncomer 1935\ndebatable 1935\nfitzhugh 1935\nfm's 1935\ninterwoven 1935\nniebla 1935\npronunciations 1935\nredhead 1935\nsda 1935\nvayalar 1935\nwhitbread 1935\nwholesome 1935\nchadian 1934\ncherish 1934\ndtm 1934\nfullest 1934\nmariko 1934\nnpr's 1934\nrie 1934\nstrada 1934\nwhit 1934\nagder 1933\naranda 1933\nbalu 1933\nblockers 1933\nbreckenridge 1933\nbullseye 1933\ndeduce 1933\ndyeing 1933\nkoen 1933\nmakarov 1933\nmlas 1933\nrigoletto 1933\nshamans 1933\ntakumi 1933\ntrabzon 1933\nvasili 1933\ncleave 1932\ndalby 1932\ndevotes 1932\ndvorak 1932\neject 1932\nhernia 1932\nhoàng 1932\nmarshfield 1932\nmikel 1932\nmohanty 1932\nnieves 1932\ntokyopop 1932\nutah's 1932\nvaledictorian 1932\nwestview 1932\nbattaglia 1931\nbil 1931\nbyproduct 1931\ncircumstantial 1931\ncotswold 1931\nelfsborg 1931\nfreezer 1931\nginn 1931\nminton 1931\noscillating 1931\npuntland 1931\nrambling 1931\nsanitarium 1931\nzahir 1931\nalo 1930\nchops 1930\ndam's 1930\ndatum 1930\nenlarging 1930\nlahiri 1930\nowen's 1930\nscreenwriters 1930\nsian 1930\ntca 1930\ntuen 1930\nbirmingham's 1929\ncupboard 1929\neffingham 1929\nmallee 1929\npavn 1929\npledging 1929\nredditch 1929\nrefuelling 1929\nsela 1929\nviterbo 1929\nwelling 1929\namidships 1928\nbromsgrove 1928\nhenriksen 1928\njanson 1928\nmatriarch 1928\nmbs 1928\nmollie 1928\nobnoxious 1928\nplainly 1928\nquintessential 1928\nsarai 1928\nslattery 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528\nreorientation 528\nreymond 528\nroman's 528\nrosato 528\nsō 528\nsargasso 528\nsarto 528\nscherrer 528\nschisms 528\nsentul 528\nsephirot 528\nshap 528\nshoegaze 528\nsiegler 528\nsonisphere 528\nsorley 528\nstrumica 528\nsummerlin 528\ntippin 528\ntopsham 528\ntransmissible 528\ntreloar 528\ntropicbirds 528\ntyron 528\nunsatisfying 528\nvarina 528\nvectis 528\nvesalius 528\nwader 528\nwesker 528\nwestborough 528\nwindowless 528\nwittering 528\nólafsson 527\nłks 527\nşanlıurfa 527\naliquippa 527\nalizée 527\narnell 527\nayana 527\nbafana 527\nbahari 527\nbeleza 527\nbellavista 527\nbenedetta 527\nberridge 527\nbleus 527\nbludgeon 527\nbroady 527\nbroc 527\nbuarque 527\nbuta 527\nbyford 527\ncahoon 527\ncapos 527\ncassander 527\ncatharus 527\ncaye 527\ncentralizing 527\nceremoniously 527\nchey 527\ncoachbuilders 527\ncobain's 527\nconasprella 527\nconon 527\ncrista 527\ndecoupled 527\ndenisa 527\ndoman 527\neccentrics 527\neconomica 527\neire 527\nellenberger 527\nerector 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527\nwakashū 527\nwillian 527\nwismut 527\nwoolworth's 527\nwsf 527\nyasna 527\nyimou 527\nacmaeodera 526\namphipods 526\nangie's 526\nannus 526\nantipova 526\nauroral 526\nbalestier 526\nbellotti 526\nbernasconi 526\nbettany 526\nbiwi 526\nblancs 526\nboilermaker 526\nboilerplate 526\nboling 526\nbouygues 526\ncanti 526\ncapsizing 526\ncarbene 526\ncattedrale 526\ncere 526\nchauncy 526\nclubiona 526\ncolloids 526\ncolumb 526\ncorris 526\ncotterell 526\ndigha 526\ndippers 526\ndoone 526\ndrummania 526\nejercito 526\nfantine 526\nferrar 526\nferrigno 526\nffsa 526\nfiemme 526\nforeknowledge 526\nfraschini 526\ngerstein 526\ngetxo 526\nghote 526\nhaken 526\nharpsichords 526\nhastert 526\nhefti 526\nholmqvist 526\nhomophones 526\nishockey 526\nivers 526\nkaepernick 526\nkakeru 526\nkutno 526\nlágrimas 526\nlaraine 526\nleinart 526\nlhota 526\nlogia 526\nlpp 526\nlto 526\nmaharajas 526\nmangione 526\nmarkley 526\nmcgaughey 526\nmeitner 526\nmicki 526\nmoquegua 526\nmusi 526\nmyrdal 526\nnẵng 526\noana 526\noldfield's 526\nomniscience 526\noutrighted 526\noverprints 526\npitot 526\nprimorac 526\npue 526\npyay 526\nróisín 526\nrendu 526\nrevivalism 526\nryukyus 526\nsalviati 526\nschack 526\nscheele 526\nscholastica 526\nscoresby 526\nscrupulously 526\nshorrock 526\nsimultaneity 526\nskat 526\nslovenská 526\nsonnenschein 526\nspécial 526\nspheroid 526\nstingaree 526\ntatasciore 526\ntimba 526\ntnc 526\ntorelli 526\ntorgau 526\ntucano 526\ntulin 526\nuitgeverij 526\nulli 526\nusba 526\nvaline 526\nvasculature 526\nvikernes 526\nvisp 526\nvolumen 526\nvung 526\nwegman 526\nwnyw 526\nwyd 526\nyueh 526\nzaynab 526\nкак 525\nabin 525\nalipore 525\naltas 525\naltham 525\namarante 525\nanonyme 525\navrohom 525\nayin 525\nbackyards 525\nbahl 525\nbidi 525\nbolas 525\nbruggen 525\ncandlemas 525\ncarnifex 525\ncavani 525\ncdps 525\ncenteno 525\ncerebrum 525\nchestertown 525\nchoix 525\ncoatesville 525\ncorporates 525\ncosmogony 525\ncronyn 525\ndébora 525\ndanielsen 525\ndebunk 525\ndecorates 525\ndjerba 525\ndonkin 525\ndwain 525\necoboost 525\nemmental 525\nepaminondas 525\nfasces 525\nfatou 525\nfifo 525\nfremen 525\ngaita 525\ngolitsyn 525\ngreenspace 525\nharting 525\nhomologues 525\nichiran 525\niits 525\ninequity 525\ninverurie 525\nironmen 525\nisport 525\nivanka 525\njovana 525\njudds 525\njuts 525\nkamphaeng 525\nkanae 525\nkincardineshire 525\nlørenskog 525\nlabyrinthine 525\nlanky 525\nlonoke 525\nlooker 525\nlwow 525\nmachilipatnam 525\nmahratta 525\nmarquard 525\nmaybelle 525\nmelanella 525\nmosque's 525\nmothering 525\nmourner 525\nnaphtali 525\nneutra 525\nnosewheel 525\nnoth 525\noceanfront 525\noil's 525\noleracea 525\noudin 525\noutlooks 525\npabna 525\npencilled 525\nperrins 525\npict 525\npirlo 525\npolgár 525\npollachi 525\npoppet 525\nquartet's 525\nraut 525\nreden 525\nregatas 525\nrenda 525\nrimless 525\nroshni 525\nrudel 525\nsapo 525\nselfie 525\nshm 525\nsiddiq 525\nsieradz 525\nsigerson 525\nsmo 525\nspck 525\nspiteri 525\nsteelmaking 525\nsynapsids 525\ntamaraws 525\ntamarine 525\ntando 525\ntechnocrats 525\ntillinghast 525\ntings 525\ntornus 525\ntorturer 525\ntrono 525\ntweeting 525\nunceremoniously 525\nvltava 525\nwarrender 525\nwetherell 525\nwhistler's 525\nwhitecross 525\nwwf's 525\nzirconia 525\nzsigmond 525\nरत 524\nacknowledgments 524\naddenda 524\nafyve 524\nalleghenys 524\nallez 524\nandolan 524\nankur 524\napicalis 524\narkona 524\narme 524\naustereo 524\nbézier 524\nbarr's 524\nbeevor 524\nbesnard 524\nbretonneux 524\ncalabro 524\ncaladenia 524\ncapercaillie 524\ncashmore 524\nchargeable 524\ncilantro 524\nciliata 524\nclaver 524\ncomet's 524\ncompulsorily 524\ncuse 524\ndfe 524\ndzungar 524\nemanuela 524\nempresas 524\nencrypting 524\nengström 524\nenvelopment 524\nessendon's 524\nfearlessness 524\nfemenino 524\nfetzer 524\nfolklorists 524\nfostoria 524\nfreshers 524\nfriesian 524\ngiresun 524\ngresik 524\ngrudziądz 524\nhalse 524\nhansraj 524\nhause 524\nhaven's 524\nhistadrut 524\nichijō 524\niniquity 524\nirreparably 524\njalapeño 524\njeh 524\njubilate 524\njun's 524\nkonyaspor 524\nkyzyl 524\nlaureline 524\nlaviolette 524\nlebar 524\nleninsky 524\nliha 524\nmünsterberg 524\nmachiko 524\nmapplethorpe 524\nmartynov 524\nmasterfully 524\nmatale 524\nmenopausal 524\nmillat 524\nmixe 524\nmoïse 524\nmoten 524\nmvps 524\nnarod 524\nnorland 524\nnurhaliza 524\nnylander 524\nocchi 524\noef 524\nolim 524\norgano 524\noughta 524\npanzerjäger 524\nperamuna 524\nperfumery 524\npietri 524\npif 524\npittodrie 524\nqueene 524\nrayford 524\nreceiver's 524\nretrenchment 524\nridicules 524\nrigi 524\nrubino 524\nrudd's 524\nsaan 524\nsatay 524\nseller's 524\nsharona 524\nshermans 524\nshudder 524\nsizzle 524\nsnowe 524\nsociopathic 524\nsolidarité 524\nsouq 524\nstrangeways 524\nsynthesizes 524\nszilágyi 524\nteichmann 524\nthảo 524\ntty 524\nturaga 524\nuaa 524\nuam 524\nvap 524\nvass 524\nvertu 524\nwarps 524\nwesterlies 524\nwizarding 524\nwxw 524\nyada 524\nyasutaka 524\nzhoushan 524\nabeille 523\nairventure 523\nakutagawa 523\nalmon 523\nalthusser 523\namia 523\nanholt 523\nank 523\nanselme 523\nautophagy 523\nbemused 523\nbirnie 523\nbretherton 523\nbusch's 523\ncableace 523\ncarre 523\ncercopithecus 523\nchadha 523\nchelating 523\nchuru 523\nclamor 523\nclase 523\nclimaxed 523\nconsolidates 523\ncorba 523\ndugas 523\ndugu 523\neases 523\nebden 523\negan's 523\nfolle 523\nfosdick 523\nfundo 523\ngergiev 523\ngitano 523\ngoddard's 523\ngoldy 523\ngrasps 523\ngreylock 523\ngrinds 523\nguion 523\nhữu 523\nhasson 523\nhenner 523\nherdsman 523\nhikr 523\nhuot 523\ninverses 523\niovine 523\nireton 523\njavanica 523\njindřichův 523\nkütahya 523\nkętrzyn 523\nkamma 523\nkanada 523\nkearsley 523\nkodachrome 523\nkranjska 523\nkubot 523\nlöw 523\nloggerheads 523\nlostock 523\nluni 523\nmackellar 523\nmaira 523\nmarson 523\nmessinger 523\nmiyan 523\nmockingbirds 523\nmorphett 523\nnate's 523\nnavami 523\nnobuko 523\nnrm 523\nohsaa 523\novations 523\npancasila 523\npenton 523\npernell 523\nplayout 523\nporco 523\npterodactyl 523\npuede 523\nquilted 523\nrawang 523\nrebukes 523\nrlfans 523\nsaberhagen 523\nsaidi 523\nsarina 523\nscandium 523\nsheikha 523\nshorted 523\nshriner 523\nsien 523\nsolen 523\nsolidaridad 523\nspikers 523\nstopes 523\nstoryboarded 523\nsynchromesh 523\nsyntactical 523\ntacuba 523\nthikkurissi 523\nthongs 523\ntitel 523\ntomaž 523\ntransomed 523\nupnp 523\nvegalta 523\nveuve 523\nwaar 523\nwib 523\nwortham 523\nabhidharma 522\nahlen 522\nalmqvist 522\nalr 522\namwell 522\nannalisa 522\nantithetical 522\nartiodactyla 522\nattainments 522\nautostrada 522\nbaldev 522\nballew 522\nbouquets 522\nbrotherhood's 522\ncaradoc 522\ncarrum 522\nchristological 522\nclémence 522\nclavichord 522\ncoppermine 522\ncoughs 522\nddm 522\ndeacon's 522\ndeist 522\ndependants 522\ndpc 522\ndragoljub 522\nerlach 522\neverett's 522\nexf 522\nfélicité 522\nfairbrother 522\nfallows 522\nfrimley 522\ngarriott 522\ngoodchild 522\ngorshkov 522\ngranulated 522\ngrunow 522\nhansbrough 522\nhibiya 522\nhinshaw 522\nhkt 522\nhoneyguide 522\nhumiliates 522\nhydrographer 522\nindologist 522\nkarlskoga 522\nkarnad 522\nkasten 522\nkhanda 522\nkristall 522\nkuniyoshi 522\nkyriakos 522\nlank 522\nliebherr 522\nlirae 522\nlorillard 522\nlowy 522\nmantes 522\nmapquest 522\nmarcellino 522\nmassinger 522\nmedii 522\nmesta 522\nmicrobrewery 522\nmilkha 522\nmodeste 522\nnaropa 522\nnbi 522\nnemec 522\nneoplastic 522\nnumidian 522\nobliqua 522\nobliterate 522\nolap 522\nostrogothic 522\npapanasam 522\npaysandu 522\npoulson 522\npsittacidae 522\npuritanical 522\npwn 522\nquadriplegic 522\nquerer 522\nquta 522\nrebroadcasters 522\nreinvested 522\nronettes 522\nrussert 522\nsammy's 522\nscience's 522\nscruton 522\nshadowlands 522\nsheepskin 522\nshefflin 522\nshinedown 522\nshuttling 522\nsoldado 522\nsolitons 522\nsteffy 522\nstiegler 522\nstilton 522\nstroking 522\nsubverting 522\nsystematized 522\ntitograd 522\ntramping 522\ntrebek 522\ntuesday's 522\nvab 522\nvaralakshmi 522\nvianna 522\nvygotsky 522\nwagnalls 522\nwaveland 522\nweirdness 522\nwildness 522\nzedillo 522\nabdullah's 521\nablaut 521\nabramowitz 521\nadq 521\nairacobra 521\nalfonsín 521\nalison's 521\nangelov 521\nbadin 521\nbardeen 521\nbarrie's 521\nbayelsa 521\nbaystars 521\nbektashi 521\nbewilderment 521\nbipin 521\nbolle 521\nbrooking 521\nbruhn 521\nbullfinch 521\ncabos 521\ncalley 521\ncastells 521\nceaseless 521\nchell 521\ndaumier 521\ndayz 521\ndombrowski 521\negovernment 521\nendosperm 521\neverwood 521\nfamiliaris 521\nfibronectin 521\nflasher 521\nforsa 521\nfulvus 521\nfuture's 521\ngoffman 521\ngpx 521\ngreiz 521\ngurdwaras 521\nhanscom 521\nhazelnuts 521\nheidenreich 521\nhomebase 521\nircam 521\njannat 521\njazze 521\nkeni 521\nkinloss 521\nkth 521\nkumbia 521\nladytron 521\nlarp 521\nlindbergh's 521\nloblaw 521\nloti 521\nloukas 521\nlozère 521\nltu 521\nlycaon 521\nmackin 521\nmanders 521\nmatchdays 521\nmeritocracy 521\nmidtempo 521\nmiscalculation 521\nmorose 521\nmottling 521\nmusette 521\nnacka 521\nnadira 521\nnigricollis 521\nnnr 521\npelister 521\npellucida 521\npersonne 521\npeur 521\nphysiol 521\nplaquemines 521\npoer 521\npolypeptides 521\npreussen 521\nquesta 521\nrecanati 521\nrfs 521\nrickover 521\nruffled 521\nrxd 521\nsarris 521\nsemicolon 521\nsennen 521\nshekel 521\nslac 521\nslackers 521\nslurred 521\nsplat 521\nsteinberger 521\nsteller's 521\nstroman 521\nsujit 521\nsung's 521\nsurvey's 521\nsvay 521\ntabak 521\ntaji 521\ntenzing 521\ntombigbee 521\nunderdark 521\nuomini 521\nurbanus 521\nvampyre 521\nvancomycin 521\nvindicate 521\nxviiie 521\nzoku 521\nákos 520\nτην 520\naddio 520\naflaq 520\naksum 520\nalburquerque 520\nalcester 520\nanggun 520\nanglicization 520\narnulfo 520\nbauru 520\nbool 520\nboursin 520\nbriarwood 520\nbroadwell 520\ncallimachus 520\ncassowary 520\ncondamine 520\ncottam 520\ndesertions 520\ndewas 520\neffusive 520\nenglert 520\nerecta 520\nfraley 520\ngarlick 520\ngeva 520\nglenora 520\ngovernorships 520\nhowell's 520\nhpi 520\nhumanitarianism 520\nirda 520\njameer 520\njarryd 520\njolanta 520\nkawamoto 520\nkeerthi 520\nladang 520\nlahaye 520\nlandlines 520\nlegarda 520\nlewitt 520\nmarge's 520\nmarieke 520\nmaryanne 520\nmegabus 520\nmenninger 520\nmerleau 520\nmicrosatellite 520\nmirwais 520\nmiyahara 520\nmohe 520\nmonomorium 520\nmultiplexer 520\nnausicaä 520\nnsn 520\nnuda 520\norlando's 520\npanavia 520\npegging 520\npetridis 520\nphaedrus 520\npind 520\npiqua 520\npolizia 520\nrejoiced 520\nreproducibility 520\nretracts 520\nritualized 520\nrjr 520\nrocs 520\nrupprecht 520\nrushville 520\nrvr 520\nsōbu 520\nscenography 520\nsenusret 520\nsexualized 520\nshaina 520\nsherrington 520\nshimabara 520\nsigue 520\nsnip 520\nsommelier 520\nsquadriglia 520\nstastny 520\nstatcan 520\nstorace 520\nsturla 520\nsymmons 520\ntabrizi 520\ntempio 520\nterpsichore 520\nterres 520\ntonsured 520\ntriggerfish 520\ntrughanacmy 520\ntychy 520\nvoorburg 520\nwdf 520\nwicketless 520\nzedong's 520\nεν 519\nakina 519\nalewife 519\nalys 519\nantiquus 519\nbaffles 519\nbaldacci 519\nbby 519\nbif 519\nbogo 519\nbookcase 519\nbotcon 519\nbothell 519\nbraincase 519\nbrepols 519\nbsm 519\ncarinata 519\nchicory 519\nchittaranjan 519\nchup 519\ncompaoré 519\ncowart 519\ndarnall 519\ndebar 519\ndeforested 519\ndemocratisation 519\ndieterich 519\ndryopteris 519\nductility 519\nedney 519\nfurius 519\ngandaki 519\ngastro 519\ngewehr 519\ngleiberman 519\ngres 519\ngurls 519\ngwaii 519\ngyn 519\nhandoff 519\nhardesty 519\nheightening 519\nhelbig 519\nherriman 519\nhessle 519\nhipsters 519\nhormel 519\niguchi 519\nird 519\nissyk 519\njeppesen 519\njmsdf 519\njolin 519\njourn 519\njoy's 519\njwh 519\nkamau 519\nkasich 519\nkaspersky 519\nkhandoba 519\nkothamangalam 519\nkras 519\nlemelson 519\nlemire 519\nlemonheads 519\nméneville 519\nmandamus 519\nmandara 519\nment 519\nmilosevic 519\nmonetization 519\nmonmouth's 519\nmontanari 519\nmoorfields 519\nmorong 519\nnaţional 519\nnasha 519\nnorthlands 519\nparoxysmal 519\npettus 519\nphalaropus 519\npinheiros 519\npollinate 519\nportalegre 519\nrégions 519\nraghuvaran 519\nraspy 519\nrefurbishments 519\nreticulate 519\nretry 519\nrnvr 519\nruhpolding 519\nrumania 519\nsacken 519\nsanchi 519\nsarathkumar 519\nsatsang 519\nseminary's 519\nsensorimotor 519\nseries's 519\nsheiks 519\nsigny 519\nslaughters 519\nslicks 519\nsonderkommando 519\nspect 519\nspicata 519\nspigot 519\nspohr 519\nsqns 519\nsteinschneider 519\nstradling 519\nsubbed 519\nsuffect 519\nsurvivalist 519\ntřinec 519\ntavastia 519\nteke 519\ntelefe 519\ntemne 519\nthừa 519\ntheodore's 519\nunisa 519\nunwrapped 519\nvastness 519\nvilloresi 519\nyh 519\nyore 519\nšarūnas 518\nобласть 518\nокруг 518\nadrianus 518\naelia 518\nalibis 518\naliquot 518\nancelotti 518\nanonymus 518\nappendectomy 518\nardija 518\nasperger's 518\nassemblers 518\nattacker's 518\nballingarry 518\nbaraga 518\nbehrman 518\nberrios 518\nblackthorne 518\nblaenavon 518\nblg 518\nborch 518\nbrockovich 518\ncakewalk 518\ncarillo 518\ncela 518\nchromis 518\ncloquet 518\nclumsiness 518\ncognitively 518\nconfiscations 518\nconstantinian 518\ncoupee 518\ncreb 518\ndianthus 518\ndicta 518\ndosti 518\neveringham 518\nfabiani 518\nfabrique 518\nfarrand 518\nfatigues 518\nferch 518\nfibrils 518\nfite 518\ngibbard 518\ngier 518\ngimbal 518\nguevara's 518\nhallock 518\nhermaphrodites 518\nhiero 518\nhoriuchi 518\nhoylake 518\nibrd 518\ninverell 518\nisca 518\njacksonville's 518\njayalalithaa 518\nkalayaan 518\nkalibo 518\nkarri 518\nkhumalo 518\nkismayo 518\nknitwear 518\nkumaratunga 518\nkvalserien 518\nlaface 518\nlandstuhl 518\nlelystad 518\nlogica 518\nlorelai 518\nlunny 518\nmahallas 518\nmckittrick 518\nmilson 518\nmoneys 518\nnadeshiko 518\nnanping 518\nnebria 518\nnubians 518\nobrist 518\noecophoridae 518\nolsen's 518\nomap 518\noreal 518\noskarshamn 518\notherness 518\npadrón 518\npanatta 518\npandering 518\nparadigmatic 518\nparticle's 518\nparticularity 518\npasteurized 518\nphysica 518\npierfrancesco 518\npolonsky 518\npopplewell 518\nprosopis 518\npru 518\npublix 518\nputumayo 518\nquizon 518\nradcliff 518\nreductionism 518\nretinol 518\nriblja 518\nripoll 518\nsaturate 518\nscrambles 518\nsee's 518\nsewickley 518\nsingam 518\nsonic's 518\nsplayed 518\nstans 518\nstereolab 518\nsubducting 518\ntelic 518\ntiglath 518\ntinder 518\ntouchline 518\ntransporte 518\nturnouts 518\nubiquinone 518\numwa 518\nunnerved 518\nvereeniging 518\nvideregående 518\nweisse 518\nwka 518\nwormholes 518\nyamane 518\nyantar 518\nacropora 517\naculeata 517\nambridge 517\nandreessen 517\narcus 517\nbackdated 517\nbasson 517\nbiedermann 517\nbitar 517\nbitton 517\nboeotian 517\ncamargue 517\nchazz 517\ncolossians 517\ncopes 517\nddrmax 517\ndelphinium 517\ndingdong 517\ndipolog 517\ndoga 517\nefate 517\nefflux 517\neira 517\nemmis 517\nendō 517\nend's 517\nengulfing 517\nerythrina 517\netonians 517\nfalcao 517\nfinery 517\nfluoxetine 517\nfreudenberg 517\ngayton 517\ngcie 517\ngedeon 517\ngelling 517\ngianmaria 517\ngrenadian 517\ngrishin 517\nhandouts 517\nhumberstone 517\ninkwell 517\ninteractively 517\nintercommunal 517\nislote 517\nison 517\njelenia 517\njigoku 517\nkomet 517\nkring 517\nlamberti 517\nlatched 517\nleishman 517\nlibbey 517\nlumberman 517\nmacdermot 517\nmacdowall 517\nmackay's 517\nmaheshwari 517\nmaktab 517\nmarka 517\nmetu 517\nmexicanos 517\nmki 517\nmomin 517\nmyasthenia 517\nnaturalness 517\nnovick 517\noph 517\npalomares 517\npattu 517\npegaso 517\npostumus 517\nqso 517\nracists 517\nrandhawa 517\nrapidan 517\nrehan 517\nrossen 517\nryun 517\nsalmi 517\nsantissima 517\nschwager 517\nschweiger 517\nsenatus 517\nsharqi 517\nshiratori 517\nshobhana 517\nsigurður 517\nskulduggery 517\nsml 517\nsouthdown 517\nspatula 517\nsportscasters 517\nsubscripts 517\ntella 517\ntemminck 517\ntilney 517\nucas 517\nuniversite 517\nuniversum 517\nusama 517\nvalby 517\nvampirella 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515\nblacklight 515\nbrookland 515\nbryk 515\nbundi 515\ncagiva 515\ncalverton 515\ncanon's 515\nchugach 515\ncig 515\nclyde's 515\ncoffea 515\ncorinthia 515\ncosmopterigidae 515\ncumulatively 515\ndadasaheb 515\ndedalus 515\ndodwell 515\ndowse 515\ndroll 515\nelefante 515\nevangelist's 515\nexpressionistic 515\nfarran 515\nfatso 515\nfelix's 515\nferragamo 515\nfindon 515\nfranti 515\nfreycinet 515\ngenealogie 515\ngolmaal 515\ngondia 515\ngrimmett 515\ngriseb 515\ngryfice 515\nguilbert 515\nhaza 515\nhej 515\ninterneurons 515\njaci 515\njetliner 515\nköprülü 515\nkarateka 515\nkavli 515\nkerrie 515\nkitna 515\nkodiaks 515\nkolev 515\nkraatz 515\nlakeman 515\nlalique 515\nmandingo 515\nmascarita 515\nmcateer 515\nmetalac 515\nminkus 515\nmohmand 515\nmontespan 515\nmoone 515\nmorgaine 515\nmunck 515\nnagaiah 515\nnefesh 515\nnicéville 515\nnipigon 515\nokita 515\nottone 515\npallidum 515\nparkgate 515\npartia 515\npenalize 515\npreludio 515\nprespa 515\npropagandists 515\nproteolysis 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514\nsemaine 514\nsparre 514\nspored 514\nsquadra 514\nstephenie 514\nswarnalatha 514\nsydvästra 514\ntakaaki 514\nthermals 514\nthika 514\ntickell 514\ntimea 514\ntoque 514\ntuncay 514\nuefa's 514\nunquestionable 514\nveltman 514\nwallen 514\nwatchlist 514\nwhiskies 514\nwmf 514\nwpial 514\nwydad 514\nyasu 514\nzeca 514\nzimmern 514\nzwart 514\nčorba 513\naigner 513\naldborough 513\naldenham 513\namd's 513\nanderen 513\nanderssen 513\nanodized 513\nasclepias 513\nazúcar 513\nbatthyány 513\nbegat 513\nbeqaa 513\nbete 513\nblunkett 513\nbothers 513\nbreakdance 513\nbroadley 513\nbrujo 513\nbrushless 513\nbusways 513\ncagefighting 513\ncalea 513\ncantab 513\ncapitalistic 513\ncengiz 513\nchangin 513\nchurchville 513\ncommandant's 513\ncribbs 513\nculross 513\ndanilov 513\ndawei 513\ndelian 513\ndiomedea 513\neady 513\neberhart 513\nesmé 513\nfévrier 513\nfarmworkers 513\nfoust 513\nfunakoshi 513\ngajanan 513\ngalvan 513\ngiveaways 513\ngral 513\ngrates 513\ngrm 513\nharlin 513\nhastie 513\nhavemeyer 513\nhawkweed 513\nhelmstedt 513\nhepatocytes 513\nhexameter 513\nhoby 513\niger 513\ninflicts 513\ninfuriates 513\ninky 513\ninvective 513\nitsrugby 513\niwona 513\njewison 513\njournée 513\nkönigssee 513\nkahana 513\nkirribilli 513\nkob 513\nkothi 513\nkunihiko 513\nlais 513\nlazcano 513\nlentulus 513\nlinas 513\nliverworts 513\nlydney 513\nmahia 513\nmcdormand 513\nmidvale 513\nmilnor 513\nmosely 513\nnanyue 513\nnegates 513\nnihilist 513\nnizhnekamsk 513\nntm 513\nobamacare 513\nobjets 513\nochiai 513\noptimizer 513\nphilipse 513\nplutonic 513\npolkas 513\nprecis 513\npreconceptions 513\npriština 513\npurist 513\nqtv 513\nregie 513\nrevitalise 513\nsanding 513\nsarit 513\nsedatives 513\nsiletz 513\nsiong 513\nsloan's 513\nspielmann 513\nstarches 513\nstared 513\ntabora 513\ntaipan 513\ntegra 513\nteun 513\ntheosophist 513\ntolga 513\ntulipa 513\ntunnel's 513\nturrell 513\nvandeweghe 513\nvanessa's 513\nvso 513\nwharton's 513\nwiggum 513\nwilliamston 513\nyousif 513\nyuncheng 513\nafrotropic 512\nagde 512\nalbumen 512\naneurin 512\narbat 512\nasphyxia 512\naurora's 512\nbalasubramanyam 512\nbalzac's 512\nbarito 512\nbearskin 512\nbedwell 512\nbelgic 512\nbergland 512\nbiblioteka 512\nbluenose 512\nbolete 512\nbror 512\nbusiek 512\ncanvassed 512\nchristina's 512\ncida 512\nclang 512\nclaude's 512\nclitic 512\ncouched 512\ncsio 512\ncultists 512\ndevaki 512\ndhara 512\ndisneyland's 512\ndugo 512\ndysphoria 512\necmascript 512\neidsvoll 512\neinleitung 512\nelrond 512\nemissivity 512\nemporio 512\nengender 512\nentrained 512\newtn 512\neyez 512\nfbk 512\nfioravanti 512\nfistfight 512\nfloruit 512\nfossey 512\nfrsc 512\ngalopp 512\ngatica 512\ngei 512\ngiridih 512\ngolems 512\ngowanus 512\ngrobišče 512\nguarino 512\nhaditha 512\nhanni 512\nhansom 512\nhartson 512\nhevc 512\nhigo 512\nhirschberg 512\nhowse 512\nhuc 512\nhughson 512\nhybridize 512\nimpales 512\nindefensible 512\nindiscretions 512\nintimated 512\nipt 512\njusticiary 512\nkakar 512\nkittiwake 512\nlandholding 512\nlangat 512\nlansdown 512\nlaundries 512\nlavi 512\nleafless 512\nlewington 512\nlifford 512\nlinge 512\nlotfi 512\nluhr 512\nmármol 512\nmajorly 512\nmarília 512\nmcsorley 512\nmelnick 512\nmirabal 512\nmontilla 512\nmossel 512\nmuhd 512\nmummers 512\nmundaring 512\nnagaur 512\nnorfolk's 512\noakfield 512\nocasek 512\nokaloosa 512\nparola 512\npedraza 512\npiscopo 512\npwl 512\nrépertoire 512\nradka 512\nrdi 512\nrepackage 512\nreplicators 512\nrnp 512\nroberti 512\nrochechouart 512\nroxboro 512\nruri 512\nsadeq 512\nsameera 512\nsaunas 512\nschönefeld 512\nseaman's 512\nsegarra 512\nsere 512\nshull 512\nsool 512\nstaterooms 512\nstellate 512\nstompers 512\nstrangways 512\nswaine 512\ntantum 512\nthamesmead 512\ntransact 512\ntyreese 512\nunmik 512\nvaas 512\nvardan 512\nvukovich 512\nwaldmann 512\nwhereafter 512\nwhippet 512\nwhs 512\nwmr 512\nxenomania 512\nxscape 512\nyasui 512\nyser 512\nzeffirelli 512\namherstburg 511\nanacortes 511\nandrić 511\naquatint 511\narial 511\naristarchus 511\natresia 511\nbabaji 511\nbajoran 511\nbemani 511\nbomb's 511\nbract 511\ncamarena 511\ncarnap 511\ncastanets 511\ncatechetical 511\ncaumont 511\nciné 511\nclepsis 511\ncorax 511\ncrato 511\ncroxton 511\ndalmau 511\ndebo 511\ndeepti 511\ndeme 511\nderose 511\ndesegregated 511\ndibdin 511\ndoli 511\nedelmann 511\nelgon 511\nemanu 511\neph 511\netcheverry 511\nfleischman 511\nforegone 511\nfrenchman's 511\nfroggatt 511\ngatlinburg 511\ngenbank 511\ngerland 511\nghibelline 511\ngompa 511\ngopalpur 511\ngunfighters 511\ngylfaginning 511\nhamsun 511\nhardliners 511\nhetchy 511\nhezb 511\nhillard 511\niat 511\njibril 511\njingzhou 511\njoensen 511\nkarisma 511\nkhanates 511\nkirtley 511\nkomárom 511\nkuchh 511\nlappin 511\nlectionaries 511\nlindfield 511\nlussier 511\nmackenna 511\nmahavamsa 511\nmalayala 511\nmambazo 511\nmanthey 511\nmelisa 511\nmellin 511\nmgo 511\nmillbrae 511\nmiscellanies 511\nmitta 511\nmusab 511\nmylothris 511\nohlsson 511\noklahoman 511\nornithine 511\nparsa 511\nphosgene 511\npierrefonds 511\nplausibly 511\npotentiation 511\npripyat 511\nranchero 511\nroarke 511\nrupturing 511\nsaadat 511\nsalona 511\nschleyer 511\nshadowland 511\nspilsby 511\nsprott 511\nstapf 511\nstratemeyer 511\nterenure 511\ntestud 511\nthornburg 511\ntickling 511\ntimmer 511\ntomson 511\ntourette's 511\ntransilien 511\ntriveni 511\nues 511\nvalium 511\nviator 511\nvictrix 511\nwashrooms 511\nwendland 511\nwgm 511\nwoodend 511\nwrestler's 511\nzealously 511\nabominations 510\naedh 510\napparatuses 510\narni 510\nasir 510\nauxin 510\navions 510\nbackflip 510\nbalao 510\nbaras 510\nbernstorff 510\nbomp 510\nbrâncuși 510\nbrčko 510\nbrasco 510\ncaddies 510\ncamby 510\ncastine 510\nchandramukhi 510\nchardy 510\ncmx 510\nconcretions 510\nconfirmations 510\ncontinentals 510\ncopolymer 510\ncopolymers 510\ncorbijn 510\ncoronas 510\ncraftspeople 510\ncrocodylus 510\ndamayanti 510\ndeafening 510\ndelenn 510\ndiggins 510\ndursley 510\neft 510\nemly 510\nenumerating 510\nfathering 510\nfilomena 510\nfolge 510\nfronde 510\ngalashiels 510\ngandolfo 510\ngreenall 510\nguimard 510\nhanauer 510\nharradine 510\nheather's 510\nherreshoff 510\nhever 510\nhimawari 510\nhong's 510\nhuangdi 510\nhudak 510\nhulking 510\nimpac 510\ninfuriating 510\nisabel's 510\nissam 510\njayewardene 510\nkempner 510\nkrizz 510\nkruk 510\nkucha 510\nkunti 510\nldh 510\nlibreoffice 510\nlitvak 510\nlozenges 510\nlusitano 510\nmäkelä 510\nmêlée 510\nmagyarország 510\nmasochistic 510\nmelhor 510\nmicrokernel 510\nmjs 510\nmonochroa 510\nmsida 510\nnagaon 510\nneuroblastoma 510\nnxc 510\noakeshott 510\nossipee 510\noverhear 510\npemex 510\npeopled 510\npkns 510\nplasa 510\npuedo 510\nqueensboro 510\nrabinovich 510\nreams 510\nreichs 510\nringtail 510\nrls 510\nrzhev 510\nsacagawea 510\nsaldívar 510\nsalters 510\nsarika 510\nscrubbers 510\nseptimania 510\nsgùrr 510\nshenmue 510\nshuck 510\nsierra's 510\nsimony 510\nsinusitis 510\nslaughterhouses 510\nsolanas 510\nsteidl 510\nstromboli 510\nstunningly 510\nsunland 510\nsunstone 510\nsuria 510\nsvenskt 510\ntannehill 510\nterras 510\ntheis 510\nthiruvalla 510\ntoasters 510\ntorquemada 510\ntownshend's 510\ntriglyceride 510\nturia 510\nucg 510\nvõru 510\nvahan 510\nvalinor 510\nvigyan 510\nwhitlow 510\nxanten 510\nabstractly 509\nactaeon 509\nagroforestry 509\nalondra 509\nantawn 509\nanzeiger 509\narpeggio 509\nasea 509\nasymmetrically 509\nbanjar 509\nbaucus 509\nbedoya 509\nbogert 509\nbry 509\ncà 509\ncapone's 509\nchomsky's 509\nchrysippus 509\ncibalia 509\nconcatenated 509\nconemaugh 509\ndeedee 509\ndeflector 509\ndiacritical 509\ndolna 509\ndowdeswell 509\ndreiser 509\nelvey 509\nemanations 509\nengrailed 509\nermisch 509\nestás 509\nfassi 509\nflan 509\ngelato 509\ngermanization 509\nghai 509\ngmm 509\ngrue 509\nguignot 509\nhanzi 509\nhatakeyama 509\nhemu 509\nherbaria 509\nherniation 509\nhighmark 509\nhipaa 509\ninterventionism 509\nionians 509\niznik 509\nkamat 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508\nreclaims 508\nreem 508\nremover 508\nrienzi 508\nrondout 508\nrugg 508\nsagittarii 508\nscriptwriters 508\nseel 508\nselway 508\nsenica 508\nsirhan 508\nskeets 508\nslaf 508\nsnickers 508\nspeidel 508\nspherically 508\nspindler 508\nsquatted 508\nstil 508\nsturmabteilung 508\nsubterranea 508\nsuperbrawl 508\nsupervalu 508\nswb 508\nswt 508\ntōgō 508\ntaieri 508\nteletubbies 508\nterwilliger 508\ntimelike 508\ntrailheads 508\ntzedek 508\nunderstorey 508\nusas 508\nutpal 508\nweekly's 508\nwendish 508\nwergeland 508\nyêu 508\nyuasa 508\nyurchenko 508\nyusa 508\nzombified 508\nackles 507\nagana 507\nagastya 507\nalleghenies 507\nannu 507\nasgardian 507\nbaber 507\nbečej 507\nbeagles 507\nbeatie 507\nbinz 507\nbish 507\nbogut 507\nbrecknock 507\nbroderip 507\nbryophytes 507\nchakravarty 507\ncharissa 507\nchil 507\nclytemnestra 507\ncnbc's 507\ncockcroft 507\ncolebrooke 507\ncondemnations 507\ncoonan 507\ncronje 507\ncrosser 507\nctd 507\ndazzled 507\ndering 507\ndipterocarp 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416\nmilitaris 416\nminns 416\nmoreno's 416\nmotul 416\nmunford 416\nmuseology 416\nnasaf 416\nnilus 416\nnkosi 416\nnspcc 416\noverspill 416\npakula 416\npapunya 416\npetrillo 416\npleydell 416\nprawer 416\npreus 416\nprover 416\nputhenchery 416\nquon 416\nrationalised 416\nraymund 416\nreconnection 416\nredshirting 416\nredwater 416\nrheinhessen 416\nrockit 416\nrocklin 416\nrono 416\nrosefinch 416\nrowlett 416\nrudess 416\nrupicola 416\nsantry 416\nsaramago 416\nsealey 416\nsexploitation 416\nshannen 416\nsheepherders 416\nshibu 416\nsiciliana 416\nsinar 416\nsnowplow 416\nstel 416\nstellaria 416\nsuperfight 416\ntamoxifen 416\nteterin 416\nthomism 416\ntindale 416\ntwiss 416\ntyphlops 416\nuncorrected 416\nunderpins 416\nusatoday 416\nvaishnav 416\nverts 416\nvrancea 416\nwasi 416\nyamal 416\nyoakum 416\nyurdu 416\nzuloaga 416\nég 415\naeons 415\nakinori 415\nallelic 415\nalzira 415\nannotate 415\narnd 415\naudi's 415\nazt 415\nbagdasarian 415\nbambusa 415\nbanhart 415\nbedrosian 415\nbikash 415\nbonamassa 415\nbrosse 415\nbrust 415\ncaillou 415\ncanas 415\ncarisbrooke 415\ncarlo's 415\ncarolingians 415\ncastlehaven 415\nchữ 415\nchachapoyas 415\nchainsaws 415\nchilies 415\ncinematics 415\ncoamo 415\ncochylis 415\ncoderre 415\ncolles 415\ncoonoor 415\ncrowd's 415\ncudmore 415\ndaar 415\ndagh 415\ndarkover 415\ndavutoğlu 415\ndepartamental 415\ndhoti 415\ndiachronic 415\ndiaphragmatic 415\ndisarms 415\ndissents 415\ndistill 415\nebadi 415\neffervescent 415\negotism 415\nfähnrich 415\nfoca 415\nforeshadow 415\nfreewheeling 415\ngabrielle's 415\ngakkō 415\ngaramond 415\ngerspr 415\ngervasi 415\ngiancana 415\ngorani 415\ngrigson 415\nhalston 415\nhardin's 415\nharjo 415\nharvill 415\nheman 415\nhesse's 415\nhidatsa 415\nhillforts 415\nhornblende 415\nhornswoggle 415\nikegami 415\nimman 415\nisocrates 415\njespersen 415\njordaens 415\njot 415\njunctional 415\nkaufmann's 415\nkef 415\nkenjiro 415\nkhm 415\nkhorramabad 415\nkilldeer 415\nlalli 415\nlamarque 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413\nolov 413\nontogeny 413\norcinus 413\noxidants 413\npélissier 413\npahrump 413\npeaceville 413\nperrysburg 413\npeterlee 413\nphebe 413\nphenytoin 413\npierrepoint 413\npitaka 413\npokemon 413\nquesto 413\nquillan 413\nrégnier 413\nradisich 413\nraghunatha 413\nrawling 413\nretried 413\nridgemont 413\nripeness 413\nrisotto 413\nromanis 413\nruane 413\nsallies 413\nschriever 413\nschweinsteiger 413\nseika 413\nseines 413\nshaef 413\nshahzada 413\nshaku 413\nsimonetta 413\nskald 413\nskoll 413\nsocialisme 413\nsolarium 413\nsorter 413\nstagflation 413\nstoopid 413\nstranahan 413\nstudiocanal 413\nsturdza 413\nsweet's 413\ntaraba 413\ntezuka's 413\nthing's 413\ntorstein 413\ntournoi 413\ntreacherously 413\ntrisetum 413\nturbojets 413\nturdoides 413\nturmholländer 413\nunheated 413\nunnerving 413\nunperformed 413\nuntie 413\nurfa 413\nvalkyria 413\nwailuku 413\nwofoo 413\nwomans 413\nwrenn 413\nwwj 413\nwyer 413\nyamamoto's 413\nyeboah 413\nzanskar 413\néclair 412\nabhorred 412\nahafo 412\napprovingly 412\narakkonam 412\narborescens 412\natreus 412\naubrey's 412\naunis 412\nbachrach 412\nbandmembers 412\nbeatboxing 412\nbeaumont's 412\nbeche 412\nberzerk 412\nbirka 412\nboltzmann's 412\nborchers 412\nbordes 412\nbrandner 412\nbranly 412\nburritos 412\ncancri 412\ncaplin 412\ncapparis 412\ncassandra's 412\ncassiodorus 412\ncentauro 412\ncerny 412\nchanler 412\nchaudhury 412\ncockles 412\ncolumbiformes 412\nconnaught's 412\ncorman's 412\ncuno 412\ndelimiter 412\ndelmenhorst 412\ndiminutives 412\ndirecto 412\ndorothee 412\ndragonette 412\neconometrica 412\negersund 412\neliz 412\neluding 412\nenchant 412\nesculenta 412\netp 412\neventuated 412\nfeige 412\nfethiye 412\nfledglings 412\nfnla 412\nfogs 412\nfrancavilla 412\nfreke 412\nfrondizi 412\nfrutos 412\nftd 412\ngebre 412\ngeorgiadis 412\ngoidelic 412\ngorodetsky 412\ngreenstreet 412\nhalit 412\nhandcock 412\nhomeownership 412\nhooky 412\nhoppin 412\nimpalement 412\nimpermissible 412\ninornata 412\nitay 412\nizvorul 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360\ncabinetry 360\ncapote's 360\ncaridina 360\ncarjacking 360\ncarmaker 360\ncaus 360\ncdd 360\nchapuis 360\ncharleton 360\nchinmoy 360\nchromaticism 360\nclothiers 360\ncommonweal 360\nconvocations 360\ncouzens 360\ncowal 360\ncranshaw 360\ncreaky 360\ncuche 360\ndąbrówka 360\ndarbyshire 360\ndecedent 360\ndelphinidae 360\ndevi's 360\ndilate 360\ndittmar 360\ndonoughmore 360\ndooku 360\ndrummondii 360\ndrypoint 360\nebscohost 360\neike 360\nenshrine 360\nepictetus 360\netb 360\nethelred 360\neunos 360\neynsham 360\nfailsafe 360\nfairlawn 360\nfarrill 360\nfelician 360\nfollo 360\nforestalled 360\nforgas 360\nfortuitously 360\nfredericktown 360\nfredriksen 360\ngabála 360\nganon 360\ngogoi 360\ngondii 360\ngorget 360\nhanika 360\nharet 360\nhaskalah 360\nhaun 360\nhearnes 360\nhemsl 360\nhimno 360\nhomebuilding 360\nhomozygote 360\nhondius 360\nhookworm 360\nimpregnate 360\nintertwine 360\njacquelin 360\njael 360\njeunet 360\njordán 360\nkarki 360\nkarski 360\nkaus 360\nkepi 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354\ngoffstown 354\ngooglebooks 354\ngraffito 354\ngreenbacks 354\ngrenz 354\ngwon 354\nhalva 354\nhamdanid 354\nharlesden 354\nhaugan 354\nhausner 354\nhearer 354\nherber 354\nhickerson 354\nhirani 354\nimatra 354\nimbalanced 354\nincendiaries 354\ninnsmouth 354\ninterposed 354\nishaan 354\njaidev 354\njarle 354\njocelin 354\njosif 354\nkōtarō 354\nkarmøy 354\nkautokeino 354\nkayako 354\nkeech 354\nkena 354\nkittu 354\nknockhill 354\nkouyaté 354\nkusari 354\nkuster 354\nlanotte 354\nleontien 354\nliberians 354\nmár 354\nmadhan 354\nmamede 354\nmaor 354\nmarham 354\nmastro 354\nmatfield 354\nmazdoor 354\nmccolgan 354\nmiño 354\nminutos 354\nmixteca 354\nmrr 354\nmufulira 354\nmultihull 354\nmumma 354\nmusicae 354\nmustelidae 354\nmycelia 354\nnagore 354\nneher 354\nnicko 354\nnihonbashi 354\nnlt 354\nnotropis 354\nnovarum 354\noakman 354\noconomowoc 354\nophidian 354\nornamentals 354\nosmund 354\noutwitted 354\nouzou 354\nparides 354\npariser 354\nparzival 354\npaura 354\npavesi 354\npecans 354\npertusaria 354\npinglu 354\nplayaz 354\npontica 354\npoulos 354\npowergen 354\nprofessionnel 354\nprogetto 354\npule 354\nqaghan 354\nqalandar 354\nquast 354\nqueluz 354\nrahel 354\nrainstorms 354\nramblings 354\nraso 354\nreuses 354\nreynolds's 354\nrgd 354\nriccione 354\nrinsing 354\nrioch 354\nrosenqvist 354\nrougier 354\nroyally 354\nrxf 354\nrydb 354\nsaban's 354\nsansi 354\nscaredy 354\nsenckenberg 354\nseybold 354\nsherborn 354\nsidestep 354\nsilversun 354\nsixteenelite 354\nslave's 354\nspleenwort 354\nspurts 354\nstag's 354\nsteiermark 354\nstubhub 354\ntapeworms 354\nteso 354\nthéologie 354\ntheophylact 354\ntheretra 354\nthrombus 354\ntottenham's 354\ntransacted 354\nturriff 354\nuccello 354\nuniqlo 354\nuzumaki 354\nvacuums 354\nvaka 354\nvalachi 354\nvarona 354\nviau 354\nviken 354\nvoegelin 354\nvolonté 354\nwaiheke 354\nwalburga 354\nwanjiru 354\nwhc 354\nwilaya 354\nwillmore 354\nwilting 354\nwmi 354\nwoodburne 354\nwookey 354\nwoos 354\nwordnet 354\nxcom 354\nxlib 354\nyamaha's 354\nyasunari 354\nzainichi 354\nzaytsev 354\nzenga 354\nzy 354\némilion 353\nabdicates 353\nachenbach 353\nacmi 353\nakgul 353\naksai 353\nalbarado 353\nalbarracín 353\nallée 353\nanastasi 353\napop 353\nardoyne 353\narghun 353\nastronomie 353\nbàsquet 353\nbagni 353\nbarakzai 353\nbarbastro 353\nbarto 353\nbasin's 353\nbedfont 353\nbegrudgingly 353\nbeilby 353\nberard 353\nbgc 353\nbiogeochemical 353\nbismil 353\nblitzstein 353\nbodoni 353\nbrecher 353\nbrosius 353\nbrownlie 353\nbybee 353\ncalluna 353\ncampylobacter 353\ncaprimulgidae 353\ncarbides 353\ncarbonation 353\nchelate 353\nchemsak 353\nciliate 353\ncnidarians 353\ncoşkun 353\ncodicil 353\ncollegehumor 353\ncollisional 353\nconseiller 353\ncrappy 353\nczersk 353\ndalgety 353\ndanilevsky 353\ndenaturation 353\ndexys 353\ndiwani 353\ndogpatch 353\ndrabble 353\ndugger 353\neberstadt 353\nelectricals 353\nenriqueta 353\nensayos 353\nesercito 353\nessec 353\neuthymius 353\neyeless 353\nfaarooq 353\nfgr 353\nfilkins 353\nfoederati 353\nfoles 353\nfreethinker 353\nfreon 353\nfreres 353\nftu 353\nfumihiko 353\nfurnivall 353\nfxx 353\ngardnerian 353\ngatien 353\ngcap 353\ngingivitis 353\nglenna 353\ngranbury 353\ngstreamer 353\ngustin 353\ngutierre 353\nhafey 353\nhardi 353\nhartono 353\nheadey 353\nhellinger 353\nhessenliga 353\nhezemans 353\nhomonyms 353\nhosny 353\nhottentot 353\nhuf 353\nhumo 353\nilu 353\ninara 353\nisam 353\nivp 353\njagdalpur 353\njubail 353\njunipers 353\nkaarlo 353\nkakapo 353\nkakegawa 353\nkashmere 353\nkennebunkport 353\nkhayyám 353\nkixx 353\nknower 353\nkujō 353\nlafcadio 353\nleoncio 353\nliberi 353\nlidstrom 353\nloening 353\nlongmen 353\nmáscaras 353\nmônica 353\nmajnun 353\nmanokwari 353\nmarmosets 353\nmaruko 353\nmeitei 353\nmeyerhoff 353\nmidsummer's 353\nmikhaylovich 353\nmilkmaid 353\nmonatsschrift 353\nmontazeri 353\nmoralizing 353\nmosier 353\nmplayer 353\nmtume 353\nmuscaria 353\nmuza 353\nna's 353\nnadesico 353\nnaras 353\nnarodowej 353\nnarula 353\nnonce 353\nobservateur 353\nokawa 353\nolyā 353\nordnung 353\norigini 353\npedi 353\npentacle 353\npimm 353\npirita 353\npnf 353\npnh 353\nportchester 353\nporton 353\nposa 353\npostmodernity 353\nproofreader 353\nputu 353\nrén 353\nrieux 353\nrifle's 353\nringo's 353\nrizzle 353\nrootless 353\nround's 353\nsaturnin 353\nselz 353\nseob 353\nseveran 353\nsgeir 353\nshoemaker's 353\nsickbay 353\nsifra 353\nslotting 353\nsnakeskin 353\nsonorama 353\nspcl 353\nstrydom 353\nsuperkick 353\nsyosset 353\nsyringa 353\ntakayoshi 353\ntalysh 353\nteats 353\ntecnológica 353\nteves 353\nthespians 353\ntockus 353\ntoninho 353\ntowler 353\ntrichouropoda 353\ntte 353\ntumbleweeds 353\nufford 353\numbo 353\nunsolvable 353\nuwm 353\nvadapalani 353\nvalentim 353\nvco 353\nvestment 353\nvra 353\nwaterdeep 353\nwenchuan 353\nwenz 353\nwhitemarsh 353\nwisma 353\nwohi 353\nzari 353\nzdeno 353\nzhongzheng 353\nđurišić 352\nγια 352\nбез 352\nиздательство 352\naak 352\nabhainn 352\naleichem 352\namando 352\narang 352\nardian 352\nasher's 352\nathletic's 352\nattractively 352\nauct 352\naudre 352\nbùi 352\nbaer's 352\nbaiji 352\nbarilla 352\nbarth's 352\nbeardtongue 352\nbehnke 352\nbelgae 352\nbelphegor 352\nberchmans 352\nbergfried 352\nblairstown 352\nbluebook 352\nblumen 352\nboalt 352\nbockwindmühle 352\nbonneau 352\nbosra 352\nbpel 352\nbrachiopod 352\nbreukelen 352\nbriere 352\nbritek 352\nbroadbridge 352\nbumthang 352\nburstall 352\ncão 352\nchéri 352\ncharanga 352\nchaw 352\nchetwode 352\nciccarelli 352\ncinecittà 352\nclarkia 352\nclery 352\ncodey 352\nconsequentially 352\ncreamfields 352\ncrowland 352\ncuan 352\ndaas 352\ndalí's 352\ndelahunt 352\ndelaroche 352\ndichroic 352\ndierdorf 352\ndistel 352\ndoberan 352\ndragunov 352\ndreamscapes 352\neinige 352\neulenburg 352\nfalmer 352\nfemtosecond 352\nfeuchtwanger 352\nfiora 352\nfroilán 352\nfromme 352\ngallimore 352\nganjawala 352\ngasset 352\ngatley 352\ngeertz 352\ngigantism 352\ngizmodo 352\ngogarty 352\ngoldbergs 352\ngondolin 352\ngotoku 352\ngrande's 352\ngratified 352\ngreco's 352\ngroundsel 352\nguglielmi 352\ngummadi 352\nguzmania 352\nharapan 352\nhayk 352\nhenoch 352\nhhb 352\nhighflyer 352\nhirsutum 352\nhmis 352\nholsters 352\nhyponatremia 352\ninattentive 352\ninformality 352\nintraparty 352\nintricacy 352\nioanna 352\nirrigable 352\nitabashi 352\niudaea 352\nivanovka 352\njanae 352\njhajjar 352\njiul 352\njoannie 352\njuicio 352\nkenworth 352\nklöden 352\nklepp 352\nkochanski 352\nkojić 352\nkoncert 352\nkremin 352\nkuaiji 352\nkubla 352\nkutt 352\nkvinnor 352\nlú 352\nlacasse 352\nlahood 352\nleveque 352\nloit 352\nlongstone 352\nlundi 352\nlycra 352\nmachinist's 352\nmaciunas 352\nmalret 352\nmangelia 352\nmarisela 352\nmarivaux 352\nmatamata 352\nmerryman 352\nmicrurus 352\nmisdiagnosis 352\nmiyazato 352\nmobbing 352\nmorariu 352\nmotivic 352\nmrcs 352\nmuldowney 352\nnakasendō 352\nnarodni 352\nnavales 352\nncea 352\nnengō 352\nnetbeans 352\nneutralizes 352\nnoboa 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352\nvientos 352\nviktorovich 352\nvividness 352\nvolksraad 352\nwenchang 352\nwilfully 352\nwiltern 352\nwindjammer 352\nwinmau 352\nwsis 352\nyevtushenko 352\nšar 351\nabedi 351\nachelous 351\nadrie 351\nadwa 351\naetolians 351\nafia 351\nahidjo 351\naigles 351\nakel 351\nalbertan 351\namilcare 351\narclight 351\narnason 351\natilius 351\naxillaris 351\nbaldassarre 351\nbartolini 351\nbezos 351\nbiały 351\nbiggio 351\nbiliran 351\nbinhai 351\nbonis 351\nboules 351\nboussac 351\nbruschi 351\nbryars 351\nbutterfly's 351\nbuxom 351\ncallies 351\ncalvario 351\ncannondale 351\ncatechesis 351\ncathail 351\ncatuvellauni 351\ncederberg 351\ncellach 351\nchelmer 351\nchevrolets 351\nchrysis 351\nclarkin 351\nclaverack 351\ncoenen 351\ncolluding 351\ncommutativity 351\nconcoct 351\nconsolacion 351\ncopyists 351\ncréditiste 351\ncrockett's 351\ncyprinid 351\ndürrenmatt 351\ndaktronics 351\ndaryle 351\ndayuan 351\ndeferential 351\ndegaussing 351\ndimock 351\ndisclaimers 351\ndnyaneshwar 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351\nsubphylum 351\nsynched 351\ntakehiko 351\ntalaash 351\ntarquinia 351\nteige 351\nteliasonera 351\nthaxton 351\ntheertham 351\ntherapsids 351\nthrottled 351\ntirhugh 351\ntobermore 351\ntrishna 351\ntroubador 351\ntsentralny 351\nvalkenburgh 351\nvariscan 351\nvatuvei 351\nvmro 351\nvoris 351\nwaded 351\nwaterberg 351\nwatten 351\nwebgl 351\nwelsch 351\nwine's 351\nwttw 351\nyuria 351\nafdb 350\nafo 350\nagneepath 350\nalbu 350\nalguien 350\nanchorite 350\nappeasing 350\narhat 350\nasa's 350\natom's 350\natrata 350\nattachés 350\nautomatons 350\navella 350\naviculture 350\nazules 350\nbřezina 350\nbeara 350\nbeartooth 350\nbeneke 350\nbertelsen 350\nbintan 350\nbivector 350\nborohydride 350\nbramston 350\nbransfield 350\nbridgton 350\nbugguide 350\nbullosa 350\nbulut 350\nbupropion 350\ncúchulainn 350\ncaffey 350\ncaldicott 350\ncaradon 350\nchel 350\nchlorpromazine 350\nciborium 350\ncomcast's 350\ncommercio 350\ncrutchley 350\ndacko 350\ndaj 350\ndargan 350\ndesdiv 350\ndessus 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343\njoyeux 343\njubba 343\nkadambas 343\nkalaw 343\nkallas 343\nkangas 343\nkaranja 343\nkarpis 343\nkemmerer 343\nkenntnis 343\nkinzer 343\nkito 343\nklimesch 343\nkmsp 343\nkonovalov 343\nkortchmar 343\nkrys 343\nkuga 343\nkuroi 343\nlaliberté 343\nlangenberg 343\nlarocca 343\nlarosa 343\nlavergne 343\nlehrte 343\nlevison 343\nliff 343\nlimited's 343\nlodomeria 343\nloosestrife 343\nmóvil 343\nmaathai 343\nmachito 343\nmargi 343\nmaric 343\nmarryat 343\nmayoress 343\nmcginnity 343\nmcteer 343\nmedusa's 343\nmehl 343\nmeningeal 343\nmennen 343\nmilkins 343\nmolchanov 343\nmolins 343\nmorongo 343\nnakama 343\nneet 343\nnike's 343\nnontoxic 343\nogv 343\noutremer 343\npegi 343\npinnately 343\npodcaster 343\npoletti 343\nportolà 343\npurchasable 343\npuspa 343\nquirin 343\nraindrop 343\nravers 343\nrcl 343\nrednex 343\nreim 343\nrevellers 343\nrockhopper 343\nroget 343\nromanzo 343\nrumson 343\nryūji 343\nrypien 343\nsanded 343\nsanyasi 343\nsaso 343\nsatirists 343\nsaturnian 343\nsavchuk 343\nsayin 343\nscholem 343\nscouters 343\nshallowly 343\nshaunavon 343\nshawe 343\nshoten's 343\nsillitoe 343\nsilurians 343\nsimonsson 343\nsixt 343\nspix 343\nspuyten 343\nsqueal 343\nsteenkamp 343\nstrahl 343\nsunnmøre 343\nsuperga 343\nsuttie 343\ntétouan 343\ntages 343\nthio 343\ntireragh 343\ntorchy 343\ntrobriand 343\ntufail 343\nuijeongbu 343\nunderbrush 343\nunhesitatingly 343\nvadi 343\nvais 343\nvcf 343\nvendor's 343\nvilá 343\nvowell 343\nwächter 343\nwaikanae 343\nwarcry 343\nwfld 343\nwhatsonstage 343\nwilliamtown 343\nwordgirl 343\nwroe 343\nximénez 343\nyeardley 343\nyoan 343\nytb 343\nyueyang 343\nzebrahead 343\nzem 343\nzio 343\nélise 342\nösterreichs 342\nτοῦ 342\nadjudicators 342\naerbin 342\nakshara 342\nalférez 342\nalioto 342\namauris 342\nanicius 342\naplomb 342\narline 342\nauscultation 342\naways 342\nballinlough 342\nbarrault 342\nbequeathing 342\nbilby 342\nbilecik 342\nbirstall 342\nbituin 342\nbochnia 342\nborujerd 342\nbousfield 342\nbowmer 342\nbrained 342\nbudworth 342\nbunjevci 342\nbushby 342\ncabrio 342\ncalder's 342\nchablais 342\nchaharmahal 342\nchandon 342\nchangjiang 342\nchesnokov 342\nchital 342\ncholan 342\ncobi 342\ncomitia 342\nconstructionist 342\ncostantini 342\ncoupar 342\ncreag 342\ncucullatus 342\ndúrcal 342\ndapitan 342\ndelocalized 342\ndespaired 342\ndexterous 342\ndialled 342\ndiggy 342\ndisputations 342\ndorso 342\ndula 342\ndvc 342\nelcho 342\nellman 342\nenigmas 342\nequitably 342\nespanto 342\neurobarometer 342\nfatehabad 342\nfattening 342\nfayez 342\nfelsted 342\nfeuille 342\nflyte 342\nfumigation 342\nfunction's 342\ngümüşhane 342\ngaétan 342\ngamification 342\ngasconade 342\ngeographie 342\ngimnasio 342\ngivewell 342\nglancy 342\nglis 342\ngnocchi 342\ngonal 342\ngorgonio 342\nguis 342\ngyri 342\nhackle 342\nhalliwell's 342\nhardouin 342\nharlan's 342\nhauran 342\nheteropoda 342\nhetton 342\nheydt 342\nhiệp 342\nhomophile 342\nhoni 342\nhybridity 342\nideogram 342\nigors 342\nincoronazione 342\ninsaan 342\ninstants 342\nitr 342\njangan 342\njeers 342\njori 342\nkalil 342\nkeffer 342\nkeladi 342\nkibo 342\nkiir 342\nkollwitz 342\nkommen 342\nkonni 342\nkoopmans 342\nkork 342\nkról 342\nkristján 342\nkunene 342\nkuruma 342\nkuzmina 342\nkvyat 342\nlastra 342\nlattes 342\nlauper's 342\nlaxatives 342\nleberecht 342\nlezama 342\nlianas 342\nlichterfelde 342\nloathes 342\nlohar 342\nlume 342\nlvii 342\nlyubomir 342\nmé 342\nmeropidae 342\nmitscher 342\nmonoceros 342\nmytton 342\nnarodnaya 342\nnayland 342\nncte 342\nnews's 342\nnocturna 342\noctoarts 342\noctoechos 342\noliverio 342\nomnibuses 342\nosmaniye 342\noutflanking 342\npanayiotis 342\npannell 342\nparapan 342\nparsimonious 342\nparticulier 342\npeaceably 342\nperforate 342\npericarditis 342\npierogi 342\npkg 342\npokhari 342\npont's 342\npoppleton 342\nprimatologist 342\nprimorye 342\npugachev 342\npuree 342\nquattrocento 342\nröber 342\nradyr 342\nrationalise 342\nrebuttals 342\nrefocusing 342\nrohana 342\nsaaristo 342\nsalen 342\nsanitorium 342\nsastha 342\nsaviano 342\nscallion 342\nschirach 342\nschwalbe 342\nscullery 342\nsekhmet 342\nshifnal 342\nshisha 342\nsiento 342\nsim's 342\nsistani 342\nslobozia 342\nsollentuna 342\nsora's 342\nspeakerphone 342\nspecialisms 342\nstargazing 342\nstifler 342\nstohl 342\nstokley 342\nstonecrop 342\nstrad 342\nstrandberg 342\nsuess 342\ntaenia 342\ntapiola 342\ntenable 342\ntendril 342\ntetrafluoride 342\nthècle 342\nthatching 342\nthorton 342\nthyristors 342\ntimoleon 342\ntindouf 342\ntlb 342\ntompkinsville 342\ntornado's 342\ntreadle 342\ntrinidad's 342\ntrireme 342\nturfan 342\nturmel 342\ntussen 342\ntuta 342\nubl 342\nuninitiated 342\nunroofed 342\nupdf 342\nurien 342\nvdcs 342\nvereinigte 342\nvisegrád 342\nvocalisations 342\nwürzburger 342\nwreathed 342\nxaverian 342\nyao's 342\nyin's 342\nzabern 342\nzat 342\nzizi 342\nτη 341\nalienates 341\nalma's 341\nancilla 341\nanett 341\naquitanian 341\narabes 341\narcachon 341\narmd 341\natlántida 341\nattelabus 341\natum 341\naustra 341\nautechre 341\nazin 341\nazua 341\nbangle 341\nbansko 341\nbarnes's 341\nbdg 341\nbeckham's 341\nbistra 341\nblockhead 341\nboehmer 341\nbonello 341\nbotaurus 341\nbradlaugh 341\nbroughty 341\nbrw 341\nbuju 341\nburnes 341\nbushra 341\ncadi 341\ncaller's 341\ncapitular 341\ncataracs 341\ncataraqui 341\ncathepsin 341\ncbg 341\ncers 341\nchampasak 341\nchatti 341\nchiat 341\nchibs 341\nclanranald 341\ncnv 341\ncommodious 341\nconcomitantly 341\ncorangamite 341\ncrassirostris 341\ncroon 341\ncupping 341\ndemarchelier 341\ndemocrazia 341\ndesmet 341\ndialup 341\ndiam 341\ndietitian 341\ndodos 341\ndomergue 341\ndoofenshmirtz 341\ndrawl 341\ndriessen 341\ndunklin 341\nebitda 341\necclesall 341\nendara 341\nennahda 341\nevil's 341\nexoticism 341\nfantoni 341\nfarro 341\nfazli 341\nfederalization 341\nfinlay's 341\nfloridanus 341\nfumaroles 341\nfutian 341\nfuxiang 341\ngaîté 341\ngaldós 341\ngherardo 341\ngiles's 341\nginepri 341\ngodhood 341\ngrazulis 341\ngrifters 341\ngumuz 341\ngwardia 341\nhalewood 341\nhaparanda 341\nharalson 341\nhatherley 341\nhebraica 341\nhercegovine 341\nheya 341\nhidayatullah 341\nhighball 341\nhiguaín 341\nhilburn 341\nhisense 341\nhri 341\nhyssop 341\nibaf 341\nifsc 341\nimperishable 341\nincredulity 341\niseki 341\njaggi 341\njalore 341\njau 341\njeepers 341\njen's 341\njerkens 341\njse 341\nkaalam 341\nkapiolani 341\nkarmann 341\nkaweah 341\nkeiō 341\nkhayat 341\nkigoma 341\nkinghorne 341\nkinglet 341\nkinnoull 341\nkirstein 341\nkissling 341\nkolya 341\nkopa 341\nkoskela 341\nkpl 341\nlakhdar 341\nlamprecht 341\nlanatus 341\nlauf 341\nlensky 341\nlewton 341\nlignes 341\nlinxia 341\nliterackie 341\nliuyang 341\nloaiza 341\nloeb's 341\nludvigsen 341\nluganda 341\nmattheus 341\nmcleay 341\nmenifee 341\nmineralisation 341\nmoffat's 341\nmoglie 341\nmonomakh 341\nmuros 341\nmurrisk 341\nmutlu 341\nnajeeb 341\nnorwich's 341\nonger 341\noolite 341\nouedraogo 341\npalooka 341\npamantasan 341\npangolins 341\nparet 341\npermaisuri 341\npescadero 341\nphthalates 341\nphysiologists 341\npicquet 341\npipelining 341\npitbulls 341\npjak 341\nplagiarised 341\nplaxico 341\nplexippus 341\npolaroids 341\npolyrhachis 341\nponcet 341\npoppel 341\nposies 341\npotentialities 341\npoto 341\npublicising 341\npvd 341\nqar 341\nquaresma 341\nquicksort 341\nraimonds 341\nrakha 341\nraspe 341\nreassembling 341\nreconnoitre 341\nrecursos 341\nrereading 341\nrevolutionists 341\nrevving 341\nrilo 341\nrlp 341\nroc's 341\nruaidhri 341\nryk 341\nryul 341\nséverin 341\nscincidae 341\nsheek 341\nshewan 341\nshikellamy 341\nshinjo 341\nsideboard 341\nsimen 341\nskyview 341\nsleepwalkers 341\nsonipat 341\nsoopafly 341\nsoucy 341\nspizella 341\nspode 341\nstammering 341\nstanbridge 341\nstrudel 341\nstryfe 341\nsunstroke 341\nsuzann 341\ntarak 341\nteodorescu 341\ntereshkova 341\nterritoriality 341\nthusly 341\ntillers 341\ntonhalle 341\ntownson 341\ntrigun 341\ntrnc 341\ntsan 341\ntut's 341\nuai 341\nuet 341\nuncharged 341\nundocking 341\nunobtainable 341\nuthai 341\nvacuous 341\nvasavi 341\nvesoul 341\nvesuvio 341\nviejas 341\nvillota 341\nvlachos 341\nwarders 341\nwarham 341\nwatie 341\nwiene 341\nwildey 341\nwinckelmann 341\nwincott 341\nxm's 341\nyarm 341\nzeno's 341\nzumbi 341\nøstre 340\nświnoujście 340\nağa 340\nabarca 340\naddlestone 340\nadumim 340\naht 340\nakashic 340\nalexy 340\namoureuse 340\nanápolis 340\nanavet 340\nanshe 340\nantithrombin 340\narieh 340\nartsy 340\nascorbate 340\nbōken 340\nbabenhausen 340\nbaganda 340\nbalor 340\nbertold 340\nbfbs 340\nbharathiraja 340\nbiedermeier 340\nbinyon 340\nbishara 340\nblemishes 340\nbluto 340\nboch 340\nbolognini 340\nborstein 340\nbrando's 340\nbrinks 340\nbrusilov 340\nbudgerigar 340\nburkert 340\ncaecilians 340\ncagematch 340\ncaryll 340\ncegep 340\ncentenarians 340\nchahal 340\nchairmanships 340\nchao's 340\nchiffchaff 340\nchilhowee 340\nchinchwad 340\nciconiidae 340\nclyburn 340\ncollies 340\nconradi 340\nconstrue 340\ncontiguity 340\nconvolutional 340\ncrucify 340\ndaco 340\ndagg 340\ndavidi 340\ndecisis 340\ndelphin 340\ndenville 340\ndevagiri 340\ndiese 340\ndolton 340\ndorner 340\ndredges 340\ndrp 340\ndrumlin 340\nduyvil 340\ndyrrhachium 340\nelectrophoretic 340\nelu 340\nemergy 340\nendogamy 340\nenvelops 340\nferritin 340\nficino 340\nfishpond 340\nfleishman 340\nflorentina 340\nformentera 340\nfoxconn 340\nfrangipani 340\nfrgs 340\nfysh 340\ngaleotti 340\ngallienne 340\nglazebrook 340\ngravest 340\ngrittier 340\ngwillimbury 340\nhadrosaur 340\nhoseyn 340\nhoym 340\nimpul 340\nindignity 340\ninhabitation 340\ninvoicing 340\niram 340\njarrold 340\njeyaretnam 340\njulfa 340\nkatee 340\nkiewit 340\nklett 340\nkode 340\nkratz 340\nlützen 340\nlatach 340\nlayang 340\nleadership's 340\nleptotes 340\nleucophrys 340\nlongspur 340\nloulé 340\nmacau's 340\nmackinlay 340\nmacrocephalus 340\nmagro 340\nmakhan 340\nmakovsky 340\nmandalas 340\nmanfredonia 340\nmarke 340\nmarmite 340\nmartinière 340\nmarzano 340\nmazurek 340\nmeštrović 340\nmeetha 340\nmetrosideros 340\nmicronation 340\nmidrashim 340\nmitchison 340\nmitts 340\nmonbiot 340\nmoneylenders 340\nmoonstruck 340\nmorrisey 340\nmoskovsky 340\nmpsf 340\nmuggsy 340\nmutis 340\nnafees 340\nnasrani 340\nnatten 340\nnavasota 340\nnbdl 340\nnevel 340\nnityananda 340\nnoémie 340\nnollaig 340\nnordics 340\noedipal 340\noguri 340\nolufsen 340\novermyer 340\npaulose 340\npeonies 340\npettah 340\nphonebook 340\nphylicia 340\nphytoseius 340\npizzey 340\npnn 340\npublicaciones 340\npublico 340\npurefoy 340\nqadim 340\nrahall 340\nritalin 340\nsabat 340\nsalivation 340\nsangallo 340\nsatanists 340\nscee 340\nschönburg 340\nscoresway 340\nselvi 340\nsenden 340\nsherpur 340\nsilents 340\nskolem 340\nslsf 340\nslutsky 340\nsoddy 340\nsonography 340\nsousa's 340\nspang 340\nstaminate 340\nstetten 340\nstruble 340\nsublimity 340\nsuji 340\nsuvi 340\nsyzygy 340\ntaché 340\ntehelka 340\nteterboro 340\nthakin 340\nthd 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339\ncarbonear 339\ncelles 339\nchōnin 339\nchelny 339\nchitarra 339\nchortle 339\ncioffi 339\ncléo 339\ncltdnq 339\ncohl 339\ncoloureds 339\ncommerce's 339\ncontainerized 339\ncriminalizes 339\ncritz 339\ncroaking 339\ncscl 339\ndúin 339\ndanka 339\ndebauched 339\ndecimalisation 339\nderbyshire's 339\ndesmoulins 339\ndisneymania 339\ndisowns 339\ndivaricata 339\ndolly's 339\ndotrice 339\ndrewes 339\ndymchurch 339\necht 339\nelbeuf 339\nemei 339\nenneagram 339\nepigraphs 339\nerhan 339\neuroa 339\nevald 339\nevm 339\nexhorts 339\nfalchuk 339\nfearnet 339\nflamenca 339\nflorine 339\nfrd 339\nfrisbie 339\nfrontages 339\nfulvescens 339\nfurano 339\ngalago 339\ngangsta's 339\ngattung 339\ngedi 339\ngemünden 339\ngleanings 339\ngoalsfor 339\ngodowsky 339\ngosho 339\nguangyuan 339\ngypsys 339\nharzer 339\nhaulers 339\nhavana's 339\nhedayat 339\nheliophanus 339\nhightstown 339\nhoku 339\nholwell 339\nhookup 339\nhumayun's 339\nhuu 339\nhypostasis 339\nicc's 339\niiit 339\nintercooled 339\niut 339\njafarabad 339\njakobstad 339\njoka 339\nkōriyama 339\nkaolinite 339\nkartar 339\nkassar 339\nkasturba 339\nkhandelwal 339\nkharian 339\nkinetoscope 339\nkingsdown 339\nkiriyama 339\nkitzhaber 339\nkokan 339\nkokugikan 339\nkorn's 339\nkristýna 339\nkurile 339\nkurti 339\nlaisse 339\nlallemand 339\nlangman 339\nlaparoscopy 339\nleaderships 339\nleaven 339\nlemmens 339\nlenhart 339\nleraut 339\nleung's 339\nlezak 339\nlipshutz 339\nloche 339\nlockable 339\nlubezki 339\nlymm 339\nlysaker 339\nmadryn 339\nmccambridge 339\nmccreadie 339\nmilovanović 339\nmiou 339\nmiroku 339\nmiscalculated 339\nmistrusted 339\nmomoi 339\nmonosaccharides 339\nmoru 339\nmossadegh 339\nmultiset 339\nmultithreaded 339\nmumble 339\nmycobacteria 339\nmyrrha 339\nnacha 339\nnakba 339\nnazarova 339\nneeley 339\nneoromicia 339\nneumayer 339\nnewsid 339\nnipmuc 339\nnobuaki 339\nnoen 339\nnogizaka 339\nnuages 339\nnyit 339\noatman 339\nolaya 339\nordoñez 339\noverconfidence 339\noz's 339\npálsson 339\npellicer 339\npemphigus 339\npervomaysky 339\nphilostratus 339\npholcus 339\nphotoresist 339\nplanch 339\npomerantz 339\npompilius 339\npopeye's 339\nprogestin 339\nprothrombin 339\npubblico 339\npuerperal 339\npuertas 339\npurée 339\npuyol 339\npyrrole 339\nqk 339\nquinceañera 339\nqurayza 339\nqxc 339\nrüppell 339\nracal 339\nrakers 339\nranjana 339\nrasi 339\nrawnsley 339\nreaganomics 339\nrille 339\nritts 339\nrodin's 339\nroeser 339\nromân 339\nrsn 339\nrubina 339\nrusskaya 339\nryker 339\nsade's 339\nsagitta 339\nsajna 339\nsankei 339\nsarabia 339\nsashi 339\nsaucon 339\nsaudia 339\nsavvas 339\nscholasticus 339\nsethumadhavan 339\nsetswana 339\nshd 339\nshill 339\nshiprock 339\nsigna 339\nsimonne 339\nsimurq 339\nsovereigntist 339\nsrihari 339\nstaplehurst 339\nstarlifter 339\nstormfront 339\nstradella 339\nstukas 339\nsubring 339\nsuperchunk 339\nsuperciliosus 339\nsuperted 339\nsuzman 339\nsvir 339\ntamazight 339\ntarboro 339\ntarplin 339\ntasim 339\ntaviani 339\nthoughtfully 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318\npego 318\npelagia 318\npentapolis 318\nperiodization 318\nperutz 318\npervade 318\nphraates 318\npipi 318\npittosporum 318\nplav 318\nploče 318\npmg 318\npremodern 318\nproteinuria 318\nprotuberances 318\nprudnik 318\nqueercore 318\nquestioner 318\nraquette 318\nredistributing 318\nrhapsodie 318\nricardian 318\nrifampicin 318\nrobbinsdale 318\nromagn 318\nrotatable 318\nrri 318\nrudbar 318\nsalacia 318\nsamphire 318\nsarpy 318\nschönebeck 318\nscobey 318\nscroller 318\nseafoods 318\nshapeless 318\nsignal's 318\nsinopec 318\nskalica 318\nsodré 318\nsordida 318\nsoulmates 318\nsoult's 318\nspringhouse 318\nsqualene 318\nstenger 318\nsterry 318\nstrongarm 318\nsuryavanshi 318\nswaledale 318\nsynchronic 318\ntalo 318\ntaue 318\ntaymiyyah 318\ntemnospondyls 318\ntenant's 318\nthorleif 318\nthredbo 318\ntominaga 318\ntopor 318\ntorey 318\ntreinta 318\ntrousseau 318\nulyanov 318\nunexposed 318\nuniversitatis 318\nutetheisa 318\nvalencia's 318\nverging 318\nwałcz 318\nwillink 318\nwilmar 318\nwrangle 318\nxinxiang 318\nyasht 318\nyenişehir 318\nyuxiang 318\nyuzhny 318\nzongo 318\nzoque 318\naasai 317\naction's 317\nahar 317\nalekseyevich 317\namália 317\namelanchier 317\nanatomica 317\nannali 317\napatin 317\narchilochus 317\narida 317\nasheton 317\nazari 317\nbéxar 317\nbathymetry 317\nbeaupre 317\nbeecher's 317\nbegović 317\nberingia 317\nbevil 317\nbirobidzhan 317\nbischofshofen 317\nblažević 317\nbolesławiec 317\nborzage 317\nbougainvillea 317\nbov 317\nbrith 317\nbrue 317\nburkhalter 317\ncallihan 317\ncardassians 317\ncatrina 317\nceltica 317\nché 317\nchangmin 317\nchernoff 317\nchiêu 317\nchirnside 317\nclasts 317\nclere 317\ncodas 317\ncombinatory 317\ncommendam 317\nconquête 317\ncorridos 317\ncrowing 317\nculp's 317\ndach 317\ndaire 317\ndebré 317\ndeepam 317\ndeez 317\ndepok 317\ndessa 317\ndibba 317\ndjenné 317\ndunajec 317\ndunked 317\ndunkerton 317\ndutchman's 317\nehess 317\nembl 317\nenablement 317\neuc 317\neuphydryas 317\nextrasensory 317\nfalsterbo 317\nfengtai 317\nfionnuala 317\nfirst's 317\nflaminius 317\nfleurieu 317\nfloridablanca 317\nfluorophores 317\nforsteri 317\nfrankenthaler 317\nfriendswood 317\ngallops 317\ngamestar 317\ngaspari 317\ngfr 317\ngigant 317\ngoodenia 317\ngrantchester 317\ngravenhage 317\ngregório 317\ngyllenhal 317\nheightens 317\nhelladic 317\nheming 317\nhiebert 317\nhilditch 317\nhyllus 317\nhymes 317\ni²c 317\niberdrola 317\nickenham 317\ninputting 317\ninsurances 317\ninterethnic 317\nishinomaki 317\njeevana 317\njunta's 317\nkā 317\nkėdainiai 317\nkairali 317\nkameez 317\nkasal 317\nkasparek 317\nkasyanov 317\nkawerau 317\nkeuper 317\nkfm 317\nkhmers 317\nkidlington 317\nkomma 317\nkristan 317\nkroenke 317\nkuranda 317\nlabo 317\nlaja 317\nlaning 317\nlavers 317\nlegno 317\nleti 317\nlindesay 317\nliteracies 317\nloxostege 317\nlync 317\nmaaseik 317\nmacrourus 317\nmadhupur 317\nmadhyamik 317\nmagas 317\nmajdan 317\nmajora's 317\nmarvelman 317\nmbta's 317\nmbti 317\nmcat 317\nmcguire's 317\nmcsherry 317\nmegillah 317\nmehsana 317\nmetonic 317\nmetroland 317\nmicrospheres 317\nmilarepa 317\nmonads 317\nmoneylender 317\nmorphou 317\nmru 317\nmurinus 317\nmuthappan 317\nnápoles 317\nneft 317\nnetivot 317\nnevelson 317\nnoether's 317\noliwa 317\noverflights 317\npaatelainen 317\npasti 317\npdes 317\npeeking 317\npersevering 317\npestana 317\nphocion 317\npierina 317\nplainmoor 317\nplaything 317\nplucks 317\npolitifact 317\npolyamide 317\nportent 317\nprenasalized 317\nprioritise 317\nprotonotary 317\nprotos 317\nprzewalski's 317\nptdins 317\npterodactylus 317\npunknews 317\npurples 317\npyridoxine 317\nquillota 317\nrajarhat 317\nredbud 317\nrefloat 317\nregan's 317\nrejewski 317\nretentive 317\nreznik 317\nribon 317\nricimer 317\nroycroft 317\nrueben 317\nruffa 317\nsabel 317\nsaguache 317\nsalomons 317\nsandin 317\nsandokan 317\nsayoko 317\nscoffs 317\nscrivens 317\nsembello 317\nsemidefinite 317\nsensorineural 317\nserc 317\nshein 317\nshiban 317\nshikshan 317\nshizhong 317\nsiar 317\nsibanda 317\nsicut 317\nsignatura 317\nsili 317\nsoglio 317\nsouthee 317\nspic 317\nsteinberg's 317\nsubsector 317\nsubstantively 317\nsucesso 317\nsullavan 317\nsurendran 317\nsutphin 317\nswaim 317\nszűcs 317\ntōma 317\ntabun 317\nteela 317\ntetras 317\nthelymitra 317\nthomasomys 317\ntransposons 317\ntrantor 317\ntsubame 317\ntugu 317\ntullaroan 317\nudder 317\nunenclosed 317\nuwi 317\nvalbuena 317\nvarichev 317\nvecinos 317\nveerappa 317\nveiling 317\nvicariates 317\nvinoba 317\nviveca 317\nvolkskrant 317\nvril 317\nwads 317\nwarbucks 317\nwdp 317\nweaverville 317\nweddington 317\nwelsh's 317\nwongteanchai 317\nzare 317\nzirid 317\nzovko 317\nünal 316\nđakovo 316\nactionaid 316\nactu 316\nafterimage 316\nagat 316\nakula 316\nalbertosaurus 316\nallopatric 316\nalmanacco 316\nambi 316\nannectens 316\naréna 316\naristotelia 316\narzoo 316\nauriculata 316\nbafa 316\nbajram 316\nbangi 316\nbarpeta 316\nbbj 316\nbeddgelert 316\nboetsch 316\nborsato 316\nbouken 316\nbreitling 316\nbuitenzorg 316\nbullata 316\nbursera 316\nbushwalking 316\nbyword 316\ncafferty 316\ncanó 316\ncarausius 316\ncarphone 316\ncastaic 316\ncayabyab 316\ncendrillon 316\ncevert 316\nchloroquine 316\nchogm 316\nchordophones 316\ncley 316\ncmn 316\ncoasted 316\ncoaxing 316\ncoliform 316\ncombretum 316\nconvivial 316\ncoprosma 316\ncorydalis 316\ncounterchanged 316\ncurrawong 316\ndalembert 316\ndanao 316\ndanjūrō 316\ndannevirke 316\ndecimating 316\ndelporte 316\ndiano 316\ndishonourable 316\ndistro 316\nduga 316\ndutcher 316\nebbinghaus 316\neliakim 316\nembroideries 316\nendoscope 316\nepitaxy 316\nerdene 316\nevangelisation 316\nfakta 316\nfelgueiras 316\nfilmes 316\nfinau 316\nflirtations 316\nfoudroyant 316\nfrasers 316\nfroelich 316\nfrothingham 316\nfulmars 316\nfuruset 316\ngórniak 316\ngalathea 316\ngammage 316\ngcu 316\ngeraldines 316\ngmsa 316\ngretzky's 316\ngroo 316\ngwp 316\nhainanese 316\nhamadani 316\nhargraves 316\nhargus 316\nharvestmen 316\nhashana 316\nhatherton 316\nhebraic 316\nhedwig's 316\nhellhammer 316\nhemisphere's 316\nhenreid 316\nhepatotoxicity 316\nhideously 316\nhindhead 316\nhinkler 316\nholcim 316\nhomophonous 316\nhongi 316\nhoose 316\nhoudini's 316\niamblichus 316\nicai 316\nitalicus 316\njhargram 316\njianguo 316\njoj 316\njozsef 316\nkamsky 316\nkarana 316\nkarataş 316\nkarz 316\nkavner 316\nkershner 316\nkhaybar 316\nkhoda 316\nkishangarh 316\nkissy 316\nkonar 316\nkuepper 316\nkullback 316\nlarfleeze 316\nlavina 316\nleprechauns 316\nleptis 316\nlinfen 316\nloeblich 316\nluen 316\nlviii 316\nmạng 316\nmacfie 316\nmahagonny 316\nmahajana 316\nmargai 316\nmarufuji 316\nmcaulay 316\nmccredie 316\nmckesson 316\nmedemblik 316\nmelati 316\nmeols 316\nmirman 316\nmonga 316\nmontara 316\nmoonchild 316\nmoscheles 316\nmycotoxins 316\nnacio 316\nnarcissa 316\nneógain 316\nneusner 316\nnitzan 316\nnyco 316\nohlendorf 316\nopernhaus 316\npakal 316\npakeha 316\nparktown 316\npashas 316\npayrolls 316\npeerce 316\npeintures 316\npekalongan 316\npelochrista 316\npereiro 316\nphilodromus 316\npiccione 316\npierzynski 316\npinard 316\npreimage 316\nprivée 316\npropertius 316\npsdi 316\npurg 316\npxst 316\nqadiri 316\nqaim 316\nquantic 316\nquarta 316\nrømer 316\nranmaru 316\nranunculaceae 316\nravenshaw 316\nreenters 316\nrefiner 316\nreginae 316\nremarrying 316\nremuera 316\nreplaying 316\nrespirators 316\nrhomb 316\nrifugio 316\nriggle 316\nroadtrip 316\nrobyns 316\nrone 316\nrouault 316\nsalvacion 316\nschleiz 316\nschwartzel 316\nscolopax 316\nseberg 316\nselanne 316\nsevak 316\nseverinus 316\nsirloin 316\nslovenly 316\nsnežana 316\nsoundproof 316\nsoundtrack's 316\nsoundz 316\nsouthcentral 316\nspearmen 316\nstasys 316\nsteeg 316\nsteilacoom 316\nstevenstone 316\nstucky 316\ntaiyou 316\ntarjei 316\ntharoor 316\nthomasine 316\ntoño 316\ntostig 316\ntotara 316\ntractates 316\ntrinitatis 316\ntruncating 316\ntunicates 316\nuitenhage 316\numbral 316\nvautrin 316\nverkehrs 316\nvijayabahu 316\nvoidable 316\nwadhurst 316\nwaitz 316\nwatabe 316\nwazirabad 316\nwelayta 316\nwiesen 316\nwiremu 316\nwitz 316\nwoodbourne 316\nwoodshed 316\nwooton 316\nwynkoop 316\nwynn's 316\nyabe 316\nyogini 316\nzaida 316\nzamek 316\nzamfara 316\nzestafoni 316\nzoff 316\nмоя 315\nцерковь 315\nabinger 315\nacc's 315\nacclimatization 315\nadud 315\naffirmatively 315\nagabus 315\nagbaje 315\naifa 315\nailly 315\naimery 315\nakbarpur 315\nalmendares 315\namerical 315\namh 315\namtsbezirk 315\nanb 315\nantiquité 315\naot 315\naponeurosis 315\nargenteus 315\naspromonte 315\nbabis 315\nbabys 315\nbahrami 315\nbarnoldswick 315\nbarrère 315\nbartonella 315\nbeavercreek 315\nbettino 315\nbircham 315\nbirchgrove 315\nbne 315\nboia 315\nbolcom 315\nbotan 315\nbreccias 315\nbremmer 315\nbrizzi 315\nbroomball 315\ncallens 315\ncaquetá 315\nceler 315\ncemex 315\ncephalophus 315\nchadic 315\nchar's 315\ncharmers 315\nchesney's 315\nchin's 315\nchug 315\ncisco's 315\nclotaire 315\ncluefinders 315\ncolerain 315\nconcentrators 315\ncopt 315\ncosets 315\ncostarred 315\ncrw 315\ncuckooshrike 315\nczolgosz 315\ndéjame 315\ndaivari 315\ndebub 315\ndenes 315\ndeorbit 315\ndesailly 315\ndevaswom 315\ndmn 315\ndragovoljac 315\nduplicitous 315\ndweezil 315\ndwyer's 315\neaf 315\neckl 315\nedifying 315\nehle 315\nensa 315\nerwan 315\nescaflowne 315\nesx 315\nettingshausen 315\neugenicist 315\nevaporators 315\newok 315\nexponentials 315\nfalko 315\nfarmersville 315\nfilson 315\nfirecrest 315\nfrazier's 315\nfrutiger 315\nfulvetta 315\nfunkhouser 315\ngallieni 315\ngauda 315\ngemilang 315\ngeologie 315\ngittenberger 315\ngopika 315\ngreyscale 315\ngulfton 315\nhackneyed 315\nhagberg 315\nhamrick 315\nharyanto 315\nhat's 315\nhauntingly 315\nhayy 315\nhedera 315\nheidke 315\nhercog 315\nheritage's 315\nherzegovina's 315\nhiba 315\nhochi 315\nhoenig 315\nhohn 315\nholanda 315\nhurtin 315\nhyemalis 315\nidoli 315\nikaruga 315\nilfov 315\njailers 315\njanthina 315\njarek 315\njennet 315\njmi 315\njulião 315\njulien's 315\njungang 315\nkaio 315\nkaraga 315\nkasab 315\nkerma 315\nkiambu 315\nknechtel 315\nkrakau 315\nkrycek 315\nkulasekara 315\nkupe 315\nlaggan 315\nlatencies 315\nlegere 315\nlegitimated 315\nlenzerheide 315\nlerkendal 315\nleucopterus 315\nlindeberg 315\nlitsinger 315\nlonghand 315\nlongji 315\nlusophone 315\nlutescens 315\nmélange 315\nmacoupin 315\nmahaveer 315\nmahboob 315\nmarjane 315\nmathématique 315\nmaud's 315\nmeazza 315\nmeldal 315\nminoo 315\nmireya 315\nmolalla 315\nmultani 315\nmuraco 315\nmyfanwy 315\nnachmanides 315\nnahan 315\nnaicker 315\nnovine 315\noctonions 315\nolecko 315\nollerton 315\noncologists 315\norchideen 315\noutsized 315\npallekele 315\npallette 315\nparga 315\npartai 315\npeening 315\npendentives 315\npflug 315\nphlogiston 315\nplagiarizing 315\npopulars 315\nportogruaro 315\nprerna 315\nprovidential 315\nprowling 315\npterostylis 315\npullman's 315\npullover 315\npurchas 315\npyp 315\nqid 315\nquintile 315\nradikal 315\nrajeswara 315\nraycroft 315\nrcac 315\nreadies 315\nrecca 315\nreinas 315\nresidencia 315\nrhod 315\nrill 315\nriom 315\nromânesc 315\nromanovsky 315\nrowboats 315\nrudkin 315\nsabmiller 315\nsadat's 315\nsahithya 315\nsanatoriums 315\nsavannahs 315\nscuffles 315\nshalu 315\nshey 315\nsiemerink 315\nsillery 315\nsmallman 315\nsnaffle 315\nsny 315\nsozialistische 315\nsteinach 315\nsteinmann 315\nstepanek 315\nstrassen 315\nstrock 315\nsubtended 315\nsurficial 315\nsweatshirts 315\nsymphoniker 315\nsyracusan 315\ntakayanagi 315\ntaringa 315\nteutenberg 315\nthanagar 315\ntheru 315\ntrekker 315\ntricare 315\ntrocadéro 315\ntrouville 315\ntuggerah 315\ntykwer 315\nuele 315\nuws 315\nuyl 315\nvaladares 315\nvardanyan 315\nverbascum 315\nvicor 315\nvishwas 315\nvolcom 315\nvolquez 315\nvora 315\nvtt 315\nwarranting 315\nwesterland 315\nwilsoni 315\nwintringham 315\nwitzel 315\nwragge 315\nxichang 315\nyamen 315\nyusaku 315\nzéro 315\nzapiski 315\nzera 315\nzoolander 315\nzvartnots 315\nštiavnica 314\naccentuating 314\nactas 314\naculeatus 314\nadsit 314\nadversary's 314\nagathis 314\nalicia's 314\namateure 314\namusingly 314\napax 314\narbitrated 314\narlovski 314\nashby's 314\naslan's 314\nauthorises 314\nautobus 314\navance 314\nbašić 314\nbabbit 314\nbahraich 314\nballasts 314\nbarnaba 314\nbasarabia 314\nbazi 314\nbeetle's 314\nbeezer 314\nberenices 314\nbiomimetic 314\nbipyramid 314\nbrachii 314\nbrevik 314\nbrink's 314\nbrockmann 314\nbuel 314\nbutyrate 314\ncalvaire 314\ncappelli 314\ncatasauqua 314\ncchs 314\ncentromere 314\ncfcf 314\nchamar 314\nchmerkovskiy 314\ncici 314\ncircuited 314\nclairefontaine 314\ncleanser 314\ncognomina 314\ncolinton 314\ncompetición 314\nconcorso 314\nconstructor's 314\ncourtice 314\ncredibly 314\ncrumbly 314\ndaibouken 314\ndanco 314\ndanon 314\ndel's 314\ndemokratie 314\ndiglossia 314\ndihydrogen 314\ndjoliba 314\ndonadoni 314\ndonnersmarck 314\ndoru 314\ndowagiac 314\ndownslope 314\ndulgheru 314\nduvalius 314\neastman's 314\nedric 314\neeuw 314\neicke 314\nellon 314\nembryological 314\nenergised 314\nepimerase 314\nerreway 314\nfacemask 314\nfahri 314\nfatalistic 314\nfawad 314\nfelina 314\nfelipa 314\nfethi 314\nfiglia 314\nflagstad 314\nflanger 314\nforeach 314\nfreisinger 314\ngemological 314\ngiáp 314\ngignac 314\ngoodsell 314\ngreenfields 314\ngwt 314\nhannett 314\nharbour's 314\nhassidic 314\nhavelange 314\nhazmi 314\nhelu 314\nhemisfair 314\nhickel 314\nhiroyasu 314\nhitchings 314\nhittin 314\nhoneycreeper 314\nhorder 314\nhuckle 314\niğdır 314\niaas 314\nicct 314\nidhu 314\ninculcated 314\ninfirmities 314\niraheta 314\nisidora 314\njägers 314\njagapathi 314\njasim 314\njefferys 314\njever 314\njugoslavije 314\nkōya 314\nkais 314\nkaiserstuhl 314\nkalashnikova 314\nkankō 314\nkhalif 314\nkhayelitsha 314\nkimani 314\nklac 314\nklebold 314\nkulbhushan 314\nlacanobia 314\nlandesman 314\nlatinization 314\nlindale 314\nlinguistica 314\nlofting 314\nlovato's 314\nlyonel 314\nmagique 314\nmalti 314\nmamet's 314\nmarktplatz 314\nmarsteller 314\nmatthey 314\nmcdavid 314\nmertiňák 314\nmetabolised 314\nmichele's 314\nminhaj 314\nmisinterpret 314\nmizusawa 314\nmontee 314\nmunim 314\nmuong 314\nnaor 314\nnarbonensis 314\nnarni 314\nneba 314\nneca 314\nneedn 314\nneshoba 314\nnordqvist 314\nnrotc 314\nnyah 314\noeynhausen 314\norganismic 314\norma 314\noseberg 314\nosiedle 314\nosservatore 314\notávio 314\npanizzi 314\nperisher 314\npeters's 314\npetliura 314\npeuples 314\nphila 314\nplexiglass 314\npoley 314\npriestman 314\nproselytize 314\nputatively 314\nqana 314\nrajbari 314\nramond 314\nrasbora 314\nrevesby 314\nriseley 314\nrizk 314\nrks 314\nroessler 314\nroundish 314\nruination 314\nrynagh's 314\nsabry 314\nsalvadore 314\nsanji 314\nschltdl 314\nselflessly 314\nsepulcher 314\nsettimo 314\nsexting 314\nshalala 314\nshirl 314\nsiac 314\nsigo 314\nsirventes 314\nsivaraman 314\nslimani 314\nsorbet 314\nsteadiness 314\nsubbuteo 314\nsurmount 314\nsymphorien 314\ntelangiectasia 314\nteste 314\ntirat 314\ntisdall 314\ntmk 314\ntransgressed 314\ntransiently 314\ntrev 314\ntshering 314\ntsk 314\ntutankhamun's 314\nunashamed 314\nunwholesome 314\nupsetters 314\nvbscript 314\nveasey 314\nvicina 314\nviegas 314\nvilleneuve's 314\nvladyslav 314\nvuuren 314\nwahi 314\nwaldsee 314\nwamu 314\nwapello 314\nwarred 314\nwhitlams 314\nwoolard 314\nwoolridge 314\nwoordenboek 314\nwti 314\nwuppertaler 314\nxiangshan 314\nzócalo 314\nzacharie 314\nzahumlje 314\nzendejas 314\nzubkov 314\néléments 313\nгг 313\naftertaste 313\nafvs 313\naise 313\naitutaki 313\nakademisk 313\nalagna 313\nalcaeus 313\nallcock 313\nalmer 313\nalphons 313\namerila 313\nantiphonal 313\nantis 313\nantoniou 313\nantwan 313\nargentan 313\nascham 313\nasokan 313\nassassinates 313\natd 313\navary 313\nbäckman 313\nböhler 313\nbędzin 313\nbachand 313\nbanastre 313\nbanesto 313\nbargeld 313\nbasketballer 313\nbatavi 313\nbeo 313\nberube 313\nbhan 313\nbharatha 313\nbiedenkopf 313\nbigard 313\nbirju 313\nblackshirt 313\nblc 313\nboardgamegeek 313\nbonser 313\nbrétigny 313\nbratchikova 313\nbredbury 313\nbrennen 313\nbromhead 313\nbucked 313\nbuonarroti 313\nburruss 313\nbuyback 313\ncabiria 313\ncalibur 313\ncannings 313\ncarma 313\ncarney's 313\ncetshwayo 313\nchartes 313\nchaser's 313\nchenda 313\nchessbase 313\nchiodos 313\nchristofer 313\ncloakroom 313\nclod 313\nclouser 313\ncobresal 313\ncolons 313\ncommitteewoman 313\nconcilium 313\nconcurrencies 313\ncontenant 313\ncuvée 313\ndactylic 313\ndafni 313\ndahlman 313\ndakini 313\ndalin 313\ndanseur 313\ndaxing 313\ndemyelination 313\ndespre 313\ndeverell 313\ndjoser 313\ndomènech 313\ndombey 313\ndrau 313\ndros 313\ndussault 313\neisleben 313\neliminatory 313\nemel 313\nendotracheal 313\nengdahl 313\nequiano 313\nessere 313\neugenides 313\nexpediting 313\nexpensively 313\nezeiza 313\nfatemeh 313\nfernande 313\nfike 313\nfine's 313\nfishhook 313\nfitters 313\nfloppies 313\nflucht 313\nfreeh 313\nfrisch's 313\nfugs 313\nfukasaku 313\ngamble's 313\ngastroliths 313\ngidding 313\nglycan 313\ngranulocyte 313\ngrenchen 313\ngutting 313\ngwenda 313\nhammoud 313\nhennes 313\nheong 313\nherberger 313\nhermitages 313\nheyl 313\nhidari 313\nhowlers 313\nhuffer 313\nhurtubise 313\nhuse 313\nhwass 313\nhyperreal 313\nianni 313\niias 313\ninfernales 313\nisoprene 313\niztapalapa 313\niztok 313\njahrgang 313\njiaozuo 313\nkananga 313\nkaon 313\nkarlsbad 313\nkatari 313\nkittyhawks 313\nknowe 313\nkudō 313\nkundara 313\nkuraki 313\nkwantlen 313\nlahad 313\nlambart 313\nlevien 313\nlexy 313\nlimericks 313\nlurianic 313\nmalahat 313\nmaniax 313\nmaryada 313\nmaryan 313\nmatadi 313\nmatteucci 313\nmaturín 313\nmayak 313\nmaykop 313\nmcglone 313\nmeisterschaft 313\nmerten 313\nmetzinger's 313\nmichelinie 313\nmillenarian 313\nminzu 313\nmondavi 313\nmontrachet 313\nmorabito 313\nmunicipalidad 313\nmyton 313\nnautiloids 313\nnervi 313\nnevo 313\nnewgrounds 313\nnmfs 313\nnnc 313\nnph 313\nnurbs 313\nolanzapine 313\npôle 313\npadden 313\npagsanjan 313\npaiutes 313\npal's 313\npalma's 313\npaphlagonia 313\nparaphilia 313\nparrotbill 313\npavao 313\npeckett 313\npester 313\npeyton's 313\nphotometer 313\npiao 313\npigmy 313\nplanalto 313\nplatformers 313\npoele 313\nportolan 313\npreparatoria 313\npropp 313\npudhu 313\npythagoreans 313\nrakovsky 313\nredoutable 313\nrepublicii 313\nretarding 313\nrevolucionaria 313\nrhabdomyolysis 313\nroamer 313\nroys 313\nruairí 313\nrustad 313\nsaosin 313\nschroth 313\nschutt 313\nselfoss 313\nseppälä 313\nserrallés 313\nshailene 313\nshawm 313\nshmoo 313\nshr 313\nsiska 313\nslovenians 313\nsniper's 313\nsobernheim 313\nsongzhi 313\nstojko 313\nsuber 313\nsusanoo 313\ntôi 313\ntabid 313\ntablo 313\ntaimur 313\ntamborine 313\ntantallon 313\ntappin 313\ntarcisio 313\ntenaga 313\ntenderfoot 313\ntenuipalpus 313\nthebarton 313\ntkachev 313\ntkb 313\ntopa 313\ntortious 313\ntrv 313\nuatu 313\numut 313\nunaccountable 313\nunburied 313\nungodly 313\nväisälä 313\nvalentyna 313\nveselinović 313\nvesely 313\nvidkun 313\nvladas 313\nvojtech 313\nvonk 313\nvouch 313\nvwf 313\nwachowskis 313\nwajima 313\nweblink 313\nwhirled 313\nwhyy 313\nwinchesters 313\nwingnut 313\nwisla 313\nwittstock 313\nworkbooks 313\nyabu 313\nyakup 313\nyarrawonga 313\nyaser 313\nyotam 313\nzaporizhian 313\nzincken 313\nöstlund 312\nšmarje 312\nэнциклопедия 312\nरम 312\naída 312\nabh 312\nadmiralties 312\nahdut 312\naltamonte 312\nanandan 312\nappelmans 312\naquin 312\narain 312\narismendi 312\narsenije 312\nartaxias 312\nasagi 312\nassynt 312\nates 312\natonality 312\naurèle 312\nauray 312\navventura 312\nbaboy 312\nbalkhash 312\nbardal 312\nbarocco 312\nbavaria's 312\nberny 312\nblaha 312\nblatty 312\nblech 312\nboardinghouse 312\nbolos 312\nbrandywell 312\nbreedon 312\nbroadleaved 312\nbundoran 312\nburdur 312\nburkart 312\nbwp 312\ncaerau 312\ncancellariidae 312\ncaret 312\nchivington 312\nchurchgoers 312\nclayburgh 312\ncleaveland 312\ncodling 312\ncommissario 312\ncompère 312\nconi 312\ncooksville 312\ncormega 312\ncountercurrent 312\ncrewkerne 312\nculley 312\ncullom 312\ndalet 312\ndaund 312\ndcn 312\ndeadlier 312\ndelà 312\ndigram 312\ndisfavour 312\ndjed 312\ndorival 312\ndow's 312\ndu's 312\ndungarpur 312\ndutronc 312\ndwarakish 312\nelaeocarpus 312\nemendations 312\nencamp 312\nengelstad 312\nenglisch 312\nenumerative 312\nepling 312\nerinaceus 312\nespeciales 312\netang 312\nexergy 312\nextracorporeal 312\nfall's 312\nfantasizes 312\nfarag 312\nfeldt 312\nfiordo 312\nfirme 312\nfiscus 312\nfishnet 312\nfluvanna 312\nfoxman 312\nfrédérick 312\nfujibayashi 312\nfukien 312\nfung's 312\nfutcher 312\ngallois 312\ngeonim 312\ngeralt 312\nghiyath 312\ngogan 312\ngrumble 312\ngunpei 312\ngusting 312\nherczeg 312\nhilden 312\nhochevar 312\nholleman 312\nhoodies 312\nhoogeveen 312\nhqs 312\nhukou 312\nidentidad 312\nifil 312\ninac 312\ninafune 312\ninteroperate 312\nionuț 312\nisaia 312\njabr 312\njanak 312\njindabyne 312\njully 312\njwrc 312\nkız 312\nkapler 312\nkastel 312\nkazuhisa 312\nkearfott 312\nkenwright 312\nkhowar 312\nkhursheed 312\nkinnell 312\nkiplinger 312\nkitai 312\nklemen 312\nkodak's 312\nkomitet 312\nkrem 312\nkuehn 312\nkuehne 312\nlèse 312\nlackeys 312\nlangage 312\nlhamo 312\nlibuše 312\nliebenberg 312\nlihue 312\nljungskile 312\nlleras 312\nlockman 312\nlovász 312\nluczak 312\nmagath 312\nmagdi 312\nmalagueña 312\nmarkham's 312\nmaroto 312\nmaudie 312\nmcanuff 312\nmcrae's 312\nmediastinum 312\nmellon's 312\nmervyn's 312\nmetonymy 312\nmidgut 312\nminiato 312\nmiyano 312\nmodano 312\nmofo 312\nmoho 312\nmoman 312\nmonstrance 312\nmorticia 312\nmunchkins 312\nnecho 312\nngā 312\nnicoleta 312\nnoncoding 312\nnuman's 312\nnwobhm 312\noberea 312\nojukwu 312\nonegai 312\nopenstep 312\noptoelectronic 312\norana 312\norienteers 312\norks 312\npakhi 312\nparlez 312\nparminder 312\npatas 312\npba's 312\nphatthalung 312\nphocoena 312\nphysalis 312\npinkpop 312\nplaisirs 312\npolitechnika 312\nponza 312\npowertrains 312\npriceline 312\npriok 312\npsara 312\npterocarpus 312\nquaderni 312\nrashly 312\nrecio 312\nreclus 312\nrichborough 312\nrixon 312\nroddam 312\nrohs 312\nrustication 312\nsagunto 312\nsambenedettese 312\nsampa 312\nsaponins 312\nsayyids 312\nscheck 312\nschwenningen 312\nseagrove 312\nseneca's 312\nsergeev 312\nserology 312\nsertraline 312\nshinran 312\nshushan 312\nsiuslaw 312\nskaven 312\nskidoo 312\nsolh 312\nstuckism 312\nsuozzi 312\nsupercapacitors 312\nsuphan 312\nsuttas 312\nswayambhu 312\nswoops 312\ntañón 312\ntarabini 312\ntauras 312\ntauride 312\nteleost 312\ntheatreworks 312\nthraupidae 312\ntiant 312\ntifa 312\ntolar 312\ntrackmasters 312\ntregear 312\ntrier's 312\ntropea 312\ntrotwood 312\ntruelove 312\nunsentimental 312\nuplb 312\nutsler 312\nvacationed 312\nvagn 312\nvanderhoof 312\nvarde 312\nvek 312\nvgs 312\nviadana 312\nvixx 312\nvohra 312\nwhizz 312\nwithe 312\nworte 312\nyü 312\nyacob 312\nyeniseian 312\nyildiz 312\nyotsuba 312\nzalău 312\nотдел 311\nabdulkadir 311\nabdurahman 311\nactuate 311\nadmir 311\naeneus 311\nagudelo 311\nahed 311\naiesec 311\nalzen 311\namundsen's 311\narianne 311\narisaema 311\nasamblea 311\nasrar 311\natrophic 311\nauclair 311\naydar 311\nbassée 311\nbeakers 311\nbenfica's 311\nberasategui 311\nbessin 311\nbhairab 311\nbigge 311\nbiographique 311\nboisvert 311\nbolívar's 311\nbombardier's 311\nbrailsford 311\nbratsk 311\nbrewin 311\nbriefcases 311\nbrunei's 311\nbukovyna 311\ncaitlyn 311\ncapitolinus 311\ncarini 311\ncenis 311\nchandrabose 311\ncharbonnier 311\ncharme 311\nchouhan 311\ncjsc 311\nclicker 311\nclincher 311\ncobbett's 311\ncollines 311\ncomplejo 311\ncomplexed 311\ncourse's 311\ncreutzfeldt 311\ndünya 311\ndasyatis 311\nddffdd 311\ndebout 311\ndecon 311\ndegelen 311\ndelagoa 311\ndemjanjuk 311\ndepolarizing 311\nderailments 311\ndeseronto 311\ndesouza 311\ndewolfe 311\ndezong's 311\ndiacylglycerol 311\ndiffusers 311\ndignitas 311\ndigo 311\ndogen 311\ndrl 311\ndsrna 311\ndynast 311\nebersol 311\nelegie 311\nelkanah 311\neois 311\nescudos 311\neutheria 311\nexclamations 311\nfeedforward 311\nfibulae 311\nfincham 311\nfixe 311\nfonz 311\nfootmen 311\nforgings 311\nfowke 311\nfrain 311\nfranciosa 311\nfreewill 311\nfrolics 311\nfuscatus 311\ngalling 311\ngebirgs 311\ngeomancy 311\ngigg 311\ngigged 311\ngirma 311\ngiustino 311\nglennamaddy 311\ngnatcatcher 311\ngolde 311\ngreases 311\ngreenbriar 311\ngrobbelaar 311\nguillet 311\nhadeln 311\nhafs 311\nhajdu 311\nhartline 311\nheigh 311\nheitman 311\nhighcroft 311\nhilandar 311\nhoofed 311\nhorb 311\nhultgren 311\nhumbucking 311\nhurstpierpoint 311\nhypersurface 311\niduna 311\nikram 311\nimaginings 311\nintorno 311\nitemized 311\niterating 311\njadi 311\njarir 311\nkaftan 311\nkawano 311\nkellyville 311\nkemeny 311\nkeonjhar 311\nkmfm 311\nkohout 311\nkompani 311\nkrannert 311\nkripal 311\nkuqi 311\nlangensalza 311\nlastclub 311\nleatham 311\nlhermitte 311\nliming 311\nlipotriches 311\nlykes 311\nmalefic 311\nmantar 311\nmarianela 311\nmaspeth 311\nmattocks 311\nmccardell 311\nmeiler 311\nmerlion 311\nmiis 311\nmilitiaman 311\nmineur 311\nmisinterprets 311\nmitchels 311\nmshc 311\nmuadzam 311\nmuh 311\nmuramasa 311\nmurderess 311\nmurie 311\nmzt 311\nnewville 311\nngaruawahia 311\nnith 311\nnnewi 311\nnovos 311\normesby 311\noryzae 311\novereating 311\noverwriting 311\npa's 311\npaise 311\npensylvanica 311\nperković 311\npgl 311\npigpen 311\npinkner 311\npoldark 311\npoteau 311\npresentational 311\npricks 311\nprocurators 311\nprologues 311\npuba 311\npyi 311\nquartette 311\nrīgas 311\nramankutty 311\nrangayana 311\nrassilon 311\nrathkenny 311\nreinstein 311\nresupplying 311\nretest 311\nrickardsson 311\nrominger 311\nrooyen 311\nroquette 311\nrunequest 311\nrvns 311\nsafdie 311\nsaidu 311\nsakyamuni 311\nsalutatorian 311\nsambandar 311\nsarrià 311\nsavernake 311\nscorpion's 311\nsesvete 311\nsgn 311\nshk 311\nshoelaces 311\nshotley 311\nshrimati 311\nsiddi 311\nsilvani 311\nsinghbhum 311\nsohmer 311\nsoricomorpha 311\nsoundclash 311\nsparkly 311\nsperafico 311\nspinebuster 311\nspingarn 311\nsportcsarnok 311\nsquatarola 311\nsrinu 311\nstayer 311\nstomatal 311\nstrachwitz 311\nsuc 311\nswiftness 311\nsympycnus 311\ntavo 311\ntenanted 311\nteos 311\ntheosis 311\nthrale 311\ntibbett 311\ntindivanam 311\ntoku 311\ntragopan 311\ntrapezius 311\ntsubomi 311\nttd 311\nturina 311\ntutta 311\ntwersky 311\ntypus 311\nudal 311\nuntangle 311\nvacher 311\nvaslav 311\nvejjajiva 311\nvelké 311\nviitorul 311\nvolstead 311\nwaarneming 311\nwaisted 311\nwargrave 311\nwellard 311\nwesterbork 311\nwestfälische 311\nwholetime 311\nwidor 311\nwortman 311\nyanbu 311\nzinman 311\nzydrunas 311\núrsula 310\nšuker 310\nabaft 310\nabdon 310\nabsheron 310\nacquittals 310\nadamsville 310\nalazraqui 310\nalexiou 310\nalhassan 310\nanabelle 310\napan 310\naraña 310\narruabarrena 310\nassayed 310\nauja 310\navus 310\nbagratid 310\nballers 310\nbargarh 310\nbarsha 310\nbartimaeus 310\nbatsman's 310\nbechdel 310\nbentwaters 310\nbersama 310\nbillung 310\nblondeau 310\nbogolyubov 310\nboor 310\nbordo 310\nborgne 310\nboromir 310\nborrero 310\nbrauerei 310\nbriles 310\nbritishers 310\nbuffo 310\nbulimulus 310\ncait 310\ncanonici 310\nceaselessly 310\nchadds 310\nchagin 310\nchakavian 310\nchalapathi 310\ncharsadda 310\nchesler 310\nchichele 310\nchopard 310\ncittern 310\nclarets 310\ncloudiness 310\nclown's 310\ncolletti 310\ncombaticons 310\ncompañia 310\ncompleta 310\nconstantinus 310\ncorll 310\ncrepidula 310\nctia 310\ndaß 310\ndarnton 310\ndeadball 310\ndeckhand 310\ndecretum 310\ndelfzijl 310\ndeyo 310\ndialogo 310\ndietitians 310\ndigiorgio 310\ndilfer 310\ndillane 310\ndooley's 310\nduckburg 310\ndullness 310\ndumm 310\ndurkan 310\nduron 310\neastfield 310\nechinata 310\neileanan 310\nenseñanza 310\nentomol 310\netheric 310\neustachio 310\nevreux 310\nexpédition 310\nexpreso 310\nféin's 310\nfacelock 310\nfarting 310\nfederman 310\nfenner's 310\nfimbriated 310\nfior 310\nforsskål 310\nfouga 310\nfourragère 310\nfredensborg 310\nfucker 310\ngaliza 310\ngastrin 310\ngauld 310\ngawai 310\nghamdi 310\ngleiwitz 310\nglorioso 310\ngodliness 310\ngolpo 310\ngoregaon 310\ngostivar 310\ngrazioso 310\ngreenlawn 310\ngrondin 310\ngsf 310\ngymnothorax 310\nhåndbold 310\nhadouken 310\nhainsworth 310\nhalka 310\nhallucinates 310\nhalvard 310\nhamal 310\nhanus 310\nhashimoto's 310\nhennadiy 310\nherk 310\nhexen 310\nhoddinott 310\nhoog 310\nhopps 310\nhorthy's 310\nibibio 310\nignatian 310\nimpinging 310\ninquests 310\ninterline 310\ninvisibly 310\nirregularis 310\nismo 310\niws 310\njõgeva 310\njurat 310\nkallon 310\nkaposvári 310\nkegel 310\nkeyboardists 310\nkhim 310\nkiddies 310\nkiselyov 310\nklausen 310\nkmox 310\nkoppen 310\nkorkut 310\nlachie 310\nlactea 310\nlagomorpha 310\nlamadrid 310\nleehom 310\nlesbia 310\nletoya 310\nliberalizing 310\nlogement 310\nludovicianus 310\nlutsenko 310\nlwr 310\nmacroura 310\nmanhã 310\nmanuva 310\nmarinovich 310\nmarwat 310\nmatanikau 310\nmazowieckie 310\nmcisaac 310\nmckendry 310\nmedrash 310\nmelanson 310\nmengal 310\nmensur 310\nmetalworks 310\nmhòr 310\nmicros 310\nmizpah 310\nmodernizations 310\nmolucca 310\nmonazite 310\nmonopolist 310\nmonreal 310\nmonteagudo 310\nmorrow's 310\nmpx 310\nmweru 310\nneckerchief 310\nnehra 310\nnerio 310\nnerys 310\nnewspeak 310\nnicoletti 310\nnizams 310\nnlaka 310\nnotman 310\nnumérique 310\noddo 310\nokeke 310\norkla 310\nosteoclasts 310\nostertag 310\noverspending 310\npadmavati 310\npapilloma 310\nparineeta 310\nparmenter 310\npeak's 310\nphalonidia 310\nphrik 310\nplautius 310\npolamalu 310\npriit 310\nprioritizes 310\nprocures 310\npronovost 310\npsychos 310\npyx 310\nquinoline 310\nréveil 310\nramprakash 310\nregenstein 310\nreggiani 310\nrenko 310\nromorantin 310\nsørlandet 310\nsükhbaatar 310\nsaenuri 310\nsagacity 310\nsalkeld 310\nsanderson's 310\nsavana 310\nschoonover 310\nseoane 310\nshield's 310\nshika 310\nskousen 310\nskylines 310\nslaine 310\nsoha 310\nstratagems 310\nsullinger 310\nsumithra 310\nsummarization 310\nsunne 310\nsupercd 310\nswami's 310\ntamo 310\ntautological 310\ntdcj 310\ntemenggong 310\nteradata 310\nthéoden 310\ntheodosian 310\ntidally 310\ntnk 310\ntomio 310\ntranspire 310\ntrecento 310\ntrewin 310\ntrimmers 310\ntritonia 310\ntukhachevsky 310\ntutwiler 310\ntyus 310\nupazila's 310\nuqam 310\nurhobo 310\nvasilije 310\nvenal 310\nverticillium 310\nvidwan 310\nviolone 310\nviscose 310\nvivants 310\nvtj 310\nvympel 310\nvyse 310\nwakhan 310\nwarnke 310\nwaterbuck 310\nwbur 310\nwend 310\nwieser 310\nwimbledon's 310\nwlb 310\nxianfeng 310\nyingying 310\nzelle 310\nämter 309\nålgård 309\nímair 309\nśiva 309\nгосударственный 309\nроссия 309\n和名未定 309\nadeel 309\nagyemang 309\naihara 309\nalaungpaya 309\nalcippe 309\nalouatta 309\nanastasija 309\nanthill 309\nargillite 309\narmorica 309\nashtead 309\nasifa 309\nateş 309\natharvaveda 309\natpases 309\navaricious 309\nbände 309\nbabysitters 309\nbaitul 309\nbandleaders 309\nbayu 309\nbelem 309\nberrocal 309\nbettors 309\nbhavan's 309\nbimba 309\nbingaman 309\nbirstein 309\nbossman 309\nboylagh 309\nbrochant 309\nbtp 309\nbucco 309\nbumpkin 309\nbupa 309\nbuprestidae 309\nburgee 309\nburri 309\ncadle 309\ncadorna 309\ncafta 309\ncalendula 309\ncanyon's 309\ncapleton 309\ncaraballo 309\ncatalytically 309\ncayor 309\nccccff 309\nceni 309\nchico's 309\nchrominance 309\nclash's 309\ncolchester's 309\ncoltan 309\nconvent's 309\ncousens 309\ncryogenics 309\nculbert 309\ncurcuma 309\ncyclisme 309\ndaewon 309\ndanil 309\ndanning 309\ndardo 309\ndavidic 309\ndeity's 309\ndennard 309\ndesmodium 309\ndevadas 309\ndifférence 309\ndisambiguate 309\ndistrusts 309\ndonnelly's 309\ndunciad 309\nelsom 309\nepdp 309\netheria 309\neuless 309\nevangelica 309\nextranjera 309\nfagot 309\nfaltermeyer 309\nfanelli 309\nfitzhardinge 309\nforesta 309\nforint 309\nfulwell 309\ngampel 309\ngandini 309\ngangas 309\ngeely 309\ngenç 309\ngibsons 309\ngile 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309\nmonacha 309\nmonistic 309\nmoomba 309\nmoschata 309\nmosco 309\nmyki 309\nnacreous 309\nnatha 309\nnavagraha 309\nnewbie 309\nnico's 309\nnikulin 309\nnipponica 309\nniqqud 309\nnonferrous 309\nnonuniform 309\nodysseas 309\nonderdonk 309\nonlooker 309\norbison's 309\nosram 309\novalcoach 309\npenas 309\nphrygians 309\npobla 309\npovich 309\nprestwood 309\npresumptions 309\nprosveta 309\npsychiatrie 309\npsychoses 309\npurism 309\nquinque 309\nrajmahal 309\nrashi's 309\nratione 309\nreconsecrated 309\nredl 309\nrighi 309\nrinds 309\nriquet 309\nrng 309\nrtk 309\nruficeps 309\nsalmonid 309\nsamobor 309\nsaraceni 309\nsardinians 309\nscintilla 309\nsellin 309\nsestao 309\nshinshū 309\nshiseido 309\nsiim 309\nsinopoli 309\nsisqó 309\nskl 309\nsnell's 309\nsnowfield 309\nsowmya 309\nspargo 309\nsparke 309\nspirostreptus 309\nsportatorium 309\nsteny 309\nstockmen 309\nsubmandibular 309\nsuhana 309\nsupriyo 309\nszombathelyi 309\ntakelot 309\ntammie 309\ntardini 309\ntemporalis 309\nthep 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308\ncapdeville 308\ncarwin 308\ncecropis 308\ncelestin 308\ncherrytree 308\ncheung's 308\nchot 308\nchrysococcyx 308\nchucks 308\ncomico 308\ncopsa 308\ncordilleras 308\ncornflower 308\ncryptanalyst 308\ncuckfield 308\ndalsland 308\ndamai 308\ndani's 308\ndatsyuk 308\ndeclensions 308\nderviş 308\ndespairs 308\ndipankar 308\ndirleton 308\ndissimilarity 308\nditson 308\ndomestics 308\ndorus 308\ndouse 308\ndouthit 308\neastmond 308\neelgrass 308\nekland 308\nelopes 308\nensnare 308\nepidermolysis 308\neverhard 308\nexcerpta 308\nexhume 308\nfacetious 308\nfahl 308\nfanjoy 308\nfatto 308\nferdie 308\nfeuilleton 308\nffm 308\nfilibusters 308\nfilipa 308\nflippin 308\nfolch 308\nfondmetal 308\nfoyers 308\nfreestylers 308\nfriðrik 308\nfti 308\ngadjah 308\ngijon 308\nglaucescens 308\nglenarm 308\ngojhl 308\ngored 308\ngreenhoff 308\ngurgen 308\nhäuser 308\nhabiba 308\nhalper 308\nharthill 308\nhatted 308\nhawar 308\nheadbands 308\nheathlands 308\nheda 308\nhenday 308\nhennigsdorf 308\nhubba 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286\nrauscher 286\nrazah 286\nreddin 286\nrendlesham 286\nriario 286\nroughened 286\nroughest 286\nrovinj 286\nruto 286\nsílvia 286\nsabz 286\nsanday 286\nsangyo 286\nsantoni 286\nsaqib 286\nsarı 286\nsatsujin 286\nscissurellidae 286\nsclerosing 286\nscotsmen 286\nserca 286\nseydoux 286\nshaffer's 286\nshels 286\nsilvermine 286\nsizer 286\nskc 286\nsolare 286\nspackman 286\nstaters 286\nstetter 286\nstreaker 286\nsucka 286\nsunless 286\nswedenborgian 286\nswisscom 286\nsylphide 286\nsynodus 286\ntʃ 286\ntabley 286\ntadanobu 286\ntanha 286\ntantō 286\ntapis 286\ntarski's 286\ntatlong 286\ntauragė 286\ntavolara 286\ntedi 286\ntempeh 286\nthalmann 286\nthereabouts 286\nthescelosaurus 286\ntijani 286\ntilly's 286\ntirunesh 286\ntoone 286\ntrachypithecus 286\ntrex 286\ntriplicane 286\ntrk 286\ntrudie 286\ntugging 286\nturca 286\nturre 286\ntvl 286\ntvri 286\nubb 286\nuhud 286\numar's 286\nunicoi 286\nushi 286\nvahdat 286\nvanian 286\nvermiculite 286\nvideoclips 286\nvient 286\nviliame 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283\nconfectioners 283\nconfidences 283\nconstrucciones 283\ncorrino 283\ncoseley 283\ncourtneidge 283\ncroatan 283\ncryme 283\ncyclopes 283\ncyriacus 283\ndakhla 283\ndatia 283\ndazaifu 283\ndehumanizing 283\ndeloria 283\ndemocracy's 283\ndeprotonated 283\ndesloge 283\ndestra 283\ndevery 283\ndews 283\ndgse 283\ndietikon 283\ndiosa 283\ndipterocarpus 283\ndotto 283\ndoukhobor 283\ndrumheads 283\ndysprosium 283\nellsbury 283\nenköpings 283\nentrees 283\nepenthetic 283\nestablishment's 283\nestrous 283\nfelices 283\nfilemon 283\nfordingbridge 283\nfranchisor 283\nfreshfield 283\nfriern 283\nfuruholmen 283\nfuseaction 283\ngametocytes 283\ngmmsf 283\ngonzi 283\ngosdin 283\ngrandaddy 283\ngrandnational 283\ngranulomas 283\ngregerson 283\nguesclin 283\nhade 283\nhaleakalā 283\nhanumangarh 283\nharischandra 283\nhauenstein 283\nheidi's 283\nheimann 283\nhelderberg 283\nheliocentrism 283\nhemelgarn 283\nherred 283\nhertsmere 283\nhika 283\nholyroodhouse 283\nhumourist 283\nhuyền 283\nhydroxyapatite 283\nhypsopygia 283\nillimani 283\nindaba 283\ninfarct 283\ninra 283\niser 283\nitaipu 283\njōji 283\njaps 283\njawara 283\njeffcoat 283\njejunum 283\njerdon 283\njerman 283\njerold 283\njewess 283\njousse 283\nkaahumanu 283\nkaci 283\nkanne 283\nkapok 283\nkarena 283\nkasson 283\nklubi 283\nkoevermans 283\nkojirō 283\nkozan 283\nkutless 283\nlauncelot 283\nleptospermum 283\nlindenhurst 283\nlish 283\nliwayway 283\nllg 283\nlncs 283\nlogographic 283\nlumberyard 283\nlunules 283\nmabou 283\nmadhavaram 283\nmaim 283\nmalouda 283\nmandocello 283\nmansell's 283\nmarbletown 283\nmarcus's 283\nmcbrain 283\nmcbryde 283\nmcgeady 283\nmedang 283\nmedlar 283\nmeq 283\nmerrivale 283\nmidamerica 283\nmildest 283\nminiskirt 283\nmolyneaux 283\nmombi 283\nmonifieth 283\nmostaganem 283\nmuslimeen 283\nmuzaffargarh 283\nnahanni 283\nnaivete 283\nnakia 283\nnatter 283\nnetborger 283\nntini 283\nnytorv 283\nobstructs 283\nonc 283\nongais 283\norania 283\noslo's 283\nosv 283\notunga 283\npapar 283\npecheneg 283\npecs 283\nperrault's 283\npeskin 283\npflag 283\npharmacotherapy 283\nphellinus 283\npicketers 283\npickpockets 283\npierhead 283\npiloto 283\nplasters 283\npookie 283\nportaferry 283\nporten 283\nprémat 283\npreflight 283\npriddis 283\nprizemoney 283\nprovine 283\nquileute 283\nramlila 283\nrayment 283\nrecommence 283\nredcoat 283\nredlining 283\nrehmat 283\nrelative's 283\nreligione 283\nrennen 283\nricos 283\nroché 283\nrre 283\nrrp 283\nrubes 283\nruncie 283\nsaddar 283\nschuester 283\nschwester 283\nscugog 283\nseadragon 283\nsektor 283\nsensus 283\nshaoguan 283\nshhh 283\nsidious 283\nsketchup 283\nsloterdijk 283\nsneferu 283\nsolenoids 283\nsolt 283\nsouring 283\nspaceways 283\nspolia 283\nsspx 283\nsubbarao 283\nsundaland 283\nsupertram 283\ntři 283\ntalim 283\ntaniyama 283\ntanvi 283\ntarazona 283\ntelegrapher 283\nterrytoons 283\nthorndyke 283\ntimerman 283\ntimesplitters 283\ntiye 283\ntoàn 283\ntoolroom 283\ntorda 283\ntosti 283\ntranquebar 283\ntrastámara 283\ntreccani 283\ntuberculatus 283\ntugwell 283\nturbopump 283\ntutorship 283\nuncertified 283\nvata 283\nveerendra 283\nvillagrán 283\nvineta 283\nvolkspolizei 283\nvorbeck 283\nwatcher's 283\nwaterwheels 283\nweet 283\nwhipsnade 283\nwhitewashing 283\nwoodchucks 283\nxda 283\nyeşilyurt 283\nyudin 283\nzebre 283\nzela 283\nzelma 283\nziering 283\nårstad 282\nōuchi 282\nاز 282\nabahlali 282\naberrans 282\nachy 282\nadipocytes 282\nagbu 282\nagnon 282\nahnenerbe 282\nainsworth's 282\nairfield's 282\nalutiiq 282\namancio 282\nanimalbase 282\nankaraspor 282\nanorexic 282\nantu 282\nanzus 282\narterials 282\nartzi 282\nasbjørnsen 282\nascanian 282\nator 282\nbäckström 282\nbabil 282\nbain's 282\nbakırköy 282\nbapt 282\nbarnack 282\nbeedi 282\nberryessa 282\nbesotted 282\nbezae 282\nbierut 282\nbizzle 282\nblokes 282\nbobobo 282\nbodrog 282\nbogomil 282\nboncompagni 282\nbonneval 282\nbothersome 282\nbrahmanical 282\nbrocard 282\nbrownsea 282\nbrzozowski 282\nburlington's 282\ncamelus 282\ncatasetum 282\ncatecholamine 282\nceca 282\ncentrepoint 282\ncenturia 282\ncerball 282\nchaudhri 282\nclampdown 282\nclete 282\nconcert's 282\ncontostavlos 282\nconurbations 282\ncorticotropin 282\ncrecd 282\ncrumpler 282\ncuffed 282\ncurso 282\ndasilva 282\ndatar 282\ndavern 282\ndeadlands 282\ndelecour 282\ndemerits 282\ndevildriver 282\ndiaby 282\ndiamine 282\ndickel 282\ndmax 282\ndolgorukov 282\ndomoto 282\ndrivas 282\nduchy's 282\nealdred 282\necobank 282\neffectually 282\neinbeck 282\nekins 282\nemslie 282\neragrostis 282\nerebor 282\nespera 282\nesses 282\nethnical 282\neudocia 282\nexacerbates 282\nexpeditiously 282\nexpiratory 282\nexr 282\nextricated 282\nfaema 282\nferrarese 282\nfestering 282\nfeux 282\nfiamma 282\nfiamme 282\nfireteam 282\nfka 282\nfriedrichstraße 282\nfusiformis 282\ngangapur 282\ngarko 282\nghoulish 282\ngibe 282\nginola 282\ngoncharova 282\ngorgona 282\ngorshin 282\ngracy 282\ngravid 282\ngrimwood 282\ngutt 282\nhaberdasher 282\nhaldi 282\nhaliclona 282\nhangovers 282\nhawise 282\nheinsberg 282\nhesperian 282\nhmy 282\nhordeum 282\nhortensis 282\nhuarte 282\nhymnody 282\niberica 282\nicebound 282\nignat 282\nindustrias 282\ninscriptional 282\nisda 282\nisotropy 282\nissara 282\nitou 282\niwase 282\njagdpanzer 282\njeanna 282\njenůfa 282\njinnah's 282\njittery 282\njonglei 282\njre 282\nkaabu 282\nkaientai 282\nkapit 282\nkarthala 282\nkattabomman 282\nkeldysh 282\nkeloid 282\nklopfer 282\nkommunefakta 282\nkopenhagen 282\nkostova 282\nkostrova 282\nkrakatau 282\nkristjánsson 282\nlaminitis 282\nlarin 282\nleitha 282\nlepomis 282\nliesel 282\nlightships 282\nlillies 282\nlivy's 282\nlizard's 282\nlubicz 282\nlustgarten 282\nlxii 282\nmärta 282\nmadou 282\nmaheswari 282\nmaidenhair 282\nmaintenant 282\nmalibran 282\nmanasi 282\nmcmahons 282\nmeadowland 282\nmentz 282\nmercurys 282\nmersea 282\nmeti 282\nmettur 282\nmikako 282\nmirah 282\nmishmar 282\nmng 282\nmolander 282\nmontis 282\nmoortown 282\nmotoren 282\nmoustakas 282\nmrca 282\nmunya 282\nnagri 282\nnasd 282\nnawada 282\nnesterenko 282\nneutralist 282\nneversoft 282\nnewars 282\nniro's 282\nnonconvex 282\nnorridgewock 282\nnritya 282\nnudibranchia 282\nnuttallii 282\noltre 282\nombudsman's 282\nomf 282\nongaro 282\nopenmp 282\noscorp 282\noshiro 282\noutlands 282\npadlocks 282\npaleofauna 282\npapuana 282\nparasitica 282\nparowan 282\npasukan 282\npenthouses 282\npetts 282\npillion 282\npittenger 282\nplatoon's 282\nplavi 282\npoiré 282\npomarine 282\npomeranians 282\npotentiometers 282\nproblème 282\nprofundo 282\npumaren 282\npurcellville 282\nqishan 282\nrüti 282\nravitch 282\nrdt 282\nrectitude 282\nrecycler 282\nreligieux 282\nrelinquishment 282\nrequisitions 282\nresentments 282\nresister 282\nrimrock 282\nrockie 282\nromanée 282\nroughed 282\nroutemasters 282\nryunosuke 282\nsabine's 282\nsabratha 282\nsalukis 282\nsangamam 282\nsasan 282\nsativus 282\nscei 282\nshadjam 282\nshaws 282\nshinai 282\nshrieve 282\nsimien 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281\nafyon 281\naggrey 281\naham 281\nalexandretta 281\nalmunia 281\nalsa 281\naltaïr 281\nalv 281\nanaplastic 281\nandon 281\nangelita 281\nangol 281\nannelise 281\nannihilus 281\napca 281\napgar 281\naptitudes 281\nargia 281\narpino 281\nasuna 281\naustenite 281\navie 281\naybar 281\naysha 281\nbârlad 281\nbabacar 281\nbachus 281\nbalon 281\nbarkas 281\nbeefy 281\nbernama 281\nbertrand's 281\nbhumika 281\nbimota 281\nbiopolymers 281\nblanck 281\nbluster 281\nbobblehead 281\nbodiam 281\nboissy 281\nbordeleau 281\nboxee 281\nbranstad 281\nbrau 281\nbraye 281\nbritains 281\nbronson's 281\nbroudie 281\nbue 281\ncabazon 281\ncahen 281\ncairngorm 281\ncanonicity 281\ncanterville 281\ncantieri 281\ncardon 281\ncasiopea 281\ncatie 281\ncdne 281\ncelui 281\ncerithiopsis 281\nchafed 281\nchameria 281\nchikafusa 281\ncladonia 281\ncongruences 281\nconnells 281\ncoveney 281\ncrighton 281\ndépôt 281\ndaguerreotypes 281\ndalvi 281\ndarlington's 281\ndehaan 281\ndehart 281\ndelahunty 281\ndevolving 281\ndewyze 281\ndhak 281\ndifferenced 281\ndishevelled 281\ndissing 281\ndodgeville 281\ndorsa 281\ndravidar 281\nduckula 281\nduris 281\ndyle 281\nedem 281\nedgley 281\neglantine 281\neisenbahnen 281\nekar 281\nelendil 281\nelpis 281\nempat 281\neosinophilia 281\nequalisation 281\neung 281\neuphemistic 281\neuskomedia 281\nexilic 281\nfairtex 281\nfanatically 281\nfien 281\nfilmore 281\nfitzclarence 281\nfiza 281\nflemmi 281\nflours 281\nfluting 281\nfotografia 281\nfountain's 281\nfrf 281\nfriml 281\nfrolich 281\ngahanna 281\ngangrel 281\ngargi 281\ngazettes 281\ngellish 281\nglenanne 281\ngompertz 281\ngonne 281\ngonzález's 281\ngoofy's 281\ngrammis 281\ngrowlanser 281\nguðjónsson 281\ngukanshō 281\ngulags 281\ngulberg 281\ngwalia 281\nhát 281\nhalloway 281\nheine's 281\nhenlow 281\nhilts 281\nhitchhike 281\nhodge's 281\nhogna 281\nhollick 281\nhollmann 281\nholzapfel 281\nhoofddorp 281\nhorlivka 281\nhorloge 281\nhrk 281\nhumorists 281\nhungover 281\nhunret 281\nhydroids 281\nibjjf 281\nifvs 281\ninds 281\ningrian 281\ninocente 281\ninvalidation 281\niriga 281\niwfl 281\njamal's 281\njamb 281\njameela 281\njawless 281\njointure 281\njsdf 281\nkalavryta 281\nkatsuyori 281\nkersh 281\nkharghar 281\nkhela 281\nkhushal 281\nkombëtar 281\nkomu 281\nkumagaya 281\nlamberg 281\nlangreo 281\nlangworthy 281\nleathernecks 281\nlelo 281\nlexcorp 281\nlgu 281\nliliom 281\nllanishen 281\nlopate 281\nlunchroom 281\nlupertazzi 281\nluuk 281\nmannington 281\nmantling 281\nmarsal 281\nmatheos 281\nmehta's 281\nmergentheim 281\nmidships 281\nmidway's 281\nmlh 281\nmohan's 281\nmontemor 281\nmopane 281\nmrinalini 281\nmudanjiang 281\nmuis 281\nmuluk 281\nmutational 281\nnanai 281\nnandurbar 281\nnarowal 281\nnavvies 281\nndm 281\nnemoto 281\nneris 281\nniaid 281\nnicomachean 281\nnoten 281\nnqakula 281\nnystad 281\nocasio 281\noecs 281\nolier 281\npacesetter 281\npanfilov 281\npantaloons 281\npaolozzi 281\nparimutuel 281\nparreira 281\npasarell 281\npasewalk 281\npassionfruit 281\npepsin 281\nperoz 281\npilson 281\npleasonton 281\nplunderers 281\npoignantly 281\npolylepis 281\nporcelains 281\npoveda 281\nprigogine 281\nprivatise 281\nquinby 281\nrécits 281\nrényi 281\nrød 281\nracewalking 281\nranier 281\nraud 281\nredmon 281\nreframing 281\nreinking 281\nrenna 281\nretailer's 281\nreverential 281\nrichlands 281\nrigours 281\nritratto 281\nriverbeds 281\nrobber's 281\nrocard 281\nrockenfeller 281\nrostom 281\nroughead 281\nruairi 281\nsœur 281\nsair 281\nsalvageable 281\nsangu 281\nsarajevo's 281\nsaravana 281\nsarreguemines 281\nsavang 281\nschönfeld 281\nschjelderup 281\nschreber 281\nscientism 281\nsdxc 281\nserjeants 281\nservicemen's 281\nshōhei 281\nshoeburyness 281\nshorenstein 281\nshrivenham 281\nshtick 281\nshwetha 281\nsikharulidze 281\nsin's 281\nsinna 281\nsitra 281\nskycable 281\nskyros 281\nslagle 281\nsollers 281\nsortland 281\nsoutine 281\nsparkbrook 281\nspittle 281\nspotland 281\nsrilankan 281\nsrtm 281\nstabilisers 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280\natharva 280\nathey 280\natropatene 280\natsimo 280\natv's 280\nautocannons 280\naveo 280\navilla 280\nbaghpat 280\nbajra 280\nbanfi 280\nbarberis 280\nbaty 280\nbernadette's 280\nbianche 280\nbisect 280\nblé 280\nblakelock 280\nblatche 280\nblears 280\nboakye 280\nbobigny 280\nbrayshaw 280\nbrazelton 280\nbroadbill 280\ncaleb's 280\ncalpurnia 280\ncaractacus 280\ncarchemish 280\ncaspary 280\ncastillejo 280\ncdrom 280\ncelos 280\ncerveza 280\nchebucto 280\ncholinesterase 280\nchown 280\ncircassia 280\ncix 280\nclague 280\ncoens 280\ncontempo 280\ncourson 280\ncradling 280\ncraigmillar 280\ncurried 280\ndarc 280\ndaryn 280\ndehlavi 280\ndeodar 280\ndesrochers 280\ndestructions 280\ndhammapada 280\ndiagne 280\ndictyna 280\ndiepholz 280\ndingos 280\ndisinvestment 280\ndismembering 280\ndisquieting 280\ndobrin 280\ndomodossola 280\ndonath 280\ndonkey's 280\ndosed 280\ndoubters 280\ndouwe 280\ndragline 280\ndrunkards 280\ndutkiewicz 280\nefsa 280\neinsteinium 280\neita 280\nelymus 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280\ntorridon 280\ntrist 280\ntrompette 280\ntsuboi 280\nttu 280\ntuchet 280\ntucholsky 280\ntyche 280\ntywin 280\nunderbody 280\nunrra 280\nvelikaya 280\nventi 280\nvictorien 280\nvigier 280\nvijayam 280\nvoltio 280\nwachau 280\nwasat 280\nwayles 280\nwedd 280\nwelcher 280\nwelham 280\nwerburgh 280\nwestberg 280\nweyler 280\nwigger 280\nwolsey's 280\nwynalda 280\nyamoussoukro 280\nyanji 280\nzìzhìxiàn 280\nzadora 280\nzamfir 280\nzarek 280\nzemlinsky 280\nzulema 280\nújezd 279\nša 279\nсоюза 279\nरस 279\naastha 279\nabbs 279\nacousmatic 279\naggressions 279\nahrweiler 279\naie 279\nalderete 279\nalexeyevich 279\namirul 279\namphipod 279\nanaerobes 279\nannelid 279\nannelies 279\napion 279\napsaras 279\napthorp 279\narbi 279\narkangel 279\narolsen 279\nasprilla 279\nautzen 279\navnet 279\naxp 279\nbabysitter's 279\nbarkingside 279\nbgo 279\nbhopali 279\nbiałowieża 279\nbichon 279\nbifid 279\nbilardo 279\nbilas 279\nbirchard 279\nbivona 279\nbiwako 279\nblaire 279\nbludgeoning 279\nbolsena 279\nboxing's 279\nbradykinin 279\nbreeder's 279\nbuba 279\ncaesariensis 279\ncelebre 279\ncenta 279\nchafin 279\ncharalambos 279\ncharcas 279\nchassidic 279\nchavdar 279\nchileno 279\nchlorosis 279\ncivilization's 279\nclappers 279\ncleckheaton 279\ncollocated 279\nconfidentially 279\nconnexxion 279\ncontinental's 279\ncorynebacterium 279\ncrimewatch 279\ncryptids 279\ncurro 279\ncuscus 279\ndöring 279\ndaevid 279\ndaffney 279\ndaffy's 279\ndarah 279\ndarey 279\ndart's 279\ndehiscence 279\ndelighting 279\ndelorenzo 279\ndichotoma 279\ndilruba 279\ndistinctness 279\ndjalma 279\ndoakes 279\ndongping 279\ndonne's 279\ndosbox 279\ndovber 279\ndraconarius 279\ndropper 279\ndujiangyan 279\nduprez 279\ndynamo's 279\neab 279\neccleshall 279\nechenique 279\neddyville 279\neem 279\nelicitation 279\nespaces 279\nestense 279\neuropéennes 279\nevildoers 279\nextempore 279\nfardc 279\nfaried 279\nferrard 279\nfev 279\nfiche 279\nflashover 279\nfluoresce 279\nfnr 279\nfodor's 279\nforcade 279\nfrío 279\nfukutoshin 279\ngabriola 279\ngagging 279\ngelre 279\ngenom 279\ngeochemist 279\ngeorgine 279\ngits 279\nglobalism 279\nglossolalia 279\nglr 279\nglutamyl 279\ngobble 279\ngoddaughter 279\ngomery 279\ngotlieb 279\ngramma 279\ngrignon 279\ngsis 279\nguare 279\nguerres 279\nguthrum 279\nharking 279\nhatch's 279\nhedgecock 279\nhendre 279\nheraclides 279\nhercynian 279\nhindukush 279\nhirsch's 279\nhizon 279\nhott 279\nhuie 279\nhumpy 279\nhwee 279\nhypergolic 279\nhypoleuca 279\nimmokalee 279\ninflexibility 279\ninforme 279\ninternee 279\ninterschool 279\ninvasores 279\nipiranga 279\nisole 279\nisshu 279\nitty 279\njeanne's 279\njibe 279\nkabadi 279\nkairuku 279\nkalachakra 279\nkallmann 279\nkataragama 279\nkatoh 279\nketoacidosis 279\nkipruto 279\nkisaeng 279\nkleinfeld 279\nkleptomania 279\nknab 279\nkneeland 279\nknitter 279\nkodavas 279\nkonik 279\nkosha 279\nkottakkal 279\nkušnirák 279\nkugimiya 279\nlamangan 279\nlanczos 279\nlegea 279\nlevein 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264\nvasiljević 264\nveluwe 264\nvespertine 264\nvessey 264\nvictrola 264\nvirgenes 264\nviswa 264\nvittoriosa 264\nvivitar 264\nwakeford 264\nwanda's 264\nwarrenville 264\nwarszawie 264\nweobley 264\nwerkbund 264\nwestleigh 264\nwetteland 264\nwitwer 264\nwoodthorpe 264\nwuerttemberg 264\nwyon 264\nxinmin 264\nyaquis 264\nyonaguni 264\nyoshifumi 264\nyounge 264\nyuhua 264\nyyz 264\nzcta 264\nçağla 263\nóbidos 263\nđôn 263\nบลราชธาน 263\naïr 263\nabbeydale 263\nabdera 263\nagit 263\nagor 263\nahola 263\nalak 263\nalboin 263\nalfort 263\nalleen 263\nanálisis 263\nanden 263\nangelicus 263\nankrum 263\nannal 263\nantipodean 263\napicella 263\nappert 263\narcadium 263\nartashat 263\naubigné 263\naugustus's 263\navezzano 263\nazéma 263\nbénédict 263\nbacio 263\nbaciu 263\nbackstories 263\nbadie 263\nbalut 263\nbaoli 263\nbarged 263\nbarnston 263\nbarzan 263\nbatajnica 263\nbaumgardner 263\nbeautifying 263\nberthiaume 263\nbisection 263\nbitz 263\nbluemont 263\nbluesfest 263\nbrabin 263\nbrame 263\nbreakable 263\nbryum 263\ncahit 263\ncaillebotte 263\ncarollo 263\ncarrà 263\ncarte's 263\ncastaldi 263\ncavaquinho 263\ncentrosome 263\nchaffin's 263\nchaguanas 263\nchaitya 263\nchasez 263\nchazy 263\nchemische 263\nchilworth 263\nchinami 263\nchissano 263\nchristophersen 263\nchynna 263\nciclismo 263\ncisa 263\nclervaux 263\ncleverley 263\ncofferdam 263\ncommandement 263\nconcentus 263\nconlee 263\ncorey's 263\ncorsicans 263\ncrawlspace 263\ncrede 263\ncrossflow 263\ncubi 263\ndamayanthi 263\ndanquah 263\ndarwinia 263\ndeak 263\ndebridement 263\ndecrypting 263\ndelacour 263\ndendera 263\ndeploring 263\ndespero 263\ndewald 263\ndiamanti 263\ndigamma 263\ndipa 263\ndisinfect 263\nditchley 263\ndoleman 263\ndoti 263\ndouwes 263\nedendale 263\neei 263\nefik 263\neliphas 263\nelveden 263\nemilius 263\nennius 263\nennore 263\nenumclaw 263\nesperantists 263\nesposa 263\nexile's 263\nférussac 263\nfargo's 263\nfarsa 263\nfatback 263\nfgw 263\nfirefinch 263\nflabby 263\nflankers 263\nfoetal 263\nfortnum 263\nfortunio 263\nfoxcatcher 263\nfreiin 263\ngalbula 263\ngampola 263\ngehrke 263\ngeiberger 263\ngekas 263\ngerasimos 263\ngigi's 263\ngik 263\nginty 263\nglenoid 263\ngoines 263\ngolovkin 263\ngonatas 263\ngründerzeit 263\ngrobler 263\ngroupoids 263\nguiné 263\ngup 263\ngupt 263\ngynecomastia 263\nhalychyna 263\nhanter 263\nharuno 263\nhasemi 263\nhassles 263\nhegan 263\nhelal 263\nherbals 263\nherrenberg 263\nhiginbotham 263\nhispánica 263\nhocret 263\nhofheinz 263\nhohaia 263\nholistically 263\nhutia 263\nhydrogel 263\nifex 263\nilahi 263\nilladopsis 263\niml 263\ninquart 263\ninspiratory 263\nintermixing 263\nisee 263\njakab 263\njobing 263\njulep 263\nkadazan 263\nkalay 263\nkalmia 263\nkamuzu 263\nkarawang 263\nkayama 263\nkellum 263\nkempson 263\nkenfig 263\nkeratoderma 263\nkhafre 263\nkillorglin 263\nkitayama 263\nkoans 263\nkoibito 263\nkokubunji 263\nkolhapure 263\nkommentar 263\nkoranic 263\nkristyn 263\nkrokodil 263\nktvk 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262\nclawing 262\nclebsch 262\ncodazzi 262\ncoelom 262\ncolegiales 262\ncolling 262\ncombust 262\ncompactor 262\ncoover 262\ncrac 262\ncronaca 262\ndagesh 262\ndallas's 262\ndangdut 262\ndasypus 262\ndebevoise 262\ndelaître 262\ndeplores 262\nderksen 262\ndetto 262\ndiatessaron 262\ndiscurso 262\ndismutase 262\ndisrespecting 262\ndodaf 262\ndomestique 262\nduars 262\nduchesnay 262\ndurcan 262\ndurutti 262\necclesiasticus 262\nehrenburg 262\neirini 262\nemond 262\nenac 262\nenercon 262\nepigastric 262\nerebuni 262\nerechthias 262\neritrea's 262\nerni 262\nesquisse 262\neuchre 262\neurobrun 262\nfeodora 262\nfiebre 262\nfipa 262\nfirouz 262\nfogelman 262\nfraile 262\nfreemans 262\nftw 262\nfukuchiyama 262\nfundazioa 262\ngadgil 262\ngaelscoil 262\ngalad 262\ngallos 262\ngalva 262\ngandzasar 262\ngarioch 262\ngendt 262\ngeomorphic 262\ngerau 262\nghola 262\nglebova 262\ngolubović 262\ngpw 262\ngrider 262\ngrimace 262\ngujaratis 262\nhässleholm 262\nhakohen 262\nhalcon 262\nhalk 262\nhanin 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249\nbenetti 249\nberkner 249\nbevo 249\nbickersteth 249\nbilt 249\nblacktail 249\nblighty 249\nbožena 249\nbobov 249\nbossiney 249\nboustany 249\nbraide 249\nbriúin 249\nbuchla 249\nbyculla 249\ncánovas 249\ncélia 249\ncabras 249\ncardone 249\ncaribbeans 249\nchamisso 249\nchandrasekharan 249\nchapin's 249\nchristophorus 249\nchromic 249\nclearlake 249\nclimaco 249\nclimber's 249\ncocteau's 249\ncomitative 249\ncontrada 249\ncorneas 249\ncoucher 249\ncowman 249\ncpc's 249\ncrinoline 249\ncromlech 249\nculverin 249\ncutlers 249\ncyn 249\ndagang 249\ndamour 249\ndarryll 249\ndedede 249\ndelict 249\ndeltona 249\ndemott 249\ndeportiu 249\nderivable 249\ndesamparados 249\ndiriangén 249\ndirlewanger 249\ndishonorably 249\ndivus 249\ndonlavey 249\ndušan's 249\nduret 249\ndynomutt 249\nebell 249\neckhoff 249\nedfu 249\nedwardians 249\neggnog 249\neichendorff 249\neiler 249\neklavya 249\nelibank 249\nelitsa 249\nelvira's 249\nenas 249\nennomos 249\nenta 249\nentangle 249\nescuintla 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249\nnachiketa 249\nnaj 249\nnakusp 249\nnapoletano 249\nnarayan's 249\nnaus 249\nnaysmith 249\nnec's 249\nneelkanth 249\nneera 249\nnellee 249\nnobler 249\nnobs 249\nnobunari 249\nnoyelles 249\nnuraghe 249\nnuva 249\nnux 249\nnybergsund 249\noakhill 249\noberhasli 249\nohira 249\nomake 249\nomt 249\nontologically 249\nonuora 249\nopara 249\norganismal 249\norgazam 249\noscillated 249\npéguy 249\npétange 249\npachyptila 249\npalmela 249\npanniers 249\npatenaude 249\npeñafiel 249\npeñuelas 249\npemigewasset 249\nperfil 249\npermatang 249\npfk 249\npicumnus 249\nponson 249\npoquoson 249\nporcellio 249\nporcia 249\npramukh 249\npromethea 249\nproselyte 249\npurdom 249\nputts 249\npytilia 249\nqods 249\nquarrington 249\nquichua 249\nrakel 249\nransdell 249\nraunds 249\nrecertification 249\nredbox 249\nrekka 249\nrichthofen's 249\nrieflin 249\nrigan 249\nriksmål 249\nrimando 249\nripoff 249\nriverworld 249\nrizla 249\nrogowski 249\nrosellini 249\nrosenquist 249\nrusts 249\nryuzo 249\nsài 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244\nmaybin 244\nmazhai 244\nmcginness 244\nmckale 244\nmckillip 244\nmeatless 244\nmeegeren 244\nmegathrust 244\nmeilleure 244\nmelanistic 244\nmendizabal 244\nmerovingians 244\nmerzifon 244\nmichalek 244\nmichinaga 244\nmicrovilli 244\nminoa 244\nmistle 244\nmontecuccoli 244\nmoretto 244\nmotmot 244\nmuiredaig 244\nmukhyamantri 244\nmulticenter 244\nmurgia 244\nmurut 244\nmustn 244\nmycenean 244\nnabbed 244\nnaogaon 244\nnazmi 244\nnecropsy 244\nneolophonotus 244\nneophron 244\nnitrification 244\nnitsch 244\nnlg 244\nnordwind 244\nnowlin 244\nnuristani 244\noberfähnrich 244\noctavianus 244\nokur 244\norientals 244\norti 244\nostentation 244\novertown 244\npérignon 244\npace's 244\npachyderm 244\npadmanabhapuram 244\npansori 244\nparmalee 244\npaulius 244\npenicillins 244\npepfar 244\npericrocotus 244\npersecutor 244\nphilippeville 244\npiarco 244\npioli 244\npittock 244\nplumptre 244\nplutons 244\npolíticas 244\npolsky 244\nporges 244\npotbelly 244\nppaca 244\nprairial 244\nprepress 244\nprovidence's 244\npulman 244\npuvis 244\nqbs 244\nquartermasters 244\nquenneville 244\nrasau 244\nraskulinecz 244\nraylene 244\nrazorbill 244\nreciprocation 244\nrees's 244\nreial 244\nreichenhall 244\nreinstates 244\nrepetto 244\nreticulation 244\nretrofits 244\nrevathy 244\nrhodopes 244\nriemenschneider 244\nriesco 244\nringmer 244\nrobat 244\nrohwer 244\nromare 244\nrtsp 244\nsachie 244\nsakigake 244\nsamatar 244\nsambuca 244\nsanaag 244\nsanguszko 244\nsantuzza 244\nsaraceno 244\nsarga 244\nsarhad 244\nscandinavica 244\nschmidti 244\nschuschnigg 244\nsciaky 244\nseka 244\nsemiclassical 244\nshamba 244\nshaz 244\nsherratt 244\nshortall 244\nshoveling 244\nshudra 244\nsiegelman 244\nsitdown 244\nskuse 244\nsmythe's 244\nsneap 244\nsnomed 244\nsonore 244\nsoward 244\nsowie 244\nspick 244\nsprayer 244\nsr_ 244\nsteadied 244\nsteinweg 244\nstilling 244\nsubtests 244\nsydal 244\nsysoev 244\ntahsin 244\ntapasya 244\ntarar 244\nterzić 244\nthần 244\nthorkildsen 244\ntierce 244\ntihanyi 244\ntilehurst 244\ntlds 244\ntonalá 244\ntotalweeks 244\ntott 244\ntraduit 244\ntrinomial 244\ntselina 244\nturnbuckles 244\ntyas 244\nudaan 244\nudayan 244\nudmurtia 244\nuja 244\nultrafilter 244\nunaccented 244\nurubamba 244\nuserspace 244\nværøy 244\nvacuuming 244\nvalérien 244\nvalenta 244\nvaren 244\nvido 244\nvigra 244\nvikram's 244\nvinayan 244\nvirton 244\nvista's 244\nwadjet 244\nwatchin 244\nwaterbodies 244\nwaterspouts 244\nweiher 244\nweisel 244\nwemba 244\nwenk 244\nwesternport 244\nwestridge 244\nwetten 244\nwhicher 244\nwhipray 244\nwillst 244\nwindscale 244\nwordsmith 244\nwspa 244\nxco 244\nxizhi 244\nxueliang 244\nyarkon 244\nysl 244\nyusoff 244\násatrú 243\nåhlund 243\núr 243\nकल 243\nधर 243\nḥasan 243\nañasco 243\nabbadie 243\nabbo 243\nadobes 243\naegyptiacus 243\nafterparty 243\nagne 243\nahlers 243\nakseli 243\nakutan 243\nallotting 243\namauri 243\nambrus 243\nanacletus 243\nankush 243\nannihilates 243\nanochetus 243\narbëreshë 243\narkiv 243\narnoldus 243\naromaticity 243\narpeggiated 243\nasman 243\nauditionees 243\navd 243\naveril 243\nazamat 243\nazat 243\nbabe's 243\nbacs 243\nbafut 243\nbaghi 243\nbannan 243\nbantayan 243\nbashir's 243\nbastet 243\nbayardo 243\nbefuddled 243\nbellator's 243\nbennink 243\nbern's 243\nbgle 243\nbhor 243\nbindery 243\nblackwing 243\nblankers 243\nblitar 243\nbloc's 243\nblythswood 243\nbongani 243\nbordighera 243\nbracero 243\nbracton 243\nbrandy's 243\nbreathers 243\nbretz 243\nbroncs 243\nbrookgreen 243\nbrunner's 243\nbuchi 243\nbuffets 243\nburnel 243\nbwgv 243\ncường 243\ncambriae 243\ncanonum 243\ncarless 243\ncashiered 243\ncatholiques 243\nccx 243\nceinture 243\nchögyam 243\nchaque 243\nchaudry 243\nchepauk 243\nchicxulub 243\nchinense 243\nchipley 243\nchobits 243\nchorthippus 243\nchukyo 243\nchuter 243\ncimitero 243\nciotat 243\ncircularity 243\nclason 243\nclaye 243\nconceptualism 243\nconjunctival 243\nconnectionist 243\nconvolutions 243\nconways 243\ncreighton's 243\ncriminal's 243\ncriminalisation 243\ncrossways 243\ncullis 243\ncya 243\ndalt 243\ndazz 243\ndecurrens 243\ndelfonics 243\ndesprez 243\ndiamanda 243\ndico 243\ndidone 243\ndilshad 243\ndisincentive 243\ndissociatives 243\ndokkum 243\ndorot 243\ndrott 243\ndumpy 243\neddine 243\neist 243\neivissa 243\nemerich 243\nencase 243\nennarea 243\nepico 243\nepihl 243\nergin 243\nernst's 243\nessarts 243\netchemin 243\nethmoid 243\nevaporite 243\nfabulosos 243\nfarhang 243\nfarooqi 243\nfedak 243\nfierceness 243\nfixations 243\nflagman 243\nflorilegium 243\nfluidic 243\nfoday 243\nforaged 243\nforbes's 243\nfruška 243\nfruela 243\nfusō 243\nfuscicollis 243\ngasps 243\ngesat 243\ngilo 243\ngismondi 243\ngoethite 243\ngrabens 243\ngrizedale 243\ngroix 243\ngubaidulina 243\ngunasekara 243\ngymnoscelis 243\nhaikai 243\nhatton's 243\nhebi 243\nhedeby 243\nhemionus 243\nhillman's 243\nhleb 243\nhongshan 243\nhoratia 243\nhorsetails 243\nhyakutake 243\nhypoallergenic 243\niasb 243\niftar 243\nilian 243\nilli 243\nimb 243\nimpellitteri 243\ninode 243\ninterlopers 243\nintermedium 243\ninterstitials 243\nisesaki 243\njõhvi 243\njavafx 243\njohnno 243\njozo 243\njuco 243\njudgeships 243\njuggles 243\nkaisers 243\nkakhovka 243\nkalender 243\nkalinić 243\nkarapet 243\nkeer 243\nkender 243\nketsana 243\nkhaleel 243\nkharijite 243\nkiew 243\nkomissarov 243\nkormos 243\nkotelawala 243\nkov 243\nksawery 243\nksb 243\nktrk 243\nlöf 243\nlachnocnema 243\nlafe 243\nlamesa 243\nlamouroux 243\nlanson 243\nlaroque 243\nlaso 243\nlastname 243\nlaxness 243\nleadbelly 243\nleafhoppers 243\nlesnoy 243\nlipoxygenase 243\nllws 243\nlogbooks 243\nloiselle 243\nlysons 243\nmétal 243\nmacmichael 243\nmacrobathra 243\nmacshane 243\nmalins 243\nmambas 243\nmanou 243\nmarchei 243\nmarji 243\nmatu 243\nmaulbetsch 243\nmccoo 243\nmckey 243\nmeiners 243\nmengo 243\nmeril 243\nmetanoia 243\nmetreveli 243\nmiani 243\nmikro 243\nmlse 243\nmome 243\nmonna 243\nmonomials 243\nmool 243\nmsy 243\nmunnings 243\nmyaskovsky 243\nmyjava 243\nmyrmica 243\nnaimark 243\nnairi 243\nnanango 243\nnaupactus 243\nnccc 243\nnermin 243\nneuruppin 243\nnevile 243\nnfi 243\nnghe 243\nniblett 243\nnordine 243\nnorthern's 243\nnurnberg 243\nnvq 243\nnwl 243\nnyeo 243\nokuyama 243\nolbrich 243\noocysts 243\noptica 243\northodontist 243\nossoliński 243\nossuaries 243\noswegatchie 243\nowensville 243\nownerships 243\npanduranga 243\npasuruan 243\npedicab 243\npeps 243\nperlo 243\npetrovski 243\nphengaris 243\nphenotypically 243\nphilobota 243\nphotoshoots 243\npichel 243\npietilä 243\npietrangeli 243\npirzada 243\nplimmerton 243\npolatsk 243\npolet 243\npolos 243\npolytrack 243\npozuelo 243\npreparative 243\nprocellariids 243\nprohaska 243\npunkt 243\nquella 243\nquitely 243\nröckel 243\nrückert 243\nrabbitte 243\nradhe 243\nradialis 243\nranee 243\nrashied 243\nrastrelli 243\nrathcline 243\nrattenbury 243\nrawsthorne 243\nreadme 243\nregna 243\nrehhagel 243\nreichlen 243\nrenjie 243\nreorient 243\nreparative 243\nreportable 243\nrichelle 243\nrichet 243\nrizza 243\nroguish 243\nrollerblading 243\nrusport 243\nryswick 243\nsalzkammergut 243\nsanctifying 243\nsatirises 243\nsaudagar 243\nsavitt 243\nscaf 243\nscheidegg 243\nschlumpf 243\nschmied 243\nschoonhoven 243\nschoorl 243\nscrymgeour 243\nseena 243\nseiyūkai 243\nselenia 243\nselfishly 243\nsenones 243\nsequined 243\nserano 243\nseras 243\nshawneetown 243\nsherlock's 243\nshinjin 243\nshinkage 243\nshuisky 243\nsipp 243\nskylarks 243\nsmallpipes 243\nsohma 243\nsoleil's 243\nsoleimani 243\nsolemnized 243\nsongtsän 243\nsoyuzmultfilm 243\nsterilizing 243\nstono 243\nstows 243\nsuhrawardi 243\nsupermoto 243\nsupertones 243\nsupervisor's 243\nsurkh 243\nsuturing 243\nsyncs 243\nsynuclein 243\ntabaluga 243\ntajikistani 243\ntaneytown 243\ntarentaise 243\ntebbetts 243\ntessar 243\nteter 243\ntetrastyle 243\nthaçi 243\nthielen 243\nthrelkeld 243\nthymic 243\ntiegs 243\ntiggy 243\ntrackpad 243\ntroparion 243\ntulln 243\nuks 243\numberleigh 243\nunemotional 243\nunmotivated 243\nunspecific 243\nupb 243\nupg 243\nurnes 243\nutamaro 243\nvéhicule 243\nvairocana 243\nvelázquez's 243\nventris 243\nverica 243\nview's 243\nviluppuram 243\nvingtaine 243\nvinne 243\nvios 243\nvirial 243\nvishu 243\nvlasic 243\nwaldo's 243\nwalram 243\nwanga 243\nwben 243\nweinreb 243\nwelten 243\nwestling 243\nwestpoint 243\nwetzler 243\nwhbq 243\nwimpfen 243\nwinnicott 243\nwinterswijk 243\nwintu 243\nwrenched 243\nwuab 243\nyorke's 243\nzakarian 243\nzoomable 243\nzuikaku 243\nzweifel 243\nözgen 242\nседмица 242\nपत 242\nabzug 242\nadbusters 242\naeroclub 242\naesthete 242\naffordances 242\nahron 242\nalbuquerque's 242\nallardice 242\naltis 242\namalekites 242\namericanist 242\napco 242\nappanoose 242\narrester 242\narye 242\nasashōryū 242\nascetical 242\nashrafi 242\naskins 242\nassim 242\natomistic 242\naubade 242\naughton 242\nauricula 242\nazolla 242\nbíró 242\nbackroads 242\nbajío 242\nbakrie 242\nballetto 242\nballsbridge 242\nbarrionuevo 242\nbartolozzi 242\nbattistelli 242\nbearsted 242\nbenzoquinone 242\nbettiah 242\nbhavya 242\nbirthplaces 242\nblanqui 242\nblistered 242\nbluejackets 242\nbootheel 242\nbouaké 242\nbourda 242\nbouzid 242\nbrioche 242\nbritishness 242\nbucksport 242\ncalamotropha 242\ncalcined 242\ncalosoma 242\ncaples 242\ncaponi 242\ncarbanion 242\ncardan 242\ncasl 242\ncassiterite 242\ncastrillo 242\ncastronovo 242\ncharioteers 242\nchinar 242\nchott 242\ncinequest 242\ncitywalk 242\nclementino 242\ncollot 242\ncomfy 242\nconcessional 242\nconsulta 242\ncornstalk 242\ncoudenhove 242\ncreed's 242\ncrimewave 242\ncristopher 242\ncrossen 242\ncryptosystems 242\ncuddington 242\ncuello 242\ncutback 242\ncuyp 242\ndaura 242\ndeafblind 242\ndesio 242\ndesson 242\ndeusto 242\ndiagonalizable 242\ndieguito 242\ndoen 242\ndragway 242\nduckweed 242\nduignan 242\nduoprism 242\ndvůr 242\ndvla 242\nearplugs 242\nelution 242\nely's 242\nenablers 242\nepiscopatuum 242\nepoxides 242\neridge 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242\nomega's 242\nonoba 242\noptimo 242\nordsall 242\norekhov 242\noverachiever 242\npacifistic 242\npadakkama 242\npagulayan 242\npaintal 242\npanathenaic 242\nparthenay 242\npatito 242\npeñasco 242\npemiscot 242\npenrhos 242\npergamino 242\nperloff 242\nperrelli 242\nperversely 242\npetróleos 242\npettifer 242\npfaffenhofen 242\nphotophobia 242\nphyllodes 242\npick's 242\npilate's 242\npilette 242\npisanello 242\npiv 242\npjm 242\nplunkett's 242\npostobón 242\nprashanta 242\npresentment 242\nprovostry 242\npullar 242\npummel 242\npumpers 242\npurush 242\nrauner 242\nredcedar 242\nregidor 242\nreinforcer 242\nreinsdorf 242\nressler 242\nreverently 242\nrhel 242\nrlif 242\nrobic 242\nrohff 242\nrokita 242\nronstadt's 242\nroyapuram 242\nsabbatarian 242\nsafecracker 242\nsaifi 242\nsannomiya 242\nsason 242\nsberbank 242\nscarboro 242\nschwassmann 242\nsebesky 242\nsecundaria 242\nseismologists 242\nsenador 242\nsettlor 242\nshūji 242\nsheni 242\nshiden 242\nshikar 242\nshinin 242\nshinozuka 242\nsicamous 242\nsicels 242\nskybolt 242\nslumbers 242\nsnh 242\nspeyeria 242\nsponsa 242\nsreten 242\nstai 242\nstriatal 242\nsumana 242\nsummertown 242\nswaddling 242\nszalay 242\ntōshō 242\ntannu 242\ntatsuno 242\nteleostei 242\ntellingly 242\ntemüjin 242\nthiem 242\nthiomersal 242\nthraco 242\ntingwell 242\ntobyhanna 242\ntondaiman 242\ntopoquest 242\ntorreon 242\ntoubou 242\ntrav 242\ntremper 242\ntriaxial 242\ntrimesters 242\ntrimix 242\ntuero 242\ntukulti 242\ntunggal 242\nturgon 242\nuchuu 242\numstead 242\nunoci 242\nvaldarno 242\nvanbiesbrouck 242\nvelibor 242\nvenizelist 242\nvesci 242\nvete 242\nvillacoublay 242\nvillaroel 242\nvinaigrette 242\nvindolanda 242\nweeper 242\nwennemars 242\nwesties 242\nwgrz 242\nwhfs 242\nwijesinghe 242\nwinneshiek 242\nwinterson 242\nwmds 242\nwujing 242\nxplorer 242\nybp 242\nyli 242\nyllana 242\nzeek 242\nzions 242\nzlatna 242\nzubarah 242\nzygotic 242\nözlem 241\nōsaki 241\nştiinţa 241\nšvitrigaila 241\nžemlja 241\nवत 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238\nmitsumasa 238\nmodernista 238\nmollohan 238\nmonarchie 238\nmoncure 238\nmonkeyflower 238\nmonopolised 238\nmontalva 238\nmukerjee 238\nmullarkey 238\nmumin 238\nmuntari 238\nmutase 238\nmws 238\nmynah 238\nnaff 238\nnanditha 238\nnanostructured 238\nnansouty 238\nnavigability 238\nncss 238\nneeru 238\nnegai 238\nnephila 238\nneukirchner 238\nneurofeedback 238\nnicodemou 238\nnoemfoor 238\nofhousehold 238\norbost 238\norigen's 238\norishas 238\norrego 238\norzeł 238\notep 238\nouarzazate 238\noutshine 238\npalapa 238\npaled 238\npalika 238\nparaphilias 238\nparce 238\nparvat 238\npastorates 238\npatai 238\npavana 238\npayyanur 238\npeattie 238\npedaliodes 238\npensar 238\npharyngitis 238\npianist's 238\npiesse 238\npintér 238\npla's 238\npmf 238\npointes 238\npoirée 238\npolytech 238\nportamento 238\npoyang 238\nprofs 238\npsidium 238\nradjabov 238\nramesan 238\nramstad 238\nranna 238\nrasc 238\nredbull 238\nregesta 238\nregn 238\nremunerative 238\nreworks 238\nrheostatics 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237\nprayerful 237\npriče 237\nprimigenius 237\nprokopenko 237\npromontories 237\nptas 237\npuddy 237\nquestia 237\nrajamani 237\nrammasun 237\nrancourt 237\nrautenbach 237\nrecliner 237\nrecoils 237\nredouble 237\nrefractories 237\nreloads 237\nrentz 237\nrevokes 237\nrheidol 237\nriegle 237\nrimer 237\nrockridge 237\nrockstars 237\nroesch 237\nrosario's 237\nroussin 237\nrudolfo 237\nrugosus 237\nrumeli 237\nsabzevar 237\nsackhoff 237\nsadanand 237\nsamsø 237\nsanhe 237\nsanitarias 237\nscarborough's 237\nschaum 237\nschizoaffective 237\nscriba 237\nscrutinizing 237\nseawell 237\nsegal's 237\nsenan 237\nsetts 237\nseyss 237\nshakoor 237\nsherritt 237\nsicknote 237\nsiegman 237\nsigils 237\nsjd 237\nskindred 237\nskylink 237\nsmer 237\nsminthopsis 237\nsmirk 237\nsoftest 237\nsolders 237\nspaceshiptwo 237\nspera 237\nstach 237\nstacia 237\nstanky 237\nstanthorpe 237\nstayers 237\nstoss 237\nstrapline 237\nsundt 237\nsunia 237\nsupaul 237\nsupercupen 237\nsuperunknown 237\nsvart 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231\nmajha 231\nmanesar 231\nmantinea 231\nmarkstein 231\nmassiah 231\nmatchings 231\nmavros 231\nmediatrix 231\nmeenachil 231\nmegaceryle 231\nmeharry 231\nmeinhardt 231\nmelanocorypha 231\nmerveilles 231\nmiędzyrzecz 231\nmiano 231\nmilivoj 231\nmily 231\nmli 231\nmobutu's 231\nmodriča 231\nmollify 231\nmomoe 231\nmortiz 231\nmotes 231\nmoyarta 231\nmukasey 231\nmuktha 231\nmulago 231\nmultijet 231\nmundesley 231\nmyelination 231\nnéel 231\nnageshwara 231\nnamigata 231\nnaqi 231\nnazri 231\nnccpg 231\nnebra 231\nneferhotep 231\nnephrectomy 231\nnewent 231\nnicolao 231\nniska 231\nnmf 231\nnscc 231\noceanarium 231\noisans 231\nomphale 231\noptimates 231\norodes 231\nosogovo 231\nosteotomy 231\npadilha 231\npalynology 231\nparli 231\npattambi 231\npaycock 231\npeñafrancia 231\npelagianism 231\npemuda 231\nperelman's 231\nperistaltic 231\npertini 231\nphags 231\npiccirilli 231\npicnickers 231\npisonia 231\npleasley 231\nplecoptera 231\npleine 231\nplesiomorphic 231\npokot 231\nponente 231\npoorman 231\npoots 231\nprader 231\npri's 231\nprie 231\nprohibitorum 231\npromulgates 231\nprosecutes 231\npulakesi 231\npunctatum 231\nqafl 231\nquackwatch 231\nramleh 231\nrangatira 231\nrazorblade 231\nredeploying 231\nredner 231\nremonstrant 231\nrenat 231\nrepetitiveness 231\nreynes 231\nrezső 231\nriquier 231\nrkm 231\nrmu 231\nromine 231\nrookeries 231\nroure 231\nsān 231\nsafdarjung 231\nsamland 231\nsanctuary's 231\nsandgren 231\nsandham 231\nsandoy 231\nsarkin 231\nsarpi 231\nsatins 231\nsaussure's 231\nsazae 231\nschmalkalden 231\nschothorst 231\nschwaller 231\nscoobies 231\nscribonius 231\nscrying 231\nsctp 231\nselk 231\nsengen 231\nserah 231\nsesimbra 231\nshahdol 231\nshortlists 231\nsiamak 231\nsidor 231\nsifl 231\nsilberling 231\nsiliwangi 231\nsilvermane 231\nsismi 231\nskehan 231\nskitch 231\nslik 231\nslovenský 231\nsmallhausen 231\nsnlf 231\nsomov 231\nsonshine 231\nspizaetus 231\nsporulation 231\nstarmaker 231\nstepanović 231\nstrakes 231\nstrandzha 231\nstunticons 231\nsturmovik 231\nsubstrata 231\nsuperion 231\nsydor 231\ntablada 231\ntael 231\ntarini 231\ntazza 231\ntenay 231\ntftp 231\nthakore 231\ntheobald's 231\nthetan 231\ntingey 231\ntithi 231\ntoarcian 231\ntrackmania 231\ntrancers 231\ntreach 231\ntruces 231\ntruls 231\ntryphaena 231\ntugendhat 231\ntunicate 231\nturspr 231\nundertown 231\nunexamined 231\nunfortified 231\nunz 231\nuserland 231\nuzen 231\nväg 231\nvalon 231\nvandalur 231\nverrucosus 231\nvexillology 231\nvicinal 231\nvidhyalaya 231\nvidyapati 231\nvincenza 231\nviris 231\nvitous 231\nvmd 231\nvmu 231\nvolha 231\nvoraciously 231\nvully 231\nwanner 231\nwecht 231\nwesters 231\nwheatus 231\nwightwick 231\nwilhem 231\nwncl 231\nworkchoices 231\nwtvf 231\nxaml 231\nxinzhou 231\nyagoda 231\nyearnings 231\nyuke's 231\nzei 231\nzgornja 231\nzikr 231\nzongmin 231\nzuffenhausen 231\nétage 230\nće 230\nścinawa 230\nşafak 230\nδg 230\nвека 230\nธรรมราช 230\nเจ 230\naabb 230\nabre 230\nadamkus 230\naestivalis 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230\ncfpl 230\ncgp 230\nchaetodontidae 230\nchangning 230\nchuikov 230\nclendenin 230\ncloninger 230\ncn's 230\ncoaxes 230\ncodebreaking 230\ncolloquialisms 230\ncolmer 230\ncommunipaw 230\ncompositionally 230\ncomprehends 230\nconchos 230\nconferral 230\nconfigures 230\nconjuration 230\ncorktown 230\ncornbury 230\ncoston 230\ncouse 230\ncrispin's 230\ncutaways 230\ndağı 230\ndahanu 230\ndarro 230\ndarwinist 230\ndbag 230\ndcw 230\ndemolay 230\ndenaro 230\ndesjardin 230\ndesjarlais 230\ndevrient 230\ndibb 230\ndicarboxylic 230\ndichloro 230\ndietrichstein 230\ndigitalism 230\ndigiti 230\ndiksha 230\ndimo 230\ndioxane 230\ndirection's 230\ndoğu 230\ndolmans 230\ndonaldo 230\ndonora 230\ndovetailed 230\ndrache 230\ndule 230\ndumaine 230\ndupplin 230\nebf 230\nelyot 230\nengal 230\nentheogenic 230\nenu 230\nescuadrón 230\nestampes 230\neubalaena 230\nevernden 230\nevinrude 230\nezhuthachan 230\nfacit 230\nfaouzi 230\nfarmall 230\nfeldzeugmeister 230\nferial 230\nferrule 230\nfesten 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222\nsalzer 222\nsamphan 222\nsandspit 222\nsansui 222\nsanwa 222\nsarmad 222\nsatem 222\nschäuble 222\nschriftsteller 222\nseabream 222\nsebree 222\nselfe 222\nsemiring 222\nsendler 222\nshaddam 222\nshiekh 222\nshinnō 222\nshv 222\nsightlines 222\nsikkimese 222\nsleiman 222\nsnowberry 222\nsolf 222\nsphaerocarpa 222\nsquirm 222\nstadtmauer 222\nstod 222\nsturdivant 222\nsturnella 222\nsubsect 222\nsuperiorly 222\nsupplément 222\nsuskind 222\nswartzia 222\nswordswoman 222\nsynthases 222\ntair 222\ntakeya 222\ntalvin 222\ntappers 222\ntattershall 222\ntausch 222\nteays 222\nteertha 222\ntejaswini 222\ntensing 222\nthackery 222\nthami 222\nthamnophilidae 222\ntheatregoers 222\nthemba 222\ntibicen 222\ntical 222\ntillet 222\ntoothy 222\ntopples 222\ntotanus 222\ntrăng 222\ntransferability 222\ntrico 222\ntrue's 222\ntsn's 222\nttb 222\nturbomachinery 222\ntzeltal 222\nufologist 222\nulanov 222\nunapproachable 222\nuneasily 222\nunvarnished 222\nupnor 222\nusvi 222\nvandamme 222\nvedda 222\nvedomosti 222\nvehari 222\nvergy 222\nviggen 222\nvillaflor 222\nvillu 222\nvilniaus 222\nvinz 222\nvitaris 222\nvitorino 222\nvorobyov 222\nvui 222\nwaldmohr 222\nwaterstone's 222\nwellhausen 222\nwickwire 222\nwinfs 222\nwithey 222\nwluk 222\nworldliness 222\nwwhl 222\nyafa 222\nyangyang 222\nyeerks 222\nyeop 222\nyigael 222\nywam 222\nzandig 222\nzenden 222\nzenger 222\nzersenay 222\nzimbardo 222\nzipcar 222\nzubeen 222\nåmål 221\nöhman 221\nčiurlionis 221\nđurđević 221\nškocjan 221\nகல 221\nabbreviata 221\naboutrika 221\nacec 221\nagénor 221\nagori 221\nakala 221\nalatriste 221\nalcmaeon 221\nalongs 221\nambergate 221\nambohimanga 221\nandaya 221\nandropogon 221\nangstroms 221\nannetta 221\nanteroom 221\nanuak 221\nappassionata 221\nappennino 221\nappii 221\naquilla 221\narvon 221\nascaris 221\nasmir 221\nastaire's 221\nastronomische 221\nasumi 221\natallah 221\natomization 221\nazizul 221\nazumanga 221\nbaccalieri 221\nbachelier 221\nbagnolet 221\nballivián 221\nbanū 221\nbanak 221\nbanggai 221\nbanjica 221\nbarash 221\nbasileuterus 221\nbaystate 221\nbeidou 221\nbellah 221\nbencao 221\nberenbaum 221\nbilirakis 221\nbishr 221\nblacken 221\nbleakness 221\nbonifaz 221\nbonners 221\nbontade 221\nbotanische 221\nbotello 221\nbowlus 221\nbragdon 221\nbreanne 221\nbressie 221\nbruix 221\nbruneck 221\nbuni 221\nburness 221\nbutene 221\nbyres 221\ncámpora 221\ncălărași 221\ncộng 221\ncairnryan 221\ncak 221\ncamulodunum 221\ncapitani 221\ncarnacki 221\ncartuja 221\ncendres 221\ncentrair 221\ncentricity 221\ncfra 221\nchads 221\nchalam 221\nchaney's 221\nchanoch 221\ncheke 221\nchemist's 221\nchernyshevsky 221\nchillingworth 221\nchimène 221\nchiprovtsi 221\ncléry 221\ncockatrice 221\ncockshutt 221\ncollomb 221\ncolori 221\ncolpo 221\nconghou 221\nconstructivists 221\ncontortion 221\ncontusions 221\ncoppel 221\ncoprolites 221\ncorrigan's 221\ncorrofin 221\ncottret 221\ncrimping 221\ncroissants 221\ncroshaw 221\ncruder 221\ncsec 221\ncuratorship 221\ncusio 221\ncyco 221\ndød 221\ndanilovich 221\ndarkblue 221\ndarmstädter 221\ndaryl's 221\ndeconcini 221\ndeerskin 221\ndemelza 221\ndeporte 221\ndermody 221\ndiaphania 221\ndominie 221\ndres 221\ndrnovšek 221\nduat 221\nducis 221\nducker 221\ndudince 221\ndufftown 221\ndynamites 221\ndysmorphic 221\necotype 221\nelgort 221\nenseigne 221\nepimetheus 221\nerlander 221\nerler 221\nerps 221\neulogius 221\newo 221\nexperimentalist 221\neynon 221\nfabulist 221\nfelonious 221\nferréol 221\nflagellate 221\nflensburger 221\nflorentius 221\nforli 221\nfranz's 221\nfrentz 221\nfrese 221\nfrossard 221\nfryxell 221\nfutami 221\nfutoshi 221\ngachibowli 221\nganteaume 221\ngaoranger 221\ngeisinger 221\ngerstlauer 221\ngesneriaceae 221\ngifs 221\nglenrowan 221\ngoalby 221\ngopro 221\ngrimmia 221\ngrune 221\ngsw 221\ngurukula 221\ngwendal 221\nhaaland 221\nhandels 221\nhandprint 221\nhappyland 221\nhassard 221\nhazeldine 221\nhearses 221\nhedya 221\nhefce 221\nhegra 221\nhelcystogramma 221\nhogfather 221\nholdren 221\nhonnef 221\nhoolock 221\nhopfield 221\nhotheaded 221\nhsinbyushin 221\nhussards 221\nhymer 221\nhymnbook 221\nianthe 221\nierapetra 221\nihsahn 221\nilife 221\nimanishi 221\nimbuing 221\ninclining 221\nindeevar 221\nindigenes 221\nintergender 221\niowans 221\nipsas 221\nishizaka 221\nitaldesign 221\njaspar 221\njayo 221\njehanabad 221\njj's 221\njoyland 221\nkaaterskill 221\nkabarett 221\nkaiping 221\nkali's 221\nkamionka 221\nkaneishi 221\nkans 221\nkarakol 221\nkarpinski 221\nkarshi 221\nkashgaria 221\nkawhi 221\nkean's 221\nkeiretsu 221\nkemalist 221\nkempt 221\nkermesse 221\nkhánum 221\nkhargone 221\nkimochi 221\nkintner 221\nkirchdorf 221\nkirkup 221\nkirsi 221\nklarion 221\nkleanthis 221\nkodjo 221\nkopassus 221\nkorbach 221\nkowa 221\nkuge 221\nkullander 221\nlì 221\nlívia 221\nlachiusa 221\nlaetus 221\nlages 221\nlandrith 221\nlapilli 221\nlehning 221\nleukodystrophy 221\nlexeme 221\nlgt 221\nliù 221\nlibrarything 221\nlickin 221\nlissie 221\nlmb 221\nlmgteam 221\nloboda 221\nloret 221\nlucilius 221\nludd 221\nluntz 221\nmădălina 221\nmaboroshi 221\nmaginn 221\nmakedonia 221\nmakhlouf 221\nmakossa 221\nmalatyaspor 221\nmalzahn 221\nmammad 221\nmanesse 221\nmangelsdorff 221\nmanito 221\nmaresme 221\nmarié 221\nmarianum 221\nmasjids 221\nmastiffs 221\nmatech 221\nmaui's 221\nmauritia 221\nmaxpreps 221\nmccudden 221\nmcdermott's 221\nmcninja 221\nmear 221\nmeiwa 221\nmelkites 221\nmetatarsus 221\nmevalonate 221\nmeyler 221\nminah 221\nmindon 221\nmistero 221\nmizuki's 221\nmogu 221\nmohabbatein 221\nmoults 221\nmountainview 221\nmrnjavčević 221\nmtbf 221\nmuire 221\nmuizenberg 221\nmultishow 221\nmuttered 221\nmxy 221\nnanometres 221\nnanteuil 221\nnarayanaswamy 221\nndn 221\nnenshi 221\nnette 221\nnginx 221\nniigaki 221\nnikica 221\nnikkatsu's 221\nnimrin 221\nnippo 221\nnntp 221\nnolo 221\nnoob 221\nnpcc 221\nodyssée 221\noertel 221\nomp 221\nomurtag 221\noorang 221\nopf 221\nopogona 221\noppressions 221\norrville 221\notomys 221\npacorus 221\npaines 221\nparri 221\nparthenogenetic 221\npascall 221\npattan 221\npauahi 221\npazienza 221\npenetrant 221\npetrochina 221\npinto's 221\npirkei 221\npitchshifter 221\nplautdietsch 221\npokkiri 221\npolymerize 221\npolynices 221\npoos 221\nprienai 221\nprio 221\nprizzi's 221\nprocellarum 221\nprogpower 221\npryderi 221\npubertal 221\nquintino 221\nrōnin 221\nradamel 221\nrahmon 221\nrajskub 221\nrandee 221\nraro 221\nrayside 221\nreclined 221\nredbreast 221\nreelviews 221\nrek 221\nrengo 221\nrennsteig 221\nretroperitoneal 221\nrevolta 221\nriblets 221\nrigoberta 221\nrmd 221\nrockery 221\nrottentomatoes 221\nroyko 221\nrpb 221\nruffels 221\nruncitruncated 221\nsøndergaard 221\nsacchini 221\nsagredo 221\nsahid 221\nsakimoto 221\nsaltires 221\nsbir 221\nschüttorf 221\nschelotto 221\nschenn 221\nschriesheim 221\nschwob 221\nseagraves 221\nsemigallia 221\nsermo 221\nserva 221\nsevmash 221\nsext 221\nshanksville 221\nshawne 221\nshehan 221\nsherley 221\nshirkuh 221\nsidetrack 221\nsignalmen 221\nsilversides 221\nskarloey 221\nskyclad 221\nslex 221\nsluggo 221\nsorption 221\nsouterrain 221\nspiceworld 221\nsportbox 221\nspringfields 221\nspurrier's 221\nsrila 221\nstarzz 221\nstewardson 221\nstrakka 221\nsubdivides 221\nsucat 221\nsuffocates 221\nsulfatide 221\nsundered 221\nsweetgrass 221\ntaggert 221\ntamam 221\ntammin 221\ntawdry 221\ntayport 221\nteac 221\ntelekinetically 221\nteleprinters 221\nteleserye 221\ntensegrity 221\nthanom 221\ntheodotus 221\nthiha 221\nthiruvambady 221\ntiësto's 221\ntijana 221\ntirthankaras 221\ntkt 221\ntotley 221\ntraudl 221\ntroas 221\ntsesarevich 221\ntunay 221\ntupa 221\nturów 221\ntvg 221\nuafa 221\nultrafiltration 221\nuniversita 221\nuwp 221\nuzeyir 221\nvago 221\nvarroa 221\nvdb 221\nvelikiy 221\nveurne 221\nvicenç 221\nvictimised 221\nvilledieu 221\nvillumsen 221\nviviers 221\nwadud 221\nwashingtonia 221\nwaterview 221\nwavers 221\nweebly 221\nwees 221\nweishaupt 221\nweisskopf 221\nwessun 221\nwetering 221\nweybourne 221\nwhatmore 221\nwillies 221\nwmec 221\nwolkenstein 221\nwoolcock 221\nwowowee 221\nxpl 221\nxscale 221\nyaropolk 221\nyawara 221\nyco 221\nyoona 221\nyulo 221\nzaide 221\nzeami 221\nzit 221\nzohn 221\nzoraida 221\nzulma 221\nzwijndrecht 221\nčz 220\nštefánik 220\nμη 220\nмихайлович 220\nלא 220\nགས 220\naéropostale 220\naaup 220\nabernant 220\nabolhassan 220\naccedes 220\nadia 220\nadrs 220\naeternus 220\naffectivity 220\nagris 220\nagudo 220\nahlam 220\nakhurst 220\nalaeddin 220\nalatau 220\nalbiventris 220\naldonza 220\nalerta 220\nalysia 220\nambareesh 220\nannamacharya 220\nannamaria 220\nanniyan 220\nantigravity 220\natomized 220\navons 220\naya's 220\nazny 220\nbaadasssss 220\nbackwash 220\nbamboozled 220\nbaylies 220\nbbd 220\nbeatific 220\nbebi 220\nbellmon 220\nbernstadt 220\nbigla 220\nbiomaterial 220\nbledel 220\nbny 220\nbolek 220\nbrémond 220\nbrahmā 220\nbretten 220\nbrookhart 220\nbublé's 220\nbudin 220\nbuginese 220\nbullet's 220\nburek 220\nbutley 220\nbve 220\ncabrales 220\ncantey 220\ncanvassers 220\ncardsyellow 220\ncategorizations 220\nceolwulf 220\ncesareo 220\nchandogya 220\nchapitre 220\nchassé 220\nchatur 220\nchauk 220\nchiusi 220\nchotiner 220\nchuchu 220\nchurston 220\ncicotte 220\nciega 220\ncirri 220\ncoactivator 220\ncock's 220\ncomeng 220\ncommercy 220\ncore's 220\ncorollaries 220\ncoulda 220\ncpw 220\ncrater's 220\ncruciata 220\ncsicop 220\ncuauhtemoc 220\ncubanas 220\ncustodianship 220\ncyrestis 220\ndörr 220\ndahlias 220\ndannemora 220\ndasharatha 220\ndeanie 220\ndehri 220\ndelhaize 220\ndelmonico 220\ndeluged 220\ndenkova 220\ndeshastha 220\ndesnoyers 220\ndglive 220\ndharmadhikari 220\ndiamantopoulos 220\ndilijan 220\ndiori 220\ndissed 220\ndissimilis 220\nditte 220\ndivine's 220\ndomestikos 220\ndomu 220\ndonnersberg 220\ndoyne 220\nduzee 220\ndyall 220\neccw 220\necko 220\neigenfunction 220\nelmaleh 220\nelz 220\nenciphered 220\nenroute 220\nentreri 220\nenvie 220\nerkenntnis 220\nessene 220\neteocles 220\nethnos 220\newig 220\neyot 220\nfaget 220\nfamosos 220\nfarmworker 220\nfatty's 220\nfforde 220\nfibra 220\nfilat 220\nfirehose 220\nflatfoot 220\nfleischer's 220\nfouta 220\nfractionated 220\nfraternitas 220\ngangu 220\ngarrotxa 220\ngeisenheim 220\nghostlight 220\ngisulf 220\nglaucocharis 220\nglobulus 220\ngoatherd 220\ngoeldi 220\ngomelsky 220\ngorodishche 220\ngorzelanny 220\ngranger's 220\ngreenberger 220\ngreenly 220\ngreenspring 220\ngrigorian 220\ngrigoryev 220\ngrimwade 220\ngualeguaychú 220\nguaracha 220\nguararapes 220\nguillemette 220\ngumm 220\ngustavsen 220\ngyrase 220\nhøegh 220\nhadham 220\nhagoromo 220\nhalebidu 220\nhaniya 220\nharahan 220\nhawkstone 220\nherberstein 220\nheydarabad 220\nhohenzollerns 220\nhollendorfer 220\nhona 220\nhospet 220\nhvc 220\nhydrolysed 220\nidempotents 220\nidlebrain 220\nigatpuri 220\nimeem 220\nimotski 220\ninao 220\nindoctrinate 220\ninhalant 220\nintergenic 220\ninveresk 220\nisard 220\njúlius 220\njacaré 220\njalwa 220\njanapada 220\njanjira 220\njayco 220\njebsen 220\njedidiah 220\njejomar 220\njernverk 220\njhajha 220\njinni 220\njitō 220\njondaryan 220\njulietta 220\nkönigswinter 220\nkaakha 220\nkafa 220\nkagemusha 220\nkammerspiele 220\nkamper 220\nkamrupi 220\nkancha 220\nkarasjok 220\nkipsang 220\nkitman 220\nknowns 220\nkolli 220\nkosambi 220\nkremlin's 220\nkurahashi 220\nkuznetsk 220\nkyzylorda 220\nlandowner's 220\nlasco 220\nlatonia 220\nlavis 220\nleporinus 220\nleuckart 220\nlewknor 220\nliaocheng 220\nlimay 220\nlintner 220\nliquefy 220\nliturgically 220\nlorikeets 220\nluohu 220\nluwu 220\nlxvi 220\nmølle 220\nmühlbach 220\nmüstair 220\nmachuca 220\nmadball 220\nmagaly 220\nmaibara 220\nmajdič 220\nmanière 220\nmanorhouse 220\nmariánské 220\nmarquês 220\nmartiri 220\nmarut 220\nmascarenes 220\nmatahari 220\nmbu 220\nmccaughan 220\nmcgloin 220\nmegazone 220\nmelanitis 220\nmicrostate 220\nmikulin 220\nmilenković 220\nmonocotyledons 220\nmonspr 220\nmonterosso 220\nmoorilla 220\nmorgoth's 220\nmsrtc 220\nmulready 220\nmurmu 220\nnagyvárad 220\nnas's 220\nnatanz 220\nnativ 220\nnawabzada 220\nndfb 220\nnelumbo 220\nnenartovich 220\nnescafé 220\nnesquik 220\nneuhof 220\nneuroscientific 220\nnhững 220\nninković 220\nnolloth 220\nnoughts 220\nnthu 220\nodo's 220\nokfuskee 220\noldowan 220\nonkelz 220\noot 220\northoclase 220\noutcrossing 220\noverflight 220\npadarn 220\npadrino 220\npaia 220\npalco 220\npallis 220\nparakrama 220\npasargadae 220\npastaza 220\npatriation 220\npecatonica 220\npennsbury 220\npethick 220\nphiloponus 220\nphysx 220\npilica 220\npineland 220\npleasuredome 220\nplouay 220\npoff 220\npoicephalus 220\npoku 220\nportugues 220\nposi 220\npotentates 220\npourri 220\npragyan 220\nprigioni 220\nprotoplasm 220\nprudentius 220\nptak 220\nquê 220\nrāga 220\nrajpura 220\nrason 220\nraychaudhuri 220\nrecoiling 220\nribchester 220\nriddle's 220\nrimmer's 220\nrobba 220\nrochat 220\nrockfill 220\nrodenberg 220\nrogaška 220\nrohan's 220\nrotha 220\nroule 220\nrpv 220\nrucci 220\nrussische 220\nruysch 220\nsabita 220\nsalcido 220\nsalesmanship 220\nsamboy 220\nsanjoy 220\nsantillan 220\nsarine 220\nsarver 220\nscaurus 220\nscdma 220\nschemers 220\nschizonts 220\nschneck 220\nschnellboot 220\nschwenk 220\nseabass 220\nseferis 220\nsehen 220\nseismometer 220\nsemiotext 220\nservicer 220\nshariatpur 220\nshawwal 220\nsherm 220\nshf 220\nshowell 220\nshowthread 220\nshrovetide 220\nsiffredi 220\nsimar 220\nskillion 220\nslags 220\nsnk's 220\nsomua 220\nsophiatown 220\nsoulsilver 220\nsoundstages 220\nsportsmaster 220\nsquab 220\nsteffensen 220\nstege 220\nstrumenti 220\nstutsman 220\nsudama 220\nswagman 220\nswamplands 220\nszamotuły 220\nszczepaniak 220\nszwarc 220\ntabar 220\ntakamoto 220\ntakita 220\ntamio 220\ntashfin 220\ntaurica 220\nteen's 220\ntehuelche 220\ntenka 220\ntergat 220\nthackerville 220\nthordarson 220\nthorolf 220\ntillyard 220\ntopľou 220\ntraición 220\ntransalta 220\ntranslucency 220\ntrasporti 220\ntravunia 220\ntreigle 220\ntrippi 220\ntsuchimikado 220\ntuazon 220\ntyphimurium 220\nudayana 220\nulbrich 220\nullal 220\nult 220\numbridge 220\nunreactive 220\nupsc 220\nusnea 220\nvårgårda 220\nvaccari 220\nvaches 220\nvahe 220\nvalcárcel 220\nvanbrugh's 220\nvannucci 220\nvanoc 220\nvatika 220\nvauquelin 220\nventure's 220\nveselkin 220\nvijayendra 220\nvillafañe 220\nvine's 220\nvlm 220\nvorm 220\nvysya 220\nwakamiya 220\nwaksman 220\nwashable 220\nwattisham 220\nwcpo 220\nwebstore 220\nweekenders 220\nweick 220\nwhodini 220\nwidder 220\nwifredo 220\nwillette 220\nxanatos 220\nxion 220\nxylophones 220\nyōichi 220\nyediot 220\nyeerk 220\nyorath 220\nyounkers 220\nyumika 220\nyuppies 220\nzahari 220\nzapad 220\nzemstvo 220\nzhongnan 220\nzierer 220\nzini 220\nzuev 220\naabenraa 219\naardvarks 219\nabbi 219\nabdiel 219\nabrostola 219\nachingly 219\nacquisitive 219\naelst 219\naii 219\nalfonsina 219\nalgonquins 219\nambitiously 219\namet 219\nanax 219\nanderston 219\nannularis 219\nantelias 219\nantiblemma 219\napink 219\nappeal's 219\nappliquée 219\napprenticing 219\nasiri 219\nassaye 219\naurobindo's 219\nautobiographer 219\navais 219\naverbukh 219\naxess 219\nbackfill 219\nbarang 219\nbaronage 219\nbarrelhouse 219\nbasecamp 219\nbav 219\nbeilin 219\nbessbrook 219\nbirzeit 219\nbiskup 219\nblasetti 219\nblasphemer 219\nboie 219\nbolly 219\nbookers 219\nbradbery 219\nbrickyards 219\nbrk 219\nbrownhill 219\nbruhns 219\nbrymer 219\nbuang 219\nburyan 219\nbusinesslike 219\nbussing 219\nbutwal 219\ncamelon 219\ncane's 219\ncapm 219\ncarcosa 219\ncatholicate 219\ncbk 219\ncentenaire 219\ncepora 219\ncerdan 219\nchappe 219\ncharlbury 219\nchatuchak 219\nchequamegon 219\nchichibunomiya 219\nchimo 219\nchirac's 219\nchiyonofuji 219\nchlamydomonas 219\nchoptank 219\nchorrera 219\nchristianorum 219\nchuseok 219\ncielito 219\ncinca 219\ncitoyenne 219\ncixous 219\nclowne 219\ncoeymans 219\ncompendious 219\ncomputron 219\nconcentrically 219\nconcussed 219\ncoraopolis 219\ncosponsor 219\ncover's 219\ncramér 219\ncrenulata 219\ncrotaline 219\ncryptomeria 219\ncutcliffe 219\ncybc 219\ndęblin 219\ndaen 219\ndats 219\ndeadbolt 219\ndentils 219\ndessner 219\ndewlap 219\ndhami 219\ndhp 219\ndiatribes 219\ndidactics 219\ndimidiatus 219\ndinneen 219\ndivini 219\ndonogh 219\ndorna 219\ndreadfully 219\ndruid's 219\nduim 219\ndyckia 219\nearlwood 219\nebeltoft 219\necclesiæ 219\necofeminism 219\nedmilson 219\neile 219\neinsteins 219\nemmeram 219\nemotionalism 219\neppendorf 219\nercall 219\nericifolia 219\nerubescens 219\nerwinia 219\neso's 219\netonian 219\netsy 219\nevms 219\nexalting 219\nfabricates 219\nfaccio 219\nfaruq 219\nfehse 219\nfeiglin 219\nfeiner 219\nfickling 219\nfilleigh 219\nflippy 219\nfolgen 219\nfreakout 219\nfrenette 219\nfrighteningly 219\nfructuoso 219\nfukada 219\nfunereus 219\ngòn 219\ngakki 219\ngalassi 219\ngarbha 219\ngarsington 219\ngastrocnemius 219\ngerwig 219\ngilberti 219\ngolok 219\ngomaa 219\ngookin 219\ngpg 219\ngrafik 219\ngranatstein 219\ngrol 219\ngrommet 219\ngrope 219\ngrr 219\nguale 219\nguana 219\ngudur 219\ngymnopus 219\nhabermann 219\nhabert 219\nhaefliger 219\nhaguro 219\nhammett's 219\nharissa 219\nhayashida 219\nheena 219\nherbison 219\nherreweghe 219\nhimmelpforten 219\nhinkel 219\nhiraki 219\nholycross 219\nhonaker 219\nhotaki 219\nhukum 219\nhunn 219\nhurlock 219\nhyaluronic 219\nhypomyces 219\nibsf 219\nigad 219\nihab 219\ninchoate 219\ninsinuate 219\nintemperance 219\ninuvialuit 219\niwama 219\njagex 219\njansky 219\njhabvala 219\njoran 219\njulich 219\njull 219\njustiça 219\nkadare 219\nkamachi 219\nkamienica 219\nkarnivool 219\nkatsuaki 219\nkaukauna 219\nkawasumi 219\nkazimierza 219\nkeowee 219\nkeziah 219\nkhmu 219\nkiaran 219\nkic 219\nkilcock 219\nkipkoech 219\nkjus 219\nkluane 219\nknaves 219\nkoes 219\nkondratiev 219\nkrižan 219\nkriegel 219\nkrla 219\nkuči 219\nkuprin 219\nkyoryuger 219\nlallana 219\nlanercost 219\nlangstroth 219\nlasswell 219\nleadup 219\nlemkin 219\nlemmer 219\nletha 219\nletwin 219\nlevonorgestrel 219\nleydon 219\nliberati 219\nliddesdale 219\nlightvessels 219\nliwan 219\nlogsdon 219\nlongtan 219\nlooby 219\nloquasto 219\nlubok 219\nluhan 219\nmaé 219\nmafikeng 219\nmahāsāṃghika 219\nmaharajganj 219\nmahishasura 219\nmaithripala 219\nmajoritarian 219\nmanute 219\nmanzai 219\nmaroua 219\nmartella 219\nmaschwitz 219\nmatyushenko 219\nmauduit 219\nmaulbronn 219\nmauren 219\nmaxene 219\nmbah 219\nmcharg 219\nmcmasters 219\nmeaninglessness 219\nmenands 219\nmeredyth 219\nmerreikh 219\nmerta 219\nmesquiteers 219\nmicromax 219\nmikimoto 219\nmillenia 219\nmitrione 219\nmizuhara 219\nmoisei 219\nmolder 219\nmontalcini 219\nmonteil 219\nmorey's 219\nmorientes 219\nmuan 219\nmuere 219\nmuhu 219\nmujtahid 219\nmusl 219\nmuthal 219\nmytilus 219\nnaissus 219\nnaiveté 219\nnakdong 219\nnanas 219\nnasib 219\nnatans 219\nnbcc 219\nnectaries 219\nneuland 219\nngarrindjeri 219\nnissar 219\nnomani 219\nnumberless 219\noakbrook 219\nodenton 219\nomentum 219\nondu 219\nopostegidae 219\norlamünde 219\norofino 219\noskari 219\notilia 219\noudemans 219\noutstripping 219\noxenham 219\npacky 219\npagès 219\npalaiseau 219\npandur 219\npapuensis 219\nparalamas 219\nparviainen 219\npasanen 219\npasqualino 219\npassu 219\npatang 219\npeds 219\npelsall 219\npergusa 219\npfarrer 219\nphaze 219\nphillie 219\nphoma 219\npimpinela 219\npisans 219\npittsgrove 219\npoblachta 219\nporträt 219\npostman's 219\npowerboats 219\npriboj 219\nprinsesa 219\npropagandhi 219\nprotection's 219\nproteomic 219\npsfs 219\npsittacus 219\npsus 219\npterophorus 219\npugu 219\npunhi 219\nputters 219\nquadripartite 219\nquarterbacked 219\nquestor 219\nradoviš 219\nragheb 219\nrajo 219\nransomes 219\nrbg 219\nreassurances 219\nrecompilation 219\nredway 219\nrefreshes 219\nregister's 219\nrelived 219\nrestorationist 219\nrethemed 219\nricardinho 219\nrighthand 219\nrinder 219\nrootsy 219\nroscius 219\nrosenfels 219\nrosenwinkel 219\nrotblat 219\nrothschildi 219\nrotundus 219\nrovi 219\nrrt 219\nrsta 219\nruffs 219\nrushent 219\nsächsischen 219\nsacrificio 219\nsalvadora 219\nsandblasting 219\nsaralee 219\nsardinella 219\nsarhadi 219\nscaddan 219\nschemel 219\nsculpteurs 219\nsculpturing 219\nseagrasses 219\nseasickness 219\nseparability 219\nsequoiadendron 219\nshadrack 219\nshahe 219\nshirey 219\nshiwen 219\nshocklee 219\nsilverleaf 219\nsinemurian 219\nskender 219\nskratch 219\nslicked 219\nsobey 219\nsoffit 219\nsomites 219\nsonor 219\nspringside 219\nsquiggle 219\nsraffa 219\nsternwheel 219\nstosch 219\nstothart 219\nstoup 219\nstrahorn 219\nstratfield 219\nstrelka 219\nstringency 219\nstringently 219\nstudiorum 219\nstyracosaurus 219\nsubburaman 219\nsubj 219\nsuccubi 219\nsulak 219\nsummerhayes 219\nsunamganj 219\nsuomenlinna 219\nsurtur 219\nsynalpheus 219\nsyndicats 219\ntöölö 219\ntabe 219\ntabulate 219\ntactfully 219\ntandragee 219\ntanimura 219\ntauzin 219\nteesri 219\nteleosts 219\ntensou 219\ntessellata 219\ntest's 219\nteulon 219\ntez 219\nthermus 219\ntheudebert 219\nthrane 219\ntibullus 219\ntinn 219\ntitley 219\ntmo 219\ntonsillectomy 219\ntouchscreens 219\ntraber 219\ntransgene 219\ntreeshrew 219\ntriumph's 219\ntsipras 219\ntuiasosopo 219\ntweedledum 219\nubique 219\nultadanga 219\nulundi 219\nundersurface 219\nundulate 219\nuptick 219\nvallathol 219\nvestiarium 219\nviharaya 219\nvilarinho 219\nvirasoro 219\nvolozhin 219\nvredenburg 219\nwahlström 219\nwakas 219\nwasaga 219\nwashingtonville 219\nwda 219\nwerdenberg 219\nwiarton 219\nwidmerpool 219\nwieners 219\nwou 219\nwrappings 219\nwut 219\nwwd 219\nwyrick 219\nyongning 219\nystradgynlais 219\nzündapp 219\nzangief 219\nzavadil 219\nzerynthia 219\nziro 219\nzomer 219\nzorak 219\nzwaan 219\nzygons 219\nécologie 218\nšu 218\nμsas 218\nкиїв 218\nнаук 218\nרו 218\nरव 218\nยว 218\naati 218\nabdulhamid 218\nabkco 218\nabma 218\naccuracies 218\nachhe 218\naequatorialis 218\naghdashloo 218\nagrabah 218\naguda 218\nairoli 218\naishc 218\nalbomarginata 218\nalbro 218\nalebrijes 218\nalexe 218\nalie 218\nalley's 218\nallwright 218\nalmásy 218\namissa 218\nangkorian 218\nanica 218\nantimicrobials 218\nantitrypsin 218\nanya's 218\nappenzeller 218\narchdiocese's 218\nargyll's 218\narkhangelsky 218\narundo 218\nascomycete 218\nasep 218\nasiaweek 218\nathalia 218\natiku 218\nattires 218\nbabbacombe 218\nbagehot 218\nbagshawe 218\nbaine 218\nbalde 218\nbalneario 218\nbandipur 218\nbaudette 218\nbaugher 218\nbearish 218\nbeloki 218\nbeobachtungen 218\nberrian 218\nbetzdorf 218\nbioreactors 218\nbirkhauser 218\nblachernae 218\nblanched 218\nblaye 218\nblisworth 218\nbludd 218\nbollée 218\nbrüel 218\nbrachystegia 218\nbreadboard 218\nbreitenfeld 218\nbriskeby 218\nbrownstones 218\nbundes 218\ncátedra 218\ncabrera's 218\ncaepio 218\ncalibra 218\ncalotype 218\ncampello 218\ncantone 218\ncanute's 218\ncanuto 218\ncappel 218\ncarthage's 218\ncashion 218\ncastelletto 218\ncatchiest 218\ncenotaphs 218\nchaffetz 218\nchhnang 218\nchibiusa 218\nchiellini 218\nchitimacha 218\nchitralekha 218\nchlorella 218\ncholecystokinin 218\nchuckwagon 218\nclairette 218\nclostera 218\ncolijn 218\ncolvin's 218\ncomtat 218\nconciliator 218\nconsilium 218\ncornwallis's 218\ncoronoid 218\ncound 218\ncounterscarp 218\ncountersuit 218\ncoxen 218\ncrassulaceae 218\ncrellin 218\ncutoffs 218\ndachsburg 218\ndadabhai 218\ndarrieux 218\ndashami 218\ndaubeney 218\ndavanagere 218\ndeadening 218\ndearman 218\ndesaturase 218\ndicho 218\ndilutions 218\ndippenaar 218\ndiscoverable 218\ndivin 218\ndivulges 218\ndjerv 218\ndonatien 218\ndonatist 218\ndondero 218\ndramatisations 218\ndreadzone 218\ndryads 218\ndudamel 218\nduetto 218\nduez 218\ndurairaj 218\ndurkheim's 218\necaha 218\nedelson 218\neglington 218\neiken 218\neléonore 218\nelfen 218\nellinger 218\nelx 218\nemka 218\nemy 218\nenchong 218\nenterprize 218\nephraemi 218\nesrange 218\nevenki 218\nexacta 218\nexpanders 218\nfarrokhzad 218\nfehrenbach 218\nfennimore 218\nfluorspar 218\nflurries 218\nforedeck 218\nfoundationalism 218\nfrancesco's 218\nfresenius 218\nfutrell 218\nfuxi 218\ngandharvas 218\ngarrigan 218\ngarrigou 218\ngarwolin 218\ngazovik 218\ngebiet 218\ngeeson 218\ngeneva's 218\ngerak 218\ngerasimenko 218\ngestern 218\nghomeshi 218\nghostwriting 218\ngilyard 218\nglessner 218\ngorgodze 218\ngostynin 218\ngovert 218\ngrandage 218\ngrella 218\nguðni 218\ngudbrandsdal 218\nguzaarish 218\nhàn 218\nhackathon 218\nhagfors 218\nhailstorms 218\nhaiyang 218\nhanawa 218\nhartsock 218\nharunobu 218\nhaslip 218\nhatfield's 218\nheighington 218\nheinen 218\nhemlocks 218\nhermaphroditism 218\nhezhou 218\nhichens 218\nhickoksports 218\nhirnyk 218\nhirsau 218\nhoklo 218\nholsey 218\nhotson 218\nhower 218\nhron 218\nhwon 218\niben 218\nicesave 218\nictus 218\nigala 218\nilonggo 218\nimpertinent 218\ninfiltrations 218\ninternat 218\nitaru 218\nizzi 218\njógvan 218\njötunn 218\njabba's 218\njangly 218\njasienica 218\njingyuan 218\njizō 218\njrb 218\njugić 218\njunagarh 218\njunjie 218\nkadisha 218\nkalasa 218\nkandalaksha 218\nkandersteg 218\nkanone 218\nkanouté 218\nkanthi 218\nkaran's 218\nkarstadt 218\nkdb 218\nkhadra 218\nkikkawa 218\nkime 218\nkingsoft 218\nkinsey's 218\nkirsipuu 218\nkissinger's 218\nkitani 218\nkittur 218\nkivimäki 218\nkleinwort 218\nklepač 218\nknowledges 218\nkokkonen 218\nkonstadinos 218\nkoppers 218\nkungfu 218\nkureishi 218\nkyōka 218\nlandgraves 218\nleister 218\nlespedeza 218\nlethally 218\nlittledale 218\nllanfyllin 218\nlobatse 218\nloganathan 218\nlolicon 218\nlombard's 218\nlome 218\nmörder 218\nmagicjack 218\nmahaweli 218\nmajles 218\nmancebo 218\nmanilow's 218\nmanotick 218\nmarkovo 218\nmarpa 218\nmarszałek 218\nmedem 218\nmedici's 218\nmedlin 218\nmegapodius 218\nmeknès 218\nmemmius 218\nmenetries 218\nmerkel's 218\nmethos 218\nmexret 218\nmhra 218\nmiša 218\nmigratorius 218\nmihailov 218\nminä 218\nminuartia 218\nmiraz 218\nmitad 218\nmitti 218\nmodul 218\nmoes 218\nmoissac 218\nmonasterium 218\nmontdidier 218\nmoundville 218\nmucci 218\nmuchacho 218\nmudi 218\nmultiplexers 218\nmusashi's 218\nmusiker 218\nmutunga 218\nmyalgia 218\nněmec 218\nnašice 218\nnajafabad 218\nnancheng 218\nnashashibi 218\nneighbourhood's 218\nnetlist 218\nnezalezhnosti 218\nnicam 218\nnivas 218\nnopcsa 218\nnoradrenergic 218\nnorthbourne 218\nnovelle 218\nocna 218\noedo 218\nontogenetic 218\norgullo 218\norlean 218\nosta 218\noverestimation 218\npaleobotany 218\npampero 218\npams 218\npanchi 218\nparachutistes 218\nparol 218\npatch's 218\npavlich 218\npeçanha 218\npendry 218\npenetrators 218\nperdy 218\npernía 218\nperplexity 218\npersia's 218\npharmacopeia 218\nphylogenic 218\npicchi 218\npictograph 218\npinaceae 218\npinicola 218\npinturicchio 218\nplebis 218\npllc 218\npoutiainen 218\nprizewinners 218\npropitiate 218\nprosiebensat 218\npseudopoda 218\nptyonoprogne 218\npublicans 218\npurab 218\nquaintance 218\nquasicrystals 218\nrịa 218\nradivojević 218\nrainald 218\nrajinikanth's 218\nrangdajied 218\nrangoni 218\nranville 218\nravenscourt 218\nravidas 218\nrayder 218\nrazdan 218\nrebutting 218\nregnans 218\nreinke 218\nrenberg 218\nreturnee 218\nrezā 218\nrixey 218\nrongbuk 218\nrootlets 218\nsōka 218\nsabco 218\nsacandaga 218\nsailin 218\nsallee 218\nsandison 218\nsanghavi 218\nsantamarina 218\nsantan 218\nsaorview 218\nsarnen 218\nsarpong 218\nscalo 218\nscatterbrain 218\nseawalls 218\nseito 218\nselfportrait 218\nsesamoid 218\nshored 218\nshotter 218\nshoutmon 218\nshunet 218\nsistemi 218\nsjenica 218\nskrifter 218\nslar 218\nsodden 218\nsonghua 218\nsonni 218\nspoelstra 218\nsportback 218\nsprue 218\nstainbank 218\nstarbuck's 218\nstarflight 218\nsteinheim 218\nstooping 218\nsturmey 218\nsturnira 218\nsubmunitions 218\nsubtyping 218\nsuhaimi 218\nsujan 218\ntường 218\ntacan 218\ntahnee 218\ntamiment 218\ntanabata 218\ntautog 218\ntearaway 218\nterem 218\nthương 218\nthirupathi 218\nthunderstone 218\ntokaji 218\ntonnant 218\ntraba 218\ntransputer 218\ntrepča 218\ntrouvé 218\nturnings 218\ntuthmosis 218\nuds 218\numashree 218\numbar 218\nunca 218\nunswept 218\nutahensis 218\nuzan 218\nvacillated 218\nvalpy 218\nvaporizing 218\nvenkataramana 218\nverksted 218\nvisi 218\nvizio 218\nvnu 218\nvolosts 218\nvolstagg 218\nvolzhsky 218\nvuoi 218\nwarsop 218\nwhiting's 218\nwillans 218\nwilm 218\nwohlgemuth 218\nwolli 218\nwoodchurch 218\nwoolgar 218\nwormatia 218\nwrp 218\nwubbzy 218\nwunna 218\nxcp 218\nxinghua 218\nyanes 218\nyeke 218\nyesvantpur 218\nyeun 218\nyuck 218\nyune 218\nyuqing 218\nzakharova 218\nzazaki 218\nzhū 218\nzhongxing 218\nzuan 218\nzub 218\náguia 217\nängelholms 217\nše 217\nελλαδοσ 217\nعلى 217\naalsmeer 217\nabendroth 217\nacrimoniously 217\naediles 217\nafranius 217\nagnete 217\nahearne 217\naidy 217\nalag 217\nalcocer 217\nalhaj 217\nalicea 217\namco 217\nami's 217\nanaprof 217\nankers 217\nantwon 217\nardclough 217\narenac 217\narenberger 217\narménie 217\narticle_ 217\naudiophiles 217\nautoren 217\nautotrophic 217\navshalom 217\nazulejos 217\nbalitang 217\nbandolero 217\nbatsuit 217\nbelasitsa 217\nbelgische 217\nbesh 217\nbezels 217\nbheinn 217\nbhuvan 217\nbibbo 217\nbiehl 217\nbiflora 217\nbilski 217\nbioethical 217\nbloedel 217\nblumenbach 217\nbodoland 217\nbondsman 217\nbrandybuck 217\nbranting 217\nbrazza 217\nbreitman 217\nbuquet 217\nburney's 217\ncalédonie 217\ncandelario 217\ncaresse 217\ncarto 217\ncavorting 217\nceefax 217\ncevat 217\nchamberland 217\nchaytor's 217\nchiappa 217\nchunar 217\ncilacap 217\nclaassen 217\nclaggett 217\nclix 217\ncobie 217\ncoffin's 217\ncolostygia 217\ncolpoys 217\ncomics's 217\ncompanc 217\nconisbrough 217\ncorreos 217\ncoté 217\ncowgate 217\ncrosswalks 217\ncunxu's 217\ndafna 217\ndanilovgrad 217\ndargie 217\ndauber 217\ndefacement 217\ndenisof 217\ndenliner 217\nderivates 217\ndetrás 217\ndeyan 217\ndhhs 217\ndhupia 217\ndicentra 217\ndiderot's 217\ndietze 217\ndigue 217\ndisinherit 217\ndiyar 217\ndka 217\ndobry 217\ndominick's 217\ndonahue's 217\ndooars 217\ndoura 217\ndraa 217\ndraves 217\ndundon 217\ndurness 217\ndwane 217\necheverri 217\necx 217\neffete 217\nelaphoidella 217\nepitomize 217\nequational 217\nered 217\nesselen 217\nethicists 217\nethnocultural 217\nfactorials 217\nfager 217\nfalkenhausen 217\nfand 217\nfarahani 217\nfasce 217\nfascias 217\nfelicien 217\nfingolfin 217\nfinnur 217\nfleitas 217\nfloor's 217\nforeshortened 217\nforis 217\nfortum 217\nfourah 217\nfourvière 217\nfrankmusik 217\nfrede 217\ngadis 217\ngaudenzi 217\ngayee 217\ngerbe 217\ngetafix 217\nghasem 217\ngibeon 217\nginger's 217\ngizmos 217\ngondwanaland 217\ngooseneck 217\ngorney 217\ngortin 217\ngrève 217\ngranddad 217\ngraus 217\ngrimoires 217\ngroupama 217\ngrunau 217\nguadagnini 217\ngusman 217\nhšk 217\nhagens 217\nhajós 217\nhardstaff 217\nharlen 217\nhattendorf 217\nhawser 217\nhayase 217\nhazrath 217\nheathkit 217\nheifers 217\nheiki 217\nhelma 217\nhelnwein 217\nhitmakers 217\nhkust 217\nhlm 217\nhobeika 217\nhomeground 217\nhostilius 217\nhtun 217\nhuastec 217\nhughleys 217\nhutong 217\nhydrosphere 217\nhyp 217\nibon 217\nimprecision 217\nindymac 217\ninternode 217\nishikawajima 217\nitalia's 217\nitokawa 217\nituzaingó 217\njahangir's 217\njanice's 217\njanklow 217\njarosz 217\njokey 217\njolas 217\njungjong 217\nkabát 217\nkabalevsky 217\nkado 217\nkaimal 217\nkajiado 217\nkalima 217\nkamio 217\nkandurata 217\nkango 217\nkangta 217\nkapfenberg 217\nkarlgren 217\nkasaravalli 217\nkasongo 217\nkelheim 217\nkenkyū 217\nkilladysert 217\nkilljoys 217\nkinman 217\nkitch 217\nklapisch 217\nknockers 217\nkołodziej 217\nkoimoi 217\nkoseki 217\nkotsu 217\nkozjak 217\nku's 217\nkuko 217\nkyrios 217\nlacquers 217\nlagman 217\nlaminations 217\nlangenhagen 217\nlarmer 217\nlegionowo 217\nlevén 217\nlightens 217\nlimia 217\nlmf 217\nlondesborough 217\nlondonhearts 217\nlouviers 217\nloviisa 217\nltgen 217\nltz 217\nlueders 217\nlugones 217\nlusitanica 217\nlxiii 217\nlynette's 217\nmadrean 217\nmahaut 217\nmainers 217\nmainyu 217\nmajere 217\nmalverne 217\nmancala 217\nmanningtree 217\nmanolescu 217\nmantlet 217\nmargolies 217\nmaribo 217\nmarkíza 217\nmarles 217\nmartín's 217\nmarvi 217\nmateer 217\nmazra 217\nmeanwood 217\nmiltonia 217\nmiva 217\nmnj 217\nmonumentum 217\nmora's 217\nmotru 217\nmursi 217\nmustached 217\nnadroga 217\nnafplio 217\nnakazato 217\nnamal 217\nnaoe 217\nnaomasa 217\nnase 217\nnazem 217\nneak 217\nneferneferuaten 217\nnepeta 217\nnewtownshandrum 217\nngk 217\nnibelung 217\nninotchka 217\nnopal 217\nnoppawan 217\nntia 217\nogeechee 217\nolavarría 217\nosk 217\nossorio 217\nostade 217\nosu's 217\noteri 217\npadamsee 217\npanait 217\npanchal 217\npanga 217\npanico 217\nparasitologist 217\npartula 217\npasaje 217\npatricios 217\npattadakal 217\npazos 217\npeetha 217\npentheus 217\npeper 217\nperga 217\nperrette 217\npetrinja 217\npirithous 217\nplacebos 217\npnt 217\npointillism 217\npolites 217\npolitzer 217\npono 217\npople 217\npot's 217\nprabandha 217\nprofiteers 217\nproietti 217\nprophetically 217\nprosopagnosia 217\npteron 217\npulli 217\npupilla 217\nqaqortoq 217\nquacker 217\nraef 217\nrajib 217\nramna 217\nratdog 217\nrazorfish 217\nrebellin 217\nrecontest 217\nredistributions 217\nredwine 217\nreifferscheid 217\nrematches 217\nretrocession 217\nrhyacionia 217\nringstraße 217\nriverrun 217\nrivoluzione 217\nronan's 217\nrosca 217\nrougeot 217\nrubecula 217\nsōji 217\nsōsuke 217\nsaawan 217\nsacem 217\nsalmerón 217\nsang's 217\nsannikov 217\nsapperton 217\nsarrail 217\nsatchmo 217\nsaxmundham 217\nsbx 217\nschmelzer 217\nschouwen 217\nschwerte 217\nsebum 217\nseeler 217\nseifuku 217\nselu 217\nsentier 217\nsextius 217\nsgarbi 217\nshabad 217\nshamefully 217\nshapell 217\nsharmarke 217\nshergar 217\nshinwa 217\nshippūden 217\nshogunate's 217\nshyster 217\nsichuanese 217\nsideroad 217\nsilversmithing 217\nsinnoh 217\nsita's 217\nsitawaka 217\nslumbering 217\nsmithkline 217\nsmorgasbord 217\nsnakepit 217\nsocialis 217\nsoil's 217\nsomesvara 217\nsorokina 217\nsoroush 217\nsoulshock 217\nsouthshore 217\nspeedman 217\nspewed 217\nsportist 217\nspot's 217\nsteeb 217\nstegmann 217\nstenus 217\nstifter 217\nstrangite 217\nsugano 217\nsuidae 217\nsupe 217\nsupercarrier 217\nsurahs 217\nsykesville 217\nsynod's 217\nsyrupy 217\ntacrolimus 217\ntaffeta 217\ntahiri 217\ntalwalkar 217\ntaven 217\ntblisi 217\ntechnicolour 217\nteicher 217\ntemerin 217\ntentera 217\ntestator's 217\ntex's 217\nthoburn 217\ntianmu 217\ntividale 217\ntobia 217\ntoles 217\ntopacio 217\ntoqui 217\ntorat 217\ntownhill 217\ntreue 217\ntriggerplant 217\ntripucka 217\ntristania 217\ntszyu 217\nunorganised 217\nunthinking 217\nunwinnable 217\nupmanship 217\nureters 217\nuuo 217\nvalia 217\nvalleyview 217\nvelen 217\nvelli 217\nveneered 217\nvenkatadri 217\nvenom's 217\nverda 217\nveri 217\nvesco 217\nveysel 217\nviše 217\nvicens 217\nvillejuif 217\nvolontaires 217\nvolscian 217\nwaldgraves 217\nwallah 217\nwallich 217\nwarrigal 217\nwaru 217\nwatani 217\nwbl 217\nweening 217\nweißenburg 217\nwheezer 217\nwhitesburg 217\nwicksteed 217\nwindbreak 217\nwinslow's 217\nwitsken 217\nwojewództwo 217\nwoodcliff 217\nworkingman's 217\nwthr 217\nxanthophyllum 217\nyeongdeungpo 217\nyglesias 217\nyorkin 217\nyoshikatsu 217\nyoungbloods 217\nyutang 217\nzipporah 217\nzits 217\níris 216\nțiriac 216\nεκκλησια 216\nσυναξαριστής 216\nукраїнське 216\nयत 216\nलक 216\nabandonware 216\nabnaki 216\nagapornis 216\nakadémia 216\nakihisa 216\nalaotra 216\nalapag 216\nalarmist 216\nallayed 216\nallhallows 216\nalmy 216\naltdorfer 216\nalzate 216\nambos 216\namylose 216\nananthan 216\nanastasiadis 216\nanguiano 216\nannadale 216\naplacental 216\narcilla 216\narzamas 216\nasarco 216\nasawa 216\naspekte 216\naudace 216\naurorae 216\nautore 216\nayllón 216\nazithromycin 216\nbahour 216\nbajević 216\nbarendrecht 216\nbarocci 216\nbarotrauma 216\nbayombong 216\nbeaudin 216\nbeida 216\nbellocchio 216\nbelu 216\nbhakkar 216\nblacklists 216\nboffa 216\nbogeys 216\nbohemund 216\nboning 216\nboreman 216\nbotella 216\nboyana 216\nbradbourne 216\nbrandname 216\nbroaching 216\nbrooker's 216\nbudd's 216\ncaerlaverock 216\ncaespitosa 216\ncalamy 216\ncalhern 216\ncampfield 216\ncampinense 216\ncardia 216\ncarquest 216\ncarretto 216\ncatenae 216\ncaterino 216\nccgt 216\ncepede 216\nchéng 216\nchachalaca 216\nchafer 216\nchanel's 216\ncheilitis 216\nchichimecas 216\nchickasaws 216\nchimu 216\ncognata 216\ncolorants 216\ncomdex 216\ncomisario 216\ncommandeers 216\ncommenters 216\nconroy's 216\ncontrastingly 216\ncooee 216\ncozart 216\ncpy 216\ncremate 216\ncroplands 216\ncrosman 216\ncursors 216\ncury 216\ncusk 216\ndamieh 216\ndarin's 216\ndarion 216\ndavoud 216\ndecazes 216\ndecussate 216\ndedić 216\ndekay 216\ndelly 216\ndenneny 216\ndeschimag 216\ndeselected 216\ndeserta 216\ndetent 216\ndeterrents 216\ndevaraja 216\ndietsch 216\ndillinger's 216\ndirektor 216\ndiscordance 216\ndish's 216\ndisorient 216\ndisyllabic 216\ndoblinger 216\ndocumentary's 216\ndogar 216\ndogville 216\ndorkin 216\ndoswell 216\ndownplays 216\ndozy 216\ndrams 216\ndreamstone 216\ndriskill 216\ndubravka 216\ndulcamara 216\nebbesen 216\neiwa 216\neks 216\nelectrotherapy 216\nemigrates 216\nemiliani 216\nemoticon 216\nepistasis 216\nepoc 216\nermolenko 216\neuropéens 216\neutychius 216\nevaporites 216\nexhibitioner 216\nführung 216\nfabritius 216\nfahn 216\nfalaschi 216\nfallingwater 216\nfernandinho 216\nfetid 216\nfgb 216\nfinniss 216\nfiordiligi 216\nflails 216\nflaxen 216\nfootages 216\nfoundlings 216\nfreeloader 216\nfriels 216\ngameiro 216\ngangaram 216\ngastaldi 216\ngateposts 216\ngearloose 216\ngeboren 216\ngentry's 216\ngenua 216\ngeoffry 216\ngeoscientists 216\ngerben 216\nghe 216\ngiard 216\nglogovac 216\ngnomic 216\ngotthilf 216\ngouvion 216\ngowerton 216\ngranicus 216\ngranulomatosis 216\ngrebo 216\ngreenshirts 216\ngrippers 216\ngroninger 216\ngruber's 216\nguyandotte 216\ngwangyang 216\ngymnase 216\nhacket 216\nhaddy 216\nhailee 216\nhalffter 216\nhartney 216\nhaslar 216\nhawiye 216\nhaydée 216\nhef 216\nheilbronner 216\nheis 216\nhephthalite 216\nheyn 216\nhmannan 216\nholbach 216\nhork 216\nhorno 216\nhrf 216\nhtin 216\nhuhn 216\nhunsur 216\nhurtle 216\nhvad 216\niberoamericano 216\nicare 216\nicfi 216\nieu 216\nilma 216\nindustriels 216\ningratiating 216\nintersubjective 216\ninterweave 216\nioe 216\niterators 216\nivanoff 216\njaheim 216\njalili 216\njansen's 216\njauhari 216\njekyll's 216\njockeying 216\njovellanos 216\njua 216\njudokas 216\nkızıltepe 216\nkamalpur 216\nkasautii 216\nkatju 216\nkazuchika 216\nkeaney 216\nkembangan 216\nketuvim 216\nkirkii 216\nklammer 216\nklier 216\nključ 216\nkneen 216\nkollur 216\nkooi 216\nkosam 216\nkovic 216\nkronfeld 216\nktu 216\nkukësi 216\nkunai 216\nkyp 216\nlabra 216\nlagun 216\nlanger's 216\nlappet 216\nlarkfield 216\nlatiff 216\nlatinisation 216\nlaurenson 216\nlifeway 216\nlimpid 216\nlinate 216\nlineola 216\nliquidambar 216\nliterare 216\nlits 216\nlns 216\nlophura 216\nlsw 216\nlucenzo 216\nlulzsec 216\nlurkers 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216\noystermouth 216\npachyramphus 216\npagliuca 216\npaho 216\npalmately 216\npanthea 216\npantnagar 216\nparatypes 216\npascu 216\npastas 216\npastern 216\npawlicki 216\npendyala 216\nperlite 216\npettibon 216\npezzo 216\nphenylketonuria 216\nphere 216\nphotoacoustic 216\nphotophores 216\npichet 216\npigsty 216\npinu 216\nplath's 216\nplym 216\npmb 216\npneumonic 216\npolinices 216\npolyphenol 216\npongau 216\nponor 216\npreachings 216\npremeditation 216\nprickett 216\npromesas 216\npromoción 216\npropublica 216\nprosumer 216\nprotista 216\npuertorriqueño 216\nqinling 216\nquieras 216\nquitter 216\nquzhou 216\nraihan 216\nrampurhat 216\nrathna 216\nrechte 216\nregarde 216\nreneau 216\nrepl 216\nreverberatory 216\nreviewer's 216\nrhetorica 216\nrheydt 216\nricciardello 216\nrichibucto 216\nriquer 216\nritam 216\nritonavir 216\nritsuryō 216\nrockwiz 216\nrossie 216\nrubbra 216\nruffle 216\nrufifrons 216\nrumi's 216\nryuga 216\nsø 216\nsacy 216\nsadder 216\nsalir 216\nsamatha 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216\nunearths 216\nunevenness 216\nunmeasured 216\nupholland 216\nursidae 216\nursule 216\nurwin 216\nvézina 216\nvaliante 216\nvandeleur 216\nvandersteen 216\nvanness 216\nvelazco 216\nvend 216\nvenning 216\nventilate 216\nveragua 216\nvha 216\nviby 216\nviiis 216\nvipr 216\nvot 216\nvrm 216\nvxr 216\nwaalo 216\nwalcott's 216\nwarka 216\nwarwickshire's 216\nwaterbeach 216\nwaterhouse's 216\nwchs 216\nwedgie 216\nwheelbarrows 216\nwhitegate 216\nwikiversity 216\nwitkiewicz 216\nwojtyla 216\nwolley 216\nxive 216\nyakuts 216\nyathrib 216\nyograj 216\nyoshkar 216\nzaal 216\nzagreus 216\nzax 216\nzebulun 216\nzillur 216\nzli 216\nzpu 216\nçınar 215\népreuve 215\núltimos 215\nживопись 215\nнас 215\nсвязей 215\nэто 215\nאת 215\nabeid 215\naccrues 215\nacetates 215\naecl 215\naffonso 215\nagenzia 215\nagetec 215\nagonistes 215\nahadith 215\nairman's 215\nairmanship 215\najuntament 215\nakritas 215\nalbelda 215\nalbertz 215\naliança 215\nalinsky 215\nallogeneic 215\nalstroemeria 215\nandalusi 215\nangham 215\nannemasse 215\nanoles 215\nantimachus 215\napóstol 215\narachis 215\narara 215\nardito 215\narihant 215\naristata 215\narscott 215\narundinacea 215\nastraeus 215\nattendre 215\nautogyros 215\navelar 215\navogadro's 215\nbašta 215\nbaaba 215\nbace 215\nbadlees 215\nbagé 215\nbaldacchino 215\nbandicoots 215\nbaruth 215\nbejar 215\nbellos 215\nbermudan 215\nbienvenu 215\nbignoniaceae 215\nboonesborough 215\nborings 215\nbr's 215\nbraničevo 215\nbratstvo 215\nbreakeven 215\nbreton's 215\nbrunning 215\nbrutalities 215\nbuckinghams 215\nbucknor 215\nbukoba 215\nbullingdon 215\nbulworth 215\nbumba 215\nbuser 215\ncamiling 215\ncantelli 215\ncapurro 215\ncarhaix 215\ncarnaross 215\ncarolsfeld 215\ncastus 215\ncatone 215\nceg 215\ncellucci 215\ncentrino 215\ncentrobasket 215\ncherrington 215\nchubbuck 215\ncinctus 215\ncinii 215\ncivis 215\nclec 215\ncleome 215\nclinches 215\nclinica 215\nclonderalaw 215\ncloss 215\ncofresí 215\ncohabit 215\ncollio 215\ncollishaw 215\ncolostrum 215\ncompagnoni 215\nconcarneau 215\nconcatenating 215\nconcords 215\nconditionality 215\nconfederacies 215\nconflates 215\ncongressman's 215\ncookie's 215\ncornewall 215\ncottony 215\ncrampons 215\ncredito 215\ncroston 215\ncrowdfunded 215\ncruzan 215\ncryotherapy 215\ncubillas 215\ncuillin 215\nculemborg 215\ncummer 215\ncushendall 215\ncyanuric 215\ncyclosporine 215\ncyornis 215\ndéputé 215\ndaimler's 215\ndainichi 215\ndalmore 215\ndalys 215\ndanian 215\ndanke 215\ndecapolis 215\ndecisionmaking 215\ndedo 215\ndega 215\ndeji 215\ndenitrification 215\ndeontological 215\ndevín 215\ndeza 215\ndgi 215\ndhammakaya 215\ndibang 215\ndiesis 215\ndismissively 215\ndodik 215\ndolaucothi 215\ndole's 215\ndonoghue's 215\ndynamit 215\nearlobe 215\neaubonne 215\necbatana 215\nedgerrin 215\neducator's 215\nekatarina 215\nelfed 215\nelixirs 215\nelsass 215\nemael 215\nemmanouil 215\nemrah 215\nenglishness 215\nennemi 215\nenrage 215\nepicondyle 215\neppingen 215\nermland 215\nethnobiology 215\nethnologie 215\nevernote 215\nexhibitionist 215\neynsford 215\nfadd 215\nfago 215\nfallersleben 215\nfarinosa 215\nfazle 215\nferina 215\nfesse 215\nffrr 215\nfhe 215\nfinanciero 215\nflagon 215\nflec 215\nfordism 215\nforegate 215\nforethought 215\nfrühgeschichte 215\nfransson 215\nfreesia 215\nfumimaro 215\ngaige 215\ngaleton 215\ngalisteo 215\ngallinari 215\ngametek 215\ngenl 215\ngerolsteiner 215\nglauchau 215\ngleick 215\ngoodeve 215\ngordy's 215\ngornyak 215\ngotovina 215\ngrape's 215\ngrottaferrata 215\nhablar 215\nhajjah 215\nhalid 215\nhalogenation 215\nhandbells 215\nhaslinger 215\nhatami 215\nhausdorf 215\nhaynesworth 215\nheadmastership 215\nhectoliters 215\nhelvering 215\nhematologist 215\nheteroatom 215\nhikurangi 215\nhobb 215\nhoffner 215\nhomewrecker 215\nhudler 215\nhypervelocity 215\nicaria 215\nichinoseki 215\nilusión 215\nimagineers 215\nimperially 215\nimst 215\nindependency 215\ninstruktori 215\nintegumentary 215\nintermodulation 215\nintoned 215\ninula 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215\nsies 215\nskerik 215\nslavoljub 215\nsolomona 215\nsprees 215\nsquibs 215\nssaa 215\nsteckler 215\nstereoscope 215\nstranda 215\nsubsampling 215\nsulpicians 215\nsurest 215\nsusser 215\nswalwell 215\nswidden 215\nszentes 215\nszilard 215\ntélétoon 215\ntadami 215\ntahuichi 215\ntakechi 215\ntamboura 215\ntanbur 215\ntapout 215\ntarpaulins 215\ntasuku 215\ntaunay 215\ntaura 215\ntawar 215\ntechsystems 215\ntejanos 215\ntesco's 215\nteshima 215\nteuku 215\ntheca 215\nthumb's 215\ntirthankar 215\nto's 215\ntombelli 215\ntopolski 215\ntrammel 215\ntremula 215\ntrial's 215\ntrnka 215\ntroubetzkoy 215\ntruc 215\nturkel 215\nuda's 215\nunfired 215\nunidade 215\nunitedhealth 215\nuntag 215\nurán 215\nusepa 215\nustka 215\nvadakara 215\nvaea 215\nvaidehi 215\nventromedial 215\nverbond 215\nvestals 215\nvicroads 215\nvitiensis 215\nvivarais 215\nvode 215\nvoya 215\nvujčić 215\nwabaunsee 215\nwalibi 215\nwaverton 215\nweyrich 215\nwiggling 215\nwildernesses 215\nwinesburg 215\nwitness's 215\nwunderhorn 215\nwvga 215\nxilie 215\nyaacob 215\nyacc 215\nyauza 215\nyazaki 215\nyid 215\nylide 215\nyoshinaka 215\nyuuji 215\nzamboangueño 215\nzandi 215\nzarand 215\nzimmerli 215\nzohan 215\nzorich 215\nétrangers 214\nîntre 214\nświdnik 214\nżółkiewski 214\nżuławski 214\nžagar 214\nвойны 214\nпесни 214\nऔर 214\naavso 214\nabal 214\nactuating 214\nadansonia 214\nadlib 214\nadulterers 214\nafdal 214\nafk 214\nafpfl 214\nagamemnon's 214\nagawa 214\nalpert's 214\nalthoff 214\namargo 214\namna 214\namorpha 214\namschel 214\nanae 214\nanandi 214\nangulatus 214\nanioma 214\nanjani 214\nankylosaur 214\nannibal 214\nansei 214\nantennaria 214\naper 214\nappiani 214\naramark 214\nashtamudi 214\nassumpta 214\nastill 214\nattac 214\naurdal 214\nbábís 214\nbäumer 214\nbabayev 214\nbadong 214\nbaluarte 214\nbangz 214\nbanisadr 214\nbaronius 214\nbasidiomycota 214\nbastelberger 214\nbatiatus 214\nbeat's 214\nbenfleet 214\nberning 214\nbethke 214\nbin's 214\nbinga 214\nblanco's 214\nbleckley 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214\ndistillates 214\ndoorsteps 214\ndpe 214\ndrassodes 214\ndreamfall 214\neaza 214\nedsger 214\neducativa 214\neidlitz 214\neisel 214\neldership 214\nepic's 214\nerast 214\nestee 214\nestyn 214\neuchromius 214\neunomia 214\neuropa's 214\nexcavates 214\nexpropriations 214\nextasy 214\nfaultline 214\nferentz 214\nfermata 214\nferula 214\nfiacre 214\nfindel 214\nfiorini 214\nfitz's 214\nflagrante 214\nflorette 214\nflyback 214\nfonder 214\nfortino 214\nfriele 214\nfrundsberg 214\nfucks 214\nfukushi 214\nfurat 214\nfurphy 214\nfutuh 214\ngörtz 214\ngaliński 214\ngalin 214\ngennes 214\ngiambologna 214\ngillot 214\ngjerstad 214\nglaister 214\ngodofredo 214\ngolding's 214\ngoldston 214\ngongadze 214\ngoose's 214\ngopis 214\ngottardo 214\ngowariker 214\ngrabbe 214\ngransden 214\ngraphophone 214\ngreenwillow 214\ngroynes 214\ngwn 214\nhainanensis 214\nhattusili 214\nhawley's 214\nheckart 214\nheinke 214\nheliade 214\nhen's 214\nheterodoxy 214\nheyde 214\nhierarchic 214\nhonorverse 214\nhscott 214\nhypatopa 214\nicheon 214\niconographie 214\nidiosyncrasy 214\nifrc 214\niler 214\ninclusively 214\ninderøy 214\ninessa 214\ninkaras 214\ninspektor 214\niof 214\nisomerases 214\njónsi 214\njahan's 214\njannah 214\njanny 214\njarett 214\njaun 214\njei 214\njenkins's 214\njinji 214\njobo 214\njustis 214\nkadosh 214\nkaikala 214\nkalemegdan 214\nkaneria 214\nkanitz 214\nkansans 214\nkashtan 214\nkatsiaryna 214\nkei's 214\nkerava 214\nkinneil 214\nkirwa 214\nkmet 214\nkohrs 214\nkollár 214\nkrasnystaw 214\nkregel 214\nkunihiro 214\nladra 214\nlakki 214\nlanang 214\nlanceolatus 214\nlapponicus 214\nleser 214\nliechtenstein's 214\nlisandra 214\nlittleport 214\nljus 214\nlons 214\nlooc 214\nludu 214\nlugosi's 214\nlyke 214\nmaharajadhiraj 214\nmajjhima 214\nmamola 214\nmanaia 214\nmanandhar 214\nmanikpur 214\nmarienlyst 214\nmarik 214\nmarmaray 214\nmarta's 214\nmassana 214\nmatina 214\nmauritiana 214\nmazrui 214\nmehler 214\nmeilland 214\nmeise 214\nmerga 214\nmessam 214\nmetrovick 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214\npodravina 214\npolesworth 214\nporęba 214\npoteat 214\npoyle 214\nprasina 214\nprionailurus 214\nproductionsop 214\nprosauropod 214\npsaltery 214\npulverised 214\nqemali 214\nquelli 214\nracines 214\nrajang 214\nrancic 214\nranx 214\nrashād 214\nrathke 214\nrayos 214\nreactos 214\nregraded 214\nreister 214\nreptil 214\nrescaling 214\nrhenus 214\nricarda 214\nrkk 214\nroatán 214\nromeoville 214\nrongotai 214\nroosa 214\nroscosmos 214\nrosenbergs 214\nrouge's 214\nroxxi 214\nruffy 214\nrupicapra 214\nrutilius 214\nryegrass 214\nsacca 214\nsakthivel 214\nsalver 214\nsamt 214\nsangye 214\nsankranthi 214\nsaponi 214\nsatyanarayan 214\nsaunière 214\nsaurav 214\nsawar 214\nsayana 214\nsccs 214\nschoolbook 214\nschrader's 214\nsesc 214\nsessue 214\nshakha 214\nshapleigh 214\nsharmell 214\nshinyanga 214\nshirayuki 214\nshiurim 214\nshoelace 214\nsigillata 214\nsindelar 214\nsinoatrial 214\nsissle 214\nskh 214\nskr 214\nsmid 214\nsoeharto 214\nsokolovsky 214\nsolara 214\nsolovki 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214\nweigle 214\nwepn 214\nweslake 214\nwiehe 214\nwilfrido 214\nwillisau 214\nwinberg 214\nworkability 214\nxilai 214\nxingyu 214\nyakety 214\nyuusuke 214\nzairian 214\nzhuo's 214\nzuma's 214\nzumeta 214\nšuma 213\nživanović 213\nэксмо 213\nகர 213\naani 213\nabovyan 213\nadamic 213\nadath 213\naen 213\naglipay 213\nahra 213\nainlay 213\najay's 213\nakhundov 213\nala's 213\nalshich 213\namenorrhea 213\nammen 213\namrut 213\nanalisi 213\nandromaque 213\nangler's 213\nantineutrino 213\nantipersonnel 213\napollyon 213\naracoeli 213\naramaki 213\narff 213\narmlock 213\naroa 213\natoned 213\nattal 213\naucuparia 213\nautomatique 213\nautons 213\nazerrad 213\nbacklogs 213\nbacton 213\nbage 213\nbagnell 213\nbahonar 213\nbakongo 213\nbaldric 213\nbalgonie 213\nbalt 213\nbalzers 213\nbananaquit 213\nbarbas 213\nbarnabé 213\nbasim 213\nbayraktar 213\nbbf 213\nbeckwourth 213\nbeloeil 213\nbeočin 213\nbernardsville 213\nberroa 213\nbestuzhev 213\nbhanj 213\nbiblique 213\nbillo 213\nbinion's 213\nbinswanger 213\nbiobank 213\nbiotrog 213\nblackground 213\nblackguard 213\nblancmange 213\nblasé 213\nblobby 213\nbodner 213\nboeke 213\nboiga 213\nbondoc 213\nbonwit 213\nborgund 213\nborknagar 213\nbouet 213\nbrachydactyla 213\nbradby 213\nbrasileiros 213\nbroni 213\nbyer 213\ncẩm 213\ncaddesi 213\ncalcul 213\ncaliche 213\ncalmar 213\ncambra 213\ncanim 213\ncapucine 213\ncarna 213\ncarnavalet 213\ncarrig 213\ncastlemartin 213\ncelik 213\ncfny 213\nchương 213\nchangshan 213\nchauraha 213\nchesson 213\nchesterman 213\nchiccarelli 213\nchuckwalla 213\nckx 213\nclaribel 213\ncobán 213\ncoccineus 213\ncojedes 213\ncoloane 213\ncomunidades 213\ncongos 213\nconjugating 213\nconowingo 213\ncoverdell 213\ncruelest 213\nculdrose 213\ncumulants 213\ncuriosa 213\ndainton 213\ndaljit 213\ndallaglio 213\ndanta 213\ndayle 213\ndayman 213\ndenaturing 213\ndenisova 213\ndenktaş 213\ndeshaun 213\ndesiré 213\ndestructors 213\ndhool 213\ndiazonium 213\ndiko 213\ndisbelieved 213\ndiscoloured 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213\nhonthorst 213\nhrdinová 213\nhuei 213\nhumko 213\nhuracan 213\nhusaren 213\nhusayn's 213\nhustled 213\nhypocalcemia 213\nhypoglycemic 213\nicehawks 213\nieper 213\nifeanyi 213\nigboland 213\niksan 213\nimines 213\nimss 213\ninha 213\ninterdicting 213\nintranasal 213\nintrathecal 213\nishita 213\nispat 213\nistiklal 213\njalousie 213\njamu 213\njanardhana 213\njangsu 213\njaubert 213\njordanus 213\njudaean 213\nkabuli 213\nkaliopi 213\nkamille 213\nkapellen 213\nkarnataki 213\nkatsuhito 213\nkeenness 213\nkennebecasis 213\nkernahan 213\nkernewek 213\nkeynesianism 213\nkeypads 213\nkhê 213\nkhonsu 213\nkidbrooke 213\nkidul 213\nkirkland's 213\nkjr 213\nkleijn 213\nkleiza 213\nklippel 213\nkniga 213\nkobza 213\nkoltsovo 213\nkorie 213\nkpp 213\nkranich 213\nkre 213\nkrishnas 213\nksitigarbha 213\nkvenland 213\nléopard 213\nléopards 213\nlév 213\nløken 213\nlüchow 213\nlabill 213\nlabillardière 213\nlakey 213\nlakshmibai 213\nlati 213\nlawbreakers 213\nldpd 213\nledra 213\nleeland 213\nleemans 213\nleid 213\nlembah 213\nlepilemur 213\nlepine 213\nleuctra 213\nlevchenko 213\nlightoller 213\nlina's 213\nlinck 213\nlinguasport 213\nlinn's 213\nlocis 213\nloncin 213\nloue 213\nludwell 213\nluteal 213\nmañalac 213\nmabuchi 213\nmachrihanish 213\nmaddie's 213\nmaharis 213\nmahl 213\nmakonde 213\nmalakhi 213\nmalapropisms 213\nmalleolus 213\nmallin 213\nmamy 213\nmaneesh 213\nmanjung 213\nmanjushri 213\nmankowitz 213\nmanukyan 213\nmap's 213\nmaraschino 213\nmartires 213\nmaruf 213\nmawangdui 213\nmccrum 213\nmebo 213\nmehrab 213\nmellander 213\nmenc 213\nmephitis 213\nmergenthaler 213\nmeszaros 213\nmgen 213\nmicroliths 213\nmicromaster 213\nmiddleburgh 213\nmihailović's 213\nmikoshi 213\nmillerites 213\nmilliard 213\nmirabile 213\nmishmash 213\nmocca 213\nmodulatory 213\nmoghadam 213\nmolfetta 213\nmonasterevin 213\nmonico 213\nmontmirail 213\nmontoya's 213\nmugar 213\nnabin 213\nnafbl 213\nnardiello 213\nnarodowe 213\nnatoma 213\nnewa 213\nnewdegate 213\nnextera 213\nneyer 213\nnicolaides 213\nniederkirchen 213\nnightride 213\nnilakantha 213\nnival 213\nnonessential 213\nnorddeutsche 213\nnoveboracensis 213\nochieng 213\noffen 213\nofficiant 213\nong's 213\nosterloh 213\nottweiler 213\noutlawry 213\nozawa's 213\npalinurus 213\npallu 213\npandiarajan 213\npanthay 213\npaolucci 213\npapini 213\nparfrey 213\npauillac 213\npaulien 213\npelagonia 213\npench 213\npendik 213\npetain 213\npillai's 213\npindari 213\npinsker 213\npirc 213\npitou 213\npluralities 213\npoincaré's 213\npragmatists 213\nprajñāpāramitā 213\npremnath 213\npuchon 213\npuddin 213\npugs 213\npwe 213\npyroelectric 213\npyrokinesis 213\nrüütel 213\nradonezh 213\nrajagopalan 213\nreallocate 213\nregulative 213\nrenzetti 213\nretinas 213\nrhoemetalces 213\nricasoli 213\nrimon 213\nriner 213\nrochas 213\nromar 213\nroog 213\nrowsley 213\nsúilleabháin 213\nsainsburys 213\nsalemi 213\nsamovar 213\nsankardev 213\nsaopha 213\nsarkisyan 213\nsatti 213\nscalene 213\nsceneries 213\nscheidegger 213\nscherrie 213\nschiaffino 213\nschneid 213\nschwartzberg 213\nschweizerischer 213\nscrimgeour 213\nsdio 213\nsedaka's 213\nseenplatte 213\nsegment's 213\nseishirō 213\nserapeum 213\nserenading 213\nserviceberry 213\nsexiness 213\nsfh 213\nshabani 213\nsharrow 213\nsheptytsky 213\nshinko 213\nshiwei 213\nshrule 213\nsibert 213\nsiegemund 213\nsiona 213\nsiryn 213\nsitric 213\nslapper 213\nslayed 213\nslovenské 213\nsoe's 213\nsofya 213\nsolitaires 213\nsonargaon 213\nspeciosum 213\nspellcasters 213\nsplotches 213\nspranger 213\nstaroffice 213\nstaudingeri 213\nstolbov 213\nstrathalbyn 213\nstratigraphical 213\nstylet 213\nsubmontane 213\nsubutai 213\nsuel 213\nsumgait 213\nsunglass 213\nsurridge 213\nswithland 213\nswordsmith 213\nsyers 213\ntailorbird 213\ntakaful 213\ntechnikon 213\nterenzi 213\ntetsurō 213\ntheobromine 213\ntherapsid 213\nthiès 213\nthymelicus 213\ntiriel 213\ntomislavgrad 213\ntoyooka 213\ntrackdown 213\ntransglobal 213\ntrapster 213\ntrezise 213\ntrifluoromethyl 213\ntriforium 213\ntrijntje 213\ntruncheon 213\ntulia 213\ntumblin 213\ntvw 213\ntyronne 213\ntytan 213\nubaidullah 213\nucar 213\nunge 213\nunitized 213\nunpromising 213\nurheimat 213\nutterson 213\nvalchek 213\nvalençay 213\nvalona 213\nvand 213\nvartiainen 213\nvasilii 213\nveatch 213\nvenkatesan 213\nvergilius 213\nveritatis 213\nveteris 213\nvitolas 213\nvlas 213\nvolksbühne 213\nvovk 213\nvraie 213\nvrinda 213\nwörgl 213\nwaage 213\nwalkability 213\nwannstedt 213\nwauconda 213\nweisbord 213\nwhitecap 213\nwindhorst 213\nwindspeed 213\nwintersun 213\nwire's 213\nwoa 213\nwojcik 213\nwolfs 213\nwombourne 213\nwops 213\nwrathchild 213\nxianzong's 213\nxpm 213\nyangs 213\nyick 213\nyocum 213\nzamzam 213\nzarin 213\nzelaya's 213\nzenta 213\nzhangke 213\nzigzagging 213\nzilberman 213\nzordon 213\nzuleika 213\nzurrieq 213\nái 212\nécrins 212\nérica 212\nčavić 212\nšerbedžija 212\nيا 212\naasmaan 212\nabdelrahman 212\nabronia 212\nacocks 212\naerith 212\naghaboe 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212\nchanghe 212\nchangqing 212\ncharvel 212\nchecchi 212\nchehab 212\nchiaramonte 212\nchinatsu 212\nchrists 212\nchromia 212\nciders 212\nclementia 212\nclunies 212\ncmaa 212\ncocciante 212\ncomplexions 212\nconfit 212\ncongesta 212\ncontepomi 212\ncontractures 212\ncorruptly 212\ncorwin's 212\ncorythosaurus 212\ncostanzi 212\ncourtown 212\ncrackles 212\ncrassidens 212\ncresskill 212\ncrozer 212\ncryptologist 212\ndakshinamurthy 212\ndansen 212\ndaredevil's 212\ndargahs 212\ndatasheets 212\ndelarue 212\ndemidova 212\ndeori 212\ndevki 212\ndhanushkodi 212\ndhoby 212\ndickey's 212\ndinorah 212\ndisassociation 212\ndistributivity 212\ndiversifolia 212\ndjuna 212\ndocbook 212\ndogleg 212\ndoppelgangers 212\ndorne 212\ndscf 212\ndysoxylum 212\nedgecliff 212\neffectives 212\nelongating 212\nemberizid 212\nengelke 212\nenschedé 212\nescapology 212\nesox 212\neuroport 212\neverleigh 212\nevey 212\nexploiters 212\nextrusions 212\nezri 212\nfals 212\nfamennian 212\nfcpa 212\nfed's 212\nfejes 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212\nmekhilta 212\nmelter 212\nmerwede 212\nmescal 212\nmht 212\nmiddlemore 212\nmikhailovsky 212\nmilhaud's 212\nmimica 212\nmindelheim 212\nminish 212\nmiroshnichenko 212\nmishmi 212\nmizuta 212\nmolana 212\nmonofilament 212\nmonthermer 212\nmonu 212\nmotorable 212\nmoviebox 212\nmsha 212\nmudguards 212\nmulticomponent 212\nmuskego 212\nnard 212\nnawab's 212\nnaylor's 212\nnazilli 212\nnecesito 212\nneelakanta 212\nnegrita 212\nneufchâtel 212\nnfca 212\nnhai 212\nniceties 212\nnihonga 212\nnontotient 212\nnoyan 212\nnunivak 212\nnuvole 212\nobservationes 212\nocularis 212\nofthe 212\nolamide 212\nool 212\noptronics 212\norbetello 212\norfs 212\norlok 212\nosian 212\noverreaching 212\novershooting 212\nozfootball 212\npêra 212\npaedophiles 212\npanguni 212\nparambrata 212\nparanjpe 212\nparceled 212\nparpola 212\npartout 212\npascaline 212\npeña's 212\npeipus 212\npendharkar 212\npergocrema 212\nphilomathean 212\npicquart 212\npinchuk 212\npiperazine 212\npisuerga 212\npj's 212\nplastiras 212\nplourde 212\nplurinational 212\npogge 212\npolyclonal 212\npolyomavirus 212\nporkchop 212\nportier 212\nportugalete 212\npostposition 212\npranas 212\nprostanthera 212\nproton's 212\npurring 212\nqissa 212\nquebecer 212\nquetiapine 212\nrabinowicz 212\nradiatus 212\nraffin 212\nrakugo 212\nramalinga 212\nramayya 212\nrandor 212\nrasha 212\nrcu 212\nrearden 212\nreasserting 212\nreawaken 212\nrebbetzin 212\nrecharges 212\nredcliff 212\nreichsgraf 212\nremorseless 212\nrenaults 212\nreverberated 212\nreynold's 212\nrft 212\nrgyud 212\nrhenen 212\nrimell 212\nritualist 212\nrosmer 212\nrostron 212\nruark 212\nrubell 212\nrubery 212\nrueter 212\nruppelt 212\nrygg 212\nsénéchal 212\nsabharwal 212\nsarathy 212\nsaundra 212\nsavion 212\nschuch 212\nschueler 212\nschwer 212\nscrollwork 212\nseguenziidae 212\nseptième 212\nshabaka 212\nsharbini 212\nshearim 212\nsheksna 212\nshellbrook 212\nshennan 212\nshumaker 212\nshuowen 212\nsiham 212\nsilvretta 212\nsimplicissimus 212\nskd 212\nsketty 212\nskogen 212\nslas 212\nslatter 212\nsleevenotes 212\nslns 212\nsluman 212\nsoffici 212\nsoldato 212\nsorcerous 212\nsoz 212\nspdy 212\nspielban 212\nstarobogatov 212\nstene 212\nstenerud 212\nstrangeland 212\nsully's 212\nsupersaturation 212\nsupplementum 212\nsynoptics 212\nszczerbiak 212\ntaastrup 212\ntamron 212\ntartakovsky 212\ntdci 212\ntdsb 212\ntejay 212\ntemesvár 212\ntenebrosa 212\nthode 212\nthorn's 212\ntianping 212\ntilikum 212\ntinkerbird 212\ntirant 212\ntison 212\ntournois 212\ntrío 212\ntramiel 212\ntrigonometrical 212\ntrussardi 212\ntunisienne 212\nturrids 212\ntutuola 212\ntwangy 212\nuglow 212\nulhasnagar 212\nunforeseeable 212\nunivalent 212\nuranie 212\nvandenbroucke 212\nvasistha 212\nvenkaiah 212\nvermaak 212\nverwood 212\nvetusta 212\nviciae 212\nvillancicos 212\nvinica 212\nvirupaksha 212\nvitara 212\nvlans 212\nvlogs 212\nvolcanically 212\nvvn 212\nwahre 212\nwcmc 212\nwentzville 212\nwerf 212\nwhisks 212\nwhittle's 212\nwiglaf 212\nwinkworth 212\nwirdheim 212\nwlwt 212\nwri 212\nwtxf 212\nwyland 212\nxander's 212\nyac 212\nyalan 212\nyashica 212\nyashiki 212\nyongding 212\nyvr 212\nzabid 212\nzavaleta 212\nzhongwu 212\nåtvidaberg 211\néchecs 211\néowyn 211\nödemiş 211\nđi 211\nžižka 211\nαπό 211\nयम 211\nabbiati 211\naduana 211\naerocar 211\nafflicts 211\nagaw 211\nagrippa's 211\nairlifting 211\nalatri 211\nalexeyevsky 211\nalroy 211\nambérieu 211\namfissa 211\nansf 211\narcona 211\narunachala 211\nascomycetes 211\naskam 211\nasterion 211\nati's 211\naudiofile 211\navray 211\nbílá 211\nbützow 211\nbackflow 211\nbackrest 211\nbailii 211\nbaje 211\nbaljit 211\nbandmember 211\nbange 211\nbarek 211\nbarzilai 211\nbasquete 211\nbataclan 211\nbaus 211\nbawdsey 211\nbeil 211\nbelittles 211\nbellomont 211\nbelt's 211\nbemerton 211\nbenchmarked 211\nbencoolen 211\nbergdahl 211\nbhang 211\nbichel 211\nbierhoff 211\nbioinformatic 211\nblata 211\nbluetones 211\nbonner's 211\nboobook 211\nbooksurge 211\nborok 211\nbousman 211\nbradberry 211\nbullpens 211\nbutai 211\nbutta 211\ncabooses 211\ncafe's 211\ncandombe 211\ncapillaris 211\ncarrouges 211\ncasabianca 211\ncathartes 211\ncavuto 211\ncenterstage 211\ncercles 211\ncernuda 211\nchakkar 211\nchastened 211\nchaura 211\ncheetos 211\nchelo 211\nchiko 211\nchilembwe 211\nchinuch 211\nchoicest 211\nchud 211\ncirkut 211\ncknw 211\nclassism 211\nclemencia 211\nclitocybe 211\nclumper 211\ncohost 211\ncolindale 211\ncollingsworth 211\ncolocasia 211\ncompositae 211\nconcordances 211\nconformally 211\nconfucians 211\nconservatorship 211\ncostache 211\ncrovan 211\ncuttin 211\ncxb 211\ncylindropuntia 211\nczukay 211\ndaira 211\ndalaras 211\ndamnatio 211\ndaniyal 211\ndargo 211\ndarstellende 211\ndauda 211\ndeconvolution 211\ndemoing 211\nderides 211\nderrell 211\ndersim 211\ndeseado 211\ndesislava 211\ndhumal 211\ndiastema 211\ndiegetic 211\ndigiulian 211\ndigte 211\ndinham 211\ndiscors 211\ndourada 211\ndragović 211\ndruidism 211\ndrydocks 211\ndumpsters 211\ndworshak 211\neastcheap 211\neasterns 211\neest 211\nehrnrooth 211\neleutherius 211\nemic 211\nenderta 211\nendodontic 211\nendura 211\nenlistees 211\nenzuigiri 211\nepicureanism 211\neroğlu 211\neuphonic 211\nevertsen 211\nexcitons 211\neyebeam 211\nförderpreis 211\nfarmerville 211\nfassbinder's 211\nfeldshuh 211\nfishermans 211\nflufftail 211\nfondest 211\nforesman 211\nformosanus 211\nfortuné 211\nfoudre 211\nfreelander 211\nfreo 211\nfuniculars 211\ngabe's 211\ngaertn 211\ngalantes 211\ngaliot 211\ngaubert 211\ngeach 211\ngedser 211\ngedung 211\ngeliebte 211\ngeluk 211\ngeniculata 211\ngetty's 211\ngirotti 211\ngisaeng 211\ngitlin 211\ngmv 211\ngoodhew 211\ngortyna 211\ngoux 211\ngp's 211\ngrímsson 211\ngrahami 211\ngraudenz 211\ngraying 211\ngreenview 211\ngreuze 211\ngriboyedov 211\ngrobe 211\ngusuku 211\nguttermouth 211\nhú 211\nhā 211\nhalmos 211\nharé 211\nharchester 211\nharda 211\nharmonists 211\nharpists 211\nhesder 211\nheshan 211\nhicks's 211\nhilum 211\nhirzel 211\nhll 211\nhoppen 211\nhorndon 211\nhougue 211\nhrushevsky 211\nhuaiguang 211\nhuckett 211\nhuell 211\nhumpbacked 211\nhxd 211\nhydra's 211\niaf's 211\nidflieg 211\nilich 211\ninagua 211\ninclusivity 211\ninfraorbital 211\ninkheart 211\ninkscape 211\ninschriften 211\nintervista 211\ninvasor 211\nirex 211\nirmãos 211\nislandica 211\nivkov 211\njallieu 211\njathedar 211\njayaprada 211\njesselton 211\njevnaker 211\njih 211\njiiva 211\njmw 211\njogia 211\njonction 211\njsw 211\njugyō 211\njungo 211\nkʷ 211\nkabira 211\nkalhor 211\nkamban 211\nkanohi 211\nkapetan 211\nkarlsplatz 211\nkasparov's 211\nkatsutoshi 211\nkawanabe 211\nkch 211\nkelch 211\nkennerly 211\nkeulemans 211\nkhandan 211\nkia's 211\nkingsclere 211\nkiong 211\nkobrin 211\nkoho 211\nkohr 211\nkolle 211\nkoneru 211\nkramarić 211\nkretinga 211\nkrupskaya 211\nkta 211\nkubicek 211\nkuniko 211\nkuwaitis 211\nléry 211\nlaliberte 211\nlandsburg 211\nlanternfish 211\nlaparotomy 211\nlawler's 211\nlcvp 211\nlefts 211\nlewenhaupt 211\nlind's 211\nliteraturii 211\nlockstep 211\nlongquan 211\nlosev 211\nlumea 211\nlwd 211\nlygia 211\nmadero's 211\nmadhhab 211\nmahadji 211\nmamu 211\nmanero 211\nmangat 211\nmanjeet 211\nmanole 211\nmaranda 211\nmarazzi 211\nmarginals 211\nmariquita 211\nmartinek 211\nmartinengo 211\nmashriq 211\nmattapoisett 211\nmattoli 211\nmaxxie 211\nmeaker 211\nmediations 211\nmenacingly 211\nmender 211\nmentalities 211\nmeridien 211\nmesne 211\nmewa 211\nmišo 211\nmidcourse 211\nmihoshi 211\nmiramontes 211\nmiscanthus 211\nmissale 211\nmitsotakis 211\nmkz 211\nmoffit 211\nmoisant 211\nmolesta 211\nmolter 211\nmongkut's 211\nmontan 211\nmorula 211\nmountstuart 211\nmuelle 211\nmuncey 211\nmunis 211\nmyroslav 211\nnaghma 211\nnanite 211\nnarducci 211\nnaticidae 211\nnchs 211\nnetsky 211\nneyra 211\nnicaragüense 211\nnkunda 211\nnoad 211\nnodosum 211\nnoosphere 211\nnuskhuri 211\noaf 211\noberalp 211\nobis 211\noquirrh 211\normes 211\noryctolagus 211\notylia 211\nouzo 211\npēteris 211\npanlalawigan 211\npaphitis 211\nparrsboro 211\npartibus 211\npattanam 211\npaugussett 211\npayton's 211\npeche 211\npeerzada 211\npeko 211\npeponocephala 211\npeppas 211\nperoxidation 211\nphibunsongkhram 211\npiezas 211\npinturas 211\nplaintexts 211\nplasmatics 211\nplatja 211\npndc 211\npodravka 211\npopocatépetl 211\npovl 211\nprasanta 211\nprednisolone 211\nprocellaria 211\nprovera 211\npsuv 211\npunj 211\nqasim's 211\nracicot 211\nrajasree 211\nraoux 211\nraymondville 211\nredgum 211\nrepenting 211\nretarder 211\nrhodamine 211\nringkøbing 211\nrippingtons 211\nrius 211\nrivervale 211\nromig 211\nrothorn 211\nrotti 211\nroubaud 211\nroxon 211\nrunan 211\nrusal 211\nrutabaga 211\nryr 211\nsénat 211\nsørum 211\nsōya 211\nsaed 211\nsaltaneh 211\nsalza 211\nsamangan 211\nsamuda 211\nsaung 211\nsavvis 211\nschio 211\nschlee 211\nschorndorf 211\nschukin 211\nschuster's 211\nschwabing 211\nscrublands 211\nseediq 211\nsems 211\nsentrum 211\nshambo 211\nshant 211\nshatuo 211\nshaya 211\nshebelle 211\nshenxin 211\nshikama 211\nshindler 211\nshinseki 211\nshipston 211\nshirehampton 211\nshivkumar 211\nshucheng 211\nsilverliner 211\nsimulant 211\nsiria 211\nsiso 211\nskan 211\nskolt 211\nslowinski 211\nsnowcat 211\nsnt 211\nsoch 211\nsolie 211\nsoundman 211\nspellmount 211\nstammerer 211\nstarbug 211\nstarc 211\nstonefish 211\nstreatley 211\nstrobes 211\nstuy 211\nsubpolar 211\nsummands 211\nsunstreaker 211\nsuperfan 211\nsusurluk 211\nsynaesthesia 211\nsynapsid 211\nsztuka 211\ntaan 211\ntabatha 211\ntafawa 211\ntaikai 211\ntakehara 211\ntakigawa 211\ntangling 211\ntasaki 211\ntasveer 211\ntazara 211\nteddie 211\ntelewizja 211\ntellegen 211\nterrigal 211\nthaicom 211\ntheodoridis 211\nthielemann 211\nthiene 211\nthoracica 211\ntias 211\ntiebreaking 211\ntiele 211\ntijara 211\ntimpanist 211\ntireurs 211\ntiryns 211\ntitisee 211\ntoccatas 211\ntokoname 211\ntollways 211\ntomte 211\ntootle 211\ntoporama 211\ntostitos 211\ntousen 211\ntoxicodendron 211\ntréguier 211\ntrebbia 211\ntresca 211\ntrysts 211\ntufo 211\ntully's 211\ntunks 211\ntuve 211\nunibet 211\nunionpay 211\nunreinforced 211\nurashima 211\nutbah 211\nuttama 211\nvölkerkunde 211\nvaden 211\nvads 211\nvalters 211\nvatsa 211\nvespertilio 211\nvillamizar 211\nvisaya 211\nwładysław's 211\nwaibel 211\nwaipa 211\nwalki 211\nwallfisch 211\nwearied 211\nwebbs 211\nweisbrod 211\nwellen 211\nwiltse 211\nwindpark 211\nwingrave 211\nwoetzel 211\nwolfensohn 211\nwomanist 211\nwotje 211\nxanthia 211\nxaviax 211\nxianzhi 211\nxiaoqing 211\nxuereb 211\nyüreğir 211\nyūbari 211\nyakubov 211\nyba 211\nyeouido 211\nyorubaland 211\nytm 211\nzając 211\nzambo 211\nzeleny 211\nzhitomir 211\nznam 211\nzollicoffer 211\nzorić 211\nōgaki 210\nśródmieście 210\nświata 210\nštybar 210\nżurrieq 210\nменя 210\nخان 210\nसत 210\nาย 210\naílton 210\naîné 210\naapa 210\nabramovitz 210\nabyssinicus 210\nacciona 210\nacromion 210\nademola 210\naegisthus 210\naggravates 210\nalbayrak 210\nalcidae 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210\nfadh 210\nfaidherbe 210\nfamiliarise 210\nfantasized 210\nfederate 210\nfederspiel 210\nfernvale 210\nfesto 210\nfeyder 210\nffynnon 210\nfi's 210\nfiddlehead 210\nfilomeno 210\nfinney's 210\nfiq 210\nfirsov 210\nflamebird 210\nflatware 210\nfluoridated 210\nfonzi 210\nforse 210\nframeline 210\nfuelwood 210\nfuling 210\ngaltung 210\ngaols 210\ngeodäsie 210\ngeorgievich 210\nghazala 210\ngillings 210\ngilsland 210\nglyceria 210\ngnt 210\ngodan 210\ngoldfaden 210\ngolijov 210\ngoossen 210\ngorenstein 210\ngorila 210\ngrünerløkka 210\ngrackles 210\ngraphiurus 210\ngravion 210\ngrechko 210\ngt's 210\ngualdo 210\nguamanian 210\nguelma 210\nguessan 210\nguidotti 210\nguille 210\ngulnara 210\ngutawa 210\nguttorm 210\ngwai 210\ngwas 210\nhadlock 210\nhakibbutz 210\nhaselden 210\nheliograph 210\nhenneman 210\nhilaria 210\nhirson 210\nhochstetter 210\nhokushin 210\nholli 210\nholvorcem 210\nhousebuilding 210\nhurtig 210\nhutan 210\nhyperkeratosis 210\nibnlive 210\nidh 210\nijsselstein 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210\nolynyk 210\nonias 210\noosterhout 210\nopérette 210\nordabasy 210\noriganum 210\noropharyngeal 210\nostende 210\notaman 210\noverlaet 210\npánuco 210\npalatines 210\npapayas 210\nparanhos 210\nparishat 210\nparlo 210\nparran 210\npccs 210\npdw 210\npequena 210\nperotti 210\npersad 210\npersonatus 210\npervasiveness 210\npharmacie 210\npiette 210\npleurota 210\nplo's 210\npodrinje 210\npoors 210\nporphyrins 210\nposets 210\npreußischen 210\npriča 210\nprivatizations 210\nproglacial 210\nproleptic 210\npuga 210\npushbutton 210\nqassem 210\nquark's 210\nquavers 210\nqz 210\nrajasekharan 210\nregionales 210\nreiteration 210\nrelativist 210\nrepaints 210\nreposted 210\nreshoots 210\nrevalued 210\nrippy 210\nrochers 210\nroimata 210\nrongji 210\nrothen 210\nroyg 210\nrva 210\nryen 210\nsaksi 210\nsantissimo 210\nsarcee 210\nsavitsky 210\nsaxa 210\nscarier 210\nschakowsky 210\nschauspiel 210\nschierke 210\nschwarzlose 210\nschwenninger 210\nscruple 210\nsebo 210\nsebold 210\nseibo 210\nseimone 210\nseismometers 210\nselfies 210\nsemenova 210\nserse 210\nsevi 210\nsfântu 210\nsfe 210\nsherpao 210\nshirota 210\nshishakli 210\nshubra 210\nsigtrygg 210\nsimonelli 210\nsinulog 210\nskard 210\nsmara 210\nsolariellidae 210\nsonos 210\nsoulier 210\nsqa 210\nsreedevi 210\nstandells 210\nstarodub 210\nstealin 210\nstinnes 210\nstockades 210\nstollen 210\nstomatology 210\nstraightness 210\nstupidest 210\nsublett 210\nsuffusions 210\nsugoroku 210\nsulfation 210\nsumbat 210\nsummerh 210\nsuono 210\nsurfaris 210\nsurfs 210\nswineherd 210\nsyncrude 210\nszakály 210\ntătărescu 210\ntakt 210\ntamsyn 210\ntanacross 210\ntapere 210\ntejgaon 210\ntelemaco 210\nteplitz 210\nthacholi 210\nthapsus 210\ntharakan 210\nthaton 210\ntheobroma 210\nthirring 210\nthp 210\ntoothpicks 210\ntorokina 210\ntpx 210\ntracey's 210\ntragödie 210\ntransmediale 210\ntrastuzumab 210\ntredici 210\ntripunithura 210\ntrumpeting 210\ntulane's 210\ntunnelled 210\nturpitude 210\ntypologically 210\nuçar 210\nunpitched 210\nunrighteous 210\nurbanity 210\nurgel 210\nverhalen 210\nvisitante 210\nvistavision 210\nvitz 210\nvlady 210\nvoloshin 210\nwadesboro 210\nwarra 210\nwaspinator 210\nwaster 210\nwatanabe's 210\nwayde 210\nwehler 210\nwhitestown 210\nwindber 210\nwolbachia 210\nwolstencroft 210\nwpro 210\nyamna 210\nyasukawa 210\nyiewsley 210\nyoulan 210\nyuno 210\nzacharia 210\nzachery 210\nzagori 210\nzakharchenko 210\nzanzibari 210\nzhiming 210\nzloty 210\nzorlu 210\nzunino 210\næthelred's 209\nçankaya 209\nóbuda 209\nīlī 209\nвнешних 209\nหนองบ 209\nabaqa 209\nabdülhamid 209\nabyan 209\nacct 209\nadigal 209\nagonized 209\naidensfield 209\naivars 209\naliyeva 209\nalldis 209\naltınordu 209\namboina 209\namoenus 209\nangiolini 209\nangklung 209\nantonian 209\naphthous 209\napichatpong 209\napplegarth 209\narachnologist 209\narcis 209\naria's 209\narpita 209\nasamiya 209\nasota 209\nattilan 209\naugite 209\nautomeris 209\naverbakh 209\navildsen 209\nbachan 209\nbackdating 209\nbadai 209\nbaddesley 209\nbaikie 209\nbalma 209\nbals 209\nbardney 209\nbazeley 209\nbbcv 209\nbeisel 209\nbelching 209\nbereza 209\nbertoldo 209\nbetaine 209\nbharathwaj 209\nbimala 209\nbirdlike 209\nbirefringent 209\nbirkhead 209\nblaschke 209\nbleich 209\nblockley 209\nboguslaw 209\nboolos 209\nboorstin 209\nbootylicious 209\nbouteille 209\nbrackus 209\nbratsberg 209\nbraunschweiger 209\nbrigman 209\nbriny 209\nbronn 209\nburditt 209\ncaha 209\ncalciatori 209\ncardinalidae 209\ncarebara 209\ncasanare 209\ncavaillon 209\ncernay 209\nceto 209\nchè 209\nchampionnats 209\nchane 209\nchanticleers 209\nchechnya's 209\ncherone 209\ncherryh's 209\nchillon 209\nchokyi 209\nchoroidal 209\nciudades 209\nclayderman 209\ncleugh 209\ncochiti 209\ncognizable 209\ncollas 209\ncomedie 209\ncomposted 209\nconjointly 209\ncontrol's 209\ncoraciidae 209\ncorpuscular 209\ncoxa 209\ncrisóstomo 209\ncrookham 209\ncuyuna 209\ncwp 209\ncwr 209\ncwu 209\ncyanoacrylate 209\ndâmbovița 209\ndadeland 209\ndakhil 209\ndangerbird 209\ndarchinyan 209\ndathan 209\ndaunted 209\ndebary 209\ndebaser 209\ndecriminalised 209\ndelain 209\ndelocalization 209\ndemetria 209\ndeshamanya 209\ndevaux 209\ndiabate 209\ndjordjevic 209\ndjurhuus 209\ndonavon 209\ndoormat 209\ndorel 209\ndrage 209\ndrumcliff 209\ndutchtown 209\nearthquake's 209\necrha 209\necton 209\necw's 209\nefford 209\nefimov 209\nehrhart 209\neileen's 209\neisaku 209\nelvington 209\nendel 209\nengle's 209\neop 209\nethio 209\neustaquio 209\nexecutrix 209\nexomars 209\nfabyan 209\nfagernes 209\nfahadh 209\nfaheem 209\nfallibility 209\nfantasmic 209\nfauxlivia 209\nfeasa 209\nfeit 209\nferrer's 209\nfeuerwehr 209\nflabellina 209\nflameback 209\nflavida 209\nfletton 209\nflipkart 209\nfoobar 209\nfootstep 209\nfouke 209\nfreistadt 209\nfrights 209\nfullpage 209\nfulop 209\nfurusato 209\ngarðar 209\ngarbajosa 209\ngarbed 209\ngarnishes 209\ngarron 209\ngarum 209\ngarvie 209\ngiel 209\ngigwise 209\ngillom 209\ngladrags 209\nglossing 209\ngonin 209\ngpgpu 209\ngracillima 209\ngrammont 209\ngraystripe 209\ngreger 209\ngrigorieva 209\ngroover 209\ngtmo 209\nguánica 209\nguiraud 209\nhagenbach 209\nhammann 209\nhamstrung 209\nhanguk 209\nhardcourts 209\nhauterive 209\nheister 209\nhenpecked 209\nhertling 209\nhertwig 209\nhgf 209\nhighspire 209\nhirakud 209\nhognose 209\nholzinger 209\nhoplodrina 209\nhoveyda 209\nhrafn 209\nhsan 209\nhuelgas 209\nhvr 209\nhydrovatus 209\nhyphy 209\nhypoventilation 209\ninoculate 209\nintonations 209\ninviscid 209\niob 209\nioda 209\niriver 209\nishino 209\nizegem 209\njabri 209\njackey 209\njacquemart 209\njaffé 209\njailbreaking 209\njamalullail 209\njecheon 209\njeng 209\njewsbury 209\njmm 209\njoeys 209\nkörberg 209\nkadmon 209\nkafe 209\nkahless 209\nkalergi 209\nkampo 209\nkangin 209\nkantakouzene 209\nkashmir's 209\nkastellaun 209\nkatydid 209\nkawasaki's 209\nkazerun 209\nkeaveney 209\nkeithley 209\nkenway 209\nkhanapur 209\nkharg 209\nkilkea 209\nkinetochores 209\nkino's 209\nkolab 209\nkoltai 209\nkornet 209\nkouassi 209\nkrausz 209\nkreisau 209\nkrips 209\nkureha 209\nkurigram 209\nkuriles 209\nkvens 209\nkyai 209\nláska 209\nladouceur 209\nlahave 209\nlavalette 209\nleakes 209\nliatris 209\nlimfjord 209\nlipset 209\nliras 209\nlissajous 209\nlissner 209\nllana 209\nllegó 209\nlomi 209\nloughridge 209\nlovington 209\nlubuntu 209\nludovici 209\nlutze 209\nlycées 209\nméridien 209\nmaalai 209\nmadeiran 209\nmagadhi 209\nmagistros 209\nmahat 209\nmalmi 209\nmandirs 209\nmangue 209\nmannheims 209\nmanoeuvering 209\nmant 209\nmantelli 209\nmarfil 209\nmarilao 209\nmartika 209\nmasqueraded 209\nmawatha 209\nmaximoff 209\nmazinkaiser 209\nmazzara 209\nmbyte 209\nmcneile 209\nmedhi 209\nmediterráneo 209\nmedvedi 209\nmeissner's 209\nmelen 209\nmeritocratic 209\nmetabolomics 209\nmetallurgists 209\nmetrobús 209\nmeye 209\nmicromollusks 209\nmilbury 209\nmillette 209\nmills's 209\nmilord 209\nminegishi 209\nmingora 209\nmithradates 209\nmmog 209\nmontrouzier 209\nmoyglare 209\nmrad 209\nmuirkirk 209\nmujahedeen 209\nmundialito 209\nmutoscope 209\nmyositis 209\nmyostatin 209\nnáedo 209\nnagu 209\nnagykanizsa 209\nnarsimha 209\nnasturtium 209\nnationen 209\nnatzweiler 209\nnavbahor 209\nnd's 209\nneot 209\nnijs 209\nnisra 209\nnjanum 209\nnkr 209\nnorcal 209\nnycteola 209\nnyle 209\nnyoman 209\nodintsovo 209\nohlson 209\nolg 209\noliquid 209\nolliver 209\nonedrive 209\nonsoranje 209\nopata 209\noratoria 209\noung 209\novenbird 209\novercapacity 209\noxa 209\npâris 209\npalang 209\npalmiro 209\npalpa 209\nparaiba 209\nparapoynx 209\npeery 209\nperdrix 209\nperfetti 209\npeschiera 209\npettirossi 209\npfr 209\nphish's 209\npierwszy 209\npilchuck 209\npilić 209\npinstriped 209\npittenweem 209\nplumbago 209\npoach 209\npodunk 209\npoedjangga 209\npoetries 209\npolarize 209\npoledouris 209\npolugaevsky 209\npolygonia 209\nponzio 209\npopiel 209\npouce 209\npouliches 209\npoulsbo 209\npredisposes 209\npreempting 209\npresidios 209\nproyas 209\npuar 209\nqmc 209\nquiambao 209\nračan 209\nramabai 209\nramgarhia 209\nramshaw 209\nrandalls 209\nranglin 209\nrathri 209\nrauxel 209\nrawles 209\nrdd 209\nrefortified 209\nresourcing 209\nrespire 209\nretread 209\nretrievable 209\nrhapsodic 209\nrichelieu's 209\nrikimaru 209\nrilski 209\nronaldsway 209\nsagarin 209\nsalamandre 209\nsalkind 209\nsaluja 209\nsalvinia 209\nsandfield 209\nsatélite 209\nsaxony's 209\nsayler 209\nscac 209\nscahill 209\nscald 209\nschuldt 209\nsebert 209\nsenger 209\nsereni 209\nseyffarth 209\nshewing 209\nshinpei 209\nshipbreaking 209\nshowjumping 209\nsilvetti 209\nsinne 209\nsirnas 209\nskokomish 209\nskopelos 209\nslipknot's 209\nsneakin 209\nsobralia 209\nsocialised 209\nsodexo 209\nsokółka 209\nsoldan 209\nsondes 209\nsotiropoulos 209\nspacehab 209\nsquirrelfish 209\nstaalplaat 209\nstarkid 209\nstarkiller 209\nstati 209\nstavans 209\nstejneger 209\nstjerne 209\nsubsumes 209\nsuffixing 209\nsulaimani 209\nsultani 209\nsundararama 209\nsurimi 209\nsxt 209\nszász 209\nsza 209\nszymczyk 209\ntache 209\ntadahiro 209\ntale's 209\ntamarindo 209\ntanumafili 209\nteather 209\ntenryō 209\nthats 209\ntheophile 209\nthermidorian 209\nthole 209\nthuringiensis 209\ntigana 209\ntilles 209\ntitlis 209\ntnstc 209\ntomasa 209\ntomjanovich 209\ntooby 209\ntorreya 209\ntost 209\ntotalseats 209\ntraficant 209\ntrampa 209\ntransloy 209\ntraversée 209\ntreepie 209\ntrilled 209\ntroisdorf 209\ntroncal 209\ntropomyosin 209\ntroxler 209\nturbat 209\ntynte 209\ntypographers 209\nuetliberg 209\nughtred 209\nuhi 209\nulúa 209\nunger's 209\nungovernable 209\nunzen 209\nuon 209\nupsy 209\nurijah 209\nvaladon 209\nvalanginian 209\nvallum 209\nvardis 209\nvatopedi 209\nvej 209\nverbenaceae 209\nvermeule 209\nviagens 209\nvieillot 209\nvillus 209\nvinoodh 209\nvisentini 209\nvlaminck 209\nvmgr 209\nvoigtländer 209\nvorstadt 209\nvorwerk 209\nvpp 209\nwèi 209\nwén 209\nwłókniarz 209\nwagle 209\nwakan 209\nwarrumbungle 209\nwaupun 209\nweichsel 209\nweidemann 209\nwhang 209\nwhitebait 209\nwillaert 209\nwindsurfers 209\nwineland 209\nwinterville 209\nwoio 209\nwriteup 209\nwwva 209\nwxga 209\nyews 209\nzambezia 209\nzamperini 209\nzebbug 209\nzerilli 209\nzoologia 209\nčd 208\nцерковных 208\nدبی 208\nகம 208\nabdominalis 208\nabis 208\nabraded 208\nacey 208\nachatina 208\nadiga 208\naeruginosus 208\nagathias 208\naharonov 208\nakaba 208\nalchemilla 208\naldh 208\nalecia 208\nallori 208\nalls 208\namatsu 208\namoraim 208\namphibien 208\nampullae 208\nancylolomia 208\nanissina 208\nansted 208\napostleship 208\napplique 208\narchibald's 208\nardi 208\nardross 208\narmfelt 208\narmii 208\nassayer 208\nastrotrain 208\natomicity 208\naudemer 208\naui 208\naurizon 208\navalokiteshvara 208\navidya 208\nazoulay 208\nbún 208\nbüyükakçay 208\nbabbington 208\nbackplate 208\nbackstabbing 208\nbajir 208\nbaluster 208\nbampfield 208\nbanaba 208\nbarbiero 208\nbardolf 208\nbarnardiston 208\nbarrosa 208\nbbo 208\nbbses 208\nbedecked 208\nbeefed 208\nbeguiled 208\nbelgisch 208\nbencic 208\nbenenson 208\nberges 208\nbertha's 208\nbesòs 208\nbesler 208\nbiathletes 208\nbibliobazaar 208\nbilingue 208\nbinyamina 208\nbinzhou 208\nbioavailable 208\nbiodiverse 208\nbizzarrini 208\nbloemaert 208\nboronic 208\nbotha's 208\nbotte 208\nbozzo 208\nbríd 208\nbraco 208\nbreydel 208\nbrochard 208\nbrovary 208\nbuderim 208\nbudhi 208\nbulatov 208\nbulganin 208\nbushe 208\ncâmpina 208\ncabatuan 208\ncanaliculata 208\ncaramon 208\ncarillion 208\ncarmenta 208\ncaro's 208\ncatalani 208\ncharlo 208\nchenna 208\nchiasm 208\nchiclana 208\nchinery 208\nchitto 208\nchod 208\ncholestasis 208\ncloying 208\ncofounders 208\ncommish 208\nconfiança 208\nconinck 208\ncoopération 208\ncorpuscle 208\ncourtesies 208\ncoyote's 208\ncrawley's 208\ncrover 208\ncunn 208\ncurvirostra 208\ncywinski 208\ndávalos 208\ndécouvertes 208\ndagsburg 208\ndahod 208\ndalu 208\ndandini 208\ndandurand 208\ndantan 208\ndarłowo 208\ndebendranath 208\ndeidamia 208\ndenari 208\ndendrimers 208\ndenio 208\ndeoxyribose 208\ndernbach 208\ndessler 208\ndetracting 208\ndhun 208\ndiem's 208\ndiferente 208\ndiputados 208\ndisfavored 208\ndither 208\ndjordje 208\ndohring 208\ndolben 208\ndom's 208\ndomachowska 208\ndominion's 208\ndongyang 208\ndorantes 208\ndorst 208\ndowels 208\ndownregulated 208\ndrinan 208\ndrishti 208\ndrobnjak 208\ndrori 208\nduology 208\nduva 208\nduyn 208\ndymaxion 208\ndynastie 208\ndynham 208\ndzierżoniów 208\neén 208\neffervescence 208\nelía 208\nelapidae 208\nelectrocutes 208\nelectrotype 208\nelfi 208\nelstow 208\nemshwiller 208\nengorged 208\nenumerations 208\neprix 208\nerschoff 208\nestrildidae 208\nestrone 208\nextensa 208\nfēng 208\nfahs 208\nfairchild's 208\nfalsifiability 208\nfenger 208\nferdynand 208\nfermentable 208\nferrell's 208\nfesch 208\nfilmways 208\nfitial 208\nfmj 208\nfoad 208\nforssa 208\nforwood 208\nfoxbat 208\nfrae 208\nfrasnian 208\nfrehley's 208\nfruta 208\nfuiste 208\ngölbaşı 208\ngabela 208\ngaillac 208\ngalanos 208\ngalbi 208\ngambaccini 208\ngapes 208\ngarga 208\ngavron 208\ngeldart 208\ngendebien 208\nggt 208\nghadi 208\ngippius 208\ngnarly 208\ngodsey 208\ngoldacre 208\ngordin 208\ngoulds 208\ngroll 208\nguardini 208\ngug 208\ngunkel 208\ngustaaf 208\ngwasanaeth 208\nhägar 208\nhaina 208\nhalawa 208\nhammou 208\nharbourmaster 208\nhargest 208\nhaunch 208\nhefer 208\nheiðar 208\nhemignathus 208\nhengshan 208\nhenrickson 208\nheol 208\nhgm 208\nhilarius 208\nhincmar 208\nhle 208\nhmat 208\nhoeneß 208\nholweck 208\nhomozygotes 208\nhostettler 208\nhowto 208\nhudlin 208\nicra 208\nicsu 208\nilleana 208\nillustrated's 208\nindeterminism 208\ninsistently 208\nirst 208\nistmo 208\njackaroo 208\njaman 208\njanitor's 208\njavaserver 208\njeschke 208\njolts 208\njoypurhat 208\njueves 208\njumeaux 208\njunit 208\njuryo 208\njusticiable 208\nkalte 208\nkanaeva 208\nkansu 208\nkealakekua 208\nkemaman 208\nkempi 208\nkeratins 208\nkilchberg 208\nkilmocomoge 208\nkingsholm 208\nkipchoge 208\nkitáb 208\nkonversations 208\nkorumburra 208\nkoryu 208\nkotcheff 208\nkrikalev 208\nkroto 208\nkuck 208\nkuruvi 208\nkyokai 208\nlaffy 208\nlanzmann 208\nlaunderette 208\nlebec 208\nleonides 208\nleprosarium 208\nleura 208\nlexington's 208\nleycester 208\nlightwood 208\nlijun 208\nlillesand 208\nliotard 208\nlishi 208\nliudolf 208\nlobkowitz 208\nlombardozzi 208\nlonelyhearts 208\nlonghaired 208\nlongwall 208\nlosar 208\nlumby 208\nluvin 208\nluzi 208\nlxiv 208\nmédailles 208\nmaata 208\nmacedonica 208\nmagnini 208\nmahone's 208\nmainshock 208\nmalcontent 208\nmanageress 208\nmandarin's 208\nmandora 208\nmante 208\nmapmakers 208\nmarani 208\nmaratta 208\nmariama 208\nmarkopoulos 208\nmartland 208\nmarzena 208\nmassaquoi 208\nmauceri 208\nmazzocchi 208\nmcbeath 208\nmedicea 208\nmelisande 208\nmerfyn 208\nmichiaki 208\nmicronations 208\nmicrotis 208\nmingrelian 208\nminky 208\nminow 208\nmonatshefte 208\nmoomins 208\nmoroto 208\nmorphometric 208\nmorwen 208\nmoster 208\nmstislavich 208\nmudanya 208\nmulattos 208\nmulching 208\nmuzzled 208\nmyon 208\nmystra 208\nnabua 208\nnanashi 208\nnanay 208\nnarsing 208\nnavarro's 208\nneeman 208\nnemirovich 208\nneoteny 208\nneurosci 208\nnewmarch 208\nnexrad 208\nnibong 208\nnojpetén 208\nnoles 208\nnordfjord 208\nnorouzi 208\nnymark 208\noblak 208\nodebrecht 208\nofbuildings 208\noksanen 208\nolímpica 208\nolkusz 208\noll 208\nondra 208\noob 208\nordina 208\nording 208\noriginalism 208\norrall 208\nosterholz 208\nostrova 208\nourique 208\noutcompete 208\noutgames 208\noutstrip 208\noverlanders 208\noyen 208\npłońsk 208\npaba 208\npalairet 208\npancoast 208\nparer 208\nparviflorum 208\npasai 208\npaschen 208\npdgf 208\npecoraro 208\npenicheiro 208\npeninsulares 208\npepe's 208\npesqueira 208\npetaled 208\npetrides 208\npetrographic 208\nphrao 208\npiatkus 208\npingris 208\npitter 208\nplacar 208\npolarizability 208\npolioptila 208\nponiente 208\npradyumna 208\nprelacy 208\npretences 208\npriorat 208\nprivileging 208\npropagators 208\nprosecco 208\nptx 208\npuppy's 208\npushnin 208\nputtkamer 208\nputto 208\npyramiden 208\nqīng 208\nqaid 208\nqst 208\nquarshie 208\nréponse 208\nrêverie 208\nrailtour 208\nrakowski 208\nramal 208\nrammell 208\nrandfontein 208\nraviv 208\nrebid 208\nrecasts 208\nredbourn 208\nredman's 208\nreep 208\nreichhardt 208\nreintroduces 208\nrff 208\nribbs 208\nrichier 208\nrockfowl 208\nroleanimated 208\nromand 208\nrosický 208\nroutley 208\nrubio's 208\nrukhsana 208\nrutilans 208\nsöder 208\nsüleymaniye 208\nsaïda 208\nsaddlebred 208\nsalame 208\nsalvages 208\nsalvator's 208\nsandwiching 208\nsangwan 208\nsansad 208\nsaskpower 208\nscenthounds 208\nschüssel 208\nschaeffer's 208\nseances 208\nsego 208\nsegui 208\nseigen 208\nsemovente 208\nseymore 208\nshadia 208\nshadowplay 208\nshahril 208\nshaka's 208\nsharpener 208\nshimen 208\nshivram 208\nsiaya 208\nsibson 208\nsidsel 208\nsigfried 208\nsike 208\nsiver 208\nslone 208\nsmartwatch 208\nsnorre 208\nsolina 208\nsospiri 208\nsouthpeak 208\nsouvanna 208\nsozomen 208\nsparser 208\nspqr 208\nsriramulu 208\nsteinn 208\nsternula 208\nstickiness 208\nstiftskirche 208\nstohr 208\nstonemasonry 208\nsubassociation 208\nsukarnoputri 208\nsulcatus 208\nsundargarh 208\nsundbyberg 208\nswanberg 208\nswitchboards 208\nsyllabuses 208\ntōyama 208\ntablecloths 208\ntalukdar 208\nteacups 208\ntehuacán 208\ntelecommuting 208\nteman 208\ntennet 208\nterboven 208\ntermez 208\nthreskiornis 208\nthurmont 208\ntimes's 208\ntimescape 208\ntimofei 208\ntirades 208\ntoei's 208\ntomaz 208\ntoplica 208\ntoprak 208\ntortricinae 208\ntradeshow 208\ntragicomic 208\ntrailblazing 208\ntrespassed 208\ntridentina 208\ntsarskoe 208\ntuamotus 208\nturini 208\ntzomet 208\nuchenna 208\nugl 208\nuhv 208\nukf 208\nunnoticeable 208\nupholsterer 208\nusaid's 208\nusana 208\nuusikaupunki 208\nvän 208\nvakula 208\nvaliquette 208\nvalori 208\nvanderkaay 208\nvarrick 208\nveneman 208\nvenir 208\nvenkatagiri 208\nverdade 208\nverni 208\nvhong 208\nviciousness 208\nvission 208\nvohwinkel 208\nvydra 208\nwüste 208\nwaly 208\nwava 208\nwaze 208\nwebobjects 208\nwesterholt 208\nwet's 208\nweyl's 208\nwhitmarsh 208\nwihl 208\nwilbur's 208\nwildbad 208\nwindeck 208\nworldnetdaily 208\nxel 208\nxibalba 208\nxiling 208\nxrs 208\nxtr 208\nyeon's 208\nyetzirah 208\nymer 208\nyoungbloodz 208\nyuraq 208\nzóbel 208\nzakuani 208\nzazu 208\nzotz 208\nzsigmondy 208\nzurna 208\nàngel 207\nال 207\nabpd 207\naccies 207\nacle 207\nadministrates 207\nalacranes 207\nalat 207\nalgemene 207\naliyot 207\naltenmarkt 207\nalvernia 207\nammonoosuc 207\namos's 207\nanaheim's 207\nandreeva 207\nandroid's 207\nannoyances 207\nansatz 207\nansible 207\napy 207\naquarelle 207\naquilani 207\narcangeli 207\narkush 207\nassiut 207\nassonet 207\nattesa 207\naurelie 207\naussig 207\nazaz 207\nbabita 207\nbaddi 207\nbadelt 207\nbadghis 207\nbadnjak 207\nbaggott 207\nbaitullah 207\nbajina 207\nbalašević 207\nbalderton 207\nbaldolini 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198\nsilveria 198\nsja 198\nsleuthing 198\nslovácko 198\nslowfox 198\nsnaking 198\nsnorri's 198\nsnowpiercer 198\nsomonauk 198\nsonatrach 198\nsorgen 198\nspath 198\nsphex 198\nspiralled 198\nstaatsgalerie 198\nstampley 198\nstaniford 198\nstauning 198\nsteingrímur 198\nstenz 198\nstieglitz's 198\nstraitjackets 198\nstratocruiser 198\nstringer's 198\nsulev 198\nsundqvist 198\nsuniti 198\nsupernanny 198\nsutermeister 198\nswanand 198\nswofford 198\nswordmaster 198\ntū 198\ntagen 198\ntassajara 198\nteatrale 198\ntefft 198\ntennoji 198\ntepuis 198\ntessari 198\nthịnh 198\nthalictrum 198\nthorby 198\ntidball 198\ntikvah 198\ntineo 198\nting's 198\ntokiko 198\ntomljanović 198\ntorrejon 198\ntréville 198\ntraks 198\ntrapezohedron 198\ntremulous 198\ntriacontahedron 198\ntribunate 198\ntsujimoto 198\ntujuh 198\ntull's 198\nturnesa 198\ntwelves 198\nuhh 198\nukal 198\nunalakleet 198\nurbie 198\nväike 198\nvaruvaayaa 198\nvenga 198\nverdienstkreuz 198\nverelst 198\nvilho 198\nvirgile 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197\ncolorant 197\ncomosa 197\ncontrive 197\ncosmographia 197\ncrash's 197\ncsanád 197\ncsny 197\ncullimore 197\nculturales 197\ncymdeithas 197\ndélétraz 197\ndanika 197\ndanites 197\ndarkforce 197\ndarwaja 197\ndatsuns 197\ndebrief 197\ndecamps 197\ndeck's 197\ndelite 197\ndenr 197\ndevelopable 197\ndilettantistica 197\ndillons 197\ndinty 197\ndiplomatist 197\ndirettore 197\ndisclaim 197\ndoğuş 197\ndobos 197\ndolný 197\ndominicensis 197\ndonatists 197\ndongxing 197\ndonlin 197\ndonnacha 197\ndoppio 197\ndorries 197\ndromard 197\ndrum's 197\ndruskininkai 197\ndubliner 197\ndudayev 197\ndunnet 197\neasdale 197\neddowes 197\nedzell 197\nembolus 197\nenquist 197\nerft 197\nerkelenz 197\neslami 197\netalk 197\nexasperating 197\nexbury 197\nförderung 197\nfaccia 197\nfallston 197\nfanie 197\nfastway 197\nfcx 197\nfener 197\nfertitta 197\nfews 197\nfinasteride 197\nfissurellidae 197\nfols 197\nforcestruc 197\nfow 197\nfrankowski 197\nfratianno 197\nfreshener 197\nfusi 197\ngéraud 197\ngüiro 197\ngaffey 197\ngapping 197\ngeisa 197\ngek 197\ngenderless 197\ngenovés 197\ngevgelija 197\nghafar 197\ngotrek 197\ngowron 197\ngrabowsky 197\ngracin 197\ngraphology 197\ngraysville 197\ngremillion 197\ngriet 197\ngrimsby's 197\ngrundmann 197\nguido's 197\ngunvessel 197\ngwennap 197\nhaldwani 197\nhammelburg 197\nharim 197\nhaseo 197\nhawkinsville 197\nhazes 197\nheadscissors 197\nheaved 197\nheiligenkreuz 197\nhelliar 197\nhelminen 197\nhelmore 197\nherkomer 197\nhezron 197\nhighspeed 197\nhijras 197\nhikaru's 197\nhindgut 197\nhistorisk 197\nhitesh 197\nhoà 197\nhockett 197\nhonkala 197\nhtut 197\nhua's 197\nhuimin 197\nhuineng 197\nhumperdinck's 197\nhutsul 197\nicpa 197\nidiops 197\nidir 197\nimamzadeh 197\nimpalas 197\nimperatoribus 197\ninagawa 197\ninamori 197\nincompletion 197\ninconveniently 197\ningegerd 197\ningermanland 197\ninstore 197\ninterlocks 197\nionotropic 197\nirisa 197\nirrorata 197\niscor 197\nitera 197\niwl 197\niyers 197\njahi 197\njdf 197\njennys 197\njhum 197\njinotega 197\njohaug 197\njokhang 197\njolson's 197\njosua 197\njowl 197\njuneteenth 197\njutras 197\nkabirov 197\nkady 197\nkagetsu 197\nkamigata 197\nkamya 197\nkandukondain 197\nkard 197\nkawānanakoa 197\nkazin 197\nkcp 197\nkembali 197\nkerogen 197\nkeulen 197\nkewpie 197\nkeysborough 197\nkfarsghab 197\nkhamsa 197\nkharan 197\nkhevenhüller 197\nkhizr 197\nkildangan 197\nkirschbaum 197\nkli 197\nkligerman 197\nknappertsbusch 197\nkonkuk 197\nkoschmin 197\nkotoka 197\nkoulikoro 197\nkrentz 197\nkresta 197\nkripo 197\nkuklinski 197\nkuldevi 197\nkury 197\nkvn 197\nkwangju 197\nlönnrot 197\nlalage 197\nlamont's 197\nlandmann 197\nlangenfeld 197\nlatam 197\nleasowe 197\nlechwe 197\nleoncito 197\nleucopogon 197\nlewdness 197\nleyser 197\nlingüística 197\nlings 197\nlixin 197\nlosch 197\nlouganis 197\nlowerhouse 197\nlugnuts 197\nlundbeck 197\nlynge 197\nmädel 197\nmacnutt 197\nmacveagh 197\nmahabir 197\nmahotella 197\nmahurin 197\nmalamute 197\nmaleki 197\nmanden 197\nmanoharlal 197\nmaragall 197\nmaranta 197\nmargerie 197\nmarise 197\nmarketa 197\nmatraca 197\nmatriarchs 197\nmatryoshka 197\nmavro 197\nmayence 197\nmaytenus 197\nmazarine 197\nmelinda's 197\nmeningioma 197\nmessidor 197\nmhi 197\nmichoacana 197\nmiedecke 197\nmiffy 197\nmilliken's 197\nmitau 197\nmithraism 197\nmitridate 197\nmonarcha 197\nmorozumi 197\nmorphettville 197\nmosty 197\nmourvèdre 197\nmoyale 197\nmrázek 197\nmudflows 197\nmultitouch 197\nmunchies 197\nmuraille 197\nnürtingen 197\nnabard 197\nnaftogaz 197\nnairo 197\nnambla 197\nnamhae 197\nnansei 197\nnarasimhavarman 197\nncpa 197\nncv 197\nnesser 197\nnewmilns 197\nnkk 197\nnoémi 197\nnordwest 197\nnoren 197\nnorthstars 197\nnouvion 197\nnrol 197\nnurit 197\nnuussuaq 197\nobersee 197\nocak 197\nochropus 197\nofficious 197\nohl's 197\nohlsdorf 197\nolenellus 197\nornithologie 197\noscroft 197\noxycontin 197\nozo 197\nozu's 197\npéruwelz 197\npablos 197\npackin 197\npanayot 197\npancyprian 197\npangan 197\nparaguarí 197\nparalia 197\npartenope 197\npeces 197\npeer's 197\npereslavl 197\nperiodismo 197\npersil 197\nphagan 197\nphylogeography 197\npicache 197\npiko 197\npilaris 197\npimen 197\npineview 197\npmos 197\npochinok 197\npointblank 197\nponomaryov 197\npontresina 197\npostbox 197\npuckapunyal 197\nquadrivium 197\nquakecon 197\nquieting 197\nqvale 197\nrádió 197\nračić 197\nraaste 197\nrakhimov 197\nramki 197\nranjitha 197\nratbat 197\nrdon 197\nrealm's 197\nrealness 197\nreconquering 197\nreferable 197\nreformated 197\nreima 197\nrenderman 197\nressel 197\nrestrengthened 197\nrevisional 197\nreynir 197\nrhenania 197\nrogel 197\nronchetti 197\nrongai 197\nroslan 197\nrotheram 197\nrumbaugh 197\nsèvre 197\nsöderhamn 197\nsaalbach 197\nsabermetrics 197\nsamyutta 197\nsapin 197\nsaraj 197\nsauchie 197\nscaler 197\nscarfiotti 197\nschmeisser 197\nschulenberg 197\nscore's 197\nscroobius 197\nseido 197\nselbstverlag 197\nsemangat 197\nseraikis 197\nsexualization 197\nshaara 197\nshadowpact 197\nshapovalov 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194\nboothroyd's 194\nbowlin 194\nbraggins 194\nbraunsberg 194\nbravi 194\nbraze 194\nbriare 194\nbristowe 194\nbronagh 194\nbrosch 194\nbuat 194\nbuceo 194\nbumblefoot 194\nbunten 194\nburgstall 194\nburrinjuck 194\nbxh 194\nbzura 194\ncabala 194\ncaillaux 194\ncandidum 194\ncantillo 194\ncarlstadt 194\ncattani 194\ncegb 194\ncegielski 194\ncerveris 194\ncfds 194\nchœur 194\nchapecoense 194\nchequer 194\nchinsurah 194\nciclistica 194\ncicognani 194\ncoki 194\ncolliders 194\nconnelly's 194\ncontactmusic 194\ncoolderry 194\ncoppicing 194\ncorea's 194\ncoronaria 194\ncorrelational 194\ncostalis 194\ncottonmouths 194\ncourbet's 194\ncoureur 194\ncraterus 194\ncristina's 194\ncrombec 194\ncrosslinks 194\ncryptanalysts 194\ncscs 194\ncumbrians 194\ncurmudgeon 194\ncynara 194\nczechoslovaks 194\ndaewongun 194\ndaneel 194\ndash's 194\ndeborah's 194\ndehavilland 194\ndeprez 194\ndermatomyositis 194\ndesideri 194\ndevadatta 194\ndhaalu 194\ndhampir 194\ndiagon 194\ndigestibility 194\ndigitalisat 194\ndika 194\ndinehart 194\ndipika 194\ndokuro 194\ndombivli 194\ndonavan 194\ndonita 194\ndramani 194\ndreger 194\ndumbrille 194\ndystopic 194\nearlene 194\nechelle 194\neega 194\nekwe 194\nelectrica 194\nelish 194\nelkmont 194\nenderlin 194\nensam 194\nenyart 194\nepidote 194\nercilla 194\nergen 194\nerhart 194\nesem 194\nestés 194\nestremadura 194\nethmiidae 194\netranger 194\nette 194\neulalio 194\nextrapolations 194\nextruder 194\nfasching 194\nfinden 194\nfkbp 194\nfogelson 194\nfokkers 194\nfrederikke 194\nfreemont 194\nfreest 194\nfregosi 194\nfreiwillige 194\nfrogmouths 194\nfumiaki 194\nfurloughs 194\nfushë 194\ngandak 194\ngaon's 194\ngarrix 194\ngauger 194\ngauja 194\ngava 194\ngazsi 194\ngedicht 194\ngeelani 194\ngeijer 194\ngenèse 194\ngerety 194\ngernon 194\nginto 194\ngloag 194\ngoblin's 194\ngoodsir 194\ngotto 194\ngoudhurst 194\ngrammodes 194\ngrangegorman 194\ngrib 194\ngrotrian 194\nguelleh 194\nhästen 194\nhē 194\nhamhung 194\nharangue 194\nharf 194\nhartal 194\nhearken 194\nheerlijkheid 194\nheleno 194\nheliodoro 194\nherschel's 194\nherzogtum 194\nhigman 194\nhindawi 194\nhingoli 194\nhokuzan 194\nhomeplace 194\nhorsewoman 194\nhoskote 194\nhoxa 194\nhtr 194\nhuaihua 194\nhugos 194\nhunny 194\nhuperzia 194\nhutner 194\nhyundai's 194\niconographical 194\nideale 194\nigh 194\nillustrierte 194\nilocanos 194\ninaho 194\nincat 194\nincenter 194\ninet 194\ninhalers 194\ninia 194\ninm 194\ninni 194\nintercessory 194\ninternus 194\nintrust 194\ninverlochy 194\ninvermere 194\ninvestigator's 194\njaam 194\njammes 194\njanneke 194\njerram 194\njerusha 194\njiande 194\njianlibao 194\njir 194\njohann's 194\njordanita 194\njoschka 194\njuxtapoz 194\nköyü 194\nkępa 194\nkanashimi 194\nkankei 194\nkantrowitz 194\nkaramzin 194\nkarunas 194\nkashmira 194\nkasr 194\nkatsuie 194\nkaty's 194\nkawal 194\nkekkaishi 194\nkempster 194\nkerrobert 194\nkhazen 194\nkhosro 194\nkiesewetter 194\nkillkenny 194\nkimmins 194\nkirra 194\nkiz 194\nknobelsdorff 194\nknoc 194\nkonkurs 194\nkookoo 194\nkornman 194\nkriž 194\nkruja 194\nkrym 194\nkudrna 194\nkundi 194\nkurakin 194\nkwanten 194\nkway 194\nkwch 194\nkyffhäuser 194\nkyphosis 194\nkyrillos 194\nlù 194\nlabé 194\nlabyrinthus 194\nlamanite 194\nlandal 194\nlarcom 194\nlemi 194\nleviev 194\nlieth 194\nlilin 194\nlinden's 194\nlinezolid 194\nliska 194\nliteralist 194\nliteratury 194\nljudi 194\nloayza 194\nlodève 194\nlongjing 194\nlopilato 194\nlosh 194\nloughner 194\nlumding 194\nlunata 194\nlushootseed 194\nmárk 194\nmølby 194\nmúm 194\nmaō 194\nmaassluis 194\nmahek 194\nmaita 194\nmajorettes 194\nmalakai 194\nmalec 194\nmalick's 194\nmammen 194\nmanza 194\nmarinens 194\nmartinsson 194\nmassu 194\nmathematischen 194\nmatheu 194\nmatobo 194\nmawar 194\nmaximums 194\nmazzoleni 194\nmcpheeters 194\nmedalwith 194\nmekka 194\nmellan 194\nmetalloid 194\nmitsunori 194\nmoacir 194\nmohite 194\nmohr's 194\nmolé 194\nmolasse 194\nmollus 194\nmolsheim 194\nmolybdate 194\nmoniuszko 194\nmonophysitism 194\nmontenotte 194\nmopan 194\nmoraea 194\nmosser 194\nmouquet 194\nmulanje 194\nmulugeta 194\nmurail 194\nmurgha 194\nnáměstí 194\nněmcová 194\nnaheed 194\nnambeesan 194\nnapthine 194\nnarooma 194\nnazira 194\nnbl's 194\nneapoli 194\nnesbitt's 194\nnields 194\nnihalani 194\nnikoleta 194\nnirs 194\nnishat 194\nniwot 194\nnixed 194\nnocton 194\nnowogard 194\nnuada 194\nnudaurelia 194\nnurettin 194\nnuti 194\nnyd 194\nnzoia 194\nobdurate 194\noctroi 194\noklahomans 194\nolliff 194\nomertà 194\noned 194\nopenside 194\norgueil 194\norol 194\northographical 194\nosmunda 194\nostapenko 194\nostentatiously 194\nostrogoth 194\nouchy 194\noutsell 194\noverbite 194\npaadal 194\npablito 194\npachauri 194\npagoh 194\npaluma 194\npanem 194\npanhala 194\npapantla 194\nparantaka 194\nparthenius 194\npasolini's 194\npasquet 194\npasteur's 194\npathshala 194\npayneham 194\npcworld 194\npedler 194\npegge 194\npembe 194\npennypack 194\npentastar 194\nperfringens 194\nperkhidmatan 194\npersonen 194\npesma 194\npeterborough's 194\npeyrefitte 194\nphata 194\npilah 194\nplanarity 194\npocatière 194\npoids 194\npolkadot 194\npolyansky 194\npolyrhythm 194\npome 194\npomponio 194\npontarddulais 194\npopovych 194\npostecoglou 194\npramoj 194\nprigent 194\nprnjavor 194\npumbedita 194\npunjab's 194\nqādisiyyah 194\nqol 194\nquigley's 194\nquoits 194\nraddatz 194\nradegast 194\nradle 194\nrangareddy 194\nraptorial 194\nravenal 194\nravshan 194\nrebeck 194\nrechlin 194\nrechter 194\nrecolonized 194\nrefinanced 194\nreiley 194\nremington's 194\nrequin 194\nrevenger 194\nrhr 194\nrichardis 194\nrichens 194\nrinku 194\nrioni 194\nrisner 194\nrobertland 194\nrodenticide 194\nromanced 194\nrondinelli 194\nrosarium 194\nrothaar 194\nrumley 194\nrupam 194\nrvp 194\nryokuchi 194\nsahib's 194\nsalaspils 194\nsamajam 194\nsamin 194\nsanitaire 194\nsardanapalus 194\nsathyamangalam 194\nschary 194\nschleifer 194\nschlesinger's 194\nschubart 194\nschweickart 194\nscintillans 194\nscolopaceus 194\nscoob 194\nscrees 194\nsdks 194\nsemai 194\nsententiae 194\nseoige 194\nseuthes 194\nseyi 194\nseyoum 194\nshadegg 194\nshafir 194\nshibani 194\nshigaraki 194\nshikra 194\nshomrim 194\nsidebands 194\nsigismond 194\nsigrún 194\nsipah 194\nsirasa 194\nskipp 194\nsmoker's 194\nsmythies 194\nsobrado 194\nsofija 194\nsonderbund 194\nsongfacts 194\nsonrisa 194\nsotherton 194\nspacewar 194\nspasojević 194\nspellbook 194\nspermaceti 194\nstaphylococci 194\nstaufen 194\nsteeldogs 194\nstodart 194\nstraume 194\nstrogoff 194\nstrychnos 194\nstura 194\nstuttered 194\nsuciu 194\nsudket 194\nsudoeste 194\nsuffolk's 194\nsugiarto 194\nsuling 194\nsunz 194\nsuperia 194\nsurinamensis 194\nsyrena 194\nsyron 194\ntaedong 194\ntakoyaki 194\ntalwandi 194\ntcnj 194\nteleri 194\nteliana 194\ntennōji 194\nterblanche 194\nterete 194\nterrelle 194\nterritoires 194\nterror's 194\ntersâne 194\ntetrameric 194\nthatcherite 194\nthicknesse 194\nthiruda 194\nthumbprint 194\nthumps 194\nthyatira 194\ntianna 194\ntidning 194\ntitanic's 194\ntopographia 194\ntoppa 194\ntrabalho 194\ntramonto 194\ntranh 194\ntransgenderism 194\ntrifoliate 194\ntroff 194\ntruchsess 194\ntuin 194\ntungning 194\ntusked 194\ntutul 194\nugljevik 194\nuliana 194\nunconstructed 194\nunderframes 194\nunquenchable 194\nurap 194\nutseya 194\nvachel 194\nvagner 194\nvalentinos 194\nvandoorne 194\nvaresine 194\nvatc 194\nvdf 194\nvengerov 194\nvideodrome 194\nvikash 194\nvionnet 194\nvitaliano 194\nvizcarra 194\nvize 194\nvoila 194\nvolkonsky 194\nvoller 194\nvonage 194\nwafu 194\nwagonway 194\nwaldkirch 194\nwallachians 194\nwambaugh 194\nwanchope 194\nwateraid 194\nwegen 194\nwesternised 194\nwestshore 194\nwestwood's 194\nwhanau 194\nwheaten 194\nwigman 194\nwijewardena 194\nwillesee 194\nwillin 194\nwithstands 194\nwizna 194\nworde 194\nworldwatch 194\nworrack 194\nworsbrough 194\nwrky 194\nxiaoyanzi 194\nxiyu 194\nxuande 194\nyaroshenko 194\nyawar 194\nyeamans 194\nyoked 194\nyolandita 194\nyoron 194\nyossef 194\nyuting 194\nzöggeler 194\nzöllner 194\nzaat 194\nzahrani 194\nzaimis 194\nzaranj 194\nzavvi 194\nzele 194\nzerbo 194\nzubeida 194\nzwiesel 194\nzygon 194\nécuyer 193\nđá 193\nľuboš 193\nδvap 193\nнад 193\nооо 193\nпетрович 193\nстепени 193\nנו 193\naafbu 193\naatish 193\nabkhazians 193\naboral 193\nacequia 193\nacgme 193\nachiral 193\nackerman's 193\nacorn's 193\nadhoc 193\nadms 193\nadsr 193\naetosaurs 193\nafterburners 193\nafters 193\nagda 193\nagpl 193\nagrius 193\naice 193\naieee 193\nairton 193\naisha's 193\naivar 193\nakhlaq 193\nakintola 193\nakki 193\nalbiston 193\nalighieri's 193\nalmont 193\nalofi 193\nalphéraky 193\nalvarado's 193\nalvino 193\nalway 193\nambiorix 193\namboyna 193\namericain 193\namte 193\nanbarabad 193\nandropadus 193\nanisotropies 193\nankyrin 193\nanouar 193\nantibonding 193\nararaquara 193\narbëresh 193\narctosa 193\narensky 193\nargenis 193\narriagada 193\nartsmark 193\narveladze 193\nashar 193\naspc 193\natalay 193\natgms 193\naustlii 193\nauxerrois 193\naxim 193\nbò 193\nbağlama 193\nbaconian 193\nbahawalnagar 193\nbahian 193\nbakili 193\nbale's 193\nballhaus 193\nbandham 193\nbandhana 193\nbankai 193\nbarah 193\nbaroclinic 193\nbason 193\nbassermann 193\nbattleship's 193\nbeattie's 193\nbelie 193\nbellus 193\nbelotti 193\nbenaderet 193\nbentvueghels 193\nbertucci 193\nbezdek 193\nbhrt 193\nbial 193\nbingyu 193\nbirinci 193\nbirnam 193\nbiyori 193\nblandin 193\nblasingame 193\nbleomycin 193\nblesseds 193\nbluestreak 193\nblurbs 193\nboatlift 193\nbobin 193\nbocelli's 193\nbogna 193\nbonaventure's 193\nborriello 193\nbosak 193\nbouché 193\nbouyon 193\nboze 193\nbpk 193\nbrûleurs 193\nbrasschaat 193\nbrinsmead 193\nbroadbills 193\nbrosque 193\nbudan 193\nbugaboo 193\nbugenhagen 193\nbunjevac 193\nbuttar 193\nbwyd 193\ncallable 193\ncalleva 193\ncampra 193\ncampylaspis 193\ncamuy 193\ncantores 193\ncarbonel 193\ncarri 193\ncasei 193\ncastellari 193\ncastrogiovanni 193\ncedd 193\ncelly 193\ncemac 193\ncentrales 193\ncerthiidae 193\nchandris 193\nchatelet 193\nchenies 193\ncherrywood 193\nchlorocebus 193\ncholoma 193\nchoma 193\nchorin 193\nchoshu 193\ncisne 193\ncitylife 193\nckua 193\ncoelotes 193\ncolère 193\ncombinedfleet 193\ncominform 193\nconsalvo 193\ncorked 193\ncormeilles 193\ncosmo's 193\ncovetous 193\ncqb 193\ncrewson 193\ncrişul 193\ncrito 193\ncuffy 193\nculdee 193\nculford 193\ncumnor 193\ncunene 193\ncuties 193\ncytological 193\ndžumhur 193\ndanfoss 193\ndawlat 193\nddx 193\ndegassing 193\ndegette 193\ndesert's 193\ndesportos 193\ndesulfurization 193\ndewulf 193\ndhakuria 193\ndickory 193\ndievoet 193\ndilton 193\ndirecte 193\ndoerge 193\ndommartin 193\ndrawbridges 193\ndron 193\ndulha 193\nduophonic 193\ndury's 193\ndutty 193\ndwdm 193\neardrums 193\nechterdingen 193\neclectus 193\neigo 193\neikon 193\nekornes 193\nellicottville 193\nempanada 193\nempiricus 193\nenclos 193\nengelmannii 193\nenmax 193\nensamble 193\nequipage 193\nesas 193\neskelin 193\nespeciais 193\netiologies 193\nexcubitor 193\nexpedición 193\nexpresscard 193\nfáilte 193\nfaberge 193\nfacchetti 193\nfaim 193\nfairlee 193\nfalköping 193\nfallsburg 193\nfassel 193\nfenninger 193\nfernandez's 193\nfernow 193\nfetting 193\nfieldiana 193\nfiges 193\nfischinger 193\nfischl 193\nfitzgibbons 193\nfkc 193\nflexors 193\nfood's 193\nfreema 193\nfresnillo 193\nfrl 193\nfrostad 193\nfulness 193\nfundulus 193\nfuntastic 193\ngagarin's 193\ngamil 193\ngardi 193\ngarraty 193\ngasterra 193\ngaudí's 193\ngaugamela 193\ngeashill 193\ngeathers 193\ngeda 193\ngenesys 193\ngenetical 193\ngermanist 193\ngesine 193\nghilzai 193\ngiacomini 193\ngianpaolo 193\ngilfach 193\nglehn 193\ngoeth 193\ngoldenes 193\ngouverneurs 193\ngreasepaint 193\ngreasers 193\ngregynog 193\ngrenze 193\ngroenlo 193\ngrousset 193\ngrunion 193\nguajeo 193\nguenevere 193\nguilbeau 193\nhaisla 193\nhalfbeak 193\nhalme 193\nhamburgische 193\nhanslick 193\nharmontown 193\nhautbois 193\nhawaiiensis 193\nhdcd 193\nheaping 193\nheckert 193\nheitinga 193\nhenthorn 193\nhikikomori 193\nhinten 193\nhistoryczny 193\nhochdorf 193\nhodood 193\nhpf 193\nhubaekje 193\nhuffaker 193\nhuffpost 193\nhurlant 193\niacopo 193\nibadi 193\nichiki 193\nicterids 193\nidolised 193\nilaiyaraja 193\nimei 193\nimke 193\nindravati 193\ninishkeel 193\ninmate's 193\ninsein 193\nirit 193\nisic 193\nisobutyl 193\nispa 193\nitraconazole 193\niturriaga 193\niuds 193\niwatsuki 193\njae's 193\njaidee 193\njanss 193\njaq 193\njec 193\njeevitha 193\njeugd 193\njhala 193\njif 193\njjj 193\njoia 193\njonassen 193\njoscelyn 193\njuchitán 193\nkörös 193\nkainan 193\nkallaste 193\nkaltenbach 193\nkanth 193\nkatv 193\nkazlauskas 193\nkebaya 193\nkeffiyeh 193\nkemmler 193\nkfyo 193\nkgb's 193\nkiesler 193\nkilliecrankie 193\nkinnitty 193\nkiratas 193\nkirman 193\nkleinow 193\nkleptomaniac 193\nklo 193\nkokopelli 193\nkoleje 193\nkomon 193\nkončar 193\nkoryū 193\nkotlas 193\nkoumi 193\nkrysta 193\nkudremukh 193\nkunstsammlungen 193\nkunstverlag 193\nkurai 193\nkyoryu 193\nléna 193\nlürssen 193\nlāčplēsis 193\nlacewing 193\nlairg 193\nlalgarh 193\nlaloo 193\nlamoni 193\nlamoriello 193\nlancy 193\nlangsdorff 193\nlaplanche 193\nlavandula 193\nlavezzi 193\nleśna 193\nleuna 193\nlevinský 193\nliben 193\nlipper 193\nlivonians 193\nlml 193\nlybius 193\nlydenburg 193\nmä 193\nmacgrath 193\nmacrossan 193\nmadhumitha 193\nmagsanoc 193\nmagyarization 193\nmaiken 193\nmajali 193\nmalis 193\nmandovi 193\nmantella 193\nmarginellidae 193\nmarianist 193\nmarkowski 193\nmartir 193\nmarudo 193\nmazamet 193\nmcalmont 193\nmccorvey 193\nmcgonigal 193\nmcgrigor 193\nmcpartlin 193\nmcx 193\nmealworms 193\nmechem 193\nmedicate 193\nmeindert 193\nmelges 193\nmenkaure 193\nmenshov 193\nmentalis 193\nmernda 193\nmetaphysik 193\nmetrically 193\nmikay 193\nmilva 193\nmimsy 193\nmirliva 193\nmiteru 193\nmlad 193\nmniszech 193\nmomina 193\nmoncoutié 193\nmontecillo 193\nmortalities 193\nmothball 193\nmoycashel 193\nmres 193\nmthethwa 193\nmudcat 193\nmusikk 193\nmymusic 193\nnachshon 193\nnajim 193\nnauplia 193\nnavio 193\nnectariniidae 193\nnieuwland 193\nnifong 193\nnilda 193\nnilp 193\nnin's 193\nnizhnevartovsk 193\nnungambakkam 193\nocypode 193\noddjob 193\noduduwa 193\nofficiis 193\nogando 193\nomulangira 193\nonganía 193\nopie's 193\nortrud 193\nottavia 193\novacık 193\npadron 193\nparrying 193\npatay 193\npaxon 193\npdv 193\npells 193\npemphigoid 193\npepsi's 193\nperdón 193\npericos 193\npersiram 193\nphala 193\nphelips 193\nphreaking 193\npiegan 193\npien 193\npignon 193\npiscivorous 193\nplayerhistory 193\npleist 193\nplumly 193\npohlman 193\npolydorus 193\npolyphosphate 193\npompallier 193\nportbury 193\nportsoy 193\npossessiveness 193\npoundland 193\nprocaccini 193\nproducción 193\nprynce 193\npseudococcus 193\npsila 193\npuò 193\npujo 193\nqabala 193\nquagmire's 193\nquartiers 193\nquintanar 193\nráth 193\nrātā 193\nrăutu 193\nracc 193\nraczyński 193\nradbourn 193\nradiographers 193\nrajnath 193\nrapsodie 193\nrautins 193\nravenstahl 193\nrazzia 193\nrbw 193\nreadjusted 193\nreald 193\nreanimates 193\nreattach 193\nreformasi 193\nreichman 193\nreinos 193\nrenderers 193\nreportages 193\nrhinestones 193\nrhynie 193\nriaan 193\nridgetop 193\nrippin 193\nriversleigh 193\nrleague 193\nrobustum 193\nrocaf 193\nroditi 193\nrolle's 193\nrosko 193\nrotarian 193\nrowrah 193\nrudisha 193\nruficauda 193\nrumelange 193\nryb 193\nsadam 193\nsainct 193\nsalvatores 193\nsampi 193\nsaraswathy 193\nscantlebury 193\nscathingly 193\nscharführer 193\nschlechte 193\nschoor 193\nschreder 193\nschutztruppe 193\nsdcc 193\nsebeok 193\nsemak 193\nsemiotician 193\nsensorial 193\nsgk 193\nsgraffito 193\nshangrao 193\nshanku 193\nshegaon 193\nshenk 193\nshimei 193\nshugden 193\nsiddig 193\nsieboldii 193\nsinuosa 193\nsivaram 193\nskall 193\nskyhorse 193\nskyship 193\nslavist 193\nslidin 193\nsnore 193\nspeedsters 193\nsrca 193\nssrc 193\nstadhuis 193\nstenka 193\nstephin 193\nstephy 193\nsternbach 193\nstilwell's 193\nstobbs 193\nstrouhal 193\nstrozier 193\nsuggestively 193\nsumatrensis 193\nsummorum 193\nsundering 193\nsunnat 193\nsupplier's 193\nsyam 193\nsymplocos 193\ntagblatt 193\ntaja 193\ntalcum 193\ntaniwha 193\ntaolu 193\ntechsters 193\ntekstilshchik 193\ntemplecombe 193\ntephrosia 193\ntestability 193\ntestimonium 193\nthalamocortical 193\ntheoretische 193\nthern 193\nthoas 193\nthuli 193\ntigar 193\ntige 193\ntitleist 193\ntoggling 193\ntokunoshima 193\ntoucanet 193\ntransair 193\ntransco 193\ntransferee 193\ntremuloides 193\ntristan's 193\ntropane 193\ntruidense 193\ntuaran 193\ntulenev 193\ntureen 193\nultrasonics 193\numbilicated 193\numf 193\numtv 193\nundeciphered 193\nuntreatable 193\nupdater 193\nurse 193\nuschrist 193\nusct 193\nutterback 193\nuttrakhand 193\nuzuki 193\nvölva 193\nvaldostan 193\nvallejo's 193\nvarkari 193\nvasylyeva 193\nvatten 193\nvazir 193\nvecino 193\nvellai 193\nveltliner 193\nvettai 193\nvieni 193\nwahed 193\nwahingdoh 193\nwarrendale 193\nwasp's 193\nwatcom 193\nwaterpower 193\nwaterproofed 193\nweissmann 193\nweitzmann 193\nwenxiu 193\nwiehen 193\nwillaston 193\nwiniarski 193\nwojnarowicz 193\nwondolowski 193\nxga 193\nxiu's 193\nxizong's 193\nyakyū 193\nyamada's 193\nyeongdong 193\nyifei 193\nzaps 193\nzarrin 193\nzeferino 193\nzoellner 193\nzygotes 193\nâu 192\némard 192\nōshū 192\nşaban 192\nκι 192\nсв 192\nabot 192\nacceso 192\nackbar 192\nacklam 192\nactriz 192\naday 192\nadventure's 192\nafterelton 192\nagapo 192\nagnes's 192\nahsoka 192\nakwasi 192\nakyab 192\nalbertino 192\nalcee 192\nalfi 192\naltaica 192\namphiprion 192\nanhanguera 192\nanthropometry 192\nanyplace 192\napley 192\napoe 192\napperley 192\napplesoft 192\narahant 192\narepa 192\nargelès 192\narquivo 192\nasiavision 192\nattas 192\nautech 192\nautobianchi 192\nazoth 192\nböke 192\nbackstreets 192\nbalck 192\nbalmes 192\nbarboursville 192\nbarent 192\nbargen 192\nbarnstone 192\nbegi 192\nbeisbol 192\nbeltinci 192\nbenburb 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192\ndagor 192\ndarktown 192\ndeamer 192\ndelibašić 192\ndersu 192\ndesfontaines 192\ndextrous 192\ndhb 192\ndhows 192\ndiaoyu 192\ndiethelm 192\ndigable 192\ndiodati 192\ndistresses 192\ndoeschate 192\ndogtooth 192\ndombeya 192\ndonnells 192\ndorn's 192\ndositej 192\ndottore 192\ndoutor 192\ndrillbit 192\ndyskobolia 192\nearias 192\nefflorescence 192\nekam 192\nelizabeta 192\neltinge 192\nendora 192\nenfield's 192\nensuite 192\neot 192\neskers 192\nesparto 192\nesquisses 192\nexell 192\nextrusive 192\neyam 192\nfāng 192\nfascinates 192\nfauntroy 192\nferocactus 192\nficker 192\nfilatima 192\nflowerdew 192\nfobs 192\nfoliosa 192\nfossen 192\nfoulness 192\nfratercula 192\nfronsac 192\nfuenmayor 192\ngándara 192\ngaggle 192\ngakushuin 192\ngalactosemia 192\ngallate 192\ngambrell 192\ngametime 192\ngarat 192\ngasholder 192\ngaskell's 192\ngasolina 192\ngayle's 192\ngeddie 192\ngeorgetowns 192\ngeppi 192\ngermanophile 192\ngersten 192\ngesicht 192\nghatge 192\ngleed 192\ngnh 192\ngordonia 192\ngorp 192\ngounaris 192\ngrgur 192\ngriffo 192\ngroesbeek 192\ngummo 192\ngymnasien 192\ngyurme 192\nhöhere 192\nhaasan's 192\nhadl 192\nhaigerloch 192\nharkavy 192\nharpactea 192\nharroun 192\nhasdai 192\nhaunches 192\nheitmann 192\nheka 192\nhenabery 192\nhetrick 192\nhetson 192\nhett 192\nheydrich's 192\nhibernates 192\nhimari 192\nhinemoa 192\nhiroyo 192\nhistrio 192\nholmertz 192\nhond 192\nhoneybourne 192\nhosoe 192\nhouseplants 192\nhuige 192\nhukam 192\nhulusi 192\nhypoponera 192\nigd 192\nijhl 192\niliacus 192\nincongruously 192\nindha 192\ninfosphere 192\niniya 192\ninsterburg 192\ninterahamwe 192\nintercedes 192\nintersectional 192\ninuzuka 192\nisachsen 192\nishall 192\nisocitrate 192\nitochu 192\njakovljević 192\njarocho 192\njaros 192\njaswinder 192\njb's 192\njordache 192\njosiah's 192\njosslyn 192\njovito 192\njurisprudential 192\njuro 192\nkærlighed 192\nkönigsegg 192\nkalbe 192\nkamaruddin 192\nkamensk 192\nkanaga 192\nkarly 192\nkarnan 192\nkarylle 192\nkawazu 192\nkeramik 192\nkex 192\nkiawah 192\nkillaz 192\nkiteboarding 192\nkitgum 192\nknxv 192\nkommunikation 192\nkondapalli 192\nkorai 192\nkossel 192\nkostanjevica 192\nkovacevic 192\nkrise 192\nkroft 192\nkutir 192\nkuzu 192\nkvinner 192\nlacertidae 192\nlandownership 192\nlapsled 192\nlasarte 192\nlasd 192\nlaurium 192\nlekki 192\nletterforms 192\nlimbatus 192\nlinet 192\nliong 192\nlongstock 192\nlorković 192\nlouanne 192\nludia 192\nluisana 192\nlukowich 192\nlurz 192\nlutin 192\nlycksele 192\nlyd 192\nméditation 192\nméduse 192\nmünter 192\nmaathu 192\nmagnus's 192\nmagon 192\nmahasamund 192\nmaheu 192\nmahfuz 192\nmalathion 192\nmanabi 192\nmantler 192\nmaróczy 192\nmargy 192\nmarii 192\nmarpat 192\nmarshaled 192\nmartianus 192\nmartinair 192\nmartines 192\nmasaniello 192\nmaslama 192\nmatn 192\nmaubourg 192\nmaxxum 192\nmedonte 192\nmedzhybizh 192\nmenocal 192\nmenstrie 192\nmeroë 192\nmethley 192\nmetrosexual 192\nmikazuki 192\nmilitarists 192\nmindstream 192\nminge 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192\nqatil 192\nqichao 192\nqieyun 192\nquino 192\nréalités 192\nrachlin 192\nradioiodine 192\nraebareli 192\nrajin 192\nrakia 192\nrale 192\nrampo 192\nranbaxy 192\nravichander 192\nrej 192\nrelgis 192\nrensselaerswyck 192\nrepudiates 192\nreturnable 192\nribonucleic 192\nrichardi 192\nriez 192\nrigondeaux 192\nritsema 192\nrocc 192\nrockcastle 192\nrodgau 192\nrogo 192\nroof's 192\nrosinski 192\nrouvroy 192\nroyen 192\nsadlers 192\nsaliers 192\nsaller 192\nsalmonidae 192\nsalomon's 192\nsangria 192\nsanosuke 192\nsaprotrophic 192\nsarde 192\nschluter 192\nschwein 192\nsecretariat's 192\nseerkazhi 192\nsegorbe 192\nselt 192\nsentech 192\nserrão 192\nsevenia 192\nshacharit 192\nshandilya 192\nshawnigan 192\nshellcode 192\nsilanes 192\nsilangan 192\nsilicones 192\nsilom 192\nsilverbird 192\nsimes 192\nskippack 192\nslean 192\nsmiljanić 192\nsniffs 192\nsoderbergh's 192\nsokurov 192\nsoleilmoon 192\nspellcaster 192\nspiridonov 192\nsplashy 192\nstannington 192\nstekelenburg 192\nstructuration 192\nstuddert 192\nsubdirectories 192\nsuckled 192\nsueca 192\nsunyata 192\nsuperferry 192\nsupplicant 192\nsuzannah 192\nswades 192\nswinfen 192\nténéré 192\ntalaq 192\ntamsoft 192\ntapanuli 192\ntareekh 192\ntattenham 192\ntavannes 192\ntavel 192\ntendinous 192\ntetrahydrate 192\nthermic 192\nthompsonville 192\nthoughtfulness 192\ntigrayan 192\ntimbrell 192\ntitres 192\ntitus's 192\ntollin 192\ntolstoi 192\ntorche 192\ntorkel 192\ntoscanini's 192\ntowline 192\ntrailway 192\ntribespeople 192\ntroglodyte 192\ntropism 192\ntrotskyite 192\ntudhaliya 192\ntuerto 192\nturfgrass 192\ntuul 192\ntwitchett 192\ntyurin 192\nufton 192\nuploader 192\nurquijo 192\nusdot 192\nusgbc 192\nvadakkan 192\nvaradaraja 192\nvelai 192\nvijender 192\nvirumaa 192\nvizsla 192\nvorrei 192\nvotesmajority 192\nwalenty 192\nwallis's 192\nwarmbloods 192\nwashouts 192\nwassell 192\nwaterlily 192\nwaxhaw 192\nwck 192\nweavings 192\nweyden's 192\nwhalsay 192\nwildsmith 192\nwildthyme 192\nwimsatt 192\nwinny 192\nwołyń 192\nwollemi 192\nwwt 192\nwyncote 192\nx¹ 192\nyanshou 192\nyaws 192\nyeremeyev 192\nyiming 192\nyoseikan 192\nysrcp 192\nyuendumu 192\nyukinori 192\nzagallo 192\nzagging 192\nzehr 192\nzetterling 192\nziegler's 192\nznf 192\nzonnebeke 192\nzoophilia 192\nzsl 192\nçatalhöyük 191\nélectrique 191\nøverland 191\nþú 191\nštúr 191\nει 191\nалександрович 191\nрф 191\nعلی 191\nabbondio 191\nabeysekera 191\nabhyankar 191\nabruzzese 191\nabseits 191\nacadien 191\nactinidia 191\nadelanto 191\nadornado 191\naeryn 191\nafis 191\nafrs 191\nagafonova 191\nagencja 191\nahane 191\naid's 191\naissa 191\nakhal 191\naleksinac 191\nalexandrovsk 191\nalexx 191\nallestree 191\nalmagor 191\nalmera 191\naloma 191\naltino 191\naltocumulus 191\nalyas 191\nameriprise 191\namerique 191\namero 191\namoo 191\namoria 191\naneka 191\nankhon 191\nanticorruption 191\nantikensammlung 191\napologie 191\narūnas 191\naraks 191\naristaeus 191\narmande 191\narturs 191\nassaad 191\nassemani 191\nastar 191\natem 191\natheling 191\nauftrag 191\naugustenborg 191\naulacophora 191\nautrement 191\naverof 191\nazpeitia 191\nbabao 191\nbabuyan 191\nbackeb 191\nbajocian 191\nbakel 191\nballygawley 191\nballymurphy 191\nbanipal 191\nbarbadians 191\nbarest 191\nbarradas 191\nbatta 191\nbeaudet 191\nbecali 191\nbechtolsheim 191\nbellfield 191\nbelser 191\nberdychiv 191\nbergey's 191\nberigan 191\nbertozzi 191\nbesco 191\nbettys 191\nbgci 191\nbidault 191\nbishopville 191\nbletcher 191\nbloodily 191\nblueback 191\nbonsor 191\nbootmaker 191\nborges's 191\nborle 191\nbouthillier 191\nbrackins 191\nbragaglia 191\nbrasse 191\nbreakthru 191\nbrener 191\nbrenly 191\nbritannique 191\nbrusca 191\nbryce's 191\nbubber 191\nbulgur 191\nbundesländer 191\nbundesstraßen 191\nbusby's 191\ncía 191\ncívica 191\ncacophonous 191\ncaffa 191\ncalore 191\ncampur 191\ncaprichos 191\ncapstick 191\ncarabanchel 191\ncarleen 191\ncarlisi 191\ncarnies 191\ncarnuntum 191\nccpa 191\ncellmates 191\nceltiberians 191\ncerra 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191\nfahan 191\nfallas 191\nfanning's 191\nfarriers 191\nfathabad 191\nfeighan 191\nferdows 191\nferryboats 191\nfespic 191\nfeyenoord's 191\nfleckenstein 191\nfnn 191\nfortinbras 191\ngörz 191\ngadara 191\ngalvis 191\ngatta 191\ngcv 191\ngdif 191\ngenizah 191\ngeschwister 191\ngilbertine 191\nginji 191\ngirėnas 191\ngittleman 191\ngiudecca 191\nglabrescens 191\ngladney 191\nglenns 191\nglitterhouse 191\nglynde 191\ngonesse 191\ngontaut 191\ngoodpaster 191\ngopalaswamy 191\ngradualism 191\ngrayer 191\ngrisham's 191\ngrizabella 191\ngualala 191\nguchi 191\ngurma 191\nhürlimann 191\nhahndorf 191\nhajjar 191\nhallward 191\nhandcar 191\nhapper 191\nhashima 191\nhatra 191\nheadbutts 191\nheadrick 191\nheadwall 191\nheartedness 191\nheen 191\nheinrici 191\nhoefler 191\nhofheim 191\nholochilus 191\nhonjin 191\nhorlogerie 191\nhupfeld 191\nhuson 191\nibad 191\nicelander 191\nichthyaetus 191\nidora 191\nihrem 191\nikar 191\nilh 191\nimmersions 191\ninma 191\ninnamorato 191\ninsulana 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191\nmait 191\nmaiz 191\nmamallapuram 191\nmancilla 191\nmantaro 191\nmarans 191\nmarchais 191\nmarico 191\nmatsoukas 191\nmcad 191\nmcalinden 191\nmcgrail 191\nmcrobbie 191\nmelanism 191\nmemory's 191\nmetastasize 191\nmetroad 191\nmieszko's 191\nmillwork 191\nmingles 191\nminhang 191\nmisstatements 191\nmitsuyoshi 191\nmitzvahs 191\nmmn 191\nmokranjac 191\nmoldejazz 191\nmonda 191\nmonken 191\nmoonshiner 191\nmorandini 191\nmossoró 191\nmoteurs 191\nmoyie 191\nmtnl 191\nmuska 191\nmustachioed 191\nmyod 191\nnádasdy 191\nnadab 191\nnagatomo 191\nnamtha 191\nnandalal 191\nnanjiani 191\nnataraj 191\nnaviaux 191\nnavqr 191\nnbo 191\nnces 191\nnecw 191\nnevison 191\nnewsmen 191\nnial 191\nningning 191\nnord's 191\nnren 191\nnrfl 191\nnudelman 191\nnukualofa 191\noblivians 191\noceanport 191\noeming 191\nohtani 191\nolecranon 191\nolivarez 191\nonaga 191\nopalia 191\normrod 191\northocenter 191\nosteitis 191\noswaldtwistle 191\nottokar's 191\novertopped 191\npafos 191\npalaszczuk 191\nparaconsistent 191\nparaneoplastic 191\nparasa 191\npardi 191\nparmi 191\nparnaby 191\nparto 191\npaternally 191\npeafowls 191\npenikett 191\npennar 191\npercival's 191\nperdóname 191\npermyak 191\npersonata 191\nphélypeaux 191\nphaltan 191\nphillippi 191\nplacoderm 191\nplasmonic 191\nplatyhelminthes 191\nplumeri 191\nplutella 191\npodiatrist 191\npohjanmaa 191\npollex 191\nprajnaparamita 191\nprassede 191\nprefuse 191\npretyman 191\npreußisch 191\nproselytization 191\nproudman 191\nprzasnysz 191\npteridium 191\npullmantur 191\npurp 191\npurulent 191\nqawali 191\nqianjiang 191\nquédate 191\nquarteira 191\nqueretaro 191\nquintos 191\nrába 191\nradegund 191\nradford's 191\nradway 191\nrantala 191\nrasam 191\nrathgar 191\nrdcs 191\nrebhorn 191\nredcap 191\nredressal 191\nreedham 191\nreedition 191\nreflexiones 191\nreinserted 191\nreles 191\nrenacimiento 191\nrenascence 191\nrestalrig 191\nretards 191\nrethought 191\nrhinecliff 191\nribatejo 191\nroadwork 191\nrobotboy 191\nrobsart 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189\nintercompany 189\ninterservice 189\nipcress 189\nipk 189\nirawan 189\nironsi 189\nistook 189\nium 189\nizh 189\njagna 189\njagst 189\njebat 189\njethmalani 189\njijé 189\njikkyou 189\njoestar 189\njohnnies 189\njover 189\njsoc 189\njuca 189\nkaministiquia 189\nkanamori 189\nkapag 189\nkaramanli 189\nkaronga 189\nkedi 189\nkerrick 189\nkeychains 189\nkhir 189\nkhudai 189\nkiseljak 189\nklage 189\nkmtv 189\nknc 189\nkolsky 189\nkompressor 189\nkondrashin 189\nkopecks 189\nkopparbergs 189\nkoregaon 189\nkpho 189\nkrahe 189\nkumashiro 189\nladybugs 189\nlaketown 189\nlambesc 189\nlambroughton 189\nlanc 189\nlandsknechts 189\nlandspout 189\nlanternshark 189\nlarysa 189\nlaxiflora 189\nlegatee 189\nleschenaultii 189\nlethrinus 189\nlibérale 189\nlichtenberger 189\nliebeslieder 189\nliebt 189\nlindenstraße 189\nlisanne 189\nlkg 189\nlochside 189\nlodbrok 189\nlorrin 189\nlowan 189\nlowth 189\nludwigia 189\nluhe 189\nlumidee 189\nlumping 189\nlyrica 189\nmåløy 189\nmaestranza 189\nmaftir 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189\nrld 189\nrockbox 189\nrockmart 189\nroyaux 189\nruggedized 189\nrummage 189\nrupnagar 189\nrute 189\nsabara 189\nsakishima 189\nsalamin 189\nsambu 189\nsanzar 189\nsarki 189\nsarvāstivāda 189\nscelsi 189\nschie 189\nsclerophyllous 189\nscornfully 189\nscotton 189\nscourged 189\nscurrying 189\nseafrance 189\nsecoli 189\nsecretase 189\nseiberg 189\nseleznev 189\nselve 189\nserviço 189\nservin 189\nsession's 189\nshadowgate 189\nshandling's 189\nsharaku 189\nshatterhand 189\nshena 189\nshiko 189\nshinchosha 189\nshippagan 189\nshirokov 189\nshockoe 189\nshulamith 189\nsickbed 189\nsihl 189\nsilwan 189\nsinitic 189\nsinsen 189\nskoko 189\nslawomir 189\nsnakeroot 189\nsolario 189\nsolec 189\nsolemnities 189\nsolomin 189\nsophomoric 189\nspånga 189\nspectrally 189\nsphenomorphus 189\nsphyrapicus 189\nssattb 189\nstannaries 189\nstecker 189\nsteinhart 189\nsteinhauser 189\nstephie 189\nstoychev 189\nströmstad 189\nstrane 189\nstria 189\nstroncone 189\nstyloid 189\nsubcomandante 189\nsubito 189\nsundaes 189\nsunwoo 189\nsuperalgebra 189\nsuperclub 189\nsuperhumanly 189\nsurya's 189\nswissôtel 189\ntafelmusik 189\ntagal 189\ntaibu 189\ntakatori 189\ntal's 189\ntamasese 189\ntamiko 189\ntarih 189\ntaroko 189\ntayong 189\nteijin 189\nterrien 189\nthumpamon 189\ntimberwolf 189\ntimelapse 189\ntimesheets 189\ntindle 189\ntitillating 189\ntobaccos 189\ntolgoi 189\ntopicality 189\ntrzcianka 189\ntsukuyomi 189\ntuki 189\ntushino 189\nuceda 189\nunderglaze 189\nunflappable 189\nurgench 189\nurquhart's 189\nvainakh 189\nvalproic 189\nvaněk 189\nvandalize 189\nverdeschi 189\nvergleichende 189\nverlorene 189\nvespidae 189\nviewfinders 189\nvilfredo 189\nvillous 189\nvinayagam 189\nvladimiro 189\nvliegen 189\nvollenweider 189\nvoorn 189\nvudu 189\nwabasca 189\nwakanohana 189\nwalchhofer 189\nwalczak 189\nwalrath 189\nwanka 189\nwarfighters 189\nwaterbody 189\nwegelius 189\nwehrle 189\nweisgerber 189\nwinifred's 189\nwittily 189\nwonderboy 189\nwoodshop 189\nwrathall 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188\ncoele 188\ncokes 188\ncolombina 188\ncomiso 188\ncompsognathus 188\nconiglio 188\nconjuror 188\ncontracta 188\ncorticaria 188\ncouderc 188\ncoughed 188\ncounihan 188\ncourtnall 188\ncraiglockhart 188\ncrania 188\ncrisper 188\ncsj 188\ncusseta 188\nczar's 188\ndöner 188\ndabu 188\ndajjal 188\ndanette 188\ndanker 188\ndaurica 188\ndechen 188\ndeconstructive 188\ndegner 188\ndelambre 188\ndemagogues 188\ndemartini 188\ndemel 188\ndenkyira 188\ndeuterostomes 188\ndevara 188\ndflp 188\ndhulia 188\ndigitivalva 188\ndillards 188\ndinn 188\ndiri 188\nditmas 188\ndogging 188\ndollaway 188\ndombasle 188\ndormael 188\ndromgoole 188\ndrumchapel 188\ndruyts 188\ndtw 188\ndubhda 188\nduloxetine 188\ndunlops 188\nduz 188\ndwb 188\neastern's 188\neastover 188\nebihara 188\neditorialist 188\nedlington 188\nelatior 188\nelmsley 188\nemulations 188\nenrique's 188\nesmée 188\nessayed 188\nethias 188\nettlinger 188\neucithara 188\nevoluzione 188\nexakta 188\nezy 188\nfaan 188\nfalsen 188\nfamished 188\nfantasque 188\nfasciola 188\nfastnacht 188\nfdb 188\nfdn 188\nfeldkamp 188\nfelinae 188\nfenagh 188\nferonia 188\nferring 188\nfeucht 188\nfibs 188\nfiorenzuola 188\nflunitrazepam 188\nfontana's 188\nforbesi 188\nforgoes 188\nfoxrock 188\nfrölich 188\nfrangipane 188\nfreedomland 188\nfreeney 188\nfrej 188\nfrodo's 188\nfrowns 188\nfuturology 188\nfvc 188\ngélin 188\ngaisford 188\ngaleodes 188\ngallopin 188\ngangseo 188\ngarnacha 188\ngehl 188\ngelehrten 188\ngenannt 188\ngihon 188\nginkel 188\ngismonti 188\ngivet 188\ngiwa 188\ngladewater 188\ngmohl 188\ngobbler 188\ngoddet 188\ngoellner 188\ngreeed 188\ngrimma 188\ngrofé 188\ngrossmünster 188\nguanxi 188\nguillermin 188\nhöger 188\nhüfner 188\nhacktivist 188\nhajacos 188\nhalothamnus 188\nhamoed 188\nhamrun 188\nhamstrings 188\nhania 188\nhanoar 188\nharperone 188\nharré 188\nheav 188\nheinola 188\nhellborg 188\nhenges 188\nheroides 188\nheterodon 188\nhigashino 188\nhistorial 188\nhogi 188\nhollandic 188\nhomebrewing 188\nhonchar 188\nhonolulu's 188\nhotwire 188\nilustres 188\nimmortelle 188\nimpel 188\nimperata 188\nimpro 188\nimpulsion 188\nincurables 188\nindicting 188\ninokashira 188\nintegración 188\ninterossei 188\niomaire 188\nirritations 188\nivanishvili 188\niwashita 188\njabali 188\njagjivan 188\njanie's 188\njankidas 188\njavanshir 188\njetstorm 188\njewishgen 188\njieyang 188\njimy 188\njinhae 188\njinzhong 188\njli 188\njosuke 188\njurgenson 188\njurowski 188\nkalhora 188\nkamboh 188\nkaraburun 188\nkarapiro 188\nkarka 188\nkaruk 188\nkatnip 188\nkaufusi 188\nkeepnews 188\nkelefa 188\nkellin 188\nkerlin 188\nkeyblade 188\nkhambhat 188\nkimoto 188\nkindu 188\nkishino 188\nklimenko 188\nklinische 188\nklown 188\nkmo 188\nknoppix 188\nkobs 188\nkofoed 188\nkoidu 188\nkokoity 188\nkonstanze 188\nkonz 188\nkorvac 188\nkountouriotis 188\nkoyamada 188\nkplr 188\nkrauth 188\nkrejci 188\nkuby 188\nkurki 188\nkushner's 188\nkywe 188\nlöb 188\nlafia 188\nlafite 188\nlapide 188\nlasiommata 188\nlaudes 188\nlausche 188\nlawan 188\nlebensborn 188\nleie 188\nleikanger 188\nlewi 188\nleyland's 188\nlezioni 188\nliebenau 188\nlilavati 188\nlockouts 188\nlocksmiths 188\nlomellina 188\nlundstrom 188\nlvr 188\nlydus 188\nmín 188\nmühsam 188\nmacrobiotic 188\nmaculosus 188\nmaesa 188\nmaflin 188\nmagdayao 188\nmainardi 188\nmaków 188\nmakabayan 188\nmancroft 188\nmanitoban 188\nmarkhor 188\nmarmorek 188\nmarquette's 188\nmartok 188\nmascolo 188\nmassart 188\nmcgeough 188\nmcnelly 188\nmealybug 188\nmeditator 188\nmegalops 188\nmelancthon 188\nmeltdowns 188\nmiddlegame 188\nmiechów 188\nmindel 188\nminitel 188\nminkler 188\nmirrabooka 188\nmiska 188\nmitchelton 188\nmiway 188\nmohiniyattam 188\nmojo's 188\nmomotaros 188\nmonastero 188\nmoonves 188\nmorata 188\nmoretus 188\nmout 188\nmurad's 188\nmurieta 188\nmusoma 188\nmuwaffaq 188\nmyf 188\nmyotonia 188\nnaanum 188\nnarm 188\nnathanaël 188\nnazli 188\nnečas 188\nneall 188\nnechayev 188\nneitzel 188\nneurovascular 188\nnevelsky 188\nngum 188\nnightcrawlers 188\nnimes 188\nnishani 188\nnooh 188\nnordhagen 188\nnorina 188\nnumeri 188\nnyai 188\nnydn 188\noberer 188\nobiora 188\nofakim 188\nojima 188\nokhla 188\noperazione 188\norb's 188\noremus 188\noreopanax 188\norganizacja 188\nosbaldeston 188\nosyth 188\novina 188\noxychloride 188\npó 188\npaffett 188\npallavolo 188\npanhandling 188\npanno 188\npappé 188\nparamo 188\nparasols 188\nparlements 188\npazyryk 188\npenjing 188\npentagrammic 188\npermanency 188\npfund 188\npharmaceutica 188\nphosphite 188\nphyl 188\nphytosanitary 188\npikachu's 188\npilcomayo 188\npimco 188\nplacencia 188\nplacentals 188\nplayschool 188\nplumper 188\npogba 188\npoka 188\npolisher 188\npollens 188\nponcia 188\nponselle 188\npoochie 188\nportman's 188\npraeludium 188\nprapakamol 188\npresbytis 188\nprospers 188\npsychoacoustics 188\npuisaye 188\npukapuka 188\npulverulenta 188\npumuckl 188\npvl 188\npxr 188\npyromaniac 188\nqshl 188\nquarmby 188\nqueasy 188\nräsänen 188\nrachida 188\nrackham's 188\nrambabu 188\nrappeneau 188\nrathangan 188\nravina 188\nrbn 188\nreagon 188\nreedsport 188\nriikka 188\nrillington 188\nriverside's 188\nroßlau 188\nrocknroll 188\nrodchenko 188\nrodden 188\nrodmond 188\nronay 188\nrooter 188\nruach 188\nrundu 188\nrutelli 188\nsabath 188\nsabic 188\nsadasiva 188\nsaintsbury 188\nsaligna 188\nsamoobrona 188\nsandifer 188\nsarjeant 188\nsatluj 188\nsaxon's 188\nschönhausen 188\nschine 188\nschmidhuber 188\nschreker 188\nschuon 188\nscolopendra 188\nscran 188\nscribe's 188\nseagram's 188\nseckau 188\nsehore 188\nselenophorus 188\nsemipalmatus 188\nsenese 188\nsercos 188\nsewart 188\nsewol 188\nshahla 188\nsheesh 188\nshiogama 188\nshirow 188\nshonali 188\nshredders 188\nsinges 188\nsissinghurst 188\nsizhao 188\nskånska 188\nskyclan 188\nslaby 188\nsoame 188\nsocialistische 188\nsolarian 188\nsoldats 188\nsommaren 188\nsouss 188\nsovacool 188\nspecifiers 188\nspired 188\nspricht 188\nsruoga 188\nssdf 188\nstatute's 188\nstieler 188\nstingless 188\nstinkers 188\nstoppa 188\nstutters 188\nsubequal 188\nsuebic 188\nsufian 188\nsunjata 188\nsupercollider 188\nsurselva 188\nsutri 188\nsyncml 188\nsyu 188\ntổ 188\ntaban 188\ntaborn 188\ntambunan 188\ntammar 188\ntangkhul 188\ntarghee 188\ntarkovsky's 188\ntarraco 188\ntartare 188\ntarvas 188\ntbo 188\ntedy 188\nteetotaller 188\ntenimyu 188\ntepa 188\ntetrahydro 188\ntfcc 188\nthöni 188\nthalaivan 188\ntheorising 188\nthornham 188\nthrombolysis 188\ntinariwen 188\ntinka 188\ntiptoft 188\ntmu 188\ntourane 188\ntragi 188\ntraianus 188\ntrashes 188\ntrenord 188\ntrialing 188\ntrombino 188\ntroye 188\ntubeway 188\ntueni 188\nturística 188\nturu 188\ntutela 188\ntuzi 188\ntweenies 188\ntwichell 188\ntyngsborough 188\nuachtaráin 188\nuncaged 188\nungu 188\nunshakeable 188\nuntruthful 188\nurla 188\nvávra 188\nvéra 188\nvaldéz 188\nvallentine 188\nvanløse 188\nvaugirard 188\nvazha 188\nvenetist 188\nverducci 188\nvibius 188\nvideogamer 188\nvillaronga 188\nvirasat 188\nvitalian 188\nvno 188\nvolusius 188\nvossius 188\nvrbanja 188\nvujadin 188\nvulpina 188\nwaliullah 188\nwalsenburg 188\nwangford 188\nwanzhou 188\nwaterkloof 188\nweever 188\nweldon's 188\nwenck 188\nwestham 188\nwhimsically 188\nwhirls 188\nwhisperers 188\nwindowpane 188\nwintney 188\nwlac 188\nwools 188\nworthley 188\nwylye 188\nyú 188\nyachats 188\nyaji 188\nyandell 188\nyanzheng 188\nyasaka 188\nyawm 188\nyuzhou 188\nzambesi 188\nzamfirescu 188\nzamorin's 188\nzhizhi 188\nzobrist 188\nzwerg 188\nçorlu 187\nübers 187\nþróttur 187\nđảo 187\nандрей 187\nжурнал 187\nवस 187\naaliya 187\naangan 187\naarberg 187\naawu 187\nabmc 187\nabrikosov 187\nacanthamoeba 187\naccessdate 187\naceveda 187\nadabi 187\naedon 187\nagoult 187\naicher 187\nakeman 187\nakosombo 187\nakpa 187\nalbeck 187\nalmirón 187\nalperton 187\nalstott 187\naltimetry 187\nambassadeurs 187\namnesties 187\nandreasberg 187\nangeliki 187\nanoaʻi 187\naoste 187\napapa 187\napba 187\naranza 187\naristagoras 187\narnošt 187\narrius 187\nassamensis 187\nasset's 187\nasthenia 187\naterciopelados 187\nathlétisme 187\natlantide 187\nayatollahs 187\naztek 187\nbabayan 187\nbaccalaureus 187\nbahya 187\nballantine's 187\nbalustrading 187\nbanović 187\nbanyumas 187\nbapat 187\nbarata 187\nbarnabe 187\nbarner 187\nbarreau 187\nbartoszewski 187\nbassianus 187\nbatou 187\nbatyr 187\nbayldon 187\nbcw 187\nbeastmen 187\nbeilein 187\nbejo 187\nbelluschi 187\nbelnap 187\nbenanti 187\nberriasian 187\nbeskid 187\nbeuthen 187\nbgu 187\nbhagwandas 187\nbhupendra 187\nbidu 187\nbissinger 187\nblairs 187\nblastocystis 187\nbluesky 187\nboddie 187\nboogerd 187\nbotanik 187\nbrakpan 187\nbrehmer 187\nbritt's 187\nbroner 187\nbrunelli 187\nbuakaw 187\nbuelna 187\nbuio 187\nbukovec 187\nbulatan 187\nbunurong 187\nburchellii 187\nbushwackers 187\nbwindi 187\ncacau 187\ncalix 187\ncalliandra 187\ncandover 187\ncarlivati 187\ncarnock 187\ncassella 187\ncastaños 187\ncaudwell 187\ncemerlang 187\ncerc 187\ncernua 187\nchador 187\nchalumeau 187\nchambres 187\ncharax 187\nchardonnet 187\ncharla 187\ncharlois 187\nchaulnes 187\nchelm 187\nchinchillas 187\nchiquimula 187\nchojna 187\nchrysoritis 187\nchunn 187\ncinereum 187\ncityhopper 187\nclarithromycin 187\nclonoulty 187\ncoalmines 187\ncockscomb 187\ncoignet 187\ncoisas 187\ncojo 187\ncompressus 187\nconventuals 187\ncooney's 187\ncordel 187\ncosel 187\ncourteeners 187\ncovariates 187\ncovenanting 187\ncresol 187\ncrevalle 187\ncrimefighter 187\ndébutante 187\ndōmei 187\ndagur 187\ndanchenko 187\ndanford 187\ndangled 187\ndarude 187\ndasaratha 187\ndast 187\ndebriefed 187\ndehumanized 187\ndelbonis 187\ndemolishes 187\ndentzel 187\ndeoxys 187\nderks 187\ndesolated 187\ndetains 187\ndeuil 187\ndevudu 187\ndhir 187\ndimpled 187\ndinakaran 187\ndingles 187\ndionysis 187\ndiopter 187\ndirtier 187\ndistin 187\ndoublespeak 187\ndrinkard 187\ndropship 187\ndrung 187\ndual_ec_drbg 187\nduhaime 187\ndundy 187\ndunno 187\nduroc 187\ndwarkanath 187\ndzbb 187\nedström 187\neim 187\nekklesia 187\nendospores 187\nendou 187\nentheogens 187\nestudiantil 187\neviscerated 187\nexch 187\nexpansa 187\nfürstin 187\nfabbro 187\nfahrudin 187\nfancy's 187\nfarbe 187\nfarinacci 187\nfddi 187\nfilangieri 187\nfilipendula 187\nfinwë 187\nfirestar's 187\nflanner 187\nfoehn 187\nforgione 187\nforn 187\nfotogramas 187\nfredegar 187\nfriburgo 187\nfuglesang 187\nfujiki 187\ngávea 187\ngammell 187\ngantner 187\ngaren 187\ngarw 187\ngavà 187\ngeatish 187\ngenerall 187\ngeneralplan 187\ngerges 187\ngetaways 187\nghati 187\ngherla 187\nghora 187\ngilsig 187\ngiocondo 187\nglauconycteris 187\nglaucum 187\nglobule 187\nglucuronate 187\ngonzaga's 187\ngossau 187\ngoulette 187\ngradišče 187\ngraflex 187\ngreenspond 187\ngrou 187\nguidobaldo 187\ngumley 187\ngunatitanand 187\ngurbanov 187\nhaarde 187\nhaldex 187\nhanby 187\nhandschuhsheim 187\nhannant 187\nhapū 187\nharawira 187\nharipad 187\nharuka's 187\nhaygood 187\nheijenoort 187\nhelpdesk 187\nhelveg 187\nhershkovitz 187\nheslov 187\nheuchera 187\nhewa 187\nhhr 187\nhighnesses 187\nhinode 187\nhiscox 187\nhodgy 187\nhomefield 187\nhoneywood 187\nhorana 187\nhounding 187\nhousden 187\nhuazhong 187\nhuch 187\nhuff's 187\nhusna 187\nhyrcania 187\niams 187\niccs 187\nicegators 187\nigfbp 187\nimminence 187\nincurved 187\nindistinctly 187\ninfringers 187\ninnkeeper's 187\ninrush 187\nintr 187\nioseliani 187\nirigaray 187\nirizar 187\nisko 187\nitto 187\nivig 187\nixs 187\njäckel 187\njaimy 187\njarndyce 187\njauron 187\njavax 187\njolliet 187\njoo's 187\njoue 187\njurby 187\nkümmel 187\nkąty 187\nkabinett 187\nkaiden 187\nkaizers 187\nkallingal 187\nkapali 187\nkaysville 187\nkazutoshi 187\nkežman 187\nkelleners 187\nkente 187\nkholmsky 187\nkiš 187\nkiessling 187\nkihachi 187\nkindl 187\nkirpan 187\nkks 187\nkoželuh 187\nkodar 187\nkoisuru 187\nkokh 187\nkonrád 187\nkontext 187\nkotohira 187\nkraichgau 187\nkribi 187\nkrp 187\nkuil 187\nlắk 187\nlaevigatus 187\nlalji 187\nlambach 187\nlandale 187\nlandru 187\nlasbela 187\nlatirostris 187\nlatrell 187\nlauzun 187\nlaybourn 187\nlesean 187\nletterboxed 187\nlevitas 187\nlifesong 187\nlineout 187\nlintz 187\nlizárraga 187\nllanharan 187\nlobau 187\nlochnagar 187\nloewi 187\nluftfahrt 187\nlundie 187\nluxembourgers 187\nluyten 187\nlydecker 187\nlyndonville 187\nmölln 187\nmackinnon's 187\nmadingley 187\nmadlock 187\nmaer 187\nmaeser 187\nmagoun 187\nmagrs 187\nmahā 187\nmahaffy 187\nmalott 187\nmalyutka 187\nmancina 187\nmanishi 187\nmanteau 187\nmaricourt 187\nmatalon 187\nmatayoshi 187\nmaude's 187\nmccalman 187\nmcdivitt 187\nmeaningfulness 187\nmeconium 187\nmeillon 187\nmeimad 187\nmelanocytic 187\nmengjiang 187\nmenzoberranzan 187\nmeranti 187\nmeshwork 187\nmessiah's 187\nmicaa 187\nmicroregions 187\nmiddleboro 187\nmikaboshi 187\nminamino 187\nmineira 187\nmitchellville 187\nmoak 187\nmoehler 187\nmoltmann 187\nmomoa 187\nmonte's 187\nmorae 187\nmowrey 187\nmtawarira 187\nmtbs 187\nmudo 187\nmuji 187\nmullery 187\nmultimeter 187\nmumun 187\nmurowana 187\nmusubi 187\nnaciones 187\nnagrania 187\nnangka 187\nnarcís 187\nnatasja 187\nnclc 187\nnecochea 187\nneedwood 187\nnemorino 187\nnesodden 187\nnighty 187\nnordbahn 187\nnorrby 187\nnotam 187\nnourrit 187\nnovitski 187\nocéane 187\nocteville 187\nohkawa 187\nolbricht 187\noltl 187\noptigan 187\nornano 187\norsett 187\nosmolality 187\nosterhaus 187\nostrya 187\notium 187\nottoline 187\nouters 187\noutpolled 187\noxytropis 187\npătrășcanu 187\npanafrican 187\npapyrology 187\nparcham 187\nparineeti 187\nparras 187\nparrington 187\npasteurella 187\npaterculus 187\npchl 187\npenitentiaries 187\npenk 187\npentarchy 187\nperabo 187\nperine 187\nperrotin 187\npetherick 187\npetroni 187\nphosphoryl 187\npilumnus 187\npinochle 187\npioche 187\nplatanistoidea 187\npleroma 187\nploshchad 187\npogost 187\npomerleau 187\nponsana 187\npontoppidan 187\npoppen 187\nportsdown 187\nposible 187\npositionin 187\npotapov 187\nprólogo 187\npulpy 187\npurdy's 187\npussyfoot 187\nqatret 187\nquietism 187\nquilley 187\nquiros 187\nröder 187\nraşit 187\nraan 187\nraeder's 187\nraghunathrao 187\nraisons 187\nraksin 187\nrangaku 187\nrebell 187\nrebrands 187\nrecuperates 187\nredactions 187\nrefurnished 187\nrehna 187\nreinickendorf 187\nrelearn 187\nremulla 187\nreorganizes 187\nrescorla 187\nrestating 187\nrestlessly 187\nreveling 187\nrewinds 187\nreyner 187\nrichy 187\nrieko 187\nrikka 187\nrockefellers 187\nroderigo 187\nromaniuk 187\nroofer 187\nrootkits 187\nsaberi 187\nsahibganj 187\nsaijo 187\nsami's 187\nsamochodzik 187\nsandile 187\nsanou 187\nsanteri 187\nsarju 187\nsatsu 187\nsayce 187\nscadoxus 187\nschlei 187\nschorsch 187\nschulten 187\nsemyorka 187\nserapio 187\nserifed 187\nsextuple 187\nshal 187\nshangani 187\nshanter 187\nshayera 187\nshehr 187\nshien 187\nshivsena 187\nsimcock 187\nsiyum 187\nskaraborg 187\nskyraiders 187\nslateford 187\nslbc 187\nslickly 187\nslimbridge 187\nsnog 187\nsnorks 187\nsoběslav 187\nsoberanes 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186\ncauchi 186\ncaulk 186\ncaylee 186\nccf's 186\nccna 186\ncercopithecidae 186\nchaldea 186\nchalle 186\nchemainus 186\ncheraman 186\nchiarello 186\nchigumbura 186\nchoreutidae 186\nchutneys 186\nclackline 186\nclardy 186\nclassing 186\nclemam 186\nclessin 186\ncloseups 186\nclsc 186\ncobbler's 186\ncodina 186\ncoly 186\nconformant 186\nconnon 186\nconstrual 186\nconvalesce 186\nconventicles 186\ncopel 186\ncoptotriche 186\ncopulas 186\ncorium 186\ncostarring 186\ncottington 186\ncoun 186\ncresset 186\ncrestfallen 186\ncrosville 186\ncryan 186\ncseszneky 186\ncyanate 186\ncyrax 186\ndagda 186\ndavion 186\ndeaflympics 186\ndebat 186\ndelaporte 186\ndeliveryman 186\ndelpech 186\ndepolarized 186\ndepts 186\ndeshields 186\ndesmarets 186\ndespain 186\ndetsen 186\ndfz 186\ndhammika 186\ndifferentiator 186\ndikmen 186\ndisaggregated 186\ndiskless 186\ndnmt 186\ndobrudzha 186\ndoisneau 186\ndolenja 186\ndoradus 186\ndoumer 186\ndozing 186\ndrivel 186\ndudukalo 186\nduenna 186\ndunas 186\ndunnes 186\ndym 186\ndysthymia 186\necgberht 186\nechinodermata 186\nediz 186\nedson's 186\nekdahl 186\nelectropositive 186\nelektrik 186\nelg 186\nellenbrook 186\nellijay 186\nelmalı 186\nelversberg 186\nemilia's 186\nempalme 186\nempting 186\nendz 186\nenjoyably 186\nennead 186\nenskilda 186\neramosa 186\nerawan 186\neriador 186\nermington 186\neunan's 186\neurobodalla 186\nexplainable 186\neyelets 186\nfaliero 186\nfarooqui 186\nfasir 186\nfaul 186\nfeakle 186\nfeatherless 186\nferrucci 186\nferzan 186\nfestoon 186\nfiammetta 186\nfirered 186\nfithian 186\nfleetingly 186\nflipmode 186\nfmea 186\nfolwark 186\nforten 186\nfostiras 186\nfreelon 186\nfromer 186\nfromholtz 186\nfrostbitten 186\nfrua 186\nftir 186\ngamekeepers 186\ngameplan 186\ngarma 186\ngelang 186\ngeltrú 186\ngervinho 186\nghatotkacha 186\ngisenyi 186\ngjertsen 186\nglasberg 186\ngoodall's 186\ngraun 186\ngrissom's 186\nguerriero 186\ngulbene 186\ngwathmey 186\ngwillim 186\nhòn 186\nhabel 186\nhachijō 186\nhadhrami 186\nhagino 186\nhaiji 186\nhaislip 186\nhalachah 186\nhanai 186\nhanfstaengl 186\nhardier 186\nharibal 186\nhasi 186\nhaydee 186\nherbes 186\nhesselberg 186\nhewish 186\nhidegkuti 186\nhili 186\nhimi 186\nhindenburg's 186\nhirate 186\nhld 186\nhocquart 186\nholey 186\nhoulding 186\nhubler 186\nhuckerby 186\nhuyck 186\nhyperborean 186\nhypomania 186\nhypsiboas 186\nicma 186\niji 186\nimmel 186\nimpassive 186\ninedito 186\ninegöl 186\ninfocom's 186\ningenting 186\ningrid's 186\ninmortales 186\ninsularity 186\ninthanon 186\niscc 186\nishtam 186\nissoire 186\niten 186\niurii 186\nizd 186\nize 186\njēkabpils 186\njagmal 186\njalaun 186\njamesburg 186\njamnalal 186\njamrud 186\njaydee 186\njeffree 186\njenckes 186\njestem 186\njhtml 186\njiaying 186\njiayu 186\njiezi 186\njimmer 186\njisshu 186\njonni 186\njonny's 186\njulus 186\nkaimuki 186\nkaino 186\nkalter 186\nkamino 186\nkapala 186\nkaramoja 186\nkasugai 186\nkeki 186\nkelm 186\nkensaku 186\nkhadir 186\nkhanji 186\nkhris 186\nkhromacheva 186\nkiewa 186\nkinkladze 186\nklaver 186\nknopfler's 186\nkołakowski 186\nkolingba 186\nkoog 186\nkopernik 186\nkorpiklaani 186\nkosača 186\nkrbava 186\nkrishnagar 186\nkrkonoše 186\nkryptos 186\nkulasekhara 186\nkulgam 186\nkumta 186\nkundiman 186\nkunstsammlung 186\nkynynmound 186\nlackadaisical 186\nladinian 186\nlaich 186\nlaminating 186\nlamphong 186\nlegatum 186\nlehrgeschwader 186\nlents 186\nleocadia 186\nlesvos 186\nliba 186\nlifo 186\nlillenas 186\nlindenbaum 186\nlindlar 186\nlipponen 186\nllobet 186\nlorant 186\nlorrha 186\nlsl 186\nluchini 186\nlucian's 186\nlulled 186\nlupins 186\nluy 186\nlxviii 186\nmacallan 186\nmachico 186\nmaestà 186\nmagellanica 186\nmagglio 186\nmahalo 186\nmaiano 186\nmaintainable 186\nmalalas 186\nmalazan 186\nmaloo 186\nmangueira 186\nmanuelito 186\nmaramon 186\nmarciniak 186\nmargaritis 186\nmartuni 186\nmartyn's 186\nmartyre 186\nmassiel 186\nmauls 186\nmauritanie 186\nmauvaise 186\nmccalebb 186\nmeddelelser 186\nmediana 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186\nskeltah 186\nskookumchuck 186\nskydrive 186\nslaveowners 186\nsnowdrops 186\nsodomites 186\nsomersby 186\nsosnovsky 186\nspecter's 186\nspj 186\nsratsimir 186\nstaatlichen 186\nstenman 186\nstenolophus 186\nstentor 186\nstoryid 186\nstreetdance 186\nsubarnarekha 186\nsubproblems 186\nsumita 186\nsuneo 186\nsuperkart 186\nsuperuser 186\nswarga 186\nsymbionese 186\nsymmonds 186\nsystematize 186\ntabler 186\ntad's 186\ntadros 186\ntaeniata 186\ntagaung 186\ntaiho 186\ntalegaon 186\ntalu 186\ntanzen 186\ntarakeswar 186\ntargowica 186\ntarsometatarsus 186\ntasered 186\ntatius 186\ntatum's 186\nteairra 186\ntemes 186\ntemesvári 186\ntenner 186\nterrell's 186\ntese 186\ntessmacher 186\ntessy 186\ntetons 186\ntetrakis 186\nthaalam 186\nthanu 186\nthelwell 186\nthk 186\ntiahrt 186\ntingler 186\ntirreni 186\ntiss 186\ntonari 186\ntoothcomb 186\ntoroid 186\ntorre's 186\ntouchstones 186\ntrotter's 186\ntsagaan 186\nturcica 186\ntwellman 186\nugi 186\nuhp 186\nuitgeest 186\nulvik 186\nunfenced 186\nuphsd 186\nupupidae 186\nvégh 186\nvaleurs 186\nvastra 186\nveldkamp 186\nvelle 186\nveracruzana 186\nvibha 186\nvideoland 186\nviduthalai 186\nvikes 186\nvikramshila 186\nviroqua 186\nvisoki 186\nvizcaino 186\nvociferus 186\nvoleurs 186\nvolgar 186\nvpm 186\nwálter 186\nwabush 186\nwahyu 186\nwaipara 186\nwajahat 186\nwakeful 186\nwarragamba 186\nwarszawy 186\nwatchman's 186\nwatl 186\nwatts's 186\nwenhui 186\nwerneri 186\nwhenuapai 186\nwherewithal 186\nwiesloch 186\nwinde 186\nwlir 186\nwojtowicz 186\nwoodlice 186\nwoolas 186\nwyszków 186\nxinsheng 186\nyarnton 186\nyawing 186\nyde 186\nyegorova 186\nyener 186\nyoka 186\nyoo's 186\nypm 186\nyuexiu 186\nyugas 186\nyumoto 186\nyuuichi 186\nzakład 186\nzanjoe 186\nzeiraphera 186\nzhi's 186\nzhuravlev 186\nzirkel 186\nzos 186\nzurg 186\nälskar 185\nčerná 185\nயம 185\n尚書省 185\nabram's 185\nacademiei 185\naccorsi 185\naccumulative 185\nachyuta 185\nadara 185\nadger 185\nadolf's 185\nadsorbate 185\nagas 185\nagnarsson 185\nahlmann 185\nalbigularis 185\nalexz 185\nalgarrobo 185\nallopathic 185\nalos 185\nalquerque 185\namah 185\nangin 185\nankrah 185\nanomalistic 185\nanthocharis 185\nanthurium 185\napcar 185\naphonopelma 185\naplenty 185\napparao 185\nappointive 185\naprs 185\naragonesa 185\naraneidae 185\nardincaple 185\narmfeldt 185\nashalata 185\naspheric 185\nathlétique 185\natlantik 185\nauditore 185\nauke 185\nautosome 185\nawestruck 185\nayer's 185\nazcárraga 185\nbabeş 185\nbabysits 185\nbagoong 185\nbagramyan 185\nbajazet 185\nbalata 185\nbalerno 185\nbalestrand 185\nbanasura 185\nbanglar 185\nbappu 185\nbarrington's 185\nbastable 185\nbatti 185\nbattlecat 185\nbeetz 185\nbenedictions 185\nbenz's 185\nberlins 185\nberrie 185\nbhuiyan 185\nbièvre 185\nbidhannagar 185\nbigs 185\nbishopsbourne 185\nbitan 185\nbitti 185\nbjarke 185\nblackwells 185\nblankenese 185\nbledsoe's 185\nbleues 185\nbluewave 185\nblythburgh 185\nbodeans 185\nbodson 185\nbodykit 185\nbogans 185\nbohairic 185\nbolz 185\nbombes 185\nbonpland 185\nbonte 185\nboomin 185\nboq 185\nbormida 185\nbottcher 185\nbouffant 185\nbrügger 185\nbrandenberg 185\nbratcher 185\nbresslaw 185\nbrestois 185\nbrigalow 185\nbritwell 185\nbrocton 185\nbuckden 185\nbudig 185\nbulletman 185\nbumbry 185\nburanda 185\nbushcraft 185\nbzp 185\nca's 185\ncaedmon's 185\ncalanda 185\ncamerini 185\ncaraş 185\ncarolinum 185\ncassaday 185\ncassard 185\ncasu 185\ncaudle 185\ncenotes 185\ncentrelink 185\ncentretown 185\ncerignola 185\nceyx 185\nchaand 185\ncharmbracelet 185\ncheena 185\nchennakeshava 185\nchiarini 185\nchis 185\nchisholm's 185\nchittlehampton 185\nchrzanowski 185\nciphertexts 185\ncoachmen 185\ncockeyed 185\ncofield 185\ncoloniae 185\ncompay 185\ncomptia 185\nconine 185\nconodont 185\nconsuegra 185\nconvery 185\ncopulations 185\ncountrywoman 185\ncrabbet 185\ncracoviensia 185\ncreation's 185\ncremonini 185\ncsps 185\ncsto 185\ncyanobacterium 185\ndébuts 185\ndžemal 185\ndairi 185\ndaishougun 185\ndalang 185\ndanegeld 185\ndarach 185\ndaradas 185\ndatz 185\ndecaocto 185\ndeepu 185\ndelfi 185\ndenilson 185\ndiabo 185\ndiann 185\ndiddley's 185\ndimmi 185\ndinga 185\ndinter 185\ndirani 185\ndirmstein 185\ndistributor's 185\ndobhair 185\ndollar's 185\ndonruss 185\ndornbusch 185\ndraisine 185\ndrenching 185\ndrylands 185\ndurin's 185\ndurlacher 185\ndyads 185\ndysplastic 185\neasterlings 185\neccentrically 185\nedtv 185\nelisabethpol 185\nelmas 185\nemmel 185\nemx 185\nenchants 185\nequidae 185\neroi 185\nerya 185\neshowe 185\nesmerelda 185\nestrid 185\newenny 185\nezel 185\nfacelifts 185\nfalcone's 185\nfarnesina 185\nfeedlot 185\nfehn 185\nfiercer 185\nfitzhamon 185\nflagrantly 185\nflatout 185\nfourfourtwo 185\nfowlie 185\nfróði 185\nfraumünster 185\nfresa 185\nfumed 185\nfusillade 185\ngaúcha 185\ngaizka 185\ngalwegians 185\nganesa 185\ngantu 185\ngaudencio 185\ngavilanes 185\ngayndah 185\ngeografico 185\ngeraes 185\nggp 185\nglavin 185\nglovers 185\ngniew 185\ngolu 185\ngoneril 185\ngrandpré 185\ngratuities 185\ngraue 185\ngreenhead 185\ngrotesk 185\ngruver 185\nguaire 185\nguayanilla 185\nguiting 185\ngunbuster 185\ngundog 185\ngwe 185\ngwynfor 185\nhaaye 185\nhabanos 185\nhaff 185\nhalidon 185\nhamani 185\nharalds 185\nharward 185\nhashiba 185\nhatreds 185\nhdlc 185\nheartened 185\nhelgeson 185\nheraldically 185\nherdecke 185\nhidage 185\nhinchman 185\nhippa 185\nhiru 185\nholmesdale 185\nhomeplug 185\nhoovers 185\nhueso 185\nhultén 185\nhypostatic 185\niddaru 185\nidolaters 185\nignatik 185\nikkō 185\nimōto 185\nimperatriz 185\nimperceptibly 185\nindhu 185\ninhales 185\ninstanton 185\ninstitucional 185\nintercessor 185\ninterpublic 185\ninvolucrata 185\nirala 185\nironworker 185\nirulan 185\nishtiaq 185\nitachi 185\nizrael 185\nizuru 185\njanas 185\njanuário 185\njatha 185\njaufre 185\njavelina 185\njeron 185\njime 185\njoba 185\njokester 185\nkörper 185\nkāne 185\nkaleo 185\nkameshwar 185\nkantaten 185\nkappu 185\nkarizma 185\nkatze 185\nkbm 185\nkeeshan 185\nkerrier 185\nkhagaria 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142\nmatucana 142\nmaufe 142\nmaury's 142\nmawi 142\nmazhabi 142\nmccluer 142\nmchardy 142\nmedavakkam 142\nmedich 142\nmedin 142\nmediterraneus 142\nmeehl 142\nmegyn 142\nmenfolk 142\nmessers 142\nmetodista 142\nmetulla 142\nmiagao 142\nmicklethwaite 142\nmicrosorum 142\nmikayla 142\nmiladin 142\nmillipore 142\nmingaladon 142\nminoxidil 142\nmiracolo 142\nmishu 142\nmitsue 142\nmittlerer 142\nmivec 142\nmocama 142\nmoi's 142\nmondor 142\nmonochord 142\nmorando 142\nmorg 142\nmorrisseau 142\nmotherlode 142\nmotovun 142\nmoultonborough 142\nmucocutaneous 142\nmullinahone 142\nmunera 142\nmuscling 142\nmusicien 142\nmuzium 142\nmyin 142\nmykelti 142\nmythen 142\nnairiku 142\nnake 142\nnanhu 142\nnarbona 142\nnavair 142\nnaville 142\nnecrophagist 142\nnectomys 142\nnergaard 142\nnesco 142\nnesler 142\nneuenstadt 142\nnicholsons 142\nnightspots 142\nnizhnyaya 142\nnlo 142\nnobiliaire 142\nnodosaurid 142\nnorales 142\nnorteña 142\nnorthen 142\nnowakowska 142\nnublu 142\nnuig 142\nnyada 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141\ncallar 141\ncallipers 141\ncammarata 141\ncampout 141\ncanalization 141\ncanedy 141\ncapek 141\ncappo 141\ncapstones 141\ncargolux 141\ncarneades 141\ncarolinae 141\ncarranger 141\ncasazza 141\ncassata 141\ncassey 141\ncastex 141\ncatel 141\ncathedrale 141\ncaudally 141\ncausewayed 141\ncedarvale 141\ncentex 141\nceratrichia 141\ncercomela 141\ncervini 141\ncfac 141\nchamfer 141\nchampassak 141\nchangfeng 141\nchaouia 141\nchassidim 141\nchaubey 141\nchemosynthetic 141\nchernyakhovsk 141\nchesnutt's 141\nchevin 141\nchimbu 141\nchingachgook 141\nchipko 141\nciołek 141\nclocher 141\nclonenagh 141\nclw 141\ncmcs 141\ncoachwhip 141\ncoagh 141\ncocchi 141\ncodrescu 141\ncoelorinchus 141\ncolimits 141\ncollectivistic 141\ncolonnello 141\ncolorable 141\ncommando's 141\ncommutated 141\ncompassvale 141\ncompilation's 141\ncomplexioned 141\nconseils 141\nconvertino 141\ncorby's 141\ncoriantumr 141\ncosc 141\ncosne 141\ncothi 141\ncrüxshadows 141\ncrewes 141\ncrocidurinae 141\ncubberley 141\ncuddy's 141\nculinaire 141\nculverden 141\ncummerbund 141\ncurrach 141\ncurtin's 141\ncybil 141\ncyclotrons 141\ndaap 141\ndallington 141\ndaroca 141\ndaryush 141\ndavidis 141\ndavita 141\ndcdcdc 141\ndebruge 141\ndenar 141\ndendron 141\ndenebola 141\ndenotified 141\ndereli 141\ndeschambault 141\ndesein 141\ndewis 141\ndeyang 141\ndhimitër 141\ndiac 141\ndiapsid 141\ndibella 141\ndices 141\ndifferentia 141\ndiggi 141\ndigitalised 141\ndilaurentis 141\ndinkha 141\ndinodata 141\ndirk's 141\ndispirito 141\ndistrowatch 141\ndjili 141\ndołęga 141\ndobrovolski 141\ndoderer 141\ndohme 141\ndokhtar 141\ndomrow 141\ndonegan's 141\ndoogan 141\ndormagen 141\ndoubront 141\ndouri 141\ndrabik 141\ndramatica 141\ndrnovice 141\ndrobiazko 141\ndromaeosaurus 141\ndruckerei 141\ndullest 141\ndumler 141\ndurdle 141\nduston 141\ndwarfish 141\ndwingeloo 141\ndya 141\neaac 141\neasyshare 141\neclac 141\neggum 141\neiche 141\neie 141\neigenen 141\neinkorn 141\nelämä 141\neldena 141\nelectroputere 141\nelectrospinning 141\nelectus 141\nemissive 141\nemplate 141\nempowerments 141\nendocardial 141\nendocytic 141\nendolymph 141\nenglebright 141\nengro 141\nenthrone 141\nephebe 141\neprints 141\nequant 141\neryri 141\nesea 141\neske 141\neslava 141\nesquirol 141\nestil 141\nestudo 141\neugster 141\neumaeus 141\neustomias 141\nexplorable 141\nextérieur 141\nezhuthu 141\nfåhraeus 141\nfarandole 141\nfaso's 141\nfelip 141\nfenham 141\nfilipstad 141\nfinalización 141\nfineberg 141\nfinitude 141\nfinnissy 141\nfinsler 141\nfionna 141\nfleeming 141\nflopsy 141\nflorimo 141\nfootscray's 141\nforegrounds 141\nfornovo 141\nforonda 141\nfoulk 141\nfoxhill 141\nfoxxy 141\nfraizer 141\nfrancheville 141\nfreenode 141\nfreightcorp 141\nfretter 141\nfriendsville 141\nfroissart's 141\nfrontieres 141\nfrusciante's 141\nfuro 141\ngénéralité 141\ngéo 141\ngaál 141\ngaadi 141\ngalagos 141\ngambaro 141\ngambell 141\nganatantra 141\ngasthof 141\ngatekeeping 141\ngattilusio 141\ngauthami 141\ngawaine 141\ngazans 141\ngenc 141\ngeorgievna 141\ngermanys 141\ngidgee 141\ngimik 141\ngiru 141\ngladstonian 141\nglasbury 141\nglenohumeral 141\nglucans 141\nglycerate 141\ngodey's 141\ngoldsbrough 141\ngoniodorididae 141\ngoodrem's 141\ngoogol 141\ngrønne 141\ngrachan 141\ngranville's 141\ngrapico 141\ngravell 141\ngreacen 141\ngreetland 141\ngreka 141\ngrishina 141\ngrita 141\ngrobman 141\ngrond 141\ngronow 141\ngui's 141\ngundel 141\ngunnislake 141\ngushan 141\ngustavian 141\ngwaith 141\nhacs 141\nhaima 141\nhakai 141\nhakkas 141\nhakurei 141\nhamelle 141\nhammerheart 141\nhanamaru 141\nhandshape 141\nhangeland 141\nhanshan 141\nharjinder 141\nhartnell's 141\nhaschbach 141\nhatake 141\nhavo 141\nhawton 141\nhaxey 141\nhazir 141\nhbm 141\nheartbreaks 141\nheavey 141\nhedwall 141\nhegesippus 141\nhelmshore 141\nhermagoras 141\nherpesviridae 141\nhevea 141\nhgt 141\nhicksite 141\nhinging 141\nhirshfield 141\nhispanophone 141\nhissed 141\nhkale 141\nhmongic 141\nhocl 141\nhodgenville 141\nhofbräuhaus 141\nholburn 141\nhollinshead 141\nholospira 141\nhomalomena 141\nhomodimers 141\nhonoo 141\nhornbæk 141\nhoulder 141\nhoundshark 141\nhuapango 141\nhuband 141\nhuebsch 141\nhumanidad 141\nhungariae 141\nhydrolyzing 141\nibraheem 141\nicaew 141\nichthyosaurus 141\nicmc 141\niddon 141\nimplementors 141\nincepted 141\nindiscernible 141\ninhalants 141\ninova 141\ninstow 141\nintellij 141\ninterprocess 141\nintestacy 141\nintoning 141\nionizes 141\nircd 141\nironworking 141\nisara 141\nismaïlia 141\nitan 141\nixtapan 141\niyanya 141\njacquerie 141\njaena 141\njaglom 141\njakie 141\njalota 141\njamario 141\njanaab 141\njanashia 141\njaniš 141\njanisch 141\njansson's 141\njaqhi 141\njayasekera 141\njayston 141\njaywick 141\njbg 141\njcd 141\njenbach 141\njfcc 141\njiguang 141\njlo 141\njoleon 141\njonbenét 141\njosephat 141\njuvenes 141\nkönigsbrunn 141\nkadison 141\nkalmthout 141\nkalua 141\nkampman 141\nkangur 141\nkanstantsin 141\nkanungo 141\nkardash 141\nkarens 141\nkarou 141\nkathu 141\nkatsoulis 141\nkavalee 141\nkazeem 141\nkebabı 141\nkelleway 141\nkelso's 141\nkhwang 141\nkilikia 141\nkimmel's 141\nkingisepp 141\nkirchhain 141\nkleyna 141\nklinkerhoffen 141\nkljestan 141\nkneževac 141\nkoaa 141\nkolah 141\nkolok 141\nkonieczny 141\nkooistra 141\nkosmina 141\nkowsar 141\nkraszewski 141\nkriegs 141\nkrila 141\nkukulkan 141\nkulturhuset 141\nkumhar 141\nkvirike 141\nkyee 141\nkylar 141\nlámh 141\nlō 141\nlaßt 141\nladygin 141\nlagana 141\nlagerstätten 141\nlaghouat 141\nlahinch 141\nlalaurie 141\nlambent 141\nlandesberg 141\nlandrover 141\nlandsbergis 141\nlaricina 141\nlatinorum 141\nlaxenburg 141\nlearoyd 141\nleclerc's 141\nleedon 141\nleffe 141\nlemo 141\nlemvig 141\nlenarduzzi 141\nlense 141\nleontios 141\nleovigild 141\nlessmann 141\nleucorhoa 141\nleuser 141\nlevenfish 141\nlevinge 141\nlews 141\nlikoma 141\nlilacina 141\nlindal 141\nlindzen 141\nlinggo 141\nlinkou 141\nlinsky 141\nliona 141\nlisco 141\nlismer 141\nlivraria 141\nllaneros 141\nlobectomy 141\nlockyer's 141\nlocomotiva 141\nlocules 141\nloda 141\nloffreda 141\nlongitarsus 141\nloriod 141\nlorris 141\nlortzing 141\nlulling 141\nlxxiv 141\nlymnaeidae 141\nlyndsy 141\nlynnville 141\nmøllergata 141\nmacpaint 141\nmaddin's 141\nmaddington 141\nmadge's 141\nmadla 141\nmafuyu 141\nmahadevar 141\nmaiar 141\nmakemake 141\nmampong 141\nmanamendra 141\nmaniaci 141\nmanovich 141\nmantegazza 141\nmapeh 141\nmapinfo 141\nmaracatu 141\nmarchibroda 141\nmargall 141\nmarienhof 141\nmartinon 141\nmartyrum 141\nmasatomo 141\nmascha 141\nmasiero 141\nmassasauga 141\nmbamba 141\nmccoubrey 141\nmcdine 141\nmcgleish 141\nmcilwain 141\nmdct 141\nmeadowlarks 141\nmedicolegal 141\nmedows 141\nmelanocortin 141\nmelkamu 141\nmendica 141\nmenntaskólinn 141\nmergansers 141\nmershon 141\nmeskheti 141\nmetha 141\nmetzen 141\nmicrotones 141\nmidilli 141\nmikl 141\nmilat 141\nmilea 141\nmimran 141\nmincham 141\nminy 141\nmiroshkin 141\nmissoulian 141\nmitose 141\nmitsukuni 141\nmitsurugi 141\nmnhn 141\nmocker 141\nmoeraki 141\nmoeran 141\nmoik 141\nmoiseev 141\nmonbetsu 141\nmondovi 141\nmonodonta 141\nmontebelluna 141\nmontebourg 141\nmorgiana 141\nmosinee 141\nmotorenwerke 141\nmozarabs 141\nmpge 141\nmsaa 141\nmuhteşem 141\nmuralidharan 141\nmurang 141\nmurrysville 141\nmusalman 141\nmuscorum 141\nmutatis 141\nmutator 141\nmyristica 141\nmyrsine 141\nmzimba 141\nnévé 141\nnabarangpur 141\nnagan 141\nnanguneri 141\nnanjo 141\nnanofabrication 141\nnarasaraopet 141\nnarayanrao 141\nnardin 141\nnarodowego 141\nnasuh 141\nnatte 141\nnccs 141\nnectarines 141\nnectars 141\nnephelomys 141\nneuendettelsau 141\nneurokinin 141\nnewsmagazines 141\nnho 141\nnichiren's 141\nniello 141\nnigrifrons 141\nninfa's 141\nnipun 141\nnitroso 141\nnizamat 141\nnje 141\nnobeoka 141\nnonintercourse 141\nnorpac 141\nnovruz 141\nnzdf 141\nobree 141\nocculting 141\nochterlony 141\nocsp 141\noctreotide 141\nodontodes 141\nofen 141\nofficered 141\nogdru 141\noidium 141\noligantha 141\nollantaytambo 141\nolympiaschanze 141\nomelettes 141\noml 141\nonkelos 141\nonslaughts 141\nonuki 141\noperabase 141\nophiothrix 141\noraz 141\noread 141\noriquen 141\noroku 141\nortman 141\nosso 141\nostiense 141\nostro 141\nostuni 141\notilio 141\nouterplanar 141\noutpourings 141\noyak 141\nozores 141\npätz 141\npékin 141\npachman 141\npadrão 141\npagewanted 141\npalaeontologica 141\npalha 141\npalmette 141\npalmtop 141\npapamichael 141\nparmeliaceae 141\nparotia 141\npasan 141\npasupathi 141\npatapon 141\npatong 141\npattoki 141\npellice 141\npenkovsky 141\npeno 141\npentagrams 141\nperfumers 141\nperiander 141\npermuting 141\npersicum 141\npestles 141\nphac 141\nphalangists 141\nphotobiology 141\nphragmatobia 141\nphv 141\nphyllobius 141\nphytic 141\npiëch 141\npiako 141\npieno 141\npiked 141\npinups 141\npippy 141\nplanckaert 141\nplattekill 141\npodbrezová 141\npolicewomen 141\npolman 141\npolyphonica 141\npoplarville 141\npoplin 141\nportal's 141\nportora 141\npostale 141\npottu 141\npoundmaker 141\npowerstation 141\npranked 141\nprateeksha 141\npratik 141\npreclassical 141\npreconditioning 141\npreludium 141\npremontane 141\npretentiousness 141\nprincipat 141\nprivalova 141\nproglottids 141\npronzini 141\nproslavery 141\nprotoform 141\nprozess 141\npseudoknot 141\npseudotooth 141\npterygopalatine 141\npuniceus 141\npunit 141\npyraloidea 141\npyrethroid 141\nquotidienne 141\nrượu 141\nrajappan 141\nraketa 141\nrakeysh 141\nrakhee 141\nranis 141\nraunkiær 141\nrcn's 141\nrebbie 141\nrecapitulated 141\nreductant 141\nregaled 141\nrelicts 141\nremoter 141\nrenfrow 141\nrengui 141\nrenominate 141\nresistants 141\nrevolutionise 141\nreyvateil 141\nrfra 141\nrhaeto 141\nribat 141\nricchi 141\nrijssen 141\nringu 141\nripper's 141\nrkl 141\nrocamadour 141\nrochelle's 141\nrocknrolla 141\nromila 141\nrousso 141\nroychoudhury 141\nrushbrooke 141\nryūō 141\nsöke 141\nsī 141\nsabaoth 141\nsabriel 141\nsabugal 141\nsaer 141\nsagua 141\nsalako 141\nsalfit 141\nsamsing 141\nsandee 141\nsanesi 141\nsanetomo 141\nsanitization 141\nsanitize 141\nsaral 141\nsarc 141\nsarid 141\nsarj 141\nsasahara 141\nsatchanalai 141\nsaunders's 141\nsauret 141\nsavaii 141\nsawat 141\nscarano 141\nschnapf 141\nschopp 141\nschwarzmann 141\nscopa 141\nseff 141\nsegetum 141\nseishiro 141\nsemipalmata 141\nsenbei 141\nsence 141\nserletic 141\nsevlievo 141\nsexson 141\nshadai 141\nshajapur 141\nshashthi 141\nshaune 141\nshawlands 141\nshernoff 141\nshiretoko 141\nshlemenko 141\nshuford 141\nsignwriting 141\nsikandra 141\nsikong 141\nsimlish 141\nsinyavsky 141\nsippin 141\nskibbe 141\nskibo 141\nslsc 141\nslyme 141\nsmagorinsky 141\nsmarmy 141\nsnarls 141\nsodasoccer 141\nsolsbury 141\nsomatochlora 141\nsouchak 141\nsouissi 141\nsoylu 141\nspaarne 141\nspaceguard 141\nspadolini 141\nspasskaya 141\nspatha 141\nspilonota 141\nsplattering 141\nspps 141\nsredets 141\nstabroek 141\nstaghounds 141\nstanhopea 141\nstankonia 141\nstarfox 141\nstarship's 141\nsteerer 141\nsteindl 141\nstonis 141\nstreichquartett 141\nstund 141\nsturdily 141\nstylite 141\nsuelo 141\nsugaring 141\nsulhamstead 141\nsundancer 141\nsundog 141\nsunniva 141\nsurčin 141\nsurbahar 141\nswarn 141\nsweated 141\nsweetmeat 141\nswerdlow 141\nsymr 141\nsyntaxes 141\nszép 141\ntéléjournal 141\ntaddy 141\ntahri 141\ntaisto 141\ntakawira 141\ntamarod 141\ntanfoglio 141\ntanki 141\ntantau 141\ntanton 141\ntaon 141\ntarantelle 141\ntavria 141\ntegin 141\ntelaga 141\ntelencephalon 141\ntemotu 141\ntendaguru 141\ntetragona 141\ntettleton 141\ntews 141\nthalaba 141\ntheofanous 141\ntheophanous 141\ntheopompus 141\nthrasyllus 141\nthundersley 141\ntibidabo 141\ntimotheos 141\ntisiphone 141\ntobis 141\ntocsin 141\ntoddla 141\ntoker 141\ntokomaru 141\ntondi 141\ntonucci 141\ntorslanda 141\ntotta 141\ntradescant 141\ntranquillitatis 141\ntremé 141\ntridiagonal 141\ntriliteral 141\ntrinitron 141\ntritter 141\ntroisgros 141\ntroitse 141\ntruncatula 141\ntsangarides 141\ntsirion 141\ntsugawa 141\ntubid 141\ntutus 141\ntva's 141\ntwofour 141\ntypifying 141\nukigumo 141\nullathorne 141\nulrikke 141\numnak 141\nunanderra 141\nunderachiever 141\nunplug 141\nunsatisfiable 141\nunshaken 141\nupads 141\nureteral 141\nusf's 141\nusov 141\nutaemon 141\nuteri 141\nvânători 141\nvadivukkarasi 141\nvajont 141\nvandor 141\nvarmus 141\nvendéens 141\nvenit 141\nvergès 141\nverpakovskis 141\nvertonghen 141\nvicto 141\nvilasa 141\nvinalopó 141\nvirgilia 141\nvkhutemas 141\nvogan 141\nvoght 141\nvoisey 141\nvotorantim 141\nwär 141\nwakui 141\nwalchuk 141\nwansbrough 141\nwasu 141\nwbc's 141\nwcsc 141\nwelman 141\nwesenberg 141\nwesham 141\nwesselényi 141\nwestpreußen 141\nwheeleri 141\nwherewith 141\nwhitbeck 141\nwhiteknights 141\nwhitewell 141\nwhut 141\nwigfall 141\nwinnipegosis 141\nwisnu 141\nwnbt 141\nwolfegg 141\nworkwear 141\nworldbank 141\nwsssa 141\nwuest 141\nxcf 141\nxema 141\nxenomorph 141\nxinshi 141\nxuyen 141\nyafei 141\nyahudi 141\nyanqi 141\nyasujiro 141\nyoungheart 141\nyuksek 141\nyusufeli 141\nzakrzewo 141\nzanten 141\nzeisel 141\nzesco 141\nzimmermann's 141\nzongyao 141\nzorori 141\nzue 141\nzulfahmi 141\nzuyev 141\næthelric 140\nélectriques 140\nñuñoa 140\nólafsdóttir 140\nćorović 140\nčervený 140\nōfunato 140\nšušak 140\nżoliborz 140\nαπ 140\nλmax 140\nкн 140\nместо 140\nнам 140\nयन 140\nरज 140\n盧龍 140\naadhaar 140\nabante 140\nabdc 140\nabderrahman 140\nabejas 140\nabhilash 140\nabily 140\nabit 140\nabobo 140\naccidente 140\naccommodative 140\nachl 140\nadorján 140\naeropostal 140\naerosystems 140\naerotropolis 140\nafdc 140\naffordance 140\nafterthoughts 140\nagapanthus 140\nagaves 140\nagham 140\nagma 140\nagrahara 140\nagriopis 140\naijaz 140\nairfreight 140\naiw 140\nakhet 140\naksakov 140\nakwamu 140\nalbas 140\nalbrechtsen 140\nalenichev 140\nalmond's 140\nalmsick 140\nalternity 140\naltkirch 140\nameena 140\namericanas 140\namqp 140\namsoil 140\nanchorites 140\nandrade's 140\nanomia 140\nanousheh 140\nansbacher 140\nanspaugh 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140\nbolivar's 140\nbonev 140\nbosenbach 140\nbosoms 140\nboukengers 140\nboulenger's 140\nbowern 140\nbowmaker 140\nboyang 140\nbrams 140\nbrata 140\nbredasdorp 140\nbretèche 140\nbreyten 140\nbrindleyplace 140\nbrković 140\nbrodersen 140\nbrothertown 140\nbryson's 140\nbugden 140\nbukol 140\nbundist 140\nbunz 140\nburghal 140\nbutch's 140\nbutman 140\nbutragueño 140\nbyt 140\ncâmpia 140\ncabagnot 140\ncabriolets 140\ncalliteara 140\ncamerota 140\ncammish 140\ncangelosi 140\ncanoers 140\ncanzo 140\ncarbocations 140\ncareproctus 140\ncargill's 140\ncarriedo 140\ncaruth 140\ncassinga 140\ncastelvetrano 140\ncatsharks 140\ncaustics 140\ncaverne 140\ncerbera 140\ncercocarpus 140\ncerl 140\ncesano 140\ncfhof 140\nchambrun 140\nchampville 140\nchanco 140\ncharismatics 140\ncharite 140\nchathams 140\nchave 140\nchaykovsky 140\ncheche 140\ncheddington 140\nchenghua 140\nchevalier's 140\nchidananda 140\nchildishly 140\nchoclair 140\ncholodenko 140\nchotanagpur 140\nchrome's 140\nchrysolepis 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127\nhinohara 127\nhispavox 127\nhitzacker 127\nhnb 127\nhodgkiss 127\nhodja 127\nhorrifies 127\nhorrobin 127\nhorror's 127\nhotkeys 127\nhotung 127\nhoundmills 127\nhras 127\nhrono 127\nhuckabees 127\nhyllestad 127\nhyperion's 127\nibolya 127\nicefish 127\nicmr 127\nictonyx 127\nidrimi 127\nigan 127\nigby 127\nigli 127\nigre 127\nillite 127\nimaret 127\nimia 127\nindigenization 127\nindomethacin 127\ninnervating 127\ninonotus 127\nintendente 127\ninterfilm 127\ninterpretatione 127\nisabey 127\nisely 127\niskusstva 127\nisophrictis 127\nissawi 127\nitabuna 127\nitek 127\nitsumo 127\njægersborg 127\njóannes 127\njawahir 127\njawlensky 127\njeannin 127\njellyroll 127\njene 127\njenova 127\njeogori 127\njerdoni 127\njerrel 127\njeudi 127\njevan 127\njezabels 127\njikkyō 127\njoliat 127\njolles 127\njorio 127\njouissance 127\njupe 127\njustirisers 127\nkabarole 127\nkaddour 127\nkafer 127\nkaihatsu 127\nkaing 127\nkaitoke 127\nkajo 127\nkalanaur 127\nkalogeropoulos 127\nkamarhati 127\nkamgar 127\nkanami 127\nkanbar 127\nkapilavastu 127\nkapsukas 127\nkarunakara 127\nkawhia 127\nkct 127\nkebra 127\nkeenleyside 127\nkenard 127\nkenret 127\nkeratinocyte 127\nkernis 127\nkff 127\nkheli 127\nkhorne 127\nkifle 127\nkikaku 127\nkilloughey 127\nkirkbymoorside 127\nkisiel 127\nkkl 127\nklestil 127\nkligman 127\nklyde 127\nknoller 127\nkodungaiyur 127\nkoetter 127\nkogaku 127\nkolomenskoye 127\nkommandos 127\nkomponist 127\nkornfeil 127\nkottler 127\nkoul 127\nkoutiala 127\nkrasnoselsky 127\nkrasnovodsk 127\nkrenwinkel 127\nkriegler 127\nkristiansson 127\nkseb 127\nkumaritashvili 127\nkumuls 127\nkunstraum 127\nkuntilanak 127\nkurbaan 127\nkurtinaitis 127\nkyll 127\nkyogoku 127\nkyrre 127\nkyuu 127\nlán 127\nlèvres 127\nlaço 127\nlaindon 127\nlancieri 127\nlandaff 127\nlanin 127\nlanoe 127\nlapatin 127\nlapstone 127\nlassonde 127\nlategan 127\nlaughin 127\nlaunders 127\nlaurenţiu 127\nlavinium 127\nleclère 127\nlecontei 127\nledóchowski 127\nledeen 127\nledrew 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127\nstatten 127\nstatyba 127\nsteiners 127\nstenen 127\nsterigmata 127\nstocklist 127\nstormberg 127\nstraffen 127\nstratotype 127\nstupids 127\nsturmmann 127\nsubassemblies 127\nsubkingdom 127\nsuboficial 127\nsuckermouth 127\nsunbittern 127\nsundararaj 127\nsuppletive 127\nsupratemporal 127\nsutherlands 127\nsvara 127\nswangard 127\nswingtown 127\nsynchronistic 127\nsynn 127\nsyntus 127\nsyrius 127\nszapolyai 127\nszentgotthárd 127\ntaborda 127\ntac's 127\ntachiki 127\ntaepodong 127\ntahirid 127\ntakanami 127\ntalentadong 127\ntamami 127\ntanguturi 127\ntannum 127\ntantrism 127\ntarab 127\ntaractrocera 127\ntchernyshev 127\ntecnicos 127\nteddies 127\nteeguarden 127\ntelmatobius 127\ntelstra's 127\nteracotona 127\nternoise 127\ntetro 127\nthadeus 127\nthaon 127\ntheresian 127\nthorius 127\nthornbills 127\nthow 127\nthuột 127\ntibbon 127\ntikbalang 127\ntikiri 127\ntimegate 127\ntka 127\ntoboso 127\ntodar 127\ntolokonnikova 127\ntone's 127\ntooie 127\ntopware 127\ntorm 127\ntorpedos 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126\nferrovial 126\nfidea 126\nfifers 126\nfinkenwerder 126\nfirestorms 126\nfirsby 126\nfitzmartin 126\nfizzles 126\nflå 126\nflipper's 126\nfluch 126\nfmsr 126\nfordwich 126\nformanek 126\nfoujita 126\nfraîche 126\nfredrich 126\nfroglet 126\nfuddy 126\nfulminans 126\nfundidora 126\ngħargħur 126\ngalindez 126\ngalinsky 126\ngallow 126\ngamemaker 126\ngandhiji's 126\nganguro 126\ngannibal 126\ngaoth 126\ngariepy 126\ngarnier's 126\ngartmore 126\ngasped 126\ngdovsky 126\ngedrick 126\ngekijōban 126\ngenos 126\ngenoud 126\ngenset 126\ngenzebe 126\ngeorg's 126\ngeschke 126\nggi 126\nghedi 126\nghiselin 126\ngilhooly 126\ngiltrap 126\nginny's 126\ngirado 126\ngising 126\nglâne 126\nglais 126\nglandore 126\ngncc 126\ngokuraku 126\ngolob 126\ngoodsall 126\ngorta 126\ngpws 126\ngrönwall 126\ngraffham 126\ngrafico 126\ngrasshopper's 126\ngrassman 126\ngratefulness 126\ngraynor 126\ngreenspoon 126\ngrimaldo 126\ngrimsthorpe 126\ngromit's 126\ngrubman 126\ngtlds 126\nguápiles 126\nguancha 126\ngudbrand 126\nguerrillero 126\nguinevere's 126\ngummidipoondi 126\ngunwales 126\ngurneyi 126\ngyeongsan 126\nhöchstädt 126\nhüttner 126\nhaith 126\nhalimah 126\nhandguards 126\nhanim 126\nhanney 126\nharilal 126\nharpole 126\nhasenclever 126\nhatzalah 126\nhaughtiness 126\nhavilland's 126\nhdtvs 126\nheadwaiter 126\nhearkens 126\nheatedly 126\nheechee 126\nheijn 126\nheindorf 126\nhekate 126\nhelmar 126\nhelvig 126\nhemmatabad 126\nhemo 126\nhenschler 126\nherges 126\nheritors 126\nherzer 126\nhetu 126\nheublein 126\nheungseon 126\nhibernaculum 126\nhillsville 126\nhiltunen 126\nhinckley's 126\nhinzman 126\nhircus 126\nhlist 126\nhoest 126\nhofgarten 126\nhominick 126\nhonegger's 126\nhongli 126\nhootkins 126\nhoppstädten 126\nhornfels 126\nhoros 126\nhoutermans 126\nhrl 126\nhudiksvalls 126\nhughan 126\nhuneker 126\nhungaricae 126\nhuntoon 126\nhushabye 126\nhuskvarna 126\nhustad 126\nhybodus 126\nhyperrealism 126\niakovakis 126\niamamiwhoami 126\niaroslav 126\niconix 126\niconoscope 126\nideomotor 126\niew 126\nije 126\nillig 126\nilustración 126\nimbibition 126\nimmelt 126\nindex's 126\ninfantilism 126\ninfarctions 126\ningressive 126\ninstitutum 126\ninstream 126\ninvar 126\nipsf 126\niroquoians 126\nishimura 126\nisoenzymes 126\niswar 126\nittehadul 126\niviron 126\njalón 126\njambase 126\njarak 126\njarvis's 126\njaula 126\njaundiced 126\njavadov 126\njaydev 126\njayne's 126\njde 126\njearl 126\njebtsundamba 126\njedermann 126\njeetu 126\njerônimo 126\njesting 126\njinbei 126\njipeng 126\njiyo 126\njode 126\njohs 126\njonesii 126\njopp 126\njowzjan 126\njox 126\nkärdla 126\nkönigreich 126\nkōwa 126\nkaaki 126\nkabukicho 126\nkacheguda 126\nkadist 126\nkaitangata 126\nkalamboli 126\nkalau 126\nkalendar 126\nkalibari 126\nkalich 126\nkamalinee 126\nkarawanks 126\nkarenin 126\nkarlsefni 126\nkedros 126\nkeesing 126\nkessock 126\nkeybank 126\nkhasis 126\nkhay 126\nkhsl 126\nkhuri 126\nkierulf 126\nkihon 126\nkindergarteners 126\nkingsdale 126\nkinlaza 126\nkisuke 126\nklingsor 126\nkns 126\nkościelny 126\nkobudō 126\nkolmar 126\nkolton 126\nkomlósi 126\nkonko 126\nkoshkonong 126\nkotah 126\nkotaku's 126\nkotb 126\nkröll 126\nkrasnoborsky 126\nkrimpen 126\nktel 126\nkubani 126\nkuchek 126\nkudirka 126\nkulturbesitz 126\nkunjan 126\nkuow 126\nkupu 126\nkurzweil's 126\nkuybyshevsky 126\nkvale 126\nkyi's 126\nkyin 126\nkyk 126\nlóránt 126\nlabidi 126\nlafitte's 126\nlafleche 126\nlaingsburg 126\nlaken 126\nlanderos 126\nlandsgemeinde 126\nlangesund 126\nlangkat 126\nlaomedon 126\nlarantuka 126\nlardner's 126\nlargeness 126\nlauingen 126\nlaupepa 126\nlavani 126\nlebih 126\nlecithoceridae 126\nlehti 126\nlenge 126\nlenticularis 126\nleukocytosis 126\nlewinski 126\nleye 126\nliebrand 126\nlilian's 126\nlimann 126\nlindhardt 126\nlindrum 126\nlinghui 126\nlinkspan 126\nliphistius 126\nlirr's 126\nlitterbug 126\nlivedoor 126\nlivs 126\nlixa 126\nljubi 126\nljungqvist 126\nllorando 126\nlloro 126\nlochearnhead 126\nlockard 126\nlogothetes 126\nloimaa 126\nloker 126\nlongowal 126\nloosing 126\nlors 126\nlousã 126\nlri 126\nluanco 126\nluath 126\nlubimov 126\nlucchinelli 126\nluhanska 126\nlundborg 126\nlymphotropic 126\nlyrebirds 126\nmón 126\nmabiala 126\nmabs 126\nmacaron 126\nmaccaferri 126\nmacrocarpus 126\nmagdolna 126\nmagistrature 126\nmagnet's 126\nmahfood 126\nmaleh 126\nmaloyaroslavets 126\nmangku 126\nmanichean 126\nmanmadhan 126\nmanubrium 126\nmargarit 126\nmarietti 126\nmarkopoulo 126\nmarkovitz 126\nmartinu 126\nmarywood 126\nmascall 126\nmasire 126\nmastercraft 126\nmathieu's 126\nmatir 126\nmatley 126\nmatricaria 126\nmattawamkeag 126\nmaurren 126\nmayuzumi 126\nmazak 126\nmbembe 126\nmbpj 126\nmccarley 126\nmcclain's 126\nmccool's 126\nmccunn 126\nmdivani 126\nmeconopsis 126\nmedenine 126\nmederos 126\nmedjez 126\nmedullinus 126\nmeeres 126\nmeghdoot 126\nmeir's 126\nmelanopogon 126\nmelchert 126\nmellus 126\nmenomena 126\nmenozzi 126\nmercuri 126\nmeritage 126\nmermet 126\nmetacercariae 126\nmezze 126\nmichl 126\nmicropentila 126\nmicrosurgical 126\nmiek 126\nmihiro 126\nmiloud 126\nminamahal 126\nminnewaska 126\nmintzberg 126\nmirar 126\nmisclassified 126\nmisfiring 126\nmishael 126\nmisprints 126\nmittleman 126\nmmw 126\nmoerdani 126\nmogliano 126\nmohib 126\nmohri 126\nmoiseiwitsch 126\nmojado 126\nmoll's 126\nmomaday 126\nmongla 126\nmoonah 126\nmoorage 126\nmoriones 126\nmorman 126\nmorshed 126\nmoscopole 126\nmoshava 126\nmotorama 126\nmotorland 126\nmouni 126\nmudry 126\nmultiform 126\nmumbly 126\nmundas 126\nmunkácsy 126\nmurakawa 126\nmuralt 126\nmusicologo 126\nnächte 126\nnära 126\nnachiyar 126\nnachruf 126\nnado 126\nnahor 126\nnamazi 126\nnamioka 126\nnanguan 126\nnanosatellite 126\nnascita 126\nnatureza 126\nnaung 126\nndau 126\nndugu 126\nnešto 126\nnebulosity 126\nnelas 126\nnellai 126\nnellikode 126\nneogeo 126\nnerazzurri 126\nnesse 126\nneutron's 126\nnevrokop 126\nnexa 126\nnextbike 126\nngon 126\nnišava 126\nnigriventris 126\nnikan 126\nnikita's 126\nnikolaisen 126\nnime 126\nnippō 126\nnirad 126\nnister 126\nniteroi 126\nniye 126\nnnamani 126\nnoapara 126\nnoctiluca 126\nnodo 126\nnoguchi's 126\nnoirmont 126\nnoncovalent 126\nnondeterminism 126\nnooru 126\nnordnes 126\nnoregs 126\nnortel's 126\nnovapex 126\nnovenas 126\nnovita 126\nnovlene 126\nnucleobase 126\nnullary 126\nnupserha 126\nnussbaumer 126\nnyamwezi 126\nnyandarua 126\noberste 126\noberwiesenthal 126\nobliterata 126\noconaluftee 126\nodžak 126\nodishaw 126\nody 126\noedipina 126\nolefination 126\nomophorion 126\noocyst 126\noooo 126\noriolo 126\norlock 126\nostwestfalen 126\noxelösund 126\noxgangs 126\noyashio 126\npacolli 126\npaidia 126\npaikiasothy 126\npalézieux 126\npalander 126\npalaver 126\npaleogeography 126\npallandt 126\npalling 126\npalmed 126\npandals 126\npanlipunan 126\npanlungsod 126\npardini 126\nparlier 126\npassey 126\npasteles 126\npastré 126\npattens 126\npaveletsky 126\npavonina 126\npawła 126\npcts 126\npeavine 126\npedrad 126\npehchan 126\npencaitland 126\npenmon 126\nperlich 126\npersée 126\npfn 126\npgb 126\nphantasms 126\npharmacodynamic 126\nphiltrum 126\nphodnq 126\nphoenicea 126\npiaţa 126\npice 126\npicturata 126\npiekarczyk 126\npiloti 126\npilzer 126\nplagios 126\nplastically 126\nplatooned 126\nplayero 126\npleiotropy 126\nplusnet 126\npodkopayeva 126\npoidevin 126\npokemouche 126\npolaron 126\npolearm 126\npolyomino 126\nporphyrius 126\nposch 126\npowelton 126\npréparatoires 126\npradesh's 126\npraktischen 126\npremade 126\npremiership's 126\nprenger 126\nprepay 126\npreserve's 126\npriebus 126\nprimitivists 126\nprocavia 126\nproprietor's 126\nprospera 126\npryce's 126\npsig 126\npulmonaria 126\npupatello 126\npust 126\nputah 126\nputus 126\nquantick 126\nquibble 126\nquiles 126\nradiotelegraph 126\nradoslaw 126\nraghallaigh 126\nrajhara 126\nrarh 126\nrationalising 126\nraymonds 126\nrayne's 126\nrecanting 126\nreci 126\nrecuperative 126\nredtop 126\nreduviidae 126\nreembarked 126\nregata 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125\nterrestrials 125\ntetraodon 125\ntfk 125\ntgb 125\nthefreelibrary 125\ntheoktistos 125\nthevenet 125\nthorkell 125\nthornhart 125\nthurgarton 125\nthutmosis 125\ntibshelf 125\ntijoux 125\ntillson 125\ntinnevelly 125\ntipps 125\ntitoist 125\ntizoc 125\ntnd 125\ntoaplan 125\ntomay 125\ntommies 125\ntomova 125\ntompall 125\ntonja 125\ntorchon 125\ntoroweap 125\ntorriente 125\ntossa 125\ntoupée 125\ntpws 125\ntractus 125\ntramples 125\ntransduced 125\ntrapanese 125\ntreleaven 125\ntribunician 125\ntridactylus 125\ntrocchi 125\ntroxy 125\ntroyon 125\ntrunk's 125\ntsotou 125\ntsune 125\ntuathail 125\ntumblehome 125\nturnov 125\ntuus 125\ntychowo 125\ntylman 125\nubar 125\nuduvil 125\nugglas 125\nuicc 125\nukai 125\numa's 125\numino 125\numran 125\nunavailing 125\nunipart 125\nuniversala 125\nuniversitaet 125\nunprinted 125\nunratified 125\nunreferenced 125\nunsaved 125\nunsourced 125\nunwatchable 125\nunzip 125\nurquell 125\nusurers 125\nutai 125\nuvr 125\nvaiko 125\nvakinankaratra 125\nvalašské 125\nvalco 125\nvalday 125\nvaleant 125\nvalluvar 125\nvalsesia 125\nvalyrian 125\nvarco 125\nvarlet 125\nvate 125\nveneziana 125\nverbände 125\nvibraphones 125\nvikingarna 125\nvillèle 125\nvinda 125\nvindob 125\nviscoelasticity 125\nviser 125\nvitālijs 125\nviva's 125\nvotum 125\nvrdolyak 125\nvyara 125\nwafs 125\nwagt 125\nwahren 125\nwaigeo 125\nwaissel 125\nwakley 125\nwaldhausen 125\nwallén 125\nwalsingham's 125\nwarchief 125\nwasielewski 125\nwathiq 125\nwawra 125\nwebcenter 125\nwelled 125\nwellford 125\nwersching 125\nweseluck 125\nwetherspoon 125\nweyi 125\nwgar 125\nwhakamaru 125\nwhiteinch 125\nwhitsitt 125\nwhittingehame 125\nwikivet 125\nwinkin 125\nwjsu 125\nwnem 125\nwof 125\nwoodcutting 125\nwoodhoopoes 125\nwoolooware 125\nwormy 125\nwoulfe 125\nwrda 125\nwsyx 125\nwtcg 125\nwung 125\nwuzhen 125\nwuzhu 125\nxavante 125\nxbiex 125\nxbs 125\nxep 125\nxiaoyan 125\nxigazê 125\nxinwen 125\nxinye 125\nyüksekova 125\nyachvili 125\nyaghnobi 125\nyahara 125\nyazata 125\nyeongju 125\nyoshiie 125\nyugto 125\nzōsen 125\nzelewski 125\nzemě 125\nzhekov 125\nzhongwei 125\nzhui 125\nzinho 125\nzuckerkandl 125\návh 124\nåsnes 124\nčajkovski 124\nśūnyatā 124\nświęta 124\nşemdinli 124\nżarnowiec 124\nигорь 124\nпремия 124\nпроблемы 124\nсовременник 124\nالثالثة 124\nانتشارات 124\nनक 124\naamina 124\nabdülkadir 124\nabergel 124\nabloy 124\nabrupta 124\nacclimatise 124\naceto 124\nachalasia 124\nacharonim 124\nachour 124\nacridine 124\nadamantina 124\nadian 124\nadjourns 124\nadriaanse 124\naema 124\naeroponic 124\nafrotheria 124\nagga 124\nagglutinin 124\naghili 124\naghnamullen 124\naimim 124\naiolia 124\nairavata 124\nairboy 124\najoynagar 124\nakher 124\nalcyonium 124\naldag 124\nalogo 124\nalopochen 124\naltertum 124\namaka 124\namares 124\namazonense 124\nameba 124\namila 124\namtk 124\nanárion 124\nanastasie 124\nanastasiia 124\nangsana 124\nanguis 124\nanner 124\nansgar's 124\nansiklopedisi 124\nantifouling 124\nanys 124\napfelbeck 124\napga 124\naphyosemion 124\napophysis 124\napopse 124\napotheke 124\narch's 124\narchitectonica 124\narcmin 124\nardoz 124\nardra 124\narfak 124\narmiya 124\narpels 124\narreton 124\narseneau 124\narticleform 124\nartifex 124\nartifices 124\nartpark 124\narundel's 124\naryabhatta 124\narzew 124\nasatiani 124\nasteroidal 124\nasthena 124\natarot 124\natherley 124\natrax 124\naubisque 124\naudiogram 124\naudiometry 124\nauditioner's 124\naudo 124\naugustae 124\naustrasian 124\naustriacus 124\navago 124\naviceda 124\navinu 124\navit 124\nazeméis 124\nazoospermia 124\nbüchler 124\nbabashoff 124\nbabauta 124\nbabović 124\nbadgerys 124\nbaham 124\nbarghash 124\nbaristas 124\nbarsham 124\nbartrum 124\nbasca 124\nbascon 124\nbashkirov 124\nbatre 124\nbaudet 124\nbayn 124\nbazmi 124\nbeñat 124\nbeadon 124\nbeechgrove 124\nbelalcázar 124\nbenchrest 124\nbentivolio 124\nbenzinger 124\nbergholz 124\nbeverley's 124\nbiconvex 124\nbighas 124\nbijedić 124\nbiometry 124\nbirnin 124\nbirutė 124\nbirzebbuga 124\nbizertin 124\nbjørvika 124\nblšany 124\nbluemotion 124\nblunsdon 124\nblustering 124\nbobrow 124\nbocchoris 124\nbodianus 124\nboerhavia 124\nbogoljub 124\nbohrium 124\nboidae 124\nbojador 124\nbolado 124\nboomhauer 124\nboort 124\nborġ 124\nborgese 124\nbornand 124\nborso 124\nborsodi 124\nbotc 124\nbourgmont 124\nboxen 124\nbrachyrhynchus 124\nbradamante 124\nbraithwaite's 124\nbranning's 124\nbreman 124\nbressane 124\nbrevicollis 124\nbrickellia 124\nbridgettine 124\nbrisé 124\nbritbowl 124\nbrittaney 124\nbrokken 124\nbromiley 124\nbrunsbüttel 124\nbrympton 124\nbrzeźnica 124\nbuchbinder 124\nbuckhaven 124\nbuczek 124\nbuddhadev 124\nbudlong 124\nbulakan 124\nbundanoon 124\nbushwacka 124\nbutere 124\nbuxbaum 124\ncaçador 124\ncablevision's 124\ncadance 124\ncadeaux 124\ncaelo 124\ncagr 124\ncaipirinha 124\ncalamian 124\ncalifone 124\ncaln 124\ncamélias 124\ncamassia 124\ncamplin 124\ncançons 124\ncaniglia 124\ncannibalization 124\ncansos 124\ncapitolio 124\ncaprotti 124\ncarabajal 124\ncarss 124\ncarterocephalus 124\ncastrén 124\ncatlike 124\ncavalaris 124\ncavus 124\ncbq 124\ncctlds 124\nceledón 124\nceltici 124\ncephonodes 124\ncesária 124\nchế 124\nchadash 124\nchagall's 124\nchagford 124\nchake 124\nchany 124\ncharalampos 124\ncharaszkiewicz 124\ncharnas 124\nchassalia 124\nchauvel's 124\nchavakkad 124\nchbc 124\nchelyuskin 124\nchepchumba 124\ncherryvale 124\nchesebrough 124\nchide 124\nchippendales 124\nchiragh 124\nchisako 124\nchlamydophila 124\nchofu 124\nchristenberry 124\nchristyi 124\nchromebooks 124\nchryst 124\nchugiak 124\nchyler 124\ncikampek 124\ncinefan 124\ncircumvallation 124\ncivili 124\nclarina 124\nclaviger 124\nclemenson 124\nclitoria 124\nclonleigh 124\nclugston 124\ncoccygeal 124\ncochliobolus 124\ncoereba 124\ncolborn 124\ncoledale 124\ncomense 124\ncommunitas 124\ncomorin 124\ncomplesso 124\ncontrapposto 124\ncoogler 124\ncopywriters 124\ncordera 124\ncorinium 124\ncorrington 124\ncotera 124\ncountercoup 124\ncoxhead 124\ncpa's 124\ncpx 124\ncríticos 124\ncraigmore 124\ncrispe 124\ncristaldo 124\ncrossbreeds 124\ncrudwell 124\ncudweed 124\ncug 124\ncurveballs 124\ncuza's 124\ncyanocephalus 124\ncyclecars 124\ncypseloides 124\ncyrrhus 124\ndälek 124\ndöllinger 124\ndabhade 124\ndadeville 124\ndager 124\ndahmen 124\ndalén 124\ndalmatic 124\ndalou 124\ndamaliscus 124\ndamilano 124\ndanacea 124\ndarkin 124\ndatei 124\ndavenham 124\ndeap 124\ndecapentaplegic 124\ndecserver 124\ndeferent 124\ndeinococcus 124\ndelima 124\ndemonyms 124\ndendrosenecio 124\ndenison's 124\nderajat 124\ndevambez 124\ndeyi 124\ndheri 124\ndiago 124\ndică 124\ndicksboro 124\ndimino 124\ndinçer 124\ndina's 124\ndingen 124\ndipu 124\ndiriyah 124\ndiscernable 124\ndisquisitiones 124\ndivinations 124\ndiwaniyah 124\ndixton 124\ndjanet 124\ndobiegniew 124\ndodgin 124\ndoinel 124\ndolgeville 124\ndongshi 124\ndonnie's 124\ndonyi 124\ndormoy 124\ndougram 124\ndouses 124\ndrouyn 124\ndrusky 124\ndsto 124\ndtds 124\ndtrace 124\ndubourdieu 124\ndubplate 124\ndudás 124\ndudo 124\ndufour's 124\ndukeries 124\ndunlea 124\ndunni 124\ndunvant 124\nduplaix 124\ndwapara 124\ndzs 124\neː 124\nearmarking 124\nearpieces 124\neccas 124\necgberhti 124\nechinops 124\nectrodactyly 124\nedghill 124\neduardo's 124\nehrgeiz 124\neitam 124\nekavian 124\nelfstedentocht 124\nelmstead 124\nelof 124\nemera 124\nemmanuele 124\nenchanté 124\nenergetik 124\nengraver's 124\nenjo 124\nentdeckung 124\nequid 124\nerbaa 124\nesmailabad 124\nests 124\nethnoreligious 124\nevansdale 124\neverist 124\neversen 124\nextorts 124\neyschen 124\nfénéon 124\nfaddeev 124\nfagerholm 124\nfanzago 124\nfao's 124\nfaustão 124\nfconcourse 124\nfeltman 124\nfely 124\nfemto 124\nfennessy 124\nfetu 124\nfewkes 124\nfiancés 124\nfieldi 124\nfilmcritic 124\nflöte 124\nflagellants 124\nflashier 124\nflouring 124\nfoda 124\nfolgefonna 124\nfootprinting 124\nfortini 124\nfotia 124\nfoule 124\nfountainhall 124\nfoxall 124\nfröhliche 124\nfranglais 124\nfrankenia 124\nfrankenstadion 124\nfredkin 124\nfreehills 124\nfucile 124\nfukumura 124\nfun's 124\nfzk 124\ngabri 124\ngacaca 124\ngaiking 124\ngaile 124\ngajdusek 124\ngalanin 124\ngalegos 124\ngallardón 124\ngambits 124\ngarabed 124\ngardan 124\ngarhwa 124\ngarl 124\ngarsdale 124\ngatenby 124\ngatti's 124\ngauchetière 124\ngaudete 124\ngaurus 124\ngavaldón 124\ngazu 124\ngeesink 124\ngeldof's 124\ngeppert 124\ngeuthner 124\nghirelli 124\ngibborim 124\ngibril 124\ngigo 124\ngilda's 124\ngilma 124\ngilston 124\ngiraffatitan 124\ngiriraj 124\ngiulietti 124\ngleevec 124\nglitterati 124\ngmpte 124\ngmx 124\ngoeldii 124\ngoldfrapp's 124\ngopal's 124\ngotv 124\ngracilirostris 124\ngramaphone 124\ngramsci's 124\ngranulina 124\ngrcret 124\ngreenhornes 124\ngrid's 124\ngrigorios 124\ngrimsel 124\ngriptonite 124\ngrlić 124\ngromova 124\ngroundings 124\ngrudem 124\ngsn's 124\nguédon 124\nguð 124\nguadalete 124\nguandong 124\nguggenheimer 124\nguilhermina 124\ngulda 124\ngulong 124\ngunbower 124\nguryevsky 124\nhaapamäki 124\nhadano 124\nhajsafi 124\nhakeswill 124\nhakushaku 124\nhalderman 124\nhamat 124\nhaotian 124\nharishankar 124\nhasp 124\nhattler 124\nhayo 124\nhcjb 124\nhearkened 124\nheaver 124\nhedjaz 124\nheelflip 124\nheeze 124\nheink 124\nheita 124\nheitersheim 124\nheliopause 124\nhelot 124\nhembree 124\nhendron 124\nhepcidin 124\nheuristically 124\nhexter 124\nhiawatha's 124\nhikasa 124\nhindmost 124\nhindson 124\nhindustantimes 124\nhirahara 124\nhirohito's 124\nhitchman 124\nhived 124\nhmdb 124\nhoffberger 124\nhoffmeyer 124\nhomonymy 124\nhopera 124\nhorea 124\nhoublon 124\nhovevei 124\nhoya's 124\nhristova 124\nhuiyao 124\nhumair 124\nhumidifiers 124\nhuskins 124\nhuttwil 124\nhydrobates 124\nhyeonjong 124\nhymenopteran 124\nhypólito 124\nhypocritically 124\nhypomagnesemia 124\nhypothermic 124\niaje 124\niggy's 124\nignatenko 124\nikhshid 124\nikuhisa 124\nimamah 124\nimtiyaz 124\ninchkeith 124\nindemnified 124\nindispensables 124\ninfluentially 124\ninsubres 124\ninterstage 124\nintimes 124\nintroitus 124\ninvalidly 124\nippolit 124\niprs 124\nipwa 124\niraklion 124\nirbil 124\nisigny 124\nislahiye 124\nissuer's 124\nistithmar 124\nitá 124\nitü 124\nitaliae 124\niwanaga 124\njáchymov 124\njöback 124\njambe 124\njamul 124\njannali 124\njedinak 124\njelf 124\njeroni 124\njkd 124\njoda 124\njoggins 124\njordanstone 124\njp's 124\njuicing 124\njungfernstieg 124\njunii 124\njushi 124\nkōsaku 124\nkachō 124\nkachiguda 124\nkahoku 124\nkakure 124\nkalac 124\nkalavati 124\nkalaya 124\nkalliopi 124\nkambia 124\nkamiizumi 124\nkamijo 124\nkandinsky's 124\nkaputt 124\nkaravelov 124\nkarikala 124\nkarutha 124\nkarygiannis 124\nkatachi 124\nkawaihae 124\nkaysone 124\nkazmierczak 124\nkazune 124\nkbw 124\nkeōua 124\nkempelen 124\nkensley 124\nkerkhove 124\nkerrii 124\nkhairabad 124\nkhami 124\nkhamti 124\nkhasiana 124\nkhouang 124\nkhuweilfe 124\nkičme 124\nkilcronaghan 124\nkilnamona 124\nkilobits 124\nkilvington 124\nkincade 124\nkinderman 124\nkingcobra 124\nkinomoto 124\nkirtlington 124\nkirundi 124\nkistner 124\nkitiara 124\nkitlv 124\nklaić 124\nklimkin 124\nklubb 124\nkluge's 124\nkmj 124\nknauth 124\nkoffice 124\nkoodu 124\nkosaraju 124\nkotlikoff 124\nkout 124\nkowie 124\nkrasue 124\nkrik 124\nkrump 124\nkswb 124\nkubiš 124\nkubilay 124\nkudumba 124\nkunwari 124\nkutiyattam 124\nkvænangen 124\nlövgren 124\nlødingen 124\nlützelstein 124\nlα 124\nlabana 124\nladye 124\nladykiller 124\nlamsweerde 124\nlanciers 124\nlandfalling 124\nlaner 124\nlaran 124\nlarena 124\nlatipes 124\nlebas 124\nledreborg 124\nlefevere 124\nleilene 124\nlengies 124\nletterheads 124\nlewisboro 124\nlickety 124\nlillibridge 124\nlilygreen 124\nlimasawa 124\nlipnicki 124\nlipnik 124\nlittd 124\nlongabaugh 124\nlongplay 124\nloredo 124\nlories 124\nlovén 124\nlubricity 124\nluchian 124\nlulia 124\nlum's 124\nluogang 124\nlusinchi 124\nluthra 124\nmånen 124\nmünchenbuchsee 124\nmableton 124\nmace's 124\nmacintosh's 124\nmackler 124\nmacmullan 124\nmadonie 124\nmadore 124\nmahasiddha 124\nmaiti 124\nmakinwa 124\nmalambo 124\nmalecon 124\nmalorie 124\nmangkunegaran 124\nmanio 124\nmanjeshwar 124\nmannheim's 124\nmansurah 124\nmarcy's 124\nmarinum 124\nmaritana 124\nmaroof 124\nmarshe 124\nmartill 124\nmashaal 124\nmassigli 124\nmassis 124\nmaximizer 124\nmayaguana 124\nmayardit 124\nmcammond 124\nmcclymonds 124\nmccraney 124\nmcgiver 124\nmclibel 124\nmcso 124\nmedgidia 124\nmegyei 124\nmeij 124\nmemorium 124\nmendonsa 124\nmennell 124\nmenzel's 124\nmeritor 124\nmetabolise 124\nmetate 124\nmethymna 124\nmetn 124\nmicronesians 124\nmiglior 124\nmilyukov 124\nmisret 124\nmisumena 124\nmitama 124\nmlstp 124\nmmtc 124\nmodasa 124\nmohorovičić 124\nmoisturizers 124\nmonark 124\nmongoloids 124\nmonopolists 124\nmonosoupape 124\nmonospar 124\nmonstruo 124\nmontesanto 124\nmontgomeries 124\nmontluc 124\nmoorad 124\nmortadella 124\nmortsel 124\nmorwenstow 124\nmotivos 124\nmpala 124\nmukhiya 124\nmullineux 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123\nnhb 123\nnhlers 123\nnickerl 123\nniea 123\nnightfighter 123\nniobate 123\nnisporeni 123\nnittel 123\nnivek 123\nnoir's 123\nnoji 123\nnonresistance 123\nnordische 123\nnoticeboard 123\nnovais 123\nnovotes 123\nntem 123\nnubi 123\nnudges 123\nnudipixel 123\nnvqs 123\nnyama 123\nnyanaponika 123\nnyingpo 123\nnzema 123\noase 123\nobizzo 123\nobomsawin 123\noctopamine 123\nodelay 123\nokiep 123\noktar 123\nolawale 123\nolbrychski 123\noleiros 123\nolleh 123\nommadawn 123\nondru 123\nopunake 123\noreades 123\norganosilicon 123\norgasmatron 123\norobanchaceae 123\notomí 123\noutboards 123\noutsmarting 123\noverpeck 123\nowino 123\npétroliers 123\npõhikool 123\npalest 123\npalladia 123\npanaca 123\npanagiotopoulos 123\npanania 123\npanarion 123\npandin 123\npandurangan 123\npania 123\npanisperna 123\npanula 123\npanunzio 123\npapillomatosis 123\nparamore's 123\nparaphilic 123\nparaya 123\nparramatta's 123\nparvedentulina 123\npasque 123\npassel 123\npassion's 123\npatrulla 123\npayatas 123\npeckoltia 123\npeile 123\npemalang 123\npencilling 123\npenclawdd 123\npeneplain 123\nperani 123\nperenne 123\nperithecia 123\npersecutory 123\npersonenlexikon 123\npesisir 123\npetitclerc 123\npetroșani 123\npettet 123\npezet 123\npgms 123\npharasmanes 123\nphdr 123\npicabo 123\npicquigny 123\npigasus 123\npinker's 123\npinte 123\npipixcan 123\npixi 123\nplatooning 123\nplessur 123\nplys 123\npodsiadly 123\npohjonen 123\npohlers 123\npoilu 123\npokorný 123\npolan 123\npollutes 123\npolt 123\npolymyositis 123\npombaline 123\npomorska 123\npondus 123\npontifices 123\npoops 123\npopworld 123\npoque 123\nportageville 123\npostigo 123\nprévoyance 123\npradakshina 123\nprapiroon 123\npredilections 123\npreemptions 123\nprefigure 123\nprefrontals 123\npresacral 123\nprina 123\nprinsipe 123\nprorector 123\nprosequi 123\nprotectively 123\nprovostship 123\npugno 123\npulqui 123\npusula 123\npxtruncated 123\npysanka 123\npysanky 123\nqtd 123\nquaedam 123\nquizmania 123\nqutab 123\nrabelo 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123\nsecretum 123\nsegodnya 123\nseiurus 123\nseja 123\nsennacherib's 123\nsessé 123\nsetthathirath 123\nseversk 123\nseybert 123\nsfatului 123\nshaad 123\nshambolic 123\nshangdong 123\nsharpei 123\nsharur 123\nshibori 123\nshijak 123\nshikan 123\nshotshell 123\nshotshells 123\nshucks 123\nshuren 123\nshurman 123\nshust 123\nsibutramine 123\nsifakas 123\nsife 123\nsilvestrii 123\nsinewy 123\nsinjin 123\nsinofsky 123\nsinor 123\nsionil 123\nsircom 123\nsirdal 123\nsiru 123\nsitemap 123\nsjogren 123\nskaf 123\nskims 123\nskyheroes 123\nskywards 123\nsmirks 123\nsnape's 123\nsnorted 123\nsobradinho 123\nsobrina 123\nsociedades 123\nsonogashira 123\nsoyza 123\nsquillari 123\nsrtio 123\nstanning 123\nstea 123\nstepless 123\nstereogram 123\nstes 123\nsthal 123\nstickam 123\nstovetop 123\nstrathblane 123\nstrawbsweb 123\nstreight 123\nstrupper 123\nstruvite 123\nstunna 123\nstupnitsky 123\nstyrbjörn 123\nsubianto 123\nsubis 123\nsugarfree 123\nsulaimaniyah 123\nsuperficialis 123\nsupergene 123\nsuppressants 123\nsutanuti 123\nsutorius 123\nswallowfield 123\nswh 123\nszenes 123\ntähkä 123\ntémoin 123\ntabara 123\ntabone 123\ntadolini 123\ntalulah 123\ntamahori 123\ntamarkin 123\ntambien 123\ntamminen 123\ntanat 123\ntanforan 123\ntangalle 123\ntanihara 123\ntanoak 123\ntarboton 123\ntarell 123\ntaskin 123\nteahupoo 123\ntebya 123\ntephritidae 123\nteraz 123\ntesler 123\nthaer 123\nthalwil 123\ntharawal 123\ntheatines 123\nthessalia 123\nthreet 123\nthylakoids 123\ntibellus 123\ntiedtke 123\ntiefland 123\ntiffen 123\ntigon 123\ntikkurila 123\ntillier 123\ntinelli 123\ntinik 123\ntionne 123\ntogiak 123\ntohil 123\ntolbukhin 123\ntorbole 123\ntortellini 123\ntowr 123\ntoxicologists 123\ntpy 123\nträumen 123\ntraineeships 123\ntramway's 123\ntransshipped 123\ntredgold 123\ntriboro 123\ntrifunctional 123\ntrigoso 123\ntrimers 123\ntroparia 123\ntrophyless 123\ntrov 123\ntrsat 123\ntsingy 123\ntsujii 123\ntsuribaka 123\ntulkarem 123\ntulsk 123\nturnier 123\nturul 123\ntutor's 123\ntyt 123\nughelli 123\nujście 123\nuncinate 123\nuncolored 123\nunderbank 123\nunderpopulated 123\nunita's 123\nunlimited's 123\nunlistenable 123\nunsane 123\nuntertürkheim 123\nupci 123\nusherette 123\nushhof 123\nusni 123\nutsukushii 123\nuza 123\nvaastu 123\nvajrasattva 123\nvalientes 123\nvallot 123\nvark 123\nvasilieva 123\nvasilyevna 123\nvasojevići 123\nvedeno 123\nvehicons 123\nvelappan 123\nvereinigten 123\nverhaal 123\nvermiculated 123\nvgt 123\nvhr 123\nvillianur 123\nvilseck 123\nvirginicus 123\nviscum 123\nviterbese 123\nvitina 123\nvizzola 123\nvoicemails 123\nvojinović 123\nvolkshochschule 123\nvoluta 123\nvoorschoten 123\nvoxx 123\nvseslav 123\nvuka 123\nwaddingtons 123\nwaggner 123\nwairakei 123\nwakakusa 123\nwapner 123\nwashingtons 123\nwasylycia 123\nwatco 123\nwaurika 123\nwebside 123\nwens 123\nwhau 123\nwikimindmap 123\nwikoff 123\nwilensky 123\nwilliamsons 123\nwinget 123\nwinkelried 123\nwinz 123\nwittes 123\nwlc 123\nwnye 123\nwolverley 123\nwoodi 123\nworldwide's 123\nworthily 123\nwssm 123\nxacml 123\nxfile 123\nxinqiao 123\nxisco 123\nxmq 123\nyakus 123\nyanic 123\nyasufumi 123\nyodfa 123\nyono 123\nyoshukai 123\nysabella 123\nzacks 123\nzafy 123\nzahn's 123\nzanobi 123\nzaripova 123\nzaurus 123\nziem 123\nzitong 123\nzolotov 123\nzonca 123\nzoroaster's 123\nzuidlaren 123\nġnien 122\nħaż 122\nōfuna 122\nśruti 122\nμl 122\nадмирал 122\nобщество 122\nхудожественная 122\nجبل 122\naabar 122\nabarema 122\nabbadia 122\nabbitt 122\nabdullaev 122\nabhiman 122\naboot 122\nacanthodactylus 122\naccessor 122\nacetylide 122\nachnatherum 122\nackee 122\nacmd 122\nadedeji 122\nadira 122\nadme 122\naetatis 122\nafrasiab 122\nafridis 122\nagfacolor 122\nahumanu 122\naia's 122\nakbayan 122\nakhnoor 122\nalaipayuthey 122\nalgérien 122\nallami 122\nallmand 122\naltenstadt 122\namaldev 122\namandala 122\namatol 122\namaziah 122\namebix 122\nanalyticity 122\nanandapur 122\nandøy 122\nangélil 122\nangelillo 122\nannamensis 122\nansdell 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122\nbertholf 122\nbertos 122\nbetha 122\nbethuel 122\nbetrayers 122\nbeuno's 122\nbezanson 122\nbhimtal 122\nbibhutibhushan 122\nbikeshare 122\nbikie 122\nbilanz 122\nbildu 122\nbilotti 122\nbindel 122\nbinningen 122\nbiopower 122\nbittle 122\nbláthnaid 122\nblando 122\nblanquet 122\nblowjob 122\nbnz 122\nboatman's 122\nboldi 122\nborassus 122\nborcea 122\nborecká 122\nborisovna 122\nbosca 122\nbotolan 122\nbovio 122\nboyson 122\nbradypus 122\nbraeswood 122\nbrejo 122\nbrillstein 122\nbrimelow 122\nbrinegar 122\nbrisingr 122\nbritama 122\nbrockholes 122\nbrunetto 122\nbuaa 122\nbudvar 122\nbuffoonish 122\nbugbears 122\nbukhoro 122\nbulvar 122\nbulweria 122\nbungarus 122\nburden's 122\nburgomasters 122\nbursey 122\nbusaiteen 122\nbuscher 122\nbuttafuoco 122\nbutterwort 122\ncíosóig 122\ncabas 122\ncacs 122\ncaetera 122\ncaisses 122\ncalderon's 122\ncalorimeters 122\ncaltrops 122\ncalumma 122\ncampylorhynchus 122\ncanaliculatus 122\ncanem 122\ncanillo 122\ncantada 122\ncapasso 122\ncapitonidae 122\ncarbapenems 122\ncarenza 122\ncaretas 122\ncasby 122\ncascine 122\ncastlebay 122\ncatani 122\ncatucci 122\ncatulle 122\nceannt 122\ncelto 122\ncementos 122\ncerin 122\ncernăuți 122\nchaam 122\nchairmans 122\nchaleurs 122\nchapelries 122\nchenchu 122\nchettikulangara 122\nchfi 122\nchhabria 122\nchianciano 122\nchieveley 122\nchinguetti 122\nchlordane 122\nchlorophenyl 122\nchokin 122\nchoten 122\nchurchbridge 122\nciannachta 122\nciit 122\ncinedigm 122\ncinninger 122\ncitronelle 122\nclain 122\nclandestino 122\nclavipes 122\nclignancourt 122\nclubby 122\nclubcard 122\ncmsa 122\ncnnfn 122\ncockleshell 122\ncolls 122\ncolugos 122\ncomala 122\nconciertos 122\nconcordats 122\nconformers 122\nconveniens 122\nconvexus 122\nconvivencia 122\ncoppull 122\ncord's 122\ncorrinne 122\ncosmosoma 122\ncossutta 122\ncotingidae 122\ncoumaric 122\ncounteraction 122\ncountships 122\ncourtlandt 122\ncouteau 122\ncpca 122\ncrang 122\ncraske 122\ncraughwell 122\ncraxton 122\ncrescens 122\ncrownpoint 122\ncryptolechia 122\ncsus 122\nctbto 122\ncuadro 122\ncubin 122\ncuchilla 122\nculgoa 122\ncumbo 122\ncurriers 122\ncutsem 122\ncyclohexene 122\ncystatin 122\ndémocrates 122\ndésirade 122\ndønna 122\ndębnica 122\ndaems 122\ndamala 122\ndande 122\ndanneskiold 122\ndanya 122\ndaphné 122\ndargomyzhsky 122\ndaskalakis 122\ndasymys 122\ndeadstar 122\ndeathspank 122\ndeblieux 122\ndecontrol 122\ndedh 122\ndeepings 122\ndefoliated 122\ndelčevo 122\ndelasalle 122\ndelgiorno 122\ndelie 122\ndemodocus 122\ndemonweb 122\ndenticulatus 122\ndeoxygenation 122\ndevanampiya 122\ndevilfish 122\ndevulapalli 122\ndewdrop 122\ndibujo 122\ndicola 122\ndilong 122\ndimerisation 122\ndimitri's 122\ndimitriades 122\ndimity 122\nditerpene 122\ndlcs 122\ndolenjske 122\ndolmatov 122\ndolpa 122\ndorćol 122\ndornhoefer 122\ndoseone 122\ndostanić 122\ndramane 122\ndregen 122\ndrillship 122\ndromaeosaurs 122\ndropwort 122\ndučić 122\nduchassaing 122\nduffer 122\ndulcimers 122\nduncraig 122\nduros 122\nduyfken 122\ndwd 122\ndword 122\nearthlike 122\neccm 122\nechinocactus 122\nechinolittorina 122\nechte 122\necológico 122\nedele 122\nedmc 122\neducacional 122\neisinger 122\nelde 122\nelectrabel 122\nelephantidae 122\neliav 122\nelledge 122\nemanem 122\nenciclopèdia 122\nendiandra 122\neninho 122\nenoree 122\nepha 122\nepigynopteryx 122\nepistaxis 122\neranakulam 122\nerazem 122\nernstthal 122\nerwadi 122\nessendine 122\nethalion 122\neulma 122\neupsilia 122\neuropan 122\neuryhaline 122\nevangelio 122\nexcusable 122\nexid 122\nexudative 122\nfabasoft 122\nfacials 122\nfacss 122\nfairie 122\nfatalist 122\nfato 122\nfehily 122\nfeleti 122\nfelica 122\nfesti 122\nfilipiak 122\nfingerholes 122\nfinse 122\nfinsternis 122\nfiodor 122\nfirenza 122\nflament 122\nflammes 122\nflattus 122\nfleurette 122\nfluyt 122\nflye 122\nfogged 122\nforgivable 122\nfork's 122\nframus 122\nfratus 122\nfreetype 122\nfrisselle 122\nfroberg 122\nfujitani 122\nfukaura 122\nfulgor 122\nfurmanov 122\nfurmint 122\nfurries 122\ngabbiano 122\ngaelle 122\ngaillot 122\ngaishi 122\ngaldhøpiggen 122\ngambianus 122\ngambino's 122\ngammas 122\ngankutsuou 122\ngansa 122\ngaramantes 122\ngardone 122\ngarreth 122\ngawande 122\ngayageum 122\ngeat 122\ngedolei 122\ngekkou 122\ngenealogica 122\ngenshi 122\ngentrifying 122\ngeo's 122\ngesenius 122\nghanaweb 122\nghumura 122\ngiangiacomo 122\ngilbertsville 122\ngillingham's 122\ngilmor 122\nglasshoughton 122\ngleim 122\ngling 122\nglochidion 122\nglossophaga 122\nglpk 122\ngojowczyk 122\ngoldfoot 122\ngoldrick 122\ngomersal 122\ngongfu 122\ngottstein 122\ngrabfeld 122\ngrandmont 122\ngreensmith 122\ngreffulhe 122\ngreive 122\ngrodner 122\ngroundskeepers 122\ngujri 122\ngull's 122\ngundula 122\ngunsmithing 122\nguttuso 122\nhäfner 122\nhärte 122\nhélary 122\nhadis 122\nhaffkine 122\nhaidee 122\nhakuōki 122\nhanak 122\nhanban 122\nhandley's 122\nhanish 122\nhardwar 122\nhardyston 122\nharir 122\nharmston 122\nharsányi 122\nhatherleigh 122\nhawridge 122\nhaynau 122\nhazaribag 122\nhazza 122\nheadshunt 122\nhechinger 122\nhegy 122\nheimberg 122\nheimir 122\nheldentenor 122\nheliozela 122\nhelmerich 122\nhendrick's 122\nhennell 122\nhenno 122\nhenslowe's 122\nhentemann 122\nhernals 122\nherpetopoma 122\nherps 122\nherriott 122\nherskovitz 122\nheselton 122\nheteroatoms 122\nheussaff 122\nhidemasa 122\nhiders 122\nhilles 122\nhindupur 122\nhirschl 122\nhjálmar 122\nholbay 122\nholdcroft 122\nholtsmark 122\nhomeworlds 122\nhongjing 122\nhoutteman 122\nhove's 122\nhoying 122\nhristić 122\nhrolf 122\nhto 122\nhuave 122\nhunahpu 122\nhunico 122\nhunsinger 122\nhuolongjing 122\nhurairah 122\nhuronia 122\nhybridised 122\niñiguez 122\nibrickane 122\nichiyo 122\nicsm 122\nictal 122\nidaten 122\nigmp 122\nihram 122\nihrd 122\niino 122\nilay 122\nimaginos 122\nimipenem 122\nimmolations 122\nimpostora 122\ninny 122\nintegris 122\ninterativo 122\ninternas 122\ninvectives 122\ninvidia 122\nirelander 122\nisostructural 122\nisstadion 122\nisu's 122\nitalyregular 122\nivanovskaya 122\niwk 122\njämtlands 122\njagadamba 122\njagi 122\njahvid 122\njaniszewski 122\njapanjapan 122\njbnhs 122\njefri 122\njetway 122\njianzhi 122\njimmo 122\njirou 122\njishin 122\njoto 122\njpop 122\njudr 122\njugalbandi 122\njuhnke 122\njuji 122\njunnosuke 122\njurm 122\nkühlungsborn 122\nkükenthal 122\nkōdansha 122\nkabocha 122\nkadee 122\nkahf 122\nkaieteur 122\nkalalau 122\nkalawao 122\nkalhana 122\nkalib 122\nkandern 122\nkasimov 122\nkasota 122\nkazee 122\nkazurinsky 122\nkcts 122\nkekaulike 122\nkellerman's 122\nkemijärvi 122\nkeratoplasty 122\nkesher 122\nkeverne 122\nkhả 122\nkhanderao 122\nkharar 122\nkhasa 122\nkhidmatgar 122\nkhuli 122\nkikō 122\nkilbom 122\nkilgeever 122\nkinfauns 122\nkingsthorpe 122\nkircheisen 122\nkiwisaver 122\nklinkhammer 122\nkmr 122\nkna 122\nkneeing 122\nknezevic 122\nknurled 122\nkofy 122\nkogane 122\nkolbeinn 122\nkolko 122\nkolobnev 122\nkoltsevaya 122\nkomugi 122\nkoodi 122\nkorderas 122\nkotara 122\nkottur 122\nkpdx 122\nkranefuss 122\nkrefelder 122\nkreislauf 122\nkrida 122\nkrieghoff 122\nkristanna 122\nkrivak 122\nkrupinski 122\nkrupnik 122\nksn 122\nkujala 122\nkulli 122\nkunama 122\nkundun 122\nkwinter 122\nkyc 122\nkymenlaakso 122\nkyoiku 122\nlacken 122\nladeside 122\nlahcen 122\nlamos 122\nlamp's 122\nlampassé 122\nlancelin 122\nlanerossi 122\nlangbaurgh 122\nlangsdorf 122\nlappas 122\nlapshin 122\nlapuente 122\nlarrivee 122\nlatheef 122\nlatro 122\nlauchlin 122\nlaurelwood 122\nlawi 122\nlazarevic 122\nleechi 122\nlegatine 122\nleger's 122\nlemnian 122\nlenco 122\nlerum 122\nleventhorpe 122\nlevier 122\nlevothyroxine 122\nlezha 122\nlhv 122\nlica 122\nlicciardi 122\nlicio 122\nliebestraum 122\nliesse 122\nliljegren 122\nlipshitz 122\nlkm 122\nlkr 122\nloanhead 122\nlongniddry 122\nlongos 122\nlontar 122\nloranger 122\nloubriel 122\nlouche 122\nlozen 122\nlrf 122\nlse's 122\nlumbard 122\nlumberkings 122\nluxair 122\nlwanga 122\nlxxxiv 122\nlyngdoh 122\nménilmontant 122\nmér 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118\nberent 118\nbershad 118\nbertier 118\nbettoni 118\nbeuzeville 118\nbfe 118\nbhangar 118\nbieberstein 118\nbiesbroeck 118\nbietak 118\nbignall 118\nbilzen 118\nbioman 118\nbiquadratic 118\nbirkby 118\nblackbourn 118\nblank's 118\nblankman 118\nbleary 118\nblit 118\nblora 118\nblountville 118\nboí 118\nboškov 118\nboas's 118\nboddingtons 118\nbolaji 118\nbollani 118\nbonera 118\nbontrager 118\nbookkeepers 118\nboom's 118\nboquillas 118\nborchgrave 118\nbordón 118\nboreray 118\nborophaginae 118\nborup 118\nbosetti 118\nboulmer 118\nbovi 118\nbpitch 118\nbrahmastra 118\nbrancacci 118\nbrandstatter 118\nbratkowski 118\nbreña 118\nbreuberg 118\nbriarcliffe 118\nbridgeport's 118\nbriet 118\nbrittin 118\nbrownings 118\nbsfc 118\nbulked 118\nbullrich 118\nbulykin 118\nbuochs 118\nbuongiorno 118\nburtonsville 118\nbuzean 118\nbyr 118\ncabrito 118\ncadaverous 118\ncalopteryx 118\ncalotropis 118\ncaltech's 118\ncamaldulensis 118\ncamerunensis 118\ncaminito 118\ncampephaga 118\ncanace 118\ncanindé 118\ncaninum 118\ncanut 118\ncapitulates 118\ncappuccilli 118\ncarasso 118\ncarillonneur 118\ncarluccio 118\ncarnaro 118\ncashen 118\ncassiel 118\ncastione 118\ncastri 118\ncataloguer 118\ncayla 118\ncbsc 118\nceasar 118\ncebeci 118\nceccato 118\nceder 118\ncenturion's 118\ncgk 118\nchagang 118\nchakki 118\nchampakulam 118\nchantier 118\nchauncey's 118\ncheckpointing 118\nchemeketa 118\nchinnar 118\nchisiza 118\nchiss 118\nchloromethyl 118\nchocim 118\nchoczewo 118\nchogyam 118\nchoisir 118\ncholuim 118\ncichlasoma 118\ncichy 118\ncircumscribe 118\ncistecephalus 118\ncius 118\nckck 118\nckvr 118\nclathrina 118\ncldc 118\nclj 118\ncllrs 118\nclusiaceae 118\ncmda 118\ncmts 118\ncoastwatchers 118\ncoccineum 118\ncoldblood 118\ncolter's 118\ncolumbella 118\ncomaroff 118\ncomins 118\nconda 118\nconnoquenessing 118\nconsignee 118\nconsolatio 118\nconstructorul 118\ncontalmaison 118\nconventionality 118\nconversazione 118\ncoolavin 118\ncopernicus's 118\ncoquerel's 118\ncortado 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118\ndufilho 118\nduflo 118\ndulbecco 118\ndumervil 118\nduval's 118\ndwayne's 118\ndybenko 118\ndykema 118\nearnock 118\neasiness 118\nebizō 118\nechad 118\nechinatus 118\nedelen 118\nedgemoor 118\nedusat 118\neffets 118\nefthimios 118\neidanger 118\nelango 118\nelectrico 118\nelegido 118\nelide 118\nellenor 118\neluard 118\nelymais 118\nemecé 118\nempel 118\nempringham 118\nendophyte 118\nengulfment 118\nenrc 118\nensign's 118\nenverga 118\nepigenome 118\nepigoni 118\neqn 118\nerdei 118\nerinaceinae 118\nerwood 118\nerzerum 118\nestopinal 118\neswaran 118\neten 118\nettal 118\neulália 118\neuropeanist 118\neustachius 118\nevangelisches 118\nevansville's 118\nexample's 118\nexperimenter's 118\nexternships 118\nezhumalai 118\nfaður 118\nfainaru 118\nfairbairn's 118\nfaiveley 118\nfamose 118\nfarseer 118\nfastcompany 118\nfellahin 118\nfendahl 118\nferrumequinum 118\nferugliotherium 118\nfesco 118\nfeser 118\nfette 118\nfidan 118\nfiedler's 118\nfinalselite 118\nfiregl 118\nflandy 118\nflautists 118\nfleurie 118\nfleury's 118\nflw 118\nfolgate 118\nforsythe's 118\nforteviot 118\nforus 118\nfoveal 118\nfróilaz 118\nfrancolini 118\nfrassati 118\nfreake 118\nfrechen 118\nfredegund 118\nfredly 118\nfreire's 118\nfriggin 118\nfroland 118\nfujimaru 118\nfulghum 118\nfulginiti 118\nfuturepop 118\nfuzzed 118\ngâteau 118\ngaard 118\ngahar 118\ngaliana 118\ngambut 118\ngandhar 118\ngandhism 118\ngapa 118\ngargždai 118\ngashed 118\ngasterosteus 118\ngateless 118\ngaudaur 118\ngauzy 118\ngavdos 118\ngazipaşa 118\ngedolot 118\ngeethapriya 118\ngentilozzi 118\nghim 118\nghuman 118\ngianetti 118\ngibbons's 118\ngiramondo 118\ngiraudet 118\ngladioli 118\ngladstein 118\nglaspaleis 118\ngleniffer 118\nglenkeen 118\nglinton 118\ngnostica 118\ngoði 118\ngolddiggers 118\ngomphidae 118\ngorguts 118\ngossow 118\ngotell 118\ngougeon 118\ngoulder 118\ngpac 118\ngraffigny 118\ngraffitti 118\ngraine 118\ngraticule 118\ngreenawalt 118\ngreenies 118\ngreenville's 118\ngriefs 118\ngriga 118\ngritos 118\ngrse 118\ngrumps 118\ngtg 118\ngué 118\ngulland 118\ngurbaksh 118\ngurgle 118\ngusenbauer 118\ngusii 118\nguthega 118\nhänsch 118\nhå 118\nhårleman 118\nhörner 118\nhötzendorf 118\nhøeg 118\nhadiqa 118\nhagalil 118\nhaiqiang 118\nhalfbreed 118\nhalfshaft 118\nhandbuilt 118\nhandman 118\nhangang 118\nhardinxveld 118\nharked 118\nharmensz 118\nharryman 118\nhartly 118\nhaslington 118\nhasnat 118\nheadcoats 118\nheartening 118\nheatherley 118\nheironeous 118\nheldon 118\nheliand 118\nhemal 118\nhenning's 118\nhierakonpolis 118\nhierba 118\nhindmargin 118\nhippomenes 118\nhirundapus 118\nhistoriated 118\nholan 118\nhollow's 118\nholyfield's 118\nhomecourt 118\nhomorod 118\nhoneywell's 118\nhongta 118\nhonza 118\nhornblower's 118\nhoshang 118\nhotdocs 118\nhouchen 118\nhoun 118\nhousman's 118\nhovels 118\nhovenden 118\nhovingham 118\nhowett 118\nhoyles 118\nhradčany 118\nhrvata 118\nhude 118\nhuke 118\nhulkling 118\nhully 118\nhun's 118\nhuncke 118\nhurok 118\nhurtgen 118\nhutin 118\nhydron 118\nhyeokgeose 118\nhymnen 118\niais 118\niang 118\nibsley 118\nidumea 118\niguatemi 118\nihei 118\nihlen 118\nilac 118\nilavarasi 118\nillertissen 118\nimbel 118\nimmunobiology 118\nindigena 118\ninshushinak 118\ninterleukins 118\nippen 118\nisidorus 118\nisrail 118\nitaipava 118\nitaljet 118\nitria 118\nival 118\nivth 118\niypt 118\njabbing 118\njacobsohn 118\njakovich 118\njalani 118\njaleo 118\njaner 118\njaric 118\njaromar 118\njarpa 118\njasius 118\njataí 118\njazeman 118\njazzier 118\njco 118\njdi 118\njeck 118\njeita 118\njellison 118\njempol 118\njenners 118\njephthah 118\njepsen's 118\njeumont 118\njiajia 118\njianjun 118\njiannan 118\njigging 118\njimeoin 118\njimtown 118\njingsheng 118\njinr 118\njissen 118\njmf 118\njoara 118\njournet 118\njuce 118\njudgepedia 118\njunkermann 118\njuxtaglomerular 118\nkadim 118\nkagayaki 118\nkahlen 118\nkaibigan 118\nkaiseki 118\nkakiiwa 118\nkaladan 118\nkaltag 118\nkalyazinsky 118\nkamiji 118\nkamuning 118\nkanye's 118\nkaons 118\nkapleau 118\nkaraisalı 118\nkarass 118\nkarloff's 118\nkarnaphuli 118\nkarpf 118\nkarponosov 118\nkasumigaura 118\nkaum 118\nkawalerowicz 118\nkazanowski 118\nkdal 118\nkeek 118\nkejache 118\nkengeri 118\nkenka 118\nkenner's 118\nkepu 118\nkerbside 118\nkerikil 118\nkerplunk 118\nkesterson 118\nketill 118\nkettlebell 118\nkettleby 118\nkfre 118\nkhaen 118\nkhalkhal 118\nkhariton 118\nkhuur 118\nkielburger 118\nkigeli 118\nkiichiro 118\nkilkivan 118\nkimitachi 118\nkimitsu 118\nkingshighway 118\nkiratpur 118\nkissan 118\nkiszko 118\nklais 118\nklinga 118\nklr 118\nkmi 118\nkmno 118\nknobler 118\nknolles 118\nkobylin 118\nkoci 118\nkodra 118\nkoenigsberg 118\nkoishiteru 118\nkojima's 118\nkokugakuin 118\nkolodziej 118\nkommuna 118\nkonec 118\nkonstantin's 118\nkornstad 118\nkorus 118\nkovak 118\nkowalewo 118\nkozelsk 118\nkozminski 118\nkramperová 118\nkrayzelburg 118\nkreises 118\nkriminalpolizei 118\nkroeker 118\nkrystina 118\nkspn 118\nkuhl's 118\nkungur 118\nkuragaki 118\nkuschel 118\nkute 118\nkvasha 118\nkvos 118\nkweskin 118\nkwiatkowska 118\nkypros 118\nkys 118\nläkerol 118\nlöbenicht 118\nlăng 118\nlabeobarbus 118\nlabyrinthodonts 118\nlagniappe 118\nlaguë 118\nlakṣmī 118\nlalamusa 118\nlambertsen 118\nlamonts 118\nlanderneau 118\nlangelinie 118\nlangmead 118\nlanos 118\nlanuza 118\nlaothoe 118\nlapolla 118\nlargen 118\nlarossi 118\nlarrey 118\nlaryngology 118\nlavabo 118\nlavatera 118\nlaveranues 118\nlazarou 118\nlbci 118\nlcme 118\nldp's 118\nleahy's 118\nleeching 118\nlegalese 118\nleili 118\nleopoldi 118\nleow 118\nlepisiota 118\nleshchenko 118\nleut 118\nleyba 118\nlezgin 118\nlfd 118\nliška 118\nliangzhu 118\nlianzhou 118\nlihula 118\nlimpele 118\nlindens 118\nlindenstrauss 118\nlineas 118\nlinnik 118\nliteraturnaya 118\nllosa's 118\nlobell 118\nlognormal 118\nlomonaco 118\nlonsdale's 118\nlotu 118\nloueke 118\nlovegood 118\nlowertown 118\nlownes 118\nlozells 118\nluban 118\nluby's 118\nlumbly 118\nlundigan 118\nlunghini 118\nlusia 118\nluten 118\nluttinen 118\nluyt 118\nlwe 118\nlycalopex 118\nlysychansk 118\nmüllenbach 118\nmaanen 118\nmaarrat 118\nmabb 118\nmaben 118\nmaccrimmon 118\nmacijauskas 118\nmacuser 118\nmagnanime 118\nmahendru 118\nmahto 118\nmaianthemum 118\nmakavejev 118\nmakhno's 118\nmakoun 118\nmalichus 118\nmamayev 118\nmanifesto's 118\nmansur's 118\nmaramba 118\nmaranan 118\nmardeev 118\nmaresfield 118\nmargalo 118\nmarid 118\nmarijn 118\nmarjoe 118\nmashan 118\nmatete 118\nmatronly 118\nmatsa 118\nmauban 118\nmauck 118\nmaurandya 118\nmauzy 118\nmayaca 118\nmayerik 118\nmayn 118\nmayya 118\nmccrory's 118\nmcmicking 118\nmcwane 118\nmehmaan 118\nmekkhala 118\nmentana 118\nmerkantil 118\nmerle's 118\nmeselson 118\nmesua 118\nmeteorcity 118\nmetrophanes 118\nmezzotints 118\nmiędzychód 118\nmichalina 118\nmickan 118\nmicrastur 118\nmilitated 118\nminev 118\nminnesinger 118\nminorum 118\nminstrel's 118\nmischke 118\nmittie 118\nmizanur 118\nmkrtich 118\nmockbuster 118\nmoderatto 118\nmoeser 118\nmofokeng 118\nmogan 118\nmolarity 118\nmolepolole 118\nmollica 118\nmonopol 118\nmonotheists 118\nmonsuno 118\nmoodies 118\nmoorfield 118\nmooy 118\nmorawetz 118\nmoray's 118\nmordenkainen 118\nmorleys 118\nmorosa 118\nmorphometry 118\nmorrígan 118\nmorselli 118\nmoste 118\nmostowski 118\nmotorshow 118\nmpac 118\nmrozek 118\nmrtv 118\nmufaddal 118\nmukomberanwa 118\nmultivitamins 118\nmumcu 118\nmungan 118\nmurderball 118\nmurni 118\nmutz 118\nnézet 118\nnöldeke 118\nnadaun 118\nnadwi 118\nnaeto 118\nnagakura 118\nnagpra 118\nnanda's 118\nnannilam 118\nnanubhai 118\nnanxi 118\nnappo 118\nnaraidi 118\nnarkhed 118\nnaryan 118\nnasipit 118\nnasko 118\nnclr 118\nnebu 118\nneemia 118\nnegin 118\nnehgs 118\nnemat 118\nnepia 118\nneritic 118\nnerium 118\nneroli 118\nnetherlanders 118\nneurites 118\nneururer 118\nneutrogena 118\nnfld 118\nngäbe 118\nnightstalker 118\nnikodim 118\nnind 118\nnjáls 118\nnodus 118\nnonliving 118\nnoorpur 118\nnorbornyl 118\nnordvik 118\nnorriton 118\nnotetaking 118\nnoton 118\nnsb's 118\nnsns 118\nnullsoft 118\nnummelin 118\noborona 118\nobote's 118\nocconeechee 118\nodeum 118\nodmark 118\noecon 118\noenopota 118\noksywie 118\nolavs 118\nolbracht 118\noldmeldrum 118\noleksandria 118\nollendorff 118\noltu 118\nolusola 118\nonjo 118\noogonia 118\noppegård 118\noppens 118\norbelian 118\noreck 118\norganisatie 118\norgaz 118\nortsgemeinden 118\nosch 118\noshikoto 118\nossowski 118\nosteocytes 118\nostfront 118\nostp 118\notherland 118\nouseph 118\nouzas 118\novalifolia 118\noverboost 118\noverby 118\noviposit 118\nowneybeg 118\npène 118\npéricard 118\npahan 118\npaifang 118\npaixhans 118\npalaniappan 118\npandiani 118\npanniculitis 118\npanoan 118\nparaganglioma 118\nparaschiv 118\nparavane 118\nparisiensis 118\nparkstadion 118\nparlby 118\nparragon 118\nparrikar 118\nparticolare 118\npasserini 118\npatent's 118\npeachpit 118\npeffer 118\npehlivan 118\npeligrosas 118\npengzhou 118\npensionable 118\npenteado 118\npeppermill 118\nperon's 118\npersema 118\npervomaisk 118\npeshwa's 118\npessanha 118\npessoa's 118\npetrovsk 118\npettingell 118\npewabic 118\nphascogale 118\nphelps's 118\nphthora 118\nphylloscartes 118\npiedimonte 118\npierrots 118\npinezhsky 118\npinhoe 118\npinholes 118\npinoteau 118\npioniere 118\npiotrovsky 118\npixote 118\nplonk 118\nplotless 118\nplusia 118\npolicja 118\npollença 118\npomel 118\npoondi 118\npopenoe 118\nportlets 118\nposie 118\npostum 118\npozzolana 118\npráxedes 118\nprasanth 118\nprashna 118\npratim 118\npraybeyt 118\nprayut 118\nprecipitator 118\npreconfigured 118\nprees 118\npremanand 118\nprobz 118\nprocyanidin 118\nprofana 118\nprofesionales 118\nprogressio 118\nprojectionists 118\npromontorium 118\npsi's 118\npsychonomic 118\npu's 118\npubnico 118\npup's 118\npupienus 118\npurwa 118\npzgr 118\nqī 118\nqad 118\nqalamoun 118\nquasicrystal 118\nqubi 118\nquisling's 118\nrötteln 118\nrıdvan 118\nradicalize 118\nraftaar 118\nrajabi 118\nrajasa 118\nrajkhowa 118\nrajpoot 118\nrallapalli 118\nramone's 118\nramsden's 118\nratboy 118\nravindranath 118\nrebana 118\nrebaptized 118\nreclassed 118\nredhills 118\nreinfection 118\nreitano 118\nremacle 118\nrenren 118\nrepopulating 118\nrestout 118\nreth 118\nrevengeful 118\nrhantus 118\nrhyton 118\nricord 118\nriggio 118\nroșca 118\nroade 118\nrobonaut 118\nroce 118\nroncal 118\nroorda 118\nroset 118\nrothstein's 118\nrucl 118\nrugosum 118\nruleville 118\nrumänien 118\nrumbold's 118\nruocco 118\nruweisat 118\nsādhanā 118\nsự 118\nsaadiq's 118\nsaiqa 118\nsalinan 118\nsalovey 118\nsamuha 118\nsandsmark 118\nsanno 118\nsanpin 118\nsantalaceae 118\nsaomai 118\nsaqqaq 118\nsase 118\nsatala 118\nsaturata 118\nsauger 118\nsawe 118\nscates 118\nschettino 118\nschierholtz 118\nschmiedeberg 118\nschmitten 118\nschollander 118\nschuko 118\nschwertern 118\nsclerocactus 118\nscozzafava 118\nscranton's 118\nscutellastra 118\nseaquarium 118\nseconding 118\nsedláček 118\nseelos 118\nsegundos 118\nseijas 118\nselvagem 118\nsennenhund 118\nsentients 118\nserenitatis 118\nserero 118\nserlin 118\nserment 118\nservaes 118\nsesser 118\nsestertius 118\nsestos 118\nseverall 118\nsexos 118\nseyffertitz 118\nshalit's 118\nshanmugham 118\nshanteau 118\nshaoyi 118\nsharib 118\nsheikhpura 118\nshekh 118\nshigemasa 118\nshiki's 118\nshimmers 118\nshirzad 118\nshorne 118\nshou's 118\nshouzhen 118\nshrimper 118\nshurmur 118\nsickels 118\nsigar 118\nsighişoara 118\nsikth 118\nsilayev 118\nsilverstein's 118\nsimos 118\nsinharaja 118\nsinno 118\nsirotkin 118\nsithi 118\nsiyun 118\nsjöstrand 118\nsketchfest 118\nsklyarov 118\nslipcased 118\nsmz 118\nsniderman 118\nsnowline 118\nsoacha 118\nsobczak 118\nsogyal 118\nsoirees 118\nsokoban 118\nsolør 118\nsolca 118\nsolovay 118\nsolovey 118\nsoneji 118\nsookmyung 118\nsoolamangalam 118\nsoryu 118\nsosnovy 118\nsourdis 118\nsouthchurch 118\nsouveraineté 118\nspanks 118\nspearritt 118\nsphingolipids 118\nspiderwebs 118\nspiderwort 118\nspiffy 118\nspilimbergo 118\nspiracular 118\nspitta 118\nspitznagel 118\nsplinting 118\nsportage 118\nsportal 118\nsportplatz 118\nsqd 118\nsqueeze's 118\nstévenin 118\nstaffroom 118\nstahlberg 118\nstainback 118\nstalker's 118\nstampers 118\nstard 118\nstarlink 118\nstarworld 118\nstebbings 118\nsteffes 118\nsterckx 118\nsteuermann 118\nstif 118\nstiller's 118\nstolin 118\nstollberg 118\nstolport 118\nstorax 118\nstratojets 118\nstrekalov 118\nstreptanthus 118\nstrumigenys 118\nsubin 118\nsubleased 118\nsublist 118\nsugarplum 118\nsulfonylureas 118\nsuperterran 118\nsuphi 118\nsurette 118\nsutilizonidae 118\nsvetly 118\nswahn 118\nsweikert 118\nswinton's 118\nsyamsul 118\nszarkowski 118\ntaenite 118\ntaijutsu 118\ntaizan 118\ntakahē 118\ntakanuva 118\ntamad 118\ntapié 118\ntarabin 118\ntarago 118\ntashian 118\ntashkurgan 118\ntaufel 118\ntaunsa 118\ntavaré 118\ntdcs 118\nteatri 118\ntebenna 118\ntecnè 118\nteffont 118\ntelevises 118\ntemporals 118\ntenar 118\ntenmu 118\nteodosio 118\nteshome 118\ntessé 118\ntetraethyllead 118\ntetri 118\nthakhek 118\nthangaraj 118\ntharavad 118\nthma 118\nthomsoni 118\nthonn 118\nthornback 118\nthottam 118\nthrombospondin 118\nthrret 118\nthulasidas 118\nthurnau 118\ntianxiang 118\ntidhar 118\ntigerlily 118\ntimsbury 118\ntirikatene 118\ntisdale's 118\ntitania's 118\ntizer 118\ntocci 118\ntoghan 118\ntoica 118\ntokamaks 118\ntokina 118\ntolsma 118\ntomine 118\ntonoloway 118\ntorkham 118\ntorrida 118\ntorrontés 118\ntorvosaurus 118\ntouqan 118\ntoutain 118\ntowage 118\ntowie 118\ntownace 118\ntoxaway 118\ntrainability 118\ntramp's 118\ntraversari 118\ntresidder 118\ntrethowan 118\ntrichrome 118\ntrigraphs 118\ntrinacria 118\ntrotternish 118\ntroude 118\ntrustmark 118\nttn 118\ntuffin 118\ntyphlodromalus 118\nuživo 118\nuaxactun 118\nubayy 118\nujjaini 118\nuko 118\nulik 118\nulker 118\nulsoor 118\nultimania 118\numjetnosti 118\nunceded 118\nundercount 118\nundervalue 118\nunderwrites 118\nunicolored 118\nunies 118\nuniversidades 118\nunmanly 118\nunprojected 118\nunruffled 118\nuntung 118\nununpentium 118\nurfey 118\nusaaf's 118\nutdc 118\nuzel 118\nvăleni 118\nvaginitis 118\nvalcke 118\nvaldano 118\nvaloret 118\nvanhoy 118\nvdd 118\nvedad 118\nventricose 118\nverbi 118\nvere's 118\nverschoyle 118\nvescovi 118\nveter 118\nviñes 118\nvicat 118\nvictrack 118\nvideotelephony 118\nviewsonic 118\nvilhjálmsson 118\nvimto 118\nvinculum 118\nviniculture 118\nvioli 118\nvirolainen 118\nviton 118\nvitrine 118\nvituperative 118\nvolodya 118\nvolsky 118\nvortexx 118\nvortigaunts 118\nvreta 118\nvrr 118\nwaapa 118\nwadl 118\nwaialae 118\nwaiata 118\nwalid's 118\nwallendorf 118\nwangfujing 118\nwaterweg 118\nwdve 118\nwechter 118\nwendorf 118\nwendron 118\nwenxi 118\nwenxian 118\nwenyuan 118\nwerenskiold 118\nwesterleigh 118\nwethers 118\nwharmby 118\nwhitespotted 118\nwickedest 118\nwierzbicki 118\nwight's 118\nwildboyz 118\nwindsheim 118\nwinfield's 118\nwinogrand 118\nwitschge 118\nwittelsbacher 118\nwivelsfield 118\nwluc 118\nwmaz 118\nwolfi 118\nwozencraft 118\nwptf 118\nwulfrun 118\nwustl 118\nwvha 118\nwxk 118\nwytwórnia 118\nxaviera 118\nxfp 118\nxiangdong 118\nxm_ 118\nxochicalco 118\nxolo 118\nxposé 118\nxrf 118\nyōji 118\nyado 118\nyakimenko 118\nyalvaç 118\nyavir 118\nyawls 118\nyayıncılık 118\nyemin 118\nyeonsan 118\nyil 118\nyizkor 118\nyolla 118\nyoungson 118\nyuanshan 118\nyukich 118\nyuru 118\nyutu 118\nzì 118\nzahav 118\nzakhm 118\nzaoyang 118\nzaporizhzhia 118\nzapovednik 118\nzarko 118\nzelikow 118\nzemo's 118\nzhenyu 118\nzipfel 118\nzisa 118\nzlatka 118\nzore 118\nzully 118\nzulm 118\nzuta 118\nécossaise 117\népernon 117\nözen 117\nāti 117\nčasopis 117\nşeyh 117\nšubašić 117\nαυτω 117\nκάτω 117\nан 117\nбольшой 117\nгоре 117\nгригорьевич 117\nмо 117\nсад 117\nсоветского 117\nіванович 117\nكتاب 117\nशक 117\nลำปาง 117\nṛta 117\n당연하지 117\naalge 117\nabanda 117\nabangan 117\nabdelatif 117\nabdulov 117\nabdulsalam 117\nabersychan 117\nabjection 117\nabsolutists 117\naccessioned 117\nacclaimedmusic 117\naccum 117\naceria 117\nacnielsen 117\nacorna 117\nactinote 117\nadaílton 117\nadastra 117\nadiutrix 117\nadjournal 117\naenigma 117\naeug 117\nafric 117\nafricae 117\nafrico 117\nafrixalus 117\nafrobasket 117\nagrifolia 117\nagueda 117\nahhotep 117\nairbridge 117\nairmont 117\naitana 117\najou 117\nalangudi 117\nalexandroni 117\nalikhan 117\nalmami 117\nalmereyda 117\naltheimer 117\naltron 117\nalundra 117\namaris 117\nambarawa 117\nambrym 117\namerikkka's 117\namfortas 117\naminoff 117\namintiri 117\nammannati 117\namritha 117\namsinck 117\nanalekta 117\nanao 117\nanatolie 117\nandrogyne 117\naniconism 117\nanma 117\nanodonta 117\nantiochos 117\nantioco 117\nantiparos 117\nantisera 117\nantitussive 117\nape's 117\napeirogonal 117\nappel's 117\nappellees 117\napprover 117\naquatints 117\narbonne 117\narchange 117\narchduke's 117\narctangent 117\nargentario 117\naridjis 117\narisbe 117\narmeni 117\narundhathi 117\narvey 117\nascalenia 117\nascani 117\nashingdon 117\nashm 117\nasilidae 117\nasoke 117\nastakhova 117\nastillero 117\nastrogeology 117\natayim 117\natcha 117\nathanaric 117\natlético's 117\naubeterre 117\naurica 117\nausfea 117\nauspiciousness 117\naussa 117\naverbuch 117\naxelos 117\naxmann 117\nayloffe 117\nazadirachta 117\naziru 117\nbaburin 117\nbadilla 117\nbaetic 117\nbagar 117\nbakayoko 117\nbakhita 117\nbakra 117\nbakul 117\nbalanda 117\nbalash 117\nbalice 117\nbaliwag 117\nballistically 117\nbangur 117\nbaozi 117\nbaqer 117\nbarbarea 117\nbarbering 117\nbasquette 117\nbatcheller 117\nbatus 117\nbayerlein 117\nbbmp 117\nbeatha 117\nbecta 117\nbedgebury 117\nbedivere 117\nbeechview 117\nbeens 117\nbeeskow 117\nbegada 117\nbegur 117\nbeik 117\nbelhar 117\nbellewstown 117\nbelying 117\nbendlets 117\nbergeijk 117\nbernes 117\nbester's 117\nbevs 117\nbhagwanpur 117\nbhv 117\nbibiane 117\nbibimbap 117\nbickenbach 117\nbigbury 117\nbigras 117\nbihar's 117\nbillotte 117\nbilthoven 117\nbiologique 117\nbirdville 117\nbisso 117\nbjerkander 117\nblackwill 117\nblaszak 117\nbleiben 117\nblkd 117\nbloudek 117\nbluebear 117\nbodanzky 117\nbodenstein 117\nboehmi 117\nbogost 117\nboksitogorsky 117\nbolthouse 117\nbomlitz 117\nborkou 117\nborwell 117\nbottari 117\nbouda 117\nbous 117\nbrătescu 117\nbracara 117\nbraceros 117\nbradd 117\nbradmore 117\nbrahmanic 117\nbramer 117\nbrasa 117\nbrassed 117\nbrazilregular 117\nbreezer 117\nbremer's 117\nbrooklawn 117\nbrunhoff 117\nbrusi 117\nbuide 117\nbulgogi 117\nburgoo 117\nburres 117\nbusemann 117\nbushidō 117\nbusinessweek's 117\nbyler 117\ncáech 117\ncẩn 117\ncửu 117\ncalan 117\ncalders 117\ncalvani 117\ncamdenton 117\ncammer 117\ncamporee 117\ncanadasoccer 117\ncansei 117\ncapitain 117\ncapodichino 117\ncapoeirista 117\ncarcharhinidae 117\ncarcillo 117\ncardiocondyla 117\ncareen 117\ncarrowkeel 117\ncartoonito 117\ncasselton 117\ncassens 117\ncassinii 117\ncbla 117\ncbre 117\ncbwt 117\ncelosia 117\ncephalanthera 117\ncerris 117\nchakka 117\nchampionnet 117\nchandini 117\nchandravanshi 117\nchapmani 117\ncharlots 117\ncharming's 117\nchartoff 117\ncheetah's 117\nchelliah 117\ncheloniidae 117\nchengdong 117\nchiatura 117\nchilko 117\nchiloquin 117\nchiltington 117\nchitalishte 117\nchloroceryle 117\nchlorurus 117\nchmiel 117\nchobin 117\nchodron 117\nchouraqui 117\nchristelijke 117\nchrom 117\nchronologique 117\nchto 117\nchumakov 117\nchupi 117\nchurro 117\ncibecue 117\nciclisme 117\ncisalpino 117\ncissp 117\nclambered 117\nclementine's 117\nclonaid 117\nclotilda 117\nclusia 117\ncnip 117\ncoelurus 117\ncoliforms 117\ncolobothea 117\ncomète 117\ncommotes 117\nconcini 117\nconcision 117\nconcorde's 117\nconfey 117\nconing 117\nconjuncta 117\nconrads 117\ncontursi 117\nconventa 117\nconventionalism 117\ncorkill 117\ncornbrook 117\ncorncrake 117\ncorrada 117\ncosm 117\ncotingas 117\ncovino 117\ncoxswains 117\ncría 117\ncriddle 117\ncrociati 117\ncrowell's 117\ncuốn 117\ncuajimalpa 117\ncupeño 117\ncuriouser 117\ncyark 117\ncybersocket 117\ncyncoed 117\ndéclaration 117\ndaang 117\ndabke 117\ndacoity 117\ndadda 117\ndafu 117\ndaihyakka 117\ndanesi 117\ndaniells 117\ndatalog 117\ndaughterboard 117\ndaughterisotope 117\ndaxue 117\ndaynara 117\ndeçan 117\ndeclercq 117\ndeemphasized 117\ndeets 117\ndelilah's 117\ndemidenko 117\ndemobilizing 117\ndemokratischen 117\ndenistone 117\ndentu 117\ndepreciating 117\nderouin 117\nderveni 117\ndesegregating 117\ndetectability 117\ndetroiters 117\ndevalaya 117\ndevnya 117\ndhamrai 117\ndharmaśāstra 117\ndhearg 117\ndibutyl 117\ndickau 117\ndidymos 117\ndieselpunk 117\ndietzen 117\ndigswell 117\ndihigo 117\ndimethylamine 117\ndimick 117\ndingus 117\ndingxi 117\ndiodon 117\ndiphthongized 117\ndipodium 117\ndiscomfiture 117\ndiscontentment 117\ndisplay's 117\ndmw 117\ndogaressa 117\ndogberry 117\ndogtanian 117\ndombal 117\ndominikovic 117\ndooher 117\ndorning 117\ndornod 117\ndossett 117\ndougherty's 117\ndoujin 117\ndrakos 117\ndramatising 117\ndrese 117\ndrewitz 117\ndrian 117\ndrikung 117\ndrniš 117\ndryobotodes 117\ndryptosaurus 117\ndukun 117\ndulness 117\ndungarees 117\ndurex 117\ndurivault 117\ndwek 117\ndystrophic 117\ndzalamidze 117\ndzehalevich 117\nebjchl 117\nebong 117\nebullition 117\negegik 117\negira 117\nehon 117\neikō 117\neldershaw 117\nelektrische 117\nelliotts 117\nemilíana 117\nencontrar 117\nendplate 117\nendrendrum 117\nengenheiros 117\nengerman 117\nenihl 117\nenik 117\nentscheidung 117\nenwezor 117\neppan 117\nermeni 117\nerrs 117\nescale 117\nesmer 117\nestriol 117\neug 117\neunjung 117\neura 117\neuroleague's 117\neusapia 117\nevraz 117\nevseev 117\nextraleague 117\nfactorisation 117\nfairrington 117\nfakhry 117\nfalekaupule 117\nfardre 117\nfariba 117\nfarnie 117\nfarragher 117\nfarstad 117\nfaylen 117\nfcra 117\nfelo 117\nferren 117\nfetteresso 117\nfictionist 117\nfilamentosa 117\nfilem 117\nfilipetti 117\nfimbristylis 117\nfirebreaks 117\nfitschen 117\nfiu's 117\nfjarðabyggð 117\nflasch 117\nflavidus 117\nflits 117\nflowerpots 117\nflugelhorns 117\nfluorescens 117\nfontanelli 117\nfootlocker 117\nforaminiferal 117\nformação 117\nforqué 117\nfortuin 117\nfosbery 117\nfröding 117\nfragariae 117\nfraiser 117\nfreecell 117\nfreno 117\nfreuler 117\nfreyja's 117\nfrezza 117\nfritzsch 117\nfukatsu 117\nfulgidus 117\nfurtiva 117\nfusuma 117\nfuzziness 117\ngécamines 117\ngökçeada 117\ngabol 117\ngalice 117\ngallager 117\ngangetica 117\ngashes 117\ngatchell 117\ngayla 117\ngdy 117\ngdzie 117\ngedik 117\ngeißler 117\ngelada 117\ngelete 117\ngennosuke 117\ngexa 117\ngiaimo 117\ngillin 117\ngintra 117\ngiorgadze 117\ngirih 117\ngjemnes 117\nglorfindel 117\nglsen 117\ngnus 117\ngoessel 117\ngomólski 117\ngommans 117\ngoodrich's 117\ngoonyella 117\ngopallawa 117\ngorleben 117\ngouldian 117\ngrallator 117\ngraveled 117\ngreasemonkey 117\ngreeff 117\ngreystock 117\ngrieb 117\ngrinnell's 117\ngrisebach 117\ngrug 117\nguangping 117\nguará 117\nguberniyas 117\nguerche 117\nguerrino 117\nguettel 117\nguilbault 117\nguman 117\ngungnir 117\ngusu 117\nguttering 117\ngwilliam 117\ngwyndaf 117\ngyaku 117\nhærens 117\nhǎo 117\nhaast's 117\nhablando 117\nhaces 117\nhaeften 117\nhaemoproteus 117\nhahnia 117\nhalba 117\nhallan 117\nhallenberg 117\nhallow's 117\nhamifratz 117\nhammet 117\nhaouz 117\nhardley 117\nhardtail 117\nhatty 117\nhauman 117\nhbar 117\nheddles 117\nhedyotis 117\nhegewisch 117\nheldburg 117\nhellerau 117\nhemchandra 117\nhemmingford 117\nhemorrhoid 117\nhengel 117\nheráldica 117\nherricks 117\nhesford 117\nhexachords 117\nheyman's 117\nhigbie 117\nhikōtai 117\nhilar 117\nhillegas 117\nhils 117\nhilson's 117\nhinnøya 117\nhisataka 117\nhistotroph 117\nhjördis 117\nhli 117\nhochhuth 117\nhockeyettan 117\nhoffert 117\nhollingdale 117\nholmenkolbanen 117\nhoogezand 117\nhoopeston 117\nhopei 117\nhorcruxes 117\nhorikita 117\nhotchkis 117\nhotwired 117\nhtpc 117\nhuacas 117\nhuffman's 117\nhuggel 117\nhuilong 117\nhumb 117\nhummel's 117\nhunding 117\nhuygen 117\nhver 117\nhygieia 117\nhysen 117\niasa 117\nifoam 117\nifoce 117\nignatiy 117\nigu 117\nijtema 117\nikatan 117\nimambargah 117\nimmoderate 117\nimpeachments 117\ninédit 117\nindianhead 117\nindological 117\ningelmunster 117\ningmann 117\ningunn 117\ninsekten 117\ninsolubility 117\ninspectorates 117\ninternasional 117\ninterpretación 117\nintikhab 117\niptl 117\nirasburg 117\nisaca 117\nisayama 117\nisere 117\nishiura 117\nisou 117\nisthmia 117\nivyland 117\njânio 117\njabel 117\njagdflieger 117\njagers 117\njeffersonians 117\njenine 117\njeth 117\njevđević 117\njhilmil 117\njitender 117\njiuzhou 117\njlr 117\njmj 117\njne 117\njonas's 117\njosephy 117\njovinus 117\njtl 117\njuvenal's 117\njuvenile's 117\nkünneth 117\nkępice 117\nkʼ 117\nkaitō 117\nkalderash 117\nkamakura's 117\nkamenetsky 117\nkandra 117\nkapus 117\nkarimova 117\nkarky 117\nkarpenisi 117\nkativik 117\nkatsukawa 117\nkatutura 117\nkefa 117\nkeikoku 117\nkeru 117\nkether 117\nketuanan 117\nkeyham 117\nkhaemwaset 117\nkharas 117\nkidulthood 117\nkingshill 117\nkirchliche 117\nkirya 117\nkiste 117\nkitano's 117\nkiti 117\nkiyonaga 117\nkjaer 117\nklassekampen 117\nklimas 117\nklon 117\nkluczynski 117\nknaths 117\nknuble 117\nkodocha 117\nkolbert 117\nkonate 117\nkongen 117\nkongo's 117\nkooti's 117\nkorbinian 117\nkorotyshkin 117\nkovačica 117\nkprp 117\nkrawiec 117\nkreipe 117\nkremser 117\nkristiine 117\nkrizan 117\nkrrc 117\nkuhle 117\nkumdo 117\nkwam 117\nkyoga 117\nlái 117\nlángara 117\nlány 117\nlépidoptères 117\nlöwenberg 117\nlabuyo 117\nlaeisz 117\nlahiya 117\nlambek 117\nlambiek's 117\nlanchkhuti 117\nlanre 117\nlantic 117\nlapébie 117\nlaudis 117\nlaughingstock 117\nlaurien 117\nlaverdure 117\nlaytown 117\nleônidas 117\nleija 117\nleinil 117\nleioa 117\nlemona 117\nlemonia 117\nleucistic 117\nlevitates 117\nlgoc 117\nliheap 117\nlilibeth 117\nlinfoot 117\nljubljani 117\nllc's 117\nlockenhaus 117\nlogistik 117\nlonegan 117\nlongrigg 117\nlorius 117\nlouderback 117\nlowfield 117\nlowri 117\nlucks 117\nlueck 117\nluek 117\nlumbago 117\nlumberjax 117\nluminaria 117\nlumpurjohor 117\nlundon 117\nlungless 117\nlutèce 117\nlutherische 117\nlutie 117\nlwt's 117\nlyran 117\nméité 117\nmabius 117\nmacatee 117\nmackeson 117\nmackle 117\nmadocks 117\nmadone 117\nmaeda's 117\nmaetel 117\nmagn 117\nmagniflex 117\nmagots 117\nmahrous 117\nmakung 117\nmaliha 117\nmaltodextrin 117\nmanassa 117\nmangalya 117\nmangope 117\nmanifiesto 117\nmanipuris 117\nmaphis 117\nmarcia's 117\nmaredsous 117\nmarehan 117\nmarier 117\nmarlan 117\nmaryboy 117\nmathpages 117\nmatulino 117\nmaubert 117\nmauritanians 117\nmayet 117\nmazzello 117\nmcarthur's 117\nmcerlane 117\nmcewen's 117\nmckinleyville 117\nmehen 117\nmehmud 117\nmehrangarh 117\nmeillet 117\nmellini 117\nmenapii 117\nmendeleev's 117\nmenichelli 117\nmerck's 117\nmerwan 117\nmetasoma 117\nmetatheory 117\nmicmacs 117\nmicrogroove 117\nmicromeria 117\nmidcentury 117\nmihails 117\nmikhaylovskoye 117\nmikrokosmos 117\nmilá 117\nmiriama 117\nmischling 117\nmitsuzawa 117\nmmtv 117\nmohon 117\nmokka 117\nmolders 117\nmonita 117\nmonstercat 117\nmontaukett 117\nmonterde 117\nmorín 117\nmoretta 117\nmountnorris 117\nmoxi 117\nmoyamba 117\nmsic 117\nmudfish 117\nmugica 117\nmulhare 117\nmultiregional 117\nmunguia 117\nmunno 117\nmutahhar 117\nmyōō 117\nmyristic 117\nnagalingam 117\nnaheen 117\nnannan 117\nnanomolar 117\nnapjainkig 117\nnarahashi 117\nnasza 117\nnatel 117\nnaters 117\nnavío 117\nncaas 117\nnecator 117\nneeye 117\nnegativa 117\nnegoiţescu 117\nneidan 117\nneigh 117\nnemacheilus 117\nnesi 117\nneuadd 117\nneutrophilic 117\nnezami 117\nnici 117\nnicotera 117\nniepołomice 117\nnightstand 117\nnigmatullin 117\nnikkanen 117\nnirankari 117\nnockamixon 117\nnohra 117\nnol's 117\nnonlinearities 117\nnorcliffe 117\nnorstar 117\nnorthglenn 117\nnorthing 117\nnovelda 117\nnugan 117\nnuncius 117\nnyctinomops 117\nnyuk 117\noakenshield 117\noarfish 117\noberkochen 117\nobovatus 117\noctopi 117\noginga 117\noib 117\nokitsu 117\nolfert 117\noltp 117\nonion's 117\noppressively 117\noptimizers 117\noragadam 117\noreodera 117\norok 117\nortego 117\norts 117\nosoba 117\noswalds 117\notterson 117\noverstimulation 117\noviraptorid 117\npašalić 117\npagu 117\npalevsky 117\npalike 117\npalliatus 117\npanca 117\npancalia 117\npanlilio 117\npantycelyn 117\npapally 117\nparaventricular 117\npargas 117\nparkman's 117\npartible 117\npascucci 117\npaska 117\npasricha 117\npassionless 117\npastoris 117\npates 117\npathare 117\npatinggi 117\npaulínia 117\npaulson's 117\npayre 117\npaysanne 117\npearlescent 117\npeart's 117\npechory 117\npedrotti 117\npeiser 117\npekoe 117\npellan 117\npellinore 117\npentile 117\nperlini 117\nperspecta 117\npeterel 117\npettyjohn 117\npevs 117\npfefferkorn 117\npheng 117\nphenylbutazone 117\nphilaret 117\nphonies 117\nphorcys 117\nphosphorite 117\nphysaria 117\npianista 117\npiaseczny 117\npickaxes 117\npigliucci 117\npiiri 117\npipe's 117\npiraz 117\npirojpur 117\npittis 117\nplötzkau 117\nplicatus 117\nplunders 117\npodebrady 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111\njosel 111\njpf 111\njpj 111\njsb 111\njubiläum 111\njujubes 111\njuliflora 111\njungwirth 111\nköpetdag 111\nköroğlu 111\nkırdar 111\nkıvanç 111\nkōkan 111\nkaer 111\nkahina 111\nkakali 111\nkalangala 111\nkalasan 111\nkamarinskaya 111\nkambe 111\nkameraden 111\nkannil 111\nkansakar 111\nkapugedera 111\nkarar 111\nkarein 111\nkaribib 111\nkarimpur 111\nkarinska 111\nkatisha 111\nkavvum 111\nkeib 111\nkelberg 111\nkemnay 111\nkerckhove 111\nkeros 111\nkesq 111\nkess 111\nkesselman 111\nkeyz 111\nkhaldoun 111\nkhanjian 111\nkhemu 111\nkhiang 111\nkhinchin 111\nkilala 111\nkilbrittain 111\nkilius 111\nkillraven 111\nkilojoules 111\nkilrossanty 111\nkimpanzu 111\nkinematograph 111\nkinnelon 111\nkireyevsky 111\nkirima 111\nkivalina 111\nklasik 111\nklb 111\nklephts 111\nklowns 111\nklymenko 111\nknibbs 111\nkobun 111\nkolaghat 111\nkollegium 111\nkollidam 111\nkomara 111\nkondrat 111\nkorowai 111\nkoryaks 111\nkosov 111\nkotik 111\nkourin 111\nkovvur 111\nkoyote 111\nkozhevnikov 111\nkrais 111\nkramskoi 111\nkraters 111\nkrek 111\nkreuzkirche 111\nkrotov 111\nktck 111\nkte 111\nkucch 111\nkuhne 111\nkulina 111\nkumudam 111\nkuns 111\nkusnezov 111\nkuusela 111\nkwwl 111\nkykuit 111\nlångholmen 111\nléglise 111\nlëtzebuerger 111\nlacrimation 111\nladee 111\nladnun 111\nlahden 111\nlakhia 111\nlakshmeshwar 111\nlammtarra 111\nlampron 111\nlamune 111\nlangkloof 111\nlangle 111\nlanglie 111\nlanherne 111\nlapinskas 111\nlarrinaga 111\nlat's 111\nlatecomers 111\nlateraled 111\nlathrup 111\nlaurenzo 111\nlawnswood 111\nlchs 111\nldm 111\nldo 111\nldpr 111\nleatherheads 111\nlechi 111\nleece 111\nleefe 111\nleibesübungen 111\nleichtathletik 111\nleina 111\nleksvik 111\nleontia 111\nlesnie 111\nlessona 111\nlevithan 111\nlewan 111\nlezignan 111\nlhn 111\nlibertate 111\nliebestod 111\nliezen 111\nlightstation 111\nlilyfield 111\nlimmatquai 111\nlinard 111\nlineaments 111\nlinos 111\nlinter 111\nlinuxtag 111\nliscannor 111\nlizarraga 111\nljungskog 111\nllyra 111\nlockseed 111\nlogania 111\nlogg 111\nloick 111\nloksatta 111\nlomana 111\nlonghena 111\nlopped 111\nlouwerens 111\nlowing 111\nlsds 111\nluani 111\nlucentio 111\nluckham 111\nluedecke 111\nluin 111\nluiten 111\nlulay 111\nlurches 111\nlurgi 111\nlushan's 111\nlutry 111\nluttinger 111\nluys 111\nlxd 111\nlygropia 111\nlyulin 111\nmén 111\nméricourt 111\nméridionale 111\nmaane 111\nmachín 111\nmachadoi 111\nmacris 111\nmadadhan 111\nmaiella 111\nmajah 111\nmajisuka 111\nmajma 111\nmajokko 111\nmalassezia 111\nmaleness 111\nmalja 111\nmallat 111\nmalmqvist 111\nmalonga 111\nmamay 111\nmamod 111\nmamut 111\nmangarap 111\nmaniero 111\nmanipulatives 111\nmankey 111\nmaozhen's 111\nmaputaland 111\nmarchiano 111\nmarcillac 111\nmaricris 111\nmarinoni 111\nmarkab 111\nmarkinson 111\nmarkups 111\nmartiño 111\nmasinde 111\nmasontown 111\nmassamba 111\nmassard 111\nmastocytosis 111\nmató 111\nmatatu 111\nmatošić 111\nmatsutomo 111\nmaxent 111\nmayhem's 111\nmaynards 111\nmazagon 111\nmccloud's 111\nmcdonaldization 111\nmcenroe's 111\nmcnew 111\nmcsa 111\nmdoc 111\nmeam 111\nmechain 111\nmedicum 111\nmeichtry 111\nmeiser 111\nmekton 111\nmelón 111\nmelecio 111\nmelite 111\nmeneage 111\nmenegazzia 111\nmeperidine 111\nmeritt 111\nmessage's 111\nmetabolisms 111\nmewis 111\nmicklem 111\nmicroanalysis 111\nmiddag 111\nmidianite 111\nmillercoors 111\nmillhauser 111\nminkkinen 111\nmischel 111\nmisreporting 111\nmittelholzer 111\nmizrahim 111\nmobis 111\nmohammadreza 111\nmohammedia 111\nmoidu 111\nmolester's 111\nmolli 111\nmollicutes 111\nmolsky 111\nmomence 111\nmomigliano 111\nmondale's 111\nmondesí 111\nmonied 111\nmonocled 111\nmonofluoride 111\nmonstruos 111\nmoominpappa 111\nmorbo 111\nmoreaux 111\nmoretti's 111\nmorphogen 111\nmorsbach 111\nmorshower 111\nmotherships 111\nmotoori 111\nmoviestar 111\nmubashir 111\nmukhtasar 111\nmukku 111\nmukthi 111\nmumbojumbo 111\nmumiy 111\nmuradov 111\nmutanabbi 111\nmuysken 111\nmyometrium 111\nmyponga 111\nnabieva 111\nnahhas 111\nnairana 111\nnakorn 111\nnammude 111\nnancie 111\nnandina 111\nnanhi 111\nnanoengineering 111\nnarathihapate 111\nnarvel 111\nnaujawan 111\nnaustdal 111\nnavajas 111\nnawabi 111\nnayagam 111\nnbatv 111\nncwa 111\nnednc 111\nnehi 111\nneofit 111\nnepheronia 111\nneukom 111\nnhã 111\nnhadau 111\nnhf 111\nniedersimmental 111\nnightshirt 111\nnightwolf 111\nnigromaculata 111\nniip 111\nnilokheri 111\nnilsen's 111\nnitroprusside 111\nnnsa 111\nnobumasa 111\nnoggle 111\nnohab 111\nnoiseworks 111\nnomade 111\nnondisjunction 111\nnonplanar 111\nnordbank 111\nnorderhov 111\nnordlys 111\nnormanville 111\nnovembers 111\nnrh 111\nnsai 111\nnsubuga 111\nnswfl 111\nntca 111\nnuernberg 111\nnulli 111\nnupponen 111\nnxnw 111\nnyaungyan 111\nożarów 111\nobernai 111\nocdsb 111\nodam 111\nodoric 111\nodoriko 111\nodyssey's 111\noee 111\nohb 111\nokno 111\nolba 111\nolema 111\noleria 111\nolstad 111\noltmans 111\nolympiades 111\nomnius 111\nonosma 111\noodles 111\noozed 111\noppelt 111\noptime 111\norizzonti 111\norlík 111\normen 111\northotic 111\nosmington 111\nostapchuk 111\notomis 111\notop 111\nottobeuren 111\noughteragh 111\noutstrips 111\nouzounian 111\noxazepam 111\npädagogische 111\npaepcke 111\npakeezah 111\npaleographer 111\npampilhosa 111\npantall 111\npapercuts 111\npapilionoidea 111\npappo 111\nparab 111\nparadisea 111\nparatriathlon 111\npardoner 111\nparijat 111\nparlar 111\npaskin 111\npastilles 111\npatita 111\npaup 111\npavic 111\npawi 111\npaydirt 111\npayscale 111\npechtold 111\npecknold 111\npellerano 111\npenateka 111\npenrhiwceiber 111\nperambulation 111\nperceval's 111\nperczel 111\nperlman's 111\nperran 111\npertevniyal 111\npervasively 111\npervaya 111\npevsner's 111\npflaum 111\npflege 111\nphật 111\npheomelanin 111\nphilipsen 111\nphomvihane 111\nphorbas 111\nphragmataecia 111\nphranc 111\nphylloporus 111\npibulsonggram 111\npierron 111\npinchgut 111\npinkman 111\npipefishes 111\npittville 111\npixel's 111\nplanifrons 111\nplantinga's 111\nplastilina 111\nplaypen 111\npolívka 111\npolenz 111\npollonera 111\npolycomb 111\npolyolefins 111\npolyus 111\nponchartrain 111\nponera 111\nponna 111\npoovellam 111\nporgera 111\npouille 111\nprado's 111\nprathama 111\nprecentral 111\npreconditioned 111\npriddle 111\nprimaticcio 111\nprims 111\nproblemy 111\nprogr 111\nprojek 111\nprovideniya 111\nprri 111\npruriens 111\nprzed 111\npterocliformes 111\npubns 111\npuchalski 111\npuerperium 111\npureza 111\npushpakumara 111\nputland 111\nputta 111\npyinmana 111\nqadiriyyah 111\nqaly 111\nqatargas 111\nquasiconformal 111\nquedo 111\nquotidiano 111\nréalisation 111\nrímac 111\nröhrig 111\nrabotnicki 111\nradičević 111\nradiochemical 111\nrahanweyn 111\nrajamundry 111\nrajauri 111\nrajnarayan 111\nralfe 111\nramsfjell 111\nrasmusen 111\nratepayer 111\nrateper 111\nratnamala 111\nratting 111\nraumfahrt 111\nrautaruukki 111\nraymart 111\nrcpi 111\nreabsorb 111\nreachin 111\nrearsby 111\nregistan 111\nregrade 111\nregulator's 111\nreigle 111\nreijseger 111\nreinjured 111\nreinsurer 111\nrelyea 111\nrenamings 111\nrenesmee 111\nreng 111\nrenunciate 111\nreorienting 111\nreproaching 111\nresidentially 111\nresponsorial 111\nretesting 111\nrexel 111\nrgk 111\nrhome 111\nricœur 111\nrightsholders 111\nrikuchū 111\nriney 111\nringgenberg 111\nrinkeby 111\nriptides 111\nrissoinidae 111\nrivers's 111\nrizki 111\nrobbr 111\nrobel 111\nrockview 111\nrollag 111\nromilda 111\nroofers 111\nrosenborg's 111\nroshei 111\nrothbard's 111\nroylance 111\nrubriceps 111\nrufius 111\nruritanian 111\nrurutu 111\nrusanov 111\nrushe 111\nrxr 111\nryofu 111\nryoko's 111\nsävehof 111\nsacrés 111\nsadayoshi 111\nsaindon 111\nsajin 111\nsalade 111\nsaliency 111\nsalvatorian 111\nsamain 111\nsambandam 111\nsamed 111\nsamperi 111\nsamtse 111\nsamundar 111\nsamura 111\nsandwicense 111\nsansho 111\nsardul 111\nsaruhan 111\nsasco 111\nsatterwhite 111\nsavov 111\nsceggs 111\nschérer 111\nschöller 111\nschjøtt 111\nschlözer 111\nschliep 111\nschouburg 111\nschurmann 111\nschynige 111\nscientiae 111\nscieszka 111\nscooba 111\nscowling 111\nscrs 111\nseawards 111\nsecl 111\nsegmentata 111\nselami 111\nselvon 111\nselye 111\nsemiothisa 111\nsenegala 111\nsennet 111\nsensitizer 111\nsentry's 111\nseskin 111\nsesshin 111\nshōta 111\nshahmukhi 111\nshaligram 111\nshann 111\nshaps 111\nsharavathi 111\nshavkat 111\nshazly 111\nsheepscot 111\nsheikhan 111\nshellard 111\nshellmound 111\nshenae 111\nshinzaki 111\nshoda 111\nshowtunes 111\nshuggazoom 111\nshyampur 111\nsiamun 111\nsiana 111\nsiapa 111\nsidhartha 111\nsierakowski 111\nsiganus 111\nsilbert 111\nsingar 111\nsirtuin 111\nsiyu 111\nskaut 111\nskautafélag 111\nskytop 111\nslager 111\nslann 111\nslavery's 111\nslavicsek 111\nslingbox 111\nslovic 111\nslurm 111\nsmolka 111\nsmolt 111\nsmurf's 111\nsneider 111\nsocky 111\nsocog 111\nsolar's 111\nsoletsky 111\nsonnenburg 111\nsorceries 111\nsosia 111\nsoun 111\nsoura 111\nsouthland's 111\nspanoudakis 111\nspartakas 111\nspase 111\nspeechwriting 111\nspelling's 111\nsphodromantis 111\nspigelia 111\nspil 111\nspineshank 111\nspooners 111\nsportsnation 111\nspotmatic 111\nsqueezer 111\nsrinagarindra 111\nstadtarchiv 111\nstarets 111\nstateira 111\nstateville 111\nstators 111\nstegeborg 111\nsteinitz's 111\nstenlake 111\nstib 111\nstickell 111\nstojanovski 111\nstolida 111\nstolperstein 111\nstoragetek 111\nstordalen 111\nstormchaser 111\nstorstad 111\nstuer 111\nstyx's 111\nsubalterns 111\nsubbotin 111\nsubheadings 111\nsubmittedto 111\nsubscapular 111\nsubte 111\nsuchinda 111\nsudah 111\nsugii 111\nsuhagan 111\nsuicidality 111\nsulphides 111\nsulphite 111\nsumeria 111\nsumo's 111\nsunila 111\nsunrunner 111\nsunshower 111\nsunya 111\nsuperband 111\nsuperbe 111\nsupernaw 111\nsupersemar 111\nsupersized 111\nsuprima 111\nsuwabe 111\nswai 111\nswammerdam 111\nswasawke 111\nsween 111\nsweetcorn 111\nswiftest 111\nswimrankings 111\nsynesius 111\nszewińska 111\nszklarska 111\ntélérama 111\ntórsvøllur 111\ntēma 111\ntaawon 111\ntabal 111\ntafaraoui 111\ntairov 111\ntalam 111\ntalavou 111\ntalmudists 111\ntamayura 111\ntamilian 111\ntanḥuma 111\ntangela 111\ntarasp 111\ntardis's 111\ntargit 111\ntargums 111\ntaruna 111\ntaschner 111\ntashard 111\ntatvan 111\ntaunton's 111\ntautomer 111\nteakwood 111\ntebing 111\nteeuw 111\ntehatta 111\nteile 111\ntell's 111\ntellurometer 111\nterreus 111\nthalbach 111\nthare 111\nthate 111\ntheage 111\nthemistoklis 111\ntheodelinda 111\nthinis 111\nthirukkural 111\nthouet 111\ntihipko 111\ntinatin 111\ntitenis 111\ntiven 111\ntjeldsund 111\ntlo 111\ntoblerone 111\ntocha 111\ntocqueville's 111\ntofik 111\ntohan 111\ntohno 111\ntokumaru 111\ntompion 111\ntorsades 111\ntrach 111\ntradesmen's 111\ntranberg 111\ntransmutations 111\ntrarza 111\ntraverser 111\ntrellech 111\ntriboelectric 111\ntricameral 111\ntridrepana 111\ntriggerman 111\ntriwizard 111\ntroilite 111\ntruncatella 111\ntruncates 111\ntrypsinogen 111\ntsonev 111\ntuchscherer 111\ntude 111\ntukey's 111\ntumarkin 111\ntuncel 111\nturnford 111\ntutton 111\ntuzun 111\ntwaron 111\ntwincam 111\ntwopence 111\ntyphonia 111\nuṣur 111\nubatuba 111\nudayabhanu 111\nudbina 111\nuefs 111\nugo's 111\nuhlich 111\nulin 111\nulivo 111\nultraseven 111\numri 111\numuc 111\nunderberg 111\nunscramble 111\nunseres 111\nunsichtbare 111\nunsynchronized 111\nupsr 111\nuribarri 111\nurteil 111\nusee 111\nusgos 111\nussc 111\nvällingby 111\nvaňková 111\nvadamarachchi 111\nvalandovo 111\nvalentinovich 111\nvallie 111\nvalmalenco 111\nvangas 111\nvarconi 111\nvashista 111\nvasiliou 111\nvawkavysk 111\nveillette 111\nvelleman 111\nverhoeven's 111\nverkhnetoyemsky 111\nverschiedenen 111\nverveer 111\nvibia 111\nvillanovense 111\nvinerian 111\nvirbhadra 111\nviroids 111\nvisagie 111\nvitsebsk 111\nvitthala 111\nvocalised 111\nvojna 111\nvolkoff's 111\nvowden 111\nvrb 111\nvrilissia 111\nvsh 111\nvučićević 111\nvvvf 111\nvytegra 111\nvyuha 111\nwünsdorf 111\nwackiest 111\nwaldnaab 111\nwalsch 111\nwaneta 111\nwankaner 111\nwankers 111\nwarui 111\nwasta 111\nwaszyński 111\nwatertender 111\nwbrz 111\nwdfn 111\nwect 111\nwellnhofer 111\nwendake 111\nwerre 111\nwesthampnett 111\nwhangaparaoa 111\nwhatculture 111\nwhimple 111\nwhinmoor 111\nwhitcoulls 111\nwhiteclay 111\nwhitecliff 111\nwickwar 111\nwidcombe 111\nwielkiego 111\nwifey 111\nwikisky 111\nwilhelmsson 111\nwilliamsburg's 111\nwimshurst 111\nwindproof 111\nwinterized 111\nwittem 111\nwnct 111\nwnox 111\nwobbled 111\nwobbler 111\nwodiczko 111\nwolfsheim 111\nwolrad 111\nwoodes 111\nwoodfill 111\nwoodwardi 111\nwooster's 111\nworldnet 111\nwpmf 111\nwrac 111\nwrangel's 111\nwreg 111\nwrnn 111\nwsai 111\nwuhlheide 111\nwurzach 111\nxgl 111\nxiāo 111\nxianghua 111\nxiaolei 111\nxie's 111\nxinan 111\nxti 111\nxuantong 111\nyüksek 111\nyūka 111\nyakusha 111\nyau's 111\nyauyos 111\nyaverland 111\nyehoram 111\nyolland 111\nyolu 111\nyorùbá 111\nyotvingians 111\nyoxall 111\nywain 111\nzaatar 111\nzachary's 111\nzahida 111\nzahniser 111\nzairol 111\nzamorins 111\nzaripov 111\nzebec 111\nzeier 111\nzeitschr 111\nzenkeri 111\nzhol 111\nzipang 111\nzohrab 111\nzophodia 111\nzorilla 111\nzsofia 111\nzura 111\nzurer 111\nzuwara 111\nzwi 111\nzwv 111\närmelband 110\néternel 110\nøksnes 110\nćuković 110\nđoković 110\nłaziska 110\nšt 110\nžupančič 110\nгодов 110\nличных 110\nнаселения 110\nолег 110\nострова 110\nجهان 110\nವರ 110\nങള 110\n河中 110\naard 110\naarushi's 110\naayudham 110\nabbans 110\nabbatis 110\nabdoh 110\nabos 110\nabramova 110\nabta 110\nabutbul 110\nacadèmia 110\nackery 110\nacxiom 110\naddanki 110\nadeleke 110\nadrano 110\naellen 110\naerogels 110\naeroshell 110\naethia 110\nafturelding 110\nagroindustrial 110\nahammed 110\nailette 110\naioli 110\najj 110\nakdağmadeni 110\nakkaya 110\nalaka 110\nalegres 110\nalemitu 110\nalfs 110\nalika 110\naljezur 110\naljofree 110\nalsterdorfer 110\namazonis 110\namsalem 110\namter 110\nanže 110\nanalysen 110\nanastasio's 110\nandara 110\nanderer 110\nandme 110\nandorians 110\nandrén 110\nanfinsen 110\nanggrek 110\nangula 110\nanison 110\nanisoptera 110\nannaliza 110\nantiparasitic 110\nanxiolytics 110\napolima 110\napplesox 110\nappunti 110\narbeidernes 110\narberry 110\narborvitae 110\narimura 110\nariyaratne 110\narohi 110\naronov 110\nartamidae 110\nartemesia 110\narthurton 110\nartturi 110\narzuiyeh 110\nasistio 110\nasselborn 110\natteridgeville 110\nauthon 110\nautograss 110\navetik 110\navialan 110\naydıncık 110\nazcárate 110\nazdi 110\nazerrail 110\nazizur 110\nazygos 110\nbäume 110\nbénézit 110\nbóltfelag 110\nbăsescu's 110\nbacnet 110\nbacsik 110\nbadakshan 110\nbaddiley 110\nbadoo 110\nbagirov 110\nbahakel 110\nbahcall 110\nbakalar 110\nbakhtawar 110\nbalbisiana 110\nbalby 110\nbalchin 110\nbalester 110\nballeny 110\nballoted 110\nballwin 110\nballycran 110\nbaneh 110\nbanovići 110\nbarnicle 110\nbarratry 110\nbarrowby 110\nbarzakh 110\nbasappa 110\nbatanaea 110\nbatholiths 110\nbaudin's 110\nbawag 110\nbayinnaung's 110\nbeïda 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109\nheirship 109\nhejinian 109\nhelike 109\nhelldorado 109\nhellige 109\nhellscream 109\nhennah 109\nhermant 109\nherzgruft 109\nheterotopic 109\nhettema 109\nheupel 109\nheusch 109\nheutigen 109\nhexed 109\nheylyn 109\nhikkaduwa 109\nhinojos 109\nhirmer 109\nhispaniolensis 109\nhjelmeland 109\nhochland 109\nhochschwarzwald 109\nhodogaya 109\nhogansville 109\nhoilett 109\nhommelvik 109\nhonea 109\nhonghu 109\nhonister 109\nhonour's 109\nhorla 109\nhorris 109\nhortobágy 109\nhotez 109\nhuipil 109\nhulkamania 109\nhumera 109\nhurme 109\nhuszár 109\nhvga 109\nhydrogues 109\nhydroxypropyl 109\nhyland's 109\nié 109\nic's 109\nichthyophis 109\nicrf 109\nideality 109\nidta 109\nidyl 109\nieso 109\nifosfamide 109\nihtiman 109\nillarion 109\nillegalism 109\nimāms 109\nimahara 109\nimcets 109\nimmaculatus 109\nimperatritsa 109\nimplosives 109\ninédito 109\ninching 109\ninconstancy 109\nindexicality 109\nindicts 109\ninhaca 109\ninikkum 109\ninlier 109\ninquilab 109\ninselbergs 109\nintellisense 109\ninteramericano 109\ninterveinal 109\nintha 109\nintoxicant 109\ninvitrogen 109\ninxile 109\nionov 109\nipho 109\niphone's 109\nipps 109\nironik 109\nisana 109\nisbert 109\nisdud 109\nispettore 109\nistiqlol 109\nistri 109\nitalyclay 109\niton 109\nivett 109\niwema 109\njabbar's 109\njambon 109\njamesport 109\njarius 109\njaxartes 109\njeczalik 109\njeito 109\njellicle 109\njerv 109\njetha 109\njeti 109\njgs 109\njianyang 109\njimmies 109\njingchu 109\njockie 109\njohnis 109\njordà 109\njovetić 109\njpe 109\njrlfc 109\njshaa 109\njuliaca 109\njuvie 109\njwm 109\nkōyō 109\nkaled 109\nkalif 109\nkamelion 109\nkamiak 109\nkamikita 109\nkampe 109\nkamut 109\nkanakas 109\nkangju 109\nkaniyapuram 109\nkapsch 109\nkardan 109\nkarson 109\nkarube 109\nkatse 109\nkauder 109\nkauz 109\nkawaiisu 109\nkawy 109\nkeaau 109\nkearley 109\nkeawe 109\nkellenberger 109\nkemetic 109\nkemper's 109\nkenpō 109\nkentuck 109\nkepis 109\nkeresley 109\nkervaire 109\nkessie 109\nkevn 109\nkhati 109\nkhgi 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109\noñati 109\noğuzeli 109\noberes 109\noberpräsident 109\nobin 109\nobus 109\nodier 109\nodjb 109\noehlen 109\noenologist 109\noertli 109\nogren 109\noguro 109\nohkay 109\nohmynews 109\noidores 109\nokada's 109\nokuka 109\noligodon 109\nolliffe 109\nomneya 109\nomnimon 109\nonboarding 109\noncologic 109\nonj 109\nonmyou 109\nonp 109\noperationalize 109\norcaella 109\norchesella 109\noreophila 109\norgazmo 109\noropa 109\nosovnikar 109\nospital 109\nosthelder 109\notumba 109\nouaa 109\nouiatenon 109\noverfield 109\noverfilled 109\noyarzún 109\npână 109\npò 109\npacifiers 109\npadfield 109\npafford 109\npakad 109\npaknam 109\npalki 109\npalmeras 109\npalmiry 109\npandera 109\npannonians 109\npaphia 109\nparaceratherium 109\nparietaria 109\nparkey 109\nparmeet 109\npartyless 109\npashkov 109\npasighat 109\npassager 109\npasscode 109\npassignano 109\npatola 109\npazcoguin 109\npeeche 109\npeeing 109\npellè 109\npellegrina 109\npeloncillo 109\npelous 109\npeluffo 109\npenhale 109\npenttilä 109\nperfekte 109\nperigona 109\nperiorbital 109\npetronila 109\npetropolitanus 109\npetrovci 109\nphasic 109\nphilosophisch 109\nphoeniceus 109\nphotobiont 109\npicker's 109\npigmentary 109\npiironen 109\npijush 109\npiki 109\npilg 109\npilosella 109\npingcheng 109\npinkins 109\npinklao 109\npinkster 109\npintel 109\npipetting 109\npiphat 109\nplagiobothrys 109\nplatyderus 109\nplaylogic 109\npliego 109\npluit 109\npnţ 109\npoglavnik 109\npokka 109\npolígono 109\npolariton 109\npolenov 109\npolicand 109\nponchielli's 109\nponty's 109\npopkomm 109\nppor 109\npracht 109\npraeclara 109\npramas 109\npravia 109\nprefazione 109\nprefectorial 109\npresentación 109\nprestadas 109\npribićević 109\nprittie 109\nprocumbent 109\nprofilin 109\npseudotelphusa 109\npsinet 109\npsycharis 109\npsychoanal 109\npsychologist's 109\npuelles 109\npurtill 109\npusong 109\nputtick 109\npwnage 109\nqlogic 109\nquants 109\nquanza 109\nquechee 109\nqueerness 109\nquimperlé 109\nrätikon 109\nrachunek 109\nraddall 109\nrafuse 109\nragusans 109\nraitt's 109\nrangeen 109\nrannap 109\nranya 109\nrareș 109\nrastra 109\nraswan 109\nratso 109\nravali 109\nrayado 109\nrazik 109\nrebreanu 109\nreddam 109\nreeltime 109\nremondis 109\nreveiz 109\nreverberi 109\nrevivo 109\nrevolucionarios 109\nrhétorique 109\nrhodesiensis 109\nrighthaven 109\nrimonabant 109\nrintelen 109\nrisako 109\nritika 109\nritzenhein 109\nroans 109\nrocha's 109\nroddy's 109\nroets 109\nrogerebert 109\nropeadope 109\nroseli 109\nroshal 109\nrosscarbery 109\nrotundifolium 109\nroughan 109\nroughwood 109\nroutable 109\nrubdown 109\nrudno 109\nrudston 109\nruhle 109\nrupinder 109\nrushbrook 109\nruyton 109\nryabov 109\nrynne 109\nryvangen 109\nsächs 109\nsós 109\nsable's 109\nsablin 109\nsaccolaimus 109\nsafai 109\nsaide 109\nsaiju 109\nsaim 109\nsalars 109\nsalmonson 109\nsalyers 109\nsandugo 109\nsangharakshita 109\nsaoura 109\nsaptapadi 109\nsarabha 109\nsaradananda 109\nsaropogon 109\nsattur 109\nsausmarez 109\nsauti 109\nsavides 109\nscagliotti 109\nscalper 109\nschat 109\nschepens 109\nschiltz 109\nschimmelbusch 109\nschmitter 109\nschommer 109\nschoolrooms 109\nschorn 109\nschwatka 109\nscitula 109\nscorekeeping 109\nscotter 109\nseanchan 109\nsecessions 109\nseculars 109\nsedevacantist 109\nseeress 109\nsehar 109\nseksyen 109\nselbitz 109\nselcuk 109\nselivanov 109\nsembcorp 109\nsemigallians 109\nsenans 109\nserf's 109\nserm 109\nserre's 109\nservicemember 109\nservomechanisms 109\nsesan 109\nsestet 109\nseyffert 109\nshaer 109\nshart 109\nshauq 109\nshibito 109\nshichuan 109\nshigekazu 109\nshillito 109\nshitsuji 109\nshkumbin 109\nshoobridge 109\nshotz 109\nsiata 109\nsibogae 109\nsiepe 109\nsigcomm 109\nsilappatikaram 109\nsilhavy 109\nsilvaplana 109\nsimonin 109\nsimpelveld 109\nsincgars 109\nsinfônica 109\nsinganallur 109\nsinsinawa 109\nsirmour 109\nsivright 109\nskatoony 109\nskibby 109\nskitters 109\nsklansky 109\nskyworks 109\nsluggishness 109\nsmythson 109\nsnet 109\nsnorkels 109\nsohlberg 109\nsomeşul 109\nsommer's 109\nsonner 109\nsonqor 109\nsorrowfully 109\nsoubry 109\nsoulsville 109\nsoundex 109\nsouza's 109\nspanisch 109\nspermatozoon 109\nsperrle 109\nsphacteria 109\nspolsky 109\nsporta 109\nspurgeon's 109\nsqualidus 109\nsrang 109\nsrdce 109\nsrpskog 109\nstahli 109\nstalham 109\nstallion's 109\nstandoffs 109\nstarpulse 109\nstassi 109\nstaudte 109\nstaver 109\nsteene 109\nstegosaurs 109\nsteina 109\nstelgidopteryx 109\nstephentown 109\nstevensons 109\nstockist 109\nstoneground 109\nstonesfield 109\nstoping 109\nstopp 109\nstps 109\nstro 109\nstryk 109\nstyli 109\nsubpixels 109\nsubstance's 109\nsubtasks 109\nsubtends 109\nsugarbeet 109\nsuhonen 109\nsujith 109\nsumerpur 109\nsundblad 109\nsunifred 109\nsunnan 109\nsuparna 109\nsuperconductive 109\nsupercop 109\nsurtout 109\nsuspenstories 109\nsux 109\nswayamvar 109\nswingate 109\nsymphonica 109\nsynchronises 109\nsysops 109\nsystembolaget 109\nsytner 109\nszeliga 109\ntúath 109\ntārā 109\ntāwhaki 109\ntůma 109\ntablero 109\ntachen 109\ntaegukgi 109\ntaffs 109\ntafuna 109\ntagge 109\ntaillamps 109\ntaivas 109\ntakam 109\ntakeji 109\ntaleh 109\ntalet 109\ntalle 109\ntarheel 109\ntatangelo 109\ntathāgata 109\ntavakoli 109\ntebaldo 109\ntechnovore 109\nteho 109\ntelecare 109\ntenille 109\ntennen 109\ntenorman 109\nterryglass 109\nterumoto 109\ntesa 109\ntetraplasandra 109\ntetsuko 109\ntfls 109\nthalía's 109\nthier 109\nthirdspace 109\nthoungthongkam 109\nthrillingly 109\nthulsa 109\nthuriya 109\nthurm 109\nthyroidectomy 109\ntibiotarsus 109\ntimberman 109\ntimberwork 109\ntimer's 109\ntimez 109\ntitanomachy 109\ntitina 109\ntmea 109\ntofield 109\ntoia 109\ntolkappiyam 109\ntolzien 109\ntondiarpet 109\ntoothill 109\ntoqgers 109\ntortonian 109\ntosha 109\ntosno 109\ntotto 109\ntounes 109\ntov's 109\ntraceless 109\ntranslocator 109\ntransparente 109\ntrefil 109\ntriterpene 109\ntroch 109\ntrochosa 109\ntrollinger 109\ntrounce 109\ntrumping 109\ntryonia 109\ntrysting 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109\nwandell 109\nwardii 109\nwarnapura 109\nwatada 109\nwaterparks 109\nwawer 109\nwazirs 109\nweihenstephan 109\nweisiger 109\nwelin 109\nwendla 109\nwerkstätte 109\nwestendorp 109\nwestrich 109\nwhaam 109\nwhall's 109\nwhitehouse's 109\nwiedman 109\nwilhelmy 109\nwillamette's 109\nwilmoth 109\nwinefride 109\nwining 109\nwinn's 109\nwinnecke 109\nwinnenden 109\nwinrow 109\nwiredu 109\nwirl 109\nwirman 109\nwissem 109\nwittliff 109\nwlaj 109\nwnic 109\nwod 109\nwoldenberg 109\nworters 109\nwpmi 109\nwuertz 109\nwunderle 109\nwusong 109\nxcr 109\nxhaka 109\nxiaogong 109\nxinping 109\nxlink 109\nyǐ 109\nyaaradi 109\nyacoob 109\nyakkha 109\nyall 109\nyanceyville 109\nyangping 109\nyavlinsky 109\nyew's 109\nyichud 109\nyorio 109\nyoungers 109\nyuran 109\nzajec 109\nzapatos 109\nzardoz 109\nzebrzydowska 109\nzechstein 109\nzeny 109\nzetsubou 109\nzhiwen 109\nziegesar 109\nziq 109\nzoabi 109\nzogg 109\nzooland 109\nzorah 109\nzoren 109\nzrh 109\nälteren 108\nælle 108\næneas 108\néchos 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108\nantartica 108\nantillia 108\nappspy 108\narborg 108\narchigram 108\nardeatine 108\nardiaei 108\narenga 108\nargenti 108\nariela 108\narimori 108\narmantrout 108\narmbreaker 108\narnol 108\narolla 108\narque 108\narrowsic 108\narshi 108\nartid 108\narturo's 108\naryeetey 108\naset 108\nasexuals 108\naspet 108\nassara 108\nassignats 108\nasystole 108\natakora 108\natiśa 108\natz 108\naudaz 108\nausländer 108\nauténtico 108\nauvs 108\navadhuta 108\naventicum 108\navoda 108\nawilda 108\nayerst 108\nbaaghi 108\nbabers 108\nbaccus 108\nbackley 108\nbadgett 108\nbadsworth 108\nbafang 108\nbairi 108\nbalestre 108\nballards 108\nballyadams 108\nballycotton 108\nbarèges 108\nbarayev 108\nbarbare 108\nbarcelonès 108\nbarle 108\nbarnehurst 108\nbarrus 108\nbasharat 108\nbaskota 108\nbastoni 108\nbatibot 108\nbatonishvili 108\nbatstone 108\nbeatallica 108\nbeethovens 108\nbehnisch 108\nbeide 108\nbektashis 108\nbelko 108\nbenefactive 108\nbergstrasser 108\nbernacchini 108\nbernson 108\nbeseler 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108\nforand 108\nfordney 108\nforegrounding 108\nforelli 108\nforepart 108\nfoshee 108\nfpk 108\nfrancklin 108\nfranziskus 108\nfreienbach 108\nfrezenberg 108\nfroide 108\nfujitsu's 108\nfusionist 108\nfutre 108\nfvm 108\ngünsberg 108\ngürel 108\ngabras 108\ngaleri 108\ngalerias 108\ngalet 108\ngalp 108\ngalvano 108\ngaly 108\ngarab 108\ngarafraxa 108\ngarbs 108\ngarbuzov 108\ngardoqui 108\ngarlot 108\ngatorzone 108\ngautamiputra 108\ngazel 108\ngekitō 108\ngelwicks 108\ngemmula 108\ngenicular 108\ngeográfica 108\ngerasa 108\ngetachew 108\ngevorkian 108\nghanadistricts 108\nghanbari 108\ngholami 108\nghostland 108\ngianbattista 108\ngikuyu 108\ngildersome 108\ngiler 108\ngirk 108\ngittern 108\nglanders 108\nglaucias 108\nglena 108\ngmes 108\ngoûter 108\ngoeslaw 108\ngogs 108\ngolab 108\ngolcar 108\ngonder 108\ngoodmayes 108\ngopy 108\ngorelik 108\ngorintō 108\ngorran 108\ngoseong 108\ngoudey 108\ngourry 108\ngozitan 108\ngrévy's 108\ngrønn 108\ngrael 108\ngrassalkovich 108\ngreason 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107\nmacgowran 107\nmacrons 107\nmaculipennis 107\nmadian 107\nmadresfield 107\nmaenan 107\nmagd 107\nmahasarakham 107\nmahmudi 107\nmahoro 107\nmajchrzak 107\nmakhmudov 107\nmakiki 107\nmalaba 107\nmalachowski 107\nmalayalees 107\nmalbank 107\nmalediction 107\nmalefactors 107\nmalheurs 107\nmallig 107\nmalvani 107\nmampali 107\nmancham 107\nmangrai 107\nmaniola 107\nmanoliu 107\nmanorialism 107\nmantel's 107\nmantid 107\nmaratea 107\nmarcello's 107\nmardini 107\nmariages 107\nmaritime's 107\nmariyadhai 107\nmarkneukirchen 107\nmarkussen 107\nmarlon's 107\nmarosvásárhely 107\nmarshon 107\nmartinetti 107\nmarudhu 107\nmarunde 107\nmashiko 107\nmasinga 107\nmasskara 107\nmasturbated 107\nmathesius 107\nmatoika 107\nmatsunaka 107\nmattannur 107\nmatten 107\nmauj 107\nmaurstad 107\nmawbray 107\nmaximalism 107\nmayanja 107\nmaydan 107\nmayrhofen 107\nmazagaon 107\nmazinho 107\nmbulu 107\nmccourty 107\nmcfly's 107\nmcmaster's 107\nmcnuggets 107\nmcvitie 107\nmdpv 107\nmeatmen 107\nmeiggs 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104\nbahadurpur 104\nbahattin 104\nbakov 104\nbalal 104\nbalb 104\nbaldon 104\nbaleztena 104\nballiol's 104\nballymacward 104\nbalticon 104\nbamboccianti 104\nbancker 104\nbanepa 104\nbankboston 104\nbanovinas 104\nbaraba 104\nbarbil 104\nbarcza 104\nbarese 104\nbargrave 104\nbarinholtz 104\nbarq 104\nbarrigada 104\nbartosiewicz 104\nbashkirtseff 104\nbassford 104\nbatarangs 104\nbatinah 104\nbaudhayana 104\nbaymax 104\nbayocean 104\nbeattyville 104\nbeaubrun 104\nbeauville 104\nbeej 104\nbeelen 104\nbelknap's 104\nbellbrook 104\nbeloye 104\nbenhamou 104\nbenon 104\nberben 104\nberehaven 104\nbersted 104\nberthold's 104\nbery 104\nbessi 104\nbeyk 104\nbhasmasura 104\nbhasya 104\nbišćan 104\nbianconeri 104\nbiconnected 104\nbifolia 104\nbijon 104\nbikutsi 104\nbilharzia 104\nbinalonan 104\nbinalong 104\nbiología 104\nbioplastic 104\nbiskupiec 104\nblackmere 104\nblacknose 104\nblancanieves 104\nblende 104\nblinked 104\nbloaters 104\nblomgren 104\nbmi's 104\nbobick 104\nbobruysk 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104\ncarpoolers 104\ncarrock 104\ncarryout 104\ncarthon 104\ncartola 104\ncascadian 104\ncassinese 104\ncassocks 104\ncastellanus 104\ncastellaro 104\ncatic 104\ncaughley 104\nccrma 104\nceibs 104\ncelsa 104\ncemile 104\ncephalorhynchus 104\ncesar's 104\ncetirizine 104\nchaeng 104\nchafetz 104\nchakhnashvili 104\nchalis 104\nchandrahasa 104\nchandram 104\ncharandas 104\ncharlery 104\ncharner 104\ncharuhasan 104\nchatrian 104\nchatrichalerm 104\nchee's 104\nchelsom 104\ncheminée 104\nchemosynthesis 104\nchervenkov 104\nchevrontexaco 104\nchevys 104\nchicanas 104\nchihi 104\nchipan 104\nchiroderma 104\nchomhairle 104\nchongzhi 104\nchromo 104\nchunari 104\ncielab 104\ncinfães 104\ncipher's 104\ncitp 104\nclarinbridge 104\nclaughaun 104\nclementines 104\nclericus 104\nclevelandii 104\ncloître 104\nclonskeagh 104\ncnsc 104\ncofferer 104\ncogan's 104\ncognitivist 104\ncoiscéim 104\ncoixet 104\ncolorno 104\ncolumbiahalle 104\ncompactified 104\ncomplementizer 104\ncondylar 104\nconfortola 104\nconocí 104\nconsistencies 104\ncontemp 104\ncontig 104\ncontrapositive 104\nconvergens 104\ncoone 104\ncoprinellus 104\ncorpi 104\ncorsano 104\ncoucou 104\ncounce 104\ncourthouse's 104\ncoxheath 104\ncpio 104\ncprf 104\ncrabeth 104\ncromac 104\ncrombeen 104\ncrouwel 104\ncrura 104\ncuckolds 104\ncuica 104\ncupreous 104\ncurbeam 104\ncutchogue 104\ncytokinin 104\ndâm 104\ndéluge 104\ndady 104\ndafen 104\ndaigunder 104\ndaneman 104\ndanielpour 104\ndaniels's 104\ndannelly 104\ndaoshu 104\ndariel 104\ndarlins 104\ndaung 104\ndaviot 104\ndavitamon 104\ndavitts 104\ndavol 104\ndawesville 104\ndazhai 104\ndbkl 104\ndcrj 104\ndeceits 104\ndeele 104\ndeemer 104\ndeil 104\ndekinai 104\ndelnaaz 104\ndelvoye 104\ndemokratikong 104\ndenitrificans 104\ndepalpur 104\ndesensitize 104\ndeslys 104\ndesmosomes 104\ndestinee 104\ndetente 104\ndettwiler 104\ndeverbal 104\ndewei 104\ndhai 104\ndhananjaya 104\ndhulikhel 104\ndhumavati 104\ndiên 104\ndiagonale 104\ndiarrhoeal 104\ndickason 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104\nentasis 104\nentoprocta 104\neojchl 104\nepigenesis 104\nequinus 104\neresia 104\neriogaster 104\nernman 104\nescravos 104\nesport 104\nestaban 104\nestarreja 104\nethertype 104\nethoxy 104\nevansburg 104\newerthon 104\nexies 104\nexogenously 104\nexor 104\nexperience's 104\neythorne 104\nfábula 104\nféval 104\nfındıklı 104\nfalacci 104\nfalconara 104\nfalkirk's 104\nfango 104\nfantazia 104\nfantomas 104\nfantuzzi 104\nfauj 104\nfayzabad 104\nfechin 104\nfederatie 104\nfeech 104\nfeeling's 104\nfelippe 104\nfelton's 104\nferb's 104\nferrissia 104\nfibbers 104\nfibred 104\nfilium 104\nfinancial's 104\nfiske's 104\nflachau 104\nflaine 104\nflateyjarbók 104\nflatpicking 104\nflesherton 104\nfliegender 104\nflorabase 104\nfogarty's 104\nfolkemuseum 104\nfondriest 104\nfootsoldier 104\nforadejogo 104\nfouchet 104\nfraboni 104\nfrancione 104\nfraterculus 104\nfrazhin 104\nfredrikshamn 104\nfreefly 104\nfreshening 104\nfrommelt 104\nfrud 104\nfuchsii 104\nfukasawa 104\nfukuro 104\nfulano 104\nfumin 104\nfunaro 104\nfunj 104\nfunmi 104\nfunshine 104\nfuzhi 104\ngéorgie 104\ngöthe 104\ngaboriau 104\ngadde 104\ngagel 104\ngager 104\ngainers 104\ngajuwaka 104\ngalipeau 104\ngamehouse 104\ngangarampur 104\ngangshan 104\nganske 104\ngante 104\ngardezi 104\ngashimov 104\ngatsu 104\ngattopardo 104\ngavins 104\ngeg 104\ngehrmann 104\ngemme 104\ngenchev 104\ngenrō 104\ngenshin 104\ngentille 104\ngeomembrane 104\ngeorgetti 104\ngeorgic 104\ngeorgij 104\ngephart 104\ngeuze 104\nghataprabha 104\ngholi 104\ngibonni 104\ngipping 104\ngiu 104\ngivhan 104\ngizenga 104\ngladstones 104\nglaser's 104\ngloyd 104\nglutaraldehyde 104\ngluzman 104\ngoeyvaerts 104\ngoitia 104\ngoloboff 104\ngonson 104\ngosavi 104\ngosub 104\ngoswin 104\ngouken 104\ngovindarajulu 104\ngoyon 104\ngrütter 104\ngrabowiec 104\ngradwell 104\ngrajeda 104\ngraveland 104\ngreenbrae 104\ngreenwald's 104\ngreifswalder 104\ngresson 104\ngrifone 104\ngriles 104\ngruhn 104\ngruwell 104\nguan's 104\nguanling 104\ngudger 104\nguemes 104\ngugga 104\ngunshu 104\ngustavsburg 104\nguyler 104\nhämsterviel 104\nhakes 104\nhalleck's 104\nhallertau 104\nhallidie 104\nhamdan's 104\nhammerich 104\nhandwerk 104\nhaney's 104\nhapoalim 104\nhaptoglobin 104\nhardham 104\nharin 104\nharlowton 104\nhartcher 104\nhashalom 104\nhasinai 104\nhasköy 104\nhassanzadeh 104\nhatology 104\nhatting 104\nhaulover 104\nhaunessy 104\nhawwa 104\nhayler 104\nheidolf 104\nheidorn 104\nheilbroner 104\nhelsing's 104\nhemsky 104\nhendthi 104\nhepatorenal 104\nheqin 104\nherelle 104\nheringi 104\nherriman's 104\nheshen 104\nheuberg 104\nheyfield 104\nhfk 104\nhietaniemi 104\nhighsmith's 104\nhildebrand's 104\nhinde's 104\nhindpool 104\nhispidula 104\nhitter's 104\nhlučín 104\nhoću 104\nhobbiton 104\nhoch's 104\nhoile 104\nholderman 104\nholne 104\nhongrois 104\nhoracek 104\nhorenbout 104\nhormiguero 104\nhornes 104\nhsph 104\nhtn 104\nhuairou 104\nhueyapan 104\nhuller 104\nhunhyun 104\nhuxleyi 104\nhwasong 104\nhylda 104\nhypocorism 104\niacr 104\nichimon 104\nicmec 104\nictalurus 104\nidelson 104\nideopsis 104\niffco 104\nikeda's 104\nikenai 104\nikkaku 104\nilike 104\nillah 104\nimporte 104\nimpugn 104\nimputations 104\ninami 104\nincendio 104\nincisional 104\nincondicional 104\nindeclinable 104\nindranil 104\nindyk 104\ninebolu 104\ninktomi 104\ninnoventions 104\ninsultingly 104\nintézete 104\ninterdistrict 104\nintermarket 104\nintrodução 104\niparhi 104\niradier 104\nishmaelites 104\nismaeel 104\nisss 104\niuu 104\nivoirian 104\nixchel 104\niyasus 104\njā 104\njackley 104\njacky's 104\njaegeri 104\njafet 104\njahoda 104\njahorina 104\njaipuria 104\njakup 104\njanequin 104\njankovich 104\njannes 104\njarmon 104\njarolím 104\njasień 104\njazzin 104\njeez 104\njennett 104\njermy 104\njfe 104\njhonen 104\njibbs 104\njiron 104\njixing 104\njuell 104\njumbos 104\njuva 104\nkügelgen 104\nkłodawa 104\nkạn 104\nkadašman 104\nkahalgaon 104\nkaijū 104\nkajri 104\nkakdwip 104\nkalaba 104\nkalaf 104\nkammath 104\nkanakadasa 104\nkandeh 104\nkangchu 104\nkangkar 104\nkannappan 104\nkarasahr 104\nkarasi 104\nkarben 104\nkardemir 104\nkarimloo 104\nkarke 104\nkaruppan 104\nkayibanda 104\nkeanan 104\nkeens 104\nkefu 104\nkehar 104\nkelucharan 104\nkenzaburo 104\nkhanan 104\nkhazan 104\nkhla 104\nkhmuic 104\nkhmum 104\nkhomeyni 104\nkhung 104\nkiao 104\nkiberlain 104\nkief 104\nkillens 104\nkimmirut 104\nkippel 104\nkirillova 104\nkirkness 104\nkirkstead 104\nkirtans 104\nklassic 104\nkmia 104\nknizia 104\nknoweth 104\nknuckled 104\nkoiwai 104\nkoktebel 104\nkomatsubara 104\nkomendant 104\nkomenský 104\nkomoriuta 104\nkongregate 104\nkonono 104\nkontact 104\nkopu 104\nkorhogo 104\nkosin 104\nkotido 104\nkrónur 104\nkrahl 104\nkratie 104\nkreiss 104\nkremerata 104\nkripik 104\nkristofferson's 104\nkrukowski 104\nkstovo 104\nkuldar 104\nkuloy 104\nkurara 104\nkuroń 104\nkweilin 104\nkyonyū 104\nkyuquot 104\nlöjtnant 104\nlabialis 104\nlachowicz 104\nlafões 104\nlakofka 104\nlakonia 104\nlamarckii 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104\nministerstwo 104\nminjok 104\nminsterley 104\nmirabell 104\nmiragaia 104\nmisantla 104\nmitcheldean 104\nmitomycin 104\nmitscher's 104\nmittra 104\nmixins 104\nmiyakonojō 104\nmmajunkie 104\nmmtb 104\nmoceanu 104\nmodularis 104\nmoellendorff 104\nmogh 104\nmohannad 104\nmolek 104\nmonetdb 104\nmoneyline 104\nmontalba 104\nmoonwatch 104\nmorda 104\nmorritt 104\nmosborough 104\nmoshiri 104\nmotley's 104\nmoto's 104\nmoulali 104\nmouvant 104\nmróz 104\nmrx 104\nmubarakmand 104\nmuehleisen 104\nmukims 104\nmuleriders 104\nmultifaith 104\nmureed 104\nmurids 104\nmuriwai 104\nmurrabit 104\nmuscle's 104\nmushaira 104\nmusquito 104\nmuztar 104\nmuzyki 104\nmyagdi 104\nmyers's 104\nmyfile 104\nmykel 104\nmykland 104\nnacd 104\nnaeba 104\nnakaoka 104\nnanakuli 104\nnanling 104\nnantel 104\nnarragansetts 104\nnarramore 104\nnarvekar 104\nnasimi 104\nnathália 104\nnavaira 104\nncell 104\nndg 104\nndungane 104\nnedspr 104\nnegocio 104\nnegrea 104\nnehantic 104\nneigher 104\nnemsa 104\nneog 104\nneox 104\nnetherne 104\nnetru 104\nneume 104\nnevski 104\nnezval 104\nnice's 104\nniederreiter 104\nnieuwkoop 104\nniggurath 104\nnigrogularis 104\nnimmervoll 104\nnkongsamba 104\nnkwe 104\nnomophila 104\nnorbert's 104\nnotonomus 104\nnoventa 104\nnoyyal 104\nnpws 104\nnshl 104\nnunspeet 104\nnunweiller 104\nnupta 104\nnurkse 104\nnute 104\nnuw 104\noatts 104\noberdan 104\nobziler 104\noderberg 104\nodhar 104\nofficielle 104\nofta 104\nogilvy's 104\nohev 104\nohren 104\noishii 104\nokkas 104\nolat 104\noldenlandia 104\noliphant's 104\nollin 104\nompa 104\nonclick 104\noneilland 104\nopstal 104\noqt 104\norans 104\noratoire 104\norduna 104\nornatum 104\noropos 104\nortaleza 104\nosterkamp 104\nostrovica 104\noswell 104\notselic 104\nottobrunn 104\noutfront 104\nouttrim 104\noverdo 104\noverwing 104\novolo 104\nowingeh 104\nozouf 104\npacôme 104\npacca 104\npachín 104\npachaiyappa's 104\npacketized 104\npadhi 104\npaenibacillus 104\npageland 104\npahner 104\npaleobotanical 104\npalley 104\npalmito 104\npalombo 104\npamphleteering 104\npanagis 104\npanayia 104\npanella 104\npangya 104\npanzerschreck 104\npapyrologie 104\nparacatu 104\nparameshwaran 104\nparangal 104\npararge 104\nparasteatoda 104\npareronia 104\nparnassia 104\nparolin 104\nparvocellular 104\npassaconaway 104\npatrilineally 104\npeccei 104\npekao 104\npendency 104\npentney 104\npentus 104\npepck 104\npeplow 104\nperceptrons 104\nperipteral 104\nperiyar's 104\nperspicua 104\nperumthachan 104\npeshtera 104\npestilential 104\npestova 104\npetia 104\npetrokimia 104\npetrozzi 104\nphụ 104\nphilippide 104\nphilotheca 104\nphleng 104\nphnum 104\nphysarum 104\nphysoderma 104\npiang 104\npianta 104\npielstick 104\npieres 104\npierotti 104\npilanesberg 104\npilesgrove 104\npilocarpine 104\npiraí 104\npithey 104\nplankinton 104\nplavšić 104\nplayzone 104\nplights 104\npocl 104\npodujevë 104\npogorilaya 104\npolfer 104\npolysulfide 104\npomacanthus 104\nponticum 104\npontigny 104\npontllanfraith 104\npoola 104\npopiełuszko 104\npopovski 104\npoppycock 104\npostcrania 104\npotentiates 104\npovoação 104\nppmv 104\nprószynski 104\nprecrime 104\npretax 104\npretender's 104\npreziosa 104\npridefest 104\nprisk 104\nproblepsis 104\nproducible 104\nprofili 104\npropellors 104\npryeri 104\npryluky 104\npsykers 104\npteropodinae 104\npudú 104\npuketapu 104\npulverize 104\npunam 104\npupu 104\npvsk 104\npwg's 104\npwrs 104\npye's 104\npyli 104\nqaen 104\nqiblah 104\nquandaries 104\nquantize 104\nquilter's 104\nquor 104\nqviding 104\nraao 104\nrabasa 104\nrabbo 104\nradiolarians 104\nragdale 104\nraizada 104\nramírez's 104\nramsingh 104\nranaudo 104\nrandalli 104\nranginui 104\nrany 104\nraoni 104\nrascel 104\nrashbam 104\nrassimov 104\nraszyn 104\nratton 104\nrayle 104\nrazvi 104\nrebuildings 104\nreddaway 104\nreemerges 104\nreestablishes 104\nreformational 104\nregionalexpress 104\nregnitz 104\nrejón 104\nrejectionist 104\nrella 104\nrelvas 104\nremits 104\nrepli 104\nrepunit 104\nresiduated 104\nrestoule 104\nrevie's 104\nrffsa 104\nrheinschild 104\nrhodoneura 104\nrhogeessa 104\nriflessioni 104\nrikon 104\nrimland 104\nrista 104\nrlx 104\nrobbins's 104\nrobosapien 104\nrockmond 104\nroddie 104\nrohrau 104\nroizen 104\nrokosz 104\nromaniote 104\nromanticize 104\nrompetrol 104\nromuva 104\nrosmah 104\nrossla 104\nrothari 104\nrougeaus 104\nrousette 104\nrozhlas 104\nrufescent 104\nruhani 104\nrunnemede 104\nrwby 104\nrydel 104\nrydzyna 104\nryes 104\nryouta 104\nryther 104\nsátoraljaújhely 104\nsânmărtean 104\nsäpo 104\nsè 104\nsaguaros 104\nsagun 104\nsahlene 104\nsai's 104\nsaitoh 104\nsalala 104\nsaldern 104\nsalination 104\nsalsabil 104\nsalsify 104\nsalsipuedes 104\nsaltos 104\nsalvors 104\nsandag 104\nsangū 104\nsanitizers 104\nsansapor 104\nsapsford 104\nsargood 104\nsarraj 104\nsarvan 104\nsassoun 104\nsavitribai 104\nsavored 104\nsayer's 104\nschanke 104\nschilsky 104\nschrage 104\nschutzhund 104\nschwebebahn 104\nschweden 104\nscorel 104\nscozia 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104\nspielrein 104\nsprawa 104\nsquaxin 104\nsravani 104\nstátní 104\nstads 104\nstaelens 104\nstankowski 104\nstanytsia 104\nsteeljaw 104\nstefany 104\nstephenites 104\nstepnoy 104\nstevanovic 104\nstompa 104\nstory_ 104\nstrangis 104\nstrobelight 104\nstuc 104\nsublimis 104\nsubmitter 104\nsubser 104\nsudhi 104\nsundaran 104\nsunuwar 104\nsupélec 104\nsuperanskaya 104\nsurili 104\nsurville 104\nsuryanath 104\nsvans 104\nsvenning 104\nswern 104\nswiftlets 104\nswirly 104\nsylvano 104\nsympathectomy 104\nsymphogear 104\nsyndicators 104\nsynodals 104\nszewczenko 104\ntè 104\ntóng 104\ntřebová 104\ntʼ 104\ntacv 104\ntacvba 104\ntaggers 104\ntaguba 104\ntajfel 104\ntalalay 104\ntaleporia 104\ntalmuds 104\ntamenglong 104\ntamr 104\ntamrat 104\ntatas 104\ntaun 104\ntaxpaying 104\ntaymouth 104\ntcat 104\nteapills 104\ntedrow 104\ntekes 104\ntelecentres 104\ntelegdi 104\ntelegraf 104\ntemognatha 104\ntempero 104\ntenancingo 104\nterell 104\nterwillegar 104\nteshie 104\nteuk 104\nthéodule 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104\nurbanismo 104\nurokinase 104\nurpo 104\nusti 104\nuwg 104\nvaccinology 104\nvadose 104\nvaino 104\nvajna 104\nvalentina's 104\nvalie 104\nvamo 104\nvanaheim 104\nvanderhoef 104\nvaniz 104\nvariazione 104\nvaritone 104\nvarne 104\nvarones 104\nvasin 104\nvaster 104\nvaynor 104\nvdk 104\nveedhi 104\nveilchen 104\nveliz 104\nvenkateswarlu 104\nvernard 104\nversilia 104\nvertigo's 104\nvetala 104\nveteres 104\nvgmss 104\nvgx 104\nvideocore 104\nvideon 104\nvierchowod 104\nvigar 104\nvillager's 104\nvillalar 104\nvineetha 104\nvipond 104\nvisarion 104\nvisiontv 104\nvitrolles 104\nvivancos 104\nvizetelly 104\nvolverás 104\nvolverte 104\nvouches 104\nvoulet 104\nvršovice 104\nvukassovich 104\nvukmir 104\nvuoso 104\nwaerea 104\nwagon's 104\nwaldenses 104\nwallmann 104\nwarwicks 104\nwashbasin 104\nwasilewska 104\nwaskom 104\nwatrin 104\nwawelberg 104\nwbmx 104\nwcal 104\nwdt 104\nwena 104\nwenjin 104\nwepner 104\nwesc 104\nwhipsnake 104\nwhisenant 104\nwhitsell 104\nwhome 104\nwhoville 104\nwiśniewska 104\nwikidot 104\nwingfoots 104\nwitte's 104\nwittei 104\nwlp 104\nwnbc's 104\nwogan's 104\nwoodlot 104\nworkes 104\nworstead 104\nwpcw 104\nwpsg 104\nwynant 104\nwytschaete 104\nxavierian 104\nxecl 104\nxerinae 104\nxià 104\nxiaoguang 104\nxiaolongnü 104\nxio 104\nxle 104\nxochitl 104\nxpcc 104\nyèvre 104\nyanıt 104\nyanagihara 104\nyayınevi 104\nyazu 104\nyealmpton 104\nyelcho 104\nyele 104\nyerby 104\nyetman 104\nyigit 104\nynysybwl 104\nyorgo 104\nyorkey 104\nyoshitane 104\nyouzhen 104\nyq 104\nytn 104\nzamaneh 104\nzangara 104\nzanqr 104\nzappala 104\nzeybek 104\nzirndorf 104\nzpa 104\nzumbrota 104\nzwol 104\nµmol 103\nåifk 103\nåsgårdstrand 103\nóláfr's 103\nøvrebø 103\nürgüp 103\nōchi 103\nśredniowiecza 103\nštrba 103\nżurakowski 103\nɣa 103\nземли 103\nмелодия 103\nмонгол 103\nје 103\nսուրբ 103\nיצחק 103\nआत 103\nपद 103\nயன 103\nทล 103\nพร 103\nเพ 103\naaiun 103\naamani 103\nabourezk 103\nabridgements 103\nachal 103\nachilleo 103\nacjc 103\nackermans 103\nadelchis 103\nadelung 103\nadenoids 103\nadorni 103\nadrenals 103\nadss 103\naduncus 103\naerate 103\naglio 103\nagun 103\naidit 103\naikines 103\najumma 103\nakenside 103\nakinyemi 103\nalberg 103\nalcian 103\naldemir 103\naldersey 103\naldingham 103\naleurone 103\nalfama 103\nalgorithmics 103\nalisia 103\nallègre 103\nallright 103\nallwine 103\naltobello 103\namedd 103\namela 103\namphenol 103\namyris 103\nanathemas 103\nanatolic 103\nanatrachyntis 103\nancares 103\nanclote 103\nancylostoma 103\nandasol 103\nanfänger 103\nangliæ 103\nangrense 103\nanhua 103\naniane 103\nannunciata 103\nanoura 103\nansai 103\nantifolk 103\nantoniadi 103\nantrim's 103\nanuraga 103\nanush 103\naphthona 103\napoa 103\naqu 103\naquitani 103\narabie 103\narattupuzha 103\narchangelgorod 103\nareti 103\narmoy 103\narnobius 103\narrasando 103\narrhythmogenic 103\narthrodire 103\nartscene 103\narvis 103\nasiatiques 103\naskance 103\naskphil 103\nassu 103\nasumu 103\nathadu 103\natimonan 103\natsumori 103\naubuchon 103\naukra 103\naupe 103\naurélia 103\naurantiacum 103\nauskick 103\nautoceste 103\nautostereogram 103\navanca 103\navants 103\navellan 103\naverre 103\navrom 103\nayant 103\nazfar 103\nazumino 103\nbánk 103\nbänden 103\nbóbr 103\nbaşaran 103\nbažant 103\nbabughat 103\nbadgering 103\nbaher 103\nbahlikas 103\nbaishakh 103\nbalans 103\nbaldies 103\nbaldwinson 103\nballygalget 103\nbalmond 103\nbaqerabad 103\nbaraguey 103\nbarbad 103\nbarilari 103\nbarkleys 103\nbarnekow 103\nbarshai 103\nbascharage 103\nbasilic 103\nbasinski 103\nbasudeb 103\nbayron 103\nbazna 103\nbeáta 103\nbeekes 103\nbeglin 103\nbeheer 103\nbeimler 103\nbelgarath 103\nbelogorie 103\nbelonogaster 103\nbenatar's 103\nbergensbanen 103\nberisso 103\nberty 103\nberurah 103\nbettinger 103\nbeukeboom 103\nbeyblades 103\nbezan 103\nbgd 103\nbham 103\nbhg 103\nbhuvaneshwari 103\nbibbidi 103\nbibliothecae 103\nbiddenham 103\nbiedma 103\nbikenibeu 103\nbikey 103\nbilar 103\nbilaterians 103\nbinder's 103\nbinnaz 103\nbiokovo 103\nbiologische 103\nbirkir 103\nbisai 103\nbishopdale 103\nbizkit's 103\nblažo 103\nblackwork 103\nblakk 103\nblameworthy 103\nblehr 103\nbloodborne 103\nblosum 103\nbognár 103\nbohler 103\nboingboing 103\nbolarum 103\nbonuspoints 103\nbonvicini 103\nbooboo 103\nborodulin 103\nbossie 103\nbotiller 103\nboulden 103\nbouligny 103\nbowbazar 103\nbowcott 103\nbowthorpe 103\nboysetsfire 103\nbpmc 103\nbrèche 103\nbréville 103\nbradlees 103\nbrandsma 103\nbrask 103\nbrassier 103\nbreaksea 103\nbridesburg 103\nbrigantia 103\nbristo 103\nbroadswords 103\nbrogren 103\nbronchodilator 103\nbronsart 103\nbrowder's 103\nbrower's 103\nbruderhof 103\nbryoria 103\nbulungan 103\nbundrage 103\nbuondelmonti 103\nbuque 103\nburaydah 103\nburfield 103\nburgada 103\nburgertime 103\nburgred 103\nburtonport 103\nbushwhacked 103\nbusis 103\nbussaglia 103\nbustier 103\nbustillos 103\nbutyrka 103\ncaapi 103\ncabadbaran 103\ncabarita 103\ncacheu 103\ncaciocavallo 103\ncadishead 103\ncalinog 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103\nfdisk 103\nfederle 103\nfefferman 103\nfeightner 103\nfeiz 103\nfeldafing 103\nfelmy 103\nfelső 103\nfendleri 103\nfenni 103\nferchar 103\nfereshteh 103\nferrites 103\nfery 103\nfeyyaz 103\nffk 103\nfightings 103\nfilmex 103\nfiloli 103\nfilología 103\nfinalista 103\nfinnieston 103\nfinschii 103\nfitzthomas 103\nflevo 103\nfluticasone 103\nfocs 103\nfoedus 103\nfolldal 103\nfoos 103\nfoppe 103\nfops 103\nforlimpopoli 103\nformosensis 103\nforneria 103\nfoucan 103\nfournaise 103\nfradkov 103\nfrazzled 103\nfreeloaders 103\nfreesheet 103\nfretheim 103\nfriendz 103\nfritzie 103\nfrontispieces 103\nfrothing 103\nfujiya 103\nfumika 103\nfutureshock 103\ngökova 103\ngülşah 103\ngītā 103\ngadret 103\ngaeru 103\ngagaga 103\ngaiter 103\ngallé 103\ngalperin 103\ngaoqi 103\ngarceau 103\ngawah 103\ngawr 103\ngazda 103\ngazela 103\ngazzard 103\ngccf 103\ngdot 103\ngearan 103\ngeelvink 103\ngefahr 103\ngeffen's 103\ngeniez 103\ngenitives 103\ngenitorturers 103\ngeodata 103\ngeomechanics 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100\npetrosus 100\npetruzzelli 100\npfalzgraf 100\npfingsten 100\npfts 100\nphụng 100\nphagspa 100\nphantosmia 100\nphasors 100\nphichai 100\nphilhealth 100\nphilotheus 100\nphowa 100\npicornavirales 100\npiedmont's 100\npimental 100\npinchers 100\npinwheels 100\npiria 100\npirtle 100\npisanu 100\npisaster 100\npisu 100\npithole 100\npitru 100\npizzolatto 100\nplayhouse's 100\nplosion 100\nplov 100\nplutocratic 100\npmca 100\npodčetrtek 100\npointlessly 100\npolemarch 100\npolishers 100\npollarded 100\npolyglutamine 100\npomerene 100\npomorza 100\npooter 100\npopovitch 100\nportmahomack 100\nposlednji 100\npostprocessing 100\npotaissa 100\npotel 100\npoussière 100\npoxviridae 100\nppcrv 100\npršo 100\npraefecti 100\npremià 100\npreparata 100\npresbyteral 100\npresper 100\nprières 100\nprié 100\nprivolzhsky 100\npriyamvada 100\npropheten 100\npropio 100\nprospal 100\nprosser's 100\nprotopriest 100\nprotus 100\nprovençale 100\nprzyjaciół 100\npuchar 100\npulci 100\npuma's 100\npunnapra 100\npurāṇa 100\npuron 100\npxv 100\npyers 100\npyran 100\nqataris 100\nqingzang 100\nqpcr 100\nqps 100\nqualquer 100\nquasimodo's 100\nquax 100\nqueets 100\nquesadilla 100\nquickstrike 100\nquie 100\nquinquagesima 100\nrădoi 100\nrạch 100\nrachell 100\nradheshyam 100\nradich 100\nradomlje 100\nradoslavov 100\nradványi 100\nradzin 100\nrafiuddin 100\nramaiya 100\nramazanov 100\nramgopal 100\nrammstein's 100\nraphaëlle 100\nraskob 100\nrasulid 100\nraviraj 100\nrbde 100\nrcsl 100\nrdos 100\nrean 100\nreata 100\nreattachment 100\nrebengiuc 100\nrecioto 100\nrecouderc 100\nredoubling 100\nreganbooks 100\nreiser's 100\nreizigers 100\nrembertów 100\nreneé 100\nrenku 100\nrepetiteur 100\nreprazent 100\nreprocess 100\nreutov 100\nrevalidated 100\nreverbs 100\nrezina 100\nrgl 100\nrhegion 100\nrhynchosia 100\nrhynchospora 100\nriflery 100\nrigdzin 100\nrigg's 100\nrigidus 100\nrigny 100\nrillieux 100\nrimpoche 100\nringebu 100\nriverdales 100\nroadrace 100\nroar's 100\nrobotica 100\nrobu 100\nrocker's 100\nrocketboom 100\nrockstar's 100\nroehrig 100\nroese 100\nrogalski 100\nrojek 100\nroleystone 100\nroncaglia 100\nronel 100\nrooibos 100\nrosero 100\nroslagsbanan 100\nrosmead 100\nrotaries 100\nrotsee 100\nroxelana 100\nroxio 100\nrpg's 100\nruapuna 100\nruatoria 100\nrubtsov 100\nrubtsovsk 100\nrudna 100\nrudrama 100\nruqayyah 100\nrzav 100\nsaabye 100\nsaac 100\nsaalfield 100\nsabacon 100\nsacker 100\nsacul 100\nsadygov 100\nsaffold 100\nsagana 100\nsaggiatore 100\nsaili 100\nsakmann 100\nsalentino 100\nsalgótarjáni 100\nsaltza 100\nsamians 100\nsamoylov 100\nsanati 100\nsanbornton 100\nsanfuentes 100\nsangbad 100\nsangrahalaya 100\nsapindus 100\nsapt 100\nsarabjit 100\nsargon's 100\nsawshark 100\nsbraccia 100\nscandlines 100\nscarpa's 100\nschapelle 100\nscheiden 100\nschindlers 100\nschoolship 100\nschrecker 100\nschwalmstadt 100\nscis 100\nsciurini 100\nscourie 100\nscriptor 100\nseacon 100\nsealaska 100\nsechlainn 100\nsedgeford 100\nseear 100\nseeley's 100\nseersucker 100\nsegoviana 100\nseguín 100\nsehanine 100\nselkup 100\nseminorms 100\nsemirechye 100\nsendel 100\nsenoculus 100\nsepr 100\nseraina 100\nsesaros 100\nseters 100\nsetina 100\nsetty 100\nsevyn 100\nshōjō 100\nshahdadkot 100\nshamrox 100\nshankle 100\nshapey 100\nsharry 100\nsheats 100\nshebeen 100\nsheepish 100\nshelest 100\nshijingshan 100\nshinkyoku 100\nshipibo 100\nshishkov 100\nshobrooke 100\nshoro 100\nshubham 100\nshumsky 100\nsibiryakov 100\nsiddapur 100\nsidedness 100\nsidoti 100\nsiegmann 100\nsierck 100\nsifuentes 100\nsigurdur 100\nsiha 100\nsillah 100\nsillinger 100\nsimdega 100\nsinitsyn 100\nsiponto 100\nsirromet 100\nsissis 100\nsitaron 100\nsivertson 100\nsivewright 100\nsixte 100\nskåneland 100\nskaha 100\nsked 100\nskerry's 100\nskimo 100\nskittering 100\nskjetten 100\nslabbert 100\nslackening 100\nslaten 100\nsluitingsprijs 100\nsmallbrook 100\nsmbc 100\nsmmc 100\nsołectwo 100\nsoapdish 100\nsobie 100\nsocietat 100\nsocolescu 100\nsoeng 100\nsoldano 100\nsomapura 100\nsomekh 100\nsomm 100\nsonex 100\nsonication 100\nsonobuoy 100\nsorgenfri 100\nsoundchecks 100\nsoupir 100\nsowe 100\nsparcs 100\nspeciosissima 100\nspeedruns 100\nspeedstar 100\nspermophora 100\nspinei 100\nspinipes 100\nspinnerette 100\nsplittorff 100\nspoon's 100\nspreydon 100\nspud's 100\nsstp 100\nstadı 100\nstahler 100\nstalock 100\nstarthistle 100\nstegosaurian 100\nsteinhof 100\nstellite 100\nstević 100\nstojiljković 100\nstoli 100\nstonewalled 100\nstrøget 100\nstriolatus 100\nstrobili 100\nstroganovs 100\nstrugo 100\nstz 100\nsubsidiary's 100\nsugu 100\nsuihua 100\nsujei 100\nsukhan 100\nsulloway 100\nsundri 100\nsunexpress 100\nsunwest 100\nsuperfamicom 100\nsuperlattices 100\nsuskin 100\nsusse 100\nsuzzara 100\nsvanholm 100\nsvensen 100\nswammerdamia 100\nswarzak 100\nswider 100\nswimbladder 100\nsylvette 100\nsynchrotrons 100\nsysplex 100\ntümed 100\ntabing 100\ntachypnea 100\ntadaima 100\ntagar 100\ntakadanobaba 100\ntakarada 100\ntakezawa 100\ntalor 100\ntanaga 100\ntandonia 100\ntaneli 100\ntanlines 100\ntaramati 100\ntarok 100\ntarong 100\ntarsney 100\ntascosa 100\ntatva 100\ntcga 100\ntebbe 100\ntechnique's 100\nteenoso 100\nteerasil 100\ntegner 100\ntellabs 100\ntenebre 100\ntersa 100\ntetch 100\ntethya 100\ntettey 100\nteulada 100\nthakuri 100\nthaumasia 100\ntheodolites 100\nthervingi 100\nthessalonika 100\nthievius 100\nthistv 100\nthorhild 100\nthormanby 100\nthornby 100\nthoughtlessly 100\nthranx 100\nthreeboot 100\nthrillville 100\nthundermans 100\nthunderstreaks 100\nthuresson 100\nthurnscoe 100\ntibba 100\ntibiofibular 100\ntigerstripe 100\ntirez 100\ntnh 100\ntnz 100\ntoady 100\ntolkan 100\ntollner 100\ntolsa 100\ntolton 100\ntolwyn 100\ntomaschek 100\ntomelilla 100\ntotalapps 100\ntotschnig 100\ntouriga 100\ntourish 100\ntournesol 100\ntrabucco 100\ntrachytes 100\ntragulus 100\ntransversality 100\ntraversa 100\ntreći 100\ntrendsetting 100\ntricarboxylic 100\ntrichiura 100\ntriclocarban 100\ntrilochan 100\ntriptans 100\ntrislander 100\ntriticeae 100\ntroeltsch 100\ntrowse 100\ntsuchi 100\ntual 100\ntunda 100\ntunebook 100\ntungamah 100\nturanga 100\nturcan 100\nturci 100\nturhal 100\nturicum 100\nturndown 100\nturves 100\ntveter 100\ntyabb 100\ntydd 100\ntypographica 100\ntyroleans 100\ntys 100\ntzrifin 100\nuccs 100\nudugama 100\nulaidh 100\nulceby 100\numid 100\numka 100\nunbanked 100\nunceta 100\nunderinvestment 100\nunidimensional 100\nuniliteral 100\nunindicted 100\nuniter 100\nuntaes 100\nupdown 100\nupis 100\nuraeginthus 100\nurak 100\nuraniidae 100\nusafss 100\nutaki 100\nvárad 100\nvästerviks 100\nvéhicules 100\nvías 100\nvölsung 100\nvainglory 100\nvajrayogini 100\nvalērijs 100\nvalaya 100\nvalerianus 100\nvalkama 100\nvallarpadam 100\nvallehermoso 100\nvalmontone 100\nvandenburg 100\nvarecia 100\nvariator 100\nvasona 100\nvdw 100\nvedast 100\nvelir 100\nvenceslau 100\nverkhovsky 100\nvermudo 100\nversors 100\nvertica 100\nvertiginidae 100\nveyre 100\nvidenov 100\nvieste 100\nvietri 100\nvifk 100\nvigalova 100\nvilági 100\nvilkaviškis 100\nvinberg 100\nvindafjord 100\nvitale's 100\nvitellinus 100\nviviendo 100\nvj's 100\nvlotho 100\nvlti 100\nvly 100\nvontobel 100\nvorstius 100\nvosne 100\nvučedol 100\nvum 100\nvyd 100\nvyttila 100\nwaer 100\nwalen 100\nwallabout 100\nwallisellen 100\nwalmesley 100\nwarbringer 100\nwarrenheip 100\nwasei 100\nwatertable 100\nwathan 100\nwayz 100\nwbbg 100\nwbn 100\nwbng 100\nwcap 100\nwdi 100\nweed's 100\nweesen 100\nweiyang 100\nwesthay 100\nwetpaint 100\nwettlaufer 100\nwetz 100\nwewa 100\nwhitsundays 100\nwice 100\nwickerman 100\nwidger 100\nwilland 100\nwillum 100\nwiniary 100\nwintersmith 100\nwitticism 100\nwjox 100\nwnol 100\nwolly 100\nwond 100\nwoodhay 100\nwoohoo 100\nwrighti 100\nwrightsman 100\nwsws 100\nwuhf 100\nwulan 100\nwuzzy 100\nwwrl 100\nwzt 100\nxawery 100\nxia's 100\nxiangtang 100\nxinjian 100\nxintang 100\nxsm 100\nxxxxi 100\nyaare 100\nyachimovich 100\nyadavindra 100\nyakupov 100\nyasai 100\nyednist 100\nyodh 100\nyomitan 100\nyoriie 100\nyoseob 100\nypbpr 100\nyuanlin 100\nyuelu 100\nyukteswar 100\nyunfu 100\nyungang 100\nzaina 100\nzais 100\nzaspa 100\nzazzau 100\nzeder 100\nzelnick 100\nzelophehad 100\nzemi 100\nzentai 100\nzfa 100\nzhangzhung 100\nziehl 100\nzillmann 100\nzisman 100\nzizou 100\nzlatev 100\nznojemští 100\nzorak's 100\nzoviet 100\nzsj 100\nzubeidaa 100\nzuiderwijk 100\nzulkarnain 100\nzuno 100\nzwide 100\n"
  },
  {
    "path": "psdvec/competitors/glove/wiki.sh",
    "content": "#!/bin/bash\n\n# Makes programs, downloads sample data, trains a GloVe model, and then evaluates it.\n# One optional argument can specify the language used for eval script: matlab, octave or [default] python\n\nCORPUS=/home/shaohua/D/corpus/cleanwiki.txt\nVOCAB_FILE=vocab-wiki.txt\nCOOCCURRENCE_FILE=cooccurrence-wiki.bin\nCOOCCURRENCE_SHUF_FILE=cooccurrence.shuf-wiki.bin\nBUILDDIR=build\nSAVE_FILE=glove-wiki.vec\nVERBOSE=2\nMEMORY=16.0\nVOCAB_MIN_COUNT=100\nVECTOR_SIZE=500\nMAX_ITER=15\nWINDOW_SIZE=3\nBINARY=0\nNUM_THREADS=8\nX_MAX=10\n\n$BUILDDIR/vocab_count -min-count $VOCAB_MIN_COUNT -verbose $VERBOSE < $CORPUS > $VOCAB_FILE\nif [[ $? -eq 0 ]]\nthen\n  $BUILDDIR/cooccur -memory $MEMORY -vocab-file $VOCAB_FILE -verbose $VERBOSE -window-size $WINDOW_SIZE < $CORPUS > $COOCCURRENCE_FILE\n  if [[ $? -eq 0 ]]\n  then\n    $BUILDDIR/shuffle -memory $MEMORY -verbose $VERBOSE < $COOCCURRENCE_FILE > $COOCCURRENCE_SHUF_FILE\n    if [[ $? -eq 0 ]]\n    then\n       $BUILDDIR/glove -save-file $SAVE_FILE -threads $NUM_THREADS -input-file $COOCCURRENCE_SHUF_FILE -x-max $X_MAX -iter $MAX_ITER -vector-size $VECTOR_SIZE -binary $BINARY -vocab-file $VOCAB_FILE -verbose $VERBOSE\n    fi\n  fi\nfi\n"
  },
  {
    "path": "psdvec/competitors/hyperwords/pmi2.sh",
    "content": "#!/bin/bash\n# A) Window size 2 with \" subsampling\nCORPUS=/home/shaohua/D/corpus/cleanwiki.txt\n\nmkdir w2.sub\npython hyperwords/corpus2pairs.py --win 2 --sub 1e-5 ${CORPUS} > w2.sub/pairs\nscripts/pairs2counts.sh w2.sub/pairs > w2.sub/counts\npython hyperwords/counts2vocab.py w2.sub/counts\n# Calculate PMI matrices for each collection of pairs\npython hyperwords/counts2pmi.py --cds 0.75 w2.sub/counts w2.sub/pmi\n"
  },
  {
    "path": "psdvec/competitors/hyperwords/pmi5-rcv1.sh",
    "content": "#!/bin/bash\n\n# B) Window size 5 with dynamic contexts and \"dirty\" subsampling\n\nCORPUS=/home/shaohua/D/corpus/rcv1clean.txt\nDIR=w5.rcv1.dyn.sub.del\nmkdir $DIR\npython hyperwords/corpus2pairs.py --win 5 --dyn --sub 1e-5 --del ${CORPUS} > $DIR/pairs\nscripts/pairs2counts.sh $DIR/pairs > $DIR/counts\npython hyperwords/counts2vocab.py $DIR/counts\n\n# Calculate PMI matrices for each collection of pairs\npython hyperwords/counts2pmi.py --cds 0.75 $DIR/counts $DIR/pmi\n"
  },
  {
    "path": "psdvec/competitors/hyperwords/pmi5.sh",
    "content": "#!/bin/bash\n\n# B) Window size 5 with dynamic contexts and \"dirty\" subsampling\n\nCORPUS=/home/shaohua/D/corpus/cleanwiki.txt\n\nmkdir w5.dyn.sub.del\npython hyperwords/corpus2pairs.py --win 5 --dyn --sub 1e-5 --del ${CORPUS} > w5.dyn.sub.del/pairs\nscripts/pairs2counts.sh w5.dyn.sub.del/pairs > w5.dyn.sub.del/counts\npython hyperwords/counts2vocab.py w5.dyn.sub.del/counts\n\n# Calculate PMI matrices for each collection of pairs\npython hyperwords/counts2pmi.py --cds 0.75 w5.dyn.sub.del/counts w5.dyn.sub.del/pmi\n"
  },
  {
    "path": "psdvec/competitors/hyperwords/svd-rcv1.sh",
    "content": "#!/bin/bash\n\n# Create embeddings with SVD\nDIR=w5.rcv1.dyn.sub.del\npython hyperwords/pmi2svd.py --dim 50 --neg 5 $DIR/pmi $DIR/svd\ncp $DIR/pmi.words.vocab $DIR/svd.words.vocab\ncp $DIR/pmi.contexts.vocab $DIR/svd.contexts.vocab\n"
  },
  {
    "path": "psdvec/competitors/hyperwords/svd.sh",
    "content": "#!/bin/bash\n\n# Create embeddings with SVD\n\nCORPUS=/home/shaohua/D/corpus/cleanwiki.txt\n\npython hyperwords/pmi2svd.py --dim 500 --neg 5 w2.sub/pmi w2.sub/svd\ncp w2.sub/pmi.words.vocab w2.sub/svd.words.vocab\ncp w2.sub/pmi.contexts.vocab w2.sub/svd.contexts.vocab\npython hyperwords/pmi2svd.py --dim 500 --neg 5 w5.dyn.sub.del/pmi w5.dyn.sub.del/svd\ncp w5.dyn.sub.del/pmi.words.vocab w5.dyn.sub.del/svd.words.vocab\ncp w5.dyn.sub.del/pmi.contexts.vocab w5.dyn.sub.del/svd.contexts.vocab\n"
  },
  {
    "path": "psdvec/competitors/hyperwords/train-rcv1.sh",
    "content": "#!/bin/sh\nrm -rf w5.rcv1.dyn.sub.del\nrm -rf w5.rcv1.dyn.sub.del\ntime ./pmi5-rcv1.sh\ntime ./svd-rcv1.sh\n\n\n\n"
  },
  {
    "path": "psdvec/competitors/hyperwords/train-wiki.sh",
    "content": "#!/bin/sh\nrm -rf w2.sub w5.dyn.sub.del\nrm -rf w2.sub w5.dyn.sub.del\ntime ./pmi2.sh\ntime ./pmi5.sh\ntime ./svd.sh\n\n\n\n"
  },
  {
    "path": "psdvec/corpus2liblinear.py",
    "content": "import numpy as np\r\nimport getopt\r\nimport sys\r\nfrom utils import *\r\nimport pdb\r\nimport time\r\nimport os\r\nimport json\r\nimport copy\r\n\r\ndef usage():\r\n    print \"\"\"Usage:\\n  corpus2liblinear.py -d doc_dir -o output_file -v vec_file [ -s sent_file ] label\r\n  corpus2liblinear.py -c config_file -n alg_name -v vec_file [ -s sent_file ]\r\nOptions:\r\n  doc_dir:      Directory of the documents to convert.\r\n  output_file:  File to save the extracted vectors.\r\n  Label:        Label of documents. Must be 1/+1/-1.\r\n  config_file:  File specifying multiple directories, labels and output files.\r\n  vec_file:     File containing embedding vectors.\r\n  alg_name:     Name of the embedding algorithm that generates vec_file. \r\n                Needed if onbly partial file name is specified in config_file.\r\n  sent_file:    File containing a list of sentiment words.\r\n\"\"\"\r\n\r\ndef parseConfigFile(configFilename):\r\n    CONF = open(configFilename)\r\n    dir_configs = []\r\n    for line in CONF:\r\n        line = line.strip()\r\n        dir_config = json.loads(line)\r\n        dir_configs.append(dir_config)\r\n    return dir_configs\r\n    \r\ndef getFileFeatures(filename, V, word2id, sentword2id, remove_stop=False):\r\n    DOC = open(filename)\r\n    doc = DOC.read()\r\n    wordsInSentences, wc = extractSentenceWords(doc, 1)\r\n    \r\n    countedWC = 0\r\n    outvocWC = 0\r\n    stopwordWC = 0\r\n    sentWC = 0\r\n    \r\n    wids = []\r\n    wid2freq = {}\r\n    BOWFeatureNum = len(sentword2id)\r\n    BOWFreqs = np.zeros(BOWFeatureNum)\r\n    \r\n    for sentence in wordsInSentences:\r\n        for w in sentence:\r\n            w = w.lower()\r\n            if remove_stop and w in stopwordDict:\r\n                stopwordWC += 1\r\n                continue\r\n                \r\n            if w in word2id:\r\n                wid = word2id[w]\r\n                wids.append( wid )\r\n                \r\n                if wid not in wid2freq:\r\n                    wid2freq[wid] = 1\r\n                else:\r\n                    wid2freq[wid] += 1\r\n                countedWC += 1\r\n            else:\r\n                outvocWC += 1\r\n    \r\n            if w in sentword2id:\r\n                id = sentword2id[w]\r\n                BOWFreqs[id] += 1\r\n                sentWC += 1\r\n                \r\n    N0 = V.shape[1]\r\n    avgv = np.zeros(N0)\r\n    \r\n    # avgv is the average embedding vector. Used in Tobias Schnabel et al. (2015) as the only features\r\n    for wid, freq in wid2freq.items():\r\n        avgv += np.log( freq + 1 ) * V[wid]\r\n        \r\n    #for wid in wids:\r\n    #    avgv += V[wid]\r\n    \r\n    avgv = normalizeF(avgv)\r\n    return avgv, BOWFreqs\r\n\r\ndef processDir( outFilename, docDir, label, appendToOutput, V, word2ID, sentword2id ):\r\n    print \"Process '%s' %s\" %( docDir, label )\r\n    \r\n    if appendToOutput:\r\n        OUT = open(outFilename, \"a\")\r\n    else:\r\n        OUT = open(outFilename, \"w\")\r\n        \r\n    filecount = 0\r\n    \r\n    for filename in os.listdir(docDir):\r\n        OUT.write(label)\r\n        fullFilename = docDir + \"/\" + filename\r\n        avgv, BOWFreqs = getFileFeatures( fullFilename, V, word2ID, sentword2id  )\r\n        for i,x in enumerate(avgv):\r\n            OUT.write( \" %d:%.4f\" %( i+1, x ) )\r\n        # i == N0 - 1 here, dimensionality of the embedding vector\r\n        i += 1\r\n        if BOWFreqs.shape[0] > 0:\r\n            for freq in BOWFreqs:\r\n                if freq > 0:\r\n                    OUT.write( \" %d:%d\" %( i+1, freq ) )\r\n                i += 1\r\n                \r\n        OUT.write(\"\\n\")\r\n        filecount += 1\r\n        if filecount % 500 == 0:\r\n            print \"\\r%d\\r\" %filecount,\r\n    \r\n    if appendToOutput:\r\n        writeMode = \"appended to\" \r\n    else:\r\n        writeMode = \"written into\"\r\n    print \"%d files processed and %s '%s'\" %( filecount, writeMode, outFilename )\r\n    \r\n    OUT.close()\r\n            \r\ndef main():\r\n    vecFilename = \"25000-180000-500-BLK-8.0.vec\"\r\n    algname = None\r\n    topword_cutoff = -1\r\n    topSentWord_cutoff = -1\r\n    \r\n    configFilename = \"\"\r\n    label = None\r\n    appendToOutput = False\r\n    sentimentWordFile = None\r\n    \r\n    try:\r\n        opts, args = getopt.getopt(sys.argv[1:],\"d:o:v:c:n:s:1ah\")\r\n        if( len(args) == 1 ):\r\n            if args[0] != \"1\" and args[0] != \"+1\":\r\n                raise getopt.GetoptError( \"Unknown free argument '%s'\" %args[0] )\r\n            label = \"+1\"\r\n        elif( len(args) > 1 ):\r\n            raise getopt.GetoptError( \"Too many free arguments '%s'\" %args )\r\n            \r\n        for opt, arg in opts:\r\n            if opt == '-1':\r\n                label = \"-1\"\r\n            \r\n            if opt == '-c':\r\n                configFilename = arg\r\n            if opt == '-s':\r\n                sentimentWordFile = arg\r\n            \r\n            if opt == '-n':\r\n                algname = arg\r\n            if opt == '-d':\r\n                docDir = arg\r\n            if opt == '-d':\r\n                docDir = arg\r\n            if opt == '-o':\r\n                outFilename = arg\r\n            if opt == '-v':\r\n                vecFilename = arg\r\n            if opt == '-a':\r\n                appendToOutput = True    \r\n            if opt == '-h':\r\n                usage()\r\n                sys.exit(0)\r\n\r\n    except getopt.GetoptError, e:\r\n        if len(e.args) == 1:\r\n            print \"Option error: %s\" %e.args[0]\r\n        usage()\r\n        sys.exit(2)\r\n\r\n    sentword2id = {}\r\n    bowSize = 0\r\n    if sentimentWordFile:\r\n        SENT = open(sentimentWordFile)\r\n        id = 0\r\n        for line in SENT:\r\n            word, freq = line.split(\"\\t\")\r\n            sentword2id[word] = id\r\n            id += 1\r\n            # if topSentWord_cutoff == -1, this equality is never satisfied, so no cut off\r\n            if id == topSentWord_cutoff:\r\n                break\r\n        bowSize = len(sentword2id)\r\n        print \"%d sentiment words loaded\" %(bowSize)  \r\n    \r\n    if configFilename:\r\n        dir_configs = parseConfigFile(configFilename)\r\n        for conf in dir_configs:\r\n            if 'outFilenameTrunk' in conf:\r\n                if not algname:\r\n                    print \"-n alg_name is needed to generate full output file name\"\r\n                    usage()\r\n                    sys.exit(2)\r\n                \r\n                if sentimentWordFile:\r\n                    conf['outFilename'] = \"%s-%s-bow%d.txt\" %( conf['outFilenameTrunk'], algname, bowSize )\r\n                else:\r\n                    conf['outFilename'] = \"%s-%s.txt\" %( conf['outFilenameTrunk'], algname )\r\n\r\n    elif not label:\r\n        print \"No config file nor label is specified\"\r\n        usage()\r\n        sys.exit(0)\r\n    else:\r\n        dir_config = { 'dir': docDir, 'outFilename': outFilename, \r\n                        'label': label, 'isAppend': appendToOutput }\r\n        dir_configs = [ dir_config ]      \r\n                      \r\n    V, vocab, word2ID, skippedWords_whatever = load_embeddings( vecFilename, topword_cutoff )\r\n\r\n    for conf in dir_configs:\r\n        processDir( conf['outFilename'], conf['docDir'], conf['label'], \r\n                    conf['appendToOutput'], V, word2ID, sentword2id )\r\n            \r\nif __name__ == '__main__':\r\n    main()\r\n    "
  },
  {
    "path": "psdvec/eval-logs/bench.log",
    "content": "shaohua@shaohua:/media/shaohua/Outerspace/corpus$ ./bench.sh \nPSD:\nRead sim testset ./testsets/ws/ws353_similarity.txt\nRead sim testset ./testsets/ws/ws353_relatedness.txt\nRead sim testset ./testsets/ws/bruni_men.txt\nRead sim testset ./testsets/ws/radinsky_mturk.txt\nRead sim testset ./testsets/ws/luong_rare.txt\nRead sim testset ./testsets/ws/simlex_999a.txt\nRead analogy testset ./testsets/analogy/google.txt\nRead analogy testset ./testsets/analogy/msr.txt\n\nLoading bigram file 'top2grams-wiki.txt':\nTotally 277025 words\n277025 words seen, top 25000 & 0 extra to keep. 25000 kept\nRead bigrams:\n25000\nCut point 4010: 4/0.000%\nCut point 2005: 23/0.000%\nCut point 1002: 123/0.000%\nCut point 501: 840/0.000%\nCut point 251: 5383/0.001%\nCut point 125: 28276/0.005%\nCut point 63: 124146/0.020%\nCut point 31: 493469/0.079%\nCut point 16: 1779998/0.285%\n493469 (0.079%) elements in Weight-1 cut off at 31.33\n\n4 iterations of EM\nBegin EM of weighted factorization by bigram freqs\n\nEM Iter 1:\nBegin unweighted factorization\n12450 positive eigenvalues, sum: 1016404.875\nEigenvalues cut at the 503-th, 186.957 ~ 186.925\nAll eigen sum: 1961478.500, Kept eigen sum: 178549.484\nnowe_factorize() elapsed: 1936.64\nL1 Weighted: Gi: 7769817.151, VV: 7350854.952, Gsym-VV: 10842411.309, G-VV: 9977720.915\nPrecompute cosine matrix, will need 2.5GB RAM... Done.\nws353_similarity: 203 test pairs, 195 valid , 0.79632\nws353_relatedness: 252 test pairs, 241 valid , 0.68286\nbruni_men: 3000 test pairs, 2639 valid , 0.77234\nradinsky_mturk: 287 test pairs, 279 valid , 0.68298\nluong_rare: 2034 test pairs, 396 valid , 0.54562\nsimlex_999a: 999 test pairs, 945 valid , 0.39870\n19500/12552/19544: Add 0.66651, Mul 0.68634\ngoogle: 19544 analogies, 12586 valid . Add Score: 0.66590, Mul Score: 0.68584\n8000/5030/8000: Add 0.54732, Mul 0.59761\nmsr: 8000 analogies, 5030 valid . Add Score: 0.54732, Mul Score: 0.59761\nEM iter 1 elapsed: 1954.66\n\nEM Iter 2:\nBegin unweighted factorization\n12338 positive eigenvalues, sum: 239171.609\nEigenvalues cut at the 502-th, 191.346 ~ 190.984\nAll eigen sum: 299793.500, Kept eigen sum: 180740.391\nnowe_factorize() elapsed: 1972.69\nL1 Weighted: Gi: 7878353.598, VV: 7242558.205, Gsym-VV: 10491300.183, G-VV: 9662005.122\nPrecompute cosine matrix, will need 2.5GB RAM... Done.\nws353_similarity: 203 test pairs, 195 valid , 0.79887\nws353_relatedness: 252 test pairs, 241 valid , 0.68380\nbruni_men: 3000 test pairs, 2639 valid , 0.77014\nradinsky_mturk: 287 test pairs, 279 valid , 0.68362\nluong_rare: 2034 test pairs, 396 valid , 0.54914\nsimlex_999a: 999 test pairs, 945 valid , 0.39755\n19500/12552/19544: Add 0.67049, Mul 0.69025\ngoogle: 19544 analogies, 12586 valid . Add Score: 0.66995, Mul Score: 0.68981\n8000/5030/8000: Add 0.54274, Mul 0.59543\nmsr: 8000 analogies, 5030 valid . Add Score: 0.54274, Mul Score: 0.59543\nEM iter 2 elapsed: 1990.84\n\nEM Iter 3:\nBegin unweighted factorization\n12334 positive eigenvalues, sum: 240251.656\nEigenvalues cut at the 501-th, 195.408 ~ 194.607\nAll eigen sum: 299763.188, Kept eigen sum: 183383.266\nnowe_factorize() elapsed: 1932.79\nL1 Weighted: Gi: 7861670.004, VV: 7228707.970, Gsym-VV: 10272011.447, G-VV: 9453111.101\nPrecompute cosine matrix, will need 2.5GB RAM... Done.\nws353_similarity: 203 test pairs, 195 valid , 0.80074\nws353_relatedness: 252 test pairs, 241 valid , 0.68146\nbruni_men: 3000 test pairs, 2639 valid , 0.76744\nradinsky_mturk: 287 test pairs, 279 valid , 0.68036\nluong_rare: 2034 test pairs, 396 valid , 0.55140\nsimlex_999a: 999 test pairs, 945 valid , 0.39524\n19500/12552/19544: Add 0.67081, Mul 0.69383\ngoogle: 19544 analogies, 12586 valid . Add Score: 0.67027, Mul Score: 0.69355\n8000/5030/8000: Add 0.53917, Mul 0.58847\nmsr: 8000 analogies, 5030 valid . Add Score: 0.53917, Mul Score: 0.58847\nEM iter 3 elapsed: 1950.92\n\nEM Iter 4:\nBegin unweighted factorization\n12339 positive eigenvalues, sum: 241826.562\nEigenvalues cut at the 500-th, 199.922 ~ 198.961\nAll eigen sum: 300269.469, Kept eigen sum: 186190.469\nnowe_factorize() elapsed: 2069.77\nL1 Weighted: Gi: 7879650.000, VV: 7250952.037, Gsym-VV: 10122656.270, G-VV: 9301310.810\nPrecompute cosine matrix, will need 2.5GB RAM... Done.\nws353_similarity: 203 test pairs, 195 valid , 0.80089\nws353_relatedness: 252 test pairs, 241 valid , 0.67612\nbruni_men: 3000 test pairs, 2639 valid , 0.76526\nradinsky_mturk: 287 test pairs, 279 valid , 0.67757\nluong_rare: 2034 test pairs, 396 valid , 0.55358\nsimlex_999a: 999 test pairs, 945 valid , 0.39342\n19500/12552/19544: Add 0.67145, Mul 0.69487\ngoogle: 19544 analogies, 12586 valid . Add Score: 0.67098, Mul Score: 0.69466\n8000/5030/8000: Add 0.53320, Mul 0.58569\nmsr: 8000 analogies, 5030 valid . Add Score: 0.53320, Mul Score: 0.58569\nEM iter 4 elapsed: 2087.96\nwe_factorize_EM() elapsed: 7987.32\n\nSave matrix 'V' into 25000-500-EM.vec\n\n\nreal\t137m4.155s\nuser\t815m28.416s\nsys\t97m3.316s\nUsing Tikhonov regularization with coeff: 2.0\nRead sim testset ./testsets/ws/ws353_similarity.txt\nRead sim testset ./testsets/ws/ws353_relatedness.txt\nRead sim testset ./testsets/ws/bruni_men.txt\nRead sim testset ./testsets/ws/radinsky_mturk.txt\nRead sim testset ./testsets/ws/luong_rare.txt\nRead sim testset ./testsets/ws/simlex_999a.txt\nRead analogy testset ./testsets/analogy/google.txt\nRead analogy testset ./testsets/analogy/msr.txt\n\nEmbeddings of all words in '25000-500-EM.vec' will be loaded as core\nLoad embedding text file '25000-500-EM.vec'\nWill load embeddings of 25000 words\n25000    25000    0    \n25000 embeddings read, 25000 kept\n2 blocks of 25000 core words and 55000 noncore words will be loaded. Skip 0 words\nLoading bigram file 'top2grams-wiki.txt' into 2 blocks. Will skip 0 words\nTotally 277025 words\n277025 words in file, top 80000 to read into vocab (25000 core, 55000 noncore), 0 skipped\nRead bigrams:\n25000 (25000 core, 0 noncore)\n25000 core words are all read.\n80000 (25000 core, 55000 noncore)\nCut point 35: 2419/0.000%\nCut point 18: 48813/0.004%\n2414 (0.000%) elements in Weight-1 cut off at 35.48\n1328 (0.000%) elements in Weight-2 cut off at 35.48\n\ndel G1, G21\nBegin finding embeddings of non-core words\n55000 / 55000. Elapsed: 5851.77/10.41 \ndel F21, WGsum, VW\nSave matrix 'V' into 25000-80000-500-BLK-2.0.vec\nTest embeddings derived from block factorization\n\nPrecompute cosine matrix, will need 25.6GB RAM... Done.\nws353_similarity: 203 test pairs, 203 valid , 0.79212\nws353_relatedness: 252 test pairs, 252 valid , 0.67948\nbruni_men: 3000 test pairs, 3000 valid , 0.76389\nradinsky_mturk: 287 test pairs, 285 valid , 0.67397\nluong_rare: 2034 test pairs, 835 valid , 0.48215\nsimlex_999a: 999 test pairs, 995 valid , 0.39890\n19500/18401/19544: Add 0.61893, Mul 0.63926\ngoogle: 19544 analogies, 18443 valid . Add Score: 0.61866, Mul Score: 0.63921\n8000/6172/8000: Add 0.49579, Mul 0.54277\nmsr: 8000 analogies, 6172 valid . Add Score: 0.49579, Mul Score: 0.54277\n\nreal\t109m26.625s\nuser\t896m3.444s\nsys\t278m1.332s\nUsing Tikhonov regularization with coeff: 4.0\nRead sim testset ./testsets/ws/ws353_similarity.txt\nRead sim testset ./testsets/ws/ws353_relatedness.txt\nRead sim testset ./testsets/ws/bruni_men.txt\nRead sim testset ./testsets/ws/radinsky_mturk.txt\nRead sim testset ./testsets/ws/luong_rare.txt\nRead sim testset ./testsets/ws/simlex_999a.txt\nRead analogy testset ./testsets/analogy/google.txt\nRead analogy testset ./testsets/analogy/msr.txt\n\nEmbeddings of top 25000 words in '25000-80000-500-BLK-2.0.vec' will be loaded as core\nLoad embedding text file '25000-80000-500-BLK-2.0.vec'\nWill load embeddings of 80000 words\n80000    80000    0    \n80000 embeddings read, 80000 kept\n2 blocks of 25000 core words and 50000 noncore words will be loaded. Skip 55000 words\nLoading bigram file 'top2grams-wiki.txt' into 2 blocks. Will skip 55000 words\nTotally 277025 words\n277025 words in file, top 75000 to read into vocab (25000 core, 50000 noncore), 55000 skipped\nRead bigrams:\n25000 (25000 core, 0 noncore)\n25000 core words are all read.\n130000 (25000 core, 50000 noncore)\n124073 (0.010%) elements in Weight-1 cut off at 6.63\n111102 (0.009%) elements in Weight-2 cut off at 6.63\n\ndel G1, G21\nBegin finding embeddings of non-core words\n50000 / 50000. Elapsed: 5291.71/10.41 \ndel F21, WGsum, VW\nSave matrix 'V' into 25000-130000-500-BLK-4.0.vec\nTest embeddings derived from block factorization\n\nws353_similarity: 203 test pairs, 203 valid , 0.79212\nws353_relatedness: 252 test pairs, 252 valid , 0.67948\nbruni_men: 3000 test pairs, 3000 valid , 0.76389\nradinsky_mturk: 287 test pairs, 286 valid , 0.67566\nluong_rare: 2034 test pairs, 1096 valid , 0.47344\nsimlex_999a: 999 test pairs, 996 valid , 0.39715\n19500/19158/19544: Add 0.60680, Mul 0.62778\ngoogle: 19544 analogies, 19202 valid . Add Score: 0.60650, Mul Score: 0.62770\n8000/6578/8000: Add 0.48100, Mul 0.52676\nmsr: 8000 analogies, 6578 valid . Add Score: 0.48100, Mul Score: 0.52676\n\nreal\t106m34.491s\nuser\t911m26.380s\nsys\t262m26.192s\nUsing Tikhonov regularization with coeff: 8.0\nRead sim testset ./testsets/ws/ws353_similarity.txt\nRead sim testset ./testsets/ws/ws353_relatedness.txt\nRead sim testset ./testsets/ws/bruni_men.txt\nRead sim testset ./testsets/ws/radinsky_mturk.txt\nRead sim testset ./testsets/ws/luong_rare.txt\nRead sim testset ./testsets/ws/simlex_999a.txt\nRead analogy testset ./testsets/analogy/google.txt\nRead analogy testset ./testsets/analogy/msr.txt\n\nEmbeddings of top 25000 words in '25000-130000-500-BLK-4.0.vec' will be loaded as core\nLoad embedding text file '25000-130000-500-BLK-4.0.vec'\nWill load embeddings of 130000 words\n130000    130000    0    \n130000 embeddings read, 130000 kept\n2 blocks of 25000 core words and 50000 noncore words will be loaded. Skip 105000 words\nLoading bigram file 'top2grams-wiki.txt' into 2 blocks. Will skip 105000 words\nTotally 277025 words\n277025 words in file, top 75000 to read into vocab (25000 core, 50000 noncore), 105000 skipped\nRead bigrams:\n25000 (25000 core, 0 noncore)\n25000 core words are all read.\n180000 (25000 core, 50000 noncore)\n191415 (0.015%) elements in Weight-1 cut off at 4.12\n183822 (0.015%) elements in Weight-2 cut off at 4.12\n\ndel G1, G21\nBegin finding embeddings of non-core words\n50000 / 50000. Elapsed: 5277.66/10.72 \ndel F21, WGsum, VW\nSave matrix 'V' into 25000-180000-500-BLK-8.0.vec\nTest embeddings derived from block factorization\n\nws353_similarity: 203 test pairs, 203 valid , 0.79212\nws353_relatedness: 252 test pairs, 252 valid , 0.67948\nbruni_men: 3000 test pairs, 3000 valid , 0.76389\nradinsky_mturk: 287 test pairs, 286 valid , 0.67566\nluong_rare: 2034 test pairs, 1260 valid , 0.45688\nsimlex_999a: 999 test pairs, 998 valid , 0.39788\n19500/19320/19544: Add 0.60041, Mul 0.62158\ngoogle: 19544 analogies, 19364 valid . Add Score: 0.60013, Mul Score: 0.62151\n8000/7054/8000: Add 0.46187, Mul 0.50383\nmsr: 8000 analogies, 7054 valid . Add Score: 0.46187, Mul Score: 0.50383\n\nreal\t111m53.430s\nuser\t964m38.856s\nsys\t271m9.988s\nword2vec:\nStarting training using file /home/shaohua/D/corpus/cleanwiki.txt\nVocab size: 289625\nWords in train file: 2000719401\nAlpha: 0.000053  Progress: 99.89%  Words/thread/sec: 65.89k  \nreal\t249m8.382s\nuser\t2530m25.988s\nsys\t4m4.680s\nglove:\nBUILDING VOCABULARY\nProcessed 2042546400 tokens.\nCounted 8527820 unique words.\nTruncating vocabulary at min count 100.\nUsing vocabulary of size 289624.\n\nCOUNTING COOCCURRENCES\nwindow size: 3\ncontext: symmetric\nmax product: 50983620\noverflow length: 152113425\nReading vocab from file \"vocab-wiki.txt\"...loaded 289624 words.\nBuilding lookup table...table contains 428261749 elements.\nProcessed 2042546400 tokens.\nWriting cooccurrences to disk..........8 files in total.\nMerging cooccurrence files: processed 652989173 lines.\n\nSHUFFLING COOCCURRENCES\narray size: 1020054732\nShuffling by chunks: processed 652989173 lines.\nWrote 1 temporary file(s).\nMerging temp files: processed 652989173 lines.\n\nTRAINING MODEL\nRead 652989173 lines.\nInitializing parameters...done.\nvector size: 500\nvocab size: 289624\nx_max: 10.000000\nalpha: 0.750000\niter: 001, cost: 0.131547\niter: 002, cost: 0.105234\niter: 003, cost: 0.090557\niter: 004, cost: 0.080886\niter: 005, cost: 0.075276\niter: 006, cost: 0.071524\niter: 007, cost: 0.068758\niter: 008, cost: 0.066837\niter: 009, cost: 0.065186\niter: 010, cost: 0.063919\niter: 011, cost: 0.062813\niter: 012, cost: 0.061871\niter: 013, cost: 0.061140\niter: 014, cost: 0.060471\niter: 015, cost: 0.059661\n\nreal\t229m37.019s\nuser\t1503m26.736s\nsys\t9m14.064s\nsingular:\nCounting words in file 1/1 .......... 6058672 types\nSliding window in file 1/1 ..........\nWriting counts\nLoading counts\nCalculating SVD\nClustering\n\nreal\t183m26.164s\nuser\t87m43.676s\nsys\t29m32.096s\n\nPMI2:\n15239.57user 3723.37system 4:37:35elapsed 113%CPU (0avgtext+0avgdata 9739144maxresident)k\n99330104inputs+7182248outputs (88major+399600177minor)pagefaults 0swaps\n59223.84user 82148.26system 36:36:14elapsed 107%CPU (0avgtext+0avgdata 31071304maxresident)k\n485837760inputs+23424872outputs (109major+7607047364minor)pagefaults 0swaps\n10169.24user 122.85system 2:53:15elapsed 99%CPU (0avgtext+0avgdata 24847900maxresident)k\n11854888inputs+4056outputs (47major+6317122minor)pagefaults 0swaps\n"
  },
  {
    "path": "psdvec/evaluate-toefl.py",
    "content": "#!/usr/bin/python\nimport getopt\nimport glob\nimport sys\nimport os.path\nfrom utils import *\nimport numpy as np\nimport copy\nimport pdb\nimport sys\n\ndef loadToeflTestset(toeflTestsetFilename):\n    TOEFL = open(toeflTestsetFilename)\n    toeflTestset = []\n    for line in TOEFL:\n        line = line.strip()\n        words = line.split(\" | \")\n        toeflTestset.append(words)\n\n    print \"%d toefl test questions are loaded\" %len(toeflTestset)\n    return toeflTestset\n    \nembeddingDir = \"./embeddings/\"\nmodelFiles = [ \"25000-180000-500-BLK-8.0.vec\", \"sparse.vec\", \"singular.vec\",  \n               \"25000-180000-500-BLK-0.0.vec\", \"word2vec.vec\", \"glove.vec\" ]\n\ntoeflTestsetFilename = \"./testsets/ws/EN-TOEFL-80.txt\"\nisHyperwordsEmbed = False\nhyperwordsType = None\n\ndef usage():\n    print \"\"\"Usage: evaluate-toefl.py [ -H -m model_file ]\nOptions:\n  -m:    Path to the model file, a \".vec\" or a Hyperwords embedding directory (with -H).\n  -H:    Hyperwords embeddings type: PPMI or SVD.\"\"\"\n    \ntry:\n    opts, args = getopt.getopt(sys.argv[1:],\"m:H:\")\n    if len(args) != 0:\n        raise getopt.GetoptError(\"\")\n    for opt, arg in opts:\n        if opt == '-m':\n            modelFiles = [ arg ]\n            embeddingDir = \"\"\n        if opt == '-H':\n            isHyperwordsEmbed = True\n            hyperwordsType = arg\n        if opt == '-h':\n            usage()\n            sys.exit(0)\n\nexcept getopt.GetoptError:\n   usage()\n   sys.exit(2)\n       \nvecNormalize = True\nloadwordCutPoint = 180000\n\nif loadwordCutPoint > 0:\n    print \"Load top %d words\" %(loadwordCutPoint)\n\ntoeflTestset = loadToeflTestset(toeflTestsetFilename)\n\nfor m,modelFile in enumerate(modelFiles):\n    modelFile = embeddingDir + modelFile\n    if not isHyperwordsEmbed:\n        V, vocab2, word2dim, skippedWords = load_embeddings( modelFile, loadwordCutPoint )\n        model = VecModel(V, vocab2, word2dim, vecNormalize=vecNormalize)\n    else:\n        model = load_embeddings_hyper(modelFile, hyperwordsType)\n        \n    questionNum = 0\n    correctNum = 0\n    for toeflQuestion in toeflTestset:\n        questionWord = toeflQuestion[0]\n        maxID = -1\n        maxsim = -100\n        for i,w in enumerate( toeflQuestion[1:] ):\n            sim = model.similarity( questionWord, w )\n            if sim > maxsim:\n                maxsim = sim\n                maxID = i\n                \n        if maxID == 0:\n            correctNum += 1\n        else:\n            question = copy.copy(toeflQuestion)\n            question[maxID+1] = '(' + question[maxID+1] + ')'\n            #if m == 0:\n            #    pdb.set_trace()\n            print \"%s: %s, %s, %s, %s\" %tuple(question)\n        questionNum += 1\n    print \"%s: %d/%d=%.1f%%\" %( modelFile, correctNum, questionNum, correctNum*100.0/questionNum )\n    \n    \n"
  },
  {
    "path": "psdvec/evaluate.py",
    "content": "#!/usr/bin/python\n\nimport getopt\nimport glob\nimport sys\nimport os.path\nfrom utils import *\nimport numpy as np\n#import pdb\n\ngetAbsentWords = False\nmodelFiles = [ \"./GoogleNews-vectors-negative300.bin\", \"./29291-500-EM.vec\", \"./100000-500-BLKEM.vec\",\n                \"./wordvecs/vec_520_forest\", \"./wiki-glove.vec2.txt\" ]\n\nisModelsBinary = [ True, False, False, False, False ]\nmodelID = -1\n\n# default is current directory\nsimTestsetDir = \"./testsets/ws/\"\n# if set to [], run all testsets\nsimTestsetNames = [ \"ws353_similarity\", \"ws353_relatedness\", \"bruni_men\", \"radinsky_mturk\", \"luong_rare\", \n                        \"simlex_999a\", \"EN-RG-65\" ]\nanaTestsetDir = \"./testsets/analogy/\"\n# if set to [], run all testsets\nanaTestsetNames = [ \"google\", \"msr\" ]\n\nunigramFilename = \"top1grams-wiki.txt\"\nvecNormalize = True\nloadwordCutPoint = -1\ntestwordCutPoint = -1\nabsentFilename = \"\"\nextraWordFilename = \"\"\n# default is in text format\nisModelBinary = False\nmodelFile = None\n# precompute the cosine similarity matrix of all pairs of words\n# need W*W*4 bytes of RAM\nprecomputeGramian = False\nskipPossessive = False\nevalVecExpectation = False\ndoAnaTest = True\nisHyperwordsEmbed = False\nhyperEmbedType = None\n\ndef usage():\n    print \"\"\"Usage: evaluate.py [ -m model_file -i builtin_model_id -e extra_word_file -a absent_file -u unigram_file ... ]\nOptions:\n  -m:    Path to the model file, a \".vec\" or \".bin\" file for word2vec\n  -b:    Model file is in binary format (default: text)\n  -d:    A directory containing the test files\n  -f:    A list of test files in the specified directory\n  -i:    Builtin model ID for the benchmark. Range: 1 (word2vec),\n         2 (PSD 29291 words), 3 (block PSD 100000 words), 4 (forest), 5(glove)\n  -P:    Do not precompute cosine matrix. When the vocab is huge, \n         it's necessary to disable computing this matrix.\n  -u:    Unigram file, for missing word check.\n         Its presence will enable checking of what words are missing\n         from the vocabulary and the model\n  -c:    Loaded Model vocabulary cut point. Load top x words from the model file\n  -t:    Vocabulary cut point for the test sets. All words in the test sets\n         whose IDs are below it will be picked out\n  -e:    Extra word file. Words in this list will be loaded anyway\n  -a:    Absent file. Words below the cut point will be saved there\n  -p:    Skip possessive analogy pairs\n  -E:    Compute the expectation of all word embeddings\n  -H:    Hyperwords embeddings type: PPMI or SVD.\"\"\"\n  \ntry:\n    opts, args = getopt.getopt(sys.argv[1:],\"m:bd:f:i:Pu:c:t:e:a:shEAH:\")\n    if len(args) != 0:\n        raise getopt.GetoptError(\"\")\n    for opt, arg in opts:\n        if opt == '-m':\n            modelID = -1\n            modelFile = arg\n        if opt == '-b':\n            isModelBinary = bool(arg)\n        if opt == '-d':\n            testsetDir = arg\n        if opt == '-f':\n            testsetNames = filter( lambda x: x, arg.split(\",\") )\n        if opt == '-i':\n            modelID = int(arg)\n        if opt == '-P':\n            precomputeGramian = False\n        if opt == '-u':\n            # unigram file is used to get a full list of words,\n            # and also to sort the absent words by their frequencies\n            unigramFilename = arg\n        if opt == '-c':\n            loadwordCutPoint = int(arg)\n        if opt == '-t':\n            testwordCutPoint = int(arg)\n        if opt == '-e':\n            extraWordFilename = arg\n        if opt == '-a':\n            getAbsentWords = True\n            absentFilename = arg\n        if opt == '-A':\n            doAnaTest = False\n        if opt == '-s':\n            skipPossessive = True\n        if opt == '-E':\n            evalVecExpectation = True\n        if opt == '-H':\n            isHyperwordsEmbed = True\n            hyperEmbedType = arg\n        if opt == '-h':\n            usage()\n            sys.exit(0)\n\n    if getAbsentWords and not unigramFilename:\n        print \"ERR: -u (Unigram file) has to be specified to get absent words\"\n        sys.exit(2)\n    # \"-\" means output to console instead of a file\n    if absentFilename == \"-\":\n        absentFilename = \"\"\n\nexcept getopt.GetoptError:\n   usage()\n   sys.exit(2)\n\nif modelID > 0:\n    modelFile = modelFiles[ modelID - 1 ]\n    isModelBinary = isModelsBinary[ modelID - 1 ]\n\nif modelFile is None:\n    usage()\n    sys.exit(2)\n    \nvocab = {}\nif unigramFilename:\n    vocab = loadUnigramFile(unigramFilename)\n\nif extraWordFilename:\n    extraWords = loadExtraWordFile(extraWordFilename)\nelse:\n    extraWords = {}\n\nif loadwordCutPoint > 0:\n    print \"Load top %d words\" %(loadwordCutPoint)\n\nif isModelBinary:\n    V, vocab2, word2dim, skippedWords = load_embeddings_bin( modelFile, loadwordCutPoint, extraWords )\nelif not isHyperwordsEmbed:\n    V, vocab2, word2dim, skippedWords =     load_embeddings( modelFile, loadwordCutPoint, extraWords )\nelse:\n    model = load_embeddings_hyper( modelFile, hyperEmbedType )\n    # the interface of hyperwords embedding class is incompatible with analogy tasks\n    # only compatible with similarity tasks\n    doAnaTest = False\n\n# if evalVecExpectation = True, compute the expectation of all embeddings\nif evalVecExpectation:\n    if unigramFilename:\n        expVec = np.zeros( len(V[0]) )\n        expVecNorm1 = 0\n        expVecNorm2 = 0\n        totalWords = 0\n        expWords = 0\n        accumProb = 0.0\n        for w in vocab2:\n            totalWords += 1\n            if w in vocab and vocab[w][0] < 180000:\n                expVec += V[ word2dim[w] ] * vocab[w][2]\n                expVecNorm1 += norm1( V[ word2dim[w] ] ) * vocab[w][2]\n                expVecNorm2 += normF( V[ word2dim[w] ] ) * vocab[w][2]\n                expWords += 1\n                accumProb += vocab[w][2]\n    \n        expVec /= accumProb\n        expVecNorm1 /= accumProb\n        expVecNorm2 /= accumProb\n        print \"totally %d words, %d words in E[v]. Accumu prob: %.2f%%.\" %( totalWords, expWords, accumProb * 100 )\n        print \"|E[v]|: %.2f/%.2f, E[|v|]: %.2f/%.2f\" %( norm1(expVec), normF(expVec), expVecNorm1, expVecNorm2 )\n\n        expMagnitude = norm1(expVec)\n        accumProb = 0\n        variance = 0\n        for w in vocab2:\n            if w in vocab and vocab[w][0] < 180000:\n                variance += ( norm1( V[ word2dim[w] ] ) - expMagnitude )**2 * vocab[w][2]\n                accumProb += vocab[w][2]\n        \n        variance /= accumProb\n        \n        # variance & standard deviation\n        print \"var(|v|): %.2f. SD: %.2f. CV: %.2f\" %( variance, np.sqrt(variance), np.sqrt(variance) / expVecNorm1 )\n                                                                                                \n        sys.exit(0)\n        \n    else:\n        print \"ERR: -u (Unigram file) has to be specified to calc expectation of embeddings\"\n        sys.exit(2)\n\nif not isHyperwordsEmbed:        \n    model = VecModel(V, vocab2, word2dim, vecNormalize=vecNormalize)\n\nif precomputeGramian:\n    isEnoughGramian, installedMemGB, requiredMemGB = isMemEnoughGramian( len(V) )\n    \n    if isEnoughGramian <= 1:\n        print \"WARN: %.1fGB mem detected, %.1fGB mem required to precompute the cosine matrix\" %( installedMemGB, requiredMemGB )\n        if isEnoughGramian == 0:\n            print \"Precomputation of the cosine matrix is disabled automatically.\"\n        else:\n            print \"In case of memory shortage, you can specify -P to disable\"\n\n    if isEnoughGramian > 0:\n        model.precomputeGramian()\n        \nprint\n\nsimTestsets = loadTestsets(loadSimTestset, simTestsetDir, simTestsetNames)\n\nif skipPossessive:\n    anaTestsets = loadTestsets( loadAnaTestset, anaTestsetDir, anaTestsetNames, { 'skipPossessive': 1 } )\nelse:\n    anaTestsets = loadTestsets( loadAnaTestset, anaTestsetDir, anaTestsetNames )\n\nprint\n\nspearmanCoeff, absentModelID2Word1, absentVocabWords1, cutVocabWords1 = \\\n            evaluate_sim( model, simTestsets, simTestsetNames, getAbsentWords, vocab, testwordCutPoint )\n\nprint\n\nif doAnaTest:\n    anaScores,     absentModelID2Word2, absentVocabWords2, cutVocabWords2 = \\\n            evaluate_ana( model, anaTestsets, anaTestsetNames, getAbsentWords, vocab, testwordCutPoint )\n\nif getAbsentWords:\n    # merge the two sets of absent words\n    absentModelID2Word1.update(absentModelID2Word2)\n    absentModelWordIDs = sorted( absentModelID2Word1.keys() )\n    absentModelWords = [ absentModelID2Word1[i] for i in absentModelWordIDs ]\n\n    absentVocabWords1.update(absentVocabWords2)\n    absentVocabWords = sorted( absentVocabWords1.keys() )\n\n    cutVocabWords1.update(cutVocabWords2)\n    # sort by ID in ascending, so that most frequent words (smaller IDs) first\n    cutVocabWords = sorted( cutVocabWords1.keys(), key=lambda w: vocab[w][0] )\n\n    print \"\\n%d words absent from the model:\" %len(absentModelWordIDs)\n    print \"ID:\"\n    print \",\".join( map( lambda i: str(i), absentModelWordIDs) )\n    print \"\\nWords:\"\n    print \",\".join(absentModelWords)\n\n    if len(absentVocabWords) > 0:\n        print \"\\n%d words absent from the vocab:\" %len(absentVocabWords)\n        print \"\\n\".join(absentVocabWords)\n\n    print\n\n    if absentFilename and len(cutVocabWords):\n        ABS = open(absentFilename, \"w\")\n        for w in cutVocabWords:\n            ABS.write( \"%s\\t%d\\n\" %( w, vocab[w][0] ) )\n        ABS.close()\n        print \"%d words saved to %s\" %( len(cutVocabWords), absentFilename )\n"
  },
  {
    "path": "psdvec/extractwiki.py",
    "content": "#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# code based on http://textminingonline.com/training-word2vec-model-on-english-wikipedia-by-gensim\r\n \r\nimport logging\r\nimport os.path\r\nimport sys\r\n \r\nfrom gensim.corpora import WikiCorpus\r\n \r\nif __name__ == '__main__':\r\n    program = os.path.basename(sys.argv[0])\r\n    logger = logging.getLogger(program)\r\n \r\n    logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s')\r\n    logging.root.setLevel(level=logging.INFO)\r\n    logger.info(\"running %s\" % ' '.join(sys.argv))\r\n \r\n    # check and process input arguments\r\n    if len(sys.argv) < 3:\r\n        print \"Usage: extractwiki.py infile_name outfile_name\"\r\n        sys.exit(1)\r\n        \r\n    infilename, outfilename = sys.argv[1:3]\r\n \r\n    if os.path.isfile(outfilename):\r\n        logger.error(\"Output file %s exists. Change the file name and try again.\" %outfilename)\r\n        sys.exit(1)\r\n        \r\n    i = 0\r\n    output = open(outfilename, 'w')\r\n    wiki = WikiCorpus(infilename, lemmatize=False, dictionary={})\r\n    for text in wiki.get_texts():\r\n        output.write( \" \".join(text) + \"\\n\")\r\n        i = i + 1\r\n        if (i % 10000 == 0):\r\n            logger.info(\"Saved \" + str(i) + \" articles\")\r\n \r\n    output.close()\r\n    logger.info(\"Finished Saved \" + str(i) + \" articles\")\r\n    "
  },
  {
    "path": "psdvec/fact-rcv1.bat",
    "content": "@echo off\r\nset N0=50\r\nrem Old way of exact factorization: \r\nrem python factorize.py -n 50 -t 28000 -e absentwords.txt top2grams-rcv1.txt\r\nrem New online fashion:\r\nrem 1. Obtain 23000 core embeddings, into 25000-50-EM.vec:\r\nrem python factorize.py -w 23000 -n %N0% top2grams-rcv1.txt\r\nrem 2. Obtain 23409 noncore embeddings, totaling 46409 (23000 core + 23409 noncore), into 25000-46409-50-BLK-2.0.vec:\r\npython factorize.py -v 23000-%N0%-EM.vec  -n %N0% -t2 top2grams-rcv1.txt\r\n"
  },
  {
    "path": "psdvec/fact-rcv1.sh",
    "content": "#!/bin/bash\nN0=50\ntime python factorize.py -w 15000 -n $N0 -E5 top2grams-rcv1.txt\ntime python factorize.py -v 15000-$N0-EM.vec  -n $N0 -t2 top2grams-rcv1.txt\n"
  },
  {
    "path": "psdvec/fact-wiki.bat",
    "content": "@echo off\r\nset N0=500\r\nrem Old way of exact factorization: \r\nrem python factorize.py -n 500 -t 28000 -e absentwords.txt top2grams-wiki.txt\r\nrem New online fashion:\r\nrem 1. Obtain 25000 core embeddings, into 25000-500-EM.vec:\r\npython factorize.py -w 25000 -n %N0% top2grams-wiki.txt\r\nrem 2. Obtain 55000 noncore embeddings, totaling 80000 (25000 core + 55000 noncore), into 25000-80000-500-BLK-2.0.vec:\r\npython factorize.py -v 25000-%N0%-EM.vec  -n %N0% -o 55000 -t2 top2grams-wiki.txt\r\nrem 3. Incrementally learn other 50000 noncore embeddings (based on 25000 core), into 25000-130000-500-BLK-4.0.vec:\r\npython factorize.py -v 25000-80000-%N0%-BLK-2.0.vec  -n %N0% -b 25000 -o 50000 -t4 top2grams-wiki.txt\r\nrem 4. Repeat 3 again to get more embeddings of rarer words.\r\npython factorize.py -v 25000-130000-%N0%-BLK-4.0.vec  -n %N0% -b 25000 -o 50000 -t8 top2grams-wiki.txt\r\n"
  },
  {
    "path": "psdvec/fact-wiki.sh",
    "content": "#!/bin/bash\nN0=500\n# Old way of exact factorization: \n# python factorize.py -n 500 -t 28000 -e absentwords.txt top2grams-wiki.txt\n# New online fashion:\n# 1. Obtain 25000 core embeddings, into 25000-500-EM.vec:\ntime python factorize.py -w 25000 -n $N0 top2grams-wiki.txt\n# 2. Obtain 55000 noncore embeddings, totaling 80000 (25000 core + 55000 noncore), into 25000-80000-500-BLK-2.0.vec:\ntime python factorize.py -v 25000-$N0-EM.vec  -n $N0 -o 55000 -t2 top2grams-wiki.txt\n# 3. Incrementally learn other 50000 noncore embeddings (based on 25000 core), into 25000-130000-500-BLK-4.0.vec:\ntime python factorize.py -v 25000-80000-$N0-BLK-2.0.vec  -n $N0 -b 25000 -o 50000 -t4 top2grams-wiki.txt\n# 4. Repeat 3 again to get more embeddings of rarer words.\ntime python factorize.py -v 25000-130000-$N0-BLK-4.0.vec  -n $N0 -b 25000 -o 50000 -t8 top2grams-wiki.txt\n"
  },
  {
    "path": "psdvec/factorize.py",
    "content": "import sys\r\nimport numpy as np\r\nfrom scipy import linalg\r\nimport getopt\r\nimport timeit\r\nimport pdb\r\nfrom utils import *\r\nimport os\r\nimport glob\r\nimport time\r\n\r\n# factorization weighted by unigram probs\r\n# MAXITERS is not used. Only for API conformity\r\n# tikhonovCoeff: coefficient of Tikhonov regularization on the unigram prob-weighted least squares\r\n# Not implemented yet\r\ndef uniwe_factorize(G, u, N0, MAXITERS=0, tikhonovCoeff=0, testenv=None):\r\n    timer = Timer( \"uniwe_factorize()\" )\r\n    print \"Begin factorization weighted by unigrams\"\r\n\r\n#    if BigramWeight is not None:\r\n#        maxwe = np.max(BigramWeight) * 1.0\r\n#        # normalize to [0,1]\r\n#        BigramWeight = BigramWeight / maxwe\r\n\r\n    Gsym  = sym(G)\r\n\r\n    power = 0.5\r\n    Utrans = np.power(u, power)\r\n    Weight = np.outer( Utrans, Utrans )\r\n\r\n    # es: eigenvalues. vs: right eigenvectors\r\n    # Weight * Gsym = vs . diag(es) . vs.T\r\n    es, vs = np.linalg.eigh( Weight * Gsym )\r\n\r\n    # find the cut point of positive eigenvalues\r\n    es2 = sorted(es, reverse=True)\r\n\r\n    d = len(es) - 1\r\n    while es2[d] <= 0 and d >= 0:\r\n        d -= 1\r\n    posEigenSum = np.sum( es2[:d+1] )\r\n    print \"%d positive eigenvalues, sum: %.3f\" %( d+1, posEigenSum )\r\n\r\n    d = N0 - 1\r\n    while es2[d] < 0 and d >= 0:\r\n        d -= 1\r\n    if d == -1:\r\n        print \"All eigenvalues are negative. Abnormal\"\r\n        sys.exit(2)\r\n\r\n    print \"Eigenvalues cut at the %d-th largest value, between %.3f-%.3f\" %( d+1, es2[d], es2[d+1] )\r\n    cutoff = es2[d]\r\n    # keep the top N0 eigenvalues, set others to 0\r\n    es_N = map( lambda x: x if x >= cutoff else 0, es )\r\n    es_N = np.array(es_N, dtype=np.float32)\r\n    print \"All eigen norm: %.3f, Kept sum: %.3f\" %( norm1(es), norm1(es_N) )\r\n\r\n    es_N_sqrt = np.diag( np.sqrt(es_N) )\r\n    # remove all-zero columns, leaving N columns\r\n    es_N_sqrt = es_N_sqrt[ :, np.flatnonzero( es_N > 0 ) ]\r\n\r\n    # vs.T / Utrans, is dividing vs.T row by row, = vs.T . diag(Utrans^-1)\r\n    # (vs.T / Utrans).T, is dividing vs column by column.\r\n    V = np.dot( (vs.T / Utrans).T, es_N_sqrt )\r\n    VV = np.dot( V, V.T )\r\n\r\n    #print \"No Weight V: %.3f, VV: %.3f, G-VV: %.3f\" %( norm1(V), norm1(VV), norm1(G - VV) )\r\n    print \"L1 Uni Weighted VV: %.3f, G-VV: %.3f\" %( norm1(VV, Weight), norm1(G - VV, Weight) )\r\n\r\n#    if BigramWeight is not None:\r\n#        print \"Freq Weighted:\"\r\n#        print \"VV: %.3f, G-VV: %.3f\" %( norm1(VV, BigramWeight), norm1(G - VV, BigramWeight) )\r\n\r\n    if testenv:\r\n        model = VecModel( V, testenv['vocab'], testenv['word2id'], vecNormalize=True, precompute_gramian=True )\r\n        evaluate_sim( model, testenv['simTestsets'], testenv['simTestsetNames'] )\r\n        evaluate_ana( model, testenv['anaTestsets'], testenv['anaTestsetNames'] )\r\n\r\n    timer.printElapseTime()\r\n\r\n    return V, VV\r\n\r\n# Factorization without weighting\r\n# N: dimension of factorization\r\n# tikhonovCoeff: coefficient of Tikhonov regularization on the unweighted least squares\r\ndef nowe_factorize(G, N, tikhonovCoeff=0):\r\n    print \"Begin unweighted factorization\"\r\n\r\n    Gsym = sym(G)\r\n\r\n    # es: eigenvalues. vs: right eigenvectors\r\n    # Gsym = vs . diag(es) . vs.T\r\n    es, vs = np.linalg.eigh(Gsym)\r\n\r\n    # es2: sorted original eigenvalues of Gsym\r\n    es2 = sorted(es, reverse=True)\r\n\r\n    d = len(es) - 1\r\n    # d points at the smallest nonnegative eigenvalue\r\n    while es2[d] <= 0 and d >= 0:\r\n        d -= 1\r\n    posEigenSum = np.sum( es2[:d+1] )\r\n    print \"%d positive eigenvalues, sum: %.3f\" %( d+1, posEigenSum )\r\n\r\n    d = N - 1\r\n    while es2[d] < 0 and d >= 0:\r\n        d -= 1\r\n    if d == -1:\r\n        print \"All eigenvalues are negative. Weird\"\r\n        sys.exit(2)\r\n\r\n    #print \"Eigenvalues:\\n%s\" %(es2)\r\n\r\n    print \"Eigenvalues cut at the %d-th, %.3f ~ %.3f\" %( d+1, es2[d], es2[d+1] )\r\n    cutoff = es2[d]\r\n    # keep the top N eigenvalues, set others to 0\r\n    es_N = map( lambda x: x if x >= cutoff else 0, es )\r\n    es_N = np.array( es_N, dtype=np.float32 )\r\n    print \"All eigen sum: %.3f, Kept eigen sum: %.3f\" %( norm1(es), norm1(es_N) )\r\n\r\n    if tikhonovCoeff > 0:\r\n        scale = 1.0 / ( 1 + tikhonovCoeff )\r\n        es_N *= scale\r\n        print \"Regularized kept eigen sum: %.3f\" %( norm1(es_N) )\r\n        \r\n    es_N_sqrt = np.diag( np.sqrt(es_N) )\r\n    # remove all-zero columns\r\n    es_N_sqrt = es_N_sqrt[ :, np.flatnonzero( es_N > 0 ) ]\r\n\r\n    V = np.dot( vs, es_N_sqrt )\r\n    VV = np.dot( V, V.T )\r\n\r\n#    print \"No Weight V: %.3f, VV: %.3f, G-VV: %.3f\" %( norm1(V), norm1(VV), norm1(G - VV) )\r\n#    print \"No Weight Gsym: %.3f, Gsym-VV: %.3f\" %( norm1(Gsym), norm1(Gsym - VV) )\r\n\r\n    timer.printElapseTime()\r\n\r\n    return V, VV\r\n\r\n# Weighted factorization by bigram freqs, optimized using Gradient Descent algorithm\r\n# Weight: nonnegative weight matrix. Assume already normalized\r\n# N0: desired rank of V\r\n# tikhonovCoeff: coefficient of Tikhonov regularization. Not implemented yet\r\ndef we_factorize_GD(G, Weight, N0, MAXITERS=5000, tikhonovCoeff=0, testenv=None):\r\n    timer1 = Timer( \"we_factorize_GD()\" )\r\n    # D is the number of words in the vocab (W in the paper)\r\n    D = len(G)\r\n\r\n    isEnoughGramian, installedMemGB, requiredMemGB = isMemEnoughGramian(D, 5)\r\n    init_with_nowe = True\r\n\r\n    if init_with_nowe:\r\n        isEnoughEigen, installedMemGB, requiredMemGB = isMemEnoughEigen(D)\r\n        # enough mem, initialize with unweighted low rank approximation\r\n        if isEnoughEigen >= 1:\r\n            # initialize V to unweighted low rank approximation\r\n            V, VV = nowe_factorize(G, N0)\r\n\r\n            if testenv:\r\n                model = VecModel( V, testenv['vocab'], testenv['word2id'], vecNormalize=True, precompute_gramian=isEnoughGramian )\r\n                evaluate_sim( model, testenv['simTestsets'], testenv['simTestsetNames'] )\r\n                if isEnoughGramian:\r\n                    evaluate_ana( model, testenv['anaTestsets'], testenv['anaTestsetNames'] )\r\n                del model\r\n                \r\n        else:\r\n            print \"Not enough RAM: %.1fGB mem detected, %.1fGB mem required.\" %( installedMemGB, requiredMemGB )\r\n            print \"Initialize V randomly\"\r\n            V = np.random.rand( D, N0 )\r\n            VV = np.dot( V, V.T )\r\n\r\n    else:\r\n        print \"Initialize V randomly\"\r\n        V = np.random.rand( D, N0 )\r\n        VV = np.dot( V, V.T )\r\n\r\n    # in-place operations to reduce the space of a matrix\r\n    G *= Weight\r\n    G_Weight = G\r\n    VV *= Weight\r\n    norm1_VV = norm1(VV)\r\n    VV -= G_Weight\r\n    # In this function, A is defined as VV-G. \r\n    # A * Weight = ( VV - G ) * Weight = VV * Weight - G * Weight\r\n    A_Weight = VV\r\n\r\n    print \"L1 Weighted: VV: %.3f, G-VV: %.3f\" %( norm1_VV, norm1(A_Weight) )\r\n    print \"\\nBegin Gradient Descent of weighted factorization by bigram freqs\"\r\n\r\n    #pdb.set_trace()\r\n\r\n    for it in xrange(MAXITERS):\r\n        timer2 = Timer( \"GD iter %d\" %(it+1) )\r\n        print \"\\nGD Iter %d:\" %( it + 1 )\r\n\r\n        # step size\r\n        gamma = 1.0 / ( it + 2 )\r\n        # Grad is D*N0, takes little memory\r\n        Grad = np.dot( A_Weight, V )\r\n\r\n        # limit the norm of the step size to no less than the norm of V, times gamma\r\n        # the gradient still converges to zero, although r is fluctuating\r\n        r = norm1(V)/norm1(Grad)\r\n        if r > 1.0:\r\n            r = 1.0\r\n\r\n        print \"Rate: %f\" %( r * gamma )\r\n\r\n        V -= r * gamma * Grad\r\n        VV = np.dot( V, V.T )\r\n        VV *= Weight\r\n        norm1_VV = norm1(VV)\r\n        VV -= G_Weight\r\n        A_Weight = VV\r\n\r\n        print \"L1 Weighted: VV: %.3f, G-VV: %.3f\" %( norm1_VV, norm1(A_Weight) )\r\n\r\n        if testenv and it % 5 == 4:\r\n            model = VecModel( V, testenv['vocab'], testenv['word2id'], vecNormalize=True, precompute_gramian=isEnoughGramian )\r\n            evaluate_sim( model, testenv['simTestsets'], testenv['simTestsetNames'] )\r\n            # evaluate_ana is very slow when we couldn't precomputeGramian()\r\n            # so in this case, only call it every 100 iterations to save training time\r\n            if isEnoughGramian or it % 100 == 99:\r\n                evaluate_ana( model, testenv['anaTestsets'], testenv['anaTestsetNames'] )\r\n            del model\r\n            \r\n        timer2.printElapseTime()\r\n\r\n    timer1.printElapseTime()\r\n\r\n    VV = np.dot( V, V.T )\r\n    return V, VV\r\n\r\n# Weighted factorization by bigram freqs, optimized using EM algorithm\r\n# if MAXITERS==1, it's identical to nowe_factorize()\r\n# Weight: nonnegative weight matrix. Assume it's already normalized\r\n# N0: desired rank\r\n# tikhonovCoeff: coefficient of Tikhonov regularization on the weighted least squares using EM\r\ndef we_factorize_EM(G, Weight, N0, MAXITERS=5, tikhonovCoeff=0, testenv=None):\r\n\r\n    timer1 = Timer( \"we_factorize_EM()\" )\r\n\r\n    D = len(G)\r\n\r\n    isEnoughEigen, installedMemGB, requiredMemGB = isMemEnoughEigen(D)\r\n    # Not enough RAM\r\n    if isEnoughEigen == 0:\r\n        print \"%.1fG RAM is required by eigendecomposition, but only %.1fG is installed\" %(requiredMemGB, installedMemGB)\r\n        print \"Proceeding may hang your computer. Please reduce the factorized vocab size (-w)\"\r\n        print \"You have 10 seconds to stop execution using Ctrl-C:\"\r\n        for i in xrange(10):\r\n            time.sleep(1)\r\n            print \"\\r%d\\r\" %i,\r\n        print \"Timeout. Proceed anyway\"\r\n        \r\n    isEnoughGramian, installedMemGB, requiredMemGB = isMemEnoughGramian(D, 5)\r\n\r\n    print \"Begin EM of weighted factorization by bigram freqs\"\r\n    Gsym  = sym(G)\r\n\r\n    alpha = 0.5\r\n    # X: low rank rep in the M step\r\n    X = alpha * G\r\n\r\n    # N is N0 plus the iteration number. Decrease 1 after each iteration\r\n    N = min( N0 + MAXITERS - 1, len(G) )\r\n\r\n    #pdb.set_trace()\r\n\r\n    for it in xrange(MAXITERS):\r\n        timer2 = Timer( \"EM iter %d\" %(it+1) )\r\n        print \"\\nEM Iter %d:\" %(it+1)\r\n\r\n        Gi = Weight * G + (1 - Weight) * X\r\n        V, VV = nowe_factorize( Gi, N, tikhonovCoeff )\r\n\r\n        # reduce the rank by one in every iteration\r\n        if N > N0:\r\n            N -= 1\r\n\r\n        X = VV\r\n\r\n        print \"L1 Weighted: Gi: %.3f, VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizes( norm1, [Gi, VV, Gsym - VV, G - VV], Weight ) )\r\n#        print \"L2 Weighted: Gi: %.3f, VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizes( normF, [Gi, VV, Gsym - VV, G - VV], Weight ) )\r\n\r\n        #X = Xnew\r\n\r\n        if testenv:\r\n            model = VecModel( V, testenv['vocab'], testenv['word2id'], vecNormalize=True, precompute_gramian=isEnoughGramian )\r\n            evaluate_sim( model, testenv['simTestsets'], testenv['simTestsetNames'] )\r\n            evaluate_ana( model, testenv['anaTestsets'], testenv['anaTestsetNames'] )\r\n\r\n        timer2.printElapseTime()\r\n\r\n    timer1.printElapseTime()\r\n\r\n    return V, VV\r\n\r\n# Weighted factorization by bigram freqs, optimized using Frank-Wolfe algorithm\r\n# Weight: nonnegative weight matrix. Assume already normalized\r\n# N0: desired rank of V\r\n# tikhonovCoeff: coefficient of Tikhonov regularization. Not implemented yet\r\ndef we_factorize_FW(G, Weight, N0, MAXITERS=6, tikhonovCoeff=0, testenv=None):\r\n\r\n    timer1 = Timer( \"we_factorize_FW()\" )\r\n\r\n    D = len(G)\r\n\r\n    Gsym  = sym(G)\r\n\r\n    # initial X in the SVD set\r\n    X = 0\r\n    #V, VV = nowe_factorize(G, N0)\r\n    #X = VV\r\n\r\n    print \"Begin Frank-Wolfe of weighted factorization by bigram freqs\"\r\n\r\n#    e, v = np.linalg.eigh( Gsym )\r\n#    print \"Eigen sum of Gsym: %.3f\" %( norm1(e) )\r\n#\r\n#    e, v = np.linalg.eigh( sym(G * Weight) )\r\n#    print \"Eigen sum of GWsym: %.3f\" %( norm1(e) )\r\n#    t = np.abs(e[0]) * 5\r\n\r\n    t = 10\r\n\r\n    print \"t=%.3f\\n\" %(t)\r\n\r\n    #pdb.set_trace()\r\n    use_power_iter = False\r\n\r\n    TRUNC_CYCLE = 3\r\n    # maximum number of largest negative eigenvalues/eigenvectors used for update\r\n    # \"-1\" means all\r\n    maxUpdVecNum = -1\r\n\r\n    for it in xrange(MAXITERS):\r\n        timer2 = Timer( \"FW iter %d\" %( it + 1 ) )\r\n        print \"\\nFW Iter %d:\" %( it + 1 )\r\n        # step size\r\n        gamma = 1.0 / ( it + 2 )\r\n        #Grad = ( X - Gsym ) * WeightSym\r\n        # symmetrized original grad performs much better than symmetric grad ( X - Gsym ) * WeightSym\r\n        Grad = sym( ( X - G ) * Weight )\r\n\r\n#        if use_power_iter:\r\n#        #if True:\r\n#            e1p, v1p = power_iter(Grad)\r\n#            if e1p > 0:\r\n#                print \"Warn: Principal eigenvalue %.3f > 0\" %e1p\r\n                #break\r\n        #else:\r\n\r\n        # largest k negative eigenvalues / eigenvectors\r\n        negEs = []\r\n        negVs = []\r\n\r\n        if True:\r\n            #eigenPos = -1\r\n            # eigenvalues are in ascending order in es\r\n            # largest (magnitude) negative eigenvalue is at the beginning of es\r\n            es, vs = np.linalg.eigh(Grad)\r\n\r\n            for i in xrange( len(es) ):\r\n                if es[i] < -0.001 and ( maxUpdVecNum == -1 or len(negEs) < maxUpdVecNum ):\r\n                    negEs.append( es[i] )\r\n                    negVs.append( vs[:, i].astype(np.float64) )\r\n                if es[i] >= 0:\r\n                    break\r\n\r\n            if len(negEs) == 0:\r\n                print \"All eigenvalues are positive. Stop\"\r\n                break\r\n\r\n            greaterPositiveCount = 0\r\n            for i in xrange( len(es) - 1, -1, -1 ):\r\n                if es[i] >= abs( negEs[0] ):\r\n                    greaterPositiveCount += 1\r\n                else:\r\n                    break\r\n\r\n            # at least 1, at most N0\r\n            # try to keep S low rank\r\n            usedEigenNum = min( max( len(negEs)/3, 1 ), N0/3 )\r\n\r\n            print \"%d -eigen used. %.3f ~ %.3f\" %( usedEigenNum, negEs[0], negEs[ usedEigenNum - 1 ] )\r\n            if greaterPositiveCount > 0:\r\n                print \"Warn: Principal eigen %.3f > 0, %d +eigen are larger\" %( es[-1], greaterPositiveCount )\r\n\r\n        #e1 = e1d\r\n        #v1 = v1d\r\n\r\n        # efft: effective t\r\n        efft = t / np.power(gamma, 0.7)\r\n        print \"eff t: %.3f\" %efft\r\n\r\n        S = np.zeros((D, D), dtype=np.float64)\r\n\r\n        # S is always PSD\r\n        for i in xrange( usedEigenNum ):\r\n            S += efft * np.outer( negVs[i], negVs[i] )\r\n\r\n        # since X is PSD, Xnew is also PSD\r\n        Xnew = X + gamma * ( S.astype(np.float32) - X )\r\n\r\n        doTrunc = False\r\n\r\n        # Truncate insignificant eigenvectors\r\n        if it%TRUNC_CYCLE == TRUNC_CYCLE - 1:\r\n            # print the old matrix sizes first for contrast to the new sizes\r\n            matSizes1old = matSizes( norm1, [Xnew, Gsym - Xnew, G - Xnew], Weight ) #matSizes( norm1, [Xnew, Gsym - X, G - X], WeightSym ) + \\\r\n\r\n#           print \"L1 SymWeighted: VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizes1old[0:3] )\r\n            print \"L1 Weighted: VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizes1old )\r\n\r\n#            matSizesFold = matSizes( normF, [Xnew, Gsym - Xnew, G - Xnew], Weight ) #matSizes( normF, [Xnew, Gsym - X, G - X], WeightSym ) + \\\r\n\r\n#           print \"L2 SymWeighted: VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizesFold[0:3] )\r\n#            print \"L2 Weighted: VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizesFold )\r\n\r\n            es, vs = np.linalg.eigh(Xnew)\r\n\r\n            es = map( lambda x: x if x >= 0 else 0, es )\r\n            es = np.array( es, dtype = np.float32 )\r\n\r\n            E_sqrt = np.diag( np.sqrt(es) )\r\n            V = vs.dot(E_sqrt)\r\n\r\n            if testenv:\r\n                model = VecModel( V, testenv['vocab'], testenv['word2id'], vecNormalize=True, precompute_gramian=True )\r\n                evaluate_sim( model, testenv['simTestsets'], testenv['simTestsetNames'] )\r\n                evaluate_ana( model, testenv['anaTestsets'], testenv['anaTestsetNames'] )\r\n\r\n            print \"Clear %d insignificant eigenvectors: %.3f ~ %.3f\" %( D - N0, es[ D - N0 - 1], es[0] )\r\n\r\n            es[ 0 : D - N0 ] = 0\r\n            Xnew = vs.dot( np.diag(es).dot( vs.T ) )\r\n\r\n            E_sqrt = np.diag( np.sqrt(es) )\r\n            V = vs.dot(E_sqrt)\r\n\r\n            doTrunc = True\r\n\r\n        X = Xnew\r\n\r\n        matSizes1 = matSizes( norm1, [Xnew, Gsym - X, G - X], Weight ) #matSizes( norm1, [Xnew, Gsym - X, G - X], WeightSym ) + \\\r\n\r\n#       print \"L1 SymWeighted: VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizes1[0:3] )\r\n        print \"L1 Weighted: VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizes1 )\r\n\r\n#        matSizesF = matSizes( normF, [Xnew, Gsym - X, G - X], Weight ) #matSizes( normF, [Xnew, Gsym - X, G - X], WeightSym ) + \\\r\n\r\n#       print \"L2 SymWeighted: VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizesF[0:3] )\r\n#        print \"L2 Weighted: VV: %.3f, Gsym-VV: %.3f, G-VV: %.3f\" %tuple( matSizesF )\r\n\r\n        if doTrunc:\r\n            matSizes1old = np.array( matSizes1old, dtype=np.float32 )\r\n#            matSizesFold = np.array( matSizesFold, dtype=np.float32 )\r\n            matSizes1    = np.array( matSizes1, dtype=np.float32 )\r\n#            matSizesF    = np.array( matSizesF, dtype=np.float32 )\r\n            ratio1 = matSizes1 / matSizes1old\r\n            print \"Trunc max ratio in norm1: %.3f\" %max(ratio1)\r\n#            ratioF = matSizesF / matSizesFold\r\n#            print \"Trunc max ratio: norm1 %.3f, normF %.3f\" %( max(ratio1), max(ratioF) )\r\n\r\n            if testenv:\r\n                model = VecModel( V, testenv['vocab'], testenv['word2id'], vecNormalize=True, precompute_gramian=True )\r\n                evaluate_sim( model, testenv['simTestsets'], testenv['simTestsetNames'] )\r\n                evaluate_ana( model, testenv['anaTestsets'], testenv['anaTestsetNames'] )\r\n\r\n#        if it > 10 and norm1(G_VV, WeightSym) / norm1(G_VV_old, WeightSym) > 1.1:\r\n#                print \"Sudden surge of G-VV norm: %.3f -> %.3f. Stop\" %( norm1(G_VV_old, WeightSym), norm1(G_VV, WeightSym) )\r\n#                X, Gsym_VV, G_VV = X_old, Gsym_VV_old, G_VV_old\r\n#                break\r\n\r\n        timer2.printElapseTime()\r\n\r\n    V, VV, vs, es = lowrank_fact(X, N0)\r\n\r\n    print \"Eigenvalue range: %f ~ %f\\n\" %(es[-1], es[0])\r\n    print \"End of Frank-Wolfe\"\r\n    print\r\n\r\n    timer1.printElapseTime()\r\n\r\n    #pdb.set_trace()\r\n    return V, VV\r\n\r\n# RawCounts is a list of numpy arrays. It may contain only one array\r\ndef normalizeWeight( RawCounts, do_weight_cutoff, cutQuantile=0.0002, zero_diagonal=True ):\r\n    for Weight in RawCounts:\r\n        np.sqrt(Weight, Weight)\r\n\r\n    idealCutPoint = getQuantileCut( RawCounts[0], cutQuantile )\r\n\r\n    maxwe = 0\r\n\r\n    for i, Weight in enumerate(RawCounts):\r\n        totalElemCount = Weight.shape[0] * Weight.shape[1]\r\n\r\n        if do_weight_cutoff:\r\n            cutEntryCount = np.sum( Weight > idealCutPoint )\r\n            Weight[ Weight > idealCutPoint ] = idealCutPoint\r\n            print \"%d (%.3f%%) elements in Weight-%d cut off at %.2f\" %( cutEntryCount,\r\n                                               cutEntryCount * 100.0 / totalElemCount, i+1, idealCutPoint )\r\n\r\n        if zero_diagonal:\r\n            for i in xrange( min(Weight.shape) ):\r\n                Weight[i,i]=0\r\n\r\n        maxwe1 = np.max(Weight) * 1.0\r\n        if maxwe1 > maxwe:\r\n            maxwe = maxwe1\r\n\r\n    for Weight in RawCounts:\r\n        # normalize to [0,1]\r\n        Weight /= maxwe\r\n\r\n    print\r\n\r\n    if len(RawCounts) == 1:\r\n        return RawCounts[0]\r\n    else:\r\n        return RawCounts\r\n\r\n# tikhonovCoeff: coefficient of Tikhonov regularization on the weighted least squares\r\n# Set to 0 to disable regularization\r\n# https://en.wikipedia.org/wiki/Tikhonov_regularization#Generalized_Tikhonov_regularization\r\ndef block_factorize( G, F, V1, N0, tikhonovCoeff, do_weight_cutoff ):\r\n    # new G12, G21, F12, F21, allocated in loadBigramFileInBlock() and passed in\r\n    # core_size * noncore_size, noncore_size * core_size\r\n    G12, G21 = G\r\n    F12, F21 = F\r\n\r\n    noncore_size = len(G21)\r\n\r\n    Weight12, Weight21 = normalizeWeight( [ F12, F21 ], do_weight_cutoff, zero_diagonal=False)\r\n\r\n    # new WGsum: noncore_size * core_size\r\n    WGsum = ( Weight12 * G12 ).T + ( Weight21 * G21 )\r\n\r\n    memLogger.debug( \"del G1, G21\" )\r\n    del G12, G21\r\n    # G1, G21 should be released\r\n\r\n    # Save memory, reuse Weight21\r\n    Weight21 += Weight12.T\r\n    Wsum = Weight21\r\n    Wsum[ np.isclose(Wsum,0) ] = 0.001\r\n    WGsum /= Wsum\r\n    Gwmean = WGsum\r\n\r\n    # only Wsum(Weight21) and Gwmean are used later\r\n    del F12, Weight12\r\n\r\n    # embeddings of noncore words\r\n    # new V2: noncore_size * N0\r\n    V2 = np.zeros( ( noncore_size, N0 ), dtype=np.float32 )\r\n    Tikhonov = np.identity(N0) * tikhonovCoeff\r\n\r\n    timer = Timer()\r\n\r\n    print \"Begin finding embeddings of non-core words\"\r\n\r\n    # Find each noncore word's embedding\r\n    for i in xrange(noncore_size):\r\n        # core_size\r\n        wi = Wsum[i]\r\n        # new VW: N0 * core_size\r\n        VW = V1.T * wi\r\n        # new VWV: N0 * N0\r\n        VWV = VW.dot(V1)\r\n        VWV_Tik = VWV + Tikhonov\r\n        V2[i] = np.linalg.inv(VWV_Tik).dot( VW.dot(Gwmean[i]) )\r\n        if i >= 0 and i % 100 == 99:\r\n            print \"\\r%d / %d.\" %(i+1,noncore_size),\r\n            print timer.getElapseTime(), \"\\r\",\r\n\r\n    print\r\n\r\n    # F21 = Weight21 = Wsum, should be released\r\n    # Gwmean = WGsum should be released\r\n    # VW should be released\r\n    # VWV is N0*N0, small, ignored\r\n    memLogger.debug( \"del F21, WGsum, VW\" )\r\n    del F21, Weight21, Wsum, Gwmean, WGsum, VW\r\n\r\n    return V2\r\n\r\n\r\ndef factorize( alg, algName, G, Weight, N0, MAX_ITERS, tikhonovCoeff, vocab, testenv, save_residuals=False ):\r\n    print \"%d iterations of %s\" %( MAX_ITERS, algName )\r\n    V, VV = alg( G, Weight, N0, MAX_ITERS, tikhonovCoeff, testenv )\r\n    print\r\n\r\n    vocab_size = len(vocab)\r\n\r\n    save_embeddings( \"%d-%d-%s.vec\" %(vocab_size, N0, algName), vocab, V, \"V\" )\r\n    print\r\n\r\n    if save_residuals:\r\n        A = G - VV\r\n        save_embeddings( \"%d-%d-%s.residual\" %(vocab_size, N0, algName), vocab, A, \"A\" )\r\n        print\r\n\r\ndef usage():\r\n    print \"\"\"factorize.py [ -n vec_dim -b num_core_words -v core_pre_vec_file ... ] bigram_file\r\nOptions:\r\n  -n:  Dimensionality of the generated embeddings. Default: 500\r\n  -b:  Do block-wise decomposition, and specify the number of core words\r\n  -v:  Existing embedding file of core words, for online learning of noncore\r\n       embeddings\r\n  -o:  Number of noncore words to generate embeddings. Default: -1 (all)\r\n  -w:  Number of words in bigram_file to generate embeddings. Default: -1 (all)\r\n  -e:  A file containing extra words to generate embeddings, even if beyond\r\n       the top words specified by -w\r\n  -k:  Kappa of Jelinek-Mercer Smoothing (in percent). Default: 2 (=0.02)\r\n  -t:  Specify a Tikhonov regularization coefficient. Default: 0 (disable)\r\n  -c:  Disable weight cutoff\r\n  -z:  Set G's elements to 0 whose corresponding weights are 0. \r\n       At the same time, set the default magnitude of vectors. Default: 8\r\n       Diagonal of G will be set to the square of the default magnitude\r\n  -E:  Number of iterations of the EM procedure. Default: 4\r\n  -F:  Use Frank-Wolfe procedure, and specify the number of iterations\r\n  -U:  Use PSD approximation with Unigram weighting\r\n  -G:  Use Gradient Descent, and specify the number of iterations\r\n\"\"\"\r\n\r\ndef main():\r\n    # degree of smoothing\r\n    kappa = 0.02\r\n    # tikhonovCoeff: coefficient of Tikhonov regularization on the weighted least squares in block_factorize()\r\n    # default is to disable it\r\n    tikhonovCoeff = 0\r\n    # vector dimensionality\r\n    N0 = 500\r\n    default_vec_len = 8\r\n    \r\n    # default -1 means to read all words\r\n    vocab_size = -1\r\n    core_size = -1\r\n    noncore_size = -1\r\n    pre_vec_file = None\r\n    test_block = False\r\n\r\n    do_smoothing = True\r\n    do_weight_cutoff = True\r\n    zero_G_elem_at_weight_0 = False\r\n    \r\n    extraWordFile = None\r\n    do_UniWeight = False\r\n    MAX_EM_ITERS = 4\r\n    MAX_FW_ITERS = 0\r\n    MAX_GD_ITERS = 0\r\n    # EM iters of the core words\r\n    MAX_CORE_EM_ITERS = 4\r\n    do_block_factorize = False\r\n    save_residuals = False\r\n\r\n    try:\r\n        opts, args = getopt.getopt(sys.argv[1:],\"n:b:v:o:w:e:k:t:cz:E:F:UG:hr\")\r\n        if len(args) != 1:\r\n            raise getopt.GetoptError(\"\")\r\n        bigram_filename = args[0]\r\n        for opt, arg in opts:\r\n            if opt == '-n':\r\n                N0 = int(arg)\r\n            if opt == '-b':\r\n                do_block_factorize = True\r\n                core_size = int(arg)\r\n            if opt == '-v':\r\n                do_block_factorize = True\r\n                pre_vec_file = arg\r\n            if opt == '-o':\r\n                do_block_factorize = True\r\n                noncore_size = int(arg)\r\n            if opt == '-w':\r\n                vocab_size = int(arg)\r\n            if opt == '-e':\r\n                extraWordFile = arg\r\n            if opt == '-k':\r\n                kappa = float(arg) / 100\r\n            if opt == '-t':\r\n                tikhonovCoeff = float(arg)\r\n                print \"Using Tikhonov regularization with coeff: %.1f\" %tikhonovCoeff\r\n            if opt == '-c':\r\n                do_weight_cutoff = False\r\n            if opt == '-z':\r\n                zero_G_elem_at_weight_0 = True\r\n                default_vec_len = float(arg)\r\n            if opt == '-E':\r\n                MAX_EM_ITERS = int(arg)\r\n                MAX_CORE_EM_ITERS = int(arg)\r\n            if opt == '-F':\r\n                MAX_FW_ITERS = int(arg)\r\n            # uniweight needs one iteration only. so no arg\r\n            if opt == '-U':\r\n                do_UniWeight = True\r\n            if opt == '-G':\r\n                MAX_GD_ITERS = int(arg)\r\n            if opt == '-r':\r\n                save_residuals = True\r\n            if opt == '-h':\r\n                usage()\r\n                sys.exit(0)\r\n\r\n    except getopt.GetoptError:\r\n        usage()\r\n        sys.exit(2)\r\n\r\n    # load testsets\r\n    simTestsetDir = \"./testsets/ws/\"\r\n    simTestsetNames = [ \"ws353_similarity\", \"ws353_relatedness\", \"bruni_men\", \"radinsky_mturk\", \"luong_rare\", \"simlex_999a\" ]\r\n    anaTestsetDir = \"./testsets/analogy/\"\r\n    anaTestsetNames = [ \"google\", \"msr\" ]\r\n\r\n    simTestsets = loadTestsets(loadSimTestset, simTestsetDir, simTestsetNames)\r\n    anaTestsets = loadTestsets(loadAnaTestset, anaTestsetDir, anaTestsetNames)\r\n    print\r\n\r\n    testenv = { 'simTestsets': simTestsets, 'simTestsetNames': simTestsetNames,\r\n                 'anaTestsets': anaTestsets, 'anaTestsetNames': anaTestsetNames }\r\n\r\n    if do_block_factorize:\r\n        if extraWordFile:\r\n            print \"Extra word file is unnecessary when doing blockwise factorization\"\r\n            sys.exit(2)\r\n\r\n        if vocab_size > 0 and core_size > 0:\r\n            # in case -o, -w, -b are all specified, check their consistency\r\n            if noncore_size > 0 and noncore_size != vocab_size - core_size:\r\n                print \"noncore_size %d + core_size %d != vocab_size %d \" %( noncore_size, core_size, vocab_size )\r\n                sys.exit(2)\r\n            else:\r\n                noncore_size = vocab_size - core_size\r\n\r\n        if not pre_vec_file:\r\n            # do_block_factorize might be accidentally enabled by -o. But without -v or -b we couldn't proceed\r\n            if core_size < 0:\r\n                print \"Neither -v nor -b is specified. Unable to determine core words\"\r\n                sys.exit(2)\r\n\r\n            # passed-in word2preID_core={}\r\n            vocab_all, word2id_all, word2id_core, coreword_preIDs, G, F, u \\\r\n                                                          = loadBigramFileInBlock( bigram_filename, core_size,\r\n                                                              noncore_size, {}, {}, kappa )\r\n            # returned coreword_preIDs = []\r\n\r\n            # Usually in the bigram file there are many more words than -b core_size\r\n            # so the actual core words read should always be core_size words. No update needed\r\n\r\n            G11 = G.pop(0)\r\n            F11 = F.pop(0)\r\n\r\n            # Weight normalization is in place. F11 couldn't be released prior to Weight11\r\n            Weight11 = normalizeWeight( [ F11 ], do_weight_cutoff)\r\n\r\n            # since pre_vec_file is not specified, according to a previous condition, we know core_size > 0\r\n            testenv['vocab'] = vocab_all[:core_size]\r\n            testenv['word2id'] = word2id_core\r\n\r\n            # obtain embeddings of core words\r\n            # new V1, VV1 in we_factorize_EM()\r\n            # core_size * N0, core_size * core_size\r\n            V1, VV1 = we_factorize_EM( G11, Weight11, N0, MAX_CORE_EM_ITERS, testenv )\r\n            print \"\\nEmbeddings of %d core words have been solved\" %core_size\r\n            memLogger.debug( \"del G11, F11, VV1\" )\r\n            # Weight11 is a reference to F11. Has to be deleted too to release F11\r\n            del G11, F11, Weight11, VV1\r\n            vocab_joint = vocab_all\r\n            V_pre_skipped = []\r\n            word2id_joint = word2id_all\r\n\r\n        else:\r\n            if core_size > 0:\r\n                print \"Embeddings of top %d words in '%s' will be loaded as core\" %( core_size, pre_vec_file )\r\n            else:\r\n                print \"Embeddings of all words in '%s' will be loaded as core\" %(pre_vec_file)\r\n\r\n            # here we don't skip words yet\r\n            # skippedWords_whatever is empty and we don't care about it\r\n            V_pre, vocab_pre, word2preID, skippedWords_whatever = load_embeddings(pre_vec_file)\r\n            N0 = V_pre.shape[1]\r\n\r\n            prewords_skipped = {}\r\n            vocab_skipped = []\r\n\r\n            # recompute core_size, initialize vocab_core & word2preID_core\r\n            # If there are less than core_size words in pre_vec_file, then the whole returned vocab is used as vocab_core\r\n            # the actual core_size is smaller than specified core_size\r\n            if core_size == -1 or core_size > len(vocab_pre):\r\n                core_size = len(vocab_pre)\r\n                vocab_core = vocab_pre\r\n                word2preID_core = word2preID\r\n            # 0 < core_size <= len(vocab_core)\r\n            # the first core_size words in vocab_pre are vocab_core\r\n            else:\r\n                vocab_core = vocab_pre[:core_size]\r\n                vocab_skipped = vocab_pre[core_size:]\r\n                for w in vocab_skipped:\r\n                    prewords_skipped[w] = 1\r\n                word2preID_core = {}\r\n                for w in vocab_core:\r\n                    word2preID_core[w] = word2preID[w]\r\n\r\n            if noncore_size > 0:\r\n                print \"2 blocks of %d core words and %d noncore words will be loaded. Skip %d words\" \\\r\n                                %( core_size, noncore_size, len(prewords_skipped) )\r\n            else:\r\n                print \"2 blocks of %d core words and all noncore words will be loaded. Skip %d words\" \\\r\n                                %( core_size, len(prewords_skipped) )\r\n\r\n            vocab_all, word2id_all, word2id_core, coreword_preIDs, G, F, u = \\\r\n                                         loadBigramFileInBlock( bigram_filename, core_size, noncore_size,\r\n                                           word2preID_core, prewords_skipped, kappa )\r\n            # the actual skipped vocab might be larger than vocab_skipped above.\r\n            # Some pre core words might not exist in this bigram file, thus not in coreword_preIDs,\r\n            # so they should be added to vocab_skipped\r\n\r\n            # update core_size to the num of pre core words existing in the bigram file\r\n            core_size = len(word2id_core)\r\n            noncore_size = len(word2id_all) - core_size\r\n            # select corresponding rows into V1 and V_pre_skipped.\r\n            # V1 is some rows within the 0:core_size rows of V_pre, but row order might change\r\n            # other rows are into V_pre_skipped\r\n            V1 = V_pre[coreword_preIDs]\r\n            skippedPreIDMask = np.ones( len(vocab_pre), np.bool )\r\n            skippedPreIDMask[coreword_preIDs] = 0\r\n            V_pre_skipped = V_pre[skippedPreIDMask]\r\n            skippedPreIDs = set( xrange( len(vocab_pre) ) ) - set(coreword_preIDs)\r\n            #pdb.set_trace()\r\n            # update vocab_skipped, add words in the pre vec file but not in the bigram file\r\n            vocab_skipped = [ vocab_pre[i] for i in skippedPreIDs ]\r\n            vocab_joint = vocab_all[:core_size] + vocab_skipped + vocab_all[core_size:]\r\n            #vocab_joint = vocab_all[:core_size] + vocab_all[core_size:]\r\n            # updated word to ID mapping\r\n            word2id_joint = { w:i for (i, w) in enumerate(vocab_joint) }\r\n\r\n        # block_factorize( G, F, V1, N0, do_weight_cutoff ):\r\n        V2 = block_factorize( G, F, V1, N0, tikhonovCoeff, True )\r\n\r\n        # concatenate vocab's and V's.\r\n        # A use case:\r\n        # On an original vocab of [ 1, ..., i1, ..., i2, ..., i3, ... ]\r\n        # 1. Factorize [ 1, ..., i1 ] as cores, and block factorize [ i1+1, ..., i2 ]\r\n        # Save to a vec file.\r\n        # vocab_all = vocab_joint = [ 1, ..., i1, ..., i2, ..., i3 ]\r\n        # vocab_skipped = []\r\n        # 2. load the vec file, use [ 1, ..., i1 ] as cores, and block factorize [ i2+1, ..., i3 ]\r\n        # So in the second run of block factorization, skipped words should be placed inbetween\r\n        # vocab_all = [ 1, ..., i1, i2+1, ..., i3 ]\r\n        # vocab_skipped = [ i1+1, ..., i2 ]\r\n        # vocab_joint = [ 1, ..., i1, i1+1, ..., i2, i2+1, ..., i3 ]\r\n\r\n        vocab_jointsize = len(vocab_joint)\r\n        #V_joint = np.concatenate( ( V1, V2 ) )\r\n        V_joint = np.concatenate( ( V1, V_pre_skipped, V2 ) )\r\n        save_embeddings( \"%d-%d-%d-%s-%.1f.vec\" %(core_size, vocab_jointsize, N0, \"BLK\", tikhonovCoeff), vocab_joint, V_joint, \"V\" )\r\n\r\n        \"\"\"\r\n        if test_block:\r\n            print \"Test EM on the complete matrix\\n\"\r\n            VV = np.dot( V, V.T )\r\n            G0 = G[3]\r\n            Weight0 = normalizeWeight( [ F[3] ], do_weight_cutoff )\r\n            print \"L1 Weighted VV: %.3f, G-VV: %.3f\" %( norm1(VV, Weight0), norm1(G0 - VV, Weight0) )\r\n\r\n            testenv['word2id'] = testenv['word2id_all']\r\n            we_factorize_EM( G0, Weight0, N0, MAXITERS, testenv )\r\n            print\r\n        \"\"\"\r\n\r\n        if testenv:\r\n            testenv['vocab'] = vocab_joint\r\n            testenv['word2id'] = word2id_joint\r\n            print \"Test embeddings derived from block factorization\\n\"\r\n            # An array of vocab_size * vocab_size is created here. Watch the amount of memory\r\n            model = VecModel( V_joint, testenv['vocab'], testenv['word2id'], vecNormalize=True, precompute_gramian=True )\r\n            evaluate_sim( model, testenv['simTestsets'], testenv['simTestsetNames'] )\r\n            evaluate_ana( model, testenv['anaTestsets'], testenv['anaTestsetNames'] )\r\n\r\n        # never save residuals when doing block factorization\r\n\r\n    else:\r\n        extraWords = {}\r\n        if extraWordFile:\r\n            with open(extraWordFile) as f:\r\n                for line in f:\r\n                    w, wid = line.strip().split('\\t')\r\n                    extraWords[w] = 1\r\n\r\n        vocab, word2id, G, F, u = loadBigramFile( bigram_filename, vocab_size, extraWords, kappa )\r\n        vocab_size = len(vocab)\r\n        testenv['vocab'] = vocab\r\n        testenv['word2id'] = word2id\r\n\r\n        # Weight modifies F in place. Memory copy is avoided\r\n        Weight = normalizeWeight( [ F ], do_weight_cutoff=do_weight_cutoff )\r\n\r\n        if zero_G_elem_at_weight_0:\r\n            NonzeroFilter = Weight != 0\r\n            G *= NonzeroFilter\r\n            nonzeroCount = np.count_nonzero(NonzeroFilter)\r\n            totalCount = G.shape[0] * G.shape[1]\r\n            zeroCount = totalCount - nonzeroCount\r\n            print \"%d (%.1f%%) nonzero elements in G are set to 0, %d left\" \\\r\n                            %( zeroCount, zeroCount * 100.0 / totalCount, nonzeroCount )\r\n            \r\n            default_vec_len_sqr = default_vec_len * default_vec_len\r\n            for i in xrange( G.shape[0] ):\r\n                G[i,i] = default_vec_len_sqr\r\n            print \"Diagnoal elements of G are set to %.1f\" %( default_vec_len_sqr )\r\n            \r\n        #we_factorize_GD( G, Weight, N0, testenv )\r\n\r\n        # factorize( alg, algName, G, Weight, N, MAX_ITERS, tikhonovCoeff, vocab, testenv )\r\n        #if do_UniWeight:\r\n        #    factorize( uniwe_factorize, \"UNI\", G, u, N0, 0, vocab, 0, testenv, save_residuals )\r\n\r\n        #if MAX_FW_ITERS > 0:\r\n        #    factorize( we_factorize_FW, \"FW\", G, Weight, N0, MAX_FW_ITERS, 0, vocab, testenv, save_residuals )\r\n\r\n        #if MAX_GD_ITERS > 0:\r\n        #    factorize( we_factorize_GD, \"GD\", G, Weight, N0, MAX_GD_ITERS, 0, vocab, testenv, save_residuals )\r\n\r\n        if MAX_EM_ITERS > 0:\r\n            factorize( we_factorize_EM, \"EM\", G, Weight, N0, MAX_EM_ITERS, tikhonovCoeff, vocab, testenv, save_residuals )\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    memLogger = initConsoleLogger(\"Mem\")\r\n    main()\r\n"
  },
  {
    "path": "psdvec/genSentDict.bat",
    "content": "@echo off\r\nset N=200\r\npython topwordsInList.py -c sent-gen-config.txt -l d:\\Dropbox\\sentiment\\positive-words.txt,d:\\Dropbox\\sentiment\\negative-words.txt -n %N% -o topSentWords%N%.txt\r\n"
  },
  {
    "path": "psdvec/gencatdata.py",
    "content": "import numpy as np\r\nimport scipy.linalg\r\nfrom scipy.special import *\r\nimport getopt\r\nimport sys\r\nfrom utils import *\r\nimport pdb\r\nimport time\r\n\r\nembed_algs = { \"PSDVec\": \"d:/corpus/embeddings/25000-180000-500-BLK-8.0.vec\", \r\n                \"word2vec\": \"d:/corpus/embeddings/word2vec2.vec\",\r\n                \"CCA\": \"d:/corpus/embeddings/182800-500-CCA.vec\"\r\n             } \r\n                    # \"50000-180000-500-BLK-8.0.vec\" }\r\ntestsetDir = \"./concept categorization\"\r\ntestsetNames = [ \"ap\", \"battig\", \"esslli\" ]\r\nmaxID = -1\r\n\r\nfor algname, vecFilename in embed_algs.iteritems():\r\n    print \"Alg %s\" %algname\r\n    if vecFilename[-4:] == \".bin\":\r\n        V, vocab, word2ID, skippedWords_whatever = load_embeddings_bin(vecFilename, 400000)\r\n    else:\r\n        V, vocab, word2ID, skippedWords_whatever = load_embeddings(vecFilename, 400000)\r\n    \r\n    for testsetName in testsetNames:\r\n        truthFilename = testsetDir + \"/\" + testsetName + \".txt\"\r\n        vecFilename = testsetDir + \"/\" + testsetName + \"-\" + algname + \".vec\"\r\n        labelFilename = testsetDir + \"/\" + testsetName + \"-\" + algname + \".label\"\r\n        \r\n        FVEC = open(vecFilename, \"w\")\r\n        ids = []\r\n        \r\n        FLABEL = open(labelFilename, \"w\")\r\n        \r\n        with open(truthFilename) as FT:\r\n            # skip header\r\n            FT.readline()\r\n            for line in FT:\r\n                line = line.strip()\r\n                fields = line.split(\"\\t\")\r\n                word, cat = fields[:2]\r\n                    \r\n                if word not in word2ID:\r\n                    print \"%s not in vocab\" %word\r\n                    continue\r\n                else:\r\n                    id = word2ID[word]\r\n                    #print \"%s: %d\" %(word, id)\r\n                    if id > maxID:\r\n                        maxID = id\r\n                    ids.append(id)\r\n                    \r\n                    FLABEL.write(\"%s\\n\" %cat)\r\n            \r\n            FVEC.write( \"%d %d\\n\" %( len(ids), V.shape[1] ) )\r\n            for id in ids:            \r\n                v = V[id]\r\n                FVEC.write(\"%.3f\" %v[0])\r\n                for d in v[1:]:\r\n                    FVEC.write(\" %.3f\" %d)\r\n                FVEC.write(\"\\n\")\r\n        \r\n            FLABEL.close()\r\n            FVEC.close()\r\n"
  },
  {
    "path": "psdvec/gram-rcv1.bat",
    "content": "set CORPUS=rcv1clean.txt\r\nset SUFFIX=rcv1\r\nperl gramcount.pl -i %CORPUS% -m1 --f1 top1grams-%SUFFIX%.txt -c --nofilter --thres1 50,0\r\nperl gramcount.pl -i %CORPUS% -m2 --f1 top1grams-%SUFFIX%.txt --nofilter -c --f2 top2grams-%SUFFIX%.txt -w 3 --thres1 50,0\r\n"
  },
  {
    "path": "psdvec/gram.bat",
    "content": "set CORPUS=reuters-train-5770.orig.txt\r\nset SUFFIX=reuters\r\nperl gramcount.pl -i %CORPUS% -m1 --f1 top1grams-%SUFFIX%.txt -c --nofilter --thres1 5,0\r\nperl gramcount.pl -i %CORPUS% -m2 --f1 top1grams-%SUFFIX%.txt --nofilter -c --f2 top2grams-%SUFFIX%.txt -w 3 --thres1 5,0\r\n"
  },
  {
    "path": "psdvec/gramcount.pl",
    "content": "use strict;\nuse warnings 'all';\nuse List::Util qw( min max sum );\nuse List::MoreUtils 'all';\nuse Getopt::Long;\nuse Inline CPP => 'DATA';\nuse Inline CPP => config => ccflags => '-std=c++11';\n\nour $processor = \\&countUnigramFreqInWindow;\nour $interestf1name;\nour $corpus_dir;\n\nour %options = ( mode => '2', nofilter => 0, c => 0, input => \"\", dir => \"\",\n                 f1 => \"top1grams.txt\", f2 => \"top2grams.txt\",\n\t\t\t\t minwords_sentence => 5, window => 3, dyn => 0, top1 => 0, # '0' means to count all words\n\t\t\t\t thres1 => '100,5e-8', thres2 => '1,0', # '5,0.0005'\n\t\t\t   );\n\n$| = 1;\n\nsub usage\n{\n\tprint <<USAGE;\nUsage:\n    $0 -d(--dir) corpus_directory [options]\n    $0 -i(--input) corpus_file [options]\n\nOptions optional:\n-c:                 Use C++ extensions to gain speedup\n--nofilter:         Disable text filtering/formatting, to speed up processing\n-m,--mode int:      '1' (unigram) or '2'(bigram) (default: $options{mode})\n--f1 file:          List of words to write or read.\n                    If the mode is '1', this is the filename to save.\n                    If the mode is '2', this file is required to be read\n                    as the counted word list\n                    (default: \"$options{f1}\")\n--f2 file:          File name of the bigrams to save (default: \"$options{f2}\")\n-s,--suffix file:   Corpus text file suffix\n--filecount int:    Number of files to process. Other files in the corpus dir\n                    are ignored.\n-n,--window int:    Size of the neighborhood for bigrams (default: $options{window})\n--dyn:              Dynamic window size ( uniformly random in [ 1,--window ] )\n--thres1 int,float: Threshods of unigrams to be considered.\n                    Format: freq,prob. Max of the two is taken.\n                    (default: $options{thres1})\n--thres2 int,float: Threshods of bigrams to be considered.\n                    Format: freq,prob. Max of the two is taken.\n                    (default: $options{thres2})\n--top1 int:         Top n words to be counted in the unigram word list file.\n                    Only applicable in the bigram mode. (default: $options{top1})\n-e,--extra file:    List of unigrams not in --top1 to count in the bigram mode\n                    These words have to be present in --f1 file.\nUSAGE\n\n\texit;\n}\n\n\nGetopt::Long::Configure(\"bundling\", \"ignore_case\");\n\nGetOptions( \\%options, 'dir|d=s', 'input|i=s', 'nofilter', 'c', 'mode|m=i', 'f1=s', 'f2=s',\n\t\t    'suffix|s=s', 'filecount=i', 'window|w=i', 'dyn', 'thres1=s', 'thres2=s',\n\t\t    'top1=i', 'extra|e=s', 'minwords_sentence=i' ) || usage();\n\nif( ! $options{'dir'} && ! $options{'input'}\n                            ||\n      $options{'dir'} && $options{'input'} ){\n\tusage();\n}\n\nour %unigram2freq;\nour %bigram2freq;\nour %interestWords;\nour $interestWordsOn = 0;\nour $allBigramOccur = 0;\n\nif( $options{mode} == 1 ){\n\t$processor = \\&countUnigramFreqInWindow;\n}\nelsif( $options{mode} == 2 ){\n\t$processor = \\&countBigramFreqInWindow;\n\tif( ! $options{'f1'} ){\n\t\tdie \"In bigram mode, the '--f1 word_list_file_name' must be specified\\n\";\n\t}\n\n\tloadlist( $options{'f1'}, \\%unigram2freq, $options{'top1'}, $options{'extra'} );\n}\nelse{\n\tdie \"Mode '$options{mode}' is unknown. Can only be '1' (for unigram) or '2' (for bigram)\\n\";\n}\n\nour ($min1gramfreq, $min1gramprob);\nour ($min2gramfreq, $min2gramprob);\n\nif( $options{'thres1'} !~ /^([\\d.]+|\\d+e-?\\d+),([\\d.]+|\\d+e-?\\d+)$/i ){\n    die \"'$options{'thres1'}': wrong number format of --thres1\\n\";\n}\nelse{\n    ($min1gramfreq, $min1gramprob) = split /,/, $options{'thres1'};\n}\n\nif( $options{'thres2'} !~ /^([\\d.]+|\\d+e-?\\d+),([\\d.]+|\\d+e-?\\d+)$/i ){\n    die \"'$options{'thres2'}': wrong number format of --thres2\\n\";\n}\nelse{\n    ($min2gramfreq, $min2gramprob) = split /,/, $options{'thres2'};\n}\n\n# use words in the file $options{'f1'} as the words of interest (words counted)\n# In bigram mode, interest words are always loaded and $interestWordsOn is always 1\n# If in unigram mode, %unigram2freq is empty, then %interestWords will also be empty\n%interestWords = %unigram2freq;\n\nif( keys %interestWords > 0 ){\n\t$interestWordsOn = 1;\n\tprint scalar keys %interestWords, \" interest words\\n\";\n}\n\nif( ! $options{nofilter} && $options{c} ){\n    print \"WARN: -c is only valid when --nofilter is specified. It will be disabled now.\\n\";\n    delete $options{c};\n}\n\nif( $options{c} ){\n    #void passParams_( int mode, char* input, char* dir, char* f1, int top1, \n    #                  int minwords_sentence, int window, char* thres1, char* thres2 )\n    passParams_( $options{mode}, $options{input}, $options{dir}, $options{f1}, $options{top1}, \n                 $options{minwords_sentence}, $options{window}, $options{dyn}, $options{thres1}, $options{thres2} );\n                 \n    if($interestWordsOn){\n        passInterestWords_( [ keys %interestWords ] );\n    }\n}\n\nour $totalWordOccur = 0;\nour $totalInterestWordCount = 0;\nour $totalSentenceCount = 0;\nour $totalNonEngWordOccur = 0;\nour $lineCount = 0;\nour $fileCount = 0;\n# '0' is no limit\nour $fileCountLimit = $options{filecount} || 0;\n\nsub processFile\n{\n    my ($filename, $fileFullname) = @_;\n    my $line;\n\n \topen(FH, \"< $fileFullname\" ) || die \"Cannot open '$fileFullname' to read: $!\\n\";\n\tprint \"$filename:\\n\";\n\tmy $sentenceCount = 0;\n    my $filesize = 0;\n    \n\twhile( $line = <FH> ){\n\t    $filesize += length($line);\n\t\tchomp $line;\n\t\tnext if !$line;\n\t\t$lineCount++;\n\t\t$line = lc($line);\n\t\tmy @sentences;\n\t\tmy $sentence;\n\t\tif( ! $options{nofilter} ){\n    \t\t$line =~ s/&[a-z];//g;\n    \t\t@sentences = split /[,;?:!\\\"()]|\\. |\\.$|--/, $line;\n    \t}\n    \telse{\n    \t    @sentences = ( $line );\n    \t}\n\t\tfor(my $i = 0; $i < @sentences; $i++){\n\t\t    $sentence = $sentences[$i];\n\t\t    $sentenceCount++;\n\t\t    $totalSentenceCount++;\n\t\t\tmy @words;\n\n    \t\tif( $options{c} ){\n\t\t\t    # if no filtering, each line is a sentence. So $. == $sentenceCount.\n\t\t\t    # no need to display $.\n                if( $sentenceCount % 5000 == 0 ){\n                    printf( \"\\r%d, %.1fM\\r\", $sentenceCount, $filesize / (1024*1024) );\n                }\n                processSentence_($sentence);\n                next;\n    \t\t}\n    \t\t\n    \t\t# old Perl code\n\t\t\tif( ! $options{nofilter} ){\n                if( $sentenceCount % 5000 == 0 ){\n                    printf( \"\\r%d, %d, %.1fM\\r\", $., $sentenceCount, $filesize / (1024*1024) );\n                }\n                #$sentence =~ s/\\'//g;\n\n    \t\t\t@words = split /\\s+|\\./, $sentence;\n    \t\t\t# remove empty entries caused by leading/trailing spaces\n    \t\t\t# not necessary for wiki corpus, as they are all removed\n    \t\t\t@words = grep { ! /^(|\\$|-|||\\/|\\%|\\@|\\#)$/  } @words;\n    \t\t}\n    \t\telse{\n\t\t\t    # if no filtering, each line is a sentence. So $. == $sentenceCount.\n\t\t\t    # no need to display $.\n                if( $sentenceCount % 5000 == 0 ){\n                    printf( \"\\r%d, %.1fM\\r\", $sentenceCount, $filesize / (1024*1024) );\n                }\n    \t\t    @words = split / /, $sentence;\n    \t\t}\n\n    \t\tmy $wc = scalar @words;\n    \t\t# remove non-English, e.g. French, Greek..., letters\n  \t\t\t@words = grep { /^[a-z0-9\\']+$/ } @words;\n\n\t\t\tmy $nonEngWc = $wc - scalar @words;\n\t\t\tnext if @words < $options{minwords_sentence};\n\n\t\t\t$totalWordOccur += @words;\n\t\t\t$totalNonEngWordOccur += $nonEngWc;\n\n\t\t\t&$processor(\\@words);\n\t\t}\n\t}\n\tprintf( \"%d, %.1fM\\n\", $sentenceCount, $filesize / (1024*1024) );\n\n    $fileCount++;\n}\n\nif( $options{'dir'} ){\n    opendir( DH, $options{'dir'} ) || die \"Failed to open $options{'dir'} as a directory: $!\\n\";\n\n    my $filename;\n    my $fileFullname;\n\n    while( $filename = readdir(DH) ){\n    \t$fileFullname = \"$options{'dir'}/$filename\";\n    \tif( ! -f $fileFullname ){\n    \t\tnext;\n    \t}\n    \tif( $options{suffix} && $filename !~ /\\.$options{suffix}$/ ){\n    \t    next;\n    \t}\n\n        processFile( $filename, $fileFullname );\n\n    \t#print \".: \", $unigram2freq{'.'} || 0, \"\\n\";\n    \tif( $fileCountLimit && $fileCount >= $fileCountLimit ){\n    \t\tlast;\n    \t}\n    }\n}\nelse{\n    processFile( $options{'input'}, $options{'input'} );\n}\n\nprint \"$fileCount files, $lineCount lines, $totalSentenceCount sentences, \";\nif( ! $options{c} ){\n    print \"$totalWordOccur words occur, $totalNonEngWordOccur non Eng, $totalInterestWordCount interest words occur\\n\";\n}\nelse{\n    printStats_();\n}\n\nif( $options{mode} == 1 ){\n\tmy $top1gramFilename = getAvailName( $options{'f1'} );\n\t\n\tif( $options{c} ){\n\t    outputTopUnigrams_($top1gramFilename);\n\t}\n\telse{\n\t    outputTopUnigrams($top1gramFilename);\n\t}\n}\nelsif( $options{mode} == 2 ){\n\tmy $topbigramFilename = getAvailName( $options{'f2'} );\n\t\n\tif( $options{c} ){\n\t    outputTopBigrams_($topbigramFilename);\n\t}\n\telse{\n\t    outputTopBigrams($topbigramFilename);\n\t}\n}\n\n# load a list of interesting words, initialize the freq to 0\nsub loadlist\n{\n\tmy ( $listFilename, $href, $topcount, $extraUnigramFile ) = @_;\n\tmy $FH;\n\topen($FH, \"< $listFilename\") || die \"Cannot open '$listFilename' to read: $!\\n\";\n\tmy @lines = <$FH>;\n\n\t%$href = map { ( split /\\t/ )[0..1] } grep { !/^\\#/ } @lines;\n\tmy @words = sort { $href->{$b} <=> $href->{$a} } keys %$href;\n\tmy $wordcount = scalar @words;\n\tprint \"$wordcount words loaded from '$listFilename'\\n\";\n\tclose($FH);\n\n\tif( $topcount && $topcount < $wordcount ){\n\t    print \"Top $topcount cuts between '$words[$topcount - 1]' and '$words[$topcount]'\\n\";\n\t    @words = @words[ 0 .. $topcount - 1 ];\n\t}\n\n    my @extraWords = ();\n    if( $extraUnigramFile ){\n    \topen($FH, \"< $extraUnigramFile\") || die \"Cannot open '$extraUnigramFile' to read: $!\\n\";\n    \tmy @lines = <$FH>;\n    \t@extraWords = map { chomp; (split /\\t/)[0] } @lines;\n    \tprint scalar @extraWords, \" extra unigrams loaded from '$extraUnigramFile'\\n\";\n    \tclose($FH);\n    }\n\n    %$href = map { $_ => 0 } ( @words, @extraWords );\n    $wordcount = scalar keys %$href;\n    print \"$wordcount valid unigrams\\n\";\n\treturn $wordcount;\n}\n\nsub countUnigramFreqInWindow\n{\n\tfor( @{$_[0]} ){\n\t\tif( ! $interestWordsOn || exists $interestWords{$_} ){\n\t\t\t$unigram2freq{$_}++;\n\t\t\t$totalInterestWordCount++;\n\t\t}\n\t\t# Otherwise, $_ is not in the interest words list. Ignore\n\t}\n}\n\nsub countBigramFreqInWindow\n{\n\tmy ($i, $j, $leftborder, $rightborder);\n\tmy $words = $_[0];\n\n\tfor( $i = 0; $i < @$words; $i++ ){\n\t\tmy $w = $words->[$i];\n\t\tnext if !exists $interestWords{$w};\n\t\t$totalInterestWordCount++;\n\t\t\n\t\tmy $windowSize;\n\t\tif( $options{dyn} ){\n\t\t    $windowSize = int( rand($options{window}) ) + 1;\n\t\t}\n\t\telse{\n\t\t    $windowSize = $options{window};\n\t\t}\n\t\t\n\t\t$leftborder = max( $i - $windowSize, 0 );\n\t\t#$rightborder = min( $i + $options{window}, scalar @$words - 1 );\n\t\t$rightborder = $i - 1;\n\n\t\tfor( $j = $leftborder; $j <= $rightborder; $j++ ){\n\t\t\tnext if $j == $i;\n\t\t\tmy $w2 = $words->[$j];\n\t\t\tnext if !exists $interestWords{$w2};\n\t\t\t# $w2 is always at the left of $w\n\t\t\t# $w: focus word, $w2: context word\n\t\t\t$bigram2freq{$w2}{$w}++;\n    \t\t$unigram2freq{$w}++;\n    \t\t$allBigramOccur++;\n\t\t}\n\t}\n}\n\nsub outputTopUnigrams\n{\n\tmy $top1gramFilename = shift;\n\tmy $OUTF;\n\n\tmy $min1gramfreq2 = int( $min1gramprob * $totalInterestWordCount );\n\t$min1gramfreq = max( $min1gramfreq, $min1gramfreq2 );\n\tprint \"Words cut-off frequency: $min1gramfreq\\n\";\n\n\topen($OUTF, \"> $top1gramFilename\") || die \"Cannot open '$top1gramFilename' to write: $!\\n\";\n\n\tmy @words = sort { $unigram2freq{$b} <=> $unigram2freq{$a} } grep { $unigram2freq{$_} >= $min1gramfreq } keys %unigram2freq;\n\twriteParams($OUTF, scalar @words);\n\n\tprint \"Saving \", scalar @words, \" words into '$top1gramFilename'...\\n\";\n\n\tfor my $w(@words){\n\t\tprint $OUTF join( \"\\t\", $w, $unigram2freq{$w},\n\t\t                   trunc( 3, log( $unigram2freq{$w} / $totalInterestWordCount ) ) ), \"\\n\";\n\t}\n\tclose($OUTF);\n\tprint \"Done.\\n\";\n}\n\n# This code is buggy. Should remove interest words with freq <= min1gramfreq\n# Please use C++ implementation instead\nsub outputTopBigrams\n{\n\tmy $topbigramFilename = shift;\n\tmy $OUTF;\n\topen($OUTF, \"> $topbigramFilename\") || die \"Cannot open '$topbigramFilename' to write: $!\\n\";\n\n\t# sort context words according to their total frequencies\n\tmy @words = sort { $unigram2freq{$b} <=> $unigram2freq{$a} } keys %bigram2freq;\n\tmy $id = 0;\n    my %word2ID = map { $_ => $id++; } @words;\n    \n\tmy $w;\n\tmy @words2;\n\tmy $wordCount = 0;\n\n\tfor $w(@words){\n\t\tmy $min2gramfreq2 = int( $min2gramprob * $unigram2freq{$w} );\n\t\t$min2gramfreq = max( $min2gramfreq, $min2gramfreq2 );\n\n        # all focus words (above freq threshold) with context $w\n\t\tmy @neighbors = grep { $bigram2freq{$w}{$_} >= $min2gramfreq }\n\t\t\t\t\t\tkeys %{ $bigram2freq{$w} };\n\n\t\t# some context words have all focus words filtered. ignore them, keep others\n\t\tif( @neighbors > 0 ){\n\t\t    push @words2, $w;\n\t\t    $wordCount++;\n\t\t}\n\t}\n\n\twriteParams( $OUTF, scalar @words2 );\n    print $OUTF \"Words:\\n\";\n\n    my $i;\n\tfor( $i = 0; $i < @words2; $i++ ){\n\t    $w = $words2[$i];\n\t    # the unigram probability is defined as the fraction of bigrams\n\t    # whose second word is $w, in all bigrams\n\t    print $OUTF join( \",\", $w, $unigram2freq{$w},\n\t                        trunc( 3, log( $unigram2freq{$w} / $allBigramOccur ) ) );\n\n\t    if( $i < @words2 - 1 ){\n\t        if( $i % 10 == 9 ){\n\t            print $OUTF \"\\n\";\n\t        }\n\t        else{\n\t            print $OUTF \"\\t\";\n            }\n\t    }\n\t}\n\n    print $OUTF \"\\n\\nBigrams:\\n\";\n\n\tmy $bigramCount = 0;\n\tmy $allKeptBigramOccurrences = 0;\n\n\tprint \"Saving bigrams from \", scalar @words2, \" words into '$topbigramFilename'...\\n\";\n\n\t$wordCount = 0;\n\n\tfor $w(@words2){\n\t\tmy $min2gramfreq2 = int( $min2gramprob * $unigram2freq{$w} );\n\t\t$min2gramfreq = max( $min2gramfreq, $min2gramfreq2 );\n\n\t\tmy @neighbors = sort { $word2ID{$a} <=> $word2ID{$b} }\n\t\t\t\t\t\tgrep { $bigram2freq{$w}{$_} >= $min2gramfreq }\n\t\t\t\t\t\tkeys %{ $bigram2freq{$w} };\n\n\t\t# neighbor words are words following $w, not preceding $w\n\t\tmy $neighborTotalOccur = sum( @{$bigram2freq{$w}}{@neighbors} );\n\t\t$wordCount++;\n\t\t$allKeptBigramOccurrences += $neighborTotalOccur;\n\n\t\tmy $neighbor;\n\t\tprint $OUTF join( \",\", $wordCount, $w, scalar @neighbors, $neighborTotalOccur, $min2gramfreq );\n\t\tprint $OUTF \"\\n\";\n\n\t\tfor(my $i = 0; $i < @neighbors; $i++){\n\t\t\t$neighbor = $neighbors[$i];\n\n\t\t\tprint $OUTF \"\\t\", join( \",\", $neighbor, $bigram2freq{$w}{$neighbor},\n\t\t\t\t\t\t# log-probability, truncated to 3 decimal places\n\t\t\t\t\t\ttrunc( 3, log( $bigram2freq{$w}{$neighbor} / $neighborTotalOccur ) ) );\n\n\t\t\t$bigramCount++;\n\t\t\t# 5 bigrams each line for humans to read easily\n\t\t\tif( $i % 5 == 4 && $i < @neighbors - 1 ){\n\t\t\t\tprint $OUTF \"\\n\";\n\t\t\t}\n\t\t}\n\t\tprint $OUTF \"\\n\";\n\n\t\tif( $wordCount % 100 == 99 && $wordCount < @words2 - 1 ){\n\t\t    print \"\\r$wordCount\\r\";\n\t\t}\n\n\t\t# release memory, to avoid using up the memory during the writing (which uses working sets)\n\t\tdelete $bigram2freq{$w};\n\t}\n\tprint $OUTF \"# Total kept bigram occurrences: $allKeptBigramOccurrences\\n\";\n\tclose($OUTF);\n\n    print \"\\n\";\n\tprint \"$bigramCount bigrams from \", $wordCount, \" words are saved into '$topbigramFilename'\\n\";\n\n\tif( $allKeptBigramOccurrences > $allBigramOccur ){\n\t    die \"BUG: kept bigram occurs $allKeptBigramOccurrences > all bigram occurs $allBigramOccur\\n\";\n\t}\n}\n\nsub writeParams\n{\n\tmy ($OUTF, $wordcount) = @_;\n\n\tmy @varNames = qw( f1 minwords_sentence  window  thres1  thres2 );\n\n\tif( exists $options{'input'} ){\n\t    unshift @varNames, \"input\";\n\t}\n\telse{\n\t    unshift @varNames, \"dir\";\n\t}\n\tif( $options{mode} == 2 ){\n\t    push @varNames, \"top1\";\n\t}\n\n\tprint $OUTF \"# $wordcount words, $totalInterestWordCount occurrences\\n\";\n\tprint $OUTF '# ', join( ', ', map { \"$_=\" . $options{$_} } @varNames ), \"\\n\";\n\tif( $options{mode} == 2 ){\n\t    print $OUTF \"# $allBigramOccur bigram occurrences\\n\";\n\t}\n}\n\n# provide $origname as the template, and return a new name if $origname is used by a file\nsub getAvailName\n{\n\tmy $origname = shift;\n\tmy $append;\n\tmy ($name, $suffix);\n\n\tif(! -e $origname){\n\t\treturn $origname;\n\t}\n\n\tif($origname =~ /^(.+)(\\.[^.]+)$/){\n\t\t$name = $1;\n\t\t$suffix = $2;\n\t}\n\telse{\n\t\t$name = $origname;\n\t\t$suffix = \"\";\n\t}\n\n\t$append = -1;\n\twhile(-e \"$name$append$suffix\"){\n\t\t$append--;\n\t}\n\treturn \"$name$append$suffix\";\n}\n\n# if $wantarray, returns @$array\n# else $array->[0]\nsub arrayOrFirst\n{\n\tmy ($wantarray, $array) = @_;\n\tif($wantarray){\n\t\treturn @$array;\n\t}\n\treturn $array->[0];\n}\n# truncate a floating point number to the $prec digits after the decimal point\nsub trunc\n{\n\tmy $prec = shift;\n\tmy @results;\n\n\tfor(@_){\n\t\tpush @results, 0 + sprintf(\"%.${prec}f\", $_);\n\t}\n\treturn arrayOrFirst(wantarray, \\@results);\n}\n\n__DATA__\n__CPP__\n\n#undef fopen\n#undef seekdir\n#undef seed\n#undef setbuf\n\n#include <hash_map>\n#include <algorithm>\n#include <cstring>\n#include <string>\n#include <vector>\n#include <cstdio>\n#include <sstream>\n#include <numeric>\n#include <random>\n#include \"perlxs.h\"\n\nusing namespace __gnu_cxx;\nusing namespace std;\n\nnamespace __gnu_cxx {\n    template <> struct hash<std::string> {\n        size_t operator() (const std::string& x) const \n        {\n            return hash<const char*>()(x.c_str());\n        }\n    };\n}\n\n/*\n\nThis operator when used with copy(), crashes mysteriously. Inspect later\n//copy( h.begin(), h.end(), pairs.begin() );\n\nnamespace std {\n    template<> \n    template<>\n    pair<string, int>& pair<string, int>::operator=(const pair<const string, int>& a)\n    {\n        printf(\"%s\\n\", a.first.c_str());\n        this->first = a.first.c_str();\n        this->second = a.second;\n        return *this;\n    }\n}\n*/\n\ntypedef hash_map<string, int> S2I;\n// maps a word to its ID\nS2I interestWord2ID;\nvector<string> ID2word;\n\nvector<int> unigram2freq;\n\ntypedef hash_map<int, int> I2I;\nvector<I2I> bigram2freq;\n\nbool interestWordsOn = false;\ndouble totalInterestWordCount = 0;\ndouble allBigramOccur = 0;\nlong long totalWordOccur = 0;\nlong long totalNonEngWordOccur = 0;\n\nstruct options{\n    int mode;\n    string input;\n    string dir;\n    string f1;\n    int top1;\n    int minwords_sentence;\n    int window;\n    int dyn;    // actually bool\n    string thres1;\n    string thres2;\n    int min1gramfreq;\n    double min1gramprob;\n    int min2gramfreq;\n    double min2gramprob;\n    int checkNonEng;\n} opt;\n\ndefault_random_engine randGen;\nuniform_int_distribution<int>* pUniform;\n\n// remove non-English, e.g. French, Greek..., letters\n// include processive forms like \"people's\" as unigrams\nbool isWordChar[256];\n\n// Checking & operating as \"existKey(); h[key]++\" takes twice time.\n// Use \"ppair = h.find(key); ppair->second++\" instead to reduce unnecessary time\ntemplate<class HashType, typename KeyType>\nbool existKey(HashType& h, KeyType key)\n{\n    if( h.find(key) != h.end() )\n\t    return true;\n    return false;\n}\n\n/*\nThis cmp function is extremely slow when used in sort(). do not know why. Inspect later\ntemplate< typename K, typename V, bool reversed=true, typename P=pair<K, V> >\nbool cmpvalue(const P& p1, const P& p2)\n{ \n    return ( p1.second < p2.second ) ^ reversed; \n}\n*/\n\ntemplate< typename K, typename V, typename P=pair<K, V> >\nvoid sortHash2Pairs( hash_map<K, V>& h, vector<P>& pairs )\n{\n    pairs.clear();\n    pairs.reserve(h.size());\n\n    for( auto iter = h.begin(); iter != h.end(); ++iter ){\n        pairs.push_back( P( iter->first, iter->second ) );\n    }\n    \n    //printf(\"Sorting %d pairs... \", pairs.size());    \n       \n    sort( pairs.begin(), pairs.end(), [](const P& p1, const P& p2) { return ( p1.second > p2.second ); } );\n    \n    //printf(\"Done.\\n\");\n}\n\n// only sort the beginning n elements in vec\ntemplate< typename V, typename P=pair<int, V> >\nvoid sortVec2Pairs( vector<V>& vec, int N, vector<P>& pairs )\n{\n    pairs.clear();\n    pairs.reserve(N);\n\n    for( int i = 0; i < N; i++ ){\n        pairs.push_back( P( i, vec[i] ) );\n    }\n    \n    //printf(\"Sorting %d pairs... \", pairs.size());    \n       \n    sort( pairs.begin(), pairs.end(), [](const P& p1, const P& p2) { return ( p1.second > p2.second ); } );\n    \n    //printf(\"Done.\\n\");\n}\n\n#define ASSIGN(field) opt.field = field;\nvoid passParams_( int mode, char* input, char* dir, char* f1, int top1, \n                  int minwords_sentence, int window, int dyn, char* thres1, char* thres2 )\n{\n    ASSIGN(mode)\n    ASSIGN(input)\n    ASSIGN(dir)\n    ASSIGN(f1)\n    ASSIGN(top1)\n    ASSIGN(minwords_sentence)\n    ASSIGN(window)\n    ASSIGN(dyn)\n    ASSIGN(thres1)\n    ASSIGN(thres2)\n    \n    stringstream ss;\n    ss << thres1;\n    ss >> opt.min1gramfreq >> opt.min1gramprob;\n\n    ss << thres2;\n    ss >> opt.min2gramfreq >> opt.min2gramprob;\n    \n    fill( isWordChar, isWordChar + 256, false );\n    // already in lower case\n    // fill( isWordChar + 'A', isWordChar + 'Z' + 1, true );\n    fill( isWordChar + 'a', isWordChar + 'z' + 1, true );\n    isWordChar['\\''] = true;\n    \n    // top 100,000 words, big enough for usual need\n    bigram2freq.reserve(100000);\n    \n    if( opt.dyn ){\n        pUniform = new uniform_int_distribution<int>( 1, opt.window );\n    }\n    \n    opt.checkNonEng = 0;\n}\n\nvoid passInterestWords_( char** pInterestWords )\n{\n    interestWordsOn = true;\n    char** pw = pInterestWords;\n    char* w;\n    int id = 0;\n    while( w = *pw ){\n        interestWord2ID[w] = id;\n        ID2word.push_back(w);\n        pw++;\n        id++;\n    }\n    \n    unigram2freq.resize( interestWord2ID.size() );\n    \n    if( opt.mode == 2 )\n        bigram2freq.resize( interestWord2ID.size() );\n        \n    printf( \"%d interest words are passed to C++ module\\n\", interestWord2ID.size() );\n}\n\nint lookupWordID(string& word)\n{\n\tauto ppair = interestWord2ID.find(word);\n\t\n\tif( ppair == interestWord2ID.end() ){\n\t    if(interestWordsOn)\n\t        return -1;\n\t    else{\n\t        // if interestWordsOn=false, then store all words in interestWord2ID\n\t        int wid = interestWord2ID.size();\n\t        \n\t        // always make unigram2freq & ID2word big enough\n\t        if( unigram2freq.size() <= wid )\n\t            unigram2freq.resize( wid * 2 + 2 );\n\t            \n\t        if( ID2word.size() <= wid )\n\t            ID2word.resize( wid * 2 + 2 );\n\t        \n\t        // unlikely to happen, as bigram2freq has been resized when passInterestWords_()\n\t        if( opt.mode == 2 && bigram2freq.size() <= wid )\n\t            bigram2freq.resize( wid * 2 + 2 );\n\t        \n\t        ID2word[wid] = word.c_str();   \n\t        interestWord2ID[word] = wid;\n\t        return wid;\n        }\n    }\n    else{\n        return ppair->second; \n    }\n}\n\nvoid countUnigramFreqInWindow_(vector<string>& words)\n{\n\tfor(int i = 0; i < words.size(); i++){\n\t    string& word = words[i];\n\t    int wid = lookupWordID(word);\n\t    if( wid >= 0 ){\n\t        unigram2freq[wid]++;\n    \t    totalInterestWordCount++;\n        }\n        \n\t\t// Otherwise, word is not in the interest words list. Ignore\n\t}\n}\n\nvoid countBigramFreqInWindow_(vector<string>& words, int window)\n{\n\tint i, j, leftborder, rightborder;\n\n\tfor( i = 0; i < words.size(); i++ ){\n\t\tstring& w = words[i];\n\t\t\n\t\tint wid = lookupWordID(w);\n        if( wid < 0 )\n            continue;\n            \n\t\ttotalInterestWordCount++;\n\t\t\n\t\tint windowSize;\n\t\tif( opt.dyn )\n\t\t    windowSize = (*pUniform)(randGen);\n\t\telse\n\t\t    windowSize = opt.window;\n\t\t    \n\t\tleftborder = max( i - windowSize, 0 );\n\t\trightborder = i - 1;\n\n\t\tfor( j = leftborder; j <= rightborder; j++ ){\n\t\t\tif( j == i )\n\t\t\t    continue;\n\t\t\tstring& w2 = words[j];\n\t\t\tint w2id = lookupWordID(w2);\n\t\t\tif( w2id < 0 )\n\t\t\t    continue;\n    \t\tbigram2freq[w2id][wid]++;\n\t\t\tunigram2freq[wid]++;\n    \t\tallBigramOccur++;\n\t\t}\n\t}\n}\n\nvoid processSentence_(char* sentence)\n{\n    vector<string> words;\n    char* w;\n    int wc = 0;\n\n    w = strtok(sentence, \" \");\n    while(w){\n        bool isLegalWord = true;\n        \n        // default is not checking\n        if( opt.checkNonEng ){\n            unsigned char* pchar = (unsigned char*)w;\n            while(*pchar){\n        \t\t// remove words containing non-English, e.g. French, Greek..., letters\n        \t\t// include processive forms like \"people's\" as unigrams\n                if( ! isWordChar[*pchar] ){\n                    isLegalWord = false;\n                    break;\n                }\n                pchar++;\n            }\n        }\n        \n        if(isLegalWord){\n            words.push_back(w);\n        }\n            \n        wc++;\n        w = strtok(NULL, \" \");\n    }\n\n    int nonEngWc = wc - words.size();\n    \n    if( words.size() < opt.minwords_sentence )\n        return;\n\n    if( opt.mode == 1 )\n        countUnigramFreqInWindow_(words);\n    else\n        countBigramFreqInWindow_( words, opt.window );\n        \n    totalWordOccur += words.size();\n    totalNonEngWordOccur += nonEngWc;\n}\n\nvoid printStats_()\n{\n    printf( \"%I64d words occur, %I64d discarded, %I64d interest words occur\\n\",\n                totalWordOccur, totalNonEngWordOccur, (long long)totalInterestWordCount );\n}\n\n#define PPARAMS(x) fprintf( OUTF, \", %s=%s\", #x, opt.x.c_str() );\n#define PPARAMD(x) fprintf( OUTF, \", %s=%d\", #x, opt.x );\n\nvoid writeParams_(FILE* OUTF, int wordcount)\n{\n\t// output: input/dir f1 minwords_sentence  window  thres1  thres2\n\n\tfprintf( OUTF, \"# %d words, %I64d occurrences\\n\", wordcount, (long long)totalInterestWordCount );\n\n\tif( opt.input.length() )\n\t    fprintf( OUTF, \"# input=%s\", opt.input.c_str() );\n\telse\n\t    fprintf( OUTF, \"# dir=%s\", opt.dir.c_str() );\n\n    PPARAMS(f1)\n    PPARAMD(minwords_sentence)\n    PPARAMD(window)\n    PPARAMD(dyn)\n    PPARAMS(thres1)\n    PPARAMS(thres2)\n    \n\tif( opt.mode == 2 )\n\t    PPARAMD(top1)\n\n    fprintf( OUTF, \"\\n\" );\n    \n    if( opt.mode == 2 )\n        fprintf( OUTF, \"# %I64d bigram occurrences\\n\", (long long)allBigramOccur );\n}\n\nvoid outputTopUnigrams_(const char* top1gramFilename)\n{\n/*\tofstream OUTF;\n\tOUTF.open( top1gramFilename, ios_base::out );\n    OUTF.setf(ios::fixed, ios::floatfield);\n    OUTF.precision(3);\n*/\n\tFILE* OUTF;\n\tOUTF = fopen( top1gramFilename, \"w\" );\n\n\tint min1gramfreq2 = int( opt.min1gramprob * totalInterestWordCount );\n\tint min1gramfreq = max( opt.min1gramfreq, min1gramfreq2 );\n\tprintf( \"Words cut-off frequency: %d\\n\", min1gramfreq );\n\n    typedef pair<int, int> P;\n    vector<P> wIDfreqs;\n    \n    sortVec2Pairs<int>( unigram2freq, interestWord2ID.size(), wIDfreqs );\n\t\n    vector<int> wordIDs;\n    for( auto iter=wIDfreqs.begin(); iter != wIDfreqs.end(); ++iter ){\n        // wIDfreqs are already ordered in descending orders by the frequencies\n        if( iter->second < min1gramfreq )\n            break;\n        wordIDs.push_back(iter->first);\n    }\n    \n\tprintf( \"Saving %d words into '%s'...\\n\", wordIDs.size(), top1gramFilename );\n    \n\twriteParams_( OUTF, wordIDs.size() );\n\n\n\tfor(auto iter = wordIDs.begin(); iter != wordIDs.end(); ++iter){\n\t    int wid = *iter;\n\t    string& w = ID2word[wid];\n\t\tfprintf( OUTF, \"%s\\t%d\\t%.3f\\n\", w.c_str(), unigram2freq[wid], log( unigram2freq[wid] / totalInterestWordCount ) );\n\t}\n\tprintf(\"Done.\\n\");\n}\n\nvoid outputTopBigrams_(const char* topbigramFilename)\n{\n\tFILE* OUTF;\n\tOUTF = fopen( topbigramFilename, \"w\" );\n\n    typedef pair<int, int> P;\n    vector<P> wIDfreqs;\n\n\tint min1gramfreq2 = int( opt.min1gramprob * totalInterestWordCount );\n\tint min1gramfreq = max( opt.min1gramfreq, min1gramfreq2 );\n\tprintf( \"Words cut-off frequency: %d\\n\", min1gramfreq );\n    \n    // unigram2freq is a vector, not a hash_map\n\t// sort focus words according to their total frequencies\n    sortVec2Pairs<int>( unigram2freq, interestWord2ID.size(), wIDfreqs );\n\n\tvector<int> outputWordIDs;\n\tint wordCount = 0;\n    double allKeptUnigramOccur = 0;\n\n    vector<int> wordID2outputRank;\n    wordID2outputRank.resize( interestWord2ID.size() );\n    \n    int i = 0;\n\tfor( auto iter = wIDfreqs.begin(); iter != wIDfreqs.end(); ++iter ){\n\t    int wid = iter->first;\n\t    i++;\n\t    \n\t    // In case some words in unigram2freq are not in bigram2freq\n\t    // Maybe this check is unnecessary\n\t    I2I& pw_hash = bigram2freq[wid];\n\t    if( pw_hash.size() == 0 )\n\t        continue;\n\n        // Some interesting words have too low frequencies, cut off here\n        if( iter->second < min1gramfreq )\n            break;\n            \n\t\tint min2gramfreq2 = int( opt.min2gramprob * unigram2freq[wid] );\n\t\tint min2gramfreq = max( opt.min2gramfreq, min2gramfreq2 );\n\n        // all focus words (above freq threshold) with context ID2word[wid]\n        // pw_hash->second is the second level hash\n        int neighborCount = count_if( pw_hash.begin(), pw_hash.end(), \n                                      [ min2gramfreq ]( const P& p2 ){ return p2.second >= min2gramfreq; } );\n                                        \n\t\t// some context words have all focus words filtered. ignore them, keep others\n\t\tif( neighborCount > 0 ){\n\t\t    outputWordIDs.push_back(wid);\n\t\t    // the hash value is the rank by unigram frequency, in the same order as the output bigram context word\n\t\t    // +1 to start from 1. To differentiate from 0 (\"not included in output\")\n\t\t    wordID2outputRank[wid] = wordCount + 1;\n\t\t    wordCount++;\n\t\t    allKeptUnigramOccur += iter->second;\n\t\t}\n\t}\n\n\twriteParams_( OUTF, wordCount );\n    fprintf( OUTF, \"Words:\\n\" );\n\n    i = 0;\n    // outputWordIDs contain all words to write in \"Unigrams\" area and all context words in \"Bigrams\" area\n\tfor( auto iter = outputWordIDs.begin(); iter != outputWordIDs.end(); ++iter ){\n\t    i++;\n\t    int wid = *iter;\n\t    string& w = ID2word[wid];\n\t    // the unigram probability is defined as the fraction of bigrams\n\t    // whose second word is w, in all bigrams\n\t    fprintf( OUTF, \"%s,%d,%.3f\", w.c_str(), unigram2freq[wid], log( unigram2freq[wid] / allKeptUnigramOccur ) );\n\n\t    if( i < wordCount ){\n\t        if( i % 10 == 0 )\n\t            fprintf( OUTF, \"\\n\" );\n    \t    else\n    \t        fprintf( OUTF, \"\\t\" );\n    \t}\n\t}\n\n    fprintf( OUTF, \"\\n\\nBigrams:\\n\" );\n\n\tint bigramCount = 0;\n\tdouble allKeptBigramOccurrences = 0;\n\n\tprintf( \"Saving bigrams of %d words into '%s'...\\n\", wordCount, topbigramFilename );\n\n    vector<P> wIDfreqs2;\n    // reserve the maximum number of words\n    wIDfreqs2.reserve(wordCount);\n\n\tint wc = 0;\n    \n\tfor( auto iter = outputWordIDs.begin(); iter != outputWordIDs.end(); ++iter ){\n\t    int wid = *iter;\n\t    string& w = ID2word[wid];\n\t\tint min2gramfreq2 = int( opt.min2gramprob * unigram2freq[wid] );\n\t\tint min2gramfreq = max( opt.min2gramfreq, min2gramfreq2 );\n\n        wIDfreqs2.clear();\n        \n        for( auto iter = bigram2freq[wid].begin(); iter != bigram2freq[wid].end(); ++iter ){\n            if( wordID2outputRank[ iter->first ] > 0 && iter->second >= min2gramfreq )\n                wIDfreqs2.push_back( P(iter->first, iter->second) );\n        }\n        \n        // all neighbors of w are below output unigram frequency threshold $min1gramfreq\n        // this is unlikely if min1gramfreq is big enough, say 100\n        if( wIDfreqs2.size() == 0 )\n            continue;\n        \n        // sort neighbors by their output order / unigram freq\n        sort( wIDfreqs2.begin(), wIDfreqs2.end(), \n                [ &wordID2outputRank ](const P& p1, const P& p2) \n                {  return ( wordID2outputRank[ p1.first ] < wordID2outputRank[ p2.first ] ); } \n            );\n        \n\t\t// neighbor words are words following w, not preceding w\n\t\t// neighbor words are focus words, w is the context word\n\t\tdouble neighborTotalOccur = 0;\n\t\t\n\t\tfor( auto iter = wIDfreqs2.begin(); iter != wIDfreqs2.end(); ++iter )\n\t\t    neighborTotalOccur += iter->second;\n\t\t\n\t\twc++;\n\t\tallKeptBigramOccurrences += neighborTotalOccur;\n\n\t\tint neighborCount = wIDfreqs2.size();\n\n        // word ID, word, number of distinct neighbors, sum of freqs of all neighbors, cut off freq\n        fprintf( OUTF, \"%d,%s,%d,%d,%d\\n\", wc, w.c_str(), neighborCount, (int)neighborTotalOccur, min2gramfreq );\n\n        int i = 0;\n\t\tfor( auto iter = wIDfreqs2.begin(); iter != wIDfreqs2.end(); ++iter ){\n\t\t\tconst P& wIDfreq2 = *iter;\n\t\t    int wid2 = wIDfreq2.first;\n\n            // word,freq,log conditional_prob\n            fprintf( OUTF, \"\\t%s,%d,%.3f\", ID2word[wid2].c_str(), \n                            wIDfreq2.second, log( wIDfreq2.second / neighborTotalOccur ) );\n\n\t\t\tbigramCount++;\n\t\t\ti++;\n\t\t\t// 5 bigrams each line for humans to read easily\n\t\t\tif( i % 5 == 0 && i < neighborCount ){\n\t\t\t\tfprintf( OUTF, \"\\n\" );\n\t\t\t}\n\t\t}\n\t\tfprintf( OUTF, \"\\n\" );\n\n\t\tif( wc % 100 == 0 && wc < wordCount ){\n\t\t    printf(\"\\r%d\\r\", wc);\n\t\t}\n\t}\n\t// for integrity check. allKeptBigramOccurrences should <= allBigramOccur\n\tfprintf( OUTF, \"# Total kept bigram occurrences: %I64d\\n\", (long long)allKeptBigramOccurrences );\n\n\tprintf( \"\\n%d bigrams of %d focus words are saved into '%s'\\n\", bigramCount, wordCount, topbigramFilename );\n\n    if( allKeptBigramOccurrences > allBigramOccur ){\n        raise(5);\n\t    // \"BUG: kept bigram occurs allKeptBigramOccurrences > all bigram occurs allBigramOccur\\n\";\n\t}\n}\n"
  },
  {
    "path": "psdvec/patch to gensim.py",
    "content": "# -*- coding: utf-8 -*-\r\n\r\n### change to \"\\Lib\\site-packages\\gensim\\corpora\\wikicorpus.py\" ###\r\n\r\n    def get_texts(self):\r\n        ....\r\n        # changed to support both compressed and uncompressed formats\r\n        if self.fname[-4:].lower() == \".bz2\":\r\n            infile = bz2.BZ2File(self.fname)\r\n        else:\r\n            infile = open(self.fname)\r\n        texts = ((text, self.lemmatize, title, pageid) for title, text, pageid in extract_pages(infile, self.filter_namespaces))\r\n        \r\n# add \"keep_poss=True\"\r\ndef tokenize(content):\r\n    return [token.encode('utf8') for token in utils.tokenize(content, lower=True, errors='ignore', keep_poss=True)\r\n            if 2 <= len(token) <= 15 and not token.startswith('_')]\r\n\r\ndef get_texts(self):\r\n    # change the maxsize=1 to maxsize=10, to increase parallalism\r\n    for group in utils.chunkize(texts, chunksize=10 * self.processes, maxsize=10):\r\n        \r\n\r\n### change to \"\\Lib\\site-packages\\gensim\\utils.py\" ###\r\n\r\n# add Possessive 's. In order to match \"? \\xE2\\x80\\x99, do\r\n# text = text.replace(\"?, \"'\") before matching\r\nPAT_ALPHABETIC_POSS = re.compile('(((?![\\d])\\w)+(?:\\'s)?)', re.UNICODE)\r\n\r\n# add \"keep_poss=False\", the new switch of whether extracts possessive forms\r\ndef tokenize(text, lowercase=False, deacc=False, errors=\"strict\", to_lower=False, lower=False, keep_poss=False):\r\n    \r\n    if keep_poss:\r\n        # utf8 of \"\"\r\n        text = text.replace(u\"?, \"'\")            # '\r\n        PAT = PAT_ALPHABETIC_POSS\r\n    else:\r\n        PAT = PAT_ALPHABETIC\r\n        \r\n    ...\r\n        \r\n    for match in PAT.finditer(text):\r\n        ....\r\n        \r\n    "
  },
  {
    "path": "psdvec/perlxs.h",
    "content": "// http://ftp.ledas.ac.uk/software/lheasoft/lheasoft6.3.1/source/heacore/pil/perl/Av_CharPtrPtr.c\r\n\r\n/* Used by the INPUT typemap for char**.\r\n * Will convert a Perl AV* (containing strings) to a C char**.\r\n */\r\nchar** XS_unpack_charPtrPtr( SV* rv )\r\n{\r\n\tAV *av;\r\n\tSV **ssv;\r\n\tchar **s;\r\n\tint avlen;\r\n\tint x;\r\n\r\n\tif( SvROK( rv ) && (SvTYPE(SvRV(rv)) == SVt_PVAV) )\r\n\t\tav = (AV*)SvRV(rv);\r\n    else {\r\n\t\twarn(\"XS_unpack_charPtrPtr: rv was not an AV ref\");\r\n\t\treturn( (char**)NULL );\r\n\t}\r\n\r\n\t/* is it empty? */\r\n\tavlen = av_len(av);\r\n\tif( avlen < 0 ){\r\n\t\twarn(\"XS_unpack_charPtrPtr: array was empty\");\r\n\t\treturn( (char**)NULL );\r\n\t}\r\n\r\n\t/* av_len+2 == number of strings, plus 1 for an end-of-array sentinel.\r\n\t */\r\n\ts = (char **)safemalloc( sizeof(char*) * (avlen + 2) );\r\n\tif( s == NULL ){\r\n\t\twarn(\"XS_unpack_charPtrPtr: unable to malloc char**\");\r\n\t\treturn( (char**)NULL );\r\n\t}\r\n\tfor( x = 0; x <= avlen; ++x ){\r\n\t\tssv = av_fetch( av, x, 0 );\r\n\t\tif( ssv != NULL ){\r\n\t\t\tif( SvPOK( *ssv ) ){\r\n\t\t\t\ts[x] = (char *)safemalloc( SvCUR(*ssv) + 1 );\r\n\t\t\t\tif( s[x] == NULL )\r\n\t\t\t\t\twarn(\"XS_unpack_charPtrPtr: unable to malloc char*\");\r\n\t\t\t\telse\r\n\t\t\t\t\tstrcpy( s[x], SvPV( *ssv, PL_na ) );\r\n\t\t\t}\r\n\t\t\telse\r\n\t\t\t\twarn(\"XS_unpack_charPtrPtr: array elem %d was not a string.\", x );\r\n\t\t}\r\n\t\telse\r\n\t\t\ts[x] = (char*)NULL;\r\n\t}\r\n\ts[x] = (char*)NULL; /* sentinel */\r\n\treturn( s );\r\n}\r\n\r\n/* Used by the OUTPUT typemap for char**.\r\n * Will convert a C char** to a Perl AV*.\r\n */\r\nvoid XS_pack_charPtrPtr( SV * st, char ** s )\r\n{\r\n\tAV *av = newAV();\r\n\tSV *sv;\r\n\tchar **c;\r\n\r\n\tfor( c = s; *c != NULL; ++c ){\r\n\t\tsv = newSVpv( *c, 0 );\r\n\t\tav_push( av, sv );\r\n\t}\r\n\tfree ( s );\r\n\tsv = newSVrv( st, NULL );\t/* upgrade stack SV to an RV */\r\n\tSvREFCNT_dec( sv );\t/* discard */\r\n\tSvRV( st ) = (SV*)av;\t/* make stack RV point at our AV */\r\n}\r\n\r\n\r\n/* cleanup the temporary char** from XS_unpack_charPtrPtr */\r\nvoid XS_release_charPtrPtr(char** s)\r\n{\r\n\tchar **c;\r\n\tfor( c = s; *c != NULL; ++c )\r\n\t\tSafefree( *c );\r\n\tSafefree( s );\r\n}\r\n"
  },
  {
    "path": "psdvec/removeDoubleNewline.pl",
    "content": "$wc = 0;\r\n$doubleNewlineCount = 0;\r\nwhile(<>){\r\n    $wc++;\r\n    if( /^\\r?\\n$/ ){\r\n        if( $lastIsNewline ){\r\n            $lastIsNewline = 0;\r\n            $doubleNewlineCount++;\r\n            next;\r\n        }\r\n        else{\r\n            print;\r\n            $lastIsNewline = 1;\r\n        }\r\n    }\r\n    else{\r\n        print;\r\n    }\r\n    if( $wc % 1000 == 0 ){\r\n        print STDERR \"\\r$wc $doubleNewlineCount\\r\";\r\n    }\r\n}\r\nprint STDERR \"$wc $doubleNewlineCount\\n\";\r\n"
  },
  {
    "path": "psdvec/sent-bench.bat",
    "content": "@echo off\r\nrem cd d:\\corpus\r\nrem python corpus2liblinear.py -d aclImdb\\test\\pos -o sent-test.txt 1\r\nrem python corpus2liblinear.py -d aclImdb\\test\\neg -o sent-test.txt -1 -a\r\nrem python corpus2liblinear.py -d aclImdb\\train\\pos -o sent-train.txt 1\r\nrem python corpus2liblinear.py -d aclImdb\\train\\neg -o sent-train.txt -1 -a\r\npushd d:\\liblinear-2.1\\windows\r\ntrain -s7 -v10 \\corpus\\sent-train-PSD-reg.txt PSD-reg.model\r\npredict \\corpus\\sent-test-PSD-reg.txt PSD-reg.model pred-output.txt\r\npopd\r\n\r\n"
  },
  {
    "path": "psdvec/sent-gen.conf",
    "content": "{ \"outFilenameTrunk\": \"sent-train\", \"docDir\": \"D:/corpus/aclImdb/train/neg\", \"label\": \"-1\", \"appendToOutput\": false }\r\n{ \"outFilenameTrunk\": \"sent-train\", \"docDir\": \"D:/corpus/aclImdb/train/pos\", \"label\": \"+1\", \"appendToOutput\": true }\r\n{ \"outFilenameTrunk\": \"sent-test\",  \"docDir\": \"D:/corpus/aclImdb/test/neg\", \"label\": \"-1\", \"appendToOutput\": false }\r\n{ \"outFilenameTrunk\": \"sent-test\",  \"docDir\": \"D:/corpus/aclImdb/test/pos\", \"label\": \"+1\", \"appendToOutput\": true }\r\n"
  },
  {
    "path": "psdvec/sentbench.py",
    "content": "import os\r\nimport re\r\nimport subprocess\r\n\r\nalg2vec = { \"PSD-reg\": \"25000-180000-500-BLK-8.0.vec\", \r\n            #\"PSD-unreg\": \"25000-180000-500-BLK-0.0.vec\", \r\n            #\"word2vec\": \"word2vec2.vec\",\r\n            #\"CCA\": \"182800-500-CCA.vec\",\r\n            \"sparse\":  \"120000-sparse.vec\"\r\n          }\r\n\r\nvecDir = \"d:/corpus/embeddings\"\r\nliblinearDir = \"D:/liblinear-2.1/windows\"\r\ntrainExePath = liblinearDir + \"/\" + \"train.exe\"\r\npredictExePath = liblinearDir + \"/\" + \"predict.exe\"\r\ndataDir = \"d:/corpus\"\r\ntrainFiletrunk = \"sent-train\"\r\ntestFiletrunk = \"sent-test\"\r\ndataGenScript = dataDir + \"/corpus2liblinear.py\"\r\ndataGenConfig = dataDir + \"/sent-gen.conf\"\r\nsentimentWordFile = dataDir + \"/topSentWords500.txt\"\r\n\r\n# code below is just to count the words in sentimentWordFile.\r\n# the count is used in file names\r\nsentword2id = {}\r\nbowSize = 0\r\nif sentimentWordFile:\r\n    SENT = open(sentimentWordFile)\r\n    id = 0\r\n    for line in SENT:\r\n        word, freq = line.split(\"\\t\")\r\n        sentword2id[word] = id\r\n        id += 1\r\n    bowSize = len(sentword2id)\r\n    SENT.close()\r\n     \r\n# L1 or L2 regularization for the logistic regression solver\r\n# Experiments show this option has little impact on the results\r\nsolverReg = 2\r\nif solverReg == 1:\r\n    solverType = \"-s6\"\r\nelif solverReg == 2:\r\n    solverType = \"-s7\"\r\n    \r\nfor algName, vecFilename in alg2vec.items():\r\n    print \"%s:\" %algName\r\n    \r\n    vecFullfilename = vecDir + \"/\" + vecFilename\r\n    \r\n    if sentimentWordFile:\r\n        trainFilename = \"%s/%s-%s-bow%d.txt\" %( dataDir, trainFiletrunk, algName, bowSize )\r\n        testFilename = \"%s/%s-%s-bow%d.txt\" %( dataDir, testFiletrunk, algName, bowSize )\r\n    else:\r\n        trainFilename = \"%s/%s-%s.txt\" %( dataDir, trainFiletrunk, algName )\r\n        testFilename = \"%s/%s-%s.txt\" %( dataDir, testFiletrunk, algName )\r\n        \r\n    if not ( os.path.isfile(trainFilename) and os.path.isfile(testFilename) ):\r\n        options = [ \"python\", dataGenScript, \"-c\", dataGenConfig, \"-n\", algName, \\\r\n                              \"-v\", vecFullfilename ]\r\n        if sentimentWordFile:\r\n            options.append(\"-s\")\r\n            options.append(sentimentWordFile)\r\n            \r\n        subprocess.call(options)\r\n\r\n    if sentimentWordFile:\r\n        modelFilename = \"%s-bow%d.model\" %( algName, bowSize )\r\n        outputFilename = \"%s-bow%d.output\" %( algName, bowSize )\r\n    else:\r\n        modelFilename = \"%s.model\" %algName\r\n        outputFilename = \"%s.output\" %algName\r\n    \r\n    print \"Training using %s\" %trainFilename\r\n    subprocess.call( [ trainExePath, solverType, \"-v10\", trainFilename, modelFilename ] )\r\n    print \"Testing using %s\" %testFilename\r\n    subprocess.call( [ predictExePath, testFilename, modelFilename, outputFilename ] )\r\n    print\r\n    "
  },
  {
    "path": "psdvec/tab2list.py",
    "content": "import sys\r\n\r\nCAT = 1\r\nWORDS = 2\r\n\r\nFTAB = open(sys.argv[1])\r\nFLIST = open(sys.argv[2], \"w\")\r\nFLIST.write(\"WORD\\tTRUECLASS\\n\")\r\n\r\nstate = CAT\r\ncatnum = 0\r\nwordnum = 0\r\n\r\nfor line in FTAB:\r\n    line = line.strip()\r\n\r\n    if not line and state == CAT:\r\n        continue\r\n\r\n    if state == CAT:\r\n        cat = line.replace(\" \", \"-\")\r\n        state = WORDS\r\n        catnum += 1\r\n        continue\r\n    if state == WORDS:\r\n        if line:\r\n            words = line.split(\", \")\r\n            for word in words:\r\n                word = word.replace(\",\", \"\")\r\n                FLIST.write( \"%s\\t%s\\n\" %(word, cat) )\r\n                wordnum += 1\r\n            continue\r\n        else:\r\n            state = CAT\r\n            continue\r\n\r\nprint \"%d words in %d categories written into %s\" %(wordnum, catnum, sys.argv[2])\r\n"
  },
  {
    "path": "psdvec/testsets/analogy/EN-TOM-ICLR13-SEM.txt",
    "content": "Athens Greece Baghdad Iraq\nAthens Greece Bangkok Thailand\nAthens Greece Beijing China\nAthens Greece Berlin Germany\nAthens Greece Bern Switzerland\nAthens Greece Cairo Egypt\nAthens Greece Canberra Australia\nAthens Greece Hanoi Vietnam\nAthens Greece Havana Cuba\nAthens Greece Helsinki Finland\nAthens Greece Islamabad Pakistan\nAthens Greece Kabul Afghanistan\nAthens Greece London England\nAthens Greece Madrid Spain\nAthens Greece Moscow Russia\nAthens Greece Oslo Norway\nAthens Greece Ottawa Canada\nAthens Greece Paris France\nAthens Greece Rome Italy\nAthens Greece Stockholm Sweden\nAthens Greece Tehran Iran\nAthens Greece Tokyo Japan\nBaghdad Iraq Bangkok Thailand\nBaghdad Iraq Beijing China\nBaghdad Iraq Berlin Germany\nBaghdad Iraq Bern Switzerland\nBaghdad Iraq Cairo Egypt\nBaghdad Iraq Canberra Australia\nBaghdad Iraq Hanoi Vietnam\nBaghdad Iraq Havana Cuba\nBaghdad Iraq Helsinki Finland\nBaghdad Iraq Islamabad Pakistan\nBaghdad Iraq Kabul Afghanistan\nBaghdad Iraq London England\nBaghdad Iraq Madrid Spain\nBaghdad Iraq Moscow Russia\nBaghdad Iraq Oslo Norway\nBaghdad Iraq Ottawa Canada\nBaghdad Iraq Paris France\nBaghdad Iraq Rome Italy\nBaghdad Iraq Stockholm Sweden\nBaghdad Iraq Tehran Iran\nBaghdad Iraq Tokyo Japan\nBaghdad Iraq Athens Greece\nBangkok Thailand Beijing China\nBangkok Thailand Berlin Germany\nBangkok Thailand Bern Switzerland\nBangkok Thailand Cairo Egypt\nBangkok Thailand Canberra Australia\nBangkok Thailand Hanoi Vietnam\nBangkok Thailand Havana Cuba\nBangkok Thailand Helsinki Finland\nBangkok Thailand Islamabad Pakistan\nBangkok Thailand Kabul Afghanistan\nBangkok Thailand London England\nBangkok Thailand Madrid Spain\nBangkok Thailand Moscow Russia\nBangkok Thailand Oslo Norway\nBangkok Thailand Ottawa Canada\nBangkok Thailand Paris France\nBangkok Thailand Rome Italy\nBangkok Thailand Stockholm Sweden\nBangkok Thailand Tehran Iran\nBangkok Thailand Tokyo Japan\nBangkok Thailand Athens Greece\nBangkok Thailand Baghdad Iraq\nBeijing China Berlin Germany\nBeijing China Bern Switzerland\nBeijing China Cairo Egypt\nBeijing China Canberra Australia\nBeijing China Hanoi Vietnam\nBeijing China Havana Cuba\nBeijing China Helsinki Finland\nBeijing China Islamabad Pakistan\nBeijing China Kabul Afghanistan\nBeijing China London England\nBeijing China Madrid Spain\nBeijing China Moscow Russia\nBeijing China Oslo Norway\nBeijing China Ottawa Canada\nBeijing China Paris France\nBeijing China Rome Italy\nBeijing China Stockholm Sweden\nBeijing China Tehran Iran\nBeijing China Tokyo Japan\nBeijing China Athens Greece\nBeijing China Baghdad Iraq\nBeijing China Bangkok Thailand\nBerlin Germany Bern Switzerland\nBerlin Germany Cairo Egypt\nBerlin Germany Canberra Australia\nBerlin Germany Hanoi Vietnam\nBerlin Germany Havana Cuba\nBerlin Germany Helsinki Finland\nBerlin Germany Islamabad Pakistan\nBerlin Germany Kabul Afghanistan\nBerlin Germany London England\nBerlin Germany Madrid Spain\nBerlin Germany Moscow Russia\nBerlin Germany Oslo Norway\nBerlin Germany Ottawa Canada\nBerlin Germany Paris France\nBerlin Germany Rome Italy\nBerlin Germany Stockholm Sweden\nBerlin Germany Tehran Iran\nBerlin Germany Tokyo Japan\nBerlin Germany Athens Greece\nBerlin Germany Baghdad Iraq\nBerlin Germany Bangkok Thailand\nBerlin Germany Beijing China\nBern Switzerland Cairo Egypt\nBern Switzerland Canberra Australia\nBern Switzerland Hanoi Vietnam\nBern Switzerland Havana Cuba\nBern Switzerland Helsinki Finland\nBern Switzerland Islamabad Pakistan\nBern Switzerland Kabul Afghanistan\nBern Switzerland London England\nBern Switzerland Madrid Spain\nBern Switzerland Moscow Russia\nBern Switzerland Oslo Norway\nBern Switzerland Ottawa Canada\nBern Switzerland Paris France\nBern Switzerland Rome Italy\nBern Switzerland Stockholm Sweden\nBern Switzerland Tehran Iran\nBern Switzerland Tokyo Japan\nBern Switzerland Athens Greece\nBern Switzerland Baghdad Iraq\nBern Switzerland Bangkok Thailand\nBern Switzerland Beijing China\nBern Switzerland Berlin Germany\nCairo Egypt Canberra Australia\nCairo Egypt Hanoi Vietnam\nCairo Egypt Havana Cuba\nCairo Egypt Helsinki Finland\nCairo Egypt Islamabad Pakistan\nCairo Egypt Kabul Afghanistan\nCairo Egypt London England\nCairo Egypt Madrid Spain\nCairo Egypt Moscow Russia\nCairo Egypt Oslo Norway\nCairo Egypt Ottawa Canada\nCairo Egypt Paris France\nCairo Egypt Rome Italy\nCairo Egypt Stockholm Sweden\nCairo Egypt Tehran Iran\nCairo Egypt Tokyo Japan\nCairo Egypt Athens Greece\nCairo Egypt Baghdad Iraq\nCairo Egypt Bangkok Thailand\nCairo Egypt Beijing China\nCairo Egypt Berlin Germany\nCairo Egypt Bern Switzerland\nCanberra Australia Hanoi Vietnam\nCanberra Australia Havana Cuba\nCanberra Australia Helsinki Finland\nCanberra Australia Islamabad Pakistan\nCanberra Australia Kabul Afghanistan\nCanberra Australia London England\nCanberra Australia Madrid Spain\nCanberra Australia Moscow Russia\nCanberra Australia Oslo Norway\nCanberra Australia Ottawa Canada\nCanberra Australia Paris France\nCanberra Australia Rome Italy\nCanberra Australia Stockholm Sweden\nCanberra Australia Tehran Iran\nCanberra Australia Tokyo Japan\nCanberra Australia Athens Greece\nCanberra Australia Baghdad Iraq\nCanberra Australia Bangkok Thailand\nCanberra Australia Beijing China\nCanberra Australia Berlin Germany\nCanberra Australia Bern Switzerland\nCanberra Australia Cairo Egypt\nHanoi Vietnam Havana Cuba\nHanoi Vietnam Helsinki Finland\nHanoi Vietnam Islamabad Pakistan\nHanoi Vietnam Kabul Afghanistan\nHanoi Vietnam London England\nHanoi Vietnam Madrid Spain\nHanoi Vietnam Moscow Russia\nHanoi Vietnam Oslo Norway\nHanoi Vietnam Ottawa Canada\nHanoi Vietnam Paris France\nHanoi Vietnam Rome Italy\nHanoi Vietnam Stockholm Sweden\nHanoi Vietnam Tehran Iran\nHanoi Vietnam Tokyo Japan\nHanoi Vietnam Athens Greece\nHanoi Vietnam Baghdad Iraq\nHanoi Vietnam Bangkok Thailand\nHanoi Vietnam Beijing China\nHanoi Vietnam Berlin Germany\nHanoi Vietnam Bern Switzerland\nHanoi Vietnam Cairo Egypt\nHanoi Vietnam Canberra Australia\nHavana Cuba Helsinki Finland\nHavana Cuba Islamabad Pakistan\nHavana Cuba Kabul Afghanistan\nHavana Cuba London England\nHavana Cuba Madrid Spain\nHavana Cuba Moscow Russia\nHavana Cuba Oslo Norway\nHavana Cuba Ottawa Canada\nHavana Cuba Paris France\nHavana Cuba Rome Italy\nHavana Cuba Stockholm Sweden\nHavana Cuba Tehran Iran\nHavana Cuba Tokyo Japan\nHavana Cuba Athens Greece\nHavana Cuba Baghdad Iraq\nHavana Cuba Bangkok Thailand\nHavana Cuba Beijing China\nHavana Cuba Berlin Germany\nHavana Cuba Bern Switzerland\nHavana Cuba Cairo Egypt\nHavana Cuba Canberra Australia\nHavana Cuba Hanoi Vietnam\nHelsinki Finland Islamabad Pakistan\nHelsinki Finland Kabul Afghanistan\nHelsinki Finland London England\nHelsinki Finland Madrid Spain\nHelsinki Finland Moscow Russia\nHelsinki Finland Oslo Norway\nHelsinki Finland Ottawa Canada\nHelsinki Finland Paris France\nHelsinki Finland Rome Italy\nHelsinki Finland Stockholm Sweden\nHelsinki Finland Tehran Iran\nHelsinki Finland Tokyo Japan\nHelsinki Finland Athens Greece\nHelsinki Finland Baghdad Iraq\nHelsinki Finland Bangkok Thailand\nHelsinki Finland Beijing China\nHelsinki Finland Berlin Germany\nHelsinki Finland Bern Switzerland\nHelsinki Finland Cairo Egypt\nHelsinki Finland Canberra Australia\nHelsinki Finland Hanoi Vietnam\nHelsinki Finland Havana Cuba\nIslamabad Pakistan Kabul Afghanistan\nIslamabad Pakistan London England\nIslamabad Pakistan Madrid Spain\nIslamabad Pakistan Moscow Russia\nIslamabad Pakistan Oslo Norway\nIslamabad Pakistan Ottawa Canada\nIslamabad Pakistan Paris France\nIslamabad Pakistan Rome Italy\nIslamabad Pakistan Stockholm Sweden\nIslamabad Pakistan Tehran Iran\nIslamabad Pakistan Tokyo Japan\nIslamabad Pakistan Athens Greece\nIslamabad Pakistan Baghdad Iraq\nIslamabad Pakistan Bangkok Thailand\nIslamabad Pakistan Beijing China\nIslamabad Pakistan Berlin Germany\nIslamabad Pakistan Bern Switzerland\nIslamabad Pakistan Cairo Egypt\nIslamabad Pakistan Canberra Australia\nIslamabad Pakistan Hanoi Vietnam\nIslamabad Pakistan Havana Cuba\nIslamabad Pakistan Helsinki Finland\nKabul Afghanistan London England\nKabul Afghanistan Madrid Spain\nKabul Afghanistan Moscow Russia\nKabul Afghanistan Oslo Norway\nKabul Afghanistan Ottawa Canada\nKabul Afghanistan Paris France\nKabul Afghanistan Rome Italy\nKabul Afghanistan Stockholm Sweden\nKabul Afghanistan Tehran Iran\nKabul Afghanistan Tokyo Japan\nKabul Afghanistan Athens Greece\nKabul Afghanistan Baghdad Iraq\nKabul Afghanistan Bangkok Thailand\nKabul Afghanistan Beijing China\nKabul Afghanistan Berlin Germany\nKabul Afghanistan Bern Switzerland\nKabul Afghanistan Cairo Egypt\nKabul Afghanistan Canberra Australia\nKabul Afghanistan Hanoi Vietnam\nKabul Afghanistan Havana Cuba\nKabul Afghanistan Helsinki Finland\nKabul Afghanistan Islamabad Pakistan\nLondon England Madrid Spain\nLondon England Moscow Russia\nLondon England Oslo Norway\nLondon England Ottawa Canada\nLondon England Paris France\nLondon England Rome Italy\nLondon England Stockholm Sweden\nLondon England Tehran Iran\nLondon England Tokyo Japan\nLondon England Athens Greece\nLondon England Baghdad Iraq\nLondon England Bangkok Thailand\nLondon England Beijing China\nLondon England Berlin Germany\nLondon England Bern Switzerland\nLondon England Cairo Egypt\nLondon England Canberra Australia\nLondon England Hanoi Vietnam\nLondon England Havana Cuba\nLondon England Helsinki Finland\nLondon England Islamabad Pakistan\nLondon England Kabul Afghanistan\nMadrid Spain Moscow Russia\nMadrid Spain Oslo Norway\nMadrid Spain Ottawa Canada\nMadrid Spain Paris France\nMadrid Spain Rome Italy\nMadrid Spain Stockholm Sweden\nMadrid Spain Tehran Iran\nMadrid Spain Tokyo Japan\nMadrid Spain Athens Greece\nMadrid Spain Baghdad Iraq\nMadrid Spain Bangkok Thailand\nMadrid Spain Beijing China\nMadrid Spain Berlin Germany\nMadrid Spain Bern Switzerland\nMadrid Spain Cairo Egypt\nMadrid Spain Canberra Australia\nMadrid Spain Hanoi Vietnam\nMadrid Spain Havana Cuba\nMadrid Spain Helsinki Finland\nMadrid Spain Islamabad Pakistan\nMadrid Spain Kabul Afghanistan\nMadrid Spain London England\nMoscow Russia Oslo Norway\nMoscow Russia Ottawa Canada\nMoscow Russia Paris France\nMoscow Russia Rome Italy\nMoscow Russia Stockholm Sweden\nMoscow Russia Tehran Iran\nMoscow Russia Tokyo Japan\nMoscow Russia Athens Greece\nMoscow Russia Baghdad Iraq\nMoscow Russia Bangkok Thailand\nMoscow Russia Beijing China\nMoscow Russia Berlin Germany\nMoscow Russia Bern Switzerland\nMoscow Russia Cairo Egypt\nMoscow Russia Canberra Australia\nMoscow Russia Hanoi Vietnam\nMoscow Russia Havana Cuba\nMoscow Russia Helsinki Finland\nMoscow Russia Islamabad Pakistan\nMoscow Russia Kabul Afghanistan\nMoscow Russia London England\nMoscow Russia Madrid Spain\nOslo Norway Ottawa Canada\nOslo Norway Paris France\nOslo Norway Rome Italy\nOslo Norway Stockholm Sweden\nOslo Norway Tehran Iran\nOslo Norway Tokyo Japan\nOslo Norway Athens Greece\nOslo Norway Baghdad Iraq\nOslo Norway Bangkok Thailand\nOslo Norway Beijing China\nOslo Norway Berlin Germany\nOslo Norway Bern Switzerland\nOslo Norway Cairo Egypt\nOslo Norway Canberra Australia\nOslo Norway Hanoi Vietnam\nOslo Norway Havana Cuba\nOslo Norway Helsinki Finland\nOslo Norway Islamabad Pakistan\nOslo Norway Kabul Afghanistan\nOslo Norway London England\nOslo Norway Madrid Spain\nOslo Norway Moscow Russia\nOttawa Canada Paris France\nOttawa Canada Rome Italy\nOttawa Canada Stockholm Sweden\nOttawa Canada Tehran Iran\nOttawa Canada Tokyo Japan\nOttawa Canada Athens Greece\nOttawa Canada Baghdad Iraq\nOttawa Canada Bangkok Thailand\nOttawa Canada Beijing China\nOttawa Canada Berlin Germany\nOttawa Canada Bern Switzerland\nOttawa Canada Cairo Egypt\nOttawa Canada Canberra Australia\nOttawa Canada Hanoi Vietnam\nOttawa Canada Havana Cuba\nOttawa Canada Helsinki Finland\nOttawa Canada Islamabad Pakistan\nOttawa Canada Kabul Afghanistan\nOttawa Canada London England\nOttawa Canada Madrid Spain\nOttawa Canada Moscow Russia\nOttawa Canada Oslo Norway\nParis France Rome Italy\nParis France Stockholm Sweden\nParis France Tehran Iran\nParis France Tokyo Japan\nParis France Athens Greece\nParis France Baghdad Iraq\nParis France Bangkok Thailand\nParis France Beijing China\nParis France Berlin Germany\nParis France Bern Switzerland\nParis France Cairo Egypt\nParis France Canberra Australia\nParis France Hanoi Vietnam\nParis France Havana Cuba\nParis France Helsinki Finland\nParis France Islamabad Pakistan\nParis France Kabul Afghanistan\nParis France London England\nParis France Madrid Spain\nParis France Moscow Russia\nParis France Oslo Norway\nParis France Ottawa Canada\nRome Italy Stockholm Sweden\nRome Italy Tehran Iran\nRome Italy Tokyo Japan\nRome Italy Athens Greece\nRome Italy Baghdad Iraq\nRome Italy Bangkok Thailand\nRome Italy Beijing China\nRome Italy Berlin Germany\nRome Italy Bern Switzerland\nRome Italy Cairo Egypt\nRome Italy Canberra Australia\nRome Italy Hanoi Vietnam\nRome Italy Havana Cuba\nRome Italy Helsinki Finland\nRome Italy Islamabad Pakistan\nRome Italy Kabul Afghanistan\nRome Italy London England\nRome Italy Madrid Spain\nRome Italy Moscow Russia\nRome Italy Oslo Norway\nRome Italy Ottawa Canada\nRome Italy Paris France\nStockholm Sweden Tehran Iran\nStockholm Sweden Tokyo Japan\nStockholm Sweden Athens Greece\nStockholm Sweden Baghdad Iraq\nStockholm Sweden Bangkok Thailand\nStockholm Sweden Beijing China\nStockholm Sweden Berlin Germany\nStockholm Sweden Bern Switzerland\nStockholm Sweden Cairo Egypt\nStockholm Sweden Canberra Australia\nStockholm Sweden Hanoi Vietnam\nStockholm Sweden Havana Cuba\nStockholm Sweden Helsinki Finland\nStockholm Sweden Islamabad Pakistan\nStockholm Sweden Kabul Afghanistan\nStockholm Sweden London England\nStockholm Sweden Madrid Spain\nStockholm Sweden Moscow Russia\nStockholm Sweden Oslo Norway\nStockholm Sweden Ottawa Canada\nStockholm Sweden Paris France\nStockholm Sweden Rome Italy\nTehran Iran Tokyo Japan\nTehran Iran Athens Greece\nTehran Iran Baghdad Iraq\nTehran Iran Bangkok Thailand\nTehran Iran Beijing China\nTehran Iran Berlin Germany\nTehran Iran Bern Switzerland\nTehran Iran Cairo Egypt\nTehran Iran Canberra Australia\nTehran Iran Hanoi Vietnam\nTehran Iran Havana Cuba\nTehran Iran Helsinki Finland\nTehran Iran Islamabad Pakistan\nTehran Iran Kabul Afghanistan\nTehran Iran London England\nTehran Iran Madrid Spain\nTehran Iran Moscow Russia\nTehran Iran Oslo Norway\nTehran Iran Ottawa Canada\nTehran Iran Paris France\nTehran Iran Rome Italy\nTehran Iran Stockholm Sweden\nTokyo Japan Athens Greece\nTokyo Japan Baghdad Iraq\nTokyo Japan Bangkok Thailand\nTokyo Japan Beijing China\nTokyo Japan Berlin Germany\nTokyo Japan Bern Switzerland\nTokyo Japan Cairo Egypt\nTokyo Japan Canberra Australia\nTokyo Japan Hanoi Vietnam\nTokyo Japan Havana Cuba\nTokyo Japan Helsinki Finland\nTokyo Japan Islamabad Pakistan\nTokyo Japan Kabul Afghanistan\nTokyo Japan London England\nTokyo Japan Madrid Spain\nTokyo Japan Moscow Russia\nTokyo Japan Oslo Norway\nTokyo Japan Ottawa Canada\nTokyo Japan Paris France\nTokyo Japan Rome Italy\nTokyo Japan Stockholm Sweden\nTokyo Japan Tehran Iran\nAbuja Nigeria Accra Ghana\nAbuja Nigeria Algiers Algeria\nAbuja Nigeria Amman Jordan\nAbuja Nigeria Ankara Turkey\nAbuja Nigeria Antananarivo Madagascar\nAbuja Nigeria Apia Samoa\nAbuja Nigeria Ashgabat Turkmenistan\nAbuja Nigeria Asmara Eritrea\nAbuja Nigeria Astana Kazakhstan\nAbuja Nigeria Athens Greece\nAbuja Nigeria Baghdad Iraq\nAbuja Nigeria Baku Azerbaijan\nAbuja Nigeria Bamako Mali\nAbuja Nigeria Bangkok Thailand\nAbuja Nigeria Banjul Gambia\nAbuja Nigeria Beijing China\nAbuja Nigeria Beirut Lebanon\nAbuja Nigeria Belgrade Serbia\nAbuja Nigeria Belmopan Belize\nAbuja Nigeria Berlin Germany\nAbuja Nigeria Bern Switzerland\nAbuja Nigeria Bishkek Kyrgyzstan\nAbuja Nigeria Bratislava Slovakia\nAbuja Nigeria Brussels Belgium\nAbuja Nigeria Bucharest Romania\nAbuja Nigeria Budapest Hungary\nAbuja Nigeria Bujumbura Burundi\nAbuja Nigeria Cairo Egypt\nAbuja Nigeria Canberra Australia\nAbuja Nigeria Caracas Venezuela\nAbuja Nigeria Chisinau Moldova\nAbuja Nigeria Conakry Guinea\nAbuja Nigeria Copenhagen Denmark\nAbuja Nigeria Dakar Senegal\nAbuja Nigeria Damascus Syria\nAbuja Nigeria Dhaka Bangladesh\nAbuja Nigeria Doha Qatar\nAbuja Nigeria Dublin Ireland\nAbuja Nigeria Dushanbe Tajikistan\nAccra Ghana Algiers Algeria\nAccra Ghana Amman Jordan\nAccra Ghana Ankara Turkey\nAccra Ghana Antananarivo Madagascar\nAccra Ghana Apia Samoa\nAccra Ghana Ashgabat Turkmenistan\nAccra Ghana Asmara Eritrea\nAccra Ghana Astana Kazakhstan\nAccra Ghana Athens Greece\nAccra Ghana Baghdad Iraq\nAccra Ghana Baku Azerbaijan\nAccra Ghana Bamako Mali\nAccra Ghana Bangkok Thailand\nAccra Ghana Banjul Gambia\nAccra Ghana Beijing China\nAccra Ghana Beirut Lebanon\nAccra Ghana Belgrade Serbia\nAccra Ghana Belmopan Belize\nAccra Ghana Berlin Germany\nAccra Ghana Bern Switzerland\nAccra Ghana Bishkek Kyrgyzstan\nAccra Ghana Bratislava Slovakia\nAccra Ghana Brussels Belgium\nAccra Ghana Bucharest Romania\nAccra Ghana Budapest Hungary\nAccra Ghana Bujumbura Burundi\nAccra Ghana Cairo Egypt\nAccra Ghana Canberra Australia\nAccra Ghana Caracas Venezuela\nAccra Ghana Chisinau Moldova\nAccra Ghana Conakry Guinea\nAccra Ghana Copenhagen Denmark\nAccra Ghana Dakar Senegal\nAccra Ghana Damascus Syria\nAccra Ghana Dhaka Bangladesh\nAccra Ghana Doha Qatar\nAccra Ghana Dublin Ireland\nAccra Ghana Dushanbe Tajikistan\nAccra Ghana Funafuti Tuvalu\nAlgiers Algeria Amman Jordan\nAlgiers Algeria Ankara Turkey\nAlgiers Algeria Antananarivo Madagascar\nAlgiers Algeria Apia Samoa\nAlgiers Algeria Ashgabat Turkmenistan\nAlgiers Algeria Asmara Eritrea\nAlgiers Algeria Astana Kazakhstan\nAlgiers Algeria Athens Greece\nAlgiers Algeria Baghdad Iraq\nAlgiers Algeria Baku Azerbaijan\nAlgiers Algeria Bamako Mali\nAlgiers Algeria Bangkok Thailand\nAlgiers Algeria Banjul Gambia\nAlgiers Algeria Beijing China\nAlgiers Algeria Beirut Lebanon\nAlgiers Algeria Belgrade Serbia\nAlgiers Algeria Belmopan Belize\nAlgiers Algeria Berlin Germany\nAlgiers Algeria Bern Switzerland\nAlgiers Algeria Bishkek Kyrgyzstan\nAlgiers Algeria Bratislava Slovakia\nAlgiers Algeria Brussels Belgium\nAlgiers Algeria Bucharest Romania\nAlgiers Algeria Budapest Hungary\nAlgiers Algeria Bujumbura Burundi\nAlgiers Algeria Cairo Egypt\nAlgiers Algeria Canberra Australia\nAlgiers Algeria Caracas Venezuela\nAlgiers Algeria Chisinau Moldova\nAlgiers Algeria Conakry Guinea\nAlgiers Algeria Copenhagen Denmark\nAlgiers Algeria Dakar Senegal\nAlgiers Algeria Damascus Syria\nAlgiers Algeria Dhaka Bangladesh\nAlgiers Algeria Doha Qatar\nAlgiers Algeria Dublin Ireland\nAlgiers Algeria Dushanbe Tajikistan\nAlgiers Algeria Funafuti Tuvalu\nAlgiers Algeria Gaborone Botswana\nAmman Jordan Ankara Turkey\nAmman Jordan Antananarivo Madagascar\nAmman Jordan Apia Samoa\nAmman Jordan Ashgabat Turkmenistan\nAmman Jordan Asmara Eritrea\nAmman Jordan Astana Kazakhstan\nAmman Jordan Athens Greece\nAmman Jordan Baghdad Iraq\nAmman Jordan Baku Azerbaijan\nAmman Jordan Bamako Mali\nAmman Jordan Bangkok Thailand\nAmman Jordan Banjul Gambia\nAmman Jordan Beijing China\nAmman Jordan Beirut Lebanon\nAmman Jordan Belgrade Serbia\nAmman Jordan Belmopan Belize\nAmman Jordan Berlin Germany\nAmman Jordan Bern Switzerland\nAmman Jordan Bishkek Kyrgyzstan\nAmman Jordan Bratislava Slovakia\nAmman Jordan Brussels Belgium\nAmman Jordan Bucharest Romania\nAmman Jordan Budapest Hungary\nAmman Jordan Bujumbura Burundi\nAmman Jordan Cairo Egypt\nAmman Jordan Canberra Australia\nAmman Jordan Caracas Venezuela\nAmman Jordan Chisinau Moldova\nAmman Jordan Conakry Guinea\nAmman Jordan Copenhagen Denmark\nAmman Jordan Dakar Senegal\nAmman Jordan Damascus Syria\nAmman Jordan Dhaka Bangladesh\nAmman Jordan Doha Qatar\nAmman Jordan Dublin Ireland\nAmman Jordan Dushanbe Tajikistan\nAmman Jordan Funafuti Tuvalu\nAmman Jordan Gaborone Botswana\nAmman Jordan Georgetown Guyana\nAnkara Turkey Antananarivo Madagascar\nAnkara Turkey Apia Samoa\nAnkara Turkey Ashgabat Turkmenistan\nAnkara Turkey Asmara Eritrea\nAnkara Turkey Astana Kazakhstan\nAnkara Turkey Athens Greece\nAnkara Turkey Baghdad Iraq\nAnkara Turkey Baku Azerbaijan\nAnkara Turkey Bamako Mali\nAnkara Turkey Bangkok Thailand\nAnkara Turkey Banjul Gambia\nAnkara Turkey Beijing China\nAnkara Turkey Beirut Lebanon\nAnkara Turkey Belgrade Serbia\nAnkara Turkey Belmopan Belize\nAnkara Turkey Berlin Germany\nAnkara Turkey Bern Switzerland\nAnkara Turkey Bishkek Kyrgyzstan\nAnkara Turkey Bratislava Slovakia\nAnkara Turkey Brussels Belgium\nAnkara Turkey Bucharest Romania\nAnkara Turkey Budapest Hungary\nAnkara Turkey Bujumbura Burundi\nAnkara Turkey Cairo Egypt\nAnkara Turkey Canberra Australia\nAnkara Turkey Caracas Venezuela\nAnkara Turkey Chisinau Moldova\nAnkara Turkey Conakry Guinea\nAnkara Turkey Copenhagen Denmark\nAnkara Turkey Dakar Senegal\nAnkara Turkey Damascus Syria\nAnkara Turkey Dhaka Bangladesh\nAnkara Turkey Doha Qatar\nAnkara Turkey Dublin Ireland\nAnkara Turkey Dushanbe Tajikistan\nAnkara Turkey Funafuti Tuvalu\nAnkara Turkey Gaborone Botswana\nAnkara Turkey Georgetown Guyana\nAnkara Turkey Hanoi Vietnam\nAntananarivo Madagascar Apia Samoa\nAntananarivo Madagascar Ashgabat Turkmenistan\nAntananarivo Madagascar Asmara Eritrea\nAntananarivo Madagascar Astana Kazakhstan\nAntananarivo Madagascar Athens Greece\nAntananarivo Madagascar Baghdad Iraq\nAntananarivo Madagascar Baku Azerbaijan\nAntananarivo Madagascar Bamako Mali\nAntananarivo Madagascar Bangkok Thailand\nAntananarivo Madagascar Banjul Gambia\nAntananarivo Madagascar Beijing China\nAntananarivo Madagascar Beirut Lebanon\nAntananarivo Madagascar Belgrade Serbia\nAntananarivo Madagascar Belmopan Belize\nAntananarivo Madagascar Berlin Germany\nAntananarivo Madagascar Bern Switzerland\nAntananarivo Madagascar Bishkek Kyrgyzstan\nAntananarivo Madagascar Bratislava Slovakia\nAntananarivo Madagascar Brussels Belgium\nAntananarivo Madagascar Bucharest Romania\nAntananarivo Madagascar Budapest Hungary\nAntananarivo Madagascar Bujumbura Burundi\nAntananarivo Madagascar Cairo Egypt\nAntananarivo Madagascar Canberra Australia\nAntananarivo Madagascar Caracas Venezuela\nAntananarivo Madagascar Chisinau Moldova\nAntananarivo Madagascar Conakry Guinea\nAntananarivo Madagascar Copenhagen Denmark\nAntananarivo Madagascar Dakar Senegal\nAntananarivo Madagascar Damascus Syria\nAntananarivo Madagascar Dhaka Bangladesh\nAntananarivo Madagascar Doha Qatar\nAntananarivo Madagascar Dublin Ireland\nAntananarivo Madagascar Dushanbe Tajikistan\nAntananarivo Madagascar Funafuti Tuvalu\nAntananarivo Madagascar Gaborone Botswana\nAntananarivo Madagascar Georgetown Guyana\nAntananarivo Madagascar Hanoi Vietnam\nAntananarivo Madagascar Harare Zimbabwe\nApia Samoa Ashgabat Turkmenistan\nApia Samoa Asmara Eritrea\nApia Samoa Astana Kazakhstan\nApia Samoa Athens Greece\nApia Samoa Baghdad Iraq\nApia Samoa Baku Azerbaijan\nApia Samoa Bamako Mali\nApia Samoa Bangkok Thailand\nApia Samoa Banjul Gambia\nApia Samoa Beijing China\nApia Samoa Beirut Lebanon\nApia Samoa Belgrade Serbia\nApia Samoa Belmopan Belize\nApia Samoa Berlin Germany\nApia Samoa Bern Switzerland\nApia Samoa Bishkek Kyrgyzstan\nApia Samoa Bratislava Slovakia\nApia Samoa Brussels Belgium\nApia Samoa Bucharest Romania\nApia Samoa Budapest Hungary\nApia Samoa Bujumbura Burundi\nApia Samoa Cairo Egypt\nApia Samoa Canberra Australia\nApia Samoa Caracas Venezuela\nApia Samoa Chisinau Moldova\nApia Samoa Conakry Guinea\nApia Samoa Copenhagen Denmark\nApia Samoa Dakar Senegal\nApia Samoa Damascus Syria\nApia Samoa Dhaka Bangladesh\nApia Samoa Doha Qatar\nApia Samoa Dublin Ireland\nApia Samoa Dushanbe Tajikistan\nApia Samoa Funafuti Tuvalu\nApia Samoa Gaborone Botswana\nApia Samoa Georgetown Guyana\nApia Samoa Hanoi Vietnam\nApia Samoa Harare Zimbabwe\nApia Samoa Havana Cuba\nAshgabat Turkmenistan Asmara Eritrea\nAshgabat Turkmenistan Astana Kazakhstan\nAshgabat Turkmenistan Athens Greece\nAshgabat Turkmenistan Baghdad Iraq\nAshgabat Turkmenistan Baku Azerbaijan\nAshgabat Turkmenistan Bamako Mali\nAshgabat Turkmenistan Bangkok Thailand\nAshgabat Turkmenistan Banjul Gambia\nAshgabat Turkmenistan Beijing China\nAshgabat Turkmenistan Beirut Lebanon\nAshgabat Turkmenistan Belgrade Serbia\nAshgabat Turkmenistan Belmopan Belize\nAshgabat Turkmenistan Berlin Germany\nAshgabat Turkmenistan Bern Switzerland\nAshgabat Turkmenistan Bishkek Kyrgyzstan\nAshgabat Turkmenistan Bratislava Slovakia\nAshgabat Turkmenistan Brussels Belgium\nAshgabat Turkmenistan Bucharest Romania\nAshgabat Turkmenistan Budapest Hungary\nAshgabat Turkmenistan Bujumbura Burundi\nAshgabat Turkmenistan Cairo Egypt\nAshgabat Turkmenistan Canberra Australia\nAshgabat Turkmenistan Caracas Venezuela\nAshgabat Turkmenistan Chisinau Moldova\nAshgabat Turkmenistan Conakry Guinea\nAshgabat Turkmenistan Copenhagen Denmark\nAshgabat Turkmenistan Dakar Senegal\nAshgabat Turkmenistan Damascus Syria\nAshgabat Turkmenistan Dhaka Bangladesh\nAshgabat Turkmenistan Doha Qatar\nAshgabat Turkmenistan Dublin Ireland\nAshgabat Turkmenistan Dushanbe Tajikistan\nAshgabat Turkmenistan Funafuti Tuvalu\nAshgabat Turkmenistan Gaborone Botswana\nAshgabat Turkmenistan Georgetown Guyana\nAshgabat Turkmenistan Hanoi Vietnam\nAshgabat Turkmenistan Harare Zimbabwe\nAshgabat Turkmenistan Havana Cuba\nAshgabat Turkmenistan Helsinki Finland\nAsmara Eritrea Astana Kazakhstan\nAsmara Eritrea Athens Greece\nAsmara Eritrea Baghdad Iraq\nAsmara Eritrea Baku Azerbaijan\nAsmara Eritrea Bamako Mali\nAsmara Eritrea Bangkok Thailand\nAsmara Eritrea Banjul Gambia\nAsmara Eritrea Beijing China\nAsmara Eritrea Beirut Lebanon\nAsmara Eritrea Belgrade Serbia\nAsmara Eritrea Belmopan Belize\nAsmara Eritrea Berlin Germany\nAsmara Eritrea Bern Switzerland\nAsmara Eritrea Bishkek Kyrgyzstan\nAsmara Eritrea Bratislava Slovakia\nAsmara Eritrea Brussels Belgium\nAsmara Eritrea Bucharest Romania\nAsmara Eritrea Budapest Hungary\nAsmara Eritrea Bujumbura Burundi\nAsmara Eritrea Cairo Egypt\nAsmara Eritrea Canberra Australia\nAsmara Eritrea Caracas Venezuela\nAsmara Eritrea Chisinau Moldova\nAsmara Eritrea Conakry Guinea\nAsmara Eritrea Copenhagen Denmark\nAsmara Eritrea Dakar Senegal\nAsmara Eritrea Damascus Syria\nAsmara Eritrea Dhaka Bangladesh\nAsmara Eritrea Doha Qatar\nAsmara Eritrea Dublin Ireland\nAsmara Eritrea Dushanbe Tajikistan\nAsmara Eritrea Funafuti Tuvalu\nAsmara Eritrea Gaborone Botswana\nAsmara Eritrea Georgetown Guyana\nAsmara Eritrea Hanoi Vietnam\nAsmara Eritrea Harare Zimbabwe\nAsmara Eritrea Havana Cuba\nAsmara Eritrea Helsinki Finland\nAsmara Eritrea Islamabad Pakistan\nAstana Kazakhstan Athens Greece\nAstana Kazakhstan Baghdad Iraq\nAstana Kazakhstan Baku Azerbaijan\nAstana Kazakhstan Bamako Mali\nAstana Kazakhstan Bangkok Thailand\nAstana Kazakhstan Banjul Gambia\nAstana Kazakhstan Beijing China\nAstana Kazakhstan Beirut Lebanon\nAstana Kazakhstan Belgrade Serbia\nAstana Kazakhstan Belmopan Belize\nAstana Kazakhstan Berlin Germany\nAstana Kazakhstan Bern Switzerland\nAstana Kazakhstan Bishkek Kyrgyzstan\nAstana Kazakhstan Bratislava Slovakia\nAstana Kazakhstan Brussels Belgium\nAstana Kazakhstan Bucharest Romania\nAstana Kazakhstan Budapest Hungary\nAstana Kazakhstan Bujumbura Burundi\nAstana Kazakhstan Cairo Egypt\nAstana Kazakhstan Canberra Australia\nAstana Kazakhstan Caracas Venezuela\nAstana Kazakhstan Chisinau Moldova\nAstana Kazakhstan Conakry Guinea\nAstana Kazakhstan Copenhagen Denmark\nAstana Kazakhstan Dakar Senegal\nAstana Kazakhstan Damascus Syria\nAstana Kazakhstan Dhaka Bangladesh\nAstana Kazakhstan Doha Qatar\nAstana Kazakhstan Dublin Ireland\nAstana Kazakhstan Dushanbe Tajikistan\nAstana Kazakhstan Funafuti Tuvalu\nAstana Kazakhstan Gaborone Botswana\nAstana Kazakhstan Georgetown Guyana\nAstana Kazakhstan Hanoi Vietnam\nAstana Kazakhstan Harare Zimbabwe\nAstana Kazakhstan Havana Cuba\nAstana Kazakhstan Helsinki Finland\nAstana Kazakhstan Islamabad Pakistan\nAstana Kazakhstan Jakarta Indonesia\nAthens Greece Baghdad Iraq\nAthens Greece Baku Azerbaijan\nAthens Greece Bamako Mali\nAthens Greece Bangkok Thailand\nAthens Greece Banjul Gambia\nAthens Greece Beijing China\nAthens Greece Beirut Lebanon\nAthens Greece Belgrade Serbia\nAthens Greece Belmopan Belize\nAthens Greece Berlin Germany\nAthens Greece Bern Switzerland\nAthens Greece Bishkek Kyrgyzstan\nAthens Greece Bratislava Slovakia\nAthens Greece Brussels Belgium\nAthens Greece Bucharest Romania\nAthens Greece Budapest Hungary\nAthens Greece Bujumbura Burundi\nAthens Greece Cairo Egypt\nAthens Greece Canberra Australia\nAthens Greece Caracas Venezuela\nAthens Greece Chisinau Moldova\nAthens Greece Conakry Guinea\nAthens Greece Copenhagen Denmark\nAthens Greece Dakar Senegal\nAthens Greece Damascus Syria\nAthens Greece Dhaka Bangladesh\nAthens Greece Doha Qatar\nAthens Greece Dublin Ireland\nAthens Greece Dushanbe Tajikistan\nAthens Greece Funafuti Tuvalu\nAthens Greece Gaborone Botswana\nAthens Greece Georgetown Guyana\nAthens Greece Hanoi Vietnam\nAthens Greece Harare Zimbabwe\nAthens Greece Havana Cuba\nAthens Greece Helsinki Finland\nAthens Greece Islamabad Pakistan\nAthens Greece Jakarta Indonesia\nAthens Greece Kabul Afghanistan\nBaghdad Iraq Baku Azerbaijan\nBaghdad Iraq Bamako Mali\nBaghdad Iraq Bangkok Thailand\nBaghdad Iraq Banjul Gambia\nBaghdad Iraq Beijing China\nBaghdad Iraq Beirut Lebanon\nBaghdad Iraq Belgrade Serbia\nBaghdad Iraq Belmopan Belize\nBaghdad Iraq Berlin Germany\nBaghdad Iraq Bern Switzerland\nBaghdad Iraq Bishkek Kyrgyzstan\nBaghdad Iraq Bratislava Slovakia\nBaghdad Iraq Brussels Belgium\nBaghdad Iraq Bucharest Romania\nBaghdad Iraq Budapest Hungary\nBaghdad Iraq Bujumbura Burundi\nBaghdad Iraq Cairo Egypt\nBaghdad Iraq Canberra Australia\nBaghdad Iraq Caracas Venezuela\nBaghdad Iraq Chisinau Moldova\nBaghdad Iraq Conakry Guinea\nBaghdad Iraq Copenhagen Denmark\nBaghdad Iraq Dakar Senegal\nBaghdad Iraq Damascus Syria\nBaghdad Iraq Dhaka Bangladesh\nBaghdad Iraq Doha Qatar\nBaghdad Iraq Dublin Ireland\nBaghdad Iraq Dushanbe Tajikistan\nBaghdad Iraq Funafuti Tuvalu\nBaghdad Iraq Gaborone Botswana\nBaghdad Iraq Georgetown Guyana\nBaghdad Iraq Hanoi Vietnam\nBaghdad Iraq Harare Zimbabwe\nBaghdad Iraq Havana Cuba\nBaghdad Iraq Helsinki Finland\nBaghdad Iraq Islamabad Pakistan\nBaghdad Iraq Jakarta Indonesia\nBaghdad Iraq Kabul Afghanistan\nBaghdad Iraq Kampala Uganda\nBaku Azerbaijan Bamako Mali\nBaku Azerbaijan Bangkok Thailand\nBaku Azerbaijan Banjul Gambia\nBaku Azerbaijan Beijing China\nBaku Azerbaijan Beirut Lebanon\nBaku Azerbaijan Belgrade Serbia\nBaku Azerbaijan Belmopan Belize\nBaku Azerbaijan Berlin Germany\nBaku Azerbaijan Bern Switzerland\nBaku Azerbaijan Bishkek Kyrgyzstan\nBaku Azerbaijan Bratislava Slovakia\nBaku Azerbaijan Brussels Belgium\nBaku Azerbaijan Bucharest Romania\nBaku Azerbaijan Budapest Hungary\nBaku Azerbaijan Bujumbura Burundi\nBaku Azerbaijan Cairo Egypt\nBaku Azerbaijan Canberra Australia\nBaku Azerbaijan Caracas Venezuela\nBaku Azerbaijan Chisinau Moldova\nBaku Azerbaijan Conakry Guinea\nBaku Azerbaijan Copenhagen Denmark\nBaku Azerbaijan Dakar Senegal\nBaku Azerbaijan Damascus Syria\nBaku Azerbaijan Dhaka Bangladesh\nBaku Azerbaijan Doha Qatar\nBaku Azerbaijan Dublin Ireland\nBaku Azerbaijan Dushanbe Tajikistan\nBaku Azerbaijan Funafuti Tuvalu\nBaku Azerbaijan Gaborone Botswana\nBaku Azerbaijan Georgetown Guyana\nBaku Azerbaijan Hanoi Vietnam\nBaku Azerbaijan Harare Zimbabwe\nBaku Azerbaijan Havana Cuba\nBaku Azerbaijan Helsinki Finland\nBaku Azerbaijan Islamabad Pakistan\nBaku Azerbaijan Jakarta Indonesia\nBaku Azerbaijan Kabul Afghanistan\nBaku Azerbaijan Kampala Uganda\nBaku Azerbaijan Kathmandu Nepal\nBamako Mali Bangkok Thailand\nBamako Mali Banjul Gambia\nBamako Mali Beijing China\nBamako Mali Beirut Lebanon\nBamako Mali Belgrade Serbia\nBamako Mali Belmopan Belize\nBamako Mali Berlin Germany\nBamako Mali Bern Switzerland\nBamako Mali Bishkek Kyrgyzstan\nBamako Mali Bratislava Slovakia\nBamako Mali Brussels Belgium\nBamako Mali Bucharest Romania\nBamako Mali Budapest Hungary\nBamako Mali Bujumbura Burundi\nBamako Mali Cairo Egypt\nBamako Mali Canberra Australia\nBamako Mali Caracas Venezuela\nBamako Mali Chisinau Moldova\nBamako Mali Conakry Guinea\nBamako Mali Copenhagen Denmark\nBamako Mali Dakar Senegal\nBamako Mali Damascus Syria\nBamako Mali Dhaka Bangladesh\nBamako Mali Doha Qatar\nBamako Mali Dublin Ireland\nBamako Mali Dushanbe Tajikistan\nBamako Mali Funafuti Tuvalu\nBamako Mali Gaborone Botswana\nBamako Mali Georgetown Guyana\nBamako Mali Hanoi Vietnam\nBamako Mali Harare Zimbabwe\nBamako Mali Havana Cuba\nBamako Mali Helsinki Finland\nBamako Mali Islamabad Pakistan\nBamako Mali Jakarta Indonesia\nBamako Mali Kabul Afghanistan\nBamako Mali Kampala Uganda\nBamako Mali Kathmandu Nepal\nBamako Mali Khartoum Sudan\nBangkok Thailand Banjul Gambia\nBangkok Thailand Beijing China\nBangkok Thailand Beirut Lebanon\nBangkok Thailand Belgrade Serbia\nBangkok Thailand Belmopan Belize\nBangkok Thailand Berlin Germany\nBangkok Thailand Bern Switzerland\nBangkok Thailand Bishkek Kyrgyzstan\nBangkok Thailand Bratislava Slovakia\nBangkok Thailand Brussels Belgium\nBangkok Thailand Bucharest Romania\nBangkok Thailand Budapest Hungary\nBangkok Thailand Bujumbura Burundi\nBangkok Thailand Cairo Egypt\nBangkok Thailand Canberra Australia\nBangkok Thailand Caracas Venezuela\nBangkok Thailand Chisinau Moldova\nBangkok Thailand Conakry Guinea\nBangkok Thailand Copenhagen Denmark\nBangkok Thailand Dakar Senegal\nBangkok Thailand Damascus Syria\nBangkok Thailand Dhaka Bangladesh\nBangkok Thailand Doha Qatar\nBangkok Thailand Dublin Ireland\nBangkok Thailand Dushanbe Tajikistan\nBangkok Thailand Funafuti Tuvalu\nBangkok Thailand Gaborone Botswana\nBangkok Thailand Georgetown Guyana\nBangkok Thailand Hanoi Vietnam\nBangkok Thailand Harare Zimbabwe\nBangkok Thailand Havana Cuba\nBangkok Thailand Helsinki Finland\nBangkok Thailand Islamabad Pakistan\nBangkok Thailand Jakarta Indonesia\nBangkok Thailand Kabul Afghanistan\nBangkok Thailand Kampala Uganda\nBangkok Thailand Kathmandu Nepal\nBangkok Thailand Khartoum Sudan\nBangkok Thailand Kiev Ukraine\nBanjul Gambia Beijing China\nBanjul Gambia Beirut Lebanon\nBanjul Gambia Belgrade Serbia\nBanjul Gambia Belmopan Belize\nBanjul Gambia Berlin Germany\nBanjul Gambia Bern Switzerland\nBanjul Gambia Bishkek Kyrgyzstan\nBanjul Gambia Bratislava Slovakia\nBanjul Gambia Brussels Belgium\nBanjul Gambia Bucharest Romania\nBanjul Gambia Budapest Hungary\nBanjul Gambia Bujumbura Burundi\nBanjul Gambia Cairo Egypt\nBanjul Gambia Canberra Australia\nBanjul Gambia Caracas Venezuela\nBanjul Gambia Chisinau Moldova\nBanjul Gambia Conakry Guinea\nBanjul Gambia Copenhagen Denmark\nBanjul Gambia Dakar Senegal\nBanjul Gambia Damascus Syria\nBanjul Gambia Dhaka Bangladesh\nBanjul Gambia Doha Qatar\nBanjul Gambia Dublin Ireland\nBanjul Gambia Dushanbe Tajikistan\nBanjul Gambia Funafuti Tuvalu\nBanjul Gambia Gaborone Botswana\nBanjul Gambia Georgetown Guyana\nBanjul Gambia Hanoi Vietnam\nBanjul Gambia Harare Zimbabwe\nBanjul Gambia Havana Cuba\nBanjul Gambia Helsinki Finland\nBanjul Gambia Islamabad Pakistan\nBanjul Gambia Jakarta Indonesia\nBanjul Gambia Kabul Afghanistan\nBanjul Gambia Kampala Uganda\nBanjul Gambia Kathmandu Nepal\nBanjul Gambia Khartoum Sudan\nBanjul Gambia Kiev Ukraine\nBanjul Gambia Kigali Rwanda\nBeijing China Beirut Lebanon\nBeijing China Belgrade Serbia\nBeijing China Belmopan Belize\nBeijing China Berlin Germany\nBeijing China Bern Switzerland\nBeijing China Bishkek Kyrgyzstan\nBeijing China Bratislava Slovakia\nBeijing China Brussels Belgium\nBeijing China Bucharest Romania\nBeijing China Budapest Hungary\nBeijing China Bujumbura Burundi\nBeijing China Cairo Egypt\nBeijing China Canberra Australia\nBeijing China Caracas Venezuela\nBeijing China Chisinau Moldova\nBeijing China Conakry Guinea\nBeijing China Copenhagen Denmark\nBeijing China Dakar Senegal\nBeijing China Damascus Syria\nBeijing China Dhaka Bangladesh\nBeijing China Doha Qatar\nBeijing China Dublin Ireland\nBeijing China Dushanbe Tajikistan\nBeijing China Funafuti Tuvalu\nBeijing China Gaborone Botswana\nBeijing China Georgetown Guyana\nBeijing China Hanoi Vietnam\nBeijing China Harare Zimbabwe\nBeijing China Havana Cuba\nBeijing China Helsinki Finland\nBeijing China Islamabad Pakistan\nBeijing China Jakarta Indonesia\nBeijing China Kabul Afghanistan\nBeijing China Kampala Uganda\nBeijing China Kathmandu Nepal\nBeijing China Khartoum Sudan\nBeijing China Kiev Ukraine\nBeijing China Kigali Rwanda\nBeijing China Kingston Jamaica\nBeirut Lebanon Belgrade Serbia\nBeirut Lebanon Belmopan Belize\nBeirut Lebanon Berlin Germany\nBeirut Lebanon Bern Switzerland\nBeirut Lebanon Bishkek Kyrgyzstan\nBeirut Lebanon Bratislava Slovakia\nBeirut Lebanon Brussels Belgium\nBeirut Lebanon Bucharest Romania\nBeirut Lebanon Budapest Hungary\nBeirut Lebanon Bujumbura Burundi\nBeirut Lebanon Cairo Egypt\nBeirut Lebanon Canberra Australia\nBeirut Lebanon Caracas Venezuela\nBeirut Lebanon Chisinau Moldova\nBeirut Lebanon Conakry Guinea\nBeirut Lebanon Copenhagen Denmark\nBeirut Lebanon Dakar Senegal\nBeirut Lebanon Damascus Syria\nBeirut Lebanon Dhaka Bangladesh\nBeirut Lebanon Doha Qatar\nBeirut Lebanon Dublin Ireland\nBeirut Lebanon Dushanbe Tajikistan\nBeirut Lebanon Funafuti Tuvalu\nBeirut Lebanon Gaborone Botswana\nBeirut Lebanon Georgetown Guyana\nBeirut Lebanon Hanoi Vietnam\nBeirut Lebanon Harare Zimbabwe\nBeirut Lebanon Havana Cuba\nBeirut Lebanon Helsinki Finland\nBeirut Lebanon Islamabad Pakistan\nBeirut Lebanon Jakarta Indonesia\nBeirut Lebanon Kabul Afghanistan\nBeirut Lebanon Kampala Uganda\nBeirut Lebanon Kathmandu Nepal\nBeirut Lebanon Khartoum Sudan\nBeirut Lebanon Kiev Ukraine\nBeirut Lebanon Kigali Rwanda\nBeirut Lebanon Kingston Jamaica\nBeirut Lebanon Libreville Gabon\nBelgrade Serbia Belmopan Belize\nBelgrade Serbia Berlin Germany\nBelgrade Serbia Bern Switzerland\nBelgrade Serbia Bishkek Kyrgyzstan\nBelgrade Serbia Bratislava Slovakia\nBelgrade Serbia Brussels Belgium\nBelgrade Serbia Bucharest Romania\nBelgrade Serbia Budapest Hungary\nBelgrade Serbia Bujumbura Burundi\nBelgrade Serbia Cairo Egypt\nBelgrade Serbia Canberra Australia\nBelgrade Serbia Caracas Venezuela\nBelgrade Serbia Chisinau Moldova\nBelgrade Serbia Conakry Guinea\nBelgrade Serbia Copenhagen Denmark\nBelgrade Serbia Dakar Senegal\nBelgrade Serbia Damascus Syria\nBelgrade Serbia Dhaka Bangladesh\nBelgrade Serbia Doha Qatar\nBelgrade Serbia Dublin Ireland\nBelgrade Serbia Dushanbe Tajikistan\nBelgrade Serbia Funafuti Tuvalu\nBelgrade Serbia Gaborone Botswana\nBelgrade Serbia Georgetown Guyana\nBelgrade Serbia Hanoi Vietnam\nBelgrade Serbia Harare Zimbabwe\nBelgrade Serbia Havana Cuba\nBelgrade Serbia Helsinki Finland\nBelgrade Serbia Islamabad Pakistan\nBelgrade Serbia Jakarta Indonesia\nBelgrade Serbia Kabul Afghanistan\nBelgrade Serbia Kampala Uganda\nBelgrade Serbia Kathmandu Nepal\nBelgrade Serbia Khartoum Sudan\nBelgrade Serbia Kiev Ukraine\nBelgrade Serbia Kigali Rwanda\nBelgrade Serbia Kingston Jamaica\nBelgrade Serbia Libreville Gabon\nBelgrade Serbia Lilongwe Malawi\nBelmopan Belize Berlin Germany\nBelmopan Belize Bern Switzerland\nBelmopan Belize Bishkek Kyrgyzstan\nBelmopan Belize Bratislava Slovakia\nBelmopan Belize Brussels Belgium\nBelmopan Belize Bucharest Romania\nBelmopan Belize Budapest Hungary\nBelmopan Belize Bujumbura Burundi\nBelmopan Belize Cairo Egypt\nBelmopan Belize Canberra Australia\nBelmopan Belize Caracas Venezuela\nBelmopan Belize Chisinau Moldova\nBelmopan Belize Conakry Guinea\nBelmopan Belize Copenhagen Denmark\nBelmopan Belize Dakar Senegal\nBelmopan Belize Damascus Syria\nBelmopan Belize Dhaka Bangladesh\nBelmopan Belize Doha Qatar\nBelmopan Belize Dublin Ireland\nBelmopan Belize Dushanbe Tajikistan\nBelmopan Belize Funafuti Tuvalu\nBelmopan Belize Gaborone Botswana\nBelmopan Belize Georgetown Guyana\nBelmopan Belize Hanoi Vietnam\nBelmopan Belize Harare Zimbabwe\nBelmopan Belize Havana Cuba\nBelmopan Belize Helsinki Finland\nBelmopan Belize Islamabad Pakistan\nBelmopan Belize Jakarta Indonesia\nBelmopan Belize Kabul Afghanistan\nBelmopan Belize Kampala Uganda\nBelmopan Belize Kathmandu Nepal\nBelmopan Belize Khartoum Sudan\nBelmopan Belize Kiev Ukraine\nBelmopan Belize Kigali Rwanda\nBelmopan Belize Kingston Jamaica\nBelmopan Belize Libreville Gabon\nBelmopan Belize Lilongwe Malawi\nBelmopan Belize Lima Peru\nBerlin Germany Bern Switzerland\nBerlin Germany Bishkek Kyrgyzstan\nBerlin Germany Bratislava Slovakia\nBerlin Germany Brussels Belgium\nBerlin Germany Bucharest Romania\nBerlin Germany Budapest Hungary\nBerlin Germany Bujumbura Burundi\nBerlin Germany Cairo Egypt\nBerlin Germany Canberra Australia\nBerlin Germany Caracas Venezuela\nBerlin Germany Chisinau Moldova\nBerlin Germany Conakry Guinea\nBerlin Germany Copenhagen Denmark\nBerlin Germany Dakar Senegal\nBerlin Germany Damascus Syria\nBerlin Germany Dhaka Bangladesh\nBerlin Germany Doha Qatar\nBerlin Germany Dublin Ireland\nBerlin Germany Dushanbe Tajikistan\nBerlin Germany Funafuti Tuvalu\nBerlin Germany Gaborone Botswana\nBerlin Germany Georgetown Guyana\nBerlin Germany Hanoi Vietnam\nBerlin Germany Harare Zimbabwe\nBerlin Germany Havana Cuba\nBerlin Germany Helsinki Finland\nBerlin Germany Islamabad Pakistan\nBerlin Germany Jakarta Indonesia\nBerlin Germany Kabul Afghanistan\nBerlin Germany Kampala Uganda\nBerlin Germany Kathmandu Nepal\nBerlin Germany Khartoum Sudan\nBerlin Germany Kiev Ukraine\nBerlin Germany Kigali Rwanda\nBerlin Germany Kingston Jamaica\nBerlin Germany Libreville Gabon\nBerlin Germany Lilongwe Malawi\nBerlin Germany Lima Peru\nBerlin Germany Lisbon Portugal\nBern Switzerland Bishkek Kyrgyzstan\nBern Switzerland Bratislava Slovakia\nBern Switzerland Brussels Belgium\nBern Switzerland Bucharest Romania\nBern Switzerland Budapest Hungary\nBern Switzerland Bujumbura Burundi\nBern Switzerland Cairo Egypt\nBern Switzerland Canberra Australia\nBern Switzerland Caracas Venezuela\nBern Switzerland Chisinau Moldova\nBern Switzerland Conakry Guinea\nBern Switzerland Copenhagen Denmark\nBern Switzerland Dakar Senegal\nBern Switzerland Damascus Syria\nBern Switzerland Dhaka Bangladesh\nBern Switzerland Doha Qatar\nBern Switzerland Dublin Ireland\nBern Switzerland Dushanbe Tajikistan\nBern Switzerland Funafuti Tuvalu\nBern Switzerland Gaborone Botswana\nBern Switzerland Georgetown Guyana\nBern Switzerland Hanoi Vietnam\nBern Switzerland Harare Zimbabwe\nBern Switzerland Havana Cuba\nBern Switzerland Helsinki Finland\nBern Switzerland Islamabad Pakistan\nBern Switzerland Jakarta Indonesia\nBern Switzerland Kabul Afghanistan\nBern Switzerland Kampala Uganda\nBern Switzerland Kathmandu Nepal\nBern Switzerland Khartoum Sudan\nBern Switzerland Kiev Ukraine\nBern Switzerland Kigali Rwanda\nBern Switzerland Kingston Jamaica\nBern Switzerland Libreville Gabon\nBern Switzerland Lilongwe Malawi\nBern Switzerland Lima Peru\nBern Switzerland Lisbon Portugal\nBern Switzerland Ljubljana Slovenia\nBishkek Kyrgyzstan Bratislava Slovakia\nBishkek Kyrgyzstan Brussels Belgium\nBishkek Kyrgyzstan Bucharest Romania\nBishkek Kyrgyzstan Budapest Hungary\nBishkek Kyrgyzstan Bujumbura Burundi\nBishkek Kyrgyzstan Cairo Egypt\nBishkek Kyrgyzstan Canberra Australia\nBishkek Kyrgyzstan Caracas Venezuela\nBishkek Kyrgyzstan Chisinau Moldova\nBishkek Kyrgyzstan Conakry Guinea\nBishkek Kyrgyzstan Copenhagen Denmark\nBishkek Kyrgyzstan Dakar Senegal\nBishkek Kyrgyzstan Damascus Syria\nBishkek Kyrgyzstan Dhaka Bangladesh\nBishkek Kyrgyzstan Doha Qatar\nBishkek Kyrgyzstan Dublin Ireland\nBishkek Kyrgyzstan Dushanbe Tajikistan\nBishkek Kyrgyzstan Funafuti Tuvalu\nBishkek Kyrgyzstan Gaborone Botswana\nBishkek Kyrgyzstan Georgetown Guyana\nBishkek Kyrgyzstan Hanoi Vietnam\nBishkek Kyrgyzstan Harare Zimbabwe\nBishkek Kyrgyzstan Havana Cuba\nBishkek Kyrgyzstan Helsinki Finland\nBishkek Kyrgyzstan Islamabad Pakistan\nBishkek Kyrgyzstan Jakarta Indonesia\nBishkek Kyrgyzstan Kabul Afghanistan\nBishkek Kyrgyzstan Kampala Uganda\nBishkek Kyrgyzstan Kathmandu Nepal\nBishkek Kyrgyzstan Khartoum Sudan\nBishkek Kyrgyzstan Kiev Ukraine\nBishkek Kyrgyzstan Kigali Rwanda\nBishkek Kyrgyzstan Kingston Jamaica\nBishkek Kyrgyzstan Libreville Gabon\nBishkek Kyrgyzstan Lilongwe Malawi\nBishkek Kyrgyzstan Lima Peru\nBishkek Kyrgyzstan Lisbon Portugal\nBishkek Kyrgyzstan Ljubljana Slovenia\nBishkek Kyrgyzstan London England\nBratislava Slovakia Brussels Belgium\nBratislava Slovakia Bucharest Romania\nBratislava Slovakia Budapest Hungary\nBratislava Slovakia Bujumbura Burundi\nBratislava Slovakia Cairo Egypt\nBratislava Slovakia Canberra Australia\nBratislava Slovakia Caracas Venezuela\nBratislava Slovakia Chisinau Moldova\nBratislava Slovakia Conakry Guinea\nBratislava Slovakia Copenhagen Denmark\nBratislava Slovakia Dakar Senegal\nBratislava Slovakia Damascus Syria\nBratislava Slovakia Dhaka Bangladesh\nBratislava Slovakia Doha Qatar\nBratislava Slovakia Dublin Ireland\nBratislava Slovakia Dushanbe Tajikistan\nBratislava Slovakia Funafuti Tuvalu\nBratislava Slovakia Gaborone Botswana\nBratislava Slovakia Georgetown Guyana\nBratislava Slovakia Hanoi Vietnam\nBratislava Slovakia Harare Zimbabwe\nBratislava Slovakia Havana Cuba\nBratislava Slovakia Helsinki Finland\nBratislava Slovakia Islamabad Pakistan\nBratislava Slovakia Jakarta Indonesia\nBratislava Slovakia Kabul Afghanistan\nBratislava Slovakia Kampala Uganda\nBratislava Slovakia Kathmandu Nepal\nBratislava Slovakia Khartoum Sudan\nBratislava Slovakia Kiev Ukraine\nBratislava Slovakia Kigali Rwanda\nBratislava Slovakia Kingston Jamaica\nBratislava Slovakia Libreville Gabon\nBratislava Slovakia Lilongwe Malawi\nBratislava Slovakia Lima Peru\nBratislava Slovakia Lisbon Portugal\nBratislava Slovakia Ljubljana Slovenia\nBratislava Slovakia London England\nBratislava Slovakia Luanda Angola\nBrussels Belgium Bucharest Romania\nBrussels Belgium Budapest Hungary\nBrussels Belgium Bujumbura Burundi\nBrussels Belgium Cairo Egypt\nBrussels Belgium Canberra Australia\nBrussels Belgium Caracas Venezuela\nBrussels Belgium Chisinau Moldova\nBrussels Belgium Conakry Guinea\nBrussels Belgium Copenhagen Denmark\nBrussels Belgium Dakar Senegal\nBrussels Belgium Damascus Syria\nBrussels Belgium Dhaka Bangladesh\nBrussels Belgium Doha Qatar\nBrussels Belgium Dublin Ireland\nBrussels Belgium Dushanbe Tajikistan\nBrussels Belgium Funafuti Tuvalu\nBrussels Belgium Gaborone Botswana\nBrussels Belgium Georgetown Guyana\nBrussels Belgium Hanoi Vietnam\nBrussels Belgium Harare Zimbabwe\nBrussels Belgium Havana Cuba\nBrussels Belgium Helsinki Finland\nBrussels Belgium Islamabad Pakistan\nBrussels Belgium Jakarta Indonesia\nBrussels Belgium Kabul Afghanistan\nBrussels Belgium Kampala Uganda\nBrussels Belgium Kathmandu Nepal\nBrussels Belgium Khartoum Sudan\nBrussels Belgium Kiev Ukraine\nBrussels Belgium Kigali Rwanda\nBrussels Belgium Kingston Jamaica\nBrussels Belgium Libreville Gabon\nBrussels Belgium Lilongwe Malawi\nBrussels Belgium Lima Peru\nBrussels Belgium Lisbon Portugal\nBrussels Belgium Ljubljana Slovenia\nBrussels Belgium London England\nBrussels Belgium Luanda Angola\nBrussels Belgium Lusaka Zambia\nBucharest Romania Budapest Hungary\nBucharest Romania Bujumbura Burundi\nBucharest Romania Cairo Egypt\nBucharest Romania Canberra Australia\nBucharest Romania Caracas Venezuela\nBucharest Romania Chisinau Moldova\nBucharest Romania Conakry Guinea\nBucharest Romania Copenhagen Denmark\nBucharest Romania Dakar Senegal\nBucharest Romania Damascus Syria\nBucharest Romania Dhaka Bangladesh\nBucharest Romania Doha Qatar\nBucharest Romania Dublin Ireland\nBucharest Romania Dushanbe Tajikistan\nBucharest Romania Funafuti Tuvalu\nBucharest Romania Gaborone Botswana\nBucharest Romania Georgetown Guyana\nBucharest Romania Hanoi Vietnam\nBucharest Romania Harare Zimbabwe\nBucharest Romania Havana Cuba\nBucharest Romania Helsinki Finland\nBucharest Romania Islamabad Pakistan\nBucharest Romania Jakarta Indonesia\nBucharest Romania Kabul Afghanistan\nBucharest Romania Kampala Uganda\nBucharest Romania Kathmandu Nepal\nBucharest Romania Khartoum Sudan\nBucharest Romania Kiev Ukraine\nBucharest Romania Kigali Rwanda\nBucharest Romania Kingston Jamaica\nBucharest Romania Libreville Gabon\nBucharest Romania Lilongwe Malawi\nBucharest Romania Lima Peru\nBucharest Romania Lisbon Portugal\nBucharest Romania Ljubljana Slovenia\nBucharest Romania London England\nBucharest Romania Luanda Angola\nBucharest Romania Lusaka Zambia\nBucharest Romania Madrid Spain\nBudapest Hungary Bujumbura Burundi\nBudapest Hungary Cairo Egypt\nBudapest Hungary Canberra Australia\nBudapest Hungary Caracas Venezuela\nBudapest Hungary Chisinau Moldova\nBudapest Hungary Conakry Guinea\nBudapest Hungary Copenhagen Denmark\nBudapest Hungary Dakar Senegal\nBudapest Hungary Damascus Syria\nBudapest Hungary Dhaka Bangladesh\nBudapest Hungary Doha Qatar\nBudapest Hungary Dublin Ireland\nBudapest Hungary Dushanbe Tajikistan\nBudapest Hungary Funafuti Tuvalu\nBudapest Hungary Gaborone Botswana\nBudapest Hungary Georgetown Guyana\nBudapest Hungary Hanoi Vietnam\nBudapest Hungary Harare Zimbabwe\nBudapest Hungary Havana Cuba\nBudapest Hungary Helsinki Finland\nBudapest Hungary Islamabad Pakistan\nBudapest Hungary Jakarta Indonesia\nBudapest Hungary Kabul Afghanistan\nBudapest Hungary Kampala Uganda\nBudapest Hungary Kathmandu Nepal\nBudapest Hungary Khartoum Sudan\nBudapest Hungary Kiev Ukraine\nBudapest Hungary Kigali Rwanda\nBudapest Hungary Kingston Jamaica\nBudapest Hungary Libreville Gabon\nBudapest Hungary Lilongwe Malawi\nBudapest Hungary Lima Peru\nBudapest Hungary Lisbon Portugal\nBudapest Hungary Ljubljana Slovenia\nBudapest Hungary London England\nBudapest Hungary Luanda Angola\nBudapest Hungary Lusaka Zambia\nBudapest Hungary Madrid Spain\nBudapest Hungary Managua Nicaragua\nBujumbura Burundi Cairo Egypt\nBujumbura Burundi Canberra Australia\nBujumbura Burundi Caracas Venezuela\nBujumbura Burundi Chisinau Moldova\nBujumbura Burundi Conakry Guinea\nBujumbura Burundi Copenhagen Denmark\nBujumbura Burundi Dakar Senegal\nBujumbura Burundi Damascus Syria\nBujumbura Burundi Dhaka Bangladesh\nBujumbura Burundi Doha Qatar\nBujumbura Burundi Dublin Ireland\nBujumbura Burundi Dushanbe Tajikistan\nBujumbura Burundi Funafuti Tuvalu\nBujumbura Burundi Gaborone Botswana\nBujumbura Burundi Georgetown Guyana\nBujumbura Burundi Hanoi Vietnam\nBujumbura Burundi Harare Zimbabwe\nBujumbura Burundi Havana Cuba\nBujumbura Burundi Helsinki Finland\nBujumbura Burundi Islamabad Pakistan\nBujumbura Burundi Jakarta Indonesia\nBujumbura Burundi Kabul Afghanistan\nBujumbura Burundi Kampala Uganda\nBujumbura Burundi Kathmandu Nepal\nBujumbura Burundi Khartoum Sudan\nBujumbura Burundi Kiev Ukraine\nBujumbura Burundi Kigali Rwanda\nBujumbura Burundi Kingston Jamaica\nBujumbura Burundi Libreville Gabon\nBujumbura Burundi Lilongwe Malawi\nBujumbura Burundi Lima Peru\nBujumbura Burundi Lisbon Portugal\nBujumbura Burundi Ljubljana Slovenia\nBujumbura Burundi London England\nBujumbura Burundi Luanda Angola\nBujumbura Burundi Lusaka Zambia\nBujumbura Burundi Madrid Spain\nBujumbura Burundi Managua Nicaragua\nBujumbura Burundi Manama Bahrain\nCairo Egypt Canberra Australia\nCairo Egypt Caracas Venezuela\nCairo Egypt Chisinau Moldova\nCairo Egypt Conakry Guinea\nCairo Egypt Copenhagen Denmark\nCairo Egypt Dakar Senegal\nCairo Egypt Damascus Syria\nCairo Egypt Dhaka Bangladesh\nCairo Egypt Doha Qatar\nCairo Egypt Dublin Ireland\nCairo Egypt Dushanbe Tajikistan\nCairo Egypt Funafuti Tuvalu\nCairo Egypt Gaborone Botswana\nCairo Egypt Georgetown Guyana\nCairo Egypt Hanoi Vietnam\nCairo Egypt Harare Zimbabwe\nCairo Egypt Havana Cuba\nCairo Egypt Helsinki Finland\nCairo Egypt Islamabad Pakistan\nCairo Egypt Jakarta Indonesia\nCairo Egypt Kabul Afghanistan\nCairo Egypt Kampala Uganda\nCairo Egypt Kathmandu Nepal\nCairo Egypt Khartoum Sudan\nCairo Egypt Kiev Ukraine\nCairo Egypt Kigali Rwanda\nCairo Egypt Kingston Jamaica\nCairo Egypt Libreville Gabon\nCairo Egypt Lilongwe Malawi\nCairo Egypt Lima Peru\nCairo Egypt Lisbon Portugal\nCairo Egypt Ljubljana Slovenia\nCairo Egypt London England\nCairo Egypt Luanda Angola\nCairo Egypt Lusaka Zambia\nCairo Egypt Madrid Spain\nCairo Egypt Managua Nicaragua\nCairo Egypt Manama Bahrain\nCairo Egypt Manila Philippines\nCanberra Australia Caracas Venezuela\nCanberra Australia Chisinau Moldova\nCanberra Australia Conakry Guinea\nCanberra Australia Copenhagen Denmark\nCanberra Australia Dakar Senegal\nCanberra Australia Damascus Syria\nCanberra Australia Dhaka Bangladesh\nCanberra Australia Doha Qatar\nCanberra Australia Dublin Ireland\nCanberra Australia Dushanbe Tajikistan\nCanberra Australia Funafuti Tuvalu\nCanberra Australia Gaborone Botswana\nCanberra Australia Georgetown Guyana\nCanberra Australia Hanoi Vietnam\nCanberra Australia Harare Zimbabwe\nCanberra Australia Havana Cuba\nCanberra Australia Helsinki Finland\nCanberra Australia Islamabad Pakistan\nCanberra Australia Jakarta Indonesia\nCanberra Australia Kabul Afghanistan\nCanberra Australia Kampala Uganda\nCanberra Australia Kathmandu Nepal\nCanberra Australia Khartoum Sudan\nCanberra Australia Kiev Ukraine\nCanberra Australia Kigali Rwanda\nCanberra Australia Kingston Jamaica\nCanberra Australia Libreville Gabon\nCanberra Australia Lilongwe Malawi\nCanberra Australia Lima Peru\nCanberra Australia Lisbon Portugal\nCanberra Australia Ljubljana Slovenia\nCanberra Australia London England\nCanberra Australia Luanda Angola\nCanberra Australia Lusaka Zambia\nCanberra Australia Madrid Spain\nCanberra Australia Managua Nicaragua\nCanberra Australia Manama Bahrain\nCanberra Australia Manila Philippines\nCanberra Australia Maputo Mozambique\nCaracas Venezuela Chisinau Moldova\nCaracas Venezuela Conakry Guinea\nCaracas Venezuela Copenhagen Denmark\nCaracas Venezuela Dakar Senegal\nCaracas Venezuela Damascus Syria\nCaracas Venezuela Dhaka Bangladesh\nCaracas Venezuela Doha Qatar\nCaracas Venezuela Dublin Ireland\nCaracas Venezuela Dushanbe Tajikistan\nCaracas Venezuela Funafuti Tuvalu\nCaracas Venezuela Gaborone Botswana\nCaracas Venezuela Georgetown Guyana\nCaracas Venezuela Hanoi Vietnam\nCaracas Venezuela Harare Zimbabwe\nCaracas Venezuela Havana Cuba\nCaracas Venezuela Helsinki Finland\nCaracas Venezuela Islamabad Pakistan\nCaracas Venezuela Jakarta Indonesia\nCaracas Venezuela Kabul Afghanistan\nCaracas Venezuela Kampala Uganda\nCaracas Venezuela Kathmandu Nepal\nCaracas Venezuela Khartoum Sudan\nCaracas Venezuela Kiev Ukraine\nCaracas Venezuela Kigali Rwanda\nCaracas Venezuela Kingston Jamaica\nCaracas Venezuela Libreville Gabon\nCaracas Venezuela Lilongwe Malawi\nCaracas Venezuela Lima Peru\nCaracas Venezuela Lisbon Portugal\nCaracas Venezuela Ljubljana Slovenia\nCaracas Venezuela London England\nCaracas Venezuela Luanda Angola\nCaracas Venezuela Lusaka Zambia\nCaracas Venezuela Madrid Spain\nCaracas Venezuela Managua Nicaragua\nCaracas Venezuela Manama Bahrain\nCaracas Venezuela Manila Philippines\nCaracas Venezuela Maputo Mozambique\nCaracas Venezuela Minsk Belarus\nChisinau Moldova Conakry Guinea\nChisinau Moldova Copenhagen Denmark\nChisinau Moldova Dakar Senegal\nChisinau Moldova Damascus Syria\nChisinau Moldova Dhaka Bangladesh\nChisinau Moldova Doha Qatar\nChisinau Moldova Dublin Ireland\nChisinau Moldova Dushanbe Tajikistan\nChisinau Moldova Funafuti Tuvalu\nChisinau Moldova Gaborone Botswana\nChisinau Moldova Georgetown Guyana\nChisinau Moldova Hanoi Vietnam\nChisinau Moldova Harare Zimbabwe\nChisinau Moldova Havana Cuba\nChisinau Moldova Helsinki Finland\nChisinau Moldova Islamabad Pakistan\nChisinau Moldova Jakarta Indonesia\nChisinau Moldova Kabul Afghanistan\nChisinau Moldova Kampala Uganda\nChisinau Moldova Kathmandu Nepal\nChisinau Moldova Khartoum Sudan\nChisinau Moldova Kiev Ukraine\nChisinau Moldova Kigali Rwanda\nChisinau Moldova Kingston Jamaica\nChisinau Moldova Libreville Gabon\nChisinau Moldova Lilongwe Malawi\nChisinau Moldova Lima Peru\nChisinau Moldova Lisbon Portugal\nChisinau Moldova Ljubljana Slovenia\nChisinau Moldova London England\nChisinau Moldova Luanda Angola\nChisinau Moldova Lusaka Zambia\nChisinau Moldova Madrid Spain\nChisinau Moldova Managua Nicaragua\nChisinau Moldova Manama Bahrain\nChisinau Moldova Manila Philippines\nChisinau Moldova Maputo Mozambique\nChisinau Moldova Minsk Belarus\nChisinau Moldova Mogadishu Somalia\nConakry Guinea Copenhagen Denmark\nConakry Guinea Dakar Senegal\nConakry Guinea Damascus Syria\nConakry Guinea Dhaka Bangladesh\nConakry Guinea Doha Qatar\nConakry Guinea Dublin Ireland\nConakry Guinea Dushanbe Tajikistan\nConakry Guinea Funafuti Tuvalu\nConakry Guinea Gaborone Botswana\nConakry Guinea Georgetown Guyana\nConakry Guinea Hanoi Vietnam\nConakry Guinea Harare Zimbabwe\nConakry Guinea Havana Cuba\nConakry Guinea Helsinki Finland\nConakry Guinea Islamabad Pakistan\nConakry Guinea Jakarta Indonesia\nConakry Guinea Kabul Afghanistan\nConakry Guinea Kampala Uganda\nConakry Guinea Kathmandu Nepal\nConakry Guinea Khartoum Sudan\nConakry Guinea Kiev Ukraine\nConakry Guinea Kigali Rwanda\nConakry Guinea Kingston Jamaica\nConakry Guinea Libreville Gabon\nConakry Guinea Lilongwe Malawi\nConakry Guinea Lima Peru\nConakry Guinea Lisbon Portugal\nConakry Guinea Ljubljana Slovenia\nConakry Guinea London England\nConakry Guinea Luanda Angola\nConakry Guinea Lusaka Zambia\nConakry Guinea Madrid Spain\nConakry Guinea Managua Nicaragua\nConakry Guinea Manama Bahrain\nConakry Guinea Manila Philippines\nConakry Guinea Maputo Mozambique\nConakry Guinea Minsk Belarus\nConakry Guinea Mogadishu Somalia\nConakry Guinea Monrovia Liberia\nCopenhagen Denmark Dakar Senegal\nCopenhagen Denmark Damascus Syria\nCopenhagen Denmark Dhaka Bangladesh\nCopenhagen Denmark Doha Qatar\nCopenhagen Denmark Dublin Ireland\nCopenhagen Denmark Dushanbe Tajikistan\nCopenhagen Denmark Funafuti Tuvalu\nCopenhagen Denmark Gaborone Botswana\nCopenhagen Denmark Georgetown Guyana\nCopenhagen Denmark Hanoi Vietnam\nCopenhagen Denmark Harare Zimbabwe\nCopenhagen Denmark Havana Cuba\nCopenhagen Denmark Helsinki Finland\nCopenhagen Denmark Islamabad Pakistan\nCopenhagen Denmark Jakarta Indonesia\nCopenhagen Denmark Kabul Afghanistan\nCopenhagen Denmark Kampala Uganda\nCopenhagen Denmark Kathmandu Nepal\nCopenhagen Denmark Khartoum Sudan\nCopenhagen Denmark Kiev Ukraine\nCopenhagen Denmark Kigali Rwanda\nCopenhagen Denmark Kingston Jamaica\nCopenhagen Denmark Libreville Gabon\nCopenhagen Denmark Lilongwe Malawi\nCopenhagen Denmark Lima Peru\nCopenhagen Denmark Lisbon Portugal\nCopenhagen Denmark Ljubljana Slovenia\nCopenhagen Denmark London England\nCopenhagen Denmark Luanda Angola\nCopenhagen Denmark Lusaka Zambia\nCopenhagen Denmark Madrid Spain\nCopenhagen Denmark Managua Nicaragua\nCopenhagen Denmark Manama Bahrain\nCopenhagen Denmark Manila Philippines\nCopenhagen Denmark Maputo Mozambique\nCopenhagen Denmark Minsk Belarus\nCopenhagen Denmark Mogadishu Somalia\nCopenhagen Denmark Monrovia Liberia\nCopenhagen Denmark Montevideo Uruguay\nDakar Senegal Damascus Syria\nDakar Senegal Dhaka Bangladesh\nDakar Senegal Doha Qatar\nDakar Senegal Dublin Ireland\nDakar Senegal Dushanbe Tajikistan\nDakar Senegal Funafuti Tuvalu\nDakar Senegal Gaborone Botswana\nDakar Senegal Georgetown Guyana\nDakar Senegal Hanoi Vietnam\nDakar Senegal Harare Zimbabwe\nDakar Senegal Havana Cuba\nDakar Senegal Helsinki Finland\nDakar Senegal Islamabad Pakistan\nDakar Senegal Jakarta Indonesia\nDakar Senegal Kabul Afghanistan\nDakar Senegal Kampala Uganda\nDakar Senegal Kathmandu Nepal\nDakar Senegal Khartoum Sudan\nDakar Senegal Kiev Ukraine\nDakar Senegal Kigali Rwanda\nDakar Senegal Kingston Jamaica\nDakar Senegal Libreville Gabon\nDakar Senegal Lilongwe Malawi\nDakar Senegal Lima Peru\nDakar Senegal Lisbon Portugal\nDakar Senegal Ljubljana Slovenia\nDakar Senegal London England\nDakar Senegal Luanda Angola\nDakar Senegal Lusaka Zambia\nDakar Senegal Madrid Spain\nDakar Senegal Managua Nicaragua\nDakar Senegal Manama Bahrain\nDakar Senegal Manila Philippines\nDakar Senegal Maputo Mozambique\nDakar Senegal Minsk Belarus\nDakar Senegal Mogadishu Somalia\nDakar Senegal Monrovia Liberia\nDakar Senegal Montevideo Uruguay\nDakar Senegal Moscow Russia\nDamascus Syria Dhaka Bangladesh\nDamascus Syria Doha Qatar\nDamascus Syria Dublin Ireland\nDamascus Syria Dushanbe Tajikistan\nDamascus Syria Funafuti Tuvalu\nDamascus Syria Gaborone Botswana\nDamascus Syria Georgetown Guyana\nDamascus Syria Hanoi Vietnam\nDamascus Syria Harare Zimbabwe\nDamascus Syria Havana Cuba\nDamascus Syria Helsinki Finland\nDamascus Syria Islamabad Pakistan\nDamascus Syria Jakarta Indonesia\nDamascus Syria Kabul Afghanistan\nDamascus Syria Kampala Uganda\nDamascus Syria Kathmandu Nepal\nDamascus Syria Khartoum Sudan\nDamascus Syria Kiev Ukraine\nDamascus Syria Kigali Rwanda\nDamascus Syria Kingston Jamaica\nDamascus Syria Libreville Gabon\nDamascus Syria Lilongwe Malawi\nDamascus Syria Lima Peru\nDamascus Syria Lisbon Portugal\nDamascus Syria Ljubljana Slovenia\nDamascus Syria London England\nDamascus Syria Luanda Angola\nDamascus Syria Lusaka Zambia\nDamascus Syria Madrid Spain\nDamascus Syria Managua Nicaragua\nDamascus Syria Manama Bahrain\nDamascus Syria Manila Philippines\nDamascus Syria Maputo Mozambique\nDamascus Syria Minsk Belarus\nDamascus Syria Mogadishu Somalia\nDamascus Syria Monrovia Liberia\nDamascus Syria Montevideo Uruguay\nDamascus Syria Moscow Russia\nDamascus Syria Muscat Oman\nDhaka Bangladesh Doha Qatar\nDhaka Bangladesh Dublin Ireland\nDhaka Bangladesh Dushanbe Tajikistan\nDhaka Bangladesh Funafuti Tuvalu\nDhaka Bangladesh Gaborone Botswana\nDhaka Bangladesh Georgetown Guyana\nDhaka Bangladesh Hanoi Vietnam\nDhaka Bangladesh Harare Zimbabwe\nDhaka Bangladesh Havana Cuba\nDhaka Bangladesh Helsinki Finland\nDhaka Bangladesh Islamabad Pakistan\nDhaka Bangladesh Jakarta Indonesia\nDhaka Bangladesh Kabul Afghanistan\nDhaka Bangladesh Kampala Uganda\nDhaka Bangladesh Kathmandu Nepal\nDhaka Bangladesh Khartoum Sudan\nDhaka Bangladesh Kiev Ukraine\nDhaka Bangladesh Kigali Rwanda\nDhaka Bangladesh Kingston Jamaica\nDhaka Bangladesh Libreville Gabon\nDhaka Bangladesh Lilongwe Malawi\nDhaka Bangladesh Lima Peru\nDhaka Bangladesh Lisbon Portugal\nDhaka Bangladesh Ljubljana Slovenia\nDhaka Bangladesh London England\nDhaka Bangladesh Luanda Angola\nDhaka Bangladesh Lusaka Zambia\nDhaka Bangladesh Madrid Spain\nDhaka Bangladesh Managua Nicaragua\nDhaka Bangladesh Manama Bahrain\nDhaka Bangladesh Manila Philippines\nDhaka Bangladesh Maputo Mozambique\nDhaka Bangladesh Minsk Belarus\nDhaka Bangladesh Mogadishu Somalia\nDhaka Bangladesh Monrovia Liberia\nDhaka Bangladesh Montevideo Uruguay\nDhaka Bangladesh Moscow Russia\nDhaka Bangladesh Muscat Oman\nDhaka Bangladesh Nairobi Kenya\nDoha Qatar Dublin Ireland\nDoha Qatar Dushanbe Tajikistan\nDoha Qatar Funafuti Tuvalu\nDoha Qatar Gaborone Botswana\nDoha Qatar Georgetown Guyana\nDoha Qatar Hanoi Vietnam\nDoha Qatar Harare Zimbabwe\nDoha Qatar Havana Cuba\nDoha Qatar Helsinki Finland\nDoha Qatar Islamabad Pakistan\nDoha Qatar Jakarta Indonesia\nDoha Qatar Kabul Afghanistan\nDoha Qatar Kampala Uganda\nDoha Qatar Kathmandu Nepal\nDoha Qatar Khartoum Sudan\nDoha Qatar Kiev Ukraine\nDoha Qatar Kigali Rwanda\nDoha Qatar Kingston Jamaica\nDoha Qatar Libreville Gabon\nDoha Qatar Lilongwe Malawi\nDoha Qatar Lima Peru\nDoha Qatar Lisbon Portugal\nDoha Qatar Ljubljana Slovenia\nDoha Qatar London England\nDoha Qatar Luanda Angola\nDoha Qatar Lusaka Zambia\nDoha Qatar Madrid Spain\nDoha Qatar Managua Nicaragua\nDoha Qatar Manama Bahrain\nDoha Qatar Manila Philippines\nDoha Qatar Maputo Mozambique\nDoha Qatar Minsk Belarus\nDoha Qatar Mogadishu Somalia\nDoha Qatar Monrovia Liberia\nDoha Qatar Montevideo Uruguay\nDoha Qatar Moscow Russia\nDoha Qatar Muscat Oman\nDoha Qatar Nairobi Kenya\nDoha Qatar Nassau Bahamas\nDublin Ireland Dushanbe Tajikistan\nDublin Ireland Funafuti Tuvalu\nDublin Ireland Gaborone Botswana\nDublin Ireland Georgetown Guyana\nDublin Ireland Hanoi Vietnam\nDublin Ireland Harare Zimbabwe\nDublin Ireland Havana Cuba\nDublin Ireland Helsinki Finland\nDublin Ireland Islamabad Pakistan\nDublin Ireland Jakarta Indonesia\nDublin Ireland Kabul Afghanistan\nDublin Ireland Kampala Uganda\nDublin Ireland Kathmandu Nepal\nDublin Ireland Khartoum Sudan\nDublin Ireland Kiev Ukraine\nDublin Ireland Kigali Rwanda\nDublin Ireland Kingston Jamaica\nDublin Ireland Libreville Gabon\nDublin Ireland Lilongwe Malawi\nDublin Ireland Lima Peru\nDublin Ireland Lisbon Portugal\nDublin Ireland Ljubljana Slovenia\nDublin Ireland London England\nDublin Ireland Luanda Angola\nDublin Ireland Lusaka Zambia\nDublin Ireland Madrid Spain\nDublin Ireland Managua Nicaragua\nDublin Ireland Manama Bahrain\nDublin Ireland Manila Philippines\nDublin Ireland Maputo Mozambique\nDublin Ireland Minsk Belarus\nDublin Ireland Mogadishu Somalia\nDublin Ireland Monrovia Liberia\nDublin Ireland Montevideo Uruguay\nDublin Ireland Moscow Russia\nDublin Ireland Muscat Oman\nDublin Ireland Nairobi Kenya\nDublin Ireland Nassau Bahamas\nDublin Ireland Niamey Niger\nDushanbe Tajikistan Funafuti Tuvalu\nDushanbe Tajikistan Gaborone Botswana\nDushanbe Tajikistan Georgetown Guyana\nDushanbe Tajikistan Hanoi Vietnam\nDushanbe Tajikistan Harare Zimbabwe\nDushanbe Tajikistan Havana Cuba\nDushanbe Tajikistan Helsinki Finland\nDushanbe Tajikistan Islamabad Pakistan\nDushanbe Tajikistan Jakarta Indonesia\nDushanbe Tajikistan Kabul Afghanistan\nDushanbe Tajikistan Kampala Uganda\nDushanbe Tajikistan Kathmandu Nepal\nDushanbe Tajikistan Khartoum Sudan\nDushanbe Tajikistan Kiev Ukraine\nDushanbe Tajikistan Kigali Rwanda\nDushanbe Tajikistan Kingston Jamaica\nDushanbe Tajikistan Libreville Gabon\nDushanbe Tajikistan Lilongwe Malawi\nDushanbe Tajikistan Lima Peru\nDushanbe Tajikistan Lisbon Portugal\nDushanbe Tajikistan Ljubljana Slovenia\nDushanbe Tajikistan London England\nDushanbe Tajikistan Luanda Angola\nDushanbe Tajikistan Lusaka Zambia\nDushanbe Tajikistan Madrid Spain\nDushanbe Tajikistan Managua Nicaragua\nDushanbe Tajikistan Manama Bahrain\nDushanbe Tajikistan Manila Philippines\nDushanbe Tajikistan Maputo Mozambique\nDushanbe Tajikistan Minsk Belarus\nDushanbe Tajikistan Mogadishu Somalia\nDushanbe Tajikistan Monrovia Liberia\nDushanbe Tajikistan Montevideo Uruguay\nDushanbe Tajikistan Moscow Russia\nDushanbe Tajikistan Muscat Oman\nDushanbe Tajikistan Nairobi Kenya\nDushanbe Tajikistan Nassau Bahamas\nDushanbe Tajikistan Niamey Niger\nDushanbe Tajikistan Nicosia Cyprus\nFunafuti Tuvalu Gaborone Botswana\nFunafuti Tuvalu Georgetown Guyana\nFunafuti Tuvalu Hanoi Vietnam\nFunafuti Tuvalu Harare Zimbabwe\nFunafuti Tuvalu Havana Cuba\nFunafuti Tuvalu Helsinki Finland\nFunafuti Tuvalu Islamabad Pakistan\nFunafuti Tuvalu Jakarta Indonesia\nFunafuti Tuvalu Kabul Afghanistan\nFunafuti Tuvalu Kampala Uganda\nFunafuti Tuvalu Kathmandu Nepal\nFunafuti Tuvalu Khartoum Sudan\nFunafuti Tuvalu Kiev Ukraine\nFunafuti Tuvalu Kigali Rwanda\nFunafuti Tuvalu Kingston Jamaica\nFunafuti Tuvalu Libreville Gabon\nFunafuti Tuvalu Lilongwe Malawi\nFunafuti Tuvalu Lima Peru\nFunafuti Tuvalu Lisbon Portugal\nFunafuti Tuvalu Ljubljana Slovenia\nFunafuti Tuvalu London England\nFunafuti Tuvalu Luanda Angola\nFunafuti Tuvalu Lusaka Zambia\nFunafuti Tuvalu Madrid Spain\nFunafuti Tuvalu Managua Nicaragua\nFunafuti Tuvalu Manama Bahrain\nFunafuti Tuvalu Manila Philippines\nFunafuti Tuvalu Maputo Mozambique\nFunafuti Tuvalu Minsk Belarus\nFunafuti Tuvalu Mogadishu Somalia\nFunafuti Tuvalu Monrovia Liberia\nFunafuti Tuvalu Montevideo Uruguay\nFunafuti Tuvalu Moscow Russia\nFunafuti Tuvalu Muscat Oman\nFunafuti Tuvalu Nairobi Kenya\nFunafuti Tuvalu Nassau Bahamas\nFunafuti Tuvalu Niamey Niger\nFunafuti Tuvalu Nicosia Cyprus\nFunafuti Tuvalu Nouakchott Mauritania\nGaborone Botswana Georgetown Guyana\nGaborone Botswana Hanoi Vietnam\nGaborone Botswana Harare Zimbabwe\nGaborone Botswana Havana Cuba\nGaborone Botswana Helsinki Finland\nGaborone Botswana Islamabad Pakistan\nGaborone Botswana Jakarta Indonesia\nGaborone Botswana Kabul Afghanistan\nGaborone Botswana Kampala Uganda\nGaborone Botswana Kathmandu Nepal\nGaborone Botswana Khartoum Sudan\nGaborone Botswana Kiev Ukraine\nGaborone Botswana Kigali Rwanda\nGaborone Botswana Kingston Jamaica\nGaborone Botswana Libreville Gabon\nGaborone Botswana Lilongwe Malawi\nGaborone Botswana Lima Peru\nGaborone Botswana Lisbon Portugal\nGaborone Botswana Ljubljana Slovenia\nGaborone Botswana London England\nGaborone Botswana Luanda Angola\nGaborone Botswana Lusaka Zambia\nGaborone Botswana Madrid Spain\nGaborone Botswana Managua Nicaragua\nGaborone Botswana Manama Bahrain\nGaborone Botswana Manila Philippines\nGaborone Botswana Maputo Mozambique\nGaborone Botswana Minsk Belarus\nGaborone Botswana Mogadishu Somalia\nGaborone Botswana Monrovia Liberia\nGaborone Botswana Montevideo Uruguay\nGaborone Botswana Moscow Russia\nGaborone Botswana Muscat Oman\nGaborone Botswana Nairobi Kenya\nGaborone Botswana Nassau Bahamas\nGaborone Botswana Niamey Niger\nGaborone Botswana Nicosia Cyprus\nGaborone Botswana Nouakchott Mauritania\nGaborone Botswana Nuuk Greenland\nGeorgetown Guyana Hanoi Vietnam\nGeorgetown Guyana Harare Zimbabwe\nGeorgetown Guyana Havana Cuba\nGeorgetown Guyana Helsinki Finland\nGeorgetown Guyana Islamabad Pakistan\nGeorgetown Guyana Jakarta Indonesia\nGeorgetown Guyana Kabul Afghanistan\nGeorgetown Guyana Kampala Uganda\nGeorgetown Guyana Kathmandu Nepal\nGeorgetown Guyana Khartoum Sudan\nGeorgetown Guyana Kiev Ukraine\nGeorgetown Guyana Kigali Rwanda\nGeorgetown Guyana Kingston Jamaica\nGeorgetown Guyana Libreville Gabon\nGeorgetown Guyana Lilongwe Malawi\nGeorgetown Guyana Lima Peru\nGeorgetown Guyana Lisbon Portugal\nGeorgetown Guyana Ljubljana Slovenia\nGeorgetown Guyana London England\nGeorgetown Guyana Luanda Angola\nGeorgetown Guyana Lusaka Zambia\nGeorgetown Guyana Madrid Spain\nGeorgetown Guyana Managua Nicaragua\nGeorgetown Guyana Manama Bahrain\nGeorgetown Guyana Manila Philippines\nGeorgetown Guyana Maputo Mozambique\nGeorgetown Guyana Minsk Belarus\nGeorgetown Guyana Mogadishu Somalia\nGeorgetown Guyana Monrovia Liberia\nGeorgetown Guyana Montevideo Uruguay\nGeorgetown Guyana Moscow Russia\nGeorgetown Guyana Muscat Oman\nGeorgetown Guyana Nairobi Kenya\nGeorgetown Guyana Nassau Bahamas\nGeorgetown Guyana Niamey Niger\nGeorgetown Guyana Nicosia Cyprus\nGeorgetown Guyana Nouakchott Mauritania\nGeorgetown Guyana Nuuk Greenland\nGeorgetown Guyana Oslo Norway\nHanoi Vietnam Harare Zimbabwe\nHanoi Vietnam Havana Cuba\nHanoi Vietnam Helsinki Finland\nHanoi Vietnam Islamabad Pakistan\nHanoi Vietnam Jakarta Indonesia\nHanoi Vietnam Kabul Afghanistan\nHanoi Vietnam Kampala Uganda\nHanoi Vietnam Kathmandu Nepal\nHanoi Vietnam Khartoum Sudan\nHanoi Vietnam Kiev Ukraine\nHanoi Vietnam Kigali Rwanda\nHanoi Vietnam Kingston Jamaica\nHanoi Vietnam Libreville Gabon\nHanoi Vietnam Lilongwe Malawi\nHanoi Vietnam Lima Peru\nHanoi Vietnam Lisbon Portugal\nHanoi Vietnam Ljubljana Slovenia\nHanoi Vietnam London England\nHanoi Vietnam Luanda Angola\nHanoi Vietnam Lusaka Zambia\nHanoi Vietnam Madrid Spain\nHanoi Vietnam Managua Nicaragua\nHanoi Vietnam Manama Bahrain\nHanoi Vietnam Manila Philippines\nHanoi Vietnam Maputo Mozambique\nHanoi Vietnam Minsk Belarus\nHanoi Vietnam Mogadishu Somalia\nHanoi Vietnam Monrovia Liberia\nHanoi Vietnam Montevideo Uruguay\nHanoi Vietnam Moscow Russia\nHanoi Vietnam Muscat Oman\nHanoi Vietnam Nairobi Kenya\nHanoi Vietnam Nassau Bahamas\nHanoi Vietnam Niamey Niger\nHanoi Vietnam Nicosia Cyprus\nHanoi Vietnam Nouakchott Mauritania\nHanoi Vietnam Nuuk Greenland\nHanoi Vietnam Oslo Norway\nHanoi Vietnam Ottawa Canada\nHarare Zimbabwe Havana Cuba\nHarare Zimbabwe Helsinki Finland\nHarare Zimbabwe Islamabad Pakistan\nHarare Zimbabwe Jakarta Indonesia\nHarare Zimbabwe Kabul Afghanistan\nHarare Zimbabwe Kampala Uganda\nHarare Zimbabwe Kathmandu Nepal\nHarare Zimbabwe Khartoum Sudan\nHarare Zimbabwe Kiev Ukraine\nHarare Zimbabwe Kigali Rwanda\nHarare Zimbabwe Kingston Jamaica\nHarare Zimbabwe Libreville Gabon\nHarare Zimbabwe Lilongwe Malawi\nHarare Zimbabwe Lima Peru\nHarare Zimbabwe Lisbon Portugal\nHarare Zimbabwe Ljubljana Slovenia\nHarare Zimbabwe London England\nHarare Zimbabwe Luanda Angola\nHarare Zimbabwe Lusaka Zambia\nHarare Zimbabwe Madrid Spain\nHarare Zimbabwe Managua Nicaragua\nHarare Zimbabwe Manama Bahrain\nHarare Zimbabwe Manila Philippines\nHarare Zimbabwe Maputo Mozambique\nHarare Zimbabwe Minsk Belarus\nHarare Zimbabwe Mogadishu Somalia\nHarare Zimbabwe Monrovia Liberia\nHarare Zimbabwe Montevideo Uruguay\nHarare Zimbabwe Moscow Russia\nHarare Zimbabwe Muscat Oman\nHarare Zimbabwe Nairobi Kenya\nHarare Zimbabwe Nassau Bahamas\nHarare Zimbabwe Niamey Niger\nHarare Zimbabwe Nicosia Cyprus\nHarare Zimbabwe Nouakchott Mauritania\nHarare Zimbabwe Nuuk Greenland\nHarare Zimbabwe Oslo Norway\nHarare Zimbabwe Ottawa Canada\nHarare Zimbabwe Paramaribo Suriname\nHavana Cuba Helsinki Finland\nHavana Cuba Islamabad Pakistan\nHavana Cuba Jakarta Indonesia\nHavana Cuba Kabul Afghanistan\nHavana Cuba Kampala Uganda\nHavana Cuba Kathmandu Nepal\nHavana Cuba Khartoum Sudan\nHavana Cuba Kiev Ukraine\nHavana Cuba Kigali Rwanda\nHavana Cuba Kingston Jamaica\nHavana Cuba Libreville Gabon\nHavana Cuba Lilongwe Malawi\nHavana Cuba Lima Peru\nHavana Cuba Lisbon Portugal\nHavana Cuba Ljubljana Slovenia\nHavana Cuba London England\nHavana Cuba Luanda Angola\nHavana Cuba Lusaka Zambia\nHavana Cuba Madrid Spain\nHavana Cuba Managua Nicaragua\nHavana Cuba Manama Bahrain\nHavana Cuba Manila Philippines\nHavana Cuba Maputo Mozambique\nHavana Cuba Minsk Belarus\nHavana Cuba Mogadishu Somalia\nHavana Cuba Monrovia Liberia\nHavana Cuba Montevideo Uruguay\nHavana Cuba Moscow Russia\nHavana Cuba Muscat Oman\nHavana Cuba Nairobi Kenya\nHavana Cuba Nassau Bahamas\nHavana Cuba Niamey Niger\nHavana Cuba Nicosia Cyprus\nHavana Cuba Nouakchott Mauritania\nHavana Cuba Nuuk Greenland\nHavana Cuba Oslo Norway\nHavana Cuba Ottawa Canada\nHavana Cuba Paramaribo Suriname\nHavana Cuba Paris France\nHelsinki Finland Islamabad Pakistan\nHelsinki Finland Jakarta Indonesia\nHelsinki Finland Kabul Afghanistan\nHelsinki Finland Kampala Uganda\nHelsinki Finland Kathmandu Nepal\nHelsinki Finland Khartoum Sudan\nHelsinki Finland Kiev Ukraine\nHelsinki Finland Kigali Rwanda\nHelsinki Finland Kingston Jamaica\nHelsinki Finland Libreville Gabon\nHelsinki Finland Lilongwe Malawi\nHelsinki Finland Lima Peru\nHelsinki Finland Lisbon Portugal\nHelsinki Finland Ljubljana Slovenia\nHelsinki Finland London England\nHelsinki Finland Luanda Angola\nHelsinki Finland Lusaka Zambia\nHelsinki Finland Madrid Spain\nHelsinki Finland Managua Nicaragua\nHelsinki Finland Manama Bahrain\nHelsinki Finland Manila Philippines\nHelsinki Finland Maputo Mozambique\nHelsinki Finland Minsk Belarus\nHelsinki Finland Mogadishu Somalia\nHelsinki Finland Monrovia Liberia\nHelsinki Finland Montevideo Uruguay\nHelsinki Finland Moscow Russia\nHelsinki Finland Muscat Oman\nHelsinki Finland Nairobi Kenya\nHelsinki Finland Nassau Bahamas\nHelsinki Finland Niamey Niger\nHelsinki Finland Nicosia Cyprus\nHelsinki Finland Nouakchott Mauritania\nHelsinki Finland Nuuk Greenland\nHelsinki Finland Oslo Norway\nHelsinki Finland Ottawa Canada\nHelsinki Finland Paramaribo Suriname\nHelsinki Finland Paris France\nHelsinki Finland Podgorica Montenegro\nIslamabad Pakistan Jakarta Indonesia\nIslamabad Pakistan Kabul Afghanistan\nIslamabad Pakistan Kampala Uganda\nIslamabad Pakistan Kathmandu Nepal\nIslamabad Pakistan Khartoum Sudan\nIslamabad Pakistan Kiev Ukraine\nIslamabad Pakistan Kigali Rwanda\nIslamabad Pakistan Kingston Jamaica\nIslamabad Pakistan Libreville Gabon\nIslamabad Pakistan Lilongwe Malawi\nIslamabad Pakistan Lima Peru\nIslamabad Pakistan Lisbon Portugal\nIslamabad Pakistan Ljubljana Slovenia\nIslamabad Pakistan London England\nIslamabad Pakistan Luanda Angola\nIslamabad Pakistan Lusaka Zambia\nIslamabad Pakistan Madrid Spain\nIslamabad Pakistan Managua Nicaragua\nIslamabad Pakistan Manama Bahrain\nIslamabad Pakistan Manila Philippines\nIslamabad Pakistan Maputo Mozambique\nIslamabad Pakistan Minsk Belarus\nIslamabad Pakistan Mogadishu Somalia\nIslamabad Pakistan Monrovia Liberia\nIslamabad Pakistan Montevideo Uruguay\nIslamabad Pakistan Moscow Russia\nIslamabad Pakistan Muscat Oman\nIslamabad Pakistan Nairobi Kenya\nIslamabad Pakistan Nassau Bahamas\nIslamabad Pakistan Niamey Niger\nIslamabad Pakistan Nicosia Cyprus\nIslamabad Pakistan Nouakchott Mauritania\nIslamabad Pakistan Nuuk Greenland\nIslamabad Pakistan Oslo Norway\nIslamabad Pakistan Ottawa Canada\nIslamabad Pakistan Paramaribo Suriname\nIslamabad Pakistan Paris France\nIslamabad Pakistan Podgorica Montenegro\nIslamabad Pakistan Quito Ecuador\nJakarta Indonesia Kabul Afghanistan\nJakarta Indonesia Kampala Uganda\nJakarta Indonesia Kathmandu Nepal\nJakarta Indonesia Khartoum Sudan\nJakarta Indonesia Kiev Ukraine\nJakarta Indonesia Kigali Rwanda\nJakarta Indonesia Kingston Jamaica\nJakarta Indonesia Libreville Gabon\nJakarta Indonesia Lilongwe Malawi\nJakarta Indonesia Lima Peru\nJakarta Indonesia Lisbon Portugal\nJakarta Indonesia Ljubljana Slovenia\nJakarta Indonesia London England\nJakarta Indonesia Luanda Angola\nJakarta Indonesia Lusaka Zambia\nJakarta Indonesia Madrid Spain\nJakarta Indonesia Managua Nicaragua\nJakarta Indonesia Manama Bahrain\nJakarta Indonesia Manila Philippines\nJakarta Indonesia Maputo Mozambique\nJakarta Indonesia Minsk Belarus\nJakarta Indonesia Mogadishu Somalia\nJakarta Indonesia Monrovia Liberia\nJakarta Indonesia Montevideo Uruguay\nJakarta Indonesia Moscow Russia\nJakarta Indonesia Muscat Oman\nJakarta Indonesia Nairobi Kenya\nJakarta Indonesia Nassau Bahamas\nJakarta Indonesia Niamey Niger\nJakarta Indonesia Nicosia Cyprus\nJakarta Indonesia Nouakchott Mauritania\nJakarta Indonesia Nuuk Greenland\nJakarta Indonesia Oslo Norway\nJakarta Indonesia Ottawa Canada\nJakarta Indonesia Paramaribo Suriname\nJakarta Indonesia Paris France\nJakarta Indonesia Podgorica Montenegro\nJakarta Indonesia Quito Ecuador\nJakarta Indonesia Rabat Morocco\nKabul Afghanistan Kampala Uganda\nKabul Afghanistan Kathmandu Nepal\nKabul Afghanistan Khartoum Sudan\nKabul Afghanistan Kiev Ukraine\nKabul Afghanistan Kigali Rwanda\nKabul Afghanistan Kingston Jamaica\nKabul Afghanistan Libreville Gabon\nKabul Afghanistan Lilongwe Malawi\nKabul Afghanistan Lima Peru\nKabul Afghanistan Lisbon Portugal\nKabul Afghanistan Ljubljana Slovenia\nKabul Afghanistan London England\nKabul Afghanistan Luanda Angola\nKabul Afghanistan Lusaka Zambia\nKabul Afghanistan Madrid Spain\nKabul Afghanistan Managua Nicaragua\nKabul Afghanistan Manama Bahrain\nKabul Afghanistan Manila Philippines\nKabul Afghanistan Maputo Mozambique\nKabul Afghanistan Minsk Belarus\nKabul Afghanistan Mogadishu Somalia\nKabul Afghanistan Monrovia Liberia\nKabul Afghanistan Montevideo Uruguay\nKabul Afghanistan Moscow Russia\nKabul Afghanistan Muscat Oman\nKabul Afghanistan Nairobi Kenya\nKabul Afghanistan Nassau Bahamas\nKabul Afghanistan Niamey Niger\nKabul Afghanistan Nicosia Cyprus\nKabul Afghanistan Nouakchott Mauritania\nKabul Afghanistan Nuuk Greenland\nKabul Afghanistan Oslo Norway\nKabul Afghanistan Ottawa Canada\nKabul Afghanistan Paramaribo Suriname\nKabul Afghanistan Paris France\nKabul Afghanistan Podgorica Montenegro\nKabul Afghanistan Quito Ecuador\nKabul Afghanistan Rabat Morocco\nKabul Afghanistan Riga Latvia\nKampala Uganda Kathmandu Nepal\nKampala Uganda Khartoum Sudan\nKampala Uganda Kiev Ukraine\nKampala Uganda Kigali Rwanda\nKampala Uganda Kingston Jamaica\nKampala Uganda Libreville Gabon\nKampala Uganda Lilongwe Malawi\nKampala Uganda Lima Peru\nKampala Uganda Lisbon Portugal\nKampala Uganda Ljubljana Slovenia\nKampala Uganda London England\nKampala Uganda Luanda Angola\nKampala Uganda Lusaka Zambia\nKampala Uganda Madrid Spain\nKampala Uganda Managua Nicaragua\nKampala Uganda Manama Bahrain\nKampala Uganda Manila Philippines\nKampala Uganda Maputo Mozambique\nKampala Uganda Minsk Belarus\nKampala Uganda Mogadishu Somalia\nKampala Uganda Monrovia Liberia\nKampala Uganda Montevideo Uruguay\nKampala Uganda Moscow Russia\nKampala Uganda Muscat Oman\nKampala Uganda Nairobi Kenya\nKampala Uganda Nassau Bahamas\nKampala Uganda Niamey Niger\nKampala Uganda Nicosia Cyprus\nKampala Uganda Nouakchott Mauritania\nKampala Uganda Nuuk Greenland\nKampala Uganda Oslo Norway\nKampala Uganda Ottawa Canada\nKampala Uganda Paramaribo Suriname\nKampala Uganda Paris France\nKampala Uganda Podgorica Montenegro\nKampala Uganda Quito Ecuador\nKampala Uganda Rabat Morocco\nKampala Uganda Riga Latvia\nKampala Uganda Rome Italy\nKathmandu Nepal Khartoum Sudan\nKathmandu Nepal Kiev Ukraine\nKathmandu Nepal Kigali Rwanda\nKathmandu Nepal Kingston Jamaica\nKathmandu Nepal Libreville Gabon\nKathmandu Nepal Lilongwe Malawi\nKathmandu Nepal Lima Peru\nKathmandu Nepal Lisbon Portugal\nKathmandu Nepal Ljubljana Slovenia\nKathmandu Nepal London England\nKathmandu Nepal Luanda Angola\nKathmandu Nepal Lusaka Zambia\nKathmandu Nepal Madrid Spain\nKathmandu Nepal Managua Nicaragua\nKathmandu Nepal Manama Bahrain\nKathmandu Nepal Manila Philippines\nKathmandu Nepal Maputo Mozambique\nKathmandu Nepal Minsk Belarus\nKathmandu Nepal Mogadishu Somalia\nKathmandu Nepal Monrovia Liberia\nKathmandu Nepal Montevideo Uruguay\nKathmandu Nepal Moscow Russia\nKathmandu Nepal Muscat Oman\nKathmandu Nepal Nairobi Kenya\nKathmandu Nepal Nassau Bahamas\nKathmandu Nepal Niamey Niger\nKathmandu Nepal Nicosia Cyprus\nKathmandu Nepal Nouakchott Mauritania\nKathmandu Nepal Nuuk Greenland\nKathmandu Nepal Oslo Norway\nKathmandu Nepal Ottawa Canada\nKathmandu Nepal Paramaribo Suriname\nKathmandu Nepal Paris France\nKathmandu Nepal Podgorica Montenegro\nKathmandu Nepal Quito Ecuador\nKathmandu Nepal Rabat Morocco\nKathmandu Nepal Riga Latvia\nKathmandu Nepal Rome Italy\nKathmandu Nepal Roseau Dominica\nKhartoum Sudan Kiev Ukraine\nKhartoum Sudan Kigali Rwanda\nKhartoum Sudan Kingston Jamaica\nKhartoum Sudan Libreville Gabon\nKhartoum Sudan Lilongwe Malawi\nKhartoum Sudan Lima Peru\nKhartoum Sudan Lisbon Portugal\nKhartoum Sudan Ljubljana Slovenia\nKhartoum Sudan London England\nKhartoum Sudan Luanda Angola\nKhartoum Sudan Lusaka Zambia\nKhartoum Sudan Madrid Spain\nKhartoum Sudan Managua Nicaragua\nKhartoum Sudan Manama Bahrain\nKhartoum Sudan Manila Philippines\nKhartoum Sudan Maputo Mozambique\nKhartoum Sudan Minsk Belarus\nKhartoum Sudan Mogadishu Somalia\nKhartoum Sudan Monrovia Liberia\nKhartoum Sudan Montevideo Uruguay\nKhartoum Sudan Moscow Russia\nKhartoum Sudan Muscat Oman\nKhartoum Sudan Nairobi Kenya\nKhartoum Sudan Nassau Bahamas\nKhartoum Sudan Niamey Niger\nKhartoum Sudan Nicosia Cyprus\nKhartoum Sudan Nouakchott Mauritania\nKhartoum Sudan Nuuk Greenland\nKhartoum Sudan Oslo Norway\nKhartoum Sudan Ottawa Canada\nKhartoum Sudan Paramaribo Suriname\nKhartoum Sudan Paris France\nKhartoum Sudan Podgorica Montenegro\nKhartoum Sudan Quito Ecuador\nKhartoum Sudan Rabat Morocco\nKhartoum Sudan Riga Latvia\nKhartoum Sudan Rome Italy\nKhartoum Sudan Roseau Dominica\nKhartoum Sudan Santiago Chile\nKiev Ukraine Kigali Rwanda\nKiev Ukraine Kingston Jamaica\nKiev Ukraine Libreville Gabon\nKiev Ukraine Lilongwe Malawi\nKiev Ukraine Lima Peru\nKiev Ukraine Lisbon Portugal\nKiev Ukraine Ljubljana Slovenia\nKiev Ukraine London England\nKiev Ukraine Luanda Angola\nKiev Ukraine Lusaka Zambia\nKiev Ukraine Madrid Spain\nKiev Ukraine Managua Nicaragua\nKiev Ukraine Manama Bahrain\nKiev Ukraine Manila Philippines\nKiev Ukraine Maputo Mozambique\nKiev Ukraine Minsk Belarus\nKiev Ukraine Mogadishu Somalia\nKiev Ukraine Monrovia Liberia\nKiev Ukraine Montevideo Uruguay\nKiev Ukraine Moscow Russia\nKiev Ukraine Muscat Oman\nKiev Ukraine Nairobi Kenya\nKiev Ukraine Nassau Bahamas\nKiev Ukraine Niamey Niger\nKiev Ukraine Nicosia Cyprus\nKiev Ukraine Nouakchott Mauritania\nKiev Ukraine Nuuk Greenland\nKiev Ukraine Oslo Norway\nKiev Ukraine Ottawa Canada\nKiev Ukraine Paramaribo Suriname\nKiev Ukraine Paris France\nKiev Ukraine Podgorica Montenegro\nKiev Ukraine Quito Ecuador\nKiev Ukraine Rabat Morocco\nKiev Ukraine Riga Latvia\nKiev Ukraine Rome Italy\nKiev Ukraine Roseau Dominica\nKiev Ukraine Santiago Chile\nKiev Ukraine Skopje Macedonia\nKigali Rwanda Kingston Jamaica\nKigali Rwanda Libreville Gabon\nKigali Rwanda Lilongwe Malawi\nKigali Rwanda Lima Peru\nKigali Rwanda Lisbon Portugal\nKigali Rwanda Ljubljana Slovenia\nKigali Rwanda London England\nKigali Rwanda Luanda Angola\nKigali Rwanda Lusaka Zambia\nKigali Rwanda Madrid Spain\nKigali Rwanda Managua Nicaragua\nKigali Rwanda Manama Bahrain\nKigali Rwanda Manila Philippines\nKigali Rwanda Maputo Mozambique\nKigali Rwanda Minsk Belarus\nKigali Rwanda Mogadishu Somalia\nKigali Rwanda Monrovia Liberia\nKigali Rwanda Montevideo Uruguay\nKigali Rwanda Moscow Russia\nKigali Rwanda Muscat Oman\nKigali Rwanda Nairobi Kenya\nKigali Rwanda Nassau Bahamas\nKigali Rwanda Niamey Niger\nKigali Rwanda Nicosia Cyprus\nKigali Rwanda Nouakchott Mauritania\nKigali Rwanda Nuuk Greenland\nKigali Rwanda Oslo Norway\nKigali Rwanda Ottawa Canada\nKigali Rwanda Paramaribo Suriname\nKigali Rwanda Paris France\nKigali Rwanda Podgorica Montenegro\nKigali Rwanda Quito Ecuador\nKigali Rwanda Rabat Morocco\nKigali Rwanda Riga Latvia\nKigali Rwanda Rome Italy\nKigali Rwanda Roseau Dominica\nKigali Rwanda Santiago Chile\nKigali Rwanda Skopje Macedonia\nKigali Rwanda Sofia Bulgaria\nKingston Jamaica Libreville Gabon\nKingston Jamaica Lilongwe Malawi\nKingston Jamaica Lima Peru\nKingston Jamaica Lisbon Portugal\nKingston Jamaica Ljubljana Slovenia\nKingston Jamaica London England\nKingston Jamaica Luanda Angola\nKingston Jamaica Lusaka Zambia\nKingston Jamaica Madrid Spain\nKingston Jamaica Managua Nicaragua\nKingston Jamaica Manama Bahrain\nKingston Jamaica Manila Philippines\nKingston Jamaica Maputo Mozambique\nKingston Jamaica Minsk Belarus\nKingston Jamaica Mogadishu Somalia\nKingston Jamaica Monrovia Liberia\nKingston Jamaica Montevideo Uruguay\nKingston Jamaica Moscow Russia\nKingston Jamaica Muscat Oman\nKingston Jamaica Nairobi Kenya\nKingston Jamaica Nassau Bahamas\nKingston Jamaica Niamey Niger\nKingston Jamaica Nicosia Cyprus\nKingston Jamaica Nouakchott Mauritania\nKingston Jamaica Nuuk Greenland\nKingston Jamaica Oslo Norway\nKingston Jamaica Ottawa Canada\nKingston Jamaica Paramaribo Suriname\nKingston Jamaica Paris France\nKingston Jamaica Podgorica Montenegro\nKingston Jamaica Quito Ecuador\nKingston Jamaica Rabat Morocco\nKingston Jamaica Riga Latvia\nKingston Jamaica Rome Italy\nKingston Jamaica Roseau Dominica\nKingston Jamaica Santiago Chile\nKingston Jamaica Skopje Macedonia\nKingston Jamaica Sofia Bulgaria\nKingston Jamaica Stockholm Sweden\nLibreville Gabon Lilongwe Malawi\nLibreville Gabon Lima Peru\nLibreville Gabon Lisbon Portugal\nLibreville Gabon Ljubljana Slovenia\nLibreville Gabon London England\nLibreville Gabon Luanda Angola\nLibreville Gabon Lusaka Zambia\nLibreville Gabon Madrid Spain\nLibreville Gabon Managua Nicaragua\nLibreville Gabon Manama Bahrain\nLibreville Gabon Manila Philippines\nLibreville Gabon Maputo Mozambique\nLibreville Gabon Minsk Belarus\nLibreville Gabon Mogadishu Somalia\nLibreville Gabon Monrovia Liberia\nLibreville Gabon Montevideo Uruguay\nLibreville Gabon Moscow Russia\nLibreville Gabon Muscat Oman\nLibreville Gabon Nairobi Kenya\nLibreville Gabon Nassau Bahamas\nLibreville Gabon Niamey Niger\nLibreville Gabon Nicosia Cyprus\nLibreville Gabon Nouakchott Mauritania\nLibreville Gabon Nuuk Greenland\nLibreville Gabon Oslo Norway\nLibreville Gabon Ottawa Canada\nLibreville Gabon Paramaribo Suriname\nLibreville Gabon Paris France\nLibreville Gabon Podgorica Montenegro\nLibreville Gabon Quito Ecuador\nLibreville Gabon Rabat Morocco\nLibreville Gabon Riga Latvia\nLibreville Gabon Rome Italy\nLibreville Gabon Roseau Dominica\nLibreville Gabon Santiago Chile\nLibreville Gabon Skopje Macedonia\nLibreville Gabon Sofia Bulgaria\nLibreville Gabon Stockholm Sweden\nLibreville Gabon Suva Fiji\nLilongwe Malawi Lima Peru\nLilongwe Malawi Lisbon Portugal\nLilongwe Malawi Ljubljana Slovenia\nLilongwe Malawi London England\nLilongwe Malawi Luanda Angola\nLilongwe Malawi Lusaka Zambia\nLilongwe Malawi Madrid Spain\nLilongwe Malawi Managua Nicaragua\nLilongwe Malawi Manama Bahrain\nLilongwe Malawi Manila Philippines\nLilongwe Malawi Maputo Mozambique\nLilongwe Malawi Minsk Belarus\nLilongwe Malawi Mogadishu Somalia\nLilongwe Malawi Monrovia Liberia\nLilongwe Malawi Montevideo Uruguay\nLilongwe Malawi Moscow Russia\nLilongwe Malawi Muscat Oman\nLilongwe Malawi Nairobi Kenya\nLilongwe Malawi Nassau Bahamas\nLilongwe Malawi Niamey Niger\nLilongwe Malawi Nicosia Cyprus\nLilongwe Malawi Nouakchott Mauritania\nLilongwe Malawi Nuuk Greenland\nLilongwe Malawi Oslo Norway\nLilongwe Malawi Ottawa Canada\nLilongwe Malawi Paramaribo Suriname\nLilongwe Malawi Paris France\nLilongwe Malawi Podgorica Montenegro\nLilongwe Malawi Quito Ecuador\nLilongwe Malawi Rabat Morocco\nLilongwe Malawi Riga Latvia\nLilongwe Malawi Rome Italy\nLilongwe Malawi Roseau Dominica\nLilongwe Malawi Santiago Chile\nLilongwe Malawi Skopje Macedonia\nLilongwe Malawi Sofia Bulgaria\nLilongwe Malawi Stockholm Sweden\nLilongwe Malawi Suva Fiji\nLilongwe Malawi Taipei Taiwan\nLima Peru Lisbon Portugal\nLima Peru Ljubljana Slovenia\nLima Peru London England\nLima Peru Luanda Angola\nLima Peru Lusaka Zambia\nLima Peru Madrid Spain\nLima Peru Managua Nicaragua\nLima Peru Manama Bahrain\nLima Peru Manila Philippines\nLima Peru Maputo Mozambique\nLima Peru Minsk Belarus\nLima Peru Mogadishu Somalia\nLima Peru Monrovia Liberia\nLima Peru Montevideo Uruguay\nLima Peru Moscow Russia\nLima Peru Muscat Oman\nLima Peru Nairobi Kenya\nLima Peru Nassau Bahamas\nLima Peru Niamey Niger\nLima Peru Nicosia Cyprus\nLima Peru Nouakchott Mauritania\nLima Peru Nuuk Greenland\nLima Peru Oslo Norway\nLima Peru Ottawa Canada\nLima Peru Paramaribo Suriname\nLima Peru Paris France\nLima Peru Podgorica Montenegro\nLima Peru Quito Ecuador\nLima Peru Rabat Morocco\nLima Peru Riga Latvia\nLima Peru Rome Italy\nLima Peru Roseau Dominica\nLima Peru Santiago Chile\nLima Peru Skopje Macedonia\nLima Peru Sofia Bulgaria\nLima Peru Stockholm Sweden\nLima Peru Suva Fiji\nLima Peru Taipei Taiwan\nLima Peru Tallinn Estonia\nLisbon Portugal Ljubljana Slovenia\nLisbon Portugal London England\nLisbon Portugal Luanda Angola\nLisbon Portugal Lusaka Zambia\nLisbon Portugal Madrid Spain\nLisbon Portugal Managua Nicaragua\nLisbon Portugal Manama Bahrain\nLisbon Portugal Manila Philippines\nLisbon Portugal Maputo Mozambique\nLisbon Portugal Minsk Belarus\nLisbon Portugal Mogadishu Somalia\nLisbon Portugal Monrovia Liberia\nLisbon Portugal Montevideo Uruguay\nLisbon Portugal Moscow Russia\nLisbon Portugal Muscat Oman\nLisbon Portugal Nairobi Kenya\nLisbon Portugal Nassau Bahamas\nLisbon Portugal Niamey Niger\nLisbon Portugal Nicosia Cyprus\nLisbon Portugal Nouakchott Mauritania\nLisbon Portugal Nuuk Greenland\nLisbon Portugal Oslo Norway\nLisbon Portugal Ottawa Canada\nLisbon Portugal Paramaribo Suriname\nLisbon Portugal Paris France\nLisbon Portugal Podgorica Montenegro\nLisbon Portugal Quito Ecuador\nLisbon Portugal Rabat Morocco\nLisbon Portugal Riga Latvia\nLisbon Portugal Rome Italy\nLisbon Portugal Roseau Dominica\nLisbon Portugal Santiago Chile\nLisbon Portugal Skopje Macedonia\nLisbon Portugal Sofia Bulgaria\nLisbon Portugal Stockholm Sweden\nLisbon Portugal Suva Fiji\nLisbon Portugal Taipei Taiwan\nLisbon Portugal Tallinn Estonia\nLisbon Portugal Tashkent Uzbekistan\nLjubljana Slovenia London England\nLjubljana Slovenia Luanda Angola\nLjubljana Slovenia Lusaka Zambia\nLjubljana Slovenia Madrid Spain\nLjubljana Slovenia Managua Nicaragua\nLjubljana Slovenia Manama Bahrain\nLjubljana Slovenia Manila Philippines\nLjubljana Slovenia Maputo Mozambique\nLjubljana Slovenia Minsk Belarus\nLjubljana Slovenia Mogadishu Somalia\nLjubljana Slovenia Monrovia Liberia\nLjubljana Slovenia Montevideo Uruguay\nLjubljana Slovenia Moscow Russia\nLjubljana Slovenia Muscat Oman\nLjubljana Slovenia Nairobi Kenya\nLjubljana Slovenia Nassau Bahamas\nLjubljana Slovenia Niamey Niger\nLjubljana Slovenia Nicosia Cyprus\nLjubljana Slovenia Nouakchott Mauritania\nLjubljana Slovenia Nuuk Greenland\nLjubljana Slovenia Oslo Norway\nLjubljana Slovenia Ottawa Canada\nLjubljana Slovenia Paramaribo Suriname\nLjubljana Slovenia Paris France\nLjubljana Slovenia Podgorica Montenegro\nLjubljana Slovenia Quito Ecuador\nLjubljana Slovenia Rabat Morocco\nLjubljana Slovenia Riga Latvia\nLjubljana Slovenia Rome Italy\nLjubljana Slovenia Roseau Dominica\nLjubljana Slovenia Santiago Chile\nLjubljana Slovenia Skopje Macedonia\nLjubljana Slovenia Sofia Bulgaria\nLjubljana Slovenia Stockholm Sweden\nLjubljana Slovenia Suva Fiji\nLjubljana Slovenia Taipei Taiwan\nLjubljana Slovenia Tallinn Estonia\nLjubljana Slovenia Tashkent Uzbekistan\nLjubljana Slovenia Tbilisi Georgia\nLondon England Luanda Angola\nLondon England Lusaka Zambia\nLondon England Madrid Spain\nLondon England Managua Nicaragua\nLondon England Manama Bahrain\nLondon England Manila Philippines\nLondon England Maputo Mozambique\nLondon England Minsk Belarus\nLondon England Mogadishu Somalia\nLondon England Monrovia Liberia\nLondon England Montevideo Uruguay\nLondon England Moscow Russia\nLondon England Muscat Oman\nLondon England Nairobi Kenya\nLondon England Nassau Bahamas\nLondon England Niamey Niger\nLondon England Nicosia Cyprus\nLondon England Nouakchott Mauritania\nLondon England Nuuk Greenland\nLondon England Oslo Norway\nLondon England Ottawa Canada\nLondon England Paramaribo Suriname\nLondon England Paris France\nLondon England Podgorica Montenegro\nLondon England Quito Ecuador\nLondon England Rabat Morocco\nLondon England Riga Latvia\nLondon England Rome Italy\nLondon England Roseau Dominica\nLondon England Santiago Chile\nLondon England Skopje Macedonia\nLondon England Sofia Bulgaria\nLondon England Stockholm Sweden\nLondon England Suva Fiji\nLondon England Taipei Taiwan\nLondon England Tallinn Estonia\nLondon England Tashkent Uzbekistan\nLondon England Tbilisi Georgia\nLondon England Tegucigalpa Honduras\nLuanda Angola Lusaka Zambia\nLuanda Angola Madrid Spain\nLuanda Angola Managua Nicaragua\nLuanda Angola Manama Bahrain\nLuanda Angola Manila Philippines\nLuanda Angola Maputo Mozambique\nLuanda Angola Minsk Belarus\nLuanda Angola Mogadishu Somalia\nLuanda Angola Monrovia Liberia\nLuanda Angola Montevideo Uruguay\nLuanda Angola Moscow Russia\nLuanda Angola Muscat Oman\nLuanda Angola Nairobi Kenya\nLuanda Angola Nassau Bahamas\nLuanda Angola Niamey Niger\nLuanda Angola Nicosia Cyprus\nLuanda Angola Nouakchott Mauritania\nLuanda Angola Nuuk Greenland\nLuanda Angola Oslo Norway\nLuanda Angola Ottawa Canada\nLuanda Angola Paramaribo Suriname\nLuanda Angola Paris France\nLuanda Angola Podgorica Montenegro\nLuanda Angola Quito Ecuador\nLuanda Angola Rabat Morocco\nLuanda Angola Riga Latvia\nLuanda Angola Rome Italy\nLuanda Angola Roseau Dominica\nLuanda Angola Santiago Chile\nLuanda Angola Skopje Macedonia\nLuanda Angola Sofia Bulgaria\nLuanda Angola Stockholm Sweden\nLuanda Angola Suva Fiji\nLuanda Angola Taipei Taiwan\nLuanda Angola Tallinn Estonia\nLuanda Angola Tashkent Uzbekistan\nLuanda Angola Tbilisi Georgia\nLuanda Angola Tegucigalpa Honduras\nLuanda Angola Tehran Iran\nLusaka Zambia Madrid Spain\nLusaka Zambia Managua Nicaragua\nLusaka Zambia Manama Bahrain\nLusaka Zambia Manila Philippines\nLusaka Zambia Maputo Mozambique\nLusaka Zambia Minsk Belarus\nLusaka Zambia Mogadishu Somalia\nLusaka Zambia Monrovia Liberia\nLusaka Zambia Montevideo Uruguay\nLusaka Zambia Moscow Russia\nLusaka Zambia Muscat Oman\nLusaka Zambia Nairobi Kenya\nLusaka Zambia Nassau Bahamas\nLusaka Zambia Niamey Niger\nLusaka Zambia Nicosia Cyprus\nLusaka Zambia Nouakchott Mauritania\nLusaka Zambia Nuuk Greenland\nLusaka Zambia Oslo Norway\nLusaka Zambia Ottawa Canada\nLusaka Zambia Paramaribo Suriname\nLusaka Zambia Paris France\nLusaka Zambia Podgorica Montenegro\nLusaka Zambia Quito Ecuador\nLusaka Zambia Rabat Morocco\nLusaka Zambia Riga Latvia\nLusaka Zambia Rome Italy\nLusaka Zambia Roseau Dominica\nLusaka Zambia Santiago Chile\nLusaka Zambia Skopje Macedonia\nLusaka Zambia Sofia Bulgaria\nLusaka Zambia Stockholm Sweden\nLusaka Zambia Suva Fiji\nLusaka Zambia Taipei Taiwan\nLusaka Zambia Tallinn Estonia\nLusaka Zambia Tashkent Uzbekistan\nLusaka Zambia Tbilisi Georgia\nLusaka Zambia Tegucigalpa Honduras\nLusaka Zambia Tehran Iran\nLusaka Zambia Thimphu Bhutan\nMadrid Spain Managua Nicaragua\nMadrid Spain Manama Bahrain\nMadrid Spain Manila Philippines\nMadrid Spain Maputo Mozambique\nMadrid Spain Minsk Belarus\nMadrid Spain Mogadishu Somalia\nMadrid Spain Monrovia Liberia\nMadrid Spain Montevideo Uruguay\nMadrid Spain Moscow Russia\nMadrid Spain Muscat Oman\nMadrid Spain Nairobi Kenya\nMadrid Spain Nassau Bahamas\nMadrid Spain Niamey Niger\nMadrid Spain Nicosia Cyprus\nMadrid Spain Nouakchott Mauritania\nMadrid Spain Nuuk Greenland\nMadrid Spain Oslo Norway\nMadrid Spain Ottawa Canada\nMadrid Spain Paramaribo Suriname\nMadrid Spain Paris France\nMadrid Spain Podgorica Montenegro\nMadrid Spain Quito Ecuador\nMadrid Spain Rabat Morocco\nMadrid Spain Riga Latvia\nMadrid Spain Rome Italy\nMadrid Spain Roseau Dominica\nMadrid Spain Santiago Chile\nMadrid Spain Skopje Macedonia\nMadrid Spain Sofia Bulgaria\nMadrid Spain Stockholm Sweden\nMadrid Spain Suva Fiji\nMadrid Spain Taipei Taiwan\nMadrid Spain Tallinn Estonia\nMadrid Spain Tashkent Uzbekistan\nMadrid Spain Tbilisi Georgia\nMadrid Spain Tegucigalpa Honduras\nMadrid Spain Tehran Iran\nMadrid Spain Thimphu Bhutan\nMadrid Spain Tirana Albania\nManagua Nicaragua Manama Bahrain\nManagua Nicaragua Manila Philippines\nManagua Nicaragua Maputo Mozambique\nManagua Nicaragua Minsk Belarus\nManagua Nicaragua Mogadishu Somalia\nManagua Nicaragua Monrovia Liberia\nManagua Nicaragua Montevideo Uruguay\nManagua Nicaragua Moscow Russia\nManagua Nicaragua Muscat Oman\nManagua Nicaragua Nairobi Kenya\nManagua Nicaragua Nassau Bahamas\nManagua Nicaragua Niamey Niger\nManagua Nicaragua Nicosia Cyprus\nManagua Nicaragua Nouakchott Mauritania\nManagua Nicaragua Nuuk Greenland\nManagua Nicaragua Oslo Norway\nManagua Nicaragua Ottawa Canada\nManagua Nicaragua Paramaribo Suriname\nManagua Nicaragua Paris France\nManagua Nicaragua Podgorica Montenegro\nManagua Nicaragua Quito Ecuador\nManagua Nicaragua Rabat Morocco\nManagua Nicaragua Riga Latvia\nManagua Nicaragua Rome Italy\nManagua Nicaragua Roseau Dominica\nManagua Nicaragua Santiago Chile\nManagua Nicaragua Skopje Macedonia\nManagua Nicaragua Sofia Bulgaria\nManagua Nicaragua Stockholm Sweden\nManagua Nicaragua Suva Fiji\nManagua Nicaragua Taipei Taiwan\nManagua Nicaragua Tallinn Estonia\nManagua Nicaragua Tashkent Uzbekistan\nManagua Nicaragua Tbilisi Georgia\nManagua Nicaragua Tegucigalpa Honduras\nManagua Nicaragua Tehran Iran\nManagua Nicaragua Thimphu Bhutan\nManagua Nicaragua Tirana Albania\nManagua Nicaragua Tokyo Japan\nManama Bahrain Manila Philippines\nManama Bahrain Maputo Mozambique\nManama Bahrain Minsk Belarus\nManama Bahrain Mogadishu Somalia\nManama Bahrain Monrovia Liberia\nManama Bahrain Montevideo Uruguay\nManama Bahrain Moscow Russia\nManama Bahrain Muscat Oman\nManama Bahrain Nairobi Kenya\nManama Bahrain Nassau Bahamas\nManama Bahrain Niamey Niger\nManama Bahrain Nicosia Cyprus\nManama Bahrain Nouakchott Mauritania\nManama Bahrain Nuuk Greenland\nManama Bahrain Oslo Norway\nManama Bahrain Ottawa Canada\nManama Bahrain Paramaribo Suriname\nManama Bahrain Paris France\nManama Bahrain Podgorica Montenegro\nManama Bahrain Quito Ecuador\nManama Bahrain Rabat Morocco\nManama Bahrain Riga Latvia\nManama Bahrain Rome Italy\nManama Bahrain Roseau Dominica\nManama Bahrain Santiago Chile\nManama Bahrain Skopje Macedonia\nManama Bahrain Sofia Bulgaria\nManama Bahrain Stockholm Sweden\nManama Bahrain Suva Fiji\nManama Bahrain Taipei Taiwan\nManama Bahrain Tallinn Estonia\nManama Bahrain Tashkent Uzbekistan\nManama Bahrain Tbilisi Georgia\nManama Bahrain Tegucigalpa Honduras\nManama Bahrain Tehran Iran\nManama Bahrain Thimphu Bhutan\nManama Bahrain Tirana Albania\nManama Bahrain Tokyo Japan\nManama Bahrain Tripoli Libya\nManila Philippines Maputo Mozambique\nManila Philippines Minsk Belarus\nManila Philippines Mogadishu Somalia\nManila Philippines Monrovia Liberia\nManila Philippines Montevideo Uruguay\nManila Philippines Moscow Russia\nManila Philippines Muscat Oman\nManila Philippines Nairobi Kenya\nManila Philippines Nassau Bahamas\nManila Philippines Niamey Niger\nManila Philippines Nicosia Cyprus\nManila Philippines Nouakchott Mauritania\nManila Philippines Nuuk Greenland\nManila Philippines Oslo Norway\nManila Philippines Ottawa Canada\nManila Philippines Paramaribo Suriname\nManila Philippines Paris France\nManila Philippines Podgorica Montenegro\nManila Philippines Quito Ecuador\nManila Philippines Rabat Morocco\nManila Philippines Riga Latvia\nManila Philippines Rome Italy\nManila Philippines Roseau Dominica\nManila Philippines Santiago Chile\nManila Philippines Skopje Macedonia\nManila Philippines Sofia Bulgaria\nManila Philippines Stockholm Sweden\nManila Philippines Suva Fiji\nManila Philippines Taipei Taiwan\nManila Philippines Tallinn Estonia\nManila Philippines Tashkent Uzbekistan\nManila Philippines Tbilisi Georgia\nManila Philippines Tegucigalpa Honduras\nManila Philippines Tehran Iran\nManila Philippines Thimphu Bhutan\nManila Philippines Tirana Albania\nManila Philippines Tokyo Japan\nManila Philippines Tripoli Libya\nManila Philippines Tunis Tunisia\nMaputo Mozambique Minsk Belarus\nMaputo Mozambique Mogadishu Somalia\nMaputo Mozambique Monrovia Liberia\nMaputo Mozambique Montevideo Uruguay\nMaputo Mozambique Moscow Russia\nMaputo Mozambique Muscat Oman\nMaputo Mozambique Nairobi Kenya\nMaputo Mozambique Nassau Bahamas\nMaputo Mozambique Niamey Niger\nMaputo Mozambique Nicosia Cyprus\nMaputo Mozambique Nouakchott Mauritania\nMaputo Mozambique Nuuk Greenland\nMaputo Mozambique Oslo Norway\nMaputo Mozambique Ottawa Canada\nMaputo Mozambique Paramaribo Suriname\nMaputo Mozambique Paris France\nMaputo Mozambique Podgorica Montenegro\nMaputo Mozambique Quito Ecuador\nMaputo Mozambique Rabat Morocco\nMaputo Mozambique Riga Latvia\nMaputo Mozambique Rome Italy\nMaputo Mozambique Roseau Dominica\nMaputo Mozambique Santiago Chile\nMaputo Mozambique Skopje Macedonia\nMaputo Mozambique Sofia Bulgaria\nMaputo Mozambique Stockholm Sweden\nMaputo Mozambique Suva Fiji\nMaputo Mozambique Taipei Taiwan\nMaputo Mozambique Tallinn Estonia\nMaputo Mozambique Tashkent Uzbekistan\nMaputo Mozambique Tbilisi Georgia\nMaputo Mozambique Tegucigalpa Honduras\nMaputo Mozambique Tehran Iran\nMaputo Mozambique Thimphu Bhutan\nMaputo Mozambique Tirana Albania\nMaputo Mozambique Tokyo Japan\nMaputo Mozambique Tripoli Libya\nMaputo Mozambique Tunis Tunisia\nMaputo Mozambique Vaduz Liechtenstein\nMinsk Belarus Mogadishu Somalia\nMinsk Belarus Monrovia Liberia\nMinsk Belarus Montevideo Uruguay\nMinsk Belarus Moscow Russia\nMinsk Belarus Muscat Oman\nMinsk Belarus Nairobi Kenya\nMinsk Belarus Nassau Bahamas\nMinsk Belarus Niamey Niger\nMinsk Belarus Nicosia Cyprus\nMinsk Belarus Nouakchott Mauritania\nMinsk Belarus Nuuk Greenland\nMinsk Belarus Oslo Norway\nMinsk Belarus Ottawa Canada\nMinsk Belarus Paramaribo Suriname\nMinsk Belarus Paris France\nMinsk Belarus Podgorica Montenegro\nMinsk Belarus Quito Ecuador\nMinsk Belarus Rabat Morocco\nMinsk Belarus Riga Latvia\nMinsk Belarus Rome Italy\nMinsk Belarus Roseau Dominica\nMinsk Belarus Santiago Chile\nMinsk Belarus Skopje Macedonia\nMinsk Belarus Sofia Bulgaria\nMinsk Belarus Stockholm Sweden\nMinsk Belarus Suva Fiji\nMinsk Belarus Taipei Taiwan\nMinsk Belarus Tallinn Estonia\nMinsk Belarus Tashkent Uzbekistan\nMinsk Belarus Tbilisi Georgia\nMinsk Belarus Tegucigalpa Honduras\nMinsk Belarus Tehran Iran\nMinsk Belarus Thimphu Bhutan\nMinsk Belarus Tirana Albania\nMinsk Belarus Tokyo Japan\nMinsk Belarus Tripoli Libya\nMinsk Belarus Tunis Tunisia\nMinsk Belarus Vaduz Liechtenstein\nMinsk Belarus Valletta Malta\nMogadishu Somalia Monrovia Liberia\nMogadishu Somalia Montevideo Uruguay\nMogadishu Somalia Moscow Russia\nMogadishu Somalia Muscat Oman\nMogadishu Somalia Nairobi Kenya\nMogadishu Somalia Nassau Bahamas\nMogadishu Somalia Niamey Niger\nMogadishu Somalia Nicosia Cyprus\nMogadishu Somalia Nouakchott Mauritania\nMogadishu Somalia Nuuk Greenland\nMogadishu Somalia Oslo Norway\nMogadishu Somalia Ottawa Canada\nMogadishu Somalia Paramaribo Suriname\nMogadishu Somalia Paris France\nMogadishu Somalia Podgorica Montenegro\nMogadishu Somalia Quito Ecuador\nMogadishu Somalia Rabat Morocco\nMogadishu Somalia Riga Latvia\nMogadishu Somalia Rome Italy\nMogadishu Somalia Roseau Dominica\nMogadishu Somalia Santiago Chile\nMogadishu Somalia Skopje Macedonia\nMogadishu Somalia Sofia Bulgaria\nMogadishu Somalia Stockholm Sweden\nMogadishu Somalia Suva Fiji\nMogadishu Somalia Taipei Taiwan\nMogadishu Somalia Tallinn Estonia\nMogadishu Somalia Tashkent Uzbekistan\nMogadishu Somalia Tbilisi Georgia\nMogadishu Somalia Tegucigalpa Honduras\nMogadishu Somalia Tehran Iran\nMogadishu Somalia Thimphu Bhutan\nMogadishu Somalia Tirana Albania\nMogadishu Somalia Tokyo Japan\nMogadishu Somalia Tripoli Libya\nMogadishu Somalia Tunis Tunisia\nMogadishu Somalia Vaduz Liechtenstein\nMogadishu Somalia Valletta Malta\nMogadishu Somalia Vienna Austria\nMonrovia Liberia Montevideo Uruguay\nMonrovia Liberia Moscow Russia\nMonrovia Liberia Muscat Oman\nMonrovia Liberia Nairobi Kenya\nMonrovia Liberia Nassau Bahamas\nMonrovia Liberia Niamey Niger\nMonrovia Liberia Nicosia Cyprus\nMonrovia Liberia Nouakchott Mauritania\nMonrovia Liberia Nuuk Greenland\nMonrovia Liberia Oslo Norway\nMonrovia Liberia Ottawa Canada\nMonrovia Liberia Paramaribo Suriname\nMonrovia Liberia Paris France\nMonrovia Liberia Podgorica Montenegro\nMonrovia Liberia Quito Ecuador\nMonrovia Liberia Rabat Morocco\nMonrovia Liberia Riga Latvia\nMonrovia Liberia Rome Italy\nMonrovia Liberia Roseau Dominica\nMonrovia Liberia Santiago Chile\nMonrovia Liberia Skopje Macedonia\nMonrovia Liberia Sofia Bulgaria\nMonrovia Liberia Stockholm Sweden\nMonrovia Liberia Suva Fiji\nMonrovia Liberia Taipei Taiwan\nMonrovia Liberia Tallinn Estonia\nMonrovia Liberia Tashkent Uzbekistan\nMonrovia Liberia Tbilisi Georgia\nMonrovia Liberia Tegucigalpa Honduras\nMonrovia Liberia Tehran Iran\nMonrovia Liberia Thimphu Bhutan\nMonrovia Liberia Tirana Albania\nMonrovia Liberia Tokyo Japan\nMonrovia Liberia Tripoli Libya\nMonrovia Liberia Tunis Tunisia\nMonrovia Liberia Vaduz Liechtenstein\nMonrovia Liberia Valletta Malta\nMonrovia Liberia Vienna Austria\nMonrovia Liberia Vientiane Laos\nMontevideo Uruguay Moscow Russia\nMontevideo Uruguay Muscat Oman\nMontevideo Uruguay Nairobi Kenya\nMontevideo Uruguay Nassau Bahamas\nMontevideo Uruguay Niamey Niger\nMontevideo Uruguay Nicosia Cyprus\nMontevideo Uruguay Nouakchott Mauritania\nMontevideo Uruguay Nuuk Greenland\nMontevideo Uruguay Oslo Norway\nMontevideo Uruguay Ottawa Canada\nMontevideo Uruguay Paramaribo Suriname\nMontevideo Uruguay Paris France\nMontevideo Uruguay Podgorica Montenegro\nMontevideo Uruguay Quito Ecuador\nMontevideo Uruguay Rabat Morocco\nMontevideo Uruguay Riga Latvia\nMontevideo Uruguay Rome Italy\nMontevideo Uruguay Roseau Dominica\nMontevideo Uruguay Santiago Chile\nMontevideo Uruguay Skopje Macedonia\nMontevideo Uruguay Sofia Bulgaria\nMontevideo Uruguay Stockholm Sweden\nMontevideo Uruguay Suva Fiji\nMontevideo Uruguay Taipei Taiwan\nMontevideo Uruguay Tallinn Estonia\nMontevideo Uruguay Tashkent Uzbekistan\nMontevideo Uruguay Tbilisi Georgia\nMontevideo Uruguay Tegucigalpa Honduras\nMontevideo Uruguay Tehran Iran\nMontevideo Uruguay Thimphu Bhutan\nMontevideo Uruguay Tirana Albania\nMontevideo Uruguay Tokyo Japan\nMontevideo Uruguay Tripoli Libya\nMontevideo Uruguay Tunis Tunisia\nMontevideo Uruguay Vaduz Liechtenstein\nMontevideo Uruguay Valletta Malta\nMontevideo Uruguay Vienna Austria\nMontevideo Uruguay Vientiane Laos\nMontevideo Uruguay Vilnius Lithuania\nMoscow Russia Muscat Oman\nMoscow Russia Nairobi Kenya\nMoscow Russia Nassau Bahamas\nMoscow Russia Niamey Niger\nMoscow Russia Nicosia Cyprus\nMoscow Russia Nouakchott Mauritania\nMoscow Russia Nuuk Greenland\nMoscow Russia Oslo Norway\nMoscow Russia Ottawa Canada\nMoscow Russia Paramaribo Suriname\nMoscow Russia Paris France\nMoscow Russia Podgorica Montenegro\nMoscow Russia Quito Ecuador\nMoscow Russia Rabat Morocco\nMoscow Russia Riga Latvia\nMoscow Russia Rome Italy\nMoscow Russia Roseau Dominica\nMoscow Russia Santiago Chile\nMoscow Russia Skopje Macedonia\nMoscow Russia Sofia Bulgaria\nMoscow Russia Stockholm Sweden\nMoscow Russia Suva Fiji\nMoscow Russia Taipei Taiwan\nMoscow Russia Tallinn Estonia\nMoscow Russia Tashkent Uzbekistan\nMoscow Russia Tbilisi Georgia\nMoscow Russia Tegucigalpa Honduras\nMoscow Russia Tehran Iran\nMoscow Russia Thimphu Bhutan\nMoscow Russia Tirana Albania\nMoscow Russia Tokyo Japan\nMoscow Russia Tripoli Libya\nMoscow Russia Tunis Tunisia\nMoscow Russia Vaduz Liechtenstein\nMoscow Russia Valletta Malta\nMoscow Russia Vienna Austria\nMoscow Russia Vientiane Laos\nMoscow Russia Vilnius Lithuania\nMoscow Russia Warsaw Poland\nMuscat Oman Nairobi Kenya\nMuscat Oman Nassau Bahamas\nMuscat Oman Niamey Niger\nMuscat Oman Nicosia Cyprus\nMuscat Oman Nouakchott Mauritania\nMuscat Oman Nuuk Greenland\nMuscat Oman Oslo Norway\nMuscat Oman Ottawa Canada\nMuscat Oman Paramaribo Suriname\nMuscat Oman Paris France\nMuscat Oman Podgorica Montenegro\nMuscat Oman Quito Ecuador\nMuscat Oman Rabat Morocco\nMuscat Oman Riga Latvia\nMuscat Oman Rome Italy\nMuscat Oman Roseau Dominica\nMuscat Oman Santiago Chile\nMuscat Oman Skopje Macedonia\nMuscat Oman Sofia Bulgaria\nMuscat Oman Stockholm Sweden\nMuscat Oman Suva Fiji\nMuscat Oman Taipei Taiwan\nMuscat Oman Tallinn Estonia\nMuscat Oman Tashkent Uzbekistan\nMuscat Oman Tbilisi Georgia\nMuscat Oman Tegucigalpa Honduras\nMuscat Oman Tehran Iran\nMuscat Oman Thimphu Bhutan\nMuscat Oman Tirana Albania\nMuscat Oman Tokyo Japan\nMuscat Oman Tripoli Libya\nMuscat Oman Tunis Tunisia\nMuscat Oman Vaduz Liechtenstein\nMuscat Oman Valletta Malta\nMuscat Oman Vienna Austria\nMuscat Oman Vientiane Laos\nMuscat Oman Vilnius Lithuania\nMuscat Oman Warsaw Poland\nMuscat Oman Windhoek Namibia\nNairobi Kenya Nassau Bahamas\nNairobi Kenya Niamey Niger\nNairobi Kenya Nicosia Cyprus\nNairobi Kenya Nouakchott Mauritania\nNairobi Kenya Nuuk Greenland\nNairobi Kenya Oslo Norway\nNairobi Kenya Ottawa Canada\nNairobi Kenya Paramaribo Suriname\nNairobi Kenya Paris France\nNairobi Kenya Podgorica Montenegro\nNairobi Kenya Quito Ecuador\nNairobi Kenya Rabat Morocco\nNairobi Kenya Riga Latvia\nNairobi Kenya Rome Italy\nNairobi Kenya Roseau Dominica\nNairobi Kenya Santiago Chile\nNairobi Kenya Skopje Macedonia\nNairobi Kenya Sofia Bulgaria\nNairobi Kenya Stockholm Sweden\nNairobi Kenya Suva Fiji\nNairobi Kenya Taipei Taiwan\nNairobi Kenya Tallinn Estonia\nNairobi Kenya Tashkent Uzbekistan\nNairobi Kenya Tbilisi Georgia\nNairobi Kenya Tegucigalpa Honduras\nNairobi Kenya Tehran Iran\nNairobi Kenya Thimphu Bhutan\nNairobi Kenya Tirana Albania\nNairobi Kenya Tokyo Japan\nNairobi Kenya Tripoli Libya\nNairobi Kenya Tunis Tunisia\nNairobi Kenya Vaduz Liechtenstein\nNairobi Kenya Valletta Malta\nNairobi Kenya Vienna Austria\nNairobi Kenya Vientiane Laos\nNairobi Kenya Vilnius Lithuania\nNairobi Kenya Warsaw Poland\nNairobi Kenya Windhoek Namibia\nNairobi Kenya Yerevan Armenia\nNassau Bahamas Niamey Niger\nNassau Bahamas Nicosia Cyprus\nNassau Bahamas Nouakchott Mauritania\nNassau Bahamas Nuuk Greenland\nNassau Bahamas Oslo Norway\nNassau Bahamas Ottawa Canada\nNassau Bahamas Paramaribo Suriname\nNassau Bahamas Paris France\nNassau Bahamas Podgorica Montenegro\nNassau Bahamas Quito Ecuador\nNassau Bahamas Rabat Morocco\nNassau Bahamas Riga Latvia\nNassau Bahamas Rome Italy\nNassau Bahamas Roseau Dominica\nNassau Bahamas Santiago Chile\nNassau Bahamas Skopje Macedonia\nNassau Bahamas Sofia Bulgaria\nNassau Bahamas Stockholm Sweden\nNassau Bahamas Suva Fiji\nNassau Bahamas Taipei Taiwan\nNassau Bahamas Tallinn Estonia\nNassau Bahamas Tashkent Uzbekistan\nNassau Bahamas Tbilisi Georgia\nNassau Bahamas Tegucigalpa Honduras\nNassau Bahamas Tehran Iran\nNassau Bahamas Thimphu Bhutan\nNassau Bahamas Tirana Albania\nNassau Bahamas Tokyo Japan\nNassau Bahamas Tripoli Libya\nNassau Bahamas Tunis Tunisia\nNassau Bahamas Vaduz Liechtenstein\nNassau Bahamas Valletta Malta\nNassau Bahamas Vienna Austria\nNassau Bahamas Vientiane Laos\nNassau Bahamas Vilnius Lithuania\nNassau Bahamas Warsaw Poland\nNassau Bahamas Windhoek Namibia\nNassau Bahamas Yerevan Armenia\nNassau Bahamas Zagreb Croatia\nNiamey Niger Nicosia Cyprus\nNiamey Niger Nouakchott Mauritania\nNiamey Niger Nuuk Greenland\nNiamey Niger Oslo Norway\nNiamey Niger Ottawa Canada\nNiamey Niger Paramaribo Suriname\nNiamey Niger Paris France\nNiamey Niger Podgorica Montenegro\nNiamey Niger Quito Ecuador\nNiamey Niger Rabat Morocco\nNiamey Niger Riga Latvia\nNiamey Niger Rome Italy\nNiamey Niger Roseau Dominica\nNiamey Niger Santiago Chile\nNiamey Niger Skopje Macedonia\nNiamey Niger Sofia Bulgaria\nNiamey Niger Stockholm Sweden\nNiamey Niger Suva Fiji\nNiamey Niger Taipei Taiwan\nNiamey Niger Tallinn Estonia\nNiamey Niger Tashkent Uzbekistan\nNiamey Niger Tbilisi Georgia\nNiamey Niger Tegucigalpa Honduras\nNiamey Niger Tehran Iran\nNiamey Niger Thimphu Bhutan\nNiamey Niger Tirana Albania\nNiamey Niger Tokyo Japan\nNiamey Niger Tripoli Libya\nNiamey Niger Tunis Tunisia\nNiamey Niger Vaduz Liechtenstein\nNiamey Niger Valletta Malta\nNiamey Niger Vienna Austria\nNiamey Niger Vientiane Laos\nNiamey Niger Vilnius Lithuania\nNiamey Niger Warsaw Poland\nNiamey Niger Windhoek Namibia\nNiamey Niger Yerevan Armenia\nNiamey Niger Zagreb Croatia\nNiamey Niger Abuja Nigeria\nNicosia Cyprus Nouakchott Mauritania\nNicosia Cyprus Nuuk Greenland\nNicosia Cyprus Oslo Norway\nNicosia Cyprus Ottawa Canada\nNicosia Cyprus Paramaribo Suriname\nNicosia Cyprus Paris France\nNicosia Cyprus Podgorica Montenegro\nNicosia Cyprus Quito Ecuador\nNicosia Cyprus Rabat Morocco\nNicosia Cyprus Riga Latvia\nNicosia Cyprus Rome Italy\nNicosia Cyprus Roseau Dominica\nNicosia Cyprus Santiago Chile\nNicosia Cyprus Skopje Macedonia\nNicosia Cyprus Sofia Bulgaria\nNicosia Cyprus Stockholm Sweden\nNicosia Cyprus Suva Fiji\nNicosia Cyprus Taipei Taiwan\nNicosia Cyprus Tallinn Estonia\nNicosia Cyprus Tashkent Uzbekistan\nNicosia Cyprus Tbilisi Georgia\nNicosia Cyprus Tegucigalpa Honduras\nNicosia Cyprus Tehran Iran\nNicosia Cyprus Thimphu Bhutan\nNicosia Cyprus Tirana Albania\nNicosia Cyprus Tokyo Japan\nNicosia Cyprus Tripoli Libya\nNicosia Cyprus Tunis Tunisia\nNicosia Cyprus Vaduz Liechtenstein\nNicosia Cyprus Valletta Malta\nNicosia Cyprus Vienna Austria\nNicosia Cyprus Vientiane Laos\nNicosia Cyprus Vilnius Lithuania\nNicosia Cyprus Warsaw Poland\nNicosia Cyprus Windhoek Namibia\nNicosia Cyprus Yerevan Armenia\nNicosia Cyprus Zagreb Croatia\nNicosia Cyprus Abuja Nigeria\nNicosia Cyprus Accra Ghana\nNouakchott Mauritania Nuuk Greenland\nNouakchott Mauritania Oslo Norway\nNouakchott Mauritania Ottawa Canada\nNouakchott Mauritania Paramaribo Suriname\nNouakchott Mauritania Paris France\nNouakchott Mauritania Podgorica Montenegro\nNouakchott Mauritania Quito Ecuador\nNouakchott Mauritania Rabat Morocco\nNouakchott Mauritania Riga Latvia\nNouakchott Mauritania Rome Italy\nNouakchott Mauritania Roseau Dominica\nNouakchott Mauritania Santiago Chile\nNouakchott Mauritania Skopje Macedonia\nNouakchott Mauritania Sofia Bulgaria\nNouakchott Mauritania Stockholm Sweden\nNouakchott Mauritania Suva Fiji\nNouakchott Mauritania Taipei Taiwan\nNouakchott Mauritania Tallinn Estonia\nNouakchott Mauritania Tashkent Uzbekistan\nNouakchott Mauritania Tbilisi Georgia\nNouakchott Mauritania Tegucigalpa Honduras\nNouakchott Mauritania Tehran Iran\nNouakchott Mauritania Thimphu Bhutan\nNouakchott Mauritania Tirana Albania\nNouakchott Mauritania Tokyo Japan\nNouakchott Mauritania Tripoli Libya\nNouakchott Mauritania Tunis Tunisia\nNouakchott Mauritania Vaduz Liechtenstein\nNouakchott Mauritania Valletta Malta\nNouakchott Mauritania Vienna Austria\nNouakchott Mauritania Vientiane Laos\nNouakchott Mauritania Vilnius Lithuania\nNouakchott Mauritania Warsaw Poland\nNouakchott Mauritania Windhoek Namibia\nNouakchott Mauritania Yerevan Armenia\nNouakchott Mauritania Zagreb Croatia\nNouakchott Mauritania Abuja Nigeria\nNouakchott Mauritania Accra Ghana\nNouakchott Mauritania Algiers Algeria\nNuuk Greenland Oslo Norway\nNuuk Greenland Ottawa Canada\nNuuk Greenland Paramaribo Suriname\nNuuk Greenland Paris France\nNuuk Greenland Podgorica Montenegro\nNuuk Greenland Quito Ecuador\nNuuk Greenland Rabat Morocco\nNuuk Greenland Riga Latvia\nNuuk Greenland Rome Italy\nNuuk Greenland Roseau Dominica\nNuuk Greenland Santiago Chile\nNuuk Greenland Skopje Macedonia\nNuuk Greenland Sofia Bulgaria\nNuuk Greenland Stockholm Sweden\nNuuk Greenland Suva Fiji\nNuuk Greenland Taipei Taiwan\nNuuk Greenland Tallinn Estonia\nNuuk Greenland Tashkent Uzbekistan\nNuuk Greenland Tbilisi Georgia\nNuuk Greenland Tegucigalpa Honduras\nNuuk Greenland Tehran Iran\nNuuk Greenland Thimphu Bhutan\nNuuk Greenland Tirana Albania\nNuuk Greenland Tokyo Japan\nNuuk Greenland Tripoli Libya\nNuuk Greenland Tunis Tunisia\nNuuk Greenland Vaduz Liechtenstein\nNuuk Greenland Valletta Malta\nNuuk Greenland Vienna Austria\nNuuk Greenland Vientiane Laos\nNuuk Greenland Vilnius Lithuania\nNuuk Greenland Warsaw Poland\nNuuk Greenland Windhoek Namibia\nNuuk Greenland Yerevan Armenia\nNuuk Greenland Zagreb Croatia\nNuuk Greenland Abuja Nigeria\nNuuk Greenland Accra Ghana\nNuuk Greenland Algiers Algeria\nNuuk Greenland Amman Jordan\nOslo Norway Ottawa Canada\nOslo Norway Paramaribo Suriname\nOslo Norway Paris France\nOslo Norway Podgorica Montenegro\nOslo Norway Quito Ecuador\nOslo Norway Rabat Morocco\nOslo Norway Riga Latvia\nOslo Norway Rome Italy\nOslo Norway Roseau Dominica\nOslo Norway Santiago Chile\nOslo Norway Skopje Macedonia\nOslo Norway Sofia Bulgaria\nOslo Norway Stockholm Sweden\nOslo Norway Suva Fiji\nOslo Norway Taipei Taiwan\nOslo Norway Tallinn Estonia\nOslo Norway Tashkent Uzbekistan\nOslo Norway Tbilisi Georgia\nOslo Norway Tegucigalpa Honduras\nOslo Norway Tehran Iran\nOslo Norway Thimphu Bhutan\nOslo Norway Tirana Albania\nOslo Norway Tokyo Japan\nOslo Norway Tripoli Libya\nOslo Norway Tunis Tunisia\nOslo Norway Vaduz Liechtenstein\nOslo Norway Valletta Malta\nOslo Norway Vienna Austria\nOslo Norway Vientiane Laos\nOslo Norway Vilnius Lithuania\nOslo Norway Warsaw Poland\nOslo Norway Windhoek Namibia\nOslo Norway Yerevan Armenia\nOslo Norway Zagreb Croatia\nOslo Norway Abuja Nigeria\nOslo Norway Accra Ghana\nOslo Norway Algiers Algeria\nOslo Norway Amman Jordan\nOslo Norway Ankara Turkey\nOttawa Canada Paramaribo Suriname\nOttawa Canada Paris France\nOttawa Canada Podgorica Montenegro\nOttawa Canada Quito Ecuador\nOttawa Canada Rabat Morocco\nOttawa Canada Riga Latvia\nOttawa Canada Rome Italy\nOttawa Canada Roseau Dominica\nOttawa Canada Santiago Chile\nOttawa Canada Skopje Macedonia\nOttawa Canada Sofia Bulgaria\nOttawa Canada Stockholm Sweden\nOttawa Canada Suva Fiji\nOttawa Canada Taipei Taiwan\nOttawa Canada Tallinn Estonia\nOttawa Canada Tashkent Uzbekistan\nOttawa Canada Tbilisi Georgia\nOttawa Canada Tegucigalpa Honduras\nOttawa Canada Tehran Iran\nOttawa Canada Thimphu Bhutan\nOttawa Canada Tirana Albania\nOttawa Canada Tokyo Japan\nOttawa Canada Tripoli Libya\nOttawa Canada Tunis Tunisia\nOttawa Canada Vaduz Liechtenstein\nOttawa Canada Valletta Malta\nOttawa Canada Vienna Austria\nOttawa Canada Vientiane Laos\nOttawa Canada Vilnius Lithuania\nOttawa Canada Warsaw Poland\nOttawa Canada Windhoek Namibia\nOttawa Canada Yerevan Armenia\nOttawa Canada Zagreb Croatia\nOttawa Canada Abuja Nigeria\nOttawa Canada Accra Ghana\nOttawa Canada Algiers Algeria\nOttawa Canada Amman Jordan\nOttawa Canada Ankara Turkey\nOttawa Canada Antananarivo Madagascar\nParamaribo Suriname Paris France\nParamaribo Suriname Podgorica Montenegro\nParamaribo Suriname Quito Ecuador\nParamaribo Suriname Rabat Morocco\nParamaribo Suriname Riga Latvia\nParamaribo Suriname Rome Italy\nParamaribo Suriname Roseau Dominica\nParamaribo Suriname Santiago Chile\nParamaribo Suriname Skopje Macedonia\nParamaribo Suriname Sofia Bulgaria\nParamaribo Suriname Stockholm Sweden\nParamaribo Suriname Suva Fiji\nParamaribo Suriname Taipei Taiwan\nParamaribo Suriname Tallinn Estonia\nParamaribo Suriname Tashkent Uzbekistan\nParamaribo Suriname Tbilisi Georgia\nParamaribo Suriname Tegucigalpa Honduras\nParamaribo Suriname Tehran Iran\nParamaribo Suriname Thimphu Bhutan\nParamaribo Suriname Tirana Albania\nParamaribo Suriname Tokyo Japan\nParamaribo Suriname Tripoli Libya\nParamaribo Suriname Tunis Tunisia\nParamaribo Suriname Vaduz Liechtenstein\nParamaribo Suriname Valletta Malta\nParamaribo Suriname Vienna Austria\nParamaribo Suriname Vientiane Laos\nParamaribo Suriname Vilnius Lithuania\nParamaribo Suriname Warsaw Poland\nParamaribo Suriname Windhoek Namibia\nParamaribo Suriname Yerevan Armenia\nParamaribo Suriname Zagreb Croatia\nParamaribo Suriname Abuja Nigeria\nParamaribo Suriname Accra Ghana\nParamaribo Suriname Algiers Algeria\nParamaribo Suriname Amman Jordan\nParamaribo Suriname Ankara Turkey\nParamaribo Suriname Antananarivo Madagascar\nParamaribo Suriname Apia Samoa\nParis France Podgorica Montenegro\nParis France Quito Ecuador\nParis France Rabat Morocco\nParis France Riga Latvia\nParis France Rome Italy\nParis France Roseau Dominica\nParis France Santiago Chile\nParis France Skopje Macedonia\nParis France Sofia Bulgaria\nParis France Stockholm Sweden\nParis France Suva Fiji\nParis France Taipei Taiwan\nParis France Tallinn Estonia\nParis France Tashkent Uzbekistan\nParis France Tbilisi Georgia\nParis France Tegucigalpa Honduras\nParis France Tehran Iran\nParis France Thimphu Bhutan\nParis France Tirana Albania\nParis France Tokyo Japan\nParis France Tripoli Libya\nParis France Tunis Tunisia\nParis France Vaduz Liechtenstein\nParis France Valletta Malta\nParis France Vienna Austria\nParis France Vientiane Laos\nParis France Vilnius Lithuania\nParis France Warsaw Poland\nParis France Windhoek Namibia\nParis France Yerevan Armenia\nParis France Zagreb Croatia\nParis France Abuja Nigeria\nParis France Accra Ghana\nParis France Algiers Algeria\nParis France Amman Jordan\nParis France Ankara Turkey\nParis France Antananarivo Madagascar\nParis France Apia Samoa\nParis France Ashgabat Turkmenistan\nPodgorica Montenegro Quito Ecuador\nPodgorica Montenegro Rabat Morocco\nPodgorica Montenegro Riga Latvia\nPodgorica Montenegro Rome Italy\nPodgorica Montenegro Roseau Dominica\nPodgorica Montenegro Santiago Chile\nPodgorica Montenegro Skopje Macedonia\nPodgorica Montenegro Sofia Bulgaria\nPodgorica Montenegro Stockholm Sweden\nPodgorica Montenegro Suva Fiji\nPodgorica Montenegro Taipei Taiwan\nPodgorica Montenegro Tallinn Estonia\nPodgorica Montenegro Tashkent Uzbekistan\nPodgorica Montenegro Tbilisi Georgia\nPodgorica Montenegro Tegucigalpa Honduras\nPodgorica Montenegro Tehran Iran\nPodgorica Montenegro Thimphu Bhutan\nPodgorica Montenegro Tirana Albania\nPodgorica Montenegro Tokyo Japan\nPodgorica Montenegro Tripoli Libya\nPodgorica Montenegro Tunis Tunisia\nPodgorica Montenegro Vaduz Liechtenstein\nPodgorica Montenegro Valletta Malta\nPodgorica Montenegro Vienna Austria\nPodgorica Montenegro Vientiane Laos\nPodgorica Montenegro Vilnius Lithuania\nPodgorica Montenegro Warsaw Poland\nPodgorica Montenegro Windhoek Namibia\nPodgorica Montenegro Yerevan Armenia\nPodgorica Montenegro Zagreb Croatia\nPodgorica Montenegro Abuja Nigeria\nPodgorica Montenegro Accra Ghana\nPodgorica Montenegro Algiers Algeria\nPodgorica Montenegro Amman Jordan\nPodgorica Montenegro Ankara Turkey\nPodgorica Montenegro Antananarivo Madagascar\nPodgorica Montenegro Apia Samoa\nPodgorica Montenegro Ashgabat Turkmenistan\nPodgorica Montenegro Asmara Eritrea\nQuito Ecuador Rabat Morocco\nQuito Ecuador Riga Latvia\nQuito Ecuador Rome Italy\nQuito Ecuador Roseau Dominica\nQuito Ecuador Santiago Chile\nQuito Ecuador Skopje Macedonia\nQuito Ecuador Sofia Bulgaria\nQuito Ecuador Stockholm Sweden\nQuito Ecuador Suva Fiji\nQuito Ecuador Taipei Taiwan\nQuito Ecuador Tallinn Estonia\nQuito Ecuador Tashkent Uzbekistan\nQuito Ecuador Tbilisi Georgia\nQuito Ecuador Tegucigalpa Honduras\nQuito Ecuador Tehran Iran\nQuito Ecuador Thimphu Bhutan\nQuito Ecuador Tirana Albania\nQuito Ecuador Tokyo Japan\nQuito Ecuador Tripoli Libya\nQuito Ecuador Tunis Tunisia\nQuito Ecuador Vaduz Liechtenstein\nQuito Ecuador Valletta Malta\nQuito Ecuador Vienna Austria\nQuito Ecuador Vientiane Laos\nQuito Ecuador Vilnius Lithuania\nQuito Ecuador Warsaw Poland\nQuito Ecuador Windhoek Namibia\nQuito Ecuador Yerevan Armenia\nQuito Ecuador Zagreb Croatia\nQuito Ecuador Abuja Nigeria\nQuito Ecuador Accra Ghana\nQuito Ecuador Algiers Algeria\nQuito Ecuador Amman Jordan\nQuito Ecuador Ankara Turkey\nQuito Ecuador Antananarivo Madagascar\nQuito Ecuador Apia Samoa\nQuito Ecuador Ashgabat Turkmenistan\nQuito Ecuador Asmara Eritrea\nQuito Ecuador Astana Kazakhstan\nRabat Morocco Riga Latvia\nRabat Morocco Rome Italy\nRabat Morocco Roseau Dominica\nRabat Morocco Santiago Chile\nRabat Morocco Skopje Macedonia\nRabat Morocco Sofia Bulgaria\nRabat Morocco Stockholm Sweden\nRabat Morocco Suva Fiji\nRabat Morocco Taipei Taiwan\nRabat Morocco Tallinn Estonia\nRabat Morocco Tashkent Uzbekistan\nRabat Morocco Tbilisi Georgia\nRabat Morocco Tegucigalpa Honduras\nRabat Morocco Tehran Iran\nRabat Morocco Thimphu Bhutan\nRabat Morocco Tirana Albania\nRabat Morocco Tokyo Japan\nRabat Morocco Tripoli Libya\nRabat Morocco Tunis Tunisia\nRabat Morocco Vaduz Liechtenstein\nRabat Morocco Valletta Malta\nRabat Morocco Vienna Austria\nRabat Morocco Vientiane Laos\nRabat Morocco Vilnius Lithuania\nRabat Morocco Warsaw Poland\nRabat Morocco Windhoek Namibia\nRabat Morocco Yerevan Armenia\nRabat Morocco Zagreb Croatia\nRabat Morocco Abuja Nigeria\nRabat Morocco Accra Ghana\nRabat Morocco Algiers Algeria\nRabat Morocco Amman Jordan\nRabat Morocco Ankara Turkey\nRabat Morocco Antananarivo Madagascar\nRabat Morocco Apia Samoa\nRabat Morocco Ashgabat Turkmenistan\nRabat Morocco Asmara Eritrea\nRabat Morocco Astana Kazakhstan\nRabat Morocco Athens Greece\nRiga Latvia Rome Italy\nRiga Latvia Roseau Dominica\nRiga Latvia Santiago Chile\nRiga Latvia Skopje Macedonia\nRiga Latvia Sofia Bulgaria\nRiga Latvia Stockholm Sweden\nRiga Latvia Suva Fiji\nRiga Latvia Taipei Taiwan\nRiga Latvia Tallinn Estonia\nRiga Latvia Tashkent Uzbekistan\nRiga Latvia Tbilisi Georgia\nRiga Latvia Tegucigalpa Honduras\nRiga Latvia Tehran Iran\nRiga Latvia Thimphu Bhutan\nRiga Latvia Tirana Albania\nRiga Latvia Tokyo Japan\nRiga Latvia Tripoli Libya\nRiga Latvia Tunis Tunisia\nRiga Latvia Vaduz Liechtenstein\nRiga Latvia Valletta Malta\nRiga Latvia Vienna Austria\nRiga Latvia Vientiane Laos\nRiga Latvia Vilnius Lithuania\nRiga Latvia Warsaw Poland\nRiga Latvia Windhoek Namibia\nRiga Latvia Yerevan Armenia\nRiga Latvia Zagreb Croatia\nRiga Latvia Abuja Nigeria\nRiga Latvia Accra Ghana\nRiga Latvia Algiers Algeria\nRiga Latvia Amman Jordan\nRiga Latvia Ankara Turkey\nRiga Latvia Antananarivo Madagascar\nRiga Latvia Apia Samoa\nRiga Latvia Ashgabat Turkmenistan\nRiga Latvia Asmara Eritrea\nRiga Latvia Astana Kazakhstan\nRiga Latvia Athens Greece\nRiga Latvia Baghdad Iraq\nRome Italy Roseau Dominica\nRome Italy Santiago Chile\nRome Italy Skopje Macedonia\nRome Italy Sofia Bulgaria\nRome Italy Stockholm Sweden\nRome Italy Suva Fiji\nRome Italy Taipei Taiwan\nRome Italy Tallinn Estonia\nRome Italy Tashkent Uzbekistan\nRome Italy Tbilisi Georgia\nRome Italy Tegucigalpa Honduras\nRome Italy Tehran Iran\nRome Italy Thimphu Bhutan\nRome Italy Tirana Albania\nRome Italy Tokyo Japan\nRome Italy Tripoli Libya\nRome Italy Tunis Tunisia\nRome Italy Vaduz Liechtenstein\nRome Italy Valletta Malta\nRome Italy Vienna Austria\nRome Italy Vientiane Laos\nRome Italy Vilnius Lithuania\nRome Italy Warsaw Poland\nRome Italy Windhoek Namibia\nRome Italy Yerevan Armenia\nRome Italy Zagreb Croatia\nRome Italy Abuja Nigeria\nRome Italy Accra Ghana\nRome Italy Algiers Algeria\nRome Italy Amman Jordan\nRome Italy Ankara Turkey\nRome Italy Antananarivo Madagascar\nRome Italy Apia Samoa\nRome Italy Ashgabat Turkmenistan\nRome Italy Asmara Eritrea\nRome Italy Astana Kazakhstan\nRome Italy Athens Greece\nRome Italy Baghdad Iraq\nRome Italy Baku Azerbaijan\nRoseau Dominica Santiago Chile\nRoseau Dominica Skopje Macedonia\nRoseau Dominica Sofia Bulgaria\nRoseau Dominica Stockholm Sweden\nRoseau Dominica Suva Fiji\nRoseau Dominica Taipei Taiwan\nRoseau Dominica Tallinn Estonia\nRoseau Dominica Tashkent Uzbekistan\nRoseau Dominica Tbilisi Georgia\nRoseau Dominica Tegucigalpa Honduras\nRoseau Dominica Tehran Iran\nRoseau Dominica Thimphu Bhutan\nRoseau Dominica Tirana Albania\nRoseau Dominica Tokyo Japan\nRoseau Dominica Tripoli Libya\nRoseau Dominica Tunis Tunisia\nRoseau Dominica Vaduz Liechtenstein\nRoseau Dominica Valletta Malta\nRoseau Dominica Vienna Austria\nRoseau Dominica Vientiane Laos\nRoseau Dominica Vilnius Lithuania\nRoseau Dominica Warsaw Poland\nRoseau Dominica Windhoek Namibia\nRoseau Dominica Yerevan Armenia\nRoseau Dominica Zagreb Croatia\nRoseau Dominica Abuja Nigeria\nRoseau Dominica Accra Ghana\nRoseau Dominica Algiers Algeria\nRoseau Dominica Amman Jordan\nRoseau Dominica Ankara Turkey\nRoseau Dominica Antananarivo Madagascar\nRoseau Dominica Apia Samoa\nRoseau Dominica Ashgabat Turkmenistan\nRoseau Dominica Asmara Eritrea\nRoseau Dominica Astana Kazakhstan\nRoseau Dominica Athens Greece\nRoseau Dominica Baghdad Iraq\nRoseau Dominica Baku Azerbaijan\nRoseau Dominica Bamako Mali\nSantiago Chile Skopje Macedonia\nSantiago Chile Sofia Bulgaria\nSantiago Chile Stockholm Sweden\nSantiago Chile Suva Fiji\nSantiago Chile Taipei Taiwan\nSantiago Chile Tallinn Estonia\nSantiago Chile Tashkent Uzbekistan\nSantiago Chile Tbilisi Georgia\nSantiago Chile Tegucigalpa Honduras\nSantiago Chile Tehran Iran\nSantiago Chile Thimphu Bhutan\nSantiago Chile Tirana Albania\nSantiago Chile Tokyo Japan\nSantiago Chile Tripoli Libya\nSantiago Chile Tunis Tunisia\nSantiago Chile Vaduz Liechtenstein\nSantiago Chile Valletta Malta\nSantiago Chile Vienna Austria\nSantiago Chile Vientiane Laos\nSantiago Chile Vilnius Lithuania\nSantiago Chile Warsaw Poland\nSantiago Chile Windhoek Namibia\nSantiago Chile Yerevan Armenia\nSantiago Chile Zagreb Croatia\nSantiago Chile Abuja Nigeria\nSantiago Chile Accra Ghana\nSantiago Chile Algiers Algeria\nSantiago Chile Amman Jordan\nSantiago Chile Ankara Turkey\nSantiago Chile Antananarivo Madagascar\nSantiago Chile Apia Samoa\nSantiago Chile Ashgabat Turkmenistan\nSantiago Chile Asmara Eritrea\nSantiago Chile Astana Kazakhstan\nSantiago Chile Athens Greece\nSantiago Chile Baghdad Iraq\nSantiago Chile Baku Azerbaijan\nSantiago Chile Bamako Mali\nSantiago Chile Bangkok Thailand\nSkopje Macedonia Sofia Bulgaria\nSkopje Macedonia Stockholm Sweden\nSkopje Macedonia Suva Fiji\nSkopje Macedonia Taipei Taiwan\nSkopje Macedonia Tallinn Estonia\nSkopje Macedonia Tashkent Uzbekistan\nSkopje Macedonia Tbilisi Georgia\nSkopje Macedonia Tegucigalpa Honduras\nSkopje Macedonia Tehran Iran\nSkopje Macedonia Thimphu Bhutan\nSkopje Macedonia Tirana Albania\nSkopje Macedonia Tokyo Japan\nSkopje Macedonia Tripoli Libya\nSkopje Macedonia Tunis Tunisia\nSkopje Macedonia Vaduz Liechtenstein\nSkopje Macedonia Valletta Malta\nSkopje Macedonia Vienna Austria\nSkopje Macedonia Vientiane Laos\nSkopje Macedonia Vilnius Lithuania\nSkopje Macedonia Warsaw Poland\nSkopje Macedonia Windhoek Namibia\nSkopje Macedonia Yerevan Armenia\nSkopje Macedonia Zagreb Croatia\nSkopje Macedonia Abuja Nigeria\nSkopje Macedonia Accra Ghana\nSkopje Macedonia Algiers Algeria\nSkopje Macedonia Amman Jordan\nSkopje Macedonia Ankara Turkey\nSkopje Macedonia Antananarivo Madagascar\nSkopje Macedonia Apia Samoa\nSkopje Macedonia Ashgabat Turkmenistan\nSkopje Macedonia Asmara Eritrea\nSkopje Macedonia Astana Kazakhstan\nSkopje Macedonia Athens Greece\nSkopje Macedonia Baghdad Iraq\nSkopje Macedonia Baku Azerbaijan\nSkopje Macedonia Bamako Mali\nSkopje Macedonia Bangkok Thailand\nSkopje Macedonia Banjul Gambia\nSofia Bulgaria Stockholm Sweden\nSofia Bulgaria Suva Fiji\nSofia Bulgaria Taipei Taiwan\nSofia Bulgaria Tallinn Estonia\nSofia Bulgaria Tashkent Uzbekistan\nSofia Bulgaria Tbilisi Georgia\nSofia Bulgaria Tegucigalpa Honduras\nSofia Bulgaria Tehran Iran\nSofia Bulgaria Thimphu Bhutan\nSofia Bulgaria Tirana Albania\nSofia Bulgaria Tokyo Japan\nSofia Bulgaria Tripoli Libya\nSofia Bulgaria Tunis Tunisia\nSofia Bulgaria Vaduz Liechtenstein\nSofia Bulgaria Valletta Malta\nSofia Bulgaria Vienna Austria\nSofia Bulgaria Vientiane Laos\nSofia Bulgaria Vilnius Lithuania\nSofia Bulgaria Warsaw Poland\nSofia Bulgaria Windhoek Namibia\nSofia Bulgaria Yerevan Armenia\nSofia Bulgaria Zagreb Croatia\nSofia Bulgaria Abuja Nigeria\nSofia Bulgaria Accra Ghana\nSofia Bulgaria Algiers Algeria\nSofia Bulgaria Amman Jordan\nSofia Bulgaria Ankara Turkey\nSofia Bulgaria Antananarivo Madagascar\nSofia Bulgaria Apia Samoa\nSofia Bulgaria Ashgabat Turkmenistan\nSofia Bulgaria Asmara Eritrea\nSofia Bulgaria Astana Kazakhstan\nSofia Bulgaria Athens Greece\nSofia Bulgaria Baghdad Iraq\nSofia Bulgaria Baku Azerbaijan\nSofia Bulgaria Bamako Mali\nSofia Bulgaria Bangkok Thailand\nSofia Bulgaria Banjul Gambia\nSofia Bulgaria Beijing China\nStockholm Sweden Suva Fiji\nStockholm Sweden Taipei Taiwan\nStockholm Sweden Tallinn Estonia\nStockholm Sweden Tashkent Uzbekistan\nStockholm Sweden Tbilisi Georgia\nStockholm Sweden Tegucigalpa Honduras\nStockholm Sweden Tehran Iran\nStockholm Sweden Thimphu Bhutan\nStockholm Sweden Tirana Albania\nStockholm Sweden Tokyo Japan\nStockholm Sweden Tripoli Libya\nStockholm Sweden Tunis Tunisia\nStockholm Sweden Vaduz Liechtenstein\nStockholm Sweden Valletta Malta\nStockholm Sweden Vienna Austria\nStockholm Sweden Vientiane Laos\nStockholm Sweden Vilnius Lithuania\nStockholm Sweden Warsaw Poland\nStockholm Sweden Windhoek Namibia\nStockholm Sweden Yerevan Armenia\nStockholm Sweden Zagreb Croatia\nStockholm Sweden Abuja Nigeria\nStockholm Sweden Accra Ghana\nStockholm Sweden Algiers Algeria\nStockholm Sweden Amman Jordan\nStockholm Sweden Ankara Turkey\nStockholm Sweden Antananarivo Madagascar\nStockholm Sweden Apia Samoa\nStockholm Sweden Ashgabat Turkmenistan\nStockholm Sweden Asmara Eritrea\nStockholm Sweden Astana Kazakhstan\nStockholm Sweden Athens Greece\nStockholm Sweden Baghdad Iraq\nStockholm Sweden Baku Azerbaijan\nStockholm Sweden Bamako Mali\nStockholm Sweden Bangkok Thailand\nStockholm Sweden Banjul Gambia\nStockholm Sweden Beijing China\nStockholm Sweden Beirut Lebanon\nSuva Fiji Taipei Taiwan\nSuva Fiji Tallinn Estonia\nSuva Fiji Tashkent Uzbekistan\nSuva Fiji Tbilisi Georgia\nSuva Fiji Tegucigalpa Honduras\nSuva Fiji Tehran Iran\nSuva Fiji Thimphu Bhutan\nSuva Fiji Tirana Albania\nSuva Fiji Tokyo Japan\nSuva Fiji Tripoli Libya\nSuva Fiji Tunis Tunisia\nSuva Fiji Vaduz Liechtenstein\nSuva Fiji Valletta Malta\nSuva Fiji Vienna Austria\nSuva Fiji Vientiane Laos\nSuva Fiji Vilnius Lithuania\nSuva Fiji Warsaw Poland\nSuva Fiji Windhoek Namibia\nSuva Fiji Yerevan Armenia\nSuva Fiji Zagreb Croatia\nSuva Fiji Abuja Nigeria\nSuva Fiji Accra Ghana\nSuva Fiji Algiers Algeria\nSuva Fiji Amman Jordan\nSuva Fiji Ankara Turkey\nSuva Fiji Antananarivo Madagascar\nSuva Fiji Apia Samoa\nSuva Fiji Ashgabat Turkmenistan\nSuva Fiji Asmara Eritrea\nSuva Fiji Astana Kazakhstan\nSuva Fiji Athens Greece\nSuva Fiji Baghdad Iraq\nSuva Fiji Baku Azerbaijan\nSuva Fiji Bamako Mali\nSuva Fiji Bangkok Thailand\nSuva Fiji Banjul Gambia\nSuva Fiji Beijing China\nSuva Fiji Beirut Lebanon\nSuva Fiji Belgrade Serbia\nTaipei Taiwan Tallinn Estonia\nTaipei Taiwan Tashkent Uzbekistan\nTaipei Taiwan Tbilisi Georgia\nTaipei Taiwan Tegucigalpa Honduras\nTaipei Taiwan Tehran Iran\nTaipei Taiwan Thimphu Bhutan\nTaipei Taiwan Tirana Albania\nTaipei Taiwan Tokyo Japan\nTaipei Taiwan Tripoli Libya\nTaipei Taiwan Tunis Tunisia\nTaipei Taiwan Vaduz Liechtenstein\nTaipei Taiwan Valletta Malta\nTaipei Taiwan Vienna Austria\nTaipei Taiwan Vientiane Laos\nTaipei Taiwan Vilnius Lithuania\nTaipei Taiwan Warsaw Poland\nTaipei Taiwan Windhoek Namibia\nTaipei Taiwan Yerevan Armenia\nTaipei Taiwan Zagreb Croatia\nTaipei Taiwan Abuja Nigeria\nTaipei Taiwan Accra Ghana\nTaipei Taiwan Algiers Algeria\nTaipei Taiwan Amman Jordan\nTaipei Taiwan Ankara Turkey\nTaipei Taiwan Antananarivo Madagascar\nTaipei Taiwan Apia Samoa\nTaipei Taiwan Ashgabat Turkmenistan\nTaipei Taiwan Asmara Eritrea\nTaipei Taiwan Astana Kazakhstan\nTaipei Taiwan Athens Greece\nTaipei Taiwan Baghdad Iraq\nTaipei Taiwan Baku Azerbaijan\nTaipei Taiwan Bamako Mali\nTaipei Taiwan Bangkok Thailand\nTaipei Taiwan Banjul Gambia\nTaipei Taiwan Beijing China\nTaipei Taiwan Beirut Lebanon\nTaipei Taiwan Belgrade Serbia\nTaipei Taiwan Belmopan Belize\nTallinn Estonia Tashkent Uzbekistan\nTallinn Estonia Tbilisi Georgia\nTallinn Estonia Tegucigalpa Honduras\nTallinn Estonia Tehran Iran\nTallinn Estonia Thimphu Bhutan\nTallinn Estonia Tirana Albania\nTallinn Estonia Tokyo Japan\nTallinn Estonia Tripoli Libya\nTallinn Estonia Tunis Tunisia\nTallinn Estonia Vaduz Liechtenstein\nTallinn Estonia Valletta Malta\nTallinn Estonia Vienna Austria\nTallinn Estonia Vientiane Laos\nTallinn Estonia Vilnius Lithuania\nTallinn Estonia Warsaw Poland\nTallinn Estonia Windhoek Namibia\nTallinn Estonia Yerevan Armenia\nTallinn Estonia Zagreb Croatia\nTallinn Estonia Abuja Nigeria\nTallinn Estonia Accra Ghana\nTallinn Estonia Algiers Algeria\nTallinn Estonia Amman Jordan\nTallinn Estonia Ankara Turkey\nTallinn Estonia Antananarivo Madagascar\nTallinn Estonia Apia Samoa\nTallinn Estonia Ashgabat Turkmenistan\nTallinn Estonia Asmara Eritrea\nTallinn Estonia Astana Kazakhstan\nTallinn Estonia Athens Greece\nTallinn Estonia Baghdad Iraq\nTallinn Estonia Baku Azerbaijan\nTallinn Estonia Bamako Mali\nTallinn Estonia Bangkok Thailand\nTallinn Estonia Banjul Gambia\nTallinn Estonia Beijing China\nTallinn Estonia Beirut Lebanon\nTallinn Estonia Belgrade Serbia\nTallinn Estonia Belmopan Belize\nTallinn Estonia Berlin Germany\nTashkent Uzbekistan Tbilisi Georgia\nTashkent Uzbekistan Tegucigalpa Honduras\nTashkent Uzbekistan Tehran Iran\nTashkent Uzbekistan Thimphu Bhutan\nTashkent Uzbekistan Tirana Albania\nTashkent Uzbekistan Tokyo Japan\nTashkent Uzbekistan Tripoli Libya\nTashkent Uzbekistan Tunis Tunisia\nTashkent Uzbekistan Vaduz Liechtenstein\nTashkent Uzbekistan Valletta Malta\nTashkent Uzbekistan Vienna Austria\nTashkent Uzbekistan Vientiane Laos\nTashkent Uzbekistan Vilnius Lithuania\nTashkent Uzbekistan Warsaw Poland\nTashkent Uzbekistan Windhoek Namibia\nTashkent Uzbekistan Yerevan Armenia\nTashkent Uzbekistan Zagreb Croatia\nTashkent Uzbekistan Abuja Nigeria\nTashkent Uzbekistan Accra Ghana\nTashkent Uzbekistan Algiers Algeria\nTashkent Uzbekistan Amman Jordan\nTashkent Uzbekistan Ankara Turkey\nTashkent Uzbekistan Antananarivo Madagascar\nTashkent Uzbekistan Apia Samoa\nTashkent Uzbekistan Ashgabat Turkmenistan\nTashkent Uzbekistan Asmara Eritrea\nTashkent Uzbekistan Astana Kazakhstan\nTashkent Uzbekistan Athens Greece\nTashkent Uzbekistan Baghdad Iraq\nTashkent Uzbekistan Baku Azerbaijan\nTashkent Uzbekistan Bamako Mali\nTashkent Uzbekistan Bangkok Thailand\nTashkent Uzbekistan Banjul Gambia\nTashkent Uzbekistan Beijing China\nTashkent Uzbekistan Beirut Lebanon\nTashkent Uzbekistan Belgrade Serbia\nTashkent Uzbekistan Belmopan Belize\nTashkent Uzbekistan Berlin Germany\nTashkent Uzbekistan Bern Switzerland\nTbilisi Georgia Tegucigalpa Honduras\nTbilisi Georgia Tehran Iran\nTbilisi Georgia Thimphu Bhutan\nTbilisi Georgia Tirana Albania\nTbilisi Georgia Tokyo Japan\nTbilisi Georgia Tripoli Libya\nTbilisi Georgia Tunis Tunisia\nTbilisi Georgia Vaduz Liechtenstein\nTbilisi Georgia Valletta Malta\nTbilisi Georgia Vienna Austria\nTbilisi Georgia Vientiane Laos\nTbilisi Georgia Vilnius Lithuania\nTbilisi Georgia Warsaw Poland\nTbilisi Georgia Windhoek Namibia\nTbilisi Georgia Yerevan Armenia\nTbilisi Georgia Zagreb Croatia\nTbilisi Georgia Abuja Nigeria\nTbilisi Georgia Accra Ghana\nTbilisi Georgia Algiers Algeria\nTbilisi Georgia Amman Jordan\nTbilisi Georgia Ankara Turkey\nTbilisi Georgia Antananarivo Madagascar\nTbilisi Georgia Apia Samoa\nTbilisi Georgia Ashgabat Turkmenistan\nTbilisi Georgia Asmara Eritrea\nTbilisi Georgia Astana Kazakhstan\nTbilisi Georgia Athens Greece\nTbilisi Georgia Baghdad Iraq\nTbilisi Georgia Baku Azerbaijan\nTbilisi Georgia Bamako Mali\nTbilisi Georgia Bangkok Thailand\nTbilisi Georgia Banjul Gambia\nTbilisi Georgia Beijing China\nTbilisi Georgia Beirut Lebanon\nTbilisi Georgia Belgrade Serbia\nTbilisi Georgia Belmopan Belize\nTbilisi Georgia Berlin Germany\nTbilisi Georgia Bern Switzerland\nTbilisi Georgia Bishkek Kyrgyzstan\nTegucigalpa Honduras Tehran Iran\nTegucigalpa Honduras Thimphu Bhutan\nTegucigalpa Honduras Tirana Albania\nTegucigalpa Honduras Tokyo Japan\nTegucigalpa Honduras Tripoli Libya\nTegucigalpa Honduras Tunis Tunisia\nTegucigalpa Honduras Vaduz Liechtenstein\nTegucigalpa Honduras Valletta Malta\nTegucigalpa Honduras Vienna Austria\nTegucigalpa Honduras Vientiane Laos\nTegucigalpa Honduras Vilnius Lithuania\nTegucigalpa Honduras Warsaw Poland\nTegucigalpa Honduras Windhoek Namibia\nTegucigalpa Honduras Yerevan Armenia\nTegucigalpa Honduras Zagreb Croatia\nTegucigalpa Honduras Abuja Nigeria\nTegucigalpa Honduras Accra Ghana\nTegucigalpa Honduras Algiers Algeria\nTegucigalpa Honduras Amman Jordan\nTegucigalpa Honduras Ankara Turkey\nTegucigalpa Honduras Antananarivo Madagascar\nTegucigalpa Honduras Apia Samoa\nTegucigalpa Honduras Ashgabat Turkmenistan\nTegucigalpa Honduras Asmara Eritrea\nTegucigalpa Honduras Astana Kazakhstan\nTegucigalpa Honduras Athens Greece\nTegucigalpa Honduras Baghdad Iraq\nTegucigalpa Honduras Baku Azerbaijan\nTegucigalpa Honduras Bamako Mali\nTegucigalpa Honduras Bangkok Thailand\nTegucigalpa Honduras Banjul Gambia\nTegucigalpa Honduras Beijing China\nTegucigalpa Honduras Beirut Lebanon\nTegucigalpa Honduras Belgrade Serbia\nTegucigalpa Honduras Belmopan Belize\nTegucigalpa Honduras Berlin Germany\nTegucigalpa Honduras Bern Switzerland\nTegucigalpa Honduras Bishkek Kyrgyzstan\nTegucigalpa Honduras Bratislava Slovakia\nTehran Iran Thimphu Bhutan\nTehran Iran Tirana Albania\nTehran Iran Tokyo Japan\nTehran Iran Tripoli Libya\nTehran Iran Tunis Tunisia\nTehran Iran Vaduz Liechtenstein\nTehran Iran Valletta Malta\nTehran Iran Vienna Austria\nTehran Iran Vientiane Laos\nTehran Iran Vilnius Lithuania\nTehran Iran Warsaw Poland\nTehran Iran Windhoek Namibia\nTehran Iran Yerevan Armenia\nTehran Iran Zagreb Croatia\nTehran Iran Abuja Nigeria\nTehran Iran Accra Ghana\nTehran Iran Algiers Algeria\nTehran Iran Amman Jordan\nTehran Iran Ankara Turkey\nTehran Iran Antananarivo Madagascar\nTehran Iran Apia Samoa\nTehran Iran Ashgabat Turkmenistan\nTehran Iran Asmara Eritrea\nTehran Iran Astana Kazakhstan\nTehran Iran Athens Greece\nTehran Iran Baghdad Iraq\nTehran Iran Baku Azerbaijan\nTehran Iran Bamako Mali\nTehran Iran Bangkok Thailand\nTehran Iran Banjul Gambia\nTehran Iran Beijing China\nTehran Iran Beirut Lebanon\nTehran Iran Belgrade Serbia\nTehran Iran Belmopan Belize\nTehran Iran Berlin Germany\nTehran Iran Bern Switzerland\nTehran Iran Bishkek Kyrgyzstan\nTehran Iran Bratislava Slovakia\nTehran Iran Brussels Belgium\nThimphu Bhutan Tirana Albania\nThimphu Bhutan Tokyo Japan\nThimphu Bhutan Tripoli Libya\nThimphu Bhutan Tunis Tunisia\nThimphu Bhutan Vaduz Liechtenstein\nThimphu Bhutan Valletta Malta\nThimphu Bhutan Vienna Austria\nThimphu Bhutan Vientiane Laos\nThimphu Bhutan Vilnius Lithuania\nThimphu Bhutan Warsaw Poland\nThimphu Bhutan Windhoek Namibia\nThimphu Bhutan Yerevan Armenia\nThimphu Bhutan Zagreb Croatia\nThimphu Bhutan Abuja Nigeria\nThimphu Bhutan Accra Ghana\nThimphu Bhutan Algiers Algeria\nThimphu Bhutan Amman Jordan\nThimphu Bhutan Ankara Turkey\nThimphu Bhutan Antananarivo Madagascar\nThimphu Bhutan Apia Samoa\nThimphu Bhutan Ashgabat Turkmenistan\nThimphu Bhutan Asmara Eritrea\nThimphu Bhutan Astana Kazakhstan\nThimphu Bhutan Athens Greece\nThimphu Bhutan Baghdad Iraq\nThimphu Bhutan Baku Azerbaijan\nThimphu Bhutan Bamako Mali\nThimphu Bhutan Bangkok Thailand\nThimphu Bhutan Banjul Gambia\nThimphu Bhutan Beijing China\nThimphu Bhutan Beirut Lebanon\nThimphu Bhutan Belgrade Serbia\nThimphu Bhutan Belmopan Belize\nThimphu Bhutan Berlin Germany\nThimphu Bhutan Bern Switzerland\nThimphu Bhutan Bishkek Kyrgyzstan\nThimphu Bhutan Bratislava Slovakia\nThimphu Bhutan Brussels Belgium\nThimphu Bhutan Bucharest Romania\nTirana Albania Tokyo Japan\nTirana Albania Tripoli Libya\nTirana Albania Tunis Tunisia\nTirana Albania Vaduz Liechtenstein\nTirana Albania Valletta Malta\nTirana Albania Vienna Austria\nTirana Albania Vientiane Laos\nTirana Albania Vilnius Lithuania\nTirana Albania Warsaw Poland\nTirana Albania Windhoek Namibia\nTirana Albania Yerevan Armenia\nTirana Albania Zagreb Croatia\nTirana Albania Abuja Nigeria\nTirana Albania Accra Ghana\nTirana Albania Algiers Algeria\nTirana Albania Amman Jordan\nTirana Albania Ankara Turkey\nTirana Albania Antananarivo Madagascar\nTirana Albania Apia Samoa\nTirana Albania Ashgabat Turkmenistan\nTirana Albania Asmara Eritrea\nTirana Albania Astana Kazakhstan\nTirana Albania Athens Greece\nTirana Albania Baghdad Iraq\nTirana Albania Baku Azerbaijan\nTirana Albania Bamako Mali\nTirana Albania Bangkok Thailand\nTirana Albania Banjul Gambia\nTirana Albania Beijing China\nTirana Albania Beirut Lebanon\nTirana Albania Belgrade Serbia\nTirana Albania Belmopan Belize\nTirana Albania Berlin Germany\nTirana Albania Bern Switzerland\nTirana Albania Bishkek Kyrgyzstan\nTirana Albania Bratislava Slovakia\nTirana Albania Brussels Belgium\nTirana Albania Bucharest Romania\nTirana Albania Budapest Hungary\nTokyo Japan Tripoli Libya\nTokyo Japan Tunis Tunisia\nTokyo Japan Vaduz Liechtenstein\nTokyo Japan Valletta Malta\nTokyo Japan Vienna Austria\nTokyo Japan Vientiane Laos\nTokyo Japan Vilnius Lithuania\nTokyo Japan Warsaw Poland\nTokyo Japan Windhoek Namibia\nTokyo Japan Yerevan Armenia\nTokyo Japan Zagreb Croatia\nTokyo Japan Abuja Nigeria\nTokyo Japan Accra Ghana\nTokyo Japan Algiers Algeria\nTokyo Japan Amman Jordan\nTokyo Japan Ankara Turkey\nTokyo Japan Antananarivo Madagascar\nTokyo Japan Apia Samoa\nTokyo Japan Ashgabat Turkmenistan\nTokyo Japan Asmara Eritrea\nTokyo Japan Astana Kazakhstan\nTokyo Japan Athens Greece\nTokyo Japan Baghdad Iraq\nTokyo Japan Baku Azerbaijan\nTokyo Japan Bamako Mali\nTokyo Japan Bangkok Thailand\nTokyo Japan Banjul Gambia\nTokyo Japan Beijing China\nTokyo Japan Beirut Lebanon\nTokyo Japan Belgrade Serbia\nTokyo Japan Belmopan Belize\nTokyo Japan Berlin Germany\nTokyo Japan Bern Switzerland\nTokyo Japan Bishkek Kyrgyzstan\nTokyo Japan Bratislava Slovakia\nTokyo Japan Brussels Belgium\nTokyo Japan Bucharest Romania\nTokyo Japan Budapest Hungary\nTokyo Japan Bujumbura Burundi\nTripoli Libya Tunis Tunisia\nTripoli Libya Vaduz Liechtenstein\nTripoli Libya Valletta Malta\nTripoli Libya Vienna Austria\nTripoli Libya Vientiane Laos\nTripoli Libya Vilnius Lithuania\nTripoli Libya Warsaw Poland\nTripoli Libya Windhoek Namibia\nTripoli Libya Yerevan Armenia\nTripoli Libya Zagreb Croatia\nTripoli Libya Abuja Nigeria\nTripoli Libya Accra Ghana\nTripoli Libya Algiers Algeria\nTripoli Libya Amman Jordan\nTripoli Libya Ankara Turkey\nTripoli Libya Antananarivo Madagascar\nTripoli Libya Apia Samoa\nTripoli Libya Ashgabat Turkmenistan\nTripoli Libya Asmara Eritrea\nTripoli Libya Astana Kazakhstan\nTripoli Libya Athens Greece\nTripoli Libya Baghdad Iraq\nTripoli Libya Baku Azerbaijan\nTripoli Libya Bamako Mali\nTripoli Libya Bangkok Thailand\nTripoli Libya Banjul Gambia\nTripoli Libya Beijing China\nTripoli Libya Beirut Lebanon\nTripoli Libya Belgrade Serbia\nTripoli Libya Belmopan Belize\nTripoli Libya Berlin Germany\nTripoli Libya Bern Switzerland\nTripoli Libya Bishkek Kyrgyzstan\nTripoli Libya Bratislava Slovakia\nTripoli Libya Brussels Belgium\nTripoli Libya Bucharest Romania\nTripoli Libya Budapest Hungary\nTripoli Libya Bujumbura Burundi\nTripoli Libya Cairo Egypt\nTunis Tunisia Vaduz Liechtenstein\nTunis Tunisia Valletta Malta\nTunis Tunisia Vienna Austria\nTunis Tunisia Vientiane Laos\nTunis Tunisia Vilnius Lithuania\nTunis Tunisia Warsaw Poland\nTunis Tunisia Windhoek Namibia\nTunis Tunisia Yerevan Armenia\nTunis Tunisia Zagreb Croatia\nTunis Tunisia Abuja Nigeria\nTunis Tunisia Accra Ghana\nTunis Tunisia Algiers Algeria\nTunis Tunisia Amman Jordan\nTunis Tunisia Ankara Turkey\nTunis Tunisia Antananarivo Madagascar\nTunis Tunisia Apia Samoa\nTunis Tunisia Ashgabat Turkmenistan\nTunis Tunisia Asmara Eritrea\nTunis Tunisia Astana Kazakhstan\nTunis Tunisia Athens Greece\nTunis Tunisia Baghdad Iraq\nTunis Tunisia Baku Azerbaijan\nTunis Tunisia Bamako Mali\nTunis Tunisia Bangkok Thailand\nTunis Tunisia Banjul Gambia\nTunis Tunisia Beijing China\nTunis Tunisia Beirut Lebanon\nTunis Tunisia Belgrade Serbia\nTunis Tunisia Belmopan Belize\nTunis Tunisia Berlin Germany\nTunis Tunisia Bern Switzerland\nTunis Tunisia Bishkek Kyrgyzstan\nTunis Tunisia Bratislava Slovakia\nTunis Tunisia Brussels Belgium\nTunis Tunisia Bucharest Romania\nTunis Tunisia Budapest Hungary\nTunis Tunisia Bujumbura Burundi\nTunis Tunisia Cairo Egypt\nTunis Tunisia Canberra Australia\nVaduz Liechtenstein Valletta Malta\nVaduz Liechtenstein Vienna Austria\nVaduz Liechtenstein Vientiane Laos\nVaduz Liechtenstein Vilnius Lithuania\nVaduz Liechtenstein Warsaw Poland\nVaduz Liechtenstein Windhoek Namibia\nVaduz Liechtenstein Yerevan Armenia\nVaduz Liechtenstein Zagreb Croatia\nVaduz Liechtenstein Abuja Nigeria\nVaduz Liechtenstein Accra Ghana\nVaduz Liechtenstein Algiers Algeria\nVaduz Liechtenstein Amman Jordan\nVaduz Liechtenstein Ankara Turkey\nVaduz Liechtenstein Antananarivo Madagascar\nVaduz Liechtenstein Apia Samoa\nVaduz Liechtenstein Ashgabat Turkmenistan\nVaduz Liechtenstein Asmara Eritrea\nVaduz Liechtenstein Astana Kazakhstan\nVaduz Liechtenstein Athens Greece\nVaduz Liechtenstein Baghdad Iraq\nVaduz Liechtenstein Baku Azerbaijan\nVaduz Liechtenstein Bamako Mali\nVaduz Liechtenstein Bangkok Thailand\nVaduz Liechtenstein Banjul Gambia\nVaduz Liechtenstein Beijing China\nVaduz Liechtenstein Beirut Lebanon\nVaduz Liechtenstein Belgrade Serbia\nVaduz Liechtenstein Belmopan Belize\nVaduz Liechtenstein Berlin Germany\nVaduz Liechtenstein Bern Switzerland\nVaduz Liechtenstein Bishkek Kyrgyzstan\nVaduz Liechtenstein Bratislava Slovakia\nVaduz Liechtenstein Brussels Belgium\nVaduz Liechtenstein Bucharest Romania\nVaduz Liechtenstein Budapest Hungary\nVaduz Liechtenstein Bujumbura Burundi\nVaduz Liechtenstein Cairo Egypt\nVaduz Liechtenstein Canberra Australia\nVaduz Liechtenstein Caracas Venezuela\nValletta Malta Vienna Austria\nValletta Malta Vientiane Laos\nValletta Malta Vilnius Lithuania\nValletta Malta Warsaw Poland\nValletta Malta Windhoek Namibia\nValletta Malta Yerevan Armenia\nValletta Malta Zagreb Croatia\nValletta Malta Abuja Nigeria\nValletta Malta Accra Ghana\nValletta Malta Algiers Algeria\nValletta Malta Amman Jordan\nValletta Malta Ankara Turkey\nValletta Malta Antananarivo Madagascar\nValletta Malta Apia Samoa\nValletta Malta Ashgabat Turkmenistan\nValletta Malta Asmara Eritrea\nValletta Malta Astana Kazakhstan\nValletta Malta Athens Greece\nValletta Malta Baghdad Iraq\nValletta Malta Baku Azerbaijan\nValletta Malta Bamako Mali\nValletta Malta Bangkok Thailand\nValletta Malta Banjul Gambia\nValletta Malta Beijing China\nValletta Malta Beirut Lebanon\nValletta Malta Belgrade Serbia\nValletta Malta Belmopan Belize\nValletta Malta Berlin Germany\nValletta Malta Bern Switzerland\nValletta Malta Bishkek Kyrgyzstan\nValletta Malta Bratislava Slovakia\nValletta Malta Brussels Belgium\nValletta Malta Bucharest Romania\nValletta Malta Budapest Hungary\nValletta Malta Bujumbura Burundi\nValletta Malta Cairo Egypt\nValletta Malta Canberra Australia\nValletta Malta Caracas Venezuela\nValletta Malta Chisinau Moldova\nVienna Austria Vientiane Laos\nVienna Austria Vilnius Lithuania\nVienna Austria Warsaw Poland\nVienna Austria Windhoek Namibia\nVienna Austria Yerevan Armenia\nVienna Austria Zagreb Croatia\nVienna Austria Abuja Nigeria\nVienna Austria Accra Ghana\nVienna Austria Algiers Algeria\nVienna Austria Amman Jordan\nVienna Austria Ankara Turkey\nVienna Austria Antananarivo Madagascar\nVienna Austria Apia Samoa\nVienna Austria Ashgabat Turkmenistan\nVienna Austria Asmara Eritrea\nVienna Austria Astana Kazakhstan\nVienna Austria Athens Greece\nVienna Austria Baghdad Iraq\nVienna Austria Baku Azerbaijan\nVienna Austria Bamako Mali\nVienna Austria Bangkok Thailand\nVienna Austria Banjul Gambia\nVienna Austria Beijing China\nVienna Austria Beirut Lebanon\nVienna Austria Belgrade Serbia\nVienna Austria Belmopan Belize\nVienna Austria Berlin Germany\nVienna Austria Bern Switzerland\nVienna Austria Bishkek Kyrgyzstan\nVienna Austria Bratislava Slovakia\nVienna Austria Brussels Belgium\nVienna Austria Bucharest Romania\nVienna Austria Budapest Hungary\nVienna Austria Bujumbura Burundi\nVienna Austria Cairo Egypt\nVienna Austria Canberra Australia\nVienna Austria Caracas Venezuela\nVienna Austria Chisinau Moldova\nVienna Austria Conakry Guinea\nVientiane Laos Vilnius Lithuania\nVientiane Laos Warsaw Poland\nVientiane Laos Windhoek Namibia\nVientiane Laos Yerevan Armenia\nVientiane Laos Zagreb Croatia\nVientiane Laos Abuja Nigeria\nVientiane Laos Accra Ghana\nVientiane Laos Algiers Algeria\nVientiane Laos Amman Jordan\nVientiane Laos Ankara Turkey\nVientiane Laos Antananarivo Madagascar\nVientiane Laos Apia Samoa\nVientiane Laos Ashgabat Turkmenistan\nVientiane Laos Asmara Eritrea\nVientiane Laos Astana Kazakhstan\nVientiane Laos Athens Greece\nVientiane Laos Baghdad Iraq\nVientiane Laos Baku Azerbaijan\nVientiane Laos Bamako Mali\nVientiane Laos Bangkok Thailand\nVientiane Laos Banjul Gambia\nVientiane Laos Beijing China\nVientiane Laos Beirut Lebanon\nVientiane Laos Belgrade Serbia\nVientiane Laos Belmopan Belize\nVientiane Laos Berlin Germany\nVientiane Laos Bern Switzerland\nVientiane Laos Bishkek Kyrgyzstan\nVientiane Laos Bratislava Slovakia\nVientiane Laos Brussels Belgium\nVientiane Laos Bucharest Romania\nVientiane Laos Budapest Hungary\nVientiane Laos Bujumbura Burundi\nVientiane Laos Cairo Egypt\nVientiane Laos Canberra Australia\nVientiane Laos Caracas Venezuela\nVientiane Laos Chisinau Moldova\nVientiane Laos Conakry Guinea\nVientiane Laos Copenhagen Denmark\nVilnius Lithuania Warsaw Poland\nVilnius Lithuania Windhoek Namibia\nVilnius Lithuania Yerevan Armenia\nVilnius Lithuania Zagreb Croatia\nVilnius Lithuania Abuja Nigeria\nVilnius Lithuania Accra Ghana\nVilnius Lithuania Algiers Algeria\nVilnius Lithuania Amman Jordan\nVilnius Lithuania Ankara Turkey\nVilnius Lithuania Antananarivo Madagascar\nVilnius Lithuania Apia Samoa\nVilnius Lithuania Ashgabat Turkmenistan\nVilnius Lithuania Asmara Eritrea\nVilnius Lithuania Astana Kazakhstan\nVilnius Lithuania Athens Greece\nVilnius Lithuania Baghdad Iraq\nVilnius Lithuania Baku Azerbaijan\nVilnius Lithuania Bamako Mali\nVilnius Lithuania Bangkok Thailand\nVilnius Lithuania Banjul Gambia\nVilnius Lithuania Beijing China\nVilnius Lithuania Beirut Lebanon\nVilnius Lithuania Belgrade Serbia\nVilnius Lithuania Belmopan Belize\nVilnius Lithuania Berlin Germany\nVilnius Lithuania Bern Switzerland\nVilnius Lithuania Bishkek Kyrgyzstan\nVilnius Lithuania Bratislava Slovakia\nVilnius Lithuania Brussels Belgium\nVilnius Lithuania Bucharest Romania\nVilnius Lithuania Budapest Hungary\nVilnius Lithuania Bujumbura Burundi\nVilnius Lithuania Cairo Egypt\nVilnius Lithuania Canberra Australia\nVilnius Lithuania Caracas Venezuela\nVilnius Lithuania Chisinau Moldova\nVilnius Lithuania Conakry Guinea\nVilnius Lithuania Copenhagen Denmark\nVilnius Lithuania Dakar Senegal\nWarsaw Poland Windhoek Namibia\nWarsaw Poland Yerevan Armenia\nWarsaw Poland Zagreb Croatia\nWarsaw Poland Abuja Nigeria\nWarsaw Poland Accra Ghana\nWarsaw Poland Algiers Algeria\nWarsaw Poland Amman Jordan\nWarsaw Poland Ankara Turkey\nWarsaw Poland Antananarivo Madagascar\nWarsaw Poland Apia Samoa\nWarsaw Poland Ashgabat Turkmenistan\nWarsaw Poland Asmara Eritrea\nWarsaw Poland Astana Kazakhstan\nWarsaw Poland Athens Greece\nWarsaw Poland Baghdad Iraq\nWarsaw Poland Baku Azerbaijan\nWarsaw Poland Bamako Mali\nWarsaw Poland Bangkok Thailand\nWarsaw Poland Banjul Gambia\nWarsaw Poland Beijing China\nWarsaw Poland Beirut Lebanon\nWarsaw Poland Belgrade Serbia\nWarsaw Poland Belmopan Belize\nWarsaw Poland Berlin Germany\nWarsaw Poland Bern Switzerland\nWarsaw Poland Bishkek Kyrgyzstan\nWarsaw Poland Bratislava Slovakia\nWarsaw Poland Brussels Belgium\nWarsaw Poland Bucharest Romania\nWarsaw Poland Budapest Hungary\nWarsaw Poland Bujumbura Burundi\nWarsaw Poland Cairo Egypt\nWarsaw Poland Canberra Australia\nWarsaw Poland Caracas Venezuela\nWarsaw Poland Chisinau Moldova\nWarsaw Poland Conakry Guinea\nWarsaw Poland Copenhagen Denmark\nWarsaw Poland Dakar Senegal\nWarsaw Poland Damascus Syria\nWindhoek Namibia Yerevan Armenia\nWindhoek Namibia Zagreb Croatia\nWindhoek Namibia Abuja Nigeria\nWindhoek Namibia Accra Ghana\nWindhoek Namibia Algiers Algeria\nWindhoek Namibia Amman Jordan\nWindhoek Namibia Ankara Turkey\nWindhoek Namibia Antananarivo Madagascar\nWindhoek Namibia Apia Samoa\nWindhoek Namibia Ashgabat Turkmenistan\nWindhoek Namibia Asmara Eritrea\nWindhoek Namibia Astana Kazakhstan\nWindhoek Namibia Athens Greece\nWindhoek Namibia Baghdad Iraq\nWindhoek Namibia Baku Azerbaijan\nWindhoek Namibia Bamako Mali\nWindhoek Namibia Bangkok Thailand\nWindhoek Namibia Banjul Gambia\nWindhoek Namibia Beijing China\nWindhoek Namibia Beirut Lebanon\nWindhoek Namibia Belgrade Serbia\nWindhoek Namibia Belmopan Belize\nWindhoek Namibia Berlin Germany\nWindhoek Namibia Bern Switzerland\nWindhoek Namibia Bishkek Kyrgyzstan\nWindhoek Namibia Bratislava Slovakia\nWindhoek Namibia Brussels Belgium\nWindhoek Namibia Bucharest Romania\nWindhoek Namibia Budapest Hungary\nWindhoek Namibia Bujumbura Burundi\nWindhoek Namibia Cairo Egypt\nWindhoek Namibia Canberra Australia\nWindhoek Namibia Caracas Venezuela\nWindhoek Namibia Chisinau Moldova\nWindhoek Namibia Conakry Guinea\nWindhoek Namibia Copenhagen Denmark\nWindhoek Namibia Dakar Senegal\nWindhoek Namibia Damascus Syria\nWindhoek Namibia Dhaka Bangladesh\nYerevan Armenia Zagreb Croatia\nYerevan Armenia Abuja Nigeria\nYerevan Armenia Accra Ghana\nYerevan Armenia Algiers Algeria\nYerevan Armenia Amman Jordan\nYerevan Armenia Ankara Turkey\nYerevan Armenia Antananarivo Madagascar\nYerevan Armenia Apia Samoa\nYerevan Armenia Ashgabat Turkmenistan\nYerevan Armenia Asmara Eritrea\nYerevan Armenia Astana Kazakhstan\nYerevan Armenia Athens Greece\nYerevan Armenia Baghdad Iraq\nYerevan Armenia Baku Azerbaijan\nYerevan Armenia Bamako Mali\nYerevan Armenia Bangkok Thailand\nYerevan Armenia Banjul Gambia\nYerevan Armenia Beijing China\nYerevan Armenia Beirut Lebanon\nYerevan Armenia Belgrade Serbia\nYerevan Armenia Belmopan Belize\nYerevan Armenia Berlin Germany\nYerevan Armenia Bern Switzerland\nYerevan Armenia Bishkek Kyrgyzstan\nYerevan Armenia Bratislava Slovakia\nYerevan Armenia Brussels Belgium\nYerevan Armenia Bucharest Romania\nYerevan Armenia Budapest Hungary\nYerevan Armenia Bujumbura Burundi\nYerevan Armenia Cairo Egypt\nYerevan Armenia Canberra Australia\nYerevan Armenia Caracas Venezuela\nYerevan Armenia Chisinau Moldova\nYerevan Armenia Conakry Guinea\nYerevan Armenia Copenhagen Denmark\nYerevan Armenia Dakar Senegal\nYerevan Armenia Damascus Syria\nYerevan Armenia Dhaka Bangladesh\nYerevan Armenia Doha Qatar\nZagreb Croatia Abuja Nigeria\nZagreb Croatia Accra Ghana\nZagreb Croatia Algiers Algeria\nZagreb Croatia Amman Jordan\nZagreb Croatia Ankara Turkey\nZagreb Croatia Antananarivo Madagascar\nZagreb Croatia Apia Samoa\nZagreb Croatia Ashgabat Turkmenistan\nZagreb Croatia Asmara Eritrea\nZagreb Croatia Astana Kazakhstan\nZagreb Croatia Athens Greece\nZagreb Croatia Baghdad Iraq\nZagreb Croatia Baku Azerbaijan\nZagreb Croatia Bamako Mali\nZagreb Croatia Bangkok Thailand\nZagreb Croatia Banjul Gambia\nZagreb Croatia Beijing China\nZagreb Croatia Beirut Lebanon\nZagreb Croatia Belgrade Serbia\nZagreb Croatia Belmopan Belize\nZagreb Croatia Berlin Germany\nZagreb Croatia Bern Switzerland\nZagreb Croatia Bishkek Kyrgyzstan\nZagreb Croatia Bratislava Slovakia\nZagreb Croatia Brussels Belgium\nZagreb Croatia Bucharest Romania\nZagreb Croatia Budapest Hungary\nZagreb Croatia Bujumbura Burundi\nZagreb Croatia Cairo Egypt\nZagreb Croatia Canberra Australia\nZagreb Croatia Caracas Venezuela\nZagreb Croatia Chisinau Moldova\nZagreb Croatia Conakry Guinea\nZagreb Croatia Copenhagen Denmark\nZagreb Croatia Dakar Senegal\nZagreb Croatia Damascus Syria\nZagreb Croatia Dhaka Bangladesh\nZagreb Croatia Doha Qatar\nZagreb Croatia Dublin Ireland\nAlgeria dinar Angola kwanza\nAlgeria dinar Argentina peso\nAlgeria dinar Armenia dram\nAlgeria dinar Brazil real\nAlgeria dinar Bulgaria lev\nAlgeria dinar Cambodia riel\nAlgeria dinar Canada dollar\nAlgeria dinar Croatia kuna\nAlgeria dinar Denmark krone\nAlgeria dinar Europe euro\nAlgeria dinar Hungary forint\nAlgeria dinar India rupee\nAlgeria dinar Iran rial\nAlgeria dinar Japan yen\nAlgeria dinar Korea won\nAlgeria dinar Latvia lats\nAlgeria dinar Lithuania litas\nAlgeria dinar Macedonia denar\nAlgeria dinar Malaysia ringgit\nAlgeria dinar Mexico peso\nAlgeria dinar Nigeria naira\nAlgeria dinar Poland zloty\nAlgeria dinar Romania leu\nAlgeria dinar Russia ruble\nAlgeria dinar Sweden krona\nAlgeria dinar Thailand baht\nAlgeria dinar Ukraine hryvnia\nAlgeria dinar USA dollar\nAlgeria dinar Vietnam dong\nAngola kwanza Argentina peso\nAngola kwanza Armenia dram\nAngola kwanza Brazil real\nAngola kwanza Bulgaria lev\nAngola kwanza Cambodia riel\nAngola kwanza Canada dollar\nAngola kwanza Croatia kuna\nAngola kwanza Denmark krone\nAngola kwanza Europe euro\nAngola kwanza Hungary forint\nAngola kwanza India rupee\nAngola kwanza Iran rial\nAngola kwanza Japan yen\nAngola kwanza Korea won\nAngola kwanza Latvia lats\nAngola kwanza Lithuania litas\nAngola kwanza Macedonia denar\nAngola kwanza Malaysia ringgit\nAngola kwanza Mexico peso\nAngola kwanza Nigeria naira\nAngola kwanza Poland zloty\nAngola kwanza Romania leu\nAngola kwanza Russia ruble\nAngola kwanza Sweden krona\nAngola kwanza Thailand baht\nAngola kwanza Ukraine hryvnia\nAngola kwanza USA dollar\nAngola kwanza Vietnam dong\nAngola kwanza Algeria dinar\nArgentina peso Armenia dram\nArgentina peso Brazil real\nArgentina peso Bulgaria lev\nArgentina peso Cambodia riel\nArgentina peso Canada dollar\nArgentina peso Croatia kuna\nArgentina peso Denmark krone\nArgentina peso Europe euro\nArgentina peso Hungary forint\nArgentina peso India rupee\nArgentina peso Iran rial\nArgentina peso Japan yen\nArgentina peso Korea won\nArgentina peso Latvia lats\nArgentina peso Lithuania litas\nArgentina peso Macedonia denar\nArgentina peso Malaysia ringgit\nArgentina peso Nigeria naira\nArgentina peso Poland zloty\nArgentina peso Romania leu\nArgentina peso Russia ruble\nArgentina peso Sweden krona\nArgentina peso Thailand baht\nArgentina peso Ukraine hryvnia\nArgentina peso USA dollar\nArgentina peso Vietnam dong\nArgentina peso Algeria dinar\nArgentina peso Angola kwanza\nArmenia dram Brazil real\nArmenia dram Bulgaria lev\nArmenia dram Cambodia riel\nArmenia dram Canada dollar\nArmenia dram Croatia kuna\nArmenia dram Denmark krone\nArmenia dram Europe euro\nArmenia dram Hungary forint\nArmenia dram India rupee\nArmenia dram Iran rial\nArmenia dram Japan yen\nArmenia dram Korea won\nArmenia dram Latvia lats\nArmenia dram Lithuania litas\nArmenia dram Macedonia denar\nArmenia dram Malaysia ringgit\nArmenia dram Mexico peso\nArmenia dram Nigeria naira\nArmenia dram Poland zloty\nArmenia dram Romania leu\nArmenia dram Russia ruble\nArmenia dram Sweden krona\nArmenia dram Thailand baht\nArmenia dram Ukraine hryvnia\nArmenia dram USA dollar\nArmenia dram Vietnam dong\nArmenia dram Algeria dinar\nArmenia dram Angola kwanza\nArmenia dram Argentina peso\nBrazil real Bulgaria lev\nBrazil real Cambodia riel\nBrazil real Canada dollar\nBrazil real Croatia kuna\nBrazil real Denmark krone\nBrazil real Europe euro\nBrazil real Hungary forint\nBrazil real India rupee\nBrazil real Iran rial\nBrazil real Japan yen\nBrazil real Korea won\nBrazil real Latvia lats\nBrazil real Lithuania litas\nBrazil real Macedonia denar\nBrazil real Malaysia ringgit\nBrazil real Mexico peso\nBrazil real Nigeria naira\nBrazil real Poland zloty\nBrazil real Romania leu\nBrazil real Russia ruble\nBrazil real Sweden krona\nBrazil real Thailand baht\nBrazil real Ukraine hryvnia\nBrazil real USA dollar\nBrazil real Vietnam dong\nBrazil real Algeria dinar\nBrazil real Angola kwanza\nBrazil real Argentina peso\nBrazil real Armenia dram\nBulgaria lev Cambodia riel\nBulgaria lev Canada dollar\nBulgaria lev Croatia kuna\nBulgaria lev Denmark krone\nBulgaria lev Europe euro\nBulgaria lev Hungary forint\nBulgaria lev India rupee\nBulgaria lev Iran rial\nBulgaria lev Japan yen\nBulgaria lev Korea won\nBulgaria lev Latvia lats\nBulgaria lev Lithuania litas\nBulgaria lev Macedonia denar\nBulgaria lev Malaysia ringgit\nBulgaria lev Mexico peso\nBulgaria lev Nigeria naira\nBulgaria lev Poland zloty\nBulgaria lev Romania leu\nBulgaria lev Russia ruble\nBulgaria lev Sweden krona\nBulgaria lev Thailand baht\nBulgaria lev Ukraine hryvnia\nBulgaria lev USA dollar\nBulgaria lev Vietnam dong\nBulgaria lev Algeria dinar\nBulgaria lev Angola kwanza\nBulgaria lev Argentina peso\nBulgaria lev Armenia dram\nBulgaria lev Brazil real\nCambodia riel Canada dollar\nCambodia riel Croatia kuna\nCambodia riel Denmark krone\nCambodia riel Europe euro\nCambodia riel Hungary forint\nCambodia riel India rupee\nCambodia riel Iran rial\nCambodia riel Japan yen\nCambodia riel Korea won\nCambodia riel Latvia lats\nCambodia riel Lithuania litas\nCambodia riel Macedonia denar\nCambodia riel Malaysia ringgit\nCambodia riel Mexico peso\nCambodia riel Nigeria naira\nCambodia riel Poland zloty\nCambodia riel Romania leu\nCambodia riel Russia ruble\nCambodia riel Sweden krona\nCambodia riel Thailand baht\nCambodia riel Ukraine hryvnia\nCambodia riel USA dollar\nCambodia riel Vietnam dong\nCambodia riel Algeria dinar\nCambodia riel Angola kwanza\nCambodia riel Argentina peso\nCambodia riel Armenia dram\nCambodia riel Brazil real\nCambodia riel Bulgaria lev\nCanada dollar Croatia kuna\nCanada dollar Denmark krone\nCanada dollar Europe euro\nCanada dollar Hungary forint\nCanada dollar India rupee\nCanada dollar Iran rial\nCanada dollar Japan yen\nCanada dollar Korea won\nCanada dollar Latvia lats\nCanada dollar Lithuania litas\nCanada dollar Macedonia denar\nCanada dollar Malaysia ringgit\nCanada dollar Mexico peso\nCanada dollar Nigeria naira\nCanada dollar Poland zloty\nCanada dollar Romania leu\nCanada dollar Russia ruble\nCanada dollar Sweden krona\nCanada dollar Thailand baht\nCanada dollar Ukraine hryvnia\nCanada dollar Vietnam dong\nCanada dollar Algeria dinar\nCanada dollar Angola kwanza\nCanada dollar Argentina peso\nCanada dollar Armenia dram\nCanada dollar Brazil real\nCanada dollar Bulgaria lev\nCanada dollar Cambodia riel\nCroatia kuna Denmark krone\nCroatia kuna Europe euro\nCroatia kuna Hungary forint\nCroatia kuna India rupee\nCroatia kuna Iran rial\nCroatia kuna Japan yen\nCroatia kuna Korea won\nCroatia kuna Latvia lats\nCroatia kuna Lithuania litas\nCroatia kuna Macedonia denar\nCroatia kuna Malaysia ringgit\nCroatia kuna Mexico peso\nCroatia kuna Nigeria naira\nCroatia kuna Poland zloty\nCroatia kuna Romania leu\nCroatia kuna Russia ruble\nCroatia kuna Sweden krona\nCroatia kuna Thailand baht\nCroatia kuna Ukraine hryvnia\nCroatia kuna USA dollar\nCroatia kuna Vietnam dong\nCroatia kuna Algeria dinar\nCroatia kuna Angola kwanza\nCroatia kuna Argentina peso\nCroatia kuna Armenia dram\nCroatia kuna Brazil real\nCroatia kuna Bulgaria lev\nCroatia kuna Cambodia riel\nCroatia kuna Canada dollar\nDenmark krone Europe euro\nDenmark krone Hungary forint\nDenmark krone India rupee\nDenmark krone Iran rial\nDenmark krone Japan yen\nDenmark krone Korea won\nDenmark krone Latvia lats\nDenmark krone Lithuania litas\nDenmark krone Macedonia denar\nDenmark krone Malaysia ringgit\nDenmark krone Mexico peso\nDenmark krone Nigeria naira\nDenmark krone Poland zloty\nDenmark krone Romania leu\nDenmark krone Russia ruble\nDenmark krone Sweden krona\nDenmark krone Thailand baht\nDenmark krone Ukraine hryvnia\nDenmark krone USA dollar\nDenmark krone Vietnam dong\nDenmark krone Algeria dinar\nDenmark krone Angola kwanza\nDenmark krone Argentina peso\nDenmark krone Armenia dram\nDenmark krone Brazil real\nDenmark krone Bulgaria lev\nDenmark krone Cambodia riel\nDenmark krone Canada dollar\nDenmark krone Croatia kuna\nEurope euro Hungary forint\nEurope euro India rupee\nEurope euro Iran rial\nEurope euro Japan yen\nEurope euro Korea won\nEurope euro Latvia lats\nEurope euro Lithuania litas\nEurope euro Macedonia denar\nEurope euro Malaysia ringgit\nEurope euro Mexico peso\nEurope euro Nigeria naira\nEurope euro Poland zloty\nEurope euro Romania leu\nEurope euro Russia ruble\nEurope euro Sweden krona\nEurope euro Thailand baht\nEurope euro Ukraine hryvnia\nEurope euro USA dollar\nEurope euro Vietnam dong\nEurope euro Algeria dinar\nEurope euro Angola kwanza\nEurope euro Argentina peso\nEurope euro Armenia dram\nEurope euro Brazil real\nEurope euro Bulgaria lev\nEurope euro Cambodia riel\nEurope euro Canada dollar\nEurope euro Croatia kuna\nEurope euro Denmark krone\nHungary forint India rupee\nHungary forint Iran rial\nHungary forint Japan yen\nHungary forint Korea won\nHungary forint Latvia lats\nHungary forint Lithuania litas\nHungary forint Macedonia denar\nHungary forint Malaysia ringgit\nHungary forint Mexico peso\nHungary forint Nigeria naira\nHungary forint Poland zloty\nHungary forint Romania leu\nHungary forint Russia ruble\nHungary forint Sweden krona\nHungary forint Thailand baht\nHungary forint Ukraine hryvnia\nHungary forint USA dollar\nHungary forint Vietnam dong\nHungary forint Algeria dinar\nHungary forint Angola kwanza\nHungary forint Argentina peso\nHungary forint Armenia dram\nHungary forint Brazil real\nHungary forint Bulgaria lev\nHungary forint Cambodia riel\nHungary forint Canada dollar\nHungary forint Croatia kuna\nHungary forint Denmark krone\nHungary forint Europe euro\nIndia rupee Iran rial\nIndia rupee Japan yen\nIndia rupee Korea won\nIndia rupee Latvia lats\nIndia rupee Lithuania litas\nIndia rupee Macedonia denar\nIndia rupee Malaysia ringgit\nIndia rupee Mexico peso\nIndia rupee Nigeria naira\nIndia rupee Poland zloty\nIndia rupee Romania leu\nIndia rupee Russia ruble\nIndia rupee Sweden krona\nIndia rupee Thailand baht\nIndia rupee Ukraine hryvnia\nIndia rupee USA dollar\nIndia rupee Vietnam dong\nIndia rupee Algeria dinar\nIndia rupee Angola kwanza\nIndia rupee Argentina peso\nIndia rupee Armenia dram\nIndia rupee Brazil real\nIndia rupee Bulgaria lev\nIndia rupee Cambodia riel\nIndia rupee Canada dollar\nIndia rupee Croatia kuna\nIndia rupee Denmark krone\nIndia rupee Europe euro\nIndia rupee Hungary forint\nIran rial Japan yen\nIran rial Korea won\nIran rial Latvia lats\nIran rial Lithuania litas\nIran rial Macedonia denar\nIran rial Malaysia ringgit\nIran rial Mexico peso\nIran rial Nigeria naira\nIran rial Poland zloty\nIran rial Romania leu\nIran rial Russia ruble\nIran rial Sweden krona\nIran rial Thailand baht\nIran rial Ukraine hryvnia\nIran rial USA dollar\nIran rial Vietnam dong\nIran rial Algeria dinar\nIran rial Angola kwanza\nIran rial Argentina peso\nIran rial Armenia dram\nIran rial Brazil real\nIran rial Bulgaria lev\nIran rial Cambodia riel\nIran rial Canada dollar\nIran rial Croatia kuna\nIran rial Denmark krone\nIran rial Europe euro\nIran rial Hungary forint\nIran rial India rupee\nJapan yen Korea won\nJapan yen Latvia lats\nJapan yen Lithuania litas\nJapan yen Macedonia denar\nJapan yen Malaysia ringgit\nJapan yen Mexico peso\nJapan yen Nigeria naira\nJapan yen Poland zloty\nJapan yen Romania leu\nJapan yen Russia ruble\nJapan yen Sweden krona\nJapan yen Thailand baht\nJapan yen Ukraine hryvnia\nJapan yen USA dollar\nJapan yen Vietnam dong\nJapan yen Algeria dinar\nJapan yen Angola kwanza\nJapan yen Argentina peso\nJapan yen Armenia dram\nJapan yen Brazil real\nJapan yen Bulgaria lev\nJapan yen Cambodia riel\nJapan yen Canada dollar\nJapan yen Croatia kuna\nJapan yen Denmark krone\nJapan yen Europe euro\nJapan yen Hungary forint\nJapan yen India rupee\nJapan yen Iran rial\nKorea won Latvia lats\nKorea won Lithuania litas\nKorea won Macedonia denar\nKorea won Malaysia ringgit\nKorea won Mexico peso\nKorea won Nigeria naira\nKorea won Poland zloty\nKorea won Romania leu\nKorea won Russia ruble\nKorea won Sweden krona\nKorea won Thailand baht\nKorea won Ukraine hryvnia\nKorea won USA dollar\nKorea won Vietnam dong\nKorea won Algeria dinar\nKorea won Angola kwanza\nKorea won Argentina peso\nKorea won Armenia dram\nKorea won Brazil real\nKorea won Bulgaria lev\nKorea won Cambodia riel\nKorea won Canada dollar\nKorea won Croatia kuna\nKorea won Denmark krone\nKorea won Europe euro\nKorea won Hungary forint\nKorea won India rupee\nKorea won Iran rial\nKorea won Japan yen\nLatvia lats Lithuania litas\nLatvia lats Macedonia denar\nLatvia lats Malaysia ringgit\nLatvia lats Mexico peso\nLatvia lats Nigeria naira\nLatvia lats Poland zloty\nLatvia lats Romania leu\nLatvia lats Russia ruble\nLatvia lats Sweden krona\nLatvia lats Thailand baht\nLatvia lats Ukraine hryvnia\nLatvia lats USA dollar\nLatvia lats Vietnam dong\nLatvia lats Algeria dinar\nLatvia lats Angola kwanza\nLatvia lats Argentina peso\nLatvia lats Armenia dram\nLatvia lats Brazil real\nLatvia lats Bulgaria lev\nLatvia lats Cambodia riel\nLatvia lats Canada dollar\nLatvia lats Croatia kuna\nLatvia lats Denmark krone\nLatvia lats Europe euro\nLatvia lats Hungary forint\nLatvia lats India rupee\nLatvia lats Iran rial\nLatvia lats Japan yen\nLatvia lats Korea won\nLithuania litas Macedonia denar\nLithuania litas Malaysia ringgit\nLithuania litas Mexico peso\nLithuania litas Nigeria naira\nLithuania litas Poland zloty\nLithuania litas Romania leu\nLithuania litas Russia ruble\nLithuania litas Sweden krona\nLithuania litas Thailand baht\nLithuania litas Ukraine hryvnia\nLithuania litas USA dollar\nLithuania litas Vietnam dong\nLithuania litas Algeria dinar\nLithuania litas Angola kwanza\nLithuania litas Argentina peso\nLithuania litas Armenia dram\nLithuania litas Brazil real\nLithuania litas Bulgaria lev\nLithuania litas Cambodia riel\nLithuania litas Canada dollar\nLithuania litas Croatia kuna\nLithuania litas Denmark krone\nLithuania litas Europe euro\nLithuania litas Hungary forint\nLithuania litas India rupee\nLithuania litas Iran rial\nLithuania litas Japan yen\nLithuania litas Korea won\nLithuania litas Latvia lats\nMacedonia denar Malaysia ringgit\nMacedonia denar Mexico peso\nMacedonia denar Nigeria naira\nMacedonia denar Poland zloty\nMacedonia denar Romania leu\nMacedonia denar Russia ruble\nMacedonia denar Sweden krona\nMacedonia denar Thailand baht\nMacedonia denar Ukraine hryvnia\nMacedonia denar USA dollar\nMacedonia denar Vietnam dong\nMacedonia denar Algeria dinar\nMacedonia denar Angola kwanza\nMacedonia denar Argentina peso\nMacedonia denar Armenia dram\nMacedonia denar Brazil real\nMacedonia denar Bulgaria lev\nMacedonia denar Cambodia riel\nMacedonia denar Canada dollar\nMacedonia denar Croatia kuna\nMacedonia denar Denmark krone\nMacedonia denar Europe euro\nMacedonia denar Hungary forint\nMacedonia denar India rupee\nMacedonia denar Iran rial\nMacedonia denar Japan yen\nMacedonia denar Korea won\nMacedonia denar Latvia lats\nMacedonia denar Lithuania litas\nMalaysia ringgit Mexico peso\nMalaysia ringgit Nigeria naira\nMalaysia ringgit Poland zloty\nMalaysia ringgit Romania leu\nMalaysia ringgit Russia ruble\nMalaysia ringgit Sweden krona\nMalaysia ringgit Thailand baht\nMalaysia ringgit Ukraine hryvnia\nMalaysia ringgit USA dollar\nMalaysia ringgit Vietnam dong\nMalaysia ringgit Algeria dinar\nMalaysia ringgit Angola kwanza\nMalaysia ringgit Argentina peso\nMalaysia ringgit Armenia dram\nMalaysia ringgit Brazil real\nMalaysia ringgit Bulgaria lev\nMalaysia ringgit Cambodia riel\nMalaysia ringgit Canada dollar\nMalaysia ringgit Croatia kuna\nMalaysia ringgit Denmark krone\nMalaysia ringgit Europe euro\nMalaysia ringgit Hungary forint\nMalaysia ringgit India rupee\nMalaysia ringgit Iran rial\nMalaysia ringgit Japan yen\nMalaysia ringgit Korea won\nMalaysia ringgit Latvia lats\nMalaysia ringgit Lithuania litas\nMalaysia ringgit Macedonia denar\nMexico peso Nigeria naira\nMexico peso Poland zloty\nMexico peso Romania leu\nMexico peso Russia ruble\nMexico peso Sweden krona\nMexico peso Thailand baht\nMexico peso Ukraine hryvnia\nMexico peso USA dollar\nMexico peso Vietnam dong\nMexico peso Algeria dinar\nMexico peso Angola kwanza\nMexico peso Armenia dram\nMexico peso Brazil real\nMexico peso Bulgaria lev\nMexico peso Cambodia riel\nMexico peso Canada dollar\nMexico peso Croatia kuna\nMexico peso Denmark krone\nMexico peso Europe euro\nMexico peso Hungary forint\nMexico peso India rupee\nMexico peso Iran rial\nMexico peso Japan yen\nMexico peso Korea won\nMexico peso Latvia lats\nMexico peso Lithuania litas\nMexico peso Macedonia denar\nMexico peso Malaysia ringgit\nNigeria naira Poland zloty\nNigeria naira Romania leu\nNigeria naira Russia ruble\nNigeria naira Sweden krona\nNigeria naira Thailand baht\nNigeria naira Ukraine hryvnia\nNigeria naira USA dollar\nNigeria naira Vietnam dong\nNigeria naira Algeria dinar\nNigeria naira Angola kwanza\nNigeria naira Argentina peso\nNigeria naira Armenia dram\nNigeria naira Brazil real\nNigeria naira Bulgaria lev\nNigeria naira Cambodia riel\nNigeria naira Canada dollar\nNigeria naira Croatia kuna\nNigeria naira Denmark krone\nNigeria naira Europe euro\nNigeria naira Hungary forint\nNigeria naira India rupee\nNigeria naira Iran rial\nNigeria naira Japan yen\nNigeria naira Korea won\nNigeria naira Latvia lats\nNigeria naira Lithuania litas\nNigeria naira Macedonia denar\nNigeria naira Malaysia ringgit\nNigeria naira Mexico peso\nPoland zloty Romania leu\nPoland zloty Russia ruble\nPoland zloty Sweden krona\nPoland zloty Thailand baht\nPoland zloty Ukraine hryvnia\nPoland zloty USA dollar\nPoland zloty Vietnam dong\nPoland zloty Algeria dinar\nPoland zloty Angola kwanza\nPoland zloty Argentina peso\nPoland zloty Armenia dram\nPoland zloty Brazil real\nPoland zloty Bulgaria lev\nPoland zloty Cambodia riel\nPoland zloty Canada dollar\nPoland zloty Croatia kuna\nPoland zloty Denmark krone\nPoland zloty Europe euro\nPoland zloty Hungary forint\nPoland zloty India rupee\nPoland zloty Iran rial\nPoland zloty Japan yen\nPoland zloty Korea won\nPoland zloty Latvia lats\nPoland zloty Lithuania litas\nPoland zloty Macedonia denar\nPoland zloty Malaysia ringgit\nPoland zloty Mexico peso\nPoland zloty Nigeria naira\nRomania leu Russia ruble\nRomania leu Sweden krona\nRomania leu Thailand baht\nRomania leu Ukraine hryvnia\nRomania leu USA dollar\nRomania leu Vietnam dong\nRomania leu Algeria dinar\nRomania leu Angola kwanza\nRomania leu Argentina peso\nRomania leu Armenia dram\nRomania leu Brazil real\nRomania leu Bulgaria lev\nRomania leu Cambodia riel\nRomania leu Canada dollar\nRomania leu Croatia kuna\nRomania leu Denmark krone\nRomania leu Europe euro\nRomania leu Hungary forint\nRomania leu India rupee\nRomania leu Iran rial\nRomania leu Japan yen\nRomania leu Korea won\nRomania leu Latvia lats\nRomania leu Lithuania litas\nRomania leu Macedonia denar\nRomania leu Malaysia ringgit\nRomania leu Mexico peso\nRomania leu Nigeria naira\nRomania leu Poland zloty\nRussia ruble Sweden krona\nRussia ruble Thailand baht\nRussia ruble Ukraine hryvnia\nRussia ruble USA dollar\nRussia ruble Vietnam dong\nRussia ruble Algeria dinar\nRussia ruble Angola kwanza\nRussia ruble Argentina peso\nRussia ruble Armenia dram\nRussia ruble Brazil real\nRussia ruble Bulgaria lev\nRussia ruble Cambodia riel\nRussia ruble Canada dollar\nRussia ruble Croatia kuna\nRussia ruble Denmark krone\nRussia ruble Europe euro\nRussia ruble Hungary forint\nRussia ruble India rupee\nRussia ruble Iran rial\nRussia ruble Japan yen\nRussia ruble Korea won\nRussia ruble Latvia lats\nRussia ruble Lithuania litas\nRussia ruble Macedonia denar\nRussia ruble Malaysia ringgit\nRussia ruble Mexico peso\nRussia ruble Nigeria naira\nRussia ruble Poland zloty\nRussia ruble Romania leu\nSweden krona Thailand baht\nSweden krona Ukraine hryvnia\nSweden krona USA dollar\nSweden krona Vietnam dong\nSweden krona Algeria dinar\nSweden krona Angola kwanza\nSweden krona Argentina peso\nSweden krona Armenia dram\nSweden krona Brazil real\nSweden krona Bulgaria lev\nSweden krona Cambodia riel\nSweden krona Canada dollar\nSweden krona Croatia kuna\nSweden krona Denmark krone\nSweden krona Europe euro\nSweden krona Hungary forint\nSweden krona India rupee\nSweden krona Iran rial\nSweden krona Japan yen\nSweden krona Korea won\nSweden krona Latvia lats\nSweden krona Lithuania litas\nSweden krona Macedonia denar\nSweden krona Malaysia ringgit\nSweden krona Mexico peso\nSweden krona Nigeria naira\nSweden krona Poland zloty\nSweden krona Romania leu\nSweden krona Russia ruble\nThailand baht Ukraine hryvnia\nThailand baht USA dollar\nThailand baht Vietnam dong\nThailand baht Algeria dinar\nThailand baht Angola kwanza\nThailand baht Argentina peso\nThailand baht Armenia dram\nThailand baht Brazil real\nThailand baht Bulgaria lev\nThailand baht Cambodia riel\nThailand baht Canada dollar\nThailand baht Croatia kuna\nThailand baht Denmark krone\nThailand baht Europe euro\nThailand baht Hungary forint\nThailand baht India rupee\nThailand baht Iran rial\nThailand baht Japan yen\nThailand baht Korea won\nThailand baht Latvia lats\nThailand baht Lithuania litas\nThailand baht Macedonia denar\nThailand baht Malaysia ringgit\nThailand baht Mexico peso\nThailand baht Nigeria naira\nThailand baht Poland zloty\nThailand baht Romania leu\nThailand baht Russia ruble\nThailand baht Sweden krona\nUkraine hryvnia USA dollar\nUkraine hryvnia Vietnam dong\nUkraine hryvnia Algeria dinar\nUkraine hryvnia Angola kwanza\nUkraine hryvnia Argentina peso\nUkraine hryvnia Armenia dram\nUkraine hryvnia Brazil real\nUkraine hryvnia Bulgaria lev\nUkraine hryvnia Cambodia riel\nUkraine hryvnia Canada dollar\nUkraine hryvnia Croatia kuna\nUkraine hryvnia Denmark krone\nUkraine hryvnia Europe euro\nUkraine hryvnia Hungary forint\nUkraine hryvnia India rupee\nUkraine hryvnia Iran rial\nUkraine hryvnia Japan yen\nUkraine hryvnia Korea won\nUkraine hryvnia Latvia lats\nUkraine hryvnia Lithuania litas\nUkraine hryvnia Macedonia denar\nUkraine hryvnia Malaysia ringgit\nUkraine hryvnia Mexico peso\nUkraine hryvnia Nigeria naira\nUkraine hryvnia Poland zloty\nUkraine hryvnia Romania leu\nUkraine hryvnia Russia ruble\nUkraine hryvnia Sweden krona\nUkraine hryvnia Thailand baht\nUSA dollar Vietnam dong\nUSA dollar Algeria dinar\nUSA dollar Angola kwanza\nUSA dollar Argentina peso\nUSA dollar Armenia dram\nUSA dollar Brazil real\nUSA dollar Bulgaria lev\nUSA dollar Cambodia riel\nUSA dollar Croatia kuna\nUSA dollar Denmark krone\nUSA dollar Europe euro\nUSA dollar Hungary forint\nUSA dollar India rupee\nUSA dollar Iran rial\nUSA dollar Japan yen\nUSA dollar Korea won\nUSA dollar Latvia lats\nUSA dollar Lithuania litas\nUSA dollar Macedonia denar\nUSA dollar Malaysia ringgit\nUSA dollar Mexico peso\nUSA dollar Nigeria naira\nUSA dollar Poland zloty\nUSA dollar Romania leu\nUSA dollar Russia ruble\nUSA dollar Sweden krona\nUSA dollar Thailand baht\nUSA dollar Ukraine hryvnia\nVietnam dong Algeria dinar\nVietnam dong Angola kwanza\nVietnam dong Argentina peso\nVietnam dong Armenia dram\nVietnam dong Brazil real\nVietnam dong Bulgaria lev\nVietnam dong Cambodia riel\nVietnam dong Canada dollar\nVietnam dong Croatia kuna\nVietnam dong Denmark krone\nVietnam dong Europe euro\nVietnam dong Hungary forint\nVietnam dong India rupee\nVietnam dong Iran rial\nVietnam dong Japan yen\nVietnam dong Korea won\nVietnam dong Latvia lats\nVietnam dong Lithuania litas\nVietnam dong Macedonia denar\nVietnam dong Malaysia ringgit\nVietnam dong Mexico peso\nVietnam dong Nigeria naira\nVietnam dong Poland zloty\nVietnam dong Romania leu\nVietnam dong Russia ruble\nVietnam dong Sweden krona\nVietnam dong Thailand baht\nVietnam dong Ukraine hryvnia\nVietnam dong USA dollar\nChicago Illinois Houston Texas\nChicago Illinois Philadelphia Pennsylvania\nChicago Illinois Phoenix Arizona\nChicago Illinois Dallas Texas\nChicago Illinois Jacksonville Florida\nChicago Illinois Indianapolis Indiana\nChicago Illinois Austin Texas\nChicago Illinois Detroit Michigan\nChicago Illinois Memphis Tennessee\nChicago Illinois Boston Massachusetts\nChicago Illinois Seattle Washington\nChicago Illinois Denver Colorado\nChicago Illinois Baltimore Maryland\nChicago Illinois Nashville Tennessee\nChicago Illinois Louisville Kentucky\nChicago Illinois Milwaukee Wisconsin\nChicago Illinois Portland Oregon\nChicago Illinois Tucson Arizona\nChicago Illinois Fresno California\nChicago Illinois Sacramento California\nChicago Illinois Mesa Arizona\nChicago Illinois Atlanta Georgia\nChicago Illinois Omaha Nebraska\nChicago Illinois Miami Florida\nChicago Illinois Tulsa Oklahoma\nChicago Illinois Oakland California\nChicago Illinois Cleveland Ohio\nChicago Illinois Minneapolis Minnesota\nChicago Illinois Wichita Kansas\nChicago Illinois Arlington Texas\nChicago Illinois Bakersfield California\nChicago Illinois Tampa Florida\nChicago Illinois Anaheim California\nChicago Illinois Honolulu Hawaii\nChicago Illinois Pittsburgh Pennsylvania\nChicago Illinois Lexington Kentucky\nChicago Illinois Stockton California\nChicago Illinois Cincinnati Ohio\nChicago Illinois Anchorage Alaska\nHouston Texas Philadelphia Pennsylvania\nHouston Texas Phoenix Arizona\nHouston Texas Jacksonville Florida\nHouston Texas Indianapolis Indiana\nHouston Texas Detroit Michigan\nHouston Texas Memphis Tennessee\nHouston Texas Boston Massachusetts\nHouston Texas Seattle Washington\nHouston Texas Denver Colorado\nHouston Texas Baltimore Maryland\nHouston Texas Nashville Tennessee\nHouston Texas Louisville Kentucky\nHouston Texas Milwaukee Wisconsin\nHouston Texas Portland Oregon\nHouston Texas Tucson Arizona\nHouston Texas Fresno California\nHouston Texas Sacramento California\nHouston Texas Mesa Arizona\nHouston Texas Atlanta Georgia\nHouston Texas Omaha Nebraska\nHouston Texas Miami Florida\nHouston Texas Tulsa Oklahoma\nHouston Texas Oakland California\nHouston Texas Cleveland Ohio\nHouston Texas Minneapolis Minnesota\nHouston Texas Wichita Kansas\nHouston Texas Bakersfield California\nHouston Texas Tampa Florida\nHouston Texas Anaheim California\nHouston Texas Honolulu Hawaii\nHouston Texas Pittsburgh Pennsylvania\nHouston Texas Lexington Kentucky\nHouston Texas Stockton California\nHouston Texas Cincinnati Ohio\nHouston Texas Anchorage Alaska\nHouston Texas Toledo Ohio\nPhiladelphia Pennsylvania Phoenix Arizona\nPhiladelphia Pennsylvania Dallas Texas\nPhiladelphia Pennsylvania Jacksonville Florida\nPhiladelphia Pennsylvania Indianapolis Indiana\nPhiladelphia Pennsylvania Austin Texas\nPhiladelphia Pennsylvania Detroit Michigan\nPhiladelphia Pennsylvania Memphis Tennessee\nPhiladelphia Pennsylvania Boston Massachusetts\nPhiladelphia Pennsylvania Seattle Washington\nPhiladelphia Pennsylvania Denver Colorado\nPhiladelphia Pennsylvania Baltimore Maryland\nPhiladelphia Pennsylvania Nashville Tennessee\nPhiladelphia Pennsylvania Louisville Kentucky\nPhiladelphia Pennsylvania Milwaukee Wisconsin\nPhiladelphia Pennsylvania Portland Oregon\nPhiladelphia Pennsylvania Tucson Arizona\nPhiladelphia Pennsylvania Fresno California\nPhiladelphia Pennsylvania Sacramento California\nPhiladelphia Pennsylvania Mesa Arizona\nPhiladelphia Pennsylvania Atlanta Georgia\nPhiladelphia Pennsylvania Omaha Nebraska\nPhiladelphia Pennsylvania Miami Florida\nPhiladelphia Pennsylvania Tulsa Oklahoma\nPhiladelphia Pennsylvania Oakland California\nPhiladelphia Pennsylvania Cleveland Ohio\nPhiladelphia Pennsylvania Minneapolis Minnesota\nPhiladelphia Pennsylvania Wichita Kansas\nPhiladelphia Pennsylvania Arlington Texas\nPhiladelphia Pennsylvania Bakersfield California\nPhiladelphia Pennsylvania Tampa Florida\nPhiladelphia Pennsylvania Anaheim California\nPhiladelphia Pennsylvania Honolulu Hawaii\nPhiladelphia Pennsylvania Lexington Kentucky\nPhiladelphia Pennsylvania Stockton California\nPhiladelphia Pennsylvania Cincinnati Ohio\nPhiladelphia Pennsylvania Anchorage Alaska\nPhiladelphia Pennsylvania Toledo Ohio\nPhiladelphia Pennsylvania Plano Texas\nPhoenix Arizona Dallas Texas\nPhoenix Arizona Jacksonville Florida\nPhoenix Arizona Indianapolis Indiana\nPhoenix Arizona Austin Texas\nPhoenix Arizona Detroit Michigan\nPhoenix Arizona Memphis Tennessee\nPhoenix Arizona Boston Massachusetts\nPhoenix Arizona Seattle Washington\nPhoenix Arizona Denver Colorado\nPhoenix Arizona Baltimore Maryland\nPhoenix Arizona Nashville Tennessee\nPhoenix Arizona Louisville Kentucky\nPhoenix Arizona Milwaukee Wisconsin\nPhoenix Arizona Portland Oregon\nPhoenix Arizona Fresno California\nPhoenix Arizona Sacramento California\nPhoenix Arizona Atlanta Georgia\nPhoenix Arizona Omaha Nebraska\nPhoenix Arizona Miami Florida\nPhoenix Arizona Tulsa Oklahoma\nPhoenix Arizona Oakland California\nPhoenix Arizona Cleveland Ohio\nPhoenix Arizona Minneapolis Minnesota\nPhoenix Arizona Wichita Kansas\nPhoenix Arizona Arlington Texas\nPhoenix Arizona Bakersfield California\nPhoenix Arizona Tampa Florida\nPhoenix Arizona Anaheim California\nPhoenix Arizona Honolulu Hawaii\nPhoenix Arizona Pittsburgh Pennsylvania\nPhoenix Arizona Lexington Kentucky\nPhoenix Arizona Stockton California\nPhoenix Arizona Cincinnati Ohio\nPhoenix Arizona Anchorage Alaska\nPhoenix Arizona Toledo Ohio\nPhoenix Arizona Plano Texas\nPhoenix Arizona Henderson Nevada\nDallas Texas Jacksonville Florida\nDallas Texas Indianapolis Indiana\nDallas Texas Detroit Michigan\nDallas Texas Memphis Tennessee\nDallas Texas Boston Massachusetts\nDallas Texas Seattle Washington\nDallas Texas Denver Colorado\nDallas Texas Baltimore Maryland\nDallas Texas Nashville Tennessee\nDallas Texas Louisville Kentucky\nDallas Texas Milwaukee Wisconsin\nDallas Texas Portland Oregon\nDallas Texas Tucson Arizona\nDallas Texas Fresno California\nDallas Texas Sacramento California\nDallas Texas Mesa Arizona\nDallas Texas Atlanta Georgia\nDallas Texas Omaha Nebraska\nDallas Texas Miami Florida\nDallas Texas Tulsa Oklahoma\nDallas Texas Oakland California\nDallas Texas Cleveland Ohio\nDallas Texas Minneapolis Minnesota\nDallas Texas Wichita Kansas\nDallas Texas Bakersfield California\nDallas Texas Tampa Florida\nDallas Texas Anaheim California\nDallas Texas Honolulu Hawaii\nDallas Texas Pittsburgh Pennsylvania\nDallas Texas Lexington Kentucky\nDallas Texas Stockton California\nDallas Texas Cincinnati Ohio\nDallas Texas Anchorage Alaska\nDallas Texas Toledo Ohio\nDallas Texas Henderson Nevada\nDallas Texas Orlando Florida\nJacksonville Florida Indianapolis Indiana\nJacksonville Florida Austin Texas\nJacksonville Florida Detroit Michigan\nJacksonville Florida Memphis Tennessee\nJacksonville Florida Boston Massachusetts\nJacksonville Florida Seattle Washington\nJacksonville Florida Denver Colorado\nJacksonville Florida Baltimore Maryland\nJacksonville Florida Nashville Tennessee\nJacksonville Florida Louisville Kentucky\nJacksonville Florida Milwaukee Wisconsin\nJacksonville Florida Portland Oregon\nJacksonville Florida Tucson Arizona\nJacksonville Florida Fresno California\nJacksonville Florida Sacramento California\nJacksonville Florida Mesa Arizona\nJacksonville Florida Atlanta 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Texas Detroit Michigan\nIrving Texas Memphis Tennessee\nIrving Texas Boston Massachusetts\nIrving Texas Seattle Washington\nIrving Texas Denver Colorado\nIrving Texas Baltimore Maryland\nIrving Texas Nashville Tennessee\nIrving Texas Louisville Kentucky\nIrving Texas Milwaukee Wisconsin\nIrving Texas Portland Oregon\nIrving Texas Tucson Arizona\nIrving Texas Fresno California\nIrving Texas Sacramento California\nIrving Texas Mesa Arizona\nIrving Texas Atlanta Georgia\nIrving Texas Omaha Nebraska\nIrving Texas Miami Florida\nFremont California Spokane Washington\nFremont California Shreveport Louisiana\nFremont California Tacoma Washington\nFremont California Akron Ohio\nFremont California Amarillo Texas\nFremont California Tallahassee Florida\nFremont California Huntsville Alabama\nFremont California Worcester Massachusetts\nFremont California Chicago Illinois\nFremont California Houston Texas\nFremont California Philadelphia Pennsylvania\nFremont California Phoenix Arizona\nFremont California Dallas Texas\nFremont California Jacksonville Florida\nFremont California Indianapolis Indiana\nFremont California Austin Texas\nFremont California Detroit Michigan\nFremont California Memphis Tennessee\nFremont California Boston Massachusetts\nFremont California Seattle Washington\nFremont California Denver Colorado\nFremont California Baltimore Maryland\nFremont California Nashville Tennessee\nFremont California Louisville Kentucky\nFremont California Milwaukee Wisconsin\nFremont California Portland Oregon\nFremont California Tucson Arizona\nFremont California Mesa Arizona\nFremont California Atlanta Georgia\nFremont California Omaha Nebraska\nFremont California Miami Florida\nFremont California Tulsa Oklahoma\nIrvine California Spokane Washington\nIrvine California Shreveport Louisiana\nIrvine California Tacoma Washington\nIrvine California Akron Ohio\nIrvine California Amarillo Texas\nIrvine California Tallahassee Florida\nIrvine California Huntsville Alabama\nIrvine California Worcester Massachusetts\nIrvine California Chicago Illinois\nIrvine California Houston Texas\nIrvine California Philadelphia Pennsylvania\nIrvine California Phoenix Arizona\nIrvine California Dallas Texas\nIrvine California Jacksonville Florida\nIrvine California Indianapolis Indiana\nIrvine California Austin Texas\nIrvine California Detroit Michigan\nIrvine California Memphis Tennessee\nIrvine California Boston Massachusetts\nIrvine California Seattle Washington\nIrvine California Denver Colorado\nIrvine California Baltimore Maryland\nIrvine California Nashville Tennessee\nIrvine California Louisville Kentucky\nIrvine California Milwaukee Wisconsin\nIrvine California Portland Oregon\nIrvine California Tucson Arizona\nIrvine California Mesa Arizona\nIrvine California Atlanta Georgia\nIrvine California Omaha Nebraska\nIrvine California Miami Florida\nIrvine California Tulsa Oklahoma\nSpokane Washington Modesto California\nSpokane Washington Shreveport Louisiana\nSpokane Washington Oxnard California\nSpokane Washington Fontana California\nSpokane Washington Akron Ohio\nSpokane Washington Amarillo Texas\nSpokane Washington Glendale California\nSpokane Washington Tallahassee Florida\nSpokane Washington Huntsville Alabama\nSpokane Washington Worcester Massachusetts\nSpokane Washington Chicago Illinois\nSpokane Washington Houston Texas\nSpokane Washington Philadelphia Pennsylvania\nSpokane Washington Phoenix Arizona\nSpokane Washington Dallas Texas\nSpokane Washington Jacksonville Florida\nSpokane Washington Indianapolis Indiana\nSpokane Washington Austin Texas\nSpokane Washington Detroit Michigan\nSpokane Washington Memphis Tennessee\nSpokane Washington Boston Massachusetts\nSpokane Washington Denver Colorado\nSpokane Washington Baltimore Maryland\nSpokane Washington Nashville Tennessee\nSpokane Washington Louisville Kentucky\nSpokane Washington Milwaukee Wisconsin\nSpokane Washington Portland Oregon\nSpokane Washington Tucson Arizona\nSpokane Washington Fresno California\nSpokane Washington Sacramento California\nSpokane Washington Mesa Arizona\nSpokane Washington Atlanta Georgia\nSpokane Washington Omaha Nebraska\nSpokane Washington Miami Florida\nSpokane Washington Tulsa Oklahoma\nSpokane Washington Oakland California\nSpokane Washington Cleveland Ohio\nModesto California Shreveport Louisiana\nModesto California Tacoma Washington\nModesto California Akron Ohio\nModesto California Amarillo Texas\nModesto California Tallahassee Florida\nModesto California Huntsville Alabama\nModesto California Worcester Massachusetts\nModesto California Chicago Illinois\nModesto California Houston Texas\nModesto California Philadelphia Pennsylvania\nModesto California Phoenix Arizona\nModesto California Dallas Texas\nModesto California Jacksonville Florida\nModesto California Indianapolis Indiana\nModesto California Austin Texas\nModesto California Detroit Michigan\nModesto California Memphis Tennessee\nModesto California Boston Massachusetts\nModesto California Seattle Washington\nModesto California Denver Colorado\nModesto California Baltimore Maryland\nModesto California Nashville Tennessee\nModesto California Louisville Kentucky\nModesto California Milwaukee Wisconsin\nModesto California Portland Oregon\nModesto California Tucson Arizona\nModesto California Mesa Arizona\nModesto California Atlanta Georgia\nModesto California Omaha Nebraska\nModesto California Miami Florida\nModesto California Tulsa Oklahoma\nModesto California Cleveland Ohio\nModesto California Minneapolis Minnesota\nShreveport Louisiana Tacoma Washington\nShreveport Louisiana Oxnard California\nShreveport Louisiana Fontana California\nShreveport Louisiana Akron Ohio\nShreveport Louisiana Amarillo Texas\nShreveport Louisiana Glendale California\nShreveport Louisiana Tallahassee Florida\nShreveport Louisiana Huntsville Alabama\nShreveport Louisiana Worcester Massachusetts\nShreveport Louisiana Chicago Illinois\nShreveport Louisiana Houston Texas\nShreveport Louisiana Philadelphia Pennsylvania\nShreveport Louisiana Phoenix Arizona\nShreveport Louisiana Dallas Texas\nShreveport Louisiana Jacksonville Florida\nShreveport Louisiana Indianapolis Indiana\nShreveport Louisiana Austin Texas\nShreveport Louisiana Detroit Michigan\nShreveport Louisiana Memphis Tennessee\nShreveport Louisiana Boston Massachusetts\nShreveport Louisiana Seattle Washington\nShreveport Louisiana Denver Colorado\nShreveport Louisiana Baltimore Maryland\nShreveport Louisiana Nashville Tennessee\nShreveport Louisiana Louisville Kentucky\nShreveport Louisiana Milwaukee Wisconsin\nShreveport Louisiana Portland Oregon\nShreveport Louisiana Tucson Arizona\nShreveport Louisiana Fresno California\nShreveport Louisiana Sacramento California\nShreveport Louisiana Mesa Arizona\nShreveport Louisiana Atlanta Georgia\nShreveport Louisiana Omaha Nebraska\nShreveport Louisiana Miami Florida\nShreveport Louisiana Tulsa Oklahoma\nShreveport Louisiana Oakland California\nShreveport Louisiana Cleveland Ohio\nShreveport Louisiana Minneapolis Minnesota\nShreveport Louisiana Wichita Kansas\nTacoma Washington Oxnard California\nTacoma Washington Fontana California\nTacoma Washington Akron Ohio\nTacoma Washington Amarillo Texas\nTacoma Washington Glendale California\nTacoma Washington Tallahassee Florida\nTacoma Washington Huntsville Alabama\nTacoma Washington Worcester Massachusetts\nTacoma Washington Chicago Illinois\nTacoma Washington Houston Texas\nTacoma Washington Philadelphia Pennsylvania\nTacoma Washington Phoenix Arizona\nTacoma Washington Dallas Texas\nTacoma Washington Jacksonville Florida\nTacoma Washington Indianapolis Indiana\nTacoma Washington Austin Texas\nTacoma Washington Detroit Michigan\nTacoma Washington Memphis Tennessee\nTacoma Washington Boston Massachusetts\nTacoma Washington Denver Colorado\nTacoma Washington Baltimore Maryland\nTacoma Washington Nashville Tennessee\nTacoma Washington Louisville Kentucky\nTacoma Washington Milwaukee Wisconsin\nTacoma Washington Portland Oregon\nTacoma Washington Tucson Arizona\nTacoma Washington Fresno California\nTacoma Washington Sacramento California\nTacoma Washington Mesa Arizona\nTacoma Washington Atlanta Georgia\nTacoma Washington Omaha Nebraska\nTacoma Washington Miami Florida\nTacoma Washington Tulsa Oklahoma\nTacoma Washington Oakland California\nTacoma Washington Cleveland Ohio\nTacoma Washington Minneapolis Minnesota\nTacoma Washington Wichita Kansas\nTacoma Washington Arlington Texas\nOxnard California Akron Ohio\nOxnard California Amarillo Texas\nOxnard California Tallahassee Florida\nOxnard California Huntsville Alabama\nOxnard California Worcester Massachusetts\nOxnard California Chicago Illinois\nOxnard California Houston Texas\nOxnard California Philadelphia Pennsylvania\nOxnard California Phoenix Arizona\nOxnard California Dallas Texas\nOxnard California Jacksonville Florida\nOxnard California Indianapolis Indiana\nOxnard California Austin Texas\nOxnard California Detroit Michigan\nOxnard California Memphis Tennessee\nOxnard California Boston Massachusetts\nOxnard California Seattle Washington\nOxnard California Denver Colorado\nOxnard California Baltimore Maryland\nOxnard California Nashville Tennessee\nOxnard California Louisville Kentucky\nOxnard California Milwaukee Wisconsin\nOxnard California Portland Oregon\nOxnard California Tucson Arizona\nOxnard California Mesa Arizona\nOxnard California Atlanta Georgia\nOxnard California Omaha Nebraska\nOxnard California Miami Florida\nOxnard California Tulsa Oklahoma\nOxnard California Cleveland Ohio\nOxnard California Minneapolis Minnesota\nOxnard California Wichita Kansas\nOxnard California Arlington Texas\nFontana California Akron Ohio\nFontana California Amarillo Texas\nFontana California Tallahassee Florida\nFontana California Huntsville Alabama\nFontana California Worcester Massachusetts\nFontana California Chicago Illinois\nFontana California Houston Texas\nFontana California Philadelphia Pennsylvania\nFontana California Phoenix Arizona\nFontana California Dallas Texas\nFontana California Jacksonville Florida\nFontana California Indianapolis Indiana\nFontana California Austin Texas\nFontana California Detroit Michigan\nFontana California Memphis Tennessee\nFontana California Boston Massachusetts\nFontana California Seattle Washington\nFontana California Denver Colorado\nFontana California Baltimore Maryland\nFontana California Nashville Tennessee\nFontana California Louisville Kentucky\nFontana California Milwaukee Wisconsin\nFontana California Portland Oregon\nFontana California Tucson Arizona\nFontana California Mesa Arizona\nFontana California Atlanta Georgia\nFontana California Omaha Nebraska\nFontana California Miami Florida\nFontana California Tulsa Oklahoma\nFontana California Cleveland Ohio\nFontana California Minneapolis Minnesota\nFontana California Wichita Kansas\nFontana California Arlington Texas\nFontana California Tampa Florida\nAkron Ohio Amarillo Texas\nAkron Ohio Glendale California\nAkron Ohio Tallahassee Florida\nAkron Ohio Huntsville Alabama\nAkron Ohio Worcester Massachusetts\nAkron Ohio Chicago Illinois\nAkron Ohio Houston Texas\nAkron Ohio Philadelphia Pennsylvania\nAkron Ohio Phoenix Arizona\nAkron Ohio Dallas Texas\nAkron Ohio Jacksonville Florida\nAkron Ohio Indianapolis Indiana\nAkron Ohio Austin Texas\nAkron Ohio Detroit Michigan\nAkron Ohio Memphis Tennessee\nAkron Ohio Boston Massachusetts\nAkron Ohio Seattle Washington\nAkron Ohio Denver Colorado\nAkron Ohio Baltimore Maryland\nAkron Ohio Nashville Tennessee\nAkron Ohio Louisville Kentucky\nAkron Ohio Milwaukee Wisconsin\nAkron Ohio Portland Oregon\nAkron Ohio Tucson Arizona\nAkron Ohio Fresno California\nAkron Ohio Sacramento California\nAkron Ohio Mesa Arizona\nAkron Ohio Atlanta Georgia\nAkron Ohio Omaha Nebraska\nAkron Ohio Miami Florida\nAkron Ohio Tulsa Oklahoma\nAkron Ohio Oakland California\nAkron Ohio Minneapolis Minnesota\nAkron Ohio Wichita Kansas\nAkron Ohio Arlington Texas\nAkron Ohio Bakersfield California\nAkron Ohio Tampa Florida\nAkron Ohio Anaheim California\nAmarillo Texas Glendale California\nAmarillo Texas Tallahassee Florida\nAmarillo Texas Huntsville Alabama\nAmarillo Texas Worcester Massachusetts\nAmarillo Texas Chicago Illinois\nAmarillo Texas Philadelphia Pennsylvania\nAmarillo Texas Phoenix Arizona\nAmarillo Texas Jacksonville Florida\nAmarillo Texas Indianapolis Indiana\nAmarillo Texas Detroit Michigan\nAmarillo Texas Memphis Tennessee\nAmarillo Texas Boston Massachusetts\nAmarillo Texas Seattle Washington\nAmarillo Texas Denver Colorado\nAmarillo Texas Baltimore Maryland\nAmarillo Texas Nashville Tennessee\nAmarillo Texas Louisville Kentucky\nAmarillo Texas Milwaukee Wisconsin\nAmarillo Texas Portland Oregon\nAmarillo Texas Tucson Arizona\nAmarillo Texas Fresno California\nAmarillo Texas Sacramento California\nAmarillo Texas Mesa Arizona\nAmarillo Texas Atlanta Georgia\nAmarillo Texas Omaha Nebraska\nAmarillo Texas Miami Florida\nAmarillo Texas Tulsa Oklahoma\nAmarillo Texas Oakland California\nAmarillo Texas Cleveland Ohio\nAmarillo Texas Minneapolis Minnesota\nAmarillo Texas Wichita Kansas\nAmarillo Texas Bakersfield California\nAmarillo Texas Tampa Florida\nAmarillo Texas Anaheim California\nAmarillo Texas Honolulu Hawaii\nGlendale California Tallahassee Florida\nGlendale California Huntsville Alabama\nGlendale California Worcester Massachusetts\nGlendale California Chicago Illinois\nGlendale California Houston Texas\nGlendale California Philadelphia Pennsylvania\nGlendale California Phoenix Arizona\nGlendale California Dallas Texas\nGlendale California Jacksonville Florida\nGlendale California Indianapolis Indiana\nGlendale California Austin Texas\nGlendale California Detroit Michigan\nGlendale California Memphis Tennessee\nGlendale California Boston Massachusetts\nGlendale California Seattle Washington\nGlendale California Denver Colorado\nGlendale California Baltimore Maryland\nGlendale California Nashville Tennessee\nGlendale California Louisville Kentucky\nGlendale California Milwaukee Wisconsin\nGlendale California Portland Oregon\nGlendale California Tucson Arizona\nGlendale California Mesa Arizona\nGlendale California Atlanta Georgia\nGlendale California Omaha Nebraska\nGlendale California Miami Florida\nGlendale California Tulsa Oklahoma\nGlendale California Cleveland Ohio\nGlendale California Minneapolis Minnesota\nGlendale California Wichita Kansas\nGlendale California Arlington Texas\nGlendale California Tampa Florida\nGlendale California Honolulu Hawaii\nGlendale California Pittsburgh Pennsylvania\nTallahassee Florida Huntsville Alabama\nTallahassee Florida Worcester Massachusetts\nTallahassee Florida Chicago Illinois\nTallahassee Florida Houston Texas\nTallahassee Florida Philadelphia Pennsylvania\nTallahassee Florida Phoenix Arizona\nTallahassee Florida Dallas Texas\nTallahassee Florida Indianapolis Indiana\nTallahassee Florida Austin Texas\nTallahassee Florida Detroit Michigan\nTallahassee Florida Memphis Tennessee\nTallahassee Florida Boston Massachusetts\nTallahassee Florida Seattle Washington\nTallahassee Florida Denver Colorado\nTallahassee Florida Baltimore Maryland\nTallahassee Florida Nashville Tennessee\nTallahassee Florida Louisville Kentucky\nTallahassee Florida Milwaukee Wisconsin\nTallahassee Florida Portland Oregon\nTallahassee Florida Tucson Arizona\nTallahassee Florida Fresno California\nTallahassee Florida Sacramento California\nTallahassee Florida Mesa Arizona\nTallahassee Florida Atlanta Georgia\nTallahassee Florida Omaha Nebraska\nTallahassee Florida Tulsa Oklahoma\nTallahassee Florida Oakland California\nTallahassee Florida Cleveland Ohio\nTallahassee Florida Minneapolis Minnesota\nTallahassee Florida Wichita Kansas\nTallahassee Florida Arlington Texas\nTallahassee Florida Bakersfield California\nTallahassee Florida Anaheim California\nTallahassee Florida Honolulu Hawaii\nTallahassee Florida Pittsburgh Pennsylvania\nTallahassee Florida Lexington Kentucky\nHuntsville Alabama Worcester Massachusetts\nHuntsville Alabama Chicago Illinois\nHuntsville Alabama Houston Texas\nHuntsville Alabama Philadelphia Pennsylvania\nHuntsville Alabama Phoenix Arizona\nHuntsville Alabama Dallas Texas\nHuntsville Alabama Jacksonville Florida\nHuntsville Alabama Indianapolis Indiana\nHuntsville Alabama Austin Texas\nHuntsville Alabama Detroit Michigan\nHuntsville Alabama Memphis Tennessee\nHuntsville Alabama Boston Massachusetts\nHuntsville Alabama Seattle Washington\nHuntsville Alabama Denver Colorado\nHuntsville Alabama Baltimore Maryland\nHuntsville Alabama Nashville Tennessee\nHuntsville Alabama Louisville Kentucky\nHuntsville Alabama Milwaukee Wisconsin\nHuntsville Alabama Portland Oregon\nHuntsville Alabama Tucson Arizona\nHuntsville Alabama Fresno California\nHuntsville Alabama Sacramento California\nHuntsville Alabama Mesa Arizona\nHuntsville Alabama Atlanta Georgia\nHuntsville Alabama Omaha Nebraska\nHuntsville Alabama Miami Florida\nHuntsville Alabama Tulsa Oklahoma\nHuntsville Alabama Oakland California\nHuntsville Alabama Cleveland Ohio\nHuntsville Alabama Minneapolis Minnesota\nHuntsville Alabama Wichita Kansas\nHuntsville Alabama Arlington Texas\nHuntsville Alabama Bakersfield California\nHuntsville Alabama Tampa Florida\nHuntsville Alabama Anaheim California\nHuntsville Alabama Honolulu Hawaii\nHuntsville Alabama Pittsburgh Pennsylvania\nHuntsville Alabama Lexington Kentucky\nHuntsville Alabama Stockton California\nWorcester Massachusetts Chicago Illinois\nWorcester Massachusetts Houston Texas\nWorcester Massachusetts Philadelphia Pennsylvania\nWorcester Massachusetts Phoenix Arizona\nWorcester Massachusetts Dallas Texas\nWorcester Massachusetts Jacksonville Florida\nWorcester Massachusetts Indianapolis Indiana\nWorcester Massachusetts Austin Texas\nWorcester Massachusetts Detroit Michigan\nWorcester Massachusetts Memphis Tennessee\nWorcester Massachusetts Seattle Washington\nWorcester Massachusetts Denver Colorado\nWorcester Massachusetts Baltimore Maryland\nWorcester Massachusetts Nashville Tennessee\nWorcester Massachusetts Louisville Kentucky\nWorcester Massachusetts Milwaukee Wisconsin\nWorcester Massachusetts Portland Oregon\nWorcester Massachusetts Tucson Arizona\nWorcester Massachusetts Fresno California\nWorcester Massachusetts Sacramento California\nWorcester Massachusetts Mesa Arizona\nWorcester Massachusetts Atlanta Georgia\nWorcester Massachusetts Omaha Nebraska\nWorcester Massachusetts Miami Florida\nWorcester Massachusetts Tulsa Oklahoma\nWorcester Massachusetts Oakland California\nWorcester Massachusetts Cleveland Ohio\nWorcester Massachusetts Minneapolis Minnesota\nWorcester Massachusetts Wichita Kansas\nWorcester Massachusetts Arlington Texas\nWorcester Massachusetts Bakersfield California\nWorcester Massachusetts Tampa Florida\nWorcester Massachusetts Anaheim California\nWorcester Massachusetts Honolulu Hawaii\nWorcester Massachusetts Pittsburgh Pennsylvania\nWorcester Massachusetts Lexington Kentucky\nWorcester Massachusetts Stockton California\nWorcester Massachusetts Cincinnati Ohio\nboy girl brother sister\nboy girl brothers sisters\nboy girl dad mom\nboy girl father mother\nboy girl grandfather grandmother\nboy girl grandpa grandma\nboy girl grandson granddaughter\nboy girl groom bride\nboy girl he she\nboy girl his her\nboy girl husband wife\nboy girl king queen\nboy girl man woman\nboy girl nephew niece\nboy girl policeman policewoman\nboy girl prince princess\nboy girl son daughter\nboy girl sons daughters\nboy girl stepbrother stepsister\nboy girl stepfather stepmother\nboy girl stepson stepdaughter\nboy girl uncle aunt\nbrother sister brothers sisters\nbrother sister dad mom\nbrother sister father mother\nbrother sister grandfather grandmother\nbrother sister grandpa grandma\nbrother sister grandson granddaughter\nbrother sister groom bride\nbrother sister he she\nbrother sister his her\nbrother sister husband wife\nbrother sister king queen\nbrother sister man woman\nbrother sister nephew niece\nbrother sister policeman policewoman\nbrother sister prince princess\nbrother sister son daughter\nbrother sister sons daughters\nbrother sister stepbrother stepsister\nbrother sister stepfather stepmother\nbrother sister stepson stepdaughter\nbrother sister uncle aunt\nbrother sister boy girl\nbrothers sisters dad mom\nbrothers sisters father mother\nbrothers sisters grandfather grandmother\nbrothers sisters grandpa grandma\nbrothers sisters grandson granddaughter\nbrothers sisters groom bride\nbrothers sisters he she\nbrothers sisters his her\nbrothers sisters husband wife\nbrothers sisters king queen\nbrothers sisters man woman\nbrothers sisters nephew niece\nbrothers sisters policeman policewoman\nbrothers sisters prince princess\nbrothers sisters son daughter\nbrothers sisters sons daughters\nbrothers sisters stepbrother stepsister\nbrothers sisters stepfather stepmother\nbrothers sisters stepson stepdaughter\nbrothers sisters uncle aunt\nbrothers sisters boy girl\nbrothers sisters brother sister\ndad mom father mother\ndad mom grandfather grandmother\ndad mom grandpa grandma\ndad mom grandson granddaughter\ndad mom groom bride\ndad mom he she\ndad mom his her\ndad mom husband wife\ndad mom king queen\ndad mom man woman\ndad mom nephew niece\ndad mom policeman policewoman\ndad mom prince princess\ndad mom son daughter\ndad mom sons daughters\ndad mom stepbrother stepsister\ndad mom stepfather stepmother\ndad mom stepson stepdaughter\ndad mom uncle aunt\ndad mom boy girl\ndad mom brother sister\ndad mom brothers sisters\nfather mother grandfather grandmother\nfather mother grandpa grandma\nfather mother grandson granddaughter\nfather mother groom bride\nfather mother he she\nfather mother his her\nfather mother husband wife\nfather mother king queen\nfather mother man woman\nfather mother nephew niece\nfather mother policeman policewoman\nfather mother prince princess\nfather mother son daughter\nfather mother sons daughters\nfather mother stepbrother stepsister\nfather mother stepfather stepmother\nfather mother stepson stepdaughter\nfather mother uncle aunt\nfather mother boy girl\nfather mother brother sister\nfather mother brothers sisters\nfather mother dad mom\ngrandfather grandmother grandpa grandma\ngrandfather grandmother grandson granddaughter\ngrandfather grandmother groom bride\ngrandfather grandmother he she\ngrandfather grandmother his her\ngrandfather grandmother husband wife\ngrandfather grandmother king queen\ngrandfather grandmother man woman\ngrandfather grandmother nephew niece\ngrandfather grandmother policeman policewoman\ngrandfather grandmother prince princess\ngrandfather grandmother son daughter\ngrandfather grandmother sons daughters\ngrandfather grandmother stepbrother stepsister\ngrandfather grandmother stepfather stepmother\ngrandfather grandmother stepson stepdaughter\ngrandfather grandmother uncle aunt\ngrandfather grandmother boy girl\ngrandfather grandmother brother sister\ngrandfather grandmother brothers sisters\ngrandfather grandmother dad mom\ngrandfather grandmother father mother\ngrandpa grandma grandson granddaughter\ngrandpa grandma groom bride\ngrandpa grandma he she\ngrandpa grandma his her\ngrandpa grandma husband wife\ngrandpa grandma king queen\ngrandpa grandma man woman\ngrandpa grandma nephew niece\ngrandpa grandma policeman policewoman\ngrandpa grandma prince princess\ngrandpa grandma son daughter\ngrandpa grandma sons daughters\ngrandpa grandma stepbrother stepsister\ngrandpa grandma stepfather stepmother\ngrandpa grandma stepson stepdaughter\ngrandpa grandma uncle aunt\ngrandpa grandma boy girl\ngrandpa grandma brother sister\ngrandpa grandma brothers sisters\ngrandpa grandma dad mom\ngrandpa grandma father mother\ngrandpa grandma grandfather grandmother\ngrandson granddaughter groom bride\ngrandson granddaughter he she\ngrandson granddaughter his her\ngrandson granddaughter husband wife\ngrandson granddaughter king queen\ngrandson granddaughter man woman\ngrandson granddaughter nephew niece\ngrandson granddaughter policeman policewoman\ngrandson granddaughter prince princess\ngrandson granddaughter son daughter\ngrandson granddaughter sons daughters\ngrandson granddaughter stepbrother stepsister\ngrandson granddaughter stepfather stepmother\ngrandson granddaughter stepson stepdaughter\ngrandson granddaughter uncle aunt\ngrandson granddaughter boy girl\ngrandson granddaughter brother sister\ngrandson granddaughter brothers sisters\ngrandson granddaughter dad mom\ngrandson granddaughter father mother\ngrandson granddaughter grandfather grandmother\ngrandson granddaughter grandpa grandma\ngroom bride he she\ngroom bride his her\ngroom bride husband wife\ngroom bride king queen\ngroom bride man woman\ngroom bride nephew niece\ngroom bride policeman policewoman\ngroom bride prince princess\ngroom bride son daughter\ngroom bride sons daughters\ngroom bride stepbrother stepsister\ngroom bride stepfather stepmother\ngroom bride stepson stepdaughter\ngroom bride uncle aunt\ngroom bride boy girl\ngroom bride brother sister\ngroom bride brothers sisters\ngroom bride dad mom\ngroom bride father mother\ngroom bride grandfather grandmother\ngroom bride grandpa grandma\ngroom bride grandson granddaughter\nhe she his her\nhe she husband wife\nhe she king queen\nhe she man woman\nhe she nephew niece\nhe she policeman policewoman\nhe she prince princess\nhe she son daughter\nhe she sons daughters\nhe she stepbrother stepsister\nhe she stepfather stepmother\nhe she stepson stepdaughter\nhe she uncle aunt\nhe she boy girl\nhe she brother sister\nhe she brothers sisters\nhe she dad mom\nhe she father mother\nhe she grandfather grandmother\nhe she grandpa grandma\nhe she grandson granddaughter\nhe she groom bride\nhis her husband wife\nhis her king queen\nhis her man woman\nhis her nephew niece\nhis her policeman policewoman\nhis her prince princess\nhis her son daughter\nhis her sons daughters\nhis her stepbrother stepsister\nhis her stepfather stepmother\nhis her stepson stepdaughter\nhis her uncle aunt\nhis her boy girl\nhis her brother sister\nhis her brothers sisters\nhis her dad mom\nhis her father mother\nhis her grandfather grandmother\nhis her grandpa grandma\nhis her grandson granddaughter\nhis her groom bride\nhis her he she\nhusband wife king queen\nhusband wife man woman\nhusband wife nephew niece\nhusband wife policeman policewoman\nhusband wife prince princess\nhusband wife son daughter\nhusband wife sons daughters\nhusband wife stepbrother stepsister\nhusband wife stepfather stepmother\nhusband wife stepson stepdaughter\nhusband wife uncle aunt\nhusband wife boy girl\nhusband wife brother sister\nhusband wife brothers sisters\nhusband wife dad mom\nhusband wife father mother\nhusband wife grandfather grandmother\nhusband wife grandpa grandma\nhusband wife grandson granddaughter\nhusband wife groom bride\nhusband wife he she\nhusband wife his her\nking queen man woman\nking queen nephew niece\nking queen policeman policewoman\nking queen prince princess\nking queen son daughter\nking queen sons daughters\nking queen stepbrother stepsister\nking queen stepfather stepmother\nking queen stepson stepdaughter\nking queen uncle aunt\nking queen boy girl\nking queen brother sister\nking queen brothers sisters\nking queen dad mom\nking queen father mother\nking queen grandfather grandmother\nking queen grandpa grandma\nking queen grandson granddaughter\nking queen groom bride\nking queen he she\nking queen his her\nking queen husband wife\nman woman nephew niece\nman woman policeman policewoman\nman woman prince princess\nman woman son daughter\nman woman sons daughters\nman woman stepbrother stepsister\nman woman stepfather stepmother\nman woman stepson stepdaughter\nman woman uncle aunt\nman woman boy girl\nman woman brother sister\nman woman brothers sisters\nman woman dad mom\nman woman father mother\nman woman grandfather grandmother\nman woman grandpa grandma\nman woman grandson granddaughter\nman woman groom bride\nman woman he she\nman woman his her\nman woman husband wife\nman woman king queen\nnephew niece policeman policewoman\nnephew niece prince princess\nnephew niece son daughter\nnephew niece sons daughters\nnephew niece stepbrother stepsister\nnephew niece stepfather stepmother\nnephew niece stepson stepdaughter\nnephew niece uncle aunt\nnephew niece boy girl\nnephew niece brother sister\nnephew niece brothers sisters\nnephew niece dad mom\nnephew niece father mother\nnephew niece grandfather grandmother\nnephew niece grandpa grandma\nnephew niece grandson granddaughter\nnephew niece groom bride\nnephew niece he she\nnephew niece his her\nnephew niece husband wife\nnephew niece king queen\nnephew niece man woman\npoliceman policewoman prince princess\npoliceman policewoman son daughter\npoliceman policewoman sons daughters\npoliceman policewoman stepbrother stepsister\npoliceman policewoman stepfather stepmother\npoliceman policewoman stepson stepdaughter\npoliceman policewoman uncle aunt\npoliceman policewoman boy girl\npoliceman policewoman brother sister\npoliceman policewoman brothers sisters\npoliceman policewoman dad mom\npoliceman policewoman father mother\npoliceman policewoman grandfather grandmother\npoliceman policewoman grandpa grandma\npoliceman policewoman grandson granddaughter\npoliceman policewoman groom bride\npoliceman policewoman he she\npoliceman policewoman his her\npoliceman policewoman husband wife\npoliceman policewoman king queen\npoliceman policewoman man woman\npoliceman policewoman nephew niece\nprince princess son daughter\nprince princess sons daughters\nprince princess stepbrother stepsister\nprince princess stepfather stepmother\nprince princess stepson stepdaughter\nprince princess uncle aunt\nprince princess boy girl\nprince princess brother sister\nprince princess brothers sisters\nprince princess dad mom\nprince princess father mother\nprince princess grandfather grandmother\nprince princess grandpa grandma\nprince princess grandson granddaughter\nprince princess groom bride\nprince princess he she\nprince princess his her\nprince princess husband wife\nprince princess king queen\nprince princess man woman\nprince princess nephew niece\nprince princess policeman policewoman\nson daughter sons daughters\nson daughter stepbrother stepsister\nson daughter stepfather stepmother\nson daughter stepson stepdaughter\nson daughter uncle aunt\nson daughter boy girl\nson daughter brother sister\nson daughter brothers sisters\nson daughter dad mom\nson daughter father mother\nson daughter grandfather grandmother\nson daughter grandpa grandma\nson daughter grandson granddaughter\nson daughter groom bride\nson daughter he she\nson daughter his her\nson daughter husband wife\nson daughter king queen\nson daughter man woman\nson daughter nephew niece\nson daughter policeman policewoman\nson daughter prince princess\nsons daughters stepbrother stepsister\nsons daughters stepfather stepmother\nsons daughters stepson stepdaughter\nsons daughters uncle aunt\nsons daughters boy girl\nsons daughters brother sister\nsons daughters brothers sisters\nsons daughters dad mom\nsons daughters father mother\nsons daughters grandfather grandmother\nsons daughters grandpa grandma\nsons daughters grandson granddaughter\nsons daughters groom bride\nsons daughters he she\nsons daughters his her\nsons daughters husband wife\nsons daughters king queen\nsons daughters man woman\nsons daughters nephew niece\nsons daughters policeman policewoman\nsons daughters prince princess\nsons daughters son daughter\nstepbrother stepsister stepfather stepmother\nstepbrother stepsister stepson stepdaughter\nstepbrother stepsister uncle aunt\nstepbrother stepsister boy girl\nstepbrother stepsister brother sister\nstepbrother stepsister brothers sisters\nstepbrother stepsister dad mom\nstepbrother stepsister father mother\nstepbrother stepsister grandfather grandmother\nstepbrother stepsister grandpa grandma\nstepbrother stepsister grandson granddaughter\nstepbrother stepsister groom bride\nstepbrother stepsister he she\nstepbrother stepsister his her\nstepbrother stepsister husband wife\nstepbrother stepsister king queen\nstepbrother stepsister man woman\nstepbrother stepsister nephew niece\nstepbrother stepsister policeman policewoman\nstepbrother stepsister prince princess\nstepbrother stepsister son daughter\nstepbrother stepsister sons daughters\nstepfather stepmother stepson stepdaughter\nstepfather stepmother uncle aunt\nstepfather stepmother boy girl\nstepfather stepmother brother sister\nstepfather stepmother brothers sisters\nstepfather stepmother dad mom\nstepfather stepmother father mother\nstepfather stepmother grandfather grandmother\nstepfather stepmother grandpa grandma\nstepfather stepmother grandson granddaughter\nstepfather stepmother groom bride\nstepfather stepmother he she\nstepfather stepmother his her\nstepfather stepmother husband wife\nstepfather stepmother king queen\nstepfather stepmother man woman\nstepfather stepmother nephew niece\nstepfather stepmother policeman policewoman\nstepfather stepmother prince princess\nstepfather stepmother son daughter\nstepfather stepmother sons daughters\nstepfather stepmother stepbrother stepsister\nstepson stepdaughter uncle aunt\nstepson stepdaughter boy girl\nstepson stepdaughter brother sister\nstepson stepdaughter brothers sisters\nstepson stepdaughter dad mom\nstepson stepdaughter father mother\nstepson stepdaughter grandfather grandmother\nstepson stepdaughter grandpa grandma\nstepson stepdaughter grandson granddaughter\nstepson stepdaughter groom bride\nstepson stepdaughter he she\nstepson stepdaughter his her\nstepson stepdaughter husband wife\nstepson stepdaughter king queen\nstepson stepdaughter man woman\nstepson stepdaughter nephew niece\nstepson stepdaughter policeman policewoman\nstepson stepdaughter prince princess\nstepson stepdaughter son daughter\nstepson stepdaughter sons daughters\nstepson stepdaughter stepbrother stepsister\nstepson stepdaughter stepfather stepmother\nuncle aunt boy girl\nuncle aunt brother sister\nuncle aunt brothers sisters\nuncle aunt dad mom\nuncle aunt father mother\nuncle aunt grandfather grandmother\nuncle aunt grandpa grandma\nuncle aunt grandson granddaughter\nuncle aunt groom bride\nuncle aunt he she\nuncle aunt his her\nuncle aunt husband wife\nuncle aunt king queen\nuncle aunt man woman\nuncle aunt nephew niece\nuncle aunt policeman policewoman\nuncle aunt prince princess\nuncle aunt son daughter\nuncle aunt sons daughters\nuncle aunt stepbrother stepsister\nuncle aunt stepfather stepmother\nuncle aunt stepson stepdaughter\n"
  },
  {
    "path": "psdvec/testsets/analogy/EN-TOM-ICLR13-SYN.txt",
    "content": "amazing amazingly apparent apparently\namazing amazingly calm calmly\namazing amazingly cheerful cheerfully\namazing amazingly complete completely\namazing amazingly efficient efficiently\namazing amazingly fortunate fortunately\namazing amazingly free freely\namazing amazingly furious furiously\namazing amazingly happy happily\namazing amazingly immediate immediately\namazing amazingly infrequent infrequently\namazing amazingly lucky luckily\namazing amazingly most mostly\namazing amazingly obvious obviously\namazing amazingly occasional occasionally\namazing amazingly possible possibly\namazing amazingly precise precisely\namazing amazingly professional professionally\namazing amazingly quick quickly\namazing amazingly quiet quietly\namazing amazingly rapid rapidly\namazing amazingly rare rarely\namazing amazingly reluctant reluctantly\namazing amazingly safe safely\namazing amazingly serious seriously\namazing amazingly slow slowly\namazing amazingly sudden suddenly\namazing amazingly swift swiftly\namazing amazingly typical typically\namazing amazingly unfortunate unfortunately\namazing amazingly usual usually\napparent apparently calm calmly\napparent apparently cheerful cheerfully\napparent apparently complete completely\napparent apparently efficient efficiently\napparent apparently fortunate fortunately\napparent apparently free freely\napparent apparently furious furiously\napparent apparently happy happily\napparent apparently immediate immediately\napparent apparently infrequent infrequently\napparent apparently lucky luckily\napparent apparently most mostly\napparent apparently obvious obviously\napparent apparently occasional occasionally\napparent apparently possible possibly\napparent apparently precise precisely\napparent apparently professional professionally\napparent apparently quick quickly\napparent apparently quiet quietly\napparent apparently rapid rapidly\napparent apparently rare rarely\napparent apparently reluctant reluctantly\napparent apparently safe safely\napparent apparently serious seriously\napparent apparently slow slowly\napparent apparently sudden suddenly\napparent apparently swift swiftly\napparent apparently typical typically\napparent apparently unfortunate unfortunately\napparent apparently usual usually\napparent apparently amazing amazingly\ncalm calmly cheerful cheerfully\ncalm calmly complete completely\ncalm calmly efficient efficiently\ncalm calmly fortunate fortunately\ncalm calmly free freely\ncalm calmly furious furiously\ncalm calmly happy happily\ncalm calmly immediate immediately\ncalm calmly infrequent infrequently\ncalm calmly lucky luckily\ncalm calmly most mostly\ncalm calmly obvious obviously\ncalm calmly occasional occasionally\ncalm calmly possible possibly\ncalm calmly precise precisely\ncalm calmly professional professionally\ncalm calmly quick quickly\ncalm calmly quiet quietly\ncalm calmly rapid rapidly\ncalm calmly rare rarely\ncalm calmly reluctant 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uninformed\nclear unclear known unknown\nclear unclear likely unlikely\nclear unclear logical illogical\nclear unclear pleasant unpleasant\nclear unclear possible impossible\nclear unclear possibly impossibly\nclear unclear productive unproductive\nclear unclear rational irrational\nclear unclear reasonable unreasonable\nclear unclear responsible irresponsible\nclear unclear sure unsure\nclear unclear tasteful distasteful\nclear unclear acceptable unacceptable\nclear unclear aware unaware\nclear unclear certain uncertain\ncomfortable uncomfortable competitive uncompetitive\ncomfortable uncomfortable consistent inconsistent\ncomfortable uncomfortable convincing unconvincing\ncomfortable uncomfortable convenient inconvenient\ncomfortable uncomfortable decided undecided\ncomfortable uncomfortable efficient inefficient\ncomfortable uncomfortable ethical unethical\ncomfortable uncomfortable fortunate unfortunate\ncomfortable uncomfortable honest dishonest\ncomfortable uncomfortable impressive unimpressive\ncomfortable uncomfortable informative uninformative\ncomfortable uncomfortable informed uninformed\ncomfortable uncomfortable known unknown\ncomfortable uncomfortable likely unlikely\ncomfortable uncomfortable logical illogical\ncomfortable uncomfortable pleasant unpleasant\ncomfortable uncomfortable possible impossible\ncomfortable uncomfortable possibly impossibly\ncomfortable uncomfortable productive unproductive\ncomfortable uncomfortable rational irrational\ncomfortable uncomfortable reasonable unreasonable\ncomfortable uncomfortable responsible irresponsible\ncomfortable uncomfortable sure unsure\ncomfortable uncomfortable tasteful distasteful\ncomfortable uncomfortable acceptable unacceptable\ncomfortable uncomfortable aware unaware\ncomfortable uncomfortable certain uncertain\ncomfortable uncomfortable clear unclear\ncompetitive uncompetitive consistent inconsistent\ncompetitive uncompetitive convincing unconvincing\ncompetitive uncompetitive convenient inconvenient\ncompetitive uncompetitive decided undecided\ncompetitive uncompetitive efficient inefficient\ncompetitive uncompetitive ethical unethical\ncompetitive uncompetitive fortunate unfortunate\ncompetitive uncompetitive honest dishonest\ncompetitive uncompetitive impressive unimpressive\ncompetitive uncompetitive informative uninformative\ncompetitive uncompetitive informed uninformed\ncompetitive uncompetitive known unknown\ncompetitive uncompetitive likely unlikely\ncompetitive uncompetitive logical illogical\ncompetitive uncompetitive pleasant unpleasant\ncompetitive uncompetitive possible impossible\ncompetitive uncompetitive possibly impossibly\ncompetitive uncompetitive productive unproductive\ncompetitive uncompetitive rational irrational\ncompetitive uncompetitive reasonable unreasonable\ncompetitive uncompetitive responsible irresponsible\ncompetitive uncompetitive sure unsure\ncompetitive uncompetitive tasteful distasteful\ncompetitive uncompetitive acceptable unacceptable\ncompetitive uncompetitive aware unaware\ncompetitive uncompetitive certain uncertain\ncompetitive uncompetitive clear unclear\ncompetitive uncompetitive comfortable uncomfortable\nconsistent inconsistent convincing unconvincing\nconsistent inconsistent convenient inconvenient\nconsistent inconsistent decided undecided\nconsistent inconsistent efficient inefficient\nconsistent inconsistent ethical unethical\nconsistent inconsistent fortunate unfortunate\nconsistent inconsistent honest dishonest\nconsistent inconsistent impressive unimpressive\nconsistent inconsistent informative uninformative\nconsistent inconsistent informed uninformed\nconsistent inconsistent known unknown\nconsistent inconsistent likely unlikely\nconsistent inconsistent logical illogical\nconsistent inconsistent pleasant unpleasant\nconsistent inconsistent possible impossible\nconsistent inconsistent possibly impossibly\nconsistent inconsistent productive unproductive\nconsistent inconsistent rational irrational\nconsistent inconsistent reasonable unreasonable\nconsistent inconsistent responsible irresponsible\nconsistent inconsistent sure unsure\nconsistent inconsistent tasteful distasteful\nconsistent inconsistent acceptable unacceptable\nconsistent inconsistent aware unaware\nconsistent inconsistent certain uncertain\nconsistent inconsistent clear unclear\nconsistent inconsistent comfortable uncomfortable\nconsistent inconsistent competitive uncompetitive\nconvincing unconvincing convenient inconvenient\nconvincing unconvincing decided undecided\nconvincing unconvincing efficient inefficient\nconvincing unconvincing ethical unethical\nconvincing unconvincing fortunate unfortunate\nconvincing unconvincing honest dishonest\nconvincing unconvincing impressive unimpressive\nconvincing unconvincing informative uninformative\nconvincing unconvincing informed uninformed\nconvincing unconvincing known unknown\nconvincing unconvincing likely unlikely\nconvincing unconvincing logical illogical\nconvincing unconvincing pleasant unpleasant\nconvincing unconvincing possible impossible\nconvincing unconvincing possibly impossibly\nconvincing unconvincing productive unproductive\nconvincing unconvincing rational irrational\nconvincing unconvincing reasonable unreasonable\nconvincing unconvincing responsible irresponsible\nconvincing unconvincing sure unsure\nconvincing unconvincing tasteful distasteful\nconvincing unconvincing acceptable unacceptable\nconvincing unconvincing aware unaware\nconvincing unconvincing certain uncertain\nconvincing unconvincing clear unclear\nconvincing unconvincing comfortable uncomfortable\nconvincing unconvincing competitive uncompetitive\nconvincing unconvincing consistent inconsistent\nconvenient inconvenient decided undecided\nconvenient inconvenient efficient inefficient\nconvenient inconvenient ethical unethical\nconvenient inconvenient fortunate unfortunate\nconvenient inconvenient honest dishonest\nconvenient inconvenient impressive unimpressive\nconvenient inconvenient informative uninformative\nconvenient inconvenient informed uninformed\nconvenient inconvenient known unknown\nconvenient inconvenient likely unlikely\nconvenient inconvenient logical illogical\nconvenient inconvenient pleasant unpleasant\nconvenient inconvenient possible impossible\nconvenient inconvenient possibly impossibly\nconvenient inconvenient productive unproductive\nconvenient inconvenient rational irrational\nconvenient inconvenient reasonable unreasonable\nconvenient inconvenient responsible irresponsible\nconvenient inconvenient sure unsure\nconvenient inconvenient tasteful distasteful\nconvenient inconvenient acceptable unacceptable\nconvenient inconvenient aware unaware\nconvenient inconvenient certain uncertain\nconvenient inconvenient clear unclear\nconvenient inconvenient comfortable uncomfortable\nconvenient inconvenient competitive uncompetitive\nconvenient inconvenient consistent inconsistent\nconvenient inconvenient convincing unconvincing\ndecided undecided efficient inefficient\ndecided undecided ethical unethical\ndecided undecided fortunate unfortunate\ndecided undecided honest dishonest\ndecided undecided impressive unimpressive\ndecided undecided informative uninformative\ndecided undecided informed uninformed\ndecided undecided known unknown\ndecided undecided likely unlikely\ndecided undecided logical illogical\ndecided undecided pleasant unpleasant\ndecided undecided possible impossible\ndecided undecided possibly impossibly\ndecided undecided productive unproductive\ndecided undecided rational irrational\ndecided undecided reasonable unreasonable\ndecided undecided responsible irresponsible\ndecided undecided sure unsure\ndecided undecided tasteful distasteful\ndecided undecided acceptable unacceptable\ndecided undecided aware unaware\ndecided undecided certain uncertain\ndecided undecided clear unclear\ndecided undecided comfortable uncomfortable\ndecided undecided competitive uncompetitive\ndecided undecided consistent inconsistent\ndecided undecided convincing unconvincing\ndecided undecided convenient inconvenient\nefficient inefficient ethical unethical\nefficient inefficient fortunate unfortunate\nefficient inefficient honest dishonest\nefficient inefficient impressive unimpressive\nefficient inefficient informative uninformative\nefficient inefficient informed uninformed\nefficient inefficient known unknown\nefficient inefficient likely unlikely\nefficient inefficient logical illogical\nefficient inefficient pleasant unpleasant\nefficient inefficient possible impossible\nefficient inefficient possibly impossibly\nefficient inefficient productive unproductive\nefficient inefficient rational irrational\nefficient inefficient reasonable unreasonable\nefficient inefficient responsible irresponsible\nefficient inefficient sure unsure\nefficient inefficient tasteful distasteful\nefficient inefficient acceptable unacceptable\nefficient inefficient aware unaware\nefficient inefficient certain uncertain\nefficient inefficient clear unclear\nefficient inefficient comfortable uncomfortable\nefficient inefficient competitive uncompetitive\nefficient inefficient consistent inconsistent\nefficient inefficient convincing unconvincing\nefficient inefficient convenient inconvenient\nefficient inefficient decided undecided\nethical unethical fortunate unfortunate\nethical unethical honest dishonest\nethical unethical impressive unimpressive\nethical unethical informative uninformative\nethical unethical informed uninformed\nethical unethical known unknown\nethical unethical likely unlikely\nethical unethical logical illogical\nethical unethical pleasant unpleasant\nethical unethical possible impossible\nethical unethical possibly impossibly\nethical unethical productive unproductive\nethical unethical rational irrational\nethical unethical reasonable unreasonable\nethical unethical responsible irresponsible\nethical unethical sure unsure\nethical unethical tasteful distasteful\nethical unethical acceptable unacceptable\nethical unethical aware unaware\nethical unethical certain uncertain\nethical unethical clear unclear\nethical unethical comfortable uncomfortable\nethical unethical competitive uncompetitive\nethical unethical consistent inconsistent\nethical unethical convincing unconvincing\nethical unethical convenient inconvenient\nethical unethical decided undecided\nethical unethical efficient inefficient\nfortunate unfortunate honest dishonest\nfortunate unfortunate impressive unimpressive\nfortunate unfortunate informative uninformative\nfortunate unfortunate informed uninformed\nfortunate unfortunate known unknown\nfortunate unfortunate likely unlikely\nfortunate unfortunate logical illogical\nfortunate unfortunate pleasant unpleasant\nfortunate unfortunate possible impossible\nfortunate unfortunate possibly impossibly\nfortunate unfortunate productive unproductive\nfortunate unfortunate rational irrational\nfortunate unfortunate reasonable unreasonable\nfortunate unfortunate responsible irresponsible\nfortunate unfortunate sure unsure\nfortunate unfortunate tasteful distasteful\nfortunate unfortunate acceptable unacceptable\nfortunate unfortunate aware unaware\nfortunate unfortunate certain uncertain\nfortunate unfortunate clear unclear\nfortunate unfortunate comfortable uncomfortable\nfortunate unfortunate competitive uncompetitive\nfortunate unfortunate consistent inconsistent\nfortunate unfortunate convincing unconvincing\nfortunate unfortunate convenient inconvenient\nfortunate unfortunate decided undecided\nfortunate unfortunate efficient inefficient\nfortunate unfortunate ethical unethical\nhonest dishonest impressive unimpressive\nhonest dishonest informative uninformative\nhonest dishonest informed uninformed\nhonest dishonest known unknown\nhonest dishonest likely unlikely\nhonest dishonest logical illogical\nhonest dishonest pleasant unpleasant\nhonest dishonest possible impossible\nhonest dishonest possibly impossibly\nhonest dishonest productive unproductive\nhonest dishonest rational irrational\nhonest dishonest reasonable unreasonable\nhonest dishonest responsible irresponsible\nhonest dishonest sure unsure\nhonest dishonest tasteful distasteful\nhonest dishonest acceptable unacceptable\nhonest dishonest aware unaware\nhonest dishonest certain uncertain\nhonest dishonest clear unclear\nhonest dishonest comfortable uncomfortable\nhonest dishonest competitive uncompetitive\nhonest dishonest consistent inconsistent\nhonest dishonest convincing unconvincing\nhonest dishonest convenient inconvenient\nhonest dishonest decided undecided\nhonest dishonest efficient inefficient\nhonest dishonest ethical unethical\nhonest dishonest fortunate unfortunate\nimpressive unimpressive informative uninformative\nimpressive unimpressive informed uninformed\nimpressive unimpressive known unknown\nimpressive unimpressive likely unlikely\nimpressive unimpressive logical illogical\nimpressive unimpressive pleasant unpleasant\nimpressive unimpressive possible impossible\nimpressive unimpressive possibly impossibly\nimpressive unimpressive productive unproductive\nimpressive unimpressive rational irrational\nimpressive unimpressive reasonable unreasonable\nimpressive unimpressive responsible irresponsible\nimpressive unimpressive sure unsure\nimpressive unimpressive tasteful distasteful\nimpressive unimpressive acceptable unacceptable\nimpressive unimpressive aware unaware\nimpressive unimpressive certain uncertain\nimpressive unimpressive clear unclear\nimpressive unimpressive comfortable uncomfortable\nimpressive unimpressive competitive uncompetitive\nimpressive unimpressive consistent inconsistent\nimpressive unimpressive convincing unconvincing\nimpressive unimpressive convenient inconvenient\nimpressive unimpressive decided undecided\nimpressive unimpressive efficient inefficient\nimpressive unimpressive ethical unethical\nimpressive unimpressive fortunate unfortunate\nimpressive unimpressive honest dishonest\ninformative uninformative informed uninformed\ninformative uninformative known unknown\ninformative uninformative likely unlikely\ninformative uninformative logical illogical\ninformative uninformative pleasant unpleasant\ninformative uninformative possible impossible\ninformative uninformative possibly impossibly\ninformative uninformative productive unproductive\ninformative uninformative rational irrational\ninformative uninformative reasonable unreasonable\ninformative uninformative responsible irresponsible\ninformative uninformative sure unsure\ninformative uninformative tasteful distasteful\ninformative uninformative acceptable unacceptable\ninformative uninformative aware unaware\ninformative uninformative certain uncertain\ninformative uninformative clear unclear\ninformative uninformative comfortable uncomfortable\ninformative uninformative competitive uncompetitive\ninformative uninformative consistent inconsistent\ninformative uninformative convincing unconvincing\ninformative uninformative convenient inconvenient\ninformative uninformative decided undecided\ninformative uninformative efficient inefficient\ninformative uninformative ethical unethical\ninformative uninformative fortunate unfortunate\ninformative uninformative honest dishonest\ninformative uninformative impressive unimpressive\ninformed uninformed known unknown\ninformed uninformed likely unlikely\ninformed uninformed logical illogical\ninformed uninformed pleasant unpleasant\ninformed uninformed possible impossible\ninformed uninformed possibly impossibly\ninformed uninformed productive unproductive\ninformed uninformed rational irrational\ninformed uninformed reasonable unreasonable\ninformed uninformed responsible irresponsible\ninformed uninformed sure unsure\ninformed uninformed tasteful distasteful\ninformed uninformed acceptable unacceptable\ninformed uninformed aware unaware\ninformed uninformed certain uncertain\ninformed uninformed clear unclear\ninformed uninformed comfortable uncomfortable\ninformed uninformed competitive uncompetitive\ninformed uninformed consistent inconsistent\ninformed uninformed convincing unconvincing\ninformed uninformed convenient inconvenient\ninformed uninformed decided undecided\ninformed uninformed efficient inefficient\ninformed uninformed ethical unethical\ninformed uninformed fortunate unfortunate\ninformed uninformed honest dishonest\ninformed uninformed impressive unimpressive\ninformed uninformed informative uninformative\nknown unknown likely unlikely\nknown unknown logical illogical\nknown unknown pleasant unpleasant\nknown unknown possible impossible\nknown unknown possibly impossibly\nknown unknown productive unproductive\nknown unknown rational irrational\nknown unknown reasonable unreasonable\nknown unknown responsible irresponsible\nknown unknown sure unsure\nknown unknown tasteful distasteful\nknown unknown acceptable unacceptable\nknown unknown aware unaware\nknown unknown certain uncertain\nknown unknown clear unclear\nknown unknown comfortable uncomfortable\nknown unknown competitive uncompetitive\nknown unknown consistent inconsistent\nknown unknown convincing unconvincing\nknown unknown convenient inconvenient\nknown unknown decided undecided\nknown unknown efficient inefficient\nknown unknown ethical unethical\nknown unknown fortunate unfortunate\nknown unknown honest dishonest\nknown unknown impressive unimpressive\nknown unknown informative uninformative\nknown unknown informed uninformed\nlikely unlikely logical illogical\nlikely unlikely pleasant unpleasant\nlikely unlikely possible impossible\nlikely unlikely possibly impossibly\nlikely unlikely productive unproductive\nlikely unlikely rational irrational\nlikely unlikely reasonable unreasonable\nlikely unlikely responsible irresponsible\nlikely unlikely sure unsure\nlikely unlikely tasteful distasteful\nlikely unlikely acceptable unacceptable\nlikely unlikely aware unaware\nlikely unlikely certain uncertain\nlikely unlikely clear unclear\nlikely unlikely comfortable uncomfortable\nlikely unlikely competitive uncompetitive\nlikely unlikely consistent inconsistent\nlikely unlikely convincing unconvincing\nlikely unlikely convenient inconvenient\nlikely unlikely decided undecided\nlikely unlikely efficient inefficient\nlikely unlikely ethical unethical\nlikely unlikely fortunate unfortunate\nlikely unlikely honest dishonest\nlikely unlikely impressive unimpressive\nlikely unlikely informative uninformative\nlikely unlikely informed uninformed\nlikely unlikely known unknown\nlogical illogical pleasant unpleasant\nlogical illogical possible impossible\nlogical illogical possibly impossibly\nlogical illogical productive unproductive\nlogical illogical rational irrational\nlogical illogical reasonable unreasonable\nlogical illogical responsible irresponsible\nlogical illogical sure unsure\nlogical illogical tasteful distasteful\nlogical illogical acceptable unacceptable\nlogical illogical aware unaware\nlogical illogical certain uncertain\nlogical illogical clear unclear\nlogical illogical comfortable uncomfortable\nlogical illogical competitive uncompetitive\nlogical illogical consistent inconsistent\nlogical illogical convincing unconvincing\nlogical illogical convenient inconvenient\nlogical illogical decided undecided\nlogical illogical efficient inefficient\nlogical illogical ethical unethical\nlogical illogical fortunate unfortunate\nlogical illogical honest dishonest\nlogical illogical impressive unimpressive\nlogical illogical informative uninformative\nlogical illogical informed uninformed\nlogical illogical known unknown\nlogical illogical likely unlikely\npleasant unpleasant possible impossible\npleasant unpleasant possibly impossibly\npleasant unpleasant productive unproductive\npleasant unpleasant rational irrational\npleasant unpleasant reasonable unreasonable\npleasant unpleasant responsible irresponsible\npleasant unpleasant sure unsure\npleasant unpleasant tasteful distasteful\npleasant unpleasant acceptable unacceptable\npleasant unpleasant aware unaware\npleasant unpleasant certain uncertain\npleasant unpleasant clear unclear\npleasant unpleasant comfortable uncomfortable\npleasant unpleasant competitive uncompetitive\npleasant unpleasant consistent inconsistent\npleasant unpleasant convincing unconvincing\npleasant unpleasant convenient inconvenient\npleasant unpleasant decided undecided\npleasant unpleasant efficient inefficient\npleasant unpleasant ethical unethical\npleasant unpleasant fortunate unfortunate\npleasant unpleasant honest dishonest\npleasant unpleasant impressive unimpressive\npleasant unpleasant informative uninformative\npleasant unpleasant informed uninformed\npleasant unpleasant known unknown\npleasant unpleasant likely unlikely\npleasant unpleasant logical illogical\npossible impossible possibly impossibly\npossible impossible productive unproductive\npossible impossible rational irrational\npossible impossible reasonable unreasonable\npossible impossible responsible irresponsible\npossible impossible sure unsure\npossible impossible tasteful distasteful\npossible impossible acceptable unacceptable\npossible impossible aware unaware\npossible impossible certain uncertain\npossible impossible clear unclear\npossible impossible comfortable uncomfortable\npossible impossible competitive uncompetitive\npossible impossible consistent inconsistent\npossible impossible convincing unconvincing\npossible impossible convenient inconvenient\npossible impossible decided undecided\npossible impossible efficient inefficient\npossible impossible ethical unethical\npossible impossible fortunate unfortunate\npossible impossible honest dishonest\npossible impossible impressive unimpressive\npossible impossible informative uninformative\npossible impossible informed uninformed\npossible impossible known unknown\npossible impossible likely unlikely\npossible impossible logical illogical\npossible impossible pleasant unpleasant\npossibly impossibly productive unproductive\npossibly impossibly rational irrational\npossibly impossibly reasonable unreasonable\npossibly impossibly responsible irresponsible\npossibly impossibly sure unsure\npossibly impossibly tasteful distasteful\npossibly impossibly acceptable unacceptable\npossibly impossibly aware unaware\npossibly impossibly certain uncertain\npossibly impossibly clear unclear\npossibly impossibly comfortable uncomfortable\npossibly impossibly competitive uncompetitive\npossibly impossibly consistent inconsistent\npossibly impossibly convincing unconvincing\npossibly impossibly convenient inconvenient\npossibly impossibly decided undecided\npossibly impossibly efficient inefficient\npossibly impossibly ethical unethical\npossibly impossibly fortunate unfortunate\npossibly impossibly honest dishonest\npossibly impossibly impressive unimpressive\npossibly impossibly informative uninformative\npossibly impossibly informed uninformed\npossibly impossibly known unknown\npossibly impossibly likely unlikely\npossibly impossibly logical illogical\npossibly impossibly pleasant unpleasant\npossibly impossibly possible impossible\nproductive unproductive rational irrational\nproductive unproductive reasonable unreasonable\nproductive unproductive responsible irresponsible\nproductive unproductive sure unsure\nproductive unproductive tasteful distasteful\nproductive unproductive acceptable unacceptable\nproductive unproductive aware unaware\nproductive unproductive certain uncertain\nproductive unproductive clear unclear\nproductive unproductive comfortable uncomfortable\nproductive unproductive competitive uncompetitive\nproductive unproductive consistent inconsistent\nproductive unproductive convincing unconvincing\nproductive unproductive convenient inconvenient\nproductive unproductive decided undecided\nproductive unproductive efficient inefficient\nproductive unproductive ethical unethical\nproductive unproductive fortunate unfortunate\nproductive unproductive honest dishonest\nproductive unproductive impressive unimpressive\nproductive unproductive informative uninformative\nproductive unproductive informed uninformed\nproductive unproductive known unknown\nproductive unproductive likely unlikely\nproductive unproductive logical illogical\nproductive unproductive pleasant unpleasant\nproductive unproductive possible impossible\nproductive unproductive possibly impossibly\nrational irrational reasonable unreasonable\nrational irrational responsible irresponsible\nrational irrational sure unsure\nrational irrational tasteful distasteful\nrational irrational acceptable unacceptable\nrational irrational aware unaware\nrational irrational certain uncertain\nrational irrational clear unclear\nrational irrational comfortable uncomfortable\nrational irrational competitive uncompetitive\nrational irrational consistent inconsistent\nrational irrational convincing unconvincing\nrational irrational convenient inconvenient\nrational irrational decided undecided\nrational irrational efficient inefficient\nrational irrational ethical unethical\nrational irrational fortunate unfortunate\nrational irrational honest dishonest\nrational irrational impressive unimpressive\nrational irrational informative uninformative\nrational irrational informed uninformed\nrational irrational known unknown\nrational irrational likely unlikely\nrational irrational logical illogical\nrational irrational pleasant unpleasant\nrational irrational possible impossible\nrational irrational possibly impossibly\nrational irrational productive unproductive\nreasonable unreasonable responsible irresponsible\nreasonable unreasonable sure unsure\nreasonable unreasonable tasteful distasteful\nreasonable unreasonable acceptable unacceptable\nreasonable unreasonable aware unaware\nreasonable unreasonable certain uncertain\nreasonable unreasonable clear unclear\nreasonable unreasonable comfortable uncomfortable\nreasonable unreasonable competitive uncompetitive\nreasonable unreasonable consistent inconsistent\nreasonable unreasonable convincing unconvincing\nreasonable unreasonable convenient inconvenient\nreasonable unreasonable decided undecided\nreasonable unreasonable efficient inefficient\nreasonable unreasonable ethical unethical\nreasonable unreasonable fortunate unfortunate\nreasonable unreasonable honest dishonest\nreasonable unreasonable impressive unimpressive\nreasonable unreasonable informative uninformative\nreasonable unreasonable informed uninformed\nreasonable unreasonable known unknown\nreasonable unreasonable likely unlikely\nreasonable unreasonable logical illogical\nreasonable unreasonable pleasant unpleasant\nreasonable unreasonable possible impossible\nreasonable unreasonable possibly impossibly\nreasonable unreasonable productive unproductive\nreasonable unreasonable rational irrational\nresponsible irresponsible sure unsure\nresponsible irresponsible tasteful distasteful\nresponsible irresponsible acceptable unacceptable\nresponsible irresponsible aware unaware\nresponsible irresponsible certain uncertain\nresponsible irresponsible clear unclear\nresponsible irresponsible comfortable uncomfortable\nresponsible irresponsible competitive uncompetitive\nresponsible irresponsible consistent inconsistent\nresponsible irresponsible convincing unconvincing\nresponsible irresponsible convenient inconvenient\nresponsible irresponsible decided undecided\nresponsible irresponsible efficient inefficient\nresponsible irresponsible ethical unethical\nresponsible irresponsible fortunate unfortunate\nresponsible irresponsible honest dishonest\nresponsible irresponsible impressive unimpressive\nresponsible irresponsible informative uninformative\nresponsible irresponsible informed uninformed\nresponsible irresponsible known unknown\nresponsible irresponsible likely unlikely\nresponsible irresponsible logical illogical\nresponsible irresponsible pleasant unpleasant\nresponsible irresponsible possible impossible\nresponsible irresponsible possibly impossibly\nresponsible irresponsible productive unproductive\nresponsible irresponsible rational irrational\nresponsible irresponsible reasonable unreasonable\nsure unsure tasteful distasteful\nsure unsure acceptable unacceptable\nsure unsure aware unaware\nsure unsure certain uncertain\nsure unsure clear unclear\nsure unsure comfortable uncomfortable\nsure unsure competitive uncompetitive\nsure unsure consistent inconsistent\nsure unsure convincing unconvincing\nsure unsure convenient inconvenient\nsure unsure decided undecided\nsure unsure efficient inefficient\nsure unsure ethical unethical\nsure unsure fortunate unfortunate\nsure unsure honest dishonest\nsure unsure impressive unimpressive\nsure unsure informative uninformative\nsure unsure informed uninformed\nsure unsure known unknown\nsure unsure likely unlikely\nsure unsure logical illogical\nsure unsure pleasant unpleasant\nsure unsure possible impossible\nsure unsure possibly impossibly\nsure unsure productive unproductive\nsure unsure rational irrational\nsure unsure reasonable unreasonable\nsure unsure responsible irresponsible\ntasteful distasteful acceptable unacceptable\ntasteful distasteful aware unaware\ntasteful distasteful certain uncertain\ntasteful distasteful clear unclear\ntasteful distasteful comfortable uncomfortable\ntasteful distasteful competitive uncompetitive\ntasteful distasteful consistent inconsistent\ntasteful distasteful convincing unconvincing\ntasteful distasteful convenient inconvenient\ntasteful distasteful decided undecided\ntasteful distasteful efficient inefficient\ntasteful distasteful ethical unethical\ntasteful distasteful fortunate unfortunate\ntasteful distasteful honest dishonest\ntasteful distasteful impressive unimpressive\ntasteful distasteful informative uninformative\ntasteful distasteful informed uninformed\ntasteful distasteful known unknown\ntasteful distasteful likely unlikely\ntasteful distasteful logical illogical\ntasteful distasteful pleasant unpleasant\ntasteful distasteful possible impossible\ntasteful distasteful possibly impossibly\ntasteful distasteful productive unproductive\ntasteful distasteful rational irrational\ntasteful distasteful reasonable unreasonable\ntasteful distasteful responsible irresponsible\ntasteful distasteful sure unsure\nbad worse big bigger\nbad worse bright brighter\nbad worse cheap cheaper\nbad worse cold colder\nbad worse cool cooler\nbad worse deep deeper\nbad worse easy easier\nbad worse fast faster\nbad worse good better\nbad worse great greater\nbad worse hard harder\nbad worse heavy heavier\nbad worse high higher\nbad worse hot hotter\nbad worse large larger\nbad worse long longer\nbad worse loud louder\nbad worse low lower\nbad worse new newer\nbad worse old older\nbad worse quick quicker\nbad worse safe safer\nbad worse sharp sharper\nbad worse short shorter\nbad worse simple simpler\nbad worse slow slower\nbad worse small smaller\nbad worse smart smarter\nbad worse strong stronger\nbad worse tall taller\nbad worse tight tighter\nbad worse tough tougher\nbad worse warm warmer\nbad worse weak weaker\nbad worse wide wider\nbad worse young younger\nbig bigger bright brighter\nbig bigger cheap cheaper\nbig bigger cold colder\nbig bigger cool cooler\nbig bigger deep deeper\nbig bigger easy easier\nbig bigger fast faster\nbig bigger good better\nbig bigger great greater\nbig bigger hard harder\nbig bigger heavy heavier\nbig bigger high higher\nbig bigger hot hotter\nbig bigger large larger\nbig bigger long longer\nbig bigger loud louder\nbig bigger low lower\nbig bigger new newer\nbig bigger old older\nbig bigger quick quicker\nbig bigger safe safer\nbig bigger sharp sharper\nbig bigger short shorter\nbig bigger simple simpler\nbig bigger slow slower\nbig bigger small smaller\nbig bigger smart smarter\nbig bigger strong stronger\nbig bigger tall taller\nbig bigger tight tighter\nbig bigger tough tougher\nbig bigger warm warmer\nbig bigger weak weaker\nbig bigger wide wider\nbig bigger young younger\nbig bigger bad worse\nbright brighter cheap cheaper\nbright brighter cold colder\nbright brighter cool cooler\nbright brighter deep deeper\nbright brighter easy easier\nbright brighter fast faster\nbright brighter good better\nbright brighter great greater\nbright brighter hard harder\nbright brighter heavy heavier\nbright brighter high higher\nbright brighter hot hotter\nbright brighter large larger\nbright brighter long longer\nbright brighter loud louder\nbright brighter low lower\nbright brighter new newer\nbright brighter old older\nbright brighter quick quicker\nbright brighter safe safer\nbright brighter sharp sharper\nbright brighter short shorter\nbright brighter simple simpler\nbright brighter slow slower\nbright brighter small smaller\nbright brighter smart smarter\nbright brighter strong stronger\nbright brighter tall taller\nbright brighter tight tighter\nbright brighter tough tougher\nbright brighter warm warmer\nbright brighter weak weaker\nbright brighter wide wider\nbright brighter young younger\nbright brighter bad worse\nbright brighter big bigger\ncheap cheaper cold colder\ncheap cheaper cool cooler\ncheap cheaper deep deeper\ncheap cheaper easy easier\ncheap cheaper fast faster\ncheap cheaper good better\ncheap cheaper great greater\ncheap cheaper hard harder\ncheap cheaper heavy heavier\ncheap cheaper high higher\ncheap cheaper hot hotter\ncheap cheaper large larger\ncheap cheaper long longer\ncheap cheaper loud louder\ncheap cheaper low lower\ncheap cheaper new newer\ncheap cheaper old older\ncheap cheaper quick quicker\ncheap cheaper safe safer\ncheap cheaper sharp sharper\ncheap cheaper short shorter\ncheap cheaper simple simpler\ncheap cheaper slow slower\ncheap cheaper small smaller\ncheap cheaper smart smarter\ncheap cheaper strong stronger\ncheap cheaper tall taller\ncheap cheaper tight tighter\ncheap cheaper tough tougher\ncheap cheaper warm warmer\ncheap cheaper weak weaker\ncheap cheaper wide wider\ncheap cheaper young younger\ncheap cheaper bad worse\ncheap cheaper big bigger\ncheap cheaper bright brighter\ncold colder cool cooler\ncold colder deep deeper\ncold colder easy easier\ncold colder fast faster\ncold colder good better\ncold colder great greater\ncold colder hard harder\ncold colder heavy heavier\ncold colder high higher\ncold colder hot hotter\ncold colder large larger\ncold colder long longer\ncold colder loud louder\ncold colder low lower\ncold colder new newer\ncold colder old older\ncold colder quick quicker\ncold colder safe safer\ncold colder sharp sharper\ncold colder short shorter\ncold colder simple simpler\ncold colder slow slower\ncold colder small smaller\ncold colder smart smarter\ncold colder strong stronger\ncold colder tall taller\ncold colder tight tighter\ncold colder tough tougher\ncold colder warm warmer\ncold colder weak weaker\ncold colder wide wider\ncold colder young younger\ncold colder bad worse\ncold colder big bigger\ncold colder bright brighter\ncold colder cheap cheaper\ncool cooler deep deeper\ncool cooler easy easier\ncool cooler fast faster\ncool cooler good better\ncool cooler great greater\ncool cooler hard harder\ncool cooler heavy heavier\ncool cooler high higher\ncool cooler hot hotter\ncool cooler large larger\ncool cooler long longer\ncool cooler loud louder\ncool cooler low lower\ncool cooler new newer\ncool cooler old older\ncool cooler quick quicker\ncool cooler safe safer\ncool cooler sharp sharper\ncool cooler short shorter\ncool cooler simple simpler\ncool cooler slow slower\ncool cooler small smaller\ncool cooler smart smarter\ncool cooler strong stronger\ncool cooler tall taller\ncool cooler tight tighter\ncool cooler tough tougher\ncool cooler warm warmer\ncool cooler weak weaker\ncool cooler wide wider\ncool cooler young younger\ncool cooler bad worse\ncool cooler big bigger\ncool cooler bright brighter\ncool cooler cheap cheaper\ncool cooler cold colder\ndeep deeper easy easier\ndeep deeper fast faster\ndeep deeper good better\ndeep deeper great greater\ndeep deeper hard harder\ndeep deeper heavy heavier\ndeep deeper high higher\ndeep deeper hot hotter\ndeep deeper large larger\ndeep deeper long longer\ndeep deeper loud louder\ndeep deeper low lower\ndeep deeper new newer\ndeep deeper old older\ndeep deeper quick quicker\ndeep deeper safe safer\ndeep deeper sharp sharper\ndeep deeper short shorter\ndeep deeper simple simpler\ndeep deeper slow slower\ndeep deeper small smaller\ndeep deeper smart smarter\ndeep deeper strong stronger\ndeep deeper tall taller\ndeep deeper tight tighter\ndeep deeper tough tougher\ndeep deeper warm warmer\ndeep deeper weak weaker\ndeep deeper wide wider\ndeep deeper young younger\ndeep deeper bad worse\ndeep deeper big bigger\ndeep deeper bright brighter\ndeep deeper cheap cheaper\ndeep deeper cold colder\ndeep deeper cool cooler\neasy easier fast faster\neasy easier good better\neasy easier great greater\neasy easier hard harder\neasy easier heavy heavier\neasy easier high higher\neasy easier hot hotter\neasy easier large larger\neasy easier long longer\neasy easier loud louder\neasy easier low lower\neasy easier new newer\neasy easier old older\neasy easier quick quicker\neasy easier safe safer\neasy easier sharp sharper\neasy easier short shorter\neasy easier simple simpler\neasy easier slow slower\neasy easier small smaller\neasy easier smart smarter\neasy easier strong stronger\neasy easier tall taller\neasy easier tight tighter\neasy easier tough tougher\neasy easier warm warmer\neasy easier weak weaker\neasy easier wide wider\neasy easier young younger\neasy easier bad worse\neasy easier big bigger\neasy easier bright brighter\neasy easier cheap cheaper\neasy easier cold colder\neasy easier cool cooler\neasy easier deep deeper\nfast faster good better\nfast faster great greater\nfast faster hard harder\nfast faster heavy heavier\nfast faster high higher\nfast faster hot hotter\nfast faster large larger\nfast faster long longer\nfast faster loud louder\nfast faster low lower\nfast faster new newer\nfast faster old older\nfast faster quick quicker\nfast faster safe safer\nfast faster sharp sharper\nfast faster short shorter\nfast faster simple simpler\nfast faster slow slower\nfast faster small smaller\nfast faster smart smarter\nfast faster strong stronger\nfast faster tall taller\nfast faster tight tighter\nfast faster tough tougher\nfast faster warm warmer\nfast faster weak weaker\nfast faster wide wider\nfast faster young younger\nfast faster bad worse\nfast faster big bigger\nfast faster bright brighter\nfast faster cheap cheaper\nfast faster cold colder\nfast faster cool cooler\nfast faster deep deeper\nfast faster easy easier\ngood better great greater\ngood better hard harder\ngood better heavy heavier\ngood better high higher\ngood better hot hotter\ngood better large larger\ngood better long longer\ngood better loud louder\ngood better low lower\ngood better new newer\ngood better old older\ngood better quick quicker\ngood better safe safer\ngood better sharp sharper\ngood better short shorter\ngood better simple simpler\ngood better slow slower\ngood better small smaller\ngood better smart smarter\ngood better strong stronger\ngood better tall taller\ngood better tight tighter\ngood better tough tougher\ngood better warm warmer\ngood better weak weaker\ngood better wide wider\ngood better young younger\ngood better bad worse\ngood better big bigger\ngood better bright brighter\ngood better cheap cheaper\ngood better cold colder\ngood better cool cooler\ngood better deep deeper\ngood better easy easier\ngood better fast faster\ngreat greater hard harder\ngreat greater heavy heavier\ngreat greater high higher\ngreat greater hot hotter\ngreat greater large larger\ngreat greater long longer\ngreat greater loud louder\ngreat greater low lower\ngreat greater new newer\ngreat greater old older\ngreat greater quick quicker\ngreat greater safe safer\ngreat greater sharp sharper\ngreat greater short shorter\ngreat greater simple simpler\ngreat greater slow slower\ngreat greater small smaller\ngreat greater smart smarter\ngreat greater strong stronger\ngreat greater tall taller\ngreat greater tight tighter\ngreat greater tough tougher\ngreat greater warm warmer\ngreat greater weak weaker\ngreat greater wide wider\ngreat greater young younger\ngreat greater bad worse\ngreat greater big bigger\ngreat greater bright brighter\ngreat greater cheap cheaper\ngreat greater cold colder\ngreat greater cool cooler\ngreat greater deep deeper\ngreat greater easy easier\ngreat greater fast faster\ngreat greater good better\nhard harder heavy heavier\nhard harder high higher\nhard harder hot hotter\nhard harder large larger\nhard harder long longer\nhard harder loud louder\nhard harder low lower\nhard harder new newer\nhard harder old older\nhard harder quick quicker\nhard harder safe safer\nhard harder sharp sharper\nhard harder short shorter\nhard harder simple simpler\nhard harder slow slower\nhard harder small smaller\nhard harder smart smarter\nhard harder strong stronger\nhard harder tall taller\nhard harder tight tighter\nhard harder tough tougher\nhard harder warm warmer\nhard harder weak weaker\nhard harder wide wider\nhard harder young younger\nhard harder bad worse\nhard harder big bigger\nhard harder bright brighter\nhard harder cheap cheaper\nhard harder cold colder\nhard harder cool cooler\nhard harder deep deeper\nhard harder easy easier\nhard harder fast faster\nhard harder good better\nhard harder great greater\nheavy heavier high higher\nheavy heavier hot hotter\nheavy heavier large larger\nheavy heavier long longer\nheavy heavier loud louder\nheavy heavier low lower\nheavy heavier new newer\nheavy heavier old older\nheavy heavier quick quicker\nheavy heavier safe safer\nheavy heavier sharp sharper\nheavy heavier short shorter\nheavy heavier simple simpler\nheavy heavier slow slower\nheavy heavier small smaller\nheavy heavier smart smarter\nheavy heavier strong stronger\nheavy heavier tall taller\nheavy heavier tight tighter\nheavy heavier tough tougher\nheavy heavier warm warmer\nheavy heavier weak weaker\nheavy heavier wide wider\nheavy heavier young younger\nheavy heavier bad worse\nheavy heavier big bigger\nheavy heavier bright brighter\nheavy heavier cheap cheaper\nheavy heavier cold colder\nheavy heavier cool cooler\nheavy heavier deep deeper\nheavy heavier easy easier\nheavy heavier fast faster\nheavy heavier good better\nheavy heavier great greater\nheavy heavier hard harder\nhigh higher hot hotter\nhigh higher large larger\nhigh higher long longer\nhigh higher loud louder\nhigh higher low lower\nhigh higher new newer\nhigh higher old older\nhigh higher quick quicker\nhigh higher safe safer\nhigh higher sharp sharper\nhigh higher short shorter\nhigh higher simple simpler\nhigh higher slow slower\nhigh higher small smaller\nhigh higher smart smarter\nhigh higher strong stronger\nhigh higher tall taller\nhigh higher tight tighter\nhigh higher tough tougher\nhigh higher warm warmer\nhigh higher weak weaker\nhigh higher wide wider\nhigh higher young younger\nhigh higher bad worse\nhigh higher big bigger\nhigh higher bright brighter\nhigh higher cheap cheaper\nhigh higher cold colder\nhigh higher cool cooler\nhigh higher deep deeper\nhigh higher easy easier\nhigh higher fast faster\nhigh higher good better\nhigh higher great greater\nhigh higher hard harder\nhigh higher heavy heavier\nhot hotter large larger\nhot hotter long longer\nhot hotter loud louder\nhot hotter low lower\nhot hotter new newer\nhot hotter old older\nhot hotter quick quicker\nhot hotter safe safer\nhot hotter sharp sharper\nhot hotter short shorter\nhot hotter simple simpler\nhot hotter slow slower\nhot hotter small smaller\nhot hotter smart smarter\nhot hotter strong stronger\nhot hotter tall taller\nhot hotter tight tighter\nhot hotter tough tougher\nhot hotter warm warmer\nhot hotter weak weaker\nhot hotter wide wider\nhot hotter young younger\nhot hotter bad worse\nhot hotter big bigger\nhot hotter bright brighter\nhot hotter cheap cheaper\nhot hotter cold colder\nhot hotter cool cooler\nhot hotter deep deeper\nhot hotter easy easier\nhot hotter fast faster\nhot hotter good better\nhot hotter great greater\nhot hotter hard harder\nhot hotter heavy heavier\nhot hotter high higher\nlarge larger long longer\nlarge larger loud louder\nlarge larger low lower\nlarge larger new newer\nlarge larger old older\nlarge larger quick quicker\nlarge larger safe safer\nlarge larger sharp sharper\nlarge larger short shorter\nlarge larger simple simpler\nlarge larger slow slower\nlarge larger small smaller\nlarge larger smart smarter\nlarge larger strong stronger\nlarge larger tall taller\nlarge larger tight tighter\nlarge larger tough tougher\nlarge larger warm warmer\nlarge larger weak weaker\nlarge larger wide wider\nlarge larger young younger\nlarge larger bad worse\nlarge larger big bigger\nlarge larger bright brighter\nlarge larger cheap cheaper\nlarge larger cold colder\nlarge larger cool cooler\nlarge larger deep deeper\nlarge larger easy easier\nlarge larger fast faster\nlarge larger good better\nlarge larger great greater\nlarge larger hard harder\nlarge larger heavy heavier\nlarge larger high higher\nlarge larger hot hotter\nlong longer loud louder\nlong longer low lower\nlong longer new newer\nlong longer old older\nlong longer quick quicker\nlong longer safe safer\nlong longer sharp sharper\nlong longer short shorter\nlong longer simple simpler\nlong longer slow slower\nlong longer small smaller\nlong longer smart smarter\nlong longer strong stronger\nlong longer tall taller\nlong longer tight tighter\nlong longer tough tougher\nlong longer warm warmer\nlong longer weak weaker\nlong longer wide wider\nlong longer young younger\nlong longer bad worse\nlong longer big bigger\nlong longer bright brighter\nlong longer cheap cheaper\nlong longer cold colder\nlong longer cool cooler\nlong longer deep deeper\nlong longer easy easier\nlong longer fast faster\nlong longer good better\nlong longer great greater\nlong longer hard harder\nlong longer heavy heavier\nlong longer high higher\nlong longer hot hotter\nlong longer large larger\nloud louder low lower\nloud louder new newer\nloud louder old older\nloud louder quick quicker\nloud louder safe safer\nloud louder sharp sharper\nloud louder short shorter\nloud louder simple simpler\nloud louder slow slower\nloud louder small smaller\nloud louder smart smarter\nloud louder strong stronger\nloud louder tall taller\nloud louder tight tighter\nloud louder tough tougher\nloud louder warm warmer\nloud louder weak weaker\nloud louder wide wider\nloud louder young younger\nloud louder bad worse\nloud louder big bigger\nloud louder bright brighter\nloud louder cheap cheaper\nloud louder cold colder\nloud louder cool cooler\nloud louder deep deeper\nloud louder easy easier\nloud louder fast faster\nloud louder good better\nloud louder great greater\nloud louder hard harder\nloud louder heavy heavier\nloud louder high higher\nloud louder hot hotter\nloud louder large larger\nloud louder long longer\nlow lower new newer\nlow lower old older\nlow lower quick quicker\nlow lower safe safer\nlow lower sharp sharper\nlow lower short shorter\nlow lower simple simpler\nlow lower slow slower\nlow lower small smaller\nlow lower smart smarter\nlow lower strong stronger\nlow lower tall taller\nlow lower tight tighter\nlow lower tough tougher\nlow lower warm warmer\nlow lower weak weaker\nlow lower wide wider\nlow lower young younger\nlow lower bad worse\nlow lower big bigger\nlow lower bright brighter\nlow lower cheap cheaper\nlow lower cold colder\nlow lower cool cooler\nlow lower deep deeper\nlow lower easy easier\nlow lower fast faster\nlow lower good better\nlow lower great greater\nlow lower hard harder\nlow lower heavy heavier\nlow lower high higher\nlow lower hot hotter\nlow lower large larger\nlow lower long longer\nlow lower loud louder\nnew newer old older\nnew newer quick quicker\nnew newer safe safer\nnew newer sharp sharper\nnew newer short shorter\nnew newer simple simpler\nnew newer slow slower\nnew newer small smaller\nnew newer smart smarter\nnew newer strong stronger\nnew newer tall taller\nnew newer tight tighter\nnew newer tough tougher\nnew newer warm warmer\nnew newer weak weaker\nnew newer wide wider\nnew newer young younger\nnew newer bad worse\nnew newer big bigger\nnew newer bright brighter\nnew newer cheap cheaper\nnew newer cold colder\nnew newer cool cooler\nnew newer deep deeper\nnew newer easy easier\nnew newer fast faster\nnew newer good better\nnew newer great greater\nnew newer hard harder\nnew newer heavy heavier\nnew newer high higher\nnew newer hot hotter\nnew newer large larger\nnew newer long longer\nnew newer loud louder\nnew newer low lower\nold older quick quicker\nold older safe safer\nold older sharp sharper\nold older short shorter\nold older simple simpler\nold older slow slower\nold older small smaller\nold older smart smarter\nold older strong stronger\nold older tall taller\nold older tight tighter\nold older tough tougher\nold older warm warmer\nold older weak weaker\nold older wide wider\nold older young younger\nold older bad worse\nold older big bigger\nold older bright brighter\nold older cheap cheaper\nold older cold colder\nold older cool cooler\nold older deep deeper\nold older easy easier\nold older fast faster\nold older good better\nold older great greater\nold older hard harder\nold older heavy heavier\nold older high higher\nold older hot hotter\nold older large larger\nold older long longer\nold older loud louder\nold older low lower\nold older new newer\nquick quicker safe safer\nquick quicker sharp sharper\nquick quicker short shorter\nquick quicker simple simpler\nquick quicker slow slower\nquick quicker small smaller\nquick quicker smart smarter\nquick quicker strong stronger\nquick quicker tall taller\nquick quicker tight tighter\nquick quicker tough tougher\nquick quicker warm warmer\nquick quicker weak weaker\nquick quicker wide wider\nquick quicker young younger\nquick quicker bad worse\nquick quicker big bigger\nquick quicker bright brighter\nquick quicker cheap cheaper\nquick quicker cold colder\nquick quicker cool cooler\nquick quicker deep deeper\nquick quicker easy easier\nquick quicker fast faster\nquick quicker good better\nquick quicker great greater\nquick quicker hard harder\nquick quicker heavy heavier\nquick quicker high higher\nquick quicker hot hotter\nquick quicker large larger\nquick quicker long longer\nquick quicker loud louder\nquick quicker low lower\nquick quicker new newer\nquick quicker old older\nsafe safer sharp sharper\nsafe safer short shorter\nsafe safer simple simpler\nsafe safer slow slower\nsafe safer small smaller\nsafe safer smart smarter\nsafe safer strong stronger\nsafe safer tall taller\nsafe safer tight tighter\nsafe safer tough tougher\nsafe safer warm warmer\nsafe safer weak weaker\nsafe safer wide wider\nsafe safer young younger\nsafe safer bad worse\nsafe safer big bigger\nsafe safer bright brighter\nsafe safer cheap cheaper\nsafe safer cold colder\nsafe safer cool cooler\nsafe safer deep deeper\nsafe safer easy easier\nsafe safer fast faster\nsafe safer good better\nsafe safer great greater\nsafe safer hard harder\nsafe safer heavy heavier\nsafe safer high higher\nsafe safer hot hotter\nsafe safer large larger\nsafe safer long longer\nsafe safer loud louder\nsafe safer low lower\nsafe safer new newer\nsafe safer old older\nsafe safer quick quicker\nsharp sharper short shorter\nsharp sharper simple simpler\nsharp sharper slow slower\nsharp sharper small smaller\nsharp sharper smart smarter\nsharp sharper strong stronger\nsharp sharper tall taller\nsharp sharper tight tighter\nsharp sharper tough tougher\nsharp sharper warm warmer\nsharp sharper weak weaker\nsharp sharper wide wider\nsharp sharper young younger\nsharp sharper bad worse\nsharp sharper big bigger\nsharp sharper bright brighter\nsharp sharper cheap cheaper\nsharp sharper cold colder\nsharp sharper cool cooler\nsharp sharper deep deeper\nsharp sharper easy easier\nsharp sharper fast faster\nsharp sharper good better\nsharp sharper great greater\nsharp sharper hard harder\nsharp sharper heavy heavier\nsharp sharper high higher\nsharp sharper hot hotter\nsharp sharper large larger\nsharp sharper long longer\nsharp sharper loud louder\nsharp sharper low lower\nsharp sharper new newer\nsharp sharper old older\nsharp sharper quick quicker\nsharp sharper safe safer\nshort shorter simple simpler\nshort shorter slow slower\nshort shorter small smaller\nshort shorter smart smarter\nshort shorter strong stronger\nshort shorter tall taller\nshort shorter tight tighter\nshort shorter tough tougher\nshort shorter warm warmer\nshort shorter weak weaker\nshort shorter wide wider\nshort shorter young younger\nshort shorter bad worse\nshort shorter big bigger\nshort shorter bright brighter\nshort shorter cheap cheaper\nshort shorter cold colder\nshort shorter cool cooler\nshort shorter deep deeper\nshort shorter easy easier\nshort shorter fast faster\nshort shorter good better\nshort shorter great greater\nshort shorter hard harder\nshort shorter heavy heavier\nshort shorter high higher\nshort shorter hot hotter\nshort shorter large larger\nshort shorter long longer\nshort shorter loud louder\nshort shorter low lower\nshort shorter new newer\nshort shorter old older\nshort shorter quick quicker\nshort shorter safe safer\nshort shorter sharp sharper\nsimple simpler slow slower\nsimple simpler small smaller\nsimple simpler smart smarter\nsimple simpler strong stronger\nsimple simpler tall taller\nsimple simpler tight tighter\nsimple simpler tough tougher\nsimple simpler warm warmer\nsimple simpler weak weaker\nsimple simpler wide wider\nsimple simpler young younger\nsimple simpler bad worse\nsimple simpler big bigger\nsimple simpler bright brighter\nsimple simpler cheap cheaper\nsimple simpler cold colder\nsimple simpler cool cooler\nsimple simpler deep deeper\nsimple simpler easy easier\nsimple simpler fast faster\nsimple simpler good better\nsimple simpler great greater\nsimple simpler hard harder\nsimple simpler heavy heavier\nsimple simpler high higher\nsimple simpler hot hotter\nsimple simpler large larger\nsimple simpler long longer\nsimple simpler loud louder\nsimple simpler low lower\nsimple simpler new newer\nsimple simpler old older\nsimple simpler quick quicker\nsimple simpler safe safer\nsimple simpler sharp sharper\nsimple simpler short shorter\nslow slower small smaller\nslow slower smart smarter\nslow slower strong stronger\nslow slower tall taller\nslow slower tight tighter\nslow slower tough tougher\nslow slower warm warmer\nslow slower weak weaker\nslow slower wide wider\nslow slower young younger\nslow slower bad worse\nslow slower big bigger\nslow slower bright brighter\nslow slower cheap cheaper\nslow slower cold colder\nslow slower cool cooler\nslow slower deep deeper\nslow slower easy easier\nslow slower fast faster\nslow slower good better\nslow slower great greater\nslow slower hard harder\nslow slower heavy heavier\nslow slower high higher\nslow slower hot hotter\nslow slower large larger\nslow slower long longer\nslow slower loud louder\nslow slower low lower\nslow slower new newer\nslow slower old older\nslow slower quick quicker\nslow slower safe safer\nslow slower sharp sharper\nslow slower short shorter\nslow slower simple simpler\nsmall smaller smart smarter\nsmall smaller strong stronger\nsmall smaller tall taller\nsmall smaller tight tighter\nsmall smaller tough tougher\nsmall smaller warm warmer\nsmall smaller weak weaker\nsmall smaller wide wider\nsmall smaller young younger\nsmall smaller bad worse\nsmall smaller big bigger\nsmall smaller bright brighter\nsmall smaller cheap cheaper\nsmall smaller cold colder\nsmall smaller cool cooler\nsmall smaller deep deeper\nsmall smaller easy easier\nsmall smaller fast faster\nsmall smaller good better\nsmall smaller great greater\nsmall smaller hard harder\nsmall smaller heavy heavier\nsmall smaller high higher\nsmall smaller hot hotter\nsmall smaller large larger\nsmall smaller long longer\nsmall smaller loud louder\nsmall smaller low lower\nsmall smaller new newer\nsmall smaller old older\nsmall smaller quick quicker\nsmall smaller safe safer\nsmall smaller sharp sharper\nsmall smaller short shorter\nsmall smaller simple simpler\nsmall smaller slow slower\nsmart smarter strong stronger\nsmart smarter tall taller\nsmart smarter tight tighter\nsmart smarter tough tougher\nsmart smarter warm warmer\nsmart smarter weak weaker\nsmart smarter wide wider\nsmart smarter young younger\nsmart smarter bad worse\nsmart smarter big bigger\nsmart smarter bright brighter\nsmart smarter cheap cheaper\nsmart smarter cold colder\nsmart smarter cool cooler\nsmart smarter deep deeper\nsmart smarter easy easier\nsmart smarter fast faster\nsmart smarter good better\nsmart smarter great greater\nsmart smarter hard harder\nsmart smarter heavy heavier\nsmart smarter high higher\nsmart smarter hot hotter\nsmart smarter large larger\nsmart smarter long longer\nsmart smarter loud louder\nsmart smarter low lower\nsmart smarter new newer\nsmart smarter old older\nsmart smarter quick quicker\nsmart smarter safe safer\nsmart smarter sharp sharper\nsmart smarter short shorter\nsmart smarter simple simpler\nsmart smarter slow slower\nsmart smarter small smaller\nstrong stronger tall taller\nstrong stronger tight tighter\nstrong stronger tough tougher\nstrong stronger warm warmer\nstrong stronger weak weaker\nstrong stronger wide wider\nstrong stronger young younger\nstrong stronger bad worse\nstrong stronger big bigger\nstrong stronger bright brighter\nstrong stronger cheap cheaper\nstrong stronger cold colder\nstrong stronger cool cooler\nstrong stronger deep deeper\nstrong stronger easy easier\nstrong stronger fast faster\nstrong stronger good better\nstrong stronger great greater\nstrong stronger hard harder\nstrong stronger heavy heavier\nstrong stronger high higher\nstrong stronger hot hotter\nstrong stronger large larger\nstrong stronger long longer\nstrong stronger loud louder\nstrong stronger low lower\nstrong stronger new newer\nstrong stronger old older\nstrong stronger quick quicker\nstrong stronger safe safer\nstrong stronger sharp sharper\nstrong stronger short shorter\nstrong stronger simple simpler\nstrong stronger slow slower\nstrong stronger small smaller\nstrong stronger smart smarter\ntall taller tight tighter\ntall taller tough tougher\ntall taller warm warmer\ntall taller weak weaker\ntall taller wide wider\ntall taller young younger\ntall taller bad worse\ntall taller big bigger\ntall taller bright brighter\ntall taller cheap cheaper\ntall taller cold colder\ntall taller cool cooler\ntall taller deep deeper\ntall taller easy easier\ntall taller fast faster\ntall taller good better\ntall taller great greater\ntall taller hard harder\ntall taller heavy heavier\ntall taller high higher\ntall taller hot hotter\ntall taller large larger\ntall taller long longer\ntall taller loud louder\ntall taller low lower\ntall taller new newer\ntall taller old older\ntall taller quick quicker\ntall taller safe safer\ntall taller sharp sharper\ntall taller short shorter\ntall taller simple simpler\ntall taller slow slower\ntall taller small smaller\ntall taller smart smarter\ntall taller strong stronger\ntight tighter tough tougher\ntight tighter warm warmer\ntight tighter weak weaker\ntight tighter wide wider\ntight tighter young younger\ntight tighter bad worse\ntight tighter big bigger\ntight tighter bright brighter\ntight tighter cheap cheaper\ntight tighter cold colder\ntight tighter cool cooler\ntight tighter deep deeper\ntight tighter easy easier\ntight tighter fast faster\ntight tighter good better\ntight tighter great greater\ntight tighter hard harder\ntight tighter heavy heavier\ntight tighter high higher\ntight tighter hot hotter\ntight tighter large larger\ntight tighter long longer\ntight tighter loud louder\ntight tighter low lower\ntight tighter new newer\ntight tighter old older\ntight tighter quick quicker\ntight tighter safe safer\ntight tighter sharp sharper\ntight tighter short shorter\ntight tighter simple simpler\ntight tighter slow slower\ntight tighter small smaller\ntight tighter smart smarter\ntight tighter strong stronger\ntight tighter tall taller\ntough tougher warm warmer\ntough tougher weak weaker\ntough tougher wide wider\ntough tougher young younger\ntough tougher bad worse\ntough tougher big bigger\ntough tougher bright brighter\ntough tougher cheap cheaper\ntough tougher cold colder\ntough tougher cool cooler\ntough tougher deep deeper\ntough tougher easy easier\ntough tougher fast faster\ntough tougher good better\ntough tougher great greater\ntough tougher hard harder\ntough tougher heavy heavier\ntough tougher high higher\ntough tougher hot hotter\ntough tougher large larger\ntough tougher long longer\ntough tougher loud louder\ntough tougher low lower\ntough tougher new newer\ntough tougher old older\ntough tougher quick quicker\ntough tougher safe safer\ntough tougher sharp sharper\ntough tougher short shorter\ntough tougher simple simpler\ntough tougher slow slower\ntough tougher small smaller\ntough tougher smart smarter\ntough tougher strong stronger\ntough tougher tall taller\ntough tougher tight tighter\nwarm warmer weak weaker\nwarm warmer wide wider\nwarm warmer young younger\nwarm warmer bad worse\nwarm warmer big bigger\nwarm warmer bright brighter\nwarm warmer cheap cheaper\nwarm warmer cold colder\nwarm warmer cool cooler\nwarm warmer deep deeper\nwarm warmer easy easier\nwarm warmer fast faster\nwarm warmer good better\nwarm warmer great greater\nwarm warmer hard harder\nwarm warmer heavy heavier\nwarm warmer high higher\nwarm warmer hot hotter\nwarm warmer large larger\nwarm warmer long longer\nwarm warmer loud louder\nwarm warmer low lower\nwarm warmer new newer\nwarm warmer old older\nwarm warmer quick quicker\nwarm warmer safe safer\nwarm warmer sharp sharper\nwarm warmer short shorter\nwarm warmer simple simpler\nwarm warmer slow slower\nwarm warmer small smaller\nwarm warmer smart smarter\nwarm warmer strong stronger\nwarm warmer tall taller\nwarm warmer tight tighter\nwarm warmer tough tougher\nweak weaker wide wider\nweak weaker young younger\nweak weaker bad worse\nweak weaker big bigger\nweak weaker bright brighter\nweak weaker cheap cheaper\nweak weaker cold colder\nweak weaker cool cooler\nweak weaker deep deeper\nweak weaker easy easier\nweak weaker fast faster\nweak weaker good better\nweak weaker great greater\nweak weaker hard harder\nweak weaker heavy heavier\nweak weaker high higher\nweak weaker hot hotter\nweak weaker large larger\nweak weaker long longer\nweak weaker loud louder\nweak weaker low lower\nweak weaker new newer\nweak weaker old older\nweak weaker quick quicker\nweak weaker safe safer\nweak weaker sharp sharper\nweak weaker short shorter\nweak weaker simple simpler\nweak weaker slow slower\nweak weaker small smaller\nweak weaker smart smarter\nweak weaker strong stronger\nweak weaker tall taller\nweak weaker tight tighter\nweak weaker tough tougher\nweak weaker warm warmer\nwide wider young younger\nwide wider bad worse\nwide wider big bigger\nwide wider bright brighter\nwide wider cheap cheaper\nwide wider cold colder\nwide wider cool cooler\nwide wider deep deeper\nwide wider easy easier\nwide wider fast faster\nwide wider good better\nwide wider great greater\nwide wider hard harder\nwide wider heavy heavier\nwide wider high higher\nwide wider hot hotter\nwide wider large larger\nwide wider long longer\nwide wider loud louder\nwide wider low lower\nwide wider new newer\nwide wider old older\nwide wider quick quicker\nwide wider safe safer\nwide wider sharp sharper\nwide wider short shorter\nwide wider simple simpler\nwide wider slow slower\nwide wider small smaller\nwide wider smart smarter\nwide wider strong stronger\nwide wider tall taller\nwide wider tight tighter\nwide wider tough tougher\nwide wider warm warmer\nwide wider weak weaker\nyoung younger bad worse\nyoung younger big bigger\nyoung younger bright brighter\nyoung younger cheap cheaper\nyoung younger cold colder\nyoung younger cool cooler\nyoung younger deep deeper\nyoung younger easy easier\nyoung younger fast faster\nyoung younger good better\nyoung younger great greater\nyoung younger hard harder\nyoung younger heavy heavier\nyoung younger high higher\nyoung younger hot hotter\nyoung younger large larger\nyoung younger long longer\nyoung younger loud louder\nyoung younger low lower\nyoung younger new newer\nyoung younger old older\nyoung younger quick quicker\nyoung younger safe safer\nyoung younger sharp sharper\nyoung younger short shorter\nyoung younger simple simpler\nyoung younger slow slower\nyoung younger small smaller\nyoung younger smart smarter\nyoung younger strong stronger\nyoung younger tall taller\nyoung younger tight tighter\nyoung younger tough tougher\nyoung younger warm warmer\nyoung younger weak weaker\nyoung younger wide wider\nbad worst big biggest\nbad worst bright brightest\nbad worst cold coldest\nbad worst cool coolest\nbad worst dark darkest\nbad worst easy easiest\nbad worst fast fastest\nbad worst good best\nbad worst great greatest\nbad worst high highest\nbad worst hot hottest\nbad worst large largest\nbad worst long longest\nbad worst low lowest\nbad worst lucky luckiest\nbad worst old oldest\nbad worst quick quickest\nbad worst sharp sharpest\nbad worst simple simplest\nbad worst short shortest\nbad worst slow slowest\nbad worst small smallest\nbad worst smart smartest\nbad worst strange strangest\nbad worst strong strongest\nbad worst sweet sweetest\nbad worst tall tallest\nbad worst tasty tastiest\nbad worst warm warmest\nbad worst weak weakest\nbad worst weird weirdest\nbad worst wide widest\nbad worst young youngest\nbig biggest bright brightest\nbig biggest cold coldest\nbig biggest cool coolest\nbig biggest dark darkest\nbig biggest easy easiest\nbig biggest fast fastest\nbig biggest good best\nbig biggest great greatest\nbig biggest high highest\nbig biggest hot hottest\nbig biggest large largest\nbig biggest long longest\nbig biggest low lowest\nbig biggest lucky luckiest\nbig biggest old oldest\nbig biggest quick quickest\nbig biggest sharp sharpest\nbig biggest simple simplest\nbig biggest short shortest\nbig biggest slow slowest\nbig biggest small smallest\nbig biggest smart smartest\nbig biggest strange strangest\nbig biggest strong strongest\nbig biggest sweet sweetest\nbig biggest tall tallest\nbig biggest tasty tastiest\nbig biggest warm warmest\nbig biggest weak weakest\nbig biggest weird weirdest\nbig biggest wide widest\nbig biggest young youngest\nbig biggest bad worst\nbright brightest cold coldest\nbright brightest cool coolest\nbright brightest dark darkest\nbright brightest easy easiest\nbright brightest fast fastest\nbright brightest good best\nbright brightest great greatest\nbright brightest high highest\nbright brightest hot hottest\nbright brightest large largest\nbright brightest long longest\nbright brightest low lowest\nbright brightest lucky luckiest\nbright brightest old oldest\nbright brightest quick quickest\nbright brightest sharp sharpest\nbright brightest simple simplest\nbright brightest short shortest\nbright brightest slow slowest\nbright brightest small smallest\nbright brightest smart smartest\nbright brightest strange strangest\nbright brightest strong strongest\nbright brightest sweet sweetest\nbright brightest tall tallest\nbright brightest tasty tastiest\nbright brightest warm warmest\nbright brightest weak weakest\nbright brightest weird weirdest\nbright brightest wide widest\nbright brightest young youngest\nbright brightest bad worst\nbright brightest big biggest\ncold coldest cool coolest\ncold coldest dark darkest\ncold coldest easy easiest\ncold coldest fast fastest\ncold coldest good best\ncold coldest great greatest\ncold coldest high highest\ncold coldest hot hottest\ncold coldest large largest\ncold coldest long longest\ncold coldest low lowest\ncold coldest lucky luckiest\ncold coldest old oldest\ncold coldest quick quickest\ncold coldest sharp sharpest\ncold coldest simple simplest\ncold coldest short shortest\ncold coldest slow slowest\ncold coldest small smallest\ncold coldest smart smartest\ncold coldest strange strangest\ncold coldest strong strongest\ncold coldest sweet sweetest\ncold coldest tall tallest\ncold coldest tasty tastiest\ncold coldest warm warmest\ncold coldest weak weakest\ncold coldest weird weirdest\ncold coldest wide widest\ncold coldest young youngest\ncold coldest bad worst\ncold coldest big biggest\ncold coldest bright brightest\ncool coolest dark darkest\ncool coolest easy easiest\ncool coolest fast fastest\ncool coolest good best\ncool coolest great greatest\ncool coolest high highest\ncool coolest hot hottest\ncool coolest large largest\ncool coolest long longest\ncool coolest low lowest\ncool coolest lucky luckiest\ncool coolest old oldest\ncool coolest quick quickest\ncool coolest sharp sharpest\ncool coolest simple simplest\ncool coolest short shortest\ncool coolest slow slowest\ncool coolest small smallest\ncool coolest smart smartest\ncool coolest strange strangest\ncool coolest strong strongest\ncool coolest sweet sweetest\ncool coolest tall tallest\ncool coolest tasty tastiest\ncool coolest warm warmest\ncool coolest weak weakest\ncool coolest weird weirdest\ncool coolest wide widest\ncool coolest young youngest\ncool coolest bad worst\ncool coolest big biggest\ncool coolest bright brightest\ncool coolest cold coldest\ndark darkest easy easiest\ndark darkest fast fastest\ndark darkest good best\ndark darkest great greatest\ndark darkest high highest\ndark darkest hot hottest\ndark darkest large largest\ndark darkest long longest\ndark darkest low lowest\ndark darkest lucky luckiest\ndark darkest old oldest\ndark darkest quick quickest\ndark darkest sharp sharpest\ndark darkest simple simplest\ndark darkest short shortest\ndark darkest slow slowest\ndark darkest small smallest\ndark darkest smart smartest\ndark darkest strange strangest\ndark darkest strong strongest\ndark darkest sweet sweetest\ndark darkest tall tallest\ndark darkest tasty tastiest\ndark darkest warm warmest\ndark darkest weak weakest\ndark darkest weird weirdest\ndark darkest wide widest\ndark darkest young youngest\ndark darkest bad worst\ndark darkest big biggest\ndark darkest bright brightest\ndark darkest cold coldest\ndark darkest cool coolest\neasy easiest fast fastest\neasy easiest good best\neasy easiest great greatest\neasy easiest high highest\neasy easiest hot hottest\neasy easiest large largest\neasy easiest long longest\neasy easiest low lowest\neasy easiest lucky luckiest\neasy easiest old oldest\neasy easiest quick quickest\neasy easiest sharp sharpest\neasy easiest simple simplest\neasy easiest short shortest\neasy easiest slow slowest\neasy easiest small smallest\neasy easiest smart smartest\neasy easiest strange strangest\neasy easiest strong strongest\neasy easiest sweet sweetest\neasy easiest tall tallest\neasy easiest tasty tastiest\neasy easiest warm warmest\neasy easiest weak weakest\neasy easiest weird weirdest\neasy easiest wide widest\neasy easiest young youngest\neasy easiest bad worst\neasy easiest big biggest\neasy easiest bright brightest\neasy easiest cold coldest\neasy easiest cool coolest\neasy easiest dark darkest\nfast fastest good best\nfast fastest great greatest\nfast fastest high highest\nfast fastest hot hottest\nfast fastest large largest\nfast fastest long longest\nfast fastest low lowest\nfast fastest lucky luckiest\nfast fastest old oldest\nfast fastest quick quickest\nfast fastest sharp sharpest\nfast fastest simple simplest\nfast fastest short shortest\nfast fastest slow slowest\nfast fastest small smallest\nfast fastest smart smartest\nfast fastest strange strangest\nfast fastest strong strongest\nfast fastest sweet sweetest\nfast fastest tall tallest\nfast fastest tasty tastiest\nfast fastest warm warmest\nfast fastest weak weakest\nfast fastest weird weirdest\nfast fastest wide widest\nfast fastest young youngest\nfast fastest bad worst\nfast fastest big biggest\nfast fastest bright brightest\nfast fastest cold coldest\nfast fastest cool coolest\nfast fastest dark darkest\nfast fastest easy easiest\ngood best great greatest\ngood best high highest\ngood best hot 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dark darkest\nwide widest easy easiest\nwide widest fast fastest\nwide widest good best\nwide widest great greatest\nwide widest high highest\nwide widest hot hottest\nwide widest large largest\nwide widest long longest\nwide widest low lowest\nwide widest lucky luckiest\nwide widest old oldest\nwide widest quick quickest\nwide widest sharp sharpest\nwide widest simple simplest\nwide widest short shortest\nwide widest slow slowest\nwide widest small smallest\nwide widest smart smartest\nwide widest strange strangest\nwide widest strong strongest\nwide widest sweet sweetest\nwide widest tall tallest\nwide widest tasty tastiest\nwide widest warm warmest\nwide widest weak weakest\nwide widest weird weirdest\nyoung youngest bad worst\nyoung youngest big biggest\nyoung youngest bright brightest\nyoung youngest cold coldest\nyoung youngest cool coolest\nyoung youngest dark darkest\nyoung youngest easy easiest\nyoung youngest fast fastest\nyoung youngest good best\nyoung youngest great 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implementing\ncode coding increase increasing\ncode coding invent inventing\ncode coding jump jumping\ncode coding listen listening\ncode coding look looking\ncode coding move moving\ncode coding play playing\ncode coding predict predicting\ncode coding read reading\ncode coding run running\ncode coding say saying\ncode coding scream screaming\ncode coding see seeing\ncode coding shuffle shuffling\ncode coding sing singing\ncode coding sit sitting\ncode coding slow slowing\ncode coding swim swimming\ncode coding think thinking\ncode coding vanish vanishing\ncode coding walk walking\ncode coding write writing\ndance dancing debug debugging\ndance dancing decrease decreasing\ndance dancing describe describing\ndance dancing discover discovering\ndance dancing enhance enhancing\ndance dancing fly flying\ndance dancing generate generating\ndance dancing go going\ndance dancing implement implementing\ndance dancing increase increasing\ndance dancing invent inventing\ndance dancing jump jumping\ndance dancing listen listening\ndance dancing look looking\ndance dancing move moving\ndance dancing play playing\ndance dancing predict predicting\ndance dancing read reading\ndance dancing run running\ndance dancing say saying\ndance dancing scream screaming\ndance dancing see seeing\ndance dancing shuffle shuffling\ndance dancing sing singing\ndance dancing sit sitting\ndance dancing slow slowing\ndance dancing swim swimming\ndance dancing think thinking\ndance dancing vanish vanishing\ndance dancing walk walking\ndance dancing write writing\ndance dancing code coding\ndebug debugging decrease decreasing\ndebug debugging describe describing\ndebug debugging discover discovering\ndebug debugging enhance enhancing\ndebug debugging fly flying\ndebug debugging generate generating\ndebug debugging go going\ndebug debugging implement implementing\ndebug debugging increase increasing\ndebug debugging invent inventing\ndebug debugging jump jumping\ndebug debugging listen 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jumping\ndecrease decreasing listen listening\ndecrease decreasing look looking\ndecrease decreasing move moving\ndecrease decreasing play playing\ndecrease decreasing predict predicting\ndecrease decreasing read reading\ndecrease decreasing run running\ndecrease decreasing say saying\ndecrease decreasing scream screaming\ndecrease decreasing see seeing\ndecrease decreasing shuffle shuffling\ndecrease decreasing sing singing\ndecrease decreasing sit sitting\ndecrease decreasing slow slowing\ndecrease decreasing swim swimming\ndecrease decreasing think thinking\ndecrease decreasing vanish vanishing\ndecrease decreasing walk walking\ndecrease decreasing write writing\ndecrease decreasing code coding\ndecrease decreasing dance dancing\ndecrease decreasing debug debugging\ndescribe describing discover discovering\ndescribe describing enhance enhancing\ndescribe describing fly flying\ndescribe describing generate generating\ndescribe describing go going\ndescribe describing implement implementing\ndescribe describing increase increasing\ndescribe describing invent inventing\ndescribe describing jump jumping\ndescribe describing listen listening\ndescribe describing look looking\ndescribe describing move moving\ndescribe describing play playing\ndescribe describing predict predicting\ndescribe describing read reading\ndescribe describing run running\ndescribe describing say saying\ndescribe describing scream screaming\ndescribe describing see seeing\ndescribe describing shuffle shuffling\ndescribe describing sing singing\ndescribe describing sit sitting\ndescribe describing slow slowing\ndescribe describing swim swimming\ndescribe describing think thinking\ndescribe describing vanish vanishing\ndescribe describing walk walking\ndescribe describing write writing\ndescribe describing code coding\ndescribe describing dance dancing\ndescribe describing debug debugging\ndescribe describing decrease decreasing\ndiscover discovering enhance enhancing\ndiscover discovering fly flying\ndiscover discovering generate generating\ndiscover discovering go going\ndiscover discovering implement implementing\ndiscover discovering increase increasing\ndiscover discovering invent inventing\ndiscover discovering jump jumping\ndiscover discovering listen listening\ndiscover discovering look looking\ndiscover discovering move moving\ndiscover discovering play playing\ndiscover discovering predict predicting\ndiscover discovering read reading\ndiscover discovering run running\ndiscover discovering say saying\ndiscover discovering scream screaming\ndiscover discovering see seeing\ndiscover discovering shuffle shuffling\ndiscover discovering sing singing\ndiscover discovering sit sitting\ndiscover discovering slow slowing\ndiscover discovering swim swimming\ndiscover discovering think thinking\ndiscover discovering vanish vanishing\ndiscover discovering walk walking\ndiscover discovering write writing\ndiscover discovering code coding\ndiscover discovering dance dancing\ndiscover discovering debug debugging\ndiscover discovering decrease decreasing\ndiscover discovering describe describing\nenhance enhancing fly flying\nenhance enhancing generate generating\nenhance enhancing go going\nenhance enhancing implement implementing\nenhance enhancing increase increasing\nenhance enhancing invent inventing\nenhance enhancing jump jumping\nenhance enhancing listen listening\nenhance enhancing look looking\nenhance enhancing move moving\nenhance enhancing play playing\nenhance enhancing predict predicting\nenhance enhancing read reading\nenhance enhancing run running\nenhance enhancing say saying\nenhance enhancing scream screaming\nenhance enhancing see seeing\nenhance enhancing shuffle shuffling\nenhance enhancing sing singing\nenhance enhancing sit sitting\nenhance enhancing slow slowing\nenhance enhancing swim swimming\nenhance enhancing think thinking\nenhance enhancing vanish vanishing\nenhance enhancing walk walking\nenhance enhancing write writing\nenhance enhancing code coding\nenhance enhancing dance dancing\nenhance enhancing debug debugging\nenhance enhancing decrease decreasing\nenhance enhancing describe describing\nenhance enhancing discover discovering\nfly flying generate generating\nfly flying go going\nfly flying implement implementing\nfly flying increase increasing\nfly flying invent inventing\nfly flying jump jumping\nfly flying listen listening\nfly flying look looking\nfly flying move moving\nfly flying play playing\nfly flying predict predicting\nfly flying read reading\nfly flying run running\nfly flying say saying\nfly flying scream screaming\nfly flying see seeing\nfly flying shuffle shuffling\nfly flying sing singing\nfly flying sit sitting\nfly flying slow slowing\nfly flying swim swimming\nfly flying think thinking\nfly flying vanish vanishing\nfly flying walk walking\nfly flying write writing\nfly flying code coding\nfly flying dance dancing\nfly flying debug debugging\nfly flying decrease decreasing\nfly flying describe describing\nfly flying discover discovering\nfly flying enhance enhancing\ngenerate generating go going\ngenerate generating implement implementing\ngenerate generating increase increasing\ngenerate generating invent inventing\ngenerate generating jump jumping\ngenerate generating listen listening\ngenerate generating look looking\ngenerate generating move moving\ngenerate generating play playing\ngenerate generating predict predicting\ngenerate generating read reading\ngenerate generating run running\ngenerate generating say saying\ngenerate generating scream screaming\ngenerate generating see seeing\ngenerate generating shuffle shuffling\ngenerate generating sing singing\ngenerate generating sit sitting\ngenerate generating slow slowing\ngenerate generating swim swimming\ngenerate generating think thinking\ngenerate generating vanish vanishing\ngenerate generating walk walking\ngenerate generating write writing\ngenerate generating code coding\ngenerate generating dance dancing\ngenerate generating debug debugging\ngenerate generating decrease decreasing\ngenerate generating describe describing\ngenerate generating discover discovering\ngenerate generating enhance enhancing\ngenerate generating fly flying\ngo going implement implementing\ngo going increase increasing\ngo going invent inventing\ngo going jump jumping\ngo going listen listening\ngo going look looking\ngo going move moving\ngo going play playing\ngo going predict predicting\ngo going read reading\ngo going run running\ngo going say saying\ngo going scream screaming\ngo going see seeing\ngo going shuffle shuffling\ngo going sing singing\ngo going sit sitting\ngo going slow slowing\ngo going swim swimming\ngo going think thinking\ngo going vanish vanishing\ngo going walk walking\ngo going write writing\ngo going code coding\ngo going dance dancing\ngo going debug debugging\ngo going decrease decreasing\ngo going describe describing\ngo going discover discovering\ngo going enhance enhancing\ngo going fly flying\ngo going generate generating\nimplement implementing increase increasing\nimplement implementing invent inventing\nimplement implementing jump jumping\nimplement implementing listen listening\nimplement implementing look looking\nimplement implementing move moving\nimplement implementing play playing\nimplement implementing predict predicting\nimplement implementing read reading\nimplement implementing run running\nimplement implementing say saying\nimplement implementing scream screaming\nimplement implementing see seeing\nimplement implementing shuffle shuffling\nimplement implementing sing singing\nimplement implementing sit sitting\nimplement implementing slow slowing\nimplement implementing swim swimming\nimplement implementing think thinking\nimplement implementing vanish vanishing\nimplement implementing walk walking\nimplement implementing write writing\nimplement implementing code coding\nimplement implementing dance dancing\nimplement implementing debug debugging\nimplement implementing decrease decreasing\nimplement implementing describe describing\nimplement implementing discover discovering\nimplement implementing enhance enhancing\nimplement implementing fly flying\nimplement implementing generate generating\nimplement implementing go going\nincrease increasing invent inventing\nincrease increasing jump jumping\nincrease increasing listen listening\nincrease increasing look looking\nincrease increasing move moving\nincrease increasing play playing\nincrease increasing predict predicting\nincrease increasing read reading\nincrease increasing run running\nincrease increasing say saying\nincrease increasing scream screaming\nincrease increasing see seeing\nincrease increasing shuffle shuffling\nincrease increasing sing singing\nincrease increasing sit sitting\nincrease increasing slow slowing\nincrease increasing swim swimming\nincrease increasing think thinking\nincrease increasing vanish vanishing\nincrease increasing walk walking\nincrease increasing write writing\nincrease increasing code coding\nincrease increasing dance dancing\nincrease increasing debug debugging\nincrease increasing decrease decreasing\nincrease increasing describe describing\nincrease increasing discover discovering\nincrease increasing enhance enhancing\nincrease increasing fly flying\nincrease increasing generate generating\nincrease increasing go going\nincrease increasing implement implementing\ninvent inventing jump jumping\ninvent inventing listen listening\ninvent inventing look looking\ninvent inventing move moving\ninvent inventing play playing\ninvent inventing predict predicting\ninvent inventing read reading\ninvent inventing run running\ninvent inventing say saying\ninvent inventing scream screaming\ninvent inventing see seeing\ninvent inventing shuffle shuffling\ninvent inventing sing singing\ninvent inventing sit sitting\ninvent inventing slow slowing\ninvent inventing swim swimming\ninvent inventing think thinking\ninvent inventing vanish vanishing\ninvent inventing walk walking\ninvent inventing write writing\ninvent inventing code coding\ninvent inventing dance dancing\ninvent inventing debug debugging\ninvent inventing decrease decreasing\ninvent inventing describe describing\ninvent inventing discover discovering\ninvent inventing enhance enhancing\ninvent inventing fly flying\ninvent inventing generate generating\ninvent inventing go going\ninvent inventing implement implementing\ninvent inventing increase increasing\njump jumping listen listening\njump jumping look looking\njump jumping move moving\njump jumping play playing\njump jumping predict predicting\njump jumping read reading\njump jumping run running\njump jumping say saying\njump jumping scream screaming\njump jumping see seeing\njump jumping shuffle shuffling\njump jumping sing singing\njump jumping sit sitting\njump jumping slow slowing\njump jumping swim swimming\njump jumping think thinking\njump jumping vanish vanishing\njump jumping walk walking\njump jumping write writing\njump jumping code coding\njump jumping dance dancing\njump jumping debug debugging\njump jumping decrease decreasing\njump jumping describe describing\njump jumping discover discovering\njump jumping enhance enhancing\njump jumping fly flying\njump jumping generate generating\njump jumping go going\njump jumping implement implementing\njump jumping increase increasing\njump jumping invent inventing\nlisten listening look looking\nlisten listening move moving\nlisten listening play playing\nlisten listening predict predicting\nlisten listening read reading\nlisten listening run running\nlisten listening say saying\nlisten listening scream screaming\nlisten listening see seeing\nlisten listening shuffle shuffling\nlisten listening sing singing\nlisten listening sit sitting\nlisten listening slow slowing\nlisten listening swim swimming\nlisten listening think thinking\nlisten listening vanish vanishing\nlisten listening walk walking\nlisten listening write writing\nlisten listening code coding\nlisten listening dance dancing\nlisten listening debug debugging\nlisten listening decrease decreasing\nlisten listening describe describing\nlisten listening discover discovering\nlisten listening enhance enhancing\nlisten listening fly flying\nlisten listening generate generating\nlisten listening go going\nlisten listening implement implementing\nlisten listening increase increasing\nlisten listening invent inventing\nlisten listening jump jumping\nlook looking move moving\nlook looking play playing\nlook looking predict predicting\nlook looking read reading\nlook looking run running\nlook looking say saying\nlook looking scream screaming\nlook looking see seeing\nlook looking shuffle shuffling\nlook looking sing singing\nlook looking sit sitting\nlook looking slow slowing\nlook looking swim swimming\nlook looking think thinking\nlook looking vanish vanishing\nlook looking walk walking\nlook looking write writing\nlook looking code coding\nlook looking dance dancing\nlook looking debug debugging\nlook looking decrease decreasing\nlook looking describe describing\nlook looking discover discovering\nlook looking enhance enhancing\nlook looking fly flying\nlook looking generate generating\nlook looking go going\nlook looking implement implementing\nlook looking increase increasing\nlook looking invent inventing\nlook looking jump jumping\nlook looking listen listening\nmove moving play playing\nmove moving predict predicting\nmove moving read reading\nmove moving run running\nmove moving say saying\nmove moving scream screaming\nmove moving see seeing\nmove moving shuffle shuffling\nmove moving sing singing\nmove moving sit sitting\nmove moving slow slowing\nmove moving swim swimming\nmove moving think thinking\nmove moving vanish vanishing\nmove moving walk walking\nmove moving write writing\nmove moving code coding\nmove moving dance dancing\nmove moving debug debugging\nmove moving decrease decreasing\nmove moving describe describing\nmove moving discover discovering\nmove moving enhance enhancing\nmove moving fly flying\nmove moving generate generating\nmove moving go going\nmove moving implement implementing\nmove moving increase increasing\nmove moving invent inventing\nmove moving jump jumping\nmove moving listen listening\nmove moving look looking\nplay playing predict predicting\nplay playing read reading\nplay playing run running\nplay playing say saying\nplay playing scream screaming\nplay playing see seeing\nplay playing shuffle shuffling\nplay playing sing singing\nplay playing sit sitting\nplay playing slow slowing\nplay playing swim swimming\nplay playing think thinking\nplay playing vanish vanishing\nplay playing walk walking\nplay playing write writing\nplay playing code coding\nplay playing dance dancing\nplay playing debug debugging\nplay playing decrease decreasing\nplay playing describe describing\nplay playing discover discovering\nplay playing enhance enhancing\nplay playing fly flying\nplay playing generate generating\nplay playing go going\nplay playing implement implementing\nplay playing increase increasing\nplay playing invent inventing\nplay playing jump jumping\nplay playing listen listening\nplay playing look looking\nplay playing move moving\npredict predicting read reading\npredict predicting run running\npredict predicting say saying\npredict predicting scream screaming\npredict predicting see seeing\npredict predicting shuffle shuffling\npredict predicting sing singing\npredict predicting sit sitting\npredict predicting slow slowing\npredict predicting swim swimming\npredict predicting think thinking\npredict predicting vanish vanishing\npredict predicting walk walking\npredict predicting write writing\npredict predicting code coding\npredict predicting dance dancing\npredict predicting debug debugging\npredict predicting decrease decreasing\npredict predicting describe describing\npredict predicting discover discovering\npredict predicting enhance enhancing\npredict predicting fly flying\npredict predicting generate generating\npredict predicting go going\npredict predicting implement implementing\npredict predicting increase increasing\npredict predicting invent inventing\npredict predicting jump jumping\npredict predicting listen listening\npredict predicting look looking\npredict predicting move moving\npredict predicting play playing\nread reading run running\nread reading say saying\nread reading scream screaming\nread reading see seeing\nread reading shuffle shuffling\nread reading sing singing\nread reading sit sitting\nread reading slow slowing\nread reading swim swimming\nread reading think thinking\nread reading vanish vanishing\nread reading walk walking\nread reading write writing\nread reading code coding\nread reading dance dancing\nread reading debug debugging\nread reading decrease decreasing\nread reading describe describing\nread reading discover discovering\nread reading enhance enhancing\nread reading fly flying\nread reading generate generating\nread reading go going\nread reading implement implementing\nread reading increase increasing\nread reading invent inventing\nread reading jump jumping\nread reading listen listening\nread reading look looking\nread reading move moving\nread reading play playing\nread reading predict predicting\nrun running say saying\nrun running scream screaming\nrun running see seeing\nrun running shuffle shuffling\nrun running sing singing\nrun running sit sitting\nrun running slow slowing\nrun running swim swimming\nrun running think thinking\nrun running vanish vanishing\nrun running walk walking\nrun running write writing\nrun running code coding\nrun running dance dancing\nrun running debug debugging\nrun running decrease decreasing\nrun running describe describing\nrun running discover discovering\nrun running enhance enhancing\nrun running fly flying\nrun running generate generating\nrun running go going\nrun running implement implementing\nrun running increase increasing\nrun running invent inventing\nrun running jump jumping\nrun running listen listening\nrun running look looking\nrun running move moving\nrun running play playing\nrun running predict predicting\nrun running read reading\nsay saying scream screaming\nsay saying see seeing\nsay saying shuffle shuffling\nsay saying sing singing\nsay saying sit sitting\nsay saying slow slowing\nsay saying swim swimming\nsay saying think thinking\nsay saying vanish vanishing\nsay saying walk walking\nsay saying write writing\nsay saying code coding\nsay saying dance dancing\nsay saying debug debugging\nsay saying decrease decreasing\nsay saying describe describing\nsay saying discover discovering\nsay saying enhance enhancing\nsay saying fly flying\nsay saying generate generating\nsay saying go going\nsay saying implement implementing\nsay saying increase increasing\nsay saying invent inventing\nsay saying jump jumping\nsay saying listen listening\nsay saying look looking\nsay saying move moving\nsay saying play playing\nsay saying predict predicting\nsay saying read reading\nsay saying run running\nscream screaming see seeing\nscream screaming shuffle shuffling\nscream screaming sing singing\nscream screaming sit sitting\nscream screaming slow slowing\nscream screaming swim swimming\nscream screaming think thinking\nscream screaming vanish vanishing\nscream screaming walk walking\nscream screaming write writing\nscream screaming code coding\nscream screaming dance dancing\nscream screaming debug debugging\nscream screaming decrease decreasing\nscream screaming describe describing\nscream screaming discover discovering\nscream screaming enhance enhancing\nscream screaming fly flying\nscream screaming generate generating\nscream screaming go going\nscream screaming implement implementing\nscream screaming increase increasing\nscream screaming invent inventing\nscream screaming jump jumping\nscream screaming listen listening\nscream screaming look looking\nscream screaming move moving\nscream screaming play playing\nscream screaming predict predicting\nscream screaming read reading\nscream screaming run running\nscream screaming say saying\nsee seeing shuffle shuffling\nsee seeing sing singing\nsee seeing sit sitting\nsee seeing slow slowing\nsee seeing swim swimming\nsee seeing think thinking\nsee seeing vanish vanishing\nsee seeing walk walking\nsee seeing write writing\nsee seeing code coding\nsee seeing dance dancing\nsee seeing debug debugging\nsee seeing decrease decreasing\nsee seeing describe describing\nsee seeing discover discovering\nsee seeing enhance enhancing\nsee seeing fly flying\nsee seeing generate generating\nsee seeing go going\nsee seeing implement implementing\nsee seeing increase increasing\nsee seeing invent inventing\nsee seeing jump jumping\nsee seeing listen listening\nsee seeing look looking\nsee seeing move moving\nsee seeing play playing\nsee seeing predict predicting\nsee seeing read reading\nsee seeing run running\nsee seeing say saying\nsee seeing scream screaming\nshuffle shuffling sing singing\nshuffle shuffling sit sitting\nshuffle shuffling slow slowing\nshuffle shuffling swim swimming\nshuffle shuffling think thinking\nshuffle shuffling vanish vanishing\nshuffle shuffling walk walking\nshuffle shuffling write writing\nshuffle shuffling code coding\nshuffle shuffling dance dancing\nshuffle shuffling debug debugging\nshuffle shuffling decrease decreasing\nshuffle shuffling describe describing\nshuffle shuffling discover discovering\nshuffle shuffling enhance enhancing\nshuffle shuffling fly flying\nshuffle shuffling generate generating\nshuffle shuffling go going\nshuffle shuffling implement implementing\nshuffle shuffling increase increasing\nshuffle shuffling invent inventing\nshuffle shuffling jump jumping\nshuffle shuffling listen listening\nshuffle shuffling look looking\nshuffle shuffling move moving\nshuffle shuffling play playing\nshuffle shuffling predict predicting\nshuffle shuffling read reading\nshuffle shuffling run running\nshuffle shuffling say saying\nshuffle shuffling scream screaming\nshuffle shuffling see seeing\nsing singing sit sitting\nsing singing slow slowing\nsing singing swim swimming\nsing singing think thinking\nsing singing vanish vanishing\nsing singing walk walking\nsing singing write writing\nsing singing code coding\nsing singing dance dancing\nsing singing debug debugging\nsing singing decrease decreasing\nsing singing describe describing\nsing singing discover discovering\nsing singing enhance enhancing\nsing singing fly flying\nsing singing generate generating\nsing singing go going\nsing singing implement implementing\nsing singing increase increasing\nsing singing invent inventing\nsing singing jump jumping\nsing singing listen listening\nsing singing look looking\nsing singing move moving\nsing singing play playing\nsing singing predict predicting\nsing singing read reading\nsing singing run running\nsing singing say saying\nsing singing scream screaming\nsing singing see seeing\nsing singing shuffle shuffling\nsit sitting slow slowing\nsit sitting swim swimming\nsit sitting think thinking\nsit sitting vanish vanishing\nsit sitting walk walking\nsit sitting write writing\nsit sitting code coding\nsit sitting dance dancing\nsit sitting debug debugging\nsit sitting decrease decreasing\nsit sitting describe describing\nsit sitting discover discovering\nsit sitting enhance enhancing\nsit sitting fly flying\nsit sitting generate generating\nsit sitting go going\nsit sitting implement implementing\nsit sitting increase increasing\nsit sitting invent inventing\nsit sitting jump jumping\nsit sitting listen listening\nsit sitting look looking\nsit sitting move moving\nsit sitting play playing\nsit sitting predict predicting\nsit sitting read reading\nsit sitting run running\nsit sitting say saying\nsit sitting scream screaming\nsit sitting see seeing\nsit sitting shuffle shuffling\nsit sitting sing singing\nslow slowing swim swimming\nslow slowing think thinking\nslow slowing vanish vanishing\nslow slowing walk walking\nslow slowing write writing\nslow slowing code coding\nslow slowing dance dancing\nslow slowing debug debugging\nslow slowing decrease decreasing\nslow slowing describe describing\nslow slowing discover discovering\nslow slowing enhance enhancing\nslow slowing fly flying\nslow slowing generate generating\nslow slowing go going\nslow slowing implement implementing\nslow slowing increase increasing\nslow slowing invent inventing\nslow slowing jump jumping\nslow slowing listen listening\nslow slowing look looking\nslow slowing move moving\nslow slowing play playing\nslow slowing predict predicting\nslow slowing read reading\nslow slowing run running\nslow slowing say saying\nslow slowing scream screaming\nslow slowing see seeing\nslow slowing shuffle shuffling\nslow slowing sing singing\nslow slowing sit sitting\nswim swimming think thinking\nswim swimming vanish vanishing\nswim swimming walk walking\nswim swimming write writing\nswim swimming code coding\nswim swimming dance dancing\nswim swimming debug debugging\nswim swimming decrease decreasing\nswim swimming describe describing\nswim swimming discover discovering\nswim swimming enhance enhancing\nswim swimming fly flying\nswim swimming generate generating\nswim swimming go going\nswim swimming implement implementing\nswim swimming increase increasing\nswim swimming invent inventing\nswim swimming jump jumping\nswim swimming listen listening\nswim swimming look looking\nswim swimming move moving\nswim swimming play playing\nswim swimming predict predicting\nswim swimming read reading\nswim swimming run running\nswim swimming say saying\nswim swimming scream screaming\nswim swimming see seeing\nswim swimming shuffle shuffling\nswim swimming sing singing\nswim swimming sit sitting\nswim swimming slow slowing\nthink thinking vanish vanishing\nthink thinking walk walking\nthink thinking write writing\nthink thinking code coding\nthink thinking dance dancing\nthink thinking debug debugging\nthink thinking decrease decreasing\nthink thinking describe describing\nthink thinking discover discovering\nthink thinking enhance enhancing\nthink thinking fly flying\nthink thinking generate generating\nthink thinking go going\nthink thinking implement implementing\nthink thinking increase increasing\nthink thinking invent inventing\nthink thinking jump jumping\nthink thinking listen listening\nthink thinking look looking\nthink thinking move moving\nthink thinking play playing\nthink thinking predict predicting\nthink thinking read reading\nthink thinking run running\nthink thinking say saying\nthink thinking scream screaming\nthink thinking see seeing\nthink thinking shuffle shuffling\nthink thinking sing singing\nthink thinking sit sitting\nthink thinking slow slowing\nthink thinking swim swimming\nvanish vanishing walk walking\nvanish vanishing write writing\nvanish vanishing code coding\nvanish vanishing dance dancing\nvanish vanishing debug debugging\nvanish vanishing decrease decreasing\nvanish vanishing describe describing\nvanish vanishing discover discovering\nvanish vanishing enhance enhancing\nvanish vanishing fly flying\nvanish vanishing generate generating\nvanish vanishing go going\nvanish vanishing implement implementing\nvanish vanishing increase increasing\nvanish vanishing invent inventing\nvanish vanishing jump jumping\nvanish vanishing listen listening\nvanish vanishing look looking\nvanish vanishing move moving\nvanish vanishing play playing\nvanish vanishing predict predicting\nvanish vanishing read reading\nvanish vanishing run running\nvanish vanishing say saying\nvanish vanishing scream screaming\nvanish vanishing see seeing\nvanish vanishing shuffle shuffling\nvanish vanishing sing singing\nvanish vanishing sit sitting\nvanish vanishing slow slowing\nvanish vanishing swim swimming\nvanish vanishing think thinking\nwalk walking write writing\nwalk walking code coding\nwalk walking dance dancing\nwalk walking debug debugging\nwalk walking decrease decreasing\nwalk walking describe describing\nwalk walking discover discovering\nwalk walking enhance enhancing\nwalk walking fly flying\nwalk walking generate generating\nwalk walking go going\nwalk walking implement implementing\nwalk walking increase increasing\nwalk walking invent inventing\nwalk walking jump jumping\nwalk walking listen listening\nwalk walking look looking\nwalk walking move moving\nwalk walking play playing\nwalk walking predict predicting\nwalk walking read reading\nwalk walking run running\nwalk walking say saying\nwalk walking scream screaming\nwalk walking see seeing\nwalk walking shuffle shuffling\nwalk walking sing singing\nwalk walking sit sitting\nwalk walking slow slowing\nwalk walking swim swimming\nwalk walking think thinking\nwalk walking vanish vanishing\nwrite writing code coding\nwrite writing dance dancing\nwrite writing debug debugging\nwrite writing decrease decreasing\nwrite writing describe describing\nwrite writing discover discovering\nwrite writing enhance enhancing\nwrite writing fly flying\nwrite writing generate generating\nwrite writing go going\nwrite writing implement implementing\nwrite writing increase increasing\nwrite writing invent inventing\nwrite writing jump jumping\nwrite writing listen listening\nwrite writing look looking\nwrite writing move moving\nwrite writing play playing\nwrite writing predict predicting\nwrite writing read reading\nwrite writing run running\nwrite writing say saying\nwrite writing scream screaming\nwrite writing see seeing\nwrite writing shuffle shuffling\nwrite writing sing singing\nwrite writing sit sitting\nwrite writing slow slowing\nwrite writing swim swimming\nwrite writing think thinking\nwrite writing vanish vanishing\nwrite writing walk walking\nAlbania Albanian Argentina Argentinean\nAlbania Albanian Australia Australian\nAlbania Albanian Austria Austrian\nAlbania Albanian Belarus Belorussian\nAlbania Albanian Brazil Brazilian\nAlbania Albanian Bulgaria Bulgarian\nAlbania Albanian Cambodia Cambodian\nAlbania Albanian Chile Chilean\nAlbania Albanian China Chinese\nAlbania Albanian Colombia Colombian\nAlbania Albanian Croatia Croatian\nAlbania Albanian Denmark Danish\nAlbania Albanian Egypt Egyptian\nAlbania Albanian England English\nAlbania Albanian France French\nAlbania Albanian Germany German\nAlbania Albanian Greece Greek\nAlbania Albanian Iceland Icelandic\nAlbania Albanian India Indian\nAlbania Albanian Ireland Irish\nAlbania Albanian Israel Israeli\nAlbania Albanian Italy Italian\nAlbania Albanian Japan Japanese\nAlbania Albanian Korea Korean\nAlbania Albanian Macedonia Macedonian\nAlbania Albanian Malta Maltese\nAlbania Albanian Mexico Mexican\nAlbania Albanian Moldova Moldovan\nAlbania Albanian Netherlands Dutch\nAlbania Albanian Norway Norwegian\nAlbania Albanian Peru Peruvian\nAlbania Albanian Poland Polish\nAlbania Albanian Portugal Portuguese\nAlbania Albanian Russia Russian\nAlbania Albanian Slovakia Slovakian\nAlbania Albanian Spain Spanish\nAlbania Albanian Sweden Swedish\nAlbania Albanian Switzerland Swiss\nAlbania Albanian Thailand Thai\nArgentina Argentinean Australia Australian\nArgentina Argentinean Austria Austrian\nArgentina Argentinean Belarus Belorussian\nArgentina Argentinean Brazil Brazilian\nArgentina Argentinean Bulgaria Bulgarian\nArgentina Argentinean Cambodia Cambodian\nArgentina Argentinean Chile Chilean\nArgentina Argentinean China Chinese\nArgentina Argentinean Colombia Colombian\nArgentina Argentinean Croatia Croatian\nArgentina Argentinean Denmark Danish\nArgentina Argentinean Egypt Egyptian\nArgentina Argentinean England English\nArgentina Argentinean France French\nArgentina Argentinean Germany German\nArgentina Argentinean Greece Greek\nArgentina Argentinean Iceland Icelandic\nArgentina Argentinean India Indian\nArgentina Argentinean Ireland Irish\nArgentina Argentinean Israel Israeli\nArgentina Argentinean Italy Italian\nArgentina Argentinean Japan Japanese\nArgentina Argentinean Korea Korean\nArgentina Argentinean Macedonia Macedonian\nArgentina Argentinean Malta Maltese\nArgentina Argentinean Mexico Mexican\nArgentina Argentinean Moldova Moldovan\nArgentina Argentinean Netherlands Dutch\nArgentina Argentinean Norway Norwegian\nArgentina Argentinean Peru Peruvian\nArgentina Argentinean Poland Polish\nArgentina Argentinean Portugal Portuguese\nArgentina Argentinean Russia Russian\nArgentina Argentinean Slovakia Slovakian\nArgentina Argentinean Spain Spanish\nArgentina Argentinean Sweden Swedish\nArgentina Argentinean Switzerland Swiss\nArgentina Argentinean Thailand Thai\nArgentina Argentinean Ukraine Ukrainian\nAustralia Australian Austria Austrian\nAustralia Australian Belarus Belorussian\nAustralia Australian Brazil Brazilian\nAustralia Australian Bulgaria Bulgarian\nAustralia Australian Cambodia Cambodian\nAustralia Australian Chile Chilean\nAustralia Australian China Chinese\nAustralia Australian Colombia Colombian\nAustralia Australian Croatia Croatian\nAustralia Australian Denmark Danish\nAustralia Australian Egypt Egyptian\nAustralia Australian England English\nAustralia Australian France French\nAustralia Australian Germany German\nAustralia Australian Greece Greek\nAustralia Australian Iceland Icelandic\nAustralia Australian India Indian\nAustralia Australian Ireland Irish\nAustralia Australian Israel Israeli\nAustralia Australian Italy Italian\nAustralia Australian Japan Japanese\nAustralia Australian Korea Korean\nAustralia Australian Macedonia Macedonian\nAustralia Australian Malta Maltese\nAustralia Australian Mexico Mexican\nAustralia Australian Moldova Moldovan\nAustralia Australian Netherlands Dutch\nAustralia Australian Norway Norwegian\nAustralia Australian Peru Peruvian\nAustralia Australian Poland Polish\nAustralia Australian Portugal Portuguese\nAustralia Australian Russia Russian\nAustralia Australian Slovakia Slovakian\nAustralia Australian Spain Spanish\nAustralia Australian Sweden Swedish\nAustralia Australian Switzerland Swiss\nAustralia Australian Thailand Thai\nAustralia Australian Ukraine Ukrainian\nAustralia Australian Albania Albanian\nAustria Austrian Belarus Belorussian\nAustria Austrian Brazil Brazilian\nAustria Austrian Bulgaria Bulgarian\nAustria Austrian Cambodia Cambodian\nAustria Austrian Chile Chilean\nAustria Austrian China Chinese\nAustria Austrian Colombia Colombian\nAustria Austrian Croatia Croatian\nAustria Austrian Denmark Danish\nAustria Austrian Egypt Egyptian\nAustria Austrian England English\nAustria Austrian France French\nAustria Austrian Germany German\nAustria Austrian Greece Greek\nAustria Austrian Iceland Icelandic\nAustria Austrian India Indian\nAustria Austrian Ireland Irish\nAustria Austrian Israel Israeli\nAustria Austrian Italy Italian\nAustria Austrian Japan Japanese\nAustria Austrian Korea Korean\nAustria Austrian Macedonia Macedonian\nAustria Austrian Malta Maltese\nAustria Austrian Mexico Mexican\nAustria Austrian Moldova Moldovan\nAustria Austrian Netherlands Dutch\nAustria Austrian Norway Norwegian\nAustria Austrian Peru Peruvian\nAustria Austrian Poland Polish\nAustria Austrian Portugal Portuguese\nAustria Austrian Russia Russian\nAustria Austrian Slovakia Slovakian\nAustria Austrian Spain Spanish\nAustria Austrian Sweden Swedish\nAustria Austrian Switzerland Swiss\nAustria Austrian Thailand Thai\nAustria Austrian Ukraine Ukrainian\nAustria Austrian Albania Albanian\nAustria Austrian Argentina Argentinean\nBelarus Belorussian Brazil Brazilian\nBelarus Belorussian Bulgaria Bulgarian\nBelarus Belorussian Cambodia Cambodian\nBelarus Belorussian Chile Chilean\nBelarus Belorussian China Chinese\nBelarus Belorussian Colombia Colombian\nBelarus Belorussian Croatia Croatian\nBelarus Belorussian Denmark Danish\nBelarus Belorussian Egypt Egyptian\nBelarus Belorussian England English\nBelarus Belorussian France French\nBelarus Belorussian Germany German\nBelarus Belorussian Greece Greek\nBelarus Belorussian Iceland Icelandic\nBelarus Belorussian India Indian\nBelarus Belorussian Ireland Irish\nBelarus Belorussian Israel Israeli\nBelarus Belorussian Italy Italian\nBelarus Belorussian Japan Japanese\nBelarus Belorussian Korea Korean\nBelarus Belorussian Macedonia Macedonian\nBelarus Belorussian Malta Maltese\nBelarus Belorussian Mexico Mexican\nBelarus Belorussian Moldova Moldovan\nBelarus Belorussian Netherlands Dutch\nBelarus Belorussian Norway Norwegian\nBelarus Belorussian Peru Peruvian\nBelarus Belorussian Poland Polish\nBelarus Belorussian Portugal Portuguese\nBelarus Belorussian Russia Russian\nBelarus Belorussian Slovakia Slovakian\nBelarus Belorussian Spain Spanish\nBelarus Belorussian Sweden Swedish\nBelarus Belorussian Switzerland Swiss\nBelarus Belorussian Thailand Thai\nBelarus Belorussian Ukraine Ukrainian\nBelarus Belorussian Albania Albanian\nBelarus Belorussian Argentina Argentinean\nBelarus Belorussian Australia Australian\nBrazil Brazilian Bulgaria Bulgarian\nBrazil Brazilian Cambodia Cambodian\nBrazil Brazilian Chile Chilean\nBrazil Brazilian China Chinese\nBrazil Brazilian Colombia Colombian\nBrazil Brazilian Croatia Croatian\nBrazil Brazilian Denmark Danish\nBrazil Brazilian Egypt Egyptian\nBrazil Brazilian England English\nBrazil Brazilian France French\nBrazil Brazilian Germany German\nBrazil Brazilian Greece Greek\nBrazil Brazilian Iceland Icelandic\nBrazil Brazilian India Indian\nBrazil Brazilian Ireland Irish\nBrazil Brazilian Israel Israeli\nBrazil Brazilian Italy Italian\nBrazil Brazilian Japan Japanese\nBrazil Brazilian Korea Korean\nBrazil Brazilian Macedonia Macedonian\nBrazil Brazilian Malta Maltese\nBrazil Brazilian Mexico Mexican\nBrazil Brazilian Moldova Moldovan\nBrazil Brazilian Netherlands Dutch\nBrazil Brazilian Norway Norwegian\nBrazil Brazilian Peru Peruvian\nBrazil Brazilian Poland Polish\nBrazil Brazilian Portugal Portuguese\nBrazil Brazilian Russia Russian\nBrazil Brazilian Slovakia Slovakian\nBrazil Brazilian Spain Spanish\nBrazil Brazilian Sweden Swedish\nBrazil Brazilian Switzerland Swiss\nBrazil Brazilian Thailand Thai\nBrazil Brazilian Ukraine Ukrainian\nBrazil Brazilian Albania Albanian\nBrazil Brazilian Argentina Argentinean\nBrazil Brazilian Australia Australian\nBrazil Brazilian Austria Austrian\nBulgaria Bulgarian Cambodia Cambodian\nBulgaria Bulgarian Chile Chilean\nBulgaria Bulgarian China Chinese\nBulgaria Bulgarian Colombia Colombian\nBulgaria Bulgarian Croatia Croatian\nBulgaria Bulgarian Denmark Danish\nBulgaria Bulgarian Egypt Egyptian\nBulgaria Bulgarian England English\nBulgaria Bulgarian France French\nBulgaria Bulgarian Germany German\nBulgaria Bulgarian Greece Greek\nBulgaria Bulgarian Iceland Icelandic\nBulgaria Bulgarian India Indian\nBulgaria Bulgarian Ireland Irish\nBulgaria Bulgarian Israel Israeli\nBulgaria Bulgarian Italy Italian\nBulgaria Bulgarian Japan Japanese\nBulgaria Bulgarian Korea Korean\nBulgaria Bulgarian Macedonia Macedonian\nBulgaria Bulgarian Malta Maltese\nBulgaria Bulgarian Mexico Mexican\nBulgaria Bulgarian Moldova Moldovan\nBulgaria Bulgarian Netherlands Dutch\nBulgaria Bulgarian Norway Norwegian\nBulgaria Bulgarian Peru Peruvian\nBulgaria Bulgarian Poland Polish\nBulgaria Bulgarian Portugal Portuguese\nBulgaria Bulgarian Russia Russian\nBulgaria Bulgarian Slovakia Slovakian\nBulgaria Bulgarian Spain Spanish\nBulgaria Bulgarian Sweden Swedish\nBulgaria Bulgarian Switzerland Swiss\nBulgaria Bulgarian Thailand Thai\nBulgaria Bulgarian Ukraine Ukrainian\nBulgaria Bulgarian Albania Albanian\nBulgaria Bulgarian Argentina Argentinean\nBulgaria Bulgarian Australia Australian\nBulgaria Bulgarian Austria Austrian\nBulgaria Bulgarian Belarus Belorussian\nCambodia Cambodian Chile Chilean\nCambodia Cambodian China Chinese\nCambodia Cambodian Colombia Colombian\nCambodia Cambodian Croatia Croatian\nCambodia Cambodian Denmark Danish\nCambodia Cambodian Egypt Egyptian\nCambodia Cambodian England English\nCambodia Cambodian France French\nCambodia Cambodian Germany German\nCambodia Cambodian Greece Greek\nCambodia Cambodian Iceland Icelandic\nCambodia Cambodian India Indian\nCambodia Cambodian Ireland Irish\nCambodia Cambodian Israel Israeli\nCambodia Cambodian Italy Italian\nCambodia Cambodian Japan Japanese\nCambodia Cambodian Korea Korean\nCambodia Cambodian Macedonia Macedonian\nCambodia Cambodian Malta Maltese\nCambodia Cambodian Mexico Mexican\nCambodia Cambodian Moldova Moldovan\nCambodia Cambodian Netherlands Dutch\nCambodia Cambodian Norway Norwegian\nCambodia Cambodian Peru Peruvian\nCambodia Cambodian Poland Polish\nCambodia Cambodian Portugal Portuguese\nCambodia Cambodian Russia Russian\nCambodia Cambodian Slovakia Slovakian\nCambodia Cambodian Spain Spanish\nCambodia Cambodian Sweden Swedish\nCambodia Cambodian Switzerland Swiss\nCambodia Cambodian Thailand Thai\nCambodia Cambodian Ukraine Ukrainian\nCambodia Cambodian Albania Albanian\nCambodia Cambodian Argentina Argentinean\nCambodia Cambodian Australia Australian\nCambodia Cambodian Austria Austrian\nCambodia Cambodian Belarus Belorussian\nCambodia Cambodian Brazil Brazilian\nChile Chilean China Chinese\nChile Chilean Colombia Colombian\nChile Chilean Croatia Croatian\nChile Chilean Denmark Danish\nChile Chilean Egypt Egyptian\nChile Chilean England English\nChile Chilean France French\nChile Chilean Germany German\nChile Chilean Greece Greek\nChile Chilean Iceland Icelandic\nChile Chilean India Indian\nChile Chilean Ireland Irish\nChile Chilean Israel Israeli\nChile Chilean Italy Italian\nChile Chilean Japan Japanese\nChile Chilean Korea Korean\nChile Chilean Macedonia Macedonian\nChile Chilean Malta Maltese\nChile Chilean Mexico Mexican\nChile Chilean Moldova Moldovan\nChile Chilean Netherlands Dutch\nChile Chilean Norway Norwegian\nChile Chilean Peru Peruvian\nChile Chilean Poland Polish\nChile Chilean Portugal Portuguese\nChile Chilean Russia Russian\nChile Chilean Slovakia Slovakian\nChile Chilean Spain Spanish\nChile Chilean Sweden Swedish\nChile Chilean Switzerland Swiss\nChile Chilean Thailand Thai\nChile Chilean Ukraine Ukrainian\nChile Chilean Albania Albanian\nChile Chilean Argentina Argentinean\nChile Chilean Australia Australian\nChile Chilean Austria Austrian\nChile Chilean Belarus Belorussian\nChile Chilean Brazil Brazilian\nChile Chilean Bulgaria Bulgarian\nChina Chinese Colombia Colombian\nChina Chinese Croatia Croatian\nChina Chinese Denmark Danish\nChina Chinese Egypt Egyptian\nChina Chinese England English\nChina Chinese France French\nChina Chinese Germany German\nChina Chinese Greece Greek\nChina Chinese Iceland Icelandic\nChina Chinese India Indian\nChina Chinese Ireland Irish\nChina Chinese Israel Israeli\nChina Chinese Italy Italian\nChina Chinese Japan Japanese\nChina Chinese Korea Korean\nChina Chinese Macedonia Macedonian\nChina Chinese Malta Maltese\nChina Chinese Mexico Mexican\nChina Chinese Moldova Moldovan\nChina Chinese Netherlands Dutch\nChina Chinese Norway Norwegian\nChina Chinese Peru Peruvian\nChina Chinese Poland Polish\nChina Chinese Portugal Portuguese\nChina Chinese Russia Russian\nChina Chinese Slovakia Slovakian\nChina Chinese Spain Spanish\nChina Chinese Sweden Swedish\nChina Chinese Switzerland Swiss\nChina Chinese Thailand Thai\nChina Chinese Ukraine Ukrainian\nChina Chinese Albania Albanian\nChina Chinese Argentina Argentinean\nChina Chinese Australia Australian\nChina Chinese Austria Austrian\nChina Chinese Belarus Belorussian\nChina Chinese Brazil Brazilian\nChina Chinese Bulgaria Bulgarian\nChina Chinese Cambodia Cambodian\nColombia Colombian Croatia Croatian\nColombia Colombian Denmark Danish\nColombia Colombian Egypt Egyptian\nColombia Colombian England English\nColombia Colombian France French\nColombia Colombian Germany German\nColombia Colombian Greece Greek\nColombia Colombian Iceland Icelandic\nColombia Colombian India Indian\nColombia Colombian Ireland Irish\nColombia Colombian Israel Israeli\nColombia Colombian Italy Italian\nColombia Colombian Japan Japanese\nColombia Colombian Korea Korean\nColombia Colombian Macedonia Macedonian\nColombia Colombian Malta Maltese\nColombia Colombian Mexico Mexican\nColombia Colombian Moldova Moldovan\nColombia Colombian Netherlands Dutch\nColombia Colombian Norway Norwegian\nColombia Colombian Peru Peruvian\nColombia Colombian Poland Polish\nColombia Colombian Portugal Portuguese\nColombia Colombian Russia Russian\nColombia Colombian Slovakia Slovakian\nColombia Colombian Spain Spanish\nColombia Colombian Sweden Swedish\nColombia Colombian Switzerland Swiss\nColombia Colombian Thailand Thai\nColombia Colombian Ukraine Ukrainian\nColombia Colombian Albania Albanian\nColombia Colombian Argentina Argentinean\nColombia Colombian Australia Australian\nColombia Colombian Austria Austrian\nColombia Colombian Belarus Belorussian\nColombia Colombian Brazil Brazilian\nColombia Colombian Bulgaria Bulgarian\nColombia Colombian Cambodia Cambodian\nColombia Colombian Chile Chilean\nCroatia Croatian Denmark Danish\nCroatia Croatian Egypt Egyptian\nCroatia Croatian England English\nCroatia Croatian France French\nCroatia Croatian Germany German\nCroatia Croatian Greece Greek\nCroatia Croatian Iceland Icelandic\nCroatia Croatian India Indian\nCroatia Croatian Ireland Irish\nCroatia Croatian Israel Israeli\nCroatia Croatian Italy Italian\nCroatia Croatian Japan Japanese\nCroatia Croatian Korea Korean\nCroatia Croatian Macedonia Macedonian\nCroatia Croatian Malta Maltese\nCroatia Croatian Mexico Mexican\nCroatia Croatian Moldova Moldovan\nCroatia Croatian Netherlands Dutch\nCroatia Croatian Norway Norwegian\nCroatia Croatian Peru Peruvian\nCroatia Croatian Poland Polish\nCroatia Croatian Portugal Portuguese\nCroatia Croatian Russia Russian\nCroatia Croatian Slovakia Slovakian\nCroatia Croatian Spain Spanish\nCroatia Croatian Sweden Swedish\nCroatia Croatian Switzerland Swiss\nCroatia Croatian Thailand Thai\nCroatia Croatian Ukraine Ukrainian\nCroatia Croatian Albania Albanian\nCroatia Croatian Argentina Argentinean\nCroatia Croatian Australia Australian\nCroatia Croatian Austria Austrian\nCroatia Croatian Belarus Belorussian\nCroatia Croatian Brazil Brazilian\nCroatia Croatian Bulgaria Bulgarian\nCroatia Croatian Cambodia Cambodian\nCroatia Croatian Chile Chilean\nCroatia Croatian China Chinese\nDenmark Danish Egypt Egyptian\nDenmark Danish England English\nDenmark Danish France French\nDenmark Danish Germany German\nDenmark Danish Greece Greek\nDenmark Danish Iceland Icelandic\nDenmark Danish India Indian\nDenmark Danish Ireland Irish\nDenmark Danish Israel Israeli\nDenmark Danish Italy Italian\nDenmark Danish Japan Japanese\nDenmark Danish Korea Korean\nDenmark Danish Macedonia Macedonian\nDenmark Danish Malta Maltese\nDenmark Danish Mexico Mexican\nDenmark Danish Moldova Moldovan\nDenmark Danish Netherlands Dutch\nDenmark Danish Norway Norwegian\nDenmark Danish Peru Peruvian\nDenmark Danish Poland Polish\nDenmark Danish Portugal Portuguese\nDenmark Danish Russia Russian\nDenmark Danish Slovakia Slovakian\nDenmark Danish Spain Spanish\nDenmark Danish Sweden Swedish\nDenmark Danish Switzerland Swiss\nDenmark Danish Thailand Thai\nDenmark Danish Ukraine Ukrainian\nDenmark Danish Albania Albanian\nDenmark Danish Argentina Argentinean\nDenmark Danish Australia Australian\nDenmark Danish Austria Austrian\nDenmark Danish Belarus Belorussian\nDenmark Danish Brazil Brazilian\nDenmark Danish Bulgaria Bulgarian\nDenmark Danish Cambodia Cambodian\nDenmark Danish Chile Chilean\nDenmark Danish China Chinese\nDenmark Danish Colombia Colombian\nEgypt Egyptian England English\nEgypt Egyptian France French\nEgypt Egyptian Germany German\nEgypt Egyptian Greece Greek\nEgypt Egyptian Iceland Icelandic\nEgypt Egyptian India Indian\nEgypt Egyptian Ireland Irish\nEgypt Egyptian Israel Israeli\nEgypt Egyptian Italy Italian\nEgypt Egyptian Japan Japanese\nEgypt Egyptian Korea Korean\nEgypt Egyptian Macedonia Macedonian\nEgypt Egyptian Malta Maltese\nEgypt Egyptian Mexico Mexican\nEgypt Egyptian Moldova Moldovan\nEgypt Egyptian Netherlands Dutch\nEgypt Egyptian Norway Norwegian\nEgypt Egyptian Peru Peruvian\nEgypt Egyptian Poland Polish\nEgypt Egyptian Portugal Portuguese\nEgypt Egyptian Russia Russian\nEgypt Egyptian Slovakia Slovakian\nEgypt Egyptian Spain Spanish\nEgypt Egyptian Sweden Swedish\nEgypt Egyptian Switzerland Swiss\nEgypt Egyptian Thailand Thai\nEgypt Egyptian Ukraine Ukrainian\nEgypt Egyptian Albania Albanian\nEgypt Egyptian Argentina Argentinean\nEgypt Egyptian Australia Australian\nEgypt Egyptian Austria Austrian\nEgypt Egyptian Belarus Belorussian\nEgypt Egyptian Brazil Brazilian\nEgypt Egyptian Bulgaria Bulgarian\nEgypt Egyptian Cambodia Cambodian\nEgypt Egyptian Chile Chilean\nEgypt Egyptian China Chinese\nEgypt Egyptian Colombia Colombian\nEgypt Egyptian Croatia Croatian\nEngland English France French\nEngland English Germany German\nEngland English Greece Greek\nEngland English Iceland Icelandic\nEngland English India Indian\nEngland English Ireland Irish\nEngland English Israel Israeli\nEngland English Italy Italian\nEngland English Japan Japanese\nEngland English Korea Korean\nEngland English Macedonia Macedonian\nEngland English Malta Maltese\nEngland English Mexico Mexican\nEngland English Moldova Moldovan\nEngland English Netherlands Dutch\nEngland English Norway Norwegian\nEngland English Peru Peruvian\nEngland English Poland Polish\nEngland English Portugal Portuguese\nEngland English Russia Russian\nEngland English Slovakia Slovakian\nEngland English Spain Spanish\nEngland English Sweden Swedish\nEngland English Switzerland Swiss\nEngland English Thailand Thai\nEngland English Ukraine Ukrainian\nEngland English Albania Albanian\nEngland English Argentina Argentinean\nEngland English Australia Australian\nEngland English Austria Austrian\nEngland English Belarus Belorussian\nEngland English Brazil Brazilian\nEngland English Bulgaria Bulgarian\nEngland English Cambodia Cambodian\nEngland English Chile Chilean\nEngland English China Chinese\nEngland English Colombia Colombian\nEngland English Croatia Croatian\nEngland English Denmark Danish\nFrance French Germany German\nFrance French Greece Greek\nFrance French Iceland Icelandic\nFrance French India Indian\nFrance French Ireland Irish\nFrance French Israel Israeli\nFrance French Italy Italian\nFrance French Japan Japanese\nFrance French Korea Korean\nFrance French Macedonia Macedonian\nFrance French Malta Maltese\nFrance French Mexico Mexican\nFrance French Moldova Moldovan\nFrance French Netherlands Dutch\nFrance French Norway Norwegian\nFrance French Peru Peruvian\nFrance French Poland Polish\nFrance French Portugal Portuguese\nFrance French Russia Russian\nFrance French Slovakia Slovakian\nFrance French Spain Spanish\nFrance French Sweden Swedish\nFrance French Switzerland Swiss\nFrance French Thailand Thai\nFrance French Ukraine Ukrainian\nFrance French Albania Albanian\nFrance French Argentina Argentinean\nFrance French Australia Australian\nFrance French Austria Austrian\nFrance French Belarus Belorussian\nFrance French Brazil Brazilian\nFrance French Bulgaria Bulgarian\nFrance French Cambodia Cambodian\nFrance French Chile Chilean\nFrance French China Chinese\nFrance French Colombia Colombian\nFrance French Croatia Croatian\nFrance French Denmark Danish\nFrance French Egypt Egyptian\nGermany German Greece Greek\nGermany German Iceland Icelandic\nGermany German India Indian\nGermany German Ireland Irish\nGermany German Israel Israeli\nGermany German Italy Italian\nGermany German Japan Japanese\nGermany German Korea Korean\nGermany German Macedonia Macedonian\nGermany German Malta Maltese\nGermany German Mexico Mexican\nGermany German Moldova Moldovan\nGermany German Netherlands Dutch\nGermany German Norway Norwegian\nGermany German Peru Peruvian\nGermany German Poland Polish\nGermany German Portugal Portuguese\nGermany German Russia Russian\nGermany German Slovakia Slovakian\nGermany German Spain Spanish\nGermany German Sweden Swedish\nGermany German Switzerland Swiss\nGermany German Thailand Thai\nGermany German Ukraine Ukrainian\nGermany German Albania Albanian\nGermany German Argentina Argentinean\nGermany German Australia Australian\nGermany German Austria Austrian\nGermany German Belarus Belorussian\nGermany German Brazil Brazilian\nGermany German Bulgaria Bulgarian\nGermany German Cambodia Cambodian\nGermany German Chile Chilean\nGermany German China Chinese\nGermany German Colombia Colombian\nGermany German Croatia Croatian\nGermany German Denmark Danish\nGermany German Egypt Egyptian\nGermany German England English\nGreece Greek Iceland Icelandic\nGreece Greek India Indian\nGreece Greek Ireland Irish\nGreece Greek Israel Israeli\nGreece Greek Italy Italian\nGreece Greek Japan Japanese\nGreece Greek Korea Korean\nGreece Greek Macedonia Macedonian\nGreece Greek Malta Maltese\nGreece Greek Mexico Mexican\nGreece Greek Moldova Moldovan\nGreece Greek Netherlands Dutch\nGreece Greek Norway Norwegian\nGreece Greek Peru Peruvian\nGreece Greek Poland Polish\nGreece Greek Portugal Portuguese\nGreece Greek Russia Russian\nGreece Greek Slovakia Slovakian\nGreece Greek Spain Spanish\nGreece Greek Sweden Swedish\nGreece Greek Switzerland Swiss\nGreece Greek Thailand Thai\nGreece Greek Ukraine Ukrainian\nGreece Greek Albania Albanian\nGreece Greek Argentina Argentinean\nGreece Greek Australia Australian\nGreece Greek Austria Austrian\nGreece Greek Belarus Belorussian\nGreece Greek Brazil Brazilian\nGreece Greek Bulgaria Bulgarian\nGreece Greek Cambodia Cambodian\nGreece Greek Chile Chilean\nGreece Greek China Chinese\nGreece Greek Colombia Colombian\nGreece Greek Croatia Croatian\nGreece Greek Denmark Danish\nGreece Greek Egypt Egyptian\nGreece Greek England English\nGreece Greek France French\nIceland Icelandic India Indian\nIceland Icelandic Ireland Irish\nIceland Icelandic Israel Israeli\nIceland Icelandic Italy Italian\nIceland Icelandic Japan Japanese\nIceland Icelandic Korea Korean\nIceland Icelandic Macedonia Macedonian\nIceland Icelandic Malta Maltese\nIceland Icelandic Mexico Mexican\nIceland Icelandic Moldova Moldovan\nIceland Icelandic Netherlands Dutch\nIceland Icelandic Norway Norwegian\nIceland Icelandic Peru Peruvian\nIceland Icelandic Poland Polish\nIceland Icelandic Portugal Portuguese\nIceland Icelandic Russia Russian\nIceland Icelandic Slovakia Slovakian\nIceland Icelandic Spain Spanish\nIceland Icelandic Sweden Swedish\nIceland Icelandic Switzerland Swiss\nIceland Icelandic Thailand Thai\nIceland Icelandic Ukraine Ukrainian\nIceland Icelandic Albania Albanian\nIceland Icelandic Argentina Argentinean\nIceland Icelandic Australia Australian\nIceland Icelandic Austria Austrian\nIceland Icelandic Belarus Belorussian\nIceland Icelandic Brazil Brazilian\nIceland Icelandic Bulgaria Bulgarian\nIceland Icelandic Cambodia Cambodian\nIceland Icelandic Chile Chilean\nIceland Icelandic China Chinese\nIceland Icelandic Colombia Colombian\nIceland Icelandic Croatia Croatian\nIceland Icelandic Denmark Danish\nIceland Icelandic Egypt Egyptian\nIceland Icelandic England English\nIceland Icelandic France French\nIceland Icelandic Germany German\nIndia Indian Ireland Irish\nIndia Indian Israel Israeli\nIndia Indian Italy Italian\nIndia Indian Japan Japanese\nIndia Indian Korea Korean\nIndia Indian Macedonia Macedonian\nIndia Indian Malta Maltese\nIndia Indian Mexico Mexican\nIndia Indian Moldova Moldovan\nIndia Indian Netherlands Dutch\nIndia Indian Norway Norwegian\nIndia Indian Peru Peruvian\nIndia Indian Poland Polish\nIndia Indian Portugal Portuguese\nIndia Indian Russia Russian\nIndia Indian Slovakia Slovakian\nIndia Indian Spain Spanish\nIndia Indian Sweden Swedish\nIndia Indian Switzerland Swiss\nIndia Indian Thailand Thai\nIndia Indian Ukraine Ukrainian\nIndia Indian Albania Albanian\nIndia Indian Argentina Argentinean\nIndia Indian Australia Australian\nIndia Indian Austria Austrian\nIndia Indian Belarus Belorussian\nIndia Indian Brazil Brazilian\nIndia Indian Bulgaria Bulgarian\nIndia Indian Cambodia Cambodian\nIndia Indian Chile Chilean\nIndia Indian China Chinese\nIndia Indian Colombia Colombian\nIndia Indian Croatia Croatian\nIndia Indian Denmark Danish\nIndia Indian Egypt Egyptian\nIndia Indian England English\nIndia Indian France French\nIndia Indian Germany German\nIndia Indian Greece Greek\nIreland Irish Israel Israeli\nIreland Irish Italy Italian\nIreland Irish Japan Japanese\nIreland Irish Korea Korean\nIreland Irish Macedonia Macedonian\nIreland Irish Malta Maltese\nIreland Irish Mexico Mexican\nIreland Irish Moldova Moldovan\nIreland Irish Netherlands Dutch\nIreland Irish Norway Norwegian\nIreland Irish Peru Peruvian\nIreland Irish Poland Polish\nIreland Irish Portugal Portuguese\nIreland Irish Russia Russian\nIreland Irish Slovakia Slovakian\nIreland Irish Spain Spanish\nIreland Irish Sweden Swedish\nIreland Irish Switzerland Swiss\nIreland Irish Thailand Thai\nIreland Irish Ukraine Ukrainian\nIreland Irish Albania Albanian\nIreland Irish Argentina Argentinean\nIreland Irish Australia Australian\nIreland Irish Austria Austrian\nIreland Irish Belarus Belorussian\nIreland Irish Brazil Brazilian\nIreland Irish Bulgaria Bulgarian\nIreland Irish Cambodia Cambodian\nIreland Irish Chile Chilean\nIreland Irish China Chinese\nIreland Irish Colombia Colombian\nIreland Irish Croatia Croatian\nIreland Irish Denmark Danish\nIreland Irish Egypt Egyptian\nIreland Irish England English\nIreland Irish France French\nIreland Irish Germany German\nIreland Irish Greece Greek\nIreland Irish Iceland Icelandic\nIsrael Israeli Italy Italian\nIsrael Israeli Japan Japanese\nIsrael Israeli Korea Korean\nIsrael Israeli Macedonia Macedonian\nIsrael Israeli Malta Maltese\nIsrael Israeli Mexico Mexican\nIsrael Israeli Moldova Moldovan\nIsrael Israeli Netherlands Dutch\nIsrael Israeli Norway Norwegian\nIsrael Israeli Peru Peruvian\nIsrael Israeli Poland Polish\nIsrael Israeli Portugal Portuguese\nIsrael Israeli Russia Russian\nIsrael Israeli Slovakia Slovakian\nIsrael Israeli Spain Spanish\nIsrael Israeli Sweden Swedish\nIsrael Israeli Switzerland Swiss\nIsrael Israeli Thailand Thai\nIsrael Israeli Ukraine Ukrainian\nIsrael Israeli Albania Albanian\nIsrael Israeli Argentina Argentinean\nIsrael Israeli Australia Australian\nIsrael Israeli Austria Austrian\nIsrael Israeli Belarus Belorussian\nIsrael Israeli Brazil Brazilian\nIsrael Israeli Bulgaria Bulgarian\nIsrael Israeli Cambodia Cambodian\nIsrael Israeli Chile Chilean\nIsrael Israeli China Chinese\nIsrael Israeli Colombia Colombian\nIsrael Israeli Croatia Croatian\nIsrael Israeli Denmark Danish\nIsrael Israeli Egypt Egyptian\nIsrael Israeli England English\nIsrael Israeli France French\nIsrael Israeli Germany German\nIsrael Israeli Greece Greek\nIsrael Israeli Iceland Icelandic\nIsrael Israeli India Indian\nItaly Italian Japan Japanese\nItaly Italian Korea Korean\nItaly Italian Macedonia Macedonian\nItaly Italian Malta Maltese\nItaly Italian Mexico Mexican\nItaly Italian Moldova Moldovan\nItaly Italian Netherlands Dutch\nItaly Italian Norway Norwegian\nItaly Italian Peru Peruvian\nItaly Italian Poland Polish\nItaly Italian Portugal Portuguese\nItaly Italian Russia Russian\nItaly Italian Slovakia Slovakian\nItaly Italian Spain Spanish\nItaly Italian Sweden Swedish\nItaly Italian Switzerland Swiss\nItaly Italian Thailand Thai\nItaly Italian Ukraine Ukrainian\nItaly Italian Albania Albanian\nItaly Italian Argentina Argentinean\nItaly Italian Australia Australian\nItaly Italian Austria Austrian\nItaly Italian Belarus Belorussian\nItaly Italian Brazil Brazilian\nItaly Italian Bulgaria Bulgarian\nItaly Italian Cambodia Cambodian\nItaly Italian Chile Chilean\nItaly Italian China Chinese\nItaly Italian Colombia Colombian\nItaly Italian Croatia Croatian\nItaly Italian Denmark Danish\nItaly Italian Egypt Egyptian\nItaly Italian England English\nItaly Italian France French\nItaly Italian Germany German\nItaly Italian Greece Greek\nItaly Italian Iceland Icelandic\nItaly Italian India Indian\nItaly Italian Ireland Irish\nJapan Japanese Korea Korean\nJapan Japanese Macedonia Macedonian\nJapan Japanese Malta Maltese\nJapan Japanese Mexico Mexican\nJapan Japanese Moldova Moldovan\nJapan Japanese Netherlands Dutch\nJapan Japanese Norway Norwegian\nJapan Japanese Peru Peruvian\nJapan Japanese Poland Polish\nJapan Japanese Portugal Portuguese\nJapan Japanese Russia Russian\nJapan Japanese Slovakia Slovakian\nJapan Japanese Spain Spanish\nJapan Japanese Sweden Swedish\nJapan Japanese Switzerland Swiss\nJapan Japanese Thailand Thai\nJapan Japanese Ukraine Ukrainian\nJapan Japanese Albania Albanian\nJapan Japanese Argentina Argentinean\nJapan Japanese Australia Australian\nJapan Japanese Austria Austrian\nJapan Japanese Belarus Belorussian\nJapan Japanese Brazil Brazilian\nJapan Japanese Bulgaria Bulgarian\nJapan Japanese Cambodia Cambodian\nJapan Japanese Chile Chilean\nJapan Japanese China Chinese\nJapan Japanese Colombia Colombian\nJapan Japanese Croatia Croatian\nJapan Japanese Denmark Danish\nJapan Japanese Egypt Egyptian\nJapan Japanese England English\nJapan Japanese France French\nJapan Japanese Germany German\nJapan Japanese Greece Greek\nJapan Japanese Iceland Icelandic\nJapan Japanese India Indian\nJapan Japanese Ireland Irish\nJapan Japanese Israel Israeli\nKorea Korean Macedonia Macedonian\nKorea Korean Malta Maltese\nKorea Korean Mexico Mexican\nKorea Korean Moldova Moldovan\nKorea Korean Netherlands Dutch\nKorea Korean Norway Norwegian\nKorea Korean Peru Peruvian\nKorea Korean Poland Polish\nKorea Korean Portugal Portuguese\nKorea Korean Russia Russian\nKorea Korean Slovakia Slovakian\nKorea Korean Spain Spanish\nKorea Korean Sweden Swedish\nKorea Korean Switzerland Swiss\nKorea Korean Thailand Thai\nKorea Korean Ukraine Ukrainian\nKorea Korean Albania Albanian\nKorea Korean Argentina Argentinean\nKorea Korean Australia Australian\nKorea Korean Austria Austrian\nKorea Korean Belarus Belorussian\nKorea Korean Brazil Brazilian\nKorea Korean Bulgaria Bulgarian\nKorea Korean Cambodia Cambodian\nKorea Korean Chile Chilean\nKorea Korean China Chinese\nKorea Korean Colombia Colombian\nKorea Korean Croatia Croatian\nKorea Korean Denmark Danish\nKorea Korean Egypt Egyptian\nKorea Korean England English\nKorea Korean France French\nKorea Korean Germany German\nKorea Korean Greece Greek\nKorea Korean Iceland Icelandic\nKorea Korean India Indian\nKorea Korean Ireland Irish\nKorea Korean Israel Israeli\nKorea Korean Italy Italian\nMacedonia Macedonian Malta Maltese\nMacedonia Macedonian Mexico Mexican\nMacedonia Macedonian Moldova Moldovan\nMacedonia Macedonian Netherlands Dutch\nMacedonia Macedonian Norway Norwegian\nMacedonia Macedonian Peru Peruvian\nMacedonia Macedonian Poland Polish\nMacedonia Macedonian Portugal Portuguese\nMacedonia Macedonian Russia Russian\nMacedonia Macedonian Slovakia Slovakian\nMacedonia Macedonian Spain Spanish\nMacedonia Macedonian Sweden Swedish\nMacedonia Macedonian Switzerland Swiss\nMacedonia Macedonian Thailand Thai\nMacedonia Macedonian Ukraine Ukrainian\nMacedonia Macedonian Albania Albanian\nMacedonia Macedonian Argentina Argentinean\nMacedonia Macedonian Australia Australian\nMacedonia Macedonian Austria Austrian\nMacedonia Macedonian Belarus Belorussian\nMacedonia Macedonian Brazil Brazilian\nMacedonia Macedonian Bulgaria Bulgarian\nMacedonia Macedonian Cambodia Cambodian\nMacedonia Macedonian Chile Chilean\nMacedonia Macedonian China Chinese\nMacedonia Macedonian Colombia Colombian\nMacedonia Macedonian Croatia Croatian\nMacedonia Macedonian Denmark Danish\nMacedonia Macedonian Egypt Egyptian\nMacedonia Macedonian England English\nMacedonia Macedonian France French\nMacedonia Macedonian Germany German\nMacedonia Macedonian Greece Greek\nMacedonia Macedonian Iceland Icelandic\nMacedonia Macedonian India Indian\nMacedonia Macedonian Ireland Irish\nMacedonia Macedonian Israel Israeli\nMacedonia Macedonian Italy Italian\nMacedonia Macedonian Japan Japanese\nMalta Maltese Mexico Mexican\nMalta Maltese Moldova Moldovan\nMalta Maltese Netherlands Dutch\nMalta Maltese Norway Norwegian\nMalta Maltese Peru Peruvian\nMalta Maltese Poland Polish\nMalta Maltese Portugal Portuguese\nMalta Maltese Russia Russian\nMalta Maltese Slovakia Slovakian\nMalta Maltese Spain Spanish\nMalta Maltese Sweden Swedish\nMalta Maltese Switzerland Swiss\nMalta Maltese Thailand Thai\nMalta Maltese Ukraine Ukrainian\nMalta Maltese Albania Albanian\nMalta Maltese Argentina Argentinean\nMalta Maltese Australia Australian\nMalta Maltese Austria Austrian\nMalta Maltese Belarus Belorussian\nMalta Maltese Brazil Brazilian\nMalta Maltese Bulgaria Bulgarian\nMalta Maltese Cambodia Cambodian\nMalta Maltese Chile Chilean\nMalta Maltese China Chinese\nMalta Maltese Colombia Colombian\nMalta Maltese Croatia Croatian\nMalta Maltese Denmark Danish\nMalta Maltese Egypt Egyptian\nMalta Maltese England English\nMalta Maltese France French\nMalta Maltese Germany German\nMalta Maltese Greece Greek\nMalta Maltese Iceland Icelandic\nMalta Maltese India Indian\nMalta Maltese Ireland Irish\nMalta Maltese Israel Israeli\nMalta Maltese Italy Italian\nMalta Maltese Japan Japanese\nMalta Maltese Korea Korean\nMexico Mexican Moldova Moldovan\nMexico Mexican Netherlands Dutch\nMexico Mexican Norway Norwegian\nMexico Mexican Peru Peruvian\nMexico Mexican Poland Polish\nMexico Mexican Portugal Portuguese\nMexico Mexican Russia Russian\nMexico Mexican Slovakia Slovakian\nMexico Mexican Spain Spanish\nMexico Mexican Sweden Swedish\nMexico Mexican Switzerland Swiss\nMexico Mexican Thailand Thai\nMexico Mexican Ukraine Ukrainian\nMexico Mexican Albania Albanian\nMexico Mexican Argentina Argentinean\nMexico Mexican Australia Australian\nMexico Mexican Austria Austrian\nMexico Mexican Belarus Belorussian\nMexico Mexican Brazil Brazilian\nMexico Mexican Bulgaria Bulgarian\nMexico Mexican Cambodia Cambodian\nMexico Mexican Chile Chilean\nMexico Mexican China Chinese\nMexico Mexican Colombia Colombian\nMexico Mexican Croatia Croatian\nMexico Mexican Denmark Danish\nMexico Mexican Egypt Egyptian\nMexico Mexican England English\nMexico Mexican France French\nMexico Mexican Germany German\nMexico Mexican Greece Greek\nMexico Mexican Iceland Icelandic\nMexico Mexican India Indian\nMexico Mexican Ireland Irish\nMexico Mexican Israel Israeli\nMexico Mexican Italy Italian\nMexico Mexican Japan Japanese\nMexico Mexican Korea Korean\nMexico Mexican Macedonia Macedonian\nMoldova Moldovan Netherlands Dutch\nMoldova Moldovan Norway Norwegian\nMoldova Moldovan Peru Peruvian\nMoldova Moldovan Poland Polish\nMoldova Moldovan Portugal Portuguese\nMoldova Moldovan Russia Russian\nMoldova Moldovan Slovakia Slovakian\nMoldova Moldovan Spain Spanish\nMoldova Moldovan Sweden Swedish\nMoldova Moldovan Switzerland Swiss\nMoldova Moldovan Thailand Thai\nMoldova Moldovan Ukraine Ukrainian\nMoldova Moldovan Albania Albanian\nMoldova Moldovan Argentina Argentinean\nMoldova Moldovan Australia Australian\nMoldova Moldovan Austria Austrian\nMoldova Moldovan Belarus Belorussian\nMoldova Moldovan Brazil Brazilian\nMoldova Moldovan Bulgaria Bulgarian\nMoldova Moldovan Cambodia Cambodian\nMoldova Moldovan Chile Chilean\nMoldova Moldovan China Chinese\nMoldova Moldovan Colombia Colombian\nMoldova Moldovan Croatia Croatian\nMoldova Moldovan Denmark Danish\nMoldova Moldovan Egypt Egyptian\nMoldova Moldovan England English\nMoldova Moldovan France French\nMoldova Moldovan Germany German\nMoldova Moldovan Greece Greek\nMoldova Moldovan Iceland Icelandic\nMoldova Moldovan India Indian\nMoldova Moldovan Ireland Irish\nMoldova Moldovan Israel Israeli\nMoldova Moldovan Italy Italian\nMoldova Moldovan Japan Japanese\nMoldova Moldovan Korea Korean\nMoldova Moldovan Macedonia Macedonian\nMoldova Moldovan Malta Maltese\nNetherlands Dutch Norway Norwegian\nNetherlands Dutch Peru Peruvian\nNetherlands Dutch Poland Polish\nNetherlands Dutch Portugal Portuguese\nNetherlands Dutch Russia Russian\nNetherlands Dutch Slovakia Slovakian\nNetherlands Dutch Spain Spanish\nNetherlands Dutch Sweden Swedish\nNetherlands Dutch Switzerland Swiss\nNetherlands Dutch Thailand Thai\nNetherlands Dutch Ukraine Ukrainian\nNetherlands Dutch Albania Albanian\nNetherlands Dutch Argentina Argentinean\nNetherlands Dutch Australia Australian\nNetherlands Dutch Austria Austrian\nNetherlands Dutch Belarus Belorussian\nNetherlands Dutch Brazil Brazilian\nNetherlands Dutch Bulgaria Bulgarian\nNetherlands Dutch Cambodia Cambodian\nNetherlands Dutch Chile Chilean\nNetherlands Dutch China Chinese\nNetherlands Dutch Colombia Colombian\nNetherlands Dutch Croatia Croatian\nNetherlands Dutch Denmark Danish\nNetherlands Dutch Egypt Egyptian\nNetherlands Dutch England English\nNetherlands Dutch France French\nNetherlands Dutch Germany German\nNetherlands Dutch Greece Greek\nNetherlands Dutch Iceland Icelandic\nNetherlands Dutch India Indian\nNetherlands Dutch Ireland Irish\nNetherlands Dutch Israel Israeli\nNetherlands Dutch Italy Italian\nNetherlands Dutch Japan Japanese\nNetherlands Dutch Korea Korean\nNetherlands Dutch Macedonia Macedonian\nNetherlands Dutch Malta Maltese\nNetherlands Dutch Mexico Mexican\nNorway Norwegian Peru Peruvian\nNorway Norwegian Poland Polish\nNorway Norwegian Portugal Portuguese\nNorway Norwegian Russia Russian\nNorway Norwegian Slovakia Slovakian\nNorway Norwegian Spain Spanish\nNorway Norwegian Sweden Swedish\nNorway Norwegian Switzerland Swiss\nNorway Norwegian Thailand Thai\nNorway Norwegian Ukraine Ukrainian\nNorway Norwegian Albania Albanian\nNorway Norwegian Argentina Argentinean\nNorway Norwegian Australia Australian\nNorway Norwegian Austria Austrian\nNorway Norwegian Belarus Belorussian\nNorway Norwegian Brazil Brazilian\nNorway Norwegian Bulgaria Bulgarian\nNorway Norwegian Cambodia Cambodian\nNorway Norwegian Chile Chilean\nNorway Norwegian China Chinese\nNorway Norwegian Colombia Colombian\nNorway Norwegian Croatia Croatian\nNorway Norwegian Denmark Danish\nNorway Norwegian Egypt Egyptian\nNorway Norwegian England English\nNorway Norwegian France French\nNorway Norwegian Germany German\nNorway Norwegian Greece Greek\nNorway Norwegian Iceland Icelandic\nNorway Norwegian India Indian\nNorway Norwegian Ireland Irish\nNorway Norwegian Israel Israeli\nNorway Norwegian Italy Italian\nNorway Norwegian Japan Japanese\nNorway Norwegian Korea Korean\nNorway Norwegian Macedonia Macedonian\nNorway Norwegian Malta Maltese\nNorway Norwegian Mexico Mexican\nNorway Norwegian Moldova Moldovan\nPeru Peruvian Poland Polish\nPeru Peruvian Portugal Portuguese\nPeru Peruvian Russia Russian\nPeru Peruvian Slovakia Slovakian\nPeru Peruvian Spain Spanish\nPeru Peruvian Sweden Swedish\nPeru Peruvian Switzerland Swiss\nPeru Peruvian Thailand Thai\nPeru Peruvian Ukraine Ukrainian\nPeru Peruvian Albania Albanian\nPeru Peruvian Argentina Argentinean\nPeru Peruvian Australia Australian\nPeru Peruvian Austria Austrian\nPeru Peruvian Belarus Belorussian\nPeru Peruvian Brazil Brazilian\nPeru Peruvian Bulgaria Bulgarian\nPeru Peruvian Cambodia Cambodian\nPeru Peruvian Chile Chilean\nPeru Peruvian China Chinese\nPeru Peruvian Colombia Colombian\nPeru Peruvian Croatia Croatian\nPeru Peruvian Denmark Danish\nPeru Peruvian Egypt Egyptian\nPeru Peruvian England English\nPeru Peruvian France French\nPeru Peruvian Germany German\nPeru Peruvian Greece Greek\nPeru Peruvian Iceland Icelandic\nPeru Peruvian India Indian\nPeru Peruvian Ireland Irish\nPeru Peruvian Israel Israeli\nPeru Peruvian Italy Italian\nPeru Peruvian Japan Japanese\nPeru Peruvian Korea Korean\nPeru Peruvian Macedonia Macedonian\nPeru Peruvian Malta Maltese\nPeru Peruvian Mexico Mexican\nPeru Peruvian Moldova Moldovan\nPeru Peruvian Netherlands Dutch\nPoland Polish Portugal Portuguese\nPoland Polish Russia Russian\nPoland Polish Slovakia Slovakian\nPoland Polish Spain Spanish\nPoland Polish Sweden Swedish\nPoland Polish Switzerland Swiss\nPoland Polish Thailand Thai\nPoland Polish Ukraine Ukrainian\nPoland Polish Albania Albanian\nPoland Polish Argentina Argentinean\nPoland Polish Australia Australian\nPoland Polish Austria Austrian\nPoland Polish Belarus Belorussian\nPoland Polish Brazil Brazilian\nPoland Polish Bulgaria Bulgarian\nPoland Polish Cambodia Cambodian\nPoland Polish Chile Chilean\nPoland Polish China Chinese\nPoland Polish Colombia Colombian\nPoland Polish Croatia Croatian\nPoland Polish Denmark Danish\nPoland Polish Egypt Egyptian\nPoland Polish England English\nPoland Polish France French\nPoland Polish Germany German\nPoland Polish Greece Greek\nPoland Polish Iceland Icelandic\nPoland Polish India Indian\nPoland Polish Ireland Irish\nPoland Polish Israel Israeli\nPoland Polish Italy Italian\nPoland Polish Japan Japanese\nPoland Polish Korea Korean\nPoland Polish Macedonia Macedonian\nPoland Polish Malta Maltese\nPoland Polish Mexico Mexican\nPoland Polish Moldova Moldovan\nPoland Polish Netherlands Dutch\nPoland Polish Norway Norwegian\nPortugal Portuguese Russia Russian\nPortugal Portuguese Slovakia Slovakian\nPortugal Portuguese Spain Spanish\nPortugal Portuguese Sweden Swedish\nPortugal Portuguese Switzerland Swiss\nPortugal Portuguese Thailand Thai\nPortugal Portuguese Ukraine Ukrainian\nPortugal Portuguese Albania Albanian\nPortugal Portuguese Argentina Argentinean\nPortugal Portuguese Australia Australian\nPortugal Portuguese Austria Austrian\nPortugal Portuguese Belarus Belorussian\nPortugal Portuguese Brazil Brazilian\nPortugal Portuguese Bulgaria Bulgarian\nPortugal Portuguese Cambodia Cambodian\nPortugal Portuguese Chile Chilean\nPortugal Portuguese China Chinese\nPortugal Portuguese Colombia Colombian\nPortugal Portuguese Croatia Croatian\nPortugal Portuguese Denmark Danish\nPortugal Portuguese Egypt Egyptian\nPortugal Portuguese England English\nPortugal Portuguese France French\nPortugal Portuguese Germany German\nPortugal Portuguese Greece Greek\nPortugal Portuguese Iceland Icelandic\nPortugal Portuguese India Indian\nPortugal Portuguese Ireland Irish\nPortugal Portuguese Israel Israeli\nPortugal Portuguese Italy Italian\nPortugal Portuguese Japan Japanese\nPortugal Portuguese Korea Korean\nPortugal Portuguese Macedonia Macedonian\nPortugal Portuguese Malta Maltese\nPortugal Portuguese Mexico Mexican\nPortugal Portuguese Moldova Moldovan\nPortugal Portuguese Netherlands Dutch\nPortugal Portuguese Norway Norwegian\nPortugal Portuguese Peru Peruvian\nRussia Russian Slovakia Slovakian\nRussia Russian Spain Spanish\nRussia Russian Sweden Swedish\nRussia Russian Switzerland Swiss\nRussia Russian Thailand Thai\nRussia Russian Ukraine Ukrainian\nRussia Russian Albania Albanian\nRussia Russian Argentina Argentinean\nRussia Russian Australia Australian\nRussia Russian Austria Austrian\nRussia Russian Belarus Belorussian\nRussia Russian Brazil Brazilian\nRussia Russian Bulgaria Bulgarian\nRussia Russian Cambodia Cambodian\nRussia Russian Chile Chilean\nRussia Russian China Chinese\nRussia Russian Colombia Colombian\nRussia Russian Croatia Croatian\nRussia Russian Denmark Danish\nRussia Russian Egypt Egyptian\nRussia Russian England English\nRussia Russian France French\nRussia Russian Germany German\nRussia Russian Greece Greek\nRussia Russian Iceland Icelandic\nRussia Russian India Indian\nRussia Russian Ireland Irish\nRussia Russian Israel Israeli\nRussia Russian Italy Italian\nRussia Russian Japan Japanese\nRussia Russian Korea Korean\nRussia Russian Macedonia Macedonian\nRussia Russian Malta Maltese\nRussia Russian Mexico Mexican\nRussia Russian Moldova Moldovan\nRussia Russian Netherlands Dutch\nRussia Russian Norway Norwegian\nRussia Russian Peru Peruvian\nRussia Russian Poland Polish\nSlovakia Slovakian Spain Spanish\nSlovakia Slovakian Sweden Swedish\nSlovakia Slovakian Switzerland Swiss\nSlovakia Slovakian Thailand Thai\nSlovakia Slovakian Ukraine Ukrainian\nSlovakia Slovakian Albania Albanian\nSlovakia Slovakian Argentina Argentinean\nSlovakia Slovakian Australia Australian\nSlovakia Slovakian Austria Austrian\nSlovakia Slovakian Belarus Belorussian\nSlovakia Slovakian Brazil Brazilian\nSlovakia Slovakian Bulgaria Bulgarian\nSlovakia Slovakian Cambodia Cambodian\nSlovakia Slovakian Chile Chilean\nSlovakia Slovakian China Chinese\nSlovakia Slovakian Colombia Colombian\nSlovakia Slovakian Croatia Croatian\nSlovakia Slovakian Denmark Danish\nSlovakia Slovakian Egypt Egyptian\nSlovakia Slovakian England English\nSlovakia Slovakian France French\nSlovakia Slovakian Germany German\nSlovakia Slovakian Greece Greek\nSlovakia Slovakian Iceland Icelandic\nSlovakia Slovakian India Indian\nSlovakia Slovakian Ireland Irish\nSlovakia Slovakian Israel Israeli\nSlovakia Slovakian Italy Italian\nSlovakia Slovakian Japan Japanese\nSlovakia Slovakian Korea Korean\nSlovakia Slovakian Macedonia Macedonian\nSlovakia Slovakian Malta Maltese\nSlovakia Slovakian Mexico Mexican\nSlovakia Slovakian Moldova Moldovan\nSlovakia Slovakian Netherlands Dutch\nSlovakia Slovakian Norway Norwegian\nSlovakia Slovakian Peru Peruvian\nSlovakia Slovakian Poland Polish\nSlovakia Slovakian Portugal Portuguese\nSpain Spanish Sweden Swedish\nSpain Spanish Switzerland Swiss\nSpain Spanish Thailand Thai\nSpain Spanish Ukraine Ukrainian\nSpain Spanish Albania Albanian\nSpain Spanish Argentina Argentinean\nSpain Spanish Australia Australian\nSpain Spanish Austria Austrian\nSpain Spanish Belarus Belorussian\nSpain Spanish Brazil Brazilian\nSpain Spanish Bulgaria Bulgarian\nSpain Spanish Cambodia Cambodian\nSpain Spanish Chile Chilean\nSpain Spanish China Chinese\nSpain Spanish Colombia Colombian\nSpain Spanish Croatia Croatian\nSpain Spanish Denmark Danish\nSpain Spanish Egypt Egyptian\nSpain Spanish England English\nSpain Spanish France French\nSpain Spanish Germany German\nSpain Spanish Greece Greek\nSpain Spanish Iceland Icelandic\nSpain Spanish India Indian\nSpain Spanish Ireland Irish\nSpain Spanish Israel Israeli\nSpain Spanish Italy Italian\nSpain Spanish Japan Japanese\nSpain Spanish Korea Korean\nSpain Spanish Macedonia Macedonian\nSpain Spanish Malta Maltese\nSpain Spanish Mexico Mexican\nSpain Spanish Moldova Moldovan\nSpain Spanish Netherlands Dutch\nSpain Spanish Norway Norwegian\nSpain Spanish Peru Peruvian\nSpain Spanish Poland Polish\nSpain Spanish Portugal Portuguese\nSpain Spanish Russia Russian\nSweden Swedish Switzerland Swiss\nSweden Swedish Thailand Thai\nSweden Swedish Ukraine Ukrainian\nSweden Swedish Albania Albanian\nSweden Swedish Argentina Argentinean\nSweden Swedish Australia Australian\nSweden Swedish Austria Austrian\nSweden Swedish Belarus Belorussian\nSweden Swedish Brazil Brazilian\nSweden Swedish Bulgaria Bulgarian\nSweden Swedish Cambodia Cambodian\nSweden Swedish Chile Chilean\nSweden Swedish China Chinese\nSweden Swedish Colombia Colombian\nSweden Swedish Croatia Croatian\nSweden Swedish Denmark Danish\nSweden Swedish Egypt Egyptian\nSweden Swedish England English\nSweden Swedish France French\nSweden Swedish Germany German\nSweden Swedish Greece Greek\nSweden Swedish Iceland Icelandic\nSweden Swedish India Indian\nSweden Swedish Ireland Irish\nSweden Swedish Israel Israeli\nSweden Swedish Italy Italian\nSweden Swedish Japan Japanese\nSweden Swedish Korea Korean\nSweden Swedish Macedonia Macedonian\nSweden Swedish Malta Maltese\nSweden Swedish Mexico Mexican\nSweden Swedish Moldova Moldovan\nSweden Swedish Netherlands Dutch\nSweden Swedish Norway Norwegian\nSweden Swedish Peru Peruvian\nSweden Swedish Poland Polish\nSweden Swedish Portugal Portuguese\nSweden Swedish Russia Russian\nSweden Swedish Slovakia Slovakian\nSwitzerland Swiss Thailand Thai\nSwitzerland Swiss Ukraine Ukrainian\nSwitzerland Swiss Albania Albanian\nSwitzerland Swiss Argentina Argentinean\nSwitzerland Swiss Australia Australian\nSwitzerland Swiss Austria Austrian\nSwitzerland Swiss Belarus Belorussian\nSwitzerland Swiss Brazil Brazilian\nSwitzerland Swiss Bulgaria Bulgarian\nSwitzerland Swiss Cambodia Cambodian\nSwitzerland Swiss Chile Chilean\nSwitzerland Swiss China Chinese\nSwitzerland Swiss Colombia Colombian\nSwitzerland Swiss Croatia Croatian\nSwitzerland Swiss Denmark Danish\nSwitzerland Swiss Egypt Egyptian\nSwitzerland Swiss England English\nSwitzerland Swiss France French\nSwitzerland Swiss Germany German\nSwitzerland Swiss Greece Greek\nSwitzerland Swiss Iceland Icelandic\nSwitzerland Swiss India Indian\nSwitzerland Swiss Ireland Irish\nSwitzerland Swiss Israel Israeli\nSwitzerland Swiss Italy Italian\nSwitzerland Swiss Japan Japanese\nSwitzerland Swiss Korea Korean\nSwitzerland Swiss Macedonia Macedonian\nSwitzerland Swiss Malta Maltese\nSwitzerland Swiss Mexico Mexican\nSwitzerland Swiss Moldova Moldovan\nSwitzerland Swiss Netherlands Dutch\nSwitzerland Swiss Norway Norwegian\nSwitzerland Swiss Peru Peruvian\nSwitzerland Swiss Poland Polish\nSwitzerland Swiss Portugal Portuguese\nSwitzerland Swiss Russia Russian\nSwitzerland Swiss Slovakia Slovakian\nSwitzerland Swiss Spain Spanish\nThailand Thai Ukraine Ukrainian\nThailand Thai Albania Albanian\nThailand Thai Argentina Argentinean\nThailand Thai Australia Australian\nThailand Thai Austria Austrian\nThailand Thai Belarus Belorussian\nThailand Thai Brazil Brazilian\nThailand Thai Bulgaria Bulgarian\nThailand Thai Cambodia Cambodian\nThailand Thai Chile Chilean\nThailand Thai China Chinese\nThailand Thai Colombia Colombian\nThailand Thai Croatia Croatian\nThailand Thai Denmark Danish\nThailand Thai Egypt Egyptian\nThailand Thai England English\nThailand Thai France French\nThailand Thai Germany German\nThailand Thai Greece Greek\nThailand Thai Iceland Icelandic\nThailand Thai India Indian\nThailand Thai Ireland Irish\nThailand Thai Israel Israeli\nThailand Thai Italy Italian\nThailand Thai Japan Japanese\nThailand Thai Korea Korean\nThailand Thai Macedonia Macedonian\nThailand Thai Malta Maltese\nThailand Thai Mexico Mexican\nThailand Thai Moldova Moldovan\nThailand Thai Netherlands Dutch\nThailand Thai Norway Norwegian\nThailand Thai Peru Peruvian\nThailand Thai Poland Polish\nThailand Thai Portugal Portuguese\nThailand Thai Russia Russian\nThailand Thai Slovakia Slovakian\nThailand Thai Spain Spanish\nThailand Thai Sweden Swedish\nUkraine Ukrainian Albania Albanian\nUkraine Ukrainian Argentina Argentinean\nUkraine Ukrainian Australia Australian\nUkraine Ukrainian Austria Austrian\nUkraine Ukrainian Belarus Belorussian\nUkraine Ukrainian Brazil Brazilian\nUkraine Ukrainian Bulgaria Bulgarian\nUkraine Ukrainian Cambodia Cambodian\nUkraine Ukrainian Chile Chilean\nUkraine Ukrainian China Chinese\nUkraine Ukrainian Colombia Colombian\nUkraine Ukrainian Croatia Croatian\nUkraine Ukrainian Denmark Danish\nUkraine Ukrainian Egypt Egyptian\nUkraine Ukrainian England English\nUkraine Ukrainian France French\nUkraine Ukrainian Germany German\nUkraine Ukrainian Greece Greek\nUkraine Ukrainian Iceland Icelandic\nUkraine Ukrainian India Indian\nUkraine Ukrainian Ireland Irish\nUkraine Ukrainian Israel Israeli\nUkraine Ukrainian Italy Italian\nUkraine Ukrainian Japan Japanese\nUkraine Ukrainian Korea Korean\nUkraine Ukrainian Macedonia Macedonian\nUkraine Ukrainian Malta Maltese\nUkraine Ukrainian Mexico Mexican\nUkraine Ukrainian Moldova Moldovan\nUkraine Ukrainian Netherlands Dutch\nUkraine Ukrainian Norway Norwegian\nUkraine Ukrainian Peru Peruvian\nUkraine Ukrainian Poland Polish\nUkraine Ukrainian Portugal Portuguese\nUkraine Ukrainian Russia Russian\nUkraine Ukrainian Slovakia Slovakian\nUkraine Ukrainian Spain Spanish\nUkraine Ukrainian Sweden Swedish\nUkraine Ukrainian Switzerland Swiss\ndancing danced decreasing decreased\ndancing danced describing described\ndancing danced enhancing enhanced\ndancing danced falling fell\ndancing danced feeding fed\ndancing danced flying flew\ndancing danced generating generated\ndancing danced going went\ndancing danced hiding hid\ndancing danced hitting hit\ndancing danced implementing implemented\ndancing danced increasing increased\ndancing danced jumping jumped\ndancing danced knowing knew\ndancing danced listening listened\ndancing danced looking looked\ndancing danced moving moved\ndancing danced paying paid\ndancing danced playing played\ndancing danced predicting predicted\ndancing danced reading read\ndancing danced running ran\ndancing danced saying said\ndancing danced screaming screamed\ndancing danced seeing saw\ndancing danced selling sold\ndancing danced shrinking shrank\ndancing danced singing sang\ndancing danced sitting sat\ndancing danced sleeping slept\ndancing danced slowing slowed\ndancing danced spending spent\ndancing danced striking struck\ndancing danced swimming swam\ndancing danced taking took\ndancing danced thinking thought\ndancing danced vanishing vanished\ndancing danced walking walked\ndancing danced writing wrote\ndecreasing decreased describing described\ndecreasing decreased enhancing enhanced\ndecreasing decreased falling fell\ndecreasing decreased feeding fed\ndecreasing decreased flying flew\ndecreasing decreased generating generated\ndecreasing decreased going went\ndecreasing decreased hiding hid\ndecreasing decreased hitting hit\ndecreasing decreased implementing implemented\ndecreasing decreased increasing increased\ndecreasing decreased jumping jumped\ndecreasing decreased knowing knew\ndecreasing decreased listening listened\ndecreasing decreased looking looked\ndecreasing decreased moving moved\ndecreasing decreased paying paid\ndecreasing decreased playing played\ndecreasing decreased predicting predicted\ndecreasing decreased reading read\ndecreasing decreased running ran\ndecreasing decreased saying said\ndecreasing decreased screaming screamed\ndecreasing decreased seeing saw\ndecreasing decreased selling sold\ndecreasing decreased shrinking shrank\ndecreasing decreased singing sang\ndecreasing decreased sitting sat\ndecreasing decreased sleeping slept\ndecreasing decreased slowing slowed\ndecreasing decreased spending spent\ndecreasing decreased striking struck\ndecreasing decreased swimming swam\ndecreasing decreased taking took\ndecreasing decreased thinking thought\ndecreasing decreased vanishing vanished\ndecreasing decreased walking walked\ndecreasing decreased writing wrote\ndecreasing decreased dancing danced\ndescribing described enhancing enhanced\ndescribing described falling fell\ndescribing described feeding fed\ndescribing described flying flew\ndescribing described generating generated\ndescribing described going went\ndescribing described hiding hid\ndescribing described hitting hit\ndescribing described implementing implemented\ndescribing described increasing increased\ndescribing described jumping jumped\ndescribing described knowing knew\ndescribing described listening listened\ndescribing described looking looked\ndescribing described moving moved\ndescribing described paying paid\ndescribing described playing played\ndescribing described predicting predicted\ndescribing described reading read\ndescribing described running ran\ndescribing described saying said\ndescribing described screaming screamed\ndescribing described seeing saw\ndescribing described selling sold\ndescribing described shrinking shrank\ndescribing described singing sang\ndescribing described sitting sat\ndescribing described sleeping slept\ndescribing described slowing slowed\ndescribing described spending spent\ndescribing described striking struck\ndescribing described swimming swam\ndescribing described taking took\ndescribing described thinking thought\ndescribing described vanishing vanished\ndescribing described walking walked\ndescribing described writing wrote\ndescribing described dancing danced\ndescribing described decreasing decreased\nenhancing enhanced falling fell\nenhancing enhanced feeding fed\nenhancing enhanced flying flew\nenhancing enhanced generating generated\nenhancing enhanced going went\nenhancing enhanced hiding hid\nenhancing enhanced hitting hit\nenhancing enhanced implementing implemented\nenhancing enhanced increasing increased\nenhancing enhanced jumping jumped\nenhancing enhanced knowing knew\nenhancing enhanced listening listened\nenhancing enhanced looking looked\nenhancing enhanced moving moved\nenhancing enhanced paying paid\nenhancing enhanced playing played\nenhancing enhanced predicting predicted\nenhancing enhanced reading read\nenhancing enhanced running ran\nenhancing enhanced saying said\nenhancing enhanced screaming screamed\nenhancing enhanced seeing saw\nenhancing enhanced selling sold\nenhancing enhanced shrinking shrank\nenhancing enhanced singing sang\nenhancing enhanced sitting sat\nenhancing enhanced sleeping slept\nenhancing enhanced slowing slowed\nenhancing enhanced spending spent\nenhancing enhanced striking struck\nenhancing enhanced swimming swam\nenhancing enhanced taking took\nenhancing enhanced thinking thought\nenhancing enhanced vanishing vanished\nenhancing enhanced walking walked\nenhancing enhanced writing wrote\nenhancing enhanced dancing danced\nenhancing enhanced decreasing decreased\nenhancing enhanced describing described\nfalling fell feeding fed\nfalling fell flying flew\nfalling fell generating generated\nfalling fell going went\nfalling fell hiding hid\nfalling fell hitting hit\nfalling fell implementing implemented\nfalling fell increasing increased\nfalling fell jumping jumped\nfalling fell knowing knew\nfalling fell listening listened\nfalling fell looking looked\nfalling fell moving moved\nfalling fell paying paid\nfalling fell playing played\nfalling fell predicting predicted\nfalling fell reading read\nfalling fell running ran\nfalling fell saying said\nfalling fell screaming screamed\nfalling fell seeing saw\nfalling fell selling sold\nfalling fell shrinking shrank\nfalling fell singing sang\nfalling fell sitting sat\nfalling fell sleeping slept\nfalling fell slowing slowed\nfalling fell spending spent\nfalling fell striking struck\nfalling fell swimming swam\nfalling fell taking took\nfalling fell thinking thought\nfalling fell vanishing vanished\nfalling fell walking walked\nfalling fell writing wrote\nfalling fell dancing danced\nfalling fell decreasing decreased\nfalling fell describing described\nfalling fell enhancing enhanced\nfeeding fed flying flew\nfeeding fed generating generated\nfeeding fed going went\nfeeding fed hiding hid\nfeeding fed hitting hit\nfeeding fed implementing implemented\nfeeding fed increasing increased\nfeeding fed jumping jumped\nfeeding fed knowing knew\nfeeding fed listening listened\nfeeding fed looking looked\nfeeding fed moving moved\nfeeding fed paying paid\nfeeding fed playing played\nfeeding fed predicting predicted\nfeeding fed reading read\nfeeding fed running ran\nfeeding fed saying said\nfeeding fed screaming screamed\nfeeding fed seeing saw\nfeeding fed selling sold\nfeeding fed shrinking shrank\nfeeding fed singing sang\nfeeding fed sitting sat\nfeeding fed sleeping slept\nfeeding fed slowing slowed\nfeeding fed spending spent\nfeeding fed striking struck\nfeeding fed swimming swam\nfeeding fed taking took\nfeeding fed thinking thought\nfeeding fed vanishing vanished\nfeeding fed walking walked\nfeeding fed writing wrote\nfeeding fed dancing danced\nfeeding fed decreasing decreased\nfeeding fed describing described\nfeeding fed enhancing enhanced\nfeeding fed falling fell\nflying flew generating generated\nflying flew going went\nflying flew hiding hid\nflying flew hitting hit\nflying flew implementing implemented\nflying flew increasing increased\nflying flew jumping jumped\nflying flew knowing knew\nflying flew listening listened\nflying flew looking looked\nflying flew moving moved\nflying flew paying paid\nflying flew playing played\nflying flew predicting predicted\nflying flew reading read\nflying flew running ran\nflying flew saying said\nflying flew screaming screamed\nflying flew seeing saw\nflying flew selling sold\nflying flew shrinking shrank\nflying flew singing sang\nflying flew sitting sat\nflying flew sleeping slept\nflying flew slowing slowed\nflying flew spending spent\nflying flew striking struck\nflying flew swimming swam\nflying flew taking took\nflying flew thinking thought\nflying flew vanishing vanished\nflying flew walking walked\nflying flew writing wrote\nflying flew dancing danced\nflying flew decreasing decreased\nflying flew describing described\nflying flew enhancing enhanced\nflying flew falling fell\nflying flew feeding fed\ngenerating generated going went\ngenerating generated hiding hid\ngenerating generated hitting hit\ngenerating generated implementing implemented\ngenerating generated increasing increased\ngenerating generated jumping jumped\ngenerating generated knowing knew\ngenerating generated listening listened\ngenerating generated looking looked\ngenerating generated moving moved\ngenerating generated paying paid\ngenerating generated playing played\ngenerating generated predicting predicted\ngenerating generated reading read\ngenerating generated running ran\ngenerating generated saying said\ngenerating generated screaming screamed\ngenerating generated seeing saw\ngenerating generated selling sold\ngenerating generated shrinking shrank\ngenerating generated singing sang\ngenerating generated sitting sat\ngenerating generated sleeping slept\ngenerating generated slowing slowed\ngenerating generated spending spent\ngenerating generated striking struck\ngenerating generated swimming swam\ngenerating generated taking took\ngenerating generated thinking thought\ngenerating generated vanishing vanished\ngenerating generated walking walked\ngenerating generated writing wrote\ngenerating generated dancing danced\ngenerating generated decreasing decreased\ngenerating generated describing described\ngenerating generated enhancing enhanced\ngenerating generated falling fell\ngenerating generated feeding fed\ngenerating generated flying flew\ngoing went hiding hid\ngoing went hitting hit\ngoing went implementing implemented\ngoing went increasing increased\ngoing went jumping jumped\ngoing went knowing knew\ngoing went listening listened\ngoing went looking looked\ngoing went moving moved\ngoing went paying paid\ngoing went playing played\ngoing went predicting predicted\ngoing went reading read\ngoing went running ran\ngoing went saying said\ngoing went screaming screamed\ngoing went seeing saw\ngoing went selling sold\ngoing went shrinking shrank\ngoing went singing sang\ngoing went sitting sat\ngoing went sleeping slept\ngoing went slowing slowed\ngoing went spending spent\ngoing went striking struck\ngoing went swimming swam\ngoing went taking took\ngoing went thinking thought\ngoing went vanishing vanished\ngoing went walking walked\ngoing went writing wrote\ngoing went dancing danced\ngoing went decreasing decreased\ngoing went describing described\ngoing went enhancing enhanced\ngoing went falling fell\ngoing went feeding fed\ngoing went flying flew\ngoing went generating generated\nhiding hid hitting hit\nhiding hid implementing implemented\nhiding hid increasing increased\nhiding hid jumping jumped\nhiding hid knowing knew\nhiding hid listening listened\nhiding hid looking looked\nhiding hid moving moved\nhiding hid paying paid\nhiding hid playing played\nhiding hid predicting predicted\nhiding hid reading read\nhiding hid running ran\nhiding hid saying said\nhiding hid screaming screamed\nhiding hid seeing saw\nhiding hid selling sold\nhiding hid shrinking shrank\nhiding hid singing sang\nhiding hid sitting sat\nhiding hid sleeping slept\nhiding hid slowing slowed\nhiding hid spending spent\nhiding hid striking struck\nhiding hid swimming swam\nhiding hid taking took\nhiding hid thinking thought\nhiding hid vanishing vanished\nhiding hid walking walked\nhiding hid writing wrote\nhiding hid dancing danced\nhiding hid decreasing decreased\nhiding hid describing described\nhiding hid enhancing enhanced\nhiding hid falling fell\nhiding hid feeding fed\nhiding hid flying flew\nhiding hid generating generated\nhiding hid going went\nhitting hit implementing implemented\nhitting hit increasing increased\nhitting hit jumping jumped\nhitting hit knowing knew\nhitting hit listening listened\nhitting hit looking looked\nhitting hit moving moved\nhitting hit paying paid\nhitting hit playing played\nhitting hit predicting predicted\nhitting hit reading read\nhitting hit running ran\nhitting hit saying said\nhitting hit screaming screamed\nhitting hit seeing saw\nhitting hit selling sold\nhitting hit shrinking shrank\nhitting hit singing sang\nhitting hit sitting sat\nhitting hit sleeping slept\nhitting hit slowing slowed\nhitting hit spending spent\nhitting hit striking struck\nhitting hit swimming swam\nhitting hit taking took\nhitting hit thinking thought\nhitting hit vanishing vanished\nhitting hit walking walked\nhitting hit writing wrote\nhitting hit dancing danced\nhitting hit decreasing decreased\nhitting hit describing described\nhitting hit enhancing enhanced\nhitting hit falling fell\nhitting hit feeding fed\nhitting hit flying flew\nhitting hit generating generated\nhitting hit going went\nhitting hit hiding hid\nimplementing implemented increasing increased\nimplementing implemented jumping jumped\nimplementing implemented knowing knew\nimplementing implemented listening listened\nimplementing implemented looking looked\nimplementing implemented moving moved\nimplementing implemented paying paid\nimplementing implemented playing played\nimplementing implemented predicting predicted\nimplementing implemented reading read\nimplementing implemented running ran\nimplementing implemented saying said\nimplementing implemented screaming screamed\nimplementing implemented seeing saw\nimplementing implemented selling sold\nimplementing implemented shrinking shrank\nimplementing implemented singing sang\nimplementing implemented sitting sat\nimplementing implemented sleeping slept\nimplementing implemented slowing slowed\nimplementing implemented spending spent\nimplementing implemented striking struck\nimplementing implemented swimming swam\nimplementing implemented taking took\nimplementing implemented thinking thought\nimplementing implemented vanishing vanished\nimplementing implemented walking walked\nimplementing implemented writing wrote\nimplementing implemented dancing danced\nimplementing implemented decreasing decreased\nimplementing implemented describing described\nimplementing implemented enhancing enhanced\nimplementing implemented falling fell\nimplementing implemented feeding fed\nimplementing implemented flying flew\nimplementing implemented generating generated\nimplementing implemented going went\nimplementing implemented hiding hid\nimplementing implemented hitting hit\nincreasing increased jumping jumped\nincreasing increased knowing knew\nincreasing increased listening listened\nincreasing increased looking looked\nincreasing increased moving moved\nincreasing increased paying paid\nincreasing increased playing played\nincreasing increased predicting predicted\nincreasing increased reading read\nincreasing increased running ran\nincreasing increased saying said\nincreasing increased screaming screamed\nincreasing increased seeing saw\nincreasing increased selling sold\nincreasing increased shrinking shrank\nincreasing increased singing sang\nincreasing increased sitting sat\nincreasing increased sleeping slept\nincreasing increased slowing slowed\nincreasing increased spending spent\nincreasing increased striking struck\nincreasing increased swimming swam\nincreasing increased taking took\nincreasing increased thinking thought\nincreasing increased vanishing vanished\nincreasing increased walking walked\nincreasing increased writing wrote\nincreasing increased dancing danced\nincreasing increased decreasing decreased\nincreasing increased describing described\nincreasing increased enhancing enhanced\nincreasing increased falling fell\nincreasing increased feeding fed\nincreasing increased flying flew\nincreasing increased generating generated\nincreasing increased going went\nincreasing increased hiding hid\nincreasing increased hitting hit\nincreasing increased implementing implemented\njumping jumped knowing knew\njumping jumped listening listened\njumping jumped looking looked\njumping jumped moving moved\njumping jumped paying paid\njumping jumped playing played\njumping jumped predicting predicted\njumping jumped reading read\njumping jumped running ran\njumping jumped saying said\njumping jumped screaming screamed\njumping jumped seeing saw\njumping jumped selling sold\njumping jumped shrinking shrank\njumping jumped singing sang\njumping jumped sitting sat\njumping jumped sleeping slept\njumping jumped slowing slowed\njumping jumped spending spent\njumping jumped striking struck\njumping jumped swimming swam\njumping jumped taking took\njumping jumped thinking thought\njumping jumped vanishing vanished\njumping jumped walking walked\njumping jumped writing wrote\njumping jumped dancing danced\njumping jumped decreasing decreased\njumping jumped describing described\njumping jumped enhancing enhanced\njumping jumped falling fell\njumping jumped feeding fed\njumping jumped flying flew\njumping jumped generating generated\njumping jumped going went\njumping jumped hiding hid\njumping jumped hitting hit\njumping jumped implementing implemented\njumping jumped increasing increased\nknowing knew listening listened\nknowing knew looking looked\nknowing knew moving moved\nknowing knew paying paid\nknowing knew playing played\nknowing knew predicting predicted\nknowing knew reading read\nknowing knew running ran\nknowing knew saying said\nknowing knew screaming screamed\nknowing knew seeing saw\nknowing knew selling sold\nknowing knew shrinking shrank\nknowing knew singing sang\nknowing knew sitting sat\nknowing knew sleeping slept\nknowing knew slowing slowed\nknowing knew spending spent\nknowing knew striking struck\nknowing knew swimming swam\nknowing knew taking took\nknowing knew thinking thought\nknowing knew vanishing vanished\nknowing knew walking walked\nknowing knew writing wrote\nknowing knew dancing danced\nknowing knew decreasing decreased\nknowing knew describing described\nknowing knew enhancing enhanced\nknowing knew falling fell\nknowing knew feeding fed\nknowing knew flying flew\nknowing knew generating generated\nknowing knew going went\nknowing knew hiding hid\nknowing knew hitting hit\nknowing knew implementing implemented\nknowing knew increasing increased\nknowing knew jumping jumped\nlistening listened looking looked\nlistening listened moving moved\nlistening listened paying paid\nlistening listened playing played\nlistening listened predicting predicted\nlistening listened reading read\nlistening listened running ran\nlistening listened saying said\nlistening listened screaming screamed\nlistening listened seeing saw\nlistening listened selling sold\nlistening listened shrinking shrank\nlistening listened singing sang\nlistening listened sitting sat\nlistening listened sleeping slept\nlistening listened slowing slowed\nlistening listened spending spent\nlistening listened striking struck\nlistening listened swimming swam\nlistening listened taking took\nlistening listened thinking thought\nlistening listened vanishing vanished\nlistening listened walking walked\nlistening listened writing wrote\nlistening listened dancing danced\nlistening listened decreasing decreased\nlistening listened describing described\nlistening listened enhancing enhanced\nlistening listened falling fell\nlistening listened feeding fed\nlistening listened flying flew\nlistening listened generating generated\nlistening listened going went\nlistening listened hiding hid\nlistening listened hitting hit\nlistening listened implementing implemented\nlistening listened increasing increased\nlistening listened jumping jumped\nlistening listened knowing knew\nlooking looked moving moved\nlooking looked paying paid\nlooking looked playing played\nlooking looked predicting predicted\nlooking looked reading read\nlooking looked running ran\nlooking looked saying said\nlooking looked screaming screamed\nlooking looked seeing saw\nlooking looked selling sold\nlooking looked shrinking shrank\nlooking looked singing sang\nlooking looked sitting sat\nlooking looked sleeping slept\nlooking looked slowing slowed\nlooking looked spending spent\nlooking looked striking struck\nlooking looked swimming swam\nlooking looked taking took\nlooking looked thinking thought\nlooking looked vanishing vanished\nlooking looked walking walked\nlooking looked writing wrote\nlooking looked dancing danced\nlooking looked decreasing decreased\nlooking looked describing described\nlooking looked enhancing enhanced\nlooking looked falling fell\nlooking looked feeding fed\nlooking looked flying flew\nlooking looked generating generated\nlooking looked going went\nlooking looked hiding hid\nlooking looked hitting hit\nlooking looked implementing implemented\nlooking looked increasing increased\nlooking looked jumping jumped\nlooking looked knowing knew\nlooking looked listening listened\nmoving moved paying paid\nmoving moved playing played\nmoving moved predicting predicted\nmoving moved reading read\nmoving moved running ran\nmoving moved saying said\nmoving moved screaming screamed\nmoving moved seeing saw\nmoving moved selling sold\nmoving moved shrinking shrank\nmoving moved singing sang\nmoving moved sitting sat\nmoving moved sleeping slept\nmoving moved slowing slowed\nmoving moved spending spent\nmoving moved striking struck\nmoving moved swimming swam\nmoving moved taking took\nmoving moved thinking thought\nmoving moved vanishing vanished\nmoving moved walking walked\nmoving moved writing wrote\nmoving moved dancing danced\nmoving moved decreasing decreased\nmoving moved describing described\nmoving moved enhancing enhanced\nmoving moved falling fell\nmoving moved feeding fed\nmoving moved flying flew\nmoving moved generating generated\nmoving moved going went\nmoving moved hiding hid\nmoving moved hitting hit\nmoving moved implementing implemented\nmoving moved increasing increased\nmoving moved jumping jumped\nmoving moved knowing knew\nmoving moved listening listened\nmoving moved looking looked\npaying paid playing played\npaying paid predicting predicted\npaying paid reading read\npaying paid running ran\npaying paid saying said\npaying paid screaming screamed\npaying paid seeing saw\npaying paid selling sold\npaying paid shrinking shrank\npaying paid singing sang\npaying paid sitting sat\npaying paid sleeping slept\npaying paid slowing slowed\npaying paid spending spent\npaying paid striking struck\npaying paid swimming swam\npaying paid taking took\npaying paid thinking thought\npaying paid vanishing vanished\npaying paid walking walked\npaying paid writing wrote\npaying paid dancing danced\npaying paid decreasing decreased\npaying paid describing described\npaying paid enhancing enhanced\npaying paid falling fell\npaying paid feeding fed\npaying paid flying flew\npaying paid generating generated\npaying paid going went\npaying paid hiding hid\npaying paid hitting hit\npaying paid implementing implemented\npaying paid increasing increased\npaying paid jumping jumped\npaying paid knowing knew\npaying paid listening listened\npaying paid looking looked\npaying paid moving moved\nplaying played predicting predicted\nplaying played reading read\nplaying played running ran\nplaying played saying said\nplaying played screaming screamed\nplaying played seeing saw\nplaying played selling sold\nplaying played shrinking shrank\nplaying played singing sang\nplaying played sitting sat\nplaying played sleeping slept\nplaying played slowing slowed\nplaying played spending spent\nplaying played striking struck\nplaying played swimming swam\nplaying played taking took\nplaying played thinking thought\nplaying played vanishing vanished\nplaying played walking walked\nplaying played writing wrote\nplaying played dancing danced\nplaying played decreasing decreased\nplaying played describing described\nplaying played enhancing enhanced\nplaying played falling fell\nplaying played feeding fed\nplaying played flying flew\nplaying played generating generated\nplaying played going went\nplaying played hiding hid\nplaying played hitting hit\nplaying played implementing implemented\nplaying played increasing increased\nplaying played jumping jumped\nplaying played knowing knew\nplaying played listening listened\nplaying played looking looked\nplaying played moving moved\nplaying played paying paid\npredicting predicted reading read\npredicting predicted running ran\npredicting predicted saying said\npredicting predicted screaming screamed\npredicting predicted seeing saw\npredicting predicted selling sold\npredicting predicted shrinking shrank\npredicting predicted singing sang\npredicting predicted sitting sat\npredicting predicted sleeping slept\npredicting predicted slowing slowed\npredicting predicted spending spent\npredicting predicted striking struck\npredicting predicted swimming swam\npredicting predicted taking took\npredicting predicted thinking thought\npredicting predicted vanishing vanished\npredicting predicted walking walked\npredicting predicted writing wrote\npredicting predicted dancing danced\npredicting predicted decreasing decreased\npredicting predicted describing described\npredicting predicted enhancing enhanced\npredicting predicted falling fell\npredicting predicted feeding fed\npredicting predicted flying flew\npredicting predicted generating generated\npredicting predicted going went\npredicting predicted hiding hid\npredicting predicted hitting hit\npredicting predicted implementing implemented\npredicting predicted increasing increased\npredicting predicted jumping jumped\npredicting predicted knowing knew\npredicting predicted listening listened\npredicting predicted looking looked\npredicting predicted moving moved\npredicting predicted paying paid\npredicting predicted playing played\nreading read running ran\nreading read saying said\nreading read screaming screamed\nreading read seeing saw\nreading read selling sold\nreading read shrinking shrank\nreading read singing sang\nreading read sitting sat\nreading read sleeping slept\nreading read slowing slowed\nreading read spending spent\nreading read striking struck\nreading read swimming swam\nreading read taking took\nreading read thinking thought\nreading read vanishing vanished\nreading read walking walked\nreading read writing wrote\nreading read dancing danced\nreading read decreasing decreased\nreading read describing described\nreading read enhancing enhanced\nreading read falling fell\nreading read feeding fed\nreading read flying flew\nreading read generating generated\nreading read going went\nreading read hiding hid\nreading read hitting hit\nreading read implementing implemented\nreading read increasing increased\nreading read jumping jumped\nreading read knowing knew\nreading read listening listened\nreading read looking looked\nreading read moving moved\nreading read paying paid\nreading read playing played\nreading read predicting predicted\nrunning ran saying said\nrunning ran screaming screamed\nrunning ran seeing saw\nrunning ran selling sold\nrunning ran shrinking shrank\nrunning ran singing sang\nrunning ran sitting sat\nrunning ran sleeping slept\nrunning ran slowing slowed\nrunning ran spending spent\nrunning ran striking struck\nrunning ran swimming swam\nrunning ran taking took\nrunning ran thinking thought\nrunning ran vanishing vanished\nrunning ran walking walked\nrunning ran writing wrote\nrunning ran dancing danced\nrunning ran decreasing decreased\nrunning ran describing described\nrunning ran enhancing enhanced\nrunning ran falling fell\nrunning ran feeding fed\nrunning ran flying flew\nrunning ran generating generated\nrunning ran going went\nrunning ran hiding hid\nrunning ran hitting hit\nrunning ran implementing implemented\nrunning ran increasing increased\nrunning ran jumping jumped\nrunning ran knowing knew\nrunning ran listening listened\nrunning ran looking looked\nrunning ran moving moved\nrunning ran paying paid\nrunning ran playing played\nrunning ran predicting predicted\nrunning ran reading read\nsaying said screaming screamed\nsaying said seeing saw\nsaying said selling sold\nsaying said shrinking shrank\nsaying said singing sang\nsaying said sitting sat\nsaying said sleeping slept\nsaying said slowing slowed\nsaying said spending spent\nsaying said striking struck\nsaying said swimming swam\nsaying said taking took\nsaying said thinking thought\nsaying said vanishing vanished\nsaying said walking walked\nsaying said writing wrote\nsaying said dancing danced\nsaying said decreasing decreased\nsaying said describing described\nsaying said enhancing enhanced\nsaying said falling fell\nsaying said feeding fed\nsaying said flying flew\nsaying said generating generated\nsaying said going went\nsaying said hiding hid\nsaying said hitting hit\nsaying said implementing implemented\nsaying said increasing increased\nsaying said jumping jumped\nsaying said knowing knew\nsaying said listening listened\nsaying said looking looked\nsaying said moving moved\nsaying said paying paid\nsaying said playing played\nsaying said predicting predicted\nsaying said reading read\nsaying said running ran\nscreaming screamed seeing saw\nscreaming screamed selling sold\nscreaming screamed shrinking shrank\nscreaming screamed singing sang\nscreaming screamed sitting sat\nscreaming screamed sleeping slept\nscreaming screamed slowing slowed\nscreaming screamed spending spent\nscreaming screamed striking struck\nscreaming screamed swimming swam\nscreaming screamed taking took\nscreaming screamed thinking thought\nscreaming screamed vanishing vanished\nscreaming screamed walking walked\nscreaming screamed writing wrote\nscreaming screamed dancing danced\nscreaming screamed decreasing decreased\nscreaming screamed describing described\nscreaming screamed enhancing enhanced\nscreaming screamed falling fell\nscreaming screamed feeding fed\nscreaming screamed flying flew\nscreaming screamed generating generated\nscreaming screamed going went\nscreaming screamed hiding hid\nscreaming screamed hitting hit\nscreaming screamed implementing implemented\nscreaming screamed increasing increased\nscreaming screamed jumping jumped\nscreaming screamed knowing knew\nscreaming screamed listening listened\nscreaming screamed looking looked\nscreaming screamed moving moved\nscreaming screamed paying paid\nscreaming screamed playing played\nscreaming screamed predicting predicted\nscreaming screamed reading read\nscreaming screamed running ran\nscreaming screamed saying said\nseeing saw selling sold\nseeing saw shrinking shrank\nseeing saw singing sang\nseeing saw sitting sat\nseeing saw sleeping slept\nseeing saw slowing slowed\nseeing saw spending spent\nseeing saw striking struck\nseeing saw swimming swam\nseeing saw taking took\nseeing saw thinking thought\nseeing saw vanishing vanished\nseeing saw walking walked\nseeing saw writing wrote\nseeing saw dancing danced\nseeing saw decreasing decreased\nseeing saw describing described\nseeing saw enhancing enhanced\nseeing saw falling fell\nseeing saw feeding fed\nseeing saw flying flew\nseeing saw generating generated\nseeing saw going went\nseeing saw hiding hid\nseeing saw hitting hit\nseeing saw implementing implemented\nseeing saw increasing increased\nseeing saw jumping jumped\nseeing saw knowing knew\nseeing saw listening listened\nseeing saw looking looked\nseeing saw moving moved\nseeing saw paying paid\nseeing saw playing played\nseeing saw predicting predicted\nseeing saw reading read\nseeing saw running ran\nseeing saw saying said\nseeing saw screaming screamed\nselling sold shrinking shrank\nselling sold singing sang\nselling sold sitting sat\nselling sold sleeping slept\nselling sold slowing slowed\nselling sold spending spent\nselling sold striking struck\nselling sold swimming swam\nselling sold taking took\nselling sold thinking thought\nselling sold vanishing vanished\nselling sold walking walked\nselling sold writing wrote\nselling sold dancing danced\nselling sold decreasing decreased\nselling sold describing described\nselling sold enhancing enhanced\nselling sold falling fell\nselling sold feeding fed\nselling sold flying flew\nselling sold generating generated\nselling sold going went\nselling sold hiding hid\nselling sold hitting hit\nselling sold implementing implemented\nselling sold increasing increased\nselling sold jumping jumped\nselling sold knowing knew\nselling sold listening listened\nselling sold looking looked\nselling sold moving moved\nselling sold paying paid\nselling sold playing played\nselling sold predicting predicted\nselling sold reading read\nselling sold running ran\nselling sold saying said\nselling sold screaming screamed\nselling sold seeing saw\nshrinking shrank singing sang\nshrinking shrank sitting sat\nshrinking shrank sleeping slept\nshrinking shrank slowing slowed\nshrinking shrank spending spent\nshrinking shrank striking struck\nshrinking shrank swimming swam\nshrinking shrank taking took\nshrinking shrank thinking thought\nshrinking shrank vanishing vanished\nshrinking shrank walking walked\nshrinking shrank writing wrote\nshrinking shrank dancing danced\nshrinking shrank decreasing decreased\nshrinking shrank describing described\nshrinking shrank enhancing enhanced\nshrinking shrank falling fell\nshrinking shrank feeding fed\nshrinking shrank flying flew\nshrinking shrank generating generated\nshrinking shrank going went\nshrinking shrank hiding hid\nshrinking shrank hitting hit\nshrinking shrank implementing implemented\nshrinking shrank increasing increased\nshrinking shrank jumping jumped\nshrinking shrank knowing knew\nshrinking shrank listening listened\nshrinking shrank looking looked\nshrinking shrank moving moved\nshrinking shrank paying paid\nshrinking shrank playing played\nshrinking shrank predicting predicted\nshrinking shrank reading read\nshrinking shrank running ran\nshrinking shrank saying said\nshrinking shrank screaming screamed\nshrinking shrank seeing saw\nshrinking shrank selling sold\nsinging sang sitting sat\nsinging sang sleeping slept\nsinging sang slowing slowed\nsinging sang spending spent\nsinging sang striking struck\nsinging sang swimming swam\nsinging sang taking took\nsinging sang thinking thought\nsinging sang vanishing vanished\nsinging sang walking walked\nsinging sang writing wrote\nsinging sang dancing danced\nsinging sang decreasing decreased\nsinging sang describing described\nsinging sang enhancing enhanced\nsinging sang falling fell\nsinging sang feeding fed\nsinging sang flying flew\nsinging sang generating generated\nsinging sang going went\nsinging sang hiding hid\nsinging sang hitting hit\nsinging sang implementing implemented\nsinging sang increasing increased\nsinging sang jumping jumped\nsinging sang knowing knew\nsinging sang listening listened\nsinging sang looking looked\nsinging sang moving moved\nsinging sang paying paid\nsinging sang playing played\nsinging sang predicting predicted\nsinging sang reading read\nsinging sang running ran\nsinging sang saying said\nsinging sang screaming screamed\nsinging sang seeing saw\nsinging sang selling sold\nsinging sang shrinking shrank\nsitting sat sleeping slept\nsitting sat slowing slowed\nsitting sat spending spent\nsitting sat striking struck\nsitting sat swimming swam\nsitting sat taking took\nsitting sat thinking thought\nsitting sat vanishing vanished\nsitting sat walking walked\nsitting sat writing wrote\nsitting sat dancing danced\nsitting sat decreasing decreased\nsitting sat describing described\nsitting sat enhancing enhanced\nsitting sat falling fell\nsitting sat feeding fed\nsitting sat flying flew\nsitting sat generating generated\nsitting sat going went\nsitting sat hiding hid\nsitting sat hitting hit\nsitting sat implementing implemented\nsitting sat increasing increased\nsitting sat jumping jumped\nsitting sat knowing knew\nsitting sat listening listened\nsitting sat looking looked\nsitting sat moving moved\nsitting sat paying paid\nsitting sat playing played\nsitting sat predicting predicted\nsitting sat reading read\nsitting sat running ran\nsitting sat saying said\nsitting sat screaming screamed\nsitting sat seeing saw\nsitting sat selling sold\nsitting sat shrinking shrank\nsitting sat singing sang\nsleeping slept slowing slowed\nsleeping slept spending spent\nsleeping slept striking struck\nsleeping slept swimming swam\nsleeping slept taking took\nsleeping slept thinking thought\nsleeping slept vanishing vanished\nsleeping slept walking walked\nsleeping slept writing wrote\nsleeping slept dancing danced\nsleeping slept decreasing decreased\nsleeping slept describing described\nsleeping slept enhancing enhanced\nsleeping slept falling fell\nsleeping slept feeding fed\nsleeping slept flying flew\nsleeping slept generating generated\nsleeping slept going went\nsleeping slept hiding hid\nsleeping slept hitting hit\nsleeping slept implementing implemented\nsleeping slept increasing increased\nsleeping slept jumping jumped\nsleeping slept knowing knew\nsleeping slept listening listened\nsleeping slept looking looked\nsleeping slept moving moved\nsleeping slept paying paid\nsleeping slept playing played\nsleeping slept predicting predicted\nsleeping slept reading read\nsleeping slept running ran\nsleeping slept saying said\nsleeping slept screaming screamed\nsleeping slept seeing saw\nsleeping slept selling sold\nsleeping slept shrinking shrank\nsleeping slept singing sang\nsleeping slept sitting sat\nslowing slowed spending spent\nslowing slowed striking struck\nslowing slowed swimming swam\nslowing slowed taking took\nslowing slowed thinking thought\nslowing slowed vanishing vanished\nslowing slowed walking walked\nslowing slowed writing wrote\nslowing slowed dancing danced\nslowing slowed decreasing decreased\nslowing slowed describing described\nslowing slowed enhancing enhanced\nslowing slowed falling fell\nslowing slowed feeding fed\nslowing slowed flying flew\nslowing slowed generating generated\nslowing slowed going went\nslowing slowed hiding hid\nslowing slowed hitting hit\nslowing slowed implementing implemented\nslowing slowed increasing increased\nslowing slowed jumping jumped\nslowing slowed knowing knew\nslowing slowed listening listened\nslowing slowed looking looked\nslowing slowed moving moved\nslowing slowed paying paid\nslowing slowed playing played\nslowing slowed predicting predicted\nslowing slowed reading read\nslowing slowed running ran\nslowing slowed saying said\nslowing slowed screaming screamed\nslowing slowed seeing saw\nslowing slowed selling sold\nslowing slowed shrinking shrank\nslowing slowed singing sang\nslowing slowed sitting sat\nslowing slowed sleeping slept\nspending spent striking struck\nspending spent swimming swam\nspending spent taking took\nspending spent thinking thought\nspending spent vanishing vanished\nspending spent walking walked\nspending spent writing wrote\nspending spent dancing danced\nspending spent decreasing decreased\nspending spent describing described\nspending spent enhancing enhanced\nspending spent falling fell\nspending spent feeding fed\nspending spent flying flew\nspending spent generating generated\nspending spent going went\nspending spent hiding hid\nspending spent hitting hit\nspending spent implementing implemented\nspending spent increasing increased\nspending spent jumping jumped\nspending spent knowing knew\nspending spent listening listened\nspending spent looking looked\nspending spent moving moved\nspending spent paying paid\nspending spent playing played\nspending spent predicting predicted\nspending spent reading read\nspending spent running ran\nspending spent saying said\nspending spent screaming screamed\nspending spent seeing saw\nspending spent selling sold\nspending spent shrinking shrank\nspending spent singing sang\nspending spent sitting sat\nspending spent sleeping slept\nspending spent slowing slowed\nstriking struck swimming swam\nstriking struck taking took\nstriking struck thinking thought\nstriking struck vanishing vanished\nstriking struck walking walked\nstriking struck writing wrote\nstriking struck dancing danced\nstriking struck decreasing decreased\nstriking struck describing described\nstriking struck enhancing enhanced\nstriking struck falling fell\nstriking struck feeding fed\nstriking struck flying flew\nstriking struck generating generated\nstriking struck going went\nstriking struck hiding hid\nstriking struck hitting hit\nstriking struck implementing implemented\nstriking struck increasing increased\nstriking struck jumping jumped\nstriking struck knowing knew\nstriking struck listening listened\nstriking struck looking looked\nstriking struck moving moved\nstriking struck paying paid\nstriking struck playing played\nstriking struck predicting predicted\nstriking struck reading read\nstriking struck running ran\nstriking struck saying said\nstriking struck screaming screamed\nstriking struck seeing saw\nstriking struck selling sold\nstriking struck shrinking shrank\nstriking struck singing sang\nstriking struck sitting sat\nstriking struck sleeping slept\nstriking struck slowing slowed\nstriking struck spending spent\nswimming swam taking took\nswimming swam thinking thought\nswimming swam vanishing vanished\nswimming swam walking walked\nswimming swam writing wrote\nswimming swam dancing danced\nswimming swam decreasing decreased\nswimming swam describing described\nswimming swam enhancing enhanced\nswimming swam falling fell\nswimming swam feeding fed\nswimming swam flying flew\nswimming swam generating generated\nswimming swam going went\nswimming swam hiding hid\nswimming swam hitting hit\nswimming swam implementing implemented\nswimming swam increasing increased\nswimming swam jumping jumped\nswimming swam knowing knew\nswimming swam listening listened\nswimming swam looking looked\nswimming swam moving moved\nswimming swam paying paid\nswimming swam playing played\nswimming swam predicting predicted\nswimming swam reading read\nswimming swam running ran\nswimming swam saying said\nswimming swam screaming screamed\nswimming swam seeing saw\nswimming swam selling sold\nswimming swam shrinking shrank\nswimming swam singing sang\nswimming swam sitting sat\nswimming swam sleeping slept\nswimming swam slowing slowed\nswimming swam spending spent\nswimming swam striking struck\ntaking took thinking thought\ntaking took vanishing vanished\ntaking took walking walked\ntaking took writing wrote\ntaking took dancing danced\ntaking took decreasing decreased\ntaking took describing described\ntaking took enhancing enhanced\ntaking took falling fell\ntaking took feeding fed\ntaking took flying flew\ntaking took generating generated\ntaking took going went\ntaking took hiding hid\ntaking took hitting hit\ntaking took implementing implemented\ntaking took increasing increased\ntaking took jumping jumped\ntaking took knowing knew\ntaking took listening listened\ntaking took looking looked\ntaking took moving moved\ntaking took paying paid\ntaking took playing played\ntaking took predicting predicted\ntaking took reading read\ntaking took running ran\ntaking took saying said\ntaking took screaming screamed\ntaking took seeing saw\ntaking took selling sold\ntaking took shrinking shrank\ntaking took singing sang\ntaking took sitting sat\ntaking took sleeping slept\ntaking took slowing slowed\ntaking took spending spent\ntaking took striking struck\ntaking took swimming swam\nthinking thought vanishing vanished\nthinking thought walking walked\nthinking thought writing wrote\nthinking thought dancing danced\nthinking thought decreasing decreased\nthinking thought describing described\nthinking thought enhancing enhanced\nthinking thought falling fell\nthinking thought feeding fed\nthinking thought flying flew\nthinking thought generating generated\nthinking thought going went\nthinking thought hiding hid\nthinking thought hitting hit\nthinking thought implementing implemented\nthinking thought increasing increased\nthinking thought jumping jumped\nthinking thought knowing knew\nthinking thought listening listened\nthinking thought looking looked\nthinking thought moving moved\nthinking thought paying paid\nthinking thought playing played\nthinking thought predicting predicted\nthinking thought reading read\nthinking thought running ran\nthinking thought saying said\nthinking thought screaming screamed\nthinking thought seeing saw\nthinking thought selling sold\nthinking thought shrinking shrank\nthinking thought singing sang\nthinking thought sitting sat\nthinking thought sleeping slept\nthinking thought slowing slowed\nthinking thought spending spent\nthinking thought striking struck\nthinking thought swimming swam\nthinking thought taking took\nvanishing vanished walking walked\nvanishing vanished writing wrote\nvanishing vanished dancing danced\nvanishing vanished decreasing decreased\nvanishing vanished describing described\nvanishing vanished enhancing enhanced\nvanishing vanished falling fell\nvanishing vanished feeding fed\nvanishing vanished flying flew\nvanishing vanished generating generated\nvanishing vanished going went\nvanishing vanished hiding hid\nvanishing vanished hitting hit\nvanishing vanished implementing implemented\nvanishing vanished increasing increased\nvanishing vanished jumping jumped\nvanishing vanished knowing knew\nvanishing vanished listening listened\nvanishing vanished looking looked\nvanishing vanished moving moved\nvanishing vanished paying paid\nvanishing vanished playing played\nvanishing vanished predicting predicted\nvanishing vanished reading read\nvanishing vanished running ran\nvanishing vanished saying said\nvanishing vanished screaming screamed\nvanishing vanished seeing saw\nvanishing vanished selling sold\nvanishing vanished shrinking shrank\nvanishing vanished singing sang\nvanishing vanished sitting sat\nvanishing vanished sleeping slept\nvanishing vanished slowing slowed\nvanishing vanished spending spent\nvanishing vanished striking struck\nvanishing vanished swimming swam\nvanishing vanished taking took\nvanishing vanished thinking thought\nwalking walked writing wrote\nwalking walked dancing danced\nwalking walked decreasing decreased\nwalking walked describing described\nwalking walked enhancing enhanced\nwalking walked falling fell\nwalking walked feeding fed\nwalking walked flying flew\nwalking walked generating generated\nwalking walked going went\nwalking walked hiding hid\nwalking walked hitting hit\nwalking walked implementing implemented\nwalking walked increasing increased\nwalking walked jumping jumped\nwalking walked knowing knew\nwalking walked listening listened\nwalking walked looking looked\nwalking walked moving moved\nwalking walked paying paid\nwalking walked playing played\nwalking walked predicting predicted\nwalking walked reading read\nwalking walked running ran\nwalking walked saying said\nwalking walked screaming screamed\nwalking walked seeing saw\nwalking walked selling sold\nwalking walked shrinking shrank\nwalking walked singing sang\nwalking walked sitting sat\nwalking walked sleeping slept\nwalking walked slowing slowed\nwalking walked spending spent\nwalking walked striking struck\nwalking walked swimming swam\nwalking walked taking took\nwalking walked thinking thought\nwalking walked vanishing vanished\nwriting wrote dancing danced\nwriting wrote decreasing decreased\nwriting wrote describing described\nwriting wrote enhancing enhanced\nwriting wrote falling fell\nwriting wrote feeding fed\nwriting wrote flying flew\nwriting wrote generating generated\nwriting wrote going went\nwriting wrote hiding hid\nwriting wrote hitting hit\nwriting wrote implementing implemented\nwriting wrote increasing increased\nwriting wrote jumping jumped\nwriting wrote knowing knew\nwriting wrote listening listened\nwriting wrote looking looked\nwriting wrote moving moved\nwriting wrote paying paid\nwriting wrote playing played\nwriting wrote predicting predicted\nwriting wrote reading read\nwriting wrote running ran\nwriting wrote saying said\nwriting wrote screaming screamed\nwriting wrote seeing saw\nwriting wrote selling sold\nwriting wrote shrinking shrank\nwriting wrote singing sang\nwriting wrote sitting sat\nwriting wrote sleeping slept\nwriting wrote slowing slowed\nwriting wrote spending spent\nwriting wrote striking struck\nwriting wrote swimming swam\nwriting wrote taking took\nwriting wrote thinking thought\nwriting wrote vanishing vanished\nwriting wrote walking walked\nbanana bananas bird birds\nbanana bananas bottle bottles\nbanana bananas building buildings\nbanana bananas car cars\nbanana bananas cat cats\nbanana bananas child children\nbanana bananas cloud clouds\nbanana bananas color colors\nbanana bananas computer computers\nbanana bananas cow cows\nbanana bananas dog dogs\nbanana bananas dollar dollars\nbanana bananas donkey donkeys\nbanana bananas dream dreams\nbanana bananas eagle eagles\nbanana bananas elephant elephants\nbanana bananas eye eyes\nbanana bananas finger fingers\nbanana bananas goat goats\nbanana bananas hand hands\nbanana bananas horse horses\nbanana bananas lion lions\nbanana bananas machine machines\nbanana bananas mango mangoes\nbanana bananas man men\nbanana bananas melon melons\nbanana bananas monkey monkeys\nbanana bananas mouse mice\nbanana bananas onion onions\nbanana bananas pear pears\nbanana bananas pig pigs\nbanana bananas pineapple pineapples\nbanana bananas rat rats\nbanana bananas road roads\nbanana bananas snake snakes\nbanana bananas woman women\nbird birds bottle bottles\nbird birds building buildings\nbird birds car cars\nbird birds cat cats\nbird birds child children\nbird birds cloud clouds\nbird birds color colors\nbird birds computer computers\nbird birds cow cows\nbird birds dog dogs\nbird birds dollar dollars\nbird birds donkey donkeys\nbird birds dream dreams\nbird birds eagle eagles\nbird birds elephant elephants\nbird birds eye eyes\nbird birds finger fingers\nbird birds goat goats\nbird birds hand hands\nbird birds horse horses\nbird birds lion lions\nbird birds machine machines\nbird birds mango mangoes\nbird birds man men\nbird birds melon melons\nbird birds monkey monkeys\nbird birds mouse mice\nbird birds onion onions\nbird birds pear pears\nbird birds pig pigs\nbird birds pineapple pineapples\nbird birds rat rats\nbird birds road roads\nbird birds snake snakes\nbird birds woman women\nbird birds banana bananas\nbottle bottles building buildings\nbottle bottles car cars\nbottle bottles cat cats\nbottle bottles child children\nbottle bottles cloud clouds\nbottle bottles color colors\nbottle bottles computer computers\nbottle bottles cow cows\nbottle bottles dog dogs\nbottle bottles dollar dollars\nbottle bottles donkey donkeys\nbottle bottles dream dreams\nbottle bottles eagle eagles\nbottle bottles elephant elephants\nbottle bottles eye eyes\nbottle bottles finger fingers\nbottle bottles goat goats\nbottle bottles hand hands\nbottle bottles horse horses\nbottle bottles lion lions\nbottle bottles machine machines\nbottle bottles mango mangoes\nbottle bottles man men\nbottle bottles melon melons\nbottle bottles monkey monkeys\nbottle bottles mouse mice\nbottle bottles onion onions\nbottle bottles pear pears\nbottle bottles pig pigs\nbottle bottles pineapple pineapples\nbottle bottles rat rats\nbottle bottles road roads\nbottle bottles snake snakes\nbottle bottles woman women\nbottle bottles banana bananas\nbottle bottles bird birds\nbuilding buildings car cars\nbuilding buildings cat cats\nbuilding buildings child children\nbuilding buildings cloud clouds\nbuilding buildings color colors\nbuilding buildings computer computers\nbuilding buildings cow cows\nbuilding buildings dog dogs\nbuilding buildings dollar dollars\nbuilding buildings donkey donkeys\nbuilding buildings dream dreams\nbuilding buildings eagle eagles\nbuilding buildings elephant elephants\nbuilding buildings eye eyes\nbuilding buildings finger fingers\nbuilding buildings goat goats\nbuilding buildings hand hands\nbuilding buildings horse horses\nbuilding buildings lion lions\nbuilding buildings machine machines\nbuilding buildings mango mangoes\nbuilding buildings man men\nbuilding buildings melon melons\nbuilding buildings monkey monkeys\nbuilding buildings mouse mice\nbuilding buildings onion onions\nbuilding buildings pear pears\nbuilding buildings pig pigs\nbuilding buildings pineapple pineapples\nbuilding buildings rat rats\nbuilding buildings road roads\nbuilding buildings snake snakes\nbuilding buildings woman women\nbuilding buildings banana bananas\nbuilding buildings bird birds\nbuilding buildings bottle bottles\ncar cars cat cats\ncar cars child children\ncar cars cloud clouds\ncar cars color colors\ncar cars computer computers\ncar cars cow cows\ncar cars dog dogs\ncar cars dollar dollars\ncar cars donkey donkeys\ncar cars dream dreams\ncar cars eagle eagles\ncar cars elephant elephants\ncar cars eye eyes\ncar cars finger fingers\ncar cars goat goats\ncar cars hand hands\ncar cars horse horses\ncar cars lion lions\ncar cars machine machines\ncar cars mango mangoes\ncar cars man men\ncar cars melon melons\ncar cars monkey monkeys\ncar cars mouse mice\ncar cars onion onions\ncar cars pear pears\ncar cars pig pigs\ncar cars pineapple pineapples\ncar cars rat rats\ncar cars road roads\ncar cars snake snakes\ncar cars woman women\ncar cars banana bananas\ncar cars bird birds\ncar cars bottle bottles\ncar cars building buildings\ncat cats child children\ncat cats cloud clouds\ncat cats color colors\ncat cats computer computers\ncat cats cow cows\ncat cats dog dogs\ncat cats dollar dollars\ncat cats donkey donkeys\ncat cats dream dreams\ncat cats eagle eagles\ncat cats elephant elephants\ncat cats eye eyes\ncat cats finger fingers\ncat cats goat goats\ncat cats hand hands\ncat cats horse horses\ncat cats lion lions\ncat cats machine machines\ncat cats mango mangoes\ncat cats man men\ncat cats melon melons\ncat cats monkey monkeys\ncat cats mouse mice\ncat cats onion onions\ncat cats pear pears\ncat cats pig pigs\ncat cats pineapple pineapples\ncat cats rat rats\ncat cats road roads\ncat cats snake snakes\ncat cats woman women\ncat cats banana bananas\ncat cats bird birds\ncat cats bottle bottles\ncat cats building buildings\ncat cats car cars\nchild children cloud clouds\nchild children color colors\nchild children computer computers\nchild children cow cows\nchild children dog dogs\nchild children dollar dollars\nchild children donkey donkeys\nchild children dream dreams\nchild children eagle eagles\nchild children elephant elephants\nchild children eye eyes\nchild children finger fingers\nchild children goat goats\nchild children hand hands\nchild children horse horses\nchild children lion lions\nchild children machine machines\nchild children mango mangoes\nchild children man men\nchild children melon melons\nchild children monkey monkeys\nchild children mouse mice\nchild children onion onions\nchild children pear pears\nchild children pig pigs\nchild children pineapple pineapples\nchild children rat rats\nchild children road roads\nchild children snake snakes\nchild children woman women\nchild children banana bananas\nchild children bird birds\nchild children bottle bottles\nchild children building buildings\nchild children car cars\nchild children cat cats\ncloud clouds color colors\ncloud clouds computer computers\ncloud clouds cow cows\ncloud clouds dog dogs\ncloud clouds dollar dollars\ncloud clouds donkey donkeys\ncloud clouds dream dreams\ncloud clouds eagle eagles\ncloud clouds elephant elephants\ncloud clouds eye eyes\ncloud clouds finger fingers\ncloud clouds goat goats\ncloud clouds hand hands\ncloud clouds horse horses\ncloud clouds lion lions\ncloud clouds machine machines\ncloud clouds mango mangoes\ncloud clouds man men\ncloud clouds melon melons\ncloud clouds monkey monkeys\ncloud clouds mouse mice\ncloud clouds onion onions\ncloud 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pineapple pineapples\ncolor colors rat rats\ncolor colors road roads\ncolor colors snake snakes\ncolor colors woman women\ncolor colors banana bananas\ncolor colors bird birds\ncolor colors bottle bottles\ncolor colors building buildings\ncolor colors car cars\ncolor colors cat cats\ncolor colors child children\ncolor colors cloud clouds\ncomputer computers cow cows\ncomputer computers dog dogs\ncomputer computers dollar dollars\ncomputer computers donkey donkeys\ncomputer computers dream dreams\ncomputer computers eagle eagles\ncomputer computers elephant elephants\ncomputer computers eye eyes\ncomputer computers finger fingers\ncomputer computers goat goats\ncomputer computers hand hands\ncomputer computers horse horses\ncomputer computers lion lions\ncomputer computers machine machines\ncomputer computers mango mangoes\ncomputer computers man men\ncomputer computers melon melons\ncomputer computers monkey monkeys\ncomputer computers mouse mice\ncomputer computers onion 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pig pigs\ncow cows pineapple pineapples\ncow cows rat rats\ncow cows road roads\ncow cows snake snakes\ncow cows woman women\ncow cows banana bananas\ncow cows bird birds\ncow cows bottle bottles\ncow cows building buildings\ncow cows car cars\ncow cows cat cats\ncow cows child children\ncow cows cloud clouds\ncow cows color colors\ncow cows computer computers\ndog dogs dollar dollars\ndog dogs donkey donkeys\ndog dogs dream dreams\ndog dogs eagle eagles\ndog dogs elephant elephants\ndog dogs eye eyes\ndog dogs finger fingers\ndog dogs goat goats\ndog dogs hand hands\ndog dogs horse horses\ndog dogs lion lions\ndog dogs machine machines\ndog dogs mango mangoes\ndog dogs man men\ndog dogs melon melons\ndog dogs monkey monkeys\ndog dogs mouse mice\ndog dogs onion onions\ndog dogs pear pears\ndog dogs pig pigs\ndog dogs pineapple pineapples\ndog dogs rat rats\ndog dogs road roads\ndog dogs snake snakes\ndog dogs woman women\ndog dogs banana bananas\ndog dogs bird birds\ndog dogs bottle bottles\ndog dogs building buildings\ndog dogs car cars\ndog dogs cat cats\ndog dogs child children\ndog dogs cloud clouds\ndog dogs color colors\ndog dogs computer computers\ndog dogs cow cows\ndollar dollars donkey donkeys\ndollar dollars dream dreams\ndollar dollars eagle eagles\ndollar dollars elephant elephants\ndollar dollars eye eyes\ndollar dollars finger fingers\ndollar dollars goat goats\ndollar dollars hand hands\ndollar dollars horse horses\ndollar dollars lion lions\ndollar dollars machine machines\ndollar dollars mango mangoes\ndollar dollars man men\ndollar dollars melon melons\ndollar dollars monkey monkeys\ndollar dollars mouse mice\ndollar dollars onion onions\ndollar dollars pear pears\ndollar dollars pig pigs\ndollar dollars pineapple pineapples\ndollar dollars rat rats\ndollar dollars road roads\ndollar dollars snake snakes\ndollar dollars woman women\ndollar dollars banana bananas\ndollar dollars bird birds\ndollar dollars bottle bottles\ndollar dollars building buildings\ndollar dollars car cars\ndollar dollars cat cats\ndollar dollars child children\ndollar dollars cloud clouds\ndollar dollars color colors\ndollar dollars computer computers\ndollar dollars cow cows\ndollar dollars dog dogs\ndonkey donkeys dream dreams\ndonkey donkeys eagle eagles\ndonkey donkeys elephant elephants\ndonkey donkeys eye eyes\ndonkey donkeys finger fingers\ndonkey donkeys goat goats\ndonkey donkeys hand hands\ndonkey donkeys horse horses\ndonkey donkeys lion lions\ndonkey donkeys machine machines\ndonkey donkeys mango mangoes\ndonkey donkeys man men\ndonkey donkeys melon melons\ndonkey donkeys monkey monkeys\ndonkey donkeys mouse mice\ndonkey donkeys onion onions\ndonkey donkeys pear pears\ndonkey donkeys pig pigs\ndonkey donkeys pineapple pineapples\ndonkey donkeys rat rats\ndonkey donkeys road roads\ndonkey donkeys snake snakes\ndonkey donkeys woman women\ndonkey donkeys banana bananas\ndonkey donkeys bird birds\ndonkey donkeys bottle bottles\ndonkey donkeys building buildings\ndonkey donkeys car cars\ndonkey donkeys cat cats\ndonkey donkeys child children\ndonkey donkeys cloud clouds\ndonkey donkeys color colors\ndonkey donkeys computer computers\ndonkey donkeys cow cows\ndonkey donkeys dog dogs\ndonkey donkeys dollar dollars\ndream dreams eagle eagles\ndream dreams elephant elephants\ndream dreams eye eyes\ndream dreams finger fingers\ndream dreams goat goats\ndream dreams hand hands\ndream dreams horse horses\ndream dreams lion lions\ndream dreams machine machines\ndream dreams mango mangoes\ndream dreams man men\ndream dreams melon melons\ndream dreams monkey monkeys\ndream dreams mouse mice\ndream dreams onion onions\ndream dreams pear pears\ndream dreams pig pigs\ndream dreams pineapple pineapples\ndream dreams rat rats\ndream dreams road roads\ndream dreams snake snakes\ndream dreams woman women\ndream dreams banana bananas\ndream dreams bird birds\ndream dreams bottle bottles\ndream dreams building buildings\ndream dreams car cars\ndream dreams cat cats\ndream dreams child children\ndream dreams cloud clouds\ndream dreams color colors\ndream dreams computer computers\ndream dreams cow cows\ndream dreams dog dogs\ndream dreams dollar dollars\ndream dreams donkey donkeys\neagle eagles elephant elephants\neagle eagles eye eyes\neagle eagles finger fingers\neagle eagles goat goats\neagle eagles hand hands\neagle eagles horse horses\neagle eagles lion lions\neagle eagles machine machines\neagle eagles mango mangoes\neagle eagles man men\neagle eagles melon melons\neagle eagles monkey monkeys\neagle eagles mouse mice\neagle eagles onion onions\neagle eagles pear pears\neagle eagles pig pigs\neagle eagles pineapple pineapples\neagle eagles rat rats\neagle eagles road roads\neagle eagles snake snakes\neagle eagles woman women\neagle eagles banana bananas\neagle eagles bird birds\neagle eagles bottle bottles\neagle eagles building buildings\neagle eagles car cars\neagle eagles cat cats\neagle eagles child children\neagle eagles cloud clouds\neagle eagles color colors\neagle eagles computer computers\neagle eagles cow cows\neagle eagles dog dogs\neagle eagles dollar dollars\neagle eagles donkey donkeys\neagle eagles dream dreams\nelephant elephants eye eyes\nelephant elephants finger fingers\nelephant elephants goat goats\nelephant elephants hand hands\nelephant elephants horse horses\nelephant elephants lion lions\nelephant elephants machine machines\nelephant elephants mango mangoes\nelephant elephants man men\nelephant elephants melon melons\nelephant elephants monkey monkeys\nelephant elephants mouse mice\nelephant elephants onion onions\nelephant elephants pear pears\nelephant elephants pig pigs\nelephant elephants pineapple pineapples\nelephant elephants rat rats\nelephant elephants road roads\nelephant elephants snake snakes\nelephant elephants woman women\nelephant elephants banana bananas\nelephant elephants bird birds\nelephant elephants bottle bottles\nelephant elephants building buildings\nelephant elephants car cars\nelephant elephants cat cats\nelephant elephants child children\nelephant elephants cloud clouds\nelephant elephants color colors\nelephant elephants computer computers\nelephant elephants cow cows\nelephant elephants dog dogs\nelephant elephants dollar dollars\nelephant elephants donkey donkeys\nelephant elephants dream dreams\nelephant elephants eagle eagles\neye eyes finger fingers\neye eyes goat goats\neye eyes hand hands\neye eyes horse horses\neye eyes lion lions\neye eyes machine machines\neye eyes mango mangoes\neye eyes man men\neye eyes melon melons\neye eyes monkey monkeys\neye eyes mouse mice\neye eyes onion onions\neye eyes pear pears\neye eyes pig pigs\neye eyes pineapple pineapples\neye eyes rat rats\neye eyes road roads\neye eyes snake snakes\neye eyes woman women\neye eyes banana bananas\neye eyes bird birds\neye eyes bottle bottles\neye eyes building buildings\neye eyes car cars\neye eyes cat cats\neye eyes child children\neye eyes cloud clouds\neye eyes color colors\neye eyes computer computers\neye eyes cow cows\neye eyes dog dogs\neye eyes dollar dollars\neye eyes donkey donkeys\neye eyes dream dreams\neye eyes eagle eagles\neye eyes elephant elephants\nfinger fingers goat goats\nfinger fingers hand hands\nfinger fingers horse horses\nfinger fingers lion lions\nfinger fingers machine machines\nfinger fingers mango mangoes\nfinger fingers man men\nfinger fingers melon melons\nfinger fingers monkey monkeys\nfinger fingers mouse mice\nfinger fingers onion onions\nfinger fingers pear pears\nfinger fingers pig pigs\nfinger fingers pineapple pineapples\nfinger fingers rat rats\nfinger fingers road roads\nfinger fingers snake snakes\nfinger fingers woman women\nfinger fingers banana bananas\nfinger fingers bird birds\nfinger fingers bottle bottles\nfinger fingers building buildings\nfinger fingers car cars\nfinger fingers cat cats\nfinger fingers child children\nfinger fingers cloud clouds\nfinger fingers color colors\nfinger fingers computer computers\nfinger fingers cow cows\nfinger fingers dog dogs\nfinger fingers dollar dollars\nfinger fingers donkey donkeys\nfinger fingers dream dreams\nfinger fingers eagle eagles\nfinger fingers elephant elephants\nfinger fingers eye eyes\ngoat goats hand hands\ngoat goats horse horses\ngoat goats lion lions\ngoat goats machine machines\ngoat goats mango mangoes\ngoat goats man men\ngoat goats melon melons\ngoat goats monkey monkeys\ngoat goats mouse mice\ngoat goats onion onions\ngoat goats pear pears\ngoat goats pig pigs\ngoat goats pineapple pineapples\ngoat goats rat rats\ngoat goats road roads\ngoat goats snake snakes\ngoat goats woman women\ngoat goats banana bananas\ngoat goats bird birds\ngoat goats bottle bottles\ngoat goats building buildings\ngoat goats car cars\ngoat goats cat cats\ngoat goats child children\ngoat goats cloud clouds\ngoat goats color colors\ngoat goats computer computers\ngoat goats cow cows\ngoat goats dog dogs\ngoat goats dollar dollars\ngoat goats donkey donkeys\ngoat goats dream dreams\ngoat goats eagle eagles\ngoat goats elephant elephants\ngoat goats eye eyes\ngoat goats finger fingers\nhand hands horse horses\nhand hands lion lions\nhand hands machine machines\nhand hands mango mangoes\nhand hands man men\nhand hands melon melons\nhand hands monkey monkeys\nhand hands mouse mice\nhand hands onion onions\nhand hands pear pears\nhand hands pig pigs\nhand hands pineapple pineapples\nhand hands rat rats\nhand hands road roads\nhand hands snake snakes\nhand hands woman women\nhand hands banana bananas\nhand hands bird birds\nhand hands bottle bottles\nhand hands building buildings\nhand hands car cars\nhand hands cat cats\nhand hands child children\nhand hands cloud clouds\nhand hands color colors\nhand hands computer computers\nhand hands cow cows\nhand hands dog dogs\nhand hands dollar dollars\nhand hands donkey donkeys\nhand hands dream dreams\nhand hands eagle eagles\nhand hands elephant elephants\nhand hands eye eyes\nhand hands finger fingers\nhand hands goat goats\nhorse horses lion lions\nhorse horses machine machines\nhorse horses mango mangoes\nhorse horses man men\nhorse horses melon melons\nhorse horses monkey monkeys\nhorse horses mouse mice\nhorse horses onion onions\nhorse horses pear pears\nhorse horses pig pigs\nhorse horses pineapple pineapples\nhorse horses rat rats\nhorse horses road roads\nhorse horses snake snakes\nhorse horses woman women\nhorse horses banana bananas\nhorse horses bird birds\nhorse horses bottle bottles\nhorse horses building buildings\nhorse horses car cars\nhorse horses cat cats\nhorse horses child children\nhorse horses cloud clouds\nhorse horses color colors\nhorse horses computer computers\nhorse horses cow cows\nhorse horses dog dogs\nhorse horses dollar dollars\nhorse horses donkey donkeys\nhorse horses dream dreams\nhorse horses eagle eagles\nhorse horses elephant elephants\nhorse horses eye eyes\nhorse horses finger fingers\nhorse horses goat goats\nhorse horses hand hands\nlion lions machine machines\nlion lions mango mangoes\nlion lions man men\nlion lions melon melons\nlion lions monkey monkeys\nlion lions mouse mice\nlion lions onion onions\nlion lions pear pears\nlion lions pig pigs\nlion lions pineapple pineapples\nlion lions rat rats\nlion lions road roads\nlion lions snake snakes\nlion lions woman women\nlion lions banana bananas\nlion lions bird birds\nlion lions bottle bottles\nlion lions building buildings\nlion lions car cars\nlion lions cat cats\nlion lions child children\nlion lions cloud clouds\nlion lions color colors\nlion lions computer computers\nlion lions cow cows\nlion lions dog dogs\nlion lions dollar dollars\nlion lions donkey donkeys\nlion lions dream dreams\nlion lions eagle eagles\nlion lions elephant elephants\nlion lions eye eyes\nlion lions finger fingers\nlion lions goat goats\nlion lions hand hands\nlion lions horse horses\nmachine machines mango mangoes\nmachine machines man men\nmachine machines melon melons\nmachine machines monkey monkeys\nmachine machines mouse mice\nmachine machines onion onions\nmachine machines pear pears\nmachine machines pig pigs\nmachine machines pineapple pineapples\nmachine machines rat rats\nmachine machines road roads\nmachine machines snake snakes\nmachine machines woman women\nmachine machines banana bananas\nmachine machines bird birds\nmachine machines bottle bottles\nmachine machines building buildings\nmachine machines car cars\nmachine machines cat cats\nmachine machines child children\nmachine machines cloud clouds\nmachine machines color colors\nmachine machines computer computers\nmachine machines cow cows\nmachine machines dog dogs\nmachine machines dollar dollars\nmachine machines donkey donkeys\nmachine machines dream dreams\nmachine machines eagle eagles\nmachine machines elephant elephants\nmachine machines eye eyes\nmachine machines finger fingers\nmachine machines goat goats\nmachine machines hand hands\nmachine machines horse horses\nmachine machines lion lions\nmango mangoes man men\nmango mangoes melon melons\nmango mangoes monkey monkeys\nmango mangoes mouse mice\nmango mangoes onion onions\nmango mangoes pear pears\nmango mangoes pig pigs\nmango mangoes pineapple pineapples\nmango mangoes rat rats\nmango mangoes road roads\nmango mangoes snake snakes\nmango mangoes woman women\nmango mangoes banana bananas\nmango mangoes bird birds\nmango mangoes bottle bottles\nmango mangoes building buildings\nmango mangoes car cars\nmango mangoes cat cats\nmango mangoes child children\nmango mangoes cloud clouds\nmango mangoes color colors\nmango mangoes computer computers\nmango mangoes cow cows\nmango mangoes dog dogs\nmango mangoes dollar dollars\nmango mangoes donkey donkeys\nmango mangoes dream dreams\nmango mangoes eagle eagles\nmango mangoes elephant elephants\nmango mangoes eye eyes\nmango mangoes finger fingers\nmango mangoes goat goats\nmango mangoes hand hands\nmango mangoes horse horses\nmango mangoes lion lions\nmango mangoes machine machines\nman men melon melons\nman men monkey monkeys\nman men mouse mice\nman men onion onions\nman men pear pears\nman men pig pigs\nman men pineapple pineapples\nman men rat rats\nman men road roads\nman men snake snakes\nman men woman women\nman men banana bananas\nman men bird birds\nman men bottle bottles\nman men building buildings\nman men car cars\nman men cat cats\nman men child children\nman men cloud clouds\nman men color colors\nman men computer computers\nman men cow cows\nman men dog dogs\nman men dollar dollars\nman men donkey donkeys\nman men dream dreams\nman men eagle eagles\nman men elephant elephants\nman men eye eyes\nman men finger fingers\nman men goat goats\nman men hand hands\nman men horse horses\nman men lion lions\nman men machine machines\nman men mango mangoes\nmelon melons monkey monkeys\nmelon melons mouse mice\nmelon melons onion onions\nmelon melons pear pears\nmelon melons pig pigs\nmelon melons pineapple pineapples\nmelon melons rat rats\nmelon melons road roads\nmelon melons snake snakes\nmelon melons woman women\nmelon melons banana bananas\nmelon melons bird birds\nmelon melons bottle bottles\nmelon melons building buildings\nmelon melons car cars\nmelon melons cat cats\nmelon melons child children\nmelon melons cloud clouds\nmelon melons color colors\nmelon melons computer computers\nmelon melons cow cows\nmelon melons dog dogs\nmelon melons dollar dollars\nmelon melons donkey donkeys\nmelon melons dream dreams\nmelon melons eagle eagles\nmelon melons elephant elephants\nmelon melons eye eyes\nmelon melons finger fingers\nmelon melons goat goats\nmelon melons hand hands\nmelon melons horse horses\nmelon melons lion lions\nmelon melons machine machines\nmelon melons mango mangoes\nmelon melons man men\nmonkey monkeys mouse mice\nmonkey monkeys onion onions\nmonkey monkeys pear pears\nmonkey monkeys pig pigs\nmonkey monkeys pineapple pineapples\nmonkey monkeys rat rats\nmonkey monkeys road roads\nmonkey monkeys snake snakes\nmonkey monkeys woman women\nmonkey monkeys banana bananas\nmonkey monkeys bird birds\nmonkey monkeys bottle bottles\nmonkey monkeys building buildings\nmonkey monkeys car cars\nmonkey monkeys cat cats\nmonkey monkeys child children\nmonkey monkeys cloud clouds\nmonkey monkeys color colors\nmonkey monkeys computer computers\nmonkey monkeys cow cows\nmonkey monkeys dog dogs\nmonkey monkeys dollar dollars\nmonkey monkeys donkey donkeys\nmonkey monkeys dream dreams\nmonkey monkeys eagle eagles\nmonkey monkeys elephant elephants\nmonkey monkeys eye eyes\nmonkey monkeys finger fingers\nmonkey monkeys goat goats\nmonkey monkeys hand hands\nmonkey monkeys horse horses\nmonkey monkeys lion lions\nmonkey monkeys machine machines\nmonkey monkeys mango mangoes\nmonkey monkeys man men\nmonkey monkeys melon melons\nmouse mice onion onions\nmouse mice pear pears\nmouse mice pig pigs\nmouse mice pineapple pineapples\nmouse mice rat rats\nmouse mice road roads\nmouse mice snake snakes\nmouse mice woman women\nmouse mice banana bananas\nmouse mice bird birds\nmouse mice bottle bottles\nmouse mice building buildings\nmouse mice car cars\nmouse mice cat cats\nmouse mice child children\nmouse mice cloud clouds\nmouse mice color colors\nmouse mice computer computers\nmouse mice cow cows\nmouse mice dog dogs\nmouse mice dollar dollars\nmouse mice donkey donkeys\nmouse mice dream dreams\nmouse mice eagle eagles\nmouse mice elephant elephants\nmouse mice eye eyes\nmouse mice finger fingers\nmouse mice goat goats\nmouse mice hand hands\nmouse mice horse horses\nmouse mice lion lions\nmouse mice machine machines\nmouse mice mango mangoes\nmouse mice man men\nmouse mice melon melons\nmouse mice monkey monkeys\nonion onions pear pears\nonion onions pig pigs\nonion onions pineapple pineapples\nonion onions rat rats\nonion onions road roads\nonion onions snake snakes\nonion onions woman women\nonion onions banana bananas\nonion onions bird birds\nonion onions bottle bottles\nonion onions building buildings\nonion onions car cars\nonion onions cat cats\nonion onions child children\nonion onions cloud clouds\nonion onions color colors\nonion onions computer computers\nonion onions cow cows\nonion onions dog dogs\nonion onions dollar dollars\nonion onions donkey donkeys\nonion onions dream dreams\nonion onions eagle eagles\nonion onions elephant elephants\nonion onions eye eyes\nonion onions finger fingers\nonion onions goat goats\nonion onions hand hands\nonion onions horse horses\nonion onions lion lions\nonion onions machine machines\nonion onions mango mangoes\nonion onions man men\nonion onions melon melons\nonion onions monkey monkeys\nonion onions mouse mice\npear pears pig pigs\npear pears pineapple pineapples\npear pears rat rats\npear pears road roads\npear pears snake snakes\npear pears woman women\npear pears banana bananas\npear pears bird birds\npear pears bottle bottles\npear pears building buildings\npear pears car cars\npear pears cat cats\npear pears child children\npear pears cloud clouds\npear pears color colors\npear pears computer computers\npear pears cow cows\npear pears dog dogs\npear pears dollar dollars\npear pears donkey donkeys\npear pears dream dreams\npear pears eagle eagles\npear pears elephant elephants\npear pears eye eyes\npear pears finger fingers\npear pears goat goats\npear pears hand hands\npear pears horse horses\npear pears lion lions\npear pears machine machines\npear pears mango mangoes\npear pears man men\npear pears melon melons\npear pears monkey monkeys\npear pears mouse mice\npear pears onion onions\npig pigs pineapple pineapples\npig pigs rat rats\npig pigs road roads\npig pigs snake snakes\npig pigs woman women\npig pigs banana bananas\npig pigs bird birds\npig pigs bottle bottles\npig pigs building buildings\npig pigs car cars\npig pigs cat cats\npig pigs child children\npig pigs cloud clouds\npig pigs color colors\npig pigs computer computers\npig pigs cow cows\npig pigs dog dogs\npig pigs dollar dollars\npig pigs donkey donkeys\npig pigs dream dreams\npig pigs eagle eagles\npig pigs elephant elephants\npig pigs eye eyes\npig pigs finger fingers\npig pigs goat goats\npig pigs hand hands\npig pigs horse horses\npig pigs lion lions\npig pigs machine machines\npig pigs mango mangoes\npig pigs man men\npig pigs melon melons\npig pigs monkey monkeys\npig pigs mouse mice\npig pigs onion onions\npig pigs pear pears\npineapple pineapples rat rats\npineapple pineapples road roads\npineapple pineapples snake snakes\npineapple pineapples woman women\npineapple pineapples banana bananas\npineapple pineapples bird birds\npineapple pineapples bottle bottles\npineapple pineapples building buildings\npineapple pineapples car cars\npineapple pineapples cat cats\npineapple pineapples child children\npineapple pineapples cloud clouds\npineapple pineapples color colors\npineapple pineapples computer computers\npineapple pineapples cow cows\npineapple pineapples dog dogs\npineapple pineapples dollar dollars\npineapple pineapples donkey donkeys\npineapple pineapples dream dreams\npineapple pineapples eagle eagles\npineapple pineapples elephant elephants\npineapple pineapples eye eyes\npineapple pineapples finger fingers\npineapple pineapples goat goats\npineapple pineapples hand hands\npineapple pineapples horse horses\npineapple pineapples lion lions\npineapple pineapples machine machines\npineapple pineapples mango mangoes\npineapple pineapples man men\npineapple pineapples melon melons\npineapple pineapples monkey monkeys\npineapple pineapples mouse mice\npineapple pineapples onion onions\npineapple pineapples pear pears\npineapple pineapples pig pigs\nrat rats road roads\nrat rats snake snakes\nrat rats woman women\nrat rats banana bananas\nrat rats bird birds\nrat rats bottle bottles\nrat rats building buildings\nrat rats car cars\nrat rats cat cats\nrat rats child children\nrat rats cloud clouds\nrat rats color colors\nrat rats computer computers\nrat rats cow cows\nrat rats dog dogs\nrat rats dollar dollars\nrat rats donkey donkeys\nrat rats dream dreams\nrat rats eagle eagles\nrat rats elephant elephants\nrat rats eye eyes\nrat rats finger fingers\nrat rats goat goats\nrat rats hand hands\nrat rats horse horses\nrat rats lion lions\nrat rats machine machines\nrat rats mango mangoes\nrat rats man men\nrat rats melon melons\nrat rats monkey monkeys\nrat rats mouse mice\nrat rats onion onions\nrat rats pear pears\nrat rats pig pigs\nrat rats pineapple pineapples\nroad roads snake snakes\nroad roads woman women\nroad roads banana bananas\nroad roads bird birds\nroad roads bottle bottles\nroad roads building buildings\nroad roads car cars\nroad roads cat cats\nroad roads child children\nroad roads cloud clouds\nroad roads color colors\nroad roads computer computers\nroad roads cow cows\nroad roads dog dogs\nroad roads dollar dollars\nroad roads donkey donkeys\nroad roads dream dreams\nroad roads eagle eagles\nroad roads elephant elephants\nroad roads eye eyes\nroad roads finger fingers\nroad roads goat goats\nroad roads hand hands\nroad roads horse horses\nroad roads lion lions\nroad roads machine machines\nroad roads mango mangoes\nroad roads man men\nroad roads melon melons\nroad roads monkey monkeys\nroad roads mouse mice\nroad roads onion onions\nroad roads pear pears\nroad roads pig pigs\nroad roads pineapple pineapples\nroad roads rat rats\nsnake snakes woman women\nsnake snakes banana bananas\nsnake snakes bird birds\nsnake snakes bottle bottles\nsnake snakes building buildings\nsnake snakes car cars\nsnake snakes cat cats\nsnake snakes child children\nsnake snakes cloud clouds\nsnake snakes color colors\nsnake snakes computer computers\nsnake snakes cow cows\nsnake snakes dog dogs\nsnake snakes dollar dollars\nsnake snakes donkey donkeys\nsnake snakes dream dreams\nsnake snakes eagle eagles\nsnake snakes elephant elephants\nsnake snakes eye eyes\nsnake snakes finger fingers\nsnake snakes goat goats\nsnake snakes hand hands\nsnake snakes horse horses\nsnake snakes lion lions\nsnake snakes machine machines\nsnake snakes mango mangoes\nsnake snakes man men\nsnake snakes melon melons\nsnake snakes monkey monkeys\nsnake snakes mouse mice\nsnake snakes onion onions\nsnake snakes pear pears\nsnake snakes pig pigs\nsnake snakes pineapple pineapples\nsnake snakes rat rats\nsnake snakes road roads\nwoman women banana bananas\nwoman women bird birds\nwoman women bottle bottles\nwoman women building buildings\nwoman women car cars\nwoman women cat cats\nwoman women child children\nwoman women cloud clouds\nwoman women color colors\nwoman women computer computers\nwoman women cow cows\nwoman women dog dogs\nwoman women dollar dollars\nwoman women donkey donkeys\nwoman women dream dreams\nwoman women eagle eagles\nwoman women elephant elephants\nwoman women eye eyes\nwoman women finger fingers\nwoman women goat goats\nwoman women hand hands\nwoman women horse horses\nwoman women lion lions\nwoman women machine machines\nwoman women mango mangoes\nwoman women man men\nwoman women melon melons\nwoman women monkey monkeys\nwoman women mouse mice\nwoman women onion onions\nwoman women pear pears\nwoman women pig pigs\nwoman women pineapple pineapples\nwoman women rat rats\nwoman women road roads\nwoman women snake snakes\ndecrease decreases describe describes\ndecrease decreases eat eats\ndecrease decreases enhance enhances\ndecrease decreases estimate estimates\ndecrease decreases find finds\ndecrease decreases generate generates\ndecrease decreases go goes\ndecrease decreases implement implements\ndecrease decreases increase increases\ndecrease decreases listen listens\ndecrease decreases play plays\ndecrease decreases predict predicts\ndecrease decreases provide provides\ndecrease decreases say says\ndecrease decreases scream screams\ndecrease decreases search searches\ndecrease decreases see sees\ndecrease decreases shuffle shuffles\ndecrease decreases sing sings\ndecrease decreases sit sits\ndecrease decreases slow slows\ndecrease decreases speak speaks\ndecrease decreases swim swims\ndecrease decreases talk talks\ndecrease decreases think thinks\ndecrease decreases vanish vanishes\ndecrease decreases walk walks\ndecrease decreases work works\ndecrease decreases write writes\ndescribe describes eat eats\ndescribe describes enhance enhances\ndescribe describes estimate estimates\ndescribe describes find finds\ndescribe describes generate generates\ndescribe describes go goes\ndescribe describes implement implements\ndescribe describes increase increases\ndescribe describes listen listens\ndescribe describes play plays\ndescribe describes predict predicts\ndescribe describes provide provides\ndescribe describes say says\ndescribe describes scream screams\ndescribe describes search searches\ndescribe describes see sees\ndescribe describes shuffle shuffles\ndescribe describes sing sings\ndescribe describes sit sits\ndescribe describes slow slows\ndescribe describes speak speaks\ndescribe describes swim swims\ndescribe describes talk talks\ndescribe describes think thinks\ndescribe describes vanish vanishes\ndescribe describes walk walks\ndescribe describes work works\ndescribe describes write writes\ndescribe describes decrease decreases\neat eats enhance enhances\neat eats estimate estimates\neat eats find finds\neat eats generate generates\neat eats go goes\neat eats implement implements\neat eats increase increases\neat eats listen listens\neat eats play plays\neat eats predict predicts\neat eats provide provides\neat eats say says\neat eats scream screams\neat eats search searches\neat eats see sees\neat eats shuffle shuffles\neat eats sing sings\neat eats sit sits\neat eats slow slows\neat eats speak speaks\neat eats swim swims\neat eats talk talks\neat eats think thinks\neat eats vanish vanishes\neat eats walk walks\neat eats work works\neat eats write writes\neat eats decrease decreases\neat eats describe describes\nenhance enhances estimate estimates\nenhance enhances find finds\nenhance enhances generate generates\nenhance enhances go goes\nenhance enhances implement implements\nenhance enhances increase increases\nenhance enhances listen listens\nenhance enhances play plays\nenhance enhances predict predicts\nenhance enhances provide provides\nenhance enhances say says\nenhance enhances scream screams\nenhance enhances search searches\nenhance enhances see sees\nenhance enhances shuffle shuffles\nenhance enhances sing sings\nenhance enhances sit sits\nenhance enhances slow slows\nenhance enhances speak speaks\nenhance enhances swim swims\nenhance enhances talk talks\nenhance enhances think thinks\nenhance enhances vanish vanishes\nenhance enhances walk walks\nenhance enhances work works\nenhance enhances write writes\nenhance enhances decrease decreases\nenhance enhances describe describes\nenhance enhances eat eats\nestimate estimates find finds\nestimate estimates generate generates\nestimate estimates go goes\nestimate estimates implement implements\nestimate estimates increase increases\nestimate estimates listen listens\nestimate estimates play plays\nestimate estimates predict predicts\nestimate estimates provide provides\nestimate estimates say says\nestimate estimates scream screams\nestimate estimates search searches\nestimate estimates see sees\nestimate estimates shuffle shuffles\nestimate estimates sing sings\nestimate estimates sit sits\nestimate estimates slow slows\nestimate estimates speak speaks\nestimate estimates swim swims\nestimate estimates talk talks\nestimate estimates think thinks\nestimate estimates vanish vanishes\nestimate estimates walk walks\nestimate estimates work works\nestimate estimates write writes\nestimate estimates decrease decreases\nestimate estimates describe describes\nestimate estimates eat eats\nestimate estimates enhance enhances\nfind finds generate generates\nfind finds go goes\nfind finds implement implements\nfind finds increase increases\nfind finds listen listens\nfind finds play plays\nfind finds predict predicts\nfind finds provide provides\nfind finds say says\nfind finds scream screams\nfind finds search searches\nfind finds see sees\nfind finds shuffle shuffles\nfind finds sing sings\nfind finds sit sits\nfind finds slow slows\nfind finds speak speaks\nfind finds swim swims\nfind finds talk talks\nfind finds think thinks\nfind finds vanish vanishes\nfind finds walk walks\nfind finds work works\nfind finds write writes\nfind finds decrease decreases\nfind finds describe describes\nfind finds eat eats\nfind finds enhance enhances\nfind finds estimate estimates\ngenerate generates go goes\ngenerate generates implement implements\ngenerate generates increase increases\ngenerate generates listen listens\ngenerate generates play plays\ngenerate generates predict predicts\ngenerate generates provide provides\ngenerate generates say says\ngenerate generates scream screams\ngenerate generates search searches\ngenerate generates see sees\ngenerate generates shuffle shuffles\ngenerate generates sing sings\ngenerate generates sit sits\ngenerate generates slow slows\ngenerate generates speak speaks\ngenerate generates swim swims\ngenerate generates talk talks\ngenerate generates think thinks\ngenerate generates vanish vanishes\ngenerate generates walk walks\ngenerate generates work works\ngenerate generates write writes\ngenerate generates decrease decreases\ngenerate generates describe describes\ngenerate generates eat eats\ngenerate generates enhance enhances\ngenerate generates estimate estimates\ngenerate generates find finds\ngo goes implement implements\ngo goes increase increases\ngo goes listen listens\ngo goes play plays\ngo goes predict predicts\ngo goes provide provides\ngo goes say says\ngo goes scream screams\ngo goes search searches\ngo goes see sees\ngo goes shuffle shuffles\ngo goes sing sings\ngo goes sit sits\ngo goes slow slows\ngo goes speak speaks\ngo goes swim swims\ngo goes talk talks\ngo goes think thinks\ngo goes vanish vanishes\ngo goes walk walks\ngo goes work works\ngo goes write writes\ngo goes decrease decreases\ngo goes describe describes\ngo goes eat eats\ngo goes enhance enhances\ngo goes estimate estimates\ngo goes find finds\ngo goes generate generates\nimplement implements increase increases\nimplement implements listen listens\nimplement implements play plays\nimplement implements predict predicts\nimplement implements provide provides\nimplement implements say says\nimplement implements scream screams\nimplement implements search searches\nimplement implements see sees\nimplement implements shuffle shuffles\nimplement implements sing sings\nimplement implements sit sits\nimplement implements slow slows\nimplement implements speak speaks\nimplement implements swim swims\nimplement implements talk talks\nimplement implements think thinks\nimplement implements vanish vanishes\nimplement implements walk walks\nimplement implements work works\nimplement implements write writes\nimplement implements decrease decreases\nimplement implements describe describes\nimplement implements eat eats\nimplement implements enhance enhances\nimplement implements estimate estimates\nimplement implements find finds\nimplement implements generate generates\nimplement implements go goes\nincrease increases listen listens\nincrease increases play plays\nincrease increases predict predicts\nincrease increases provide provides\nincrease increases say says\nincrease increases scream screams\nincrease increases search searches\nincrease increases see sees\nincrease increases shuffle shuffles\nincrease increases sing sings\nincrease increases sit sits\nincrease increases slow slows\nincrease increases speak speaks\nincrease increases swim swims\nincrease increases talk talks\nincrease increases think thinks\nincrease increases vanish vanishes\nincrease increases walk walks\nincrease increases work works\nincrease increases write writes\nincrease increases decrease decreases\nincrease increases describe describes\nincrease increases eat eats\nincrease increases enhance enhances\nincrease increases estimate estimates\nincrease increases find finds\nincrease increases generate generates\nincrease increases go goes\nincrease increases implement implements\nlisten listens play plays\nlisten listens predict predicts\nlisten listens provide provides\nlisten listens say says\nlisten listens scream screams\nlisten listens search searches\nlisten listens see sees\nlisten listens shuffle shuffles\nlisten listens sing sings\nlisten listens sit sits\nlisten listens slow slows\nlisten listens speak speaks\nlisten listens swim swims\nlisten listens talk talks\nlisten listens think thinks\nlisten listens vanish vanishes\nlisten listens walk walks\nlisten listens work works\nlisten listens write writes\nlisten listens decrease decreases\nlisten listens describe describes\nlisten listens eat eats\nlisten listens enhance enhances\nlisten listens estimate estimates\nlisten listens find finds\nlisten listens generate generates\nlisten listens go goes\nlisten listens implement implements\nlisten listens increase increases\nplay plays predict predicts\nplay plays provide provides\nplay plays say says\nplay plays scream screams\nplay plays search searches\nplay plays see sees\nplay plays shuffle shuffles\nplay plays sing sings\nplay plays sit sits\nplay plays slow slows\nplay plays speak speaks\nplay plays swim swims\nplay plays talk talks\nplay plays think thinks\nplay plays vanish vanishes\nplay plays walk walks\nplay plays work works\nplay plays write writes\nplay plays decrease decreases\nplay plays describe describes\nplay plays eat eats\nplay plays enhance enhances\nplay plays estimate estimates\nplay plays find finds\nplay plays generate generates\nplay plays go goes\nplay plays implement implements\nplay plays increase increases\nplay plays listen listens\npredict predicts provide provides\npredict predicts say says\npredict predicts scream screams\npredict predicts search searches\npredict predicts see sees\npredict predicts shuffle shuffles\npredict predicts sing sings\npredict predicts sit sits\npredict predicts slow slows\npredict predicts speak speaks\npredict predicts swim swims\npredict predicts talk talks\npredict predicts think thinks\npredict predicts vanish vanishes\npredict predicts walk walks\npredict predicts work works\npredict predicts write writes\npredict predicts decrease decreases\npredict predicts describe describes\npredict predicts eat eats\npredict predicts enhance enhances\npredict predicts estimate estimates\npredict predicts find finds\npredict predicts generate generates\npredict predicts go goes\npredict predicts implement implements\npredict predicts increase increases\npredict predicts listen listens\npredict predicts play plays\nprovide provides say says\nprovide provides scream screams\nprovide provides search searches\nprovide provides see sees\nprovide provides shuffle shuffles\nprovide provides sing sings\nprovide provides sit sits\nprovide provides slow slows\nprovide provides speak speaks\nprovide provides swim swims\nprovide provides talk talks\nprovide provides think thinks\nprovide provides vanish vanishes\nprovide provides walk walks\nprovide provides work works\nprovide provides write writes\nprovide provides decrease decreases\nprovide provides describe describes\nprovide provides eat eats\nprovide provides enhance enhances\nprovide provides estimate estimates\nprovide provides find finds\nprovide provides generate generates\nprovide provides go goes\nprovide provides implement implements\nprovide provides increase increases\nprovide provides listen listens\nprovide provides play plays\nprovide provides predict predicts\nsay says scream screams\nsay says search searches\nsay says see sees\nsay says shuffle shuffles\nsay says sing sings\nsay says sit sits\nsay says slow slows\nsay says speak speaks\nsay says swim swims\nsay says talk talks\nsay says think thinks\nsay says vanish vanishes\nsay says walk walks\nsay says work works\nsay says write writes\nsay says decrease decreases\nsay says describe describes\nsay says eat eats\nsay says enhance enhances\nsay says estimate estimates\nsay says find finds\nsay says generate generates\nsay says go goes\nsay says implement implements\nsay says increase increases\nsay says listen listens\nsay says play plays\nsay says predict predicts\nsay says provide provides\nscream screams search searches\nscream screams see sees\nscream screams shuffle shuffles\nscream screams sing sings\nscream screams sit sits\nscream screams slow slows\nscream screams speak speaks\nscream screams swim swims\nscream screams talk talks\nscream screams think thinks\nscream screams vanish vanishes\nscream screams walk walks\nscream screams work works\nscream screams write writes\nscream screams decrease decreases\nscream screams describe describes\nscream screams eat eats\nscream screams enhance enhances\nscream screams estimate estimates\nscream screams find finds\nscream screams generate generates\nscream screams go goes\nscream screams implement implements\nscream screams increase increases\nscream screams listen listens\nscream screams play plays\nscream screams predict predicts\nscream screams provide provides\nscream screams say says\nsearch searches see sees\nsearch searches shuffle shuffles\nsearch searches sing sings\nsearch searches sit sits\nsearch searches slow slows\nsearch searches speak speaks\nsearch searches swim swims\nsearch searches talk talks\nsearch searches think thinks\nsearch searches vanish vanishes\nsearch searches walk walks\nsearch searches work works\nsearch searches write writes\nsearch searches decrease decreases\nsearch searches describe describes\nsearch searches eat eats\nsearch searches enhance enhances\nsearch searches estimate estimates\nsearch searches find finds\nsearch searches generate generates\nsearch searches go goes\nsearch searches implement implements\nsearch searches increase increases\nsearch searches listen listens\nsearch searches play plays\nsearch searches predict predicts\nsearch searches provide provides\nsearch searches say says\nsearch searches scream screams\nsee sees shuffle shuffles\nsee sees sing sings\nsee sees sit sits\nsee sees slow slows\nsee sees speak speaks\nsee sees swim swims\nsee sees talk talks\nsee sees think thinks\nsee sees vanish vanishes\nsee sees walk walks\nsee sees work works\nsee sees write writes\nsee sees decrease decreases\nsee sees describe describes\nsee sees eat eats\nsee sees enhance enhances\nsee sees estimate estimates\nsee sees find finds\nsee sees generate generates\nsee sees go goes\nsee sees implement implements\nsee sees increase increases\nsee sees listen listens\nsee sees play plays\nsee sees predict predicts\nsee sees provide provides\nsee sees say says\nsee sees scream screams\nsee sees search searches\nshuffle shuffles sing sings\nshuffle shuffles sit sits\nshuffle shuffles slow slows\nshuffle shuffles speak speaks\nshuffle shuffles swim swims\nshuffle shuffles talk talks\nshuffle shuffles think thinks\nshuffle shuffles vanish vanishes\nshuffle shuffles walk walks\nshuffle shuffles work works\nshuffle shuffles write writes\nshuffle shuffles decrease decreases\nshuffle shuffles describe describes\nshuffle shuffles eat eats\nshuffle shuffles enhance enhances\nshuffle shuffles estimate estimates\nshuffle shuffles find finds\nshuffle shuffles generate generates\nshuffle shuffles go goes\nshuffle shuffles implement implements\nshuffle shuffles increase increases\nshuffle shuffles listen listens\nshuffle shuffles play plays\nshuffle shuffles predict predicts\nshuffle shuffles provide provides\nshuffle shuffles say says\nshuffle shuffles scream screams\nshuffle shuffles search searches\nshuffle shuffles see sees\nsing sings sit sits\nsing sings slow slows\nsing sings speak speaks\nsing sings swim swims\nsing sings talk talks\nsing sings think thinks\nsing sings vanish vanishes\nsing sings walk walks\nsing sings work works\nsing sings write writes\nsing sings decrease decreases\nsing sings describe describes\nsing sings eat eats\nsing sings enhance enhances\nsing sings estimate estimates\nsing sings find finds\nsing sings generate generates\nsing sings go goes\nsing sings implement implements\nsing sings increase increases\nsing sings listen listens\nsing sings play plays\nsing sings predict predicts\nsing sings provide provides\nsing sings say says\nsing sings scream screams\nsing sings search searches\nsing sings see sees\nsing sings shuffle shuffles\nsit sits slow slows\nsit sits speak speaks\nsit sits swim swims\nsit sits talk talks\nsit sits think thinks\nsit sits vanish vanishes\nsit sits walk walks\nsit sits work works\nsit sits write writes\nsit sits decrease decreases\nsit sits describe describes\nsit sits eat eats\nsit sits enhance enhances\nsit sits estimate estimates\nsit sits find finds\nsit sits generate generates\nsit sits go goes\nsit sits implement implements\nsit sits increase increases\nsit sits listen listens\nsit sits play plays\nsit sits predict predicts\nsit sits provide provides\nsit sits say says\nsit sits scream screams\nsit sits search searches\nsit sits see sees\nsit sits shuffle shuffles\nsit sits sing sings\nslow slows speak speaks\nslow slows swim swims\nslow slows talk talks\nslow slows think thinks\nslow slows vanish vanishes\nslow slows walk walks\nslow slows work works\nslow slows write writes\nslow slows decrease decreases\nslow slows describe describes\nslow slows eat eats\nslow slows enhance enhances\nslow slows estimate estimates\nslow slows find finds\nslow slows generate generates\nslow slows go goes\nslow slows implement implements\nslow slows increase increases\nslow slows listen listens\nslow slows play plays\nslow slows predict predicts\nslow slows provide provides\nslow slows say says\nslow slows scream screams\nslow slows search searches\nslow slows see sees\nslow slows shuffle shuffles\nslow slows sing sings\nslow slows sit sits\nspeak speaks swim swims\nspeak speaks talk talks\nspeak speaks think thinks\nspeak speaks vanish vanishes\nspeak speaks walk walks\nspeak speaks work works\nspeak speaks write writes\nspeak speaks decrease decreases\nspeak speaks describe describes\nspeak speaks eat eats\nspeak speaks enhance enhances\nspeak speaks estimate estimates\nspeak speaks find finds\nspeak speaks generate generates\nspeak speaks go goes\nspeak speaks implement implements\nspeak speaks increase increases\nspeak speaks listen listens\nspeak speaks play plays\nspeak speaks predict predicts\nspeak speaks provide provides\nspeak speaks say says\nspeak speaks scream screams\nspeak speaks search searches\nspeak speaks see sees\nspeak speaks shuffle shuffles\nspeak speaks sing sings\nspeak speaks sit sits\nspeak speaks slow slows\nswim swims talk talks\nswim swims think thinks\nswim swims vanish vanishes\nswim swims walk walks\nswim swims work works\nswim swims write writes\nswim swims decrease decreases\nswim swims describe describes\nswim swims eat eats\nswim swims enhance enhances\nswim swims estimate estimates\nswim swims find finds\nswim swims generate generates\nswim swims go goes\nswim swims implement implements\nswim swims increase increases\nswim swims listen listens\nswim swims play plays\nswim swims predict predicts\nswim swims provide provides\nswim swims say says\nswim swims scream screams\nswim swims search searches\nswim swims see sees\nswim swims shuffle shuffles\nswim swims sing sings\nswim swims sit sits\nswim swims slow slows\nswim swims speak speaks\ntalk talks think thinks\ntalk talks vanish vanishes\ntalk talks walk walks\ntalk talks work works\ntalk talks write writes\ntalk talks decrease decreases\ntalk talks describe describes\ntalk talks eat eats\ntalk talks enhance enhances\ntalk talks estimate estimates\ntalk talks find finds\ntalk talks generate generates\ntalk talks go goes\ntalk talks implement implements\ntalk talks increase increases\ntalk talks listen listens\ntalk talks play plays\ntalk talks predict predicts\ntalk talks provide provides\ntalk talks say says\ntalk talks scream screams\ntalk talks search searches\ntalk talks see sees\ntalk talks shuffle shuffles\ntalk talks sing sings\ntalk talks sit sits\ntalk talks slow slows\ntalk talks speak speaks\ntalk talks swim swims\nthink thinks vanish vanishes\nthink thinks walk walks\nthink thinks work works\nthink thinks write writes\nthink thinks decrease decreases\nthink thinks describe describes\nthink thinks eat eats\nthink thinks enhance enhances\nthink thinks estimate estimates\nthink thinks find finds\nthink thinks generate generates\nthink thinks go goes\nthink thinks implement implements\nthink thinks increase increases\nthink thinks listen listens\nthink thinks play plays\nthink thinks predict predicts\nthink thinks provide provides\nthink thinks say says\nthink thinks scream screams\nthink thinks search searches\nthink thinks see sees\nthink thinks shuffle shuffles\nthink thinks sing sings\nthink thinks sit sits\nthink thinks slow slows\nthink thinks speak speaks\nthink thinks swim swims\nthink thinks talk talks\nvanish vanishes walk walks\nvanish vanishes work works\nvanish vanishes write writes\nvanish vanishes decrease decreases\nvanish vanishes describe describes\nvanish vanishes eat eats\nvanish vanishes enhance enhances\nvanish vanishes estimate estimates\nvanish vanishes find finds\nvanish vanishes generate generates\nvanish vanishes go goes\nvanish vanishes implement implements\nvanish vanishes increase increases\nvanish vanishes listen listens\nvanish vanishes play plays\nvanish vanishes predict predicts\nvanish vanishes provide provides\nvanish vanishes say says\nvanish vanishes scream screams\nvanish vanishes search searches\nvanish vanishes see sees\nvanish vanishes shuffle shuffles\nvanish vanishes sing sings\nvanish vanishes sit sits\nvanish vanishes slow slows\nvanish vanishes speak speaks\nvanish vanishes swim swims\nvanish vanishes talk talks\nvanish vanishes think thinks\nwalk walks work works\nwalk walks write writes\nwalk walks decrease decreases\nwalk walks describe describes\nwalk walks eat eats\nwalk walks enhance enhances\nwalk walks estimate estimates\nwalk walks find finds\nwalk walks generate generates\nwalk walks go goes\nwalk walks implement implements\nwalk walks increase increases\nwalk walks listen listens\nwalk walks play plays\nwalk walks predict predicts\nwalk walks provide provides\nwalk walks say says\nwalk walks scream screams\nwalk walks search searches\nwalk walks see sees\nwalk walks shuffle shuffles\nwalk walks sing sings\nwalk walks sit sits\nwalk walks slow slows\nwalk walks speak speaks\nwalk walks swim swims\nwalk walks talk talks\nwalk walks think thinks\nwalk walks vanish vanishes\nwork works write writes\nwork works decrease decreases\nwork works describe describes\nwork works eat eats\nwork works enhance enhances\nwork works estimate estimates\nwork works find finds\nwork works generate generates\nwork works go goes\nwork works implement implements\nwork works increase increases\nwork works listen listens\nwork works play plays\nwork works predict predicts\nwork works provide provides\nwork works say says\nwork works scream screams\nwork works search searches\nwork works see sees\nwork works shuffle shuffles\nwork works sing sings\nwork works sit sits\nwork works slow slows\nwork works speak speaks\nwork works swim swims\nwork works talk talks\nwork works think thinks\nwork works vanish vanishes\nwork works walk walks\nwrite writes decrease decreases\nwrite writes describe describes\nwrite writes eat eats\nwrite writes enhance enhances\nwrite writes estimate estimates\nwrite writes find finds\nwrite writes generate generates\nwrite writes go goes\nwrite writes implement implements\nwrite writes increase increases\nwrite writes listen listens\nwrite writes play plays\nwrite writes predict predicts\nwrite writes provide provides\nwrite writes say says\nwrite writes scream screams\nwrite writes search searches\nwrite writes see sees\nwrite writes shuffle shuffles\nwrite writes sing sings\nwrite writes sit sits\nwrite writes slow slows\nwrite writes speak speaks\nwrite writes swim swims\nwrite writes talk talks\nwrite writes think thinks\nwrite writes vanish vanishes\nwrite writes walk walks\nwrite writes work works\n"
  },
  {
    "path": "psdvec/testsets/analogy/google.txt",
    "content": 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ana\tbujumbura\tburundi\r\naccra\tghana\tcairo\tegypt\r\naccra\tghana\tcanberra\taustralia\r\naccra\tghana\tcaracas\tvenezuela\r\naccra\tghana\tchisinau\tmoldova\r\naccra\tghana\tconakry\tguinea\r\naccra\tghana\tcopenhagen\tdenmark\r\naccra\tghana\tdakar\tsenegal\r\naccra\tghana\tdamascus\tsyria\r\naccra\tghana\tdhaka\tbangladesh\r\naccra\tghana\tdoha\tqatar\r\naccra\tghana\tdublin\tireland\r\naccra\tghana\tdushanbe\ttajikistan\r\naccra\tghana\tfunafuti\ttuvalu\r\nalgiers\talgeria\tamman\tjordan\r\nalgiers\talgeria\tankara\tturkey\r\nalgiers\talgeria\tantananarivo\tmadagascar\r\nalgiers\talgeria\tapia\tsamoa\r\nalgiers\talgeria\tashgabat\tturkmenistan\r\nalgiers\talgeria\tasmara\teritrea\r\nalgiers\talgeria\tastana\tkazakhstan\r\nalgiers\talgeria\tathens\tgreece\r\nalgiers\talgeria\tbaghdad\tiraq\r\nalgiers\talgeria\tbaku\tazerbaijan\r\nalgiers\talgeria\tbamako\tmali\r\nalgiers\talgeria\tbangkok\tthailand\r\nalgiers\talgeria\tbanjul\tgambia\r\nalgiers\talgeria\tbeijing\tchina\r\nalgiers\talgeria\tbeirut\tlebanon\r\nalgiers\talgeria\tbelgrade\tserbia\r\nalgiers\talgeria\tbelmopan\tbelize\r\nalgiers\talgeria\tberlin\tgermany\r\nalgiers\talgeria\tbern\tswitzerland\r\nalgiers\talgeria\tbishkek\tkyrgyzstan\r\nalgiers\talgeria\tbratislava\tslovakia\r\nalgiers\talgeria\tbrussels\tbelgium\r\nalgiers\talgeria\tbucharest\tromania\r\nalgiers\talgeria\tbudapest\thungary\r\nalgiers\talgeria\tbujumbura\tburundi\r\nalgiers\talgeria\tcairo\tegypt\r\nalgiers\talgeria\tcanberra\taustralia\r\nalgiers\talgeria\tcaracas\tvenezuela\r\nalgiers\talgeria\tchisinau\tmoldova\r\nalgiers\talgeria\tconakry\tguinea\r\nalgiers\talgeria\tcopenhagen\tdenmark\r\nalgiers\talgeria\tdakar\tsenegal\r\nalgiers\talgeria\tdamascus\tsyria\r\nalgiers\talgeria\tdhaka\tbangladesh\r\nalgiers\talgeria\tdoha\tqatar\r\nalgiers\talgeria\tdublin\tireland\r\nalgiers\talgeria\tdushanbe\ttajikistan\r\nalgiers\talgeria\tfunafuti\ttuvalu\r\nalgiers\talgeria\tgaborone\tbotswana\r\namman\tjordan\tankara\tturkey\r\namman\tjordan\tantananarivo\tmadagascar\r\namman\tjordan\tapia\tsamoa\r\namman\tjordan\tashgabat\tturkmenistan\r\namman\tjordan\tasmara\teritrea\r\namman\tjordan\tastana\tkazakhstan\r\namman\tjordan\tathens\tgreece\r\namman\tjordan\tbaghdad\tiraq\r\namman\tjordan\tbaku\tazerbaijan\r\namman\tjordan\tbamako\tmali\r\namman\tjordan\tbangkok\tthailand\r\namman\tjordan\tbanjul\tgambia\r\namman\tjordan\tbeijing\tchina\r\namman\tjordan\tbeirut\tlebanon\r\namman\tjordan\tbelgrade\tserbia\r\namman\tjordan\tbelmopan\tbelize\r\namman\tjordan\tberlin\tgermany\r\namman\tjordan\tbern\tswitzerland\r\namman\tjordan\tbishkek\tkyrgyzstan\r\namman\tjordan\tbratislava\tslovakia\r\namman\tjordan\tbrussels\tbelgium\r\namman\tjordan\tbucharest\tromania\r\namman\tjordan\tbudapest\thungary\r\namman\tjordan\tbujumbura\tburundi\r\namman\tjordan\tcairo\tegypt\r\namman\tjordan\tcanberra\taustralia\r\namman\tjordan\tcaracas\tvenezuela\r\namman\tjordan\tchisinau\tmoldova\r\namman\tjordan\tconakry\tguinea\r\namman\tjordan\tcopenhagen\tdenmark\r\namman\tjordan\tdakar\tsenegal\r\namman\tjordan\tdamascus\tsyria\r\namman\tjordan\tdhaka\tbangladesh\r\namman\tjordan\tdoha\tqatar\r\namman\tjordan\tdublin\tireland\r\namman\tjordan\tdushanbe\ttajikistan\r\namman\tjordan\tfunafuti\ttuvalu\r\namman\tjordan\tgaborone\tbotswana\r\namman\tjordan\tgeorgetown\tguyana\r\nankara\tturkey\tantananarivo\tmadagascar\r\nankara\tturkey\tapia\tsamoa\r\nankara\tturkey\tashgabat\tturkmenistan\r\nankara\tturkey\tasmara\teritrea\r\nankara\tturkey\tastana\tkazakhstan\r\nankara\tturkey\tathens\tgreece\r\nankara\tturkey\tbaghdad\tiraq\r\nankara\tturkey\tbaku\tazerbaijan\r\nankara\tturkey\tbamako\tmali\r\nankara\tturkey\tbangkok\tthailand\r\nankara\tturkey\tbanjul\tgambia\r\nankara\tturkey\tbeijing\tchina\r\nankara\tturkey\tbeirut\tlebanon\r\nankara\tturkey\tbelgrade\tserbia\r\nankara\tturkey\tbelmopan\tbelize\r\nankara\tturkey\tberlin\tgermany\r\nankara\tturkey\tbern\tswitzerland\r\nankara\tturkey\tbishkek\tkyrgyzstan\r\nankara\tturkey\tbratislava\tslovakia\r\nankara\tturkey\tbrussels\tbelgium\r\nankara\tturkey\tbucharest\tromania\r\nankara\tturkey\tbudapest\thungary\r\nankara\tturkey\tbujumbura\tburundi\r\nankara\tturkey\tcairo\tegypt\r\nankara\tturkey\tcanberra\taustralia\r\nankara\tturkey\tcaracas\tvenezuela\r\nankara\tturkey\tchisinau\tmoldova\r\nankara\tturkey\tconakry\tguinea\r\nankara\tturkey\tcopenhagen\tdenmark\r\nankara\tturkey\tdakar\tsenegal\r\nankara\tturkey\tdamascus\tsyria\r\nankara\tturkey\tdhaka\tbangladesh\r\nankara\tturkey\tdoha\tqatar\r\nankara\tturkey\tdublin\tireland\r\nankara\tturkey\tdushanbe\ttajikistan\r\nankara\tturkey\tfunafuti\ttuvalu\r\nankara\tturkey\tgaborone\tbotswana\r\nankara\tturkey\tgeorgetown\tguyana\r\nankara\tturkey\thanoi\tvietnam\r\nantananarivo\tmadagascar\tapia\tsamoa\r\nantananarivo\tmadagascar\tashgabat\tturkmenistan\r\nantananarivo\tmadagascar\tasmara\teritrea\r\nantananarivo\tmadagascar\tastana\tkazakhstan\r\nantananarivo\tmadagascar\tathens\tgreece\r\nantananarivo\tmadag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anda\tangola\trabat\tmorocco\r\nluanda\tangola\triga\tlatvia\r\nluanda\tangola\trome\titaly\r\nluanda\tangola\troseau\tdominica\r\nluanda\tangola\tsantiago\tchile\r\nluanda\tangola\tskopje\tmacedonia\r\nluanda\tangola\tsofia\tbulgaria\r\nluanda\tangola\tstockholm\tsweden\r\nluanda\tangola\tsuva\tfiji\r\nluanda\tangola\ttaipei\ttaiwan\r\nluanda\tangola\ttallinn\testonia\r\nluanda\tangola\ttashkent\tuzbekistan\r\nluanda\tangola\ttbilisi\tgeorgia\r\nluanda\tangola\ttegucigalpa\thonduras\r\nluanda\tangola\ttehran\tiran\r\nlusaka\tzambia\tmadrid\tspain\r\nlusaka\tzambia\tmanagua\tnicaragua\r\nlusaka\tzambia\tmanama\tbahrain\r\nlusaka\tzambia\tmanila\tphilippines\r\nlusaka\tzambia\tmaputo\tmozambique\r\nlusaka\tzambia\tminsk\tbelarus\r\nlusaka\tzambia\tmogadishu\tsomalia\r\nlusaka\tzambia\tmonrovia\tliberia\r\nlusaka\tzambia\tmontevideo\turuguay\r\nlusaka\tzambia\tmoscow\trussia\r\nlusaka\tzambia\tmuscat\toman\r\nlusaka\tzambia\tnairobi\tkenya\r\nlusaka\tzambia\tnassau\tbahamas\r\nlusaka\tzambia\tniamey\tniger\r\nlusaka\tzambia\tnicosia\tcyprus\r\nlusaka\tzambia\tnouakchott\tmauritania\r\nlusaka\tzambia\tnuuk\tgreenland\r\nlusaka\tzambia\toslo\tnorway\r\nlusaka\tzambia\tottawa\tcanada\r\nlusaka\tzambia\tparamaribo\tsuriname\r\nlusaka\tzambia\tparis\tfrance\r\nlusaka\tzambia\tpodgorica\tmontenegro\r\nlusaka\tzambia\tquito\tecuador\r\nlusaka\tzambia\trabat\tmorocco\r\nlusaka\tzambia\triga\tlatvia\r\nlusaka\tzambia\trome\titaly\r\nlusaka\tzambia\troseau\tdominica\r\nlusaka\tzambia\tsantiago\tchile\r\nlusaka\tzambia\tskopje\tmacedonia\r\nlusaka\tzambia\tsofia\tbulgaria\r\nlusaka\tzambia\tstockholm\tsweden\r\nlusaka\tzambia\tsuva\tfiji\r\nlusaka\tzambia\ttaipei\ttaiwan\r\nlusaka\tzambia\ttallinn\testonia\r\nlusaka\tzambia\ttashkent\tuzbekistan\r\nlusaka\tzambia\ttbilisi\tgeorgia\r\nlusaka\tzambia\ttegucigalpa\thonduras\r\nlusaka\tzambia\ttehran\tiran\r\nlusaka\tzambia\tthimphu\tbhutan\r\nmadrid\tspain\tmanagua\tnicaragua\r\nmadrid\tspain\tmanama\tbahrain\r\nmadrid\tspain\tmanila\tphilippines\r\nmadrid\tspain\tmaputo\tmozambique\r\nmadrid\tspain\tminsk\tbelarus\r\nmadrid\tspain\tmogadishu\tsomalia\r\nmadrid\tspain\tmonrovia\tliberia\r\nmadrid\tspain\tmontevideo\turuguay\r\nmadrid\tspain\tmoscow\trussia\r\nmadrid\tspain\tmuscat\toman\r\nmadrid\tspain\tnairobi\tkenya\r\nmadrid\tspain\tnassau\tbahamas\r\nmadrid\tspain\tniamey\tniger\r\nmadrid\tspain\tnicosia\tcyprus\r\nmadrid\tspain\tnouakchott\tmauritania\r\nmadrid\tspain\tnuuk\tgreenland\r\nmadrid\tspain\toslo\tnorway\r\nmadrid\tspain\tottawa\tcanada\r\nmadrid\tspain\tparamaribo\tsuriname\r\nmadrid\tspain\tparis\tfrance\r\nmadrid\tspain\tpodgorica\tmontenegro\r\nmadrid\tspain\tquito\tecuador\r\nmadrid\tspain\trabat\tmorocco\r\nmadrid\tspain\triga\tlatvia\r\nmadrid\tspain\trome\titaly\r\nmadrid\tspain\troseau\tdominica\r\nmadrid\tspain\tsantiago\tchile\r\nmadrid\tspain\tskopje\tmacedonia\r\nmadrid\tspain\tsofia\tbulgaria\r\nmadrid\tspain\tstockholm\tsweden\r\nmadrid\tspain\tsuva\tfiji\r\nmadrid\tspain\ttaipei\ttaiwan\r\nmadrid\tspain\ttallinn\testonia\r\nmadrid\tspain\ttashkent\tuzbekistan\r\nmadrid\tspain\ttbilisi\tgeorgia\r\nmadrid\tspain\ttegucigalpa\thonduras\r\nmadrid\tspain\ttehran\tiran\r\nmadrid\tspain\tthimphu\tbhutan\r\nmadrid\tspain\ttirana\talbania\r\nmanagua\tnicaragua\tmanama\tbahrain\r\nmanagua\tnicaragua\tmanila\tphilippines\r\nmanagua\tnicaragua\tmaputo\tmozambique\r\nmanagua\tnicaragua\tminsk\tbelarus\r\nmanagua\tnicaragua\tmogadishu\tsomalia\r\nmanagua\tnicaragua\tmonrovia\tliberia\r\nmanagua\tnicaragua\tmontevideo\turuguay\r\nmanagua\tnicaragua\tmoscow\trussia\r\nmanagua\tnicaragua\tmuscat\toman\r\nmanagua\tnicaragua\tnairobi\tkenya\r\nmanagua\tnicaragua\tnassau\tbahamas\r\nmanagua\tnicaragua\tniamey\tniger\r\nmanagua\tnicaragua\tnicosia\tcyprus\r\nmanagua\tnicaragua\tnouakchott\tmauritania\r\nmanagua\tnicaragua\tnuuk\tgreenland\r\nmanagua\tnicaragua\toslo\tnorway\r\nmanagua\tnicaragua\tottawa\tcanada\r\nmanagua\tnicaragua\tparamaribo\tsuriname\r\nmanagua\tnicaragua\tparis\tfrance\r\nmanagua\tnicaragua\tpodgorica\tmontenegro\r\nmanagua\tnicaragua\tquito\tecuador\r\nmanagua\tnicaragua\trabat\tmorocco\r\nmanagua\tnicaragua\triga\tlatvia\r\nmanagua\tnicaragua\trome\titaly\r\nmanagua\tnicaragua\troseau\tdominica\r\nmanagua\tnicaragua\tsantiago\tchile\r\nmanagua\tnicaragua\tskopje\tmacedonia\r\nmanagua\tnicaragua\tsofia\tbulgaria\r\nmanagua\tnicaragua\tstockholm\tsweden\r\nmanagua\tnicaragua\tsuva\tfiji\r\nmanagua\tnicaragua\ttaipei\ttaiwan\r\nmanagua\tnicaragua\ttallinn\testonia\r\nmanagua\tnicaragua\ttashkent\tuzbekistan\r\nmanagua\tnicaragua\ttbilisi\tgeorgia\r\nmanagua\tnicaragua\ttegucigalpa\thonduras\r\nmanagua\tnicaragua\ttehran\tiran\r\nmanagua\tnicaragua\tthimphu\tbhutan\r\nmanagua\tnicaragua\ttirana\talbania\r\nmanagua\tnicaragua\ttokyo\tjapan\r\nmanama\tbahrain\tmanila\tphilippines\r\nmanama\tbahrain\tmaputo\tmozambique\r\nmanama\tbahrain\tminsk\tbelarus\r\nmanama\tbahrain\tmogadishu\tsomalia\r\nmanama\tbahrain\tmonrovia\tliberia\r\nmanama\tbahr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uras\tbeijing\tchina\r\ntegucigalpa\thonduras\tbeirut\tlebanon\r\ntegucigalpa\thonduras\tbelgrade\tserbia\r\ntegucigalpa\thonduras\tbelmopan\tbelize\r\ntegucigalpa\thonduras\tberlin\tgermany\r\ntegucigalpa\thonduras\tbern\tswitzerland\r\ntegucigalpa\thonduras\tbishkek\tkyrgyzstan\r\ntegucigalpa\thonduras\tbratislava\tslovakia\r\ntehran\tiran\tthimphu\tbhutan\r\ntehran\tiran\ttirana\talbania\r\ntehran\tiran\ttokyo\tjapan\r\ntehran\tiran\ttripoli\tlibya\r\ntehran\tiran\ttunis\ttunisia\r\ntehran\tiran\tvaduz\tliechtenstein\r\ntehran\tiran\tvalletta\tmalta\r\ntehran\tiran\tvienna\taustria\r\ntehran\tiran\tvientiane\tlaos\r\ntehran\tiran\tvilnius\tlithuania\r\ntehran\tiran\twarsaw\tpoland\r\ntehran\tiran\twindhoek\tnamibia\r\ntehran\tiran\tyerevan\tarmenia\r\ntehran\tiran\tzagreb\tcroatia\r\ntehran\tiran\tabuja\tnigeria\r\ntehran\tiran\taccra\tghana\r\ntehran\tiran\talgiers\talgeria\r\ntehran\tiran\tamman\tjordan\r\ntehran\tiran\tankara\tturkey\r\ntehran\tiran\tantananarivo\tmadagascar\r\ntehran\tiran\tapia\tsamoa\r\ntehran\tiran\tashgabat\tturkmenistan\r\ntehran\tiran\tasmara\teritrea\r\ntehran\tiran\tastana\tkazakhstan\r\ntehran\tiran\tathens\tgreece\r\ntehran\tiran\tbaghdad\tiraq\r\ntehran\tiran\tbaku\tazerbaijan\r\ntehran\tiran\tbamako\tmali\r\ntehran\tiran\tbangkok\tthailand\r\ntehran\tiran\tbanjul\tgambia\r\ntehran\tiran\tbeijing\tchina\r\ntehran\tiran\tbeirut\tlebanon\r\ntehran\tiran\tbelgrade\tserbia\r\ntehran\tiran\tbelmopan\tbelize\r\ntehran\tiran\tberlin\tgermany\r\ntehran\tiran\tbern\tswitzerland\r\ntehran\tiran\tbishkek\tkyrgyzstan\r\ntehran\tiran\tbratislava\tslovakia\r\ntehran\tiran\tbrussels\tbelgium\r\nthimphu\tbhutan\ttirana\talbania\r\nthimphu\tbhutan\ttokyo\tjapan\r\nthimphu\tbhutan\ttripoli\tlibya\r\nthimphu\tbhutan\ttunis\ttunisia\r\nthimphu\tbhutan\tvaduz\tliechtenstein\r\nthimphu\tbhutan\tvalletta\tmalta\r\nthimphu\tbhutan\tvienna\taustria\r\nthimphu\tbhutan\tvientiane\tlaos\r\nthimphu\tbhutan\tvilnius\tlithuania\r\nthimphu\tbhutan\twarsaw\tpoland\r\nthimphu\tbhutan\twindhoek\tnamibia\r\nthimphu\tbhutan\tyerevan\tarmenia\r\nthimphu\tbhutan\tzagreb\tcroatia\r\nthimphu\tbhutan\tabuja\tnigeria\r\nthimphu\tbhutan\taccra\tghana\r\nthimphu\tbhutan\talgiers\talgeria\r\nthimphu\tbhutan\tamman\tjordan\r\nthimphu\tbhutan\tankara\tturkey\r\nthimphu\tbhutan\tantananarivo\tmadagascar\r\nthimphu\tbhutan\tapia\tsamoa\r\nthimphu\tbhutan\tashgabat\tturkmenistan\r\nthimphu\tbhutan\tasmara\teritrea\r\nthimphu\tbhutan\tastana\tkazakhstan\r\nthimphu\tbhutan\tathens\tgreece\r\nthimphu\tbhutan\tbaghdad\tiraq\r\nthimphu\tbhutan\tbaku\tazerbaijan\r\nthimphu\tbhutan\tbamako\tmali\r\nthimphu\tbhutan\tbangkok\tthailand\r\nthimphu\tbhutan\tbanjul\tgambia\r\nthimphu\tbhutan\tbeijing\tchina\r\nthimphu\tbhutan\tbeirut\tlebanon\r\nthimphu\tbhutan\tbelgrade\tserbia\r\nthimphu\tbhutan\tbelmopan\tbelize\r\nthimphu\tbhutan\tberlin\tgermany\r\nthimphu\tbhutan\tbern\tswitzerland\r\nthimphu\tbhutan\tbishkek\tkyrgyzstan\r\nthimphu\tbhutan\tbratislava\tslovakia\r\nthimphu\tbhutan\tbrussels\tbelgium\r\nthimphu\tbhutan\tbucharest\tromania\r\ntirana\talbania\ttokyo\tjapan\r\ntirana\talbania\ttripoli\tlibya\r\ntirana\talbania\ttunis\ttunisia\r\ntirana\talbania\tvaduz\tliechtenstein\r\ntirana\talbania\tvalletta\tmalta\r\ntirana\talbania\tvienna\taustria\r\ntirana\talbania\tvientiane\tlaos\r\ntirana\talbania\tvilnius\tlithuania\r\ntirana\talbania\twarsaw\tpoland\r\ntirana\talbania\twindhoek\tnamibia\r\ntirana\talbania\tyerevan\tarmenia\r\ntirana\talbania\tzagreb\tcroatia\r\ntirana\talbania\tabuja\tnigeria\r\ntirana\talbania\taccra\tghana\r\ntirana\talbania\talgiers\talgeria\r\ntirana\talbania\tamman\tjordan\r\ntirana\talbania\tankara\tturkey\r\ntirana\talbania\tantananarivo\tmadagascar\r\ntirana\talbania\tapia\tsamoa\r\ntirana\talbania\tashgabat\tturkmenistan\r\ntirana\talbania\tasmara\teritrea\r\ntirana\talbania\tastana\tkazakhstan\r\ntirana\talbania\tathens\tgreece\r\ntirana\talbania\tbaghdad\tiraq\r\ntirana\talbania\tbaku\tazerbaijan\r\ntirana\talbania\tbamako\tmali\r\ntirana\talbania\tbangkok\tthailand\r\ntirana\talbania\tbanjul\tgambia\r\ntirana\talbania\tbeijing\tchina\r\ntirana\talbania\tbeirut\tlebanon\r\ntirana\talbania\tbelgrade\tserbia\r\ntirana\talbania\tbelmopan\tbelize\r\ntirana\talbania\tberlin\tgermany\r\ntirana\talbania\tbern\tswitzerland\r\ntirana\talbania\tbishkek\tkyrgyzstan\r\ntirana\talbania\tbratislava\tslovakia\r\ntirana\talbania\tbrussels\tbelgium\r\ntirana\talbania\tbucharest\tromania\r\ntirana\talbania\tbudapest\thungary\r\ntokyo\tjapan\ttripoli\tlibya\r\ntokyo\tjapan\ttunis\ttunisia\r\ntokyo\tjapan\tvaduz\tliechtenstein\r\ntokyo\tjapan\tvalletta\tmalta\r\ntokyo\tjapan\tvienna\taustria\r\ntokyo\tjapan\tvientiane\tlaos\r\ntokyo\tjapan\tvilnius\tlithuania\r\ntokyo\tjapan\twarsaw\tpoland\r\ntokyo\tjapan\twindhoek\tnamibia\r\ntokyo\tjapan\tyerevan\tarmenia\r\ntokyo\tjapan\tzagreb\tcroatia\r\ntokyo\tjapan\tabuja\tnigeria\r\ntokyo\tjapan\taccra\tghana\r\ntokyo\tjapan\talgiers\talgeria\r\ntokyo\tjapan\tamman\tjordan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iet\tquietly\tsudden\tsuddenly\r\nquiet\tquietly\tswift\tswiftly\r\nquiet\tquietly\ttypical\ttypically\r\nquiet\tquietly\tunfortunate\tunfortunately\r\nquiet\tquietly\tusual\tusually\r\nquiet\tquietly\tamazing\tamazingly\r\nquiet\tquietly\tapparent\tapparently\r\nquiet\tquietly\tcalm\tcalmly\r\nquiet\tquietly\tcheerful\tcheerfully\r\nquiet\tquietly\tcomplete\tcompletely\r\nquiet\tquietly\tefficient\tefficiently\r\nquiet\tquietly\tfortunate\tfortunately\r\nquiet\tquietly\tfree\tfreely\r\nquiet\tquietly\tfurious\tfuriously\r\nquiet\tquietly\thappy\thappily\r\nquiet\tquietly\timmediate\timmediately\r\nquiet\tquietly\tinfrequent\tinfrequently\r\nquiet\tquietly\tlucky\tluckily\r\nquiet\tquietly\tmost\tmostly\r\nquiet\tquietly\tobvious\tobviously\r\nquiet\tquietly\toccasional\toccasionally\r\nquiet\tquietly\tpossible\tpossibly\r\nquiet\tquietly\tprecise\tprecisely\r\nquiet\tquietly\tprofessional\tprofessionally\r\nquiet\tquietly\tquick\tquickly\r\nrapid\trapidly\trare\trarely\r\nrapid\trapidly\treluctant\treluctantly\r\nrapid\trapidly\tsafe\tsafely\r\nrapid\trapidly\tserious\tseriously\r\nrapid\trapidly\tslow\tslowly\r\nrapid\trapidly\tsudden\tsuddenly\r\nrapid\trapidly\tswift\tswiftly\r\nrapid\trapidly\ttypical\ttypically\r\nrapid\trapidly\tunfortunate\tunfortunately\r\nrapid\trapidly\tusual\tusually\r\nrapid\trapidly\tamazing\tamazingly\r\nrapid\trapidly\tapparent\tapparently\r\nrapid\trapidly\tcalm\tcalmly\r\nrapid\trapidly\tcheerful\tcheerfully\r\nrapid\trapidly\tcomplete\tcompletely\r\nrapid\trapidly\tefficient\tefficiently\r\nrapid\trapidly\tfortunate\tfortunately\r\nrapid\trapidly\tfree\tfreely\r\nrapid\trapidly\tfurious\tfuriously\r\nrapid\trapidly\thappy\thappily\r\nrapid\trapidly\timmediate\timmediately\r\nrapid\trapidly\tinfrequent\tinfrequently\r\nrapid\trapidly\tlucky\tluckily\r\nrapid\trapidly\tmost\tmostly\r\nrapid\trapidly\tobvious\tobviously\r\nrapid\trapidly\toccasional\toccasionally\r\nrapid\trapidly\tpossible\tpossibly\r\nrapid\trapidly\tprecise\tprecisely\r\nrapid\trapidly\tprofessional\tprofessionally\r\nrapid\trapidly\tquick\tquickly\r\nrapid\trapidly\tquiet\tquietly\r\nrare\trarely\treluctant\treluctantly\r\nrare\trarely\tsafe\tsafely\r\nrare\trarely\tserious\tseriously\r\nrare\trarely\tslow\tslowly\r\nrare\trarely\tsudden\tsuddenly\r\nrare\trarely\tswift\tswiftly\r\nrare\trarely\ttypical\ttypically\r\nrare\trarely\tunfortunate\tunfortunately\r\nrare\trarely\tusual\tusually\r\nrare\trarely\tamazing\tamazingly\r\nrare\trarely\tapparent\tapparently\r\nrare\trarely\tcalm\tcalmly\r\nrare\trarely\tcheerful\tcheerfully\r\nrare\trarely\tcomplete\tcompletely\r\nrare\trarely\tefficient\tefficiently\r\nrare\trarely\tfortunate\tfortunately\r\nrare\trarely\tfree\tfreely\r\nrare\trarely\tfurious\tfuriously\r\nrare\trarely\thappy\thappily\r\nrare\trarely\timmediate\timmediately\r\nrare\trarely\tinfrequent\tinfrequently\r\nrare\trarely\tlucky\tluckily\r\nrare\trarely\tmost\tmostly\r\nrare\trarely\tobvious\tobviously\r\nrare\trarely\toccasional\toccasionally\r\nrare\trarely\tpossible\tpossibly\r\nrare\trarely\tprecise\tprecisely\r\nrare\trarely\tprofessional\tprofessionally\r\nrare\trarely\tquick\tquickly\r\nrare\trarely\tquiet\tquietly\r\nrare\trarely\trapid\trapidly\r\nreluctant\treluctantly\tsafe\tsafely\r\nreluctant\treluctantly\tserious\tseriously\r\nreluctant\treluctantly\tslow\tslowly\r\nreluctant\treluctantly\tsudden\tsuddenly\r\nreluctant\treluctantly\tswift\tswiftly\r\nreluctant\treluctantly\ttypical\ttypically\r\nreluctant\treluctantly\tunfortunate\tunfortunately\r\nreluctant\treluctantly\tusual\tusually\r\nreluctant\treluctantly\tamazing\tamazingly\r\nreluctant\treluctantly\tapparent\tapparently\r\nreluctant\treluctantly\tcalm\tcalmly\r\nreluctant\treluctantly\tcheerful\tcheerfully\r\nreluctant\treluctantly\tcomplete\tcompletely\r\nreluctant\treluctantly\tefficient\tefficiently\r\nreluctant\treluctantly\tfortunate\tfortunately\r\nreluctant\treluctantly\tfree\tfreely\r\nreluctant\treluctantly\tfurious\tfuriously\r\nreluctant\treluctantly\thappy\thappily\r\nreluctant\treluctantly\timmediate\timmediately\r\nreluctant\treluctantly\tinfrequent\tinfrequently\r\nreluctant\treluctantly\tlucky\tluckily\r\nreluctant\treluctantly\tmost\tmostly\r\nreluctant\treluctantly\tobvious\tobviously\r\nreluctant\treluctantly\toccasional\toccasionally\r\nreluctant\treluctantly\tpossible\tpossibly\r\nreluctant\treluctantly\tprecise\tprecisely\r\nreluctant\treluctantly\tprofessional\tprofessionally\r\nreluctant\treluctantly\tquick\tquickly\r\nreluctant\treluctantly\tquiet\tquietly\r\nreluctant\treluctantly\trapid\trapidly\r\nreluctant\treluctantly\trare\trarely\r\nsafe\tsafely\tserious\tseriously\r\nsafe\tsafely\tslow\tslowly\r\nsafe\tsafely\tsudden\tsuddenly\r\nsafe\tsafely\tswift\tswiftly\r\nsafe\tsafely\ttypical\ttypically\r\nsafe\tsafely\tunfortunate\tunfortunately\r\nsafe\tsafely\tusual\tusually\r\nsafe\tsafely\tamazing\tamazingly\r\nsafe\tsafely\tapparent\tapparently\r\nsafe\tsafely\tcalm\tcalmly\r\nsafe\tsafely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ly\tmost\tmostly\r\nswift\tswiftly\tobvious\tobviously\r\nswift\tswiftly\toccasional\toccasionally\r\nswift\tswiftly\tpossible\tpossibly\r\nswift\tswiftly\tprecise\tprecisely\r\nswift\tswiftly\tprofessional\tprofessionally\r\nswift\tswiftly\tquick\tquickly\r\nswift\tswiftly\tquiet\tquietly\r\nswift\tswiftly\trapid\trapidly\r\nswift\tswiftly\trare\trarely\r\nswift\tswiftly\treluctant\treluctantly\r\nswift\tswiftly\tsafe\tsafely\r\nswift\tswiftly\tserious\tseriously\r\nswift\tswiftly\tslow\tslowly\r\nswift\tswiftly\tsudden\tsuddenly\r\ntypical\ttypically\tunfortunate\tunfortunately\r\ntypical\ttypically\tusual\tusually\r\ntypical\ttypically\tamazing\tamazingly\r\ntypical\ttypically\tapparent\tapparently\r\ntypical\ttypically\tcalm\tcalmly\r\ntypical\ttypically\tcheerful\tcheerfully\r\ntypical\ttypically\tcomplete\tcompletely\r\ntypical\ttypically\tefficient\tefficiently\r\ntypical\ttypically\tfortunate\tfortunately\r\ntypical\ttypically\tfree\tfreely\r\ntypical\ttypically\tfurious\tfuriously\r\ntypical\ttypically\thappy\thappily\r\ntypical\ttypically\timmediate\timmediately\r\ntypical\ttypically\tinfrequent\tinfrequently\r\ntypical\ttypically\tlucky\tluckily\r\ntypical\ttypically\tmost\tmostly\r\ntypical\ttypically\tobvious\tobviously\r\ntypical\ttypically\toccasional\toccasionally\r\ntypical\ttypically\tpossible\tpossibly\r\ntypical\ttypically\tprecise\tprecisely\r\ntypical\ttypically\tprofessional\tprofessionally\r\ntypical\ttypically\tquick\tquickly\r\ntypical\ttypically\tquiet\tquietly\r\ntypical\ttypically\trapid\trapidly\r\ntypical\ttypically\trare\trarely\r\ntypical\ttypically\treluctant\treluctantly\r\ntypical\ttypically\tsafe\tsafely\r\ntypical\ttypically\tserious\tseriously\r\ntypical\ttypically\tslow\tslowly\r\ntypical\ttypically\tsudden\tsuddenly\r\ntypical\ttypically\tswift\tswiftly\r\nunfortunate\tunfortunately\tusual\tusually\r\nunfortunate\tunfortunately\tamazing\tamazingly\r\nunfortunate\tunfortunately\tapparent\tapparently\r\nunfortunate\tunfortunately\tcalm\tcalmly\r\nunfortunate\tunfortunately\tcheerful\tcheerfully\r\nunfortunate\tunfortunately\tcomplete\tcompletely\r\nunfortunate\tunfortunately\tefficient\tefficiently\r\nunfortunate\tunfortunately\tfortunate\tfortunately\r\nunfortunate\tunfortunately\tfree\tfreely\r\nunfortunate\tunfortunately\tfurious\tfuriously\r\nunfortunate\tunfortunately\thappy\thappily\r\nunfortunate\tunfortunately\timmediate\timmediately\r\nunfortunate\tunfortunately\tinfrequent\tinfrequently\r\nunfortunate\tunfortunately\tlucky\tluckily\r\nunfortunate\tunfortunately\tmost\tmostly\r\nunfortunate\tunfortunately\tobvious\tobviously\r\nunfortunate\tunfortunately\toccasional\toccasionally\r\nunfortunate\tunfortunately\tpossible\tpossibly\r\nunfortunate\tunfortunately\tprecise\tprecisely\r\nunfortunate\tunfortunately\tprofessional\tprofessionally\r\nunfortunate\tunfortunately\tquick\tquickly\r\nunfortunate\tunfortunately\tquiet\tquietly\r\nunfortunate\tunfortunately\trapid\trapidly\r\nunfortunate\tunfortunately\trare\trarely\r\nunfortunate\tunfortunately\treluctant\treluctantly\r\nunfortunate\tunfortunately\tsafe\tsafely\r\nunfortunate\tunfortunately\tserious\tseriously\r\nunfortunate\tunfortunately\tslow\tslowly\r\nunfortunate\tunfortunately\tsudden\tsuddenly\r\nunfortunate\tunfortunately\tswift\tswiftly\r\nunfortunate\tunfortunately\ttypical\ttypically\r\nusual\tusually\tamazing\tamazingly\r\nusual\tusually\tapparent\tapparently\r\nusual\tusually\tcalm\tcalmly\r\nusual\tusually\tcheerful\tcheerfully\r\nusual\tusually\tcomplete\tcompletely\r\nusual\tusually\tefficient\tefficiently\r\nusual\tusually\tfortunate\tfortunately\r\nusual\tusually\tfree\tfreely\r\nusual\tusually\tfurious\tfuriously\r\nusual\tusually\thappy\thappily\r\nusual\tusually\timmediate\timmediately\r\nusual\tusually\tinfrequent\tinfrequently\r\nusual\tusually\tlucky\tluckily\r\nusual\tusually\tmost\tmostly\r\nusual\tusually\tobvious\tobviously\r\nusual\tusually\toccasional\toccasionally\r\nusual\tusually\tpossible\tpossibly\r\nusual\tusually\tprecise\tprecisely\r\nusual\tusually\tprofessional\tprofessionally\r\nusual\tusually\tquick\tquickly\r\nusual\tusually\tquiet\tquietly\r\nusual\tusually\trapid\trapidly\r\nusual\tusually\trare\trarely\r\nusual\tusually\treluctant\treluctantly\r\nusual\tusually\tsafe\tsafely\r\nusual\tusually\tserious\tseriously\r\nusual\tusually\tslow\tslowly\r\nusual\tusually\tsudden\tsuddenly\r\nusual\tusually\tswift\tswiftly\r\nusual\tusually\ttypical\ttypically\r\nusual\tusually\tunfortunate\tunfortunately\r\nacceptable\tunacceptable\taware\tunaware\r\nacceptable\tunacceptable\tcertain\tuncertain\r\nacceptable\tunacceptable\tclear\tunclear\r\nacceptable\tunacceptable\tcomfortable\tuncomfortable\r\nacceptable\tunacceptable\tcompetitive\tuncompetitive\r\nacceptable\tunacceptable\tconsistent\tinconsistent\r\nacceptable\tunacceptable\tconvincing\tunconvincing\r\nacceptable\tunacceptable\tconvenient\tinconvenient\r\nacceptable\tunacceptable\tdecided\tundecided\r\nacceptable\tunacce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eeper\r\nbright\tbrighter\teasy\teasier\r\nbright\tbrighter\tfast\tfaster\r\nbright\tbrighter\tgood\tbetter\r\nbright\tbrighter\tgreat\tgreater\r\nbright\tbrighter\thard\tharder\r\nbright\tbrighter\theavy\theavier\r\nbright\tbrighter\thigh\thigher\r\nbright\tbrighter\thot\thotter\r\nbright\tbrighter\tlarge\tlarger\r\nbright\tbrighter\tlong\tlonger\r\nbright\tbrighter\tloud\tlouder\r\nbright\tbrighter\tlow\tlower\r\nbright\tbrighter\tnew\tnewer\r\nbright\tbrighter\told\tolder\r\nbright\tbrighter\tquick\tquicker\r\nbright\tbrighter\tsafe\tsafer\r\nbright\tbrighter\tsharp\tsharper\r\nbright\tbrighter\tshort\tshorter\r\nbright\tbrighter\tsimple\tsimpler\r\nbright\tbrighter\tslow\tslower\r\nbright\tbrighter\tsmall\tsmaller\r\nbright\tbrighter\tsmart\tsmarter\r\nbright\tbrighter\tstrong\tstronger\r\nbright\tbrighter\ttall\ttaller\r\nbright\tbrighter\ttight\ttighter\r\nbright\tbrighter\ttough\ttougher\r\nbright\tbrighter\twarm\twarmer\r\nbright\tbrighter\tweak\tweaker\r\nbright\tbrighter\twide\twider\r\nbright\tbrighter\tyoung\tyounger\r\nbright\tbrighter\tbad\tworse\r\nbright\tbrighter\tbig\tbigger\r\ncheap\tcheaper\tcold\tcolder\r\ncheap\tcheaper\tcool\tcooler\r\ncheap\tcheaper\tdeep\tdeeper\r\ncheap\tcheaper\teasy\teasier\r\ncheap\tcheaper\tfast\tfaster\r\ncheap\tcheaper\tgood\tbetter\r\ncheap\tcheaper\tgreat\tgreater\r\ncheap\tcheaper\thard\tharder\r\ncheap\tcheaper\theavy\theavier\r\ncheap\tcheaper\thigh\thigher\r\ncheap\tcheaper\thot\thotter\r\ncheap\tcheaper\tlarge\tlarger\r\ncheap\tcheaper\tlong\tlonger\r\ncheap\tcheaper\tloud\tlouder\r\ncheap\tcheaper\tlow\tlower\r\ncheap\tcheaper\tnew\tnewer\r\ncheap\tcheaper\told\tolder\r\ncheap\tcheaper\tquick\tquicker\r\ncheap\tcheaper\tsafe\tsafer\r\ncheap\tcheaper\tsharp\tsharper\r\ncheap\tcheaper\tshort\tshorter\r\ncheap\tcheaper\tsimple\tsimpler\r\ncheap\tcheaper\tslow\tslower\r\ncheap\tcheaper\tsmall\tsmaller\r\ncheap\tcheaper\tsmart\tsmarter\r\ncheap\tcheaper\tstrong\tstronger\r\ncheap\tcheaper\ttall\ttaller\r\ncheap\tcheaper\ttight\ttighter\r\ncheap\tcheaper\ttough\ttougher\r\ncheap\tcheaper\twarm\twarmer\r\ncheap\tcheaper\tweak\tweaker\r\ncheap\tcheaper\twide\twider\r\ncheap\tcheaper\tyoung\tyounger\r\ncheap\tcheaper\tbad\tworse\r\ncheap\tcheaper\tbig\tbigger\r\ncheap\tcheaper\tbright\tbrighter\r\ncold\tcolder\tcool\tcooler\r\ncold\tcolder\tdeep\tdeeper\r\ncold\tcolder\teasy\teasier\r\ncold\tcolder\tfast\tfaster\r\ncold\tcolder\tgood\tbetter\r\ncold\tcolder\tgreat\tgreater\r\ncold\tcolder\thard\tharder\r\ncold\tcolder\theavy\theavier\r\ncold\tcolder\thigh\thigher\r\ncold\tcolder\thot\thotter\r\ncold\tcolder\tlarge\tlarger\r\ncold\tcolder\tlong\tlonger\r\ncold\tcolder\tloud\tlouder\r\ncold\tcolder\tlow\tlower\r\ncold\tcolder\tnew\tnewer\r\ncold\tcolder\told\tolder\r\ncold\tcolder\tquick\tquicker\r\ncold\tcolder\tsafe\tsafer\r\ncold\tcolder\tsharp\tsharper\r\ncold\tcolder\tshort\tshorter\r\ncold\tcolder\tsimple\tsimpler\r\ncold\tcolder\tslow\tslower\r\ncold\tcolder\tsmall\tsmaller\r\ncold\tcolder\tsmart\tsmarter\r\ncold\tcolder\tstrong\tstronger\r\ncold\tcolder\ttall\ttaller\r\ncold\tcolder\ttight\ttighter\r\ncold\tcolder\ttough\ttougher\r\ncold\tcolder\twarm\twarmer\r\ncold\tcolder\tweak\tweaker\r\ncold\tcolder\twide\twider\r\ncold\tcolder\tyoung\tyounger\r\ncold\tcolder\tbad\tworse\r\ncold\tcolder\tbig\tbigger\r\ncold\tcolder\tbright\tbrighter\r\ncold\tcolder\tcheap\tcheaper\r\ncool\tcooler\tdeep\tdeeper\r\ncool\tcooler\teasy\teasier\r\ncool\tcooler\tfast\tfaster\r\ncool\tcooler\tgood\tbetter\r\ncool\tcooler\tgreat\tgreater\r\ncool\tcooler\thard\tharder\r\ncool\tcooler\theavy\theavier\r\ncool\tcooler\thigh\thigher\r\ncool\tcooler\thot\thotter\r\ncool\tcooler\tlarge\tlarger\r\ncool\tcooler\tlong\tlonger\r\ncool\tcooler\tloud\tlouder\r\ncool\tcooler\tlow\tlower\r\ncool\tcooler\tnew\tnewer\r\ncool\tcooler\told\tolder\r\ncool\tcooler\tquick\tquicker\r\ncool\tcooler\tsafe\tsafer\r\ncool\tcooler\tsharp\tsharper\r\ncool\tcooler\tshort\tshorter\r\ncool\tcooler\tsimple\tsimpler\r\ncool\tcooler\tslow\tslower\r\ncool\tcooler\tsmall\tsmaller\r\ncool\tcooler\tsmart\tsmarter\r\ncool\tcooler\tstrong\tstronger\r\ncool\tcooler\ttall\ttaller\r\ncool\tcooler\ttight\ttighter\r\ncool\tcooler\ttough\ttougher\r\ncool\tcooler\twarm\twarmer\r\ncool\tcooler\tweak\tweaker\r\ncool\tcooler\twide\twider\r\ncool\tcooler\tyoung\tyounger\r\ncool\tcooler\tbad\tworse\r\ncool\tcooler\tbig\tbigger\r\ncool\tcooler\tbright\tbrighter\r\ncool\tcooler\tcheap\tcheaper\r\ncool\tcooler\tcold\tcolder\r\ndeep\tdeeper\teasy\teasier\r\ndeep\tdeeper\tfast\tfaster\r\ndeep\tdeeper\tgood\tbetter\r\ndeep\tdeeper\tgreat\tgreater\r\ndeep\tdeeper\thard\tharder\r\ndeep\tdeeper\theavy\theavier\r\ndeep\tdeeper\thigh\thigher\r\ndeep\tdeeper\thot\thotter\r\ndeep\tdeeper\tlarge\tlarger\r\ndeep\tdeeper\tlong\tlonger\r\ndeep\tdeeper\tloud\tlouder\r\ndeep\tdeeper\tlow\tlower\r\ndeep\tdeeper\tnew\tnewer\r\ndeep\tdeeper\told\tolder\r\ndeep\tdeeper\tquick\tquicker\r\ndeep\tdeeper\tsafe\tsafer\r\ndeep\tdeeper\tsharp\tsharper\r\n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eland\ticelandic\r\nitaly\titalian\tindia\tindian\r\nitaly\titalian\tireland\tirish\r\njapan\tjapanese\tkorea\tkorean\r\njapan\tjapanese\tmacedonia\tmacedonian\r\njapan\tjapanese\tmalta\tmaltese\r\njapan\tjapanese\tmexico\tmexican\r\njapan\tjapanese\tmoldova\tmoldovan\r\njapan\tjapanese\tnetherlands\tdutch\r\njapan\tjapanese\tnorway\tnorwegian\r\njapan\tjapanese\tperu\tperuvian\r\njapan\tjapanese\tpoland\tpolish\r\njapan\tjapanese\tportugal\tportuguese\r\njapan\tjapanese\trussia\trussian\r\njapan\tjapanese\tslovakia\tslovakian\r\njapan\tjapanese\tspain\tspanish\r\njapan\tjapanese\tsweden\tswedish\r\njapan\tjapanese\tswitzerland\tswiss\r\njapan\tjapanese\tthailand\tthai\r\njapan\tjapanese\tukraine\tukrainian\r\njapan\tjapanese\talbania\talbanian\r\njapan\tjapanese\targentina\targentinean\r\njapan\tjapanese\taustralia\taustralian\r\njapan\tjapanese\taustria\taustrian\r\njapan\tjapanese\tbelarus\tbelorussian\r\njapan\tjapanese\tbrazil\tbrazilian\r\njapan\tjapanese\tbulgaria\tbulgarian\r\njapan\tjapanese\tcambodia\tcambodian\r\njapan\tjapanese\tchile\tchilean\r\njapan\tjapanese\tchina\tchinese\r\njapan\tjapanese\tcolombia\tcolombian\r\njapan\tjapanese\tcroatia\tcroatian\r\njapan\tjapanese\tdenmark\tdanish\r\njapan\tjapanese\tegypt\tegyptian\r\njapan\tjapanese\tengland\tenglish\r\njapan\tjapanese\tfrance\tfrench\r\njapan\tjapanese\tgermany\tgerman\r\njapan\tjapanese\tgreece\tgreek\r\njapan\tjapanese\ticeland\ticelandic\r\njapan\tjapanese\tindia\tindian\r\njapan\tjapanese\tireland\tirish\r\njapan\tjapanese\tisrael\tisraeli\r\nkorea\tkorean\tmacedonia\tmacedonian\r\nkorea\tkorean\tmalta\tmaltese\r\nkorea\tkorean\tmexico\tmexican\r\nkorea\tkorean\tmoldova\tmoldovan\r\nkorea\tkorean\tnetherlands\tdutch\r\nkorea\tkorean\tnorway\tnorwegian\r\nkorea\tkorean\tperu\tperuvian\r\nkorea\tkorean\tpoland\tpolish\r\nkorea\tkorean\tportugal\tportuguese\r\nkorea\tkorean\trussia\trussian\r\nkorea\tkorean\tslovakia\tslovakian\r\nkorea\tkorean\tspain\tspanish\r\nkorea\tkorean\tsweden\tswedish\r\nkorea\tkorean\tswitzerland\tswiss\r\nkorea\tkorean\tthailand\tthai\r\nkorea\tkorean\tukraine\tukrainian\r\nkorea\tkorean\talbania\talbanian\r\nkorea\tkorean\targentina\targentinean\r\nkorea\tkorean\taustralia\taustralian\r\nkorea\tkorean\taustria\taustrian\r\nkorea\tkorean\tbelarus\tbelorussian\r\nkorea\tkorean\tbrazil\tbrazilian\r\nkorea\tkorean\tbulgaria\tbulgarian\r\nkorea\tkorean\tcambodia\tcambodian\r\nkorea\tkorean\tchile\tchilean\r\nkorea\tkorean\tchina\tchinese\r\nkorea\tkorean\tcolombia\tcolombian\r\nkorea\tkorean\tcroatia\tcroatian\r\nkorea\tkorean\tdenmark\tdanish\r\nkorea\tkorean\tegypt\tegyptian\r\nkorea\tkorean\tengland\tenglish\r\nkorea\tkorean\tfrance\tfrench\r\nkorea\tkorean\tgermany\tgerman\r\nkorea\tkorean\tgreece\tgreek\r\nkorea\tkorean\ticeland\ticelandic\r\nkorea\tkorean\tindia\tindian\r\nkorea\tkorean\tireland\tirish\r\nkorea\tkorean\tisrael\tisraeli\r\nkorea\tkorean\titaly\titalian\r\nmacedonia\tmacedonian\tmalta\tmaltese\r\nmacedonia\tmacedonian\tmexico\tmexican\r\nmacedonia\tmacedonian\tmoldova\tmoldovan\r\nmacedonia\tmacedonian\tnetherlands\tdutch\r\nmacedonia\tmacedonian\tnorway\tnorwegian\r\nmacedonia\tmacedonian\tperu\tperuvian\r\nmacedonia\tmacedonian\tpoland\tpolish\r\nmacedonia\tmacedonian\tportugal\tportuguese\r\nmacedonia\tmacedonian\trussia\trussian\r\nmacedonia\tmacedonian\tslovakia\tslovakian\r\nmacedonia\tmacedonian\tspain\tspanish\r\nmacedonia\tmacedonian\tsweden\tswedish\r\nmacedonia\tmacedonian\tswitzerland\tswiss\r\nmacedonia\tmacedonian\tthailand\tthai\r\nmacedonia\tmacedonian\tukraine\tukrainian\r\nmacedonia\tmacedonian\talbania\talbanian\r\nmacedonia\tmacedonian\targentina\targentinean\r\nmacedonia\tmacedonian\taustralia\taustralian\r\nmacedonia\tmacedonian\taustria\taustrian\r\nmacedonia\tmacedonian\tbelarus\tbelorussian\r\nmacedonia\tmacedonian\tbrazil\tbrazilian\r\nmacedonia\tmacedonian\tbulgaria\tbulgarian\r\nmacedonia\tmacedonian\tcambodia\tcambodian\r\nmacedonia\tmacedonian\tchile\tchilean\r\nmacedonia\tmacedonian\tchina\tchinese\r\nmacedonia\tmacedonian\tcolombia\tcolombian\r\nmacedonia\tmacedonian\tcroatia\tcroatian\r\nmacedonia\tmacedonian\tdenmark\tdanish\r\nmacedonia\tmacedonian\tegypt\tegyptian\r\nmacedonia\tmacedonian\tengland\tenglish\r\nmacedonia\tmacedonian\tfrance\tfrench\r\nmacedonia\tmacedonian\tgermany\tgerman\r\nmacedonia\tmacedonian\tgreece\tgreek\r\nmacedonia\tmacedonian\ticeland\ticelandic\r\nmacedonia\tmacedonian\tindia\tindian\r\nmacedonia\tmacedonian\tireland\tirish\r\nmacedonia\tmacedonian\tisrael\tisraeli\r\nmacedonia\tmacedonian\titaly\titalian\r\nmacedonia\tmacedonian\tjapan\tjapanese\r\nmalta\tmaltese\tmexico\tmexican\r\nmalta\tmaltese\tmoldova\tmoldovan\r\nmalta\tmaltese\tnetherlands\tdutch\r\nmalta\tmaltese\tnorway\tnorwegian\r\nmalta\tmaltese\tperu\tperuvian\r\nmalta\tmaltese\tpoland\tpolish\r\nmalta\tmaltese\tportugal\tportuguese\r\nmalta\tmaltese\trussia\trussian\r\nmalta\tmaltese\tslovakia\tslovakian\r\nmalta\tmaltese\tspain\tspanish\r\nmalta\tmaltese\tswede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  },
  {
    "path": "psdvec/testsets/analogy/msr.txt",
    "content": "good\tbetter\trough\trougher\r\nbetter\tgood\trougher\trough\r\ngood\tbest\trough\troughest\r\nbest\tgood\troughest\trough\r\nbest\tbetter\troughest\trougher\r\nbetter\tbest\trougher\troughest\r\ngood\tbetter\tmean\tmeaner\r\nbetter\tgood\tmeaner\tmean\r\ngood\tbest\tmean\tmeanest\r\nbest\tgood\tmeanest\tmean\r\nbest\tbetter\tmeanest\tmeaner\r\nbetter\tbest\tmeaner\tmeanest\r\ngood\tbetter\theavy\theavier\r\nbetter\tgood\theavier\theavy\r\ngood\tbest\theavy\theaviest\r\nbest\tgood\theaviest\theavy\r\nbest\tbetter\theaviest\theavier\r\nbetter\tbest\theavier\theaviest\r\ngood\tbetter\tmighty\tmightier\r\nbetter\tgood\tmightier\tmighty\r\ngood\tbest\tmighty\tmightiest\r\nbest\tgood\tmightiest\tmighty\r\nbest\tbetter\tmightiest\tmightier\r\nbetter\tbest\tmightier\tmightiest\r\ngood\tbetter\tnoisy\tnoisier\r\nbetter\tgood\tnoisier\tnoisy\r\ngood\tbest\tnoisy\tnoisiest\r\nbest\tgood\tnoisiest\tnoisy\r\nbest\tbetter\tnoisiest\tnoisier\r\nbetter\tbest\tnoisier\tnoisiest\r\nhigh\thigher\tbad\tworse\r\nhigher\thigh\tworse\tbad\r\nhigh\thighest\tbad\tworst\r\nhighest\thigh\tworst\tbad\r\nhighest\thigher\tworst\tworse\r\nhigher\thighest\tworse\tworst\r\nhigh\thigher\tsad\tsadder\r\nhigher\thigh\tsadder\tsad\r\nhigh\thighest\tsad\tsaddest\r\nhighest\thigh\tsaddest\tsad\r\nhighest\thigher\tsaddest\tsadder\r\nhigher\thighest\tsadder\tsaddest\r\nhigh\thigher\twild\twilder\r\nhigher\thigh\twilder\twild\r\nhigh\thighest\twild\twildest\r\nhighest\thigh\twildest\twild\r\nhighest\thigher\twildest\twilder\r\nhigher\thighest\twilder\twildest\r\nhigh\thigher\tstrict\tstricter\r\nhigher\thigh\tstricter\tstrict\r\nhigh\thighest\tstrict\tstrictest\r\nhighest\thigh\tstrictest\tstrict\r\nhighest\thigher\tstrictest\tstricter\r\nhigher\thighest\tstricter\tstrictest\r\nhigh\thigher\tnarrow\tnarrower\r\nhigher\thigh\tnarrower\tnarrow\r\nhigh\thighest\tnarrow\tnarrowest\r\nhighest\thigh\tnarrowest\tnarrow\r\nhighest\thigher\tnarrowest\tnarrower\r\nhigher\thighest\tnarrower\tnarrowest\r\ngreat\tgreater\tclassy\tclassier\r\ngreater\tgreat\tclassier\tclassy\r\ngreat\tgreatest\tclassy\tclassiest\r\ngreatest\tgreat\tclassiest\tclassy\r\ngreatest\tgreater\tclassiest\tclassier\r\ngreater\tgreatest\tclassier\tclassiest\r\ngreat\tgreater\tearly\tearlier\r\ngreater\tgreat\tearlier\tearly\r\ngreat\tgreatest\tearly\tearliest\r\ngreatest\tgreat\tearliest\tearly\r\ngreatest\tgreater\tearliest\tearlier\r\ngreater\tgreatest\tearlier\tearliest\r\ngreat\tgreater\tfat\tfatter\r\ngreater\tgreat\tfatter\tfat\r\ngreat\tgreatest\tfat\tfattest\r\ngreatest\tgreat\tfattest\tfat\r\ngreatest\tgreater\tfattest\tfatter\r\ngreater\tgreatest\tfatter\tfattest\r\ngreat\tgreater\tfull\tfuller\r\ngreater\tgreat\tfuller\tfull\r\ngreat\tgreatest\tfull\tfullest\r\ngreatest\tgreat\tfullest\tfull\r\ngreatest\tgreater\tfullest\tfuller\r\ngreater\tgreatest\tfuller\tfullest\r\ngreat\tgreater\tcostly\tcostlier\r\ngreater\tgreat\tcostlier\tcostly\r\ngreat\tgreatest\tcostly\tcostliest\r\ngreatest\tgreat\tcostliest\tcostly\r\ngreatest\tgreater\tcostliest\tcostlier\r\ngreater\tgreatest\tcostlier\tcostliest\r\nlow\tlower\ttough\ttougher\r\nlower\tlow\ttougher\ttough\r\nlow\tlowest\ttough\ttoughest\r\nlowest\tlow\ttoughest\ttough\r\nlowest\tlower\ttoughest\ttougher\r\nlower\tlowest\ttougher\ttoughest\r\nlow\tlower\tgentle\tgentler\r\nlower\tlow\tgentler\tgentle\r\nlow\tlowest\tgentle\tgentlest\r\nlowest\tlow\tgentlest\tgentle\r\nlowest\tlower\tgentlest\tgentler\r\nlower\tlowest\tgentler\tgentlest\r\nlow\tlower\tshort\tshorter\r\nlower\tlow\tshorter\tshort\r\nlow\tlowest\tshort\tshortest\r\nlowest\tlow\tshortest\tshort\r\nlowest\tlower\tshortest\tshorter\r\nlower\tlowest\tshorter\tshortest\r\nlow\tlower\tsoft\tsofter\r\nlower\tlow\tsofter\tsoft\r\nlow\tlowest\tsoft\tsoftest\r\nlowest\tlow\tsoftest\tsoft\r\nlowest\tlower\tsoftest\tsofter\r\nlower\tlowest\tsofter\tsoftest\r\nlow\tlower\told\tolder\r\nlower\tlow\tolder\told\r\nlow\tlowest\told\toldest\r\nlowest\tlow\toldest\told\r\nlowest\tlower\toldest\tolder\r\nlower\tlowest\tolder\toldest\r\nlarge\tlarger\trich\tricher\r\nlarger\tlarge\tricher\trich\r\nlarge\tlargest\trich\trichest\r\nlargest\tlarge\trichest\trich\r\nlargest\tlarger\trichest\tricher\r\nlarger\tlargest\tricher\trichest\r\nlarge\tlarger\tweak\tweaker\r\nlarger\tlarge\tweaker\tweak\r\nlarge\tlargest\tweak\tweakest\r\nlargest\tlarge\tweakest\tweak\r\nlargest\tlarger\tweakest\tweaker\r\nlarger\tlargest\tweaker\tweakest\r\nlarge\tlarger\tpretty\tprettier\r\nlarger\tlarge\tprettier\tpretty\r\nlarge\tlargest\tpretty\tprettiest\r\nlargest\tlarge\tprettiest\tpretty\r\nlargest\tlarger\tprettiest\tprettier\r\nlarger\tlargest\tprettier\tprettiest\r\nlarge\tlarger\tcool\tcooler\r\nlarger\tlarge\tcooler\tcool\r\nlarge\tlargest\tcool\tcoolest\r\nlargest\tlarge\tcoolest\tcool\r\nlargest\tlarger\tcoolest\tcooler\r\nlarger\tlargest\tcooler\tcoolest\r\nlarge\tlarger\tlucky\tluckier\r\nlarger\tlarge\tluckier\tlucky\r\nlarge\tlargest\tlucky\tluckiest\r\nlargest\tlarge\tluckiest\tlucky\r\nlargest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  },
  {
    "path": "psdvec/testsets/ws/EN-RG-65.txt",
    "content": "gem\tjewel\t3.94\nmidday\tnoon\t3.94\nautomobile\tcar\t3.92\ncemetery\tgraveyard\t3.88\ncushion\tpillow\t3.84\nboy\tlad\t3.82\ncock\trooster\t3.68\nimplement\ttool\t3.66\nforest\twoodland\t3.65\ncoast\tshore\t3.60\nautograph\tsignature\t3.59\njourney\tvoyage\t3.58\nserf\tslave\t3.46\ngrin\tsmile\t3.46\nglass\ttumbler\t3.45\ncord\tstring\t3.41\nhill\tmound\t3.29\nmagician\twizard\t3.21\nfurnace\tstove\t3.11\nasylum\tmadhouse\t3.04\nbrother\tmonk\t2.74\nfood\tfruit\t2.69\nbird\tcock\t2.63\nbird\tcrane\t2.63\noracle\tsage\t2.61\nsage\twizard\t2.46\nbrother\tlad\t2.41\ncrane\timplement\t2.37\nmagician\toracle\t1.82\nglass\tjewel\t1.78\ncemetery\tmound\t1.69\ncar\tjourney\t1.55\nhill\twoodland\t1.48\ncrane\trooster\t1.41\nfurnace\timplement\t1.37\ncoast\thill\t1.26\nbird\twoodland\t1.24\nshore\tvoyage\t1.22\ncemetery\twoodland\t1.18\nfood\trooster\t1.09\nforest\tgraveyard\t1.00\nlad\twizard\t0.99\nmound\tshore\t0.97\nautomobile\tcushion\t0.97\nboy\tsage\t0.96\nmonk\toracle\t0.91\nshore\twoodland\t0.90\ngrin\tlad\t0.88\ncoast\tforest\t0.85\nasylum\tcemetery\t0.79\nmonk\tslave\t0.57\ncushion\tjewel\t0.45\nboy\trooster\t0.44\nglass\tmagician\t0.44\ngraveyard\tmadhouse\t0.44\nasylum\tmonk\t0.39\nasylum\tfruit\t0.19\ngrin\timplement\t0.18\nmound\tstove\t0.14\nautomobile\twizard\t0.11\nautograph\tshore\t0.06\nfruit\tfurnace\t0.05\nnoon\tstring\t0.04\nrooster\tvoyage\t0.04\ncord\tsmile\t0.02"
  },
  {
    "path": "psdvec/testsets/ws/EN-TOEFL-80.txt",
    "content": "enormously | tremendously | appropriately | uniquely | decidedly\nprovisions | stipulations | jurisdictions | interrelations | interpretations\nhaphazardly | randomly | linearly | dangerously | densely\nprominent | conspicuous | ancient | mysterious | battered\nzenith | pinnacle | completion | decline | outset\nflawed | imperfect | tiny | crude | lustrous\nurgently | desperately | conceivably | typically | tentatively\nconsumed | eaten | bred | caught | supplied\nadvent | coming | stability | financing | arrest\nconcisely | succinctly | freely | positively | powerfully\nsalutes | greetings | privileges | ceremonies | information\nsolitary | alone | fearless | alert | restless\nhasten | accelerate | accompany | determine | permit\nperseverance | endurance | skill | generosity | disturbance\nfanciful | imaginative | logical | familiar | apparent\nshowed | demonstrated | published | repeated | postponed\nconstantly | continually | accidentally | rapidly | instantly\nissues | subjects | training | benefits | salaries\nfurnish | supply | protect | advise | impress\ncostly | expensive | beautiful | popular | complicated\nrecognized | acknowledged | welcomed | depicted | successful\nspot | location | climate | latitude | sea\nmake | earn | print | trade | borrow\noften | frequently | definitely | chemically | hardly\neasygoing | relaxed | boring | frontier | farming\ndebate | argument | competition | war | election\nnarrow | thin | poisonous | freezing | clear\narranged | planned | discarded | studied | explained\ninfinite | limitless | structural | unusual | relative\nshowy | striking | prickly | incidental | entertaining\nlevied | imposed | believed | correlated | requested\ndeftly | skillfully | occasionally | prudently | humorously\ndistribute | circulate | commercialize | acknowledge | research\ndiscrepancies | differences | weights | wavelengths | deposits\nprolific | productive | capable | serious | promising\nunmatched | unequaled | emulated | alienated | unrecognized\npeculiarly | uniquely | suspiciously | patriotically | partly\nhue | color | contrast | scent | glare\nhind | rear | curved | muscular | hairy\nhighlight | accentuate | alter | restore | imitate\nhastily | hurriedly | habitually | shrewdly | chronologically\ntemperate | mild | short | windy | cold\ngrin | smile | exercise | rest | joke\nverbally | orally | verbosely | overtly | fittingly\nphysician | doctor | chemist | nurse | pharmacist\nessentially | basically | eagerly | ordinarily | possibly\nkeen | sharp | useful | simple | famous\nsituated | positioned | rotating | emptying | isolated\nprincipal | major | exceptional | numerous | most\nslowly | gradually | effectively | continuously | rarely\nbuilt | constructed | proposed | organized | financed\ntasks | jobs | customers | shops | materials\nunlikely | improbable | disagreeable | different | unpopular\nhalfheartedly | apathetically | unconventionally | bipartisanly | customarily\nannals | chronicles | homes | trails | songs\nwildly | furiously | mysteriously | abruptly | distinctively\nhailed | acclaimed | judged | remembered | addressed\ncommand | mastery | observation | love | awareness\nconcocted | devised | supervised | requested | cleaned\nprospective | potential | prominent | particular | prudent\ngenerally | broadly | controversially | accurately | descriptively\nsustained | prolonged | analyzed | refined | lowered\nperilous | dangerous | offensive | binding | exciting\ntranquillity | peacefulness | weariness | harshness | happiness\ndissipate | disperse | isolate | photograph | disguise\nprimarily | chiefly | consistently | occasionally | cautiously\ncolloquial | conversational | incorrect | recorded | misunderstood\nresolved | settled | examined | forgotten | publicized\nfeasible | possible | evident | permitted | equitable\nexpeditiously | rapidly | frequently | repeatedly | actually\npercentage | proportion | sample | profit | volume\nterminated | ended | posed | evaluated | postponed\nuniform | alike | sharp | hard | complex\nfigure | solve | list | express | divide\nsufficient | enough | valuable | physiological | recent\nfashion | manner | fathom | craze | ration\nmarketed | sold | sweetened | diluted | frozen\nbigger | larger | steadier | closer | better\nroots | origins | function | rituals | cure\nnormally | ordinarily | periodically | haltingly | permanently\n"
  },
  {
    "path": "psdvec/testsets/ws/bruni_men.txt",
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  },
  {
    "path": "psdvec/testsets/ws/luong_rare.txt",
    "content": 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connect\tintercommunicate\t7\r\ninterconnect\tconnect\t8.22\r\nnondescripts\tperson\t1.25\r\namorphous\tunformed\t8.75\r\namorphous\tinorganic\t1.89\r\nuncreative\tsterile\t4.8\r\nforeigners\tgringo\t6.83\r\nunaffected\tunimpressed\t8.14\r\nunaffected\tinsensitive\t2.86\r\ngravitated\tmove\t5.4\r\ngravitated\ttend\t4.6\r\nrespectable\treputable\t8.62\r\nrespectable\tworthy\t7.75\r\nreproduce\tphotocopy\t8\r\nreproduce\tpropagate\t8.57\r\nschnauzer\tgiant\t0.33\r\nstimuli\tstimulation\t8.5\r\nastronomical\tlarge\t8.8\r\nmicrobalance\tbalance\t5\r\nsubjoined\tappend\t7.17\r\nunquestioned\tuncontroversial\t6.29\r\nloveless\tunloving\t7.71\r\nloveless\tunloved\t8\r\npostmarks\tmarker\t5.5\r\npostmarks\tstamp\t8.33\r\npresenting\tbring\t7\r\npresenting\targue\t0.71\r\nfiddled\tembezzle\t0.12\r\nfiddled\tslack\t0.75\r\ntransfused\tbreathe\t0.11\r\ntransfused\tpour\t1.38\r\ninterchanging\tshift\t6\r\ninterchanging\ttrade\t6.5\r\nantisubmarine\tdefensive\t4.75\r\ndispleases\trepel\t7\r\nreproachful\tunfavorable\t6\r\nindependently\tworker\t2.8\r\nindependently\tindividualist\t8.12\r\nextrajudicial\tillegal\t5.4\r\nexterminated\tdestroy\t9.33\r\nexterminated\tkill\t9.78\r\nintercede\tnegociate\t6.86\r\npostdates\tfollow\t5.6\r\npostdates\tchronologize\t6.71\r\ncomport\tabout\t0\r\ncomport\tmisbehave\t1.62\r\nstockers\tanimal\t0\r\nceremonious\tformal\t8.33\r\nauthorship\tinitiation\t6.2\r\nslacken\tdecrease\t7.6\r\nslacken\tweaken\t6.33\r\nfreshen\tregenerate\t8.44\r\nfreshen\twash\t7.14\r\nobserved\tcomment\t5.71\r\nobserved\tdiscover\t6.83\r\nhydrolysed\tchange\t5.43\r\nenjoins\tinstruct\t5\r\nreplacements\tstand-in\t9.25\r\nreplacements\tsupplanting\t6\r\nbengali\tethnic\t6.38\r\ntranssexual\tperson\t8\r\nautomates\tchange\t2\r\namateurish\tunprofessional\t8.5\r\nsponsorship\tsupport\t8.2\r\nejector\tperson\t0.75\r\nejector\tmechanism\t7.62\r\nrehashing\trecycle\t8.33\r\npreassembled\tproduce\t5.5\r\nfascinate\tmatter\t1.71\r\nfascinate\tinterest\t8.29\r\nriskless\tsafe\t9.44\r\nincombustible\tfireproof\t9\r\nrareness\tscarcity\t9\r\nlengthy\tlong\t10\r\nlordship\tauthority\t8\r\nlordship\ttitle\t7.38\r\ngrinder\tsandwich\t9.33\r\ngrinder\twisdom\t0\r\nalgebraic\tquadratics\t7.25\r\ncongeniality\tfriendliness\t9.5\r\ncongeniality\tcompatibility\t6.29\r\nportioned\tdistribute\t5.8\r\npiquancy\tspiciness\t9.25\r\npiquancy\tquality\t3.33\r\ncommenting\tnote\t9\r\ncommenting\texplain\t7.86\r\nreprocessing\treclaim\t5.33\r\nroosted\tsettle\t7.67\r\nroosted\tsit\t6.86\r\nimmobilizing\tbeat\t1.5\r\nimmobilizing\twithhold\t3.8\r\npromised\tdeclare\t6.2\r\nemployments\tstate\t3.75\r\nemployments\tpopulace\t3.33\r\ntransposable\texchangeable\t7.17\r\nprotractors\tdrafting\t5.83\r\nreligiousness\tpiety\t7.5\r\nreligiousness\tconscientiousness\t4\r\nconcerts\tsettle\t0.44\r\nconcerts\tplan\t0.5\r\npostholes\thole\t5.5\r\nliveable\thabitable\t10\r\nbesieging\tattack\t8.22\r\nbesieging\tdistress\t5.5\r\nirregardless\tlook\t0\r\nirregardless\tprize\t0\r\nattendance\tfrequency\t4.83\r\nattendance\tpresence\t8.25\r\ncomputer\texpert\t0.71\r\ncomputer\tserver\t6.67\r\nsubtend\tshepherd\t5.33\r\nsubtend\tsuffer\t0.38\r\nirrelevance\tinapplicability\t8.33\r\ndesiccating\tdry\t8.56\r\ndesiccating\tpreserve\t5.86\r\ntransforming\ttransubstantiate\t7.17\r\ntransforming\tchange\t8.5\r\nprisoners\tinternee\t6.17\r\ncosponsors\tsponsor\t8.67\r\nunconvincing\timplausible\t7\r\nunconvincing\tunpersuasive\t10\r\nanalogous\tsimilar\t8.17\r\npreheating\theat\t7.12\r\nirrigate\thush\t0.5\r\nirrigate\ttreat\t4\r\nimmortalize\tremind\t4.33\r\nimmortalize\tchange\t2.33\r\nunploughed\tuntilled\t8.6\r\nsyntaxes\tsystem\t6.12\r\nsyntaxes\tstructure\t7.38\r\nenforcing\texecute\t7.86\r\nenforcing\tcompel\t7.25\r\ndevilish\tplayful\t6.38\r\ndevilish\tevil\t8.89\r\nganging\tgroup\t7.67\r\ndissimulate\tdisguise\t7.25\r\nenhancement\timprovement\t9.75\r\nreconstructs\tconstruct\t7.14\r\nentrench\tfasten\t4.2\r\nentrench\ttrespass\t1.75\r\nspherical\tround\t9\r\nimpolitic\tinexpedient\t5.14\r\nlondoners\tperson\t5.17\r\npredetermination\tdecision\t6.17\r\ndestabilization\tchange\t6.14\r\nreasonable\tmoderate\t7.67\r\nreasonable\trational\t8.33\r\npottery\tlusterware\t7.57\r\npottery\ttrade\t2.5\r\nunenthusiastic\thalfhearted\t8.5\r\nvirility\tmasculinity\t7\r\nvirility\tmaleness\t7\r\ndiscordance\tdissonance\t7.67\r\ndiscordance\tstrife\t7.86\r\nplundered\tdestroy\t8.71\r\nplundered\tsteal\t9.75\r\ntransvestite\tperson\t4.86\r\ntransvestite\thomosexual\t3.67\r\nretraction\tmotion\t5.83\r\nretraction\twithdrawal\t8.75\r\nenslaves\tsubjugate\t8.78\r\ncarburettors\tmechanical\t7.57\r\nexplorer\tdiver\t6.86\r\nperfectible\tperfect\t6.67\r\nstimulates\tprompt\t4.5\r\nstimulates\tquicken\t6.43\r\nconcurrencies\tagreement\t9.11\r\nconcurrencies\tcooperation\t6.83\r\nemulsify\tchange\t5\r\ninternationalisms\tscope\t0.43\r\ninternationalisms\tdoctrine\t3.8\r\ncylindric\trounded\t8.12\r\nsexually\tsexy\t8\r\nafghani\tiranian\t5.2\r\nanimalism\tdoctrine\t0.86\r\nanimalism\tdisposition\t3.25\r\nlatinist\tclassicist\t6.29\r\npunjabi\tsanskrit\t6\r\npunjabi\tindian\t6.83\r\nexterminate\tdestroy\t9.67\r\nexterminate\tkill\t9.88\r\nconsign\tcheck\t2.67\r\nrespectively\tindividual\t3.75\r\nreceiving\tfence\t0.14\r\nunicycle\twheel\t7\r\nunicycle\tbicycle\t7.29\r\nincised\tcut\t8.78\r\nincised\tcompound\t1.14\r\nperfective\tfuture\t0.62\r\nperfective\taspect\t3.4\r\napprenticeship\tposition\t5.75\r\nreporters\treporter\t9.78\r\nhouseful\tcontainerful\t4.71\r\nirreverence\tevil\t1.62\r\nunostentatious\ttasteful\t7.5\r\ndisadvantaged\tunderprivileged\t8.14\r\ncombatted\twrestle\t6.6\r\nsnooper\teavesdropper\t8.89\r\nresearchers\tfieldworker\t6.5\r\nresides\tpopulate\t6.5\r\nindividualize\tdistinguish\t8.29\r\nindividualize\tchange\t4.25\r\ncoinsurance\tinsurance\t6.5\r\nmicrometer\tnanometer\t7.14\r\nmicrometer\tcaliper\t2.83\r\npostcode\taddress\t8\r\nencrusted\tcoat\t7.29\r\nencrusted\tdecorate\t5.83\r\nundissolved\tunmelted\t9.22\r\nfastness\tfast\t8.12\r\nremainder\tsell\t2.17\r\nremainder\tpart\t6.17\r\nmarginality\tposition\t6.14\r\nunmanned\tfaze\t1.62\r\nrefuels\tfuel\t7.86\r\nsidewinder\trattlesnake\t7.29\r\nsidewinder\tmissile\t7.29\r\nfederalize\tunite\t7.43\r\ncasteless\tunwanted\t7.5\r\nanimalize\tchange\t3\r\nreproves\tknock\t5.5\r\ncharacters\tbeing\t4.4\r\ncharacters\tscratch\t0\r\nconjecture\thypothesis\t10\r\nsymmetrical\tbalanced\t10\r\nallurement\ttemptation\t9.78\r\nallurement\tinvitation\t8.86\r\nbinging\teat\t7.43\r\nextractor\tforceps\t8.5\r\nphotographer\tpaparazzo\t7.6\r\nperspectives\teye\t4.67\r\nperspectives\tpoint\t6.43\r\ndisinflation\teconomic\t5.83\r\ninterplanetary\tinternational\t5.62\r\ninterplanetary\tunsettled\t2\r\ntrilogies\ttrio\t8.33\r\npoisoning\tcorrupt\t7.25\r\npoisoning\tpoison\t9.62\r\nbobbers\tfloat\t9\r\nportrayer\tpainter\t7.29\r\ninvariable\thard-and-fast\t6.57\r\ninvariable\tparameter\t3.6\r\nconstrict\tastringe\t6.33\r\nconstrict\tchoke\t8.4\r\nextraversion\tsociability\t8.71\r\nfrivolously\tsuperficial\t7.86\r\nremounted\tmount\t7.6\r\nremounted\thop\t0.29\r\nreviles\tabuse\t5.86\r\ndiscounters\tmercantile\t5.67\r\nconfinement\trestraint\t9.33\r\nconsciousness\tknowing\t8.2\r\nconsciousness\tself\t5.86\r\nbelieving\tfeel\t6\r\nbelieving\tbelieve\t9.75\r\nregimental\tcontrol\t7\r\nregimental\tform\t5\r\nsubdivided\tsubdivide\t9.5\r\nsubdivided\tdivide\t6\r\nsustainable\tcontinue\t7.86\r\nstandardise\tgauge\t6.5\r\nstandardise\tmeasure\t7.12\r\ndefrayed\tpay\t3.8\r\ndevilishly\tcook\t0.22\r\ndevilishly\tantagonize\t5.5\r\ndisapproving\tdiscountenance\t5.4\r\nsubspaces\tmathematical\t6.57\r\nconnoting\timply\t7.67\r\nconnoting\texpress\t5.57\r\ninheritance\ttransfer\t5.71\r\ninheritance\tacquisition\t7\r\narchery\tsport\t7\r\nsufficed\tserve\t5.2\r\nbelligerence\thostility\t8.67\r\nprocreating\tbrood\t7.17\r\ngelatinous\tthick\t8\r\nvillainous\twicked\t9.75\r\nharmony\tcongruity\t8.25\r\nharmony\tmusic\t7.25\r\ninoffensive\tinnocuous\t8.83\r\ninsurrectionist\tyoung\t3\r\ninquisitive\tcurious\t8.4\r\ninquisitive\tinquiring\t8.43\r\nextraterrestrial\thypothetical\t3.2\r\ntriclinic\tmonoclinic\t5.4\r\nmurderer\tkiller\t9.78\r\nconcurrency\tagreement\t9\r\nconcurrency\tcooperation\t7.33\r\nhistorically\treal\t4.71\r\nnontoxic\tantitoxic\t7\r\nnontoxic\tedible\t6.43\r\nstrengthened\tsandbag\t5\r\nstrengthened\tbrace\t7.17\r\nincontrovertible\tundeniable\t7.5\r\nincontrovertible\tincontestable\t7.5\r\nrumbled\tsound\t6.5\r\nintragroup\tintramural\t6.38\r\nexceptionally\textraordinary\t8.8\r\nirredeemable\twicked\t6.43\r\nirredeemable\tinconvertible\t6.57\r\nsnickering\tlaugh\t7.71\r\n"
  },
  {
    "path": "psdvec/testsets/ws/radinsky_mturk.txt",
    "content": "episcopal\trussia\t2.75\r\nwater\tshortage\t2.714285714\r\nhorse\twedding\t2.266666667\r\nplays\tlosses\t3.2\r\nclassics\tadvertiser\t2.25\r\nlatin\tcredit\t2.0625\r\nship\tballots\t2.3125\r\nmistake\terror\t4.352941176\r\ndisease\tplague\t4.117647059\r\nsake\tshade\t2.529411765\r\nsaints\tobservatory\t1.9375\r\ntreaty\twheat\t1.8125\r\ntexas\tdeath\t1.533333333\r\nrepublicans\tchallenge\t2.3125\r\nbody\tpeaceful\t2.058823529\r\nadmiralty\tintensity\t2.647058824\r\nbody\timproving\t2.117647059\r\nheroin\tmarijuana\t3.375\r\nscottish\tcommuters\t2.6875\r\napollo\tmyth\t2.6\r\nfilm\tcautious\t2.125\r\nexhibition\tart\t4.117647059\r\nchocolate\tcandy\t3.764705882\r\nrepublic\tcandidate\t2.8125\r\ngospel\tchurch\t4.0625\r\nmomentum\tdesirable\t2.4\r\nsingapore\tsanctions\t2.117647059\r\nenglish\tfrench\t3.823529412\r\nexile\tchurch\t2.941176471\r\nnavy\tcoordinator\t2.235294118\r\nadventure\tflood\t2.4375\r\nradar\tplane\t3.235294118\r\npacific\tocean\t4.266666667\r\nscotch\tliquor\t4.571428571\r\nkennedy\tgun\t3\r\ngarfield\tcat\t2.866666667\r\nscale\tbudget\t3.5\r\nrhythm\tblues\t3.071428571\r\nrich\tprivileges\t3.2\r\nnavy\twithdrawn\t1.571428571\r\nmarble\tmarching\t2.615384615\r\npolo\tcharged\t2.125\r\nmark\tmissing\t2.333333333\r\nbattleship\tarmy\t4.235294118\r\nmedium\torganization\t2.5625\r\npennsylvania\twriter\t1.466666667\r\nhamlet\tpoet\t3.882352941\r\nbattle\tprisoners\t3.705882353\r\nguild\tsmith\t2.75\r\nmud\tsoil\t4.235294118\r\ncrime\tassaulted\t3.941176471\r\nmussolini\tstability\t2.133333333\r\nlincoln\tdivision\t2.4375\r\nslaves\tinsured\t2.2\r\nsummer\twinter\t4.375\r\nintegration\tdignity\t3.058823529\r\nmoney\tquota\t2.5\r\nhonolulu\tvacation\t3.6875\r\nlibya\tforged\t2.461538462\r\ncheers\tmusician\t2.823529412\r\nsession\tsurprises\t1.8125\r\nbillion\tcampaigning\t2.571428571\r\nperjury\tsoybean\t2.0625\r\nforswearing\tperjury\t3.3125\r\ncostume\thalloween\t3.4375\r\nbulgarian\tnurses\t1.941176471\r\ncostume\tultimate\t2.5\r\nfaith\tjudging\t2.235294118\r\nfrance\tbridges\t2.235294118\r\ncitizenship\tcasey\t2.2\r\nrecreation\tdish\t1.4\r\nintelligence\ttroubles\t1.625\r\ngermany\tworst\t1.4375\r\nchaos\tdeath\t2.75\r\nsydney\thancock\t2.857142857\r\nsabbath\tstevenson\t2.214285714\r\nespionage\tpassport\t2.3125\r\npolitical\ttoday\t1.6875\r\npipe\tconvertible\t2\r\nscouting\tdemonstrate\t2.5625\r\nsalute\tpatterns\t2.235294118\r\nreichstag\tgermany\t2.285714286\r\nradiation\tcostumes\t1.5625\r\nhorace\tgrief\t1.764705882\r\nsale\trental\t3.470588235\r\nopen\tclose\t4.058823529\r\nphotography\tproving\t2.375\r\npropaganda\tgermany\t1.705882353\r\nassassination\tforbes\t2.071428571\r\nmirror\tduel\t1.928571429\r\nprobability\thanging\t2.058823529\r\nafrica\ttheater\t1.5\r\nhell\theaven\t4.117647059\r\nmussolini\titaly\t3\r\ncomposer\tbeethoven\t3.647058824\r\nminister\tforthcoming\t1.764705882\r\nbrussels\tsweden\t3.176470588\r\nneutral\tparish\t1.6\r\nemotion\ttaxation\t1.733333333\r\nlouisiana\tsimple\t2\r\nquarantine\tdisease\t3\r\ncannon\timprisoned\t2.625\r\nbronze\tsuspicion\t2\r\npearl\tinterim\t2.352941176\r\nartist\tpaint\t4.117647059\r\nrelay\tfamily\t2.0625\r\nart\tmortality\t2.294117647\r\nfood\tinvestment\t2.25\r\nalt\ttenor\t2.692307692\r\ncatholics\tprotestant\t3.5625\r\nmilitia\tlandlord\t3.0625\r\nbattle\twarships\t4.176470588\r\nalcohol\tfleeing\t2.5625\r\ncoil\tashes\t3.117647059\r\npoland\trussia\t4\r\nexplosive\tbuilders\t2.4375\r\naeronautics\tplane\t4.277777778\r\ncharge\tsentence\t3.133333333\r\npet\tretiring\t2\r\ndrink\talcohol\t4.352941176\r\nstability\tspecies\t2.375\r\ncolonies\tdepression\t2\r\neaster\tpreference\t2.0625\r\ngenius\tintellect\t4.090909091\r\ndiamond\tkilled\t1.555555556\r\nslavery\tafrican\t2.8\r\njurisdiction\tlaw\t4.454545455\r\nsaints\trepeal\t1.555555556\r\nconspiracy\tcampaign\t2.166666667\r\noperator\textracts\t2.214285714\r\nphysician\taction\t2.153846154\r\nelectronics\tguess\t1.916666667\r\nslavery\tdiamond\t2.285714286\r\nquarterback\tsport\t3.142857143\r\nassassination\tkilled\t4.285714286\r\nslavery\tklan\t2.230769231\r\nheroin\tshoot\t2.692307692\r\nbirds\tdisturbances\t1.692307692\r\npalestinians\tturks\t2.5\r\ncitizenship\tcourt\t2.5\r\nimmunity\tviolation\t2.076923077\r\nalternative\tcontend\t2.461538462\r\nchile\tplates\t2.692307692\r\nabraham\tstranger\t1.846153846\r\nkansas\tcity\t3.769230769\r\nmonth\tyear\t3.857142857\r\nmonth\tday\t3.857142857\r\namateur\tactor\t2.333333333\r\nafghanistan\twar\t3.384615385\r\ntransmission\tmaxwell\t2.25\r\nmanchester\tambitious\t1.923076923\r\nprogram\tbattered\t1.928571429\r\ndrawing\tmusic\t2.583333333\r\nexile\tpledges\t2.307692308\r\nadventure\tsixteen\t1.538461538\r\nexile\tthreats\t2.166666667\r\nconcrete\twings\t1.428571429\r\nseizure\tbishops\t2\r\nsubmarine\tsea\t3.857142857\r\nvilla\tmayor\t2.25\r\ntrade\tfarley\t2.375\r\nnature\tforest\t3.636363636\r\nchronicle\tyoung\t1.9\r\nradical\tbishops\t1.818181818\r\npakistan\tradical\t2.875\r\nfire\twater\t4.266666667\r\ngossip\tnuisance\t3.0625\r\ncon\texaminer\t2.266666667\r\nsatellite\tspace\t3.75\r\nessay\tboston\t2\r\nminiature\tstatue\t3.6\r\nspill\tpollution\t3.5\r\nminister\tcouncil\t3.5625\r\nlandscape\tmountain\t3.5625\r\nreligion\tremedy\t2.5625\r\nship\tstorm\t3.5\r\ncollege\tscientist\t2.8125\r\ncrystal\toldest\t2.5625\r\nafghanistan\twise\t2.066666667\r\ntrinity\treligion\t3.133333333\r\nhomer\todyssey\t2.857142857\r\nparish\tclue\t2.4375\r\nactress\tactor\t4.0625\r\npatent\tprofessionals\t2.375\r\nchaos\thorrible\t3.066666667\r\nacre\tearthquake\t2.125\r\ngovernment\timmunity\t2\r\nfootball\tjustice\t1.8\r\ngambling\tmoney\t3.75\r\ncorruption\tnervous\t1.875\r\ncardinals\tvillages\t2.375\r\nlife\tdeath\t4.103448276\r\nartillery\tsanctions\t2.428571429\r\njerusalem\tmurdered\t2.357142857\r\ncell\tbrick\t3.285714286\r\nknowledge\tpromoter\t2.642857143\r\nadventure\trails\t2.571428571\r\nhouston\tcrash\t2.357142857\r\noxford\tsubcommittee\t2.642857143\r\nmilitia\tweapon\t3.785714286\r\nmanufacturer\tmeat\t1.857142857\r\ndamages\treaction\t3.071428571\r\nsea\tfishing\t4.357142857\r\natomic\tclash\t2.785714286\r\nbroadcasting\tathletics\t3\r\nmystery\texpedition\t2.538461538\r\nkremlin\tsoviets\t3.166666667\r\npig\tblaze\t1.75\r\nriverside\tvietnamese\t2.25\r\nbitter\tprotective\t1.923076923\r\ndisaster\tannounced\t2.384615385\r\npork\tblaze\t2.230769231\r\nfeet\tinternational\t1.916666667\r\nradical\tuniform\t2.5\r\ngossip\tcondemned\t2.692307692\r\nmozart\twagner\t3.166666667\r\nsoccer\tboxing\t3.4\r\nradical\troles\t2.75\r\nrescued\tslaying\t3\r\nresearchers\ttested\t3.538461538\r\nsales\tseason\t2.307692308\r\nhomeless\trefugees\t3.615384615\r\npakistan\trepair\t1.75\r\nathens\tpainting\t2.294117647\r\ntiger\twoods\t3.375\r\naircraft\tplane\t4.473684211\r\nsolar\tcarbon\t2.842105263\r\nenterprise\tbankruptcy\t2.5\r\nhomer\tspringfield\t2.833333333\r\ncoin\tawards\t2.166666667\r\nrhodes\tnative\t2.25\r\nsoccer\tcurator\t2.125\r\ngasoline\tstock\t2.888888889\r\nguilt\textended\t2.105263158\r\nrapid\tsingapore\t1.764705882\r\ncoin\tbanker\t3.631578947\r\nlondon\tcorrespondence\t1.944444444\r\npop\tsex\t2.6\r\nmedicine\tbread\t2.176470588\r\nasia\tanimal\t1.555555556\r\npop\tclubhouse\t3.210526316\r\nnazi\tdefensive\t2.055555556\r\nearth\tpoles\t3.421052632\r\nthailand\tcrowded\t2.166666667\r\nday\tindependence\t3.473684211\r\ncontroversy\tpitch\t2.375\r\nstock\tgasoline\t3.166666667\r\ncomposers\tmozart\t3.833333333\r\ntone\tpiano\t3.722222222\r\nparis\tchef\t2.111111111\r\nprofession\tresponsible\t2.722222222\r\nbankruptcy\tchronicle\t2\r\nlebanon\twar\t2.722222222\r\nisrael\tterror\t3.055555556\r\nangola\tmilitary\t2.941176471\r\nchemistry\tpatients\t2.357142857\r\nmunich\tconstitution\t3.071428571\r\npiano\ttheater\t3.266666667\r\npoetry\tartist\t3.8\r\nacre\tburned\t1.769230769\r\nreligion\tabortion\t2.076923077\r\njazz\tmusic\t4.533333333\r\ngovernment\ttransportation\t3\r\ncolor\twine\t2.533333333\r\njackson\tquota\t1.692307692\r\nshariff\tdeputy\t3.642857143\r\nboat\tnegroes\t2\r\nshooting\tsentenced\t2.933333333\r\nrepublicans\tfriedman\t2.416666667\r\npolitics\tbrokerage\t2.5\r\nrussian\tstalin\t3.357142857\r\nlove\tphilip\t2.5\r\nnuclear\tplant\t3.733333333\r\njamaica\tqueens\t3.076923077\r\ndollar\tasylum\t1.846153846\r\nbridge\trowing\t2.785714286\r\nberlin\tgermany\t4\r\nfuneral\tdeath\t4.714285714\r\nalbert\teinstein\t4.266666667\r\ngulf\tshore\t3.857142857\r\necuador\targentina\t3.266666667\r\nbritain\tfrance\t3.714285714\r\nsports\tscore\t3.866666667\r\nsocialism\tcapitalism\t3.785714286\r\ntreaty\tpeace\t4.166666667\r\nexchange\tmarket\t4.266666667\r\nmarriage\tanniversary\t4.333333333\r\n"
  },
  {
    "path": "psdvec/testsets/ws/simlex_999a.txt",
    "content": "old\tnew\t1.58\nsmart\tintelligent\t9.2\nhard\tdifficult\t8.77\nhappy\tcheerful\t9.55\nhard\teasy\t0.95\nfast\trapid\t8.75\nhappy\tglad\t9.17\nshort\tlong\t1.23\nstupid\tdumb\t9.58\nweird\tstrange\t8.93\nwide\tnarrow\t1.03\nbad\tawful\t8.42\neasy\tdifficult\t0.58\nbad\tterrible\t7.78\nhard\tsimple\t1.38\nsmart\tdumb\t0.55\ninsane\tcrazy\t9.57\nhappy\tmad\t0.95\nlarge\thuge\t9.47\nhard\ttough\t8.05\nnew\tfresh\t6.83\nsharp\tdull\t0.6\nquick\trapid\t9.7\ndumb\tfoolish\t6.67\nwonderful\tterrific\t8.63\nstrange\todd\t9.02\nhappy\tangry\t1.28\nnarrow\tbroad\t1.18\nsimple\teasy\t9.4\nold\tfresh\t0.87\napparent\tobvious\t8.47\ninexpensive\tcheap\t8.72\nnice\tgenerous\t5\nweird\tnormal\t0.72\nweird\todd\t9.2\nbad\timmoral\t7.62\nsad\tfunny\t0.95\nwonderful\tgreat\t8.05\nguilty\tashamed\t6.38\nbeautiful\twonderful\t6.5\nconfident\tsure\t8.27\ndumb\tdense\t7.27\nlarge\tbig\t9.55\nnice\tcruel\t0.67\nimpatient\tanxious\t6.03\nbig\tbroad\t6.73\nstrong\tproud\t3.17\nunnecessary\tnecessary\t0.63\nrestless\tyoung\t1.6\ndumb\tintelligent\t0.75\nbad\tgreat\t0.35\ndifficult\tsimple\t0.87\nnecessary\timportant\t7.37\nbad\tterrific\t0.65\nmad\tglad\t1.45\nhonest\tguilty\t1.18\neasy\ttough\t0.52\neasy\tflexible\t4.1\ncertain\tsure\t8.42\nessential\tnecessary\t8.97\ndifferent\tnormal\t1.08\nsly\tclever\t7.25\ncrucial\timportant\t8.82\nharsh\tcruel\t8.18\nchildish\tfoolish\t5.5\nscarce\trare\t9.17\nfriendly\tgenerous\t5.9\nfragile\tfrigid\t2.38\nlong\tnarrow\t3.57\nbig\theavy\t6.18\nrough\tfrigid\t2.47\nbizarre\tstrange\t9.37\nillegal\timmoral\t4.28\nbad\tguilty\t4.2\nmodern\tancient\t0.73\nnew\tancient\t0.23\ndull\tfunny\t0.55\nhappy\tyoung\t2\neasy\tbig\t1.12\ngreat\tawful\t1.17\ntiny\thuge\t0.6\npolite\tproper\t7.63\nmodest\tashamed\t2.65\nexotic\trare\t8.05\ndumb\tclever\t1.17\ndelightful\twonderful\t8.65\nnoticeable\tobvious\t8.48\nafraid\tanxious\t5.07\nformal\tproper\t8.02\ndreary\tdull\t8.25\ndelightful\tcheerful\t6.58\nunhappy\tmad\t5.95\nsad\tterrible\t5.4\nsick\tcrazy\t3.57\nviolent\tangry\t6.98\nladen\theavy\t5.9\ndirty\tcheap\t1.6\nelastic\tflexible\t7.78\nhard\tdense\t5.9\nrecent\tnew\t7.05\nbold\tproud\t3.97\nsly\tstrange\t1.97\nstrange\tsly\t2.07\ndumb\trare\t0.48\nsly\ttough\t0.58\nterrific\tmad\t0.4\nmodest\tflexible\t0.98\nfresh\twide\t0.4\nhuge\tdumb\t0.48\nlarge\tflexible\t0.48\ndirty\tnarrow\t0.3\nwife\thusband\t2.3\nbook\ttext\t6.35\ngroom\tbride\t3.17\nnight\tday\t1.88\nsouth\tnorth\t2.2\nplane\tairport\t3.65\nuncle\taunt\t5.5\nhorse\tmare\t8.33\nbottom\ttop\t0.7\nfriend\tbuddy\t8.78\nstudent\tpupil\t9.35\nworld\tglobe\t6.67\nleg\tarm\t2.88\nplane\tjet\t8.1\nwoman\tman\t3.33\nhorse\tcolt\t7.07\nactress\tactor\t7.12\nteacher\tinstructor\t9.25\nmovie\tfilm\t8.87\nbird\thawk\t7.85\nword\tdictionary\t3.68\nmoney\tsalary\t7.88\ndog\tcat\t1.75\narea\tregion\t9.47\nnavy\tarmy\t6.43\nbook\tliterature\t7.53\nclothes\tcloset\t3.27\nsunset\tsunrise\t2.47\nchild\tadult\t2.98\ncow\tcattle\t9.52\nbook\tstory\t5.63\nwinter\tsummer\t2.38\ntaxi\tcab\t9.2\ntree\tmaple\t5.53\nbed\tbedroom\t3.4\nroof\tceiling\t7.58\ndisease\tinfection\t7.15\narm\tshoulder\t4.85\nsheep\tlamb\t8.42\nlady\tgentleman\t3.42\nboat\tanchor\t2.25\npriest\tmonk\t6.28\ntoe\tfinger\t4.68\nriver\tstream\t7.3\nanger\tfury\t8.73\ndate\tcalendar\t4.42\nsea\tocean\t8.27\nsecond\tminute\t4.62\nhand\tthumb\t3.88\nwood\tlog\t7.3\nmud\tdirt\t7.32\nhallway\tcorridor\t9.28\nway\tmanner\t7.62\nmouse\tcat\t1.12\ncop\tsheriff\t9.05\ndeath\tburial\t4.93\nmusic\tmelody\t6.98\nbeer\talcohol\t7.5\nmouth\tlip\t7.1\nstorm\thurricane\t6.38\ntax\tincome\t2.38\nflower\tviolet\t6.95\npaper\tcardboard\t5.38\nfloor\tceiling\t1.73\nbeach\tseashore\t8.33\nrod\tcurtain\t3.03\nhound\tfox\t2.38\nstreet\talley\t5.48\nboat\tdeck\t4.28\ncar\thorn\t2.57\nfriend\tguest\t4.25\nemployer\temployee\t3.65\nhand\twrist\t3.97\nball\tcannon\t2.58\nalcohol\tbrandy\t6.98\nvictory\ttriumph\t8.98\ntelephone\tbooth\t3.63\ndoor\tdoorway\t5.4\nmotel\tinn\t8.17\nclothes\tcloth\t5.47\nsteak\tmeat\t7.47\nnail\tthumb\t3.55\nband\torchestra\t7.08\nbook\tbible\t5\nbusiness\tindustry\t7.02\nwinter\tseason\t6.27\ndecade\tcentury\t3.48\nalcohol\tgin\t8.65\nhat\tcoat\t2.67\nwindow\tdoor\t3.33\narm\twrist\t3.57\nhouse\tapartment\t5.8\nglass\tcrystal\t6.27\nwine\tbrandy\t5.15\ncreator\tmaker\t9.62\ndinner\tbreakfast\t3.33\narm\tmuscle\t3.72\nbubble\tsuds\t8.57\nbread\tflour\t3.33\ndeath\ttragedy\t5.8\nabsence\tpresence\t0.4\ngun\tcannon\t5.68\ngrass\tblade\t4.57\nball\tbasket\t1.67\nhose\tgarden\t1.67\nboy\tkid\t7.5\nchurch\tchoir\t2.95\nclothes\tdrawer\t3.02\ntower\tbell\t1.9\nfather\tparent\t7.07\nschool\tgrade\t4.42\nparent\tadult\t5.37\nbar\tjail\t1.9\ncar\thighway\t3.4\ndictionary\tdefinition\t6.25\ndoor\tcellar\t1.97\narmy\tlegion\t5.95\nmetal\taluminum\t7.25\nchair\tbench\t6.67\ncloud\tfog\t6\nboy\tson\t6.75\nwater\tice\t6.47\nbed\tblanket\t3.02\nattorney\tlawyer\t9.35\narea\tzone\t8.33\nbusiness\tcompany\t9.02\nclothes\tfabric\t5.87\nsweater\tjacket\t7.15\nmoney\tcapital\t6.67\nhand\tfoot\t4.17\nalcohol\tco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ment\tagreement\t1.45\ndepth\tmagnitude\t6.12\nmedium\tnews\t3.65\nwinner\tcandidate\t2.78\nbirthday\tdate\t5.08\nfee\tpayment\t7.15\nbible\thymn\t5.15\nexit\tdoorway\t5.5\nman\tsentry\t3.25\naisle\thall\t6.35\nwhiskey\tgin\t6.28\nblood\tmarrow\t3.4\noil\tmink\t1.23\nfloor\tdeck\t5.55\nroof\tfloor\t2.62\ndoor\tfloor\t1.67\nshoulder\thead\t3.42\nwagon\tcarriage\t7.7\ncar\tcarriage\t5.13\nelbow\tankle\t3.13\nwealth\tfame\t4.02\nsorrow\tshame\t4.77\nadministration\tmanagement\t7.25\ncommunication\tconversation\t8.02\npollution\tatmosphere\t4.25\nanatomy\tbiology\t5.33\ncollege\tprofession\t3.12\nbook\ttopic\t2.07\nformula\tequation\t7.95\nbook\tinformation\t5\nboy\tpartner\t1.9\nsky\tuniverse\t4.68\npopulation\tpeople\t7.68\ncollege\tclass\t4.13\nchief\tmayor\t4.85\nrabbi\tminister\t7.62\nmeter\tinch\t5.08\npolyester\tcotton\t5.63\nlawyer\tbanker\t1.88\nviolin\tinstrument\t6.58\ncamp\tcabin\t4.2\npot\tappliance\t2.53\nlinen\tfabric\t7.47\nwhiskey\tchampagne\t5.33\ngirl\tchild\t5.38\ncottage\tcabin\t7.72\nbird\then\t7.03\nracket\tnoise\t8.1\nsunset\tevening\t5.98\ndrizzle\train\t9.17\nadult\tbaby\t2.22\ncharcoal\tcoal\t7.63\nbody\tspine\t4.78\nhead\tnail\t2.47\nlog\ttimber\t8.05\nspoon\tcup\t2.02\nbody\tnerve\t3.13\nman\thusband\t5.32\nbone\tneck\t2.53\nfrustration\tanger\t6.5\nriver\tsea\t5.72\ntask\tjob\t8.87\nclub\tsociety\t5.23\nreflection\timage\t7.27\nprince\tking\t5.92\nsnow\tweather\t5.48\npeople\tparty\t2.2\nboy\tbrother\t6.67\nroot\tgrass\t3.55\nbrow\teye\t3.82\nmoney\tpearl\t2.1\nmoney\tdiamond\t3.42\nvehicle\tbus\t6.47\ncab\tbus\t5.6\nhouse\tbarn\t4.33\nfinger\tpalm\t3.33\ncar\tbridge\t0.95\neffort\tdifficulty\t4.45\nfact\tinsight\t4.77\njob\tmanagement\t3.97\ncancer\tsickness\t7.93\nword\tnewspaper\t2.47\ncomposer\twriter\t6.58\nactor\tsinger\t4.52\nshelter\thut\t6.47\nbathroom\tkitchen\t3.1\ncabin\thut\t6.53\ndoor\tkitchen\t1.67\nvalue\tbelief\t7.07\nwisdom\tintelligence\t7.47\nignorance\tintelligence\t1.5\nhappiness\tluck\t2.38\nidea\tscheme\t6.75\nmood\temotion\t8.12\nhappiness\tpeace\t6.03\ndespair\tmisery\t7.22\nlogic\tarithmetic\t3.97\ndenial\tconfession\t1.03\nargument\tcriticism\t5.08\naggression\thostility\t8.48\nhysteria\tconfusion\t6.33\nchemistry\ttheory\t3.17\ntrial\tverdict\t3.33\ncomfort\tsafety\t5.8\nconfidence\tself\t3.12\nvision\tperception\t6.88\nera\tdecade\t5.4\nbiography\tfiction\t1.38\ndiscussion\targument\t5.48\ncode\tsymbol\t6.03\ndanger\tdisease\t3\naccident\tcatastrophe\t5.9\njourney\ttrip\t8.88\nactivity\tmovement\t7.15\ngossip\tnews\t5.22\nfather\tgod\t3.57\naction\tcourse\t5.45\nfever\tillness\t7.65\naviation\tflight\t8.18\ngame\taction\t4.85\nmolecule\tair\t3.05\nhome\tstate\t2.58\nword\tliterature\t4.77\nadult\tguardian\t6.9\nnewspaper\tinformation\t5.65\ncommunication\ttelevision\t5.6\ncousin\tuncle\t4.63\nauthor\treader\t1.6\nguy\tpartner\t3.57\narea\tcorner\t2.07\nballad\tsong\t7.53\nwall\tdecoration\t2.62\nword\tpage\t2.92\nnurse\tscientist\t2.08\npolitician\tpresident\t7.38\npresident\tmayor\t5.68\nbook\tessay\t4.72\nman\twarrior\t4.72\narticle\tjournal\t6.18\nbreakfast\tsupper\t4.4\ncrowd\tparade\t3.93\naisle\thallway\t6.75\nteacher\trabbi\t4.37\nhip\tlip\t1.43\nbook\tarticle\t5.43\nroom\tcell\t4.58\nbox\tbooth\t3.8\ndaughter\tkid\t4.17\nlimb\tleg\t6.9\nliver\tlung\t2.7\nclassroom\thallway\t2\nmountain\tledge\t3.73\ncar\televator\t1.03\nbed\tcouch\t3.42\nclothes\tbutton\t2.3\nclothes\tcoat\t5.35\nkidney\torgan\t6.17\napple\tsauce\t1.43\nchicken\tsteak\t3.73\ncar\those\t0.87\ntobacco\tcigarette\t7.5\nstudent\tprofessor\t1.95\nbaby\tdaughter\t5\npipe\tcigar\t6.03\nmilk\tjuice\t4.05\nbox\tcigar\t1.25\napartment\thotel\t3.33\ncup\tcone\t3.17\nhorse\tox\t3.02\nthroat\tnose\t2.8\nbone\tteeth\t4.17\nbone\telbow\t3.78\nbacon\tbean\t1.22\ncup\tjar\t5.13\nproof\tfact\t7.3\nappointment\tengagement\t6.75\nbirthday\tyear\t1.67\nword\tclue\t2.53\nauthor\tcreator\t8.02\natom\tcarbon\t3.1\narchbishop\tbishop\t7.05\nletter\tparagraph\t4\npage\tparagraph\t3.03\nsteeple\tchapel\t7.08\nmuscle\tbone\t3.65\nmuscle\ttongue\t5\nboy\tsoldier\t2.15\nbelly\tabdomen\t8.13\nguy\tgirl\t3.33\nbed\tchair\t3.5\nclothes\tjacket\t5.15\ngun\tknife\t3.65\ntin\tmetal\t5.63\nbottle\tcontainer\t7.93\nhen\tturkey\t6.13\nmeat\tbread\t1.67\narm\tbone\t3.83\nneck\tspine\t5.32\napple\tlemon\t4.05\nagony\tgrief\t7.63\nassignment\ttask\t8.7\nnight\tdawn\t2.95\ndinner\tsoup\t3.72\ncalf\tbull\t4.93\nsnow\tstorm\t4.8\nnail\thand\t3.42\ndog\thorse\t2.38\narm\tneck\t1.58\nball\tglove\t1.75\nflu\tfever\t6.08\nfee\tsalary\t3.72\nnerve\tbrain\t3.88\nbeast\tanimal\t7.83\ndinner\tchicken\t2.85\ngirl\tmaid\t2.93\nchild\tboy\t5.75\nalcohol\twine\t7.42\nnose\tmouth\t3.73\nstreet\tcar\t2.38\nbell\tdoor\t2.2\nbox\that\t1.3\nbelief\timpression\t5.95\nbias\topinion\t5.6\nattention\tawareness\t8.73\nanger\tmood\t4.1\nelegance\tstyle\t5.72\nbeauty\tage\t1.58\nbook\ttheme\t2.58\nfriend\tmother\t2.53\nvitamin\tiron\t5.55\ncar\tfactory\t2.75\npact\tcondition\t2.45\nchapter\tchoice\t0.48\narithmetic\trhythm\t2.35\nwinner\tpresence\t1.08\nbelief\tflower\t0.4\nwinner\tgoal\t3.23\ntrick\tsize\t0.48\nchoice\tvein\t0.98\nhymn\tconquest\t0.68\nendurance\tband\t0.4\njail\tchoice\t1.08\ncondition\tboy\t0.48\nflower\tendurance\t0.4\nhole\tagreement\t0.3\ndoctor\ttemper\t0.48\nfraternity\tdoor\t0.68\ntask\twoman\t0.68\nfraternity\tbaseball\t0.88\ncent\tsize\t0.4\npresence\tdoor\t0.48\nmouse\tmanagement\t0.48\ntask\thighway\t0.48\nliquor\tcentury\t0.4\ntask\tstraw\t0.68\nisland\ttask\t0.3\nnight\tchapter\t0.48\npollution\tpresident\t0.68\ngun\ttrick\t0.48\nbath\ttrick\t0.58\ndiet\tapple\t1.18\ncent\twife\t0.58\nchapter\ttail\t0.3\ncourse\tstomach\t0.58\nhymn\tstraw\t0.4\ndentist\tcolonel\t0.4\nwife\tstraw\t0.4\nhole\twife\t0.68\npupil\tpresident\t0.78\nbath\twife\t0.48\npeople\tcent\t0.48\nformula\tlog\t1.77\nwoman\tfur\t0.58\napple\tsunshine\t0.58\ngun\tdawn\t1.18\nmeal\twaist\t0.98\ncamera\tpresident\t0.48\nliquor\tband\t0.68\nstomach\tvein\t2.35\ngun\tfur\t0.3\ncouch\tbaseball\t0.88\nworker\tcamera\t0.68\ndeck\tmouse\t0.48\nrice\tboy\t0.4\npeople\tgun\t0.68\ncliff\ttail\t0.3\nankle\twindow\t0.3\nprincess\tisland\t0.3\ncontainer\tmouse\t0.3\nwagon\tcontainer\t2.65\npeople\tballoon\t0.48\ndollar\tpeople\t0.4\nbath\tballoon\t0.4\nstomach\tbedroom\t0.4\nbicycle\tbedroom\t0.4\nlog\tbath\t0.4\nbowl\ttail\t0.48\ngo\tcome\t2.42\ntake\tsteal\t6.18\nlisten\thear\t8.17\nthink\trationalize\t8.25\noccur\thappen\t9.32\nvanish\tdisappear\t9.8\nmultiply\tdivide\t1.75\nplead\tbeg\t9.08\nbegin\toriginate\t8.2\nprotect\tdefend\t9.13\nkill\tdestroy\t5.9\ncreate\tmake\t8.72\naccept\treject\t0.83\nignore\tavoid\t6.87\ncarry\tbring\t5.8\nleave\tenter\t0.95\nchoose\telect\t7.62\nlose\tfail\t7.33\nencourage\tdiscourage\t1.58\nachieve\taccomplish\t8.57\nmake\tconstruct\t8.33\nlisten\tobey\t4.93\ninform\tnotify\t9.25\nreceive\tgive\t1.47\nborrow\tbeg\t2.62\ntake\tobtain\t7.1\nadvise\trecommend\t8.1\nimitate\tportray\t6.75\nwin\tsucceed\t7.9\nthink\tdecide\t5.13\ngreet\tmeet\t6.17\nagree\targue\t0.77\nenjoy\tentertain\t5.92\ndestroy\tmake\t1.6\nsave\tprotect\t6.58\ngive\tlend\t7.22\nunderstand\tknow\t7.47\ntake\treceive\t5.08\naccept\tacknowledge\t6.88\ndecide\tchoose\t8.87\naccept\tbelieve\t6.75\nkeep\tpossess\t8.27\nroam\twander\t8.83\nsucceed\tfail\t0.83\nspend\tsave\t0.55\nleave\tgo\t7.63\ncome\tattend\t8.1\nknow\tbelieve\t5.5\ngather\tmeet\t7.3\nmake\tearn\t7.62\nforget\tignore\t3.07\nmultiply\tadd\t2.7\nshrink\tgrow\t0.23\narrive\tleave\t1.33\nsucceed\ttry\t3.98\naccept\tdeny\t1.75\narrive\tcome\t7.05\nagree\tdiffer\t1.05\nsend\treceive\t1.08\nwin\tdominate\t5.68\nadd\tdivide\t2.3\nkill\tchoke\t4.92\nacquire\tget\t8.82\nparticipate\tjoin\t7.7\nleave\tremain\t2.53\ngo\tenter\t4\ntake\tcarry\t5.23\nforget\tlearn\t1.18\nappoint\telect\t8.17\nengage\tmarry\t5.43\nask\tpray\t3.72\ngo\tsend\t3.75\ntake\tdeliver\t4.37\nspeak\thear\t3.02\nanalyze\tevaluate\t8.03\nargue\trationalize\t4.2\nlose\tkeep\t1.05\ncompare\tanalyze\t8.1\ndisorganize\torganize\t1.45\ngo\tallow\t3.62\ntake\tpossess\t7.2\nlearn\tlisten\t3.88\ndestroy\tconstruct\t0.92\ncreate\tbuild\t8.48\nsteal\tbuy\t1.13\nkill\thang\t4.45\nforget\tknow\t0.92\ncreate\timagine\t5.13\ndo\thappen\t4.23\nwin\taccomplish\t7.85\ngive\tdeny\t1.43\ndeserve\tearn\t5.8\nget\tput\t1.98\nlocate\tfind\t8.73\nappear\tattend\t6.28\nknow\tcomprehend\t7.63\npretend\timagine\t8.47\nsatisfy\tplease\t7.67\ncherish\tkeep\t4.85\nargue\tdiffer\t5.15\novercome\tdominate\t6.25\nbehave\tobey\t7.3\ncooperate\tparticipate\t6.43\nachieve\ttry\t4.42\nfail\tdiscourage\t3.33\nbegin\tquit\t1.28\nsay\tparticipate\t3.82\ncome\tbring\t2.42\ndeclare\tannounce\t9.08\nread\tcomprehend\t4.7\ntake\tleave\t2.47\nproclaim\tannounce\t8.18\nacquire\tobtain\t8.57\nconclude\tdecide\t7.75\nplease\tplead\t2.98\nargue\tprove\t4.83\nask\tplead\t6.47\nfind\tdisappear\t0.77\ninspect\texamine\t8.75\nverify\tjustify\t4.08\nassume\tpredict\t4.85\nlearn\tevaluate\t4.17\nargue\tjustify\t5\nmake\tbecome\t4.77\ndiscover\toriginate\t4.83\nachieve\tsucceed\t7.5\ngive\tput\t3.65\nunderstand\tlisten\t4.68\nexpand\tgrow\t8.27\nborrow\tsell\t1.73\nkeep\tprotect\t5.4\nexplain\tprove\t4.1\nassume\tpretend\t3.72\nagree\tplease\t4.13\nforgive\tforget\t3.92\nclarify\texplain\t8.33\nunderstand\tforgive\t4.87\nremind\tforget\t0.87\nget\tremain\t1.6\nrealize\tdiscover\t7.47\nrequire\tinquire\t1.82\nignore\task\t1.07\nthink\tinquire\t4.77\nreject\tavoid\t4.78\nargue\tpersuade\t6.23\npursue\tpersuade\t3.17\naccept\tforgive\t3.73\ndo\tquit\t1.17\ninvestigate\texamine\t8.1\ndiscuss\texplain\t6.67\nowe\tlend\t2.32\nexplore\tdiscover\t8.48\ncomplain\targue\t4.8\nwithdraw\treject\t6.38\nkeep\tborrow\t2.25\nbeg\task\t6\narrange\torganize\t8.27\nreduce\tshrink\t8.02\nspeak\tacknowledge\t4.67\ngive\tborrow\t2.22\nkill\tdefend\t2.63\ndisappear\tshrink\t5.8\ndeliver\tcarry\t3.88\nbreathe\tchoke\t1.37\nacknowledge\tnotify\t5.3\nbecome\tseem\t2.63\npretend\tseem\t4.68\naccomplish\tbecome\t4\ncontemplate\tthink\t8.82\ndetermine\tpredict\t5.8\nplease\tentertain\t5\nremain\tretain\t5.75\npretend\tportray\t7.03\nforget\tretain\t0.63\nwant\tchoose\t4.78\nlose\tget\t0.77\ntry\tthink\t2.62\nbecome\tappear\t4.77\nleave\tignore\t4.42\naccept\trecommend\t2.75\nleave\twander\t3.57\nkeep\tgive\t1.05\ngive\tallow\t5.15\nbring\tsend\t2.97\nabsorb\tlearn\t5.48\nacquire\tfind\t6.38\nleave\tappear\t0.97\ncreate\tdestroy\t0.63\nbegin\tgo\t7.42\nget\tbuy\t5.08\ncollect\tsave\t6.67\nreplace\trestore\t5.73\njoin\tadd\t8.1\njoin\tmarry\t5.35\naccept\tdeliver\t1.58\nattach\tjoin\t7.75\nput\thang\t3\ngo\tsell\t0.97\ncommunicate\tpray\t3.55\ngive\tsteal\t0.5\nadd\tbuild\t4.92\nbring\trestore\t2.62\ncomprehend\tsatisfy\t2.55\nportray\tdecide\t1.18\norganize\tbecome\t1.77\ngive\tknow\t0.88\nsay\tverify\t4.9\ncooperate\tjoin\t5.18\narrange\trequire\t0.98\nborrow\twant\t1.77\ninvestigate\tpursue\t7.15\nignore\texplore\t0.4\nbring\tcomplain\t0.98\nenter\towe\t0.68\nportray\tnotify\t0.78\nremind\tsell\t0.4\nabsorb\tpossess\t5\njoin\tacquire\t2.85\nsend\tattend\t1.67\ngather\tattend\t4.8\nabsorb\twithdraw\t2.97\nattend\tarrive\t6.08\n"
  },
  {
    "path": "psdvec/testsets/ws/ws353.txt",
    "content": "love\tsex\t6.77\r\ntiger\tcat\t7.35\r\ntiger\ttiger\t10.00\r\nbook\tpaper\t7.46\r\ncomputer\tkeyboard\t7.62\r\ncomputer\tinternet\t7.58\r\nplane\tcar\t5.77\r\ntrain\tcar\t6.31\r\ntelephone\tcommunication\t7.50\r\ntelevision\tradio\t6.77\r\nmedia\tradio\t7.42\r\ndrug\tabuse\t6.85\r\nbread\tbutter\t6.19\r\ncucumber\tpotato\t5.92\r\ndoctor\tnurse\t7.00\r\nprofessor\tdoctor\t6.62\r\nstudent\tprofessor\t6.81\r\nsmart\tstudent\t4.62\r\nsmart\tstupid\t5.81\r\ncompany\tstock\t7.08\r\nstock\tmarket\t8.08\r\nstock\tphone\t1.62\r\nstock\tCD\t1.31\r\nstock\tjaguar\t0.92\r\nstock\tegg\t1.81\r\nfertility\tegg\t6.69\r\nstock\tlive\t3.73\r\nstock\tlife\t0.92\r\nbook\tlibrary\t7.46\r\nbank\tmoney\t8.12\r\nwood\tforest\t7.73\r\nmoney\tcash\t9.15\r\nprofessor\tcucumber\t0.31\r\nking\tcabbage\t0.23\r\nking\tqueen\t8.58\r\nking\trook\t5.92\r\nbishop\trabbi\t6.69\r\nJerusalem\tIsrael\t8.46\r\nJerusalem\tPalestinian\t7.65\r\nholy\tsex\t1.62\r\nfuck\tsex\t9.44\r\nMaradona\tfootball\t8.62\r\nfootball\tsoccer\t9.03\r\nfootball\tbasketball\t6.81\r\nfootball\ttennis\t6.63\r\ntennis\tracket\t7.56\r\nArafat\tpeace\t6.73\r\nArafat\tterror\t7.65\r\nArafat\tJackson\t2.50\r\nlaw\tlawyer\t8.38\r\nmovie\tstar\t7.38\r\nmovie\tpopcorn\t6.19\r\nmovie\tcritic\t6.73\r\nmovie\ttheater\t7.92\r\nphysics\tproton\t8.12\r\nphysics\tchemistry\t7.35\r\nspace\tchemistry\t4.88\r\nalcohol\tchemistry\t5.54\r\nvodka\tgin\t8.46\r\nvodka\tbrandy\t8.13\r\ndrink\tcar\t3.04\r\ndrink\tear\t1.31\r\ndrink\tmouth\t5.96\r\ndrink\teat\t6.87\r\nbaby\tmother\t7.85\r\ndrink\tmother\t2.65\r\ncar\tautomobile\t8.94\r\ngem\tjewel\t8.96\r\njourney\tvoyage\t9.29\r\nboy\tlad\t8.83\r\ncoast\tshore\t9.10\r\nasylum\tmadhouse\t8.87\r\nmagician\twizard\t9.02\r\nmidday\tnoon\t9.29\r\nfurnace\tstove\t8.79\r\nfood\tfruit\t7.52\r\nbird\tcock\t7.10\r\nbird\tcrane\t7.38\r\ntool\timplement\t6.46\r\nbrother\tmonk\t6.27\r\ncrane\timplement\t2.69\r\nlad\tbrother\t4.46\r\njourney\tcar\t5.85\r\nmonk\toracle\t5.00\r\ncemetery\twoodland\t2.08\r\nfood\trooster\t4.42\r\ncoast\thill\t4.38\r\nforest\tgraveyard\t1.85\r\nshore\twoodland\t3.08\r\nmonk\tslave\t0.92\r\ncoast\tforest\t3.15\r\nlad\twizard\t0.92\r\nchord\tsmile\t0.54\r\nglass\tmagician\t2.08\r\nnoon\tstring\t0.54\r\nrooster\tvoyage\t0.62\r\nmoney\tdollar\t8.42\r\nmoney\tcash\t9.08\r\nmoney\tcurrency\t9.04\r\nmoney\twealth\t8.27\r\nmoney\tproperty\t7.57\r\nmoney\tpossession\t7.29\r\nmoney\tbank\t8.50\r\nmoney\tdeposit\t7.73\r\nmoney\twithdrawal\t6.88\r\nmoney\tlaundering\t5.65\r\nmoney\toperation\t3.31\r\ntiger\tjaguar\t8.00\r\ntiger\tfeline\t8.00\r\ntiger\tcarnivore\t7.08\r\ntiger\tmammal\t6.85\r\ntiger\tanimal\t7.00\r\ntiger\torganism\t4.77\r\ntiger\tfauna\t5.62\r\ntiger\tzoo\t5.87\r\npsychology\tpsychiatry\t8.08\r\npsychology\tanxiety\t7.00\r\npsychology\tfear\t6.85\r\npsychology\tdepression\t7.42\r\npsychology\tclinic\t6.58\r\npsychology\tdoctor\t6.42\r\npsychology\tFreud\t8.21\r\npsychology\tmind\t7.69\r\npsychology\thealth\t7.23\r\npsychology\tscience\t6.71\r\npsychology\tdiscipline\t5.58\r\npsychology\tcognition\t7.48\r\nplanet\tstar\t8.45\r\nplanet\tconstellation\t8.06\r\nplanet\tmoon\t8.08\r\nplanet\tsun\t8.02\r\nplanet\tgalaxy\t8.11\r\nplanet\tspace\t7.92\r\nplanet\tastronomer\t7.94\r\nprecedent\texample\t5.85\r\nprecedent\tinformation\t3.85\r\nprecedent\tcognition\t2.81\r\nprecedent\tlaw\t6.65\r\nprecedent\tcollection\t2.50\r\nprecedent\tgroup\t1.77\r\nprecedent\tantecedent\t6.04\r\ncup\tcoffee\t6.58\r\ncup\ttableware\t6.85\r\ncup\tarticle\t2.40\r\ncup\tartifact\t2.92\r\ncup\tobject\t3.69\r\ncup\tentity\t2.15\r\ncup\tdrink\t7.25\r\ncup\tfood\t5.00\r\ncup\tsubstance\t1.92\r\ncup\tliquid\t5.90\r\njaguar\tcat\t7.42\r\njaguar\tcar\t7.27\r\nenergy\tsecretary\t1.81\r\nsecretary\tsenate\t5.06\r\nenergy\tlaboratory\t5.09\r\ncomputer\tlaboratory\t6.78\r\nweapon\tsecret\t6.06\r\nFBI\tfingerprint\t6.94\r\nFBI\tinvestigation\t8.31\r\ninvestigation\teffort\t4.59\r\nMars\twater\t2.94\r\nMars\tscientist\t5.63\r\nnews\treport\t8.16\r\ncanyon\tlandscape\t7.53\r\nimage\tsurface\t4.56\r\ndiscovery\tspace\t6.34\r\nwater\tseepage\t6.56\r\nsign\trecess\t2.38\r\nWednesday\tnews\t2.22\r\nmile\tkilometer\t8.66\r\ncomputer\tnews\t4.47\r\nterritory\tsurface\t5.34\r\natmosphere\tlandscape\t3.69\r\npresident\tmedal\t3.00\r\nwar\ttroops\t8.13\r\nrecord\tnumber\t6.31\r\nskin\teye\t6.22\r\nJapanese\tAmerican\t6.50\r\ntheater\thistory\t3.91\r\nvolunteer\tmotto\t2.56\r\nprejudice\trecognition\t3.00\r\ndecoration\tvalor\t5.63\r\ncentury\tyear\t7.59\r\ncentury\tnation\t3.16\r\ndelay\tracism\t1.19\r\ndelay\tnews\t3.31\r\nminister\tparty\t6.63\r\npeace\tplan\t4.75\r\nminority\tpeace\t3.69\r\nattempt\tpeace\t4.25\r\ngovernment\tcrisis\t6.56\r\ndeployment\tdeparture\t4.25\r\ndeployment\twithdrawal\t5.88\r\nenergy\tcrisis\t5.94\r\nannouncement\tnews\t7.56\r\nannouncement\teffort\t2.75\r\nstroke\thospital\t7.03\r\ndisability\tdeath\t5.47\r\nvictim\temergency\t6.47\r\ntreatment\trecovery\t7.91\r\njournal\tassociation\t4.97\r\ndoctor\tpersonnel\t5.00\r\ndoctor\tliability\t5.19\r\nliability\tinsurance\t7.03\r\nschool\tcenter\t3.44\r\nreason\thypertension\t2.31\r\nreason\tcriterion\t5.91\r\nhundred\tpercent\t7.38\r\nHarvard\tYale\t8.13\r\nhospital\tinfrastructure\t4.63\r\ndeath\trow\t5.25\r\ndeath\tinmate\t5.03\r\nlawyer\tevidence\t6.69\r\nlife\tdeath\t7.88\r\nlife\tterm\t4.50\r\nword\tsimilarity\t4.75\r\nboard\trecommendation\t4.47\r\ngovernor\tinterview\t3.25\r\nOPEC\tcountry\t5.63\r\npeace\tatmosphere\t3.69\r\npeace\tinsurance\t2.94\r\nterritory\tkilometer\t5.28\r\ntravel\tactivity\t5.00\r\ncompetition\tprice\t6.44\r\nconsumer\tconfidence\t4.13\r\nconsumer\tenergy\t4.75\r\nproblem\tairport\t2.38\r\ncar\tflight\t4.94\r\ncredit\tcard\t8.06\r\ncredit\tinformation\t5.31\r\nhotel\treservation\t8.03\r\ngrocery\tmoney\t5.94\r\nregistration\tarrangement\t6.00\r\narrangement\taccommodation\t5.41\r\nmonth\thotel\t1.81\r\ntype\tkind\t8.97\r\narrival\thotel\t6.00\r\nbed\tcloset\t6.72\r\ncloset\tclothes\t8.00\r\nsituation\tconclusion\t4.81\r\nsituation\tisolation\t3.88\r\nimpartiality\tinterest\t5.16\r\ndirection\tcombination\t2.25\r\nstreet\tplace\t6.44\r\nstreet\tavenue\t8.88\r\nstreet\tblock\t6.88\r\nstreet\tchildren\t4.94\r\nlisting\tproximity\t2.56\r\nlisting\tcategory\t6.38\r\ncell\tphone\t7.81\r\nproduction\thike\t1.75\r\nbenchmark\tindex\t4.25\r\nmedia\ttrading\t3.88\r\nmedia\tgain\t2.88\r\ndividend\tpayment\t7.63\r\ndividend\tcalculation\t6.48\r\ncalculation\tcomputation\t8.44\r\ncurrency\tmarket\t7.50\r\nOPEC\toil\t8.59\r\noil\tstock\t6.34\r\nannouncement\tproduction\t3.38\r\nannouncement\twarning\t6.00\r\nprofit\twarning\t3.88\r\nprofit\tloss\t7.63\r\ndollar\tyen\t7.78\r\ndollar\tbuck\t9.22\r\ndollar\tprofit\t7.38\r\ndollar\tloss\t6.09\r\ncomputer\tsoftware\t8.50\r\nnetwork\thardware\t8.31\r\nphone\tequipment\t7.13\r\nequipment\tmaker\t5.91\r\nluxury\tcar\t6.47\r\nfive\tmonth\t3.38\r\nreport\tgain\t3.63\r\ninvestor\tearning\t7.13\r\nliquid\twater\t7.89\r\nbaseball\tseason\t5.97\r\ngame\tvictory\t7.03\r\ngame\tteam\t7.69\r\nmarathon\tsprint\t7.47\r\ngame\tseries\t6.19\r\ngame\tdefeat\t6.97\r\nseven\tseries\t3.56\r\nseafood\tsea\t7.47\r\nseafood\tfood\t8.34\r\nseafood\tlobster\t8.70\r\nlobster\tfood\t7.81\r\nlobster\twine\t5.70\r\nfood\tpreparation\t6.22\r\nvideo\tarchive\t6.34\r\nstart\tyear\t4.06\r\nstart\tmatch\t4.47\r\ngame\tround\t5.97\r\nboxing\tround\t7.61\r\nchampionship\ttournament\t8.36\r\nfighting\tdefeating\t7.41\r\nline\tinsurance\t2.69\r\nday\tsummer\t3.94\r\nsummer\tdrought\t7.16\r\nsummer\tnature\t5.63\r\nday\tdawn\t7.53\r\nnature\tenvironment\t8.31\r\nenvironment\tecology\t8.81\r\nnature\tman\t6.25\r\nman\twoman\t8.30\r\nman\tgovernor\t5.25\r\nmurder\tmanslaughter\t8.53\r\nsoap\topera\t7.94\r\nopera\tperformance\t6.88\r\nlife\tlesson\t5.94\r\nfocus\tlife\t4.06\r\nproduction\tcrew\t6.25\r\ntelevision\tfilm\t7.72\r\nlover\tquarrel\t6.19\r\nviewer\tserial\t2.97\r\npossibility\tgirl\t1.94\r\npopulation\tdevelopment\t3.75\r\nmorality\timportance\t3.31\r\nmorality\tmarriage\t3.69\r\nMexico\tBrazil\t7.44\r\ngender\tequality\t6.41\r\nchange\tattitude\t5.44\r\nfamily\tplanning\t6.25\r\nopera\tindustry\t2.63\r\nsugar\tapproach\t0.88\r\npractice\tinstitution\t3.19\r\nministry\tculture\t4.69\r\nproblem\tchallenge\t6.75\r\nsize\tprominence\t5.31\r\ncountry\tcitizen\t7.31\r\nplanet\tpeople\t5.75\r\ndevelopment\tissue\t3.97\r\nexperience\tmusic\t3.47\r\nmusic\tproject\t3.63\r\nglass\tmetal\t5.56\r\naluminum\tmetal\t7.83\r\nchance\tcredibility\t3.88\r\nexhibit\tmemorabilia\t5.31\r\nconcert\tvirtuoso\t6.81\r\nrock\tjazz\t7.59\r\nmuseum\ttheater\t7.19\r\nobservation\tarchitecture\t4.38\r\nspace\tworld\t6.53\r\npreservation\tworld\t6.19\r\nadmission\tticket\t7.69\r\nshower\tthunderstorm\t6.31\r\nshower\tflood\t6.03\r\nweather\tforecast\t8.34\r\ndisaster\tarea\t6.25\r\ngovernor\toffice\t6.34\r\narchitecture\tcentury\t3.78\r\n"
  },
  {
    "path": "psdvec/testsets/ws/ws353_relatedness.txt",
    "content": "computer\tkeyboard\t7.62\nJerusalem\tIsrael\t8.46\nplanet\tgalaxy\t8.11\ncanyon\tlandscape\t7.53\nOPEC\tcountry\t5.63\nday\tsummer\t3.94\nday\tdawn\t7.53\ncountry\tcitizen\t7.31\nplanet\tpeople\t5.75\nenvironment\tecology\t8.81\nMaradona\tfootball\t8.62\nOPEC\toil\t8.59\nmoney\tbank\t8.50\ncomputer\tsoftware\t8.50\nlaw\tlawyer\t8.38\nweather\tforecast\t8.34\nnetwork\thardware\t8.31\nnature\tenvironment\t8.31\nFBI\tinvestigation\t8.31\nmoney\twealth\t8.27\npsychology\tFreud\t8.21\nnews\treport\t8.16\nwar\ttroops\t8.13\nphysics\tproton\t8.12\nbank\tmoney\t8.12\nstock\tmarket\t8.08\nplanet\tconstellation\t8.06\ncredit\tcard\t8.06\nhotel\treservation\t8.03\ncloset\tclothes\t8.00\nsoap\topera\t7.94\nplanet\tastronomer\t7.94\nplanet\tspace\t7.92\nmovie\ttheater\t7.92\ntreatment\trecovery\t7.91\nbaby\tmother\t7.85\nmoney\tdeposit\t7.73\ntelevision\tfilm\t7.72\npsychology\tmind\t7.69\ngame\tteam\t7.69\nadmission\tticket\t7.69\nJerusalem\tPalestinian\t7.65\nArafat\tterror\t7.65\nboxing\tround\t7.61\ncomputer\tinternet\t7.58\nmoney\tproperty\t7.57\ntennis\tracket\t7.56\ntelephone\tcommunication\t7.50\ncurrency\tmarket\t7.50\npsychology\tcognition\t7.48\nseafood\tsea\t7.47\nbook\tpaper\t7.46\nbook\tlibrary\t7.46\npsychology\tdepression\t7.42\nfighting\tdefeating\t7.41\nmovie\tstar\t7.38\nhundred\tpercent\t7.38\ndollar\tprofit\t7.38\nmoney\tpossession\t7.29\ncup\tdrink\t7.25\npsychology\thealth\t7.23\nsummer\tdrought\t7.16\ninvestor\tearning\t7.13\ncompany\tstock\t7.08\nstroke\thospital\t7.03\nliability\tinsurance\t7.03\ngame\tvictory\t7.03\npsychology\tanxiety\t7.00\ngame\tdefeat\t6.97\nFBI\tfingerprint\t6.94\nmoney\twithdrawal\t6.88\npsychology\tfear\t6.85\ndrug\tabuse\t6.85\nconcert\tvirtuoso\t6.81\ncomputer\tlaboratory\t6.78\nlove\tsex\t6.77\nproblem\tchallenge\t6.75\nmovie\tcritic\t6.73\nArafat\tpeace\t6.73\nbed\tcloset\t6.72\nlawyer\tevidence\t6.69\nfertility\tegg\t6.69\nprecedent\tlaw\t6.65\nminister\tparty\t6.63\npsychology\tclinic\t6.58\ncup\tcoffee\t6.58\nwater\tseepage\t6.56\ngovernment\tcrisis\t6.56\nspace\tworld\t6.53\ndividend\tcalculation\t6.48\nvictim\temergency\t6.47\nluxury\tcar\t6.47\ntool\timplement\t6.46\ncompetition\tprice\t6.44\npsychology\tdoctor\t6.42\ngender\tequality\t6.41\nlisting\tcategory\t6.38\nvideo\tarchive\t6.34\noil\tstock\t6.34\ngovernor\toffice\t6.34\ndiscovery\tspace\t6.34\nrecord\tnumber\t6.31\nbrother\tmonk\t6.27\nproduction\tcrew\t6.25\nnature\tman\t6.25\nfamily\tplanning\t6.25\ndisaster\tarea\t6.25\nfood\tpreparation\t6.22\npreservation\tworld\t6.19\nmovie\tpopcorn\t6.19\nlover\tquarrel\t6.19\ngame\tseries\t6.19\ndollar\tloss\t6.09\nweapon\tsecret\t6.06\nshower\tflood\t6.03\nregistration\tarrangement\t6.00\narrival\thotel\t6.00\nannouncement\twarning\t6.00\ngame\tround\t5.97\nbaseball\tseason\t5.97\ndrink\tmouth\t5.96\nlife\tlesson\t5.94\ngrocery\tmoney\t5.94\nenergy\tcrisis\t5.94\nreason\tcriterion\t5.91\nequipment\tmaker\t5.91\ncup\tliquid\t5.90\ndeployment\twithdrawal\t5.88\ntiger\tzoo\t5.87\njourney\tcar\t5.85\nmoney\tlaundering\t5.65\nsummer\tnature\t5.63\ndecoration\tvalor\t5.63\nMars\tscientist\t5.63\nalcohol\tchemistry\t5.54\ndisability\tdeath\t5.47\nchange\tattitude\t5.44\narrangement\taccommodation\t5.41\nterritory\tsurface\t5.34\nsize\tprominence\t5.31\nexhibit\tmemorabilia\t5.31\ncredit\tinformation\t5.31\nterritory\tkilometer\t5.28\ndeath\trow\t5.25\ndoctor\tliability\t5.19\nimpartiality\tinterest\t5.16\nenergy\tlaboratory\t5.09\nsecretary\tsenate\t5.06\ndeath\tinmate\t5.03\nmonk\toracle\t5.00\ncup\tfood\t5.00\njournal\tassociation\t4.97\nstreet\tchildren\t4.94\ncar\tflight\t4.94\nspace\tchemistry\t4.88\nsituation\tconclusion\t4.81\nword\tsimilarity\t4.75\npeace\tplan\t4.75\nconsumer\tenergy\t4.75\nministry\tculture\t4.69\nsmart\tstudent\t4.62\ninvestigation\teffort\t4.59\nimage\tsurface\t4.56\nlife\tterm\t4.50\nstart\tmatch\t4.47\ncomputer\tnews\t4.47\nboard\trecommendation\t4.47\nlad\tbrother\t4.46\nobservation\tarchitecture\t4.38\ncoast\thill\t4.38\ndeployment\tdeparture\t4.25\nbenchmark\tindex\t4.25\nattempt\tpeace\t4.25\nconsumer\tconfidence\t4.13\nstart\tyear\t4.06\nfocus\tlife\t4.06\ndevelopment\tissue\t3.97\ntheater\thistory\t3.91\nsituation\tisolation\t3.88\nprofit\twarning\t3.88\nmedia\ttrading\t3.88\nchance\tcredibility\t3.88\nprecedent\tinformation\t3.85\narchitecture\tcentury\t3.78\npopulation\tdevelopment\t3.75\nstock\tlive\t3.73\npeace\tatmosphere\t3.69\nmorality\tmarriage\t3.69\nminority\tpeace\t3.69\natmosphere\tlandscape\t3.69\nreport\tgain\t3.63\nmusic\tproject\t3.63\nseven\tseries\t3.56\nexperience\tmusic\t3.47\nschool\tcenter\t3.44\nfive\tmonth\t3.38\nannouncement\tproduction\t3.38\nmorality\timportance\t3.31\nmoney\toperation\t3.31\ndelay\tnews\t3.31\ngovernor\tinterview\t3.25\npractice\tinstitution\t3.19\ncentury\tnation\t3.16\ncoast\tforest\t3.15\nshore\twoodland\t3.08\ndrink\tcar\t3.04\npresident\tmedal\t3.00\nprejudice\trecognition\t3.00\nviewer\tserial\t2.97\npeace\tinsurance\t2.94\nMars\twater\t2.94\nmedia\tgain\t2.88\nprecedent\tcognition\t2.81\nannouncement\teffort\t2.75\nline\tinsurance\t2.69\ncrane\timplement\t2.69\ndrink\tmother\t2.65\nopera\tindustry\t2.63\nvolunteer\tmotto\t2.56\nlisting\tproximity\t2.56\nprecedent\tcollection\t2.50\ncup\tarticle\t2.40\nsign\trecess\t2.38\nproblem\tairport\t2.38\nreason\thypertension\t2.31\ndirection\tcombination\t2.25\nWednesday\tnews\t2.22\nglass\tmagician\t2.08\ncemetery\twoodland\t2.08\npossibility\tgirl\t1.94\ncup\tsubstance\t1.92\nforest\tgraveyard\t1.85\nstock\tegg\t1.81\nmonth\thotel\t1.81\nenergy\tsecretary\t1.81\nprecedent\tgroup\t1.77\nproduction\thike\t1.75\nstock\tphone\t1.62\nholy\tsex\t1.62\nstock\tCD\t1.31\ndrink\tear\t1.31\ndelay\tracism\t1.19\nstock\tlife\t0.92\nstock\tjaguar\t0.92\nmonk\tslave\t0.92\nlad\twizard\t0.92\nsugar\tapproach\t0.88\nrooster\tvoyage\t0.62\nnoon\tstring\t0.54\nchord\tsmile\t0.54\nprofessor\tcucumber\t0.31\nking\tcabbage\t0.23\n"
  },
  {
    "path": "psdvec/testsets/ws/ws353_similarity.txt",
    "content": "tiger\tcat\t7.35\ntiger\ttiger\t10.00\nplane\tcar\t5.77\ntrain\tcar\t6.31\ntelevision\tradio\t6.77\nmedia\tradio\t7.42\nbread\tbutter\t6.19\ncucumber\tpotato\t5.92\ndoctor\tnurse\t7.00\nprofessor\tdoctor\t6.62\nstudent\tprofessor\t6.81\nsmart\tstupid\t5.81\nwood\tforest\t7.73\nmoney\tcash\t9.15\nking\tqueen\t8.58\nking\trook\t5.92\nbishop\trabbi\t6.69\nfuck\tsex\t9.44\nfootball\tsoccer\t9.03\nfootball\tbasketball\t6.81\nfootball\ttennis\t6.63\nArafat\tJackson\t2.50\nphysics\tchemistry\t7.35\nvodka\tgin\t8.46\nvodka\tbrandy\t8.13\ndrink\teat\t6.87\ncar\tautomobile\t8.94\ngem\tjewel\t8.96\njourney\tvoyage\t9.29\nboy\tlad\t8.83\ncoast\tshore\t9.10\nasylum\tmadhouse\t8.87\nmagician\twizard\t9.02\nmidday\tnoon\t9.29\nfurnace\tstove\t8.79\nfood\tfruit\t7.52\nbird\tcock\t7.10\nbird\tcrane\t7.38\nfood\trooster\t4.42\nmoney\tdollar\t8.42\nmoney\tcurrency\t9.04\ntiger\tjaguar\t8.00\ntiger\tfeline\t8.00\ntiger\tcarnivore\t7.08\ntiger\tmammal\t6.85\ntiger\tanimal\t7.00\ntiger\torganism\t4.77\ntiger\tfauna\t5.62\npsychology\tpsychiatry\t8.08\npsychology\tscience\t6.71\npsychology\tdiscipline\t5.58\nplanet\tstar\t8.45\nplanet\tmoon\t8.08\nplanet\tsun\t8.02\nprecedent\texample\t5.85\nprecedent\tantecedent\t6.04\ncup\ttableware\t6.85\ncup\tartifact\t2.92\ncup\tobject\t3.69\ncup\tentity\t2.15\njaguar\tcat\t7.42\njaguar\tcar\t7.27\nmile\tkilometer\t8.66\nskin\teye\t6.22\nJapanese\tAmerican\t6.50\ncentury\tyear\t7.59\nannouncement\tnews\t7.56\ndoctor\tpersonnel\t5.00\nHarvard\tYale\t8.13\nhospital\tinfrastructure\t4.63\nlife\tdeath\t7.88\ntravel\tactivity\t5.00\ntype\tkind\t8.97\nstreet\tplace\t6.44\nstreet\tavenue\t8.88\nstreet\tblock\t6.88\ncell\tphone\t7.81\ndividend\tpayment\t7.63\ncalculation\tcomputation\t8.44\nprofit\tloss\t7.63\ndollar\tyen\t7.78\ndollar\tbuck\t9.22\nphone\tequipment\t7.13\nliquid\twater\t7.89\nmarathon\tsprint\t7.47\nseafood\tfood\t8.34\nseafood\tlobster\t8.70\nlobster\tfood\t7.81\nlobster\twine\t5.70\nchampionship\ttournament\t8.36\nman\twoman\t8.30\nman\tgovernor\t5.25\nmurder\tmanslaughter\t8.53\nopera\tperformance\t6.88\nMexico\tBrazil\t7.44\nglass\tmetal\t5.56\naluminum\tmetal\t7.83\nrock\tjazz\t7.59\nmuseum\ttheater\t7.19\nshower\tthunderstorm\t6.31\nmonk\toracle\t5.00\ncup\tfood\t5.00\njournal\tassociation\t4.97\nstreet\tchildren\t4.94\ncar\tflight\t4.94\nspace\tchemistry\t4.88\nsituation\tconclusion\t4.81\nword\tsimilarity\t4.75\npeace\tplan\t4.75\nconsumer\tenergy\t4.75\nministry\tculture\t4.69\nsmart\tstudent\t4.62\ninvestigation\teffort\t4.59\nimage\tsurface\t4.56\nlife\tterm\t4.50\nstart\tmatch\t4.47\ncomputer\tnews\t4.47\nboard\trecommendation\t4.47\nlad\tbrother\t4.46\nobservation\tarchitecture\t4.38\ncoast\thill\t4.38\ndeployment\tdeparture\t4.25\nbenchmark\tindex\t4.25\nattempt\tpeace\t4.25\nconsumer\tconfidence\t4.13\nstart\tyear\t4.06\nfocus\tlife\t4.06\ndevelopment\tissue\t3.97\ntheater\thistory\t3.91\nsituation\tisolation\t3.88\nprofit\twarning\t3.88\nmedia\ttrading\t3.88\nchance\tcredibility\t3.88\nprecedent\tinformation\t3.85\narchitecture\tcentury\t3.78\npopulation\tdevelopment\t3.75\nstock\tlive\t3.73\npeace\tatmosphere\t3.69\nmorality\tmarriage\t3.69\nminority\tpeace\t3.69\natmosphere\tlandscape\t3.69\nreport\tgain\t3.63\nmusic\tproject\t3.63\nseven\tseries\t3.56\nexperience\tmusic\t3.47\nschool\tcenter\t3.44\nfive\tmonth\t3.38\nannouncement\tproduction\t3.38\nmorality\timportance\t3.31\nmoney\toperation\t3.31\ndelay\tnews\t3.31\ngovernor\tinterview\t3.25\npractice\tinstitution\t3.19\ncentury\tnation\t3.16\ncoast\tforest\t3.15\nshore\twoodland\t3.08\ndrink\tcar\t3.04\npresident\tmedal\t3.00\nprejudice\trecognition\t3.00\nviewer\tserial\t2.97\npeace\tinsurance\t2.94\nMars\twater\t2.94\nmedia\tgain\t2.88\nprecedent\tcognition\t2.81\nannouncement\teffort\t2.75\nline\tinsurance\t2.69\ncrane\timplement\t2.69\ndrink\tmother\t2.65\nopera\tindustry\t2.63\nvolunteer\tmotto\t2.56\nlisting\tproximity\t2.56\nprecedent\tcollection\t2.50\ncup\tarticle\t2.40\nsign\trecess\t2.38\nproblem\tairport\t2.38\nreason\thypertension\t2.31\ndirection\tcombination\t2.25\nWednesday\tnews\t2.22\nglass\tmagician\t2.08\ncemetery\twoodland\t2.08\npossibility\tgirl\t1.94\ncup\tsubstance\t1.92\nforest\tgraveyard\t1.85\nstock\tegg\t1.81\nmonth\thotel\t1.81\nenergy\tsecretary\t1.81\nprecedent\tgroup\t1.77\nproduction\thike\t1.75\nstock\tphone\t1.62\nholy\tsex\t1.62\nstock\tCD\t1.31\ndrink\tear\t1.31\ndelay\tracism\t1.19\nstock\tlife\t0.92\nstock\tjaguar\t0.92\nmonk\tslave\t0.92\nlad\twizard\t0.92\nsugar\tapproach\t0.88\nrooster\tvoyage\t0.62\nnoon\tstring\t0.54\nchord\tsmile\t0.54\nprofessor\tcucumber\t0.31\nking\tcabbage\t0.23\n"
  },
  {
    "path": "psdvec/topwordsInList.py",
    "content": "import numpy as np\r\nimport getopt\r\nimport sys\r\nfrom utils import *\r\nimport pdb\r\nimport time\r\nimport os\r\nimport json\r\n\r\ndef usage():\r\n    print \"\"\"Usage:\\n  topsentwords.py -c config_file -l f1,f2... -o out_file -n count\r\nOptions:\r\n  config_file:  Same config file used by corpus2liblinear.py, \r\n                which specifying multiple document directories.\r\n  f1,f2:        Files containg lists of interesting words.\r\n  out_file:     Output file to save top interesting words. \r\n                Default: 'topwords.txt'\r\n  count:        Top k words that will be counted. Default: 1000.\r\n\"\"\"\r\n\r\ndef parseConfigFile(configFilename):\r\n    CONF = open(configFilename)\r\n    dir_configs = []\r\n    for line in CONF:\r\n        line = line.strip()\r\n        dir_config = json.loads(line)\r\n        dir_configs.append(dir_config)\r\n    return dir_configs\r\n\r\ndef getListWordCount( docPath, word2freq ):\r\n    DOC = open(docPath)\r\n    doc = DOC.read()\r\n    wordsInSentences, wc = extractSentenceWords(doc, 1)\r\n    \r\n    interestingWc = 0\r\n    for sentence in wordsInSentences:\r\n        for w in sentence:\r\n            w = w.lower()\r\n            if w in word2freq:\r\n                word2freq[w] += 1\r\n                interestingWc += 1\r\n\r\n    return wc, interestingWc\r\n    \r\ndef processDir( docDir, word2freq ):\r\n    print \"Processing '%s'\" %( docDir )\r\n    \r\n    filecount = 0\r\n    totalwc = 0\r\n    totalInterestingWc = 0\r\n    \r\n    for filename in os.listdir(docDir):\r\n        docPath = docDir + \"/\" + filename\r\n        wc, interestingWc = getListWordCount( docPath, word2freq )\r\n        \r\n        totalwc += wc\r\n        totalInterestingWc += interestingWc\r\n        filecount += 1\r\n        \r\n        if filecount % 500 == 0:\r\n            print \"\\r%d\\r\" %filecount,\r\n    \r\n    print \"%d files scanned, totally %d words, %d are interesting\" %( filecount, totalwc, totalInterestingWc )\r\n            \r\ndef main():\r\n    topword_cutoff = 1000\r\n    \r\n    configFilename = None\r\n    listFilenames = None\r\n    outFilename = \"topwords.txt\"\r\n    \r\n    try:\r\n        opts, args = getopt.getopt(sys.argv[1:],\"c:l:o:n:h\")\r\n            \r\n        for opt, arg in opts:\r\n            if opt == '-c':\r\n                configFilename = arg\r\n            if opt == '-o':\r\n                outFilename = arg\r\n            if opt == '-n':\r\n                topword_cutoff = int(arg)\r\n            if opt == '-l':\r\n                listFilenames = arg.split(\",\")\r\n            if opt == '-h':\r\n                usage()\r\n                sys.exit(0)\r\n\r\n    except getopt.GetoptError, e:\r\n        if len(e.args) == 1:\r\n            print \"Option error: %s\" %e.args[0]\r\n        usage()\r\n        sys.exit(2)\r\n    \r\n    if not configFilename or not listFilenames:\r\n        usage()\r\n        sys.exit(2)\r\n        \r\n    dir_configs = parseConfigFile(configFilename)\r\n    \r\n    word2freq = {}\r\n    \r\n    totalwc = 0\r\n    for listFilename in listFilenames:\r\n        filewc = 0\r\n        LIST = open(listFilename)\r\n        for line in LIST:\r\n            if line[0] == ';':\r\n                continue\r\n            line = line.strip()\r\n            if not line:\r\n                continue\r\n            word2freq[line] = 0\r\n            filewc += 1\r\n            totalwc += 1\r\n        print \"%d words loaded from '%s'\" %( filewc, listFilename )\r\n    \r\n    print \"%d words loaded from %d files\" %( totalwc, len(listFilenames) )        \r\n\r\n    for conf in dir_configs:\r\n        processDir( conf['docDir'], word2freq )\r\n\r\n    words = sorted( word2freq.keys(), key=lambda w: word2freq[w], reverse=True )\r\n    topwords = words[:topword_cutoff]\r\n    OUT = open(outFilename, \"w\")\r\n    for w in topwords:\r\n        OUT.write( \"%s\\t%d\\n\" %( w, word2freq[w] ) )\r\n    print \"%d words written into '%s'\" %( len(topwords), outFilename )\r\n                    \r\nif __name__ == '__main__':\r\n    main()\r\n    "
  },
  {
    "path": "psdvec/utils.py",
    "content": "# -*- coding=GBK -*-\r\n\r\nimport numpy as np\r\nimport scipy.linalg\r\nfrom scipy.stats.stats import spearmanr\r\nimport time\r\nimport re\r\nimport pdb\r\nimport sys\r\nimport os\r\nimport glob\r\nimport logging\r\nfrom psutil import virtual_memory\r\nimport os.path\r\nimport random\r\nimport unicodedata\r\nimport sys\r\n\r\nunicode_punc_tbl = dict.fromkeys( i for i in xrange(128, sys.maxunicode)\r\n                      if unicodedata.category(unichr(i)).startswith('P') )\r\n\r\nlogging.basicConfig( level=logging.DEBUG )\r\nfor handler in logging.root.handlers[:]:\r\n    logging.root.removeHandler(handler)\r\n\r\ndef str2dict(s):\r\n    wordlist = re.split( \"\\s+\", s )\r\n    return dict.fromkeys(wordlist, 1)\r\n\r\nstopwordStr = '''a about above across after again against all almost alone along also\r\nalthough always am among an and another any anybody anyone anything\r\napart are around as  at away be because been before behind being below\r\nbesides between beyond both but by can cannot could  did do does doing done\r\ndown  during each either else enough etc  ever every everybody\r\neveryone except far few for  from get gets got had has have having\r\nhe her here herself him himself his how however if in indeed instead into\r\nis it its itself just kept me maybe might  more most mostly much must\r\nmy myself  neither  no nobody none nor not nothing  of off often on one\r\nonly onto or other others ought our ours out  own  please\r\npp quite rather really said seem  shall she should since so\r\nsome somebody somewhat still such than that the their theirs them themselves\r\nthen there therefore these they this thorough thoroughly those through thus to\r\ntogether too toward towards until up upon was we well were what\r\nwhatever when whenever where whether which while who whom whose why will with\r\nwithin would yet you your yourself\r\nre d ll m ve t s'''\r\n\r\nstopwordDict = str2dict(stopwordStr)\r\n\r\nnp.seterr(all=\"raise\")\r\nnp.set_printoptions(suppress=True, threshold=np.nan, precision=3)\r\n\r\ndef initConsoleLogger(loggerName):\r\n    consoleLogger = logging.getLogger(loggerName)\r\n    streamHandler = logging.StreamHandler()\r\n    consoleLogger.addHandler(streamHandler)  \r\n    return consoleLogger\r\n    \r\ndef initFileLogger(loggerName, isAppending=False):\r\n    loggerName = os.path.splitext(loggerName)[0]\r\n    currDate = timeToStr( time.time(), \"%m.%d\" )\r\n    filename = \"%s-%s.log\" %( loggerName, currDate )\r\n    sn = 0\r\n    while os.path.isfile(filename):\r\n        sn += 1\r\n        filename = \"%s-%s-%d.log\" %( loggerName, currDate, sn )\r\n  \r\n    fileLogger = logging.getLogger(loggerName)\r\n    if isAppending:\r\n        mode = 'a'\r\n    else:\r\n        mode = 'w'\r\n        \r\n    fileHandler = logging.FileHandler(filename, mode=mode)\r\n    fileLogger.addHandler(fileHandler)\r\n    return fileLogger\r\n    \r\ndef warning(*objs):\r\n    sys.stderr.write(*objs)\r\n    \r\nclass Timer(object):\r\n    def __init__(self, name=None):\r\n        self.name = name\r\n        self.tstart = time.time()\r\n        self.tlast = self.tstart\r\n        self.firstCall = True\r\n\r\n    def getElapseTime(self, isStr=True):\r\n        totalElapsed = time.time() - self.tstart\r\n        # elapsed time since last call\r\n        interElapsed = time.time() - self.tlast\r\n        self.tlast = time.time()\r\n\r\n        firstCall = self.firstCall\r\n        self.firstCall = False\r\n\r\n        if isStr:\r\n            if self.name:\r\n                if firstCall:\r\n                    return '%s elapsed: %.2f' % ( self.name, totalElapsed )\r\n                return '%s elapsed: %.2f/%.2f' % ( self.name, totalElapsed, interElapsed )\r\n            else:\r\n                if firstCall:\r\n                    return 'Elapsed: %.2f' % ( totalElapsed )\r\n                return 'Elapsed: %.2f/%.2f' % ( totalElapsed, interElapsed )\r\n        else:\r\n            return totalElapsed, interElapsed\r\n\r\n    def printElapseTime(self):\r\n        print self.getElapseTime()\r\n\r\ndef timeToStr(timeNum, fmt=\"%H:%M:%S\"):\r\n    timeStr = time.strftime(fmt, time.localtime(timeNum))\r\n    return timeStr\r\n\r\n# Weight: nonnegative real matrix. If not specified, return the unweighted norm\r\ndef norm1(M, Weight=None):\r\n    if len(M.shape) == 1:\r\n        if Weight is not None:\r\n            return np.sum( np.abs( M * Weight ) )\r\n        else:\r\n            return np.sum( np.abs(M) )\r\n\r\n    s = 0\r\n    \r\n    if Weight is not None:\r\n        for i in xrange( len(M) ):\r\n            # row by row calculation. \r\n            # If doing matrix multiplication, a big temporary matrix will be generated, consuming a lot RAM\r\n            row = np.abs( M[i] * Weight[i] )\r\n            s += np.sum(row)\r\n    else:\r\n        for i in xrange( len(M) ):\r\n            row = np.abs( M[i] )\r\n            s += np.sum(row)\r\n\r\n    return s\r\n\r\n# F-norm of a vector or a matrix\r\ndef normF(M, Weight=None):\r\n    if len(M.shape) == 1:\r\n        if Weight is not None:\r\n            # M*M makes all elems positive, and all elems of Weight are nonnegative. So no need to take abs()\r\n            return np.sqrt( np.sum( M * M * Weight ) )\r\n        else:\r\n            return np.sqrt( np.sum( M * M ) )\r\n    \r\n    s = 0\r\n    \r\n    if Weight is not None:\r\n        for i in xrange( len(M) ):\r\n            # row by row calculation. \r\n            # If doing matrix multiplication, a big temporary matrix will be generated, consuming a lot RAM\r\n            row = M[i] * M[i] * Weight[i]\r\n            s += np.sum(row)\r\n    else:\r\n        for i in xrange( len(M) ):\r\n            row = M[i] * M[i]\r\n            s += np.sum(row)\r\n\r\n    return np.sqrt(s)\r\n\r\n# normalize a 1-d or 2-d array of nonnegative numbers: \r\n# keep the original array intact, return a copy of normalized array\r\n# when array is 2d:\r\n# axis=0: normalize columns. axis=1: normalize rows (default)\r\ndef normalize(data, axis=1):\r\n    if np.min(data) < 0:\r\n        raise RuntimeError(\"Negative element in data passed to normalize()\")\r\n    if data.ndim == 1:\r\n        return data / np.sum(data)\r\n    if axis == 0:\r\n        s = np.sum(data, axis=0)\r\n        return data / s\r\n    elif axis == 1:\r\n        ss = np.sum(data, axis=1)\r\n        return data / np.tile(ss, (data.shape[1],1)).T\r\n    else:\r\n        raise RuntimeError('function normalize: axis must be 0/1')\r\n\r\n# normalize a 1-d or 2-d array of numbers, w.r.t. F-norm\r\ndef normalizeF(data, axis=1):\r\n    if data.ndim == 1:\r\n        return data / normF(data) \r\n    \r\n    data2 = np.copy(data)\r\n    # normalize each column of data\r\n    if axis == 0:\r\n        for i in xrange(data2.shape[1]):\r\n            if normF(data2[:,i]) > 0:\r\n                data2[:,i] /= normF(data2[:,i])\r\n        return data2\r\n    \r\n    # normalize each row of data     \r\n    elif axis == 1:\r\n        norms = np.array( [ normF(x) for x in data2 ] )\r\n        norms[ norms==0 ] = 1\r\n        data2 /= norms[:, None]\r\n    else:\r\n        raise RuntimeError('function normalize: axis must be 0/1')\r\n    return data2\r\n\r\ndef cosine(x, y):\r\n    x2 = normalizeF(x)\r\n    y2 = normalizeF(y)\r\n    return np.dot(x2, y2)\r\n\r\n# Given a list of matrices, return a list of their norms\r\ndef matSizes( norm, Ms, Weight=None ):\r\n    sizes = []\r\n    for M in Ms:\r\n        sizes.append( norm(M, Weight) )\r\n\r\n    return sizes\r\n\r\ndef sym(M):\r\n    return ( M + M.T ) / 2.0\r\n\r\ndef skew(M):\r\n    return ( M - M.T ) / 2.0\r\n\r\n# Assume A has been approximately sorted by rows, and in each row, sorted by columns\r\n# matrix F returned from loadBigramFile satisfies this\r\n# print the number of elements >= A[0,0]/2^n\r\n# return the idea cut point above which there are at least \"fraction\" of the elements\r\n# these elements will be cut off to this upper limit\r\ndef getQuantileCut(A, fraction):\r\n    totalNonzeroElemCount = np.sum( A > 0 ) #A.shape[0] * A.shape[1]\r\n    maxElem = A[0,0]\r\n    cutPoint = maxElem\r\n    idealCutPoint = cutPoint\r\n    idealFound = False\r\n    \r\n    while cutPoint >= 10:\r\n        aboveElemCount = np.sum( A >= cutPoint )\r\n        print \"Cut point %.0f: %d/%.3f%%\" %( cutPoint, aboveElemCount, aboveElemCount * 100.0 / totalNonzeroElemCount )\r\n        if not idealFound and aboveElemCount >= totalNonzeroElemCount * fraction:\r\n            idealCutPoint = cutPoint\r\n            idealFound = True\r\n        cutPoint /= 2.0\r\n\r\n    return idealCutPoint\r\n\r\n# find the principal eigenvalue/eigenvector: e1 & v1.\r\n# if e1 < 0, then the left principal singular vector is -v1, and the right is v1.\r\n# much faster than numpy.linalg.eig / scipy.linalg.eigh\r\ndef power_iter(M):\r\n    MAXITER = 100\r\n    epsilon = 1e-6\r\n    vec = np.random.rand(len(M))\r\n    old_vec = vec\r\n\r\n    for i in xrange(MAXITER):\r\n        vec2 = np.dot( M, vec )\r\n        magnitude = np.linalg.norm(vec2)\r\n        vec2 /= magnitude\r\n        vec = vec2\r\n\r\n        if i%2 == 1:\r\n            error = np.linalg.norm( vec2 - old_vec )\r\n            #print \"%d: %f, %f\" %( i+1, magnitude, error )\r\n            if error < epsilon:\r\n                break\r\n            old_vec = vec2\r\n\r\n    vec2 = np.dot( M, vec )\r\n    if np.sum(vec2)/np.sum(vec) > 0:\r\n        eigen = magnitude\r\n    else:\r\n        eigen = -magnitude\r\n\r\n    return eigen, vec\r\n\r\n# each column of vs is an eigenvector\r\n# It's a prerequisite that all eigenvalues are already nonnegative\r\n# This requirement is guaranteed after nowe_factorize()\r\ndef lowrank_fact(VV, N0):\r\n    timer1 = Timer( \"lowrank_fact()\" )\r\n\r\n    es, vs = np.linalg.eigh(VV)\r\n    es = es[-N0:]\r\n    vs = vs[ :, -N0: ]\r\n    E_sqrt = np.diag( np.sqrt(es) )\r\n    V = vs.dot(E_sqrt)\r\n    VV = V.dot(V.T)\r\n\r\n    return V, VV, vs,es\r\n\r\ndef save_embeddings( filename, vocab, V, matrixName ):\r\n    FMAT = open(filename, \"wb\")\r\n    print \"Save matrix '%s' into %s\" %(matrixName, filename)\r\n\r\n    vocab_size = len(vocab)\r\n    N = len(V[0])\r\n\r\n    #pdb.set_trace()\r\n\r\n    FMAT.write( \"%d %d\\n\" %(vocab_size, N) )\r\n    for i in xrange(vocab_size):\r\n        line = vocab[i]\r\n        for j in xrange(N):\r\n            line += \" %.5f\" %V[i,j]\r\n        FMAT.write(\"%s\\n\" %line)\r\n\r\n    FMAT.close()\r\n\r\ndef save_matrix_as_text( filename, rowTypeName, T, *extraCols, **kwargs ):\r\n    FMAT = open(filename, \"wb\")\r\n    print \"Save %s matrix into '%s'\" %(rowTypeName, filename)\r\n    colSep = kwargs.get(\"colSep\", \" \")\r\n    \r\n    K, N = T.shape\r\n\r\n    #pdb.set_trace()\r\n    extraColNum = len(extraCols)\r\n    \r\n    FMAT.write( \"%d %d %d\\n\" %( K, N, extraColNum ) )\r\n    for i in xrange(K):\r\n        # if rowNames is provided, print the corresponding row name at the beginning of each line\r\n        line = \"\"\r\n        for j in xrange(extraColNum):\r\n            col = str( extraCols[j][i] )\r\n            line += col + colSep\r\n        line += \"%.5f\" %T[i,0]\r\n            \r\n        for j in xrange(1, N):\r\n            line += \" %.5f\" %T[i,j]\r\n        FMAT.write(\"%s\\n\" %line)\r\n\r\n    FMAT.close()\r\n    print \"%d rows of %s(s) (%d-d each) saved\" %( K, rowTypeName, N )\r\n\r\ndef load_matrix_from_text( filename, rowTypeName, colSep=\" \" ):\r\n    FMAT = open(filename)\r\n    print \"Load %s matrix from '%s'\" %(rowTypeName, filename)\r\n    precision = np.float64\r\n    extraCols = []\r\n    extraColNum = 0\r\n    lineno = 0\r\n    \r\n    try:\r\n        header = FMAT.readline()\r\n        rowID = 0\r\n        lineno += 1\r\n        match = re.match( r\"(\\d+) (\\d+) (\\d+)\", header)\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        K = int(match.group(1))\r\n        N = int(match.group(2))\r\n        extraColNum = int(match.group(3))\r\n        print \"'%s': %dx%d, %d extra columns\" %( filename, K, N, extraColNum )\r\n        \r\n        for i in xrange(extraColNum):\r\n            extraCols.append([])\r\n        \r\n        M = np.zeros( (K, N), dtype=precision )\r\n\r\n        for line in FMAT:\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                if rowID != K:\r\n                    raise ValueError( lineno, \"%d rows declared in header, but %d read\" %( K, rowID ) )\r\n                break\r\n\r\n            row_extraCols = []\r\n            fields = line.split(colSep)\r\n            for i in xrange(extraColNum):\r\n                extraCols[i].append(fields[i])\r\n                \r\n            matFields = fields[extraColNum:]\r\n            # matrix values are always concatenated by \" \"\r\n            # if colSep is not \" \", matrix values should take one column\r\n            if colSep != \" \":\r\n                if len(matFields) > 1:\r\n                    raise ValueError( lineno, \"%d columns of matrix values when colSep is not space\" %( len(matFields) ) )\r\n                else:\r\n                    matFields = matFields[0].split(\" \")\r\n                    \r\n            M[rowID] = np.array( [ float(x) for x in matFields ], dtype=precision )\r\n            rowID += 1\r\n                \r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            warning( \"Unknown line %d:\\n%s\\n\" %( e.args[0], e.args[1] ) )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            warning( \"Source line %d - %s on File line %d:\\n%s\\n\" %( tb.tb_lineno, e, lineno, line ) )\r\n        exit(2)\r\n\r\n    FMAT.close()\r\n    warning( \"%dx%d %s matrix loaded from '%s'\\n\" %(K, N, rowTypeName, filename) )\r\n\r\n    if len(extraCols) == 0:\r\n        return M\r\n    else:\r\n        return M, extraCols\r\n        \r\n# load top maxWordCount words, plus extraWords\r\ndef load_embeddings( filename, maxWordCount=-1, extraWords={}, record_skipped=False ):\r\n    FMAT = open(filename)\r\n    warning( \"Load embedding text file '%s'\\n\" %(filename) )\r\n    \r\n    V = []\r\n    word2id = {}\r\n    skippedWords = {}\r\n\r\n    vocab = []\r\n    precision = np.float32\r\n\r\n    try:\r\n        header = FMAT.readline()\r\n        lineno = 1\r\n        match = re.match( r\"(\\d+) (\\d+)\", header)\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        vocab_size = int(match.group(1))\r\n        N = int(match.group(2))\r\n\r\n        if maxWordCount > 0:\r\n            maxWordCount = min(maxWordCount, vocab_size)\r\n        else:\r\n            maxWordCount = vocab_size\r\n\r\n        warning( \"Will load embeddings of %d words\" %maxWordCount )\r\n        if len(extraWords) > 0:\r\n            warning( \", plus %d extra words\" %(len(extraWords)) )\r\n        warning(\"\\n\")\r\n\r\n        # maxWordCount + len(extraWords) is the maximum num of words.\r\n        # V may contain extra rows that will be removed at the end\r\n        V = np.zeros( (maxWordCount + len(extraWords), N), dtype=precision )\r\n        wid = 0\r\n        orig_wid = 0\r\n\r\n        for line in FMAT:\r\n            lineno += 1\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                if orig_wid != vocab_size:\r\n                    raise ValueError( lineno, \"%d words declared in header, but %d read\" %( vocab_size, orig_wid ) )\r\n                break\r\n\r\n            fields = line.split(' ')\r\n            # remove empty fields\r\n            fields = filter( lambda x: x, fields )\r\n            w = fields[0]\r\n\r\n            if w in extraWords:\r\n                del extraWords[w]\r\n                isInterested = True\r\n            elif orig_wid < maxWordCount:\r\n                isInterested = True\r\n            elif record_skipped:\r\n                isInterested = False\r\n                skippedWords[w] = 1\r\n            else:\r\n                break\r\n\t\t\t\t\t\t\t\r\n            orig_wid += 1\r\n\r\n            if isInterested:\r\n                V[wid] = np.array( [ float(x) for x in fields[1:] ], dtype=precision )\r\n                word2id[w] = wid\r\n                vocab.append(w)\r\n                wid += 1\r\n\r\n            if orig_wid % 1000 == 0:\r\n                warning( \"\\r%d    %d    %d    \\r\" %( orig_wid, wid, len(extraWords) ) )\r\n\r\n            if orig_wid > vocab_size:\r\n                raise ValueError( \"%d words declared in header, but more are read\" %(vocab_size) )\r\n\r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            warning( \"Unknown line %d:\\n%s\\n\" %( e.args[0], e.args[1] ) )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            warning( \"Source line %d - %s on File line %d:\\n%s\\n\" %( tb.tb_lineno, e, lineno, line ) )\r\n        exit(2)\r\n\r\n    FMAT.close()\r\n    warning( \"\\n%d embeddings read, %d kept\\n\" %(orig_wid, wid) )\r\n\r\n    #pdb.set_trace()\r\n\r\n    if wid < len(V):\r\n        V = V[:wid]\r\n\r\n    # V: embeddings, vocab: array of words, word2id: dict of word to index in V\r\n    return V, vocab, word2id, skippedWords\r\n\r\n# borrowed from gensim.models.word2vec\r\n# load top maxWordCount words, plus extraWords\r\ndef load_embeddings_bin( filename, maxWordCount=-1, extraWords={}, record_skipped=False ):\r\n    print \"Load embedding binary file '%s'\" %(filename)\r\n    word2id = {}\r\n    skippedWords = {}\r\n    vocab = []\r\n    #origWord2id = {}\r\n    #origVocab = []\r\n    precision = np.float32\r\n\r\n    with open(filename, \"rb\") as fin:\r\n        header = fin.readline()\r\n        vocab_size, N = map(int, header.split())\r\n\r\n        if maxWordCount > 0:\r\n            maxWordCount = min(maxWordCount, vocab_size)\r\n        else:\r\n            maxWordCount = vocab_size\r\n\r\n        print \"Will load embeddings of %d words\" %maxWordCount,\r\n        if len(extraWords) > 0:\r\n            print \"\\b, plus %d extra words\" %(len(extraWords))\r\n        else:\r\n            print\r\n\r\n        # maxWordCount + len(extraWords) is the maximum num of words.\r\n        # V may contain extra rows that will be removed at the end\r\n        V = np.zeros( (maxWordCount + len(extraWords), N), dtype=precision )\r\n\r\n        full_binvec_len = np.dtype(precision).itemsize * N\r\n\r\n        #pdb.set_trace()\r\n        orig_wid = 0\r\n        wid = 0\r\n        while True:\r\n            # mixed text and binary: read text first, then binary\r\n            word = []\r\n            while True:\r\n                ch = fin.read(1)\r\n                if ch == ' ':\r\n                    break\r\n                if ch != '\\n':  # ignore newlines in front of words (some binary files have newline, some don't)\r\n                    word.append(ch)\r\n            word = b''.join(word)\r\n\r\n            if word[0].isupper():\r\n                word2 = word.lower()\r\n                # if the lowercased word hasn't been read, treat the embedding as the lowercased word's\r\n                # otherwise, add the capitalized word to V\r\n                if word2 not in word2id:\r\n                    word = word2\r\n\r\n            #origWord2id[word] = orig_wid\r\n            #origVocab.append(word)\r\n\r\n            if w in extraWords:\r\n                del extraWords[w]\r\n                isInterested = True\r\n            elif orig_wid < maxWordCount:\r\n                isInterested = True\r\n            elif record_skipped:\r\n                isInterested = False\r\n                skippedWords[w] = 1\r\n            else:\r\n                break\r\n\r\n            orig_wid += 1\r\n\r\n            if isInterested:\r\n                word2id[word] = wid\r\n                vocab.append(word)\r\n                V[wid] = np.fromstring( fin.read(full_binvec_len), dtype=precision )\r\n                wid += 1\r\n            else:\r\n                fin.read(full_binvec_len)\r\n\r\n            if orig_wid % 1000 == 0:\r\n                print \"\\r%d    %d    %d    \\r\" %( orig_wid, wid, len(extraWords) ),\r\n\r\n            if orig_wid > vocab_size:\r\n                raise ValueError( \"%d words declared in header, but more are read\" %(vocab_size) )\r\n\r\n    if wid < len(V):\r\n        V = V[:wid]\r\n    print \"\\n%d embeddings read, %d embeddings kept\" %(orig_wid, wid)\r\n\r\n    # V: embeddings, vocab: array of words, word2id: dict of word to index in V\r\n    return V, vocab, word2id, skippedWords\r\n\r\n# load Hyperwords embeddings\r\ndef load_embeddings_hyper(modelPath, vecType):\r\n    sys.path.append('./hyperwords/hyperwords')\r\n    from representations.explicit import PositiveExplicit\r\n    from representations.embedding import SVDEmbedding\r\n    print \"Load Hyperwords(%s) embedding file '%s'\" %(vecType, modelPath)\r\n    if vecType == 'PPMI':\r\n        base = PositiveExplicit\r\n    else:\r\n        base = SVDEmbedding\r\n        \r\n    class HyperEmbed(base):\r\n        def __contains__(self, w):\r\n            return w in self.wi\r\n    \r\n    model = HyperEmbed(modelPath, True)\r\n\r\n    print \"Done.\"\r\n    return model\r\n    \r\n# load residuals\r\n# the dict word2id is to ensure the same word is mapped to the same id as in the embedding file\r\n# in other words, the embedding file and residual file had to be generated in the same batch\r\ndef load_residuals( filename, word2id={}, maxRowCount=-1, maxColCount=-1 ):\r\n    FMAT = open(filename)\r\n    warning( \"Load residual file '%s'\\n\" %(filename) )\r\n    \r\n    precision = np.float32\r\n\r\n    try:\r\n        header = FMAT.readline()\r\n        lineno = 1\r\n        match = re.match( r\"(\\d+) (\\d+)\", header)\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        vocab_size = int(match.group(1))\r\n        vocab_size2 = int(match.group(2))\r\n\r\n        if maxRowCount > 0:\r\n            maxRowCount = min(maxRowCount, vocab_size)\r\n        else:\r\n            maxRowCount = vocab_size\r\n\r\n        if maxColCount > 0:\r\n            maxColCount = min(maxColCount, vocab_size2)\r\n        else:\r\n            maxColCount = vocab_size2\r\n\r\n        warning( \"Will load residuals of %dx%d words\" %( maxRowCount, maxColCount ) )\r\n\r\n        A = np.zeros( (maxRowCount, maxColCount), dtype=precision )\r\n\r\n        for line in FMAT:\r\n            lineno += 1\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                if lineno != vocab_size:\r\n                    raise ValueError( lineno, \"%d rows declared in header, but %d read\" %( vocab_size, lineno ) )\r\n                break\r\n\r\n            fields = line.split(' ')\r\n            fields = filter( lambda x: x, fields )\r\n            w = fields[0]\r\n\r\n            if len(word2id) > 0:\r\n                if w not in word2id or word2id[w] != lineno - 1:\r\n                    raise ValueError(\"ID of '%s' is inconsistent between '%s' and the loaded embeddings. \"\r\n                                     \"Make sure they were generated in the same batch\" %(w, filename) )\r\n            A[ lineno - 1 ] = np.array( [ float(x) for x in fields[1:] ], dtype=precision )\r\n\r\n            if lineno % 1000 == 0:\r\n                warning( \"\\r%d\\r\" %lineno )\r\n\r\n            if lineno >= vocab_size:\r\n                raise ValueError( \"%d words declared in header, but more are read\" %(vocab_size) )\r\n\r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            warning( \"Unknown line %d:\\n%s\\n\" %( e.args[0], e.args[1] ) )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            warning( \"Source line %d - %s on File line %d:\\n%s\\n\" %( tb.tb_lineno, e, lineno, line ) )\r\n        exit(2)\r\n\r\n    FMAT.close()\r\n    warning( \"\\n%d rows read, each row %d words\\n\" %(lineno, vocab_size2) )\r\n\r\n    #pdb.set_trace()\r\n\r\n    return A\r\n\r\ndef loadBigramFile( bigram_filename, topWordNum, extraWords, kappa=0.01 ):\r\n    print \"Loading bigram file '%s':\" %bigram_filename\r\n    BIGRAM = open(bigram_filename)\r\n    lineno = 0\r\n    vocab = []\r\n    word2id = {}\r\n    # 1: headers, 2: bigrams. for error msg printing\r\n    stage = 1\r\n    # In order to disable smoothing, just set kappa to 0.\r\n    # But when smoothing is disabled, some entries in logb_i will be log of 0\r\n    # After smoothing, entries in b_i are always positive, thus logb_i is fine\r\n    # do_smoothing=True\r\n\r\n    timer1 = Timer( \"loadBigramFile()\" )\r\n\r\n    try:\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n        match = re.match( r\"#\\s+(\\d+) words,\\s+\\d+ occurrences\", header )\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        wholeVocabSize = int(match.group(1))\r\n        print \"Totally %d words\"  %wholeVocabSize\r\n        # If topWordNum < 0, read all focus words\r\n        if topWordNum < 0:\r\n            topWordNum = wholeVocabSize\r\n\r\n        # skip params\r\n        header = BIGRAM.readline()\r\n        header = BIGRAM.readline()\r\n        lineno += 2\r\n\r\n        match = re.match( r\"#\\s+(\\d+) bigram occurrences\", header)\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        if header[0:6] != \"Words:\":\r\n            raise ValueError(lineno, header)\r\n\r\n        # vector log_u, unigram log-probs\r\n        log_u = []\r\n\r\n        i = 0\r\n        wc = 0\r\n        # Read the word list, build the word2id mapping\r\n        # Keep first topWordNum words and words in extraWords, if any\r\n        while True:\r\n            header = BIGRAM.readline()\r\n            lineno += 1\r\n            header = header.rstrip()\r\n\r\n            # \"Words\" field ends\r\n            if not header:\r\n                break\r\n\r\n            words = header.split(\"\\t\")\r\n            for word in words:\r\n                w, freq, log_ui = word.split(\",\")\r\n                if i < topWordNum or w in extraWords:\r\n                    word2id[w] = i\r\n                    log_u.append(float(log_ui))\r\n                    vocab.append(w)\r\n                    i += 1\r\n                wc += 1\r\n\r\n        # Usually these two should match, unless the bigram file is corrupted\r\n        if wc != wholeVocabSize:\r\n            raise ValueError( \"%d words declared in header, but %d seen\" %(wholeVocabSize, wc) )\r\n\r\n        vocab_size = len(vocab)\r\n        print \"%d words seen, top %d & %d extra to keep. %d kept\" %( wholeVocabSize, topWordNum, len(extraWords), vocab_size )\r\n\r\n        log_u = np.array(log_u)\r\n        u = np.exp(log_u)\r\n        # renormalize unigram probs\r\n        if topWordNum < wholeVocabSize:\r\n            u = u / np.sum(u)\r\n            log_u = np.log(u)\r\n\r\n        k_u = kappa * u\r\n        # original B, without smoothing\r\n        #B = []\r\n        G = np.zeros( (vocab_size, vocab_size), dtype=np.float32 )\r\n        F = np.zeros( (vocab_size, vocab_size), dtype=np.float32 )\r\n\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        if header[0:8] != \"Bigrams:\":\r\n            raise ValueError(lineno, header)\r\n\r\n        print \"Read bigrams:\"\r\n        stage = 2\r\n\r\n        line = BIGRAM.readline()\r\n        lineno += 1\r\n        contextWID = 0\r\n\r\n        #pdb.set_trace()\r\n\r\n        while True:\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                break\r\n\r\n            # If we have read the bigrams of all the wanted words\r\n            if contextWID == vocab_size:\r\n                # if some words in extraWords are not read, there is bug\r\n                break\r\n\r\n            # word ID, word, number of distinct neighbors, sum of freqs of all neighbors, cut off freq\r\n            orig_wid, w, neighborCount, neighborTotalOccur, cutoffFreq = line.split(\",\")\r\n            orig_wid = int(orig_wid)\r\n            neighborTotalOccur = float(neighborTotalOccur)\r\n\r\n            if orig_wid % 200 == 0:\r\n                print \"\\r%d\\r\" %orig_wid,\r\n\r\n            if orig_wid <= topWordNum or w in extraWords:\r\n                recordCurrWord = True\r\n                # remove it from the extra list, as a double-check measure\r\n                # when all wanted words are read, the extra list should be empty\r\n                if w in extraWords:\r\n                    del extraWords[w]\r\n            else:\r\n                recordCurrWord = False\r\n\r\n            # x_{.j}\r\n            x_i = np.zeros(vocab_size, dtype=np.float32)\r\n            skipRemainingNeighbors = False\r\n\r\n            while True:\r\n                line = BIGRAM.readline()\r\n                lineno += 1\r\n\r\n                # Empty line. Should be end of file\r\n                if not line:\r\n                    break\r\n\r\n                # A comment. Just in case of future extension\r\n                # Currently only the last line in the file is a comment\r\n                if line[0] == '#':\r\n                    continue\r\n\r\n                # beginning of the next word. Continue at the outer loop\r\n                # Neighbor lines always start with '\\t'\r\n                if line[0] != '\\t':\r\n                    break\r\n\r\n                # if the current context word is not wanted, skip these lines\r\n                if not recordCurrWord or skipRemainingNeighbors:\r\n                    continue\r\n\r\n                line = line.strip()\r\n                neighbors = line.split(\"\\t\")\r\n                for neighbor in neighbors:\r\n                    w2, freq2, log_bij = neighbor.split(\",\")\r\n                    if w2 in word2id:\r\n                        i = word2id[w2]\r\n                        x_i[i] = int(freq2)\r\n                    # when meeting the first focus word not in vocab, all following focus words are not in vocab\r\n                    # since neighbors are sorted ascendingly by ID\r\n                    # So they are skipped to speed up reading\r\n                    else:\r\n                        skipRemainingNeighbors = True\r\n                        break\r\n\r\n            # only save in F & G when this word is wanted\r\n            if recordCurrWord:\r\n                # Question: whether set F to the original freq or smoothed freq (assign F before or after smoothing)?\r\n                F[contextWID] = x_i\r\n\r\n                \"\"\"\r\n                x_i_norm1 = np.sum(x_i)\r\n                utrans = x_i_norm1 * k_u\r\n                x_i = x_i * (1 - kappa) + utrans\r\n\r\n                # the smoothing shoudn't change the norm1 of x_i\r\n                # i.e. x_i_norm1 = np.sum(x_i)\r\n                # After normalization, b_i = ( normalized x_i )*( 1 - kappa ) + u * kappa\r\n                b_i = x_i / np.sum(x_i)\r\n                \"\"\"\r\n\r\n                x_i /= neighborTotalOccur\r\n                b_i = x_i *( 1 - kappa ) + k_u\r\n                g_i = np.log(b_i) - log_u\r\n                G[contextWID] = g_i\r\n                contextWID += 1\r\n\r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            print \"Unknown line %d:\\n%s\" %( e.args[0], e.args[1] )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            print \"Source line %d: %s\" %(tb.tb_lineno, e)\r\n            if stage == 1:\r\n                print header\r\n            else:\r\n                print line\r\n        exit(0)\r\n\r\n    print\r\n    BIGRAM.close()\r\n\r\n    return vocab, word2id, G, F, u\r\n\r\n# If noncore_size == -1, all noncore words are loaded into the upperright and lowerleft blocks\r\n# word2preID_core are the IDs of words in the pretrained embedding file\r\n# If vocab_core and word2preID_core are specified, core words are limited to words in them\r\n# Otherwise the top core_size words are core words\r\ndef loadBigramFileInBlock( bigram_filename, core_size, noncore_size=-1, word2preID_core={}, prewords_skipped={}, kappa=0.02 ):\r\n\r\n    # corewords_specified means the list of core words are specified in word2preID_core\r\n    \r\n    if len(word2preID_core) > 0:\r\n        corewords_specified = True\r\n        # recordUpperleft is always the negation of corewords_specified. But sometimes is semantically clearer\r\n        recordUpperleft = False\r\n        # this core_size is used in comparsion with the total word count in the header\r\n        # this size might be inaccurate, as some words in word2preID_core might be missing from this bigram file\r\n        core_size = len(word2preID_core)\r\n    else:\r\n        corewords_specified = False\r\n        recordUpperleft = True\r\n        # if core words are not specified, core_size should always > 0,\r\n        # otherwise I don't know how many words are core words\r\n        if core_size < 0:\r\n            raise ValueError( \"Argument error: core_size = %d < 0 when word2preID_core is not specified\" %core_size )\r\n        if len(prewords_skipped) > 0:\r\n            raise ValueError( \"Argument error: word2preID_core is empty but prewords_skipped is not\" )\r\n\r\n    # if corewords_specified, return a list of coreword IDs in the pretrained mapping\r\n    # otherwise, return empty list (just for return value conformity)\r\n    coreword_preIDs = []\r\n\r\n    if not recordUpperleft:\r\n        # do not record G11/F11\r\n        print \"Loading bigram file '%s' into 2 blocks. Will skip %d words\" \\\r\n                                    %( bigram_filename, len(prewords_skipped) )\r\n    else:\r\n        print \"Loading bigram file '%s' into 3 blocks.\" %bigram_filename\r\n\r\n    BIGRAM = open(bigram_filename)\r\n\r\n    lineno = 0\r\n    vocab_all = []\r\n    vocab_core = []\r\n    vocab_noncore = []\r\n\r\n    word2id_all = {}\r\n    # origID is the original ID in this bigram file\r\n    # preID is the ID in the pretrained vec file\r\n    word2origID_all = {}\r\n    word2id_noncore = {}\r\n    word2id_core = {}\r\n\r\n    # stage 1: header and unigrams, stage 2: bigrams. for error msg printing\r\n    stage = 1\r\n    # do_smoothing must be True. Otherwise some entries in logb_i will be log of 0\r\n    # After smoothing, entries in b_i are always positive, thus logb_i is fine\r\n    # To reduce code modifications, this flag is not removed\r\n    timer1 = Timer( \"loadBigramFileInBlock()\" )\r\n\r\n    #pdb.set_trace()\r\n\r\n    try:\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n        match = re.match( r\"#\\s+(\\d+) words,\\s+\\d+ occurrences\", header )\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        wholeVocabSize = int(match.group(1))\r\n        print \"Totally %d words\"  %wholeVocabSize\r\n\r\n        if core_size >= wholeVocabSize:\r\n            raise ValueError( \"%d core words, but vocabulary only declares %d words in header\" %( core_size, wholeVocabSize ) )\r\n\r\n        # at least consider one noncore word\r\n        min_vocab_size = core_size + max( noncore_size, 1 )\r\n        if min_vocab_size > wholeVocabSize:\r\n            warning( \"%d (%d + %d) words demanded, but only %d declared in header\" %( min_vocab_size, core_size,\r\n                                                                                             max( noncore_size, 1 ), wholeVocabSize) )\r\n            min_vocab_size = wholeVocabSize\r\n            noncore_size2 = wholeVocabSize - core_size\r\n            warning( \"Noncore words adjusted from %d to %d\" %( noncore_size, noncore_size2 ) )\r\n            noncore_size = noncore_size2\r\n            \r\n        # all the words are included in the vocab_all\r\n        # in this case, vocab_size needs to be initialized\r\n        # otherwise corewords_specified, noncore_size & vocab_size will be computed later, \r\n        # needn't to be initialized\r\n        if not corewords_specified:\r\n            if noncore_size < 0:\r\n                vocab_size = wholeVocabSize\r\n                noncore_size = vocab_size - core_size\r\n            else:\r\n                # core_size will be updated later\r\n                # some core words in word2preID_core may not be present in this bigram file\r\n                vocab_size = core_size + noncore_size\r\n\r\n        # skip params\r\n        header = BIGRAM.readline()\r\n        header = BIGRAM.readline()\r\n        lineno += 2\r\n\r\n        match = re.match( r\"#\\s+(\\d+) bigram occurrences\", header )\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        if header[0:6] != \"Words:\":\r\n            raise ValueError(lineno, header)\r\n\r\n        # vector log_u, log-probs of all unigrams (at most vocab_size unigrams)\r\n        log_u0 = []\r\n        log_u0_core = []\r\n        log_u0_noncore = []\r\n        wc = 0\r\n        core_wc = 0\r\n        noncore_wc = 0\r\n        skipped_wc = 0\r\n        # maximum ID in the original order of core words\r\n        max_core_origID = 0\r\n\r\n        # Read the focus list, build the word2id_all / word2id_core mapping\r\n        # Read all context words of the core_size words\r\n        # Read top core_size context words of remaining words\r\n        while True:\r\n            header = BIGRAM.readline()\r\n            lineno += 1\r\n            header = header.rstrip()\r\n\r\n            # \"Words\" field ends\r\n            if not header:\r\n                break\r\n\r\n            words = header.split(\"\\t\")\r\n            for word in words:\r\n                w, freq, log_ui = word.split(\",\")\r\n\r\n                # load core words only in word2preID_core, other words as noncore\r\n                # core words may not be consecutive, they may be interspersed by noncore words\r\n                # So put them into two sets of arrays\r\n                if corewords_specified:\r\n                    if w in word2preID_core:\r\n                        word2id_core[w] = core_wc\r\n                        core_wc += 1\r\n                        coreword_preIDs.append( word2preID_core[w] )\r\n                        log_u0_core.append(float(log_ui))\r\n                        vocab_core.append(w)\r\n\r\n                        if wc > max_core_origID:\r\n                            max_core_origID = wc\r\n\r\n                    elif w in prewords_skipped:\r\n                        skipped_wc += 1\r\n                    elif noncore_size < 0 or noncore_wc < noncore_size :\r\n                        word2id_noncore[w] = noncore_wc\r\n                        noncore_wc += 1\r\n                        log_u0_noncore.append(float(log_ui))\r\n                        vocab_noncore.append(w)\r\n                        \r\n                    word2origID_all[w] = wc\r\n                    wc += 1\r\n\r\n                # load fresh core words\r\n                else:\r\n                    if wc == vocab_size:\r\n                        break\r\n\r\n                    word2id_all[w] = wc\r\n                    if wc < core_size:\r\n                        word2id_core[w] = wc\r\n                    else:\r\n                        word2id_noncore[w] = wc - core_size\r\n\r\n                    log_u0.append(float(log_ui))\r\n                    vocab_all.append(w)\r\n                    wc += 1\r\n\r\n        if corewords_specified:\r\n            # some core words may be missing from the bigram file. So recompute core_size\r\n            # If these two don't match, then some specified core words don't appear in the unigram list\r\n            if core_size != core_wc:\r\n                print \"WARN: %d core words demanded, but only %d read\" %(core_size, core_wc)\r\n                core_size = core_wc\r\n\r\n            noncore_size = noncore_wc\r\n            vocab_size = core_size + noncore_size\r\n            log_u0 = log_u0_core + log_u0_noncore\r\n            vocab_all = vocab_core + vocab_noncore\r\n\r\n            word2id_all = word2id_core.copy()\r\n            # insert noncore words into word2id_all\r\n            # core ID \\in [ 0, coresize - 1 ]\r\n            # noncore ID \\in [ core_size, ... ]\r\n            for w in word2id_noncore:\r\n                word2id_all[w] = word2id_noncore[w] + core_size\r\n\r\n        else:\r\n            # core words are consecutive. So orig ID = id\r\n            max_core_origID = core_size\r\n            word2origID_all = word2id_all\r\n            if vocab_size > 0 and wc < vocab_size:\r\n                print \"WARN: %d words demanded, but only %d read\" %(vocab_size, wc)\r\n                vocab_size = wc\r\n\r\n        print \"%d words in file, top %d to read into vocab (%d core, %d noncore), %d skipped\" \\\r\n               %( wholeVocabSize, vocab_size, core_size, noncore_size, skipped_wc )\r\n\r\n        # unigram prob & logprob of all the words\r\n        log_u0 = np.array(log_u0)\r\n        u0 = np.exp(log_u0)\r\n        # re-normalization is needed, as some unigrams may be out of vocab_all\r\n        u0 /= np.sum(u0)\r\n\r\n        log_u0 = np.log(u0)\r\n        log_u_core    = log_u0[:core_size]\r\n        log_u_noncore = log_u0[core_size:]\r\n\r\n        k_u0 = kappa * u0\r\n        k_u_core    = k_u0[:core_size]\r\n        k_u_noncore = k_u0[core_size:]\r\n\r\n        ### Reading bigrams begins ###\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        if header[0:8] != \"Bigrams:\":\r\n            raise ValueError(lineno, header)\r\n\r\n        print \"Read bigrams:\"\r\n        stage = 2\r\n\r\n        line = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        contextWID = 0\r\n\r\n        # new G12, F12\r\n        # G12/F12: the upperright block\r\n        G12 = np.zeros( (core_size, noncore_size), dtype=np.float32 )\r\n        F12 = np.zeros( (core_size, noncore_size), dtype=np.float32 )\r\n        # new G21, F21\r\n        # G21/F21: the lowerleft block\r\n        # the lower right block (biggest) G22/F22 is ignored\r\n        G21 = np.zeros( (noncore_size, core_size), dtype=np.float32 )\r\n        F21 = np.zeros( (noncore_size, core_size), dtype=np.float32 )\r\n\r\n        # when reading core words, keep the upperleft block, the whole vocab\r\n        if recordUpperleft:\r\n            # new G11, F11\r\n            # G11/F11: the upperleft block\r\n            G11 = np.zeros( (core_size, core_size), dtype=np.float32 )\r\n            F11 = np.zeros( (core_size, core_size), dtype=np.float32 )\r\n            rowLength = vocab_size\r\n            k_u = k_u0\r\n            log_u = log_u0\r\n        # when reading core words, only keep the upperright block, noncore_size words\r\n        else:\r\n            rowLength = noncore_size\r\n            k_u = k_u_noncore\r\n            log_u = log_u_noncore\r\n\r\n        # focusIDLimit: the limit of neighbor wid\r\n        # In the beginning, read all neighbors\r\n        focusIDLimit = vocab_size\r\n\r\n        # vars are initialized like those of a core word,\r\n        # So pretend the previous nonexistent word is a core word\r\n        lastContextIsCore = True\r\n\r\n        #pdb.set_trace()\r\n        core_readcount = 0\r\n        noncore_readcount = 0\r\n        coreMsg_printed = False\r\n\r\n        while True:\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                break\r\n\r\n            # We have read the bigrams of all the wanted words\r\n            if contextWID == vocab_size:\r\n                break\r\n\r\n            # word ID, word, number of distinct neighbors, sum of freqs of all neighbors, cut off freq\r\n            orig_wid, w, neighborCount, neighborTotalOccur, cutoffFreq = line.split(\",\")\r\n            orig_wid = int(orig_wid)\r\n            neighborTotalOccur = float(neighborTotalOccur)\r\n\r\n            if w in word2id_core:\r\n                # the current context word is a core word\r\n                contextIsCore = True\r\n                wid = word2id_core[w]\r\n                # context type switches from noncore to core, update relevant variables\r\n                # if context type doesn't change (last context is also core), then just keep vars unchanged\r\n                # context type switching should only happen very few times\r\n                if not lastContextIsCore:\r\n                    focusIDLimit = vocab_size\r\n\r\n                    if recordUpperleft:\r\n                        rowLength = vocab_size\r\n                        k_u = k_u0\r\n                        log_u = log_u0\r\n                    # when reading core words, only keep the upperright block, noncore_size words\r\n                    else:\r\n                        rowLength = noncore_size\r\n                        k_u = k_u_noncore\r\n                        log_u = log_u_noncore\r\n\r\n                    lastContextIsCore = True\r\n\r\n            elif w in word2id_noncore:\r\n                contextIsCore = False\r\n                wid = word2id_noncore[w]\r\n                # context type switches from core to noncore, update relevant variables\r\n                # if context type doesn't change (last context is also noncore), then just keep vars unchanged\r\n                # context type switching should only happen very few times\r\n                if lastContextIsCore:\r\n                    # in a row of noncore (context) word, only freqs of core (focus) words are recorded\r\n                    focusIDLimit = max_core_origID\r\n                    rowLength = core_size\r\n\r\n                    k_u = k_u_core\r\n                    log_u = log_u_core\r\n\r\n                    lastContextIsCore = False\r\n\r\n            # x_{i.}\r\n            x_i = np.zeros(rowLength, dtype=np.float32)\r\n\r\n            if w in prewords_skipped:\r\n                saveCurrRow = False\r\n                skipRemainingNeighbors = True\r\n            else:\r\n                saveCurrRow = True\r\n                skipRemainingNeighbors = False\r\n\r\n            while True:\r\n                line = BIGRAM.readline()\r\n                lineno += 1\r\n\r\n                # Empty line. Should be end of file\r\n                if not line:\r\n                    break\r\n\r\n                # Encounter a comment. Just in case of future extension\r\n                # Currently only the last line in the file is a comment after the header\r\n                if line[0] == '#':\r\n                    continue\r\n\r\n                # Beginning of the next word. Continue at the outer loop\r\n                # Neighbor lines always start with '\\t'\r\n                if line[0] != '\\t':\r\n                    break\r\n\r\n                if skipRemainingNeighbors:\r\n                    continue\r\n\r\n                line = line.strip()\r\n                neighbors = line.split(\"\\t\")\r\n\r\n                for neighbor in neighbors:\r\n                    w2, freq2, log_bij = neighbor.split(\",\")\r\n\r\n                    # w2 in skip list, and surely not in word2id_all\r\n                    # so check here to avoid setting skipRemainingNeighbors\r\n                    if w2 in prewords_skipped:\r\n                        continue\r\n                        \r\n                    # when meeting the first focus word out of vocab_all, all following focus words are not in vocab_all\r\n                    # since neighbors are sorted ascendingly by ID\r\n                    # So they are skipped to speed up reading\r\n                    if w2 not in word2id_all:\r\n                        skipRemainingNeighbors = True\r\n                        break\r\n\r\n                    origID = word2origID_all[w2]\r\n                    # On a noncore row. Should have focus word orig ID <= max_core_origID\r\n                    # origIDs of core words may be interspersed by origIDs of noncore words\r\n                    # but IDs of core words are consecutive, and preceding IDs of noncore words\r\n                    if not contextIsCore and origID > max_core_origID:\r\n                        skipRemainingNeighbors = True\r\n                        break\r\n\r\n                    freq2 = int(freq2)\r\n                    # On a core (context) row.\r\n                    # If recordUpperleft, use the map from whole vocab to IDs;\r\n                    # otherwise use the map from core words to IDs\r\n                    if contextIsCore:\r\n                        if recordUpperleft:\r\n                            # w2id: id of w2\r\n                            w2id = word2id_all[w2]\r\n                            x_i[w2id] = freq2\r\n                        # don't keep upperleft block. core (focus) words are discarded\r\n                        # w2id \\in [ 0, noncore_size - 1 ]\r\n                        elif w2 in word2id_noncore:\r\n                            w2id = word2id_noncore[w2]\r\n                            x_i[w2id] = freq2\r\n                    # On a noncore (context) row. Only record core (focus) words\r\n                    elif w2 in word2id_core:\r\n                        w2id = word2id_core[w2]\r\n                        x_i[w2id] = freq2\r\n\r\n            if not saveCurrRow:\r\n                continue\r\n                \r\n            # Question: whether set F to the original freq or smoothed freq (assign F before or after smoothing)?\r\n            if contextIsCore:\r\n                if recordUpperleft:\r\n                    F11[core_readcount] = x_i[:core_size]\r\n                    F12[core_readcount] = x_i[core_size:]\r\n                else:\r\n                    # As w2id \\in [ 0, noncore_size - 1 ], no offset is needed\r\n                    F12[core_readcount] = x_i\r\n            else:\r\n                F21[noncore_readcount] = x_i\r\n\r\n            \"\"\"\r\n            x_i_norm1 = np.sum(x_i)\r\n            utrans = x_i_norm1 * k_u\r\n            x_i = x_i * (1 - kappa) + utrans\r\n\r\n            # the smoothing shoudn't change the norm1 of x_i\r\n            # i.e. x_i_norm1 = np.sum(x_i)\r\n            # normalization\r\n            b_i = x_i / np.sum(x_i)\r\n            \"\"\"\r\n\r\n            x_i /= neighborTotalOccur\r\n            b_i = x_i * ( 1 - kappa ) + k_u\r\n            g_i = np.log(b_i) - log_u\r\n\r\n            if contextIsCore:\r\n                if recordUpperleft:\r\n                    G11[core_readcount] = g_i[:core_size]\r\n                    G12[core_readcount] = g_i[core_size:]\r\n                else:\r\n                    # As w2id \\in [ 0, noncore_size - 1 ], no offset is needed\r\n                    G12[core_readcount] = g_i\r\n            else:\r\n                G21[noncore_readcount] = g_i\r\n\r\n            contextWID += 1\r\n            if contextIsCore:\r\n                core_readcount += 1\r\n            else:\r\n                noncore_readcount += 1\r\n\r\n            if orig_wid % 200 == 0:\r\n                print \"\\r%d (%d core, %d noncore)\\r\" %( orig_wid, core_readcount, noncore_readcount ),\r\n            if not coreMsg_printed and core_readcount == core_size:\r\n                print \"\\n%d core words are all read.\" %(core_size)\r\n                coreMsg_printed = True\r\n\r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            print \"Unknown line %d:\\n%s\" %( e.args[0], e.args[1] )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            print \"Source line %d: %s\" %(tb.tb_lineno, e)\r\n            if stage == 1:\r\n                print header\r\n            else:\r\n                print line\r\n        exit(0)\r\n\r\n    print\r\n    BIGRAM.close()\r\n\r\n    if recordUpperleft:\r\n        G = [ G11, G12, G21 ]\r\n        F = [ F11, F12, F21 ]\r\n    else:\r\n        G = [ G12, G21 ]\r\n        F = [ F12, F21 ]\r\n\r\n    return vocab_all, word2id_all, word2id_core, coreword_preIDs, G, F, u0\r\n\r\ndef loadUnigramFile(filename):\r\n    UNI = open(filename)\r\n    vocab_dict = {}\r\n    wid = 1\r\n    for line in UNI:\r\n        line = line.strip()\r\n        if line[0] == '#':\r\n            continue\r\n        fields = line.split(\"\\t\")\r\n                             # id, freq, prob\r\n        vocab_dict[ fields[0] ] = ( wid, int(fields[1]), np.exp(float(fields[2])) )\r\n        wid += 1\r\n\r\n    print \"%d words loaded from unigram file %s\" %(wid, filename)\r\n    return vocab_dict\r\n\r\ndef loadExtraWordFile(filename):\r\n    extraWords = {}\r\n    with open(filename) as f:\r\n        for line in f:\r\n            w, wid = line.strip().split('\\t')\r\n            extraWords[w] = 1\r\n\r\n    print \"%d words loaded from extra word file %s\" %( len(extraWords), filename)\r\n    return extraWords\r\n\r\n# borrowed from Omer Levy's code\r\n# extraArgs is not used, only for API conformity\r\ndef loadSimTestset(path, extraArgs=None):\r\n    testset = []\r\n    print \"Read sim testset \" + path\r\n    with open(path) as f:\r\n        for line in f:\r\n            x, y, sim = line.strip().lower().split()\r\n            testset.append( [ x, y, float(sim) ] )\r\n    return testset\r\n\r\ndef loadAnaTestset(path, extraArgs=None):\r\n    testset = []\r\n    print \"Read analogy testset \" + path\r\n\r\n    if extraArgs is not None and 'skipPossessive' in extraArgs:\r\n        skipPossessive = True\r\n        possessive = 0\r\n    else:\r\n        skipPossessive = False\r\n\r\n    with open(path) as f:\r\n        for line in f:\r\n            # skip possessive forms\r\n            if skipPossessive and line.find(\"'\") >= 0:\r\n                possessive += 1\r\n                continue\r\n            a, a2, b, b2 = line.strip().lower().split()\r\n            testset.append( [ a, a2, b, b2 ] )\r\n\r\n    if skipPossessive:\r\n        print \"%d possessive pairs skipped\" %possessive\r\n\r\n    return testset\r\n\r\n# available loaders: loadSimTestset, loadAnaTestset\r\ndef loadTestsets(loader, testsetDir, testsetNames, extraArgs=None):\r\n    # always use unix style path\r\n    testsetDir = testsetDir.replace(\"\\\\\", \"/\")\r\n    if testsetDir[-1] != '/':\r\n        testsetDir += '/'\r\n\r\n    if not os.path.isdir(testsetDir):\r\n        print \"ERR: Test set dir does not exist or is not a dir:\\n\" + testsetDir\r\n        sys.exit(2)\r\n\r\n    testsets = []\r\n    if len(testsetNames) == 0:\r\n        testsetNames = glob.glob( testsetDir + '*.txt' )\r\n        if len(testsetNames) == 0:\r\n            print \"No testset ended with '.txt' is found in \" + testsetDir\r\n            sys.exit(2)\r\n        testsetNames = map( lambda x: os.path.basename(x)[:-4], testsetNames )\r\n\r\n    for testsetName in testsetNames:\r\n        testset = loader( testsetDir + testsetName + \".txt\", extraArgs )\r\n        testsets.append(testset)\r\n\r\n    return testsets\r\n\r\n# \"model\" in methods below has to support two methods:\r\n# model[w]: return the embedding of w\r\n# model.similarity(x, y): return the cosine similarity between the embeddings of x and y\r\n# realb2 is passed in only for debugging purpose\r\ndef predict_ana( model, a, a2, b, realb2 ):\r\n    questWordIndices = [ model.word2id[x] for x in (a,a2,b) ]\r\n    # b2 is effectively iterating through the vocab. The row is all the cosine values\r\n    b2a2 = model.sim_row(a2)\r\n    b2a  = model.sim_row(a)\r\n    b2b  = model.sim_row(b)\r\n    addsims = b2a2 - b2a + b2b\r\n\r\n    addsims[questWordIndices] = -10000\r\n\r\n    iadd = np.nanargmax(addsims)\r\n    b2add  = model.vocab[iadd]\r\n\r\n    # For debugging purposes\r\n    ia = model.word2id[a]\r\n    ia2 = model.word2id[a2]\r\n    ib = model.word2id[b]\r\n    ib2 = model.word2id[realb2]\r\n    realaddsim = addsims[ib2]\r\n\r\n    mulsims = ( b2a2 + 1 ) * ( b2b + 1 ) / ( b2a + 1.001 )\r\n    mulsims[questWordIndices] = -10000\r\n    imul = np.nanargmax(mulsims)\r\n    b2mul  = model.vocab[imul]\r\n\r\n    return b2add, b2mul\r\n\r\n''' baa2 = model[b] - model[a] + model[a2]\r\n    baa2 = baa2/normF(baa2)\r\n    sims2 = model.V.dot(baa2)\r\n    dists1 = np.abs( model.V - baa2 ).dot( np.ones( model.V.shape[1] ) )\r\n\r\n    sims2[questWordIndices] = -10000\r\n    dists1[questWordIndices] = 10000\r\n\r\n    i2 = np.nanargmax(sims2)\r\n    b22 = model.vocab[i2]\r\n    i1 = np.nanargmin(dists1)\r\n    b21 = model.vocab[i1]\r\n\r\n    realsim2 = sims2[ib2]\r\n    realdist1 = dists1[ib2]\r\n\r\n    # F-norm (L2)\r\n    topIDs2 = sims2.argsort()[-5:][::-1]\r\n    topwords2 = [ model.vocab[i] for i in topIDs2 ]\r\n    topsims2 = sims2[topIDs2]\r\n\r\n    # Manhattan distance (L1)\r\n    topIDs1 = sims1.argsort()[-5:][::-1]\r\n    topwords1 = [ model.vocab[i] for i in topIDs1 ]\r\n    topsims1 = sims1[topIDs1]\r\n\r\n    if b22 != realb2:\r\n        print \"%s,%s\\t%s,[%s]\" %(a,a2,b,realb2)\r\n        print \"%s,%f\\t%s\\t%s\" %(b21,realsim1, str(topsims1), str(topwords1))\r\n        print \"%s,%f\\t%s\\t%s\" %(b22,realsim2, str(topsims2), str(topwords2))\r\n        print\r\n        #pdb.set_trace()\r\n    return b2add, b2mul, b21, b22\r\n    '''\r\n\r\n# vocab_dict is a vocabulary dict, usually bigger than model.vocab, loaded from a unigram file\r\n# its purpose is to find absent words in the model\r\ndef evaluate_sim(model, testsets, testsetNames, getAbsentWords=False, vocab_dict=None, cutPoint=-1 ):\r\n    # words in absentModelID2Word and words in absentVocabWords don't overlap\r\n\r\n    # words in the vocab but not in the model\r\n    absentModelID2Word = {}\r\n    # words not in the vocab (of coz not in the model)\r\n    absentVocabWords = {}\r\n    # words in the vocab but below the cutPoint (id > cutPoint), may be in or out of the model\r\n    cutVocabWords = {}\r\n    # a set of spearman coeffs, in the same order as in testsets\r\n    spearmanCoeff = []\r\n\r\n    for i,testset in enumerate(testsets):\r\n        modelResults = []\r\n        groundtruth = []\r\n\r\n        for x, y, sim in testset:\r\n            if vocab_dict and x in vocab_dict:\r\n                xid = vocab_dict[x][0]\r\n                if cutPoint > 0 and xid > cutPoint:\r\n                    cutVocabWords[x] = 1\r\n\r\n            if vocab_dict and y in vocab_dict:\r\n                yid = vocab_dict[y][0]\r\n                if cutPoint > 0 and yid > cutPoint:\r\n                    cutVocabWords[y] = 1\r\n\r\n            if x not in model:\r\n                if getAbsentWords and x in vocab_dict:\r\n                    absentModelID2Word[xid] = x\r\n                else:\r\n                    absentVocabWords[x] = 1\r\n            elif y not in model:\r\n                if getAbsentWords and y in vocab_dict:\r\n                    absentModelID2Word[yid] = y\r\n                else:\r\n                    absentVocabWords[y] = 1\r\n            else:\r\n                modelResults.append( model.similarity(x, y) )\r\n                groundtruth.append(sim)\r\n                #print \"%s %s: %.3f %.3f\" %(x, y, modelResults[-1], sim)\r\n        print \"%s: %d test pairs, %d valid\" %( testsetNames[i], len(testset), len(modelResults) ),\r\n        spearmanCoeff.append( spearmanr(modelResults, groundtruth)[0] )\r\n        print \", %.5f\" %spearmanCoeff[-1]\r\n\r\n    # return hashes directly, for ease of merge\r\n    return spearmanCoeff, absentModelID2Word, absentVocabWords, cutVocabWords\r\n\r\n# vocab_dict is a vocabulary dict, usually bigger than model.vocab, loaded from a unigram file\r\n# its purpose is to find absent words in the model\r\ndef evaluate_ana(model, testsets, testsetNames, getAbsentWords=False, vocab_dict=None, cutPoint=-1 ):\r\n    # for words in the vocab but not in the model. mapping from words to IDs\r\n    absentModelID2Word = {}\r\n    # words not in the vocab (of coz not in the model)\r\n    absentVocabWords = {}\r\n    # words in the vocab but below the cutPoint (id > cutPoint), may be in or out of the model\r\n    cutVocabWords = {}\r\n    # a set of scores, in the same order as in testsets\r\n    # each is a tuple (add_score, mul_score)\r\n    anaScores = []\r\n\r\n    #pdb.set_trace()\r\n\r\n    for i,testset in enumerate(testsets):\r\n        modelResults = []\r\n        groundtruth = []\r\n\r\n        correct_add = 0.0\r\n        correct_mul = 0.0\r\n        validPairNum = 0\r\n        currentScores = np.array( [ 0.0, 0.0 ] )\r\n\r\n        for j,analogy in enumerate(testset):\r\n\r\n            allWordsPresent = True\r\n            watchWhenWrong = False\r\n\r\n            # check presence of all words in this test pair\r\n            for x in analogy:\r\n                if vocab_dict and x in vocab_dict:\r\n                    xid = vocab_dict[x][0]\r\n                    if cutPoint > 0 and xid > cutPoint:\r\n                        cutVocabWords[x] = 1\r\n                        watchWhenWrong = True\r\n\r\n                if x not in model:\r\n                    if vocab_dict and x in vocab_dict:\r\n                        absentModelID2Word[ vocab_dict[x][0] ] = x\r\n                    else:\r\n                        absentVocabWords[x] = 1\r\n                    allWordsPresent = False\r\n\r\n            if allWordsPresent:\r\n                a, a2, b, b2 = analogy\r\n                b2add, b2mul = predict_ana( model, a, a2, b, b2 )\r\n                validPairNum += 1\r\n                if b2add == b2:\r\n                    correct_add += 1\r\n                elif watchWhenWrong:\r\n                    print \"%s~%s = %s~%s,%.3f (%s,%3f)\" %( a, a2, b, b2, model.similarity(b,b2), b2_add, model.similarity(b,b2_add) )\r\n\r\n                if b2mul == b2:\r\n                    correct_mul += 1\r\n\r\n                \"\"\"if b2_mul == b2:\r\n                    correct_mul += 1\r\n                if b2_L1 == b2:\r\n                    correct_L1 += 1\r\n                if b2_L2 == b2:\r\n                    correct_L2 += 1\r\n                currentScores = np.array([ correct_add, correct_mul, correct_L1, correct_L2 ]) / validPairNum\r\n                \"\"\"\r\n\r\n                # latest cumulative scores\r\n                currentScores = np.array( [ correct_add, correct_mul ] ) / validPairNum\r\n\r\n            if j % 500 == 499:\r\n                print \"\\r%i/%i/%i: Add %.5f, Mul %.5f\\r\" %( j + 1, validPairNum, len(testset),\r\n                                                            currentScores[0], currentScores[1] ),\r\n\r\n        print \"\\n%s: %d analogies, %d valid\" %( testsetNames[i], len(testset), validPairNum ),\r\n        anaScores.append(currentScores)\r\n        print \". Add Score: %.5f, Mul Score: %.5f\" %( currentScores[0], currentScores[1] )\r\n\r\n    return anaScores, absentModelID2Word, absentVocabWords, cutVocabWords\r\n\r\ndef bench(func, N, topEigenNum=0):\r\n    print \"Begin to factorize a %dx%d matrix\" %(N,N)\r\n    a = np.random.randn(N, N)\r\n    a = (a+a.T)/2\r\n    tic = time.clock()\r\n    func(a)\r\n    toc = time.clock()\r\n    diff = toc - tic\r\n    print \"Elapsed time is %.3f\" %diff\r\n    return diff\r\n\r\n# return a flag indicating whether installed mem is enough to computing a D*D dimensional Gramian matrix,\r\n# plus installed amounts and required amounts of mem\r\n# extraVarsRatio: the required mem of other existing variables (as a ratio of the decomposed matrix)\r\n# e.g. in evaluate.py, only the Gramian (cosine) matrix is present, extraVarsRatio = 0\r\n# in factorize.py, if there are 4 similar sized matrices other than the Gramian, \r\n# then extraVarsRatio=4 may be reasonable\r\ndef isMemEnoughGramian(D, extraVarsRatio=0):\r\n    mem = virtual_memory()\r\n    installedMemGB = round( mem.total * 1.0 / (1<<30) )\r\n    # some overhead for np.array, so not divided by 1024^3\r\n    requiredMemGB = D * D * 4.0 * ( extraVarsRatio + 1 ) / 1000000000\r\n    \r\n    # installed mem is enough\r\n    if requiredMemGB <= installedMemGB:\r\n        isEnough = 2\r\n        \r\n    # give a warning, will use some paging file and make the computer very slow\r\n    elif requiredMemGB <= installedMemGB * 1.2:\r\n        isEnough = 1\r\n    # not enough\r\n    else:\r\n        isEnough = 0\r\n\r\n    return isEnough, installedMemGB, requiredMemGB\r\n\r\n# return a flag indicating whether installed mem is enough to computing eigendecomposition of a D*D matrix\r\n# plus installed amounts and required amounts of mem\r\n# extraVarsRatio: the required mem of other existing variables (as a ratio of the decomposed matrix)\r\n# assume eigendecomposition alone takes 10 times of the mem of the decomposed matrix\r\n# e.g. when only doing eigendecomposition of the matrix is present, extraVarsRatio = 0, allVarsRatio = 10\r\n# if there are 4 similar sized matrices other than the decomposed matrix, then allVarsRatio = 10 + 4 = 14\r\n# In factorize.py, when doing eigendecomposition, usually there are at least 5 other matrices of similar sizes\r\n# so by default extraVarsRatio=5\r\ndef isMemEnoughEigen(D, extraVarsRatio=5):\r\n    mem = virtual_memory()\r\n    installedMemGB = round( mem.total * 1.0 / (1<<30) )\r\n    # 15 is an empirical estimation. when D=30K, it takes around 50GB mem\r\n    requiredMemGB = D * D * 4.0 * ( extraVarsRatio + 8 ) / 1000000000\r\n    \r\n    # installed mem is enough\r\n    if requiredMemGB <= installedMemGB:\r\n        isEnough = 2\r\n        \r\n    # give a warning, will use some paging file and make the computer very slow\r\n    elif requiredMemGB <= installedMemGB * 1.2:\r\n        isEnough = 1\r\n    # not enough\r\n    else:\r\n        isEnough = 0\r\n\r\n    return isEnough, installedMemGB, requiredMemGB\r\n\r\ndef extractSentenceWords(doc, remove_url=True, remove_punc=\"utf-8\", min_length=1):\r\n    if remove_punc:\r\n        # ensure doc_u is in unicode\r\n        if not isinstance(doc, unicode):\r\n            encoding = remove_punc\r\n            doc_u = doc.decode(encoding, errors='ignore')\r\n        else:\r\n            doc_u = doc\r\n        # remove unicode punctuation marks, keep ascii punctuation marks\r\n        doc_u = doc_u.translate(unicode_punc_tbl)\r\n        if not isinstance(doc, unicode):\r\n            doc = doc_u.encode(encoding)\r\n        else:\r\n            doc = doc_u\r\n            \r\n    if remove_url:\r\n        re_url = r\"(https?:\\/\\/)?(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0-9@:%_\\+.~#?&//=]*)\"\r\n        doc = re.sub( re_url, \"\", doc )\r\n            \r\n    sentences = re.split( r\"\\s*[,;:`\\\"()?!{}]\\s*|--+|\\s*-\\s+|''|\\.\\s|\\.$|\\.\\.+||\", doc ) #\"\r\n    wc = 0\r\n    wordsInSentences = []\r\n    \r\n    for sentence in sentences:\r\n        if sentence == \"\":\r\n            continue\r\n\r\n        if not re.search( \"[A-Za-z0-9]\", sentence ):\r\n            continue\r\n\r\n        words = re.split( r\"\\s+\\+|^\\+|\\+?[\\-*\\/&%=<>\\[\\]~\\|\\@\\$]+\\+?|\\'\\s+|\\'s\\s+|\\'s$|\\s+\\'|^\\'|\\'$|\\$|\\\\|\\s+\", sentence )\r\n\r\n        words = filter( lambda w: w, words )\r\n\r\n        if len(words) >= min_length:\r\n            wordsInSentences.append(words)\r\n            wc += len(words)\r\n\r\n    #print \"%d words extracted\" %wc\r\n    return wordsInSentences, wc\r\n                            \r\ndef randomsample( X, n ):\r\n    \"\"\" random.sample of the rows of X\r\n        X may be sparse -- best csr\r\n    \"\"\"\r\n    sampleix = random.sample( xrange( X.shape[0] ), int(n) )\r\n    return X[sampleix]\r\n\r\ndef relu(v, bias):\r\n    v2 = np.copy(v)\r\n    v2[ v < bias ] = 0\r\n    return v2\r\n\r\ndef maxpool(vs):\r\n    vs = np.array(vs)\r\n    max_indices = np.argmax( np.abs(vs), axis=0 )\r\n    N = vs.shape[1]\r\n    max_v = vs[ max_indices, range(N) ]\r\n    return max_v\r\n\r\ndef avgpool(vs):\r\n    vs = np.array(vs)\r\n    avg_v = np.sum( vs, axis=0 )\r\n    avg_v /= vs.shape[0]\r\n    return avg_v\r\n                                    \r\nclass VecModel:\r\n    def __init__(self, V, vocab, word2id, vecNormalize=True, precompute_gramian=False):\r\n        self.Vorig = V\r\n        self.Vnorm = np.array( [ normF(x) for x in self.Vorig ], dtype=np.float32 )\r\n        for i, w in enumerate(vocab):\r\n            if self.Vnorm[i] == 0:\r\n                print \"WARN: %s norm is 0\" %w\r\n                # set to 1 to avoid \"divided by 0 exception\"\r\n                self.Vnorm[i] = 1\r\n                \r\n        self.V = self.Vorig / self.Vnorm[:, None]\r\n        self.word2id = word2id\r\n        self.vecNormalize = vecNormalize\r\n        self.vocab = vocab\r\n        self.iterIndex = 0\r\n        self.cosTable = None\r\n        \r\n        if precompute_gramian:\r\n            isEnough, installedMemGB, requiredMemGB = isMemEnoughGramian( len(V) )\r\n            if isEnough == 2:\r\n                self.precomputeGramian()\r\n            \r\n    def __contains__(self, w):\r\n        return w in self.word2id\r\n\r\n    def __getitem__(self, w):\r\n        if w not in self:\r\n            return None\r\n        else:\r\n            if self.vecNormalize:\r\n                return self.V[ self.word2id[w] ]\r\n            else:\r\n                return self.Vorig[ self.word2id[w] ]\r\n\r\n    def orig(self, w):\r\n        if w not in self:\r\n            return None\r\n        else:\r\n            return self.Vorig[ self.word2id[w] ]\r\n\r\n    def precomputeGramian(self):\r\n        print \"Precompute cosine matrix, will need %.1fGB RAM...\" %( len(self.V) * len(self.V) * 4.0 / 1000000000 ),\r\n        self.cosTable = np.dot( self.V, self.V.T )\r\n        print \"Done.\"\r\n\r\n    def similarity(self, x, y):\r\n        if x not in self or y not in self:\r\n            return 0\r\n\r\n        if self.vecNormalize:\r\n            if self.cosTable is not None:\r\n                ix = self.word2id[x]\r\n                iy = self.word2id[y]\r\n                return self.cosTable[ix,iy]\r\n            return np.dot( self[x], self[y] )\r\n\r\n        # when vectors are not normalized, return the raw dot product\r\n        vx = self[x]\r\n        vy = self[y]\r\n        # vector too short. the similarity doesn't make sense\r\n        if normF(vx) <= 1e-6 or normF(vy) <= 1e-6:\r\n            return 0\r\n\r\n        return np.dot( self[x], self[y] )\r\n\r\n    def sim_row(self, x):\r\n        if x not in self:\r\n            return 0\r\n\r\n        if self.vecNormalize:\r\n            if self.cosTable is not None:\r\n                ix = self.word2id[x]\r\n                return self.cosTable[ix]\r\n            return self.V.dot(self[x])\r\n            \r\n        vx = self[x]\r\n        # vector too short. the dot product similarity doesn't make sense\r\n        if normF(vx) <= 1e-6:\r\n            return np.zeros( len(self.vocab) )\r\n\r\n        return self.V.dot(vx)\r\n\r\n    def most_similar(self, vx, top_num=1):\r\n        vx2 = normalizeF(vx)\r\n        if self.vecNormalize:\r\n            sim_row = self.V.dot(vx2)\r\n            top_indices = sim_row.argsort()[-top_num:][::-1]\r\n            word_sims = [ ( self.vocab[i], sim_row[i] ) for i in top_indices ]\r\n            return word_sims\r\n        else:\r\n            raise NotImplementedError\r\n\r\n                "
  },
  {
    "path": "psdvec/vecnorms.py",
    "content": "#!/usr/bin/python\n\n# this simple script is to find patterns of the norms (L1) of the learned embeddings\nfrom utils import *\nimport sys\nimport operator\nimport os\nimport getopt\nimport math\nimport pdb\n\ndef usage():\n    print \"Usage: vecnorms.py [-s -1 first_block_count -2 second_block_count ] embedding_filename\"\n\ndef expectation(value_probs):\n    accuProb = 0\n    accuExp = 0\n    for v, p in value_probs:\n        accuExp += v * p\n        accuProb += p\n\n    return accuExp / accuProb\n\ndef var_div(value_probs):\n    expect = expectation(value_probs)\n    accuVar = 0\n    accuProb = 0\n    for v, p in value_probs:\n        accuVar += (v - expect)**2 * p\n        accuProb += p\n    var = accuVar / accuProb\n    div = math.sqrt(var)\n    return var, div\n\nif len(sys.argv) == 1:\n    usage()\n    sys.exit(1)\n\ndoSort = False\nfirst_block_count = -1\nsecond_block_count = -1\nunigramFilename = 'top1grams-wiki.txt'\n\ntry:\n    opts, args = getopt.getopt(sys.argv[1:],\"s1:2:\")\n    if len(args) != 1:\n        raise getopt.GetoptError(\"\")\n    embeddingFilename = args[0]\n    for opt, arg in opts:\n        if opt == '-s':\n            doSort = True\n        if opt == '-1':\n            first_block_count = int(arg)\n            print 'First block: 1-%d' %first_block_count\n        if opt == '-2':\n            second_block_count = int(arg)\n            print 'Second block: %d-%d' %(first_block_count, second_block_count)\n        if opt == '-u':\n            # unigram file is used to get a full list of words,\n            # and also to sort the absent words by their frequencies\n            unigramFilename = arg\n            \nexcept getopt.GetoptError:\n     usage()\n     sys.exit(2)\n\nvocab_prob = loadUnigramFile(unigramFilename)\nV, vocab, word2id, skippedWords = load_embeddings( embeddingFilename, second_block_count )\nwarning(\"\\nCompute norms...\")\n\nword2norm = {}\nwordnorms = []\nword_probs1 = []\nword_probs2 = []\n\nfor i in xrange( len(V) ):\n    w = vocab[i]\n    if w not in vocab_prob:\n        warning( \"%s not in vocab, skip\\n\" %w )\n        continue\n    \n    mag = norm1( V[i] )\n    word2norm[w] = mag\n    prob = vocab_prob[w][2]\n    wordnorms.append( [ w, mag ] )\n    if i < first_block_count:\n        word_probs1.append( [ mag, prob ] )\n    elif i < second_block_count:\n        word_probs2.append( [ mag, prob ] )\n\nwarning(\"Done\\n\")\n\nif len(word_probs1) > 0:\n    var1, div1 = var_div(word_probs1)\n    expect = expectation(word_probs1)\n    print \"First block: %d words, exp: %.2f, var: %.2f, div: %.2f\" %( len(word_probs1), expect, var1, div1 )\nif len(word_probs2) > 0:\n    var2, div2 = var_div(word_probs2)\n    expect = expectation(word_probs2)\n    print \"Second block: %d words, exp: %.2f, var: %.2f, div: %.2f\" %( len(word_probs2), expect, var2, div2 )\n\n\nif doSort:\n    warning(\"Done\\nSorting words ascendingly by norm...\")\n    # sort ascendingly by the norm length\n    sorted_wordnorms = sorted( wordnorms, key=operator.itemgetter(1) )\n    wordnorms = sorted_wordnorms\n    \nembeddingFilename = os.path.basename(embeddingFilename)\nembeddingFilename = os.path.splitext(embeddingFilename)[0]\n\nnormFilename = \"norms_\" + embeddingFilename + \"-%d.txt\" %( len(V) )\n\nwarning( \"Save norms into %s\\n\" %normFilename )\nNORM = open(normFilename, \"w\")\n\nwc = 0\nfor word_norm in wordnorms:\n    word, norm = word_norm\n    NORM.write( \"%i %s: %.2f\\n\" %( word2id[word], word, norm ) )\n    wc += 1\n\nwarning( \"%d words saved\\n\" %wc )    \n"
  },
  {
    "path": "psdvec/xml2corpus.pl",
    "content": "use strict; \r\nuse warnings;\r\nuse XML::LibXML;\r\nuse File::Find;\r\n\r\nmy $rootdir = \"D:/corpus/rcv1/\";\r\nmy $fc = 0;\r\nmy $totalbytes = 0;\r\n\r\nfind({ wanted => \\&process_file, no_chdir => 1 }, $rootdir);\r\nmy $totalMB = int( $totalbytes / 1024 / 1024 );\r\nprint STDERR \"$fc files processed, totally $totalMB MB\\n\";\r\n\r\nsub process_file {\r\n    if ( /\\.xml$/ ) {\r\n        my $doc = XML::LibXML->load_xml(location => $_);\r\n        for my $textnode ( $doc->findnodes('/newsitem/text') ){\r\n            print $textnode->textContent();\r\n            $totalbytes += length( $textnode->textContent() );\r\n            $totalMB = int( $totalbytes / 1024 / 1024 );\r\n        }\r\n        $fc++;\r\n        if( $fc % 500 == 0 ){\r\n            print STDERR \"\\r$fc $totalMB\\r\";\r\n        }\r\n    }\r\n}\r\n"
  },
  {
    "path": "reuters.bat",
    "content": "python topicExp.py -s reuters train\r\npython topicExp.py -i reuters-train-5770-sep91-em150-best.topic.vec reuters train,test\r\npython classEval.py reuters topicprop\r\npython classEval.py reuters topic-wvavg\r\n"
  },
  {
    "path": "snippet2topic.py",
    "content": "import numpy as np\r\nimport getopt\r\nimport sys\r\nimport pdb\r\nimport os\r\nfrom topicvecDir import topicvecDir\r\nfrom utils import *\r\n\r\ncustomStopwords = \"based via using approach learning multi algorithm algorithms\"\r\n\r\nconfig = dict(  snip_filenames = None,\r\n                short_name = None,\r\n                unigramFilename = \"top1grams-wiki.txt\",\r\n                word_vec_file = \"25000-180000-500-BLK-8.0.vec\",\r\n                K = 20,\r\n                N0 = 500,\r\n                max_l = 5,\r\n                init_l = 1,\r\n                max_grad_norm = 0,\r\n                # cap the sum of Em when updating topic embeddings\r\n                # to avoid too big gradients\r\n                grad_scale_Em_base = 2500,\r\n                topW = 30,\r\n                topTopicMassFracPrintThres = 0.1,\r\n                alpha0 = 0.1,\r\n                alpha1 = 0.1,\r\n                iniDelta = 0.1,\r\n                MAX_EM_ITERS = 100,\r\n                topicDiff_tolerance = 2e-3,\r\n                printTopics_iterNum = 10,\r\n                zero_topic0 = True,\r\n                useDrdtApprox = False,\r\n                customStopwords = customStopwords,\r\n                remove_stop = True,\r\n                normalize_vecs = False,\r\n                # shift all embeddings in a document, so that their average is 0\r\n                rebase_vecs = True,\r\n                rebase_norm_thres = 0.2,\r\n                evalKmeans = False,\r\n                verbose = 1,\r\n                seed = 0\r\n            )\r\n\r\ndef usage():\r\n    print \"\"\"topicvecDir.py [ -v vec_file -a alpha ... ] snip_file\r\nOptions:\r\n  -k:  Number of topic embeddings to extract. Default: 20\r\n  -v:  Existing embedding file of all words.\r\n  -r:  Existing residual file of core words.\r\n  -a:  Hyperparameter alpha. Default: 0.1.\r\n  -i:  Number of iterations of the EM procedure. Default: 100\r\n  -u:  Unigram file, to obtain unigram probs.\r\n  -l:  Magnitude of topic embeddings.\r\n  -A:  Append to the old log file.\r\n  -s:  Seed the random number generator to x. Used to repeat experiments\r\n  -n:  Nickname (short name) for the snip_file\r\n\"\"\"\r\n\r\ndef getOptions():\r\n    global config\r\n\r\n    try:\r\n        opts, args = getopt.getopt(sys.argv[1:],\"k:v:i:u:l:s:n:Ah\")\r\n        if len(args) != 1:\r\n            raise getopt.GetoptError(\"\")\r\n        config['snip_filename'] = args[0]\r\n            \r\n        for opt, arg in opts:\r\n            if opt == '-k':\r\n                config['K'] = int(arg)\r\n            if opt == '-v':\r\n                config['vec_file'] = arg\r\n            if opt == '-a':\r\n                config['alpha1'] = float(opt)\r\n            if opt == '-i':\r\n                config['MAX_EM_ITERS'] = int(arg)\r\n            if opt == '-u':\r\n                config['unigramFilename'] = arg\r\n            if opt == '-l':\r\n                config['max_l'] = int(arg)\r\n            if opt == '-s':\r\n                config['seed'] = int(arg)\r\n            if opt == '-A':\r\n                config['appendLogfile'] = True\r\n            if opt == '-n':\r\n                config['short_name'] = arg\r\n            if opt == '-r':\r\n                config['useDrdtApprox'] = True\r\n            if opt == '-h':\r\n                usage()\r\n                sys.exit(0)\r\n\r\n        basename = os.path.basename(args[0])\r\n        if config['short_name']:\r\n            config['logfilename'] = config['short_name']\r\n        elif len(args) > 1:\r\n            config['logfilename'] = \"(%d)%s\" %( len(args), basename )\r\n        else:\r\n            config['logfilename'] = basename\r\n\r\n    except getopt.GetoptError:\r\n        usage()\r\n        sys.exit(2)\r\n\r\n    return config\r\n\r\ndef main():\r\n\r\n    config = getOptions()\r\n    snip_filename = config['snip_filename']\r\n    snips_words = []\r\n    snips_name = []\r\n    \r\n    with open(snip_filename) as DOC:\r\n        snip_lines = []\r\n        snipcount = 0\r\n        snips_wc = 0\r\n        for line in DOC:\r\n            line = line.strip()\r\n            if line:\r\n                snip_lines.append(line)\r\n            else:\r\n                sniptext = \" \".join(snip_lines)\r\n                wordsInSentences, wc = extractSentenceWords(sniptext, remove_punc=\"iso-8859-1\")\r\n                snips_wc += wc\r\n                snipcount += 1\r\n                snips_words.append(wordsInSentences)\r\n                snips_name.append( \"%s-row%d\" %(snip_filename, snipcount) )\r\n                \r\n    snipfile_avgwc = snips_wc * 1.0 / snipcount\r\n    print \"%d words extracted from %d snippets in '%s'. Avg %.1f words each row\" %( snips_wc, \r\n                snipcount, snip_filename, snipfile_avgwc )\r\n    \r\n    topicvec = topicvecDir(**config)\r\n    topicvec.setDocs( snips_words, snips_name )\r\n    \r\n    best_last_Ts, Em, docs_Em, Pi = topicvec.inference()\r\n\r\n    basename = os.path.basename(config['logfilename'])\r\n    basetrunk = os.path.splitext(basename)[0]\r\n\r\n    best_it, best_T, best_loglike = best_last_Ts[0]\r\n    save_matrix_as_text( basetrunk + \"-em%d-best.topic.vec\" %best_it, \"topic\", best_T  )\r\n\r\n    if best_last_Ts[1]:\r\n        last_it, last_T, last_loglike = best_last_Ts[1]\r\n        save_matrix_as_text( basetrunk + \"-em%d-last.topic.vec\" %last_it, \"topic\", last_T  )\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n"
  },
  {
    "path": "test-docs/Drug Goes From 13.50 a Tablet to 750, Overnight.txt",
    "content": "Specialists in infectious disease are protesting a gigantic overnight increase in the price of a 62-year-old drug that is the standard of care for treating a life-threatening parasitic infection.\n\nThe drug, called Daraprim, was acquired in August by Turing Pharmaceuticals, a start-up run by a former hedge fund manager. Turing immediately raised the price to $750 a tablet from $13.50, bringing the annual cost of treatment for some patients to hundreds of thousands of dollars.\n\n“What is it that they are doing differently that has led to this dramatic increase?” said Dr. Judith Aberg, the chief of the division of infectious diseases at the Icahn School of Medicine at Mount Sinai. She said the price increase could force hospitals to use “alternative therapies that may not have the same efficacy.”\n\nTuring’s price increase is not an isolated example. While most of the attention on pharmaceutical prices has been on new drugs for diseases like cancer, hepatitis C and high cholesterol, there is also growing concern about huge price increases on older drugs, some of them generic, that have long been mainstays of treatment.\n\nAlthough some price increases have been caused by shortages, others have resulted from a business strategy of buying old neglected drugs and turning them into high-priced “specialty drugs.”\n\nCycloserine, a drug used to treat dangerous multidrug-resistant tuberculosis, was just increased in price to $10,800 for 30 pills from $500 after its acquisition by Rodelis Therapeutics. Scott Spencer, general manager of Rodelis, said the company needed to invest to make sure the supply of the drug remained reliable. He said the company provided the drug free to certain needy patients.\n\nIn August, two members of Congress investigating generic drug price increases wrote to Valeant Pharmaceuticals after that company acquired two heart drugs, Isuprel and Nitropress, from Marathon Pharmaceuticals and promptly raised their prices by 525 percent and 212 percent respectively. Marathon had acquired the drugs from another company in 2013 and had quintupled their prices, according to the lawmakers, Senator Bernie Sanders, the Vermont independent who is seeking the Democratic nomination for president, and Representative Elijah E. Cummings, Democrat of Maryland.\n\nDoxycycline, an antibiotic, went from $20 a bottle in October 2013 to $1,849 by April 2014, according to the two lawmakers.\n\nThe Infectious Diseases Society of America and the HIV Medicine Association sent a joint letter to Turing earlier this month calling the price increase for Daraprim “unjustifiable for the medically vulnerable patient population” and “unsustainable for the health care system.” An organization representing the directors of state AIDS programs has also been looking into the price increase, according to doctors and patient advocates.\n\nDaraprim, known generically as pyrimethamine, is used mainly to treat toxoplasmosis, a parasite infection that can cause serious or even life-threatening problems for babies born to women who become infected during pregnancy, and also for people with compromised immune systems, like AIDS patients and certain cancer patients.\n\nMartin Shkreli, the founder and chief executive of Turing, said that the drug is so rarely used that the impact on the health system would be minuscule and that Turing would use the money it earns to develop better treatments for toxoplasmosis, with fewer side effects.\n\n“This isn’t the greedy drug company trying to gouge patients, it is us trying to stay in business,” Mr. Shkreli said. He said that many patients use the drug for far less than a year and that the price was now more in line with those of other drugs for rare diseases.\n\n“This is still one of the smallest pharmaceutical products in the world,” he said. “It really doesn’t make sense to get any criticism for this.”\n\nThis is not the first time the 32-year-old Mr. Shkreli, who has a reputation for both brilliance and brashness, has been the center of controversy. He started MSMB Capital, a hedge fund company, in his 20s and drew attention for urging the Food and Drug Administration not to approve certain drugs made by companies whose stock he was shorting.\n\nIn 2011, Mr. Shkreli started Retrophin, which also acquired old neglected drugs and sharply raised their prices. Retrophin’s board fired Mr. Shkreli a year ago. Last month, it filed a complaint in Federal District Court in Manhattan, accusing him of using Retrophin as a personal piggy bank to pay back angry investors in his hedge fund.\n\nMr. Shkreli has denied the accusations. He has filed for arbitration against his old company, which he says owes him at least $25 million in severance. “They are sort of concocting this wild and crazy and unlikely story to swindle me out of the money,” he said.\n\nDaraprim, which is also used to treat malaria, was approved by the F.D.A. in 1953 and has long been made by GlaxoSmithKline. Glaxo sold United States marketing rights to CorePharma in 2010. Last year, Impax Laboratories agreed to buy Core and affiliated companies for $700 million. In August, Impax sold Daraprim to Turing for $55 million, a deal announced the same day Turing said it had raised $90 million from Mr. Shkreli and other investors in its first round of financing.\n\nDaraprim cost only about $1 a tablet several years ago, but the drug’s price rose sharply after CorePharma acquired it. According to IMS Health, which tracks prescriptions, sales of the drug jumped to $6.3 million in 2011 from $667,000 in 2010, even as prescriptions held steady at about 12,700. In 2014, after further price increases, sales were $9.9 million, as the number of prescriptions shrank to 8,821. The figures do not include inpatient use in hospitals.\n\nTuring’s price increase could bring sales to tens or even hundreds of millions of dollars a year if use remains constant. Medicaid and certain hospitals will be able to get the drug inexpensively under federal rules for discounts and rebates. But private insurers, Medicare and hospitalized patients would have to pay an amount closer to the list price.\n\n\nSome doctors questioned Turing’s claim that there was a need for better drugs, saying the side effects, while potentially serious, could be managed.\n\n“I certainly don’t think this is one of those diseases where we have been clamoring for better therapies,” said Dr. Wendy Armstrong, professor of infectious diseases at Emory University in Atlanta.\n\nWith the price now high, other companies could conceivably make generic copies, since patents have long expired. One factor that could discourage that option is that Daraprim’s distribution is now tightly controlled, making it harder for generic companies to get the samples they need for the required testing.\n\nThe switch from drugstores to controlled distribution was made in June by Impax, not by Turing. Still, controlled distribution was a strategy Mr. Shkreli talked about at his previous company as a way to thwart generics.\n\nSome hospitals say they now have trouble getting the drug. “We’ve not had access to the drug for a few months,” said Dr. Armstrong, who also works at Grady Memorial Hospital, a huge public treatment center in Atlanta that serves many low-income patients.\n\nBut Dr. Rima McLeod, medical director of the toxoplasmosis center at the University of Chicago, said that Turing had been good about delivering drugs quickly to patients, sometimes without charge.\n\n“They have jumped every time I’ve called,” she said. The situation, she added, “seems workable” despite the price increase.\n\nDaraprim is the standard first treatment for toxoplasmosis, in combination with an antibiotic called sulfadiazine. There are alternative treatments, but there is less data supporting their efficacy.\n\nDr. Aberg of Mount Sinai said some hospitals will now find Daraprim too expensive to keep in stock, possibly resulting in treatment delays. She said that Mount Sinai was continuing to use the drug, but each use now required a special review.\n\n“This seems to be all profit-driven for somebody,” Dr. Aberg said, “and I just think it’s a very dangerous process.”\n"
  },
  {
    "path": "test-docs/VR-mitrv.txt",
    "content": "Though virtual reality is still far from mainstream, 2015 was a big year for the industry as new headsets were introducedsome full-featured and powerful, some simple and portableand companies announced new ways to control and capture VR imagery, too. Throughout the year, investors poured money into companies developing the technology, content creators figured out how to make everything from films to advertisements in VR, and millions of Americans experienced virtual-reality technology for the very first time.   \r\n\r\nWith all that happened, it can be hard to sift out whats most important. We cut through the virtual noise to bring you the six most significant events in virtual reality this year.\r\n\r\n1. HTC and Valve show off the Vive VR headset\r\n\r\nIn March, smartphone maker HTC and video-game company Valve Software pulled back the curtains on their collaborative virtual-reality effort, the Vive headset. Vive has a tracking system that uses lasers to keep an eye on your location within a space as large as 15 by 15 feet, making it possible to roam around while using the headset. The Vive is slated for commercial release in April.\r\n\r\n2. Oculus unveils its first consumer headset, Rift, and Oculus Touch hand controllers\r\n\r\nIn June, Facebook-owned Oculus trotted out its first consumer headset, Rift, and a pair of half-moon, button-bedecked controllers, both of which it plans to release next year (see Oculus Shows Its First Consumer Headset, Circular Hand Controls).\r\n\r\nThe matte black headset has two OLED displays, a wide field of view, and a speaker over each ear; it will need to physically connect to a powerful computer, along with a separate sensor that tracks the users motions. The controls, meanwhile, enable hand gestures like dragging objects, pointing, and waving.\r\n\r\nThe company hasnt publicly stated a specific release date for the headset, though it has said it will come in the first quarter of 2016. The hand controls are slated for release in the second quarter.\r\n\r\n3. Microsoft shows off speedy progress with HoloLens, its holographic headset\r\n\r\nMicrosofts HoloLens, which is more correctly referred to as augmented reality than virtual reality since it is intended to blend 3-D virtual visuals with the real world, debuted in January. At the time, Microsoft invited a number of reporters to try out HoloLens, which it hopes could eventually be used to do things like play games and work on 3-D models. While Microsoft presented it as a sort of futuristic set of ski goggles, reporters were only able to test a much clunkier version that was still tethered to a stationary computer, and required a so-called holographic processing unit worn around the neck.\r\n\r\nBut at the companys Build developer conference in May, Microsoft let reporters try out a fully self-contained prototype version of the device that looked much more polished (see Microsoft Making Fast Progress with HoloLens). This prototype gave a much better sense of the hard work required to fit a lot of technology into a compact, good-looking, untethered head-mounted display. Its not clear when HoloLens will be released to the public; Microsoft has only said that a $3,000 developer edition will begin shipping early next year.\r\n\r\n4. Cameras envision new ways to film virtual reality\r\n\r\nAt Googles I/O developer conference in June, the company announced a new kind of camera for taking live-action 3-D virtual-reality videos. Called Jump, it includes 16 cameras in a circular array, making it possible to capture each point from three perspectives; software can then convert the footage into 3-D. Google released the Jump design for free, and GoPro started selling a fully assembled version of the device late this year.\r\n\r\nAlso this year, photography startup Lytrowhich has struggled to popularize its camera for consumers that lets you refocus images after taking themsaid in November that its working on a spherical camera for capturing live-action 3-D films for virtual reality, too (see Lytro Is Building a Camera to Capture Live-Action Virtual Reality).\r\n\r\n\r\n5. Google Cardboard goes out to New York Times Sunday print subscribers\r\n\r\nIn November, the New York Times sent over a million Google Cardboard virtual-reality kits to its subscribers to promote its virtual-reality documentary The Displaced, which details what happens to children displaced by war. Though Google Cardboarda foldable, handheld virtual-reality headset with plastic lenses that users must slip a smartphone into to power itwas introduced back in 2014, this move brought a simple, immersive experience into many more homes  (see Google Aims to Make VR Hardware Irrelevant Before it Even Gets Going).\r\n\r\n6. Samsung starts selling its new Gear VR\r\n\r\nAlso in November, Samsung released the latest version of its Gear VR headset, a $100 headset developed with Oculus that uses a Samsung smartphone as its computer and display. The newest Gear VR costs half as much as its predecessor did when it came out in 2014, and supports more of Samsungs smartphones (the Galaxy Note 5, Galaxy S6, Galaxy S6 Edge, and Galaxy S6 Edge+). It still lacks important functions like positional tracking, which means games and other VR experiences cant track your head movements, but it shows Samsung and Oculus are serious about marrying virtual reality and mobile devices.\r\n"
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  {
    "path": "test-docs/batman-v-superman.txt",
    "content": "Batman v Superman: Dawn of Justice begins where its 2013 DC-Comics predecessor, Man of Steel, ended: The titular Kryptonian is fighting his nemesis, General Zod, over the streets of Metropolis and, in the process, laying waste to much of the city. What we didnt know before is that down there, amid the rubble, is the billionaire Bruce Wayne, trying with limited success to rescue workers at a local branch of Wayne Enterprises from the collateral destruction.\r\n\r\nTheres the germ of an interesting idea here. The Man of Steel director Zack Snyder took a fair amount of grief for the way that movie casually depopulated an American city, however fictional. In Snyders retelling here, Bruce Wayne (a.k.a., obviously, Batman) essentially becomes the voice of those critics. Is Superman really a hero? Or is he just some alien interloper who brought his extraterrestrial vendettas to our humble planet, knocking down an entire urban skyline in the process? In the hands of another directorthe Christopher Nolan of the Dark Knight movies or the Bryan Singer of the X-Men films come immediately to mindthis and other moral quandaries might have been drawn out in intriguing ways.\r\n\r\nInstead, alas, we have Snyder, whose idea of a moral quandary is should I make this scene grimor grimmer? Loudor louder? Violentor more violent still? Its thus no surprise that after all its early, ostentatious handwringing, Batman v Superman ends almost exactly as its predecessor did, with another dull, city-smashing duel between super-beings. The only surprise is that the movie recalls its animating premise vaguely enough to bother explaining that the neighborhood being leveled this time around is uninhabited. No harm, no foul.\r\n\r\n\r\nSuch thematic carelessness is on constant display, which wouldnt loom as nearly so large a problem if Snyders film didnt advertise its aspirations to Moral Seriousness in almost every plodding, humorless frame. There are endless disquisitions about whether Batman is good for Gotham and Superman good for the Earth, and tedious evocations of the roles of gods and men. Yet the actual characters themselves never come to life as anything other than symbols to be clumsily bickered over.\r\n\r\nWe know, for instance, that Lex Luthor (played by Jesse Eisenberg as a young tech magnate) hates Superman (Henry Cavill) primarily because we knew as much before the movie even started. And other than a kind of whiny, inchoate envy, thats all the motivation he evidently needs. Likewise, we know that Lois Lane (a thoroughly wasted Amy Adams) loves Superman because a) she keeps saying so; and b) that is her principal plot function. (Well, that and requiring the Man of Steel to rescue her from certain death at frequent intervalsthere are times when its hard to believe that this movie was made in 2016.) As for Loiss heartfelt question, I just dont know if its possible for you to love me and be you, I would refer her to Larry Nivens dispositive 1969 essay, Man of Steel, Woman of Kleenex.\r\n\r\nAnd when it comes to Diana Prince (a.k.a., Wonder Woman, and played by Gal Gadot), over the course of two and a half hours we learn precisely nothing about her other than she fought in World War I, became disillusioned with mankinds senseless cruelty, and has now reappeared 100 years later with a taste for high-end couture that displays large, strategically selected expanses of bare skin. (She reserves her famous red-and-blue one-piece for the final act.)\r\n\r\nWhich brings us to Batman/Bruce Wayne, played with grumpy intensityand poor shaving habitsby Ben Affleck. The idea of the clash between him and Superman is loosely lifted from Frank Millers seminal 1986 comic series The Dark Knight Returns. In that telling, Batman was a bitter, 55-year-old super-retiree, whose vigilante methods were of such concern to the U.S. government that it enlisted Superman to rein him in. This time around, however, the roles are mostly inverted, with Superman as the potential threat to ordera U.S. Senator played by Holly Hunter pops up periodically to raise this questionand Batman the potential remedy.\r\n\r\n\r\nBut following the initial loss of life at Wayne Enterprises, Batmans motivations become vaguely bordering on incomprehensible. The closest he comes to articulating them is when he explains to his butler Alfred (now played by Jeremy Irons, in a perhaps inevitable passing of the generational torch from Michael Caine): He has the power to wipe out the human race. And if we think that theres even a one percent chance that hes our enemy, we have to treat it as an absolute certainty. This may be the most ostentatiously shoddy logic deployed by a theoretically brilliant character in recent movie history.\r\n\r\nMake no mistake: Batman doesnt want merely to limit Supermans super-prerogatives, or to come up with a contingency plan in case he goes rogue, or even to confine him somewhere. He wants, quite explicitly, to kill Superman. Again, in the proper hands (such as Frank Millers 30 years ago), the idea of a quasi-insane Batman might bear interesting fruit. But Snyders Batman is all over the map, suave and rational one moment and snarling belligerent threats the next. At one point, he taunts Superman like an overly theatrical WWF heel: Tell me, do you bleed? You will. What has Superman done to merit this degree of hatred? Leveled another city? Killed the president? Declared war on puppies? Nope. Alien monster that he is, hes messed up the Batmobile.\r\n\r\nI will not go further into the plot of the movie, in part because Snyder has made an explicit plea that reviews avoid spoilers and in part because the screenplay, by Chris Terrio and David S. Goyer, is a tangled mess of awkwardly entwined storylines. Even on questions as straightforward as whether this Batman is a continuation of Nolans Dark Knight or a reboot, the movie seems unable to decide. On the one hand, it presents us yet again with the robbery-murder of young Bruce Waynes parents, perhaps the scene in all popular cinema that least needed to be portrayed anew. (This time, though, his mothers pearls tumble to the ground in slow motion.) On the other, it presumes that we already know all about Alfred, the Bat Cave and so on.\r\n\r\nAs its subtitle announces, Batman v Superman is a setup for Snyders upcoming Justice League movie, in conscious apery of the world-building that Marvel Studios has been undertaking for almost a decade. But whereas Marvel produced five superhero features before assembling its heroes in The Avengers, Warner Bros. has no such patience with its DC properties. (Justice League Part One is due out next year.) Nowhere is this impatience more evident than in a remarkably lame narrative shortcut in which Bruce Wayne happens upon a metahuman supercut of footage that introduces Wonder Woman, the Flash, Aquaman, and Cyborg in the course of approximately two minutes. Glad thats out of the way, guys!\r\n\r\nSuch shoddiness is characteristic of the entire script. Though there are several scenes illustrating the extent of Supermans super-speed, he never seems to use it when its most neededfor example, as he stands around watching Batman prepare to hurl what he already knows to be a deadly kryptonite grenade. One crucial plot twist depends entirely on the coincidence of two characters having relatives with the same first name. (Though the moment is intended to be one of profound emotion, there was open laughter in the screening I attended.) And the biggest twist of all is one that was rendered implausible by a vision of the future that Batman had already experienceda vision, incidentally, that will be utterly incomprehensible to all but the most devout DC fans.\r\n\r\nAffleck is solid as Batman, at least insofar as the script allows him to be. Cavill, though, again fails to sell the Man of Steel persuasively, and Gadot makes almost no impression at all in her underwritten role. But its Eisenberg who really stands out in the film, and not in a good way. Ive been a fan of the actor dating back to 2002s Roger Dodger, but his twerpy take on Lex Luthor is almost unwatchable: Petulant, melodramatic, hyperactive, and entitled, he comes across like an improbableand thoroughly unappetizingblend of Tracy Flick and the Joker. One almost wonders whether Eisenbergs mocking sendup of film reviewers in The New Yorker last December was intended as a bit of critical inoculation.\r\n\r\nUltimately though, the central flaw of Batman v Superman is Snyders trademark tone, which alternates between angry and maudlin with little in between. Almost the entire film seems to be set at night, as if it were taking place in Mordor, or perhaps Anchorage in December. And Hans Zimmers clamorous, punishing score was still reverberating in my fillings for hours after the movie was over. In the end, Batman v Superman is a tiresome, ill-tempered film, and one too lazy even to earn its dismal outlook.\r\n\r\n"
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  },
  {
    "path": "test-docs/beijing-haze-news.txt",
    "content": "BEIJING  Wu Yiling, a 15-year-old student with a love of hamburgers and Hello Kitty, celebrated when she heard that her school was canceling classes for three days because of air pollution. Finally, she would be able to stay up late watching her favorite Korean soap operas, she imagined, and playing computer games with friends.\r\n\r\nYet at the crack of dawn on Wednesday, Yilings mother jolted her out of bed.\r\n\r\nHer literature teacher had assigned 100 pages of reading, including an intricate Song dynasty poem. Her mother had arranged a tutoring session with a math instructor. An English teacher had announced that there would be a six-part exam on Friday.\r\n\r\nI quickly learned that theres no such thing as a day off, said Yiling, who attends a high school in eastern Beijing. Even when we can barely breathe outside, somehow we are supposed to do our schoolwork.\r\n\r\nContinue reading the main story\r\nRELATED COVERAGE\r\n\r\nPeople filming a flag-raising ceremony during heavy smog at Tiananmen Square in Beijing on Wednesday.As Beijing Shuts Down Over Smog Alert, Worse-Off Neighbors Carry OnDEC. 9, 2015\r\nSmog blanketed Tiananmen Square in Beijing on Wednesday.Sinosphere: Chinese-Language Readers Speak Out on the Emissions ChallengeDEC. 9, 2015\r\nAs U.S. and Europe Pass the Hat at Climate Talks, China Clings to Developing-Nation StatusDEC. 9, 2015\r\nClimate Negotiators Face Hurdles on Key Issues as Deadline LoomsDEC. 9, 2015\r\nSecretary of State John Kerry, in Paris this week, said, The Negotiations: Kerry in Familiar Spot: Trying to Coax Deal in Meetings Closing DaysDEC. 9, 2015\r\nFor Indians, Smog and Poverty Are Higher Priorities Than Talks in ParisDEC. 9, 2015\r\nA building with a screen on it in Beijing on Tuesday was obscured by smog that led to the citys top level of air pollution warning.Smog So Thick, Beijing Comes to a StandstillDEC. 8, 2015\r\nAs smog blanketed this city for a second day on Wednesday, reviving calls for officials to take action, millions of families grappled with the unexpected closing of schools across the city.\r\n\r\nPhoto\r\n\r\nMa Chenlei, her daughter Yifan, 7, and son Yichen, 3, at home. Credit Li Qiang for The New York Times\r\nParents searched frantically for day care options. Teachers drew up impromptu lesson plans for use at home. Students who dreamed of leisurely breaks and trips to the mall braced for hours of drills and review sessions instead.\r\n\r\nIts hard to be at home and doing nonstop work, said Zhang Wei, 14, a student at a middle school in central Beijing. I wish the skies were clear and we could play outside.\r\n\r\nIn online forums, students bemoaned the amount of work they had to complete over the three-day break.\r\n\r\nYou may never know how badly we will be tortured by the teachers in the three days that we are home, an unidentified student wrote on a widely circulated post on Weibo, a microblogging service. Kids in Beijing, go cry.\r\n\r\nSeveral parents and students said they were concerned that school closings would become more frequent as the severe air pollution persisted. While Beijing has historically kept its schools open, no matter how poisonous the air, the government this week issued what it called a red alert for the first time, advising schools to close down from Tuesday through Thursday.\r\n\r\nAdvertisement\r\n\r\nContinue reading the main story\r\nFactories were also shut and half of all cars were kept off the roads. But in other cities across northern China, tens of millions of people, including schoolchildren, went about their daily routines in toxic air that was far worse than Beijings. Smog will be the norm in the future, so what are you going to do about it? said Zhang Lili, 38, a university instructor in Beijing and the mother of an 8-year-old son. Are we supposed to suspend classes whenever there is smog? Am I supposed to become a stay-at-home mother?\r\n\r\nAs smog levels reached hazardous levels on Wednesday, parents hoarded face masks and bought air purifiers. Some said it was unsustainable to have students studying at home and questioned whether it was an effective safety precaution.\r\n\r\nSchools and homes share the same air pollution problem, said Chen Xiao, 35, a translator who is the father of a 9-year-old boy. My son can still be hurt by smog.\r\n\r\nAdvertisement\r\n\r\nContinue reading the main story\r\nMr. Chen said the pollution had gotten so bad recently that his family was considering leaving Beijing.\r\n\r\nChinese parents invest extraordinary amounts of time and money to ensure that their children succeed academically, and many said they worried their children might fall behind if they were kept out of school too long.\r\n\r\nSchool closings in Beijing are rare, given the citys dry climate and lack of snow. An exception was the outbreak of the SARS respiratory virus in 2003, when the city suspended classes for several weeks.\r\n\r\nThe red alert led residents to take more precautions, but the lack of official responses in cities like Anyang, in Henan Province, and Handan, in Hebei Province, both with hazardous levels of smog, pointed to a major shortcoming of efforts by Chinese officials to battle air pollution and protect people from its effects.\r\n\r\nSome experts said that Beijings red alert was a watershed moment, though, and that other provinces could feel pressure to impose stricter measures  if only to lessen some of the pollutants from their factories blowing to Beijing, a showcase metropolis and home to more than 20 million people  including the Communist Party leadership.\r\n\r\nThere will be pressure on them, but whether they will do it or not is a question, Wang Tao, an energy and climate scholar at the Carnegie-Tsinghua Center for Global Policy, said of provincial and local officials.\r\n\r\nIn issuing the red alert this week, Beijing officials told parents that while classes were suspended, students should continue studying. They encouraged parents to use the time to teach their children life and safety lessons. The government also unveiled a database of 5,000 lecture videos, covering topics like Chinese history and biology, on a state-run digital education platform.\r\n\r\nPrivate tutoring companies, a booming industry in China, seized on the disruption caused by the smog to make a pitch for their products. Some offered extra tutoring sessions to help prepare for college exams. Others advertised online courses, saying that parents could return to work with peace of mind while their children studied at home.\r\n\r\nAt Beijing Jingshan School, a prestigious public school, teachers led online classes for students in subjects like math and Chinese. One class on Wednesday focused on the history of air pollution in China, making the argument that it was present even in ancient times, when fog and dust storms were common in Beijing.\r\n\r\nAt some of Beijings most competitive schools, parents were asked to provide pictures of their children completing school assignments as proof that they were still studying. They happily complied, flooding online forums with snapshots of their children seated at desks and next to calculators.\r\n\r\nHe Mei, 40, a housewife in Beijing, said her daughter, He Xiang, 8, could have a healthier lifestyle by studying at home, taking more breaks and eating more fruit.\r\n\r\nBeijing is dirty and dry, Ms. He said. Staying at home is good for her health.\r\n\r\nAdvertisement\r\n\r\nContinue reading the main story\r\nAdvertisement\r\n\r\nContinue reading the main story\r\nHer daughter disagreed. I dont like the smog, she said. I want to go outside and play with my classmates.\r\n\r\nWhen Ma Chenlei, 44, an engineer, and Wang Yanhui, 46, a salesman, first heard that the pollution in Beijing would reach hazardous levels this week, they decided to stay home with their two children until classes resumed. They have alternated study time with play breaks and occasional walks around the block, to make up for missed exercise classes.\r\n\r\nI dont like it, I cough a lot, said their 7-year-old daughter, Yifan. When I wear a mask, I feel a little suffocated.\r\n\r\nInside the familys apartment on Wednesday, Yifan showed off her completed math and English homework, which she had sent to her teachers, while her 3-year-old brother, Yichen, played with racecars.\r\n\r\nMs. Ma said her daughters school was planning to install air filters next year. In the meantime, she said, there was nothing they could do to alleviate the pollution.\r\n\r\nWe hope that every day the air is clean, she said. But it will take much longer than one or two days to solve this problem."
  },
  {
    "path": "test-docs/brain-scar.txt",
    "content": "Brain images taken from hundreds of soldiers diagnosed with mild traumatic brain injuries suggest that methods for diagnosing concussions are inadequate in detecting damage. The results, part of the largest-ever imaging study of traumatic brain injury in the military, provide evidence that even brain injuries commonly classified as mild may lead to long-lasting damage.\r\n\r\nResearchers at Walter Reed National Medical Military Center observed abnormalities in the white matterthe part of the brain responsible for transmitting signals between different regionsof more than half the participants, most of whom had been diagnosed with at least one concussion. Gerard Riedy, a neuroradiologist at Walter Reed who led the research, says the large number of abnormalities seen in this study was surprising, and it undermines the conventional wisdom that a person with mild traumatic brain injury should have normal brain images.\r\n\r\nMore than 300,000 U.S. service members have been diagnosed with traumatic brain injury since 2000, often the result of blast-related trauma. Using imaging to detect damage could help doctors determine the most appropriate treatment. Often in concussion cases neither a CT scan nor an MRI reveals any signs of brain damage. And the clinical tools available for assessing the injury, which include a patients history, evaluations of cognitive skills like memory and attention, and tests of certain motor skills, require a large degree of subjective interpretation, says Riedy. Further, those assessments can be muddled by other conditions like post-traumatic stress disorder, which can cause many of the same symptoms.\r\n\r\nRiedy and his colleagues used an advanced form of MRI to look for abnormalities in the wiring of the brain, and they found them in nearly 52 percent of the more than 800 soldiers who participated in the study. The medical significance of the findings is not yet fully understood, but they are definitely abnormal, says Riedy. Something has happened to that section of the brain, and the body has come in and tried to repair it, and it leaves a little scar.\r\n\r\nThe group has also collected data from the same  soldiers using other imaging techniques: one that can reveal disruptions in the way that white-matter fibers, or axons, are organized, and another that evaluates brain functionality by measuring blood flow to given regions.  We see abnormalities on those also, says Riedy.\r\n"
  },
  {
    "path": "test-docs/britain-EU.txt",
    "content": "Would Britain be better off economically outside the European Union? The answer is most likely no, which is why Britons need to weigh the evidence carefully when they go to the polls in June to vote on leaving the E.U.\r\n\r\nProponents of an exit criticize the union for taking too much power away from member nations and for issuing unnecessary and excessive regulations. This, the argument goes, has hurt Britains economy. But the data does not back that up. It is far from clear that leaving would lead to faster growth and better living standards. In fact, it could have the opposite effect.\r\n\r\nIf E.U. regulations were as onerous as British critics say, those rules would hurt all 28 member countries. Yet the economies of other members like Germany, the Netherlands and Ireland have been more productive than Britain. Those countries had a gross domestic product per hour worked that was nearly the same as the United States in 2014, while Britains comparable figure was much lower, according to the Organization for Economic Cooperation and Development.\r\n\r\nIf Britain left the E.U. it would have to replace the unions rules with its own regulations in areas like the environment, banking and social services. It is improbable that when it came down to specific rules, the British public would want the risks that come with a more laissez-faire approach. In some areas like policies to reduce greenhouse gases, British regulations are already tougher than those of the E.U., according to an analysis by Open Europe, a London-based research group.\r\n\r\nSupporters of an exit argue that Britain would be able to maintain an open market with the union, its biggest trading partner. But that seems like wishful thinking. The E.U. would have no obligation to continue giving Britain tariff-free access to its market without securing big concessions in return. For example, Norway, which is not a member, pays the E.U. a fee and abides by most of its regulations to get access to its market.\r\n\r\nAs far as trading with the rest of the world, Britain would most likely be worse off on its own. The European Union has trade agreements with countries that account for 60 percent of Britains trade, according to Open Europe. Britain would probably have to negotiate new pacts with those countries, and the terms would very likely be less favorable than those secured by the E.U. The union, after all, is the worlds largest economy with a G.D.P. of $18.5 trillion in 2014; Britains G.D.P. was $3 trillion. For the United States, which is negotiating a trade agreement with the E.U., a pact with Britain would no doubt be a lower priority.\r\n"
  },
  {
    "path": "test-docs/drugstory.log",
    "content": "topicvecDir() init at Thu Mar 17 14:45:54 2016\n1 docs scanned, 1 kept. 654 words kept, 405 unique. 548 stop words, 113 out voc\nTop words:\nprice(1535): 17 drug(1942): 15 drugs(3528): 10 patients(2468): 9 turing(18313): 9 company(151): 8 increase(1265): 8 use(176): 7 year(54): 6 now(169): 6 diseases(4439): 6 million(357): 6 treatment(1460): 5 dr(570): 5 hospitals(4777): 5 mr(757): 5 old(204): 5 acquired(1447): 5 toxoplasmosis(125001): 4 used(82): 4 increases(4242): 4 prices(4345): 4 infectious(12595): 4 according(331): 4 companies(939): 4 certain(1095): 4 raised(1286): 4 generic(7602): 4 first(26): 3 two(37): 3 \n20 topics.\n'drugstory' inference starts at 14:47:26\nEM Iter 0:\nEm:\n[ 22.42513049  31.94466586  31.1619016   33.11611494  33.03238088\n  35.44686424  31.72498834  30.70455034  31.22343986  32.27387376\n  31.33918348  30.22820011  36.41553558  33.12208627  38.59218148\n  33.05118468  34.77249142  33.53407919  35.08483692  34.80631055]\n\nEM Iter 1:\nEm:\n[ 16.67578105  32.31893478  30.69428264  33.45501942  33.07694558\n  35.60827353  31.63191089  29.50517304  30.04487049  31.97660854\n  30.67902955  29.11488785  38.20366332  33.8647681   39.83253969\n  33.99530301  35.95971922  34.21572809  37.7853764   35.3611848 ]\n\nEM Iter 2:\nEm:\n[ 13.16854201  34.04145502  31.03806302  34.1659293   32.79319253\n  34.16999106  31.985903    29.21662462  29.4447374   32.53248789\n  30.19529453  28.93848403  38.331214    33.56927367  39.27630003\n  35.86227957  35.93185527  34.48179943  40.44831835  34.40825527]\n\nEM Iter 3:\nEm:\n[ 10.5326401   35.86979501  31.19478481  34.68651523  32.47052565\n  32.54495389  32.13450145  29.08754538  28.66441582  33.00413208\n  29.53609946  29.07690908  38.14965474  32.95005769  38.3746014\n  38.00194928  35.61070403  34.51368495  44.44506119  33.15146876]\n\nEM Iter 4:\nEm:\n[  8.50212579  37.71040875  31.18828393  35.0594849   31.990186\n  30.93905665  32.15926958  28.80520499  27.77495359  33.35182703\n  28.69566074  29.11916465  37.80851414  32.13186955  37.31229026\n  40.24713187  34.99438545  34.37650211  50.0937598   31.73992022]\n\nEM Iter 5:\nEm:\n[  6.9343874   39.44140038  31.03432663  35.31104626  31.23567792\n  29.44835222  32.10389795  28.40908645  26.82112074  33.5374257\n  27.66675086  29.08999569  37.38273736  31.18733392  36.18607366\n  42.3160612   34.08263548  34.11226403  57.44336597  30.25606017]\n\nEM Iter 6:\nEm:\n[  5.72271109  41.00232242  30.78084396  35.49964307  30.18555715\n  28.14844539  32.02207941  27.9533021   25.85885304  33.5769724\n  26.49222524  29.03433587  36.94663126  30.19377209  35.08454728\n  43.9603864   32.9382514   33.78876752  66.03199433  28.77835859]\n\nEM Iter 7:\nEm:\n[  4.78698232  42.43842573  30.49839394  35.70445089  28.92288533\n  27.08835268  31.96855858  27.49559216  24.94325291  33.53882893\n  25.25279297  29.00797224  36.55674247  29.21753521  34.07409178\n  45.11995121  31.67083684  33.48321982  74.86148306  27.36965094]\n\nEM Iter 8:\nEm:\n[  4.06606679  43.86704936  30.25261988  35.99694438  27.57180791\n  26.28494608  31.98245941  27.07907948  24.11167346  33.50650433\n  24.02870001  29.05773553  36.2349779   28.29735188  33.18563779\n  45.92446564  30.38781109  33.25582252  82.84406831  26.06427823]\n\nEM Iter 9:\nEm:\n[  3.51224926  45.40837701  30.0824987   36.418958    26.22961319\n  25.72572704  32.07663873  26.72128215  23.37551399  33.54096877\n  22.87014432  29.2068489   35.96533163  27.43988813  32.41366655\n  46.55883472  29.15733312  33.13375912  89.29670862  24.86565805]\n\nEM Iter 10:\nEm:\n[  3.0878245   47.14789787  29.99891507  36.98169633  24.94667447\n  25.38039423  32.24135216  26.41777874  22.7258759   33.67008986\n  21.79483943  29.45539483  35.70776189  26.63020979  31.72953332\n  47.16411314  28.00581411  33.11495873  94.04204309  23.75683253]\n\nEM Iter 11:\nEm:\n[  2.76308274  49.13470543  29.99500076  37.67712516  23.73824082\n  25.21304957  32.45556102  26.15297726  22.14462575  33.8971149\n  20.79999814  29.78961073  35.41769674  25.84642681  31.0981298\n  47.81885564  26.93338132  33.18138975  97.22877561  22.71425204]\n\nEM Iter 12:\nEm:\n[  2.51483557  51.39196338  30.05646786  38.48942797  22.60090332\n  25.18934671  32.69676182  25.90918213  21.61279311  34.21213583\n  19.87401116  30.19088721  35.06026674  25.06964972  30.4888976\n  48.55477382  25.92872904  33.31054942  99.131542    21.71687558]\n\nEM Iter 13:\nEm:\n[   2.32513929   53.92615096   30.16782162   39.4019761    21.52377921\n   25.27872358   32.94623649   25.67135134   21.1145511    34.59989882\n   19.00336504   30.6411312    34.61653553   24.28789164   29.88006812\n   49.3747321    24.97785597   33.4817674   100.03135276   20.74967175]\n\nEM Iter 14:\nEm:\n[   2.18018715   56.73244166   30.31510315   40.40041093   20.49444543\n   25.45412256   33.19066866   25.42860923   20.63820376   35.04386418\n   18.17576137   31.12505593   34.08368086   23.49609319   29.258666\n   50.26508974   24.06796666   33.67852831  100.16739646   19.80370478]\n\nEM Iter 15:\nEm:\n[  2.06939226  59.7972452   30.48672182  41.47353424  19.50167994\n  25.69115258  33.421834    25.17410793  20.17579606  35.52801334\n  17.38117926  31.63075602  33.47183795  22.69450475  28.61874765\n  51.20316142  23.18876988  33.88870814  99.72800826  18.8748493 ]\n\nEM Iter 16:\nEm:\n[  1.98465577  63.09928777  30.67347174  42.61324238  18.53655999\n  25.96761136  33.6355638   24.90428376  19.72230949  36.03758628\n  16.6120249   32.14953207  32.7996686   21.88675258  27.95929371\n  52.16178881  22.33263915  34.1039866   98.85747853  17.96226271]\n\nEM Iter 17:\nEm:\n[  1.91979426  66.61018023  30.86828333  43.81414406  17.59275962\n  26.26352817  33.83059598  24.61801392  19.2748642   36.55938695\n  15.8629309   32.67545435  32.08995715  21.0781704   27.28235165\n  53.11241181  21.49435546  34.31905313  97.66669517  17.06706925]\n\nEM Iter 18:\nEm:\n[  1.87009554  70.29502163  31.06592997  45.07312242  16.66645204\n  26.561519    34.00756026  24.31586426  18.83205967  37.08193286\n  15.13044057  33.20486615  31.36594963  20.27456579  26.59159471\n  54.02736596  20.67072998  34.53084644  96.24271299  16.19137014]\n\nEM Iter 19:\nEm:\n[  1.83197508  74.11331519  31.26275466  46.38891823  15.75600808\n  26.84716611  34.16816459  23.99947647  18.39346667  37.59554313\n  14.41266453  33.73589419  30.64873649  19.48140752  25.89128755\n  54.88170834  19.86021227  34.73789507  94.65584558  15.33756027]\n\nT[:,10]:\n[[ 0.          0.          0.          0.          0.          0.          0.\n   0.          0.          0.        ]\n [ 0.0118022  -0.02589011  0.06636742 -0.02732162 -0.03207544  0.02501733\n   0.03020483 -0.010835   -0.04846409  0.00364001]\n [ 0.00918208 -0.02292093  0.07708674  0.04281715 -0.09520832  0.01093115\n   0.06072116  0.0150073  -0.0583028   0.01097153]\n [ 0.01404123 -0.02724448  0.12158989  0.03804148 -0.04402607  0.05624154\n   0.09115467  0.04264906 -0.06528525  0.01105764]\n [-0.01594965 -0.02347516  0.05772781  0.00705458 -0.0511815   0.03002521\n   0.03491682  0.0046473  -0.02231415  0.03161692]\n [ 0.01893166  0.01594318  0.06178     0.00804629 -0.02460504 -0.01997418\n   0.02451225 -0.00651891 -0.0968537   0.00300578]\n [ 0.03927523 -0.01458148  0.11396262  0.06725097 -0.00939741  0.00880774\n   0.03945074 -0.02658016 -0.07120358  0.00450482]\n [ 0.02466559 -0.02812296  0.0627749  -0.01539987 -0.02113124  0.00021817\n   0.04140584  0.01059798 -0.13152911  0.02487502]\n [ 0.00757743  0.03177872  0.11676658  0.01689815 -0.04921332  0.03637627\n   0.06006395 -0.02597941 -0.06833992  0.02288234]\n [ 0.03139762  0.00654507  0.06806538  0.0454825  -0.05484947  0.04635491\n   0.04511475  0.04716904 -0.0114017   0.02100199]\n [ 0.04647012 -0.01303862  0.09762131 -0.01714125 -0.02531205  0.0233857\n   0.06969647 -0.02207201 -0.07448613  0.00684893]\n [ 0.01001768  0.01993036  0.12816079  0.00713317 -0.00378545  0.00570591\n   0.08248835 -0.04534903 -0.07203994  0.01259309]\n [ 0.01638241  0.00540389  0.08902986  0.04678924 -0.01624505  0.02978762\n   0.03293116 -0.03270301 -0.07475792 -0.02217778]\n [ 0.0127572  -0.02085729  0.06849897  0.05131651 -0.02999911  0.0352009\n   0.04930354  0.00869155 -0.07820439  0.00639415]\n [ 0.02478932 -0.00127118  0.05462858  0.02824942 -0.04559811  0.00537276\n   0.04034342 -0.03319763 -0.08345083  0.02326234]\n [ 0.00044789 -0.02948613  0.06635768  0.02068053 -0.03784395  0.05408524\n   0.08143771  0.00857089 -0.06614132  0.04160593]\n [ 0.00402001 -0.03708822  0.08928191  0.0269045  -0.06697044  0.06028367\n   0.07880183 -0.00860535 -0.05795833  0.01330504]\n [ 0.02523645 -0.00411075  0.10643977  0.02427419 -0.06262636  0.0276704\n   0.08120745  0.01304656 -0.07600836  0.03182495]\n [ 0.00668924  0.02528352  0.06158351 -0.02236388 -0.07002944  0.08843788\n   0.05509688 -0.00127315 -0.01834244 -0.00323235]\n [ 0.00859478  0.02689469  0.07079655  0.06420877 -0.02177496  0.00978898\n   0.06106631 -0.03116674 -0.09244583  0.02214464]]\nr:\n[ -1.33004718e-13  -4.59116456e-01  -4.43488338e-01  -4.34566179e-01\n  -5.51647034e-01  -6.75962763e-01  -4.80468252e-01  -4.85047676e-01\n  -5.02036959e-01  -4.42277334e-01  -5.03524593e-01  -4.53579846e-01\n  -7.08444560e-01  -5.46051252e-01  -5.87860443e-01  -4.73523423e-01\n  -5.34555390e-01  -4.66656682e-01  -4.80231452e-01  -5.67964421e-01]\n\nTopic magnitudes:\n[ 0.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.\n  3.  3.]\n\nTopic 18 (3.00): 14.5%\nMost relevant words:\nprice (1535,17): 0.947/16.093/0.904/7.897 prices (4345,4): 0.852/3.406/0.798/7.809 gouge (81038,1): 0.749/0.749/0.191/4.012 priced (14909,1): 0.675/0.675/0.529/4.444 stock (1878,2): 0.438/0.877/0.408/3.909 sales (1485,3): 0.379/1.138/0.467/4.498 rose (1336,1): 0.378/0.378/0.165/1.204 sold (614,2): 0.373/0.745/0.348/2.875 swindle (42243,1): 0.360/0.360/0.209/2.919 sharply (12378,2): 0.345/0.690/0.237/2.023 piggy (34457,1): 0.324/0.324/0.154/2.102 buy (3433,1): 0.319/0.319/0.471/3.864 buying (7669,1): 0.308/0.308/0.489/4.051 scott (1062,1): 0.304/0.304/0.079/0.507 discounts (28868,1): 0.296/0.296/0.397/4.710 martin (778,1): 0.295/0.295/0.019/0.124 born (106,1): 0.291/0.291/-0.068/-0.586 line (181,1): 0.291/0.291/0.025/0.206 april (148,1): 0.289/0.289/0.044/0.313 high (104,3): 0.286/0.858/0.142/1.267 pay (1653,2): 0.285/0.570/0.442/3.804 june (140,1): 0.284/0.284/0.036/0.253 cost (1178,2): 0.282/0.563/0.525/4.149 spencer (4173,1): 0.281/0.281/0.102/0.765 october (131,1): 0.278/0.278/0.029/0.221 low (612,1): 0.268/0.268/0.285/2.384 august (149,3): 0.267/0.800/0.016/0.122 round (273,1): 0.256/0.256/-0.054/-0.484 year (54,6): 0.253/1.520/0.088/0.645 armstrong (4767,1): 0.249/0.249/0.050/0.424 \nMost similar words in vocab:\nprice: 0.904/7.897 prices: 0.798/7.809 pricing: 0.597/5.223 inflation: 0.539/4.985 priced: 0.529/4.444 purchases: 0.526/4.054 cost: 0.525/4.149 exorbitant: 0.525/6.255 costs: 0.514/4.326 discount: 0.511/4.417 commodity: 0.507/4.229 purchasing: 0.504/4.002 profits: 0.501/4.120 resale: 0.490/5.611 buying: 0.489/4.051 dollars: 0.486/4.156 purchase: 0.476/3.894 discounted: 0.476/3.171 buy: 0.471/3.864 undervalued: 0.470/5.281 skyrocketed: 0.469/5.317 sales: 0.467/4.498 repayments: 0.465/4.994 buyers: 0.456/3.884 rates: 0.455/4.229 rebates: 0.451/5.066 commodities: 0.449/3.884 market: 0.446/3.645 sale: 0.445/3.757 capitalisation: 0.444/5.621 \n\nTopic 1 (3.00): 11.3%\nMost relevant words:\nmillion (357,6): 0.478/2.869/0.539/5.266 company (151,8): 0.320/2.556/0.336/2.708 millions (5429,1): 0.316/0.316/0.433/3.926 dollars (4434,2): 0.309/0.619/0.564/4.821 fund (2014,3): 0.302/0.906/0.455/4.002 investors (5976,2): 0.288/0.577/0.481/4.272 percent (1651,2): 0.272/0.544/0.441/4.024 companies (939,4): 0.267/1.070/0.445/3.515 invest (11944,1): 0.266/0.266/0.448/3.167 acquired (1447,5): 0.252/1.262/0.285/2.464 financing (8017,1): 0.252/0.252/0.444/3.655 hedge (14848,3): 0.251/0.753/0.289/2.548 profit (2747,1): 0.247/0.247/0.445/3.660 acquisition (4356,1): 0.245/0.245/0.360/3.131 raised (1286,4): 0.241/0.965/0.236/2.102 filed (3883,2): 0.241/0.482/0.282/2.691 rebates (60162,1): 0.233/0.233/0.535/6.007 clamoring (148365,1): 0.232/0.232/0.268/4.054 capital (735,1): 0.230/0.230/0.193/1.808 icahn (83210,1): 0.229/0.229/0.343/6.709 money (808,2): 0.226/0.452/0.407/3.459 thousands (2861,1): 0.221/0.221/0.256/2.227 sales (1485,3): 0.220/0.660/0.441/4.247 sold (614,2): 0.217/0.434/0.318/2.629 us (220,1): 0.216/0.216/0.226/2.063 huge (3131,2): 0.214/0.428/0.217/1.903 bank (640,1): 0.210/0.210/0.193/1.756 annual (895,1): 0.209/0.209/0.196/1.917 deal (1260,1): 0.204/0.204/0.342/2.611 hundreds (3443,2): 0.200/0.401/0.213/1.738 \nMost similar words in vocab:\nbillion: 0.588/5.868 dollars: 0.564/4.821 profits: 0.554/4.548 million: 0.539/5.266 rebates: 0.535/6.007 multibillion: 0.521/8.074 investments: 0.508/4.418 costs: 0.505/4.252 reimburse: 0.502/5.530 cost: 0.502/3.965 refinancing: 0.502/5.495 reinvest: 0.500/6.656 defrauded: 0.497/5.564 funds: 0.493/4.327 prices: 0.491/4.804 revenues: 0.487/4.244 multimillion: 0.484/6.634 investors: 0.481/4.272 overvalued: 0.478/6.877 purchasing: 0.478/3.793 indymac: 0.477/6.928 wellpoint: 0.477/7.368 purchases: 0.477/3.675 investment: 0.474/4.081 adrs: 0.474/7.397 payments: 0.473/4.132 untaxed: 0.473/6.944 surtax: 0.471/6.469 kickbacks: 0.470/5.754 incentivize: 0.469/6.329 \n\nTopic 15 (3.00): 8.4%\nMost relevant words:\nmillion (357,6): 0.233/1.396/0.494/4.824 rebates (60162,1): 0.228/0.228/0.558/6.264 shorting (108317,1): 0.226/0.226/0.362/6.337 dollars (4434,2): 0.220/0.441/0.557/4.761 increase (1265,8): 0.213/1.707/0.516/3.999 amount (1588,1): 0.204/0.204/0.427/3.463 cost (1178,2): 0.198/0.396/0.553/4.367 percent (1651,2): 0.195/0.389/0.435/3.970 clamoring (148365,1): 0.194/0.194/0.275/4.156 millions (5429,1): 0.192/0.192/0.409/3.709 increased (1167,1): 0.185/0.185/0.430/3.469 investors (5976,2): 0.185/0.369/0.462/4.105 shrank (46494,1): 0.181/0.181/0.282/3.241 pay (1653,2): 0.180/0.359/0.455/3.915 income (874,1): 0.179/0.179/0.413/2.988 severance (36986,1): 0.176/0.176/0.311/4.120 sales (1485,3): 0.175/0.524/0.446/4.294 money (808,2): 0.174/0.349/0.410/3.478 increases (4242,4): 0.174/0.695/0.425/3.650 inexpensively (98181,1): 0.174/0.174/0.283/4.680 annual (895,1): 0.171/0.171/0.204/1.994 huge (3131,2): 0.166/0.331/0.220/1.927 financing (8017,1): 0.165/0.165/0.427/3.512 fund (2014,3): 0.161/0.484/0.415/3.652 capital (735,1): 0.160/0.160/0.184/1.726 profit (2747,1): 0.159/0.159/0.425/3.500 discounts (28868,1): 0.157/0.157/0.391/4.648 invest (11944,1): 0.155/0.155/0.412/2.907 overnight (9082,1): 0.147/0.147/0.165/1.511 hedge (14848,3): 0.147/0.440/0.260/2.290 \nMost similar words in vocab:\nprices: 0.588/5.757 profits: 0.560/4.604 costs: 0.558/4.697 rebates: 0.558/6.264 dollars: 0.557/4.761 billion: 0.555/5.541 cost: 0.553/4.367 price: 0.540/4.715 refinancing: 0.533/5.836 exorbitant: 0.520/6.203 increase: 0.516/3.999 premiums: 0.505/5.794 repayments: 0.504/5.407 surtax: 0.503/6.912 overvalued: 0.501/7.208 reinvest: 0.500/6.662 reimburse: 0.497/5.479 multibillion: 0.497/7.699 million: 0.494/4.824 incentivize: 0.494/6.659 revenues: 0.492/4.288 purchases: 0.491/3.785 refunds: 0.491/5.658 payments: 0.490/4.283 purchasing: 0.490/3.889 untaxed: 0.484/7.108 investments: 0.483/4.195 adrs: 0.483/7.539 outlay: 0.482/5.118 recouping: 0.481/6.998 \n\nTopic 3 (3.00): 7.1%\nMost relevant words:\nhospitals (4777,5): 0.164/0.821/0.437/4.198 hospital (723,1): 0.160/0.160/0.346/3.171 care (1069,2): 0.156/0.312/0.496/4.225 medical (679,1): 0.152/0.152/0.489/4.190 health (502,3): 0.149/0.446/0.483/4.085 doctors (4958,2): 0.146/0.293/0.420/3.974 medicine (1528,2): 0.142/0.284/0.390/3.626 patients (2468,9): 0.134/1.206/0.647/5.842 patient (3777,2): 0.130/0.259/0.532/4.712 pharmaceutical (8897,2): 0.127/0.254/0.520/4.847 dr (570,5): 0.120/0.601/0.182/1.442 inpatient (33848,1): 0.120/0.120/0.399/4.979 glaxosmithkline (55181,1): 0.118/0.118/0.492/6.617 therapies (21018,2): 0.116/0.233/0.507/4.209 treating (10124,1): 0.113/0.113/0.522/4.216 center (223,3): 0.113/0.338/0.099/0.886 pharmaceuticals (16906,3): 0.112/0.337/0.454/4.193 specialists (10857,1): 0.112/0.112/0.250/2.139 cancer (1871,2): 0.111/0.222/0.485/4.688 therapeutics (36356,1): 0.110/0.110/0.474/5.915 glaxo (101914,1): 0.109/0.109/0.331/6.410 treatment (1460,5): 0.109/0.544/0.575/5.185 treat (5814,3): 0.109/0.326/0.515/4.382 laboratories (7537,1): 0.108/0.108/0.285/2.657 sinai (13035,3): 0.108/0.323/0.145/1.170 heart (869,1): 0.106/0.106/0.165/1.375 advocates (8690,1): 0.105/0.105/0.239/1.951 treatments (9357,2): 0.105/0.210/0.570/5.196 association (345,1): 0.105/0.105/0.083/0.728 prescriptions (34657,3): 0.105/0.314/0.494/6.130 \nMost similar words in vocab:\npatients: 0.647/5.842 medication: 0.623/5.404 medications: 0.620/5.249 drugs: 0.603/5.795 antiretroviral: 0.596/6.785 ivig: 0.587/7.330 drug: 0.575/5.756 treatment: 0.575/5.185 treatments: 0.570/5.196 prophylactic: 0.560/6.406 medicines: 0.556/4.826 diabetes: 0.556/4.831 methadone: 0.549/6.582 diarrheal: 0.546/6.884 antiepileptic: 0.541/6.657 prescription: 0.540/4.692 vioxx: 0.536/8.223 patient: 0.532/4.712 regimens: 0.531/6.194 antipsychotic: 0.528/5.922 healthcare: 0.525/4.739 contraindicated: 0.523/5.482 treating: 0.522/4.216 pharmaceutical: 0.520/4.847 vaccine: 0.520/4.837 hiv: 0.516/5.027 praziquantel: 0.516/6.991 treat: 0.515/4.382 bevacizumab: 0.512/7.226 dosages: 0.512/5.522 \n\nTopic 9 (3.00): 5.7%\nMost relevant words:\ndrugstores (120806,1): 0.121/0.121/0.376/5.962 glaxo (101914,1): 0.110/0.110/0.342/6.626 generics (85144,1): 0.107/0.107/0.376/6.381 companies (939,4): 0.103/0.412/0.403/3.186 insurers (32340,1): 0.102/0.102/0.431/4.814 concocting (179559,1): 0.096/0.096/0.283/4.774 icahn (83210,1): 0.095/0.095/0.330/6.457 lawmakers (27291,2): 0.094/0.188/0.257/2.896 medicaid (25609,1): 0.093/0.093/0.396/5.330 products (1127,1): 0.092/0.092/0.386/3.213 invest (11944,1): 0.090/0.090/0.384/2.714 patents (8554,1): 0.090/0.090/0.264/2.487 seeking (3864,1): 0.089/0.089/0.272/2.186 acquisition (4356,1): 0.089/0.089/0.315/2.745 acquired (1447,5): 0.089/0.444/0.236/2.044 marketing (2693,1): 0.088/0.088/0.306/2.759 clamoring (148365,1): 0.087/0.087/0.244/3.698 trying (2359,2): 0.086/0.172/0.191/1.412 expensive (4449,1): 0.086/0.086/0.329/3.147 filed (3883,2): 0.086/0.172/0.239/2.285 directors (2573,1): 0.085/0.085/0.158/1.263 fund (2014,3): 0.084/0.253/0.381/3.349 ims (30708,1): 0.083/0.083/0.156/2.238 rebates (60162,1): 0.083/0.083/0.499/5.604 company (151,8): 0.083/0.663/0.246/1.983 business (354,2): 0.083/0.165/0.220/1.671 approved (2389,1): 0.082/0.082/0.308/2.676 better (968,3): 0.082/0.245/0.228/1.621 financing (8017,1): 0.082/0.082/0.383/3.155 federal (626,2): 0.082/0.163/0.207/1.843 \nMost similar words in vocab:\nivig: 0.540/6.748 antiretroviral: 0.530/6.030 vioxx: 0.529/8.109 drugs: 0.519/4.987 medication: 0.511/4.431 adrs: 0.508/7.933 medications: 0.507/4.288 pfizer: 0.500/6.546 rebates: 0.499/5.604 drug: 0.498/4.985 methadone: 0.497/5.964 incentivize: 0.497/6.700 novartis: 0.496/6.430 patients: 0.495/4.472 medicines: 0.492/4.266 sanofi: 0.489/5.850 reimburse: 0.487/5.365 tamiflu: 0.487/7.014 pharmaceutical: 0.486/4.528 prescription: 0.485/4.214 dhhs: 0.484/6.441 healthcare: 0.483/4.361 diarrheal: 0.482/6.076 uninsured: 0.481/5.664 bevacizumab: 0.479/6.758 thiopental: 0.478/7.003 glaxosmithkline: 0.475/6.396 praziquantel: 0.475/6.437 prescriptions: 0.474/5.881 prophylactic: 0.473/5.405 \n\nTopic 17 (3.00): 5.3%\nMost relevant words:\ncancer (1871,2): 0.139/0.278/0.539/5.213 tuberculosis (11017,1): 0.120/0.120/0.542/4.717 diseases (4439,6): 0.119/0.712/0.566/5.464 disease (1780,1): 0.114/0.114/0.551/5.080 malaria (13222,1): 0.112/0.112/0.505/4.503 hepatitis (19494,1): 0.112/0.112/0.550/4.668 infectious (12595,4): 0.110/0.441/0.499/4.701 hiv (6116,1): 0.106/0.106/0.557/5.428 patients (2468,9): 0.106/0.950/0.654/5.904 therapeutics (36356,1): 0.104/0.104/0.494/6.161 medicine (1528,2): 0.104/0.207/0.389/3.613 patient (3777,2): 0.103/0.205/0.540/4.779 hospital (723,1): 0.103/0.103/0.330/3.023 aids (4693,2): 0.101/0.202/0.475/4.555 multidrug (112412,1): 0.099/0.099/0.422/6.733 treating (10124,1): 0.099/0.099/0.543/4.380 treat (5814,3): 0.098/0.294/0.538/4.581 treatment (1460,5): 0.096/0.482/0.594/5.365 infection (6037,2): 0.096/0.192/0.502/4.580 therapies (21018,2): 0.096/0.192/0.520/4.316 immune (8538,1): 0.094/0.094/0.383/3.484 hospitalized (18161,1): 0.094/0.094/0.345/2.396 treatments (9357,2): 0.094/0.188/0.591/5.387 glaxosmithkline (55181,1): 0.094/0.094/0.497/6.684 medical (679,1): 0.093/0.093/0.467/3.995 health (502,3): 0.092/0.277/0.462/3.908 antibiotic (24098,2): 0.092/0.183/0.510/3.685 parasitic (18146,1): 0.091/0.091/0.245/2.041 heart (869,1): 0.090/0.090/0.182/1.511 infected (8254,1): 0.089/0.089/0.336/3.227 \nMost similar words in vocab:\npatients: 0.654/5.904 medications: 0.632/5.344 medication: 0.631/5.470 drugs: 0.626/6.020 ivig: 0.617/7.706 antiretroviral: 0.606/6.893 drug: 0.602/6.021 treatment: 0.594/5.365 diabetes: 0.594/5.160 treatments: 0.591/5.387 diarrheal: 0.590/7.436 prophylactic: 0.572/6.534 antiepileptic: 0.569/7.002 chronic: 0.569/5.015 diseases: 0.566/5.464 vaccine: 0.558/5.196 hiv: 0.557/5.428 contraindicated: 0.556/5.830 medicines: 0.554/4.809 disease: 0.551/5.080 hepatitis: 0.550/4.668 antibiotics: 0.549/4.284 regimens: 0.548/6.394 methadone: 0.548/6.577 asthma: 0.545/4.162 treating: 0.543/4.380 antipsychotic: 0.543/6.087 chemotherapy: 0.542/4.181 tuberculosis: 0.542/4.717 patient: 0.540/4.779 \n\nTopic 6 (3.00): 5.2%\nMost relevant words:\nmedical (679,1): 0.112/0.112/0.492/4.217 medicine (1528,2): 0.110/0.220/0.398/3.700 drug (1942,15): 0.107/1.607/0.704/7.045 hospital (723,1): 0.107/0.107/0.338/3.095 aids (4693,2): 0.104/0.207/0.480/4.607 drugs (3528,10): 0.102/1.022/0.701/6.740 therapies (21018,2): 0.101/0.201/0.530/4.395 treatment (1460,5): 0.098/0.492/0.600/5.415 health (502,3): 0.096/0.288/0.471/3.976 pills (18688,1): 0.095/0.095/0.400/3.252 medically (28191,1): 0.095/0.095/0.360/5.232 care (1069,2): 0.095/0.190/0.476/4.057 pharmaceutical (8897,2): 0.094/0.188/0.524/4.878 therapeutics (36356,1): 0.094/0.094/0.488/6.080 patient (3777,2): 0.094/0.187/0.533/4.715 hiv (6116,1): 0.092/0.092/0.545/5.312 doctors (4958,2): 0.092/0.183/0.405/3.833 treatments (9357,2): 0.091/0.182/0.591/5.383 cancer (1871,2): 0.091/0.181/0.498/4.816 hospitals (4777,5): 0.091/0.453/0.410/3.934 treat (5814,3): 0.090/0.271/0.532/4.526 rima (42963,1): 0.090/0.090/-0.014/-0.197 inpatient (33848,1): 0.089/0.089/0.401/5.014 treating (10124,1): 0.089/0.089/0.533/4.303 center (223,3): 0.086/0.259/0.106/0.948 patients (2468,9): 0.086/0.774/0.634/5.728 dr (570,5): 0.086/0.428/0.181/1.432 hospitalized (18161,1): 0.084/0.084/0.334/2.316 laboratories (7537,1): 0.084/0.084/0.293/2.734 investigating (8587,1): 0.084/0.084/0.235/2.006 \nMost similar words in vocab:\ndrug: 0.704/7.045 drugs: 0.701/6.740 medication: 0.664/5.762 medications: 0.651/5.509 patients: 0.634/5.728 antiretroviral: 0.623/7.084 treatment: 0.600/5.415 ivig: 0.596/7.440 treatments: 0.591/5.383 prescription: 0.588/5.106 methadone: 0.586/7.035 medicines: 0.574/4.977 diabetes: 0.569/4.950 antiepileptic: 0.569/7.002 antipsychotic: 0.565/6.344 prophylactic: 0.563/6.431 diarrheal: 0.550/6.935 contraindicated: 0.550/5.765 therapeutic: 0.546/4.759 hiv: 0.545/5.312 regimens: 0.545/6.354 chronic: 0.540/4.766 antiviral: 0.537/6.292 analgesics: 0.536/5.950 antibiotics: 0.535/4.175 treating: 0.533/4.303 patient: 0.533/4.715 treat: 0.532/4.526 therapies: 0.530/4.395 vaccine: 0.527/4.908 \n\nTopic 11 (3.00): 5.2%\nMost relevant words:\ninpatient (33848,1): 0.136/0.136/0.435/5.429 hospital (723,1): 0.115/0.115/0.346/3.165 tuberculosis (11017,1): 0.110/0.110/0.535/4.660 patients (2468,9): 0.108/0.974/0.659/5.955 infectious (12595,4): 0.106/0.422/0.498/4.684 hospitals (4777,5): 0.105/0.523/0.424/4.074 malaria (13222,1): 0.105/0.105/0.500/4.462 hospitalized (18161,1): 0.103/0.103/0.363/2.515 serious (2375,2): 0.102/0.203/0.298/2.651 infection (6037,2): 0.101/0.202/0.511/4.658 care (1069,2): 0.100/0.200/0.482/4.109 treatment (1460,5): 0.099/0.496/0.601/5.419 infected (8254,1): 0.099/0.099/0.350/3.359 disease (1780,1): 0.097/0.097/0.537/4.952 patient (3777,2): 0.097/0.193/0.536/4.744 diseases (4439,6): 0.096/0.576/0.547/5.279 hepatitis (19494,1): 0.093/0.093/0.531/4.507 sinai (13035,3): 0.092/0.276/0.166/1.339 threatening (7927,2): 0.091/0.183/0.256/2.280 resistant (8950,1): 0.090/0.090/0.303/2.994 vulnerable (6874,1): 0.089/0.089/0.197/1.863 pregnancy (7979,1): 0.088/0.088/0.418/3.867 health (502,3): 0.087/0.260/0.458/3.871 heart (869,1): 0.087/0.087/0.180/1.495 cancer (1871,2): 0.086/0.172/0.493/4.759 problems (1188,1): 0.086/0.086/0.296/2.534 medical (679,1): 0.086/0.086/0.460/3.942 rare (2251,1): 0.085/0.085/0.170/1.624 treat (5814,3): 0.085/0.254/0.524/4.461 treating (10124,1): 0.084/0.084/0.526/4.245 \nMost similar words in vocab:\npatients: 0.659/5.955 ivig: 0.608/7.596 treatment: 0.601/5.419 diarrheal: 0.596/7.514 medication: 0.592/5.135 medications: 0.586/4.959 antiretroviral: 0.582/6.626 treatments: 0.574/5.226 prophylactic: 0.573/6.555 nosocomial: 0.567/7.545 diabetes: 0.561/4.879 chronic: 0.555/4.893 treatable: 0.549/5.977 vaccine: 0.547/5.092 diseases: 0.547/5.279 antiepileptic: 0.543/6.692 infections: 0.543/5.026 regimens: 0.543/6.329 contraindicated: 0.542/5.681 disease: 0.537/4.952 patient: 0.536/4.744 prophylaxis: 0.536/6.271 tuberculosis: 0.535/4.660 hiv: 0.535/5.211 hemodialysis: 0.533/6.132 hepatitis: 0.531/4.507 asthma: 0.530/4.046 praziquantel: 0.530/7.186 monotherapy: 0.527/6.989 treating: 0.526/4.245 \n\nTopic 2 (3.00): 4.8%\nMost relevant words:\nicahn (83210,1): 0.177/0.177/0.371/7.257 glaxo (101914,1): 0.102/0.102/0.347/6.727 generics (85144,1): 0.092/0.092/0.377/6.402 glaxosmithkline (55181,1): 0.088/0.088/0.498/6.705 concocting (179559,1): 0.087/0.087/0.288/4.850 lawmakers (27291,2): 0.087/0.173/0.266/2.995 prescriptions (34657,3): 0.080/0.239/0.503/6.241 ims (30708,1): 0.079/0.079/0.165/2.358 drugstores (120806,1): 0.078/0.078/0.360/5.698 medicaid (25609,1): 0.077/0.077/0.396/5.324 insurers (32340,1): 0.077/0.077/0.422/4.715 pharmaceutical (8897,2): 0.077/0.154/0.508/4.730 doctors (4958,2): 0.077/0.154/0.393/3.713 sinai (13035,3): 0.077/0.230/0.151/1.217 hospitals (4777,5): 0.074/0.370/0.394/3.786 affiliated (3843,1): 0.074/0.074/0.113/0.993 controlled (2257,3): 0.074/0.221/0.211/1.957 approved (2389,1): 0.073/0.073/0.315/2.740 advocates (8690,1): 0.071/0.071/0.238/1.946 therapeutics (36356,1): 0.071/0.071/0.470/5.856 medical (679,1): 0.071/0.071/0.445/3.809 medicare (18132,1): 0.070/0.070/0.436/3.283 thwart (31967,1): 0.069/0.069/0.157/1.927 complaint (9280,1): 0.068/0.068/0.220/1.792 pharmaceuticals (16906,3): 0.068/0.203/0.441/4.069 review (783,1): 0.068/0.068/0.109/0.934 medicine (1528,2): 0.067/0.135/0.351/3.264 investigating (8587,1): 0.067/0.067/0.214/1.829 turing (18313,9): 0.067/0.599/0.052/0.391 generic (7602,4): 0.066/0.266/0.218/1.908 \nMost similar words in vocab:\ndrugs: 0.593/5.707 medication: 0.579/5.022 drug: 0.578/5.779 medications: 0.573/4.852 antiretroviral: 0.571/6.498 ivig: 0.568/7.100 methadone: 0.550/6.595 vioxx: 0.548/8.404 patients: 0.545/4.917 prophylactic: 0.540/6.172 prescription: 0.540/4.686 antiepileptic: 0.533/6.561 treatments: 0.521/4.746 antipsychotic: 0.520/5.835 medicines: 0.518/4.492 regimens: 0.516/6.022 novartis: 0.514/6.661 pfizer: 0.512/6.705 diarrheal: 0.512/6.444 praziquantel: 0.511/6.928 pharmaceutical: 0.508/4.730 dosages: 0.507/5.477 bevacizumab: 0.505/7.121 treatment: 0.505/4.553 prescriptions: 0.503/6.241 sanofi: 0.500/5.978 adrs: 0.499/7.800 dhhs: 0.499/6.646 monotherapy: 0.498/6.612 glaxosmithkline: 0.498/6.705 \n\nTopic 12 (3.00): 4.7%\nMost relevant words:\ndrug (1942,15): 0.487/7.311/0.887/8.869 drugs (3528,10): 0.379/3.794/0.869/8.361 pills (18688,1): 0.158/0.158/0.500/4.066 cholesterol (20400,1): 0.089/0.089/0.363/2.914 dangerous (3584,2): 0.080/0.161/0.262/2.413 efficacy (14568,1): 0.076/0.076/0.461/3.860 effects (1380,2): 0.075/0.150/0.341/3.124 rima (42963,1): 0.073/0.073/-0.007/-0.097 antibiotic (24098,2): 0.072/0.143/0.523/3.777 investigating (8587,1): 0.071/0.071/0.251/2.142 generic (7602,4): 0.070/0.282/0.253/2.222 pharmaceutical (8897,2): 0.069/0.139/0.524/4.881 pregnancy (7979,1): 0.069/0.069/0.425/3.928 controlled (2257,3): 0.068/0.205/0.231/2.136 therapies (21018,2): 0.067/0.134/0.518/4.299 side (287,2): 0.065/0.131/-0.024/-0.172 use (176,7): 0.064/0.450/0.288/1.999 administration (991,1): 0.062/0.062/0.252/2.066 pharmaceuticals (16906,3): 0.062/0.186/0.459/4.237 samples (4875,1): 0.062/0.062/0.177/1.816 testing (2814,1): 0.060/0.060/0.323/2.880 known (97,1): 0.059/0.059/-0.019/-0.115 used (82,4): 0.058/0.233/0.126/0.951 isolated (4860,1): 0.057/0.057/0.169/1.529 hiv (6116,1): 0.056/0.056/0.527/5.130 women (463,1): 0.056/0.056/0.127/1.031 immune (8538,1): 0.056/0.056/0.362/3.293 combination (2674,1): 0.054/0.054/0.243/1.811 medicine (1528,2): 0.054/0.108/0.355/3.295 story (367,1): 0.054/0.054/-0.029/-0.222 \nMost similar words in vocab:\ndrug: 0.887/8.869 drugs: 0.869/8.361 medication: 0.693/6.010 medications: 0.686/5.802 prescription: 0.659/5.725 antiretroviral: 0.641/7.292 antipsychotic: 0.623/6.987 methadone: 0.610/7.323 opiates: 0.603/6.951 antiepileptic: 0.599/7.372 steroids: 0.598/4.032 ivig: 0.588/7.343 medicines: 0.584/5.062 antiviral: 0.580/6.805 diazepam: 0.577/6.103 anticancer: 0.576/6.454 analgesics: 0.575/6.381 cocaine: 0.574/5.993 stimulants: 0.570/6.265 treatments: 0.566/5.155 therapeutic: 0.565/4.927 anticoagulant: 0.563/6.190 opioids: 0.561/6.429 antidepressant: 0.560/6.298 painkillers: 0.559/6.871 nsaids: 0.558/6.166 warfarin: 0.557/5.948 patients: 0.555/5.014 treatment: 0.555/5.008 antibiotics: 0.554/4.326 \n\nTopic 5 (3.00): 4.1%\nMost relevant words:\ntoxoplasmosis (125001,4): 0.795/3.180/0.692/10.183 infection (6037,2): 0.271/0.541/0.668/6.088 disease (1780,1): 0.224/0.224/0.676/6.232 diseases (4439,6): 0.219/1.317/0.679/6.553 multidrug (112412,1): 0.217/0.217/0.500/7.989 infected (8254,1): 0.205/0.205/0.473/4.535 infectious (12595,4): 0.180/0.720/0.602/5.664 malaria (13222,1): 0.180/0.180/0.611/5.451 hepatitis (19494,1): 0.180/0.180/0.661/5.614 parasitic (18146,1): 0.174/0.174/0.379/3.160 immune (8538,1): 0.172/0.172/0.501/4.558 parasite (16531,1): 0.171/0.171/0.374/2.898 tuberculosis (11017,1): 0.165/0.165/0.632/5.507 medically (28191,1): 0.142/0.142/0.418/6.076 hiv (6116,1): 0.126/0.126/0.624/6.073 rare (2251,1): 0.116/0.116/0.250/2.382 cancer (1871,2): 0.115/0.230/0.569/5.499 cause (1310,1): 0.111/0.111/0.409/3.177 pregnancy (7979,1): 0.110/0.110/0.491/4.538 resistant (8950,1): 0.105/0.105/0.363/3.587 isolated (4860,1): 0.104/0.104/0.251/2.269 patients (2468,9): 0.103/0.925/0.703/6.350 treating (10124,1): 0.097/0.097/0.600/4.840 rarely (4300,1): 0.095/0.095/0.258/2.110 antibiotic (24098,2): 0.095/0.190/0.580/4.192 effects (1380,2): 0.095/0.190/0.381/3.490 aids (4693,2): 0.093/0.185/0.515/4.939 treatment (1460,5): 0.091/0.453/0.640/5.777 treat (5814,3): 0.089/0.268/0.583/4.959 hospitalized (18161,1): 0.087/0.087/0.402/2.790 \nMost similar words in vocab:\npatients: 0.703/6.350 toxoplasmosis: 0.692/10.183 diarrheal: 0.690/8.689 infections: 0.681/6.300 diseases: 0.679/6.553 disease: 0.676/6.232 ulcerative: 0.674/6.465 ivig: 0.672/8.399 infection: 0.668/6.088 chronic: 0.663/5.850 hepatitis: 0.661/5.614 antiepileptic: 0.660/8.127 treatable: 0.659/7.174 crohn's: 0.647/6.549 contraindicated: 0.643/6.738 diabetes: 0.642/5.581 treatment: 0.640/5.777 medications: 0.638/5.397 medication: 0.635/5.504 prophylactic: 0.633/7.233 tuberculosis: 0.632/5.507 treatments: 0.630/5.742 antiretroviral: 0.629/7.152 endocarditis: 0.628/6.479 nosocomial: 0.625/8.329 hiv: 0.624/6.073 neuroleptic: 0.623/8.059 prophylaxis: 0.623/7.291 asthma: 0.623/4.751 colitis: 0.620/6.573 \n\nTopic 14 (3.00): 4.0%\nMost relevant words:\ndiseases (4439,6): 0.141/0.849/0.624/6.024 multidrug (112412,1): 0.122/0.122/0.459/7.323 disease (1780,1): 0.122/0.122/0.600/5.532 infectious (12595,4): 0.121/0.483/0.550/5.175 hiv (6116,1): 0.120/0.120/0.610/5.939 infection (6037,2): 0.118/0.236/0.567/5.171 medically (28191,1): 0.116/0.116/0.398/5.784 hepatitis (19494,1): 0.114/0.114/0.597/5.069 tuberculosis (11017,1): 0.112/0.112/0.578/5.031 treatments (9357,2): 0.111/0.221/0.651/5.934 patients (2468,9): 0.109/0.980/0.700/6.319 immune (8538,1): 0.107/0.107/0.439/3.994 malaria (13222,1): 0.107/0.107/0.543/4.840 treatment (1460,5): 0.104/0.519/0.645/5.823 treat (5814,3): 0.104/0.311/0.590/5.021 treating (10124,1): 0.103/0.103/0.596/4.808 cancer (1871,2): 0.101/0.202/0.546/5.280 aids (4693,2): 0.101/0.202/0.515/4.939 infected (8254,1): 0.101/0.101/0.389/3.737 pregnancy (7979,1): 0.099/0.099/0.470/4.343 antibiotic (24098,2): 0.095/0.190/0.568/4.104 therapies (21018,2): 0.094/0.188/0.564/4.678 patient (3777,2): 0.092/0.184/0.571/5.054 medicine (1528,2): 0.088/0.176/0.413/3.834 effects (1380,2): 0.087/0.174/0.362/3.315 resistant (8950,1): 0.083/0.083/0.330/3.261 parasitic (18146,1): 0.081/0.081/0.277/2.309 efficacy (14568,1): 0.079/0.079/0.472/3.954 therapeutics (36356,1): 0.078/0.078/0.502/6.259 cholesterol (20400,1): 0.077/0.077/0.350/2.816 \nMost similar words in vocab:\npatients: 0.700/6.319 drugs: 0.697/6.705 medications: 0.694/5.869 medication: 0.691/5.995 drug: 0.665/6.653 antiretroviral: 0.659/7.493 treatments: 0.651/5.934 ivig: 0.650/8.123 treatment: 0.645/5.823 diarrheal: 0.633/7.972 antiepileptic: 0.632/7.784 prophylactic: 0.629/7.191 diabetes: 0.629/5.465 diseases: 0.624/6.024 chronic: 0.619/5.461 contraindicated: 0.618/6.477 hiv: 0.610/5.939 antibiotics: 0.606/4.732 regimens: 0.606/7.063 disease: 0.600/5.532 infections: 0.598/5.528 hepatitis: 0.597/5.069 treating: 0.596/4.808 treatable: 0.595/6.481 antipsychotic: 0.595/6.674 prophylaxis: 0.593/6.943 asthma: 0.593/4.525 anticoagulant: 0.591/6.506 treat: 0.590/5.021 medicines: 0.589/5.109 \n\nTopic 7 (3.00): 3.7%\nMost relevant words:\neffects (1380,2): 0.073/0.146/0.342/3.128 potentially (6379,1): 0.073/0.073/0.433/3.808 cause (1310,1): 0.071/0.071/0.337/2.623 multidrug (112412,1): 0.070/0.070/0.424/6.764 threatening (7927,2): 0.070/0.141/0.265/2.363 dangerous (3584,2): 0.070/0.139/0.250/2.303 resistant (8950,1): 0.070/0.070/0.312/3.078 serious (2375,2): 0.069/0.138/0.293/2.606 antibiotic (24098,2): 0.067/0.135/0.518/3.746 use (176,7): 0.066/0.463/0.296/2.060 medically (28191,1): 0.065/0.065/0.358/5.197 pregnancy (7979,1): 0.064/0.064/0.421/3.889 immune (8538,1): 0.063/0.063/0.379/3.451 vulnerable (6874,1): 0.062/0.062/0.196/1.853 parasitic (18146,1): 0.062/0.062/0.243/2.027 treat (5814,3): 0.062/0.186/0.528/4.492 develop (1982,1): 0.062/0.062/0.290/2.394 conceivably (69563,1): 0.062/0.062/0.269/3.070 treatments (9357,2): 0.062/0.123/0.586/5.336 problems (1188,1): 0.062/0.062/0.298/2.545 generically (48146,1): 0.060/0.060/0.186/2.126 used (82,4): 0.060/0.239/0.133/1.007 caused (1150,1): 0.060/0.060/0.265/2.282 hepatitis (19494,1): 0.059/0.059/0.519/4.403 possibly (2456,1): 0.058/0.058/0.150/1.155 rare (2251,1): 0.058/0.058/0.167/1.594 pills (18688,1): 0.058/0.058/0.382/3.105 isolated (4860,1): 0.058/0.058/0.174/1.574 parasite (16531,1): 0.057/0.057/0.220/1.704 treatment (1460,5): 0.057/0.286/0.578/5.214 \nMost similar words in vocab:\ndrugs: 0.653/6.282 medications: 0.645/5.455 medication: 0.644/5.585 drug: 0.629/6.289 ivig: 0.625/7.805 antiretroviral: 0.616/7.005 patients: 0.610/5.511 antiepileptic: 0.602/7.417 treatments: 0.586/5.336 diarrheal: 0.584/7.354 prophylactic: 0.583/6.666 treatment: 0.578/5.214 methadone: 0.568/6.818 antipsychotic: 0.562/6.309 medicines: 0.559/4.853 antibiotics: 0.558/4.352 prescription: 0.557/4.839 contraindicated: 0.556/5.823 regimens: 0.551/6.427 treatable: 0.545/5.933 vaccine: 0.545/5.069 praziquantel: 0.544/7.377 antiviral: 0.543/6.365 diabetes: 0.542/4.716 anticoagulant: 0.542/5.967 dosages: 0.542/5.855 analgesics: 0.539/5.981 monotherapy: 0.539/7.147 vioxx: 0.537/8.239 warfarin: 0.537/5.730 \n\nTopic 16 (3.00): 3.0%\nMost relevant words:\nconcocting (179559,1): 0.141/0.141/0.347/5.838 glaxo (101914,1): 0.111/0.111/0.377/7.312 drugstores (120806,1): 0.088/0.088/0.399/6.329 generics (85144,1): 0.086/0.086/0.403/6.837 prescriptions (34657,3): 0.081/0.242/0.545/6.752 inexpensively (98181,1): 0.073/0.073/0.292/4.837 conceivably (69563,1): 0.064/0.064/0.292/3.328 shorting (108317,1): 0.063/0.063/0.347/6.081 thwart (31967,1): 0.060/0.060/0.187/2.286 clamoring (148365,1): 0.058/0.058/0.263/3.971 insurers (32340,1): 0.058/0.058/0.441/4.921 generically (48146,1): 0.056/0.056/0.198/2.260 lawmakers (27291,2): 0.053/0.106/0.267/3.002 generic (7602,4): 0.051/0.202/0.244/2.136 discounts (28868,1): 0.050/0.050/0.381/4.527 ims (30708,1): 0.050/0.050/0.168/2.397 using (379,1): 0.049/0.049/0.196/1.416 medicaid (25609,1): 0.048/0.048/0.398/5.357 glaxosmithkline (55181,1): 0.048/0.048/0.491/6.607 icahn (83210,1): 0.048/0.048/0.330/6.458 products (1127,1): 0.048/0.048/0.390/3.247 rebates (60162,1): 0.047/0.047/0.509/5.713 brilliance (27594,1): 0.046/0.046/0.076/0.971 needed (1661,1): 0.046/0.046/0.311/2.192 alternative (1705,1): 0.046/0.046/0.217/1.822 mainstays (61442,1): 0.046/0.046/0.109/1.248 food (807,1): 0.046/0.046/0.302/2.770 piggy (34457,1): 0.046/0.046/0.128/1.744 swindle (42243,1): 0.045/0.045/0.175/2.432 strategy (2853,2): 0.045/0.089/0.244/2.160 \nMost similar words in vocab:\ndrugs: 0.585/5.628 antiretroviral: 0.571/6.499 ivig: 0.569/7.112 methadone: 0.560/6.723 drug: 0.560/5.598 prescriptions: 0.545/6.752 medication: 0.544/4.721 vioxx: 0.543/8.330 prescription: 0.536/4.656 medications: 0.532/4.506 thiopental: 0.525/7.685 antiepileptic: 0.523/6.446 medicines: 0.521/4.517 adrs: 0.520/8.119 antipsychotic: 0.516/5.794 dosages: 0.514/5.552 prophylactic: 0.514/5.879 placebos: 0.510/7.381 rebates: 0.509/5.713 tamiflu: 0.506/7.297 contraceptives: 0.505/6.143 pfizer: 0.505/6.622 patients: 0.503/4.539 sanofi: 0.502/6.011 incentivize: 0.500/6.749 bevacizumab: 0.500/7.056 regimens: 0.500/5.825 praziquantel: 0.500/6.774 novartis: 0.499/6.459 pharmaceutical: 0.496/4.622 \n\nTopic 13 (3.00): 3.0%\nMost relevant words:\ndrugs (3528,10): 0.087/0.872/0.745/7.168 drug (1942,15): 0.075/1.123/0.727/7.273 treatments (9357,2): 0.069/0.137/0.624/5.687 antibiotic (24098,2): 0.068/0.136/0.554/4.000 therapies (21018,2): 0.064/0.129/0.546/4.534 multidrug (112412,1): 0.064/0.064/0.433/6.910 efficacy (14568,1): 0.062/0.062/0.471/3.947 pills (18688,1): 0.062/0.062/0.419/3.408 hiv (6116,1): 0.062/0.062/0.565/5.500 treatment (1460,5): 0.060/0.300/0.610/5.506 effects (1380,2): 0.060/0.120/0.347/3.174 cholesterol (20400,1): 0.059/0.059/0.347/2.787 immune (8538,1): 0.058/0.058/0.397/3.616 treating (10124,1): 0.058/0.058/0.553/4.465 pregnancy (7979,1): 0.057/0.057/0.435/4.023 therapeutics (36356,1): 0.057/0.057/0.495/6.167 hepatitis (19494,1): 0.055/0.055/0.539/4.574 pharmaceutical (8897,2): 0.054/0.109/0.528/4.918 resistant (8950,1): 0.054/0.054/0.311/3.071 aids (4693,2): 0.054/0.108/0.474/4.545 medically (28191,1): 0.054/0.054/0.361/5.249 treat (5814,3): 0.054/0.161/0.540/4.593 cancer (1871,2): 0.053/0.107/0.504/4.873 medicine (1528,2): 0.053/0.105/0.382/3.549 testing (2814,1): 0.052/0.052/0.337/3.013 pharmaceuticals (16906,3): 0.052/0.156/0.471/4.342 generic (7602,4): 0.051/0.206/0.249/2.186 patients (2468,9): 0.051/0.463/0.642/5.800 isolated (4860,1): 0.051/0.051/0.188/1.701 prescriptions (34657,3): 0.051/0.153/0.510/6.323 \nMost similar words in vocab:\ndrugs: 0.745/7.168 drug: 0.727/7.273 medications: 0.694/5.870 medication: 0.691/5.992 antiretroviral: 0.659/7.498 ivig: 0.646/8.075 patients: 0.642/5.800 treatments: 0.624/5.687 antiepileptic: 0.620/7.638 antipsychotic: 0.613/6.879 prescription: 0.612/5.312 treatment: 0.610/5.506 prophylactic: 0.602/6.887 medicines: 0.599/5.193 methadone: 0.595/7.139 antibiotics: 0.592/4.618 contraindicated: 0.591/6.195 antiviral: 0.589/6.902 diarrheal: 0.587/7.397 regimens: 0.583/6.797 anticoagulant: 0.580/6.375 analgesics: 0.575/6.381 anticancer: 0.574/6.424 diabetes: 0.573/4.982 dosages: 0.572/6.176 therapeutic: 0.570/4.971 warfarin: 0.569/6.078 praziquantel: 0.567/7.684 nsaids: 0.566/6.247 isoniazid: 0.565/7.452 \n\nTopic 8 (3.00): 2.8%\nMost relevant words:\ntreatments (9357,2): 0.059/0.118/0.611/5.565 treatment (1460,5): 0.055/0.276/0.604/5.455 efficacy (14568,1): 0.055/0.055/0.460/3.851 testing (2814,1): 0.054/0.054/0.344/3.068 therapies (21018,2): 0.053/0.106/0.527/4.373 pharmaceuticals (16906,3): 0.052/0.155/0.473/4.361 generically (48146,1): 0.051/0.051/0.195/2.227 pharmaceutical (8897,2): 0.050/0.100/0.522/4.866 patients (2468,9): 0.050/0.446/0.642/5.794 prescriptions (34657,3): 0.049/0.147/0.509/6.315 treat (5814,3): 0.048/0.145/0.531/4.522 patient (3777,2): 0.048/0.097/0.528/4.671 therapeutics (36356,1): 0.048/0.048/0.484/6.030 glaxosmithkline (55181,1): 0.048/0.048/0.494/6.650 samples (4875,1): 0.047/0.047/0.182/1.862 generic (7602,4): 0.047/0.188/0.242/2.127 care (1069,2): 0.046/0.093/0.465/3.961 generics (85144,1): 0.046/0.046/0.370/6.277 use (176,7): 0.046/0.321/0.283/1.969 laboratories (7537,1): 0.046/0.046/0.294/2.741 effects (1380,2): 0.045/0.091/0.320/2.928 inpatient (33848,1): 0.045/0.045/0.396/4.942 hospitals (4777,5): 0.045/0.224/0.401/3.846 treating (10124,1): 0.045/0.045/0.525/4.234 health (502,3): 0.045/0.134/0.453/3.826 problems (1188,1): 0.045/0.045/0.292/2.496 potentially (6379,1): 0.044/0.044/0.407/3.585 certain (1095,4): 0.044/0.176/0.309/2.127 used (82,4): 0.044/0.175/0.129/0.974 medical (679,1): 0.044/0.044/0.455/3.893 \nMost similar words in vocab:\nmedication: 0.654/5.672 medications: 0.650/5.498 patients: 0.642/5.794 drugs: 0.632/6.082 ivig: 0.632/7.897 antiretroviral: 0.621/7.070 treatments: 0.611/5.565 treatment: 0.604/5.455 drug: 0.604/6.039 antiepileptic: 0.603/7.426 prophylactic: 0.595/6.805 antipsychotic: 0.577/6.471 regimens: 0.574/6.697 methadone: 0.573/6.876 medicines: 0.571/4.956 diarrheal: 0.568/7.157 dosages: 0.566/6.107 contraindicated: 0.565/5.920 prescription: 0.561/4.872 bevacizumab: 0.556/7.845 praziquantel: 0.553/7.496 diabetes: 0.549/4.776 infliximab: 0.547/7.086 vioxx: 0.547/8.385 monotherapy: 0.546/7.242 analgesics: 0.544/6.039 bisphosphonates: 0.542/6.948 anticoagulant: 0.542/5.962 antibiotics: 0.539/4.205 antiviral: 0.538/6.311 \n\nTopic 4 (3.00): 2.4%\nMost relevant words:\ninexpensively (98181,1): 0.147/0.147/0.349/5.775 shorting (108317,1): 0.100/0.100/0.387/6.784 clamoring (148365,1): 0.076/0.076/0.296/4.477 rebates (60162,1): 0.067/0.067/0.561/6.299 insurers (32340,1): 0.061/0.061/0.467/5.216 discounts (28868,1): 0.055/0.055/0.410/4.864 thwart (31967,1): 0.053/0.053/0.195/2.386 conceivably (69563,1): 0.052/0.052/0.294/3.355 drugstores (120806,1): 0.052/0.052/0.381/6.038 severance (36986,1): 0.052/0.052/0.314/4.158 piggy (34457,1): 0.051/0.051/0.153/2.088 financing (8017,1): 0.051/0.051/0.437/3.595 swindle (42243,1): 0.049/0.049/0.197/2.752 generics (85144,1): 0.048/0.048/0.383/6.491 investors (5976,2): 0.048/0.095/0.451/4.011 amount (1588,1): 0.047/0.047/0.402/3.260 shrank (46494,1): 0.047/0.047/0.274/3.149 buying (7669,1): 0.047/0.047/0.483/3.999 money (808,2): 0.047/0.093/0.403/3.418 acquisition (4356,1): 0.046/0.046/0.345/3.000 expensive (4449,1): 0.046/0.046/0.359/3.430 millions (5429,1): 0.044/0.044/0.386/3.494 buy (3433,1): 0.044/0.044/0.452/3.711 option (3370,1): 0.043/0.043/0.294/2.594 cost (1178,2): 0.043/0.085/0.518/4.095 dollars (4434,2): 0.042/0.084/0.510/4.365 fund (2014,3): 0.042/0.126/0.405/3.566 profit (2747,1): 0.041/0.041/0.415/3.413 acquired (1447,5): 0.041/0.206/0.253/2.191 invest (11944,1): 0.041/0.041/0.402/2.840 \nMost similar words in vocab:\nrebates: 0.561/6.299 refinancing: 0.539/5.900 prices: 0.536/5.246 costs: 0.533/4.488 adrs: 0.533/8.319 ivig: 0.530/6.624 incentivize: 0.525/7.077 profits: 0.520/4.276 premiums: 0.520/5.959 cost: 0.518/4.095 overvalued: 0.518/7.451 vioxx: 0.518/7.944 disincentive: 0.513/7.273 reimburse: 0.513/5.649 exorbitant: 0.512/6.107 antiretroviral: 0.512/5.822 dollars: 0.510/4.365 multibillion: 0.508/7.870 untaxed: 0.506/7.436 illiquid: 0.506/7.053 uninsured: 0.505/5.950 refunds: 0.505/5.823 billion: 0.496/4.950 overcharging: 0.495/8.429 reinvest: 0.495/6.588 kickbacks: 0.495/6.056 anticompetitive: 0.493/7.214 thiopental: 0.493/7.214 subsidize: 0.493/5.453 purchasing: 0.491/3.900 \n\nTopic 19 (3.00): 2.3%\nMost relevant words:\nmultidrug (112412,1): 0.067/0.067/0.451/7.207 therapeutics (36356,1): 0.054/0.054/0.510/6.359 antibiotic (24098,2): 0.051/0.101/0.548/3.957 treatments (9357,2): 0.050/0.099/0.616/5.612 treatment (1460,5): 0.049/0.244/0.615/5.547 patients (2468,9): 0.049/0.437/0.664/5.991 testing (2814,1): 0.048/0.048/0.356/3.182 medically (28191,1): 0.048/0.048/0.371/5.389 infectious (12595,4): 0.048/0.191/0.502/4.728 immune (8538,1): 0.047/0.047/0.402/3.657 treating (10124,1): 0.046/0.046/0.556/4.490 patient (3777,2): 0.046/0.092/0.547/4.845 infection (6037,2): 0.046/0.092/0.517/4.711 generics (85144,1): 0.046/0.046/0.383/6.493 hepatitis (19494,1): 0.046/0.046/0.546/4.638 infected (8254,1): 0.045/0.045/0.356/3.416 hiv (6116,1): 0.045/0.045/0.558/5.437 therapies (21018,2): 0.045/0.090/0.533/4.424 treat (5814,3): 0.045/0.134/0.547/4.658 resistant (8950,1): 0.045/0.045/0.316/3.124 efficacy (14568,1): 0.044/0.044/0.460/3.857 effects (1380,2): 0.044/0.088/0.340/3.118 generically (48146,1): 0.044/0.044/0.201/2.295 disease (1780,1): 0.043/0.043/0.540/4.981 diseases (4439,6): 0.043/0.259/0.551/5.317 malaria (13222,1): 0.043/0.043/0.494/4.407 prescriptions (34657,3): 0.042/0.126/0.514/6.380 tuberculosis (11017,1): 0.042/0.042/0.520/4.529 parasite (16531,1): 0.042/0.042/0.242/1.880 pregnancy (7979,1): 0.042/0.042/0.428/3.955 \nMost similar words in vocab:\nmedication: 0.668/5.798 medications: 0.665/5.625 patients: 0.664/5.991 drugs: 0.659/6.338 ivig: 0.654/8.166 antiretroviral: 0.651/7.407 drug: 0.639/6.390 antiepileptic: 0.624/7.683 diarrheal: 0.617/7.769 treatments: 0.616/5.612 prophylactic: 0.615/7.034 treatment: 0.615/5.547 regimens: 0.599/6.986 antipsychotic: 0.593/6.659 contraindicated: 0.590/6.180 methadone: 0.586/7.031 antibiotics: 0.580/4.530 antiviral: 0.580/6.805 diabetes: 0.580/5.038 praziquantel: 0.579/7.856 dosages: 0.578/6.241 anticoagulant: 0.577/6.352 infliximab: 0.577/7.470 isoniazid: 0.575/7.585 prophylaxis: 0.575/6.727 chronic: 0.573/5.056 medicines: 0.571/4.955 vaccine: 0.570/5.310 analgesics: 0.570/6.322 prescription: 0.569/4.938 \n\nTopic 10 (3.00): 2.2%\nMost relevant words:\nmedicaid (25609,1): 0.057/0.057/0.431/5.799 drugstores (120806,1): 0.054/0.054/0.386/6.125 rebates (60162,1): 0.053/0.053/0.544/6.109 clamoring (148365,1): 0.048/0.048/0.269/4.066 discounts (28868,1): 0.048/0.048/0.402/4.767 severance (36986,1): 0.046/0.046/0.309/4.088 insurers (32340,1): 0.046/0.046/0.446/4.977 generics (85144,1): 0.045/0.045/0.382/6.489 inexpensively (98181,1): 0.044/0.044/0.279/4.611 prescriptions (34657,3): 0.041/0.122/0.513/6.356 concocting (179559,1): 0.039/0.039/0.287/4.828 lawmakers (27291,2): 0.037/0.074/0.260/2.933 conceivably (69563,1): 0.036/0.036/0.266/3.029 inpatient (33848,1): 0.036/0.036/0.395/4.933 products (1127,1): 0.035/0.035/0.387/3.227 required (967,2): 0.035/0.069/0.264/1.909 medicare (18132,1): 0.034/0.034/0.447/3.368 tablet (10396,2): 0.034/0.067/0.142/1.407 increases (4242,4): 0.034/0.134/0.386/3.316 federal (626,2): 0.033/0.067/0.216/1.919 companies (939,4): 0.033/0.134/0.383/3.026 seeking (3864,1): 0.033/0.033/0.268/2.153 acquisition (4356,1): 0.033/0.033/0.312/2.713 claim (2099,1): 0.033/0.033/0.169/1.359 rules (1377,1): 0.032/0.032/0.091/0.822 profit (2747,1): 0.032/0.032/0.391/3.215 glaxo (101914,1): 0.032/0.032/0.328/6.360 increase (1265,8): 0.032/0.257/0.441/3.418 needed (1661,1): 0.032/0.032/0.300/2.111 provided (793,1): 0.032/0.032/0.175/1.241 \nMost similar words in vocab:\nivig: 0.568/7.090 antiretroviral: 0.563/6.403 rebates: 0.544/6.109 adrs: 0.544/8.495 vioxx: 0.543/8.331 drugs: 0.542/5.212 medication: 0.541/4.692 methadone: 0.532/6.388 medications: 0.530/4.488 patients: 0.523/4.719 prescription: 0.517/4.491 drug: 0.517/5.172 thiopental: 0.516/7.557 prescriptions: 0.513/6.356 uninsured: 0.511/6.019 prophylactic: 0.510/5.836 antiepileptic: 0.506/6.231 dosages: 0.506/5.460 praziquantel: 0.502/6.813 diarrheal: 0.501/6.309 reimburse: 0.500/5.511 antipsychotic: 0.498/5.589 incentivize: 0.497/6.707 bevacizumab: 0.496/7.006 costs: 0.496/4.177 medicines: 0.495/4.298 contraceptives: 0.495/6.013 bisphosphonates: 0.492/6.307 dhhs: 0.491/6.542 tamiflu: 0.490/7.072 \n\nTopic 0 (0.00): 0.3%\nMost relevant words:\nborn (106,1): 0.021/0.021/0.000/0.000 round (273,1): 0.017/0.017/0.000/0.000 first (26,3): 0.015/0.044/0.000/0.000 two (37,3): 0.014/0.043/0.000/0.000 university (61,2): 0.013/0.027/0.000/0.000 rima (42963,1): 0.013/0.013/0.000/0.000 world (56,1): 0.013/0.013/0.000/0.000 last (237,2): 0.012/0.024/0.000/0.000 side (287,2): 0.012/0.024/0.000/0.000 former (185,1): 0.012/0.012/0.000/0.000 held (230,1): 0.011/0.011/0.000/0.000 tracks (907,1): 0.011/0.011/0.000/0.000 martin (778,1): 0.010/0.010/0.000/0.000 district (167,1): 0.010/0.010/0.000/0.000 story (367,1): 0.010/0.010/0.000/0.000 known (97,1): 0.010/0.010/0.000/0.000 school (49,1): 0.010/0.010/0.000/0.000 division (236,1): 0.010/0.010/0.000/0.000 line (181,1): 0.009/0.009/0.000/0.000 august (149,3): 0.009/0.028/0.000/0.000 back (193,1): 0.009/0.009/0.000/0.000 wrote (536,1): 0.009/0.009/0.000/0.000 works (341,1): 0.009/0.009/0.000/0.000 force (351,1): 0.009/0.009/0.000/0.000 old (204,5): 0.009/0.045/0.000/0.000 october (131,1): 0.009/0.009/0.000/0.000 june (140,1): 0.009/0.009/0.000/0.000 list (186,1): 0.009/0.009/0.000/0.000 judith (8272,1): 0.009/0.009/0.000/0.000 called (155,3): 0.009/0.026/0.000/0.000 \nEM Iter 20:\nEm:\n[  1.8027104   78.02029567  31.45642255  47.76173967  14.86158862\n  27.10921395  34.31457686  23.67109005  17.95924665  38.09238302\n  13.70894114  34.26798497  29.95569525  18.703356    25.18559404\n  55.65464868  19.06251671  34.93976389  92.96433893  14.50789295]\n\nEM Iter 21:\nEm:\n[  1.78023594  81.96864025  31.64569023  49.19288557  13.98469099\n  27.33950334  34.44897372  23.33318065  17.52987078  38.5664582\n  13.01951432  34.80147277  29.29983815  17.94404813  24.47815484\n  56.33056666  18.27827696  35.13659553  91.21717183  13.70423116]\n\nEM Iter 22:\nEm:\n[  1.76298487  85.91044342  31.83018451  50.68437225  13.12769306\n  27.53267317  34.57322701  22.98819542  17.10591658  39.01355007\n  12.3452382   35.33718469  28.68983261  17.20605741  23.77186887\n  56.89957894  17.50873395  35.3287368   89.45559596  12.92793222]\n\nEM Iter 23:\nEm:\n[  1.74976746  89.79927843  32.01018938  52.2385629   12.29342972\n  27.68571575  34.68870258  22.63836958  16.68792648  39.43108972\n  11.68731715  35.87609012  28.13045563  16.49096584  23.06882347\n  57.3576471   16.75546309  35.51644405  87.71393782  12.1798237 ]\n\nEM Iter 24:\nEm:\n[  1.73967807  93.59214719  32.18644522  53.85780685  11.48482758\n  27.79748107  34.79615154  22.28561091  16.27631686  39.81797696\n  11.04708813  36.41900342  27.62327697  15.79949968  22.37032862\n  57.70626188  16.02014642  35.69966465  86.02004981  11.46023816]\n\nEM Iter 25:\nEm:\n[  1.7320244   97.25113577  32.3599671   55.54410173  10.70461448\n  27.86820638  34.89567799  21.93144083  15.87132863  40.17435739\n  10.42584959  36.96634763  27.16741783  15.1316946   21.67701922\n  57.95178287  15.30439333  35.87789214  84.3956654   10.7690827 ]\n\nEM Iter 26:\nEm:\n[   1.72627422  100.74463823   32.53188951   57.29879318    9.95511082\n   27.89911725   34.9867686    21.57698148   15.47301147   40.5013754\n    9.8247377    37.5179847    26.76028271   14.48706657   20.9889972\n   58.10454122   14.60961088   36.05009168   82.85680186   10.10592531]\n\nEM Iter 27:\nEm:\n[   1.72201554  104.0480752    32.70334292   59.12232567    9.2381006\n   27.89211785   35.0683706    21.22297832   15.08123387   40.80092207\n    9.24464738   38.07311347   26.39820487   13.86477271   20.30599084\n   58.17782322   13.93692111   36.21468926   81.4142693     9.47008519]\n\nEM Iter 28:\nEm:\n[   1.71892708  107.1441013    32.87536465   61.01405409    8.55477351\n   27.84956764   35.13900455   20.86984787   14.69571132   41.07539521\n    8.68619218   38.63023241   26.07697737   13.26375252   19.62751364\n   58.18684341   13.28711971   36.3696156    80.07428792    8.86071803]\n\nEM Iter 29:\nEm:\n[   1.71675659  110.02235373   33.04884333   62.97212165    7.90572466\n   27.77412935   35.19689824   20.51774084   14.31604559   41.32748492\n    8.14969577   39.18716012   25.79226277   12.68284475   18.95300971\n   58.14779444   12.66066824   36.51239316   78.8391818     8.27689035]\n\nEM Iter 30:\nEm:\n[   1.71530489  112.67883548   33.22449334   64.9934043     7.29099676\n   27.66866865   35.24012938   20.1666119    13.94176851   41.55999342\n    7.63520666   39.74110367   25.53988755   12.12087799   18.28197654\n   58.07703244   12.05771147   36.64025324   77.70810392    7.71763986]\n\nEM Iter 31:\nEm:\n[   1.71441424  115.1150466    33.40285375   67.07351821    6.71014999\n   27.53618663   35.2667653    19.8162887    13.57238507   41.77569242\n    7.14252835   40.28876305   25.31603511   11.57673607   17.61405994\n   57.99042788   11.47811114   36.75027007   76.67774684    7.18202064]\n\nEM Iter 32:\nEm:\n[   1.71395959  117.33697804   33.58430509   69.20688341    6.16234648\n   27.37976979   35.27499071   19.46653476   13.20741178   41.97721746\n    6.67125798   40.82645976   25.11735421   11.04940063   16.94911914\n   57.90288736   10.9214888    36.83949965   75.7430014     6.66913395]\n\nEM Iter 33:\nEm:\n[   1.7138421   119.3540695    33.7690976    71.38683502    5.64643929\n   27.2025471    35.26321637   19.11710257   12.84640802   42.16699535\n    6.22082814   41.35027791   24.9410011    10.53797434   16.28726291\n   57.82803422   10.38727184   36.90511281   74.89753767    6.17814613]\n\nEM Iter 34:\nEm:\n[   1.71398409  121.17821099   33.95738493   73.60577211    5.16105834\n   27.00764815   35.2301643    18.76777498   12.48899882   42.34719903\n    5.79054792   41.85620772   24.78463269   10.04168874   15.62885963\n   57.77802427    9.87473832   36.94451447   74.1342942     5.7082963 ]\n\nEM Iter 35:\nEm:\n[   1.71432499  122.82284399   34.14925858   75.85533424    4.70468897\n   26.79816024   35.17492761   18.41839446   12.13488916   42.51972382\n    5.3796408    42.34028296   24.64636631    9.55990054   14.97452541\n   57.76346867    9.38305785   36.9554429    73.44587233    5.25889616]\n\nEM Iter 36:\nEm:\n[   1.71481811  124.30219457   34.34477946   78.126596      4.2757404\n   26.57708476   35.09700487   18.06888082   11.78387027   42.68617892\n    4.98727801   42.79870568   24.52471927    9.09208002   14.32509553\n   57.7934354     8.9113272    36.93604591   72.82483997    4.82932483]\n\nEM Iter 37:\nEm:\n[   1.71542786  125.63065205   34.54400444   80.4102703     3.87260369\n   26.34729451   34.99631085   17.71923884   11.43581905   42.84788917\n    4.61260704   43.2279537    24.41853877    8.6377947    13.68158406\n   57.87550351    8.45859994   36.88493232   72.26395433    4.41902087]\n\nEM Iter 38:\nEm:\n[   1.71612752  126.82229185   34.74700639   82.69691214    3.49369972\n   26.11149442   34.8731665    17.36955758   11.09069222   43.00590287\n    4.25477541   43.62486783   24.32693002    8.19669079   13.04513675\n   58.01584863    8.02391043   36.80119921   71.7563161     4.02747363]\n\nEM Iter 39:\nEm:\n[   1.71689721  127.89053168   34.95388775   84.97711539    3.13751836\n   25.87218757   34.72827182   17.02000331   10.74851642   43.16100287\n    3.9129503    43.98671754   24.2491883     7.76847441   12.41698132\n   58.21934301    7.60629272   36.68443675   71.29546767    3.65421561]\n\nT[:,10]:\n[[  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n    0.00000000e+00   0.00000000e+00]\n [ -2.48034913e-04  -5.77582529e-02   5.63983269e-02  -3.55741671e-02\n   -3.51406347e-02   2.30279681e-02   3.15228358e-02   1.37860576e-02\n   -8.68349676e-03   1.54963227e-02]\n [  6.92556601e-03  -2.43292477e-02   7.81581534e-02   4.93556731e-02\n   -8.49071721e-02   3.26582520e-03   6.97567542e-02   9.63693630e-03\n   -6.14407700e-02   1.69539403e-02]\n [  1.74246363e-02  -1.35020653e-02   1.10133672e-01   5.04530635e-02\n   -4.39673336e-02   3.17057992e-02   8.62609986e-02   1.99704375e-02\n   -6.90631468e-02   1.50090334e-02]\n [ -1.67045337e-02  -2.51942964e-02   5.70502553e-02   9.06953601e-03\n   -4.93940932e-02   2.97169770e-02   3.76302866e-02   5.32822964e-03\n   -2.27095971e-02   3.09887746e-02]\n [  2.28730852e-02   2.00328222e-02   4.25045239e-02   1.59298982e-03\n   -1.66444028e-02  -2.79634612e-02   1.88386962e-03  -5.49695110e-05\n   -1.00390651e-01   2.11098357e-02]\n [  3.36437644e-02  -1.10372689e-02   1.04711897e-01   6.19394693e-02\n   -2.06807699e-02   1.04317130e-02   4.44649044e-02  -2.63564805e-02\n   -7.60447956e-02   2.34473513e-03]\n [  2.49683429e-02  -2.42679674e-02   6.25976608e-02  -2.71503280e-03\n   -2.35522960e-02  -5.21808779e-04   4.55960793e-02   3.29568876e-03\n   -1.23993722e-01   2.48565966e-02]\n [  8.65795748e-03   2.55297367e-02   1.10009226e-01   2.28018055e-02\n   -4.72296659e-02   3.05217431e-02   6.20346771e-02  -2.48037847e-02\n   -7.05218753e-02   2.28614453e-02]\n [  2.17608385e-02  -3.13116722e-03   7.08356897e-02   4.08854967e-02\n   -5.50040608e-02   3.26210781e-02   5.97621389e-02   3.42540021e-02\n   -2.16708458e-02   2.49116528e-02]\n [  4.27598991e-02  -1.27700346e-02   9.49814576e-02  -1.27724972e-02\n   -2.65203444e-02   2.21308883e-02   7.02540382e-02  -2.08753676e-02\n   -7.30972522e-02   7.22719858e-03]\n [  1.02234851e-02   1.51230838e-02   1.09948382e-01   2.39300535e-02\n   -1.36678352e-02   3.59098746e-03   8.07607762e-02  -2.66963866e-02\n   -7.70059638e-02   1.83642127e-02]\n [  1.59122736e-02   7.36195760e-03   8.13434505e-02   4.50130401e-02\n   -1.99447264e-02   3.35872534e-02   1.08653146e-02  -4.40428531e-02\n   -7.56621623e-02  -4.03682756e-02]\n [  1.54123575e-02  -1.86926764e-02   6.88149183e-02   4.99943204e-02\n   -3.05457976e-02   3.03368266e-02   4.72817454e-02  -3.98912586e-04\n   -8.06230932e-02   4.19661547e-03]\n [  2.66330617e-02  -1.89636695e-03   5.64444175e-02   3.34957594e-02\n   -3.97489935e-02   3.22099261e-03   4.16147642e-02  -3.01490885e-02\n   -8.46827565e-02   2.18410368e-02]\n [ -1.63878021e-02  -2.30386735e-02   5.53083220e-02   9.18474106e-03\n   -3.20797423e-02   4.61246332e-02   6.89004351e-02   2.49244491e-02\n   -5.41155335e-02   2.27148610e-02]\n [  4.99483237e-03  -3.47116497e-02   8.61100695e-02   3.02503008e-02\n   -6.57365827e-02   5.15926376e-02   7.99889692e-02  -1.10679859e-02\n   -5.78905235e-02   1.54723537e-02]\n [  2.41475139e-02  -3.70704409e-03   9.63523429e-02   3.52173645e-02\n   -5.38551452e-02   1.72919203e-02   7.63386937e-02   6.86400394e-03\n   -7.80439778e-02   3.08558424e-02]\n [  6.63559666e-03   3.87775779e-02   5.87860131e-02  -1.81072690e-02\n   -7.75257250e-02   8.99805395e-02   5.72779273e-02  -8.39826128e-03\n   -1.89497967e-02  -8.60455931e-03]\n [  1.02648170e-02   2.23825404e-02   6.97485854e-02   6.20646479e-02\n   -2.25064448e-02   8.23854230e-03   6.02984701e-02  -3.00825631e-02\n   -9.16976721e-02   2.23841875e-02]]\nr:\n[ -1.33004718e-13  -5.31244623e-01  -4.71919926e-01  -4.82623926e-01\n  -5.73798845e-01  -7.00817524e-01  -5.19816242e-01  -5.22457353e-01\n  -5.24096167e-01  -4.80627123e-01  -5.17596898e-01  -5.03506876e-01\n  -8.58453837e-01  -5.69852819e-01  -6.06971254e-01  -5.43282039e-01\n  -5.63931539e-01  -5.09962637e-01  -5.00896433e-01  -5.81896472e-01]\n\nTopic magnitudes:\n[ 0.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.\n  3.  3.]\n\nTopic 1 (3.00): 19.6%\nMost relevant words:\nmillion (357,6): 0.780/4.678/0.609/5.942 company (151,8): 0.773/6.181/0.479/3.862 investors (5976,2): 0.614/1.229/0.548/4.867 companies (939,4): 0.610/2.440/0.534/4.221 acquired (1447,5): 0.595/2.974/0.367/3.170 fund (2014,3): 0.594/1.782/0.513/4.515 sold (614,2): 0.574/1.148/0.396/3.276 dollars (4434,2): 0.569/1.137/0.599/5.127 invest (11944,1): 0.546/0.546/0.514/3.628 millions (5429,1): 0.539/0.539/0.456/4.133 acquisition (4356,1): 0.538/0.538/0.416/3.624 bank (640,1): 0.527/0.527/0.266/2.422 hedge (14848,3): 0.516/1.547/0.332/2.924 capital (735,1): 0.512/0.512/0.248/2.325 financing (8017,1): 0.502/0.502/0.487/4.010 filed (3883,2): 0.497/0.994/0.328/3.129 profit (2747,1): 0.484/0.484/0.481/3.960 raised (1286,4): 0.461/1.844/0.262/2.332 announced (568,1): 0.456/0.456/0.238/1.772 money (808,2): 0.455/0.910/0.441/3.745 sales (1485,3): 0.455/1.365/0.462/4.448 business (354,2): 0.452/0.904/0.314/2.387 icahn (83210,1): 0.446/0.446/0.362/7.086 private (597,1): 0.443/0.443/0.274/2.357 us (220,1): 0.442/0.442/0.254/2.324 stock (1878,2): 0.439/0.878/0.387/3.706 buy (3433,1): 0.418/0.418/0.474/3.890 manager (830,2): 0.402/0.803/0.115/1.009 percent (1651,2): 0.397/0.795/0.426/3.891 deal (1260,1): 0.396/0.396/0.365/2.786 \nMost similar words in vocab:\nbillion: 0.645/6.442 million: 0.609/5.942 dollars: 0.599/5.127 profits: 0.580/4.761 investments: 0.578/5.024 investors: 0.548/4.867 multibillion: 0.543/8.411 investment: 0.543/4.677 funds: 0.541/4.743 companies: 0.534/4.221 defrauded: 0.531/5.944 reinvest: 0.528/7.025 shareholders: 0.525/4.286 rebates: 0.524/5.888 revenues: 0.520/4.538 invest: 0.514/3.628 fund: 0.513/4.515 invested: 0.511/3.859 refinancing: 0.511/5.598 multimillion: 0.509/6.975 reimburse: 0.505/5.568 billions: 0.503/3.413 purchasing: 0.502/3.985 unitedhealth: 0.500/6.992 assets: 0.500/4.482 indymac: 0.499/7.249 purchase: 0.499/4.082 usd: 0.498/4.652 buyouts: 0.497/5.725 wellpoint: 0.496/7.663 \n\nTopic 3 (3.00): 13.0%\nMost relevant words:\nhospitals (4777,5): 0.383/1.917/0.492/4.724 hospital (723,1): 0.373/0.373/0.404/3.702 medical (679,1): 0.366/0.366/0.549/4.699 medicine (1528,2): 0.355/0.709/0.449/4.173 care (1069,2): 0.350/0.699/0.542/4.616 doctors (4958,2): 0.344/0.689/0.468/4.422 patients (2468,9): 0.331/2.976/0.708/6.391 health (502,3): 0.325/0.976/0.521/4.401 patient (3777,2): 0.324/0.649/0.589/5.217 inpatient (33848,1): 0.307/0.307/0.436/5.450 therapies (21018,2): 0.283/0.566/0.554/4.594 treatment (1460,5): 0.282/1.408/0.630/5.688 treatments (9357,2): 0.278/0.557/0.625/5.694 treating (10124,1): 0.270/0.270/0.570/4.597 treat (5814,3): 0.267/0.800/0.563/4.793 therapeutics (36356,1): 0.257/0.257/0.495/6.176 cancer (1871,2): 0.252/0.505/0.521/5.030 medically (28191,1): 0.250/0.250/0.372/5.407 dr (570,5): 0.248/1.241/0.206/1.630 pharmaceutical (8897,2): 0.247/0.494/0.526/4.896 prescriptions (34657,3): 0.242/0.727/0.509/6.308 specialists (10857,1): 0.238/0.238/0.275/2.355 aids (4693,2): 0.235/0.470/0.473/4.540 efficacy (14568,1): 0.233/0.233/0.443/3.708 heart (869,1): 0.231/0.231/0.195/1.619 hospitalized (18161,1): 0.228/0.228/0.352/2.444 center (223,3): 0.228/0.683/0.120/1.072 sinai (13035,3): 0.226/0.678/0.174/1.405 laboratories (7537,1): 0.225/0.225/0.306/2.852 hiv (6116,1): 0.222/0.222/0.548/5.339 \nMost similar words in vocab:\npatients: 0.708/6.391 medication: 0.665/5.766 medications: 0.661/5.592 treatment: 0.630/5.688 treatments: 0.625/5.694 antiretroviral: 0.615/6.999 drugs: 0.610/5.864 diabetes: 0.598/5.194 ivig: 0.597/7.453 prophylactic: 0.595/6.801 patient: 0.589/5.217 medicines: 0.580/5.035 drug: 0.575/5.748 treating: 0.570/4.597 diarrheal: 0.567/7.147 regimens: 0.567/6.613 contraindicated: 0.567/5.938 pediatric: 0.566/5.026 clinical: 0.565/5.119 methadone: 0.565/6.774 treat: 0.563/4.793 antiepileptic: 0.563/6.930 therapeutic: 0.556/4.850 prescription: 0.555/4.820 therapies: 0.554/4.594 chemotherapy: 0.552/4.257 healthcare: 0.550/4.968 chronic: 0.550/4.853 medical: 0.549/4.699 hiv: 0.548/5.339 \n\nTopic 18 (3.00): 10.9%\nMost relevant words:\nprice (1535,17): 0.961/16.340/0.944/8.243 prices (4345,4): 0.865/3.458/0.814/7.964 gouge (81038,1): 0.803/0.803/0.212/4.454 priced (14909,1): 0.656/0.656/0.542/4.552 rose (1336,1): 0.318/0.318/0.178/1.302 sharply (12378,2): 0.306/0.612/0.247/2.105 high (104,3): 0.267/0.800/0.157/1.401 scott (1062,1): 0.259/0.259/0.099/0.637 low (612,1): 0.254/0.254/0.294/2.457 martin (778,1): 0.252/0.252/0.043/0.274 stock (1878,2): 0.251/0.502/0.385/3.688 born (106,1): 0.244/0.244/-0.049/-0.422 piggy (34457,1): 0.241/0.241/0.153/2.096 swindle (42243,1): 0.239/0.239/0.202/2.812 spencer (4173,1): 0.237/0.237/0.115/0.867 line (181,1): 0.225/0.225/0.031/0.260 discounts (28868,1): 0.220/0.220/0.379/4.504 april (148,1): 0.210/0.210/0.048/0.346 round (273,1): 0.209/0.209/-0.041/-0.368 sanders (7915,1): 0.207/0.207/0.084/0.583 tracks (907,1): 0.205/0.205/0.005/0.039 october (131,1): 0.202/0.202/0.033/0.250 mcleod (15404,1): 0.201/0.201/0.080/0.602 june (140,1): 0.201/0.201/0.039/0.274 free (381,1): 0.200/0.200/0.143/1.274 judith (8272,1): 0.198/0.198/0.028/0.219 armstrong (4767,1): 0.196/0.196/0.057/0.483 democrat (3309,1): 0.196/0.196/0.082/0.755 shortages (16485,1): 0.195/0.195/0.267/2.201 wendy (8642,1): 0.194/0.194/0.051/0.388 \nMost similar words in vocab:\nprice: 0.944/8.243 prices: 0.814/7.964 pricing: 0.602/5.264 priced: 0.542/4.552 inflation: 0.539/4.986 commodity: 0.503/4.200 exorbitant: 0.503/6.003 discount: 0.501/4.329 cost: 0.497/3.930 purchases: 0.492/3.792 costs: 0.482/4.061 discounted: 0.478/3.187 resale: 0.474/5.423 purchasing: 0.467/3.705 buying: 0.455/3.765 profits: 0.449/3.691 buyers: 0.444/3.783 undervalued: 0.444/4.991 rates: 0.443/4.114 skyrocketed: 0.443/5.015 commodities: 0.440/3.807 purchase: 0.438/3.586 buy: 0.438/3.596 repayments: 0.438/4.702 dollars: 0.434/3.715 premium: 0.432/4.331 market: 0.431/3.523 demand: 0.431/3.553 sales: 0.428/4.126 asset's: 0.426/5.918 \n\nTopic 15 (3.00): 8.9%\nMost relevant words:\nshorting (108317,1): 0.363/0.363/0.385/6.743 increase (1265,8): 0.344/2.756/0.562/4.360 rebates (60162,1): 0.334/0.334/0.594/6.665 clamoring (148365,1): 0.325/0.325/0.320/4.843 amount (1588,1): 0.299/0.299/0.467/3.790 increased (1167,1): 0.290/0.290/0.470/3.791 inexpensively (98181,1): 0.287/0.287/0.307/5.077 shrank (46494,1): 0.284/0.284/0.320/3.681 increases (4242,4): 0.275/1.099/0.456/3.922 cost (1178,2): 0.261/0.522/0.568/4.493 percent (1651,2): 0.245/0.490/0.460/4.204 discounts (28868,1): 0.243/0.243/0.409/4.861 severance (36986,1): 0.238/0.238/0.332/4.397 pay (1653,2): 0.232/0.463/0.481/4.142 income (874,1): 0.229/0.229/0.438/3.171 dollars (4434,2): 0.224/0.449/0.584/4.994 huge (3131,2): 0.221/0.441/0.260/2.281 sales (1485,3): 0.210/0.629/0.464/4.468 money (808,2): 0.204/0.408/0.440/3.737 steady (7656,1): 0.197/0.197/0.223/1.943 annual (895,1): 0.196/0.196/0.223/2.183 millions (5429,1): 0.195/0.195/0.432/3.912 expensive (4449,1): 0.179/0.179/0.351/3.352 financing (8017,1): 0.175/0.175/0.456/3.756 buying (7669,1): 0.173/0.173/0.480/3.975 overnight (9082,1): 0.173/0.173/0.177/1.622 resulted (2001,1): 0.172/0.172/0.291/2.128 lawmakers (27291,2): 0.171/0.342/0.278/3.133 profit (2747,1): 0.167/0.167/0.449/3.695 swindle (42243,1): 0.166/0.166/0.194/2.702 \nMost similar words in vocab:\nprofits: 0.596/4.898 rebates: 0.594/6.665 dollars: 0.584/4.994 costs: 0.582/4.898 billion: 0.577/5.759 prices: 0.576/5.634 cost: 0.568/4.493 increase: 0.562/4.360 refinancing: 0.561/6.145 reinvest: 0.545/7.257 exorbitant: 0.544/6.487 repayments: 0.536/5.759 revenues: 0.531/4.632 surtax: 0.530/7.284 premiums: 0.527/6.035 outlay: 0.525/5.578 incentivize: 0.523/7.058 refunds: 0.523/6.034 multibillion: 0.523/8.102 recouping: 0.521/7.571 million: 0.518/5.061 overvalued: 0.517/7.437 reimburse: 0.516/5.690 payments: 0.515/4.500 untaxed: 0.513/7.537 disincentive: 0.509/7.206 disburse: 0.509/6.745 shortfall: 0.507/5.712 price: 0.506/4.423 subsidize: 0.506/5.600 \n\nTopic 11 (3.00): 6.7%\nMost relevant words:\ninpatient (33848,1): 0.185/0.185/0.449/5.603 patients (2468,9): 0.177/1.591/0.712/6.427 tuberculosis (11017,1): 0.173/0.173/0.577/5.029 infectious (12595,4): 0.167/0.666/0.542/5.106 treatment (1460,5): 0.161/0.804/0.642/5.790 malaria (13222,1): 0.161/0.161/0.536/4.785 diseases (4439,6): 0.160/0.958/0.599/5.783 disease (1780,1): 0.157/0.157/0.583/5.376 hospitalized (18161,1): 0.154/0.154/0.391/2.715 patient (3777,2): 0.153/0.307/0.580/5.131 infection (6037,2): 0.153/0.305/0.555/5.063 hospital (723,1): 0.151/0.151/0.378/3.463 infected (8254,1): 0.151/0.151/0.384/3.689 serious (2375,2): 0.150/0.301/0.310/2.757 hepatitis (19494,1): 0.147/0.147/0.569/4.831 hiv (6116,1): 0.144/0.144/0.572/5.568 cancer (1871,2): 0.141/0.282/0.529/5.111 resistant (8950,1): 0.140/0.140/0.319/3.154 treat (5814,3): 0.140/0.420/0.565/4.809 care (1069,2): 0.140/0.279/0.511/4.360 treating (10124,1): 0.139/0.139/0.570/4.598 pregnancy (7979,1): 0.138/0.138/0.443/4.090 medically (28191,1): 0.138/0.138/0.377/5.474 hospitals (4777,5): 0.138/0.689/0.454/4.363 aids (4693,2): 0.137/0.274/0.486/4.662 threatening (7927,2): 0.134/0.269/0.269/2.390 vulnerable (6874,1): 0.132/0.132/0.209/1.980 heart (869,1): 0.132/0.132/0.207/1.720 treatments (9357,2): 0.131/0.262/0.615/5.603 cause (1310,1): 0.131/0.131/0.336/2.615 \nMost similar words in vocab:\npatients: 0.712/6.427 treatment: 0.642/5.790 medication: 0.624/5.414 diarrheal: 0.619/7.802 medications: 0.619/5.240 ivig: 0.618/7.720 treatments: 0.615/5.603 diabetes: 0.603/5.241 prophylactic: 0.600/6.864 antiretroviral: 0.599/6.819 diseases: 0.599/5.783 chronic: 0.595/5.251 infections: 0.588/5.438 nosocomial: 0.587/7.819 treatable: 0.584/6.363 disease: 0.583/5.376 patient: 0.580/5.131 tuberculosis: 0.577/5.029 contraindicated: 0.576/6.038 asthma: 0.572/4.366 hiv: 0.572/5.568 regimens: 0.570/6.651 treating: 0.570/4.598 hepatitis: 0.569/4.831 prophylaxis: 0.569/6.660 vaccine: 0.568/5.287 antiepileptic: 0.567/6.988 illnesses: 0.567/4.377 treat: 0.565/4.809 pediatric: 0.555/4.928 \n\nTopic 9 (3.00): 6.6%\nMost relevant words:\ngenerics (85144,1): 0.201/0.201/0.399/6.775 drugstores (120806,1): 0.200/0.200/0.397/6.291 glaxo (101914,1): 0.193/0.193/0.365/7.067 concocting (179559,1): 0.177/0.177/0.308/5.188 insurers (32340,1): 0.146/0.146/0.450/5.023 medicaid (25609,1): 0.137/0.137/0.408/5.486 lawmakers (27291,2): 0.133/0.266/0.277/3.121 inexpensively (98181,1): 0.129/0.129/0.273/4.512 icahn (83210,1): 0.124/0.124/0.350/6.840 products (1127,1): 0.120/0.120/0.392/3.265 glaxosmithkline (55181,1): 0.118/0.118/0.491/6.611 expensive (4449,1): 0.116/0.116/0.330/3.156 ims (30708,1): 0.114/0.114/0.169/2.413 seeking (3864,1): 0.114/0.114/0.283/2.270 clamoring (148365,1): 0.113/0.113/0.266/4.023 patents (8554,1): 0.112/0.112/0.276/2.600 thwart (31967,1): 0.110/0.110/0.164/2.003 pharmaceuticals (16906,3): 0.108/0.325/0.451/4.162 trying (2359,2): 0.108/0.215/0.199/1.466 approved (2389,1): 0.107/0.107/0.318/2.763 strategy (2853,2): 0.107/0.214/0.243/2.150 rebates (60162,1): 0.107/0.107/0.513/5.761 marketing (2693,1): 0.106/0.106/0.318/2.867 medicare (18132,1): 0.105/0.105/0.439/3.305 conceivably (69563,1): 0.104/0.104/0.251/2.858 discounts (28868,1): 0.103/0.103/0.357/4.238 pharmaceutical (8897,2): 0.102/0.204/0.501/4.668 better (968,3): 0.102/0.305/0.230/1.642 needed (1661,1): 0.101/0.101/0.299/2.104 prescriptions (34657,3): 0.101/0.303/0.491/6.088 \nMost similar words in vocab:\nivig: 0.537/6.704 vioxx: 0.536/8.230 antiretroviral: 0.534/6.081 pfizer: 0.515/6.747 rebates: 0.513/5.761 incentivize: 0.513/6.921 novartis: 0.512/6.637 drugs: 0.512/4.927 adrs: 0.512/7.992 medication: 0.509/4.413 methadone: 0.505/6.065 sanofi: 0.505/6.042 medications: 0.502/4.250 pharmaceutical: 0.501/4.668 healthcare: 0.499/4.505 reimburse: 0.498/5.494 dhhs: 0.498/6.629 medicines: 0.494/4.281 prescription: 0.491/4.268 glaxosmithkline: 0.491/6.611 prescriptions: 0.491/6.088 drug: 0.490/4.902 thiopental: 0.488/7.149 patients: 0.488/4.405 tamiflu: 0.488/7.030 multibillion: 0.487/7.540 uninsured: 0.486/5.725 wellpoint: 0.486/7.512 refinancing: 0.482/5.285 disincentive: 0.481/6.819 \n\nTopic 17 (3.00): 5.6%\nMost relevant words:\ncancer (1871,2): 0.168/0.336/0.565/5.464 diseases (4439,6): 0.154/0.926/0.614/5.925 tuberculosis (11017,1): 0.147/0.147/0.579/5.042 hiv (6116,1): 0.145/0.145/0.590/5.750 disease (1780,1): 0.145/0.145/0.593/5.469 hepatitis (19494,1): 0.144/0.144/0.588/4.991 infectious (12595,4): 0.143/0.571/0.545/5.129 multidrug (112412,1): 0.139/0.139/0.447/7.129 malaria (13222,1): 0.138/0.138/0.539/4.809 aids (4693,2): 0.130/0.261/0.499/4.791 therapeutics (36356,1): 0.128/0.128/0.507/6.320 patients (2468,9): 0.128/1.148/0.695/6.278 treat (5814,3): 0.127/0.380/0.575/4.889 treatments (9357,2): 0.126/0.252/0.630/5.743 treating (10124,1): 0.126/0.126/0.580/4.677 treatment (1460,5): 0.126/0.628/0.634/5.721 antibiotic (24098,2): 0.125/0.250/0.545/3.935 immune (8538,1): 0.125/0.125/0.414/3.766 therapies (21018,2): 0.123/0.246/0.555/4.601 infection (6037,2): 0.119/0.239/0.548/4.994 medicine (1528,2): 0.119/0.239/0.423/3.924 patient (3777,2): 0.119/0.238/0.571/5.054 resistant (8950,1): 0.116/0.116/0.318/3.142 pregnancy (7979,1): 0.114/0.114/0.441/4.073 hospitalized (18161,1): 0.111/0.111/0.370/2.569 efficacy (14568,1): 0.111/0.111/0.455/3.809 infected (8254,1): 0.110/0.110/0.370/3.551 heart (869,1): 0.108/0.108/0.204/1.700 cholesterol (20400,1): 0.108/0.108/0.328/2.635 parasitic (18146,1): 0.104/0.104/0.268/2.235 \nMost similar words in vocab:\npatients: 0.695/6.278 medications: 0.660/5.589 medication: 0.658/5.704 treatment: 0.634/5.721 drugs: 0.631/6.067 treatments: 0.630/5.743 diabetes: 0.628/5.460 ivig: 0.627/7.834 antiretroviral: 0.622/7.077 diseases: 0.614/5.925 diarrheal: 0.613/7.722 chronic: 0.604/5.327 prophylactic: 0.601/6.875 drug: 0.600/6.003 disease: 0.593/5.469 hiv: 0.590/5.750 antiepileptic: 0.589/7.247 hepatitis: 0.588/4.991 contraindicated: 0.587/6.148 asthma: 0.584/4.455 vaccine: 0.583/5.428 antibiotics: 0.583/4.551 infections: 0.583/5.390 treating: 0.580/4.677 tuberculosis: 0.579/5.042 chemotherapy: 0.578/4.455 regimens: 0.575/6.705 treat: 0.575/4.889 treatable: 0.573/6.246 patient: 0.571/5.054 \n\nTopic 2 (3.00): 5.3%\nMost relevant words:\nicahn (83210,1): 0.171/0.171/0.377/7.369 glaxo (101914,1): 0.155/0.155/0.364/7.053 generics (85144,1): 0.152/0.152/0.395/6.700 concocting (179559,1): 0.137/0.137/0.305/5.139 glaxosmithkline (55181,1): 0.128/0.128/0.512/6.896 prescriptions (34657,3): 0.124/0.373/0.524/6.501 medicaid (25609,1): 0.109/0.109/0.407/5.469 drugstores (120806,1): 0.107/0.107/0.371/5.874 pharmaceutical (8897,2): 0.105/0.210/0.526/4.903 lawmakers (27291,2): 0.104/0.208/0.273/3.080 ims (30708,1): 0.097/0.097/0.172/2.456 therapeutics (36356,1): 0.096/0.096/0.485/6.049 controlled (2257,3): 0.093/0.280/0.217/2.015 generic (7602,4): 0.093/0.371/0.233/2.042 pharmaceuticals (16906,3): 0.093/0.278/0.457/4.213 approved (2389,1): 0.091/0.091/0.322/2.799 insurers (32340,1): 0.090/0.090/0.425/4.747 thwart (31967,1): 0.088/0.088/0.162/1.983 medicare (18132,1): 0.087/0.087/0.441/3.319 advocates (8690,1): 0.086/0.086/0.247/2.020 conceivably (69563,1): 0.086/0.086/0.252/2.874 generically (48146,1): 0.086/0.086/0.177/2.024 laboratories (7537,1): 0.085/0.085/0.293/2.734 sinai (13035,3): 0.084/0.253/0.159/1.281 doctors (4958,2): 0.084/0.168/0.409/3.872 administration (991,1): 0.082/0.082/0.239/1.955 alternative (1705,1): 0.082/0.082/0.204/1.717 investigating (8587,1): 0.082/0.082/0.216/1.844 complaint (9280,1): 0.081/0.081/0.227/1.849 turing (18313,9): 0.080/0.722/0.062/0.467 \nMost similar words in vocab:\ndrugs: 0.602/5.794 medication: 0.596/5.171 medications: 0.589/4.983 antiretroviral: 0.586/6.667 drug: 0.582/5.821 ivig: 0.576/7.197 methadone: 0.564/6.764 patients: 0.560/5.055 prophylactic: 0.557/6.362 prescription: 0.555/4.816 vioxx: 0.554/8.505 antiepileptic: 0.543/6.691 treatments: 0.536/4.888 medicines: 0.536/4.647 antipsychotic: 0.536/6.010 regimens: 0.531/6.189 novartis: 0.528/6.837 pharmaceutical: 0.526/4.903 pfizer: 0.525/6.873 prescriptions: 0.524/6.501 praziquantel: 0.518/7.028 treatment: 0.518/4.670 dosages: 0.517/5.576 diarrheal: 0.516/6.495 sanofi: 0.513/6.141 glaxosmithkline: 0.512/6.896 bevacizumab: 0.511/7.211 healthcare: 0.510/4.607 contraceptives: 0.510/6.197 dhhs: 0.506/6.743 \n\nTopic 6 (3.00): 5.3%\nMost relevant words:\ndrugs (3528,10): 0.155/1.546/0.734/7.060 drug (1942,15): 0.132/1.979/0.729/7.292 pills (18688,1): 0.127/0.127/0.423/3.444 therapies (21018,2): 0.118/0.236/0.557/4.623 medicine (1528,2): 0.111/0.222/0.422/3.916 pharmaceutical (8897,2): 0.108/0.217/0.534/4.977 aids (4693,2): 0.108/0.216/0.486/4.668 treatments (9357,2): 0.108/0.216/0.620/5.651 treatment (1460,5): 0.107/0.533/0.623/5.620 therapeutics (36356,1): 0.105/0.105/0.496/6.189 efficacy (14568,1): 0.105/0.105/0.455/3.812 hiv (6116,1): 0.102/0.102/0.561/5.469 antibiotic (24098,2): 0.102/0.205/0.526/3.800 medically (28191,1): 0.101/0.101/0.372/5.409 cholesterol (20400,1): 0.101/0.101/0.328/2.637 medical (679,1): 0.100/0.100/0.503/4.304 pregnancy (7979,1): 0.100/0.100/0.433/4.005 treating (10124,1): 0.099/0.099/0.557/4.496 treat (5814,3): 0.099/0.296/0.553/4.702 pharmaceuticals (16906,3): 0.096/0.287/0.465/4.287 dangerous (3584,2): 0.095/0.190/0.238/2.198 investigating (8587,1): 0.095/0.095/0.237/2.027 cancer (1871,2): 0.095/0.189/0.512/4.952 patient (3777,2): 0.093/0.187/0.551/4.875 testing (2814,1): 0.093/0.093/0.326/2.915 rima (42963,1): 0.093/0.093/-0.005/-0.077 effects (1380,2): 0.091/0.182/0.320/2.928 health (502,3): 0.089/0.266/0.474/4.005 administration (991,1): 0.088/0.088/0.252/2.066 hospitalized (18161,1): 0.086/0.086/0.342/2.375 \nMost similar words in vocab:\ndrugs: 0.734/7.060 drug: 0.729/7.292 medication: 0.694/6.024 medications: 0.685/5.796 patients: 0.655/5.919 antiretroviral: 0.642/7.307 treatment: 0.623/5.620 treatments: 0.620/5.651 prescription: 0.609/5.289 ivig: 0.608/7.598 methadone: 0.600/7.196 medicines: 0.595/5.164 antiepileptic: 0.592/7.287 antipsychotic: 0.590/6.618 prophylactic: 0.586/6.704 diabetes: 0.586/5.096 therapeutic: 0.578/5.042 contraindicated: 0.576/6.041 regimens: 0.568/6.627 antibiotics: 0.565/4.412 analgesics: 0.563/6.252 hiv: 0.561/5.469 antiviral: 0.560/6.572 diarrheal: 0.560/7.055 chronic: 0.557/4.916 treating: 0.557/4.496 therapies: 0.557/4.623 chemotherapy: 0.556/4.287 treat: 0.553/4.702 anticoagulant: 0.551/6.066 \n\nTopic 5 (3.00): 4.0%\nMost relevant words:\ntoxoplasmosis (125001,4): 0.949/3.796/0.796/11.721 infection (6037,2): 0.350/0.700/0.724/6.606 infected (8254,1): 0.288/0.288/0.527/5.053 parasite (16531,1): 0.282/0.282/0.452/3.508 parasitic (18146,1): 0.282/0.282/0.452/3.768 disease (1780,1): 0.259/0.259/0.715/6.588 diseases (4439,6): 0.250/1.500/0.719/6.944 multidrug (112412,1): 0.236/0.236/0.513/8.190 malaria (13222,1): 0.223/0.223/0.653/5.826 immune (8538,1): 0.214/0.214/0.532/4.843 infectious (12595,4): 0.213/0.850/0.644/6.063 hepatitis (19494,1): 0.211/0.211/0.696/5.909 tuberculosis (11017,1): 0.180/0.180/0.664/5.783 rare (2251,1): 0.167/0.167/0.290/2.765 cause (1310,1): 0.136/0.136/0.433/3.369 isolated (4860,1): 0.131/0.131/0.280/2.527 medically (28191,1): 0.123/0.123/0.418/6.071 rarely (4300,1): 0.122/0.122/0.289/2.363 resistant (8950,1): 0.121/0.121/0.377/3.720 hiv (6116,1): 0.111/0.111/0.618/6.021 caused (1150,1): 0.106/0.106/0.335/2.889 pregnancy (7979,1): 0.106/0.106/0.491/4.537 rima (42963,1): 0.103/0.103/0.036/0.505 possibly (2456,1): 0.100/0.100/0.230/1.766 effects (1380,2): 0.099/0.199/0.381/3.489 cancer (1871,2): 0.094/0.188/0.561/5.418 antibiotic (24098,2): 0.093/0.186/0.577/4.173 hospitalized (18161,1): 0.088/0.088/0.413/2.864 factor (2518,1): 0.086/0.086/0.253/2.228 vulnerable (6874,1): 0.083/0.083/0.235/2.226 \nMost similar words in vocab:\ntoxoplasmosis: 0.796/11.721 infections: 0.732/6.775 infection: 0.724/6.606 diseases: 0.719/6.944 disease: 0.715/6.588 ulcerative: 0.704/6.753 diarrheal: 0.702/8.849 hepatitis: 0.696/5.909 falciparum: 0.674/7.469 endocarditis: 0.673/6.947 chronic: 0.671/5.922 treatable: 0.670/7.296 patients: 0.666/6.012 tuberculosis: 0.664/5.783 crohn's: 0.664/6.725 bacteremia: 0.655/7.947 malaria: 0.653/5.826 ivig: 0.647/8.077 histolytica: 0.646/7.616 colitis: 0.646/6.846 infectious: 0.644/6.063 malignancies: 0.643/6.630 nosocomial: 0.643/8.562 antiepileptic: 0.642/7.905 sepsis: 0.640/6.933 pylori: 0.639/6.664 chlamydia: 0.635/7.189 staphylococcal: 0.633/8.823 blastocystis: 0.633/8.335 pneumoniae: 0.632/6.609 \n\nTopic 12 (3.00): 3.7%\nMost relevant words:\ndrug (1942,15): 0.571/8.561/0.945/9.458 drugs (3528,10): 0.387/3.865/0.902/8.678 pills (18688,1): 0.136/0.136/0.517/4.210 cholesterol (20400,1): 0.063/0.063/0.356/2.859 dangerous (3584,2): 0.062/0.124/0.268/2.470 investigating (8587,1): 0.056/0.056/0.258/2.202 generic (7602,4): 0.054/0.215/0.256/2.242 controlled (2257,3): 0.053/0.158/0.236/2.184 side (287,2): 0.052/0.104/-0.003/-0.024 effects (1380,2): 0.049/0.099/0.329/3.017 rima (42963,1): 0.048/0.048/-0.002/-0.023 use (176,7): 0.047/0.326/0.285/1.984 efficacy (14568,1): 0.044/0.044/0.436/3.651 crazy (5181,1): 0.044/0.044/0.093/0.742 story (367,1): 0.043/0.043/-0.007/-0.057 known (97,1): 0.042/0.042/-0.006/-0.037 administration (991,1): 0.042/0.042/0.247/2.025 used (82,4): 0.042/0.167/0.127/0.959 samples (4875,1): 0.041/0.041/0.168/1.725 force (351,1): 0.040/0.040/0.002/0.015 testing (2814,1): 0.039/0.039/0.308/2.747 antibiotic (24098,2): 0.038/0.076/0.486/3.512 talked (11444,1): 0.038/0.038/0.077/0.484 combination (2674,1): 0.037/0.037/0.233/1.740 pharmaceutical (8897,2): 0.037/0.074/0.495/4.607 two (37,3): 0.037/0.111/-0.062/-0.470 like (189,2): 0.036/0.072/0.078/0.524 possibly (2456,1): 0.036/0.036/0.127/0.975 former (185,1): 0.036/0.036/-0.035/-0.280 tightly (15059,1): 0.036/0.036/0.022/0.191 \nMost similar words in vocab:\ndrug: 0.945/9.458 drugs: 0.902/8.678 medication: 0.660/5.729 prescription: 0.659/5.721 medications: 0.650/5.497 cocaine: 0.637/6.646 steroids: 0.616/4.154 heroin: 0.612/5.885 antipsychotic: 0.607/6.808 antiretroviral: 0.607/6.901 opiates: 0.605/6.971 methadone: 0.591/7.087 cannabis: 0.575/5.221 antiepileptic: 0.567/6.987 stimulants: 0.567/6.233 diazepam: 0.566/5.984 marijuana: 0.565/5.379 painkillers: 0.564/6.927 morphine: 0.559/4.416 psychotropic: 0.559/6.651 ketamine: 0.557/6.539 anticancer: 0.557/6.239 antiviral: 0.556/6.525 opioids: 0.556/6.362 analgesics: 0.552/6.122 barbiturates: 0.549/6.335 addiction: 0.549/5.156 medicines: 0.548/4.750 antidepressant: 0.544/6.122 methamphetamine: 0.544/6.975 \n\nTopic 7 (3.00): 2.6%\nMost relevant words:\neffects (1380,2): 0.062/0.123/0.355/3.247 potentially (6379,1): 0.061/0.061/0.440/3.873 dangerous (3584,2): 0.058/0.115/0.261/2.405 multidrug (112412,1): 0.057/0.057/0.439/7.001 resistant (8950,1): 0.056/0.056/0.322/3.181 antibiotic (24098,2): 0.055/0.109/0.536/3.877 cause (1310,1): 0.055/0.055/0.345/2.686 use (176,7): 0.054/0.380/0.308/2.141 serious (2375,2): 0.054/0.107/0.300/2.674 threatening (7927,2): 0.053/0.105/0.270/2.402 generically (48146,1): 0.052/0.052/0.199/2.272 conceivably (69563,1): 0.051/0.051/0.273/3.111 medically (28191,1): 0.049/0.049/0.371/5.394 pregnancy (7979,1): 0.049/0.049/0.433/3.999 problems (1188,1): 0.049/0.049/0.306/2.615 pills (18688,1): 0.049/0.049/0.391/3.185 immune (8538,1): 0.048/0.048/0.394/3.583 treatments (9357,2): 0.048/0.095/0.607/5.535 vulnerable (6874,1): 0.047/0.047/0.201/1.900 efficacy (14568,1): 0.047/0.047/0.444/3.724 used (82,4): 0.047/0.189/0.144/1.086 treat (5814,3): 0.047/0.140/0.548/4.661 combination (2674,1): 0.047/0.047/0.264/1.968 caused (1150,1): 0.047/0.047/0.266/2.297 develop (1982,1): 0.045/0.045/0.294/2.423 possibly (2456,1): 0.045/0.045/0.157/1.206 prescriptions (34657,3): 0.045/0.136/0.503/6.241 testing (2814,1): 0.045/0.045/0.324/2.890 cholesterol (20400,1): 0.044/0.044/0.313/2.517 isolated (4860,1): 0.044/0.044/0.185/1.676 \nMost similar words in vocab:\ndrugs: 0.669/6.435 medications: 0.664/5.622 medication: 0.663/5.752 drug: 0.639/6.395 ivig: 0.638/7.970 antiretroviral: 0.632/7.197 patients: 0.632/5.704 antiepileptic: 0.620/7.634 treatments: 0.607/5.535 prophylactic: 0.604/6.900 treatment: 0.598/5.395 diarrheal: 0.597/7.518 antipsychotic: 0.581/6.515 methadone: 0.580/6.957 contraindicated: 0.577/6.047 antibiotics: 0.577/4.500 regimens: 0.573/6.676 medicines: 0.572/4.966 prescription: 0.566/4.918 treatable: 0.563/6.129 anticoagulant: 0.560/6.163 analgesics: 0.560/6.213 diabetes: 0.559/4.859 antiviral: 0.559/6.551 praziquantel: 0.558/7.565 dosages: 0.557/6.013 warfarin: 0.556/5.934 vaccine: 0.556/5.172 chronic: 0.554/4.882 nsaids: 0.552/6.090 \n\nTopic 14 (3.00): 1.9%\nMost relevant words:\ndiseases (4439,6): 0.070/0.418/0.650/6.275 multidrug (112412,1): 0.069/0.069/0.474/7.564 hiv (6116,1): 0.063/0.063/0.623/6.066 disease (1780,1): 0.062/0.062/0.625/5.762 hepatitis (19494,1): 0.059/0.059/0.618/5.245 infectious (12595,4): 0.059/0.236/0.572/5.388 immune (8538,1): 0.058/0.058/0.455/4.146 infection (6037,2): 0.058/0.115/0.593/5.407 medically (28191,1): 0.056/0.056/0.406/5.899 treatments (9357,2): 0.056/0.112/0.666/6.070 tuberculosis (11017,1): 0.055/0.055/0.597/5.197 malaria (13222,1): 0.054/0.054/0.561/5.009 antibiotic (24098,2): 0.053/0.106/0.584/4.222 treat (5814,3): 0.053/0.158/0.605/5.152 treating (10124,1): 0.052/0.052/0.612/4.942 treatment (1460,5): 0.052/0.259/0.663/5.980 aids (4693,2): 0.052/0.103/0.522/5.006 pregnancy (7979,1): 0.051/0.051/0.479/4.423 cancer (1871,2): 0.051/0.102/0.561/5.418 infected (8254,1): 0.050/0.050/0.408/3.917 effects (1380,2): 0.050/0.099/0.371/3.402 patients (2468,9): 0.049/0.443/0.717/6.471 resistant (8950,1): 0.048/0.048/0.344/3.401 therapies (21018,2): 0.047/0.094/0.577/4.784 efficacy (14568,1): 0.044/0.044/0.480/4.022 cholesterol (20400,1): 0.043/0.043/0.356/2.865 patient (3777,2): 0.042/0.084/0.582/5.154 isolated (4860,1): 0.041/0.041/0.219/1.981 parasitic (18146,1): 0.041/0.041/0.294/2.450 cause (1310,1): 0.040/0.040/0.353/2.746 \nMost similar words in vocab:\npatients: 0.717/6.471 medications: 0.700/5.927 medication: 0.696/6.039 drugs: 0.690/6.632 treatments: 0.666/6.070 treatment: 0.663/5.980 antiretroviral: 0.659/7.498 drug: 0.653/6.531 ivig: 0.653/8.155 diseases: 0.650/6.275 diabetes: 0.643/5.589 diarrheal: 0.643/8.099 prophylactic: 0.638/7.293 chronic: 0.638/5.624 antiepileptic: 0.638/7.851 contraindicated: 0.630/6.600 disease: 0.625/5.762 infections: 0.623/5.766 hiv: 0.623/6.066 antibiotics: 0.621/4.848 hepatitis: 0.618/5.245 regimens: 0.614/7.153 treating: 0.612/4.942 treatable: 0.611/6.661 asthma: 0.611/4.663 prophylaxis: 0.606/7.098 ulcerative: 0.606/5.813 treat: 0.605/5.152 chemotherapy: 0.600/4.631 anticoagulant: 0.599/6.594 \n\nTopic 8 (3.00): 1.6%\nMost relevant words:\nefficacy (14568,1): 0.036/0.036/0.466/3.903 testing (2814,1): 0.035/0.035/0.348/3.107 treatments (9357,2): 0.035/0.070/0.624/5.686 generically (48146,1): 0.035/0.035/0.203/2.324 prescriptions (34657,3): 0.035/0.104/0.518/6.427 pharmaceuticals (16906,3): 0.032/0.097/0.474/4.369 generic (7602,4): 0.032/0.129/0.250/2.190 generics (85144,1): 0.032/0.032/0.374/6.353 therapies (21018,2): 0.032/0.064/0.539/4.473 treatment (1460,5): 0.032/0.158/0.617/5.566 pharmaceutical (8897,2): 0.031/0.063/0.526/4.903 use (176,7): 0.031/0.217/0.293/2.037 effects (1380,2): 0.031/0.061/0.328/3.003 therapeutics (36356,1): 0.031/0.031/0.490/6.111 samples (4875,1): 0.030/0.030/0.185/1.891 glaxosmithkline (55181,1): 0.030/0.030/0.495/6.657 potentially (6379,1): 0.030/0.030/0.411/3.613 certain (1095,4): 0.029/0.117/0.312/2.149 treat (5814,3): 0.029/0.086/0.544/4.631 cholesterol (20400,1): 0.029/0.029/0.315/2.534 used (82,4): 0.028/0.114/0.137/1.034 problems (1188,1): 0.028/0.028/0.296/2.529 combination (2674,1): 0.028/0.028/0.257/1.919 concocting (179559,1): 0.028/0.028/0.282/4.756 dangerous (3584,2): 0.028/0.055/0.230/2.122 antibiotic (24098,2): 0.027/0.055/0.504/3.644 alternative (1705,1): 0.027/0.027/0.214/1.801 resistant (8950,1): 0.027/0.027/0.294/2.904 patient (3777,2): 0.027/0.053/0.541/4.785 using (379,1): 0.027/0.027/0.186/1.347 \nMost similar words in vocab:\nmedication: 0.667/5.785 medications: 0.663/5.614 patients: 0.655/5.918 drugs: 0.642/6.174 ivig: 0.639/7.985 antiretroviral: 0.632/7.193 treatments: 0.624/5.686 treatment: 0.617/5.566 antiepileptic: 0.613/7.544 drug: 0.610/6.098 prophylactic: 0.607/6.944 antipsychotic: 0.587/6.586 regimens: 0.587/6.842 methadone: 0.582/6.978 medicines: 0.581/5.036 contraindicated: 0.578/6.056 diarrheal: 0.575/7.250 dosages: 0.574/6.191 prescription: 0.570/4.947 praziquantel: 0.560/7.599 bevacizumab: 0.560/7.900 diabetes: 0.559/4.858 analgesics: 0.557/6.180 infliximab: 0.555/7.185 monotherapy: 0.552/7.328 anticoagulant: 0.552/6.073 bisphosphonates: 0.551/7.067 antibiotics: 0.551/4.302 therapeutic: 0.551/4.799 warfarin: 0.550/5.870 \n\nTopic 13 (3.00): 1.2%\nMost relevant words:\ndrugs (3528,10): 0.045/0.448/0.763/7.338 drug (1942,15): 0.033/0.498/0.743/7.428 pills (18688,1): 0.030/0.030/0.432/3.516 antibiotic (24098,2): 0.030/0.059/0.564/4.073 efficacy (14568,1): 0.028/0.028/0.480/4.018 effects (1380,2): 0.028/0.056/0.356/3.258 cholesterol (20400,1): 0.028/0.028/0.355/2.855 treatments (9357,2): 0.027/0.053/0.633/5.771 therapies (21018,2): 0.026/0.052/0.557/4.621 multidrug (112412,1): 0.026/0.026/0.439/7.016 hiv (6116,1): 0.024/0.024/0.569/5.547 pregnancy (7979,1): 0.024/0.024/0.443/4.094 generic (7602,4): 0.024/0.095/0.256/2.244 immune (8538,1): 0.024/0.024/0.404/3.679 testing (2814,1): 0.023/0.023/0.342/3.056 therapeutics (36356,1): 0.023/0.023/0.497/6.193 resistant (8950,1): 0.023/0.023/0.315/3.116 pharmaceutical (8897,2): 0.023/0.046/0.530/4.937 prescriptions (34657,3): 0.022/0.067/0.512/6.346 treating (10124,1): 0.022/0.022/0.561/4.528 dangerous (3584,2): 0.022/0.045/0.246/2.268 treatment (1460,5): 0.022/0.112/0.618/5.574 pharmaceuticals (16906,3): 0.022/0.067/0.471/4.348 aids (4693,2): 0.022/0.043/0.476/4.571 use (176,7): 0.021/0.149/0.290/2.019 isolated (4860,1): 0.021/0.021/0.193/1.741 combination (2674,1): 0.021/0.021/0.264/1.975 treat (5814,3): 0.021/0.063/0.548/4.664 samples (4875,1): 0.021/0.021/0.181/1.859 medically (28191,1): 0.021/0.021/0.367/5.328 \nMost similar words in vocab:\ndrugs: 0.763/7.338 drug: 0.743/7.428 medications: 0.706/5.975 medication: 0.703/6.101 antiretroviral: 0.667/7.586 ivig: 0.650/8.114 patients: 0.647/5.841 treatments: 0.633/5.771 antiepileptic: 0.630/7.757 antipsychotic: 0.623/6.994 prescription: 0.620/5.386 treatment: 0.618/5.574 prophylactic: 0.610/6.969 medicines: 0.606/5.256 methadone: 0.603/7.230 antibiotics: 0.602/4.696 contraindicated: 0.601/6.293 antiviral: 0.597/7.005 regimens: 0.591/6.894 anticoagulant: 0.589/6.483 diarrheal: 0.589/7.416 analgesics: 0.588/6.525 therapeutic: 0.583/5.086 anticancer: 0.583/6.525 warfarin: 0.580/6.197 diabetes: 0.579/5.032 dosages: 0.579/6.247 nsaids: 0.576/6.364 opiates: 0.572/6.595 isoniazid: 0.572/7.543 \n\nTopic 16 (3.00): 1.2%\nMost relevant words:\nconcocting (179559,1): 0.078/0.078/0.365/6.152 glaxo (101914,1): 0.049/0.049/0.386/7.479 generics (85144,1): 0.046/0.046/0.417/7.082 drugstores (120806,1): 0.041/0.041/0.410/6.491 prescriptions (34657,3): 0.038/0.115/0.557/6.904 inexpensively (98181,1): 0.036/0.036/0.304/5.030 conceivably (69563,1): 0.032/0.032/0.304/3.466 shorting (108317,1): 0.028/0.028/0.354/6.205 thwart (31967,1): 0.028/0.028/0.196/2.405 generically (48146,1): 0.026/0.026/0.211/2.414 discounts (28868,1): 0.025/0.025/0.387/4.597 insurers (32340,1): 0.024/0.024/0.448/5.001 generic (7602,4): 0.023/0.092/0.254/2.228 clamoring (148365,1): 0.022/0.022/0.276/4.169 lawmakers (27291,2): 0.022/0.044/0.275/3.100 medicaid (25609,1): 0.021/0.021/0.403/5.419 using (379,1): 0.021/0.021/0.206/1.492 glaxosmithkline (55181,1): 0.021/0.021/0.496/6.670 products (1127,1): 0.021/0.021/0.394/3.279 brilliance (27594,1): 0.020/0.020/0.085/1.083 ims (30708,1): 0.020/0.020/0.172/2.466 food (807,1): 0.020/0.020/0.302/2.774 alternative (1705,1): 0.020/0.020/0.223/1.877 expensive (4449,1): 0.020/0.020/0.331/3.167 needed (1661,1): 0.020/0.020/0.319/2.248 use (176,7): 0.020/0.137/0.280/1.944 mainstays (61442,1): 0.019/0.019/0.117/1.330 piggy (34457,1): 0.019/0.019/0.135/1.842 strategy (2853,2): 0.019/0.038/0.250/2.211 rules (1377,1): 0.019/0.019/0.103/0.931 \nMost similar words in vocab:\ndrugs: 0.586/5.632 antiretroviral: 0.577/6.563 ivig: 0.573/7.158 methadone: 0.567/6.797 drug: 0.557/5.574 prescriptions: 0.557/6.904 vioxx: 0.549/8.420 medication: 0.546/4.740 prescription: 0.540/4.691 thiopental: 0.534/7.813 medications: 0.533/4.514 antiepileptic: 0.531/6.538 adrs: 0.524/8.177 antipsychotic: 0.523/5.872 medicines: 0.523/4.536 prophylactic: 0.522/5.965 placebos: 0.522/7.543 dosages: 0.520/5.618 rebates: 0.516/5.793 contraceptives: 0.514/6.249 incentivize: 0.510/6.884 pfizer: 0.510/6.686 tamiflu: 0.509/7.340 unapproved: 0.508/6.137 regimens: 0.508/5.917 sanofi: 0.507/6.065 praziquantel: 0.507/6.870 novartis: 0.503/6.520 pharmacies: 0.503/5.960 bevacizumab: 0.503/7.094 \n\nTopic 10 (3.00): 0.6%\nMost relevant words:\nmedicaid (25609,1): 0.018/0.018/0.435/5.850 drugstores (120806,1): 0.017/0.017/0.392/6.207 discounts (28868,1): 0.017/0.017/0.405/4.804 generics (85144,1): 0.015/0.015/0.389/6.597 rebates (60162,1): 0.015/0.015/0.548/6.158 inexpensively (98181,1): 0.014/0.014/0.284/4.701 insurers (32340,1): 0.014/0.014/0.452/5.044 prescriptions (34657,3): 0.013/0.039/0.519/6.435 severance (36986,1): 0.013/0.013/0.311/4.115 concocting (179559,1): 0.013/0.013/0.294/4.955 conceivably (69563,1): 0.012/0.012/0.271/3.095 clamoring (148365,1): 0.012/0.012/0.276/4.168 increases (4242,4): 0.011/0.043/0.387/3.322 lawmakers (27291,2): 0.011/0.022/0.267/3.005 products (1127,1): 0.011/0.011/0.388/3.234 medicare (18132,1): 0.010/0.010/0.450/3.391 required (967,2): 0.010/0.021/0.269/1.943 expensive (4449,1): 0.010/0.010/0.323/3.089 tablet (10396,2): 0.010/0.020/0.141/1.400 increase (1265,8): 0.010/0.078/0.442/3.426 needed (1661,1): 0.010/0.010/0.305/2.150 rules (1377,1): 0.010/0.010/0.094/0.852 seeking (3864,1): 0.010/0.010/0.272/2.188 supply (1905,1): 0.010/0.010/0.281/2.470 increased (1167,1): 0.010/0.010/0.373/3.009 certain (1095,4): 0.009/0.038/0.291/2.003 access (935,1): 0.009/0.009/0.213/1.803 glaxo (101914,1): 0.009/0.009/0.332/6.439 option (3370,1): 0.009/0.009/0.260/2.298 thwart (31967,1): 0.009/0.009/0.157/1.927 \nMost similar words in vocab:\nivig: 0.569/7.109 antiretroviral: 0.565/6.433 rebates: 0.548/6.158 vioxx: 0.546/8.376 adrs: 0.545/8.512 medication: 0.541/4.696 drugs: 0.541/5.205 methadone: 0.535/6.423 medications: 0.530/4.486 patients: 0.523/4.719 thiopental: 0.519/7.604 prescription: 0.519/4.509 prescriptions: 0.519/6.435 drug: 0.516/5.158 uninsured: 0.514/6.056 prophylactic: 0.513/5.864 antiepileptic: 0.509/6.267 dosages: 0.508/5.480 praziquantel: 0.505/6.848 incentivize: 0.504/6.802 reimburse: 0.504/5.551 diarrheal: 0.501/6.311 antipsychotic: 0.500/5.613 contraceptives: 0.499/6.060 bevacizumab: 0.498/7.021 costs: 0.497/4.185 medicines: 0.497/4.307 dhhs: 0.495/6.591 bisphosphonates: 0.495/6.342 refinancing: 0.493/5.402 \n\nTopic 19 (3.00): 0.6%\nMost relevant words:\nmultidrug (112412,1): 0.016/0.016/0.459/7.321 therapeutics (36356,1): 0.013/0.013/0.513/6.391 antibiotic (24098,2): 0.013/0.026/0.555/4.012 testing (2814,1): 0.013/0.013/0.358/3.201 effects (1380,2): 0.012/0.024/0.347/3.177 efficacy (14568,1): 0.012/0.012/0.466/3.905 generics (85144,1): 0.012/0.012/0.381/6.463 treatments (9357,2): 0.012/0.024/0.626/5.705 generically (48146,1): 0.012/0.012/0.205/2.347 resistant (8950,1): 0.012/0.012/0.322/3.183 immune (8538,1): 0.012/0.012/0.409/3.722 medically (28191,1): 0.011/0.011/0.378/5.488 prescriptions (34657,3): 0.011/0.034/0.517/6.409 treatment (1460,5): 0.011/0.056/0.624/5.631 hiv (6116,1): 0.011/0.011/0.565/5.505 treating (10124,1): 0.011/0.011/0.566/4.566 combination (2674,1): 0.011/0.011/0.277/2.068 cholesterol (20400,1): 0.011/0.011/0.332/2.669 therapies (21018,2): 0.011/0.022/0.542/4.497 treat (5814,3): 0.011/0.032/0.557/4.740 generic (7602,4): 0.011/0.043/0.249/2.183 hepatitis (19494,1): 0.011/0.011/0.554/4.703 pregnancy (7979,1): 0.010/0.010/0.434/4.015 potentially (6379,1): 0.010/0.010/0.415/3.654 problems (1188,1): 0.010/0.010/0.307/2.623 isolated (4860,1): 0.010/0.010/0.197/1.780 cause (1310,1): 0.010/0.010/0.331/2.570 patients (2468,9): 0.010/0.092/0.673/6.077 infectious (12595,4): 0.010/0.040/0.511/4.806 patient (3777,2): 0.010/0.020/0.555/4.912 \nMost similar words in vocab:\nmedication: 0.676/5.864 medications: 0.673/5.696 patients: 0.673/6.077 drugs: 0.663/6.381 ivig: 0.658/8.219 antiretroviral: 0.656/7.465 drug: 0.640/6.403 antiepileptic: 0.631/7.766 treatments: 0.626/5.705 treatment: 0.624/5.631 prophylactic: 0.623/7.123 diarrheal: 0.622/7.831 regimens: 0.606/7.070 contraindicated: 0.599/6.279 antipsychotic: 0.599/6.719 methadone: 0.589/7.069 antibiotics: 0.589/4.596 diabetes: 0.588/5.107 antiviral: 0.585/6.865 anticoagulant: 0.584/6.427 praziquantel: 0.583/7.906 prophylaxis: 0.583/6.820 dosages: 0.582/6.283 isoniazid: 0.582/7.667 infliximab: 0.581/7.529 chronic: 0.581/5.124 analgesics: 0.577/6.408 treatable: 0.576/6.279 medicines: 0.576/4.997 vaccine: 0.575/5.354 \n\nTopic 4 (3.00): 0.5%\nMost relevant words:\ninexpensively (98181,1): 0.039/0.039/0.360/5.961 shorting (108317,1): 0.025/0.025/0.397/6.959 clamoring (148365,1): 0.015/0.015/0.309/4.666 discounts (28868,1): 0.014/0.014/0.415/4.932 rebates (60162,1): 0.014/0.014/0.570/6.403 insurers (32340,1): 0.013/0.013/0.474/5.292 conceivably (69563,1): 0.013/0.013/0.303/3.453 thwart (31967,1): 0.012/0.012/0.202/2.469 drugstores (120806,1): 0.012/0.012/0.388/6.148 generics (85144,1): 0.012/0.012/0.389/6.598 expensive (4449,1): 0.011/0.011/0.363/3.472 piggy (34457,1): 0.011/0.011/0.159/2.172 severance (36986,1): 0.011/0.011/0.319/4.223 swindle (42243,1): 0.011/0.011/0.204/2.849 buying (7669,1): 0.010/0.010/0.488/4.043 concocting (179559,1): 0.010/0.010/0.296/4.991 shrank (46494,1): 0.010/0.010/0.281/3.233 amount (1588,1): 0.010/0.010/0.406/3.290 option (3370,1): 0.010/0.010/0.297/2.624 cost (1178,2): 0.010/0.020/0.520/4.113 buy (3433,1): 0.009/0.009/0.457/3.750 supply (1905,1): 0.009/0.009/0.302/2.657 financing (8017,1): 0.009/0.009/0.442/3.640 increases (4242,4): 0.009/0.034/0.390/3.347 distribution (1335,3): 0.009/0.026/0.222/2.056 money (808,2): 0.009/0.017/0.407/3.456 lawmakers (27291,2): 0.009/0.017/0.269/3.025 required (967,2): 0.008/0.016/0.273/1.974 pay (1653,2): 0.008/0.016/0.429/3.691 products (1127,1): 0.008/0.008/0.388/3.232 \nMost similar words in vocab:\nrebates: 0.570/6.403 refinancing: 0.548/6.002 costs: 0.536/4.515 prices: 0.535/5.233 adrs: 0.534/8.347 incentivize: 0.534/7.206 ivig: 0.527/6.586 profits: 0.526/4.318 overvalued: 0.525/7.560 premiums: 0.525/6.013 disincentive: 0.522/7.399 cost: 0.520/4.113 exorbitant: 0.519/6.189 vioxx: 0.519/7.962 reimburse: 0.519/5.717 multibillion: 0.517/8.008 untaxed: 0.516/7.573 illiquid: 0.513/7.155 refunds: 0.513/5.912 dollars: 0.512/4.377 antiretroviral: 0.509/5.795 uninsured: 0.508/5.977 reinvest: 0.506/6.732 kickbacks: 0.500/6.129 subsidize: 0.500/5.538 overcharging: 0.500/8.514 anticompetitive: 0.499/7.301 undervalued: 0.499/5.614 billion: 0.497/4.960 disburse: 0.497/6.589 \n\nTopic 0 (0.00): 0.3%\nMost relevant words:\nborn (106,1): 0.019/0.019/0.000/0.000 round (273,1): 0.016/0.016/0.000/0.000 first (26,3): 0.013/0.040/0.000/0.000 two (37,3): 0.013/0.039/0.000/0.000 world (56,1): 0.012/0.012/0.000/0.000 side (287,2): 0.012/0.023/0.000/0.000 university (61,2): 0.012/0.023/0.000/0.000 last (237,2): 0.011/0.023/0.000/0.000 rima (42963,1): 0.011/0.011/0.000/0.000 former (185,1): 0.010/0.010/0.000/0.000 tracks (907,1): 0.010/0.010/0.000/0.000 held (230,1): 0.010/0.010/0.000/0.000 story (367,1): 0.010/0.010/0.000/0.000 martin (778,1): 0.010/0.010/0.000/0.000 known (97,1): 0.010/0.010/0.000/0.000 district (167,1): 0.009/0.009/0.000/0.000 line (181,1): 0.009/0.009/0.000/0.000 school (49,1): 0.009/0.009/0.000/0.000 back (193,1): 0.009/0.009/0.000/0.000 force (351,1): 0.009/0.009/0.000/0.000 division (236,1): 0.009/0.009/0.000/0.000 wrote (536,1): 0.009/0.009/0.000/0.000 august (149,3): 0.008/0.025/0.000/0.000 old (204,5): 0.008/0.042/0.000/0.000 list (186,1): 0.008/0.008/0.000/0.000 works (341,1): 0.008/0.008/0.000/0.000 judith (8272,1): 0.008/0.008/0.000/0.000 october (131,1): 0.008/0.008/0.000/0.000 called (155,3): 0.008/0.024/0.000/0.000 june (140,1): 0.008/0.008/0.000/0.000 \nEM Iter 40:\nEm:\n[   1.71772225  128.84790419   35.16478757   87.24169635    2.80265093\n   25.63164822   34.56266559   16.67080807   10.40937595   43.31371876\n    3.58633483   44.3112449    24.18473821    7.35289399   11.79837888\n   58.48965784    7.20479509   36.53471312   70.87544818    3.29881708]\n\nEM Iter 41:\nEm:\n[   1.71859174  129.70592664   35.37988296   89.48185854    2.48781801\n   25.3919027    34.37767599   16.32225538   10.07339923   43.46433916\n    3.2741818    44.59668785   24.13308222    6.94972474   11.19057892\n   58.82935949    6.818491     36.3525432    70.49081622    2.9608842 ]\n\nEM Iter 42:\nEm:\n[   1.71949742  130.47504761   35.59938574   91.68933455    2.19189534\n   25.15471826   34.17486566   15.97466482    9.7407452    43.6129234\n    2.97580597   44.84178438   24.09375893    6.55875562   10.59478004\n   59.23999465    6.44648723   36.13884415   70.13664944    2.64006159]\n\nEM Iter 43:\nEm:\n[   1.72043266  131.16465169   35.82353514   93.85650144    1.91394088\n   24.92159958   33.95597452   15.62837633    9.41159022   43.75931229\n    2.69059576   45.04575984   24.0663108     6.17977923   10.01209725\n   59.72216164    6.08792995   35.89488181   69.80852846    2.33604051]\n\nEM Iter 44:\nEm:\n[   1.72139165  131.78310454   36.05258742   95.97646723    1.65322697\n   24.69379194   33.7228627    15.28373527    9.08611621   43.90313786\n    2.41802559   45.20829981   24.05026056    5.81258425    9.44353654\n   60.27556687    5.74200895   35.62221086   69.50251052    2.04857424]\n\nEM Iter 45:\nEm:\n[   1.72236875  132.3378226    36.28680256   98.04312657    1.40928261\n   24.47228977   33.47745562   14.94107847    8.7645002    44.04383157\n    2.15767025   45.32951066   24.04509476    5.4569505     8.88997657\n   60.89906647    5.40796083   35.32261173   69.21509662    1.77750288]\n\nEM Iter 46:\nEm:\n[   1.72335789  132.83535346   36.52642822  100.0511838     1.18195264\n   24.25784923   33.22169206   14.60072162    8.44690542   44.18063037\n    1.90922311   45.40987012   24.05025323    5.1126461     8.35215694\n   61.59069323    5.08507099   34.99802621   68.94319445    1.52279091]\n\nEM Iter 47:\nEm:\n[   1.72435201  133.28145441   36.77168019  101.99614212    0.97148142\n   24.05100319   32.95747569   14.26294773    8.13347378   44.31257932\n    1.67252063   45.45016918   24.0651227     4.77942661    7.83067244\n   62.34766916    4.77267496   34.65049347   68.68407827    1.28458273]\n\nEM Iter 48:\nEm:\n[   1.72534249  133.68115894   37.02271851  103.8742573     0.77862713\n   23.85207708   32.68662979   13.92799647    7.82431962   44.43852928\n    1.44757687   45.45144689   24.08903297    4.45703558    7.32597238\n   63.16640349    4.47015905   34.28208721   68.43534614    1.06328282]\n\nEM Iter 49:\nEm:\n[   1.72631859  134.03882591   37.27961826  105.6824566     0.6047993\n   23.66120477   32.41085494   13.59605425    7.5195246    44.55712873\n    1.2346328    45.41491937   24.12125464    4.14520656    6.83836417\n   64.04247735    4.17696073   33.89485513   68.19487575    0.85966754]\n\nEM Iter 50:\nEm:\n[   1.72726698  134.3581794    37.5423376   107.41823135    0.45216665\n   23.47834458   32.1316915    13.26724582    7.2191342    44.66681292\n    1.03422699   45.34190791   24.16099811    3.84366667    6.36802094\n   64.97062298    3.89256919   33.49076353   67.960784      0.67502867]\n\nEM Iter 51:\nEm:\n[   1.72817181  134.64238023   37.81069429  109.07953449    0.32356734\n   23.30329971   31.85049439   12.94163102    6.92315767   44.76580551\n    0.84729434   45.23377965   24.20741745    3.55214244    5.91499374\n   65.94472607    3.61652788   33.07165602   67.73140688    0.51131906]\n\nEM Iter 52:\nEm:\n[   1.72901644  134.89422837   38.08438155  110.66476186    0.22186811\n   23.13575352   31.56843978   12.61921536    6.63157593   44.85217266\n    0.67529336   45.09193248   24.2596283     3.27036982    5.4792305\n   66.95792239    3.34844147   32.63924722   67.50533537    0.37118548]\n\nEM Iter 53:\nEm:\n[   1.72978813  135.11662283   38.36307126  112.17294032    0.14841854\n   22.97533543   31.28659249   12.29998624    6.34436262   44.92398811\n    0.5203381    44.91787013   24.31675534    2.99810988    5.06060372\n   68.00289625    3.0879898    32.19518103   67.28154798    0.25760179]\n\nEM Iter 54:\nEm:\n[  1.73048469e+00   1.35313224e+02   3.86466169e+01   1.13604168e+02\n   1.01120903e-01   2.28217179e+01   3.10060381e+01   1.19839740e+01\n   6.06151621e+00   4.49796165e+01   3.85241261e-01   4.47133757e+01\n   2.43780128e+01   2.73516835e+00   4.65894347e+00   6.90724003e+01\n   2.83494569e+00   3.17411556e+01   6.70596039e+01   1.72675665e-01]\n\nEM Iter 55:\nEm:\n[  1.73111805e+00   1.35488856e+02   3.89352526e+01   1.14960066e+02\n   7.41199732e-02   2.26747012e+01   3.07280092e+01   1.16713045e+01\n   5.78308418e+00   4.50179685e+01   2.73234681e-01   4.44806773e+01\n   2.44427828e+01   2.48141073e+00   4.27406182e+00   7.01597451e+01\n   2.58918502e+00   3.12790397e+01   6.68397101e+01   1.15672745e-01]\n\nEM Iter 56:\nEm:\n[  1.73170965e+00   1.35649192e+02   3.92296343e+01   1.16243848e+02\n   6.01833529e-02   2.25342220e+01   3.04539028e+01   1.13621997e+01\n   5.50915874e+00   4.50385377e+01   1.86998208e-01   4.42224475e+01\n   2.45106297e+01   2.23676682e+00   3.90575625e+00   7.12589253e+01\n   2.35068085e+00   3.08108731e+01   6.66225128e+01   8.18217711e-02]\n\nEM Iter 57:\nEm:\n[  1.73228136e+00   1.35800050e+02   3.95307234e+01   1.17459909e+02\n   5.34554721e-02   2.24002760e+01   3.01851787e+01   1.10569312e+01\n   5.23984969e+00   4.50412483e+01   1.26913495e-01   4.39416209e+01\n   2.45812295e+01   2.00123022e+00   3.55379807e+00   7.23644498e+01\n   2.11949145e+00   3.03387430e+01   6.64087721e+01   6.38480456e-02]\n\nEM Iter 58:\nEm:\n[  1.73284923e+00   1.35946903e+02   3.98396437e+01   1.18613243e+02\n   5.03211600e-02   2.22728154e+01   2.99232278e+01   1.07557655e+01\n   4.97525728e+00   4.50262962e+01   8.96547949e-02   4.36411612e+01\n   2.46542646e+01   1.77486540e+00   3.21792358e+00   7.34712007e+01\n   1.89575733e+00   2.98646310e+01   6.61991697e+01   5.50498300e-02]\n\nEM Iter 59:\nEm:\n[  1.73342050e+00   1.36094604e+02   4.01575501e+01   1.19708865e+02\n   4.88794673e-02   2.21516709e+01   2.96692582e+01   1.04589211e+01\n   4.71545292e+00   4.49940165e+01   6.89894450e-02   4.33238535e+01\n   2.47293333e+01   1.55782870e+00   2.89783642e+00   7.45743495e+01\n   1.67971061e+00   2.93902840e+01   6.59942278e+01   5.09483171e-02]\n\nT[:,10]:\n[[  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n    0.00000000e+00   0.00000000e+00]\n [ -4.52278001e-03  -7.49727225e-02   5.34804034e-02  -4.62007730e-02\n   -3.28729004e-02   2.35333886e-02   2.92777251e-02   1.91730230e-02\n    9.52414221e-03   1.95833277e-02]\n [  6.87958269e-03  -2.37450769e-02   7.64971707e-02   5.38127568e-02\n   -7.96700184e-02  -2.20403097e-03   7.47112539e-02   4.28010851e-03\n   -6.32566341e-02   2.08470975e-02]\n [  1.97615131e-02  -3.77375522e-03   9.98591323e-02   5.77277298e-02\n   -3.96756197e-02   1.73242892e-02   7.92619481e-02   5.04083617e-03\n   -7.19667878e-02   1.69282717e-02]\n [ -1.67840810e-02  -2.52358369e-02   5.69737786e-02   9.27866446e-03\n   -4.92412591e-02   2.97174818e-02   3.78511533e-02   5.35705121e-03\n   -2.27742887e-02   3.09402837e-02]\n [  2.34659426e-02   2.20189722e-02   3.30830355e-02  -2.88543777e-03\n   -1.35628411e-02  -3.17098059e-02  -9.08153011e-03   3.73206311e-03\n   -1.01303585e-01   2.82990017e-02]\n [  3.14538422e-02  -1.03779008e-02   9.81883123e-02   5.91259047e-02\n   -2.53015507e-02   1.10512299e-02   4.42736083e-02  -2.85087300e-02\n   -7.87610740e-02   1.06822783e-05]\n [  2.50957408e-02  -2.29102185e-02   6.17391882e-02   1.62441146e-03\n   -2.43758209e-02  -8.60519047e-04   4.67549139e-02   3.24612786e-04\n   -1.21561856e-01   2.47382091e-02]\n [  8.99719692e-03   2.38180193e-02   1.07618256e-01   2.44547537e-02\n   -4.66874969e-02   2.87554478e-02   6.24852690e-02  -2.47351606e-02\n   -7.12771765e-02   2.29212624e-02]\n [  1.69600337e-02  -5.41227017e-03   7.02531025e-02   4.11949994e-02\n   -5.56619958e-02   2.35998855e-02   6.92046730e-02   2.48537716e-02\n   -2.71597364e-02   2.84436342e-02]\n [  4.23562467e-02  -1.26345987e-02   9.46251955e-02  -1.22713170e-02\n   -2.66315742e-02   2.19904450e-02   7.03436800e-02  -2.08001234e-02\n   -7.30056264e-02   7.27390705e-03]\n [  1.15512782e-02   1.30332000e-02   9.75765863e-02   3.22518203e-02\n   -1.75025848e-02   1.64559921e-03   7.78689079e-02  -1.85876356e-02\n   -7.95686692e-02   2.19578599e-02]\n [  1.51200423e-02   9.86625924e-03   7.86923401e-02   4.41969291e-02\n   -1.97371676e-02   3.54611944e-02   2.10953046e-03  -4.76780088e-02\n   -7.45176506e-02  -4.75247987e-02]\n [  1.58591768e-02  -1.84227648e-02   6.85949143e-02   4.97382395e-02\n   -3.07384736e-02   2.95019636e-02   4.68114169e-02  -2.18986238e-03\n   -8.11170645e-02   3.66571149e-03]\n [  2.68713623e-02  -2.15534788e-03   5.62806198e-02   3.43438184e-02\n   -3.86918256e-02   2.75047668e-03   4.18634136e-02  -2.96285293e-02\n   -8.49828843e-02   2.17925509e-02]\n [ -2.64847914e-02  -1.13682581e-02   4.63235941e-02   6.15007724e-03\n   -2.56895393e-02   4.26464926e-02   6.01649399e-02   3.52671192e-02\n   -5.07405464e-02   7.70606915e-03]\n [  5.22507074e-03  -3.39892211e-02   8.52468349e-02   3.10746130e-02\n   -6.55441356e-02   4.98973059e-02   8.02887547e-02  -1.17877743e-02\n   -5.80123104e-02   1.59954827e-02]\n [  2.39538973e-02  -3.28828046e-03   8.94876889e-02   3.93764616e-02\n   -4.92548324e-02   1.20939267e-02   7.29904491e-02   3.30011223e-03\n   -7.93773074e-02   3.11282061e-02]\n [  9.26020422e-03   4.65776014e-02   5.62036477e-02  -1.41332716e-02\n   -8.07923536e-02   8.92201245e-02   5.72095783e-02  -1.21661385e-02\n   -1.92566770e-02  -1.03517262e-02]\n [  1.03847762e-02   2.20162287e-02   6.95877568e-02   6.19001475e-02\n   -2.25725947e-02   8.10938783e-03   6.02441640e-02  -3.00269748e-02\n   -9.16608358e-02   2.24072144e-02]]\nr:\n[ -1.33004718e-13  -5.36877497e-01  -4.98790776e-01  -5.26616766e-01\n  -5.75811828e-01  -6.95685195e-01  -5.47198933e-01  -5.38629555e-01\n  -5.31982997e-01  -5.24534572e-01  -5.19603932e-01  -5.40449549e-01\n  -9.09576613e-01  -5.75121909e-01  -6.11381760e-01  -5.96789424e-01\n  -5.71727687e-01  -5.36200418e-01  -5.10488851e-01  -5.83280208e-01]\n\nTopic magnitudes:\n[ 0.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.\n  3.  3.]\n\nTopic 1 (3.00): 20.8%\nMost relevant words:\ncompany (151,8): 0.892/7.134/0.551/4.448 million (357,6): 0.802/4.809/0.607/5.926 acquired (1447,5): 0.743/3.713/0.413/3.570 companies (939,4): 0.719/2.876/0.568/4.495 sold (614,2): 0.708/1.416/0.432/3.573 investors (5976,2): 0.689/1.379/0.566/5.027 fund (2014,3): 0.661/1.982/0.523/4.606 bank (640,1): 0.656/0.656/0.300/2.732 acquisition (4356,1): 0.635/0.635/0.438/3.813 invest (11944,1): 0.612/0.612/0.527/3.726 hedge (14848,3): 0.608/1.825/0.352/3.100 filed (3883,2): 0.596/1.191/0.348/3.326 capital (735,1): 0.591/0.591/0.266/2.494 announced (568,1): 0.567/0.567/0.270/2.010 business (354,2): 0.566/1.131/0.348/2.645 dollars (4434,2): 0.563/1.127/0.584/4.994 manager (830,2): 0.547/1.093/0.158/1.386 private (597,1): 0.540/0.540/0.299/2.568 financing (8017,1): 0.538/0.538/0.489/4.025 stock (1878,2): 0.537/1.074/0.405/3.880 profit (2747,1): 0.521/0.521/0.480/3.952 millions (5429,1): 0.518/0.518/0.435/3.941 us (220,1): 0.507/0.507/0.261/2.380 founder (1298,1): 0.501/0.501/0.178/1.431 icahn (83210,1): 0.497/0.497/0.364/7.125 raised (1286,4): 0.487/1.948/0.259/2.310 sales (1485,3): 0.482/1.445/0.457/4.401 executive (876,1): 0.477/0.477/0.192/1.516 directors (2573,1): 0.472/0.472/0.245/1.958 buy (3433,1): 0.472/0.472/0.476/3.909 \nMost similar words in vocab:\nbillion: 0.643/6.415 million: 0.607/5.926 investments: 0.595/5.175 dollars: 0.584/4.994 companies: 0.568/4.495 investors: 0.566/5.027 profits: 0.565/4.640 investment: 0.562/4.837 shareholders: 0.553/4.520 company: 0.551/4.448 invested: 0.540/4.073 funds: 0.539/4.732 multibillion: 0.530/8.215 defrauded: 0.529/5.920 invest: 0.527/3.726 fund: 0.523/4.606 assets: 0.521/4.664 unitedhealth: 0.515/7.192 reinvest: 0.513/6.834 equity: 0.513/4.793 company's: 0.508/4.189 revenues: 0.506/4.413 purchase: 0.506/4.135 purchasing: 0.498/3.956 multimillion: 0.498/6.817 usd: 0.498/4.647 buyouts: 0.497/5.733 stockholders: 0.497/5.603 indymac: 0.497/7.212 buyout: 0.496/3.298 \n\nTopic 3 (3.00): 18.3%\nMost relevant words:\nhospitals (4777,5): 0.566/2.830/0.516/4.954 hospital (723,1): 0.566/0.566/0.434/3.973 medicine (1528,2): 0.551/1.101/0.482/4.472 medical (679,1): 0.548/0.548/0.575/4.926 patients (2468,9): 0.544/4.895/0.751/6.780 patient (3777,2): 0.526/1.052/0.627/5.547 doctors (4958,2): 0.521/1.043/0.493/4.661 care (1069,2): 0.520/1.041/0.564/4.805 inpatient (33848,1): 0.494/0.494/0.457/5.711 treatment (1460,5): 0.485/2.425/0.670/6.046 health (502,3): 0.478/1.435/0.535/4.519 treatments (9357,2): 0.477/0.954/0.662/6.031 therapies (21018,2): 0.460/0.920/0.582/4.829 treating (10124,1): 0.452/0.452/0.605/4.882 medically (28191,1): 0.452/0.452/0.400/5.813 treat (5814,3): 0.451/1.352/0.598/5.089 cancer (1871,2): 0.418/0.835/0.544/5.252 aids (4693,2): 0.380/0.760/0.489/4.693 hospitalized (18161,1): 0.378/0.378/0.382/2.648 therapeutics (36356,1): 0.377/0.377/0.496/6.189 hiv (6116,1): 0.374/0.374/0.569/5.546 heart (869,1): 0.373/0.373/0.219/1.819 efficacy (14568,1): 0.371/0.371/0.457/3.827 pregnancy (7979,1): 0.365/0.365/0.442/4.082 dr (570,5): 0.361/1.806/0.213/1.689 tuberculosis (11017,1): 0.359/0.359/0.555/4.830 specialists (10857,1): 0.358/0.358/0.287/2.457 antibiotic (24098,2): 0.346/0.692/0.521/3.768 sinai (13035,3): 0.341/1.024/0.187/1.512 prescriptions (34657,3): 0.338/1.014/0.509/6.314 \nMost similar words in vocab:\npatients: 0.751/6.780 medication: 0.687/5.956 medications: 0.683/5.778 treatment: 0.670/6.046 treatments: 0.662/6.031 patient: 0.627/5.547 diabetes: 0.624/5.424 antiretroviral: 0.622/7.076 prophylactic: 0.616/7.040 drugs: 0.605/5.821 treating: 0.605/4.882 clinical: 0.602/5.460 pediatric: 0.602/5.338 ivig: 0.601/7.508 treat: 0.598/5.089 contraindicated: 0.598/6.262 regimens: 0.591/6.887 therapeutic: 0.588/5.123 medicines: 0.585/5.077 chemotherapy: 0.584/4.509 diarrheal: 0.583/7.347 therapies: 0.582/4.829 chronic: 0.582/5.133 diseases: 0.579/5.590 antiepileptic: 0.578/7.113 asthma: 0.576/4.393 medical: 0.575/4.926 prophylaxis: 0.575/6.732 treatable: 0.570/6.215 antibiotics: 0.570/4.447 \n\nTopic 15 (3.00): 11.4%\nMost relevant words:\nincrease (1265,8): 0.566/4.529/0.618/4.796 shorting (108317,1): 0.566/0.566/0.405/7.101 clamoring (148365,1): 0.540/0.540/0.354/5.355 rebates (60162,1): 0.507/0.507/0.616/6.921 increased (1167,1): 0.486/0.486/0.518/4.182 inexpensively (98181,1): 0.476/0.476/0.332/5.488 increases (4242,4): 0.475/1.899/0.510/4.379 shrank (46494,1): 0.470/0.470/0.355/4.081 amount (1588,1): 0.468/0.468/0.503/4.079 discounts (28868,1): 0.374/0.374/0.429/5.090 cost (1178,2): 0.370/0.740/0.579/4.575 percent (1651,2): 0.359/0.717/0.473/4.320 huge (3131,2): 0.340/0.680/0.291/2.546 severance (36986,1): 0.338/0.338/0.343/4.538 income (874,1): 0.321/0.321/0.452/3.272 pay (1653,2): 0.316/0.633/0.490/4.218 steady (7656,1): 0.310/0.310/0.255/2.217 dollars (4434,2): 0.278/0.555/0.580/4.963 resulted (2001,1): 0.263/0.263/0.318/2.323 money (808,2): 0.262/0.525/0.446/3.784 sales (1485,3): 0.262/0.785/0.464/4.468 resulting (1955,1): 0.258/0.258/0.304/2.223 millions (5429,1): 0.256/0.256/0.432/3.913 annual (895,1): 0.256/0.256/0.229/2.244 lawmakers (27291,2): 0.255/0.511/0.298/3.351 conceivably (69563,1): 0.254/0.254/0.293/3.339 low (612,1): 0.249/0.249/0.296/2.478 thwart (31967,1): 0.249/0.249/0.196/2.401 expensive (4449,1): 0.249/0.249/0.356/3.402 swindle (42243,1): 0.247/0.247/0.210/2.928 \nMost similar words in vocab:\nincrease: 0.618/4.796 rebates: 0.616/6.921 profits: 0.607/4.991 costs: 0.601/5.061 dollars: 0.580/4.963 cost: 0.579/4.575 prices: 0.577/5.642 refinancing: 0.574/6.292 reinvest: 0.566/7.536 billion: 0.563/5.624 exorbitant: 0.563/6.715 repayments: 0.559/6.000 surtax: 0.550/7.563 outlay: 0.548/5.829 incentivize: 0.547/7.376 recouping: 0.547/7.949 revenues: 0.545/4.752 premiums: 0.543/6.223 refunds: 0.541/6.246 disincentive: 0.538/7.624 shortfall: 0.533/6.008 overvalued: 0.530/7.625 untaxed: 0.530/7.781 disburse: 0.530/7.028 multibillion: 0.527/8.171 skyrocketed: 0.523/5.923 subsidize: 0.522/5.784 payments: 0.522/4.558 recoup: 0.520/5.966 increased: 0.518/4.182 \n\nTopic 18 (3.00): 10.1%\nMost relevant words:\nprice (1535,17): 0.967/16.433/0.956/8.352 prices (4345,4): 0.866/3.463/0.818/7.998 gouge (81038,1): 0.826/0.826/0.222/4.664 priced (14909,1): 0.662/0.662/0.544/4.570 rose (1336,1): 0.305/0.305/0.180/1.315 sharply (12378,2): 0.293/0.586/0.249/2.123 high (104,3): 0.268/0.805/0.163/1.456 low (612,1): 0.252/0.252/0.300/2.507 scott (1062,1): 0.245/0.245/0.104/0.669 martin (778,1): 0.238/0.238/0.049/0.314 spencer (4173,1): 0.227/0.227/0.118/0.891 born (106,1): 0.224/0.224/-0.045/-0.387 piggy (34457,1): 0.211/0.211/0.150/2.055 line (181,1): 0.205/0.205/0.032/0.265 swindle (42243,1): 0.200/0.200/0.196/2.732 sanders (7915,1): 0.198/0.198/0.088/0.611 free (381,1): 0.197/0.197/0.146/1.300 mcleod (15404,1): 0.196/0.196/0.085/0.640 shortages (16485,1): 0.194/0.194/0.268/2.208 round (273,1): 0.193/0.193/-0.038/-0.343 judith (8272,1): 0.191/0.191/0.034/0.271 tracks (907,1): 0.190/0.190/0.007/0.061 discounts (28868,1): 0.189/0.189/0.373/4.427 stock (1878,2): 0.188/0.376/0.368/3.525 wendy (8642,1): 0.185/0.185/0.055/0.421 armstrong (4767,1): 0.183/0.183/0.059/0.500 democrat (3309,1): 0.183/0.183/0.082/0.755 grady (13903,1): 0.181/0.181/0.125/0.873 april (148,1): 0.179/0.179/0.044/0.312 dramatic (3872,1): 0.178/0.178/0.086/0.767 \nMost similar words in vocab:\nprice: 0.956/8.352 prices: 0.818/7.998 pricing: 0.602/5.261 priced: 0.544/4.570 inflation: 0.537/4.966 commodity: 0.499/4.164 exorbitant: 0.494/5.888 discount: 0.494/4.270 cost: 0.486/3.838 discounted: 0.477/3.177 purchases: 0.474/3.654 costs: 0.470/3.955 resale: 0.467/5.348 purchasing: 0.448/3.559 rates: 0.440/4.089 buying: 0.437/3.618 buyers: 0.436/3.717 commodities: 0.433/3.752 undervalued: 0.432/4.856 skyrocketed: 0.430/4.867 demand: 0.427/3.527 premium: 0.426/4.270 repayments: 0.425/4.561 profits: 0.424/3.485 market: 0.422/3.446 asset's: 0.421/5.855 buy: 0.421/3.455 purchase: 0.419/3.429 valuations: 0.418/4.776 wages: 0.417/3.773 \n\nTopic 9 (3.00): 6.9%\nMost relevant words:\ngenerics (85144,1): 0.299/0.299/0.422/7.165 drugstores (120806,1): 0.283/0.283/0.413/6.547 glaxo (101914,1): 0.278/0.278/0.380/7.372 concocting (179559,1): 0.271/0.271/0.335/5.644 insurers (32340,1): 0.183/0.183/0.463/5.174 medicaid (25609,1): 0.167/0.167/0.417/5.608 lawmakers (27291,2): 0.165/0.329/0.296/3.331 inexpensively (98181,1): 0.161/0.161/0.291/4.820 glaxosmithkline (55181,1): 0.152/0.152/0.498/6.700 icahn (83210,1): 0.151/0.151/0.360/7.029 products (1127,1): 0.138/0.138/0.396/3.296 thwart (31967,1): 0.138/0.138/0.182/2.227 expensive (4449,1): 0.136/0.136/0.337/3.217 ims (30708,1): 0.133/0.133/0.177/2.536 conceivably (69563,1): 0.133/0.133/0.273/3.113 seeking (3864,1): 0.131/0.131/0.293/2.355 clamoring (148365,1): 0.131/0.131/0.288/4.357 prescriptions (34657,3): 0.130/0.390/0.510/6.328 discounts (28868,1): 0.126/0.126/0.372/4.420 pharmaceuticals (16906,3): 0.126/0.377/0.453/4.181 strategy (2853,2): 0.125/0.251/0.253/2.239 approved (2389,1): 0.125/0.125/0.324/2.818 patents (8554,1): 0.124/0.124/0.283/2.658 rebates (60162,1): 0.123/0.123/0.528/5.926 trying (2359,2): 0.122/0.244/0.209/1.544 pharmaceutical (8897,2): 0.119/0.237/0.505/4.707 medicare (18132,1): 0.118/0.118/0.440/3.319 needed (1661,1): 0.117/0.117/0.314/2.212 option (3370,1): 0.115/0.115/0.266/2.349 food (807,1): 0.114/0.114/0.284/2.605 \nMost similar words in vocab:\nvioxx: 0.545/8.365 antiretroviral: 0.542/6.169 ivig: 0.541/6.760 incentivize: 0.529/7.139 rebates: 0.528/5.926 pfizer: 0.522/6.833 novartis: 0.519/6.720 methadone: 0.517/6.209 adrs: 0.517/8.068 drugs: 0.514/4.945 sanofi: 0.511/6.110 prescriptions: 0.510/6.328 medication: 0.509/4.414 reimburse: 0.507/5.582 pharmaceutical: 0.505/4.707 dhhs: 0.505/6.728 thiopental: 0.503/7.367 healthcare: 0.501/4.526 medications: 0.500/4.229 disincentive: 0.499/7.070 prescription: 0.498/4.329 glaxosmithkline: 0.498/6.700 multibillion: 0.497/7.706 wellpoint: 0.496/7.664 refinancing: 0.495/5.421 medicines: 0.495/4.293 uninsured: 0.491/5.784 prophylactic: 0.491/5.611 tamiflu: 0.490/7.066 drug: 0.488/4.883 \n\nTopic 11 (3.00): 6.6%\nMost relevant words:\ndiseases (4439,6): 0.195/1.173/0.632/6.105 tuberculosis (11017,1): 0.195/0.195/0.601/5.238 infectious (12595,4): 0.193/0.771/0.570/5.364 disease (1780,1): 0.191/0.191/0.613/5.654 malaria (13222,1): 0.186/0.186/0.559/4.986 infection (6037,2): 0.184/0.368/0.585/5.337 infected (8254,1): 0.175/0.175/0.408/3.911 hepatitis (19494,1): 0.174/0.174/0.593/5.036 patients (2468,9): 0.173/1.558/0.737/6.652 hiv (6116,1): 0.169/0.169/0.593/5.771 treatment (1460,5): 0.164/0.821/0.663/5.980 serious (2375,2): 0.164/0.328/0.319/2.839 multidrug (112412,1): 0.162/0.162/0.447/7.140 resistant (8950,1): 0.162/0.162/0.333/3.293 inpatient (33848,1): 0.161/0.161/0.449/5.607 hospitalized (18161,1): 0.161/0.161/0.405/2.811 cancer (1871,2): 0.158/0.316/0.548/5.296 aids (4693,2): 0.154/0.308/0.501/4.807 pregnancy (7979,1): 0.151/0.151/0.456/4.217 cause (1310,1): 0.150/0.150/0.355/2.757 treat (5814,3): 0.150/0.449/0.588/5.003 treating (10124,1): 0.150/0.150/0.594/4.793 patient (3777,2): 0.149/0.299/0.599/5.304 immune (8538,1): 0.146/0.146/0.414/3.766 threatening (7927,2): 0.145/0.290/0.276/2.461 medically (28191,1): 0.145/0.145/0.392/5.691 vulnerable (6874,1): 0.144/0.144/0.219/2.068 potentially (6379,1): 0.139/0.139/0.421/3.705 heart (869,1): 0.139/0.139/0.222/1.847 treatments (9357,2): 0.137/0.275/0.637/5.803 \nMost similar words in vocab:\npatients: 0.737/6.652 treatment: 0.663/5.980 medication: 0.639/5.542 treatments: 0.637/5.803 medications: 0.635/5.378 diarrheal: 0.634/7.986 diseases: 0.632/6.105 diabetes: 0.625/5.430 ivig: 0.624/7.798 chronic: 0.620/5.469 infections: 0.617/5.711 prophylactic: 0.616/7.037 disease: 0.613/5.654 antiretroviral: 0.607/6.909 treatable: 0.606/6.606 tuberculosis: 0.601/5.238 patient: 0.599/5.304 nosocomial: 0.599/7.972 contraindicated: 0.596/6.245 asthma: 0.596/4.548 illnesses: 0.594/4.584 treating: 0.594/4.793 hepatitis: 0.593/5.036 hiv: 0.593/5.771 prophylaxis: 0.588/6.889 treat: 0.588/5.003 regimens: 0.586/6.829 infection: 0.585/5.337 antiepileptic: 0.583/7.175 vaccine: 0.579/5.391 \n\nTopic 2 (3.00): 6.1%\nMost relevant words:\nglaxo (101914,1): 0.228/0.228/0.375/7.273 generics (85144,1): 0.224/0.224/0.411/6.973 concocting (179559,1): 0.197/0.197/0.322/5.419 icahn (83210,1): 0.191/0.191/0.376/7.360 glaxosmithkline (55181,1): 0.185/0.185/0.520/6.998 prescriptions (34657,3): 0.176/0.529/0.543/6.730 drugstores (120806,1): 0.147/0.147/0.378/5.993 pharmaceutical (8897,2): 0.144/0.287/0.536/4.996 medicaid (25609,1): 0.143/0.143/0.413/5.553 pharmaceuticals (16906,3): 0.128/0.384/0.465/4.295 generic (7602,4): 0.126/0.503/0.249/2.188 therapeutics (36356,1): 0.126/0.126/0.494/6.155 lawmakers (27291,2): 0.125/0.250/0.280/3.152 ims (30708,1): 0.120/0.120/0.177/2.528 controlled (2257,3): 0.118/0.354/0.224/2.070 approved (2389,1): 0.116/0.116/0.326/2.835 generically (48146,1): 0.113/0.113/0.193/2.211 insurers (32340,1): 0.110/0.110/0.426/4.760 laboratories (7537,1): 0.108/0.108/0.304/2.834 medicare (18132,1): 0.108/0.108/0.441/3.324 conceivably (69563,1): 0.107/0.107/0.263/2.996 thwart (31967,1): 0.107/0.107/0.169/2.071 advocates (8690,1): 0.104/0.104/0.253/2.064 alternative (1705,1): 0.103/0.103/0.214/1.794 administration (991,1): 0.102/0.102/0.243/1.993 investigating (8587,1): 0.101/0.101/0.218/1.863 pills (18688,1): 0.099/0.099/0.364/2.966 complaint (9280,1): 0.098/0.098/0.230/1.878 tablet (10396,2): 0.098/0.196/0.135/1.337 strategy (2853,2): 0.097/0.194/0.235/2.078 \nMost similar words in vocab:\ndrugs: 0.613/5.893 medication: 0.607/5.269 medications: 0.599/5.070 antiretroviral: 0.598/6.800 drug: 0.587/5.875 ivig: 0.584/7.300 methadone: 0.576/6.907 prophylactic: 0.570/6.512 prescription: 0.567/4.925 patients: 0.565/5.098 vioxx: 0.560/8.588 antiepileptic: 0.555/6.835 antipsychotic: 0.550/6.174 medicines: 0.548/4.751 treatments: 0.545/4.967 regimens: 0.543/6.332 prescriptions: 0.543/6.730 pharmaceutical: 0.536/4.996 novartis: 0.534/6.922 pfizer: 0.531/6.954 dosages: 0.528/5.699 praziquantel: 0.526/7.136 treatment: 0.523/4.717 contraceptives: 0.522/6.345 sanofi: 0.520/6.223 glaxosmithkline: 0.520/6.998 diarrheal: 0.519/6.537 bevacizumab: 0.518/7.302 healthcare: 0.516/4.654 thiopental: 0.512/7.498 \n\nTopic 6 (3.00): 4.5%\nMost relevant words:\ndrugs (3528,10): 0.179/1.790/0.761/7.321 drug (1942,15): 0.135/2.019/0.751/7.511 pills (18688,1): 0.133/0.133/0.441/3.593 pharmaceutical (8897,2): 0.104/0.208/0.538/5.009 cholesterol (20400,1): 0.099/0.099/0.340/2.732 therapies (21018,2): 0.099/0.198/0.566/4.696 efficacy (14568,1): 0.099/0.099/0.467/3.909 antibiotic (24098,2): 0.097/0.194/0.539/3.897 pharmaceuticals (16906,3): 0.093/0.280/0.468/4.318 therapeutics (36356,1): 0.093/0.093/0.497/6.190 dangerous (3584,2): 0.091/0.182/0.249/2.300 effects (1380,2): 0.090/0.179/0.334/3.059 aids (4693,2): 0.089/0.179/0.484/4.648 pregnancy (7979,1): 0.089/0.089/0.441/4.076 investigating (8587,1): 0.088/0.088/0.240/2.053 hiv (6116,1): 0.087/0.087/0.564/5.492 treatments (9357,2): 0.087/0.174/0.629/5.733 testing (2814,1): 0.086/0.086/0.333/2.972 generic (7602,4): 0.083/0.334/0.241/2.115 treatment (1460,5): 0.080/0.400/0.626/5.648 glaxosmithkline (55181,1): 0.080/0.080/0.482/6.491 use (176,7): 0.080/0.557/0.281/1.954 medicine (1528,2): 0.079/0.159/0.424/3.938 treating (10124,1): 0.079/0.079/0.562/4.538 administration (991,1): 0.078/0.078/0.252/2.064 treat (5814,3): 0.078/0.234/0.556/4.735 medically (28191,1): 0.077/0.077/0.375/5.444 rima (42963,1): 0.076/0.076/-0.002/-0.032 immune (8538,1): 0.075/0.075/0.383/3.487 cancer (1871,2): 0.075/0.150/0.511/4.940 \nMost similar words in vocab:\ndrugs: 0.761/7.321 drug: 0.751/7.511 medication: 0.708/6.145 medications: 0.701/5.931 patients: 0.654/5.902 antiretroviral: 0.653/7.428 treatments: 0.629/5.733 treatment: 0.626/5.648 prescription: 0.623/5.415 ivig: 0.616/7.690 methadone: 0.608/7.296 antipsychotic: 0.606/6.802 antiepileptic: 0.606/7.461 medicines: 0.606/5.255 prophylactic: 0.596/6.809 therapeutic: 0.592/5.159 contraindicated: 0.587/6.156 diabetes: 0.586/5.096 analgesics: 0.579/6.430 antibiotics: 0.579/4.521 regimens: 0.578/6.743 antiviral: 0.574/6.735 therapies: 0.566/4.696 chemotherapy: 0.565/4.362 anticoagulant: 0.565/6.219 hiv: 0.564/5.492 treating: 0.562/4.538 warfarin: 0.562/6.004 diarrheal: 0.561/7.074 chronic: 0.560/4.940 \n\nTopic 17 (3.00): 4.5%\nMost relevant words:\ndiseases (4439,6): 0.142/0.852/0.638/6.162 cancer (1871,2): 0.141/0.283/0.576/5.562 multidrug (112412,1): 0.138/0.138/0.461/7.355 hepatitis (19494,1): 0.134/0.134/0.607/5.152 disease (1780,1): 0.133/0.133/0.615/5.669 hiv (6116,1): 0.132/0.132/0.605/5.898 infectious (12595,4): 0.129/0.516/0.567/5.340 tuberculosis (11017,1): 0.128/0.128/0.596/5.190 malaria (13222,1): 0.125/0.125/0.556/4.963 immune (8538,1): 0.118/0.118/0.432/3.929 antibiotic (24098,2): 0.115/0.231/0.562/4.064 aids (4693,2): 0.115/0.230/0.510/4.892 infection (6037,2): 0.112/0.223/0.572/5.215 therapeutics (36356,1): 0.110/0.110/0.510/6.352 resistant (8950,1): 0.109/0.109/0.332/3.280 treat (5814,3): 0.105/0.314/0.590/5.023 treating (10124,1): 0.104/0.104/0.596/4.806 treatments (9357,2): 0.103/0.207/0.647/5.894 therapies (21018,2): 0.102/0.203/0.568/4.714 treatment (1460,5): 0.100/0.502/0.650/5.866 pregnancy (7979,1): 0.100/0.100/0.453/4.182 infected (8254,1): 0.099/0.099/0.388/3.724 efficacy (14568,1): 0.098/0.098/0.464/3.889 cholesterol (20400,1): 0.097/0.097/0.336/2.703 effects (1380,2): 0.095/0.191/0.340/3.113 patients (2468,9): 0.093/0.841/0.710/6.412 hospitalized (18161,1): 0.092/0.092/0.379/2.633 parasitic (18146,1): 0.092/0.092/0.283/2.359 cause (1310,1): 0.090/0.090/0.338/2.625 isolated (4860,1): 0.090/0.090/0.206/1.863 \nMost similar words in vocab:\npatients: 0.710/6.412 medications: 0.672/5.684 medication: 0.667/5.787 treatment: 0.650/5.866 treatments: 0.647/5.894 diabetes: 0.642/5.581 diseases: 0.638/6.162 ivig: 0.633/7.907 drugs: 0.632/6.079 antiretroviral: 0.629/7.152 diarrheal: 0.625/7.878 chronic: 0.621/5.478 prophylactic: 0.615/7.035 disease: 0.615/5.669 infections: 0.607/5.613 hepatitis: 0.607/5.152 hiv: 0.605/5.898 contraindicated: 0.601/6.297 asthma: 0.601/4.587 antibiotics: 0.600/4.683 antiepileptic: 0.599/7.380 drug: 0.597/5.971 tuberculosis: 0.596/5.190 treating: 0.596/4.806 vaccine: 0.594/5.530 chemotherapy: 0.593/4.573 treatable: 0.591/6.443 treat: 0.590/5.023 regimens: 0.588/6.853 ulcerative: 0.584/5.598 \n\nTopic 12 (3.00): 3.8%\nMost relevant words:\ndrug (1942,15): 0.638/9.572/0.962/9.624 drugs (3528,10): 0.412/4.121/0.906/8.713 pills (18688,1): 0.142/0.142/0.518/4.215 dangerous (3584,2): 0.061/0.123/0.267/2.462 cholesterol (20400,1): 0.061/0.061/0.349/2.801 investigating (8587,1): 0.059/0.059/0.259/2.209 controlled (2257,3): 0.054/0.162/0.236/2.183 generic (7602,4): 0.053/0.212/0.253/2.219 side (287,2): 0.053/0.105/0.004/0.028 crazy (5181,1): 0.047/0.047/0.100/0.800 effects (1380,2): 0.045/0.091/0.320/2.933 use (176,7): 0.045/0.316/0.280/1.947 rima (42963,1): 0.045/0.045/-0.001/-0.008 story (367,1): 0.044/0.044/0.001/0.008 force (351,1): 0.042/0.042/0.010/0.085 administration (991,1): 0.042/0.042/0.244/1.994 known (97,1): 0.041/0.041/-0.002/-0.014 used (82,4): 0.040/0.162/0.124/0.939 efficacy (14568,1): 0.038/0.038/0.420/3.521 talked (11444,1): 0.038/0.038/0.081/0.511 samples (4875,1): 0.038/0.038/0.163/1.669 two (37,3): 0.037/0.112/-0.053/-0.404 testing (2814,1): 0.036/0.036/0.298/2.665 combination (2674,1): 0.036/0.036/0.226/1.687 possibly (2456,1): 0.035/0.035/0.125/0.962 first (26,3): 0.035/0.106/-0.073/-0.485 discourage (22551,1): 0.035/0.035/0.187/1.166 like (189,2): 0.035/0.070/0.078/0.520 wild (1617,1): 0.035/0.035/0.008/0.074 tightly (15059,1): 0.035/0.035/0.023/0.204 \nMost similar words in vocab:\ndrug: 0.962/9.624 drugs: 0.906/8.713 cocaine: 0.657/6.859 prescription: 0.652/5.659 medication: 0.639/5.542 heroin: 0.631/6.069 medications: 0.626/5.299 steroids: 0.616/4.153 opiates: 0.600/6.911 antipsychotic: 0.593/6.658 antiretroviral: 0.586/6.668 cannabis: 0.583/5.297 marijuana: 0.580/5.518 methadone: 0.577/6.923 morphine: 0.566/4.471 painkillers: 0.560/6.878 stimulants: 0.559/6.147 narcotics: 0.555/4.998 diazepam: 0.555/5.871 psychotropic: 0.555/6.597 ketamine: 0.555/6.506 addiction: 0.553/5.199 antiepileptic: 0.548/6.754 opioids: 0.547/6.262 alcohol: 0.547/5.620 methamphetamine: 0.546/6.999 barbiturates: 0.545/6.286 anticancer: 0.543/6.084 illicit: 0.543/4.372 antiviral: 0.540/6.336 \n\nTopic 5 (3.00): 3.4%\nMost relevant words:\ntoxoplasmosis (125001,4): 0.968/3.871/0.837/12.324 infection (6037,2): 0.321/0.642/0.738/6.726 parasite (16531,1): 0.308/0.308/0.485/3.757 parasitic (18146,1): 0.304/0.304/0.481/4.009 infected (8254,1): 0.278/0.278/0.543/5.209 disease (1780,1): 0.218/0.218/0.718/6.615 diseases (4439,6): 0.203/1.220/0.723/6.977 malaria (13222,1): 0.201/0.201/0.661/5.894 immune (8538,1): 0.198/0.198/0.538/4.900 multidrug (112412,1): 0.192/0.192/0.510/8.144 hepatitis (19494,1): 0.186/0.186/0.699/5.934 infectious (12595,4): 0.181/0.722/0.651/6.131 rare (2251,1): 0.168/0.168/0.305/2.908 tuberculosis (11017,1): 0.148/0.148/0.665/5.793 cause (1310,1): 0.126/0.126/0.439/3.412 isolated (4860,1): 0.120/0.120/0.288/2.602 rarely (4300,1): 0.114/0.114/0.299/2.448 resistant (8950,1): 0.108/0.108/0.377/3.723 caused (1150,1): 0.105/0.105/0.344/2.965 rima (42963,1): 0.097/0.097/0.046/0.658 possibly (2456,1): 0.096/0.096/0.241/1.852 effects (1380,2): 0.084/0.167/0.375/3.436 pregnancy (7979,1): 0.084/0.084/0.482/4.458 hiv (6116,1): 0.083/0.083/0.604/5.886 medically (28191,1): 0.082/0.082/0.410/5.954 factor (2518,1): 0.080/0.080/0.255/2.248 wild (1617,1): 0.078/0.078/0.084/0.763 vulnerable (6874,1): 0.076/0.076/0.239/2.258 antibiotic (24098,2): 0.075/0.151/0.566/4.092 hospitalized (18161,1): 0.071/0.071/0.408/2.828 \nMost similar words in vocab:\ntoxoplasmosis: 0.837/12.324 infections: 0.742/6.864 infection: 0.738/6.726 diseases: 0.723/6.977 disease: 0.718/6.615 ulcerative: 0.706/6.770 hepatitis: 0.699/5.934 diarrheal: 0.698/8.800 falciparum: 0.693/7.676 endocarditis: 0.683/7.043 tuberculosis: 0.665/5.793 treatable: 0.664/7.230 bacteremia: 0.663/8.039 chronic: 0.661/5.834 malaria: 0.661/5.894 histolytica: 0.661/7.787 crohn's: 0.660/6.690 infectious: 0.651/6.131 colitis: 0.647/6.862 chlamydia: 0.646/7.317 pylori: 0.644/6.718 sepsis: 0.644/6.979 malignancies: 0.642/6.619 nosocomial: 0.641/8.542 pneumoniae: 0.641/6.709 blastocystis: 0.641/8.436 staphylococcal: 0.636/8.858 patients: 0.632/5.704 ivig: 0.627/7.834 antiepileptic: 0.625/7.702 \n\nTopic 7 (3.00): 1.6%\nMost relevant words:\npotentially (6379,1): 0.042/0.042/0.444/3.908 effects (1380,2): 0.041/0.082/0.361/3.303 dangerous (3584,2): 0.038/0.076/0.266/2.456 multidrug (112412,1): 0.038/0.038/0.444/7.090 resistant (8950,1): 0.037/0.037/0.326/3.224 use (176,7): 0.036/0.252/0.315/2.187 antibiotic (24098,2): 0.036/0.071/0.543/3.923 cause (1310,1): 0.035/0.035/0.349/2.715 serious (2375,2): 0.035/0.070/0.304/2.702 threatening (7927,2): 0.034/0.068/0.271/2.416 generically (48146,1): 0.033/0.033/0.205/2.347 pills (18688,1): 0.033/0.033/0.396/3.225 conceivably (69563,1): 0.032/0.032/0.276/3.150 problems (1188,1): 0.032/0.032/0.309/2.645 immune (8538,1): 0.031/0.031/0.399/3.629 efficacy (14568,1): 0.031/0.031/0.450/3.770 combination (2674,1): 0.031/0.031/0.269/2.010 pregnancy (7979,1): 0.031/0.031/0.437/4.034 used (82,4): 0.031/0.122/0.149/1.128 caused (1150,1): 0.031/0.031/0.268/2.308 vulnerable (6874,1): 0.030/0.030/0.203/1.920 cholesterol (20400,1): 0.030/0.030/0.318/2.555 testing (2814,1): 0.030/0.030/0.328/2.929 generic (7602,4): 0.029/0.117/0.239/2.095 prescriptions (34657,3): 0.029/0.088/0.508/6.298 develop (1982,1): 0.029/0.029/0.295/2.434 possibly (2456,1): 0.029/0.029/0.161/1.234 drugs (3528,10): 0.028/0.284/0.677/6.507 samples (4875,1): 0.028/0.028/0.180/1.842 medically (28191,1): 0.028/0.028/0.376/5.459 \nMost similar words in vocab:\ndrugs: 0.677/6.507 medications: 0.671/5.682 medication: 0.670/5.811 drug: 0.644/6.444 ivig: 0.644/8.040 antiretroviral: 0.639/7.271 patients: 0.637/5.750 antiepileptic: 0.628/7.728 treatments: 0.614/5.599 prophylactic: 0.611/6.987 treatment: 0.603/5.446 diarrheal: 0.601/7.572 antipsychotic: 0.589/6.604 contraindicated: 0.585/6.128 methadone: 0.585/7.015 antibiotics: 0.583/4.552 regimens: 0.581/6.771 medicines: 0.577/5.007 prescription: 0.570/4.953 treatable: 0.569/6.197 analgesics: 0.568/6.308 anticoagulant: 0.567/6.243 antiviral: 0.565/6.624 dosages: 0.564/6.086 warfarin: 0.564/6.020 praziquantel: 0.564/7.641 diabetes: 0.563/4.891 nsaids: 0.559/6.177 prophylaxis: 0.559/6.541 vaccine: 0.558/5.198 \n\nTopic 8 (3.00): 0.7%\nMost relevant words:\nefficacy (14568,1): 0.016/0.016/0.468/3.920 testing (2814,1): 0.016/0.016/0.350/3.122 pharmaceuticals (16906,3): 0.016/0.048/0.474/4.371 prescriptions (34657,3): 0.016/0.048/0.522/6.468 generically (48146,1): 0.015/0.015/0.207/2.366 pharmaceutical (8897,2): 0.015/0.031/0.527/4.911 glaxosmithkline (55181,1): 0.015/0.015/0.494/6.654 generic (7602,4): 0.015/0.061/0.253/2.220 generics (85144,1): 0.015/0.015/0.376/6.387 use (176,7): 0.015/0.102/0.297/2.067 potentially (6379,1): 0.014/0.014/0.412/3.629 therapeutics (36356,1): 0.014/0.014/0.492/6.129 effects (1380,2): 0.014/0.028/0.331/3.032 treatments (9357,2): 0.014/0.028/0.627/5.717 samples (4875,1): 0.014/0.014/0.186/1.904 cholesterol (20400,1): 0.013/0.013/0.317/2.549 certain (1095,4): 0.013/0.054/0.314/2.163 therapies (21018,2): 0.013/0.027/0.542/4.497 combination (2674,1): 0.013/0.013/0.260/1.944 problems (1188,1): 0.013/0.013/0.297/2.542 used (82,4): 0.013/0.052/0.140/1.059 dangerous (3584,2): 0.013/0.025/0.232/2.143 pills (18688,1): 0.013/0.013/0.376/3.057 antibiotic (24098,2): 0.013/0.025/0.508/3.669 resistant (8950,1): 0.012/0.012/0.296/2.924 treatment (1460,5): 0.012/0.062/0.619/5.590 alternative (1705,1): 0.012/0.012/0.216/1.817 food (807,1): 0.012/0.012/0.286/2.629 using (379,1): 0.012/0.012/0.190/1.371 controlled (2257,3): 0.012/0.037/0.211/1.950 \nMost similar words in vocab:\nmedication: 0.671/5.818 medications: 0.667/5.648 patients: 0.658/5.941 drugs: 0.646/6.210 ivig: 0.642/8.019 antiretroviral: 0.636/7.233 treatments: 0.627/5.717 treatment: 0.619/5.590 antiepileptic: 0.616/7.589 drug: 0.612/6.121 prophylactic: 0.611/6.987 antipsychotic: 0.591/6.629 regimens: 0.591/6.888 methadone: 0.585/7.013 medicines: 0.583/5.061 contraindicated: 0.582/6.096 diarrheal: 0.578/7.278 dosages: 0.577/6.226 prescription: 0.573/4.974 praziquantel: 0.563/7.637 bevacizumab: 0.561/7.922 analgesics: 0.561/6.229 diabetes: 0.561/4.873 infliximab: 0.557/7.218 anticoagulant: 0.555/6.111 antibiotics: 0.555/4.331 bisphosphonates: 0.555/7.111 monotherapy: 0.555/7.360 therapeutic: 0.554/4.833 warfarin: 0.554/5.915 \n\nTopic 14 (3.00): 0.4%\nMost relevant words:\nmultidrug (112412,1): 0.017/0.017/0.477/7.618 diseases (4439,6): 0.016/0.094/0.655/6.319 hiv (6116,1): 0.015/0.015/0.625/6.088 disease (1780,1): 0.014/0.014/0.630/5.802 immune (8538,1): 0.014/0.014/0.459/4.178 hepatitis (19494,1): 0.014/0.014/0.621/5.276 infection (6037,2): 0.013/0.027/0.598/5.451 infectious (12595,4): 0.013/0.053/0.576/5.425 antibiotic (24098,2): 0.013/0.026/0.588/4.247 malaria (13222,1): 0.013/0.013/0.565/5.038 tuberculosis (11017,1): 0.013/0.013/0.600/5.223 effects (1380,2): 0.012/0.025/0.374/3.426 aids (4693,2): 0.012/0.025/0.523/5.016 pregnancy (7979,1): 0.012/0.012/0.480/4.437 resistant (8950,1): 0.012/0.012/0.348/3.434 treatments (9357,2): 0.012/0.024/0.669/6.092 cancer (1871,2): 0.012/0.024/0.563/5.437 infected (8254,1): 0.012/0.012/0.412/3.949 medically (28191,1): 0.012/0.012/0.407/5.916 treat (5814,3): 0.012/0.035/0.608/5.173 treating (10124,1): 0.012/0.012/0.615/4.962 cholesterol (20400,1): 0.011/0.011/0.358/2.878 treatment (1460,5): 0.011/0.055/0.665/6.002 efficacy (14568,1): 0.011/0.011/0.482/4.036 therapies (21018,2): 0.011/0.021/0.579/4.801 cause (1310,1): 0.010/0.010/0.356/2.771 isolated (4860,1): 0.010/0.010/0.222/2.003 parasitic (18146,1): 0.010/0.010/0.297/2.480 therapeutics (36356,1): 0.010/0.010/0.503/6.266 patients (2468,9): 0.010/0.086/0.718/6.487 \nMost similar words in vocab:\npatients: 0.718/6.487 medications: 0.701/5.936 medication: 0.697/6.045 drugs: 0.689/6.622 treatments: 0.669/6.092 treatment: 0.665/6.002 antiretroviral: 0.659/7.501 diseases: 0.655/6.319 ivig: 0.654/8.168 drug: 0.651/6.509 diabetes: 0.645/5.607 diarrheal: 0.645/8.125 chronic: 0.641/5.652 prophylactic: 0.640/7.314 antiepileptic: 0.639/7.870 contraindicated: 0.632/6.622 disease: 0.630/5.802 infections: 0.628/5.809 hiv: 0.625/6.088 antibiotics: 0.624/4.872 hepatitis: 0.621/5.276 regimens: 0.615/7.173 treating: 0.615/4.962 treatable: 0.615/6.696 asthma: 0.614/4.686 ulcerative: 0.609/5.844 prophylaxis: 0.609/7.128 treat: 0.608/5.173 chemotherapy: 0.603/4.653 anticoagulant: 0.601/6.615 \n\nTopic 0 (0.00): 0.3%\nMost relevant words:\nborn (106,1): 0.019/0.019/0.000/0.000 round (273,1): 0.015/0.015/0.000/0.000 first (26,3): 0.013/0.039/0.000/0.000 two (37,3): 0.013/0.038/0.000/0.000 world (56,1): 0.012/0.012/0.000/0.000 side (287,2): 0.012/0.023/0.000/0.000 last (237,2): 0.012/0.023/0.000/0.000 university (61,2): 0.011/0.022/0.000/0.000 rima (42963,1): 0.010/0.010/0.000/0.000 tracks (907,1): 0.010/0.010/0.000/0.000 story (367,1): 0.010/0.010/0.000/0.000 martin (778,1): 0.010/0.010/0.000/0.000 former (185,1): 0.010/0.010/0.000/0.000 known (97,1): 0.010/0.010/0.000/0.000 held (230,1): 0.010/0.010/0.000/0.000 district (167,1): 0.009/0.009/0.000/0.000 line (181,1): 0.009/0.009/0.000/0.000 force (351,1): 0.009/0.009/0.000/0.000 back (193,1): 0.009/0.009/0.000/0.000 school (49,1): 0.009/0.009/0.000/0.000 wrote (536,1): 0.009/0.009/0.000/0.000 old (204,5): 0.008/0.042/0.000/0.000 division (236,1): 0.008/0.008/0.000/0.000 list (186,1): 0.008/0.008/0.000/0.000 judith (8272,1): 0.008/0.008/0.000/0.000 called (155,3): 0.008/0.024/0.000/0.000 works (341,1): 0.008/0.008/0.000/0.000 august (149,3): 0.008/0.024/0.000/0.000 october (131,1): 0.008/0.008/0.000/0.000 mount (1283,3): 0.008/0.023/0.000/0.000 \nTopic 16 (3.00): 0.3%\nMost relevant words:\nconcocting (179559,1): 0.018/0.018/0.370/6.232 glaxo (101914,1): 0.012/0.012/0.387/7.511 generics (85144,1): 0.011/0.011/0.421/7.141 drugstores (120806,1): 0.010/0.010/0.412/6.525 prescriptions (34657,3): 0.009/0.026/0.560/6.943 inexpensively (98181,1): 0.008/0.008/0.307/5.082 conceivably (69563,1): 0.007/0.007/0.308/3.507 thwart (31967,1): 0.006/0.006/0.199/2.438 generically (48146,1): 0.006/0.006/0.215/2.456 shorting (108317,1): 0.006/0.006/0.356/6.240 insurers (32340,1): 0.006/0.006/0.449/5.017 discounts (28868,1): 0.006/0.006/0.389/4.618 glaxosmithkline (55181,1): 0.005/0.005/0.496/6.677 generic (7602,4): 0.005/0.022/0.257/2.252 medicaid (25609,1): 0.005/0.005/0.404/5.432 products (1127,1): 0.005/0.005/0.394/3.285 food (807,1): 0.005/0.005/0.302/2.776 lawmakers (27291,2): 0.005/0.010/0.277/3.124 using (379,1): 0.005/0.005/0.209/1.513 expensive (4449,1): 0.005/0.005/0.333/3.180 ims (30708,1): 0.005/0.005/0.173/2.480 alternative (1705,1): 0.005/0.005/0.225/1.892 use (176,7): 0.005/0.032/0.282/1.963 brilliance (27594,1): 0.005/0.005/0.087/1.113 strategy (2853,2): 0.005/0.009/0.251/2.224 needed (1661,1): 0.005/0.005/0.322/2.264 supply (1905,1): 0.005/0.005/0.288/2.534 mainstays (61442,1): 0.004/0.004/0.118/1.351 pharmaceuticals (16906,3): 0.004/0.013/0.447/4.120 rules (1377,1): 0.004/0.004/0.105/0.952 \nMost similar words in vocab:\ndrugs: 0.586/5.637 antiretroviral: 0.578/6.580 ivig: 0.574/7.174 methadone: 0.568/6.818 prescriptions: 0.560/6.943 drug: 0.557/5.571 vioxx: 0.550/8.441 medication: 0.547/4.745 prescription: 0.541/4.701 thiopental: 0.536/7.849 medications: 0.534/4.515 antiepileptic: 0.533/6.565 antipsychotic: 0.525/5.894 placebos: 0.525/7.589 adrs: 0.524/8.189 prophylactic: 0.524/5.988 medicines: 0.523/4.540 dosages: 0.522/5.640 rebates: 0.518/5.813 contraceptives: 0.516/6.278 incentivize: 0.513/6.916 pfizer: 0.511/6.693 unapproved: 0.511/6.174 tamiflu: 0.510/7.349 regimens: 0.510/5.942 praziquantel: 0.509/6.896 sanofi: 0.507/6.069 pharmacies: 0.505/5.985 novartis: 0.504/6.525 bevacizumab: 0.504/7.105 \n\nTopic 13 (3.00): 0.2%\nMost relevant words:\ndrugs (3528,10): 0.010/0.099/0.767/7.381 drug (1942,15): 0.007/0.101/0.746/7.467 pills (18688,1): 0.007/0.007/0.435/3.543 antibiotic (24098,2): 0.006/0.012/0.565/4.085 cholesterol (20400,1): 0.006/0.006/0.357/2.869 efficacy (14568,1): 0.006/0.006/0.481/4.031 effects (1380,2): 0.006/0.012/0.358/3.277 multidrug (112412,1): 0.005/0.005/0.440/7.031 pharmaceutical (8897,2): 0.005/0.010/0.530/4.941 generic (7602,4): 0.005/0.020/0.258/2.261 pharmaceuticals (16906,3): 0.005/0.015/0.472/4.351 testing (2814,1): 0.005/0.005/0.343/3.064 therapeutics (36356,1): 0.005/0.005/0.497/6.192 therapies (21018,2): 0.005/0.010/0.558/4.632 resistant (8950,1): 0.005/0.005/0.316/3.123 hiv (6116,1): 0.005/0.005/0.570/5.548 immune (8538,1): 0.005/0.005/0.405/3.688 pregnancy (7979,1): 0.005/0.005/0.444/4.104 treatments (9357,2): 0.005/0.010/0.634/5.780 dangerous (3584,2): 0.005/0.009/0.248/2.286 use (176,7): 0.005/0.032/0.293/2.038 prescriptions (34657,3): 0.005/0.014/0.512/6.352 combination (2674,1): 0.004/0.004/0.266/1.987 aids (4693,2): 0.004/0.009/0.476/4.570 controlled (2257,3): 0.004/0.013/0.221/2.047 investigating (8587,1): 0.004/0.004/0.233/1.991 potentially (6379,1): 0.004/0.004/0.404/3.556 samples (4875,1): 0.004/0.004/0.183/1.869 glaxosmithkline (55181,1): 0.004/0.004/0.483/6.502 isolated (4860,1): 0.004/0.004/0.193/1.747 \nMost similar words in vocab:\ndrugs: 0.767/7.381 drug: 0.746/7.467 medications: 0.708/5.994 medication: 0.706/6.120 antiretroviral: 0.668/7.604 ivig: 0.650/8.122 patients: 0.646/5.836 treatments: 0.634/5.780 antiepileptic: 0.632/7.781 antipsychotic: 0.626/7.021 prescription: 0.622/5.406 treatment: 0.618/5.578 prophylactic: 0.611/6.981 medicines: 0.607/5.268 methadone: 0.604/7.250 antibiotics: 0.603/4.709 contraindicated: 0.602/6.308 antiviral: 0.599/7.025 regimens: 0.593/6.910 anticoagulant: 0.591/6.505 analgesics: 0.591/6.555 diarrheal: 0.588/7.412 therapeutic: 0.586/5.104 anticancer: 0.585/6.547 warfarin: 0.582/6.221 dosages: 0.580/6.264 diabetes: 0.579/5.032 nsaids: 0.579/6.390 opiates: 0.575/6.625 isoniazid: 0.573/7.558 \n\nTopic 10 (3.00): 0.0%\nMost relevant words:\nmedicaid (25609,1): 0.000/0.000/0.436/5.857 drugstores (120806,1): 0.000/0.000/0.392/6.216 discounts (28868,1): 0.000/0.000/0.405/4.811 generics (85144,1): 0.000/0.000/0.390/6.611 insurers (32340,1): 0.000/0.000/0.452/5.051 rebates (60162,1): 0.000/0.000/0.549/6.165 prescriptions (34657,3): 0.000/0.001/0.520/6.447 severance (36986,1): 0.000/0.000/0.311/4.119 inexpensively (98181,1): 0.000/0.000/0.285/4.715 concocting (179559,1): 0.000/0.000/0.295/4.973 conceivably (69563,1): 0.000/0.000/0.272/3.106 products (1127,1): 0.000/0.000/0.388/3.235 medicare (18132,1): 0.000/0.000/0.450/3.394 expensive (4449,1): 0.000/0.000/0.324/3.094 lawmakers (27291,2): 0.000/0.000/0.268/3.014 required (967,2): 0.000/0.000/0.270/1.948 supply (1905,1): 0.000/0.000/0.281/2.472 tablet (10396,2): 0.000/0.000/0.141/1.400 food (807,1): 0.000/0.000/0.282/2.595 seeking (3864,1): 0.000/0.000/0.273/2.192 distribution (1335,3): 0.000/0.001/0.203/1.878 needed (1661,1): 0.000/0.000/0.306/2.156 glaxo (101914,1): 0.000/0.000/0.333/6.445 clamoring (148365,1): 0.000/0.000/0.277/4.182 option (3370,1): 0.000/0.000/0.261/2.302 increases (4242,4): 0.000/0.001/0.387/3.326 certain (1095,4): 0.000/0.001/0.291/2.007 access (935,1): 0.000/0.000/0.213/1.807 federal (626,2): 0.000/0.000/0.216/1.925 rules (1377,1): 0.000/0.000/0.095/0.858 \nMost similar words in vocab:\nivig: 0.570/7.115 antiretroviral: 0.566/6.438 rebates: 0.549/6.165 vioxx: 0.546/8.383 adrs: 0.545/8.515 medication: 0.542/4.697 drugs: 0.541/5.207 methadone: 0.536/6.429 medications: 0.530/4.487 patients: 0.523/4.719 thiopental: 0.520/7.613 prescriptions: 0.520/6.447 prescription: 0.520/4.512 drug: 0.516/5.158 uninsured: 0.515/6.060 prophylactic: 0.513/5.870 antiepileptic: 0.510/6.275 dosages: 0.508/5.486 praziquantel: 0.506/6.855 incentivize: 0.505/6.814 reimburse: 0.504/5.555 diarrheal: 0.501/6.313 antipsychotic: 0.501/5.618 contraceptives: 0.499/6.068 bevacizumab: 0.498/7.025 costs: 0.497/4.188 medicines: 0.497/4.308 dhhs: 0.495/6.596 bisphosphonates: 0.495/6.349 refinancing: 0.494/5.409 \n\nTopic 19 (3.00): 0.0%\nMost relevant words:\nmultidrug (112412,1): 0.000/0.000/0.459/7.331 therapeutics (36356,1): 0.000/0.000/0.513/6.392 antibiotic (24098,2): 0.000/0.000/0.556/4.017 testing (2814,1): 0.000/0.000/0.359/3.203 effects (1380,2): 0.000/0.000/0.348/3.183 efficacy (14568,1): 0.000/0.000/0.467/3.910 resistant (8950,1): 0.000/0.000/0.323/3.188 generics (85144,1): 0.000/0.000/0.381/6.463 immune (8538,1): 0.000/0.000/0.409/3.727 cholesterol (20400,1): 0.000/0.000/0.333/2.673 generically (48146,1): 0.000/0.000/0.206/2.354 prescriptions (34657,3): 0.000/0.000/0.517/6.413 combination (2674,1): 0.000/0.000/0.277/2.071 potentially (6379,1): 0.000/0.000/0.416/3.658 generic (7602,4): 0.000/0.001/0.249/2.187 glaxosmithkline (55181,1): 0.000/0.000/0.491/6.610 hiv (6116,1): 0.000/0.000/0.566/5.509 problems (1188,1): 0.000/0.000/0.307/2.626 treatments (9357,2): 0.000/0.000/0.627/5.711 cause (1310,1): 0.000/0.000/0.331/2.574 hepatitis (19494,1): 0.000/0.000/0.554/4.707 pregnancy (7979,1): 0.000/0.000/0.435/4.019 controlled (2257,3): 0.000/0.000/0.222/2.054 isolated (4860,1): 0.000/0.000/0.197/1.784 pharmaceutical (8897,2): 0.000/0.000/0.512/4.773 develop (1982,1): 0.000/0.000/0.296/2.439 pharmaceuticals (16906,3): 0.000/0.000/0.454/4.185 dangerous (3584,2): 0.000/0.000/0.237/2.191 use (176,7): 0.000/0.001/0.285/1.981 therapies (21018,2): 0.000/0.000/0.543/4.503 \nMost similar words in vocab:\nmedication: 0.677/5.869 medications: 0.674/5.702 patients: 0.674/6.082 drugs: 0.664/6.387 ivig: 0.658/8.225 antiretroviral: 0.657/7.471 drug: 0.640/6.405 antiepileptic: 0.631/7.774 treatments: 0.627/5.711 treatment: 0.625/5.637 prophylactic: 0.624/7.131 diarrheal: 0.622/7.835 regimens: 0.607/7.077 contraindicated: 0.600/6.287 antipsychotic: 0.599/6.726 antibiotics: 0.590/4.602 methadone: 0.590/7.073 diabetes: 0.588/5.111 antiviral: 0.586/6.870 anticoagulant: 0.585/6.434 praziquantel: 0.583/7.912 prophylaxis: 0.583/6.828 dosages: 0.583/6.288 isoniazid: 0.582/7.674 infliximab: 0.582/7.534 chronic: 0.581/5.129 analgesics: 0.578/6.416 treatable: 0.577/6.287 medicines: 0.577/5.001 vaccine: 0.575/5.356 \n\nEM Iter 60:\nEm:\n[  1.73399134e+00   1.36247116e+02   4.04854858e+01   1.20751283e+02\n   4.82125086e-02   2.20365094e+01   2.94242056e+01   1.01665341e+01\n   4.46046626e+00   4.49447295e+01   5.84678235e-02   4.29921335e+01\n   2.48058945e+01   1.35040590e+00   2.59322091e+00   7.56692066e+01\n   1.47169686e+00   2.89171178e+01   6.57942365e+01   4.90864586e-02]\n\nEM Iter 61:\nEm:\n[  1.73454761e+00   1.36407389e+02   4.08242905e+01   1.21744210e+02\n   4.78952436e-02   2.19268480e+01   2.91887044e+01   9.87864776e+00\n   4.21028442e+00   4.48786372e+01   5.33902380e-02   4.26480173e+01\n   2.48832687e+01   1.15307149e+00   2.30376811e+00   7.67511138e+01\n   1.27221550e+00   2.84461936e+01   6.55992520e+01   4.82545878e-02]\n\nEM Iter 62:\nEm:\n[  1.73506876e+00   1.36577490e+02   4.11745942e+01   1.22690573e+02\n   4.77347173e-02   2.18221096e+01   2.89631250e+01   9.59522632e+00\n   3.96486281e+00   4.47958150e+01   5.10081125e-02   4.22931372e+01\n   2.49606793e+01   9.66573118e-01   2.02921126e+00   7.78154745e+01\n   1.08198297e+00   2.79782634e+01   6.54091825e+01   4.78883614e-02]\n\nEM Iter 63:\nEm:\n[  1.73553178e+00   1.36758804e+02   4.15368407e+01   1.23592612e+02\n   4.76441736e-02   2.17216768e+01   2.87476228e+01   9.31617414e+00\n   3.72413788e+00   4.46962392e+01   4.99036636e-02   4.19288085e+01\n   2.50372913e+01   7.92042268e-01   1.76936580e+00   7.88578461e+01\n   9.02020721e-01   2.75138311e+01   6.52238766e+01   4.77309875e-02]\n\nEM Iter 64:\nEm:\n[  1.73591336e+00   1.36952173e+02   4.19112979e+01   1.24451951e+02\n   4.75848035e-02   2.16249247e+01   2.85421676e+01   9.04134859e+00\n   3.48803719e+00   4.45798015e+01   4.93917573e-02   4.15560748e+01\n   2.51122231e+01   6.31127009e-01   1.52417512e+00   7.98740107e+01\n   7.33769751e-01   2.70531953e+01   6.50431665e+01   4.76668386e-02]\n\nEM Iter 65:\nEm:\n[  1.73619058e+00   1.37157970e+02   4.22980437e+01   1.25269595e+02\n   4.75392901e-02   2.15312344e+01   2.83465526e+01   8.77056632e+00\n   3.25648684e+00   4.44463005e+01   4.91516568e-02   4.11757282e+01\n   2.51845365e+01   4.86115708e-01   1.29376812e+00   8.08600084e+01\n   5.79222514e-01   2.65964720e+01   6.48668743e+01   4.76439815e-02]\n\nEM Iter 66:\nEm:\n[  1.73634114e+00   1.37376140e+02   4.26969451e+01   1.26045922e+02\n   4.74999051e-02   2.14399993e+01   2.81603968e+01   8.50360786e+00\n   3.02941873e+00   4.42954268e+01   4.90354038e-02   4.07883227e+01\n   2.52532266e+01   3.59945311e-01   1.07853714e+00   8.18121526e+01\n   4.41022661e-01   2.61436124e+01   6.46948083e+01   4.76389999e-02]\n\nEM Iter 67:\nEm:\n[  1.73634446e+00   1.37606308e+02   4.31076653e+01   1.26780765e+02\n   4.74632056e-02   2.13506397e+01   2.79831715e+01   8.24022825e+00\n   2.80678065e+00   4.41267775e+01   4.89751873e-02   4.03942140e+01\n   2.53172462e+01   2.55848763e-01   8.79244217e-01   8.27270805e+01\n   3.22386347e-01   2.56944357e+01   6.45267837e+01   4.76412358e-02]\n\nEM Iter 68:\nEm:\n[  1.73618526e+00   1.37848032e+02   4.35297622e+01   1.27473744e+02\n   4.74277457e-02   2.12626458e+01   2.78142911e+01   7.98018385e+00\n   2.58855192e+00   4.39399555e+01   4.89400917e-02   3.99936713e+01\n   2.53756186e+01   1.76304257e-01   6.97157899e-01   8.36019207e+01\n   2.26551185e-01   2.52487099e+01   6.43627005e+01   4.76458489e-02]\n\nEM Iter 69:\nEm:\n[  1.73585959e+00   1.38101240e+02   4.39629192e+01   1.28124923e+02\n   4.73931660e-02   2.11756480e+01   2.76532898e+01   7.72327765e+00\n   2.37476435e+00   4.37347850e+01   4.89162847e-02   3.95870756e+01\n   2.54276589e+01   1.21344575e-01   5.34197395e-01   8.44346255e+01\n   1.55463168e-01   2.48062864e+01   6.42026816e+01   4.76508564e-02]\n\nEM Iter 70:\nEm:\n[  1.73537914e+00   1.38366597e+02   4.44072161e+01   1.28735454e+02\n   4.73598002e-02   2.10894656e+01   2.74999987e+01   7.46939907e+00\n   2.16551976e+00   4.35115470e+01   4.88979632e-02   3.91750970e+01\n   2.54731910e+01   8.74576295e-02   3.92990427e-01   8.52243093e+01\n   1.08084838e-01   2.43672179e+01   6.40471621e+01   4.76558232e-02]\n\nEM Iter 71:\nEm:\n[  1.73476676e+00   1.38645351e+02   4.48631976e+01   1.29307557e+02\n   4.73282937e-02   2.10040568e+01   2.73545568e+01   7.21851785e+00\n   1.96099454e+00   4.32710126e+01   4.88829984e-02   3.87586640e+01\n   2.55125401e+01   6.86609268e-02   2.76605492e-01   8.59712391e+01\n   7.99029162e-02   2.39317382e+01   6.38967667e+01   4.76610272e-02]\n\nEM Iter 72:\nEm:\n[  1.73404548e+00   1.38938695e+02   4.53316830e+01   1.29843850e+02\n   4.72991950e-02   2.09194082e+01   2.72172465e+01   6.97063714e+00\n   1.76143816e+00   4.30142311e+01   4.88706909e-02   3.83387527e+01\n   2.55462891e+01   5.90334918e-02   1.87564755e-01   8.66764168e+01\n   6.47102135e-02   2.35001261e+01   6.37520354e+01   4.76668174e-02]\n\nEM Iter 73:\nEm:\n[  1.73323266e+00   1.39247343e+02   4.58135873e+01   1.30346971e+02\n   4.72728000e-02   2.08355098e+01   2.70883721e+01   6.72576289e+00\n   1.56718564e+00   4.27423319e+01   4.88607824e-02   3.79162856e+01\n   2.55750622e+01   5.43451786e-02   1.26020624e-01   8.73411975e+01\n   5.70673697e-02   2.30726377e+01   6.36132804e+01   4.76734017e-02]\n\nEM Iter 74:\nEm:\n[  1.73234192e+00   1.39571538e+02   4.63099084e+01   1.30819826e+02\n   4.72492427e-02   2.07524289e+01   2.69682738e+01   6.48390746e+00\n   1.37868913e+00   4.24564751e+01   4.88531829e-02   3.74922218e+01\n   2.55994724e+01   5.21285024e-02   8.82648164e-02   8.79671691e+01\n   5.33774225e-02   2.26495584e+01   6.34806359e+01   4.76809698e-02]\n\nEM Iter 75:\nEm:\n[  1.73138370e+00   1.39911051e+02   4.68216876e+01   1.31265675e+02\n   4.72285273e-02   2.06703323e+01   2.68573229e+01   6.24508705e+00\n   1.19656128e+00   4.21577904e+01   4.88478295e-02   3.70675883e+01\n   2.56200913e+01   5.11008568e-02   6.76044525e-02   8.85560217e+01\n   5.16347825e-02   2.22312188e+01   6.33540828e+01   4.76897031e-02]\n\nEM Iter 76:\nEm:\n[  1.73036177e+00   1.40265030e+02   4.73498419e+01   1.31687628e+02\n   4.72104642e-02   2.05893976e+01   2.67558208e+01   6.00929734e+00\n   1.02163355e+00   4.18472361e+01   4.88445163e-02   3.66433354e+01\n   2.56373555e+01   5.06342153e-02   5.72445932e-02   8.91093021e+01\n   5.08214285e-02   2.18179063e+01   6.32333985e+01   4.76995946e-02]\n\nEM Iter 77:\nEm:\n[  1.72927216e+00   1.40632032e+02   4.78950225e+01   1.32088109e+02\n   4.71947010e-02   2.05097224e+01   2.66639069e+01   5.77649553e+00\n   8.55041771e-01   4.15255124e+01   4.88428672e-02   3.62201922e+01\n   2.56514992e+01   5.04297502e-02   5.23260164e-02   8.96282876e+01\n   5.04446565e-02   2.14097799e+01   6.31181780e+01   4.77103793e-02]\n\nEM Iter 78:\nEm:\n[  1.72810602e+00   1.41010271e+02   4.84575919e+01   1.32468702e+02\n   4.71808411e-02   2.04313023e+01   2.65815459e+01   5.54660210e+00\n   6.98341805e-01   4.11930802e+01   4.88424414e-02   3.57986332e+01\n   2.56625687e+01   5.03470491e-02   5.00597580e-02   9.01140423e+01\n   5.02713723e-02   2.10068550e+01   6.30079353e+01   4.77216377e-02]\n\nEM Iter 79:\nEm:\n[  1.72685311e+00   1.41397869e+02   4.90376558e+01   1.32830236e+02\n   4.71685283e-02   2.03540521e+01   2.65085559e+01   5.31951150e+00\n   5.53639116e-01   4.08502160e+01   4.88428253e-02   3.53789132e+01\n   2.56704746e+01   5.03204258e-02   4.90322352e-02   9.05675163e+01\n   5.01924183e-02   2.06090275e+01   6.29021905e+01   4.77329180e-02]\n\nT[:,10]:\n[[  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n    0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00\n    0.00000000e+00   0.00000000e+00]\n [ -5.79407919e-03  -8.41797304e-02   5.32884264e-02  -5.26145160e-02\n   -3.08820231e-02   2.40274124e-02   2.92626458e-02   1.99543484e-02\n    1.79639601e-02   2.21005006e-02]\n [  8.25564738e-03  -2.34482035e-02   7.39515788e-02   5.74016241e-02\n   -7.64096838e-02  -7.29013790e-03   7.79235772e-02  -1.50557198e-03\n   -6.38996390e-02   2.43217962e-02]\n [  2.09018938e-02   4.83829894e-04   9.30338575e-02   5.94211750e-02\n   -3.47079102e-02   1.03617274e-02   7.34586772e-02  -1.78902379e-03\n   -7.31544980e-02   1.87281371e-02]\n [ -1.67859630e-02  -2.52355995e-02   5.69710745e-02   9.28393898e-03\n   -4.92378254e-02   2.97168024e-02   3.78582629e-02   5.35640327e-03\n   -2.27768562e-02   3.09404038e-02]\n [  2.33970162e-02   2.34393078e-02   2.81443609e-02  -6.11491811e-03\n   -1.18764654e-02  -3.38456747e-02  -1.59422058e-02   6.23621086e-03\n   -1.01725480e-01   3.16728611e-02]\n [  3.05271870e-02  -1.08932027e-02   9.36804641e-02   5.72694262e-02\n   -2.81015817e-02   1.14773283e-02   4.29744253e-02  -3.06162701e-02\n   -8.01264277e-02  -1.99177337e-03]\n [  2.52063093e-02  -2.24635756e-02   6.12693382e-02   3.25440705e-03\n   -2.47784575e-02  -1.05540586e-03   4.71433461e-02  -9.37431823e-04\n   -1.20580637e-01   2.47599095e-02]\n [  9.10188151e-03   2.34088684e-02   1.07024837e-01   2.48231781e-02\n   -4.65966314e-02   2.83151770e-02   6.25835180e-02  -2.47534523e-02\n   -7.14329928e-02   2.29775114e-02]\n [  1.48703523e-02  -6.19734347e-03   6.88690227e-02   4.26215638e-02\n   -5.70318732e-02   1.65128396e-02   7.56420504e-02   1.68009277e-02\n   -3.01397077e-02   3.21513525e-02]\n [  4.23483680e-02  -1.26310486e-02   9.46174646e-02  -1.22610577e-02\n   -2.66345811e-02   2.19868792e-02   7.03461323e-02  -2.07999586e-02\n   -7.30035934e-02   7.27633748e-03]\n [  1.30080825e-02   1.15828800e-02   8.98170147e-02   3.60316156e-02\n   -1.92798142e-02  -5.83087747e-05   7.53128075e-02  -1.51030473e-02\n   -8.06200135e-02   2.48330084e-02]\n [  1.46415113e-02   1.13784816e-02   7.74216601e-02   4.37137102e-02\n   -1.93970138e-02   3.64265461e-02  -2.37176315e-03  -4.93737605e-02\n   -7.35414535e-02  -5.11242938e-02]\n [  1.58919213e-02  -1.84113034e-02   6.85718079e-02   4.97191192e-02\n   -3.07600163e-02   2.94433805e-02   4.67784202e-02  -2.31501250e-03\n   -8.11482330e-02   3.63177173e-03]\n [  2.68933330e-02  -2.18987293e-03   5.62431262e-02   3.44152639e-02\n   -3.86126580e-02   2.70095262e-03   4.18841885e-02  -2.95842855e-02\n   -8.50089026e-02   2.18038695e-02]\n [ -3.29803619e-02   8.20159116e-04   3.78817794e-02   5.04027823e-03\n   -2.06213518e-02   3.94046751e-02   5.18675760e-02   4.29053704e-02\n   -4.93369608e-02  -4.66500623e-03]\n [  5.24726514e-03  -3.39279837e-02   8.51751864e-02   3.11374015e-02\n   -6.55349871e-02   4.97610655e-02   8.03154126e-02  -1.18536294e-02\n   -5.80207844e-02   1.60427397e-02]\n [  2.41016832e-02  -3.33843834e-03   8.54938147e-02   4.09779435e-02\n   -4.71771525e-02   9.33844088e-03   7.09963763e-02   1.48339528e-03\n   -7.99030188e-02   3.18515922e-02]\n [  1.11552385e-02   5.02984169e-02   5.50046344e-02  -1.22089932e-02\n   -8.25693630e-02   8.86538004e-02   5.68445401e-02  -1.43610721e-02\n   -1.91265021e-02  -1.06426755e-02]\n [  1.03878693e-02   2.20071797e-02   6.95830529e-02   6.18960791e-02\n   -2.25751374e-02   8.10576540e-03   6.02429979e-02  -3.00261952e-02\n   -9.16595968e-02   2.24087387e-02]]\nr:\n[ -1.33004718e-13  -5.26796598e-01  -5.22659486e-01  -5.50115116e-01\n  -5.75867098e-01  -6.86477035e-01  -5.66269687e-01  -5.44973018e-01\n  -5.33806050e-01  -5.63637117e-01  -5.19645538e-01  -5.60809805e-01\n  -9.30674924e-01  -5.75489382e-01  -6.11776830e-01  -6.42125303e-01\n  -5.72331313e-01  -5.48707087e-01  -5.13770356e-01  -5.83312757e-01]\n\nTopic magnitudes:\n[ 0.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.\n  3.  3.]\n\nTopic 1 (3.00): 21.6%\nMost relevant words:\ncompany (151,8): 0.935/7.478/0.590/4.761 acquired (1447,5): 0.821/4.107/0.441/3.816 million (357,6): 0.807/4.845/0.594/5.795 sold (614,2): 0.773/1.546/0.449/3.712 companies (939,4): 0.771/3.085/0.581/4.596 bank (640,1): 0.729/0.729/0.318/2.897 investors (5976,2): 0.722/1.444/0.567/5.040 fund (2014,3): 0.700/2.100/0.523/4.606 acquisition (4356,1): 0.688/0.688/0.448/3.895 hedge (14848,3): 0.664/1.992/0.362/3.186 filed (3883,2): 0.664/1.328/0.362/3.453 manager (830,2): 0.651/1.301/0.189/1.655 announced (568,1): 0.642/0.642/0.290/2.163 invest (11944,1): 0.641/0.641/0.526/3.718 business (354,2): 0.634/1.268/0.365/2.776 capital (735,1): 0.629/0.629/0.271/2.547 founder (1298,1): 0.607/0.607/0.211/1.692 private (597,1): 0.600/0.600/0.310/2.660 stock (1878,2): 0.585/1.169/0.410/3.927 executive (876,1): 0.570/0.570/0.219/1.730 us (220,1): 0.545/0.545/0.261/2.385 directors (2573,1): 0.543/0.543/0.262/2.094 financing (8017,1): 0.543/0.543/0.481/3.956 dollars (4434,2): 0.537/1.073/0.561/4.795 profit (2747,1): 0.531/0.531/0.471/3.880 icahn (83210,1): 0.525/0.525/0.365/7.131 june (140,1): 0.519/0.519/0.098/0.686 marketing (2693,1): 0.518/0.518/0.365/3.289 president (211,1): 0.514/0.514/0.129/1.161 august (149,3): 0.512/1.536/0.079/0.600 \nMost similar words in vocab:\nbillion: 0.630/6.293 investments: 0.597/5.189 million: 0.594/5.795 company: 0.590/4.761 companies: 0.581/4.596 investors: 0.567/5.040 investment: 0.564/4.859 shareholders: 0.563/4.601 dollars: 0.561/4.795 invested: 0.546/4.122 profits: 0.543/4.459 funds: 0.528/4.636 invest: 0.526/3.718 assets: 0.524/4.697 fund: 0.523/4.606 unitedhealth: 0.522/7.299 equity: 0.519/4.855 defrauded: 0.519/5.811 company's: 0.518/4.272 buyout: 0.514/3.423 multibillion: 0.513/7.940 shareholder: 0.507/3.863 investor: 0.506/4.345 purchase: 0.501/4.099 interpublic: 0.499/6.226 acquisitions: 0.497/3.919 investing: 0.495/3.816 wellpoint: 0.494/7.628 subsidiaries: 0.492/4.090 firms: 0.491/4.095 \n\nTopic 3 (3.00): 20.3%\nMost relevant words:\npatients (2468,9): 0.668/6.010/0.774/6.988 hospital (723,1): 0.661/0.661/0.445/4.078 hospitals (4777,5): 0.643/3.215/0.521/4.998 medicine (1528,2): 0.636/1.271/0.493/4.577 patient (3777,2): 0.633/1.266/0.643/5.694 medical (679,1): 0.623/0.623/0.580/4.971 treatment (1460,5): 0.609/3.047/0.692/6.244 care (1069,2): 0.600/1.199/0.569/4.850 doctors (4958,2): 0.598/1.196/0.500/4.729 treatments (9357,2): 0.590/1.180/0.680/6.198 inpatient (33848,1): 0.583/0.583/0.463/5.782 medically (28191,1): 0.565/0.565/0.414/6.015 treating (10124,1): 0.564/0.564/0.626/5.048 treat (5814,3): 0.564/1.691/0.618/5.259 therapies (21018,2): 0.556/1.111/0.595/4.933 health (502,3): 0.552/1.657/0.537/4.534 cancer (1871,2): 0.533/1.065/0.559/5.404 hiv (6116,1): 0.480/0.480/0.584/5.686 hospitalized (18161,1): 0.479/0.479/0.402/2.787 tuberculosis (11017,1): 0.476/0.476/0.579/5.044 aids (4693,2): 0.472/0.943/0.498/4.782 heart (869,1): 0.464/0.464/0.235/1.954 pregnancy (7979,1): 0.455/0.455/0.453/4.186 diseases (4439,6): 0.450/2.699/0.610/5.887 efficacy (14568,1): 0.445/0.445/0.462/3.873 infectious (12595,4): 0.445/1.779/0.545/5.127 antibiotic (24098,2): 0.434/0.868/0.535/3.869 disease (1780,1): 0.428/0.428/0.584/5.381 therapeutics (36356,1): 0.416/0.416/0.491/6.116 hepatitis (19494,1): 0.415/0.415/0.564/4.791 \nMost similar words in vocab:\npatients: 0.774/6.988 treatment: 0.692/6.244 medication: 0.691/5.990 medications: 0.688/5.821 treatments: 0.680/6.198 patient: 0.643/5.694 diabetes: 0.640/5.564 treating: 0.626/5.048 prophylactic: 0.623/7.123 clinical: 0.620/5.618 antiretroviral: 0.619/7.046 treat: 0.618/5.259 pediatric: 0.618/5.482 contraindicated: 0.612/6.415 diseases: 0.610/5.887 chronic: 0.605/5.332 chemotherapy: 0.602/4.647 ivig: 0.600/7.495 therapeutic: 0.600/5.226 regimens: 0.599/6.986 asthma: 0.597/4.556 therapies: 0.595/4.933 diarrheal: 0.594/7.480 drugs: 0.592/5.693 prophylaxis: 0.591/6.922 treatable: 0.590/6.430 disease: 0.584/5.381 hiv: 0.584/5.686 antibiotics: 0.583/4.547 illnesses: 0.582/4.489 \n\nTopic 15 (3.00): 13.8%\nMost relevant words:\nincrease (1265,8): 0.737/5.894/0.667/5.175 shorting (108317,1): 0.699/0.699/0.419/7.335 clamoring (148365,1): 0.695/0.695/0.379/5.731 increases (4242,4): 0.662/2.646/0.560/4.807 increased (1167,1): 0.660/0.660/0.561/4.529 shrank (46494,1): 0.633/0.633/0.384/4.409 rebates (60162,1): 0.628/0.628/0.628/7.053 inexpensively (98181,1): 0.622/0.622/0.352/5.824 amount (1588,1): 0.612/0.612/0.531/4.307 discounts (28868,1): 0.479/0.479/0.441/5.242 huge (3131,2): 0.465/0.929/0.315/2.758 cost (1178,2): 0.460/0.919/0.583/4.605 percent (1651,2): 0.457/0.913/0.478/4.369 steady (7656,1): 0.432/0.432/0.283/2.461 severance (36986,1): 0.420/0.420/0.347/4.589 income (874,1): 0.401/0.401/0.458/3.314 pay (1653,2): 0.386/0.771/0.490/4.213 conceivably (69563,1): 0.383/0.383/0.326/3.716 resulting (1955,1): 0.376/0.376/0.342/2.494 thwart (31967,1): 0.364/0.364/0.221/2.712 resulted (2001,1): 0.363/0.363/0.342/2.498 low (612,1): 0.353/0.353/0.324/2.708 lawmakers (27291,2): 0.335/0.671/0.311/3.500 fewer (5556,1): 0.329/0.329/0.344/2.813 sharply (12378,2): 0.329/0.657/0.243/2.077 swindle (42243,1): 0.327/0.327/0.220/3.073 constant (2943,1): 0.325/0.325/0.197/1.816 dollars (4434,2): 0.318/0.636/0.565/4.836 required (967,2): 0.314/0.628/0.328/2.372 millions (5429,1): 0.313/0.313/0.425/3.853 \nMost similar words in vocab:\nincrease: 0.667/5.175 rebates: 0.628/7.053 costs: 0.613/5.160 profits: 0.607/4.988 cost: 0.583/4.605 refinancing: 0.578/6.330 prices: 0.575/5.625 reinvest: 0.575/7.652 exorbitant: 0.575/6.852 repayments: 0.571/6.132 dollars: 0.565/4.836 increasing: 0.565/4.401 recouping: 0.563/8.191 incentivize: 0.562/7.587 surtax: 0.562/7.722 increased: 0.561/4.529 outlay: 0.561/5.958 disincentive: 0.561/7.944 increases: 0.560/4.807 premiums: 0.552/6.325 shortfall: 0.551/6.214 refunds: 0.550/6.340 tenfold: 0.547/6.347 revenues: 0.547/4.772 disburse: 0.541/7.179 billion: 0.539/5.384 untaxed: 0.537/7.891 overvalued: 0.537/7.730 skyrocketed: 0.534/6.051 tripling: 0.534/6.214 \n\nTopic 18 (3.00): 9.6%\nMost relevant words:\nprice (1535,17): 0.969/16.471/0.961/8.399 prices (4345,4): 0.858/3.433/0.818/7.998 gouge (81038,1): 0.817/0.817/0.226/4.754 priced (14909,1): 0.666/0.666/0.545/4.577 rose (1336,1): 0.296/0.296/0.180/1.319 sharply (12378,2): 0.271/0.542/0.248/2.114 high (104,3): 0.263/0.790/0.165/1.475 scott (1062,1): 0.235/0.235/0.107/0.689 low (612,1): 0.231/0.231/0.301/2.515 martin (778,1): 0.227/0.227/0.053/0.338 spencer (4173,1): 0.220/0.220/0.120/0.905 born (106,1): 0.207/0.207/-0.042/-0.365 free (381,1): 0.195/0.195/0.147/1.313 mcleod (15404,1): 0.193/0.193/0.088/0.662 sanders (7915,1): 0.192/0.192/0.090/0.627 line (181,1): 0.192/0.192/0.032/0.266 piggy (34457,1): 0.191/0.191/0.148/2.023 shortages (16485,1): 0.188/0.188/0.267/2.202 judith (8272,1): 0.187/0.187/0.038/0.301 round (273,1): 0.182/0.182/-0.037/-0.332 tracks (907,1): 0.181/0.181/0.008/0.071 grady (13903,1): 0.180/0.180/0.128/0.890 wendy (8642,1): 0.180/0.180/0.057/0.440 swindle (42243,1): 0.176/0.176/0.192/2.681 armstrong (4767,1): 0.175/0.175/0.060/0.511 democrat (3309,1): 0.174/0.174/0.082/0.755 democratic (731,1): 0.171/0.171/0.050/0.475 dramatic (3872,1): 0.170/0.170/0.089/0.790 crazy (5181,1): 0.170/0.170/0.092/0.733 good (462,1): 0.166/0.166/0.125/0.861 \nMost similar words in vocab:\nprice: 0.961/8.399 prices: 0.818/7.998 pricing: 0.601/5.253 priced: 0.545/4.577 inflation: 0.534/4.939 commodity: 0.496/4.141 discount: 0.490/4.235 exorbitant: 0.488/5.817 cost: 0.479/3.785 discounted: 0.476/3.170 purchases: 0.465/3.581 resale: 0.463/5.303 costs: 0.462/3.892 purchasing: 0.439/3.484 rates: 0.437/4.057 buyers: 0.432/3.681 commodities: 0.430/3.722 buying: 0.428/3.543 undervalued: 0.425/4.779 demand: 0.424/3.501 premium: 0.423/4.234 skyrocketed: 0.422/4.777 asset's: 0.417/5.803 market: 0.417/3.405 repayments: 0.417/4.476 tariff: 0.416/3.356 valuations: 0.414/4.732 cents: 0.413/3.815 volatility: 0.413/5.274 wages: 0.413/3.736 \n\nTopic 2 (3.00): 7.5%\nMost relevant words:\nglaxo (101914,1): 0.326/0.326/0.386/7.483 generics (85144,1): 0.320/0.320/0.427/7.237 glaxosmithkline (55181,1): 0.272/0.272/0.527/7.097 concocting (179559,1): 0.268/0.268/0.337/5.675 prescriptions (34657,3): 0.265/0.794/0.560/6.942 icahn (83210,1): 0.220/0.220/0.375/7.327 pharmaceutical (8897,2): 0.209/0.418/0.546/5.086 drugstores (120806,1): 0.204/0.204/0.385/6.102 medicaid (25609,1): 0.194/0.194/0.417/5.605 pharmaceuticals (16906,3): 0.184/0.551/0.475/4.381 generic (7602,4): 0.178/0.713/0.267/2.340 therapeutics (36356,1): 0.178/0.178/0.501/6.248 ims (30708,1): 0.157/0.157/0.181/2.595 generically (48146,1): 0.155/0.155/0.209/2.389 controlled (2257,3): 0.154/0.462/0.229/2.122 approved (2389,1): 0.153/0.153/0.330/2.867 lawmakers (27291,2): 0.151/0.301/0.284/3.202 laboratories (7537,1): 0.148/0.148/0.313/2.917 medicare (18132,1): 0.139/0.139/0.440/3.313 insurers (32340,1): 0.137/0.137/0.426/4.757 pills (18688,1): 0.137/0.137/0.374/3.046 alternative (1705,1): 0.136/0.136/0.222/1.868 administration (991,1): 0.134/0.134/0.247/2.019 advocates (8690,1): 0.132/0.132/0.256/2.091 investigating (8587,1): 0.130/0.130/0.220/1.881 tablet (10396,2): 0.129/0.258/0.141/1.400 use (176,7): 0.126/0.881/0.269/1.871 conceivably (69563,1): 0.126/0.126/0.272/3.106 thwart (31967,1): 0.125/0.125/0.175/2.145 complaint (9280,1): 0.125/0.125/0.232/1.896 \nMost similar words in vocab:\ndrugs: 0.623/5.992 medication: 0.616/5.346 medications: 0.608/5.141 antiretroviral: 0.607/6.910 drug: 0.593/5.929 ivig: 0.591/7.383 methadone: 0.586/7.027 prophylactic: 0.580/6.633 prescription: 0.579/5.028 antiepileptic: 0.565/6.958 patients: 0.565/5.100 vioxx: 0.564/8.649 antipsychotic: 0.563/6.319 prescriptions: 0.560/6.942 medicines: 0.559/4.848 regimens: 0.553/6.446 treatments: 0.551/5.020 pharmaceutical: 0.546/5.086 novartis: 0.540/7.000 dosages: 0.538/5.808 pfizer: 0.536/7.026 contraceptives: 0.533/6.478 praziquantel: 0.533/7.226 glaxosmithkline: 0.527/7.097 sanofi: 0.527/6.302 treatment: 0.524/4.732 thiopental: 0.523/7.659 bevacizumab: 0.523/7.380 prescribing: 0.523/6.534 placebos: 0.522/7.553 \n\nTopic 9 (3.00): 6.2%\nMost relevant words:\ngenerics (85144,1): 0.339/0.339/0.442/7.499 drugstores (120806,1): 0.322/0.322/0.427/6.764 concocting (179559,1): 0.316/0.316/0.359/6.045 glaxo (101914,1): 0.306/0.306/0.393/7.626 insurers (32340,1): 0.188/0.188/0.473/5.278 medicaid (25609,1): 0.172/0.172/0.423/5.688 lawmakers (27291,2): 0.164/0.328/0.310/3.490 glaxosmithkline (55181,1): 0.159/0.159/0.503/6.766 icahn (83210,1): 0.151/0.151/0.366/7.158 inexpensively (98181,1): 0.144/0.144/0.306/5.068 prescriptions (34657,3): 0.144/0.431/0.527/6.534 products (1127,1): 0.133/0.133/0.399/3.323 thwart (31967,1): 0.133/0.133/0.197/2.408 ims (30708,1): 0.132/0.132/0.184/2.629 expensive (4449,1): 0.132/0.132/0.342/3.269 conceivably (69563,1): 0.127/0.127/0.292/3.324 seeking (3864,1): 0.127/0.127/0.301/2.420 approved (2389,1): 0.124/0.124/0.329/2.856 pharmaceuticals (16906,3): 0.124/0.371/0.454/4.190 strategy (2853,2): 0.123/0.245/0.261/2.307 discounts (28868,1): 0.120/0.120/0.384/4.564 pharmaceutical (8897,2): 0.119/0.239/0.508/4.731 patents (8554,1): 0.118/0.118/0.287/2.701 trying (2359,2): 0.118/0.235/0.218/1.610 medicare (18132,1): 0.113/0.113/0.439/3.312 clamoring (148365,1): 0.113/0.113/0.306/4.623 rebates (60162,1): 0.113/0.113/0.538/6.045 needed (1661,1): 0.112/0.112/0.325/2.290 option (3370,1): 0.111/0.111/0.273/2.411 generic (7602,4): 0.111/0.444/0.236/2.070 \nMost similar words in vocab:\nvioxx: 0.552/8.466 antiretroviral: 0.549/6.242 ivig: 0.545/6.806 incentivize: 0.541/7.297 rebates: 0.538/6.045 methadone: 0.528/6.330 prescriptions: 0.527/6.534 pfizer: 0.526/6.895 novartis: 0.523/6.777 adrs: 0.520/8.120 drugs: 0.517/4.976 thiopental: 0.516/7.548 sanofi: 0.515/6.157 reimburse: 0.512/5.642 disincentive: 0.512/7.252 dhhs: 0.510/6.787 medication: 0.509/4.414 pharmaceutical: 0.508/4.731 multibillion: 0.505/7.822 prescription: 0.504/4.380 refinancing: 0.503/5.513 glaxosmithkline: 0.503/6.766 wellpoint: 0.502/7.753 pharmacies: 0.501/5.933 placebos: 0.501/7.239 healthcare: 0.500/4.517 anticompetitive: 0.500/7.305 prophylactic: 0.500/5.712 contraceptives: 0.499/6.071 medications: 0.498/4.212 \n\nTopic 11 (3.00): 5.4%\nMost relevant words:\ndiseases (4439,6): 0.184/1.104/0.654/6.316 disease (1780,1): 0.179/0.179/0.633/5.832 infectious (12595,4): 0.178/0.712/0.588/5.535 tuberculosis (11017,1): 0.175/0.175/0.616/5.367 infection (6037,2): 0.173/0.346/0.605/5.517 malaria (13222,1): 0.172/0.172/0.574/5.122 hepatitis (19494,1): 0.162/0.162/0.609/5.174 infected (8254,1): 0.159/0.159/0.423/4.055 hiv (6116,1): 0.157/0.157/0.605/5.893 multidrug (112412,1): 0.157/0.157/0.458/7.318 resistant (8950,1): 0.149/0.149/0.343/3.393 serious (2375,2): 0.145/0.289/0.325/2.894 cancer (1871,2): 0.141/0.282/0.559/5.398 aids (4693,2): 0.140/0.279/0.509/4.889 patients (2468,9): 0.138/1.245/0.746/6.736 immune (8538,1): 0.138/0.138/0.429/3.906 hospitalized (18161,1): 0.136/0.136/0.412/2.855 treatment (1460,5): 0.136/0.678/0.672/6.064 cause (1310,1): 0.135/0.135/0.366/2.846 pregnancy (7979,1): 0.133/0.133/0.463/4.283 treating (10124,1): 0.128/0.128/0.606/4.891 treat (5814,3): 0.128/0.384/0.599/5.101 threatening (7927,2): 0.127/0.255/0.281/2.502 vulnerable (6874,1): 0.126/0.126/0.224/2.124 inpatient (33848,1): 0.123/0.123/0.444/5.553 potentially (6379,1): 0.123/0.123/0.425/3.741 antibiotic (24098,2): 0.122/0.245/0.543/3.926 patient (3777,2): 0.121/0.242/0.606/5.361 medically (28191,1): 0.120/0.120/0.399/5.790 parasitic (18146,1): 0.119/0.119/0.295/2.462 \nMost similar words in vocab:\npatients: 0.746/6.736 treatment: 0.672/6.064 diseases: 0.654/6.316 treatments: 0.647/5.897 medication: 0.644/5.587 diarrheal: 0.643/8.096 medications: 0.642/5.435 infections: 0.637/5.890 diabetes: 0.636/5.531 chronic: 0.634/5.594 disease: 0.633/5.832 ivig: 0.627/7.830 prophylactic: 0.623/7.120 treatable: 0.619/6.743 tuberculosis: 0.616/5.367 illnesses: 0.610/4.707 antiretroviral: 0.610/6.940 hepatitis: 0.609/5.174 asthma: 0.609/4.650 treating: 0.606/4.891 patient: 0.606/5.361 contraindicated: 0.606/6.345 nosocomial: 0.605/8.061 hiv: 0.605/5.893 infection: 0.605/5.517 treat: 0.599/5.101 prophylaxis: 0.599/7.009 regimens: 0.592/6.908 ulcerative: 0.592/5.680 antiepileptic: 0.590/7.264 \n\nTopic 6 (3.00): 4.1%\nMost relevant words:\ndrugs (3528,10): 0.198/1.982/0.782/7.523 pills (18688,1): 0.138/0.138/0.455/3.707 drug (1942,15): 0.136/2.047/0.768/7.686 pharmaceutical (8897,2): 0.104/0.207/0.540/5.033 cholesterol (20400,1): 0.097/0.097/0.347/2.792 efficacy (14568,1): 0.095/0.095/0.472/3.957 pharmaceuticals (16906,3): 0.093/0.280/0.472/4.351 antibiotic (24098,2): 0.092/0.184/0.546/3.946 therapies (21018,2): 0.088/0.175/0.568/4.716 dangerous (3584,2): 0.088/0.175/0.257/2.369 effects (1380,2): 0.087/0.173/0.342/3.135 therapeutics (36356,1): 0.086/0.086/0.495/6.170 investigating (8587,1): 0.083/0.083/0.243/2.076 pregnancy (7979,1): 0.082/0.082/0.444/4.101 generic (7602,4): 0.081/0.323/0.251/2.197 testing (2814,1): 0.081/0.081/0.336/2.999 aids (4693,2): 0.078/0.156/0.480/4.610 use (176,7): 0.077/0.539/0.292/2.027 hiv (6116,1): 0.076/0.076/0.562/5.475 glaxosmithkline (55181,1): 0.075/0.075/0.479/6.454 treatments (9357,2): 0.074/0.147/0.631/5.746 administration (991,1): 0.073/0.073/0.252/2.062 controlled (2257,3): 0.071/0.213/0.216/1.996 immune (8538,1): 0.069/0.069/0.386/3.514 samples (4875,1): 0.069/0.069/0.178/1.826 prescriptions (34657,3): 0.068/0.205/0.503/6.235 used (82,4): 0.068/0.272/0.135/1.019 food (807,1): 0.067/0.067/0.279/2.563 combination (2674,1): 0.066/0.066/0.250/1.864 resistant (8950,1): 0.066/0.066/0.292/2.882 \nMost similar words in vocab:\ndrugs: 0.782/7.523 drug: 0.768/7.686 medication: 0.715/6.206 medications: 0.709/6.001 antiretroviral: 0.658/7.492 patients: 0.646/5.830 prescription: 0.634/5.505 treatments: 0.631/5.746 treatment: 0.623/5.618 ivig: 0.619/7.729 antipsychotic: 0.616/6.917 antiepileptic: 0.614/7.556 methadone: 0.613/7.356 medicines: 0.612/5.309 prophylactic: 0.598/6.839 therapeutic: 0.598/5.212 contraindicated: 0.591/6.197 analgesics: 0.589/6.533 antibiotics: 0.586/4.575 antiviral: 0.583/6.831 diabetes: 0.582/5.058 regimens: 0.582/6.781 anticoagulant: 0.573/6.303 warfarin: 0.570/6.090 therapies: 0.568/4.716 anticancer: 0.568/6.363 chemotherapy: 0.568/4.382 nsaids: 0.565/6.241 opiates: 0.565/6.506 hiv: 0.562/5.475 \n\nTopic 12 (3.00): 3.9%\nMost relevant words:\ndrug (1942,15): 0.681/10.210/0.969/9.693 drugs (3528,10): 0.436/4.358/0.906/8.711 pills (18688,1): 0.153/0.153/0.517/4.207 dangerous (3584,2): 0.064/0.128/0.266/2.451 investigating (8587,1): 0.063/0.063/0.259/2.209 cholesterol (20400,1): 0.063/0.063/0.344/2.764 controlled (2257,3): 0.057/0.172/0.235/2.178 side (287,2): 0.055/0.110/0.007/0.054 generic (7602,4): 0.054/0.217/0.251/2.200 crazy (5181,1): 0.050/0.050/0.104/0.829 story (367,1): 0.047/0.047/0.005/0.042 use (176,7): 0.046/0.325/0.276/1.921 effects (1380,2): 0.045/0.090/0.315/2.881 administration (991,1): 0.045/0.045/0.241/1.976 force (351,1): 0.044/0.044/0.015/0.121 rima (42963,1): 0.044/0.044/-0.000/-0.003 known (97,1): 0.042/0.042/-0.000/-0.003 used (82,4): 0.041/0.165/0.122/0.923 talked (11444,1): 0.040/0.040/0.083/0.523 two (37,3): 0.039/0.116/-0.049/-0.369 efficacy (14568,1): 0.038/0.038/0.411/3.444 samples (4875,1): 0.038/0.038/0.160/1.636 testing (2814,1): 0.037/0.037/0.293/2.617 discourage (22551,1): 0.037/0.037/0.187/1.161 wild (1617,1): 0.037/0.037/0.010/0.088 first (26,3): 0.036/0.109/-0.068/-0.455 combination (2674,1): 0.036/0.036/0.221/1.653 like (189,2): 0.036/0.072/0.077/0.516 possibly (2456,1): 0.036/0.036/0.124/0.953 food (807,1): 0.036/0.036/0.254/2.333 \nMost similar words in vocab:\ndrug: 0.969/9.693 drugs: 0.906/8.711 cocaine: 0.667/6.955 prescription: 0.646/5.613 heroin: 0.640/6.153 medication: 0.626/5.431 steroids: 0.615/4.142 medications: 0.612/5.183 opiates: 0.596/6.863 cannabis: 0.587/5.325 marijuana: 0.586/5.579 antipsychotic: 0.585/6.564 antiretroviral: 0.574/6.531 methadone: 0.569/6.823 morphine: 0.569/4.488 narcotics: 0.566/5.096 painkillers: 0.556/6.836 addiction: 0.554/5.211 stimulants: 0.554/6.086 ketamine: 0.552/6.472 illicit: 0.551/4.437 psychotropic: 0.551/6.552 diazepam: 0.548/5.798 alcohol: 0.548/5.633 methamphetamine: 0.546/6.997 barbiturates: 0.541/6.244 opioids: 0.541/6.194 antiepileptic: 0.537/6.616 anticancer: 0.535/5.989 antiviral: 0.531/6.222 \n\nTopic 17 (3.00): 3.2%\nMost relevant words:\nmultidrug (112412,1): 0.109/0.109/0.468/7.477 diseases (4439,6): 0.106/0.634/0.651/6.287 cancer (1871,2): 0.103/0.205/0.580/5.605 hepatitis (19494,1): 0.102/0.102/0.617/5.236 hiv (6116,1): 0.101/0.101/0.613/5.971 disease (1780,1): 0.100/0.100/0.626/5.772 infectious (12595,4): 0.097/0.388/0.579/5.451 malaria (13222,1): 0.095/0.095/0.566/5.047 tuberculosis (11017,1): 0.094/0.094/0.605/5.265 immune (8538,1): 0.091/0.091/0.441/4.017 antibiotic (24098,2): 0.089/0.178/0.572/4.131 aids (4693,2): 0.087/0.174/0.515/4.940 resistant (8950,1): 0.085/0.085/0.340/3.359 infection (6037,2): 0.085/0.170/0.585/5.331 therapeutics (36356,1): 0.084/0.084/0.510/6.364 pregnancy (7979,1): 0.075/0.075/0.458/4.231 treat (5814,3): 0.074/0.223/0.597/5.080 infected (8254,1): 0.074/0.074/0.398/3.815 efficacy (14568,1): 0.074/0.074/0.468/3.923 cholesterol (20400,1): 0.074/0.074/0.341/2.737 therapies (21018,2): 0.074/0.147/0.574/4.760 treating (10124,1): 0.074/0.074/0.602/4.860 treatments (9357,2): 0.073/0.146/0.654/5.957 effects (1380,2): 0.072/0.145/0.347/3.175 treatment (1460,5): 0.070/0.348/0.656/5.921 parasitic (18146,1): 0.068/0.068/0.292/2.432 cause (1310,1): 0.068/0.068/0.345/2.681 hospitalized (18161,1): 0.066/0.066/0.383/2.656 isolated (4860,1): 0.066/0.066/0.212/1.914 heart (869,1): 0.064/0.064/0.218/1.816 \nMost similar words in vocab:\npatients: 0.715/6.453 medications: 0.676/5.720 medication: 0.670/5.814 treatment: 0.656/5.921 treatments: 0.654/5.957 diseases: 0.651/6.287 diabetes: 0.648/5.634 ivig: 0.636/7.939 drugs: 0.632/6.081 diarrheal: 0.632/7.957 antiretroviral: 0.631/7.181 chronic: 0.629/5.549 disease: 0.626/5.772 prophylactic: 0.622/7.107 infections: 0.620/5.730 hepatitis: 0.617/5.236 hiv: 0.613/5.971 asthma: 0.609/4.648 antibiotics: 0.608/4.749 contraindicated: 0.607/6.361 tuberculosis: 0.605/5.265 antiepileptic: 0.604/7.439 treating: 0.602/4.860 treatable: 0.600/6.536 vaccine: 0.600/5.583 chemotherapy: 0.599/4.624 treat: 0.597/5.080 drug: 0.595/5.948 regimens: 0.593/6.915 ulcerative: 0.592/5.681 \n\nTopic 5 (3.00): 3.1%\nMost relevant words:\ntoxoplasmosis (125001,4): 0.976/3.902/0.861/12.667 parasite (16531,1): 0.323/0.323/0.501/3.884 parasitic (18146,1): 0.316/0.316/0.495/4.127 infection (6037,2): 0.296/0.591/0.740/6.744 infected (8254,1): 0.267/0.267/0.549/5.264 disease (1780,1): 0.189/0.189/0.713/6.574 immune (8538,1): 0.187/0.187/0.538/4.900 malaria (13222,1): 0.186/0.186/0.660/5.889 diseases (4439,6): 0.170/1.019/0.718/6.926 hepatitis (19494,1): 0.169/0.169/0.696/5.907 rare (2251,1): 0.169/0.169/0.312/2.977 multidrug (112412,1): 0.165/0.165/0.505/8.058 infectious (12595,4): 0.159/0.638/0.650/6.115 tuberculosis (11017,1): 0.129/0.129/0.661/5.752 cause (1310,1): 0.119/0.119/0.439/3.414 isolated (4860,1): 0.114/0.114/0.291/2.629 rarely (4300,1): 0.109/0.109/0.304/2.488 caused (1150,1): 0.102/0.102/0.347/2.990 resistant (8950,1): 0.101/0.101/0.374/3.692 rima (42963,1): 0.095/0.095/0.053/0.749 possibly (2456,1): 0.093/0.093/0.247/1.896 wild (1617,1): 0.079/0.079/0.093/0.840 effects (1380,2): 0.076/0.151/0.370/3.384 factor (2518,1): 0.075/0.075/0.255/2.250 pregnancy (7979,1): 0.074/0.074/0.475/4.390 vulnerable (6874,1): 0.073/0.073/0.239/2.264 hiv (6116,1): 0.069/0.069/0.592/5.765 remains (1135,1): 0.068/0.068/0.106/0.814 antibiotic (24098,2): 0.067/0.134/0.556/4.018 samples (4875,1): 0.065/0.065/0.210/2.155 \nMost similar words in vocab:\ntoxoplasmosis: 0.861/12.667 infections: 0.742/6.862 infection: 0.740/6.744 diseases: 0.718/6.926 disease: 0.713/6.574 ulcerative: 0.703/6.742 falciparum: 0.700/7.756 hepatitis: 0.696/5.907 diarrheal: 0.692/8.723 endocarditis: 0.684/7.054 histolytica: 0.666/7.851 bacteremia: 0.664/8.048 tuberculosis: 0.661/5.752 malaria: 0.660/5.889 treatable: 0.656/7.151 crohn's: 0.655/6.630 chronic: 0.651/5.740 infectious: 0.650/6.115 chlamydia: 0.649/7.354 colitis: 0.645/6.833 pylori: 0.644/6.712 pneumoniae: 0.643/6.728 sepsis: 0.643/6.968 blastocystis: 0.642/8.454 malignancies: 0.637/6.570 nosocomial: 0.637/8.481 staphylococcal: 0.634/8.836 mycoplasma: 0.626/6.832 malarial: 0.625/7.749 tetanus: 0.624/7.562 \n\nTopic 7 (3.00): 0.8%\nMost relevant words:\npotentially (6379,1): 0.022/0.022/0.446/3.922 effects (1380,2): 0.022/0.043/0.363/3.326 dangerous (3584,2): 0.020/0.041/0.269/2.478 multidrug (112412,1): 0.020/0.020/0.446/7.125 resistant (8950,1): 0.020/0.020/0.328/3.241 use (176,7): 0.019/0.134/0.318/2.208 antibiotic (24098,2): 0.019/0.038/0.545/3.942 serious (2375,2): 0.018/0.037/0.305/2.713 cause (1310,1): 0.018/0.018/0.351/2.725 pills (18688,1): 0.018/0.018/0.398/3.243 threatening (7927,2): 0.018/0.036/0.272/2.421 problems (1188,1): 0.017/0.017/0.311/2.656 generically (48146,1): 0.017/0.017/0.208/2.379 efficacy (14568,1): 0.017/0.017/0.452/3.788 immune (8538,1): 0.016/0.016/0.401/3.646 combination (2674,1): 0.016/0.016/0.272/2.028 cholesterol (20400,1): 0.016/0.016/0.320/2.571 pregnancy (7979,1): 0.016/0.016/0.438/4.047 used (82,4): 0.016/0.064/0.152/1.148 vulnerable (6874,1): 0.016/0.016/0.204/1.929 testing (2814,1): 0.016/0.016/0.330/2.944 caused (1150,1): 0.016/0.016/0.268/2.312 develop (1982,1): 0.016/0.016/0.296/2.437 generic (7602,4): 0.015/0.062/0.242/2.119 prescriptions (34657,3): 0.015/0.046/0.510/6.321 drugs (3528,10): 0.015/0.154/0.680/6.540 investigating (8587,1): 0.015/0.015/0.227/1.937 possibly (2456,1): 0.015/0.015/0.162/1.246 controlled (2257,3): 0.015/0.044/0.216/2.000 samples (4875,1): 0.015/0.015/0.181/1.857 \nMost similar words in vocab:\ndrugs: 0.680/6.540 medications: 0.674/5.706 medication: 0.673/5.834 drug: 0.646/6.466 ivig: 0.646/8.067 antiretroviral: 0.642/7.301 patients: 0.638/5.762 antiepileptic: 0.631/7.766 treatments: 0.617/5.622 prophylactic: 0.614/7.019 treatment: 0.605/5.461 diarrheal: 0.603/7.591 antipsychotic: 0.592/6.641 contraindicated: 0.588/6.159 methadone: 0.587/7.038 antibiotics: 0.586/4.574 regimens: 0.584/6.807 medicines: 0.579/5.025 prescription: 0.572/4.969 analgesics: 0.572/6.347 treatable: 0.571/6.221 anticoagulant: 0.570/6.274 antiviral: 0.568/6.655 warfarin: 0.567/6.054 dosages: 0.566/6.115 praziquantel: 0.566/7.671 diabetes: 0.564/4.901 nsaids: 0.563/6.212 prophylaxis: 0.561/6.572 chronic: 0.560/4.937 \n\nTopic 0 (0.00): 0.3%\nMost relevant words:\nborn (106,1): 0.018/0.018/0.000/0.000 round (273,1): 0.015/0.015/0.000/0.000 first (26,3): 0.013/0.038/0.000/0.000 two (37,3): 0.013/0.038/0.000/0.000 side (287,2): 0.012/0.023/0.000/0.000 world (56,1): 0.012/0.012/0.000/0.000 last (237,2): 0.011/0.023/0.000/0.000 university (61,2): 0.011/0.022/0.000/0.000 story (367,1): 0.010/0.010/0.000/0.000 tracks (907,1): 0.010/0.010/0.000/0.000 rima (42963,1): 0.010/0.010/0.000/0.000 martin (778,1): 0.010/0.010/0.000/0.000 known (97,1): 0.010/0.010/0.000/0.000 former (185,1): 0.009/0.009/0.000/0.000 held (230,1): 0.009/0.009/0.000/0.000 district (167,1): 0.009/0.009/0.000/0.000 line (181,1): 0.009/0.009/0.000/0.000 force (351,1): 0.009/0.009/0.000/0.000 back (193,1): 0.009/0.009/0.000/0.000 wrote (536,1): 0.009/0.009/0.000/0.000 school (49,1): 0.009/0.009/0.000/0.000 list (186,1): 0.008/0.008/0.000/0.000 old (204,5): 0.008/0.042/0.000/0.000 judith (8272,1): 0.008/0.008/0.000/0.000 called (155,3): 0.008/0.024/0.000/0.000 works (341,1): 0.008/0.008/0.000/0.000 division (236,1): 0.008/0.008/0.000/0.000 mount (1283,3): 0.008/0.023/0.000/0.000 august (149,3): 0.008/0.023/0.000/0.000 remained (770,1): 0.008/0.008/0.000/0.000 \nTopic 8 (3.00): 0.1%\nMost relevant words:\npharmaceuticals (16906,3): 0.002/0.006/0.474/4.373 pharmaceutical (8897,2): 0.002/0.004/0.528/4.914 efficacy (14568,1): 0.002/0.002/0.468/3.923 testing (2814,1): 0.002/0.002/0.350/3.125 glaxosmithkline (55181,1): 0.002/0.002/0.494/6.654 prescriptions (34657,3): 0.002/0.006/0.522/6.478 generic (7602,4): 0.002/0.007/0.254/2.228 therapeutics (36356,1): 0.002/0.002/0.492/6.133 potentially (6379,1): 0.002/0.002/0.413/3.633 use (176,7): 0.002/0.012/0.298/2.075 generically (48146,1): 0.002/0.002/0.208/2.376 effects (1380,2): 0.002/0.003/0.332/3.039 cholesterol (20400,1): 0.002/0.002/0.317/2.552 samples (4875,1): 0.002/0.002/0.186/1.907 certain (1095,4): 0.002/0.006/0.315/2.167 pills (18688,1): 0.002/0.002/0.376/3.064 problems (1188,1): 0.002/0.002/0.298/2.545 generics (85144,1): 0.002/0.002/0.377/6.397 combination (2674,1): 0.002/0.002/0.261/1.950 treatments (9357,2): 0.002/0.003/0.628/5.723 food (807,1): 0.002/0.002/0.286/2.630 used (82,4): 0.002/0.006/0.141/1.066 therapies (21018,2): 0.002/0.003/0.543/4.503 dangerous (3584,2): 0.002/0.003/0.233/2.149 antibiotic (24098,2): 0.002/0.003/0.509/3.676 medicaid (25609,1): 0.002/0.002/0.389/5.230 resistant (8950,1): 0.002/0.002/0.296/2.928 controlled (2257,3): 0.001/0.004/0.211/1.954 alternative (1705,1): 0.001/0.001/0.217/1.821 laboratories (7537,1): 0.001/0.001/0.298/2.780 \nMost similar words in vocab:\nmedication: 0.672/5.826 medications: 0.668/5.656 patients: 0.658/5.944 drugs: 0.647/6.219 ivig: 0.643/8.027 antiretroviral: 0.637/7.243 treatments: 0.628/5.723 treatment: 0.620/5.594 antiepileptic: 0.617/7.600 drug: 0.613/6.128 prophylactic: 0.612/6.998 antipsychotic: 0.592/6.640 regimens: 0.592/6.899 methadone: 0.585/7.021 medicines: 0.584/5.068 contraindicated: 0.583/6.105 diarrheal: 0.578/7.283 dosages: 0.578/6.234 prescription: 0.574/4.981 praziquantel: 0.564/7.646 analgesics: 0.562/6.241 bevacizumab: 0.562/7.927 diabetes: 0.561/4.875 infliximab: 0.558/7.225 anticoagulant: 0.556/6.120 antibiotics: 0.556/4.338 bisphosphonates: 0.556/7.121 therapeutic: 0.555/4.840 monotherapy: 0.555/7.367 warfarin: 0.555/5.926 \n\nTopic 13 (3.00): 0.0%\nMost relevant words:\ndrugs (3528,10): 0.000/0.003/0.768/7.384 pills (18688,1): 0.000/0.000/0.436/3.545 drug (1942,15): 0.000/0.003/0.747/7.470 antibiotic (24098,2): 0.000/0.000/0.565/4.085 cholesterol (20400,1): 0.000/0.000/0.357/2.870 efficacy (14568,1): 0.000/0.000/0.481/4.032 effects (1380,2): 0.000/0.000/0.358/3.278 pharmaceutical (8897,2): 0.000/0.000/0.531/4.941 pharmaceuticals (16906,3): 0.000/0.001/0.472/4.352 therapeutics (36356,1): 0.000/0.000/0.497/6.192 multidrug (112412,1): 0.000/0.000/0.441/7.032 generic (7602,4): 0.000/0.001/0.258/2.263 testing (2814,1): 0.000/0.000/0.343/3.064 resistant (8950,1): 0.000/0.000/0.316/3.123 pregnancy (7979,1): 0.000/0.000/0.444/4.104 hiv (6116,1): 0.000/0.000/0.570/5.548 immune (8538,1): 0.000/0.000/0.405/3.689 dangerous (3584,2): 0.000/0.000/0.248/2.288 therapies (21018,2): 0.000/0.000/0.558/4.633 glaxosmithkline (55181,1): 0.000/0.000/0.483/6.502 use (176,7): 0.000/0.001/0.293/2.039 prescriptions (34657,3): 0.000/0.000/0.512/6.353 treatments (9357,2): 0.000/0.000/0.634/5.781 investigating (8587,1): 0.000/0.000/0.233/1.992 potentially (6379,1): 0.000/0.000/0.404/3.556 combination (2674,1): 0.000/0.000/0.266/1.988 controlled (2257,3): 0.000/0.000/0.221/2.048 aids (4693,2): 0.000/0.000/0.476/4.569 samples (4875,1): 0.000/0.000/0.183/1.870 used (82,4): 0.000/0.001/0.140/1.056 \nMost similar words in vocab:\ndrugs: 0.768/7.384 drug: 0.747/7.470 medications: 0.708/5.996 medication: 0.706/6.122 antiretroviral: 0.668/7.605 ivig: 0.650/8.122 patients: 0.646/5.835 treatments: 0.634/5.781 antiepileptic: 0.632/7.783 antipsychotic: 0.626/7.023 prescription: 0.623/5.407 treatment: 0.618/5.578 prophylactic: 0.611/6.982 medicines: 0.607/5.269 methadone: 0.604/7.251 antibiotics: 0.603/4.710 contraindicated: 0.602/6.309 antiviral: 0.599/7.027 regimens: 0.593/6.911 anticoagulant: 0.591/6.506 analgesics: 0.591/6.557 diarrheal: 0.588/7.412 therapeutic: 0.586/5.106 anticancer: 0.585/6.549 warfarin: 0.583/6.222 dosages: 0.580/6.265 nsaids: 0.579/6.392 diabetes: 0.579/5.031 opiates: 0.575/6.627 isoniazid: 0.573/7.559 \n\nTopic 16 (3.00): 0.0%\nMost relevant words:\nconcocting (179559,1): 0.000/0.000/0.370/6.239 glaxo (101914,1): 0.000/0.000/0.388/7.513 drugstores (120806,1): 0.000/0.000/0.412/6.527 generics (85144,1): 0.000/0.000/0.421/7.146 prescriptions (34657,3): 0.000/0.001/0.560/6.947 conceivably (69563,1): 0.000/0.000/0.308/3.510 inexpensively (98181,1): 0.000/0.000/0.307/5.086 glaxosmithkline (55181,1): 0.000/0.000/0.496/6.677 insurers (32340,1): 0.000/0.000/0.449/5.019 thwart (31967,1): 0.000/0.000/0.199/2.440 generically (48146,1): 0.000/0.000/0.215/2.459 generic (7602,4): 0.000/0.001/0.257/2.255 medicaid (25609,1): 0.000/0.000/0.404/5.433 food (807,1): 0.000/0.000/0.302/2.777 products (1127,1): 0.000/0.000/0.394/3.286 discounts (28868,1): 0.000/0.000/0.389/4.620 expensive (4449,1): 0.000/0.000/0.333/3.181 using (379,1): 0.000/0.000/0.209/1.514 shorting (108317,1): 0.000/0.000/0.356/6.242 pharmaceuticals (16906,3): 0.000/0.000/0.447/4.121 ims (30708,1): 0.000/0.000/0.173/2.481 supply (1905,1): 0.000/0.000/0.288/2.534 lawmakers (27291,2): 0.000/0.000/0.278/3.126 alternative (1705,1): 0.000/0.000/0.225/1.893 strategy (2853,2): 0.000/0.000/0.252/2.225 use (176,7): 0.000/0.001/0.283/1.964 pharmaceutical (8897,2): 0.000/0.000/0.501/4.668 needed (1661,1): 0.000/0.000/0.322/2.265 controlled (2257,3): 0.000/0.000/0.213/1.969 delivering (9451,1): 0.000/0.000/0.227/2.004 \nMost similar words in vocab:\ndrugs: 0.586/5.637 antiretroviral: 0.578/6.581 ivig: 0.574/7.175 methadone: 0.568/6.820 prescriptions: 0.560/6.947 drug: 0.557/5.571 vioxx: 0.550/8.443 medication: 0.547/4.745 prescription: 0.541/4.701 thiopental: 0.536/7.852 medications: 0.534/4.515 antiepileptic: 0.533/6.567 antipsychotic: 0.525/5.896 placebos: 0.525/7.593 adrs: 0.524/8.190 prophylactic: 0.524/5.990 medicines: 0.523/4.540 dosages: 0.523/5.642 rebates: 0.518/5.815 contraceptives: 0.517/6.280 incentivize: 0.513/6.918 unapproved: 0.511/6.177 pfizer: 0.511/6.693 regimens: 0.510/5.944 tamiflu: 0.510/7.350 praziquantel: 0.509/6.898 sanofi: 0.507/6.069 pharmacies: 0.505/5.987 novartis: 0.504/6.526 bevacizumab: 0.504/7.106 \n\nTopic 14 (3.00): 0.0%\nMost relevant words:\nmultidrug (112412,1): 0.000/0.000/0.478/7.623 hiv (6116,1): 0.000/0.000/0.625/6.090 diseases (4439,6): 0.000/0.002/0.655/6.323 immune (8538,1): 0.000/0.000/0.459/4.181 hepatitis (19494,1): 0.000/0.000/0.622/5.278 disease (1780,1): 0.000/0.000/0.630/5.805 antibiotic (24098,2): 0.000/0.000/0.588/4.249 infection (6037,2): 0.000/0.000/0.598/5.454 infectious (12595,4): 0.000/0.001/0.577/5.429 malaria (13222,1): 0.000/0.000/0.565/5.041 aids (4693,2): 0.000/0.000/0.523/5.017 effects (1380,2): 0.000/0.000/0.374/3.428 resistant (8950,1): 0.000/0.000/0.348/3.437 pregnancy (7979,1): 0.000/0.000/0.480/4.438 tuberculosis (11017,1): 0.000/0.000/0.600/5.225 cancer (1871,2): 0.000/0.000/0.563/5.438 infected (8254,1): 0.000/0.000/0.412/3.952 cholesterol (20400,1): 0.000/0.000/0.358/2.879 treatments (9357,2): 0.000/0.000/0.669/6.094 efficacy (14568,1): 0.000/0.000/0.482/4.037 treating (10124,1): 0.000/0.000/0.615/4.964 treat (5814,3): 0.000/0.001/0.608/5.174 medically (28191,1): 0.000/0.000/0.407/5.917 therapies (21018,2): 0.000/0.000/0.579/4.802 therapeutics (36356,1): 0.000/0.000/0.503/6.266 treatment (1460,5): 0.000/0.001/0.665/6.004 cause (1310,1): 0.000/0.000/0.357/2.774 parasitic (18146,1): 0.000/0.000/0.298/2.482 isolated (4860,1): 0.000/0.000/0.222/2.005 potentially (6379,1): 0.000/0.000/0.419/3.691 \nMost similar words in vocab:\npatients: 0.718/6.487 medications: 0.702/5.937 medication: 0.697/6.045 drugs: 0.688/6.622 treatments: 0.669/6.094 treatment: 0.665/6.004 antiretroviral: 0.659/7.502 diseases: 0.655/6.323 ivig: 0.654/8.169 drug: 0.651/6.507 diabetes: 0.645/5.608 diarrheal: 0.645/8.128 chronic: 0.641/5.654 prophylactic: 0.640/7.316 antiepileptic: 0.639/7.872 contraindicated: 0.632/6.624 disease: 0.630/5.805 infections: 0.629/5.813 hiv: 0.625/6.090 antibiotics: 0.625/4.875 hepatitis: 0.622/5.278 regimens: 0.615/7.174 treating: 0.615/4.964 treatable: 0.615/6.699 asthma: 0.614/4.687 ulcerative: 0.609/5.847 prophylaxis: 0.609/7.131 treat: 0.608/5.174 chemotherapy: 0.603/4.655 anticoagulant: 0.601/6.617 \n\nTopic 10 (3.00): 0.0%\nMost relevant words:\nmedicaid (25609,1): 0.000/0.000/0.436/5.857 drugstores (120806,1): 0.000/0.000/0.392/6.216 discounts (28868,1): 0.000/0.000/0.405/4.811 insurers (32340,1): 0.000/0.000/0.452/5.051 generics (85144,1): 0.000/0.000/0.390/6.612 prescriptions (34657,3): 0.000/0.001/0.520/6.447 severance (36986,1): 0.000/0.000/0.311/4.119 rebates (60162,1): 0.000/0.000/0.549/6.166 medicare (18132,1): 0.000/0.000/0.450/3.394 products (1127,1): 0.000/0.000/0.388/3.235 expensive (4449,1): 0.000/0.000/0.324/3.094 concocting (179559,1): 0.000/0.000/0.295/4.973 food (807,1): 0.000/0.000/0.282/2.595 supply (1905,1): 0.000/0.000/0.281/2.472 tablet (10396,2): 0.000/0.000/0.141/1.400 conceivably (69563,1): 0.000/0.000/0.272/3.106 distribution (1335,3): 0.000/0.000/0.203/1.878 lawmakers (27291,2): 0.000/0.000/0.268/3.015 required (967,2): 0.000/0.000/0.270/1.948 inexpensively (98181,1): 0.000/0.000/0.285/4.715 seeking (3864,1): 0.000/0.000/0.273/2.192 glaxosmithkline (55181,1): 0.000/0.000/0.468/6.302 option (3370,1): 0.000/0.000/0.261/2.303 federal (626,2): 0.000/0.000/0.216/1.925 access (935,1): 0.000/0.000/0.213/1.807 needed (1661,1): 0.000/0.000/0.306/2.156 certain (1095,4): 0.000/0.000/0.291/2.007 approved (2389,1): 0.000/0.000/0.300/2.605 claim (2099,1): 0.000/0.000/0.172/1.385 delivering (9451,1): 0.000/0.000/0.213/1.880 \nMost similar words in vocab:\nivig: 0.570/7.115 antiretroviral: 0.566/6.438 rebates: 0.549/6.166 vioxx: 0.546/8.383 adrs: 0.545/8.515 medication: 0.542/4.697 drugs: 0.541/5.207 methadone: 0.536/6.429 medications: 0.530/4.487 patients: 0.523/4.719 thiopental: 0.520/7.613 prescriptions: 0.520/6.447 prescription: 0.520/4.512 drug: 0.516/5.158 uninsured: 0.515/6.060 prophylactic: 0.513/5.870 antiepileptic: 0.510/6.275 dosages: 0.508/5.486 praziquantel: 0.506/6.855 incentivize: 0.505/6.814 reimburse: 0.504/5.555 diarrheal: 0.501/6.313 antipsychotic: 0.501/5.618 contraceptives: 0.499/6.068 bevacizumab: 0.498/7.025 costs: 0.497/4.188 medicines: 0.497/4.308 dhhs: 0.495/6.596 bisphosphonates: 0.495/6.349 refinancing: 0.494/5.409 \n\nTopic 19 (3.00): 0.0%\nMost relevant words:\nmultidrug (112412,1): 0.000/0.000/0.459/7.331 therapeutics (36356,1): 0.000/0.000/0.513/6.392 testing (2814,1): 0.000/0.000/0.359/3.203 antibiotic (24098,2): 0.000/0.000/0.556/4.017 effects (1380,2): 0.000/0.000/0.348/3.183 efficacy (14568,1): 0.000/0.000/0.467/3.910 resistant (8950,1): 0.000/0.000/0.323/3.188 glaxosmithkline (55181,1): 0.000/0.000/0.491/6.609 cholesterol (20400,1): 0.000/0.000/0.333/2.673 immune (8538,1): 0.000/0.000/0.409/3.727 potentially (6379,1): 0.000/0.000/0.416/3.658 prescriptions (34657,3): 0.000/0.000/0.517/6.413 combination (2674,1): 0.000/0.000/0.277/2.071 generic (7602,4): 0.000/0.001/0.249/2.187 pharmaceutical (8897,2): 0.000/0.000/0.512/4.773 pharmaceuticals (16906,3): 0.000/0.000/0.454/4.185 hiv (6116,1): 0.000/0.000/0.566/5.509 problems (1188,1): 0.000/0.000/0.307/2.626 generically (48146,1): 0.000/0.000/0.206/2.354 generics (85144,1): 0.000/0.000/0.381/6.463 cause (1310,1): 0.000/0.000/0.331/2.574 pregnancy (7979,1): 0.000/0.000/0.435/4.019 controlled (2257,3): 0.000/0.000/0.222/2.054 develop (1982,1): 0.000/0.000/0.296/2.439 hepatitis (19494,1): 0.000/0.000/0.554/4.707 use (176,7): 0.000/0.001/0.285/1.981 dangerous (3584,2): 0.000/0.000/0.238/2.191 pills (18688,1): 0.000/0.000/0.376/3.060 isolated (4860,1): 0.000/0.000/0.197/1.784 treatments (9357,2): 0.000/0.000/0.627/5.712 \nMost similar words in vocab:\nmedication: 0.677/5.869 medications: 0.674/5.702 patients: 0.674/6.082 drugs: 0.664/6.387 ivig: 0.658/8.225 antiretroviral: 0.657/7.471 drug: 0.640/6.405 antiepileptic: 0.631/7.775 treatments: 0.627/5.712 treatment: 0.625/5.637 prophylactic: 0.624/7.131 diarrheal: 0.622/7.835 regimens: 0.607/7.078 contraindicated: 0.600/6.287 antipsychotic: 0.599/6.726 antibiotics: 0.590/4.602 methadone: 0.590/7.074 diabetes: 0.588/5.111 antiviral: 0.586/6.871 anticoagulant: 0.585/6.434 praziquantel: 0.584/7.912 prophylaxis: 0.583/6.828 dosages: 0.583/6.288 isoniazid: 0.582/7.674 infliximab: 0.582/7.534 chronic: 0.581/5.129 analgesics: 0.578/6.417 treatable: 0.577/6.287 medicines: 0.577/5.001 vaccine: 0.575/5.356 \n\nEM Iter 80:\nEm:\n[  1.72550378e+00   1.41792997e+02   4.96350956e+01   1.33172925e+02\n   4.71574659e-02   2.02778356e+01   2.64446413e+01   5.09510258e+00\n   4.23675800e-01   4.04970562e+01   4.88436633e-02   3.49611137e+01\n   2.56750422e+01   5.03191420e-02   4.85711138e-02   9.09896047e+01\n   5.01568612e-02   2.02161050e+01   6.28005091e+01   4.77438011e-02]\n\nEM Iter 81:\nEm:\n[  1.72405008e+00   1.42193947e+02   5.02495979e+01   1.33496520e+02\n   4.71474148e-02   2.02024940e+01   2.63894230e+01   4.87324780e+00\n   3.11727071e-01   4.01336333e+01   4.88446638e-02   3.45451911e+01\n   2.56760535e+01   5.03287634e-02   4.83661753e-02   9.13811828e+01\n   5.01409314e-02   1.98278359e+01   6.27025143e+01   4.77539305e-02]\n\nEM Iter 82:\nEm:\n[  1.72248726e+00   1.42599231e+02   5.08807162e+01   1.33800547e+02\n   4.71382122e-02   2.01278791e+01   2.63424833e+01   4.65382429e+00\n   2.21030416e-01   3.97599318e+01   4.88456253e-02   3.41310425e+01\n   2.56732936e+01   5.03425074e-02   4.82762825e-02   9.17431747e+01\n   5.01335361e-02   1.94439494e+01   6.26079099e+01   4.77630559e-02]\n\nEM Iter 83:\nEm:\n[  1.72081639e+00   1.43007748e+02   5.15280052e+01   1.34084721e+02\n   4.71298269e-02   2.00538973e+01   2.63034428e+01   4.43672875e+00\n   1.53511964e-01   3.93759879e+01   4.88465037e-02   3.37186085e+01\n   2.56666167e+01   5.03572892e-02   4.82377300e-02   9.20766997e+01\n   5.01295868e-02   1.90642140e+01   6.25165299e+01   4.77711158e-02]\n\nEM Iter 84:\nEm:\n[  1.71904703e+00   1.43418955e+02   5.21912055e+01   1.34349441e+02\n   4.71224047e-02   1.99805525e+01   2.62720554e+01   4.22189249e+00\n   1.08235330e-01   3.89820097e+01   4.88474703e-02   3.33079888e+01\n   2.56560151e+01   5.03719239e-02   4.82220536e-02   9.23832471e+01\n   5.01269381e-02   1.86885024e+01   6.24283837e+01   4.77783092e-02]\n\nEM Iter 85:\nEm:\n[  1.71719611e+00   1.43832805e+02   5.28703452e+01   1.34596015e+02\n   4.71162064e-02   1.99079448e+01   2.62482495e+01   4.00928469e+00\n   8.10274358e-02   3.85784201e+01   4.88488494e-02   3.28994813e+01\n   2.56416319e+01   5.03861917e-02   4.82167584e-02   9.26646962e+01\n   5.01248782e-02   1.83168102e+01   6.23436150e+01   4.77850330e-02]\n\nEM Iter 86:\nEm:\n[  1.71528188e+00   1.44249384e+02   5.35656407e+01   1.34826315e+02\n   4.71114309e-02   1.98362070e+01   2.62320648e+01   3.79889877e+00\n   6.61516135e-02   3.81657610e+01   4.88509214e-02   3.24934875e+01\n   2.56236822e+01   5.04001998e-02   4.82164168e-02   9.29230749e+01\n   5.01232455e-02   1.79492010e+01   6.22623551e+01   4.77916606e-02]\n\nEM Iter 87:\nEm:\n[  1.71331793e+00   1.44668548e+02   5.42772988e+01   1.35042207e+02\n   4.71080926e-02   1.97654316e+01   2.62235436e+01   3.59073541e+00\n   5.85448895e-02   3.77445497e+01   4.88537710e-02   3.20903728e+01\n   2.56023495e+01   5.04140001e-02   4.82185768e-02   9.31602604e+01\n   5.01219465e-02   1.75857257e+01   6.21845993e+01   4.77983588e-02]\n\nEM Iter 88:\nEm:\n[  1.71131178e+00   1.45089843e+02   5.50053933e+01   1.35245210e+02\n   4.71060527e-02   1.96956434e+01   2.62226704e+01   3.38479600e+00\n   5.48103791e-02   3.73152080e+01   4.88573054e-02   3.16903938e+01\n   2.55777394e+01   5.04274992e-02   4.82220086e-02   9.33778553e+01\n   5.01208178e-02   1.72263836e+01   6.21102027e+01   4.78050888e-02]\n\nEM Iter 89:\nEm:\n[  1.70926686e+00   1.45512631e+02   5.57498441e+01   1.35436464e+02\n   4.71051165e-02   1.96268131e+01   2.62293657e+01   3.18108588e+00\n   5.30187064e-02   3.68780658e+01   4.88613542e-02   3.12936999e+01\n   2.55498877e+01   5.04405261e-02   4.82260216e-02   9.35772220e+01\n   5.01196604e-02   1.68711234e+01   6.20389470e+01   4.78117083e-02]\n\nEM Iter 90:\nEm:\n[  1.70718482e+00   1.45936232e+02   5.65104402e+01   1.35616821e+02\n   4.71051014e-02   1.95588814e+01   2.62435039e+01   2.97962066e+00\n   5.21711279e-02   3.64333898e+01   4.88657467e-02   3.09003610e+01\n   2.55187873e+01   5.04529060e-02   4.82302014e-02   9.37595561e+01\n   5.01182913e-02   1.65198593e+01   6.19706015e+01   4.78180588e-02]\n\nEM Iter 91:\nEm:\n[  1.70506692e+00   1.46360008e+02   5.72868648e+01   1.35786936e+02\n   4.71058593e-02   1.94917769e+01   2.62649306e+01   2.78043274e+00\n   5.17748757e-02   3.59814087e+01   4.88703418e-02   3.05103944e+01\n   2.54844107e+01   5.04644970e-02   4.82342918e-02   9.39259405e+01\n   5.01165647e-02   1.61724861e+01   6.19049539e+01   4.78240059e-02]\n\nEM Iter 92:\nEm:\n[  1.70291463e+00   1.46783401e+02   5.80787130e+01   1.35947339e+02\n   4.71072752e-02   1.94254277e+01   2.62934744e+01   2.58357751e+00\n   5.15925082e-02   3.55223294e+01   4.88750319e-02   3.01237832e+01\n   2.54467241e+01   5.04751957e-02   4.82381330e-02   9.40773759e+01\n   5.01143723e-02   1.58288886e+01   6.18418190e+01   4.78294480e-02]\n\nEM Iter 93:\nEm:\n[  1.70072982e+00   1.47205937e+02   5.88855012e+01   1.36098486e+02\n   4.71092586e-02   1.93597667e+01   2.63289548e+01   2.38914002e+00\n   5.15108979e-02   3.50563471e+01   4.88797357e-02   2.97404882e+01\n   2.54056951e+01   5.04849320e-02   4.82416240e-02   9.42147960e+01\n   5.01116360e-02   1.54889488e+01   6.17810376e+01   4.78343130e-02]\n\nEM Iter 94:\nEm:\n[  1.69851468e+00   1.47627219e+02   5.97066733e+01   1.36240777e+02\n   4.71117354e-02   1.92947334e+01   2.63711854e+01   2.19724280e+00\n   5.14764272e-02   3.45836520e+01   4.88843910e-02   2.93604556e+01\n   2.53612956e+01   5.04936602e-02   4.82446999e-02   9.43390740e+01\n   5.01082989e-02   1.51525485e+01   6.17224713e+01   4.78385502e-02]\n\nEM Iter 95:\nEm:\n[  1.69627166e+00   1.48046921e+02   6.05416020e+01   1.36374582e+02\n   4.71146423e-02   1.92302745e+01   2.64199766e+01   2.00805586e+00\n   5.14637411e-02   3.41044330e+01   4.88889484e-02   2.89836207e+01\n   2.53135031e+01   5.05013506e-02   4.82473176e-02   9.44510267e+01\n   5.01043192e-02   1.48195720e+01   6.16659979e+01   4.78421241e-02]\n\nEM Iter 96:\nEm:\n[  1.69400329e+00   1.48464776e+02   6.13895899e+01   1.36500245e+02\n   4.71179229e-02   1.91663429e+01   2.64751363e+01   1.82180989e+00\n   5.14608591e-02   3.36188810e+01   4.88933669e-02   2.86099108e+01\n   2.52623002e+01   5.05079840e-02   4.82494465e-02   9.45514162e+01\n   5.00996653e-02   1.44899069e+01   6.16115070e+01   4.78450090e-02]\n\nEM Iter 97:\nEm:\n[  1.69171211e+00   1.48880563e+02   6.22498678e+01   1.36618087e+02\n   4.71215250e-02   1.91028966e+01   2.65364691e+01   1.63881434e+00\n   5.14621186e-02   3.31271903e+01   4.88976108e-02   2.82392460e+01\n   2.52076738e+01   5.05135464e-02   4.82510623e-02   9.46409510e+01\n   5.00943119e-02   1.41634443e+01   6.15588964e+01   4.78471843e-02]\n\nEM Iter 98:\nEm:\n[  1.68940050e+00   1.49294101e+02   6.31215927e+01   1.36728406e+02\n   4.71253990e-02   1.90398966e+01   2.66037754e+01   1.45948241e+00\n   5.14648393e-02   3.26295603e+01   4.89016472e-02   2.78715394e+01\n   2.51496126e+01   5.05180254e-02   4.82521426e-02   9.47202858e+01\n   5.00882375e-02   1.38400790e+01   6.15080699e+01   4.78486314e-02]\n\nEM Iter 99:\nEm:\n[  1.68707065e+00   1.49705235e+02   6.40038427e+01   1.36831464e+02\n   4.71294958e-02   1.89773055e+01   2.66768493e+01   1.28436596e+00\n   5.14677457e-02   3.21261952e+01   4.89054436e-02   2.75066960e+01\n   2.50881058e+01   5.05214065e-02   4.82526626e-02   9.47900199e+01\n   5.00814217e-02   1.35197082e+01   6.14589341e+01   4.78493307e-02]\n\nT[:,10]:\n[[ 0.          0.          0.          0.          0.          0.          0.\n   0.          0.          0.        ]\n [-0.0068679  -0.08903761  0.05394089 -0.05615922 -0.02908937  0.02449681\n   0.03065432  0.02002353  0.02203169  0.02401383]\n [ 0.01074333 -0.02346159  0.07061641  0.06070656 -0.07384103 -0.0122768\n   0.07991715 -0.00813502 -0.06384013  0.02747665]\n [ 0.02120409  0.00266273  0.08906918  0.05905987 -0.03092194  0.00735507\n   0.06983235 -0.00373274 -0.07322774  0.02051344]\n [-0.0167872  -0.02523479  0.05696896  0.0092872  -0.04923571  0.02971589\n   0.03786381  0.0053552  -0.02277895  0.03094122]\n [ 0.02311682  0.0245541   0.02528513 -0.00854753 -0.01072564 -0.03522006\n  -0.02076179  0.0080715  -0.10199925  0.0334311 ]\n [ 0.02995815 -0.01167053  0.09025553  0.05582201 -0.03016525  0.01197336\n   0.04138166 -0.03248085 -0.08088802 -0.0039524 ]\n [ 0.02525235 -0.02233053  0.06111422  0.003773   -0.02492631 -0.00112654\n   0.0472579  -0.00136125 -0.12023992  0.02477794]\n [ 0.00910667  0.02339277  0.10700167  0.02483726 -0.04659393  0.0282974\n   0.06258715 -0.02475474 -0.07143793  0.02298058]\n [ 0.01405494 -0.00639364  0.06745876  0.04399187 -0.05831578  0.01157777\n   0.07959895  0.010621   -0.03177424  0.03504706]\n [ 0.04234283 -0.01262814  0.09461195 -0.01225372 -0.02663707  0.02198404\n   0.07034802 -0.02080047 -0.07300182  0.00727875]\n [ 0.01407799  0.01065582  0.08516627  0.03791625 -0.02021301 -0.00120022\n   0.07348445 -0.01338178 -0.08097128  0.02679221]\n [ 0.01433467  0.01237742  0.07682994  0.04342048 -0.01908747  0.03698103\n  -0.00490346 -0.05026525 -0.07282264 -0.0531181 ]\n [ 0.01589499 -0.01841069  0.06856969  0.04971737 -0.03076251  0.02943812\n   0.0467756  -0.00232588 -0.08115037  0.00362921]\n [ 0.02689417 -0.00219131  0.05624169  0.03441788 -0.0386103   0.00269904\n   0.04188492 -0.02958265 -0.08500947  0.02180449]\n [-0.03650575  0.01063767  0.03125081  0.00449365 -0.01744535  0.0364484\n   0.04530424  0.04782295 -0.04888128 -0.01333685]\n [ 0.00524921 -0.03392317  0.08516961  0.03114163 -0.06553443  0.04975051\n   0.08031732 -0.01185935 -0.05802119  0.01604652]\n [ 0.02423399 -0.00341843  0.0833728   0.04169755 -0.04618244  0.0079045\n   0.06986772  0.00058411 -0.08006354  0.03235701]\n [ 0.01243752  0.05224404  0.05440155 -0.01114312 -0.08356868  0.08836758\n   0.05636417 -0.01571466 -0.0188332  -0.01055636]\n [ 0.01039039  0.02200017  0.0695795   0.06189284 -0.02257742  0.00810283\n   0.06024205 -0.03002574 -0.09165826  0.02241017]]\nr:\n[ -1.33004718e-13  -5.17144796e-01  -5.45711965e-01  -5.62045305e-01\n  -5.75905702e-01  -6.77978372e-01  -5.82179348e-01  -5.46965271e-01\n  -5.33873839e-01  -5.92736389e-01  -5.19674357e-01  -5.71703190e-01\n  -9.40740198e-01  -5.75520156e-01  -6.11790138e-01  -6.76649516e-01\n  -5.72371988e-01  -5.54759557e-01  -5.15335872e-01  -5.83336798e-01]\n\nTopic magnitudes:\n[ 0.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.\n  3.  3.]\n\nTopic 1 (3.00): 22.9%\nMost relevant words:\ncompany (151,8): 0.955/7.642/0.610/4.917 acquired (1447,5): 0.866/4.331/0.457/3.949 million (357,6): 0.832/4.995/0.581/5.671 sold (614,2): 0.815/1.630/0.456/3.768 companies (939,4): 0.809/3.237/0.584/4.621 bank (640,1): 0.782/0.782/0.329/2.989 investors (5976,2): 0.761/1.521/0.564/5.015 fund (2014,3): 0.746/2.238/0.522/4.593 acquisition (4356,1): 0.732/0.732/0.451/3.926 manager (830,2): 0.725/1.451/0.210/1.838 filed (3883,2): 0.721/1.443/0.371/3.542 hedge (14848,3): 0.715/2.145/0.367/3.231 announced (568,1): 0.703/0.703/0.304/2.264 business (354,2): 0.688/1.376/0.375/2.847 founder (1298,1): 0.685/0.685/0.234/1.875 invest (11944,1): 0.677/0.677/0.522/3.685 capital (735,1): 0.670/0.670/0.273/2.561 private (597,1): 0.651/0.651/0.315/2.704 executive (876,1): 0.646/0.646/0.239/1.886 stock (1878,2): 0.629/1.259/0.410/3.928 directors (2573,1): 0.607/0.607/0.274/2.189 president (211,1): 0.599/0.599/0.149/1.343 june (140,1): 0.586/0.586/0.117/0.818 us (220,1): 0.586/0.586/0.260/2.379 august (149,3): 0.581/1.742/0.097/0.736 financing (8017,1): 0.568/0.568/0.472/3.886 october (131,1): 0.565/0.565/0.097/0.732 april (148,1): 0.565/0.565/0.110/0.784 marketing (2693,1): 0.564/0.564/0.367/3.309 profit (2747,1): 0.560/0.560/0.463/3.812 \nMost similar words in vocab:\nbillion: 0.619/6.179 company: 0.610/4.917 investments: 0.594/5.166 companies: 0.584/4.621 million: 0.581/5.671 shareholders: 0.566/4.625 investors: 0.564/5.015 investment: 0.563/4.845 invested: 0.546/4.117 dollars: 0.542/4.633 unitedhealth: 0.527/7.357 profits: 0.524/4.308 buyout: 0.524/3.485 assets: 0.523/4.684 fund: 0.522/4.593 invest: 0.522/3.685 equity: 0.521/4.872 company's: 0.521/4.296 funds: 0.519/4.551 shareholder: 0.518/3.942 investor: 0.513/4.402 interpublic: 0.512/6.386 defrauded: 0.510/5.712 subsidiaries: 0.506/4.203 acquisitions: 0.502/3.962 multibillion: 0.498/7.710 purchase: 0.495/4.047 investing: 0.493/3.799 wellpoint: 0.492/7.601 cendant: 0.490/7.253 \n\nTopic 3 (3.00): 20.9%\nMost relevant words:\npatients (2468,9): 0.736/6.620/0.786/7.097 hospital (723,1): 0.706/0.706/0.450/4.121 patient (3777,2): 0.685/1.369/0.650/5.752 treatment (1460,5): 0.676/3.382/0.704/6.349 hospitals (4777,5): 0.672/3.362/0.519/4.983 medicine (1528,2): 0.660/1.320/0.495/4.593 medical (679,1): 0.643/0.643/0.578/4.946 treatments (9357,2): 0.641/1.283/0.688/6.268 care (1069,2): 0.632/1.264/0.568/4.845 treating (10124,1): 0.624/0.624/0.637/5.140 treat (5814,3): 0.624/1.871/0.629/5.351 doctors (4958,2): 0.623/1.246/0.500/4.727 inpatient (33848,1): 0.619/0.619/0.463/5.780 medically (28191,1): 0.612/0.612/0.420/6.098 cancer (1871,2): 0.611/1.222/0.571/5.520 therapies (21018,2): 0.596/1.191/0.599/4.967 health (502,3): 0.586/1.759/0.536/4.525 tuberculosis (11017,1): 0.568/0.568/0.599/5.217 diseases (4439,6): 0.556/3.335/0.635/6.127 hiv (6116,1): 0.554/0.554/0.595/5.795 hospitalized (18161,1): 0.543/0.543/0.416/2.885 infectious (12595,4): 0.534/2.134/0.564/5.313 disease (1780,1): 0.530/0.530/0.608/5.602 aids (4693,2): 0.529/1.057/0.505/4.848 heart (869,1): 0.519/0.519/0.246/2.049 pregnancy (7979,1): 0.504/0.504/0.459/4.244 hepatitis (19494,1): 0.499/0.499/0.583/4.951 malaria (13222,1): 0.492/0.492/0.541/4.827 antibiotic (24098,2): 0.483/0.965/0.544/3.929 multidrug (112412,1): 0.477/0.477/0.443/7.075 \nMost similar words in vocab:\npatients: 0.786/7.097 treatment: 0.704/6.349 treatments: 0.688/6.268 medication: 0.686/5.952 medications: 0.685/5.795 diabetes: 0.651/5.657 patient: 0.650/5.752 treating: 0.637/5.140 diseases: 0.635/6.127 treat: 0.629/5.351 clinical: 0.627/5.686 pediatric: 0.626/5.553 prophylactic: 0.624/7.133 chronic: 0.621/5.478 contraindicated: 0.618/6.475 antiretroviral: 0.613/6.976 asthma: 0.612/4.669 chemotherapy: 0.612/4.719 disease: 0.608/5.602 treatable: 0.603/6.566 diarrheal: 0.602/7.582 therapeutic: 0.601/5.242 illnesses: 0.601/4.637 regimens: 0.600/6.999 infections: 0.600/5.549 prophylaxis: 0.599/7.014 tuberculosis: 0.599/5.217 therapies: 0.599/4.967 ivig: 0.597/7.458 hiv: 0.595/5.795 \n\nTopic 15 (3.00): 14.5%\nMost relevant words:\nincrease (1265,8): 0.811/6.489/0.700/5.433 clamoring (148365,1): 0.761/0.761/0.394/5.965 increases (4242,4): 0.755/3.020/0.596/5.119 shorting (108317,1): 0.751/0.751/0.426/7.457 increased (1167,1): 0.743/0.743/0.591/4.771 shrank (46494,1): 0.713/0.713/0.403/4.630 inexpensively (98181,1): 0.692/0.692/0.366/6.057 amount (1588,1): 0.678/0.678/0.549/4.450 rebates (60162,1): 0.670/0.670/0.631/7.085 huge (3131,2): 0.530/1.060/0.332/2.904 discounts (28868,1): 0.516/0.516/0.448/5.314 steady (7656,1): 0.500/0.500/0.303/2.641 percent (1651,2): 0.490/0.980/0.478/4.363 cost (1178,2): 0.489/0.978/0.581/4.593 conceivably (69563,1): 0.462/0.462/0.351/4.005 resulting (1955,1): 0.448/0.448/0.370/2.702 severance (36986,1): 0.442/0.442/0.346/4.578 thwart (31967,1): 0.430/0.430/0.240/2.937 income (874,1): 0.425/0.425/0.457/3.308 resulted (2001,1): 0.418/0.418/0.360/2.627 low (612,1): 0.416/0.416/0.345/2.887 pay (1653,2): 0.401/0.801/0.484/4.165 constant (2943,1): 0.393/0.393/0.222/2.045 fewer (5556,1): 0.378/0.378/0.359/2.938 sharply (12378,2): 0.373/0.746/0.257/2.193 lawmakers (27291,2): 0.366/0.733/0.318/3.582 swindle (42243,1): 0.362/0.362/0.226/3.154 required (967,2): 0.358/0.717/0.345/2.492 less (615,2): 0.358/0.716/0.356/2.470 delays (10116,1): 0.350/0.350/0.271/2.308 \nMost similar words in vocab:\nincrease: 0.700/5.433 rebates: 0.631/7.085 costs: 0.617/5.197 increasing: 0.600/4.673 profits: 0.600/4.931 increases: 0.596/5.119 increased: 0.591/4.771 cost: 0.581/4.593 exorbitant: 0.579/6.909 reinvest: 0.575/7.660 refinancing: 0.575/6.301 repayments: 0.575/6.175 disincentive: 0.575/8.147 recouping: 0.572/8.310 prices: 0.571/5.583 incentivize: 0.570/7.695 tenfold: 0.567/6.577 surtax: 0.565/7.773 outlay: 0.564/5.996 shortfall: 0.561/6.324 decrease: 0.556/4.437 premiums: 0.554/6.351 refunds: 0.550/6.349 amount: 0.549/4.450 tripling: 0.548/6.386 dollars: 0.547/4.679 disburse: 0.545/7.229 revenues: 0.542/4.729 overvalued: 0.540/7.766 skyrocketed: 0.539/6.104 \n\nTopic 2 (3.00): 9.8%\nMost relevant words:\ngenerics (85144,1): 0.467/0.467/0.442/7.507 glaxo (101914,1): 0.459/0.459/0.396/7.683 prescriptions (34657,3): 0.404/1.211/0.577/7.159 glaxosmithkline (55181,1): 0.385/0.385/0.534/7.189 concocting (179559,1): 0.379/0.379/0.351/5.918 pharmaceutical (8897,2): 0.305/0.610/0.556/5.181 drugstores (120806,1): 0.295/0.295/0.392/6.209 medicaid (25609,1): 0.274/0.274/0.420/5.642 pharmaceuticals (16906,3): 0.265/0.795/0.485/4.475 generic (7602,4): 0.264/1.058/0.286/2.506 therapeutics (36356,1): 0.259/0.259/0.508/6.337 icahn (83210,1): 0.256/0.256/0.371/7.257 generically (48146,1): 0.228/0.228/0.225/2.574 ims (30708,1): 0.216/0.216/0.186/2.662 laboratories (7537,1): 0.211/0.211/0.321/2.995 controlled (2257,3): 0.210/0.631/0.235/2.174 approved (2389,1): 0.210/0.210/0.333/2.895 lawmakers (27291,2): 0.196/0.391/0.287/3.230 pills (18688,1): 0.195/0.195/0.384/3.129 alternative (1705,1): 0.190/0.190/0.232/1.945 medicare (18132,1): 0.189/0.189/0.437/3.293 insurers (32340,1): 0.184/0.184/0.425/4.744 use (176,7): 0.182/1.272/0.285/1.979 administration (991,1): 0.181/0.181/0.249/2.039 tablet (10396,2): 0.180/0.360/0.149/1.472 advocates (8690,1): 0.177/0.177/0.258/2.108 investigating (8587,1): 0.173/0.173/0.222/1.894 rules (1377,1): 0.172/0.172/0.113/1.023 testing (2814,1): 0.170/0.170/0.317/2.829 turing (18313,9): 0.165/1.483/0.086/0.647 \nMost similar words in vocab:\ndrugs: 0.633/6.093 medication: 0.624/5.416 antiretroviral: 0.616/7.012 medications: 0.616/5.210 drug: 0.598/5.980 ivig: 0.597/7.453 methadone: 0.595/7.136 prescription: 0.591/5.135 prophylactic: 0.590/6.740 prescriptions: 0.577/7.159 antipsychotic: 0.576/6.459 antiepileptic: 0.574/7.072 medicines: 0.571/4.950 vioxx: 0.566/8.687 patients: 0.563/5.081 regimens: 0.562/6.550 pharmaceutical: 0.556/5.181 treatments: 0.556/5.063 dosages: 0.548/5.913 novartis: 0.546/7.069 contraceptives: 0.544/6.609 pfizer: 0.541/7.086 praziquantel: 0.539/7.308 prescribing: 0.538/6.723 placebos: 0.536/7.752 glaxosmithkline: 0.534/7.189 thiopental: 0.534/7.812 sanofi: 0.533/6.378 analgesics: 0.531/5.888 bisphosphonates: 0.530/6.796 \n\nTopic 18 (3.00): 9.4%\nMost relevant words:\nprice (1535,17): 0.972/16.525/0.964/8.424 prices (4345,4): 0.863/3.451/0.817/7.995 gouge (81038,1): 0.806/0.806/0.228/4.789 priced (14909,1): 0.680/0.680/0.546/4.584 rose (1336,1): 0.292/0.292/0.180/1.319 high (104,3): 0.261/0.783/0.166/1.480 sharply (12378,2): 0.259/0.518/0.246/2.100 scott (1062,1): 0.226/0.226/0.109/0.700 martin (778,1): 0.218/0.218/0.055/0.351 low (612,1): 0.217/0.217/0.300/2.511 spencer (4173,1): 0.215/0.215/0.121/0.912 free (381,1): 0.195/0.195/0.148/1.321 born (106,1): 0.192/0.192/-0.041/-0.353 mcleod (15404,1): 0.191/0.191/0.090/0.675 sanders (7915,1): 0.188/0.188/0.092/0.636 shortages (16485,1): 0.187/0.187/0.266/2.194 line (181,1): 0.185/0.185/0.032/0.267 judith (8272,1): 0.184/0.184/0.040/0.318 piggy (34457,1): 0.182/0.182/0.146/1.998 grady (13903,1): 0.180/0.180/0.129/0.899 wendy (8642,1): 0.176/0.176/0.059/0.450 tracks (907,1): 0.176/0.176/0.009/0.076 round (273,1): 0.174/0.174/-0.036/-0.327 armstrong (4767,1): 0.170/0.170/0.061/0.516 crazy (5181,1): 0.168/0.168/0.092/0.737 democrat (3309,1): 0.168/0.168/0.082/0.752 democratic (731,1): 0.166/0.166/0.051/0.483 swindle (42243,1): 0.166/0.166/0.190/2.646 dramatic (3872,1): 0.164/0.164/0.090/0.797 good (462,1): 0.164/0.164/0.126/0.866 \nMost similar words in vocab:\nprice: 0.964/8.424 prices: 0.817/7.995 pricing: 0.600/5.249 priced: 0.546/4.584 inflation: 0.531/4.918 commodity: 0.495/4.129 discount: 0.487/4.215 exorbitant: 0.484/5.774 discounted: 0.475/3.166 cost: 0.475/3.754 resale: 0.461/5.277 purchases: 0.459/3.540 costs: 0.458/3.854 rates: 0.434/4.032 purchasing: 0.434/3.444 buyers: 0.430/3.662 commodities: 0.428/3.706 buying: 0.423/3.503 demand: 0.422/3.482 undervalued: 0.421/4.734 premium: 0.421/4.213 skyrocketed: 0.417/4.719 tariff: 0.415/3.354 asset's: 0.415/5.766 market: 0.414/3.383 volatility: 0.412/5.266 repayments: 0.412/4.423 cents: 0.412/3.801 valuations: 0.412/4.704 wages: 0.410/3.710 \n\nTopic 9 (3.00): 4.9%\nMost relevant words:\ndrugstores (120806,1): 0.296/0.296/0.437/6.923 generics (85144,1): 0.289/0.289/0.456/7.736 concocting (179559,1): 0.284/0.284/0.376/6.338 glaxo (101914,1): 0.253/0.253/0.402/7.797 insurers (32340,1): 0.165/0.165/0.479/5.346 medicaid (25609,1): 0.148/0.148/0.427/5.736 lawmakers (27291,2): 0.139/0.277/0.319/3.596 glaxosmithkline (55181,1): 0.129/0.129/0.505/6.804 prescriptions (34657,3): 0.123/0.370/0.539/6.682 icahn (83210,1): 0.123/0.123/0.370/7.235 inexpensively (98181,1): 0.114/0.114/0.316/5.237 expensive (4449,1): 0.110/0.110/0.346/3.304 ims (30708,1): 0.109/0.109/0.188/2.692 products (1127,1): 0.109/0.109/0.401/3.340 thwart (31967,1): 0.107/0.107/0.207/2.531 seeking (3864,1): 0.105/0.105/0.307/2.463 approved (2389,1): 0.102/0.102/0.331/2.880 strategy (2853,2): 0.101/0.203/0.266/2.351 conceivably (69563,1): 0.101/0.101/0.304/3.468 discounts (28868,1): 0.100/0.100/0.393/4.661 pharmaceuticals (16906,3): 0.098/0.294/0.454/4.192 pharmaceutical (8897,2): 0.097/0.194/0.509/4.743 trying (2359,2): 0.097/0.193/0.224/1.656 rebates (60162,1): 0.095/0.095/0.545/6.121 medicare (18132,1): 0.094/0.094/0.438/3.300 generic (7602,4): 0.094/0.374/0.248/2.177 mainstays (61442,1): 0.093/0.093/0.130/1.481 patents (8554,1): 0.093/0.093/0.290/2.728 option (3370,1): 0.093/0.093/0.278/2.453 food (807,1): 0.091/0.091/0.288/2.643 \nMost similar words in vocab:\nvioxx: 0.556/8.527 antiretroviral: 0.553/6.291 incentivize: 0.548/7.398 ivig: 0.547/6.832 rebates: 0.545/6.121 prescriptions: 0.539/6.682 methadone: 0.535/6.413 pfizer: 0.529/6.929 novartis: 0.526/6.809 thiopental: 0.524/7.670 adrs: 0.522/8.149 disincentive: 0.520/7.368 drugs: 0.520/4.999 sanofi: 0.517/6.182 reimburse: 0.515/5.680 dhhs: 0.512/6.817 placebos: 0.512/7.401 pharmacies: 0.510/6.048 multibillion: 0.509/7.891 pharmaceutical: 0.509/4.743 medication: 0.509/4.413 prescription: 0.509/4.417 refinancing: 0.508/5.568 contraceptives: 0.508/6.170 anticompetitive: 0.508/7.421 prophylactic: 0.506/5.780 glaxosmithkline: 0.505/6.804 wellpoint: 0.505/7.799 unapproved: 0.504/6.089 vha: 0.502/7.711 \n\nTopic 11 (3.00): 4.2%\nMost relevant words:\ndiseases (4439,6): 0.154/0.923/0.668/6.448 disease (1780,1): 0.150/0.150/0.645/5.942 infectious (12595,4): 0.149/0.596/0.599/5.643 infection (6037,2): 0.146/0.291/0.617/5.628 malaria (13222,1): 0.145/0.145/0.584/5.209 tuberculosis (11017,1): 0.143/0.143/0.625/5.447 hepatitis (19494,1): 0.136/0.136/0.620/5.260 multidrug (112412,1): 0.136/0.136/0.465/7.426 infected (8254,1): 0.132/0.132/0.432/4.145 hiv (6116,1): 0.132/0.132/0.612/5.966 resistant (8950,1): 0.125/0.125/0.350/3.457 serious (2375,2): 0.118/0.236/0.329/2.926 immune (8538,1): 0.117/0.117/0.439/3.992 aids (4693,2): 0.116/0.232/0.515/4.938 cancer (1871,2): 0.115/0.230/0.565/5.456 cause (1310,1): 0.110/0.110/0.373/2.898 pregnancy (7979,1): 0.109/0.109/0.467/4.318 hospitalized (18161,1): 0.108/0.108/0.415/2.878 patients (2468,9): 0.106/0.957/0.750/6.769 treatment (1460,5): 0.106/0.530/0.676/6.102 threatening (7927,2): 0.104/0.207/0.284/2.526 treating (10124,1): 0.103/0.103/0.612/4.941 treat (5814,3): 0.103/0.308/0.605/5.151 antibiotic (24098,2): 0.102/0.205/0.551/3.984 vulnerable (6874,1): 0.102/0.102/0.228/2.157 potentially (6379,1): 0.100/0.100/0.427/3.760 parasitic (18146,1): 0.099/0.099/0.305/2.540 problems (1188,1): 0.096/0.096/0.322/2.756 medically (28191,1): 0.095/0.095/0.402/5.837 patient (3777,2): 0.095/0.189/0.608/5.379 \nMost similar words in vocab:\npatients: 0.750/6.769 treatment: 0.676/6.102 diseases: 0.668/6.448 treatments: 0.652/5.944 infections: 0.649/6.001 diarrheal: 0.648/8.160 medication: 0.646/5.601 medications: 0.645/5.459 disease: 0.645/5.942 diabetes: 0.643/5.586 chronic: 0.642/5.664 ivig: 0.628/7.839 prophylactic: 0.626/7.160 treatable: 0.626/6.818 tuberculosis: 0.625/5.447 illnesses: 0.620/4.781 hepatitis: 0.620/5.260 infection: 0.617/5.628 asthma: 0.617/4.709 hiv: 0.612/5.966 treating: 0.612/4.941 antiretroviral: 0.611/6.949 contraindicated: 0.610/6.393 nosocomial: 0.609/8.112 patient: 0.608/5.379 treat: 0.605/5.151 prophylaxis: 0.604/7.073 ulcerative: 0.601/5.762 infectious: 0.599/5.643 regimens: 0.595/6.941 \n\nTopic 6 (3.00): 4.1%\nMost relevant words:\ndrugs (3528,10): 0.233/2.331/0.800/7.690 drug (1942,15): 0.155/2.328/0.783/7.835 pills (18688,1): 0.154/0.154/0.467/3.800 pharmaceutical (8897,2): 0.108/0.217/0.542/5.049 cholesterol (20400,1): 0.103/0.103/0.353/2.836 efficacy (14568,1): 0.100/0.100/0.476/3.985 pharmaceuticals (16906,3): 0.097/0.292/0.474/4.377 antibiotic (24098,2): 0.095/0.191/0.550/3.972 dangerous (3584,2): 0.092/0.185/0.262/2.419 effects (1380,2): 0.091/0.183/0.348/3.186 therapies (21018,2): 0.088/0.176/0.568/4.716 therapeutics (36356,1): 0.086/0.086/0.492/6.139 investigating (8587,1): 0.086/0.086/0.245/2.094 generic (7602,4): 0.084/0.334/0.258/2.259 pregnancy (7979,1): 0.083/0.083/0.445/4.108 testing (2814,1): 0.083/0.083/0.337/3.013 use (176,7): 0.081/0.570/0.299/2.082 aids (4693,2): 0.076/0.151/0.476/4.567 administration (991,1): 0.075/0.075/0.252/2.061 controlled (2257,3): 0.074/0.222/0.220/2.036 hiv (6116,1): 0.074/0.074/0.559/5.445 glaxosmithkline (55181,1): 0.072/0.072/0.477/6.416 treatments (9357,2): 0.071/0.143/0.629/5.735 used (82,4): 0.071/0.283/0.141/1.066 food (807,1): 0.071/0.071/0.281/2.579 samples (4875,1): 0.070/0.070/0.181/1.851 immune (8538,1): 0.069/0.069/0.387/3.522 combination (2674,1): 0.068/0.068/0.254/1.899 resistant (8950,1): 0.067/0.067/0.293/2.890 prescriptions (34657,3): 0.065/0.196/0.503/6.242 \nMost similar words in vocab:\ndrugs: 0.800/7.690 drug: 0.783/7.835 medication: 0.720/6.243 medications: 0.714/6.045 antiretroviral: 0.662/7.530 prescription: 0.642/5.578 patients: 0.636/5.747 treatments: 0.629/5.735 antipsychotic: 0.624/6.998 ivig: 0.620/7.742 antiepileptic: 0.618/7.612 treatment: 0.618/5.573 methadone: 0.616/7.395 medicines: 0.616/5.346 therapeutic: 0.601/5.239 prophylactic: 0.598/6.837 analgesics: 0.595/6.602 contraindicated: 0.593/6.210 antibiotics: 0.590/4.604 antiviral: 0.588/6.894 regimens: 0.582/6.788 anticoagulant: 0.578/6.354 diabetes: 0.576/5.009 warfarin: 0.575/6.142 anticancer: 0.574/6.431 opiates: 0.574/6.616 nsaids: 0.571/6.310 therapies: 0.568/4.716 chemotherapy: 0.568/4.382 diazepam: 0.567/6.003 \n\nTopic 12 (3.00): 3.8%\nMost relevant words:\ndrug (1942,15): 0.679/10.192/0.972/9.727 drugs (3528,10): 0.423/4.232/0.905/8.702 pills (18688,1): 0.152/0.152/0.516/4.198 investigating (8587,1): 0.063/0.063/0.259/2.208 dangerous (3584,2): 0.062/0.125/0.265/2.443 cholesterol (20400,1): 0.062/0.062/0.341/2.741 controlled (2257,3): 0.056/0.169/0.235/2.174 side (287,2): 0.054/0.108/0.009/0.067 generic (7602,4): 0.051/0.205/0.249/2.187 crazy (5181,1): 0.050/0.050/0.106/0.844 story (367,1): 0.046/0.046/0.008/0.061 use (176,7): 0.045/0.315/0.274/1.904 administration (991,1): 0.045/0.045/0.240/1.965 force (351,1): 0.044/0.044/0.017/0.141 effects (1380,2): 0.043/0.086/0.311/2.849 rima (42963,1): 0.042/0.042/-0.000/-0.000 known (97,1): 0.041/0.041/0.001/0.003 used (82,4): 0.040/0.160/0.121/0.913 talked (11444,1): 0.040/0.040/0.085/0.530 two (37,3): 0.038/0.113/-0.046/-0.349 efficacy (14568,1): 0.037/0.037/0.406/3.397 samples (4875,1): 0.037/0.037/0.158/1.616 discourage (22551,1): 0.036/0.036/0.186/1.157 wild (1617,1): 0.036/0.036/0.011/0.096 testing (2814,1): 0.036/0.036/0.290/2.588 food (807,1): 0.036/0.036/0.252/2.312 first (26,3): 0.035/0.105/-0.066/-0.438 like (189,2): 0.035/0.070/0.077/0.513 possibly (2456,1): 0.035/0.035/0.123/0.947 tracks (907,1): 0.035/0.035/-0.027/-0.227 \nMost similar words in vocab:\ndrug: 0.972/9.727 drugs: 0.905/8.702 cocaine: 0.672/7.006 heroin: 0.645/6.197 prescription: 0.643/5.584 medication: 0.618/5.364 steroids: 0.613/4.132 medications: 0.604/5.113 opiates: 0.593/6.831 marijuana: 0.590/5.610 cannabis: 0.588/5.337 antipsychotic: 0.580/6.505 narcotics: 0.572/5.148 morphine: 0.569/4.495 antiretroviral: 0.567/6.448 methadone: 0.564/6.762 illicit: 0.555/4.471 addiction: 0.555/5.214 painkillers: 0.554/6.807 stimulants: 0.550/6.046 ketamine: 0.550/6.448 psychotropic: 0.548/6.521 alcohol: 0.548/5.636 methamphetamine: 0.546/6.991 diazepam: 0.544/5.751 barbiturates: 0.539/6.215 opioids: 0.537/6.150 antiepileptic: 0.530/6.532 anticancer: 0.530/5.931 antiviral: 0.525/6.153 \n\nTopic 5 (3.00): 2.9%\nMost relevant words:\ntoxoplasmosis (125001,4): 0.979/3.917/0.876/12.897 parasite (16531,1): 0.326/0.326/0.510/3.957 parasitic (18146,1): 0.316/0.316/0.503/4.192 infection (6037,2): 0.268/0.536/0.738/6.729 infected (8254,1): 0.252/0.252/0.550/5.281 immune (8538,1): 0.175/0.175/0.536/4.883 malaria (13222,1): 0.170/0.170/0.657/5.863 rare (2251,1): 0.166/0.166/0.317/3.018 disease (1780,1): 0.163/0.163/0.707/6.516 hepatitis (19494,1): 0.153/0.153/0.691/5.866 multidrug (112412,1): 0.144/0.144/0.499/7.972 diseases (4439,6): 0.142/0.850/0.710/6.856 infectious (12595,4): 0.141/0.562/0.645/6.075 tuberculosis (11017,1): 0.113/0.113/0.655/5.703 cause (1310,1): 0.112/0.112/0.438/3.402 isolated (4860,1): 0.108/0.108/0.292/2.640 rarely (4300,1): 0.104/0.104/0.307/2.511 caused (1150,1): 0.097/0.097/0.347/2.997 resistant (8950,1): 0.093/0.093/0.370/3.655 rima (42963,1): 0.092/0.092/0.057/0.812 possibly (2456,1): 0.090/0.090/0.250/1.922 wild (1617,1): 0.078/0.078/0.098/0.887 factor (2518,1): 0.070/0.070/0.255/2.247 vulnerable (6874,1): 0.070/0.070/0.239/2.262 effects (1380,2): 0.069/0.138/0.365/3.341 pregnancy (7979,1): 0.068/0.068/0.469/4.336 remains (1135,1): 0.067/0.067/0.111/0.852 samples (4875,1): 0.061/0.061/0.210/2.149 antibiotic (24098,2): 0.061/0.122/0.547/3.954 hiv (6116,1): 0.060/0.060/0.581/5.663 \nMost similar words in vocab:\ntoxoplasmosis: 0.876/12.897 infections: 0.739/6.832 infection: 0.738/6.729 diseases: 0.710/6.856 disease: 0.707/6.516 falciparum: 0.703/7.789 ulcerative: 0.699/6.706 hepatitis: 0.691/5.866 diarrheal: 0.687/8.649 endocarditis: 0.682/7.041 histolytica: 0.668/7.876 bacteremia: 0.662/8.033 malaria: 0.657/5.863 tuberculosis: 0.655/5.703 chlamydia: 0.650/7.361 treatable: 0.650/7.081 crohn's: 0.649/6.573 infectious: 0.645/6.075 pneumoniae: 0.642/6.720 blastocystis: 0.642/8.446 chronic: 0.641/5.658 pylori: 0.641/6.689 colitis: 0.641/6.796 sepsis: 0.640/6.942 malignancies: 0.632/6.518 nosocomial: 0.632/8.418 staphylococcal: 0.632/8.802 mycoplasma: 0.628/6.847 meningitis: 0.624/7.500 malarial: 0.623/7.727 \n\nTopic 17 (3.00): 2.1%\nMost relevant words:\nmultidrug (112412,1): 0.077/0.077/0.472/7.541 diseases (4439,6): 0.070/0.420/0.658/6.351 hepatitis (19494,1): 0.070/0.070/0.622/5.280 hiv (6116,1): 0.069/0.069/0.617/6.008 cancer (1871,2): 0.068/0.137/0.582/5.626 disease (1780,1): 0.067/0.067/0.632/5.825 infectious (12595,4): 0.065/0.261/0.585/5.509 malaria (13222,1): 0.064/0.064/0.571/5.091 immune (8538,1): 0.063/0.063/0.446/4.062 tuberculosis (11017,1): 0.062/0.062/0.609/5.303 antibiotic (24098,2): 0.061/0.123/0.576/4.165 aids (4693,2): 0.060/0.120/0.517/4.964 resistant (8950,1): 0.059/0.059/0.344/3.400 infection (6037,2): 0.058/0.115/0.591/5.390 therapeutics (36356,1): 0.057/0.057/0.511/6.369 pregnancy (7979,1): 0.051/0.051/0.460/4.254 efficacy (14568,1): 0.051/0.051/0.470/3.939 cholesterol (20400,1): 0.051/0.051/0.343/2.754 infected (8254,1): 0.050/0.050/0.402/3.862 therapies (21018,2): 0.050/0.100/0.576/4.782 effects (1380,2): 0.049/0.099/0.350/3.206 treat (5814,3): 0.049/0.147/0.600/5.106 treating (10124,1): 0.049/0.049/0.605/4.885 treatments (9357,2): 0.049/0.097/0.657/5.985 parasitic (18146,1): 0.046/0.046/0.296/2.471 cause (1310,1): 0.046/0.046/0.349/2.710 treatment (1460,5): 0.045/0.227/0.659/5.946 hospitalized (18161,1): 0.044/0.044/0.384/2.666 isolated (4860,1): 0.044/0.044/0.215/1.941 parasite (16531,1): 0.043/0.043/0.274/2.128 \nMost similar words in vocab:\npatients: 0.716/6.468 medications: 0.678/5.735 medication: 0.671/5.824 treatment: 0.659/5.946 diseases: 0.658/6.351 treatments: 0.657/5.985 diabetes: 0.651/5.659 ivig: 0.637/7.953 diarrheal: 0.635/7.997 chronic: 0.633/5.583 antiretroviral: 0.632/7.194 drugs: 0.632/6.080 disease: 0.632/5.825 infections: 0.626/5.790 prophylactic: 0.625/7.140 hepatitis: 0.622/5.280 hiv: 0.617/6.008 asthma: 0.613/4.677 antibiotics: 0.613/4.783 contraindicated: 0.610/6.389 tuberculosis: 0.609/5.303 antiepileptic: 0.606/7.465 treating: 0.605/4.885 treatable: 0.604/6.582 vaccine: 0.603/5.610 chemotherapy: 0.602/4.648 treat: 0.600/5.106 ulcerative: 0.596/5.722 regimens: 0.596/6.943 illnesses: 0.595/4.586 \n\nTopic 0 (0.00): 0.3%\nMost relevant words:\nborn (106,1): 0.017/0.017/0.000/0.000 round (273,1): 0.015/0.015/0.000/0.000 first (26,3): 0.012/0.037/0.000/0.000 two (37,3): 0.012/0.036/0.000/0.000 side (287,2): 0.011/0.023/0.000/0.000 world (56,1): 0.011/0.011/0.000/0.000 last (237,2): 0.011/0.022/0.000/0.000 university (61,2): 0.010/0.021/0.000/0.000 story (367,1): 0.010/0.010/0.000/0.000 tracks (907,1): 0.010/0.010/0.000/0.000 rima (42963,1): 0.009/0.009/0.000/0.000 known (97,1): 0.009/0.009/0.000/0.000 martin (778,1): 0.009/0.009/0.000/0.000 district (167,1): 0.009/0.009/0.000/0.000 force (351,1): 0.009/0.009/0.000/0.000 back (193,1): 0.009/0.009/0.000/0.000 held (230,1): 0.009/0.009/0.000/0.000 line (181,1): 0.009/0.009/0.000/0.000 wrote (536,1): 0.008/0.008/0.000/0.000 list (186,1): 0.008/0.008/0.000/0.000 former (185,1): 0.008/0.008/0.000/0.000 school (49,1): 0.008/0.008/0.000/0.000 old (204,5): 0.008/0.041/0.000/0.000 judith (8272,1): 0.008/0.008/0.000/0.000 called (155,3): 0.008/0.024/0.000/0.000 works (341,1): 0.008/0.008/0.000/0.000 mount (1283,3): 0.008/0.023/0.000/0.000 division (236,1): 0.007/0.007/0.000/0.000 wild (1617,1): 0.007/0.007/0.000/0.000 remained (770,1): 0.007/0.007/0.000/0.000 \nTopic 7 (3.00): 0.2%\nMost relevant words:\npotentially (6379,1): 0.006/0.006/0.446/3.927 effects (1380,2): 0.005/0.011/0.364/3.334 dangerous (3584,2): 0.005/0.010/0.269/2.485 multidrug (112412,1): 0.005/0.005/0.447/7.136 resistant (8950,1): 0.005/0.005/0.329/3.246 antibiotic (24098,2): 0.005/0.009/0.546/3.948 use (176,7): 0.005/0.033/0.319/2.216 serious (2375,2): 0.005/0.009/0.305/2.716 pills (18688,1): 0.005/0.005/0.399/3.249 threatening (7927,2): 0.004/0.009/0.272/2.422 cause (1310,1): 0.004/0.004/0.351/2.728 efficacy (14568,1): 0.004/0.004/0.453/3.794 problems (1188,1): 0.004/0.004/0.311/2.660 cholesterol (20400,1): 0.004/0.004/0.320/2.575 pregnancy (7979,1): 0.004/0.004/0.438/4.050 immune (8538,1): 0.004/0.004/0.401/3.652 combination (2674,1): 0.004/0.004/0.272/2.033 testing (2814,1): 0.004/0.004/0.330/2.949 used (82,4): 0.004/0.016/0.153/1.154 vulnerable (6874,1): 0.004/0.004/0.204/1.931 generically (48146,1): 0.004/0.004/0.209/2.389 develop (1982,1): 0.004/0.004/0.296/2.438 caused (1150,1): 0.004/0.004/0.268/2.313 drugs (3528,10): 0.004/0.038/0.681/6.551 investigating (8587,1): 0.004/0.004/0.227/1.939 generic (7602,4): 0.004/0.015/0.243/2.127 controlled (2257,3): 0.004/0.011/0.216/2.004 prescriptions (34657,3): 0.004/0.011/0.510/6.328 pharmaceutical (8897,2): 0.004/0.007/0.497/4.630 possibly (2456,1): 0.004/0.004/0.163/1.249 \nMost similar words in vocab:\ndrugs: 0.681/6.551 medications: 0.675/5.714 medication: 0.673/5.842 drug: 0.647/6.474 ivig: 0.646/8.075 antiretroviral: 0.642/7.310 patients: 0.638/5.765 antiepileptic: 0.632/7.778 treatments: 0.618/5.629 prophylactic: 0.615/7.030 treatment: 0.606/5.466 diarrheal: 0.603/7.597 antipsychotic: 0.593/6.653 contraindicated: 0.589/6.168 methadone: 0.587/7.045 antibiotics: 0.587/4.581 regimens: 0.585/6.818 medicines: 0.580/5.031 analgesics: 0.573/6.360 prescription: 0.573/4.975 treatable: 0.572/6.229 anticoagulant: 0.571/6.285 antiviral: 0.568/6.664 warfarin: 0.568/6.064 dosages: 0.567/6.124 praziquantel: 0.566/7.680 diabetes: 0.564/4.904 nsaids: 0.564/6.223 prophylaxis: 0.562/6.582 chronic: 0.560/4.941 \n\nTopic 8 (3.00): 0.0%\nMost relevant words:\npharmaceuticals (16906,3): 0.000/0.001/0.474/4.374 pharmaceutical (8897,2): 0.000/0.000/0.528/4.914 efficacy (14568,1): 0.000/0.000/0.468/3.924 testing (2814,1): 0.000/0.000/0.350/3.125 glaxosmithkline (55181,1): 0.000/0.000/0.494/6.654 therapeutics (36356,1): 0.000/0.000/0.492/6.134 potentially (6379,1): 0.000/0.000/0.413/3.633 prescriptions (34657,3): 0.000/0.001/0.522/6.479 generic (7602,4): 0.000/0.001/0.254/2.229 use (176,7): 0.000/0.001/0.298/2.075 effects (1380,2): 0.000/0.000/0.332/3.039 cholesterol (20400,1): 0.000/0.000/0.318/2.552 generically (48146,1): 0.000/0.000/0.208/2.377 food (807,1): 0.000/0.000/0.286/2.630 samples (4875,1): 0.000/0.000/0.186/1.907 pills (18688,1): 0.000/0.000/0.376/3.064 medicaid (25609,1): 0.000/0.000/0.389/5.230 problems (1188,1): 0.000/0.000/0.298/2.545 certain (1095,4): 0.000/0.001/0.315/2.167 combination (2674,1): 0.000/0.000/0.261/1.951 antibiotic (24098,2): 0.000/0.000/0.509/3.676 therapies (21018,2): 0.000/0.000/0.543/4.503 used (82,4): 0.000/0.001/0.141/1.066 treatments (9357,2): 0.000/0.000/0.628/5.723 dangerous (3584,2): 0.000/0.000/0.233/2.149 resistant (8950,1): 0.000/0.000/0.296/2.929 laboratories (7537,1): 0.000/0.000/0.298/2.780 controlled (2257,3): 0.000/0.000/0.211/1.954 alternative (1705,1): 0.000/0.000/0.217/1.821 medicare (18132,1): 0.000/0.000/0.417/3.144 \nMost similar words in vocab:\nmedication: 0.672/5.826 medications: 0.668/5.657 patients: 0.658/5.944 drugs: 0.647/6.220 ivig: 0.643/8.027 antiretroviral: 0.637/7.244 treatments: 0.628/5.723 treatment: 0.620/5.594 antiepileptic: 0.617/7.600 drug: 0.613/6.128 prophylactic: 0.612/6.998 antipsychotic: 0.592/6.640 regimens: 0.592/6.900 methadone: 0.585/7.022 medicines: 0.584/5.068 contraindicated: 0.583/6.106 diarrheal: 0.578/7.284 dosages: 0.578/6.235 prescription: 0.574/4.981 praziquantel: 0.564/7.646 analgesics: 0.562/6.241 bevacizumab: 0.562/7.928 diabetes: 0.561/4.875 infliximab: 0.558/7.226 anticoagulant: 0.556/6.121 antibiotics: 0.556/4.339 bisphosphonates: 0.556/7.121 therapeutic: 0.555/4.841 monotherapy: 0.555/7.367 warfarin: 0.555/5.926 \n\nTopic 13 (3.00): 0.0%\nMost relevant words:\ndrugs (3528,10): 0.000/0.003/0.768/7.385 pills (18688,1): 0.000/0.000/0.436/3.546 drug (1942,15): 0.000/0.003/0.747/7.470 cholesterol (20400,1): 0.000/0.000/0.357/2.870 antibiotic (24098,2): 0.000/0.000/0.565/4.086 efficacy (14568,1): 0.000/0.000/0.481/4.032 effects (1380,2): 0.000/0.000/0.358/3.278 pharmaceutical (8897,2): 0.000/0.000/0.531/4.941 pharmaceuticals (16906,3): 0.000/0.001/0.472/4.352 therapeutics (36356,1): 0.000/0.000/0.497/6.192 testing (2814,1): 0.000/0.000/0.343/3.064 multidrug (112412,1): 0.000/0.000/0.441/7.032 resistant (8950,1): 0.000/0.000/0.316/3.123 generic (7602,4): 0.000/0.001/0.258/2.263 pregnancy (7979,1): 0.000/0.000/0.444/4.104 hiv (6116,1): 0.000/0.000/0.570/5.548 immune (8538,1): 0.000/0.000/0.405/3.689 dangerous (3584,2): 0.000/0.000/0.248/2.288 therapies (21018,2): 0.000/0.000/0.558/4.633 glaxosmithkline (55181,1): 0.000/0.000/0.483/6.502 use (176,7): 0.000/0.001/0.293/2.039 investigating (8587,1): 0.000/0.000/0.233/1.992 potentially (6379,1): 0.000/0.000/0.404/3.556 aids (4693,2): 0.000/0.000/0.476/4.569 controlled (2257,3): 0.000/0.000/0.221/2.048 treatments (9357,2): 0.000/0.000/0.634/5.781 combination (2674,1): 0.000/0.000/0.266/1.988 prescriptions (34657,3): 0.000/0.000/0.512/6.353 samples (4875,1): 0.000/0.000/0.183/1.870 used (82,4): 0.000/0.001/0.140/1.056 \nMost similar words in vocab:\ndrugs: 0.768/7.385 drug: 0.747/7.470 medications: 0.708/5.996 medication: 0.706/6.122 antiretroviral: 0.668/7.605 ivig: 0.650/8.122 patients: 0.646/5.835 treatments: 0.634/5.781 antiepileptic: 0.632/7.783 antipsychotic: 0.626/7.023 prescription: 0.623/5.407 treatment: 0.618/5.578 prophylactic: 0.611/6.982 medicines: 0.607/5.269 methadone: 0.604/7.251 antibiotics: 0.603/4.710 contraindicated: 0.602/6.309 antiviral: 0.599/7.027 regimens: 0.593/6.911 anticoagulant: 0.591/6.506 analgesics: 0.591/6.557 diarrheal: 0.588/7.412 therapeutic: 0.586/5.106 anticancer: 0.585/6.549 warfarin: 0.583/6.222 dosages: 0.580/6.265 nsaids: 0.579/6.392 diabetes: 0.579/5.031 opiates: 0.575/6.627 isoniazid: 0.573/7.559 \n\nTopic 16 (3.00): 0.0%\nMost relevant words:\nconcocting (179559,1): 0.000/0.000/0.370/6.239 drugstores (120806,1): 0.000/0.000/0.412/6.528 glaxo (101914,1): 0.000/0.000/0.388/7.513 prescriptions (34657,3): 0.000/0.001/0.560/6.947 generics (85144,1): 0.000/0.000/0.421/7.146 insurers (32340,1): 0.000/0.000/0.449/5.019 glaxosmithkline (55181,1): 0.000/0.000/0.496/6.677 medicaid (25609,1): 0.000/0.000/0.404/5.433 conceivably (69563,1): 0.000/0.000/0.308/3.510 food (807,1): 0.000/0.000/0.302/2.777 products (1127,1): 0.000/0.000/0.394/3.286 generic (7602,4): 0.000/0.001/0.257/2.255 generically (48146,1): 0.000/0.000/0.215/2.460 inexpensively (98181,1): 0.000/0.000/0.307/5.086 thwart (31967,1): 0.000/0.000/0.199/2.440 expensive (4449,1): 0.000/0.000/0.333/3.181 discounts (28868,1): 0.000/0.000/0.389/4.620 pharmaceuticals (16906,3): 0.000/0.000/0.447/4.121 supply (1905,1): 0.000/0.000/0.288/2.534 using (379,1): 0.000/0.000/0.209/1.514 pharmaceutical (8897,2): 0.000/0.000/0.501/4.668 strategy (2853,2): 0.000/0.000/0.252/2.225 alternative (1705,1): 0.000/0.000/0.225/1.893 ims (30708,1): 0.000/0.000/0.173/2.481 use (176,7): 0.000/0.001/0.283/1.964 lawmakers (27291,2): 0.000/0.000/0.278/3.126 delivering (9451,1): 0.000/0.000/0.227/2.004 controlled (2257,3): 0.000/0.000/0.213/1.969 needed (1661,1): 0.000/0.000/0.322/2.265 bottle (7312,1): 0.000/0.000/0.142/1.387 \nMost similar words in vocab:\ndrugs: 0.586/5.637 antiretroviral: 0.578/6.582 ivig: 0.574/7.175 methadone: 0.568/6.820 prescriptions: 0.560/6.947 drug: 0.557/5.571 vioxx: 0.550/8.443 medication: 0.547/4.745 prescription: 0.541/4.702 thiopental: 0.536/7.852 medications: 0.534/4.515 antiepileptic: 0.533/6.567 antipsychotic: 0.526/5.896 placebos: 0.525/7.593 adrs: 0.524/8.190 prophylactic: 0.524/5.990 medicines: 0.523/4.540 dosages: 0.523/5.642 rebates: 0.518/5.815 contraceptives: 0.517/6.281 incentivize: 0.513/6.918 unapproved: 0.511/6.177 pfizer: 0.511/6.693 regimens: 0.510/5.945 tamiflu: 0.510/7.350 praziquantel: 0.509/6.898 sanofi: 0.507/6.069 pharmacies: 0.505/5.988 novartis: 0.504/6.526 bevacizumab: 0.504/7.106 \n\nTopic 10 (3.00): 0.0%\nMost relevant words:\nmedicaid (25609,1): 0.000/0.000/0.436/5.857 drugstores (120806,1): 0.000/0.000/0.392/6.216 insurers (32340,1): 0.000/0.000/0.452/5.051 discounts (28868,1): 0.000/0.000/0.405/4.811 severance (36986,1): 0.000/0.000/0.311/4.119 medicare (18132,1): 0.000/0.000/0.450/3.394 rebates (60162,1): 0.000/0.000/0.549/6.166 products (1127,1): 0.000/0.000/0.388/3.235 prescriptions (34657,3): 0.000/0.000/0.520/6.447 generics (85144,1): 0.000/0.000/0.390/6.612 expensive (4449,1): 0.000/0.000/0.324/3.094 food (807,1): 0.000/0.000/0.282/2.595 supply (1905,1): 0.000/0.000/0.281/2.472 tablet (10396,2): 0.000/0.000/0.141/1.400 distribution (1335,3): 0.000/0.000/0.203/1.878 seeking (3864,1): 0.000/0.000/0.273/2.192 option (3370,1): 0.000/0.000/0.261/2.303 federal (626,2): 0.000/0.000/0.216/1.925 access (935,1): 0.000/0.000/0.213/1.807 glaxosmithkline (55181,1): 0.000/0.000/0.468/6.302 required (967,2): 0.000/0.000/0.270/1.948 lawmakers (27291,2): 0.000/0.000/0.268/3.015 approved (2389,1): 0.000/0.000/0.300/2.605 needed (1661,1): 0.000/0.000/0.306/2.156 delivering (9451,1): 0.000/0.000/0.213/1.880 certain (1095,4): 0.000/0.001/0.291/2.007 claim (2099,1): 0.000/0.000/0.172/1.385 pay (1653,2): 0.000/0.000/0.401/3.455 potentially (6379,1): 0.000/0.000/0.376/3.310 concocting (179559,1): 0.000/0.000/0.295/4.974 \nMost similar words in vocab:\nivig: 0.570/7.115 antiretroviral: 0.566/6.438 rebates: 0.549/6.166 vioxx: 0.546/8.383 adrs: 0.545/8.515 medication: 0.542/4.697 drugs: 0.541/5.207 methadone: 0.536/6.429 medications: 0.530/4.487 patients: 0.523/4.719 thiopental: 0.520/7.613 prescriptions: 0.520/6.447 prescription: 0.520/4.512 drug: 0.516/5.158 uninsured: 0.515/6.060 prophylactic: 0.513/5.870 antiepileptic: 0.510/6.275 dosages: 0.508/5.486 praziquantel: 0.506/6.855 incentivize: 0.505/6.814 reimburse: 0.504/5.555 diarrheal: 0.501/6.313 antipsychotic: 0.501/5.619 contraceptives: 0.499/6.069 bevacizumab: 0.498/7.025 costs: 0.497/4.188 medicines: 0.497/4.309 dhhs: 0.495/6.596 bisphosphonates: 0.495/6.349 refinancing: 0.494/5.409 \n\nTopic 14 (3.00): 0.0%\nMost relevant words:\nmultidrug (112412,1): 0.000/0.000/0.478/7.623 hiv (6116,1): 0.000/0.000/0.625/6.090 immune (8538,1): 0.000/0.000/0.459/4.181 hepatitis (19494,1): 0.000/0.000/0.622/5.278 diseases (4439,6): 0.000/0.001/0.655/6.323 antibiotic (24098,2): 0.000/0.000/0.588/4.249 disease (1780,1): 0.000/0.000/0.630/5.805 aids (4693,2): 0.000/0.000/0.523/5.017 effects (1380,2): 0.000/0.000/0.374/3.428 pregnancy (7979,1): 0.000/0.000/0.480/4.438 resistant (8950,1): 0.000/0.000/0.348/3.437 infection (6037,2): 0.000/0.000/0.598/5.454 malaria (13222,1): 0.000/0.000/0.565/5.041 infectious (12595,4): 0.000/0.001/0.577/5.429 tuberculosis (11017,1): 0.000/0.000/0.600/5.225 cholesterol (20400,1): 0.000/0.000/0.358/2.879 cancer (1871,2): 0.000/0.000/0.563/5.438 efficacy (14568,1): 0.000/0.000/0.482/4.037 infected (8254,1): 0.000/0.000/0.412/3.952 treatments (9357,2): 0.000/0.000/0.669/6.094 treating (10124,1): 0.000/0.000/0.615/4.964 treat (5814,3): 0.000/0.001/0.608/5.174 therapeutics (36356,1): 0.000/0.000/0.503/6.266 medically (28191,1): 0.000/0.000/0.407/5.917 therapies (21018,2): 0.000/0.000/0.579/4.803 cause (1310,1): 0.000/0.000/0.357/2.774 treatment (1460,5): 0.000/0.001/0.665/6.004 parasitic (18146,1): 0.000/0.000/0.298/2.483 isolated (4860,1): 0.000/0.000/0.222/2.005 potentially (6379,1): 0.000/0.000/0.419/3.691 \nMost similar words in vocab:\npatients: 0.718/6.487 medications: 0.702/5.937 medication: 0.697/6.045 drugs: 0.688/6.622 treatments: 0.669/6.094 treatment: 0.665/6.004 antiretroviral: 0.659/7.502 diseases: 0.655/6.323 ivig: 0.654/8.169 drug: 0.651/6.507 diabetes: 0.645/5.608 diarrheal: 0.645/8.128 chronic: 0.641/5.654 prophylactic: 0.640/7.317 antiepileptic: 0.639/7.872 contraindicated: 0.632/6.624 disease: 0.630/5.805 infections: 0.629/5.813 hiv: 0.625/6.090 antibiotics: 0.625/4.875 hepatitis: 0.622/5.278 regimens: 0.615/7.174 treating: 0.615/4.964 treatable: 0.615/6.700 asthma: 0.614/4.687 ulcerative: 0.609/5.847 prophylaxis: 0.609/7.131 treat: 0.608/5.174 chemotherapy: 0.603/4.655 anticoagulant: 0.601/6.617 \n\nTopic 19 (3.00): 0.0%\nMost relevant words:\nmultidrug (112412,1): 0.000/0.000/0.459/7.331 therapeutics (36356,1): 0.000/0.000/0.513/6.392 testing (2814,1): 0.000/0.000/0.359/3.203 antibiotic (24098,2): 0.000/0.000/0.556/4.017 efficacy (14568,1): 0.000/0.000/0.467/3.910 effects (1380,2): 0.000/0.000/0.348/3.184 resistant (8950,1): 0.000/0.000/0.323/3.189 cholesterol (20400,1): 0.000/0.000/0.333/2.673 glaxosmithkline (55181,1): 0.000/0.000/0.491/6.609 potentially (6379,1): 0.000/0.000/0.416/3.658 immune (8538,1): 0.000/0.000/0.409/3.727 pharmaceutical (8897,2): 0.000/0.000/0.512/4.773 combination (2674,1): 0.000/0.000/0.277/2.072 pharmaceuticals (16906,3): 0.000/0.000/0.454/4.185 problems (1188,1): 0.000/0.000/0.307/2.626 hiv (6116,1): 0.000/0.000/0.566/5.509 generic (7602,4): 0.000/0.001/0.249/2.187 prescriptions (34657,3): 0.000/0.000/0.517/6.413 develop (1982,1): 0.000/0.000/0.296/2.440 pregnancy (7979,1): 0.000/0.000/0.435/4.019 controlled (2257,3): 0.000/0.000/0.222/2.054 cause (1310,1): 0.000/0.000/0.331/2.574 hepatitis (19494,1): 0.000/0.000/0.554/4.707 generically (48146,1): 0.000/0.000/0.206/2.354 use (176,7): 0.000/0.001/0.285/1.982 pills (18688,1): 0.000/0.000/0.376/3.060 dangerous (3584,2): 0.000/0.000/0.238/2.192 aids (4693,2): 0.000/0.000/0.471/4.519 serious (2375,2): 0.000/0.000/0.279/2.482 therapies (21018,2): 0.000/0.000/0.543/4.503 \nMost similar words in vocab:\nmedication: 0.677/5.870 medications: 0.674/5.702 patients: 0.674/6.082 drugs: 0.664/6.387 ivig: 0.658/8.225 antiretroviral: 0.657/7.471 drug: 0.640/6.405 antiepileptic: 0.631/7.775 treatments: 0.627/5.712 treatment: 0.625/5.637 prophylactic: 0.624/7.131 diarrheal: 0.622/7.836 regimens: 0.607/7.078 contraindicated: 0.600/6.287 antipsychotic: 0.599/6.726 antibiotics: 0.590/4.602 methadone: 0.590/7.074 diabetes: 0.588/5.111 antiviral: 0.586/6.871 anticoagulant: 0.585/6.434 praziquantel: 0.584/7.912 prophylaxis: 0.583/6.828 dosages: 0.583/6.288 isoniazid: 0.582/7.674 infliximab: 0.582/7.534 chronic: 0.582/5.129 analgesics: 0.578/6.417 treatable: 0.577/6.287 medicines: 0.577/5.001 vaccine: 0.575/5.357 \n\nEm:\n[  1.68707065e+00   1.49705235e+02   6.40038427e+01   1.36831464e+02\n   4.71294958e-02   1.89773055e+01   2.66768493e+01   1.28436596e+00\n   5.14677457e-02   3.21261952e+01   4.89054436e-02   2.75066960e+01\n   2.50881058e+01   5.05214065e-02   4.82526626e-02   9.47900199e+01\n   5.00814217e-02   1.35197082e+01   6.14589341e+01   4.78493307e-02]\n\n\nTopic magnitudes:\n[ 0.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.  3.\n  3.  3.]\n\nTopic 1 (3.00): 22.9%\nMost relevant words:\ncompany (151,8): 0.955/7.642/0.610/4.917 acquired (1447,5): 0.866/4.331/0.457/3.949 million (357,6): 0.832/4.995/0.581/5.671 sold (614,2): 0.815/1.630/0.456/3.768 companies (939,4): 0.809/3.237/0.584/4.621 bank (640,1): 0.782/0.782/0.329/2.989 investors (5976,2): 0.761/1.521/0.564/5.015 fund (2014,3): 0.746/2.238/0.522/4.593 acquisition (4356,1): 0.732/0.732/0.451/3.926 manager (830,2): 0.725/1.451/0.210/1.838 filed (3883,2): 0.721/1.443/0.371/3.542 hedge (14848,3): 0.715/2.145/0.367/3.231 announced (568,1): 0.703/0.703/0.304/2.264 business (354,2): 0.688/1.376/0.375/2.847 founder (1298,1): 0.685/0.685/0.234/1.875 invest (11944,1): 0.677/0.677/0.522/3.685 capital (735,1): 0.670/0.670/0.273/2.561 private (597,1): 0.651/0.651/0.315/2.704 executive (876,1): 0.646/0.646/0.239/1.886 stock (1878,2): 0.629/1.259/0.410/3.928 directors (2573,1): 0.607/0.607/0.274/2.189 president (211,1): 0.599/0.599/0.149/1.343 june (140,1): 0.586/0.586/0.117/0.818 us (220,1): 0.586/0.586/0.260/2.379 august (149,3): 0.581/1.742/0.097/0.736 financing (8017,1): 0.568/0.568/0.472/3.886 october (131,1): 0.565/0.565/0.097/0.732 april (148,1): 0.565/0.565/0.110/0.784 marketing (2693,1): 0.564/0.564/0.367/3.309 profit (2747,1): 0.560/0.560/0.463/3.812 \nMost similar words in vocab:\nbillion: 0.619/6.179 company: 0.610/4.917 investments: 0.594/5.166 companies: 0.584/4.621 million: 0.581/5.671 shareholders: 0.566/4.625 investors: 0.564/5.015 investment: 0.563/4.845 invested: 0.546/4.117 dollars: 0.542/4.633 unitedhealth: 0.527/7.357 profits: 0.524/4.308 buyout: 0.524/3.485 assets: 0.523/4.684 fund: 0.522/4.593 invest: 0.522/3.685 equity: 0.521/4.872 company's: 0.521/4.296 funds: 0.519/4.551 shareholder: 0.518/3.942 investor: 0.513/4.402 interpublic: 0.512/6.386 defrauded: 0.510/5.712 subsidiaries: 0.506/4.203 acquisitions: 0.502/3.962 multibillion: 0.498/7.710 purchase: 0.495/4.047 investing: 0.493/3.799 wellpoint: 0.492/7.601 cendant: 0.490/7.253 \n\nTopic 3 (3.00): 20.9%\nMost relevant words:\npatients (2468,9): 0.736/6.620/0.786/7.097 hospital (723,1): 0.706/0.706/0.450/4.121 patient (3777,2): 0.685/1.369/0.650/5.752 treatment (1460,5): 0.676/3.382/0.704/6.349 hospitals (4777,5): 0.672/3.362/0.519/4.983 medicine (1528,2): 0.660/1.320/0.495/4.593 medical (679,1): 0.643/0.643/0.578/4.946 treatments (9357,2): 0.641/1.283/0.688/6.268 care (1069,2): 0.632/1.264/0.568/4.845 treating (10124,1): 0.624/0.624/0.637/5.140 treat (5814,3): 0.624/1.871/0.629/5.351 doctors (4958,2): 0.623/1.246/0.500/4.727 inpatient (33848,1): 0.619/0.619/0.463/5.780 medically (28191,1): 0.612/0.612/0.420/6.098 cancer (1871,2): 0.611/1.222/0.571/5.520 therapies (21018,2): 0.596/1.191/0.599/4.967 health (502,3): 0.586/1.759/0.536/4.525 tuberculosis (11017,1): 0.568/0.568/0.599/5.217 diseases (4439,6): 0.556/3.335/0.635/6.127 hiv (6116,1): 0.554/0.554/0.595/5.795 hospitalized (18161,1): 0.543/0.543/0.416/2.885 infectious (12595,4): 0.534/2.134/0.564/5.313 disease (1780,1): 0.530/0.530/0.608/5.602 aids (4693,2): 0.529/1.057/0.505/4.848 heart (869,1): 0.519/0.519/0.246/2.049 pregnancy (7979,1): 0.504/0.504/0.459/4.244 hepatitis (19494,1): 0.499/0.499/0.583/4.951 malaria (13222,1): 0.492/0.492/0.541/4.827 antibiotic (24098,2): 0.483/0.965/0.544/3.929 multidrug (112412,1): 0.477/0.477/0.443/7.075 \nMost similar words in vocab:\npatients: 0.786/7.097 treatment: 0.704/6.349 treatments: 0.688/6.268 medication: 0.686/5.952 medications: 0.685/5.795 diabetes: 0.651/5.657 patient: 0.650/5.752 treating: 0.637/5.140 diseases: 0.635/6.127 treat: 0.629/5.351 clinical: 0.627/5.686 pediatric: 0.626/5.553 prophylactic: 0.624/7.133 chronic: 0.621/5.478 contraindicated: 0.618/6.475 antiretroviral: 0.613/6.976 asthma: 0.612/4.669 chemotherapy: 0.612/4.719 disease: 0.608/5.602 treatable: 0.603/6.566 diarrheal: 0.602/7.582 therapeutic: 0.601/5.242 illnesses: 0.601/4.637 regimens: 0.600/6.999 infections: 0.600/5.549 prophylaxis: 0.599/7.014 tuberculosis: 0.599/5.217 therapies: 0.599/4.967 ivig: 0.597/7.458 hiv: 0.595/5.795 \n\nTopic 15 (3.00): 14.5%\nMost relevant words:\nincrease (1265,8): 0.811/6.489/0.700/5.433 clamoring (148365,1): 0.761/0.761/0.394/5.965 increases (4242,4): 0.755/3.020/0.596/5.119 shorting (108317,1): 0.751/0.751/0.426/7.457 increased (1167,1): 0.743/0.743/0.591/4.771 shrank (46494,1): 0.713/0.713/0.403/4.630 inexpensively (98181,1): 0.692/0.692/0.366/6.057 amount (1588,1): 0.678/0.678/0.549/4.450 rebates (60162,1): 0.670/0.670/0.631/7.085 huge (3131,2): 0.530/1.060/0.332/2.904 discounts (28868,1): 0.516/0.516/0.448/5.314 steady (7656,1): 0.500/0.500/0.303/2.641 percent (1651,2): 0.490/0.980/0.478/4.363 cost (1178,2): 0.489/0.978/0.581/4.593 conceivably (69563,1): 0.462/0.462/0.351/4.005 resulting (1955,1): 0.448/0.448/0.370/2.702 severance (36986,1): 0.442/0.442/0.346/4.578 thwart (31967,1): 0.430/0.430/0.240/2.937 income (874,1): 0.425/0.425/0.457/3.308 resulted (2001,1): 0.418/0.418/0.360/2.627 low (612,1): 0.416/0.416/0.345/2.887 pay (1653,2): 0.401/0.801/0.484/4.165 constant (2943,1): 0.393/0.393/0.222/2.045 fewer (5556,1): 0.378/0.378/0.359/2.938 sharply (12378,2): 0.373/0.746/0.257/2.193 lawmakers (27291,2): 0.366/0.733/0.318/3.582 swindle (42243,1): 0.362/0.362/0.226/3.154 required (967,2): 0.358/0.717/0.345/2.492 less (615,2): 0.358/0.716/0.356/2.470 delays (10116,1): 0.350/0.350/0.271/2.308 \nMost similar words in vocab:\nincrease: 0.700/5.433 rebates: 0.631/7.085 costs: 0.617/5.197 increasing: 0.600/4.673 profits: 0.600/4.931 increases: 0.596/5.119 increased: 0.591/4.771 cost: 0.581/4.593 exorbitant: 0.579/6.909 reinvest: 0.575/7.660 refinancing: 0.575/6.301 repayments: 0.575/6.175 disincentive: 0.575/8.147 recouping: 0.572/8.310 prices: 0.571/5.583 incentivize: 0.570/7.695 tenfold: 0.567/6.577 surtax: 0.565/7.773 outlay: 0.564/5.996 shortfall: 0.561/6.324 decrease: 0.556/4.437 premiums: 0.554/6.351 refunds: 0.550/6.349 amount: 0.549/4.450 tripling: 0.548/6.386 dollars: 0.547/4.679 disburse: 0.545/7.229 revenues: 0.542/4.729 overvalued: 0.540/7.766 skyrocketed: 0.539/6.104 \n\nTopic 2 (3.00): 9.8%\nMost relevant words:\ngenerics (85144,1): 0.467/0.467/0.442/7.507 glaxo (101914,1): 0.459/0.459/0.396/7.683 prescriptions (34657,3): 0.404/1.211/0.577/7.159 glaxosmithkline (55181,1): 0.385/0.385/0.534/7.189 concocting (179559,1): 0.379/0.379/0.351/5.918 pharmaceutical (8897,2): 0.305/0.610/0.556/5.181 drugstores (120806,1): 0.295/0.295/0.392/6.209 medicaid (25609,1): 0.274/0.274/0.420/5.642 pharmaceuticals (16906,3): 0.265/0.795/0.485/4.475 generic (7602,4): 0.264/1.058/0.286/2.506 therapeutics (36356,1): 0.259/0.259/0.508/6.337 icahn (83210,1): 0.256/0.256/0.371/7.257 generically (48146,1): 0.228/0.228/0.225/2.574 ims (30708,1): 0.216/0.216/0.186/2.662 laboratories (7537,1): 0.211/0.211/0.321/2.995 controlled (2257,3): 0.210/0.631/0.235/2.174 approved (2389,1): 0.210/0.210/0.333/2.895 lawmakers (27291,2): 0.196/0.391/0.287/3.230 pills (18688,1): 0.195/0.195/0.384/3.129 alternative (1705,1): 0.190/0.190/0.232/1.945 medicare (18132,1): 0.189/0.189/0.437/3.293 insurers (32340,1): 0.184/0.184/0.425/4.744 use (176,7): 0.182/1.272/0.285/1.979 administration (991,1): 0.181/0.181/0.249/2.039 tablet (10396,2): 0.180/0.360/0.149/1.472 advocates (8690,1): 0.177/0.177/0.258/2.108 investigating (8587,1): 0.173/0.173/0.222/1.894 rules (1377,1): 0.172/0.172/0.113/1.023 testing (2814,1): 0.170/0.170/0.317/2.829 turing (18313,9): 0.165/1.483/0.086/0.647 \nMost similar words in vocab:\ndrugs: 0.633/6.093 medication: 0.624/5.416 antiretroviral: 0.616/7.012 medications: 0.616/5.210 drug: 0.598/5.980 ivig: 0.597/7.453 methadone: 0.595/7.136 prescription: 0.591/5.135 prophylactic: 0.590/6.740 prescriptions: 0.577/7.159 antipsychotic: 0.576/6.459 antiepileptic: 0.574/7.072 medicines: 0.571/4.950 vioxx: 0.566/8.687 patients: 0.563/5.081 regimens: 0.562/6.550 pharmaceutical: 0.556/5.181 treatments: 0.556/5.063 dosages: 0.548/5.913 novartis: 0.546/7.069 contraceptives: 0.544/6.609 pfizer: 0.541/7.086 praziquantel: 0.539/7.308 prescribing: 0.538/6.723 placebos: 0.536/7.752 glaxosmithkline: 0.534/7.189 thiopental: 0.534/7.812 sanofi: 0.533/6.378 analgesics: 0.531/5.888 bisphosphonates: 0.530/6.796 \n\nTopic 18 (3.00): 9.4%\nMost relevant words:\nprice (1535,17): 0.972/16.525/0.964/8.424 prices (4345,4): 0.863/3.451/0.817/7.995 gouge (81038,1): 0.806/0.806/0.228/4.789 priced (14909,1): 0.680/0.680/0.546/4.584 rose (1336,1): 0.292/0.292/0.180/1.319 high (104,3): 0.261/0.783/0.166/1.480 sharply (12378,2): 0.259/0.518/0.246/2.100 scott (1062,1): 0.226/0.226/0.109/0.700 martin (778,1): 0.218/0.218/0.055/0.351 low (612,1): 0.217/0.217/0.300/2.511 spencer (4173,1): 0.215/0.215/0.121/0.912 free (381,1): 0.195/0.195/0.148/1.321 born (106,1): 0.192/0.192/-0.041/-0.353 mcleod (15404,1): 0.191/0.191/0.090/0.675 sanders (7915,1): 0.188/0.188/0.092/0.636 shortages (16485,1): 0.187/0.187/0.266/2.194 line (181,1): 0.185/0.185/0.032/0.267 judith (8272,1): 0.184/0.184/0.040/0.318 piggy (34457,1): 0.182/0.182/0.146/1.998 grady (13903,1): 0.180/0.180/0.129/0.899 wendy (8642,1): 0.176/0.176/0.059/0.450 tracks (907,1): 0.176/0.176/0.009/0.076 round (273,1): 0.174/0.174/-0.036/-0.327 armstrong (4767,1): 0.170/0.170/0.061/0.516 crazy (5181,1): 0.168/0.168/0.092/0.737 democrat (3309,1): 0.168/0.168/0.082/0.752 democratic (731,1): 0.166/0.166/0.051/0.483 swindle (42243,1): 0.166/0.166/0.190/2.646 dramatic (3872,1): 0.164/0.164/0.090/0.797 good (462,1): 0.164/0.164/0.126/0.866 \nMost similar words in vocab:\nprice: 0.964/8.424 prices: 0.817/7.995 pricing: 0.600/5.249 priced: 0.546/4.584 inflation: 0.531/4.918 commodity: 0.495/4.129 discount: 0.487/4.215 exorbitant: 0.484/5.774 discounted: 0.475/3.166 cost: 0.475/3.754 resale: 0.461/5.277 purchases: 0.459/3.540 costs: 0.458/3.854 rates: 0.434/4.032 purchasing: 0.434/3.444 buyers: 0.430/3.662 commodities: 0.428/3.706 buying: 0.423/3.503 demand: 0.422/3.482 undervalued: 0.421/4.734 premium: 0.421/4.213 skyrocketed: 0.417/4.719 tariff: 0.415/3.354 asset's: 0.415/5.766 market: 0.414/3.383 volatility: 0.412/5.266 repayments: 0.412/4.423 cents: 0.412/3.801 valuations: 0.412/4.704 wages: 0.410/3.710 \n\nTopic 9 (3.00): 4.9%\nMost relevant words:\ndrugstores (120806,1): 0.296/0.296/0.437/6.923 generics (85144,1): 0.289/0.289/0.456/7.736 concocting (179559,1): 0.284/0.284/0.376/6.338 glaxo (101914,1): 0.253/0.253/0.402/7.797 insurers (32340,1): 0.165/0.165/0.479/5.346 medicaid (25609,1): 0.148/0.148/0.427/5.736 lawmakers (27291,2): 0.139/0.277/0.319/3.596 glaxosmithkline (55181,1): 0.129/0.129/0.505/6.804 prescriptions (34657,3): 0.123/0.370/0.539/6.682 icahn (83210,1): 0.123/0.123/0.370/7.235 inexpensively (98181,1): 0.114/0.114/0.316/5.237 expensive (4449,1): 0.110/0.110/0.346/3.304 ims (30708,1): 0.109/0.109/0.188/2.692 products (1127,1): 0.109/0.109/0.401/3.340 thwart (31967,1): 0.107/0.107/0.207/2.531 seeking (3864,1): 0.105/0.105/0.307/2.463 approved (2389,1): 0.102/0.102/0.331/2.880 strategy (2853,2): 0.101/0.203/0.266/2.351 conceivably (69563,1): 0.101/0.101/0.304/3.468 discounts (28868,1): 0.100/0.100/0.393/4.661 pharmaceuticals (16906,3): 0.098/0.294/0.454/4.192 pharmaceutical (8897,2): 0.097/0.194/0.509/4.743 trying (2359,2): 0.097/0.193/0.224/1.656 rebates (60162,1): 0.095/0.095/0.545/6.121 medicare (18132,1): 0.094/0.094/0.438/3.300 generic (7602,4): 0.094/0.374/0.248/2.177 mainstays (61442,1): 0.093/0.093/0.130/1.481 patents (8554,1): 0.093/0.093/0.290/2.728 option (3370,1): 0.093/0.093/0.278/2.453 food (807,1): 0.091/0.091/0.288/2.643 \nMost similar words in vocab:\nvioxx: 0.556/8.527 antiretroviral: 0.553/6.291 incentivize: 0.548/7.398 ivig: 0.547/6.832 rebates: 0.545/6.121 prescriptions: 0.539/6.682 methadone: 0.535/6.413 pfizer: 0.529/6.929 novartis: 0.526/6.809 thiopental: 0.524/7.670 adrs: 0.522/8.149 disincentive: 0.520/7.368 drugs: 0.520/4.999 sanofi: 0.517/6.182 reimburse: 0.515/5.680 dhhs: 0.512/6.817 placebos: 0.512/7.401 pharmacies: 0.510/6.048 multibillion: 0.509/7.891 pharmaceutical: 0.509/4.743 medication: 0.509/4.413 prescription: 0.509/4.417 refinancing: 0.508/5.568 contraceptives: 0.508/6.170 anticompetitive: 0.508/7.421 prophylactic: 0.506/5.780 glaxosmithkline: 0.505/6.804 wellpoint: 0.505/7.799 unapproved: 0.504/6.089 vha: 0.502/7.711 \n\nTopic 11 (3.00): 4.2%\nMost relevant words:\ndiseases (4439,6): 0.154/0.923/0.668/6.448 disease (1780,1): 0.150/0.150/0.645/5.942 infectious (12595,4): 0.149/0.596/0.599/5.643 infection (6037,2): 0.146/0.291/0.617/5.628 malaria (13222,1): 0.145/0.145/0.584/5.209 tuberculosis (11017,1): 0.143/0.143/0.625/5.447 hepatitis (19494,1): 0.136/0.136/0.620/5.260 multidrug (112412,1): 0.136/0.136/0.465/7.426 infected (8254,1): 0.132/0.132/0.432/4.145 hiv (6116,1): 0.132/0.132/0.612/5.966 resistant (8950,1): 0.125/0.125/0.350/3.457 serious (2375,2): 0.118/0.236/0.329/2.926 immune (8538,1): 0.117/0.117/0.439/3.992 aids (4693,2): 0.116/0.232/0.515/4.938 cancer (1871,2): 0.115/0.230/0.565/5.456 cause (1310,1): 0.110/0.110/0.373/2.898 pregnancy (7979,1): 0.109/0.109/0.467/4.318 hospitalized (18161,1): 0.108/0.108/0.415/2.878 patients (2468,9): 0.106/0.957/0.750/6.769 treatment (1460,5): 0.106/0.530/0.676/6.102 threatening (7927,2): 0.104/0.207/0.284/2.526 treating (10124,1): 0.103/0.103/0.612/4.941 treat (5814,3): 0.103/0.308/0.605/5.151 antibiotic (24098,2): 0.102/0.205/0.551/3.984 vulnerable (6874,1): 0.102/0.102/0.228/2.157 potentially (6379,1): 0.100/0.100/0.427/3.760 parasitic (18146,1): 0.099/0.099/0.305/2.540 problems (1188,1): 0.096/0.096/0.322/2.756 medically (28191,1): 0.095/0.095/0.402/5.837 patient (3777,2): 0.095/0.189/0.608/5.379 \nMost similar words in vocab:\npatients: 0.750/6.769 treatment: 0.676/6.102 diseases: 0.668/6.448 treatments: 0.652/5.944 infections: 0.649/6.001 diarrheal: 0.648/8.160 medication: 0.646/5.601 medications: 0.645/5.459 disease: 0.645/5.942 diabetes: 0.643/5.586 chronic: 0.642/5.664 ivig: 0.628/7.839 prophylactic: 0.626/7.160 treatable: 0.626/6.818 tuberculosis: 0.625/5.447 illnesses: 0.620/4.781 hepatitis: 0.620/5.260 infection: 0.617/5.628 asthma: 0.617/4.709 hiv: 0.612/5.966 treating: 0.612/4.941 antiretroviral: 0.611/6.949 contraindicated: 0.610/6.393 nosocomial: 0.609/8.112 patient: 0.608/5.379 treat: 0.605/5.151 prophylaxis: 0.604/7.073 ulcerative: 0.601/5.762 infectious: 0.599/5.643 regimens: 0.595/6.941 \n\nTopic 6 (3.00): 4.1%\nMost relevant words:\ndrugs (3528,10): 0.233/2.331/0.800/7.690 drug (1942,15): 0.155/2.328/0.783/7.835 pills (18688,1): 0.154/0.154/0.467/3.800 pharmaceutical (8897,2): 0.108/0.217/0.542/5.049 cholesterol (20400,1): 0.103/0.103/0.353/2.836 efficacy (14568,1): 0.100/0.100/0.476/3.985 pharmaceuticals (16906,3): 0.097/0.292/0.474/4.377 antibiotic (24098,2): 0.095/0.191/0.550/3.972 dangerous (3584,2): 0.092/0.185/0.262/2.419 effects (1380,2): 0.091/0.183/0.348/3.186 therapies (21018,2): 0.088/0.176/0.568/4.716 therapeutics (36356,1): 0.086/0.086/0.492/6.139 investigating (8587,1): 0.086/0.086/0.245/2.094 generic (7602,4): 0.084/0.334/0.258/2.259 pregnancy (7979,1): 0.083/0.083/0.445/4.108 testing (2814,1): 0.083/0.083/0.337/3.013 use (176,7): 0.081/0.570/0.299/2.082 aids (4693,2): 0.076/0.151/0.476/4.567 administration (991,1): 0.075/0.075/0.252/2.061 controlled (2257,3): 0.074/0.222/0.220/2.036 hiv (6116,1): 0.074/0.074/0.559/5.445 glaxosmithkline (55181,1): 0.072/0.072/0.477/6.416 treatments (9357,2): 0.071/0.143/0.629/5.735 used (82,4): 0.071/0.283/0.141/1.066 food (807,1): 0.071/0.071/0.281/2.579 samples (4875,1): 0.070/0.070/0.181/1.851 immune (8538,1): 0.069/0.069/0.387/3.522 combination (2674,1): 0.068/0.068/0.254/1.899 resistant (8950,1): 0.067/0.067/0.293/2.890 prescriptions (34657,3): 0.065/0.196/0.503/6.242 \nMost similar words in vocab:\ndrugs: 0.800/7.690 drug: 0.783/7.835 medication: 0.720/6.243 medications: 0.714/6.045 antiretroviral: 0.662/7.530 prescription: 0.642/5.578 patients: 0.636/5.747 treatments: 0.629/5.735 antipsychotic: 0.624/6.998 ivig: 0.620/7.742 antiepileptic: 0.618/7.612 treatment: 0.618/5.573 methadone: 0.616/7.395 medicines: 0.616/5.346 therapeutic: 0.601/5.239 prophylactic: 0.598/6.837 analgesics: 0.595/6.602 contraindicated: 0.593/6.210 antibiotics: 0.590/4.604 antiviral: 0.588/6.894 regimens: 0.582/6.788 anticoagulant: 0.578/6.354 diabetes: 0.576/5.009 warfarin: 0.575/6.142 anticancer: 0.574/6.431 opiates: 0.574/6.616 nsaids: 0.571/6.310 therapies: 0.568/4.716 chemotherapy: 0.568/4.382 diazepam: 0.567/6.003 \n\nTopic 12 (3.00): 3.8%\nMost relevant words:\ndrug (1942,15): 0.679/10.192/0.972/9.727 drugs (3528,10): 0.423/4.232/0.905/8.702 pills (18688,1): 0.152/0.152/0.516/4.198 investigating (8587,1): 0.063/0.063/0.259/2.208 dangerous (3584,2): 0.062/0.125/0.265/2.443 cholesterol (20400,1): 0.062/0.062/0.341/2.741 controlled (2257,3): 0.056/0.169/0.235/2.174 side (287,2): 0.054/0.108/0.009/0.067 generic (7602,4): 0.051/0.205/0.249/2.187 crazy (5181,1): 0.050/0.050/0.106/0.844 story (367,1): 0.046/0.046/0.008/0.061 use (176,7): 0.045/0.315/0.274/1.904 administration (991,1): 0.045/0.045/0.240/1.965 force (351,1): 0.044/0.044/0.017/0.141 effects (1380,2): 0.043/0.086/0.311/2.849 rima (42963,1): 0.042/0.042/-0.000/-0.000 known (97,1): 0.041/0.041/0.001/0.003 used (82,4): 0.040/0.160/0.121/0.913 talked (11444,1): 0.040/0.040/0.085/0.530 two (37,3): 0.038/0.113/-0.046/-0.349 efficacy (14568,1): 0.037/0.037/0.406/3.397 samples (4875,1): 0.037/0.037/0.158/1.616 discourage (22551,1): 0.036/0.036/0.186/1.157 wild (1617,1): 0.036/0.036/0.011/0.096 testing (2814,1): 0.036/0.036/0.290/2.588 food (807,1): 0.036/0.036/0.252/2.312 first (26,3): 0.035/0.105/-0.066/-0.438 like (189,2): 0.035/0.070/0.077/0.513 possibly (2456,1): 0.035/0.035/0.123/0.947 tracks (907,1): 0.035/0.035/-0.027/-0.227 \nMost similar words in vocab:\ndrug: 0.972/9.727 drugs: 0.905/8.702 cocaine: 0.672/7.006 heroin: 0.645/6.197 prescription: 0.643/5.584 medication: 0.618/5.364 steroids: 0.613/4.132 medications: 0.604/5.113 opiates: 0.593/6.831 marijuana: 0.590/5.610 cannabis: 0.588/5.337 antipsychotic: 0.580/6.505 narcotics: 0.572/5.148 morphine: 0.569/4.495 antiretroviral: 0.567/6.448 methadone: 0.564/6.762 illicit: 0.555/4.471 addiction: 0.555/5.214 painkillers: 0.554/6.807 stimulants: 0.550/6.046 ketamine: 0.550/6.448 psychotropic: 0.548/6.521 alcohol: 0.548/5.636 methamphetamine: 0.546/6.991 diazepam: 0.544/5.751 barbiturates: 0.539/6.215 opioids: 0.537/6.150 antiepileptic: 0.530/6.532 anticancer: 0.530/5.931 antiviral: 0.525/6.153 \n\nTopic 5 (3.00): 2.9%\nMost relevant words:\ntoxoplasmosis (125001,4): 0.979/3.917/0.876/12.897 parasite (16531,1): 0.326/0.326/0.510/3.957 parasitic (18146,1): 0.316/0.316/0.503/4.192 infection (6037,2): 0.268/0.536/0.738/6.729 infected (8254,1): 0.252/0.252/0.550/5.281 immune (8538,1): 0.175/0.175/0.536/4.883 malaria (13222,1): 0.170/0.170/0.657/5.863 rare (2251,1): 0.166/0.166/0.317/3.018 disease (1780,1): 0.163/0.163/0.707/6.516 hepatitis (19494,1): 0.153/0.153/0.691/5.866 multidrug (112412,1): 0.144/0.144/0.499/7.972 diseases (4439,6): 0.142/0.850/0.710/6.856 infectious (12595,4): 0.141/0.562/0.645/6.075 tuberculosis (11017,1): 0.113/0.113/0.655/5.703 cause (1310,1): 0.112/0.112/0.438/3.402 isolated (4860,1): 0.108/0.108/0.292/2.640 rarely (4300,1): 0.104/0.104/0.307/2.511 caused (1150,1): 0.097/0.097/0.347/2.997 resistant (8950,1): 0.093/0.093/0.370/3.655 rima (42963,1): 0.092/0.092/0.057/0.812 possibly (2456,1): 0.090/0.090/0.250/1.922 wild (1617,1): 0.078/0.078/0.098/0.887 factor (2518,1): 0.070/0.070/0.255/2.247 vulnerable (6874,1): 0.070/0.070/0.239/2.262 effects (1380,2): 0.069/0.138/0.365/3.341 pregnancy (7979,1): 0.068/0.068/0.469/4.336 remains (1135,1): 0.067/0.067/0.111/0.852 samples (4875,1): 0.061/0.061/0.210/2.149 antibiotic (24098,2): 0.061/0.122/0.547/3.954 hiv (6116,1): 0.060/0.060/0.581/5.663 \nMost similar words in vocab:\ntoxoplasmosis: 0.876/12.897 infections: 0.739/6.832 infection: 0.738/6.729 diseases: 0.710/6.856 disease: 0.707/6.516 falciparum: 0.703/7.789 ulcerative: 0.699/6.706 hepatitis: 0.691/5.866 diarrheal: 0.687/8.649 endocarditis: 0.682/7.041 histolytica: 0.668/7.876 bacteremia: 0.662/8.033 malaria: 0.657/5.863 tuberculosis: 0.655/5.703 chlamydia: 0.650/7.361 treatable: 0.650/7.081 crohn's: 0.649/6.573 infectious: 0.645/6.075 pneumoniae: 0.642/6.720 blastocystis: 0.642/8.446 chronic: 0.641/5.658 pylori: 0.641/6.689 colitis: 0.641/6.796 sepsis: 0.640/6.942 malignancies: 0.632/6.518 nosocomial: 0.632/8.418 staphylococcal: 0.632/8.802 mycoplasma: 0.628/6.847 meningitis: 0.624/7.500 malarial: 0.623/7.727 \n\nTopic 17 (3.00): 2.1%\nMost relevant words:\nmultidrug (112412,1): 0.077/0.077/0.472/7.541 diseases (4439,6): 0.070/0.420/0.658/6.351 hepatitis (19494,1): 0.070/0.070/0.622/5.280 hiv (6116,1): 0.069/0.069/0.617/6.008 cancer (1871,2): 0.068/0.137/0.582/5.626 disease (1780,1): 0.067/0.067/0.632/5.825 infectious (12595,4): 0.065/0.261/0.585/5.509 malaria (13222,1): 0.064/0.064/0.571/5.091 immune (8538,1): 0.063/0.063/0.446/4.062 tuberculosis (11017,1): 0.062/0.062/0.609/5.303 antibiotic (24098,2): 0.061/0.123/0.576/4.165 aids (4693,2): 0.060/0.120/0.517/4.964 resistant (8950,1): 0.059/0.059/0.344/3.400 infection (6037,2): 0.058/0.115/0.591/5.390 therapeutics (36356,1): 0.057/0.057/0.511/6.369 pregnancy (7979,1): 0.051/0.051/0.460/4.254 efficacy (14568,1): 0.051/0.051/0.470/3.939 cholesterol (20400,1): 0.051/0.051/0.343/2.754 infected (8254,1): 0.050/0.050/0.402/3.862 therapies (21018,2): 0.050/0.100/0.576/4.782 effects (1380,2): 0.049/0.099/0.350/3.206 treat (5814,3): 0.049/0.147/0.600/5.106 treating (10124,1): 0.049/0.049/0.605/4.885 treatments (9357,2): 0.049/0.097/0.657/5.985 parasitic (18146,1): 0.046/0.046/0.296/2.471 cause (1310,1): 0.046/0.046/0.349/2.710 treatment (1460,5): 0.045/0.227/0.659/5.946 hospitalized (18161,1): 0.044/0.044/0.384/2.666 isolated (4860,1): 0.044/0.044/0.215/1.941 parasite (16531,1): 0.043/0.043/0.274/2.128 \nMost similar words in vocab:\npatients: 0.716/6.468 medications: 0.678/5.735 medication: 0.671/5.824 treatment: 0.659/5.946 diseases: 0.658/6.351 treatments: 0.657/5.985 diabetes: 0.651/5.659 ivig: 0.637/7.953 diarrheal: 0.635/7.997 chronic: 0.633/5.583 antiretroviral: 0.632/7.194 drugs: 0.632/6.080 disease: 0.632/5.825 infections: 0.626/5.790 prophylactic: 0.625/7.140 hepatitis: 0.622/5.280 hiv: 0.617/6.008 asthma: 0.613/4.677 antibiotics: 0.613/4.783 contraindicated: 0.610/6.389 tuberculosis: 0.609/5.303 antiepileptic: 0.606/7.465 treating: 0.605/4.885 treatable: 0.604/6.582 vaccine: 0.603/5.610 chemotherapy: 0.602/4.648 treat: 0.600/5.106 ulcerative: 0.596/5.722 regimens: 0.596/6.943 illnesses: 0.595/4.586 \n\nTopic 0 (0.00): 0.3%\nMost relevant words:\nborn (106,1): 0.017/0.017/0.000/0.000 round (273,1): 0.015/0.015/0.000/0.000 first (26,3): 0.012/0.037/0.000/0.000 two (37,3): 0.012/0.036/0.000/0.000 side (287,2): 0.011/0.023/0.000/0.000 world (56,1): 0.011/0.011/0.000/0.000 last (237,2): 0.011/0.022/0.000/0.000 university (61,2): 0.010/0.021/0.000/0.000 story (367,1): 0.010/0.010/0.000/0.000 tracks (907,1): 0.010/0.010/0.000/0.000 rima (42963,1): 0.009/0.009/0.000/0.000 known (97,1): 0.009/0.009/0.000/0.000 martin (778,1): 0.009/0.009/0.000/0.000 district (167,1): 0.009/0.009/0.000/0.000 force (351,1): 0.009/0.009/0.000/0.000 back (193,1): 0.009/0.009/0.000/0.000 held (230,1): 0.009/0.009/0.000/0.000 line (181,1): 0.009/0.009/0.000/0.000 wrote (536,1): 0.008/0.008/0.000/0.000 list (186,1): 0.008/0.008/0.000/0.000 former (185,1): 0.008/0.008/0.000/0.000 school (49,1): 0.008/0.008/0.000/0.000 old (204,5): 0.008/0.041/0.000/0.000 judith (8272,1): 0.008/0.008/0.000/0.000 called (155,3): 0.008/0.024/0.000/0.000 works (341,1): 0.008/0.008/0.000/0.000 mount (1283,3): 0.008/0.023/0.000/0.000 division (236,1): 0.007/0.007/0.000/0.000 wild (1617,1): 0.007/0.007/0.000/0.000 remained (770,1): 0.007/0.007/0.000/0.000 \nTopic 7 (3.00): 0.2%\nMost relevant words:\npotentially (6379,1): 0.006/0.006/0.446/3.927 effects (1380,2): 0.005/0.011/0.364/3.334 dangerous (3584,2): 0.005/0.010/0.269/2.485 multidrug (112412,1): 0.005/0.005/0.447/7.136 resistant (8950,1): 0.005/0.005/0.329/3.246 antibiotic (24098,2): 0.005/0.009/0.546/3.948 use (176,7): 0.005/0.033/0.319/2.216 serious (2375,2): 0.005/0.009/0.305/2.716 pills (18688,1): 0.005/0.005/0.399/3.249 threatening (7927,2): 0.004/0.009/0.272/2.422 cause (1310,1): 0.004/0.004/0.351/2.728 efficacy (14568,1): 0.004/0.004/0.453/3.794 problems (1188,1): 0.004/0.004/0.311/2.660 cholesterol (20400,1): 0.004/0.004/0.320/2.575 pregnancy (7979,1): 0.004/0.004/0.438/4.050 immune (8538,1): 0.004/0.004/0.401/3.652 combination (2674,1): 0.004/0.004/0.272/2.033 testing (2814,1): 0.004/0.004/0.330/2.949 used (82,4): 0.004/0.016/0.153/1.154 vulnerable (6874,1): 0.004/0.004/0.204/1.931 generically (48146,1): 0.004/0.004/0.209/2.389 develop (1982,1): 0.004/0.004/0.296/2.438 caused (1150,1): 0.004/0.004/0.268/2.313 drugs (3528,10): 0.004/0.038/0.681/6.551 investigating (8587,1): 0.004/0.004/0.227/1.939 generic (7602,4): 0.004/0.015/0.243/2.127 controlled (2257,3): 0.004/0.011/0.216/2.004 prescriptions (34657,3): 0.004/0.011/0.510/6.328 pharmaceutical (8897,2): 0.004/0.007/0.497/4.630 possibly (2456,1): 0.004/0.004/0.163/1.249 \nMost similar words in vocab:\ndrugs: 0.681/6.551 medications: 0.675/5.714 medication: 0.673/5.842 drug: 0.647/6.474 ivig: 0.646/8.075 antiretroviral: 0.642/7.310 patients: 0.638/5.765 antiepileptic: 0.632/7.778 treatments: 0.618/5.629 prophylactic: 0.615/7.030 treatment: 0.606/5.466 diarrheal: 0.603/7.597 antipsychotic: 0.593/6.653 contraindicated: 0.589/6.168 methadone: 0.587/7.045 antibiotics: 0.587/4.581 regimens: 0.585/6.818 medicines: 0.580/5.031 analgesics: 0.573/6.360 prescription: 0.573/4.975 treatable: 0.572/6.229 anticoagulant: 0.571/6.285 antiviral: 0.568/6.664 warfarin: 0.568/6.064 dosages: 0.567/6.124 praziquantel: 0.566/7.680 diabetes: 0.564/4.904 nsaids: 0.564/6.223 prophylaxis: 0.562/6.582 chronic: 0.560/4.941 \n\nTopic 8 (3.00): 0.0%\nMost relevant words:\npharmaceuticals (16906,3): 0.000/0.001/0.474/4.374 pharmaceutical (8897,2): 0.000/0.000/0.528/4.914 efficacy (14568,1): 0.000/0.000/0.468/3.924 testing (2814,1): 0.000/0.000/0.350/3.125 glaxosmithkline (55181,1): 0.000/0.000/0.494/6.654 therapeutics (36356,1): 0.000/0.000/0.492/6.134 potentially (6379,1): 0.000/0.000/0.413/3.633 prescriptions (34657,3): 0.000/0.001/0.522/6.479 generic (7602,4): 0.000/0.001/0.254/2.229 use (176,7): 0.000/0.001/0.298/2.075 effects (1380,2): 0.000/0.000/0.332/3.039 cholesterol (20400,1): 0.000/0.000/0.318/2.552 generically (48146,1): 0.000/0.000/0.208/2.377 food (807,1): 0.000/0.000/0.286/2.630 samples (4875,1): 0.000/0.000/0.186/1.907 pills (18688,1): 0.000/0.000/0.376/3.064 medicaid (25609,1): 0.000/0.000/0.389/5.230 problems (1188,1): 0.000/0.000/0.298/2.545 certain (1095,4): 0.000/0.001/0.315/2.167 combination (2674,1): 0.000/0.000/0.261/1.951 antibiotic (24098,2): 0.000/0.000/0.509/3.676 therapies (21018,2): 0.000/0.000/0.543/4.503 used (82,4): 0.000/0.001/0.141/1.066 treatments (9357,2): 0.000/0.000/0.628/5.723 dangerous (3584,2): 0.000/0.000/0.233/2.149 resistant (8950,1): 0.000/0.000/0.296/2.929 laboratories (7537,1): 0.000/0.000/0.298/2.780 controlled (2257,3): 0.000/0.000/0.211/1.954 alternative (1705,1): 0.000/0.000/0.217/1.821 medicare (18132,1): 0.000/0.000/0.417/3.144 \nMost similar words in vocab:\nmedication: 0.672/5.826 medications: 0.668/5.657 patients: 0.658/5.944 drugs: 0.647/6.220 ivig: 0.643/8.027 antiretroviral: 0.637/7.244 treatments: 0.628/5.723 treatment: 0.620/5.594 antiepileptic: 0.617/7.600 drug: 0.613/6.128 prophylactic: 0.612/6.998 antipsychotic: 0.592/6.640 regimens: 0.592/6.900 methadone: 0.585/7.022 medicines: 0.584/5.068 contraindicated: 0.583/6.106 diarrheal: 0.578/7.284 dosages: 0.578/6.235 prescription: 0.574/4.981 praziquantel: 0.564/7.646 analgesics: 0.562/6.241 bevacizumab: 0.562/7.928 diabetes: 0.561/4.875 infliximab: 0.558/7.226 anticoagulant: 0.556/6.121 antibiotics: 0.556/4.339 bisphosphonates: 0.556/7.121 therapeutic: 0.555/4.841 monotherapy: 0.555/7.367 warfarin: 0.555/5.926 \n\nTopic 13 (3.00): 0.0%\nMost relevant words:\ndrugs (3528,10): 0.000/0.003/0.768/7.385 pills (18688,1): 0.000/0.000/0.436/3.546 drug (1942,15): 0.000/0.003/0.747/7.470 cholesterol (20400,1): 0.000/0.000/0.357/2.870 antibiotic (24098,2): 0.000/0.000/0.565/4.086 efficacy (14568,1): 0.000/0.000/0.481/4.032 effects (1380,2): 0.000/0.000/0.358/3.278 pharmaceutical (8897,2): 0.000/0.000/0.531/4.941 pharmaceuticals (16906,3): 0.000/0.001/0.472/4.352 therapeutics (36356,1): 0.000/0.000/0.497/6.192 testing (2814,1): 0.000/0.000/0.343/3.064 multidrug (112412,1): 0.000/0.000/0.441/7.032 resistant (8950,1): 0.000/0.000/0.316/3.123 generic (7602,4): 0.000/0.001/0.258/2.263 pregnancy (7979,1): 0.000/0.000/0.444/4.104 hiv (6116,1): 0.000/0.000/0.570/5.548 immune (8538,1): 0.000/0.000/0.405/3.689 dangerous (3584,2): 0.000/0.000/0.248/2.288 therapies (21018,2): 0.000/0.000/0.558/4.633 glaxosmithkline (55181,1): 0.000/0.000/0.483/6.502 use (176,7): 0.000/0.001/0.293/2.039 investigating (8587,1): 0.000/0.000/0.233/1.992 potentially (6379,1): 0.000/0.000/0.404/3.556 aids (4693,2): 0.000/0.000/0.476/4.569 controlled (2257,3): 0.000/0.000/0.221/2.048 treatments (9357,2): 0.000/0.000/0.634/5.781 combination (2674,1): 0.000/0.000/0.266/1.988 prescriptions (34657,3): 0.000/0.000/0.512/6.353 samples (4875,1): 0.000/0.000/0.183/1.870 used (82,4): 0.000/0.001/0.140/1.056 \nMost similar words in vocab:\ndrugs: 0.768/7.385 drug: 0.747/7.470 medications: 0.708/5.996 medication: 0.706/6.122 antiretroviral: 0.668/7.605 ivig: 0.650/8.122 patients: 0.646/5.835 treatments: 0.634/5.781 antiepileptic: 0.632/7.783 antipsychotic: 0.626/7.023 prescription: 0.623/5.407 treatment: 0.618/5.578 prophylactic: 0.611/6.982 medicines: 0.607/5.269 methadone: 0.604/7.251 antibiotics: 0.603/4.710 contraindicated: 0.602/6.309 antiviral: 0.599/7.027 regimens: 0.593/6.911 anticoagulant: 0.591/6.506 analgesics: 0.591/6.557 diarrheal: 0.588/7.412 therapeutic: 0.586/5.106 anticancer: 0.585/6.549 warfarin: 0.583/6.222 dosages: 0.580/6.265 nsaids: 0.579/6.392 diabetes: 0.579/5.031 opiates: 0.575/6.627 isoniazid: 0.573/7.559 \n\nTopic 16 (3.00): 0.0%\nMost relevant words:\nconcocting (179559,1): 0.000/0.000/0.370/6.239 drugstores (120806,1): 0.000/0.000/0.412/6.528 glaxo (101914,1): 0.000/0.000/0.388/7.513 prescriptions (34657,3): 0.000/0.001/0.560/6.947 generics (85144,1): 0.000/0.000/0.421/7.146 insurers (32340,1): 0.000/0.000/0.449/5.019 glaxosmithkline (55181,1): 0.000/0.000/0.496/6.677 medicaid (25609,1): 0.000/0.000/0.404/5.433 conceivably (69563,1): 0.000/0.000/0.308/3.510 food (807,1): 0.000/0.000/0.302/2.777 products (1127,1): 0.000/0.000/0.394/3.286 generic (7602,4): 0.000/0.001/0.257/2.255 generically (48146,1): 0.000/0.000/0.215/2.460 inexpensively (98181,1): 0.000/0.000/0.307/5.086 thwart (31967,1): 0.000/0.000/0.199/2.440 expensive (4449,1): 0.000/0.000/0.333/3.181 discounts (28868,1): 0.000/0.000/0.389/4.620 pharmaceuticals (16906,3): 0.000/0.000/0.447/4.121 supply (1905,1): 0.000/0.000/0.288/2.534 using (379,1): 0.000/0.000/0.209/1.514 pharmaceutical (8897,2): 0.000/0.000/0.501/4.668 strategy (2853,2): 0.000/0.000/0.252/2.225 alternative (1705,1): 0.000/0.000/0.225/1.893 ims (30708,1): 0.000/0.000/0.173/2.481 use (176,7): 0.000/0.001/0.283/1.964 lawmakers (27291,2): 0.000/0.000/0.278/3.126 delivering (9451,1): 0.000/0.000/0.227/2.004 controlled (2257,3): 0.000/0.000/0.213/1.969 needed (1661,1): 0.000/0.000/0.322/2.265 bottle (7312,1): 0.000/0.000/0.142/1.387 \nMost similar words in vocab:\ndrugs: 0.586/5.637 antiretroviral: 0.578/6.582 ivig: 0.574/7.175 methadone: 0.568/6.820 prescriptions: 0.560/6.947 drug: 0.557/5.571 vioxx: 0.550/8.443 medication: 0.547/4.745 prescription: 0.541/4.702 thiopental: 0.536/7.852 medications: 0.534/4.515 antiepileptic: 0.533/6.567 antipsychotic: 0.526/5.896 placebos: 0.525/7.593 adrs: 0.524/8.190 prophylactic: 0.524/5.990 medicines: 0.523/4.540 dosages: 0.523/5.642 rebates: 0.518/5.815 contraceptives: 0.517/6.281 incentivize: 0.513/6.918 unapproved: 0.511/6.177 pfizer: 0.511/6.693 regimens: 0.510/5.945 tamiflu: 0.510/7.350 praziquantel: 0.509/6.898 sanofi: 0.507/6.069 pharmacies: 0.505/5.988 novartis: 0.504/6.526 bevacizumab: 0.504/7.106 \n\nTopic 10 (3.00): 0.0%\nMost relevant words:\nmedicaid (25609,1): 0.000/0.000/0.436/5.857 drugstores (120806,1): 0.000/0.000/0.392/6.216 insurers (32340,1): 0.000/0.000/0.452/5.051 discounts (28868,1): 0.000/0.000/0.405/4.811 severance (36986,1): 0.000/0.000/0.311/4.119 medicare (18132,1): 0.000/0.000/0.450/3.394 rebates (60162,1): 0.000/0.000/0.549/6.166 products (1127,1): 0.000/0.000/0.388/3.235 prescriptions (34657,3): 0.000/0.000/0.520/6.447 generics (85144,1): 0.000/0.000/0.390/6.612 expensive (4449,1): 0.000/0.000/0.324/3.094 food (807,1): 0.000/0.000/0.282/2.595 supply (1905,1): 0.000/0.000/0.281/2.472 tablet (10396,2): 0.000/0.000/0.141/1.400 distribution (1335,3): 0.000/0.000/0.203/1.878 seeking (3864,1): 0.000/0.000/0.273/2.192 option (3370,1): 0.000/0.000/0.261/2.303 federal (626,2): 0.000/0.000/0.216/1.925 access (935,1): 0.000/0.000/0.213/1.807 glaxosmithkline (55181,1): 0.000/0.000/0.468/6.302 required (967,2): 0.000/0.000/0.270/1.948 lawmakers (27291,2): 0.000/0.000/0.268/3.015 approved (2389,1): 0.000/0.000/0.300/2.605 needed (1661,1): 0.000/0.000/0.306/2.156 delivering (9451,1): 0.000/0.000/0.213/1.880 certain (1095,4): 0.000/0.001/0.291/2.007 claim (2099,1): 0.000/0.000/0.172/1.385 pay (1653,2): 0.000/0.000/0.401/3.455 potentially (6379,1): 0.000/0.000/0.376/3.310 concocting (179559,1): 0.000/0.000/0.295/4.974 \nMost similar words in vocab:\nivig: 0.570/7.115 antiretroviral: 0.566/6.438 rebates: 0.549/6.166 vioxx: 0.546/8.383 adrs: 0.545/8.515 medication: 0.542/4.697 drugs: 0.541/5.207 methadone: 0.536/6.429 medications: 0.530/4.487 patients: 0.523/4.719 thiopental: 0.520/7.613 prescriptions: 0.520/6.447 prescription: 0.520/4.512 drug: 0.516/5.158 uninsured: 0.515/6.060 prophylactic: 0.513/5.870 antiepileptic: 0.510/6.275 dosages: 0.508/5.486 praziquantel: 0.506/6.855 incentivize: 0.505/6.814 reimburse: 0.504/5.555 diarrheal: 0.501/6.313 antipsychotic: 0.501/5.619 contraceptives: 0.499/6.069 bevacizumab: 0.498/7.025 costs: 0.497/4.188 medicines: 0.497/4.309 dhhs: 0.495/6.596 bisphosphonates: 0.495/6.349 refinancing: 0.494/5.409 \n\nTopic 14 (3.00): 0.0%\nMost relevant words:\nmultidrug (112412,1): 0.000/0.000/0.478/7.623 hiv (6116,1): 0.000/0.000/0.625/6.090 immune (8538,1): 0.000/0.000/0.459/4.181 hepatitis (19494,1): 0.000/0.000/0.622/5.278 diseases (4439,6): 0.000/0.001/0.655/6.323 antibiotic (24098,2): 0.000/0.000/0.588/4.249 disease (1780,1): 0.000/0.000/0.630/5.805 aids (4693,2): 0.000/0.000/0.523/5.017 effects (1380,2): 0.000/0.000/0.374/3.428 pregnancy (7979,1): 0.000/0.000/0.480/4.438 resistant (8950,1): 0.000/0.000/0.348/3.437 infection (6037,2): 0.000/0.000/0.598/5.454 malaria (13222,1): 0.000/0.000/0.565/5.041 infectious (12595,4): 0.000/0.001/0.577/5.429 tuberculosis (11017,1): 0.000/0.000/0.600/5.225 cholesterol (20400,1): 0.000/0.000/0.358/2.879 cancer (1871,2): 0.000/0.000/0.563/5.438 efficacy (14568,1): 0.000/0.000/0.482/4.037 infected (8254,1): 0.000/0.000/0.412/3.952 treatments (9357,2): 0.000/0.000/0.669/6.094 treating (10124,1): 0.000/0.000/0.615/4.964 treat (5814,3): 0.000/0.001/0.608/5.174 therapeutics (36356,1): 0.000/0.000/0.503/6.266 medically (28191,1): 0.000/0.000/0.407/5.917 therapies (21018,2): 0.000/0.000/0.579/4.803 cause (1310,1): 0.000/0.000/0.357/2.774 treatment (1460,5): 0.000/0.001/0.665/6.004 parasitic (18146,1): 0.000/0.000/0.298/2.483 isolated (4860,1): 0.000/0.000/0.222/2.005 potentially (6379,1): 0.000/0.000/0.419/3.691 \nMost similar words in vocab:\npatients: 0.718/6.487 medications: 0.702/5.937 medication: 0.697/6.045 drugs: 0.688/6.622 treatments: 0.669/6.094 treatment: 0.665/6.004 antiretroviral: 0.659/7.502 diseases: 0.655/6.323 ivig: 0.654/8.169 drug: 0.651/6.507 diabetes: 0.645/5.608 diarrheal: 0.645/8.128 chronic: 0.641/5.654 prophylactic: 0.640/7.317 antiepileptic: 0.639/7.872 contraindicated: 0.632/6.624 disease: 0.630/5.805 infections: 0.629/5.813 hiv: 0.625/6.090 antibiotics: 0.625/4.875 hepatitis: 0.622/5.278 regimens: 0.615/7.174 treating: 0.615/4.964 treatable: 0.615/6.700 asthma: 0.614/4.687 ulcerative: 0.609/5.847 prophylaxis: 0.609/7.131 treat: 0.608/5.174 chemotherapy: 0.603/4.655 anticoagulant: 0.601/6.617 \n\nTopic 19 (3.00): 0.0%\nMost relevant words:\nmultidrug (112412,1): 0.000/0.000/0.459/7.331 therapeutics (36356,1): 0.000/0.000/0.513/6.392 testing (2814,1): 0.000/0.000/0.359/3.203 antibiotic (24098,2): 0.000/0.000/0.556/4.017 efficacy (14568,1): 0.000/0.000/0.467/3.910 effects (1380,2): 0.000/0.000/0.348/3.184 resistant (8950,1): 0.000/0.000/0.323/3.189 cholesterol (20400,1): 0.000/0.000/0.333/2.673 glaxosmithkline (55181,1): 0.000/0.000/0.491/6.609 potentially (6379,1): 0.000/0.000/0.416/3.658 immune (8538,1): 0.000/0.000/0.409/3.727 pharmaceutical (8897,2): 0.000/0.000/0.512/4.773 combination (2674,1): 0.000/0.000/0.277/2.072 pharmaceuticals (16906,3): 0.000/0.000/0.454/4.185 problems (1188,1): 0.000/0.000/0.307/2.626 hiv (6116,1): 0.000/0.000/0.566/5.509 generic (7602,4): 0.000/0.001/0.249/2.187 prescriptions (34657,3): 0.000/0.000/0.517/6.413 develop (1982,1): 0.000/0.000/0.296/2.440 pregnancy (7979,1): 0.000/0.000/0.435/4.019 controlled (2257,3): 0.000/0.000/0.222/2.054 cause (1310,1): 0.000/0.000/0.331/2.574 hepatitis (19494,1): 0.000/0.000/0.554/4.707 generically (48146,1): 0.000/0.000/0.206/2.354 use (176,7): 0.000/0.001/0.285/1.982 pills (18688,1): 0.000/0.000/0.376/3.060 dangerous (3584,2): 0.000/0.000/0.238/2.192 aids (4693,2): 0.000/0.000/0.471/4.519 serious (2375,2): 0.000/0.000/0.279/2.482 therapies (21018,2): 0.000/0.000/0.543/4.503 \nMost similar words in vocab:\nmedication: 0.677/5.870 medications: 0.674/5.702 patients: 0.674/6.082 drugs: 0.664/6.387 ivig: 0.658/8.225 antiretroviral: 0.657/7.471 drug: 0.640/6.405 antiepileptic: 0.631/7.775 treatments: 0.627/5.712 treatment: 0.625/5.637 prophylactic: 0.624/7.131 diarrheal: 0.622/7.836 regimens: 0.607/7.078 contraindicated: 0.600/6.287 antipsychotic: 0.599/6.726 antibiotics: 0.590/4.602 methadone: 0.590/7.074 diabetes: 0.588/5.111 antiviral: 0.586/6.871 anticoagulant: 0.585/6.434 praziquantel: 0.584/7.912 prophylaxis: 0.583/6.828 dosages: 0.583/6.288 isoniazid: 0.582/7.674 infliximab: 0.582/7.534 chronic: 0.582/5.129 analgesics: 0.578/6.417 treatable: 0.577/6.287 medicines: 0.577/5.001 vaccine: 0.575/5.357 \n\n'drugstory' inference ends at 14:51:18. Lasts 232 seconds.\n"
  },
  {
    "path": "test-docs/hillary-speech.txt",
    "content": "Hello, Madison!\r\nWell, thank you all very much. Thank you. Thank you and please please be seated.\r\nI am delighted to be back at this beautiful school, in this wonderful city and to have a chance to talk with all of you. I want to recognize former Governor Doyle. Thank you so much for being here.\r\nSomebody asked me, So why are you going to Madison.  I said, Why wouldnt I? I love going to Madison. Ive been to Madison quite a few times. Well, I mean, its a place where your opponent is very competitive. I said, Yeah thats true, but Im here because, not only do I want to compete for every vote, I respect the people of Madison and Dane county. I want a chance to talk with you and I want you to know where I am coming from with respect to one of the most important issues facing our country. As someone whos been fighting for progressive causes her whole life.  I think its important that we take a broad view about whats at stake in this election.\r\nIve been making the case in this campaign that were not a single-issue country.  Our next President has to be able to break down all the barriers that are holding us back, not just some of them.  And there are so many challenges we need to take on that dont always get the attention they deserve on the campaign trail.\r\nSo today Im going to talk about one of those challenges C something that matters a great deal to our future, to your future, the future of our country: and that is the Supreme Court.\r\nStop and think about it -- and how many law students are here? Do we have some law students are here, so you think about this.\r\nBut if you do stop and think this, the Court shapes virtually every aspect of life in the United States C from whether you can marry the person you love, to whether you can get healthcare, to whether your classmates can carry guns around this campus.  A lot of Americans are concerned about money in politics, and rightly so. Its a serious problem that we have to address.\r\nBut Supreme Court justices are appointed for life.  Theyre not making decisions based on campaign contributions; theyre making them based on legal philosophy C and in some cases, ideology.  And for a long time now, the ideological bent of the Court has led our country in the wrong direction, especially when it comes to stacking the deck in favor of the already wealthy and powerful.\r\nIf were serious about fighting for progressive causes, we need to focus on the Court:  who sits on it, how we choose them, and how much we let politics -- partisan politics -- dominate that process.\r\nAnd I cant think of a better place for this discussion than right here in Madison, because these decisions will affect you.\r\nBefore I was a Senator from New York or a Secretary of State, and even before I was a wife or mother, for that matter C I was a lawyer.\r\nI was drawn to the law for the same reason a lot of young people are: I put my faith in justice and fairness. And I saw the profound impact that our justice system has on peoples lives, for better or for worse.  And I wanted to help make it for the better.\r\nSo when I was in law school I volunteered for the New Haven legal aid association. After I graduated, I put my legal education to work at the Childrens Defense Fund. I ran the legal aid clinic at the University of Arkansas Law School where I taught, supervising students providing legal assistance to prison inmates and poor families.\r\nAnd when President Carter appointed me to the Legal Services Corporation, which is the largest single provider of civil legal aid in America, it was one of the greatest honors of my life.  And we fought hard to convince Congress that using the law to help poor families was a just and necessary cause.  And we won that fight. We hired an army of lawyers to work on behalf of more than a million poor clients across the country C helping families avoid eviction, fight discrimination, receive their earned federal benefits, and so much more.\r\nBut I also learned, when the administration changed, and President Carter went out, and President Reagan went in, that you couldnt ever rest in the fight for justice and fairness. And we kept fighting. And thankfully we had increased legal services before that time because its been pretty much static ever since. Because of these experiences, I come to the issue of the Supreme Court not just as a former Senator who took my constitutional responsibility to advise and consent seriously, but also as a lawyer who spent years fighting for people who werent getting a fair deal in our system.\r\nAnd I carry all these experiences with me -- all those clients and all those cases C every single day.\r\nSo today, I want to share some of my thoughts on the Supreme Court, and then I would love to hear from you.\r\nWe start with that basic premise: that I already stated: the Court matters.\r\nAt its best, the Court is a place where the least powerful voices in our society are heard and protected C whether they be African Americans trying to vote or people getting an education in the era of segregated schools and poll taxes, or women trying to make our own health decisions in the face of humiliating laws that would strip that right away.\r\nNow this may be hard for you today in 2016 to really believe, but I was in high school. The Supreme Court decided a case called Griswold v. Connecticut. That case recognized that women have the right to make the personal choice of whether to use birth control.  Before that in some places in our country like Connecticut, it could be a criminal offense.\r\nSo that case left a powerful impression on me. Along with all the civil rights cases from the 1950s forward, I saw the Court as a place where wrongs were righted, and where everyday folks could stand on equal footing with the most powerful people in the land.\r\nIn recent years, the Court has made a lot of high-profile decisions C some that uphold this progressive tradition and some that tarnish it.  It effectively declared George W. Bush President.  It cut the heart out of the Voting Rights Act, it overturned commonsense laws addressing gun violence, and said that certain employers get to decide whether their female workers can access free birth control under the Affordable Care Act.\r\nBut it also made same-sex marriage legal nationwide, preserved the Affordable Care Act--\r\n--not once but twice, and ensured equal access to education for women.\r\nThe death of Justice Scalia marked the end of an era.  Now as you know, theres a fight over whether President Obama should nominate a replacement, as the Constitution requires.  And that fight is revealing the worst of our politics C the same obstructionism that weve seen from Republicans since the beginning of the Obama Administration, the same disregard for the rule of the law thats given rise to the extremist candidacies of Donald Trump and Ted Cruz.\r\nIts corroding our democracy, and it has to stop.  For those of you who arent following the saga, the story is pretty straightforward.\r\nPresident Obama has done his job, and nominated Merrick Garland, one of the most respected judges in the country, to become the ninth justice.  Democrats admire him.  Republicans do, too.  In fact, a few years ago, when another seat was open on the Court, Senator Orrin Hatch of Utah C whos not exactly a liberal C said that if the President named Judge Garland, there was no question that hed be confirmed.  In fact Senator Hatch called the judge a consensus nominee.\r\nNow normally, this is when the Senate would do its job -- hold hearings, consider the nomination, call for a vote.  But Republicans say they wont.  They wont even hold a hearing.   It doesnt matter how how qualified the Presidents nominee is, or what the Constitution says, or what our country needs.  This is their job!  But they refuse to do it.\r\nSenator Chuck Grassley, the head of the judiciary committee, could hold a hearing tomorrow if he wanted.  But he says we should wait for a new President because, and I quote, the American people shouldnt be denied a voice.  Well, as one of the more than 65 million Americans who voted to re-elect Barack Obama, Id say my voice is being ignored right now because of their obstructionism.\r\nWe chose a President.  We chose him twice.  And now Republicans in the Senate are acting like our votes didnt count, and that President Obama is not still our nations leader.  And Ill tell you, those are not high-minded principles C they are low-minded politics.  And today,\r\nIm adding my voice to the chorus asking Senator Grassley to step up and do his job.\r\nHe should hold a hearing C and he should schedule it as soon as the Senate returns from recess.\r\nBut lets keep in mind C this battle is bigger than just one empty seat on the court.\r\nBy Election Day, two justices will be more than 80 years old, past the Courts average retirement age.  The next President could end up nominating multiple justices.  That means whoever America elects this fall will help determine the future of the Court for decades to come.\r\nJust look at the courts docket, the cases that its hearing this term alone.\r\nThe Court is reviewing how public sector unions collect the fees they use to do their work.  The economic security of millions of teachers, social workers and first responders is at stake.  This is something the people of Wisconsin know all too well.  Because your governor has repeatedly attacked and bullied public sector unions and working families have paid the price.  I think thats wrong, and it should stop.\r\nThe Court is reviewing a Texas law imposing unnecessary, expensive requirements on doctors who perform abortions.  If that law is allowed to stand, there will only be 10 or so health centers left where women can get safe, legal abortions in the whole state of Texas, a state with about 5.4 million women of reproductive age.  So it will effectively end the legal right to choose for millions of women.\r\nThe Court is also reviewing whether Texas should have to exclude non-voters when drawing its electoral map.  That would leave out, among others, legal residents, people with felony convictions and children.  The fair representation of everyone in our society C including 75 million children C hangs in the balance.\r\nAnd on top of all that, the Court is reviewing affirmative action and President Obamas executive actions on immigration, which called for halting the deportation of DREAMers and undocumented parents of citizens and legal residents. Its also put a hold on the Presidents clean-power plan.  Either America can limit how much carbon pollution we produce, or we cant.  And if we cant, then our ability to work with other nations to meet the threat of climate change under the Paris agreement is greatly diminished.\r\nIn short: in a single term, the Supreme Court could demolish pillars of the progressive movement.  And as someone who has worked on every single one of these issues for decades, I see this as a make-or-break moment.  If you care about the fairness of elections, the future of unions, racial disparities in universities, the rights of women, or the future of our planet, you should care about who wins the Presidency and appoints the next Supreme Court justices.\r\nAnd consider, if you will, the dangerous turn the Court has taken in recent years toward protecting the rights of corporations over those of people.\r\nNow you may have heard of the case Citizens United.  The Court ruled that corporations have an unfettered right to free speech, just like you and me.  That means no limit on what corporations can spend independently to influence elections.  And C big surprise C a flood of money from rich people, corporations and special interests has poured into our politics.\r\nCitizens United opened the door to the creation of Super-PACs and between the 2008 and 2012 presidential elections, spending by outside groups tripled.  In 2014, the top 100 donors to super PACs spent nearly as much as all 4-million-750-thousand small donors in the country combined.\r\nNow the idea, I believe, that money is speech turns our Constitution upside down.  Wealth should not be privileged in the courts C in fact, it should have no privilege.  Yet at a time when inequality between working Americans and those at the top is starker than ever, the Supreme Court has given the wealthiest Americans even greater power to affect what happens in our democracy.\r\nJustice Ginsburg says if there were one recent decision shed overrule, its this one.  Im with her.  And I hope she gets the chance to do just that. Now--\r\nNow people forget this, but the Citizens United case actually began with yet another a right-wing attack on me.  It grew out of a Wisconsin case about whether corporations can run issue ads, so-called issue ads close to an election.  So we all have a personal stake in this.  If the Court doesnt overturn Citizens United, I will fight for a Constitutional amendment to limit the influence of money in elections.  It is dangerous to our country and poisonous to our politics.\r\nBut it doesnt stop with Citizens United.  This Court has voted on the side of corporations C against the interests of workers, unions, consumers and the general public C in case after case.\r\nIts made it harder for consumers to band together to sue a corporation, even if they are collectively suffering from corporate behavior.  So 2 million Comcast subscribers in Philadelphia were told they each had to hire a lawyer if they wanted to sue for fairer prices. One-and-a-half million women working at Walmart each had to hire a lawyer if they wanted to sue for sex discrimination.  Thats a burden that the vast majority of people cannot afford.\r\nI know this might sound a little technical.  But it points to an alarming trend.  The Court used to, in the 20th century anyway, protect the little guy against the rich and powerful.  More and more, its doing the opposite -- protecting the rich and powerful against the little guy.  One study found that between 2009 and 2012, the one party most likely to convince the Court to hear a case, out of all the top petitioners, from every part of our society and economy, was the U.S. Chamber of Commerce.  So the Court was more likely to take up cases concerning corporate interests and then to decide in favor of those corporate interests.\r\nIf Im fortunate enough to be President, I will appoint justices who will make sure the scales of justice are not tipped away from individuals toward corporations and special interests, who will protect the constitutional principles of liberty and equality for all, regardless of race, gender, sexual orientation or political viewpoint, who will protect a womans right to choose, rather than billionaires right to buy elections and who will see the Constitution as a blueprint for progress, not a barrier to it.\r\nSo I hope you and everyone across Wisconsin, everyone across America, keeps the Court in mind when you vote.  Now, some of you may already have decided to support me, some of you may have decided differently -- I will keep working to earn your vote.  But even if you are decided or undecided, I will be for you, but I ask you this: Please make sure the court factors into your decision.\r\nConservatives know exactly how high the stakes are.  For years, they have used aggressive legal strategies to accomplish through the courts what theyve failed to accomplish through legislation.  They couldnt pass a repeal of the Affordable Care Act, so they tried to get the Courts to do it.  They couldnt stop the Presidents Clean Power Plan, and they couldnt pass immigration reform, and they didnt want the President to act, so they got the courts to step in.  Now they are fighting hard to make sure the Supreme Court includes as many right-wing justices as possible.\r\nAs scary as it might be, ask yourselves:  what kind of Justice will a President Trump appoint?  Or for that matter, what kind of Attorney General?  What kind of lower court judges?\r\nAnd as you know, he believes Muslims should be banned from entering this country because of their faith.  What would that mean for a nation founded on religious freedom?\r\nHe wants to round up 11 million immigrants and kick them out.  What would that mean for a nation built by immigrants?\r\nHe says wages for working people are too high and we shouldnt raise the minimum wage.  What would that mean for working people, and a Court thats already tilting in favor of powerful corporations?\r\nLets be clear about whats really going on here.  The current fight over Judge Garland is just the latest in a long line of actions aimed at disrupting our government and undermining our President.  And the result is an America thats more divided, more dysfunctional, and less secure.\r\nConsider how Republicans, led by Ted Cruz, shutdown the entire federal government in 2013, rather than fund the Affordable Care Act.  Or how they almost shut down the government again last fall over trying to defund Planned Parenthood.\r\nRemember what Mitch McConnell, the Republican leader in the Senate, said back in 2010 C that the single most important priority for Republicans was making Barack Obama a one-term president.  Now some people thought that was hyperbole.  But I always remember Maya Angelous advice: When someone shows you who they are, believe them. Right? And today --\r\nToday, Republican leaders have been showing us who they are, in fact, for a long time.  Blocking Judge Garland is just the latest evidence and we should start believing them.\r\nIf you want to know where that kind of obstruction and recklessness leads, just look at the Republican race for the Presidency.\r\nNow every day, another Republican bemoans the rise of Donald Trump. They say a Trump nomination will set their party back decades.  I agree.  It will set the Republican Party back if Donald Trump is their standard-bearer.\r\nBut Donald Trump didnt come out of nowhere.  What the Republicans have sown with their extremist tactics, they are now reaping with Donald Trumps candidacy.\r\nIt wasnt long after Senator McConnell said his number-one goal was to prevent the Presidents reelection that Donald Trump started his racist campaign to discredit the Presidents citizenship -- remember the birther movement? And Ted Cruz embarked on his strategy of holding the government hostage to get his way.\r\nThese things are connected.\r\nWhen you have leaders willing to bring the whole of government to a halt to make headlines, you may just give rise to candidates who promise to do even more radical and dangerous things C because once you make the extreme normal, you open the door to even worse.\r\nAnd when you have a party dead-set on demonizing the President, you may just end up with a candidate who says the President never legally was the President at all.\r\nEnough is enough.  Its time for us to take a stand and you can start right here in Wisconsin.\r\nYour Senator, Ron Johnson, is bragging about blocking the President.  Hes in a tight race against former Senator Russ Feingold, an exceptional public servant.\r\nDuring his time on the Senate Judiciary Committee, Senator Feingold actually helped build a stronger judiciary.  So when you leave here, I urge you to call the office of Senator Johnson, email him, contact him, if he has a Facebook page, go on and express your opinion.  Tell him to stop playing games with the Supreme Court.\r\nAnd remember this, remember this in November, when you choose who stands for you in the Senate.  The incumbent will vote for corporations and against working people.  His opponent will vote for a Court that will listen to you.\r\nThen, keep these larger issues in mind when you go to the polls on April 5.\r\nIts time to get back to what makes America already great C respect for the rule of law, statesmanship over showmanship, and people working together across party lines for the good of the nation.\r\nThats what this Court fight is really about.  Thats what this election is about C whether we, as a country, are able to come together to meet the challenges we face and break down all the barriers holding people back C or whether we will be paralyzed by deadlock and divided from each other by bitter partisanship.\r\nAt our best, America has united behind the ideal that everyone deserves a fair shot, no matter who we are or where we started out.  And at its best, the Supreme Court has defended that ideal.\r\nLike in 1954, when the Court abolished segregation in our schools.\r\nOr 1973, when it ruled that women have the right to make intimate health decisions for ourselves.\r\nOr 1977, when the Court paved the way for public sector unions.\r\nOr 1982, when it ruled that undocumented children had the right to go to school.\r\nOr just last year, when the Court ruled that marriage equality was the law of the land.\r\nYou know, all Jim Obergefell wanted was to marry his partner of more than 20 years, John Arthur, before John Arthur died of ALS.  They ended up flying to Maryland, because their home state of Ohio didnt recognize same-sex marriage.  John was so sick, he couldnt even leave the plane C but they got married right there on the tarmac in Baltimore, then flew straight home.  And when John died three months later, Ohio listed him as single on his death certificate.  For the partner who had loved and cared for him, this was a bitter, painful blow, a rejection of the life they had built together over decades C until the Supreme Court ruled that Jim and Johns marriage was legal everywhere in the United States of America.\r\nAnd in that decision, Justice Kennedy wrote, The nature of injustice is that we may not always see it in our own times.  The generations that wrote and ratified the Bill of Rights and the Fourteenth Amendment did not presume to know the extent of freedom in all its dimensions. So they entrusted to future generations a charge protecting the right of all persons to enjoy liberty as we learn its meaning.\r\nThat decision is the latest reminder of what the Court can do when it stand for equality, or against it. When it make America a fairer place, or roll back the progress weve worked so hard to achieve.\r\nIt depends on what the Court decides, and it depends on whos deciding.\r\nWhich, in the end, means it depends on all of us.\r\nSo think hard about the Court. For years people have tried to make the Court a voting issues, and its not easy to do. People are rightly concerned about their economic well-being, about the education of their children, about their health care, about their social security payment. About all the other issues that keep people up at night around millions of kitchen tables.\r\nBut this election has ripped away the curtain and made it absolutely clear to everyone how essential the Supreme Court is to those decisions as well. I will keep talking about it, and advocating, and calling on the Senate to do its job, and I hope there will be a great chorus of voices across our land that will do the same.\r\nIts our Constitution, its our Court, and its our future.\r\nThank you all very much. Thank you. Thank you so much.\r\nWell I know some people may have to leave for classes or other activities, but I would be happy to take some questions.\r\n"
  },
  {
    "path": "test-docs/hillary-speech2.txt",
    "content": "Thank you. Today, today you proved once again theres no place like home. You know, in this campaign weve won in every region of the country. From New York to the South to the East to the West. But this ones personal. New Yorkers, youve alwaysyouve always had my back. And Ive always tried to have yours. Today, together, we did it again and I am deeply, deeply grateful. I want to thank everyone who came out and voted, and to all of you across New York whove known me and worked with me for so long. It is humbling that you trust me with the awesome responsibilities that await our next president. And to all the people who supported Senator Sanders, I believe there is much more that unites us than divides us.\r\nYou know, we started this race not far from here on Roosevelt Island. Pledging to build on the progressive tradition thats done so much for America, from Franklin Roosevelt to Barack Obama. And tonight, a little less than a year later, the race for the Democratic nomination is in the home stretch and victory is in sight.\r\nI want to say to all of my supporters and all of the voters, you have carried us every step of the way with passion and determination that some critics tried to dismiss. Because of you, this campaign is the only one, Democrat or Republican, to win more than 10 million votes. Im going forward because more voices remain to be heard, and tomorrow its on to Connecticut, Delaware, Maryland, Pennsylvania, and Rhode Island. We need you to be volunteering. I hope you will join the 1.1 million people whove already contributed at HillaryClinton.comand by the way, most with less than $100because we have more work to do.\r\nUnder the bright lights of New York, we have seen that its not enough to diagnose problems. You have to explain how you have to resolve them. Thats what we have to do together for our kids, for each other, for our country. So I want you with me to imagine a tomorrow where no barriers hold you back, and all of our people can share in the promise of America. Imagine a tomorrow where every parent can find a good job and every grandparent can enjoy a secure retirement, where no child grows up in the shadow of discrimination or under the specter of deportation, where hard work is honored, families are supported, and communities are strong, a tomorrow where we trust and respect each other despite our differences because were going to make positive differences in peoples lives. That is what this is supposed to be about, actually helping people.\r\nNow, we all knowwe all know many people who are still hurting. I see it everywhere I go. The Great Recession wiped out jobs, homes, and savings, and a lot of Americans havent yet recovered. But I still believe with all my heart that as another greater Democratic President once said, theres nothing wrong with America that cant be cured by whats right with America. That is, after all, what weve always done. Its who we are. America is a problem-solving nation. And in this campaign, we are setting bold progressive goals backed up by real plans that will improve lives, creating more good jobs that provide dignity and pride in a middle class life, raising wages and reducing inequality, making sure all our kids get a good education no matter what zip code they live in, building ladders of opportunity and empowerment so all of our people can go as far as their hard work and talent will take them.\r\nLets revitalize places that have been left out and left behind, from inner cities to coal country to Indian country. And lets put Americans to work rebuilding our crumbling infrastructure, including our failing water systems like the one in Flint, Michigan. There are many places across our country where children and families are at risk from the water they drink and the air they breathe. Lets combat climate change and make America the clean energy superpower of the 21st century. Lets take on the challenge of systemic racism, invest in communities of color, and finally pass comprehensive immigration reform. And once and for all, lets guarantee equal pay for women.\r\nAnd we are going to keep our families safe and our country strong, and were going to defend our rightscivil rights, voting rights, workers rights, womens rights, LGBT rights, and rights for people with disabilities. Those are, after all, New York values, and they are American values. And just as we did in this primary campaign, we need to stand up for them through the general election and every day after that.\r\nYou know, its becoming clearer that this may be one of the most consequential elections of our lifetimes. Donald Trump and Ted Cruz are pushing a vision for America thats divisive and frankly dangerous, returning to trickle-down economics, opposing any increase in the minimum wage, restricting a womans right to make her own health care decisions, promising to round up millions of immigrants, threatening to ban all Muslims from entering the country, planning to treat American Muslims like criminals. These things go against everything America stands for.\r\nAnd we have a very different vision. Its about lifting each other up, not tearing each other down. So instead of building walls, were going to break down barriers. And in this campaign, Ive seen again our remarkable diversity and determination. This is a state and a country. A big-hearted, open-minded, straight talking, hard-working people. Like John, the firefighter from the South Bronx that I met shortly after 9/11 as he searched for survivors at Ground Zero, and like so many others, John got sick breathing the toxic air. When we met again last week, he gave me a replica of his FDNY badge and thanked me for helping our first responders get the healthcare they need. We have to keep fighting for John and all of our firefighters and our police officers, our emergency responders, and the construction workers who did so much.\r\nOr Maxine. Maxine, a 27-year-old single mom from Staten Island whos here tonight. She shared with me how she her way out of poverty, graduated from college. Thanks in part to the help she got for her child from the Childrens Health Insurance Program. Or Mikey, from Stuyvesant Town, who spentis Mikey here? Well, Ill tell you, Mikey spent six months in Rikers for a low-level drug offense, and he found out how hard it is for people whove done their time to find jobs when they get out. Mikey managed to start his own ice cream shop. I took a lot of in or [inaudible] through the media there yesterday. I highly recommend it, as you might have seen. I couldnt stop myself from eating it, as soon as I had it. By the way, he made a concoction for me called Victory.\r\nBut Mikey is one of the many reasons why we have to reform our criminal justice system. And ban the box so others have a fair chance of succeeding. New Yorkers and Americans speak every language, follow every faith, hail from every continent. Our diversity is one of our greatest strengths in the 21st century. Not a weakness. As Robert Kennedy, whose Senate seat I was honored to hold, once said, we are a great country, an unselfish country, and a compassionate country. And no matter what anyone tells you, or what you might hear from others running for president, that is still true today. America is great, and we can do great things if we do them together. So please join us. Text JOIN, 47246, go to HillaryClinton.com. Be part of this campaign.\r\nI know how important it is that we get the campaigns resources from people just like you, who go in and chip in $5, $25. I am grateful to every one of you. And to the volunteers who have worked their hearts out, to the community leaders, members of the state Senate and assembly, county executives, mayors of cities large and small, and to the mayor of New York. Our borough presidents, and our city council members. And to our governor, our senators, our congressional members. And all of my friends across this wonderful state of ours, thank you. Were going to go up against some powerful forces that will do, say, and spend whatever it takes to stop us. But remember, its not whether you get knocked down, its whether you get back up. And finally, finally, let me say this.\r\nFinally, let me say this. There is a remarkable young woman here tonight. Her name is Erica. Erica Smegielski. She lives the truth of what Ive been saying every day. Ericas mother, Dawn, was the principal of Sandy Hook Elementary School. And she died trying to protect children, her students. Erica was devastated, as any family member is, and she couldnt imagine life without her mom but then she got thinking. She got back up. Shed never been involved in politics before, but she has made it her mission to advocate for common sense gun safety reform. Like the mothers of Eric Garner, and Trayvon Martin, and so many others, Erica has turned her sorrow into a strategy, and her mourning into a movement. It isnt easy, but as Erica said the other day, what if everyone who faced tough odds said, its hard, so Im going to walk away? Thats not the type of world I want to live in. Erica, its not the type of world we want to live in, and we refuse to live in that.\r\nSo, my friends, thats the spirit that makes this country great. Its how New Yorkers pull together and rebuild our city after the worst terrorist attack in our history. Its how Americans worked our way back from the worst economic crisis in our lifetimes, and it is how were going to break down all the barriers holding us back. The motto of this state is Excelsior, ever upward. So lets go out and win this election.\r\n"
  },
  {
    "path": "test-docs/nips-wiki.txt",
    "content": "Conference on Neural Information Processing Systems\r\nAbbreviation\tNIPS\r\nDiscipline\tMachine learning, statistics, artificial intelligence, computational neuroscience\r\nPublication details\r\nHistory\t1987C\r\nFrequency\tAnnual\r\nWebsite\thttps://nips.cc/\r\nThe Conference and Workshop on Neural Information Processing Systems (NIPS) is a machine learning and computational neuroscience conference held every December. The conference is a single track meeting that includes invited talks as well as oral and poster presentations of refereed papers, followed by parallel-track workshops that up to 2013 were held at ski resorts. According to Microsoft Academic Search, NIPS is the top conference on machine learning.1\r\n\r\nContents hide\r\n1\tHistory\r\n2\tTopics\r\n3\tThe NIPS Experiment\r\n4\tEditions\r\n5\tSee also\r\n6\tExternal links\r\n7\tNotes\r\nHistory\r\nNIPS began in 1987 as a connectionist neural networks conference, and was held in Denver, United States until 2000. Since then, the conference has been held in Vancouver, Canada (2001-2010), Granada, Spain (2011), and Lake Tahoe, United States (2012-2013). In 2014, the conference was held in Montreal, Canada.\r\nThe NIPS Conference is organized by the NIPS Foundation, which was founded by Ed Posner in 1987. Terrence Sejnowski has been the President of the NIPS Foundation since 1993, when Ed had a tragic bicycle accident. The Board of Trustees consists of previous General Chairs of the NIPS Conference.\r\nPapers in early NIPS proceedings tended to use neural networks as a tool for understanding how the human brain works, which attracted researchers with interests in biological learning systems as well as those interested in artificial learning systems. Since then, the biological and artificial systems research streams have diverged, and recent NIPS proceedings are dominated by papers on machine learning, artificial intelligence and statistics, although computational neuroscience remains an aspect of the conference.\r\nThe proceedings from the conferences have been published in book form by Morgan Kaufmann (1987-1993), MIT Press (1994-2004) and Curran Associates (2005-2013) under the name Advances in Neural Information Processing Systems. Papers from all NIPS conferences are available at http://papers.nips.cc/. NIPS tutorials and conference presentations from 2013 are at http://nips.cc/Conferences/2013/Videos/.\r\nTopics\r\nThe conference continues to span a wide range of topics and continues to grow, with over 2,500 registered participants in 2014. Besides machine learning and neuroscience, other fields represented at NIPS include cognitive science, psychology, computer vision, statistical linguistics, and information theory. Although the 'Neural' in the NIPS acronym was something of a historical relic, the recent resurgence of deep learning in neural networks, which has a wide range of practical applications in speech recognition, object recognition, natural language processing and brain imaging, has revived the initial impetus for the conference.\r\nThe NIPS Experiment\r\nIn NIPS 2014, the program chairs decided to duplicate 10% of all submissions and send them through separate reviewers to evaluate randomness in the reviewing process.2 Several researchers interpreted the result.34 In response to the question whether the decision in NIPS is completely random or not, John Langford writes: \"Clearly nota purely random decision would have arbitrariness of ~78%. It is, however, quite notable that 60% is much closer to 78% than 0%.\" He concludes that the result of the reviewing process is mostly arbitrary. 5"
  },
  {
    "path": "test-docs/sanders-speeches.txt",
    "content": "I am honored to be with you today and was pleased to receive your invitation to speak to this conference of The Pontifical Academy of Social Sciences. Today we celebrate the encyclical Centesimus Annus and reflect on its meaning for our world a quarter-century after it was presented by Pope John Paul II. With the fall of Communism, Pope John Paul II gave a clarion call for human freedom in its truest sense: freedom that defends the dignity of every person and that is always oriented towards the common good.\r\nThe Churchs social teachings, stretching back to the first modern encyclical about the industrial economy, Rerum Novarum in 1891, to Centesimus Annus, to Pope Franciss inspiring encyclical Laudato Si this past year, have grappled with the challenges of the market economy. There are few places in modern thought that rival the depth and insight of the Churchs moral teachings on the market economy.\r\nOver a century ago, Pope Leo XIII highlighted economic issues and challenges in Rerum Novarum that continue to haunt us today, such as what he called the enormous wealth of a few as opposed to the poverty of the many.\r\nAnd let us be clear. That situation is worse today. In the year 2016, the top one percent of the people on this planet own more wealth than the bottom 99 percent, while the wealthiest 60 people C 60 people C own more than the bottom half C 3 1/2 billion people. At a time when so few have so much, and so many have so little, we must reject the foundations of this contemporary economy as immoral and unsustainable.\r\nThe words of Centesimus Annus likewise resonate with us today. One striking example:\r\nFurthermore, society and the State must ensure wage levels adequate for the maintenance of the worker and his family, including a certain amount for savings. This requires a continuous effort to improve workers training and capability so that their work will be more skilled and productive, as well as careful controls and adequate legislative measures to block shameful forms of exploitation, especially to the disadvantage of the most vulnerable workers, of immigrants and of those on the margins of society. The role of trade unions in negotiating minimum salaries and working conditions is decisive in this area. (Para15)\r\nThe essential wisdom of Centesimus Annus is this: A market economy is beneficial for productivity and economic freedom. But if we let the quest for profits dominate society; if workers become disposable cogs of the financial system; if vast inequalities of power and wealth lead to marginalization of the poor and the powerless; then the common good is squandered and the market economy fails us. Pope John Paul II puts it this way: profit that is the result of illicit exploitation, speculation, or the breaking of solidarity among working people . . . has not justification, and represents an abuse in the sight of God and man. (Para43).\r\nWe are now twenty-five years after the fall of Communist rule in Eastern Europe. Yet we have to acknowledge that Pope John Pauls warnings about the excesses of untrammeled finance were deeply prescient. Twenty-five years after Centesimus Annus, speculation, illicit financial flows, environmental destruction, and the weakening of the rights of workers is far more severe than it was a quarter century ago. Financial excesses, indeed widespread financial criminality on Wall Street, played a direct role in causing the worlds worst financial crisis since the Great Depression.\r\nWe need a political analysis as well as a moral and anthropological analysis to understand what has happened since 1991. We can say that with unregulated globalization, a world market economy built on speculative finance burst through the legal, political, and moral constraints that had once served to protect the common good. In my country, home of the worlds largest financial markets, globalization was used as a pretext to deregulate the banks, ending decades of legal protections for working people and small businesses. Politicians joined hands with the leading bankers to allow the banks to become too big to fail. The result: eight years ago the American economy and much of the world was plunged into the worst economic decline since the 1930s. Working people lost their jobs, their homes and their savings, while the government bailed out the banks.\r\nInexplicably, the United States political system doubled down on this reckless financial deregulation, when the U.S. Supreme Court in a series of deeply misguided decisions, unleashed an unprecedented flow of money into American politics. These decisions culminated in the infamous Citizen United case, which opened the financial spigots for huge campaign donations by billionaires and large corporations to turn the U.S. political system to their narrow and greedy advantage. It has established a system in which billionaires can buy elections. Rather than an economy aimed at the common good, we have been left with an economy operated for the top 1 percent, who get richer and richer as the working class, the young and the poor fall further and further behind. And the billionaires and banks have reaped the returns of their campaign investments, in the form of special tax privileges, imbalanced trade agreements that favor investors over workers, and that even give multinational companies extra-judicial power over governments that are trying to regulate them.\r\nBut as both Pope John Paul II and Pope Francis have warned us and the world, the consequences have been even direr than the disastrous effects of financial bubbles and falling living standards of working-class families. Our very soul as a nation has suffered as the public lost faith in political and social institutions. As Pope Francis has stated: Man is not in charge today, money is in charge, money rules. And the Pope has also stated: We have created new idols. The worship of the golden calf of old has found a new and heartless image in the cult of money and the dictatorship of an economy which is faceless and lacking any truly humane goal.\r\nAnd further: While the income of a minority is increasing exponentially, that of the majority is crumbling. This imbalance results from ideologies which uphold the absolute autonomy of markets and financial speculation, and thus deny the right of control to States, which are themselves charged with providing for the common good.\r\nPope Francis has called on the world to say: No to a financial system that rules rather than serves in Evangeli Gaudium. And he called upon financial executives and political leaders to pursue financial reform that is informed by ethical considerations. He stated plainly and powerfully that the role of wealth and resources in a moral economy must be that of servant, not master.\r\nThe widening gaps between the rich and poor, the desperation of the marginalized, the power of corporations over politics, is not a phenomenon of the United States alone. The excesses of the unregulated global economy have caused even more damage in the developing countries. They suffer not only from the boom-bust cycles on Wall Street, but from a world economy that puts profits over pollution, oil companies over climate safety, and arms trade over peace. And as an increasing share of new wealth and income goes to a small fraction of those at the top, fixing this gross inequality has become a central challenge. The issue of wealth and income inequality is the great economic issue of our time, the great political issue of our time, and the great moral issue of our time. It is an issue that we must confront in my nation and across the world.\r\nPope Francis has given the most powerful name to the predicament of modern society: the Globalization of Indifference. Almost without being aware of it, he noted, we end up being incapable of feeling compassion at the outcry of the poor, weeping for other peoples pain, and feeling a need to help them, as though all this were someone elses responsibility and not our own. We have seen on Wall Street that financial fraud became not only the norm but in many ways the new business model. Top bankers have shown no shame for their bad behavior and have made no apologies to the public. The billions and billions of dollars of fines they have paid for financial fraud are just another cost of doing business, another short cut to unjust profits.\r\nSome might feel that it is hopeless to fight the economic juggernaut, that once the market economy escaped the boundaries of morality it would be impossible to bring the economy back under the dictates of morality and the common good. I am told time and time again by the rich and powerful, and the mainstream media that represent them, that we should be practical, that we should accept the status quo; that a truly moral economy is beyond our reach. Yet Pope Francis himself is surely the worlds greatest demonstration against such a surrender to despair and cynicism. He has opened the eyes of the world once again to the claims of mercy, justice and the possibilities of a better world. He is inspiring the world to find a new global consensus for our common home.\r\nI see that hope and sense of possibility every day among Americas young people. Our youth are no longer satisfied with corrupt and broken politics and an economy of stark inequality and injustice. They are not satisfied with the destruction of our environment by a fossil fuel industry whose greed has put short term profits ahead of climate change and the future of our planet. They want to live in harmony with nature, not destroy it. They are calling out for a return to fairness; for an economy that defends the common good by ensuring that every person, rich or poor, has access to quality health care, nutrition and education.\r\nAs Pope Francis made powerfully clear last year in Laudato Si, we have the technology and know-how to solve our problems C from poverty to climate change to health care to protection of biodiversity. We also have the vast wealth to do so, especially if the rich pay their way in fair taxes rather than hiding their funds in the worlds tax and secrecy havens- as the Panama Papers have shown.\r\nThe challenges facing our planet are not mainly technological or even financial, because as a world we are rich enough to increase our investments in skills, infrastructure, and technological know-how to meet our needs and to protect the planet. Our challenge is mostly a moral one, to redirect our efforts and vision to the common good. Centesimus Annus, which we celebrate and reflect on today, and Laudato Si, are powerful, eloquent and hopeful messages of this possibility. It is up to us to learn from them, and to move boldly toward the common good in our time.\r\n\r\nAs we all remember, the last time Republicans occupied the White House, their trickle down economic policies drove us into the worst economic downturn since the depression of the 1930's. No, we will not allow huge tax breaks for billionaires, we will not allow packed -- huge cuts to social security, veterans needs, Medicare, MedicAid, and education. No, we will not allow back into the White House a political party which is so beholden to the fossil fuel industry that they cannot even acknowledge the scientific reality of climate change (INAUDIBLE).\r\n\r\nThe people of New Hampshire have sent a profound message to the political establishment, to the economic establishment, and by the way, to the media establishment.\r\n\r\nWhat the people here have said is given the enormous crises facing our country, it is just too late for the same old, same old establishment politics, and establishment economics. The people want real change. What the American people are saying -- and, by the way, I hear this not just from progressives, but from conservatives and from moderates, is that we can no longer continue to have a campaign finances system in which Wall Street and the billionaire class are able to buy elections.\r\n\r\nAmericans, no matter what their political view may be, understand that that is not what democracy is about. That is what oligarchy is about, and we will not allow that to continue. I do not have a Super PAC, and I do not want a Super PAC. I am overwhelmed, and I am deeply moved far more than I can express in words by the fact that our campaigns financial support comes from more than one million Americans who have made more than 3.7 million individual contributions. That is more individual contributions than any candidate in the history of the United States up until this point in an election.\r\n\r\n\r\nAnd, you know what that average contribution was? $27 dollars.\r\n\r\nI am going to New York City tonight and tomorrow, but I'm not going to New York City to hold a fundraiser on Wall Street. Instead, I'm going to hold a fundraiser right here, right now, across America. My request is please go to Berniesanders.com and contribute. Please help us raise the funds we need, whether it's $10 bucks, $20 bucks, or $50 bucks. Help up us raise the money we need to take the fight to Nevada, South Carolina, and the states on Super Tuesday.\r\n\r\nSo, there it is, that's our fundraiser. Pretty quick.\r\n\r\nNow, what the American people understand is that our great country was based on a simple principal, and that principle is fairness. Let me be very clear, it is not fair when we have more income and wealth inequality today than almost any major country on Earth. And, when the top one-tenth of 1% now owns almost as much wealth as the bottom 90%, that's not fair. It is not fair when the 20 wealthiest people in this country now own more wealth than the bottom half of the American people.\r\n\r\nSo, are you guys ready for a radical idea? Together we are going to create an economy that works for all of us, not just the 1%. And, when millions of our people are working for starvation wages, yep, we're going to raise the minimum wage to $15 bucks an hour. And, we are going to bring pay equity for women. And, when we need the best educated workforce in the world, yes, we are going to make public colleges and universities tuition free. And, for the millions of Americans struggling with horrendous levels of student debt, we are going to substantially ease that burden.\r\n\r\nIn America people should be financially distressed for decades for the crime, the crime of trying to get a higher education, that's absurd.\r\n\r\nWell, my critics say, you know, Bernie, that's a great idea, you're into all this free stuff. How are you going to pay for it? I will tell you how we're going to pay for it. We're going to impose a tax on Wall Street speculation. The greed, the recklessness, and the illegal behavior drove our economy to its knees. The American people bailed out Wall Street, now it's Wall Street's time to help the middle class.\r\n\r\n\r\nAnd, when we talk about transforming America, it means ending the disgrace of this country having more people in jail than any other country in the world, disproportionately African-American, and Latino. Not only are we going to fight to end institutional racism, and a broken criminal justice system, we are going to provide jobs and education for our young people, not jails and incarceration.\r\n\r\nAnd, Let me say that as a member of the energy committee in the Senate, and the environmental committee, the debate is over. Climate change is real. It is caused by human activity, and it is already causing devastating problems in this country and around the world. We have a moral responsibility to work with countries throughout the world to transform our energy system away from fossil fuel to energy efficiency and sustainable energy.\r\n\r\nNow, I have been criticized during this campaign for many, many things. Every single day, that's OK, that's alright. They're throwing everything at me except the kitchen sink, and I have the feeling that kitchen sink is coming pretty soon as well. But, what our campaign is about is thinking big, not small. It's about having the courage to reject the status quo. It's about saying that in a time when every major country on Earth guarantees healthcare to all of their people, we should be doing the same in our great country.\r\n\r\nIn my view, under President Obama's leadership, the Affordable Care Act has been an important step forward, no question about it. But, we can, and must, do better. Twenty-nine million Americans should not remain uninsured, an even greater number should remain under- insured with large deductibles and co-payments. We should not be paying by far the highest prices in the world for prescription drugs at a time -- listen to this, when the top three drug companies in this country made $45 billion dollars in profit last year. That is an obscenity, and let me tell you something. When we make it to the White House, the pharmaceutical industry will not continue to rip-off the American people.\r\n\r\nFurther, it makes no sense that as a nation we continue to spend far, far more per capita than do the people of any other nation, all of whom guarantee healthcare to all of their people. That is why I believe in a Medicare for all, single-payer program which will not only guarantee healthcare for all, but will save the average middle class family thousands of dollars a year in health care costs.\r\n\r\n\r\nMy friends, we all know that we live in a dangerous and complex world. As president I will defend this nation, but I will do it responsibly. I voted against the war in Iraq, and that was the right vote. While we must be relentless in combating terrorist who would do us harm, we cannot, and should not be the policeman of the world. Nor should we bear the burden of fighting terrorism alone.\r\n\r\nIn the Middle East, the United States must remain part of an international coalition sustained by nations in the region that have the means to protect themselves. Together we must, and will, destroy ISIS, but we should do it in a way that does not put our young men and women in the military into perpetual warfare in the quagmire of the Middle East.\r\n\r\nMy friends, we must fix our broken immigration system that divides families, and create a path towards citizenship for hardworking people who are living in the shadows.\r\n\r\nWe must strengthen and expand Social Security, and increase the benefits that seniors, and disabled vets receive so that people can live in dignity in their retirement. We must rebuild our crumbling infrastructure, and when we do that, we create millions of decent paying jobs.\r\n\r\nWe must pursue the fight for women's rights, for gay rights, for disability rights. We must against stronger, and stronger, opposition protect the right of a woman to control her own body.\r\n\r\nWe must protect the men and women who serve our nation in uniform, and protect our veterans who put their lives on the line to defend us.\r\n\r\nMy friends, we must tell the billionaire class and the 1% that they cannot have it all at a time of massive wealth and income inequality, the wealthiest people and largest corporations in this country will start their paying their fair share of taxes.\r\n\r\nMy friends, I am the son of a Polish immigrant who came to this country speaking no English, and having no money. My father worked everyday of his life, and he never made a whole lot. My Mom and Dad, and brother and I grew up in a small three and a half room, rent controlled apartment in Brooklyn, New York. My Mother, who died at a young age, always dreamed of moving out of that apartment, getting a home of her own, but she never realized that dream.\r\n\r\nThe truth is that neither one of my parents could ever have dreamed that I would be here tonight standing before you as a candidate for President of the United States.\r\n\r\nThis is the promise of America, and this is the promise we must keep alive for future generations. What began last week in Iowa, what voters here in New Hampshire confirm tonight, is nothing short of the beginning of a political revolution.\r\n\r\nIt is a political revolution that will bring tens of millions of our people together. It will bring together working people who have given up on the political process. It will bring together young people who have never participated in the political process. It will bring together blacks, and whites, latinos, Asian-Americans, Native Americans, straight and gay, male and female. People who were born in America, and people who immigrated here.\r\n\r\nWe will all come together to say loudly, and clearly that the government of our great nation belongs to all of us, not just a few wealthy campaign contributors. That is what this campaign is about, that is what the political revolution is about. So, New Hampshire, thank you again. And, now it's time.\r\n\r\nThank you, New Hampshire. And, now it's on to Nevada, South Carolina, and beyond.\r\n"
  },
  {
    "path": "test-docs/spacex-news.txt",
    "content": "Some of you may have been following our recent attempts to vertically land the first stage of our Falcon 9 rocket back on Earth.\r\n\r\nThere was this attempt in January, followed by this one in April.\r\nCRS-6 first stage approaching Just Read the InstructionsThese landing attempts move us toward our goal of producing a fully and rapidly reusable rocket system, which will dramatically reduce the cost of space transport.\r\n\r\nA jumbo jet costs about the same as one of our Falcon 9 rockets, but airlines don't junk a plane after a one-way trip from LA to New York. Yet when it comes to space travel, rockets fly only onceeven though the rocket itself represents the majority of launch cost.\r\n\r\nThe Space Shuttle was technically reusable, but its giant fuel tank was discarded after each launch, and its side boosters parachuted into corrosive salt water every flight, beginning a long and involved process of retrieval and reprocessing. So, what if we could mitigate those factors by landing rockets gently and precisely on land? Refurbishment time and cost would be dramatically reduced.\r\n\r\nHistorically, most rockets have needed to use all of their available fuel in order to get their payload into space. SpaceX rockets were built from the beginning with reusability in mindthey have enough built-in fuel margin to deliver a Dragon to the space station and return the first-stage to Earth. That extra fuel is needed to reignite the engines a few times to slow the rocket down and ultimately land the first stage after it has sent the spacecraft on its way.\r\n\r\nIn addition to extra fuel, weve added a few critical features to our Falcon 9 first stage for reusabilitys sake. Our rocket has small, foldable heat-resistant wings called grid fins needed for steering the first-stage as it plummets from the edge of space through Earths atmosphere, cold-gas thrusters on the top of the first-stage that are used to flip the rocket around as it begins its journey back to Earth, and strong but lightweight carbon fiber landing legs that deploy as it approaches touchdown. All of these systems, while built and programmed by humans, are totally automated once the rocket is launchedand are reacting and adjusting their behavior based on incoming, real-time data.\r\n\r\nSo, what have we learned from the most recent landing attempts?\r\n\r\nThe first attempt to land on a drone ship in the Atlantic was in January, and while we came close, the first stage prematurely ran out of the hydraulic fluid that is used to steer the small fins that help control the rockets descent. The vehicle has now been equipped with much more of that critical fluid for steering purposes.\r\n\r\nOur second attempt was in April, and we came close to sticking this landing. Check out this previously unreleased, longer video from our tracking camera. It shows the stages descent through the atmosphere, when the vehicle is traveling faster than the speed of sound, all the way to touchdown.\r\n\r\n\r\n\r\nThat controlled descent was successful, but about 10 seconds before landing, a valve controlling the rockets engine power (thrust) temporarily stopped responding to commands as quickly as it should have. As a result, it throttled down a few seconds later than commanded, andwith the rocket weighing about 67,000 lbs and traveling nearly 200 mph at this pointa few seconds can be a very long time. With the throttle essentially stuck on high and the engine firing longer than it was supposed to, the vehicle temporarily lost control and was unable to recover in time for landing, eventually tipping over.\r\n\r\nLast-second tilt aside, the landing attempt happened pretty much exactly as planned. Shortly after stage separation (when the second stage leaves the first stage behind and goes on to carry Dragon to orbit), cold gas thrusters fired to flip the stage to reorient it for reentry. Then, three engines lit for a boostback burn that slows the rocket and brings it toward the landing site.\r\n\r\nThe engines then re-lit to slow the stage for reentry through Earths atmosphere, and grid fins (this time with much more hydraulic fluid) extended to steer the lift produced by the stage. Our atmosphere is like molasses to an object traveling at Mach 4, and the grid fins are essential for landing with precision. The final landing burn ignited, and together the grid fins, cold gas thrusters and steerable engines controlled the vehicle, keeping the stage within 15 meters of its target trajectory throughout the landing burn. The vehicles legs deployed just before it reached our drone ship, Just Read the Instructions, where the stage landed within 10 meters of the target, albeit a bit too hard to stay upright. Rocket Re-usability Infographic Post-launch analysis has confirmed the throttle valve as the sole cause of this hard landing. The team has made changes to help prevent, and be able to rapidly recover from, similar issues for the next attempt, which will be on our next launchthe eighth Falcon 9 and Dragon cargo mission to the space station, currently scheduled for this Sunday.\r\n\r\nEven given everything weve learned, the odds of succeeding on our third attempt to land on a drone ship (a new one named Of Course I Still Love You) are uncertain, but tune in here this Sunday as we try to get one step closer toward a fully and rapidly reusable rocket.\r\n"
  },
  {
    "path": "test-docs/trump-speech.txt",
    "content": "Wow. Whoa. That is some group of people. Thousands.\r\n\r\nSo nice, thank you very much. Thats really nice. Thank you. Its great to be at Trump Tower. Its great to be in a wonderful city, New York. And its an honor to have everybody here. This is beyond anybodys expectations. Theres been no crowd like this.\r\n\r\nAnd, I can tell, some of the candidates, they went in. They didnt know the air-conditioner didnt work. They sweated like dogs.\r\n\r\nThey didnt know the room was too big, because they didnt have anybody there. How are they going to beat ISIS? I dont think its gonna happen.\r\n\r\nOur country is in serious trouble. We dont have victories anymore. We used to have victories, but we dont have them. When was the last time anybody saw us beating, lets say, China in a trade deal? They kill us. I beat China all the time. All the time.\r\n\r\nWhen did we beat Japan at anything? They send their cars over by the millions, and what do we do? When was the last time you saw a Chevrolet in Tokyo? It doesnt exist, folks. They beat us all the time.\r\n\r\nWhen do we beat Mexico at the border? Theyre laughing at us, at our stupidity. And now they are beating us economically. They are not our friend, believe me. But theyre killing us economically.\r\n\r\nThe U.S. has become a dumping ground for everybody elses problems.\r\n\r\nThank you. Its true, and these are the best and the finest. When Mexico sends its people, theyre not sending their best. Theyre not sending you. Theyre not sending you. Theyre sending people that have lots of problems, and theyre bringing those problems with us. Theyre bringing drugs. Theyre bringing crime. Theyre rapists. And some, I assume, are good people.\r\n\r\nBut I speak to border guards and they tell us what were getting. And it only makes common sense. It only makes common sense. Theyre sending us not the right people.\r\n\r\nIts coming from more than Mexico. Its coming from all over South and Latin America, and its coming probably probably from the Middle East. But we dont know. Because we have no protection and we have no competence, we dont know whats happening. And its got to stop and its got to stop fast.\r\n\r\nIslamic terrorism is eating up large portions of the Middle East. Theyve become rich. Im in competition with them.\r\n\r\nThey just built a hotel in Syria. Can you believe this? They built a hotel. When I have to build a hotel, I pay interest. They dont have to pay interest, because they took the oil that, when we left Iraq, I said we shouldve taken.\r\n\r\nSo now ISIS has the oil, and what they dont have, Iran has. And in 19 and I will tell you this, and I said it very strongly, years ago, I said and I love the military, and I want to have the strongest military that weve ever had, and we need it more now than ever. But I said, Dont hit Iraq, because youre going to totally destabilize the Middle East. Iran is going to take over the Middle East, Iran and somebody else will get the oil, and it turned out that Iran is now taking over Iraq. Think of it. Iran is taking over Iraq, and theyre taking it over big league.\r\n\r\nWe spent $2 trillion in Iraq, $2 trillion. We lost thousands of lives, thousands in Iraq. We have wounded soldiers, who I love, I love  theyre great  all over the place, thousands and thousands of wounded soldiers.\r\n\r\nAnd we have nothing. We cant even go there. We have nothing. And every time we give Iraq equipment, the first time a bullet goes off in the air, they leave it.\r\n \r\nLast week, I read 2,300 Humvees these are big vehicles were left behind for the enemy. 2,000? You would say maybe two, maybe four? 2,300 sophisticated vehicles, they ran, and the enemy took them.\r\n\r\nLast quarter, it was just announced our gross domestic product a sign of strength, right? But not for us. It was below zero. Whoever heard of this? Its never below zero.\r\n\r\nOur labor participation rate was the worst since 1978. But think of it, GDP below zero, horrible labor participation rate.\r\n\r\nAnd our real unemployment is anywhere from 18 to 20 percent. Dont believe the 5.6. Dont believe it.\r\n\r\nThats right. A lot of people up there cant get jobs. They cant get jobs, because there are no jobs, because China has our jobs and Mexico has our jobs. They all have jobs.\r\n\r\nBut the real number, the real number is anywhere from 18 to 19 and maybe even 21 percent, and nobody talks about it, because its a statistic thats full of nonsense.\r\n\r\nOur enemies are getting stronger and stronger by the way, and we as a country are getting weaker. Even our nuclear arsenal doesnt work.\r\n\r\nIt came out recently they have equipment that is 30 years old. They dont know if it worked. And I thought it was horrible when it was broadcast on television, because boy, does that send signals to Putin and all of the other people that look at us and they say, That is a group of people, and that is a nation that truly has no clue. They dont know what theyre doing. They dont know what theyre doing.\r\n\r\nWe have a disaster called the big lie: Obamacare. Obamacare.\r\n\r\nYesterday, it came out that costs are going for people up 29, 39, 49, and even 55 percent, and deductibles are through the roof. You have to be hit by a tractor, literally, a tractor, to use it, because the deductibles are so high, its virtually useless. Its virtually useless. It is a disaster.\r\n\r\nAnd remember the $5 billion website? $5 billion we spent on a website, and to this day it doesnt work. A $5 billion website.\r\n\r\nI have so many websites, I have them all over the place. I hire people, they do a website. It costs me $3. $5 billion website.\r\n\r\nWell, you need somebody, because politicians are all talk, no action. Nothings gonna get done. They will not bring us believe me to the promised land. They will not.\r\n\r\nAs an example, Ive been on the circuit making speeches, and I hear my fellow Republicans. And theyre wonderful people. I like them. They all want me to support them. They dont know how to bring it about. They come up to my office. Im meeting with three of them in the next week. And they dont know Are you running? Are you not running? Could we have your support? What do we do? How do we do it?\r\n\r\nI like them. And I hear their speeches. And they dont talk jobs and they dont talk China. When was the last time you heard China is killing us? Theyre devaluing their currency to a level that you wouldnt believe. It makes it impossible for our companies to compete, impossible. Theyre killing us.\r\n\r\nBut you dont hear that from anybody else. You dont hear it from anybody else. And I watch the speeches.\r\n\r\nI watch the speeches of these people, and they say the sun will rise, the moon will set, all sorts of wonderful things will happen. And people are saying, Whats going on? I just want a job. Just get me a job. I dont need the rhetoric. I want a job.\r\n\r\nAnd thats whats happening. And its going to get worse, because remember, Obamacare really kicks in in 16, 2016. Obama is going to be out playing golf. He might be on one of my courses. I would invite him, I actually would say. I have the best courses in the world, so Id say, you what, if he wants to I have one right next to the White House, right on the Potomac. If hed like to play, thats fine.\r\n\r\nIn fact, Id love him to leave early and play, that would be a very good thing.\r\n\r\nBut Obamacare kicks in in 2016. Really big league. It is going to be amazingly destructive. Doctors are quitting. I have a friend whos a doctor, and he said to me the other day, Donald, I never saw anything like it. I have more accountants than I have nurses. Its a disaster. My patients are beside themselves. They had a plan that was good. They have no plan now.\r\n\r\nWe have to repeal Obamacare, and it can be and and it can be replaced with something much better for everybody. Let it be for everybody. But much better and much less expensive for people and for the government. And we can do it.\r\n\r\nSo Ive watched the politicians. Ive dealt with them all my life. If you cant make a good deal with a politician, then theres something wrong with you. Youre certainly not very good. And thats what we have representing us. They will never make America great again. They dont even have a chance. Theyre controlled fully theyre controlled fully by the lobbyists, by the donors, and by the special interests, fully.\r\n\r\nYes, they control them. Hey, I have lobbyists. I have to tell you. I have lobbyists that can produce anything for me. Theyre great. But you know what? it wont happen. It wont happen. Because we have to stop doing things for some people, but for this country, its destroying our country. We have to stop, and it has to stop now.\r\n\r\nNow, our country needs our country needs a truly great leader, and we need a truly great leader now. We need a leader that wrote The Art of the Deal.\r\n\r\nWe need a leader that can bring back our jobs, can bring back our manufacturing, can bring back our military, can take care of our vets. Our vets have been abandoned.\r\n\r\nAnd we also need a cheerleader.\r\n\r\nYou know, when President Obama was elected, I said, Well, the one thing, I think hell do well. I think hell be a great cheerleader for the country. I think hed be a great spirit.\r\n\r\nHe was vibrant. He was young. I really thought that he would be a great cheerleader.\r\n\r\nHes not a leader. Thats true. Youre right about that.\r\n\r\nBut he wasnt a cheerleader. Hes actually a negative force. Hes been a negative force. He wasnt a cheerleader; he was the opposite.\r\n\r\nWe need somebody that can take the brand of the United States and make it great again. Its not great again.\r\n\r\nWe need we need somebody we need somebody that literally will take this country and make it great again. We can do that.\r\n\r\nAnd, I will tell you, I love my life. I have a wonderful family. Theyre saying, Dad, youre going to do something thats going to be so tough.\r\n\r\nYou know, all of my life, Ive heard that a truly successful person, a really, really successful person and even modestly successful cannot run for public office. Just cant happen. And yet thats the kind of mindset that you need to make this country great again.\r\n\r\nSo ladies and gentlemenI am officially running for president of the United States, and we are going to make our country great again.\r\n\r\nIt can happen. Our country has tremendous potential. We have tremendous people.\r\n\r\nWe have people that arent working. We have people that have no incentive to work. But theyre going to have incentive to work, because the greatest social program is a job. And theyll be proud, and theyll love it, and theyll make much more than they wouldve ever made, and theyll be theyll be doing so well, and were going to be thriving as a country, thriving. It can happen.\r\n\r\nI will be the greatest jobs president that God ever created. I tell you that.\r\n\r\nIll bring back our jobs from China, from Mexico, from Japan, from so many places. Ill bring back our jobs, and Ill bring back our money.\r\n\r\nRight now, think of this: We owe China $1.3 trillion. We owe Japan more than that. So they come in, they take our jobs, they take our money, and then they loan us back the money, and we pay them in interest, and then the dollar goes up so their deals even better.\r\n\r\nHow stupid are our leaders? How stupid are these politicians to allow this to happen? How stupid are they?\r\n\r\nIm going to tell you thank you. Im going to tell you a couple of stories about trade, because Im totally against the trade bill for a number of reasons.\r\n\r\nNumber one, the people negotiating dont have a clue. Our president doesnt have a clue. Hes a bad negotiator.\r\n\r\nHes the one that did Bergdahl. We get Bergdahl, they get five killer terrorists that everybody wanted over there.\r\n\r\nWe get Bergdahl. We get a traitor. We get a no-good traitor, and they get the five people that they wanted for years, and those people are now back on the battlefield trying to kill us. Thats the negotiator we have.\r\n\r\nTake a look at the deal hes making with Iran. He makes that deal, Israel maybe wont exist very long. Its a disaster, and we have to protect Israel. But\r\n\r\nSo we need people Im a free trader. But the problem with free trade is you need really talented people to negotiate for you. If you dont have talented people, if you dont have great leadership, if you dont have people that know business, not just a political hack that got the job because he made a contribution to a campaign, which is the way all jobs, just about, are gotten, free trade terrible.\r\n\r\nFree trade can be wonderful if you have smart people, but we have people that are stupid. We have people that arent smart. And we have people that are controlled by special interests. And its just not going to work.\r\n\r\nSo, heres a couple of stories happened recently. A friend of mine is a great manufacturer. And, you know, China comes over and they dump all their stuff, and I buy it. I buy it, because, frankly, I have an obligation to buy it, because they devalue their currency so brilliantly, they just did it recently, and nobody thought they could do it again.\r\n\r\nBut with all our problems with Russia, with all our problems with everything everything, they got away with it again. And its impossible for our people here to compete.\r\n\r\nSo I want to tell you this story. A friend of mine whos a great manufacturer, calls me up a few weeks ago. Hes very upset. I said, Whats your problem?\r\n\r\nHe said, You know, I make great product.\r\n\r\nAnd I said, I know. I know that because I buy the product.\r\n\r\nHe said, I cant get it into China. They wont accept it. I sent a boat over and they actually sent it back. They talked about environmental, they talked about all sorts of crap that had nothing to do with it.\r\n\r\nI said, Oh, wait a minute, thats terrible. Does anyone know this?\r\n\r\nHe said, Yeah, they do it all the time with other people.\r\n\r\nI said, They send it back?\r\n\r\nYeah. So I finally got it over there and they charged me a big tariff. Theyre not supposed to be doing that. I told them.\r\n\r\nNow, they do charge you tariff on trucks, when we send trucks and other things over there.\r\n\r\nAsk Boeing. They wanted Boeings secrets. They wanted their patents and all their secrets before they agreed to buy planes from Boeing.\r\n\r\nHey, Im not saying theyre stupid. I like China. I sell apartments for I just sold an apartment for $15 million to somebody from China. Am I supposed to dislike them? I own a big chunk of the Bank of America Building at 1290 Avenue of the Americas, that I got from China in a war. Very valuable.\r\n\r\nI love China. The biggest bank in the world is from China. You know where their United States headquarters is located? In this building, in Trump Tower. I love China. People say, Oh, you dont like China?\r\n\r\nNo, I love them. But their leaders are much smarter than our leaders, and we cant sustain ourself with that. Theres too much its like its like take the New England Patriots and Tom Brady and have them play your high school football team. Thats the difference between Chinas leaders and our leaders.\r\n\r\nThey are ripping us. We are rebuilding China. Were rebuilding many countries. China, you go there now, roads, bridges, schools, you never saw anything like it. They have bridges that make the George Washington Bridge look like small potatoes. And theyre all over the place.\r\n\r\nWe have all the cards, but we dont know how to use them. We dont even know that we have the cards, because our leaders dont understand the game. We could turn off that spigot by charging them tax until they behave properly.\r\n\r\nNow theyre going militarily. Theyre building a military island in the middle of the South China sea. A military island. Now, our country could never do that because wed have to get environmental clearance, and the environmentalist wouldnt let our country we would never build in an ocean. They built it in about one year, this massive military port.\r\n\r\nTheyre building up their military to a point that is very scary. You have a problem with ISIS. You have a bigger problem with China.\r\n\r\nAnd, in my opinion, the new China, believe it or not, in terms of trade, is Mexico.\r\n\r\nSo this man tells me about the manufacturing. I say, Thats a terrible story. I hate to hear it.\r\n\r\nBut I have another one, Ford.\r\n\r\nSo Mexico takes a company, a car company that was going to build in Tennessee, rips it out. Everybody thought the deal was dead. Reported it in the Wall Street Journal recently. Everybody thought it was a done deal. Its going in and thats going to be it, going into Tennessee. Great state, great people.\r\n\r\nAll of a sudden, at the last moment, this big car manufacturer, foreign, announces theyre not going to Tennessee. Theyre gonna spend their $1 billion in Mexico instead. Not good.\r\n\r\nNow, Ford announces a few weeks ago that Ford is going to build a $2.5 billion car and truck and parts manufacturing plant in Mexico. $2.5 billion, its going to be one of the largest in the world. Ford. Good company.\r\n\r\nSo I announced that Im running for president. I would\r\n\r\n one of the early things I would do, probably before I even got in and I wouldnt even use you know, I have I know the smartest negotiators in the world. I know the good ones. I know the bad ones. I know the overrated ones.\r\n\r\nYou get a lot of them that are overrated. Theyre not good. They think they are. They get good stories, because the newspapers get buffaloed. But theyre not good.\r\n\r\nBut I know the negotiators in the world, and I put them one for each country. Believe me, folks. We will do very, very well, very, very well.\r\n\r\nBut I wouldnt even waste my time with this one. I would call up the head of Ford, who I know. If I was president, Id say, Congratulations. I understand that youre building a nice $2.5 billion car factory in Mexico and that youre going to take your cars and sell them to the United States zero tax, just flow them across the border.\r\n\r\nAnd you say to yourself, How does that help us, right? How does that help us? Where is that good? Its not.\r\n\r\nSo I would say, Congratulations. Thats the good news. Let me give you the bad news. Every car and every truck and every part manufactured in this plant that comes across the border, were going to charge you a 35-percent tax, and that tax is going to be paid simultaneously with the transaction, and thats it.\r\n\r\nNow, heres what is going to happen. If its not me in the position, its one of these politicians that were running against, you know, the 400 people that were (inaudible). And heres whats going to happen. Theyre not so stupid. They know its not a good thing, and they may even be upset by it. But then theyre going to get a call from the donors or probably from the lobbyist for Ford and say, You cant do that to Ford, because Ford takes care of me and I take care of you, and you cant do that to Ford.\r\n\r\nAnd guess what? No problem. Theyre going to build in Mexico. Theyre going to take away thousands of jobs. Its very bad for us.\r\n\r\nSo under President Trump, heres what would happen:\r\n\r\nThe head of Ford will call me back, I would say within an hour after I told them the bad news. But it could be hed want to be cool, and hell wait until the next day. You know, they want to be a little cool.\r\n\r\nAnd hell say, Please, please, please. Hell beg for a little while, and Ill say, No interest. Then hell call all sorts of political people, and Ill say, Sorry, fellas. No interest, because I dont need anybodys money. Its nice. I dont need anybodys money.\r\n\r\nIm using my own money. Im not using the lobbyists. Im not using donors. I dont care. Im really rich. I (inaudible).\r\n\r\nAnd by the way, Im not even saying thats the kind of mindset, thats the kind of thinking you need for this country.\r\n\r\nSo because we got to make the country rich.\r\n\r\nIt sounds crass. Somebody said, Oh, thats crass. Its not crass.\r\n\r\nWe got $18 trillion in debt. We got nothing but problems.\r\n\r\nWe got a military that needs equipment all over the place. We got nuclear weapons that are obsolete.\r\n\r\nWeve got nothing. Weve got Social Security thats going to be destroyed if somebody like me doesnt bring money into the country. All these other people want to cut the hell out of it. Im not going to cut it at all; Im going to bring money in, and were going to save it.\r\n\r\nBut heres whats going to happen:\r\n\r\nAfter Im called by 30 friends of mine who contributed to different campaigns, after Im called by all of the special interests and by the the donors and by the lobbyists and they have zero chance at convincing me, zero Ill get a call the next day from the head of Ford. Hell say. Please reconsider, Ill say no.\r\n\r\nHell say, Mr. President, weve decided to move the plant back to the United States, and were not going to build it in Mexico. Thats it. They have no choice. They have no choice.\r\n\r\nThere are hundreds of things like that. Ill give you another example.\r\n\r\nSaudi Arabia, they make $1 billion a day. $1 billion a day. I love the Saudis. Many are in this building. They make a billion dollars a day. Whenever they have problems, we send over the ships. We say were gonna protect. What are we doing? Theyve got nothing but money.\r\n\r\nIf the right person asked them, theyd pay a fortune. They wouldnt be there except for us.\r\n\r\nAnd believe me, you look at the border with Yemen. You remember Obama a year ago, Yemen was a great victory. Two weeks later, the place was blown up. Everybody got out and they kept our equipment.\r\n\r\nThey always keep our equipment. We ought to send used equipment, right? They always keep our equipment. We ought to send some real junk, because, frankly, it would be we ought to send our surplus. Were always losing this gorgeous brand-new stuff.\r\n\r\nBut look at that border with Saudi Arabia. Do you really think that these people are interested in Yemen? Saudi Arabia without us is gone. Theyre gone.\r\n\r\nAnd Im the one that made all of the right predictions about Iraq. You know, all of these politicians that Im running against now its so nice to say Im running as opposed to if I run, if I run. Im running.\r\n\r\nBut all of these politicians that Im running against now, theyre trying to disassociate. I mean, you looked at Bush, it took him five days to answer the question on Iraq. He couldnt answer the question. He didnt know. I said, Is he intelligent?\r\n\r\nThen I looked at Rubio. He was unable to answer the question, is Iraq a good thing or bad thing? He didnt know. He couldnt answer the question.\r\n\r\nHow are these people gonna lead us? How are we gonna how are we gonna go back and make it great again? We cant. They dont have a clue. They cant lead us. They cant. They cant even answer simple questions. It was terrible.\r\n\r\nBut Saudi Arabia is in big, big trouble. Now, thanks to fracking and other things, the oil is all over the place. And I used to say it, there are ships at sea, and this was during the worst crisis, that were loaded up with oil, and the cartel kept the price up, because, again, they were smarter than our leaders. They were smarter than our leaders.\r\n\r\nThere is so much wealth out there that can make our country so rich again, and therefore make it great again. Because we need money. Were dying. Were dying. We need money. We have to do it. And we need the right people.\r\n\r\nSo Ford will come back. Theyll all come back. And I will say this, this is going to be an election, in my opinion, thats based on competence.\r\n\r\nSomebody said  thank you, darlin.\r\n\r\nSomebody said to me the other day, a reporter, a very nice reporter, But, Mr. Trump, youre not a nice person.\r\n\r\nThats true. But actually I am. I think I am a nice person. People that know me, like me. Does my family like me? I think so, right. Look at my family. Im proud of my family.\r\n\r\nBy the way, speaking of my family, Melania, Barron, Kai, Donnie, Don, Vanessa, Tiffany, Evanka did a great job. Did she do a great job?\r\n\r\nGreat. Jared, Laura and Eric, Im very proud of my family. Theyre a great family.\r\n\r\nSo the reporter said to me the other day, But, Mr. Trump, youre not a nice person. How can you get people to vote for you?\r\n\r\nI said, I dont know. I said, I think that number one, I am a nice person. I give a lot of money away to charities and other things. I think Im actually a very nice person.\r\n\r\nBut, I said, This is going to be an election thats based on competence, because people are tired of these nice people. And theyre tired of being ripped off by everybody in the world. And theyre tired of spending more money on education than any nation in the world per capita, than any nation in the world, and we are 26th in the world, 25 countries are better than us in education. And some of them are like third world countries. But were becoming a third word country, because of our infrastructure, our airports, our roads, everything. So one of the things I did, and I said, you know what Ill do. Ill do it. Because a lot of people said, Hell never run. Number one, he wont want to give up his lifestyle.\r\n\r\nTheyre right about that, but Im doing it.\r\n\r\nNumber two, Im a private company, so nobody knows what Im worth. And the one thing is that when you run, you have to announce and certify to all sorts of governmental authorities your net worth.\r\n\r\nSo I said, Thats OK. Im proud of my net worth. Ive done an amazing job.\r\n\r\nI started off thank you I started off in a small office with my father in Brooklyn and Queens, and my father said  and I love my father. I learned so much. He was a great negotiator. I learned so much just sitting at his feet playing with blocks listening to him negotiate with subcontractors. But I learned a lot.\r\n\r\nBut he used to say, Donald, dont go into Manhattan. Thats the big leagues. We dont know anything about that. Dont do it.\r\n\r\nI said, I gotta go into Manhattan. I gotta build those big buildings. I gotta do it, Dad. Ive gotta do it.\r\n\r\nAnd after four or five years in Brooklyn, I ventured into Manhattan and did a lot of great deals the Grand Hyatt Hotel. I was responsible for the convention center on the west side. I did a lot of great deals, and I did them early and young. And now Im building all over the world, and I love what Im doing.\r\n\r\nBut they all said, a lot of the pundits on television, Well, Donald will never run, and one of the main reasons is hes private and hes probably not as successful as everybody thinks.\r\n\r\nSo I said to myself, you know, nobodys ever going to know unless I run, because Im really proud of my success. I really am.\r\n\r\nIve employed Ive employed tens of thousands of people over my lifetime. That means medical. That means education. That means everything.\r\n\r\nSo a large accounting firm and my accountants have been working for months, because its big and complex, and theyve put together a statement, a financial statement, just a summary. But everything will be filed eventually with the government, and we dont [use] extensions or anything. Well be filing it right on time. We dont need anything.\r\n\r\nAnd it was even reported incorrectly yesterday, because they said, He had assets of $9 billion. So I said, No, thats the wrong number. Thats the wrong number. Not assets.\r\n\r\nSo they put together this. And before I say it, I have to say this. I made it the old-fashioned way. Its real estate. You know, its real estate.\r\n\r\nIts labor, and its unions good and some bad and lots of people that arent in unions, and its all over the place and building all over the world.\r\n\r\nAnd I have assets big accounting firm, one of the most highly respected 9 billion 240 million dollars.\r\n\r\nAnd I have liabilities of about $500 million. Thats long-term debt, very low interest rates.\r\n\r\nIn fact, one of the big banks came to me and said, Donald, you dont have enough borrowings. Could we loan you $4 billion? I said, I dont need it. I dont want it. And Ive been there. I dont want it.\r\n\r\nBut in two seconds, they give me whatever I wanted. So I have a total net worth, and now with the increase, itll be well-over $10 billion. But here, a total net worth ofnet worth, not assets, not a net worth, after all debt, after all expenses, the greatest assets Trump Tower, 1290 Avenue of the Americas, Bank of America building in San Francisco, 40 Wall Street, sometimes referred to as the Trump building right opposite the New York many other places all over the world.\r\n\r\nSo the total is $8,737,540,00.\r\n\r\nNow Im not doing that\r\n\r\nIm not doing that to brag, because you know what? I dont have to brag. I dont have to, believe it or not.\r\n\r\nIm doing that to say that thats the kind of thinking our country needs. We need that thinking. We have the opposite thinking.\r\n\r\nWe have losers. We have losers. We have people that dont have it. We have people that are morally corrupt. We have people that are selling this country down the drain.\r\n\r\nSo I put together this statement, and the only reason Im telling you about it today is because we really do have to get going, because if we have another three or four years you know, were at $8 trillion now. Were soon going to be at $20 trillion.\r\n\r\nAccording to the economists who Im not big believers in, but, nevertheless, this is what theyre saying that $24 trillion were very close thats the point of no return. $24 trillion. We will be there soon. Thats when we become Greece. Thats when we become a country thats unsalvageable. And were gonna be there very soon. Were gonna be there very soon.\r\n\r\nSo, just to sum up, I would do various things very quickly. I would repeal and replace the big lie, Obamacare.\r\n\r\nI would build a great wall, and nobody builds walls better than me, believe me, and Ill build them very inexpensively, I will build a great, great wall on our southern border. And I will have Mexico pay for that wall.\r\n\r\nMark my words.\r\n\r\nNobody would be tougher on ISIS than Donald Trump. Nobody.\r\n\r\nI will find  within our military, I will find the General Patton or I will find General MacArthur, I will find the right guy. I will find the guy thats going to take that military and make it really work. Nobody, nobody will be pushing us around.\r\n\r\nI will stop Iran from getting nuclear weapons. And we wont be using a man like Secretary Kerry that has absolutely no concept of negotiation, whos making a horrible and laughable deal, whos just being tapped along as they make weapons right now, and then goes into a bicycle race at 72 years old, and falls and breaks his leg. I wont be doing that. And I promise I will never be in a bicycle race. That I can tell you.\r\n\r\nI will immediately terminate President Obamas illegal executive order on immigration, immediately.\r\n\r\nFully support and back up the Second Amendment.\r\n\r\nNow, its very interesting. Today I heard it. Through stupidity, in a very, very hard core prison, interestingly named Clinton, two vicious murderers, two vicious people escaped, and nobody knows where they are. And a woman was on television this morning, and she said, You know, Mr. Trump, and she was telling other people, and I actually called her, and she said, You know, Mr. Trump, I always was against guns. I didnt want guns. And now since this happened its up in the prison area my husband and I are finally in agreement, because he wanted the guns. We now have a gun on every table. Were ready to start shooting.\r\n\r\nI said, Very interesting.\r\n\r\nSo protect the Second Amendment.\r\n\r\nEnd end Common Core. Common Core should it is a disaster. Bush is totally in favor of Common Core. I dont see how he can possibly get the nomination. Hes weak on immigration. Hes in favor of Common Core. How the hell can you vote for this guy? You just cant do it. We have to end education has to be local.\r\n\r\nRebuild the countrys infrastructure.\r\n\r\nNobody can do that like me. Believe me. It will be done on time, on budget, way below cost, way below what anyone ever thought.\r\n\r\nI look at the roads being built all over the country, and I say I can build those things for one-third. What they do is unbelievable, how bad.\r\n\r\nYou know, were building on Pennsylvania Avenue, the Old Post Office, were converting it into one of the worlds great hotels. Its gonna be the best hotel in Washington, D.C. We got it from the General Services Administration in Washington. The Obama administration. We got it. It was the most highly sought after or one of them, but I think the most highly sought after project in the history of General Services. We got it. People were shocked, Trump got it.\r\n\r\nWell, I got it for two reasons. Number one, were really good. Number two, we had a really good plan. And Ill add in the third, we had a great financial statement. Because the General Services, who are terrific people, by the way, and talented people, they wanted to do a great job. And they wanted to make sure it got built.\r\n\r\nSo we have to rebuild our infrastructure, our bridges, our roadways, our airports. You come into La Guardia Airport, its like were in a third world country. You look at the patches and the 40-year-old floor. They throw down asphalt, and they throw.\r\n\r\nYou look at these airports, we are like a third world country. And I come in from China and I come in from Qatar and I come in from different places, and they have the most incredible airports in the world. You come to back to this country and you have LAX, disaster. You have all of these disastrous airports. We have to rebuild our infrastructure.\r\n\r\nSave Medicare, Medicaid and Social Security without cuts. Have to do it.\r\n\r\nGet rid of the fraud. Get rid of the waste and abuse, but save it. People have been paying it for years. And now many of these candidates want to cut it. You save it by making the United States, by making us rich again, by taking back all of the money thats being lost.\r\n\r\nRenegotiate our foreign trade deals.\r\n\r\nReduce our $18 trillion in debt, because, believe me, were in a bubble. We have artificially low interest rates. We have a stock market that, frankly, has been good to me, but I still hate to see whats happening. We have a stock market that is so bloated.\r\n\r\nBe careful of a bubble because what youve seen in the past might be small potatoes compared to what happens. So be very, very careful.\r\n\r\nAnd strengthen our military and take care of our vets. So, so important.\r\n\r\nSadly, the American dream is dead.\r\n\r\nBut if I get elected president I will bring it back bigger and better and stronger than ever before, and we will make America great again.\r\n\r\nThank you. Thank you very much.\r\n"
  },
  {
    "path": "topic-competitors/LDA/Readme.txt",
    "content": ": sklearnԴ20 newsgroups (~20000ƪĵ)nltkԴreuters(10788ƪĵ)\r\n\r\nldaExp.py: gensimָ(20newsgroup reutersָ֮ͨһ)ѧϰdoc-topicֲΪ '-train-ĵ.svm-lda.txt'  '-test-ĵ.svm-lda.txt'\r\n\r\nclassEval.py: ldaExp.pyɵ '-train-ĵ.svm-lda.txt' Ϊļѵ '-test-ĵ.svm-lda.txt' ϲԷЧ\r\n\r\ncorpusLoader.py: sklearn20newsgroupsnltkreutersͳһϷʽӿڡ\r\n\r\nʵ:\r\n- python ldaExp.py 20news\r\n '20news-train-11314.svm-lda.txt'  '20news-test-7532.svm-lda.txt'\r\n- python classEval.py 20news lda\r\nѵģЧ\r\n"
  },
  {
    "path": "topic-competitors/LDA/classEval.py",
    "content": "from sklearn import svm, metrics\r\nfrom sklearn.datasets import load_svmlight_file\r\nimport sys\r\n\r\n# precision, recall, f1, accuracy\r\ndef getScores( true_classes, pred_classes, average):\r\n    precision = metrics.precision_score( true_classes, pred_classes, average=average )\r\n    recall = metrics.recall_score( true_classes, pred_classes, average=average )\r\n    f1 = metrics.f1_score( true_classes, pred_classes, average=average )\r\n    accuracy = metrics.accuracy_score( true_classes, pred_classes )\r\n    return precision, recall, f1, accuracy\r\n\r\n# : python classEval.py  ļ(lda, bowȵ)     \r\ncorpus = sys.argv[1]\r\nfiletype = sys.argv[2]\r\n# (ѡ)ָֻʹһ\r\n# selected feature dimensions can be specified in the last argument as:\r\n# 1-400 (starting from 1)\r\nif len(sys.argv) > 3:\r\n    dims = sys.argv[3].split(\"-\")\r\n    dims[0] = int(dims[0]) - 1\r\n    dims[1] = int(dims[1])\r\nelse:\r\n    dims = None\r\n\r\n# ģ壬õtrainingtestļ        \r\nif corpus == '20news':\r\n    train_file = \"20news-train-11314.svm-%s.txt\" %filetype\r\n    test_file = \"20news-test-7532.svm-%s.txt\" %filetype\r\nelse:\r\n    train_file = \"reuters-train-5770.svm-%s.txt\" %filetype\r\n    test_file = \"reuters-test-2255.svm-%s.txt\" %filetype\r\n\r\n# trainingtestļ\r\ntrain_features_sparse, true_train_classes = load_svmlight_file(train_file)\r\ntest_features_sparse, true_test_classes = load_svmlight_file(test_file)\r\n\r\n# ȱʡΪϡתΪͨnumpy array\r\ntrain_features = train_features_sparse.toarray()\r\ntest_features = test_features_sparse.toarray()\r\n\r\nprint \"Train: %dx%d. Test: %dx%d\" %( tuple( train_features.shape + test_features.shape ) )\r\n\r\nif dims:\r\n    train_features = train_features[ :, dims[0]:dims[1] ]\r\n    test_features = test_features[ :, dims[0]:dims[1] ]\r\n    print \"Choose only features %d-%d\" %( dims[0]+1, dims[1] )\r\nelse:\r\n    train_features = train_features[ :, : ]\r\n    test_features = test_features[ :, : ]\r\n\r\n# SVML1        \r\nmodel = svm.LinearSVC(penalty='l1', dual=False)\r\n\r\n# trainingļѵ\r\nprint \"Training...\",\r\nmodel.fit( train_features, true_train_classes )\r\nprint \"Done.\"\r\n\r\n# testļԤ\r\npred_train_classes = model.predict( train_features )\r\npred_test_classes = model.predict( test_features )\r\n\r\n# 㱨\r\nprint metrics.classification_report(true_train_classes, pred_train_classes, digits=3)\r\nprint metrics.classification_report(true_test_classes, pred_test_classes, digits=3)\r\n\r\nfor average in ['micro', 'macro']:\r\n    train_precision, train_recall, train_f1, train_acc = getScores( true_train_classes, pred_train_classes, average )\r\n    print \"Train Prec (%s average): %.3f, recall: %.3f, F1: %.3f, Acc: %.3f\" %( average, \r\n                        train_precision, train_recall, train_f1, train_acc )\r\n    \r\n    test_precision, test_recall, test_f1, test_acc = getScores( true_test_classes, pred_test_classes, average )\r\n    print \"Test Prec (%s average): %.3f, recall: %.3f, F1: %.3f, Acc: %.3f\" %(  average, \r\n                        test_precision, test_recall, test_f1, test_acc )\r\n"
  },
  {
    "path": "topic-competitors/LDA/corpusLoader.py",
    "content": "# -*- coding=GBK -*-\r\n\r\nfrom sklearn.datasets import fetch_20newsgroups\r\nfrom nltk.corpus import reuters\r\nimport HTMLParser\r\nimport os\r\nimport sys\r\nimport unicodedata\r\nimport re\r\nimport pdb\r\n\r\nunicode_punc_tbl = dict.fromkeys( i for i in xrange(128, sys.maxunicode)\r\n                      if unicodedata.category(unichr(i)).startswith('P') )\r\n\r\n# : һĵ\r\n# : ȰŷֳɾӣȻÿ䰴ʱִ߽\r\ndef extractSentenceWords(doc, remove_url=True, remove_punc=\"utf-8\", min_length=1):\r\n    # ȥַָ(ȱʡȥutf-8)еı\r\n    if remove_punc:\r\n        # ensure doc_u is in unicode\r\n        if not isinstance(doc, unicode):\r\n            encoding = remove_punc\r\n            doc_u = doc.decode(encoding)\r\n        else:\r\n            doc_u = doc\r\n        # remove unicode punctuation marks, keep ascii punctuation marks\r\n        doc_u = doc_u.translate(unicode_punc_tbl)\r\n        if not isinstance(doc, unicode):\r\n            doc = doc_u.encode(encoding)\r\n        else:\r\n            doc = doc_u\r\n    \r\n    # ȥıеURL(ѡ)        \r\n    if remove_url:\r\n        re_url = r\"(https?:\\/\\/)?(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0-9@:%_\\+.~#?&//=]*)\"\r\n        doc = re.sub( re_url, \"\", doc )\r\n    \r\n    # ӱ־        \r\n    sentences = re.split( r\"\\s*[,;:`\\\"()?!{}]\\s*|--+|\\s*-\\s+|''|\\.\\s|\\.$|\\.\\.+||\", doc ) #\"\r\n    wc = 0\r\n    wordsInSentences = []\r\n    \r\n    for sentence in sentences:\r\n        if sentence == \"\":\r\n            continue\r\n\r\n        if not re.search( \"[A-Za-z0-9]\", sentence ):\r\n            continue\r\n\r\n        # ʱִ߽\r\n        words = re.split( r\"\\s+\\+|^\\+|\\+?[\\-*\\/&%=<>\\[\\]~\\|\\@\\$]+\\+?|\\'\\s+|\\'s\\s+|\\'s$|\\s+\\'|^\\'|\\'$|\\$|\\\\|\\s+\", sentence )\r\n\r\n        words = filter( lambda w: w, words )\r\n\r\n        if len(words) >= min_length:\r\n            wordsInSentences.append(words)\r\n            wc += len(words)\r\n\r\n    #print \"%d words extracted\" %wc\r\n    return wordsInSentences, wc\r\n    \r\ndef load_20news(setName):\r\n    newsgroups_subset = fetch_20newsgroups(subset=setName, remove=('headers', 'footers')) #, 'quotes'\r\n    totalLineNum = 0\r\n    readDocNum = 0\r\n    print \"Loading 20 newsgroup %s data...\" %setName\r\n        \r\n    setDocNum = len(newsgroups_subset.data)\r\n    orig_docs_name = []\r\n    orig_docs_cat = []\r\n    orig_docs_words = []\r\n    \r\n    catNum = len(newsgroups_subset.target_names)\r\n    cats_docsWords = [ [] for i in xrange(catNum) ]\r\n    cats_docNames = [ [] for i in xrange(catNum) ]\r\n    \r\n    emptyFileNum = 0\r\n    \r\n    for d, text in enumerate(newsgroups_subset.data):\r\n        if d % 50 == 49 or d == setDocNum - 1:\r\n            print \"\\r%d %d\\r\" %( d + 1, totalLineNum ),\r\n        text = text.encode(\"utf-8\")\r\n        lines = text.split(\"\\n\")\r\n        if len(text) == 0 or len(lines) == 0:\r\n            emptyFileNum += 1\r\n            continue\r\n    \r\n        readDocNum += 1\r\n        totalLineNum += len(lines)\r\n    \r\n        catID = newsgroups_subset.target[d]\r\n        category = newsgroups_subset.target_names[catID]\r\n    \r\n        text = \" \".join(lines)\r\n    \r\n        wordsInSentences, wc = extractSentenceWords(text)\r\n        filename = newsgroups_subset.filenames[d]\r\n        filename = os.path.basename(filename)\r\n        orig_docs_words.append( wordsInSentences )\r\n        orig_docs_name.append(filename)\r\n        orig_docs_cat.append(catID)\r\n        cats_docsWords[catID].append(wordsInSentences)\r\n        cats_docNames[catID].append(filename)\r\n    \r\n    print \"Done. %d docs read, %d empty docs skipped. Totally %d lines\" %(readDocNum, emptyFileNum, totalLineNum)\r\n    return setDocNum, orig_docs_words, orig_docs_name, orig_docs_cat, \\\r\n                cats_docsWords, cats_docNames, newsgroups_subset.target_names\r\n    \r\ndef load_reuters(setName):\r\n    html = HTMLParser.HTMLParser()\r\n    doc_ids = reuters.fileids()\r\n    cat2all_ids = {}\r\n    cat2train_ids = {}\r\n    cat2test_ids = {}\r\n    cat2all_num = {}\r\n    cand_docNum = 0\r\n    \r\n    for doc_id in doc_ids:\r\n        # only choose docs belonging in one category\r\n        if len( reuters.categories(doc_id) ) == 1:\r\n            cat = reuters.categories(doc_id)[0]\r\n            cand_docNum += 1\r\n            \r\n            # 'train'ͷĵtraining\r\n            if doc_id.startswith(\"train\"):\r\n                cat2set_ids = cat2train_ids\r\n            # 򣬷ŵtest\r\n            else:\r\n                cat2set_ids = cat2test_ids\r\n                \r\n            if cat in cat2set_ids:\r\n                cat2set_ids[cat].append(doc_id)\r\n            else:\r\n                cat2set_ids[cat] = [ doc_id ]\r\n            \r\n            # both train and test doc_ids are put in cat2all_ids\r\n            if cat in cat2all_ids:\r\n                cat2all_ids[cat].append(doc_id)\r\n            else:\r\n                cat2all_ids[cat] = [ doc_id ]\r\n            if cat in cat2all_num:\r\n                cat2all_num[cat] += 1\r\n            else:\r\n                cat2all_num[cat] = 1\r\n            \r\n    print \"Totally %d docs, %d single-category docs in %d categories\" %( len(doc_ids), \r\n                    cand_docNum, len(cat2train_ids) )\r\n                    \r\n    sorted_cats = sorted( cat2all_num.keys(), key=lambda cat: cat2all_num[cat],\r\n                            reverse=True )\r\n                            \r\n    catNum = 10\r\n    cats_docsWords = [ [] for i in xrange(catNum) ]\r\n    cats_docNames = [ [] for i in xrange(catNum) ]\r\n                            \r\n    topN_cats = sorted_cats[:catNum]\r\n    print \"Top 10 categories:\"\r\n    keptAllDocNum = 0\r\n    keptTrainDocNum = 0\r\n    keptTestDocNum = 0\r\n    \r\n    for cat in topN_cats:\r\n        print \"%s: %d/%d\" %( cat, len(cat2train_ids[cat]), len(cat2test_ids[cat]) )\r\n        keptTrainDocNum += len(cat2train_ids[cat])\r\n        keptTestDocNum += len(cat2test_ids[cat])\r\n        keptAllDocNum += len(cat2train_ids[cat]) + len(cat2test_ids[cat])\r\n        \r\n    print \"Totally %d docs kept, %d in train, %d in test\" %( keptAllDocNum, \r\n                        keptTrainDocNum, keptTestDocNum )    \r\n    \r\n    if setName == \"train\":\r\n        cat2set_ids = cat2train_ids\r\n        setDocNum = keptTrainDocNum\r\n    elif setName == \"test\":\r\n        cat2set_ids = cat2test_ids\r\n        setDocNum = keptTestDocNum\r\n    elif setName == \"all\":\r\n        cat2set_ids = cat2all_ids\r\n        setDocNum = keptAllDocNum\r\n    else:\r\n        raise Exception(\"Unknown set name %s\" %setName)\r\n            \r\n    orig_docs_name = []\r\n    orig_docs_cat = []\r\n    orig_docs_words = []\r\n    readDocNum = 0\r\n    totalLineNum = 0\r\n    emptyFileNum = 0\r\n    \r\n    for cat_id, cat in enumerate(topN_cats):\r\n        for doc_id in cat2set_ids[cat]:\r\n            if readDocNum % 50 == 49 or readDocNum == setDocNum - 1:\r\n                print \"\\r%d %d\\r\" %( readDocNum + 1, totalLineNum ),\r\n            text = html.unescape( reuters.raw(doc_id) )\r\n            text = text.encode(\"utf-8\")\r\n            lines = text.split(\"\\n\")\r\n            if len(text) == 0 or len(lines) == 0:\r\n                emptyFileNum += 1\r\n                continue\r\n        \r\n            readDocNum += 1\r\n            totalLineNum += len(lines)\r\n        \r\n            text = \" \".join(lines)\r\n            wordsInSentences, wc = extractSentenceWords(text)\r\n            \r\n            filename = doc_id\r\n            orig_docs_words.append( wordsInSentences )\r\n            orig_docs_name.append(filename)\r\n            orig_docs_cat.append(cat_id)\r\n            cats_docsWords[cat_id].append(wordsInSentences)\r\n            cats_docNames[cat_id].append(filename)\r\n            \r\n    print \"Done. %d docs read, %d empty docs skipped. Totally %d lines\" %(readDocNum, emptyFileNum, totalLineNum)\r\n    return setDocNum, orig_docs_words, orig_docs_name, orig_docs_cat, \\\r\n                cats_docsWords, cats_docNames, topN_cats\r\n    "
  },
  {
    "path": "topic-competitors/LDA/ldaExp.py",
    "content": "import sys\r\nimport pdb\r\nimport os\r\nimport getopt\r\nimport time\r\nimport gensim\r\nfrom sklearn.datasets import load_svmlight_file\r\nfrom scipy.sparse import csr_matrix\r\nfrom corpusLoader import *\r\n\r\n# Ͽӳ䵽غ֮\r\ncorpus2loader = { '20news': load_20news, 'reuters': load_reuters }\r\n\r\ndef usage():\r\n    print \"\"\"Usage: ldaExp.py corpus_name\"\"\"\r\n\r\ncorpusName = sys.argv[1]\r\n# غ\r\nloader = corpus2loader[corpusName]\r\n\r\n# 20newsĵЩһЩ\r\nif corpusName == \"20news\":\r\n    topicNum = 100\r\nelse:\r\n    topicNum = 50\r\n\r\n# ϶ѷֳtraintestϡֱ\r\nsetNames = [ 'train', 'test' ]\r\nbasenames = []\r\nsubcorpora = []\r\ncorpus = []\r\nword2id = {}\r\nid2word = {}\r\nmaxWID = 0\r\n\r\nfor setName in setNames:\r\n    print \"Process set '%s':\" %setName\r\n    \r\n    # ϵtraintestӼԾΪλ orig_docs_wordsȷ orig_docs_cat\r\n    setDocNum, orig_docs_words, orig_docs_name, orig_docs_cat, cats_docsWords, \\\r\n            cats_docNames, category_names = loader(setName)\r\n    # ļǰ׺\r\n    basename = \"%s-%s-%d\" %( corpusName, setName, setDocNum )\r\n    basenames.append(basename)\r\n    \r\n    # ǰѭӼһlistlistÿlistԪضӦһĵ\r\n    # ÿڲlistΪһ (word_id, frequency) pair\r\n    # ָʽgensimı׼ʽ\r\n    subcorpus = []\r\n    \r\n    # ԭʼıԹ˲鿴\r\n    orig_filename = \"%s.orig.txt\" %basename\r\n    ORIG = open( orig_filename, \"w\" )\r\n\r\n    # ÿ wordsInSentences Ӧһĵ\r\n    # ÿ wordsInSentences ɣÿһlist of words\r\n    for wordsInSentences in orig_docs_words:\r\n        # ͳƵǰĵÿʵƵ\r\n        doc_wid2freq = {}\r\n        # ѭȡǰĵһ\r\n        for sentence in wordsInSentences:\r\n            for w in sentence:\r\n                w = w.lower()\r\n                ORIG.write( \"%s \" %w )\r\n                \r\n                # wword2idӳУӳwid\r\n                if w in word2id:\r\n                    wid = word2id[w]\r\n                # 򣬰wӳӳwid\r\n                else:\r\n                    wid = maxWID\r\n                    word2id[w] = maxWID\r\n                    id2word[maxWID] = w\r\n                    maxWID += 1\r\n                \r\n                # ͳ wid Ƶ\r\n                if wid in doc_wid2freq:\r\n                    doc_wid2freq[wid] += 1\r\n                else:\r\n                    doc_wid2freq[wid] = 1\r\n                    \r\n        ORIG.write(\"\\n\")\r\n        # ĵгֵwididС\r\n        sorted_wids = sorted( doc_wid2freq.keys() )\r\n        doc_pairs = []\r\n        #  (wid, frequency) Ķ׷ӵǰĵlist\r\n        for wid in sorted_wids:\r\n            doc_pairs.append( (wid, doc_wid2freq[wid]) )\r\n        \r\n        # ǰĵlistѾȫɣsubcorpusӼlist    \r\n        subcorpus.append(doc_pairs)\r\n\r\n    ORIG.close()\r\n    print \"%d original docs saved in '%s'\" %( setDocNum, orig_filename )\r\n\r\n    # Ӽlist֮ǰlistϲõһtraintestϵĵļ\r\n    corpus += subcorpus\r\n    # traintestϷֿţ֮Ѳͬϵÿĵġdoc-topicɲͬļ\r\n    subcorpora.append( (subcorpus, orig_docs_cat) )\r\n    \r\nprint \"Training LDA...\"\r\nstartTime = time.time()\r\n# LDAѵʱǰtraintestһѵ(ϸİ취Ӧֻtrainѵ)\r\nlda = gensim.models.ldamodel.LdaModel( corpus=corpus, num_topics=topicNum, passes=20 )\r\nendTime = time.time()\r\nprint \"Finished in %.1f seconds\" %( endTime - startTime )\r\n\r\nfor i in xrange(2):\r\n    lda_filename = \"%s.svm-lda.txt\" %basenames[i]\r\n    LDA = open( lda_filename, \"w\" )\r\n    print \"Saving topic proportions into '%s'...\" %lda_filename\r\n    \r\n    # óһӼ (traintest)\r\n    subcorpus, labels = subcorpora[i]\r\n\r\n    # Ӽÿĵ\r\n    for d, doc_pairs in enumerate(subcorpus):\r\n        label = labels[d]\r\n        # ѵǰĵΪ룬ѵõLDAģdoc-topic\r\n        topic_props = lda.get_document_topics( doc_pairs, minimum_probability=0.001 )\r\n        LDA.write( \"%d\" %label )\r\n        # KKsvmlightʽ\r\n        for k, prop in topic_props:\r\n            LDA.write(\" %d:%.3f\" %(k, prop) )\r\n        LDA.write(\"\\n\")\r\n    LDA.close()\r\n    print \"%d docs saved\" %len(subcorpus)\r\n"
  },
  {
    "path": "topic-competitors/doc2vec.py",
    "content": "import gensim.models.doc2vec as doc2vec\nimport sys\nimport pdb\n\ncorpus = sys.argv[1]\n\nif corpus == '20news':\n    all_words_file = \"20news-all-18791.gibbslda-bow.txt\"\n    train_label_file = \"20news-train-11314.slda-label.txt\"\n    train_docvec_file = \"20news-train-11314.svm-doc2vec.txt\"\n    test_label_file = \"20news-test-7532.slda-label.txt\"\n    test_docvec_file = \"20news-test-7532.svm-doc2vec.txt\"\n    all_count = 18791\n    train_count = 11285\n    test_count = 7506\nelse:\n    all_words_file = \"reuters-all-8025.gibbslda-bow.txt\"\n    train_label_file = \"reuters-train-5770.slda-label.txt\"\n    train_docvec_file = \"reuters-train-5770.svm-doc2vec.txt\"\n    test_label_file = \"reuters-test-2255.slda-label.txt\"\n    test_docvec_file = \"reuters-test-2255.svm-doc2vec.txt\"\n    all_count = 8025\n    train_count = 5770\n    test_count = 2255\n\ndim = 400\ncorpus = doc2vec.TaggedLineDocument(all_words_file)\nmodel = doc2vec.Doc2Vec(corpus,size=dim, window=8, min_count=5, workers=4)\nTRAIN_DOC2VEC = open(train_docvec_file, \"w\")\nTRAIN_LABEL = open(train_label_file)\n\n#pdb.set_trace()\n\nfor d in xrange(1, train_count + 1):\n    doc_vec = model.docvecs[d]\n    label_line = TRAIN_LABEL.readline().strip()\n    label = int(label_line)\n\n    TRAIN_DOC2VEC.write( \"%d\" %(label+1) )\n\n    for k in xrange(dim):\n        TRAIN_DOC2VEC.write( \" %d:%.3f\" %( k + 1, doc_vec[k] ) )\n\n    TRAIN_DOC2VEC.write(\"\\n\")\n\nTRAIN_DOC2VEC.close()\n\nprint \"%d doc vecs written in svm format into '%s'\" %( train_count, train_docvec_file )\n\nTEST_DOC2VEC = open(test_docvec_file, \"w\")\nTEST_LABEL = open(test_label_file)\nfor d in xrange(train_count + 1, all_count + 1):\n    doc_vec = model.docvecs[d]\n    label_line = TEST_LABEL.readline().strip()\n    label = int(label_line)\n\n    TEST_DOC2VEC.write( \"%d\" %(label+1) )\n\n    for k in xrange(dim):\n        TEST_DOC2VEC.write( \" %d:%.3f\" %( k + 1, doc_vec[k] ) )\n\n    TEST_DOC2VEC.write(\"\\n\")\n\nTEST_DOC2VEC.close()\n\nprint \"%d doc vecs written in svm format into '%s'\" %( test_count, test_docvec_file )\n"
  },
  {
    "path": "topic-competitors/kmeans.py",
    "content": "#!/usr/bin/env python\r\n# kmeans.py using any of the 20-odd metrics in scipy.spatial.distance\r\n# kmeanssample 2 pass, first sample sqrt(N)\r\n\r\nfrom __future__ import division\r\nimport random\r\nimport numpy as np\r\nfrom scipy.spatial.distance import cdist  # $scipy/spatial/distance.py\r\n    # http://docs.scipy.org/doc/scipy/reference/spatial.html\r\nfrom scipy.sparse import issparse  # $scipy/sparse/csr.py\r\n\r\n__date__ = \"2011-11-17 Nov denis\"\r\n    # X sparse, any cdist metric: real app ?\r\n    # centres get dense rapidly, metrics in high dim hit distance whiteout\r\n    # vs unsupervised / semi-supervised svm\r\n\r\n#...............................................................................\r\ndef kmeans( X, centres, delta=.001, maxiter=10, metric=\"euclidean\", p=2, verbose=1 ):\r\n    \"\"\" centres, Xtocentre, distances = kmeans( X, initial centres ... )\r\n    in:\r\n        X N x dim  may be sparse\r\n        centres k x dim: initial centres, e.g. random.sample( X, k )\r\n        delta: relative error, iterate until the average distance to centres\r\n            is within delta of the previous average distance\r\n        maxiter\r\n        metric: any of the 20-odd in scipy.spatial.distance\r\n            \"chebyshev\" = max, \"cityblock\" = L1, \"minkowski\" with p=\r\n            or a function( Xvec, centrevec ), e.g. Lqmetric below\r\n        p: for minkowski metric -- local mod cdist for 0 < p < 1 too\r\n        verbose: 0 silent, 2 prints running distances\r\n    out:\r\n        centres, k x dim\r\n        Xtocentre: each X -> its nearest centre, ints N -> k\r\n        distances, N\r\n    see also: kmeanssample below, class Kmeans below.\r\n    \"\"\"\r\n\r\n    if verbose:\r\n        print \"kmeans: X %s  centres %s  delta=%.2g  maxiter=%d  metric=%s\" % (\r\n            X.shape, centres.shape, delta, maxiter, metric)\r\n    allx = np.arange(N)\r\n    prevdist = 0\r\n    for jiter in range( 1, maxiter+1 ):\r\n        D = cdist( X, centres, metric=metric, p=p )  # |X| x |centres|\r\n        xtoc = D.argmin(axis=1)  # X -> nearest centre\r\n        distances = D[allx,xtoc]\r\n        avdist = distances.mean()  # median ?\r\n        if verbose >= 2:\r\n            print \"kmeans: av |X - nearest centre| = %.4g\" % avdist\r\n        if (1 - delta) * prevdist <= avdist <= prevdist \\\r\n        or jiter == maxiter:\r\n            break\r\n        prevdist = avdist\r\n        for jc in range(k):  # (1 pass in C)\r\n            c = np.where( xtoc == jc )[0]\r\n            if len(c) > 0:\r\n                centres[jc] = X[c].mean( axis=0 )\r\n    if verbose:\r\n        print \"kmeans: %d iterations  cluster sizes:\" % jiter, np.bincount(xtoc)\r\n    if verbose >= 2:\r\n        r50 = np.zeros(k)\r\n        r90 = np.zeros(k)\r\n        for j in range(k):\r\n            dist = distances[ xtoc == j ]\r\n            if len(dist) > 0:\r\n                r50[j], r90[j] = np.percentile( dist, (50, 90) )\r\n        print \"kmeans: cluster 50 % radius\", r50.astype(int)\r\n        print \"kmeans: cluster 90 % radius\", r90.astype(int)\r\n            # scale L1 / dim, L2 / sqrt(dim) ?\r\n    return centres, xtoc, distances\r\n\r\ndef randomsample( X, n ):\r\n    \"\"\" random.sample of the rows of X\r\n        X may be sparse -- best csr\r\n    \"\"\"\r\n    sampleix = random.sample( xrange( X.shape[0] ), int(n) )\r\n    return X[sampleix]\r\n\r\nif __name__ == \"__main__\":\r\n    import random\r\n    import sys\r\n    from time import time\r\n\r\n    N = 10000\r\n    dim = 10\r\n    ncluster = 10\r\n    kmdelta = .001\r\n    kmiter = 10\r\n    metric = \"cosine\"\r\n    seed = 1\r\n\r\n    np.set_printoptions( 1, threshold=200, edgeitems=5, suppress=True )\r\n    np.random.seed(seed)\r\n    random.seed(seed)\r\n\r\n    unigramFilename = \"top1grams-wiki.txt\"\r\n    word_vec_file = \"25000-180000-500-BLK-8.0.vec\"\r\n                \r\n    vocab_dict = loadUnigramFile(unigramFilename)\r\n    V, vocab, word2ID, skippedWords_whatever = load_embeddings(word_vec_file)\r\n    # map of word -> id of all words with embeddings\r\n    vocab_dict2 = {}\r\n    \r\n    if normalize_vecs:\r\n        Vnorm = np.array( [ normF(x) for x in V ] )\r\n        for i,w in enumerate(vocab):\r\n            if Vnorm[i] == 0:\r\n                print \"WARN: %s norm is 0\" %w\r\n                # set to 1 to avoid \"divided by 0 exception\"\r\n                Vnorm[i] = 1\r\n            \r\n        V /= Vnorm[:, None]\r\n        \r\n    # dimensionality of topic/word embeddings\r\n    N0 = V.shape[1]\r\n\r\n    customStopwordList = re.split( \"\\s+\", self.customStopwords )\r\n    for stop_w in customStopwordList:\r\n        stopwordDict[stop_w] = 1\r\n    print \"Custom stopwords: %s\" %( \", \".join(customStopwordList) )\r\n                \r\n    print \"N %d  dim %d  ncluster %d  metric %s\" % (N, dim, ncluster, metric)\r\n    t0 = time()\r\n\r\n    randomcentres = randomsample( X, ncluster )\r\n    centres, xtoc, dist = kmeans( X, randomcentres,\r\n        delta=kmdelta, maxiter=kmiter, metric=metric, verbose=2 )\r\n    print \"%.0f msec\" % ((time() - t0) * 1000)\r\n    "
  },
  {
    "path": "topic-competitors/labelEval.py",
    "content": "from sklearn import metrics\r\nimport sys\r\n\r\ndef getScores( true_classes, pred_classes, average):\r\n    precision = metrics.precision_score( true_classes, pred_classes, average=average )\r\n    recall = metrics.recall_score( true_classes, pred_classes, average=average )\r\n    f1 = metrics.f1_score( true_classes, pred_classes, average=average )\r\n    accuracy = metrics.accuracy_score( true_classes, pred_classes )\r\n    return precision, recall, f1, accuracy\r\n\r\ntrue_labelfile = sys.argv[1]\r\npred_labelfile = sys.argv[2]\r\n\r\nTRUE = open(true_labelfile)\r\nPRED = open(pred_labelfile)\r\n\r\ntrue_classes = []\r\npred_classes = []\r\n\r\nfor line in TRUE:\r\n    line = line.strip()\r\n    label = int(line)\r\n    true_classes.append(label)\r\n\r\nfor line in PRED:\r\n    line = line.strip()\r\n    label = int(line)\r\n    pred_classes.append(label)\r\n\r\nprint metrics.classification_report(true_classes, pred_classes, digits=3)\r\n\r\nfor average in ['micro', 'macro']:\r\n    precision, recall, f1, acc = getScores( true_classes, pred_classes, average )\r\n    print \"Prec (%s average): %.3f, recall: %.3f, F1: %.3f, Acc: %.3f\" %(  average, \r\n                        precision, recall, f1, acc )\r\n"
  },
  {
    "path": "topic-competitors/lftm2svm.py",
    "content": "import sys\n\nlftm_file = sys.argv[1]\n\ntrain_words_file = \"reuters-train-5770.gibbslda-words.txt\"\ntrain_label_file = \"reuters-train-5770.slda-label.txt\"\ntest_words_file = \"reuters-test-2255.gibbslda-words.txt\"\ntest_label_file = \"reuters-test-2255.slda-label.txt\"\n\nLFTM_TOPIC = open(lftm_file)\nTRAIN_WORDS = open(train_words_file)\nTEST_WORDS = open(test_words_file)\nTRAIN_LABELS = open(train_label_file)\nTEST_LABELS = open(test_label_file)\n\nfor i in xrange(2):\n\tWORDS =  [ TRAIN_WORDS, TEST_WORDS ][i]\n\tLABELS = [TRAIN_LABELS, TEST_LABELS][i]\n\tif i == 0:\n\t\toutput_file = \"reuters-train-5770.svm-lftm.txt\"\n\telse:\n\t\toutput_file = \"reuters-test-2255.svm-lftm.txt\"\n\n\tOUTPUT = open(output_file, \"w\")\n\t\n\tsetName = [\"train\", \"test\"][i]\n\n\tlineno = 0\n\tvalidDocNum = 0\n\tfor line in WORDS:\n\t\tlineno += 1 \n\t\tline = line.strip()\n\t\tlabel_line = LABELS.readline().strip()\n\t\tif not line:\n\t\t\tprint \"Empty doc %s-%d skipped\" %(setName, lineno)\n\t\t\tcontinue\n\t\tlabel = int(label_line)\n\t\tOUTPUT.write( \"%d\" %(label+1) )\n\t\tlftm_topic_line = LFTM_TOPIC.readline().strip()\n\t\tlftm_topicprops = lftm_topic_line.split(\" \")\n\t\tfor k in xrange(50):\n\t\t\ttopicprop = float(lftm_topicprops[k])\n\t\t\tOUTPUT.write( \" %d:%.3f\" %(k+1, topicprop) )\n\t\tOUTPUT.write(\"\\n\")\n\t\tvalidDocNum += 1\n\tprint \"%d %s docs, %d written into '%s'\" %(lineno, setName, validDocNum, output_file)\n\tOUTPUT.close()\n\nlineno = 0\nfor line in LFTM_TOPIC:\n\tlineno += 1\n\nif lineno > 0:\n\tprint \"Warn: %d lines left in '%s'\" %(lineno, lftm_file)\n"
  },
  {
    "path": "topic-competitors/liu-doc2vec.py",
    "content": "from utils import *\nimport pdb\n\ndef genDocEmbedding( setName, words_file, topics_file, label_file, V, word2ID, T ):\n    WORDS = open(words_file)\n    TOPICS = open(topics_file)\n    LABEL = open(label_file)\n\n    filename_trunk = words_file.split('.')[0]\n    docvec_file = \".\".join( [ filename_trunk, \"svm-liu\", \"txt\" ] )\n    docvecbow_file = \".\".join( [ filename_trunk, \"svm-liubow\", \"txt\" ] )\n\n    DOCVEC = open( docvec_file, \"w\" )\n    DOCVECBOW = open( docvecbow_file, \"w\" )\n\n    dim = V.shape[1] + T.shape[1]\n\n    lineno = 0\n    emptyDocIds = []\n\n    for word_line in WORDS:\n        lineno += 1\n        word_line = word_line.strip()\n        topic_line = TOPICS.readline().strip()\n        label_line = LABEL.readline().strip()\n        # encounter an empty doc\n        if not word_line:\n            words = []\n            topics = []\n        else:\n            words = word_line.split(\" \")\n            topics = topic_line.split(\" \")\n        assert len(words) == len(topics), \\\n            \"Words number %d != topic number %d in line %d\" %( len(words), len(topics), lineno )\n        label = int(label_line)\n\n        sum_vec = np.zeros(dim)\n        doc_vec = np.zeros(dim)\n        validWordNum = 0\n\n        wid2freq = {}\n\n        for i in xrange(len(words)):\n            word = words[i]\n            topic = int(topics[i])\n\n            if word not in word2ID:\n                continue\n            validWordNum += 1\n            wid = word2ID[word]\n            sum_vec += np.concatenate( [ V[wid], T[topic] ] )\n\n            if wid in wid2freq:\n                wid2freq[wid] += 1\n            else:\n                wid2freq[wid] = 1\n\n        if validWordNum > 0:\n            doc_vec = sum_vec / validWordNum\n        else:\n            emptyDocIds.append(lineno)\n\n        sorted_wids = sorted( wid2freq.keys() )\n\n        DOCVEC.write( \"%d\" %(label+1) )\n        DOCVECBOW.write( \"%d\" %(label+1) )\n        \n        for k in xrange(dim):\n            DOCVEC.write( \" %d:%.3f\" %( k + 1, doc_vec[k] ) )\n            DOCVECBOW.write( \" %d:%.3f\" %( k + 1, doc_vec[k] ) )\n\n        for wid in sorted_wids:\n            # first dim indices are reserved for topic features, so add dim here\n            # add 1 to make wid start from 1\n            DOCVECBOW.write( \" %d:%d\" %( wid + dim + 1, wid2freq[wid] ) )\n\n        DOCVEC.write(\"\\n\")\n        DOCVECBOW.write(\"\\n\")\n\n    print \"%d %s docs converted to Liu et al's docvec in svm format.\" %( lineno, setName )\n    if len(emptyDocIds) > 0:\n        print \"Empty docs: %s\" %emptyDocIds\n\n    DOCVEC.close()\n    DOCVECBOW.close()\n    WORDS.close()\n    TOPICS.close()\n    LABEL.close()\n\ncorpus = sys.argv[1]\n\nif corpus == '20news':\n    train_words_file = \"20news-train-11314.gibbslda-words.txt\"\n    train_topics_file = \"20news-train-11314.gibbslda-topics.txt\"\n    train_wordvec_file = \"20news-train-11314.liu-wordvec2.txt\"\n    train_topicvec_file = \"20news-train-11314.liu-topicvec2.txt\"\n    train_label_file = \"20news-train-11314.slda-label.txt\"\n    test_words_file = \"20news-test-7532.gibbslda-words.txt\"\n    test_topics_file = \"20news-test-7532.gibbslda-topics.txt\"\n    test_label_file = \"20news-test-7532.slda-label.txt\"\nelse:\n    train_words_file = \"reuters-train-5770.gibbslda-words.txt\"\n    train_topics_file = \"reuters-train-5770.gibbslda-topics.txt\"\n    train_wordvec_file = \"reuters-train-5770.liu-wordvec2.txt\"\n    train_topicvec_file = \"reuters-train-5770.liu-topicvec2.txt\"\n    train_label_file = \"reuters-train-5770.slda-label.txt\"\n    test_words_file = \"reuters-test-2255.gibbslda-words.txt\"\n    test_topics_file = \"reuters-test-2255.gibbslda-topics.txt\"\n    test_label_file = \"reuters-test-2255.slda-label.txt\"\n\nV, vocab, word2ID, skippedWords_whatever = load_embeddings(train_wordvec_file)\nT = load_matrix_from_text( train_topicvec_file, \"topic embedding\" )\ngenDocEmbedding( \"train\", train_words_file, train_topics_file, train_label_file, V, word2ID, T )\ngenDocEmbedding( \"test\", test_words_file, test_topics_file, test_label_file, V, word2ID, T )\n"
  },
  {
    "path": "topic-competitors/slda/20news-test-7532.slda-bow.txt",
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  {
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  },
  {
    "path": "topic-competitors/slda/Makefile",
    "content": "CC = g++\r\nLDFLAGS = -lgsl -lm -lgslcblas\r\n\r\n\r\nLSOURCE = main.cpp corpus.cpp slda.cpp utils.cpp opt.cpp\r\nLHEADER = corpus.h slda.h utils.h opt.h settings.h\r\n\r\nslda: $(LSOURCE) $(HEADER)\r\n\t  $(CC) $(LSOURCE) -o $@ $(LDFLAGS)\r\n\r\nclean:\r\n\t-rm -f *.o slda\r\n"
  },
  {
    "path": "topic-competitors/slda/corpus.cpp",
    "content": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This file is part of slda.\n\n// slda is free software; you can redistribute it and/or modify it under\n// the terms of the GNU General Public License as published by the Free\n// Software Foundation; either version 2 of the License, or (at your\n// option) any later version.\n\n// slda is distributed in the hope that it will be useful, but WITHOUT\n// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License\n// for more details.\n\n// You should have received a copy of the GNU General Public License\n// along with this program; if not, write to the Free Software\n// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\n// USA\n\n#include \"corpus.h\"\n#include <assert.h>\n#include <stdio.h>\n\ncorpus::corpus()\n{\n    num_docs = 0;\n    size_vocab = 0;\n    num_classes = 0;\n    num_total_words = 0;\n}\n\ncorpus::~corpus()\n{\n    for (int i = 0; i < num_docs; i ++)\n    {\n        document * doc = docs[i];\n        delete doc;\n    }\n    docs.clear();\n\n    num_docs = 0;\n    size_vocab = 0;\n    num_classes = 0;\n    num_total_words = 0;\n}\n\nvoid corpus::read_data(const char * data_filename,\n                       const char * label_filename)\n{\n    int OFFSET = 0;\n    int length = 0, count = 0, word = 0,\n        n = 0, nd = 0, nw = 0, label = -1;\n\n    FILE * fileptr;\n    fileptr = fopen(data_filename, \"r\");\n    printf(\"\\nreading data from %s\\n\", data_filename);\n    nd = 0;\n    nw = 0;\n\n    while ((fscanf(fileptr, \"%10d\", &length) != EOF))\n    {\n        document * doc = new document(length);\n        for (n = 0; n < length; n++)\n        {\n            fscanf(fileptr, \"%10d:%10d\", &word, &count);\n            word = word - OFFSET;\n            doc->words[n] = word;\n            doc->counts[n] = count;\n            doc->total += count;\n            if (word >= nw)\n            {\n                nw = word + 1;\n            }\n        }\n        num_total_words += doc->total;\n        docs.push_back(doc);\n        nd++;\n    }\n    fclose(fileptr);\n    num_docs = nd;\n    size_vocab = nw;\n    printf(\"number of docs  : %d\\n\", nd);\n    printf(\"number of terms : %d\\n\", nw);\n    printf(\"number of total words : %d\\n\", num_total_words);\n\n    fileptr = fopen(label_filename, \"r\");\n    printf(\"\\nreading labels from %s\\n\", label_filename);\n    nd = 0;\n    while ((fscanf(fileptr, \"%10d\", &label) != EOF))\n    {\n        document * doc = docs[nd];\n        doc->label = label;\n        if (label >= num_classes)\n        {\n            num_classes = label + 1;\n        }\n        nd ++;\n    }\n    assert(nd == int(docs.size()));\n    printf(\"number of classes : %d\\n\\n\", num_classes);\n}\n\nint corpus::max_corpus_length() {\n    int max_length = 0;\n\n    for (int d = 0; d < num_docs; d++) {\n        if (docs[d]->length > max_length)\n            max_length = docs[d]->length;\n    }\n    return max_length;\n}\n"
  },
  {
    "path": "topic-competitors/slda/corpus.h",
    "content": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\r\n\r\n// written by Chong Wang, chongw@cs.princeton.edu\r\n\r\n// This file is part of slda.\r\n\r\n// slda is free software; you can redistribute it and/or modify it under\r\n// the terms of the GNU General Public License as published by the Free\r\n// Software Foundation; either version 2 of the License, or (at your\r\n// option) any later version.\r\n\r\n// slda is distributed in the hope that it will be useful, but WITHOUT\r\n// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\r\n// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License\r\n// for more details.\r\n\r\n// You should have received a copy of the GNU General Public License\r\n// along with this program; if not, write to the Free Software\r\n// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\r\n// USA\r\n\r\n#ifndef CORPUS_H\r\n#define CORPUS_H\r\n\r\n#include <cstddef>\r\n#include <vector>\r\nusing namespace std;\r\n\r\nclass document\r\n{\r\npublic:\r\n    int * words;\r\n    int * counts;\r\n    int length;\r\n    int total;\r\n    int label;\r\npublic:\r\n    document()\r\n    {\r\n        words = NULL;\r\n        counts = NULL;\r\n        length = 0;\r\n        total = 0;\r\n        label = -1;\r\n    }\r\n    document(int len)\r\n    {\r\n        length = len;\r\n        words = new int [length];\r\n        counts = new int [length];\r\n        total = 0;\r\n        label = -1;\r\n    }\r\n    ~document()\r\n    {\r\n        if (words != NULL)\r\n        {\r\n            delete [] words;\r\n            delete [] counts;\r\n            length = 0;\r\n            total = 0;\r\n            label = -1;\r\n        }\r\n    }\r\n};\r\n\r\nclass corpus\r\n{\r\npublic:\r\n    corpus();\r\n    ~corpus();\r\n    void read_data(const char * data_filename, const char * label_filename);\r\n    int max_corpus_length();\r\npublic:\r\n    int num_docs;\r\n    int size_vocab;\r\n    int num_classes;\r\n    int num_total_words;\r\n    vector<document*> docs;\r\n};\r\n\r\n#endif // CORPUS_H\r\n"
  },
  {
    "path": "topic-competitors/slda/main.cpp",
    "content": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This file is part of slda.\n\n// slda is free software; you can redistribute it and/or modify it under\n// the terms of the GNU General Public License as published by the Free\n// Software Foundation; either version 2 of the License, or (at your\n// option) any later version.\n\n// slda is distributed in the hope that it will be useful, but WITHOUT\n// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License\n// for more details.\n\n// You should have received a copy of the GNU General Public License\n// along with this program; if not, write to the Free Software\n// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\n// USA\n\n#include <stdio.h>\n#include <string.h>\n#include \"corpus.h\"\n#include \"utils.h\"\n#include \"slda.h\"\n\nvoid help( void ) {\n    printf(\"usage: slda [est] [data] [label] [settings] [alpha] [k] [random/seeded/model_path] [directory]\\n\");\n    printf(\"       slda [inf] [data] [label] [settings] [model] [directory]\\n\");\n}\n\nint main(int argc, char* argv[])\n{\n    if (argc < 2)\n    {\n        help();\n        return 0;\n    }\n    if (strcmp(argv[1], \"est\") == 0)\n    {\n        corpus c;\n        char * data_filename = argv[2];\n        char * label_filename = argv[3];\n        c.read_data(data_filename, label_filename);\n        settings setting;\n        char * setting_filename = argv[4];\n        setting.read_settings(setting_filename);\n\n        double alpha = atof(argv[5]);\n        int num_topics = atoi(argv[6]);\n        printf(\"number of topics is %d\\n\", num_topics);\n        char * init_method = argv[7];\n        char * directory = argv[8];\n        printf(\"models will be saved in %s\\n\", directory);\n        make_directory(directory);\n\n        slda model;\n        model.init(alpha, num_topics, &c);\n        model.v_em(&c, &setting, init_method, directory);\n    }\n\n    if (strcmp(argv[1], \"inf\") == 0)\n    {\n        corpus c;\n        char * data_filename = argv[2];\n        char * label_filename = argv[3];\n        c.read_data(data_filename, label_filename);\n        settings setting;\n        char * setting_filename = argv[4];\n        setting.read_settings(setting_filename);\n\n        char * model_filename = argv[5];\n        char * directory = argv[6];\n        printf(\"\\nresults will be saved in %s\\n\", directory);\n        make_directory(directory);\n\n        slda model;\n        model.load_model(model_filename);\n        model.infer_only(&c, &setting, directory);\n    }\n\n    return 0;\n}\n"
  },
  {
    "path": "topic-competitors/slda/opt.cpp",
    "content": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This file is part of slda.\n\n// slda is free software; you can redistribute it and/or modify it under\n// the terms of the GNU General Public License as published by the Free\n// Software Foundation; either version 2 of the License, or (at your\n// option) any later version.\n\n// slda is distributed in the hope that it will be useful, but WITHOUT\n// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License\n// for more details.\n\n// You should have received a copy of the GNU General Public License\n// along with this program; if not, write to the Free Software\n// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\n// USA\n#include \"opt.h\"\n#include \"slda.h\"\n#include \"utils.h\"\n/*\n * Here the implementation is slightly different from the equations\n * in the paper, we instead use a second-order taylor expansion to approximate\n * the second line in eqaution (6).\n */\n\ndouble softmax_f(const gsl_vector * x, void * opt_param)\n{\n    opt_parameter * gsl_param = (opt_parameter *)opt_param;\n    double PENALTY = gsl_param->PENALTY;\n    slda * model = gsl_param->model;\n    suffstats * ss = gsl_param->ss;\n\n    double f, t, a1 = 0.0, a2 = 0.0;\n\n    int k, d, j, l, idx;\n\n    double f_regularization = 0.0;\n\n\n    for (l = 0; l < model->num_classes-1; l ++)\n    {\n        for (k = 0; k < model->num_topics; k ++)\n        {\n            model->eta[l][k] = gsl_vector_get(x, l*model->num_topics + k);\n            f_regularization -= pow(model->eta[l][k], 2) * PENALTY/2.0;\n        }\n    }\n    f = 0.0; //log likelihood\n    for (d = 0; d < ss->num_docs; d ++)\n    {\n        for (k = 0; k < model->num_topics; k ++)\n        {\n            if (ss->labels[d] < model->num_classes-1)\n            {\n                f += model->eta[ss->labels[d]][k] * ss->z_bar[d].z_bar_m[k];\n            }\n        }\n\n        t = 0.0; // in log space,  1+exp()+exp()...\n        for (l = 0; l < model->num_classes-1; l ++)\n        {\n            a1 = 0.0; // \\eta_k^T * \\bar{\\phi}_d\n            a2 = 0.0; // 1 + 0.5 * \\eta_k^T * Var(z_bar)\\eta_k\n            for (k = 0; k < model->num_topics; k ++)\n            {\n                a1 += model->eta[l][k] * ss->z_bar[d].z_bar_m[k];\n                for (j = 0; j < model->num_topics; j ++)\n                {\n                    idx = map_idx(k, j, model->num_topics);\n                    a2 += model->eta[l][k] * ss->z_bar[d].z_bar_var[idx] * model->eta[l][j];\n                }\n            }\n            a2 = 1.0 + 0.5 * a2;\n            t = log_sum(t, a1 + log(a2));\n        }\n        f -= t; \n    }\n\n    return -(f + f_regularization);\n}\nvoid softmax_df(const gsl_vector * x, void * opt_param, gsl_vector * df)\n{\n\n    opt_parameter * gsl_param = (opt_parameter *)opt_param;\n    double PENALTY = gsl_param->PENALTY;\n    slda * model = gsl_param->model;\n    suffstats * ss = gsl_param->ss;\n    gsl_vector_set_zero(df);\n    gsl_vector * df_tmp = gsl_vector_alloc(df->size);\n\n    double t, a1 = 0.0, a2 = 0.0, g;\n    int k, d, j, l, idx;\n\n    double * eta_aux = new double [model->num_topics];\n\n    for (l = 0; l < model->num_classes-1; l ++)\n    {\n        for (k = 0; k < model->num_topics; k ++)\n        {\n            idx = l*model->num_topics + k;\n            model->eta[l][k] = gsl_vector_get(x, idx); \n            g = -PENALTY * model->eta[l][k];\n            gsl_vector_set(df, idx, g);\n        }\n    }\n    for (d = 0; d < ss->num_docs; d ++)\n    {\n        for (k = 0; k < model->num_topics; k ++)\n        {\n            l = ss->labels[d];\n            if (l < model->num_classes-1)\n            {\n                idx = l*model->num_topics + k;\n                g = gsl_vector_get(df, idx) + ss->z_bar[d].z_bar_m[k];\n                gsl_vector_set(df, idx, g);\n            }\n        }\n\n        t = 0.0; // in log space, 1+exp()+exp()+....\n        gsl_vector_memcpy(df_tmp, df);\n        gsl_vector_set_zero(df);\n        for (l = 0; l < model->num_classes-1; l ++)\n        {\n            memset(eta_aux, 0, sizeof(double)*model->num_topics);\n            a1 = 0.0; // \\eta_k^T * \\bar{\\phi}_d\n            a2 = 0.0; // 1 + 0.5*\\eta_k^T * Var(z_bar)\\eta_k\n            for (k = 0; k < model->num_topics; k ++)\n            {\n                a1 += model->eta[l][k] * ss->z_bar[d].z_bar_m[k];\n                for (j = 0; j < model->num_topics; j ++)\n                {\n                    idx = map_idx(k, j, model->num_topics);\n                    a2 += model->eta[l][k] * ss->z_bar[d].z_bar_var[idx] * model->eta[l][j];\n                    eta_aux[k] += ss->z_bar[d].z_bar_var[idx] * model->eta[l][j];\n                }\n            }\n            a2 = 1.0 + 0.5 * a2;\n            t = log_sum(t, a1 + log(a2));\n\n            for (k = 0; k < model->num_topics; k ++)\n            {\n                idx = l*model->num_topics + k;\n                g =  gsl_vector_get(df, idx) -\n                     exp(a1) * (ss->z_bar[d].z_bar_m[k] * a2 + eta_aux[k]);\n                gsl_vector_set(df, idx, g);\n            }\n        }\n        gsl_vector_scale(df, exp(-t));\n        gsl_vector_add(df, df_tmp);\n    }\n    gsl_vector_scale(df, -1.0);\n    delete [] eta_aux;\n    gsl_vector_free(df_tmp);\n}\nvoid softmax_fdf(const gsl_vector * x, void * opt_param, double * f, gsl_vector * df)\n{\n    opt_parameter * gsl_param = (opt_parameter *)opt_param;\n    double PENALTY = gsl_param->PENALTY;\n    slda * model = gsl_param->model;\n    suffstats * ss = gsl_param->ss;\n    gsl_vector_set_zero(df);\n    gsl_vector * df_tmp = gsl_vector_alloc(df->size);\n\n    double t, a1 = 0.0, a2 = 0.0, g;\n    int k, d, j, l, idx;\n\n    double f_regularization = 0.0;\n\n    double* eta_aux = new double [model->num_topics];\n\n    for (l = 0; l < model->num_classes-1; l ++)\n    {\n        for (k = 0; k < model->num_topics; k ++)\n        {\n            model->eta[l][k] = gsl_vector_get(x, l*model->num_topics + k);\n            f_regularization -= pow(model->eta[l][k], 2) * PENALTY/2.0;\n            idx = l*model->num_topics + k;\n            g = -PENALTY * model->eta[l][k];\n            gsl_vector_set(df, idx, g);\n        }\n    }\n    *f = 0.0; //log likelihood\n    for (d = 0; d < ss->num_docs; d ++)\n    {\n        for (k = 0; k < model->num_topics; k ++)\n        {\n            l = ss->labels[d];\n            if (l < model->num_classes-1)\n            {\n                *f += model->eta[l][k] * ss->z_bar[d].z_bar_m[k];\n                idx = l*model->num_topics + k;\n                g = gsl_vector_get(df, idx) + ss->z_bar[d].z_bar_m[k];\n                gsl_vector_set(df, idx, g);\n            }\n        }\n        t = 0.0; // in log space,  base class 1+exp()+exp()\n        gsl_vector_memcpy(df_tmp, df);\n        gsl_vector_set_zero(df);\n        for (l = 0; l < model->num_classes-1; l ++)\n        {\n            memset(eta_aux, 0, sizeof(double)*model->num_topics);\n            a1 = 0.0; // \\eta_k^T * \\bar{\\phi}_d\n            a2 = 0.0; // 1 + 0.5 * \\eta_k^T * Var(z_bar)\\eta_k\n            for (k = 0; k < model->num_topics; k ++)\n            {\n                a1 += model->eta[l][k] * ss->z_bar[d].z_bar_m[k];\n                for (j = 0; j < model->num_topics; j ++)\n                {\n                    idx = map_idx(k, j, model->num_topics);\n                    a2 += model->eta[l][k] * ss->z_bar[d].z_bar_var[idx] * model->eta[l][j];\n                    eta_aux[k] += ss->z_bar[d].z_bar_var[idx] * model->eta[l][j];\n                }\n            }\n            a2 = 1.0 + 0.5 * a2;\n            t = log_sum(t, a1 + log(a2));\n\n            for (k = 0; k < model->num_topics; k ++)\n            {\n                idx = l*model->num_topics + k;\n                g =  gsl_vector_get(df, idx) -\n                     exp(a1) * (ss->z_bar[d].z_bar_m[k] * a2 + eta_aux[k]);\n                gsl_vector_set(df, idx, g);\n            }\n        }\n        gsl_vector_scale(df, exp(-t));\n        gsl_vector_add(df, df_tmp);\n        *f -= t; \n    }\n    gsl_vector_scale(df, -1.0);\n    *f = -(*f + f_regularization);\n    delete [] eta_aux;\n    gsl_vector_free(df_tmp);\n}\n\n"
  },
  {
    "path": "topic-competitors/slda/opt.h",
    "content": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This file is part of slda.\n\n// slda is free software; you can redistribute it and/or modify it under\n// the terms of the GNU General Public License as published by the Free\n// Software Foundation; either version 2 of the License, or (at your\n// option) any later version.\n\n// slda is distributed in the hope that it will be useful, but WITHOUT\n// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License\n// for more details.\n\n// You should have received a copy of the GNU General Public License\n// along with this program; if not, write to the Free Software\n// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\n// USA\n#ifndef OPT_H_INCLUDED\n#define OPT_H_INCLUDED\n#include <gsl/gsl_vector.h>\n#include \"slda.h\"\n\n/*\n * structure for the gsl optimization routine\n *\n */\n\nstruct opt_parameter\n{\n\tsuffstats * ss;\n\tslda * model;\n\tdouble PENALTY;\n};\n\n/*\n * function to compute the value of the obj function, then \n * return it\n */\n\ndouble softmax_f(const gsl_vector * x, void * opt_param);\n\n/*\n * function to compute the derivatives of function \n *\n */\n\nvoid softmax_df(const gsl_vector * x, void * opt_param, gsl_vector * df);\n\n/*\n * function to compute the value and derivatives of the function \n *\n */\n\nvoid softmax_fdf(const gsl_vector * x, void * opt_param, double * f, gsl_vector * df);\n\n#endif // OPT_H_INCLUDED\n\n"
  },
  {
    "path": "topic-competitors/slda/readme.txt",
    "content": "**********************************************************\nSUPERVISED LATENT DIRICHLET ALLOCATION FOR CLASSIFICATION\n**********************************************************\n\n(C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\nwritten by Chong Wang, chongw@cs.princeton.edu, part of code\nis from http://www.cs.princeton.edu/~blei/lda-c/index.html.\n\nThis file is part of slda.\n\nslda is free software; you can redistribute it and/or modify it under\nthe terms of the GNU General Public License as published by the Free\nSoftware Foundation; either version 2 of the License, or (at your\noption) any later version.\n\nslda is distributed in the hope that it will be useful, but WITHOUT\nANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\nFITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License\nfor more details.\n\nYou should have received a copy of the GNU General Public License\nalong with this program; if not, write to the Free Software\nFoundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\nUSA\n\n\n------------------------------------------------------------------------\n\nThis is a C++ implementation of supervised latent Dirichlet allocation (sLDA) \nfor classification.\n\nNote that this code requires the Gnu Scientific Library, http://www.gnu.org/software/gsl/\n\n------------------------------------------------------------------------\n\n\nTABLE OF CONTENTS\n\n\nA. COMPILING\n\nB. ESTIMATION\n\nC. INFERENCE\n\n\n------------------------------------------------------------------------\n\nA. COMPILING\n\nType \"make\" in a shell. Make sure the GSL is installed.\n\n\n------------------------------------------------------------------------\n\nB. ESTIMATION\n\nEstimate the model by executing:\n\n     slda [est] [data] [label] [settings] [alpha] [k] [seeded/random/model_path] [directory]\n\nThe saved models are in two files:\n\n     <iteration>.model is the model saved in the binary format, which is easy and\n     fast to use for inference.\n\n     <iteration>.model.txt is the model saved in the text format, which is\n     convenient for printing topics or analysis using python.\n     \n\nThe variational posterior Dirichlets are in:\n\n     <iteration>.gamma\n\n\nData format\n\n(1) [data] is a file where each line is of the form:\n\n     [M] [term_1]:[count] [term_2]:[count] ...  [term_N]:[count]\n\nwhere [M] is the number of unique terms in the document, and the\n[count] associated with each term is how many times that term appeared\nin the document. \n\n(2) [label] is a file where each line is the corresponding label for [data].\nThe labels must be 0, 1, ..., C-1, if we have C classes.\n\n\n------------------------------------------------------------------------\n\nC. INFERENCE\n\nTo perform inference on a different set of data (in the same format as\nfor estimation), execute:\n\n     slda [inf] [data] [label] [settings] [model] [directory]\n    \nwhere [model] is the binary file from the estimation.\n     \nThe predictive labels are in:\n\n     inf-labels.dat\n\nThe variational posterior Dirichlets are in:\n\n     inf-gamma.dat\n\n"
  },
  {
    "path": "topic-competitors/slda/reuters-test-2255.slda-bow.txt",
    "content": "41 1:1 2:2 5:2 56:1 76:1 150:2 244:1 580:2 660:1 703:3 718:1 901:1 914:1 1044:1 1179:1 1227:1 1503:2 1512:1 1525:1 1799:1 2051:1 2182:1 2453:1 2457:1 2724:2 3094:2 3267:1 5219:1 5791:1 6462:1 6659:1 7220:2 7449:1 7654:2 8106:1 8140:1 8373:1 11197:1 11510:1 15077:2 16078:1\r\n32 43:1 73:1 258:1 362:1 471:1 687:1 816:1 923:2 1032:13 1349:1 1571:1 1803:2 1982:1 2197:1 2328:1 2332:3 2453:1 2667:2 3104:1 3853:1 4340:1 4610:1 5701:1 6455:1 6659:1 6804:1 8630:1 11956:4 14878:2 15077:19 16876:2 17337:1\r\n66 1:2 21:1 39:1 73:2 86:1 111:1 148:1 201:1 220:1 266:1 268:1 280:1 357:1 380:1 382:1 413:1 436:1 489:1 547:1 590:1 603:1 630:1 697:1 702:1 931:1 1102:1 1378:1 1571:1 1593:1 1646:1 1856:2 1975:1 2182:1 2332:4 2369:1 2453:2 2781:1 2987:1 3080:1 3094:2 3152:1 3197:1 3515:1 3934:1 4385:1 4442:1 4646:1 5070:1 5189:1 5557:1 5658:1 5701:2 5832:2 6370:1 6516:1 6659:2 7127:1 7633:1 7860:1 8028:1 8106:3 12471:1 14878:4 14889:1 15077:10 16876:4\r\n84 2:1 6:4 9:1 12:1 21:1 31:1 33:1 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  },
  {
    "path": "topic-competitors/slda/settings.h",
    "content": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\r\n\r\n// written by Chong Wang, chongw@cs.princeton.edu\r\n\r\n// This file is part of slda.\r\n\r\n// slda is free software; you can redistribute it and/or modify it under\r\n// the terms of the GNU General Public License as published by the Free\r\n// Software Foundation; either version 2 of the License, or (at your\r\n// option) any later version.\r\n\r\n// slda is distributed in the hope that it will be useful, but WITHOUT\r\n// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\r\n// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License\r\n// for more details.\r\n\r\n// You should have received a copy of the GNU General Public License\r\n// along with this program; if not, write to the Free Software\r\n// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\r\n// USA\r\n#ifndef SETTINGS_H\r\n#define SETTINGS_H\r\n#include <stdio.h>\r\n#include <string.h>\r\n\r\nstruct settings\r\n{\r\n    float VAR_CONVERGED;\r\n    int   VAR_MAX_ITER;\r\n    float EM_CONVERGED;\r\n    int   EM_MAX_ITER;\r\n    int   ESTIMATE_ALPHA;\r\n    float PENALTY;\r\n\r\n    void read_settings(char* filename)\r\n    {\r\n        FILE * fileptr;\r\n        char alpha_action[100];\r\n\r\n        fileptr = fopen(filename, \"r\");\r\n        fscanf(fileptr, \"var max iter %d\\n\", &this->VAR_MAX_ITER);\r\n        fscanf(fileptr, \"var convergence %f\\n\", &this->VAR_CONVERGED);\r\n        fscanf(fileptr, \"em max iter %d\\n\", &this->EM_MAX_ITER);\r\n        fscanf(fileptr, \"em convergence %f\\n\", &this->EM_CONVERGED);\r\n        fscanf(fileptr, \"L2 penalty %f\\n\", &this->PENALTY);\r\n\r\n        fscanf(fileptr, \"alpha %s\", alpha_action);\r\n        if (strcmp(alpha_action, \"fixed\") == 0)\r\n        {\r\n            this->ESTIMATE_ALPHA = 0;\r\n            printf(\"alpha is fixed ...\\n\");\r\n        }\r\n        else\r\n        {\r\n            this->ESTIMATE_ALPHA = 1;\r\n            printf(\"alpha is esimated ...\\n\");\r\n        }\r\n        fclose(fileptr);\r\n        printf(\"var max iter %d\\n\", this->VAR_MAX_ITER);\r\n        printf(\"var convergence %.2E\\n\", this->VAR_CONVERGED);\r\n        printf(\"em max iter %d\\n\", this->EM_MAX_ITER);\r\n        printf(\"em convergence %.2E\\n\", this->EM_CONVERGED);\r\n        printf(\"L2 penalty %.2E\\n\", this->PENALTY);\r\n    }\r\n};\r\n\r\n#endif // SETTINGS_H\r\n\r\n"
  },
  {
    "path": "topic-competitors/slda/settings.txt",
    "content": "var max iter 20\nvar convergence 1e-3\nem max iter 50\nem convergence 1e-4\nL2 penalty 0.01\nalpha fixed\n"
  },
  {
    "path": "topic-competitors/slda/slda.cpp",
    "content": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This file is part of slda.\n\n// slda is free software; you can redistribute it and/or modify it under\n// the terms of the GNU General Public License as published by the Free\n// Software Foundation; either version 2 of the License, or (at your\n// option) any later version.\n\n// slda is distributed in the hope that it will be useful, but WITHOUT\n// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License\n// for more details.\n\n// You should have received a copy of the GNU General Public License\n// along with this program; if not, write to the Free Software\n// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\n// USA\n\n#include \"slda.h\"\n#include <time.h>\n#include \"utils.h\"\n#include \"assert.h\"\n#include \"opt.h\"\n#include <gsl/gsl_multimin.h>\n#include <gsl/gsl_rng.h>\n#include <gsl/gsl_randist.h>\n\nconst int NUM_INIT = 50;\nconst int LAG = 10;\nconst int LDA_INIT_MAX = 0;\nconst int MSTEP_MAX_ITER = 50;\n\nslda::slda()\n{\n    //ctor\n    alpha = 1.0;\n    num_topics = 0;\n    num_classes = 0;\n    size_vocab = 0;\n\n    log_prob_w = NULL;\n    eta = NULL;\n}\n\nslda::~slda()\n{\n    free_model();\n}\n\n/*\n * init the model\n */\n\nvoid slda::init(double alpha_, int num_topics_,\n                const corpus * c)\n{\n    alpha = alpha_;\n    num_topics = num_topics_;\n    size_vocab = c->size_vocab;\n    num_classes = c->num_classes;\n\n    log_prob_w = new double * [num_topics];\n    for (int k = 0; k < num_topics; k++)\n    {\n        log_prob_w[k] = new double [size_vocab];\n        memset(log_prob_w[k], 0, sizeof(double)*size_vocab);\n    }\n    //no need to train slda if we only have on class\n    if (num_classes > 1)\n    {\n        eta = new double * [num_classes-1];\n        for (int i = 0; i < num_classes-1; i ++)\n        {\n            eta[i] = new double [num_topics];\n            memset(eta[i], 0, sizeof(double)*num_topics);\n        }\n    }\n}\n\n/*\n * free the model\n */\n\nvoid slda::free_model()\n{\n    if (log_prob_w != NULL)\n    {\n        for (int k = 0; k < num_topics; k++)\n        {\n            delete [] log_prob_w[k];\n        }\n        delete [] log_prob_w;\n        log_prob_w = NULL;\n    }\n    if (eta != NULL)\n    {\n        for (int i = 0; i < num_classes-1; i ++)\n        {\n            delete [] eta[i];\n        }\n        delete [] eta;\n        eta = NULL;\n    }\n}\n\n/*\n * save the model in the binary format\n */\n\nvoid slda::save_model(const char * filename)\n{\n    FILE * file = NULL;\n    file = fopen(filename, \"wb\");\n    fwrite(&alpha, sizeof (double), 1, file);\n    fwrite(&num_topics, sizeof (int), 1, file);\n    fwrite(&size_vocab, sizeof (int), 1, file);\n    fwrite(&num_classes, sizeof (int), 1, file);\n\n    for (int k = 0; k < num_topics; k++)\n    {\n        fwrite(log_prob_w[k], sizeof(double), size_vocab, file);\n    }\n    if (num_classes > 1)\n    {\n        for (int i = 0; i < num_classes-1; i ++)\n        {\n            fwrite(eta[i], sizeof(double), num_topics, file);\n        }\n    }\n\n    fflush(file);\n    fclose(file);\n}\n\n/*\n * load the model in the binary format\n */\n\nvoid slda::load_model(const char * filename)\n{\n    FILE * file = NULL;\n    file = fopen(filename, \"rb\");\n    fread(&alpha, sizeof (double), 1, file);\n    fread(&num_topics, sizeof (int), 1, file);\n    fread(&size_vocab, sizeof (int), 1, file);\n    fread(&num_classes, sizeof (int), 1, file);\n\n    log_prob_w = new double * [num_topics];\n    for (int k = 0; k < num_topics; k++)\n    {\n        log_prob_w[k] = new double [size_vocab];\n        fread(log_prob_w[k], sizeof(double), size_vocab, file);\n    }\n    if (num_classes > 1)\n    {\n        eta = new double * [num_classes-1];\n        for (int i = 0; i < num_classes-1; i ++)\n        {\n            eta[i] = new double [num_topics];\n            fread(eta[i], sizeof(double), num_topics, file);\n        }\n    }\n\n    fflush(file);\n    fclose(file);\n}\n\n/*\n * save the model in the text format\n */\n\nvoid slda::save_model_text(const char * filename)\n{\n    FILE * file = NULL;\n    file = fopen(filename, \"w\");\n    fprintf(file, \"alpha: %lf\\n\", alpha);\n    fprintf(file, \"number of topics: %d\\n\", num_topics);\n    fprintf(file, \"size of vocab: %d\\n\", size_vocab);\n    fprintf(file, \"number of classes: %d\\n\", num_classes);\n\n    fprintf(file, \"betas: \\n\"); // in log space\n    for (int k = 0; k < num_topics; k++)\n    {\n        for (int j = 0; j < size_vocab; j ++)\n        {\n            fprintf(file, \"%lf \", log_prob_w[k][j]);\n        }\n        fprintf(file, \"\\n\");\n    }\n    if (num_classes > 1)\n    {\n        fprintf(file, \"etas: \\n\");\n        for (int i = 0; i < num_classes-1; i ++)\n        {\n            for (int j = 0; j < num_topics; j ++)\n            {\n                fprintf(file, \"%lf \", eta[i][j]);\n            }\n            fprintf(file, \"\\n\");\n        }\n    }\n\n    fflush(file);\n    fclose(file);\n}\n\n/*\n * create the data structure for sufficient statistic \n */\n\nsuffstats * slda::new_suffstats(int num_docs)\n{\n    suffstats * ss = new suffstats;\n    ss->num_docs = num_docs;\n    ss->word_total_ss = new double [num_topics];\n    memset(ss->word_total_ss, 0, sizeof(double)*num_topics);\n    ss->word_ss = new double * [num_topics];\n    for (int k = 0; k < num_topics; k ++)\n    {\n        ss->word_ss[k] = new double [size_vocab];\n        memset(ss->word_ss[k], 0, sizeof(double)*size_vocab);\n    }\n\n    int num_var_entries = num_topics*(num_topics+1)/2;\n    ss->z_bar =  new z_stat [num_docs];\n    for (int d = 0; d < num_docs; d ++)\n    {\n        ss->z_bar[d].z_bar_m = new double [num_topics];\n        ss->z_bar[d].z_bar_var = new double [num_var_entries];\n        memset(ss->z_bar[d].z_bar_m, 0, sizeof(double)*num_topics);\n        memset(ss->z_bar[d].z_bar_var, 0, sizeof(double)*num_var_entries);\n    }\n    ss->labels = new int [num_docs];\n    memset(ss->labels, 0, sizeof(int)*(num_docs));\n    ss->tot_labels = new int [num_classes];\n    memset(ss->tot_labels, 0, sizeof(int)*(num_classes));\n\n    return(ss);\n}\n\n\n/*\n * initialize the sufficient statistics with zeros\n */\n\nvoid slda::zero_initialize_ss(suffstats * ss)\n{\n    memset(ss->word_total_ss, 0, sizeof(double)*num_topics);\n    for (int k = 0; k < num_topics; k ++)\n    {\n        memset(ss->word_ss[k], 0, sizeof(double)*size_vocab);\n    }\n\n    int num_var_entries = num_topics*(num_topics+1)/2;\n    for (int d = 0; d < ss->num_docs; d ++)\n    {\n        memset(ss->z_bar[d].z_bar_m, 0, sizeof(double)*num_topics);\n        memset(ss->z_bar[d].z_bar_var, 0, sizeof(double)*num_var_entries);\n    }\n    ss->num_docs = 0;\n}\n\n\n/*\n * initialize the sufficient statistics with random numbers \n */\n\nvoid slda::random_initialize_ss(suffstats * ss, corpus* c)\n{\n    int num_docs = ss->num_docs;\n    gsl_rng * rng = gsl_rng_alloc(gsl_rng_taus);\n    time_t seed;\n    time(&seed);\n    gsl_rng_set(rng, (long) seed);\n    int k, w, d, j, idx;\n    for (k = 0; k < num_topics; k++)\n    {\n        for (w = 0; w < size_vocab; w++)\n        {\n            ss->word_ss[k][w] = 1.0/size_vocab + 0.1*gsl_rng_uniform(rng);\n            ss->word_total_ss[k] += ss->word_ss[k][w];\n        }\n    }\n\n    for (d = 0; d < num_docs; d ++)\n    {\n        document * doc = c->docs[d];\n        ss->labels[d] = doc->label;\n        ss->tot_labels[doc->label] ++;\n\n        double total = 0.0;\n        for (k = 0; k < num_topics; k ++)\n        {\n            ss->z_bar[d].z_bar_m[k] = gsl_rng_uniform(rng);\n            total += ss->z_bar[d].z_bar_m[k];\n        }\n        for (k = 0; k < num_topics; k ++)\n        {\n            ss->z_bar[d].z_bar_m[k] /= total;\n        }\n        for (k = 0; k < num_topics; k ++)\n        {\n            for (j = k; j < num_topics; j ++)\n            {\n                idx = map_idx(k, j, num_topics);\n                if (j == k)\n                    ss->z_bar[d].z_bar_var[idx] = ss->z_bar[d].z_bar_m[k] / (double)(doc->total);\n                else\n                    ss->z_bar[d].z_bar_var[idx] = 0.0;\n\n                ss->z_bar[d].z_bar_var[idx] -=\n                    ss->z_bar[d].z_bar_m[k] * ss->z_bar[d].z_bar_m[j] / (double)(doc->total);\n            }\n        }\n    }\n\n    gsl_rng_free(rng);\n}\n\nvoid slda::corpus_initialize_ss(suffstats* ss, corpus* c)\n{\n    int num_docs = ss->num_docs;\n    gsl_rng * rng = gsl_rng_alloc(gsl_rng_taus);\n    time_t seed;\n    time(&seed);\n    gsl_rng_set(rng, (long) seed);\n    int k, n, d, j, idx, i, w;\n\n    for (k = 0; k < num_topics; k++)\n    {\n        for (i = 0; i < NUM_INIT; i++)\n        {\n            d = (int)(floor(gsl_rng_uniform(rng) * num_docs));\n            printf(\"initialized with document %d\\n\", d);\n            document * doc = c->docs[d];\n            for (n = 0; n < doc->length; n++)\n            {\n                ss->word_ss[k][doc->words[n]] += doc->counts[n];\n            }\n        }\n        for (w = 0; w < size_vocab; w++)\n        {\n            ss->word_ss[k][w] = 2*ss->word_ss[k][w] + 5 + gsl_rng_uniform(rng);\n            ss->word_total_ss[k] = ss->word_total_ss[k] + ss->word_ss[k][w];\n        }\n    }\n\n    for (d = 0; d < num_docs; d ++)\n    {\n        document * doc = c->docs[d];\n        ss->labels[d] = doc->label;\n        ss->tot_labels[doc->label] ++;\n\n        double total = 0.0;\n        for (k = 0; k < num_topics; k ++)\n        {\n            ss->z_bar[d].z_bar_m[k] = gsl_rng_uniform(rng);\n            total += ss->z_bar[d].z_bar_m[k];\n        }\n        for (k = 0; k < num_topics; k ++)\n        {\n            ss->z_bar[d].z_bar_m[k] /= total;\n        }\n        for (k = 0; k < num_topics; k ++)\n        {\n            for (j = k; j < num_topics; j ++)\n            {\n                idx = map_idx(k, j, num_topics);\n                if (j == k)\n                    ss->z_bar[d].z_bar_var[idx] = ss->z_bar[d].z_bar_m[k] / (double)(doc->total);\n                else\n                    ss->z_bar[d].z_bar_var[idx] = 0.0;\n\n                ss->z_bar[d].z_bar_var[idx] -=\n                    ss->z_bar[d].z_bar_m[k] * ss->z_bar[d].z_bar_m[j] / (double)(doc->total);\n            }\n        }\n    }\n    gsl_rng_free(rng);\n}\n\nvoid slda::load_model_initialize_ss(suffstats* ss, corpus * c)\n{\n    int num_docs = ss->num_docs;                                                                         \n    for (int d = 0; d < num_docs; d ++)       \n    {                                                                                                    \n       document * doc = c->docs[d];\n       ss->labels[d] = doc->label;\n       ss->tot_labels[doc->label] ++;\n    }     \n}\n\nvoid slda::free_suffstats(suffstats * ss)\n{\n    delete [] ss->word_total_ss;\n\n    for (int k = 0; k < num_topics; k ++)\n    {\n        delete [] ss->word_ss[k];\n    }\n    delete [] ss->word_ss;\n\n    for (int d = 0; d < ss->num_docs; d ++)\n    {\n        delete [] ss->z_bar[d].z_bar_m;\n        delete [] ss->z_bar[d].z_bar_var;\n    }\n    delete [] ss->z_bar;\n    delete [] ss->labels;\n    delete [] ss->tot_labels;\n\n    delete ss;\n}\n\nvoid slda::v_em(corpus * c, const settings * setting,\n                const char * start, const char * directory)\n{\n    char filename[100];\n    int max_length = c->max_corpus_length();\n    double **var_gamma, **phi, **lambda;\n    double likelihood, likelihood_old = 0, converged = 1;\n    int d, n, i;\n    double L2penalty = setting->PENALTY;\n    // allocate variational parameters\n    var_gamma = new double * [c->num_docs];\n    for (d = 0; d < c->num_docs; d++)\n        var_gamma[d] = new double [num_topics];\n\n    phi = new double * [max_length];\n    for (n = 0; n < max_length; n++)\n        phi[n] = new double [num_topics];\n\n    printf(\"initializing ...\\n\");\n    suffstats * ss = new_suffstats(c->num_docs);\n    if (strcmp(start, \"seeded\") == 0)\n    {\n        corpus_initialize_ss(ss, c);\n        mle(ss, 0, setting);\n    }\n    else if (strcmp(start, \"random\") == 0)\n    {\n        random_initialize_ss(ss, c);\n        mle(ss, 0, setting);\n    }\n    else\n    {\n        load_model(start);\n        load_model_initialize_ss(ss, c);\n    }\n\n    FILE * likelihood_file = NULL;\n    sprintf(filename, \"%s/likelihood.dat\", directory);\n    likelihood_file = fopen(filename, \"w\");\n\n    int ETA_UPDATE = 0;\n\n    i = 0;\n    while (((converged < 0) || (converged > setting->EM_CONVERGED) || (i <= LDA_INIT_MAX+2)) && (i <= setting->EM_MAX_ITER))\n    {\n        printf(\"**** em iteration %d ****\\n\", ++i);\n        likelihood = 0;\n        zero_initialize_ss(ss);\n        if (i > LDA_INIT_MAX) ETA_UPDATE = 1;\n        // e-step\n        printf(\"**** e-step ****\\n\");\n        for (d = 0; d < c->num_docs; d++)\n        {\n            if ((d % 100) == 0) printf(\"document %d\\n\", d);\n            likelihood += doc_e_step(c->docs[d], var_gamma[d], phi, ss, ETA_UPDATE, setting);\n        }\n\n        printf(\"likelihood: %10.10f\\n\", likelihood);\n        // m-step\n        printf(\"**** m-step ****\\n\");\n        mle(ss, ETA_UPDATE, setting);\n\n        // check for convergence\n        converged = fabs((likelihood_old - likelihood) / (likelihood_old));\n        //if (converged < 0) VAR_MAX_ITER = VAR_MAX_ITER * 2;\n        likelihood_old = likelihood;\n\n        // output model and likelihood\n        fprintf(likelihood_file, \"%10.10f\\t%5.5e\\n\", likelihood, converged);\n        fflush(likelihood_file);\n        if ((i % LAG) == 0)\n        {\n            sprintf(filename, \"%s/%03d.model\", directory, i);\n            save_model(filename);\n            sprintf(filename, \"%s/%03d.model.text\", directory, i);\n            save_model_text(filename);\n            sprintf(filename, \"%s/%03d.gamma\", directory, i);\n            save_gamma(filename, var_gamma, c->num_docs);\n        }\n    }\n\n    // output the final model\n    sprintf(filename, \"%s/final.model\", directory);\n    save_model(filename);\n    sprintf(filename, \"%s/final.model.text\", directory);\n    save_model_text(filename);\n    sprintf(filename, \"%s/final.gamma\", directory);\n    save_gamma(filename, var_gamma, c->num_docs);\n\n\n    fclose(likelihood_file);\n    FILE * w_asgn_file = NULL;\n    sprintf(filename, \"%s/word-assignments.dat\", directory);\n    w_asgn_file = fopen(filename, \"w\");\n    for (d = 0; d < c->num_docs; d ++)\n    {\n        //final inference\n        if ((d % 100) == 0) printf(\"final e step document %d\\n\", d);\n        likelihood += slda_inference(c->docs[d], var_gamma[d], phi, setting);\n        write_word_assignment(w_asgn_file, c->docs[d], phi);\n\n    }\n    fclose(w_asgn_file);\n\n    free_suffstats(ss);\n    for (d = 0; d < c->num_docs; d++)\n        delete [] var_gamma[d];\n    delete [] var_gamma;\n\n    for (n = 0; n < max_length; n++)\n        delete [] phi[n];\n    delete [] phi;\n}\nvoid slda::mle(suffstats * ss, int eta_update, const settings * setting)\n{\n    int k, w;\n\n    for (k = 0; k < num_topics; k++)\n    {\n        for (w = 0; w < size_vocab; w++)\n        {\n            if (ss->word_ss[k][w] > 0)\n                log_prob_w[k][w] = log(ss->word_ss[k][w]) - log(ss->word_total_ss[k]);\n            else\n                log_prob_w[k][w] = -100.0;\n        }\n    }\n    if (eta_update == 0) return;\n\n    //the label part goes here\n    printf(\"maximizing ...\\n\");\n\tdouble f = 0.0;\n\tint status;\n\tint opt_iter;\n\tint opt_size = (num_classes-1) * num_topics;\n\tint l;\n\n\topt_parameter param;\n\tparam.ss = ss;\n\tparam.model = this;\n\tparam.PENALTY = setting->PENALTY;\n\n\tconst gsl_multimin_fdfminimizer_type * T;\n\tgsl_multimin_fdfminimizer * s;\n\tgsl_vector * x;\n\tgsl_multimin_function_fdf opt_fun;\n\topt_fun.f = &softmax_f;\n\topt_fun.df = &softmax_df;\n\topt_fun.fdf = &softmax_fdf;\n\topt_fun.n = opt_size;\n\topt_fun.params = (void*)(&param);\n\tx = gsl_vector_alloc(opt_size);\n\n\n\tfor (l = 0; l < num_classes-1; l ++)\n\t{\n\t\tfor (k = 0; k < num_topics; k ++)\n\t\t{\n\t\t\tgsl_vector_set(x, l*num_topics + k, eta[l][k]);\n\t\t}\n\t}\n\n\tT = gsl_multimin_fdfminimizer_vector_bfgs;\n\ts = gsl_multimin_fdfminimizer_alloc(T, opt_size);\n\tgsl_multimin_fdfminimizer_set(s, &opt_fun, x, 0.02, 1e-4);\n\n\topt_iter = 0;\n\tdo\n\t{\n\t\topt_iter ++;\n\t\tstatus = gsl_multimin_fdfminimizer_iterate(s);\n\t\tif (status)\n\t\t\tbreak;\n\t\tstatus = gsl_multimin_test_gradient(s->gradient, 1e-3);\n\t\tif (status == GSL_SUCCESS)\n\t\t\tbreak;\n\t\tf = -s->f;\n\t\tif ((opt_iter-1) % 10 == 0)\n\t\t\tprintf(\"step: %02d -> f: %f\\n\", opt_iter-1, f);\n\t} while (status == GSL_CONTINUE && opt_iter < MSTEP_MAX_ITER);\n\n\tfor (l = 0; l < num_classes-1; l ++)\n\t{\n\t\tfor (k = 0; k < num_topics; k ++)\n\t\t{\n\t\t\teta[l][k] = gsl_vector_get(s->x, l*num_topics + k);\n\t\t}\n\t}\n\n\tgsl_multimin_fdfminimizer_free (s);\n\tgsl_vector_free (x);\n\n\tprintf(\"final f: %f\\n\", f);\n}\n\ndouble slda::doc_e_step(document* doc, double* gamma, double** phi,\n                        suffstats * ss, int eta_update, const settings * setting)\n{\n    double likelihood = 0.0;\n    if (eta_update == 1)\n        likelihood = slda_inference(doc, gamma, phi, setting);\n    else\n        likelihood = lda_inference(doc, gamma, phi, setting);\n\n    int d = ss->num_docs;\n\n    int n, k, i, idx, m;\n\n    // update sufficient statistics\n\n    for (n = 0; n < doc->length; n++)\n    {\n        for (k = 0; k < num_topics; k++)\n        {\n            ss->word_ss[k][doc->words[n]] += doc->counts[n]*phi[n][k];\n            ss->word_total_ss[k] += doc->counts[n]*phi[n][k];\n\n            //statistics for each document of the supervised part\n            ss->z_bar[d].z_bar_m[k] += doc->counts[n] * phi[n][k]; //mean\n            for (i = k; i < num_topics; i ++) //variance\n            {\n                idx = map_idx(k, i, num_topics);\n                if (i == k)\n                    ss->z_bar[d].z_bar_var[idx] +=\n                        doc->counts[n] * doc->counts[n] * phi[n][k]; \n\n                ss->z_bar[d].z_bar_var[idx] -=\n                    doc->counts[n] * doc->counts[n] * phi[n][k] * phi[n][i];\n            }\n        }\n    }\n    for (k = 0; k < num_topics; k++)\n    {\n        ss->z_bar[d].z_bar_m[k] /= (double)(doc->total);\n    }\n    for (i = 0; i < num_topics*(num_topics+1)/2; i ++)\n    {\n        ss->z_bar[d].z_bar_var[i] /= (double)(doc->total * doc->total);\n    }\n\n    ss->num_docs = ss->num_docs + 1; //because we need it for store statistics for each docs\n\n    return (likelihood);\n}\n\ndouble slda::lda_inference(document* doc, double* var_gamma, double** phi, const settings * setting)\n{\n    int k, n, var_iter;\n    double converged = 1, phisum = 0, likelihood = 0, likelihood_old = 0;\n\n    double *oldphi = new double [num_topics];\n    double *digamma_gam = new double [num_topics];\n\n    // compute posterior dirichlet\n    for (k = 0; k < num_topics; k++)\n    {\n        var_gamma[k] = alpha + (doc->total/((double) num_topics));\n        digamma_gam[k] = digamma(var_gamma[k]);\n        for (n = 0; n < doc->length; n++)\n            phi[n][k] = 1.0/num_topics;\n    }\n    var_iter = 0;\n\n    while (converged > setting->VAR_CONVERGED && (var_iter < setting->VAR_MAX_ITER || setting->VAR_MAX_ITER == -1))\n    {\n        var_iter++;\n        for (n = 0; n < doc->length; n++)\n        {\n            phisum = 0;\n            for (k = 0; k < num_topics; k++)\n            {\n                oldphi[k] = phi[n][k];\n                phi[n][k] = digamma_gam[k] + log_prob_w[k][doc->words[n]];\n\n                if (k > 0)\n                    phisum = log_sum(phisum, phi[n][k]);\n                else\n                    phisum = phi[n][k]; // note, phi is in log space\n            }\n\n            for (k = 0; k < num_topics; k++)\n            {\n                phi[n][k] = exp(phi[n][k] - phisum);\n                var_gamma[k] = var_gamma[k] + doc->counts[n]*(phi[n][k] - oldphi[k]);\n                digamma_gam[k] = digamma(var_gamma[k]);\n            }\n        }\n\n        likelihood = lda_compute_likelihood(doc, phi, var_gamma);\n        assert(!isnan(likelihood));\n        converged = (likelihood_old - likelihood) / likelihood_old;\n        likelihood_old = likelihood;\n    }\n\n    delete [] oldphi;\n    delete [] digamma_gam;\n\n    return likelihood;\n}\n\ndouble slda::lda_compute_likelihood(document* doc, double** phi, double* var_gamma)\n{\n    double likelihood = 0, digsum = 0, var_gamma_sum = 0;\n    double *dig = new double [num_topics];\n    int k, n;\n    double alpha_sum = num_topics * alpha;\n    for (k = 0; k < num_topics; k++)\n    {\n        dig[k] = digamma(var_gamma[k]);\n        var_gamma_sum += var_gamma[k];\n    }\n    digsum = digamma(var_gamma_sum);\n\n    likelihood = lgamma(alpha_sum) - lgamma(var_gamma_sum);\n\n    for (k = 0; k < num_topics; k++)\n    {\n        likelihood += - lgamma(alpha) + (alpha - 1)*(dig[k] - digsum) +\n                      lgamma(var_gamma[k]) - (var_gamma[k] - 1)*(dig[k] - digsum);\n\n        for (n = 0; n < doc->length; n++)\n        {\n            if (phi[n][k] > 0)\n            {\n                likelihood += doc->counts[n]*(phi[n][k]*((dig[k] - digsum) -\n                                              log(phi[n][k]) + log_prob_w[k][doc->words[n]]));\n            }\n        }\n    }\n\n    delete [] dig;\n    return likelihood;\n}\n\ndouble slda::slda_compute_likelihood(document* doc, double** phi, double* var_gamma)\n{\n    double likelihood = 0, digsum = 0, var_gamma_sum = 0, t = 0.0, t1 = 0.0, t2 = 0.0;\n    double * dig = new double [num_topics];\n    int k, n, l;\n    int flag;\n    double alpha_sum = num_topics * alpha;\n    for (k = 0; k < num_topics; k++)\n    {\n        dig[k] = digamma(var_gamma[k]);\n        var_gamma_sum += var_gamma[k];\n    }\n    digsum = digamma(var_gamma_sum);\n\n    likelihood = lgamma(alpha_sum) - lgamma(var_gamma_sum);\n    t = 0.0;\n    for (k = 0; k < num_topics; k++)\n    {\n        likelihood += -lgamma(alpha) + (alpha - 1)*(dig[k] - digsum) + lgamma(var_gamma[k]) - (var_gamma[k] - 1)*(dig[k] - digsum);\n\n        for (n = 0; n < doc->length; n++)\n        {\n            if (phi[n][k] > 0)\n            {\n                likelihood += doc->counts[n]*(phi[n][k]*((dig[k] - digsum) - log(phi[n][k]) + log_prob_w[k][doc->words[n]]));\n                if (doc->label < num_classes-1)\n                    t += eta[doc->label][k] * doc->counts[n] * phi[n][k];\n            }\n        }\n    }\n    likelihood += t / (double)(doc->total); \t//eta_k*\\bar{\\phi}\n\n    t = 1.0; //the class model->num_classes-1\n    for (l = 0; l < num_classes-1; l ++)\n    {\n        t1 = 1.0; \n        for (n = 0; n < doc->length; n ++)\n        {\n            t2 = 0.0;\n            for (k = 0; k < num_topics; k ++)\n            {\n                t2 += phi[n][k] * exp(eta[l][k] * doc->counts[n]/(double)(doc->total));\n            }\n            t1 *= t2; \n        }\n        t += t1; \n    }\n    likelihood -= log(t); \n    delete [] dig;\n    //printf(\"%lf\\n\", likelihood);\n    return likelihood;\n}\n\ndouble slda::slda_inference(document* doc, double* var_gamma, double** phi, const settings * setting)\n{\n    int k, n, var_iter, l;\n    int FP_MAX_ITER = 10;\n    int fp_iter = 0;\n    double converged = 1, phisum = 0, likelihood = 0, likelihood_old = 0;\n    double * oldphi = new double [num_topics];\n    double * digamma_gam = new double [num_topics];\n    double * sf_params = new double [num_topics];\n    double * sf_aux = new double [num_classes-1];\n    double sf_val = 0.0;\n\n    // compute posterior dirichlet\n    for (k = 0; k < num_topics; k++)\n    {\n        var_gamma[k] = alpha + (doc->total/((double) num_topics));\n        digamma_gam[k] = digamma(var_gamma[k]);\n        for (n = 0; n < doc->length; n++)\n            phi[n][k] = 1.0/(double)(num_topics);\n    }\n\n    double t = 0.0;\n    for (l = 0; l < num_classes-1; l ++)\n    {\n        sf_aux[l] = 1.0; // the quantity for equation 6 of each class\n        for (n = 0; n < doc->length; n ++)\n        {\n            t = 0.0;\n            for (k = 0; k < num_topics; k ++)\n            {\n                t += phi[n][k] * exp(eta[l][k] * doc->counts[n]/(double)(doc->total));\n            }\n            sf_aux[l] *= t;\n        }\n    }\n\n    var_iter = 0;\n\n    while ((converged > setting->VAR_CONVERGED) && ((var_iter < setting->VAR_MAX_ITER) || (setting->VAR_MAX_ITER == -1)))\n    {\n        var_iter++;\n        for (n = 0; n < doc->length; n++)\n        {\n            //compute sf_params\n            memset(sf_params, 0, sizeof(double)*num_topics); //in log space\n            for (l = 0; l < num_classes-1; l ++)\n            {\n                t = 0.0;\n                for (k = 0; k < num_topics; k ++)\n                {\n                    t += phi[n][k] * exp(eta[l][k] * doc->counts[n]/(double)(doc->total));\n                }\n                sf_aux[l] /= t; //take out word n\n\n                for (k = 0; k < num_topics; k ++)\n                {\n                    //h in the paper\n                    sf_params[k] += sf_aux[l]*exp(eta[l][k] * doc->counts[n]/(double)(doc->total));\n                }\n            }\n            //\n            for (k = 0; k < num_topics; k++)\n            {\n                oldphi[k] = phi[n][k];\n            }\n            for (fp_iter = 0; fp_iter < FP_MAX_ITER; fp_iter ++) //fixed point update\n            {\n                sf_val = 1.0; // the base class, in log space\n                for (k = 0; k < num_topics; k++)\n                {\n                    sf_val += sf_params[k]*phi[n][k];\n                }\n\n                phisum = 0;\n                for (k = 0; k < num_topics; k++)\n                {\n                    phi[n][k] = digamma_gam[k] + log_prob_w[k][doc->words[n]];\n\n                    //added softmax parts\n                    if (doc->label < num_classes-1)\n                        phi[n][k] += eta[doc->label][k]/(double)(doc->total);\n                    phi[n][k] -= sf_params[k]/(sf_val*(double)(doc->counts[n]));\n\n                    if (k > 0)\n                        phisum = log_sum(phisum, phi[n][k]);\n                    else\n                        phisum = phi[n][k]; // note, phi is in log space\n                }\n                for (k = 0; k < num_topics; k++)\n                {\n                    phi[n][k] = exp(phi[n][k] - phisum); //normalize\n                }\n            }\n            //back to sf_aux value\n            for (l = 0; l < num_classes-1; l ++)\n            {\n                t = 0.0;\n                for (k = 0; k < num_topics; k ++)\n                {\n                    t += phi[n][k] * exp(eta[l][k] * doc->counts[n]/(double)(doc->total));\n                }\n                sf_aux[l] *= t;\n            }\n            for (k = 0; k < num_topics; k++)\n            {\n                var_gamma[k] = var_gamma[k] + doc->counts[n]*(phi[n][k] - oldphi[k]);\n                digamma_gam[k] = digamma(var_gamma[k]);\n            }\n        }\n\n        likelihood = slda_compute_likelihood(doc, phi, var_gamma);\n        assert(!isnan(likelihood));\n        converged = fabs((likelihood_old - likelihood) / likelihood_old);\n        likelihood_old = likelihood;\n    }\n\n    delete [] oldphi;\n    delete [] digamma_gam;\n    delete [] sf_params;\n    delete [] sf_aux;\n    return likelihood;\n}\n\nvoid slda::infer_only(corpus * c, const settings * setting, const char * directory)\n{\n    int i, k, d, n;\n    double **var_gamma, likelihood, **phi;\n    double* phi_m;\n    char filename[100];\n    double base_score, score;\n    int label;\n    int num_correct = 0;\n    int max_length = c->max_corpus_length();\n\n\n    var_gamma = new double * [c->num_docs];\n    for (i = 0; i < c->num_docs; i++)\n        var_gamma[i] = new double [num_topics];\n\n\n    phi = new double * [max_length];\n    for (n = 0; n < max_length; n++)\n        phi[n] = new double [num_topics];\n\n    phi_m = new double [num_topics];\n\n    FILE * likelihood_file = NULL;\n    sprintf(filename, \"%s/inf-likelihood.dat\", directory);\n    likelihood_file = fopen(filename, \"w\");\n    FILE * inf_label_file = NULL;\n    sprintf(filename, \"%s/inf-labels.dat\", directory);\n    inf_label_file = fopen(filename, \"w\");\n\n    for (d = 0; d < c->num_docs; d++)\n    {\n        if ((d % 100) == 0)\n            printf(\"document %d\\n\", d);\n\n        document * doc = c->docs[d];\n        likelihood = lda_inference(doc, var_gamma[d], phi, setting);\n\n        memset(phi_m, 0, sizeof(double)*num_topics); //zero_initialize\n        for (n = 0; n < doc->length; n++)\n        {\n            for (k = 0; k < num_topics; k ++)\n            {\n                phi_m[k] += doc->counts[n] * phi[n][k];\n            }\n        }\n        for (k = 0; k < num_topics; k ++)\n        {\n            phi_m[k] /= (double)(doc->total);\n        }\n\n        //do classification\n        label = num_classes-1;\n        base_score = 0.0;\n        for (i = 0; i < num_classes-1; i ++)\n        {\n            score = 0.0;\n            for (k = 0; k < num_topics; k ++)\n            {\n                score += eta[i][k] * phi_m[k];\n            }\n            if (score > base_score)\n            {\n                base_score = score;\n                label = i;\n            }\n        }\n        if (label == doc->label)\n            num_correct ++;\n\n        fprintf(likelihood_file, \"%5.5f\\n\", likelihood);\n        fprintf(inf_label_file, \"%d\\n\", label);\n    }\n\n    printf(\"average accuracy: %.3f\\n\", (double)num_correct / (double) c->num_docs);\n\n    sprintf(filename, \"%s/inf-gamma.dat\", directory);\n    save_gamma(filename, var_gamma, c->num_docs);\n\n    for (d = 0; d < c->num_docs; d++)\n        delete [] var_gamma[d];\n    delete [] var_gamma;\n\n    for (n = 0; n < max_length; n++)\n        delete [] phi[n];\n    delete [] phi;\n\n    delete [] phi_m;\n}\n\nvoid slda::save_gamma(char* filename, double** gamma, int num_docs)\n{\n    int d, k;\n\n    FILE* fileptr = fopen(filename, \"w\");\n    for (d = 0; d < num_docs; d++)\n    {\n        fprintf(fileptr, \"%5.10f\", gamma[d][0]);\n        for (k = 1; k < num_topics; k++)\n            fprintf(fileptr, \" %5.10f\", gamma[d][k]);\n        fprintf(fileptr, \"\\n\");\n    }\n    fclose(fileptr);\n}\n\nvoid slda::write_word_assignment(FILE* f, document* doc, double** phi)\n{\n    int n;\n\n    fprintf(f, \"%03d\", doc->length);\n    for (n = 0; n < doc->length; n++)\n    {\n        fprintf(f, \" %04d:%02d\", doc->words[n], argmax(phi[n], num_topics));\n    }\n    fprintf(f, \"\\n\");\n    fflush(f);\n}\n"
  },
  {
    "path": "topic-competitors/slda/slda.h",
    "content": "// (C) Copyright 2009, Chong Wang, David Blei and Li Fei-Fei\n\n// written by Chong Wang, chongw@cs.princeton.edu\n\n// This file is part of slda.\n\n// slda is free software; you can redistribute it and/or modify it under\n// the terms of the GNU General Public License as published by the Free\n// Software Foundation; either version 2 of the License, or (at your\n// option) any later version.\n\n// slda is distributed in the hope that it will be useful, but WITHOUT\n// ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n// FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License\n// for more details.\n\n// You should have received a copy of the GNU General Public License\n// along with this program; if not, write to the Free Software\n// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307\n// USA\n\n#ifndef SLDA_H\n#define SLDA_H\n#include \"settings.h\"\n#include \"corpus.h\"\n\ntypedef struct {\n    double * z_bar_m;\n    double * z_bar_var;\n} z_stat;\n\ntypedef struct {\n    double ** word_ss;\n    double * word_total_ss;\n    int num_docs;\n    z_stat * z_bar;\n    int * labels;\n    int * tot_labels;\n} suffstats;\n\nclass slda\n{\npublic:\n    slda();\n    ~slda();\n    void free_model();\n    void init(double alpha_, int num_topics_, const corpus * c);\n    void v_em(corpus * c, const settings * setting,\n              const char * start, const char * directory);\n\n    void save_model(const char * filename);\n    void save_model_text(const char * filename);\n    void load_model(const char * model_filename);\n    void infer_only(corpus * c, const settings * setting,\n                    const char * directory);\n\n    suffstats * new_suffstats(int num_docs);\n    void free_suffstats(suffstats * ss);\n    void zero_initialize_ss(suffstats * ss);\n    void random_initialize_ss(suffstats * ss, corpus * c);\n    void corpus_initialize_ss(suffstats* ss, corpus * c);\n    void load_model_initialize_ss(suffstats* ss, corpus * c);\n    void mle(suffstats * ss, int eta_update, const settings * setting);\n\n    double doc_e_step(document* doc, double* gamma, double** phi, suffstats * ss, int eta_update, const settings * setting);\n\n    double lda_inference(document* doc, double* var_gamma, double** phi, const settings * setting);\n    double lda_compute_likelihood(document* doc, double** phi, double* var_gamma);\n    double slda_inference(document* doc, double* var_gamma, double** phi, const settings * setting);\n    double slda_compute_likelihood(document* doc, double** phi, double* var_gamma);\n\n    void save_gamma(char* filename, double** gamma, int num_docs);\n    void write_word_assignment(FILE* f, document* doc, double** phi);\n\n\npublic:\n    double alpha; // the parameter for the dirichlet\n    int num_topics;\n    int num_classes;\n    int size_vocab;\n\n    double ** log_prob_w; //the log of the topic distribution\n    double ** eta; //softmax regression, in general, there are num_classes-1 etas, we don't need a intercept here, since \\sum_i \\bar{z_i} = 1\n};\n\n#endif // SLDA_H\n\n"
  },
  {
    "path": "topic-competitors/slda/utils.cpp",
    "content": "#include \"utils.h\"\r\n\r\n/*\r\n * given log(a) and log(b), return log(a + b)\r\n *\r\n */\r\n\r\ndouble log_sum(double log_a, double log_b)\r\n{\r\n    double v;\r\n\r\n    if (log_a < log_b)\r\n        v = log_b+log(1 + exp(log_a-log_b));\r\n    else\r\n        v = log_a+log(1 + exp(log_b-log_a));\r\n\r\n    return v;\r\n}\r\n\r\n/**\r\n * Proc to calculate the value of the trigamma, the second\r\n * derivative of the loggamma function. Accepts positive matrices.\r\n * From Abromowitz and Stegun.  Uses formulas 6.4.11 and 6.4.12 with\r\n * recurrence formula 6.4.6.  Each requires workspace at least 5\r\n * times the size of X.\r\n *\r\n **/\r\n\r\ndouble trigamma(double x)\r\n{\r\n    double p;\r\n    int i;\r\n\r\n    x = x+6;\r\n    p = 1/(x*x);\r\n    p = (((((0.075757575757576*p-0.033333333333333)*p+0.0238095238095238)*p-0.033333333333333)*p+0.166666666666667)*p+1)/x+0.5*p;\r\n    for (i=0; i<6 ;i++)\r\n    {\r\n        x = x-1;\r\n        p = 1/(x*x)+p;\r\n    }\r\n    return p;\r\n}\r\n\r\n\r\n/*\r\n * taylor approximation of first derivative of the log gamma function\r\n *\r\n */\r\n\r\ndouble digamma(double x)\r\n{\r\n    double p;\r\n    x = x+6;\r\n    p = 1/(x*x);\r\n    p = (((0.004166666666667*p-0.003968253986254)*p+0.008333333333333)*p-0.083333333333333)*p;\r\n    p = p+log(x)-0.5/x-1/(x-1)-1/(x-2)-1/(x-3)-1/(x-4)-1/(x-5)-1/(x-6);\r\n    return p;\r\n}\r\n\r\n/*\r\n * this log gamma function has the implementation of this function\r\n *\r\n */\r\n\r\n/* double lgamma(double x)\r\n * {\r\n * double x0,x2,xp,gl,gl0;\r\n * int n,k;\r\n * static double a[] = {\r\n * 8.333333333333333e-02,\r\n * -2.777777777777778e-03,\r\n * 7.936507936507937e-04,\r\n * -5.952380952380952e-04,\r\n * 8.417508417508418e-04,\r\n * -1.917526917526918e-03,\r\n * 6.410256410256410e-03,\r\n * -2.955065359477124e-02,\r\n * 1.796443723688307e-01,\r\n * -1.39243221690590\r\n * };\r\n *\r\n * x0 = x;\r\n * if (x <= 0.0) return 1e308;\r\n * else if ((x == 1.0) || (x == 2.0)) return 0.0;\r\n * else if (x <= 7.0) {\r\n * n = (int)(7-x);\r\n * x0 = x+n;\r\n * }\r\n * x2 = 1.0/(x0*x0);\r\n * xp = 2.0*M_PI;\r\n * gl0 = a[9];\r\n * for (k=8;k>=0;k--) {\r\n * gl0 = gl0*x2 + a[k];\r\n * }\r\n * gl = gl0/x0+0.5*log(xp)+(x0-0.5)*log(x0)-x0;\r\n * if (x <= 7.0) {\r\n * for (k=1;k<=n;k++) {\r\n * gl -= log(x0-1.0);\r\n * x0 -= 1.0;\r\n * }\r\n * }\r\n * return gl;\r\n * }\r\n */\r\n\r\n\r\n\r\n/*\r\n * make directory\r\n *\r\n */\r\n\r\nvoid make_directory(char* name)\r\n{\r\n    mkdir(name, S_IRUSR|S_IWUSR|S_IXUSR);\r\n}\r\n\r\n\r\n/*\r\n * argmax\r\n *\r\n */\r\n\r\nint argmax(double* x, int n)\r\n{\r\n    int i, argmax = 0;\r\n    double max = x[0];\r\n\r\n    for (i = 1; i < n; i++)\r\n    {\r\n        if (x[i] > max)\r\n        {\r\n            max = x[i];\r\n            argmax = i;\r\n        }\r\n    }\r\n    return argmax;\r\n}\r\n\r\n/*\r\n * return the correponding index in the n(n+1)/2 given row and col\r\n * this is a upper triangle matrix, we can do this since this is \r\n * a symmetric matrix\r\n *\r\n */\r\n\r\nint map_idx(int row, int col, int dim)\r\n{\r\n    int swap, idx;\r\n    if (row > col)\r\n    {\r\n        swap = row;\r\n        row  = col;\r\n        col  = swap;\r\n    }\r\n    //now row <= col\r\n    idx = (2*dim - row + 1)*row/2 + col - row;\r\n    return idx;\r\n}\r\n\r\n"
  },
  {
    "path": "topic-competitors/slda/utils.h",
    "content": "#ifndef UTILS_H\n#define UTILS_H\n\n#include <stdio.h>\n#include <math.h>\n#include <float.h>\n#include <stdlib.h>\n#include <sys/stat.h>\n#include <sys/types.h>\n\ndouble log_sum(double log_a, double log_b);\ndouble trigamma(double x);\ndouble digamma(double x);\n//double lgamma(double x);\nvoid make_directory(char* name);\nint argmax(double* x, int n);\nint map_idx(int row, int col, int dim); \n\n#endif\n\n"
  },
  {
    "path": "topic-cosine.py",
    "content": "import numpy as np\r\nimport sys\r\nimport pdb\r\nfrom utils import *\r\n\r\ntopic_vec_file = sys.argv[1]\r\nT = load_matrix_from_text( topic_vec_file, \"topic\" )\r\nK = T.shape[0]\r\ncosine_mat = []\r\nfor x in xrange(K):\r\n    for y in xrange(x):\r\n        if normF(T[x]) < 1e-6 or normF(T[y]) < 1e-6:\r\n            continue\r\n        cosine = np.dot( T[x], T[y] ) / normF(T[x]) / normF(T[y])\r\n        cosine_mat.append( [ cosine, x, y ] )\r\n\r\ncosine_sum = 0\r\nfor i in xrange( len(cosine_mat) ):\r\n    cosine_sum += cosine_mat[i][0]\r\n\r\nprint \"Avg: %.5f\" %( cosine_sum / len(cosine_mat) )\r\ncosine_sorted = sorted( cosine_mat, key=lambda cosine_tuple: cosine_tuple[0], reverse=True )\r\nfor i in xrange(10):\r\n    cosine, x, y = cosine_sorted[i]\r\n    print \"%d,%d: %.5f\" %( x, y, cosine )\r\n    print \"%d: %s\" %( x, T[x][:10] )\r\n    print \"%d: %s\" %( y, T[y][:10] )\r\n    print\r\n            "
  },
  {
    "path": "topicExp.py",
    "content": "import sys\r\nimport pdb\r\nimport os\r\nimport getopt\r\nfrom corpusLoader import *\r\nfrom utils import *\r\nfrom topicvecDir import topicvecDir\r\n\r\nconfig = dict(  unigramFilename = \"top1grams-wiki.txt\",\r\n                word_vec_file = \"25000-180000-500-BLK-8.0.vec\",\r\n                #word_vec_file = \"word2vec.vec\",\r\n                load_embedding_word_count = 180000,\r\n                K = 100,\r\n                # for separate category training, each category has 10 topics, totalling 200\r\n                sepK_20news = 15,\r\n                sepK_reuters = 10,\r\n                # set it to 0 to disable the removal of very small topics\r\n                topTopicMassFracThres = 0.05,\r\n                N0 = 500,\r\n                # don't set it too big, e.g. 5.\r\n                # otherwise derived topics will be too specific, and classification accuracy will drop.\r\n                max_l = 3,\r\n                init_l = 1,\r\n                # cap the norm of the gradient of topics to avoid too big gradients\r\n                max_grad_norm = 1,\r\n                Mstep_sample_topwords = 25000,\r\n                # normalize by the sum of Em when updating topic embeddings\r\n                # to avoid too big gradients\r\n                grad_scale_Em_base = 20000,\r\n                topW = 12,\r\n                # when topTopicMassFracPrintThres = 0, print all topics\r\n                topTopicMassFracPrintThres = 0,\r\n                alpha0 = 0.1,\r\n                alpha1 = 0.1,\r\n                delta = 0.1,\r\n                MAX_EM_ITERS = 150,\r\n                MAX_TopicProp_ITERS = 1,\r\n                topicDiff_tolerance = 1e-2,\r\n                zero_topic0 = True,\r\n                remove_stop = True,\r\n                useDrdtApprox = False,\r\n                verbose = 0,\r\n                seed = 0,\r\n                printTopics_iterNum = 10,\r\n                calcSum_pi_v_iterNum = 1,\r\n                VStep_iterNum = 5\r\n            )\r\n\r\ndef usage():\r\n    print \"\"\"Usage: topicExp.py -s                corpus_name set_name(s)\r\n                   -i topic_vec_file corpus_name set_name(s)\r\n                   [ -w ]            corpus_name set_name(s)\r\n                   (Optional) -t max_iter_num ...\r\n  corpus_name: '20news' or  'reuters'\r\n  set_name(s): 'train', 'test' or 'train,test' (will save in separate files)\r\n  -s:          Train on separate categories\r\n  -i:          Do inference on a corpus given a topic vec file\r\n  -w:          Dump words only (no inference of topics)\r\n  -t:          Specify the maximum number of iterations\"\"\"\r\n  \r\ncorpusName = None\r\ncorpus2loader = { '20news': load_20news, 'reuters': load_reuters }\r\n    \r\nsubsetNames = [ ]\r\ntopic_vec_file = None\r\nMAX_ITERS = -1\r\nonlyDumpWords = False\r\nseparateCatTraining = False\r\nonlyInferTopicProp = False\r\ntopicTraitStr = \"\"\r\nonlyGetOriginalText = False\r\n\r\ntry:\r\n    opts, args = getopt.getopt( sys.argv[1:], \"i:t:wso\" )\r\n\r\n    if len(args) == 0:\r\n        raise getopt.GetoptError(\"Not enough free arguments\")\r\n    corpusName = args[0]\r\n    if len(args) == 2:\r\n        subsetNames = args[1].split(\",\")\r\n    if len(args) > 2:\r\n        raise getopt.GetoptError(\"Too many free arguments\")\r\n\r\n    for opt, arg in opts:\r\n        if opt == '-i':\r\n            onlyInferTopicProp = True\r\n            topic_vec_file = arg\r\n            # if 'useDrdtApprox' == True, will precompute matrix Evv, which is very slow\r\n            # disable to speed up\r\n            config['useDrdtApprox'] = False\r\n        if opt == '-t':\r\n            MAX_ITERS = int(arg)\r\n        if opt == '-w':\r\n            onlyDumpWords = True\r\n            # if 'useDrdtApprox' == True, will precompute matrix Evv, which is very slow\r\n            # disable to speed up\r\n            config['useDrdtApprox'] = False\r\n        if opt == '-s':\r\n            separateCatTraining = True\r\n        if opt == '-o':\r\n            onlyGetOriginalText = True\r\n            \r\nexcept getopt.GetoptError, e:\r\n    print e.msg\r\n    usage()\r\n    sys.exit(2)\r\n\r\nif not onlyGetOriginalText:\r\n# The leading 'all-mapping' is only to get word mappings from the original IDs in \r\n# the embedding file to a compact word ID list, to speed up computation of sLDA\r\n# The mapping has to be done on 'all' to include all words in train and test sets\r\n    subsetNames = [ 'all-mapping' ] + subsetNames\r\n    \r\nif MAX_ITERS > 0:\r\n    if onlyInferTopicProp:\r\n        MAX_TopicProp_ITERS = MAX_ITERS\r\n    else:\r\n        config['MAX_EM_ITERS'] = MAX_ITERS\r\n\r\nloader = corpus2loader[corpusName]\r\nwid2compactId = {}\r\ncompactId_words = []\r\nhasIdMapping = False\r\n\r\nif onlyInferTopicProp:\r\n    topicfile_trunk = topic_vec_file.split(\".\")[0]\r\n    topicTraits = topicfile_trunk.split(\"-\")[3:]\r\n    topicTraitStr = \"-\".join(topicTraits)\r\n    T = load_matrix_from_text( topic_vec_file, \"topic\" )\r\n    config['K'] = T.shape[0]\r\n\r\nconfig['logfilename'] = corpusName\r\ntopicvec = topicvecDir(**config)\r\nout = topicvec.genOutputter(0)\r\n\r\nfor si, subsetName in enumerate(subsetNames):       \r\n    print \"Process subset '%s':\" %subsetName\r\n    if subsetName == 'all-mapping':\r\n        subsetName = 'all'\r\n        onlyGetWidMapping = True\r\n    else:\r\n        onlyGetWidMapping = False\r\n        \r\n    subsetDocNum, orig_docs_words, orig_docs_name, orig_docs_cat, cats_docsWords, \\\r\n            cats_docNames, category_names = loader(subsetName)\r\n    catNum = len(category_names)\r\n    basename = \"%s-%s-%d\" %( corpusName, subsetName, subsetDocNum )\r\n\r\n    # dump original words (without filtering)\r\n    orig_filename = \"%s.orig.txt\" %basename\r\n    ORIG = open( orig_filename, \"w\" )\r\n    for wordsInSentences in orig_docs_words:\r\n        for sentence in wordsInSentences:\r\n            for w in sentence:\r\n                w = w.lower()        \r\n                ORIG.write( \"%s \" %w )\r\n        ORIG.write(\"\\n\")\r\n    ORIG.close()\r\n    print \"%d original docs saved in '%s'\" %( subsetDocNum, orig_filename )\r\n\r\n    if onlyGetOriginalText:\r\n        continue\r\n        \r\n    docs_idx = topicvec.setDocs( orig_docs_words, orig_docs_name )\r\n    docs_name = [ orig_docs_name[i] for i in docs_idx ]\r\n    docs_cat = [ orig_docs_cat[i] for i in docs_idx ]\r\n    readDocNum = len(docs_idx)\r\n    out( \"%d docs left after filtering empty docs\" %(readDocNum) )\r\n    assert readDocNum == topicvec.D, \"Returned %d doc idx != %d docs in Topicvec\" %(readDocNum, topicvec.D)\r\n    \r\n    # executed when subsetName == 'all-mapping'\r\n    if onlyGetWidMapping:\r\n        sorted_wids = sorted( topicvec.wid2freq.keys() )\r\n        uniq_wid_num = len(sorted_wids)\r\n        for i, wid in enumerate(sorted_wids):\r\n            # svm feature index cannot be 0\r\n            # +1 to avoid 0 being used as a feature index\r\n            wid2compactId[wid] = i + 1\r\n            compactId_words.append( topicvec.vocab[wid] )\r\n            \r\n        hasIdMapping = True\r\n        onlyGetWidMapping = False\r\n        print \"Word mapping created: %d -> %d\" %( sorted_wids[-1], uniq_wid_num )\r\n        id2word_filename = \"%s.id2word.txt\" %basename\r\n        ID2WORD = open( id2word_filename, \"w\" )\r\n        for i in xrange(uniq_wid_num):\r\n            ID2WORD.write( \"%d\\t%s\\n\" %( i, compactId_words[i] ) )\r\n        ID2WORD.close()\r\n        continue\r\n            \r\n    # dump words in stanford classifier format\r\n    stanford_filename = \"%s.stanford-bow.txt\" %basename\r\n    STANFORD = open( stanford_filename, \"w\" )\r\n    for i in xrange(readDocNum):\r\n        wids = topicvec.docs_wids[i]\r\n        words = [ topicvec.vocab[j] for j in wids ]\r\n        text = \" \".join(words)\r\n        catID = docs_cat[i]\r\n        category = category_names[catID]\r\n        doc_name = docs_name[i]\r\n        STANFORD.write( \"%s\\t%s\\t%s\\n\" %( category, doc_name, text ) )\r\n    \r\n    STANFORD.close()\r\n    print \"%d docs saved in '%s' in stanford bow format\" %( readDocNum, stanford_filename )\r\n\r\n    # dump words in sLDA format\r\n    slda_bow_filename = \"%s.slda-bow.txt\" %basename\r\n    slda_label_filename = \"%s.slda-label.txt\" %basename\r\n    SLDA_BOW = open( slda_bow_filename, \"w\" )\r\n    SLDA_LABEL = open( slda_label_filename, \"w\" )\r\n    \r\n    for i in xrange(readDocNum):\r\n        wids = topicvec.docs_wids[i]\r\n        # compact wid to freq\r\n        cwid2freq = {}\r\n        for wid in wids:\r\n            cwid = wid2compactId[wid]\r\n            if cwid in cwid2freq:\r\n                cwid2freq[cwid] += 1\r\n            else:\r\n                cwid2freq[cwid] = 1\r\n        catID = docs_cat[i]\r\n        sorted_cwids = sorted( cwid2freq.keys() )\r\n        uniq_wid_num = len(sorted_cwids)\r\n        # sLDA requires class lables to start from 0\r\n        SLDA_LABEL.write( \"%d\\n\" %catID )\r\n        SLDA_BOW.write( \"%d\" %uniq_wid_num )\r\n        for cwid in sorted_cwids:\r\n            SLDA_BOW.write( \" %d:%d\" %( cwid, cwid2freq[cwid] ) )\r\n        SLDA_BOW.write(\"\\n\")\r\n            \r\n    SLDA_BOW.close()\r\n    SLDA_LABEL.close()\r\n    \r\n    print \"%d docs saved in '%s' and '%s' in sLDA bow format\" %( readDocNum, \r\n                slda_bow_filename, slda_label_filename )\r\n        \r\n    # dump words in libsvm/svmlight format\r\n    svmbow_filename = \"%s.svm-bow.txt\" %basename\r\n    SVMBOW = open( svmbow_filename, \"w\" )\r\n    for i in xrange(readDocNum):\r\n        wids = topicvec.docs_wids[i]\r\n        cwid2freq = {}\r\n        for wid in wids:\r\n            cwid = wid2compactId[wid]\r\n            if cwid in cwid2freq:\r\n                cwid2freq[cwid] += 1\r\n            else:\r\n                cwid2freq[cwid] = 1\r\n        catID = docs_cat[i]\r\n        sorted_cwids = sorted( cwid2freq.keys() )\r\n        SVMBOW.write( \"%d\" %(catID+1) )\r\n        for cwid in sorted_cwids:\r\n            SVMBOW.write( \" %d:%d\" %( cwid, cwid2freq[cwid] ) )\r\n        SVMBOW.write(\"\\n\")\r\n    \r\n    SVMBOW.close()\r\n    print \"%d docs saved in '%s' in svm bow format\" %( readDocNum, svmbow_filename )\r\n        \r\n    if onlyDumpWords:\r\n        continue\r\n    \r\n    # load topics from a file, infer the topic proportions, and save the proportions\r\n    if onlyInferTopicProp:\r\n        docs_Em, docs_Pi = topicvec.inferTopicProps(T, config['MAX_TopicProp_ITERS'])\r\n        # dump the topic proportions in my own matrix format\r\n        save_matrix_as_text( basename + \"-%s-i%d.topic.prop\" %(topicTraitStr, config['MAX_TopicProp_ITERS']), \r\n                                \"topic proportion\", docs_Em, docs_cat, docs_name, colSep=\"\\t\" )\r\n        \r\n        # dump the topic proportions into SVMTOPIC_PROP in libsvm/svmlight format\r\n        # dump the mix of word freqs and topic proportions into SVMTOPIC_BOW in libsvm/svmlight format   \r\n        svmtopicprop_filename = \"%s.svm-topicprop.txt\" %basename\r\n        # topic props + weighted sum of topic vectors\r\n        svmtopicbow_filename = \"%s.svm-topicbow.txt\" %basename\r\n        svmtopic_wvavg_filename = \"%s.svm-topic-wvavg.txt\" %basename\r\n        \r\n        SVMTOPIC_PROP = open( svmtopicprop_filename, \"w\" )\r\n        SVMTOPIC_BOW = open( svmtopicbow_filename, \"w\" )\r\n        SVMTOPIC_WVAVG = open( svmtopic_wvavg_filename, \"w\" )\r\n        \r\n        wordvec_avg = np.zeros( topicvec.N0 )\r\n        \r\n        for i in xrange(readDocNum):\r\n            wids = topicvec.docs_wids[i]\r\n            cwid2freq = {}\r\n            for wid in wids:\r\n                cwid = wid2compactId[wid]\r\n                if cwid in cwid2freq:\r\n                    cwid2freq[cwid] += 1\r\n                else:\r\n                    cwid2freq[cwid] = 1\r\n                \r\n                wordvec_avg += topicvec.V[wid]\r\n                    \r\n            catID = docs_cat[i]\r\n            sorted_cwids = sorted( cwid2freq.keys() )\r\n            \r\n            SVMTOPIC_PROP.write( \"%d\" %(catID+1) )\r\n            SVMTOPIC_BOW.write( \"%d\" %(catID+1) )\r\n            SVMTOPIC_WVAVG.write( \"%d\" %(catID+1) )\r\n            \r\n            for k in xrange(topicvec.K):\r\n                SVMTOPIC_PROP.write( \" %d:%.3f\" %( k+1, docs_Em[i][k] ) )\r\n                SVMTOPIC_BOW.write( \" %d:%.3f\" %( k+1, docs_Em[i][k] ) )\r\n                SVMTOPIC_WVAVG.write( \" %d:%.3f\" %( k+1, docs_Em[i][k] ) )\r\n            \r\n            for cwid in sorted_cwids:\r\n                # first K indices are reserved for topic features, so add topicvec.K here\r\n                SVMTOPIC_BOW.write( \" %d:%d\" %( cwid + topicvec.K, cwid2freq[cwid] ) )\r\n            \r\n            wordvec_avg /= topicvec.docs_L[i]\r\n            for n in xrange(topicvec.N0):\r\n                SVMTOPIC_WVAVG.write( \" %d:%.3f\" %( n + 1 + topicvec.K, wordvec_avg[n] ) )\r\n                \r\n            SVMTOPIC_PROP.write(\"\\n\")    \r\n            SVMTOPIC_BOW.write(\"\\n\")\r\n            SVMTOPIC_WVAVG.write(\"\\n\")\r\n            \r\n        SVMTOPIC_PROP.close()\r\n        SVMTOPIC_BOW.close()\r\n        SVMTOPIC_WVAVG.close()\r\n        \r\n        print \"%d docs saved in '%s' in svm topicProp format\" %( readDocNum, svmtopicprop_filename )\r\n        print \"%d docs saved in '%s' in svm topicProp-BOW format\" %( readDocNum, svmtopicbow_filename )\r\n        print \"%d docs saved in '%s' in svm topicProp-WordvecAvg format\" %( readDocNum, svmtopic_wvavg_filename )\r\n        \r\n    # infer topics from docs, and save topics and their proportions in each doc\r\n    else:\r\n        if not separateCatTraining:\r\n            best_last_Ts, Em, docs_Em, Pi = topicvec.inference()\r\n\r\n            best_it, best_T, best_loglike = best_last_Ts[0]\r\n            last_it, last_T, last_loglike = best_last_Ts[1]\r\n            \r\n            save_matrix_as_text( basename + \"-em%d-best.topic.vec\" %best_it, \"best topics\", best_T  )\r\n            save_matrix_as_text( basename + \"-em%d-last.topic.vec\" %last_it, \"last topics\", last_T  )\r\n                \r\n            save_matrix_as_text( basename + \"-em%d.topic.prop\" %config['MAX_EM_ITERS'], \"topic proportion\", docs_Em, docs_cat, docs_name, colSep=\"\\t\" )\r\n\r\n        else:\r\n            # infer topics for each category, combine them and save in one file\r\n            if corpusName == \"20news\":\r\n                topicvec.setK( config['sepK_20news'] )\r\n            else:\r\n                topicvec.setK( config['sepK_reuters'] )\r\n                \r\n            best_T = []\r\n            last_T = []\r\n            slim_T = []\r\n            totalDocNum = 0\r\n            #pdb.set_trace()\r\n            \r\n            for catID in xrange(catNum):\r\n                out(\"\")\r\n                out( \"Inference on category %d:\" %( catID+1 ) )\r\n                cat_docs_idx = topicvec.setDocs( cats_docsWords[catID], cats_docNames[catID] )\r\n                totalDocNum += len(cat_docs_idx)\r\n                cat_best_last_Ts, cat_Em, cat_docs_Em, cat_Pi = topicvec.inference()\r\n                cat_best_it, cat_best_T, cat_best_loglike = cat_best_last_Ts[0]\r\n                if cat_best_last_Ts[1]:\r\n                    cat_last_it, cat_last_T, cat_last_loglike = cat_best_last_Ts[1]\r\n                else:\r\n                    cat_last_it, cat_last_T, cat_last_loglike = cat_best_last_Ts[0]\r\n                    \r\n                # normalize by the number of documents \r\n                cat_Em2 = cat_Em / len(cat_docs_Em)\r\n                \r\n                if catID > 0 and config['zero_topic0']:\r\n                    # remove the redundant null topic\r\n                    removeNullTopic = True\r\n                    best_T.append( cat_best_T[1:] )\r\n                    last_T.append( cat_last_T[1:] )\r\n                else:\r\n                    # keep null topic\r\n                    removeNullTopic = False\r\n                    best_T.append( cat_best_T )\r\n                    last_T.append( cat_last_T )\r\n \r\n                sorted_tids = sorted( range(topicvec.K), key=lambda k: cat_Em[k], reverse=True )\r\n                out(\"Topic normalized mass:\")\r\n                s = \"\"\r\n                for tid in sorted_tids:\r\n                    s += \"%d: %.3f \" %( tid, cat_Em2[tid] )\r\n                out(s)\r\n                \r\n                if config['topTopicMassFracThres'] > 0:\r\n                    cat_Em2_thres = np.sum(cat_Em2) / topicvec.K * config['topTopicMassFracThres']\r\n                    out( \"Topic normalized mass thres: %.3f\" %cat_Em2_thres )\r\n                    top_tids = []\r\n                    for i,tid in enumerate(sorted_tids):\r\n                        if cat_Em2[tid] <= cat_Em2_thres:\r\n                            break\r\n                        if removeNullTopic and tid == 0:\r\n                            continue\r\n                        top_tids.append(tid)\r\n                        \r\n                    out( \"Keep top %d topics:\" %len(top_tids) )\r\n                    s = \"\"\r\n                    for tid in top_tids:\r\n                        s += \"%d: %.3f \" %( tid, cat_Em2[tid] )\r\n                    out(s)\r\n                    \r\n                    slim_cat_T = cat_last_T[top_tids]\r\n                    slim_T.append(slim_cat_T)\r\n                \r\n            out( \"Done inference on %d docs in %d categories\" %(totalDocNum, catNum) )\r\n\r\n            best_T = np.concatenate(best_T)\r\n            last_T = np.concatenate(last_T)\r\n            save_matrix_as_text( \"%s-sep%d-em%d-best.topic.vec\" %( basename, best_T.shape[0], \r\n                                            topicvec.MAX_EM_ITERS ), \"best topics\", best_T )\r\n            save_matrix_as_text( \"%s-sep%d-em%d-last.topic.vec\" %( basename, last_T.shape[0], \r\n                                            topicvec.MAX_EM_ITERS ), \"last topics\", last_T )\r\n\r\n            if config['topTopicMassFracThres'] > 0:\r\n                slim_T = np.concatenate(slim_T)\r\n                save_matrix_as_text( \"%s-sep%d-em%d-slim.topic.vec\" %( basename, slim_T.shape[0], topicvec.MAX_EM_ITERS ), \r\n                                            \"slim topics\", slim_T )\r\n            "
  },
  {
    "path": "topicvecDir.py",
    "content": "import numpy as np\r\nimport scipy.linalg\r\n# import for using gammaln, psi\r\nfrom scipy.special import *\r\nimport getopt\r\nimport sys\r\nfrom utils import *\r\nimport pdb\r\nimport time\r\nimport re\r\nimport os\r\nfrom scipy.spatial.distance import cdist\r\n\r\n# V: W x N0\r\n# T: K x N0\r\n# VT: W x K\r\n# u: W x 1\r\n# r: K x 1\r\n# Pi: L x K\r\n# sum_pi_v: K x N0\r\n# X = Evv\r\n\r\nclass topicvecDir:\r\n    def __init__(self, **kwargs):\r\n        self.unigramFilename = kwargs.get( 'unigramFilename', \"top1grams-wiki.txt\" )\r\n        self.word_vec_file = kwargs.get( 'word_vec_file', \"25000-500-EM.vec\" )\r\n        self.topic_vec_file = kwargs.get( 'topic_vec_file', None )\r\n        self.W = kwargs.get( 'load_embedding_word_count', -1 )\r\n        K = kwargs.get( 'K', 30 )\r\n\r\n        self.max_l = kwargs.get( 'max_l', 5 )\r\n        self.init_l = kwargs.get( 'init_l', 1 )\r\n        self.max_grad_norm = kwargs.get( 'max_grad_norm', 1.0 )\r\n        self.max_grad_norm_fraction = kwargs.get( 'max_grad_norm_fraction', 0.2 )\r\n        self.grad_scale_Em_base = kwargs.get( 'grad_scale_Em_base', 0 )\r\n        # number of top words to output into logfile\r\n        self.topW = kwargs.get( 'topW', 12 )\r\n        # output the first 'topDim' dimensions of T, for debugging\r\n        self.topDim = kwargs.get( 'topDim', 10 )\r\n        self.topTopicMassFracPrintThres = kwargs.get( 'topTopicMassFracPrintThres', 1 )\r\n        # Dirichlet parameter for the null topic\r\n        self.alpha0 = kwargs.get( 'alpha0', 5 )\r\n        # Dirichlet parameter for all other topics\r\n        self.alpha1 = kwargs.get( 'alpha1', 1 )\r\n        # initial learning rate\r\n        self.delta = self.iniDelta = kwargs.get( 'iniDelta', 0.1 )\r\n        self.MAX_EM_ITERS = kwargs.get( 'MAX_EM_ITERS', 200 )\r\n        self.topicDiff_tolerance = kwargs.get( 'topicDiff_tolerance', 1e-2 )\r\n        # whether fix topic 0 to null topic\r\n        self.zero_topic0 = kwargs.get( 'zero_topic0', True )\r\n        self.appendLogfile = kwargs.get( 'appendLogfile', False )\r\n        self.customStopwords = kwargs.get( 'customStopwords', \"\" )\r\n        self.remove_stop = kwargs.get( 'remove_stop', True )\r\n        self.seed = kwargs.get( 'seed', 0 )\r\n        self.verbose = kwargs.get( 'verbose', 1 )\r\n        # print topics every so many iters\r\n        self.printTopics_iterNum = kwargs.get( 'printTopics_iterNum', 20 )\r\n        # compute sum_pi_v is slow. Approximate it by calculating it every few iters to speed up\r\n        self.calcSum_pi_v_iterNum = kwargs.get( 'calcSum_pi_v_iterNum', 1 )\r\n        # do V-step every few M-steps to speed up. Default: 1 (each M-step)\r\n        self.VStep_iterNum = kwargs.get( 'VStep_iterNum', 1 )\r\n        self.calcLike_iterNum = kwargs.get( 'calcLike_iterNum', 1 )\r\n\r\n        self.useDrdtApprox = kwargs.get( 'useDrdtApprox', False )\r\n        self.Mstep_sample_topwords = kwargs.get( 'Mstep_sample_topwords', 0 )\r\n        self.normalize_vecs = kwargs.get( 'normalize_vecs', False )\r\n        self.rebase_vecs = kwargs.get( 'rebase_vecs', False )\r\n        self.rebase_norm_thres = kwargs.get( 'rebase_norm_thres', 0 )\r\n        self.evalKmeans = kwargs.get( 'evalKmeans', False )\r\n        \r\n        self.D = 0\r\n        self.docsName = \"Uninitialized\"\r\n\r\n        #self.alpha = np.array( [ self.alpha1 ] * self.K )\r\n        #if self.zero_topic0:\r\n        #    self.alpha[0] = self.alpha0\r\n\r\n        self.vocab_dict = loadUnigramFile(self.unigramFilename)\r\n        \r\n        embedding_npyfile = self.word_vec_file + \".npy\"\r\n        if os.path.isfile(embedding_npyfile):\r\n            print \"Load embeddings from npy file '%s'\" %embedding_npyfile\r\n            embedding_arrays = np.load(embedding_npyfile)\r\n            self.V, self.vocab, self.word2ID, skippedWords_whatever = embedding_arrays\r\n        else:\r\n            self.V, self.vocab, self.word2ID, skippedWords_whatever = load_embeddings(self.word_vec_file, self.W)\r\n            embedding_arrays = np.array( [ self.V, self.vocab, self.word2ID, skippedWords_whatever ] )\r\n            print \"Save embeddings to npy file '%s'\" %embedding_npyfile\r\n            np.save( embedding_npyfile, embedding_arrays )\r\n            \r\n        # map of word -> id of all words with embeddings\r\n        vocab_dict2 = {}\r\n        \r\n        if self.normalize_vecs:\r\n            self.V = normalizeF(self.V)\r\n            \r\n        # dimensionality of topic/word embeddings\r\n        self.N0 = self.V.shape[1]\r\n        # number of all words\r\n        self.vocab_size = self.V.shape[0]\r\n        \r\n        # set unigram probs\r\n        u2 = []    \r\n        oovcount = 0\r\n        unigram_oov_prior = 0.000001\r\n        for wid,w in enumerate(self.vocab):\r\n            if w not in self.vocab_dict:\r\n                oovcount += 1\r\n                u2.append(unigram_oov_prior)\r\n            else:\r\n                u2.append( self.vocab_dict[w][2] )\r\n                vocab_dict2[w] = wid\r\n\r\n        if oovcount > 0:\r\n            print \"%d words in '%s' but not in '%s'. Unigram prob set to oov prior %.3g\" %(oovcount, self.word_vec_file, \r\n                    self.unigramFilename, unigram_oov_prior)\r\n            \r\n        u2 = np.array(u2)\r\n        self.u = normalize(u2)\r\n        # structure of vocab_dict changed here. Original vocab_dict is w->[id, freq, unigram_prob]\r\n        # now vocab_dict is only w->id\r\n        self.vocab_dict = vocab_dict2\r\n\r\n        # u2 is the top \"Mstep_sample_topwords\" words of u, \r\n        # used for a sampling inference (i.e. only the most \r\n        # important \"Mstep_sample_topwords\" words are used) in the M-step\r\n        # if Mstep_sample_topwords == 0, sampling is disabled\r\n        if self.Mstep_sample_topwords == 0:\r\n            self.Mstep_sample_topwords = self.vocab_size\r\n            self.u2 = self.u\r\n            self.V2 = self.V\r\n        else:\r\n            self.u2 = self.u[:self.Mstep_sample_topwords]\r\n            self.u2 = normalize(self.u2)\r\n            self.V2 = self.V[:self.Mstep_sample_topwords]\r\n            \r\n        customStopwordList = re.split( r\"\\s+\", self.customStopwords )\r\n        for stop_w in customStopwordList:\r\n            stopwordDict[stop_w] = 1\r\n        print \"Custom stopwords: %s\" %( \", \".join(customStopwordList) )\r\n        \r\n        if 'fileLogger' not in kwargs:\r\n            self.logfilename = kwargs.get( 'logfilename', \"topicvecDir\" )\r\n            self.fileLogger = initFileLogger( self.logfilename, self.appendLogfile )\r\n        else:\r\n            self.fileLogger = kwargs['fileLogger']\r\n\r\n        self.fileLogger.debug( \"topicvecDir() init at %s\", time.ctime() )\r\n        self.precompute()\r\n        self.setK(K)\r\n\r\n        self.docs_name = []\r\n        self.docs_idx = []\r\n        self.docs_wids = []\r\n        self.wid2freq = []\r\n        self.wids_freq = []\r\n        self.expVT = None\r\n        self.T = self.r = self.sum_pi_v = None\r\n        self.docs_L = []\r\n        self.docs_Pi = []\r\n        self.docs_theta = []\r\n        self.totalL = 0\r\n        self.kmeans_xtoc = self.kmeans_distances = None\r\n        # current iteration number\r\n        self.it = 0\r\n\r\n    def setK(self, K):\r\n        self.K = K\r\n        self.alpha = np.array( [ self.alpha1 ] * self.K )\r\n        if self.zero_topic0:\r\n            self.alpha[0] = self.alpha0\r\n        # K rows of Ev\r\n        # EV: K x N0\r\n        if self.useDrdtApprox:\r\n            self.EV = np.tile( self.Ev, (self.K, 1) )\r\n\r\n    def precompute(self):\r\n        print \"Precompute matrix u_V\"\r\n        # each elem of u multiplies each row of V\r\n        # Pw_V: Mstep_sample_topwords x N0\r\n        self.Pw_V = self.u2[:, None] * self.V2\r\n\r\n        if self.useDrdtApprox:\r\n            print \"Precompute vector Ev\"\r\n            self.Ev = np.dot(self.u, self.V)\r\n            print \"Precompute matrix Evv...\",\r\n            self.Evv = np.zeros( (self.N0, self.N0) )\r\n            for wid in xrange(self.vocab_size):\r\n                self.Evv += self.u[wid] * np.outer( self.V[wid], self.V[wid] )\r\n            print \"Done.\"\r\n                    \r\n    def calcEm(self, docs_Pi):\r\n        Em = np.zeros(self.K)\r\n        for d in xrange( len(docs_Pi) ):\r\n            Em += np.sum( docs_Pi[d], axis=0 )\r\n        return Em\r\n\r\n    # this actually computes the variational lowerbound, as an approximation of the (intractable) data log-likelihood\r\n    def calcLoglikelihood(self):\r\n        totalLoglike = 0\r\n\r\n        for d in xrange(self.D):\r\n            theta = self.docs_theta[d]\r\n            Pi = self.docs_Pi[d]\r\n\r\n            theta0 = np.sum(theta)\r\n            entropy = np.sum( gammaln(theta) ) - gammaln(theta0)\r\n            entropy += (theta0 - self.K) * psi(theta0) - np.sum( (theta - 1) * psi(theta) )\r\n            entropy -= np.sum( Pi * np.log(Pi) )\r\n            # this Em is not the total Em calculated by calcEm()\r\n            # Em[k] = sum_j Pi[j][k]\r\n            Em = np.sum( Pi, axis=0 )\r\n            Em_Ephi = ( Em + self.alpha - 1 ) * ( psi(theta) - psi(theta0) )\r\n            sum_r_pi = np.dot( Em, self.r )\r\n            loglike = entropy + np.sum(Em_Ephi) + np.trace( np.dot( self.T, self.sum_pi_v.T ) ) + sum_r_pi\r\n\r\n            totalLoglike += loglike\r\n        return totalLoglike\r\n\r\n    def updateTheta(self):\r\n        for d in xrange(self.D):\r\n            self.docs_theta[d] = np.sum( self.docs_Pi[d], axis=0 ) + self.alpha\r\n            \r\n    def updatePi(self, docs_theta):\r\n        docs_Pi = []\r\n        psiDocs_theta = psi(docs_theta)\r\n\r\n        for d in xrange(self.D):\r\n            if d % 50 == 49 or d == self.D - 1:\r\n                print \"\\r%d\" %(d+1),\r\n\r\n            wids = self.docs_wids[d]\r\n            L = self.docs_L[d]\r\n            \r\n            # faster computation, more memory\r\n            if L <= 20000:\r\n                # Vd: L x N0\r\n                Vd = self.V[wids]\r\n                TV = np.dot( Vd, self.T.T )\r\n                Pi = np.exp( psiDocs_theta[d] + TV + self.r )\r\n            # slower but avoids using up memory    \r\n            else:\r\n                Pi = np.zeros( (L, self.K) )\r\n    \r\n                for i,wid in enumerate(wids):\r\n                    v = self.V[wid]\r\n                    Tv = np.dot( self.T, v )\r\n                    Pi[i] = np.exp( psiDocs_theta[d] + Tv + self.r )\r\n\r\n            Pi = normalize(Pi)\r\n            docs_Pi.append(Pi)\r\n\r\n        return docs_Pi\r\n\r\n    # T is fed as an argument to provide more flexibility\r\n    def calcTopicResiduals(self, T):\r\n        # VT_{i,j} = v_wi' t_j\r\n        VT = np.dot(self.V2, T.T)\r\n\r\n        # expVT_{i,j} = exp(v_wi' t_j)\r\n        # used in the computation of drdt\r\n        # expVT: Mstep_sample_topwords x K\r\n        self.expVT = np.exp(VT)\r\n\r\n        r = -np.log( np.dot(self.u2, self.expVT) )\r\n\r\n        return r\r\n\r\n    def updateTopicEmbeddings(self):\r\n        Em = self.calcEm( self.docs_Pi )\r\n        if self.grad_scale_Em_base > 0 and np.sum(Em) > self.grad_scale_Em_base:\r\n            grad_scale = self.grad_scale_Em_base / np.sum(Em)\r\n        else:\r\n            grad_scale = 1\r\n                                \r\n        # Em: 1 x K vector\r\n        # r: 1 x K vector\r\n        # Em_exp_r: 1 x K vector\r\n        Em_exp_r = Em * np.exp(self.r)\r\n\r\n        # d_EwVT_dT: K x N0\r\n        d_EwVT_dT = np.dot( self.expVT.T, self.Pw_V )\r\n        # Em_drdT_exact: N0 x K\r\n        Em_drdT_exact = d_EwVT_dT.T * Em_exp_r\r\n\r\n        # Em_drdT: K x N0\r\n        Em_drdT = Em_drdT_exact.T\r\n\r\n        # dLdT, gradT: K x N0\r\n        dLdT = self.sum_pi_v - Em_drdT\r\n        gradT = dLdT * self.delta * grad_scale\r\n        \r\n        gradTNorms = np.linalg.norm( gradT, axis=1 )\r\n        TNorms = np.linalg.norm( self.T, axis=1 )\r\n        TNorms[ TNorms < 1e-2 ] = 1.0\r\n            \r\n        gradTScale = np.ones(self.K)\r\n        gradFractions = gradTNorms / TNorms\r\n        for k,fraction in enumerate(gradFractions):\r\n            if self.max_grad_norm_fraction > 0 and fraction > self.max_grad_norm_fraction:\r\n                gradTScale[k] = self.max_grad_norm_fraction / fraction\r\n            if self.max_grad_norm > 0 and TNorms[k] > self.max_grad_norm:\r\n                gradTScale[k] = min( gradTScale[k], self.max_grad_norm / TNorms[k] )\r\n                    \r\n        gradT *= gradTScale[:, None]\r\n        T2 = self.T + gradT\r\n\r\n        maxTStep = np.max( np.linalg.norm( gradT, axis=1 ) )\r\n\r\n        # self.max_l == 0: do not do normalization\r\n        if self.max_l > 0:\r\n            for k in xrange( self.K ):\r\n                # do normalization only if the magnitude > self.max_l\r\n                if np.linalg.norm( T2[k] ) > self.max_l:\r\n                    T2[k] = self.max_l * normalizeF( T2[k] )\r\n\r\n        if self.zero_topic0:\r\n            T2[0] = np.zeros(self.N0)\r\n\r\n        r2 = self.calcTopicResiduals(T2)\r\n        topicDiffNorm = np.linalg.norm( self.T - T2 )\r\n        return T2, r2, topicDiffNorm, maxTStep\r\n\r\n    # Pi: L x K\r\n    # sum_pi_v: K x N0\r\n    def calcSum_pi_v(self):\r\n        self.sum_pi_v = np.zeros( (self.K, self.N0) )\r\n\r\n        for d in xrange(self.D):\r\n            Pi = self.docs_Pi[d]\r\n            wids = self.docs_wids[d]\r\n            #L = self.docs_L[d]\r\n            #for i in xrange(L):\r\n            #    self.sum_pi_v += np.outer( Pi[i], self.V[ wids[i] ] )\r\n            self.sum_pi_v += np.dot( Pi.T, self.V[wids] )\r\n            \r\n    # the returned outputter always output to the log file\r\n    # screenVerboseThres controls when the generated outputter will output to screen\r\n    # when self.verbose >= screenVerboseThres, screen output is enabled\r\n    # in the batch mode for multiple files, typically self.verbose == 0\r\n    # then by default no screen output anyway\r\n    # in the single file mode, typically self.verbose == 1\r\n    # then in printTopWordsInTopics(),\r\n    #   outputToScreen == True => screenVerboseThres == 1\r\n    #       with screen output\r\n    #   outputToScreen == False => screenVerboseThres == 2\r\n    #       no screen output\r\n    # in other places by default screenVerboseThres==1, with screen output\r\n    def genOutputter(self, screenVerboseThres=1):\r\n        def screen_log_output(s):\r\n            self.fileLogger.debug(s)\r\n            if self.verbose >= screenVerboseThres:\r\n                print s\r\n        return screen_log_output\r\n\r\n    def genProgressor(self):\r\n        def screen_log_progress(s):\r\n            self.fileLogger.debug(s)\r\n            if self.verbose == 0:\r\n                print \"\\r%s    \\r\" %s,\r\n            else:\r\n                print s\r\n        return screen_log_progress\r\n\r\n    # topTopicMassFracPrintThres: when a topic's fraction Em[k]/L > topTopicMassFracPrintThres/K, print it\r\n    def printTopWordsInTopics( self, docs_theta, outputToScreen=False ):\r\n        wids2 = self.wid2freq.keys()\r\n        wids_topics_sim = np.dot( normalizeF( self.V[wids2] ), normalizeF(self.T).T )\r\n        wids_topics_dot = np.dot( self.V[wids2], self.T.T )\r\n\r\n        # row ID: de-duplicated id, also the row idx in the \r\n        # matrices wids_topics_sim and wids_topics_dot\r\n        wid2rowID = {}\r\n        for i, wid in enumerate(wids2):\r\n            wid2rowID[wid] = i\r\n\r\n        # the topic prop of each word, indexed by the row ID\r\n        row_topicsProp = np.zeros( wids_topics_sim.shape )\r\n        # word occurrences, indexed bythe row ID\r\n        row_wordOccur = np.array( self.wid2freq.values() )\r\n\r\n        if self.evalKmeans:\r\n            Em = np.bincount(self.kmeans_xtoc)\r\n        else:\r\n            docs_Pi = self.updatePi(docs_theta)\r\n            Em = self.calcEm(docs_Pi)\r\n\r\n        # tids is sorted topic IDs from most frequent to least frequent\r\n        tids = sorted( range(self.K), key=lambda k: Em[k], reverse=True )\r\n        for i,k in enumerate(tids):\r\n            # below the average proportion * topTopicMassFracPrintThres\r\n            if Em[k] < self.topTopicMassFracPrintThres * self.totalL / self.K:\r\n                break\r\n\r\n        # cut_i is the cut point of tids: tids[:cut_i] will be printed\r\n        # if i==0, no topic has enough proportion to be printed.\r\n        # this may happen when topicThres is too big. in this case, print the principal topic\r\n        if i == 0:\r\n            cut_i = 1\r\n        else:\r\n            cut_i = i\r\n\r\n        for d in xrange(self.D):\r\n            for i in xrange(self.docs_L[d]):\r\n                wid = self.docs_wids[d][i]\r\n                rowID = wid2rowID[wid]\r\n                if self.evalKmeans:\r\n                    k = self.kmeans_xtoc[rowID]\r\n                    row_topicsProp[rowID][k] += 1\r\n                else:\r\n                    row_topicsProp[rowID] += docs_Pi[d][i]\r\n            \r\n        # the topic prop of each word, indexed by the row ID\r\n        # take account of the word freq, but dampen it with sqrt\r\n        # so that more similar, less frequent words have chance to be selected\r\n        # doing average does not consider freq, not good either\r\n        row_topicsDampedProp = row_topicsProp / np.sqrt(row_wordOccur)[:,None]\r\n\r\n        W = len(self.vocab)\r\n        # number of unique words in the docs\r\n        W2 = len(wids2)\r\n\r\n        if outputToScreen:\r\n            out = self.genOutputter(1)\r\n        else:\r\n            out = self.genOutputter(2)\r\n\r\n        out(\"\")\r\n        out( \"Em:\\n%s\\n\" %Em )\r\n        out(\"Topic magnitudes:\")\r\n\r\n        topicMagnitudes = np.linalg.norm(self.T, axis=1)\r\n\r\n        out(topicMagnitudes)\r\n        out(\"\")\r\n\r\n        # selected tids to output\r\n        selTids = tids[:cut_i]\r\n        selTids = np.array(selTids)\r\n\r\n        # always output topic 0\r\n        # if topic 0 is not in selTids, append it\r\n        if len( np.where(selTids == 0)[0] ) == 0:\r\n            selTids = np.append( selTids, 0 )\r\n\r\n        for k in selTids:\r\n            out( \"Topic %d (%.2f): %.1f%%\" %( k, np.linalg.norm( self.T[k] ), 100 * Em[k] / self.totalL ) )\r\n\r\n            rowID_sorted = sorted( range(W2), key=lambda rowID: row_topicsDampedProp[rowID, k], reverse=True )\r\n\r\n            out(\"Most relevant words:\")\r\n\r\n            line = \"\"\r\n            for rowID in rowID_sorted[:self.topW]:\r\n                wid = wids2[rowID]\r\n                topicDampedProp = row_topicsDampedProp[rowID, k]\r\n                topicProp = row_topicsProp[rowID, k]\r\n                sim = wids_topics_sim[rowID, k]\r\n                dotprod = wids_topics_dot[rowID, k]\r\n\r\n                line += \"%s (%d,%d): %.2f/%.2f/%.2f/%.2f \" %( self.vocab[wid], wid, self.wid2freq[wid],\r\n                                    topicDampedProp, topicProp, sim, dotprod )\r\n\r\n            out(line)\r\n\r\n            if np.linalg.norm( self.T[k] ) == 0:\r\n                continue\r\n\r\n            V_topic_dot = np.dot( self.V2, self.T[k] )\r\n            V_topic_sim = V_topic_dot / np.linalg.norm( self.V2, axis=1 ) / np.linalg.norm( self.T[k] )\r\n\r\n            wid_sorted = sorted( xrange(self.Mstep_sample_topwords), \r\n                                key=lambda wid: V_topic_sim[wid], reverse=True )\r\n\r\n            out(\"Most similar words in vocab:\")\r\n\r\n            line = \"\"\r\n            for wid in wid_sorted[:self.topW]:\r\n                sim = V_topic_sim[wid]\r\n                dotprod = V_topic_dot[wid]\r\n                line += \"%s: %.2f/%.2f \" %( self.vocab[wid], sim, dotprod )\r\n\r\n            out(line)\r\n            out(\"\")\r\n\r\n    def docSentences2wids( self, docs_wordsInSentences ):\r\n        docs_wids = []\r\n        docs_idx = []\r\n        countedWC = 0\r\n        outvocWC = 0\r\n        stopwordWC = 0\r\n        wid2freq = {}\r\n        wids_freq = np.zeros(self.vocab_size)\r\n\r\n        for d, wordsInSentences in enumerate(docs_wordsInSentences):\r\n            wids = []\r\n            for sentence in wordsInSentences:\r\n                for w in sentence:\r\n                    w = w.lower()\r\n                    if self.remove_stop and w in stopwordDict:\r\n                        stopwordWC += 1\r\n                        continue\r\n\r\n                    if w in self.vocab_dict:\r\n                        wid = self.vocab_dict[w]\r\n                        wids.append(wid)\r\n                        wids_freq[wid] += 1\r\n\r\n                        if wid not in wid2freq:\r\n                            wid2freq[wid] = 1\r\n                        else:\r\n                            wid2freq[wid] += 1\r\n                        countedWC += 1\r\n                    else:\r\n                        outvocWC += 1\r\n\r\n            # skip empty documents\r\n            if len(wids) > 0:\r\n                docs_wids.append(wids)\r\n                docs_idx.append(d)\r\n\r\n        # out0 prints both to screen and to log file, regardless of the verbose level\r\n        out0 = self.genOutputter(0)\r\n        out1 = self.genOutputter(1)\r\n\r\n        out0( \"%d docs scanned, %d kept. %d words kept, %d unique. %d stop words, %d out voc\" %( len(docs_wordsInSentences),\r\n                                                            len(docs_idx), countedWC, len(wid2freq), stopwordWC, outvocWC ) )\r\n\r\n        wid_freqs = sorted( wid2freq.items(), key=lambda kv: kv[1], reverse=True )\r\n        out1(\"Top words:\")\r\n        line = \"\"\r\n        for wid, freq in wid_freqs[:30]:\r\n            line += \"%s(%d): %d \" %( self.vocab[wid], wid, freq )\r\n        out1(line)\r\n        return docs_idx, docs_wids, wid2freq, wids_freq\r\n\r\n    def setDocs( self, docs_wordsInSentences, docs_name ):\r\n        self.totalL = 0\r\n        self.docs_L = []\r\n        self.docs_name = []\r\n\r\n        self.docs_idx, self.docs_wids, self.wid2freq, self.wids_freq = \\\r\n                                    self.docSentences2wids(docs_wordsInSentences)\r\n\r\n        for doc_idx in self.docs_idx:\r\n            self.docs_name.append( docs_name[doc_idx] )\r\n        for wids in self.docs_wids:\r\n            self.docs_L.append( len(wids) )\r\n        self.totalL = sum(self.docs_L)\r\n\r\n        avgV = np.zeros(self.N0)\r\n        sum_freq = 0\r\n        for wid, freq in self.wid2freq.iteritems():\r\n            avgV += self.V[wid] * freq\r\n            sum_freq += freq\r\n        avgV /= sum_freq\r\n        norm_avgV = np.linalg.norm(avgV)\r\n        print \"Norm of avg vector: %.2f\" %norm_avgV\r\n        if self.rebase_vecs and norm_avgV >= self.rebase_norm_thres:\r\n            self.V -= avgV\r\n            # update the precomputed matrices/vectors\r\n            self.precompute()\r\n            \r\n#        if self.useLocalU:\r\n#            self.local_u = self.wids_freq / self.totalL\r\n#            assert abs( np.sum(self.local_u) - 1 ) < 1e-5, \\\r\n#                \"Local unigram empirical prob vector local_u wrongly normalized: sum=%.3f != 1\" %np.sum(self.local_u)\r\n\r\n        self.D = len(self.docs_name)\r\n        if self.D == 0:\r\n            print \"WARN: Document set is empty after preprocessing.\"\r\n        if self.D == 1:\r\n            self.docsName = \"'%s'\" %(docs_name[0])\r\n        else:\r\n            self.docsName = \"'%s'...(%d docs)\" %( docs_name[0], self.D )\r\n\r\n        return self.docs_idx\r\n        \r\n    def kmeans( self, maxiter=10 ):\r\n        \"\"\" centers, Xtocentre, distances = topicvec.kmeans( ... )\r\n        in:\r\n            X: M x N0\r\n            centers K x N0: initial centers, e.g. random.sample( X, K )\r\n            iterate until the change of the average distance to centers\r\n                is within topicDiff_tolerance of the previous average distance\r\n            maxiter\r\n            metric: cosine\r\n            self.verbose: 0 silent, 2 prints running distances\r\n        out:\r\n            centers, K x N0\r\n            Xtocentre: each X -> its nearest center, ints M -> K\r\n            distances, M\r\n        \"\"\"\r\n    \r\n        wids2 = self.wid2freq.keys()\r\n        weights = np.array( self.wid2freq.values() )\r\n        \r\n        X = normalizeF( self.V[wids2] )\r\n        centers = randomsample( X, self.K )\r\n        \r\n        if self.verbose:\r\n            print \"kmeans: X %s  centers %s  tolerance=%.2g  maxiter=%d\" %(\r\n                X.shape, centers.shape, self.topicDiff_tolerance, maxiter )\r\n        \r\n        M = X.shape[0]\r\n        allx = np.arange(M)\r\n        prevdist = 0\r\n        \r\n        for jiter in range( 1, maxiter+1 ):\r\n            D = cdist( X, centers, metric='cosine' )  # |X| x |centers|\r\n            xtoc = D.argmin(axis=1)  # X -> nearest center\r\n            distances = D[allx,xtoc]\r\n            #avdist = distances.mean()  # median ?\r\n            avdist = (distances * weights).sum() / weights.sum()\r\n            \r\n            if self.verbose >= 2:\r\n                print \"kmeans: av |X - nearest center| = %.4g\" % avdist\r\n                \r\n            if (1 - self.topicDiff_tolerance) * prevdist <= avdist <= prevdist \\\r\n            or jiter == maxiter:\r\n                break\r\n                \r\n            prevdist = avdist\r\n            \r\n            for jc in range(self.K):  # (1 pass in C)\r\n                c = np.where( xtoc == jc )[0]\r\n                if len(c) > 0:\r\n                    centers[jc] = ( X[c] * weights[c, None] ).mean( axis=0 )\r\n                    \r\n        if self.verbose:\r\n            print \"kmeans: %d iterations  cluster sizes:\" % jiter, np.bincount(xtoc)\r\n            \r\n        if self.verbose >= 2:\r\n            r50 = np.zeros(self.K)\r\n            r90 = np.zeros(self.K)\r\n            for j in range(self.K):\r\n                dist = distances[ xtoc == j ]\r\n                if len(dist) > 0:\r\n                    r50[j], r90[j] = np.percentile( dist, (50, 90) )\r\n            print \"kmeans: cluster 50% radius\", r50.astype(int)\r\n            print \"kmeans: cluster 90% radius\", r90.astype(int)\r\n        \r\n        self.T = centers\r\n        self.kmeans_xtoc = xtoc\r\n        self.kmeans_distances = distances    \r\n    \r\n    def inferTopicProps( self, T, MAX_ITERS=5 ):\r\n\r\n        self.T = T\r\n        self.r = self.calcTopicResiduals(T)\r\n        # uniform prior\r\n        self.docs_theta = np.ones( (self.D, self.K) )\r\n        loglike = 0\r\n\r\n        for i in xrange(MAX_ITERS):\r\n            iterStartTime = time.time()\r\n            docs_Pi2 = self.docs_Pi\r\n            self.docs_Pi = self.updatePi( self.docs_theta )\r\n            self.updateTheta()\r\n            self.calcSum_pi_v()\r\n            \r\n            if i > 0:\r\n                docs_Pi_diff = np.zeros(self.D)\r\n                for d in xrange(self.D):\r\n                    docs_Pi_diff[d] = np.linalg.norm( self.docs_Pi[d] - docs_Pi2[d] )\r\n                max_Pi_diff = np.max(docs_Pi_diff)\r\n                total_Pi_diff = np.sum(docs_Pi_diff)\r\n            else:\r\n                max_Pi_diff = 0\r\n                total_Pi_diff = 0\r\n\r\n            iterDur = time.time() - iterStartTime\r\n            loglike = self.calcLoglikelihood()\r\n            print \"Iter %d loglike %.2f, Pi diff total %.3f, max %.3f. %.1fs\" %( i, \r\n                                 loglike, total_Pi_diff, max_Pi_diff, iterDur )\r\n\r\n        docs_Em = np.zeros( (self.D, self.K) )\r\n        for d, Pi in enumerate(self.docs_Pi):\r\n            docs_Em[d] = np.sum( Pi, axis=0 )\r\n\r\n        return docs_Em, self.docs_Pi\r\n\r\n    def inference(self):\r\n        if self.D == 0:\r\n            print \"document set is empty or uninitialized\"\r\n            return None, None, None, None\r\n\r\n        startTime = time.time()\r\n        startTimeStr = timeToStr(startTime)\r\n\r\n        # out0 prints both to screen and to log file, regardless of the verbose level\r\n        out0 = self.genOutputter(0)\r\n        out1 = self.genOutputter(1)\r\n\r\n        out0( \"%d topics.\" %(self.K) )\r\n        out0( \"%s inference starts at %s\" %( self.docsName, startTimeStr ) )\r\n\r\n        self.T = np.zeros( ( self.K, self.N0 ) )\r\n\r\n        if self.seed != 0:\r\n            np.random.seed(self.seed)\r\n            out0( \"Seed: %d\" %self.seed )\r\n\r\n        for k in xrange(0, self.K):\r\n            self.T[k] = np.random.randn(self.N0)\r\n            if self.init_l > 0:\r\n                self.T[k] = self.init_l * normalizeF(self.T[k])\r\n\r\n        if self.zero_topic0:\r\n            self.T[0] = np.zeros(self.N0)\r\n\r\n    #    sum_v = np.zeros(N0)\r\n    #    for wid in wids:\r\n    #        sum_v += V[wid]\r\n    #\r\n    #    T[0] = self.max_l * normalizeF(sum_v)\r\n        #self.fileLogger.debug(\"avg_v:\")\r\n        #self.fileLogger.debug(T[0])\r\n\r\n        self.r = self.calcTopicResiduals(self.T)\r\n        # initialized as uniform over topics\r\n        self.docs_theta = np.ones( (self.D, self.K) )\r\n\r\n        lastIterEndTime = time.time()\r\n        print \"Initial learning rate: %.2f\" %(self.iniDelta)\r\n\r\n        self.docs_Pi = self.updatePi( self.docs_theta )\r\n        self.updateTheta()\r\n\r\n        self.calcSum_pi_v()\r\n        loglike = self.calcLoglikelihood()\r\n\r\n        self.it = 0\r\n\r\n        iterDur = time.time() - lastIterEndTime\r\n        lastIterEndTime = time.time()\r\n\r\n        print \"Iter %d: loglike %.2f, %.1fs\" %( self.it, loglike, iterDur )\r\n\r\n        # an arbitrary number to satisfy pylint\r\n        topicDiffNorm = 100000\r\n\r\n        unif_docs_theta = np.ones( (self.D, self.K) )\r\n        Ts_loglikes = []\r\n\r\n        while self.it == 0 or ( self.it < self.MAX_EM_ITERS and topicDiffNorm > self.topicDiff_tolerance ):\r\n            self.it += 1\r\n            self.fileLogger.debug( \"EM Iter %d:\", self.it )\r\n\r\n            self.delta = self.iniDelta / ( self.it + 1 )\r\n            # T, r not updated inside updateTopicEmbeddings()\r\n            # because sometimes we want to keep the original T, r\r\n            self.T, self.r, topicDiffNorm, maxTStep = self.updateTopicEmbeddings()\r\n            \r\n            if self.it % self.VStep_iterNum == 0:\r\n                # does it matter to swap updatePi() & updateTheta()?\r\n                self.docs_Pi = self.updatePi( self.docs_theta )\r\n                self.updateTheta()\r\n\r\n            # calcSum_pi_v() takes a long time on a large corpus\r\n            # so it can be done once every a few iters, with slight loss of performance\r\n            # on 20news and reuters, calcSum_pi_v() is fast enough and this acceleration is unnecessary\r\n            if self.it <= 5 or self.it == self.MAX_EM_ITERS or self.it % self.calcSum_pi_v_iterNum == 0:\r\n                self.calcSum_pi_v()\r\n\r\n            loglike = self.calcLoglikelihood()\r\n\r\n            iterDur = time.time() - lastIterEndTime\r\n            lastIterEndTime = time.time()\r\n\r\n            iterStatusMsg = \"Iter %d: loglike %.2f, topicDiffNorm %.4f, maxTStep %.3f, %.1fs\" %( self.it,\r\n                                           loglike, topicDiffNorm, maxTStep, iterDur )\r\n\r\n            if self.it % self.printTopics_iterNum == 0:\r\n                out0(iterStatusMsg)\r\n\r\n                if self.verbose >= 2:\r\n                    self.fileLogger.debug( \"T[:,%d]:\", self.topDim )\r\n                    self.fileLogger.debug( self.T[ :, :self.topDim ] )\r\n\r\n                    self.fileLogger.debug(\"r:\")\r\n                    self.fileLogger.debug(self.r)\r\n\r\n                self.printTopWordsInTopics(self.docs_theta, False)\r\n            else:\r\n                # not using out0 because the \"\\r\" in the console output shouldn't be in the log file\r\n                print \"%s  \\r\" %iterStatusMsg,\r\n                self.fileLogger.debug(iterStatusMsg)\r\n                Em = self.calcEm( self.docs_Pi )\r\n                self.fileLogger.debug( \"Em:\\n%s\\n\", Em )\r\n                \r\n            Ts_loglikes.append( [ self.it, self.T, loglike ] )\r\n            \r\n        if self.verbose >= 1:\r\n            # if == 0, topics has just been printed in the while loop\r\n            if self.it % self.printTopics_iterNum != 0:\r\n                #self.printTopWordsInTopics(unif_docs_theta, False)\r\n                self.printTopWordsInTopics(self.docs_theta, False)\r\n\r\n        endTime = time.time()\r\n        endTimeStr = timeToStr(endTime)\r\n        inferDur = int(endTime - startTime)\r\n        \r\n        print\r\n        out0( \"%s inference ends at %s. %d iters, %d seconds.\" %( self.docsName, endTimeStr, self.it, inferDur ) )\r\n\r\n        # Em: the global (all documents) distribution of topic mass \r\n        Em = self.calcEm( self.docs_Pi )\r\n        # docs_Em: the document-wise distribution of topic mass \r\n        docs_Em = np.zeros( (self.D, self.K) )\r\n        for d, Pi in enumerate(self.docs_Pi):\r\n            docs_Em[d] = np.sum( Pi, axis=0 )\r\n\r\n        # sort according to loglike\r\n        Ts_loglikes_sorted = sorted( Ts_loglikes, key=lambda T_loglike: T_loglike[2], reverse=True )\r\n        # best T could be the last T. \r\n        # In that case, the two elements in best_last_Ts are the same\r\n        best_last_Ts = [ Ts_loglikes_sorted[0], Ts_loglikes[-1] ]\r\n\r\n        return best_last_Ts, Em, docs_Em, self.docs_Pi\r\n\r\n"
  },
  {
    "path": "utils.py",
    "content": "# -*- coding=GBK -*-\r\n\r\nimport numpy as np\r\nimport scipy.linalg\r\nfrom scipy.stats.stats import spearmanr\r\nimport time\r\nimport re\r\nimport pdb\r\nimport sys\r\nimport os\r\nimport glob\r\nimport logging\r\nfrom psutil import virtual_memory\r\nimport os.path\r\nimport random\r\nimport unicodedata\r\nimport sys\r\n\r\nunicode_punc_tbl = dict.fromkeys( i for i in xrange(128, sys.maxunicode)\r\n                      if unicodedata.category(unichr(i)).startswith('P') )\r\n\r\nlogging.basicConfig( level=logging.DEBUG )\r\nfor handler in logging.root.handlers[:]:\r\n    logging.root.removeHandler(handler)\r\n\r\ndef str2dict(s):\r\n    wordlist = re.split( \"\\s+\", s )\r\n    return dict.fromkeys(wordlist, 1)\r\n\r\nstopwordStr = '''a about above across after again against all almost alone along also\r\nalthough always am among an and another any anybody anyone anything\r\napart are around as  at away be because been before behind being below\r\nbesides between beyond both but by can cannot could  did do does doing done\r\ndown  during each either else enough etc  ever every everybody\r\neveryone except far few for  from get gets got had has have having\r\nhe her here herself him himself his how however if in indeed instead into\r\nis it its itself just kept me maybe might  more most mostly much must\r\nmy myself  neither  no nobody none nor not nothing  of off often on one\r\nonly onto or other others ought our ours out  own  please\r\npp quite rather really said seem  shall she should since so\r\nsome somebody somewhat still such than that the their theirs them themselves\r\nthen there therefore these they this thorough thoroughly those through thus to\r\ntogether too toward towards until up upon was we well were what\r\nwhatever when whenever where whether which while who whom whose why will with\r\nwithin would yet you your yourself\r\nre d ll m ve t s'''\r\n\r\nstopwordDict = str2dict(stopwordStr)\r\n\r\nnp.seterr(all=\"raise\")\r\nnp.set_printoptions(suppress=True, threshold=np.nan, precision=3)\r\n\r\ndef initConsoleLogger(loggerName):\r\n    consoleLogger = logging.getLogger(loggerName)\r\n    streamHandler = logging.StreamHandler()\r\n    consoleLogger.addHandler(streamHandler)  \r\n    return consoleLogger\r\n    \r\ndef initFileLogger(loggerName, isAppending=False):\r\n    loggerName = os.path.splitext(loggerName)[0]\r\n    currDate = timeToStr( time.time(), \"%m.%d\" )\r\n    filename = \"%s-%s.log\" %( loggerName, currDate )\r\n    sn = 0\r\n    while os.path.isfile(filename):\r\n        sn += 1\r\n        filename = \"%s-%s-%d.log\" %( loggerName, currDate, sn )\r\n  \r\n    fileLogger = logging.getLogger(loggerName)\r\n    if isAppending:\r\n        mode = 'a'\r\n    else:\r\n        mode = 'w'\r\n        \r\n    fileHandler = logging.FileHandler(filename, mode=mode)\r\n    fileLogger.addHandler(fileHandler)\r\n    return fileLogger\r\n    \r\ndef warning(*objs):\r\n    sys.stderr.write(*objs)\r\n    \r\nclass Timer(object):\r\n    def __init__(self, name=None):\r\n        self.name = name\r\n        self.tstart = time.time()\r\n        self.tlast = self.tstart\r\n        self.firstCall = True\r\n\r\n    def getElapseTime(self, isStr=True):\r\n        totalElapsed = time.time() - self.tstart\r\n        # elapsed time since last call\r\n        interElapsed = time.time() - self.tlast\r\n        self.tlast = time.time()\r\n\r\n        firstCall = self.firstCall\r\n        self.firstCall = False\r\n\r\n        if isStr:\r\n            if self.name:\r\n                if firstCall:\r\n                    return '%s elapsed: %.2f' % ( self.name, totalElapsed )\r\n                return '%s elapsed: %.2f/%.2f' % ( self.name, totalElapsed, interElapsed )\r\n            else:\r\n                if firstCall:\r\n                    return 'Elapsed: %.2f' % ( totalElapsed )\r\n                return 'Elapsed: %.2f/%.2f' % ( totalElapsed, interElapsed )\r\n        else:\r\n            return totalElapsed, interElapsed\r\n\r\n    def printElapseTime(self):\r\n        print self.getElapseTime()\r\n\r\ndef timeToStr(timeNum, fmt=\"%H:%M:%S\"):\r\n    timeStr = time.strftime(fmt, time.localtime(timeNum))\r\n    return timeStr\r\n\r\n# Weight: nonnegative real matrix. If not specified, return the unweighted norm\r\ndef norm1(M, Weight=None):\r\n    if len(M.shape) == 1:\r\n        if Weight is not None:\r\n            return np.sum( np.abs( M * Weight ) )\r\n        else:\r\n            return np.sum( np.abs(M) )\r\n\r\n    s = 0\r\n    \r\n    if Weight is not None:\r\n        for i in xrange( len(M) ):\r\n            # row by row calculation. \r\n            # If doing matrix multiplication, a big temporary matrix will be generated, consuming a lot RAM\r\n            row = np.abs( M[i] * Weight[i] )\r\n            s += np.sum(row)\r\n    else:\r\n        for i in xrange( len(M) ):\r\n            row = np.abs( M[i] )\r\n            s += np.sum(row)\r\n\r\n    return s\r\n\r\n# F-norm of a vector or a matrix\r\ndef normF(M, Weight=None):\r\n    if len(M.shape) == 1:\r\n        if Weight is not None:\r\n            # M*M makes all elems positive, and all elems of Weight are nonnegative. So no need to take abs()\r\n            return np.sqrt( np.sum( M * M * Weight ) )\r\n        else:\r\n            return np.sqrt( np.sum( M * M ) )\r\n    \r\n    s = 0\r\n    \r\n    if Weight is not None:\r\n        for i in xrange( len(M) ):\r\n            # row by row calculation. \r\n            # If doing matrix multiplication, a big temporary matrix will be generated, consuming a lot RAM\r\n            row = M[i] * M[i] * Weight[i]\r\n            s += np.sum(row)\r\n    else:\r\n        for i in xrange( len(M) ):\r\n            row = M[i] * M[i]\r\n            s += np.sum(row)\r\n\r\n    return np.sqrt(s)\r\n\r\n# normalize a 1-d or 2-d array of nonnegative numbers: \r\n# keep the original array intact, return a copy of normalized array\r\n# when array is 2d:\r\n# axis=0: normalize columns. axis=1: normalize rows (default)\r\ndef normalize(data, axis=1):\r\n    if np.min(data) < 0:\r\n        raise RuntimeError(\"Negative element in data passed to normalize()\")\r\n    if data.ndim == 1:\r\n        return data / np.sum(data)\r\n    if axis == 0:\r\n        s = np.sum(data, axis=0)\r\n        return data / s\r\n    elif axis == 1:\r\n        ss = np.sum(data, axis=1)\r\n        return data / np.tile(ss, (data.shape[1],1)).T\r\n    else:\r\n        raise RuntimeError('function normalize: axis must be 0/1')\r\n\r\n# normalize a 1-d or 2-d array of numbers, w.r.t. F-norm\r\ndef normalizeF(data, axis=1):\r\n    if data.ndim == 1:\r\n        return data / normF(data) \r\n    \r\n    data2 = np.copy(data)\r\n    # normalize each column of data\r\n    if axis == 0:\r\n        for i in xrange(data2.shape[1]):\r\n            if normF(data2[:,i]) > 0:\r\n                data2[:,i] /= normF(data2[:,i])\r\n        return data2\r\n    \r\n    # normalize each row of data     \r\n    elif axis == 1:\r\n        norms = np.array( [ normF(x) for x in data2 ] )\r\n        norms[ norms==0 ] = 1\r\n        data2 /= norms[:, None]\r\n    else:\r\n        raise RuntimeError('function normalize: axis must be 0/1')\r\n    return data2\r\n\r\ndef cosine(x, y):\r\n    x2 = normalizeF(x)\r\n    y2 = normalizeF(y)\r\n    return np.dot(x2, y2)\r\n\r\n# Given a list of matrices, return a list of their norms\r\ndef matSizes( norm, Ms, Weight=None ):\r\n    sizes = []\r\n    for M in Ms:\r\n        sizes.append( norm(M, Weight) )\r\n\r\n    return sizes\r\n\r\ndef sym(M):\r\n    return ( M + M.T ) / 2.0\r\n\r\ndef skew(M):\r\n    return ( M - M.T ) / 2.0\r\n\r\n# Assume A has been approximately sorted by rows, and in each row, sorted by columns\r\n# matrix F returned from loadBigramFile satisfies this\r\n# print the number of elements >= A[0,0]/2^n\r\n# return the idea cut point above which there are at least \"fraction\" of the elements\r\n# these elements will be cut off to this upper limit\r\ndef getQuantileCut(A, fraction):\r\n    totalNonzeroElemCount = np.sum( A > 0 ) #A.shape[0] * A.shape[1]\r\n    maxElem = A[0,0]\r\n    cutPoint = maxElem\r\n    idealCutPoint = cutPoint\r\n    idealFound = False\r\n    \r\n    while cutPoint >= 10:\r\n        aboveElemCount = np.sum( A >= cutPoint )\r\n        print \"Cut point %.0f: %d/%.3f%%\" %( cutPoint, aboveElemCount, aboveElemCount * 100.0 / totalNonzeroElemCount )\r\n        if not idealFound and aboveElemCount >= totalNonzeroElemCount * fraction:\r\n            idealCutPoint = cutPoint\r\n            idealFound = True\r\n        cutPoint /= 2.0\r\n\r\n    return idealCutPoint\r\n\r\n# find the principal eigenvalue/eigenvector: e1 & v1.\r\n# if e1 < 0, then the left principal singular vector is -v1, and the right is v1.\r\n# much faster than numpy.linalg.eig / scipy.linalg.eigh\r\ndef power_iter(M):\r\n    MAXITER = 100\r\n    epsilon = 1e-6\r\n    vec = np.random.rand(len(M))\r\n    old_vec = vec\r\n\r\n    for i in xrange(MAXITER):\r\n        vec2 = np.dot( M, vec )\r\n        magnitude = np.linalg.norm(vec2)\r\n        vec2 /= magnitude\r\n        vec = vec2\r\n\r\n        if i%2 == 1:\r\n            error = np.linalg.norm( vec2 - old_vec )\r\n            #print \"%d: %f, %f\" %( i+1, magnitude, error )\r\n            if error < epsilon:\r\n                break\r\n            old_vec = vec2\r\n\r\n    vec2 = np.dot( M, vec )\r\n    if np.sum(vec2)/np.sum(vec) > 0:\r\n        eigen = magnitude\r\n    else:\r\n        eigen = -magnitude\r\n\r\n    return eigen, vec\r\n\r\n# each column of vs is an eigenvector\r\n# It's a prerequisite that all eigenvalues are already nonnegative\r\n# This requirement is guaranteed after nowe_factorize()\r\ndef lowrank_fact(VV, N0):\r\n    timer1 = Timer( \"lowrank_fact()\" )\r\n\r\n    es, vs = np.linalg.eigh(VV)\r\n    es = es[-N0:]\r\n    vs = vs[ :, -N0: ]\r\n    E_sqrt = np.diag( np.sqrt(es) )\r\n    V = vs.dot(E_sqrt)\r\n    VV = V.dot(V.T)\r\n\r\n    return V, VV, vs,es\r\n\r\ndef save_embeddings( filename, vocab, V, matrixName ):\r\n    FMAT = open(filename, \"wb\")\r\n    print \"Save matrix '%s' into %s\" %(matrixName, filename)\r\n\r\n    vocab_size = len(vocab)\r\n    N = len(V[0])\r\n\r\n    #pdb.set_trace()\r\n\r\n    FMAT.write( \"%d %d\\n\" %(vocab_size, N) )\r\n    for i in xrange(vocab_size):\r\n        line = vocab[i]\r\n        for j in xrange(N):\r\n            line += \" %.5f\" %V[i,j]\r\n        FMAT.write(\"%s\\n\" %line)\r\n\r\n    FMAT.close()\r\n\r\ndef save_matrix_as_text( filename, rowTypeName, T, *extraCols, **kwargs ):\r\n    FMAT = open(filename, \"wb\")\r\n    print \"Save %s matrix into '%s'\" %(rowTypeName, filename)\r\n    colSep = kwargs.get(\"colSep\", \" \")\r\n    \r\n    K, N = T.shape\r\n\r\n    #pdb.set_trace()\r\n    extraColNum = len(extraCols)\r\n    \r\n    FMAT.write( \"%d %d %d\\n\" %( K, N, extraColNum ) )\r\n    for i in xrange(K):\r\n        # if rowNames is provided, print the corresponding row name at the beginning of each line\r\n        line = \"\"\r\n        for j in xrange(extraColNum):\r\n            col = str( extraCols[j][i] )\r\n            line += col + colSep\r\n        line += \"%.5f\" %T[i,0]\r\n            \r\n        for j in xrange(1, N):\r\n            line += \" %.5f\" %T[i,j]\r\n        FMAT.write(\"%s\\n\" %line)\r\n\r\n    FMAT.close()\r\n    print \"%d rows of %s(s) (%d-d each) saved\" %( K, rowTypeName, N )\r\n\r\ndef load_matrix_from_text( filename, rowTypeName, colSep=\" \" ):\r\n    FMAT = open(filename)\r\n    print \"Load %s matrix from '%s'\" %(rowTypeName, filename)\r\n    precision = np.float64\r\n    extraCols = []\r\n    extraColNum = 0\r\n    lineno = 0\r\n    \r\n    try:\r\n        header = FMAT.readline()\r\n        rowID = 0\r\n        lineno += 1\r\n        match = re.match( r\"(\\d+) (\\d+) (\\d+)\", header)\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        K = int(match.group(1))\r\n        N = int(match.group(2))\r\n        extraColNum = int(match.group(3))\r\n        print \"'%s': %dx%d, %d extra columns\" %( filename, K, N, extraColNum )\r\n        \r\n        for i in xrange(extraColNum):\r\n            extraCols.append([])\r\n        \r\n        M = np.zeros( (K, N), dtype=precision )\r\n\r\n        for line in FMAT:\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                if rowID != K:\r\n                    raise ValueError( lineno, \"%d rows declared in header, but %d read\" %( K, rowID ) )\r\n                break\r\n\r\n            row_extraCols = []\r\n            fields = line.split(colSep)\r\n            for i in xrange(extraColNum):\r\n                extraCols[i].append(fields[i])\r\n                \r\n            matFields = fields[extraColNum:]\r\n            # matrix values are always concatenated by \" \"\r\n            # if colSep is not \" \", matrix values should take one column\r\n            if colSep != \" \":\r\n                if len(matFields) > 1:\r\n                    raise ValueError( lineno, \"%d columns of matrix values when colSep is not space\" %( len(matFields) ) )\r\n                else:\r\n                    matFields = matFields[0].split(\" \")\r\n                    \r\n            M[rowID] = np.array( [ float(x) for x in matFields ], dtype=precision )\r\n            rowID += 1\r\n                \r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            warning( \"Unknown line %d:\\n%s\\n\" %( e.args[0], e.args[1] ) )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            warning( \"Source line %d - %s on File line %d:\\n%s\\n\" %( tb.tb_lineno, e, lineno, line ) )\r\n        exit(2)\r\n\r\n    FMAT.close()\r\n    warning( \"%dx%d %s matrix loaded from '%s'\\n\" %(K, N, rowTypeName, filename) )\r\n\r\n    if len(extraCols) == 0:\r\n        return M\r\n    else:\r\n        return M, extraCols\r\n        \r\n# load top maxWordCount words, plus extraWords\r\ndef load_embeddings( filename, maxWordCount=-1, extraWords={}, record_skipped=False ):\r\n    FMAT = open(filename)\r\n    warning( \"Load embedding text file '%s'\\n\" %(filename) )\r\n    \r\n    V = []\r\n    word2id = {}\r\n    skippedWords = {}\r\n\r\n    vocab = []\r\n    precision = np.float32\r\n\r\n    try:\r\n        header = FMAT.readline()\r\n        lineno = 1\r\n        match = re.match( r\"(\\d+) (\\d+)\", header)\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        vocab_size = int(match.group(1))\r\n        N = int(match.group(2))\r\n\r\n        if maxWordCount > 0:\r\n            maxWordCount = min(maxWordCount, vocab_size)\r\n        else:\r\n            maxWordCount = vocab_size\r\n\r\n        warning( \"Will load embeddings of %d words\" %maxWordCount )\r\n        if len(extraWords) > 0:\r\n            warning( \", plus %d extra words\" %(len(extraWords)) )\r\n        warning(\"\\n\")\r\n\r\n        # maxWordCount + len(extraWords) is the maximum num of words.\r\n        # V may contain extra rows that will be removed at the end\r\n        V = np.zeros( (maxWordCount + len(extraWords), N), dtype=precision )\r\n        wid = 0\r\n        orig_wid = 0\r\n\r\n        for line in FMAT:\r\n            lineno += 1\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                if orig_wid != vocab_size:\r\n                    raise ValueError( lineno, \"%d words declared in header, but %d read\" %( vocab_size, orig_wid ) )\r\n                break\r\n\r\n            fields = line.split(' ')\r\n            # remove empty fields\r\n            fields = filter( lambda x: x, fields )\r\n            w = fields[0]\r\n\r\n            if w in extraWords:\r\n                del extraWords[w]\r\n                isInterested = True\r\n            elif orig_wid < maxWordCount:\r\n                isInterested = True\r\n            elif record_skipped:\r\n                isInterested = False\r\n                skippedWords[w] = 1\r\n            else:\r\n                break\r\n\t\t\t\t\t\t\t\r\n            orig_wid += 1\r\n\r\n            if isInterested:\r\n                V[wid] = np.array( [ float(x) for x in fields[1:] ], dtype=precision )\r\n                word2id[w] = wid\r\n                vocab.append(w)\r\n                wid += 1\r\n\r\n            if orig_wid % 1000 == 0:\r\n                warning( \"\\r%d    %d    %d    \\r\" %( orig_wid, wid, len(extraWords) ) )\r\n\r\n            if orig_wid > vocab_size:\r\n                raise ValueError( \"%d words declared in header, but more are read\" %(vocab_size) )\r\n\r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            warning( \"Unknown line %d:\\n%s\\n\" %( e.args[0], e.args[1] ) )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            warning( \"Source line %d - %s on File line %d:\\n%s\\n\" %( tb.tb_lineno, e, lineno, line ) )\r\n        exit(2)\r\n\r\n    FMAT.close()\r\n    warning( \"\\n%d embeddings read, %d kept\\n\" %(orig_wid, wid) )\r\n\r\n    #pdb.set_trace()\r\n\r\n    if wid < len(V):\r\n        V = V[:wid]\r\n\r\n    # V: embeddings, vocab: array of words, word2id: dict of word to index in V\r\n    return V, vocab, word2id, skippedWords\r\n\r\n# borrowed from gensim.models.word2vec\r\n# load top maxWordCount words, plus extraWords\r\ndef load_embeddings_bin( filename, maxWordCount=-1, extraWords={}, record_skipped=False ):\r\n    print \"Load embedding binary file '%s'\" %(filename)\r\n    word2id = {}\r\n    skippedWords = {}\r\n    vocab = []\r\n    #origWord2id = {}\r\n    #origVocab = []\r\n    precision = np.float32\r\n\r\n    with open(filename, \"rb\") as fin:\r\n        header = fin.readline()\r\n        vocab_size, N = map(int, header.split())\r\n\r\n        if maxWordCount > 0:\r\n            maxWordCount = min(maxWordCount, vocab_size)\r\n        else:\r\n            maxWordCount = vocab_size\r\n\r\n        print \"Will load embeddings of %d words\" %maxWordCount,\r\n        if len(extraWords) > 0:\r\n            print \"\\b, plus %d extra words\" %(len(extraWords))\r\n        else:\r\n            print\r\n\r\n        # maxWordCount + len(extraWords) is the maximum num of words.\r\n        # V may contain extra rows that will be removed at the end\r\n        V = np.zeros( (maxWordCount + len(extraWords), N), dtype=precision )\r\n\r\n        full_binvec_len = np.dtype(precision).itemsize * N\r\n\r\n        #pdb.set_trace()\r\n        orig_wid = 0\r\n        wid = 0\r\n        while True:\r\n            # mixed text and binary: read text first, then binary\r\n            word = []\r\n            while True:\r\n                ch = fin.read(1)\r\n                if ch == ' ':\r\n                    break\r\n                if ch != '\\n':  # ignore newlines in front of words (some binary files have newline, some don't)\r\n                    word.append(ch)\r\n            word = b''.join(word)\r\n\r\n            if word[0].isupper():\r\n                word2 = word.lower()\r\n                # if the lowercased word hasn't been read, treat the embedding as the lowercased word's\r\n                # otherwise, add the capitalized word to V\r\n                if word2 not in word2id:\r\n                    word = word2\r\n\r\n            #origWord2id[word] = orig_wid\r\n            #origVocab.append(word)\r\n\r\n            if w in extraWords:\r\n                del extraWords[w]\r\n                isInterested = True\r\n            elif orig_wid < maxWordCount:\r\n                isInterested = True\r\n            elif record_skipped:\r\n                isInterested = False\r\n                skippedWords[w] = 1\r\n            else:\r\n                break\r\n\r\n            orig_wid += 1\r\n\r\n            if isInterested:\r\n                word2id[word] = wid\r\n                vocab.append(word)\r\n                V[wid] = np.fromstring( fin.read(full_binvec_len), dtype=precision )\r\n                wid += 1\r\n            else:\r\n                fin.read(full_binvec_len)\r\n\r\n            if orig_wid % 1000 == 0:\r\n                print \"\\r%d    %d    %d    \\r\" %( orig_wid, wid, len(extraWords) ),\r\n\r\n            if orig_wid > vocab_size:\r\n                raise ValueError( \"%d words declared in header, but more are read\" %(vocab_size) )\r\n\r\n    if wid < len(V):\r\n        V = V[:wid]\r\n    print \"\\n%d embeddings read, %d embeddings kept\" %(orig_wid, wid)\r\n\r\n    # V: embeddings, vocab: array of words, word2id: dict of word to index in V\r\n    return V, vocab, word2id, skippedWords\r\n\r\n# load Hyperwords embeddings\r\ndef load_embeddings_hyper(modelPath, vecType):\r\n    sys.path.append('./hyperwords/hyperwords')\r\n    from representations.explicit import PositiveExplicit\r\n    from representations.embedding import SVDEmbedding\r\n    print \"Load Hyperwords(%s) embedding file '%s'\" %(vecType, modelPath)\r\n    if vecType == 'PPMI':\r\n        base = PositiveExplicit\r\n    else:\r\n        base = SVDEmbedding\r\n        \r\n    class HyperEmbed(base):\r\n        def __contains__(self, w):\r\n            return w in self.wi\r\n    \r\n    model = HyperEmbed(modelPath, True)\r\n\r\n    print \"Done.\"\r\n    return model\r\n    \r\n# load residuals\r\n# the dict word2id is to ensure the same word is mapped to the same id as in the embedding file\r\n# in other words, the embedding file and residual file had to be generated in the same batch\r\ndef load_residuals( filename, word2id={}, maxRowCount=-1, maxColCount=-1 ):\r\n    FMAT = open(filename)\r\n    warning( \"Load residual file '%s'\\n\" %(filename) )\r\n    \r\n    precision = np.float32\r\n\r\n    try:\r\n        header = FMAT.readline()\r\n        lineno = 1\r\n        match = re.match( r\"(\\d+) (\\d+)\", header)\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        vocab_size = int(match.group(1))\r\n        vocab_size2 = int(match.group(2))\r\n\r\n        if maxRowCount > 0:\r\n            maxRowCount = min(maxRowCount, vocab_size)\r\n        else:\r\n            maxRowCount = vocab_size\r\n\r\n        if maxColCount > 0:\r\n            maxColCount = min(maxColCount, vocab_size2)\r\n        else:\r\n            maxColCount = vocab_size2\r\n\r\n        warning( \"Will load residuals of %dx%d words\" %( maxRowCount, maxColCount ) )\r\n\r\n        A = np.zeros( (maxRowCount, maxColCount), dtype=precision )\r\n\r\n        for line in FMAT:\r\n            lineno += 1\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                if lineno != vocab_size:\r\n                    raise ValueError( lineno, \"%d rows declared in header, but %d read\" %( vocab_size, lineno ) )\r\n                break\r\n\r\n            fields = line.split(' ')\r\n            fields = filter( lambda x: x, fields )\r\n            w = fields[0]\r\n\r\n            if len(word2id) > 0:\r\n                if w not in word2id or word2id[w] != lineno - 1:\r\n                    raise ValueError(\"ID of '%s' is inconsistent between '%s' and the loaded embeddings. \"\r\n                                     \"Make sure they were generated in the same batch\" %(w, filename) )\r\n            A[ lineno - 1 ] = np.array( [ float(x) for x in fields[1:] ], dtype=precision )\r\n\r\n            if lineno % 1000 == 0:\r\n                warning( \"\\r%d\\r\" %lineno )\r\n\r\n            if lineno >= vocab_size:\r\n                raise ValueError( \"%d words declared in header, but more are read\" %(vocab_size) )\r\n\r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            warning( \"Unknown line %d:\\n%s\\n\" %( e.args[0], e.args[1] ) )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            warning( \"Source line %d - %s on File line %d:\\n%s\\n\" %( tb.tb_lineno, e, lineno, line ) )\r\n        exit(2)\r\n\r\n    FMAT.close()\r\n    warning( \"\\n%d rows read, each row %d words\\n\" %(lineno, vocab_size2) )\r\n\r\n    #pdb.set_trace()\r\n\r\n    return A\r\n\r\ndef loadBigramFile( bigram_filename, topWordNum, extraWords, kappa=0.01 ):\r\n    print \"Loading bigram file '%s':\" %bigram_filename\r\n    BIGRAM = open(bigram_filename)\r\n    lineno = 0\r\n    vocab = []\r\n    word2id = {}\r\n    # 1: headers, 2: bigrams. for error msg printing\r\n    stage = 1\r\n    # In order to disable smoothing, just set kappa to 0.\r\n    # But when smoothing is disabled, some entries in logb_i will be log of 0\r\n    # After smoothing, entries in b_i are always positive, thus logb_i is fine\r\n    # do_smoothing=True\r\n\r\n    timer1 = Timer( \"loadBigramFile()\" )\r\n\r\n    try:\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n        match = re.match( r\"#\\s+(\\d+) words,\\s+\\d+ occurrences\", header )\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        wholeVocabSize = int(match.group(1))\r\n        print \"Totally %d words\"  %wholeVocabSize\r\n        # If topWordNum < 0, read all focus words\r\n        if topWordNum < 0:\r\n            topWordNum = wholeVocabSize\r\n\r\n        # skip params\r\n        header = BIGRAM.readline()\r\n        header = BIGRAM.readline()\r\n        lineno += 2\r\n\r\n        match = re.match( r\"#\\s+(\\d+) bigram occurrences\", header)\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        if header[0:6] != \"Words:\":\r\n            raise ValueError(lineno, header)\r\n\r\n        # vector log_u, unigram log-probs\r\n        log_u = []\r\n\r\n        i = 0\r\n        wc = 0\r\n        # Read the word list, build the word2id mapping\r\n        # Keep first topWordNum words and words in extraWords, if any\r\n        while True:\r\n            header = BIGRAM.readline()\r\n            lineno += 1\r\n            header = header.rstrip()\r\n\r\n            # \"Words\" field ends\r\n            if not header:\r\n                break\r\n\r\n            words = header.split(\"\\t\")\r\n            for word in words:\r\n                w, freq, log_ui = word.split(\",\")\r\n                if i < topWordNum or w in extraWords:\r\n                    word2id[w] = i\r\n                    log_u.append(float(log_ui))\r\n                    vocab.append(w)\r\n                    i += 1\r\n                wc += 1\r\n\r\n        # Usually these two should match, unless the bigram file is corrupted\r\n        if wc != wholeVocabSize:\r\n            raise ValueError( \"%d words declared in header, but %d seen\" %(wholeVocabSize, wc) )\r\n\r\n        vocab_size = len(vocab)\r\n        print \"%d words seen, top %d & %d extra to keep. %d kept\" %( wholeVocabSize, topWordNum, len(extraWords), vocab_size )\r\n\r\n        log_u = np.array(log_u)\r\n        u = np.exp(log_u)\r\n        # renormalize unigram probs\r\n        if topWordNum < wholeVocabSize:\r\n            u = u / np.sum(u)\r\n            log_u = np.log(u)\r\n\r\n        k_u = kappa * u\r\n        # original B, without smoothing\r\n        #B = []\r\n        G = np.zeros( (vocab_size, vocab_size), dtype=np.float32 )\r\n        F = np.zeros( (vocab_size, vocab_size), dtype=np.float32 )\r\n\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        if header[0:8] != \"Bigrams:\":\r\n            raise ValueError(lineno, header)\r\n\r\n        print \"Read bigrams:\"\r\n        stage = 2\r\n\r\n        line = BIGRAM.readline()\r\n        lineno += 1\r\n        contextWID = 0\r\n\r\n        #pdb.set_trace()\r\n\r\n        while True:\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                break\r\n\r\n            # If we have read the bigrams of all the wanted words\r\n            if contextWID == vocab_size:\r\n                # if some words in extraWords are not read, there is bug\r\n                break\r\n\r\n            # word ID, word, number of distinct neighbors, sum of freqs of all neighbors, cut off freq\r\n            orig_wid, w, neighborCount, neighborTotalOccur, cutoffFreq = line.split(\",\")\r\n            orig_wid = int(orig_wid)\r\n            neighborTotalOccur = float(neighborTotalOccur)\r\n\r\n            if orig_wid % 200 == 0:\r\n                print \"\\r%d\\r\" %orig_wid,\r\n\r\n            if orig_wid <= topWordNum or w in extraWords:\r\n                recordCurrWord = True\r\n                # remove it from the extra list, as a double-check measure\r\n                # when all wanted words are read, the extra list should be empty\r\n                if w in extraWords:\r\n                    del extraWords[w]\r\n            else:\r\n                recordCurrWord = False\r\n\r\n            # x_{.j}\r\n            x_i = np.zeros(vocab_size, dtype=np.float32)\r\n            skipRemainingNeighbors = False\r\n\r\n            while True:\r\n                line = BIGRAM.readline()\r\n                lineno += 1\r\n\r\n                # Empty line. Should be end of file\r\n                if not line:\r\n                    break\r\n\r\n                # A comment. Just in case of future extension\r\n                # Currently only the last line in the file is a comment\r\n                if line[0] == '#':\r\n                    continue\r\n\r\n                # beginning of the next word. Continue at the outer loop\r\n                # Neighbor lines always start with '\\t'\r\n                if line[0] != '\\t':\r\n                    break\r\n\r\n                # if the current context word is not wanted, skip these lines\r\n                if not recordCurrWord or skipRemainingNeighbors:\r\n                    continue\r\n\r\n                line = line.strip()\r\n                neighbors = line.split(\"\\t\")\r\n                for neighbor in neighbors:\r\n                    w2, freq2, log_bij = neighbor.split(\",\")\r\n                    if w2 in word2id:\r\n                        i = word2id[w2]\r\n                        x_i[i] = int(freq2)\r\n                    # when meeting the first focus word not in vocab, all following focus words are not in vocab\r\n                    # since neighbors are sorted ascendingly by ID\r\n                    # So they are skipped to speed up reading\r\n                    else:\r\n                        skipRemainingNeighbors = True\r\n                        break\r\n\r\n            # only save in F & G when this word is wanted\r\n            if recordCurrWord:\r\n                # Question: whether set F to the original freq or smoothed freq (assign F before or after smoothing)?\r\n                F[contextWID] = x_i\r\n\r\n                \"\"\"\r\n                x_i_norm1 = np.sum(x_i)\r\n                utrans = x_i_norm1 * k_u\r\n                x_i = x_i * (1 - kappa) + utrans\r\n\r\n                # the smoothing shoudn't change the norm1 of x_i\r\n                # i.e. x_i_norm1 = np.sum(x_i)\r\n                # After normalization, b_i = ( normalized x_i )*( 1 - kappa ) + u * kappa\r\n                b_i = x_i / np.sum(x_i)\r\n                \"\"\"\r\n\r\n                x_i /= neighborTotalOccur\r\n                b_i = x_i *( 1 - kappa ) + k_u\r\n                g_i = np.log(b_i) - log_u\r\n                G[contextWID] = g_i\r\n                contextWID += 1\r\n\r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            print \"Unknown line %d:\\n%s\" %( e.args[0], e.args[1] )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            print \"Source line %d: %s\" %(tb.tb_lineno, e)\r\n            if stage == 1:\r\n                print header\r\n            else:\r\n                print line\r\n        exit(0)\r\n\r\n    print\r\n    BIGRAM.close()\r\n\r\n    return vocab, word2id, G, F, u\r\n\r\n# If noncore_size == -1, all noncore words are loaded into the upperright and lowerleft blocks\r\n# word2preID_core are the IDs of words in the pretrained embedding file\r\n# If vocab_core and word2preID_core are specified, core words are limited to words in them\r\n# Otherwise the top core_size words are core words\r\ndef loadBigramFileInBlock( bigram_filename, core_size, noncore_size=-1, word2preID_core={}, prewords_skipped={}, kappa=0.02 ):\r\n\r\n    # corewords_specified means the list of core words are specified in word2preID_core\r\n    \r\n    if len(word2preID_core) > 0:\r\n        corewords_specified = True\r\n        # recordUpperleft is always the negation of corewords_specified. But sometimes is semantically clearer\r\n        recordUpperleft = False\r\n        # this core_size is used in comparsion with the total word count in the header\r\n        # this size might be inaccurate, as some words in word2preID_core might be missing from this bigram file\r\n        core_size = len(word2preID_core)\r\n    else:\r\n        corewords_specified = False\r\n        recordUpperleft = True\r\n        # if core words are not specified, core_size should always > 0,\r\n        # otherwise I don't know how many words are core words\r\n        if core_size < 0:\r\n            raise ValueError( \"Argument error: core_size = %d < 0 when word2preID_core is not specified\" %core_size )\r\n        if len(prewords_skipped) > 0:\r\n            raise ValueError( \"Argument error: word2preID_core is empty but prewords_skipped is not\" )\r\n\r\n    # if corewords_specified, return a list of coreword IDs in the pretrained mapping\r\n    # otherwise, return empty list (just for return value conformity)\r\n    coreword_preIDs = []\r\n\r\n    if not recordUpperleft:\r\n        # do not record G11/F11\r\n        print \"Loading bigram file '%s' into 2 blocks. Will skip %d words\" \\\r\n                                    %( bigram_filename, len(prewords_skipped) )\r\n    else:\r\n        print \"Loading bigram file '%s' into 3 blocks.\" %bigram_filename\r\n\r\n    BIGRAM = open(bigram_filename)\r\n\r\n    lineno = 0\r\n    vocab_all = []\r\n    vocab_core = []\r\n    vocab_noncore = []\r\n\r\n    word2id_all = {}\r\n    # origID is the original ID in this bigram file\r\n    # preID is the ID in the pretrained vec file\r\n    word2origID_all = {}\r\n    word2id_noncore = {}\r\n    word2id_core = {}\r\n\r\n    # stage 1: header and unigrams, stage 2: bigrams. for error msg printing\r\n    stage = 1\r\n    # do_smoothing must be True. Otherwise some entries in logb_i will be log of 0\r\n    # After smoothing, entries in b_i are always positive, thus logb_i is fine\r\n    # To reduce code modifications, this flag is not removed\r\n    timer1 = Timer( \"loadBigramFileInBlock()\" )\r\n\r\n    #pdb.set_trace()\r\n\r\n    try:\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n        match = re.match( r\"#\\s+(\\d+) words,\\s+\\d+ occurrences\", header )\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        wholeVocabSize = int(match.group(1))\r\n        print \"Totally %d words\"  %wholeVocabSize\r\n\r\n        if core_size >= wholeVocabSize:\r\n            raise ValueError( \"%d core words, but vocabulary only declares %d words in header\" %( core_size, wholeVocabSize ) )\r\n\r\n        # at least consider one noncore word\r\n        min_vocab_size = core_size + max( noncore_size, 1 )\r\n        if min_vocab_size > wholeVocabSize:\r\n            warning( \"%d (%d + %d) words demanded, but only %d declared in header\" %( min_vocab_size, core_size,\r\n                                                                                             max( noncore_size, 1 ), wholeVocabSize) )\r\n            min_vocab_size = wholeVocabSize\r\n            noncore_size2 = wholeVocabSize - core_size\r\n            warning( \"Noncore words adjusted from %d to %d\" %( noncore_size, noncore_size2 ) )\r\n            noncore_size = noncore_size2\r\n            \r\n        # all the words are included in the vocab_all\r\n        # in this case, vocab_size needs to be initialized\r\n        # otherwise corewords_specified, noncore_size & vocab_size will be computed later, \r\n        # needn't to be initialized\r\n        if not corewords_specified:\r\n            if noncore_size < 0:\r\n                vocab_size = wholeVocabSize\r\n                noncore_size = vocab_size - core_size\r\n            else:\r\n                # core_size will be updated later\r\n                # some core words in word2preID_core may not be present in this bigram file\r\n                vocab_size = core_size + noncore_size\r\n\r\n        # skip params\r\n        header = BIGRAM.readline()\r\n        header = BIGRAM.readline()\r\n        lineno += 2\r\n\r\n        match = re.match( r\"#\\s+(\\d+) bigram occurrences\", header )\r\n        if not match:\r\n            raise ValueError(lineno, header)\r\n\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        if header[0:6] != \"Words:\":\r\n            raise ValueError(lineno, header)\r\n\r\n        # vector log_u, log-probs of all unigrams (at most vocab_size unigrams)\r\n        log_u0 = []\r\n        log_u0_core = []\r\n        log_u0_noncore = []\r\n        wc = 0\r\n        core_wc = 0\r\n        noncore_wc = 0\r\n        skipped_wc = 0\r\n        # maximum ID in the original order of core words\r\n        max_core_origID = 0\r\n\r\n        # Read the focus list, build the word2id_all / word2id_core mapping\r\n        # Read all context words of the core_size words\r\n        # Read top core_size context words of remaining words\r\n        while True:\r\n            header = BIGRAM.readline()\r\n            lineno += 1\r\n            header = header.rstrip()\r\n\r\n            # \"Words\" field ends\r\n            if not header:\r\n                break\r\n\r\n            words = header.split(\"\\t\")\r\n            for word in words:\r\n                w, freq, log_ui = word.split(\",\")\r\n\r\n                # load core words only in word2preID_core, other words as noncore\r\n                # core words may not be consecutive, they may be interspersed by noncore words\r\n                # So put them into two sets of arrays\r\n                if corewords_specified:\r\n                    if w in word2preID_core:\r\n                        word2id_core[w] = core_wc\r\n                        core_wc += 1\r\n                        coreword_preIDs.append( word2preID_core[w] )\r\n                        log_u0_core.append(float(log_ui))\r\n                        vocab_core.append(w)\r\n\r\n                        if wc > max_core_origID:\r\n                            max_core_origID = wc\r\n\r\n                    elif w in prewords_skipped:\r\n                        skipped_wc += 1\r\n                    elif noncore_size < 0 or noncore_wc < noncore_size :\r\n                        word2id_noncore[w] = noncore_wc\r\n                        noncore_wc += 1\r\n                        log_u0_noncore.append(float(log_ui))\r\n                        vocab_noncore.append(w)\r\n                        \r\n                    word2origID_all[w] = wc\r\n                    wc += 1\r\n\r\n                # load fresh core words\r\n                else:\r\n                    if wc == vocab_size:\r\n                        break\r\n\r\n                    word2id_all[w] = wc\r\n                    if wc < core_size:\r\n                        word2id_core[w] = wc\r\n                    else:\r\n                        word2id_noncore[w] = wc - core_size\r\n\r\n                    log_u0.append(float(log_ui))\r\n                    vocab_all.append(w)\r\n                    wc += 1\r\n\r\n        if corewords_specified:\r\n            # some core words may be missing from the bigram file. So recompute core_size\r\n            # If these two don't match, then some specified core words don't appear in the unigram list\r\n            if core_size != core_wc:\r\n                print \"WARN: %d core words demanded, but only %d read\" %(core_size, core_wc)\r\n                core_size = core_wc\r\n\r\n            noncore_size = noncore_wc\r\n            vocab_size = core_size + noncore_size\r\n            log_u0 = log_u0_core + log_u0_noncore\r\n            vocab_all = vocab_core + vocab_noncore\r\n\r\n            word2id_all = word2id_core.copy()\r\n            # insert noncore words into word2id_all\r\n            # core ID \\in [ 0, coresize - 1 ]\r\n            # noncore ID \\in [ core_size, ... ]\r\n            for w in word2id_noncore:\r\n                word2id_all[w] = word2id_noncore[w] + core_size\r\n\r\n        else:\r\n            # core words are consecutive. So orig ID = id\r\n            max_core_origID = core_size\r\n            word2origID_all = word2id_all\r\n            if vocab_size > 0 and wc < vocab_size:\r\n                print \"WARN: %d words demanded, but only %d read\" %(vocab_size, wc)\r\n                vocab_size = wc\r\n\r\n        print \"%d words in file, top %d to read into vocab (%d core, %d noncore), %d skipped\" \\\r\n               %( wholeVocabSize, vocab_size, core_size, noncore_size, skipped_wc )\r\n\r\n        # unigram prob & logprob of all the words\r\n        log_u0 = np.array(log_u0)\r\n        u0 = np.exp(log_u0)\r\n        # re-normalization is needed, as some unigrams may be out of vocab_all\r\n        u0 /= np.sum(u0)\r\n\r\n        log_u0 = np.log(u0)\r\n        log_u_core    = log_u0[:core_size]\r\n        log_u_noncore = log_u0[core_size:]\r\n\r\n        k_u0 = kappa * u0\r\n        k_u_core    = k_u0[:core_size]\r\n        k_u_noncore = k_u0[core_size:]\r\n\r\n        ### Reading bigrams begins ###\r\n        header = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        if header[0:8] != \"Bigrams:\":\r\n            raise ValueError(lineno, header)\r\n\r\n        print \"Read bigrams:\"\r\n        stage = 2\r\n\r\n        line = BIGRAM.readline()\r\n        lineno += 1\r\n\r\n        contextWID = 0\r\n\r\n        # new G12, F12\r\n        # G12/F12: the upperright block\r\n        G12 = np.zeros( (core_size, noncore_size), dtype=np.float32 )\r\n        F12 = np.zeros( (core_size, noncore_size), dtype=np.float32 )\r\n        # new G21, F21\r\n        # G21/F21: the lowerleft block\r\n        # the lower right block (biggest) G22/F22 is ignored\r\n        G21 = np.zeros( (noncore_size, core_size), dtype=np.float32 )\r\n        F21 = np.zeros( (noncore_size, core_size), dtype=np.float32 )\r\n\r\n        # when reading core words, keep the upperleft block, the whole vocab\r\n        if recordUpperleft:\r\n            # new G11, F11\r\n            # G11/F11: the upperleft block\r\n            G11 = np.zeros( (core_size, core_size), dtype=np.float32 )\r\n            F11 = np.zeros( (core_size, core_size), dtype=np.float32 )\r\n            rowLength = vocab_size\r\n            k_u = k_u0\r\n            log_u = log_u0\r\n        # when reading core words, only keep the upperright block, noncore_size words\r\n        else:\r\n            rowLength = noncore_size\r\n            k_u = k_u_noncore\r\n            log_u = log_u_noncore\r\n\r\n        # focusIDLimit: the limit of neighbor wid\r\n        # In the beginning, read all neighbors\r\n        focusIDLimit = vocab_size\r\n\r\n        # vars are initialized like those of a core word,\r\n        # So pretend the previous nonexistent word is a core word\r\n        lastContextIsCore = True\r\n\r\n        #pdb.set_trace()\r\n        core_readcount = 0\r\n        noncore_readcount = 0\r\n        coreMsg_printed = False\r\n\r\n        while True:\r\n            line = line.strip()\r\n            # end of file\r\n            if not line:\r\n                break\r\n\r\n            # We have read the bigrams of all the wanted words\r\n            if contextWID == vocab_size:\r\n                break\r\n\r\n            # word ID, word, number of distinct neighbors, sum of freqs of all neighbors, cut off freq\r\n            orig_wid, w, neighborCount, neighborTotalOccur, cutoffFreq = line.split(\",\")\r\n            orig_wid = int(orig_wid)\r\n            neighborTotalOccur = float(neighborTotalOccur)\r\n\r\n            if w in word2id_core:\r\n                # the current context word is a core word\r\n                contextIsCore = True\r\n                wid = word2id_core[w]\r\n                # context type switches from noncore to core, update relevant variables\r\n                # if context type doesn't change (last context is also core), then just keep vars unchanged\r\n                # context type switching should only happen very few times\r\n                if not lastContextIsCore:\r\n                    focusIDLimit = vocab_size\r\n\r\n                    if recordUpperleft:\r\n                        rowLength = vocab_size\r\n                        k_u = k_u0\r\n                        log_u = log_u0\r\n                    # when reading core words, only keep the upperright block, noncore_size words\r\n                    else:\r\n                        rowLength = noncore_size\r\n                        k_u = k_u_noncore\r\n                        log_u = log_u_noncore\r\n\r\n                    lastContextIsCore = True\r\n\r\n            elif w in word2id_noncore:\r\n                contextIsCore = False\r\n                wid = word2id_noncore[w]\r\n                # context type switches from core to noncore, update relevant variables\r\n                # if context type doesn't change (last context is also noncore), then just keep vars unchanged\r\n                # context type switching should only happen very few times\r\n                if lastContextIsCore:\r\n                    # in a row of noncore (context) word, only freqs of core (focus) words are recorded\r\n                    focusIDLimit = max_core_origID\r\n                    rowLength = core_size\r\n\r\n                    k_u = k_u_core\r\n                    log_u = log_u_core\r\n\r\n                    lastContextIsCore = False\r\n\r\n            # x_{i.}\r\n            x_i = np.zeros(rowLength, dtype=np.float32)\r\n\r\n            if w in prewords_skipped:\r\n                saveCurrRow = False\r\n                skipRemainingNeighbors = True\r\n            else:\r\n                saveCurrRow = True\r\n                skipRemainingNeighbors = False\r\n\r\n            while True:\r\n                line = BIGRAM.readline()\r\n                lineno += 1\r\n\r\n                # Empty line. Should be end of file\r\n                if not line:\r\n                    break\r\n\r\n                # Encounter a comment. Just in case of future extension\r\n                # Currently only the last line in the file is a comment after the header\r\n                if line[0] == '#':\r\n                    continue\r\n\r\n                # Beginning of the next word. Continue at the outer loop\r\n                # Neighbor lines always start with '\\t'\r\n                if line[0] != '\\t':\r\n                    break\r\n\r\n                if skipRemainingNeighbors:\r\n                    continue\r\n\r\n                line = line.strip()\r\n                neighbors = line.split(\"\\t\")\r\n\r\n                for neighbor in neighbors:\r\n                    w2, freq2, log_bij = neighbor.split(\",\")\r\n\r\n                    # w2 in skip list, and surely not in word2id_all\r\n                    # so check here to avoid setting skipRemainingNeighbors\r\n                    if w2 in prewords_skipped:\r\n                        continue\r\n                        \r\n                    # when meeting the first focus word out of vocab_all, all following focus words are not in vocab_all\r\n                    # since neighbors are sorted ascendingly by ID\r\n                    # So they are skipped to speed up reading\r\n                    if w2 not in word2id_all:\r\n                        skipRemainingNeighbors = True\r\n                        break\r\n\r\n                    origID = word2origID_all[w2]\r\n                    # On a noncore row. Should have focus word orig ID <= max_core_origID\r\n                    # origIDs of core words may be interspersed by origIDs of noncore words\r\n                    # but IDs of core words are consecutive, and preceding IDs of noncore words\r\n                    if not contextIsCore and origID > max_core_origID:\r\n                        skipRemainingNeighbors = True\r\n                        break\r\n\r\n                    freq2 = int(freq2)\r\n                    # On a core (context) row.\r\n                    # If recordUpperleft, use the map from whole vocab to IDs;\r\n                    # otherwise use the map from core words to IDs\r\n                    if contextIsCore:\r\n                        if recordUpperleft:\r\n                            # w2id: id of w2\r\n                            w2id = word2id_all[w2]\r\n                            x_i[w2id] = freq2\r\n                        # don't keep upperleft block. core (focus) words are discarded\r\n                        # w2id \\in [ 0, noncore_size - 1 ]\r\n                        elif w2 in word2id_noncore:\r\n                            w2id = word2id_noncore[w2]\r\n                            x_i[w2id] = freq2\r\n                    # On a noncore (context) row. Only record core (focus) words\r\n                    elif w2 in word2id_core:\r\n                        w2id = word2id_core[w2]\r\n                        x_i[w2id] = freq2\r\n\r\n            if not saveCurrRow:\r\n                continue\r\n                \r\n            # Question: whether set F to the original freq or smoothed freq (assign F before or after smoothing)?\r\n            if contextIsCore:\r\n                if recordUpperleft:\r\n                    F11[core_readcount] = x_i[:core_size]\r\n                    F12[core_readcount] = x_i[core_size:]\r\n                else:\r\n                    # As w2id \\in [ 0, noncore_size - 1 ], no offset is needed\r\n                    F12[core_readcount] = x_i\r\n            else:\r\n                F21[noncore_readcount] = x_i\r\n\r\n            \"\"\"\r\n            x_i_norm1 = np.sum(x_i)\r\n            utrans = x_i_norm1 * k_u\r\n            x_i = x_i * (1 - kappa) + utrans\r\n\r\n            # the smoothing shoudn't change the norm1 of x_i\r\n            # i.e. x_i_norm1 = np.sum(x_i)\r\n            # normalization\r\n            b_i = x_i / np.sum(x_i)\r\n            \"\"\"\r\n\r\n            x_i /= neighborTotalOccur\r\n            b_i = x_i * ( 1 - kappa ) + k_u\r\n            g_i = np.log(b_i) - log_u\r\n\r\n            if contextIsCore:\r\n                if recordUpperleft:\r\n                    G11[core_readcount] = g_i[:core_size]\r\n                    G12[core_readcount] = g_i[core_size:]\r\n                else:\r\n                    # As w2id \\in [ 0, noncore_size - 1 ], no offset is needed\r\n                    G12[core_readcount] = g_i\r\n            else:\r\n                G21[noncore_readcount] = g_i\r\n\r\n            contextWID += 1\r\n            if contextIsCore:\r\n                core_readcount += 1\r\n            else:\r\n                noncore_readcount += 1\r\n\r\n            if orig_wid % 200 == 0:\r\n                print \"\\r%d (%d core, %d noncore)\\r\" %( orig_wid, core_readcount, noncore_readcount ),\r\n            if not coreMsg_printed and core_readcount == core_size:\r\n                print \"\\n%d core words are all read.\" %(core_size)\r\n                coreMsg_printed = True\r\n\r\n    except ValueError, e:\r\n        if len( e.args ) == 2:\r\n            print \"Unknown line %d:\\n%s\" %( e.args[0], e.args[1] )\r\n        else:\r\n            exc_type, exc_obj, tb = sys.exc_info()\r\n            print \"Source line %d: %s\" %(tb.tb_lineno, e)\r\n            if stage == 1:\r\n                print header\r\n            else:\r\n                print line\r\n        exit(0)\r\n\r\n    print\r\n    BIGRAM.close()\r\n\r\n    if recordUpperleft:\r\n        G = [ G11, G12, G21 ]\r\n        F = [ F11, F12, F21 ]\r\n    else:\r\n        G = [ G12, G21 ]\r\n        F = [ F12, F21 ]\r\n\r\n    return vocab_all, word2id_all, word2id_core, coreword_preIDs, G, F, u0\r\n\r\ndef loadUnigramFile(filename):\r\n    UNI = open(filename)\r\n    vocab_dict = {}\r\n    wid = 1\r\n    for line in UNI:\r\n        line = line.strip()\r\n        if line[0] == '#':\r\n            continue\r\n        fields = line.split(\"\\t\")\r\n                             # id, freq, prob\r\n        vocab_dict[ fields[0] ] = ( wid, int(fields[1]), np.exp(float(fields[2])) )\r\n        wid += 1\r\n\r\n    print \"%d words loaded from unigram file %s\" %(wid, filename)\r\n    return vocab_dict\r\n\r\ndef loadExtraWordFile(filename):\r\n    extraWords = {}\r\n    with open(filename) as f:\r\n        for line in f:\r\n            w, wid = line.strip().split('\\t')\r\n            extraWords[w] = 1\r\n\r\n    print \"%d words loaded from extra word file %s\" %( len(extraWords), filename)\r\n    return extraWords\r\n\r\n# borrowed from Omer Levy's code\r\n# extraArgs is not used, only for API conformity\r\ndef loadSimTestset(path, extraArgs=None):\r\n    testset = []\r\n    print \"Read sim testset \" + path\r\n    with open(path) as f:\r\n        for line in f:\r\n            x, y, sim = line.strip().lower().split()\r\n            testset.append( [ x, y, float(sim) ] )\r\n    return testset\r\n\r\ndef loadAnaTestset(path, extraArgs=None):\r\n    testset = []\r\n    print \"Read analogy testset \" + path\r\n\r\n    if extraArgs is not None and 'skipPossessive' in extraArgs:\r\n        skipPossessive = True\r\n        possessive = 0\r\n    else:\r\n        skipPossessive = False\r\n\r\n    with open(path) as f:\r\n        for line in f:\r\n            # skip possessive forms\r\n            if skipPossessive and line.find(\"'\") >= 0:\r\n                possessive += 1\r\n                continue\r\n            a, a2, b, b2 = line.strip().lower().split()\r\n            testset.append( [ a, a2, b, b2 ] )\r\n\r\n    if skipPossessive:\r\n        print \"%d possessive pairs skipped\" %possessive\r\n\r\n    return testset\r\n\r\n# available loaders: loadSimTestset, loadAnaTestset\r\ndef loadTestsets(loader, testsetDir, testsetNames, extraArgs=None):\r\n    # always use unix style path\r\n    testsetDir = testsetDir.replace(\"\\\\\", \"/\")\r\n    if testsetDir[-1] != '/':\r\n        testsetDir += '/'\r\n\r\n    if not os.path.isdir(testsetDir):\r\n        print \"ERR: Test set dir does not exist or is not a dir:\\n\" + testsetDir\r\n        sys.exit(2)\r\n\r\n    testsets = []\r\n    if len(testsetNames) == 0:\r\n        testsetNames = glob.glob( testsetDir + '*.txt' )\r\n        if len(testsetNames) == 0:\r\n            print \"No testset ended with '.txt' is found in \" + testsetDir\r\n            sys.exit(2)\r\n        testsetNames = map( lambda x: os.path.basename(x)[:-4], testsetNames )\r\n\r\n    for testsetName in testsetNames:\r\n        testset = loader( testsetDir + testsetName + \".txt\", extraArgs )\r\n        testsets.append(testset)\r\n\r\n    return testsets\r\n\r\n# \"model\" in methods below has to support two methods:\r\n# model[w]: return the embedding of w\r\n# model.similarity(x, y): return the cosine similarity between the embeddings of x and y\r\n# realb2 is passed in only for debugging purpose\r\ndef predict_ana( model, a, a2, b, realb2 ):\r\n    questWordIndices = [ model.word2id[x] for x in (a,a2,b) ]\r\n    # b2 is effectively iterating through the vocab. The row is all the cosine values\r\n    b2a2 = model.sim_row(a2)\r\n    b2a  = model.sim_row(a)\r\n    b2b  = model.sim_row(b)\r\n    addsims = b2a2 - b2a + b2b\r\n\r\n    addsims[questWordIndices] = -10000\r\n\r\n    iadd = np.nanargmax(addsims)\r\n    b2add  = model.vocab[iadd]\r\n\r\n    # For debugging purposes\r\n    ia = model.word2id[a]\r\n    ia2 = model.word2id[a2]\r\n    ib = model.word2id[b]\r\n    ib2 = model.word2id[realb2]\r\n    realaddsim = addsims[ib2]\r\n\r\n    mulsims = ( b2a2 + 1 ) * ( b2b + 1 ) / ( b2a + 1.001 )\r\n    mulsims[questWordIndices] = -10000\r\n    imul = np.nanargmax(mulsims)\r\n    b2mul  = model.vocab[imul]\r\n\r\n    return b2add, b2mul\r\n\r\n''' baa2 = model[b] - model[a] + model[a2]\r\n    baa2 = baa2/normF(baa2)\r\n    sims2 = model.V.dot(baa2)\r\n    dists1 = np.abs( model.V - baa2 ).dot( np.ones( model.V.shape[1] ) )\r\n\r\n    sims2[questWordIndices] = -10000\r\n    dists1[questWordIndices] = 10000\r\n\r\n    i2 = np.nanargmax(sims2)\r\n    b22 = model.vocab[i2]\r\n    i1 = np.nanargmin(dists1)\r\n    b21 = model.vocab[i1]\r\n\r\n    realsim2 = sims2[ib2]\r\n    realdist1 = dists1[ib2]\r\n\r\n    # F-norm (L2)\r\n    topIDs2 = sims2.argsort()[-5:][::-1]\r\n    topwords2 = [ model.vocab[i] for i in topIDs2 ]\r\n    topsims2 = sims2[topIDs2]\r\n\r\n    # Manhattan distance (L1)\r\n    topIDs1 = sims1.argsort()[-5:][::-1]\r\n    topwords1 = [ model.vocab[i] for i in topIDs1 ]\r\n    topsims1 = sims1[topIDs1]\r\n\r\n    if b22 != realb2:\r\n        print \"%s,%s\\t%s,[%s]\" %(a,a2,b,realb2)\r\n        print \"%s,%f\\t%s\\t%s\" %(b21,realsim1, str(topsims1), str(topwords1))\r\n        print \"%s,%f\\t%s\\t%s\" %(b22,realsim2, str(topsims2), str(topwords2))\r\n        print\r\n        #pdb.set_trace()\r\n    return b2add, b2mul, b21, b22\r\n    '''\r\n\r\n# vocab_dict is a vocabulary dict, usually bigger than model.vocab, loaded from a unigram file\r\n# its purpose is to find absent words in the model\r\ndef evaluate_sim(model, testsets, testsetNames, getAbsentWords=False, vocab_dict=None, cutPoint=-1 ):\r\n    # words in absentModelID2Word and words in absentVocabWords don't overlap\r\n\r\n    # words in the vocab but not in the model\r\n    absentModelID2Word = {}\r\n    # words not in the vocab (of coz not in the model)\r\n    absentVocabWords = {}\r\n    # words in the vocab but below the cutPoint (id > cutPoint), may be in or out of the model\r\n    cutVocabWords = {}\r\n    # a set of spearman coeffs, in the same order as in testsets\r\n    spearmanCoeff = []\r\n\r\n    for i,testset in enumerate(testsets):\r\n        modelResults = []\r\n        groundtruth = []\r\n\r\n        for x, y, sim in testset:\r\n            if vocab_dict and x in vocab_dict:\r\n                xid = vocab_dict[x][0]\r\n                if cutPoint > 0 and xid > cutPoint:\r\n                    cutVocabWords[x] = 1\r\n\r\n            if vocab_dict and y in vocab_dict:\r\n                yid = vocab_dict[y][0]\r\n                if cutPoint > 0 and yid > cutPoint:\r\n                    cutVocabWords[y] = 1\r\n\r\n            if x not in model:\r\n                if getAbsentWords and x in vocab_dict:\r\n                    absentModelID2Word[xid] = x\r\n                else:\r\n                    absentVocabWords[x] = 1\r\n            elif y not in model:\r\n                if getAbsentWords and y in vocab_dict:\r\n                    absentModelID2Word[yid] = y\r\n                else:\r\n                    absentVocabWords[y] = 1\r\n            else:\r\n                modelResults.append( model.similarity(x, y) )\r\n                groundtruth.append(sim)\r\n                #print \"%s %s: %.3f %.3f\" %(x, y, modelResults[-1], sim)\r\n        print \"%s: %d test pairs, %d valid\" %( testsetNames[i], len(testset), len(modelResults) ),\r\n        spearmanCoeff.append( spearmanr(modelResults, groundtruth)[0] )\r\n        print \", %.5f\" %spearmanCoeff[-1]\r\n\r\n    # return hashes directly, for ease of merge\r\n    return spearmanCoeff, absentModelID2Word, absentVocabWords, cutVocabWords\r\n\r\n# vocab_dict is a vocabulary dict, usually bigger than model.vocab, loaded from a unigram file\r\n# its purpose is to find absent words in the model\r\ndef evaluate_ana(model, testsets, testsetNames, getAbsentWords=False, vocab_dict=None, cutPoint=-1 ):\r\n    # for words in the vocab but not in the model. mapping from words to IDs\r\n    absentModelID2Word = {}\r\n    # words not in the vocab (of coz not in the model)\r\n    absentVocabWords = {}\r\n    # words in the vocab but below the cutPoint (id > cutPoint), may be in or out of the model\r\n    cutVocabWords = {}\r\n    # a set of scores, in the same order as in testsets\r\n    # each is a tuple (add_score, mul_score)\r\n    anaScores = []\r\n\r\n    #pdb.set_trace()\r\n\r\n    for i,testset in enumerate(testsets):\r\n        modelResults = []\r\n        groundtruth = []\r\n\r\n        correct_add = 0.0\r\n        correct_mul = 0.0\r\n        validPairNum = 0\r\n        currentScores = np.array( [ 0.0, 0.0 ] )\r\n\r\n        for j,analogy in enumerate(testset):\r\n\r\n            allWordsPresent = True\r\n            watchWhenWrong = False\r\n\r\n            # check presence of all words in this test pair\r\n            for x in analogy:\r\n                if vocab_dict and x in vocab_dict:\r\n                    xid = vocab_dict[x][0]\r\n                    if cutPoint > 0 and xid > cutPoint:\r\n                        cutVocabWords[x] = 1\r\n                        watchWhenWrong = True\r\n\r\n                if x not in model:\r\n                    if vocab_dict and x in vocab_dict:\r\n                        absentModelID2Word[ vocab_dict[x][0] ] = x\r\n                    else:\r\n                        absentVocabWords[x] = 1\r\n                    allWordsPresent = False\r\n\r\n            if allWordsPresent:\r\n                a, a2, b, b2 = analogy\r\n                b2add, b2mul = predict_ana( model, a, a2, b, b2 )\r\n                validPairNum += 1\r\n                if b2add == b2:\r\n                    correct_add += 1\r\n                elif watchWhenWrong:\r\n                    print \"%s~%s = %s~%s,%.3f (%s,%3f)\" %( a, a2, b, b2, model.similarity(b,b2), b2_add, model.similarity(b,b2_add) )\r\n\r\n                if b2mul == b2:\r\n                    correct_mul += 1\r\n\r\n                \"\"\"if b2_mul == b2:\r\n                    correct_mul += 1\r\n                if b2_L1 == b2:\r\n                    correct_L1 += 1\r\n                if b2_L2 == b2:\r\n                    correct_L2 += 1\r\n                currentScores = np.array([ correct_add, correct_mul, correct_L1, correct_L2 ]) / validPairNum\r\n                \"\"\"\r\n\r\n                # latest cumulative scores\r\n                currentScores = np.array( [ correct_add, correct_mul ] ) / validPairNum\r\n\r\n            if j % 500 == 499:\r\n                print \"\\r%i/%i/%i: Add %.5f, Mul %.5f\\r\" %( j + 1, validPairNum, len(testset),\r\n                                                            currentScores[0], currentScores[1] ),\r\n\r\n        print \"\\n%s: %d analogies, %d valid\" %( testsetNames[i], len(testset), validPairNum ),\r\n        anaScores.append(currentScores)\r\n        print \". Add Score: %.5f, Mul Score: %.5f\" %( currentScores[0], currentScores[1] )\r\n\r\n    return anaScores, absentModelID2Word, absentVocabWords, cutVocabWords\r\n\r\ndef bench(func, N, topEigenNum=0):\r\n    print \"Begin to factorize a %dx%d matrix\" %(N,N)\r\n    a = np.random.randn(N, N)\r\n    a = (a+a.T)/2\r\n    tic = time.clock()\r\n    func(a)\r\n    toc = time.clock()\r\n    diff = toc - tic\r\n    print \"Elapsed time is %.3f\" %diff\r\n    return diff\r\n\r\n# return a flag indicating whether installed mem is enough to computing a D*D dimensional Gramian matrix,\r\n# plus installed amounts and required amounts of mem\r\n# extraVarsRatio: the required mem of other existing variables (as a ratio of the decomposed matrix)\r\n# e.g. in evaluate.py, only the Gramian (cosine) matrix is present, extraVarsRatio = 0\r\n# in factorize.py, if there are 4 similar sized matrices other than the Gramian, \r\n# then extraVarsRatio=4 may be reasonable\r\ndef isMemEnoughGramian(D, extraVarsRatio=0):\r\n    mem = virtual_memory()\r\n    installedMemGB = round( mem.total * 1.0 / (1<<30) )\r\n    # some overhead for np.array, so not divided by 1024^3\r\n    requiredMemGB = D * D * 4.0 * ( extraVarsRatio + 1 ) / 1000000000\r\n    \r\n    # installed mem is enough\r\n    if requiredMemGB <= installedMemGB:\r\n        isEnough = 2\r\n        \r\n    # give a warning, will use some paging file and make the computer very slow\r\n    elif requiredMemGB <= installedMemGB * 1.2:\r\n        isEnough = 1\r\n    # not enough\r\n    else:\r\n        isEnough = 0\r\n\r\n    return isEnough, installedMemGB, requiredMemGB\r\n\r\n# return a flag indicating whether installed mem is enough to computing eigendecomposition of a D*D matrix\r\n# plus installed amounts and required amounts of mem\r\n# extraVarsRatio: the required mem of other existing variables (as a ratio of the decomposed matrix)\r\n# assume eigendecomposition alone takes 10 times of the mem of the decomposed matrix\r\n# e.g. when only doing eigendecomposition of the matrix is present, extraVarsRatio = 0, allVarsRatio = 10\r\n# if there are 4 similar sized matrices other than the decomposed matrix, then allVarsRatio = 10 + 4 = 14\r\n# In factorize.py, when doing eigendecomposition, usually there are at least 5 other matrices of similar sizes\r\n# so by default extraVarsRatio=5\r\ndef isMemEnoughEigen(D, extraVarsRatio=5):\r\n    mem = virtual_memory()\r\n    installedMemGB = round( mem.total * 1.0 / (1<<30) )\r\n    # 15 is an empirical estimation. when D=30K, it takes around 50GB mem\r\n    requiredMemGB = D * D * 4.0 * ( extraVarsRatio + 8 ) / 1000000000\r\n    \r\n    # installed mem is enough\r\n    if requiredMemGB <= installedMemGB:\r\n        isEnough = 2\r\n        \r\n    # give a warning, will use some paging file and make the computer very slow\r\n    elif requiredMemGB <= installedMemGB * 1.2:\r\n        isEnough = 1\r\n    # not enough\r\n    else:\r\n        isEnough = 0\r\n\r\n    return isEnough, installedMemGB, requiredMemGB\r\n\r\ndef extractSentenceWords(doc, remove_url=True, remove_punc=\"utf-8\", min_length=1):\r\n    if remove_punc:\r\n        # ensure doc_u is in unicode\r\n        if not isinstance(doc, unicode):\r\n            encoding = remove_punc\r\n            doc_u = doc.decode(encoding, errors='ignore')\r\n        else:\r\n            doc_u = doc\r\n        # remove unicode punctuation marks, keep ascii punctuation marks\r\n        doc_u = doc_u.translate(unicode_punc_tbl)\r\n        if not isinstance(doc, unicode):\r\n            doc = doc_u.encode(encoding)\r\n        else:\r\n            doc = doc_u\r\n            \r\n    if remove_url:\r\n        re_url = r\"(https?:\\/\\/)?(www\\.)?[-a-zA-Z0-9@:%._\\+~#=]{2,256}\\.[a-z]{2,6}\\b([-a-zA-Z0-9@:%_\\+.~#?&//=]*)\"\r\n        doc = re.sub( re_url, \"\", doc )\r\n            \r\n    sentences = re.split( r\"\\s*[,;:`\\\"()?!{}]\\s*|--+|\\s*-\\s+|''|\\.\\s|\\.$|\\.\\.+||\", doc ) #\"\r\n    wc = 0\r\n    wordsInSentences = []\r\n    \r\n    for sentence in sentences:\r\n        if sentence == \"\":\r\n            continue\r\n\r\n        if not re.search( \"[A-Za-z0-9]\", sentence ):\r\n            continue\r\n\r\n        words = re.split( r\"\\s+\\+|^\\+|\\+?[\\-*\\/&%=<>\\[\\]~\\|\\@\\$]+\\+?|\\'\\s+|\\'s\\s+|\\'s$|\\s+\\'|^\\'|\\'$|\\$|\\\\|\\s+\", sentence )\r\n\r\n        words = filter( lambda w: w, words )\r\n\r\n        if len(words) >= min_length:\r\n            wordsInSentences.append(words)\r\n            wc += len(words)\r\n\r\n    #print \"%d words extracted\" %wc\r\n    return wordsInSentences, wc\r\n                            \r\ndef randomsample( X, n ):\r\n    \"\"\" random.sample of the rows of X\r\n        X may be sparse -- best csr\r\n    \"\"\"\r\n    sampleix = random.sample( xrange( X.shape[0] ), int(n) )\r\n    return X[sampleix]\r\n\r\ndef relu(v, bias):\r\n    v2 = np.copy(v)\r\n    v2[ v < bias ] = 0\r\n    return v2\r\n\r\ndef maxpool(vs):\r\n    vs = np.array(vs)\r\n    max_indices = np.argmax( np.abs(vs), axis=0 )\r\n    N = vs.shape[1]\r\n    max_v = vs[ max_indices, range(N) ]\r\n    return max_v\r\n\r\ndef avgpool(vs):\r\n    vs = np.array(vs)\r\n    avg_v = np.sum( vs, axis=0 )\r\n    avg_v /= vs.shape[0]\r\n    return avg_v\r\n                                    \r\nclass VecModel:\r\n    def __init__(self, V, vocab, word2id, vecNormalize=True, precompute_gramian=False):\r\n        self.Vorig = V\r\n        self.Vnorm = np.array( [ normF(x) for x in self.Vorig ], dtype=np.float32 )\r\n        for i, w in enumerate(vocab):\r\n            if self.Vnorm[i] == 0:\r\n                print \"WARN: %s norm is 0\" %w\r\n                # set to 1 to avoid \"divided by 0 exception\"\r\n                self.Vnorm[i] = 1\r\n                \r\n        self.V = self.Vorig / self.Vnorm[:, None]\r\n        self.word2id = word2id\r\n        self.vecNormalize = vecNormalize\r\n        self.vocab = vocab\r\n        self.iterIndex = 0\r\n        self.cosTable = None\r\n        \r\n        if precompute_gramian:\r\n            isEnough, installedMemGB, requiredMemGB = isMemEnoughGramian( len(V) )\r\n            if isEnough == 2:\r\n                self.precomputeGramian()\r\n            \r\n    def __contains__(self, w):\r\n        return w in self.word2id\r\n\r\n    def __getitem__(self, w):\r\n        if w not in self:\r\n            return None\r\n        else:\r\n            if self.vecNormalize:\r\n                return self.V[ self.word2id[w] ]\r\n            else:\r\n                return self.Vorig[ self.word2id[w] ]\r\n\r\n    def orig(self, w):\r\n        if w not in self:\r\n            return None\r\n        else:\r\n            return self.Vorig[ self.word2id[w] ]\r\n\r\n    def precomputeGramian(self):\r\n        print \"Precompute cosine matrix, will need %.1fGB RAM...\" %( len(self.V) * len(self.V) * 4.0 / 1000000000 ),\r\n        self.cosTable = np.dot( self.V, self.V.T )\r\n        print \"Done.\"\r\n\r\n    def similarity(self, x, y):\r\n        if x not in self or y not in self:\r\n            return 0\r\n\r\n        if self.vecNormalize:\r\n            if self.cosTable is not None:\r\n                ix = self.word2id[x]\r\n                iy = self.word2id[y]\r\n                return self.cosTable[ix,iy]\r\n            return np.dot( self[x], self[y] )\r\n\r\n        # when vectors are not normalized, return the raw dot product\r\n        vx = self[x]\r\n        vy = self[y]\r\n        # vector too short. the similarity doesn't make sense\r\n        if normF(vx) <= 1e-6 or normF(vy) <= 1e-6:\r\n            return 0\r\n\r\n        return np.dot( self[x], self[y] )\r\n\r\n    def sim_row(self, x):\r\n        if x not in self:\r\n            return 0\r\n\r\n        if self.vecNormalize:\r\n            if self.cosTable is not None:\r\n                ix = self.word2id[x]\r\n                return self.cosTable[ix]\r\n            return self.V.dot(self[x])\r\n            \r\n        vx = self[x]\r\n        # vector too short. the dot product similarity doesn't make sense\r\n        if normF(vx) <= 1e-6:\r\n            return np.zeros( len(self.vocab) )\r\n\r\n        return self.V.dot(vx)\r\n\r\n    def most_similar(self, vx, top_num=1):\r\n        vx2 = normalizeF(vx)\r\n        if self.vecNormalize:\r\n            sim_row = self.V.dot(vx2)\r\n            top_indices = sim_row.argsort()[-top_num:][::-1]\r\n            word_sims = [ ( self.vocab[i], sim_row[i] ) for i in top_indices ]\r\n            return word_sims\r\n        else:\r\n            raise NotImplementedError\r\n\r\n                "
  }
]