[ { "path": ".gitignore", "content": ".directory\n*checkpoint.ipynb\ntemp_*\n*.pyc\n" }, { "path": "LICENSE", "content": "The MIT License (MIT)\n\nCopyright (c) 2016 Alejandro Correa Bahnsen\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "README.md", "content": "## Short Course\n# Applied Machine Learning for Risk Management\n\nThe use of statistical models in computer algorithms allows computers to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Machine learning is the interdisciplinary field at the intersection of statistics and computer science which develops such statistical models and interweaves them with computer algorithms. It underpins many modern technologies, such as speech recognition, Internet search, bioinformatics and computer vision—Amazon’s recommender system, Google’s driverless car and the most recent imaging systems for cancer diagnosis are all based on Machine Learning technology. \n\nThis course on Machine Learning will explain how to build systems that learn and adapt using real-world applications. Some of the topics to be covered include linear regression, logistic regression, deep neural networks, clustering, and so forth. The course will be project-oriented, with emphasis placed on writing software implementations of learning algorithms applied to real-world problems, in particular, Credit Risk, Collections Management and Fraud Detection.\n\n*Instructors:* \n\n*Dr. Alejandro Correa Bahnsen*\n\n- email: \n- twitter: [@albahnsen](https://twitter.com/albahnsen)\n- github: [albahnsen](http://github.com/albahnsen)\n\n*Iván Torroledo*\n\n- email: \n- github: [torroledo](http://github.com/torroledo)\n\n## Requiriments \n* [Python](http://www.python.org) version 3.5;\n* [Numpy](http://www.numpy.org), the core numerical extensions for linear algebra and multidimensional arrays;\n* [Scipy](http://www.scipy.org), additional libraries for scientific programming;\n* [Matplotlib](http://matplotlib.sf.net), excellent plotting and graphing libraries;\n* [IPython](http://ipython.org), with the additional libraries required for the notebook interface.\n* [Pandas](http://pandas.pydata.org/), Python version of R dataframe\n* [scikit-learn](http://scikit-learn.org), Machine learning library!\n\nA good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the [Anaconda](https://www.continuum.io/).\n\nGIT!! Unfortunatelly out of the scope of this class, but please take a look at these [tutorials](https://help.github.com/articles/good-resources-for-learning-git-and-github/)\n\n## Sessions\n\n| Session | Notebook link | Exercises |\n| :------------- | :------------- | ----| \n| 1 | [Introduction to Machine Learning](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/01-IntroMachineLearning.ipynb)|\n| 2 | [Introduction to Python for Data Analysis](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/02-IntroPython_Numpy_Scypy_Pandas.ipynb) | [Python & Numpy & Pandas](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/exercises/02-Python&Numpy&Pandas.ipynb)|\n| 3 | [Linear Regression](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/04-linear_regression.ipynb) | [Income Prediction Rent](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/exercises/03-IncomePrediction.ipynb) \n| 4 | [Logistic Regression](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/05-logistic_regression.ipynb) | [Credit Scoring](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/exercises/04-CreditScoring.ipynb) \n| 5 | [Data Preparation and Model Evaluation](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/06-data_preparation_evaluation.ipynb) | [Credit Scoring V2](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/exercises/05-creditscoring_cross_validation.ipynb) |\n| 6 | [Unbalance Datasets](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/08_Unbalanced_Datasets.ipynb) | [Fraud Detection](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/exercises/06-fraud_detection_sampling.ipynb) |\n| 7 | [Decision Trees](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/09_decision_trees.ipynb) | [Fraud Detection V2](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/exercises/07-fraud_detection_DT.ipynb) |\n| 8 | [Ensemble Methods - Bagging](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/10_EnsembleMethods_Bagging.ipynb) | [Fraud Detection V3](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/exercises/08-fraud_ensemble_bagging.ipynb) |\n| 9 | [Statistical Inference](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/09_StatisticalInference.ipynb) | |\n| 10 | [Cost-Sensitive Classification](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/10_CostSensitiveClassification.ipynb) | [Credit Scoring V4](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/exercises/10_CS_Churn.ipynb) |\n| 11 | [Model Deployment](http://nbviewer.jupyter.org/github/albahnsen/ML_RiskManagement/blob/master/notebooks/11_Model_Deployment.ipynb) |\n" }, { "path": "datasets/churn.csv", "content": "State,Account Length,Area Code,Phone,Int'l Plan,VMail Plan,VMail Message,Day Mins,Day Calls,Day Charge,Eve Mins,Eve Calls,Eve Charge,Night Mins,Night Calls,Night Charge,Intl Mins,Intl Calls,Intl Charge,CustServ Calls,Churn?\r\nKS,128,415,382-4657,no,yes,25,265.100000,110,45.070000,197.400000,99,16.780000,244.700000,91,11.010000,10.000000,3,2.700000,1,False.\r\nOH,107,415,371-7191,no,yes,26,161.600000,123,27.470000,195.500000,103,16.620000,254.400000,103,11.450000,13.700000,3,3.700000,1,False.\r\nNJ,137,415,358-1921,no,no,0,243.400000,114,41.380000,121.200000,110,10.300000,162.600000,104,7.320000,12.200000,5,3.290000,0,False.\r\nOH,84,408,375-9999,yes,no,0,299.400000,71,50.900000,61.900000,88,5.260000,196.900000,89,8.860000,6.600000,7,1.780000,2,False.\r\nOK,75,415,330-6626,yes,no,0,166.700000,113,28.340000,148.300000,122,12.610000,186.900000,121,8.410000,10.100000,3,2.730000,3,False.\r\nAL,118,510,391-8027,yes,no,0,223.400000,98,37.980000,220.600000,101,18.750000,203.900000,118,9.180000,6.300000,6,1.700000,0,False.\r\nMA,121,510,355-9993,no,yes,24,218.200000,88,37.090000,348.500000,108,29.620000,212.600000,118,9.570000,7.500000,7,2.030000,3,False.\r\nMO,147,415,329-9001,yes,no,0,157.000000,79,26.690000,103.100000,94,8.760000,211.800000,96,9.530000,7.100000,6,1.920000,0,False.\r\nLA,117,408,335-4719,no,no,0,184.500000,97,31.370000,351.600000,80,29.890000,215.800000,90,9.710000,8.700000,4,2.350000,1,False.\r\nWV,141,415,330-8173,yes,yes,37,258.600000,84,43.960000,222.000000,111,18.870000,326.400000,97,14.690000,11.200000,5,3.020000,0,False.\r\nIN,65,415,329-6603,no,no,0,129.100000,137,21.950000,228.500000,83,19.420000,208.800000,111,9.400000,12.700000,6,3.430000,4,True.\r\nRI,74,415,344-9403,no,no,0,187.700000,127,31.910000,163.400000,148,13.890000,196.000000,94,8.820000,9.100000,5,2.460000,0,False.\r\nIA,168,408,363-1107,no,no,0,128.800000,96,21.900000,104.900000,71,8.920000,141.100000,128,6.350000,11.200000,2,3.020000,1,False.\r\nMT,95,510,394-8006,no,no,0,156.600000,88,26.620000,247.600000,75,21.050000,192.300000,115,8.650000,12.300000,5,3.320000,3,False.\r\nIA,62,415,366-9238,no,no,0,120.700000,70,20.520000,307.200000,76,26.110000,203.000000,99,9.140000,13.100000,6,3.540000,4,False.\r\nNY,161,415,351-7269,no,no,0,332.900000,67,56.590000,317.800000,97,27.010000,160.600000,128,7.230000,5.400000,9,1.460000,4,True.\r\nID,85,408,350-8884,no,yes,27,196.400000,139,33.390000,280.900000,90,23.880000,89.300000,75,4.020000,13.800000,4,3.730000,1,False.\r\nVT,93,510,386-2923,no,no,0,190.700000,114,32.420000,218.200000,111,18.550000,129.600000,121,5.830000,8.100000,3,2.190000,3,False.\r\nVA,76,510,356-2992,no,yes,33,189.700000,66,32.250000,212.800000,65,18.090000,165.700000,108,7.460000,10.000000,5,2.700000,1,False.\r\nTX,73,415,373-2782,no,no,0,224.400000,90,38.150000,159.500000,88,13.560000,192.800000,74,8.680000,13.000000,2,3.510000,1,False.\r\nFL,147,415,396-5800,no,no,0,155.100000,117,26.370000,239.700000,93,20.370000,208.800000,133,9.400000,10.600000,4,2.860000,0,False.\r\nCO,77,408,393-7984,no,no,0,62.400000,89,10.610000,169.900000,121,14.440000,209.600000,64,9.430000,5.700000,6,1.540000,5,True.\r\nAZ,130,415,358-1958,no,no,0,183.000000,112,31.110000,72.900000,99,6.200000,181.800000,78,8.180000,9.500000,19,2.570000,0,False.\r\nSC,111,415,350-2565,no,no,0,110.400000,103,18.770000,137.300000,102,11.670000,189.600000,105,8.530000,7.700000,6,2.080000,2,False.\r\nVA,132,510,343-4696,no,no,0,81.100000,86,13.790000,245.200000,72,20.840000,237.000000,115,10.670000,10.300000,2,2.780000,0,False.\r\nNE,174,415,331-3698,no,no,0,124.300000,76,21.130000,277.100000,112,23.550000,250.700000,115,11.280000,15.500000,5,4.190000,3,False.\r\nWY,57,408,357-3817,no,yes,39,213.000000,115,36.210000,191.100000,112,16.240000,182.700000,115,8.220000,9.500000,3,2.570000,0,False.\r\nMT,54,408,418-6412,no,no,0,134.300000,73,22.830000,155.500000,100,13.220000,102.100000,68,4.590000,14.700000,4,3.970000,3,False.\r\nMO,20,415,353-2630,no,no,0,190.000000,109,32.300000,258.200000,84,21.950000,181.500000,102,8.170000,6.300000,6,1.700000,0,False.\r\nHI,49,510,410-7789,no,no,0,119.300000,117,20.280000,215.100000,109,18.280000,178.700000,90,8.040000,11.100000,1,3.000000,1,False.\r\nIL,142,415,416-8428,no,no,0,84.800000,95,14.420000,136.700000,63,11.620000,250.500000,148,11.270000,14.200000,6,3.830000,2,False.\r\nNH,75,510,370-3359,no,no,0,226.100000,105,38.440000,201.500000,107,17.130000,246.200000,98,11.080000,10.300000,5,2.780000,1,False.\r\nLA,172,408,383-1121,no,no,0,212.000000,121,36.040000,31.200000,115,2.650000,293.300000,78,13.200000,12.600000,10,3.400000,3,False.\r\nAZ,12,408,360-1596,no,no,0,249.600000,118,42.430000,252.400000,119,21.450000,280.200000,90,12.610000,11.800000,3,3.190000,1,True.\r\nOK,57,408,395-2854,no,yes,25,176.800000,94,30.060000,195.000000,75,16.580000,213.500000,116,9.610000,8.300000,4,2.240000,0,False.\r\nGA,72,415,362-1407,no,yes,37,220.000000,80,37.400000,217.300000,102,18.470000,152.800000,71,6.880000,14.700000,6,3.970000,3,False.\r\nAK,36,408,341-9764,no,yes,30,146.300000,128,24.870000,162.500000,80,13.810000,129.300000,109,5.820000,14.500000,6,3.920000,0,False.\r\nMA,78,415,353-3305,no,no,0,130.800000,64,22.240000,223.700000,116,19.010000,227.800000,108,10.250000,10.000000,5,2.700000,1,False.\r\nAK,136,415,402-1381,yes,yes,33,203.900000,106,34.660000,187.600000,99,15.950000,101.700000,107,4.580000,10.500000,6,2.840000,3,False.\r\nNJ,149,408,332-9891,no,no,0,140.400000,94,23.870000,271.800000,92,23.100000,188.300000,108,8.470000,11.100000,9,3.000000,1,False.\r\nGA,98,408,372-9976,no,no,0,126.300000,102,21.470000,166.800000,85,14.180000,187.800000,135,8.450000,9.400000,2,2.540000,3,False.\r\nMD,135,408,383-6029,yes,yes,41,173.100000,85,29.430000,203.900000,107,17.330000,122.200000,78,5.500000,14.600000,15,3.940000,0,True.\r\nAR,34,510,353-7289,no,no,0,124.800000,82,21.220000,282.200000,98,23.990000,311.500000,78,14.020000,10.000000,4,2.700000,2,False.\r\nID,160,415,390-7274,no,no,0,85.800000,77,14.590000,165.300000,110,14.050000,178.500000,92,8.030000,9.200000,4,2.480000,3,False.\r\nWI,64,510,352-1237,no,no,0,154.000000,67,26.180000,225.800000,118,19.190000,265.300000,86,11.940000,3.500000,3,0.950000,1,False.\r\nOR,59,408,353-3061,no,yes,28,120.900000,97,20.550000,213.000000,92,18.110000,163.100000,116,7.340000,8.500000,5,2.300000,2,False.\r\nMI,65,415,363-5450,no,no,0,211.300000,120,35.920000,162.600000,122,13.820000,134.700000,118,6.060000,13.200000,5,3.560000,3,False.\r\nDE,142,408,364-1995,no,no,0,187.000000,133,31.790000,134.600000,74,11.440000,242.200000,127,10.900000,7.400000,5,2.000000,2,False.\r\nID,119,415,398-1294,no,no,0,159.100000,114,27.050000,231.300000,117,19.660000,143.200000,91,6.440000,8.800000,3,2.380000,5,True.\r\nWY,97,415,405-7146,no,yes,24,133.200000,135,22.640000,217.200000,58,18.460000,70.600000,79,3.180000,11.000000,3,2.970000,1,False.\r\nIA,52,408,413-4957,no,no,0,191.900000,108,32.620000,269.800000,96,22.930000,236.800000,87,10.660000,7.800000,5,2.110000,3,False.\r\nIN,60,408,420-5645,no,no,0,220.600000,57,37.500000,211.100000,115,17.940000,249.000000,129,11.210000,6.800000,3,1.840000,1,False.\r\nVA,10,408,349-4396,no,no,0,186.100000,112,31.640000,190.200000,66,16.170000,282.800000,57,12.730000,11.400000,6,3.080000,2,False.\r\nUT,96,415,404-3211,no,no,0,160.200000,117,27.230000,267.500000,67,22.740000,228.500000,68,10.280000,9.300000,5,2.510000,2,False.\r\nWY,87,415,353-3759,no,no,0,151.000000,83,25.670000,219.700000,116,18.670000,203.900000,127,9.180000,9.700000,3,2.620000,5,True.\r\nIN,81,408,363-5947,no,no,0,175.500000,67,29.840000,249.300000,85,21.190000,270.200000,98,12.160000,10.200000,3,2.750000,1,False.\r\nCO,141,415,340-5121,no,no,0,126.900000,98,21.570000,180.000000,62,15.300000,140.800000,128,6.340000,8.000000,2,2.160000,1,False.\r\nCO,121,408,370-7574,no,yes,30,198.400000,129,33.730000,75.300000,77,6.400000,181.200000,77,8.150000,5.800000,3,1.570000,3,True.\r\nWI,68,415,403-9733,no,no,0,148.800000,70,25.300000,246.500000,164,20.950000,129.800000,103,5.840000,12.100000,3,3.270000,3,False.\r\nOK,125,408,355-7251,no,no,0,229.300000,103,38.980000,177.400000,126,15.080000,189.300000,95,8.520000,12.000000,8,3.240000,1,False.\r\nID,174,408,359-5893,no,no,0,192.100000,97,32.660000,169.900000,94,14.440000,166.600000,54,7.500000,11.400000,4,3.080000,1,False.\r\nCA,116,415,405-3371,no,yes,34,268.600000,83,45.660000,178.200000,142,15.150000,166.300000,106,7.480000,11.600000,3,3.130000,2,False.\r\nMN,74,510,344-5117,no,yes,33,193.700000,91,32.930000,246.100000,96,20.920000,138.000000,92,6.210000,14.600000,3,3.940000,2,False.\r\nSD,149,408,332-8160,no,yes,28,180.700000,92,30.720000,187.800000,64,15.960000,265.500000,53,11.950000,12.600000,3,3.400000,3,False.\r\nNC,38,408,359-4081,no,no,0,131.200000,98,22.300000,162.900000,97,13.850000,159.000000,106,7.150000,8.200000,6,2.210000,2,False.\r\nWA,40,415,352-8305,no,yes,41,148.100000,74,25.180000,169.500000,88,14.410000,214.100000,102,9.630000,6.200000,5,1.670000,2,False.\r\nWY,43,415,329-9847,yes,no,0,251.500000,105,42.760000,212.800000,104,18.090000,157.800000,67,7.100000,9.300000,4,2.510000,0,False.\r\nMN,113,408,365-9011,yes,no,0,125.200000,93,21.280000,206.400000,119,17.540000,129.300000,139,5.820000,8.300000,8,2.240000,0,False.\r\nUT,126,408,338-9472,no,no,0,211.600000,70,35.970000,216.900000,80,18.440000,153.500000,60,6.910000,7.800000,1,2.110000,1,False.\r\nTX,150,510,374-8042,no,no,0,178.900000,101,30.410000,169.100000,110,14.370000,148.600000,100,6.690000,13.800000,3,3.730000,4,True.\r\nNJ,138,408,359-1231,no,no,0,241.800000,93,41.110000,170.500000,83,14.490000,295.300000,104,13.290000,11.800000,7,3.190000,3,False.\r\nMN,162,510,413-7170,no,yes,46,224.900000,97,38.230000,188.200000,84,16.000000,254.600000,61,11.460000,12.100000,2,3.270000,0,False.\r\nNM,147,510,415-2935,no,no,0,248.600000,83,42.260000,148.900000,85,12.660000,172.500000,109,7.760000,8.000000,4,2.160000,3,False.\r\nNV,90,415,399-4246,no,no,0,203.400000,146,34.580000,226.700000,117,19.270000,152.400000,105,6.860000,7.300000,4,1.970000,1,False.\r\nHI,85,415,362-5889,no,no,0,235.800000,109,40.090000,157.200000,94,13.360000,188.200000,99,8.470000,12.000000,3,3.240000,0,False.\r\nMN,50,415,350-8921,no,no,0,157.100000,90,26.710000,223.300000,72,18.980000,181.400000,111,8.160000,6.100000,2,1.650000,1,False.\r\nDC,82,415,374-5353,no,no,0,300.300000,109,51.050000,181.000000,100,15.390000,270.100000,73,12.150000,11.700000,4,3.160000,0,True.\r\nNY,144,408,360-1171,no,no,0,61.600000,117,10.470000,77.100000,85,6.550000,173.000000,99,7.790000,8.200000,7,2.210000,4,True.\r\nMN,46,415,355-8887,no,no,0,214.100000,72,36.400000,164.400000,104,13.970000,177.500000,113,7.990000,8.200000,3,2.210000,2,False.\r\nMD,70,408,333-1967,no,no,0,170.200000,98,28.930000,155.200000,102,13.190000,228.600000,76,10.290000,15.000000,2,4.050000,1,False.\r\nWV,144,415,354-4577,no,no,0,201.100000,99,34.190000,303.500000,74,25.800000,224.000000,119,10.080000,13.200000,2,3.560000,1,False.\r\nOR,116,415,331-7425,yes,no,0,215.400000,104,36.620000,204.800000,79,17.410000,278.500000,109,12.530000,12.600000,5,3.400000,3,False.\r\nCO,55,408,419-2637,no,yes,25,165.600000,123,28.150000,136.100000,95,11.570000,175.700000,90,7.910000,11.000000,2,2.970000,3,False.\r\nGA,70,415,411-1530,no,yes,24,249.500000,101,42.420000,259.700000,98,22.070000,222.700000,68,10.020000,9.800000,4,2.650000,1,False.\r\nTX,106,510,395-3026,no,no,0,210.600000,96,35.800000,249.200000,85,21.180000,191.400000,88,8.610000,12.400000,1,3.350000,2,True.\r\nVT,128,510,388-6441,no,yes,29,179.300000,104,30.480000,225.900000,86,19.200000,323.000000,78,14.540000,8.600000,7,2.320000,0,False.\r\nIN,94,408,402-1251,no,no,0,157.900000,105,26.840000,155.000000,101,13.180000,189.600000,84,8.530000,8.000000,5,2.160000,4,True.\r\nWV,111,510,412-9997,no,no,0,214.300000,118,36.430000,208.500000,76,17.720000,182.400000,98,8.210000,12.000000,2,3.240000,1,False.\r\nKY,74,415,346-7302,no,yes,35,154.100000,104,26.200000,123.400000,84,10.490000,202.100000,57,9.090000,10.900000,9,2.940000,2,False.\r\nNJ,128,415,358-9095,no,no,0,237.900000,125,40.440000,247.600000,93,21.050000,208.900000,68,9.400000,13.900000,4,3.750000,1,True.\r\nDC,82,510,400-9770,no,no,0,143.900000,61,24.460000,194.900000,105,16.570000,109.600000,94,4.930000,11.100000,2,3.000000,1,False.\r\nLA,155,415,334-1275,no,no,0,203.400000,100,34.580000,190.900000,104,16.230000,196.000000,119,8.820000,8.900000,4,2.400000,0,True.\r\nAR,80,415,340-4953,no,no,0,124.300000,100,21.130000,173.000000,107,14.710000,253.200000,62,11.390000,7.900000,9,2.130000,1,False.\r\nME,78,415,400-9510,no,no,0,252.900000,93,42.990000,178.400000,112,15.160000,263.900000,105,11.880000,9.500000,7,2.570000,3,False.\r\nAZ,90,415,387-6103,no,no,0,179.100000,71,30.450000,190.600000,81,16.200000,127.700000,91,5.750000,10.600000,7,2.860000,3,False.\r\nAK,104,408,366-4467,no,no,0,278.400000,106,47.330000,81.000000,113,6.890000,163.200000,137,7.340000,9.800000,5,2.650000,1,False.\r\nMT,73,415,370-3450,no,no,0,160.100000,110,27.220000,213.300000,72,18.130000,174.100000,72,7.830000,13.000000,4,3.510000,0,False.\r\nAZ,99,415,327-3954,no,no,0,198.200000,87,33.690000,207.300000,76,17.620000,190.900000,113,8.590000,8.700000,3,2.350000,4,False.\r\nMS,120,408,355-6291,no,no,0,212.100000,131,36.060000,209.400000,104,17.800000,167.200000,96,7.520000,5.300000,5,1.430000,1,True.\r\nID,77,415,362-9748,no,no,0,251.800000,72,42.810000,205.700000,126,17.480000,275.200000,109,12.380000,9.800000,7,2.650000,2,True.\r\nIA,98,510,379-6506,no,yes,21,161.200000,114,27.400000,252.200000,83,21.440000,160.200000,92,7.210000,4.400000,8,1.190000,4,False.\r\nMA,108,415,347-7741,no,no,0,178.300000,137,30.310000,189.000000,76,16.070000,129.100000,102,5.810000,14.600000,5,3.940000,0,False.\r\nVT,135,415,354-3783,no,no,0,151.700000,82,25.790000,119.000000,105,10.120000,180.000000,100,8.100000,10.500000,6,2.840000,0,False.\r\nKY,95,408,401-7594,no,no,0,135.000000,99,22.950000,183.600000,106,15.610000,245.300000,102,11.040000,12.500000,9,3.380000,1,False.\r\nIN,122,408,397-4976,no,no,0,170.500000,94,28.990000,173.700000,109,14.760000,248.600000,75,11.190000,11.300000,2,3.050000,1,False.\r\nAZ,95,408,334-2577,no,no,0,238.100000,65,40.480000,187.200000,98,15.910000,190.000000,115,8.550000,11.800000,4,3.190000,4,False.\r\nMI,36,510,400-3637,no,yes,29,281.400000,102,47.840000,202.200000,76,17.190000,187.200000,113,8.420000,9.000000,6,2.430000,2,False.\r\nNM,93,510,383-4361,no,yes,21,117.900000,131,20.040000,164.500000,115,13.980000,217.000000,86,9.760000,9.800000,3,2.650000,1,False.\r\nCO,141,415,371-4306,no,yes,32,148.600000,91,25.260000,131.100000,97,11.140000,219.400000,142,9.870000,10.100000,1,2.730000,1,False.\r\nUT,157,408,403-4298,no,no,0,229.800000,90,39.070000,147.900000,121,12.570000,241.400000,108,10.860000,9.600000,7,2.590000,3,False.\r\nMI,120,408,409-3786,no,no,0,165.000000,100,28.050000,317.200000,83,26.960000,119.200000,86,5.360000,8.300000,8,2.240000,1,False.\r\nMA,103,415,337-4697,no,no,0,185.000000,117,31.450000,223.300000,94,18.980000,222.800000,91,10.030000,12.600000,2,3.400000,2,False.\r\nAL,98,408,383-1509,no,no,0,161.000000,117,27.370000,190.900000,113,16.230000,227.700000,113,10.250000,12.100000,4,3.270000,4,False.\r\nDE,125,408,359-9794,no,no,0,126.700000,108,21.540000,206.000000,90,17.510000,247.800000,114,11.150000,13.300000,7,3.590000,1,False.\r\nAZ,63,415,407-7035,no,no,0,58.900000,125,10.010000,169.600000,59,14.420000,211.400000,88,9.510000,9.400000,3,2.540000,1,False.\r\nME,36,510,363-1069,yes,yes,42,196.800000,89,33.460000,254.900000,122,21.670000,138.300000,126,6.220000,20.000000,6,5.400000,0,True.\r\nNJ,64,510,391-4652,no,no,0,162.600000,83,27.640000,152.300000,109,12.950000,57.500000,122,2.590000,14.200000,3,3.830000,1,False.\r\nNV,74,415,355-6837,no,no,0,282.500000,114,48.030000,219.900000,48,18.690000,170.000000,115,7.650000,9.400000,4,2.540000,1,True.\r\nMO,112,510,409-1244,no,yes,36,113.700000,117,19.330000,157.500000,82,13.390000,177.600000,118,7.990000,10.000000,3,2.700000,2,False.\r\nID,97,408,328-3266,no,no,0,239.800000,125,40.770000,214.800000,111,18.260000,143.300000,81,6.450000,8.700000,5,2.350000,2,False.\r\nNE,46,408,352-7072,no,no,0,210.200000,92,35.730000,227.300000,77,19.320000,200.100000,116,9.000000,13.100000,7,3.540000,1,False.\r\nTX,41,408,370-7550,no,yes,22,213.800000,102,36.350000,141.800000,86,12.050000,142.200000,123,6.400000,7.200000,3,1.940000,0,False.\r\nMD,121,510,369-5526,no,no,0,190.700000,103,32.420000,183.500000,117,15.600000,220.800000,103,9.940000,9.800000,4,2.650000,3,False.\r\nMS,193,415,329-4391,no,no,0,170.900000,124,29.050000,132.300000,95,11.250000,112.900000,89,5.080000,11.600000,3,3.130000,1,False.\r\nNV,130,510,408-4195,no,no,0,154.200000,119,26.210000,110.200000,98,9.370000,227.400000,117,10.230000,9.200000,5,2.480000,2,False.\r\nAZ,85,408,354-4445,no,no,0,201.400000,52,34.240000,229.400000,104,19.500000,252.500000,106,11.360000,12.000000,3,3.240000,1,False.\r\nMS,162,415,335-4858,no,no,0,70.700000,108,12.020000,157.500000,87,13.390000,154.800000,82,6.970000,9.100000,3,2.460000,4,True.\r\nMS,61,510,414-8718,no,yes,27,187.500000,124,31.880000,146.600000,103,12.460000,225.700000,129,10.160000,6.400000,6,1.730000,4,True.\r\nTX,92,408,409-5939,no,no,0,91.700000,90,15.590000,193.700000,123,16.460000,175.000000,86,7.880000,9.200000,4,2.480000,2,False.\r\nNE,131,408,331-4902,no,yes,36,214.200000,115,36.410000,161.700000,117,13.740000,264.700000,102,11.910000,9.500000,4,2.570000,3,False.\r\nNE,90,415,353-6870,no,no,0,145.500000,92,24.740000,217.700000,114,18.500000,146.900000,123,6.610000,10.900000,2,2.940000,3,False.\r\nCA,75,408,355-2909,no,no,0,166.300000,125,28.270000,158.200000,86,13.450000,256.700000,80,11.550000,6.100000,5,1.650000,1,False.\r\nNJ,78,415,390-6101,no,no,0,231.000000,115,39.270000,230.400000,140,19.580000,261.400000,120,11.760000,9.500000,3,2.570000,1,False.\r\nTX,82,408,400-3446,no,no,0,200.300000,96,34.050000,201.200000,102,17.100000,206.100000,60,9.270000,7.100000,1,1.920000,4,False.\r\nAR,163,408,411-5859,no,no,0,197.000000,109,33.490000,202.600000,128,17.220000,206.400000,80,9.290000,9.100000,10,2.460000,1,False.\r\nAL,91,510,387-2919,yes,no,0,129.900000,112,22.080000,173.300000,83,14.730000,247.200000,130,11.120000,11.200000,3,3.020000,3,False.\r\nNY,75,415,374-8525,no,yes,21,175.800000,97,29.890000,217.500000,106,18.490000,237.500000,134,10.690000,5.300000,4,1.430000,5,False.\r\nFL,91,510,379-5592,no,no,0,203.100000,106,34.530000,210.100000,113,17.860000,195.600000,129,8.800000,12.000000,3,3.240000,3,False.\r\nAK,127,510,345-8237,no,yes,36,183.200000,117,31.140000,126.800000,76,10.780000,263.300000,71,11.850000,11.200000,8,3.020000,1,False.\r\nNV,113,415,422-6690,no,yes,23,205.000000,101,34.850000,152.000000,60,12.920000,158.600000,59,7.140000,10.200000,5,2.750000,2,False.\r\nDE,110,510,346-2359,no,no,0,148.500000,115,25.250000,276.400000,84,23.490000,193.600000,112,8.710000,12.400000,3,3.350000,1,False.\r\nMD,120,415,374-3534,no,yes,39,200.300000,68,34.050000,220.400000,97,18.730000,253.800000,116,11.420000,10.500000,4,2.840000,0,False.\r\nMI,157,415,381-4756,no,yes,28,192.600000,107,32.740000,195.500000,74,16.620000,109.700000,139,4.940000,6.800000,5,1.840000,3,False.\r\nVT,103,510,390-2805,no,no,0,246.500000,47,41.910000,195.500000,84,16.620000,200.500000,96,9.020000,11.700000,4,3.160000,1,False.\r\nVT,117,408,390-2390,yes,no,0,167.100000,86,28.410000,177.500000,87,15.090000,249.400000,132,11.220000,14.100000,7,3.810000,2,True.\r\nMI,140,415,419-9097,no,no,0,231.900000,101,39.420000,160.100000,94,13.610000,110.400000,98,4.970000,14.300000,6,3.860000,3,False.\r\nWA,127,408,386-7281,no,no,0,146.700000,91,24.940000,203.500000,78,17.300000,203.400000,110,9.150000,13.700000,3,3.700000,1,False.\r\nUT,83,408,380-3561,yes,no,0,271.500000,87,46.160000,216.300000,126,18.390000,121.100000,105,5.450000,11.700000,4,3.160000,1,False.\r\nLA,121,408,390-8760,no,no,0,181.500000,121,30.860000,218.400000,98,18.560000,161.600000,103,7.270000,8.500000,5,2.300000,1,False.\r\nRI,145,408,366-6730,no,yes,43,257.700000,97,43.810000,162.100000,95,13.780000,286.900000,86,12.910000,11.100000,4,3.000000,2,False.\r\nIA,113,408,395-5285,no,no,0,193.800000,99,32.950000,221.400000,125,18.820000,172.300000,67,7.750000,10.600000,6,2.860000,1,False.\r\nNE,117,415,354-3436,no,no,0,102.800000,119,17.480000,206.700000,91,17.570000,299.000000,105,13.460000,10.100000,7,2.730000,1,False.\r\nOH,65,408,336-7600,no,no,0,187.900000,116,31.940000,157.600000,117,13.400000,227.300000,86,10.230000,7.500000,6,2.030000,1,False.\r\nRI,56,415,383-6293,no,no,0,226.000000,112,38.420000,248.500000,118,21.120000,140.500000,142,6.320000,6.900000,11,1.860000,1,False.\r\nOK,96,415,362-4596,no,no,0,260.400000,115,44.270000,146.000000,46,12.410000,269.500000,87,12.130000,11.500000,4,3.110000,5,False.\r\nLA,151,408,401-3926,no,no,0,178.700000,116,30.380000,292.100000,138,24.830000,265.900000,101,11.970000,9.800000,4,2.650000,0,False.\r\nOH,83,415,370-9116,no,no,0,337.400000,120,57.360000,227.400000,116,19.330000,153.900000,114,6.930000,15.800000,7,4.270000,0,True.\r\nVA,139,510,328-6289,no,yes,23,157.600000,129,26.790000,247.000000,96,21.000000,259.200000,112,11.660000,13.700000,2,3.700000,0,False.\r\nMO,6,510,350-9994,no,no,0,183.600000,117,31.210000,256.700000,72,21.820000,178.600000,79,8.040000,10.200000,2,2.750000,1,False.\r\nFL,115,510,351-4616,no,yes,24,142.100000,124,24.160000,183.400000,129,15.590000,164.800000,114,7.420000,9.600000,4,2.590000,1,False.\r\nSC,87,415,360-5779,no,no,0,136.300000,97,23.170000,172.200000,108,14.640000,137.500000,101,6.190000,7.100000,5,1.920000,0,False.\r\nVA,141,415,417-4885,no,no,0,217.100000,110,36.910000,241.500000,111,20.530000,253.500000,103,11.410000,12.000000,6,3.240000,0,False.\r\nIA,141,510,406-4710,no,yes,36,187.500000,99,31.880000,241.400000,116,20.520000,229.500000,105,10.330000,10.500000,5,2.840000,3,False.\r\nMI,62,415,409-8743,no,no,0,98.900000,103,16.810000,135.400000,122,11.510000,236.600000,82,10.650000,12.200000,1,3.290000,1,False.\r\nOK,146,415,335-4584,no,no,0,206.300000,151,35.070000,148.600000,89,12.630000,167.200000,91,7.520000,6.100000,3,1.650000,1,False.\r\nDE,92,415,361-9845,no,yes,33,243.100000,92,41.330000,213.800000,92,18.170000,228.700000,104,10.290000,12.100000,2,3.270000,2,False.\r\nGA,185,510,366-5699,no,yes,31,189.800000,126,32.270000,163.300000,133,13.880000,264.800000,126,11.920000,7.500000,3,2.030000,1,False.\r\nDC,148,415,329-9364,no,no,0,202.000000,102,34.340000,243.200000,128,20.670000,261.300000,90,11.760000,10.900000,3,2.940000,1,False.\r\nAZ,94,408,390-7434,no,yes,38,170.100000,124,28.920000,193.300000,116,16.430000,105.900000,73,4.770000,12.800000,4,3.460000,1,False.\r\nAL,32,510,404-9680,no,no,0,230.900000,87,39.250000,187.400000,90,15.930000,154.000000,53,6.930000,6.300000,2,1.700000,0,False.\r\nCO,68,408,338-9398,no,no,0,237.100000,105,40.310000,223.500000,105,19.000000,97.400000,79,4.380000,13.200000,2,3.560000,1,False.\r\nNH,64,408,394-2445,no,yes,27,182.100000,91,30.960000,169.700000,98,14.420000,164.700000,86,7.410000,10.600000,5,2.860000,2,False.\r\nNM,25,415,381-2709,no,no,0,119.300000,87,20.280000,211.500000,101,17.980000,268.900000,86,12.100000,10.500000,4,2.840000,3,False.\r\nOR,65,415,397-5060,no,no,0,116.800000,87,19.860000,178.900000,93,15.210000,182.400000,150,8.210000,14.100000,2,3.810000,1,False.\r\nLA,179,408,415-2393,no,no,0,219.200000,92,37.260000,149.400000,125,12.700000,244.700000,104,11.010000,6.100000,5,1.650000,0,False.\r\nNE,94,415,377-1765,no,no,0,252.600000,104,42.940000,169.000000,125,14.370000,170.900000,106,7.690000,11.100000,7,3.000000,2,False.\r\nMN,62,415,409-2111,no,no,0,147.100000,91,25.010000,190.400000,107,16.180000,195.200000,115,8.780000,12.200000,3,3.290000,0,False.\r\nMI,127,415,401-3170,no,no,0,202.100000,103,34.360000,229.400000,86,19.500000,195.200000,113,8.780000,11.500000,3,3.110000,2,False.\r\nAR,116,408,405-5681,no,no,0,173.500000,93,29.500000,194.100000,76,16.500000,208.000000,112,9.360000,16.200000,10,4.370000,3,False.\r\nKS,70,408,411-4582,no,no,0,232.100000,122,39.460000,292.300000,112,24.850000,201.200000,112,9.050000,0.000000,0,0.000000,3,False.\r\nWV,94,510,355-5009,yes,yes,23,197.100000,125,33.510000,214.500000,136,18.230000,282.200000,103,12.700000,9.500000,5,2.570000,4,False.\r\nAK,126,415,372-3750,no,no,0,58.200000,94,9.890000,138.700000,118,11.790000,136.800000,91,6.160000,11.900000,1,3.210000,5,True.\r\nNY,67,408,405-2888,no,yes,36,115.600000,111,19.650000,237.700000,94,20.200000,169.900000,103,7.650000,9.900000,12,2.670000,2,False.\r\nNH,19,408,361-3337,no,no,0,186.100000,98,31.640000,254.300000,57,21.620000,214.000000,127,9.630000,14.600000,7,3.940000,2,False.\r\nVA,170,510,350-1639,yes,no,0,259.900000,68,44.180000,245.000000,122,20.830000,134.400000,121,6.050000,8.400000,3,2.270000,3,False.\r\nNM,73,415,333-3221,no,no,0,214.300000,145,36.430000,268.500000,135,22.820000,241.200000,92,10.850000,10.800000,13,2.920000,1,False.\r\nNY,106,408,422-1471,no,no,0,158.700000,74,26.980000,64.300000,139,5.470000,198.500000,103,8.930000,10.200000,4,2.750000,1,False.\r\nAZ,93,415,399-7865,no,no,0,271.600000,71,46.170000,229.400000,108,19.500000,77.300000,121,3.480000,10.900000,3,2.940000,2,False.\r\nWY,164,510,373-4819,no,no,0,160.600000,111,27.300000,163.200000,126,13.870000,187.100000,112,8.420000,9.000000,3,2.430000,1,False.\r\nWA,51,408,338-6981,no,no,0,232.400000,109,39.510000,187.400000,95,15.930000,231.200000,107,10.400000,9.100000,3,2.460000,1,False.\r\nCO,107,415,418-4365,no,no,0,133.800000,85,22.750000,180.500000,94,15.340000,112.200000,115,5.050000,8.900000,4,2.400000,0,False.\r\nTX,130,415,359-5461,no,no,0,176.900000,109,30.070000,90.700000,104,7.710000,238.000000,69,10.710000,9.500000,2,2.570000,1,False.\r\nKY,80,408,375-3586,no,no,0,209.900000,74,35.680000,195.100000,77,16.580000,208.200000,119,9.370000,8.800000,4,2.380000,2,False.\r\nMT,94,415,407-8376,no,no,0,137.500000,118,23.380000,203.200000,88,17.270000,150.000000,131,6.750000,13.400000,2,3.620000,0,False.\r\nOK,118,408,408-6496,no,yes,23,289.500000,52,49.220000,166.600000,111,14.160000,119.100000,88,5.360000,9.500000,4,2.570000,1,False.\r\nMD,117,415,385-7688,no,yes,23,198.100000,86,33.680000,177.000000,86,15.050000,180.500000,92,8.120000,6.800000,6,1.840000,1,False.\r\nTN,78,415,332-6934,no,no,0,149.700000,119,25.450000,182.200000,115,15.490000,261.500000,126,11.770000,9.700000,8,2.620000,0,False.\r\nTX,208,510,378-3625,no,no,0,326.500000,67,55.510000,176.300000,113,14.990000,181.700000,102,8.180000,10.700000,6,2.890000,2,True.\r\nME,131,510,353-7292,yes,yes,26,292.900000,101,49.790000,199.700000,97,16.970000,255.300000,127,11.490000,13.800000,7,3.730000,4,True.\r\nDC,63,408,399-6786,no,no,0,83.000000,64,14.110000,177.000000,106,15.050000,245.700000,89,11.060000,13.000000,3,3.510000,0,False.\r\nMN,53,415,358-3261,no,yes,24,145.700000,146,24.770000,220.500000,136,18.740000,249.900000,96,11.250000,13.100000,5,3.540000,3,False.\r\nDE,62,408,377-9932,no,no,0,182.300000,101,30.990000,328.200000,93,27.900000,245.000000,131,11.030000,11.200000,1,3.020000,2,False.\r\nMD,97,415,397-4030,no,no,0,218.000000,86,37.060000,184.000000,94,15.640000,240.500000,110,10.820000,6.400000,8,1.730000,3,False.\r\nMI,105,510,367-1062,no,no,0,140.600000,109,23.900000,178.600000,51,15.180000,217.000000,83,9.760000,6.800000,3,1.840000,2,False.\r\nWA,157,415,341-8467,no,no,0,152.700000,105,25.960000,257.500000,80,21.890000,198.100000,93,8.910000,9.400000,4,2.540000,1,False.\r\nMO,66,415,339-9453,no,yes,36,106.700000,76,18.140000,209.800000,77,17.830000,190.400000,117,8.570000,12.100000,2,3.270000,1,False.\r\nIN,122,415,344-3388,no,no,0,243.800000,98,41.450000,83.900000,72,7.130000,179.800000,84,8.090000,13.700000,8,3.700000,2,False.\r\nOR,38,415,375-8013,no,no,0,194.400000,94,33.050000,186.700000,95,15.870000,223.300000,90,10.050000,10.800000,5,2.920000,3,False.\r\nMD,106,510,408-4142,no,no,0,213.900000,95,36.360000,151.900000,70,12.910000,260.100000,124,11.700000,12.200000,5,3.290000,3,False.\r\nRI,99,510,386-3671,no,no,0,217.200000,112,36.920000,246.700000,89,20.970000,226.100000,89,10.170000,15.800000,7,4.270000,3,False.\r\nLA,99,415,411-2284,no,no,0,241.100000,72,40.990000,155.600000,98,13.230000,188.200000,109,8.470000,11.600000,10,3.130000,1,False.\r\nAZ,144,510,346-7795,yes,no,0,203.500000,100,34.600000,247.600000,103,21.050000,194.300000,94,8.740000,11.900000,11,3.210000,0,False.\r\nPA,82,415,333-5609,no,yes,24,155.200000,131,26.380000,244.500000,106,20.780000,122.400000,68,5.510000,10.700000,3,2.890000,1,False.\r\nAZ,86,408,405-1842,no,yes,31,167.600000,139,28.490000,113.000000,118,9.610000,246.900000,121,11.110000,12.200000,6,3.290000,1,False.\r\nFL,70,510,366-6345,yes,no,0,226.700000,98,38.540000,228.100000,115,19.390000,73.200000,93,3.290000,17.600000,4,4.750000,2,True.\r\nLA,93,415,337-9345,no,no,0,179.300000,93,30.480000,178.600000,98,15.180000,225.200000,131,10.130000,11.500000,6,3.110000,3,False.\r\nFL,93,415,328-6770,no,no,0,151.400000,89,25.740000,186.400000,76,15.840000,172.500000,120,7.760000,10.900000,3,2.940000,0,False.\r\nFL,120,415,380-7321,no,no,0,180.000000,80,30.600000,224.200000,82,19.060000,265.400000,91,11.940000,4.700000,7,1.270000,3,False.\r\nMD,136,415,375-1476,no,no,0,250.200000,121,42.530000,267.100000,118,22.700000,151.000000,114,6.800000,13.000000,2,3.510000,1,True.\r\nAL,106,415,356-1567,no,no,0,223.000000,121,37.910000,110.100000,98,9.360000,188.700000,107,8.490000,7.100000,12,1.920000,0,False.\r\nWA,81,415,422-6685,no,no,0,183.600000,116,31.210000,152.600000,98,12.970000,212.200000,99,9.550000,12.200000,6,3.290000,3,False.\r\nTN,127,408,336-1090,no,yes,22,166.000000,114,28.220000,174.500000,103,14.830000,244.900000,68,11.020000,10.200000,6,2.750000,1,False.\r\nMS,65,415,343-2095,no,no,0,136.100000,112,23.140000,272.900000,96,23.200000,220.200000,104,9.910000,4.400000,2,1.190000,1,False.\r\nME,35,408,345-3934,no,no,0,149.300000,113,25.380000,242.200000,122,20.590000,174.300000,104,7.840000,8.900000,6,2.400000,2,False.\r\nOK,88,408,338-8050,no,no,0,65.400000,97,11.120000,168.200000,76,14.300000,236.000000,113,10.620000,13.800000,1,3.730000,2,False.\r\nIN,65,415,388-9568,no,no,0,213.400000,111,36.280000,234.500000,94,19.930000,250.100000,123,11.250000,2.700000,4,0.730000,1,False.\r\nMO,123,415,402-6591,no,no,0,206.900000,85,35.170000,244.700000,78,20.800000,221.500000,136,9.970000,7.700000,2,2.080000,3,False.\r\nIA,126,408,403-6419,no,yes,27,186.200000,78,31.650000,189.600000,83,16.120000,76.500000,139,3.440000,9.600000,3,2.590000,2,False.\r\nVA,104,415,386-9790,no,yes,23,280.200000,136,47.630000,220.500000,92,18.740000,136.900000,102,6.160000,13.300000,3,3.590000,4,False.\r\nKY,45,415,378-5692,no,yes,22,196.600000,84,33.420000,313.200000,92,26.620000,163.300000,108,7.350000,11.900000,3,3.210000,0,False.\r\nMD,93,408,360-3324,yes,no,0,312.000000,109,53.040000,129.400000,100,11.000000,217.600000,74,9.790000,10.500000,2,2.840000,0,True.\r\nOH,63,415,410-3719,yes,yes,36,199.000000,110,33.830000,291.300000,111,24.760000,197.600000,92,8.890000,11.000000,6,2.970000,1,False.\r\nOK,100,415,352-4221,no,no,0,203.100000,96,34.530000,217.000000,126,18.450000,180.900000,122,8.140000,13.500000,2,3.650000,3,False.\r\nNV,53,415,327-6179,no,no,0,168.800000,97,28.700000,220.300000,87,18.730000,154.300000,113,6.940000,10.900000,2,2.940000,0,False.\r\nID,92,415,359-6196,yes,no,0,173.100000,140,29.430000,240.300000,105,20.430000,233.200000,117,10.490000,9.000000,5,2.430000,1,False.\r\nMN,139,510,374-9107,no,no,0,134.400000,106,22.850000,211.300000,98,17.960000,193.600000,125,8.710000,10.200000,2,2.750000,5,True.\r\nSD,110,408,357-4078,no,yes,40,202.600000,103,34.440000,118.800000,128,10.100000,234.900000,98,10.570000,9.000000,9,2.430000,2,False.\r\nIL,110,408,366-5780,no,no,0,74.500000,117,12.670000,200.800000,98,17.070000,192.200000,101,8.650000,9.800000,7,2.650000,3,False.\r\nWY,215,510,393-9619,no,no,0,83.600000,148,14.210000,120.900000,91,10.280000,226.600000,110,10.200000,10.700000,9,2.890000,0,False.\r\nAL,73,415,355-9295,no,no,0,192.200000,86,32.670000,168.600000,116,14.330000,139.800000,87,6.290000,9.400000,6,2.540000,1,False.\r\nNJ,138,510,400-5751,no,no,0,220.200000,89,37.430000,88.300000,125,7.510000,195.300000,79,8.790000,12.900000,5,3.480000,0,False.\r\nNV,137,415,338-1027,yes,no,0,135.100000,95,22.970000,134.100000,102,11.400000,223.100000,81,10.040000,12.300000,2,3.320000,2,True.\r\nIN,36,415,405-8867,no,no,0,253.400000,77,43.080000,182.400000,151,15.500000,275.800000,103,12.410000,8.400000,2,2.270000,1,False.\r\nWV,85,408,336-5616,no,no,0,225.000000,81,38.250000,176.900000,63,15.040000,194.300000,110,8.740000,7.100000,2,1.920000,3,False.\r\nVA,108,408,335-1697,no,no,0,198.500000,99,33.750000,267.800000,60,22.760000,354.900000,75,15.970000,9.400000,3,2.540000,0,True.\r\nSC,22,408,331-5138,no,no,0,110.300000,107,18.750000,166.500000,93,14.150000,202.300000,96,9.100000,9.500000,5,2.570000,0,False.\r\nRI,107,415,385-8240,no,yes,37,60.000000,102,10.200000,102.200000,80,8.690000,261.800000,106,11.780000,11.100000,3,3.000000,0,False.\r\nIN,51,510,348-1359,no,no,0,214.800000,94,36.520000,149.700000,58,12.720000,283.400000,66,12.750000,10.200000,5,2.750000,0,False.\r\nAZ,94,408,354-7339,no,no,0,181.800000,85,30.910000,202.400000,98,17.200000,245.900000,97,11.070000,9.200000,2,2.480000,4,False.\r\nNM,119,510,349-1687,no,yes,23,154.000000,114,26.180000,278.000000,137,23.630000,228.400000,112,10.280000,11.800000,4,3.190000,2,False.\r\nOR,33,415,380-2558,no,yes,29,157.400000,99,26.760000,117.900000,80,10.020000,279.200000,79,12.560000,13.900000,11,3.750000,4,True.\r\nNJ,106,415,365-2153,no,no,0,207.900000,91,35.340000,172.000000,109,14.620000,191.800000,143,8.630000,14.400000,7,3.890000,4,False.\r\nMS,82,408,345-6043,no,no,0,207.000000,90,35.190000,232.900000,83,19.800000,172.400000,108,7.760000,9.100000,8,2.460000,3,False.\r\nMI,86,510,349-2808,no,yes,41,119.000000,101,20.230000,230.000000,134,19.550000,236.900000,58,10.660000,9.500000,3,2.570000,0,False.\r\nTX,97,415,411-1715,yes,no,0,143.700000,117,24.430000,273.000000,82,23.210000,178.300000,81,8.020000,10.900000,3,2.940000,0,False.\r\nFL,106,408,385-2488,no,yes,32,165.900000,126,28.200000,216.500000,93,18.400000,173.100000,86,7.790000,14.100000,8,3.810000,4,False.\r\nDC,108,510,377-7177,no,no,0,138.600000,122,23.560000,172.300000,117,14.650000,231.600000,92,10.420000,9.800000,3,2.650000,1,False.\r\nTX,114,415,342-1099,no,no,0,84.700000,118,14.400000,249.900000,86,21.240000,193.400000,95,8.700000,14.500000,8,3.920000,1,False.\r\nKS,92,408,386-4170,yes,no,0,62.600000,111,10.640000,180.600000,126,15.350000,221.700000,80,9.980000,10.400000,2,2.810000,1,True.\r\nUT,59,510,413-1269,no,no,0,155.200000,79,26.380000,235.300000,123,20.000000,169.400000,80,7.620000,8.700000,4,2.350000,1,False.\r\nMN,24,510,396-4460,no,yes,25,164.900000,110,28.030000,209.300000,105,17.790000,231.200000,55,10.400000,6.700000,9,1.810000,1,False.\r\nIL,151,408,334-2730,no,no,0,134.500000,88,22.870000,143.100000,112,12.160000,223.900000,61,10.080000,15.400000,1,4.160000,1,False.\r\nNM,117,415,340-3182,no,no,0,143.300000,103,24.360000,211.300000,108,17.960000,185.200000,96,8.330000,11.500000,3,3.110000,1,False.\r\nSC,78,510,377-8608,no,no,0,168.300000,110,28.610000,221.200000,73,18.800000,241.000000,136,10.850000,12.500000,1,3.380000,1,False.\r\nNC,155,408,417-3676,no,no,0,262.400000,55,44.610000,194.600000,113,16.540000,146.500000,85,6.590000,8.300000,6,2.240000,2,False.\r\nWV,114,510,417-6774,no,yes,30,206.200000,79,35.050000,260.000000,91,22.100000,291.600000,83,13.120000,11.400000,6,3.080000,1,False.\r\nRI,114,510,411-9554,no,yes,28,225.800000,94,38.390000,193.000000,117,16.410000,232.400000,100,10.460000,8.400000,9,2.270000,4,False.\r\nNH,119,408,420-3192,no,no,0,138.300000,89,23.510000,170.500000,78,14.490000,263.900000,98,11.880000,13.500000,6,3.650000,3,False.\r\nMO,64,510,389-1475,no,yes,48,94.400000,104,16.050000,136.200000,101,11.580000,147.400000,89,6.630000,4.500000,4,1.220000,0,False.\r\nMA,118,408,343-7734,yes,no,0,160.000000,123,27.200000,175.400000,96,14.910000,184.800000,99,8.320000,9.900000,3,2.670000,2,False.\r\nPA,101,415,410-3390,no,no,0,206.600000,105,35.120000,224.900000,117,19.120000,249.900000,100,11.250000,14.600000,3,3.940000,0,False.\r\nOK,117,415,344-6495,no,no,0,134.700000,121,22.900000,180.000000,83,15.300000,200.900000,104,9.040000,7.700000,3,2.080000,1,False.\r\nAL,49,415,331-6229,no,yes,28,214.400000,78,36.450000,235.200000,100,19.990000,206.200000,107,9.280000,8.000000,13,2.160000,3,False.\r\nWY,139,415,337-7501,no,no,0,192.800000,104,32.780000,234.400000,96,19.920000,203.200000,101,9.140000,13.000000,3,3.510000,3,False.\r\nPA,92,408,339-9631,no,yes,28,151.100000,90,25.690000,194.800000,79,16.560000,239.200000,114,10.760000,10.000000,3,2.700000,1,False.\r\nWA,83,415,369-4384,no,no,0,221.400000,103,37.640000,231.800000,103,19.700000,122.500000,100,5.510000,9.800000,5,2.650000,3,False.\r\nHI,148,510,416-3915,yes,no,0,218.900000,88,37.210000,208.000000,85,17.680000,203.300000,99,9.150000,11.100000,4,3.000000,0,False.\r\nSD,144,408,339-3049,no,yes,48,189.800000,96,32.270000,123.400000,67,10.490000,214.200000,106,9.640000,6.500000,2,1.760000,2,True.\r\nAL,131,415,361-7998,no,yes,25,192.700000,85,32.760000,225.900000,105,19.200000,254.200000,59,11.440000,10.900000,6,2.940000,2,False.\r\nVT,146,510,355-4842,yes,no,0,204.400000,135,34.750000,219.100000,90,18.620000,222.700000,114,10.020000,10.500000,6,2.840000,3,False.\r\nMT,143,415,387-6440,no,no,0,172.300000,97,29.290000,174.000000,108,14.790000,188.200000,119,8.470000,13.000000,4,3.510000,2,False.\r\nMN,81,415,369-2625,no,no,0,198.400000,93,33.730000,210.900000,108,17.930000,193.300000,71,8.700000,10.400000,6,2.810000,2,False.\r\nAK,48,415,389-7073,no,yes,37,211.700000,115,35.990000,159.900000,84,13.590000,144.100000,80,6.480000,12.200000,1,3.290000,1,False.\r\nMI,86,415,370-8463,no,yes,28,221.600000,74,37.670000,288.400000,100,24.510000,240.300000,105,10.810000,9.000000,2,2.430000,1,False.\r\nDE,71,415,362-7318,no,no,0,197.900000,108,33.640000,181.500000,109,15.430000,281.400000,56,12.660000,6.700000,5,1.810000,3,False.\r\nSD,145,408,412-1194,no,yes,24,147.500000,90,25.080000,175.700000,108,14.930000,252.100000,102,11.340000,15.600000,3,4.210000,2,False.\r\nMI,137,510,355-9508,no,no,0,206.400000,122,35.090000,128.000000,102,10.880000,194.500000,84,8.750000,8.800000,5,2.380000,2,False.\r\nKS,137,408,352-8202,no,no,0,205.900000,88,35.000000,209.300000,86,17.790000,289.900000,84,13.050000,14.500000,4,3.920000,2,False.\r\nAL,167,510,335-5882,no,no,0,207.600000,88,35.290000,132.400000,63,11.250000,255.200000,98,11.480000,14.100000,5,3.810000,0,False.\r\nOK,89,510,352-6976,no,no,0,303.900000,95,51.660000,260.900000,114,22.180000,312.100000,89,14.040000,5.300000,3,1.430000,1,True.\r\nCT,199,415,393-6733,no,yes,34,230.600000,121,39.200000,219.400000,99,18.650000,299.300000,94,13.470000,8.000000,2,2.160000,0,False.\r\nNE,132,510,335-1838,no,no,0,99.500000,110,16.920000,129.100000,80,10.970000,125.100000,124,5.630000,9.700000,3,2.620000,0,False.\r\nWI,94,510,355-6930,no,no,0,177.100000,112,30.110000,194.000000,112,16.490000,146.700000,108,6.600000,5.900000,4,1.590000,1,False.\r\nCT,96,415,387-5860,no,yes,37,172.700000,93,29.360000,120.100000,116,10.210000,216.100000,86,9.720000,10.300000,5,2.780000,5,True.\r\nWI,96,510,343-2605,no,yes,18,172.700000,86,29.360000,133.400000,113,11.340000,259.500000,70,11.680000,9.800000,3,2.650000,1,False.\r\nIN,166,510,350-6759,no,no,0,204.200000,115,34.710000,179.900000,152,15.290000,216.800000,109,9.760000,9.500000,5,2.570000,1,False.\r\nDC,74,415,371-1514,no,no,0,85.700000,83,14.570000,247.700000,67,21.050000,142.400000,85,6.410000,10.100000,5,2.730000,2,False.\r\nAR,36,415,346-9317,no,no,0,157.600000,117,26.790000,184.300000,58,15.670000,240.400000,99,10.820000,11.900000,1,3.210000,0,False.\r\nME,113,415,398-4313,no,no,0,215.500000,129,36.640000,218.700000,117,18.590000,207.100000,91,9.320000,6.600000,9,1.780000,4,False.\r\nMN,94,415,412-4399,no,no,0,181.500000,98,30.860000,199.900000,88,16.990000,287.700000,114,12.950000,6.600000,5,1.780000,1,False.\r\nMD,67,415,330-1835,no,no,0,171.700000,80,29.190000,110.400000,81,9.380000,195.400000,111,8.790000,11.900000,4,3.210000,2,False.\r\nFL,127,415,416-1676,no,no,0,266.600000,106,45.320000,264.800000,168,22.510000,207.200000,119,9.320000,5.900000,2,1.590000,1,True.\r\nRI,121,408,329-7347,no,no,0,170.400000,108,28.970000,350.500000,68,29.790000,297.000000,87,13.370000,11.200000,3,3.020000,0,True.\r\nIA,158,415,360-6868,no,no,0,158.000000,106,26.860000,292.500000,114,24.860000,241.100000,89,10.850000,9.100000,4,2.460000,1,False.\r\nAZ,136,510,405-6641,no,no,0,92.000000,117,15.640000,253.600000,77,21.560000,214.100000,90,9.630000,10.300000,10,2.780000,1,False.\r\nMO,196,415,393-2373,no,no,0,234.000000,109,39.780000,249.500000,114,21.210000,173.100000,70,7.790000,9.100000,5,2.460000,2,False.\r\nVT,113,415,419-1714,no,no,0,272.100000,111,46.260000,268.500000,118,22.820000,213.800000,105,9.620000,8.500000,10,2.300000,1,True.\r\nIN,122,408,336-3819,no,no,0,296.400000,99,50.390000,214.800000,89,18.260000,133.900000,107,6.030000,11.400000,3,3.080000,4,True.\r\nRI,112,510,341-3464,no,no,0,194.400000,101,33.050000,190.300000,82,16.180000,183.400000,107,8.250000,11.400000,2,3.080000,0,False.\r\nSD,209,415,413-5310,no,no,0,227.200000,128,38.620000,258.400000,92,21.960000,183.500000,74,8.260000,8.900000,4,2.400000,3,False.\r\nMN,62,415,366-7912,no,no,0,248.700000,109,42.280000,220.000000,118,18.700000,265.700000,78,11.960000,13.200000,2,3.560000,1,True.\r\nTX,110,415,399-8845,no,yes,38,236.300000,102,40.170000,195.900000,112,16.650000,183.500000,82,8.260000,9.700000,6,2.620000,1,False.\r\nVA,16,510,368-2583,no,no,0,205.600000,69,34.950000,169.500000,93,14.410000,220.100000,64,9.900000,10.900000,3,2.940000,0,False.\r\nMA,73,408,360-6309,no,no,0,94.100000,136,16.000000,280.300000,122,23.830000,205.000000,77,9.230000,9.800000,4,2.650000,0,False.\r\nID,128,408,359-5890,no,no,0,125.200000,99,21.280000,205.400000,107,17.460000,254.400000,111,11.450000,18.900000,2,5.100000,0,False.\r\nMA,39,408,332-2462,no,no,0,60.400000,158,10.270000,306.200000,120,26.030000,123.900000,46,5.580000,12.400000,3,3.350000,1,False.\r\nGA,103,415,381-9196,no,yes,28,121.000000,105,20.570000,270.400000,100,22.980000,160.500000,76,7.220000,7.700000,4,2.080000,2,False.\r\nRI,119,415,329-3222,no,yes,29,117.800000,66,20.030000,256.800000,114,21.830000,147.600000,76,6.640000,7.600000,3,2.050000,3,False.\r\nID,173,510,363-5819,no,yes,21,232.400000,96,39.510000,211.900000,118,18.010000,273.000000,102,12.290000,5.000000,5,1.350000,3,False.\r\nSD,128,510,413-9269,yes,yes,32,223.500000,81,38.000000,188.800000,74,16.050000,154.900000,101,6.970000,9.400000,2,2.540000,2,True.\r\nMA,86,510,330-7483,no,no,0,176.300000,79,29.970000,259.200000,97,22.030000,287.400000,78,12.930000,6.200000,3,1.670000,0,False.\r\nWY,114,415,403-7775,no,yes,32,125.200000,79,21.280000,177.800000,105,15.110000,232.400000,89,10.460000,12.900000,3,3.480000,1,False.\r\nVA,104,408,360-2479,no,no,0,138.700000,107,23.580000,256.900000,113,21.840000,234.900000,74,10.570000,10.000000,3,2.700000,0,False.\r\nOR,148,415,394-3791,no,no,0,86.300000,134,14.670000,246.600000,92,20.960000,251.600000,91,11.320000,11.300000,4,3.050000,1,False.\r\nVA,129,408,384-2632,no,no,0,207.000000,91,35.190000,154.900000,121,13.170000,245.100000,112,11.030000,13.400000,5,3.620000,3,False.\r\nME,100,510,359-8466,no,yes,30,58.800000,104,10.000000,219.500000,107,18.660000,152.300000,118,6.850000,7.100000,3,1.920000,0,False.\r\nAL,121,408,331-8909,no,yes,35,68.700000,95,11.680000,209.200000,69,17.780000,197.400000,42,8.880000,11.400000,4,3.080000,1,False.\r\nGA,143,408,359-5160,no,yes,33,239.200000,109,40.660000,235.500000,112,20.020000,156.300000,95,7.030000,9.500000,4,2.570000,1,False.\r\nIA,76,510,330-9833,no,no,0,198.300000,130,33.710000,217.100000,86,18.450000,188.400000,96,8.480000,12.500000,3,3.380000,0,False.\r\nAZ,158,510,362-2314,no,no,0,205.200000,97,34.880000,240.600000,77,20.450000,79.700000,108,3.590000,14.400000,12,3.890000,0,False.\r\nFL,116,510,338-8478,no,no,0,192.100000,98,32.660000,312.900000,135,26.600000,130.200000,94,5.860000,7.900000,2,2.130000,3,False.\r\nMT,54,415,387-5453,no,no,0,272.600000,83,46.340000,248.700000,74,21.140000,197.400000,111,8.880000,9.500000,2,2.570000,1,True.\r\nAL,86,415,380-3437,no,no,0,128.300000,121,21.810000,197.100000,93,16.750000,138.400000,152,6.230000,12.200000,5,3.290000,7,True.\r\nDE,108,510,365-8779,no,no,0,169.600000,99,28.830000,264.100000,87,22.450000,206.300000,78,9.280000,9.300000,4,2.510000,0,False.\r\nMT,66,510,407-2750,no,no,0,201.300000,95,34.220000,152.800000,66,12.990000,233.200000,101,10.490000,7.500000,4,2.030000,1,False.\r\nKY,151,408,396-8265,no,yes,17,214.700000,97,36.500000,138.500000,90,11.770000,169.100000,44,7.610000,8.600000,4,2.320000,1,False.\r\nSC,99,510,397-4304,no,no,0,169.200000,70,28.760000,271.500000,77,23.080000,170.200000,104,7.660000,10.600000,2,2.860000,0,False.\r\nWA,55,415,333-2611,no,no,0,194.100000,121,33.000000,176.600000,110,15.010000,302.800000,136,13.630000,7.000000,7,1.890000,2,False.\r\nOR,77,510,409-8814,no,no,0,233.800000,104,39.750000,266.500000,94,22.650000,212.700000,104,9.570000,7.600000,3,2.050000,2,False.\r\nAK,78,408,336-5406,no,no,0,225.100000,67,38.270000,199.200000,127,16.930000,175.500000,102,7.900000,14.600000,2,3.940000,0,False.\r\nGA,89,415,343-6940,no,no,0,213.000000,63,36.210000,176.600000,71,15.010000,262.600000,126,11.820000,9.100000,1,2.460000,1,True.\r\nMN,101,415,361-9923,no,no,0,183.900000,115,31.260000,255.900000,101,21.750000,275.000000,145,12.380000,10.800000,11,2.920000,1,False.\r\nIL,44,415,350-6639,no,yes,34,221.800000,105,37.710000,161.700000,85,13.740000,227.700000,62,10.250000,14.000000,7,3.780000,0,False.\r\nIN,98,408,376-4300,no,yes,21,64.600000,98,10.980000,176.100000,86,14.970000,244.800000,84,11.020000,0.000000,0,0.000000,2,False.\r\nSC,64,510,349-6567,no,yes,37,154.600000,92,26.280000,83.400000,103,7.090000,165.900000,99,7.470000,13.300000,3,3.590000,1,False.\r\nVA,141,415,333-7749,no,no,0,260.200000,131,44.230000,179.200000,120,15.230000,135.000000,119,6.080000,7.200000,8,1.940000,3,False.\r\nWI,81,415,408-6089,no,yes,33,161.600000,117,27.470000,123.000000,90,10.460000,261.300000,101,11.760000,12.200000,5,3.290000,1,False.\r\nVT,162,510,375-2165,no,no,0,220.600000,117,37.500000,155.200000,121,13.190000,186.700000,89,8.400000,10.500000,11,2.840000,1,False.\r\nAZ,83,415,400-6999,no,yes,41,155.900000,122,26.500000,162.300000,107,13.800000,127.600000,105,5.740000,13.100000,5,3.540000,3,False.\r\nFL,100,510,420-7823,no,no,0,107.000000,63,18.190000,105.700000,67,8.980000,243.100000,74,10.940000,12.800000,3,3.460000,4,True.\r\nAK,59,510,366-5241,no,no,0,182.500000,104,31.030000,204.700000,95,17.400000,229.900000,100,10.350000,11.300000,8,3.050000,4,False.\r\nAR,179,415,413-3412,yes,yes,38,220.100000,78,37.420000,234.300000,71,19.920000,237.300000,85,10.680000,10.100000,4,2.730000,4,False.\r\nAR,79,408,406-2752,no,no,0,152.200000,112,25.870000,177.200000,132,15.060000,96.400000,87,4.340000,5.300000,3,1.430000,1,False.\r\nAK,117,415,337-8078,no,no,0,181.500000,95,30.860000,205.100000,88,17.430000,204.000000,82,9.180000,14.700000,9,3.970000,2,False.\r\nMS,64,408,402-1942,yes,no,0,236.200000,77,40.150000,218.600000,85,18.580000,194.100000,97,8.730000,13.200000,2,3.560000,2,True.\r\nME,31,415,371-7917,no,no,0,166.100000,105,28.240000,79.300000,93,6.740000,213.700000,98,9.620000,12.700000,2,3.430000,1,False.\r\nCA,124,408,343-6374,yes,no,0,244.600000,89,41.580000,188.800000,80,16.050000,206.000000,114,9.270000,11.300000,4,3.050000,1,False.\r\nNM,122,408,385-8730,no,yes,23,134.200000,85,22.810000,227.300000,132,19.320000,122.400000,96,5.510000,8.500000,2,2.300000,2,False.\r\nNE,37,408,393-7892,yes,yes,39,149.700000,122,25.450000,211.100000,75,17.940000,114.300000,90,5.140000,9.200000,4,2.480000,1,False.\r\nSC,90,408,407-6748,no,yes,29,150.100000,109,25.520000,264.700000,103,22.500000,178.400000,97,8.030000,5.800000,4,1.570000,1,False.\r\nCO,159,408,341-4463,yes,no,0,257.100000,53,43.710000,312.200000,127,26.540000,183.000000,82,8.240000,8.800000,6,2.380000,1,True.\r\nDE,148,415,351-2587,no,no,0,124.400000,83,21.150000,179.700000,81,15.270000,253.000000,99,11.390000,11.300000,6,3.050000,0,False.\r\nOH,39,415,421-9752,no,yes,36,141.700000,121,24.090000,232.300000,113,19.750000,222.100000,131,9.990000,12.000000,5,3.240000,1,False.\r\nMS,77,408,356-4001,no,no,0,230.000000,87,39.100000,103.200000,138,8.770000,309.600000,136,13.930000,11.300000,3,3.050000,2,False.\r\nOK,194,408,328-9869,no,no,0,162.300000,88,27.590000,213.700000,118,18.160000,192.100000,81,8.640000,10.900000,2,2.940000,0,False.\r\nCO,154,415,343-5709,no,no,0,350.800000,75,59.640000,216.500000,94,18.400000,253.900000,100,11.430000,10.100000,9,2.730000,1,True.\r\nNC,112,415,334-1872,no,no,0,193.300000,96,32.860000,264.100000,123,22.450000,128.600000,115,5.790000,9.100000,3,2.460000,4,False.\r\nMD,45,415,350-1040,no,no,0,78.200000,127,13.290000,253.400000,108,21.540000,255.000000,100,11.480000,18.000000,3,4.860000,1,False.\r\nKS,132,415,369-3214,no,no,0,83.400000,110,14.180000,232.200000,137,19.740000,146.700000,114,6.600000,7.600000,5,2.050000,1,False.\r\nMA,128,415,385-6778,no,no,0,195.600000,99,33.250000,267.800000,120,22.760000,164.900000,76,7.420000,16.000000,2,4.320000,0,False.\r\nNC,135,415,383-7689,no,no,0,201.800000,81,34.310000,225.000000,114,19.130000,204.400000,82,9.200000,10.300000,6,2.780000,1,False.\r\nNM,56,408,385-5722,no,no,0,197.000000,110,33.490000,222.800000,102,18.940000,225.300000,91,10.140000,10.600000,6,2.860000,2,False.\r\nCA,151,415,357-1909,yes,no,0,218.000000,57,37.060000,114.400000,88,9.720000,269.200000,95,12.110000,12.400000,1,3.350000,0,True.\r\nNY,32,415,364-3567,no,no,0,164.800000,98,28.020000,229.900000,96,19.540000,167.300000,108,7.530000,14.800000,2,4.000000,2,False.\r\nAZ,90,415,422-4241,no,no,0,179.200000,77,30.460000,210.700000,99,17.910000,276.900000,58,12.460000,9.200000,6,2.480000,2,False.\r\nSD,87,415,370-2957,no,yes,21,214.000000,113,36.380000,180.000000,114,15.300000,134.500000,82,6.050000,10.600000,5,2.860000,0,False.\r\nDC,138,415,329-6562,no,no,0,170.500000,87,28.990000,118.200000,116,10.050000,187.900000,111,8.460000,11.200000,7,3.020000,2,False.\r\nND,79,408,363-3515,no,no,0,205.700000,123,34.970000,214.500000,108,18.230000,226.100000,106,10.170000,6.700000,18,1.810000,1,False.\r\nMO,95,415,374-7787,yes,no,0,165.500000,84,28.140000,286.200000,112,24.330000,198.900000,89,8.950000,11.500000,2,3.110000,1,True.\r\nKS,127,415,345-2931,no,no,0,221.000000,100,37.570000,160.700000,113,13.660000,233.100000,96,10.490000,6.800000,4,1.840000,2,False.\r\nSD,137,510,373-5732,no,no,0,242.100000,118,41.160000,191.000000,93,16.240000,218.600000,50,9.840000,14.700000,2,3.970000,3,False.\r\nOK,97,415,348-7437,no,no,0,151.600000,107,25.770000,155.400000,96,13.210000,240.000000,112,10.800000,14.700000,4,3.970000,1,False.\r\nOR,149,415,332-9460,yes,no,0,176.200000,87,29.950000,145.000000,81,12.330000,249.500000,92,11.230000,5.700000,4,1.540000,0,False.\r\nIN,117,415,355-6531,yes,yes,22,196.000000,82,33.320000,322.700000,82,27.430000,225.600000,120,10.150000,3.700000,5,1.000000,1,False.\r\nOH,84,408,336-9390,no,no,0,159.500000,125,27.120000,247.100000,90,21.000000,187.900000,82,8.460000,7.200000,4,1.940000,2,False.\r\nKS,137,415,346-8581,no,no,0,230.200000,113,39.130000,220.400000,79,18.730000,204.700000,111,9.210000,10.700000,7,2.890000,4,False.\r\nCT,99,415,363-8824,no,no,0,146.700000,64,24.940000,274.000000,99,23.290000,321.300000,98,14.460000,8.900000,1,2.400000,3,False.\r\nNH,54,510,353-3351,no,no,0,210.500000,102,35.790000,204.500000,83,17.380000,127.800000,53,5.750000,8.500000,5,2.300000,1,False.\r\nWI,85,415,360-4320,no,no,0,102.000000,95,17.340000,270.200000,139,22.970000,148.200000,105,6.670000,10.700000,3,2.890000,1,False.\r\nMS,150,510,417-6252,no,no,0,126.000000,99,21.420000,238.500000,73,20.270000,285.100000,100,12.830000,10.200000,6,2.750000,3,False.\r\nWV,43,415,393-4949,no,no,0,168.400000,125,28.630000,243.800000,89,20.720000,214.700000,102,9.660000,11.100000,2,3.000000,1,False.\r\nMA,35,415,401-3156,no,no,0,105.600000,129,17.950000,258.200000,129,21.950000,213.100000,77,9.590000,8.700000,3,2.350000,0,False.\r\nMD,98,415,338-6283,no,no,0,206.500000,92,35.110000,176.200000,152,14.980000,232.800000,115,10.480000,12.400000,5,3.350000,5,False.\r\nPA,112,510,352-9017,no,no,0,217.100000,76,36.910000,205.200000,100,17.440000,185.700000,91,8.360000,9.400000,3,2.540000,2,False.\r\nWI,16,510,405-5305,no,no,0,229.600000,78,39.030000,205.700000,108,17.480000,166.200000,91,7.480000,10.800000,2,2.920000,0,True.\r\nTN,98,415,376-9249,no,yes,22,278.300000,89,47.310000,93.400000,143,7.940000,107.600000,42,4.840000,9.700000,5,2.620000,0,False.\r\nTX,84,408,339-7139,no,no,0,138.600000,102,23.560000,199.000000,93,16.920000,204.100000,137,9.180000,7.800000,4,2.110000,0,False.\r\nOR,94,415,328-6011,no,no,0,234.400000,103,39.850000,279.300000,109,23.740000,234.200000,121,10.540000,2.000000,2,0.540000,1,True.\r\nIL,84,510,378-1303,no,no,0,181.500000,129,30.860000,130.700000,112,11.110000,186.500000,118,8.390000,8.500000,4,2.300000,1,False.\r\nDC,66,415,402-5155,no,no,0,167.300000,91,28.440000,230.000000,68,19.550000,191.700000,118,8.630000,10.600000,5,2.860000,1,True.\r\nGA,98,415,333-5430,no,yes,31,121.000000,105,20.570000,218.900000,98,18.610000,226.700000,110,10.200000,12.000000,1,3.240000,1,False.\r\nID,74,415,365-9696,no,no,0,221.100000,124,37.590000,110.800000,94,9.420000,240.100000,112,10.800000,10.600000,3,2.860000,0,False.\r\nUT,96,408,410-4023,no,yes,26,145.800000,108,24.790000,192.200000,89,16.340000,165.100000,96,7.430000,9.900000,2,2.670000,1,False.\r\nKY,119,510,411-7649,no,no,0,222.800000,122,37.880000,163.200000,107,13.870000,160.600000,112,7.230000,11.200000,6,3.020000,1,False.\r\nOH,73,415,338-4065,no,no,0,183.400000,80,31.180000,242.000000,115,20.570000,201.400000,100,9.060000,7.500000,3,2.030000,4,False.\r\nWI,92,415,421-9401,yes,no,0,264.300000,91,44.930000,160.900000,115,13.680000,198.600000,73,8.940000,9.300000,5,2.510000,0,False.\r\nIL,21,415,343-9658,no,no,0,146.000000,78,24.820000,109.700000,79,9.320000,247.400000,108,11.130000,6.800000,7,1.840000,0,False.\r\nDE,122,510,332-5521,no,no,0,157.100000,134,26.710000,184.900000,122,15.720000,197.200000,59,8.870000,8.500000,5,2.300000,4,True.\r\nRI,133,510,349-4369,yes,no,0,127.300000,108,21.640000,251.300000,81,21.360000,135.000000,88,6.080000,10.300000,3,2.780000,1,False.\r\nTX,145,415,351-4288,no,no,0,187.900000,110,31.940000,197.000000,117,16.750000,167.000000,108,7.520000,4.800000,4,1.300000,2,False.\r\nOR,25,408,422-5874,no,no,0,178.800000,90,30.400000,141.200000,72,12.000000,203.000000,99,9.140000,8.400000,5,2.270000,2,False.\r\nNV,64,415,396-2324,no,no,0,97.200000,80,16.520000,186.200000,90,15.830000,189.000000,92,8.500000,10.400000,6,2.810000,2,False.\r\nNE,85,415,416-5662,no,no,0,259.800000,85,44.170000,242.300000,117,20.600000,168.800000,72,7.600000,5.400000,1,1.460000,0,False.\r\nMS,126,415,363-9663,no,no,0,256.500000,112,43.610000,199.500000,90,16.960000,188.300000,122,8.470000,7.000000,5,1.890000,3,False.\r\nOR,76,415,410-9477,no,no,0,169.500000,77,28.820000,124.000000,87,10.540000,219.400000,92,9.870000,10.000000,3,2.700000,0,False.\r\nDE,113,415,352-4418,no,no,0,239.700000,47,40.750000,282.900000,110,24.050000,238.400000,88,10.730000,8.700000,3,2.350000,2,True.\r\nDE,224,510,361-6563,yes,no,0,171.500000,99,29.160000,160.000000,103,13.600000,212.400000,102,9.560000,5.000000,2,1.350000,1,True.\r\nAZ,117,408,417-4404,no,no,0,239.900000,84,40.780000,174.800000,106,14.860000,209.500000,93,9.430000,9.800000,2,2.650000,0,False.\r\nSD,128,408,372-8048,no,yes,34,142.300000,73,24.190000,194.800000,79,16.560000,239.300000,81,10.770000,16.000000,6,4.320000,1,False.\r\nNV,115,415,356-3646,no,no,0,184.100000,98,31.300000,327.000000,73,27.800000,212.500000,106,9.560000,7.500000,6,2.030000,2,False.\r\nNM,141,415,351-9604,no,yes,28,206.900000,126,35.170000,264.400000,126,22.470000,171.800000,124,7.730000,9.300000,11,2.510000,2,False.\r\nMN,51,510,355-9581,no,no,0,259.900000,114,44.180000,176.200000,94,14.980000,77.200000,112,3.470000,15.300000,1,4.130000,1,False.\r\nNJ,100,415,396-5189,no,no,0,203.800000,122,34.650000,283.100000,76,24.060000,197.300000,83,8.880000,12.500000,3,3.380000,0,False.\r\nIN,96,415,356-9187,no,yes,45,248.800000,124,42.300000,140.300000,77,11.930000,263.600000,102,11.860000,10.300000,2,2.780000,3,False.\r\nDC,112,415,394-5537,no,yes,16,221.600000,110,37.670000,130.200000,123,11.070000,200.000000,108,9.000000,11.300000,3,3.050000,1,False.\r\nMA,129,510,408-2712,yes,no,0,192.900000,131,32.790000,185.500000,101,15.770000,205.200000,130,9.230000,10.900000,4,2.940000,1,False.\r\nME,163,415,404-4486,no,no,0,122.400000,129,20.810000,113.400000,108,9.640000,180.200000,97,8.110000,12.500000,7,3.380000,1,False.\r\nNH,67,415,355-1113,no,yes,40,104.900000,65,17.830000,216.300000,93,18.390000,217.400000,128,9.780000,9.600000,9,2.590000,1,False.\r\nAZ,140,408,411-4674,no,no,0,173.200000,91,29.440000,196.800000,106,16.730000,209.300000,128,9.420000,11.200000,5,3.020000,3,False.\r\nOR,49,510,376-4519,no,no,0,119.400000,69,20.300000,273.300000,92,23.230000,214.400000,153,9.650000,12.400000,7,3.350000,2,False.\r\nKS,46,510,365-5979,no,no,0,250.300000,100,42.550000,260.600000,90,22.150000,195.000000,104,8.780000,13.300000,2,3.590000,2,True.\r\nNE,148,415,382-2879,no,no,0,178.300000,98,30.310000,282.600000,110,24.020000,181.000000,98,8.150000,11.400000,4,3.080000,1,False.\r\nMI,112,510,420-1383,no,no,0,243.400000,77,41.380000,182.100000,97,15.480000,259.200000,94,11.660000,12.800000,2,3.460000,1,False.\r\nSC,78,415,411-7390,no,no,0,155.000000,106,26.350000,175.300000,101,14.900000,155.600000,125,7.000000,11.800000,5,3.190000,2,False.\r\nPA,61,408,383-8848,no,yes,31,288.700000,101,49.080000,203.800000,102,17.320000,203.200000,49,9.140000,8.600000,3,2.320000,0,False.\r\nMT,58,510,387-9301,no,yes,29,240.400000,80,40.870000,118.900000,91,10.110000,164.200000,108,7.390000,11.200000,3,3.020000,1,False.\r\nNM,155,415,399-3164,no,no,0,190.300000,123,32.350000,301.300000,96,25.610000,214.600000,134,9.660000,8.000000,3,2.160000,1,False.\r\nOH,100,510,385-8997,no,no,0,278.000000,76,47.260000,176.700000,74,15.020000,219.500000,126,9.880000,8.300000,4,2.240000,0,True.\r\nWY,113,510,352-6573,no,no,0,155.000000,93,26.350000,330.600000,106,28.100000,189.400000,123,8.520000,13.500000,3,3.650000,1,False.\r\nMI,81,415,408-3384,no,no,0,153.500000,99,26.100000,197.600000,102,16.800000,198.500000,86,8.930000,6.300000,2,1.700000,2,False.\r\nAR,135,510,419-6033,no,yes,27,273.400000,141,46.480000,154.000000,99,13.090000,245.800000,112,11.060000,12.300000,6,3.320000,1,False.\r\nFL,99,408,336-2090,no,no,0,155.300000,93,26.400000,265.700000,95,22.580000,145.700000,67,6.560000,12.400000,4,3.350000,0,False.\r\nAR,59,510,343-7242,no,yes,29,133.100000,114,22.630000,221.200000,82,18.800000,131.600000,103,5.920000,6.800000,3,1.840000,1,False.\r\nMO,135,510,376-1713,no,no,0,246.800000,129,41.960000,187.800000,121,15.960000,154.500000,109,6.950000,12.600000,5,3.400000,1,False.\r\nWI,85,408,381-5878,yes,no,0,165.400000,107,28.120000,196.000000,126,16.660000,349.200000,110,15.710000,9.600000,7,2.590000,2,False.\r\nTX,70,510,390-5470,no,no,0,59.500000,103,10.120000,257.200000,106,21.860000,208.300000,86,9.370000,11.100000,6,3.000000,0,False.\r\nTX,88,510,414-4803,no,no,0,138.300000,116,23.510000,236.000000,138,20.060000,179.100000,110,8.060000,9.600000,4,2.590000,3,False.\r\nNM,55,510,382-5478,no,no,0,286.700000,100,48.740000,134.400000,121,11.420000,192.900000,122,8.680000,6.900000,5,1.860000,2,False.\r\nGA,75,415,333-7637,no,no,0,117.300000,114,19.940000,201.100000,61,17.090000,107.900000,82,4.860000,12.200000,3,3.290000,1,False.\r\nID,79,510,341-1647,no,yes,21,264.300000,79,44.930000,202.800000,118,17.240000,173.400000,92,7.800000,6.300000,3,1.700000,4,False.\r\nAL,85,408,411-4232,no,no,0,127.900000,107,21.740000,271.200000,124,23.050000,202.200000,76,9.100000,12.500000,5,3.380000,0,False.\r\nKS,86,408,339-2616,no,yes,23,225.500000,107,38.340000,246.300000,105,20.940000,245.700000,81,11.060000,9.800000,2,2.650000,0,False.\r\nSD,91,510,327-3850,no,no,0,149.000000,115,25.330000,245.300000,105,20.850000,260.000000,94,11.700000,8.300000,3,2.240000,0,False.\r\nLA,149,415,328-7209,no,yes,20,198.900000,77,33.810000,274.000000,88,23.290000,190.700000,76,8.580000,14.300000,9,3.860000,1,False.\r\nOH,97,408,405-6189,no,no,0,256.400000,125,43.590000,273.900000,100,23.280000,222.700000,101,10.020000,11.100000,1,3.000000,1,True.\r\nMA,88,415,418-6737,no,no,0,264.800000,124,45.020000,245.400000,112,20.860000,160.500000,115,7.220000,14.800000,2,4.000000,1,True.\r\nAZ,60,415,366-2212,no,no,0,98.200000,88,16.690000,180.500000,69,15.340000,223.600000,69,10.060000,9.300000,2,2.510000,2,False.\r\nKY,54,408,356-1420,no,no,0,159.800000,99,27.170000,264.000000,64,22.440000,115.700000,70,5.210000,9.700000,7,2.620000,2,False.\r\nDC,11,415,343-1323,no,yes,28,190.600000,86,32.400000,220.100000,122,18.710000,180.300000,80,8.110000,6.000000,3,1.620000,3,False.\r\nWA,109,415,361-8239,no,no,0,184.000000,120,31.280000,120.400000,119,10.230000,153.700000,86,6.920000,11.000000,3,2.970000,0,False.\r\nUT,90,415,384-1621,no,no,0,261.800000,128,44.510000,220.600000,104,18.750000,136.600000,91,6.150000,9.600000,5,2.590000,1,False.\r\nRI,115,408,360-3525,no,no,0,147.900000,109,25.140000,228.400000,117,19.410000,299.700000,90,13.490000,9.600000,9,2.590000,3,False.\r\nOH,144,415,392-3813,no,yes,18,106.400000,109,18.090000,108.100000,113,9.190000,208.400000,111,9.380000,10.100000,5,2.730000,1,False.\r\nNV,91,408,337-6898,no,no,0,133.700000,75,22.730000,195.300000,87,16.600000,280.500000,89,12.620000,5.900000,2,1.590000,0,False.\r\nND,105,415,366-8036,no,yes,23,193.500000,85,32.900000,220.200000,90,18.720000,272.400000,111,12.260000,8.500000,5,2.300000,0,False.\r\nNV,71,415,352-8327,yes,no,0,178.200000,113,30.290000,167.800000,94,14.260000,182.100000,111,8.190000,13.600000,3,3.670000,3,True.\r\nFL,132,510,334-9505,no,yes,36,226.200000,103,38.450000,181.600000,125,15.440000,258.800000,102,11.650000,10.500000,5,2.840000,3,True.\r\nMD,112,415,336-5702,no,no,0,170.400000,103,28.970000,200.200000,71,17.020000,258.300000,100,11.620000,11.600000,4,3.130000,1,False.\r\nAZ,86,415,392-2381,no,yes,32,70.900000,163,12.050000,166.700000,121,14.170000,244.900000,105,11.020000,11.100000,5,3.000000,3,False.\r\nAL,41,510,369-6880,no,yes,34,194.400000,63,33.050000,254.900000,110,21.670000,160.200000,115,7.210000,17.200000,9,4.640000,2,False.\r\nNE,44,415,416-8697,no,no,0,240.300000,146,40.850000,164.600000,83,13.990000,240.700000,106,10.830000,10.600000,2,2.860000,1,False.\r\nNV,78,408,345-3451,no,no,0,75.000000,116,12.750000,248.700000,87,21.140000,176.000000,83,7.920000,9.500000,6,2.570000,3,False.\r\nIL,149,408,379-2514,no,no,0,69.100000,117,11.750000,136.300000,100,11.590000,181.700000,53,8.180000,6.300000,3,1.700000,1,False.\r\nWV,72,510,418-6651,no,yes,33,96.600000,59,16.420000,315.400000,98,26.810000,163.300000,117,7.350000,6.200000,4,1.670000,4,True.\r\nMI,139,415,421-3528,no,yes,20,214.600000,101,36.480000,235.100000,132,19.980000,162.800000,132,7.330000,14.800000,12,4.000000,0,False.\r\nAR,74,510,329-9046,no,no,0,148.500000,111,25.250000,146.500000,42,12.450000,289.200000,83,13.010000,9.900000,6,2.670000,3,False.\r\nUT,50,510,406-3890,no,no,0,258.100000,106,43.880000,161.400000,106,13.720000,225.100000,110,10.130000,11.700000,5,3.160000,1,False.\r\nGA,141,510,403-8904,no,yes,23,149.700000,112,25.450000,162.500000,118,13.810000,220.300000,115,9.910000,7.600000,2,2.050000,3,False.\r\nAZ,140,408,393-4086,no,no,0,149.800000,134,25.470000,164.400000,98,13.970000,294.700000,124,13.260000,8.100000,2,2.190000,1,False.\r\nID,99,408,400-1367,no,no,0,190.400000,102,32.370000,158.100000,107,13.440000,271.500000,92,12.220000,11.200000,4,3.020000,2,False.\r\nHI,166,408,377-9473,no,no,0,181.400000,108,30.840000,253.800000,54,21.570000,112.300000,94,5.050000,11.600000,6,3.130000,1,False.\r\nNV,124,408,396-3068,no,no,0,151.100000,123,25.690000,187.400000,104,15.930000,255.400000,93,11.490000,5.300000,3,1.430000,1,False.\r\nMD,74,415,331-9293,no,no,0,155.700000,116,26.470000,173.700000,63,14.760000,257.400000,97,11.580000,8.100000,4,2.190000,0,False.\r\nGA,117,510,347-1914,no,no,0,149.900000,95,25.480000,256.100000,110,21.770000,212.700000,92,9.570000,13.300000,13,3.590000,2,False.\r\nGA,85,510,395-1962,no,no,0,222.300000,132,37.790000,231.500000,101,19.680000,223.500000,75,10.060000,11.000000,2,2.970000,3,False.\r\nUT,36,415,401-5485,no,yes,16,149.400000,111,25.400000,131.800000,113,11.200000,132.700000,87,5.970000,6.700000,2,1.810000,0,False.\r\nMA,102,510,355-6560,yes,no,0,233.800000,103,39.750000,221.600000,131,18.840000,146.900000,106,6.610000,12.800000,3,3.460000,0,False.\r\nIN,76,415,363-3911,no,no,0,204.200000,100,34.710000,292.600000,139,24.870000,244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,367-7039,no,no,0,47.400000,125,8.060000,167.800000,90,14.260000,163.100000,107,7.340000,10.500000,8,2.840000,2,False.\r\nMI,30,510,391-6607,no,no,0,227.400000,88,38.660000,182.500000,100,15.510000,191.700000,134,8.630000,12.500000,3,3.380000,0,False.\r\nNJ,95,415,379-6652,no,yes,22,40.900000,126,6.950000,133.400000,90,11.340000,264.200000,91,11.890000,11.900000,7,3.210000,0,False.\r\nID,46,415,384-1833,no,no,0,124.800000,133,21.220000,157.300000,143,13.370000,199.300000,72,8.970000,8.600000,4,2.320000,2,False.\r\nDC,100,510,403-2455,yes,no,0,68.500000,110,11.650000,337.100000,115,28.650000,205.200000,99,9.230000,12.100000,9,3.270000,0,False.\r\nNY,47,415,391-1348,no,yes,37,163.500000,77,27.800000,203.100000,102,17.260000,232.000000,87,10.440000,7.800000,4,2.110000,2,False.\r\nAL,77,415,408-4174,no,no,0,163.000000,112,27.710000,219.100000,89,18.620000,233.400000,66,10.500000,6.700000,3,1.810000,2,False.\r\nOR,98,415,366-4334,no,yes,38,213.700000,61,36.330000,253.000000,104,21.510000,207.700000,73,9.350000,10.700000,5,2.890000,2,False.\r\nOK,125,415,406-5059,no,yes,36,201.300000,117,34.220000,42.200000,78,3.590000,125.700000,104,5.660000,5.400000,3,1.460000,1,False.\r\nLA,67,510,373-6784,no,no,0,310.400000,97,52.770000,66.500000,123,5.650000,246.500000,99,11.090000,9.200000,10,2.480000,4,False.\r\nNE,194,408,408-3532,no,no,0,48.400000,101,8.230000,281.100000,138,23.890000,218.500000,87,9.830000,18.200000,1,4.910000,1,False.\r\nTX,128,415,350-8680,no,yes,40,171.200000,88,29.100000,145.700000,109,12.380000,196.800000,93,8.860000,14.000000,6,3.780000,1,False.\r\nUT,190,415,398-9870,no,yes,22,166.500000,93,28.310000,183.000000,92,15.560000,121.000000,102,5.440000,8.500000,3,2.300000,0,False.\r\nOR,165,415,343-3356,no,no,0,216.600000,126,36.820000,190.800000,104,16.220000,224.700000,123,10.110000,12.400000,8,3.350000,0,False.\r\nNY,59,408,415-4609,no,no,0,107.800000,113,18.330000,216.600000,125,18.410000,217.500000,92,9.790000,9.900000,3,2.670000,2,False.\r\nAL,47,408,404-5387,no,yes,28,141.300000,94,24.020000,168.000000,108,14.280000,113.500000,84,5.110000,7.800000,2,2.110000,1,False.\r\nRI,150,415,415-8151,no,yes,29,209.900000,77,35.680000,158.000000,52,13.430000,141.900000,113,6.390000,6.600000,1,1.780000,0,False.\r\nMN,152,415,416-2778,yes,yes,20,237.500000,120,40.380000,253.400000,94,21.540000,265.200000,80,11.930000,14.200000,3,3.830000,9,True.\r\nNC,26,415,393-3300,no,no,0,234.500000,109,39.870000,216.500000,129,18.400000,191.600000,94,8.620000,3.500000,6,0.950000,3,False.\r\nMD,79,510,391-7661,no,yes,31,103.100000,90,17.530000,243.000000,135,20.660000,76.400000,92,3.440000,12.200000,8,3.290000,3,False.\r\nRI,95,510,339-4317,no,yes,27,129.500000,106,22.020000,248.900000,90,21.160000,268.000000,115,12.060000,11.900000,3,3.210000,1,False.\r\nWI,69,510,418-6455,yes,no,0,279.800000,90,47.570000,248.700000,91,21.140000,171.000000,118,7.690000,8.400000,10,2.270000,2,True.\r\nVT,95,510,378-3508,yes,yes,41,136.800000,91,23.260000,200.800000,61,17.070000,133.700000,67,6.020000,10.300000,9,2.780000,5,True.\r\nCT,31,415,390-9359,no,yes,31,100.100000,54,17.020000,246.300000,97,20.940000,255.000000,131,11.480000,5.900000,3,1.590000,0,False.\r\nOK,121,408,364-2495,no,yes,31,237.100000,63,40.310000,205.600000,117,17.480000,196.700000,85,8.850000,10.100000,5,2.730000,4,False.\r\nAK,111,415,364-7719,no,no,0,172.800000,58,29.380000,183.100000,108,15.560000,158.800000,104,7.150000,7.900000,3,2.130000,4,True.\r\nNY,157,415,421-1189,no,no,0,224.500000,111,38.170000,200.700000,99,17.060000,116.600000,118,5.250000,11.500000,2,3.110000,2,False.\r\nGA,44,510,419-8987,no,no,0,288.100000,112,48.980000,258.000000,92,21.930000,192.400000,90,8.660000,10.200000,4,2.750000,3,True.\r\nUT,61,510,402-9980,yes,no,0,78.200000,103,13.290000,195.900000,149,16.650000,108.000000,100,4.860000,10.100000,6,2.730000,2,False.\r\nNM,65,415,376-5908,no,no,0,148.700000,80,25.280000,259.000000,94,22.020000,149.500000,107,6.730000,12.700000,6,3.430000,2,False.\r\nNE,74,415,400-3150,no,yes,25,194.600000,84,33.080000,119.900000,103,10.190000,175.500000,75,7.900000,13.100000,2,3.540000,2,False.\r\nNJ,123,408,336-1749,no,no,0,159.500000,77,27.120000,303.800000,92,25.820000,226.900000,120,10.210000,12.000000,4,3.240000,0,False.\r\nTX,58,408,420-1259,no,yes,20,194.500000,110,33.070000,213.700000,89,18.160000,236.600000,92,10.650000,9.500000,2,2.570000,1,False.\r\nMT,74,408,339-7541,no,no,0,174.100000,96,29.600000,251.100000,94,21.340000,257.600000,123,11.590000,8.300000,5,2.240000,2,True.\r\nCO,125,415,378-9029,no,no,0,131.800000,97,22.410000,136.700000,100,11.620000,308.200000,119,13.870000,7.700000,6,2.080000,2,False.\r\nVT,80,415,342-7514,no,no,0,160.600000,103,27.300000,237.000000,109,20.150000,245.100000,88,11.030000,10.700000,1,2.890000,1,False.\r\nRI,53,408,422-4956,no,yes,18,146.800000,107,24.960000,310.000000,84,26.350000,178.700000,130,8.040000,7.200000,7,1.940000,0,False.\r\nWY,99,408,389-8606,no,yes,28,200.700000,88,34.120000,264.200000,116,22.460000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0000,237.400000,118,10.680000,7.500000,3,2.030000,0,False.\r\nMT,13,415,347-9421,no,yes,31,265.300000,94,45.100000,147.600000,95,12.550000,259.300000,117,11.670000,12.900000,1,3.480000,1,False.\r\nDE,106,415,419-3167,no,no,0,128.600000,83,21.860000,134.000000,114,11.390000,210.600000,113,9.480000,11.400000,2,3.080000,0,False.\r\nND,88,415,414-4162,no,no,0,161.500000,92,27.460000,173.500000,108,14.750000,206.200000,95,9.280000,7.900000,4,2.130000,2,False.\r\nVA,74,408,416-5341,no,no,0,165.300000,120,28.100000,198.500000,106,16.870000,208.500000,102,9.380000,9.800000,3,2.650000,1,False.\r\nIL,83,415,368-8600,no,no,0,195.000000,92,33.150000,210.500000,83,17.890000,180.600000,92,8.130000,11.000000,13,2.970000,0,False.\r\nOK,49,415,336-6085,no,no,0,213.800000,79,36.350000,265.100000,93,22.530000,239.800000,128,10.790000,15.600000,7,4.210000,0,False.\r\nCO,111,510,377-1479,no,yes,24,205.500000,114,34.940000,219.300000,99,18.640000,215.900000,95,9.720000,14.000000,4,3.780000,1,False.\r\nMT,50,415,360-2107,no,yes,22,252.900000,112,42.990000,177.900000,99,15.120000,158.400000,146,7.130000,8.500000,4,2.300000,3,False.\r\nWV,153,408,405-9384,no,yes,28,235.600000,74,40.050000,227.900000,37,19.370000,170.300000,103,7.660000,15.400000,9,4.160000,0,False.\r\nME,88,415,420-5179,no,no,0,192.000000,91,32.640000,127.600000,127,10.850000,155.600000,125,7.000000,7.500000,5,2.030000,1,False.\r\nWI,131,415,331-3174,no,yes,39,69.100000,122,11.750000,101.300000,136,8.610000,104.800000,94,4.720000,9.100000,4,2.460000,0,False.\r\nMO,79,408,411-5958,no,no,0,261.700000,97,44.490000,210.600000,48,17.900000,256.700000,83,11.550000,6.000000,3,1.620000,3,True.\r\nNY,140,415,333-8180,no,no,0,235.500000,81,40.040000,257.200000,130,21.860000,103.100000,111,4.640000,11.500000,4,3.110000,2,False.\r\nCT,105,408,357-2679,no,no,0,213.400000,100,36.280000,204.900000,52,17.420000,179.700000,93,8.090000,9.500000,6,2.570000,1,False.\r\nAR,54,415,396-2867,no,yes,39,206.900000,143,35.170000,127.800000,72,10.860000,199.200000,120,8.960000,9.200000,1,2.480000,3,False.\r\nWY,87,415,341-9443,no,yes,22,263.800000,65,44.850000,103.400000,115,8.790000,208.100000,109,9.360000,8.500000,3,2.300000,3,False.\r\nCA,96,510,341-4103,no,yes,31,183.400000,126,31.180000,195.500000,106,16.620000,180.100000,93,8.100000,10.500000,5,2.840000,1,False.\r\nCA,79,510,416-8701,no,no,0,157.600000,85,26.790000,194.100000,92,16.500000,231.500000,86,10.420000,9.400000,10,2.540000,5,True.\r\nMN,55,415,397-6109,no,no,0,175.600000,147,29.850000,161.800000,118,13.750000,289.500000,55,13.030000,9.300000,4,2.510000,0,False.\r\nAK,130,415,392-5587,no,no,0,242.500000,101,41.230000,102.800000,114,8.740000,142.400000,89,6.410000,9.300000,2,2.510000,2,False.\r\nVA,34,415,392-9342,no,no,0,151.000000,102,25.670000,131.400000,101,11.170000,186.600000,86,8.400000,9.900000,7,2.670000,0,False.\r\nCO,139,415,368-2845,no,no,0,138.100000,103,23.480000,164.500000,100,13.980000,134.900000,63,6.070000,8.300000,2,2.240000,1,False.\r\nMT,109,408,405-4920,no,no,0,264.700000,69,45.000000,305.000000,120,25.930000,197.400000,86,8.880000,9.500000,9,2.570000,1,True.\r\nSD,65,408,348-7484,no,yes,31,282.300000,70,47.990000,152.000000,89,12.920000,225.500000,93,10.150000,12.000000,4,3.240000,1,False.\r\nNE,63,415,338-5207,no,no,0,211.200000,80,35.900000,237.700000,93,20.200000,259.200000,58,11.660000,12.300000,2,3.320000,0,False.\r\nVT,152,415,418-7846,no,no,0,197.100000,126,33.510000,130.100000,76,11.060000,78.100000,100,3.510000,7.400000,4,2.000000,3,False.\r\nND,147,408,358-8729,no,no,0,205.300000,95,34.900000,166.700000,128,14.170000,240.600000,84,10.830000,7.800000,4,2.110000,1,False.\r\nGA,112,415,349-1943,no,yes,22,181.800000,110,30.910000,228.100000,123,19.390000,262.700000,141,11.820000,9.200000,4,2.480000,2,False.\r\nOR,120,415,368-8283,no,no,0,252.000000,120,42.840000,150.200000,106,12.770000,151.800000,96,6.830000,9.600000,1,2.590000,2,False.\r\nMT,27,510,345-1419,no,no,0,193.800000,102,32.950000,118.900000,104,10.110000,135.900000,124,6.120000,9.200000,3,2.480000,0,False.\r\nWY,171,415,358-8025,no,no,0,231.200000,135,39.300000,188.700000,74,16.040000,206.900000,124,9.310000,12.300000,1,3.320000,1,False.\r\nGA,101,415,383-8695,no,yes,33,200.100000,108,34.020000,188.900000,122,16.060000,205.100000,90,9.230000,15.500000,4,4.190000,0,False.\r\nWV,32,408,370-7565,no,yes,26,266.700000,109,45.340000,232.300000,107,19.750000,212.800000,98,9.580000,16.300000,4,4.400000,1,False.\r\nCT,3,415,401-6162,no,yes,36,118.100000,117,20.080000,221.500000,125,18.830000,103.900000,89,4.680000,11.900000,6,3.210000,2,False.\r\nIL,151,408,386-5303,no,no,0,175.300000,106,29.800000,144.300000,87,12.270000,160.200000,88,7.210000,11.800000,5,3.190000,0,False.\r\nCO,60,408,351-6552,no,no,0,125.100000,99,21.270000,248.800000,62,21.150000,211.300000,79,9.510000,11.200000,3,3.020000,3,False.\r\nDE,119,415,345-5338,no,no,0,176.800000,90,30.060000,224.700000,81,19.100000,204.600000,77,9.210000,7.500000,15,2.030000,1,False.\r\nLA,43,415,330-2849,no,no,0,241.900000,101,41.120000,129.400000,121,11.000000,264.800000,104,11.920000,5.900000,3,1.590000,1,False.\r\nMA,42,408,364-6801,no,no,0,241.200000,134,41.000000,116.500000,114,9.900000,152.200000,91,6.850000,10.600000,4,2.860000,0,False.\r\nIN,84,408,375-3003,no,no,0,217.100000,99,36.910000,236.000000,68,20.060000,118.300000,120,5.320000,9.400000,4,2.540000,1,False.\r\nNY,65,510,383-8878,no,no,0,195.400000,110,33.220000,181.200000,109,15.400000,178.500000,105,8.030000,8.900000,4,2.400000,0,False.\r\nTX,75,415,384-2372,yes,no,0,222.400000,78,37.810000,327.000000,111,27.800000,208.000000,104,9.360000,8.700000,9,2.350000,1,True.\r\nKS,116,510,377-7107,no,no,0,189.500000,90,32.220000,189.800000,118,16.130000,205.800000,83,9.260000,13.100000,2,3.540000,1,False.\r\nWV,107,415,361-1581,no,no,0,123.100000,100,20.930000,158.400000,82,13.460000,256.100000,82,11.520000,9.300000,5,2.510000,0,False.\r\nNE,189,415,417-7888,no,yes,38,256.700000,98,43.640000,150.500000,120,12.790000,123.000000,87,5.540000,11.400000,3,3.080000,3,False.\r\nND,123,408,383-8364,no,no,0,159.100000,94,27.050000,241.600000,119,20.540000,202.400000,120,9.110000,6.500000,1,1.760000,1,False.\r\nAK,110,408,396-2335,no,no,0,100.100000,90,17.020000,233.300000,93,19.830000,204.400000,57,9.200000,11.100000,8,3.000000,3,False.\r\nCO,63,415,408-4530,no,yes,32,30.900000,113,5.250000,187.000000,113,15.900000,230.800000,101,10.390000,8.600000,7,2.320000,1,False.\r\nME,176,415,408-6621,no,no,0,223.200000,76,37.940000,214.400000,131,18.220000,154.400000,80,6.950000,10.100000,2,2.730000,3,False.\r\nSC,108,510,393-7522,no,no,0,187.400000,101,31.860000,199.900000,126,16.990000,216.100000,107,9.720000,12.600000,8,3.400000,1,False.\r\nMN,13,510,338-7120,no,yes,21,315.600000,105,53.650000,208.900000,71,17.760000,260.100000,123,11.700000,12.100000,3,3.270000,3,False.\r\nCO,71,415,357-4265,no,no,0,277.500000,104,47.180000,131.800000,121,11.200000,126.900000,101,5.710000,8.200000,2,2.210000,1,False.\r\nKS,88,415,398-8801,no,no,0,189.800000,111,32.270000,197.300000,101,16.770000,234.500000,111,10.550000,14.900000,3,4.020000,2,False.\r\nMS,137,510,346-2347,no,no,0,147.200000,119,25.020000,192.800000,91,16.390000,172.700000,105,7.770000,10.200000,4,2.750000,1,False.\r\nNE,82,408,343-2741,no,no,0,185.800000,36,31.590000,276.500000,134,23.500000,192.100000,104,8.640000,5.700000,7,1.540000,4,False.\r\nNJ,92,510,420-8242,no,yes,29,155.400000,110,26.420000,188.500000,104,16.020000,254.900000,118,11.470000,8.000000,4,2.160000,3,False.\r\nWI,165,510,402-7746,no,no,0,154.200000,91,26.210000,268.600000,108,22.830000,188.800000,99,8.500000,10.900000,4,2.940000,6,False.\r\nMT,96,415,332-1494,no,no,0,97.600000,98,16.590000,105.500000,118,8.970000,220.200000,105,9.910000,11.600000,9,3.130000,1,False.\r\nAR,156,415,388-6223,no,no,0,178.800000,94,30.400000,178.400000,97,15.160000,169.200000,77,7.610000,7.500000,3,2.030000,1,False.\r\nWA,63,408,404-9539,no,no,0,149.300000,104,25.380000,273.600000,75,23.260000,206.600000,72,9.300000,9.100000,4,2.460000,0,False.\r\nNH,37,415,341-7332,no,no,0,206.000000,89,35.020000,186.000000,88,15.810000,307.100000,86,13.820000,8.400000,11,2.270000,0,False.\r\nIA,98,415,338-7886,no,no,0,216.800000,86,36.860000,190.800000,114,16.220000,187.500000,79,8.440000,11.000000,9,2.970000,0,False.\r\nWV,121,415,332-5596,no,no,0,103.300000,110,17.560000,129.100000,82,10.970000,167.100000,113,7.520000,10.700000,3,2.890000,0,False.\r\nRI,94,415,348-9945,no,no,0,139.400000,95,23.700000,159.100000,92,13.520000,128.200000,129,5.770000,7.700000,3,2.080000,0,False.\r\nKS,99,415,407-1896,no,no,0,191.200000,110,32.500000,163.900000,102,13.930000,243.600000,114,10.960000,14.100000,3,3.810000,1,False.\r\nMT,163,510,398-9408,no,yes,23,160.000000,104,27.200000,189.400000,64,16.100000,229.900000,118,10.350000,10.400000,7,2.810000,1,False.\r\nMO,161,510,369-8005,no,no,0,221.700000,95,37.690000,193.000000,82,16.410000,194.100000,113,8.730000,6.500000,4,1.760000,3,False.\r\nHI,99,415,346-2530,no,no,0,62.900000,81,10.690000,231.000000,64,19.640000,168.900000,121,7.600000,8.500000,5,2.300000,1,False.\r\nCO,108,415,400-5984,no,no,0,215.600000,78,36.650000,195.300000,119,16.600000,194.400000,65,8.750000,3.600000,5,0.970000,1,False.\r\nCT,84,510,351-1007,no,yes,42,165.300000,97,28.100000,223.500000,118,19.000000,260.800000,72,11.740000,7.600000,7,2.050000,3,False.\r\nID,83,415,345-5980,yes,yes,32,94.700000,111,16.100000,154.400000,98,13.120000,200.400000,109,9.020000,10.600000,6,2.860000,2,False.\r\nDC,139,510,368-8964,no,no,0,203.200000,81,34.540000,152.500000,99,12.960000,197.800000,76,8.900000,9.700000,3,2.620000,2,False.\r\nTN,69,510,358-1912,no,no,0,195.300000,70,33.200000,216.700000,108,18.420000,259.900000,119,11.700000,12.500000,4,3.380000,3,False.\r\nWY,129,510,379-3132,no,no,0,143.700000,114,24.430000,297.800000,98,25.310000,212.600000,86,9.570000,11.400000,8,3.080000,4,False.\r\nMO,106,415,340-9910,no,no,0,114.400000,104,19.450000,78.300000,101,6.660000,232.700000,78,10.470000,0.000000,0,0.000000,2,False.\r\nVA,158,415,396-2719,no,no,0,222.800000,101,37.880000,203.000000,128,17.260000,210.600000,106,9.480000,6.900000,2,1.860000,2,False.\r\nSD,168,415,369-6204,no,yes,22,175.900000,70,29.900000,211.700000,105,17.990000,174.500000,81,7.850000,7.300000,5,1.970000,2,False.\r\nWV,115,510,420-9971,yes,no,0,249.900000,95,42.480000,242.500000,104,20.610000,151.700000,121,6.830000,15.300000,6,4.130000,1,True.\r\nGA,57,408,410-3782,yes,yes,30,234.500000,130,39.870000,195.200000,116,16.590000,268.800000,94,12.100000,11.400000,4,3.080000,2,False.\r\nAZ,67,415,404-4481,no,no,0,210.700000,116,35.820000,219.200000,86,18.630000,179.700000,83,8.090000,7.200000,6,1.940000,1,False.\r\nAK,127,408,383-9255,no,no,0,182.300000,124,30.990000,169.900000,110,14.440000,184.000000,116,8.280000,9.300000,3,2.510000,1,False.\r\nAK,78,510,418-9385,no,no,0,190.300000,88,32.350000,194.500000,89,16.530000,256.500000,109,11.540000,11.700000,5,3.160000,2,False.\r\nCT,100,415,360-9676,no,yes,38,177.100000,88,30.110000,163.700000,108,13.910000,242.700000,72,10.920000,7.400000,2,2.000000,0,False.\r\nUT,103,510,327-3587,no,yes,36,87.200000,92,14.820000,169.300000,110,14.390000,166.700000,80,7.500000,10.900000,5,2.940000,6,True.\r\nKY,113,415,385-4715,no,no,0,215.600000,96,36.650000,193.400000,127,16.440000,105.400000,115,4.740000,13.500000,3,3.650000,1,False.\r\nMI,78,510,414-2695,no,no,0,137.400000,109,23.360000,237.600000,49,20.200000,206.700000,136,9.300000,14.000000,11,3.780000,3,False.\r\nOR,129,510,331-5999,no,yes,36,192.800000,103,32.780000,177.000000,83,15.050000,216.500000,118,9.740000,16.400000,5,4.430000,1,False.\r\nTN,57,510,337-7739,no,no,0,149.300000,100,25.380000,200.200000,110,17.020000,231.700000,101,10.430000,11.900000,3,3.210000,2,False.\r\nWV,82,408,388-6658,no,no,0,143.700000,116,24.430000,170.700000,99,14.510000,287.700000,95,12.950000,7.800000,5,2.110000,1,False.\r\nNJ,64,415,405-6943,no,no,0,224.800000,111,38.220000,190.000000,101,16.150000,221.400000,110,9.960000,9.200000,2,2.480000,1,False.\r\nMS,86,510,382-4084,no,yes,39,261.200000,122,44.400000,214.200000,101,18.210000,154.900000,101,6.970000,12.700000,5,3.430000,2,False.\r\nME,151,415,352-8249,no,yes,26,196.500000,98,33.410000,175.800000,111,14.940000,221.800000,124,9.980000,13.400000,5,3.620000,0,False.\r\nWY,94,510,353-8363,no,no,0,271.200000,105,46.100000,202.600000,105,17.220000,221.600000,51,9.970000,11.500000,3,3.110000,3,True.\r\nWY,90,415,416-2825,no,no,0,207.200000,121,35.220000,292.500000,104,24.860000,226.300000,103,10.180000,8.000000,1,2.160000,2,False.\r\nIN,48,510,342-6696,no,no,0,300.400000,94,51.070000,133.200000,103,11.320000,197.400000,94,8.880000,7.200000,5,1.940000,2,False.\r\nNM,85,408,338-9210,no,yes,37,229.600000,123,39.030000,132.300000,90,11.250000,211.900000,76,9.540000,9.500000,8,2.570000,2,False.\r\nNJ,93,415,328-1768,yes,yes,20,187.500000,110,31.880000,169.800000,94,14.430000,175.300000,127,7.890000,12.100000,4,3.270000,1,False.\r\nDC,169,415,406-5870,yes,no,0,57.100000,98,9.710000,199.700000,78,16.970000,274.700000,103,12.360000,6.500000,6,1.760000,3,False.\r\nUT,68,415,398-3834,no,no,0,162.100000,86,27.560000,155.000000,86,13.180000,189.700000,87,8.540000,11.000000,9,2.970000,5,True.\r\nKY,91,415,330-7754,yes,no,0,145.000000,89,24.650000,175.800000,102,14.940000,223.700000,151,10.070000,16.700000,3,4.510000,2,True.\r\nKS,68,510,414-9054,no,no,0,159.500000,123,27.120000,240.800000,93,20.470000,210.300000,76,9.460000,11.400000,3,3.080000,1,False.\r\nMI,101,510,350-2832,no,no,0,190.700000,72,32.420000,208.600000,103,17.730000,203.800000,111,9.170000,8.800000,8,2.380000,1,False.\r\nUT,67,510,414-9027,no,yes,20,230.600000,40,39.200000,189.100000,58,16.070000,162.200000,115,7.300000,9.400000,2,2.540000,1,False.\r\nNE,66,415,337-1225,no,no,0,34.000000,133,5.780000,278.600000,61,23.680000,129.600000,120,5.830000,11.500000,3,3.110000,0,False.\r\nFL,116,415,394-6577,no,yes,17,193.400000,112,32.880000,240.600000,131,20.450000,248.100000,98,11.160000,11.400000,3,3.080000,5,False.\r\nLA,158,408,359-6995,no,no,0,202.000000,126,34.340000,163.500000,86,13.900000,195.400000,84,8.790000,10.400000,6,2.810000,1,False.\r\nMT,78,415,377-7561,no,no,0,191.700000,122,32.590000,241.400000,88,20.520000,203.500000,86,9.160000,9.100000,5,2.460000,1,False.\r\nWA,119,415,380-6631,no,yes,26,161.300000,97,27.420000,250.300000,110,21.280000,142.400000,92,6.410000,6.600000,8,1.780000,1,False.\r\nMI,120,415,390-8876,no,no,0,150.600000,85,25.600000,119.000000,128,10.120000,232.900000,123,10.480000,6.400000,2,1.730000,1,False.\r\nKY,155,510,413-2201,no,no,0,184.600000,102,31.380000,196.000000,117,16.660000,226.500000,122,10.190000,7.800000,1,2.110000,1,False.\r\nLA,106,408,374-2073,no,no,0,220.700000,120,37.520000,270.200000,95,22.970000,121.600000,113,5.470000,8.700000,5,2.350000,1,False.\r\nNM,87,510,417-1272,yes,no,0,167.300000,119,28.440000,198.500000,119,16.870000,133.100000,88,5.990000,11.000000,6,2.970000,1,False.\r\nAL,146,415,358-1129,no,yes,32,154.000000,80,26.180000,185.500000,91,15.770000,148.200000,107,6.670000,8.200000,4,2.210000,3,False.\r\nMO,101,415,394-1211,no,yes,29,121.100000,116,20.590000,186.400000,100,15.840000,241.700000,75,10.880000,10.100000,6,2.730000,0,False.\r\nCO,22,510,327-1319,no,yes,23,182.100000,94,30.960000,164.600000,59,13.990000,128.800000,102,5.800000,12.700000,4,3.430000,3,False.\r\nTX,90,415,399-4413,no,no,0,109.600000,88,18.630000,137.600000,108,11.700000,159.700000,121,7.190000,11.000000,5,2.970000,2,False.\r\nNY,41,415,393-9985,no,no,0,209.900000,105,35.680000,121.900000,105,10.360000,253.700000,104,11.420000,9.600000,4,2.590000,1,False.\r\nOR,69,415,401-8377,no,no,0,167.500000,76,28.480000,242.100000,92,20.580000,101.200000,103,4.550000,11.400000,4,3.080000,2,False.\r\nWY,33,415,331-3202,no,no,0,213.900000,88,36.360000,239.800000,119,20.380000,148.700000,71,6.690000,9.800000,14,2.650000,2,False.\r\nUT,112,415,358-5953,no,no,0,115.800000,108,19.690000,243.300000,111,20.680000,184.600000,78,8.310000,13.100000,5,3.540000,1,False.\r\nLA,108,510,380-7624,no,yes,30,276.600000,99,47.020000,220.100000,113,18.710000,177.900000,95,8.010000,9.800000,6,2.650000,2,False.\r\nNV,136,415,416-5261,no,yes,21,179.400000,88,30.500000,181.100000,97,15.390000,320.700000,120,14.430000,9.500000,4,2.570000,2,False.\r\nNC,128,510,417-5067,no,no,0,187.300000,84,31.840000,270.800000,95,23.020000,206.400000,68,9.290000,10.100000,5,2.730000,1,False.\r\nNC,27,408,345-6515,no,no,0,201.200000,128,34.200000,227.200000,100,19.310000,145.800000,91,6.560000,8.400000,3,2.270000,2,False.\r\nWY,161,415,406-1349,yes,no,0,189.600000,78,32.230000,267.400000,117,22.730000,184.500000,137,8.300000,1.300000,6,0.350000,1,False.\r\nTN,33,415,360-9038,no,yes,35,186.800000,124,31.760000,261.000000,69,22.190000,317.800000,103,14.300000,15.000000,5,4.050000,0,False.\r\nFL,120,415,348-3444,no,yes,31,153.500000,83,26.100000,219.100000,96,18.620000,237.400000,76,10.680000,11.400000,4,3.080000,0,False.\r\nCA,113,415,370-2892,no,no,0,187.600000,97,31.890000,208.200000,118,17.700000,158.900000,101,7.150000,8.700000,6,2.350000,2,False.\r\nPA,122,415,383-4061,yes,no,0,230.900000,132,39.250000,243.200000,99,20.670000,182.400000,57,8.210000,11.000000,2,2.970000,0,True.\r\nWV,148,415,391-7937,no,yes,26,244.900000,150,41.630000,118.000000,138,10.030000,236.000000,91,10.620000,15.200000,4,4.100000,2,False.\r\nNJ,74,415,389-4083,no,no,0,230.900000,93,39.250000,223.000000,78,18.960000,157.800000,101,7.100000,9.700000,2,2.620000,3,False.\r\nMT,106,415,410-9633,no,no,0,187.100000,104,31.810000,250.200000,117,21.270000,144.900000,81,6.520000,11.000000,3,2.970000,1,False.\r\nMN,179,415,418-9502,no,no,0,170.700000,54,29.020000,191.100000,108,16.240000,214.600000,107,9.660000,13.300000,4,3.590000,1,False.\r\nWI,149,415,339-6637,yes,yes,28,126.900000,97,21.570000,166.900000,102,14.190000,145.200000,77,6.530000,8.800000,3,2.380000,5,True.\r\nID,77,510,356-3403,no,no,0,189.500000,112,32.220000,207.000000,95,17.600000,214.100000,91,9.630000,9.200000,7,2.480000,0,False.\r\nMA,127,408,371-9457,yes,no,0,176.900000,110,30.070000,167.900000,100,14.270000,182.200000,138,8.200000,7.700000,2,2.080000,1,True.\r\nOR,80,415,391-8087,no,no,0,161.100000,99,27.390000,198.800000,81,16.900000,228.400000,116,10.280000,10.600000,4,2.860000,1,False.\r\nMT,106,510,392-6420,no,no,0,169.400000,107,28.800000,197.200000,71,16.760000,202.200000,79,9.100000,10.700000,4,2.890000,1,False.\r\nAL,61,415,399-4094,no,yes,20,254.400000,133,43.250000,161.700000,96,13.740000,251.400000,91,11.310000,10.500000,4,2.840000,0,False.\r\nND,135,510,378-4013,yes,yes,24,127.700000,54,21.710000,215.000000,105,18.280000,234.300000,84,10.540000,5.800000,4,1.570000,2,False.\r\nLA,115,415,386-6306,no,yes,26,170.500000,107,28.990000,217.200000,77,18.460000,225.700000,71,10.160000,13.600000,5,3.670000,6,False.\r\nND,167,408,359-3618,yes,no,0,219.100000,100,37.250000,242.900000,90,20.650000,168.900000,101,7.600000,10.100000,4,2.730000,2,False.\r\nMS,107,510,340-8875,yes,no,0,273.500000,104,46.500000,183.800000,68,15.620000,153.800000,67,6.920000,11.000000,9,2.970000,2,False.\r\nWV,112,415,330-2693,yes,no,0,161.900000,138,27.520000,200.900000,114,17.080000,134.000000,134,6.030000,10.700000,4,2.890000,1,False.\r\nWI,35,510,403-7627,no,yes,27,241.700000,87,41.090000,142.000000,101,12.070000,288.900000,68,13.000000,9.400000,4,2.540000,1,False.\r\nKS,103,408,342-3678,yes,no,0,62.800000,124,10.680000,170.400000,66,14.480000,280.200000,78,12.610000,9.400000,4,2.540000,3,False.\r\nMO,107,415,344-9943,no,yes,22,281.100000,83,47.790000,143.700000,130,12.210000,239.400000,128,10.770000,11.200000,9,3.020000,1,False.\r\nPA,69,415,390-5686,no,no,0,228.200000,70,38.790000,263.700000,80,22.410000,142.600000,60,6.420000,10.700000,5,2.890000,3,False.\r\nSD,85,408,358-5826,no,no,0,209.800000,82,35.670000,194.500000,94,16.530000,200.400000,85,9.020000,11.300000,3,3.050000,0,False.\r\nNJ,24,408,393-7826,no,no,0,265.600000,86,45.150000,208.800000,102,17.750000,182.500000,105,8.210000,11.100000,6,3.000000,2,True.\r\nAL,90,415,335-9786,no,no,0,214.900000,97,36.530000,117.800000,117,10.010000,133.700000,78,6.020000,11.800000,2,3.190000,2,False.\r\nME,137,510,368-9860,no,no,0,110.500000,79,18.790000,223.200000,111,18.970000,169.500000,64,7.630000,10.500000,3,2.840000,3,False.\r\nAZ,92,415,416-9522,yes,yes,45,281.100000,88,47.790000,198.000000,103,16.830000,94.300000,76,4.240000,7.500000,3,2.030000,0,False.\r\nVT,38,415,416-7307,no,no,0,137.800000,86,23.430000,286.300000,76,24.340000,167.000000,77,7.520000,14.100000,3,3.810000,2,False.\r\nNV,69,510,397-6789,yes,yes,33,271.500000,98,46.160000,253.400000,102,21.540000,165.400000,85,7.440000,8.200000,2,2.210000,1,True.\r\nWI,45,408,335-9501,no,no,0,112.800000,108,19.180000,218.800000,120,18.600000,240.200000,106,10.810000,9.000000,3,2.430000,2,False.\r\nHI,73,408,388-1250,no,no,0,187.300000,118,31.840000,239.700000,90,20.370000,167.500000,108,7.540000,15.100000,2,4.080000,1,False.\r\nDE,92,415,386-1374,no,no,0,197.000000,84,33.490000,269.300000,105,22.890000,158.900000,105,7.150000,10.800000,4,2.920000,1,False.\r\nAZ,113,415,346-8112,no,yes,32,180.400000,89,30.670000,129.400000,124,11.000000,166.900000,124,7.510000,8.400000,2,2.270000,1,False.\r\nVA,68,408,364-9040,yes,no,0,148.500000,126,25.250000,219.400000,125,18.650000,198.500000,121,8.930000,14.500000,7,3.920000,1,True.\r\nGA,135,415,366-3944,no,yes,22,197.100000,113,33.510000,259.400000,95,22.050000,134.700000,135,6.060000,14.600000,5,3.940000,2,False.\r\nAZ,100,415,331-9861,no,yes,26,153.700000,115,26.130000,137.800000,146,11.710000,213.500000,104,9.610000,15.900000,5,4.290000,1,False.\r\nMN,96,415,330-2881,no,yes,27,261.300000,96,44.420000,220.900000,101,18.780000,179.400000,97,8.070000,11.300000,2,3.050000,1,False.\r\nME,108,510,402-9558,no,no,0,246.200000,102,41.850000,202.400000,134,17.200000,180.100000,95,8.100000,9.400000,5,2.540000,1,False.\r\nFL,84,510,341-3180,no,no,0,191.000000,88,32.470000,318.800000,119,27.100000,247.300000,79,11.130000,6.500000,4,1.760000,0,False.\r\nWA,134,408,371-8598,no,no,0,208.300000,86,35.410000,253.600000,89,21.560000,291.000000,86,13.100000,12.600000,3,3.400000,1,False.\r\nMT,72,415,398-8385,no,no,0,253.000000,73,43.010000,219.300000,78,18.640000,210.800000,89,9.490000,9.800000,4,2.650000,0,False.\r\nAL,83,408,333-4154,no,no,0,202.300000,87,34.390000,201.500000,111,17.130000,101.700000,82,4.580000,6.800000,4,1.840000,0,False.\r\nWV,137,408,330-3589,no,no,0,174.400000,120,29.650000,156.300000,98,13.290000,136.500000,121,6.140000,10.200000,5,2.750000,0,False.\r\nGA,56,415,417-1477,no,yes,30,127.100000,89,21.610000,172.100000,116,14.630000,194.600000,111,8.760000,12.100000,3,3.270000,1,False.\r\nOH,61,510,327-5525,yes,yes,16,143.500000,76,24.400000,242.600000,58,20.620000,147.700000,95,6.650000,11.300000,3,3.050000,0,False.\r\nDE,171,510,363-8244,no,yes,17,186.900000,94,31.770000,240.000000,138,20.400000,200.900000,64,9.040000,5.800000,3,1.570000,1,False.\r\nNE,123,510,419-9104,no,no,0,194.000000,118,32.980000,242.000000,114,20.570000,146.300000,108,6.580000,12.100000,4,3.270000,1,False.\r\nFL,58,510,363-1560,no,no,0,234.800000,89,39.920000,106.800000,131,9.080000,178.500000,122,8.030000,9.900000,6,2.670000,0,False.\r\nAK,156,510,341-4075,no,no,0,123.700000,96,21.030000,103.000000,80,8.760000,189.400000,82,8.520000,13.100000,4,3.540000,1,False.\r\nWI,166,510,366-9074,no,no,0,173.900000,103,29.560000,276.400000,83,23.490000,190.800000,113,8.590000,15.300000,5,4.130000,0,False.\r\nWA,75,510,367-1424,no,yes,41,130.900000,115,22.250000,203.400000,110,17.290000,171.700000,68,7.730000,12.400000,4,3.350000,1,False.\r\nKY,75,415,341-1191,no,no,0,314.600000,102,53.480000,169.800000,86,14.430000,285.100000,100,12.830000,5.700000,3,1.540000,2,True.\r\nOH,83,510,342-9480,no,no,0,227.900000,78,38.740000,207.500000,115,17.640000,211.700000,100,9.530000,12.100000,5,3.270000,1,False.\r\nUT,243,510,355-9360,no,no,0,95.500000,92,16.240000,163.700000,63,13.910000,264.200000,118,11.890000,6.600000,6,1.780000,2,False.\r\nNM,153,408,343-1538,no,no,0,185.300000,127,31.500000,208.000000,73,17.680000,206.100000,124,9.270000,15.100000,3,4.080000,1,False.\r\nMN,150,415,335-2331,no,no,0,146.300000,133,24.870000,202.700000,95,17.230000,234.700000,103,10.560000,13.100000,3,3.540000,1,False.\r\nWV,92,510,335-7257,no,yes,16,184.000000,99,31.280000,76.400000,134,6.490000,185.100000,96,8.330000,12.700000,3,3.430000,2,False.\r\nMN,80,415,332-2137,no,no,0,105.800000,110,17.990000,43.900000,88,3.730000,189.600000,87,8.530000,13.100000,5,3.540000,0,False.\r\nAL,134,415,352-2998,no,no,0,178.000000,110,30.260000,153.800000,64,13.070000,236.600000,105,10.650000,11.700000,4,3.160000,1,False.\r\nPA,77,510,346-6941,no,yes,24,149.400000,74,25.400000,123.900000,72,10.530000,174.300000,84,7.840000,10.100000,6,2.730000,1,False.\r\nDE,147,510,400-2203,no,no,0,209.400000,104,35.600000,132.500000,78,11.260000,149.400000,123,6.720000,11.300000,3,3.050000,2,False.\r\nMO,74,415,421-2955,no,no,0,172.100000,105,29.260000,211.700000,99,17.990000,182.200000,105,8.200000,11.600000,6,3.130000,1,False.\r\nIL,138,510,331-6629,yes,no,0,169.300000,82,28.780000,217.900000,147,18.520000,184.200000,77,8.290000,9.400000,9,2.540000,1,False.\r\nFL,143,415,343-6314,no,no,0,119.100000,117,20.250000,287.700000,136,24.450000,223.000000,100,10.040000,12.200000,4,3.290000,0,False.\r\nHI,64,415,414-6638,no,no,0,194.200000,147,33.010000,173.400000,87,14.740000,268.700000,114,12.090000,5.500000,2,1.490000,2,False.\r\nME,120,510,350-5883,no,no,0,198.800000,56,33.800000,230.100000,73,19.560000,119.800000,81,5.390000,9.900000,3,2.670000,2,False.\r\nCO,121,408,409-4447,yes,no,0,167.700000,94,28.510000,93.700000,121,7.960000,241.300000,115,10.860000,13.400000,1,3.620000,3,True.\r\nNH,88,415,376-4856,no,no,0,202.200000,86,34.370000,216.800000,93,18.430000,239.400000,99,10.770000,11.800000,2,3.190000,2,False.\r\nSC,87,408,335-1874,no,no,0,322.500000,106,54.830000,204.600000,93,17.390000,186.200000,128,8.380000,9.400000,4,2.540000,2,True.\r\nIN,100,510,397-6255,no,no,0,216.200000,107,36.750000,215.600000,84,18.330000,138.400000,127,6.230000,10.200000,3,2.750000,0,False.\r\nFL,104,415,381-5047,no,no,0,76.400000,116,12.990000,115.600000,74,9.830000,226.300000,94,10.180000,9.400000,3,2.540000,3,False.\r\nGA,27,510,403-6850,no,no,0,72.700000,75,12.360000,208.600000,117,17.730000,65.800000,71,2.960000,9.900000,3,2.670000,1,False.\r\nIL,81,415,375-3658,no,yes,31,210.400000,100,35.770000,225.500000,97,19.170000,168.700000,120,7.590000,9.700000,4,2.620000,0,False.\r\nNC,64,510,341-2603,yes,yes,33,127.200000,93,21.620000,162.900000,104,13.850000,247.400000,109,11.130000,8.100000,13,2.190000,0,False.\r\nVT,107,510,342-5062,no,yes,28,201.800000,79,34.310000,304.900000,128,25.920000,225.600000,133,10.150000,11.900000,8,3.210000,1,False.\r\nDC,88,415,354-1558,no,yes,17,219.500000,78,37.320000,222.100000,94,18.880000,188.300000,92,8.470000,16.100000,5,4.350000,1,False.\r\nVT,111,408,351-9537,no,no,0,99.300000,112,16.880000,270.500000,136,22.990000,225.300000,94,10.140000,9.000000,6,2.430000,3,False.\r\nNV,77,415,401-1252,no,no,0,239.200000,114,40.660000,150.000000,115,12.750000,160.800000,81,7.240000,10.300000,2,2.780000,5,False.\r\nOR,67,415,366-9538,yes,no,0,120.900000,58,20.550000,235.000000,88,19.980000,95.100000,130,4.280000,11.400000,11,3.080000,2,False.\r\nAL,102,408,364-7622,no,no,0,224.700000,81,38.200000,129.400000,112,11.000000,167.600000,109,7.540000,15.800000,6,4.270000,1,False.\r\nND,146,408,393-9918,no,yes,19,176.600000,88,30.020000,162.700000,66,13.830000,215.500000,98,9.700000,14.600000,6,3.940000,1,False.\r\nFL,144,415,376-4484,no,yes,51,283.900000,98,48.260000,192.000000,109,16.320000,196.300000,85,8.830000,10.000000,4,2.700000,1,False.\r\nNE,96,415,410-6791,no,no,0,180.600000,92,30.700000,190.900000,114,16.230000,295.600000,125,13.300000,10.300000,4,2.780000,1,True.\r\nND,70,415,343-2392,no,yes,31,125.900000,101,21.400000,196.400000,102,16.690000,252.700000,75,11.370000,10.300000,4,2.780000,1,False.\r\nME,149,408,408-4323,no,no,0,237.600000,79,40.390000,192.400000,107,16.350000,207.400000,111,9.330000,9.100000,9,2.460000,0,False.\r\nIL,129,415,395-1718,no,no,0,198.400000,91,33.730000,264.700000,106,22.500000,111.400000,101,5.010000,9.200000,2,2.480000,2,False.\r\nWA,166,408,354-9492,no,no,0,274.300000,110,46.630000,52.900000,109,4.500000,246.100000,119,11.070000,10.900000,5,2.940000,0,False.\r\nMA,136,408,367-8168,yes,no,0,199.600000,89,33.930000,211.400000,96,17.970000,72.400000,84,3.260000,11.000000,4,2.970000,3,True.\r\nKS,149,510,340-3500,no,no,0,217.700000,91,37.010000,273.500000,74,23.250000,226.900000,99,10.210000,9.600000,3,2.590000,3,False.\r\nRI,70,415,369-4962,no,no,0,134.700000,96,22.900000,235.900000,90,20.050000,260.200000,113,11.710000,7.600000,6,2.050000,3,False.\r\nMO,120,415,334-8967,no,yes,24,212.700000,73,36.160000,257.500000,103,21.890000,227.800000,119,10.250000,9.700000,13,2.620000,2,False.\r\nIA,66,510,402-2377,no,no,0,256.300000,135,43.570000,180.200000,106,15.320000,187.300000,135,8.430000,6.200000,7,1.670000,2,False.\r\nWY,104,408,366-3917,no,no,0,183.600000,133,31.210000,120.700000,98,10.260000,215.100000,112,9.680000,12.700000,2,3.430000,1,False.\r\nNV,160,415,333-3531,no,no,0,176.200000,90,29.950000,196.000000,115,16.660000,263.900000,95,11.880000,9.200000,4,2.480000,1,False.\r\nWI,129,415,333-8954,no,yes,37,205.000000,94,34.850000,165.400000,103,14.060000,185.000000,81,8.320000,11.700000,8,3.160000,1,False.\r\nAL,93,408,374-9203,no,no,0,267.900000,114,45.540000,223.000000,74,18.960000,262.700000,90,11.820000,11.300000,3,3.050000,3,True.\r\nHI,169,415,334-3289,no,no,0,179.200000,111,30.460000,175.200000,130,14.890000,228.600000,92,10.290000,9.900000,6,2.670000,2,False.\r\nMO,58,415,353-7822,no,no,0,149.400000,145,25.400000,196.500000,105,16.700000,209.500000,108,9.430000,14.900000,3,4.020000,1,False.\r\nCA,75,510,350-1422,no,yes,38,163.600000,132,27.810000,146.700000,113,12.470000,345.800000,115,15.560000,13.100000,3,3.540000,3,False.\r\nMO,45,408,385-8406,no,no,0,207.600000,71,35.290000,152.700000,94,12.980000,217.800000,125,9.800000,12.400000,13,3.350000,1,False.\r\nCT,155,510,380-7277,no,no,0,165.400000,108,28.120000,183.700000,103,15.610000,80.200000,108,3.610000,8.900000,4,2.400000,3,False.\r\nMD,52,415,352-1798,no,no,0,209.800000,114,35.670000,171.300000,82,14.560000,154.600000,119,6.960000,9.900000,9,2.670000,4,False.\r\nOH,119,415,385-7922,no,yes,27,220.100000,128,37.420000,268.200000,133,22.800000,146.500000,80,6.590000,11.100000,3,3.000000,0,False.\r\nNV,86,510,353-7730,no,no,0,141.300000,72,24.020000,154.300000,95,13.120000,210.600000,91,9.480000,8.200000,5,2.210000,1,False.\r\nMD,42,408,337-7163,no,no,0,196.500000,89,33.410000,241.300000,123,20.510000,143.200000,105,6.440000,4.000000,7,1.080000,0,False.\r\nNE,127,510,348-5567,yes,no,0,180.900000,114,30.750000,209.500000,118,17.810000,249.900000,105,11.250000,7.400000,4,2.000000,2,False.\r\nOH,123,408,420-9575,no,no,0,105.000000,150,17.850000,251.600000,90,21.390000,258.000000,93,11.610000,14.900000,5,4.020000,0,False.\r\nMA,98,510,366-3358,no,no,0,271.400000,119,46.140000,190.400000,102,16.180000,284.700000,118,12.810000,11.100000,6,3.000000,4,True.\r\nOK,149,510,359-9972,no,yes,43,206.700000,79,35.140000,174.600000,122,14.840000,241.500000,80,10.870000,10.900000,3,2.940000,1,False.\r\nMA,160,408,387-3332,no,no,0,166.800000,109,28.360000,236.000000,117,20.060000,307.600000,77,13.840000,9.300000,1,2.510000,1,False.\r\nWA,103,415,354-6960,no,no,0,204.900000,107,34.830000,135.200000,102,11.490000,208.200000,106,9.370000,10.400000,3,2.810000,5,False.\r\nHI,132,415,405-3335,no,yes,15,154.600000,128,26.280000,245.600000,106,20.880000,148.600000,90,6.690000,9.100000,4,2.460000,1,False.\r\nCO,137,415,379-4257,no,no,0,127.000000,107,21.590000,323.200000,75,27.470000,143.900000,127,6.480000,7.500000,2,2.030000,1,False.\r\nFL,129,415,355-4992,yes,no,0,267.400000,78,45.460000,204.200000,85,17.360000,111.700000,146,5.030000,5.900000,4,1.590000,1,False.\r\nWI,62,415,383-6373,no,no,0,281.000000,66,47.770000,160.600000,108,13.650000,77.900000,74,3.510000,0.000000,0,0.000000,1,False.\r\nID,122,510,382-7993,no,yes,33,270.800000,96,46.040000,220.400000,110,18.730000,169.900000,104,7.650000,11.800000,8,3.190000,4,False.\r\nWY,32,408,422-5865,no,no,0,171.200000,82,29.100000,185.600000,102,15.780000,203.300000,64,9.150000,10.200000,7,2.750000,1,False.\r\nGA,86,510,410-9961,no,no,0,124.100000,82,21.100000,202.600000,120,17.220000,289.600000,119,13.030000,6.700000,8,1.810000,3,False.\r\nFL,130,415,343-9946,no,no,0,162.800000,113,27.680000,290.300000,111,24.680000,114.900000,140,5.170000,7.200000,3,1.940000,1,False.\r\nWY,42,408,357-7060,no,no,0,146.300000,84,24.870000,255.900000,113,21.750000,45.000000,117,2.030000,8.000000,12,2.160000,1,False.\r\nDE,73,415,355-9541,no,no,0,254.800000,85,43.320000,143.400000,80,12.190000,153.900000,102,6.930000,15.000000,7,4.050000,2,False.\r\nME,66,408,378-4145,no,yes,26,254.900000,108,43.330000,243.200000,135,20.670000,190.800000,95,8.590000,5.400000,3,1.460000,2,False.\r\nDC,103,510,386-2317,no,yes,31,107.700000,124,18.310000,188.900000,104,16.060000,196.200000,98,8.830000,8.900000,3,2.400000,0,False.\r\nIA,128,408,335-8146,no,no,0,158.800000,75,27.000000,264.800000,91,22.510000,270.000000,77,12.150000,7.600000,7,2.050000,1,False.\r\nCO,104,415,377-2235,no,no,0,182.900000,113,31.090000,239.600000,85,20.370000,229.800000,104,10.340000,5.500000,4,1.490000,2,False.\r\nMN,103,415,386-9141,no,no,0,198.500000,112,33.750000,42.500000,90,3.610000,179.200000,124,8.060000,12.400000,5,3.350000,0,False.\r\nVT,124,415,416-5623,no,no,0,178.400000,72,30.330000,233.600000,134,19.860000,179.400000,91,8.070000,12.000000,2,3.240000,0,False.\r\nAZ,87,510,327-3053,no,no,0,110.900000,91,18.850000,158.500000,115,13.470000,207.500000,131,9.340000,6.200000,5,1.670000,1,False.\r\nLA,109,415,395-6195,no,yes,27,166.900000,85,28.370000,221.200000,92,18.800000,197.300000,97,8.880000,12.300000,4,3.320000,1,True.\r\nMO,167,415,397-8772,yes,no,0,244.800000,91,41.620000,60.800000,105,5.170000,176.700000,110,7.950000,10.700000,3,2.890000,2,False.\r\nME,97,510,346-7656,no,no,0,120.800000,96,20.540000,169.800000,101,14.430000,194.100000,63,8.730000,11.900000,3,3.210000,4,True.\r\nMD,106,415,343-2350,no,no,0,165.300000,118,28.100000,210.000000,101,17.850000,187.200000,93,8.420000,8.500000,3,2.300000,2,False.\r\nVT,125,415,372-4722,no,no,0,126.700000,113,21.540000,155.500000,131,13.220000,206.200000,112,9.280000,14.400000,7,3.890000,2,False.\r\nDC,108,408,399-8615,no,yes,35,215.900000,106,36.700000,200.600000,107,17.050000,195.400000,107,8.790000,15.500000,7,4.190000,0,False.\r\nWY,125,415,379-8248,no,no,0,140.100000,132,23.820000,209.600000,126,17.820000,264.100000,77,11.880000,8.000000,2,2.160000,1,False.\r\nVA,89,415,414-6219,no,yes,32,209.900000,113,35.680000,249.800000,104,21.230000,224.200000,92,10.090000,8.700000,7,2.350000,1,False.\r\nVA,72,510,387-1343,yes,yes,29,139.800000,114,23.770000,138.200000,91,11.750000,221.000000,88,9.950000,5.500000,6,1.490000,0,False.\r\nCT,23,510,370-5527,no,no,0,321.600000,107,54.670000,251.600000,115,21.390000,141.100000,158,6.350000,11.300000,3,3.050000,2,True.\r\nHI,149,510,393-8736,no,no,0,166.600000,61,28.320000,218.800000,107,18.600000,208.300000,131,9.370000,8.200000,6,2.210000,7,False.\r\nWI,73,415,402-7626,no,no,0,214.200000,90,36.410000,196.800000,78,16.730000,157.900000,112,7.110000,5.900000,8,1.590000,0,False.\r\nMD,61,415,370-2688,no,no,0,260.000000,123,44.200000,210.500000,127,17.890000,234.700000,70,10.560000,9.000000,3,2.430000,1,True.\r\nWV,161,415,418-9036,no,no,0,191.900000,113,32.620000,70.900000,87,6.030000,204.800000,107,9.220000,13.400000,4,3.620000,4,True.\r\nVT,73,408,417-2035,no,no,0,213.000000,95,36.210000,188.800000,104,16.050000,136.200000,89,6.130000,13.500000,3,3.650000,0,False.\r\nUT,118,415,355-3602,no,yes,24,118.100000,83,20.080000,109.600000,72,9.320000,245.500000,73,11.050000,16.900000,2,4.560000,1,False.\r\nCO,23,408,393-4027,no,no,0,190.200000,89,32.330000,166.400000,108,14.140000,219.800000,73,9.890000,15.000000,4,4.050000,6,False.\r\nNC,127,415,418-5141,no,yes,25,82.200000,95,13.970000,163.300000,109,13.880000,264.900000,104,11.920000,5.100000,6,1.380000,0,False.\r\nNJ,42,415,406-1247,no,yes,32,163.800000,80,27.850000,177.800000,123,15.110000,190.400000,106,8.570000,8.100000,5,2.190000,0,False.\r\nAR,118,415,402-3892,no,no,0,267.800000,145,45.530000,316.400000,121,26.890000,208.600000,91,9.390000,14.400000,11,3.890000,5,True.\r\nIA,45,510,332-2965,no,no,0,159.800000,91,27.170000,120.400000,86,10.230000,163.000000,93,7.340000,10.600000,3,2.860000,2,False.\r\nGA,50,408,377-1218,no,yes,24,214.300000,129,36.430000,289.800000,55,24.630000,312.500000,130,14.060000,10.600000,4,2.860000,1,False.\r\nMO,179,510,355-2464,no,no,0,287.300000,123,48.840000,288.000000,114,24.480000,266.000000,112,11.970000,10.500000,4,2.840000,0,True.\r\nMO,152,408,404-4611,no,no,0,101.200000,122,17.200000,141.600000,87,12.040000,198.500000,124,8.930000,7.500000,3,2.030000,2,False.\r\nWY,105,415,373-2339,no,no,0,102.800000,74,17.480000,281.700000,125,23.940000,228.100000,113,10.260000,13.200000,5,3.560000,2,False.\r\nHI,72,415,410-3503,no,no,0,109.100000,97,18.550000,115.700000,96,9.830000,295.800000,84,13.310000,8.300000,6,2.240000,0,False.\r\nPA,52,408,410-4739,no,no,0,215.900000,67,36.700000,217.000000,108,18.450000,342.800000,130,15.430000,5.200000,2,1.400000,1,False.\r\nTX,125,415,365-3562,no,no,0,203.400000,110,34.580000,128.700000,97,10.940000,190.500000,113,8.570000,11.000000,4,2.970000,1,False.\r\nVA,143,510,387-7641,no,no,0,110.100000,113,18.720000,169.000000,59,14.370000,166.700000,94,7.500000,9.200000,2,2.480000,1,False.\r\nRI,65,415,385-9744,no,no,0,111.000000,51,18.870000,219.800000,84,18.680000,202.000000,89,9.090000,4.400000,14,1.190000,1,False.\r\nWI,80,415,398-5006,no,no,0,239.900000,121,40.780000,142.300000,51,12.100000,364.300000,106,16.390000,9.300000,5,2.510000,1,False.\r\nMS,1,415,408-3977,no,no,0,144.800000,107,24.620000,112.500000,66,9.560000,218.700000,79,9.840000,13.800000,3,3.730000,1,False.\r\nKY,60,408,334-2729,no,no,0,135.400000,134,23.020000,205.900000,85,17.500000,204.000000,103,9.180000,7.900000,4,2.130000,1,False.\r\nNC,43,415,334-7685,no,no,0,84.200000,134,14.310000,80.800000,103,6.870000,196.100000,79,8.820000,10.800000,2,2.920000,1,False.\r\nNV,143,415,350-9228,no,no,0,209.100000,127,35.550000,106.100000,80,9.020000,179.600000,90,8.080000,14.000000,6,3.780000,0,False.\r\nUT,81,415,407-5774,no,yes,24,130.100000,117,22.120000,196.000000,61,16.660000,139.300000,123,6.270000,11.400000,5,3.080000,0,False.\r\nME,205,510,413-4039,no,yes,24,175.800000,139,29.890000,155.000000,98,13.180000,180.700000,64,8.130000,7.800000,5,2.110000,2,False.\r\nHI,24,415,343-2077,no,no,0,241.900000,104,41.120000,145.200000,112,12.340000,214.500000,105,9.650000,6.600000,5,1.780000,1,False.\r\nOH,74,415,336-5661,no,no,0,136.700000,106,23.240000,228.600000,105,19.430000,265.300000,114,11.940000,9.800000,4,2.650000,0,False.\r\nWV,77,510,355-4143,no,no,0,67.700000,68,11.510000,195.700000,86,16.630000,236.500000,137,10.640000,12.000000,2,3.240000,1,False.\r\nOK,74,415,366-5918,no,no,0,200.400000,87,34.070000,309.200000,105,26.280000,152.100000,118,6.840000,10.000000,2,2.700000,1,False.\r\nKY,74,510,368-7555,yes,no,0,125.800000,103,21.390000,207.700000,96,17.650000,207.400000,143,9.330000,14.100000,4,3.810000,1,True.\r\nAL,200,408,408-2119,no,no,0,128.200000,87,21.790000,133.200000,105,11.320000,177.600000,123,7.990000,11.200000,2,3.020000,1,False.\r\nMD,86,408,329-2789,no,no,0,226.300000,88,38.470000,223.000000,107,18.960000,255.600000,92,11.500000,13.000000,3,3.510000,4,False.\r\nNE,91,510,334-1508,no,yes,37,162.300000,107,27.590000,233.900000,115,19.880000,277.400000,94,12.480000,9.200000,4,2.480000,0,False.\r\nDC,76,415,337-1506,no,no,0,224.400000,121,38.150000,147.900000,97,12.570000,183.800000,74,8.270000,6.700000,2,1.810000,2,False.\r\nTX,130,415,396-8400,no,no,0,120.500000,127,20.490000,189.700000,52,16.120000,270.100000,107,12.150000,14.300000,2,3.860000,1,False.\r\nOH,56,408,349-2654,no,no,0,91.100000,90,15.490000,179.300000,115,15.240000,300.700000,89,13.530000,11.900000,8,3.210000,2,False.\r\nDE,117,415,417-2716,no,no,0,168.800000,137,28.700000,241.400000,107,20.520000,204.800000,106,9.220000,15.500000,4,4.190000,0,False.\r\nIA,63,510,402-1725,no,no,0,153.500000,81,26.100000,287.300000,115,24.420000,230.200000,85,10.360000,6.500000,5,1.760000,2,False.\r\nVT,126,415,403-3229,no,no,0,226.200000,88,38.450000,140.300000,114,11.930000,208.900000,110,9.400000,6.400000,2,1.730000,0,False.\r\nOR,132,510,353-6056,no,no,0,191.900000,107,32.620000,206.900000,127,17.590000,272.000000,88,12.240000,12.600000,2,3.400000,1,False.\r\nNV,81,415,399-9802,no,yes,28,167.900000,147,28.540000,190.700000,105,16.210000,193.000000,103,8.690000,9.200000,6,2.480000,4,True.\r\nNC,122,415,366-7069,no,no,0,180.000000,88,30.600000,145.000000,77,12.330000,233.700000,120,10.520000,11.500000,6,3.110000,2,False.\r\nNJ,46,408,332-5949,no,no,0,257.400000,67,43.760000,261.100000,91,22.190000,204.400000,107,9.200000,13.400000,5,3.620000,2,True.\r\nMN,150,415,393-6376,no,yes,28,174.400000,75,29.650000,169.900000,80,14.440000,201.600000,130,9.070000,11.000000,4,2.970000,1,False.\r\nID,99,408,354-7025,no,no,0,159.700000,83,27.150000,155.400000,121,13.210000,255.700000,114,11.510000,8.400000,3,2.270000,1,False.\r\nAL,87,408,351-6585,no,no,0,237.200000,124,40.320000,222.600000,87,18.920000,173.300000,81,7.800000,11.000000,3,2.970000,1,False.\r\nAK,108,415,330-5462,no,no,0,103.000000,129,17.510000,242.300000,103,20.600000,170.200000,89,7.660000,7.900000,3,2.130000,1,False.\r\nVT,101,415,407-2292,no,no,0,153.800000,89,26.150000,234.000000,89,19.890000,196.300000,77,8.830000,11.600000,2,3.130000,4,False.\r\nPA,53,415,340-3011,no,no,0,205.100000,86,34.870000,160.500000,95,13.640000,149.500000,142,6.730000,10.700000,2,2.890000,3,False.\r\nAK,132,415,345-9153,no,yes,39,175.700000,93,29.870000,187.200000,94,15.910000,225.500000,118,10.150000,8.600000,3,2.320000,2,False.\r\nCA,158,510,379-5503,no,no,0,155.900000,123,26.500000,224.200000,112,19.060000,221.000000,116,9.950000,8.600000,8,2.320000,2,False.\r\nMS,114,415,389-6790,no,yes,34,154.400000,109,26.250000,221.400000,142,18.820000,208.500000,103,9.380000,10.300000,5,2.780000,0,False.\r\nAR,77,415,408-3610,no,yes,23,209.700000,73,35.650000,183.600000,63,15.610000,205.500000,111,9.250000,7.100000,3,1.920000,2,False.\r\nNV,144,415,375-8238,yes,no,0,150.000000,69,25.500000,285.900000,73,24.300000,190.600000,121,8.580000,9.400000,15,2.540000,0,False.\r\nAL,91,510,378-5633,no,yes,23,232.400000,97,39.510000,186.000000,88,15.810000,190.500000,128,8.570000,12.300000,3,3.320000,3,False.\r\nGA,58,408,389-9120,no,no,0,165.400000,100,28.120000,115.700000,87,9.830000,193.800000,118,8.720000,12.800000,5,3.460000,2,False.\r\nAR,5,415,380-2758,no,no,0,199.200000,106,33.860000,187.300000,12,15.920000,214.000000,85,9.630000,13.300000,3,3.590000,3,False.\r\nMA,97,408,402-2728,no,no,0,217.600000,81,36.990000,320.500000,51,27.240000,150.700000,110,6.780000,4.200000,3,1.130000,0,False.\r\nNJ,107,415,354-9062,no,no,0,212.100000,95,36.060000,150.100000,88,12.760000,219.800000,111,9.890000,7.700000,2,2.080000,3,False.\r\nMO,142,415,417-2054,no,yes,30,154.000000,75,26.180000,165.800000,97,14.090000,270.000000,83,12.150000,10.800000,5,2.920000,1,False.\r\nNY,9,408,353-1941,no,yes,31,193.800000,130,32.950000,202.600000,98,17.220000,191.200000,102,8.600000,13.300000,2,3.590000,1,False.\r\nAZ,73,415,328-1522,no,no,0,175.400000,130,29.820000,248.100000,105,21.090000,122.400000,85,5.510000,12.200000,4,3.290000,0,False.\r\nNJ,48,510,408-4529,no,yes,22,152.000000,63,25.840000,258.800000,131,22.000000,263.200000,109,11.840000,15.700000,5,4.240000,2,True.\r\nWV,43,408,417-5320,no,no,0,230.200000,147,39.130000,186.700000,121,15.870000,128.400000,100,5.780000,9.200000,3,2.480000,0,False.\r\nNM,122,408,370-9755,no,yes,33,174.900000,103,29.730000,248.200000,105,21.100000,164.600000,116,7.410000,13.500000,3,3.650000,1,True.\r\nSC,93,415,372-4835,no,no,0,190.200000,68,32.330000,262.200000,64,22.290000,130.000000,92,5.850000,8.800000,4,2.380000,0,False.\r\nVT,85,415,334-6605,no,no,0,176.400000,122,29.990000,224.900000,123,19.120000,219.600000,50,9.880000,11.500000,1,3.110000,1,False.\r\nTN,59,415,399-5564,no,no,0,160.900000,95,27.350000,251.200000,65,21.350000,273.400000,97,12.300000,5.000000,5,1.350000,3,False.\r\nLA,87,415,392-2887,no,no,0,228.700000,90,38.880000,163.000000,99,13.860000,154.100000,90,6.930000,11.800000,3,3.190000,1,False.\r\nOH,137,408,328-2110,no,no,0,144.000000,90,24.480000,181.600000,100,15.440000,128.100000,93,5.760000,12.300000,2,3.320000,0,False.\r\nOR,21,510,383-5976,no,yes,31,135.900000,90,23.100000,271.000000,84,23.040000,179.100000,89,8.060000,9.500000,7,2.570000,6,False.\r\nDE,129,510,332-6181,no,no,0,334.300000,118,56.830000,192.100000,104,16.330000,191.000000,83,8.590000,10.400000,6,2.810000,0,True.\r\nKY,104,415,330-5255,no,no,0,130.500000,77,22.190000,131.200000,117,11.150000,264.700000,63,11.910000,13.000000,6,3.510000,0,False.\r\nGA,93,408,413-5190,no,yes,21,134.200000,105,22.810000,162.500000,128,13.810000,186.600000,90,8.400000,11.800000,2,3.190000,4,True.\r\nVT,63,415,394-7447,no,no,0,278.000000,102,47.260000,266.400000,114,22.640000,224.100000,118,10.080000,13.100000,4,3.540000,4,True.\r\nOR,161,415,353-7096,no,no,0,105.400000,70,17.920000,214.800000,122,18.260000,223.600000,126,10.060000,7.800000,5,2.110000,0,False.\r\nTX,50,510,395-6002,no,no,0,188.900000,94,32.110000,203.900000,104,17.330000,151.800000,124,6.830000,11.600000,8,3.130000,3,False.\r\nMO,103,408,372-9816,no,yes,24,111.800000,85,19.010000,239.600000,102,20.370000,268.300000,81,12.070000,6.900000,4,1.860000,1,False.\r\nND,84,415,400-7253,no,yes,33,159.100000,106,27.050000,149.800000,101,12.730000,213.400000,108,9.600000,13.000000,18,3.510000,1,False.\r\nMN,92,510,344-7470,no,no,0,212.400000,105,36.110000,224.600000,118,19.090000,221.300000,105,9.960000,9.000000,4,2.430000,1,False.\r\nNV,77,415,378-8572,no,no,0,142.300000,112,24.190000,306.300000,111,26.040000,196.500000,82,8.840000,9.900000,1,2.670000,1,False.\r\nNY,64,415,345-9140,yes,no,0,346.800000,55,58.960000,249.500000,79,21.210000,275.400000,102,12.390000,13.300000,9,3.590000,1,True.\r\nFL,159,415,340-5460,no,yes,15,113.900000,102,19.360000,145.300000,146,12.350000,195.200000,137,8.780000,11.800000,9,3.190000,1,False.\r\nKS,110,415,369-8024,yes,yes,27,267.900000,103,45.540000,263.300000,74,22.380000,178.100000,106,8.010000,8.300000,2,2.240000,1,True.\r\nNV,138,510,395-8595,no,no,0,171.400000,117,29.140000,115.200000,128,9.790000,224.500000,115,10.100000,17.000000,4,4.590000,3,False.\r\nNV,178,408,359-4587,no,no,0,275.400000,150,46.820000,187.500000,62,15.940000,147.100000,126,6.620000,13.600000,3,3.670000,1,False.\r\nSC,38,415,375-5439,no,yes,31,197.200000,118,33.520000,249.900000,70,21.240000,298.900000,104,13.450000,3.900000,2,1.050000,0,False.\r\nMI,50,415,361-3779,no,yes,35,192.600000,97,32.740000,135.200000,101,11.490000,216.200000,101,9.730000,7.900000,2,2.130000,2,False.\r\nMI,45,510,375-8934,no,yes,26,91.700000,104,15.590000,150.600000,119,12.800000,63.300000,103,2.850000,7.700000,5,2.080000,1,False.\r\nTN,70,510,395-4757,no,no,0,126.300000,99,21.470000,141.600000,106,12.040000,255.900000,96,11.520000,9.600000,2,2.590000,0,False.\r\nNY,147,510,421-7205,no,yes,33,251.500000,107,42.760000,234.100000,110,19.900000,213.400000,87,9.600000,10.400000,6,2.810000,3,False.\r\nNV,94,510,379-8805,no,no,0,190.600000,108,32.400000,152.300000,95,12.950000,144.700000,97,6.510000,7.500000,5,2.030000,1,False.\r\nIL,179,510,348-2150,no,no,0,116.100000,101,19.740000,201.800000,99,17.150000,181.900000,103,8.190000,11.600000,5,3.130000,0,False.\r\nMS,116,415,417-9128,no,no,0,217.300000,91,36.940000,216.100000,95,18.370000,148.100000,76,6.660000,11.300000,3,3.050000,2,False.\r\nND,59,510,351-4226,no,no,0,179.400000,80,30.500000,232.500000,99,19.760000,175.800000,105,7.910000,14.700000,3,3.970000,0,False.\r\nNC,165,415,330-6630,no,no,0,207.700000,109,35.310000,164.800000,94,14.010000,54.500000,91,2.450000,7.900000,3,2.130000,0,False.\r\nMI,133,408,387-9137,no,no,0,277.300000,138,47.140000,228.400000,117,19.410000,117.300000,103,5.280000,12.800000,4,3.460000,2,True.\r\nTN,140,415,372-3987,no,no,0,125.300000,84,21.300000,167.600000,121,14.250000,260.600000,94,11.730000,8.400000,4,2.270000,1,False.\r\nVT,93,408,408-5183,no,yes,32,138.100000,91,23.480000,167.300000,72,14.220000,238.900000,115,10.750000,6.800000,3,1.840000,2,False.\r\nOK,52,510,412-9357,no,yes,38,169.300000,88,28.780000,225.900000,97,19.200000,172.000000,86,7.740000,8.200000,3,2.210000,0,False.\r\nDE,64,415,402-3599,no,yes,27,201.300000,101,34.220000,143.800000,89,12.220000,150.200000,127,6.760000,12.300000,3,3.320000,1,False.\r\nND,12,510,379-5211,yes,no,0,216.700000,117,36.840000,116.500000,126,9.900000,220.000000,110,9.900000,9.800000,4,2.650000,2,False.\r\nOR,48,415,405-9217,no,no,0,190.400000,92,32.370000,317.500000,85,26.990000,133.400000,113,6.000000,8.300000,4,2.240000,2,False.\r\nNY,181,408,340-9200,no,no,0,143.300000,91,24.360000,195.500000,58,16.620000,223.300000,95,10.050000,6.000000,7,1.620000,1,False.\r\nMO,168,415,339-9026,no,yes,42,97.400000,57,16.560000,203.600000,98,17.310000,173.900000,124,7.830000,11.400000,2,3.080000,2,False.\r\nFL,155,415,343-4772,no,no,0,181.400000,111,30.840000,167.700000,92,14.250000,168.500000,122,7.580000,11.300000,3,3.050000,3,False.\r\nND,105,510,345-2108,no,no,0,246.400000,83,41.890000,256.200000,101,21.780000,169.000000,151,7.610000,3.800000,4,1.030000,0,False.\r\nNY,11,415,401-4650,no,no,0,143.400000,130,24.380000,289.400000,50,24.600000,194.000000,100,8.730000,9.700000,6,2.620000,2,False.\r\nND,182,415,400-3945,no,no,0,104.900000,111,17.830000,198.500000,120,16.870000,258.200000,91,11.620000,8.000000,5,2.160000,2,False.\r\nNY,104,415,338-3781,no,no,0,156.200000,93,26.550000,193.000000,54,16.410000,222.700000,94,10.020000,13.100000,5,3.540000,1,False.\r\nOH,102,415,342-6316,no,no,0,114.800000,125,19.520000,81.900000,126,6.960000,304.300000,101,13.690000,12.000000,4,3.240000,0,False.\r\nAL,122,415,336-5920,no,no,0,232.500000,96,39.530000,205.500000,120,17.470000,213.700000,91,9.620000,11.900000,2,3.210000,0,False.\r\nSC,41,415,332-1060,no,no,0,143.600000,117,24.410000,152.400000,108,12.950000,194.400000,110,8.750000,8.600000,3,2.320000,1,False.\r\nGA,132,415,418-3426,no,no,0,176.700000,132,30.040000,244.100000,80,20.750000,176.300000,120,7.930000,9.100000,4,2.460000,2,False.\r\nWY,76,415,408-6326,no,no,0,263.400000,148,44.780000,230.300000,69,19.580000,170.600000,101,7.680000,11.400000,5,3.080000,1,True.\r\nWV,13,415,334-6142,no,no,0,146.400000,74,24.890000,148.500000,92,12.620000,216.700000,96,9.750000,11.300000,3,3.050000,1,False.\r\nHI,115,415,336-6128,no,yes,33,145.000000,72,24.650000,194.500000,157,16.530000,242.300000,138,10.900000,14.200000,3,3.830000,2,False.\r\nWV,67,408,406-6708,no,no,0,167.800000,91,28.530000,167.700000,69,14.250000,110.300000,71,4.960000,8.400000,12,2.270000,1,False.\r\nLA,154,510,388-8670,no,no,0,166.900000,99,28.370000,154.900000,97,13.170000,189.400000,89,8.520000,7.200000,5,1.940000,0,False.\r\nAR,100,510,363-5853,no,no,0,142.500000,87,24.230000,195.700000,88,16.630000,122.100000,117,5.490000,7.800000,8,2.110000,2,False.\r\nAK,146,510,383-6544,yes,no,0,133.000000,65,22.610000,262.800000,93,22.340000,214.300000,128,9.640000,11.200000,3,3.020000,1,False.\r\nDC,148,415,378-2940,no,yes,11,252.900000,129,42.990000,284.300000,88,24.170000,262.800000,99,11.830000,12.300000,1,3.320000,1,False.\r\nAL,67,415,338-7683,no,yes,28,95.000000,94,16.150000,291.200000,73,24.750000,159.600000,114,7.180000,10.000000,2,2.700000,2,False.\r\nUT,161,510,329-2786,yes,no,0,194.200000,106,33.010000,249.400000,105,21.200000,254.900000,129,11.470000,12.900000,1,3.480000,1,True.\r\nKS,70,415,369-9465,no,no,0,222.800000,114,37.880000,215.900000,113,18.350000,223.500000,122,10.060000,0.000000,0,0.000000,1,False.\r\nMN,116,510,337-3769,no,no,0,201.800000,82,34.310000,231.500000,95,19.680000,226.100000,130,10.170000,16.500000,5,4.460000,0,False.\r\nVA,99,415,400-6257,no,yes,42,216.000000,125,36.720000,232.300000,104,19.750000,215.500000,100,9.700000,9.300000,4,2.510000,2,True.\r\nUT,87,415,411-6663,no,no,0,146.300000,108,24.870000,171.800000,102,14.600000,167.500000,66,7.540000,5.300000,9,1.430000,1,False.\r\nIN,87,510,384-3101,no,no,0,234.800000,85,39.920000,140.900000,91,11.980000,204.300000,93,9.190000,9.500000,5,2.570000,1,False.\r\nCT,70,408,339-5329,no,no,0,198.600000,111,33.760000,213.900000,115,18.180000,171.200000,105,7.700000,10.600000,6,2.860000,2,False.\r\nDE,131,408,388-9944,no,no,0,94.400000,80,16.050000,215.100000,101,18.280000,179.700000,108,8.090000,13.100000,9,3.540000,2,False.\r\nVT,119,510,403-1769,no,no,0,190.400000,74,32.370000,215.600000,113,18.330000,161.200000,111,7.250000,10.000000,1,2.700000,2,False.\r\nMO,119,408,390-1612,no,yes,32,142.600000,77,24.240000,208.200000,126,17.700000,171.000000,102,7.690000,12.000000,2,3.240000,3,False.\r\nRI,87,510,421-7214,yes,no,0,134.200000,80,22.810000,165.000000,71,14.030000,173.100000,102,7.790000,10.700000,5,2.890000,0,False.\r\nCA,112,415,338-6962,no,no,0,111.900000,92,19.020000,114.000000,143,9.690000,146.800000,79,6.610000,14.100000,3,3.810000,5,True.\r\nRI,75,415,333-9826,no,no,0,122.800000,89,20.880000,211.300000,104,17.960000,261.400000,91,11.760000,10.700000,2,2.890000,2,False.\r\nCT,150,510,411-8549,no,no,0,189.300000,77,32.180000,220.900000,105,18.780000,238.700000,117,10.740000,9.200000,5,2.480000,4,False.\r\nIN,161,510,381-9234,no,yes,38,240.400000,112,40.870000,201.800000,102,17.150000,206.100000,112,9.270000,16.100000,6,4.350000,0,False.\r\nFL,91,510,387-9855,yes,yes,24,93.500000,112,15.900000,183.400000,128,15.590000,240.700000,133,10.830000,9.900000,3,2.670000,0,False.\r\nKS,124,415,371-6990,no,no,0,158.600000,104,26.960000,211.200000,77,17.950000,179.300000,104,8.070000,10.200000,8,2.750000,3,False.\r\nNY,94,510,417-3046,yes,no,0,243.200000,109,41.340000,147.000000,88,12.500000,94.900000,99,4.270000,7.200000,4,1.940000,4,False.\r\nTX,217,408,385-7082,no,no,0,176.400000,115,29.990000,158.800000,128,13.500000,306.600000,107,13.800000,9.300000,3,2.510000,4,False.\r\nRI,158,510,365-5886,no,no,0,220.900000,129,37.550000,242.200000,108,20.590000,233.300000,75,10.500000,6.400000,5,1.730000,0,False.\r\nNV,102,415,373-5196,no,no,0,144.400000,87,24.550000,266.500000,128,22.650000,217.600000,59,9.790000,7.100000,7,1.920000,0,False.\r\nUT,85,510,327-6194,no,no,0,212.300000,107,36.090000,228.400000,103,19.410000,163.300000,116,7.350000,7.700000,3,2.080000,0,False.\r\nOK,79,510,381-4565,no,no,0,147.000000,72,24.990000,165.700000,102,14.080000,243.200000,107,10.940000,8.400000,9,2.270000,1,False.\r\nMN,139,510,357-9832,no,yes,25,96.200000,112,16.350000,178.900000,70,15.210000,182.100000,84,8.190000,12.900000,10,3.480000,2,False.\r\nNC,103,415,396-4845,no,no,0,263.400000,118,44.780000,179.100000,69,15.220000,214.700000,112,9.660000,10.300000,2,2.780000,0,False.\r\nOR,98,415,378-6772,yes,no,0,12.500000,67,2.130000,256.600000,90,21.810000,169.400000,88,7.620000,7.700000,9,2.080000,1,False.\r\nNH,78,408,353-4296,no,no,0,162.300000,116,27.590000,192.400000,86,16.350000,240.600000,100,10.830000,10.100000,3,2.730000,2,False.\r\nAK,50,408,362-8331,no,no,0,183.600000,107,31.210000,58.600000,118,4.980000,202.600000,99,9.120000,8.700000,3,2.350000,1,False.\r\nOR,161,415,362-4685,no,no,0,178.100000,109,30.280000,146.500000,86,12.450000,137.600000,78,6.190000,8.500000,2,2.300000,1,False.\r\nKS,67,408,383-1431,no,no,0,201.400000,101,34.240000,97.600000,122,8.300000,202.500000,119,9.110000,7.000000,3,1.890000,0,False.\r\nWV,86,415,332-2258,no,yes,38,123.000000,158,20.910000,133.900000,119,11.380000,138.200000,103,6.220000,13.300000,4,3.590000,1,False.\r\nGA,92,510,375-8304,no,no,0,208.000000,125,35.360000,198.900000,76,16.910000,76.400000,97,3.440000,8.600000,6,2.320000,0,False.\r\nNM,174,415,340-5580,no,no,0,239.200000,72,40.660000,188.500000,124,16.020000,105.600000,116,4.750000,8.800000,3,2.380000,1,False.\r\nOH,124,510,362-1490,no,no,0,193.000000,97,32.810000,89.800000,99,7.630000,172.800000,104,7.780000,15.300000,3,4.130000,1,False.\r\nND,132,415,336-4281,no,yes,31,174.500000,101,29.670000,245.600000,105,20.880000,172.800000,76,7.780000,10.300000,9,2.780000,1,False.\r\nRI,190,415,361-1315,no,yes,26,116.700000,71,19.840000,145.900000,88,12.400000,175.100000,103,7.880000,9.900000,3,2.670000,1,False.\r\nHI,101,510,342-5906,no,no,0,93.800000,127,15.950000,150.000000,104,12.750000,241.100000,116,10.850000,10.700000,2,2.890000,1,False.\r\nWY,185,415,405-7904,yes,yes,30,154.100000,114,26.200000,118.700000,106,10.090000,258.400000,105,11.630000,12.900000,3,3.480000,2,False.\r\nNY,68,415,349-4762,no,yes,29,239.500000,82,40.720000,203.800000,105,17.320000,167.800000,70,7.550000,9.900000,6,2.670000,0,False.\r\nKS,117,510,385-3263,no,yes,25,216.000000,140,36.720000,224.100000,69,19.050000,267.900000,112,12.060000,11.800000,4,3.190000,0,False.\r\nLA,118,408,405-9496,no,no,0,187.400000,97,31.860000,177.800000,89,15.110000,233.400000,97,10.500000,12.200000,6,3.290000,1,False.\r\nPA,124,415,396-6775,no,no,0,167.400000,119,28.460000,233.200000,143,19.820000,109.600000,115,4.930000,10.300000,5,2.780000,0,False.\r\nNV,22,510,393-6475,no,no,0,160.400000,108,27.270000,218.100000,88,18.540000,192.900000,115,8.680000,12.500000,4,3.380000,1,False.\r\nMN,75,415,379-7779,no,no,0,143.200000,92,24.340000,209.100000,142,17.770000,173.000000,96,7.790000,11.900000,9,3.210000,1,False.\r\nPA,134,408,408-8650,no,no,0,205.300000,122,34.900000,240.500000,155,20.440000,179.100000,107,8.060000,5.000000,9,1.350000,1,False.\r\nMO,164,408,400-3497,no,yes,25,219.100000,88,37.250000,151.500000,99,12.880000,50.100000,60,2.250000,14.300000,6,3.860000,1,False.\r\nNY,44,510,407-9244,no,no,0,143.200000,77,24.340000,169.800000,114,14.430000,215.800000,77,9.710000,7.600000,4,2.050000,1,False.\r\nME,177,415,406-8809,no,no,0,232.800000,106,39.580000,175.200000,97,14.890000,212.200000,77,9.550000,12.500000,7,3.380000,2,False.\r\nNH,110,408,358-1778,no,no,0,162.000000,81,27.540000,247.500000,89,21.040000,155.500000,99,7.000000,8.900000,8,2.400000,0,False.\r\nWY,53,415,337-4339,no,yes,27,25.900000,119,4.400000,206.500000,96,17.550000,228.100000,64,10.260000,6.500000,7,1.760000,1,False.\r\nNY,108,415,344-7197,no,no,0,154.200000,123,26.210000,112.300000,86,9.550000,246.400000,75,11.090000,15.400000,4,4.160000,4,True.\r\nME,80,408,333-7631,no,no,0,322.300000,113,54.790000,222.000000,95,18.870000,162.800000,123,7.330000,6.700000,8,1.810000,0,True.\r\nMN,158,408,372-6623,no,no,0,209.900000,112,35.680000,221.300000,82,18.810000,210.000000,93,9.450000,8.200000,3,2.210000,1,False.\r\nOH,114,415,363-2602,no,no,0,191.500000,88,32.560000,175.200000,78,14.890000,220.300000,118,9.910000,0.000000,0,0.000000,0,False.\r\nNC,64,408,387-7757,no,yes,19,291.100000,150,49.490000,226.700000,123,19.270000,219.100000,67,9.860000,7.500000,2,2.030000,1,False.\r\nSD,88,415,397-5381,no,no,0,215.600000,115,36.650000,216.200000,85,18.380000,171.300000,65,7.710000,11.800000,1,3.190000,3,False.\r\nUT,82,510,406-4604,yes,no,0,208.800000,101,35.500000,213.700000,87,18.160000,175.100000,86,7.880000,12.400000,6,3.350000,3,False.\r\nKY,111,415,376-9513,no,no,0,255.900000,97,43.500000,204.100000,129,17.350000,171.300000,84,7.710000,12.300000,5,3.320000,3,False.\r\nMT,60,408,360-1852,no,no,0,252.700000,97,42.960000,221.100000,121,18.790000,109.900000,100,4.950000,12.400000,4,3.350000,2,False.\r\nNJ,113,408,421-7270,no,no,0,132.100000,72,22.460000,247.500000,107,21.040000,246.200000,123,11.080000,6.900000,6,1.860000,3,False.\r\nFL,109,408,371-9482,no,no,0,217.000000,115,36.890000,207.000000,142,17.600000,268.000000,106,12.060000,8.200000,4,2.210000,1,False.\r\nIN,105,510,337-4101,no,yes,42,101.900000,79,17.320000,223.100000,97,18.960000,241.600000,77,10.870000,12.900000,2,3.480000,0,False.\r\nDE,85,510,420-2796,no,no,0,211.500000,100,35.960000,184.600000,88,15.690000,164.300000,131,7.390000,13.300000,4,3.590000,2,False.\r\nAL,131,510,366-4225,no,no,0,153.400000,86,26.080000,198.500000,81,16.870000,164.400000,83,7.400000,10.400000,3,2.810000,0,False.\r\nME,59,510,398-4567,no,no,0,166.300000,95,28.270000,239.300000,87,20.340000,123.200000,108,5.540000,10.000000,3,2.700000,2,False.\r\nSD,148,408,388-4571,no,no,0,185.200000,87,31.480000,170.400000,96,14.480000,165.100000,104,7.430000,9.500000,13,2.570000,1,False.\r\nVA,210,408,360-8666,no,no,0,104.600000,121,17.780000,149.500000,71,12.710000,255.100000,67,11.480000,6.500000,8,1.760000,2,False.\r\nAK,115,415,333-3704,no,no,0,245.200000,105,41.680000,159.000000,109,13.520000,229.900000,74,10.350000,7.200000,8,1.940000,0,False.\r\nID,106,510,383-2566,no,no,0,274.400000,120,46.650000,198.600000,82,16.880000,160.800000,62,7.240000,6.000000,3,1.620000,1,False.\r\nRI,93,415,406-5584,no,no,0,98.400000,78,16.730000,249.600000,129,21.220000,248.200000,114,11.170000,14.200000,4,3.830000,1,False.\r\nKY,57,415,344-4691,no,yes,29,279.900000,121,47.580000,223.100000,109,18.960000,251.700000,94,11.330000,13.000000,2,3.510000,1,False.\r\nND,98,510,347-9737,no,no,0,187.200000,127,31.820000,195.600000,88,16.630000,181.800000,129,8.180000,5.100000,4,1.380000,1,False.\r\nHI,157,415,333-7961,no,no,0,276.200000,95,46.950000,165.800000,119,14.090000,151.600000,79,6.820000,2.200000,4,0.590000,3,False.\r\nWI,116,415,364-2439,no,yes,35,200.400000,104,34.070000,272.800000,89,23.190000,214.500000,100,9.650000,8.300000,4,2.240000,1,False.\r\nWV,30,510,411-8043,no,no,0,162.300000,96,27.590000,244.000000,122,20.740000,180.100000,89,8.100000,9.100000,4,2.460000,2,False.\r\nNJ,111,510,332-5084,no,no,0,176.900000,128,30.070000,102.800000,56,8.740000,213.700000,84,9.620000,10.500000,2,2.840000,4,True.\r\nKS,52,415,413-4831,no,no,0,165.500000,78,28.140000,205.500000,89,17.470000,213.600000,124,9.610000,12.200000,6,3.290000,0,False.\r\nAL,72,415,343-4806,no,no,0,217.800000,93,37.030000,189.700000,113,16.120000,182.600000,91,8.220000,10.400000,5,2.810000,4,False.\r\nNJ,135,510,401-8735,no,yes,28,201.400000,100,34.240000,246.500000,117,20.950000,154.800000,131,6.970000,12.900000,4,3.480000,2,True.\r\nNC,86,510,391-8626,no,no,0,190.500000,115,32.390000,179.600000,130,15.270000,258.500000,89,11.630000,10.100000,5,2.730000,3,False.\r\nDE,98,415,327-5817,no,yes,29,179.900000,97,30.580000,189.200000,89,16.080000,164.300000,76,7.390000,12.800000,7,3.460000,3,False.\r\nWY,151,510,381-4712,no,no,0,235.900000,104,40.100000,80.600000,91,6.850000,212.800000,116,9.580000,5.800000,2,1.570000,3,False.\r\nID,118,415,335-3320,no,no,0,140.400000,112,23.870000,187.100000,60,15.900000,207.900000,155,9.360000,7.900000,1,2.130000,0,False.\r\nNY,117,415,415-8780,no,no,0,144.600000,115,24.580000,258.800000,66,22.000000,253.200000,113,11.390000,7.400000,9,2.000000,2,False.\r\nMO,55,510,362-1146,no,no,0,189.000000,100,32.130000,118.500000,99,10.070000,248.100000,87,11.160000,17.100000,6,4.620000,0,False.\r\nID,82,408,352-7413,no,no,0,101.000000,93,17.170000,155.600000,104,13.230000,304.400000,93,13.700000,13.300000,2,3.590000,0,False.\r\nIA,152,415,387-6716,no,no,0,206.300000,98,35.070000,292.800000,82,24.890000,43.700000,121,1.970000,10.600000,4,2.860000,1,False.\r\nTN,108,408,352-1127,no,yes,15,165.100000,85,28.070000,267.000000,93,22.700000,250.700000,114,11.280000,10.900000,4,2.940000,1,False.\r\nOH,98,408,368-1288,no,no,0,165.000000,129,28.050000,202.600000,113,17.220000,172.300000,94,7.750000,12.500000,7,3.380000,1,True.\r\nIL,130,415,403-5279,no,no,0,155.900000,95,26.500000,256.100000,97,21.770000,262.900000,103,11.830000,11.700000,3,3.160000,3,False.\r\nFL,136,408,397-9333,yes,no,0,199.200000,122,33.860000,214.700000,114,18.250000,150.900000,105,6.790000,11.800000,7,3.190000,1,False.\r\nMA,47,415,411-7353,no,no,0,155.300000,116,26.400000,188.200000,85,16.000000,247.000000,73,11.120000,12.300000,4,3.320000,3,False.\r\nOK,189,415,383-2537,no,no,0,208.300000,106,35.410000,236.700000,123,20.120000,179.100000,120,8.060000,11.300000,5,3.050000,3,False.\r\nKY,107,415,330-4419,no,no,0,157.100000,79,26.710000,162.600000,124,13.820000,150.000000,138,6.750000,12.100000,6,3.270000,1,False.\r\nMI,91,415,390-7930,no,no,0,154.400000,165,26.250000,168.300000,121,14.310000,239.900000,81,10.800000,11.700000,4,3.160000,5,True.\r\nNE,159,415,362-5111,no,no,0,189.100000,105,32.150000,246.100000,147,20.920000,242.000000,106,10.890000,10.400000,5,2.810000,1,True.\r\nVA,11,408,358-6796,no,yes,24,131.500000,98,22.360000,230.200000,111,19.570000,283.700000,87,12.770000,10.000000,3,2.700000,2,False.\r\nNY,167,415,409-7494,no,no,0,166.400000,85,28.290000,243.200000,135,20.670000,229.200000,95,10.310000,9.900000,5,2.670000,1,False.\r\nMT,111,408,400-1636,no,no,0,142.300000,75,24.190000,122.800000,106,10.440000,229.500000,94,10.330000,12.800000,9,3.460000,2,False.\r\nVT,99,408,389-2747,no,yes,19,87.700000,103,14.910000,223.000000,86,18.960000,182.300000,112,8.200000,7.300000,4,1.970000,2,False.\r\nKS,159,415,415-2176,no,yes,19,184.100000,78,31.300000,194.500000,71,16.530000,225.600000,101,10.150000,16.900000,3,4.560000,0,False.\r\nVA,114,415,377-8067,yes,yes,31,174.500000,104,29.670000,224.200000,92,19.060000,116.300000,91,5.230000,12.300000,10,3.320000,2,False.\r\nAL,71,415,362-7835,no,no,0,103.300000,103,17.560000,138.500000,79,11.770000,164.800000,98,7.420000,9.000000,2,2.430000,2,False.\r\nPA,122,415,361-5225,no,no,0,35.100000,62,5.970000,180.800000,89,15.370000,251.600000,58,11.320000,12.700000,2,3.430000,1,False.\r\nAL,100,408,333-3447,no,yes,25,246.600000,94,41.920000,141.400000,112,12.020000,189.800000,109,8.540000,13.000000,5,3.510000,1,False.\r\nWV,83,415,341-3044,no,yes,37,78.500000,109,13.350000,210.500000,101,17.890000,179.700000,102,8.090000,11.800000,4,3.190000,1,False.\r\nNV,64,408,350-7306,no,no,0,148.100000,73,25.180000,164.900000,101,14.020000,216.000000,125,9.720000,12.300000,2,3.320000,5,True.\r\nTN,105,408,353-8849,no,no,0,206.200000,84,35.050000,256.400000,138,21.790000,117.100000,91,5.270000,9.000000,3,2.430000,1,False.\r\nID,144,415,402-3476,no,yes,33,251.600000,87,42.770000,197.600000,118,16.800000,209.200000,97,9.410000,12.200000,3,3.290000,2,False.\r\nWY,106,415,338-6018,yes,yes,26,270.300000,111,45.950000,215.200000,90,18.290000,254.000000,133,11.430000,14.400000,7,3.890000,1,True.\r\nTX,19,510,409-3520,no,yes,34,156.600000,97,26.620000,224.200000,97,19.060000,260.900000,135,11.740000,11.300000,1,3.050000,1,False.\r\nMA,46,408,357-1085,no,no,0,139.400000,81,23.700000,223.700000,113,19.010000,173.100000,77,7.790000,13.600000,6,3.670000,1,False.\r\nIL,127,510,353-3285,no,no,0,220.200000,108,37.430000,188.400000,124,16.010000,172.700000,113,7.770000,11.700000,3,3.160000,2,False.\r\nLA,9,415,409-9885,no,yes,39,214.100000,108,36.400000,169.200000,115,14.380000,189.700000,117,8.540000,10.100000,3,2.730000,1,False.\r\nCO,157,415,388-7701,no,no,0,196.000000,74,33.320000,213.400000,96,18.140000,196.800000,81,8.860000,7.900000,6,2.130000,1,False.\r\nUT,105,415,385-8184,no,no,0,106.400000,71,18.090000,240.100000,83,20.410000,147.700000,114,6.650000,5.300000,4,1.430000,6,True.\r\nMI,105,415,345-2863,no,yes,29,179.400000,113,30.500000,275.400000,100,23.410000,246.100000,105,11.070000,10.000000,5,2.700000,0,False.\r\nNH,155,408,353-6300,no,no,0,216.700000,30,36.840000,144.300000,125,12.270000,135.300000,106,6.090000,10.800000,1,2.920000,2,False.\r\nID,31,415,389-5649,no,no,0,177.300000,129,30.140000,152.800000,105,12.990000,162.900000,92,7.330000,5.100000,2,1.380000,0,False.\r\nWA,161,415,378-8137,no,no,0,151.600000,117,25.770000,219.400000,87,18.650000,224.700000,68,10.110000,4.000000,5,1.080000,1,False.\r\nMN,95,408,340-4627,no,yes,32,262.200000,123,44.570000,165.200000,82,14.040000,194.300000,57,8.740000,10.600000,5,2.860000,0,False.\r\nNY,122,415,352-6833,no,no,0,173.600000,110,29.510000,91.700000,84,7.790000,211.700000,103,9.530000,9.700000,7,2.620000,3,False.\r\nMD,37,415,420-2000,yes,no,0,106.600000,76,18.120000,147.400000,89,12.530000,235.800000,113,10.610000,9.600000,8,2.590000,2,False.\r\nGA,132,415,368-5437,no,no,0,193.300000,106,32.860000,128.300000,94,10.910000,162.100000,119,7.290000,11.600000,4,3.130000,5,True.\r\nHI,119,408,418-8170,no,yes,24,217.200000,94,36.920000,138.700000,52,11.790000,139.300000,85,6.270000,11.300000,4,3.050000,0,False.\r\nAL,16,408,403-9417,no,no,0,209.500000,89,35.620000,172.800000,85,14.690000,94.100000,102,4.230000,8.800000,4,2.380000,1,False.\r\nCT,99,408,330-6165,no,no,0,95.400000,105,16.220000,207.200000,101,17.610000,136.000000,117,6.120000,5.600000,5,1.510000,1,False.\r\nCO,76,408,412-4185,no,yes,26,214.600000,110,36.480000,205.200000,87,17.440000,134.600000,140,6.060000,8.100000,2,2.190000,1,False.\r\nKS,167,415,409-4734,no,no,0,131.600000,120,22.370000,211.300000,96,17.960000,168.300000,97,7.570000,11.100000,4,3.000000,4,True.\r\nNJ,129,415,348-9828,no,no,0,168.400000,117,28.630000,217.100000,129,18.450000,81.600000,100,3.670000,11.800000,7,3.190000,1,False.\r\nFL,116,408,418-8850,no,no,0,146.400000,123,24.890000,176.600000,113,15.010000,212.600000,102,9.570000,7.800000,5,2.110000,1,False.\r\nCA,60,415,330-8351,yes,no,0,183.000000,110,31.110000,206.700000,93,17.570000,203.800000,119,9.170000,11.100000,6,3.000000,1,False.\r\nKS,128,415,347-7773,no,no,0,103.300000,122,17.560000,245.900000,123,20.900000,161.100000,95,7.250000,6.400000,7,1.730000,0,False.\r\nTX,47,408,392-6841,no,yes,28,112.200000,70,19.070000,154.800000,106,13.160000,166.700000,105,7.500000,10.600000,2,2.860000,0,False.\r\nMN,40,510,354-2189,yes,no,0,170.700000,55,29.020000,179.100000,108,15.220000,281.900000,89,12.690000,8.200000,9,2.210000,3,False.\r\nAK,173,510,349-9060,no,no,0,172.500000,78,29.330000,142.600000,91,12.120000,102.000000,63,4.590000,10.900000,2,2.940000,0,False.\r\nOR,157,510,334-1311,no,yes,30,194.300000,107,33.030000,243.200000,108,20.670000,322.200000,114,14.500000,7.100000,5,1.920000,1,False.\r\nMS,66,408,415-3120,no,yes,32,187.800000,117,31.930000,129.800000,90,11.030000,132.300000,113,5.950000,12.000000,3,3.240000,2,False.\r\nVT,50,415,387-5891,yes,yes,26,307.100000,94,52.210000,289.400000,78,24.600000,174.900000,109,7.870000,8.000000,3,2.160000,0,False.\r\nUT,72,415,368-8026,no,no,0,118.200000,106,20.090000,167.200000,136,14.210000,214.200000,106,9.640000,12.200000,3,3.290000,3,False.\r\nKS,130,510,332-9446,no,no,0,154.000000,95,26.180000,205.900000,106,17.500000,233.700000,75,10.520000,12.900000,1,3.480000,1,False.\r\nNV,143,408,393-5284,no,no,0,155.500000,101,26.440000,213.400000,89,18.140000,237.900000,61,10.710000,7.600000,11,2.050000,1,False.\r\nDE,89,510,376-1677,yes,no,0,125.600000,108,21.350000,213.000000,90,18.110000,181.700000,108,8.180000,5.400000,5,1.460000,1,False.\r\nIN,108,415,392-2268,no,no,0,199.300000,104,33.880000,224.200000,92,19.060000,140.100000,57,6.300000,15.200000,2,4.100000,0,False.\r\nTX,32,408,396-4311,no,no,0,157.900000,88,26.840000,180.800000,132,15.370000,132.500000,102,5.960000,12.800000,3,3.460000,1,False.\r\nMA,166,415,363-4005,no,no,0,203.400000,81,34.580000,167.700000,110,14.250000,132.000000,124,5.940000,9.200000,5,2.480000,2,False.\r\nVT,109,408,344-9966,no,no,0,222.200000,113,37.770000,218.500000,122,18.570000,266.000000,88,11.970000,10.900000,5,2.940000,1,False.\r\nCA,72,408,337-7377,no,yes,39,92.800000,98,15.780000,271.200000,115,23.050000,167.100000,83,7.520000,5.800000,7,1.570000,1,False.\r\nIA,134,415,373-7037,no,yes,32,216.800000,78,36.860000,102.200000,111,8.690000,174.000000,83,7.830000,8.600000,2,2.320000,0,False.\r\nNH,13,415,356-7580,no,no,0,193.200000,89,32.840000,194.400000,90,16.520000,186.500000,104,8.390000,9.700000,2,2.620000,4,False.\r\nGA,90,415,390-3401,no,no,0,113.200000,108,19.240000,189.300000,63,16.090000,271.800000,124,12.230000,14.100000,4,3.810000,3,False.\r\nWI,111,415,350-9313,no,yes,36,166.200000,54,28.250000,238.800000,109,20.300000,108.800000,92,4.900000,11.200000,2,3.020000,3,False.\r\nCO,101,408,420-9009,no,yes,23,262.200000,101,44.570000,157.000000,80,13.350000,129.100000,100,5.810000,7.300000,14,1.970000,1,False.\r\nSC,72,415,415-2641,no,no,0,207.800000,92,35.330000,195.700000,110,16.630000,184.800000,124,8.320000,13.100000,4,3.540000,0,False.\r\nMI,67,510,392-4929,no,yes,35,245.400000,89,41.720000,148.200000,102,12.600000,274.000000,136,12.330000,7.500000,6,2.030000,1,False.\r\nMD,172,408,346-5068,no,no,0,287.100000,108,48.810000,178.400000,125,15.160000,153.200000,98,6.890000,14.400000,2,3.890000,3,True.\r\nIN,154,510,389-2631,no,yes,32,192.300000,82,32.690000,165.300000,134,14.050000,205.000000,74,9.230000,9.000000,1,2.430000,2,False.\r\nME,69,510,368-3808,no,no,0,194.200000,122,33.010000,242.100000,81,20.580000,215.800000,80,9.710000,9.700000,3,2.620000,2,False.\r\nDC,123,415,406-8599,no,no,0,211.000000,92,35.870000,217.000000,102,18.450000,214.800000,104,9.670000,9.800000,7,2.650000,3,False.\r\nTN,130,415,386-7456,no,yes,12,141.900000,92,24.120000,228.900000,102,19.460000,195.100000,101,8.780000,8.700000,5,2.350000,0,False.\r\nFL,142,415,357-4936,no,yes,26,220.500000,94,37.490000,239.500000,126,20.360000,254.300000,109,11.440000,5.900000,9,1.590000,2,False.\r\nWA,29,415,397-7411,no,no,0,157.400000,122,26.760000,145.000000,75,12.330000,281.800000,92,12.680000,9.300000,2,2.510000,1,False.\r\nHI,87,408,353-6218,no,yes,28,143.500000,106,24.400000,223.500000,147,19.000000,175.400000,69,7.890000,11.200000,5,3.020000,0,False.\r\nNE,149,415,369-5942,no,no,0,156.000000,56,26.520000,56.000000,116,4.760000,163.300000,104,7.350000,8.900000,8,2.400000,0,False.\r\nTN,146,408,416-8543,yes,no,0,160.100000,63,27.220000,208.400000,112,17.710000,177.600000,98,7.990000,9.200000,3,2.480000,2,False.\r\nMD,88,415,358-4576,yes,no,0,235.100000,98,39.970000,251.800000,79,21.400000,285.900000,76,12.870000,7.200000,2,1.940000,4,True.\r\nNM,119,415,352-5118,yes,yes,15,160.000000,95,27.200000,209.500000,110,17.810000,82.300000,107,3.700000,8.700000,5,2.350000,5,True.\r\nVT,48,510,408-2621,no,no,0,188.400000,63,32.030000,165.900000,89,14.100000,205.700000,71,9.260000,13.200000,2,3.560000,1,False.\r\nOR,135,415,353-3994,no,no,0,194.800000,97,33.120000,235.300000,118,20.000000,174.400000,126,7.850000,11.000000,3,2.970000,1,False.\r\nIN,100,510,367-4277,no,no,0,247.800000,117,42.130000,130.000000,95,11.050000,134.300000,125,6.040000,6.900000,2,1.860000,1,False.\r\nMO,98,415,354-3237,no,no,0,221.200000,80,37.600000,213.600000,104,18.160000,291.800000,89,13.130000,11.900000,3,3.210000,4,False.\r\nFL,75,510,394-8504,no,yes,26,118.500000,86,20.150000,213.900000,118,18.180000,132.600000,99,5.970000,13.400000,3,3.620000,2,False.\r\nDC,180,415,370-4139,no,yes,33,231.800000,78,39.410000,232.900000,79,19.800000,206.900000,121,9.310000,7.600000,4,2.050000,1,False.\r\nVT,100,415,382-6135,no,yes,25,215.900000,90,36.700000,257.900000,92,21.920000,180.200000,157,8.110000,11.600000,4,3.130000,1,False.\r\nOH,119,415,359-5718,no,yes,35,217.100000,92,36.910000,220.800000,134,18.770000,249.500000,93,11.230000,8.000000,5,2.160000,2,False.\r\nMO,86,415,385-3111,no,no,0,83.500000,96,14.200000,221.100000,63,18.790000,349.700000,75,15.740000,12.600000,3,3.400000,0,False.\r\nLA,155,408,353-4880,no,yes,39,183.300000,106,31.160000,205.100000,101,17.430000,263.700000,90,11.870000,5.100000,7,1.380000,1,False.\r\nIL,78,415,343-7019,yes,no,0,236.800000,141,40.260000,265.300000,101,22.550000,152.400000,77,6.860000,9.500000,2,2.570000,1,True.\r\nNJ,153,415,419-6133,no,no,0,193.800000,90,32.950000,195.300000,121,16.600000,182.700000,108,8.220000,8.500000,3,2.300000,1,False.\r\nIA,92,510,350-7344,no,yes,25,134.000000,112,22.780000,206.000000,111,17.510000,180.600000,118,8.130000,9.700000,4,2.620000,0,False.\r\nWA,13,510,397-6064,no,yes,25,176.600000,65,30.020000,172.700000,96,14.680000,104.500000,128,4.700000,11.300000,5,3.050000,2,False.\r\nNE,154,415,363-6896,no,no,0,191.400000,93,32.540000,205.400000,119,17.460000,205.700000,121,9.260000,10.200000,3,2.750000,3,False.\r\nCT,144,510,416-9021,yes,yes,35,174.800000,127,29.720000,219.600000,93,18.670000,255.800000,90,11.510000,12.800000,3,3.460000,0,False.\r\nWV,48,408,408-3269,no,no,0,275.200000,67,46.780000,180.200000,108,15.320000,159.000000,110,7.150000,7.900000,2,2.130000,1,False.\r\nND,94,408,408-9463,no,no,0,174.000000,85,29.580000,241.100000,114,20.490000,207.800000,94,9.350000,7.900000,1,2.130000,1,False.\r\nUT,139,415,340-7062,no,no,0,165.000000,132,28.050000,249.700000,86,21.220000,170.300000,128,7.660000,12.600000,8,3.400000,1,False.\r\nMS,126,415,417-4309,no,no,0,228.700000,102,38.880000,168.700000,99,14.340000,223.500000,100,10.060000,11.800000,4,3.190000,1,False.\r\nMN,122,415,421-2659,no,no,0,107.900000,88,18.340000,235.800000,109,20.040000,228.600000,119,10.290000,9.500000,3,2.570000,2,False.\r\nNH,139,510,383-2017,no,no,0,221.300000,140,37.620000,157.800000,89,13.410000,192.500000,89,8.660000,11.300000,6,3.050000,1,False.\r\nLA,95,415,411-6294,no,no,0,141.100000,84,23.990000,211.400000,108,17.970000,103.700000,127,4.670000,5.900000,6,1.590000,3,False.\r\nME,80,408,332-9525,no,yes,31,166.400000,92,28.290000,238.300000,74,20.260000,150.700000,84,6.780000,10.700000,4,2.890000,4,False.\r\nKS,131,415,401-5012,no,yes,28,249.600000,87,42.430000,227.200000,138,19.310000,239.900000,92,10.800000,7.600000,3,2.050000,3,False.\r\nKS,36,510,368-8835,no,no,0,178.600000,83,30.360000,213.100000,103,18.110000,198.000000,119,8.910000,10.900000,5,2.940000,1,False.\r\nNV,180,510,351-1382,no,no,0,139.000000,96,23.630000,224.900000,64,19.120000,170.800000,118,7.690000,15.700000,5,4.240000,2,False.\r\nMT,25,415,359-7694,no,no,0,134.300000,98,22.830000,202.300000,109,17.200000,195.900000,100,8.820000,12.600000,5,3.400000,2,False.\r\nMT,113,415,419-5505,no,no,0,215.900000,93,36.700000,240.100000,85,20.410000,156.700000,123,7.050000,4.900000,5,1.320000,3,False.\r\nID,88,415,341-4570,no,yes,31,181.600000,91,30.870000,213.200000,120,18.120000,207.800000,104,9.350000,11.400000,4,3.080000,1,False.\r\nAK,120,415,366-6991,no,no,0,178.400000,97,30.330000,168.300000,113,14.310000,120.500000,93,5.420000,9.300000,9,2.510000,1,False.\r\nUT,74,415,377-7399,no,no,0,106.400000,84,18.090000,140.200000,104,11.920000,90.900000,81,4.090000,11.400000,3,3.080000,1,False.\r\nAR,109,510,377-9092,no,no,0,170.700000,101,29.020000,240.200000,82,20.420000,119.000000,112,5.360000,11.400000,4,3.080000,2,False.\r\nLA,162,510,373-6681,no,yes,33,184.500000,139,31.370000,183.200000,78,15.570000,127.400000,106,5.730000,12.300000,6,3.320000,0,False.\r\nKS,124,510,417-7736,no,yes,37,161.200000,109,27.400000,204.200000,79,17.360000,231.500000,87,10.420000,8.900000,4,2.400000,1,False.\r\nOR,177,408,393-2812,no,no,0,84.900000,77,14.430000,257.500000,109,21.890000,210.500000,66,9.470000,7.500000,5,2.030000,2,False.\r\nWI,91,510,398-3176,no,no,0,217.900000,71,37.040000,230.100000,116,19.560000,232.100000,110,10.440000,10.600000,2,2.860000,1,False.\r\nCO,105,408,334-8694,no,no,0,270.900000,98,46.050000,226.200000,110,19.230000,178.800000,60,8.050000,8.800000,5,2.380000,0,True.\r\nKS,24,510,369-5449,no,no,0,243.000000,91,41.310000,183.900000,77,15.630000,184.300000,109,8.290000,15.300000,6,4.130000,0,True.\r\nIL,48,510,380-5246,no,no,0,128.200000,71,21.790000,48.100000,78,4.090000,116.300000,80,5.230000,8.900000,3,2.400000,0,False.\r\nIA,86,408,390-3873,no,no,0,126.300000,115,21.470000,168.800000,112,14.350000,154.600000,95,6.960000,9.800000,7,2.650000,2,False.\r\nAZ,163,510,354-4568,no,no,0,178.700000,56,30.380000,215.700000,79,18.330000,152.700000,84,6.870000,10.600000,2,2.860000,4,False.\r\nNE,91,510,339-6968,no,no,0,159.000000,109,27.030000,255.100000,142,21.680000,82.400000,73,3.710000,10.100000,4,2.730000,0,False.\r\nID,56,510,379-5933,no,no,0,150.900000,79,25.650000,161.800000,87,13.750000,167.700000,115,7.550000,11.700000,5,3.160000,3,False.\r\nOH,147,415,365-5682,yes,yes,24,219.900000,118,37.380000,208.500000,116,17.720000,352.500000,111,15.860000,8.100000,4,2.190000,3,False.\r\nTX,64,415,382-8518,no,no,0,168.000000,116,28.560000,192.400000,94,16.350000,166.500000,98,7.490000,10.100000,3,2.730000,2,False.\r\nTN,108,510,356-8449,no,yes,34,162.100000,83,27.560000,171.800000,117,14.600000,259.800000,76,11.690000,9.600000,3,2.590000,4,True.\r\nOK,159,510,333-3460,no,no,0,198.800000,107,33.800000,195.500000,91,16.620000,213.300000,120,9.600000,16.500000,7,4.460000,5,False.\r\nND,136,510,375-8596,yes,no,0,256.800000,90,43.660000,230.100000,104,19.560000,143.600000,82,6.460000,9.100000,10,2.460000,3,False.\r\nMT,116,415,384-5907,no,yes,35,182.800000,122,31.080000,212.700000,119,18.080000,193.800000,103,8.720000,11.000000,2,2.970000,1,False.\r\nNC,45,408,373-2903,no,yes,38,196.800000,92,33.460000,254.200000,108,21.610000,261.800000,85,11.780000,7.700000,2,2.080000,1,False.\r\nMN,122,415,361-7702,no,no,0,140.100000,120,23.820000,231.400000,128,19.670000,188.100000,127,8.460000,11.200000,5,3.020000,2,False.\r\nMN,138,415,388-5850,no,no,0,194.300000,83,33.030000,189.900000,97,16.140000,232.200000,102,10.450000,9.000000,3,2.430000,5,False.\r\nMA,132,415,405-6298,no,no,0,117.600000,66,19.990000,214.000000,108,18.190000,239.500000,94,10.780000,8.800000,5,2.380000,1,False.\r\nPA,101,415,368-2074,yes,no,0,193.700000,108,32.930000,186.600000,98,15.860000,223.000000,100,10.040000,11.600000,8,3.130000,0,False.\r\nGA,58,510,328-5050,no,no,0,243.100000,105,41.330000,231.400000,108,19.670000,180.900000,120,8.140000,7.800000,4,2.110000,2,False.\r\nNV,81,415,395-5783,no,no,0,145.400000,132,24.720000,129.300000,91,10.990000,186.400000,109,8.390000,5.200000,4,1.400000,1,False.\r\nTX,87,415,420-7301,no,no,0,169.100000,105,28.750000,169.900000,102,14.440000,244.900000,106,11.020000,9.900000,10,2.670000,3,False.\r\nME,116,510,328-2478,no,no,0,229.300000,93,38.980000,184.500000,111,15.680000,168.200000,91,7.570000,8.800000,3,2.380000,2,False.\r\nRI,85,415,381-2460,yes,no,0,197.200000,97,33.520000,211.700000,115,17.990000,210.100000,133,9.450000,8.300000,4,2.240000,4,False.\r\nMN,62,510,390-9811,no,yes,33,186.400000,84,31.690000,201.000000,136,17.090000,286.700000,103,12.900000,11.100000,3,3.000000,2,True.\r\nNH,90,415,373-5670,no,no,0,76.100000,121,12.940000,290.300000,73,24.680000,236.900000,89,10.660000,10.800000,3,2.920000,0,False.\r\nTN,98,415,351-7016,no,no,0,162.800000,65,27.680000,185.000000,109,15.730000,219.500000,104,9.880000,6.000000,3,1.620000,2,False.\r\nUT,73,415,394-9934,no,no,0,182.300000,115,30.990000,199.200000,97,16.930000,120.200000,113,5.410000,18.000000,5,4.860000,1,False.\r\nRI,107,510,422-8268,yes,no,0,194.400000,83,33.050000,247.800000,84,21.060000,245.400000,93,11.040000,11.200000,7,3.020000,2,False.\r\nNH,55,408,373-7690,no,yes,20,189.300000,95,32.180000,118.600000,113,10.080000,250.200000,102,11.260000,12.500000,4,3.380000,2,False.\r\nAK,76,415,366-9781,no,yes,22,160.100000,107,27.220000,168.700000,136,14.340000,23.200000,102,1.040000,9.500000,4,2.570000,3,False.\r\nNC,30,510,404-5427,no,no,0,145.000000,76,24.650000,240.700000,112,20.460000,197.100000,134,8.870000,7.100000,4,1.920000,3,False.\r\nAZ,157,415,389-9783,no,no,0,220.700000,105,37.520000,119.300000,127,10.140000,165.100000,113,7.430000,11.500000,7,3.110000,4,False.\r\nMA,40,408,351-7005,no,yes,31,224.700000,69,38.200000,134.500000,81,11.430000,120.300000,104,5.410000,7.500000,5,2.030000,1,True.\r\nTN,72,408,348-2009,no,no,0,147.000000,79,24.990000,162.300000,103,13.800000,162.900000,80,7.330000,10.500000,4,2.840000,1,False.\r\nWY,95,415,340-4236,no,yes,39,260.800000,130,44.340000,213.400000,111,18.140000,195.600000,97,8.800000,10.100000,5,2.730000,1,False.\r\nIA,42,415,348-1528,no,no,0,155.400000,127,26.420000,164.100000,45,13.950000,157.700000,128,7.100000,9.000000,3,2.430000,0,False.\r\nIN,86,415,365-5039,no,no,0,166.200000,112,28.250000,255.300000,81,21.700000,228.100000,97,10.260000,5.400000,7,1.460000,1,False.\r\nNJ,131,415,411-1810,no,no,0,211.800000,115,36.010000,260.500000,102,22.140000,144.200000,96,6.490000,10.800000,7,2.920000,0,False.\r\nFL,55,510,364-7644,no,yes,45,130.500000,114,22.190000,208.400000,94,17.710000,141.600000,114,6.370000,11.000000,5,2.970000,4,True.\r\nMT,74,415,335-9066,no,no,0,162.700000,102,27.660000,292.000000,105,24.820000,183.300000,80,8.250000,8.700000,6,2.350000,0,False.\r\nND,81,408,362-7581,yes,yes,37,237.100000,76,40.310000,264.200000,125,22.460000,271.300000,120,12.210000,7.900000,3,2.130000,1,False.\r\nMI,81,408,346-1095,no,no,0,166.200000,102,28.250000,217.600000,112,18.500000,220.200000,68,9.910000,13.200000,2,3.560000,4,False.\r\nMT,28,415,357-9136,no,no,0,121.700000,48,20.690000,125.800000,112,10.690000,261.600000,122,11.770000,8.300000,2,2.240000,6,True.\r\nAL,111,510,390-7863,no,no,0,176.400000,62,29.990000,201.000000,124,17.090000,150.400000,138,6.770000,11.200000,2,3.020000,0,False.\r\nNV,3,510,344-2416,no,yes,27,67.400000,116,11.460000,244.000000,78,20.740000,281.100000,93,12.650000,11.400000,2,3.080000,2,False.\r\nMI,51,415,373-1448,no,no,0,229.700000,129,39.050000,336.000000,104,28.560000,192.800000,128,8.680000,9.600000,1,2.590000,1,True.\r\nFL,68,415,360-7076,no,yes,24,176.000000,118,29.920000,277.900000,116,23.620000,174.700000,71,7.860000,14.700000,7,3.970000,1,False.\r\nNY,163,408,413-2241,no,no,0,247.700000,77,42.110000,269.500000,108,22.910000,167.300000,82,7.530000,9.600000,7,2.590000,0,True.\r\nKS,87,510,343-3961,no,no,0,115.400000,90,19.620000,262.600000,68,22.320000,245.700000,69,11.060000,13.100000,5,3.540000,2,False.\r\nNC,58,510,375-4107,no,no,0,112.200000,95,19.070000,209.600000,108,17.820000,260.900000,78,11.740000,13.900000,1,3.750000,0,True.\r\nMN,109,408,414-7410,no,no,0,162.600000,138,27.640000,154.000000,109,13.090000,209.700000,118,9.440000,11.500000,4,3.110000,0,False.\r\nRI,111,415,351-2535,no,no,0,229.400000,107,39.000000,214.100000,99,18.200000,289.600000,95,13.030000,10.400000,6,2.810000,4,False.\r\nUT,144,415,416-9615,no,no,0,139.600000,96,23.730000,124.200000,93,10.560000,95.600000,75,4.300000,15.000000,4,4.050000,2,False.\r\nOR,135,415,377-1293,no,no,0,263.800000,66,44.850000,251.300000,116,21.360000,200.100000,112,9.000000,8.400000,2,2.270000,5,True.\r\nSC,109,415,388-6479,no,yes,46,217.500000,123,36.980000,233.700000,84,19.860000,163.900000,99,7.380000,9.000000,3,2.430000,4,False.\r\nIL,107,415,390-2755,no,yes,14,114.300000,132,19.430000,199.800000,91,16.980000,194.700000,74,8.760000,7.500000,8,2.030000,1,False.\r\nOH,149,408,340-5930,no,no,0,196.300000,108,33.370000,136.800000,96,11.630000,154.700000,87,6.960000,7.700000,3,2.080000,2,False.\r\nMA,56,510,401-3622,no,no,0,253.200000,95,43.040000,188.000000,116,15.980000,142.000000,133,6.390000,4.400000,4,1.190000,1,False.\r\nOR,129,408,382-1104,no,no,0,98.000000,99,16.660000,240.700000,62,20.460000,254.800000,123,11.470000,10.500000,4,2.840000,0,False.\r\nCA,92,408,355-9324,no,no,0,249.400000,118,42.400000,211.500000,95,17.980000,169.000000,116,7.610000,9.100000,3,2.460000,0,False.\r\nWV,67,415,393-4843,no,yes,30,129.600000,107,22.030000,233.000000,104,19.810000,297.000000,93,13.370000,14.500000,5,3.920000,1,False.\r\nVT,120,415,401-4052,no,no,0,221.300000,106,37.620000,267.600000,98,22.750000,111.500000,80,5.020000,9.300000,7,2.510000,0,False.\r\nID,166,415,385-1830,no,no,0,220.700000,106,37.520000,177.800000,118,15.110000,206.100000,102,9.270000,12.400000,9,3.350000,1,False.\r\nOR,66,408,348-7409,no,no,0,87.600000,76,14.890000,262.000000,111,22.270000,184.600000,125,8.310000,9.200000,5,2.480000,1,False.\r\nGA,76,408,361-4910,no,no,0,203.600000,61,34.610000,161.700000,127,13.740000,175.900000,97,7.920000,8.400000,3,2.270000,0,False.\r\nME,79,415,415-6578,no,no,0,213.600000,110,36.310000,234.900000,121,19.970000,229.600000,157,10.330000,8.800000,3,2.380000,2,False.\r\nHI,98,415,395-5015,no,yes,31,181.600000,112,30.870000,220.700000,100,18.760000,236.300000,121,10.630000,12.900000,4,3.480000,2,False.\r\nAZ,141,510,369-6012,no,yes,22,215.400000,123,36.620000,328.700000,98,27.940000,160.500000,89,7.220000,7.800000,6,2.110000,1,False.\r\nUT,49,415,394-4520,no,no,0,266.300000,90,45.270000,207.800000,117,17.660000,205.000000,98,9.230000,14.000000,2,3.780000,2,True.\r\nIA,46,510,380-5873,no,no,0,199.200000,111,33.860000,175.100000,83,14.880000,210.600000,84,9.480000,10.200000,2,2.750000,3,False.\r\nCT,137,415,372-5384,no,no,0,115.000000,130,19.550000,137.800000,83,11.710000,224.000000,61,10.080000,7.300000,4,1.970000,3,False.\r\nWA,171,408,419-1863,no,no,0,270.500000,69,45.990000,230.000000,112,19.550000,136.000000,111,6.120000,9.600000,5,2.590000,1,True.\r\nVA,10,415,352-5697,no,no,0,222.200000,127,37.770000,153.100000,125,13.010000,227.400000,80,10.230000,12.900000,4,3.480000,1,False.\r\nCO,88,510,373-4274,no,no,0,61.900000,78,10.520000,262.600000,114,22.320000,212.500000,110,9.560000,8.800000,2,2.380000,3,False.\r\nLA,89,415,382-4024,no,no,0,141.100000,92,23.990000,249.100000,126,21.170000,136.000000,73,6.120000,10.800000,2,2.920000,2,False.\r\nTX,82,415,395-9215,no,no,0,189.200000,81,32.160000,184.400000,117,15.670000,255.800000,83,11.510000,10.600000,5,2.860000,3,True.\r\nSD,139,510,331-9149,no,no,0,196.000000,135,33.320000,186.000000,146,15.810000,153.000000,92,6.890000,9.800000,1,2.650000,3,False.\r\nVA,87,415,347-3958,no,no,0,171.600000,119,29.170000,205.000000,107,17.430000,170.600000,114,7.680000,13.800000,4,3.730000,0,False.\r\nNY,137,415,389-2540,yes,no,0,174.000000,123,29.580000,161.300000,115,13.710000,260.700000,98,11.730000,11.400000,3,3.080000,2,False.\r\nWA,45,510,399-3083,no,no,0,78.600000,106,13.360000,187.300000,110,15.920000,184.200000,111,8.290000,7.400000,5,2.000000,1,True.\r\nWI,90,415,420-8308,no,no,0,200.900000,92,34.150000,164.300000,91,13.970000,249.000000,98,11.210000,8.900000,7,2.400000,1,False.\r\nTN,103,415,384-7724,no,no,0,141.300000,123,24.020000,253.600000,87,21.560000,215.800000,96,9.710000,6.400000,2,1.730000,1,False.\r\nCT,100,415,389-2114,no,no,0,235.800000,130,40.090000,176.000000,69,14.960000,63.600000,122,2.860000,7.300000,1,1.970000,2,False.\r\nWA,110,510,335-4414,no,no,0,185.100000,100,31.470000,165.100000,88,14.030000,111.600000,104,5.020000,6.300000,4,1.700000,3,False.\r\nNH,124,415,370-5361,no,no,0,254.300000,113,43.230000,78.900000,104,6.710000,153.200000,69,6.890000,11.800000,2,3.190000,2,False.\r\nMT,10,510,374-5965,no,no,0,183.000000,103,31.110000,214.800000,77,18.260000,206.400000,73,9.290000,8.700000,6,2.350000,2,False.\r\nNE,89,415,420-6414,no,yes,29,163.500000,80,27.800000,274.800000,136,23.360000,381.900000,147,17.190000,7.500000,5,2.030000,2,False.\r\nWA,121,408,392-2708,no,no,0,207.900000,98,35.340000,210.500000,96,17.890000,109.600000,114,4.930000,7.700000,2,2.080000,2,False.\r\nWI,101,415,352-7234,no,no,0,248.600000,102,42.260000,174.900000,93,14.870000,207.200000,86,9.320000,6.100000,3,1.650000,3,False.\r\nAR,103,415,337-9878,no,yes,31,185.400000,105,31.520000,197.600000,126,16.800000,147.100000,110,6.620000,14.500000,4,3.920000,2,False.\r\nWI,51,408,350-7288,no,no,0,197.800000,60,33.630000,221.000000,64,18.790000,168.600000,134,7.590000,8.900000,5,2.400000,1,False.\r\nDE,2,415,415-8448,yes,no,0,132.100000,42,22.460000,138.900000,88,11.810000,192.600000,119,8.670000,9.100000,1,2.460000,2,True.\r\nNH,111,510,392-6331,no,no,0,197.100000,117,33.510000,227.800000,128,19.360000,214.000000,101,9.630000,9.300000,11,2.510000,0,False.\r\nVA,118,415,392-3315,no,no,0,154.600000,112,26.280000,184.200000,105,15.660000,217.400000,102,9.780000,12.600000,5,3.400000,2,False.\r\nTN,17,510,382-5401,no,yes,31,153.100000,115,26.030000,185.900000,59,15.800000,224.300000,102,10.090000,10.000000,1,2.700000,6,True.\r\nVA,130,408,389-7012,no,no,0,211.200000,119,35.900000,231.100000,120,19.640000,220.900000,80,9.940000,6.300000,9,1.700000,2,False.\r\nID,193,415,411-4714,no,no,0,96.800000,92,16.460000,142.600000,103,12.120000,210.100000,115,9.450000,10.900000,5,2.940000,2,True.\r\nTN,114,510,343-3846,no,no,0,172.000000,145,29.240000,276.400000,101,23.490000,193.700000,100,8.720000,10.100000,9,2.730000,1,False.\r\nAZ,137,415,370-4395,no,no,0,141.100000,91,23.990000,147.200000,100,12.510000,254.700000,75,11.460000,8.000000,7,2.160000,2,False.\r\nMT,185,408,422-4394,no,yes,29,151.100000,121,25.690000,244.700000,88,20.800000,154.400000,91,6.950000,13.800000,2,3.730000,2,False.\r\nOK,101,408,405-1780,no,no,0,209.600000,107,35.630000,228.800000,96,19.450000,172.400000,85,7.760000,7.600000,2,2.050000,3,False.\r\nID,95,415,393-2220,no,yes,32,247.000000,109,41.990000,125.600000,91,10.680000,226.500000,90,10.190000,10.500000,4,2.840000,3,False.\r\nNV,7,408,355-8299,no,yes,30,221.400000,114,37.640000,165.800000,116,14.090000,247.000000,105,11.120000,10.800000,12,2.920000,1,False.\r\nKS,126,408,379-8681,no,no,0,321.300000,99,54.620000,167.900000,93,14.270000,193.600000,106,8.710000,8.000000,4,2.160000,1,True.\r\nWY,71,415,409-7034,no,no,0,243.700000,124,41.430000,60.000000,90,5.100000,189.000000,129,8.500000,11.300000,2,3.050000,0,False.\r\nMS,124,415,358-1922,no,no,0,251.500000,85,42.760000,214.200000,98,18.210000,186.100000,71,8.370000,11.100000,6,3.000000,3,False.\r\nWY,97,510,346-1629,yes,no,0,236.900000,107,40.270000,157.600000,105,13.400000,241.000000,120,10.850000,7.300000,2,1.970000,0,True.\r\nTX,28,415,347-1870,no,no,0,159.700000,79,27.150000,216.700000,131,18.420000,206.700000,116,9.300000,9.300000,3,2.510000,2,False.\r\nWA,90,415,374-9576,yes,no,0,148.200000,96,25.190000,220.400000,111,18.730000,134.200000,97,6.040000,9.200000,1,2.480000,4,True.\r\nHI,190,408,380-1096,no,no,0,150.900000,86,25.650000,268.600000,129,22.830000,179.900000,73,8.100000,14.700000,1,3.970000,1,False.\r\nIL,31,415,396-5790,no,yes,28,210.500000,101,35.790000,250.500000,86,21.290000,241.600000,125,10.870000,11.500000,2,3.110000,1,False.\r\nAK,52,415,356-5244,no,yes,24,170.900000,71,29.050000,201.400000,80,17.120000,159.000000,124,7.150000,4.100000,5,1.110000,2,False.\r\nMD,73,510,341-1412,no,no,0,254.700000,80,43.300000,90.200000,79,7.670000,153.400000,60,6.900000,10.600000,8,2.860000,0,False.\r\nMA,111,415,387-7371,no,no,0,284.400000,89,48.350000,157.000000,113,13.350000,242.800000,91,10.930000,8.400000,8,2.270000,0,True.\r\nSD,98,415,392-2555,no,no,0,0.000000,0,0.000000,159.600000,130,13.570000,167.100000,88,7.520000,6.800000,1,1.840000,4,True.\r\nPA,106,408,403-9167,yes,no,0,133.700000,45,22.730000,187.800000,107,15.960000,181.900000,89,8.190000,10.700000,2,2.890000,1,True.\r\nNC,111,408,338-6550,no,no,0,224.900000,117,38.230000,191.900000,127,16.310000,229.900000,97,10.350000,10.300000,3,2.780000,0,False.\r\nVT,59,408,357-5801,no,no,0,151.800000,98,25.810000,209.900000,92,17.840000,266.900000,86,12.010000,11.900000,5,3.210000,1,False.\r\nKY,71,510,403-1953,no,yes,22,141.400000,107,24.040000,163.000000,105,13.860000,220.000000,99,9.900000,5.600000,3,1.510000,2,False.\r\nWA,55,408,357-6039,no,no,0,285.700000,124,48.570000,230.900000,106,19.630000,230.700000,140,10.380000,14.800000,7,4.000000,0,True.\r\nLA,13,415,388-9653,no,no,0,58.400000,121,9.930000,262.200000,64,22.290000,159.000000,115,7.150000,11.900000,5,3.210000,1,False.\r\nWA,136,415,359-2915,no,yes,16,90.400000,105,15.370000,201.300000,109,17.110000,227.100000,115,10.220000,13.100000,4,3.540000,0,False.\r\nME,123,408,381-4562,no,no,0,114.400000,91,19.450000,216.600000,123,18.410000,250.600000,102,11.280000,11.000000,3,2.970000,0,False.\r\nWI,105,408,406-2213,no,no,0,147.700000,103,25.110000,222.700000,78,18.930000,163.500000,102,7.360000,12.800000,3,3.460000,2,False.\r\nTX,50,408,330-6436,no,yes,31,302.700000,93,51.460000,240.500000,119,20.440000,193.900000,103,8.730000,13.600000,14,3.670000,3,False.\r\nIA,118,415,332-4289,no,no,0,136.100000,120,23.140000,204.200000,103,17.360000,228.200000,90,10.270000,11.300000,4,3.050000,1,False.\r\nAZ,97,408,386-3596,no,no,0,169.700000,84,28.850000,165.900000,86,14.100000,191.900000,83,8.640000,12.800000,6,3.460000,3,False.\r\nND,51,510,337-3740,no,no,0,227.200000,89,38.620000,194.400000,106,16.520000,243.400000,126,10.950000,14.900000,2,4.020000,0,False.\r\nVT,73,415,414-1496,no,no,0,217.800000,91,37.030000,220.600000,97,18.750000,277.300000,89,12.480000,10.300000,6,2.780000,1,True.\r\nHI,35,415,349-7291,no,no,0,124.200000,102,21.110000,123.900000,115,10.530000,135.700000,100,6.110000,13.100000,8,3.540000,2,False.\r\nWY,64,415,385-1985,no,no,0,206.200000,76,35.050000,232.400000,76,19.750000,251.600000,96,11.320000,13.600000,2,3.670000,1,False.\r\nWV,63,510,329-7102,no,no,0,132.900000,122,22.590000,67.000000,62,5.700000,160.400000,121,7.220000,9.900000,2,2.670000,3,False.\r\nOK,117,415,394-2553,no,yes,31,104.900000,115,17.830000,237.600000,125,20.200000,263.400000,104,11.850000,7.700000,6,2.080000,3,False.\r\nCT,115,415,339-1330,no,no,0,245.000000,97,41.650000,250.700000,75,21.310000,270.200000,124,12.160000,13.700000,8,3.700000,1,True.\r\nUT,162,408,398-1959,no,no,0,184.500000,118,31.370000,224.000000,95,19.040000,180.500000,82,8.120000,11.600000,3,3.130000,1,False.\r\nNY,89,415,408-7015,no,no,0,89.500000,66,15.220000,179.300000,104,15.240000,225.100000,116,10.130000,12.300000,1,3.320000,3,False.\r\nVA,94,415,384-9254,yes,no,0,235.600000,131,40.050000,194.800000,107,16.560000,170.600000,93,7.680000,8.600000,4,2.320000,1,False.\r\nVT,129,408,355-9475,no,no,0,186.000000,127,31.620000,262.300000,96,22.300000,98.900000,63,4.450000,11.500000,6,3.110000,4,False.\r\nSD,86,415,334-1337,no,no,0,223.900000,75,38.060000,155.700000,109,13.230000,150.200000,143,6.760000,7.300000,9,1.970000,1,False.\r\nPA,96,510,359-1441,no,no,0,179.500000,125,30.520000,162.300000,139,13.800000,264.500000,133,11.900000,6.600000,2,1.780000,1,False.\r\nND,190,415,391-5442,no,no,0,169.400000,102,28.800000,253.500000,113,21.550000,197.100000,93,8.870000,8.900000,5,2.400000,1,False.\r\nCT,80,408,374-1551,no,no,0,118.100000,90,20.080000,144.300000,77,12.270000,225.100000,86,10.130000,8.200000,6,2.210000,1,False.\r\nSC,108,415,399-6233,no,no,0,112.000000,105,19.040000,193.700000,110,16.460000,208.900000,93,9.400000,4.100000,4,1.110000,4,True.\r\nMI,97,408,337-4749,no,yes,32,168.400000,129,28.630000,225.900000,97,19.200000,191.800000,95,8.630000,8.500000,7,2.300000,0,False.\r\nVT,84,415,403-5552,no,yes,42,214.300000,112,36.430000,188.200000,107,16.000000,333.500000,117,15.010000,11.300000,10,3.050000,0,False.\r\nOH,65,415,405-3097,no,no,0,245.700000,139,41.770000,241.900000,113,20.560000,285.300000,117,12.840000,4.200000,5,1.130000,4,True.\r\nVT,131,415,364-9240,no,yes,34,156.600000,134,26.620000,71.000000,95,6.040000,261.700000,120,11.780000,13.400000,10,3.620000,1,False.\r\nIL,58,415,404-9348,yes,yes,43,142.800000,96,24.280000,272.300000,100,23.150000,193.400000,105,8.700000,8.900000,4,2.400000,1,False.\r\nMO,36,415,336-1462,no,no,0,202.400000,115,34.410000,230.700000,115,19.610000,202.000000,127,9.090000,10.200000,2,2.750000,3,False.\r\nWI,54,415,364-8981,no,no,0,116.800000,119,19.860000,123.100000,123,10.460000,217.500000,101,9.790000,12.000000,2,3.240000,1,False.\r\nNJ,45,510,412-7606,no,no,0,155.700000,110,26.470000,260.300000,103,22.130000,192.200000,98,8.650000,11.000000,1,2.970000,1,False.\r\nGA,125,415,380-6342,no,yes,39,236.100000,107,40.140000,289.200000,110,24.580000,175.400000,107,7.890000,9.100000,4,2.460000,2,False.\r\nVT,72,415,418-3017,no,yes,21,138.100000,113,23.480000,260.100000,83,22.110000,135.400000,118,6.090000,8.200000,2,2.210000,2,False.\r\nCT,141,408,367-3648,no,no,0,51.900000,108,8.820000,162.000000,83,13.770000,223.500000,115,10.060000,10.100000,3,2.730000,3,False.\r\nAZ,113,510,341-5892,no,no,0,81.300000,116,13.820000,220.600000,124,18.750000,235.700000,113,10.610000,8.900000,3,2.400000,0,False.\r\nSD,20,415,334-4678,no,yes,35,171.500000,98,29.160000,153.100000,127,13.010000,165.600000,125,7.450000,7.100000,3,1.920000,0,False.\r\nCT,212,415,366-6751,no,no,0,126.000000,96,21.420000,144.300000,80,12.270000,302.800000,102,13.630000,7.600000,3,2.050000,1,False.\r\nIN,99,415,397-8512,yes,no,0,197.200000,127,33.520000,156.000000,92,13.260000,204.100000,99,9.180000,9.900000,6,2.670000,4,False.\r\nOH,94,510,367-9495,no,no,0,194.100000,62,33.000000,227.200000,54,19.310000,190.400000,115,8.570000,15.300000,4,4.130000,1,False.\r\nNY,40,510,379-2991,no,no,0,115.700000,105,19.670000,127.800000,113,10.860000,107.500000,91,4.840000,9.300000,6,2.510000,1,False.\r\nNE,86,510,356-4832,no,yes,29,157.200000,118,26.720000,196.300000,136,16.690000,226.700000,109,10.200000,8.400000,5,2.270000,3,False.\r\nOK,101,415,413-4040,no,no,0,269.700000,85,45.850000,207.600000,86,17.650000,214.200000,107,9.640000,4.500000,15,1.220000,3,True.\r\nNC,170,415,366-4444,no,no,0,246.400000,107,41.890000,228.100000,124,19.390000,166.400000,95,7.490000,9.100000,8,2.460000,0,False.\r\nHI,105,510,394-3806,no,no,0,227.400000,121,38.660000,268.500000,89,22.820000,143.300000,82,6.450000,13.000000,4,3.510000,1,False.\r\nUT,103,415,368-5647,no,no,0,189.800000,110,32.270000,115.500000,83,9.820000,191.300000,103,8.610000,12.200000,4,3.290000,0,False.\r\nMD,140,415,412-1076,yes,yes,27,188.900000,124,32.110000,160.900000,102,13.680000,197.700000,100,8.900000,11.500000,5,3.110000,4,False.\r\nVT,101,510,413-7655,no,no,0,0.000000,0,0.000000,192.100000,119,16.330000,168.800000,95,7.600000,7.200000,4,1.940000,1,False.\r\nTX,98,408,371-2316,no,yes,19,110.500000,87,18.790000,227.800000,97,19.360000,243.600000,84,10.960000,11.000000,4,2.970000,1,False.\r\nAZ,104,408,420-3346,no,no,0,167.600000,116,28.490000,219.200000,112,18.630000,215.900000,94,9.720000,11.700000,2,3.160000,4,False.\r\nVA,115,415,367-3971,no,no,0,132.000000,90,22.440000,197.500000,75,16.790000,175.800000,114,7.910000,0.000000,0,0.000000,3,False.\r\nWI,112,408,417-5813,no,no,0,167.800000,88,28.530000,247.900000,81,21.070000,155.100000,108,6.980000,11.900000,4,3.210000,0,False.\r\nNE,70,415,421-8535,no,no,0,213.400000,86,36.280000,204.700000,77,17.400000,256.600000,101,11.550000,5.700000,4,1.540000,1,False.\r\nKY,126,510,375-1721,no,no,0,175.400000,120,29.820000,98.300000,71,8.360000,201.900000,93,9.090000,10.600000,1,2.860000,0,False.\r\nNV,87,510,331-8484,no,yes,39,82.600000,113,14.040000,224.400000,63,19.070000,163.600000,88,7.360000,9.500000,1,2.570000,3,False.\r\nMT,125,510,366-5829,no,no,0,143.200000,80,24.340000,88.100000,94,7.490000,233.200000,135,10.490000,8.800000,7,2.380000,4,True.\r\nMO,86,415,367-7906,no,no,0,125.500000,139,21.340000,269.800000,93,22.930000,235.800000,110,10.610000,8.900000,8,2.400000,1,False.\r\nMS,73,415,412-2520,no,yes,31,82.300000,105,13.990000,256.100000,91,21.770000,229.600000,98,10.330000,11.800000,2,3.190000,6,True.\r\nNM,232,408,386-9177,no,no,0,165.600000,104,28.150000,195.900000,115,16.650000,118.300000,77,5.320000,11.800000,3,3.190000,1,False.\r\nNJ,1,415,420-6780,no,yes,30,183.100000,95,31.130000,232.600000,110,19.770000,248.300000,110,11.170000,8.400000,2,2.270000,0,False.\r\nNH,133,408,401-1454,no,no,0,162.100000,91,27.560000,212.100000,94,18.030000,260.400000,78,11.720000,12.200000,5,3.290000,1,False.\r\nNC,103,510,379-2508,no,no,0,166.600000,84,28.320000,192.400000,91,16.350000,167.900000,115,7.560000,7.700000,6,2.080000,1,False.\r\nMT,131,415,353-3492,no,yes,24,135.900000,60,23.100000,233.200000,78,19.820000,210.600000,121,9.480000,9.400000,4,2.540000,1,False.\r\nSD,95,408,406-4840,no,yes,20,165.700000,78,28.170000,215.600000,94,18.330000,243.300000,91,10.950000,9.800000,6,2.650000,0,False.\r\nVA,182,415,391-7982,no,no,0,176.100000,90,29.940000,174.900000,106,14.870000,234.700000,134,10.560000,9.700000,4,2.620000,1,False.\r\nLA,99,510,379-9821,no,no,0,142.300000,89,24.190000,204.500000,95,17.380000,203.100000,114,9.140000,9.100000,1,2.460000,0,False.\r\nNV,27,510,398-7414,no,no,0,177.600000,121,30.190000,296.800000,92,25.230000,192.900000,106,8.680000,7.600000,3,2.050000,3,False.\r\nAK,141,408,338-8566,no,no,0,83.200000,74,14.140000,190.600000,104,16.200000,150.500000,79,6.770000,10.700000,7,2.890000,1,False.\r\nOH,29,415,397-3058,yes,yes,37,235.000000,101,39.950000,183.300000,79,15.580000,139.800000,106,6.290000,5.700000,7,1.540000,2,False.\r\nNM,65,415,389-8096,no,no,0,105.700000,95,17.970000,141.800000,100,12.050000,180.500000,105,8.120000,6.600000,12,1.780000,2,False.\r\nMI,81,415,393-6840,yes,no,0,149.400000,68,25.400000,171.900000,98,14.610000,214.500000,97,9.650000,17.900000,3,4.830000,3,True.\r\nMN,37,415,360-7404,no,yes,20,264.700000,81,45.000000,216.500000,110,18.400000,210.700000,102,9.480000,10.400000,7,2.810000,0,False.\r\nWY,107,510,411-5740,no,yes,31,160.300000,45,27.250000,221.500000,70,18.830000,261.600000,109,11.770000,5.600000,1,1.510000,1,False.\r\nWY,127,415,412-3726,yes,yes,28,95.900000,117,16.300000,159.500000,131,13.560000,152.800000,132,6.880000,10.400000,3,2.810000,1,False.\r\nWA,78,408,372-7326,no,no,0,140.700000,77,23.920000,195.200000,114,16.590000,252.900000,107,11.380000,11.700000,5,3.160000,0,False.\r\nNM,55,510,338-9873,no,no,0,119.700000,148,20.350000,231.800000,96,19.700000,222.300000,113,10.000000,4.600000,2,1.240000,2,False.\r\nVA,86,415,383-4322,no,yes,30,99.900000,84,16.980000,263.500000,125,22.400000,254.700000,90,11.460000,9.800000,7,2.650000,2,False.\r\nRI,176,415,401-7654,no,no,0,250.900000,108,42.650000,171.400000,100,14.570000,148.600000,85,6.690000,9.600000,6,2.590000,2,False.\r\nAL,96,415,410-5455,yes,no,0,200.600000,117,34.100000,289.500000,120,24.610000,98.300000,95,4.420000,11.200000,5,3.020000,2,False.\r\nWV,11,510,419-4310,no,yes,38,209.800000,130,35.670000,196.600000,84,16.710000,233.000000,79,10.490000,7.000000,7,1.890000,1,False.\r\nWV,48,415,367-2056,no,yes,34,198.000000,70,33.660000,273.700000,121,23.260000,217.900000,71,9.810000,7.600000,4,2.050000,1,False.\r\nNJ,127,510,363-6695,no,no,0,239.800000,107,40.770000,128.900000,121,10.960000,249.900000,110,11.250000,11.300000,5,3.050000,1,False.\r\nTN,63,415,374-6217,no,no,0,164.500000,75,27.970000,147.900000,118,12.570000,252.700000,97,11.370000,11.200000,2,3.020000,0,False.\r\nMI,79,510,337-9569,no,no,0,220.900000,107,37.550000,192.200000,97,16.340000,161.000000,74,7.250000,12.200000,2,3.290000,1,False.\r\nUT,47,408,350-9720,no,yes,37,112.800000,150,19.180000,243.900000,97,20.730000,178.700000,112,8.040000,13.200000,6,3.560000,2,False.\r\nIL,89,415,380-4080,yes,yes,19,112.600000,114,19.140000,261.700000,132,22.240000,123.500000,116,5.560000,11.100000,2,3.000000,0,True.\r\nMI,83,510,333-7460,no,yes,26,226.400000,117,38.490000,234.700000,97,19.950000,133.600000,82,6.010000,10.800000,7,2.920000,1,False.\r\nWI,126,415,378-3722,yes,yes,34,244.900000,118,41.630000,219.600000,105,18.670000,210.800000,136,9.490000,9.700000,6,2.620000,4,False.\r\nND,60,510,353-9339,no,no,0,203.200000,99,34.540000,235.800000,131,20.040000,224.900000,112,10.120000,15.100000,6,4.080000,2,False.\r\nVT,122,415,386-6535,no,no,0,136.700000,115,23.240000,243.100000,137,20.660000,188.900000,110,8.500000,8.600000,4,2.320000,0,False.\r\nWA,136,408,403-5575,no,no,0,152.600000,97,25.940000,208.900000,85,17.760000,119.100000,99,5.360000,5.000000,10,1.350000,1,False.\r\nNC,172,408,331-5962,no,yes,47,274.900000,102,46.730000,186.600000,118,15.860000,245.000000,123,11.030000,8.800000,2,2.380000,1,False.\r\nME,102,510,390-9627,no,no,0,195.700000,116,33.270000,209.100000,87,17.770000,201.100000,73,9.050000,8.300000,3,2.240000,1,True.\r\nSD,113,415,406-4560,yes,no,0,204.300000,82,34.730000,188.800000,115,16.050000,139.400000,97,6.270000,9.200000,7,2.480000,1,False.\r\nWV,79,415,359-6931,no,no,0,222.300000,99,37.790000,146.200000,82,12.430000,275.600000,82,12.400000,8.900000,4,2.400000,3,False.\r\nID,55,510,331-7342,no,yes,8,222.500000,104,37.830000,171.500000,94,14.580000,377.500000,114,16.990000,9.700000,2,2.620000,1,False.\r\nLA,111,415,367-2227,no,yes,28,128.800000,104,21.900000,157.300000,52,13.370000,147.400000,76,6.630000,10.300000,2,2.780000,2,False.\r\nGA,160,415,335-8836,no,no,0,174.300000,105,29.630000,171.300000,107,14.560000,220.800000,131,9.940000,8.300000,2,2.240000,0,False.\r\nFL,110,415,398-6703,no,no,0,242.500000,110,41.230000,162.300000,140,13.800000,184.100000,86,8.280000,7.800000,3,2.110000,4,False.\r\nCO,192,415,370-8379,no,no,0,221.600000,101,37.670000,285.200000,50,24.240000,167.400000,83,7.530000,12.700000,6,3.430000,4,False.\r\nNV,93,408,335-3880,no,no,0,114.300000,100,19.430000,221.100000,103,18.790000,126.300000,88,5.680000,10.900000,9,2.940000,0,False.\r\nIN,101,415,375-8761,no,yes,33,219.700000,137,37.350000,186.800000,94,15.880000,184.500000,113,8.300000,9.500000,2,2.570000,2,False.\r\nVA,77,510,367-1398,no,no,0,144.900000,136,24.630000,151.300000,115,12.860000,252.400000,73,11.360000,12.300000,3,3.320000,2,False.\r\nUT,105,415,395-7857,no,yes,40,236.500000,111,40.210000,117.000000,110,9.950000,221.100000,115,9.950000,8.100000,3,2.190000,2,False.\r\nUT,133,408,398-8745,no,yes,44,174.000000,80,29.580000,209.400000,113,17.800000,224.100000,87,10.080000,14.100000,7,3.810000,2,True.\r\nMO,131,408,386-3717,no,no,0,109.500000,95,18.620000,332.100000,48,28.230000,258.600000,108,11.640000,6.600000,7,1.780000,1,False.\r\nCT,106,510,330-1175,no,yes,33,81.600000,120,13.870000,235.600000,85,20.030000,150.900000,113,6.790000,9.900000,4,2.670000,1,False.\r\nHI,118,415,418-6752,no,no,0,133.400000,113,22.680000,121.000000,92,10.290000,254.700000,129,11.460000,5.900000,4,1.590000,1,False.\r\nMD,125,408,349-6464,no,no,0,137.100000,94,23.310000,209.800000,83,17.830000,238.400000,114,10.730000,8.600000,4,2.320000,1,False.\r\nVA,95,415,366-7331,no,no,0,197.000000,88,33.490000,190.400000,68,16.180000,211.900000,104,9.540000,16.100000,8,4.350000,0,False.\r\nMT,80,415,361-8288,no,no,0,198.100000,160,33.680000,156.700000,87,13.320000,182.100000,76,8.190000,9.300000,3,2.510000,3,False.\r\nSC,145,408,377-6635,no,no,0,39.500000,78,6.720000,264.300000,106,22.470000,185.800000,90,8.360000,10.000000,6,2.700000,0,False.\r\nCO,37,408,408-1513,no,no,0,199.500000,107,33.920000,207.500000,110,17.640000,83.900000,123,3.780000,8.100000,4,2.190000,2,False.\r\nID,87,415,370-7546,no,no,0,156.800000,93,26.660000,215.800000,68,18.340000,223.300000,77,10.050000,7.600000,6,2.050000,1,False.\r\nAL,69,415,389-4278,no,no,0,183.400000,85,31.180000,237.600000,100,20.200000,228.000000,94,10.260000,9.000000,5,2.430000,3,False.\r\nCO,83,510,379-3012,no,no,0,132.400000,120,22.510000,121.600000,101,10.340000,197.700000,84,8.900000,8.600000,2,2.320000,1,False.\r\nUT,195,415,355-3620,no,no,0,63.200000,108,10.740000,220.200000,88,18.720000,184.000000,99,8.280000,5.100000,3,1.380000,0,False.\r\nDE,67,415,413-7743,yes,yes,35,181.100000,59,30.790000,215.900000,116,18.350000,216.300000,106,9.730000,16.900000,4,4.560000,0,True.\r\nOH,75,510,372-2296,no,yes,27,117.500000,102,19.980000,206.800000,127,17.580000,194.400000,114,8.750000,4.200000,7,1.130000,3,False.\r\nRI,123,415,333-9728,no,yes,27,218.700000,79,37.180000,163.400000,78,13.890000,173.800000,116,7.820000,15.000000,1,4.050000,0,False.\r\nFL,41,415,415-6110,no,yes,41,207.300000,95,35.240000,137.300000,120,11.670000,115.700000,74,5.210000,5.900000,3,1.590000,1,False.\r\nOH,75,415,340-9803,no,no,0,150.600000,99,25.600000,301.500000,83,25.630000,158.700000,104,7.140000,8.100000,5,2.190000,0,False.\r\nMD,76,415,400-7002,yes,no,0,273.300000,66,46.460000,263.600000,121,22.410000,165.200000,84,7.430000,12.000000,7,3.240000,1,True.\r\nIL,86,415,395-7435,yes,no,0,266.100000,120,45.240000,182.000000,92,15.470000,206.500000,103,9.290000,10.300000,4,2.780000,1,False.\r\nPA,140,408,336-7143,no,no,0,112.800000,89,19.180000,156.700000,65,13.320000,249.600000,85,11.230000,16.300000,6,4.400000,0,False.\r\nAZ,70,415,352-2175,no,no,0,104.700000,112,17.800000,82.200000,104,6.990000,169.400000,110,7.620000,15.800000,7,4.270000,3,False.\r\nNH,121,510,346-6352,no,yes,35,193.800000,62,32.950000,197.600000,97,16.800000,218.800000,95,9.850000,5.900000,4,1.590000,0,False.\r\nRI,112,415,405-7467,no,no,0,168.600000,102,28.660000,298.000000,117,25.330000,194.700000,110,8.760000,9.800000,5,2.650000,1,False.\r\nHI,118,415,379-8526,no,no,0,253.200000,122,43.040000,201.000000,78,17.090000,195.300000,108,8.790000,9.700000,7,2.620000,2,False.\r\nNJ,66,415,410-5713,no,yes,16,174.700000,92,29.700000,232.100000,105,19.730000,305.400000,98,13.740000,8.900000,2,2.400000,1,False.\r\nWI,78,408,408-5916,no,no,0,87.000000,102,14.790000,193.600000,64,16.460000,205.800000,120,9.260000,11.000000,5,2.970000,0,False.\r\nMD,129,415,370-5626,no,yes,34,204.500000,79,34.770000,132.800000,113,11.290000,190.100000,117,8.550000,14.800000,9,4.000000,2,False.\r\nOR,6,408,408-1331,no,no,0,226.500000,93,38.510000,152.100000,122,12.930000,164.400000,98,7.400000,9.400000,4,2.540000,3,False.\r\nNV,107,510,419-9688,yes,no,0,234.100000,91,39.800000,163.100000,105,13.860000,282.500000,100,12.710000,10.000000,3,2.700000,1,False.\r\nAR,107,415,343-5219,yes,no,0,133.300000,106,22.660000,182.900000,89,15.550000,241.100000,123,10.850000,12.900000,2,3.480000,3,True.\r\nMT,138,415,401-5586,no,no,0,133.900000,87,22.760000,166.400000,110,14.140000,193.500000,139,8.710000,15.400000,3,4.160000,1,False.\r\nCT,103,510,377-9178,no,no,0,160.200000,104,27.230000,138.900000,70,11.810000,312.500000,97,14.060000,9.700000,2,2.620000,0,False.\r\nUT,116,415,345-5639,no,yes,44,230.600000,94,39.200000,224.100000,103,19.050000,244.000000,76,10.980000,11.100000,2,3.000000,0,False.\r\nGA,189,408,336-3488,no,no,0,227.400000,84,38.660000,176.000000,81,14.960000,206.100000,120,9.270000,6.300000,4,1.700000,2,False.\r\nNV,161,415,414-6426,no,no,0,72.800000,120,12.380000,267.100000,120,22.700000,222.500000,91,10.010000,11.800000,2,3.190000,2,False.\r\nTN,1,415,335-5591,no,no,0,196.100000,107,33.340000,296.500000,82,25.200000,211.500000,91,9.520000,7.000000,2,1.890000,1,False.\r\nDE,89,408,421-9144,no,no,0,197.100000,110,33.510000,165.900000,115,14.100000,227.300000,106,10.230000,12.800000,3,3.460000,1,False.\r\nNY,64,408,422-7728,no,no,0,219.600000,126,37.330000,303.300000,100,25.780000,154.500000,65,6.950000,9.700000,5,2.620000,1,False.\r\nMT,126,415,344-3466,no,yes,30,153.400000,90,26.080000,151.400000,97,12.870000,153.800000,97,6.920000,12.800000,4,3.460000,4,True.\r\nIA,129,415,398-7978,no,no,0,216.000000,85,36.720000,186.900000,114,15.890000,210.700000,109,9.480000,4.900000,10,1.320000,2,False.\r\nVT,128,510,346-8368,no,yes,32,222.900000,136,37.890000,262.000000,80,22.270000,191.400000,101,8.610000,10.800000,4,2.920000,0,False.\r\nLA,81,415,392-2722,no,yes,36,115.900000,120,19.700000,236.600000,95,20.110000,255.000000,90,11.480000,11.700000,6,3.160000,3,False.\r\nMT,114,510,393-3274,no,no,0,189.800000,101,32.270000,147.700000,80,12.550000,172.700000,121,7.770000,10.600000,5,2.860000,1,False.\r\nNH,50,408,339-4636,no,no,0,154.700000,102,26.300000,298.000000,108,25.330000,210.200000,95,9.460000,11.100000,3,3.000000,0,False.\r\nWV,86,415,349-7138,no,no,0,136.400000,104,23.190000,202.500000,110,17.210000,230.700000,86,10.380000,11.500000,1,3.110000,3,False.\r\nID,96,408,363-3295,no,no,0,170.500000,86,28.990000,277.500000,88,23.590000,162.500000,117,7.310000,12.200000,6,3.290000,1,False.\r\nAZ,72,510,407-9830,no,no,0,272.400000,88,46.310000,107.900000,125,9.170000,185.500000,81,8.350000,12.700000,2,3.430000,0,False.\r\nSC,64,510,333-8822,no,yes,40,210.000000,116,35.700000,232.700000,89,19.780000,168.800000,94,7.600000,5.900000,4,1.590000,8,False.\r\nWV,57,415,419-6418,yes,yes,17,236.500000,94,40.210000,163.100000,94,13.860000,236.700000,117,10.650000,12.200000,3,3.290000,2,False.\r\nOH,65,510,351-8955,no,no,0,153.900000,117,26.160000,220.100000,122,18.710000,280.500000,147,12.620000,8.500000,3,2.300000,2,False.\r\nMD,163,408,338-1840,no,no,0,223.000000,120,37.910000,227.000000,98,19.300000,188.300000,125,8.470000,8.800000,5,2.380000,1,False.\r\nMD,136,415,336-6997,no,no,0,252.400000,74,42.910000,167.900000,81,14.270000,248.300000,110,11.170000,10.700000,3,2.890000,2,False.\r\nMN,116,408,408-6266,no,no,0,197.900000,84,33.640000,168.100000,113,14.290000,239.800000,145,10.790000,12.000000,6,3.240000,1,False.\r\nNE,93,408,332-4291,no,no,0,152.400000,74,25.910000,274.600000,88,23.340000,252.200000,120,11.350000,6.600000,5,1.780000,3,False.\r\nMN,142,510,355-7895,no,yes,40,237.400000,105,40.360000,175.900000,93,14.950000,210.300000,110,9.460000,9.200000,3,2.480000,3,False.\r\nNY,92,408,348-2916,no,no,0,265.600000,82,45.150000,180.700000,75,15.360000,211.100000,113,9.500000,8.600000,2,2.320000,1,False.\r\nHI,70,415,339-8132,no,no,0,197.300000,91,33.540000,305.800000,81,25.990000,171.000000,105,7.690000,6.700000,6,1.810000,1,False.\r\nMO,22,408,374-1684,no,yes,14,199.100000,100,33.850000,221.800000,103,18.850000,65.700000,91,2.960000,4.200000,1,1.130000,1,False.\r\nNV,37,415,362-7604,no,no,0,233.700000,114,39.730000,207.900000,109,17.670000,212.700000,101,9.570000,12.000000,2,3.240000,2,False.\r\nMA,51,415,389-3206,no,no,0,183.100000,99,31.130000,160.100000,107,13.610000,311.800000,121,14.030000,7.000000,3,1.890000,1,False.\r\nNH,174,408,336-2829,no,no,0,139.400000,96,23.700000,143.400000,108,12.190000,225.200000,107,10.130000,10.000000,5,2.700000,2,False.\r\nUT,68,415,403-8916,no,no,0,213.900000,112,36.360000,260.500000,100,22.140000,233.800000,97,10.520000,8.400000,3,2.270000,1,True.\r\nFL,130,415,384-1135,no,no,0,207.100000,70,35.210000,200.100000,115,17.010000,194.200000,100,8.740000,12.400000,2,3.350000,1,False.\r\nWA,104,415,390-2320,no,no,0,139.700000,78,23.750000,202.600000,119,17.220000,203.600000,102,9.160000,11.300000,5,3.050000,2,False.\r\nCT,134,408,398-8578,no,no,0,177.200000,91,30.120000,228.700000,105,19.440000,194.300000,113,8.740000,8.900000,3,2.400000,2,False.\r\nKY,108,415,393-9424,no,yes,35,169.800000,136,28.870000,173.700000,101,14.760000,214.600000,105,9.660000,9.500000,7,2.570000,2,False.\r\nNM,103,415,417-6330,no,no,0,173.500000,83,29.500000,244.300000,65,20.770000,221.600000,66,9.970000,9.700000,2,2.620000,3,False.\r\nND,62,510,340-6339,no,no,0,159.900000,100,27.180000,172.200000,99,14.640000,263.200000,109,11.840000,5.600000,4,1.510000,1,False.\r\nNV,162,415,380-6571,no,no,0,115.100000,89,19.570000,196.800000,111,16.730000,212.400000,98,9.560000,11.400000,3,3.080000,2,False.\r\nCA,93,510,368-6488,no,yes,19,136.800000,113,23.260000,179.500000,105,15.260000,71.100000,95,3.200000,12.500000,3,3.380000,2,False.\r\nID,42,415,363-2193,no,no,0,92.200000,108,15.670000,211.200000,120,17.950000,129.100000,73,5.810000,13.100000,6,3.540000,1,False.\r\nOK,155,415,328-1206,no,yes,23,243.900000,112,41.460000,133.000000,106,11.310000,213.700000,123,9.620000,13.400000,11,3.620000,2,False.\r\nIA,36,510,385-3540,no,no,0,117.100000,94,19.910000,235.400000,117,20.010000,221.300000,108,9.960000,9.000000,2,2.430000,0,False.\r\nOH,143,415,337-7167,no,no,0,223.300000,99,37.960000,167.100000,128,14.200000,203.000000,84,9.140000,4.500000,4,1.220000,0,True.\r\nNJ,197,510,372-8405,no,no,0,154.800000,111,26.320000,171.500000,102,14.580000,227.300000,86,10.230000,10.600000,2,2.860000,3,False.\r\nIA,81,510,377-1273,no,no,0,261.400000,141,44.440000,215.700000,102,18.330000,271.800000,96,12.230000,8.000000,6,2.160000,1,True.\r\nDE,138,510,380-7816,yes,no,0,46.500000,104,7.910000,186.000000,114,15.810000,167.500000,95,7.540000,9.600000,4,2.590000,4,True.\r\nCA,103,415,402-6744,no,yes,18,149.900000,84,25.480000,170.900000,84,14.530000,171.500000,112,7.720000,11.500000,7,3.110000,0,True.\r\nWY,127,510,400-2181,yes,no,0,242.200000,102,41.170000,226.100000,80,19.220000,252.000000,96,11.340000,13.900000,5,3.750000,2,True.\r\nOR,136,510,366-1613,no,no,0,259.400000,99,44.100000,172.700000,125,14.680000,293.700000,78,13.220000,10.700000,6,2.890000,1,True.\r\nME,99,415,347-8205,no,no,0,222.400000,102,37.810000,185.800000,89,15.790000,237.700000,81,10.700000,12.000000,8,3.240000,2,False.\r\nAR,95,415,328-2982,no,no,0,69.400000,79,11.800000,190.800000,109,16.220000,219.900000,102,9.900000,8.900000,5,2.400000,0,False.\r\nME,118,408,384-8723,yes,yes,21,156.500000,122,26.610000,209.200000,125,17.780000,158.700000,81,7.140000,11.100000,3,3.000000,4,True.\r\nWV,113,415,341-7686,no,no,0,61.200000,111,10.400000,92.300000,88,7.850000,197.400000,114,8.880000,13.700000,3,3.700000,5,True.\r\nPA,128,408,353-6038,yes,no,0,245.200000,112,41.680000,101.500000,101,8.630000,152.300000,116,6.850000,10.700000,5,2.890000,0,False.\r\nHI,117,408,416-8827,no,no,0,102.300000,100,17.390000,135.200000,104,11.490000,199.700000,93,8.990000,15.700000,10,4.240000,3,False.\r\nMT,48,415,418-8450,no,yes,36,230.900000,92,39.250000,167.600000,121,14.250000,270.000000,87,12.150000,7.600000,4,2.050000,3,False.\r\nDC,81,510,385-7861,yes,no,0,227.400000,105,38.660000,211.500000,120,17.980000,258.200000,113,11.620000,11.900000,3,3.210000,0,False.\r\nAR,57,510,393-3507,no,no,0,192.800000,68,32.780000,158.000000,86,13.430000,235.500000,105,10.600000,12.700000,6,3.430000,1,False.\r\nMS,140,408,372-5262,no,no,0,162.600000,98,27.640000,206.200000,109,17.530000,141.600000,66,6.370000,8.200000,2,2.210000,1,False.\r\nOH,107,510,411-3095,no,yes,38,219.400000,92,37.300000,180.500000,73,15.340000,104.100000,91,4.680000,11.000000,1,2.970000,2,False.\r\nMO,56,415,331-5919,no,no,0,137.200000,111,23.320000,165.900000,119,14.100000,182.300000,72,8.200000,14.300000,4,3.860000,1,False.\r\nTX,159,415,402-1556,no,no,0,87.700000,103,14.910000,278.200000,97,23.650000,170.600000,93,7.680000,10.500000,10,2.840000,1,False.\r\nMD,102,415,349-7362,no,no,0,271.100000,80,46.090000,172.000000,133,14.620000,169.200000,105,7.610000,10.300000,5,2.780000,1,False.\r\nCT,107,408,339-2734,no,no,0,103.400000,94,17.580000,189.300000,125,16.090000,227.200000,125,10.220000,14.400000,3,3.890000,1,False.\r\nSC,106,408,330-4914,no,no,0,52.200000,106,8.870000,220.100000,113,18.710000,112.300000,95,5.050000,11.400000,2,3.080000,2,False.\r\nMI,225,415,371-2500,no,no,0,165.400000,106,28.120000,273.700000,109,23.260000,210.000000,93,9.450000,8.700000,3,2.350000,0,True.\r\nSD,75,408,335-3681,no,no,0,147.500000,110,25.080000,191.700000,97,16.290000,135.000000,68,6.080000,16.400000,3,4.430000,2,False.\r\nCO,86,415,405-1132,no,no,0,217.800000,93,37.030000,214.700000,95,18.250000,228.700000,70,10.290000,11.300000,7,3.050000,0,False.\r\nID,169,415,399-9239,no,no,0,235.700000,79,40.070000,136.900000,85,11.640000,220.900000,97,9.940000,13.300000,10,3.590000,1,False.\r\nAZ,122,510,350-7227,no,yes,22,204.500000,92,34.770000,139.600000,121,11.870000,205.000000,103,9.230000,8.600000,5,2.320000,2,False.\r\nFL,106,408,384-6654,no,no,0,178.400000,143,30.330000,247.000000,123,21.000000,259.900000,105,11.700000,9.600000,2,2.590000,0,False.\r\nMN,52,415,376-4271,no,yes,32,130.100000,68,22.120000,247.200000,77,21.010000,289.400000,87,13.020000,13.500000,5,3.650000,2,False.\r\nDE,79,415,391-8124,no,yes,34,103.700000,100,17.630000,236.300000,78,20.090000,256.600000,102,11.550000,14.800000,4,4.000000,2,False.\r\nMI,135,415,393-2524,no,no,0,239.900000,91,40.780000,177.100000,104,15.050000,217.200000,118,9.770000,5.900000,3,1.590000,2,False.\r\nMS,70,408,384-4385,no,no,0,148.400000,110,25.230000,267.100000,90,22.700000,151.500000,101,6.820000,8.900000,4,2.400000,0,False.\r\nMA,80,408,377-8266,no,no,0,148.600000,106,25.260000,210.800000,65,17.920000,203.700000,86,9.170000,10.000000,2,2.700000,2,False.\r\nCA,37,415,345-1243,no,no,0,191.100000,69,32.490000,129.200000,113,10.980000,207.500000,117,9.340000,12.900000,1,3.480000,0,False.\r\nMN,161,415,394-8086,no,yes,39,218.500000,76,37.150000,112.700000,94,9.580000,205.100000,121,9.230000,7.300000,4,1.970000,1,False.\r\nVT,137,510,348-9145,no,no,0,97.500000,95,16.580000,195.800000,82,16.640000,288.800000,78,13.000000,0.000000,0,0.000000,1,False.\r\nCT,123,415,376-5201,no,no,0,128.700000,126,21.880000,117.600000,94,10.000000,198.400000,132,8.930000,10.800000,5,2.920000,0,False.\r\nWV,80,415,356-2093,no,yes,38,236.600000,69,40.220000,197.500000,68,16.790000,209.500000,102,9.430000,9.500000,10,2.570000,2,False.\r\nWV,94,415,353-2080,no,no,0,85.900000,113,14.600000,226.700000,91,19.270000,279.600000,110,12.580000,15.600000,16,4.210000,0,False.\r\nNE,105,415,397-7500,no,yes,27,141.200000,96,24.000000,167.700000,94,14.250000,274.400000,101,12.350000,11.400000,2,3.080000,1,False.\r\nNC,73,415,414-5786,no,yes,31,194.400000,104,33.050000,176.000000,84,14.960000,230.100000,110,10.350000,11.500000,3,3.110000,0,False.\r\nNE,112,415,388-4282,no,no,0,167.600000,100,28.490000,154.500000,90,13.130000,281.400000,107,12.660000,17.300000,3,4.670000,2,False.\r\nIL,179,408,415-5132,no,no,0,234.500000,134,39.870000,164.200000,94,13.960000,191.400000,72,8.610000,6.100000,4,1.650000,1,False.\r\nMA,57,510,352-4541,no,no,0,154.200000,78,26.210000,196.700000,85,16.720000,253.500000,97,11.410000,10.100000,9,2.730000,1,False.\r\nAZ,127,415,373-5928,no,yes,14,143.200000,99,24.340000,169.900000,91,14.440000,221.600000,77,9.970000,11.600000,1,3.130000,1,False.\r\nSD,122,415,406-7737,yes,yes,40,216.400000,80,36.790000,249.700000,90,21.220000,185.900000,99,8.370000,12.700000,4,3.430000,1,False.\r\nMT,33,510,332-7607,no,yes,35,161.900000,85,27.520000,151.200000,82,12.850000,191.000000,131,8.590000,8.500000,2,2.300000,1,False.\r\nVT,94,408,359-7788,no,no,0,118.700000,90,20.180000,205.100000,57,17.430000,172.200000,100,7.750000,10.400000,6,2.810000,3,False.\r\nUT,100,408,384-1549,no,no,0,179.100000,123,30.450000,196.600000,132,16.710000,186.700000,116,8.400000,10.200000,10,2.750000,1,False.\r\nHI,106,415,352-8508,no,no,0,147.900000,97,25.140000,209.300000,99,17.790000,162.100000,80,7.290000,8.800000,5,2.380000,2,False.\r\nDC,148,415,404-1002,no,yes,38,209.200000,110,35.560000,116.600000,73,9.910000,109.600000,105,4.930000,16.500000,4,4.460000,2,False.\r\nWI,120,415,414-2905,no,yes,29,244.300000,140,41.530000,322.300000,89,27.400000,166.800000,83,7.510000,10.600000,6,2.860000,0,False.\r\nUT,91,415,380-9849,no,yes,34,175.300000,96,29.800000,262.300000,122,22.300000,143.900000,76,6.480000,5.600000,11,1.510000,1,False.\r\nWA,86,510,387-6498,no,no,0,150.500000,92,25.590000,120.300000,95,10.230000,271.200000,96,12.200000,9.000000,2,2.430000,1,False.\r\nSD,78,415,360-6024,no,yes,25,197.400000,73,33.560000,295.700000,113,25.130000,211.700000,73,9.530000,13.200000,2,3.560000,0,False.\r\nMT,94,510,352-5815,no,no,0,163.500000,136,27.800000,143.700000,111,12.210000,253.400000,82,11.400000,12.600000,5,3.400000,1,False.\r\nNJ,85,415,366-2273,no,no,0,236.900000,93,40.270000,197.700000,113,16.800000,309.100000,78,13.910000,11.400000,7,3.080000,2,True.\r\nCT,89,415,414-9119,no,no,0,82.300000,77,13.990000,167.200000,80,14.210000,194.700000,70,8.760000,7.200000,4,1.940000,1,False.\r\nVA,128,415,409-8796,no,no,0,216.000000,111,36.720000,153.700000,115,13.060000,227.000000,74,10.220000,12.700000,4,3.430000,1,False.\r\nNC,115,415,337-2442,yes,no,0,180.000000,119,30.600000,198.800000,126,16.900000,217.100000,70,9.770000,12.400000,3,3.350000,1,False.\r\nAK,76,415,404-1931,no,no,0,143.700000,55,24.430000,173.100000,108,14.710000,239.100000,95,10.760000,5.800000,6,1.570000,1,False.\r\nMD,75,415,367-9765,no,yes,39,198.200000,107,33.690000,280.400000,132,23.830000,129.600000,73,5.830000,11.300000,7,3.050000,1,False.\r\nIL,90,415,378-7299,no,yes,29,185.600000,106,31.550000,219.700000,113,18.670000,152.100000,120,6.840000,11.100000,5,3.000000,2,False.\r\nCT,30,408,410-5192,no,no,0,137.600000,108,23.390000,162.000000,80,13.770000,187.700000,126,8.450000,5.800000,10,1.570000,3,False.\r\nKS,105,415,405-1108,yes,no,0,273.900000,119,46.560000,278.600000,103,23.680000,255.300000,90,11.490000,10.900000,7,2.940000,1,True.\r\nMA,102,415,392-1734,no,yes,31,125.300000,92,21.300000,141.200000,108,12.000000,168.200000,68,7.570000,6.300000,2,1.700000,3,False.\r\nNJ,83,415,395-6030,no,no,0,178.800000,102,30.400000,167.900000,84,14.270000,178.900000,65,8.050000,8.600000,4,2.320000,3,False.\r\nAR,63,510,330-5168,no,yes,49,214.900000,86,36.530000,198.200000,89,16.850000,170.800000,139,7.690000,8.200000,5,2.210000,0,False.\r\nMS,155,408,334-3142,no,no,0,163.000000,93,27.710000,203.900000,102,17.330000,159.000000,109,7.150000,15.100000,4,4.080000,2,False.\r\nND,82,415,362-9983,no,yes,29,163.800000,77,27.850000,134.900000,112,11.470000,79.300000,95,3.570000,8.800000,2,2.380000,2,False.\r\nIN,87,510,414-2606,no,no,0,189.500000,113,32.220000,204.900000,100,17.420000,221.700000,93,9.980000,13.400000,3,3.620000,1,False.\r\nMI,115,415,402-4501,no,yes,26,155.200000,110,26.380000,230.900000,133,19.630000,261.600000,100,11.770000,4.500000,4,1.220000,0,False.\r\nAR,99,510,387-2604,yes,no,0,242.300000,102,41.190000,350.900000,102,29.830000,163.100000,93,7.340000,11.300000,3,3.050000,0,True.\r\nVT,121,415,400-3343,yes,yes,44,254.100000,127,43.200000,180.200000,108,15.320000,196.200000,129,8.830000,8.700000,4,2.350000,3,False.\r\nWV,54,510,353-2450,no,yes,33,112.000000,90,19.040000,208.000000,112,17.680000,150.300000,83,6.760000,11.300000,4,3.050000,2,False.\r\nME,105,408,406-2032,no,no,0,115.500000,73,19.640000,267.300000,83,22.720000,114.200000,90,5.140000,13.300000,5,3.590000,3,False.\r\nIA,73,415,409-4462,no,no,0,137.100000,102,23.310000,210.800000,114,17.920000,191.400000,120,8.610000,11.100000,4,3.000000,1,False.\r\nCT,95,415,392-5941,no,no,0,198.400000,113,33.730000,235.900000,144,20.050000,325.600000,99,14.650000,10.100000,3,2.730000,0,False.\r\nNM,21,415,334-9182,no,yes,19,132.700000,94,22.560000,204.600000,101,17.390000,154.700000,78,6.960000,12.900000,7,3.480000,3,False.\r\nOR,163,408,346-3445,no,yes,25,219.600000,99,37.330000,210.400000,99,17.880000,242.700000,88,10.920000,13.800000,8,3.730000,2,False.\r\nVT,57,415,368-9507,no,no,0,169.600000,96,28.830000,234.700000,112,19.950000,285.400000,83,12.840000,11.200000,4,3.020000,0,False.\r\nRI,104,408,382-3966,yes,no,0,160.400000,73,27.270000,293.900000,103,24.980000,306.600000,90,13.800000,12.600000,5,3.400000,4,False.\r\nRI,83,415,334-5844,no,yes,20,95.000000,89,16.150000,167.900000,92,14.270000,200.600000,79,9.030000,11.200000,2,3.020000,0,False.\r\nNM,141,415,362-9411,no,no,0,160.100000,87,27.220000,256.700000,120,21.820000,270.000000,107,12.150000,7.000000,1,1.890000,2,False.\r\nAL,95,415,390-3565,no,no,0,194.600000,114,33.080000,232.800000,106,19.790000,173.400000,92,7.800000,3.800000,2,1.030000,3,False.\r\nMT,184,415,417-4810,no,no,0,236.400000,73,40.190000,287.300000,120,24.420000,192.000000,94,8.640000,13.800000,4,3.730000,1,True.\r\nCT,74,408,384-3389,no,no,0,157.100000,95,26.710000,213.100000,36,18.110000,280.400000,77,12.620000,7.600000,3,2.050000,2,False.\r\nTN,67,415,414-9717,no,no,0,179.800000,125,30.570000,173.200000,86,14.720000,272.800000,97,12.280000,10.900000,4,2.940000,0,False.\r\nID,104,415,357-1700,yes,no,0,148.200000,108,25.190000,161.800000,113,13.750000,259.300000,103,11.670000,11.000000,4,2.970000,0,False.\r\nTX,71,415,376-7207,no,yes,39,183.200000,103,31.140000,209.400000,111,17.800000,172.400000,109,7.760000,11.900000,6,3.210000,1,False.\r\nNH,149,415,368-7706,no,no,0,119.200000,88,20.260000,168.300000,110,14.310000,204.700000,119,9.210000,12.200000,6,3.290000,4,True.\r\nND,154,408,346-4216,no,yes,35,224.000000,102,38.080000,192.000000,99,16.320000,163.100000,100,7.340000,9.600000,2,2.590000,0,False.\r\nSC,138,510,370-9533,no,yes,21,19.500000,149,3.320000,140.900000,109,11.980000,179.700000,111,8.090000,7.900000,1,2.130000,0,False.\r\nKS,117,415,372-1493,no,no,0,184.800000,83,31.420000,248.600000,101,21.130000,133.100000,113,5.990000,9.600000,8,2.590000,1,False.\r\nME,130,408,387-6031,no,no,0,176.300000,140,29.970000,201.000000,104,17.090000,161.900000,123,7.290000,11.300000,5,3.050000,1,False.\r\nRI,73,415,366-6248,no,no,0,241.700000,115,41.090000,168.500000,133,14.320000,169.800000,122,7.640000,11.100000,2,3.000000,2,False.\r\nWI,100,510,369-3756,no,yes,38,224.700000,121,38.200000,294.000000,131,24.990000,290.000000,61,13.050000,9.800000,6,2.650000,0,False.\r\nNC,149,510,363-1719,no,no,0,207.300000,115,35.240000,198.400000,82,16.860000,114.100000,83,5.130000,8.600000,4,2.320000,1,False.\r\nOH,29,408,402-6666,no,no,0,196.800000,81,33.460000,168.000000,110,14.280000,132.600000,98,5.970000,12.700000,7,3.430000,2,False.\r\nWY,131,510,408-9779,no,no,0,110.900000,74,18.850000,115.600000,90,9.830000,190.500000,114,8.570000,15.800000,9,4.270000,1,False.\r\nNJ,153,510,407-2441,no,no,0,122.500000,145,20.830000,273.300000,103,23.230000,197.800000,71,8.900000,8.000000,3,2.160000,2,False.\r\nND,84,510,384-5027,no,no,0,226.900000,144,38.570000,201.600000,122,17.140000,130.200000,121,5.860000,13.200000,5,3.560000,2,False.\r\nWI,133,510,380-3161,no,no,0,187.000000,65,31.790000,141.400000,128,12.020000,238.200000,108,10.720000,10.000000,8,2.700000,2,False.\r\nKY,112,415,360-8135,no,no,0,170.500000,113,28.990000,193.200000,129,16.420000,188.000000,91,8.460000,11.200000,6,3.020000,0,False.\r\nNY,87,415,399-5426,no,no,0,204.800000,101,34.820000,161.000000,80,13.690000,285.700000,89,12.860000,9.500000,3,2.570000,0,False.\r\nMO,72,415,385-2564,no,no,0,165.900000,114,28.200000,235.900000,97,20.050000,210.100000,120,9.450000,12.000000,5,3.240000,2,False.\r\nAZ,66,510,337-8618,no,no,0,154.000000,133,26.180000,198.900000,121,16.910000,151.900000,100,6.840000,9.500000,3,2.570000,4,True.\r\nMN,65,510,354-8491,no,yes,29,158.100000,104,26.880000,322.200000,81,27.390000,210.000000,96,9.450000,8.900000,6,2.400000,1,False.\r\nCO,74,415,394-6278,no,no,0,225.200000,93,38.280000,215.100000,120,18.280000,241.800000,95,10.880000,9.100000,2,2.460000,2,False.\r\nMD,116,408,405-2276,no,no,0,159.400000,79,27.100000,179.500000,88,15.260000,167.800000,71,7.550000,9.700000,2,2.620000,6,True.\r\nAR,68,510,376-1000,no,no,0,172.700000,95,29.360000,139.100000,90,11.820000,174.300000,99,7.840000,11.700000,1,3.160000,2,False.\r\nTN,68,415,397-1659,no,no,0,222.800000,99,37.880000,175.800000,85,14.940000,202.000000,111,9.090000,11.000000,3,2.970000,3,False.\r\nDE,54,415,379-3953,yes,no,0,214.100000,77,36.400000,240.500000,94,20.440000,188.900000,75,8.500000,10.100000,3,2.730000,1,False.\r\nTN,99,408,418-6512,no,no,0,54.800000,92,9.320000,173.000000,103,14.710000,195.100000,125,8.780000,7.500000,3,2.030000,1,False.\r\nWI,107,408,392-5296,no,no,0,134.000000,104,22.780000,174.500000,94,14.830000,311.100000,79,14.000000,7.300000,3,1.970000,3,False.\r\nWV,124,510,355-3814,no,no,0,184.800000,74,31.420000,175.100000,84,14.880000,158.200000,95,7.120000,10.500000,6,2.840000,1,False.\r\nCT,95,415,375-2098,no,yes,36,283.100000,112,48.130000,286.200000,86,24.330000,261.700000,129,11.780000,11.300000,3,3.050000,3,False.\r\nMN,173,510,372-7990,no,no,0,291.800000,143,49.610000,214.300000,134,18.220000,151.200000,119,6.800000,9.900000,2,2.670000,0,True.\r\nMO,110,408,356-4558,no,no,0,222.700000,94,37.860000,105.800000,98,8.990000,214.800000,78,9.670000,13.500000,4,3.650000,1,False.\r\nVA,102,510,398-5788,no,no,0,174.500000,79,29.670000,236.800000,136,20.130000,270.400000,110,12.170000,8.500000,5,2.300000,0,False.\r\nNH,130,408,390-4003,no,no,0,68.400000,86,11.630000,193.300000,110,16.430000,171.500000,139,7.720000,10.400000,4,2.810000,0,False.\r\nOK,91,408,332-8103,no,yes,31,273.000000,78,46.410000,215.500000,98,18.320000,104.700000,114,4.710000,9.600000,2,2.590000,1,False.\r\nCT,64,415,406-9926,yes,no,0,225.300000,134,38.300000,108.200000,87,9.200000,139.600000,132,6.280000,17.300000,9,4.670000,1,True.\r\nTN,176,415,418-2402,no,yes,23,283.200000,130,48.140000,162.600000,74,13.820000,177.700000,104,8.000000,7.200000,6,1.940000,1,False.\r\nMD,93,510,384-3299,yes,no,0,131.400000,78,22.340000,219.700000,106,18.670000,155.700000,103,7.010000,11.100000,2,3.000000,1,True.\r\nWI,84,510,378-9090,no,yes,12,89.700000,87,15.250000,138.600000,73,11.780000,165.800000,114,7.460000,10.700000,2,2.890000,1,False.\r\nSD,138,510,350-6473,no,no,0,127.100000,102,21.610000,247.700000,106,21.050000,207.700000,75,9.350000,5.000000,3,1.350000,3,False.\r\nND,101,415,379-4583,no,yes,28,105.900000,132,18.000000,231.700000,107,19.690000,281.300000,120,12.660000,10.700000,5,2.890000,1,False.\r\nVA,136,408,411-5078,no,no,0,142.300000,79,24.190000,158.000000,113,13.430000,177.500000,75,7.990000,6.000000,11,1.620000,2,False.\r\nUT,111,510,347-4982,no,no,0,191.300000,80,32.520000,138.500000,94,11.770000,246.000000,107,11.070000,6.400000,3,1.730000,2,False.\r\nMA,132,408,341-9274,no,yes,36,201.900000,93,34.320000,156.300000,75,13.290000,131.300000,92,5.910000,13.700000,5,3.700000,0,False.\r\nSD,128,415,353-7461,no,no,0,247.300000,91,42.040000,182.700000,60,15.530000,143.200000,112,6.440000,14.700000,2,3.970000,3,False.\r\nAL,92,408,371-7366,no,yes,38,242.200000,96,41.170000,159.700000,144,13.570000,210.000000,108,9.450000,8.900000,1,2.400000,1,False.\r\nAL,197,415,395-7923,yes,no,0,127.300000,80,21.640000,222.300000,115,18.900000,173.900000,95,7.830000,13.700000,5,3.700000,5,True.\r\nWV,191,408,351-8398,no,no,0,162.000000,104,27.540000,241.200000,120,20.500000,210.400000,83,9.470000,10.900000,7,2.940000,1,False.\r\nSC,99,415,329-2204,no,yes,33,179.100000,93,30.450000,238.300000,102,20.260000,165.700000,96,7.460000,10.600000,1,2.860000,2,False.\r\nFL,106,415,407-7507,no,yes,31,197.400000,125,33.560000,123.400000,110,10.490000,115.600000,101,5.200000,12.300000,4,3.320000,3,False.\r\nKY,88,415,405-8075,no,no,0,148.200000,82,25.190000,308.700000,67,26.240000,235.400000,79,10.590000,6.400000,4,1.730000,2,False.\r\nUT,78,415,390-9698,no,no,0,193.100000,85,32.830000,172.100000,105,14.630000,129.600000,119,5.830000,10.200000,1,2.750000,0,False.\r\nNY,98,408,403-4917,no,no,0,171.700000,99,29.190000,174.800000,87,14.860000,189.600000,130,8.530000,7.800000,6,2.110000,1,False.\r\nMS,17,408,391-6709,no,yes,35,198.500000,123,33.750000,270.600000,74,23.000000,209.900000,130,9.450000,8.100000,10,2.190000,1,False.\r\nNH,56,415,389-5988,no,yes,24,121.700000,87,20.690000,184.000000,76,15.640000,266.600000,98,12.000000,12.700000,3,3.430000,1,False.\r\nVA,84,415,372-1534,no,no,0,130.200000,105,22.130000,278.000000,60,23.630000,305.400000,74,13.740000,14.000000,6,3.780000,2,False.\r\nVT,95,510,395-6369,no,no,0,203.400000,96,34.580000,168.600000,61,14.330000,173.000000,105,7.790000,13.700000,3,3.700000,2,False.\r\nWY,16,415,400-3197,no,no,0,174.700000,83,29.700000,280.800000,122,23.870000,171.700000,80,7.730000,10.500000,8,2.840000,5,False.\r\nNV,76,510,377-4169,yes,no,0,241.000000,120,40.970000,231.800000,96,19.700000,220.200000,67,9.910000,9.900000,1,2.670000,1,True.\r\nWV,93,415,384-5343,no,no,0,141.700000,95,24.090000,221.000000,100,18.790000,227.100000,71,10.220000,10.200000,3,2.750000,0,False.\r\nWA,83,408,338-4472,no,no,0,134.800000,96,22.920000,167.200000,78,14.210000,161.500000,123,7.270000,7.700000,5,2.080000,2,False.\r\nKS,123,415,332-2126,no,no,0,163.100000,119,27.730000,249.400000,51,21.200000,168.200000,77,7.570000,9.000000,10,2.430000,1,False.\r\nVT,64,408,349-2157,no,no,0,145.500000,116,24.740000,228.400000,110,19.410000,273.400000,91,12.300000,8.900000,8,2.400000,1,False.\r\nOK,82,510,393-4823,no,no,0,329.800000,73,56.070000,208.300000,120,17.710000,267.100000,102,12.020000,10.600000,6,2.860000,0,True.\r\nGA,107,510,385-2683,no,no,0,194.500000,97,33.070000,186.300000,131,15.840000,178.300000,106,8.020000,12.700000,1,3.430000,2,False.\r\nCO,110,510,345-8350,no,no,0,131.900000,93,22.420000,272.700000,106,23.180000,192.800000,105,8.680000,7.100000,4,1.920000,1,False.\r\nAK,96,408,334-4506,no,yes,29,150.000000,91,25.500000,159.400000,75,13.550000,228.100000,55,10.260000,8.500000,3,2.300000,1,False.\r\nTN,47,415,332-3544,no,yes,30,196.600000,93,33.420000,241.400000,140,20.520000,226.000000,118,10.170000,12.900000,4,3.480000,2,False.\r\nKY,115,510,380-5102,no,no,0,99.700000,107,16.950000,145.100000,96,12.330000,149.400000,99,6.720000,14.100000,4,3.810000,2,False.\r\nPA,69,415,395-6149,no,no,0,143.600000,88,24.410000,141.800000,86,12.050000,194.000000,83,8.730000,10.800000,5,2.920000,3,False.\r\nCT,163,408,398-8122,no,yes,40,231.900000,56,39.420000,211.800000,91,18.000000,268.500000,74,12.080000,12.300000,3,3.320000,2,False.\r\nCT,90,415,334-4438,no,no,0,37.800000,80,6.430000,155.300000,105,13.200000,175.000000,111,7.880000,14.200000,5,3.830000,3,False.\r\nMN,98,415,384-7459,no,no,0,72.800000,107,12.380000,186.400000,103,15.840000,175.300000,110,7.890000,10.500000,4,2.840000,3,False.\r\nWY,90,408,368-3931,no,yes,39,94.800000,89,16.120000,219.100000,91,18.620000,197.400000,65,8.880000,11.400000,5,3.080000,1,False.\r\nPA,174,415,353-1352,no,yes,15,221.800000,143,37.710000,210.600000,115,17.900000,221.800000,109,9.980000,12.400000,9,3.350000,1,False.\r\nOR,95,415,348-5725,no,no,0,269.000000,120,45.730000,233.700000,120,19.860000,179.300000,61,8.070000,7.300000,4,1.970000,2,True.\r\nPA,79,415,365-2008,no,no,0,268.300000,114,45.610000,185.500000,111,15.770000,264.600000,88,11.910000,6.300000,7,1.700000,1,True.\r\nOK,123,415,393-3635,no,yes,27,198.700000,127,33.780000,249.000000,105,21.170000,173.200000,124,7.790000,12.500000,5,3.380000,1,False.\r\nVT,99,415,380-8727,no,no,0,115.500000,75,19.640000,218.100000,111,18.540000,254.900000,98,11.470000,11.500000,7,3.110000,7,True.\r\nID,114,415,381-2376,no,no,0,202.100000,100,34.360000,195.700000,102,16.630000,291.800000,120,13.130000,13.300000,5,3.590000,2,False.\r\nPA,141,510,365-8114,no,no,0,215.600000,113,36.650000,200.600000,81,17.050000,153.800000,107,6.920000,12.400000,6,3.350000,1,False.\r\nNM,132,408,415-5008,no,no,0,169.900000,107,28.880000,209.400000,121,17.800000,206.100000,79,9.270000,11.500000,2,3.110000,1,False.\r\nFL,133,510,392-8318,no,no,0,201.700000,85,34.290000,169.400000,116,14.400000,286.300000,80,12.880000,6.000000,4,1.620000,0,False.\r\nTX,133,408,401-4007,no,no,0,221.100000,133,37.590000,160.200000,140,13.620000,161.800000,84,7.280000,8.400000,3,2.270000,4,False.\r\nVT,93,510,338-7709,no,yes,32,218.700000,117,37.180000,115.000000,61,9.780000,192.700000,85,8.670000,9.400000,5,2.540000,2,False.\r\nMA,34,415,374-1981,no,no,0,293.700000,89,49.930000,272.500000,71,23.160000,178.200000,76,8.020000,11.000000,10,2.970000,2,True.\r\nOR,140,415,333-5101,no,no,0,120.300000,108,20.450000,240.400000,84,20.430000,216.400000,74,9.740000,7.700000,3,2.080000,4,True.\r\nDE,96,415,345-3734,no,yes,26,175.800000,96,29.890000,206.600000,84,17.560000,178.000000,105,8.010000,11.100000,2,3.000000,2,False.\r\nFL,144,510,384-5004,no,no,0,278.500000,95,47.350000,240.700000,90,20.460000,120.000000,90,5.400000,11.600000,5,3.130000,1,True.\r\nID,24,408,341-9396,no,yes,29,236.300000,105,40.170000,190.800000,114,16.220000,129.000000,105,5.810000,7.200000,2,1.940000,3,False.\r\nMD,54,415,408-6302,no,no,0,273.800000,113,46.550000,119.600000,156,10.170000,267.600000,117,12.040000,11.700000,3,3.160000,1,False.\r\nWV,50,408,348-7193,no,no,0,131.100000,129,22.290000,160.500000,94,13.640000,206.900000,88,9.310000,5.600000,9,1.510000,5,True.\r\nID,92,415,417-4063,no,yes,23,167.400000,83,28.460000,258.600000,129,21.980000,116.400000,110,5.240000,11.200000,3,3.020000,4,False.\r\nNV,96,408,375-6911,no,no,0,197.700000,68,33.610000,250.500000,53,21.290000,181.200000,67,8.150000,10.500000,3,2.840000,3,False.\r\nOH,146,415,358-3604,no,no,0,169.500000,93,28.820000,230.900000,71,19.630000,269.800000,115,12.140000,9.000000,7,2.430000,2,False.\r\nID,138,415,339-7485,yes,yes,17,225.200000,116,38.280000,173.400000,88,14.740000,145.800000,99,6.560000,11.700000,4,3.160000,0,False.\r\nSC,102,408,368-3078,no,no,0,174.500000,73,29.670000,213.700000,114,18.160000,164.700000,116,7.410000,10.300000,5,2.780000,4,False.\r\nMO,76,510,418-7055,no,no,0,129.700000,84,22.050000,177.500000,80,15.090000,228.900000,87,10.300000,7.500000,3,2.030000,5,True.\r\nNE,99,415,386-9981,no,no,0,200.000000,66,34.000000,107.900000,104,9.170000,233.700000,82,10.520000,11.400000,2,3.080000,3,False.\r\nNC,83,510,366-2541,no,yes,36,95.900000,87,16.300000,261.600000,105,22.240000,228.600000,109,10.290000,13.300000,4,3.590000,0,False.\r\nME,36,510,335-3110,no,yes,25,152.800000,110,25.980000,242.800000,67,20.640000,147.400000,74,6.630000,9.100000,2,2.460000,1,False.\r\nMI,70,510,400-7809,no,no,0,129.900000,102,22.080000,208.700000,133,17.740000,231.400000,93,10.410000,14.300000,3,3.860000,1,False.\r\nAZ,109,415,404-3106,yes,no,0,268.400000,85,45.630000,150.600000,131,12.800000,297.900000,84,13.410000,9.700000,8,2.620000,2,True.\r\nAZ,100,415,333-2337,no,no,0,188.500000,152,32.050000,148.300000,115,12.610000,179.800000,88,8.090000,15.200000,5,4.100000,2,False.\r\nHI,104,408,353-6482,no,no,0,170.600000,97,29.000000,162.100000,111,13.780000,210.700000,131,9.480000,6.100000,1,1.650000,1,False.\r\nNJ,106,415,397-8162,no,no,0,191.400000,124,32.540000,200.700000,116,17.060000,230.100000,76,10.350000,8.200000,3,2.210000,1,False.\r\nMS,84,510,380-6722,no,no,0,75.300000,96,12.800000,179.900000,113,15.290000,193.800000,134,8.720000,12.300000,1,3.320000,1,False.\r\nMA,80,510,329-2918,no,no,0,149.800000,123,25.470000,276.300000,75,23.490000,241.400000,75,10.860000,10.900000,7,2.940000,2,False.\r\nSC,100,510,348-8022,no,no,0,115.900000,87,19.700000,111.300000,56,9.460000,170.200000,77,7.660000,7.100000,4,1.920000,1,False.\r\nMN,99,408,388-4459,no,no,0,128.800000,86,21.900000,203.900000,105,17.330000,282.600000,131,12.720000,14.100000,4,3.810000,2,False.\r\nWV,50,510,358-3114,no,no,0,131.700000,108,22.390000,216.500000,103,18.400000,196.100000,126,8.820000,11.000000,5,2.970000,1,False.\r\nMS,105,415,343-9654,no,no,0,101.400000,48,17.240000,159.100000,119,13.520000,259.200000,53,11.660000,12.200000,2,3.290000,1,False.\r\nVA,113,415,401-9909,no,yes,23,149.000000,104,25.330000,235.800000,67,20.040000,201.800000,76,9.080000,9.500000,5,2.570000,4,False.\r\nMS,111,415,404-9978,no,yes,36,96.800000,123,16.460000,170.600000,105,14.500000,166.000000,85,7.470000,13.400000,4,3.620000,2,False.\r\nNM,161,408,397-8011,no,no,0,107.500000,121,18.280000,256.400000,46,21.790000,247.200000,131,11.120000,12.600000,3,3.400000,2,False.\r\nTX,70,415,341-8719,no,no,0,232.800000,95,39.580000,303.400000,111,25.790000,255.600000,104,11.500000,12.900000,7,3.480000,0,True.\r\nHI,97,415,408-1242,no,yes,43,121.100000,105,20.590000,260.200000,115,22.120000,222.400000,100,10.010000,8.300000,5,2.240000,3,False.\r\nWA,130,510,406-7726,no,no,0,124.300000,70,21.130000,270.700000,99,23.010000,239.500000,83,10.780000,3.500000,6,0.950000,0,False.\r\nWI,92,415,351-2773,no,no,0,157.700000,101,26.810000,298.600000,100,25.380000,216.900000,99,9.760000,13.800000,3,3.730000,1,False.\r\nCO,119,408,368-6174,no,no,0,124.300000,68,21.130000,207.100000,88,17.600000,157.400000,93,7.080000,14.800000,1,4.000000,0,False.\r\nNV,115,415,334-5029,no,no,0,286.400000,125,48.690000,205.700000,74,17.480000,191.400000,141,8.610000,6.900000,6,1.860000,1,True.\r\nRI,134,415,413-1789,no,no,0,141.700000,95,24.090000,205.600000,101,17.480000,218.500000,60,9.830000,8.800000,6,2.380000,0,False.\r\nVA,127,408,414-1246,no,yes,25,173.000000,91,29.410000,245.800000,64,20.890000,300.000000,99,13.500000,4.800000,3,1.300000,0,False.\r\nNJ,80,415,330-4978,no,no,0,268.700000,120,45.680000,301.000000,147,25.590000,167.000000,140,7.520000,5.800000,1,1.570000,2,True.\r\nND,153,415,386-1631,no,yes,31,218.500000,130,37.150000,134.200000,103,11.410000,118.900000,105,5.350000,9.400000,6,2.540000,0,False.\r\nMN,85,415,363-1208,no,no,0,255.300000,114,43.400000,194.600000,83,16.540000,276.600000,78,12.450000,3.700000,5,1.000000,3,False.\r\nHI,79,415,334-5263,no,no,0,41.900000,124,7.120000,211.000000,95,17.940000,237.900000,55,10.710000,11.400000,5,3.080000,1,False.\r\nND,35,415,361-4137,no,no,0,260.800000,87,44.340000,258.100000,78,21.940000,131.300000,123,5.910000,5.800000,2,1.570000,1,False.\r\nWI,120,408,374-8187,no,yes,26,239.400000,94,40.700000,259.400000,88,22.050000,238.000000,132,10.710000,7.700000,3,2.080000,0,False.\r\nMN,68,510,370-1525,no,no,0,226.700000,94,38.540000,168.400000,129,14.310000,188.700000,117,8.490000,10.200000,1,2.750000,0,False.\r\nDC,60,408,355-3801,no,no,0,179.300000,147,30.480000,208.900000,89,17.760000,248.200000,98,11.170000,13.500000,6,3.650000,1,True.\r\nKS,120,510,392-5605,no,no,0,158.000000,110,26.860000,197.000000,103,16.750000,154.900000,132,6.970000,10.000000,5,2.700000,1,False.\r\nMT,71,510,363-1366,no,yes,23,175.700000,82,29.870000,258.900000,136,22.010000,268.400000,154,12.080000,14.100000,7,3.810000,1,False.\r\nWV,124,415,358-5274,no,no,0,157.400000,107,26.760000,167.800000,112,14.260000,188.800000,102,8.500000,8.800000,3,2.380000,3,False.\r\nME,23,510,376-9607,no,no,0,113.100000,74,19.230000,168.800000,95,14.350000,262.900000,126,11.830000,6.900000,2,1.860000,1,True.\r\nWY,225,415,374-1213,no,no,0,182.700000,142,31.060000,246.500000,63,20.950000,218.000000,103,9.810000,8.800000,2,2.380000,1,False.\r\nNY,181,415,421-8537,yes,no,0,161.300000,83,27.420000,124.400000,83,10.570000,262.000000,98,11.790000,14.100000,3,3.810000,0,True.\r\nVT,63,415,351-5576,no,no,0,142.500000,92,24.230000,208.300000,102,17.710000,228.900000,120,10.300000,7.500000,2,2.030000,2,False.\r\nNC,54,415,407-7258,yes,no,0,190.500000,108,32.390000,259.700000,108,22.070000,141.500000,111,6.370000,9.700000,2,2.620000,2,True.\r\nMO,80,408,405-4420,yes,yes,15,159.300000,110,27.080000,170.600000,120,14.500000,141.200000,82,6.350000,11.900000,5,3.210000,1,False.\r\nNC,118,408,340-2855,yes,yes,39,153.800000,106,26.150000,123.300000,111,10.480000,117.800000,103,5.300000,9.200000,6,2.480000,1,False.\r\nNJ,42,408,342-8002,yes,no,0,180.700000,127,30.720000,174.600000,94,14.840000,165.300000,114,7.440000,12.000000,6,3.240000,2,False.\r\nOH,134,408,355-6826,no,no,0,202.700000,105,34.460000,224.900000,90,19.120000,253.900000,108,11.430000,12.100000,7,3.270000,0,False.\r\nTX,66,415,402-3886,no,yes,35,190.800000,100,32.440000,261.300000,93,22.210000,209.500000,108,9.430000,8.900000,6,2.400000,0,False.\r\nWA,66,415,336-5900,no,no,0,205.100000,102,34.870000,232.700000,109,19.780000,259.900000,95,11.700000,9.200000,6,2.480000,2,False.\r\nTN,127,415,339-7684,no,yes,28,235.600000,124,40.050000,236.800000,113,20.130000,241.200000,127,10.850000,7.700000,2,2.080000,1,False.\r\nHI,146,510,390-2433,no,no,0,189.300000,77,32.180000,155.900000,128,13.250000,186.000000,83,8.370000,7.400000,3,2.000000,0,False.\r\nWY,93,408,360-7246,no,yes,42,166.900000,101,28.370000,273.200000,84,23.220000,171.000000,106,7.690000,11.500000,1,3.110000,1,False.\r\nCT,77,415,335-6508,no,no,0,245.200000,87,41.680000,254.100000,83,21.600000,239.400000,91,10.770000,7.500000,4,2.030000,0,True.\r\nNM,111,415,348-6720,no,no,0,132.600000,125,22.540000,221.100000,67,18.790000,127.900000,101,5.760000,12.700000,2,3.430000,4,True.\r\nNJ,125,415,406-6400,no,no,0,182.300000,64,30.990000,139.800000,121,11.880000,171.600000,96,7.720000,11.600000,7,3.130000,2,False.\r\nAL,115,510,390-7370,no,yes,14,192.300000,86,32.690000,88.700000,90,7.540000,229.400000,120,10.320000,10.500000,3,2.840000,2,False.\r\nMN,115,510,390-5055,yes,no,0,122.000000,110,20.740000,220.200000,100,18.720000,179.700000,124,8.090000,10.800000,2,2.920000,2,True.\r\nNC,114,408,405-7542,no,no,0,193.000000,101,32.810000,250.000000,81,21.250000,133.300000,79,6.000000,9.600000,2,2.590000,2,False.\r\nOH,106,415,364-4927,no,no,0,158.600000,112,26.960000,220.000000,114,18.700000,252.900000,106,11.380000,9.100000,3,2.460000,0,False.\r\nND,118,415,329-3458,no,yes,39,91.500000,125,15.560000,219.900000,113,18.690000,229.000000,99,10.310000,12.700000,8,3.430000,2,False.\r\nCO,59,510,331-3842,no,no,0,153.600000,92,26.110000,205.500000,88,17.470000,114.500000,89,5.150000,12.500000,10,3.380000,1,False.\r\nND,87,415,343-4147,yes,yes,40,221.600000,79,37.670000,157.100000,74,13.350000,222.400000,124,10.010000,11.500000,3,3.110000,1,False.\r\nNY,21,415,335-2274,no,no,0,244.700000,81,41.600000,168.000000,117,14.280000,281.500000,87,12.670000,6.600000,1,1.780000,1,False.\r\nWI,142,408,343-3227,no,yes,24,239.800000,103,40.770000,285.900000,65,24.300000,256.700000,106,11.550000,9.500000,4,2.570000,0,False.\r\nWY,62,415,336-6907,no,no,0,172.400000,132,29.310000,230.500000,100,19.590000,228.200000,109,10.270000,11.000000,5,2.970000,0,False.\r\nOR,149,415,331-1391,no,no,0,242.500000,83,41.230000,245.400000,97,20.860000,219.600000,80,9.880000,10.000000,3,2.700000,3,True.\r\nCO,54,510,360-1643,no,yes,39,117.600000,82,19.990000,159.200000,60,13.530000,236.400000,113,10.640000,11.300000,10,3.050000,2,False.\r\nLA,112,510,410-2518,no,no,0,174.500000,127,29.670000,259.300000,71,22.040000,170.500000,120,7.670000,11.300000,7,3.050000,1,False.\r\nAL,68,510,344-4970,no,no,0,157.300000,83,26.740000,220.900000,85,18.780000,218.900000,129,9.850000,12.000000,7,3.240000,1,False.\r\nTX,201,415,408-1486,no,yes,21,192.000000,97,32.640000,239.100000,81,20.320000,116.100000,125,5.220000,15.100000,3,4.080000,1,False.\r\nPA,88,510,396-1648,no,no,0,218.200000,76,37.090000,169.300000,60,14.390000,141.100000,99,6.350000,8.000000,1,2.160000,1,False.\r\nIL,85,415,391-2022,no,yes,29,144.600000,97,24.580000,140.000000,102,11.900000,165.400000,148,7.440000,10.900000,3,2.940000,1,False.\r\nDE,51,415,420-6465,yes,no,0,153.600000,108,26.110000,232.900000,85,19.800000,214.200000,92,9.640000,14.100000,4,3.810000,0,True.\r\nMO,45,510,398-2628,no,yes,29,135.800000,104,23.090000,222.500000,101,18.910000,235.600000,92,10.600000,7.900000,6,2.130000,2,False.\r\nAR,116,510,409-5519,no,no,0,160.700000,69,27.320000,146.800000,106,12.480000,287.800000,144,12.950000,8.200000,5,2.210000,0,False.\r\nOH,146,408,391-8554,no,yes,31,202.500000,91,34.430000,241.400000,108,20.520000,169.600000,77,7.630000,7.800000,2,2.110000,1,False.\r\nWI,63,510,395-1693,no,yes,34,152.200000,119,25.870000,227.100000,91,19.300000,195.700000,103,8.810000,12.300000,5,3.320000,1,False.\r\nGA,133,510,393-3194,no,no,0,227.400000,90,38.660000,73.200000,135,6.220000,114.300000,99,5.140000,4.700000,7,1.270000,0,False.\r\nKY,125,408,328-3402,no,no,0,191.600000,115,32.570000,205.600000,108,17.480000,210.200000,123,9.460000,9.200000,3,2.480000,2,False.\r\nOH,72,510,411-4781,no,no,0,138.900000,111,23.610000,211.600000,102,17.990000,179.500000,91,8.080000,10.800000,3,2.920000,1,False.\r\nTN,130,408,401-2581,no,no,0,127.000000,102,21.590000,206.900000,107,17.590000,231.700000,99,10.430000,6.100000,6,1.650000,0,False.\r\nSD,97,415,385-1214,no,no,0,168.600000,87,28.660000,259.200000,105,22.030000,279.800000,123,12.590000,7.300000,4,1.970000,1,False.\r\nNY,54,415,348-6853,no,no,0,286.600000,73,48.720000,223.200000,108,18.970000,203.700000,107,9.170000,11.500000,5,3.110000,1,True.\r\nGA,160,415,341-8412,no,yes,29,164.600000,121,27.980000,262.800000,108,22.340000,123.800000,131,5.570000,15.200000,4,4.100000,1,False.\r\nTX,79,415,330-8142,no,no,0,144.000000,90,24.480000,135.800000,91,11.540000,212.400000,129,9.560000,13.000000,4,3.510000,1,False.\r\nWV,92,415,361-1404,no,yes,47,141.600000,95,24.070000,207.900000,130,17.670000,203.600000,95,9.160000,10.200000,11,2.750000,0,False.\r\nMI,59,415,375-9671,no,no,0,204.300000,65,34.730000,247.300000,123,21.020000,214.700000,94,9.660000,12.000000,4,3.240000,1,False.\r\nND,132,510,372-1824,no,no,0,163.200000,80,27.740000,167.600000,90,14.250000,87.500000,90,3.940000,6.200000,10,1.670000,1,False.\r\nNE,21,510,408-3606,no,no,0,225.000000,110,38.250000,244.200000,111,20.760000,221.200000,93,9.950000,10.700000,4,2.890000,0,False.\r\nSD,93,415,333-3595,no,no,0,176.100000,103,29.940000,199.700000,130,16.970000,263.900000,96,11.880000,8.500000,6,2.300000,2,False.\r\nNJ,147,415,379-7009,no,yes,36,254.200000,78,43.210000,228.100000,105,19.390000,98.000000,125,4.410000,13.800000,7,3.730000,5,False.\r\nAK,101,510,411-4940,no,no,0,174.900000,105,29.730000,262.000000,75,22.270000,210.000000,93,9.450000,8.500000,5,2.300000,1,False.\r\nCT,125,415,409-7523,yes,no,0,187.300000,118,31.840000,160.700000,111,13.660000,263.800000,112,11.870000,9.600000,2,2.590000,0,True.\r\nCO,63,415,408-6725,no,no,0,211.800000,84,36.010000,230.900000,137,19.630000,217.100000,99,9.770000,10.700000,9,2.890000,3,False.\r\nMD,107,415,350-2384,no,no,0,241.900000,102,41.120000,126.900000,117,10.790000,185.600000,92,8.350000,11.700000,6,3.160000,0,False.\r\nND,110,408,348-1706,no,no,0,196.100000,103,33.340000,199.700000,123,16.970000,135.900000,71,6.120000,12.900000,1,3.480000,3,False.\r\nNH,83,415,415-6145,no,no,0,231.300000,100,39.320000,210.400000,84,17.880000,217.400000,106,9.780000,12.400000,2,3.350000,3,False.\r\nMN,117,408,373-3731,no,no,0,161.600000,104,27.470000,196.300000,119,16.690000,294.800000,111,13.270000,13.800000,2,3.730000,1,False.\r\nKY,124,415,341-3349,no,no,0,194.000000,103,32.980000,241.000000,116,20.490000,227.500000,153,10.240000,11.900000,5,3.210000,0,False.\r\nNH,115,510,399-8859,no,no,0,109.700000,148,18.650000,223.800000,87,19.020000,240.300000,96,10.810000,15.400000,8,4.160000,3,False.\r\nCO,156,408,377-4518,yes,no,0,277.000000,119,47.090000,238.300000,106,20.260000,94.400000,96,4.250000,8.300000,3,2.240000,1,False.\r\nKY,89,408,341-1594,no,no,0,192.100000,83,32.660000,163.600000,88,13.910000,169.700000,138,7.640000,6.100000,3,1.650000,0,False.\r\nKY,72,415,418-8770,no,no,0,198.400000,147,33.730000,216.900000,121,18.440000,112.800000,125,5.080000,13.100000,4,3.540000,0,False.\r\nIN,101,415,332-9118,no,yes,42,209.200000,82,35.560000,159.700000,74,13.570000,181.600000,100,8.170000,9.500000,3,2.570000,0,False.\r\nOR,53,415,386-1418,no,no,0,184.800000,98,31.420000,216.400000,125,18.390000,141.100000,116,6.350000,18.400000,3,4.970000,2,False.\r\nSD,116,408,393-3535,no,no,0,167.800000,119,28.530000,142.000000,123,12.070000,190.700000,128,8.580000,7.300000,4,1.970000,2,False.\r\nDE,78,408,328-9006,no,no,0,139.200000,140,23.660000,191.400000,113,16.270000,286.500000,125,12.890000,11.800000,3,3.190000,3,False.\r\nOR,117,415,402-2482,no,yes,17,221.300000,82,37.620000,167.600000,100,14.250000,262.700000,87,11.820000,4.400000,4,1.190000,0,False.\r\nNE,56,510,408-4865,no,no,0,121.600000,84,20.670000,165.300000,115,14.050000,243.900000,95,10.980000,8.900000,2,2.400000,1,False.\r\nOH,123,408,396-6247,no,yes,39,270.400000,99,45.970000,245.100000,110,20.830000,108.900000,113,4.900000,15.400000,7,4.160000,1,False.\r\nOH,127,408,396-9462,no,no,0,139.600000,94,23.730000,240.900000,112,20.480000,127.100000,88,5.720000,8.800000,4,2.380000,2,False.\r\nAR,116,415,396-9279,no,yes,23,253.000000,78,43.010000,138.900000,121,11.810000,277.800000,104,12.500000,11.800000,3,3.190000,2,False.\r\nKS,138,510,363-8715,no,yes,26,183.900000,83,31.260000,240.700000,93,20.460000,185.700000,125,8.360000,15.000000,3,4.050000,1,False.\r\nTX,120,415,356-1358,no,no,0,203.300000,108,34.560000,259.900000,66,22.090000,115.900000,103,5.220000,7.800000,2,2.110000,3,False.\r\nWI,102,408,360-7839,no,no,0,200.600000,106,34.100000,152.500000,127,12.960000,199.400000,128,8.970000,7.700000,2,2.080000,3,False.\r\nOR,95,415,364-8774,no,no,0,167.600000,96,28.490000,176.000000,89,14.960000,250.900000,113,11.290000,13.400000,6,3.620000,2,False.\r\nVA,102,408,348-5038,no,no,0,156.500000,67,26.610000,204.300000,103,17.370000,141.900000,72,6.390000,9.900000,2,2.670000,2,False.\r\nAR,89,415,365-4728,no,yes,25,215.100000,140,36.570000,197.400000,69,16.780000,162.100000,117,7.290000,10.600000,10,2.860000,1,False.\r\nCT,50,408,351-9037,no,no,0,301.700000,82,51.290000,167.100000,118,14.200000,72.200000,89,3.250000,10.500000,6,2.840000,1,False.\r\nOH,93,415,397-9184,no,yes,42,152.300000,90,25.890000,267.500000,102,22.740000,266.900000,130,12.010000,11.300000,5,3.050000,7,False.\r\nWY,68,510,398-4538,no,no,0,195.400000,116,33.220000,212.100000,101,18.030000,138.400000,134,6.230000,15.100000,11,4.080000,1,False.\r\nIL,70,408,382-6827,no,no,0,208.700000,97,35.480000,275.500000,83,23.420000,182.500000,122,8.210000,8.000000,3,2.160000,2,False.\r\nMO,138,415,408-1340,no,yes,29,190.100000,87,32.320000,223.200000,123,18.970000,256.200000,130,11.530000,14.200000,6,3.830000,0,False.\r\nDC,141,415,333-9511,no,yes,37,185.400000,87,31.520000,178.500000,128,15.170000,218.300000,107,9.820000,8.000000,3,2.160000,4,False.\r\nMA,112,415,358-7379,no,yes,17,183.200000,95,31.140000,252.800000,125,21.490000,156.700000,95,7.050000,9.700000,3,2.620000,0,False.\r\nNH,117,510,397-1766,yes,no,0,54.200000,100,9.210000,303.200000,84,25.770000,171.800000,84,7.730000,8.600000,2,2.320000,1,True.\r\nIA,1,408,331-2144,no,yes,26,208.000000,115,35.360000,185.000000,113,15.730000,177.700000,144,8.000000,8.100000,9,2.190000,1,False.\r\nAL,70,415,345-7014,no,no,0,230.300000,110,39.150000,77.900000,87,6.620000,247.100000,105,11.120000,13.200000,4,3.560000,1,False.\r\nOR,87,510,395-1898,no,yes,22,240.800000,102,40.940000,75.900000,106,6.450000,224.600000,115,10.110000,7.100000,3,1.920000,2,False.\r\nWV,52,510,373-8920,no,yes,21,195.700000,119,33.270000,106.200000,95,9.030000,157.400000,94,7.080000,5.300000,3,1.430000,2,False.\r\nWA,97,408,373-8908,no,no,0,276.100000,82,46.940000,201.100000,106,17.090000,231.300000,73,10.410000,8.900000,4,2.400000,0,True.\r\nNV,105,408,415-1203,no,no,0,166.100000,93,28.240000,175.900000,106,14.950000,243.500000,55,10.960000,16.200000,3,4.370000,2,False.\r\nSC,77,510,369-7017,no,yes,28,135.900000,117,23.100000,244.500000,102,20.780000,207.500000,74,9.340000,11.500000,3,3.110000,4,True.\r\nNC,80,415,420-8435,yes,no,0,189.100000,122,32.150000,223.200000,92,18.970000,269.000000,116,12.110000,13.900000,3,3.750000,2,True.\r\nNH,120,510,395-2579,no,yes,43,177.900000,117,30.240000,175.100000,70,14.880000,161.300000,117,7.260000,11.500000,4,3.110000,1,False.\r\nWY,54,408,405-7850,no,yes,39,143.900000,73,24.460000,210.300000,117,17.880000,129.200000,117,5.810000,12.500000,8,3.380000,2,False.\r\nFL,148,510,394-7710,yes,no,0,148.200000,138,25.190000,159.600000,123,13.570000,197.400000,62,8.880000,8.600000,3,2.320000,2,False.\r\nPA,119,408,342-4122,no,no,0,287.100000,115,48.810000,159.300000,99,13.540000,216.800000,86,9.760000,13.900000,1,3.750000,2,True.\r\nNC,162,408,340-1876,no,yes,26,179.700000,144,30.550000,218.100000,129,18.540000,212.300000,105,9.550000,9.300000,8,2.510000,1,True.\r\nMO,85,510,383-6095,no,no,0,165.800000,96,28.190000,190.000000,141,16.150000,144.000000,116,6.480000,10.900000,3,2.940000,5,True.\r\nKS,101,510,413-1061,no,yes,25,144.100000,144,24.500000,167.600000,105,14.250000,240.000000,107,10.800000,14.500000,3,3.920000,1,False.\r\nKY,172,415,343-5347,no,no,0,172.500000,85,29.330000,253.100000,71,21.510000,221.600000,113,9.970000,5.900000,6,1.590000,0,False.\r\nDE,80,415,376-4861,no,no,0,199.800000,138,33.970000,167.100000,91,14.200000,271.800000,94,12.230000,5.500000,4,1.490000,1,False.\r\nWI,67,510,417-2265,no,no,0,109.100000,134,18.550000,142.300000,76,12.100000,91.200000,86,4.100000,10.900000,5,2.940000,2,False.\r\nCO,86,408,419-7415,no,no,0,171.800000,106,29.210000,301.700000,44,25.640000,139.400000,108,6.270000,9.700000,5,2.620000,1,False.\r\nNM,107,415,407-2259,no,no,0,222.300000,101,37.790000,286.000000,111,24.310000,249.400000,117,11.220000,12.100000,4,3.270000,1,True.\r\nDE,133,510,333-2906,no,no,0,245.800000,102,41.790000,264.700000,90,22.500000,111.700000,103,5.030000,11.200000,7,3.020000,0,False.\r\nIL,116,510,360-7477,no,no,0,164.600000,110,27.980000,270.600000,103,23.000000,230.400000,109,10.370000,8.000000,3,2.160000,0,False.\r\nWA,63,408,342-5243,no,no,0,211.700000,107,35.990000,271.700000,77,23.090000,203.300000,108,9.150000,7.400000,7,2.000000,0,False.\r\nMA,119,408,417-3999,yes,yes,16,147.200000,103,25.020000,160.100000,96,13.610000,184.000000,120,8.280000,7.700000,2,2.080000,0,True.\r\nOH,133,408,379-1720,yes,no,0,254.700000,103,43.300000,252.200000,80,21.440000,178.100000,103,8.010000,8.000000,3,2.160000,0,True.\r\nTN,94,408,368-3117,yes,no,0,170.100000,113,28.920000,271.800000,94,23.100000,110.700000,78,4.980000,8.700000,4,2.350000,1,False.\r\nMA,69,510,352-5000,no,no,0,195.100000,91,33.170000,261.500000,57,22.230000,203.800000,90,9.170000,11.400000,5,3.080000,0,False.\r\nMI,146,408,405-7676,no,no,0,149.300000,83,25.380000,187.100000,130,15.900000,149.800000,100,6.740000,7.900000,4,2.130000,7,True.\r\nTX,119,510,361-2349,no,no,0,81.900000,75,13.920000,253.800000,114,21.570000,213.100000,125,9.590000,8.900000,1,2.400000,2,True.\r\nNH,142,408,383-2901,yes,yes,25,191.100000,109,32.490000,149.600000,120,12.720000,227.800000,60,10.250000,9.800000,3,2.650000,0,False.\r\nMD,123,408,369-7049,no,no,0,206.900000,115,35.170000,224.400000,86,19.070000,197.400000,60,8.880000,8.300000,2,2.240000,3,False.\r\nMS,101,408,387-5533,no,no,0,239.000000,156,40.630000,273.000000,106,23.210000,278.200000,93,12.520000,13.500000,8,3.650000,1,True.\r\nAZ,43,415,362-3660,no,no,0,179.300000,97,30.480000,252.700000,126,21.480000,227.500000,114,10.240000,8.000000,5,2.160000,0,False.\r\nIN,69,408,357-3577,no,no,0,185.300000,91,31.500000,219.100000,88,18.620000,243.600000,107,10.960000,5.500000,5,1.490000,0,False.\r\nNY,15,510,394-3312,yes,no,0,141.400000,80,24.040000,123.900000,76,10.530000,323.500000,88,14.560000,8.100000,3,2.190000,2,False.\r\nWI,107,510,395-8330,no,yes,25,248.600000,91,42.260000,119.300000,115,10.140000,194.300000,83,8.740000,12.000000,1,3.240000,1,False.\r\nWV,67,510,373-8895,no,no,0,152.500000,131,25.930000,252.400000,107,21.450000,185.400000,104,8.340000,4.900000,3,1.320000,2,False.\r\nNY,99,415,386-4581,no,no,0,145.600000,102,24.750000,230.900000,87,19.630000,181.500000,86,8.170000,11.400000,7,3.080000,1,False.\r\nOK,46,415,354-8191,no,no,0,164.200000,116,27.910000,196.200000,153,16.680000,236.100000,119,10.620000,8.100000,1,2.190000,1,False.\r\nNC,55,408,359-7562,yes,no,0,221.000000,115,37.570000,165.400000,97,14.060000,235.400000,117,10.590000,9.700000,4,2.620000,1,False.\r\nKY,39,415,359-4336,no,no,0,295.400000,126,50.220000,232.100000,117,19.730000,204.400000,123,9.200000,11.500000,2,3.110000,1,True.\r\nTX,92,510,336-9901,no,no,0,139.800000,98,23.770000,174.900000,143,14.870000,201.600000,135,9.070000,9.400000,7,2.540000,2,False.\r\nCT,56,415,406-3069,no,no,0,162.300000,99,27.590000,149.100000,78,12.670000,255.500000,115,11.500000,14.800000,1,4.000000,4,True.\r\nNE,76,415,334-6519,no,no,0,272.700000,97,46.360000,236.400000,95,20.090000,235.500000,105,10.600000,7.700000,2,2.080000,0,True.\r\nHI,132,408,361-8113,yes,yes,33,200.300000,75,34.050000,226.600000,67,19.260000,198.800000,91,8.950000,12.900000,3,3.480000,2,False.\r\nSC,140,510,347-9769,no,yes,28,157.100000,77,26.710000,172.400000,97,14.650000,184.500000,94,8.300000,11.100000,9,3.000000,1,False.\r\nAK,51,510,352-9130,yes,yes,12,135.800000,60,23.090000,200.600000,134,17.050000,192.400000,98,8.660000,12.300000,7,3.320000,2,False.\r\nSD,27,408,378-4557,no,no,0,236.700000,110,40.240000,231.900000,92,19.710000,164.700000,85,7.410000,12.700000,6,3.430000,1,False.\r\nID,224,510,360-8919,no,no,0,111.400000,133,18.940000,175.000000,66,14.880000,217.200000,106,9.770000,5.500000,6,1.490000,3,False.\r\nOK,105,510,405-4109,yes,yes,28,156.100000,89,26.540000,107.100000,114,9.100000,167.700000,95,7.550000,14.700000,3,3.970000,0,True.\r\nWA,117,408,381-2498,no,no,0,191.100000,93,32.490000,282.800000,56,24.040000,84.800000,118,3.820000,12.000000,4,3.240000,2,False.\r\nSD,91,415,357-5696,no,no,0,153.000000,123,26.010000,141.100000,127,11.990000,171.500000,76,7.720000,10.300000,15,2.780000,1,True.\r\nOH,135,415,412-2947,no,no,0,218.800000,123,37.200000,242.800000,64,20.640000,85.800000,80,3.860000,10.300000,3,2.780000,4,False.\r\nVA,146,415,363-3571,no,no,0,205.400000,101,34.920000,134.900000,77,11.470000,310.500000,83,13.970000,10.300000,2,2.780000,3,False.\r\nWI,147,415,405-5403,yes,no,0,225.200000,111,38.280000,184.900000,98,15.720000,143.200000,146,6.440000,9.900000,1,2.670000,0,True.\r\nIN,68,510,330-9354,no,no,0,249.900000,127,42.480000,254.500000,118,21.630000,273.200000,98,12.290000,8.900000,6,2.400000,2,True.\r\nNM,68,408,396-7091,no,no,0,131.600000,89,22.370000,137.000000,109,11.650000,256.300000,107,11.530000,10.200000,5,2.750000,3,False.\r\nHI,86,408,398-3004,no,yes,21,197.900000,99,33.640000,165.600000,100,14.080000,208.000000,120,9.360000,10.100000,9,2.730000,0,False.\r\nKY,131,415,400-4020,no,no,0,166.500000,129,28.310000,210.200000,107,17.870000,257.200000,93,11.570000,9.900000,5,2.670000,1,False.\r\nOH,86,415,356-3448,no,yes,29,225.400000,79,38.320000,187.100000,112,15.900000,281.100000,112,12.650000,12.900000,3,3.480000,1,False.\r\nVT,159,415,335-2019,no,no,0,275.800000,103,46.890000,189.500000,108,16.110000,223.900000,93,10.080000,7.400000,5,2.000000,2,True.\r\nAZ,134,415,332-6633,no,yes,40,142.900000,105,24.290000,88.600000,61,7.530000,290.000000,96,13.050000,10.800000,6,2.920000,1,False.\r\nVT,113,510,359-7648,no,no,0,207.200000,113,35.220000,256.000000,80,21.760000,211.000000,87,9.490000,9.900000,1,2.670000,1,False.\r\nMO,132,408,412-9190,no,no,0,206.200000,100,35.050000,211.200000,118,17.950000,196.200000,122,8.830000,10.200000,6,2.750000,1,False.\r\nAL,85,415,368-9007,no,no,0,210.300000,66,35.750000,195.800000,76,16.640000,221.600000,82,9.970000,11.200000,7,3.020000,1,False.\r\nNJ,93,510,384-5632,yes,yes,38,225.700000,117,38.370000,119.600000,122,10.170000,193.200000,125,8.690000,14.000000,7,3.780000,1,True.\r\nWA,174,408,352-6068,no,yes,33,167.800000,91,28.530000,205.300000,91,17.450000,130.000000,132,5.850000,14.500000,4,3.920000,4,True.\r\nNY,61,415,343-9645,no,no,0,197.700000,118,33.610000,152.200000,96,12.940000,221.000000,93,9.950000,7.000000,3,1.890000,2,False.\r\nDC,91,415,384-7873,no,yes,39,169.800000,105,28.870000,65.200000,116,5.540000,144.400000,92,6.500000,10.900000,4,2.940000,1,False.\r\nNE,88,408,396-2187,no,yes,28,190.600000,104,32.400000,237.300000,105,20.170000,211.600000,116,9.520000,9.800000,1,2.650000,2,False.\r\nMA,88,408,383-5109,no,yes,45,80.300000,140,13.650000,153.300000,101,13.030000,309.200000,123,13.910000,12.800000,3,3.460000,2,False.\r\nVT,195,415,377-7843,no,yes,36,231.700000,110,39.390000,225.100000,88,19.130000,201.700000,89,9.080000,12.100000,2,3.270000,0,False.\r\nNM,182,415,382-7999,no,no,0,69.100000,114,11.750000,230.300000,109,19.580000,256.700000,96,11.550000,6.500000,4,1.760000,0,False.\r\nCO,118,408,328-1222,no,no,0,188.800000,60,32.100000,217.400000,64,18.480000,220.100000,100,9.900000,8.200000,7,2.210000,4,False.\r\nNH,103,408,371-1727,no,no,0,150.600000,125,25.600000,169.100000,126,14.370000,221.200000,104,9.950000,10.400000,8,2.810000,8,True.\r\nIL,65,510,369-8871,no,no,0,192.000000,89,32.640000,139.500000,88,11.860000,187.400000,102,8.430000,5.500000,4,1.490000,2,False.\r\nUT,61,408,335-9726,no,yes,25,163.700000,78,27.830000,113.200000,112,9.620000,134.100000,118,6.030000,9.900000,3,2.670000,3,False.\r\nWV,172,415,357-3709,no,no,0,211.700000,100,35.990000,198.700000,101,16.890000,301.700000,136,13.580000,6.500000,9,1.760000,1,False.\r\nNJ,72,415,422-9964,no,no,0,175.500000,103,29.840000,132.300000,120,11.250000,242.900000,96,10.930000,11.800000,3,3.190000,1,False.\r\nNM,113,510,366-9211,no,no,0,150.100000,120,25.520000,200.100000,85,17.010000,266.700000,105,12.000000,11.000000,3,2.970000,2,False.\r\nND,177,408,384-9033,no,no,0,189.500000,99,32.220000,176.300000,117,14.990000,225.900000,112,10.170000,14.200000,2,3.830000,1,False.\r\nWA,100,408,382-4932,no,no,0,70.800000,94,12.040000,215.600000,102,18.330000,230.800000,125,10.390000,9.500000,1,2.570000,6,True.\r\nNM,67,415,404-7518,no,no,0,215.500000,102,36.640000,190.700000,95,16.210000,214.500000,106,9.650000,8.600000,6,2.320000,1,False.\r\nDE,136,415,353-1954,no,no,0,101.700000,105,17.290000,202.800000,99,17.240000,136.200000,119,6.130000,9.400000,6,2.540000,3,False.\r\nGA,71,415,391-7166,no,no,0,258.400000,132,43.930000,126.800000,119,10.780000,182.400000,87,8.210000,9.700000,8,2.620000,4,False.\r\nHI,134,408,370-9000,no,no,0,242.400000,126,41.210000,152.900000,115,13.000000,318.300000,115,14.320000,11.800000,6,3.190000,1,False.\r\nCT,124,415,332-3642,no,no,0,131.800000,82,22.410000,284.300000,119,24.170000,305.500000,101,13.750000,11.300000,2,3.050000,1,False.\r\nNJ,84,415,412-3898,no,no,0,190.200000,102,32.330000,197.700000,141,16.800000,247.500000,102,11.140000,9.800000,6,2.650000,2,False.\r\nME,39,408,366-5640,no,no,0,154.100000,104,26.200000,204.200000,112,17.360000,196.200000,92,8.830000,9.800000,4,2.650000,2,False.\r\nOK,110,510,356-2302,no,no,0,188.000000,127,31.960000,90.500000,118,7.690000,150.300000,64,6.760000,15.300000,3,4.130000,3,False.\r\nTN,102,510,345-9018,no,no,0,103.100000,70,17.530000,275.000000,129,23.380000,141.100000,92,6.350000,11.200000,5,3.020000,1,False.\r\nWY,70,415,365-6205,no,no,0,175.400000,130,29.820000,159.500000,130,13.560000,260.600000,96,11.730000,11.600000,4,3.130000,0,False.\r\nNY,142,415,337-1151,no,no,0,145.400000,93,24.720000,209.100000,98,17.770000,214.000000,96,9.630000,10.900000,1,2.940000,1,False.\r\nDE,81,510,374-4664,yes,no,0,250.600000,85,42.600000,187.900000,50,15.970000,120.300000,131,5.410000,7.800000,5,2.110000,1,False.\r\nRI,17,415,396-9656,no,no,0,161.500000,123,27.460000,214.200000,81,18.210000,315.000000,106,14.180000,8.600000,5,2.320000,1,False.\r\nPA,119,408,377-5043,no,no,0,260.100000,101,44.220000,256.500000,68,21.800000,229.100000,89,10.310000,10.000000,2,2.700000,1,True.\r\nHI,105,415,401-7359,no,no,0,281.300000,124,47.820000,301.500000,96,25.630000,202.800000,109,9.130000,8.700000,3,2.350000,0,True.\r\nMD,108,415,375-2184,yes,yes,42,130.100000,90,22.120000,167.000000,128,14.200000,244.700000,80,11.010000,13.600000,5,3.670000,3,True.\r\nVA,90,415,367-6005,no,no,0,102.000000,118,17.340000,113.300000,134,9.630000,188.600000,105,8.490000,11.400000,3,3.080000,2,False.\r\nIN,100,415,364-2166,no,yes,33,218.700000,104,37.180000,155.000000,144,13.180000,99.000000,117,4.460000,12.100000,4,3.270000,1,False.\r\nOR,155,408,414-4741,no,yes,30,128.500000,86,21.850000,188.400000,91,16.010000,254.400000,85,11.450000,6.800000,6,1.840000,1,False.\r\nAZ,113,510,403-9719,no,no,0,128.700000,100,21.880000,227.100000,67,19.300000,178.100000,135,8.010000,9.200000,4,2.480000,2,True.\r\nWI,123,415,371-8452,no,no,0,172.200000,92,29.270000,162.600000,76,13.820000,250.300000,101,11.260000,8.700000,4,2.350000,1,False.\r\nVA,145,408,392-6239,no,no,0,199.200000,124,33.860000,126.000000,86,10.710000,289.200000,135,13.010000,7.600000,3,2.050000,1,False.\r\nMO,42,415,410-5250,no,no,0,184.500000,98,31.370000,200.500000,93,17.040000,279.200000,91,12.560000,8.800000,3,2.380000,2,False.\r\nNV,125,510,336-1574,no,no,0,168.600000,99,28.660000,175.600000,107,14.930000,243.300000,92,10.950000,10.900000,7,2.940000,0,False.\r\nHI,131,415,406-8324,no,yes,30,174.000000,118,29.580000,205.300000,81,17.450000,218.200000,90,9.820000,6.700000,3,1.810000,1,False.\r\nWA,107,415,411-7110,no,no,0,230.400000,65,39.170000,257.400000,80,21.880000,107.300000,88,4.830000,8.500000,3,2.300000,1,False.\r\nIL,48,408,341-9907,no,no,0,198.200000,73,33.690000,202.800000,115,17.240000,146.400000,73,6.590000,5.100000,5,1.380000,1,False.\r\nIL,76,510,400-8952,no,no,0,186.100000,96,31.640000,211.600000,100,17.990000,230.600000,100,10.380000,8.000000,4,2.160000,0,False.\r\nLA,128,415,333-9266,no,no,0,148.500000,105,25.250000,243.000000,106,20.660000,255.200000,114,11.480000,6.800000,2,1.840000,1,False.\r\nWI,73,415,419-4894,no,no,0,157.100000,109,26.710000,268.800000,83,22.850000,181.500000,91,8.170000,10.000000,8,2.700000,0,False.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0000,138.600000,106,6.240000,10.200000,4,2.750000,0,False.\r\nAZ,59,408,385-9657,no,no,0,150.200000,70,25.530000,185.700000,98,15.780000,212.500000,128,9.560000,12.100000,2,3.270000,1,False.\r\nMT,124,415,420-5652,no,yes,30,144.500000,35,24.570000,262.300000,101,22.300000,226.500000,82,10.190000,12.000000,7,3.240000,2,False.\r\nDE,99,415,415-1141,no,no,0,140.700000,88,23.920000,210.900000,98,17.930000,229.900000,125,10.350000,12.400000,4,3.350000,2,False.\r\nVA,150,510,334-5634,no,no,0,169.200000,123,28.760000,216.800000,83,18.430000,179.400000,107,8.070000,12.600000,3,3.400000,1,False.\r\nMA,81,510,403-4200,no,no,0,220.800000,77,37.540000,148.500000,87,12.620000,183.900000,100,8.280000,7.600000,3,2.050000,2,False.\r\nIN,86,510,357-7893,no,no,0,216.300000,96,36.770000,266.300000,77,22.640000,214.000000,110,9.630000,4.500000,3,1.220000,0,False.\r\nMD,84,510,369-2899,no,no,0,169.500000,96,28.820000,157.600000,94,13.400000,98.200000,70,4.420000,10.600000,7,2.860000,0,False.\r\nNV,118,510,381-1026,no,yes,35,256.300000,119,43.570000,258.100000,91,21.940000,215.500000,130,9.700000,11.700000,1,3.160000,1,False.\r\nCO,89,415,388-8722,no,no,0,179.700000,128,30.550000,299.800000,92,25.480000,185.300000,120,8.340000,7.600000,3,2.050000,1,False.\r\nKS,93,415,418-3135,no,no,0,266.000000,120,45.220000,130.100000,84,11.060000,165.800000,63,7.460000,13.100000,6,3.540000,3,False.\r\nAR,85,415,380-3974,no,no,0,96.700000,97,16.440000,193.800000,95,16.470000,171.700000,88,7.730000,9.700000,3,2.620000,2,False.\r\nWY,160,408,338-7232,no,no,0,82.700000,116,14.060000,194.600000,95,16.540000,159.000000,54,7.150000,10.900000,9,2.940000,0,False.\r\nPA,28,415,334-5223,no,no,0,168.200000,87,28.590000,161.700000,92,13.740000,192.400000,112,8.660000,10.100000,3,2.730000,3,False.\r\nTX,73,408,340-8323,no,no,0,286.400000,109,48.690000,178.200000,67,15.150000,214.200000,152,9.640000,10.700000,14,2.890000,1,True.\r\nNY,156,408,337-6851,no,no,0,174.300000,95,29.630000,186.600000,128,15.860000,258.200000,105,11.620000,12.900000,5,3.480000,3,False.\r\nOR,33,415,344-5973,yes,no,0,190.600000,100,32.400000,161.700000,104,13.740000,189.900000,136,8.550000,13.000000,6,3.510000,1,False.\r\nCA,77,510,335-2261,no,no,0,175.500000,86,29.840000,205.100000,78,17.430000,245.200000,100,11.030000,17.800000,3,4.810000,4,False.\r\nNY,119,415,343-1458,no,no,0,133.400000,102,22.680000,204.600000,71,17.390000,196.900000,103,8.860000,11.100000,7,3.000000,1,False.\r\nAR,91,510,415-4875,no,yes,27,204.600000,96,34.780000,136.000000,93,11.560000,210.500000,82,9.470000,6.600000,2,1.780000,3,False.\r\nMI,102,510,381-2726,no,no,0,242.200000,88,41.170000,233.200000,89,19.820000,188.500000,121,8.480000,6.200000,6,1.670000,3,False.\r\nOK,86,415,395-3852,no,yes,33,253.100000,112,43.030000,210.100000,94,17.860000,95.000000,98,4.270000,11.900000,4,3.210000,3,False.\r\nTX,82,415,358-7914,no,no,0,130.000000,110,22.100000,185.300000,88,15.750000,178.700000,105,8.040000,8.300000,4,2.240000,0,False.\r\nNC,89,408,332-6958,no,no,0,105.900000,151,18.000000,189.600000,142,16.120000,170.900000,67,7.690000,12.700000,7,3.430000,0,False.\r\nID,86,415,355-1019,no,no,0,194.200000,98,33.010000,193.800000,95,16.470000,192.000000,123,8.640000,9.300000,7,2.510000,3,False.\r\nIL,134,408,382-9447,no,no,0,183.800000,111,31.250000,123.500000,92,10.500000,160.700000,105,7.230000,6.100000,2,1.650000,1,False.\r\nOR,92,415,386-8536,no,no,0,196.500000,82,33.410000,190.000000,89,16.150000,163.200000,99,7.340000,10.800000,2,2.920000,2,False.\r\nSD,87,510,363-3818,no,no,0,184.500000,81,31.370000,172.000000,103,14.620000,183.400000,96,8.250000,13.700000,3,3.700000,3,False.\r\nNE,64,408,360-6416,no,no,0,261.900000,113,44.520000,148.100000,99,12.590000,145.200000,74,6.530000,13.800000,4,3.730000,0,False.\r\nRI,80,510,332-8764,no,no,0,202.400000,118,34.410000,260.200000,67,22.120000,177.400000,112,7.980000,9.200000,5,2.480000,3,False.\r\nMD,165,415,398-4814,no,yes,39,167.400000,113,28.460000,172.700000,94,14.680000,192.600000,113,8.670000,9.500000,4,2.570000,3,False.\r\nID,153,415,410-5963,no,yes,22,167.700000,104,28.510000,246.800000,91,20.980000,203.900000,117,9.180000,7.500000,11,2.030000,1,False.\r\nME,41,415,399-6642,no,yes,30,191.700000,109,32.590000,193.000000,86,16.410000,149.400000,93,6.720000,11.100000,4,3.000000,1,False.\r\nSD,108,415,390-9986,no,no,0,240.200000,78,40.830000,230.300000,109,19.580000,217.000000,83,9.760000,5.200000,1,1.400000,2,False.\r\nNY,104,415,391-1793,no,yes,26,189.100000,112,32.150000,178.200000,97,15.150000,199.300000,104,8.970000,11.100000,4,3.000000,1,False.\r\nDE,115,408,352-5542,no,no,0,127.700000,67,21.710000,182.900000,90,15.550000,172.900000,92,7.780000,10.600000,7,2.860000,1,False.\r\nOK,87,415,386-8118,no,no,0,205.200000,106,34.880000,99.500000,122,8.460000,189.500000,75,8.530000,13.400000,3,3.620000,1,False.\r\nSC,159,415,394-9825,no,yes,23,153.600000,93,26.110000,216.900000,88,18.440000,161.300000,91,7.260000,12.600000,3,3.400000,2,False.\r\nIN,119,510,382-4952,no,no,0,154.500000,129,26.270000,193.600000,87,16.460000,180.900000,145,8.140000,13.400000,3,3.620000,2,False.\r\nNV,69,415,387-2698,no,no,0,153.700000,109,26.130000,194.000000,105,16.490000,256.100000,114,11.520000,14.100000,6,3.810000,1,False.\r\nIL,87,408,417-1360,yes,yes,36,171.200000,138,29.100000,185.800000,102,15.790000,227.600000,97,10.240000,10.800000,3,2.920000,1,False.\r\nSD,93,510,408-4836,no,no,0,328.100000,106,55.780000,151.700000,89,12.890000,303.500000,114,13.660000,8.700000,3,2.350000,1,True.\r\nOK,154,415,374-8329,yes,no,0,145.900000,69,24.800000,208.200000,141,17.700000,180.900000,106,8.140000,14.400000,10,3.890000,0,True.\r\nKS,57,415,363-8424,no,yes,37,201.200000,76,34.200000,280.100000,122,23.810000,154.200000,110,6.940000,11.800000,1,3.190000,1,False.\r\nIA,130,510,408-8910,no,no,0,139.100000,72,23.650000,246.000000,112,20.910000,207.200000,121,9.320000,11.400000,9,3.080000,5,False.\r\nNJ,151,415,399-3840,no,no,0,118.900000,128,20.210000,278.300000,65,23.660000,194.800000,61,8.770000,13.200000,10,3.560000,2,False.\r\nNJ,162,408,367-8692,no,no,0,217.600000,87,36.990000,279.000000,71,23.720000,250.700000,65,11.280000,10.400000,4,2.810000,2,True.\r\nMT,60,415,387-4504,no,no,0,145.000000,133,24.650000,209.100000,92,17.770000,328.500000,112,14.780000,14.600000,2,3.940000,1,False.\r\nIA,81,510,328-2647,no,no,0,203.500000,89,34.600000,289.600000,69,24.620000,212.900000,71,9.580000,8.700000,3,2.350000,3,False.\r\nWA,132,415,369-7903,no,no,0,240.100000,115,40.820000,180.400000,91,15.330000,133.400000,122,6.000000,8.000000,6,2.160000,3,False.\r\nNE,86,408,399-6852,no,no,0,83.800000,121,14.250000,240.200000,96,20.420000,158.600000,108,7.140000,6.700000,8,1.810000,1,False.\r\nTX,136,408,335-4888,no,no,0,269.800000,106,45.870000,228.800000,101,19.450000,257.500000,106,11.590000,10.100000,8,2.730000,1,True.\r\nMS,121,415,344-2260,no,yes,21,126.300000,84,21.470000,209.600000,102,17.820000,192.500000,129,8.660000,10.600000,2,2.860000,1,False.\r\nIA,105,510,349-4070,no,yes,15,88.100000,125,14.980000,175.900000,142,14.950000,269.900000,85,12.150000,9.700000,1,2.620000,2,False.\r\nWI,105,415,394-6505,no,yes,34,218.500000,61,37.150000,196.700000,74,16.720000,151.100000,103,6.800000,9.900000,4,2.670000,2,False.\r\nMT,51,415,419-3612,no,yes,26,236.800000,61,40.260000,263.400000,97,22.390000,181.100000,91,8.150000,11.200000,8,3.020000,1,False.\r\nGA,64,408,356-1952,no,no,0,124.100000,117,21.100000,192.800000,108,16.390000,162.900000,84,7.330000,6.400000,5,1.730000,3,False.\r\nMT,80,510,416-7866,yes,yes,30,184.200000,132,31.310000,167.500000,109,14.240000,212.800000,114,9.580000,10.000000,10,2.700000,0,False.\r\nND,56,415,398-1759,no,no,0,222.700000,133,37.860000,277.000000,89,23.550000,101.800000,94,4.580000,13.600000,4,3.670000,4,False.\r\nVT,120,415,338-9950,no,no,0,149.200000,98,25.360000,193.600000,88,16.460000,248.900000,119,11.200000,11.100000,5,3.000000,1,False.\r\nSD,103,510,412-7278,no,no,0,206.500000,125,35.110000,180.200000,113,15.320000,220.600000,95,9.930000,12.200000,4,3.290000,3,False.\r\nOH,164,510,347-1263,no,yes,27,159.700000,102,27.150000,168.800000,113,14.350000,244.100000,127,10.980000,9.600000,9,2.590000,3,False.\r\nOH,116,415,386-5684,no,yes,27,204.700000,118,34.800000,209.400000,91,17.800000,212.900000,67,9.580000,7.000000,2,1.890000,2,False.\r\nMT,121,408,334-4354,no,no,0,213.200000,79,36.240000,120.700000,116,10.260000,244.400000,102,11.000000,7.500000,4,2.030000,1,False.\r\nIL,55,415,398-5970,yes,no,0,269.600000,121,45.830000,171.700000,91,14.590000,219.000000,98,9.860000,8.200000,6,2.210000,1,False.\r\nNV,183,415,330-3429,no,no,0,116.700000,92,19.840000,213.800000,112,18.170000,214.300000,112,9.640000,9.700000,6,2.620000,2,False.\r\nUT,104,408,418-4637,no,no,0,263.400000,101,44.780000,235.500000,117,20.020000,102.000000,146,4.590000,13.000000,4,3.510000,0,False.\r\nNH,90,408,393-7322,no,no,0,140.200000,97,23.830000,213.900000,102,18.180000,120.000000,126,5.400000,7.100000,2,1.920000,1,False.\r\nLA,82,415,353-5557,no,no,0,197.700000,101,33.610000,127.600000,83,10.850000,142.100000,103,6.390000,13.500000,3,3.650000,1,False.\r\nVT,101,415,411-5334,no,no,0,136.200000,92,23.150000,220.900000,110,18.780000,196.900000,116,8.860000,13.300000,7,3.590000,3,False.\r\nNY,9,415,398-8588,no,yes,16,88.500000,87,15.050000,178.800000,108,15.200000,228.700000,96,10.290000,11.500000,3,3.110000,2,False.\r\nCT,97,415,374-7285,no,no,0,215.300000,58,36.600000,242.400000,91,20.600000,279.800000,105,12.590000,12.100000,9,3.270000,0,False.\r\nKS,94,408,379-7215,no,no,0,269.200000,104,45.760000,193.800000,144,16.470000,257.600000,61,11.590000,8.900000,2,2.400000,3,True.\r\nMI,127,510,357-7875,no,yes,25,203.800000,118,34.650000,267.100000,48,22.700000,225.100000,105,10.130000,7.300000,5,1.970000,0,False.\r\nSD,125,510,393-9677,no,yes,34,268.400000,112,45.630000,222.200000,108,18.890000,117.600000,102,5.290000,10.300000,3,2.780000,0,False.\r\nME,140,415,345-9598,no,no,0,159.100000,104,27.050000,269.800000,106,22.930000,220.400000,116,9.920000,10.300000,4,2.780000,1,False.\r\nMD,90,415,353-3203,no,no,0,114.400000,122,19.450000,127.700000,154,10.850000,253.100000,109,11.390000,10.100000,5,2.730000,2,False.\r\nVA,67,415,330-7486,no,no,0,138.900000,65,23.610000,208.900000,109,17.760000,232.400000,82,10.460000,9.200000,3,2.480000,2,False.\r\nNJ,113,415,397-6425,no,no,0,186.000000,55,31.620000,237.400000,105,20.180000,148.100000,83,6.660000,12.200000,6,3.290000,1,False.\r\nTN,121,510,338-1815,no,yes,26,170.400000,91,28.970000,254.500000,90,21.630000,219.600000,122,9.880000,15.100000,5,4.080000,0,False.\r\nDC,93,408,406-5023,no,no,0,164.500000,95,27.970000,230.900000,87,19.630000,149.900000,91,6.750000,9.900000,3,2.670000,4,False.\r\nDC,121,408,368-2458,no,no,0,168.600000,121,28.660000,168.600000,94,14.330000,95.300000,59,4.290000,12.300000,4,3.320000,1,False.\r\nOR,53,408,400-8375,no,no,0,261.200000,119,44.400000,250.800000,105,21.320000,176.000000,112,7.920000,9.800000,2,2.650000,0,True.\r\nRI,75,415,387-8201,no,no,0,190.500000,91,32.390000,178.400000,75,15.160000,162.400000,113,7.310000,13.100000,5,3.540000,1,False.\r\nAK,132,510,346-4360,no,no,0,181.100000,121,30.790000,314.400000,109,26.720000,246.700000,81,11.100000,4.200000,9,1.130000,2,False.\r\nWI,162,408,412-8811,no,no,0,177.100000,131,30.110000,114.700000,122,9.750000,153.600000,88,6.910000,6.500000,6,1.760000,3,False.\r\nIN,140,415,413-3990,no,no,0,160.500000,114,27.290000,240.500000,103,20.440000,233.500000,121,10.510000,11.300000,4,3.050000,1,False.\r\nMI,91,408,345-2448,no,no,0,134.700000,116,22.900000,295.300000,98,25.100000,195.500000,121,8.800000,6.600000,5,1.780000,2,False.\r\nID,73,510,394-4512,no,yes,28,198.200000,107,33.690000,139.100000,123,11.820000,199.100000,139,8.960000,8.800000,1,2.380000,2,False.\r\nNH,95,408,400-8538,yes,no,0,228.900000,134,38.910000,255.700000,71,21.730000,208.000000,120,9.360000,10.100000,2,2.730000,4,True.\r\nMN,145,408,412-8769,no,no,0,241.700000,137,41.090000,135.800000,100,11.540000,277.600000,123,12.490000,13.100000,3,3.540000,0,False.\r\nAZ,100,415,390-1552,no,no,0,131.100000,108,22.290000,176.200000,81,14.980000,89.700000,81,4.040000,4.300000,4,1.160000,1,False.\r\nMN,122,415,389-2477,no,no,0,234.100000,101,39.800000,200.200000,121,17.020000,237.400000,89,10.680000,13.100000,9,3.540000,2,False.\r\nMO,109,415,389-4695,no,no,0,200.100000,72,34.020000,300.900000,120,25.580000,236.000000,68,10.620000,11.900000,5,3.210000,0,False.\r\nAL,82,408,406-8037,no,no,0,154.000000,107,26.180000,94.400000,114,8.020000,287.600000,95,12.940000,10.100000,7,2.730000,1,False.\r\nPA,65,415,382-9138,no,yes,23,224.200000,106,38.110000,189.600000,100,16.120000,222.800000,75,10.030000,9.800000,4,2.650000,0,False.\r\nAK,52,510,414-7942,no,no,0,148.300000,83,25.210000,181.600000,79,15.440000,155.600000,104,7.000000,8.300000,6,2.240000,3,False.\r\nMS,136,415,348-7071,no,yes,24,174.600000,76,29.680000,176.600000,114,15.010000,214.400000,91,9.650000,8.800000,5,2.380000,2,False.\r\nIA,75,415,404-2942,no,no,0,138.500000,110,23.550000,153.200000,86,13.020000,215.600000,103,9.700000,11.100000,7,3.000000,1,False.\r\nWY,146,408,348-3581,no,no,0,109.000000,69,18.530000,265.800000,98,22.590000,228.300000,80,10.270000,12.600000,2,3.400000,1,False.\r\nNE,105,408,327-6764,no,no,0,162.300000,99,27.590000,212.500000,95,18.060000,214.700000,114,9.660000,11.100000,8,3.000000,4,False.\r\nND,48,415,405-2831,no,no,0,210.800000,84,35.840000,189.600000,98,16.120000,157.600000,99,7.090000,16.400000,3,4.430000,2,False.\r\nCT,45,415,416-4351,no,no,0,142.400000,107,24.210000,318.700000,78,27.090000,224.100000,108,10.080000,11.100000,7,3.000000,1,False.\r\nNC,106,415,419-3196,no,yes,37,223.500000,104,38.000000,235.100000,99,19.980000,140.100000,90,6.300000,10.600000,5,2.860000,2,False.\r\nCT,33,510,411-6211,no,no,0,182.500000,65,31.030000,232.100000,96,19.730000,149.200000,82,6.710000,7.500000,2,2.030000,2,False.\r\nND,68,408,391-8369,no,no,0,219.600000,97,37.330000,141.100000,144,11.990000,205.700000,101,9.260000,10.800000,4,2.920000,2,False.\r\nWA,106,408,416-4464,no,no,0,193.600000,66,32.910000,238.200000,82,20.250000,176.400000,107,7.940000,12.900000,3,3.480000,0,False.\r\nNV,141,415,347-1814,no,no,0,192.400000,111,32.710000,156.900000,87,13.340000,175.800000,82,7.910000,11.000000,6,2.970000,0,False.\r\nCO,98,408,386-7337,no,no,0,236.200000,122,40.150000,189.400000,110,16.100000,153.600000,104,6.910000,13.300000,4,3.590000,0,False.\r\nKS,94,510,375-8505,no,yes,28,233.200000,88,39.640000,113.300000,102,9.630000,118.000000,71,5.310000,16.100000,4,4.350000,0,False.\r\nCO,65,510,407-5056,no,no,0,158.800000,53,27.000000,188.500000,132,16.020000,189.300000,87,8.520000,9.800000,4,2.650000,2,False.\r\nMO,85,415,367-8924,no,no,0,126.100000,112,21.440000,274.700000,126,23.350000,184.400000,95,8.300000,9.800000,4,2.650000,1,False.\r\nMA,71,510,419-5171,no,no,0,290.400000,108,49.370000,253.900000,92,21.580000,263.300000,126,11.850000,10.100000,5,2.730000,3,True.\r\nNY,112,408,396-7687,no,yes,30,60.600000,113,10.300000,165.900000,96,14.100000,132.800000,99,5.980000,13.300000,7,3.590000,0,False.\r\nAK,110,415,394-4548,no,no,0,148.400000,95,25.230000,193.800000,98,16.470000,206.000000,106,9.270000,6.900000,6,1.860000,0,False.\r\nWI,111,415,382-6438,no,no,0,246.500000,108,41.910000,216.300000,89,18.390000,179.600000,99,8.080000,12.700000,3,3.430000,2,False.\r\nNH,74,408,413-2194,no,no,0,298.100000,112,50.680000,201.300000,100,17.110000,214.700000,88,9.660000,9.700000,4,2.620000,2,True.\r\nTN,105,510,366-2622,no,no,0,119.300000,82,20.280000,185.100000,111,15.730000,157.000000,74,7.070000,10.900000,4,2.940000,2,False.\r\nNY,40,408,416-7591,no,no,0,242.500000,82,41.230000,232.900000,97,19.800000,154.000000,86,6.930000,9.600000,7,2.590000,0,False.\r\nGA,128,408,355-2634,yes,yes,18,222.100000,89,37.760000,160.600000,109,13.650000,218.800000,102,9.850000,13.600000,2,3.670000,0,True.\r\nMN,123,408,422-5350,no,no,0,236.200000,135,40.150000,273.900000,88,23.280000,227.000000,77,10.220000,10.100000,6,2.730000,2,True.\r\nMI,122,510,329-2388,no,no,0,144.200000,87,24.510000,212.200000,74,18.040000,169.300000,87,7.620000,9.500000,4,2.570000,0,False.\r\nID,114,408,381-5273,no,yes,19,154.600000,100,26.280000,241.600000,109,20.540000,160.000000,112,7.200000,12.600000,1,3.400000,3,False.\r\nCT,102,415,421-6694,no,yes,25,137.400000,100,23.360000,176.700000,83,15.020000,188.200000,93,8.470000,10.200000,6,2.750000,2,False.\r\nNC,126,415,342-1702,no,no,0,103.700000,93,17.630000,127.000000,107,10.800000,329.300000,66,14.820000,14.400000,1,3.890000,0,False.\r\nLA,150,415,381-4029,no,no,0,136.600000,112,23.220000,209.400000,81,17.800000,161.100000,78,7.250000,12.200000,2,3.290000,4,True.\r\nNJ,60,408,335-2967,no,no,0,289.800000,101,49.270000,255.600000,115,21.730000,242.800000,76,10.930000,11.700000,4,3.160000,2,True.\r\nTX,123,408,416-6594,no,no,0,260.900000,85,44.350000,168.500000,103,14.320000,178.300000,91,8.020000,13.300000,5,3.590000,3,False.\r\nCA,138,510,388-6026,yes,no,0,196.200000,129,33.350000,176.500000,86,15.000000,232.400000,108,10.460000,15.200000,1,4.100000,0,True.\r\nMD,29,510,367-1024,no,no,0,195.600000,71,33.250000,126.400000,74,10.740000,148.600000,87,6.690000,14.200000,4,3.830000,1,False.\r\nWY,111,415,386-7118,no,no,0,222.200000,96,37.770000,162.500000,111,13.810000,184.900000,120,8.320000,11.900000,7,3.210000,4,False.\r\nTX,37,510,346-2020,yes,no,0,172.900000,119,29.390000,183.000000,86,15.560000,226.400000,100,10.190000,9.800000,1,2.650000,0,True.\r\nCA,111,408,329-9067,no,no,0,249.800000,109,42.470000,242.400000,106,20.600000,231.800000,78,10.430000,11.600000,4,3.130000,0,True.\r\nUT,81,510,329-6144,no,no,0,154.500000,84,26.270000,216.200000,91,18.380000,229.800000,82,10.340000,13.700000,3,3.700000,1,False.\r\nWA,46,510,332-1502,no,no,0,90.400000,108,15.370000,276.200000,77,23.480000,146.500000,111,6.590000,12.700000,2,3.430000,1,False.\r\nMS,69,510,342-8320,no,yes,27,268.800000,78,45.700000,246.600000,89,20.960000,271.900000,102,12.240000,16.400000,3,4.430000,0,False.\r\nOH,125,408,411-5748,no,no,0,106.100000,95,18.040000,157.600000,113,13.400000,192.500000,69,8.660000,8.100000,3,2.190000,1,False.\r\nKS,43,415,381-9367,no,no,0,27.000000,117,4.590000,160.900000,97,13.680000,279.500000,96,12.580000,10.700000,3,2.890000,3,False.\r\nRI,127,415,400-1280,no,yes,27,140.100000,59,23.820000,223.400000,111,18.990000,257.900000,73,11.610000,3.800000,10,1.030000,1,False.\r\nIN,94,510,360-5794,no,no,0,245.000000,112,41.650000,180.400000,91,15.330000,262.900000,105,11.830000,9.700000,6,2.620000,1,False.\r\nVT,46,408,373-3538,no,no,0,196.700000,85,33.440000,205.900000,74,17.500000,216.600000,112,9.750000,11.200000,5,3.020000,3,False.\r\nMT,73,408,394-9942,no,yes,26,131.200000,98,22.300000,106.500000,97,9.050000,221.700000,96,9.980000,10.200000,6,2.750000,2,False.\r\nCT,146,408,380-3329,no,yes,23,149.600000,96,25.430000,239.800000,124,20.380000,293.500000,135,13.210000,7.400000,4,2.000000,2,False.\r\nNM,93,415,334-7618,no,no,0,239.800000,70,40.770000,251.800000,99,21.400000,168.600000,112,7.590000,10.900000,10,2.940000,1,False.\r\nOH,52,408,327-9289,no,yes,31,142.100000,77,24.160000,193.000000,97,16.410000,253.400000,88,11.400000,11.000000,4,2.970000,1,False.\r\nGA,202,510,351-2589,no,no,0,115.400000,137,19.620000,178.700000,70,15.190000,185.700000,113,8.360000,6.000000,3,1.620000,3,False.\r\nMN,129,510,368-6892,no,yes,31,193.000000,99,32.810000,224.800000,87,19.110000,197.600000,91,8.890000,10.300000,8,2.780000,2,False.\r\nCT,94,415,337-9303,no,no,0,206.100000,49,35.040000,224.600000,115,19.090000,256.700000,74,11.550000,13.000000,1,3.510000,1,False.\r\nAL,100,415,377-5258,no,no,0,160.300000,138,27.250000,221.300000,92,18.810000,150.400000,120,6.770000,11.200000,2,3.020000,0,False.\r\nWV,43,415,348-5767,no,no,0,199.900000,108,33.980000,288.400000,80,24.510000,180.600000,103,8.130000,11.300000,7,3.050000,1,False.\r\nNH,130,415,373-3549,no,no,0,213.100000,105,36.230000,206.200000,108,17.530000,163.400000,93,7.350000,8.900000,3,2.400000,0,False.\r\nWY,124,415,422-8344,no,no,0,178.300000,102,30.310000,235.000000,120,19.980000,239.700000,119,10.790000,10.900000,1,2.940000,3,False.\r\nVA,92,510,411-2958,yes,no,0,252.300000,120,42.890000,207.000000,112,17.600000,284.600000,95,12.810000,12.000000,5,3.240000,3,True.\r\nVT,48,415,384-2908,no,no,0,197.700000,64,33.610000,136.700000,126,11.620000,244.400000,81,11.000000,13.200000,5,3.560000,4,False.\r\nOH,98,510,347-6393,no,yes,29,111.100000,105,18.890000,217.900000,101,18.520000,248.100000,108,11.160000,6.600000,3,1.780000,2,False.\r\nMT,100,415,385-7148,no,no,0,96.500000,86,16.410000,210.200000,133,17.870000,146.400000,106,6.590000,12.500000,3,3.380000,1,True.\r\nMA,79,415,419-2767,no,no,0,156.900000,109,26.670000,122.200000,87,10.390000,189.100000,103,8.510000,11.300000,5,3.050000,3,False.\r\nVA,164,415,375-1746,no,no,0,123.300000,78,20.960000,170.000000,85,14.450000,165.900000,78,7.470000,12.700000,2,3.430000,1,False.\r\nNM,105,415,362-7870,no,no,0,193.700000,108,32.930000,183.200000,124,15.570000,293.700000,72,13.220000,10.800000,5,2.920000,1,False.\r\nAR,89,408,410-3725,yes,no,0,206.900000,134,35.170000,167.700000,105,14.250000,155.700000,86,7.010000,10.900000,4,2.940000,0,False.\r\nNE,126,415,387-1535,no,no,0,249.800000,96,42.470000,261.900000,92,22.260000,166.800000,108,7.510000,12.700000,4,3.430000,3,True.\r\nWY,96,408,329-2045,no,no,0,144.000000,102,24.480000,224.700000,73,19.100000,227.700000,91,10.250000,10.000000,7,2.700000,1,False.\r\nIA,120,415,341-6743,no,yes,33,299.500000,83,50.920000,163.400000,84,13.890000,146.700000,88,6.600000,11.600000,5,3.130000,0,False.\r\nSC,212,415,336-8343,no,no,0,226.000000,127,38.420000,304.600000,83,25.890000,181.200000,132,8.150000,12.600000,4,3.400000,2,True.\r\nNC,72,415,368-5758,no,no,0,137.600000,106,23.390000,143.500000,94,12.200000,273.700000,110,12.320000,9.600000,6,2.590000,2,False.\r\nHI,155,415,346-8362,yes,yes,26,211.700000,121,35.990000,139.200000,123,11.830000,146.700000,89,6.600000,11.100000,3,3.000000,1,False.\r\nUT,89,415,345-9690,no,no,0,89.700000,80,15.250000,179.800000,81,15.280000,145.700000,120,6.560000,9.500000,4,2.570000,2,False.\r\nWY,126,408,339-9798,yes,no,0,197.600000,126,33.590000,246.500000,112,20.950000,285.300000,104,12.840000,12.500000,8,3.380000,2,False.\r\nAL,172,408,359-5731,no,no,0,270.000000,102,45.900000,256.600000,111,21.810000,168.500000,104,7.580000,12.000000,5,3.240000,2,True.\r\nVA,75,415,373-2091,no,no,0,224.700000,116,38.200000,192.000000,79,16.320000,212.200000,98,9.550000,11.300000,11,3.050000,3,False.\r\nWI,143,510,367-3439,no,no,0,194.300000,99,33.030000,123.600000,133,10.510000,229.500000,99,10.330000,10.200000,2,2.750000,2,False.\r\nFL,166,510,367-1681,yes,no,0,47.700000,89,8.110000,264.400000,95,22.470000,235.200000,97,10.580000,13.200000,3,3.560000,0,True.\r\nKS,132,415,420-9973,no,no,0,190.100000,105,32.320000,182.200000,116,15.490000,279.800000,105,12.590000,13.000000,2,3.510000,1,False.\r\nNV,94,408,351-4025,yes,no,0,89.500000,94,15.220000,339.900000,106,28.890000,172.900000,76,7.780000,7.900000,1,2.130000,1,True.\r\nNY,99,415,393-5897,no,no,0,182.600000,83,31.040000,154.500000,111,13.130000,196.000000,57,8.820000,12.100000,5,3.270000,0,False.\r\nVA,136,415,384-7216,no,yes,35,205.500000,86,34.940000,298.500000,119,25.370000,214.200000,104,9.640000,6.900000,4,1.860000,1,False.\r\nKS,119,415,384-4595,no,no,0,231.500000,82,39.360000,266.900000,97,22.690000,211.000000,118,9.490000,7.400000,10,2.000000,1,False.\r\nNC,115,510,329-9667,yes,no,0,251.300000,69,42.720000,252.500000,96,21.460000,118.300000,112,5.320000,9.900000,1,2.670000,3,True.\r\nMO,160,415,347-5063,no,no,0,171.200000,103,29.100000,243.500000,121,20.700000,178.200000,92,8.020000,13.000000,3,3.510000,2,False.\r\nND,166,510,345-8433,no,no,0,197.900000,89,33.640000,251.000000,113,21.340000,138.300000,85,6.220000,11.200000,2,3.020000,2,False.\r\nCA,120,510,339-7602,no,no,0,134.800000,94,22.920000,204.100000,106,17.350000,238.400000,109,10.730000,6.700000,8,1.810000,1,False.\r\nWV,173,415,332-1109,no,no,0,191.400000,114,32.540000,168.500000,138,14.320000,109.300000,99,4.920000,10.300000,3,2.780000,1,False.\r\nIL,156,415,343-3296,no,no,0,174.500000,65,29.670000,197.400000,116,16.780000,238.500000,86,10.730000,10.600000,2,2.860000,0,False.\r\nNY,70,415,366-2536,no,no,0,177.400000,125,30.160000,226.200000,104,19.230000,254.100000,72,11.430000,10.900000,4,2.940000,0,False.\r\nNV,41,510,355-2293,no,no,0,182.100000,89,30.960000,211.500000,104,17.980000,207.400000,124,9.330000,6.800000,1,1.840000,1,False.\r\nAL,132,408,350-9318,no,no,0,222.400000,85,37.810000,165.400000,76,14.060000,208.400000,97,9.380000,11.200000,4,3.020000,0,False.\r\nKS,47,510,418-5300,yes,no,0,47.800000,120,8.130000,178.900000,123,15.210000,152.600000,96,6.870000,13.300000,7,3.590000,0,True.\r\nND,160,510,395-2626,no,no,0,121.800000,97,20.710000,89.300000,97,7.590000,150.700000,92,6.780000,10.300000,5,2.780000,1,False.\r\nOK,180,415,402-7372,no,no,0,143.500000,121,24.400000,189.300000,111,16.090000,174.900000,82,7.870000,8.800000,5,2.380000,3,False.\r\nUT,93,415,337-9710,no,no,0,164.900000,68,28.030000,210.400000,86,17.880000,229.400000,104,10.320000,7.800000,4,2.110000,2,False.\r\nOH,109,415,363-4967,no,no,0,193.600000,58,32.910000,148.700000,115,12.640000,282.500000,105,12.710000,13.100000,1,3.540000,2,False.\r\nWY,80,510,400-5389,no,no,0,101.100000,121,17.190000,263.200000,110,22.370000,137.700000,74,6.200000,7.300000,5,1.970000,0,False.\r\nNM,54,415,416-9162,no,yes,24,92.300000,88,15.690000,193.100000,98,16.410000,99.300000,119,4.470000,11.600000,3,3.130000,2,False.\r\nAL,121,415,414-6541,no,no,0,168.900000,128,28.710000,123.900000,99,10.530000,266.300000,105,11.980000,2.900000,7,0.780000,2,False.\r\nDC,157,510,392-6647,no,yes,29,219.200000,102,37.260000,206.000000,109,17.510000,192.400000,117,8.660000,15.000000,5,4.050000,1,False.\r\nID,170,510,343-2465,no,yes,37,178.100000,130,30.280000,242.800000,103,20.640000,243.000000,93,10.930000,13.000000,4,3.510000,0,False.\r\nAR,138,510,338-9171,no,no,0,146.500000,101,24.910000,284.500000,142,24.180000,176.000000,98,7.920000,14.000000,6,3.780000,3,False.\r\nID,92,415,405-4606,no,yes,31,172.300000,116,29.290000,266.200000,91,22.630000,228.200000,90,10.270000,11.800000,5,3.190000,1,False.\r\nTX,126,415,386-9711,no,no,0,190.900000,143,32.450000,149.700000,72,12.720000,191.400000,87,8.610000,13.000000,3,3.510000,1,False.\r\nNM,41,415,327-8495,no,no,0,232.100000,74,39.460000,327.100000,88,27.800000,226.500000,119,10.190000,10.900000,2,2.940000,3,True.\r\nWA,167,415,416-5660,no,no,0,169.200000,124,28.760000,173.300000,108,14.730000,216.500000,64,9.740000,12.400000,4,3.350000,4,True.\r\nUT,91,510,370-3032,no,no,0,123.800000,107,21.050000,319.000000,125,27.120000,237.600000,78,10.690000,7.300000,4,1.970000,2,False.\r\nRI,127,510,331-8462,no,no,0,96.000000,117,16.320000,177.000000,68,15.050000,162.200000,127,7.300000,9.700000,4,2.620000,3,False.\r\nNC,88,408,414-4037,no,yes,27,93.400000,106,15.880000,252.000000,92,21.420000,189.000000,104,8.500000,10.900000,1,2.940000,1,False.\r\nRI,113,415,415-2865,no,no,0,90.600000,130,15.400000,170.600000,100,14.500000,137.400000,74,6.180000,5.400000,9,1.460000,1,False.\r\nNY,78,510,362-2353,no,no,0,152.900000,81,25.990000,256.600000,82,21.810000,173.600000,112,7.810000,5.300000,6,1.430000,2,False.\r\nTX,123,408,329-5114,no,no,0,257.900000,92,43.840000,211.600000,71,17.990000,189.300000,104,8.520000,9.400000,2,2.540000,2,False.\r\nDE,136,408,351-1389,yes,yes,29,85.200000,98,14.480000,230.400000,85,19.580000,243.600000,104,10.960000,9.000000,3,2.430000,2,False.\r\nMS,68,415,375-3668,no,yes,34,160.000000,72,27.200000,184.500000,119,15.680000,208.300000,101,9.370000,6.100000,10,1.650000,1,False.\r\nOH,132,415,375-5414,no,yes,10,182.900000,54,31.090000,292.400000,68,24.850000,142.300000,116,6.400000,11.500000,4,3.110000,0,False.\r\nLA,133,415,360-7079,no,no,0,216.200000,67,36.750000,222.200000,133,18.890000,192.000000,95,8.640000,3.100000,1,0.840000,2,False.\r\nFL,127,415,344-9302,no,no,0,261.700000,105,44.490000,181.800000,107,15.450000,100.900000,131,4.540000,3.300000,5,0.890000,0,False.\r\nWA,110,415,418-1775,no,no,0,241.200000,105,41.000000,174.300000,85,14.820000,245.300000,59,11.040000,8.500000,4,2.300000,2,False.\r\nWV,121,510,401-2468,no,no,0,177.200000,142,30.120000,123.500000,88,10.500000,213.200000,51,9.590000,8.400000,6,2.270000,1,False.\r\nNY,116,510,346-4984,no,no,0,89.500000,128,15.220000,180.800000,137,15.370000,193.100000,94,8.690000,14.000000,3,3.780000,2,False.\r\nNE,112,415,351-2928,yes,yes,16,200.300000,72,34.050000,197.800000,91,16.810000,151.100000,92,6.800000,10.400000,3,2.810000,1,False.\r\nPA,97,510,365-7774,yes,no,0,145.000000,103,24.650000,294.300000,93,25.020000,239.800000,120,10.790000,11.000000,2,2.970000,4,True.\r\nMS,43,510,358-3691,no,no,0,159.500000,99,27.120000,119.700000,149,10.170000,173.900000,126,7.830000,6.800000,3,1.840000,2,False.\r\nIN,110,415,364-9059,no,no,0,151.800000,106,25.810000,138.000000,126,11.730000,233.500000,112,10.510000,11.200000,8,3.020000,3,False.\r\nVA,67,408,356-7208,no,no,0,176.200000,120,29.950000,236.000000,138,20.060000,152.500000,104,6.860000,10.600000,4,2.860000,1,False.\r\nMN,166,408,333-5551,no,no,0,152.100000,95,25.860000,121.000000,105,10.290000,198.000000,126,8.910000,9.800000,5,2.650000,0,False.\r\nDE,129,408,362-6528,no,no,0,161.300000,122,27.420000,220.600000,95,18.750000,224.700000,104,10.110000,9.600000,3,2.590000,1,False.\r\nOR,103,510,394-2560,yes,no,0,171.700000,78,29.190000,144.500000,86,12.280000,157.900000,106,7.110000,6.800000,3,1.840000,3,False.\r\nUT,71,415,367-3220,no,no,0,278.900000,110,47.410000,190.200000,67,16.170000,255.200000,84,11.480000,11.700000,7,3.160000,0,True.\r\nCT,112,415,418-5708,no,yes,27,213.000000,121,36.210000,226.200000,101,19.230000,189.800000,99,8.540000,11.100000,3,3.000000,4,False.\r\nAL,8,415,421-2245,no,yes,36,242.900000,67,41.290000,170.900000,59,14.530000,177.300000,130,7.980000,4.800000,12,1.300000,1,False.\r\nTX,98,415,406-2242,no,no,0,217.200000,121,36.920000,303.400000,73,25.790000,197.100000,71,8.870000,12.400000,2,3.350000,0,True.\r\nCT,90,415,347-6994,no,no,0,175.900000,111,29.900000,285.200000,115,24.240000,150.800000,122,6.790000,13.000000,7,3.510000,1,False.\r\nMS,13,415,413-7468,no,no,0,303.200000,133,51.540000,170.500000,86,14.490000,227.600000,80,10.240000,11.500000,3,3.110000,0,True.\r\nAR,58,415,389-6082,no,no,0,238.900000,107,40.610000,187.200000,88,15.910000,181.100000,84,8.150000,11.800000,3,3.190000,2,False.\r\nCA,137,415,415-3689,no,yes,22,189.600000,42,32.230000,179.000000,137,15.220000,179.600000,126,8.080000,11.400000,5,3.080000,2,False.\r\nMI,116,408,379-2503,no,no,0,133.300000,94,22.660000,247.800000,126,21.060000,219.000000,78,9.860000,11.300000,5,3.050000,5,True.\r\nWV,94,415,396-1106,no,yes,28,92.700000,107,15.760000,127.800000,86,10.860000,225.600000,86,10.150000,9.900000,4,2.670000,3,False.\r\nDE,87,415,379-4372,no,no,0,177.200000,72,30.120000,248.900000,105,21.160000,200.800000,87,9.040000,8.600000,7,2.320000,3,False.\r\nFL,120,415,336-3738,no,no,0,184.500000,103,31.370000,209.000000,86,17.770000,169.700000,70,7.640000,10.200000,6,2.750000,2,False.\r\nAK,97,415,380-2600,no,yes,24,176.100000,109,29.940000,159.400000,81,13.550000,269.100000,94,12.110000,12.100000,9,3.270000,0,False.\r\nID,134,415,345-4473,no,no,0,204.700000,108,34.800000,143.100000,105,12.160000,165.800000,84,7.460000,11.000000,4,2.970000,6,False.\r\nOH,68,510,380-9990,no,no,0,143.600000,80,24.410000,134.300000,65,11.420000,215.600000,84,9.700000,15.500000,5,4.190000,2,False.\r\nNH,93,408,411-1045,no,no,0,179.300000,93,30.480000,188.800000,65,16.050000,253.200000,88,11.390000,12.100000,5,3.270000,1,False.\r\nMA,120,415,413-5306,no,no,0,137.300000,100,23.340000,212.200000,129,18.040000,152.700000,92,6.870000,10.500000,2,2.840000,1,False.\r\nSC,41,408,417-6906,no,no,0,237.800000,92,40.430000,223.500000,155,19.000000,217.400000,90,9.780000,10.200000,6,2.750000,2,False.\r\nOR,80,510,331-4807,no,no,0,203.700000,92,34.630000,216.400000,97,18.390000,154.200000,66,6.940000,7.600000,5,2.050000,2,False.\r\nOH,83,415,376-5375,no,yes,25,191.300000,95,32.520000,250.700000,136,21.310000,249.400000,86,11.220000,17.600000,5,4.750000,2,False.\r\nNC,109,510,361-9839,yes,no,0,209.100000,141,35.550000,205.000000,93,17.430000,119.400000,111,5.370000,7.800000,3,2.110000,2,False.\r\nKY,66,510,348-8679,no,yes,33,88.800000,104,15.100000,109.600000,94,9.320000,172.700000,107,7.770000,7.100000,9,1.920000,3,False.\r\nID,104,510,403-6565,no,no,0,97.200000,88,16.520000,155.600000,85,13.230000,261.600000,105,11.770000,12.400000,5,3.350000,0,False.\r\nWA,89,510,346-5287,no,no,0,137.900000,96,23.440000,192.600000,63,16.370000,255.700000,125,11.510000,11.000000,5,2.970000,1,False.\r\nWV,127,510,413-6769,no,no,0,224.300000,112,38.130000,185.700000,103,15.780000,159.400000,83,7.170000,10.000000,1,2.700000,2,False.\r\nRI,117,408,370-5042,no,yes,13,207.600000,65,35.290000,152.700000,77,12.980000,232.800000,95,10.480000,9.700000,3,2.620000,1,False.\r\nKS,128,510,397-9486,no,no,0,268.100000,95,45.580000,120.500000,126,10.240000,220.800000,121,9.940000,14.400000,6,3.890000,2,False.\r\nNV,88,415,364-3286,no,no,0,166.700000,61,28.340000,179.300000,88,15.240000,242.700000,131,10.920000,6.800000,7,1.840000,4,True.\r\nNE,61,408,420-8897,no,no,0,267.100000,104,45.410000,180.400000,131,15.330000,230.600000,106,10.380000,17.300000,4,4.670000,1,True.\r\nFL,22,415,378-9506,no,no,0,181.800000,108,30.910000,198.600000,148,16.880000,206.600000,96,9.300000,9.300000,3,2.510000,2,False.\r\nWY,78,415,399-6259,no,no,0,147.100000,80,25.010000,199.700000,100,16.970000,160.700000,106,7.230000,13.700000,7,3.700000,0,False.\r\nWA,56,415,335-5806,no,yes,29,37.700000,115,6.410000,144.100000,111,12.250000,226.600000,101,10.200000,4.900000,3,1.320000,1,False.\r\nCO,192,415,401-6392,no,no,0,185.000000,88,31.450000,224.900000,98,19.120000,212.400000,105,9.560000,11.400000,3,3.080000,2,False.\r\nWI,70,415,379-9859,no,no,0,156.400000,108,26.590000,171.000000,116,14.540000,196.100000,96,8.820000,8.600000,4,2.320000,2,False.\r\nKS,148,510,415-4051,no,no,0,239.300000,84,40.680000,195.700000,85,16.630000,232.600000,104,10.470000,10.900000,3,2.940000,1,False.\r\nRI,65,415,368-5612,no,yes,29,215.500000,129,36.640000,161.900000,77,13.760000,128.300000,91,5.770000,8.800000,5,2.380000,2,False.\r\nMT,119,510,374-5301,no,no,0,134.900000,70,22.930000,211.500000,74,17.980000,188.500000,105,8.480000,11.300000,6,3.050000,1,False.\r\nCO,80,415,406-5710,no,no,0,194.800000,116,33.120000,209.900000,93,17.840000,194.100000,100,8.730000,12.800000,3,3.460000,0,False.\r\nCT,152,408,354-7077,no,yes,20,239.100000,105,40.650000,209.100000,111,17.770000,268.200000,130,12.070000,13.300000,3,3.590000,5,False.\r\nFL,113,510,343-3340,no,no,0,92.600000,85,15.740000,177.600000,92,15.100000,159.800000,72,7.190000,14.400000,4,3.890000,3,False.\r\nVT,75,510,377-8267,no,no,0,209.400000,133,35.600000,211.500000,121,17.980000,291.200000,123,13.100000,7.200000,4,1.940000,1,False.\r\nOH,80,415,382-2453,no,no,0,197.600000,83,33.590000,164.500000,86,13.980000,94.000000,98,4.230000,6.400000,6,1.730000,1,False.\r\nNH,148,408,333-7449,no,no,0,17.600000,121,2.990000,161.700000,125,13.740000,203.100000,82,9.140000,10.600000,6,2.860000,1,False.\r\nRI,63,415,366-4287,yes,no,0,62.900000,112,10.690000,202.900000,111,17.250000,259.000000,58,11.660000,8.900000,8,2.400000,1,False.\r\nFL,97,415,415-2285,no,yes,28,202.300000,97,34.390000,69.200000,84,5.880000,257.600000,64,11.590000,6.700000,3,1.810000,1,False.\r\nMD,166,415,381-1328,no,no,0,136.100000,116,23.140000,181.400000,93,15.420000,131.400000,108,5.910000,11.300000,4,3.050000,0,False.\r\nWY,94,408,344-4022,no,no,0,207.000000,109,35.190000,167.400000,80,14.230000,238.200000,117,10.720000,2.600000,6,0.700000,1,False.\r\nFL,85,408,415-6601,no,yes,33,207.900000,95,35.340000,233.500000,88,19.850000,221.300000,92,9.960000,13.500000,3,3.650000,1,False.\r\nTN,80,415,351-7309,yes,no,0,276.500000,122,47.010000,195.600000,79,16.630000,210.300000,78,9.460000,7.200000,3,1.940000,1,True.\r\nNC,210,415,363-7802,no,yes,31,313.800000,87,53.350000,147.700000,103,12.550000,192.700000,97,8.670000,10.100000,7,2.730000,3,False.\r\nIN,88,415,408-4870,yes,yes,25,288.500000,114,49.050000,203.400000,74,17.290000,228.400000,117,10.280000,13.000000,5,3.510000,1,False.\r\nIA,100,408,378-9478,no,no,0,210.900000,85,35.850000,329.300000,69,27.990000,127.100000,78,5.720000,9.400000,5,2.540000,4,False.\r\nWV,154,408,401-4778,no,yes,35,64.900000,76,11.030000,184.100000,91,15.650000,151.600000,75,6.820000,14.600000,1,3.940000,1,False.\r\nUT,32,510,370-9563,no,yes,26,243.500000,137,41.400000,236.800000,108,20.130000,173.300000,149,7.800000,9.000000,9,2.430000,1,False.\r\nGA,18,408,394-6382,no,no,0,197.000000,97,33.490000,203.700000,107,17.310000,202.000000,105,9.090000,8.700000,3,2.350000,3,False.\r\nAK,126,415,333-5295,no,yes,31,278.000000,88,47.260000,253.200000,65,21.520000,223.200000,114,10.040000,8.700000,4,2.350000,0,False.\r\nUT,144,510,370-2451,no,yes,37,219.900000,102,37.380000,222.100000,77,18.880000,118.500000,111,5.330000,10.000000,4,2.700000,1,False.\r\nMS,29,510,401-6982,no,no,0,313.200000,103,53.240000,216.300000,151,18.390000,218.400000,106,9.830000,12.800000,4,3.460000,2,True.\r\nAR,86,408,329-8115,no,yes,16,145.700000,88,24.770000,191.000000,129,16.240000,215.500000,82,9.700000,11.300000,7,3.050000,0,False.\r\nAK,138,415,340-3409,no,yes,37,75.800000,102,12.890000,173.600000,147,14.760000,162.600000,96,7.320000,8.200000,13,2.210000,0,False.\r\nAK,146,415,397-5911,no,no,0,195.900000,86,33.300000,228.600000,82,19.430000,303.500000,94,13.660000,12.200000,4,3.290000,3,False.\r\nME,175,415,415-6127,no,no,0,132.000000,95,22.440000,231.200000,74,19.650000,313.400000,108,14.100000,8.700000,10,2.350000,1,False.\r\nWV,74,510,392-6073,no,no,0,124.000000,102,21.080000,262.100000,101,22.280000,268.200000,98,12.070000,11.700000,2,3.160000,2,False.\r\nCT,48,415,419-6564,no,no,0,171.900000,98,29.220000,159.000000,127,13.520000,139.500000,101,6.280000,7.600000,3,2.050000,2,False.\r\nGA,74,510,340-8245,no,yes,31,249.400000,70,42.400000,209.500000,59,17.810000,180.600000,75,8.130000,9.900000,2,2.670000,4,False.\r\nNV,105,415,376-4540,yes,no,0,228.400000,100,38.830000,145.100000,108,12.330000,245.300000,140,11.040000,7.700000,7,2.080000,0,False.\r\nVT,157,510,361-5936,no,no,0,168.600000,71,28.660000,205.100000,48,17.430000,175.800000,88,7.910000,5.900000,2,1.590000,3,False.\r\nDC,217,415,421-9846,no,no,0,123.700000,138,21.030000,248.500000,105,21.120000,269.600000,78,12.130000,13.300000,4,3.590000,0,False.\r\nTN,68,415,356-1582,no,no,0,178.700000,61,30.380000,252.300000,84,21.450000,255.700000,76,11.510000,8.400000,4,2.270000,1,False.\r\nOR,80,415,375-4900,no,no,0,113.200000,86,19.240000,185.500000,97,15.770000,237.300000,145,10.680000,9.500000,5,2.570000,1,False.\r\nMS,38,415,420-8953,no,yes,25,142.400000,106,24.210000,313.700000,109,26.660000,126.600000,117,5.700000,13.400000,6,3.620000,2,False.\r\nNC,107,415,376-4035,no,yes,38,204.200000,57,34.710000,205.900000,92,17.500000,286.500000,80,12.890000,8.300000,4,2.240000,0,False.\r\nCO,140,415,344-5206,no,no,0,149.700000,71,25.450000,212.500000,97,18.060000,245.900000,67,11.070000,12.600000,4,3.400000,3,False.\r\nAR,98,415,328-7833,no,no,0,227.100000,116,38.610000,120.500000,103,10.240000,117.000000,102,5.270000,4.700000,4,1.270000,5,True.\r\nPA,114,415,417-4266,no,no,0,155.300000,75,26.400000,169.900000,87,14.440000,207.000000,133,9.320000,12.600000,5,3.400000,2,False.\r\nMN,46,408,351-6574,no,no,0,156.400000,105,26.590000,185.500000,98,15.770000,226.700000,96,10.200000,11.800000,3,3.190000,1,False.\r\nNM,118,415,372-8925,no,yes,42,148.700000,105,25.280000,167.300000,105,14.220000,270.600000,105,12.180000,10.400000,7,2.810000,0,False.\r\nUT,37,510,340-5678,no,no,0,271.700000,112,46.190000,155.100000,96,13.180000,199.500000,97,8.980000,6.600000,4,1.780000,3,False.\r\nNE,34,415,361-6814,no,no,0,193.700000,74,32.930000,126.900000,84,10.790000,221.200000,166,9.950000,8.800000,4,2.380000,0,False.\r\nMS,98,415,336-7155,yes,yes,23,245.500000,54,41.740000,292.700000,83,24.880000,184.000000,90,8.280000,10.800000,7,2.920000,1,False.\r\nNV,113,510,342-8167,no,no,0,245.300000,108,41.700000,259.900000,140,22.090000,204.300000,115,9.190000,10.700000,2,2.890000,3,True.\r\nOR,69,408,375-9180,no,no,0,196.100000,87,33.340000,236.800000,66,20.130000,182.300000,75,8.200000,11.900000,1,3.210000,0,False.\r\nVA,121,415,357-7064,no,no,0,134.100000,112,22.800000,195.100000,104,16.580000,159.600000,139,7.180000,10.500000,2,2.840000,2,False.\r\nNJ,59,510,347-5354,yes,yes,31,225.000000,78,38.250000,191.300000,79,16.260000,226.700000,79,10.200000,9.100000,3,2.460000,1,False.\r\nWV,59,510,362-9391,no,no,0,189.700000,100,32.250000,115.900000,133,9.850000,220.600000,115,9.930000,7.400000,4,2.000000,0,False.\r\nOR,190,415,386-8984,no,no,0,142.900000,96,24.290000,177.900000,96,15.120000,113.300000,117,5.100000,6.600000,4,1.780000,0,False.\r\nKY,109,415,384-6372,no,no,0,175.600000,80,29.850000,238.000000,94,20.230000,198.400000,103,8.930000,10.200000,6,2.750000,1,False.\r\nMO,136,415,358-1329,no,no,0,92.400000,109,15.710000,219.000000,115,18.620000,212.600000,80,9.570000,12.900000,4,3.480000,2,False.\r\nTX,86,510,400-7987,no,no,0,92.800000,92,15.780000,159.600000,87,13.570000,148.700000,115,6.690000,8.800000,5,2.380000,1,False.\r\nMN,100,415,327-8732,no,yes,27,221.700000,100,37.690000,236.100000,70,20.070000,192.700000,91,8.670000,8.000000,4,2.160000,0,False.\r\nFL,106,510,389-6955,no,no,0,159.600000,94,27.130000,276.800000,118,23.530000,223.500000,65,10.060000,8.800000,3,2.380000,0,False.\r\nPA,104,415,396-1800,no,no,0,144.500000,107,24.570000,180.500000,85,15.340000,226.000000,94,10.170000,17.000000,6,4.590000,2,False.\r\nWV,129,415,349-4979,no,no,0,159.100000,100,27.050000,202.500000,90,17.210000,233.100000,96,10.490000,11.500000,6,3.110000,2,False.\r\nIN,205,510,361-5864,no,no,0,49.900000,123,8.480000,150.700000,81,12.810000,188.200000,67,8.470000,10.100000,4,2.730000,2,False.\r\nOK,93,415,418-4658,no,yes,32,116.900000,120,19.870000,232.400000,97,19.750000,127.700000,112,5.750000,11.000000,9,2.970000,0,False.\r\nNE,123,415,330-6208,no,no,0,150.000000,98,25.500000,89.800000,95,7.630000,326.000000,91,14.670000,11.100000,3,3.000000,3,False.\r\nDE,99,415,416-6628,no,no,0,254.400000,120,43.250000,159.300000,92,13.540000,264.400000,94,11.900000,6.000000,5,1.620000,1,False.\r\nMI,61,415,349-5617,no,yes,33,270.700000,53,46.020000,200.700000,116,17.060000,201.700000,102,9.080000,10.900000,3,2.940000,3,False.\r\nIL,71,415,397-8051,no,no,0,207.000000,112,35.190000,173.800000,96,14.770000,178.400000,61,8.030000,12.100000,3,3.270000,1,False.\r\nUT,4,510,413-6346,yes,no,0,145.300000,89,24.700000,303.800000,93,25.820000,206.100000,82,9.270000,8.900000,4,2.400000,0,False.\r\nIL,148,408,395-9270,no,yes,25,230.700000,102,39.220000,233.800000,109,19.870000,215.800000,90,9.710000,13.500000,2,3.650000,3,False.\r\nWA,141,415,401-7575,no,no,0,151.500000,104,25.760000,242.200000,114,20.590000,304.200000,109,13.690000,10.800000,2,2.920000,1,False.\r\nNM,56,408,332-5964,no,no,0,146.100000,57,24.840000,196.200000,97,16.680000,310.100000,110,13.950000,9.200000,3,2.480000,0,False.\r\nMD,160,415,348-3338,no,no,0,256.000000,111,43.520000,187.400000,61,15.930000,119.100000,81,5.360000,11.500000,4,3.110000,3,False.\r\nVA,43,408,387-5411,no,yes,35,200.200000,105,34.030000,244.400000,88,20.770000,207.200000,97,9.320000,11.600000,4,3.130000,3,False.\r\nMT,42,415,378-7872,no,no,0,150.700000,52,25.620000,246.700000,96,20.970000,103.800000,118,4.670000,7.000000,4,1.890000,2,False.\r\nCT,135,415,389-6037,yes,no,0,186.000000,107,31.620000,66.000000,94,5.610000,213.100000,105,9.590000,12.900000,4,3.480000,1,False.\r\nAL,106,415,349-3732,no,no,0,212.900000,110,36.190000,187.000000,69,15.900000,128.100000,71,5.760000,6.300000,3,1.700000,3,False.\r\nWV,106,510,347-9738,no,no,0,194.800000,133,33.120000,213.400000,73,18.140000,190.800000,92,8.590000,11.500000,7,3.110000,0,False.\r\nMO,83,415,380-6074,no,yes,30,272.500000,105,46.330000,253.000000,83,21.510000,180.800000,123,8.140000,8.700000,6,2.350000,3,False.\r\nND,110,415,338-4307,no,no,0,135.100000,109,22.970000,205.200000,99,17.440000,166.300000,119,7.480000,11.700000,4,3.160000,1,False.\r\nAR,153,408,339-3636,no,no,0,154.600000,56,26.280000,263.000000,84,22.360000,367.700000,89,16.550000,15.500000,2,4.190000,1,False.\r\nGA,109,510,328-9315,no,yes,35,230.500000,116,39.190000,265.800000,130,22.590000,269.700000,69,12.140000,10.600000,6,2.860000,5,False.\r\nFL,31,510,402-3634,no,no,0,165.400000,84,28.120000,203.700000,107,17.310000,201.700000,65,9.080000,8.200000,1,2.210000,1,False.\r\nLA,124,510,348-4316,no,no,0,143.300000,120,24.360000,230.700000,111,19.610000,214.300000,91,9.640000,7.800000,2,2.110000,4,True.\r\nUT,110,415,375-3826,no,no,0,271.100000,108,46.090000,237.000000,122,20.150000,239.900000,122,10.800000,9.800000,5,2.650000,2,True.\r\nAZ,124,415,360-1406,no,no,0,253.500000,104,43.100000,117.900000,123,10.020000,248.500000,104,11.180000,14.000000,2,3.780000,0,False.\r\nNY,82,415,356-5475,no,no,0,167.100000,77,28.410000,131.800000,79,11.200000,187.400000,98,8.430000,9.400000,1,2.540000,6,True.\r\nKY,122,415,363-9969,no,no,0,168.300000,96,28.610000,87.600000,91,7.450000,247.200000,87,11.120000,8.400000,6,2.270000,1,False.\r\nAL,137,415,350-4367,no,no,0,104.700000,115,17.800000,249.800000,144,21.230000,192.300000,99,8.650000,8.900000,2,2.400000,1,False.\r\nIN,69,510,348-1592,no,no,0,135.400000,101,23.020000,238.100000,124,20.240000,195.600000,102,8.800000,10.600000,2,2.860000,1,False.\r\nIN,46,415,368-9751,no,yes,34,191.400000,102,32.540000,361.800000,96,30.750000,147.500000,132,6.640000,7.200000,2,1.940000,1,False.\r\nFL,103,415,380-6413,no,no,0,158.700000,90,26.980000,198.400000,117,16.860000,181.100000,76,8.150000,10.500000,4,2.840000,1,False.\r\nNM,16,510,367-9259,no,no,0,144.800000,84,24.620000,164.900000,141,14.020000,231.500000,75,10.420000,8.200000,4,2.210000,2,False.\r\nAL,119,415,404-8765,no,no,0,98.800000,97,16.800000,146.900000,68,12.490000,190.700000,105,8.580000,10.000000,4,2.700000,3,False.\r\nMN,124,510,371-6284,yes,no,0,157.800000,71,26.830000,203.200000,114,17.270000,168.700000,82,7.590000,10.000000,2,2.700000,3,True.\r\nNY,122,415,403-9468,no,yes,37,163.000000,107,27.710000,312.800000,118,26.590000,200.000000,85,9.000000,11.600000,5,3.130000,1,False.\r\nMD,139,415,335-3133,no,no,0,181.600000,119,30.870000,335.700000,118,28.530000,149.800000,64,6.740000,8.300000,6,2.240000,4,False.\r\nKS,67,510,366-4426,no,no,0,129.000000,78,21.930000,188.000000,116,15.980000,235.000000,102,10.580000,11.200000,3,3.020000,2,False.\r\nWV,84,408,354-4752,no,no,0,86.000000,83,14.620000,260.700000,86,22.160000,98.600000,109,4.440000,8.900000,4,2.400000,1,False.\r\nID,101,510,406-4768,no,yes,17,193.900000,71,32.960000,189.800000,81,16.130000,196.300000,97,8.830000,12.600000,7,3.400000,0,False.\r\nLA,40,510,367-9257,no,no,0,109.400000,107,18.600000,244.700000,102,20.800000,276.900000,123,12.460000,7.100000,7,1.920000,0,False.\r\nMI,61,415,342-8348,no,no,0,188.900000,105,32.110000,153.600000,116,13.060000,213.300000,106,9.600000,10.200000,2,2.750000,2,False.\r\nTN,120,408,410-7611,yes,no,0,179.900000,72,30.580000,170.000000,98,14.450000,190.600000,89,8.580000,13.800000,2,3.730000,1,True.\r\nCA,95,415,341-3270,no,no,0,183.400000,98,31.180000,281.300000,95,23.910000,105.200000,113,4.730000,8.200000,8,2.210000,1,False.\r\nFL,98,408,416-7452,no,no,0,288.100000,101,48.980000,137.900000,93,11.720000,206.500000,88,9.290000,0.000000,0,0.000000,0,False.\r\nLA,114,415,356-8982,no,no,0,169.200000,96,28.760000,149.900000,83,12.740000,196.900000,119,8.860000,4.600000,4,1.240000,2,False.\r\nIL,68,415,340-6908,yes,yes,29,195.500000,113,33.240000,171.600000,96,14.590000,204.000000,85,9.180000,13.500000,9,3.650000,1,True.\r\nAL,149,415,348-6659,no,yes,20,264.400000,102,44.950000,219.600000,123,18.670000,200.400000,89,9.020000,11.300000,3,3.050000,2,False.\r\nCT,22,408,345-2401,no,no,0,207.700000,116,35.310000,210.600000,99,17.900000,238.200000,88,10.720000,9.600000,5,2.590000,0,False.\r\nPA,176,415,422-5264,no,no,0,169.500000,151,28.820000,112.900000,84,9.600000,56.600000,99,2.550000,8.700000,4,2.350000,0,False.\r\nTX,152,415,422-1799,no,no,0,141.500000,102,24.060000,263.000000,94,22.360000,207.100000,113,9.320000,3.400000,4,0.920000,2,False.\r\nVA,118,408,404-2877,no,no,0,154.800000,71,26.320000,244.000000,73,20.740000,159.600000,81,7.180000,12.800000,4,3.460000,0,False.\r\nMO,101,415,417-7913,yes,no,0,133.500000,51,22.700000,219.600000,96,18.670000,210.000000,74,9.450000,11.700000,4,3.160000,1,False.\r\nMT,102,408,399-2457,no,no,0,273.200000,85,46.440000,211.100000,82,17.940000,203.700000,129,9.170000,13.100000,7,3.540000,2,True.\r\nND,118,408,419-3427,no,no,0,224.600000,94,38.180000,225.900000,120,19.200000,269.000000,105,12.110000,12.500000,8,3.380000,2,False.\r\nFL,105,415,358-2490,no,no,0,273.800000,97,46.550000,289.700000,106,24.620000,269.100000,126,12.110000,5.800000,3,1.570000,2,True.\r\nWI,153,510,349-3112,no,no,0,159.500000,103,27.120000,275.500000,90,23.420000,176.700000,126,7.950000,10.100000,2,2.730000,1,True.\r\nKY,71,415,414-5422,no,no,0,104.000000,92,17.680000,197.000000,125,16.750000,110.100000,123,4.950000,14.600000,8,3.940000,0,False.\r\nMD,71,415,386-3766,no,yes,31,115.400000,90,19.620000,217.400000,78,18.480000,239.900000,102,10.800000,13.100000,4,3.540000,1,False.\r\nIN,68,415,386-9724,no,no,0,222.100000,107,37.760000,199.400000,102,16.950000,162.400000,107,7.310000,9.400000,3,2.540000,2,False.\r\nMA,66,415,416-7393,no,no,0,116.400000,98,19.790000,95.600000,74,8.130000,181.500000,94,8.170000,10.500000,3,2.840000,3,False.\r\nND,101,415,395-1380,no,no,0,217.700000,118,37.010000,231.700000,128,19.690000,185.300000,128,8.340000,0.000000,0,0.000000,3,False.\r\nVT,116,415,408-4911,no,no,0,129.400000,84,22.000000,157.300000,89,13.370000,215.500000,77,9.700000,13.300000,3,3.590000,0,False.\r\nCT,54,415,387-4064,no,yes,33,161.800000,73,27.510000,273.000000,58,23.210000,153.900000,76,6.930000,13.700000,4,3.700000,0,False.\r\nVA,112,408,380-5667,no,yes,29,198.800000,122,33.800000,238.600000,114,20.280000,289.500000,69,13.030000,11.500000,5,3.110000,1,False.\r\nMS,122,408,402-8930,no,yes,45,147.800000,85,25.130000,147.400000,93,12.530000,203.500000,110,9.160000,14.000000,5,3.780000,1,False.\r\nAK,74,415,336-6533,no,no,0,262.300000,114,44.590000,198.900000,96,16.910000,165.900000,90,7.470000,6.600000,5,1.780000,3,False.\r\nWY,90,415,359-9992,no,no,0,246.400000,83,41.890000,160.300000,88,13.630000,170.900000,99,7.690000,7.600000,7,2.050000,1,False.\r\nNY,112,415,391-1737,no,no,0,174.300000,123,29.630000,140.200000,124,11.920000,215.400000,89,9.690000,9.000000,6,2.430000,4,True.\r\nNC,85,510,404-2871,no,no,0,183.400000,111,31.180000,168.800000,98,14.350000,199.700000,97,8.990000,9.900000,4,2.670000,4,False.\r\nIL,100,415,420-6121,no,no,0,191.900000,95,32.620000,200.900000,101,17.080000,271.900000,74,12.240000,18.200000,3,4.910000,1,False.\r\nOH,114,415,369-4012,no,no,0,187.800000,109,31.930000,154.600000,97,13.140000,213.900000,102,9.630000,10.100000,3,2.730000,2,False.\r\nRI,83,510,357-2294,no,no,0,259.700000,106,44.150000,152.700000,116,12.980000,224.700000,92,10.110000,10.200000,2,2.750000,0,False.\r\nWY,157,415,348-9938,yes,no,0,180.400000,123,30.670000,194.000000,98,16.490000,227.300000,88,10.230000,8.400000,5,2.270000,0,False.\r\nOR,51,510,394-3023,no,no,0,51.800000,107,8.810000,230.200000,104,19.570000,227.500000,118,10.240000,10.400000,4,2.810000,2,False.\r\nNV,42,415,352-5466,no,no,0,303.900000,106,51.660000,232.200000,54,19.740000,147.100000,76,6.620000,5.800000,3,1.570000,1,True.\r\nND,101,415,364-5510,no,yes,36,123.700000,125,21.030000,172.600000,106,14.670000,280.500000,127,12.620000,8.800000,4,2.380000,1,True.\r\nOR,112,510,396-6462,no,no,0,206.200000,122,35.050000,164.500000,94,13.980000,140.300000,101,6.310000,12.600000,7,3.400000,3,False.\r\nND,56,510,384-5335,no,no,0,164.300000,92,27.930000,233.700000,107,19.860000,187.300000,104,8.430000,11.800000,1,3.190000,2,False.\r\nNJ,53,408,416-6886,no,no,0,228.600000,117,38.860000,132.800000,123,11.290000,227.200000,124,10.220000,10.100000,2,2.730000,9,True.\r\nWV,64,415,357-2748,no,yes,22,200.400000,80,34.070000,131.100000,84,11.140000,230.700000,67,10.380000,7.600000,5,2.050000,1,False.\r\nVA,123,408,386-7976,no,no,0,154.300000,107,26.230000,183.000000,111,15.560000,54.000000,134,2.430000,10.900000,8,2.940000,0,False.\r\nID,68,510,403-9199,no,yes,30,122.900000,93,20.890000,233.500000,91,19.850000,199.500000,144,8.980000,9.600000,2,2.590000,2,False.\r\nCT,40,510,361-1900,yes,no,0,220.800000,100,37.540000,265.700000,106,22.580000,212.800000,94,9.580000,6.400000,3,1.730000,0,False.\r\nNM,132,408,405-3848,no,no,0,214.600000,78,36.480000,251.700000,98,21.390000,240.800000,88,10.840000,13.900000,2,3.750000,0,False.\r\nCT,120,408,344-1136,yes,no,0,202.000000,123,34.340000,184.300000,78,15.670000,176.000000,89,7.920000,7.400000,2,2.000000,2,True.\r\nMI,108,408,378-6276,no,yes,32,209.500000,108,35.620000,109.600000,64,9.320000,189.700000,145,8.540000,9.100000,6,2.460000,6,True.\r\nSC,161,510,343-2592,no,no,0,297.900000,141,50.640000,238.100000,107,20.240000,240.500000,93,10.820000,8.900000,5,2.400000,1,True.\r\nSC,130,415,396-4410,no,no,0,212.800000,102,36.180000,189.800000,137,16.130000,170.100000,105,7.650000,10.600000,4,2.860000,0,True.\r\nNY,122,510,397-3943,no,no,0,145.600000,102,24.750000,284.700000,111,24.200000,228.200000,91,10.270000,12.200000,5,3.290000,0,False.\r\nMT,130,408,347-3821,no,yes,19,152.900000,87,25.990000,213.200000,99,18.120000,205.300000,114,9.240000,10.800000,6,2.920000,2,False.\r\nWY,90,510,400-8069,no,no,0,125.400000,158,21.320000,269.100000,83,22.870000,238.600000,103,10.740000,11.000000,7,2.970000,1,False.\r\nNE,139,415,346-5349,no,yes,25,138.300000,96,23.510000,80.600000,79,6.850000,163.700000,83,7.370000,8.300000,8,2.240000,0,False.\r\nIN,57,415,345-5089,no,no,0,189.300000,157,32.180000,174.900000,70,14.870000,221.900000,117,9.990000,11.200000,5,3.020000,3,False.\r\nCO,128,510,410-4613,no,no,0,199.300000,86,33.880000,194.800000,102,16.560000,298.200000,82,13.420000,14.300000,2,3.860000,4,False.\r\nWY,127,510,356-4706,yes,no,0,247.500000,99,42.080000,108.500000,118,9.220000,232.000000,72,10.440000,10.600000,3,2.860000,2,False.\r\nMD,107,415,347-3406,no,no,0,294.900000,71,50.130000,192.800000,78,16.390000,148.100000,87,6.660000,13.200000,5,3.560000,1,True.\r\nOK,177,408,333-9133,no,no,0,175.400000,99,29.820000,155.300000,83,13.200000,179.400000,86,8.070000,11.500000,3,3.110000,1,False.\r\nSD,121,415,392-2459,no,no,0,179.400000,70,30.500000,143.000000,93,12.160000,116.300000,113,5.230000,11.200000,5,3.020000,1,False.\r\nSC,99,510,401-3685,yes,yes,39,126.800000,94,21.560000,293.600000,115,24.960000,174.100000,91,7.830000,8.400000,4,2.270000,0,False.\r\nNY,126,415,352-7752,yes,no,0,239.700000,87,40.750000,281.700000,92,23.940000,183.500000,113,8.260000,11.400000,1,3.080000,1,True.\r\nNY,77,415,388-9285,no,yes,33,143.000000,101,24.310000,212.200000,102,18.040000,104.900000,120,4.720000,15.300000,4,4.130000,5,True.\r\nWV,21,415,332-5582,no,no,0,91.900000,109,15.620000,198.400000,111,16.860000,171.700000,125,7.730000,13.000000,7,3.510000,2,False.\r\nWA,56,408,376-2550,no,no,0,210.400000,80,35.770000,176.600000,96,15.010000,149.700000,56,6.740000,15.500000,4,4.190000,1,False.\r\nID,92,415,333-4594,no,yes,29,201.300000,130,34.220000,203.700000,115,17.310000,129.900000,113,5.850000,6.400000,6,1.730000,1,True.\r\nMN,81,510,375-2522,no,no,0,145.600000,59,24.750000,287.900000,131,24.470000,181.700000,121,8.180000,9.200000,4,2.480000,2,False.\r\nTX,139,510,388-2240,yes,yes,31,203.500000,82,34.600000,200.300000,72,17.030000,214.000000,112,9.630000,13.400000,6,3.620000,1,True.\r\nAZ,68,415,420-1782,no,no,0,232.400000,76,39.510000,153.300000,103,13.030000,214.600000,107,9.660000,10.500000,2,2.840000,1,False.\r\nDE,183,415,384-8890,no,yes,8,86.500000,119,14.710000,285.200000,97,24.240000,180.400000,133,8.120000,8.700000,2,2.350000,2,False.\r\nCO,90,408,371-4788,no,no,0,109.900000,102,18.680000,220.800000,114,18.770000,104.000000,133,4.680000,10.900000,6,2.940000,0,False.\r\nWI,165,408,360-5636,no,no,0,156.000000,88,26.520000,276.100000,81,23.470000,175.900000,94,7.920000,9.300000,5,2.510000,1,False.\r\nWI,89,415,373-4264,no,no,0,326.300000,112,55.470000,165.100000,110,14.030000,162.900000,97,7.330000,7.500000,1,2.030000,1,True.\r\nNJ,59,510,352-9836,no,no,0,195.000000,58,33.150000,198.500000,88,16.870000,304.300000,110,13.690000,14.800000,9,4.000000,0,False.\r\nIL,16,415,342-2013,yes,no,0,110.000000,91,18.700000,147.300000,75,12.520000,190.500000,73,8.570000,6.400000,7,1.730000,0,False.\r\nDC,114,415,354-5689,no,no,0,147.100000,119,25.010000,161.000000,111,13.690000,275.900000,106,12.420000,9.000000,3,2.430000,5,True.\r\nIA,113,510,335-8427,no,no,0,156.000000,141,26.520000,256.800000,72,21.830000,175.300000,123,7.890000,11.900000,5,3.210000,2,False.\r\nGA,120,408,409-6753,no,no,0,98.200000,99,16.690000,186.700000,85,15.870000,146.700000,96,6.600000,9.300000,4,2.510000,2,False.\r\nAK,115,415,349-1756,no,no,0,210.600000,120,35.800000,153.100000,84,13.010000,262.200000,79,11.800000,11.000000,5,2.970000,0,False.\r\nCA,37,510,328-8980,no,no,0,239.900000,120,40.780000,261.600000,88,22.240000,207.100000,88,9.320000,8.900000,4,2.400000,2,True.\r\nMN,100,415,399-2151,yes,no,0,159.900000,94,27.180000,179.900000,95,15.290000,154.400000,102,6.950000,11.600000,2,3.130000,2,True.\r\nCO,132,415,379-9524,no,no,0,197.800000,66,33.630000,133.900000,119,11.380000,177.300000,94,7.980000,10.900000,3,2.940000,4,True.\r\nKY,38,408,352-9947,no,yes,36,115.400000,98,19.620000,166.200000,83,14.130000,184.700000,79,8.310000,15.200000,6,4.100000,2,False.\r\nSC,1,408,336-1043,no,no,0,123.800000,113,21.050000,236.200000,77,20.080000,73.200000,81,3.290000,3.700000,2,1.000000,0,False.\r\nMT,97,415,416-7013,no,yes,15,117.600000,97,19.990000,196.300000,126,16.690000,157.400000,113,7.080000,6.400000,3,1.730000,1,False.\r\nKY,55,415,345-4551,no,yes,28,105.300000,82,17.900000,197.400000,109,16.780000,187.500000,91,8.440000,8.600000,6,2.320000,2,False.\r\nWV,75,415,405-9864,no,no,0,111.700000,121,18.990000,237.300000,119,20.170000,253.500000,110,11.410000,13.100000,6,3.540000,1,False.\r\nID,83,415,350-4297,no,no,0,159.300000,104,27.080000,202.300000,98,17.200000,229.000000,73,10.310000,9.500000,3,2.570000,2,False.\r\nMN,40,510,350-7114,no,no,0,81.700000,123,13.890000,210.200000,108,17.870000,212.000000,64,9.540000,11.300000,3,3.050000,6,True.\r\nMA,101,415,400-4244,no,yes,21,238.000000,88,40.460000,209.600000,84,17.820000,233.000000,95,10.490000,10.000000,5,2.700000,2,False.\r\nMD,120,415,417-2608,no,yes,40,128.100000,99,21.780000,247.700000,78,21.050000,199.700000,121,8.990000,15.600000,3,4.210000,0,False.\r\nSD,183,415,372-2990,no,yes,31,171.200000,104,29.100000,193.600000,74,16.460000,196.500000,85,8.840000,10.200000,4,2.750000,1,False.\r\nPA,75,510,353-9998,no,no,0,109.000000,88,18.530000,259.300000,120,22.040000,182.100000,119,8.190000,13.300000,3,3.590000,4,True.\r\nKY,80,415,330-3008,no,no,0,220.000000,114,37.400000,207.700000,76,17.650000,168.400000,137,7.580000,12.100000,3,3.270000,2,False.\r\nID,88,415,384-8629,no,no,0,55.600000,65,9.450000,242.700000,121,20.630000,176.300000,134,7.930000,11.300000,4,3.050000,0,False.\r\nNY,112,415,404-9504,no,yes,23,286.600000,79,48.720000,315.300000,102,26.800000,193.900000,101,8.730000,10.300000,6,2.780000,1,False.\r\nNM,63,510,405-8753,no,no,0,207.600000,96,35.290000,229.000000,112,19.470000,162.600000,131,7.320000,13.300000,2,3.590000,1,False.\r\nCA,105,415,409-9911,no,yes,31,109.600000,108,18.630000,249.300000,119,21.190000,321.200000,101,14.450000,8.300000,4,2.240000,4,True.\r\nID,92,415,394-4260,no,no,0,197.200000,113,33.520000,242.300000,116,20.600000,192.000000,76,8.640000,11.000000,5,2.970000,2,False.\r\nWY,177,415,380-9063,no,no,0,175.700000,120,29.870000,168.600000,90,14.330000,198.900000,110,8.950000,14.600000,4,3.940000,1,False.\r\nNM,118,510,395-4509,no,no,0,205.200000,115,34.880000,184.800000,137,15.710000,176.100000,115,7.920000,7.000000,6,1.890000,0,False.\r\nHI,111,408,401-6671,no,yes,13,193.100000,104,32.830000,111.600000,98,9.490000,227.400000,94,10.230000,12.100000,4,3.270000,1,False.\r\nID,82,510,405-7204,no,yes,34,232.600000,121,39.540000,153.200000,115,13.020000,286.700000,77,12.900000,4.700000,3,1.270000,3,False.\r\nNC,74,415,329-5377,no,no,0,102.700000,89,17.460000,149.300000,100,12.690000,188.100000,114,8.460000,11.000000,5,2.970000,0,False.\r\nTX,121,415,408-9572,no,yes,31,263.100000,70,44.730000,279.300000,118,23.740000,127.100000,143,5.720000,9.700000,4,2.620000,5,False.\r\nGA,131,408,380-9879,no,no,0,197.000000,79,33.490000,201.000000,114,17.090000,151.200000,111,6.800000,11.600000,5,3.130000,1,False.\r\nAL,125,408,384-9243,no,no,0,169.300000,90,28.780000,156.000000,138,13.260000,210.800000,106,9.490000,11.600000,6,3.130000,2,False.\r\nME,19,415,404-5597,no,no,0,201.500000,123,34.260000,129.200000,110,10.980000,220.600000,98,9.930000,12.900000,4,3.480000,1,False.\r\nVA,138,415,359-7521,no,no,0,251.000000,119,42.670000,91.200000,96,7.750000,142.200000,87,6.400000,13.800000,3,3.730000,3,False.\r\nID,119,415,327-4795,no,no,0,230.400000,117,39.170000,225.000000,101,19.130000,198.500000,111,8.930000,7.600000,6,2.050000,3,False.\r\nNY,137,510,338-7955,no,no,0,109.800000,120,18.670000,230.500000,86,19.590000,255.800000,103,11.510000,11.900000,6,3.210000,1,False.\r\nNC,182,415,379-6970,no,no,0,279.500000,118,47.520000,203.200000,113,17.270000,174.200000,101,7.840000,10.700000,4,2.890000,2,True.\r\nOH,135,415,351-7807,no,no,0,173.400000,107,29.480000,222.000000,84,18.870000,64.200000,94,2.890000,13.700000,6,3.700000,1,False.\r\nHI,134,415,342-9394,no,yes,38,214.400000,93,36.450000,211.700000,57,17.990000,165.000000,79,7.430000,10.000000,8,2.700000,1,False.\r\nDC,45,415,384-6264,no,no,0,96.100000,103,16.340000,246.800000,134,20.980000,229.700000,92,10.340000,9.700000,4,2.620000,1,False.\r\nTN,129,408,352-4534,no,no,0,101.400000,145,17.240000,249.100000,116,21.170000,157.600000,107,7.090000,7.100000,6,1.920000,1,False.\r\nVT,142,415,378-4617,no,no,0,232.500000,74,39.530000,181.800000,142,15.450000,203.100000,86,9.140000,10.400000,6,2.810000,4,False.\r\nMD,130,415,364-9567,no,yes,45,174.500000,120,29.670000,217.500000,95,18.490000,220.300000,67,9.910000,12.200000,2,3.290000,1,False.\r\nLA,163,408,371-5875,no,yes,23,224.000000,126,38.080000,233.500000,89,19.850000,293.900000,104,13.230000,8.800000,4,2.380000,1,False.\r\nHI,105,415,383-6489,no,no,0,211.100000,99,35.890000,176.700000,66,15.020000,221.500000,96,9.970000,14.700000,7,3.970000,4,False.\r\nFL,119,415,345-7117,no,no,0,109.200000,96,18.560000,153.100000,80,13.010000,240.000000,102,10.800000,9.800000,5,2.650000,2,False.\r\nWY,78,408,384-3902,no,no,0,220.000000,95,37.400000,179.900000,121,15.290000,188.200000,109,8.470000,11.500000,5,3.110000,0,False.\r\nNE,92,415,386-2759,no,no,0,181.400000,98,30.840000,164.500000,98,13.980000,171.000000,110,7.690000,10.900000,4,2.940000,2,False.\r\nWY,146,415,356-1270,no,yes,11,180.700000,82,30.720000,173.700000,90,14.760000,231.500000,89,10.420000,10.100000,4,2.730000,2,False.\r\nOR,125,408,379-1336,no,yes,32,96.500000,109,16.410000,145.800000,109,12.390000,174.400000,82,7.850000,9.400000,2,2.540000,1,False.\r\nIN,88,415,354-7201,no,no,0,183.500000,93,31.200000,170.500000,80,14.490000,193.800000,88,8.720000,8.300000,5,2.240000,3,False.\r\nMD,83,408,404-5057,no,yes,38,107.900000,90,18.340000,140.400000,94,11.930000,253.600000,79,11.410000,10.500000,2,2.840000,0,False.\r\nTN,3,510,407-8012,yes,no,0,161.000000,96,27.370000,244.900000,82,20.820000,180.800000,103,8.140000,7.700000,6,2.080000,1,False.\r\nWV,152,510,332-6139,yes,yes,41,146.800000,128,24.960000,285.600000,96,24.280000,213.600000,80,9.610000,4.300000,2,1.160000,1,True.\r\nWA,48,510,328-1373,no,no,0,149.200000,146,25.360000,161.900000,109,13.760000,197.900000,109,8.910000,8.300000,2,2.240000,3,False.\r\nMS,189,415,411-6501,no,no,0,227.800000,124,38.730000,169.500000,112,14.410000,201.100000,91,9.050000,5.600000,4,1.510000,3,False.\r\nOH,95,415,329-8056,no,yes,23,160.300000,87,27.250000,202.400000,101,17.200000,191.100000,122,8.600000,7.400000,3,2.000000,0,False.\r\nIN,129,415,415-4564,no,no,0,137.800000,120,23.430000,225.800000,110,19.190000,145.200000,95,6.530000,10.200000,6,2.750000,1,True.\r\nCO,66,408,329-6192,no,yes,40,141.700000,87,24.090000,268.300000,89,22.810000,241.300000,68,10.860000,8.500000,7,2.300000,0,False.\r\nTX,80,510,384-3904,no,yes,22,196.400000,115,33.390000,150.300000,109,12.780000,176.200000,75,7.930000,9.300000,1,2.510000,0,False.\r\nAK,1,408,373-1028,no,no,0,175.200000,74,29.780000,151.700000,79,12.890000,230.500000,109,10.370000,5.300000,3,1.430000,1,False.\r\nWV,84,408,369-1220,no,no,0,146.800000,133,24.960000,171.700000,73,14.590000,234.500000,69,10.550000,9.900000,3,2.670000,1,False.\r\nMA,96,415,359-9369,no,no,0,173.900000,111,29.560000,287.400000,105,24.430000,204.800000,91,9.220000,9.100000,7,2.460000,1,False.\r\nTN,123,415,415-3016,no,yes,34,305.200000,80,51.880000,156.500000,109,13.300000,280.000000,81,12.600000,13.200000,7,3.560000,1,False.\r\nID,116,510,414-7090,yes,yes,29,162.300000,91,27.590000,279.300000,79,23.740000,192.700000,131,8.670000,11.700000,2,3.160000,3,True.\r\nDE,105,415,350-2250,yes,no,0,150.000000,106,25.500000,293.800000,123,24.970000,250.700000,65,11.280000,10.300000,7,2.780000,3,False.\r\nVA,80,415,383-9355,no,no,0,197.500000,114,33.580000,206.900000,119,17.590000,163.600000,109,7.360000,11.300000,4,3.050000,1,False.\r\nMT,157,408,417-3257,no,no,0,240.200000,67,40.830000,153.000000,98,13.010000,249.000000,72,11.210000,10.200000,6,2.750000,2,False.\r\nID,67,510,336-8010,no,yes,30,186.200000,117,31.650000,286.700000,76,24.370000,164.300000,113,7.390000,12.900000,3,3.480000,2,False.\r\nIN,141,415,354-7718,no,yes,39,116.900000,127,19.870000,276.500000,88,23.500000,289.900000,125,13.050000,12.300000,2,3.320000,0,False.\r\nMD,79,415,358-4412,no,yes,17,236.700000,95,40.240000,263.500000,56,22.400000,259.600000,107,11.680000,12.000000,4,3.240000,1,False.\r\nMS,76,408,368-8972,no,no,0,173.200000,93,29.440000,131.200000,80,11.150000,170.900000,104,7.690000,5.400000,3,1.460000,0,False.\r\nWA,111,510,407-9841,no,no,0,152.200000,114,25.870000,137.200000,102,11.660000,185.900000,97,8.370000,9.800000,3,2.650000,0,False.\r\nOH,94,415,393-5208,no,no,0,181.300000,135,30.820000,182.400000,108,15.500000,180.600000,103,8.130000,6.700000,2,1.810000,0,False.\r\nRI,143,408,332-2889,no,no,0,167.800000,72,28.530000,211.000000,99,17.940000,153.500000,109,6.910000,10.500000,6,2.840000,4,True.\r\nAR,109,510,374-5530,no,no,0,175.400000,125,29.820000,250.700000,87,21.310000,289.300000,74,13.020000,9.800000,9,2.650000,1,False.\r\nAZ,138,415,332-3381,no,no,0,87.600000,112,14.890000,266.900000,107,22.690000,214.600000,104,9.660000,9.800000,10,2.650000,2,False.\r\nSC,73,415,344-9347,no,no,0,203.300000,45,34.560000,141.900000,87,12.060000,200.700000,71,9.030000,8.500000,6,2.300000,0,False.\r\nKY,21,415,412-1991,no,no,0,92.600000,95,15.740000,161.900000,70,13.760000,285.000000,78,12.830000,11.300000,3,3.050000,5,True.\r\nAL,148,415,393-4528,no,yes,21,262.900000,135,44.690000,149.500000,96,12.710000,140.500000,109,6.320000,8.100000,4,2.190000,1,False.\r\nNE,103,408,347-2378,no,no,0,160.800000,91,27.340000,155.800000,82,13.240000,254.300000,103,11.440000,8.500000,3,2.300000,1,False.\r\nMT,143,408,385-2699,no,yes,22,141.800000,116,24.110000,167.300000,99,14.220000,178.100000,130,8.010000,7.800000,3,2.110000,1,False.\r\nMN,79,408,383-4319,no,yes,32,50.600000,62,8.600000,201.400000,87,17.120000,146.800000,121,6.610000,4.200000,4,1.130000,2,False.\r\nNV,89,415,352-7915,no,no,0,134.900000,59,22.930000,156.000000,152,13.260000,197.500000,112,8.890000,10.200000,5,2.750000,1,False.\r\nCA,120,415,375-5547,no,no,0,252.100000,110,42.860000,226.100000,103,19.220000,155.600000,83,7.000000,13.800000,3,3.730000,1,False.\r\nUT,121,415,337-2348,no,yes,41,215.500000,95,36.640000,241.800000,92,20.550000,147.000000,108,6.610000,9.600000,3,2.590000,1,False.\r\nIL,101,415,342-8702,no,no,0,124.800000,66,21.220000,257.200000,85,21.860000,193.200000,115,8.690000,13.400000,4,3.620000,0,False.\r\nDC,115,408,393-5802,no,no,0,178.700000,114,30.380000,271.000000,96,23.040000,245.900000,94,11.070000,16.400000,5,4.430000,2,False.\r\nIN,168,415,384-2219,no,no,0,183.200000,131,31.140000,179.200000,73,15.230000,292.800000,100,13.180000,9.900000,5,2.670000,2,False.\r\nNM,90,415,347-6164,no,no,0,167.500000,96,28.480000,139.100000,104,11.820000,138.400000,87,6.230000,13.000000,1,3.510000,1,False.\r\nMS,70,510,376-9940,no,no,0,147.100000,105,25.010000,200.000000,135,17.000000,234.900000,65,10.570000,12.500000,9,3.380000,3,False.\r\nVT,138,415,354-4352,no,no,0,230.100000,107,39.120000,212.000000,120,18.020000,174.900000,119,7.870000,13.200000,4,3.560000,1,False.\r\nVT,43,408,331-8713,no,no,0,135.800000,125,23.090000,163.200000,88,13.870000,229.800000,106,10.340000,12.600000,3,3.400000,0,False.\r\nUT,117,510,341-3663,no,yes,20,205.700000,98,34.970000,136.100000,107,11.570000,159.400000,147,7.170000,8.700000,3,2.350000,2,False.\r\nKY,108,408,376-4665,no,no,0,73.800000,105,12.550000,143.400000,114,12.190000,170.200000,98,7.660000,10.900000,3,2.940000,2,False.\r\nVA,118,408,421-9034,no,no,0,189.300000,119,32.180000,233.500000,112,19.850000,270.900000,104,12.190000,10.000000,1,2.700000,2,False.\r\nOH,169,408,401-5169,no,no,0,147.200000,115,25.020000,161.900000,123,13.760000,142.100000,103,6.390000,7.200000,6,1.940000,3,False.\r\nAZ,62,408,370-8262,no,yes,42,137.300000,95,23.340000,184.200000,94,15.660000,231.400000,70,10.410000,10.200000,3,2.750000,0,False.\r\nNY,86,510,387-2041,no,no,0,70.700000,125,12.020000,211.000000,113,17.940000,174.600000,107,7.860000,0.000000,0,0.000000,2,False.\r\nVA,44,408,356-4146,no,no,0,204.600000,117,34.780000,205.200000,94,17.440000,164.600000,84,7.410000,10.700000,5,2.890000,0,False.\r\nMD,111,510,372-8883,no,no,0,123.100000,88,20.930000,213.900000,84,18.180000,184.900000,88,8.320000,12.000000,2,3.240000,5,True.\r\nMA,127,510,336-1880,no,yes,19,129.700000,115,22.050000,160.800000,101,13.670000,265.000000,63,11.930000,12.200000,3,3.290000,2,False.\r\nIL,151,415,347-5843,yes,no,0,198.700000,70,33.780000,209.500000,106,17.810000,281.900000,126,12.690000,12.400000,4,3.350000,0,False.\r\nLA,53,415,370-8023,no,no,0,145.100000,116,24.670000,233.700000,82,19.860000,208.700000,95,9.390000,7.900000,5,2.130000,2,False.\r\nMO,15,415,417-9814,no,no,0,135.200000,101,22.980000,152.500000,79,12.960000,224.800000,83,10.120000,8.400000,5,2.270000,2,False.\r\nDC,123,408,387-3422,no,yes,28,124.700000,105,21.200000,250.400000,78,21.280000,216.400000,128,9.740000,7.800000,8,2.110000,1,False.\r\nPA,137,415,365-1664,no,no,0,215.900000,76,36.700000,145.400000,118,12.360000,186.900000,129,8.410000,12.100000,4,3.270000,1,False.\r\nTN,106,415,367-2436,no,no,0,119.200000,142,20.260000,228.400000,139,19.410000,197.900000,61,8.910000,8.400000,9,2.270000,2,False.\r\nNJ,88,510,344-6258,no,no,0,144.300000,116,24.530000,156.400000,74,13.290000,214.700000,90,9.660000,7.800000,10,2.110000,3,False.\r\nVA,106,415,353-8928,no,no,0,235.200000,121,39.980000,220.600000,87,18.750000,236.300000,91,10.630000,11.800000,4,3.190000,1,False.\r\nTN,95,510,365-7784,no,no,0,174.000000,57,29.580000,281.100000,118,23.890000,197.200000,94,8.870000,9.700000,2,2.620000,0,False.\r\nNJ,57,510,330-2635,yes,no,0,115.000000,65,19.550000,122.300000,96,10.400000,245.000000,75,11.030000,6.400000,1,1.730000,0,True.\r\nWA,184,408,344-3131,no,no,0,151.700000,93,25.790000,178.500000,77,15.170000,229.100000,111,10.310000,13.100000,8,3.540000,2,False.\r\nAR,109,510,378-4294,no,no,0,153.100000,102,26.030000,234.100000,77,19.900000,329.200000,74,14.810000,9.900000,9,2.670000,3,False.\r\nWI,127,415,343-9365,no,no,0,218.600000,93,37.160000,149.900000,130,12.740000,204.600000,131,9.210000,9.200000,5,2.480000,2,False.\r\nWA,82,510,362-5579,no,no,0,265.200000,122,45.080000,178.700000,102,15.190000,174.700000,90,7.860000,10.700000,9,2.890000,2,False.\r\nRI,180,415,366-7616,no,no,0,143.300000,134,24.360000,180.500000,113,15.340000,184.200000,87,8.290000,10.100000,4,2.730000,1,False.\r\nME,174,415,397-2870,no,no,0,190.300000,98,32.350000,252.700000,70,21.480000,220.600000,97,9.930000,7.200000,9,1.940000,2,False.\r\nCO,92,415,408-3262,no,no,0,184.700000,60,31.400000,262.000000,73,22.270000,239.500000,120,10.780000,12.300000,6,3.320000,2,True.\r\nCO,81,408,372-9091,no,no,0,115.300000,99,19.600000,224.700000,117,19.100000,152.500000,98,6.860000,18.000000,2,4.860000,1,False.\r\nRI,125,408,410-3159,no,no,0,113.000000,108,19.210000,169.200000,107,14.380000,156.600000,61,7.050000,9.200000,5,2.480000,2,True.\r\nCT,119,408,344-5181,no,no,0,294.200000,100,50.010000,232.500000,53,19.760000,195.000000,64,8.780000,9.000000,1,2.430000,0,True.\r\nNC,122,415,396-8662,no,no,0,215.600000,86,36.650000,167.800000,59,14.260000,207.000000,67,9.320000,6.400000,8,1.730000,3,False.\r\nWY,34,408,339-6446,no,no,0,128.800000,80,21.900000,208.700000,93,17.740000,202.100000,103,9.090000,14.000000,7,3.780000,1,False.\r\nOR,138,415,384-7236,yes,yes,28,211.200000,117,35.900000,312.500000,98,26.560000,178.000000,118,8.010000,10.700000,2,2.890000,3,True.\r\nFL,90,415,353-5257,no,yes,24,71.200000,82,12.100000,181.600000,103,15.440000,186.900000,111,8.410000,12.900000,1,3.480000,1,False.\r\nKY,73,408,369-7295,no,no,0,94.900000,121,16.130000,253.200000,83,21.520000,175.100000,86,7.880000,14.200000,2,3.830000,2,False.\r\nSC,19,510,408-5322,no,no,0,259.400000,116,44.100000,269.700000,109,22.920000,175.300000,130,7.890000,9.500000,3,2.570000,1,True.\r\nWA,120,408,344-9620,no,yes,28,215.800000,123,36.690000,285.200000,76,24.240000,192.100000,78,8.640000,6.900000,3,1.860000,1,False.\r\nMT,160,415,329-8436,no,no,0,97.500000,113,16.580000,268.100000,69,22.790000,255.300000,62,11.490000,13.200000,4,3.560000,3,False.\r\nPA,141,510,414-6739,no,no,0,146.500000,121,24.910000,169.900000,125,14.440000,238.800000,112,10.750000,8.200000,5,2.210000,0,False.\r\nMA,90,408,406-1730,no,no,0,157.900000,72,26.840000,234.000000,93,19.890000,210.000000,86,9.450000,12.200000,5,3.290000,2,False.\r\nVT,72,415,336-9327,no,no,0,139.900000,117,23.780000,223.600000,96,19.010000,240.800000,93,10.840000,12.700000,4,3.430000,2,False.\r\nMN,117,408,378-7418,no,yes,21,153.200000,112,26.040000,263.300000,110,22.380000,135.000000,85,6.080000,11.900000,7,3.210000,1,False.\r\nIL,79,408,412-6019,yes,no,0,103.500000,134,17.600000,319.300000,111,27.140000,239.900000,124,10.800000,8.400000,4,2.270000,2,False.\r\nAR,87,408,390-4789,no,no,0,185.800000,119,31.590000,192.300000,83,16.350000,200.000000,96,9.000000,6.600000,4,1.780000,1,False.\r\nMD,102,415,386-9774,no,no,0,129.500000,56,22.020000,354.200000,118,30.110000,145.500000,93,6.550000,10.900000,3,2.940000,1,False.\r\nMT,49,408,353-8970,no,no,0,236.600000,91,40.220000,220.900000,146,18.780000,146.800000,114,6.610000,8.900000,2,2.400000,1,False.\r\nVT,67,408,410-5370,no,no,0,260.400000,107,44.270000,208.200000,104,17.700000,207.900000,115,9.360000,10.000000,2,2.700000,6,False.\r\nCO,107,415,404-4421,no,no,0,167.300000,100,28.440000,163.900000,79,13.930000,185.900000,100,8.370000,6.700000,5,1.810000,2,False.\r\nNC,190,408,409-3353,no,no,0,182.200000,101,30.970000,212.300000,95,18.050000,233.000000,123,10.490000,9.300000,4,2.510000,2,False.\r\nWA,118,510,422-2571,no,no,0,113.000000,80,19.210000,150.100000,87,12.760000,204.300000,115,9.190000,10.800000,4,2.920000,2,False.\r\nTN,120,415,412-3404,no,no,0,185.700000,133,31.570000,235.100000,149,19.980000,256.400000,78,11.540000,16.900000,6,4.560000,0,False.\r\nAR,94,408,333-2964,no,no,0,136.200000,114,23.150000,165.100000,118,14.030000,137.900000,71,6.210000,9.600000,5,2.590000,0,False.\r\nDC,115,510,406-6669,no,yes,29,222.600000,81,37.840000,190.300000,109,16.180000,201.200000,87,9.050000,11.500000,2,3.110000,1,False.\r\nMN,61,415,409-8802,no,no,0,197.300000,67,33.540000,264.500000,106,22.480000,210.500000,116,9.470000,9.000000,6,2.430000,1,False.\r\nIA,143,510,354-6183,no,yes,33,141.400000,130,24.040000,186.400000,114,15.840000,210.000000,111,9.450000,7.700000,6,2.080000,1,False.\r\nKS,110,510,354-2434,no,no,0,208.000000,69,35.360000,95.100000,94,8.080000,178.500000,129,8.030000,8.000000,11,2.160000,1,False.\r\nND,104,415,389-7620,no,no,0,118.500000,92,20.150000,177.800000,109,15.110000,255.700000,98,11.510000,12.100000,4,3.270000,1,False.\r\nMT,16,510,338-1724,no,no,0,153.200000,65,26.040000,229.700000,90,19.520000,148.200000,94,6.670000,10.700000,8,2.890000,1,False.\r\nIL,183,510,399-1750,no,no,0,108.300000,87,18.410000,183.600000,116,15.610000,176.600000,109,7.950000,13.500000,2,3.650000,0,False.\r\nDC,147,408,354-8914,no,no,0,168.600000,92,28.660000,187.700000,107,15.950000,216.500000,95,9.740000,14.400000,8,3.890000,2,False.\r\nKY,58,415,409-2983,no,no,0,247.200000,116,42.020000,303.700000,103,25.810000,105.400000,94,4.740000,9.300000,2,2.510000,2,True.\r\nMS,102,510,329-9689,yes,no,0,224.200000,81,38.110000,243.300000,90,20.680000,147.800000,66,6.650000,12.000000,8,3.240000,3,False.\r\nTN,123,415,337-8950,no,no,0,166.900000,98,28.370000,221.800000,77,18.850000,243.900000,114,10.980000,12.800000,4,3.460000,3,False.\r\nIA,64,415,374-1836,no,yes,43,118.400000,100,20.130000,144.100000,108,12.250000,158.100000,91,7.110000,8.500000,6,2.300000,1,False.\r\nAK,103,510,359-9454,no,no,0,190.900000,62,32.450000,226.600000,53,19.260000,230.100000,96,10.350000,7.800000,3,2.110000,2,False.\r\nMN,152,415,378-9542,no,no,0,317.800000,60,54.030000,152.900000,100,13.000000,123.400000,63,5.550000,10.400000,7,2.810000,1,True.\r\nWV,124,415,344-1970,no,no,0,312.000000,112,53.040000,180.000000,109,15.300000,168.600000,94,7.590000,12.800000,4,3.460000,1,True.\r\nOR,97,415,417-2774,no,no,0,146.000000,121,24.820000,203.000000,141,17.260000,151.800000,120,6.830000,13.300000,2,3.590000,1,False.\r\nMS,131,415,333-9002,no,no,0,131.600000,95,22.370000,179.300000,109,15.240000,251.200000,129,11.300000,15.500000,3,4.190000,1,True.\r\nME,57,415,369-8576,no,yes,33,193.400000,105,32.880000,231.600000,79,19.690000,226.200000,90,10.180000,11.100000,11,3.000000,2,False.\r\nMN,157,510,372-6920,no,no,0,185.100000,92,31.470000,213.000000,85,18.110000,196.100000,85,8.820000,8.500000,5,2.300000,2,False.\r\nGA,194,510,333-6575,no,no,0,193.300000,106,32.860000,169.000000,150,14.370000,225.200000,122,10.130000,11.800000,4,3.190000,0,False.\r\nDC,66,415,410-1190,no,no,0,146.400000,107,24.890000,196.500000,99,16.700000,230.100000,106,10.350000,7.800000,2,2.110000,1,False.\r\nGA,155,510,376-1641,no,no,0,71.200000,90,12.100000,304.400000,119,25.870000,183.300000,103,8.250000,8.600000,4,2.320000,0,False.\r\nNY,123,415,329-6731,no,no,0,123.200000,104,20.940000,190.000000,117,16.150000,170.300000,95,7.660000,12.900000,5,3.480000,4,True.\r\nOK,116,510,393-3976,no,no,0,205.000000,90,34.850000,140.900000,114,11.980000,272.600000,96,12.270000,7.500000,4,2.030000,2,False.\r\nOK,63,415,388-7355,no,no,0,128.700000,78,21.880000,240.800000,133,20.470000,237.700000,121,10.700000,12.800000,6,3.460000,1,False.\r\nGA,64,510,412-7791,no,no,0,216.900000,78,36.870000,211.000000,115,17.940000,179.800000,116,8.090000,11.400000,5,3.080000,3,False.\r\nNJ,96,510,368-6111,no,no,0,150.000000,122,25.500000,218.500000,116,18.570000,212.400000,89,9.560000,9.800000,1,2.650000,3,False.\r\nMN,53,415,401-9420,no,no,0,164.100000,106,27.900000,206.000000,56,17.510000,194.700000,124,8.760000,11.400000,2,3.080000,1,False.\r\nME,105,510,352-5750,no,no,0,212.000000,113,36.040000,226.600000,128,19.260000,193.600000,114,8.710000,8.900000,7,2.400000,3,False.\r\nMI,53,510,417-3702,no,yes,37,167.300000,99,28.440000,194.700000,99,16.550000,236.700000,112,10.650000,12.000000,9,3.240000,2,False.\r\nMT,101,415,353-5714,no,no,0,154.400000,130,26.250000,217.200000,101,18.460000,185.400000,52,8.340000,13.900000,4,3.750000,1,False.\r\nNE,129,510,409-9494,no,yes,30,177.300000,95,30.140000,211.800000,102,18.000000,240.200000,108,10.810000,9.300000,7,2.510000,3,False.\r\nND,122,408,395-1901,no,no,0,231.200000,141,39.300000,267.800000,136,22.760000,240.300000,100,10.810000,8.800000,5,2.380000,1,True.\r\nVA,163,415,378-8342,no,no,0,202.900000,100,34.490000,178.600000,46,15.180000,203.800000,116,9.170000,12.800000,3,3.460000,5,False.\r\nVT,93,408,417-6044,no,no,0,149.600000,120,25.430000,200.700000,85,17.060000,181.200000,107,8.150000,14.300000,9,3.860000,0,False.\r\nOH,115,510,348-1163,yes,no,0,345.300000,81,58.700000,203.400000,106,17.290000,217.500000,107,9.790000,11.800000,8,3.190000,1,True.\r\nAL,25,408,337-4600,no,no,0,264.900000,80,45.030000,281.200000,66,23.900000,166.100000,80,7.470000,8.400000,4,2.270000,1,True.\r\nDC,73,408,355-5922,no,no,0,122.000000,92,20.740000,138.300000,114,11.760000,224.200000,128,10.090000,5.800000,5,1.570000,1,False.\r\nND,120,415,369-5810,no,no,0,177.200000,88,30.120000,270.400000,99,22.980000,231.500000,90,10.420000,14.000000,2,3.780000,2,False.\r\nTN,196,415,340-8291,no,no,0,133.100000,80,22.630000,206.500000,120,17.550000,221.600000,96,9.970000,10.300000,8,2.780000,1,False.\r\nDE,97,510,354-7397,no,no,0,225.100000,90,38.270000,279.500000,127,23.760000,233.800000,103,10.520000,8.800000,4,2.380000,0,True.\r\nNY,148,408,407-7464,no,no,0,208.400000,120,35.430000,174.400000,99,14.820000,310.700000,105,13.980000,11.200000,4,3.020000,1,False.\r\nAL,85,408,386-6411,no,yes,30,173.100000,107,29.430000,247.200000,101,21.010000,158.700000,104,7.140000,11.500000,5,3.110000,1,False.\r\nOK,86,510,397-3746,yes,no,0,162.400000,131,27.610000,167.000000,102,14.200000,128.900000,118,5.800000,11.400000,2,3.080000,2,True.\r\nMS,78,415,410-5236,no,yes,13,281.200000,93,47.800000,178.200000,101,15.150000,244.200000,129,10.990000,6.400000,5,1.730000,2,False.\r\nMD,106,415,409-2412,no,no,0,208.300000,89,35.410000,169.400000,67,14.400000,102.000000,90,4.590000,15.900000,4,4.290000,3,False.\r\nNE,147,415,400-7280,no,yes,38,243.400000,126,41.380000,273.800000,109,23.270000,282.900000,91,12.730000,14.100000,8,3.810000,2,False.\r\nAR,145,415,332-5820,no,no,0,224.200000,89,38.110000,314.900000,121,26.770000,182.900000,121,8.230000,16.100000,3,4.350000,1,True.\r\nIL,91,415,373-4483,no,no,0,189.300000,100,32.180000,239.300000,107,20.340000,89.700000,89,4.040000,9.900000,3,2.670000,3,False.\r\nIN,81,408,347-6717,no,yes,46,168.300000,124,28.610000,270.900000,103,23.030000,222.500000,98,10.010000,6.700000,2,1.810000,4,False.\r\nUT,116,415,380-2929,no,yes,24,232.900000,90,39.590000,152.100000,94,12.930000,344.300000,82,15.490000,10.700000,6,2.890000,1,False.\r\nLA,69,415,420-7692,no,yes,37,155.000000,98,26.350000,142.400000,105,12.100000,143.700000,117,6.470000,5.900000,4,1.590000,1,False.\r\nID,135,510,380-6437,no,no,0,154.400000,130,26.250000,203.800000,90,17.320000,158.700000,59,7.140000,11.800000,3,3.190000,0,False.\r\nKY,73,510,377-8309,no,no,0,234.700000,102,39.900000,195.700000,110,16.630000,253.400000,71,11.400000,8.400000,8,2.270000,2,False.\r\nMI,48,415,407-2718,no,no,0,240.000000,88,40.800000,141.000000,117,11.990000,128.900000,137,5.800000,7.100000,9,1.920000,1,False.\r\nNH,125,415,357-1938,yes,no,0,298.400000,78,50.730000,270.500000,142,22.990000,107.300000,84,4.830000,12.200000,2,3.290000,0,True.\r\nWV,100,415,381-3735,no,no,0,166.000000,102,28.220000,236.100000,97,20.070000,134.300000,93,6.040000,10.900000,4,2.940000,1,False.\r\nOR,165,415,409-8453,no,yes,33,111.600000,140,18.970000,213.300000,111,18.130000,267.600000,115,12.040000,16.000000,3,4.320000,0,False.\r\nSD,64,415,395-6758,no,no,0,174.500000,98,29.670000,180.200000,103,15.320000,179.000000,89,8.060000,10.700000,2,2.890000,2,False.\r\nMD,116,510,399-5424,yes,yes,27,175.500000,137,29.840000,210.600000,60,17.900000,294.800000,121,13.270000,6.900000,5,1.860000,1,False.\r\nND,147,408,409-4671,yes,yes,35,157.500000,109,26.780000,189.600000,67,16.120000,227.000000,76,10.220000,11.100000,2,3.000000,3,True.\r\nTN,115,415,374-6525,no,no,0,206.200000,113,35.050000,176.400000,102,14.990000,297.100000,119,13.370000,11.000000,7,2.970000,1,False.\r\nMO,84,415,406-8665,no,yes,35,207.500000,138,35.280000,201.000000,116,17.090000,164.500000,107,7.400000,7.500000,16,2.030000,4,False.\r\nIL,86,510,342-7716,no,yes,16,144.800000,105,24.620000,206.200000,111,17.530000,255.400000,117,11.490000,11.600000,2,3.130000,4,False.\r\nUT,134,415,417-2221,no,no,0,258.800000,85,44.000000,129.500000,114,11.010000,193.600000,106,8.710000,10.900000,7,2.940000,2,False.\r\nMI,105,415,376-5213,no,no,0,226.900000,106,38.570000,182.200000,77,15.490000,203.900000,107,9.180000,11.600000,2,3.130000,0,True.\r\nAR,88,408,348-7448,no,no,0,152.900000,119,25.990000,171.200000,107,14.550000,257.000000,106,11.570000,12.000000,5,3.240000,2,False.\r\nTX,90,408,328-8179,no,yes,27,156.700000,51,26.640000,236.500000,118,20.100000,123.200000,111,5.540000,12.600000,6,3.400000,2,False.\r\nAK,86,408,389-4602,no,no,0,150.800000,85,25.640000,295.900000,88,25.150000,247.200000,104,11.120000,12.500000,1,3.380000,1,False.\r\nTN,37,415,413-2238,no,no,0,221.000000,126,37.570000,204.500000,110,17.380000,118.000000,98,5.310000,6.800000,3,1.840000,4,False.\r\nNH,141,415,402-3370,no,yes,32,322.400000,92,54.810000,283.200000,107,24.070000,209.500000,111,9.430000,6.700000,3,1.810000,1,True.\r\nNM,148,408,348-6008,no,no,0,153.600000,148,26.110000,262.100000,87,22.280000,225.500000,99,10.150000,9.800000,3,2.650000,2,False.\r\nMN,163,408,371-5655,no,yes,22,215.100000,91,36.570000,138.900000,102,11.810000,146.200000,109,6.580000,12.400000,2,3.350000,2,False.\r\nIA,89,415,374-5224,no,yes,35,174.400000,108,29.650000,196.700000,100,16.720000,127.400000,74,5.730000,11.800000,3,3.190000,1,False.\r\nRI,63,415,371-1187,no,no,0,180.500000,126,30.690000,230.000000,98,19.550000,232.500000,73,10.460000,10.600000,3,2.860000,0,False.\r\nAL,102,415,337-1100,no,no,0,123.100000,106,20.930000,182.000000,102,15.470000,244.600000,75,11.010000,12.600000,7,3.400000,2,False.\r\nNC,76,510,421-8141,no,no,0,165.700000,94,28.170000,257.400000,80,21.880000,170.800000,114,7.690000,10.000000,4,2.700000,1,False.\r\nSD,104,408,406-2678,no,no,0,200.200000,92,34.030000,118.700000,87,10.090000,236.600000,65,10.650000,6.000000,6,1.620000,2,False.\r\nMT,109,510,415-9649,no,no,0,154.800000,82,26.320000,287.700000,109,24.450000,208.400000,80,9.380000,5.900000,9,1.590000,3,False.\r\nHI,105,510,364-8128,no,no,0,125.400000,116,21.320000,261.500000,95,22.230000,241.600000,104,10.870000,11.400000,9,3.080000,2,False.\r\nMT,63,415,356-7817,no,yes,33,184.200000,111,31.310000,312.600000,89,26.570000,264.000000,55,11.880000,12.200000,4,3.290000,1,False.\r\nKY,105,415,404-6357,no,yes,24,274.700000,99,46.700000,193.500000,118,16.450000,299.600000,109,13.480000,10.800000,3,2.920000,3,False.\r\nDC,68,415,398-2138,yes,yes,39,142.000000,140,24.140000,241.600000,89,20.540000,302.000000,72,13.590000,11.300000,5,3.050000,1,False.\r\nCO,63,408,378-8029,yes,yes,21,151.500000,99,25.760000,147.800000,89,12.560000,210.400000,114,9.470000,10.000000,4,2.700000,1,False.\r\nMI,74,415,386-4215,no,no,0,124.800000,114,21.220000,133.000000,121,11.310000,160.300000,85,7.210000,10.600000,7,2.860000,3,False.\r\nAL,76,415,367-8156,no,no,0,179.200000,85,30.460000,222.900000,66,18.950000,188.200000,113,8.470000,12.400000,2,3.350000,0,False.\r\nKS,91,408,382-8079,yes,no,0,246.400000,110,41.890000,182.000000,98,15.470000,157.600000,106,7.090000,12.100000,2,3.270000,2,True.\r\nNC,101,415,354-2985,no,no,0,232.700000,114,39.560000,186.400000,123,15.840000,153.300000,122,6.900000,11.500000,6,3.110000,5,False.\r\nSC,116,408,373-6922,no,no,0,288.000000,120,48.960000,255.800000,90,21.740000,233.400000,99,10.500000,13.400000,4,3.620000,0,True.\r\nCO,131,415,397-7125,no,yes,23,170.800000,145,29.040000,236.700000,93,20.120000,294.500000,100,13.250000,12.700000,1,3.430000,1,False.\r\nMN,84,415,333-6296,no,no,0,216.100000,114,36.740000,197.500000,107,16.790000,217.800000,104,9.800000,9.800000,3,2.650000,1,False.\r\nWY,104,415,365-6022,no,no,0,138.700000,100,23.580000,215.400000,58,18.310000,164.300000,98,7.390000,4.900000,4,1.320000,2,False.\r\nFL,108,510,365-1688,no,no,0,210.700000,112,35.820000,238.700000,73,20.290000,253.600000,90,11.410000,9.200000,5,2.480000,3,False.\r\nNY,111,415,382-4872,no,no,0,181.800000,117,30.910000,158.100000,91,13.440000,266.200000,123,11.980000,9.700000,9,2.620000,0,False.\r\nOK,155,408,367-6136,no,yes,30,61.600000,103,10.470000,255.100000,110,21.680000,225.900000,96,10.170000,12.400000,5,3.350000,1,False.\r\nME,66,510,404-3592,no,no,0,207.700000,85,35.310000,196.700000,112,16.720000,261.700000,83,11.780000,6.800000,3,1.840000,1,False.\r\nNE,64,510,415-2949,no,no,0,219.200000,73,37.260000,167.000000,65,14.200000,161.400000,119,7.260000,10.000000,5,2.700000,1,False.\r\nOH,69,415,375-8880,no,no,0,227.000000,122,38.590000,258.700000,111,21.990000,169.700000,87,7.640000,8.900000,1,2.400000,2,False.\r\nCT,116,415,335-6832,no,no,0,245.900000,73,41.800000,240.100000,87,20.410000,158.700000,89,7.140000,8.900000,5,2.400000,3,False.\r\nDC,101,415,361-8367,no,no,0,257.300000,84,43.740000,184.800000,115,15.710000,108.900000,109,4.900000,13.500000,7,3.650000,0,False.\r\nOK,15,415,408-2002,no,no,0,121.100000,130,20.590000,216.000000,86,18.360000,235.100000,33,10.580000,16.100000,5,4.350000,2,False.\r\nNJ,88,415,347-8659,no,no,0,301.500000,136,51.260000,257.700000,72,21.900000,132.900000,118,5.980000,13.400000,2,3.620000,4,True.\r\nIA,197,415,376-2922,no,no,0,233.900000,96,39.760000,218.900000,111,18.610000,182.900000,109,8.230000,9.500000,3,2.570000,0,False.\r\nVA,50,415,382-2182,yes,no,0,99.600000,108,16.930000,308.700000,102,26.240000,161.200000,62,7.250000,13.700000,6,3.700000,2,True.\r\nVA,172,510,408-2089,no,no,0,169.800000,123,28.870000,183.100000,94,15.560000,395.000000,72,17.770000,12.700000,7,3.430000,2,False.\r\nNM,188,415,369-6890,yes,yes,26,198.800000,115,33.800000,166.600000,67,14.160000,198.500000,118,8.930000,14.400000,3,3.890000,1,True.\r\nFL,85,408,347-2951,yes,no,0,116.200000,86,19.750000,229.700000,127,19.520000,204.200000,109,9.190000,10.100000,3,2.730000,3,False.\r\nRI,103,510,420-6324,yes,no,0,255.900000,128,43.500000,140.900000,92,11.980000,308.900000,130,13.900000,12.100000,2,3.270000,1,True.\r\nNJ,136,408,402-7650,no,yes,27,187.700000,84,31.910000,221.000000,147,18.790000,145.700000,110,6.560000,10.000000,4,2.700000,3,False.\r\nNE,155,408,391-2702,no,yes,21,195.900000,91,33.300000,213.900000,84,18.180000,88.200000,111,3.970000,8.600000,4,2.320000,0,False.\r\nWV,145,415,383-3375,no,no,0,129.400000,97,22.000000,185.400000,101,15.760000,204.700000,106,9.210000,1.100000,2,0.300000,2,False.\r\nWY,116,510,392-2733,no,yes,12,221.000000,108,37.570000,151.000000,118,12.840000,179.000000,80,8.060000,9.000000,6,2.430000,2,False.\r\nSC,152,408,397-9933,no,no,0,140.500000,92,23.890000,186.800000,96,15.880000,227.000000,89,10.220000,9.500000,5,2.570000,2,False.\r\nMS,65,415,383-9306,yes,no,0,277.900000,123,47.240000,155.800000,112,13.240000,256.900000,71,11.560000,9.200000,10,2.480000,0,True.\r\nND,180,415,369-1929,no,no,0,224.900000,105,38.230000,250.000000,101,21.250000,216.100000,73,9.720000,6.700000,5,1.810000,3,True.\r\nIL,67,415,369-4377,no,no,0,109.100000,117,18.550000,217.400000,124,18.480000,188.400000,141,8.480000,12.800000,6,3.460000,0,False.\r\nOR,60,415,366-9430,no,no,0,207.800000,109,35.330000,123.500000,112,10.500000,291.600000,115,13.120000,5.700000,9,1.540000,0,False.\r\nUT,138,510,353-7407,no,no,0,205.900000,96,35.000000,257.100000,94,21.850000,209.000000,63,9.400000,12.100000,8,3.270000,0,False.\r\nIA,44,415,359-7426,no,no,0,308.600000,139,52.460000,150.800000,94,12.820000,198.700000,66,8.940000,7.300000,3,1.970000,4,True.\r\nME,25,510,332-7391,no,no,0,242.600000,69,41.240000,209.000000,117,17.770000,219.700000,82,9.890000,14.400000,6,3.890000,2,False.\r\nWY,145,408,405-6559,no,no,0,229.600000,82,39.030000,138.100000,103,11.740000,250.800000,109,11.290000,3.300000,3,0.890000,1,False.\r\nWI,122,510,338-8784,no,yes,28,166.000000,62,28.220000,233.900000,88,19.880000,170.100000,84,7.650000,7.700000,3,2.080000,2,False.\r\nSC,121,415,415-6347,no,no,0,144.800000,126,24.620000,200.600000,82,17.050000,208.800000,81,9.400000,13.300000,9,3.590000,0,True.\r\nDC,55,510,354-5058,yes,no,0,106.100000,77,18.040000,123.500000,100,10.500000,96.400000,92,4.340000,12.900000,3,3.480000,0,False.\r\nCT,77,415,342-5701,no,no,0,221.800000,84,37.710000,166.000000,125,14.110000,210.200000,72,9.460000,13.200000,4,3.560000,1,False.\r\nOR,12,415,378-4179,no,no,0,204.600000,98,34.780000,212.500000,90,18.060000,182.100000,95,8.190000,9.800000,7,2.650000,2,False.\r\nOR,64,510,407-6391,no,no,0,213.500000,93,36.300000,166.600000,114,14.160000,122.000000,78,5.490000,14.100000,3,3.810000,3,False.\r\nNV,92,415,404-3105,no,yes,44,152.000000,95,25.840000,274.900000,73,23.370000,162.400000,121,7.310000,10.000000,1,2.700000,2,False.\r\nMN,125,415,390-9735,yes,yes,29,260.800000,81,44.340000,163.700000,112,13.910000,271.700000,117,12.230000,17.000000,6,4.590000,1,True.\r\nOK,160,408,350-4820,no,no,0,166.400000,117,28.290000,317.000000,129,26.950000,160.400000,121,7.220000,10.000000,2,2.700000,1,False.\r\nKS,79,415,383-8807,no,no,0,177.900000,83,30.240000,167.300000,84,14.220000,223.700000,142,10.070000,15.200000,8,4.100000,0,False.\r\nRI,36,415,366-8382,no,no,0,235.100000,97,39.970000,196.800000,104,16.730000,259.700000,110,11.690000,7.000000,7,1.890000,3,False.\r\nDC,102,415,402-9704,no,no,0,186.800000,92,31.760000,173.700000,123,14.760000,250.900000,131,11.290000,9.700000,4,2.620000,2,False.\r\nIL,138,408,405-2209,yes,no,0,268.400000,81,45.630000,174.400000,115,14.820000,193.500000,96,8.710000,11.600000,4,3.130000,1,False.\r\nUT,164,510,397-3939,no,no,0,192.100000,95,32.660000,249.800000,94,21.230000,132.600000,100,5.970000,7.300000,3,1.970000,3,False.\r\nMN,125,415,343-2689,no,no,0,240.700000,82,40.920000,269.400000,85,22.900000,187.100000,74,8.420000,10.100000,3,2.730000,0,True.\r\nWI,72,408,383-9448,no,no,0,179.900000,113,30.580000,149.800000,112,12.730000,168.200000,79,7.570000,9.800000,7,2.650000,2,False.\r\nMI,74,415,359-6232,no,no,0,314.100000,86,53.400000,222.400000,99,18.900000,259.000000,121,11.660000,12.300000,5,3.320000,3,True.\r\nMI,134,415,369-9772,no,yes,41,162.000000,82,27.540000,324.700000,77,27.600000,160.100000,112,7.200000,11.900000,5,3.210000,0,False.\r\nMA,145,415,381-7003,no,no,0,175.800000,89,29.890000,274.300000,119,23.320000,226.600000,69,10.200000,12.400000,4,3.350000,1,False.\r\nAL,136,510,352-6732,no,no,0,109.400000,91,18.600000,207.500000,111,17.640000,135.000000,107,6.080000,11.600000,5,3.130000,0,False.\r\nSC,209,510,388-7540,no,no,0,255.100000,124,43.370000,230.600000,110,19.600000,218.000000,69,9.810000,8.500000,5,2.300000,3,True.\r\nWI,66,415,356-3333,yes,no,0,208.700000,84,35.480000,173.300000,88,14.730000,264.700000,107,11.910000,8.300000,3,2.240000,3,False.\r\nVT,152,510,333-9664,no,yes,20,214.600000,108,36.480000,96.600000,82,8.210000,170.700000,145,7.680000,7.900000,5,2.130000,1,False.\r\nCT,162,408,363-3763,no,no,0,49.200000,121,8.360000,143.900000,136,12.230000,203.000000,97,9.140000,12.100000,13,3.270000,1,False.\r\nOR,72,510,345-7900,no,no,0,141.300000,133,24.020000,134.900000,96,11.470000,227.500000,97,10.240000,11.200000,3,3.020000,2,False.\r\nHI,101,415,400-5511,no,no,0,253.200000,89,43.040000,237.900000,114,20.220000,154.300000,85,6.940000,9.700000,7,2.620000,4,False.\r\nWV,125,415,381-7597,no,no,0,206.000000,128,35.020000,198.100000,71,16.840000,135.900000,116,6.120000,13.200000,3,3.560000,0,False.\r\nRI,46,408,404-9775,no,no,0,40.400000,105,6.870000,172.400000,83,14.650000,145.100000,89,6.530000,9.000000,2,2.430000,2,False.\r\nMI,132,408,389-4608,no,no,0,291.200000,104,49.500000,234.200000,132,19.910000,191.700000,87,8.630000,8.900000,3,2.400000,1,True.\r\nME,193,415,403-1742,no,yes,31,71.200000,58,12.100000,124.700000,105,10.600000,155.500000,108,7.000000,11.700000,3,3.160000,0,False.\r\nWV,63,510,328-9797,no,no,0,261.800000,69,44.510000,245.000000,135,20.830000,202.100000,94,9.090000,14.700000,4,3.970000,0,True.\r\nNE,124,510,359-9223,no,no,0,191.300000,134,32.520000,261.500000,113,22.230000,182.300000,111,8.200000,10.000000,3,2.700000,1,False.\r\nDC,144,415,336-7696,no,no,0,133.300000,101,22.660000,255.500000,127,21.720000,228.600000,68,10.290000,11.600000,2,3.130000,0,False.\r\nNH,116,408,369-2214,no,yes,24,183.600000,138,31.210000,203.800000,90,17.320000,166.900000,89,7.510000,6.000000,3,1.620000,2,False.\r\nMD,189,415,411-1325,no,yes,30,155.200000,116,26.380000,195.500000,50,16.620000,170.100000,108,7.650000,15.400000,6,4.160000,1,False.\r\nNH,97,408,410-7553,no,yes,28,283.100000,93,48.130000,185.400000,98,15.760000,312.800000,78,14.080000,6.100000,8,1.650000,1,False.\r\nWV,137,510,376-4284,no,yes,50,186.500000,94,31.710000,178.000000,106,15.130000,215.600000,100,9.700000,12.100000,4,3.270000,2,False.\r\nIL,142,415,334-2800,no,yes,38,163.300000,104,27.760000,136.000000,114,11.560000,249.100000,127,11.210000,4.300000,6,1.160000,0,False.\r\nOK,84,510,369-1904,no,no,0,203.400000,125,34.580000,182.900000,88,15.550000,213.700000,121,9.620000,13.800000,2,3.730000,1,False.\r\nNH,119,415,359-3833,no,yes,19,178.100000,110,30.280000,212.800000,100,18.090000,226.300000,123,10.180000,10.000000,6,2.700000,1,False.\r\nMI,158,415,348-5569,no,no,0,195.900000,103,33.300000,89.100000,95,7.570000,302.200000,82,13.600000,10.300000,3,2.780000,1,False.\r\nND,50,415,342-1960,no,no,0,295.300000,127,50.200000,127.400000,100,10.830000,166.800000,105,7.510000,9.600000,6,2.590000,1,False.\r\nLA,98,408,352-9050,no,no,0,136.100000,82,23.140000,156.300000,118,13.290000,158.800000,83,7.150000,10.100000,5,2.730000,2,False.\r\nHI,101,415,390-5316,no,yes,24,114.100000,95,19.400000,161.500000,86,13.730000,176.300000,90,7.930000,13.000000,9,3.510000,2,False.\r\nNJ,182,415,418-8568,no,no,0,279.100000,124,47.450000,180.500000,108,15.340000,217.500000,104,9.790000,9.500000,11,2.570000,2,True.\r\nWV,51,408,401-4844,no,no,0,169.300000,111,28.780000,139.500000,69,11.860000,197.000000,87,8.870000,12.000000,3,3.240000,0,False.\r\nNC,117,510,376-5471,no,no,0,214.400000,94,36.450000,138.000000,149,11.730000,148.700000,102,6.690000,9.900000,1,2.670000,2,False.\r\nPA,92,415,409-2917,yes,no,0,255.800000,125,43.490000,142.700000,111,12.130000,181.200000,101,8.150000,11.700000,3,3.160000,0,False.\r\nMI,86,408,369-6308,no,no,0,148.200000,71,25.190000,285.100000,91,24.230000,166.400000,155,7.490000,6.200000,3,1.670000,2,False.\r\nWY,122,415,357-7385,no,no,0,119.300000,93,20.280000,223.900000,103,19.030000,211.900000,122,9.540000,8.700000,4,2.350000,2,False.\r\nNJ,156,408,405-7119,no,yes,27,192.300000,137,32.690000,199.900000,115,16.990000,244.200000,112,10.990000,14.800000,8,4.000000,1,False.\r\nNJ,127,510,405-3309,no,no,0,245.200000,91,41.680000,217.200000,92,18.460000,243.100000,128,10.940000,13.900000,6,3.750000,0,True.\r\nNC,130,408,384-4938,yes,no,0,216.200000,106,36.750000,363.700000,86,30.910000,126.700000,123,5.700000,16.900000,2,4.560000,5,True.\r\nNM,158,408,377-2725,no,no,0,172.400000,114,29.310000,256.600000,69,21.810000,235.300000,104,10.590000,0.000000,0,0.000000,2,False.\r\nMS,145,510,405-6398,yes,yes,30,175.300000,107,29.800000,153.300000,116,13.030000,233.600000,85,10.510000,11.100000,3,3.000000,1,False.\r\nTX,90,415,355-9366,yes,yes,26,169.000000,104,28.730000,188.800000,104,16.050000,213.300000,76,9.600000,13.300000,3,3.590000,0,True.\r\nOK,127,510,403-1128,no,yes,27,2.600000,113,0.440000,254.000000,102,21.590000,242.700000,156,10.920000,9.200000,5,2.480000,3,False.\r\nID,109,415,384-9682,no,no,0,184.100000,143,31.300000,211.700000,105,17.990000,243.000000,116,10.930000,9.900000,2,2.670000,1,False.\r\nAL,88,415,352-5393,no,no,0,181.900000,90,30.920000,151.500000,87,12.880000,143.000000,100,6.440000,7.500000,3,2.030000,1,False.\r\nWY,101,510,395-1229,no,yes,9,160.100000,116,27.220000,210.000000,121,17.850000,139.100000,65,6.260000,10.800000,9,2.920000,0,False.\r\nHI,171,510,361-9195,no,no,0,189.800000,122,32.270000,173.700000,85,14.760000,257.100000,84,11.570000,10.300000,1,2.780000,0,False.\r\nVA,21,415,351-6366,no,no,0,223.200000,142,37.940000,216.500000,114,18.400000,214.700000,111,9.660000,12.400000,2,3.350000,1,False.\r\nWV,145,408,346-4919,no,yes,31,216.000000,94,36.720000,225.100000,123,19.130000,234.700000,109,10.560000,10.700000,1,2.890000,2,False.\r\nDE,90,415,354-9068,no,no,0,198.500000,124,33.750000,266.600000,100,22.660000,243.300000,80,10.950000,8.000000,7,2.160000,2,False.\r\nCA,33,408,369-2743,no,no,0,159.500000,115,27.120000,195.400000,118,16.610000,102.400000,86,4.610000,7.100000,7,1.920000,1,False.\r\nPA,61,408,343-1347,no,yes,40,105.000000,78,17.850000,180.600000,100,15.350000,174.100000,115,7.830000,10.200000,2,2.750000,2,True.\r\nCO,107,415,336-5495,no,no,0,204.500000,108,34.770000,162.400000,110,13.800000,155.000000,102,6.980000,13.400000,1,3.620000,3,False.\r\nMD,147,408,376-4292,no,no,0,274.000000,92,46.580000,231.800000,82,19.700000,283.600000,83,12.760000,6.200000,1,1.670000,0,True.\r\nAL,117,510,391-8677,no,no,0,158.700000,84,26.980000,181.700000,91,15.440000,177.300000,67,7.980000,7.700000,10,2.080000,2,False.\r\nAL,95,415,350-7273,no,no,0,229.900000,116,39.080000,202.400000,110,17.200000,171.400000,105,7.710000,14.200000,6,3.830000,1,False.\r\nKS,186,510,400-6454,no,no,0,137.800000,97,23.430000,187.700000,118,15.950000,146.400000,85,6.590000,8.700000,6,2.350000,1,False.\r\nMI,128,415,422-3052,no,no,0,179.400000,94,30.500000,270.400000,92,22.980000,191.000000,88,8.590000,7.900000,4,2.130000,0,False.\r\nAK,55,408,365-6756,no,yes,39,139.300000,101,23.680000,178.300000,117,15.160000,246.500000,104,11.090000,8.100000,1,2.190000,3,False.\r\nOH,134,415,406-4158,no,no,0,7.800000,86,1.330000,171.400000,100,14.570000,186.500000,80,8.390000,12.900000,2,3.480000,2,False.\r\nIN,96,415,383-4641,no,yes,23,183.100000,88,31.130000,147.400000,89,12.530000,350.200000,108,15.760000,11.300000,7,3.050000,1,False.\r\nSC,107,415,368-5165,no,no,0,206.900000,79,35.170000,262.400000,117,22.300000,149.300000,69,6.720000,10.700000,3,2.890000,0,False.\r\nKS,123,415,378-2432,no,no,0,140.000000,106,23.800000,153.700000,101,13.060000,50.100000,87,2.250000,12.500000,1,3.380000,2,False.\r\nOK,35,415,362-4159,no,no,0,179.200000,59,30.460000,283.300000,101,24.080000,285.400000,83,12.840000,5.800000,7,1.570000,2,False.\r\nWI,74,408,363-7979,no,no,0,177.400000,136,30.160000,240.300000,104,20.430000,237.300000,133,10.680000,12.000000,3,3.240000,0,False.\r\nIN,130,408,334-9818,no,no,0,115.600000,129,19.650000,167.800000,104,14.260000,141.800000,124,6.380000,12.600000,9,3.400000,1,False.\r\nIL,137,408,352-5787,yes,no,0,237.300000,103,40.340000,176.700000,84,15.020000,263.400000,81,11.850000,14.200000,4,3.830000,0,True.\r\nTN,88,415,332-3617,no,no,0,181.500000,116,30.860000,187.000000,119,15.900000,220.300000,96,9.910000,10.500000,7,2.840000,1,False.\r\nDC,80,408,327-9957,no,no,0,51.500000,90,8.760000,164.000000,98,13.940000,169.400000,80,7.620000,9.500000,4,2.570000,3,False.\r\nNC,116,408,338-7527,no,yes,19,155.700000,104,26.470000,185.400000,118,15.760000,192.700000,116,8.670000,8.200000,2,2.210000,3,False.\r\nRI,123,510,348-8711,no,yes,23,245.000000,88,41.650000,265.000000,105,22.530000,239.700000,108,10.790000,14.900000,3,4.020000,2,False.\r\nMS,120,415,421-3226,no,no,0,131.700000,99,22.390000,163.100000,109,13.860000,201.100000,116,9.050000,10.700000,3,2.890000,1,False.\r\nVA,146,415,391-4358,yes,no,0,111.100000,126,18.890000,313.400000,95,26.640000,215.700000,82,9.710000,10.500000,6,2.840000,1,False.\r\nKY,106,510,379-2523,no,yes,9,88.500000,100,15.050000,324.800000,109,27.610000,79.900000,86,3.600000,8.200000,4,2.210000,3,False.\r\nNV,121,408,419-2369,no,yes,44,116.000000,85,19.720000,150.100000,120,12.760000,246.800000,98,11.110000,12.000000,2,3.240000,1,False.\r\nWI,137,510,382-1227,no,no,0,155.500000,81,26.440000,133.100000,94,11.310000,253.100000,77,11.390000,9.100000,2,2.460000,1,False.\r\nNH,84,408,409-5749,no,yes,30,106.500000,65,18.110000,225.700000,108,19.180000,188.600000,61,8.490000,5.700000,3,1.540000,2,False.\r\nNE,67,510,362-7951,no,yes,31,175.200000,68,29.780000,199.200000,73,16.930000,219.800000,99,9.890000,13.200000,6,3.560000,1,False.\r\nWI,161,408,415-3537,no,no,0,154.700000,84,26.300000,177.800000,125,15.110000,172.900000,90,7.780000,5.900000,2,1.590000,4,True.\r\nNJ,134,510,373-3959,no,yes,34,247.200000,105,42.020000,225.500000,133,19.170000,186.300000,76,8.380000,6.100000,5,1.650000,2,True.\r\nME,62,415,358-1346,yes,yes,32,218.400000,93,37.130000,236.700000,132,20.120000,192.200000,137,8.650000,13.200000,3,3.560000,0,True.\r\nWY,120,415,381-8422,no,yes,24,227.500000,81,38.680000,234.900000,71,19.970000,166.400000,128,7.490000,9.000000,13,2.430000,1,False.\r\nIN,130,408,360-9005,no,yes,30,185.000000,117,31.450000,249.500000,141,21.210000,157.800000,103,7.100000,7.400000,7,2.000000,0,False.\r\nWI,20,408,344-5967,no,no,0,186.800000,89,31.760000,253.400000,51,21.540000,273.100000,105,12.290000,12.300000,6,3.320000,2,False.\r\nVT,68,415,396-6390,no,no,0,158.800000,119,27.000000,211.800000,105,18.000000,198.100000,101,8.910000,10.300000,3,2.780000,1,False.\r\nCA,112,415,346-5036,no,no,0,208.700000,150,35.480000,212.800000,104,18.090000,178.100000,98,8.010000,8.500000,4,2.300000,0,False.\r\nIN,77,408,328-7252,no,no,0,185.900000,95,31.600000,212.000000,98,18.020000,282.300000,81,12.700000,11.300000,4,3.050000,3,False.\r\nSC,109,415,360-1745,no,no,0,222.500000,74,37.830000,169.700000,75,14.420000,264.300000,94,11.890000,9.000000,3,2.430000,2,False.\r\nIN,108,415,358-2046,no,no,0,201.100000,101,34.190000,170.700000,86,14.510000,237.400000,113,10.680000,11.600000,3,3.130000,3,False.\r\nIA,79,415,344-6935,no,yes,17,167.900000,114,28.540000,243.700000,93,20.710000,211.900000,114,9.540000,9.100000,2,2.460000,1,False.\r\nMD,119,408,401-9665,no,no,0,239.100000,88,40.650000,243.500000,79,20.700000,230.900000,92,10.390000,10.900000,3,2.940000,3,True.\r\nMN,38,510,399-5291,no,no,0,175.700000,109,29.870000,211.800000,97,18.000000,137.900000,109,6.210000,9.200000,3,2.480000,5,True.\r\nAR,109,415,409-6588,no,yes,29,111.200000,90,18.900000,263.500000,98,22.400000,224.700000,128,10.110000,9.000000,6,2.430000,6,True.\r\nMS,78,415,358-5721,no,no,0,87.700000,74,14.910000,214.800000,58,18.260000,201.300000,147,9.060000,10.800000,6,2.920000,1,False.\r\nMN,134,415,414-7446,no,no,0,244.100000,99,41.500000,246.900000,111,20.990000,200.000000,133,9.000000,7.200000,2,1.940000,0,False.\r\nWA,47,415,329-9517,no,yes,27,165.000000,89,28.050000,127.300000,118,10.820000,284.400000,95,12.800000,7.700000,4,2.080000,2,False.\r\nMS,59,408,415-9553,no,yes,27,127.400000,110,21.660000,103.300000,99,8.780000,164.200000,73,7.390000,9.100000,3,2.460000,0,False.\r\nID,151,415,413-3177,no,no,0,194.800000,106,33.120000,292.700000,103,24.880000,224.600000,82,10.110000,5.500000,3,1.490000,0,False.\r\nNC,129,415,347-5113,no,no,0,54.700000,131,9.300000,256.100000,105,21.770000,176.600000,135,7.950000,11.100000,4,3.000000,1,False.\r\nVA,107,510,330-2662,no,yes,27,283.400000,104,48.180000,224.100000,152,19.050000,241.300000,63,10.860000,14.400000,7,3.890000,2,False.\r\nMT,137,408,330-5824,yes,no,0,258.000000,112,43.860000,246.500000,117,20.950000,173.200000,100,7.790000,10.900000,3,2.940000,0,True.\r\nMI,76,510,411-1261,no,no,0,90.500000,142,15.390000,211.700000,75,17.990000,194.900000,76,8.770000,9.300000,2,2.510000,1,False.\r\nHI,24,415,329-8788,no,no,0,235.600000,132,40.050000,115.900000,129,9.850000,185.400000,136,8.340000,16.200000,2,4.370000,0,False.\r\nNC,169,408,333-7869,no,no,0,142.500000,82,24.230000,231.400000,110,19.670000,131.200000,67,5.900000,10.000000,4,2.700000,2,False.\r\nMN,30,408,399-4800,no,no,0,54.000000,68,9.180000,179.300000,96,15.240000,247.200000,101,11.120000,10.200000,8,2.750000,1,False.\r\nWV,70,415,402-2072,no,no,0,214.800000,87,36.520000,131.000000,114,11.140000,216.900000,104,9.760000,9.400000,3,2.540000,3,False.\r\nSD,52,510,403-6187,yes,no,0,251.400000,118,42.740000,196.600000,80,16.710000,192.000000,53,8.640000,11.000000,2,2.970000,0,True.\r\nHI,3,408,355-2872,no,no,0,139.000000,99,23.630000,250.700000,108,21.310000,286.200000,87,12.880000,6.100000,3,1.650000,4,False.\r\nMS,38,415,386-2970,no,no,0,117.300000,114,19.940000,208.700000,105,17.740000,203.400000,98,9.150000,14.400000,2,3.890000,2,False.\r\nNY,104,415,389-6081,no,no,0,264.000000,108,44.880000,132.200000,75,11.240000,177.700000,91,8.000000,10.600000,8,2.860000,3,False.\r\nLA,27,408,348-7556,no,no,0,82.600000,105,14.040000,204.000000,99,17.340000,224.200000,122,10.090000,9.100000,4,2.460000,1,False.\r\nKS,166,415,334-9163,yes,yes,28,175.800000,126,29.890000,253.600000,76,21.560000,128.500000,72,5.780000,11.400000,5,3.080000,1,False.\r\nMA,13,408,411-4293,no,no,0,220.400000,100,37.470000,211.200000,79,17.950000,259.300000,112,11.670000,13.600000,8,3.670000,2,False.\r\nAK,52,408,375-5562,no,no,0,217.000000,104,36.890000,152.300000,83,12.950000,134.300000,109,6.040000,11.800000,4,3.190000,2,False.\r\nSD,114,415,386-3823,no,yes,25,129.000000,77,21.930000,290.000000,110,24.650000,177.100000,110,7.970000,11.600000,5,3.130000,1,False.\r\nCO,156,408,364-6445,no,no,0,150.500000,106,25.590000,152.900000,112,13.000000,215.900000,86,9.720000,3.500000,3,0.950000,1,False.\r\nNH,90,415,383-2251,no,yes,42,193.300000,66,32.860000,263.300000,85,22.380000,214.400000,97,9.650000,11.100000,4,3.000000,0,False.\r\nWV,62,415,351-3169,no,no,0,189.500000,122,32.220000,103.800000,95,8.820000,180.600000,106,8.130000,10.800000,5,2.920000,2,False.\r\nAZ,82,415,413-6380,no,yes,33,137.800000,95,23.430000,235.500000,128,20.020000,268.100000,70,12.060000,11.000000,6,2.970000,2,False.\r\nME,52,510,380-9674,no,no,0,129.300000,80,21.980000,142.700000,101,12.130000,258.300000,89,11.620000,12.300000,4,3.320000,3,False.\r\nNH,146,510,345-2319,no,no,0,115.600000,77,19.650000,213.600000,100,18.160000,218.400000,72,9.830000,10.700000,6,2.890000,1,False.\r\nRI,120,415,377-5441,no,yes,23,221.900000,114,37.720000,254.700000,84,21.650000,250.500000,117,11.270000,7.200000,5,1.940000,2,False.\r\nID,130,415,358-3692,no,no,0,263.700000,113,44.830000,186.500000,103,15.850000,195.300000,99,8.790000,18.300000,6,4.940000,1,True.\r\nWI,90,408,400-5831,no,no,0,61.300000,91,10.420000,194.400000,94,16.520000,143.100000,80,6.440000,11.400000,9,3.080000,1,False.\r\nNM,147,408,357-5995,yes,no,0,183.800000,113,31.250000,164.700000,110,14.000000,111.000000,87,5.000000,10.100000,4,2.730000,1,False.\r\nWY,159,415,391-2159,no,no,0,167.400000,68,28.460000,143.800000,74,12.220000,140.100000,111,6.300000,10.300000,3,2.780000,0,True.\r\nIL,74,510,398-5954,no,yes,27,154.100000,122,26.200000,195.300000,150,16.600000,276.700000,86,12.450000,13.200000,2,3.560000,4,False.\r\nTX,130,408,385-6175,no,no,0,252.000000,101,42.840000,170.200000,105,14.470000,209.200000,64,9.410000,5.700000,5,1.540000,0,False.\r\nRI,155,408,334-2961,yes,no,0,163.100000,94,27.730000,291.700000,108,24.790000,96.400000,111,4.340000,11.200000,3,3.020000,0,False.\r\nMI,87,415,405-4303,no,no,0,198.300000,80,33.710000,187.000000,89,15.900000,133.500000,96,6.010000,16.600000,4,4.480000,2,False.\r\nOR,81,415,406-4100,no,no,0,324.700000,48,55.200000,236.400000,82,20.090000,187.600000,78,8.440000,13.100000,5,3.540000,0,True.\r\nVA,99,510,352-4401,no,no,0,128.300000,78,21.810000,215.300000,120,18.300000,143.700000,140,6.470000,14.300000,9,3.860000,2,False.\r\nSD,131,510,347-1473,no,no,0,187.900000,110,31.940000,200.500000,101,17.040000,202.600000,125,9.120000,10.200000,11,2.750000,2,False.\r\nAL,89,510,347-2016,no,no,0,129.200000,71,21.960000,214.100000,68,18.200000,214.900000,100,9.670000,10.300000,4,2.780000,5,True.\r\nMS,123,415,388-8948,yes,no,0,125.500000,106,21.340000,128.900000,96,10.960000,251.900000,129,11.340000,6.300000,6,1.700000,4,True.\r\nMS,130,510,402-5509,no,yes,26,257.200000,108,43.720000,224.300000,122,19.070000,204.000000,118,9.180000,12.600000,4,3.400000,1,False.\r\nHI,99,415,367-2598,no,no,0,124.600000,90,21.180000,146.400000,70,12.440000,169.400000,95,7.620000,10.500000,6,2.840000,1,False.\r\nWV,36,408,370-5001,no,no,0,175.100000,144,29.770000,216.900000,69,18.440000,243.700000,146,10.970000,9.900000,3,2.670000,1,False.\r\nWV,87,415,332-3693,no,no,0,124.300000,91,21.130000,173.400000,105,14.740000,256.300000,109,11.530000,7.500000,5,2.030000,3,False.\r\nWV,139,415,403-9766,no,no,0,271.600000,130,46.170000,156.000000,131,13.260000,136.300000,108,6.130000,11.600000,9,3.130000,2,False.\r\nIN,189,510,363-2407,no,no,0,219.900000,80,37.380000,143.300000,117,12.180000,130.600000,69,5.880000,11.700000,7,3.160000,1,False.\r\nNM,96,415,395-9214,no,yes,33,183.300000,115,31.160000,201.400000,87,17.120000,177.400000,84,7.980000,10.400000,15,2.810000,3,False.\r\nDE,112,408,351-8894,no,no,0,101.100000,119,17.190000,214.400000,67,18.220000,179.500000,112,8.080000,10.300000,5,2.780000,2,False.\r\nNC,75,408,406-5003,no,no,0,203.300000,70,34.560000,228.900000,97,19.460000,222.200000,118,10.000000,14.300000,3,3.860000,1,False.\r\nNM,178,415,398-1332,no,yes,35,175.400000,88,29.820000,190.000000,65,16.150000,138.700000,94,6.240000,10.500000,3,2.840000,2,False.\r\nSC,112,415,363-8033,no,no,0,266.000000,97,45.220000,214.600000,94,18.240000,306.200000,100,13.780000,14.200000,2,3.830000,2,True.\r\nNJ,108,415,333-1012,no,yes,41,171.600000,110,29.170000,136.100000,78,11.570000,183.400000,103,8.250000,10.800000,7,2.920000,0,False.\r\nAZ,100,510,391-6260,no,no,0,78.700000,98,13.380000,225.600000,102,19.180000,150.400000,106,6.770000,14.000000,8,3.780000,0,False.\r\nRI,121,510,336-1353,no,yes,20,211.900000,110,36.020000,215.100000,120,18.280000,238.500000,107,10.730000,9.400000,2,2.540000,0,False.\r\nSD,116,415,365-5629,no,no,0,63.700000,101,10.830000,195.800000,95,16.640000,210.100000,87,9.450000,10.000000,6,2.700000,1,False.\r\nNH,161,415,349-4397,no,no,0,173.400000,100,29.480000,213.700000,74,18.160000,141.500000,69,6.370000,11.500000,4,3.110000,1,False.\r\nAL,19,415,380-3910,yes,no,0,237.700000,98,40.410000,207.100000,121,17.600000,182.200000,95,8.200000,4.500000,4,1.220000,0,False.\r\nWV,104,415,354-7820,no,no,0,225.900000,123,38.400000,162.800000,106,13.840000,272.100000,85,12.240000,10.100000,4,2.730000,1,False.\r\nID,119,415,338-9952,yes,yes,32,173.000000,101,29.410000,209.400000,93,17.800000,231.100000,91,10.400000,12.200000,4,3.290000,0,False.\r\nMD,125,415,405-1821,no,no,0,224.900000,102,38.230000,143.800000,87,12.220000,198.900000,105,8.950000,8.000000,2,2.160000,0,False.\r\nIN,156,510,329-8669,no,no,0,237.700000,122,40.410000,181.500000,91,15.430000,185.700000,151,8.360000,7.700000,4,2.080000,1,False.\r\nWY,109,415,328-8808,no,no,0,137.000000,128,23.290000,217.000000,116,18.450000,182.100000,86,8.190000,10.000000,5,2.700000,2,False.\r\nNH,95,510,409-3018,no,no,0,142.500000,109,24.230000,176.100000,107,14.970000,189.600000,88,8.530000,8.200000,3,2.210000,2,False.\r\nMN,90,408,421-5994,no,no,0,142.400000,126,24.210000,126.200000,118,10.730000,274.200000,71,12.340000,4.600000,4,1.240000,1,False.\r\nMA,105,415,354-4448,no,yes,21,147.000000,112,24.990000,197.300000,43,16.770000,267.400000,93,12.030000,8.700000,3,2.350000,2,False.\r\nNH,101,510,352-5081,no,no,0,220.300000,124,37.450000,188.600000,101,16.030000,278.400000,98,12.530000,10.600000,4,2.860000,1,False.\r\nMN,95,415,401-7803,no,no,0,149.200000,96,25.360000,260.700000,116,22.160000,201.000000,120,9.050000,8.100000,2,2.190000,1,False.\r\nIA,123,415,420-6052,no,no,0,204.400000,88,34.750000,137.500000,111,11.690000,226.000000,100,10.170000,10.000000,4,2.700000,0,False.\r\nNY,160,408,352-6084,no,no,0,216.800000,77,36.860000,207.300000,117,17.620000,228.600000,117,10.290000,5.600000,2,1.510000,1,False.\r\nAL,141,510,388-8583,no,yes,28,308.000000,123,52.360000,247.800000,128,21.060000,152.900000,103,6.880000,7.400000,3,2.000000,1,False.\r\nWV,87,415,415-3158,no,no,0,58.000000,125,9.860000,67.500000,116,5.740000,185.900000,136,8.370000,11.500000,3,3.110000,0,False.\r\nNM,81,415,402-9304,no,no,0,173.200000,80,29.440000,236.200000,94,20.080000,240.200000,84,10.810000,11.800000,6,3.190000,2,False.\r\nCA,75,415,341-1916,no,yes,19,210.300000,90,35.750000,241.800000,87,20.550000,215.700000,102,9.710000,13.100000,3,3.540000,4,False.\r\nMA,126,408,381-2745,no,yes,24,58.900000,125,10.010000,305.500000,90,25.970000,158.900000,73,7.150000,12.100000,6,3.270000,0,False.\r\nME,28,415,402-5014,no,no,0,236.800000,102,40.260000,167.100000,87,14.200000,280.200000,115,12.610000,9.700000,3,2.620000,3,False.\r\nFL,153,415,399-8846,no,no,0,228.900000,102,38.910000,160.700000,136,13.660000,203.100000,109,9.140000,12.500000,2,3.380000,0,False.\r\nNH,97,408,328-9267,no,yes,32,90.000000,87,15.300000,276.300000,113,23.490000,185.200000,107,8.330000,8.600000,6,2.320000,2,True.\r\nIN,115,408,348-7224,no,no,0,146.700000,128,24.940000,106.200000,74,9.030000,197.700000,104,8.900000,11.100000,4,3.000000,2,False.\r\nWI,95,408,336-2190,no,no,0,237.300000,83,40.340000,154.000000,65,13.090000,237.000000,105,10.670000,11.200000,6,3.020000,1,False.\r\nMA,17,415,376-4705,yes,no,0,162.800000,118,27.680000,229.600000,91,19.520000,332.700000,94,14.970000,13.600000,3,3.670000,0,True.\r\nNH,105,415,381-4076,no,yes,20,186.900000,114,31.770000,256.300000,91,21.790000,334.700000,104,15.060000,8.900000,2,2.400000,1,False.\r\nTX,121,415,348-8464,no,no,0,86.100000,100,14.640000,259.800000,113,22.080000,148.000000,79,6.660000,9.100000,9,2.460000,2,False.\r\nNC,125,408,412-7020,no,no,0,212.300000,89,36.090000,215.400000,127,18.310000,186.800000,73,8.410000,11.300000,2,3.050000,2,False.\r\nCT,124,415,386-8432,no,no,0,151.000000,98,25.670000,120.600000,119,10.250000,152.800000,81,6.880000,9.200000,2,2.480000,2,False.\r\nNJ,35,408,337-1802,no,no,0,158.600000,67,26.960000,130.400000,96,11.080000,229.800000,80,10.340000,6.900000,5,1.860000,2,False.\r\nWY,134,510,366-1084,no,no,0,296.000000,93,50.320000,226.400000,117,19.240000,246.800000,98,11.110000,12.300000,10,3.320000,0,True.\r\nLA,123,510,352-4182,no,yes,32,212.300000,77,36.090000,251.500000,78,21.380000,208.700000,85,9.390000,6.600000,2,1.780000,3,False.\r\nNV,124,415,368-5628,no,no,0,234.400000,61,39.850000,179.300000,111,15.240000,285.500000,117,12.850000,10.400000,6,2.810000,3,False.\r\nWV,133,510,333-8996,no,no,0,176.800000,92,30.060000,187.500000,97,15.940000,196.800000,88,8.860000,6.500000,3,1.760000,2,False.\r\nAR,185,415,353-2557,no,yes,19,157.300000,123,26.740000,257.700000,94,21.900000,190.400000,107,8.570000,9.600000,6,2.590000,0,False.\r\nSC,1,415,356-8621,no,yes,26,146.600000,68,24.920000,172.800000,67,14.690000,173.800000,113,7.820000,10.000000,2,2.700000,1,False.\r\nKS,107,415,354-6942,no,no,0,260.500000,108,44.290000,102.400000,110,8.700000,129.700000,148,5.840000,9.800000,5,2.650000,1,False.\r\nNY,91,415,377-9829,no,yes,20,146.100000,98,24.840000,277.400000,104,23.580000,137.700000,100,6.200000,6.200000,3,1.670000,1,False.\r\nMI,178,408,348-4660,yes,no,0,124.500000,134,21.170000,141.200000,78,12.000000,268.200000,113,12.070000,11.400000,2,3.080000,2,True.\r\nMA,123,415,410-5199,no,no,0,209.400000,49,35.600000,237.400000,117,20.180000,239.200000,98,10.760000,9.800000,11,2.650000,1,False.\r\nUT,170,415,397-6542,no,no,0,285.700000,44,48.570000,167.500000,144,14.240000,260.000000,97,11.700000,8.700000,4,2.350000,1,True.\r\nHI,135,415,333-4492,no,no,0,190.900000,44,32.450000,161.400000,109,13.720000,231.900000,100,10.440000,8.400000,2,2.270000,1,False.\r\nPA,85,408,405-9573,no,no,0,144.400000,88,24.550000,264.600000,105,22.490000,185.400000,94,8.340000,9.900000,3,2.670000,1,False.\r\nOR,134,415,359-7255,no,yes,50,208.800000,130,35.500000,132.900000,104,11.300000,136.700000,107,6.150000,11.100000,4,3.000000,2,False.\r\nTN,148,415,419-5501,no,yes,36,77.600000,141,13.190000,207.000000,60,17.600000,255.700000,115,11.510000,10.900000,2,2.940000,1,False.\r\nCT,93,415,404-4809,no,no,0,271.100000,101,46.090000,237.400000,133,20.180000,145.400000,103,6.540000,8.400000,6,2.270000,1,True.\r\nAZ,138,415,344-6334,no,no,0,240.800000,104,40.940000,144.500000,92,12.280000,125.700000,98,5.660000,11.600000,1,3.130000,0,False.\r\nOR,159,510,400-1899,no,no,0,114.800000,98,19.520000,192.600000,101,16.370000,259.000000,108,11.660000,12.200000,5,3.290000,0,False.\r\nDE,103,415,346-5053,no,yes,34,138.800000,80,23.600000,142.000000,108,12.070000,183.800000,77,8.270000,11.800000,7,3.190000,1,False.\r\nMA,150,408,398-2148,no,yes,27,209.800000,112,35.670000,155.000000,80,13.180000,251.500000,111,11.320000,7.200000,6,1.940000,0,False.\r\nCT,37,408,347-7675,no,no,0,134.900000,98,22.930000,248.400000,130,21.110000,236.200000,113,10.630000,14.700000,2,3.970000,3,False.\r\nAR,33,415,411-8956,yes,no,0,164.000000,99,27.880000,153.100000,102,13.010000,123.800000,104,5.570000,6.400000,4,1.730000,0,False.\r\nSD,55,415,390-3761,no,no,0,245.500000,130,41.740000,192.700000,54,16.380000,141.700000,83,6.380000,9.100000,4,2.460000,1,False.\r\nCT,134,408,377-3876,no,yes,32,80.300000,94,13.650000,199.900000,124,16.990000,170.800000,117,7.690000,16.600000,3,4.480000,0,False.\r\nCT,107,408,345-2476,no,no,0,90.700000,90,15.420000,207.500000,109,17.640000,169.400000,96,7.620000,5.600000,5,1.510000,2,False.\r\nMA,80,408,337-7879,no,yes,36,190.300000,115,32.350000,256.600000,78,21.810000,214.900000,145,9.670000,3.800000,4,1.030000,1,False.\r\nMS,78,408,361-7283,no,no,0,108.600000,108,18.460000,209.900000,126,17.840000,222.600000,117,10.020000,7.900000,5,2.130000,1,True.\r\nMT,85,408,372-4868,no,yes,17,89.800000,88,15.270000,233.200000,75,19.820000,165.700000,116,7.460000,9.300000,7,2.510000,4,True.\r\nAK,61,415,346-8863,no,yes,15,252.400000,106,42.910000,187.800000,69,15.960000,259.600000,137,11.680000,10.000000,3,2.700000,2,False.\r\nDE,97,415,390-5267,no,yes,32,183.400000,94,31.180000,269.100000,120,22.870000,203.500000,38,9.160000,6.700000,4,1.810000,5,False.\r\nOH,136,408,392-1547,yes,no,0,183.400000,103,31.180000,141.900000,113,12.060000,200.400000,122,9.020000,10.400000,9,2.810000,2,False.\r\nMN,135,408,340-8177,no,no,0,155.200000,100,26.380000,135.900000,84,11.550000,184.600000,82,8.310000,3.800000,9,1.030000,3,False.\r\nCA,87,415,383-4802,no,yes,19,165.800000,122,28.190000,186.900000,89,15.890000,249.700000,78,11.240000,0.000000,0,0.000000,1,False.\r\nOH,165,510,378-8567,no,no,0,209.400000,67,35.600000,273.800000,89,23.270000,150.200000,88,6.760000,12.800000,1,3.460000,0,False.\r\nNV,148,415,406-7844,no,no,0,279.300000,104,47.480000,201.600000,87,17.140000,280.800000,99,12.640000,7.900000,2,2.130000,2,True.\r\nSC,99,415,402-9173,no,no,0,174.100000,102,29.600000,99.100000,118,8.420000,211.600000,126,9.520000,7.700000,2,2.080000,2,False.\r\nWV,123,415,352-3440,no,no,0,175.700000,78,29.870000,184.600000,96,15.690000,156.900000,92,7.060000,9.100000,2,2.460000,2,False.\r\nNM,127,415,363-1413,yes,no,0,256.500000,87,43.610000,222.100000,101,18.880000,156.700000,122,7.050000,13.000000,3,3.510000,1,False.\r\nWY,151,415,394-8861,no,no,0,170.200000,89,28.930000,187.500000,83,15.940000,119.500000,100,5.380000,4.300000,3,1.160000,0,False.\r\nCA,185,408,358-4036,no,no,0,139.600000,92,23.730000,250.200000,115,21.270000,158.100000,79,7.110000,10.800000,4,2.920000,1,False.\r\nKS,65,415,392-4680,no,yes,34,208.800000,119,35.500000,142.100000,106,12.080000,214.600000,87,9.660000,12.500000,4,3.380000,4,False.\r\nWY,58,510,354-2762,no,no,0,210.100000,126,35.720000,248.900000,108,21.160000,158.600000,88,7.140000,14.400000,2,3.890000,4,False.\r\nOK,104,415,371-5811,no,no,0,113.600000,87,19.310000,158.600000,98,13.480000,187.700000,87,8.450000,10.500000,6,2.840000,2,False.\r\nUT,44,415,387-2014,no,no,0,202.600000,89,34.440000,163.000000,96,13.860000,268.100000,151,12.060000,8.300000,3,2.240000,0,False.\r\nMA,58,408,411-6598,no,no,0,174.400000,112,29.650000,265.800000,122,22.590000,182.400000,87,8.210000,0.000000,0,0.000000,4,False.\r\nUT,108,415,355-9356,no,no,0,210.600000,117,35.800000,164.200000,103,13.960000,201.400000,68,9.060000,9.400000,5,2.540000,0,False.\r\nMN,132,510,406-4720,no,no,0,121.500000,88,20.660000,253.000000,124,21.510000,195.700000,120,8.810000,10.700000,4,2.890000,1,False.\r\nNE,80,415,356-7239,no,no,0,127.800000,67,21.730000,181.600000,112,15.440000,197.300000,63,8.880000,15.900000,2,4.290000,2,False.\r\nOH,162,415,328-8747,no,no,0,135.200000,98,22.980000,242.000000,107,20.570000,246.900000,96,11.110000,10.200000,2,2.750000,2,False.\r\nKY,110,510,388-9464,no,no,0,99.400000,62,16.900000,275.000000,86,23.380000,212.100000,94,9.540000,16.700000,3,4.510000,2,False.\r\nWA,96,415,406-2866,no,no,0,276.900000,105,47.070000,246.900000,94,20.990000,254.400000,107,11.450000,10.300000,3,2.780000,1,True.\r\nNY,168,408,333-5729,no,no,0,163.400000,134,27.780000,240.100000,87,20.410000,164.000000,147,7.380000,11.600000,2,3.130000,2,True.\r\nIN,72,510,391-1499,no,no,0,287.400000,116,48.860000,235.300000,126,20.000000,292.100000,114,13.140000,5.000000,3,1.350000,4,True.\r\nCO,125,408,386-8690,no,yes,23,120.500000,104,20.490000,227.800000,115,19.360000,158.500000,100,7.130000,10.200000,3,2.750000,1,False.\r\nDC,170,510,391-2231,no,no,0,184.100000,106,31.300000,204.900000,70,17.420000,224.300000,133,10.090000,9.800000,3,2.650000,2,False.\r\nAK,71,510,332-2275,no,no,0,185.000000,84,31.450000,232.500000,129,19.760000,191.100000,82,8.600000,14.900000,4,4.020000,3,False.\r\nSC,124,415,340-4028,no,no,0,160.900000,109,27.350000,144.200000,152,12.260000,120.400000,97,5.420000,12.900000,12,3.480000,1,False.\r\nKS,68,415,363-3486,no,no,0,207.600000,68,35.290000,251.600000,123,21.390000,191.600000,100,8.620000,10.900000,6,2.940000,2,False.\r\nUT,97,415,418-3181,no,no,0,209.200000,134,35.560000,0.000000,0,0.000000,175.400000,94,7.890000,11.800000,6,3.190000,1,False.\r\nIL,98,510,351-3316,yes,no,0,158.400000,71,26.930000,306.600000,66,26.060000,144.200000,93,6.490000,2.100000,4,0.570000,1,False.\r\nDC,24,408,369-3626,no,no,0,149.000000,73,25.330000,131.000000,81,11.140000,238.600000,69,10.740000,8.600000,3,2.320000,2,True.\r\nDC,136,510,353-2763,no,no,0,204.500000,63,34.770000,208.800000,95,17.750000,224.000000,119,10.080000,9.800000,2,2.650000,0,False.\r\nOK,44,408,358-7165,no,no,0,288.800000,86,49.100000,175.900000,87,14.950000,215.400000,106,9.690000,9.500000,2,2.570000,0,True.\r\nKY,96,415,353-3223,no,yes,40,108.600000,90,18.460000,206.400000,154,17.540000,126.300000,118,5.680000,13.400000,4,3.620000,0,False.\r\nNE,31,415,338-9044,no,no,0,97.500000,129,16.580000,260.400000,78,22.130000,88.700000,100,3.990000,7.000000,5,1.890000,1,False.\r\nAL,72,415,341-7296,no,no,0,166.500000,102,28.310000,261.000000,103,22.190000,262.700000,85,11.820000,13.300000,5,3.590000,0,False.\r\nHI,24,415,398-4431,no,no,0,156.200000,104,26.550000,90.000000,101,7.650000,205.100000,116,9.230000,7.300000,5,1.970000,1,False.\r\nSC,112,415,408-5601,no,yes,31,225.200000,89,38.280000,256.800000,117,21.830000,249.700000,87,11.240000,11.500000,1,3.110000,4,False.\r\nGA,117,415,328-5188,yes,no,0,287.400000,118,48.860000,259.600000,84,22.070000,153.200000,86,6.890000,10.000000,3,2.700000,1,True.\r\nKS,137,415,329-4474,no,yes,19,175.300000,96,29.800000,241.300000,146,20.510000,211.400000,109,9.510000,7.800000,2,2.110000,0,False.\r\nPA,136,408,357-4573,no,no,0,102.100000,75,17.360000,219.500000,97,18.660000,73.700000,92,3.320000,9.800000,5,2.650000,0,False.\r\nUT,95,415,341-7112,no,no,0,157.900000,103,26.840000,259.600000,90,22.070000,230.000000,117,10.350000,14.000000,2,3.780000,0,False.\r\nOR,82,415,400-3147,no,yes,19,146.500000,73,24.910000,246.400000,65,20.940000,199.000000,114,8.960000,4.100000,4,1.110000,1,False.\r\nND,145,415,378-1936,no,no,0,245.800000,116,41.790000,286.700000,91,24.370000,240.700000,115,10.830000,9.000000,13,2.430000,1,True.\r\nKY,56,415,347-1640,yes,no,0,177.700000,114,30.210000,215.600000,110,18.330000,236.700000,67,10.650000,10.500000,5,2.840000,1,False.\r\nMN,155,408,374-9531,yes,no,0,250.800000,146,42.640000,152.500000,105,12.960000,148.100000,104,6.660000,10.000000,5,2.700000,2,False.\r\nOH,133,408,380-4374,no,no,0,117.800000,100,20.030000,199.200000,105,16.930000,244.100000,119,10.980000,11.800000,4,3.190000,0,True.\r\nTX,53,415,380-9409,no,no,0,119.700000,113,20.350000,189.700000,84,16.120000,256.200000,108,11.530000,12.900000,7,3.480000,2,False.\r\nWY,123,415,387-1116,no,no,0,242.200000,87,41.170000,226.100000,101,19.220000,268.600000,121,12.090000,8.200000,3,2.210000,5,True.\r\nGA,136,415,400-7509,no,no,0,163.400000,83,27.780000,249.300000,119,21.190000,249.700000,90,11.240000,9.800000,4,2.650000,7,False.\r\nTX,57,415,403-6225,no,no,0,161.000000,113,27.370000,208.000000,134,17.680000,208.100000,81,9.360000,8.400000,4,2.270000,3,False.\r\nWV,62,408,382-8274,no,no,0,128.700000,111,21.880000,169.500000,104,14.410000,193.600000,97,8.710000,10.300000,5,2.780000,1,False.\r\nNM,112,415,354-5764,no,no,0,81.600000,94,13.870000,268.100000,112,22.790000,140.800000,75,6.340000,8.600000,18,2.320000,1,False.\r\nMI,55,415,387-6912,no,yes,20,207.700000,91,35.310000,199.700000,113,16.970000,216.500000,110,9.740000,7.300000,1,1.970000,1,False.\r\nNC,95,408,384-3413,no,no,0,128.600000,115,21.860000,216.200000,88,18.380000,255.300000,96,11.490000,6.300000,2,1.700000,6,True.\r\nNY,125,415,367-3950,no,no,0,233.300000,65,39.660000,209.800000,93,17.830000,210.600000,109,9.480000,9.100000,4,2.460000,1,False.\r\nTX,1,415,396-4254,no,no,0,182.100000,106,30.960000,134.900000,106,11.470000,152.300000,75,6.850000,10.000000,3,2.700000,5,True.\r\nKY,98,415,333-3010,no,yes,36,168.000000,81,28.560000,163.200000,125,13.870000,172.700000,120,7.770000,8.000000,2,2.160000,6,True.\r\nSD,105,415,393-1891,no,no,0,251.600000,88,42.770000,175.100000,103,14.880000,184.400000,112,8.300000,5.400000,5,1.460000,1,False.\r\nID,113,415,336-9053,no,yes,30,183.800000,102,31.250000,183.400000,123,15.590000,235.000000,52,10.580000,11.600000,7,3.130000,0,False.\r\nOR,99,408,353-3372,no,no,0,256.400000,44,43.590000,214.500000,105,18.230000,233.700000,75,10.520000,7.900000,1,2.130000,3,True.\r\nWI,103,415,386-8943,no,no,0,180.200000,134,30.630000,97.700000,85,8.300000,181.700000,134,8.180000,8.400000,3,2.270000,1,False.\r\nWV,177,408,376-9716,no,no,0,227.800000,81,38.730000,161.800000,97,13.750000,217.000000,106,9.760000,8.000000,5,2.160000,1,False.\r\nSC,149,415,370-8676,no,yes,20,147.800000,132,25.130000,276.800000,94,23.530000,149.900000,110,6.750000,10.200000,6,2.750000,0,False.\r\nCT,160,415,360-2329,no,no,0,234.900000,136,39.930000,270.800000,134,23.020000,219.300000,101,9.870000,13.900000,2,3.750000,1,True.\r\nNV,116,408,360-1320,no,no,0,110.900000,54,18.850000,213.400000,82,18.140000,186.200000,116,8.380000,7.900000,2,2.130000,2,False.\r\nND,90,415,329-8638,no,yes,22,124.500000,94,21.170000,231.700000,90,19.690000,222.200000,108,10.000000,6.400000,12,1.730000,1,False.\r\nMI,148,415,385-1118,yes,no,0,233.500000,81,39.700000,187.700000,71,15.950000,122.300000,97,5.500000,9.600000,2,2.590000,0,True.\r\nMT,147,415,408-8269,no,yes,35,197.300000,134,33.540000,141.100000,99,11.990000,212.100000,90,9.540000,10.100000,4,2.730000,2,True.\r\nNE,95,510,391-2334,no,no,0,58.200000,96,9.890000,202.100000,126,17.180000,210.500000,97,9.470000,10.400000,5,2.810000,0,False.\r\nUT,201,510,373-8900,no,no,0,212.700000,72,36.160000,225.200000,90,19.140000,195.100000,99,8.780000,7.000000,6,1.890000,1,False.\r\nWV,80,415,382-3512,no,no,0,151.500000,89,25.760000,131.700000,78,11.190000,235.300000,131,10.590000,11.800000,4,3.190000,0,False.\r\nAR,122,415,420-4089,yes,no,0,146.300000,117,24.870000,218.700000,93,18.590000,236.000000,97,10.620000,11.500000,5,3.110000,1,False.\r\nMT,132,408,406-8465,no,no,0,195.100000,100,33.170000,148.800000,95,12.650000,224.500000,117,10.100000,6.700000,2,1.810000,0,False.\r\nUT,83,510,386-6114,no,no,0,208.900000,71,35.510000,214.800000,92,18.260000,247.900000,108,11.160000,13.000000,5,3.510000,1,False.\r\nHI,99,408,413-9328,no,no,0,135.700000,107,23.070000,208.400000,103,17.710000,209.000000,95,9.400000,8.800000,3,2.380000,7,True.\r\nKS,84,415,335-7144,no,no,0,225.900000,86,38.400000,275.600000,105,23.430000,201.400000,108,9.060000,14.300000,3,3.860000,3,True.\r\nNY,46,415,414-5177,no,no,0,122.200000,67,20.770000,167.200000,62,14.210000,194.800000,98,8.770000,9.700000,6,2.620000,1,False.\r\nOH,87,510,350-5993,no,no,0,153.300000,106,26.060000,224.500000,117,19.080000,273.400000,152,12.300000,8.900000,5,2.400000,2,False.\r\nHI,150,415,370-1465,no,no,0,214.000000,117,36.380000,192.400000,89,16.350000,242.600000,99,10.920000,7.900000,4,2.130000,1,False.\r\nKS,73,408,385-2370,no,no,0,194.800000,112,33.120000,167.200000,85,14.210000,100.300000,61,4.510000,10.800000,6,2.920000,1,False.\r\nIN,7,415,358-9146,no,no,0,206.700000,87,35.140000,281.100000,83,23.890000,158.500000,77,7.130000,11.000000,5,2.970000,3,False.\r\nOR,89,415,357-8515,no,yes,12,188.000000,105,31.960000,151.300000,107,12.860000,201.900000,132,9.090000,10.500000,3,2.840000,2,False.\r\nNY,131,408,406-8995,yes,no,0,122.300000,83,20.790000,118.800000,94,10.100000,147.900000,95,6.660000,13.700000,3,3.700000,3,True.\r\nVA,105,415,344-3145,no,no,0,259.300000,96,44.080000,175.200000,97,14.890000,222.400000,36,10.010000,12.000000,5,3.240000,3,False.\r\nMI,108,408,341-9890,yes,no,0,115.100000,114,19.570000,211.300000,70,17.960000,136.100000,85,6.120000,13.800000,3,3.730000,2,True.\r\nID,47,415,365-4387,no,yes,28,172.900000,109,29.390000,137.600000,94,11.700000,203.800000,109,9.170000,8.300000,6,2.240000,1,False.\r\nMO,101,415,375-3341,yes,no,0,156.400000,116,26.590000,130.400000,114,11.080000,207.300000,109,9.330000,7.300000,5,1.970000,1,False.\r\nAL,182,415,418-3096,no,yes,24,128.100000,104,21.780000,143.400000,127,12.190000,191.000000,98,8.590000,11.600000,3,3.130000,1,False.\r\nOR,161,408,378-3879,no,no,0,196.600000,73,33.420000,170.200000,79,14.470000,194.300000,79,8.740000,12.500000,3,3.380000,1,False.\r\nVT,128,408,344-1362,no,no,0,227.900000,130,38.740000,302.600000,71,25.720000,191.500000,82,8.620000,5.500000,7,1.490000,1,True.\r\nAZ,69,415,419-3937,no,yes,31,194.900000,63,33.130000,191.600000,90,16.290000,153.000000,129,6.890000,13.200000,2,3.560000,2,False.\r\nVA,113,408,348-4961,no,yes,34,44.900000,63,7.630000,134.200000,82,11.410000,168.400000,118,7.580000,13.300000,3,3.590000,1,False.\r\nPA,87,408,329-1410,no,yes,30,262.800000,114,44.680000,215.800000,130,18.340000,154.800000,88,6.970000,7.800000,2,2.110000,0,False.\r\nCO,71,415,332-9896,no,no,0,211.200000,70,35.900000,252.700000,122,21.480000,225.800000,104,10.160000,12.300000,3,3.320000,0,False.\r\nKY,76,415,407-8575,no,no,0,204.000000,69,34.680000,225.100000,110,19.130000,240.300000,85,10.810000,9.600000,5,2.590000,1,False.\r\nNJ,87,510,387-2799,no,no,0,223.200000,109,37.940000,127.500000,86,10.840000,289.300000,83,13.020000,14.500000,4,3.920000,3,False.\r\nIL,117,408,373-9108,no,no,0,119.000000,82,20.230000,187.500000,108,15.940000,189.300000,97,8.520000,11.500000,3,3.110000,1,False.\r\nWA,177,415,345-3947,no,no,0,266.100000,91,45.240000,225.200000,79,19.140000,224.700000,58,10.110000,8.900000,8,2.400000,3,True.\r\nWV,95,415,356-7511,no,no,0,134.400000,104,22.850000,152.400000,95,12.950000,236.500000,80,10.640000,9.400000,3,2.540000,1,False.\r\nRI,76,415,343-4516,no,no,0,171.100000,78,29.090000,257.200000,83,21.860000,91.600000,92,4.120000,16.200000,3,4.370000,1,False.\r\nOH,66,415,408-6305,no,no,0,170.500000,103,28.990000,254.300000,77,21.620000,197.300000,138,8.880000,10.500000,2,2.840000,2,False.\r\nMO,110,415,338-7305,no,no,0,178.500000,124,30.350000,146.900000,141,12.490000,217.100000,102,9.770000,9.900000,7,2.670000,1,False.\r\nMD,204,510,401-3077,no,no,0,205.200000,145,34.880000,154.800000,95,13.160000,191.400000,77,8.610000,14.100000,5,3.810000,3,False.\r\nOH,32,415,401-6977,no,yes,31,232.800000,97,39.580000,183.500000,111,15.600000,206.800000,111,9.310000,13.000000,2,3.510000,0,False.\r\nVA,133,408,385-1464,no,yes,39,239.900000,107,40.780000,253.800000,77,21.570000,128.700000,85,5.790000,6.700000,3,1.810000,5,False.\r\nFL,185,408,417-5034,no,no,0,55.600000,97,9.450000,288.700000,83,24.540000,111.200000,110,5.000000,12.100000,3,3.270000,2,False.\r\nCO,103,415,420-7066,no,yes,37,153.500000,78,26.100000,241.900000,108,20.560000,244.700000,110,11.010000,10.600000,3,2.860000,1,False.\r\nNY,91,510,394-8256,no,no,0,109.800000,100,18.670000,189.600000,104,16.120000,206.700000,85,9.300000,11.100000,3,3.000000,1,False.\r\nWV,131,415,362-5044,no,no,0,196.100000,89,33.340000,185.500000,87,15.770000,250.000000,132,11.250000,5.200000,6,1.400000,2,False.\r\nLA,153,510,350-2075,no,no,0,166.800000,127,28.360000,143.500000,121,12.200000,210.700000,130,9.480000,11.800000,4,3.190000,0,False.\r\nMA,132,415,343-5372,no,yes,25,113.200000,96,19.240000,269.900000,107,22.940000,229.100000,87,10.310000,7.100000,7,1.920000,2,False.\r\nUT,148,510,377-9520,no,no,0,203.000000,92,34.510000,150.900000,125,12.830000,245.500000,131,11.050000,14.600000,9,3.940000,1,False.\r\nAL,141,408,391-6773,no,no,0,242.800000,90,41.280000,234.100000,80,19.900000,211.500000,104,9.520000,6.000000,3,1.620000,5,False.\r\nME,105,415,403-4442,no,no,0,156.500000,102,26.610000,140.200000,134,11.920000,227.400000,111,10.230000,12.200000,2,3.290000,2,False.\r\nTX,169,408,379-5885,no,no,0,266.700000,105,45.340000,158.200000,88,13.450000,287.700000,111,12.950000,13.800000,3,3.730000,3,True.\r\nND,127,415,399-1021,no,yes,23,182.000000,80,30.940000,216.100000,85,18.370000,156.900000,82,7.060000,9.800000,4,2.650000,1,False.\r\nCO,57,415,342-4004,no,no,0,85.900000,92,14.600000,193.900000,127,16.480000,231.500000,93,10.420000,10.100000,2,2.730000,0,False.\r\nLA,123,415,382-7659,no,yes,33,146.600000,87,24.920000,114.800000,59,9.760000,220.400000,99,9.920000,2.900000,7,0.780000,0,False.\r\nMT,103,510,342-1004,no,yes,35,110.500000,101,18.790000,208.300000,81,17.710000,87.400000,77,3.930000,13.900000,2,3.750000,4,True.\r\nOR,101,415,398-5851,no,no,0,118.600000,89,20.160000,199.600000,97,16.970000,53.300000,61,2.400000,11.500000,5,3.110000,1,False.\r\nNH,123,415,396-4869,no,yes,22,197.600000,105,33.590000,80.000000,86,6.800000,120.800000,82,5.440000,15.600000,12,4.210000,2,False.\r\nNE,78,510,422-8333,no,yes,32,210.300000,116,35.750000,192.200000,83,16.340000,246.100000,92,11.070000,10.800000,4,2.920000,6,False.\r\nWV,101,415,367-9127,no,yes,28,220.300000,96,37.450000,285.800000,72,24.290000,203.000000,111,9.140000,9.400000,6,2.540000,4,False.\r\nNV,129,415,420-3028,no,no,0,150.000000,98,25.500000,232.400000,101,19.750000,261.200000,123,11.750000,12.500000,6,3.380000,1,False.\r\nMA,67,415,357-6348,no,yes,34,161.700000,114,27.490000,207.600000,115,17.650000,205.700000,114,9.260000,9.200000,4,2.480000,0,False.\r\nMI,37,415,386-1131,no,no,0,191.400000,116,32.540000,167.400000,99,14.230000,216.500000,112,9.740000,14.000000,5,3.780000,3,False.\r\nKY,64,415,349-8391,yes,no,0,146.700000,83,24.940000,148.300000,91,12.610000,238.600000,69,10.740000,12.500000,3,3.380000,3,False.\r\nWV,173,510,421-1484,no,no,0,109.400000,103,18.600000,101.300000,111,8.610000,167.300000,106,7.530000,7.800000,7,2.110000,1,False.\r\nKY,135,510,414-2663,no,no,0,144.100000,115,24.500000,249.800000,68,21.230000,211.400000,82,9.510000,13.600000,3,3.670000,1,False.\r\nNJ,75,415,327-6989,no,yes,42,248.900000,93,42.310000,170.800000,108,14.520000,104.500000,91,4.700000,11.200000,8,3.020000,1,False.\r\nME,88,415,405-5513,no,no,0,85.700000,112,14.570000,221.600000,70,18.840000,190.600000,75,8.580000,11.600000,3,3.130000,4,True.\r\nTX,112,415,345-9168,no,no,0,214.800000,112,36.520000,209.700000,104,17.820000,164.400000,97,7.400000,9.400000,5,2.540000,3,False.\r\nMN,113,408,417-5146,no,no,0,158.900000,137,27.010000,242.800000,109,20.640000,247.800000,97,11.150000,6.500000,4,1.760000,0,False.\r\nVA,121,510,339-2792,no,yes,28,110.000000,94,18.700000,141.500000,76,12.030000,237.300000,87,10.680000,6.400000,3,1.730000,2,False.\r\nDC,70,415,345-8397,no,no,0,152.800000,145,25.980000,183.600000,102,15.610000,151.800000,75,6.830000,10.500000,2,2.840000,1,False.\r\nMD,90,415,344-6404,no,no,0,145.600000,103,24.750000,197.100000,137,16.750000,294.500000,83,13.250000,10.500000,4,2.840000,0,False.\r\nRI,39,408,417-9455,no,no,0,93.300000,83,15.860000,199.600000,114,16.970000,206.200000,104,9.280000,6.500000,4,1.760000,0,False.\r\nMA,142,408,343-1009,no,no,0,216.800000,134,36.860000,187.800000,106,15.960000,138.100000,108,6.210000,8.300000,2,2.240000,0,False.\r\nMD,176,408,365-3493,no,no,0,201.900000,101,34.320000,154.700000,78,13.150000,164.400000,79,7.400000,9.000000,2,2.430000,1,False.\r\nNM,105,408,376-7043,no,no,0,146.400000,81,24.890000,225.100000,80,19.130000,230.100000,117,10.350000,8.500000,2,2.300000,1,False.\r\nMN,57,415,348-5728,no,no,0,272.700000,74,46.360000,224.900000,85,19.120000,178.200000,104,8.020000,10.500000,3,2.840000,2,True.\r\nMI,110,510,357-5784,no,no,0,18.900000,92,3.210000,258.400000,81,21.960000,109.600000,74,4.930000,14.800000,4,4.000000,1,False.\r\nAZ,88,415,417-9844,no,no,0,172.800000,81,29.380000,193.400000,90,16.440000,89.600000,107,4.030000,12.800000,5,3.460000,2,False.\r\nAL,95,408,333-7225,no,no,0,190.200000,119,32.330000,157.100000,70,13.350000,181.500000,120,8.170000,14.000000,6,3.780000,0,False.\r\nMI,147,415,382-4943,no,no,0,130.600000,83,22.200000,208.100000,144,17.690000,204.600000,72,9.210000,15.600000,3,4.210000,3,False.\r\nSC,101,415,345-4589,no,no,0,158.400000,92,26.930000,188.000000,117,15.980000,219.700000,125,9.890000,13.500000,4,3.650000,4,True.\r\nMS,115,415,404-6337,no,no,0,166.500000,111,28.310000,236.200000,98,20.080000,205.600000,92,9.250000,15.600000,2,4.210000,1,False.\r\nMS,103,415,412-1470,no,no,0,129.300000,103,21.980000,202.800000,89,17.240000,233.000000,126,10.490000,12.900000,2,3.480000,2,False.\r\nCA,82,415,394-9220,no,no,0,199.300000,112,33.880000,193.400000,120,16.440000,254.400000,117,11.450000,7.000000,10,1.890000,0,False.\r\nMD,141,415,364-5362,no,no,0,185.100000,126,31.470000,233.000000,98,19.810000,152.200000,106,6.850000,9.100000,7,2.460000,0,False.\r\nND,149,408,372-9852,no,no,0,175.400000,80,29.820000,197.400000,127,16.780000,188.200000,102,8.470000,9.700000,2,2.620000,2,False.\r\nIL,131,510,394-9984,no,no,0,263.400000,123,44.780000,151.900000,74,12.910000,218.500000,101,9.830000,10.700000,2,2.890000,2,False.\r\nSD,119,510,402-1668,no,no,0,94.200000,108,16.010000,264.100000,100,22.450000,203.700000,79,9.170000,7.300000,3,1.970000,1,False.\r\nAL,112,510,339-5659,no,no,0,189.400000,83,32.200000,219.000000,89,18.620000,168.000000,116,7.560000,7.100000,8,1.920000,3,False.\r\nNV,116,510,341-7279,no,yes,35,118.000000,103,20.060000,167.200000,106,14.210000,205.700000,102,9.260000,11.800000,2,3.190000,2,False.\r\nLA,94,415,371-3236,no,no,0,212.100000,98,36.060000,189.400000,89,16.100000,352.200000,95,15.850000,8.400000,5,2.270000,3,False.\r\nVA,90,408,343-5679,no,no,0,222.000000,93,37.740000,187.000000,103,15.900000,282.300000,124,12.700000,12.400000,6,3.350000,2,False.\r\nDE,114,415,347-4626,no,yes,31,222.800000,98,37.880000,180.500000,105,15.340000,151.300000,101,6.810000,13.000000,4,3.510000,0,False.\r\nCT,63,408,344-8498,no,yes,25,190.000000,137,32.300000,116.600000,76,9.910000,141.500000,110,6.370000,12.200000,2,3.290000,1,False.\r\nCO,130,408,349-3005,no,no,0,271.800000,129,46.210000,237.200000,128,20.160000,210.100000,91,9.450000,8.700000,2,2.350000,4,True.\r\nMA,122,408,371-3498,no,yes,29,195.400000,83,33.220000,268.200000,93,22.800000,168.000000,95,7.560000,8.400000,6,2.270000,3,False.\r\nOR,166,510,367-4853,no,no,0,199.600000,93,33.930000,214.300000,99,18.220000,196.800000,110,8.860000,7.200000,5,1.940000,3,False.\r\nWA,62,415,422-3454,no,no,0,100.000000,98,17.000000,173.500000,95,14.750000,218.000000,122,9.810000,10.100000,4,2.730000,0,False.\r\nSC,78,415,403-8915,no,yes,21,160.600000,85,27.300000,223.100000,79,18.960000,124.000000,92,5.580000,9.500000,1,2.570000,2,False.\r\nIN,148,415,371-2418,no,yes,26,158.700000,91,26.980000,160.500000,127,13.640000,218.300000,88,9.820000,9.900000,3,2.670000,1,False.\r\nMD,154,510,411-2977,no,no,0,154.500000,122,26.270000,214.200000,71,18.210000,178.000000,105,8.010000,12.000000,2,3.240000,3,True.\r\nNV,110,408,389-8163,no,yes,34,192.300000,114,32.690000,129.300000,114,10.990000,136.300000,102,6.130000,6.300000,12,1.700000,1,False.\r\nTX,75,415,417-4456,no,no,0,305.100000,106,51.870000,188.000000,115,15.980000,235.400000,116,10.590000,8.500000,5,2.300000,0,True.\r\nND,84,408,351-1894,no,yes,38,193.000000,106,32.810000,153.600000,106,13.060000,260.400000,87,11.720000,7.400000,5,2.000000,2,False.\r\nWV,113,510,386-6408,no,no,0,72.500000,88,12.330000,204.000000,112,17.340000,117.900000,118,5.310000,6.600000,3,1.780000,1,False.\r\nCO,181,510,370-9592,no,yes,40,105.200000,61,17.880000,341.300000,79,29.010000,165.700000,97,7.460000,6.300000,3,1.700000,2,False.\r\nTX,51,415,397-9251,no,no,0,180.500000,88,30.690000,134.700000,102,11.450000,170.700000,97,7.680000,10.000000,3,2.700000,2,False.\r\nFL,102,408,395-6913,no,yes,29,214.700000,86,36.500000,314.300000,109,26.720000,280.200000,110,12.610000,14.300000,2,3.860000,0,False.\r\nAL,107,408,332-3804,no,no,0,86.800000,95,14.760000,108.100000,85,9.190000,204.300000,87,9.190000,13.200000,3,3.560000,1,False.\r\nWV,88,510,421-1326,no,no,0,131.500000,99,22.360000,174.800000,128,14.860000,184.200000,83,8.290000,7.900000,2,2.130000,5,True.\r\nMI,82,415,415-8200,no,no,0,135.400000,102,23.020000,237.100000,122,20.150000,118.300000,91,5.320000,17.500000,4,4.730000,0,False.\r\nNY,204,415,371-9414,no,no,0,174.300000,85,29.630000,254.100000,95,21.600000,176.400000,96,7.940000,5.900000,3,1.590000,6,False.\r\nMS,130,510,347-3895,no,no,0,203.900000,63,34.660000,191.800000,93,16.300000,132.500000,125,5.960000,12.100000,4,3.270000,3,False.\r\nMO,174,510,342-5854,no,no,0,235.500000,108,40.040000,142.300000,143,12.100000,316.700000,131,14.250000,12.500000,5,3.380000,0,False.\r\nAR,129,415,379-7192,no,no,0,157.000000,113,26.690000,256.900000,97,21.840000,185.500000,126,8.350000,12.100000,2,3.270000,2,False.\r\nMS,190,415,394-5753,yes,no,0,111.900000,55,19.020000,223.000000,124,18.960000,243.200000,81,10.940000,10.000000,7,2.700000,3,False.\r\nNY,54,510,390-6932,yes,no,0,236.300000,91,40.170000,152.800000,130,12.990000,160.300000,98,7.210000,11.200000,8,3.020000,3,False.\r\nSD,78,408,394-3171,no,no,0,163.600000,88,27.810000,283.400000,93,24.090000,262.100000,108,11.790000,8.600000,9,2.320000,0,False.\r\nAK,100,415,394-5202,no,yes,29,213.600000,127,36.310000,175.900000,82,14.950000,207.200000,100,9.320000,8.900000,3,2.400000,1,False.\r\nWV,70,510,348-3777,no,yes,30,143.400000,72,24.380000,170.000000,92,14.450000,127.900000,68,5.760000,9.400000,4,2.540000,3,False.\r\nSC,111,510,407-3949,no,no,0,78.300000,119,13.310000,198.200000,94,16.850000,248.500000,94,11.180000,12.100000,4,3.270000,1,False.\r\nVA,117,408,363-4779,no,no,0,97.100000,98,16.510000,228.000000,131,19.380000,240.000000,111,10.800000,10.600000,3,2.860000,1,False.\r\nMS,68,415,340-2239,no,no,0,94.100000,93,16.000000,147.600000,80,12.550000,213.500000,85,9.610000,10.100000,2,2.730000,0,False.\r\nSD,27,510,359-3423,no,no,0,226.300000,95,38.470000,274.300000,109,23.320000,242.700000,119,10.920000,8.200000,3,2.210000,2,True.\r\nMN,91,415,382-9297,no,no,0,133.800000,61,22.750000,158.800000,96,13.500000,189.600000,92,8.530000,10.500000,2,2.840000,1,False.\r\nAL,181,415,330-9294,no,yes,27,190.300000,93,32.350000,249.000000,127,21.170000,215.700000,82,9.710000,10.600000,4,2.860000,1,False.\r\nCO,118,415,362-8763,no,yes,36,294.900000,106,50.130000,165.700000,115,14.080000,189.200000,63,8.510000,9.800000,5,2.650000,3,False.\r\nME,112,415,403-4816,no,no,0,185.400000,114,31.520000,191.400000,119,16.270000,144.000000,78,6.480000,10.000000,11,2.700000,2,False.\r\nGA,93,415,371-2155,no,no,0,179.500000,121,30.520000,191.900000,131,16.310000,165.500000,125,7.450000,12.000000,4,3.240000,0,False.\r\nAZ,102,408,334-1339,no,no,0,158.000000,94,26.860000,207.900000,100,17.670000,190.400000,120,8.570000,10.100000,10,2.730000,0,False.\r\nMA,93,415,341-7412,no,no,0,173.000000,131,29.410000,190.400000,108,16.180000,290.000000,66,13.050000,10.400000,2,2.810000,0,False.\r\nAZ,107,415,375-4770,no,yes,32,134.200000,101,22.810000,211.900000,145,18.010000,167.600000,138,7.540000,8.200000,5,2.210000,1,False.\r\nIN,100,415,406-7643,no,yes,32,125.200000,123,21.280000,230.900000,101,19.630000,192.000000,106,8.640000,12.600000,9,3.400000,3,False.\r\nDE,115,415,415-8164,no,no,0,195.900000,111,33.300000,227.000000,108,19.300000,313.200000,113,14.090000,13.200000,1,3.560000,2,False.\r\nWI,63,510,354-3545,no,yes,13,214.200000,61,36.410000,181.200000,88,15.400000,174.000000,68,7.830000,10.300000,2,2.780000,0,False.\r\nME,57,415,377-3139,no,no,0,221.100000,101,37.590000,236.700000,65,20.120000,252.300000,137,11.350000,9.500000,1,2.570000,0,False.\r\nDC,119,408,418-7478,no,yes,26,132.000000,100,22.440000,173.300000,121,14.730000,203.500000,108,9.160000,11.600000,5,3.130000,1,False.\r\nGA,73,408,385-6952,no,no,0,157.600000,92,26.790000,198.300000,87,16.860000,364.900000,106,16.420000,9.100000,4,2.460000,1,False.\r\nHI,98,408,381-8593,no,yes,30,110.300000,71,18.750000,182.400000,108,15.500000,183.800000,88,8.270000,11.000000,8,2.970000,2,False.\r\nVA,139,415,365-9371,yes,no,0,161.500000,121,27.460000,192.900000,137,16.400000,168.300000,96,7.570000,11.200000,13,3.020000,0,False.\r\nNY,31,408,401-7335,no,yes,28,171.800000,116,29.210000,240.700000,125,20.460000,245.500000,80,11.050000,10.600000,7,2.860000,1,False.\r\nPA,129,510,364-5126,no,yes,32,211.000000,99,35.870000,155.100000,89,13.180000,234.800000,96,10.570000,11.400000,5,3.080000,1,False.\r\nAR,115,415,385-7157,no,no,0,139.300000,89,23.680000,192.300000,95,16.350000,151.000000,75,6.800000,9.300000,3,2.510000,7,True.\r\nHI,108,415,385-4766,no,no,0,291.600000,99,49.570000,221.100000,93,18.790000,229.200000,110,10.310000,14.000000,9,3.780000,2,True.\r\nDE,139,408,390-1760,no,no,0,139.000000,110,23.630000,132.900000,93,11.300000,272.000000,120,12.240000,12.100000,1,3.270000,0,False.\r\nWV,102,408,365-8831,no,no,0,234.800000,125,39.920000,199.200000,99,16.930000,163.200000,88,7.340000,10.000000,1,2.700000,4,False.\r\nOK,149,408,353-4002,no,no,0,187.600000,83,31.890000,201.400000,81,17.120000,264.200000,79,11.890000,8.800000,1,2.380000,1,False.\r\nOR,113,415,367-5923,no,no,0,159.800000,143,27.170000,210.100000,93,17.860000,175.100000,86,7.880000,13.100000,7,3.540000,2,False.\r\nND,131,408,393-9548,no,yes,33,177.100000,100,30.110000,194.000000,85,16.490000,253.400000,124,11.400000,5.200000,5,1.400000,1,False.\r\nMO,83,408,362-2356,no,no,0,117.900000,101,20.040000,160.400000,92,13.630000,235.300000,150,10.590000,11.400000,10,3.080000,0,False.\r\nAR,96,415,365-2341,no,yes,21,247.600000,95,42.090000,256.300000,150,21.790000,158.600000,72,7.140000,10.800000,6,2.920000,2,False.\r\nGA,98,408,388-8797,no,no,0,169.900000,77,28.880000,138.300000,155,11.760000,142.600000,105,6.420000,8.500000,7,2.300000,1,False.\r\nTN,3,415,400-4713,no,no,0,185.000000,120,31.450000,203.700000,129,17.310000,170.500000,89,7.670000,14.100000,3,3.810000,3,False.\r\nMA,77,408,420-3042,no,yes,17,204.900000,84,34.830000,201.000000,102,17.090000,219.700000,97,9.890000,11.300000,5,3.050000,0,False.\r\nND,75,408,396-4171,no,yes,24,225.500000,119,38.340000,182.000000,108,15.470000,270.900000,106,12.190000,9.400000,2,2.540000,3,False.\r\nIA,40,510,389-8417,no,no,0,169.700000,115,28.850000,141.400000,123,12.020000,253.000000,115,11.390000,10.500000,3,2.840000,4,True.\r\nNJ,108,415,339-4068,no,no,0,239.300000,102,40.680000,223.400000,127,18.990000,251.400000,104,11.310000,10.600000,6,2.860000,0,False.\r\nMT,100,415,341-4873,no,no,0,113.300000,96,19.260000,197.900000,89,16.820000,284.500000,93,12.800000,11.700000,2,3.160000,4,True.\r\nAL,16,415,336-2322,no,no,0,161.900000,100,27.520000,230.100000,138,19.560000,148.800000,78,6.700000,10.200000,11,2.750000,3,False.\r\nNY,115,510,402-1607,no,yes,16,133.300000,110,22.660000,185.700000,111,15.780000,161.500000,113,7.270000,5.600000,4,1.510000,2,False.\r\nPA,108,510,379-3037,no,yes,25,170.700000,88,29.020000,109.900000,113,9.340000,165.700000,99,7.460000,8.700000,1,2.350000,3,False.\r\nVT,107,510,382-1399,no,no,0,189.700000,76,32.250000,156.100000,65,13.270000,244.000000,91,10.980000,8.300000,3,2.240000,5,False.\r\nNC,161,415,394-5489,no,no,0,322.300000,100,54.790000,230.400000,135,19.580000,241.500000,104,10.870000,7.800000,5,2.110000,2,True.\r\nCT,147,415,387-6065,no,no,0,124.400000,74,21.150000,320.900000,78,27.280000,157.200000,126,7.070000,10.400000,4,2.810000,2,False.\r\nMO,107,415,327-6087,no,no,0,146.900000,94,24.970000,114.300000,111,9.720000,114.500000,97,5.150000,11.400000,5,3.080000,3,False.\r\nWV,120,510,341-8667,no,no,0,192.600000,123,32.740000,206.400000,105,17.540000,283.200000,93,12.740000,10.800000,3,2.920000,1,False.\r\nNJ,107,408,338-9612,no,yes,36,96.300000,83,16.370000,179.600000,91,15.270000,166.300000,121,7.480000,10.300000,2,2.780000,1,False.\r\nAK,58,510,364-1134,no,no,0,131.900000,96,22.420000,167.600000,107,14.250000,205.900000,106,9.270000,14.700000,5,3.970000,3,False.\r\nLA,91,408,413-4811,no,no,0,147.200000,121,25.020000,175.200000,87,14.890000,136.300000,80,6.130000,13.300000,3,3.590000,2,False.\r\nAL,13,415,354-4333,no,no,0,143.100000,139,24.330000,239.600000,88,20.370000,221.700000,123,9.980000,7.100000,5,1.920000,2,False.\r\nIN,104,408,382-2026,no,no,0,280.400000,127,47.670000,179.400000,79,15.250000,150.600000,77,6.780000,15.200000,6,4.100000,5,False.\r\nMA,93,415,368-3287,no,yes,31,237.200000,85,40.320000,213.100000,100,18.110000,192.700000,87,8.670000,10.700000,4,2.890000,1,False.\r\nDE,95,510,367-8298,no,no,0,184.200000,95,31.310000,181.600000,101,15.440000,143.400000,113,6.450000,12.800000,4,3.460000,2,False.\r\nSC,104,415,391-1783,no,no,0,109.100000,141,18.550000,187.100000,140,15.900000,216.600000,100,9.750000,10.000000,4,2.700000,3,False.\r\nNH,35,408,393-8762,no,no,0,138.100000,115,23.480000,158.200000,82,13.450000,215.700000,118,9.710000,10.300000,2,2.780000,5,True.\r\nLA,62,415,385-1423,no,no,0,186.800000,94,31.760000,207.600000,92,17.650000,195.000000,98,8.780000,8.800000,4,2.380000,3,False.\r\nMS,143,510,406-7670,no,no,0,155.400000,112,26.420000,290.900000,92,24.730000,228.400000,91,10.280000,13.900000,5,3.750000,1,False.\r\nNM,62,415,339-5423,no,no,0,245.300000,91,41.700000,122.900000,130,10.450000,228.400000,102,10.280000,8.500000,4,2.300000,4,False.\r\nWA,60,415,366-8939,yes,no,0,205.900000,97,35.000000,277.400000,117,23.580000,202.000000,139,9.090000,11.000000,2,2.970000,0,True.\r\nSC,41,510,353-2391,no,no,0,207.200000,138,35.220000,214.100000,83,18.200000,193.000000,105,8.690000,11.900000,4,3.210000,1,False.\r\nMT,34,415,372-4203,no,yes,14,151.500000,100,25.760000,248.700000,126,21.140000,199.800000,120,8.990000,10.700000,5,2.890000,2,False.\r\nME,56,408,385-5688,no,no,0,221.900000,112,37.720000,278.200000,122,23.650000,288.100000,85,12.960000,7.100000,5,1.920000,0,True.\r\nNM,183,415,397-7453,no,no,0,190.000000,100,32.300000,246.600000,78,20.960000,304.200000,107,13.690000,9.500000,4,2.570000,1,False.\r\nAZ,94,415,366-9015,no,no,0,220.800000,111,37.540000,156.200000,67,13.280000,187.900000,89,8.460000,10.500000,4,2.840000,2,False.\r\nCT,73,415,356-1654,no,yes,47,173.700000,117,29.530000,204.000000,114,17.340000,174.600000,94,7.860000,6.300000,3,1.700000,2,False.\r\nIL,123,408,337-3932,no,no,0,114.800000,94,19.520000,150.000000,104,12.750000,268.600000,119,12.090000,9.600000,4,2.590000,2,False.\r\nIN,64,408,350-1126,no,no,0,113.800000,97,19.350000,192.300000,97,16.350000,214.900000,89,9.670000,10.400000,1,2.810000,3,False.\r\nAR,127,415,416-3649,yes,no,0,143.200000,60,24.340000,179.500000,159,15.260000,171.800000,122,7.730000,6.200000,4,1.670000,4,True.\r\nRI,33,415,349-1726,no,no,0,184.400000,111,31.350000,203.800000,110,17.320000,237.400000,100,10.680000,9.300000,5,2.510000,3,False.\r\nND,27,415,405-1589,no,no,0,227.400000,67,38.660000,248.000000,115,21.080000,61.400000,109,2.760000,7.800000,6,2.110000,1,False.\r\nNH,123,408,366-7560,no,no,0,224.000000,99,38.080000,210.700000,80,17.910000,231.900000,75,10.440000,2.100000,5,0.570000,0,False.\r\nNV,148,510,333-9643,no,no,0,216.200000,95,36.750000,185.700000,105,15.780000,300.000000,143,13.500000,10.000000,5,2.700000,2,False.\r\nUT,81,415,355-6422,no,no,0,129.900000,121,22.080000,230.100000,105,19.560000,140.500000,123,6.320000,13.300000,3,3.590000,0,False.\r\nNC,122,510,329-5400,no,yes,30,230.100000,108,39.120000,287.600000,76,24.450000,177.100000,85,7.970000,6.900000,3,1.860000,2,False.\r\nMI,52,415,383-6356,no,no,0,204.400000,97,34.750000,273.200000,128,23.220000,179.600000,118,8.080000,11.000000,5,2.970000,1,False.\r\nWI,91,408,377-7276,no,yes,44,216.600000,101,36.820000,173.100000,98,14.710000,242.100000,95,10.890000,9.100000,3,2.460000,1,False.\r\nAR,54,415,337-1586,no,no,0,247.500000,85,42.080000,225.400000,93,19.160000,244.300000,132,10.990000,10.200000,2,2.750000,2,True.\r\nNH,152,510,336-9273,no,no,0,228.100000,93,38.780000,136.400000,106,11.590000,197.300000,107,8.880000,9.000000,2,2.430000,1,False.\r\nTX,201,415,415-5476,no,no,0,225.900000,110,38.400000,299.100000,86,25.420000,251.300000,81,11.310000,11.200000,4,3.020000,1,True.\r\nID,78,415,332-2650,no,no,0,103.500000,115,17.600000,117.900000,102,10.020000,201.000000,94,9.050000,12.000000,3,3.240000,4,True.\r\nCT,67,415,418-8257,no,no,0,115.500000,70,19.640000,252.200000,143,21.440000,208.900000,91,9.400000,7.500000,6,2.030000,0,False.\r\nNH,100,408,407-3121,no,no,0,218.800000,125,37.200000,148.300000,102,12.610000,277.800000,97,12.500000,9.700000,6,2.620000,1,False.\r\nWY,41,510,381-2413,no,no,0,223.800000,67,38.050000,244.800000,74,20.810000,223.800000,156,10.070000,12.300000,5,3.320000,3,False.\r\nOR,133,415,378-1144,no,no,0,143.800000,71,24.450000,184.000000,131,15.640000,275.500000,132,12.400000,12.900000,4,3.480000,3,False.\r\nSC,36,408,359-5091,no,yes,43,29.900000,123,5.080000,129.100000,117,10.970000,325.900000,105,14.670000,8.600000,6,2.320000,2,False.\r\nMD,51,510,378-6986,no,yes,28,276.700000,121,47.040000,203.700000,99,17.310000,246.200000,88,11.080000,8.300000,3,2.240000,0,False.\r\nNY,122,415,386-6580,no,no,0,141.400000,128,24.040000,146.400000,70,12.440000,123.000000,75,5.540000,8.100000,4,2.190000,0,False.\r\nNM,84,408,419-9713,no,yes,41,153.900000,102,26.160000,140.700000,117,11.960000,217.700000,101,9.800000,12.800000,5,3.460000,1,False.\r\nLA,91,415,382-6153,no,no,0,190.500000,128,32.390000,205.500000,103,17.470000,130.700000,63,5.880000,13.800000,5,3.730000,0,False.\r\nUT,110,408,332-1690,no,no,0,192.600000,102,32.740000,178.900000,118,15.210000,214.600000,74,9.660000,9.400000,4,2.540000,1,False.\r\nAL,91,408,348-9383,yes,no,0,151.800000,115,25.810000,103.600000,116,8.810000,156.300000,86,7.030000,12.200000,4,3.290000,1,False.\r\nDE,121,408,420-3857,no,no,0,215.600000,74,36.650000,192.900000,98,16.400000,144.000000,103,6.480000,10.100000,4,2.730000,5,False.\r\nWV,109,415,405-2653,no,no,0,180.000000,100,30.600000,229.000000,103,19.470000,139.400000,105,6.270000,7.800000,8,2.110000,3,False.\r\nKY,95,510,417-9278,no,no,0,157.300000,116,26.740000,197.500000,77,16.790000,128.200000,111,5.770000,8.400000,4,2.270000,2,False.\r\nNC,72,415,352-5663,no,no,0,196.500000,88,33.410000,158.600000,129,13.480000,269.300000,118,12.120000,6.800000,3,1.840000,0,False.\r\nWV,73,415,370-8786,no,no,0,240.300000,130,40.850000,162.500000,83,13.810000,231.900000,136,10.440000,11.900000,3,3.210000,0,False.\r\nAZ,108,415,415-6333,no,no,0,193.300000,126,32.860000,154.700000,85,13.150000,174.800000,98,7.870000,9.400000,6,2.540000,3,False.\r\nWV,58,408,391-6558,no,yes,39,211.900000,40,36.020000,274.400000,76,23.320000,210.500000,139,9.470000,5.400000,4,1.460000,1,False.\r\nND,148,415,396-4234,yes,no,0,218.700000,111,37.180000,155.600000,133,13.230000,277.400000,62,12.480000,8.200000,5,2.210000,1,False.\r\nWA,76,510,345-6961,yes,no,0,246.800000,110,41.960000,206.300000,63,17.540000,208.400000,123,9.380000,13.200000,5,3.560000,0,True.\r\nID,103,415,346-5992,no,no,0,174.700000,151,29.700000,148.000000,56,12.580000,168.200000,109,7.570000,15.800000,3,4.270000,6,True.\r\nCT,87,415,402-3908,no,no,0,240.000000,83,40.800000,134.100000,106,11.400000,189.100000,84,8.510000,9.300000,2,2.510000,0,True.\r\nOK,35,510,350-2340,no,yes,37,181.200000,76,30.800000,177.600000,98,15.100000,228.000000,136,10.260000,5.000000,3,1.350000,2,False.\r\nIA,88,415,410-2015,no,no,0,113.700000,67,19.330000,165.100000,127,14.030000,141.500000,142,6.370000,10.800000,3,2.920000,1,False.\r\nNE,67,415,380-3311,no,yes,41,174.700000,86,29.700000,160.600000,93,13.650000,155.300000,108,6.990000,13.400000,1,3.620000,0,False.\r\nID,77,510,399-7029,no,yes,29,211.100000,89,35.890000,223.500000,97,19.000000,148.400000,106,6.680000,9.700000,9,2.620000,2,False.\r\nOR,124,510,337-3868,no,no,0,169.300000,108,28.780000,178.600000,91,15.180000,242.300000,82,10.900000,12.200000,3,3.290000,1,False.\r\nSD,30,415,354-8088,no,no,0,247.400000,107,42.060000,175.900000,76,14.950000,287.400000,90,12.930000,11.300000,2,3.050000,0,False.\r\nDE,53,415,416-9723,no,yes,32,131.200000,63,22.300000,227.400000,125,19.330000,178.900000,105,8.050000,12.800000,2,3.460000,2,False.\r\nWA,152,510,337-4403,no,no,0,161.400000,84,27.440000,163.600000,88,13.910000,153.200000,121,6.890000,11.800000,5,3.190000,1,False.\r\nCT,100,510,416-1536,yes,no,0,107.200000,98,18.220000,86.800000,122,7.380000,156.200000,117,7.030000,9.700000,4,2.620000,1,False.\r\nMN,59,408,386-3796,no,yes,32,211.900000,120,36.020000,202.900000,136,17.250000,213.500000,95,9.610000,8.800000,5,2.380000,1,False.\r\nWA,143,510,340-4989,no,no,0,160.400000,120,27.270000,285.900000,104,24.300000,182.500000,85,8.210000,6.900000,4,1.860000,3,False.\r\nPA,142,510,340-6221,no,yes,40,230.700000,101,39.220000,256.800000,88,21.830000,263.900000,92,11.880000,6.400000,3,1.730000,1,False.\r\nID,105,408,363-3469,no,no,0,232.600000,96,39.540000,253.400000,117,21.540000,154.000000,101,6.930000,10.500000,9,2.840000,1,False.\r\nMS,111,408,345-3787,no,no,0,294.700000,90,50.100000,294.600000,72,25.040000,260.100000,121,11.700000,10.800000,3,2.920000,1,True.\r\nWA,143,510,362-3107,no,no,0,133.400000,107,22.680000,223.900000,117,19.030000,180.400000,85,8.120000,10.200000,13,2.750000,1,False.\r\nDC,93,408,345-1994,no,yes,22,306.200000,123,52.050000,189.700000,83,16.120000,240.300000,107,10.810000,11.700000,2,3.160000,0,False.\r\nKY,79,415,377-5417,no,no,0,236.800000,135,40.260000,186.400000,87,15.840000,126.900000,112,5.710000,10.400000,5,2.810000,2,False.\r\nOH,68,415,369-8574,yes,yes,24,125.700000,92,21.370000,275.900000,98,23.450000,214.500000,108,9.650000,14.200000,6,3.830000,3,True.\r\nTN,93,510,344-6847,yes,no,0,168.400000,114,28.630000,276.000000,127,23.460000,196.200000,48,8.830000,11.400000,3,3.080000,1,False.\r\nID,103,415,391-7528,no,no,0,70.900000,134,12.050000,134.500000,112,11.430000,168.800000,164,7.600000,12.000000,6,3.240000,2,False.\r\nWV,144,510,393-6053,no,yes,38,105.000000,86,17.850000,121.800000,123,10.350000,221.500000,122,9.970000,3.700000,4,1.000000,0,False.\r\nWI,93,415,392-6286,no,no,0,152.100000,141,25.860000,215.500000,107,18.320000,262.400000,111,11.810000,12.000000,7,3.240000,1,False.\r\nOK,149,510,365-9079,yes,no,0,180.900000,79,30.750000,194.900000,83,16.570000,197.800000,109,8.900000,8.800000,9,2.380000,3,False.\r\nWV,23,510,399-3089,no,yes,31,156.600000,84,26.620000,161.500000,96,13.730000,294.600000,107,13.260000,9.400000,6,2.540000,1,False.\r\nSD,221,510,365-2192,no,yes,24,180.500000,85,30.690000,224.100000,92,19.050000,205.700000,103,9.260000,2.400000,3,0.650000,0,False.\r\nKS,164,510,394-3051,no,yes,30,238.800000,100,40.600000,230.000000,121,19.550000,206.300000,66,9.280000,13.200000,8,3.560000,1,False.\r\nNC,104,415,357-2429,no,yes,18,182.100000,66,30.960000,213.600000,65,18.160000,193.000000,108,8.690000,13.400000,9,3.620000,2,False.\r\nNY,150,415,421-6268,no,yes,35,139.600000,72,23.730000,332.800000,170,28.290000,213.800000,105,9.620000,8.800000,2,2.380000,2,False.\r\nWI,184,408,401-5915,no,yes,12,200.300000,76,34.050000,253.600000,105,21.560000,149.300000,93,6.720000,10.200000,5,2.750000,0,False.\r\nSC,88,408,348-6057,no,no,0,153.500000,94,26.100000,251.700000,118,21.390000,182.200000,99,8.200000,8.500000,6,2.300000,1,False.\r\nUT,61,415,349-3843,yes,yes,29,128.200000,119,21.790000,171.700000,83,14.590000,250.900000,114,11.290000,11.700000,6,3.160000,3,False.\r\nNC,110,408,396-5561,no,no,0,159.500000,145,27.120000,202.300000,101,17.200000,256.000000,96,11.520000,16.700000,2,4.510000,2,False.\r\nIN,115,415,370-9622,no,no,0,226.400000,101,38.490000,276.800000,60,23.530000,213.400000,82,9.600000,12.300000,4,3.320000,3,True.\r\nAR,33,408,371-9602,no,no,0,251.900000,81,42.820000,194.600000,96,16.540000,211.200000,87,9.500000,8.400000,3,2.270000,2,False.\r\nME,100,510,351-2815,no,no,0,264.500000,117,44.970000,194.000000,111,16.490000,262.700000,111,11.820000,7.500000,4,2.030000,2,True.\r\nNY,209,415,369-8703,no,no,0,153.700000,105,26.130000,188.600000,87,16.030000,200.800000,95,9.040000,10.700000,2,2.890000,0,False.\r\nOR,27,510,355-2840,no,no,0,232.100000,81,39.460000,210.800000,101,17.920000,165.400000,87,7.440000,15.000000,6,4.050000,5,False.\r\nIL,117,415,372-1115,no,no,0,201.900000,86,34.320000,212.300000,96,18.050000,176.900000,98,7.960000,7.800000,10,2.110000,1,False.\r\nMA,87,408,337-2986,no,no,0,186.900000,79,31.770000,182.600000,105,15.520000,143.100000,90,6.440000,4.200000,14,1.130000,1,False.\r\nCT,129,510,404-3238,no,yes,27,196.600000,89,33.420000,180.600000,95,15.350000,245.000000,83,11.030000,6.600000,5,1.780000,1,False.\r\nWI,142,510,397-4968,no,no,0,232.100000,102,39.460000,168.200000,110,14.300000,197.300000,120,8.880000,9.900000,3,2.670000,1,False.\r\nOK,112,415,327-1058,no,no,0,166.000000,79,28.220000,74.600000,100,6.340000,247.900000,74,11.160000,6.300000,7,1.700000,0,False.\r\nDE,75,510,419-9509,no,yes,28,200.600000,96,34.100000,164.100000,111,13.950000,169.600000,153,7.630000,2.500000,5,0.680000,1,False.\r\nAZ,97,408,349-7282,no,yes,25,141.000000,101,23.970000,212.000000,85,18.020000,175.200000,138,7.880000,4.900000,2,1.320000,3,False.\r\nAK,121,408,382-5743,no,yes,34,245.000000,95,41.650000,216.900000,66,18.440000,112.400000,125,5.060000,7.500000,8,2.030000,0,False.\r\nMI,142,415,358-2694,yes,no,0,140.800000,140,23.940000,228.600000,119,19.430000,152.900000,88,6.880000,10.900000,7,2.940000,1,False.\r\nWA,121,510,378-1884,no,no,0,255.100000,93,43.370000,266.900000,97,22.690000,197.700000,118,8.900000,8.800000,3,2.380000,3,True.\r\nSD,87,415,330-1627,no,yes,33,125.000000,99,21.250000,235.300000,81,20.000000,215.300000,95,9.690000,10.200000,7,2.750000,2,False.\r\nSD,34,408,392-5716,no,no,0,180.600000,65,30.700000,280.400000,99,23.830000,292.400000,105,13.160000,5.000000,3,1.350000,1,False.\r\nAK,177,415,384-6132,yes,no,0,248.700000,118,42.280000,172.300000,73,14.650000,191.900000,87,8.640000,11.300000,2,3.050000,1,True.\r\nMA,58,415,359-2740,no,yes,30,178.100000,111,30.280000,236.700000,109,20.120000,264.000000,118,11.880000,8.400000,2,2.270000,0,False.\r\nAR,113,415,338-6714,yes,no,0,122.200000,112,20.770000,131.700000,94,11.190000,169.500000,106,7.630000,10.300000,9,2.780000,5,True.\r\nKS,101,415,347-9968,no,no,0,231.300000,87,39.320000,224.700000,88,19.100000,214.600000,69,9.660000,7.200000,7,1.940000,1,False.\r\nOR,89,415,343-3399,no,no,0,111.200000,101,18.900000,122.100000,94,10.380000,180.800000,85,8.140000,12.600000,2,3.400000,3,False.\r\nNC,77,408,334-6129,yes,yes,44,103.200000,117,17.540000,236.300000,86,20.090000,203.500000,101,9.160000,11.900000,2,3.210000,0,True.\r\nOK,146,510,377-4975,no,no,0,138.400000,104,23.530000,158.900000,122,13.510000,47.400000,73,2.130000,3.900000,9,1.050000,4,True.\r\nNJ,93,415,405-3533,no,no,0,146.300000,85,24.870000,216.600000,95,18.410000,233.000000,82,10.490000,11.500000,3,3.110000,0,False.\r\nOH,160,415,337-9326,no,no,0,206.300000,66,35.070000,241.100000,109,20.490000,227.800000,102,10.250000,11.700000,6,3.160000,0,False.\r\nNM,55,415,338-6556,no,no,0,132.000000,103,22.440000,279.600000,114,23.770000,180.000000,74,8.100000,13.500000,4,3.650000,0,False.\r\nOH,88,408,354-3040,no,no,0,274.600000,105,46.680000,161.100000,121,13.690000,194.400000,123,8.750000,9.200000,4,2.480000,2,False.\r\nMI,63,510,396-1278,no,no,0,185.300000,87,31.500000,225.300000,87,19.150000,194.300000,93,8.740000,11.700000,3,3.160000,0,False.\r\nKS,127,415,354-6810,no,yes,24,154.800000,69,26.320000,177.200000,105,15.060000,207.600000,102,9.340000,9.000000,4,2.430000,1,False.\r\nIL,57,415,403-6237,no,yes,30,179.200000,105,30.460000,283.200000,83,24.070000,228.100000,77,10.260000,14.700000,5,3.970000,1,False.\r\nRI,138,510,411-6823,yes,no,0,286.200000,61,48.650000,187.200000,60,15.910000,146.200000,114,6.580000,11.000000,4,2.970000,2,True.\r\nAR,115,408,338-1400,no,no,0,268.000000,115,45.560000,153.600000,106,13.060000,232.300000,65,10.450000,17.000000,1,4.590000,3,False.\r\nNY,171,415,412-6245,no,no,0,137.500000,110,23.380000,198.100000,109,16.840000,292.700000,131,13.170000,13.300000,5,3.590000,2,False.\r\nWY,148,408,377-3417,no,no,0,243.000000,115,41.310000,191.800000,91,16.300000,117.800000,93,5.300000,13.400000,5,3.620000,2,False.\r\nNC,127,510,343-2597,no,no,0,134.900000,79,22.930000,221.500000,114,18.830000,113.800000,118,5.120000,15.000000,2,4.050000,1,False.\r\nOR,61,415,388-8282,no,no,0,234.200000,76,39.810000,216.700000,108,18.420000,130.600000,122,5.880000,13.900000,2,3.750000,1,False.\r\nVT,131,415,416-8394,no,no,0,175.100000,73,29.770000,171.900000,116,14.610000,131.100000,94,5.900000,7.300000,6,1.970000,1,False.\r\nSD,88,408,343-6643,no,no,0,142.200000,107,24.170000,262.400000,84,22.300000,139.200000,99,6.260000,10.10000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94,21.610000,228.300000,116,19.410000,166.700000,108,7.500000,7.100000,3,1.920000,1,False.\r\nCA,84,415,417-1488,no,no,0,280.000000,113,47.600000,202.200000,90,17.190000,156.800000,103,7.060000,10.400000,4,2.810000,0,True.\r\nLA,75,510,358-9898,yes,no,0,153.200000,78,26.040000,210.800000,99,17.920000,153.500000,100,6.910000,7.800000,3,2.110000,1,False.\r\nWI,114,415,373-7308,no,yes,26,137.100000,88,23.310000,155.700000,125,13.230000,247.600000,94,11.140000,11.500000,7,3.110000,2,False.\r\nIL,71,510,330-7137,yes,no,0,186.100000,114,31.640000,198.600000,140,16.880000,206.500000,80,9.290000,13.800000,5,3.730000,4,True.\r\nIN,58,415,406-8445,no,yes,22,224.100000,127,38.100000,238.800000,85,20.300000,174.200000,86,7.840000,11.500000,7,3.110000,2,False.\r\nAL,106,408,404-5283,no,yes,29,83.600000,131,14.210000,203.900000,131,17.330000,229.500000,73,10.330000,8.100000,3,2.190000,1,False.\r\nOK,172,408,398-3632,no,no,0,203.900000,109,34.660000,234.000000,123,19.890000,160.700000,65,7.230000,17.800000,4,4.810000,4,False.\r\nIA,45,415,399-5763,no,no,0,211.300000,87,35.920000,165.700000,97,14.080000,265.900000,72,11.970000,13.300000,6,3.590000,1,False.\r\nVT,100,408,340-9449,yes,no,0,219.400000,112,37.300000,225.700000,102,19.180000,255.300000,95,11.490000,12.000000,4,3.240000,4,False.\r\nNY,94,415,363-1123,no,no,0,190.400000,91,32.370000,92.000000,107,7.820000,224.800000,108,10.120000,13.600000,17,3.670000,2,False.\r\nLA,128,415,361-2170,no,no,0,147.700000,94,25.110000,283.300000,83,24.080000,188.300000,124,8.470000,6.900000,5,1.860000,2,False.\r\nSC,181,408,406-6304,no,no,0,229.900000,130,39.080000,144.400000,93,12.270000,262.400000,110,11.810000,14.200000,4,3.830000,2,False.\r\nID,127,408,392-5090,no,no,0,102.800000,128,17.480000,143.700000,95,12.210000,191.400000,97,8.610000,10.000000,5,2.700000,1,False.\r\nMO,89,415,373-7713,no,no,0,178.700000,81,30.380000,233.700000,74,19.860000,131.900000,120,5.940000,9.100000,4,2.460000,1,False.\r\nME,149,415,392-1376,no,yes,18,148.500000,106,25.250000,114.500000,106,9.730000,178.300000,98,8.020000,6.500000,4,1.760000,0,False.\r\nMS,103,510,390-6388,no,yes,29,164.100000,111,27.900000,219.100000,96,18.620000,220.300000,108,9.910000,12.300000,9,3.320000,0,False.\r\nSD,163,415,379-7290,yes,no,0,197.200000,90,33.520000,188.500000,113,16.020000,211.100000,94,9.500000,7.800000,8,2.110000,1,False.\r\nOK,52,415,397-9928,no,no,0,124.900000,131,21.230000,300.500000,118,25.540000,192.500000,106,8.660000,11.600000,4,3.130000,2,False.\r\nWY,89,415,378-6924,no,no,0,115.400000,99,19.620000,209.900000,115,17.840000,280.900000,112,12.640000,15.900000,6,4.290000,3,False.\r\nGA,122,510,411-5677,yes,no,0,140.000000,101,23.800000,196.400000,77,16.690000,120.100000,133,5.400000,9.700000,4,2.620000,4,True.\r\nVT,60,415,400-2738,no,no,0,193.900000,118,32.960000,85.000000,110,7.230000,210.100000,134,9.450000,13.200000,8,3.560000,3,False.\r\nMD,62,408,409-1856,no,no,0,321.100000,105,54.590000,265.500000,122,22.570000,180.500000,72,8.120000,11.500000,2,3.110000,4,True.\r\nIN,117,415,362-5899,no,no,0,118.400000,126,20.130000,249.300000,97,21.190000,227.000000,56,10.220000,13.600000,3,3.670000,5,True.\r\nWV,159,415,377-1164,no,no,0,169.800000,114,28.870000,197.700000,105,16.800000,193.700000,82,8.720000,11.600000,4,3.130000,1,False.\r\nOH,78,408,368-8555,no,no,0,193.400000,99,32.880000,116.900000,88,9.940000,243.300000,109,10.950000,9.300000,4,2.510000,2,False.\r\nOH,96,415,347-6812,no,no,0,106.600000,128,18.120000,284.800000,87,24.210000,178.900000,92,8.050000,14.900000,7,4.020000,1,False.\r\nSC,79,415,348-3830,no,no,0,134.700000,98,22.900000,189.700000,68,16.120000,221.400000,128,9.960000,11.800000,5,3.190000,2,False.\r\nAZ,192,415,414-4276,no,yes,36,156.200000,77,26.550000,215.500000,126,18.320000,279.100000,83,12.560000,9.900000,6,2.670000,2,False.\r\nWV,68,415,370-3271,no,no,0,231.100000,57,39.290000,153.400000,55,13.040000,191.300000,123,8.610000,9.600000,4,2.590000,3,False.\r\nRI,28,510,328-8230,no,no,0,180.800000,109,30.740000,288.800000,58,24.550000,191.900000,91,8.640000,14.100000,6,3.810000,2,False.\r\nCT,184,510,364-6381,yes,no,0,213.800000,105,36.350000,159.600000,84,13.570000,139.200000,137,6.260000,5.000000,10,1.350000,2,False.\r\nTN,74,415,400-4344,no,yes,25,234.400000,113,39.850000,265.900000,82,22.600000,241.400000,77,10.860000,13.700000,4,3.700000,0,False.\r\n" }, { "path": "exercises/02-Python&Numpy&Pandas.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 02.1\\n\",\n \"\\n\",\n \"Create a function that receives two inputs a and b, and returns the product of the a decimal of pi and the b decimal of pi.\\n\",\n \"\\n\",\n \"```\\n\",\n \"i.e, \\n\",\n \"pi = 3.14159\\n\",\n \"if a = 2 and b = 4\\n\",\n \"result = 4 * 5\\n\",\n \"result = 20\\n\",\n \"```\\n\",\n \"\\n\",\n \"Caveats:\\n\",\n \"- a and b are between 1 and 15\\n\",\n \"- decimals positions 1 and 2 are 1 and 4, respectively. (remember that python start indexing in 0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from math import pi\\n\",\n \"def mult_dec_pi(a, b):\\n\",\n \" \\n\",\n \" # Add the solution here\\n\",\n \" \\n\",\n \" result = ''\\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mult_dec_pi(a=2, b=4)\\n\",\n \"# 20.0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mult_dec_pi(a=5, b=10)\\n\",\n \"# 45.0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mult_dec_pi(a=14, b=1)\\n\",\n \"# 9.0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"mult_dec_pi(a=6, b=8)\\n\",\n \"# 10.0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Bonus\\n\",\n \"mult_dec_pi(a=16, b=4)\\n\",\n \"# 'Error'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 02.2\\n\",\n \"\\n\",\n \"Using the given dataset. Estimate a linear regression between Employed and GNP.\\n\",\n \"\\n\",\n \"$$Employed = b_0 + b_1 * GNP $$\\n\",\n \"\\n\",\n \"$$\\\\hat b = (X^TX)^{-1}X^TY$$\\n\",\n \"$$Y = Employed$$\\n\",\n \"$$X = [1 \\\\quad GNP]$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'Employed')\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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AJgpqpquY2r6kBVTVfV9O7duzssU5L6rbMgqKq7gbuSnNM2XQjcmuR5\\nwBuAF1XV8a4+X5I0mFM6fv9LgdkkpwK3A5cAnwUeAFyfBOCGqnp5x3VIklbQaRBU1c3A9JLmn+zy\\nMyVJ6+PIYknqOYNAknrOIJCknjMIJKnnDAJJ6jmDQJJ6ziCQpJ4zCCSp5wyCMTU7C1NTsGNH8zi7\\n7K35JGnzDIIN6nJHPTsLe/fC0aNQ1Tzu3WsYSOqGQbABXe+o9+2D40tux3f8eNMuSVvNINiArnfU\\nd965vnZJ2gyDYAO63lHv2bO+dknaDINgA7reUe/fDzt33rdt586mXZK2mkGwAV3vqGdm4MABOPNM\\nSJrHAweadknaal1PTDORFnbI+/Y1h4P27GlCYCt31DMz7vglDYdBsEHuqCVNCg8NSVLPGQSS1HMG\\ngST1nEEgST1nEEhSzxkEktRzBoEk9dzEBoH385ekwUzkgLKF20Qv3CF04TbR4CAwSVpqInsE3s9f\\nkgY3kUHQ9W2iPewkaZJMZBB0eZtop5GUNGkmMgi6vE20h50kTZpOgyDJriQHk9yW5EiSpyV5aJLr\\nk3y5fXzIVn9ul/fzdxpJSZOm6x7BFcB1VfU44FzgCHAZ8ImqeizwiXZ5y83MwB13wIkTzeNWXS3k\\nNJKSJk1nQZDkNOAC4CqAqrqnqr4NvBi4pl3tGuAlXdXQBaeRlDRpuuwRnA3MA1cnuSnJlUkeBDyi\\nqr4O0D4+fLmNk+xNMpdkbn5+vsMy18dpJCVNmlRVN2+cTAM3AE+vqkNJrgC+C1xaVbsWrfdXVbXq\\neYLp6emam5vrpE5JmlRJDlfV9FrrddkjOAYcq6pD7fJB4HzgG0keBdA+frPDGiRJa+gsCKrqbuCu\\nJOe0TRcCtwIfAS5u2y4GPtxVDZKktXV9r6FLgdkkpwK3A5fQhM+1Sf4JcCfwCx3XIElaRadBUFU3\\nA8sdn7qwy8+VJA1uIkcWS5IG19lVQ1spyTxwdAQffTrwrRF87lbZzvVb++hs5/q3c+2w9fWfWVW7\\n11ppWwTBqCSZG+TSq3G1neu39tHZzvVv59phdPV7aEiSes4gkKSeMwhWd2DUBWzSdq7f2kdnO9e/\\nnWuHEdXvOQJJ6jl7BJLUc70NgiSPSfKpdsKcLyb55bb9TUn+IsnN7d/zF21zeZKvJPlSkr89uuoh\\nyQOTfCbJLW39b27bz0pyqJ345w/bUd0keUC7/JX29akxrP33k/z5ou/+vLY9Sf5dW/vnkpw/qtoX\\nS3K/9s66H22Xx/67X7BM7dvmu09yR5LPt3XOtW3LTng1bvWvUPvo9zlV1cs/4FHA+e3zBwN/Bjwe\\neBPw2mXWfzxwC/AA4Czgq8D9Rlh/gB9vn98fOAQ8FbgWuKhtfxfwivb5vwDe1T6/CPjDMaz994GX\\nLbP+84GPtds9FTg06v9+2rpeA7wP+Gi7PPbf/Sq1b5vvHrgDOH1J21uBy9rnlwG/MY71r1D7yPc5\\nve0RVNXXq+rG9vn/opk97dGrbPJi4P1V9f2q+nPgK8DPdF/p8qrxv9vF+7d/BTyb5k6vcN+JfxZP\\nCHQQuDBJhlTufaxS+0peDPxBu90NwK6FO9iOSpIzgL8DXNkuh23w3cOP1r6GsfvuV7DShFfbpf7l\\nDG2f09sgWKztqj+Z5pcpwC+13ch35+Scyo8G7lq02TFWD47Otd37m2lu5X09zS+Gb1fVve0qi2v8\\nYf3t698BHjbcik9aWnudvF35/va7/80kD2jbxu67B34LeD1wol1+GNvku+dHa1+wXb77Aj6e5HCS\\nvW3bShNejVv9y9UOI97n9D4Ikvw48EfAq6vqu8A7gb8GnAd8Hfi3C6sus/lIL7mqqh9U1XnAGTS/\\nFP76cqu1j2NV/9LakzwRuBx4HPDTwEOBN7Srj1XtSV4AfLOqDi9uXmbVsfvuV6gdtsl333p6VZ0P\\n/BzwyiQXrLLuuNW/XO0j3+f0OgiS3J8mBGar6gMAVfWNdid1Avg9TnbFjgGPWbT5GcDXhlnvSqqZ\\nC/rTNMdAdyVZuKvs4hp/WH/7+k8AfzncSn/Uotqf1x6uq6r6PnA14/vdPx14UZI7gPfTHBL6LbbH\\nd/8jtSd57zb67qmqr7WP3wQ+SFPrShNejVX9y9U+Dvuc3gZBe4z2KuBIVb1jUfvi44cvBb7QPv8I\\ncFF7BchZwGOBzwyr3qWS7E6yq33+Y8DfojnP8SngZe1qiyf+WTwh0MuAT1Z7RmrYVqj9tkX/I4fm\\nGO/i7/4ftleAPBX4zsJhgFGoqsur6oyqmqI5+fvJqpphG3z3K9T+97fLd5/kQUkevPAceC5NrStN\\neDU29a9U+zjsc7qemGacPR34B8Dn22PVAP8S+MU0l84VzRn+fw5QVV9Mci3NLGv3Aq+sqh8MveqT\\nHgVck+R+tJP9VNVHk9wKvD/JrwE30YQd7eN7knyF5tfoRaMourVS7Z9MspumS3wz8PJ2/f9Ec/XH\\nV4DjNBMcjaM3MP7f/Upmt8l3/wjgg+259lOA91XVdUk+y/ITXo1T/SvV/p5R73McWSxJPdfbQ0OS\\npIZBIEk9ZxBIUs8ZBJLUcwaBJPWcQSCtIMkjkrwvye3tLQH+NMlLkzwrSSV54aJ1P5rkWe3zT7d3\\ni7wlyX9Pcs7I/hHSAAwCaRntwKoPAX9cVWdX1VNorv8/o13lGLBvlbeYqapzaW6A9rZOi5U2ySCQ\\nlvds4J6qetdCQ1UdrarfbhdvAb6T5DlrvM8fAz/ZUY3SljAIpOU9AbhxjXV+DfhXa6zzQuDzW1KR\\n1BGDQBpAkt9pj/l/dqGtqv6kfe0Zy2wy29665OnAa4dUprQhfb7XkLSaLwJ/d2Ghql6Z5HRgbsl6\\n+2nOFdy7pH2mqpauK40lewTS8j4JPDDJKxa17Vy6UlV9HHgIcO6wCpO2mkEgLaO9TfRLgGemmdT9\\nMzRXAL1hmdX3c/JqImnb8e6jktRz9ggkqecMAknqOYNAknrOIJCknjMIJKnnDAJJ6jmDQJJ6ziCQ\\npJ77/zkjhHUhbcJ/AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"# Import data\\n\",\n \"raw_data = \\\"\\\"\\\"\\n\",\n \"Year,Employed,GNP\\n\",\n \"1947,60.323,234.289\\n\",\n \"1948,61.122,259.426\\n\",\n \"1949,60.171,258.054\\n\",\n \"1950,61.187,284.599\\n\",\n \"1951,63.221,328.975\\n\",\n \"1952,63.639,346.999\\n\",\n \"1953,64.989,365.385\\n\",\n \"1954,63.761,363.112\\n\",\n \"1955,66.019,397.469\\n\",\n \"1956,67.857,419.18\\n\",\n \"1957,68.169,442.769\\n\",\n \"1958,66.513,444.546\\n\",\n \"1959,68.655,482.704\\n\",\n \"1960,69.564,502.601\\n\",\n \"1961,69.331,518.173\\n\",\n \"1962,70.551,554.894\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"data = []\\n\",\n \"for line in raw_data.splitlines()[2:]:\\n\",\n \" words = line.split(',')\\n\",\n \" data.append(words)\\n\",\n \"data = np.array(data, dtype=np.float)\\n\",\n \"n_obs = data.shape[0]\\n\",\n \"plt.plot(data[:, 2], data[:, 1], 'bo')\\n\",\n \"plt.xlabel(\\\"GNP\\\")\\n\",\n \"plt.ylabel(\\\"Employed\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Exercise 02.3\\n\",\n \"\\n\",\n \"Analyze the baby names dataset using pandas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"# Load dataset\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/baby-names2.csv.zip', 'r') as z:\\n\",\n \" f = z.open('baby-names2.csv')\\n\",\n \" names = pd.io.parsers.read_table(f, sep=',')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearnamepropsexsoundex
01880John0.081541boyJ500
11880William0.080511boyW450
21880James0.050057boyJ520
31880Charles0.045167boyC642
41880George0.043292boyG620
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year name prop sex soundex\\n\",\n \"0 1880 John 0.081541 boy J500\\n\",\n \"1 1880 William 0.080511 boy W450\\n\",\n \"2 1880 James 0.050057 boy J520\\n\",\n \"3 1880 Charles 0.045167 boy C642\\n\",\n \"4 1880 George 0.043292 boy G620\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"names.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearnamepropsexsoundex
1130001993Michael0.024010boyM240
1130011993Christopher0.018572boyC623
1130021993Matthew0.017332boyM300
1130031993Joshua0.016268boyJ200
1130041993Tyler0.014439boyT460
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year name prop sex soundex\\n\",\n \"113000 1993 Michael 0.024010 boy M240\\n\",\n \"113001 1993 Christopher 0.018572 boy C623\\n\",\n \"113002 1993 Matthew 0.017332 boy M300\\n\",\n \"113003 1993 Joshua 0.016268 boy J200\\n\",\n \"113004 1993 Tyler 0.014439 boy T460\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"names[names.year == 1993].head() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### segment the data into boy and girl names\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"boys = names[names.sex == 'boy'].copy() \\n\",\n \"girls = names[names.sex == 'girl'].copy()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Analyzing the popularity of a name over time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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U6J8fTJTGXH3grufWc5RbvLPxeflZbE0UO6c8/by9laUs6vzxkZtdxF\\nYo2Kfjvz7eMG07NzCjc+N48v3/fBp+szOyVyfmge/senr+G9pVu49LD+HDskm7gWLqhvLd7CL15Y\\ngBkkxBkVVZ/tvR82MIubzxhOVloSM1Zto6LSOWt0H1IS4/jjG0u5790VbN9dzl0XjSElMb5F8xQR\\nsLb28zovL8/z8/OjnUabt2B9MasKdzMyJ4PvPf0xW3aW8s6PjmNt0R7OuOd/uENltXNgz3Qe+9oE\\nemWktEge7s45906jaE85b//wOBLj4yitqGJTcSl7K6oY2qszZvV/6UyauorbXl7EYQOz+PvleXRO\\nSWyRPEU6OjOb7e55DcWpT7+dGpmTwVmj+5DbPY2bTh3KxuJSHpm2mhufm0d6cgJTbzyBOy4YzarC\\n3dz/3opmPZe7M2t1EXe8uZStu8o+t+29ZVuZW1DMdccNJjE++HNKSYxnQPc0hvXust+CD3D1Ubnc\\ndeEY8ldv56IHp7OztKJZuYrI/ql7pwM4fFA3jjsomz++sYRqhzsvHE2vjBTOHdeXD1ds46lZa/nO\\nCYPplp7MP2es5ZFpqzhnbA5nj+7DrNVFPDVzHWWVVXxpWE/yBmSxsrCEhRt2UlJaiQMLNxSzcutu\\nACbP3cDjV0+gX1Yn3J173vqEnMxUzh3Xt8n5nzM2h4zURL7xWD7ffmIOj1x16KdfICISWere6SAW\\nb9zJ6ff8j2MPzOaRKw/9dA97+ZZdfOmO97nhhMGcNLwX5/5tGl07JbGlxh77wO5pZHRK5KO1Oz5d\\nl5GaSNdOiZgZPbsk85VxfemTmcq3nphNalI8Xzsyl1WFu3lq1jpuO2dkRC7v+OzsAn7077mcf0hf\\nvnPCEDbtLGVAt0706NIyXVMiHUm43Tsq+h3IgvXF5HZPIy358z/grnksnxmriujaKZGyympeveFo\\nivaU88bCTYzpl8nhA7thZmzeWcqC9cUM6dGZflmpdXbNLNm0kysensnmnWV0Soonb0AWD371kIgd\\nhL1zyjLufuuTT+9npCbywnVHkts9LSLti3RUKvryqTlrt3PufR8QZ/DUNYczPjerWe2VVVZRWl5N\\nl9SEBvvsG8vdeXneRvZWVNElJZGfPT+fzNREnv/2kWR00kFekfqEW/TVpx8Dxh3QlW8cnUtu9/Rm\\nF3wILumYnNAywyvNjLNG9/n0flZaEpc+NJ1v/3M2/7hqvPr6RZpJ/4NixM/PGM4lEw6IdhqNNj43\\ni99++WCmLd/GH15fEu10RNq9sIq+mZ1qZkvNbLmZ3VTH9mQzezq0fYaZDai1/QAzKzGzH0UmbYkl\\n5+f14/LD+/P3/63ilXkbo52OSLvWYNE3s3jgXuA0YDhwsZkNrxV2NbDd3QcDdwK319p+J/Ba89OV\\nWHXzGcMZe0AmP3l2Lsu37Ip2OiLtVjh7+uOB5e6+0t3LgaeAibViJgKPhpafBU600BE+MzsHWAks\\njEzKEouSEuK479JxpCTG87V/5FNYUtbwg0TkC8Ip+jnAuhr3C0Lr6oxx90qgGOhmZmnAjcAt+3sC\\nM7vGzPLNLH/r1q3h5i4xpndGKg9dkceWXaVc/Wg+e8o1PbNIY4VT9Osak1d7nGd9MbcAd7p7yf6e\\nwN0fdPc8d8/Lzs4OIyWJVWMP6Mo9F41lfsEOrntyDqUVLXNtgZKySn2pSIcUTtEvAPrVuN8X2FBf\\njJklABlAETAB+IOZrQa+B/zMzK5vZs4S404e0YvbzhnJu8u2cuGD078wH1BzzVpdxPF/epcJv32L\\nP7y+JOLti0RTOEV/FjDEzHLNLAm4CJhcK2YycEVo+TzgbQ8c7e4D3H0AcBfwW3f/a4Rylxh26YT+\\n/O3SQ1i6aSfn3DuN/8wpaPZef2lFFZOmruLiB6eTlhTPUYO787f3VnDMH97h8elrdMEX6RDCOiPX\\nzE4nKNrxwMPu/hszuxXId/fJZpYCPA6MJdjDv8jdV9Zq41dAibv/aX/PpTNypTHmFxTz3ac+YmXh\\nbrqkJPCl4T0ZPyCLIwZ154BuncJqY/rKbTz24WreXbqVPeVVfGlYD/58wRgyUhNZubWEX720iPeX\\nbeW4g7L50/mj6Z6e3LIvSqQJNA2DxIzqamf6qm08PWsd//uk8NMrdZ05qjffP+lAUhPjmVdQzJpt\\nu9m6q4ySskoGZaczuEc6T89ax+sLN9E9PYmTR/TilBG9OHpw989deMbdeXz6Gn776mJyMlP51zcO\\n0yRw0uao6EtMcndWbN3NCx+t5+Fpq9hT6yLyKYlxpCUlsC30xdApKZ5vHzeIrx89sMFJ42auKuLK\\nR2bSKyOFp1T4pY1R0ZeYV1hSxtOz1tE5JYGRORkM6ZFOenIwSdy2kjKWbtrF4J7p9OgcfvGetbqI\\nKx+eSZU7Q3t14eCcDI49MJsjB3cnNUmXe5ToUdEXaSELNxTz3Oz1LNxQzIL1xewuryIlMY7vfelA\\nrj12ULTTkxilWTZFWsiIPhmM6JMBQHllNTNXFfHItFX8/rUl9M5IYeKY2ucuirQdmmVTpBmSEuI4\\nakh3/nbZIYzPzeLHz85jztrt0U5LpF4q+iIRkJQQx/2XHUKvLilc+fBMHp++hqrqttV1KgIq+iIR\\nk5WWxONXj2dY7y784oUFnHHP//h43Y6GHyjSilT0RSKof7c0nrrmMO69ZBzFeyv4yt8+4M9vLqW8\\nsjraqYkAKvoiEWdmnDGqN298/xjOGZPDX95ezql3v8/zHxVQWaXiL9Gloi/SQrqkJPLnC0Yz6Yo8\\nkuLj+P7Tczn5rvdZsmlntFOTGKaiL9LCThzWk1dvOJr7LxtHSWklX7nvA/67aHO005IYpaIv0gri\\n4oxTR/Zm8vVHMTA7nW88ns8j01ZFOy2JQSr6Iq2oV0YKz3zzcE4a1pNbXlrEPW99oimbpVWp6Iu0\\nstSkeO67dBznjsvhjinL+P1rS1T4pdVoGgaRKEiIj+NP540mLSmBB95fSVpyAjecOCTaaUkMUNEX\\niZK4OOOWs0ewp7yKO6YsI7NTIpcfPiDaaUkHp6IvEkVxccbtXzmY4r0V/N+LC4mPMy6d0D/aaUkH\\npj59kShLiI/jr5eM5YShPfj58wv427srop2SdGAq+iJtQEpiPA989RDOHt2H219fwp1TlkU7Jemg\\n1L0j0kYkxsdx54VjSE6I4+63PqFrp0SuPDI32mlJB6OiL9KGxMcZvzs36OO/5eVFdEtP5qzRfaKd\\nlnQg6t4RaWMS4uO45+KxHNo/ix8+M5cF64ujnZJ0ICr6Im1QSmI893/1ELqmJXLDUx+xp7wy2ilJ\\nB6GiL9JGZaUlcecFY1hVuJtbJi+KdjrSQajoi7RhRwzuzreOHcTT+eu4+7+f6BKM0mwq+iJt3PdP\\nOpCJY/pw53+XcelD09lUXBrtlKQdU9EXaeMS4+O468Ix/PG8UcxdV8yFD37IztKKaKcl7ZSKvkg7\\nYGacn9ePx64eT8H2vfz0P/M1M6c0iYq+SDty6IAsfnTyQbwybyNPzFgb7XSkHVLRF2lnvnnMQI47\\nKJvbXlrEa/M3RjsdaWdU9EXambg4464LxzAypwvfenIOD7y3Ql09EjYVfZF2KLNTEv/8xmGcMao3\\nv3ttCZdNmsHUTwpV/KVBKvoi7VRKYjx/uWgsvzhzOMs2l3DZpBlc8MCHbNmlIZ1SP2trewZ5eXme\\nn58f7TRE2pWyyiqenV3Ar19eTFZaEpOuzCMnM5Ulm3Yxa3URHyzfxo695XznhCGcPLwnZhbtlCXC\\nzGy2u+c1GBdO0TezU4G7gXjgIXf/fa3tycBjwCHANuBCd19tZicBvweSgHLgx+7+9v6eS0VfpOkW\\nrC/m6kdnsa2knMoaZ+8e1LMzldXVrNi6m6MGd+ek4T3pk5lKckIc2/eUU1ZRzbj+mQzKTtcXQjsV\\nbtFvcGplM4sH7gVOAgqAWWY22d1rTgZyNbDd3Qeb2UXA7cCFQCFwlrtvMLORwBtATuNfjoiEY2RO\\nBi9edxQPvr+SbulJHNSzM6P7ZZLdOZnKqmqenLGWu/67jKnLC+t8fO+MFI4c3J2jh3Qnb0AWvbqk\\nEB+nL4GOpME9fTM7HPiVu58Suv9TAHf/XY2YN0IxH5pZArAJyPYajVuw+1AI9HH3svqeT3v6Ii3L\\n3SksKWf9jr2UV1aTlZZEnMGMVUVM/aSQaSsK2bEnOOM3Ic7ol9WJm88YxonDekY5c9mfiO3pE+yZ\\nr6txvwCYUF+Mu1eaWTHQjaDI7/MV4KP9FXwRaXlmRnbnZLI7J39u/cDsdC4efwBV1c7CDcXMKyhm\\nw469vL1kC19/LJ+fnz6Mq4/KVfdPOxdO0a/rE67982C/MWY2gqDL5+Q6n8DsGuAagAMOOCCMlESk\\npcTHGaP6ZjKqbyYA3zlhCD945mN+/cpiNhWXcvOZw6OcoTRHOEM2C4B+Ne73BTbUFxPq3skAikL3\\n+wLPA5e7+4q6nsDdH3T3PHfPy87ObtwrEJEWlZoUz72XjOPKIwbw0NRVPDJtVbRTkmYIp+jPAoaY\\nWa6ZJQEXAZNrxUwGrggtnwe87e5uZpnAK8BP3X1apJIWkdYVF2f835nDOWVET259eRFTFm2OdkrS\\nRA0WfXevBK4nGHmzGHjG3Rea2a1mdnYobBLQzcyWAz8Abgqtvx4YDPzCzD4O3XpE/FWISIsLpn8Y\\ny8E5GXznX3N4bnZBtFOSJtDJWSLSKIUlZVz/zzlMX1nExeP78cuzRpCSGB/ttGJeuKN3NA2DiDRK\\n9/Rknrh6At8+bhD/mrmOc+/7gDXbdkc7LQmTir6INFpCfBw/OXUok67IY/2OvZz5l6m8vkDTPLcH\\nKvoi0mQnDuvJy985itzuaVz7xBx+/vx89pZXRTst2Q8VfRFpln5ZnXj22iO45piBPDljLWf/dSpL\\nNu2MdlpSDxV9EWm2pIQ4fnb6MB772ni276ng7L9O49EPVmt+/zZIRV9EIuaYA7N5/XtHc+Sgbvxy\\n8kLOuXcak+duoKKqOtqpSYiGbIpIxLk7z+Sv4/73VrKqcDfZnZM5ZURPThvZmwm5WSTEa38z0iI6\\nn35rUtEX6Tiqq513lm7huTkFvLNkK3srqsjslMhJw3oyqEc6e8qrSEmM47xD+tKjc8qnj3N3Siuq\\nqXYnLTmcKcJERV9E2pS95VW8t2wrbyzcxH8Xb2ZXaeWn25IS4jh3bA6V1c6s1UWsLdrDvtI09oBM\\nThvZi7NH59ArI6We1kVFX0TarIqqasorq0lNjGdt0R4eeH8Fz81eT3pKAnn9u3Jgz86kJSdQWlHF\\nW0s2s2D9ThLijFNG9uK8cX0ZmJ1G74xUkhLUTbSPir6ItCulFVUkJ8TVOV//6sLdPDljDU/PWsfO\\n0C+EOIOhvbqQN6ArRwzqznEHZcf0dBAq+iLS4ewpr+TjdTso2L6XNdt28/G6HXy0dgd7yqtIS4rn\\n+KE9OHJwdybkZpHbPS2mLvgSyStniYi0CZ2SEjhiUPfPrausqmb6yiJemb+BKYu28PK8YDqIjNRE\\nRvTpwuh+mRwxqBuHDsiK6V8C+2hPX0Q6DHdnZeFuZqwsYv76YhZuKGbRhp1UVjtJ8XGM65/JkYO6\\nc2huFiNzMkjvQCODtKcvIjHHzBiUnc6g7PRP1+0uq2Tm6iI+WF7I1OXb+POUZaFYGJSdzqicDEb1\\nzeDM0X3onp5cX9Mdhvb0RSSmFO0uZ+66HcwrKGZewQ7mFhRTWFJGSmIcFx16ACNzMphXsIP12/cy\\ntHdnDs7JZGPxXmauKqKwpIweXVLIyUxlTL9MDh2Q9YULzEeLDuSKiITB3VmxtYT731vJCx+tp7La\\nSUuKp09mKisLd1NVHdTIvl1TyclMZcuuMtbv2Et5ZTC1RJ+MFAb1SCcnM5Vqd6odcruncXBOBt3T\\nk9ldXkl8nDG2X2aLHlhW0RcRaaRNxaXsKq1gYHY68XHG3vIqlmzaSc8uKfTJTP00rqKqmvnri5m5\\nqoglG3eyfGsJm3eWkRBnVLuzeWfZF9o+pH9XfnHmcMb0y2yR3FX0RUSipHhvBQvXF1O8t4L0lATW\\nbNvDXf/9hMKSMvL6d+X4oT2YkJtFzy4pZHdOjsioIhV9EZE2pKSskn9MW8VrCzaxcMPnrzfQOSWB\\nHp2TOWVEL35y6tAmta/ROyIibUh6cgLXnzCE608YwuadpSzasJOtu8rYWlIW/LurjC6piS2eh4q+\\niEgr69klhZ5dojN5nGYrEhGJISr6IiIxREVfRCSGqOiLiMQQFX0RkRiioi8iEkNU9EVEYoiKvohI\\nDFHRFxGJISr6IiIxREVfRCTPw98rAAAMq0lEQVSGqOiLiMQQFX0RkRgSVtE3s1PNbKmZLTezm+rY\\nnmxmT4e2zzCzATW2/TS0fqmZnRK51EVEpLEaLPpmFg/cC5wGDAcuNrPhtcKuBra7+2DgTuD20GOH\\nAxcBI4BTgftC7YmISBSEs6c/Hlju7ivdvRx4CphYK2Yi8Gho+VngRAuuADwReMrdy9x9FbA81J6I\\niERBOBdRyQHW1bhfAEyoL8bdK82sGOgWWj+91mNzaj+BmV0DXBO6W2JmS8PKvm7dgcIWiFV8dOPb\\nUi6Kbz+5dIT4cPUPJyicom91rKt9Yd36YsJ5LO7+IPBgGLk0yMzyw7lOZGNjFR/d+LaUi+LbTy4d\\nIT7SwuneKQD61bjfF9hQX4yZJQAZQFGYjxURkVYSTtGfBQwxs1wzSyI4MDu5Vsxk4IrQ8nnA2+7u\\nofUXhUb35AJDgJmRSV1ERBqrwe6dUB/99cAbQDzwsLsvNLNbgXx3nwxMAh43s+UEe/gXhR670Mye\\nARYBlcB17l7VQq9ln8Z0EzW2S0nx0YtvS7koPnptx2J8RFmwQy4iIrFAZ+SKiMQQFX0RkRiioi8i\\nEkNU9EVEYkg4J2eJiEiImWUQzCWWQ3Cy6QbgDXffEcZjc4GxwCJ3X9KiidaXQ3sfvdOSH4CZDSWY\\nP6hm25PdfXEYbR9FMM/QAnd/s56Ylm7/FOCcWu2/6O6v1xHb3d0La9y/bF/7wN+9jj+UVmi/Se+P\\n3vu23X57fu/N7HLgl8CbwPrQ6r7AScAt7v5YrfgX3P2c0PJE4C7gXeAI4Hfu/o+GXnOktevundAH\\nMAc4DugEpAHHA7ND22rHv1BjeSLwNnAW8KKZXVkr9kaCyeWM4ISyWaHlf9UzvfTMGsvfAP4KdAZ+\\nWU98S7d/F/Bd4D3gD8AfQ8s3mNndteMJ/oj3PfZm4KvAbII/5jui0H7Y74/e+/bTfnt/74GfA4e4\\n+7fc/deh27VAHnBzHfE158O5ETjB3a8CjgS+X0d8y3P3dnsDlgKZdazvCiyrY/1HNZY/AHJDy92B\\nubVilwGJdbSRBHzSQNuzgOzQchowv474Fm+/nvfMwmh/DpAWWk6MVvvhvj9679tP+x3hvQcy6lif\\nUU/7c2osz6zvuVvz1t779I06JnADqml4srcED6Z7xt0Lzay6jjb6AGtqre8d2lZbnJl1Jfj1ZO6+\\nNdT2bjOrrCfHlmy/1MzGu3vtaS8OBUrriE81s7Gh9uPdfXeo/Qozq+ss6pZuvzHvj9779tN+e3/v\\nfwPMMbM3+Wz24QMIfhncVkf8aDPbSVCPks2sl7tvCk1pE5Vri7T3ot+SH8D3gLfM7JNabQ8Grq+j\\n7QyCn4UGeI2206n7C6il278S+JuZdSaY+A6Cye92hrbVtpHPfs4WmVlvd99oZt0IptBo7fYb8/7o\\nvY9s+1cRXPAo3PY3NaL9dv3eu/ujZjYZOIXgmIER9NH/1N231xFfX2HvBHyznm0tqiMcyO3K5z+A\\nAoIDuV/4APbTRiYwzN0/rLU+juCgTs22Z3kj5g8ys05Az32/Klqz/dD2XjXbd/dN4bYdenw8kOzu\\ne1q7/ea+P3rvG2w/Dkhp7c+2g7z3PalxoNjdNzfQXqPiW1JHKPoJ7l4ZWk4HhgIr3b0oQvHZBEfn\\nK4FV7l7SQD5hx5uZ8dkf/75RBjO9ng+lsfH7ed6h3ojhYvuLN7NEd6+ote5zIyKaGh8qDrh7dejX\\n2EhgdV2fVWNi68nr2+5+XzixjY0P/Z0dSPB3Fs6osv3Gh15fxb7P3cyOB8YBC73uESr1xS9y99fq\\niB/l7vPCeW1NjD8A2OnuOyy4nnYesNjdFzYifom7L9jPc+QR7OFXEvS17/fvPdx4MxsD3E/wC6SA\\n4EulL7AD+La7z6kVPxb4Wyi+5mifHcC33P2j/eXVIhp7EKAt3Qh+rm0jOLhyGrASeIvgZ+PFzYkn\\nuB7wfwku8VgOzABWAf+g7gM5jY0/ORT7GvBQ6PZ6aN3JzY1v4H1b29x4glFSBcBWghEQA2psmxOB\\n+HOAzQQ/vyeG3s+3Q22c1dTYUPwP6rgV7lsOI/6HDcTfV2P5KGAt8E7o7+z0CMTPBbqGln9MMCjh\\nZmAKwTDAxsT/vo74qtDf1W3A8DD+PsKOB24K/b9YAnw99O8kYGE972Vj448F8gn+L24HXgamEXTB\\n9ItA/MfAhDrWH0atwSBNiW+NW6s/YUSTh/kEI29yCfrsBoXW9wTmNSee4DKPB4WWxwOPhpa/ATxb\\nR9uNjV9MjcJXY30uwV5Pc+Pvqef2F4K9pubGzwJGhJbPAz4BDgvd/8KohCbEfwT0qvFZ7Xtv+xNM\\n6d2k2ND6XcDTwP8RjLn+Zeg//C+BX0YgvuaIjXeAcaHlgfXk09j4BTWW84HU0HJC7b/jJsZ/RPBL\\n6TcExXwuQfH9wt9fY+MJinUqweVUd/H50TgLIhD/UY2YXOD50PJJwJsRiP/CCJ0a25Y3N741bu16\\nnD5Q5e6FHvTrlbj7CgCvv7+sMfGp7r40tH0mcHBo+e8Ee/XNjU/gswNNNa0nGC7W3PirCE4wmV3r\\nlk/wS6S58Uke+jnu7s8S7G0/amZfpu4RVY2Nx903hT6rtTXe2zXUcX5JY2KBEQQH7tOAP7r7LcB2\\nd78ltNzc+Jq6eOgnv7uvpOERG+HE7zSzkaHlQiAltJxA3a+3sfHu7gvc/efuPphgx6UH8D8z+6CZ\\n8VXuvpege2MvwS9vPDRqpg6NjY/30Agfgl9M/UPxU6jj+txNiH/NzF4xswvN7IjQ7UIze4Xgl3dz\\n41tcex+9s9bMfkdwssYSM/sz8B/gSwQ/9ZsTv8LMfkHQ/XMuwc80zCyRut+3xsY/DMwys6f4bBRD\\nP4IL0EyKQPwsgj2hL/wnNbNfRSC+Yt9ICvj0gjknEvw8HhSBeMwszt2rga/VWBdPMKa7ybHuvhY4\\nz4IT9KaY2Z11PX9T44GhZjaPoL93gJl1dfftoeMOdX1BNzb+WuBJM5sLbAHyzew9YBTw2wjEf25U\\nTGgnZqaZ/RA4ppnxc8zsnwRfoG8RfPG/DpxAcLGl2hobn29mk0KxEwm6afYd+K3rC7RR8e5+g5md\\nxmdnFO87EH2vu7/a3PjW0K4P5JpZF+A6gj3FvxJMx3AlwTf2be6+sanxFozo+RnBXvpcgr7PXRZM\\n+zDM3afXartR8aHHDAfO5vN/DJPdva4/5kbFm1kWUOr1jD6IQPyXgK3uPrfW+kyCK6T9ppnxhxKc\\nHFNaa/0A4Ch3f6IpsXW8jjTgVwT9rnUVtEbHm1n/Wqs2unu5mXUHjnH3/zQnPvSYeILjPAfy2a/A\\neqcfaUy8mV3i7v+sq5162g473oJraJ9P8H/wWWACcDHB/8F7a+/BNyE+keCXxr7/hw+7e5WZpQI9\\nQr/+mhzfEbTroi8i0ppCO3E/Jdhz7xFavQV4kWBHb0dz4ltDu+7TN7M8M3vHzJ4ws35mNsXMdpjZ\\nrNBQqSbH1xNb3Mi29xefbma3mtnCUNxWM5tuteYAUnzD8W0pF8U3+bO6opFtNxS/oJG5hxUPPENw\\nEP94d+/m7t0IRqbtAP4dgfiW19wjwdG8EUzYdBrBz711wHmh9ScCHzYnviXbDq1/kaBrqS/B0L9f\\nAEOAR4HfKj78+LaUi+I7/Ge1dD/16AvbGhvfGrdWf8KIJv/5yZLW1retKfEt2XZoXe0J3maF/o0j\\nOPFE8WHGt6VcFN/hP6s3gZ8QnA28b11Pghk0/9vc+Na4tevuHYLJlU42s/MJ5uXYN2/1sQQnjDQn\\nviXbBthtwdzgmNlZQBEEZ5RCnXOKKL7++LaUi+I79md1IcE5A++Z2XYzKyIY8ZMFXBCB+JYXjW+a\\nSN2A0cAbBGepDgXuJugrWwgc0Zz4lmw7FD+KoEtoBzAVODC0Phu4QfHhx7elXBTfsT+r0LahBMO8\\n02utPzUS8S19a/UnbLUXBle1VHxLtq14fVaxGt+WcqkvHriB4DoeLwCrgYk1ttU1nUij4lvj1upP\\n2GovLALzy0SjbcXrs4rV+LaUS33xBFO5pIeWBxCcsf7d0P26jt01Kr41bu36jFwLzmKscxPBwZIm\\nx7dk24qPbHxbykXx+49vS7k0JZ5g2oYSAHdfbWbHAc9acIJdXccAGhvf4tp10Sf4UE4hGAdbkxHM\\nJNic+JZsW/GRjW9LuSh+//FtKZemxG8yszHu/jGAu5eY2ZkE06QcHIH4Ftfei/7LBD+dPq69wcze\\nbWZ8S7at+MjGt6VcFL//+LaUS1PiL6fWFbU8uD7H5Wb2QATiW5ymYRARiSHtfZy+iIg0goq+iEgM\\nUdEXEYkhKvoiIjFERV9EJIb8P+G/MymBxQkMAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"william = boys[boys['name']=='William']\\n\",\n \"\\n\",\n \"plt.plot(range(william.shape[0]), william['prop'])\\n\",\n \"plt.xticks(range(william.shape[0])[::5], william['year'].values[::5], rotation='vertical')\\n\",\n \"plt.ylim([0, 0.1])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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jvuo0JfRHK2aXczX7/3FY6ePpbvX7CA1o4kAKOL8hlfWsDE0UMnVGN5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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"Daniel = boys[boys['name']=='Daniel']\\n\",\n \"\\n\",\n \"plt.plot(range(Daniel.shape[0]), Daniel['prop'])\\n\",\n \"plt.xticks(range(Daniel.shape[0])[::5], Daniel['year'].values[::5], rotation='vertical')\\n\",\n \"plt.ylim([0, 0.1])\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 02.3\\n\",\n \"\\n\",\n \"Which has been the most popular boy name every decade?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 02.4\\n\",\n \"\\n\",\n \"Which has been the most popular girl name?\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 02.5\\n\",\n \"\\n\",\n \"What is the most popular new girl name? (new is a name that appears only in the 2000's)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "exercises/03-IncomePrediction.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 03\\n\",\n \"\\n\",\n \"Estimate a regression using the Income data\\n\",\n \"\\n\",\n \"\\n\",\n \"## Forecast of income\\n\",\n \"\\n\",\n \"We'll be working with a dataset from US Census indome ([data dictionary](https://archive.ics.uci.edu/ml/datasets/Adult)).\\n\",\n \"\\n\",\n \"Many businesses would like to personalize their offer based on customer’s income. High-income customers could be, for instance, exposed to premium products. As a customer’s income is not always explicitly known, predictive model could estimate income of a person based on other information.\\n\",\n \"\\n\",\n \"Our goal is to create a predictive model that will be able to output an estimation of a person income.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
AgeWorkclassfnlwgtEducationEducation-NumMartial StatusOccupationRelationshipRaceSexCapital GainCapital LossHours per weekCountryIncome
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States51806.0
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States68719.0
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States51255.0
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States47398.0
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba30493.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Age Workclass fnlwgt Education Education-Num \\\\\\n\",\n \"0 39 State-gov 77516 Bachelors 13 \\n\",\n \"1 50 Self-emp-not-inc 83311 Bachelors 13 \\n\",\n \"2 38 Private 215646 HS-grad 9 \\n\",\n \"3 53 Private 234721 11th 7 \\n\",\n \"4 28 Private 338409 Bachelors 13 \\n\",\n \"\\n\",\n \" Martial Status Occupation Relationship Race Sex \\\\\\n\",\n \"0 Never-married Adm-clerical Not-in-family White Male \\n\",\n \"1 Married-civ-spouse Exec-managerial Husband White Male \\n\",\n \"2 Divorced Handlers-cleaners Not-in-family White Male \\n\",\n \"3 Married-civ-spouse Handlers-cleaners Husband Black Male \\n\",\n \"4 Married-civ-spouse Prof-specialty Wife Black Female \\n\",\n \"\\n\",\n \" Capital Gain Capital Loss Hours per week Country Income \\n\",\n \"0 2174 0 40 United-States 51806.0 \\n\",\n \"1 0 0 13 United-States 68719.0 \\n\",\n \"2 0 0 40 United-States 51255.0 \\n\",\n \"3 0 0 40 United-States 47398.0 \\n\",\n \"4 0 0 40 Cuba 30493.0 \"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"# read the data and set the datetime as the index\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/income.csv.zip', 'r') as z:\\n\",\n \" f = z.open('income.csv')\\n\",\n \" income = pd.read_csv(f, index_col=0)\\n\",\n \"\\n\",\n \"income.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(32561, 15)\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"income.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 4.1 \\n\",\n \"\\n\",\n \"What is the relation between the age and Income?\\n\",\n \"\\n\",\n \"For a one percent increase in the Age how much the income increases?\\n\",\n \"\\n\",\n \"Using sklearn estimate a linear regression and predict the income when the Age is 30 and 40 years\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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4dFBTLOsuNhUkCD6Yxv0i70WQR8nVKznVOBij+7URnJ90PjR69sNBUX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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"income.plot(x='Age', y='Income', kind='scatter')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 03.2\\n\",\n \"\\n\",\n \"Evaluate the model using the MSE\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"# Exercise 03.3\\n\",\n \"\\n\",\n \"Run a regression model using as features the Age and Age$^2$ using the OLS equations\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 03.4\\n\",\n \"\\n\",\n \"\\n\",\n \"Estimate a regression using more features.\\n\",\n \"\\n\",\n \"How is the performance compared to using only the Age?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "exercises/04-CreditScoring.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 04\\n\",\n \"\\n\",\n \"## Logistic regression for credit scoring\\n\",\n \"\\n\",\n \"\\n\",\n \"Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. \\n\",\n \"\\n\",\n \"Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. [Dataset](https://www.kaggle.com/c/GiveMeSomeCredit)\\n\",\n \"\\n\",\n \"Attribute Information:\\n\",\n \"\\n\",\n \"|Variable Name\\t|\\tDescription\\t|\\tType|\\n\",\n \"|----|----|----|\\n\",\n \"|SeriousDlqin2yrs\\t|\\tPerson experienced 90 days past due delinquency or worse \\t|\\tY/N|\\n\",\n \"|RevolvingUtilizationOfUnsecuredLines\\t|\\tTotal balance on credit divided by the sum of credit limits\\t|\\tpercentage|\\n\",\n \"|age\\t|\\tAge of borrower in years\\t|\\tinteger|\\n\",\n \"|NumberOfTime30-59DaysPastDueNotWorse\\t|\\tNumber of times borrower has been 30-59 days past due |\\tinteger|\\n\",\n \"|DebtRatio\\t|\\tMonthly debt payments\\t|\\tpercentage|\\n\",\n \"|MonthlyIncome\\t|\\tMonthly income\\t|\\treal|\\n\",\n \"|NumberOfOpenCreditLinesAndLoans\\t|\\tNumber of Open loans |\\tinteger|\\n\",\n \"|NumberOfTimes90DaysLate\\t|\\tNumber of times borrower has been 90 days or more past due.\\t|\\tinteger|\\n\",\n \"|NumberRealEstateLoansOrLines\\t|\\tNumber of mortgage and real estate loans\\t|\\tinteger|\\n\",\n \"|NumberOfTime60-89DaysPastDueNotWorse\\t|\\tNumber of times borrower has been 60-89 days past due |integer|\\n\",\n \"|NumberOfDependents\\t|\\tNumber of dependents in family\\t|\\tinteger|\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Read the data into Pandas \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SeriousDlqin2yrsRevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependents
010.76612745.02.00.8029829120.013.00.06.00.02.0
100.95715140.00.00.1218762600.04.00.00.00.01.0
200.65818038.01.00.0851133042.02.01.00.00.00.0
300.23381030.00.00.0360503300.05.00.00.00.00.0
400.90723949.01.00.02492663588.07.00.01.00.00.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" SeriousDlqin2yrs RevolvingUtilizationOfUnsecuredLines age \\\\\\n\",\n \"0 1 0.766127 45.0 \\n\",\n \"1 0 0.957151 40.0 \\n\",\n \"2 0 0.658180 38.0 \\n\",\n \"3 0 0.233810 30.0 \\n\",\n \"4 0 0.907239 49.0 \\n\",\n \"\\n\",\n \" NumberOfTime30-59DaysPastDueNotWorse DebtRatio MonthlyIncome \\\\\\n\",\n \"0 2.0 0.802982 9120.0 \\n\",\n \"1 0.0 0.121876 2600.0 \\n\",\n \"2 1.0 0.085113 3042.0 \\n\",\n \"3 0.0 0.036050 3300.0 \\n\",\n \"4 1.0 0.024926 63588.0 \\n\",\n \"\\n\",\n \" NumberOfOpenCreditLinesAndLoans NumberOfTimes90DaysLate \\\\\\n\",\n \"0 13.0 0.0 \\n\",\n \"1 4.0 0.0 \\n\",\n \"2 2.0 1.0 \\n\",\n \"3 5.0 0.0 \\n\",\n \"4 7.0 0.0 \\n\",\n \"\\n\",\n \" NumberRealEstateLoansOrLines NumberOfTime60-89DaysPastDueNotWorse \\\\\\n\",\n \"0 6.0 0.0 \\n\",\n \"1 0.0 0.0 \\n\",\n \"2 0.0 0.0 \\n\",\n \"3 0.0 0.0 \\n\",\n \"4 1.0 0.0 \\n\",\n \"\\n\",\n \" NumberOfDependents \\n\",\n \"0 2.0 \\n\",\n \"1 1.0 \\n\",\n \"2 0.0 \\n\",\n \"3 0.0 \\n\",\n \"4 0.0 \"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"pd.set_option('display.max_columns', 500)\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/KaggleCredit2.csv.zip', 'r') as z:\\n\",\n \" f = z.open('KaggleCredit2.csv')\\n\",\n \" data = pd.read_csv(f, index_col=0)\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(112915, 11)\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Drop na\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SeriousDlqin2yrs 0\\n\",\n \"RevolvingUtilizationOfUnsecuredLines 0\\n\",\n \"age 4267\\n\",\n \"NumberOfTime30-59DaysPastDueNotWorse 0\\n\",\n \"DebtRatio 0\\n\",\n \"MonthlyIncome 0\\n\",\n \"NumberOfOpenCreditLinesAndLoans 0\\n\",\n \"NumberOfTimes90DaysLate 0\\n\",\n \"NumberRealEstateLoansOrLines 0\\n\",\n \"NumberOfTime60-89DaysPastDueNotWorse 0\\n\",\n \"NumberOfDependents 4267\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.isnull().sum(axis=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(108648, 11)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.dropna(inplace=True)\\n\",\n \"data.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create X and y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = data['SeriousDlqin2yrs']\\n\",\n \"X = data.drop('SeriousDlqin2yrs', axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.06742876076872101\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y.mean()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 4.1 \\n\",\n \"\\n\",\n \"Split the data into training and testing sets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 4.2\\n\",\n \"\\n\",\n \"Fit a logistic regression model and examine the coefficients\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 4.3\\n\",\n \"\\n\",\n \"Make predictions on the testing set and calculate the accuracy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 4.4\\n\",\n \"\\n\",\n \"Confusion matrix of predictions\\n\",\n \"\\n\",\n \"What is the percentage of detected bad custumers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 4.5\\n\",\n \"\\n\",\n \"Increase sensitivity by lowering the threshold for predicting a bad customer\\n\",\n \"\\n\",\n \"Create a new classifier by changing the probability threshold to 0.3\\n\",\n \"\\n\",\n \"What is the new confusion matrix?\\n\",\n \"\\n\",\n \"What is the new percentage of detected bad customers?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "exercises/05-creditscoring_cross_validation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 05\\n\",\n \"\\n\",\n \"## Data preparation and model evaluation exercise with credit scoring\\n\",\n \"\\n\",\n \"Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. \\n\",\n \"\\n\",\n \"Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. [Dataset](https://www.kaggle.com/c/GiveMeSomeCredit)\\n\",\n \"\\n\",\n \"Attribute Information:\\n\",\n \"\\n\",\n \"|Variable Name\\t|\\tDescription\\t|\\tType|\\n\",\n \"|----|----|----|\\n\",\n \"|SeriousDlqin2yrs\\t|\\tPerson experienced 90 days past due delinquency or worse \\t|\\tY/N|\\n\",\n \"|RevolvingUtilizationOfUnsecuredLines\\t|\\tTotal balance on credit divided by the sum of credit limits\\t|\\tpercentage|\\n\",\n \"|age\\t|\\tAge of borrower in years\\t|\\tinteger|\\n\",\n \"|NumberOfTime30-59DaysPastDueNotWorse\\t|\\tNumber of times borrower has been 30-59 days past due |\\tinteger|\\n\",\n \"|DebtRatio\\t|\\tMonthly debt payments\\t|\\tpercentage|\\n\",\n \"|MonthlyIncome\\t|\\tMonthly income\\t|\\treal|\\n\",\n \"|NumberOfOpenCreditLinesAndLoans\\t|\\tNumber of Open loans |\\tinteger|\\n\",\n \"|NumberOfTimes90DaysLate\\t|\\tNumber of times borrower has been 90 days or more past due.\\t|\\tinteger|\\n\",\n \"|NumberRealEstateLoansOrLines\\t|\\tNumber of mortgage and real estate loans\\t|\\tinteger|\\n\",\n \"|NumberOfTime60-89DaysPastDueNotWorse\\t|\\tNumber of times borrower has been 60-89 days past due |integer|\\n\",\n \"|NumberOfDependents\\t|\\tNumber of dependents in family\\t|\\tinteger|\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Read the data into Pandas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Unnamed: 0SeriousDlqin2yrsRevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependents
0010.76612745.02.00.8029829120.013.00.06.00.02.0
1100.95715140.00.00.1218762600.04.00.00.00.01.0
2200.65818038.01.00.0851133042.02.01.00.00.00.0
3300.23381030.00.00.0360503300.05.00.00.00.00.0
4400.90723949.01.00.02492663588.07.00.01.00.00.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Unnamed: 0 SeriousDlqin2yrs RevolvingUtilizationOfUnsecuredLines age \\\\\\n\",\n \"0 0 1 0.766127 45.0 \\n\",\n \"1 1 0 0.957151 40.0 \\n\",\n \"2 2 0 0.658180 38.0 \\n\",\n \"3 3 0 0.233810 30.0 \\n\",\n \"4 4 0 0.907239 49.0 \\n\",\n \"\\n\",\n \" NumberOfTime30-59DaysPastDueNotWorse DebtRatio MonthlyIncome \\\\\\n\",\n \"0 2.0 0.802982 9120.0 \\n\",\n \"1 0.0 0.121876 2600.0 \\n\",\n \"2 1.0 0.085113 3042.0 \\n\",\n \"3 0.0 0.036050 3300.0 \\n\",\n \"4 1.0 0.024926 63588.0 \\n\",\n \"\\n\",\n \" NumberOfOpenCreditLinesAndLoans NumberOfTimes90DaysLate \\\\\\n\",\n \"0 13.0 0.0 \\n\",\n \"1 4.0 0.0 \\n\",\n \"2 2.0 1.0 \\n\",\n \"3 5.0 0.0 \\n\",\n \"4 7.0 0.0 \\n\",\n \"\\n\",\n \" NumberRealEstateLoansOrLines NumberOfTime60-89DaysPastDueNotWorse \\\\\\n\",\n \"0 6.0 0.0 \\n\",\n \"1 0.0 0.0 \\n\",\n \"2 0.0 0.0 \\n\",\n \"3 0.0 0.0 \\n\",\n \"4 1.0 0.0 \\n\",\n \"\\n\",\n \" NumberOfDependents \\n\",\n \"0 2.0 \\n\",\n \"1 1.0 \\n\",\n \"2 0.0 \\n\",\n \"3 0.0 \\n\",\n \"4 0.0 \"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"pd.set_option('display.max_columns', 500)\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/KaggleCredit2.csv.zip', 'r') as z:\\n\",\n \" f = z.open('KaggleCredit2.csv')\\n\",\n \" data = pd.io.parsers.read_table(f, sep=',')\\n\",\n \"\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"y = data['SeriousDlqin2yrs']\\n\",\n \"X = data.drop('SeriousDlqin2yrs', axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 5.1\\n\",\n \"\\n\",\n \"Input the missing values of the Age and Number of Dependents \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 5.2\\n\",\n \"\\n\",\n \"From the set of features\\n\",\n \"\\n\",\n \"Select the features that maximize the **F1Score** the model using K-Fold cross-validation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 5.3\\n\",\n \"\\n\",\n \"Now which is the best set of features selected by AUC\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "exercises/06-fraud_detection_sampling.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 06\\n\",\n \"\\n\",\n \"# Fraud Detection\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Introduction\\n\",\n \"\\n\",\n \"- Fraud Detection Dataset from Microsoft Azure: [data](http://gallery.cortanaintelligence.com/Experiment/8e9fe4e03b8b4c65b9ca947c72b8e463)\\n\",\n \"\\n\",\n \"Fraud detection is one of the earliest industrial applications of data mining and machine learning. Fraud detection is typically handled as a binary classification problem, but the class population is unbalanced because instances of fraud are usually very rare compared to the overall volume of transactions. Moreover, when fraudulent transactions are discovered, the business typically takes measures to block the accounts from transacting to prevent further losses. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"from sklearn.model_selection import cross_val_score\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"from sklearn import metrics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/fraud_detection.csv.zip', 'r') as z:\\n\",\n \" f = z.open('15_fraud_detection.csv')\\n\",\n \" data = pd.io.parsers.read_table(f, index_col=0, sep=',')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
accountAgedigitalItemCountsumPurchaseCount1DaysumPurchaseAmount1DaysumPurchaseAmount30DaypaymentBillingPostalCode - LogOddsForClass_0accountPostalCode - LogOddsForClass_0paymentBillingState - LogOddsForClass_0accountState - LogOddsForClass_0paymentInstrumentAgeInAccountipState - LogOddsForClass_0transactionAmounttransactionAmountUSDipPostalCode - LogOddsForClass_0localHour - LogOddsForClass_0Label
02000000.00720.255.0645330.4212141.3121860.5663953279.5743061.218157599.00626.1646501.2595434.7454020
162111185.442530.370.5389960.4818384.4013704.50015761.9701394.0356011185.441185.4400003.9811184.9213490
22000000.000.005.0645335.0963963.0563573.1552260.0000003.31418632.0932.0900005.0084904.7423030
31100.000.005.0645335.0963963.3311543.3312390.0000003.529398133.28132.7295541.3249254.7454020
41100.00132.735.4128850.3429455.5636774.0869650.0013893.529398543.66543.6600002.6934514.8767710
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" accountAge digitalItemCount sumPurchaseCount1Day sumPurchaseAmount1Day \\\\\\n\",\n \"0 2000 0 0 0.00 \\n\",\n \"1 62 1 1 1185.44 \\n\",\n \"2 2000 0 0 0.00 \\n\",\n \"3 1 1 0 0.00 \\n\",\n \"4 1 1 0 0.00 \\n\",\n \"\\n\",\n \" sumPurchaseAmount30Day paymentBillingPostalCode - LogOddsForClass_0 \\\\\\n\",\n \"0 720.25 5.064533 \\n\",\n \"1 2530.37 0.538996 \\n\",\n \"2 0.00 5.064533 \\n\",\n \"3 0.00 5.064533 \\n\",\n \"4 132.73 5.412885 \\n\",\n \"\\n\",\n \" accountPostalCode - LogOddsForClass_0 \\\\\\n\",\n \"0 0.421214 \\n\",\n \"1 0.481838 \\n\",\n \"2 5.096396 \\n\",\n \"3 5.096396 \\n\",\n \"4 0.342945 \\n\",\n \"\\n\",\n \" paymentBillingState - LogOddsForClass_0 accountState - LogOddsForClass_0 \\\\\\n\",\n \"0 1.312186 0.566395 \\n\",\n \"1 4.401370 4.500157 \\n\",\n \"2 3.056357 3.155226 \\n\",\n \"3 3.331154 3.331239 \\n\",\n \"4 5.563677 4.086965 \\n\",\n \"\\n\",\n \" paymentInstrumentAgeInAccount ipState - LogOddsForClass_0 \\\\\\n\",\n \"0 3279.574306 1.218157 \\n\",\n \"1 61.970139 4.035601 \\n\",\n \"2 0.000000 3.314186 \\n\",\n \"3 0.000000 3.529398 \\n\",\n \"4 0.001389 3.529398 \\n\",\n \"\\n\",\n \" transactionAmount transactionAmountUSD ipPostalCode - LogOddsForClass_0 \\\\\\n\",\n \"0 599.00 626.164650 1.259543 \\n\",\n \"1 1185.44 1185.440000 3.981118 \\n\",\n \"2 32.09 32.090000 5.008490 \\n\",\n \"3 133.28 132.729554 1.324925 \\n\",\n \"4 543.66 543.660000 2.693451 \\n\",\n \"\\n\",\n \" localHour - LogOddsForClass_0 Label \\n\",\n \"0 4.745402 0 \\n\",\n \"1 4.921349 0 \\n\",\n \"2 4.742303 0 \\n\",\n \"3 4.745402 0 \\n\",\n \"4 4.876771 0 \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"((138721, 16), 797, 0.0057453449730033666)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.shape, data.Label.sum(), data.Label.mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"X = data.drop(['Label'], axis=1)\\n\",\n \"y = data['Label']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 06.1\\n\",\n \"\\n\",\n \"Estimate a Logistic Regression\\n\",\n \"\\n\",\n \"Evaluate using the following metrics:\\n\",\n \"* Accuracy\\n\",\n \"* F1-Score\\n\",\n \"* F_Beta-Score (Beta=10)\\n\",\n \"\\n\",\n \"Comment about the results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 06.2\\n\",\n \"\\n\",\n \"Under-sample the negative class using random-under-sampling\\n\",\n \"\\n\",\n \"Which is parameter for target_percentage did you choose?\\n\",\n \"How the results change?\\n\",\n \"\\n\",\n \"**Only apply under-sampling to the training set, evaluate using the whole test set**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 06.3\\n\",\n \"\\n\",\n \"Now using random-over-sampling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 06.4\\n\",\n \"Evaluate the results using SMOTE\\n\",\n \"\\n\",\n \"Which parameters did you choose?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "exercises/07-fraud_detection_DT.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 07\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Forecast of income using DT\\n\",\n \"\\n\",\n \"We'll be working with a dataset from US Census indome ([data dictionary](https://archive.ics.uci.edu/ml/datasets/Adult)).\\n\",\n \"\\n\",\n \"Many businesses would like to personalize their offer based on customer’s income. High-income customers could be, for instance, exposed to premium products. As a customer’s income is not always explicitly known, predictive model could estimate income of a person based on other information.\\n\",\n \"\\n\",\n \"Our goal is to create a predictive model that will be able to output an estimation of a person income.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
AgeWorkclassfnlwgtEducationEducation-NumMartial StatusOccupationRelationshipRaceSexCapital GainCapital LossHours per weekCountryIncome
039State-gov77516Bachelors13Never-marriedAdm-clericalNot-in-familyWhiteMale2174040United-States51806.0
150Self-emp-not-inc83311Bachelors13Married-civ-spouseExec-managerialHusbandWhiteMale0013United-States68719.0
238Private215646HS-grad9DivorcedHandlers-cleanersNot-in-familyWhiteMale0040United-States51255.0
353Private23472111th7Married-civ-spouseHandlers-cleanersHusbandBlackMale0040United-States47398.0
428Private338409Bachelors13Married-civ-spouseProf-specialtyWifeBlackFemale0040Cuba30493.0
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\"\n ],\n \"text/plain\": [\n \" Age Workclass fnlwgt Education Education-Num \\\\\\n\",\n \"0 39 State-gov 77516 Bachelors 13 \\n\",\n \"1 50 Self-emp-not-inc 83311 Bachelors 13 \\n\",\n \"2 38 Private 215646 HS-grad 9 \\n\",\n \"3 53 Private 234721 11th 7 \\n\",\n \"4 28 Private 338409 Bachelors 13 \\n\",\n \"\\n\",\n \" Martial Status Occupation Relationship Race Sex \\\\\\n\",\n \"0 Never-married Adm-clerical Not-in-family White Male \\n\",\n \"1 Married-civ-spouse Exec-managerial Husband White Male \\n\",\n \"2 Divorced Handlers-cleaners Not-in-family White Male \\n\",\n \"3 Married-civ-spouse Handlers-cleaners Husband Black Male \\n\",\n \"4 Married-civ-spouse Prof-specialty Wife Black Female \\n\",\n \"\\n\",\n \" Capital Gain Capital Loss Hours per week Country Income \\n\",\n \"0 2174 0 40 United-States 51806.0 \\n\",\n \"1 0 0 13 United-States 68719.0 \\n\",\n \"2 0 0 40 United-States 51255.0 \\n\",\n \"3 0 0 40 United-States 47398.0 \\n\",\n \"4 0 0 40 Cuba 30493.0 \"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"# read the data and set the datetime as the index\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/income.csv.zip', 'r') as z:\\n\",\n \" f = z.open('income.csv')\\n\",\n \" income = pd.read_csv(f, index_col=0)\\n\",\n \"\\n\",\n \"income.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 07.1\\n\",\n \"\\n\",\n \"it a linear regression model to the entire dataset, using \\\"Income\\\" as the response and \\\"Education-Num\\\" and \\\"Age\\\" as the only features. Then, print the coefficients and interpret them. What are the limitations of linear regression in this instance?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 07.2\\n\",\n \"\\n\",\n \"Use 10-fold cross-validation to calculate the RMSE for the linear regression model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 07.3\\n\",\n \"\\n\",\n \"Use 10-fold cross-validation to evaluate a decision tree model with those same features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 07.4\\n\",\n \"\\n\",\n \"Select the max_depth that minimizes the RMSE\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 07.5\\n\",\n \"\\n\",\n \"Fit a decision tree model to the entire dataset using \\\"max_depth=3\\\", and create a tree diagram using Graphviz. Then, figure out what each leaf represents. What did the decision tree learn that a linear regression model could not learn?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "exercises/08-fraud_ensemble_bagging.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 08\\n\",\n \"\\n\",\n \"\\n\",\n \"- Fraud Detection Dataset from Microsoft Azure: [data](http://gallery.cortanaintelligence.com/Experiment/8e9fe4e03b8b4c65b9ca947c72b8e463)\\n\",\n \"\\n\",\n \"Fraud detection is one of the earliest industrial applications of data mining and machine learning. Fraud detection is typically handled as a binary classification problem, but the class population is unbalanced because instances of fraud are usually very rare compared to the overall volume of transactions. Moreover, when fraudulent transactions are discovered, the business typically takes measures to block the accounts from transacting to prevent further losses. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
accountAgedigitalItemCountsumPurchaseCount1DaysumPurchaseAmount1DaysumPurchaseAmount30DaypaymentBillingPostalCode - LogOddsForClass_0accountPostalCode - LogOddsForClass_0paymentBillingState - LogOddsForClass_0accountState - LogOddsForClass_0paymentInstrumentAgeInAccountipState - LogOddsForClass_0transactionAmounttransactionAmountUSDipPostalCode - LogOddsForClass_0localHour - LogOddsForClass_0Label
02000000.00720.255.0645330.4212141.3121860.5663953279.5743061.218157599.00626.1646501.2595434.7454020
162111185.442530.370.5389960.4818384.4013704.50015761.9701394.0356011185.441185.4400003.9811184.9213490
22000000.000.005.0645335.0963963.0563573.1552260.0000003.31418632.0932.0900005.0084904.7423030
31100.000.005.0645335.0963963.3311543.3312390.0000003.529398133.28132.7295541.3249254.7454020
41100.00132.735.4128850.3429455.5636774.0869650.0013893.529398543.66543.6600002.6934514.8767710
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" accountAge digitalItemCount sumPurchaseCount1Day sumPurchaseAmount1Day \\\\\\n\",\n \"0 2000 0 0 0.00 \\n\",\n \"1 62 1 1 1185.44 \\n\",\n \"2 2000 0 0 0.00 \\n\",\n \"3 1 1 0 0.00 \\n\",\n \"4 1 1 0 0.00 \\n\",\n \"\\n\",\n \" sumPurchaseAmount30Day paymentBillingPostalCode - LogOddsForClass_0 \\\\\\n\",\n \"0 720.25 5.064533 \\n\",\n \"1 2530.37 0.538996 \\n\",\n \"2 0.00 5.064533 \\n\",\n \"3 0.00 5.064533 \\n\",\n \"4 132.73 5.412885 \\n\",\n \"\\n\",\n \" accountPostalCode - LogOddsForClass_0 \\\\\\n\",\n \"0 0.421214 \\n\",\n \"1 0.481838 \\n\",\n \"2 5.096396 \\n\",\n \"3 5.096396 \\n\",\n \"4 0.342945 \\n\",\n \"\\n\",\n \" paymentBillingState - LogOddsForClass_0 accountState - LogOddsForClass_0 \\\\\\n\",\n \"0 1.312186 0.566395 \\n\",\n \"1 4.401370 4.500157 \\n\",\n \"2 3.056357 3.155226 \\n\",\n \"3 3.331154 3.331239 \\n\",\n \"4 5.563677 4.086965 \\n\",\n \"\\n\",\n \" paymentInstrumentAgeInAccount ipState - LogOddsForClass_0 \\\\\\n\",\n \"0 3279.574306 1.218157 \\n\",\n \"1 61.970139 4.035601 \\n\",\n \"2 0.000000 3.314186 \\n\",\n \"3 0.000000 3.529398 \\n\",\n \"4 0.001389 3.529398 \\n\",\n \"\\n\",\n \" transactionAmount transactionAmountUSD ipPostalCode - LogOddsForClass_0 \\\\\\n\",\n \"0 599.00 626.164650 1.259543 \\n\",\n \"1 1185.44 1185.440000 3.981118 \\n\",\n \"2 32.09 32.090000 5.008490 \\n\",\n \"3 133.28 132.729554 1.324925 \\n\",\n \"4 543.66 543.660000 2.693451 \\n\",\n \"\\n\",\n \" localHour - LogOddsForClass_0 Label \\n\",\n \"0 4.745402 0 \\n\",\n \"1 4.921349 0 \\n\",\n \"2 4.742303 0 \\n\",\n \"3 4.745402 0 \\n\",\n \"4 4.876771 0 \"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/fraud_detection.csv.zip', 'r') as z:\\n\",\n \" f = z.open('15_fraud_detection.csv')\\n\",\n \" data = pd.io.parsers.read_table(f, index_col=0, sep=',')\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 0.994255\\n\",\n \"1 0.005745\\n\",\n \"Name: Label, dtype: float64\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X = data.drop(['Label'], axis=1)\\n\",\n \"y = data['Label']\\n\",\n \"y.value_counts(normalize=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 08.1\\n\",\n \"\\n\",\n \"Estimate a Logistic Regression, GaussianNB, K-nearest neighbors and a Decision Tree **Classifiers**\\n\",\n \"\\n\",\n \"Evaluate using the following metrics:\\n\",\n \"* Accuracy\\n\",\n \"* F1-Score\\n\",\n \"* F_Beta-Score (Beta=10)\\n\",\n \"\\n\",\n \"Comment about the results\\n\",\n \"\\n\",\n \"Combine the classifiers and comment\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"from sklearn.naive_bayes import GaussianNB\\n\",\n \"from sklearn.neighbors import KNeighborsClassifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"models = {'lr': LogisticRegression(),\\n\",\n \" 'dt': DecisionTreeClassifier(),\\n\",\n \" 'nb': GaussianNB(),\\n\",\n \" 'nn': KNeighborsClassifier()}\\n\",\n \"\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2)\\n\",\n \"# Train all the models\\n\",\n \"for model in models.keys():\\n\",\n \" models[model].fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
lrdtnbnn
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\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" lr dt nb nn\\n\",\n \"111018 0 0 0 0\\n\",\n \"120018 0 0 0 0\\n\",\n \"24895 0 0 0 0\\n\",\n \"23525 0 0 0 0\\n\",\n \"29535 0 0 0 0\\n\",\n \"52150 0 0 1 0\\n\",\n \"127077 0 0 0 0\\n\",\n \"83261 0 0 0 0\\n\",\n \"26716 0 0 0 0\\n\",\n \"45260 0 0 0 0\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# predict test for each model\\n\",\n \"y_pred = pd.DataFrame(index=X_test.index, columns=models.keys())\\n\",\n \"for model in models.keys():\\n\",\n \" y_pred[model] = models[model].predict(X_test)\\n\",\n \"y_pred.sample(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"y_pred_ensemble1 = (y_pred.mean(axis=1) > 0.5).astype(int)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.00020183962400161472\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_pred_ensemble1.mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import accuracy_score, f1_score, recall_score, precision_score\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"stats = {'acc': accuracy_score,\\n\",\n \" 'f1': f1_score,\\n\",\n \" 'rec': recall_score,\\n\",\n \" 'pre': precision_score}\\n\",\n \"res = pd.DataFrame(index=models.keys(), columns=stats.keys())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for model in models.keys():\\n\",\n \" for stat in stats.keys():\\n\",\n \" res.loc[model, stat] = stats[stat](y_test, y_pred[model])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
accf1recpre
lr0.993829000
dt0.9879180.1215930.1367920.109434
nb0.9236470.03145570.202830.01705
nn0.9937140.08403360.04716980.384615
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" acc f1 rec pre\\n\",\n \"lr 0.993829 0 0 0\\n\",\n \"dt 0.987918 0.121593 0.136792 0.109434\\n\",\n \"nb 0.923647 0.0314557 0.20283 0.01705\\n\",\n \"nn 0.993714 0.0840336 0.0471698 0.384615\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"res\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"res.loc['ensemble1'] = 0\\n\",\n \"for stat in stats.keys():\\n\",\n \" res.loc['ensemble1', stat] = stats[stat](y_test, y_pred_ensemble1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
accf1recpre
lr0.993829000
dt0.9879180.1215930.1367920.109434
nb0.9236470.03145570.202830.01705
nn0.9937140.08403360.04716980.384615
ensemble10.9940310.05479450.02830190.857143
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" acc f1 rec pre\\n\",\n \"lr 0.993829 0 0 0\\n\",\n \"dt 0.987918 0.121593 0.136792 0.109434\\n\",\n \"nb 0.923647 0.0314557 0.20283 0.01705\\n\",\n \"nn 0.993714 0.0840336 0.0471698 0.384615\\n\",\n \"ensemble1 0.994031 0.0547945 0.0283019 0.857143\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"res\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 08.2\\n\",\n \"\\n\",\n \"Apply random-undersampling with a target percentage of 0.5\\n\",\n \"\\n\",\n \"how does the results change\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 08.3\\n\",\n \"\\n\",\n \"For each model estimate a BaggingClassifier of 100 models using the under sampled datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 08.4\\n\",\n \"\\n\",\n \"Using the under-sampled dataset\\n\",\n \"\\n\",\n \"Evaluate a RandomForestClassifier and compare the results\\n\",\n \"\\n\",\n \"change n_estimators=100, what happened\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "exercises/10_CS_Churn.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercise 10- CostSensitive Churn\\n\",\n \"\\n\",\n \"[paper](http://download.springer.com/static/pdf/125/art%253A10.1186%252Fs40165-015-0014-6.pdf?originUrl=http%3A%2F%2Fdecisionanalyticsjournal.springeropen.com%2Farticle%2F10.1186%2Fs40165-015-0014-6&token2=exp=1462974790~acl=%2Fstatic%2Fpdf%2F125%2Fart%25253A10.1186%25252Fs40165-015-0014-6.pdf*~hmac=05041d990b7e5a5e70d6efc1fbb29c2a380465c6edc84be1feda1b6d49588a1a)\\n\",\n \"[slides](http://www.slideshare.net/albahnsen/maximizing-a-churn-campaigns-profitability-with-cost-sensitive-predictive-analytics)\\n\",\n \"\\n\",\n \"Customer churn predictive modeling deals with predicting the probability of a customer defecting \\n\",\n \"using historical, behavioral and socio-economical information. This tool is of great benefit to \\n\",\n \"subscription based companies allowing them to maximize the results of retention campaigns. The \\n\",\n \"problem of churn predictive modeling has been widely studied by the data mining and machine learning\\n\",\n \"communities. It is usually tackled by using classification algorithms in order to learn the \\n\",\n \"different patterns of both the churners and non-churners. Nevertheless, current state-of-the-art \\n\",\n \"classification algorithms are not well aligned with commercial goals, in the sense that, the models \\n\",\n \"miss to include the real financial costs and benefits during the training and evaluation phases. In \\n\",\n \"the case of churn, evaluating a model based on a traditional measure such as accuracy or predictive \\n\",\n \"power, does not yield to the best results when measured by the actual financial cost, i.e., \\n\",\n \"investment per subscriber on a loyalty campaign and the financial impact of failing to detect a \\n\",\n \"real churner versus wrongly predicting a non-churner as a churner.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The two main objectives of subscription-based companies are to acquire new subscribers and \\n\",\n \"retain those they already have, mainly because profits are directly linked with the number of \\n\",\n \"subscribers. In order to maximize the profit, companies must increase the customer base by \\n\",\n \"incrementing sales while decreasing the number of churners. Furthermore, it is common knowledge \\n\",\n \"that retaining a customer is about five times less expensive than acquiring a new one , this creates pressure to have better and more effective churn campaigns.\\n\",\n \"\\n\",\n \"A typical churn campaign consists in identifying from the current customer base which ones are \\n\",\n \"more likely to leave the company, and make an offer in order to avoid that behavior.\\n\",\n \"With this in mind the companies use intelligence to create and improve retention and collection\\n\",\n \"strategies. In the first case, this usually implies an offer that can be either a discount or a \\n\",\n \"free upgrade during certain span of time. In both cases the company has to \\tassume a cost for that \\n\",\n \"offer, therefore, accurate prediction of the churners becomes important. The logic of this flow is \\n\",\n \"shown in the following figure.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![fig1](../notebooks/images/ch5_fig1.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The churn campaign process starts with the sales that every month increase the customer \\n\",\n \"base, however, monthly there is a group of customers that decide to leave the company for many \\n\",\n \"reasons. Then the objective of a churn model is to identify those customers before they take the \\n\",\n \"decision of defecting.\\n\",\n \"\\n\",\n \"Using a churn model, those customers more likely to leave are predicted as churners and \\n\",\n \"an offer is made in order to retain them. However, it is known that not all customers will accept \\n\",\n \"the offer, in the case when a customer is planning to defect, it is possible that the offer is not \\n\",\n \"good enough to retain him or that the reason for defecting can not be influenced by an offer.\\n\",\n \"Using historical information, it is estimated that a customer will accept the offer with \\n\",\n \"probability $\\\\gamma$.\\n\",\n \"On the other hand, there is the case in which the churn model misclassified a non-churner as \\n\",\n \"churner, also known as false positives, in that case the customer will always accept the offer that \\n\",\n \"means and additional cost to the company since those misclassified customers do not have the \\n\",\n \"intentions of leaving.\\n\",\n \"\\n\",\n \"In the case were the churn model predicts customers as non-churners, there is also the possibility \\n\",\n \"of a misclassification, in this case an actual churner is predicted as non-churner, since \\n\",\n \"these customers do not receive an offer and they will leave the company, these cases are known as \\n\",\n \"false negatives. Lastly, there is the case were the customers are actually non-churners, then \\n\",\n \"there is no need to make a retention offer to these customers since they will continue to be part \\n\",\n \"of the customer base.\\n\",\n \"\\n\",\n \"It can be seen that a churn campaign (or churn model) has three main points. First, avoid false \\n\",\n \"positives since there is a financial cost of making an offer where it is not needed. Second, find \\n\",\n \"the right offer to give to those customers identified as churners. And lastly, to decrease \\n\",\n \"the number of false negatives.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the following figure, the financial impact of a churn model is shown. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![fig1](../notebooks/images/ch5_fig2.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note than we take \\n\",\n \"into account the costs and not the profit in each case.\\n\",\n \"When a customer is predicted to be a churner, an offer is made with the objective of avoiding \\n\",\n \"the customer defecting. However, if a customer is actually a churner, he may or not accept the \\n\",\n \"offer with a probability $\\\\gamma_i$. If the customer accepts the offer, the financial impact is \\n\",\n \"equal to the cost of the offer ($C_{o_i}$) plus the administrative cost of contacting the \\n\",\n \"customer ($C_a$). On the other hand, if the customer declines the offer, the cost is the \\n\",\n \"expected \\tincome that the clients would otherwise generate, also called customer lifetime value \\n\",\n \"($CLV_i$), \\tplus $C_a$. Lastly, if the customer is not actually a churner, he will be happy to \\n\",\n \"accept the \\toffer and the cost will be $C_{o_i}$ plus $C_a$.\\n\",\n \"\\t\\n\",\n \"In the case that the customer is predicted as non-churner, there are two possible outcomes. \\n\",\n \"Either the customer is not a churner, then the cost is zero, or the customer is a churner and the \\n\",\n \"cost is $CLV_i$. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"| \\t| Actual Positive ($y_i=1$) \\t| Actual Negative \\t($y_i=0$)|\\n\",\n \"|---\\t|:-:\\t|:-:\\t|\\n\",\n \"| Predicted Positive ($c_i=1$)\\t| $C_{TP_i}=\\\\gamma_iC_{o_i}+(1-\\\\gamma_i)(CLV_i+C_a)$\\t| $C_{FP_i}=C_{o_i}+C_a$ |\\n\",\n \"| Predicted Negative ($c_i=0$) \\t| $C_{FN_i}=CLV_i$\\t| $C_{TN_i}=0$\\t|\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/cost_sensitive_classification_churn.csv.zip', 'r') as z:\\n\",\n \" f = z.open('cost_sensitive_classification_churn.csv')\\n\",\n \" data = pd.read_csv(f, index_col=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
x1x2x3x4x5x6x7x8x9x10...x42x43x44x45x46C_FPC_FNC_TPC_TNtarget
id
00.01.01.01.00.01.01.00.00.01.0...1.01.05.02.02.074.0000001028.571429121.8285710.00.0
10.01.01.01.00.01.01.00.00.01.0...3.01.05.02.04.053.4285711028.57142982.7428570.00.0
21.01.01.01.00.01.01.00.00.01.0...1.08.03.01.04.066.2857141285.714286102.9285710.00.0
31.01.01.00.00.01.01.00.00.01.0...1.08.04.03.02.092.0000001285.714286151.7857140.00.0
40.01.01.01.00.01.01.00.00.01.0...1.07.05.02.04.053.4285711028.57142982.7428570.00.0
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5 rows × 51 columns

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\"\n ],\n \"text/plain\": [\n \" x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 ... x42 x43 x44 \\\\\\n\",\n \"id ... \\n\",\n \"0 0.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 ... 1.0 1.0 5.0 \\n\",\n \"1 0.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 ... 3.0 1.0 5.0 \\n\",\n \"2 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 ... 1.0 8.0 3.0 \\n\",\n \"3 1.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 ... 1.0 8.0 4.0 \\n\",\n \"4 0.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 ... 1.0 7.0 5.0 \\n\",\n \"\\n\",\n \" x45 x46 C_FP C_FN C_TP C_TN target \\n\",\n \"id \\n\",\n \"0 2.0 2.0 74.000000 1028.571429 121.828571 0.0 0.0 \\n\",\n \"1 2.0 4.0 53.428571 1028.571429 82.742857 0.0 0.0 \\n\",\n \"2 1.0 4.0 66.285714 1285.714286 102.928571 0.0 0.0 \\n\",\n \"3 3.0 2.0 92.000000 1285.714286 151.785714 0.0 0.0 \\n\",\n \"4 2.0 4.0 53.428571 1028.571429 82.742857 0.0 0.0 \\n\",\n \"\\n\",\n \"[5 rows x 51 columns]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.0 0.952127\\n\",\n \"1.0 0.047873\\n\",\n \"Name: target, dtype: float64\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.target.value_counts(normalize=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"X =data[['x'+str(i) for i in range(1, 47)]]\\n\",\n \"y = data.target\\n\",\n \"cost_mat = data[['C_FP','C_FN','C_TP','C_TN']].values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\\n\",\n \" \\\"This module will be removed in 0.20.\\\", DeprecationWarning)\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.cross_validation import train_test_split\\n\",\n \"temp = train_test_split(X, y, cost_mat, test_size=0.33, random_state=42)\\n\",\n \"X_train, X_test, y_train, y_test, cost_mat_train, cost_mat_test = temp\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 10.1\\n\",\n \"\\n\",\n \"\\n\",\n \"* Train 4 different models to predict target (Churn) using x1-x46 as features\\n\",\n \"1. Logistic Regression\\n\",\n \"2. Ensemble\\n\",\n \"3. Under-sampling LR\\n\",\n \"4. Under-sampling Ensemble\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 10.2\\n\",\n \"\\n\",\n \"* Calculate the savings of the different models\\n\",\n \"* Compare F1Score and Savings\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 10.3\\n\",\n \"\\n\",\n \"Using the probabilities of each model estimate a BMR classifier\\n\",\n \"\\n\",\n \"Compare the savings and F1Score\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Exercice 10.4\\n\",\n \"\\n\",\n \"Estimate a CostSensitiveDecisionTreeClassifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/01-IntroMachineLearning.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 01 - Introduction to Machine Learning\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Jake Vanderplas](http://www.vanderplas.com)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What is Machine Learning?\\n\",\n \"\\n\",\n \"In this section we will begin to explore the basic principles of machine learning.\\n\",\n \"Machine Learning is about building programs with **tunable parameters** (typically an\\n\",\n \"array of floating point values) that are adjusted automatically so as to improve\\n\",\n \"their behavior by **adapting to previously seen data.**\\n\",\n \"\\n\",\n \"Machine Learning can be considered a subfield of **Artificial Intelligence** since those\\n\",\n \"algorithms can be seen as building blocks to make computers learn to behave more\\n\",\n \"intelligently by somehow **generalizing** rather that just storing and retrieving data items\\n\",\n \"like a database system would do.\\n\",\n \"\\n\",\n \"We'll take a look at two very simple machine learning tasks here.\\n\",\n \"The first is a **classification** task: the figure shows a\\n\",\n \"collection of two-dimensional data, colored according to two different class\\n\",\n \"labels. \\n\",\n \"\\n\",\n \"### Type of Learning\\n\",\n \"\\n\",\n \"Usually, ML problems can be classified according to the learning type. Two of the more importants are:\\n\",\n \"\\n\",\n \"* **Supervised Learning:**\\n\",\n \"\\n\",\n \"Supervised Learning models is the set of algorithms that tries to approximate a function $f(x)$ that represents the relation between a dependent variable $Y$ (label, target) and an independent set of variables $X$. This characteristic makes supervised algorithms a type of predictive models, such that given a set of $X$ data, it can be forecasted dependent $Y$ variable. \\n\",\n \"\\n\",\n \"According to the type of values in $Y$, it can be define two main types of problems and algorithms to analyze data:\\n\",\n \"\\n\",\n \"- Classification Problems:\\n\",\n \"\\n\",\n \"Whether dependent variable $Y$ define a group of categories (not ordered values) like approved and denied or good and bad, the task of predict $Y$ variable can be defined as a classification problem. The output variables are often called labels or categories.\\n\",\n \"\\n\",\n \"Some examples of Classification algorithms are:\\n\",\n \"```\\n\",\n \"Logistic Regression, Decision Trees, Random Forest, KNN and Suppor Vector Machine.\\n\",\n \"```\\n\",\n \"\\n\",\n \"- Regression Problems:\\n\",\n \"\\n\",\n \"A regression problem is the task of predict a dependent (target) variable $Y$ with continous values. Some examples of regression problems can be: the prediction of the price of a stock or the number of potential customers for a product.\\n\",\n \"\\n\",\n \"Some examples of Regression algorithms are:\\n\",\n \"```\\n\",\n \"Linear Regression, Decision Trees, Neural Networks. \\n\",\n \"```\\n\",\n \"\\n\",\n \"* **Unsupervised Learning:**\\n\",\n \" \\n\",\n \"Opposite to Supervised Learnig, in Unsupervised Learning dependent variable or labeled data $Y$ is not included in the data set. Then insted of predict a variable, this set of algorithms tries to use techniques in the input data $X$ to detect patterns, find rules, or summarize and group data. Usually, Unsupervised algorithms are mainly used in descriptive anlysis and modelling, where is particularly useful to find out insights and information that human expert doesn't know. In Unsupervised Learning, there are two main tasks:\\n\",\n \"\\n\",\n \"- Dimensionality Reduction: \\n\",\n \"\\n\",\n \"Dimensionality reduction is the methodology to reduce the amount of variables in dataset $X$ that are into consideration. One of the most important methods applied in dimensionality reduction is the Principal Component Analysis (PCA). \\n\",\n \"\\n\",\n \"- Clustering Analysis: \\n\",\n \"\\n\",\n \"Cluster analysis or clustering is the task of grouping a set of data $X$ in such way that segments of $X$ in the same group are more similar between them than to those in other groups (clusters). It is the most common technique for exploratory data mining and data analysis. An example of this algorithms are k-means.\\n\",\n \"\\n\",\n \"\\n\",\n \"#### Other Types of Learing\\n\",\n \"\\n\",\n \"Despite Supervised and Unsupervised Learning cover a vast amount of techiniques to use, there area other less common but usefull Learning types like Semi-Supervised Learning and Reinforcement Learnig. However these are outside the scope of this course. To read more about:\\n\",\n \"\\n\",\n \"https://medium.com/machine-learning-for-humans/reinforcement-learning-6eacf258b265\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Import libraries\\n\",\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib as mpl\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 1. Supervised Learning \\n\",\n \"\\n\",\n \"### 1.1 Classification Problem\\n\",\n \"\\n\",\n \"The first task we are going to study is a **classification** problem: the figure shows a collection of two-dimensional data, colored according to two different class labels.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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P/U/bZ5vp2nzKeQSOLKVsRO+KpePgABo3qG/PjiPQhCUGklD0O3YUppxzEV598Q05+Nv1LCpMew4idhnHJX8+xpS5/jp+TrzyaF/44l42rKvB4LQaPGsi5t57a7W4wIZwmCUGkHK/Xw/Adh7odhm32mz6RPQ/flflzv8CfncneR+1Ohl++eiL1yKdSiCTIzs/i4NMnux2GEFuU1vchCCGEsI8kBCGEEIAkBOGAlqYWmf9HiDQk1xCEbQKNAR761VMs/3I1odYQA7cbwE9vVwwb13suEAvRm8kZgrDN3694kvmvLaJ8dSVV62tY/Oky7rvkUYIJppgWQqQeSQhimxlj2qanbqpvZsXXq+PWWb+sjIVvfJns0IQQ20C6jES3BZpaeOTq51i2aCUmFKZk7BDUdccTbImfoiEUDFNbXudClEKI7pKEILrtgcsf5/M3v2p7XbaygtqKevqXFFFTFvvj339oIZOO3yvZIQohtoF0GYluqS2vY8WiVXHla35Yx5Qz92PImEFYnsiUDEVDCjjyokPk+RRCpAk5QxDdUl/dSKAxfpbOlsYWsvOzuOWNq/jvSwuor27kgJMnbnG6aSFEapGEILplyOiB9C8pjLvPYMDwInY9eDyZ2ZkccuZ+LkUnhOgJ6TIS3eLxejjpV9MYMGzzA+r7DcznsHMPJLcwx8XIhBA9JWcIPbR+eRkv/HEuzdXN5BXncso1x1I8rL/bYTlqn2P3ZNyk7Zn3yAe0NLVw2HkHMnjUwK1vmATrlpXxv5cXMmhkMZOO2xNfpnzEhegq+bb0QOmS9dx9zt/ZuLqyrWzp5yv5jb6c/kOTP49/R8YYQq0hR34UCwb249Srj7W93p548v9e4JOXFlBX2YDX5+G1+99h5iMXMnDEALdDEyItSJdRD7xw59yYZACwYflGXvzjXJci2uxf983jhiPu4OqDbuGmY//E/Ne+cDskRy37YiUfvTCfusoGIHL/w5rvS3n0Wm1bG62BIOFw2Lb6hEg1cobQA53dcFW5rjrJkcT6z9Mf8+pf59HcEACgYm01T9zwPOMn7Ej+kGxXY3PKe898QmNN/IR6HR9duS1+nL+MZ299hcp1Vfhz/Ox68I5cce9FPa5XiFQjCaEHCgYmHl8/oKQoYXmyfPzCZ23JYJOajXU8f9crnH/X6S5F5az8AXkJyzP8GT2qt66ynlkzn6RsZUVb2caV5eQX5HPilUf2qG4hUo10GfXAKVcfy8DtYvunB48eyMlXHeNSRBGtgdaE5Y11yZuSesWXq7nj9Pu45uDbuPGYu3jj7/92tL2jLjyE4g7XCjw+DzvtP7ZH9b75j/dikgFAsDXEwnkyP5PofeQMoQeGjhnMVU9fyot3zaWxqpn8gbmcevWxFA1x92askrFDWPZF7N3EHq+HCUfsnpT2a8vruO/SR2J+SNctLcPyWhx14RRH2szvn8eFd5+J/v2rVK6rxp+dwU77j+OsG0/qUb3VG2oSlrc0x9+cJ0S6k4TQQ4NHDeTSe8+juLiY8vKe91fb4awbp7N28XpWfbuWUGsIf24m4/fdgaMvPIyq6irH25/74LtxR9WBhgCfvLTA9oTww6dLeenuN6irrCevKJfTfnMco3YbQWZWhi2jqw4+YzKfzV1EU11zTPmQ0YN6XLcQqUYSQi+UW5jDb1+eyaevfs7yL1ez+9Sd2OWgHfH6vElpv7OL6ommvOiJVd+u5YHLHqNq3eaj+HVLNnDFPy5kzF4jbWlj3D7bM+n4vfjstS9oqGnC47UYusNgLv3LT22pX4hUIgmhl/L6vOw3fSL7TZ+Y9Lb3PnJXFrz+Zdx02O3vbrbDK395KyYZAFRvqOXVv73FzNn2jQL62Z1ncNh5B/LJSwsYNGogB5yyDyXDh6TMGaEQdnE8ISilVgB1QAgIaq2T/wslkmrS8Xvx8Yuf8e1HP9LaHLnAPXTMIM7+3cm2ttNQ05iwPNHw054auctwRu4y3PZ6hUglyTpDmKq1lsOpPsLj8TDzkYtY+OZXLHj9SwaPLubIC6aQ08/eeyCGjhnEtx8ujisfPDo1ptEQm/n4ijzrWSyaaTXjqOc8IMvtsEQH0mUkHOHxeJh49B5MPHoPx9o45apj+XH+clZ9u7atbPj4oZx27fGOtSm6z8+/6Wf9Fa8VGdDgtxaQab6i0tzjcmSio2QkBAO8pZQywN+11rOS0KboA3ILc7h+zi95Y9a/Wf1dKSXjhnDMxYeSnS9Hnqkk1/pnWzLYJIPvyeId4Gx3ghIJWZseku4UpVSJ1rpUKTUImAf8Qmv9frvlM4AZAFrrCS0t8SNRfD4fwWD883pTicRon3SIU2LsOm/VNCyzJq48lDkdb+EfUiLGLUmV/bglmZmZAFZP63E8IbSnlLoJqNda39XJKqa0tDSuMJXG+HdGYrRPOsQpMXZdf+sSMq3vY8qM8VJjriSv+NyUiHFLUmU/bklJSQnYkBAcnbpCKZWrlMrf9G/gSOBrJ9sUQqSWRjOdkIm9e7+VcTQjc0GlGqevIQwG5iilNrX1tNb6DYfbFEKkkGaOImz6k8PzWDQTNGOo5wJkTEvqcfQd0VovA5wbZiKESAst7EOL2cftMMRWyGynQgghAEkIQgghoiQhCCGEACQhCCGEiJKEIIQQApCEIIQQIkoSghBCCEASghBCiChJCEIIIQBJCEIIIaIkIQghhAAkIQghhIiShCCEEAKQhCCEECJKEoIQQghAEoIQQogoSQhCCCEASQhCCCGiJCEIIYQAJCEIIYSIkoQghBACkIQghBAiyud2AEII0ZGH9eTwCoZsGjkBQ4HbIfUJkhCEECklh2fJtTReqxKAbPMateYKWtjf5ch6P+kyEkKkDItacqw5bckAwGetJ996GAi7F1gfIWcIQsQxZDOHLOs9LIK0mvHUMQPwux1Yr+fnU3zWhrhyL2V4KSXEcBei6jskIQjRQR5/J8eag8cKAJDBN/hYQZW5C7DcDa6XCzGAsPG37ftNDH7C5LkUVd8hXUZCxAiQZX0Y84NkWeDjO3x842JcfUMrexBkVILycRgKkx9QHyMJQYh2PFRjURdX7rUayWCxCxH1NR6qzW0EzCSCZghBM4wmcyg15rduB9YnSJeR6CMMPr7BRyktTCRM/4RrhRlAmCK81MSUh0w/Wtg7GYH2eWGKqTJ3AiEiXXRy3JoskhBEr2dRR5F1HT6W4LGaCJqBNJsjqGdGgrV9NJnj8PIYHitypmCMjxYmEkrQleGeEBl8j8FLkHH0zh9Nr9sB9DmSEESv18+6m0zrq7bXPmsjObxKszmIIDvFrd/IqbSaHcjlBaCVgDmQJo5JYsRblsEX9LP+gpc1gIcQI6g21xNitNuhiTQnCUH0ej6Wx5V5rDpyzEvUJkgIAK3sSbXZ0+nQtkGQftY9ZFgr2ko8LKGA26k0DyKjoERPOJ4QlFLTgL8QOf/7h9b6dqfbFCJW4q4HQ1aS4+i5DBbhY3VcuY+1eFlNiO1ciEr0Fo52PCqlvMB9wNHAzsCZSqmdnWxTiI5azG4YE3vkHDIDaES5FFFPWCQ+C+isXIiuc/pK1CRgidZ6mda6BXgWONHhNoWIUcflNHMoQTOIsMmj1YyizlxEiGFuh9ZtrexOkBFx5UGGy128osec7jIaBjHnt2uAfduvoJSaAZHhHlpriouL4yrx+XwJy1OJxGgfZ+L8C4RrCJsqLM9w8ixfj+57dXVftt6GafwdhFYBHvCOwpN7B8W+gakTYxdJjKnF6YSQ6BzWtH+htZ4FzNq0rLy8PG6D4uJiEpWnEonRPs7GmQdU97gWd/dlCfAAPpYCXoLh0VBtAbHxOBmjRQP53IvPWgpk0GwOopHT6W63VTp8JtMhxpKSElvqcTohrIGY89vhQKnDbQrRB3gIMtaltg1F1jVkWl+3lfj4EY+ppp5LXIpJ2MHphDAfGKuUGg2sBc4AznK4TSHSUDP5PECG9QPgJWAm08DZOHOZL0AWHwAtBDgEQ263ts7kU3z8GFPmsVrI4mPqzQVAhn2hiqRyNCForYNKqcuBN4mM/ZuttZYZwoTooMi6Hr+1oO11BovxmHLq+FWXtvdQRi5P4bGqCZiDaObQhOv5+IpC687oTW2GEE9Qby6gmcO7HGsGS+JmIwWwqMeiQSahS2OO34egtZ4LzHW6HSHSlY/vyOD7mDLLasXPfOpNI4acLW6fyUIKrNvxWmUA+PmYLP4N5sEOaxoKrL/iszaP8/CxjjweodkcCF28LyPAvuSaZ9um9tgkTBGGfl2qQ6Sm3jgBihBpxcdSPFZDXLmHOjxUbXX7POvhtmQA4LFayWQBtH4Ss56X1XhZF7e9l1Iy+azL8QbZgQCTCJvNXUMhU0SDOQ35SUlvMnWFEC5rZQIhU4TXiv3xD9GfEIO3srXBQ0VcqcdqxrTOg3YXng1ZmARfeYOv29cRaswNZPE2WbxH2GTRyOkuXuQWdpGEIITLQgwlYPYni7fb+uZDph9N5ni2/hW1Ev6YG2OBd1xMWZhBBBmNl887tD+SVvboZtQWzRxBszmim9uJVCYJQYgUUMuvCZiJZPMOhgwazKkE2aULW4YAMCbyZLdNgozG8k+H+th+/mpzI4Xcgo+VQJgQJdSYa5CuHgGSEIRIERYBphIwU7u1VR6z8bEiJhkY46Xe/IR8yw8dnv5mKKTK/AmLGixCnT4oSPRNclggRBrLtD7HskIxZZYVIst6b4vbGQokGYg4khCESGsmYalFOMlxiN5AEoIQaazF7Bo3tXfYZNNojnUpIpHO5BqCEGmsnhl4WUem+RqvVU3QDKLZTKGFyW6HJtKQJAQh0loGNeZWvKzDa9bSyg4ydYTYZpIQhOgFQgwlxFC3wxBpTq4hCCGEACQhCCGEiJKEIIQQApCEIIQQIkoSghBCCEBGGYkU5uN78q2/46WcMDk0m8Np5DS3wxLCZgaLuuiDkNz9SZaEIFKSh0oKrd/hszY/0MXHaozx0cR0FyMTwj5+3iHXegYvVRhyaDb7U88lgLXVbZ0gXUYiJeXydEwyAPBYjWRb81yKSAh7eVlBP+s+Mq0leK0KfNZqcqw55PK0azFJQhApyWOVJyy3aEpyJEI4I5dn8FqVMWUeqwW/9ZFLEUlCECkqYPbDmPgezRBDXIhGCPtZVuKDG4vWJEeymSQEkZKaOZwAE2Ie5B4021Fnfu5iVELYp9lMJWwy48qDjHQhmgi5qCxSlJdq8wf8vEcWHxE0I2jkFAx5bge2VRaNZPMaHjbSxDGEGOV2SCIFBZhCgI/wm//hseoIm0yCjKHW/NK1mCQhiBTm2abHSrrJxw8UWrfgZQ2WBdnmDZrMUdRzmduhiZRjUWNuwMeP+M1/CTKGAPvhZseNJAQhbNTPuheftabttdeqJZu3aDLHE2I7FyNzQhAfSzDk9ML/W/IEGUuQsW6HAUhCEMJGATxsiCv1WjVkmzep5yIXYnJGJh+Rbz2El1LATysjqTE3y3Oa05xcVBaiU0Es6ujsucXxfIA/rtQYixAD7QzMVRYN9LPuI8NagcdqwWPV4be+psC6ze3QRA/JGYIQcQx5PEiW9TEWjYQYQIM5mwCHbGU7LwGzB17WYlmbH3IfZARNHO1syFE+vsfPp7SyI5hjHGkji3eiZwaxvKzCoj4tLvyLxCQhCNFBLk+SY83BY7UA4KUCL3+l0owhxPAtblvHTDAWfr4AWggxhFozk0RnDvYKU2DdhJ+FeKx6wsYPdc9icVt0jhw7GSx3ZlYQDpOEIEQHfuvjtmSwideqINc8Qy1XbWVrH3VcSZ0xQIhkfcWymUsWH2NZQQA8VgCCn5PP/dTya1vbauYwcs2zcVOLhBghZwdpTq4hCNFBZ3eKWlZDt2pJ5vGW39qcDNrzWUttb8uQR525hFazHcZ4CZscWsxO1JhrbW9LJJecIQjRQZARZLAkpixs/DSbQ12KaOtMJ11ShoyE5T0V4BACZn8y+A5DLkG2x60ZOoV9HEsISqmbgIuAjdGi67TWc51qTwi71JqZeCnFxzI8Vithk0+AyQQ4yO3QOtVgTieTz/Fa1W1lhmyazRQHW82gld0drF8km9NnCH/WWt/lcBtC2MpQQKW5Hz8f4jPLCXBAytw41Jnu0ZA7AAANYUlEQVQg46kzF5PL83ioJkwunuxpNDWm1rMjvCwnk0W0Mp4g490OR3QgXUZCJOSNdItsdahp6mjmaJrNUdGhnzkU5wyBxsTTiCdfiALrZvx8jseqJWzyaGEXMA+6HZhox+mEcLlS6lzgM+BKrXWVw+0J0cd5MPRzO4g4Ocwhiw+xrBAAHqsev/mUcNN9wE/cDU60sYzp6l2Y8ZRSb0PCCeqvBz4Byonc5nkLMFRr/bMEdcwAZgBorSe0tLR0XAWfz0cwGD+CIpVIjPZJhzglxu7x1F6MJ5jgwS8ZEwnmP5r0eLojlfZjZzIzM8GGq/o9SghdpZQaBfxLa73rVlY1paXxd0AWFxdTXp4qp76JSYz2SYc4JcbuKbSuJcv6b1x52LcvZS13uBBR16XSfuxMSUkJ2JAQHLsPQSk1tN3L6cDXTrUlhEhtjeYEwiY3pixssjCZyZnSQ3SNk9cQ7lRK7Umky2gFcLGDbQkhUlgL+1FvziKbt/BQjSGfZnMwWVmnQn1qH333JY4lBK31OU7VLYTYMi+rIbgR6A943Q4HgEbOptEoPFQQpgjwk+V2UCKGDDsVohfxUE6hdRM+VmLVBhlgDaHOXE4LE9wOLSqDcMJxKCIVyFxGQvQiBdZtZFpf47HqsGgiw1pOvnUPEHA7NJEGJCEI0UtYVONjVVy5jzVk8YELEYl0I11GQvQSFmE6f7pbaIvb+vmAbOs1LEIEzL40cjJyvNj3SEIQopcI058gw/BSGVMeooQAB3e6XS6Pk2M9hzc6vXcmC8nga2rMTU6GK1KQHAII0YvUmGtpMTtGxvjjodVsR625FEN2J1sEyLbmtSUDAMsKkcnnZPJRwkdltmdRF33utOgN5AxBiF4kTAmV5kF8LKYwP4OK2hGwhWcieCnHQ3V8uVVDIb/DkE2IEVSbGwkzsG25hwoKrNvwsRowBBlBjbkuZh2RfuQMQYhexyLIjpA5iS0lA4AQxYQ7mQzPY7XgtWrItL6mwLo1Zlmh9X/4rYV4rY14rXL81ucUWjfS+TUMkQ4kIQjRp/lpNgcTNpufuJZoejMfq/BEn3XlZSU+ViZYZyU+ljsWqXCedBkJ0cfVczEhM4ws3sVDRcIfewizaaSSh0YS3ddg0YxFo6OxCmdJQhDCRhbVZPMG4KWJaRjy3Q6pS5o4jiZzHNBMsXUhPtbELA9R0naHcStjCVGCp0PiCDKM1nZPQcvgC/KsJ/BQQ5gC6s05tLKn4/8Xse0kIQhhkyzeIN96BK+1AYAc8yK15jJaONDlyLoji1pzKfk8iI+1GHyEGEGNuabdOj7qzAz6cV/bKKQQJdSZi9j0k+JjSeSis7Wx3VYrqTK/J8i4JP5/RHdIQhDCFgHyrCfbkgGAz1pHPg9TYSaTTl+1Fg6gwuxDJosw+GllVzpeboyssxd+3gUgwKEYctqW51qPxyQDAK9VTh5PUG1ucfz/ILZN+nxKhUhhmXyFl7Vx5V7W4WM5Qca6EFVPZNLCPltcw5BDM8clXObp5N4EuWchtckoIyFsEKYAk2AyZ4MfQ54LEbkraIZ3q1ykBkkIQtggyA4EGZWgfDQhhsZv0MvVcyGtZvuYslazPfVc6FJEoiuky0gIW1hUm1sp4A94WQV4CDKaGnOt24G5wlBApfkbOeY5MqzltJpRNHIGhtytbyxcIwlBCJuEGUCVuQtoJvK8c/9WtujdDLk08DO5eTmNSEIQwnbyYEiRnuQaghBCCEASghBCiChJCEIIIQBJCEIIIaIkIQghhAAkIQhhG4s6MvkMb4eZQoVIFzLsVAgb5PEwWdY8fNZ6QqaAVsZTbW7GrnsRLOrJZi4W9TRxLGEG21KvEO1JQhCihzJYRI41B49VD0SeR+wx/yOfe6njyh7Xn8lC+ll34bMiU03nmNdoMKfSyJk9rluI9qTLSIgeyrFeaUsGm1gWZFrf2VC7Id96sC0ZAHitCnKsOVhU21C/EJtJQhCih4xJ/DUyNny9PJThYUNcuc8qw8+HPa5fiPYkIQjRQ42cTMj0iykzxqLV7Nrjug05GDLjysMmgzDFPa5fiPYkIQjRQ0F2osGcQdAMI2wyCJkBNHMQdVza47oN+THPKd4kxMitPsBGiO6Si8pC2KCRs2gy0/GykjADCTPAtrprzPXAHWSwGAgSYhg15teA17Y2hABJCELYxpBNMMHRfM9lUWNuBIJAGBJ0IQlhhx4lBKXUacBNwE7AJK31Z+2WXQtcAISAK7TWb/akLSGEHL8JZ/X0GsLXwMnA++0LlVI7A2cAuwDTgPuVUnJ+K4QQKaxHCUFr/Z3W+ocEi04EntVaB7TWy4ElwKSetCWEEMJZTp2DDgM+afd6TbQsjlJqBjADQGtNcXH8UDqfz5ewPJVIjPZJhzglRntIjKllqwlBKfU2MCTBouu11i93spmVoCzhk1W11rOAWZvWKS8vj1unuLiYROWpRGK0TzrEKTHaQ2K0R0lJiS31bDUhaK0P34Z61wAj2r0eDpR2sq4QQogU4FSX0SvA00qpu4ESYCzwaVc27CzT2ZUBnSQx2icd4pQY7SExpo4eXVRWSk1XSq0B9gNeU0q9CaC1/gbQwLfAG8BlWutQF6q0Ev1RSi3obFmq/JEY+1acEqPEmEp/ojH2WI/OELTWc4A5nSy7DbitJ/ULIYRIHpnLSAghBJA+CWHW1ldxncRon3SIU2K0h8RoD1titIxJOBpUCCFEH5MuZwhCCCEclpKzZSmlngN2jL4sBKq11nsmWG8FUEdkAr2g1npiEmO8CbgI2Bgtuk5rPTfBetOAvxCZq/gfWuvbkxjjH4HjgRZgKXC+1jruuYtu7Met7RellB94HJgAVACna61XOB1Xu/ZHRNsfQmSK0Vla6790WGcK8DKwPFr0otb65mTF2C6OFWzh/VNKWUT29TFAI/BTrfXCJMa3I/Bcu6Ltgf/TWt/Tbp0pJHlfKqVmA8cBZVrrXaNl/aOxjgJWAEprXZVg2/OAG6Ivb9VaP5bEGB37XqdkQtBan77p30qpPwE1W1h9qtbardsI/6y1vquzhdEJ/e4DjiBys958pdQrWutvkxTfPOBarXVQKXUHcC1wTSfrJm0/dnG/XABUaa13UEqdAdwBnB5fm2OCwJVa64VKqXxggVJqXoL37gOt9XFJjKszW3r/jiZyL9BYYF/ggejfSRGd72xPaHvv15J4dGKy9+WjwL1EEv8mvwHe0VrfrpT6TfR1zHcmmjRuBCYSmYFhQfTzG5c4HIrRse91SncZRY9sFPCM27Fso0nAEq31Mq11C/AskYn/kkJr/ZbWOhh9+QmRO8ZTQVf2y4nApqOu54HDop+HpNBar9t0FK21rgO+o5P5uNLAicDjWmujtf4EKFRKDXUplsOApVrrlS6130Zr/T5Q2aG4/efuMeCkBJseBczTWldGk8A8IrM6JyVGJ7/XKZ0QgIOADVrrHztZboC3lFILopPkJdvlSqkvlVKzlVJFCZYPA1a3e93pJH9J8DPg9U6WJXs/dmW/tK0T/fDXgI2PIesGpdQoYC/gfwkW76eUWqSUel0ptUtyI2uztfcvlT6HZ9D5AV4q7MvBWut1EDkoAAYlWCeV9qet32vXuoy6OGnemWz57OAArXWpUmoQME8p9X00ozoeI5HT7luI7PRbgD8ReXPaS3REa+uwrq7sR6XU9US6QJ7qpBpH92MCXdkvju+7rlBK5QEvADO11rUdFi8ERmqt65VSxwAvEemWSbatvX+psi8zgROIdHF0lCr7sitSZX/a/r12LSFsbdI8pZSPyMN3JmyhjtLo32VKqTlEuiJs+yHr6sR+SqmHgH8lWOT4JH9d2I/nEbkodZjWurMZZx3djwl0Zb9sWmdN9LNQQPzpvaOUUhlEksFTWusXOy5vnyC01nOVUvcrpYqTfU2rC+9fqkw2eTSwUGu9oeOCVNmXwAal1FCt9bpot1pZgnXWAFPavR4O/CcJsbVx6nudyl1GhwPfa63XJFqolMqNXuxDKZULHEnkCW5J0aEPdnonbc8HxiqlRkePjs4gMvFfUkRH8lwDnKC1buxkHTf2Y1f2yyvAedF/nwq829kH3wnR6xUPA99pre/uZJ0hm65rKKUmEfk+VSQrxmi7XXn/XgHOVUpZSqnJQM2mbpEk6/SMPxX2ZVT7z915REY+dfQmcKRSqijaVXxktCwpnPxep+Qoo6i4vkalVAmRIYrHAIOBOUopiPw/ntZav5HE+O5USu1J5FRxBXBxxxijowAuJ/Jh8QKzoxP/Jcu9gJ/I6SLAJ1rrS9zej53tF6XUzcBnWutXiPwYP6GUWkLkzOAMJ2NK4ADgHOArpdQX0bLrgO2i/4cHiSSqS5VSQaAJOCOZSSsq4funlLqkXZxziQw5XUJk2On5SY4RpVQOkVFlF7crax9j0velUuoZIkf6xSoySeeNwO2AVkpdAKwCTouuOxG4RGt9oda6Uil1C5EDG4CbtdaOnL12EuO1OPS9ljuVhRBCAKndZSSEECKJJCEIIYQAJCEIIYSIkoQghBACkIQghBAiShKCEEIIQBKCEEKIKEkIQgghAPj/eaikFxbzK6wAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Create a random set of examples\\n\",\n \"from sklearn.datasets.samples_generator import make_blobs\\n\",\n \"X, Y = make_blobs(n_samples=50, centers=2,random_state=23, cluster_std=2.90)\\n\",\n \"\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=Y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A classification algorithm may be used to draw a dividing boundary\\n\",\n \"between the two clusters of points:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SGDClassifier(alpha=0.01, average=False, class_weight=None, epsilon=0.1,\\n\",\n \" eta0=0.0, fit_intercept=True, l1_ratio=0.15,\\n\",\n \" learning_rate='optimal', loss='hinge', max_iter=200, n_iter=None,\\n\",\n \" n_jobs=1, penalty='l2', power_t=0.5, random_state=None,\\n\",\n \" shuffle=True, tol=None, verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import SGDClassifier\\n\",\n \"clf = SGDClassifier(loss=\\\"hinge\\\", alpha=0.01, max_iter=200, fit_intercept=True)\\n\",\n \"clf.fit(X, Y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Plot the decision boundary. For that, we will assign a color to each\\n\",\n \"# point in the mesh [x_min, m_max]x[y_min, y_max].\\n\",\n \"x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\\n\",\n \"y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\\n\",\n \"xx, yy = np.meshgrid(np.arange(x_min, x_max, .05), np.arange(y_min, y_max, .05))\\n\",\n \"Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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+P+56H7vicoXHZ5LutpHvG0bNpO3KzPeRSvePXh/frUf6UNTExsVrb1bawH9F1PaGst54AVF414RRlpeSwf1MSCe2aeH2GP2EOpRTjH/uSdQs3k5eRT0DIbFp1acbjk/5F0AludKopTdN46MO72LFqD6tmraNZh6b889o+2Bz183xvwmlNSDitidkxANiyLYmFGR/x5D1raOUoYH1OE6KDJ+CwhpkdTRyntu/2acAdwBtlf0+t6Y6OLoTw5/xNZB7OJjw2lI7/aMcDY27Hajv1SaWE/1g+fS0rZvxJSXHp6ZHi/GK2r9jNly/+wL2jbvbJMdv3aUP7Pm3KH8+Z8Csrpq/FWVhCfOtYbnt1MJHxsihydU39fgmdLn6XlzulkOWyM+3A2fRNfAFNs5sdTVThVIY7fg0MAGJ1XU8CXqC0oBu6rt8D7AeG1DTIku9Xsfi7lZSUnRvNSctj9ez1TBs9h2ufuKymuxV+YMX0teVF/VgHNh/02jEK84qwB9irnFly2ntzmDF2PsX5pRdQ929O5sjeVF6Y/sQpTzPcGD377ds8f818WgYUcKg4GI/1e/o1858b0URl1X5XG4ZxosGzXlmlYPn0teVF/SiPy8PmP3Zw7RPeOIIwiyOw6l6dzVH7T2LbVuxiyitTyTiUhSPIzulntueO4UMqfMpbMf3P8qJ+VPL2wyz+bjnn3+J/E5b5i3V/7mE1Yxk9ZC0hFjcLDvWnU/xrWGV1I7/nN9+hE11H0yz1525BUbVB/zqfTYu2k5P214yQNoeNHgO71mq/eZn5jP/Pl6TuSy9vS92fjs1u5fbXSj88ejweCnIKKz3X4/J49RNDQzP9l+VoHSfydMttFHqszD44mB5NHzY7lqgmvyns5+j92bJkB8UFf/WsrHYr3S/obGIq4Q2turZg8FNXMPeT38hJzSUkIpi4VtGERYeQn1VASGTwyXdShfmf/V6hqAN43IotS3aWP7ZYLEQ1jSA9ueIN0QHBDpK2HWLY+cOxWCy079uGW1++vt5eZPWWnIw8Jm59nSfOX0WcrZi9RSG4LSPo0bR2v4RF3fKb2R37XNadi+48h7iWMdgDbcQ0i+Ks6/sy6P4LzI4mvOC8G//BK7P/j/veuRmrzcrG37bx6f+m8Pygkfz2zbIa7TM7Na/K9hJnxVN61zw+iOjEyPLHNocVm8PGtmW7OLjjCEnbDrHwyyWM+/fkGuU4ntvlJj05g+J6NiPj1DnLWJD5P944+w/ibMXMTNIJdMwgxCZFvb7xq+6J/vRVXPnIxRzek0pci2hCo2Te7IZEs2j88NYskrb9dQok7UAG096bS9/Lup/yzJADbj6T5dNWk59V8VRLk1YVbybpdt7pPP39o8wYM4+8zAKan57ArI8WVNyZgp2r95CXmV+r992vXy5h3sTfyUrNITgsiDPO78Str1zv1xOQKaX4dOpMLho4kX5h6SQXBbOv4AF6JV5pdjRRQ35V2AGCwgJpc0YLs2MIH8g4lFVhOb2j0vans2bOBs4e0u+U9teqa3POur4fS39cRW5GPharhYR2Tbjj9cpraMa1iOGuETcCsHTq6gqn/I4qzCsmJy23xoV938YDfPfmz+Sml36SyMvI59evlhAeG8rVjx0/zZJ/WLpkK8nxY3j46q2EW11szosmOvgb2jocZkcTteB3hV00XI7AqocjWu0WgiNqNnzulpeu44I7zmb51NXEtojhH1f1Oul58i5ndyQ6MbLSZGHRTSNo0qbmK0DNGrewvKgf5XK6Wbdgs18W9vE/TqXDP3/i4YQ9ZLvsLDzyMJ3iBpsdS3iBFHZRZ8KiQ2neMYHMw9kV2pu2bVKri+QJbeO55j+Dqr19eEwo5+j9+eWzReRllU4aFtkknMseuLBWN8NVNVYfwF3iXzMyut1uXvn0E1646wcS7EX8mRNDaMBrdIo73exowkuksIs69cDYO/jk8a/ZvWE/bpe79BTJmzfU+d3F1/33Mnpd0o35ny3CEWhn0P0X1HoKizOv6cP6hVtwFh43AVmXZrXarzcVFTiZuPVZ3r13FYEWD4vTOtAu+uOTP1HUK1LYRZ0KiQzm5WlPsm/nfkqcLlNv62/drYVXpzToPegM+v/Siz/nbyI3PY+AYAetu7Xglpeu89oxaspV4mL0tC+4YMB8Xu6TDMCanKdpF32xycmEL0hhF6ao6dh1f6ZpGveOupkje1LZ8NtWWnRKpEO/tqaPiCkqKOaZOaP5fPBMrBr8kZZIWOCTJIZ2NzWX8B0p7EJ4WZM2cbW6COtN65buIKX1CCZdtwuFxpKsKzkt+nGzYwkfk8IuRAP12tefcsMVs7gtIoVct419Re/TNlwWlm4MpLAL0cBkpOYyfuVYntF/pam9iN9SWtMicjixQdVbpEHUf34zpYAQovbGTppLZuDdjLp8NpFWJ3MP9aJj7KcE26SoNybSYxenZNef+1gzaz3NT0+g35U9ZREUP1GUX8QTM5/ivhsW0TU4m50FEVit4zijSVOzowkTSGEX1aKU4qNHvmD9gs0U5BRitVuZNW4h/518P+GxsjSamXbuPMjUgx/w5mVLCLB4+DmpOz2avo7V0vBGHonqkVMxolpWz1rP6tnry+c2d5e42bcxic+f/c7kZI3bc198REyL+xk5YDHFysLCVJ3eiaOlqDdy0mMXVcrNyCM7JYembeOxOWwsn7am0gpXAId31Xr9clED+/akMDX5fUbcsoxoWwmLUjrSr/1ndNKKzY4m/IAUdlGBy+ni48cms2PlHgpyC4lKiOTiu8874SRdNlkztM4tW7WVbeEfMvzcdbiUxsykC+iV+BxWaxgghV3IqRhxnK9e+pEVM/4k41AWRXnFHNpxhB/fnkXvy7oTlRBRYVt7gI1eF3czKWnj9P5H02nf7X88efo6UpwB7Cx+lV6JzwP+O9+7qHvS3RIVbF+5B+VRFdpyUnNZOeNPbn9tCNNGzyUrJYeg0AB6XtyNKx8ZaFLSxmXx0s3sCR3Po/dtpom9mK150UQEGUTa5EdYVCbvClGB8lQ9xazL6aLXxd3oObArzkIn9gA7Fqt84KsL0+YtpU3f9/i/6ENkuhzMOHANvRMfQdNkqKmomhR2UUHLzs1I2nqoQltweBAX3nEOUDrRVUBwgBnRGqVHJ3zEuLu/J8pWwv6iEBz2r+nTzLwZMUX9IIVdVHD78CFkHslm74YkCnMKiU6M5J/X9aVdr9ZmR2tUvp31KwEdp/DJPTsIs7pYlnEGrSNHI+fSRXVIYa/C9pW7WTj5D8Ijwjj/zn/StG282ZHqTFBoIE9NeZi9Gw+Qui+djv9oR3hMqNmxKnAWlZC8/TCR8eFENY04+RPqEaUUo8Z/zQN3fsVpgXnsLAhnRc6NdIn33rzxouGTwn6cb16dym9fLaEgpwiApTNWM+SpKzhH729ysrrVumsLWnf1v0XFf5m0iLkTfycjOZOg8CDa9mzFg2PvwBFoNztarbndbt5c/BKv3reEMKuLNVmJJIZ/SZdA6aWLUyNXv46RdSSbZT+tLi/qANkpOcwatxC3y21isr9kpeQw4fGveEMfw5j7PyV5+6GTP6mBOLjzMD+9O4fDu1JwFpWQnZLD2jkb+OK5+n/3648zV7Dw8L94e8DvhFldLE27m8Twr5BTL6ImpMd+jI2/b6u00DJA1uFsUvenm35KJic9jzf0MRzaeaS8bffaffz7k3uJHRBrYrK6MWf8r+Sk5lZq3712n1f2X1xQzPT357F3/QGCI4K46tFLiD3bt6+r2+XmvZ++YuDAmVwUdYjDzkD25j1Jm+jzfXpc0bBJYT9GfOtYAoIdFBc4K7QHhQUQ5gfnmae+O7tCUQdIT87kh7dm0ntAD5NS1R2PW1XZrlTV7aeipNjFiBs/YNeaveVt21fs5omJD9LiDN/MkLh/x2GWaq/x8uBN2DUPyzMSSAh/h9aRMiOjqB05FXOM9n3a0Kpr8wptFqtGh36nERJh/qRKqfvSq2zPSc+r4yRQXOhkzie/8dlTBpv/2O6V4noyA+8+l9CokErtLTs3q/W+f/t6KXvWVez5Zx7O5pvXf6z1vqvyzhSDjJgn+HeHDWgoVmW/RqvIr3FYpKiL2pMe+zE0TeOxT+/j86e/JWnrIRyBDk7r3Yqbnr/G7GgAxLWMqbK9rketpOxP4907x5O84zAoWPLDSroN6MTD4+7y6cLNLTs345L7BvD7N0tJ3Z9BcEQQrbu14I7X9Vrve8fK3VV+IshOzan1vo9VmF/Ee3Mn8Oh1s2gTkM+azHiCAl6ipSxZJ7xICvtxQiKCeWDsHQDExsaSlpZmcqK/XP2fS9m0eHuF0zExzaK47r+X1WmOyc99T/L2w+WPiwucrFuwmTVzN9D7kjN8cswDWw6SsjeV82/9JxfdeQ47V+8htnk0ie2908Nt0bkZy6atgeNqe2hk5U8INTXl+z84bcBHjLwuCYCFRzrQKe4j5IOz8DYp7PVIeEwoT015iO9G/ExaUgah0SFc+/ilNOuQUKc50g5kVGorKSphxbS1Xi/sRfnFjL57Ans37Kcgp6j8hqkhT13h1eMMvOsclk1dzYHNB8vbwmJCuOrhS2u9b1eJixFTx3HzpQs5MzyNpOJgclzv0imuQ633LURVpLDXM5FNIrj3bXNvVrGfYMx4RLz3b3X/4rnv2PzH9vLHGQezWPD5Ynpc2Jn2fdt67TgBwQH87+uHmPLaVA7vTiUwJIBLhw7g3Ov/UatPbekp2XywbAwjrl9AqNXNvMPt6BT7JtH2aK9lF+J4UtjFKet7WXcO7jiMs/CvhTfiWkRz2QMXev1Y+zYmVWoryClkwRd/eLWwQ+knovvevsVr+9uz6zBbA55n7DXbcXoszD8ykG7xz3ht/0KciFcKu67re4FcwA24DMPo4439Cv90+UMX4XK6WDVrPYV5RUQlRHLDsCuJ9EGP3WKp+mKsxea/56UzUnOYsHYMd527gruCsyjyWEhxTaJrnP/dySsaJm/22M83DMN/rjQKn9E0jWseH8Q1jw9CKeXTkTDt+7Zl/+aDFeaID48N5dL7/PMGnh07DjI9533evnQpANMP9KBH05dw2BrWnDbVoZFHGKOxa7tR2HCqPuRxD3Kx2PfkVIyoFV8WdYCbnr+WnLQ8dq7aTUFOEVFNI7jwznNo0SnRp8etiSlf/Uafq8bwZsdUsl129jr/Td9m3r3IW38oorRhOLQN5S12dqGpHHJ5wsRcjYPmjRtLdF3fA2RSOlhsnGEYH1exzVBgKIBhGL2dTufxm/gdm82Gy+UyO0a1NPSs2ak5ZBzOonmHBOwBdTfhV3WyKqV45JOnefLyBbQJyCepKIS4Jr9gtdbt/QV+9R4oWYk190E0Cis0eyzN8UT8gM0e7j9ZT8KfXleHwwHVmEDIW4U90TCMg7quxwPzgEcMw/j9b56iDh48+Ddf9g/+No7970hW3zhZ1qUrNrPV8Qn/7vEnwRY3s5LOonvT57FY6n4xEn96XYP4iQjLu5Xa3SqCdDWO6NiufpP1ZPzpdU1MTIRqFHavnOwyDONg2d8pwI9AP2/sVwh/9n8fTKZvrycY1ms1uS47cw5dT8/E10wp6v7GSX/cKqpSu4cYPMSZkKhxqXVh13U9RNf1sKP/Bi4GNtZ2v0L4q9RDWby3bBij/vUFzR2FrM5qjcc6g+5NHjE7mt9wk0CROgePCvqrTUWRr4YAslarr3nj4mkT4Edd14/u7yvDMGZ7Yb9C+J2f5i3B0/pLhv9zEwA/H7iW3s0eQUZ6VJbLfyhWZxHEbJQKJB8dN23MjtUo1LqwG4axG+juhSxC+C2lFC/8/AavX7aQGJuT3YWhpBY/SO9mdTtPT/2i4aQ/TtW4Vh/zBzLcUYiT+G3RJtISPuT9KzYTYPHw+5HL6BD3f7QKkNWNhH+Swi7ECSilGP3FV7To8xUPNdlLlsvOH6kP0DluiNnRhPhbUtiFqILb7ea58RN4Y+gPxNuK2ZAbTahjJJ3jTjM7mhAnJYVdiON88sMc2p/5DWPv30eQxcPitE60i/7Q7FhCVJsUdiHKuF1uRv/0Jbdf8T1dg7PZkR+B0/Ec7aJlTjtRv0hhF4LSNVyf+2UUnw2Zg0WDZRnNaR35KbGRRkylAAAgAElEQVSxCX5z16EQ1SWDb0WjppRipPEZWwvvYuKVc/GgsSJ7MK0jJwN1NyeNEN4kPXbRaBXkFTFi+gcMv2E2MTYni9KaExn0FC3DupodTYhakcIuGqWMtFxmZTzDBzevB2BBSl86x440OZUQ3iGnYkSj8+5nM8gMuIcnTl+PQmNX0TtS1EWDIj120WikHs7is/Ufct9NSzk9KIetedHY7e8THtDM7GhCeJX02EWj8OvcdexzPMKoS+fQKiCfmcndiAj6ihC7FHXR8EiPXTR4z38zln9fM5cuwdlklDjIZTK9EuLNjiWEz0hhFw3W3l0pTDv0PiP0ZUTZSvjlUBc6xL5OgDXc7GhC+JQUdtEgrVqzg52R7zH83A2UKI2ZyRfQK+E5qrGqmBD1nhR20aDkZhfw4aqRPHrucq4IKCC1JIBchtMrobfZ0YSoM1LYRYOxZPkWtgWOZ+RFa7BqMG3/+fRt9jTBmtxBKhoXKeyiQZg+dwnt/zGaYZFHSCsJYHPO7fRrfovZsYQwhRR2Ua+t+3MPayxjuOfCjcTbi0kuDsZi+5EOUQFmRxPCNDKOXdRb38/5nb3RIxjWYzVhVhczkwZjtf+MpklRF42b9NhFvaOUYvgHX/PE0C9pHZDP7sIwXJZX6ZUoa6oLAVLYRT3jdrl5Z/kLvPbgMsKtLlZnNadZ+BfIMEYh/iKFXdQLSinG/zSN9v2m8ebZu7Bo8OuRf3F63E1mRxPC70hhF37P5XLx+JSxfHHLVAIsHtbnRFPgfojT4y40O5oQfkkKu/BbSim2r9/PhrCRfH7LZgIsHpZknkfbiJfMjiZ8wk0oE3FoqwFFiepALg8BgWYHq3eksAu/5CpxMXralwwYMI+H45Io9FhZnvESbSPPMzua8JFw3iJIm4emuQCwsw0bh8hUb5mcrP6Rwi78hlIKTdMoKnAyfuvzvDZ4OQ5NsSQ9kejg52gV2cknx9UoRGEBZJikWTRycWhryos6gKaBXW3Fxk5ctDMxXf0jhV2Y7mhBB/hsym90u2g8r/VJAmB97pO0jRrkk+NaOESE9iZWkgELLtqRrYahCPHJ8cSJWcjAQl7ldi0Pm9orhf0USWEXfqHE6eKtn8dx8xUL6R+Wxr7CUHI9I4kP8U0vHTxEas/j0HaUt9g4DLxMlhrho2OKE3GTgJsYLORXbFexOJH7E06V3Hkq6pxSCqVU+ePMtDxenfUmb1z7Pf3D0ph/+DRs9snEBvmqqIOdtdjYW0X7Tixk+uy44kQcFKqrcKu/5sr3qCCK1Dl4iDMxV/0kPXZRZ44t5kdPvbz25WfccvUMPrwmDaeysDDtMrrG/8/nWaxkY9FKKrVrONHIB6J8nkFUVMBgnKoLIXwHuChUl+LkTLNj1UtS2EWd0jQNTdNIO5LNxHVjefrG30mwF7EktRVNwl+nU3RirfZv5RB2NlJCO9y0OeF2xfTDpZpi0w5XaHfTBDeyDqpZXHQiWz1ndox6Twq78JljL4rCX7307duS+ObIa7x96Qo8CmYf7E2PpiOp3ZlBRTgjCNCWY9Uy8agwnHQlS70MVJ6PXRFKvrqJECZj01JRCtwkkqsewB+nJ9AoxEoybpqgCDM7jvBzUthFnfps0i8MGPwhr/ZII8dtI6nkSXo0HVjr/QYyi0Dtl/LTKxYtlwC1jFAmkse/qnxOIVdTrM4hSM3GQxBFXIIiuNZZvC2UiaX/NzLwEEGx6ksuj+OPv4CEf5DCLnzm2N76lq1JLMz8iDtvWkvbwHz2FoZhs39NtCPUK8cK1H6vdM5c0xQONoA6wZMAD9Hkc7NXMvhCAEsI0r7DqhUAYKEQK7Nxq0QKkHlyRNW8Uth1Xb8UGA1YgQmGYbzhjf2K+qGqi6LHWr5qK57Ww3m5837y3DZ+Th5AzyZPYbF481Zxa9XZTtBeXwRpM8qL+lGaVkIASylQUthF1Wo93FHXdSswFhgEdAZu0nW9c233K+qHo0X96EXR4/133Kf06vEfro3fT3qJg1xlcEm3MV4u6lCgrsKjKt5Y5FEOitVZXj1O3av644b2dx9DRKPnjR57P2CnYRi7AXRd/wa4GtjshX0LP1dVMQdYtGQDe0M/ZfR9G4mxOVmZeRrNw8ehab45++ekP/nqBoKYV3YuOpxi9Q8KGOKT49WVIjUAB6uxaM7yNqUsFCu5aUecmDd+ypoBB455nAT0P34jXdeHAkMBDMMgNjbWC4f2LZvNVi9ygn9lffPdb7hq8PsMCc7mkDOQeUeGcP7pz5dO/oEvsz4O6kE8noNgaUKAFlLr2V9Mf13Vzaj8HaiS39BUKkqLQtl7ERT6X4KO+yVpetZTIFl9yxuFvaouW6XPiYZhfAx8fPTraWlpXji0b8XGxlIfcoJ/ZFVK8cqc4bx08+/E2YrZmhdNRJBBtzgbaenp5dv5Pms4UFj2p3b84XWFh7FwAza24qYNbndzKM6qtFVdZNXIIojZgJ1CLkFRs4vf/vG6Vo8/ZU1MrN59Ht4o7ElAi2MeNwcOemG/wg8cPxb9RH75bR3ZzT7m3UGbcWiKXw5eRZem/0GG5HmHhzicJt9aH8gswrTPsGpHAAhW35GjHsHJP03NJSrzRmFfCbTXdb0NkAzcCH48fkxUy7EjXU623afTZtHl7K+5Je4AmS4Hi9PvoUvTG3ycsL5zYWcdGgonPfD3kccahYRqk8uLOoBNO0QY40lX/TnRqCRhjlq/mwzDcOm6/jAwh9Lv7kTDMDbVOpkwVXV66Qf3pTErZziPXrWeMKuLTblRhAeNoXOc3JL/d2xsIkIbia3s0pSLFuSo/1Dio1kMbWwkVJuMhXxcKoE87sdD9Cntw846rFV8ELdyGBv7cNHWW3GFF3ilm2AYxkxgpjf2JcxR3VMuR038cRZt+3/LM91341aw4MgjdI673ocJGwoPEdoo7Nre8hY7ewnnXdLVBKrX8y0mnHexa1sAhYu25KgngMoX+BysIkJ7HatWeo3DoW3ArraTod4/pakJPISjCECjqEK7IhCPzF/vd/z785/wO263m/e+/5K7rv6O04Ny2JwbhVN7is5xlQZCiSrY2HmCnm8SdrZRwslvAYnUXiaAP44OMsLOPixkAF9W2jZE+7K8qB9l1/YSor4hj/uqndtFJ1y0wsG249rb4KFJtfcj6oYUdgFU79TLjNkrCTpjHC/pu3FoHlZkNKdl5OfItP6nwkbVr5cVVY0fRwvp2NnK8d8uOzvxuLYBMcdtn1t1Cm3/3061UJlGlnqVCN7AygFKV5xqQ7Yadio7EXVECrs4KY/Hw6jvv+DigXO4JPogKc5A9hY8S8vIs82OVu+4aIOLFlX0fFvgov1Jn28ho2y++OPatXyU5wjHF3YPkZW2VQpK1KkvNechjkw1StaIrQekqyX+VlFBMc99+S7PDZ7MJdEHWZKeSKH2CS3DpajXjEa2eganOh2PCiudXlh1JFs9Q3WGhrpohbuKYY8u1RRl61GpPVfdhVtV3L6E9rW6I1cRhBR1/yY9dnFC6UeymZ/3LONu34iGYkHqP+gcI/O71ZablmSoD8sW0Va4aU51x/tbSUGjCKXKb+TFrQIpUFcSbAkHKt5I46ILGWokoXyOhRxcqjV53OGX0xML75HCLiopyC3kvQXjGXLBIv6dkArA5vz36RzTzeRkDYlWVtBPTbg2EpuWelxrMIVcfsJS7aY12er5Uz6WqL+ksIsKMlJzeX/L23x47UIA5h0+jTaRLxAd1NLkZEIjHxvJldqtWgZBai5w6ufNRcMkhV2Umz1tNU3OeZsPByRT6LGwpeBeusXLTcT+QmGtcn55pbSy895ClJLCLjh8MIPJmz7i9oErOCMkiyPOQAqYRGKwjE/2L4G4OA0bRyq0ukmkiIvkNiFRTkbFNHI7dx5ktWcYb10yl9ODcpiR1B2nNoUgmxR1f5StnqZY9cOtYvCocEpUe7LV/0mPXVQgPfZG7KVx3/LonV9wT1AO+W4rGZ7x9EmUOT/8mSKUTPUmFjLRKMRNAjKDpjieFPZGKCM1h083vcOwe5bT0lHAupymxAVPwG71zsLSwvc8RAFRZscQfkoKeyOzbOVWNtomMOL8VVg0mH7gXPokPu+zJeuEEHVPfpobkee+f4vnrl7ANY4CUpwB7My/l77N6veaoEKIyqSwNwJ/rt3NWutYRl//J8EWN/MP/ZMu8S/TNkK+/UI0RPKT3cBNm7cUe+eJDGuxgwK3ldnJOj0SHjQ7lhDCh6SwN2DPv/8lzz8wmeaOQvYWhqKsb9Ej4XSzYwkhfEwKewP0w+yl2Dp8zpsP7yTSWsLKrJa0CJ+Efw6Lc2NnAxrOsrU/HWYHEqLek8LegCil+OSn6VxyyST6hqZzoCiEVal30dVPpwWwsptI7TWs7EfDjYtm5KoHZNV70ejY2ISDzTjpUa15+U++P9EguN1unvhuFBOHzCTQ4mFTbgwxId/QNd5udrQTUERob2LXdpW32DlAGB+Qrnoj832LxsFJpPYMDjZj0fLxqDCK6UG2epHqrX9bNZlSoJ5TSvHRjz/y06Zr+bSsqC/LvIiYkO8Bfy3qpavbV7X2p41kHKw0IZEQdS+UTwlgJRatdFUsi5ZLIEsI5vta7Vd67PWY2+Xm5Ynjeemen2hqL2JtdizF7gdpHXmB2dFOqnSWwqr6FRb8+ReSEN5k1zZVWr9W09wEsJICpdd4v1LY66nC/GIm7XyW94auJsDi4Y+MLpwWOdbsWNXmIR4XLbCSVaHdRQuc9DYplRB1reoSrGrZuZFTMfXQ+G/ms7noHl7suZIAi4c12c/Rv90Us2Odsmz1HE7VFbeKwKNCy2Yq/B/S3xCNRZG6CI+qODOnR4VSoK6u1X7lJ6geKcwvYvS8T7j96vn0Cslkd0EY+Wo4iWH1c8k6D/FkqDFl59pLcNMS/xySeWI2dhLA77hoRTHnIT9S4lQUchlWdZhAFqKRi4dICtWlOOlfq/3Ku7CeWPPHdjLajuCta3ejgIUpbekQPZpgS5jZ0WrNTaLZEWpAEc4IArU/sGi5KGXDxTdkqjfwEGN2OFGP5HE3eeo2LGSVzdpZ+7Isp2Lqgde+mkjkGc9wa7NdFHqs7CgYR6fYiVgbQFGvrxysIFD7FYuWC4CmubBrOwjX3jE5WV0oAorNDtHA2PEQh7f62tJj92PpKdl8suYDnr3hV5rYi/n1SBtaRr1GdFB97OE2LEHaXCxaUaV2axWLTTcUFlKJ0N7AygHAgos2ZKunUUgHw99Ij91PvffZLLKD7+Xty+YQbi1hzqE+nB73KcE2Ker+wKPCq2yv7WgG/6WI1J4nQFuNTUvBph0mUFtKpPaC2cFEFaTH7mcK8wp557f3+NdNf9AlKIft+VEE2MfRvUm82dEapADmEqzNRsOFU51OHvdQnbte87mJQLUYq5Za3qaUHaeq3UWvU+dGIx+Fb1e/srENG3uqaN+DhSN4kDVy/YkUdj+yY8dBZhway1tXLMGhKX5O6kGPpq9jtchCxb4QwheEaF9j0QoAsLMeOzvJVKM42egcD/FkqycI5TOspKEIokj1J4+76yB5qWC+JEibh4VcPESgFd0MXOSTY1nIRqvivLpGERbypLD7GSnsfmLpks3YTn+dER0PkOu2sSRzCL0T/2V2rAbMRaD2S3lRB9A0sKst2FlLCb1Ougcn/yBD9UejAEUAdfnjFMBcQrUvy/NbSUcVvo+d6GplP1VOuuMmEdtx1xDcNMVFa68fT9SOFHaT7d15hBmp73Nv37W0Dsgny2WngB84PVouSPmSRh4Wciq1W7RCHGrzKRRHDUWId8NVQ7A2u8IvJQBNZROifU+W8n5hh0Dy1K2E8ik2LQUAl0ogV91LbSarEr4hhd1ES1dsZmfUOF49ax0lSmPa/gH0SRyGxSIzG/qaIgwPkVjJqNDuUSEU09OkVNWnUXKCdqfPjlnEIIrVmQSrmSjsFHKpjIjxU1LYTfLOh1O58+5PuDYoh8POQLLVS/RrXtcX3hozKwXqCsL4tHwsulIWnJyBiy4mZzu5EtURh7ahQpvCSrHy7Tw7ikjy8c/5/cVfalXYdV1/EbgPODo04GnDMGbWNlRD5na5eWPhcJ4buph4ezFb8mKIDJpChCa/Y+taIdfhVi0I5kc0SihWvShgiNmxqiWX+7CpndjZikUrwq2C0ex9KHAONjua8APeqCbvGIbxlhf20+D9OHcxqs3XvH3xZuyaYlbSdfRMfIT6Nj9KQ+KkL07V1+wYNRBApnoHO6txqE046UVE+PmQlmZ2sEpsbCdE+wqNQpyqJwXILx9fk25iHVBK8e9PP+Lju34g0lrC7sJQ9uXeRs/EG8yOJuo1jRL6UEIfs4OcUAALCdfew6pllj1egYNVoD41OVnDpimlavzkslMxdwI5wCrgCcMwMk+w7VBgKIBhGL2dTt9d5PEWm82Gy+Wq1T52bDrA1LSnebjzekKtblZk96FX60+pNLt+LXkja12RrL7hj1mt2TeiuTdWaFMEoUW8i8t6lkmpTo0/va4OhwOq8RH/pIVd1/X5QNMqvvQMsAxIAxTwCpBgGEZ17tBQBw9WXhbN38TGxpJWw4+2Sik+mTqdNn2ncUuLnRR7LCxKGUrX+Bu9nLJUbbLWNcnqG/6X1UWsdgs27Uilr7gDbiG18D4TMp06f3pdExMToRqF/aSnYgzDqNatbLqujwdmVGfbhs7tdvPWkhd55eolhFrdbMiNRlmepGu8jHoRjYm1bKqDioVdKRvY6ucaAvVFrSYB03U94ZiH1wIbT7RtY/HdjKX8dngob527iFCrm6Vp9xEX8gPxQVLURWOjUagG4VYVb+AqoR3KcalJmRqH2l48fVPX9R6UnorZCzTae+A9Hg/vfj+ZQZf8zPmRRzjsDGJf7pO0iR5gdjQhTFPAYNwqgmBmolGMS7Uhl/uJkeG9PlWrV9cwjNu8FaQ+Ky508ty3Y/ngthmEWt0sy0ikefi7tIqSGRlF3dDIAY9/DpstZiDFaqDZMRoV+bVZS29/PYULL5nGJ3ccRAN+TT+b06NeNTuWaCQsZBKhvYqNfViyIVpLIFs9iZvmZkcTJpLCXkMFeUW8N288jw2eTeuAfFZlNCUw4DlOj/L/29FFwxGhvUiAtq70gQKHlkYEL5GhxiHr6DReUthrICMtl/E73uTd6xcBsPDI6XSK+wD5QRJ1ycJhbOyt1G5jH3Y2UsIZdR9K+AWpRKegxOni9R/GkB14N++ctYhij4UNBY/QKe4j5KUUdU2jCI3KN85olKBRUMUzKrKxnRAmEcBiwOODhMIs0mOvptTDWYxbPYYR1y0kxOJmzsH2tIt5kbjAZmZHE42Um5a4aYqFXce1N8P5t1MPKyK0VwhgBRYtD4+y46I9mWqETMPbQEg3sxp2bT/EGs//MebK+dhQzE+5hO5NxxNil6IuzGQhRz2CS7VAKQ0FuFQiuepu/m7d1gAWEMAiLFpe6V60EhzaZsJ4v25iC5+THvtJvDDhKx69dQp3BGdT6LGQ6v6CrrFS0IV/KKEHaWoCgfxGWEgg6Xm9T7qiU5D2Kxat8kIdAdoyongMRRh56g5ctKu0jY2NhGjfoeGkSJ1HERcjs5P6HynsJ7BtWxJjljzH83euopmjkDVZzYgL+RCHNdzsaEIcJ4AiLiY0MBaVd/I5TTwqqMpabNVysPInADa2kKVexEXX8q8H8ROh2kSsWumSgg5W4lCryOEZ7/w3hNfIqZgqTP5iIY7mj/Hq2YsIsbiYfuBsEsImYZeiLhqAfG7GraL/dhublkaoNumYFhfB2tTyog6lp3ACtBVY2eejpKKmpMd+DKUUL04dybAbf6V1QAH7i0Kx2qbQt1ndL1YshK+4aU2OeogQvsFKBhoFWLTCSttZyS7/t4V0LMetDwtg1bJxqNUU0sqnmcWpkcJeZvXqXay3fsi716whyOJhZtI5DOw6mvSMyivZC1HfFXMhxeoCNPIIYTKh2pRK27iJKP+3h8iymRqzK2zjUcG4aF/puVYOlu0j0bvBRbVIYQdmLFhOSJdxDEvYTb7bypyDOr0SH0SzOMyOJvyUlWRC+AyrloVLtSKPO+rhUEENRRj53EqAWoFd21P+FZeKI0/decy2ARSpfxLMVCzaX4vklNCRkmPOw1s5QIQ2HCvJQOnQy2w1DDctff2fEcdo1IU9JTkDI3kkj563hnh7MfuLQtCsY+jetI3Z0YQfs7KTKO2Z8gUkArSVONRaMtR7Jx2R4o8UYWSodwhVE7BpyXjKRsW4Oa3Cdnk8iEfFEMgfgIsS1YFcHuCvK7GKCO1VHNq28udYySGCV8umOJDRM3Wl0Rb2H+ctQbWZzKv/2IxHwc8HhtC72YPIm0+cTJg2odKqQHZtF8Hqa/K516RUtaOIJJf/lk7AfUIaBdxIgap6FTAbu7Cxv4r2A9jYWeUpG+Ebja6wK6X45NvpXH7lp/QMyWRvYShHnA/Tu5lM/C+qx0qVy/pi13afpDA2dC7AXWV7VVMfCN9pVIV94e8byW72AY8O2U641cWW3BiiQr6jVYD00kX1eQitst2tGveFQhftcdMcC7srtLtpTgkdTErVODWKcexKKcb/OBVH55Hc334zbqWx4PBDRIV8j5x6EacqT92GW8VWaHOpFuRxq0mJ/IWVHPVvSlQrlLKilBWXakmO+jdgNTtco9Lge+xut5tnx49n5NAfiLU5WZ8TQ3jg23SOl3G3omZK6EGmepFQJmMhDzfx5Kr7UUSaHc10JXQnXX2CgzWAwklvGkGZ8TsN+hXPzy7k6/3P8uH9awm0eFiU3oX2UWPNjiUaABddyVJvmB3DT9lw0s/sEI1agyzsrhIXo6dN5vwB83m2exIAyzNepH3UAHODCSFEHWhwhb2ooJiXfn+LTwbPw6LBH+mJhAf8j1aRPcyOJoQQdaJBFfbFv2yipMsoJgzajQeN1TlDOC3qQbNjCSFEnWoQhb0gt5D3Foxn8AWLOTsihQyXg4PFo2kR2snsaEIIUefqfWFPT8lhbs6zjLpmPQDzD59Gu+hXiQ1KMDmZEEKYo16PY3/702lkB9/DfzqsR6Gxp/g9usZ/QqBNiroQovGqlz32/JwC3l/0Pg/cspiOgblsyYshwD6GUIcUdCGEqHeFffeuw/yQPJZRVyzCqsHM5O70bDoCTQs0O5poFBSBzCFQW4CGokidSyFXIHcwC39Srwr7C1PG8OjV83irfTY5bhvrs2+hV8JdZscSjUgY7xOkzcSiFQFg509salvpzIheYmUnYdoELGSWTaF7Oy7O8Nr+RcNXLwr7np2H+TllDCP05URaS5h3sBud4l+nXWTVkzEJ4QsaOQRof5QXdShd9zOQZeSrdDzE1PoYVpIqzPUOYGcvmeolXHSp9f5F4+D3F09XrN7GKtsrvHr2YoI1FzOTB9Kt6fvYLFLURd2ycQAr6ZXarVoaNi8t6BzCpEpzvVu1NEK1yV7Zv2gc/LbHnpOZz7g/R/Lvs1dwVUABKSUB5DGCXglyB6kwh4tmuInGRkqFdreKweWlpd+sWtVzvVvI88r+RePglz32RUs28uOB53jzgl9pGVDAtP0X4Lb8TLBVirowjyKSYtUPj/prLVylbBSr3niI/ZtnVp9LVf0Lwk28V/YvGge/67H/NHsxnc96jyERKaSWBLA15076Nb/J7FhCAJDLE7hU67J1P6FY9acA3Wv7z+MuHGoddm1XeZtLtSBX/ctrxxANn18V9icmv8f7N08n2lbCgaIQbPYfaB8VYHYsIY6hUchgCtVgn+y9dGHp0YSor7Bpe3GrBPK4TeZ6F6fEbwp7dlouF5y/kWhbCUvT+9Im6k1kbLBojBSh5DG0ka+fKmpDU8qUd4+8ZYUQomZO2uM16+KpVh/+6Lq+2uwMklWySlbJetyfk/LLUTFCCCFqTgq7EEI0MFLY/97HZgc4BZLVNySrb0hWHzLr4qkQQggfkR67EEI0MH4zjt0f6Lr+InAfkFrW9LRhGDOr2O5SYDRgBSYYhvFGnYX8K8NI4ErACewC7jIMI6uK7fYCuYAbcBmG0aeO8v3ta6TregDwOdAbSAduMAxjb11kOy5Hi7IcTQEP8LFhGKOP22YAMBXYU9b0g2EYL9dlzuPy7OVvvqe6rmuUvvaXAQXAnYZhrDEhZ0dgyjFNbYHnDcN495htBmDSa6vr+kTgCiDFMIyuZW3RZZlbA3sB3TCMShP46Lp+B/Bs2cNXDcOYVBeZq0sKe2XvGIbx1om+qOu6FRgLDASSgJW6rk8zDGNzXQUsMw8YZhiGS9f1EcAw4MkTbHu+YRhpdRWsmq/RPUCmYRjtdF2/ERgB3FBXGY/hAp4wDGONruthwGpd1+dV8f1cZBjGFSbkO5G/+54OAtqX/ekPfFj2d50yDGMb0APK3xPJwI9VbGrWa/sZMIbSX+xHPQX8YhjGG7quP1X2uMLPVVnxfwHoQ+k9OavL3t9Vz+BmAjkVc+r6ATsNw9htGIYT+Aa4uq5DGIYx1zAMV9nDZUDzus7wN6rzGl0NHO3lfAdcWNbTrFOGYRw62ps1DCMX2AI0q+scXnY18LlhGMowjGVApK7rZq8beSGwyzCMfSbnKGcYxu9AxnHNx74vJwHXVPHUS4B5hmFklBXzecClPgtaA1LYK3tY1/X1uq5P1HU9qoqvNwMOHPM4CfMLwd3ArBN8TQFzdV1frev60DrKU53XqHybsl9Q2eCFlSpqQdf11kBPYHkVXz5T1/V1uq7P0nXd7BUvTvY99cf36I3A1yf4mj+9tk0MwzgEpb/0ocppNf3x9a2g0Z2K0XV9PqXnU4/3DKUfWV+h9AfnFWAUpUXzWFX1Kn0ytOjvshqGMbVsm2coPZ3w5Ql2c5ZhGAd1XY8H5um6vrWsp+JL1XmN6ux1rA5d10OB74HHDMPIOe7La4BWhmHk6bp+GfATpac5zHKy7zHwq8wAAAJ+SURBVKm/vbYO4CpKTxcez99e2+rwq9e3Ko2usBuGcVF1ttN1fTwwo4ovJQEtjnncHDjohWiVnCxr2QWcK4ALDcOo8o1lGMbBsr9TdF3/kdLTJL4u7NV5jY5uk6Trug2IoPLH4jqh67qd0qL+pWEYPxz/9WMLvWEYM3Vd/0DX9di6vG5xXJ6TfU/r7D1aTYOANYZhHDn+C/722gJHdF1PMAzjUNnpq5QqtkkCBhzzuDnwax1kq7ZGV9j/ztFvaNnDa4GNVWy2Evj/9u4XRKooiuP4FxUErQuKzWA3iGWDbYNZf4yCiCjsCHbBuIIsBpPBokVQ+RVxgmixajAIslg2iFgsgghaBA3nKk+cB1tmnjx+nzLDmxsO9z7O/fPO8I5IOkw9DJoAZ5cU4h+t6uQqcML2t542+4Fdtr+272vAMioOdtJHM+A88BI4Bbzom5wWqZ3r3wXe2b7V0+Yg8Mn2T0nHqSPMf9+RtwQ7HNMZdaT4iHpo+qVzXw/hDD3HMP9T3za/78vN9vlkTpvnwI3OUe0a83cjg0li/9tNSUepbdV7YB1A0iGqZO9kq0K5Qg3ubuCe7a0BYr0N7KW24gCvbE+7sQIHgMft9z3AA9vPFh1YXx9J2gBe255RyfS+pG1qpT5ZdFw9VoFzwFtJb9q1a1DvurN9h5p4Lkv6AXwHJkNMQs3cMZU07cT7lCp13KbKHS8MFCuS9lHVUeuda91YB+tbSQ+plfeKpI9UpcsmYEkXgQ/A6db2GDC1fcn2Z0nXqQUMwIbtQXabffLP04iIkUlVTETEyCSxR0SMTBJ7RMTIJLFHRIxMEntExMgksUdEjEwSe0TEyCSxR0SMzC8aDJfJTNCsJgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.contour(xx, yy, Z)\\n\",\n \"plt.scatter(X[:, 0], X[:, 1], c=Y)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This may seem like a trivial task, but it is a simple version of a very important concept.\\n\",\n \"By drawing this separating line, we have learned a model which can **generalize** to new\\n\",\n \"data: if you were to drop another point onto the plane which is unlabeled, this algorithm\\n\",\n \"could now **predict** whether it's a blue or a red point.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The next simple task we'll look at is a **regression** task: a simple best-fit line\\n\",\n \"to a set of data:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1.2 Regression Problem\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"a = 0.5\\n\",\n \"b = 1.0\\n\",\n \"\\n\",\n \"# x from 0 to 10\\n\",\n \"x = 30 * np.random.random(20)\\n\",\n \"\\n\",\n \"# y = a*x + b with noise\\n\",\n \"y = a * x + b + np.random.normal(size=x.shape)\\n\",\n \"\\n\",\n \"plt.scatter(x, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"clf = LinearRegression()\\n\",\n \"clf.fit(x[:, None], y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0.45686433]\\n\",\n \"1.3280248907068941\\n\"\n ]\n }\n ],\n \"source\": [\n \"# underscore at the end indicates a fit parameter\\n\",\n \"print(clf.coef_)\\n\",\n \"print(clf.intercept_)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x_new = np.linspace(0, 30, 100)\\n\",\n \"y_new = clf.predict(x_new[:, None])\\n\",\n \"plt.scatter(x, y)\\n\",\n \"plt.plot(x_new, y_new)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Again, this is an example of fitting a model to data, such that the model can make\\n\",\n \"generalizations about new data. The model has been **learned** from the training\\n\",\n \"data, and can be used to predict the result of test data:\\n\",\n \"here, we might be given an x-value, and the model would\\n\",\n \"allow us to predict the y value. Again, this might seem like a trivial problem,\\n\",\n \"but it is a basic example of a type of operation that is fundamental to\\n\",\n \"machine learning tasks.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Representation of Data in Scikit-learn\\n\",\n \"\\n\",\n \"Machine learning is about creating models from data: for that reason, we'll start by\\n\",\n \"discussing how data can be represented in order to be understood by the computer. Along\\n\",\n \"with this, we'll build on our matplotlib examples from the previous section and show some\\n\",\n \"examples of how to visualize data.\\n\",\n \"\\n\",\n \"Most machine learning algorithms implemented in scikit-learn expect data to be stored in a\\n\",\n \"**two-dimensional array or matrix**. The arrays can be\\n\",\n \"either ``numpy`` arrays, or in some cases ``scipy.sparse`` matrices.\\n\",\n \"The size of the array is expected to be `[n_samples, n_features]`\\n\",\n \"\\n\",\n \"- **n_samples:** The number of samples: each sample is an item to process (e.g. classify).\\n\",\n \" A sample can be a document, a picture, a sound, a video, an astronomical object,\\n\",\n \" a row in database or CSV file,\\n\",\n \" or whatever you can describe with a fixed set of quantitative traits.\\n\",\n \"- **n_features:** The number of features or distinct traits that can be used to describe each\\n\",\n \" item in a quantitative manner. Features are generally real-valued, but may be boolean or\\n\",\n \" discrete-valued in some cases.\\n\",\n \"\\n\",\n \"The number of features must be fixed in advance. However it can be very high dimensional\\n\",\n \"(e.g. millions of features) with most of them being zeros for a given sample. This is a case\\n\",\n \"where `scipy.sparse` matrices can be useful, in that they are\\n\",\n \"much more memory-efficient than numpy arrays.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## A Simple Example: the Iris Dataset\\n\",\n \"\\n\",\n \"As an example of a simple dataset, we're going to take a look at the\\n\",\n \"iris data stored by scikit-learn.\\n\",\n \"The data consists of measurements of three different species of irises.\\n\",\n \"There are three species of iris in the dataset, which we can picture here:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Iris Setosa\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Iris Versicolor\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Iris Virginica\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Iris versicolor and the petal and sepal width and length\\n\",\n \"From, Python Data Analytics, Apress, 2015.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from IPython.core.display import Image, display\\n\",\n \"\\n\",\n \"display(Image(url='images/iris_setosa.jpg'))\\n\",\n \"print(\\\"Iris Setosa\\\\n\\\")\\n\",\n \"\\n\",\n \"display(Image(url='images/iris_versicolor.jpg'))\\n\",\n \"print(\\\"Iris Versicolor\\\\n\\\")\\n\",\n \"\\n\",\n \"display(Image(url='images/iris_virginica.jpg'))\\n\",\n \"print(\\\"Iris Virginica\\\")\\n\",\n \"\\n\",\n \"display(Image(url='images/iris_with_length.png'))\\n\",\n \"print('Iris versicolor and the petal and sepal width and length')\\n\",\n \"print('From, Python Data Analytics, Apress, 2015.')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Quick Question:\\n\",\n \"\\n\",\n \"**If we want to design an algorithm to recognize iris species, what might the data be?**\\n\",\n \"\\n\",\n \"Remember: we need a 2D array of size `[n_samples x n_features]`.\\n\",\n \"\\n\",\n \"- What would the `n_samples` refer to?\\n\",\n \"\\n\",\n \"- What might the `n_features` refer to?\\n\",\n \"\\n\",\n \"Remember that there must be a **fixed** number of features for each sample, and feature\\n\",\n \"number ``i`` must be a similar kind of quantity for each sample.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Loading the Iris Data with Scikit-Learn\\n\",\n \"\\n\",\n \"Scikit-learn has a very straightforward set of data on these iris species. The data consist of\\n\",\n \"the following:\\n\",\n \"\\n\",\n \"- Features in the Iris dataset:\\n\",\n \"\\n\",\n \" 1. sepal length in cm\\n\",\n \" 2. sepal width in cm\\n\",\n \" 3. petal length in cm\\n\",\n \" 4. petal width in cm\\n\",\n \"\\n\",\n \"- Target classes to predict:\\n\",\n \"\\n\",\n \" 1. Iris Setosa\\n\",\n \" 2. Iris Versicolour\\n\",\n \" 3. Iris Virginica\\n\",\n \" \\n\",\n \"``scikit-learn`` embeds a copy of the iris CSV file along with a helper function to load it into numpy arrays:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.datasets import load_iris\\n\",\n \"iris = load_iris()\\n\",\n \"iris.keys()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(150, 4)\\n\",\n \"[5.1 3.5 1.4 0.2]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n_samples, n_features = iris.data.shape\\n\",\n \"print((n_samples, n_features))\\n\",\n \"print(iris.data[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(150, 4)\\n\",\n \"(150,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(iris.data.shape)\\n\",\n \"print(iris.target.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n\",\n \" 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\\n\",\n \" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\\n\",\n \" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\\n\",\n \" 2 2]\\n\",\n \"['setosa' 'versicolor' 'virginica']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(iris.target)\\n\",\n \"print(iris.target_names)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 2. Unsupervised Learning \\n\",\n \"\\n\",\n \"### 2.1 Dimensionality Reduction: PCA\\n\",\n \"\\n\",\n \"Principle Component Analysis (PCA) is a dimension reduction technique that can find the combinations of variables that explain the most variance.\\n\",\n \"\\n\",\n \"Consider the iris dataset. It cannot be visualized in a single 2D plot, as it has 4 features. We are going to extract 2 combinations of sepal and petal dimensions to visualize it:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X, y = iris.data, iris.target\\n\",\n \"from sklearn.decomposition import PCA\\n\",\n \"pca = PCA(n_components=3)\\n\",\n \"pca.fit(X)\\n\",\n \"X_reduced = pca.transform(X)\\n\",\n \"plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X, y = iris.data, iris.target\\n\",\n \"from sklearn.manifold import Isomap\\n\",\n \"pca = Isomap(n_components=3)\\n\",\n \"pca.fit(X)\\n\",\n \"X_reduced2 = pca.transform(X)\\n\",\n \"plt.scatter(X_reduced2[:, 0], X_reduced2[:, 1], c=y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n \"fig = plt.figure()\\n\",\n \"ax = Axes3D(fig)\\n\",\n \"ax.set_title('Iris Dataset by PCA', size=14)\\n\",\n \"ax.scatter(X_reduced[:,0],X_reduced[:,1],X_reduced[:,2], c=y)\\n\",\n \"ax.set_xlabel('First eigenvector')\\n\",\n \"ax.set_ylabel('Second eigenvector')\\n\",\n \"ax.set_zlabel('Third eigenvector')\\n\",\n \"ax.w_xaxis.set_ticklabels(())\\n\",\n \"ax.w_yaxis.set_ticklabels(())\\n\",\n \"ax.w_zaxis.set_ticklabels(())\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2.2 Clustering: K-means\\n\",\n \"\\n\",\n \"Clustering groups together observations that are homogeneous with respect to a given criterion, finding ''clusters'' in the data.\\n\",\n \"\\n\",\n \"Note that these clusters will uncover relevent hidden structure of the data only if the criterion used highlights it.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.cluster import KMeans\\n\",\n \"k_means = KMeans(n_clusters=3, random_state=0) # Fixing the RNG in kmeans\\n\",\n \"k_means.fit(X)\\n\",\n \"y_pred = k_means.predict(X)\\n\",\n \"\\n\",\n \"plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y_pred);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lets then evaluate the performance of the clustering versus the ground truth\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 0 50 0]\\n\",\n \" [48 0 2]\\n\",\n \" [14 0 36]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"\\n\",\n \"# Compute confusion matrix\\n\",\n \"cm = confusion_matrix(y, y_pred)\\n\",\n \"np.set_printoptions(precision=2)\\n\",\n \"print(cm)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\\n\",\n \" plt.imshow(cm, interpolation='nearest', cmap=cmap)\\n\",\n \" plt.title(title)\\n\",\n \" plt.colorbar()\\n\",\n \" tick_marks = np.arange(len(iris.target_names))\\n\",\n \" plt.xticks(tick_marks, iris.target_names, rotation=45)\\n\",\n \" plt.yticks(tick_marks, iris.target_names)\\n\",\n \" plt.tight_layout()\\n\",\n \" plt.ylabel('True label')\\n\",\n \" plt.xlabel('Predicted label')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure()\\n\",\n \"plot_confusion_matrix(cm)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### A First Model for Classification: Logistic Regression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7066666666666667 0.6 0.6241 0.015221001573118932\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"\\n\",\n \"from sklearn import model_selection\\n\",\n \"\\n\",\n \"errors = []\\n\",\n \"for i in range(1000):\\n\",\n \" X_train, X_test, y_train, y_test = model_selection.train_test_split(iris.data, iris.target, test_size=0.4, random_state=i)\\n\",\n \"\\n\",\n \" clf = LogisticRegression()\\n\",\n \" clf.fit(X_train, y_train)\\n\",\n \" y_pred = clf.predict(X_test)\\n\",\n \"\\n\",\n \" acc = float((y_pred == y_test).sum())\\n\",\n \" err = 1- acc / n_samples\\n\",\n \" errors.append(err)\\n\",\n \"\\n\",\n \"plt.plot(list(range(1000)), errors)\\n\",\n \"\\n\",\n \"errors = np.array(errors)\\n\",\n \"print(errors.max(), errors.min(), errors.mean(), errors.std())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.6666666666666667 0.6 0.6203333333333333 0.009017267386027282\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"\\n\",\n \"errors = []\\n\",\n \"for i in range(1000):\\n\",\n \" X_train, X_test, y_train, y_test = model_selection.train_test_split(iris.data, iris.target, test_size=0.4, random_state=i)\\n\",\n \"\\n\",\n \" clf = RandomForestClassifier()\\n\",\n \" clf.fit(X_train, y_train)\\n\",\n \" y_pred = clf.predict(X_test)\\n\",\n \"\\n\",\n \" acc = float((y_pred == y_test).sum())\\n\",\n \" err = 1- acc / n_samples\\n\",\n \" errors.append(err)\\n\",\n \"plt.plot(list(range(1000)), errors)\\n\",\n \"\\n\",\n \"errors = np.array(errors)\\n\",\n \"print(errors.max(), errors.min(), errors.mean(), errors.std())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Recap: Scikit-learn's estimator interface\\n\",\n \"\\n\",\n \"Scikit-learn strives to have a uniform interface across all methods,\\n\",\n \"and we'll see examples of these below. Given a scikit-learn *estimator*\\n\",\n \"object named `model`, the following methods are available:\\n\",\n \"\\n\",\n \"- Available in **all Estimators**\\n\",\n \" + `model.fit()` : fit training data. For supervised learning applications,\\n\",\n \" this accepts two arguments: the data `X` and the labels `y` (e.g. `model.fit(X, y)`).\\n\",\n \" For unsupervised learning applications, this accepts only a single argument,\\n\",\n \" the data `X` (e.g. `model.fit(X)`).\\n\",\n \"- Available in **supervised estimators**\\n\",\n \" + `model.predict()` : given a trained model, predict the label of a new set of data.\\n\",\n \" This method accepts one argument, the new data `X_new` (e.g. `model.predict(X_new)`),\\n\",\n \" and returns the learned label for each object in the array.\\n\",\n \" + `model.predict_proba()` : For classification problems, some estimators also provide\\n\",\n \" this method, which returns the probability that a new observation has each categorical label.\\n\",\n \" In this case, the label with the highest probability is returned by `model.predict()`.\\n\",\n \" + `model.score()` : for classification or regression problems, most (all?) estimators implement\\n\",\n \" a score method. Scores are between 0 and 1, with a larger score indicating a better fit.\\n\",\n \"- Available in **unsupervised estimators**\\n\",\n \" + `model.predict()` : predict labels in clustering algorithms.\\n\",\n \" + `model.transform()` : given an unsupervised model, transform new data into the new basis.\\n\",\n \" This also accepts one argument `X_new`, and returns the new representation of the data based\\n\",\n \" on the unsupervised model.\\n\",\n \" + `model.fit_transform()` : some estimators implement this method,\\n\",\n \" which more efficiently performs a fit and a transform on the same input data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Flow Chart: How to Choose your Estimator\\n\",\n \"\\n\",\n \"This is a flow chart created by scikit-learn super-contributor [Andreas Mueller](https://github.com/amueller) which gives a nice summary of which algorithms to choose in various situations. Keep it around as a handy reference!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from IPython.display import Image\\n\",\n \"Image(url=\\\"http://scikit-learn.org/dev/_static/ml_map.png\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Original source on the [scikit-learn website](http://scikit-learn.org/stable/tutorial/machine_learning_map/)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Machine Learning for Risk Management\\n\",\n \"\\n\",\n \"There are several applications of machine learning for Risk Management\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Credit Scoring\\n\",\n \"\\n\",\n \"The objective in credit scoring is to classify which potential\\n\",\n \"customers are likely to default a contracted financial obli-\\n\",\n \"gation based on the customer’s past financial experience,\\n\",\n \"and with that information decide whether to approve or\\n\",\n \"decline a loan.\\n\",\n \"\\n\",\n \"![Intrusion Detection](images/credit-score.jpg)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Income Prediction\\n\",\n \"\\n\",\n \"Many businesses would like to personalize their offer based on customer’s income. High-income customers could be, for instance, exposed to premium products. As a customer’s income is not always explicitly known, predictive model could estimate income of a person based on other information.\\n\",\n \"![Income](images/income.jpg)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Fraud Detection\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Fraud detection is one of the earliest industrial applications of data mining and machine learning. \\n\",\n \"Fraud detection is typically handled as a binary classification problem, but the class population is unbalanced because instances of fraud are usually very rare compared to the overall volume of transactions. Moreover, when fraudulent transactions are discovered, the business typically takes measures to block the accounts from transacting to prevent further losses. Therefore, model performance is measured by using account-level metrics, which will be discussed in detail later.\\n\",\n \"![fraud Detection](images/fraud.png)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/02-IntroPython_Numpy_Scypy_Pandas.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 02 - Introduction to Python for Data Analysis\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Rick Muller](http://www.cs.sandia.gov/~rmuller/), Sandia National Laboratories\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Why Python?\\n\",\n \"Python is the programming language of choice for many scientists to a large degree because it offers a great deal of power to analyze and model scientific data with relatively little overhead in terms of learning, installation or development time. It is a language you can pick up in a weekend, and use for the rest of one's life.\\n\",\n \"\\n\",\n \"The [Python Tutorial](http://docs.python.org/3/tutorial/) is a great place to start getting a feel for the language. To complement this material, I taught a [Python Short Course](http://www.wag.caltech.edu/home/rpm/python_course/) years ago to a group of computational chemists during a time that I was worried the field was moving too much in the direction of using canned software rather than developing one's own methods. I wanted to focus on what working scientists needed to be more productive: parsing output of other programs, building simple models, experimenting with object oriented programming, extending the language with C, and simple GUIs. \\n\",\n \"\\n\",\n \"I'm trying to do something very similar here, to cut to the chase and focus on what scientists need. In the last year or so, the [Jupyter Project](http://jupyter.org) has put together a notebook interface that I have found incredibly valuable. A large number of people have released very good IPython Notebooks that I have taken a huge amount of pleasure reading through. Some ones that I particularly like include:\\n\",\n \"\\n\",\n \"* Rick Muller [A Crash Course in Python for Scientists](http://nbviewer.jupyter.org/gist/rpmuller/5920182)\\n\",\n \"* Rob Johansson's [excellent notebooks](http://jrjohansson.github.io/), including [Scientific Computing with Python](https://github.com/jrjohansson/scientific-python-lectures) and [Computational Quantum Physics with QuTiP](https://github.com/jrjohansson/qutip-lectures) lectures;\\n\",\n \"* [XKCD style graphs in matplotlib](http://nbviewer.ipython.org/url/jakevdp.github.com/downloads/notebooks/XKCD_plots.ipynb);\\n\",\n \"* [A collection of Notebooks for using IPython effectively](https://github.com/ipython/ipython/tree/master/examples/notebooks#a-collection-of-notebooks-for-using-ipython-effectively)\\n\",\n \"* [A gallery of interesting IPython Notebooks](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks)\\n\",\n \"\\n\",\n \"I find Jupyter notebooks an easy way both to get important work done in my everyday job, as well as to communicate what I've done, how I've done it, and why it matters to my coworkers. In the interest of putting more notebooks out into the wild for other people to use and enjoy, I thought I would try to recreate some of what I was trying to get across in the original Python Short Course, updated by 15 years of Python, Numpy, Scipy, Pandas, Matplotlib, and IPython development, as well as my own experience in using Python almost every day of this time.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Why Python for Data Analysis?\\n\",\n \"\\n\",\n \"- Python is great for scripting and applications.\\n\",\n \"- The `pandas` library offers imporved library support.\\n\",\n \"- Scraping, web APIs\\n\",\n \"- Strong High Performance Computation support\\n\",\n \" - Load balanceing tasks\\n\",\n \" - MPI, GPU\\n\",\n \" - MapReduce\\n\",\n \"- Strong support for abstraction\\n\",\n \" - Intel MKL\\n\",\n \" - HDF5\\n\",\n \"- Environment\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## But we already know R\\n\",\n \"\\n\",\n \"...Which is better? Hard to answer\\n\",\n \"\\n\",\n \"http://www.kdnuggets.com/2015/05/r-vs-python-data-science.html\\n\",\n \"\\n\",\n \"http://www.kdnuggets.com/2015/03/the-grammar-data-science-python-vs-r.html\\n\",\n \"\\n\",\n \"https://www.datacamp.com/community/tutorials/r-or-python-for-data-analysis\\n\",\n \"\\n\",\n \"https://www.dataquest.io/blog/python-vs-r/\\n\",\n \"\\n\",\n \"http://www.dataschool.io/python-or-r-for-data-science/\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What You Need to Install\\n\",\n \"\\n\",\n \"There are two branches of current releases in Python: the older-syntax Python 2, and the newer-syntax Python 3. This schizophrenia is largely intentional: when it became clear that some non-backwards-compatible changes to the language were necessary, the Python dev-team decided to go through a five-year (or so) transition, during which the new language features would be introduced and the old language was still actively maintained, to make such a transition as easy as possible. \\n\",\n \"\\n\",\n \"Nonetheless, I'm going to write these notes with Python 3 in mind, since this is the version of the language that I use in my day-to-day job, and am most comfortable with. \\n\",\n \"\\n\",\n \"With this in mind, these notes assume you have a Python distribution that includes:\\n\",\n \"\\n\",\n \"* [Python](http://www.python.org) version 3.5;\\n\",\n \"* [Numpy](http://www.numpy.org), the core numerical extensions for linear algebra and multidimensional arrays;\\n\",\n \"* [Scipy](http://www.scipy.org), additional libraries for scientific programming;\\n\",\n \"* [Matplotlib](http://matplotlib.sf.net), excellent plotting and graphing libraries;\\n\",\n \"* [IPython](http://ipython.org), with the additional libraries required for the notebook interface.\\n\",\n \"* [Pandas](http://pandas.pydata.org/), Python version of R dataframe\\n\",\n \"* [scikit-learn](http://scikit-learn.org), Machine learning library!\\n\",\n \"\\n\",\n \"A good, easy to install option that supports Mac, Windows, and Linux, and that has all of these packages (and much more) is the [Anaconda](https://www.continuum.io/).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Checking your installation\\n\",\n \"\\n\",\n \"You can run the following code to check the versions of the packages on your system:\\n\",\n \"\\n\",\n \"(in IPython notebook, press `shift` and `return` together to execute the contents of a cell)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Python version: 3.6.3 |Anaconda custom (64-bit)| (default, Oct 13 2017, 12:02:49) \\n\",\n \"[GCC 7.2.0]\\n\",\n \"IPython: 6.1.0\\n\",\n \"numpy: 1.14.0\\n\",\n \"scipy: 0.19.1\\n\",\n \"matplotlib: 2.1.0\\n\",\n \"pandas: 0.20.3\\n\",\n \"scikit-learn: 0.19.1\\n\"\n ]\n }\n ],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"print('Python version:', sys.version)\\n\",\n \"\\n\",\n \"import IPython\\n\",\n \"print('IPython:', IPython.__version__)\\n\",\n \"\\n\",\n \"import numpy\\n\",\n \"print('numpy:', numpy.__version__)\\n\",\n \"\\n\",\n \"import scipy\\n\",\n \"print('scipy:', scipy.__version__)\\n\",\n \"\\n\",\n \"import matplotlib\\n\",\n \"print('matplotlib:', matplotlib.__version__)\\n\",\n \"\\n\",\n \"import pandas\\n\",\n \"print('pandas:', pandas.__version__)\\n\",\n \"\\n\",\n \"import sklearn\\n\",\n \"print('scikit-learn:', sklearn.__version__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# I. Python Overview\\n\",\n \"This is a quick introduction to Python. There are lots of other places to learn the language more thoroughly. I have collected a list of useful links, including ones to other learning resources, at the end of this notebook. If you want a little more depth, [Python Tutorial](http://docs.python.org/2/tutorial/) is a great place to start, as is Zed Shaw's [Learn Python the Hard Way](http://learnpythonthehardway.org/book/).\\n\",\n \"\\n\",\n \"The lessons that follow make use of the IPython notebooks. There's a good introduction to notebooks [in the IPython notebook documentation](http://ipython.org/notebook.html) that even has a [nice video](http://www.youtube.com/watch?v=H6dLGQw9yFQ#!) on how to use the notebooks. You should probably also flip through the [IPython tutorial](http://ipython.org/ipython-doc/dev/interactive/tutorial.html) in your copious free time.\\n\",\n \"\\n\",\n \"Briefly, notebooks have code cells (that are generally followed by result cells) and text cells. The text cells are the stuff that you're reading now. The code cells start with \\\"In []:\\\" with some number generally in the brackets. If you put your cursor in the code cell and hit Shift-Enter, the code will run in the Python interpreter and the result will print out in the output cell. You can then change things around and see whether you understand what's going on. If you need to know more, see the [IPython notebook documentation](http://ipython.org/notebook.html) or the [IPython tutorial](http://ipython.org/ipython-doc/dev/interactive/tutorial.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Using Python as a Calculator\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Many of the things I used to use a calculator for, I now use Python for:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"4\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"2+2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5.0\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"(50-5*6)/4\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"(If you're typing this into an IPython notebook, or otherwise using notebook file, you hit shift-Enter to evaluate a cell.)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the last few lines, we have sped by a lot of things that we should stop for a moment and explore a little more fully. We've seen, however briefly, two different data types: **integers**, also known as *whole numbers* to the non-programming world, and **floating point numbers**, also known (incorrectly) as *decimal numbers* to the rest of the world.\\n\",\n \"\\n\",\n \"We've also seen the first instance of an **import** statement. Python has a huge number of libraries included with the distribution. To keep things simple, most of these variables and functions are not accessible from a normal Python interactive session. Instead, you have to import the name. For example, there is a **math** module containing many useful functions. To access, say, the square root function, you can either first\\n\",\n \"\\n\",\n \" from math import sqrt\\n\",\n \"\\n\",\n \"and then\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'sqrt' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0msqrt\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m81\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'sqrt' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"sqrt(81)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"9.0\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from math import sqrt\\n\",\n \"sqrt(81)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"or you can simply import the math library itself\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"9.0\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import math\\n\",\n \"math.sqrt(81)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can define variables using the equals (=) sign:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1256.6370614359173\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"radius = 20\\n\",\n \"pi = math.pi\\n\",\n \"area = pi * radius ** 2 \\n\",\n \"area\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can name a variable *almost* anything you want. It needs to start with an alphabetical character or \\\"\\\\_\\\", can contain alphanumeric charcters plus underscores (\\\"\\\\_\\\"). Certain words, however, are reserved for the language:\\n\",\n \"\\n\",\n \" and, as, assert, break, class, continue, def, del, elif, else, except, \\n\",\n \" exec, finally, for, from, global, if, import, in, is, lambda, not, or,\\n\",\n \" pass, print, raise, return, try, while, with, yield\\n\",\n \"\\n\",\n \"Trying to define a variable using one of these will result in a syntax error:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"invalid syntax (, line 1)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;36m File \\u001b[0;32m\\\"\\\"\\u001b[0;36m, line \\u001b[0;32m1\\u001b[0m\\n\\u001b[0;31m return = 0\\u001b[0m\\n\\u001b[0m ^\\u001b[0m\\n\\u001b[0;31mSyntaxError\\u001b[0m\\u001b[0;31m:\\u001b[0m invalid syntax\\n\"\n ]\n }\n ],\n \"source\": [\n \"return = 0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The [Python Tutorial](http://docs.python.org/2/tutorial/introduction.html#using-python-as-a-calculator) has more on using Python as an interactive shell. The [IPython tutorial](http://ipython.org/ipython-doc/dev/interactive/tutorial.html) makes a nice complement to this, since IPython has a much more sophisticated iteractive shell.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Strings\\n\",\n \"Strings are lists of printable characters, and can be defined using either single quotes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Hello, World!'\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'Hello, World!'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"or double quotes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Hello, World!'\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"Hello, World!\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just like the other two data objects we're familiar with (ints and floats), you can assign a string to a variable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"greeting = \\\"Hello, World!\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The **print** statement is often used for printing character strings:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello, World!\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(greeting)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But it can also print data types other than strings:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"must be str, not float\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"The area is \\\"\\u001b[0m \\u001b[0;34m+\\u001b[0m \\u001b[0marea\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m: must be str, not float\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"The area is \\\" + area)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The area is 1256.6370614359173\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"The area is \\\" + str(area))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In the above snipped, the number 600 (stored in the variable \\\"area\\\") is converted into a string before being printed out.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can use the + operator to concatenate strings together:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Don't forget the space between the strings, if you want one there. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello, World!\\n\"\n ]\n }\n ],\n \"source\": [\n \"statement = \\\"Hello, \\\" + \\\"World!\\\"\\n\",\n \"print(statement)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you have a lot of words to concatenate together, there are other, more efficient ways to do this. But this is fine for linking a few strings together.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Lists\\n\",\n \"Very often in a programming language, one wants to keep a group of similar items together. Python does this using a data type called **lists**.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"days_of_the_week = [\\\"Sunday\\\",\\\"Monday\\\",\\\"Tuesday\\\",\\\"Wednesday\\\",\\\"Thursday\\\",\\\"Friday\\\",\\\"Saturday\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can access members of the list using the **index** of that item:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Tuesday'\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"days_of_the_week[2]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Python lists, like C, but unlike Fortran, use 0 as the index of the first element of a list. Thus, in this example, the 0 element is \\\"Sunday\\\", 1 is \\\"Monday\\\", and so on. If you need to access the *n*th element from the end of the list, you can use a negative index. For example, the -1 element of a list is the last element:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Saturday'\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"days_of_the_week[-1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can add additional items to the list using the .append() command:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Fortran', 'C', 'C++', 'Python']\\n\"\n ]\n }\n ],\n \"source\": [\n \"languages = [\\\"Fortran\\\",\\\"C\\\",\\\"C++\\\"]\\n\",\n \"languages.append(\\\"Python\\\")\\n\",\n \"print(languages)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The **range()** command is a convenient way to make sequential lists of numbers:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(range(10))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that range(n) starts at 0 and gives the sequential list of integers less than n. If you want to start at a different number, use range(start,stop)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[2, 3, 4, 5, 6, 7]\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"list(range(2,8))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The lists created above with range have a *step* of 1 between elements. You can also give a fixed step size via a third command:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"evens = list(range(0,20,2))\\n\",\n \"evens\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"6\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"evens[3]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lists do not have to hold the same data type. For example,\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Today', 7, 99.3, '']\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[\\\"Today\\\",7,99.3,\\\"\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"However, it's good (but not essential) to use lists for similar objects that are somehow logically connected. If you want to group different data types together into a composite data object, it's best to use **tuples**, which we will learn about below.\\n\",\n \"\\n\",\n \"You can find out how long a list is using the **len()** command:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on built-in function len in module builtins:\\n\",\n \"\\n\",\n \"len(obj, /)\\n\",\n \" Return the number of items in a container.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(len)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"10\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(evens)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Iteration, Indentation, and Blocks\\n\",\n \"One of the most useful things you can do with lists is to *iterate* through them, i.e. to go through each element one at a time. To do this in Python, we use the **for** statement:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Sunday\\n\",\n \"Monday\\n\",\n \"Tuesday\\n\",\n \"Wednesday\\n\",\n \"Thursday\\n\",\n \"Friday\\n\",\n \"Saturday\\n\"\n ]\n }\n ],\n \"source\": [\n \"for day in days_of_the_week:\\n\",\n \" print(day)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This code snippet goes through each element of the list called **days_of_the_week** and assigns it to the variable **day**. It then executes everything in the indented block (in this case only one line of code, the print statement) using those variable assignments. When the program has gone through every element of the list, it exists the block.\\n\",\n \"\\n\",\n \"(Almost) every programming language defines blocks of code in some way. In Fortran, one uses END statements (ENDDO, ENDIF, etc.) to define code blocks. In C, C++, and Perl, one uses curly braces {} to define these blocks.\\n\",\n \"\\n\",\n \"Python uses a colon (\\\":\\\"), followed by indentation level to define code blocks. Everything at a higher level of indentation is taken to be in the same block. In the above example the block was only a single line, but we could have had longer blocks as well:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Today is Sunday\\n\",\n \"Today is Monday\\n\",\n \"Today is Tuesday\\n\",\n \"Today is Wednesday\\n\",\n \"Today is Thursday\\n\",\n \"Today is Friday\\n\",\n \"Today is Saturday\\n\"\n ]\n }\n ],\n \"source\": [\n \"for day in days_of_the_week:\\n\",\n \" statement = \\\"Today is \\\" + day\\n\",\n \" print(statement)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The **range()** command is particularly useful with the **for** statement to execute loops of a specified length:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The square of 0 is 0\\n\",\n \"The square of 1 is 1\\n\",\n \"The square of 2 is 4\\n\",\n \"The square of 3 is 9\\n\",\n \"The square of 4 is 16\\n\",\n \"The square of 5 is 25\\n\",\n \"The square of 6 is 36\\n\",\n \"The square of 7 is 49\\n\",\n \"The square of 8 is 64\\n\",\n \"The square of 9 is 81\\n\",\n \"The square of 10 is 100\\n\",\n \"The square of 11 is 121\\n\",\n \"The square of 12 is 144\\n\",\n \"The square of 13 is 169\\n\",\n \"The square of 14 is 196\\n\",\n \"The square of 15 is 225\\n\",\n \"The square of 16 is 256\\n\",\n \"The square of 17 is 289\\n\",\n \"The square of 18 is 324\\n\",\n \"The square of 19 is 361\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i in range(20):\\n\",\n \" print(\\\"The square of \\\",i,\\\" is \\\",i*i)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Slicing\\n\",\n \"Lists and strings have something in common that you might not suspect: they can both be treated as sequences. You already know that you can iterate through the elements of a list. You can also iterate through the letters in a string:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"S\\n\",\n \"u\\n\",\n \"n\\n\",\n \"d\\n\",\n \"a\\n\",\n \"y\\n\"\n ]\n }\n ],\n \"source\": [\n \"for letter in \\\"Sunday\\\":\\n\",\n \" print(letter)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is only occasionally useful. Slightly more useful is the *slicing* operation, which you can also use on any sequence. We already know that we can use *indexing* to get the first element of a list:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'Sunday'\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"days_of_the_week[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we want the list containing the first two elements of a list, we can do this via\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Sunday', 'Monday']\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"days_of_the_week[0:2]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"or simply\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Sunday', 'Monday']\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"days_of_the_week[:2]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we want the last items of the list, we can do this with negative slicing:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Friday', 'Saturday']\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"days_of_the_week[-2:]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"which is somewhat logically consistent with negative indices accessing the last elements of the list.\\n\",\n \"\\n\",\n \"You can do:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\\n\"\n ]\n }\n ],\n \"source\": [\n \"workdays = days_of_the_week[1:6]\\n\",\n \"print(workdays)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since strings are sequences, you can also do this to them:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Sun\\n\"\n ]\n }\n ],\n \"source\": [\n \"day = \\\"Sunday\\\"\\n\",\n \"abbreviation = day[:3]\\n\",\n \"print(abbreviation)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we really want to get fancy, we can pass a third element into the slice, which specifies a step length (just like a third argument to the **range()** function specifies the step):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38]\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"numbers = list(range(0,40))\\n\",\n \"evens = numbers[2::2]\\n\",\n \"evens\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Note that in this example I was even able to omit the second argument, so that the slice started at 2, went to the end of the list, and took every second element, to generate the list of even numbers less that 40.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Booleans and Truth Testing\\n\",\n \"We have now learned a few data types. We have integers and floating point numbers, strings, and lists to contain them. We have also learned about lists, a container that can hold any data type. We have learned to print things out, and to iterate over items in lists. We will now learn about **boolean** variables that can be either True or False.\\n\",\n \"\\n\",\n \"We invariably need some concept of *conditions* in programming to control branching behavior, to allow a program to react differently to different situations. If it's Monday, I'll go to work, but if it's Sunday, I'll sleep in. To do this in Python, we use a combination of **boolean** variables, which evaluate to either True or False, and **if** statements, that control branching based on boolean values.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Sleep in\\n\"\n ]\n }\n ],\n \"source\": [\n \"if day == \\\"Sunday\\\":\\n\",\n \" print(\\\"Sleep in\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Go to work\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"(Quick quiz: why did the snippet print \\\"Go to work\\\" here? What is the variable \\\"day\\\" set to?)\\n\",\n \"\\n\",\n \"Let's take the snippet apart to see what happened. First, note the statement\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"day == \\\"Sunday\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we evaluate it by itself, as we just did, we see that it returns a boolean value, False. The \\\"==\\\" operator performs *equality testing*. If the two items are equal, it returns True, otherwise it returns False. In this case, it is comparing two variables, the string \\\"Sunday\\\", and whatever is stored in the variable \\\"day\\\", which, in this case, is the other string \\\"Saturday\\\". Since the two strings are not equal to each other, the truth test has the false value.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The if statement that contains the truth test is followed by a code block (a colon followed by an indented block of code). If the boolean is true, it executes the code in that block. Since it is false in the above example, we don't see that code executed.\\n\",\n \"\\n\",\n \"The first block of code is followed by an **else** statement, which is executed if nothing else in the above if statement is true. Since the value was false, this code is executed, which is why we see \\\"Go to work\\\".\\n\",\n \"\\n\",\n \"You can compare any data types in Python:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"1 == 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"50 == 2*25\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"3 < 3.14159\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"1 == 1.0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"1 != 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"1 <= 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"1 >= 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We see a few other boolean operators here, all of which which should be self-explanatory. Less than, equality, non-equality, and so on.\\n\",\n \"\\n\",\n \"Particularly interesting is the 1 == 1.0 test, which is true, since even though the two objects are different data types (integer and floating point number), they have the same *value*. There is another boolean operator **is**, that tests whether two objects are the same object:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"1 is 1.0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can do boolean tests on lists as well:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[1,2,3] == [1,2,4]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[1,2,3] < [1,2,4]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Finally, note that you can also string multiple comparisons together, which can result in very intuitive tests:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"hours = 5\\n\",\n \"0 < hours < 24\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If statements can have **elif** parts (\\\"else if\\\"), in addition to if/else parts. For example:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Sleep in\\n\"\n ]\n }\n ],\n \"source\": [\n \"if day == \\\"Sunday\\\":\\n\",\n \" print(\\\"Sleep in\\\")\\n\",\n \"elif day == \\\"Saturday\\\":\\n\",\n \" print(\\\"Do chores\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Go to work\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Of course we can combine if statements with for loops, to make a snippet that is almost interesting:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Today is Sunday\\n\",\n \" Sleep in\\n\",\n \"Today is Monday\\n\",\n \" Go to work\\n\",\n \"Today is Tuesday\\n\",\n \" Go to work\\n\",\n \"Today is Wednesday\\n\",\n \" Go to work\\n\",\n \"Today is Thursday\\n\",\n \" Go to work\\n\",\n \"Today is Friday\\n\",\n \" Go to work\\n\",\n \"Today is Saturday\\n\",\n \" Do chores\\n\"\n ]\n }\n ],\n \"source\": [\n \"for day in days_of_the_week:\\n\",\n \" statement = \\\"Today is \\\" + day\\n\",\n \" print(statement)\\n\",\n \" if day == \\\"Sunday\\\":\\n\",\n \" print(\\\" Sleep in\\\")\\n\",\n \" elif day == \\\"Saturday\\\":\\n\",\n \" print(\\\" Do chores\\\")\\n\",\n \" else:\\n\",\n \" print(\\\" Go to work\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is something of an advanced topic, but ordinary data types have boolean values associated with them, and, indeed, in early versions of Python there was not a separate boolean object. Essentially, anything that was a 0 value (the integer or floating point 0, an empty string \\\"\\\", or an empty list []) was False, and everything else was true. You can see the boolean value of any data object using the **bool()** function.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bool(1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bool(0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bool([\\\"This \\\",\\\" is \\\",\\\" a \\\",\\\" list\\\"])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Code Example: The Fibonacci Sequence\\n\",\n \"The [Fibonacci sequence](http://en.wikipedia.org/wiki/Fibonacci_number) is a sequence in math that starts with 0 and 1, and then each successive entry is the sum of the previous two. Thus, the sequence goes 0,1,1,2,3,5,8,13,21,34,55,89,...\\n\",\n \"\\n\",\n \"A very common exercise in programming books is to compute the Fibonacci sequence up to some number **n**. First I'll show the code, then I'll discuss what it is doing.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = 10\\n\",\n \"sequence = [0,1]\\n\",\n \"for i in range(2,n): # This is going to be a problem if we ever set n <= 2!\\n\",\n \" sequence.append(sequence[i-1]+sequence[i-2])\\n\",\n \"print(sequence)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's go through this line by line. First, we define the variable **n**, and set it to the integer 20. **n** is the length of the sequence we're going to form, and should probably have a better variable name. We then create a variable called **sequence**, and initialize it to the list with the integers 0 and 1 in it, the first two elements of the Fibonacci sequence. We have to create these elements \\\"by hand\\\", since the iterative part of the sequence requires two previous elements.\\n\",\n \"\\n\",\n \"We then have a for loop over the list of integers from 2 (the next element of the list) to **n** (the length of the sequence). After the colon, we see a hash tag \\\"#\\\", and then a **comment** that if we had set **n** to some number less than 2 we would have a problem. Comments in Python start with #, and are good ways to make notes to yourself or to a user of your code explaining why you did what you did. Better than the comment here would be to test to make sure the value of **n** is valid, and to complain if it isn't; we'll try this later.\\n\",\n \"\\n\",\n \"In the body of the loop, we append to the list an integer equal to the sum of the two previous elements of the list.\\n\",\n \"\\n\",\n \"After exiting the loop (ending the indentation) we then print out the whole list. That's it!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Functions\\n\",\n \"We might want to use the Fibonacci snippet with different sequence lengths. We could cut an paste the code into another cell, changing the value of **n**, but it's easier and more useful to make a function out of the code. We do this with the **def** statement in Python:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def fibonacci(sequence_length):\\n\",\n \" \\\"Return the Fibonacci sequence of length *sequence_length*\\\"\\n\",\n \" sequence = [0,1]\\n\",\n \" if sequence_length < 1:\\n\",\n \" print(\\\"Fibonacci sequence only defined for length 1 or greater\\\")\\n\",\n \" return\\n\",\n \" if 0 < sequence_length < 3:\\n\",\n \" return sequence[:sequence_length]\\n\",\n \" for i in range(2,sequence_length): \\n\",\n \" sequence.append(sequence[i-1]+sequence[i-2])\\n\",\n \" return sequence\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can now call **fibonacci()** for different sequence_lengths:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0, 1]\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fibonacci(2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"fibonacci(12)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We've introduced a several new features here. First, note that the function itself is defined as a code block (a colon followed by an indented block). This is the standard way that Python delimits things. Next, note that the first line of the function is a single string. This is called a **docstring**, and is a special kind of comment that is often available to people using the function through the python command line:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on function fibonacci in module __main__:\\n\",\n \"\\n\",\n \"fibonacci(sequence_length)\\n\",\n \" Return the Fibonacci sequence of length *sequence_length*\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(fibonacci)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you define a docstring for all of your functions, it makes it easier for other people to use them, since they can get help on the arguments and return values of the function.\\n\",\n \"\\n\",\n \"Next, note that rather than putting a comment in about what input values lead to errors, we have some testing of these values, followed by a warning if the value is invalid, and some conditional code to handle special cases.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Two More Data Structures: Tuples and Dictionaries\\n\",\n \"Before we end the Python overview, I wanted to touch on two more data structures that are very useful (and thus very common) in Python programs.\\n\",\n \"\\n\",\n \"A **tuple** is a sequence object like a list or a string. It's constructed by grouping a sequence of objects together with commas, either without brackets, or with parentheses:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1, 2, 'hi', 9.0)\"\n ]\n },\n \"execution_count\": 61,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t = (1,2,'hi',9.0)\\n\",\n \"t\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Tuples are like lists, in that you can access the elements using indices:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2\"\n ]\n },\n \"execution_count\": 62,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t[1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"However, tuples are *immutable*, you can't append to them or change the elements of them:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'tuple' object has no attribute 'append'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mappend\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m7\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mAttributeError\\u001b[0m: 'tuple' object has no attribute 'append'\"\n ]\n }\n ],\n \"source\": [\n \"t.append(7)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'tuple' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mt\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m77\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m: 'tuple' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"t[1]=77\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Tuples are useful anytime you want to group different pieces of data together in an object, but don't want to create a full-fledged class (see below) for them. For example, let's say you want the Cartesian coordinates of some objects in your program. Tuples are a good way to do this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"('Bob', 0.0, 21.0)\"\n ]\n },\n \"execution_count\": 65,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"('Bob',0.0,21.0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Again, it's not a necessary distinction, but one way to distinguish tuples and lists is that tuples are a collection of different things, here a name, and x and y coordinates, whereas a list is a collection of similar things, like if we wanted a list of those coordinates:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"positions = [\\n\",\n \" ('Bob',0.0,21.0),\\n\",\n \" ('Cat',2.5,13.1),\\n\",\n \" ('Dog',33.0,1.2)\\n\",\n \" ]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Tuples can be used when functions return more than one value. Say we wanted to compute the smallest x- and y-coordinates of the above list of objects. We could write:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.0 1.2\\n\"\n ]\n }\n ],\n \"source\": [\n \"def minmax(objects):\\n\",\n \" minx = 1e20 # These are set to really big numbers\\n\",\n \" miny = 1e20\\n\",\n \" for obj in objects:\\n\",\n \" name,x,y = obj\\n\",\n \" if x < minx: \\n\",\n \" minx = x\\n\",\n \" if y < miny:\\n\",\n \" miny = y\\n\",\n \" return minx,miny\\n\",\n \"\\n\",\n \"x,y = minmax(positions)\\n\",\n \"print(x,y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Dictionaries** are an object called \\\"mappings\\\" or \\\"associative arrays\\\" in other languages. Whereas a list associates an integer index with a set of objects:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"mylist = [1,2,9,21]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The index in a dictionary is called the *key*, and the corresponding dictionary entry is the *value*. A dictionary can use (almost) anything as the key. Whereas lists are formed with square brackets [], dictionaries use curly brackets {}:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Rick's age is 46\\n\"\n ]\n }\n ],\n \"source\": [\n \"ages = {\\\"Rick\\\": 46, \\\"Bob\\\": 86, \\\"Fred\\\": 21}\\n\",\n \"print(\\\"Rick's age is \\\",ages[\\\"Rick\\\"])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There's also a convenient way to create dictionaries without having to quote the keys.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'Bob': 86, 'Fred': 20, 'Rick': 46}\"\n ]\n },\n \"execution_count\": 70,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dict(Rick=46,Bob=86,Fred=20)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The **len()** command works on both tuples and dictionaries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"4\"\n ]\n },\n \"execution_count\": 71,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 72,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(ages)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Conclusion of the Python Overview\\n\",\n \"There is, of course, much more to the language than I've covered here. I've tried to keep this brief enough so that you can jump in and start using Python to simplify your life and work. My own experience in learning new things is that the information doesn't \\\"stick\\\" unless you try and use it for something in real life.\\n\",\n \"\\n\",\n \"You will no doubt need to learn more as you go. I've listed several other good references, including the [Python Tutorial](http://docs.python.org/2/tutorial/) and [Learn Python the Hard Way](http://learnpythonthehardway.org/book/). Additionally, now is a good time to start familiarizing yourself with the [Python Documentation](http://docs.python.org/2.7/), and, in particular, the [Python Language Reference](http://docs.python.org/2.7/reference/index.html).\\n\",\n \"\\n\",\n \"Tim Peters, one of the earliest and most prolific Python contributors, wrote the \\\"Zen of Python\\\", which can be accessed via the \\\"import this\\\" command:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The Zen of Python, by Tim Peters\\n\",\n \"\\n\",\n \"Beautiful is better than ugly.\\n\",\n \"Explicit is better than implicit.\\n\",\n \"Simple is better than complex.\\n\",\n \"Complex is better than complicated.\\n\",\n \"Flat is better than nested.\\n\",\n \"Sparse is better than dense.\\n\",\n \"Readability counts.\\n\",\n \"Special cases aren't special enough to break the rules.\\n\",\n \"Although practicality beats purity.\\n\",\n \"Errors should never pass silently.\\n\",\n \"Unless explicitly silenced.\\n\",\n \"In the face of ambiguity, refuse the temptation to guess.\\n\",\n \"There should be one-- and preferably only one --obvious way to do it.\\n\",\n \"Although that way may not be obvious at first unless you're Dutch.\\n\",\n \"Now is better than never.\\n\",\n \"Although never is often better than *right* now.\\n\",\n \"If the implementation is hard to explain, it's a bad idea.\\n\",\n \"If the implementation is easy to explain, it may be a good idea.\\n\",\n \"Namespaces are one honking great idea -- let's do more of those!\\n\"\n ]\n }\n ],\n \"source\": [\n \"import this\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"No matter how experienced a programmer you are, these are words to meditate on.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# II. Numpy and Scipy\\n\",\n \"\\n\",\n \"[Numpy](http://numpy.org) contains core routines for doing fast vector, matrix, and linear algebra-type operations in Python. [Scipy](http://scipy) contains additional routines for optimization, special functions, and so on. Both contain modules written in C and Fortran so that they're as fast as possible. Together, they give Python roughly the same capability that the [Matlab](http://www.mathworks.com/products/matlab/) program offers. (In fact, if you're an experienced Matlab user, there a [guide to Numpy for Matlab users](http://www.scipy.org/NumPy_for_Matlab_Users) just for you.)\\n\",\n \"\\n\",\n \"## Making vectors and matrices\\n\",\n \"Fundamental to both Numpy and Scipy is the ability to work with vectors and matrices. You can create vectors from lists using the **array** command:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import scipy as sp\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4, 5, 6])\"\n ]\n },\n \"execution_count\": 75,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"array = np.array([1,2,3,4,5,6])\\n\",\n \"array\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"size of the array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(6,)\"\n ]\n },\n \"execution_count\": 76,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"array.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To build matrices, you can either use the array command with lists of lists:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0, 1],\\n\",\n \" [1, 0]])\"\n ]\n },\n \"execution_count\": 77,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mat = np.array([[0,1],[1,0]])\\n\",\n \"mat\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Add a column of ones to mat\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0., 1., 1.],\\n\",\n \" [1., 0., 1.]])\"\n ]\n },\n \"execution_count\": 78,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mat2 = np.c_[mat, np.ones(2)]\\n\",\n \"mat2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"size of a matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(2, 3)\"\n ]\n },\n \"execution_count\": 79,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mat2.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"You can also form empty (zero) matrices of arbitrary shape (including vectors, which Numpy treats as vectors with one row), using the **zeros** command:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0., 0., 0.],\\n\",\n \" [0., 0., 0.],\\n\",\n \" [0., 0., 0.]])\"\n ]\n },\n \"execution_count\": 80,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.zeros((3,3))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There's also an **identity** command that behaves as you'd expect:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 0., 0., 0.],\\n\",\n \" [0., 1., 0., 0.],\\n\",\n \" [0., 0., 1., 0.],\\n\",\n \" [0., 0., 0., 1.]])\"\n ]\n },\n \"execution_count\": 81,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.identity(4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"as well as a **ones** command.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Linspace, matrix functions, and plotting\\n\",\n \"The **linspace** command makes a linear array of points from a starting to an ending value.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0. , 0.02040816, 0.04081633, 0.06122449, 0.08163265,\\n\",\n \" 0.10204082, 0.12244898, 0.14285714, 0.16326531, 0.18367347,\\n\",\n \" 0.20408163, 0.2244898 , 0.24489796, 0.26530612, 0.28571429,\\n\",\n \" 0.30612245, 0.32653061, 0.34693878, 0.36734694, 0.3877551 ,\\n\",\n \" 0.40816327, 0.42857143, 0.44897959, 0.46938776, 0.48979592,\\n\",\n \" 0.51020408, 0.53061224, 0.55102041, 0.57142857, 0.59183673,\\n\",\n \" 0.6122449 , 0.63265306, 0.65306122, 0.67346939, 0.69387755,\\n\",\n \" 0.71428571, 0.73469388, 0.75510204, 0.7755102 , 0.79591837,\\n\",\n \" 0.81632653, 0.83673469, 0.85714286, 0.87755102, 0.89795918,\\n\",\n \" 0.91836735, 0.93877551, 0.95918367, 0.97959184, 1. ])\"\n ]\n },\n \"execution_count\": 82,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.linspace(0,1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you provide a third argument, it takes that as the number of points in the space. If you don't provide the argument, it gives a length 50 linear space.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])\"\n ]\n },\n \"execution_count\": 83,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.linspace(0,1,11)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**linspace** is an easy way to make coordinates for plotting. Functions in the numpy library (all of which are imported into IPython notebook) can act on an entire vector (or even a matrix) of points at once. Thus,\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0.00000000e+00, 1.27877162e-01, 2.53654584e-01, 3.75267005e-01,\\n\",\n \" 4.90717552e-01, 5.98110530e-01, 6.95682551e-01, 7.81831482e-01,\\n\",\n \" 8.55142763e-01, 9.14412623e-01, 9.58667853e-01, 9.87181783e-01,\\n\",\n \" 9.99486216e-01, 9.95379113e-01, 9.74927912e-01, 9.38468422e-01,\\n\",\n \" 8.86599306e-01, 8.20172255e-01, 7.40277997e-01, 6.48228395e-01,\\n\",\n \" 5.45534901e-01, 4.33883739e-01, 3.15108218e-01, 1.91158629e-01,\\n\",\n \" 6.40702200e-02, -6.40702200e-02, -1.91158629e-01, -3.15108218e-01,\\n\",\n \" -4.33883739e-01, -5.45534901e-01, -6.48228395e-01, -7.40277997e-01,\\n\",\n \" -8.20172255e-01, -8.86599306e-01, -9.38468422e-01, -9.74927912e-01,\\n\",\n \" -9.95379113e-01, -9.99486216e-01, -9.87181783e-01, -9.58667853e-01,\\n\",\n \" -9.14412623e-01, -8.55142763e-01, -7.81831482e-01, -6.95682551e-01,\\n\",\n \" -5.98110530e-01, -4.90717552e-01, -3.75267005e-01, -2.53654584e-01,\\n\",\n \" -1.27877162e-01, -2.44929360e-16])\"\n ]\n },\n \"execution_count\": 84,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.linspace(0,2*np.pi)\\n\",\n \"np.sin(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In conjunction with **matplotlib**, this is a nice way to plot things:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 86,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x,np.sin(x))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Matrix operations\\n\",\n \"Matrix objects act sensibly when multiplied by scalars:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0.125, 0. , 0. ],\\n\",\n \" [0. , 0.125, 0. ],\\n\",\n \" [0. , 0. , 0.125]])\"\n ]\n },\n \"execution_count\": 87,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"0.125*np.identity(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"as well as when you add two matrices together. (However, the matrices have to be the same shape.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[2., 1.],\\n\",\n \" [1., 3.]])\"\n ]\n },\n \"execution_count\": 88,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.identity(2) + np.array([[1,1],[1,2]])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Something that confuses Matlab users is that the times (*) operator give element-wise multiplication rather than matrix multiplication:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 0.],\\n\",\n \" [0., 1.]])\"\n ]\n },\n \"execution_count\": 89,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.identity(2)*np.ones((2,2))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To get matrix multiplication, you need the **dot** command:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1., 1.],\\n\",\n \" [1., 1.]])\"\n ]\n },\n \"execution_count\": 90,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.dot(np.identity(2),np.ones((2,2)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**dot** can also do dot products (duh!):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5.0\"\n ]\n },\n \"execution_count\": 91,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v = np.array([3,4])\\n\",\n \"np.sqrt(np.dot(v,v))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"as well as matrix-vector products.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are **determinant**, **inverse**, and **transpose** functions that act as you would suppose. Transpose can be abbreviated with \\\".T\\\" at the end of a matrix object:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 3],\\n\",\n \" [2, 4]])\"\n ]\n },\n \"execution_count\": 92,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m = np.array([[1,2],[3,4]])\\n\",\n \"m.T\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[-2. , 1. ],\\n\",\n \" [ 1.5, -0.5]])\"\n ]\n },\n \"execution_count\": 93,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.linalg.inv(m)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There's also a **diag()** function that takes a list or a vector and puts it along the diagonal of a square matrix. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 94,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 0, 0, 0, 0],\\n\",\n \" [0, 2, 0, 0, 0],\\n\",\n \" [0, 0, 3, 0, 0],\\n\",\n \" [0, 0, 0, 4, 0],\\n\",\n \" [0, 0, 0, 0, 5]])\"\n ]\n },\n \"execution_count\": 94,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.diag([1,2,3,4,5])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll find this useful later on.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Least squares fitting\\n\",\n \"Very often we deal with some data that we want to fit to some sort of expected behavior. Say we have the following:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 95,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"raw_data = \\\"\\\"\\\"\\\\\\n\",\n \"3.1905781584582433,0.028208609537968457\\n\",\n \"4.346895074946466,0.007160804747670053\\n\",\n \"5.374732334047101,0.0046962988461934805\\n\",\n \"8.201284796573875,0.0004614473299618756\\n\",\n \"10.899357601713055,0.00005038370219939726\\n\",\n \"16.295503211991434,4.377451812785309e-7\\n\",\n \"21.82012847965739,3.0799922117601088e-9\\n\",\n \"32.48394004282656,1.524776208284536e-13\\n\",\n \"43.53319057815846,5.5012073588707224e-18\\\"\\\"\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There's a section below on parsing CSV data. We'll steal the parser from that. For an explanation, skip ahead to that section. Otherwise, just assume that this is a way to parse that text into a numpy array that we can plot and do other analyses with.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 96,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"data = []\\n\",\n \"for line in raw_data.splitlines():\\n\",\n \" words = line.split(',')\\n\",\n \" data.append(words)\\n\",\n \"data = np.array(data, dtype=np.float)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 97,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[3.19057816e+00, 2.82086095e-02],\\n\",\n \" [4.34689507e+00, 7.16080475e-03],\\n\",\n \" [5.37473233e+00, 4.69629885e-03],\\n\",\n \" [8.20128480e+00, 4.61447330e-04],\\n\",\n \" [1.08993576e+01, 5.03837022e-05],\\n\",\n \" [1.62955032e+01, 4.37745181e-07],\\n\",\n \" [2.18201285e+01, 3.07999221e-09],\\n\",\n \" [3.24839400e+01, 1.52477621e-13],\\n\",\n \" [4.35331906e+01, 5.50120736e-18]])\"\n ]\n },\n \"execution_count\": 97,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 98,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 3.19057816, 4.34689507, 5.37473233, 8.2012848 , 10.8993576 ,\\n\",\n \" 16.29550321, 21.82012848, 32.48394004, 43.53319058])\"\n ]\n },\n \"execution_count\": 98,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data[:, 0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 99,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.title(\\\"Raw Data\\\")\\n\",\n \"plt.xlabel(\\\"Distance\\\")\\n\",\n \"plt.plot(data[:,0],data[:,1],'bo')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since we expect the data to have an exponential decay, we can plot it using a semi-log plot.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 100,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 100,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.title(\\\"Raw Data\\\")\\n\",\n \"plt.xlabel(\\\"Distance\\\")\\n\",\n \"plt.semilogy(data[:,0],data[:,1],'bo')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For a pure exponential decay like this, we can fit the log of the data to a straight line. The above plot suggests this is a good approximation. Given a function\\n\",\n \"$$ y = Ae^{-ax} $$\\n\",\n \"$$ \\\\log(y) = \\\\log(A) - ax$$\\n\",\n \"Thus, if we fit the log of the data versus x, we should get a straight line with slope $a$, and an intercept that gives the constant $A$.\\n\",\n \"\\n\",\n \"There's a numpy function called **polyfit** that will fit data to a polynomial form. We'll use this to fit to a straight line (a polynomial of order 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"params = sp.polyfit(data[:,0],np.log(data[:,1]),1)\\n\",\n \"a = params[0]\\n\",\n \"A = np.exp(params[1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's see whether this curve fits the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 102,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 102,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"x = np.linspace(1,45)\\n\",\n \"plt.title(\\\"Raw Data\\\")\\n\",\n \"plt.xlabel(\\\"Distance\\\")\\n\",\n \"plt.semilogy(data[:,0],data[:,1],'bo')\\n\",\n \"plt.semilogy(x,A*np.exp(a*x),'b-')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If we have more complicated functions, we may not be able to get away with fitting to a simple polynomial. Consider the following data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 103,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 103,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"gauss_data = \\\"\\\"\\\"\\\\\\n\",\n \"-0.9902286902286903,1.4065274110372852e-19\\n\",\n \"-0.7566104566104566,2.2504438576596563e-18\\n\",\n \"-0.5117810117810118,1.9459459459459454\\n\",\n \"-0.31887271887271884,10.621621621621626\\n\",\n \"-0.250997150997151,15.891891891891893\\n\",\n \"-0.1463309463309464,23.756756756756754\\n\",\n \"-0.07267267267267263,28.135135135135133\\n\",\n \"-0.04426734426734419,29.02702702702703\\n\",\n \"-0.0015939015939017698,29.675675675675677\\n\",\n \"0.04689304689304685,29.10810810810811\\n\",\n \"0.0840994840994842,27.324324324324326\\n\",\n \"0.1700546700546699,22.216216216216214\\n\",\n \"0.370878570878571,7.540540540540545\\n\",\n \"0.5338338338338338,1.621621621621618\\n\",\n \"0.722014322014322,0.08108108108108068\\n\",\n \"0.9926849926849926,-0.08108108108108646\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"data = []\\n\",\n \"for line in gauss_data.splitlines():\\n\",\n \" words = line.split(',')\\n\",\n \" data.append(words)\\n\",\n \"data = np.array(data, dtype=np.float)\\n\",\n \"\\n\",\n \"plt.plot(data[:,0],data[:,1],'bo')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This data looks more Gaussian than exponential. If we wanted to, we could use **polyfit** for this as well, but let's use the **curve_fit** function from Scipy, which can fit to arbitrary functions. You can learn more using help(curve_fit).\\n\",\n \"\\n\",\n \"First define a general Gaussian function to fit to.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 104,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def gauss(x,A,a): \\n\",\n \" return A*np.exp(a*x**2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now fit to it using **curve_fit**:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 105,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 105,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from scipy.optimize import curve_fit\\n\",\n \"\\n\",\n \"params,conv = curve_fit(gauss,data[:,0],data[:,1])\\n\",\n \"x = np.linspace(-1,1)\\n\",\n \"plt.plot(data[:,0],data[:,1],'bo')\\n\",\n \"A,a = params\\n\",\n \"plt.plot(x,gauss(x,A,a),'b-')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The **curve_fit** routine we just used is built on top of a very good general **minimization** capability in Scipy. You can learn more [at the scipy documentation pages](http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Monte Carlo and random numbers\\n\",\n \"Many methods in scientific computing rely on Monte Carlo integration, where a sequence of (pseudo) random numbers are used to approximate the integral of a function. Python has good random number generators in the standard library. The **random()** function gives pseudorandom numbers uniformly distributed between 0 and 1:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 106,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 106,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from random import random\\n\",\n \"rands = []\\n\",\n \"for i in range(100):\\n\",\n \" rands.append(random())\\n\",\n \"plt.plot(rands)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**random()** uses the [Mersenne Twister](http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html) algorithm, which is a highly regarded pseudorandom number generator. There are also functions to generate random integers, to randomly shuffle a list, and functions to pick random numbers from a particular distribution, like the normal distribution:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 107,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 107,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from random import gauss\\n\",\n \"grands = []\\n\",\n \"for i in range(100):\\n\",\n \" grands.append(gauss(0,1))\\n\",\n \"plt.plot(grands)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is generally more efficient to generate a list of random numbers all at once, particularly if you're drawing from a non-uniform distribution. Numpy has functions to generate vectors and matrices of particular types of random distributions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 108,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 108,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(np.random.rand(100))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# III. Introduction to Pandas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 109,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Series\\n\",\n \"\\n\",\n \"A Series is a one-dimensional array-like object containing an array of data and an associated array of data labels. The data can be any NumPy data type and the labels are the Series' index.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a Series:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 110,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 1\\n\",\n \"1 1\\n\",\n \"2 2\\n\",\n \"3 -3\\n\",\n \"4 -5\\n\",\n \"5 8\\n\",\n \"6 13\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 110,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_1 = pd.Series([1, 1, 2, -3, -5, 8, 13])\\n\",\n \"ser_1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get the array representation of a Series:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 111,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 1, 1, 2, -3, -5, 8, 13])\"\n ]\n },\n \"execution_count\": 111,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_1.values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Index objects are immutable and hold the axis labels and metadata such as names and axis names.\\n\",\n \"\\n\",\n \"Get the index of the Series:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 112,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RangeIndex(start=0, stop=7, step=1)\"\n ]\n },\n \"execution_count\": 112,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_1.index\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a Series with a custom index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 113,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 1\\n\",\n \"b 1\\n\",\n \"c 2\\n\",\n \"d -3\\n\",\n \"e -5\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 113,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_2 = pd.Series([1, 1, 2, -3, -5], index=['a', 'b', 'c', 'd', 'e'])\\n\",\n \"ser_2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get a value from a Series:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 114,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 114,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_2[4] == ser_2['e']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get a set of values from a Series by passing in a list:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 115,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"c 2\\n\",\n \"a 1\\n\",\n \"b 1\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 115,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_2[['c', 'a', 'b']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Get values great than 0:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 116,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 1\\n\",\n \"b 1\\n\",\n \"c 2\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 116,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_2[ser_2 > 0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Scalar multiply:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 117,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 2\\n\",\n \"b 2\\n\",\n \"c 4\\n\",\n \"d -6\\n\",\n \"e -10\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 117,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_2 * 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Apply a numpy math function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 118,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 2.718282\\n\",\n \"b 2.718282\\n\",\n \"c 7.389056\\n\",\n \"d 0.049787\\n\",\n \"e 0.006738\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 118,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.exp(ser_2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"A Series is like a fixed-length, ordered dict. \\n\",\n \"\\n\",\n \"Create a series by passing in a dict:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 119,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"bar 200\\n\",\n \"baz 300\\n\",\n \"foo 100\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 119,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dict_1 = {'foo' : 100, 'bar' : 200, 'baz' : 300}\\n\",\n \"ser_3 = pd.Series(dict_1)\\n\",\n \"ser_3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Re-order a Series by passing in an index (indices not found are NaN):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 120,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"foo 100.0\\n\",\n \"bar 200.0\\n\",\n \"baz 300.0\\n\",\n \"qux NaN\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 120,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"index = ['foo', 'bar', 'baz', 'qux']\\n\",\n \"ser_4 = pd.Series(dict_1, index=index)\\n\",\n \"ser_4\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Check for NaN with the pandas method:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 121,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"foo False\\n\",\n \"bar False\\n\",\n \"baz False\\n\",\n \"qux True\\n\",\n \"dtype: bool\"\n ]\n },\n \"execution_count\": 121,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.isnull(ser_4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Check for NaN with the Series method:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 122,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"foo False\\n\",\n \"bar False\\n\",\n \"baz False\\n\",\n \"qux True\\n\",\n \"dtype: bool\"\n ]\n },\n \"execution_count\": 122,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_4.isnull()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Series automatically aligns differently indexed data in arithmetic operations:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 123,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"bar 400.0\\n\",\n \"baz 600.0\\n\",\n \"foo 200.0\\n\",\n \"qux NaN\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 123,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_3 + ser_4\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Name a Series:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 124,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ser_4.name = 'foobarbazqux'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Name a Series index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 125,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"ser_4.index.name = 'label'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 126,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"label\\n\",\n \"foo 100.0\\n\",\n \"bar 200.0\\n\",\n \"baz 300.0\\n\",\n \"qux NaN\\n\",\n \"Name: foobarbazqux, dtype: float64\"\n ]\n },\n \"execution_count\": 126,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_4\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Rename a Series' index in place:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 127,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"fo 100.0\\n\",\n \"br 200.0\\n\",\n \"bz 300.0\\n\",\n \"qx NaN\\n\",\n \"Name: foobarbazqux, dtype: float64\"\n ]\n },\n \"execution_count\": 127,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_4.index = ['fo', 'br', 'bz', 'qx']\\n\",\n \"ser_4\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## DataFrame\\n\",\n \"\\n\",\n \"A DataFrame is a tabular data structure containing an ordered collection of columns. Each column can have a different type. DataFrames have both row and column indices and is analogous to a dict of Series. Row and column operations are treated roughly symmetrically. Columns returned when indexing a DataFrame are views of the underlying data, not a copy. To obtain a copy, use the Series' copy method.\\n\",\n \"\\n\",\n \"Create a DataFrame:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 128,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
popstateyear
05.0VA2012
15.1VA2013
25.2VA2014
34.0MD2014
44.1MD2015
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" pop state year\\n\",\n \"0 5.0 VA 2012\\n\",\n \"1 5.1 VA 2013\\n\",\n \"2 5.2 VA 2014\\n\",\n \"3 4.0 MD 2014\\n\",\n \"4 4.1 MD 2015\"\n ]\n },\n \"execution_count\": 128,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_1 = {'state' : ['VA', 'VA', 'VA', 'MD', 'MD'],\\n\",\n \" 'year' : [2012, 2013, 2014, 2014, 2015],\\n\",\n \" 'pop' : [5.0, 5.1, 5.2, 4.0, 4.1]}\\n\",\n \"df_1 = pd.DataFrame(data_1)\\n\",\n \"df_1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 129,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepop
02012VA5.0
12013VA5.1
22014VA5.2
32014MD4.0
42015MD4.1
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year state pop\\n\",\n \"0 2012 VA 5.0\\n\",\n \"1 2013 VA 5.1\\n\",\n \"2 2014 VA 5.2\\n\",\n \"3 2014 MD 4.0\\n\",\n \"4 2015 MD 4.1\"\n ]\n },\n \"execution_count\": 129,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_2 = pd.DataFrame(data_1, columns=['year', 'state', 'pop'])\\n\",\n \"df_2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Like Series, columns that are not present in the data are NaN:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 130,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
02012VA5.0NaN
12013VA5.1NaN
22014VA5.2NaN
32014MD4.0NaN
42015MD4.1NaN
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"0 2012 VA 5.0 NaN\\n\",\n \"1 2013 VA 5.1 NaN\\n\",\n \"2 2014 VA 5.2 NaN\\n\",\n \"3 2014 MD 4.0 NaN\\n\",\n \"4 2015 MD 4.1 NaN\"\n ]\n },\n \"execution_count\": 130,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3 = pd.DataFrame(data_1, columns=['year', 'state', 'pop', 'unempl'])\\n\",\n \"df_3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Retrieve a column by key, returning a Series:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 131,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 VA\\n\",\n \"1 VA\\n\",\n \"2 VA\\n\",\n \"3 MD\\n\",\n \"4 MD\\n\",\n \"Name: state, dtype: object\"\n ]\n },\n \"execution_count\": 131,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3['state']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Retrive a column by attribute, returning a Series:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 132,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 2012\\n\",\n \"1 2013\\n\",\n \"2 2014\\n\",\n \"3 2014\\n\",\n \"4 2015\\n\",\n \"Name: year, dtype: int64\"\n ]\n },\n \"execution_count\": 132,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3.year\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Retrieve a row by position:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 133,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"year 2012\\n\",\n \"state VA\\n\",\n \"pop 5\\n\",\n \"unempl NaN\\n\",\n \"Name: 0, dtype: object\"\n ]\n },\n \"execution_count\": 133,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3.iloc[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Update a column by assignment:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 134,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
02012VA5.00
12013VA5.11
22014VA5.22
32014MD4.03
42015MD4.14
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"0 2012 VA 5.0 0\\n\",\n \"1 2013 VA 5.1 1\\n\",\n \"2 2014 VA 5.2 2\\n\",\n \"3 2014 MD 4.0 3\\n\",\n \"4 2015 MD 4.1 4\"\n ]\n },\n \"execution_count\": 134,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3['unempl'] = np.arange(5)\\n\",\n \"df_3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Assign a Series to a column (note if assigning a list or array, the length must match the DataFrame, unlike a Series):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 135,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
02012VA5.0NaN
12013VA5.1NaN
22014VA5.26.0
32014MD4.06.0
42015MD4.16.1
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"0 2012 VA 5.0 NaN\\n\",\n \"1 2013 VA 5.1 NaN\\n\",\n \"2 2014 VA 5.2 6.0\\n\",\n \"3 2014 MD 4.0 6.0\\n\",\n \"4 2015 MD 4.1 6.1\"\n ]\n },\n \"execution_count\": 135,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"unempl = pd.Series([6.0, 6.0, 6.1], index=[2, 3, 4])\\n\",\n \"df_3['unempl'] = unempl\\n\",\n \"df_3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Assign a new column that doesn't exist to create a new column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 136,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunemplstate_dup
02012VA5.0NaNVA
12013VA5.1NaNVA
22014VA5.26.0VA
32014MD4.06.0MD
42015MD4.16.1MD
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year state pop unempl state_dup\\n\",\n \"0 2012 VA 5.0 NaN VA\\n\",\n \"1 2013 VA 5.1 NaN VA\\n\",\n \"2 2014 VA 5.2 6.0 VA\\n\",\n \"3 2014 MD 4.0 6.0 MD\\n\",\n \"4 2015 MD 4.1 6.1 MD\"\n ]\n },\n \"execution_count\": 136,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3['state_dup'] = df_3['state']\\n\",\n \"df_3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Delete a column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 137,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
02012VA5.0NaN
12013VA5.1NaN
22014VA5.26.0
32014MD4.06.0
42015MD4.16.1
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"0 2012 VA 5.0 NaN\\n\",\n \"1 2013 VA 5.1 NaN\\n\",\n \"2 2014 VA 5.2 6.0\\n\",\n \"3 2014 MD 4.0 6.0\\n\",\n \"4 2015 MD 4.1 6.1\"\n ]\n },\n \"execution_count\": 137,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"del df_3['state_dup']\\n\",\n \"df_3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Transpose the DataFrame:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 138,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
01234
year20122013201420142015
stateVAVAVAMDMD
pop55.15.244.1
unemplNaNNaN666.1
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" 0 1 2 3 4\\n\",\n \"year 2012 2013 2014 2014 2015\\n\",\n \"state VA VA VA MD MD\\n\",\n \"pop 5 5.1 5.2 4 4.1\\n\",\n \"unempl NaN NaN 6 6 6.1\"\n ]\n },\n \"execution_count\": 138,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3.T\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a DataFrame from a nested dict of dicts (the keys in the inner dicts are unioned and sorted to form the index in the result, unless an explicit index is specified):\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 139,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MDVA
2013NaN5.1
20144.05.2
20154.1NaN
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" MD VA\\n\",\n \"2013 NaN 5.1\\n\",\n \"2014 4.0 5.2\\n\",\n \"2015 4.1 NaN\"\n ]\n },\n \"execution_count\": 139,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pop = {'VA' : {2013 : 5.1, 2014 : 5.2},\\n\",\n \" 'MD' : {2014 : 4.0, 2015 : 4.1}}\\n\",\n \"df_4 = pd.DataFrame(pop)\\n\",\n \"df_4\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a DataFrame from a dict of Series:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 140,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MDVA
2014NaN5.2
20154.1NaN
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" MD VA\\n\",\n \"2014 NaN 5.2\\n\",\n \"2015 4.1 NaN\"\n ]\n },\n \"execution_count\": 140,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_2 = {'VA' : df_4['VA'][1:],\\n\",\n \" 'MD' : df_4['MD'][2:]}\\n\",\n \"df_5 = pd.DataFrame(data_2)\\n\",\n \"df_5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set the DataFrame index name:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 141,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MDVA
year
2014NaN5.2
20154.1NaN
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" MD VA\\n\",\n \"year \\n\",\n \"2014 NaN 5.2\\n\",\n \"2015 4.1 NaN\"\n ]\n },\n \"execution_count\": 141,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_5.index.name = 'year'\\n\",\n \"df_5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set the DataFrame columns name:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 142,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
stateMDVA
year
2014NaN5.2
20154.1NaN
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \"state MD VA\\n\",\n \"year \\n\",\n \"2014 NaN 5.2\\n\",\n \"2015 4.1 NaN\"\n ]\n },\n \"execution_count\": 142,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_5.columns.name = 'state'\\n\",\n \"df_5\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Return the data contained in a DataFrame as a 2D ndarray:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 143,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[nan, 5.2],\\n\",\n \" [4.1, nan]])\"\n ]\n },\n \"execution_count\": 143,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_5.values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If the columns are different dtypes, the 2D ndarray's dtype will accomodate all of the columns:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 144,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[2012, 'VA', 5.0, nan],\\n\",\n \" [2013, 'VA', 5.1, nan],\\n\",\n \" [2014, 'VA', 5.2, 6.0],\\n\",\n \" [2014, 'MD', 4.0, 6.0],\\n\",\n \" [2015, 'MD', 4.1, 6.1]], dtype=object)\"\n ]\n },\n \"execution_count\": 144,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3.values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Reindexing\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a new object with the data conformed to a new index. Any missing values are set to NaN.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 145,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
02012VA5.0NaN
12013VA5.1NaN
22014VA5.26.0
32014MD4.06.0
42015MD4.16.1
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\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"0 2012 VA 5.0 NaN\\n\",\n \"1 2013 VA 5.1 NaN\\n\",\n \"2 2014 VA 5.2 6.0\\n\",\n \"3 2014 MD 4.0 6.0\\n\",\n \"4 2015 MD 4.1 6.1\"\n ]\n },\n \"execution_count\": 145,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Reindexing rows returns a new frame with the specified index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 146,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
5NaNNaNNaNNaN
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22014.0VA5.26.0
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\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"5 NaN NaN NaN NaN\\n\",\n \"4 2015.0 MD 4.1 6.1\\n\",\n \"3 2014.0 MD 4.0 6.0\\n\",\n \"2 2014.0 VA 5.2 6.0\\n\",\n \"1 2013.0 VA 5.1 NaN\\n\",\n \"0 2012.0 VA 5.0 NaN\"\n ]\n },\n \"execution_count\": 146,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3.reindex(list(reversed(range(0, 6))))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Reindex columns:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 147,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
statepopunemplyear
0VA5.0NaN2012
1VA5.1NaN2013
2VA5.26.02014
3MD4.06.02014
4MD4.16.12015
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\"\n ],\n \"text/plain\": [\n \" state pop unempl year\\n\",\n \"0 VA 5.0 NaN 2012\\n\",\n \"1 VA 5.1 NaN 2013\\n\",\n \"2 VA 5.2 6.0 2014\\n\",\n \"3 MD 4.0 6.0 2014\\n\",\n \"4 MD 4.1 6.1 2015\"\n ]\n },\n \"execution_count\": 147,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3.reindex(columns=['state', 'pop', 'unempl', 'year'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Dropping Entries\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Drop rows from a Series or DataFrame:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 148,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
22014VA5.26.0
32014MD4.06.0
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\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"2 2014 VA 5.2 6.0\\n\",\n \"3 2014 MD 4.0 6.0\\n\",\n \"4 2015 MD 4.1 6.1\"\n ]\n },\n \"execution_count\": 148,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_7 = df_3.drop([0, 1])\\n\",\n \"df_7\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 149,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepop
22014VA5.2
32014MD4.0
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\"\n ],\n \"text/plain\": [\n \" year state pop\\n\",\n \"2 2014 VA 5.2\\n\",\n \"3 2014 MD 4.0\\n\",\n \"4 2015 MD 4.1\"\n ]\n },\n \"execution_count\": 149,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_7 = df_7.drop('unempl', axis=1)\\n\",\n \"df_7\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Indexing, Selecting, Filtering\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Pandas supports indexing into a DataFrame.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 150,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
02012VA5.0NaN
12013VA5.1NaN
22014VA5.26.0
32014MD4.06.0
42015MD4.16.1
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"0 2012 VA 5.0 NaN\\n\",\n \"1 2013 VA 5.1 NaN\\n\",\n \"2 2014 VA 5.2 6.0\\n\",\n \"3 2014 MD 4.0 6.0\\n\",\n \"4 2015 MD 4.1 6.1\"\n ]\n },\n \"execution_count\": 150,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Select specified columns from a DataFrame:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 151,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
popunempl
05.0NaN
15.1NaN
25.26.0
34.06.0
44.16.1
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\"\n ],\n \"text/plain\": [\n \" pop unempl\\n\",\n \"0 5.0 NaN\\n\",\n \"1 5.1 NaN\\n\",\n \"2 5.2 6.0\\n\",\n \"3 4.0 6.0\\n\",\n \"4 4.1 6.1\"\n ]\n },\n \"execution_count\": 151,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3[['pop', 'unempl']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Select a slice from a DataFrame:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 152,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
02012VA5.0NaN
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\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"0 2012 VA 5.0 NaN\\n\",\n \"1 2013 VA 5.1 NaN\"\n ]\n },\n \"execution_count\": 152,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3[:2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 153,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
12013VA5.1NaN
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\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"1 2013 VA 5.1 NaN\\n\",\n \"2 2014 VA 5.2 6.0\"\n ]\n },\n \"execution_count\": 153,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3.iloc[1:3]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Select from a DataFrame based on a filter:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 154,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
12013VA5.1NaN
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\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"1 2013 VA 5.1 NaN\\n\",\n \"2 2014 VA 5.2 6.0\"\n ]\n },\n \"execution_count\": 154,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3[df_3['pop'] > 5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Select a slice of rows from a specific column of a DataFrame:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 155,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
yearstatepopunempl
02012VA5.0NaN
12013VA5.1NaN
22014VA5.26.0
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\"\n ],\n \"text/plain\": [\n \" year state pop unempl\\n\",\n \"0 2012 VA 5.0 NaN\\n\",\n \"1 2013 VA 5.1 NaN\\n\",\n \"2 2014 VA 5.2 6.0\\n\",\n \"3 2014 MD 4.0 6.0\\n\",\n \"4 2015 MD 4.1 6.1\"\n ]\n },\n \"execution_count\": 155,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_3.loc[0:2, 'pop']\\n\",\n \"df_3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Arithmetic and Data Alignment\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Adding DataFrame objects results in the union of index pairs for rows and columns if the pairs are not the same, resulting in NaN for indices that do not overlap:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 156,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
abc
00.5488140.7151890.602763
10.5448830.4236550.645894
20.4375870.8917730.963663
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\"\n ],\n \"text/plain\": [\n \" a b c\\n\",\n \"0 0.548814 0.715189 0.602763\\n\",\n \"1 0.544883 0.423655 0.645894\\n\",\n \"2 0.437587 0.891773 0.963663\"\n ]\n },\n \"execution_count\": 156,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.random.seed(0)\\n\",\n \"df_8 = pd.DataFrame(np.random.rand(9).reshape((3, 3)),\\n\",\n \" columns=['a', 'b', 'c'])\\n\",\n \"df_8\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 157,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
bcd
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10.3023330.1467560.092339
20.1862600.3455610.396767
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
abcd
0NaN1.1322111.323088NaN
1NaN0.7259870.792650NaN
2NaN1.0780331.309223NaN
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\"\n ],\n \"text/plain\": [\n \" a b c d\\n\",\n \"0 NaN 1.132211 1.323088 NaN\\n\",\n \"1 NaN 0.725987 0.792650 NaN\\n\",\n \"2 NaN 1.078033 1.309223 NaN\"\n ]\n },\n \"execution_count\": 158,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_8 + df_9\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set a fill value instead of NaN for indices that do not overlap:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 159,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" a b c d\\n\",\n \"0 0.548814 1.132211 1.323088 0.000114\\n\",\n \"1 0.544883 0.725987 0.792650 0.092339\\n\",\n \"2 0.437587 1.078033 1.309223 0.396767\"\n ]\n },\n \"execution_count\": 159,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_10 = df_8.add(df_9, fill_value=0)\\n\",\n \"df_10\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Like NumPy, pandas supports arithmetic operations between DataFrames and Series.\\n\",\n \"\\n\",\n \"Match the index of the Series on the DataFrame's columns, broadcasting down the rows:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 160,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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abcd
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\"\n ],\n \"text/plain\": [\n \" a b c d\\n\",\n \"0 0.000000 0.000000 0.000000 0.000000\\n\",\n \"1 -0.003930 -0.406224 -0.530438 0.092224\\n\",\n \"2 -0.111226 -0.054178 -0.013864 0.396653\"\n ]\n },\n \"execution_count\": 160,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_8 = df_10.iloc[0]\\n\",\n \"df_11 = df_10 - ser_8\\n\",\n \"df_11\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Match the index of the Series on the DataFrame's columns, broadcasting down the rows and union the indices that do not match:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 161,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 0\\n\",\n \"d 1\\n\",\n \"e 2\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 161,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ser_9 = pd.Series(range(3), index=['a', 'd', 'e'])\\n\",\n \"ser_9\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 162,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
abcde
00.000000NaNNaN-1.000000NaN
1-0.003930NaNNaN-0.907776NaN
2-0.111226NaNNaN-0.603347NaN
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\"\n ],\n \"text/plain\": [\n \" a b c d e\\n\",\n \"0 0.000000 NaN NaN -1.000000 NaN\\n\",\n \"1 -0.003930 NaN NaN -0.907776 NaN\\n\",\n \"2 -0.111226 NaN NaN -0.603347 NaN\"\n ]\n },\n \"execution_count\": 162,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_11 - ser_9\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Function Application and Mapping\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"NumPy ufuncs (element-wise array methods) operate on pandas objects:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 163,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
abcd
00.0000000.0000000.0000000.000000
10.0039300.4062240.5304380.092224
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\"\n ],\n \"text/plain\": [\n \" a b c d\\n\",\n \"0 0.000000 0.000000 0.000000 0.000000\\n\",\n \"1 0.003930 0.406224 0.530438 0.092224\\n\",\n \"2 0.111226 0.054178 0.013864 0.396653\"\n ]\n },\n \"execution_count\": 163,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_11 = np.abs(df_11)\\n\",\n \"df_11\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Apply a function on 1D arrays to each column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 164,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a 0.115157\\n\",\n \"b 0.460402\\n\",\n \"c 0.544302\\n\",\n \"d 0.488877\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 164,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_11.apply(sum)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Apply a function on 1D arrays to each row:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 165,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 0.000000\\n\",\n \"1 1.032816\\n\",\n \"2 0.575922\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 165,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_11.apply(sum, axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Apply an element-wise Python function to a DataFrame:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 166,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
abcd
00.000.000.000.00
10.000.410.530.09
20.110.050.010.40
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\"\n ],\n \"text/plain\": [\n \" a b c d\\n\",\n \"0 0.00 0.00 0.00 0.00\\n\",\n \"1 0.00 0.41 0.53 0.09\\n\",\n \"2 0.11 0.05 0.01 0.40\"\n ]\n },\n \"execution_count\": 166,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"def func_3(x): \\n\",\n \" return '%.2f' %x\\n\",\n \"df_11.applymap(func_3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Sorting\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 167,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
cabd
three0123
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two891011
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" c a b d\\n\",\n \"three 0 1 2 3\\n\",\n \"one 4 5 6 7\\n\",\n \"two 8 9 10 11\"\n ]\n },\n \"execution_count\": 167,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_12 = pd.DataFrame(np.arange(12).reshape((3, 4)),\\n\",\n \" index=['three', 'one', 'two'],\\n\",\n \" columns=['c', 'a', 'b', 'd'])\\n\",\n \"df_12\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sort a DataFrame by its index:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 168,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
cabd
one4567
three0123
two891011
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" c a b d\\n\",\n \"one 4 5 6 7\\n\",\n \"three 0 1 2 3\\n\",\n \"two 8 9 10 11\"\n ]\n },\n \"execution_count\": 168,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_12.sort_index()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sort a DataFrame by columns in descending order:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 169,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
dcba
three3021
one7465
two118109
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" d c b a\\n\",\n \"three 3 0 2 1\\n\",\n \"one 7 4 6 5\\n\",\n \"two 11 8 10 9\"\n ]\n },\n \"execution_count\": 169,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_12.sort_index(axis=1, ascending=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sort a DataFrame's values by column:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 170,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
cabd
three0123
one4567
two891011
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" c a b d\\n\",\n \"three 0 1 2 3\\n\",\n \"one 4 5 6 7\\n\",\n \"two 8 9 10 11\"\n ]\n },\n \"execution_count\": 170,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_12.sort_values(by=['d', 'c'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Summarizing and Computing Descriptive Statistics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Unlike NumPy arrays, Pandas descriptive statistics automatically exclude missing data. NaN values are excluded unless the entire row or column is NA.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 171,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
abccat1cat2
0-2.3634691.135345-1.0170140.01.0
10.637362-0.8599071.7726082.01.0
2-1.1103630.1812140.5643450.01.0
3-0.5665100.7299760.3729942.01.0
40.533811-0.0919731.9138202.01.0
50.3307971.141943-1.1295950.01.0
6-0.8500520.960820-0.2174181.00.0
70.1585150.873418-0.1113832.00.0
8-1.038039-1.009480-1.0582571.01.0
90.656284-0.062492-1.7386540.00.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" a b c cat1 cat2\\n\",\n \"0 -2.363469 1.135345 -1.017014 0.0 1.0\\n\",\n \"1 0.637362 -0.859907 1.772608 2.0 1.0\\n\",\n \"2 -1.110363 0.181214 0.564345 0.0 1.0\\n\",\n \"3 -0.566510 0.729976 0.372994 2.0 1.0\\n\",\n \"4 0.533811 -0.091973 1.913820 2.0 1.0\\n\",\n \"5 0.330797 1.141943 -1.129595 0.0 1.0\\n\",\n \"6 -0.850052 0.960820 -0.217418 1.0 0.0\\n\",\n \"7 0.158515 0.873418 -0.111383 2.0 0.0\\n\",\n \"8 -1.038039 -1.009480 -1.058257 1.0 1.0\\n\",\n \"9 0.656284 -0.062492 -1.738654 0.0 0.0\"\n ]\n },\n \"execution_count\": 171,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_15 = pd.DataFrame(np.random.randn(10, 3),\\n\",\n \" columns=['a', 'b', 'c'])\\n\",\n \"df_15['cat1'] = (np.random.rand(10) * 3).round(0)\\n\",\n \"df_15['cat2'] = (np.random.rand(10)).round(0)\\n\",\n \"df_15\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Sum and Mean\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 172,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a -3.611664\\n\",\n \"b 2.998865\\n\",\n \"c -0.648555\\n\",\n \"cat1 10.000000\\n\",\n \"cat2 7.000000\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 172,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_15.sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 173,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 -1.245137\\n\",\n \"1 4.550063\\n\",\n \"2 0.635196\\n\",\n \"3 3.536459\\n\",\n \"4 5.355658\\n\",\n \"5 1.343144\\n\",\n \"6 0.893349\\n\",\n \"7 2.920550\\n\",\n \"8 -1.105775\\n\",\n \"9 -1.144862\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 173,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_15.sum(axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 174,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"a -0.361166\\n\",\n \"b 0.299886\\n\",\n \"c -0.064856\\n\",\n \"cat1 1.000000\\n\",\n \"cat2 0.700000\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 174,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_15.mean(axis=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Descriptive analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 175,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"count 10.000000\\n\",\n \"mean -0.361166\\n\",\n \"std 0.993980\\n\",\n \"min -2.363469\\n\",\n \"25% -0.991042\\n\",\n \"50% -0.203998\\n\",\n \"75% 0.483057\\n\",\n \"max 0.656284\\n\",\n \"Name: a, dtype: float64\"\n ]\n },\n \"execution_count\": 175,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_15['a'].describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 176,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2.0 4\\n\",\n \"0.0 4\\n\",\n \"1.0 2\\n\",\n \"Name: cat1, dtype: int64\"\n ]\n },\n \"execution_count\": 176,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_15['cat1'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Pivot tables\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### group by cat1 and calculate mean\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 177,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
abccat2
cat1
0.0-0.6216880.599003-0.8302300.75
1.0-0.944046-0.024330-0.6378370.50
2.00.1907940.1628780.9870100.75
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" a b c cat2\\n\",\n \"cat1 \\n\",\n \"0.0 -0.621688 0.599003 -0.830230 0.75\\n\",\n \"1.0 -0.944046 -0.024330 -0.637837 0.50\\n\",\n \"2.0 0.190794 0.162878 0.987010 0.75\"\n ]\n },\n \"execution_count\": 177,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.pivot_table(df_15, index='cat1', aggfunc=np.mean)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [conda env:tf]\",\n \"language\": \"python\",\n \"name\": \"conda-env-tf-py\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/03-linear_regression.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 03 - Linear Regression\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Kevin Markham](https://github.com/justmarkham)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from mpl_toolkits.mplot3d import Axes3D\\n\",\n \"from matplotlib import cm\\n\",\n \"import itertools\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/javascript\": [\n \"IPython.OutputArea.auto_scroll_threshold = 99999;\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%%javascript\\n\",\n \"IPython.OutputArea.auto_scroll_threshold = 99999;\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
areabedroomprice
021043399900
116003329900
224003369000
314162232000
430004539900
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" area bedroom price\\n\",\n \"0 2104 3 399900\\n\",\n \"1 1600 3 329900\\n\",\n \"2 2400 3 369000\\n\",\n \"3 1416 2 232000\\n\",\n \"4 3000 4 539900\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Test Dataset\\n\",\n \"# Load dataset\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/houses_portland.csv.zip', 'r') as z:\\n\",\n \" f = z.open('houses_portland.csv')\\n\",\n \" data = pd.io.parsers.read_table(f, sep=',')\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['area', 'bedroom', ' price'], dtype='object')\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'Price')\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y = data[' price'].values\\n\",\n \"X = data['area'].values\\n\",\n \"plt.scatter(X, y,c='b')\\n\",\n \"plt.xlabel('Area')\\n\",\n \"plt.ylabel('Price')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Normalize data\\n\",\n \"\\n\",\n \"## $$ x = \\\\frac{x -\\\\overline x}{\\\\sigma_x} $$ \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'Price')\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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cAqSQJhIi4NyIuiohfi4j3RsTHU9RRhaJHUvYNmqZ6+unSm28yyAygXHQZlWyrs4cH2bz24LzzsiB59dVigQIA4yIQSjbOmMDGaapnnSW99dapv98qUABgXARCySYdE2CQGUBVCISSTbofEYPMAKpCIJRs0v2I2OAOQFWS7GXUNZPsRzTpXkgAUFQnWghN3xto3DOOAWAUrW8hsDcQABTT+hbCqOsAAKCrWh8I40zbbHoXEwCMo/WBMOq0zVG3mgCAtmh9IIw6bZMuJgBd1fpAGHUdACuDAXRV62cZSaOtA9i9e/ABNawMBtB2rW8hjIqVwQC6ikDYZNKtJgCgqTrRZTSqSbaaAICmooUAAJBEIAAAcgQCAEASgQAAyBEIAABJBAIAIOeISF1DYbbXJA1YR1x750t6JXURNcD7kOF94D3oq+p9mIuI2e0e1KhAaCrbKxGxkLqO1HgfMrwPvAd9dXsf6DICAEgiEAAAOQKhGkupC6gJ3ocM7wPvQV+t3gfGEAAAkmghAAByBEJFbN9s+7Dtddu1mVVQBdv7bD9v+wXbX0pdTwq2b7f9su1nU9eSku2LbT9o+0j+/4fPp64pBdtn2H7M9tP5+/Dl1DVJBEKVnpV0k6SHUxdSJds7Jf2DpBskXSZpv+3L0laVxB2S9qUuogbelvTFiLhU0rWSbuno/x5+KWlvRFwpaY+kfbavTVwTgVCViDgSEc+nriOBayS9EBEvRsSvJH1d0icT11S5iHhY0mup60gtIn4aEU/mP/9c0hFJF6atqnqReTO/uSu/JB/QJRBQtgslvbTh9nF18AMA72Z7XtJVkh5NW0katnfafkrSy5IeiIjk7wMnpk2R7e9Iet+AXx2MiG9WXU9NeMB9yb8JIS3bZ0m6R9IXIuKN1PWkEBEnJe2xfY6ke21fHhFJx5gIhCmKiOtT11BDxyVdvOH2RZJ+kqgW1IDtXcrCYDkivpG6ntQi4nXbDykbY0oaCHQZoWyPS/ot25fYPl3Sn0n6t8Q1IRHblnSbpCMR8ZXU9aRiezZvGcj2mZKul/TDtFURCJWx/SnbxyV9SNK3bd+fuqYqRMTbkv5S0v3KBhD/JSIOp62qera/Jum/Jf227eO2P5u6pkSuk/QZSXttP5VfbkxdVAIXSHrQ9jPKvjQ9EBHfSlwTK5UBABlaCAAASQQCACBHIAAAJBEIAIAcgQAAkEQgANvKpwyH7d9JXQtQJgIB2N5+SY8oW1R3inw3V6AVCARgC/meO9dJ+qzyQLD90XxP/7sl/SC/7y/y/e2fsv3P/aCw/U+2V+q05z0wDIEAbO2PJf1HRPxI0mu2fy+//xplmxZeZvtSSX8q6bqI2CPppKRe/riDEbEg6QpJH7F9RcX1A4URCMDW9is7w0H59f7858ci4sf5zx+TdLWkx/PtjD8m6f357/7E9pOS/kfSB5QdEgTUErudAkPYPk/SXkmX2w5JO5Vt3X2fpF9sfKikOyPirzb9/SWSbpX0wYj4me07JJ1RRe3AOGghAMN9WtJXI2IuIuYj4mJJP5b0+5se91+SPm37NyTJ9rm25ySdrSw4/s/2e5UdIwrUFoEADLdf0r2b7rtH0p9vvCMinpP015L+M9+98gFJF0TE08q6ig5Lul3Sd0uvGJgAu50CACTRQgAA5AgEAIAkAgEAkCMQAACSCAQAQI5AAABIIhAAADkCAQAgSfp/sGm0VIoew5oAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"y_mean, y_std = y.mean(), y.std()\\n\",\n \"X_mean, X_std = X.mean(), X.std()\\n\",\n \"\\n\",\n \"y = (y - y_mean)/ y_std\\n\",\n \"X = (X - X_mean)/ X_std\\n\",\n \"\\n\",\n \"plt.scatter(X, y,c='b')\\n\",\n \"plt.xlabel('Area')\\n\",\n \"plt.ylabel('Price')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Form of linear regression\\n\",\n \"\\n\",\n \"## $$h_\\\\beta(x) = \\\\beta_0 + \\\\beta_1x_1 + \\\\beta_2x_2 + ... + \\\\beta_nx_n$$\\n\",\n \"\\n\",\n \"- $h_\\\\beta(x)$ is the response\\n\",\n \"- $\\\\beta_0$ is the intercept\\n\",\n \"- $\\\\beta_1$ is the coefficient for $x_1$ (the first feature)\\n\",\n \"- $\\\\beta_n$ is the coefficient for $x_n$ (the nth feature)\\n\",\n \"\\n\",\n \"The $\\\\beta$ values are called the **model coefficients**:\\n\",\n \"\\n\",\n \"- These values are estimated (or \\\"learned\\\") during the model fitting process using the **least squares criterion**.\\n\",\n \"- Specifically, we are find the line (mathematically) which minimizes the **sum of squared residuals** (or \\\"sum of squared errors\\\").\\n\",\n \"- And once we've learned these coefficients, we can use the model to predict the response.\\n\",\n \"\\n\",\n \"![Estimating coefficients](images/estimating_coefficients.png)\\n\",\n \"\\n\",\n \"In the diagram above:\\n\",\n \"\\n\",\n \"- The black dots are the **observed values** of x and y.\\n\",\n \"- The blue line is our **least squares line**.\\n\",\n \"- The red lines are the **residuals**, which are the vertical distances between the observed values and the least squares line.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Cost function\\n\",\n \"\\n\",\n \"The goal became to estimate the parameters $\\\\beta$ that minimisse the sum of squared residuals\\n\",\n \"\\n\",\n \"## $$J(\\\\beta_0, \\\\beta_1)=\\\\frac{1}{2n}\\\\sum_{i=1}^n (h_\\\\beta(x_i)-y_i)^2$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create X and y\\n\",\n \"n_samples = X.shape[0]\\n\",\n \"X_ = np.c_[np.ones(n_samples), X]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lets suppose the following betas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"beta_ini = np.array([-1, 1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# h\\n\",\n \"def lr_h(beta,x):\\n\",\n \" return np.dot(beta, x.T)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'Price')\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# scatter plot\\n\",\n \"plt.scatter(X, y,c='b')\\n\",\n \"\\n\",\n \"# Plot the linear regression\\n\",\n \"x = np.c_[np.ones(2), [X.min(), X.max()]]\\n\",\n \"plt.plot(x[:, 1], lr_h(beta_ini, x), 'r', lw=5)\\n\",\n \"plt.xlabel('Area')\\n\",\n \"plt.ylabel('Price')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lets calculate the error of such regression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.6450124071218747\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Cost function\\n\",\n \"def lr_cost_func(beta, x, y):\\n\",\n \" # Can be vectorized\\n\",\n \" res = 0\\n\",\n \" for i in range(x.shape[0]):\\n\",\n \" res += (lr_h(beta,x[i, :]) - y[i]) ** 2\\n\",\n \" res *= 1 / (2*x.shape[0])\\n\",\n \" return res\\n\",\n \"lr_cost_func(beta_ini, X_, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Understanding the cost function\\n\",\n \"\\n\",\n \"Lets see how the cost function looks like for different values of $\\\\beta$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"beta0 = np.arange(-15, 20, 1)\\n\",\n \"beta1 = 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'J(beta)')\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cost_func=[]\\n\",\n \"for beta_0 in beta0:\\n\",\n \" cost_func.append(lr_cost_func(np.array([beta_0, beta1]), X_, y) )\\n\",\n \"\\n\",\n \"plt.plot(beta0, cost_func)\\n\",\n \"plt.xlabel('beta_0')\\n\",\n \"plt.ylabel('J(beta)')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"beta0 = 0\\n\",\n \"beta1 = np.arange(-15, 20, 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'J(beta)')\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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RA9heW8vTsdM7o156TerX1Oo7njuYYxEgzO/XQLdDBwomZMeW8FHLyS3heA/mJhK2nZqdTWFLOLyf08zpKSDhiQZjZ94E5wMfA/b6v9wU2VvgZ1q015w/uyNQ5m9idW+x1HBE5SlVDamTwveFdddq6T122IH4InABsdc6NBYYCOmXHj19O6EdlJfz5k/VeRxGRo/Tox+uJjLBGMZVoXdWlIIqdc8UAZtbUObcO6BvYWOGpa5vm3DA6iXeW7mDNzlyv44hIHa3ccYCZK3by/TE96dAyxus4IaMuBbHDzFoB7wOfmtkH+BlET6rcPrY3rZpF8eC/0qi6OFxEQplzjodnraNNbDS3ntbT6zgh5YgF4Zy72Dl3wDl3H/Ab4CWqJvcRP1o2i+KH45KZt2kvX6zLPvILRMRTX27IYf7mvdx9Rm/iGumQGjWpy0HqVw/dd8595ZybCUwLaKowN2lUd3q2i+WhWWmUVVR6HUdEalBaXskfPlpLUtvmXH1i4x1SoyZ12cU0oPoDM4sEhgcmTsMQFRnBPef0Y1NOIW8u3OZ1HBGpwYx5GWzKKeQ35/dv1ENq1KS2oTbuNbN8YLCZ5ZlZvu9xNv8/h4PUYHz/RE7s0YbHPttIXnGZ13FE5DDZecU8/vlGxvZNYFxKotdxQlKNBeGce9g5Fwc86pyLd87F+W5tnXP3BjFjWDIzfn1ef/YVlvLMbM1fLRJqHvnPOkrLK/ntBQOO/ORGqi7bVFPM7Boz+w2AmXU1s5EBztUgDOrSkkuGdmbaN1vYvk8zz4mEiiVb9/Hu0kxuOqUHPdrFeh0nZNWlIJ4GTgKu9j0u8C2TOvj5hL40iTDu/3CN11FEhKq5Hn43cw0d4mO4c2xvr+OEtLoUxInOuTuAYgDn3H4gOqCpGpCOLZvxw3HJfJaWzWdrs7yOI9Lo/WPRdlZn5nHvuf00sOYR1KUgynxnLjkAM0sAdO7mUbhxTA+S27fg/o/WUFxW4XUckUbrQFEpj368jpE92nDhkE5exwl5dSmIJ4D3gEQzexD4GngooKkamKjICB6YOJDt+w7yzJc6YC3ilb9+uoHcg2Xcd8EAzBr3XA91ccTtK+fc62a2BBjnW3SRc06TMB+lk3q1ZWJqJ577ahOXDO1Mkg6MiQTV2p15vLZgK5NO7E7/TvFexwkLdb0ypDkQ6Xt+s+NZoZn1NbPl1W55ZvYjM7vPzDKrLT/3eNYTin51bgrRkRHc9+EajdMkEkTOOe6buYaWzaL46VkarbWu6jLUxm+BGUAboB3wspn9+lhX6Jxb75xLdc6lUnVFdhFVu7AAHjv0PefcrGNdR6hKjI/hR2cm8+X6HD7RAWuRoJm5YicLM/bxs7P70qq5zrGpq7psQVwFnOCcu8859ztgFDCpntY/DtjknNtaTz8v5F1/chL9OsTxwIdrKSot9zqOSINXWFLOQ7PSGNg5nitP6OZ1nLBSl4LIAKoPkN4UqK8jrVcCb1R7fKeZrTSzaWbWup7WEVKa+A5YZx44yNOz072OI9LgPTU7nay8Eu6/cACRETowfTRqG4vpSTN7AigB1pjZdDN7GVhN1cVyx8XMooELgbd8i54FegGpwC7gLzW87hYzW2xmi3NywnNiu5E92nDJsM5MnbOZTTnH/VaKSA227CnkpblbuGRoZ4Z3b+N1nLBT2xbEYmAJVccHfgXMBr4EpgD/rod1nwMsdc5lATjnspxzFc65SuAFwO9wHs65qc65Ec65EQkJCfUQwxv3npNCTFQk983UAWuRQHDO8dsPVhMVadxzTj+v44SlGk9zdc7NCPC6r6La7iUz6+ic2+V7eDFVWyoNVkJcU352Vl9+N3MNs1bt5rzBHb2OJNKgvLcsk7kb9/DAxAG0j9c0oseitl1MH5rZBWb2nSmWzKynmT1gZjcey0rNrDkwHni32uI/mdkqM1sJjAV+fCw/O5xMOrEb/TvG8/uP1lJYogPWIvVlT0EJD3y0lmHdWnGNJgI6ZrXtYroZOAVYZ2aLzGyWmc02sy3A81TtHjqmmeWcc0W+YcNzqy271jk3yDk32Dl3YbWtiQarSWQEv79oILvzinni841exxFpMA790fXHSwcToQPTx6y2XUy7gV8AvzCzJKADcBDY4Jw7GJR0jcDw7q25fEQXXvp6CxcN7UxKR13hKXI8Zq/L5oPlO/nhuGSSE+O8jhPWatvFlO+7yjkPWAl8CswFsswsx8wWmNm4ml4vdXfvOSm0ah7Fz99eQbnmsBY5ZgUl5Ux5bxW927fg9rG9vI4T9mqbUS7ON5Pcodt/H1O1NXEr8HjQkjZgrWOjeWDiQFZn5jF17mav44iErT9/vJ5decX88dJBNG0S6XWcsHdMs3T7TkddATxZz3karXMHdeScgR3422cbSc/O9zqOSNhZtm0/M+ZncO2o7rrmoZ4cU0Ec4px7vr6CCNw/cQDNoyP5xdsrqajUtREidVVaXsk976yiQ3wMPz+7r9dxGozjKgipX+3jYvjdBf1Zuu0A0+dleB1HJGw8/9Um1mfl84eLBhIX850z8+UYqSBCzEWpnTmjX3se/XgdW/cWeh1HJOSlZxfw5BfpnD+4I+NSEr2O06CoIEKMmfHgxQOJiojgl++spFK7mkRqVFnpuPfdlTSLjuR3FwzwOk6Do4IIQR1bNmPKeSks2LyP1xdu8zqOSMh6Y9E2FmXs59fnpZAQ19TrOA2OCiJEXXFCV8b0bscjs9LYsb/I6zgiIWd3bjGPzFrH6N5tuWx4F6/jNEgqiBBlZjx8ySAccO+7qzTiq0g1zjnueXclZZWVPHTxIMw0nEYgqCBCWNc2zbnnnH7M3biHt5bs8DqOSMh4bcFWvlyfw6/OTaF721iv4zRYKogQd82J3RmZ1Ibff7SWrLxir+OIeC49u4AHZ6VxWp8Erh2lkVoDSQUR4iIijD9eNpjS8kqmvKddTdK4lZZX8uN/LKdZVCSPXjZYu5YCTAURBnq0i+XnZ/fls7Rs/rFou9dxRDzzxOcbWZWZy8OXDNYkQEGggggTN47uwcm92nL/h2tJz9Y81tL4LM7YxzNfpvO94V2YMLCD13EaBRVEmIiIMB67IpWYqAh++OYySsorvI4kEjT5xWX8+J/L6dy6Gb+7UBfEBYsKIowkxsfwp8uGsGZnHo/+Z73XcUSC5oEP15K5/yCPXZ5Ki6Y1znMm9UwFEWbG90/k2lHdefHrLXy1IcfrOCIB95/Vu3hryQ5uP703I5I0jHcwqSDC0JTzUuiT2IKf/nMFewpKvI4jEjDZecXc++4qBnVuyQ/PTPY6TqOjgghDMVGRPHHVUPKKy/j5Wyt06qs0SM45fv72Sg6WVfDYFalERerXVbB59o6bWYaZrTKz5Wa22LesjZl9amYbfV9be5Uv1PXrEM+Uc1OYvT5Hc0dIg/Tqgq18tSGHKeem0Lt9C6/jNEpeV/JY51yqc26E7/E9wOfOuWTgc99jqcF1J3VnXL/2PDxrHWm78ryOI1Jv0rPzefBfaZzeN4FrdLW0Z7wuiMNNBGb47s8ALvIwS8gzM/502WBaNo/i7jeWcbBUp75K+CssKecHry0ltmkT/nSprpb2kpcF4YBPzGyJmd3iW5bonNsF4Pva3rN0YaJti6b89fIhbMwu4MFZa72OI3JcnHPc++4q0nMKeOLKobpa2mNeFsRo59ww4BzgDjM7tS4vMrNbzGyxmS3OydFpngCnJCdwy6k9eW3BNj5es9vrOCLH7NUFW5m5Yic/Hd+HMcntvI7T6HlWEM65nb6v2cB7wEggy8w6Avi+Zvt53VTn3Ajn3IiEhIRgRg5pPzurLwM7x/PLd1ayfZ8mGJLws3Tbfn7/0VrO6Nee20/v7XUcwaOCMLNYM4s7dB84C1gNzAQm+542GfjAi3zhKLpJBE9dNYzKSsetry7R8QgJK3sLSrjj9aUkxsfw2OWpRETouEMo8GoLIhH42sxWAAuBfznn/gM8Aow3s43AeN9jqaOkdrE8fuVQ0nbnce+7K3V9hISFikrHj/6xnL2FpTx3zXBaNo/yOpL4eDKoiXNuMzDEz/K9wLjgJ2o4xvZrz0/H9+HPn2xgUJdW3DSmh9eRRGr1+GcbmLtxD3+8dBADO7f0Oo5UE2qnuUo9uP303pw9IJGHZqUxb9Mer+OI1OiLdVk88UXVEN5XnNDN6zhyGBVEAxQRYfzl8lR6tIvlzr8vI/PAQa8jiXzH9n1F/PgfK0jpGM/vLxrodRzxQwXRQLVo2oTnrx1OWXklt726hOIyHbSW0FFcVsEPXl9CpXM8d80wYqIivY4kfqggGrBeCS147IpUVmXm8ivNZy0h5P4P17A6M4+/Xp5K97axXseRGqggGrgz+yfyozOTeXdpJq/M3+p1HBHeWLiNNxZu5/bTezG+f6LXcaQWKohG4O4zkjkzpT2//2gtC7fs8zqONGJzNuTw6/dXc2qfBH4yvo/XceQIVBCNQESE8dcrUunWpjm3v76EXbk6aC3Bt353Pre/vpTk9i14+uqhNNH8DiFP/4UaifiYKKZeN5yDpRXcpiutJciy84q54eWFxDaNZNr1JxAXo4vhwoEKohHp3T6Ov105lFWZudz1xlLKKyq9jiSNQFFpOTfNWMyBg2W8NPkEOrVq5nUkqSMVRCMzvn8i908cyGdp2fzmg9U6s0kCqqLScfcby1mzM5cnrxqqK6XDjCdDbYi3rh3Vnd25B3l69iY6xDfTZPASMH/411o+S8vi/gsHMC5FZyyFGxVEI/Wzs/qyO7eExz7bQGJ8U64cqWEOpH5N/2YLL3+TwY2jezD55CSv48gxUEE0UmbGI5cOIqeghCnvryYhrqn+wpN683laFg98tJbx/ROZcl6K13HkGOkYRCMWFRnBs5OG0b9jPHf8fSnLtu33OpI0AKszc7nz78sY2Lklj1+ZSqTmdghbKohGLrZpE6ZdfwLt42K4acZiNucUeB1JwljmgYPcOH0RbWKjeXHyCJpHaydFOFNBCAlxTZlx40gAJr+8kOz8Yo8TSTjanVvM1S8s4GBZxX//6JDwpoIQAHq0i2Xa9SewJ7+UG6cvoqCk3OtIEkay86vKYW9BKa/edCJ9O8R5HUnqgQpC/iu1ayuenjSUtF35GiJc6mxvQQmTXviW3XnFTL/hBFK7tvI6ktQTFYT8jzP6JfLIJYP4ZtMebn5lsUpCarW/sJRJL37L9v1FTLv+BEYktfE6ktQjFYR8x/dGdOWPlw7m6/Q93Dh9kcZtEr9yi8q45qVv2bynkBevO4FRPdt6HUnqmQpC/Lp8RFf+fNkQ5m/eyw3TF1JUqmMS8v/yi8u47uWFbMwqYOq1wxmT3M7rSBIAQS8IM+tqZrPNLM3M1pjZD33L7zOzTDNb7rudG+xs8r8uHd6Fxy5PZeGWfVw/bRGFOnAtQGFJOde/vIg1mbk8M2kYp/dt73UkCRAvtiDKgZ8651KAUcAdZtbf973HnHOpvtssD7LJYS4a2pm/XTmUJdv2M3naQp3d1MgVlZZzw/RFLN9+gCevGsqZmhGuQQt6QTjndjnnlvru5wNpQOdg55C6u3BIJ564cijLth/gupe+Jb+4zOtI4oHisgpufmUxizP28dgVqZwzqKPXkSTAPD0GYWZJwFDgW9+iO81spZlNM7PWngWT7zhvcEeevnooK3fkcu1LC8lTSTQqh85WmrdpL49eNoQLh3TyOpIEgWcFYWYtgHeAHznn8oBngV5AKrAL+EsNr7vFzBab2eKcnJyg5RWYMLAjz0waxpqduVz74rfkFqkkGoMd+4u47Ll5rMrM5emrh3Hp8C5eR5Ig8aQgzCyKqnJ43Tn3LoBzLss5V+GcqwReAEb6e61zbqpzboRzbkRCQkLwQgsAZw3owLOThpO2K5+rXljA7lwNy9GQrdmZyyXPzCMnv4RXbxzJudqt1Kh4cRaTAS8Bac65v1ZbXv2TdzGwOtjZpG7O7J/IC5NHsHVvIROf/prVmbleR5IA+CZ9D1c8v4DICOPtH5zMibrOodHxYgtiNHAtcMZhp7T+ycxWmdlKYCzwYw+ySR2d1ieBt39wMpFmXP78fD5bm+V1JKlH7y/L5PqXF9K5VTPevf1k+iRqbKXGyMJ5TuIRI0aj3VMSAAANRklEQVS4xYsXex2jUcvOK+amGYtZvTOXX5/XnxtHJ1G1kSjhyDnH1Dmbefjf6xjVsw3PXzuCls2ivI4l9czMljjnRhzpebqSWo5L+/gY/nHrKM7qn8jvP1rLbz9YQ3lFpdex5BhUVjoe+GgtD/97HecN7siMG0eqHBo5FYQct+bRTXh20nBuPa0nry7Yyo0zFutaiTBTXFbBXW8s4+VvMrhpTA+evHIoTZtEeh1LPKaCkHoREWHce04KD18yiHnpe7js2fns2F/kdSypg805BVz09Df8a9Uuppybwm/O70+EpgkVVBBSz64a2Y3pN4xkZ+5BLnp6nua5DnEfrdzJhU99w+68Yl6+4QRuPrWn15EkhKggpN6NSW7He7efTLPoCC5/fj5T52yisjJ8T4ZoiErKK/jdB6u58+/L6JPYgll3n8JYDbonh1FBSED0bh/HzDvGcEa/9jw0a13VXNd5uqguFGzfV8T3npvPjPlbufmUHvzj1pPo1KqZ17EkBKkgJGBax0bz3DXDeejiQSzK2MeEx+fyeZqul/DSJ2t2c94Tc8nYU8jUa4cz5bz+REXq14D4p0+GBJSZcfWJ3fjorjF0iI/hphmL+e0HqzWVaZCVVVTyh4/WcsurS0hqF8u/7j6FswZ08DqWhDgVhARF7/ZxvHfHyXx/TA9emb+VC5/6mnW787yO1Shszingiufn8+LXW5h8Unfeuu0kurZp7nUsCQMqCAmapk0i+fX5/Zlx40j2FZZx4VPfMGNeBuF8NX8oKymv4PHPNjLh8blszC7gqauHcv/Egbq+QepMQ22IJ/YUlPDzt1Ywe30OpyS3474LB9AroYXXsRqM+Zv2MuX9VWzOKeSCIZ34zfkptI+L8TqWhIi6DrWhghDPOOd4dcFWHv3Peg6WVXDD6CTuHpdMXIyGdzhW+wpLefBfabyzdAdd2zTjDxcN4rQ+GhZf/pcKQsLGnoISHv3Pev65ZDttY5vyiwl9uWxYF13NexScc7y1ZAcPz0ojv7icW07tyV1nJNMsWruT5LtUEBJ2Vu44wH0z17B02wGGdG3FfRf0Z2g3zTx7JOnZBUx5bxXfbtnHiO6tefDiQfTtoOG5pWYqCAlLzjneX57Jw7PWkZ1fwqXDuvDLc/pq/7kf2/YW8cyX6byzdAfNoiK599wUrhjRVVteckR1LYgmwQgjUldmxsVDuzC+fweenp3OS3O38PGa3dxyak+uHdWd1rHRXkf03OacAp6evYn3l2cSGWFcNbIbd52RTEJcU6+jSQOjLQgJaRl7CnlwVhqfrs0iJiqC7w3vyk1jepDULtbraEG3ISufp75I56OVO4luEsGkE7tzy6k9SYzX1pUcHe1ikgZlQ1Y+L87dzPvLdlJWWcn4lERuPrUnI7q3bvAz2K3ZmctTX6Tz79W7aR4dybUndefmU3rSroW2GOTYqCCkQcrOL+aVeVt57dutHCgqY0jXVtxySk/OHpBIkwY0plBJeQWz1+Xw1uLtfL4um7imTbh+dBI3ju6h3Wxy3FQQ0qAVlZbzzpIdvPT1FjL2FtGldTOuPKErEwZ2oHf78DyDp7LSsWDLXmYu38msVbvIKy6nXYtorh2VxPWjkzT9p9QbFYQ0ChWVjs/Ssnhp7hYWZuwDoGdCLGf178DZAxIZ0qVVSJ/V45xj7a48Pli+k5nLd7I7r5jY6EjOHtCBiUM7M7pX2wa1ZSShIWwLwswmAI8DkcCLzrlHanquCkKq251bzKdrd/PxmiwWbN5LeaWjQ3wM4/sncvaADpzYs01IDG1dWFLOyh25LMrYx4crdrIxu4AmEcZpfRKYOLQz41MSdYGbBFRYFoSZRQIbgPHADmARcJVzbq2/56sgpCa5RWV8vi6Lj9fs5qsNORSXVRIf04SRPdqQ0jGefh3iSekYR/e2sUQGcAvDOceWPYUs23aApdv2s2zbAdbtzuPQBHsjurdm4tDOnDeoI210bEGCJFyvgxgJpDvnNgOY2ZvARMBvQYjUpGXzKC4Z1oVLhnXhYGkFczfm8MnaLFZsP8Ds9TlU+H5DN4uKpE+HOFI6xPmKI44OLWNoHt2E2KaRNIuKrPUsKecceQfLySkoYW9BCXsLS9lbUEJ2fgmrM3NZtv0AB4rKAIhr2oTUbq24c2xvhnZvTWqXVjrgLCEt1AqiM7C92uMdwIkeZZEGoll0JGcN6PDfCXKKyypIzy5g7a481u3KJ21XHv9Zs5s3F23/zmvNoHlUJLFNmxDbtAnNoyNpHh1JYUkFewtL2FtQSrmf+bbNoHdCC87qn8iwbq0Z1r01vRJaBHRrRaS+hVpB+Pu/53/+7zOzW4BbALp16xaMTNLAxERFMrBzSwZ2bvnfZc45svJKSNudx/7CUgpLKygsKaeopJzC0gqKSsspLKlaVlhaTseWMQzsHE/bFk1pGxtNQlxT2sY2pW2LaNq2iKZN82gdXJawF2oFsQPoWu1xF2Bn9Sc456YCU6HqGETwoklDZmZ0aBlDh5a6KlnkkFD7E2cRkGxmPcwsGrgSmOlxJhGRRimktiCcc+VmdifwMVWnuU5zzq3xOJaISKMUUgUB4JybBczyOoeISGMXaruYREQkRKggRETELxWEiIj4pYIQERG/VBAiIuJXSA3Wd7TMLAfYehw/oh2wp57iBEO45QVlDpZwyxxueaFhZe7unEs40ovDuiCOl5ktrsuIhqEi3PKCMgdLuGUOt7zQODNrF5OIiPilghAREb8ae0FM9TrAUQq3vKDMwRJumcMtLzTCzI36GISIiNSssW9BiIhIDRpdQZjZ98xsjZlVmtmIasuTzOygmS333Z7zMmd1NWX2fe9eM0s3s/VmdrZXGWtjZveZWWa19/ZcrzP5Y2YTfO9jupnd43WeujCzDDNb5XtfQ3KCdjObZmbZZra62rI2ZvapmW30fW3tZcbD1ZA5ZD/HZtbVzGabWZrvd8UPfcuP631udAUBrAYuAeb4+d4m51yq73ZbkHPVxm9mM+tP1ZwZA4AJwDNmFhn8eHXyWLX3NuRG6/W9b08D5wD9gat87284GOt7X0P1FMzpVH0+q7sH+Nw5lwx87nscSqbz3cwQup/jcuCnzrkUYBRwh+/ze1zvc6MrCOdcmnNuvdc5jkYtmScCbzrnSpxzW4B0YGRw0zUYI4F059xm51wp8CZV768cJ+fcHGDfYYsnAjN892cAFwU11BHUkDlkOed2OeeW+u7nA2lAZ47zfW50BXEEPcxsmZl9ZWaneB2mDjoD26s93uFbForuNLOVvk33kNqd4BNO72V1DvjEzJb45msPF4nOuV1Q9csNaO9xnroK9c8xZpYEDAW+5Tjf5wZZEGb2mZmt9nOr7S/CXUA359xQ4CfA380sPjiJjzmz+VnmyWlpR8j/LNALSKXqff6LFxmPIGTey6M02jk3jKpdY3eY2aleB2rAQv5zbGYtgHeAHznn8o7354XcjHL1wTl35jG8pgQo8d1fYmabgD5AUA78HUtmqv7K7VrtcRdgZ/0kOjp1zW9mLwAfBTjOsQiZ9/JoOOd2+r5mm9l7VO0q83d8LdRkmVlH59wuM+sIZHsd6Eicc1mH7ofi59jMoqgqh9edc+/6Fh/X+9wgtyCOhZklHDrAa2Y9gWRgs7epjmgmcKWZNTWzHlRlXuhxpu/wfTAPuZiqg+6hZhGQbGY9zCyaqoP/Mz3OVCszizWzuEP3gbMIzffWn5nAZN/9ycAHHmapk1D+HJuZAS8Bac65v1b71vG9z865RnWj6j/sDqq2FrKAj33LLwXWACuApcAFXmc9Umbf96YAm4D1wDleZ60h/6vAKmCl7wPb0etMNeQ8F9jgez+neJ2nDnl7+j6vK3yf3ZDMDLxB1S6ZMt/n+CagLVVn1Wz0fW3jdc46ZA7ZzzEwhqpdoiuB5b7bucf7PutKahER8Uu7mERExC8VhIiI+KWCEBERv1QQIiLilwpCRET8UkGIiIhfKgiRGviGgK/zxVBmdr2ZdTrGddU4pLuIV1QQIvXneuCYCoLah6EX8YQKQqR2Tcxshm8Ez7fNrLmZDfeN+LvEzD42s45mdhkwAnjdN5lMMzP7rZkt8g1aONU3HIJfLgyHoZeGTwUhUru+wFTn3GAgD7gDeBK4zDk3HJgGPOice5uqgR0nuarJZA4CTznnTnDODQSaAed7808QOTYNcjRXkXq03Tn3je/+a8CvgIHAp74NgkiqxuzxZ6yZ/QJoDrSharykDwMbV6T+qCBEanf4YGX5wBrn3Em1vcjMYoBngBHOue1mdh8QE5iIIoGhXUwitetmZofK4CpgAZBwaJmZRZnZAN/384E43/1DZbDHN4nLZcEKLFJfVBAitUsDJpvZSqp2Ez1J1S/7P5rZCqqGVT7Z99zpwHNmtpyqodlfoGp46Pepmm+iRmZ2sZntAE4C/mVmHwfg3yJyVDTct4iI+KUtCBER8UsHqUWCyMyeBkYftvhx59zLXuQRqY12MYmIiF/axSQiIn6pIERExC8VhIiI+KWCEBERv1QQIiLi1/8B1ldq/PaHNkYAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"cost_func=[]\\n\",\n \"for beta_1 in beta1:\\n\",\n \" cost_func.append(lr_cost_func(np.array([beta0, beta_1]), X_, y) )\\n\",\n \"\\n\",\n \"plt.plot(beta1, cost_func)\\n\",\n \"plt.xlabel('beta_1')\\n\",\n \"plt.ylabel('J(beta)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Analyzing both at the same time\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"beta0 = np.arange(-5, 7, 0.2)\\n\",\n \"beta1 = np.arange(-5, 7, 0.2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"cost_func = pd.DataFrame(index=beta0, columns=beta1)\\n\",\n \"\\n\",\n \"for beta_0 in beta0:\\n\",\n \" for beta_1 in beta1:\\n\",\n \" cost_func.loc[beta_0, beta_1] = lr_cost_func(np.array([beta_0, beta_1]), X_, y) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"betas = np.transpose([np.tile(beta0, beta1.shape[0]), np.repeat(beta1, beta0.shape[0])])\\n\",\n \"fig = plt.figure(figsize=(10, 10))\\n\",\n \"ax = fig.gca(projection='3d')\\n\",\n \"ax.plot_trisurf(betas[:, 0], betas[:, 1], cost_func.T.values.flatten(), cmap=cm.jet, linewidth=0.1)\\n\",\n \"ax.set_xlabel('beta_0')\\n\",\n \"ax.set_ylabel('beta_1')\\n\",\n \"ax.set_zlabel('J(beta)')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It can also be seen as a contour plot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'beta_1')\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"contour_levels = [0, 0.1, 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 5, 7, 10, 12, 15, 20]\\n\",\n \"plt.contour(beta0, beta1, cost_func.T.values, contour_levels)\\n\",\n \"plt.xlabel('beta_0')\\n\",\n \"plt.ylabel('beta_1')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lets understand how different values of betas are observed on the contour plot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"betas = np.array([[0, 0],\\n\",\n \" [-1, -1],\\n\",\n \" [-5, 5],\\n\",\n \" [3, -2]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [0 0]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-1 -1]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-5 5]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [ 3 -2]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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1vcqcZZutzmJe5gL1l+/HX+nN5q1EMDR+MZws4lVBKicFiptxooNyoR28xYbJYMEkrJqsFk9WCVUrcNW54aNxw17ih1WjwcnMnwN2TQA8vfN090TTRNcf1IaVkf9lBFmYv5lR1BpFeEVzTZiI9g7o1qTXV0pJv+//FfOzsxtym9+DZlKgTEOurUydYsQKGDoXq6j9f+/lnuOEG+PZbcGtaT5qKoigXKlm7xdaxQAQgguci3Nu5uqTflZvKWXhmCesLNuHp5snVrScwMnJos5mJtkpJXk0F6ZUl5FSXk6evJLemktyaCvL0FZTW6qkwGjBaLY0aRyME/u6e6Dy8ifT2J9LHn0ifAKJ8/In0DiDOP4gYvyA83Zp2XBFC0C0oha66Luwu3cf8rAW8n/oh7f2TuDbmahL84l1dIgDCLQKCv0GWP4SseB5pyUf4PdCkAn9z1LRfnc7Wpw8sXgyjR9va5/3R99+DTgeffALqRacoiuJSUr/MdjS4Nh4RNNMWEpqAWkstK/NWsyxnBSZpZljEJYyPvrzJHvEtpSRPX8mhkjyOluWTVlHEqYoS0iuLMVjMv3+dAEK9fInyCSDOL5ieoT4EengT6OFFgLsXgR5eeGvdf5+B/u2HEGC22maqjVYLZqsVvcVEpdFwdkbbQIXJQGmtnnx9JXuKssnTH8Vktf4+tkYI2vjqiA8IId4/hA66cDoHR5LgH4KbRuOC37W/JoSgV3B3ugelsL5gEwvPLOb5w6/SL6QPV7e+knCvMFeXiND4gu5DZMXzUP0x0loIAS+qExMbQf3O/a9hw2DuXJg4Ef7wPzMAM2ZASAi8+qpralMURVGQ1bNtJ72597JtptIEuLokpJTsKt3DNxnzKDaW0CuoO5PaXEWUd9PqCFVlqmVPUTa7i7I4VJLHwZJcimtrAFtgbn02tPaLiCXeP4T4gGBa++oI8/LDw0mfzFqlpKS2hpyaCk5XlHCysoj0imJOVhSzJe/U77Ph3m7udAyKoHNQJN1Doukd3oYoH9e/FgDchBvDIi7hotB+/JSzkhV5q9hdsocxrS5jbNQoly/zEUILAS8hNWFQ/SHSWgq6d5v8oUZNlVoz/Ve+/BJuvvn81958Ex49bycoRVGU81JrphtPSnn2aPCPwHO47WjwJvCXf74hn1mnv+Vg+WHa+LRmauwUOgQ0jSUnerOJHYWZbMvPYFtBBodL87BIiUYIEgNC6RwUSefgSDoHR9FRF46PtmnvDbJYrZysLOZQSR6HSnM5VJLHkdJ89BYTAG18dfQLj6VfRCwXRcQR7t00TiwsMZYyN/MHthZvJ9QjhKlxU+ge1DT6n8vqOcjKl2yt84JmIDRN81MUV1AbEO3h3XfhoYfOf+2zz+DWW51Xi6IozZoK040jpURWvgo1s84eQPESwsXdEsxWMz/lrmRx9jLchJaJbcYzPGKIy7s45NZU8EtOGmuzU9lakEGtxYy7RkO3kGj6hMXQNzyGbiHR+Lo37eBcVxarlWPlBewoyGRHYSbbCzIpNxoA6BwUybDoJIa0SqRzUKTL1wYfrTjOrNPfkK3PoXdQT66Pm0ywh+vb70r9cmT5I6Btjwj+AqFxfU1NgQrT9vLMM/Dyy+f+ukYD8+bZloMoiqL8AxWmG84WpF+Amm/BZxrC/98uD0XpVaeYkf4l2foc+gT34vrYyQR56FxWz6mKYpZlHmHVmRMcKcsHIMZXx9DoJC6JSqBXWBu8tU2v77YjWKXkaFk+G3PTWZeTxt6iM0ggwtuPoa2SGBPTkT5hMS5bb222mlme+zOLspfhJtyYEjuJS8IGufw1LWvXI0vvse1DCP6qyR545EwqTNuLlHDPPfDRR+dec3eHn36CESOcW5OiKM2OCtMNI6UVWfEi6L8F31sRfo+6NHSYrCYWZS9lWc5KAt0Duant9S77uD6nuoKfso6wNOMIh0vzEECP0NYMi05iaKtEEgNCXR7QmoJiQzXrc0+yLjuVDbnp6C0mwr38GBPTkbGxnega3Molv0/5hgI+PzWboxXH6BKYzC1tbyTE07UBVtZutvWi1sYhgmdd8IFahWl7slrh+uvhu+/OvebjA2vWQP/+zq9LUZRmQ4Xp+rMF6edAPw98b0P4PeLScHiq+jQzTn7JGX02F4cNYErMNfhqfZxag95sYmXWMean72d7YSYAKcFRjI3pxOiYjk1mA15TVWM2si4njWUZR9iQexKj1UKMr46r4lOY2LYrkT7OXS9slVbWFWxgbuYPaISGKTGTGBw20KWvc1m7BVl6J2hjEUGzEG4hLqvF1VSYtjeTCcaPh+XLz70WFAQbNkCXLs6vS1GUZkGF6fqxBelnQD8ffO9A+D3ksoBhtppZnLOMJdnLCXQP4Ob4qXTTpTi1hqOl+cxL38ei04eoNNUS6xfEhLZdGBvTiTj/C3v2sKEqjAZWnTnBotMH2VqQgUYILolK4JqEblwSlYjWictACgyFfJb+JccqT5AS2Jlb46e5dNmQrP31bKBujQiajXALdVktrqTCtCPU1NhOQ9y8+dxrkZGwZQvEN43G7IqiNC0qTNedlNLWA1f/Hfje5dJDJYpqi/kwbQZpVScZGNqf62Mn46v1dcrYFquVNdkn+OzYdvYWZ+OhcWNUmw5ck9CNPmExagmHHWVUljL/1H4WpB+gwFBFpLc/U9v1YkpCd/w9nNMxxiqtrM1fz9ysH/DUeHJnwi2k6Do7ZezzkbXbkGV3gFsbRPDXF+SmRBWmHaWsDIYMgX37zr0WH28L2lFRzq9LUZQmTYXpurNWvgXVM1y+tGNP6T4+S/8Ss9XCzfFT6R/SxynjGswmFp4+yMxj28moKiXGV8cN7XoxIa4LOk9vp9RwoTJbrazLSWX2iV1sLcjAT+vBNQndual9b6ctocnW5/BB6qec0WdzeavRXNV6nMs6xNhmqG8H9w62JR8a5zxINhUqTDtSfj4MGgSpqede69zZtuQjWH3spijK/1Nhum5k9efIyjfAezIi4AWXBGmz1cz3WQtYkbeaWJ823JN0J5Fejj9hscJoYNaJXcxO3UVJbQ0pwVHc1qEfl7Zu3+RO+rNKSWVtLaUGPWV6A2UGAzUmE0arBZPFgslixWgxI8F2GqKbBg8326mIXlotgV5e6P7ww91JB8LUx6GSPD47to0VWUcRCC6PTebOjv1JDHT8kodaSy1zMuayvnAT7fyTuDvhNoJdtDlRGtYiy+4Bj36IoE8RomW0VKwLFaYdLSMDBgyA7Oxzr/XrZ9uU6HthPcEpivLXVJj+Z7JmAbLi3+A1ChH4jkv6SBfXlvBB2iekVaUzLHwIU2In4aFxbEu5SlMtXx3fyRfHd1BhMnBJVAK3dehH33DXLeWwSklWeTlpJcVklZeTU1lJdmUF2RUV5FRWUqLXY7VjfvD38CTK349W/gFEBwQQ7e9PdEAACUHBJAQH46l13YHNZ6rK+OLEDr4/uR+DxcTlscncmzyQ+ADHb8zbUrSVL0/NwV3jzr8SbqOLLtnhY56P1C9Elj/h0v83XUGFaWc4etQ2Q11cfO61ESNg6VLwdO2RoYqiNA0qTP89aViNLLsXPPq7bPbrWMUJpqd+jNFq5Nb4afQN6e3Q8apMtcw+sYuZx7dTbjQwPLod93ceRKcgx8+C/1G10cjB/HwO5OdxoriYE8XFpJUUYzCbf/8aTze3syE3gFYB/oT5+KLz8rbNLHt7ofP0wtfDAw83N9sMtJsGd40bQoDJYsVktc1UmyxW9GYT5YZaygx6Sg0Gyg0GSvQ15FT8f2Avr639fWyNEMQEBtIuJIR2IaEkh4fTPSqKcF/nnm5YbKhm5rHtfJ26m1qrmXGxnbkneYDDN4Dm6HOZnvoJ2focJrWZwJioy1zykCWrv0BWvu7ST42cTYVpZ9m1y7aGuqrq3GsTJ8LcudAEP75SFMW5VJj+a9K4E1lyk0vXZa7J/4U5GXMJ9wzj/nb/Itq7lcPGMlutfJO2m+mHNlNq1DOkVSL3dx5El2Dn7LfJr6ri16xMdufksCc3hxPFxb/PMkf4+pIUEkq7kBCSQkJICg4hRqcjxNvbqeGpymjkTEU5J0tKOFFcTOrZH6fLSrGcrTXK358ekVH0aNWKvtGt6RgW5pQaiwzVzDi6jTlpuzFbLUxs25WHUgYT6uW4163BUsvM9K/YXrKTPsG9uD3+JjzdnD9ZZ618G6o/Bd9/ofF/wOnjO5sK0870yy8wahT84Un6d7feCjNmwAXwBKcoyl9TYfr8pPkksvga0IQgQuY6vWOA2WpmTsZc1hasp7uuK3cm3IKPA3tHb8k7xUt7V5NaXsRFEXE8knIJXUMcF9wBas1mdmRnsynjNBszTnPi7Kepfh4edIuMontUFN0jo+gaGUmQd9Pe4FhrNnOksIC9uXnsy8tlT24OOZWVAIT6+HBxbByDYmMZGBNLiI9je4AX6Kv4+MivfJu2B2+tO/d1HsQNST1x1zhmAk1KyfLcn5mXtYBYnzY82O4ep6+jtnXaeRr08xEBLyN8Jjl1fGdTYdrZFi+Gq64Ci+Xca489Bm+84fyaFEVpMlSYPpe0liCLJ4GsRgR/j9C2cWJ1UG2uZnrqJxyuOMqYqMuY1GYCGuGYjX6ZVaW8unctq7NP0MZXx5PdhzEiup3DZlIramtZl36SFampbMrMwGA24+HmRq9W0Vx8Nmy2Dw1tchsbGyK3spJfszLZmHGazRkZlBoMCCAlIpLLkpIYlZRETKDjejanlRfx8t41bMpLJyEghKe7j+DiKMe1yd1XeoAP02bg5ebJg+3uId6vrcPGOh8pzcjSO8C4FRH0OcKz5R5ap8K0K8yaBdOmnf/a66/D4487tRxFUZoOFab/TEojsmQamA7Yeth6dHdqbUW1xbx1/H3yDPnc0nYqg8IGOGScWouZj4/8yqdHt6LVaLir40Xc0qEvnm7231BXbjCw+uRJVqSdYHNGBiarlSg/P0YkJDI4Lo6+rdvg4+7YzZSuZpWSQwX5bMrIYPXJNA7k5wOQHBbOZUlJjE5qR9sg+3/6IaXkl5w0Xt67hoyqUoZHJ/Fcj0tp5euYdnpnarJ5+8R0KkwV3JN4h9OPtJfWSmTJNWApQITMR2idG+idRYVpV3n/fXjgL9YRzZgBt93m3HoURWkSVJj+f7aPiv8N+oW2zgDeY51aV0Z1Jm8dfx+j1cj9SXfTKbCDQ8bZXpDB0ztXkF5ZwuUxnXii2zC7H1dtsVrZkpnJD0cOs+pkGkaLhWj/AEadnZHtGhmF5gJeZnimvJyVJ9NYmXqCPbm5APSMasXE5GRGJ7XD385NAmotZr46sZPphzajEYIHu1zM1KReDvkEoNxUztvH/8vp6kymxV3P0IjBdh/j70hzFrJ4ImgCESHfIzSuO7HRUVSYdqXnnoMXXzz314WwbUic1LLXGCmKci4Vpv+frPoMWfUf8L0bjf/9Tq3pYPlh/nviY3y03jzS/gHa+ETbfYyyWj1v7F/H9+n7aeOr48Vel9n9Y/+MsjLmHz7Ej0ePkFtVhc7Liyvad+DKjp1IiYi4IDot1FduZSVLjx/jhyOHSSspwUurZVRSEld36kzf1q3t+nuWVVXGs7tXsjE3nZTgKF7pPdohXVoMFgMfpH3K/rKDXNFqDBNbj3fqn7007kaWTAWPnoigmS2uB7UK064kJdx3H3zwwbnX3N1tLfMuvdT5dSmK4jIqTNtIwy/IsjvP9qt916l/8W8r3sEnJz+nlVckj7S/3yGbt9Zmp/LUzuWU1NZwS/u+3Nd5EN5a+yytkFKyOzeHmbt3s/pkGkIIBsXGcnWnzgyLj3dpL+bmRErJ/rw85h85zNLjx6gyGukQGsqtPXoxtn17POzUgUtKybLMI7y0ZzXlRgN3durPPckD7b5B0SItfHVqDusLNzE4bCA3t53qsLX/5yP1i5Hlj4L3tWgCX3DauM6gwrSrWa0wdSp8882513x8YPVquOgi59elKIpLqDAN0nwKWXwVuMUiQr5DCC+n1bK+YBNfnJpNO/9EHmx3L7527tihN5t4bd9avknbQwddOG/2HUtyUKRd7m22WlmVlsbMPbvYl5eHzsuL61K6cl1KCpF+9l02cqExmE0sPX6cz/fs5kTrEiAXAAAgAElEQVRxMRG+vkzt1p0pXVII9LLP67OsVs9Le1ez6PQhuga34t3+44j1t++6bSklC7MXsyh7Gb2De/KvhNvQapz3cGWt/A9Uf9biOnyoMN0UmEwwYQIsW3buNZ3Odux4Sorz61IUxeku9DAtrTXIkqvBUogIXYRwc2w7uD9ak/8Ls05/Q0pgZ+5Lusvu/XmPlubzwNZFpFUUc0v7vjycMtguGwxNFgs/HjvKRzu2k1leTmxgIDf36MlVnZJb/EZCZ5NSsikjg8/27GJLZiY+7u5cn9KV23r2sluLveWZR3lq5wos0spzPUcyIa6L3T+ZWZG7im8zv6dnUHfuSbzDaYFaSguy9HYwbkOEzEO4d3bKuI6mwnRTodfDZZfBxo3nXouIgM2bITHR+XUpiuJUF3KYllLaPgY2LEUEfYHwdEznjPP5OW8NczLm0l3XlXuT7sTdjkeDW6XkqxM7+c/+X9B5ePOffpczMLLxXQ3MViuLjh7lgx3byCwvp3N4BP/q04cR8QktopVdU3e0sJBPdu1g2fHjeLu7c0PXbtzWsxfBdujBnVNdwSPblrC9MJMxMR15udcoAjzs+wnN6rx1zM74lm66FO5NugsPO77m/460liCLJoDQIEIWtogNiSpMNyXl5bZTEvfuPfdaXJwtUEfbfxOMoihNxwUdpmu+Q1Y8h/C7H+F3t9PGX577M99lzqdXUHfutvMsXWltDQ9vW8qG3JMMj07itT5jCPZs3AymxWpl8bFjTN++lYzycpLDwnmgf3+Gto1XGwpdIK2kmP9u28ZPJ47j4+7O1G7dua1nT3RejQvVFquVT49u5f1Dmwj39mP6gCvpFmLfDLAufwNfnv6aLoHJPNDubjw0ztkYKI37kSVTwHMAQvcJwolrtx1BhemmpqAABg2CEyfOvZacbJu5DnbuSUaKojjPhRqmd279ynbCoUd/RNAMp/3l+lPOSuZm/UCf4F7clXCrXYP0gZJc7t68gCJDNU91H851iT0aHXa3ZmXy0ob1HCsqolNYGPf368/w+AQVopuAE8VFfLB9Oz+dOE6Apyf39+vPdSldcW/kRsX9xTnc9+uP5Osreab7CKbY4XX0R7/tE+gU0IGH2t/rvEBd8w2y4gWE34MIv7ucMqajqDDdFGVmwoABcObMudf69oU1a8DPz/l1KYricBdmmO4hd6wIBmmyrZN20lHhq/LW8nXGd/QL6cOdCbfgJuzXPWHR6UM8uXM5IZ4+fDjwKlKCoxp1v+yKCl7btJHlqSeI9g/g8YGDGNPOcScjKg13tLCQVzdtYEtmJu1CQnhm8BAGxMQ06p5ltXoe3raE9bknuSa+G8/1HGnXA302FW7hs/Sv6BKYzIPt7nHKGmrbsq6HwbACETwH4dHT4WM6igrTTdWxY7YZ6qKic68NH27brGjnJvKKorjeBRmmu0fKHSt0Z/9Cdc5/+pairXxy8nN6BnXn3qQ77RakzVYrb+5fx+fHd9A3LIbpA64kxMu3wferNZv5dNdOPtm1E4C7evfhtp498bJTGz3FMaSUrD55klc2biCropzLEpN46uLBRAc0/KRDi9XKu4c28vGRX+kR2poPB0wg3Nt+E2u/FGzki1Oz6R/ShzsTbnVK2zxprUIWjwdpPvsg3TzXT9f1fbt5L2Zpjjp0gJUrwf887YzWrIEpU8Bsdn5diqIo9mYtQ/jd67Qgva/0AJ+lf0XHgA78K/F2uwXpCqOBWzbM4/PjO7ghqSezhlzbqCC9/UwWo7/5mve2bWVY23hWT53GvX37qSDdDAghGJmYyKqpN/LwRQPYcPoUl349iy/37sFitTbonm4aDY+kXML0i67kaGk+41d9wcGSXLvVPCT8Yq5pcxVbi3cwJ2MuzphEFRo/hO4dsBYgy592ypiupMK0K/TsaTu45Xwz0AsXwh132A5+URRFac60CeB7p1OGOlGZyvS0T2jjHc2DSXfbrYNBdnU5k9bMZltBBq/1Hs3zPS9t8KEbFQYDT61ZzbU/zMdssTJ7wlVMHzO2UbOaimt4arXc3acvq6ZOo090a17asJ6J8+ZyrKiwwfccHdORH0bciJvQcO3aOazNTrVbvWOiLmNU5EhW56/jx+yldrvv3xHuKQj/h6F2FegXOGVMV1Fh2lUGD4b58+F8Gxi++AIefVQFakVRmjfhjbDjeuW/klVzhrePTyfYI4hHOjyAt7bxLczAttFwwuqvyNNX8uUlk5mU0K3B9/o5LZWRX89i3uFD3N6zFytvmMrAmFi71Km4TnRAAJ+PG897l43mTEU5V3z7De/8uoXaBn7C3EEXzsIR00gMDOXOzT8w68ROu9QphODamKsZFHoRP2YvYU3+L3a57z/yuQk8+iErX0aaM5wzpguoMO1Kl18OX311/mtvvw2vv+7UchRFUZqb4toS/nPsPTw1njze4SEC3e0zy7sxN50pa+fgqdEyf/hULoqIa9B9qoxGHv55BXctW0qojw8/Tp7CE4MuxrsFH7pitlipMRgprdJTUllDlb4Wo8ncYj/qF0JwRYcOrJo6jSvad+CDHdsZ9903pBYXN+h+Yd5+fDv0Ooa2SuTFPat5Y986u/zeCSG4Jf5Geui6Mfv0t+ws2d3oe/7zmBpE4BuAFln+GFJaHD6mK6gNiE3B9Olw333nv/bxx3Cncz4mVRTFcS7IDYgOfs82WAy8eOR1imqLeabTE7TxsU+v3iWnD/Po9qUkBYby5eDJhDVwM9jhggLuXb6MzPJy7unTl7v79G10OzVXMZktZBeXk1FQSmZBGQVlVZRW1lBcWUNJpZ6SymqqDEaMJgvWv8kVnu5ueHm4E+znQ5C/NyH+PgT7+xAS4EubMB2x4TpiwoPw9XJOGzdHWH/qFI+uWkm1ycRzlwxhUnLnBnVnsVitvLBnFd+k7WFi2xRe6T0arR0O7TFajbx29G0yqjN5utNjxPs1/qChfyL1i5HljyL8n0D43uzw8exFdfNobl54AZ5//txfFwK+/RYmT3Z6SYqi2I8K0/ZllVbeT/2IfaUHeKT9/XTRJdvlvt+m7eGZXSvpGx7DpwMn4t+A0+mklMzat5fXN28i2Nubdy8bRd/WbexSnzMUV1RzOCOfwxl5HM0q4HReCTklFVis/58XvNy1BAf4EOLvQ5C/7Wc/b0883d3w0Grx0LrhrnVDIwRGswWT2UKt2YzJbKGm1kRJZc3vP4oraqjU1/6phtAAH2LCg2jfOozk2EiSYyOICQtCo2keLQMLqqt4aOVKfs3KZGy79rwybDj+DejUJaVk+uHNvH9oEyNbt+P9/lfiYYcHsgpTJc8dfgWz1cQLnZ8m2MOxbSullMiyf0HtZkToYoQ23qHj2YsK082NlPDAA/Df/557Tau1bVi87DLn16Uoil2oMG1f32ctZGnOcm6IvZaRkcPscs8vj+/g5b1rGNIqkQ8HTGhQv99SvZ7HV69iTfpJhraN582Rl9rlGGpHkVJyKr+EHcey2JmaxeHTeeSXVQGgEYK2kcEkRIUQEx5ETLiO2PAgYsJ0BPp62bUXtt5o4kxhOZmFpb/Pfp/OL+H4mUIMRtv6Yz8vDzrGRNA9oRV9O8TQJS4Kd23Tnem3WK18smsn7239leiAAN4fNYaukZENutdXJ3by0p7VjXpt/q+smmxePPwqUd5RPNXxUTzdHNuWV1oKkEVjQJuACP7GKfspGkuF6ebIaoVp0+Drr8+95u0Nq1fbDn1RFKXZUWHafn7rJT00fDDT4q63S6j79OhW3tz/S6Nm/w4V5HPX0qUUVFfxxKCLmdate5M8fKW0Ss+Ww6fYfjyT7ccyKSyvBqBVSAApbaNIjo2kU0w4HVqH4+Pi5RZmi5VTeSUcybTNlB/KyON4ViFWKfH2cKdHYjR9O8QwIDmO+MgQl9b6V3blZPPAiuUUVlfz/JChXNslpUH3+e1Tk0GRbfl44ES87dBKcU/pPt478SF9gntxd+LtDn+9NrflHipMN1cmE0ycCEuWnHstMBA2bICuXZ1fl6IojaLCtH2kVaXz6pE3SfRL4LEOD9rlRLcPDm/m3YMbGRvTibf6Xd6g1neLjx3lidWrCPbx4aMxlzd4BtJRSqv0rN2byuo9J9iVegarlOj8vOnTvg1928fQp30bWoc2j4M1KmsM7Eo9w/Zjmew4nsWp/BIA2kYEM7xHEpf2bE9CVNMK1mUGPQ+uWMGGjNNM7tyF54cMbdAD2w/pB3hixzL6hMcw8+JJ+Ggb/7CzLGcF87IWcFXrcYyPvrzR9/s7f17usQyhbdodbVSYbs4MBhg1CtavP/daRARs2gRJSU4vS1GUhlNhuvHKTRU8c/Al3DRuvJj8NP7ujT8l7rcZ6fFxnXmzz1jc6rnByyol7279lQ93bKdvdGumjxlLqI9Po+uyB6PJzNr9aSzbdoTtxzOxWCVxEUEM757E0K6JtG8d3mzWIP+d/NJKNhxMZ83eVHaffVBIbBXCqN4dGNcvmZCAhh+wY08Wq5V3t/7KRzt30De6NR+NvZygBiwBWnL6MA9vX0LfMFugbuxhP1JKPj35Ob8Wb+fh9vfRVdelUff7x/Es+ciiUeDeHRE0s0l+evMbFaabu4oKGDoUdp+ndU1sLGzZAtH22bmuKIrjqTDdOFZp5fVj75BWeZJnk/9NnG9Mo+85+8QuXtiziitik3mr7+X1DtJ6k4lHVq1kRWoq13TuzAtDhtllc1hjZRaUsXDLARZvO0JZlZ5WIQFc2rM9l/ZsT7vo0CYdXhqrqLyaNXtT+Xn3cfal56DVaBjSNYGJg1Lo3a5Nk/hvX3zsKI+vXkWUnx8zx11JQnBwve/x46mDPLp9KYOjEvhk0MQGHyT0m1pL7e+dcV7q/AzhXmGNut8/kdWzkJWvIHTTEV6XOnSsxlBhuiUoLISLL4Zjx8691rEjbNwIoaHOr0tRlHpTYbpxFpxZzKLspdwWfxMXhzV+78iSjMM8uHUxw6OT+HDAVfVuOVZUU8Oti3/kYH4+/754MLd07+HSoGa1SjYcTGfexn1sP5aJm0YwOCWBqwem0Kd9TIuYga6v0/klLNh8kKXbj1BebSAmXMfEgSlceVFn/Lwdu9nun+zJyeGOZUswmi3MuOKKBnV7+S5tL0/vWsG42GTe6ncFmka+/goMhTxz6EUivSJ4ptMTdllC9VekNCOLJ4C1FBG6EqFpGp8e/C8VpluKrCzbpsOsrHOv9e4Na9eCv7/z61IUpV5UmG64Q+VHePPYuwwM7c/tCY3ftLQh9yS3b5xPj9Bovhw8ud4fk2eWl3HjjwvJr6pi+ugxDItPaHRNDWUyW1i+8xizVu/iVH4JkUH+TBjQhXH9kwnXNX4ZTEtQazKzZm8qP2w6wL70HPy8PZk0KIUpQ7q7dAlIdkUFNy1aSGZ5Oe+NGs1lifVfvvnh4S28c3AD09r15unuwxv9QLezZDf/Tf2YSyOHc32sY1vySuNeZMk14HsrGv/HHDpWQ6kw3ZIcPw6DBtlmqv/X0KHw00/gVf9eqIqiOI8K0w1TZizn6UMv4Kf14/nkp/BqZPuuPUVnmPrLd7T1D+bbodfVu4/04YICblq0ELPVyswrxtOjVatG1dNQ1QYjCzYf5Jtf9lBQVkW76DBuGtmb4d2T0Lqpw43/yuGMPL5ctZN1+9Nwd3Pjin7JTB3ekzZhrtl8WWbQc+viRezNzeWFIcO4vp4NBqSUvLx3DV+d2MlDXQZzd3LjP7WZffpbVuev44Gku+kZ3L3R9/s71vInQb8IEbIY4d709oKpMN3S7NkDQ4bY1lL/r/HjYf58Wz9qRVGaJBWm688qrbx57F1Sq07yQvJTtG7kCYenKkuYuHoWgR5efD/8BkK96jdzuzM7m1sW/4i/pyezrpxAYrDzO0aYLVYWbjnIxz9tpaxKT6+k1tw0sjf9O8Y2ifXAzUVGfimz1uxi2Y6jWK1WrhqYwh2j+xHs7/zNo3qTiXuX/8S6U+k8fNEA7u7Tt17fb5WSR7ctZVHGId7oM4aJ8Y3r+GWymnjpyOvkGwp5rcvzBHvWf013XUlrCbLwMnDvgAia1eRew3V931aPr81Fjx62g1vONwO9aBHcdputT7WiKEoLsSpvLYcrjnJd7DWNDtIltTXcvGEeGgFfDp5c7yC9OTODaT8uIMzXlx8mTXZ6kJZSsunQKSa9+jWvzVtHQlQIsx6ZzGcPXM1FneKaXAhp6mIjgnj2uhEse+FmrhqYwoLNBxj3/JfMWr2LWpPZqbV4u7vzyeVXcGXHjrz96xb+s2Uz9Zno1AjB633HMDCiLU/tXMGWvFONqsdd487dibdjkRY+Tf8Cq3RcthCaYITffWDcBrVrHDaOo7k8TAsh3IQQe4UQy1xdS5N38cXwww/nn4H+6it45BHbSYqKoijNXFbNGb7PWkAPXTeGhF3cqHsZLRbu3ryAfH0lnw66mlj/+h2dvDkzg1sXLyJGp2PuxElEOXmfSmp2Ef/6YCH3fbwIs8XKO7dfzmf3TySlbZRT62iJwnV+/PuaoXz/1A10T4zmvUWbmPDSLFbtPl6vQNtYWo2G/4y8jGu7pPDxzh28sXlTvcZ317jxwYAriQ8I4Z4tP3KqsqRR9UR4RXB97GSOVBzj5zwHh1yfyaBNRFa+gZRGx47lIC4P08D9wFFXF9FsjBkDs2bB+WYh3n0XXn3V+TUpinLBcMYEiMlq4uO0z/DR+nBL/I2NnnV9cc8qdhRm8XrvMfQIbV2v791+JovblyymbVAQ3151NWG+ztuwVmMw8vaCDUx+bQ5HMvN5dOJgFjw9lSFdE9VMtJ3FR4bw37vG8/G9E/D18uDxL5Zz538XkFFQ6rQaNELw8tBhXJfSlRm7d/Hetl/r9f3+Hl7MGHQ1bkJwx6b5VBoNjapncNhAegZ15/ushWTWnKcJgp0IoUX4/xssmVAzx2HjOJJLw7QQojUwBpjpyjqanSlTYPr08197+mn46CPn1qMoyoXE4RMgP2YvJUufza1tpxHg3rhZ4G9Sd/Pdyb3c0bE/V8Ql1+t7d+Vkc8viRbQJCOTrCRMbdMBGQ206lM5VL89mzro9jL8omSXP38yUIT1w17q+j3VL1q9DLN89cR3/vmYoRzLzmfTK13y2Yjsms8Up4wsheGHIUK7p3Jnp27czffu2en1/Gz8dHwyYQEZlKQ9sXYylEcs/hRDc0nYqvlofZpz8ArPVcctfhOcg8BiIrPoYaS132DiO4uqZ6feAx4C//NMWQtwuhNglhNhVeL5uFhequ++Gl146/7V77oFvv3VuPYqitHjOmAA5WZXOspwVXBw2kG5BKY261/aCDF7cs5ohrRJ5uMvgen3v/rxcbl70IxF+fsy5aqLTTjUsqazh8c9/4r6PF+Pj6c4XD07imSkjCPRVHZucxU2jYdLFXVn47I0M7hLPR8t+5ZrX5rA/Pccp42uE4JVhI5jQsRPvbv2VT3ftrNf394uI5dkeI1ife5K3D25oVC3+7v7cFHcDGTVZLMn5qVH3+ifC/zGQFciqjx06jiO4rP2DEGIsUCCl3C2EuOSvvk5KOQOYAbad4U4qr3l46ikoLob33vvzr0sJN94IgYG2ZSGKoij28dsEyF9OFwshbgduB4iJqd8phWarmc/TZ6Fz13FdzKTG1Em+vpJ7t/xIjJ+Od/tdUa/TDdNKirlp0Y8Ee3vzzVUTnba0Y+PBdJ6bs4pqg5F/jb2IaSN6NbmZaKtVkl9YQV5BOUUlVRSWVFFUXEVhSSWVVQZqjWZqjWaMRjO1tWasUuLhocXzDz98vD0IDfYjJNiPsGA/wkL8CQ/1p3VUEO7uTee/NyzQjzdvHcumQ+m8OncdN7/zPTdf2ps7Rvd3ePtBjRC8MWIkRouFNzZvQuflxTWd637M93VJPTlSls+nR7fSLaQVI1u3b3AtPYO70z+kL0tyltMnuFejNwP/FeHeAek1HmrmIH2nIdwiHTKOI7iyl9oA4AohxGjACwgQQsyRUl7vwpqaFyHg7behtNS2jvqPzGaYOBFWrbL1qFYURWkEZ0yArMhbRZY+mwfb3YOPtuEzwSarhft/XUSN2cScevaSzquq5MaFC9BqNMy68ioi/Ry/2dBktvD+4s18s24P7VuH8cq0USREOb/t3v+qNZo5npZH6qkC0jMKSc8o4mRGIXqD6U9f5+3lTliIPwH+Xnh6aAnw87YFZ08tQgiMJluw/i1k5xdWcPhELmXlNX+6j5ubhpjoYOJjQ4mPCSMhLozk9lHoApzfru6PBnWOZ/5T0by1YAMzV+5g14kzvHbzaCKDHPvacNNoeOvSy6isreWptWsI9vZmREJinb//2R4jOVyaz6Pbl9E+MLzeG2//6PrYyRwsP8QXp2bzdKfH0QjHPEwIv3uQhqXIqk8Qgc87ZAxHaBJ9ps++MT8ipRz7d193QfeZ/ju/BefFi8+9FhAA69dDd8c2XlcU5e819z7TQojXgBsAM2cnQICFfzcBUp/37HxDAf8+8BxddV24v92/GlXrm/t/4dOjW3m337h6rZOuMhqZPH8emeXlzJ04iU7h4Y2qoy6yCst44ovlHMnM55rB3XjwykF4urtmnstQa+LQsRz2Hc5i3+Esjp7IxWiyrRX29/MiPjaUhNgw4mNDiY7UERrsT1iIH74+DTtIx2gyU3R2ZjuvsIJTmUWkZxSRnlFIXuH/n6nQNiaUbsmt6Zbchm6d2xCsc92phSt2HuPl79bgrnXj+etHckmK40+/rDGZuO6H+RwvLmLu1ZNIiaj7jO2ZqjKuWPUF0T6B/DDiRjzdGv7a2lS4hRnpX3JT3A0Mjajfsqn6sJY/B/ofbMeMa+t/zLo9NatDW1SYtgODAUaPhl9+OfdaWBhs3gzt2jm/LkVRgOYfpv/I3u/ZUkreOPYO6dWneT3lRYI9Gj6Dtik3nWkb5jI5oRuv9B5d5+8zW63cvmQxmzJO8/m4K7k4Lq7BNdTVz7uP8/K3axBC8Nz1IxjWzfknwJWWVbNpRxqbtqWy+0AmJrMFjUaQ1Db89/DaPiGC0GA/p3YQqa6pJe1UIfuPnGHf4SwOHcv+fUY8qW04g/olMbhfEm1jQp3e2SSjoJQnvljOsawCpgzpzv3jBuLh4Aegwupqrpr3HbVmCwsnX0t0QECdv/eXnDRu3fg9N7brxbM9Rja4Biklrx97m1PVGbyR8hJBHo45NVJa8pCFw8H7cjSBrzlkjLpqVmG6rlSY/geVlTBsGOw8z2aFmBjYsgVa168tlKIo9qHC9F/bXLiVT9M/58a46xgeMaTBdRUZqhiz8nOCPL1ZNOImvLTudfo+KSXP/bKOOQf28/LQ4UxJadzGx39islh4d+FGvlu/jy5to3j9plG0Cgl06Jh/VFRSxdrNx9i0LZUDR88gJURFBDKobyK9UuLo0jEaP9/GHdtub2azhePp+ew7lMXmHWkcPp6DlNA6SsegvkkMG9iB9onOW2NrNJl5b9Emvlu/j86xkbx9++WE6+p3EFB9pRYXM3HeXKL8/fh+0mQCPOv+Z/TyntV8eWInMwZdzbDohj+05RnyefLAc3QL6sp9SXc1+D7/xFrxKtTMRoSuQGjbOmycf6LC9IWqqMh2uMvR83Su6tABNm2C0FDn16UoF7iWFKbrqi7v2dXmah7d/xQRXhE804i1mFJKbtn4PdsKMvhxxDTa6+q+RGP2vr08v/4XbuvZk38PctzH1wCV+loem7mMbccyuW5Id+6/chDubo7fdCelZO+hLH5csZdN21KxWCXxsaEM7teOi/slkRAX1qx6VxeVVLF5Rxobt6Wy52AmFouVDomRjB/VjWEDO+DlWbcHqcZauy+VZ2f/jI+XB9PvGk+HNo5dGrQlM5ObFi1kQJsYZo4bX+eNtbUWMxNXzyK3poIVo24jzLvhwX9J9k/MP/MjD7e/j246xzx4SksxsmgYeA5Do3vbIWPUhQrTF7IzZ2DgQMjIOPdar16wdq1tLbWiKE6jwvT5fZMxj5/z1vBS52eJ9W34+sh5J/fx5M7lPNtjBDe2613n79uWlcUNC3/gkrZt+fTycWgcGChzSyq47+NFnM4r5ekpwxnXv359rxuiuqaWFesOs2jlXjLOlBDg78XoYV24fEQKMdHBDh/fGSqrDKzeeIRFK/dzKrMIfz8vRg/tzJWjuhEd1fAlQ3WVllPEvR8torzawBu3jGFQZ8fOpH538ABPrV3Dnb1689jAujcYOFlRxOU/f8GgyLZ8MnBigx+ezFYzTx58HoBXuzyPVuOYJS7Wyjeh+ouza6fjHDLGP6nr+7ar+0wrjtC6NaxeDefbPLNrF4wbZ1tjrSiK4kJ5hnxW569jcNigRgXpM1VlvLJ3Df3DY7khqe7PK9kVFdyzfBlxuiDeuXSUQ4P00cx8pv7nO/JKKvng7isdHqQrqwx8Oe9Xrr59Bu/PXIuvjydP3T+KhTPv5O5pl7SYIA22zZETRvdg1vvTmP7KZHp3i+OHn/Yw5e7PefHdZZzOKnbo+ImtQpn96GTiIoJ44JPFfL9xv0PHu7ZLClO6pPDJrp38dOJ4nb8vISCUR1IuYU12KgtOHWzw+FqNlmtjribXkMe6gsb1sf47wucmQIusbvrn+rmyNZ7iSElJ8PPPMHgwVFT8+dr69XDNNbBgAWjVS0BRFNf4LnM+7kLLVa3HNfgeVil5bMcyBPBG37F1DsRGi4X7lv+E0WLh08uvwL8e60/ra/PhUzw28ycCfb348uGrSGzluKV25RV6vl+6iwU/7aG6xsjAPoncMLEfndpFOWzMpkIIYds0mdyGopIqvl+yi0Ur97Fm41Eu6d+eqZP6kRjnmGUYYYF+zHzgap74cjmvzVtHTnE5948f5LClM89eMoTjRUU8sXoVHUPDiA+u28PRtHa9WX3mOC/tXc1FEbG08m3YWv1uuhSSAzryY/YSBvwfe+cdFtXVve37zFwWyYMAACAASURBVDD0JogKIoogNhQr9i4q9hp7N3aNJrZYE2M3dmOPvcbeC4ode0EBCyiCIggiSK9zvj8m/j5fBaYwat43574uLyJn77X3QMZ5zjprP6tgTcwM9O+wIsjtEE06Qeo+RPPRCPIv766jK1KZx/86V65As2aQmvr5tT59YNMm0KKZgYSEhG5IZR7/SdD7x8x9/DtdHDvStqjmrhufsvnpLX6768M8z1Z0Kemh8bxZFy+w8d5dVrZsTcsv6HR05s4Tpmw+hYuDLSuGt8fO6sscUktJzWDHgRvsPXqH1LRMGtZ2o2+XWrg6f1kBkpKSTnR0Im9jEkhITCM9LZP09EyVr3R6JtnZIsbGCgwNDTA2VmBkZICpqREF7SwoVMgSKyuTL1qrHZ+Qwl9H7rD/+F1SUjNoULMUg3vX/2KZ+axsJQv2nmfv5Qd0qO3OlO5NtGoYpA2RiYm02bkdOzMzDnTtjolCszrx8KQ4Wp3aQCXbomxp2F3nJzLhKS+Z+nAmzYs0pWfxrjrFUIeYFY74thmYDURmMf6LrJEXmv67LaUl/9epWxf27VOVdmRl/ee1rVuhQAFYskTVAEZCQkLiK6AUlewM34OtoQ0t7JvqHCc8KY6F/udpaO9CZ2fND0KdCQlh47279K1U6YsK6cPXApm5wwePkvYsG9YeCxP9Z7+VSpGTvgGs236Jd/EpNK5bhn7f1cLZSb/Z73fvknjyJJKnT6IICX5DZGQ80dEJJCen5yuuQiHHzs6CwoWtcC5ph1tpe0qXtsfR0QaZLP+fS9aWpgzuVY9u7aqx79hd9hy5zZVbm+joXZkB3evo3bXEQC7j566NsTIzZsOpmySnZzCrb4svcsjU3sKCJS286XfwADPO+7KgWXON5jmZF+DnSk2YdvsUe57dp7urbn0onEyL0cCuLj5vfGlSuCFFjAvrFCcvBAMnROPmkLIL0WwoguzLN1HSBUlM/xto2VIlnHv2VLUa/5hly8DWFqZN+zZ7k5CQ+NdxLfYmYSkvGebyPYYyQ51iiKLIL3fOIBdkzKrurXF2MyopkUlnz1ChcGEm1a2v09qacORaIL/uOEPNMsVZNLgNJob6d5cICY1m0RofAp68xr20A3Mmd6C8m4NeYkdFxXPr5nNu3w7l8aNI3r5NBEAmE3B0tMGxmA0elZyws7PEzs4Cu7+zzMZGCoz+zkAbGhogkwn/11r8Q8Y6KSmNtzGJREcnEBOTSExMAlGR7zl+7D4H9queZJiaGlKqVBEqVXbC09OFUm5FkOejhbelhQkDutehvXclNuy4wr7jdzh35REj+jfCq35ZvWbHBUFgRJs6mBsbsfTQZRBhdj/vL9KCvF7xEozwrMHKmzdoUKIErdw0axve3aUyx8KDWOB/nmaObtga61am0cmxPddib7Lv5UFGlhqqUwx1CGbfI6adhNR9YNb/i6yRXyQx/W+he3eIj4fhOXQWmz5dlaEeOfLr70tCQuJfRZYyiwOvDuNkWoyatpq7bnzK2YhgLkY+Y3KlJtibauZOpBRFxp85TXpWFktatMToC50ZOXojiF/+FtJLhrTVe0fD9PRM1u+4wt5jd7C0MOHnUd60aFQ+X5nc7Gwl9+6FcfPGM27dfE54uOrQXpEiVlSq7ISbWxHcStvj6loYExPtboCMjBQYGSkAk///zTI57yE8PJYnTyIJfhrFo6AItm65wpbNV7CyMqFaNWeq13ChZk0XLCxMPg+gATbWZkwY0Zy2zT1YvNaH35Yc5/jZh0wc0RyHIvptQtLXqxqCILDk4CVkMoHf+rT4IoJ6VI2aXAkPY+q5s1Sxd8DeQn32VhAEfq3anNan/mSh/wXm1Wil09rWhla0KOLF4dfHaJ0cTgkzJ53i5IWgcEdUVEVM2QamfRCEL28lqS1SzfS/jTlzYMqUnK9t367KXktISOgdqWZahe+bi2x6sY2f3EZTqYBuHrWpWZk0P7EOU4WCo80HopBp9uG68e5dZl26wJwmTelW4cv445689ZgpW07iWdqJpUPaYWyoXyH9OCSKWUuPE/bqHe2aezCkd30szI11iiWKIsHBb/A58xBf3yDi41JQKOR4eDjhWaMknp4uOBaz+ab+0+/fp3D7Vii3bj7n1q3nxMer9lizliteXu541nBBodBNXGVnKzl29gFrtlwiW6lk5IBGtPGqqPfXu+nMLZYfvkKr6mWY2aeFXspXPuVFfBytd2zHo0gRtnXsrHEd9Lz7vqx/fJ29TftQpaBuTd2Ss1L46f4kXC1cGFf6B51iqENMO4UYPxrBehWCse6lYdoi1UxL5MzPP8O7d7AoBxP0vn1V/tNt2nz9fUlISPzPk6HM4FDEUdzMXfGwrqBznNVBfkSkvGdn454aC+nHb2NYcPUyXiVd6Oqu+9p5cTkglOlbT1PV1ZElQ9rqVUhnZWWzbd91tvx1DdsC5iz5tQvVPEroFCsuLplTpx7gczqAsLC3/ydOm3q5U62aM8bGX6fhiSZYWZnSpGl5mjQtj1Ip8vRJJOfOBeLrG8TlS0+wtDShUeOyeLf0oFQp7TogyuUy2jWvRM0qJZm74iQLV53h8o0QJo5oTkEb/R0U7d+sOtlKJX8c9cPc1JiJXRrqXbCXsC7AtAYN+fmsDxvv3mFQVc3u20eWr8PRsEBm3D7NoWb9dTosaWZgSmsHb/a83M+TxGBKW+jeYTFXjJqCzAExZetXFdOaImWm/42IIgwaBBs3fn7N2BhOnVJZ6klISOgNKTMNJyJPsyt8L1PKTqCMpW4H/0IT39Hy5Hq8i5VhcS3NLPUys7PpuHsXUclJnOrVB1tTU53Wzgv/568Zunw/zkVsWPdDZ8z1eNgw7FUss5ae4HFIFM0alGPM9010yka/CI1h375bnPUJIDMzG3d3R5p6udOwURmdyya+FVlZ2dy+FYqPTwB+V4PJyMjCo5ITnbt4UrOmq9bZX6VS5MCJe6zeehFjIwPGDW1Gozqa1R9rgiiKLD5wie2+dxneujbfe9fQW+yP1xh27AgXXrzgWM9euNrYajTveHgQo/0O8UvVZlr5tH9MenY64/wnU9i4MFPKjv8iTzPEpPWISQsRbI8gKHKoE/oCSB0QJfImKwu++w4OHvz8moWFyou6SpWvvi0Jif9V/u1iOj07nR/vT8LJtBgTy/6oc8xBl/7iVsxLzrYconFL5D9u3mCR31XWtG5LM1dXndfOjbA3cfRdtBtLU2M2/fgdtpb689w9f/UJs5edwNhIwU/DvGhUW3uBFxQUwY7tfly/FoKRkQHNm1egY6fqFHPSTGz900lKSuPECX8O7r9NdHQCTk629OhZi8ZNymt9aDHsVSyzl53gUXAUHbwrMXpgYwwM9FOjq1SKTN92muM3H/FLr2ZfpHFPTHIyzbdtwdm6AHu7dtOo3EMURfpe2MWDd5H4th6GjZFuN5s+Ub5sDdvJ5LLjKGupf7ErKuMRo+uDSRtkVrP1Hj8npA6IEnljYAA7d0KTJp9fS0yE5s3hieadlSQkJCTy4lLMVRKyEmlXtLXOMa5GhXL+dQgjytXRWEiHv49n5Y3reJcq9UWE9PvkNEavOYQgCKwa2UFvQloURTbtvsr0hUcoVbIQm5f101pIh4fHMvnnvxg1YiuBga/o178eu/aM4IexLf5nhDSAubkx331Xg207hjJlalvkBjLmzT1G/77ruHrlKdokDYs72rJqbg+6ta/OwZP3GTdzH4lJ+ukYLJMJzOjlRc0yTszaeZbbT1/qJe7H2JmZMa1BQ+5FRbL7oWZdDgVBYFoVL5KzMlgZeEXntRsUqoeVwpKjr0/qHCMvBJk1mLSGtOOIyqQvsoauSGL634yxsSoz7en5+bW3b8HLC17q/80uISHx7yJbzOZE1BlczUvqXE+pFEXm3ffF0cyKvm6aJfhFUWSGry8KuZxpDRrqtG5eZGZlM37DMSLfJbJkcBscC+rHDSI9PZNfFh1j424/WjQqz7LfumpVw5uQkMrKFT4MGrCBhw9eMuj7huzaPYLefepiZaX/Epd/CgYGcho3Kc+69QOZ+Vsn5AYypk/bz/ifdvHsWbRWcUb0a8jPo7zxD3rFkAnbCY94p5c9KuRyFgxsRTE7a8ZtOEZYdJxe4n5M+zJlqenoyIKrl3mbkqLRnFJWdnQtWYkdwXcJTdTttRrKFDQr3JSH7wMJSw7XKYY6BJOuIKZA2tEvEl9XJDH9b8fCAk6cgHLlPr/28qVKUMfEfP19SUhI/M9wM/Y2b9Pf0specz/oTzkWFkRQ/Bt+qtAQI7lmB/tOhgRzMewFP9aqTRFz/TZ7EEWReX/5cuvpS2b09KKSS1G9xH37LomRU3Zz/upjhvapz+TR3hhqaK2XlZXNwQO36dNrDYcP3cG7pQdbtw+le49aWtvZ/TcjkwnUqevG+g0DGTXai5Bnbxg6eCOLF50kLi5Z4zgtm7izbGZXEpLSGDJhO7f9X+hlfxamxiwb1g4B+GH1YRJT9JP5/oAgCPzWuCmpmZnMvXxR43k/uNfDUC5nyUPN53xKk8INMZYZczzylM4x8kRREQxKI6bs/TLxdUSqmZZQERGh6pb44sXn16pUgfPnVU4fEhISOvFvrZm+desWUwNmkqXMZG7FmcgE7XM4GdnZND+xFjOFIUeaD9SoDjQxPZ1mWzerWi1364GBnls677/ygFm7zjGwuScj29bRS8yQF9GMn7mf5JR0pv/YmrqempelPH0SyYIFxwl9HkOVKiUYOrwJLi5frpV4ZmYW0a/fExkRR9Tff+LjkklLzSQ9NYPUlAzSUjNRKpUYmxhibKL4v69m5sYUdrDG3tGGIkULUKSoNWY62vtpQkJCKtu2XuHwobsYGysYOqwJ3i01t8CLfPOeSbMPEPYqlh+HetG2meZt6/PibsgrhizbT+1yxVkypJ3eLfMW+11l5c0b7OjUmVrFNPN/XvzgIn8EXeVwswG422jnjvKBXeF7ORl5ht895lDI2E6nGHkhJm9FTJz1VQ4iSgcQJbQnJEQlqN+8+fxagwZw8iSY/Hed+JaQ+KfwbxXTG89uZuGTpQxy7keDQnV1irMj+A7T75xmQ/3vaOSgmcCceeE8W+7f40C3HngU0U0U5EbAiyj6L96Dp1sxlg9vr5Od2Kc8fBzBhJn7MTFRsGBaJ1xLaCaEMzOz2bL5Mnt2X6eAjRmjRzejTl03vbopKJVKXoRE8/BuGI8evORxQARRr+L+oxZZoZBjbWuuEs3GhpiYqoSzTCYjLU0lrNP+FtmJCamkJP1nG3KrAma4lXOgTAVH3KsUp4x7UYz1nE0PD49lyaKTPHjwkqrVSjBufCsKFdIsSZSSmsGMhUe4fjeUIb3r06uTftw4dl+4z/y95xnaqhZDWtbUS8wPpGVl0mLbVgxkMo737K1Rk6LEjDQaHltNBRt7NjfsptO67zLi+PH+JBra1aefs/57V4jKOMToumDaHZnlVL3H/xjJZ1pCe1xd4cwZlXCOj//PaxcvQteusH8/KP45HqQSEhL/bE5F+WCtsKJ2Qd3ER3p2FquC/KhW0JGG9i4azQmOjWWb/326V6iodyGdmJrOxI3HsbMyZ07/lnoR0g8evWLcr/uwtTFnyS9dKFLISqN5L8NjmT3rCMHBUbRoUZFhI5pgrqcMb1JiKtfOP+GKbxABd8NISlSVItgUtKBsRUcae1fAvqgNRRwLYO9YAJuC5si0+FkkJqQS9UqV0Y6MiOPVi7c8Dojgtl8IoihiYCCnVDkHatZ3o55XeYrq4cCkk5Mti5b05OiRu6xbe57Bg/5k7E/eNGigPrtpamLI3MkdmLP8JGu3XSI7W0nf72rle09dG3gQ8CKSdSeuU62UI1VL6dY4JSeMDRT80rAxAw4fZJv/fY28py0MjRlSthbz/X25+/aVTo1cbAwLUKdgLS6/vUrnYu0wN9CfZzeAICuAaNwUUo8iWkxEEL69JpEy0xKf4+cHTZtCaurn13r1gi1bQM+PTCUk/tf5N2amPap4iBVXV6eTY3va6+jisTPkLtNun2Jrw+7UKeKs0ZyBhw9yO+I15/sPwEaPT9NEUWTy5pP43H3Kxh+7UtHZPt8xPwjpgjbmLJ/VTeODhqdOPmDF8jMoDOWMG9+KunV18+3+mA8C+tLZQO5ee0ZWVjaFilhRtbYr7pWL417FicL21l+0I2JSYiqB918ScDcM/9uhPAmIAMC1jD31mpbTm7COiHjH7FlHePI4Eu+WHowa7fV3y/O8yc5WMnfFSU5fCOL7nnXp0yX/gjolLYPu83eQnpHFnsm9sTLTb8lL/0MHuPs6kosDBmBtrP79kJKVQf0jf1DR1oGNDbrqtGZY8kumBvxKN6cutLJvrlOMvBDTfBHjhyJYr0UwbqT3+B+QMtMSulO7Nhw4AG3bQmbmf17bvh0KFIBly+AbtpiVkJD455OQlYiBYECjQvV1mp+pzGZN0DUq2TpQu3AJjebcfPWK86GhTKhTV69CGuDojSBO3X7CiDa1v5mQzszMZtVKH44cuUflysWZ+HMb7Ozyd7gyOOg1h3Zd5+JpVTOXQkWsaN+jBvW8ylO6fNGv2k7c3MKEGvXcqFFPdXMQHRnP5bNBXPYJZNPKc2xaeY7ylZ1o370mdRqVQa6jB3TRojYsX9GbLZuvsGunHyHBb5g5q5Pasg+5XMbPo7wBWL9DZSOXX0FtamzInH7e9Fu0h5k7ffh9UGu9/swn1a1Py+1bWX3rJj/XU9+QzdTAkAGlPVn08CIP30VSwUb7/9eLmxWjtIUbZ9+cx7uIl05nJfLEqC4I1ohpR7+omNYUKb0okTMtWsC2bTkL5hUr4Ndfv/6eJCQk/qtIykyipm11rBS6HV4+/CKAiJT3jCxfVyNxIYoi869cpoi5Of0qV9ZpzdwIi45j3p7zVCvlSP9m1fMdL+hp5P+VdmgqpOPikpkwbhdHjtyja7eazF/YTWchnZWZzYXTAYztt4GRPddy1fcRLTpWZdnW79l6Yizfj21OGXfHryqkc6KQvTWdetdm6dbv2XZiLIPGeBEbncjsCX/Rp/VSdv95ifdaOHR8jIGBnIGDGvDb7M5ERLxj2NBNPHyo3g72g6Bu1qAc63dcYceBGzqt/zHlixdhZJs6+N4P4cBVzfyhNaV0wYJ0LFeeLffvE5GQoNGc3m7VsFQY80fgVZ3XbV6kCW/T33Ivzl/nGLkhCIZg7A3p5xCVuv3+9YkkpiVyp2tXWL0652u//grLl3/d/UhISPxXoUSkWZEcGkNpQJZSyaogP8oXKKJxrfSZZyHci4rkh5q1MDbQXx1lZnY2kzedxFAhZ1bfFvmuk37xMpYJv+2ngJUpKzQU0k+fRjF86GYeP45k8pS2DB7SSOvufgAZGVkc2nmdPq2WMHfSXuLeJTN0fAt2nPqJkZNaUabCtxfQuVHI3poufeuy8fBofl3ag2IlCrJp5Tl6tljM8tlHeRutmVD8lFq1SrFyVV/MzIwY9+NOjh65p3aOXC5j8mhvmtYry5qtlzjm80CntT+md5Oq1CzjxO/7LhIapR9f6w+MqanKni+7fk2j8RYKI/q5VcMn4imP4zX36P6YKgUqYWtog88bX53mq0MwaQNiKqSf/SLxtUES0xJ5M2QIzJ2b87UfflBlryUkJCRywMnUEWezEjrNPfHyEWFJcYwoX0cjcZetVLLI7youBWzoVE6/bZq3+NwmKPwNU7s3pXCB/JVUxMYlMW7mPuRygcW/dNFISF+98pQxo7chiiLLlveiSVPtX58oilzyCWRQ+xWsXniSosVtmbm8JxsPjaJDj1qYWXw5azp9I5fLqNmgNPPW9GXtvhF4tfHg9KF79G+7jC2rfElLzdA6ZvHiBVm1uh+Vq5Rg6ZJTrFx+huxspdp9TB7tjWflEixcfYZrt5/r+pIAlT/2zD7NMTI0YMa202Qr815fG4paWtLHoxIHHgUR8i5Wozl93apjZmDI6iA/ndaUC3IaF2pAYMIjIlOjdIqRJ4oqICuKmPrtG7hIYlpCPRMnwvjxOV/r3x+OHPm6+5GQkPivQC7oVs8qiiIbHt+gpIUNXkU1O1h3KiSYkHfvGFurtl49pV/GxLP+5A2aVi5F08q6dW/8QHpGFlPmHSL+fQoLpnWmqH0BtXN8zwXyy4wDODvbsWpNf9xKa1+/+uxJJOMHbWL2hL8wNTdizuo+LFjXjxr13LRy4PgnUsKlED9MbcuGgyOp3bAMO9dfZGD7FfieeKBVG3FQtSWfPacLnTpX5+DBOyxccFytoFYo5Mya2A6XEnb8uvgooS/f5uflYGdlzvjODXn4Ikrv5R7DqntibGDAHzc1K0uxNjKhu2tlTr58xOvk9zqtWd+uDgICl9/qJsjzQhBkYOINGdcQlbo9ldAX/93vIomvgyDA/PkwcODn17Kz4bvv4MKFr74tCQmJ/03uvH1FYFwU/Ut7atSgRSmKrLhxHVcbG1qUyp/g/RhVl8PzKAzkjO/cMN+xFq46TeCTSKaOaUlpl8Jq55w7G8jcOUdxd3fk98U9sLEx02rNtNQMVi84wYjuawl7HsPoKa35Y9dQqtZ0+ceWcuiKvaMNP8/rwqKNA7C2NWP+lP38NHAjEeGaZWE/IJfLGD6iKX371cPnTAAL56sX1CbGhsz9uQNGhgZMmnWA9wk5OGFpQcvqZajuVowVh68Sm6C/euACJib0qujB0SdPCI3TrI1531IqI4ttwXd0WtPa0JqK1u5cibmGUtRfpv0DglFzIBPSv0wpiaZIYlpCMwQB1q6Fzp0/v5aernL+kGwLJSQk9MDW4NtYKIxoX8Jdo/E+z0J4GhvLcM8aGolvTTl7Lxi/oBcMb12bQtb588rdefAmpy8EMbB7HRrWLq1+bZ8A5s09SoWKxZgz7zut24EH+b9keLc1HNp1g9bfVWfj4dG06lxdpzrr/ybcKxdnxfbBjJ3elhfB0Qzrupqjf91EqWXJRJ++denXvx4+PpoJ6sJ2lsyZ3IG375KYtuAwmZnZOr8GQRCY3K0xaZlZLDlwSec4OTGwSlUM5XJW3bqp0XgHMyu8irrx13N/0rIy1U/IgXoF6xCXGUdgwiOd5ueJogLICiOmndF/bC34335XSegXuVxljefl9fm1xESVA8ijL/BmkZCQ+NfwJjWR0y+f0KWkB6YG6gWkKIqsvHGDEtbWtHZTL1I1JSk1nYX7LlCmWCG+q5+/9tF+t5+xdtslGtcto1GjDx+fAObPO0ZFDydmz+milZDOyMhi43IffhrwJ5kZWcxf25eRk1phYfnv6V4rk8lo0aEqa/cNx72yEyvnHmfy8G1ER2lXqtC7T136D6iPj08AC+YfUyuoy7s5MHFkC+4FvGTZn+fy8xIoUdiGvk2rcfzWY249Ve8woil2ZmZ0r1CRQ4+CePles59H71LViM9I5Wh4kE5rVi7ggZnclMsxX6jUw7gZpF/+pq4ekpiW0A4jI5UHdc0c2p7GxkKzZhAe/vX3JSHxhdmxA0qUUPUrKlFC9XcJ/bMr5B7ZopJerlU1Gu8bGkpgTDTDq9fQa6306uPXeJuQzJRuTTDIRzb39Zt4fltyHFfnQvw8qoXa8oqzPgEs0FFIhwa/YXSvdezZdIVmbSuzZu9wKnmW1Hnv+SUjPZPU5HSta5f1hV1hK2b/0ZvRU1rz6MErhnT5g3PHtbNp69W7DgMG1uesTyDz56kX1M0alKNnR08On/Ln1PmA/Gyfgc09cSxoxZzd58jIzMpXrI8ZXLUacpmM1Rpmp2sUcsLNyo6tT2/r9Ls0lCmoaVuD2+/ukpKVovV8dQjGzYF0yNBvFl8bpKYtEtpjbg7Hj6vajgd88o/Fq1eqzPXly1Co0LfZn4SEntmxAwYPhpS/PwfCwlR/B+jZ89vt63+NLKWSv57fp769C8Ut1B/OA1h/5xZFLSxpV0Z9S2hNeR4Vy56L9+lUpyLuJXRvR56drWTW0hOIosisCe0wVtNh786d0P/ISBsba27vd+tKMLMn/oWJqSG/LutBzfr6y9LnRFZmNq+eR/PiaRQvHkcS9jSS2DfvSU5M+/tPKlkZqlIHmUzA1NwYUwtjzCyMsbQxx8m1MCVK2+Ncxh6nUkW+mJuIIAi06lydKjVd+H36IRZMPUBoyBsGjGqq8eHLnr3qAAIb/7yItbUpw0c0zXP8oJ71CHwSyeK1Z6lY1hGHItY67d3Y0IBJ3zVm5KqD7L7oT5+mmt1gqqOwuTldyruzNzCAsbVqY2eWdy2+IAj0LlWVabdP4f/uNZVsi2q9Zj27WpyLPs/tuLvUt6ur69ZzRlEVZDaIaWcRjL31G1tDJDEtoRs2NnD6NNStC6Gh/3nt6VNVycf582Bl9W32JyGhR6ZM+f9C+gMpKarvS2Jaf1yOes6b1CRmVNWs/fCjmBhuRkQwuV59FHLdnENyYtnBy5gYKhjepna+4uw+fIuHjyKYOqalWkH16tU7fvv1EMWLF+S3WZ20EtIn9t9mxdzjlCxVmJnLemCrpoufLmRnK3n6IJxbvo+4ffERoY8jyfq7Llgml+FY0o7CjjYUdbbD1MIEMwtjTM2NkctlpCSpBHZKUhopiWm8i0ng7P5bpCan/1/8IsVsqFTHDc9G5ahUpxQmZkZ63b+9ow0L1vVl1YKT7N18lejX7xk3sz2GGrQQB+jZqzZxccns33eLkiUL0cK7Yq5jDeQypo5pSd8fNjF72QmWz+qmc616nfIlqF2uBBtO3aBtzXJYm+unXKd/pcrseODPX4EBjPCsoXZ8m+LlmX3vLPueP9BJTJc0c8bOqCA3Ym/rXUwLghzRsK6q1ENUqko/vjKSmJbQHQcH8PFRCeqoTzwk792DNm1UglvPLX0lJL42uVUuSRVN+mXfc39sjExpZO+q0fjt/vcxksvpXF5/vtJ3gl9xKSCU0e3qUiAfwiXkRTQbdl6hQS03mjUoZv1wyQAAIABJREFUl+fYpKQ0pk3dhyAI/Da7M6ammglJpVLJ5j982bPxMtXrlGLKgi6YaDhXEzLSM7nuE8j1swHcufSEhLhkZDKBslVK0GFgA1VmubQ9RUsWwtBIOzmhVCqJjojjxZMoXjyJ5OmDcC4eu8ep3dcxMJRTwdOF6o3K0qB1ZWz0dHMgN5Az8udWFClqzYalPryNSeCXxd2xtDbVaP6w4U0IC3vL0iWnKFbMhvLujrmOLWxnyZjvmzB72Un2HLlNjw6eOu97bId6dJ2znT9P3+SnTurbgWtCSRsb6joVZ+cDf4ZUq662RMpCYUSLYmU4Gh7ElMpNMdGyKZIgCNSwqcbJKB8SM5OwUOTvQO9n8Y3qI6YdgaxA1aHEr4wkpiXyh4sLnDkD9etDfPx/Xrt8Gbp0gYMHQaG/bmQSEl8bJydVaUdO35fQD7FpyZx7HUyfUtUw1CDLnJCWxqHHj2hXpizWxvq5YRdFkZVHrmJnZUb3hrq3I8/IzGLW0hNYmhszbphXnnXS2dlK5sw+QsSrOBb83g17e81KAjIyslg04xAXTj2kZadqjJzUErmBfrLzEaExnNx1DZ99t0iIS8bSxozqDctSrVFZqtZzw8JaO4u+nJDJZBQpZkuRYrbU/LsJTWZGFkF3Qrnp+4hbFx6x7rfD/Dn3KLW8KtCyZy08arnm2xdbEAS69K1LIXtrFk47yNh+G5i1shf2jjZq58rlMqZNb8+IYZuZMf0Aq9b0o1AeQr95w/JcvhHChh1XqFHZGZcSdjrt2dWhIC09y/DXJX96N6mab2eZD/TxqMTgo4c5+/wZLVzVW0p2cfbg0IsATr96orHTzsfUsPXkWOQp7sTdpWGh+rpsOXeM6gICpF/6JmJaOoAokX8qVIATJ8A0h7v748ehXz/QYycnCYmvzezZn//vbWqq+r6EfjgSFkimUkkn59wfn3/MvqAgUrOy6O1RSW97uBr0gvvPX/O9dw2MDXXPNW3c7cezFzFMGNECa8u8s54b/7zIjevPGDnKi0qVimsUPzUlnSkjtnHh1EMGjG7K6Cmt8y2ks7OVXD31gMm91jCo8VwObbpEhRouzN46hJ03f2Xc4h40bFNZL0I6NxSGBnjUKsX3U9qyzmci689Ool2/+vhfC2ZyrzUMbjKf/esvkJRPH2eABs3cmbemD+/jU/ihzwaePYnUaJ6lpQm/ze5Cenom06fuJy0td7s4QRAYN6wZ5mZGzFp6PF92eUNb1kIpiqw/qVnDFU1o5OxMUQtLtt2/r9F4z0JOOJlZs/e5doc4P1DctBiFjQpxI1b/NrqCzAYU7ojpl/UeWxMkMS2hH2rVyj0DvXMnjB4N3+hEt4REfunZE9atg+LFVZbrxYur/i7VS+uPfaEP8LBxoLS1+oPLoiiy/cF9qto7UF5PB51FUWTVUT8cC1rRvpb2WbcPBD59za6DN2ntVZE61V3yHHvh/CN277pOm7aVaduuikbx09MymTZqBwH3wpk4pxNd+9fLdwMW/2vBjGy1iFnDNhMRGkOfn7zZenU6U1f3o0q90t/Mm9rRpRDfT2nL9uszGL+4B1YFzdkw5wj9683i8KZL/1ezrSvulYuzdMsgDI0MmDhkCy+eRWs0r0SJgkye2paQkCgW/34iT4eLAlamTBjRnJAXMWz565rOey1a0IqOdSpwyC+AiLe6dSP8FLlMRs+KFbn26iXBseqb28gEgU4lK3I9OoyXSfFqx3+KIAjUsK1OUMJjEjMTddly3hjWh8z7iEr9/Hy0QRLTEvqjWTOVcM7pMdwff8CMGV9/TxISeqJnT3jxQvWQ5cULSUjrk+D3MTyOj9b40fHt1695ER9Ptwr6e5x7/XE4j15GM7C5Jwods7xKpciStWexLWDGyP4N8xwbGRnP4kUnKVvOgREjc/Duz4HsrGzmTd5HwL1wJvzWkcZ5HILThOiIOOaM2MKkHqtJTU5n0orebLw0he4jvfRWp6wPDI0UNO5QjUV7R7Hi2I+UqliMNTMPMbL1Iu77BecrtmPxgixc3w+FQs6UEZp7UdeqVYq+/epx7lwQp0/n3fa7rqcrzRqUY8fBG0REatZ5MCcGNvcEAbad060bYU50Lu+OgUzG/qBAjca3L656jx7X0XO6uk0VlCi5H/9Ap/l5IRjVBpSQcUvvsdUhiWkJ/dK5s6pTYk789hssXfp19yMhIfGP53j4I2SCgHcxzezt9gcFYqpQaFTnqSmbz9zCzsqMltV1t9g7fSGQJ8/eMLRPA8zyOAioVIosnH8cgKnT2qFQqBfvoiiyct5x/M4/Zui4FjTy1v1GIiM9kx3LTzO46Txu+gbRe2wL1vpMoEHryvnKQouiSHJiKtGv3vE88BUP/J5y79Jjnt4P43VoNAnvktT6NKvDtbwjs7cOYdra/qSlZPBzz9XMGraZ6AjdRaq9ow2zVvYmJTmdKcO3kfBeMy/kHj1r4+HhxB8rfHjzJm8RPqxvAxQGclZvuajzPgtZm9OqelkOXwvkXaJ+/JoLmprSsIQzBx89IkuDckxHc2sq2xbleLhuDdqKmzpRQFGAe3H6F9MoPAAjxAzN/LP1iXQAUUL/DBoE797BxImfXxs7FgoUgL59v/6+JCQk/nGIosjx8CA87ZywM1F/sColM5MTwU/xLuWGmaF2LbZzIzAsiptPXzK2Qz0MFbp9LKakZrB22yXKudnTtF7ZPMceOXwHf/9wfhrXkiIaehBvX3uBE/vv0LV/Xdr3yKFploaEPY1i/g/bCH0cSf1WlRj4cxsKFdXM0/tj4mISCHnwkpCHL3n2UPU15nUcSg3EsmUBM0q6O+JaoRguFYrhWqEYDs52Gh8uFASB2s0qULV+aQ6sv8ieVWe5d/Upo2Z1pmFbzcplPsWldBF+WdKdKcO3MeOHncxd3QdjNQ1z5HIZ4ye2YtCADSz6/QTzF3TLteSmoI05vTrVYP2OK9x7GE7lCrqdXu7jVZXD1wPZffE+w1vnz7rxA53KlePs82dcCQujobOz2vGtnMoy695ZnifEUtLSVqu1BEGgcoGK+L29TqYyE4VMf+YEgmCIaFgZMq7rLaamSGJa4sswYYJKUM+f//m1gQNV/tPt23/9fUlISPyjeBwfzfPEdwword7rFuBMSDBJGRl0Lpe33Zw2bPa5jbmJER3r6J7t3XnwJrFxycya2A6ZLPca5tcRcaxfd4HqniXxbqlZmcbxfbfYvvYCzdpVpv+ovBuG5IYoihzf7sf62YcxMTfm1z8H4dlY85+hUqnkyd0XXDl2D7+T/kSF//8a26IlC1G6SgkadayOubUJ5pammFuZYmZlglwuIzkxjaT4FJITUkh6n8LbyPc8C3jJoQ0XyMpQdfYztTDGs6k7dVpWolrj8hibqr9RMjI2pPsoLxq1r8LCsTuY/8N2bl98zPBfO2Jqrn0TGI9qzkyc3YnZE/cyd9I+pi/qqvZgp729NYOHNGL5sjOcOO5Pq9a5H4jt2rYaR848YMWm86xf2FunpwAli9jSyMOFPRfv069pNUyN839D2ci5JAWMjdkfFKiRmPYuVpbZ985yPDyIUe71tF6vknVFfKMv8jQxhPJWed94aotgWBMxaRmiMh5BpluzHF2QxLTEl2PuXJWgXr/+P7+fnQ1du8LJk9C48bfZm4SExD+CEy9VJR7NHN00Gr//URDFLK2oXjR3j19tCIuO49z9YPp7VcfcRDeP5jcxCew6dIum9criXib3hhZKpcjvC08gl8v48SdvjQ4O3rkWwsq5x/Gs58aYqW10OmyYEJfM4vG7uXEukGoNyjB2YTds7DSriX50J5TzB27hd+I+sVHvMVDIqdKgLG36N8C1ohMu7o6YWepmTZiVmU3400hCHr4k8OYzrp9+yIWDtzEyMaRa43LUa1OF2t4eKNQ4qxQpZsuC3SPYtdKHXSt8CLwdyqRlvSitoTvKx9TzKs+IuGRWzj3OuiVnGDZefUe9Nm2rcOnSE9asPke16s4ULpxzszIjIwVD+9Tn10XHOHU+kFZNdbt56+dVnfP+zzjoF0DPxrpl4j/GUC6nXZmy7Hz4gPdpaVgZ530jUsTUgmp2xTj58rFOYrqsZRkUggH+8Q/0LqYx9AREyLgJxs30GzsPpJppiS+HIMDq1Sqv6U/JyIB27eDW1z8oICEh8c/BJ+IpnnZO2Bqrt1yLTk7CLzyc9mXLIsung8UHdl24j4Fcni9f6U17/EAUGdonb+/cM6cf4u8fzpBhjfP0J/7Au7eJzJ+8H6eSdkye11kn+7uwp1H80G4Jdy4/ZvC0dvy6cZBGQvrx3VAmd13Bj61/5/ROP9wqlWD8yr7sDpjPr9uG0XFoEyrWLqWzkAYwUMgpWd6RZt1qMXZxL3b6z2Hu3tF4da1J0K3nzBu6kcH1ZuLz13W1tdZyAzm9xrRg/u4RKLOVjOu6krP7dft8afOdJ+171OTQzutc9VVfGyyTCYwb3xKlUuSPlWfzHNukbhnKl7bnz11XyMjM0ml/FZ3tqexSlJ0X7pGtJ9vZjuXKkZGdzakQzQ50NncszZP3MYQnaV+rbiw3ooxlaR681+zQo1YoKqKqm9a//V5eSGJa4ssil8P27Sqnj09JSgJvbwjS7VSwhITEfzevk98T/P4tjRw063h4KiQEEWjtVlov66ekZXD8RhBelUtR0Eo3/+TXUfGcOh9I2+YeFM5DpCYlpbFh/QXKlS+Kt7eH2riiKLJoxiFSUzOYMv87nTobhgS8YnzXlWSkZ7Fw90g6DGigti45NCiCX/quYWyr33kW8IrvZ3Rk18N5TN80mMadPPMlntUhN5BTqW5pRsztyra7s/l12zDMrUxZ/MM2hjWcxeWjd1GqEY/u1Uuy8tiPuFcvyaJxuzi86ZJOexk0xotSZR1Y+tsRYqMT1I63t7emZ6/aXL3ylNu3Q3MdJwgCg3rUIyY2ieM+ebuA5EW3Bh68jk3ALyiHblI6UN6uEMWtrDgZ/FSj8Q3/fs9ejHym03rlLMsQkfqa95n6tbETBENQuEOmZt7Z+kIS0xJfHkNDOHBA5UX9KbGxKqH94sVX35aEhMS35cLfH8QN7fP2Y/7AqeCnuNrYUMpWu0NPuXHm7lOS0jLoXFd3i7mt+64jlwn07Jh3zff2bVeJj09m5CivPGuqP3Bk9w1u+4Uw+MfmOJXUvnPeo7svmNRjFSZmRvy+dxRlKudd8vA2Mp55wzYyvMkcAq6H0GdCazbd+JWOQ5voVH+cX+RyGZ5N3Vl+eiJT1g8CQWDO4D8Z1Xw+D/zyFnwW1mbM3Pg9tZtXYM3MQ/y15pzW6ysUBkyc04n0tEx+n3FIrYgH6NzFEwcHa/5Y6UNWVu4e2FUrOlGxbFG27b+uc3a6kYcrtham7L+iH1cMQRBoUcoNv5cviU9T3xTH2cKG4uYFuPBaNzFd1lJ1Q/wo4YlO8/PEsDJkBiKKGfqPnQuSmJb4OpiZqboh5uQLGxEBXl7w5s3X35eEhMQ342Lkc4qaWuGigSNAbEoKNyMi8C6lPzu8w9cCcS5sQyUXB53mf5yVLmiTuxNJeHgsB/bfxrulB6VL26uN+yLkDeuX+uBZz43WXaprva8H10OY3HsNVrbmLNwzEnun3H++oihyZvc1hjacxfVTD+g6qhmbbsyk+1jvbyKiP0UQBOq2rszq81MYt7wPye9TmdhpGasm7yE1OS3XeQpDA35e0YeGbSuzaf5xti89lWdzlZwoVqIgQ8e14O71Zxzapb7zoKGhAcNGNCU8LJZDB3P3ghYEgf7d6uQrO60wkNOmZjmuBIby9n2yTjE+xbtUKbKUSnyf555Z/5gG9i5cjw4jLSv3LpC5UcKsOMYy4y8ipgWFB5AJmV+gjCQXJDEt8fUoUABOnwaXHLJQISHQvDnEa99VSUJC4r+P9Ows/N6E0tDBRaNDdedCn6MURZq56EdMh0a94/7z17SrVV7nDoKaZqXXrfXF2FjBwEEN1cbMSM9k3uT9mJkb8dMv7bTe251LT5jefz12DgVYuGdknrZ3byPjmdF7NUvGbse5rAOrfCfTb3I7LKzzboH+LZDLZTTpUoM1F6bSblAjjm2+zPDGc3iQR9MWA4WccYt74tXFkx3LzrBx/jGtBbV3x6rUaliGjct8eP40Su34WrVcqe5Zkm1br5KQR9tzfWSn29YsT7ZS5PhN3TyfP6VCocLYm5tz5plmddMNHVxIy87iRky41mvJBTllLN0ISnis9Vy1KP52VPmKpR6SmJb4utjbg4+P6uun+PtDmzaQoh8zegkJiX8ud96+IiUrkwYalnicffYMBwsLytlpX/KQE0euByKXCbSuoZubwNt3SZy+EEhrr4p5ZqUDHr7iml8I3brXxFoDkbpllS+hwW/46Zf2WOcRNyfuXn7CL99voKizHQt2Dc+zi+HVE/cZ1mgWD64+ZcjMzsw/MAYHZ/20Zv+SGJsaMvS3ziw4MAZBJjCx01I2zDyQa2txuVzGmHnf0apXbfatPc+fc49qtZ4gCIyd3hYLKxPmT9lPphrhKwgCg4c0Ijk5jd27cvc7FgSBfl1rExObxJkLup0bci5iQ6WSDhy+Fqj1TUJue/JyceVSmGbZ5hp2ThjJDXSumy5rWZqotDe8y9C94U5OCPLCIHNA/IpiWrLGk/j6ODvDmTNQvz7EffImunJF1UXx0CFVrbWEhMT/JNfevEAuCNQopL55RUZ2Nn4vw+lQtpzOWeSPEUWR03eeUqtsCWwtdTt4eNTnAVlZSr5rUzXPcTt3+GFlZUKHjtXUxnzxLJoDO67j3bEqnvU0swr8QGR4LHNHbsXRuRDzdg7Dwjr317Vn+Wk2zz2CW6XiTFzVL18iOjtbyfMH4dy/9IiIkDfEv00gPiaR9zEJxMUkkJWRhbWdJVYFLbC2s8TazgK7oja41y6Ne61SGJvpZkfoXtOVVeem8OdvB9m/+hzPAyOYtvF7TMw+L02RyWSMmNkJAYH96y/gXMaBJhr8Pj5gVcCMMdPbMX30Dg7tvE6XvnXzHF+yZCEaNirLkcN36d6jJhYWOR/arOZRnJJOBTl06j6tvXSr22/lWZbZu88R/PotbkXzf6PZyNmZrf73uRURQb3iJfIca2ygoHrBYlx/o9shyNIWqqdMIYnP8LTV/PehEYoKUpmHxL8Ad3c4cUJVS/0pJ0+qOiRm536AQ0JC4r+bm9HhuBewx1yhXkzdj4wkJTOTuk66dY37lKDwN0S+S8Crsm4lI9nZSo75PKC6R3GK2udeRvH8eTQ3bjyjQ8dqmKjppgfw59IzmJgYMkDLxizpaRnMHr4ZgOnrBuQqpEVRZNPsw2yee4RGHauz8NBYnYT062dvOLz2LDN7rqCr82hGNZzJn9P3cuO0P9EvYzE1N6aMpwve/RrQcUQzqjQuT0GHAiTFJxPgF8zeZaeY2mkxnYuP5KcWc9k25xABfk81OuT3McamhoyY25WxS3rhf+UJU7qtJCmXVuCCIDB0Rnsq1nRhxZS9hD56rdVaNeq54VnPjV0bLvM+Tn2NcrfutUhNzeDI4Xu5jhEEgbbNPXjy7A1PQtSXkORE40quyAQBn7ualWaow7OoI4ZyOZfDNBPInoWcePI+hrh07Z8oFzd1QiEYEJL0XOu56hAU5SA7HFGZqPfYOSFlpiW+HTVrqjLQrVqpfKc/ZvduVY31H3+o/KolJCS+OYIgFAO2AkUAJbBOFMVl2sZJzcrE/91r+rt5ajT+SngYckGglmMxbZfKEZ+7TzGQy2jooVmJyafcuBdK9NtERg5olOe4v3bfwNhYQbv2eWevAe5ef8bNK8EMGtsMSy1rllfPOMizwAh+2TAw18OGSqWS1VP2cmzzJVr2qcuIuV01bt/9Yf7tswEcXnOWO+cCALAvYUfddtXwaFCGinXLYKtha/S05HSCboRw/2IQ9y89ZtfCo+yYf4SiroVpN6QpTbvXxjSXbG5ONOtWC1NzY+YP38TEzsuYvWsk1gUtPhsnN5AzaXkfRrZexKzhm1l2eCzmWlj9DfrBi6HfrWLn+osMm9Ayz7GuroWpXt2ZAwdu0eU7TwxzaTzTrEE5Vm+5yJEz/ox3LaLxXj5gY2FKtVKOnL33lOGta+X7yY2JQkFVBweuhGsmpj88WboZ85LmjtpZVhrIDChhVpxnX0BMo/i7fCvrMRhqf4hXW6TMtMS3pWlT2LULcvpHffVqmDbt6+9JQkIiN7KAn0RRLAvUBEYIgqB1X+97sRFkKpV4alDiAXA5LAyPIkWwVNOZTRNEUcTnXjA1SjthaapbvCOn/bGxNqWeZ+7+2G/evMfXN4hWrSthqUawKZVKNiw9Q2EHa9p11ewG4wOn91zn9F836DaiKTWalM9xTHZWNovHbOfY5kt0Ht6UkfO6aSykkxNSObTGh0FVJzO9y1JeBL2i79QObPafzyb/+YxZ0Y9GnWtqLKQBjM2MqNK4PAN+7cLy89P4K3QF49d9j7mVKavG76BXuXGs/XkXr59p7vBUt3VlftkylIiQN0zosISY1znX4Raws2DyH3158+odi8ft0qrWuLhLIVp0qMrRvbeICItVO75r91rEx6Vw+lTujh0W5sY0rlsGn0uPSEnVzcqtaZVSvHgTR8hr9XvShLpOxXn89i0xyeoz8BVtHDCWG3AzWvtDiACu5iUJTX5BllK3Q5i5YvC3mM7Uz+FMdUhiWuLb07Hj5y3HPzB7Nixa9HX3IyEhkSOiKEaKonj37/9OBB4BuffPzoWb0eHIBIFqduozze/T0ngY/YY6Ttq3hs6JoPA3vI5NwKuKdjXJH4h+m8i1O8/xblIBgzw6Eu7bexNQeQ+r49zxBzx7EkX/kU0xNFJovJeQgFf8Mf0Aleu40WtsixzHZGZkMXfoRs7tvUGfCa0ZMLW9RtlLpVLJ4bVn6eM+jjUTd2FV0IJJfw5hy8MFdB/fhiIl9HMQFMDc2pQmXWuxzHcaS89NoUZzD46s82Vg1cksGbmJhHdJGsWp2qgcs3aN5G3Ue8a3X0Jk2Nscx5Wv5sygyW255hPA3rW+Wu2199CGKBQGbFyRd6dDgEqVnChdxp69f93Is4Nj22YVSU3L5Owl3YRfk0qlVKUe9zRruKKOun+/166+VC+QDeVyKtsW1VlMu5i7kClmEZ7yUqf5uSIrBDJbxKyv0xROEtMS/wwGDIDff8/52rhxsHHj192PhIREngiCUAKoDNz45PuDBUG4LQjC7ZiYmBzn3o55SVnrwlhoUC99M+IVSlGkjp7qpS88eIZcJtCwom4lHqcvBKJUirTN48BYamoGJ088oHHjcmrbhmdnZbN97QXcyjnQoHnOmeWcyEjPZMGY7VjZmjNhWS/k8pw/zlf9vIerx+8z+NdOdB/rrZGQfvs6jsntFrF6wk5KVynJMt+pLPGZQsPONTBQqK8OzczIIvrlWx7dDMH/0iNeBUeSmpS7J/THlKnmwsQNg9kasIAOI7w4u8uPYbWnc/+iZkLTvaYr8/aOJiUpjandV5KcmLM9Xbt+9ajfqhJbfj9JSOArjWID2BS0oEvfOlw5F0TI48g8xwqCQNduNYmIiOPG9dwdL8qXdqCkU0FO+OrmOW1jYUoV16Kc9w/Raf5n+ylUCGtjY/zCNRPINQo58Sj+DYmZ6Vqv5WLuDMCzJM28rTVFEARVdjrzC1jv5YAkpiX+Ofz0E0yenPO1779XdVGUkJD45giCYA7sB8aIovgfvZZFUVwnimI1URSr2eVgY6cURR7GRVHJVrNGKXdev0Yhk+FRWPt60py4GvSCis72WOXg+qAJ568+oXxpexzyKGs47/uI1NQM2rSrrDbeJZ8goiLi6D5Ifavvj9m1woeXz6IZM68r1rY5W+j57rvJqZ1+dB3VjA6DG2sU1+/YXYbVns7jO88Zu7I/sw/+SOmqJXMdn5KYyqUDN1kwcA3Dak7lu+IjaF1gAL3L/MiYRjOZ4D2XgZUm0r7wYDoUGczAyhOZ2uF3Dq8+w5vwnDPHALb2BRg8uxvLfKdiYm7Mz+1+Z+OMvWRmqC8HcKtUnOmbBhMVHsvycTtzLOUQBIGRsztjZWPGkgl7crXWy4n2PWpgambE3s1X1I6tU6cUNjZmnDzhn+sYQRBoWr8sgU8iiYrWrb123fLOhLyOJTpesyx+XsgEgSr2DtyN1OyQZkVbB0Qg4F3eNxc5YWtog5nclPAUzW9oNMbABbJDEUXtDrbqgiSmJf5ZzJoFQ4Z8/n2lErp3h3Pat4WVkJDQH4IgKFAJ6R2iKGp9hxuaGEtSZjoVbdR3AgS4E/ka90KFMTLI/3n5d4kpPAqPpna5EjrND494R3BoNI3qlMlz3IkT9ylevCDlyuVdASOKIn9tvoKTsx01G2hedhIRGsO+dedp0rEaVevnfOjrZXAUKybuwr2GC70ntFYbMyM9k+VjtjKz50qKFC/IykszaN67Xo6Z7HdR8Rzb4MuU9r/zndMIZvdeyS2fB9g52lCnXTV6T+3ImD8G8Nv+n5h3bCITNgxh4G9dada7Ps7uxYh8EcOqcdvpU/ZHhteaytZZB3jmn/OBN1eP4qy8OIMWferx19KT/NhsDpGh0Wpfj3sNV/pOasOlI3c5vvVyjmMsrEwZMbMTz4MiOLHTT23MD5hbmNCqczUu+QQS+epdnmMNDOQ0b1GR69dDePs2d2eJRnVUv8cLalql50atcqrSDL+gFzrN/5Qq9g48j4vjXar61uIV/n4vP9BBTAuCgJNpMf2XeQCCQUkQU0Gpm1OKNkhuHhL/LARB5eARHw979vzntYwMaNdOJahr5N1x7J/Mjh0wZQqEh4OTk6osvGfPb70rCQn1CCpl9SfwSBTFxbrEeBCr+sCtoIGYTs/K4uGbN/T2qKTLUp9x/ZFKsNUqq1v99QU/VevjhrVyF76hoTE8CnrNsOFN1JZU3Ln2jOdPo/jxl/ZaZaU3zDmCwsjbhvfuAAAgAElEQVSAgZPa5Hg9LSWDOYP/xNDYkImrByDPo7YbVNnlmT1Xcv/iI7r84E2fqR1Q5OA+ERsZz+7fj3By4wUyM7JwcClM26Fe1GpdhXI1XNWu8zGvgiO5dvwe147fZdf8w+yYe4jKjcrTd3onyn5ysNPYzIgflvejatMKLB21mR+bzWH2gR8pWSHv0p/Ow5vy8Fowa6fvp0wVZ1wrfF6jX7t5BSrXcWP70tM0alclT3/uj+nQoyaHdl5n/zY/Rv6c981KC++K7Np5jTOnH9KjZ+0cxzjaF8CtZGF8rz6hW3vt3SdKORSkoJUZ1x6F0b62u9bzP6Wqg+r9eS8ykiYlc38yAWBjZEoxM2se6iCmAZzMinE++hJKUYlM0GOOV/73vrNCQa7ZkzBdkTLTEv885HLYuhVa5HCgJjkZWraEwK9nxq5PduyAwYMhLAxEUfV18GDV9yUk/guoA/QGGguCcP/vP3l7hH3Cg3eRmBoocLUsqHZsUEw0GdnZVMmpY6oO+D0Kw9rchLLFCus03/fqEyqULUphu9zroE+e8MfAQIZXM/WCZu+WKxQsZEnjlhU03sO9K0+5fjaQbiOaUsDuc/s3gDXT9vLi8WsmrOxLQfu8XTbexyYysc1CHlx5wvh13zNwZpfPhHR8dAJrJ+2kn/tPHN9wHq9e9Vh7aw4b/RcweG53KtQprZWQBnAsZU+XMS1Z7DOVXc9XMHhud54/DGdMo5lM67iI4Huf19DWbVuVJT6TkSvkjG81nwA1WVyZTMa45X2xsjVnzuANOdZPC4LA91PbkpyQyvalpzXev20hS5q08uD04XvEv8vb9cLR0QaPSk6cPOGfp3tI47qleRQcSeQb7Us9BEGgdtniXH8c9v/YO8/oJq61Cz8jWbbce68UGzDF9BI6CS29XNIg4SY3yU3vpPfeSL9feu8hFQg1BULoAYdmuivuvcqyJc33YyRjW6PRWJaBBD1reQGaM6MjWYP27HnPfjF3Ma9bjiHRMWgFQXWpx+Cw2LYL5a6S5JdIi6WF0mbndxy6hJdUj43JtQ6NXcEjpj2cmHh7w7ffwvjx9tuqqmDGDMhx74KFY8H999t3S29qkh734OFERxTFP0RRFERRHCKK4lDrz7KuHGNXVTGDQmPQqnBitxVLX84j4rrvKomiyMa9eYzrn4xG0/Us3vzCKg7nljNtvOMsXbPZwi+/7GHcuFSCg5Wzog9mFfHXlhzOmzsWnYpFfSAlbLz9xA/EJIVz7hWTZces+eFPVn6+gYtunsmIqcqphTUVdSyY/Qx5ewt5+PMbOfWicXbP9+XzS5g/6A5++N9KJl8whvf+epZbXruClPQEt3SjBAiJDOKCm2fz0Z6FXPnoHLK2HOLGCQ/zxGWvU1PeoSSfxLRYXlx5H6FRwdx33kIynbTiDg4P4J43rqC0oIrX7/5Sdkyv/nHMvnQcSz/dQEEX4vj+NX88rS1mFn+12enYWbOGUFRUw57dhQ7H2Eo9frPeAekq4wakUNdkZE+e+tfgCF+djvSoKLYVOZ5vezLC4yhsqqWy2XmcXmeS/KQ7Bu5P9IgEIQDR3AM51p2fqsefwQGCICQKgvCbIAh7BUHYIwjCLcdrLh5OUPz8YOlSyMiw31ZUBNOnQ0nP10K5E0eLo1Uumvbg4W+NRRTZX1vGgFB1zvDu0lJiAgKI8pdfYNcV8sqqqapvYmRagkv7b9omfSFPUMiW3ptVRE11E5OnKNdUA/y89C+8fbyYfZ7zhi42Nq7eTe7+EubfeTrePvYCvKG2ibce/IZ+w5K5bMEZisdqbjLyyEWvUpJXwZPf3c6YWR1LaVqMrTxzxRt88MgiRpw2mLe3PcOdb19DbDdajzvDN0DPRXeexcdZLzLvvnPZ9FMmt536OEXZHcVhVGI4L6y4h7jeUTw+73WydyuLsEFj+nLp7bNZ8/2fbFsjnwoy79ZZ6Ly9WPSm+qi8xJQIRozrw89LlR1ngPET0vDy0rBhg+NOhXHRIfRNiWTzdteMotH9JFGaeUidAHbGkOgYssrLsajI4h4QIn0u9tV03V2O841FQKDQ4Jqz7QhBEECbDCb312N35ng6024J//fwDyckBFauhL4yX2CHD8PMmVAtH85/IuIo3ctNqV8ePJzQFDXW0mRqJTVIXUbxvopyBsgkgrjCzmzpizqjt2su99a/ckmKDyMmKtjhmE2bDqHRCIwarVxjajZb+H31HkaNT8U/UF2qiCiKLHrzV2KSwpk4Wz6W79Pnf6KuqoEbn7lYsezCbLbw3NXvsH9bDne9cw2DO7ntDTWN3H/O86z9ZjNXPXkxD352E4lpXSu1EUWR6tJaSvPKMZvUJ2UA+Af5ctn95/Pssnuor27gtmmPs39bR3cxJCKIxxfdhm+AnofmvEx5ofJCwDk3TCe+dxRv3P+1bCJISHgAMy8czW8/bqe8uEb1XKfMHExpUQ37FRxnAH9/HwYPTmTzJuX4ulFDU9i59wiG5q43cAkL9CMxIpidOe4RpQMiImloaaGwrs7p2LRg6Tw9WOc4ocUR3hod4d5hlDR331G3QxsPZvdcXChx3MS0u8L/PZwEREfD6tUgd6t3504480yplvpvwJNPSoZ7e/z8pMdtfPYZpKRITSFTUjz11B7+Odi+aFODnddLG00msqurGRDhHjG9K7eEAL03vaLDurxva6uZv/YcYWSG8sLFzZsOM2hQAgEBygJ51/Y8qioamDJLfa307i3Z7P8rnwuumiIrlEvyK/jp43XMmjuevkOUr84/f24xG5Zu579PX8yEszs642VHKrn9tCfI2nSQu9+/ljm3nu60nMPQ0Myqj3/ntZs/4IFznuOqIQs4O+QKLkq8jstSb+GMwPlclnozC6Y/wQtXvcnXLyyhNE8+g7w9A8em8tIvD+Hj582CWU+xZUXHeLnIhDAeX3QrTfUGHp/3P0ytjmPzvH10XPPYBRRml7HyC/nkjvOvmoLFIvLD+787nZuNU6b2R6fTsmal84zo0WP7kJtbQWmJ45roUUNTMJks/LXbtai4wb1i2ZXrJjEdKZ2nex3kxbcnQu9PiLcvB2udj5UjRh/do2K6K50uXeGEqJl2FP5v3ea0AYCHk4CUFElQh8l8EW7YABdcIKV9nODMnQtvvw3JyVJwSXKy9G9bmsfJskDRc8FwcnKwVr2YPlRVhclicZszvTuvhIEpMS7VS2cdKKbZ2MoIBZFaXl5HdnYZY8Y6LgOxsXblLvS+3oyekKp6Dove+pXg8ACmz5FPevj0hWVotBouvX224nF2bzzAF88t4dSLx3HuddM7bMvZXcBtUx+jvLCKJ39YwLSL5JMnQHKed6/fx8Kr3+KixOt44ao3+e3L9VQV15DYL44z/3sa1780n9vevJqL7zqbgaf0w9RqYvsvu3n3vi+4PO1W7p79FL9+sR6jQhvtxLRYXv71IRJTY3n4wpdY+dHaDtt7D07i9v9dyYHtOXzy1I+Kr33UtIEMGtOHL15aQXOT/XNGJ4Qx6cyhLP9iI/W1TTJHsMc/UM+oCamsXblHscshwFjrZ2PzZscL4gYPiMfLS8P23a7V/g1KiaG8tpHSascxfGrpFxGJAOytcK69BEEgNTii7RzvKjG+kph2t+gVtAmAESzuabXuiOMejacU/g9SAwDgbYCRI0f27KWFhxOb9HRYvhymTbN3oleuhMsvl1SZtmuryo81c+c6jsJTWqD4T4nPs10w2F6n7YIB/jmv0YM8h+oqiNT7E+zt63SszQ1zhzNtaGnlYGE5V8zoeuQYwPbd+QgCDB3ouP25rcPd2LHKnRVNrWbW/ZzFuCn90Pt6q3r+nH1FbP1tL5ffPhsfvf0+efuL+PWbLVxw3amEKzSTaahp4rmr3yE6KYIbXpjXYdvBzFzuPuMZ9H4+LFx1v8PYObPJzA//W8nSt36m8FAJvgF6plw4jpnzJ5M+Lk3VosSS3HJWf/I7qz75nWfm/w//YD+mXTKeufedR5jM/MNiQnh+5X08Mfc1Xrz+PRrrDJx/09G0pwnnjGTmZRP5+qVlDJ+aTsakAbLPKwgC8+85mwXnvcSSD9Yy54bpdmPmXDOVNT9u56dPN3DxDac5fS0Ak2cOZsNv+9iTmc+QkSkOxyUmhhEbF8KmTYc4+5zhsmP0PjoGpsWRucs1MT04RSrH2ZVbQnSofNqLWvx0OpJDQlQ50wB9gsJZUbAfURS7vDg1Vh+DwWygzlRHsM5xKVWX0VrXSJgLQev8It5Vjqsz3d3wfw8nIaNHw48/SmkfnfnqK7jhBsnS/ZtyMixQ9CSanLxk11XSOyhc1dj9lRX4aLUkhyhHu6nhYGEFZotIepJrXRR3Zh2hT3IkQYGOLwL+/DOHqOggklOUv7B3bc+jvtbApOnqs4CXfPwHPr7enHmZvFP89Wur0Pv7yIrD9nz0xHdUFFVz93vX4NfutTTWGXj80lcJCPbj5d8edCik66sbuO+sZ3lrwaeERgdz57vX8mX+/3H7W9cw8JR+qgVUTEoklz14AR/te4lnV97P2DOGs+L937h+zH3s/1PetfUL9OWxb29n4nmjePveL9i1vmPixXXPXkpcnyhevulDxS6Jg8b2ZeS0dL75v59pMbbabe+dHs+ISf1Y+skfTp1mG2MnpeGj17H+V+VkEUEQGDOmD39l5mFSqCMfNjiRgzllNCk49o5Ii4/AS6shK989JRP9IyLZX6nObe4TFEFNi4HqFueNXjoTrZcWMLo9Hs+WL93DddPHM82j2+H/Hk5STj0VvvxSqhHozFtv/a1V2cmwQPFkuGDwIE9hYy2J/urEcV5NDckhIaoi9JxxuFi6xZsar07It0cURQ5klzIg1fECPFEUydpTyOBBzuPiMjcfRuulYdiYXqqev7XFxLplOzhlxiDZhiJ1VQ2sW5rJaXNGExTmOPXkyKESln2wltP/PZn+Izu65+/e/yVlBZXc/cF1RCXKXwwU7C/i5vEPsev3vdzxzjW8+NvDzLh8Er5O6sOV0Gg0DJs6kLs/vJ7XNz6BzkfHHdMeY83XG2XHe+m8uOPNq4lOjmDhte/Q3Ghs26b39+G6Zy+lOLecn97/TfF5z716GnVVDWxYLt/ie8acMVSW1rFLoRyjPXpfbwYNS2L7ZucRbIMGJWA0mjh82LFo7N83FotF5FBu14Wlt86LpMgQsovdU9bQKzSUwro6TCqyqxP8JUf5SGPXc7LDvaUSzqoWNwcKaK3pMxbXyk/Ucjyd6W6H/3s4iTnvPHj3XfltTz8Nzz9/bOfjJtQsUPy7czJcMHiwx2g2Udbc0PaF64y8mhpS3OBKA+SUVOGj0xIb5rjZiiNKy+uoq28mrY/jOL+y0joqKxtIH+g8di9zSw79ByXg6+ej6vl3bjpMQ62BSWfId4H8ZdEWWo0mZl82QfE4Hz76LTofL+bec3aHx7f9sptl7//G+TfNYuBY+RrubT/v4paJD9FQ08hzq+5n5vwpqubeFXoNTuK19Y+TNqI3T817jY8eWYRFRsD5Bui5/Y2rKM4u44OHF3XYNuLUQQydPIDPn11Co0LN87BJ/YhJCmf5J3/Ibh99ajo+eh0bVuxUPf+ho3uTn11OZblyrfLAQdJnJGuPY6c0rbckAA8oCG4leseGk1OqnG6iluSQEEwWC0X1zhM9EqwXykUuiOkw71CgB8S0EAzoEC1udrw7cTzTPLod/u/hJOeKK+BFBzc17rrLsdg+gXG2QPGfwMlwweDBnqIm6cs4ToWYtogiebU1JAW7R0xnF1eSEh3mkst9wJpxnNrbcb7ynixJGKUPVA6kaqg3cGhvERmj1LnSABtW7ULv583wSfbNYkRRZNknf9B/RC96DXD83FmbD/HH4m3MuWU2oe2i/RrrDLx0/bskpMUy/6ELZPdd/MYq7j/rWSLiw3ltwxMMGu88Q9tVQiKDeGbFfcycP5nPnvqeJy55leYmo924jEkDOOe66fzwxip2/bGv7XFBEPjPYxdSV9XAVy85lhMajYZZc8ezc8NBjsg0adH7Su/3htW7ZQW9HEOtv9MdW5UzoqOigoiICFQU0xFhAYQG+7V99rpK75gwjpTXYlRIN1FLivUczKtxHhcY7y9drLriTPtqfdFrfNwupgVBA5rwf7Qz7cFD97ntNnjgAflt//0vfPPNsZ2PG5g7F3JzwWKR/vwnCWk4OS4YPNhjc6vUONOlDQ20mM3uE9MlVfSO6XqJB8CB7DK0GoG+yY4XQmbtKUSv19Gnj3JDk51/5mKxiAxzkkNtw2KxsGn1bkZOHoC3j85u++5NhzhyuJTTL5PpFGtFFEXeffBrQqODOf+GGR22vXPfF1QWVXPnW1fjI7MY8qd3f+H1Wz5k9KyhvPz7I8SkuCdZRQlvHx23v30N1zw3l/U/bOXpea/L1i5f+eiFxPaOYuG173Yo90gdmszUC8fywxurKT/i2J2dfvFYtF4aln+6Xnb7KTMGU1lSy8Gd6hp+9OkfS0Cgnswtzks90gfGk5XlWEwLgkBq7ygOuiime8WESxekZd0XprY1C7kqxHSQTk+AzodCF8S0IAiEeYdSZeyBvhHaSDD3bBqcR0x7+Pvz2GNw/fX2j1sscOmlUqSehxOKY3HB4InfO7GwuVXxfs7FdEGtNDY5pPur+g3GVoqr6ugd2/V8aZCc6eSEcHxkxKyNrKxC+vWLRatV/kr9a0sOPnod/Yeo68K4/698qsrqOGWm/GLF5Z+uxz/Il4lnOe6iuGnZX2RtPsRl957Tob75rzVZLP9gDRfcPJsBMl0dd/6+l9du+oCRMzN4eNFtHRYs9jSCIPCvW8/g+pcuZ+PSbXz44Fd2Y/T+PtzxxlUU55Tx4aMdTZP5D5yPaBH55OkfHD5HWFQw42Zl8PNXm2QXIo45NR2NVsOGVc7zowG0Wg0Zo3rxl4q66fT0eEpKaqmsbHA4Jq13NDkFlRgVFlM6wvZZzy7ufqlHpL8/Plpt2zmphCAIxPsFuSSmAcK8w6hscU95Sgc0kfBPLfPw4MFtCAK89poknDvT2grnngubNh37eXk4bpwsed1/J8oMknCI8nUe11XcINWdxgV2vca5M0WVVkc8wjWXO/9IFSlJjhM6LBaR3Jxy+qY6b5F+YE8h/QbG4+2tLpV2p7Vb3sjJ9qUVZpOZLT/vZsKZw9D7OY7YW/npOiLiQ5l52cQOj3/69PdEJoRx+YPn2+3T2mLilRveIyYlkvs/u0mxm2JPcs71M5l95VQWvbiU7J32K5QHT+jPrPmTWfrur1SVHHVOY5IjmD53Amu/3SJbJmLj1H+Npq66kayt9gI4MMSf/sOS2blJ3SJEgIFDkygrqaWmyrFIBkhLk1JlcrIdC7xeSRGYzRaKStV3Y7SRGCl91gsrXRO17dEIArGBgZQ0KL8mGzF+QZQ1qxvbmWDvYOpM3c/HtkMTBpae7ZTsEdMe/hloNPDhh3C6zBrWpibp8d27j/m02uNxSo8dnvg9ewRBWH48n7/S2EiQTo+3ihz4CusvL7Jzcb0LlFRLX+wxoY6TLhxhMlsoKa8jXiG7ubysDqPRRFKSchmJxWIh51AZvdOci24b+zLzSOgdJZvisW97Lo11BkZMlc9UBqkz4bZfdjP+rBEdBPG+rYfZ9cd+zr9pFt4yudU/vL6Cgv1FXLfwcvyDuv47qCqpZu2ijfzv5vd5dv5rLH1rNXlZBS415PjPU5cQEOrP67d+KLv/hbefganFxI9vdrwDOfHckRgNLWz7xfH/+xkT0vDSadm2Zq/s9gHDkjm05wgtRnXucG+rSM4+oFyekZgkOccFBY5dWNtnrkihW6IjfL11BPvr3dK4BSR3uqJJXZfhMB8/Kptd60gc4OVPQ6trQlwRIRDEHhDp7fCIaQ//HHQ6WLQIJsisaq+uhhkzINv5LbiewOOUHltO1vg9QRCGO/gZAcjHQRwjKpubCNerE2bljY14a7UE+qhLvFCitEb6EnWlgUVZRR1ms4W4aMflJvn5UgSZMzFdfKSaZkMLvVQ42CDVOu/LzKP/MPkW5tvW7EWjERg6wfGCwD9/3k2r0cT4TmUgP733K74Bemb/e4rdPpVF1Xz6xHeMOX0YY04fpmquAJm/7mLhVW9wRf+buSjuGp646EVWvP8r21bt4JXr3uaqQbczJ/o/PHL+cyx5cxVmhZzl9gSFBXDlExez+499/PalfRvw+L4xjDl9KCs/+r1DO/HB49MIDPVnw9LtDo/t668nfVRvtq+Rz4fuPywZU4uZbIX65vbYfrc5B5XFdGioP/7+Pm2fHTni2sR0151pgKiQAPeJaT9/yhvVdYQM1/tRaWxy6cIpwCuAZkszJkv3F062R9AEg2hAFHuuS7JHTHv4Z+HnB0uWwFAZ3VBcDNOnS38eYzxO6bHlJI7f2wq8ACzs9PMC4J7VfC5SaWwk3EedmK5oaiLCz6/LXdTkKKmuRyMIRATbu7vOsLmCcQrOdEGBJIgSEpXFtE1g9UpT1zimrLCamsoG+g2V/9BuX7uXfsNTCAxx/J6uX7KN4PAABo47GnlnaGjm9++2MPmCMbIZ0e/e9wWmFhPXLbxc1TxbjK38360fcNdpj7H++80k9Ivjmucu47VNT/F99Yd8VfQOHx54lTveu54xZ44ge2c+r17/DndMfZjSPHWLwmb+ewppI3rzzr2f01Rv3xBk5uWTqC6r5c+fj9Y3e+m8GDN7KJuX71Bs4jJiSjrZewqpKrN3gG0XMvv/ylM1z5Awf8IiApyKaUEQSEwM44iCMx0S5IuvXudSmQdATGhg212Z7hLh50e5Smc63Mcfo9lEo6nrwjXASzpHG0xudqcFa7mYxXm8n6t0SUwLgtD1/408eDjWhITAihWQKpOZmp0tOdRVPbDIQYGT1Sk9XpzE8Xt7gf+Koji18w/Qs9lQTqgyNhHWBWc60s89Xzdl1Q2EB/mhU1Fe0hmbkFEq8ygoqMLf34fQUOXXlnOwFI1GILm3ukSMvZmSgJNzppsamjm4I5+hE+zj8my0GFvZsnIHY08f1mFh5B8/bKW50cj0efZ38Hb9sY9fPv+DOXecSZxCrraN/H2F3DT2Xr5/dRnn3jSbLwvf5vEf72HOnWfTf3QqXjovaVFa31hmXTGVBe/fwMeHXufeT28mZ1c+/x16J2u+kk/TaI9Wq+GGV/5NZVE1nz9lv6hw9MwMQiKDWPXJug6Pjz9rOA21Texct89uHxsZE6X3cNfGQ3bbImJCiIgNYZ9KMQ2SO+1MTAMkJoa3XYjJIQgCcTEhLpV5AESHBFJW474yjzqjEaPJuWMcZr1grjKqc7LbE+gl3T2qN7lWJuIQjfXOktj9GnKHT6FmkCAIpwiCkIX0HzWCIGQIgvB/PTYrDx66S3S0lOIRL5O9uns3nHEGNLr5hFXgJHZKjwsncfzeIzj+f/2mYzgPO6qamwj1ViemKw1NhLuhXhqgrLaBqJCu10sDlJTVodVqiFDoLFhUWE1CYphTFz3vcBmxCWHoZSLo5Di4swBvHy969bPvvHjgrzwsZgsDx9incNjI2nSIpvpmTjmzY6nG799tIbZ3FAPHpdnt8/Fj3xCREMbFd5/jdH5lBRXcPulBKgureHzxPdzwypWy9ddyTLt0Im9mPk/SgHievORl1n5tX77RmQGj+zJz/mS+e3UZdZUdRaKXzoupF45j87LMDgsOh08diI+vN1tXO26+0ndQAj6+3uyVWYQI0C8jkYM7j6h6XQApfaPJyy53mk+dkBhGeXk9RpkkERtx0cEUyzjmaogODaCmsdktWdMR1nOx0uC8TbitlMsVMW1zphvdLqZtznTP1U2rdaZfAmYClQCiKO4AJvXUpDx4cAvJyZKgDpe5/bppk9RF0eh4pbc7OYmd0uPGPz2vWw5RFL8RRXG/g21tlp4gCPOP3awkGk0tBHqrq4FuaGkh0EedMHNGXVMzwf6uxbrV1DYRHKhXjLyrqmokPNy5WK8oqycyRn06SemRSqITwmSTNIqsKRBJqY5LRrJ3S/nI/UYczbQWRZG9Ww+RMXGAnfivLq1l1+97mTV/Cnon3RlNrSaevPglWppbeWnd44w903E0nyNie0WzcM2jpI9L48Wr3+TIgSKn+5x13XRMrWY2Lt1mty1j8gBMrWYO7zjqIvv4etNrYALZux2LYa2XlvjekRTlypecxCZHUFpUpbp5S2RMEK0tJuprlYVnmPUCrabasegMCfKjtq7rohQgyE8q4alXSDNRS4C3dC42tDg/VoCXNLaxtetlHj5a6XPXbGnu8r6KCNbzX3R+MeAqqss8RFHsnFyubvWABw/HkwEDpJKPAJkvu9WrYd48MPf8R/kkdko9nJjcciyfzGyx0Gw24e+lTiA3trTgr3OPmK43GAn0dW0hY129gSAn+cq1tU2EKNQt26gqryc8Uv0iyLLCaqLiQ2W3FedX4uXtRViM44WRuVlHCIkMIiTyqIAvyS2nvqqRtBH2HRi3/bwLi0Vk3NnOhfF7935O1sYD3PHudST2U+76qITOW8f9X96Gl7cXj81ZiNGgLNZSh/UiIj6MP1faO81pw6WLhv3bOjrMKekJ5O45orggLjY5kpI8+Sqo6PgwTC1mairU1fGGW99vZ23FQ6xlQTUKbc+DgnyprW92aTFfoPWCqN7Je6oG27nY2OJcIPvZxrpQM+2jkeZsNLt7oaD1/BfdLNLboVZMFwiCcAogCoLgLQjCnVhLPjx4OOEZORIWLwa5ZIBvvoFrr5UiNnqYk9Ep9XDC0v2VfV2gySzdyvZTKaYbWlra3LDuUt/kupiurW8mWEFMWywiNTVNhMhE17VHFEWqKuoJi1AvpksVxHRJXgUxieGKjnnOniOkpHcUujah2d6ttrFjbRaBof70yZBPD7FRnFPKdy8v5YyrT2Pyhac4exlOiUqM4K6PbiRnVz4r3v9NcawgCAydkjVLg8sAACAASURBVM5fa7PsBGZ4bAgR8WEc2NaxnXfKwHjqqhqoKnVcLhGTHE5JQaWs+2z7HZQWqltnExYhGTdVTsR3qPUCrKbacUlDcKAes9lCk6Hr4tL2mXeLmG5zph2XpNiwneNNJudjO+OjkfY1Wtx8x1iwLbQ9/mL6WuAGIB44ghSxdENPTcqDB7czdSp89RXILUJ69124995jPycPHo4fPX/12A6DySamHXcRtGGyWDCazW1f4N1BFEXqDUYCVNYpd6auwUCgTOKFjYaGZsxmi1Nnur7WQGurmTCVznRzk5G6qkai4uW7NhbnVhCT7Dg9xGy2kL+viF6DEjs8vv/PbLz1OlIG2ndg3LF2D4MnDkCjUZYF3730ExqthnkP/UvFK1HH6NnDGDA2lW9fWorZyZ3CjCnp1JbXkbvHvnSj34heds50r3TptcqNtxGbHEmr0USlzGI/m5guK1TX9MN298GZMx1s/cxU1yg40wHShVxdfddFYIBNTLuxzENN6YZ/m5h2pcxD2rfF4mZnuq3M4ziLaVEUK0RRnCuKYrQoilGiKM4TRdHxMlQPHk5EzjkH3ntPftuzz0o/HjycHBxTZ9p2y1dNmYftVrI7yjyMrWZMZkvbLe+uUufEma6xCiFnSR5VFZKwUutMlxVJKSJyzrQoipTkVxCb7LgrY0luOUZDC706OdMHtufQZ0gSXrqOHRhL88opySknY0q64rzqqxtY8f6vTL10AhHxylGAXUEQBObceQ7F2aWs/36L4tiMyQMByUnvTNrw3hQdLqW+ndtru3DIzXIspmOs72Vxrn2pR2ScVUwfUSembb9j2+/cEaGh0t0MpZrpoEDpQq5OJg7QGTZnuqHZHc60dBGsqszDesHsmjNtK/NwtzNtK/PouTVSatM8PhIEIaTdv0MFQXi/x2blwUNPMX8+f857WX7bPffAO+8c2/l48HB8cJ5H5kZazFKigJruh0brWB83tLButt6W1uucO+Ky+ze34uvreF+D9fa7r0I7b4DGBulLPDDIscvdngZrHW1wqH35iNlkobHOQEiE48WMlcVWMZ7YUXCXH6kkro/9okXbgsbeg5XjhbJ35tHcZGTaJRMVx7nCKeeMxMfXm6yNBxTHxaRE4hfkS3G2ffxcfF8pzq/8yFGvLzg8EB8/byoVmp+ERUnvZZ1MG3D/QD06by/qatQlTPjodei8vWhsUHZB9XodggAGhTIMP+sdlabmrgtTX2vL+maFjG21+GilYxlVNNrx1ljHmrv+vF6CtG+r6N6mLQjWc7gHm7Z4OR8CwBBRFNs+iaIoVguCoL41kgcPJwiffQbXfHcLd1HFwzxmP+C//4XgYLjwwmM/OQ8e3IggCGcAA4E2BSeK4mPWP288lnOxWOtbNYJz/8Y2VuuGhi1tx9K4diyzxYJWoezBbJZqbL2cXCSYrCJELplDdnyrNN7L2368rcufzsfx13eLVXx5d7oQMDa1oPe3d+mbrcLPN1BZ7NeWS00vwhRyt11Fq9USEhVMbYXzxhq+AXqaG+1FqI/VjW3pJD599N52j7XHSye9z7b3XW672aQuzQPAy0uDuVV5vCAIaLUazGbHFVe2khu1SSJy+5pd2NfuWNZz0aJibZG2C2Ptn8d2rrm7Cq3n+xOqfQaNIAht95sEQQhDvRD34OGEwdaJ8BEe4TVk9IQoSgkfK1ce+8l58OAmBEF4E7gIKVtaAOYAyivLehCL9ctRo6K6xGL9HnVH90ObkNC4LKZFxUV+NjGtNAZoE2JeXuq+cltbrGJaZ/81a9um83b8FdxqzS729ukkpg0t+MjUjxtsYtpfWUzXlElCNyRKfcRfVwiJCqJGRa6yr7++bc7t8bHeIWjuVCfsrdcpi2nre9nqIJPZS6fF1AWHV+ulbftsKI7TahTHabVWYWpxRZi6vq/DY6kQubaxoguCWMB1Ia6M7bzr/oWFs2dwxkJggyAIjwuC8DiwAXiux2blwUMPcbTjoMAtvMKnyERqtLbC+efDBueNBDx4OEE5RRTFy4FqURQfBcYBiU726TGOOtMqxLRNALvDmbbYnGnXnCmz2aIoxLsqpp2NOzreJqbtnelWa+mKnGttw2gtP/HWHxXToihibDLKi+lGq5hWWGwJR53poHD1qSRdITgyqO05lNAH+CiKaWOn9Audj45Wo2MxbLswcTTGS6dtu7ugBq1Wo2q8UzFtc5dVCHP7fd0nTI8Kc+fzELrhTAvdEOLKnCBiWhTFj4ELgFKgDDhfFMVPemxWHjz0EO07DopouIIPWMKZ9gObmqQuiTsdd87y4OEExrZiqUkQhDigFbAPFz5GiF0R09jKPLp/a9ZsUf+8snOxWFQ50xqt8vFtwspLZZmHzSGVE9MmFc50i4wzbWo1Y7GIbYKzPW3OtBMxXV1WS2BYgKxj7g5CooLb3G8lHJZ5WDswtnQS0z6+TpzptjIPBTHtoAREdryXRlVZiFarwaLoTNtKNVxwpt1Y5nG0dEPlcwuCyyJeQHApV1sdPRdipPi/lSAIQdY/w4AS4HPgM6DE+pgHD38rOnciNKHj375fU9pfpqFnTQ3MmAGHDx+7CXrw4B6WWheNPw9sB3KBL4/XZNTcHm4ba/0idYMxje3L05WSEVEUEUXleVjM6sS6zSEXVJabtB1XZvxRAa8g8lvta7RtpR9ygr7VKjS1MuK9PU11TbLOtrvw8fWhUaGJiQ0vby879xmOzr+zw6zVah0KZWm7VXg6qHPWaNSJ47bxWo0qESsIgqJQFrrgCNvNwebyukOYdqHMA6S6sq6c851xvzNtO4+Ok5hGEs8A24A/2/3Y/u3Bw98KuU6Er77jS/SmxTBMZk1taSlMnw5FzlvdevBwAvGcKIo1oih+i1Qr3R944nhNRqexihyLijQAa3JAixs6k+qswrHF1PV0AEEQ8Pb2wqhQHqC3LvBrNiqnLdjSPgxN6tIE/KwOcaNMvnBAsOQGNCjkEwdb49mq29Uf+wbo8Q3QU1FkH/EWlSSlfpTkyLfUtpE2sg8VhVWySRruYNe6LNJG9XE6riSnjOgk+2jASutrC4vtuECyury27T2Ro94q4AMcRBw21hvwD1Lfkt7QaMRPRRyj0diKXu84LcZo/VzpfbqeRmO0Xjz4uOEuQqv1XFSTxmMRRcyiiLem62k8FtGCiIhW6H6ST0ds/5f03FI/RTEtiuKZgnRpNFkUxd7tfnqJomjfQsmDB5V89hmkpIBGI/352WfH7rllOxEGB0ttx9PS7HfIyZEc6ip1HbA8eDgB2Gj7iyiKRlEUa9s/dqzxscZltagQ0z7WL2yjG8S03pqP62o8mK9eh0GhPKAtuqxRWSQHBDoWx3IEh0mReHUy3fECgn3RemmoVcgxjrQ2e2kvnAVBICYlkpJce8GcNEDKo87fV6g4r9GnDwdgy/JMJ6+g65Tll5O7u4DRs4crjjMaWijJKSexf5zdtuJcKeIvtldU22OmVhPVpXVEJji+mW57L0NkasHNJjMNtQaCnGSJ27BYLDQ2GPF3koxiMpkxGk34KcQq2j57vgqC2xG2z7ztHOgOtnPRR4WYtsVg2uL0uoLJGomn07hZ9IrWc1jo/nvhCKdFaaJ0j+D7HpuBh5OOzz6Da66BvDwpPCMvT/r3sRTUskRFwerVkGDfHYw9e+D006FBuUWsBw/HE0EQYgRBGAH4CoIwTBCE4dafKYA6NdADeLcJZOei1sfLlmnb/axZfTezdv303oqus581Zq7RSZc5m7BqUCmmA63d8eplxLQgCASHB1JT6fj/oghrs5fyTi2wHYnphLRYwLmYTkiNJa5vDFuWb1d+AS6wZflfAIw+XTl198iBYkRRJKl/vN22ktwKtF5aItp1jqwsrkEURSLiFMS09b0MDg+w21ZfKy0/CJLJ/JbD0NiCKIptF1COaLLepVBysA3N1hxzfddLa5pbra62Qm296mOZbNnvzo9ltFgz5V1wplut+9rypt2H7Rw+jmLayiZBEEb12Cw8nFTY4una09QkPe5OXHK/k5IkQR0h011s82Y47zww9lwXJQ8euslM4AUgAXgRKYlpIXAbcN/xmpRPF0o32pxpN4hpnVaLl0bT1rylq/jqdW2NWeSwuYpNMovh2nPUmVbXye6oMy1fyhESEUCNgjMdHBGIztuLik4tsGNTIinOLbOro/X11xOdHEHBPuflbKNnD+OvX3djVGg24gpblm8nJiVSViS3xyb45cV0GVFJ4R0Wjdrc+UiZbpI2bO9liEwpSL21WYtaMW27YHIupqX3z18m99tGmzOt0DjIEW3OtIsNi9pjOxfVONNHXWzXnWm3i+kTwZm2MhVJUB8WBGGnIAi7BEHwxBz0EMezBOJYcDSeTt3jrtAt97t/f6nkI1Cmxu7nn+HSS8ENX/QePLgbURQ/EkVxKvBvURSntvs5RxTF747XvGwulc21UsJLo0EjCG4p8wDJmWtWWHymhK+vTrH7nM1VVBLcAP4BXXOmfXy9pa57Ms40SF39lMS0RqMhIi7U3pnuFYWxqUU2MSOxf7xTZxqkUo+W5la2rXafBDA0NpP58y5GzR7udLFowb4iNBqBhDT7To7FOeXEpkR1eMx2QdHere6M7b2Uc6Zrq6TfQWBI18S0szIPmzOt1D2zW850i/uc6bbSDRXOdFu3UxecaZOlh8o8TiBnejbQG5gGnAWcaf3Tg5s5YUsg3Ej7eDo1j7tCt93vESNgyRLwkXENvvtO6pTYY/E9Hjx0m/WCILwnCMJyAEEQ0gVB+M/xmoyfl9XBbXW+AE8QBPx0Ohpb3NP611/vTb2TMgxHBPrrqa1z7CbrdFr8/LyprlJuNe2l0xIY7EtFmWMB3B5BEIiIDab0iPw6jaiEMIpzyhWTGhJSY8jeXdDhsV4DpajxvVsO2Y3vP6oPObvyqShUXhuSMTmdyIRwFv7nDfZuPujspTilvrqB+2Y/idHQwrRLxjsdv3XVDnoPSca7k8BsbjSSs7uAlPSOjvXhXflovbTEJMvcbbRSnFdBcHiAbFJJmfV3EBEbrOblUFUu/Y5DZYR5e2qsdx2CFBY21tpcbhWLGTtTb71zEOCCEO9Mg1WY+6twuRtN1gsAr64L1xaLNR9d0/XXq4hovYgV3HzcdjiLxtMLgnArsACYBRSKophn++mxWZ3EHKsSiONJ53g6kP795JPuew63uN+TJ8OiRSB3a+v992HBAo+g9nCi8gGwErCt0joA3Hq8JuOj9UKv9aK2VZ0zG6r3pbpZXUmEM8KC/Kisdx63JkdEeCAVVcoCODo6mNJS59nI8UnhFBWoX8SckhZL7oES2W19BydSV91IRVGNw/2HTOhP/r6iDoke6WP74h/sx8af7GueT5s7EYAf/2+V4ry89d48/+vD+Af7cdepj7J1heuLESsKK7l90kPs33KI+7+4lUETBiiO37PxAPu3HmbG/Ml227b/uptWYytjZnesud65bh/9RvZCryBID+0qoO9g+Z5GuQdK8PLWEp8SqeIVQWF+JQDxScrpwSUl0u8lJsaxSC+vqCc02A+dk8hCOSrrpM98WFD3l0rUWM/FEL3zRJPaFukcD/FWn35io9EszdlP2/V9FRGtF7uCursLruDMmf4IGAnsQnKnF/bYTDwAx6YE4ngjF0/39tvWVA3cU+biNvf7rLPgww/lty1cCM8808UDevBwTIgQRfFrrC2/RFE0cTQf6rgQ7K2nrkWdmA7x1VPTrG6sM8ID/alyUUxHhgVQXduk2M0uKjqI0lLnLbDjEsMosgotNSSnxVCYU06LTDRfn0HSIulDuxx/MQyZ2B+AHb/va3vMS+fFqOlD2LJih112cVyfaCaeP4Ylb66moUbZaY/vG8vLfzxOfFosD579LKs/Wav6ddnI31fILeMfoCy/gieX3cfkC09xus+Xz/5IUHgAM/9tL6Y3LcvEP9iPQeOPJjI11hk4kJlLhvW9kKOluZW8fUWOxfT+YhJ7R8k20JGjML8SP38fgp3UWJeW1qLRCERGOo7sK69qIMKJw+0I22c+NKD7YrraIJ2Lob7KpStwVEwHeTsf2xmDSRLtfl5uFtMW62JdjWvvpRqciel0URTniaL4FvAvYGKPzcQDcGxKIE4EZOPpcF+Zi1vd73nz4NVX5bfddx+89ZYLB/XgoUdpFAQhHGuXAkEQxgLOFV8PEuStp6ZFndsc4qOnxuAuMe1HZZ2yOHREZEQgogiVDmqXQXIW1TnTYZSX1rV1J3RGSv9YLGYLBYftM517pcej0Qgc2lUgs6dE34wk/IJ82bFuX4fHx5w+lJryOvb/mW23z0ULzqap3sCSN1c7nV9YTCgL1zzK4EkDeG7+6yx6YbGKVyWRtekAt054gFZjKwvXPMqwaYOd7pOzK5/NyzI598ZZ+Pp3FGoWi4UtK3YwavqQDt0Zd288gMVsIWOiY8c7d18RZpOFPkMci+mUfrEqXxkUFlQRlxTmtPa7tLSWiIhAxa6YFZX1RLnYur2yrokQfz1eKlvYK2FzpoN91IvpYBfEdJPVmfbVujl46ARwptvOequz4aGHORYlECcy7ipzceZ+d5mbboJHHpHfdt118NVXLh7Yg4ce4XZgMdBbEIT1wMfATcdzQsE6fdsXrTMkZ9p9ZR7V9Ya2LoRdITJMcrLKKh2XekRHB9PQ0ExDg/Jri0sKRxRFio7YN02Rwybg8vYX223T+/mQmBrDgb8cV1tqvbQMPiWNnZ3E9KgZGWi0GjYtsy/P6DsshVGzMvjutRU0q6gz9w/y48mf7mPSnHG8fdcnPHbhQtZ9t5nGOvs7AaZWE3s27Ofdez7lrlMfJTDUn5f/eIK+w9R1uf/y+cX4Bug55/oZdtsObMuhuqyWMacP7fD4zt/3ofP2YsBox41gDu6Q3P3UIfaOVWOdgfKimi6J6aL8SuITw52OKy2pJSo6SHFMeWX3nOmwQPeI0urmZvx0OlULEOtauyOmpXPe3+1lHlZnugfFtLN3JkMQBNslt4CUXVpn/bsoiqLyJ8FDl7GJvfvvl0o7kpIkIe2yCPyb4c4yl7lz3fy+PfSQ1Lils0stipJ7HRwMs2a58Qk9eHCZLKT+AE1APfADUt30cSPEx5eCBsc1vu0J8/Wlyk3OdESQHyaLhZpGQ5fFRaTVFSxTSM6w1bwWF9eQmmqfMGEjIUkSWAU5FaT0iXI4zkZ8SiRe3loO7ylk2nkj7bYPGNGLdUszMbWaHZYgZEwawOYVOyjKLiOut/ScgaH+DBrfj3Xfb+HyBy/oECMHcPFd53DHtMf4+oUlXP7Qv5zO09tHx32f30Jc72iWvrWadd9skuafGkvfYSk0NxqpLq0lZ1c+rcZWtF5aRs7M4I53ryM0OsTJ0SX2bT3E2q83cv6tpxMYai8u1367GY1Ww8jpQ9oeE0WRP3/ZTf/RfRRboO/efIiQiECiZQRw9l4pKlCtmDY2t1JaVMPUWc6d9pKSWoZkyLvhIHXVrK03uOxMV9Q1Eh7kHvFYbTAQolcnjquNTWgEgUBd18V0o0lykH293O1M28T0cSrzEEVRK4pikPUnUBRFr3Z/9wjpHsJRCcTJwAld5iII8NJLcNll9ttMJjj/fFi//tjPy4MHez5GaiH+FPAakAp8cjwnFKkPoLxZXdOjaP8A6luMbkn0iA2TxG5RpfNSjM4kxIag0QjkH3Fc65zSS1qYln24TPFYKX2j8PLSsn/3EVXP7aXTkj48hb822idvAIyZMZjGOgOZv++T3Q4w8dyRaDQCqz5d1+HxM6+aRuGhUn7u9DjA4An9mXbJeD5/6nt2/r5X1Vy1Wi3/eXoui0rf5YVfH+Hfj11Mr8FJ7N96mNLccvyD/Tj3xlk88NXtfFP2Hk8suVe1kG6oaeSpea8RkRDOJXefY7e9rKCCpe/8wpR/jSUo7KhY2r8tm7y9hUz91xiHxzYaWti8ahdjZgyWLcv4a8NBNBqBASNSVM314N4iLBaR1HT7DOz21NUZKCura/vsyJFvTVVJVIj0U6Kwopb4CHUJJM4obWggNkCdqC83NBLh44/GSZmLHHWt9XhrvNG7Oc1DtFQDuuPqTHvwcEx58kmpRrp9qccJVeai0cB770FtLSzuVCNoMMAZZ8DatZCRcXzm58GDRD9RFNt/CH8TBGHHcZsNEO0bSJXRgNFsctrQIdaa8V7cUE/fMOe3zJVIipQERUFFDYNSHDvHcvj46IiLDiZHYeFgQkIYer2OQ4dKmalwLG8fHakDYsna6bjOuTPDJ/bjw+eXUV1eT2inhWrDpwwgINiXtT9uY9SpA2X3j4wPY9SMIaz8ZB2X3nU23ta21JPOH833/1vJB498w4TzRuPfKZ7t5tevZP/Wwzx9+eu8+efTBEeo8868dF5kTBlIxhT5+XQVURR56dp3KMuv5MXfHpJ1pd974GsArni0o4u+9L016P19mKIgprf8shtDo5HJ546Q3b593X7SMpIIDFbnlO61/m7TM2S66Lbj8CGpDj61b7TDMTn5FQD0SnQc6eeIxuYWKuubSHCTmC6qr2NwtLpzp8RQT7Sfa256bWstwbogp/XmXcZSBZpQ9x+3Hd2vTPfgwY24vda5J9DppBrpKVPst9XWwsyZcLD7+asePHSDTOuiQwAEQRgDHNfbJjHWL9gyg3N3Oi5QEm/F9epymZWIj5Ac0IJydSUmnUlJjCCnoMLhdq1WQ6/ekRw6aL9QsDMDMhI5sKeIVpVNZIZP7AdA5nr7Ch1vHx2nnD6UDct3YFRoGnPOtadRXVbHL19uaHtMEASue34e1WW1fPm8/cJBv0BfHvj8Zuoq6nnhqrcU86x7kp/e+YV1323hiscuJH1smt32PRsPsOabTfzr1tOJaic6y49U8dvXm5gxbwJ+gY7rb9f+sI3QyECGnGJ/7PraJg7syG/7Hagha0cBcYlhhIQplxMctIrpvgplQTkFlXh5aUiIVefgt+dIhbTWOCmy6/t2RhRFihsaiJNrYiZDqaGeaF9XxXQdwTr3XAB0wFIFGtccfrV4xLSHE46/RZmLXg8//ig1d+lMaSlMnw6FzruJefDgTtp1px0DbBAEIVcQhBxgIzDpeM7N9gVbanAukG1f3IV1XS/N6Ize24vokIBuiOlwCoqqaW11HI+X2jeaw4fLnC5yTB+SSGuLicP75fOjO9NnYDxBof5sX7dfdvuUc0diaGjmz1+zHB5j2JR0Uocms+iV5ZjNR+Pw+o3ozWmXjuf711dSlG1/IdBnaArXPDeXzcsyeffeL465oN728y7euOMTRs4Ywpw7zrTbbrFYeGPBp0TEhXLR7R23f/v6CgAuuNHxvYLGegNbft7NxLOH29WNA+zYcBCLRVQtpkVRZO/OIwxwkArSnkMHS4mICCQkxLHjnZtfQWJcmGLahyNsn/VEN4jpSoOBFrO57W6RM0oN9cT4ulabXNNaS4iuByqIPWLag4cTmKAgWL5caj/embw8mDEDKtXnynrw4AZs3WlnAb2AycAU69/POH7TgmjrF2yJCjEdHRCARhAocoMzDZAQGcKRcteSAXslRWA2WzhS7DiFo29qDI2NRooVxgCkWxecZe1QV+qh0WgYOj6VzD8OyIrZIaekEhIRyJof/nR4DEEQuOj2MyjKLmPd91s7bLvysQvR6rS87UAsn33dDM7872ksenEpr9/yIa0txybU6/dvN/PQuc+TmBbL3R9ej0ZjL1VWf7KOg5m5/Ofxi9D7H62xra2sZ/lHvzN1zhiikxyXSGxcvpNWo4nJ59gv7gTI/OMAvgE+9MtQt2CnpLCa6sqGtt+xEgcPltA31XGJB0hlHr1UpILIYRPT7ijzsF3QqnGmDaZWaluaT1BnunvlYs7wiGkPHrpDZCSsWiW/QjIrC04/HdwkCDx4cEb7DrVyP8dzbnF+kuNU2Ohc1HppNMQFBpJb45qb3JmUqFCySypdclf7JEuLxPYfduwm9+svpT3s2a18Nyo8KojYxDAyN9tnPDti1JQBVJXVkbUt126b1kvLlPNGsmnlTsoLHQv5U84aTkp6PO89vIim+qORg+Gxocy791w2Lt3O0nd+sdtPEARuevUK/nXbGSx5czW3TnqYgv1FqufeVQwNzbx07Ts8cckr9BmawnOrH5Ct187fV8gbd33GoPH9mHrRuA7b3n3wa0wtZi687XSHzyOKIks/XEtsSgQDRtpH85lazWxYuYvhE/qpbtZi+50OGp6sOK6uzkBBfiX9FBJC6huaKSqtpY/KroudyS6pJCLYnwDf7i/ky6+1utzBzkWu7dyO9eu6u9xiaaHB1ECId/fd9PaIogiWCo+Y9uDhhCcxEVavloR1Z7ZsgXPPBTd1c/Pg4e9KoLeeMB8/cuvV5Sz3DQvncJX69ttKpCVEUtdkpNhJa3A5UhLD8ffzZreCiOzdO4qgIF8yM51fr4ydlEbm5mwMKnKcAcbPGoLez5sVX26S3X7u1VOxWES+e8teDNvQaDTc/Mp8Kgqr+eiJ7ztsu+CW2YyemcGbd31G1mb7tR6CIHDNs3N56OvbKMkt5/ox9/PTu7+4vexj/5+HuX70faz4YA0XLTibhb8+1CGdw0ZjnYFHL3kVH19v7v3wug6Lyrb/tofVn61nzi2zSOoX5/C5dm44yP7MPP513Wmyi9I2/7qHmsoGTjtf3rWWY+OafcQmhJLcW1kA7/grH1GEYQqie4/1szaov3IqiCP2F5TTL941Id6ZQ1WVaASBXiGhTsfmNUjndkpg10sqyo3SuoRIn64vuFRErAPRgKDt2uLjruIR0x48uIO0NFixQir96Myvv8Ill0jxeR48nMSkBIaR16BOIKeGh3G4ugpzp7bXrtA/UcpY3n9EOb5ODq1WQ3paHLv3ORbTGo3A0GHJZG7PdSoyx07uT2uLie2b1LnTvv4+TDtvJL8vzaROphNjdGI4U88byfJPKytKQQAAIABJREFU11NX5XhxZ/rovpx19TQWv/ULe7cebjd3DXe9dy2RCeE8Mfd1Korkfz8Tzh3F29ufJX1cKq9c/x63TnqY377c0K3SD1EU2bv5IE9f/jq3TnoEY3MLz626n/88eTE6b/vEF7PZwsL/vk3R4VLu+/gGIuKOirbmJiOv3vIR8X2jufSusxWf9+vXVhEaGchpF46V3f7TJxuIjAth1FTHnRPbY2gykrklh7GT+zlNjNi+PRe9Xkf//o7F/u59RWg1Av37dl0AGltN5JRUtX3mu8vByiqSQ0JUNWzJrZc+OymBzoV3Z46KaTc7yGZr0yOt+sY7ruAR0x48uIvhw2HJEmlxYmd++AGuvlpaVenBw98YQRBmCYKwXxCEQ4Ig3NOVfZMDQsnrgjPdYjZTUNf9Luip8RFoBIF9BV0X0wCD+8eRnVdOQ6NjN3n48GTKy+s5ckT5YmHQ0CQCgnzZuNZxPnRnzpx3Ci1GE6u/2Sq7fc6N0zEaWljywVrF4/z7ofMJjwvh5Zs61j8Hhvrz4Oc3Y2gwsGDW05QXyr+G8LhQnv7pHm5+/UpqK+t5+vLXuazvzXzy+LdUlagvyWkxtrL6k9+56ZQHuWXiw2z+KZOzr5vOW9ueIWNyuuw+ZpOZ5696i/WLt3HN05eQMamj0P3kqR8pyavgllf+3RYBKMfBHflsX7uXc6+ZJjvuyOEyMtcfYPYl49CqXPy3beNhWltMjJsis36mE5nbcxmSkYhOoXxk175C+qRE4afQbMYRh4srMVks9EtwnzOdGqbOac5rqCbYW0+oT9ebrpQ3S2I6ysc9827DJqY1HjHt4W/KZ59BSooUzZySIv37H8+kSfDNNyB3Ff/hh3DnnVLHRA8e/oYIgqAF/gfMBtKBSwRBkFc/MqQEhlJiqKfJ5LwZS6o1X/pQZfdLPXy9dSRHh7LvSLlL+w/qH48oQtYBx+70sOEpAGRuVy718NJpGT0hlS2/H+iQrqFEr/5xDBrVm58+W49F5oI8uV8cY2cO5sf31mJodFxS5hfoy00vXkbe3kK+fnlZh219hiTx5I8LqCmrZcGspxwKao1Gw5nXnMb7uxfyxOK76DM0mU8e/5Z5fW7i7llP8upN7/PNyz+xcck28rKOUJxdSuavu1n6zi+8fc9nPHzBQub2vonn//MmhoZmbnz1Cj7PfZ3rFl4umyMNkpB+7qq3+O3rjVzxyBzOu6FjSsfBzFy+/99KZv97MkMmKKdvLPrfKvwC9Zxx+UTZ7T99vgEvnZZZF8m71nJsXLOPgCBfBjpZrFheXk9BQVXbZ0UOk9nC3oPFDB7gWomH7YKxX0L3nekWs5ncmhrVWe+59VWkBLiWmlFuLEcn6Ny/ANFiTarxlHl4+Dvy2WdS85W8PEk75uVJ/z4pBPUZZ8BHH0lB2Z156SV46qljPycPHtzDaOCQKIrZoii2AF8C9m3pHNDLWkuZU+9cIPexumH7Kx1nPHeF/glRLjvTA/vFodEI7MxyvMAwPj6UqKggtm3LcXq8sZP7UVvTxG4VNdY2zrhsPMV5lWz5Vb4r4YU3zqC+upEl7/+ueJwxs4Yy+YLRfPn8Urb/tqfDtvQxqTy1+C5qK+q5/dTHOZiZ6/A4Go2G0bOG8uTiu/lgz0LOvn4GTfXNrF20ibfvkkTz1UPvYn7/27h71lO8esN7LP6/VRQdLmXolHSeXnYv7+58nrOvna6YBV1f3cgjF73MmkWbuPKxC7l4wVkdtleX1fLMVW8REhXEfx6do/jac/cV8cfSvzhz/iS7RjUADXUGVi/awvhZQ+ya5DiitdXE5nUHGD0h1eliRdtnY7iCmD6UU4ahuZVBCjXfSuwrKMNf7+2WJI/cmmpMFgt9VTrT2fVVLpV4AJQay4nSR7q9sYpoLga0oHGz490JTwdEDz3C/fd37GII0r/vv/8EzY12N5deCtXVcOON9tseeABCQ+H664/9vDx46B7xQPtctyNImdZtCIJwDXANQFKnlJt+wZJbtq+mjIGhyk5RoI8PycHB7C5z3gxFDRm9Y1n+5z6X2iz7+XozsF8cG7Yd5qq5E2THCILAuFP6smL5TgyGFnwVbtGPmZiGn78Pq37MJEMmTUKOCbOG8HFyOJ+8uJzR0wbYxcUNGNmbcTOH8PmLyzjl9AwS+jiOXrvhhXnk7Svi0Ute49GvbmHo5KMlEwNG9+W5Zffw6MWvcNupj3PTy/OZeblyRHl8aizXPn9Z27/rqhooOlxC0aFSBI1AaHQwcX1iiIgPlY25c0T2rnweu+RVyo9UcuPL8znr6lM7bK8pr+Oes1+goqiaJ7+7nQCF3GazycxLt31KYIgf5197quyY795ZQ2N9M//671TVc9y0Zj/1tQamzhrsdOwf6/YTFRVEnz6OXeMNWw+j0QiMyFBOBXHE9kOFDE6JQaPpvijdVSqdewOjlGP8AGqMBoqb6ugX4pojXmQoJsHXNTdeEXM+aOOQbqr1HB5n2kOPkJ/ftcf/kdxwAzz+uPy2G2+EL744tvPx4KH7yH1Dd6hbEkXxbVEUR4qiODKyU8JNr8AwfLU6sqrVCeTB0THsLHWPmB6ZJuX/bj2gvp13eyaM7svB7DJKyx03kpk0uT9Go4ktmw87HAOg9/VmyqzBrPs5i8Z6dUk/Xjot826dRfbeItYtk+8Mf+OzF+Pt680LN3+M2eS4yUxQWADPLL6T2F6RPHzRK+xY17F+O3VYL17/4zEGjkvlxeve5dGLX6HwkLpGM7bj9x/Vl2mXjGfqRacwdMpAohLDVQvppnoDHz76DbdMeZSW5laeX3mfvZCuqOOes5+nJLecx76+lUHj7LsYtuerV1dx4K88bnzuYoLD7ctJairq+f79tUw6cyh9Byq3A2/Pih+2ExEVxIhT+iqOa2ho5s+tOUycpLxI8Y8thxjUL45QlS3M21NV38Th4kpG9XOeda2GnaUl+Ot09A517jZn1UjnaXqIc+HdmRZLK6XNZST4uebGK2LKA61rFyZdwSOmPfQIcrHLSo//Y7n/frjtNvvHRREuvxyWLbPf5sHDicsRoP03dQKgOnhYq9HQPySKvSrF9JCYGIrr6ylvtE+x6Cq9Y8IID/Tjz26IaZDEjiMGD04kJNSPtWucLy48/YIRGJtbWbUkU/Ucppw9jJR+sXyycIWsWA6LDuaGpy9i//Zcvn59teKxQiKCeGbxAqKTI3jowpfZtb5jl8WQyCCe+nEBVz46h8zf9nD1iHt5Y8Gniokh3cVstrDsg9+4cshdfPHcYk45cwSv//Eo6WNSO4yrrazn3nNeoCi7jEe/uoWMicoL/w7uyOfzl5Yx5byRTDxzuOyYr974hRajictum6V6voX5lWzbeJiZ5w6T7aLYnk0bD9HaamaywiLFkrJaDuaUMWGMsjB3hO2zPSrVTWK6pJRBUdFoVVwE2c7pAaFdF9PFhmJEROJ93SumRVEEcx54ecS0h78pTz4Jfp0urP38pMdPKgQBXngB5s+332YywQUXwLp1x35eHjy4xlYgVRCEXoIgeAMXA4u7coABoVFk1ZSqyikeEi19Me8sVe+KOkIQBEakJbDlQIFLGclJ8WEkxYcpimmtVsOkif3YtOkQTU5ypFMHxDFgSCKLv9wiu6hQDo1Gw/w7ZlOYW87P38one0w+ZwSTzx3BZwt/4tAu5QuHkMggnl2ygKiEcB6c8zK7Nxzo+Hq8tFx051m8v+M5Zl4+icVvruaKIQv47rUVtBhbVc1ZDaIosu3nXdww7kFeufEDYntH8cqah7n3o+sJi+nYxKOuqoF7z3mBwkOlPPLlzR1KVORoaW7lhZs/IiQikOufukh2TFlhNUs/Xc/0C0aR0Ft9mcLSr7ei0QqccYHzPOpff80iMjKQAQoLC//YIt3RGD/KNTG99cAR/PXeDEjquqDtTIvZzN6KcobEqDtWVnUp0b4BROj9u/xcRwzS9Xi8u8s8xGoQ6xE8zrSHvytz58Lbb0NysqQnk5Olf58U9dKd0Wjg3XfhHJl1Ws3NcOaZkKnenfLg4XghiqIJuBFYCewFvhZFcY/yXh1JD4mhvtXIERWdEAdFRaMRBLeIaZAcu/LaRvLLXOusOGF0XzJ3F1Df4Lg049TTBmE0mvhj3QGHY2ycc/EYigqq2LZBuSykPWNOG0i/oUl8+soqh4L2hqcuIjg8kBdu+gijQTk5JTQqmGeWLCAiLpQH57zEhqXb7caExYRwy2tX8H8bnyBtWC/euudzLu51Ey9c8zbrfthKQ03X7xyYWk3s+mMf7z/0NVdm3MV95zyPobGZ+z+5kRd/foD+o/rY7VN0uJR7zn6eggPFPPz5TQyfOtDp83z0zGLyD5Rw24vzCHRQU/35q6sAuPTmGarn32xoYeWPmUw8NZ3wKOWOf7W1Tfy5NYdppw5UrGVev/UQyQnSRZsr/HmwgGH/z955R0V1fW34GToICqKoqGBD7Ipi770ba+wxiTVqosYWNbEldqOxG2Mv2Htv2DuIHRQVFQSR3uvM+f6Yn/kMTrkzDNZ51prFWtzT7gzc2Xfffd63ZGHMtGTJpRAQGUG6XE6lAtJUMB7EhlNOyz4IdbxMCcVUZkohq+zfBPyHzP9t8H0PwbRxA6KRHKN37y80eFaFmRls26a0Fz9z5r/H4uOhZUu4eFFp/mLEyEeMEOIIoHd9Uvm8yi/c29GhFLXVbB1sY25OaUdHfEPD9J3uP9Qso6wzu/TgGa4FdFcdaFDLDa+91zl/LZC2TVVvOCtfoTDOzvYcPnyLFi01b0qr16wsjgvt2Lr2PJ51S0lSMpDJZHw/vh3jey7Ha/EJvh3b9p02dg65GLWgN7/1Xs6iMV6MXdpP49iOBe2Zc3AsU7ovZnrvpTTsUoMf5vbCPouVd/EKRZl5YCy3z/lzyusilw/d5OSWi5iYmlC2RkmKujtTuGRBHJxy41AgDw4F7DG3MCXmdTwx4XFEh8cS+zqe4Edh3Dp7n+SEVExMTajcoCzdR7ejSY86WFi+q/0sz5SzZ9kJNs3ch7mlGVO3/kS1phW0vldn9/mw529v2n3bgGqNVSs4PvAN4sTO63T8vgFOhaX/TRzccZ2kxFS+6qldQu/Y0TvI5Qqat1C/5ujYJPzuvqBHpxqS1/A2oVFxPAuPoXNd7RshpeAXpvyfq1JQuz5zUkY6T+IjaVFEv++v4OQQCloVwMzEwCFp5v+Udd5DmYcxmDbyybFli7IU+cULZQ32jBmfSNBuZQX790PTpnAjyyPaiAho3hwuXYIi0je/GDHyqVHW3glrU3NuRobQzkW7RHWtIkXZdu8u6XI5FqbZ25FfNL89xQvm5dydJ/Rq7KFz/3KlC1HE2YFj3vfVBtMymYyvOlZjxfLTPHr0itKl1WfrzM3N6DmgAUtnHebm1SdUqy3t8X6lWqVo3q0GO1Z4U7W+O5VqvdvPs0l5+v3Sng2zD+LqXojuP7VUMdL/41jIgYWnJrFz0VG2zj2I35kHDJnTk8bdav0nEJfJZFRpVI4qjcqRmZGJ//Un+J66y4OrgZzdeZVUDcY2oNxImb+oIw271qJas4p4NCqHrb360oCn94JZOHwdgX7PqN3Wg+F/9sGxkPag9+Gt5ywctZkKNUsyaFoXlW2S4lOYO3ILToUd6D1C8/vzNslJaexYf4lqdUpRrrLm+mS5XMG+fb5UruJC8eLq5dlOnHuAXCFo1Uh7tl0VZ+8on240rPhuRl8froYEUyR3bgqrcvXNwq2ol8iFoGo+3b+7hBA8TQyikr1hbgL+M3bmI8DCmJk2oh+fbLApgTf61W9k997oV8Mnco52dspNhw0agH8WvdgXL5QB9YULkC/fh1mfESM5jLmJKZUcC+EbESKpfc0iRVh/y4874a/wdM5+TWWjSiXZeMqH+ORUctuocCvVgEwmo1Wj8qz2ukhoeCzOBVRn1lu1rsS6tefZt9eHcePbaRyzZceq7Fh3kQ3Lvalaq6Rknd0hkzty/8ZT5v3sxfKjY7BTof7Q/aeWPH8YxvpZB7Czt6GNGqOSN5hbmNFrbHvqtq/KwuHrmTvwH87svMagmd0p6vZuhtLM3IyKdd2pWPf/jVJSElOJeR33v0x0HBlpGeQtYP+/THUe7BxySTrHpLhkdi4+xs6/jmLnkIuJ63+gfkdPSX2D/F8yudcyHPLnZtLqgSqtyQGWTt5NRFgs83cMJ5ed9L+FfVuvEh+bzDc/aJfQu3I5kNfh8Qwb1kxju2Nn7lPWrSDFiupnp332zlNKFMyLi5Pmpz1SUAjB9ZAQmpSQFpjfjAxBBlR11P3/Mzo9hvjMBErkKqZzX61kBoJZyRyXxQNjzfRnx+dulqJJv/qTIV8+OHFCWUielYAAaN1aWfphxMhnSrV8RfCPDScpQ7sTYo3CRZAB10KkBd/aaFSpJHKF4OI97eYqqmjZSJlNP372gdo2trZWtGhZEe/TD4iJ0VxPbGFhRq+BDXl47yXXzmuvs36Dja0V4/7qQ0xEPIsn7FS5qVImk/HzX32p0awCS8Zv47jXZUlju5YpzJ/HJzBkdk/uXAxgUPVfmd57KQE+2mu7rW2tcC5RgPK1S1O/Y3WadK9DlUblcC1bmNx5bbUGw1FhMaz+bQd9y49h2/xDNOxSg1XX/6BBp+qSAunnD8OY0G0x5pZmzNzxI/b5VJuveO/14ez+m/T+qQVlNZioZCUxIYXdGy9Tq4E7ZSpoz8Tu2e1DgQJ5qF3HTW2bwKfhPHkWQSsJNeCqiEtK5ebjEBpVMkxW+lFUJDGpqdSS+JTUNzKE0nnyY2eh280pQFDSMwCK2xbTua9WMh+C2fspnTQG058Zn0WwqYHPRr+6SBE4eRKcVOwc9/GBjh2VmxONGPkMqZqvCHIhuBOtXVXPwdqaMvnyczVEP0m7rFRwLYijnQ3n7j7Vq39BpzxUrejC8TP3NaqCdOrsSUaGnMOHbmkds3n7KhQq4sDGFd6SlT0A3Cu78M3o1lw8epsTO6+rbGNuYcakfwZQrVFZFo3x4uSOq5LGNjU1oeMPzdlwZy49xrTlzsWHjGw6g7Ft53D9xB29FFE0ERwYxsLh6/i20nj2LD1O9RaVWHJuCuNWDSR3XtU24++O8YoJ3RZhYmrC7F0jcC6muqwi9HkkSyfvpkL1EnTXkjHOyu5NV0hMSOWbodqz0k8eh3P79gs6dqqmUTrv6Jn7mJuZ0rSeZmUSdVy6H4RcIQwWTL+5ca1VRLvEnlyhwC8qVK8SD1AG06YyU1xsDFveKBSxoHiNzFyzvbyhMAbTnxmfTbCphs9Kv9rNDY4fhzwq3NjOnIEePZTyeUaMfGZUzad8HOyjQ6mHb2goaQb4fzAxkdGgYgkuPXhGWoZ+47VuUp6Xr2K5dV/9+l1cHKlevTgH9t8kPV3zPGbmpvQZ3JgnD19x/oRO4ih0HdSYynXcWDF1L0/V2J1bWJnz29pBVKnvzsKRmzm8Qbocp33+3PT7tTMb781l0MwehAW9ZnK3v+jpNorf+yxj7/ITBPo902gSkxUhBK+eR3Jq22UW/bSegdUnMdBzEmd2XqXlN/VZc3MWE9YOwa2K9FrXx3eDGd91EULA7J0j1DpApqakM/unTZjIZIxd2FurPvTbREcmsM/rKvWblaOku/aNebt23cDKypzWbSqpbZOekcnJ8/7UqV6SPCoszqXgffsx+XLbUN5VPzWNrFwJll4v/SgugsSMNKrpGUw/TXxGYWtnLEzUO4bqReb/nvK8p8y0sWb6M8PFRVnaoer3mvhU6qxnzPhvzTR84vrVVarAoUPQogWkpPz32P79MGAArF2rlNczYuQzIY+FNeXsC3Al/Bk/VlBtz/029V1dWX/Lj+svQ6jvWizb87eoWpq9l+9x/u5TmlfV/cu2UR13lqw9w+7DN/GooD57171HLcaM3srhQ7fo1FmzFnHj1hXZu+UK/yw8Qc0GpbG2sZS0FhMTE8Yt7M1PXy1k6sA1/LVvJHnzvxsEWVpbMHndYGYNXsPSX7YR9jyC7yZ1lBxM2thZ03lYC9oPbMLF/T74nLrLvcuBXDroC4C1rSVORfNRwMWR3I525MlrS25HW8wszIiPSiQ+KoG4qETiohIIfx5JZGgMALnyWFOuphst+tSjea+62KtYuzaunbzL7CFrsbPPxe9eQ3FRs+lToVDw5+itPL4bwuS/v9NJvQNg9V8nSU/L5Nvhqu3I3+bly2hOnbxHx07VsLNTHyR7XwwgNi6ZDi3UB9yaSEhJ48K9IDrXrWgQC/F0uZxLL57ToYy0LPnl8GcA1HTSfZOfXMh5nPiEuvlq69xXKxl3lT/Ntau+GAJjMP2ZoU+wqe+mvg8RgL8Z/1MI/CVTrx7s2qXUoc6aeduwAeztYeFCpWC3ESOfCXULFmf9o+skZaSTy1xzVqp20aJYmZnhHfTUIMF0dfei5MuTi8PX/fUKpq0szWnfojJb914nLDyOQgVUPF0Cqni4UrmyC15bLtOmbWUsVci+vcHU1IThE9oy6ts1rF18imG/vCt5p468TrmZuro/Y7otZWr/1cza/AO5VGQ5rWwsmLxuECt+3cnuFacJ8H3G2KX9KKDDpjdzCzMad6tF425KSbiIl9Hcu/KIJ7dfEHQ/hOhXcTx78JK4qATSkpU18aZmpuR2tCWPoy15HO2oWM+dsjVKUqF2aVzLFtYpO/w26akZrJu5n33/nKFkhaJM2zQEx4KqN+AJIVgz8yAXj95mwMQO1GquW5Dle+Uxpw/fpkf/+hRx1b5BfNPGS5ibm9Kzl/pAUQjBjoO+FCvqSPUqxXRazxtO+QWSnimnbQ39SkSycv1lCEkZGTQpXlxS+4vhQZSwy4tzLt1vgl4kB5OqSMPdTn09ub6IjDtgUhiZiX6a3bpPKMQn86pWrZowop3Nm4VwdRVCJlP+3LxZc3tXVyGU2xX/+3J11TyHjc1/29vYqJ5L1/V8sXh5Kd8kVR/G9OkfenVGsgngIz6C6+j7fGm6Zl8IeypKbJ0hvF8GSnr/vt+3RzRcu1ooFApJ7bXx5+5zwnP4XyI6IVmv/uER8aJhp3liyRpvje1u+T0XTRrNFLt2XpM07vK5R0SLKpPF7RtBOq/p2un7op3bGDGq8yKRlJCise3pXddEp1I/i85uP4sze27oPJcUUpJSRUJMksE+s7d5FvBS/NBkhmhVcKhYNnG7SE1OU9tWoVCIdXMPi1bFRollk3frvJ7EhBTRu9WfYkCnJSItNV1r+xcvIkWzJrPE8mWnNLbzu/dC1Ptqrth//JZO63mbAQt3iK+mrjPYezz97BlRZvFfIjld+3mmZmaIcjvmiCk+x/Sa62joCdHnan8RlRqlV39NyF83FvLoH7M9jtTr9gd9diyTyVrJZLKHMpnssUwm++VDruVzondvePYMFArlT21ZW33qrKVudPzc1UUMSs+esGyZ6mOTJ8PSpe93PUaM5CCe+YpgYWLKpVfSVDUaFyvBi7g4nsbEGGT+tjXKkqlQcPKmdAWNt3HKZ0fjumU4cOK2RkfEylVc8PBwxcvrCila3AgBvhvelEJF87Jg2j5SJbR/mxpNyvHLkr48vP2CKf1Xk6rB0rxJlxosPz2BYmWcmTN0HfOGrycpPkVte32wsrHE1t5GstyfFIQQHFh7lp9azSXmdTzTNv/A0BlfY2mt/umG15ITbF9+ilY9ajFkSked1/PPwhNEvY5n9LSOKk1lsrJx/UUsLMzo3kOzoYvX3uvkyW1Ni4ba9dZVERYdj09gCG1qlDHYe3wm6Cm1ihbF2lz7ed6MDCFVnkm9gtKy2Fl5mBBIPst85LU0bPZYKKJBHoLM3PDa1er4YMG0TCn8twxoDZQDespkMv3+ooxkC3029UkNwD93dRGD88MP6mtyfvzReBdi5LPBysyc6vmLcjFcYjD9v8fO3kH6qXBkpXThfJRyduTwdX/tjdXQq1N1UlIz2HvUT2O77wc0JDYmmR3br2kd08ragtFTviIsJIY1i07qvKa6LSsx7q/ePPAJYurAtRoD8oIu+Zi7ZyR9xrbl7D5fhjWbxaXDt3RSFHmfBPm/5Ldey1gxaSeV6rix3HsiNbQ4Ie5YcZrNC4/TrEt1fpzRFRMd95/4Xn3C0T2+dPmmDmUqat9k9+hhGN7eD+jU2ZO8edWb0Tx5FsEVn6d0bVsVKwkBuiqO+gQA0KZ6Gb36Z+VpTAzPYmNpXLyEpPaXXj3DVCbTq15aCMHDhMAcKfEg457y55cQTAM1gMdCiKdCiHRgG/DVB1zPF8uMGcq66rfRVmctNQD/3NVFcoQJE2D0aNXH+vVTblg0YuQzoH6hEgTGRRKaFKe1beHcuSmXPz/HAgMNMrdMJqNdjXLcCQrjSWikXmO4lShArarF2X7AR2N2uly5wjRsWIZtW68S+lJ7Zr1itWJ07FWLA9uvc+VsgM7ratjOg5/n9eTu1cdM6L2CuOhEtW1NzUzp/XMb5u8bhYWlGX8M+IehTWZyZs8NnRQ6cpKHt54z/du/GdpkJg98gvhhRjembx6Kg4bNigqFgtUzD7Bu7mEadfBg5JzuOgfS0ZEJzPt1Dy7F8/PNEO1SeHK5gkV/Hcfe3oYeWmzG1267hLWVOZ3b6O7ECaBQCPZfuY9HSWeK5Mu+UQvA0UDlU5qmEoPpc2FP8HAsjJ25tM2ybxOaEkZCZgJlcqJeOv0WYPLeNh/Chw2mCwNvC4eG/O93/0Emkw2SyWQ+MpnMJyIi4r0t7lNhyxYoVkwp9lCsmH6Jy969YdUqpYeITKb82a+fMnusblypAbiUoNsQ5/BZIZPBvHnw/ffvHpPLoVs3OH/+/a/LiBED09RZ+UV66qW0ALm9exn8XoXxIi7WIPN3qF0eS3NTvM5qzixrYmCf+sQnpOK1V7XO8xt+GNYMMzMT/vrrGMpSTM30/6kZbmWdmT95L2Eh0Tqvq2lnTyYu68eT+y+UsGCnAAAgAElEQVQZ3WUJQQGaNb3LepZgxZlJjF/+HQBzh61nYP3pHN5wgTQdy00MgRCCO5cfManHUka2nsvda4/pM6YNG65Pp8P3jTSWNSQlpDJr+CZ2/3OW9n3rMmaBbhJ4APJMObMm7CI5KY1Jc7tJKu84eOAmAQFhDB3WDFtb9QYm9wJecv5qID071SC3BqUPTVy8H8SL17F0a1BZr/5ZEUJwICCAas7OkiTxXibF8SA2nKaF9ZOeuxunlICskEc/oxqNZPiAWRlkJtL0yQ3BhwymVf0nvHOFEUKsEkJ4CiE88+dX72v/JWLIeuS366xnzFCKSGgaV1UAvmrVu/XZ2oJuY021GmQy+Ptv6Nz53WOpqdC+Pdy8+f7XZcSIASmR25ESdnk59VJa3XK70koDhgMBumdrVeFga03bGmU5fN2fmET96oVLlyhAs/pl2XnQl0gNGeD8+e3oP6ARvj7POHVKu5a0haU5v877GoA/xu0gPS1D57XVbVWJmZuHkJyUyqhOi9Qau7zB1MyURp08We49kd/WDsI2tw1Lf9lGz0oTmP/jBq6fvkdmRs5lq4UQPLkXzNoZ+/i2xmTGd1nE0/shfP9rRzbc+J3eo9ti56C+dALg6YOXjOiwkMsn7tJ/Qnt+mNZZL7WQDSvOcMfnGT9ObEexUqo1q98mIiKe1f+cw9OzOE2aqq9YFUKwctN5HPLY0L2DZrlETWzxvkkBe1uaeRgmsxsQGUlgdBQd3KWVjLz5n21eWL/578bdp5BVQfJZ6mefrg4hMiDjNljo/97qw4cMpkOAtwU6iwDa7bCM/EtO1SNLHVfKRkdtQbexploDZmbg5QVNVWiaxsdDq1bw8OH7X5cRIwakWeHSXHv9gvh07Y6fhXPnpkbhwuwPCJCU3ZVCr8ZVScuQs/viHb3HGNi7HnKFgvXbNdt1t+/gQdlyzqxYdoq4uGSNbQEKFnZgzPROPPYPY/GMQ3qdc4XqJVh2eAxlqhZj4bhtLBi7VevGRhMTE+q0rsyiY+OYt28UDTpU5drJu0zps4JelSewaIwXlw7fIuJlTLY/h6T4FG5dfMjm+YcZ3OB3hjefze4Vp3FxK8jPi/qy/vp0ug1rjo2GTC8og9Rj264yqvMiUlPSmOM1lK6DGuu1Me/CyftsX3uB1p2r0bx9FUl9liw+iUKhYMSolhrnvOobxO37IXzXvQ42GjZNauLRywiuPwqme8MqmJua6jVGVvYH+GNmYkLb0tIcA0+9DKRkbkeK59Y9GM5QZBCQ8IgKeXJgm1zGAxApyN5zMP0hdaZvAG4ymaw48BLoAfT6gOv55MipemRDj9u7t3pFEWNNtRYsLWHvXmjWDK5nySpFREDz5nDpEhTVbvtqxMjHSPMipVkVcJWzoU/oUEz7I98O7mX51fsU9yNeU8FJe8ZQGyULOVKnrCvbz9+mXzNPzM10D06cC9rzVcsq7Dvqx9cdPHEprFqdwNTUhJ9Ht2bIoHX8vdKbcePbaR27dqMy9B3SiE0rz+LskpdeAxrqvD6H/HbM2DgYr8Un2LrkJIF3g5m4rB9F1bgEvkEmk1GhZikq1CzFsFnduXnOn3P7fDm79wbHtlz6d2y3Kq6UruxC4ZIFsLPPhZ29Dbb2NtjZ58LM3ITE2BQSYpNIiE0mMTaZiNAYAm8/59GtF4Q8Cf93roq1S9FxYGPqtvUgj6P0R/SpyWks/XU3p/f64FG3NOP+6o19Pjud3yeAgLshzP1tD+UqF2XouNaS+ly88JBLFx8xcFBjnJ3VG8HI5QpWbjpHkUL2tNfTpAXAy9sPKwszOtc1zAY7hRAcfPSQei6u5LXWXnYSl57C9dcv6F+mpl7zPUp4TLoinYo5VeIBYF7N8GNr4IMF00KITJlMNhw4DpgCa4UQuvmofmFkNUnJmxeiot5tl11rbX1cFPU1cNHXsVFXPhWHR5XY2cGRI9CgATx48N9jwcHKgPrCBTCWQRn5BKniWJgC1rYcDfGXFEy3dnNj2llv9vn7GySYBmV2evjyvRz1CaBDLf2+4Pt1q8WR03dZufEcMyd0UtuuRAknvu5ek61eV2jStDyentplxXoPakRocDQblnmT3ykPzTtIy5a+jampCX1HtaJcteLMHbWZH9svpNuQxnQZ0AgrCW6L5hZm1GxekZrNK5KelkHQg5c8uvX839eNU/d1ylI7FsxD6cquNOlag9JVXHGrVJTceXWrcRVCcOnYHdbOOcSrF9H0GdWSHsOa620CE/I8kikjvcibz44pC3pKqpOOj09h8eITlCzpRNdu1TW2PX72AU+fRzJ1THvM9LhpA4iMS+KITwAda5cnTy7N2XqpXA8JISwhgXF1tbuRApwMCSRTKGhVRFoWOyt34u5hKjOlTG79+mtCpPuAqSsy0/f7ffhBHRCFEEeAIx9yDZ8KqlwKLSzA3Bwy3iqlM4S1tq4uivo6KOozlz5kZ30fDY6OcOKE0i3x2bP/Hnv4UFnyceYMSNg4YsTIx4SJTEbromXxenyTuPQU8lhozow5WFvTtERJ9vo/YGzdeliaZf9rrE45V9wK52Pt8Ru0rVEWUx1VHwAc7HPRt2stVm2+wFXfp9Sqpl4Roe83dbl06RFzZx9i1er+2NvbqG0LyqztyMlfERWRyIJp+7CwMqNhC/2UCqo1cGfZ4dGsnLaPzQuPc2TLFfqOakXzrtUxlRjgWVia4+5RDHePYv/+LjkxlcjQ2P9koBNik8jMkGP3vyy1Mlttg31+O/I6qXaNlMoD3yBWzzyI/81nuJYuyKwtQ6hcW//64VcvYxg/aANCwO9LemOvQdbuDUIIFsw/SmxMMn/80VVjgJyQmMqKjeco716IxnX0DyLXn/JBoVDQp4nhMq/b79/F1sKC5iVLSWq///k9XHLZUzFvIZ3nEkLgG+1HudxlsDY1zM3A/4+dCenXwKqNQceVOPmHd8mS+vqSHRDVuRQ6OuaMu6AuroX6OCjqO5c+ZHd9HxWBgUIUKKD6hBo2FCJZPzc3IzkPRgdEtdyJChUlts4QWwJ9JbU//yxIFF/4pzgQ4C+pvRRO3nwkqgxdIA5fe6D3GGnpGaLX0NWi26C/RXKKekc+IYR4HPhKtGoxV4wetUVkZsoljZ+SnCZGfbdatPacKi55Z//cH/gGiZ+7LBKtio0Sg1vMEVdP3csRt0JDEvwkXPw+ZK1oVWyU6FVjiji27YrIzMjM1pjhYbGib5sFonODWeJxQJjkfju2XxNNGs0U27Ze0dp27rJjokGneeLRk1d6rzMyLlHUGrFY/LrhqN5jZCU6OVm4L/5LTPbW7Nb4htCkOFFy6wyx8M45veZ7kRQs+lztL06Hn9WrvyYUaTeFPMxNKFKOGGxMqdftD+qA+KWRHQk4dTXE0dG6uR1KXd+kScrssKpxs56HqjINTWvOiq6Ojbqibh3Pn3+CknylSikz1PYqdEXPnYPu3f/7qMKIkU+ACg4FKZXbkX3P7klqX9fFlSK5c7Pt7l2DraFJ5VKUcnbkn2PXkOtpWmJhbsbYoS0IC4/jny0XNbYtWaoAI0a1xM/vOevWSpO6tLK24PfFvSnpXoiZ43dw42L2NLfLVi3G/J0/8uuKb8lMlzN1wBqGtp7HEa8rOrsv5iRCCPwuPmLqgDUMajaHmxce0XdUK9acmUDL7rUkZ9RVEfU6nl8GbyAhPoVZy/tS0r2gpH53br9g1d/e1Ktfmq+7a64d9r3znAMn7vB1+2q4ldC/NGnDKV/SM+UMaKVfrbIq9vr7ky6X07OitBruA8/vI4Cviun3ZORG9E1kyKjmoHupklbSLwEysNCs8Z0TGINpA6EtUM6uBJw+LoW6IHV9qtqp27hs6LpnfVG3DpnsE5Xkq1QJDh8GVRtFDh6E/v2VdyZGjHwiyGQyOhariG9kCC8StZuamMhkdK9QkSshwQazFzcxkTGodS2ehcdwQk+LcYAq5YvSqXUVdh3y5V7AS41tW7WqRLv2Hmz1usLFi9LmzGVrxYxlfXAt6cTUUVs5eyx7NxQymYy6rSqx8sS4f41NlkzaSd/a01g98wDhemhcG4qUpDQObbrE4BZzmdh3JQG3ntNjWDPWnJlIr59aSKr11sTL51GM+m4N0ZEJ/LGkD6XLv2N1oZLIyASmT9uHc2EHxo1vp1G9IyU1nTnLjlPE2YEBvaTVJKsiOiGZnedv09rTHVcn9ZscdUEIwbZ7d/AoWIgy+bTXGAsh2PfsHh6OhSlup58FuE+0L6XtSpHHPHtlPqoQaZfBrDwyE8O8P7pgDKYNgJRANLsScPq4FOqC1PWpaifEuwG1oeues4Oq904mU677bT4pSb46dZQqH+YqNshs2gQjR757gkaMfMR85VoeGbBfYnb66/IVMDMxYfs9/SXtstK0ihslCznyz1H9s9MAQ75piFO+3Mxeeoy09EyNbYcNb4Z7mULMnX2I4GAVO8pVkDuPDXNW9aNMxSLMnrib/du025Rrw8zclJZf12Tp4dHM2z4cj7ql2bv2PN83nMHY7kvZteoMzx6GIXL4uhIdEc/pPT7MHLaB3jWnsmzybqyszRk9vycbL/3GN6Nb45BfP6WOtwl8EMqo79aQmpzO3FXfUr6KtOxPRoac36ftIyUlnWnTOpMrl+aA/p/NFwkLj2P8sJZY6mkbDrDxlI8yK93acFnpG6EveRwdTY+K0lRB/GNf8ygugk56ZqVfpYYTnPIST4eqevXXhFAkQsYtsKxj8LGlYAymDYCUQDS7EnBSTVL0Rer61LUTIufWll1UvXfqvg8+KUm+li2VgbOqrMiSJTB9+vtfkxEjeuKcKw+1nFzZHXQXhYSALX+uXDQrUZKd9++TYqDSJmV2uiZBr6I5dM1f73FsrC0YN7QFz0OiWbnxnMa2FhZmTJnaCTMzEyZN3ClJfxrA1s6amcv7UquhO8vnHGHp7MMGMVSRyWRUqFGCicv6sf78JHr+2JzkhFTWzDrID63m0bf2NOaM2MThLZe5d/0p8TFJes+VlprOkwcvObPfl9UzDzCszXx615jK/NFe3LvxlIbtPViw+ycW7R9Fsy7VJalrSOH8yfuM7r8WS0sz/lzXX3JGWgjBwgVHuXcvhNFj21CsuOZs7nW/IHYe8qVzGw+qlNdfvvRVTALbz92mlac7xQrolxFWxebbt7GzsPzXDEkbO5/exsLElDYuZfWa73qUUrbOM6/hg2nSrwGZyCzqGn5sCXxQNY/PBSmBqCEk4DTpNevLG8k4dd9dWden7jxcXd8VmVA314eQp8v63qmr9f5YSlMk0707xMbCkCHvHps6FRwc4Kef3vuyjBjRhx6lPBhxeR8XXwXRoJB6NYw3fOdRlWOPA9nj/4DelQxjq9y8amm2nPFj8f6LNKlSCjtr/UoJangUp1u7auw85ItnJVfq1lCvlFCgQB6m/96VMaO9mPzrbub92RMLC+1fz5ZW5vw2vztrFp1k96bLBAdFMGnO1+TWog4ilfzODvQZ2Yo+I1sRERrDzQsPuXnhEXeuPuHsgf+3YLd3tMXFrQD5nR2wsbVSvuwssbG1wtTUhJSkNJITU0lOTCM5IZWYyASCn4Tz6kX0v5luM3NTynsW57txbala350S5Zwx0UNVRRMKhYLNf59ly6pzlK1UlCkLeuCgg571po2XOH7sLt/0q0eTJpoNR6Jjk5ix6AjFXfLxQz/dtcHfZuEeZU39sPaGCxRfxsdzNPAR/atWw1rVE84sJGems/fZXVoVLYODpe5/X0IILkVepbSdm8FdDwFEmjfIbMHi/epLv8EYTBsAKYHy+5CA05WsknFZUbU+fc/jY5On+xg/D70ZPBhiYmDChHePjRihDKj79n3/6zJiREdaFHYnr6UNXo9vSgqmPZ2dqVSgAKtv+tKjQkW9JO2yIpPJGP91Y/rM9WLVkauM7qJ/IDSkXwNu3Q9m1pJjrPurH/kd1ZcnVKhYhAkT2zN92j6mTtnDtOldMDfXvrHO1NSEQT+3pHipAiz64wDDeq5k/MwuVPBw1Xvdqsjv7EDL7rVo2b0WCoWCiNBYXjwO50VgOC8ev+JFYDh3rz35N2hWyN8tk7G2VQbYdnlsKFW+CE06VsOlVEFcSxfEuVg+zCXcQOhLdGQCf07Zh8/lxzTvUIWfJrWXdMPyhn17fdmw/gLNW1Tgm36aa58VCsGMRUdITE5n4bSvscpGRt3nUTAnbj5iSNvaODsaTvp0rd9NAPpWlrYR8PALfxIy0uhZ0kOv+Z4nBxOaGsZ3BQ3/XSSEAtLOgmV9ZDL9XCUNsIgPL58k9fWxSuNt3iyEjY34j0qZjc27Em85LQGnK+ok497Ixqlbnz7n8THK031sn0e2UCiEGDtW9ZtsairE/v0feoVfPBil8SQx55a3cNs2U4QmxUlqf/jRQ1F84Z/i8MOHOs+lielbTgjP4X+JJ2GR2RrneUiUaN59oRg+cavIkCCBd2C/r2jSaKaYOmWPZMm8N/jfDRb92i0UrapOEeuXnhIZ6dmTjNMXhUIhUpLTRNTrOPH6ZbRIjE8Rcrlu52JIrpwLEN0azxHtak4X+7dd1Vn+78jhW6JJo5ni10k7RYYEGb4te66Jel/NFXuO3NR3yUIIITIy5aLbHxtFm99Wi5S0jGyN9TbRycmi3JJF4udj0iXkOh1fJ1ocXqm3dOKWZ9tEv2uDREJGgl79NaFI81NK4iUb/ntO6nXbWDNtAKTWM+e0BJyuqCtPkck0r0+f8/gYbcM/ts8jW8hkMGeOUskjK3I5fP01nD373pdlxIiu9ChZBbkQ7Hx6W1L7liVLUczenpU+11F+9xmGYe3rYm1pztydZ7M1rkvhvPw8uBm37gfz9ybtEnjtO1Tlh6FNOX8ugHlzDqNQSJ+7TIUiLNs6hKZtK+O1+jyj+68lNPj9q3HIZDKsrC3Imz83+Z0dyGVnZfCSDSmkpWawdPZhpozwIm8+W5ZsGUyH7jU1qm9kxfv0ff6cfwRPz+L8NrmjVufCm3dfsGrTeRrWLk3HVtmTf9t14Q6BoZGM7twQKwNm7TfevkVKZiaDqml2bHzDvehX3I4OpWepqjq9d29QCAVXoq5T2b4itma6uVxKQaSdAkzBMnvlNNnBGEwbiE8xMMtpub0PNdcXi0wGf/8NXbu+eywtDTp0AF/f978uI0Z0wMXWgfoFS7D9yS0yFNo31JmamDDYszr3Xr/m4gs1ovd6kNfOhqHt6nAt4AWn/LKn59yqcQU6ta7Ctn03OHHugdb2XbvV4Pv+DTh58h5/zj+CXEXJhDpy2VoxZnonJszuRnBQJEN7rODgjuvIM7O/OfFT4p7fc4b3+puD26/TuXdtFm8aSLGSTjqNcfaMP7NmHqRSJRem/d5Fa1lI6KtYJs87QBHnvEz8qbVegecbouKTWH7oMrXKuNC4ckm9x8lKUno6G2/50bRECdzz5ZPUx+vxTaxMzfRW8bgf709sRhx1HA2v/yyEgNRTYFEdmYnh5fakYgymv2ByWm7vQ831RWNqCps3Q/Pm7x5LSFDajgcEvP91GTGiA33dqvEqJYGjwdL+VjuWKUshOzsWXrls0Ox01/qVKFPUibk7zxCfnJqtsX78vglVyhdl9pJj+N0L1tq+d5+6fNOvHseO3mHmjANk6KjU0ahlBVbu+AH3CkVYOuswQ75ewZWzAQZ9fz5GXgRFMO3nrYz+fi0pKenMXN6XwWNa6awEcvTIbWb8sZ/yFYrwx8yuWFlp7h+fkMLY33ejUAhmTuiIjXX2anfn7DhDakYm47o1zlZQnpX1t/yISU1laPUaktpHpSax7/k9OrhWII+FCm8DCZx7fZFcpjZ4OEgzhtGJzEcgf4rMqqXhx9YBYzD9BZPTcnsfaq4vHktL2LMHaqnIAkRGKgPtT0oD0MiXRmPnUpTM7cgq/yuSgj9LMzN+rFGTW69e4R0UZLB1mJmaMKV3c2ISU1iwR5pLoTrMzU2ZOaEjzgXtmThrL88kaEr3+7Y+g4c04ewZfyb+soOEhBSd5nQqZM/sld8wZUEPFAoFU0dtZUz/dQTcDdH3ND5aoiMTWDzjIIO7LefW9SD6DWvC6j3DqVZbvYqKKoQQbNxwkfnzjlCtWjFmz+mOjRZzmLT0TCbM3EtYeByzJnTCpXD25Ou8bz3mpF8gg1vXonhBw0nhxaem8o+vD02Kl8CjkLOkPhsDfUiXZ9LfXVrwnZWEjAR8Y/yom682FiaG3xwoUo8AJmBpDKY/W7JjH/6+0LU8JTvn9CmWwnyy2NoqXRIrqHgsFxKiDKhfv37/6zLyRSHQLwtqIpMxqEwt/GNfc+GVtOC4S7nyuOTJw8IrlyTpVEulTFEnvm1enf1X7nPVP3tlJHa2Vsz7rQsW5qaMnb6LqJhErX2+7l6TcePbcufOC34avonQl7o5PspkMuo0LsvfO4fx48R2hDyPZMQ3//DH2O0E+ofqeyofDTFRiaxfdprvOizm2L6btOvqyboDP9FrQEOsdMwOp6dnMmvGATasv0CLlhWZ/of2jLRCIZi5+Ch3/F8yaWQbKpcvkp3TIT45lVnbT+NeJD/fNDeszNsaP1/i09IYVVuasUlSRjqbAn1pVrg0pfJIKwnJysXIK2SKTBo5NdCrvyaUJR5HwKIWMlP91mcojMF0DpFd+/APjaqg+VM/py+OvHnh+HEoXvzdY48eKUs+4uLe/7qMfDG8SArmZYp+AVsH1woUtLZjpf9lSe3NTU0ZUas2DyIiOPY4ezXOWRnYuibFC+Rl6uYT2S73KFQgD3N/7UJsfArj/9hDckq61j4tW1Vi3vyexMQmMWzoBu7c1v3Jkpm5Ke26VWfdgRH0GdwI3ytPGN7rb8YOXMfVcw9RZMPx8UPw7MlrFkzdR9/WC9i25gLV65bin13DGfZLW+zz6r7JLSYmidE/e3H69AMGDGzEuPFttdZICyFYsfEc3hcD+KFfQ5rWK6Pv6fzLrO3exCamMqV3c8xNtUsjSiU6JYW1N2/S2s2N8k7Sase3P71FXHoqg8vW1mtOIQTer89RyrYkRW2kGePoROYDkD9HZtXG8GPriDGYloiuGVl97cPfRzZb2xzqguYRI7JniW7kA+DsDCdPQsGC7x7z84P27SFFt0fHRoxIRSDwDtfsAKgOC1NT+pepybXXL7gV9VJSnw7uZSjpkJdFV65kyw48K5bmZvzeryVR8cnM3n4m2+O5lyrItLHteRz0mnF/7CY1TbuDY6XKLixb3o88eawZO2Yrx4/pZ6Nuk8uSvkMas/nozwwc1YKwkBimjPRiYOel7N96lfhYaQ6MH4KMjEyuX3jExGGbGNx1GWeP36Nlx6qs3jucX+d1p7CrfmYgQUERDB+6gSePw5kytRM9e9WWVKe8cedVtu27Qec2HvTsKE0ZQxPHfB5yzOchg9rUoqxLgWyP9zarfG6QnJHByFrSstLpcjlrAq5R08kFj3z6BcIP4gN4lRpOU6dGevXXhrLEwwysWuTI+DohRT/vY3l9KJ1pqTrSbyOTiXfkfkH5e0POkxPnokl/WtdzMvKRcOeOEPb2qj/Atm2FSE//0Cv87OEL1Jl2qeAqBt4YLpIzkvV6zxLT04TH7j9F/3PbJfd5ozu9/d5dvebUxN9HrogqQxeI/ZfvGWS8k+cfiAad5olhE71EUnKapD7x8clizGgv0aTRTPHnvCMiKSk1W2vISM8UZ47eEcN7rRQtPaaIVtWminGD1on9266KiHBpWt85SUpymrhw6r6YPXGX6FRvpmhRZbLo3nSu2PLPWREbnZitsRUKhTh0yE+0aTVPdOuyWAQEhErut8broqj31Vzx+8JDQi7XT3v5bV68jhH1Ri8TfedJ0yPXhZfx8aLM4r/EqKPSdaW9Am+KEltniLOhj/Wed8HDJWKIzwiRJjf894tCIRfy8AZCHj3A4GO/jdTrtkzZ9tPA09NT+Pj4vPd51VlPa7LQNmQfU1NlnbE6C25dbLqlrMvERL29uCqkWIkb+Qi4cgWaNVNtedmrF2zapPzwjeQIMpnMVwjh+aHX8T6pWLWiqLKiJr1cvqZ1If2yR8vvX+LPu+fY1ayfpAyZEIKu27cREh/PqX7fYmepnx24KuQKBT8s2cPdoDA2jetJKefs12meuuDPHwsPU7pkQf6c0hU7Wyvt65ArWLvmHNu3XaVgQXvGjW9LpcrZ0xkVQvDk4SsunLrPJW9/goMiAXCvUBiPmiUoV9mFspWKkDuPYazK1ZGenslj/zAe3H7B3ZvP8bv2lLTUDOzyWFOroTv1mpSjau2SOrkXqiIqKpE/5x/h2tUnVPFwZcLE9uTLp96h8g1CCJZvOMe2fTdo3aQC44e1xNQ0e9fNtIxMvvtzOyGRcWyb0BtnR8NKvI04epjjjx9zut93FM6t3UUxTZ5J08MrKWBty65m/fRSE3mVGs6427/SwbktXYt21GfZGhFplxEx3yLLsxCZdVuDj/8GqddtYzAtAXXBpUymDHLh3YC2TRvYsOFdu2pNChZSgtisY6iyBNc0j5RzURdwy2Tv9tV2TkY+Mk6cgHbtIEPFY+Vhw2DJEuUHbcTgfInBtKenp+i4sRtRadHMrzITU5nuNaBJGek0OrSMcvYF2dC4p6Q+t1+9ovM2L/pXrcbEBoY1coiMS6Ln7M3YWluyeVwvclllX6HgwrVApsw7iGuRvCyY2g0H+1yS+t29G8yc2Yd4FRZLl6416D+gYbaDzDe8eBrBpTP+XDkbQKB/2L/24C7F81OuclGKuTlRqEhenIvkpWBhe52l5xQKBZHh8YSGxBAWHE3ws0j87wQT+CD0XxnAQkUcqF7XjbpNy1HRwwVTLYYpUvH2fsDiv46TlpbJwEGN6NjJExMT7dc9hUKwcNUp9h27Rec2HowY0FRSP23M3HaanRfusHBwBxpVMpymNMC1kGB67trJjzVrSd54uPGRD9NunmBDo57UK8sfd7wAACAASURBVKhiz40E1gdt4VzEBRZWmYO9heH1nxWxYyHNG5nTZWQyw90wZ8UYTBsQbdlcdQFtv35w5Ii0jLGmedTNK2Vtup4LqD4fVTg6wqJFxkD6k2PXLuje/f/vnt5m8mSYNu39r+kL4EsNplee+IdFgcsYXmowNR31qyv9J+Aqs295s61JH6o7ScvATjh1gt0PHnC4d1/cHPWrpVWHz6NgBi/eTfOqpZn1XfbMOd5w3S+IibP2UdApN39O6UaB/NoziAApKemsWunNgQN+uLg6Mv6XdpQpI032TCqpKek8uh/Kg9svuH87GP87wSTE/f9eC5lMhqOTHY757bCytsDa2gIrGwusbSwwMZGRmpJBako6KcnKV0J8CuEvY/6jnW1ubkqpss6Ur1L03yx4XgmZYl2Ii0tm8aITnD3jT9myzoz/pR1FXaT9bWRmypmz7DjHztynV6caDPmmgUE+96M+AUxcd5R+zaoxspNhFS8yFQrabdlEUnoGJ77ph7W59hue1MwMGh9agaudA1ub9NHrHBMyEhl5axy1HKszsMR3+ixdI0KRiIioC1ZfYZJnusHHfxtjMG1AtGV/9SnpUDXHiBEQpV169D9ZZCmZ5qzzSMlkv51pNzFROlJnxVje8QmzejUMHKj62MKFMHLk+13PF8CXGkxfv3Gdcbd/JZdZLqaWn6jXl3NKZgaNDy2nhJ0jW5r0ljRGVHIyTTeso4JTATZ17mJQ4wuANceus/TgJSZ0b8LXDSobZMzb90MYP2M31lYWzPm1M6VLSN+E5nPjKfPmHiEqKoE2bavQf0BD8uRQSYYQgriYJEKDYwgLiSYsJJrQ4BjiYpJISUknNTmd1JQMUpLTkcsVWP8vsLaytsDK2hxbOysKFnagUBEHZXa7aF7yF8htsMxzVuRyBQcP+LF+3XlSUtL5pl99evSsJbk8IzEpjclz93Pj9nP696xLv6+lbVDURtCraPrM9aJ04fysGtnVoOodAOv9bjL93FlWtGtPy1JukvqsCbjGzFun8WrSm5pOrnrNu//lIXaF7GNmxWk5ouIhkncj4icgy7sdmYWHwcd/G8nXbSmF1R/L60NtQBRCuUHP1VW52c7V9b8b9tRtNoR326obO+umQBDCxET9mG9Qt1nw7Ta6nIsq9NlMaeQTYM4c9X+469d/6NV9dvAFbkB8c80++cpb9LnaXwTEPdT7/Vv38LoosXWGuBj2VHKfDbf8RPGFf4pDDwP0nlcdcrlCDFu6R3gO/0vceBRssHGfPHstOvdfIZp3XyjOXXmkU9+EhBSxbOlJ0azJLPFV+wVi545rIi0tw2Br+xS5cf2JGPD9atGk0UwxetQWEfT0tU79Q0KjxTc/rhUNO88Xh07dMdi6YhNTxFdT14nG41aIV9HxBhv3Da8TE0XFZUvEN7t3CYVC2gbJxPQ04blnoejjvUXvedPk6WKY789ijv8CvcfQhjyyl5C/biH5vLKD1Ou2cbeRRDQZjrhoeOooRYtZlYwegIODdgtufWy6dTVPUXd+ms7byCfAuHEwfrzqY/37w75973c9Rj5b6uerg52ZHQdCj+g9Rs+SHhS2ycPMW6fJlCh717tiJSo4OTHt7BliUw0rAWliImPmd60pmt+en/8+wJPQSIOMW8I1P3/P7UOxovmYNHsfq70uolBIe4Jsa2vF0GHN+Gd1f0q7F2TF8tN8128VJ0/cRS7/tHSks0uAfyhjx2xl/LjtpKSkM3lqJ+b92ZNixfNLHuOaXxADx24mIjqR+ZO70rZpRYOsLS0jk1F/7yc0Op4/B7angINhy1kApp71Jl0uZ0rjJpKz6CseXCY6LZnRlfTfZ3A+4iJxGXG0LdRK7zE0ITIfQ8YNZNadDf60KTsYg2kDoCqgfRttWszqnJ2jo7VbcL8Pm259AnYjnwizZqku95DLlXXV3t7vf01GPjssTS1pXag5d+Lu8TRRP7tvS1MzfqnShIDY12x/4iepj6mJCbObtSAmJYWZ57NnB66K3DZWLB3WEUtzU4Yv38frWO2OhlLIl9eWJTN60LZpRTbsuML4GXtISJRuFlOseH7mzuvJnLndyZXLktmzDvFNn5Xs2+tDigSTmE8VIQTXrj3h51FbGDZ0A4GBrxg6rClr1w+kYcMykoMvhUKwaddVxk7fhVM+O1bP74tnZf1KHlSNPWn9UfyehDKjXys8Shm+DOLE48ccDQzkx5q1KOHgIKnP84QY1jy8RkfXClRx1G9NmYpMDoUepZRtScrlzr6BjSpE8hbAHGy65cj4+mKsmTYQb2qM1W0gVFfDDIapuc5pdJHfM/KJIZdDz56wc+e7x2xtlQF19ewbEnzpfKk102+u2SmZKYy6NR733KUZVXq4XuMJIejtvYWHcRGcbjsEe0trSf3mXrzASp8bbOzchXouhgmK3iYg+DX9F+6gSD571ozqhq21YdQFhBDsP36bRatP4+hgy68j21ClfFGdxlAoBFcuB7J921Xu339J7tzWdOxUjY6dquVYTfX7JjNTzpkz/mzfdpWgpxHky2dHl27Vadu2Crly6fZZREYnMnPxUW7cekaz+mUZN6wF1gZQbHnD/F1n2XLGj9FdGtKnSVWDjfuG+NRUWmzagKO1Dft69pJchz34wi4uhwdxqu0QCljrlyk/9/oiq4PWM8Z9BJXtDZPFfxvlxsP6YNkME/t5Bh9fFVKv28bMtIF4UzrhquY6raok4o0T4fPn76qRfWyZX11LQ4x8QpiawubN0EKFDnBiIrRuDQ8evP91GfmssDazpkXBZtyMucXzJN3tsEGpGjG5WgviM1JZdO+C5H4/1apFMXt7Jp06RVK64TOzZYo6MW9AO56GRTFm9SEyMlXs2NYDmUxGx1ZVWD6rF+ZmJvz06zZWbb5Apg7jm5jIqFuvNIuXfsOixX2pUKEIGzdcpHu3pfzx+z58bjz9ZEtAnj+PZNXfZ+jVYzmzZx5EKATjf2nHZq8f+PrrmjoH0hevP+bbkeu58yCEMT80Z/LPbQ0aSG/2vsmWM370buyRI4E0wOyLF4hMTmZWc+l25BdfBXHq5SOGlqurdyCdqcjkQOhhiudypVKeCnqNoZXU/SCSkNn0yZnxs4ExmDYwUksi3rbsBuWurzcBdU6UahgxohELC9izB2rXfvdYVJQy0Jai22jEiAZaFmyGjak1e14e0HuMMvZO9CzpwZbHvjyMfS2pj5WZObObtyAkPo45F6UH4bpQp1wxfuvVjGsBL5i2+YTkOmcplHUrxJoF/WjTpCKbdl1l6AQvgkNjdB6nQsUi/D6jK2vXD6RN28r43Ahi/Ljt9O65nDWrz/HihQQ5qQ9MfHwKBw/4MXzoBr7/9h927riGu3shZszsxuq1A2jRsiLm5rqpYqSmZTB/5QkmzNyLk6Mdq//8hq9aVjFoTe4xn4f8ufsczTzc+LmzYbXP33DuWRDb7t1lQNVqVCpQUFKfDIWcP26exCWXPd+719B77kuRV3mdFkGnwh1ypJZZCKEs8TCrAOaVDD5+djGWeeQAUkoiPoXSDiNfIDEx0LAh3L377jE3N7hwAQpIl+sy8v986WUeb9j38iC7Q/YzpdwEStnpZ1ARk5ZM08Mrcc+THy8dtHBnnj/H6pu+/NOhI01LlNBrbm2sPnaNZQcv06FWOSb3bo6pgV1Fz1x+yLzlJ0hPz+S7HnX4ur2nzsHjG9LTM7lyOZBjx+7gcyMIhULg7GyPp2dxqlUvQZUqLthKcGTMSTIz5QT4h+Hj8xRf32cE+IeiUAiKFctHy9aVaNasAnnzSjO5UcU1vyAW/n2Kl69i6dGxOgN718PC3DDGN2845vOQXzccpVJxZ1b82BlLA48PEJGURLstm3GwtmJ/z95YmkmbY3XANWbdOs3Kel1pXqS0XnOnKzIYd3sSecxzM7X8pJwJpt84Huaehcymi8HHV4fU67bhP1EjklC36VDd740YeS84OMDx41C/Pjx58t9jgYHQqhWcOQP29h9mfUY+eVoVbM6p8DNsC97FpLLj9PridbC0YVzlxky6cZTdQXfoWkKazvPoOnW5EhzMuBPHONS7L4XsDK+i0L9lDeRyBSuPXCUjU8H0b1pilk276bdpXMedCu7OLPj7FCs3nufQqbv89H0TanvqfnNgYWFGw0ZladioLJGRCVy+FMi1a084ceIeBw74YWIio2xZZ8qVL4ybW0FKuRWgSJG82bbP1kRUVCKBj14RGPiKhw/DuHM7mKSkNExMZLi7F6JX7zrUreuGW+mC2QraQsJiWLr2DJduPKFIIXsW/94dj4qGl6g6eO0BUzedoEpJZxb/0DFHAmmFEIw9cYz4tFQ2dO4sOZAOSYzlr7vnaeJcimaFpelQq+JkuDdR6dEMKvF9jilsiKS1YJIPrNvlyPjZxZiZNjBSTVHUZaZNTZV1ycZNfkY+KEFBULcuhIW9e6xePWXArUnCxsg7GDPT/493+DnWPdvEqNLDqepQRa+xFULQ8/QmHsdHcaLNIBytpGUnn8bE0MFrMxWcnNjSpZvBM8dvWHP8OksPXKKZhxszv2ttcEMOgKu+T1m8xpvg0BhqVSvBj983xqVw3myPm5Ehx//BS3x8grh58xmPA8P/dSu0sjKnZEknihZ1xKlAbpyccv/7097eBktLc8zNTVUGVZmZctLSMklMTCXidQKvX8cR/jqe1+HxhIXF8jgwnJiYJEBZ9li4SF4qV3bB07M4HlVdsbOTtuFUE8kp6WzcdZUd+30wMzOh39e16da+msGz0QB7Lt3lj62nqO5WlL+GfIW1BMv1hATYvl2Zu3BzU4oqabvnW+Vzg9kXL/BHk2b0qiStBEIIwYDzO7ge8YLjrQfhnEs/y+/EzERG35qIm11JxriP0GsMbYiMQERUW2S2I5HZDs2ROdRhdED8QEgt35Bi2a0qCDdi5L1x7x40aKAs/chK69ZKHWoLw23O+dz51INpmUw2D2gPpANPgO+EELGa+qi7ZsuFnAl3piADZlaahqlMv0AzMC6C9sfX0KZoWRbU/kpyv73+Dxh9/BgjatVmRC0V+wQMxKbTvizYc56GFUswt3/bHAnYMjLk7D58k3XbL5OekUnnNh706VwTB3v9Sx+ykpkp58WLqP9ljMN5/Dic0NAYoqMSURVCyGRgaWmOlZU5JiYy0tIySU3NULvR0c7OigIF8lCipBNubgVwK12QkiWdsLExjCrKm3M4euY+a7wuEhWTRKvG5RnctwH58toabI632X7uFrN3nKFOuWL8ObA9VhbaP/uLF6FNG2VCLSkJcuVSOhAfOaLMYajiVlgYX+/cTvOSJVnapp3kzPDhFw/46fI+Jnk0y1attNfzHRx7dZIZFafmiNshgCJuIqQcQuZ0DpmJNKk/Q2EMpj8Quth7Gy27jXz0XL0KzZopr+xZ6dFDqQKSAxm3z5HPIJhuAXgLITJlMtkcACGEGtcfJZqu2b7RfvwVuIzviveliZP+G7L+unueJfcv8k/9bjTR4VH1mOPH2Bfgz5YuXalZRDe5OV14O6iaP6CdpOykPkTFJLJq8wWOnbmPhbkZndt40LNTdexz59wTpIwMOZGRCbwOj+f16zji41NJS8sgLS2D1NRM0tKUAbSVlfm/wbWlpTk2Nhbkd7LDySkPTk52Bg2as5IpV3D87H027LhCWHgcZd0KMWJAE8q7O+fYnBtP+bJwr243UQkJULiw8mdW7OwgNFSpVPo28amptPPajBBwuHcfcltJq2+PSk2i1dF/cLbJze7m32Km59OZiLRIxt3+lTr5ajGwxLd6jaENIY9ARDQCm68xyT0lR+bQhDGY/kDou7FQlyDciJH3yqlT0LYtqJIU++EHWLbsXW1HI+/wqQfTbyOTyToBXYUQGp+babpmCyH4w38u4anhzKs8E2tT/Ta6pcvldDyxltj0FI60GihZezopPZ32XptJycjgYO++5MvBsqU9l+4yY+tpyrkUYOHgDuTLY7iscVZevIxm/fbLnLrgj5WlOR1bVeHrDp45loH9WEnPyOT42Qds2X2Nl69icS9ZgO971qV2tRI5VtcrVyhYuOc8W8740dzDjRnftsbcTFqyYfVqGDlSdd4iVy5YtEhpTPsGIQTDjxzixOPHbO/WnarO0m8Ofrq8lxMhD9nf4nvc7Z0k98vK8sf/4Bvjx7xKf5DXMvvlRapQJCyApL+R5TuBzMzwGvHaMOpMfyD0dQs0WnYb+Whp1gy2blXe8WVlxQr47bf3vyYjH5rvgaOqDshkskEymcxHJpP5REREqB1AJpPR06UbcRnxHHh5SO+FWJiaMrdme6JSk/nN5xhSE0S5LCxY2qYdcWlp/HDwAGmZmXqvQRud61Zk/sB2PA6LpO+8rTwMUf++ZBeX/2PvvqOiutY+jn/3MPTeFKSpgBVj711jS0zRmJim6bkxvSc3PTf1Tc9N7z3GqNHYotHErrFiRxFBQATpDMMwTNvvH6O5RlEpUwD3Zy0WCYc55zmKw2/27P3smDCeeXAi3/73Zgb3TWLWgq1cefsnPPPaArbuPOzQln1NUU5eKR98vYrJt3zMax8sI8Dfm1efmMRnb0xjUJ9EpwVpfXUN93+8gB9WpnLNiB68cvNFdQ7SYJ8jXVuQBvvXMzL++bX3Nv3FbwcP8sjgIfUK0r8e3sPinDTu6Tq0UUE6vfIgG0s2MT5qjNOCtLRVguEH8B7rliBdHy06TJ/YFEWjsX/+4QfnX7Oh23urLbuVJm3yZPjss9qPvfQSvPWWa+tRnEIIsUIIsaeWj8tO+p4nAQtQ6zOqlPJTKWUfKWWfyMjIs14vKaA9QyIG8VvBcvKrCxpcd0pYFPd3G8aS3DTmHd5T58d1adWKN8aOZ1v+UZ74Y3mdg3hDjOyexJcPXIVNSm56cxYrd2ac+0GN0DYunGcfmsgPH9zC5It6sm13Ng88O5trZnzG93M3UVp+huTWDJnMFlasTePep37iuru+YPaCrfToEsvbz1/JZ29MY3C/JKeFaIAjxeXc+OYsNqYd5omrR/HolSPrvbA1Odk+Al0bf39ISvrf/y9OP8A7f21kcucu3Na77m92HdGX88y2ZfSOiOWOzg1fK2CTNr49PJNQz1AuaXNRg89zToZvQFYiAmY47xoO0mKnedS1q0ZTorbsVpq8N9+Ehx+u/diXX8JNN7m2nmakJUzzEELcANwBjJZSnmX5tF1dnrPLTRU8uusp2vu35bFODzY49FhtNq5f+QP7yo6xaPytxAXUvX3je5v+4u2NG3hk8BBm9G34Yqy6KKrQ8+AnC9mbU8Ddlw7mpjF9nRr0TqgxWVizMZ0Fv+9ix95cPDSCHilxjBjUkWEDkglz4IJFV6ipMbN5x2FWbUhnw5ZD6A01RLcO5pIxFzBhVIrLprVszzjCQ58twmaz8fqtE+nXsWFvJ9d1zvTOggKunj2LlNat+X7ylDq3wbPabFy38gfSGvDv41S/F/zBd9kzuSvpdgaEO+ffi7TpkEUjwas/mtAPnXKNujjv50w3ZO6yCrOKUgdPPgkvv3z61zUamDMHJk1yfU3NQHMP00KI8cBbwHApZZ3mKdT1OXt5wZ98m/1jo38551VVcPHSz0kOjmTmqOvrvLBKSsl9vy1hUfoBPpp4CeOSGt5zty6MJgvPff87y7YdYFSPJJ67fiyBvs5bhHeqnLxSlq7cy6oNB8g9WoYQcEGXWEYM7ECf7m1JiA1zScCvr7LyKrbvyWXNXwfZuPUQ1UYzgQE+DO2fxIVDO9P7ggQ0GtfULaXk+z+389/564iNDOadOy4joVXjOk2cq5tHgb6Sy2f+iKfGg3nXXFuvef4f7l3Pm7tX80b/S5jUrluDayw3lfPorqdJ9G/Ho50ecF5faf17SP17iPD5CM8uTrlGXZz3Ybq+C/qa40i2oriFlHDnnfDxx6cf8/KyP/OPHu36upq4FhCmMwBv4MSe039JKe8422Pq+pxtkzae2/sSZaZyXrvgRXy1De8nvODwXh7461fuTxnGPSln6CdWC6PFzDWzZ5NeUszPV11N11YNn09aFyeHseiwQF67dSKd4px7zdpqyMopZtWGdFZvTCczpxiAsFB/eqbE0Sslnh4pccRGh7ospJ6srLyKXWl5bN+dQ+qeXLKO1xcS7Mew/kmMGNSRnilxaOsxN9kRKg1Gnv3+d1buPMSo7kk8N81xL4b0enuf6YwM+9SOqVPtI9LVZjNTZ88iq6yM2VOvplPE2adQnWxXaT5XLv+GcXEdeXfg5Y0KwB9mfMrW0u28fMHzRPk4Zzfc/41KD0AT+oFTrlFX532Yru/ItNreW1HqwWq1v8qcNev0Y/7+8Mcf0L+/6+tqwpp7mG6I+jxnZ+oP89zelxjVajg3tru+Udd9YOOvLMrZxzcjrmFQ67Z1flxhlZ5JM3/EJiVzpl5DTFBQo+qoix2H8njsyyWU6au597IhXDuip1uCK0Befhnb9+SSujuH7btzKDm+gYqvjydt48JpFx/x90eb1iFEhPnj69O4XvMWi5WSsiqOFes4nFtCVk7x3x+l5fbRLR9vT7p1jqFXt3h6doujU2KUU3dhPJvUjDye/GYpReV67ps0lOtG9nT6KL7FZuOuRQtZkXmITy+9nNHt677bZVmNgUm/f43ZZmXJhFsJ9mr4C9Vd5Xt4/cA7TIq5hMmxde/rXl+2yneg6kO3j0qDCtP1HmlWrekUpZ5MJrjsMli69PRjYWGwZg107er6upooFabP7fvsn1hWsIInOj9C56CODb6u3lzDlOXfUFJTxfyxNxNTj93d0oqKuHr2z4T7+TLryqlEnmlVmAOVVhr4zw/LWb07k4GdE3j2ujG0DnX8Vuf1IaUk92gZe/Yf5WDWsdMC7gn+fl5EhAUQHhpAUKAP3l5avDy1eHtr8fbSIoTAZLZQY7JQU2PBZLZiMNRQUlZFcamecp3hH797/xHc4yLo0jGazknReHq6t5+9yWzhs6Wb+HLZFmLCg3jpxgl0axft9Oue2Cp8Xloaz40YyfQePev8WKvNxs2rZ7G5KIeZo6+nR3jDN1WptlTz793P4q3x5sVuz+CpcU6/dGktQhZfCN4j0YS845Rr1Md5H6ahfnOg1ci0ojSAwQBjx8L69acfa9PGPgmwXTvX19UEqTB9bkZrDU/ufg6B4KVuz+Lt0fC3zrMqS5n0+1fEB4Ty8+hp+Gjr/st/29Gj3DBvLnHBwcycciUhPo3fxvpcpJTMXbebN+euxsNDw32XD+GKwRe4bZT6TMp1BrJySjhWpKOkTE9x6fGPEj2VVTV/B2dTjQWT2YLVJu0B28serr29tPh4exIRFkBEmL89iIcF0Co8kPjYMKIig5vcPe/Kyuf5738ns6CUSwd04dErR+LfyBH5upBS8szKP/lh104eHDiIu/sPqNfjX9u5kk/SNvJK34u4KrFHo2r5Out7/ixczTNdHicpMLFR5zobm+45MPyMiPitSbTDU2G6ntScaUVpoPJyGDECdu48/Vhioj1QR0W5vKymRoXputlXsZ9X9r/BhKixXJtwVaOu/2feQW5bO5tJbbvxev+6b7UMsD4nh1t+nUeniAi+mzyFQG/XLBDMLSrnxR9XsDk9l15JMTxz7RgSWrt2C2XFzmA08cGiDcxclUqr4ACevGY0Q1PqPsWiMaSU/N+6tXy6bSu39+7DY0OG1uvnd0lOGvdsmMc1iT15se+ERtWSptvPy2lvMD5qDNclTG3Uuc5GWrKRxRPA9yo0wc857Tr1oTZtqaeG9odWlPNeSAgsW/bPRqgnHDoE48bZA7ei1EGX4E6MajWcpQXLydBnNupco2KSuT9lKPMO7+bbg/UL9YPj4/ng4onsKyritgXzqTabG1VLXcVFhvDxvVfwzHVjSM8r5qqXv+PL3zdjtlpdcn3F7q+0bK58+Tt+XJnKlUO7M+ep6S4L0gAfbN7Ep9u2cv0F3esdpA+UF/LY5kX0DI/h6V5jGlWH0VrD55nf0Mo7kimxlzfqXOci9e+A8EQE3OXU6ziDCtMnue46+5QOm83+WQVpRamj1q1h+XL71I5T7dpl3478TNt7Kcopro6bQphXKJ8c+gKjtaZR57qr6xAujEnm5dQ/2FRYy1y+sxjdPpE3x41nS14eMxY5d5fEkwkhmDQohV+ens6QlHa89+t6rn75e9buyXLqxjIKZBeW8dBnC5nx/i94aT348oGr+PfUUQS4sHXhF9u38dbGDUzu3IXnRo6qV5Aur6lmxrq5+Gu9+GDIZLw96taH+kx+yplDYU0Rt7a/oVHTrs5FmveAcTH43YjwqHunkqZChWlFURyjbVt7oA6rZWvZDRtgyhT7okVFOQdfrS+3t7+ZY8ZCfsj+qVHn0gjBG/0vISEwlDvX/cIhXXG9Hn9Jx068MmYsa7KzufnXeehd+DMcGRzAm7ddwlu3X4rZauPej+Zz+7tz2Jvd8N0ildqV6Kp4ZdafTHnhWzbuy2bGxIH89O/r6ZnU8EV79SWl5IPNm3hpzWomJCfz6pixaOoRpGusFu5cP5d8g473B0+mtW/jFrFuK03lj8KVjI8aQ+egTo0619lIKZG6F0EThvC/1WnXcSYVphVFcZwuXeC33+yNUU+1dClMn25vq6co59AluBMT20xgVdFaNpVsadS5Ar18+GzoVXgIwU2rZnGsupZt5s7iqq4pvDluPJuPHOH6ubMpq65uVD31NbJ7InOfns7jV43kUH4J1782k0c/X0T2sTKX1tESVVbX8MnijVz63FfMXbeLyUO6sfD5m7h9wgC8PRs3qlsfNil5ac1q3tywnss7dead8RfVedOhE49/+K+FbCrM4dV+F9MnMq5R9ZTWlPJ51tck+MVzVdzkRp3rnIy/gnk7IuBhhMa9XWwaSi1AVBTF8f74w76VV22jeP/6F3z0kX1xwnlELUCsP4vNwov7XiPfmM+LNPqQNAAAIABJREFU3Z4l0juiUfXsLs3n2j+/JyEglJmjrifQy6dej19x6BB3L1lEfHAw30y6guhA1//irzKa+HbFVr79Yxsms5ULeyVz89h+dIxtfm+Nu1OZvpofV6by0+od6KtrGN0jiXsuHeKWxZ4Wm41/L/+duWn7uLFHT54aPqJeI9JSSl5MXcHX6Vt4vMcobutUv64fp7JJG6+kvUFWVTYvpDxNtK/zFpBLmx5ZPA48ohFhPyNE0xrjVQsQFUVxn9Gj4aef7A3cT/XJJ/aelYpyDlqNljuTbkNi33nNYmvcnOVuYdF8OPgKDlYUM2PdXGqs9TvfhYmJfDNpMgV6PVf+/BOZZa4fGfb38WLGxEEsev5mbhjTh/V7D3P1K99z30fz2ZWV7/J6mpvCcj1vzF3NRU9/zudLN9GvYxw/PHYtb9x2iVuCdI3Fwl2LFjI3bR/3DxjI0/UM0gCf7d/E1+lbuKlDX27t2PjNshYcXcz+ynSmt73WqUEaQOo/AFsRIvDpJhek60ONTCuK4jxffQU331z7sddeg0cecW09bqRGphvur5LNfJDxKZe2uYgrHfCW87ys3Ty8aSET47vw9sDL6h1e9hQe46Z5vwDw1aTJpLRyzrbKdaEzGPlp9Q5+XJlKRZWRXkkxTB3WnZHdk/B08TbbTZWUkr3Zx/h5zU6WbjuAzWZjfJ9O3DS2L4nR4W6rq7KmhjsWLmDjkdx6b8hywvzDe3jorwVcHN+ZdwZeXu+f5VOlVx7kxX2vMSC8HzMSb3Xq7o7SkoEsvhR8L0cT/LLTrtMYqs+0oihNw9tvw4MP1n7ss8/g1ua54KS+VJhunM8yv2Zt0Xoe7ngfF4SkNPp8n6Rt5LWdK5mW3Jtne42td2jILCtj+i9zKDcaeWPceMYnJTe6psYwGE38smEPP61KJa9ER2iAL5f078Llg1JoF1XLouDzgM5gZMnm/czbsIf0vCL8vD2Z2L8L00b3IjYixK21ZZeXc/uCX8kqL+O1seO4vFPnep9j5dEM7lg7hz6RcXw5fGqjO3dUmit5es8LaISGl1KexVfrvM2KpJTIshvAvBcR8TvCw30vas5GhWlFUZqOp5+GF188/esaDfz8M1xxhetrcjEVphvHaK3hP3tfptRUxvMpT9Hap1Wjziel5JUdf/LFgU3c3LEfT/QYXe9AfUyv546FC9h5rIB7+w/g3gEDGz0y2Fg2m2RD2mHmbdjDml2ZWGw2urePZmyvjlzYM5lWIbUsDm5Bqk1m1u3JYkXqQVbvPkSN2Urn+FZMGpTChD6dXNri7kzWZmdz75JFCCF476KJDI6Pr/c5Vucf4o61c+gQHMn3I6+t9/z/U1lsFl478A4ZlRk81eVx2ge0bdT5zkVWz0dWPIoIeg7hd61Tr9UYKkwritJ0SAl33w0ffnj6MS8vWLQIxjRuc4GmToXpxis0FvHs3hcJ9gzm2a5P4OvRuAAhpeSF1OV8k76Vmzr05cmeF9Y7UNdYLDz1xwrmpu1jbGIib4ybQICX87earoviiioWbtrHb1v2c/CovSVgj/ZtGNMrmVE9kokKbZ6dE05VXWNm3d4slm9PZ+3eLIwmC2GBfozpmczlg1LoFNe4F16OIqXky9TtvLJ2DUlhYXx66WXEB9d/hHzV0UPMWDeH5OAIvh1xLSHejR9B/vbwjyw/9if/an8LQyIHNvp8ZyNtZcii8aBNQIT91KTnSqswrShK02KzwbRp8OOPpx/z94cVK2BA41ahN2UqTDvGnop9vLb/bXqH9uCe5BloGvmL+OROCDd26MtTDQjUUkq+Sk3l5bWrSQoL45NLLiMhxL3TCE51+Fgpy7cfZHlqOgfz7ME6MTqcAZ0TGNApnt5Jsfh6e7q5yrqx2SRpucf4a38Of6VlszMrH7PFSligH6N7JDGmVwd6JcXgUY/Wcs72zxddSbw5bjz+DXjRtfJoBneum0tycCTfjrjGIUF6TdE6Psv82unbhZ9gK38MjAsQ4fMRnh2dfr3GUGFaUZSmx2yGSZNg8eLTj4WGwpo1kNL4+bBNkQrTjrM0fzk/5MxicsxlTIq9pNHnk1LyUuoKvkrfwg0d+vB0zzENWni1LiebexYvQgKvjx3HmMSkRtfmDIePlbJq1yH+2p9DakYeJosVT60H3dtF0719G7omRJHStjWRwU1jSojBaCItt5C92QXsyspn28EjlFcZAegQE8mATvEMTWlHzyYWoE/ILi/nniWL2FNYyH0DBnJP/wENmg7kjCCdUXmIl9Jep2NgMo90uh8P4dxFq7JmLbLsFvCfgSbwAadeyxFUmFYUpWkyGGDcOFi37vRj0dH2r7dv7/q6nEyFaceRUvJp5lesK97Afcl30Ses/l0Qajvnyzv+4MsDm5mW3JtnetVv97kTcirKuXvxYvYUHuPmnr14ZPAQvLWu2/yjvowmC6mH8vgrLZvN6bkczCvCarPngtYhAXSKa0VyTARxkSHEtwolPjKE0ABfp3R5qK4xk1NUTk5hGbnF5WQfK2NfzjEy80uxHc8q0WFB9E6OYWCnBPp3iic8yN/hdTjSkoPpPL78d/tOnGPHc2FiYoPO80feQe5e/wsdgiP5duQ1BHs1PkiXmsp4ds+LeGm8eD7lSQK0zn3xZO8pPRGEDyLiV4Rw//z1c6nr83bT/ReuKErL5OcHCxfCyJGwY8c/j+Xn2+dOr1tnD9aKUgshBDe1m8bR6nw+OvQZT3s/Rlv/hEaf84keo9EIwef7N1FhMvJqv4vr3SEhPjiE2VdN5ZW1a/gydTt/HcnlnQkXkRTWNLsV+HhpGdg5gYGd7X9+1SYzB44UsfdwAXuyCziQW8S6vVl/B2yAAB8vWoUEEBboR1igH6GBfoQF+hLg44WXVouX1gNPTw+8tB5ohMBksWKyWDFbrJjMVqpNZsr01ZRWGiirNFCqr6a4Qk+xzvCP2iKC/OgQE8mo7kmktI2ia0IUYYF+Lv3zaagqk4nnV61kzr699IiK4r8TLiY2OLhB55qduZMntyyha2gUX4+42iFB2mit4a0D72G0Gnm00wNOD9IAsvIVsBUgwmY2iyBdH24ZmRZCvA5cApiAQ8BNUsrycz1OjUwrSgty7BgMHQoHD55+LCUFVq+GsJbT0kuNTDteuamC5/a+hEVaeabL47TyafwugFJKPk7byBu7VjGgVQIfDbmCoAZ2Svgj8xCPLf8dg9nME0OHc90FFzi1b6+zmC1WjpbqyCksJ7eonJyicoor9JRWVlOmN1BaaUBnqKnXOX29PAkL9CU0wB7Ew4L8iY0IJj4yhPjIEGIjQ/D3aRoLOetrR34+Dyz7jZzycu7s1597+w/A06P+0yeklLy3dx3v7lnL0Kh2vD94MgGejQ+hFpuFdw5+wK7yPTzY8R56hFzQ6HOeizSuRJb/C/xvRxP4sNOv5yhNepqHEGIs8KeU0iKE+D8AKeVj53qcCtOK0sJkZ8PgwZCXd/qxAQPsixL9m/bbuHWlwrRz5BmO8sK+Vwn0DOTpLo8T5OmYDhXzsnbz+ObFtA8K58vhU4n2C2rQeQqr9DyybBlrc7IZFBfPKxeOIa6BI5RNmdlixVBjxmSxYLZYqTHbR6OllHhq7aPU3p5aPLUe+Hhp8fVqHosd68NoMfPOxo18vn0brQMCeHvcBPrFxjboXGablWe2LuXnzJ1MbtuNl/tdhKem8fOZpZR8lvkVa4s3cFO7aYxqNbzR5zznNW2l9ukdmnBE+FyEaD4vkpp0mP5HAUJMAqZIKa871/eqMK0oLVBamn2EuqTk9GNjx8KCBeDd/N8SVGHaedIrD/Jq2lvE+cXy784P4+PhmJ+X9QVZ3LluLgGe3nw5fCodQxrWYs0mJT/t3s2r69Zgtdl4dMhQpnXv4fae1IrjbMnL47HlyzhcXs41Kd14bOgwghr4vFVlNnHPhnmszj/E3V0Hc3/KMIe9ozE79xcWHF3CpJhLmRx7qUPOeTZSSmT5vVDzpz1Ie3Zy+jUdqa7P201h2evNwG9nOiiEuF0IsVUIsbWoqMiFZSmK4hKdO8PSpRBQy5y933+3t9OzWl1fl+IAFlwxYNMhMJm7km4nq+ow72d8jMVmcch5B0e146fR05BIrvrjO9YXZDXoPBohuPaCC/jt+un0i4nl+VUrmTp7FpmlpQ6pU3Ef+9zoP7l69iwsNhvfTZ7CSxeOaXCQLqzWc+2f37O2IJMX+0zggW7DHRaklxf8yYKjSxjZahiTYhrfBadOjAuhZhki4L5mF6Trw2kj00KIFUBULYeelFL+evx7ngT6AJNlHQpRI9OK0oKtXAkTJkBNLXMvb7sNPvkEmvFI3nk5Mt3dT27Z+C3Cb4pLrvdn4Wq+yvqOweEDuD3x5kb3oD7haFUFN6+ZRaauhMd7jOamDn0bHHCklPySto8XVq/CaLFwS6/ezOjbr8ls9KLUjZSSBQf289r6dRRUVnJDj548NGhwg3pHn7Ct+Ah3r/sFvaWGdwdezqgYx21Rv7F4Ex8d+pyeod25N3mG01vgAUhLDrLkctB2QIT9gHDBNR2tyU/zEELcANwBjJZSGs71/aDCtKK0eL/+at9avLaR6Mceg1dfdX1NDnJehukeEXLz0mhE+ByXbc7wa94i5hyZz6hWw7mx7fUOG9WrNNfw6KaF/H4knYvjO/NK34vx92x4cCqs0vPq2rXM359GhJ8fDw0azJQuXZtkn2Tln1Lzj/LC6lXsKCiga2Qrnh05kj5tYhp8Pikl32ds46XUFUT7BfPRkCvo1MApRbXZXraDd9M/pENgEo90uh8vjfNfuElZgyyZCtY8RMR8hEfD/3zcqUmHaSHEeOAtYLiUss5zN1SYVpTzwLffwg031H7s1VftoboZOi/DdJ8ecvMSfxABiPBfEBrnLyaVUvJz7i8syv+NC1uPZHrCtQ4L1FJKPknbyJu7V5MYFM5HQ6bQLrBxHWd2FuTzwupVbM/Pp3NEJE8NH87AuHiH1Ks4Vp5Ox+vr17HgwH4i/fx5ZPAQJnfp0qi579UWM09t/Y35h/cwsk0Sbw24tMHdY2qzvWwH/z34EQl+8Tze+SF8PRx37rOx6Z4Dw4+IkI8RPqNcck1naOphOgPwBk6sOPpLSnnHuR6nwrSinCfefRfuv7/2Y59+ap/20cycn2G6j9yy/gNk2Q3gMxER/LpLWsNJKfkpdw5L8pc5PFCDfWHifRvmY5Y2Xu8/kbGxjRt1l1KyOD2dV9et4WhlJcPbtuXBgYPp1rq1gypWGqOoqoqPt27hh107EcCtvftwR5++jZrSAZBdWcbd638hrfwY96UM5a6uQxy6KPV/QTqORzs9iL/WNT26ZfViZMUD4HcLmqDmOfhxQpMO0w2lwrSinEeefRb+85/Tvy4E/PQTXHWV62tqhPM1TG/duhWp/xCpfwcR9ALCb6pLrn1yoB7TehTTEq5xaKDOq6rgrvW/sLs0n2nJvfl3j9H13uDlVEaLmW927OCTrVsoNxoZm5jI/QMH0Smi8f2zlforN1bz6datfLMjlRqrlcmdu3DfgIHEBDWsTeLJFmTv5ektv6ERGt4aeCkj2zh26/nUsp28e/BD1wdpy2FkySTQdkSEfYcQzbsFogrTiqI0b1LCvffC+++ffszT076L4rhxrq+rgc7rMC1tyLJbwLQFEf4zwrOLS64vpWRmzmx+K/idMa1HcX3C1Q5blAhQY7Xw+s6VfJW+hc4hrXhn4OUkBUc0+ryVNTV8lbqdz7dvo8pk4uIOHZnRtx+dI1WodoUSg4Fvd+7gq9TtVJlMTOzYkfv6D6S9AzaR0ptreGH7cuZk7aJ3RCxvD7yMGH/H9h1324i0NCJLrgJrgX27cI/mv4utCtOKojR/NhtMnw4//HD6MT8/WL4cBg1yfV0NcD6HaQBpLUGWXAbCy95vVhPqkhpODtTDI4dwc7vpDg3UACuPZvDopoVUWcw82n0k05P7OOTt+nJjNZ9v28bXO1IxmM0MjI3jpp69GNmunVqo6AT7i4v4OjWV+fvTMFmtjE1M4v6BAx32zsDWolwe+WshRwwVzOg8iHtThqJ18N/jppItfHToczcEaYmseBSMvyJCP0V4j3DJdZ1NhWlFUVoGsxkmT4ZFi04/FhJi33b8Audvh9tY53uYBpCmncjSa8GrNyL0C5e9BSyl5Je8X5mft4jeoT25M+k2h3c0KKrW8+8tS1h5NINBrdvyUt8JxAc45gVDhdHIrD27+WbnDvIrK0kIDuaGHj25oktXAlvAhkbuZLXZWJ19mC+3b2dDbg4+Wi2TO3fhxp49SQoLd8g1qi1m3t2zli8ObCLGL5g3BlxCn8g4h5z7ZMsL/uS77Jl0CEzioQ734qv1dfg1zkRWfYGs/D9EwL2IgLtddl1nU2FaUZSWo7oaxo+HNWtOPxYVBevWQWKi6+uqBxWm7WT1PGTFY+B3LZqg51xaz7KCFfyQPYsOgUk80OEeh4/aSSn56dAOXt3xB1YpeaDbMG7s0Ndho8gWm41lGQf5KnU72/Pz8dVquahDR67qmkKfNm1csrizpThSUcHsfXuZs28v+ZWVRAUEMK17D65O6Uaor+NC6MZjh3liy2/k6Mu4qn0Pnuw5mgBPx74AklIy58h8FhxdTK/QHtyVdLtL2t/9ff2aVciyf4HPeETw2wgHv/PjTipMK4rSslRUwKhRsH376cfatbMH6jZtXF9XHakw/T+2yteg6nNE0HMIv2tdWtNfJZv5+NAXRPtE8Uin+wnzcvx0k6NVOp7dtpQ/j2ZwQVg0r/a7uMFbkZ/JzoICZu3ZzcID+6kym2kfGsqVXVOY1Lkzrfxr2U1UwWgxs+JQJj/v3cP6nGwAhsQncFVKCmMTk/D0cNymIjqTkVd3/MmszB3EB4TyUt8JDGrd1mHnP8EqrXyZ9R1ritYxInIYN7a7ziUbspwgLRn2edIe8YjwmQjhutFwV1BhWlGUlqewEIYOhfT004917WofuXbAIiFnUGH6f6S0IstnQM1aROhXCO8BLq1rb0Ua76R/gL/Wj0c63U+Mr+NfhEkpWZSzj/9sX47OZORfnQdyZ5dB+GgdO7WlymRiycF0ft67h21HjyKA/rGxXJTckXFJSUT6O7+3d1NWY7GwJvswi9PT+SPzEFVmMzGBQUzp2pUpXbo6pDPHyaSULDtygOe3/U5xTRW3dOzPfSlD8XXw3ztAjbWG9zM+YUf5Li6PuYTJMZe69N0JaStDllwJ0mBfB9ECFhyeSoVpRVFappwcGDwYjhw5/Vj//rBiBQQ0vZE5Fab/Sdr09l/EthJE+CyEtp1LaztclcMbB97BYrNwT/IMugZ3dsp1ymoMvJi6gvmH99DGL4hHuo/kkvguTgk9maWlLDiwn8Xp6RwqK0UjBH3atGFUu/aMaNeO5LDw82IqSFFVFetzsvkzK4uVWZlUmc2E+PgwPimZizt0ZGBcnEP7OZ+wr+wYL6euYGNhNp1DWvFKv4vpFuacgFluKued9A/JrMpiettrubD1SKdc50ykNCHLbgXTNkTY9wivni69vquoMK0oSsu1f799hLq4+PRjF15oX6zYxBZmqTB9OmnJsQdqTaC9ZZ7Gte8qFBqLeCv9PfKrC7guYSpjWo9yWtjcVJjNS6kr2Ft2jJ7hMTzZ80J6Rjhni2UpJeklJSw5mM7vhzJILy5GAq39/Rkcn8CQ+HgGxye0mFHrarOZrUfzWJudzbqcbPYff14I9/VlTGIS45OSGRgX59BpHCcrqtbz1u7VzM7cSbCXL/elDOXapF4O79RxwiF9Ju+mf4jBWs0dibfSJ8y1QdbeueMxMM63b8Tke5lLr+9KKkwritKybdsGI0dCZeXpxyZPhlmzQNu4TTQcSYXp2klTKrJ0Onh2QIR+65Itx09WbTXy8aHP2V62g2GRg7mx7fV4apzTZcRqszHv8G7e2LWKImMVl8R34YFuw0kIdG6bwDydjnU52azLzmZDbg5lRiMA8cHB9IyOpmdUND2j29ApIsJpgdNRpJTk6ipIzc8nNT+f7fn57C8uwmKz4eXhQe/oNvYXDAnxdI1s5dQWglVmE1+lb+bTtL8w2SxMS+7D3V0HE+zlvHnDa4vW81XWd4R4hfBAh7uJ84t12rXOxFb5BlR92uI6d9RGhWlFUVq+1avtG7fU1Jx+7JZb4LPP7DsmNgEqTJ+ZNP6BLL8LvAYjQj92+a5pNmljXt5C5uctJCmgPfcl30mIV4jTrldlNvFJ2ka+OLAJs83KlHbdubvrYNo4ePOO2tikZG9hIX8dyWV7/lFS8/MprKoCwEerJSksnI7h4XSMiKBDeATJ4eFEBQS4ZXqIzmjkYGkp6SXF9o/iEg6UFFNaXQ2An6cn3VtH0TM6mr4xMfSNicXP0/k/O0aLmR8ytvNx2kZKawxcGNOBx3uMol2g895ZsUorM3Nms6xgBV2COnF30h0Eerp+Opus+hZZ+SL4Xo0Ier7FTxtSYVpRlPPDwoUwaRJYracfe/hheO21JhGoVZg+O2n4Gal7CnwuRQS/5pb2WltKt/HJoS/x9fDh3uQ7SQ50brvFwmo9H+5bz0+HUhEIrk7syZ1dBhHp67qQJKXkaGUlqfn57CjIJ72khPSS4r8DNoC3hwdtgoKICQwiNiiImKAgIvz8CPXxJcTXx/7Zx4cALy+8PDzOOBpskxKz1YrRYqHMWE15tdH+2WikpLqaozodeTodeZU6juh06E56kezv6UlyeDjJ4eF0axVFr+hoOkREOG0qRW1MViuzM3fwwb71HKvWM7h1Wx7oNtxp03VO0Jkr+TDjU/bq0hgXdSHXxF/p0o4dJ8jqRciKh8D7QkTIfxFuqMHVVJhWFOX88f33MG1a7cdeeQUef9y19dRChelzk/oPkfp3wO9mROBjbhn1yjUc4Z30DygxlTI17grGR41xeh15VRW8v3c9c7N24qnxYGpiD27q0I+4AOeNjp9LWXU1B0vsI8FHdBXk6SrtnysrKTEYzvpYjRB4ajR4enigEQKz1YrZZsNis531cf6ensQcD+sngnvi8ZHyNkFBTlk0WBdVZhNzsnbyxf7N5Bkq6B0Ry4PdhjOgdYLTr51eeZD3D36K3lLJje2uZ1jkEKdfszayZj2y7Hbw7I4I+wohmtaaFGdRYVpRlPPLe+/BvffWfuzjj+Ff/3JtPadQYfrcpJTIyv+A4QdEwAOIgBlOrO7MqiwGPs/8iq1lqXQP6cbt7W8myDPQ6dfNqizlw73rWZC9FxuS8bEdubljf6ePfNZXtdlMaXU1ZcZqyqqNlBurKTMaMZhMmG02zFYrpuMfEomXhweeGg88j3/21nqcNKrtQ4iPL2G+vgR5ezepaQP5Bh3fHdzGzIxUdGYjvSJiubvrYIZFtXd6nTZpY8HRxfxyZAGR3hHckzyDtv7xTr3mmUhTKrLsJvCIRYT9iNA4tp1gU6bCtKIo55/nn4fnnjv960LAzJkwdarLS/pfCSpM14WUNmTFo2BcgAh8AuF/o3OKO2cdkhXHVjIz52f8tf7ckXir09rnnarAUMm3B7f+HeJ6hscwPbkP4+I64u3RdBbVtkRSSraX5PHjwe0sytmHDcm42I7c3LEfvSJcs9iv1FTGxxmfk1Z5gIHh/bmp7fUu3Rr8ZNK8F1l6A2hC7UHaI9ItdbiLCtOKopx/pIT774f//vf0Y1qtfX71+PGurwsVputDSguy/AGoWYYIfArhP90J1dVNdlUuH2Z8Qr7xGBOix3JF7OV4Oanbx6lOTC/4+sAWcqrKCfXyZXK7blyd2JP2QeEuqeF8UWGqZv7hPfx0aAfpFUX4a724sn13buzQ16XTbTaXbOWrw99jtpm5oe21DIkY5LbRemneZw/SIgAR/j3Co2m9Q+IKKkwrinJ+stngxhvhu+9OP+brC8uX2zd9cTEVputHSjOy/D6oWYEIehbhd52Dq6s7o7WGH7JnsapoDTG+bbgj8VaXvuVuk5INxw7z06FUlh9JxyJt9I+MZ0r77oyJ7UCg5/kxf9XRrDYbm4py+CVrN0ty06ixWugWFs01iT2ZGN8Ff08vl9Wit+j59vBMNpZsop1/AjMSbyPaN8pl1z+VNKcdD9J+iLDvENo4t9XiTipMK4py/jKbYcoUWLDg9GPBwfaWet27u7QkFabrT0rT8UD9ByLoPwi/qx1YXf3tKN/FF5nfUGnRc3nMRC5pc5HLuyoUVeuZm7WLWYd2kFNVjreHltFtkrk0oSvDoxPxauJ9ot1NSsmesgIWZO9lUfY+Co16ArReXNq2K1Pb9yQlzPUBdmf5br7I/AadpZLL2kzkkjYT0GrcN51Hmg8gS6eB8LHvbqh1z1ztpkCFaUVRzm9GI0yYAKtWnX6sdWtYtw6SklxWjgrTDWMP1HdDzSp7X1u/axxUXcPYRxB/ZGPJZtr5J3BLuxtJ8Hf9qN2Jub0Ls/eyOGcfpTXVBHn6MComiZFtkhgW1Z4gLx+X19UUmW1WthUdYWV+BivyDnK4shRPjYbh0UlcltCVkW2S8NW6trc5QJWlipk5s1ldtO74Ox630Nbf+R1Czkaa0+yLDfE8HqTdW4+7qTCtKIqi08GoUfbdEk/Vtq09UMe4Zh6gCtMNZw/U90DNSkTg4wj/mx1QXeNsLtnKN4d/QG+pYmzUaCbHXoavh3vCq9lmZX3BYRbl7GPl0QzKTdV4CEGfiDhGtElieHR7OgRHNqlOGc5WVK1nbUEWK49msLYgk0pzDZ4aDf0i47kovjMT4jo5dafCs5FSsr54Iz/mzKbKUsVF0WOZFHuZy+bin7Eu005k2a0gfBFh3yK0bd1aT1OgwrSiKApAUREMGwb7959+rEsXWLMGwp2/mEuF6caxB+qHoWYp+N9hb53n5nCot+iZnTuPlYVrCPEM5rqEqfQL6+PWuiw2GztK8lh19BAr8zPYX14IQJi3L/0i4+nXKp6dWq7BAAAYbElEQVT+rRLoEBzptr7NzlBgqGRzYQ6birLZVJhDVmUpAJE+/oyITmJkm0QGRbVz+/zyXEMe3xz+ngOVB0kKaM+Nba8nwU0t704ma9bbdyHVhCNCvz5v50ifSoVpRVGUE3JzYcgQyMk5/Vi/frBiBQQ6t49wSwnTQoiHgdeBSCll8dm+19HP2VJakbpnofrn49sZP9skdmHL0Gfyddb3ZBty6BrUmRvaXufWxWMnO1qlY/2xLDYV5rC5MIc8QwUAwV4+dAuNpltYNN3CougW1oZov0C3v0Cpi0qTkd1lBewuzWdPaT67SwvIrSoHIMDTm76RcfSLjGdg6wS6hkY1iRcNRquReXkLWZq/HD+tL1PjpjAscjAaN+z0eSppXIosfwi07RGhXyA8Wrm7pCZDhWlFUZSTpafbA3VR0enHRo2CxYvBx3lv07eEMC2EiAM+BzoBvV0dpuH4xi76N6HqU/CZgAh+HSFc13XhTGzSxh/HVjHnyDxqbCYubD2SSTGX4K/1d3dp/3BEX86mohy2Fh1hT2k+6RVFWKR9Z8Iwbz+SgyNIDAqnfWA4SUH2/27tG3jGLcKdRUpJuamazMpSMnUlZOiKj38uIUdf9vf3xfmH0C0siu7hMfRvFU+XkNYur/VsbNLGmqL1zDkynwpzBSMih3JV3GQCXbAJUF1Iwyyk7hnw7IkI/QShCXZ3SU2KCtOKoiin2r4dRo60z6U+1eWXw+zZ9n7UTtBCwvQc4AXgV6CPO8L0CbLqC2Tl/4HXYETI+whN0witFeYK5uT+yuqitfhr/bg85hJGtxrh1u4MZ2O0mNlfUcie0gL2lBaQoSvmkK4Endn49/dohYZWvgFE+QUR7RdIa99AInz8CfbyIdjLhyBPH4K8fPDXeqHVaOy7HR7/LBCYbSe2E7ditlmpsVnRmYxUmIzozEZ0JiNlNdUUVFeSb9BRYNCRb6ik2mr+uwYvjQftAsNIDAqnc0hruoVFkxIWRai3nzv+2OpkT8U+ZubMJseQS1JAItclTCUpoL27ywLsL1ao+tT+wtRrKCLkPYSm6f5ZuosK04qiKLVZswbGjbN3+zjVjTfCF1+AE0a2mnuYFkJcCoyWUt4nhDjMGcK0EOJ24HaA+Pj43tnZ2U6rSRrmInVPgbYjIvRjhEfTmFoBkGPI5cfsn9mrSyPSO4IpsZMYEN63Sbytfy5SSkpqDGTqSjikKyHPUEG+QXf8o5LC6kqMVotDrymAVr4BRPsF/ePjRICO8QtuUiPOZ5NVdZjZufPYXbGXCO8Iro67wu1z6U8mpRmpexGqZ4LPRETwq03i3Z2mSIVpRVGUM1m82D4SbaklEDzwALz5pn0LcgdqDmFaCLECqC2RPgk8AYyVUlacLUyfzBXP2bJmlX23ROFvD9SeKU69Xn1IKdldsZdZuXPJMeTSxieKy2IuaTah+kyklBgs5r9HlXUmIxVmIwaLGYvNhtlmxWKzYrJZkYCnRoNWeOCp8cBL44GXhweBnt4Ee/naR7a9vAnQejebsHwmWVWHmXdkIanlOwnQ+nNpm4u5sPVIPN3cpeNk0qaz9243rQf/2xABDyGa8c+is6kwrSiKcjY//gjXX2/fgvxUL74ITz7p0Ms1hzB9JkKIbsAfgOH4l2KBo0A/KWXBmR7nqudsaT6ALLsDZCki+DWEzzinX7M+bNLGltLtzM9byJHqPKJ9orgsZiIDw/s161Ct2GXqs5iXt5Ad5bvw9/BjfPRYxrYehZ+2aU2bkJYcZNntYM09vgnSFe4uqclTYVpRFOVcPvgA7r679mMffggzZjjsUs05TJ+qKY1MnyCtxcjyO8G8AxHwEPjf3mTeVj/BJm1sPR6qc6vziPJpzUXR4xgcMdDtPYaV+pFSkqY7wOL8peyq2EOA1p8JUWMZ03oUvlr39K8+G2nagiy7CwAR+j7Cq5+bK2oe6vq83TRXRCiKorjCXXdBWRk8/XTtx0JC4Br37rin1I3wiICw75AV/7YvqrJkQvB/EMK9fYVPphEa+oX3oU9YL7aVpbIgbzFfZn3LnNx5jIkazehWIwj0DHB3mcpZWKWVzSVbWZK/jMOGHIK0gVwZO5kxUaPctmnPudjXFjwDHrGI0E/P+10NnUGFaUVRzm9PPgmlpfD22//8upQwfToEB8NFF7mntiZKStnW3TXURghvCH4TtIlI/btIy0EI+S9CG+vu0v5BIzT0DetNn9BepOkOsKRgGXOPzGfh0SUMiRjIqFYj3LJFuXJmFWYda4vWs+LYSkpMpUT7RHFLu+kMasLvKkhpQupehuofwWsQIuRd1frOSVSYVhTl/CYEvPGGfYT666//ecxigSuugN9/h6FD3VKeUj9CCAi4C7SdkBWPIksmQcjrCO8R7i7tNEIIugR3oktwJ44Y8vitYDlri9bzZ+Fq2vu3ZWSrYQwI74dPEx3xbOls0sbeijRWFq1he9kOrNJK58COTG97LT1CLmjS892l5Qiy/F6w7AH/WxEBDyKEinzOouZMK4qigD04X3klzJ9/+rGgIFi1Cnr2bPDpW9Kc6bpy93O2tOQgy+8Gy37wn4EIuLdJ7Jh4NpVmPeuLN7KqaC151Ufx0XgzILw/QyMHkRyQ2OTmgbdERTXFbCj+i9VF6yiqKSZAG8CQiIGMiBxKjF8bd5d3TvYON48AVkTw/yF8xri7pGZLLUBUFEWpL6PRPqVj5crTj0VGwrp10KFDg06twrR7SGlE6v4D1XPAa6B9x8RmsF2ylJKD+kOsKlzDptKtmGwmIrwjGBTej4Hh/Yn1i3F3iS2KzlzJ5tKtbCzeRLo+A4DOQZ0Y2WoYfUJ7Nqn2dmcipQWpfw+qPgJtJ/tGLGp+dKOoMK0oitIQlZUwejRs2XL6sfh4WL8eYus/B1eFafeShjn2UK3xs4/WeQ93d0l1Vm01sq10OxtKNrGnYh8SSZxfLAPC+tI7tCdtfKPViHUD6MyVpJbtZEvZNvZU7MMqrcT4tmFQ+AAGhPellU+ku0usM2k5gqx4CMyp4DsFEfQMQqjpQY2lwrSiKEpDFRfDsGGQlnb6sc6d7bsoRkTU65QqTLuftGTYN3ixHAC/GxGBDze7nd8qzBVsKtnKxpJNZOgzAYjyaU2v0B70CulOUmAiHk18Kos7FRiPkVq2k21lqaRXZiCRhHuFMSC8H4Mi+hPnG9vsXphI42/IiqcAae8f7TvR3SW1GCpMK4qiNMaRIzBkCNS2HXafPvDnnxAYWOfTqTDdNEhZg6x8FQw/2LchD34D4dnR3WU1SKmpjO1lO9hetoN9uv1YpRU/D186BXWka1BnugR1Isa3TbMLh45UbqogTbefvbo09un2U1Rjb40e5xdL79Ce9A7tSYJfXLP8M5K2SqTuBTDOB8/uiOA3Edp4d5fVoqgwrSiK0lgHD9oDdWHh6cdGjoQlS8Cnbm+lqjDdtEjjSqTuCbDpEAH3gv8tzbrbgcFiYHfFPvZU7GWvLu3v0BjsGUznoI4kBbQnKaA9CX7xaDXN9z7PRkrJsZpCMiozydAfYn9lOnnVRwHw8/Clc1AnugZ1pntIt2Y1haM2sma9fTTaVgD+dyAC7kSIpj+vu7lRm7YoiqI0VnIyLFsGI0ZARcU/j+3dCzk5DV6QqLiX8BkJXouRFc/aN3kxLoPgVxGezfPv00/rR//wPvQPt//eLzQWsU+3n326NPbrDvJXyWYAPIWWtv4JJAa0J94vlni/ONr4RjeLBXYns0kbxTUl5BhyyTEcIavqMBn6TPQWPQA+Gm+SAhIZHDGArkGdaeuf0KRb2dWVtFXa31mpng0e7RBhMxFePdxd1nlPhWlFUZSz6dEDFi2CsWOhutr+tfh4WL5cBelmTmjCEKHv2eec6p6396QOuAv8b2v2o3ytfCJp5RPJiFb2/uilNaVk6DPJ0GdySJ/JH8dWYZZmADRoiPaNIs4vliif1rT2bkVrH/tHoDbArVMgjNYaCmsKOWY88VHEkeo8jhiOYLTVACAQRPtE0Su0x98j8DG+bVpEeD6ZrFmNrHgabIX2n9GAe9QiwyZChWlFUZRzGTIE5s6FSy+FpCR7kG5ARw+laRI+E8CrP1L3AlL/zvFR6pcQninuLs1hwrzD6OcdRr/jI9dWaeWYsZBcwxFyDEfINRwhQ5/JppItSP43/dPXw5cwr1BCPIMJ8Qoh9PjnAG0Afh6+9g+tH74evnhrvNAKLR4aDzyEBxo0CCGwSRsWacUqrVilBbPNgsFqoNpSjcFajcFqoMpioNxcQbmpgnJzOeWmcsrM5VSYdf+4j0BtADG+bRgaOZh4v1ji/GKJ8Y3Bx6PpbBvvaNJWitT9HxjngTYZEfo+wvMCd5elnESFaUVRlLqYMAF+/RX69at3Jw+l6ROaMETI20jjRUjdc8iSKUi/axEB9yM0Qe4uz+E8hAdtfKNp4xtN//C+f3/dbDNTXFNiHwU+PiJcZiqn3FzOAV06ZeZyrNJa5+sIxD/C+bm+N8gz8O/g3tY/gUjviL9HyVt5R+Kn9av3vTZXUtqgeg6y8g2Q+uMbD93V7DrQnA9UmFYURamriy5ydwWKkwmfMfZRav3bYPgRaVwCAY+C7+WIFjZtoDaeGk+ifaOI9o2q9biUEr1Fj95SRbW1mqq/R5gN1NhMx0ef//chpT24ewgPPDQeaIUHWqHFz8MXX60vfh5+f49uB2kDW+ziyPqS5t1I3fNg3gWevRFBzzfb+fznA/VTqyiKoignEZogRNCzSN8r7XOpdY9D9SwIerpFTf1oCCEEgZ6BBHrWvS2kUnfSVoqsfMf+86aJQAS/Dj6XNsvWfeeTlv8yW1EURVEaQHh2sXdLCHoVrDnIksnYyh9BWvPdXZrSwkhZg9R/hiy60N6pw+8GRMQyhO9lKkg3A2pkWlEURVHOQAgN+E0Gn7HIqo+h6mukcSnS/2aE/20ITYC7S1SaMSklGBcjK98EWx54j0IEPoLQJrq7NKUe1Mi0oiiKopyD0ASgCXwYEbkMfMZA1UfI4rFIw0ykNLm7PKUZkqbNyNKrkBUPgiYIEfoNmtCPVZBuhlSYVhRFUZQ6Eh4xaELeQoTNBo94pO5ZZNE4pGGWCtVKnUjTFmyl05Cl14M1HxH0KiL8F4T3QHeXpjSQCtOKoiiKUk/Cq7t9PnXo5+ARjtQ9jSwejzT8jDy+GYqinMweoqcjS68DSyYi8ElE5AqE32SE8HB3eUojqDnTiqIoitIAQgjwHgZeQ8G0Bqn/L1L3FOg/Av+bwO9KhPB1d5mKG0kpj/9sfAbmzfYOHYFPgN/VavfCFkSFaUVRFEVpBHuoHg5ew8C0Gqn/GFn5IujfB/9p4Hc9QhPq7jIVF5LSAsbfkFWfgWU/aKKOh+ip6gVWC6TCtKIoiqI4gD1Uj0B4j0CatiGrPkPq34Oqz5G+VyL8piG0Ce4uU3EiadND9S9Iw9dgPQIeiYjgV8Fnotq5sAVTYVpRFEVRHEx49UZ49Uaa05FVn4NhJtLwPUSuQnjUvrug0vzJ0ulg2QOevRCBT4L3yPNi58zznZBSuruGOhNCFAHZ7q6jDiKAYncX4ULn0/2eT/cK6n4dKUFKGemkczdJLnzObmk/py3pflrSvUDLup+WdC/gnPup0/N2swrTzYUQYquUso+763CV8+l+z6d7BXW/SvPQ0v7eWtL9tKR7gZZ1Py3pXsC996Pee1AURVEURVGUBlJhWlEURVEURVEaSIVp5/jU3QW42Pl0v+fTvYK6X6V5aGl/by3pflrSvUDLup+WdC/gxvtRc6YVRVEURVEUpYHUyLSiKIqiKIqiNJAK04qiKIqiKIrSQCpMO4kQ4kohxF4hhE0I0WJaz5xMCDFeCHFACJEhhHjc3fU4kxDiSyFEoRBij7trcQUhRJwQYqUQIu34z/F97q7JWYQQPkKIzUKIncfv9Xl316Q0nBDiYSGEFEJEuLuWhhJCvC6E2C+E2CWEmCeECHF3TQ3RUn5HtNTnQyGEhxAiVQixyN21NIYQIkQIMef4v5k0IcRAV9egwrTz7AEmA2vcXYgzCCE8gA+ACUAX4BohRBf3VuVUXwPj3V2EC1mAh6SUnYEBwF0t+O+3BhglpewO9ADGCyEGuLkmpQGEEHHAGCDH3bU00nIgRUp5AZAO/NvN9dRbC/sd0VKfD+8D0txdhAO8CyyVUnYCuuOGe1Jh2kmklGlSygPursOJ+gEZUspMKaUJ+Am4zM01OY2Ucg1Q6u46XEVKmS+l3H78vyuxPznFuLcq55B2+uP/63n8Q63Mbp7eBh6lmf/9SSl/l1Jajv/vX0CsO+tpoBbzO6IlPh8KIWKBi4HP3V1LYwghgoBhwBcAUkqTlLLc1XWoMK00VAyQe9L/H6GZP7kotRNCtAV6ApvcW4nzHH+7cwdQCCyXUrbYe22phBCXAnlSyp3ursXBbgZ+c3cRDdAif0e0oOfDd7C/8LS5u5BGag8UAV8dn7LyuRDC39VFaF19wZZECLECiKrl0JNSyl9dXY+LiVq+1qxHg5TTCSECgLnA/VJKnbvrcRYppRXocXxu6jwhRIqU8ryYH9+cnO05F3gCGOvaihquLr8/hBBPYp9i8IMra3OQFvc7oqU8HwohJgKFUsptQogR7q6nkbRAL+AeKeUmIcS7wOPA064u4v/bu9cQK+o4jOPfBzNUyijR2PCyBmV5Q/ICZtDFiIqwoq00DQVfVu+iMCPqRb6MLnZTuhhdJKyoKLLFrCgKNVNrLfJFF5duhF2kfBVPL2aMbcusYc8Z95znA8uZmXNm5zkvzvx/Z85//v+oyPb5dWeoUS8wrs/6WODrmrJEA0gaStFwPGX7+brzNIPtnyS9SdE/PsX0EeZQ51xJ04CJwE5JUJyPtkuaY/vbJkb8zw7XfkhaClwCzPfgnBCipdqIFjsfzgMWSLoYGAaMlPSk7SU156qiF+jt82viBopiuqnSzSOq2gqcImmipKOBhcBLNWeKAaKiInkE+MT2XXXnaSRJow+OliBpOHA+8Gm9qeL/sP2R7TG2O213UjSwZxyphfThSLoQuBlYYPu3uvNU1DJtRKudD22vsD22/KwsBN4YpIU05Wd8r6RJ5ab5wO5m50gx3SCSLpfUC8wFXpG0se5MA6m8OeZ6YCPFzRjP2u6pN1XjSHoGeA+YJKlX0vK6MzXYPOBa4DxJO8q/i+sO1SAdwGZJuygKgG7bg3qoqBj0VgPHAt3lZ++hugP9Xy3WRrTT+XAwugF4qjyHzwBWNTtAphOPiIiIiKgoV6YjIiIiIipKMR0RERERUVGK6YiIiIiIilJMR0RERERUlGI6IiIiIqKiFNPRFsqhCi3ptLqzREREROtIMR3tYhHwDsUA9X8haUjz40REtDdJnZL+80yjkpZJOqnisU6Q1C1pT/l4fJX/E/FPUkxHy5N0DMWg+8spi2lJ50jaLOlp4KNy2xJJW8oB+R8+WGRLelDSNkk9ku6o631ERLS5ZUClYppiiulNtk8BNlHDlNPRulJMRzu4DHjN9mfAPklnlNvnACttT5Z0OnA1MM/2DOB3YHH5upW2ZwHTgbMlTW9y/oiIVnWUpHWSdknaIGmEpJmS3pL0gaSNkjokdQGzKGa62yFpuKTbJG2V9LGkNeW034dyKbCuXF5H0S5EDIgU09EOFgHry+X15TrAFtufl8vzgZnAVkk7yvWTy+eukrQd+BCYAkxuSuqIiNY3CVhjezrwC3AdcB/QZXsm8Chwp+0NwDZgse0Ztg8Aq23Ptj0VGA5c8i/HOdH2NwDl45jGvaVoN0fVHSCikSSNAs4DpkoyMAQw8Crwa9+XAutsr+i3/0TgRmC27R8lPQ4Ma0b2iIg2sNf2u+Xyk8AtwFSgu7zQPAT45hD7nivpJmAEcALQA7zc2LgRf5cr09HquoAnbE+w3Wl7HPA5cFa/120CuiSNgT9vVpkAjKQoun+WdCJwUROzR0S0Ovdb3w/0lFefZ9ieZvuC/jtJGgY8QHEFexqwln+/0PGdpI5y3w7g+4GJH5FiOlrfIuCFftueA67pu8H2buBW4HVJu4BuoMP2ToruHT0UPze+S0REDJTxkuaWy4uA94HRB7dJGippSvn8fuDYcvlg4fxDeZN512GO8xKwtFxeCrw4EOEjAGT3/1IYERER0ViSOim63L0NnAnsAa4FTgXuBY6j6I56t+21kq4AVgEHgLnASooRmr4A9gJf2r79EMcaBTwLjAe+Aq60va8x7yzaTYrpiIiIiIiK0s0jIiIiIqKijOYRERERLUHS/RSTdPV1j+3H6sgT7SHdPCIiIiIiKko3j4iIiIiIilJMR0RERERUlGI6IiIiIqKiFNMRERERERX9AdO1GgcMEmKZAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for beta in betas:\\n\",\n \" print('\\\\n\\\\nLinear Regression with betas ', beta)\\n\",\n \" f, (ax1, ax2) = plt.subplots(1,2, figsize=(12, 6))\\n\",\n \" ax2.contour(beta0, beta1, cost_func.T.values, contour_levels)\\n\",\n \" ax2.set_xlabel('beta_0')\\n\",\n \" ax2.set_ylabel('beta_1')\\n\",\n \" ax2.scatter(beta[0], beta[1],c='b', s=50)\\n\",\n \"\\n\",\n \" # scatter plot\\n\",\n \" ax1.scatter(X, y,c='b')\\n\",\n \"\\n\",\n \" # Plot the linear regression\\n\",\n \" x = np.c_[np.ones(2), [X.min(), X.max()]]\\n\",\n \" ax1.plot(x[:, 1], lr_h(beta, x), 'r', lw=5)\\n\",\n \" ax1.set_xlabel('Area')\\n\",\n \" ax1.set_ylabel('Price')\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Gradient descent\\n\",\n \"\\n\",\n \"Have some function $J(\\\\beta_0, \\\\beta_1)$\\n\",\n \"\\n\",\n \"Want $\\\\min_{\\\\beta_0, \\\\beta_1}J(\\\\beta_0, \\\\beta_1)$\\n\",\n \"\\n\",\n \"Process:\\n\",\n \"\\n\",\n \"* Start with some $\\\\beta_0, \\\\beta_1$\\n\",\n \"\\n\",\n \"* Keep changing $\\\\beta_0, \\\\beta_1$ to reduce $J(\\\\beta_0, \\\\beta_1)$\\n\",\n \"until hopefully end up at a minimum\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Gradient descent algorithm\\n\",\n \"\\n\",\n \"Repeat until convergence{\\n\",\n \"\\n\",\n \"## $$ \\\\beta_j := \\\\beta_j - \\\\alpha \\\\frac{\\\\partial }{\\\\partial \\\\beta_j} J(\\\\beta_0, \\\\beta_1)$$\\n\",\n \"\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"while simuntaneously update j=0 and j=1\\n\",\n \"\\n\",\n \"$\\\\alpha$ is refered as the learning rate\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the particular case of linear regression with one variable and one intercept the gradient is calculated as:\\n\",\n \"\\n\",\n \"\\n\",\n \"### $$\\\\frac{\\\\partial }{\\\\partial \\\\beta_j} J(\\\\beta_0, \\\\beta_1) = \\\\frac{\\\\partial }{\\\\partial \\\\beta_j} \\\\frac{1}{2n}\\\\sum_{i=1}^n (h_\\\\beta(x_i)-y_i)^2$$\\n\",\n \"\\n\",\n \"### $$\\\\frac{\\\\partial }{\\\\partial \\\\beta_j} J(\\\\beta_0, \\\\beta_1) = \\\\frac{\\\\partial }{\\\\partial \\\\beta_j} \\\\frac{1}{2n}\\\\sum_{i=1}^n (\\\\beta_0 + \\\\beta_1x_i-y_i)^2$$\\n\",\n \"\\n\",\n \"### $ j = 0: \\\\frac{\\\\partial }{\\\\partial \\\\beta_0} = \\\\frac{1}{n}\\\\sum_{i=1}^n (\\\\beta_0 + \\\\beta_1x_i-y_i)$\\n\",\n \"\\n\",\n \"### $ j = 1: \\\\frac{\\\\partial }{\\\\partial \\\\beta_1} = \\\\frac{1}{n}\\\\sum_{i=1}^n (\\\\beta_0 + \\\\beta_1x_i-y_i) \\\\cdot x_i$\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Gradient descent algorithm\\n\",\n \"\\n\",\n \"Repeat until convergence{\\n\",\n \"\\n\",\n \"### $ \\\\beta_0 := \\\\beta_0- \\\\alpha \\\\frac{1}{n}\\\\sum_{i=1}^n (\\\\beta_0 + \\\\beta_1x_i-y_i)$\\n\",\n \"\\n\",\n \"### $ \\\\beta_1 := \\\\beta_1- \\\\alpha \\\\frac{1}{n}\\\\sum_{i=1}^n (\\\\beta_0 + \\\\beta_1x_i-y_i) \\\\cdot x_i$\\n\",\n \"}\\n\",\n \"\\n\",\n \"simultaneously!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Calculate gradient\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-1.5 , -0.85498759])\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# gradient calculation\\n\",\n \"beta_ini = np.array([-1.5, 0.])\\n\",\n \"\\n\",\n \"def gradient(beta, x, y):\\n\",\n \" # Not vectorized\\n\",\n \" gradient_0 = 1 / x.shape[0] * ((lr_h(beta, x) - y).sum())\\n\",\n \" gradient_1 = 1 / x.shape[0] * ((lr_h(beta, x) - y)* x[:, 1]).sum()\\n\",\n \"\\n\",\n \" return np.array([gradient_0, gradient_1])\\n\",\n \"\\n\",\n \"gradient(beta_ini, X_, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Gradient descent algorithm\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def gradient_descent(x, y, beta_ini, alpha, iters): \\n\",\n \" betas = np.zeros((iters, beta_ini.shape[0] + 1))\\n\",\n \"\\n\",\n \" beta = beta_ini\\n\",\n \" for iter_ in range(iters):\\n\",\n \"\\n\",\n \" betas[iter_, :-1] = beta\\n\",\n \" betas[iter_, -1] = lr_cost_func(beta, x, y)\\n\",\n \" beta -= alpha * gradient(beta, x, y)\\n\",\n \" \\n\",\n \" return betas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"iters = 100\\n\",\n \"alpha = 0.05\\n\",\n \"beta_ini = np.array([-4., -4.])\\n\",\n \"\\n\",\n \"betas = gradient_descent(X_, y, beta_ini, alpha, iters)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lets see the evolution of the cost per iteration\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(range(iters), betas[:, -1])\\n\",\n \"plt.xlabel('iteration')\\n\",\n \"plt.ylabel('J(beta)');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Understanding what it is doing in each iteration\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-4. -4.]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-2.39494776 -2.05187282]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-1.43394369 -0.88545711]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-0.85855506 -0.18708094]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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kNCCE5OIzEoDD8tN4dV4QQ9AxLp4ehO7sq9vJN7gLeyXiPTsGpXJN4NSlB7T1dIgBCGwPh85FVf0VWP4u0FyGC7veawN9aeferU1EURVF+hzQudx4NrmvvnJHWxni6JMDZTm1V4RqW56/EKm2MihnOFfGXeu0R31JKCo01HCwv5EhlEZnVpZysLierpgyT3fbr4wQQ6R9InD6E5KBw+kTqCfUNINTXnxAff0J9/QnQ+fw6A/3LlxBgczhnqi0OOzaHA6PdSo3FdGZG20S11USF2UiRsYbdpXkUGo9gdTh+HVsjBG0DDbQPiaB9cASdDdF0C48lJTgCrUbjgT+1PyaEoG94L3qFpfNT8UYWnl7Cs4f+zsCI/lydcCXR/lGeLhGhCQTDe8jqZ6HufaSjBEKeVycmNoP6k1MURVFaFVk313nSm09f52YqTYinS0JKyc6K3czP/ooySzl9w3oxue1VxAXEerq0/1JrNbO7NI9dpbkcLC/kQHkBZeZ6wBmYE86E1oExSbQPjqB9SDgJgQai/IPw1bpnDbBDSsrN9eTXV3OqupwTNaVkVZdxorqMzYUnf50ND9D60CUshm5hsfSKiKdfdFvi9J5/LQBohZZRMcO5IHIg3+WvYmXhanaV72ZCm4u5JG6cx5f5CKGDkBeQmiioew/pqADDW15/qJG3UhsQFUVR3EBtQGw+KeWZo8H/BX6jnUeDe8E//kWmIuac+pwDVYdoq0/ghqRr6RziHUtOjDYr20ty2FqUzdbibA5VFGKXEo0QdAiJpFtYLN3CY+kWHkcXQzR6nXf3IbY7HJyoKeNgeSEHKwo4WF7I4YoijHYrAG0DDQyMTmJgTBIXxCQTHeAdJxaWWyr4MudbtpRtI9I3ghuSr6VXmHf0P5d185A1L4BPb0TYLITGOz9F8QTVzUNRFMWLqDDdPFJKZM3foX7OmaPBX0B4uFuCzWHju4JVLMlbjlbomNT2CkbHjPB4F4eC+mp+zM/kh7wMthRnY7bb8NFo6BkRT/+oRAZEJ9IzIp5AH+8Ozg1ldzg4WlXM9uIctpfksK04hyqLCYBuYbGMik9lRJsOdAuL9fja4CPVx5hzaj55xnz6hfXhuuSphPuGebQmAGlcgax6CHSdEOGfIDSer8kbqDCtKIriRVSYbjpnkH4O6j8H/XRE8GMeD0VZtSeZlfUpecZ8+of35bqkqYT5GjxWz8nqMpbnHGb16eMcriwCIDHQwMj4VIbHpdA3qi0BOu/ru+0KDik5UlnEhoIs1uVnsqf0NBKICQhiZJtUJiR2oX9UosfWW9scNlYUfM/ivOVohZZrkyYzPGqox1/T0vwTsuJu5z6E8M+89sAjd1JhWlEUxYuoMN00UjqQ1c+D8XMIvBUR9LBHQ4fVYWVx3jKW568i1CeUm9pd57GP6/Prqvku9zDLsg9zqKIQAfSOTGBUfCoj23SgQ0ikxwOaNygz1fFTwQnW5WWwviALo91KtH8QExK7cElSV3qEt/HIn1ORqZiPT87lSPVRuoemcUu7G4nw82yAleZNzl7UumRE+JzzPlCrMK0oiuJFVJhuPGeQfgaMX0HgbYighzwaDk/WnWLWiU85bczjwqjBXJs4hUCd3q01GG1WVuUe5ZusfWwryQEgPTyOSxK7Mj6xi9dswPNW9TYL6/IzWZ59mPUFJ7A47CQGGriqfTqT2vUgVu/e9cIO6WBd8Xq+zPkWjdBwbeJkhkUN8ejrXJo3O/tQ65IQYXMQ2giP1eJpKkwriqJ4ERWmG8cZpJ8C4zcQeDsi6K8eCxg2h40l+ctZmreCUJ8Qbm5/Az0N6W6t4UhFEV9l7WXxqYPUWM0kBYUxsV13LknsSnLw+T172FTVFhOrTx9n8akDbCnORiMEw+NSmJLSk+FxHdC5cRlIsamEj7I+5WjNcdJDu3Fr++keXTYkzT+fCdQJiLC5CG2kx2rxJBWmFUVRvIgK0w0npXT2wDV+AYF3evRQiVJzGe9lziKz9gRDIgdxXdJUAnWBbhnb7nCwNu84Hx3dxp6yPHw1Wsa17cyUlJ70j0pUSzhaUHZNBd+c3MeCrP0Um2qJDQjmho59uTalF8G+7ukY45AOfij6iS9zv8VP48cdKbeQbujmlrF/jzRvRVbeDtq2iPB/n5ebElWYVhRF8SIqTDeco+YfUDfL40s7dlfs5aOsT7E57Nzc/gYGRfR3y7gmm5WFpw4w++g2smsrSAw0cH3HvkxM7o7BL8AtNZyvbA4H6/IzmHt8J1uKswnS+TIlpRc3derntiU0ecZ83s34kNPGPC5tM56rEi73WIcY5wz1DPDp7FzyoXHPjaS3UGFaURTFi6gw3TCy7mNkzasQMBUR8pxHgrTNYePr3AWsLFxDkr4td6feQay/609YrLaYmHN8J3MzdlJuric9PI7bOg/kooROXnfSn0NKasxmKkxGKo0mKk0m6q1WLA47Vrsdq92BxW5DgvM0RK0GX63zVER/nY5Qf38Mv/nycdOBMI1xsLyQj45uZWXuEQSCS5PSuKPLIDqEun7Jg9luZl72l/xUspGOwanclXIb4R7anChNPyAr7wbfgYiwDxHi3Gip2BAqTCuKongRFabPTtYvQFY/Bv7jEKFveqSPdJm5nHczPyCzNotR0SO4NmkyvhrXtpSrsZr57NgOPjm2nWqrieFxKdzWeSADoj23lMMhJblVVWSWl5FbVUV+TQ15NdXkVVeTX1NDudGIowXzQ7CvH3HBQbQJDiE+JIT44GDiQ0JICQsnJTwcP53nDmw+XVvJJ8e38/WJfZjsVi5NSuOetCG0D3H9xrzNpVv49OQ8fDQ+/CXlNrob0lw+5u+RxoXIqkc9+v+mJ6gwrSiK4kVUmP5z0rQGWXkP+A7y2OzX0erjzMx4H4vDwq3tpzMgop9Lx6u1mpl7fCezj22jymJidHxH7us2lK5hrp8F/606i4UDRUXsLyrkeFkZx8vKyCwvw2Sz/foYP632TMgNoU1IMFH6QAz+Ac6Z5QB/DH7+BPr64qvVOmegtRp8NFqEAKvdgdXhnKm22h0YbVaqTGYqTUYqTCaqTCbKjfXkV/8nsFeZzb+OrRGCxNBQOkZE0DEikrToaHrFxREd6N7TDctMdcw+uo1/Z+zC7LBxeVI37k4b7PINoPnGAmZmfECeMZ/JbScyIe5ij9xkybpPkDWvePRTI3dTYVpRFMWLqDD9x6RlB7L8Jo+uy1xb9CPzsr8k2i+K+zr+hfiANi4by+ZwMD9zFzMPbqLCYmREmw7c120o3cPjXDbmbxXV1vJzbg678vPZXZDP8bKyX2eZYwIDSY2IpGNEBKkREaSGR5BoMBAREODW8FRrsXC6uooT5eUcLysj48zXqcoK7GdqjQsOpndsHL3btGFAfAJdoqLcUmOpqY5ZR7YyL3MXNoedSe168Nf0YUT6u+51a7KbmZ31GdvKd9A/vC8z2t+En9bPZeP9EUfNG1D3IQT+BU3w/W4f391UmFYURfEiKkz/Pmk7gSybApoIRMSXbu8YYHPYmJf9JT8U/0QvQw/uSLkFvQt7R28uPMkLe9aQUVXKBTHJPJQ+nB4RrgvuAGabje15eWzMPsWG7FMcLysDIMjXl56xcfSKi6NXbBw9YmMJC/DuDY5mm43DJcXsKShkb2EBuwvyya+pASBSr+fCpGSGJiUxJDGJCL1re4AXG2t5//DPfJ65mwCdD/d2G8r1qX3w0bhmCYSUkhUF3/NV7gKS9G15oOPdbl9H7ey08yQYv0GEvIjQT3br+O6mwrSiKIoXUWH6/5OOcmTZZJB1iPCvEbq2bqwO6mx1zMz4gEPVR5gQdzGT205EI1yz0S+ntoK/7/mBNXnHaRto4PFeoxgT39FlM6nVZjPrsk6wMiODjTnZmGw2fLVa+raJ58IzYbNTZKTXbWxsioKaGn7OzWFD9ik2ZWdTYTIhgPSYWC5OTWVcaiqJoa7r2ZxZVcqLe9aysTCLlJAInuw1hgvj2rtsvL0V+3kvcxb+Wj8e6Hg37YPauWys3yOlDVlxO1i2IMI+RvgNcuv47qTCtKIoihdRYfq/SWlBlk8H635nD1vfXm6trdRcxj+OvUOhqYhb2t3A0KjBLhnHbLfx/uGf+fDIFnQaDXd2uYBbOg/AT9vyG+qqTCbWnDjByszjbMrOxupwEBcUxJiUDgxLTmZAQlv0Pq7dTOlpDik5WFzExuxs1pzIZH9REQBpUdFcnJrK+NSOtAtr+U8/pJT8mJ/Ji3vWkl1bwej4VJ7pfRFtAl3TTu90fR5vHJ9JtbWauzvc7vYj7aWjBlk+BezFiIhvEDr3Bnp3UWFaURTFi6gw/R/Oj4ofA+NCZ2eAgEvcWld2XQ7/OPYOFoeF+1LvomtoZ5eMs604myd3rCSrppxLE7vyaM9RLX5ctd3hYHNODt8ePsTqE5lY7Hbig0MYd2ZGtkdsHJrzYKPYHzldVcWqE5msyjjO7oICAPrEtWFSWhrjUzsS7Ney647NdhufHd/BzIOb0AjBA90v5IbUvi75BKDKWsUbx/7Jqbocpidfx8iYYS0+xp+Rtlxk2STQhCIivkZoPHdio6uoMK0oiuJFVJj+D1n7EbL2dQi8C03wfW6t6UDVIf55/H30ugAe6nQ/bfXxLT5GpdnIq/vW8XXWPtoGGni+78Ut/rF/dmUl3xw6yKIjhymorcXg789lnTpzZZeupMfEnBedFhqroKaGZceO8u3hQ2SWl+Ov0zEuNZWru3ZjQEJCi/6Z5dZW8vSuVWwoyCI9PI6X+o13SZcWk93Eu5kfsq/yAJe1mcCkhCvc+ncvLbuQ5TeAbx9E2Oxzrge1CtOKoiheRIVpJ2n6EVl5x5l+tW+59R/+rWXb+eDEx7Txj+WhTve5ZPPWD3kZPLFjBeXmem7pNIB7uw0lQNcySyuklOwqyGf2rl2sOZGJEIKhSUlc3bUbo9q392gv5tZESsm+wkK+OXyIZceOUmux0Dkyklt79+WSTp3wbaEDZKSULM85zAu711BlMXFH10HcnTakxTco2qWdz07O46eSjQyLGsLN7W5w2dr/3yONS5BVD0PANWhCn3PbuO6gwrSiKIoXUWEapO0ksuwq0CYhIr5ACH+31fJT8UY+OTmXjsEdeKDjPQS2cMcOo83Ky3t/YH7mbjobonltwCWkhcW2yLVtDgerMzOZvXsnewsLMfj7My29B9PS04kNatllI+cbk83KsmPH+Hj3Lo6XlRETGMgNPXtxbfd0Qv1b5vVZaTbywp41LD51kB7hbXhr0OUkBbfsum0pJQvzlrA4bzn9wvvwl5Tb0Gncd3PlqHkd6j465zp8qDCtKIriRc73MC0d9cjyq8FegohcjNC6th3cb60t+pE5p+aTHtqNe1PvbPH+vEcqirh/y2Iyq8u4pdMAHkwf1iIbDK12O4uOHuFf27eRU1VFUmgoN/fuw1Vd0875jYTuJqVkY3Y2H+3eyeacHPQ+PlyX3oPb+vRtsRZ7K3KO8MSOldilg2f6jGVicvcW/2RmZcFqPs/5mj5hvbi7w+1uC9RS2pEVM8CyFRHxFcKnm1vGdTUVphVFUbzI+RympZTOj4FNyxBhnyD8XNM54/d8X7iWedlf0svQg3tS78CnBY8Gd0jJZ8d38Pq+HzH4BvD6wEsZEtv8rgY2h4PFR47w7vat5FRV0S06hr/078+Y9innRCs7b3ekpIQPdm5n+bFjBPj4cH2PntzWpy/hLdCDO7+umoe2LmVbSQ4TErvwYt9xhPi27Cc0awrXMTf7c3oa0rkn9U58W/A1/2ekoxxZOhGEBhGx8JzYkKjCtKIoihc5r8N0/RfI6mcQQfchgu5y2/grCr7ni5xv6BvWi7taeJauwlzPg1uXsb7gBKPjU3m5/wTC/Zo3g2l3OFhy9Cgzt20hu6qKtKho7h80iJHt2qsNhR6QWV7GP7du5bvjx9D7+HBDz17c1qcPBv/mhWq7w8GHR7bwzsGNRAcEMXPwlfSMaNmNsOuK1vPpqX/TPTSN+zveha/GPRsDpWUfsvxa8BuMMHyAcOPabVdQYVpRFMWLnK9heseWz5wnHPoOQoTNcts/rt/lr+LL3G/pH96XO1NubdEgvb+8gLs2LaDUVMcTvUYzrUPDn9rEAAAgAElEQVTvZofdLbk5vLD+J46WltI1Kor7Bg5idPsUFaK9wPGyUt7dto3vjh8jxM+P+wYOYlp6D3yauVFxX1k+9/68iCJjDU/1GsO1LfA6+q1f9gl0DenMXzvd475AXT8fWf0cIugBRNCdbhnTVVSYVhRF8SLnZ5juLbevDAdpda6TdtNR4asLf+Df2V8wMKI/d6Tcgla0XPeExacO8viOFUT46XlvyFWkh8c163p51dW8vHEDKzKOEx8cwt+GDGVCR9edjKg03ZGSEv6+cT2bc3LoGBHBU8NGMDgxsVnXrDQbeXDrUn4qOMGU9j15ps/YFj3QZ2PJZj7K+ozuoWk80PFut6yhdi7rehBMKxHh8xC+fVw+pquoMK0oiuJFzssw3StWbl9pOPMPqnt+65tLt/DBiY/pE9aLe1LvaLEgbXM4eG3fOj4+tp0BUYnMHHwlEf6BTb6e2Wbjw507+GDnDgDu7Nef2/r0wb+F2ugpriGlZM2JE7y0YT251VVc3CGVJy4cRnxI0086tDscvHVwA+8f/pnekQm8N3gi0QFBLVbzj8Ub+OTkXAZF9OeOlFvd0jZPOmqRZVeAtJ25kW6d66cb+r6tmlIqiqIoruGoRAQ95bYgvbdiPx9lfUaXkM78pcOMFgvS1RYT92xexKaik1yf2ocneo1uVq/gbadzefyHtZysqGBCakceHXphs8KY4j5CCMZ2cB7PPnv3Lv61fRsbsk/x4AWDuaFHzyZtENVqNDyUPpyuhhge2bacK1Z/wodDr6Z7Mz/1+MWI6Aups9XxVe4CgnRBXJ90jcs/+RCaIDC8iSybiqx6Egwzz+lPW1r3ynBFURTFe+lSIPAOtwx1vCaDmZkf0DYgngdS72qxDgZ5dVVMXjuXrcXZvNxvPM/2uajJQbraZOKJtWu45ttvsNkdzJ14FTMnXKKCdCvkp9NxV/8BrL5hOv3jE3hh/U9M+upLjpaWNPma4xO78O2YG9EKDdf8MI8f8jJarN4JcRczLnYsa4rWsShvWYtd988In3RE8INgXg3GBW4Z01NUmFYURVFcQwQgWnC98h/JrT/NG8dmEu4bxkOd7ydA1/wWZuDcaDhxzWcUGmv4dPhUJqf0bPK1vs/MYOy/5/DVoYPM6NOXVdffwJDEpBapU/Gc+JAQPr78Ct6+eDynq6u47PP5vPnzZsw2W5Ou19kQzcIx0+kQGskdm75lzvEdLVKnEIJrEq9maOQFLMpbytqiH1vkumelvwl8ByJrXkTast0zpgeoMK0oiqK0WmXmcl4/+jZ+Gj/+1vmvhPq0zCzvhoIsrv1hHn4aHd+MvoELYpKbdJ1ai4UHv1/JncuXEanXs2jqtTw69EICzuFDV2x2B/UmCxW1Rspr6qk1mrFYbbSmPVqNIYTgss6dWX3DdC7r1Jl3t2/j8i/mk1FW1qTrRQUE8fnIaYxs04Hnd6/h1b3rWuTPTgjBLe1vpLehJ3NPfc6O8l3NvubZx9QgQl8FdMiqR5DS7vIxPUFtQFQURXGD83IDoovfs012E88ffoVScxlPdX2UtvqW6dW79NQhHt62jNTQSD4dNpWoJm4GO1RczD0rlpNTVcXd/QdwV/8BzW6n5ilWm528siqyiyvIKa6kuLKWipp6ymrqKa8xUl5TR63JgsVqx/EnucLPR4u/rw/hQXrCggOICNYTHqwnIiSQtlEGkqINJEaHEejvnjZurvDTyZM8vHoVdVYrzwwfweS0bk1aL2x3OHhu92rmZ+5mUrt0Xuo3Hl0LHNpjcVh4+cgbZNfl8GTXR2gf1PyDhs5GGpcgqx5GBD+KCLzZ5eO1FNXNQ1EUxYuoMN2yHNLBOxn/Ym/Ffh7qdB/dDWktct3PM3fz1M5VDIhO5MMhkwhuwul0Ukrm7N3DK5s2Eh4QwFsXj2NAQtsWqc8dyqrrOJRdxKHsQo7kFnOqsJz88mrsjv/kBX8fHeEheiKC9YQFO38MCvDDz0eLr06Hr06Lj06LRggsNjtWmx2zzYbVZqfebKW8pv7Xr7LqemqM5v+qITJET2J0GJ0SokhLiiUtKYbEqDA0mtaxia24rpa/rlrFz7k5XNKxEy+NGk2wX+OPsZdSMvPQJt45uJGxCR15Z9CV+LbADVm1tYZnDr2EzWHluW5PEu7r2raVUkpk5V/AvAkRuQSha+/S8VqKCtOKoiheRIXplvV17kKW5a/g+qRrGBs7qkWu+emx7by4Zy0j2nTgvcETm9Tvt8Jo5G9rVrM26wQj27XntbEXtcgx1K4ipeRkUTnbj+ayIyOXQ6cKKaqsBUAjBO1iw0mJiyAxOozEaANJ0WEkRhkIDfRv0e4MRouV0yVV5JRU/Dr7faqonGOnSzBZnOuPg/x96ZIYQ6+UNgzonEj35Dh8dN470293OPhg5w7e3vIz8SEhvDNuAj1iY5t0rc+O7+CF3Wua9dr8X7n1eTx/6O/EBcTxRJeH8dM2Puw3hrQXI0sngC4FET7fLfspmkuFaUVRFC+iwnTL+aWX9MjoYUxPvq5FQt2HR7bw2r4fmzX7d7C4iDuXLaO4rpZHh17I9J69vLIdWEWtkc2HTrLtWA7bjuZQUlUHQJuIENLbxZGWFEvXxGg6J0Sj9/ByC5vdwcnCcg7nOGfKD2YXciy3BIeUBPj60LtDPAM6JzI4LZn2sREerfWP7MzP4/6VKyipq+PZESO5pnt6k67zy6cmQ2Pb8f6QSQS0QE/y3RV7efv4e/QP78tdHWa4/PXa2pZ7qDCtKIriRVSYbhmZtVn8/fBrdAhK4ZHOD7TIiW7vHtrEWwc2cEliV/4x8NImtb5bcvQIj65ZTbhez78mXNrkGUhXqag18sOeDNbsPs7OjNM4pMQQFED/Tm0Z0CmR/p3akhDZOg7WqKk3sTPjNNuO5rD9WC4ni8oBaBcTzujeqVzUpxMpcd4VrCtNRh5YuZL12aeY2q07z44Y2aQbtm+z9vPo9uX0j05k9oWT0euaf7OzPH8lX+Uu4KqEy7ki/tJmX+/P/Pdyj+UInXd3tFFhWlEUxYuoMN18VdZqnjrwAlqNlufTniTYp/mnxP0yI31Fcjde639Jow/dcEjJW1t+5r3t2xgQn8DMCZcQqdc3u66WYLHa+GFfJsu3HmbbsRzsDklyTBije6UyskcHOiVEt5o1yH+mqKKG9QeyWLsng11nbhQ6tIlgXL/OXD4wjYiQpp9U2ZLsDgdvbfmZf+3YzoD4BP51yaWENWEJ0NJTh3hw21IGRDkDdXNPzZRS8uGJj/m5bBsPdrqXHobuzbreWcezFyFLx4FPL0TYbK/89OYXKkwriqJ4ERWmm8chHbxy9E0ya07wdNpjJAcmNvuac4/v5Lndq7ksKY1/DLi00UHaaLXy0OpVrMzIYEq3bjw3YlSLbA5rrpziShZu3s+SrYeprDXSJiKEi/p04qI+negYH+nV4aW5SqvqWLsng+93HWNvVj46jYYRPVKYNDSdfh3besXvfcnRI/xtzWrigoKYffmVpISHN/oai04e4OFtyxgWl8IHQyc160ROALPd/GtnnBe6PUW0f1Szrnc2sm4OsuYlhGEmwv8il47VHCpMK4qXmj8fnngCcnIgMRFeegmmTfN0VYqrqTDdPAtOL2Fx3jJua38TF0YNbvb1lmYf4oEtSxgdn8p7g69qdMux0vp6bl2yiANFRTx24TBu6dXbo0HN4ZCsP5DFVxv2su1oDlqNYFh6ClcPSad/p8RzYga6sU4VlbNg0wGWbTtMVZ2JxGgDk4akc+UF3QgKcO1mu7PZnZ/P7cuXYrHZmXXZZU3q9vJF5h6e3LmSy5PS+MfAy9A08/VXbCrhqYPPE+sfw1NdH22RJVR/REobsmwiOCoQkasQGu/49OB/qTCtKF5o/nyYMQPq6//zPb0eZs1Sgfpcp8J00x2sOsxrR99iSOQgZqQ0f9PS+oITzNjwDb0j4/l02NRGf0yeU1XJjYsWUlRby8zxExjVPqXZNTWV1WZnxY6jzFmzk5NF5cSGBTNxcHcuH5RGtKH5y2DOBWarjbV7Mvh24372ZuUTFODH5KHpXDuil0eXgORVV3PT4oXkVFXx9rjxXNwhtdHXeO/QZt48sJ7pHfvxZK/Rzb6h21G+i39mvM9FsaO5Lmlqs651NtKyB1k+BQJvRRP8iEvHaioVphXFCyUnQ/bvnKialASnTrm7GsWdVJhumkpLFU8efI4gXRDPpj2BfzPbd+0uPc0NP35Bu+BwPh85rdF9pA8VF3PT4oXYHA5mX3YFvdu0aVY9TVVnsrBg0wHm/7ib4spaOsZHcdPYfozulYpOqw43/iOHsgv5dPUO1u3LxEer5bKBadwwug9tozyz+bLSZOTWJYvZU1DAcyNGcV2PHo16vpSSF/es5bPjO/hr92Hcldb8T23mnvqcNUXruD/1LvqE92r29f6Mo+pxMC5GRCxB+DT+ZsLVVJhWFC+k0cDv/S8nBDgc7q9HcR8VphvPIR28dvQtMmpP8FzaEyQ084TDkzXlTFozh1Bff74efT2R/o2bud2Rl8ctSxYR7OfHnCsn0iHc/R0jbHYHCzcf4P3vtlBZa6RvagI3je3HoC5JXrEeuLXILqpgztqdLN9+BIfDwVVD0rl9/EDCg92/edRotXLPiu9YdzKLBy8YzF39BzTq+Q4peXjrMhZnH+TV/hOY1L5xgfx/WR1WXjj8CkWmEl7u/izhfo1f091Q0lGOLLkYfDojwuZ43Wu4oe/b6vZVUdwo8Q/2TP3R9xXlfLa68AcOVR9hWtKUZgfpcnM9N6//Co2AT4dNbXSQ3pSTzfRFC4gKDOTbyVPdHqSllGw8eJLJf/83L3+1jpS4COY8NJWP7r+aC7ome10I8XZJMWE8PW0My5+7mauGpLNg034uf/ZT5qzZidlqc2stAT4+fHDpZVzZpQtv/LyZ1zdvojETnRoheGXABIbEtOOJHSvZXHiyWfX4aHy4q8MM7NLOh1mf4JCum+kRmnBE0L1g2QrmtS4bx9VUmFYUN3rpJeca6d/S653fVxTlP3LrT/N17gJ6G3oyIurCZl3LYrdz16YFFBlr+HDo1SQFN+7o5E052dy6ZDGJBgNfTppMXHBws+pprIy8Uv7y7kLufX8xNruDN2dcykf3TSK9XZxb6zgXRRuCeGzKSL5+4np6dYjn7cUbmfjCHFbvOtaoQNtcOo2G18dezDXd03l/x3Ze3bSxUeP7aLS8O/hK2odEcPfmRZysKW9WPTH+MVyXNJXD1Uf5vtDFIVc/FXQdkDWvIqXFtWO5iMfCtBCirRDiRyHEESHEISHEfZ6qRVHcZdo052bDpCTn0o6kJLX5UGldhBBaIcQeIcRyV41hdVh5P/Mj9Do9t7S/sdmzrs/vXs32klxe6TeB3pEJjXruttO5zFi6hHZhYXx+1dVEBbpvw1q9ycIbC9Yz9eV5HM4p4uFJw1jw5A2M6NFBzUS3sPaxEfzzzit4/56JBPr78rdPVnDHPxeQXVzhtho0QvDiyFFMS+/BrF07eXvrz416frCvP7OGXo1WCG7f+A01FlOz6hkWNYQ+Yb34OnchOfW5zbrWnxFChwh+DOw5UD/PZeO4kidnpm3Ag1LKLsBA4C4hRFcP1qMobjFtmnOzocPh/FEFaaWVuQ844soBFuUtI9eYx63tphPi07xZ4PkZu/jixB5u7zKIy5LTGvXcnfl53LJkMW1DQvn3xElNOmCjqTYezOKqF+cyb91urrggjaXP3sy1I3rjo/N8H+tz2cDOSXzx6DQemzKSwzlFTH7p33y0chtWm90t4wsheG7ESKZ068bMbduYuW1ro57fNsjAu4Mnkl1Twf1blmBvxmYcIQS3tLuBQJ2eWSc+weZw3fIX4TcUfIcga99HOqpcNo6reCxMSykLpJS7z/x3Dc435+YtilMURVFcRgiRAEwAZrtqjBO1WSzPX8mFUUPoGZberGttK87m+d1rGNGmAw92H9ao5+4rLODmxYuICQpi3lWT3HaqYXlNPX/7+DvufX8Jej8fPnlgMk9dO4bQwMZ1HVGaTqvRMPnCHix8+kaGdW/Pv5b/zJSX57EvK98t42uE4KVRY5jYpStvbfmZD3fuaNTzB8Yk8XTvMfxUcII3DqxvVi3BPsHclHw92fW5LM3/rlnXOhsR/AjIamTt+y4dxxVc15G7EYQQyUAvYNvv/NoMYAZAotqlpSiK4klvA48Afzhd3Jz3bJvDxsdZczD4GJiWOLk5dVJkrOGezYtIDDLw1sDLGnW6YWZ5GTctXkR4QADzr5rktqUdGw5k8cy81dSZLPzlkguYPqav181EOxySopJqCourKC2vpaS8ltKyWkrKa6ipNWG22DBbbFgsNsxmGw4p8fXV4febL32AL5HhQUSEBxEVHkRURDDRkcEkxIXh4+M9v9+o0CBeu/USNh7M4u9fruPmN7/m5ov6cfv4QS5vP6gRglfHjMVit/Pqpo0Y/P2Z0q3hx3xPS+3D4coiPjyyhZ4RbRib0KnJtfQJ78WgiAEszV9B//C+zd4M/EeET2ek/xVQPw8ZOB2hjXXJOK7g8TAthAgCFgD3Symr//fXpZSzgFngbLPk5vIUxe3UCYmKNxJCXAIUSyl3CSGG/9HjmvOevbJwNbnGPB7oeDd6XdNngq0OO/f9vJh6m5V5jewlXVhbw40LF6DTaJhz5VXEBrl+s6HVZuedJZuYv243nRKieGn6OFLi3N9273+ZLTaOZRaScbKYrOwSsrJLOZFdgtFk/a/HBfj7EBURTEiwP36+OkKCApzB2U+HEAKL1RmsfwnZRSXVHDpeQGVV/X9dR6vVkBgfTvukSNonRpGSHEVapzgMIe5vV/dbQ7u155sn4vnHgvXMXrWdncdP8/LN44kNc+1rQ6vR8I+LLqbGbOaJH9YSHhDAmJQODX7+073HcqiiiIe3LadTaHSjN97+1nVJUzlQdZBPTs7lya5/QyNcczMhgu5GmpYhaz9AhD7rkjFcwaN9poUQPsBy4Hsp5Ztne7zqM62c69QJieeu1t5nWgjxMnA9zv0u/kAIsFBKed0fPacx79lFpmIe2/8MPQzdua/jX5pV62v7fuTDI1t4a+DljVonXWuxMPWbr8ipquLLSZPpGh3drDoaIrekkkc/WcHhnCKmDOvJA1cOxc/HM/NcJrOVg0fz2Xsol72HcjlyvACL1blWODjIn/ZJkaQkRdE+KZL4WAOR4cFERQQRqG/aQToWq43SMzPbhSXVnMwpJSu7lKzsEgpL/jO31i4xkp5pCfRMa0vPbm0JN3ju1MKVO47y4hdr8dFpefa6sQxPd/3pl/VWK9O+/YZjZaV8efVk0mMaPmN7uraSy1Z/Qrw+lG/H3IiftumvrY0lm5mV9Sk3JV/PyJjGLZtqDEfVM2D81nnMuK7xx6y3JK8/tEU4tyLPAcqllPc35DkqTCvnOnVC4rmrtYfp3zozM/2QlPKSP3tcQ9+zpZS8evRNsupO8Ur684T7Nn0GbWNBFtPXf8nUlJ681G98g59ncziYsXQJG7NP8fHlV3JhcnKTa2io73cd48XP1yKE4JnrxjCqp/tPgKuorGPj9kw2bs1g1/4crDY7Go0gtV30r+G1U0oMkeFBbu0gUldvJvNkCfsOn2bvoVwOHs37dUY8tV00QwemMmxgKu0SI93e2SS7uIJHP1nB0dxirh3Ri/suH4Kvi2+ASurquOqrLzDb7Cyceg3xISENfu6P+ZncuuFrbuzYl6d7j21yDVJKXjn6Bifrsnk1/QXCfF1zaqS0FyJLRkPApWhCX3bJGA3VGsL0EGAjcAD4Zbvp41LKFX/0HBWmlXPd+XJC4vm4lEWF6T+2qWQLH2Z9zI3J0xgdM6LJdZWaapmw6mPC/AJYPOYm/HU+DXqelJJnflzHvP37eHHkaK5Nb97Gx7Ox2u28tXADX/y0l+7t4njlpnG0iQh16Zi/VVpeyw+bjrJxawb7j5xGSoiLCWXogA70TU+me5d4ggKbd2x7S7PZ7BzLKmLvwVw2bc/k0LF8pISEOANDB6QyakhnOnVw3xpbi9XG24s38sVPe+mWFMsbMy4l2tC4g4AaK6OsjElffUlccBBfT55KiF/D/45e3L2GT4/vYNbQqxkV3/SbtkJTEY/vf4aeYT24N/XOJl/nbBzVf4f6uYjIlQhdO5eNczZeH6abQoVp5Vx3PsxMn69LWc6lMN1QDXnPrrPV8fC+J4jxj+GpZqzFlFJyy4av2VqczaIx0+lkaPgSjbl79/DsTz9yW58+PDbUdR9fA9QYzTwyezlbj+YwbUQv7rtyKD5a12+6k1Ky52Aui1buYePWDOwOSfukSIYN7MiFA1NJSY5qVb2rS8tr2bQ9kw1bM9h9IAe73UHnDrFcMa4no4Z0xt+vYTdSzfXD3gyenvs9en9fZt55BZ3bunZp0OacHG5avJDBbROZffkVDd5Ya7bbmLRmDgX11awcdxtRAU0P/kvzvuOb04t4sNO99DS45sZT2suQpaPAbxQawxsuGaMhVJhWlFbofAia58MNw+9RYfr3zc/+iu8L1/JCt6dJCmz6+sivTuzl8R0reLr3GG7s2K/Bz9uam8v1C79leLt2fHjp5WhcGCgLyqu59/3FnCqs4MlrR3P5oMb1vW6KunozK9cdYvGqPWSfLick2J/xo7pz6Zh0EuPDXT6+O9TUmliz4TCLV+3jZE4pwUH+jB/ZjSvH9SQ+rulLhhoqM7+Ue/61mKo6E6/eMoGh3Vw7k/rFgf088cNa7ujbj0eGDG3w805Ul3Lp958wNLYdHwyZ1OSbJ5vDxuMHngXg792fRadxzRIXR81rUPfJmbXTyS4Z42wa+r6tjhNXFC9yPpyQmJPTuO8r565CUxFritYxLGpos4L06dpKXtqzlkHRSVyf2vD7lbzqau5esZxkQxhvXjTOpUH6SE4RN7z+BYXlNbx715UuD9I1tSY+/epnrp4xi3dm/0Cg3o8n7hvHwtl3cNf04edMkAbn5siJ43sz553pzHxpKv16JvPtd7u59q6Pef6t5ZzKLXPp+B3aRDL34akkx4Rx/wdL+HrDPpeOd033dK7tns4HO3fw3fFjDX5eSkgkD6UPZ21eBgtOHmjy+DqNjmsSr6bAVMi64ub1sf4zQn8ToEPWuaytfYvxeGs8RVH+27Rp51Z4/l+Jib8/M63ayJ9/vsj5Bh+h46qEy5t8DYeUPLJ9OQJ4dcAlDQ7EFrude1d8h8Vu58NLLyO4EetPG2vToZM8Mvs7QgP9+fTBq+jQJtJlY1VVG/l62U4WfLebunoLQ/p34PpJA+naMc5lY3oLIYRz02RaW0rLa/l66U4Wr9rL2g1HGD6oEzdMHkiHZNcsw4gKDWL2/Vfz6KcrePmrdeSXVXHfFUNdtnTm6eEjOFZayqNrVtMlMor24Q27OZresR9rTh/jhT1ruCAmiTaBTVur39OQTlpIFxblLWVw5EACdS3fYUVoo5ABV4HxW2TQvQit67vrNJVa5qEoiludD0tZfo9a5vHfDlcd5eWj/+DqhIlcFt/wrhv/67PjO3hh9xpe6T+Bq9v3aPDzXlz/E5/s2c274y9hfMeOTR7/bFbvOsYTn60ipU0EM/9yBVGhrtmkVm+0MH/hNr5ZtgujycrwCzpy49WD6NDOtQGkvt5McXENpSXVVNeYMJusmM1WZ19psxW7XeLv74Ovrw5/fx/8/HTo9X5ERgUTHR1CaGiAS9dqV1bX8/XSXSz4bjf1RgvDBqYy4/oLXTYzb7M7eO2bH/lm436uvKAbT1wzqlEHBjVGQU0Nl34+j6jAQBZOuYYAn4atE8+prWDCqtn0jIhnzvBrmvyJTE59Lk8eeJ6LYkczLWlKk65xNtKWgywdC4G3oAl+2CVj/JmGvm+rmWlFUdzql8B8vnXzUP7DIR18nvMVEb7hXBw3usnXyamt4PV9PzI8LoVJ7Rq+EWp1Ziaf7NnNjT17ujRIL9lyiOfnr6FH+zjeufMKggNafvbb4ZCsXHeQWfM2UF5Zz8ghnZk+eRDtElt29ru8vJZjxwo4fqyQzIwiCgoqKS6upq7O3Kzr+vhoiYoKJiYmlHbto+jYKY5OneJISAhHo2l+yDaE6Jlx3VCmXt6Xb5fv5qulO9m041MmjuvFzdcMbvGuJTqthsemjCQ00J/Zq7ZTZ7bw4o0Xu2STaVxwMG9dPI7pixbyzI/reG3sRQ16XmJQGI/1HMVTO1fx1Ym9XNOhV5PGT9S3ZVjUENYUrWNUzHBi/WOadJ0/I3SJSP+LoP4LZOAdCI3rD1FqCjUzrSiK4gZqZvo/Npdu5YMTs7kz5TYuiBzQpGv/0r1jZ0ku34+fQZy+YX13C2trGD/v37QNDeXrq6fgp3PNnNLSLYd4dv5qBnZO4o0ZlxLg2/LdJTJPFvPGB2s4eCyfbp3acPctI0jr2KZFrl1YWMmO7Vns3HmSo0cKKC2tAUCjESQkhJPQNpzo6BCiokKIigom6swss7+fD35nZqB9fXVoNOLXo8V/mbGurTVRWlJDcXE1JSU1lJRUU1hQRVZWMWazDQC93pfU1Fh69kqkf/8UUjvGom2BI7zLK+uYPX8Ty9fuJyxUz103jWDMhV1cMjs+Z81O3l68kbG9O/LS9HEuO4L8zZ838+72bcwcP4EJHRt2bLiUkmk/zudIRTFrJ9xOhH/TlmlUWqp4aN/j9DR05+7UO5p0jbOR1oPIsomI4McQgTe5ZIw/omamFUVRFK9jc9hYeHoJifq2DIxoeNeN/7U2L4P1BSf4P/bOOyyqq3vb95lh6E0QFAQEFRRFsaLYu2Kvsbdo7DGaWBI1mthL7MYee429F+wFsStSVFAERZoKCtKZ8/1BfD9fA9MYNb83574urknGvdfeAwzznHXWftbEyk00FtJKUWTcqZNk5uSwqGWrTyakD18L/Y+QXjSknd47GmZmZrN22wbNJLIAACAASURBVGV2H7mFpYUJP33rR8tGFQqVyc3NVXLnThTXrz3mxvUnREfnHdorXtyKylVc8PAojkdZB8qUKYaJiaFWsY2MFBgZKQCT//9kufz3EB39iocPYwl/FEdYaAybN11m08bLWFmZUL26GzVqlqZWrdJYWJj8PYAG2FibMX5EC9q18Gbhan+mLzrK0dP3mTCiBY7F9duEpF+z6giCwKL9F5HJBKb3bflJBPW3NWtxOTqKyWdOU9XBEQcL9dlbQRD4tVoL2pz4g/n3zjOnZmud1rY2tKJl8WYcfHGENu+icTXT/+EXQeGFqKiGmLYFTPsiCJ/eSlJbpMy0hISExGdAykzncTb+AhuebuEHj1FULqKbR216TjYtjq3BVKHgcIuBKGSafbiuv32bGRfPM6tJU7pX/DT+uMdvPGDSpuP4lHVh8ZD2GBvqV0g/iIhjxuKjRD1/TfsW3gzpUx8Lc2OdYomiSHh4PP6n7nP2bCjJSWkoFHK8vV3wqVkKH5/SODnbfFH/6Tdv0rh5I5Ib159w48YTkpPz9ljLtwzNmnnhU7M0CoVu4io3V8mR00Gs2nSRXKWSkV83om2zSnp/vRtO3WDpwcu0rlGOaX1b6qV85WOeJifRZttWvIsXZ0unLhrXQc+5e5a1DwLZ3bQvVYs66bT2u5w0frj7I2UsSjO27Hc6xVCHmHECMXkUgvUKBGPdS8O0RcpMS0hISEj8o8hSZnEg5jAe5mXwtq6oc5yVoQHEpL1he+NeGgvpBy8TmXflEs1Klaabl+5rq+JScCRTNp+kWhknFg1pp1chnZOTy5Y9gWz68yq2RcxZ9GtXqnu76hQrKekdJ04E4X8ymKiol/8Rp02beVG9uhvGxp+n4YkmWFmZ0qRpBZo0rYBSKfLoYSxnzoRw9mwoly4+xNLShEaNPfFr5Y27u3YdEOVyGe1bVKZW1VLMXnac+StOcelaBBNGtKCojf4Oig5oXoNcpZLfDwdgbmrMhK4N9S7YXa2L8HODhvx02p/1t28xqJpm1+0jK9ThcFQIU2+e5EDzATodljQzMKWNox+7nu3lYUo4ZS1077BYIEZNQeaImLb5s4ppTZEy0xISEhKfASkzDcdiT7IjejeTPMdTzlK3g3+RKa9pdXwtfs7lWOirmaVedm4unXbuIO5dKid698XW1FSntVVx78kLhi7di1txG9Z81wVzPR42jHr+ihmLj/EgIo7mDcoz+psmOmWjn0YmsmfPDU77B5OdnYuXlxNNm3nRsFE5ncsmvhQ5ObncvBGJv38wAVfCycrKwbuyC126+lCrVhmts79Kpci+Y3dYufkCxkYGjB3anEZ1NKs/1gRRFFm47yJbz95meJvafOOn21kBdWsMO3KI80+fcqRXb8rY2Go072h0KKMCDvBLteZa+bR/SGZuJmPvTaSYcTEmeY77JHczxNS1iKnzEWwPISjyqRP6BEiZaQkJCQmJfwyZuZkcfXECL8vyOgtpgJl3TmMoN+Cnyk00nrPm1k1CEhNY1abdJxHSUfFJfLfqIHbW5iwb3kGvQvrclYfMXHIMYyMF08a3o1Ft7QVeaGgM27YGEHg1AiMjA/z8KtGpcw2cXTQTW/9EDAzysum1fMuQmprBsWP32L/3Jj9P2oOLiy09e/nSuEkFjQ8tymQCXdpUpUblksxccowp8w/RMbgyowY2xsCg8DW6giAwpmN9klLTWXEkAHtrc7037hEEgemNm9JiyyYmnDrF7m7dNSr3aOXsya5id1kQdIHWLuWxMdL+PWIkN6KdY2s2R23nQcpDPC0/gdg17QqpyxDTtiBYzdR//EIgdUCUkJCQkPjkXEy8wtucFNqXaKNzjCtxkZx7EcGI8nWwM9HsNnz0m2SWXwvEz92d5mXK6Lx2Qbx5l8GoVQcQBIEVIztia6mf5hWiKLJh5xWmzD+Eeyl7Ni7pr7WQjo5+xcSf/uTbEZsJCXlO/wH12LFrBN+Nafl/Wkh/jLm5MV99VZMt24YyaXI75AYy5sw+woB+a7hy+RHa3IEv6WTLitk96d6hBvuP32XstD2kpGboZZ8ymcDU3s2oVc6FGdtPc/PRM73E/RA7MzN+btCQO3Gx7LyvWZdDQRD4uWoz3uVksTzkss5rN7Cvh5XCksMvjuscQxWCzBpM2kDGUURl6idZQ1ckMS0hIfE/ybZt4OoKMlne47ZtX3pH/15yxVyOxZ2ijHkpnesplaLInLtncTKzop+HZreiRVFk6tmzKORyfm7QUKd1VZGdk8u4dUeIfZ3CosFtcSqqHzeIzMxsfllwhPU7A2jZqAJLpnfTqob37dt0li/zZ9DX67gf9IxB3zRkx84R9OlbFysr/Wfm/ykYGMhp3KQCa9YOZNr0zsgNZEz5eS/jftjB48cJWsUZ0b8hP33rx73Q5wwZv5XomNd62aNCLmfewNY421kzdt0RohKS9BL3QzqU86SWkxPzrlzi5YfdsVTgbmVHt1KV2RZ+m8gU3V6roUxB82JNuf8mhKh30TrFUIdg0g3ENMg4/Eni64okpiUkJP7neN9lMSoKRDHvcfBgSVB/Ka6/usnLzJe0dvDTuZbySFQoocnx/FCxIUZyzSoUj0eEcyHqKd/71qa4uX6bPYiiyJw/z3Lj0TOm9mpG5dIl9BL35etURk7aybkrDxjatz4TR/lhqKG1Xk5OLvv33aRv71UcPHALv1bebN46lB49fbW2s/u/jEwmUKeuB2vXDeTbUc2IeBzP0MHrWbjgOElJ7zSO06qJF0umdeNtagZDxm/l5r2netmfhakxS4a1RwC+W3mQlDT9ZL7f877cIz07m9mXLmg87zuvehjK5Sy6r/mcj2lSrCHGMmOOxp7QOYZKFJXAoCxi2u5PE19HpAOIEhIS/3O4uuYJ6I8pWRKePv3cu8nj33oA8caNG0wOnkaOMpvZlaYhE7TP4WTl5tLi2GrMFIYcajFQozrQlMxMmm/emNdquXtPDPTc0nnv5SBm7DjDwBY+jGxXRy8xI54mMG7aXt6lZTLl+zbU9dG8LOXRw1jmzTtK5JNEqlZ1ZejwJpQu/elaiWdn55Dw4g2xMUnE/fWVnPSOjPRsMtOzSE/LIiM9G6VSibGJIcYmiv88mpkbU8zRGgcnG4qXKELxEtaY6Wjvpwlv36azZfNlDh64jbGxgqHDmuDXSnMLvNj4N/w4cx9Rz1/x/dBmtGuuedt6VdyOeM6QJXupXb4ki4a017tl3vtmLts6d8HXWTP/54VBF/g99AoHm3+Nl4127ijv2RG9m+Oxp/jNexb2xnY6xVCF+G4zYsqMz3IQUdO/25KYlpCQ+J9DJsvLSH+MIIBS+fn3k7f2v1NMrz+9kfkPFzPIrT8N7OvqFGdb+C2m3DrJuvpf0chRM4E57fw5Nt29w77uPfEurpsoKIjgp3EMWLgLHw9nlg7voJOd2MfcfxDD+Gl7MTFRMO/nzpRx1UwIZ2fnsmnjJXbtDKSIjRmjRjWnTl0PvbopKJVKnkYkcP92FGFBz3gQHEPc86T/qkVWKORY25rniWZjQ0xM84SzTCYjIyNPWGf8JbJT3qaTlvrfbcitipjhUd6RchWd8KpaknJeJTDWczY9OvoVixYcJyjoGdWquzJ2XGvs7TVr+JOWnsXU+YcIvB3JkD716d1ZP24cO8/fZe7ucwxt7cuQVrX0EvM9GTnZtNyyGQOZjKO9+mjUpCglK4OGR1ZS0caBjQ2767Tu66wkvr/7Iw3t6tPfrZdOMVQhKpMQE+qCaQ9klpP1Hv9DJDcPCQmJfy0uLvlnpl3035xLQg0n4vyxVljp3DY8MzeHFaEBVC/qREOH0hrNCX/1ii337tKjYiW9C+mU9EwmrD+KnZU5swa00ouQDgp7zthf92BrY86iX7pS3N5Ko3nPol8xc8YhwsPjaNmyEsNGNMFcTxne1JR0rp57yOWzoQTfjiI1Ja8UwaaoBZ6VnGjsVxGHEjYUdyqCg1MRbIqaI9Pie5HyNp2453kZ7diYJJ4/fcmD4BhuBkQgiiIGBnLcyztSq74H9ZpVoIQeDky6uNiyYFEvDh+6zZrV5xg86A/G/OBHgwbqs5umJobMntiRWUuPs3rLRXJzlfT7yrfQe+rWwJvgp7GsORZIdXcnqrnr1jglP4wNFPzSsDFfH9zPlnt3NfKetjA0ZoinL3PvneX2y+c6NXKxMSxCnaK+XHp5hS7O7TE30J9nN4AgK4Jo3BTSDyNaTEAQvrwvuiSmJSQ+E9u2waRJEB2dJ+pmzoRe+r9olyDvezt4MHx49sbUNO95ic9HtjKb+29C6OzUAYVMtw+8vZFBxKWnMK9mG42zrXMuX8RUYcj3tfVTfvEeURSZtfMM8UkprP++G1ZmhReu74V0URtzls7orvFBwxPHg1i29BQKQzm/Tu9M3bq62w2+572Avng6hNtXH5OTk4t9cSvqNauAV5WSeFV1oZiDtV6y3haWJliUN8G9vOPf9hBy9xnBt6O4dzOSDcvPsGH5GcqUc6Be0/KFFtYymUD7DtWoXsONmTMOMe2X/fi18ubbUc3+anleMAYGciZ91wqZTGDd9ssIAvTtWjhBLQgCE7s34X5UHJM2HmfXxD56+b16T0M3Nxq4urLs2jW6VKiAtbF6P/He7lVZE3aV5SFXWN+gm07rNi/WhIuJl7mQeIXWDi10iqEKwbg9YsZxyLwMxo30Hl9bJDEtIfEZeH8g7r24e38gDiRB/Sl4/z2VLl6+LG9zUjAQDGhkX1+n+dnKXFaFXqWyrSO1i7lqNOf68+eci4xkfJ262JjotxHJ4WuhnLj5kBFta1PJzaHQ8XQR0tnZuaxY7s+hQ3eoUqUkE35qi51d4Q5Xhoe+4MCOQC6czGvmYl/cig49a1KvWQXKVijxWduJm1uYULOeBzXr5V0cJMQmc+l0KJf8Q/4jrCtUcaFDj1rUaVQOuY4e0CVK2LB0WR82bbzMju0BRITHM21GZ7VlH3K5jJ++9QNg7bY8G7nCCmpTY0Nm9fej/4JdTNvuz2+DNL9w1IQf69an1dbNrLxxnZ/qNVC/HwNDvi7rw4L7F7j/OpaKNtr/rpc0c6ashQen48/hV7yZTmclVGJUFwRrxIzDCP8AMS3VTEtIfAb+iQfiJD4v/8aaaTtPe3HGkdkMKT1Qp/l7ntxjwvWjGtdKi6JIl107iU1N4Wz/ARgb6O/2b1RCEj1mb6NCyWKsGtW50OUdoY9iGT1lF7Y25izTUEgnJb1j2i/7CQp6RrfutRg4qIHGTUk+Jic7l8tnwzi4I5DQe88wMTWkadvKNG3tTVmvzyugNSUhNpkLp4I5svsmcTFJFC1mSduuNfDrVA2rIrr7e1+9Gs6sGYcwNDLgl187UbGis9o5ublKZi09zqkLoQztW59enQpfQ73J/yaLD1xico8mdK5bqdDxPmTcqZMcfviAM/0GUMJSfZ14SnYm9Q/9Tk17F1bV66LTmjde32Jp+EpGu4+gmk0VnWKoQvlmKmQcQLALQJDpx9/9YzT9uy1Z40lIfAaiC7DcLOh5CYn/BZSINC+ueafCD8lRKlkRGkCFIsU1rpU+9TiCO3GxfFfLV69COjs3l4kbjmOokDOjX8tCC+mnz14xfvpeiliZaiykHz2KY/jQjTx4EMvESe0YPKSRTkI6KyuHA9sD6dt6EbN/3E3S63cMHdeSbSd+YOSPrSlX0ekfKaQB7B2s6dqvLusPjuLXxT1xdi3KhuVn6NVyIUtnHuZlwlud4vr6urN8RT/MzIwY+/12Dh+6o3aOXC5j4ig/mtbzZNXmixzxD9Jp7Q/p06Qatcq58NueC0TG6cfX+j2ja+Vlz5cEXtVovIXCiP4e1fGPecSDZM09uj+kapHK2Bra4B9/Vqf56hBM2oKYDpmnP0l8bZDEtITEZ6Cgg2/SgTiJ/2VcTJ1wM3PVae6xZ2FEpSYxokIdjcRdrlLJgoArlC5iQ+fy+m3TvMn/JqHR8Uzu0ZRiRQpXUvEqKZWx0/Yglwss/KWrRkL6yuVHjB61BVEUWbK0N02aav/6RFHkon8IgzosY+X845Qoacu0pb1Yf+BbOvb0xczi01nT6Ru5XEatBmWZs6ofq/eMoFlbb04euMOAdkvYtOIsGelZWscsWbIoK1b2p0pVVxYvOsHypafIzVVt/fNeUPtUcWX+ylNcvflE15cE5NVzT+vbAiNDA6ZuOUmuHq2HSlha0te7MvvCQol4/UqjOf08amBmYMjK0ACd1pQLchrbNyDkbRix6XE6xVCJoirISiCmf/kGLpKYlpD4DMycmXcA7kOkA3ES/+vIBd3qWUVRZN2Da5SysKFZCc0O1p2ICCfi9WvG+NbWq6f0s8Rk1h6/RtMq7jStolv3xvdkZuUwac4Bkt+kMe/nLpRwKKJ2ztkzIfwydR9ubnasWDUAj7La168+fhjLuEEbmDn+T0zNjZi1si/z1vSnZj0PrRw4/om4lrbnu8ntWLd/JLUblmP72gsM7LCMs8eCtGojDnltyWfO6krnLjXYv/8W8+cdVSuoFQo5Mya0p7SrHb8uPEzks5eFeTnYWZkzrktD7j+NY98VzdqBa8qwGj4YGxjw+/VrGo23NjKhR5kqHH8Wxot3b3Ras75dHQQELr3UTZCrQhBkYOIHWVcRlbrdldAX/7ffRRIS/0fo1QvWrMmrkRaEvMc1a6QDcRIS+XHr5XNCkuIYUNZHowYtSlFk2bVAytjY0NK9cIL3Q/K6HJ5DYSBnXJeGhY41f8VJQh7GMnl0K8qWLqZ2zpnTIcyedRgvLyd+W9gTGxvt6kIz0rNYOe8YI3qsJupJIqMmteH3HUOpVqv0P7aUQ1ccnGz4aU5XFqz/GmtbM+ZO2ssPA9cTE61ZFvY9crmM4SOa0q9/PfxPBTN/rnpBbWJsyOyfOmJkaMCPM/bx5m16YV4KrWqUo4aHM8sOXuHVW807NqqjiIkJvSt5c/jhQyKTNGtj3s89r1x4S/gtnda0NrSmkrUXlxOvohT1b/IvGLUAsiHz05SSaIokpiUkPhO9euUdNlQq8x4lIS0hkT+bw29ioTCig6uXRuP9H0fw6NUrhvvU1Eh8a8rpO+EEhD5leJva2FsXzit3+/7rnDwfysAedWhYu6z6tf2DmTP7MBUrOTNrzldatwMPvfeM4d1XcWDHNdp8VYP1B0fRuksNnQ8s/l/Bq0pJlm0dzJgp7XgansCwbis5/Od1lFqWTPTtV5f+A+rh76+ZoC5mZ8msiR15+TqVn+cdJDs7V+fXkGeX15iM7BwW7buoc5z8GFi1GoZyOStuXNdovKOZFc1KePDnk3tk5GTrtGa9onVIyk4i5G2YTvNVoqgIsmKIGaf0H1sL/rffVRISEhIS/6eIT0/h5LOHdC3ljamBegEpiiLLr13D1dqaNh7qRaqmpKZnMn/Peco52/NV/cK1jw64+ZjVWy7SuG45jRp9+PsHM3fOESp5uzBzVlethHRWVg7rl/rzw9d/kJ2Vw9zV/Rj5Y2ssLPVrE/hPRiaT0bJjNVbvGY5XFReWzz7KxOFbSIjTrlShT9+6DPi6Pv7+wcybe0StoK7g4ciEkS25E/yMJX+cKcxLwLWYDf2aVufojQfcePSsULE+xM7MjB4VK3EgLJRnbzT7fvRxr05yVjqHo0N1WrNKEW/M5KZcSvxEpR7GzSHzEqJSf1l8bZHEtISEhITEP4YdEXfIFZX0LlNNo/FnIyMJSUxgeI2aeq2VXnn0Ki/fvmNS9yYYFCKb+yI+memLjlLGzZ6fvm2ptrzitH8w83QU0pHh8YzqvYZdGy7TvF0VVu0eTmWfUjrvvbBkZWaT/i5T69plfWFXzIqZv/dh1KQ2hAU9Z0jX3zlz9J5WMXr3qcPXA+tz2j+EuXPUC+rmDcrTq5MPB0/c48S54MJsn4EtfHAqasWsnWfIys4pVKwPGVytOnKZjJUaZqdr2rvgYWXH5kc3dfpZGsoU1LKtyc3Xt0nLSVM/QUsE4xZAJmTpN4uvDVLTFgkJCQmJfwQ5SiV/PrlLfYfSlLRQfzgPYO2tG5SwsKR9OfUtoTXlSdwrdl24S+c6lfBy1b0deW6ukhmLjyGKIjPGt8dYTYe9W7ci/ysjbWysub3fjcvhzJzwJyamhvy6pCe16usvS58fOdm5PH+SwNNHcTx9EEvUo1hexb/hXUrGX1/p5GTllTrIZAKm5saYWhhjZmGMpY05LmWK4VrWAbdyDri4F/9kbiKCINC6Sw2q1irNb1MOMG/yPiIj4vn626YaH77s1bsOILD+jwtYW5syfERTleMH9apHyMNYFq4+TSVPJxyLW+u0d2NDA378qjEjV+xn54V79G2q2QWmOoqZm9O1ghe7Q4IZ41sbOzPVtfiCINDHvRo/3zzBvdcvqGxbQus169n5cibhHDeTblPfrq6uW88fRTWQ2SBmnEYw9tNvbA2RxLSEhISExD+CS3FPiE9PZWo1zdoPhyUmcj0mhon16qOQ6+Yckh9L9l/CxFDB8La1CxVn58Eb3A+LYfLoVmoF1fPnr5n+6wFKlizK9BmdtRLSx/beZNnso5RyL8a0JT2xVdPFTxdyc5U8Cormxtkwbl4II/JBLDl/1QXL5DKcStlRzMmGEm52mFqYYGZhjKm5MXK5jLTUPIGdlppBWkoGrxPfcnrvDdLfZf4nfnFnGyrX8cCnUXkq13HHxMxIr/t3cLJh3pp+rJh3nN0br5Dw4g1jp3XAUM0Fznt69a5NUtI79u65QalS9rT0K7ipioFcxuTRrej33QZmLjnG0hndda5Vr1PBldrlXVl34hrtapXH2lw/5ToDKldhW9A9/gwJZoSP+oYzbUtWYOad0+x5EqSTmC5l5oadUVGuvbqpdzEtCHJEw7p5pR6iMq/04zMjiWkJCQkJiX8Ee57cw8bIlEYO6rsdAmy9dxcjuZwuFfTnK30r/DkXgyMZ1b4uRQohXCKeJrBu+2Ua+HrQvEF5lWNTUzP4efIeBEFg+swumJpqJiSVSiUbfz/LrvWXqFHHnUnzumKi4VxNyMrMJtA/hMDTwdy6+JC3Se+QyQQ8q7rScWCDvMxyWQdKlLLH0Eg7OaFUKkmISeLpwziePozlUVA0F47c4cTOQAwM5VT0KU2NRp40aFMFGz1dHMgN5Iz8qTXFS1izbrE/LxPf8svCHlham6qfDAwb3oSoqJcsXnQCZ2cbKng5FTi2mJ0lo79pwswlx9l16CY9O/rovO8xHevRbdZW/jh5nR86q28HrgmlbGyo61KS7UH3GFK9htoSKQuFES2dy3E4OpRJVZpiomVTJEEQqGlTneNx/qRkp2KhKNyB3r/FN6qPmHEIckLyDiV+ZiQxLSEhISHxxXmV8Y4zL8Lp614dQw2yzG8zMjjwIIz25TyxNtZPtk4URZYfuoKdlRk9Gure/jgrO4cZi49haW7M2GHNVNZJ5+YqmTXzEDHPk5j3W3ccHDQrCcjKymHB1AOcP3GfVp2rM/LHVsgN9JOdj4lM5PiOq/jvucHbpHdY2phRo6En1Rt5Uq2eBxbWhW/dLJPJKO5sS3FnW2r91YQmOyuH0FuRXD8bxo3zYayZfpA/Zh/Gt1lFWvXyxdu3TKF9sQVBoGu/utg7WDP/5/2M6b+OGct74+Bko3auXC7j5ykdGDFsI1On7GPFqv7YqxD6LRpW4NK1CNZtu0zNKm6UdrXTac9lHIvSyqccf168R58m1QrtLPOevt6VGXz4IKefPKZlGfWWkl3dvDnwNJiTzx9q7LTzITVtfTgSe4JbSbdpaF9fly0XjFFdQIDMi19ETEsHECUkJCQkvjiHokLIVirp7Fbw7fMP2RMaSnpODn28K+ttD1dCn3L3yQu+8auJsaHuuab1OwN4/DSR8SNaYm2pOuu5/o8LXAt8zMhvm1G5ckmN4qenZTJpxBbOn7jP16OaMmpSm0IL6dxcJVdOBDGx9yoGNZ7NgQ0XqVizNDM3D2H79V8Zu7AnDdtW0YuQLgiFoQHevu58M6kda/wnsPb0j7TvX597V8OZ2HsVg5vMZe/a86QW0scZoEFzL+as6sub5DS+67uOxw9jNZpnaWnC9JldyczMZsrkvWRkFGwXJwgCY4c1x9zMiBmLjxbKLm9oK1+Uosja45o1XNGERm5ulLCwZMvduxqN97F3wcXMmt1PtDvE+Z6Sps4UM7Ln2qubOs1XhSCzAYUXYuYlvcfWBElMS0hISEh8cfZEBuFt40hZa3u1Y0VRZGvQXao5OFLBXv14TRBFkRWHA3AqakUHX+2zbu8JefSCHfuv06ZZJerUKK1y7PlzYezcEUjbdlVo176qRvEzM7L5+dttBN+JZsKsznQbUK/QDVjuXQ1nZOsFzBi2kZjIRPr+4MfmK1OYvLI/VeuV/WLe1E6l7flmUju2Bk5l3MKeWBU1Z92sQwyoN4ODGy7+p2ZbV7yqlGTxpkEYGhkwYcgmnj5O0Gieq2tRJk5uR0REHAt/O6bS4aKIlSnjR7Qg4mkim/68qvNeSxS1olOdihwICCbmpW7dCD9GLpPRq1Ilrj5/Rvgr9c1tZIJA51KVCEyI4llqstbrCYJATdsahL59QEp2ii5bVo1hfci+i6jUz/dHGyQxLSEhISHxRQl/k8iD5ASNbx3ffPGCp8nJdK+ov9u5gQ+iCXuWwMAWPih0zPIqlSKLVp/GtogZIwc0VDk2NjaZhQuO41nekREjm2kUPzcnlzkT9xB8J5rx0zvRWMUhOE1IiEli1ohN/NhzJenvMvlxWR/WX5xEj5HN9FanrA8MjRQ07lidBbu/ZdmR73Gv5MyqaQcY2WYBdwPCCxXbqWRR5q/tj0IhZ9IIzb2ofX3d6de/HmfOhHLypOq233V9ytC8QXm27b9GTKxmnQfzY2ALHxBgyxnduhHmR5cKXhjIZOwNDdFofIeSee/Rozp6TtewqYoSJXeTg3SarwrBzTorqQAAIABJREFUqDaghKwbeo+tDklMS0hISEh8UY5GhyETBPycNbO32xsagqlCoVGdp6ZsPHUDOyszWtXQ3WLv5PkQHj6OZ2jfBpipOAioVIrMn3sUgMk/t0ehUC/eRVFk+ZyjBJx7wNCxLWnkp/uFRFZmNtuWnmRw0zlcPxtKnzEtWe0/ngZtqhQqCy2KIu9S0kl4/ponIc8JCnjEnYsPeHQ3iheRCbx9narWp1kdZSo4MXPzEH5ePYCMtCx+6rWSGcM2khCju0h1cLJhxvI+pL3LZNLwLbx9o5kXcs9etfH2duH3Zf7Ex6sW4cP6NUBhIGflpgs679Pe2pzWNTw5eDWE1yn68WsuampKQ1c39oeFkaNBl0gnc2uq2JbgaLRu3QxLmrpQRFGEO0n6F9MovAEjxCzN/LP1iXQAUUJCQkLiiyGKIkejQ/Gxc8HORP3BqrTsbI6FP8LP3QMzQ+1abBdESFQc1x89Y0zHehgqdPtYTEvPYvWWi5T3cKBpPU+VYw8dvMW9e9H8MLYVxTX0IN66+jzH9t6i24C6dOhZS6c9AkQ9imPud1uIfBBL/daVGfhTW+xLaObp/SFJiW+JCHpGxP1nPL6f95j4IgmlBmLZsogZpbycKFPRmdIVnSlT0RlHNzuNDxcKgkDt5hWpVr8s+9ZeYNeK09y58ohvZ3ShYTvNymU+pnTZ4vyyqAeThm9h6nfbmb2yL8ZqGubI5TLGTWjNoK/XseC3Y8yd173AkpuiNub07lyTtdsuc+d+NFUquui0z77NqnEwMISdF+4yvE3hrBvf07l8eU4/eczlqCgaurmpHd/axZMZd07z5O0rSlnaarWWIAhUKVKJgJeBZCuzUci0cwVRHdsQ0bAKZAXqLaamSGJaQkJCQuKL8SA5gScpr/m6rHqvW4BTEeGkZmXRpbxquzlt2Oh/E3MTIzrV0T3bu33/dV4lvWPGhPbIZAXXML+ISWLtmvPU8CmFXyvNyjSO7rnB1tXnad6+CgO+Vd0wpCBEUeTo1gDWzjyIibkxv/4xCJ/Gmn8PlUolD28/5fKROwQcv0dc9P+vsS1Ryp6yVV1p1KkG5tYmmFuaYm5lipmVCXK5jHcpGaQmp/HubRqpb9J4GfuGx8HPOLDuPDlZeZ39TC2M8WnqRZ1WlaneuALGpuovlIyMDenxbTMadajK/DHbmPvdVm5eeMDwXzthaq59Exjv6m5MmNmZmRN2M/vHPUxZ0E3twU4HB2sGD2nE0iWnOHb0Hq3bFHwgtlu76hw6FcSyDedYO7+PTncBShW3pZF3aXZduEv/ptUxNS78BWUjt1IUMTZmb2iIRmLaz9mTmXdOczQ6lG+96mm9XmXrSpxNuMCjlAgqWKm+8NQWwbAWYuoSRGUygky3Zjm6IIlpCQkJCYkvxrFneSUezZ08NBq/NywUZ0srapQo2ONXG6ISkjhzN5wBzWpgbqKbR3N84lt2HLhB03qeeJUruKGFUiny2/xjyOUyvv/BT6ODg7euRrB89lF86nkwenJbnQ4bvk16x8JxO7l2JoTqDcoxZn53bOw0q4kOuxXJuX03CDh2l1dxbzBQyKnawJO2AxpQppILpb2cMLPUzZowJzuX6EexRNx/Rsj1xwSevM/5/TcxMjGkeuPy1Gtbldp+3ijUOKsUd7Zl3s4R7Fjuz45l/oTcjOTHJb0pq6E7yofUa1aBEUnvWD77KGsWnWLYOPUd9dq2q8rFiw9ZtfIM1Wu4UayYVb7jjIwUDO1bn18XHOHEuRBaN9Xt4q1/sxqcu/eY/QHB9GqsWyb+QwzlctqX82T7/SDeZGRgZaz6QqS4qQXV7Zw5/uyBTmLa07IcCsGAe8lBehfTGPoAImRdB+Pm+o2tAqlmWkJCQkLii+Ef8wgfOxdsjdVbriW8SyUgOpoOnp7ICulg8Z4d5+9iIJcXyld6w64AEEWG9lXtnXvq5H3u3YtmyLDGKv2J3/P6ZQpzJ+7FpZQdE+d00cn+LupRHN+1X8StSw8Y/HN7fl0/SCMh/eB2JBO7LeP7Nr9xcnsAHpVdGbe8HzuD5/LrlmF0GtqESrXddRbSAAYKOaUqONG8uy9jFvZm+71ZzN49imbdahF64wlzhq5ncL1p+P8ZqLbWWm4gp/folszdOQJlrpKx3ZZzeq9uB9HafuVDh561OLA9kCtn1dcGy2QCY8e1QqkU+X35aZVjm9QtR4WyDvyx4zJZ2Tk67a+SmwNVSpdg+/k75GpQ56wJncqXJys3lxMRmh3obOFUlodvEolO1b5W3VhuRDnLsgS90ezQo1YoKpFXN61/+z1VSGJa4l/Htm3g6goyWd7jtm1fekcSEv9OXrx7Q/iblzRy1Kzj4YmICESgjUdZvayflpHF0WuhNKviTlEr3fyTX8Qlc+JcCO1aeFNMhUhNTc1g3drzlK9QAj8/b7VxRVFkwdQDpKdnMWnuVzp1NowIfs64bsvJysxh/s6RdPy6gdq65MjQGH7pt4oxrX/jcfBzvpnaiR335zBlw2Aad/YplHhWh9xATuW6ZRkxuxtbbs/k1y3DMLcyZeF3WxjWcAaXDt9GqUY8etUoxfIj3+NVoxQLxu7g4IaLOu1l0OhmuHs6snj6IV4lvFU73sHBml69a3Pl8iNu3owscJwgCAzqWY/EV6kc9VftAqKK7g28efHqLQGhUTrH+JAKdvaUtLLiePgjjcY3/Os9eyH2sU7rlbcsR0z6C95k69fGThAMQeEF2Zp5Z+sLSUxL/KvYtg0GD4aoKBDFvMfBgyVBLSHxJTj/1wdxQwfVfszvORH+iDI2NrjbanfoqSBO3X5EakYWXerqbjG3eU8gcplAr06qa763brlCcvI7Rn7bTGVN9XsO7bzGzYAIBn/fApdS2nfOC7v9lB97rsDEzIjfdn9LuSqqSx5exiYzZ9h6hjeZRXBgBH3Ht2HDtV/pNLSJTvXHhUUul+HT1IulJycwae0gEARmDf6Db1vMJShAteCzsDZj2vpvqN2iIqumHeDPVWe0Xl+hMGDCrM5kZmTz29QDakU8QJeuPjg6WvP7cn9ycgr2wK5WyYVKniXYsjdQ5+x0I+8y2FqYsveyflwxBEGgpbsHAc+ekZyhvimOm4UNJc2LcP6FbmLa0zLvgjjs7UOd5qvEsApkhyCKWfqPXQCSmJb4VzFpEqR95CiUlpb3vISExOflQuwTSphaUVoDR4BXaWlcj4nBz11/dngHr4bgVsyGyqUddZr/YVa6qE3BTiTR0a/Yt/cmfq28KVvWQW3cpxHxrF3sj089D9p0raH1voICI5jYZxVWtubM3zUSB5eCv7+iKHJq51WGNpxB4Ikgun3bnA3XptFjjN8XEdEfIwgCddtUYeW5SYxd2pd3b9KZ0HkJKybuIv1dRoHzFIYG/LSsLw3bVWHD3KNsXXxCZXOV/HB2LcrQsS25HfiYAzvUdx40NDRg2IimREe94sD+gr2gBUFgQPc6hcpOKwzktK1Vnsshkbx8806nGB/j5+5OjlLJ2ScFZ9Y/pIFDaQITosjIKbgLZEG4mpXEWGb8ScS0oPAGsiH7E5SRFIAkpiX+VURHa/e8hITEpyEzN4eA+EgaOpbW6FDdmcgnKEWR5qX1I6Yj415z98kL2vtW0LmDoKZZ6TWrz2JsrGDgoIZqY2ZlZjNn4l7MzI344Zf2Wu/t1sWHTBmwFjvHIszfNVKl7d3L2GSm9lnJojFbcfN0ZMXZifSf2B4La9Ut0L8EcrmMJl1rsur8ZNoPasSRjZcY3ngWQSqathgo5Ixd2ItmXX3YtuQU6+ce0VpQ+3Wqhm/Dcqxf4s+TR3Fqx/v6lqGGTym2bL7CWxVtz/WRnW5XqwK5SpGj13XzfP6YivbFcDA359RjzeqmGzqWJiM3h2uJ2n+AygU55Sw9CH37QOu5alH85ajyGUs9JDEt8a/CpQBrz4Kel5CQ+DTcevmctJxsGmhY4nH68WMcLSwob6d9yUN+HAoMQS4TaFNTNzeBl69TOXk+hDbNKqnMSgfff87VgAi696iFtQYiddOKs0SGx/PDLx2wVhE3P25fesgv36yjhJsd83YMV9nF8MqxuwxrNIOgK48YMq0Lc/eNxtFNP63ZPyXGpoYMnd6FeftGI8gEJnRezLpp+wpsLS6Xyxg95yta967NntXn+GP2Ya3WEwSBMVPaYWFlwtxJe8lWI3wFQWDwkEa8e5fBzh0F+x0LgkD/brVJfJXKqfO6dRN0K25D5VKOHLwaovVFQkF7ala6DBejNMs217RzwUhuoHPdtKdlWeIy4nmdpXvDnfwQ5MVA5oj4GcW0ZI0n8a9i5sy8GukPSz1MTfOel5CQ+HxcjX+KXBCoaa/+SjYrN5eAZ9F09Cyvcxb5Q0RR5OStR/h6umJrqdvBw8P+QeTkKPmqbTWV47ZvC8DKyoSOnaqrjfn0cQL7tgXi16kaPvU0swp8T2z0K2aP3IyTmz1ztg/Dwrrg17Vr6Uk2zj6ER+WSTFjRv1AiOjdXyZOgaO5eDCMmIp7kl29JTkzhTeJbkhLfkpOVg7WdJVZFLbC2s8TazgK7EjZ41S6Ll687xma62RF61SrDijOT+GP6fvauPMOTkBh+Xv8NJmZ/L02RyWSMmNYZAYG9a8/jVs6RJhr8PN5jVcSM0VPaM2XUNg5sD6Rrv7oqx5cqZU/DRp4cOnibHj1rYWGR/6HN6t4lKeVSlAMn7tKmmW51+619PJm58wzhL17iUaLwF5qN3NzYfO8uN2JiqFfSVeVYYwMFNYo6Exiv2yHIshZ5d5kiUh7jY6v5z0MjFBU/a5mHJKYl/lX06pX3OGlSXmmHi0uekH7/vISExOfhekI0XkUcMFeoF1N3Y2NJy86mrp5uIYVGxxP7+i1DW+nWSTA3V8kR/yBqeJekhEPBZRRPniRw7dpj+g+oh4mabnoAfyw+hYmJIV9r2ZglMyOLmcM3AjBlzdcFCmlRFNk46xB/Lj9Fo041GL2wF4ZG2nege/E4nhun73PvYhhBlx6S+lf77SLFrChib4l1UUsc3OywtrNEoZCT/DKFNy9TSE58y/PwOF6+SGLngqMYKOSUrV6KyvU9qdKwPOVrldG4CyLkZalHzO6Gu7cLS37YxqTuy5m2dTjmVn+/AyAIAkOndiA6Io5lk3ZTytMRN0/Na+Vr1vPAp54HO9Zdonm7KlgVUX0R1r2HL+fOhnHo4B169c6/U6EgCLRr4c3itWd4GBFH2TLFNd7PexpXLsPsXWfxvx2uFzHtU8IJQ7mcS1FRasU0gI+9CwvvXyApM40iRtqVB5U0dUEhGBCR+kTvYlpQlEfMPImoTEGQWeg1dn5IYlriX0evXpJ4lpDQBUEQnIHNQHFACawRRXGJtnHSc7K59/oFAzx8NBp/OToKuSDg6+Ss7VL54n/7EQZyGQ29NSsx+ZhrdyJJeJnCyK8bqRz3585rGBsraN9BdfYa4HbgY65fDmfQmOZYalmzvHLqfh6HxPDLuoEFHjZUKpWsnLSbIxsv0qpvXUbM7qaVcFUqldw8HczBVae5dSYYAAdXO+q2r453g3JUqlsOWw1bo2e8yyT0WgR3L4Ry9+IDdsw/zLa5hyhRphjthzSlaY/amBaQzc2P5t19MTU3Zu7wDUzosoSZO0ZiXfTvAkpuIOfHpX0Z2WYBM4ZvZMnBMZhrYfU36LtmDP1qBdvXXmDY+FYqx5YpU4waNdzYt+8GXb/ywbCAxjPNG5Rn5aYLHDp1j3E6iGkbC1Oquztx+s4jhrfxLfSdGxOFgmqOjlyO1izb/P7O0vXEZ7Rw0s6y0kBmgKtZSR6nPtF6n2pR/FW+lfMADLU/xKstUs20hISEhISm5AA/iKLoCdQCRgiCoHVf7zuvYshWKvHRoMQD4FJUFN7Fi2OppjObJoiiiP+dcGqWdcHSVLd4h07ew8balHo+Bftjx8e/4ezZUFq3qYylGsGmVCpZt/gUxRytad9NswuM95zcFcjJP6/RfURTajapkO+Y3JxcFo7eypGNF+kyvCkj53TXWEi/e5vOgVX+DKo2kSldF/M09Dn9Jndk4725bLg3l9HL+tOoSy2NhTSAsZkRVRtX4Otfu7L03M/8GbmMcWu+wdzKlBXjttG7/FhW/7SDF4/jNY5Zt00Vftk0lJiIeMZ3XETii/zrcIvYWTDx937EP3/NwrE7tKo1LlnanpYdq3F49w1iol6pHd+thy/JSWmcPFGwY4eFuTGN65bD/2IYaem6Wbk1rerO0/gkIl6o35Mm1HUpyYOXL0l8p94lpJKNI8ZyA64n6HaKv4x5KSLfPSVHqdshzAIx+EtMZ+vncKY6JDEtISEhIaERoijGiqJ4+6//TgHCgIL7ZxfA9YRoZIJAdTv1meY3GRncT4injov2raHzIzQ6nhev3tKsqnY1ye9JeJnC1VtP8GtSEQMVHQn37L4O5HkPq+PM0SAeP4xjwMimWpVdRAQ/5/cp+6hSx4PeY1rmOyY7K4fZQ9dzZvc1+o5vw9eTO2iUvVQqlRxcfZq+XmNZNWEHVkUt+PGPIWy6P48e49pS3FU/B0EBzK1NadLNlyVnf2bxmUnUbOHNoTVnGVhtIotGbuDt61SN4lRrVJ4ZO0byMu4N4zosIjbqZb7jKlR3Y9DEdlz1D2b36rNa7bXP0IYoFAasX6a60yFA5coulC3nwO4/r6ns4NiueSXSM7I5fVE34deksjsyQcD/jmYNV9RR96/32pVn6gWyoVxOFdsSOovp0ualyRZziE57ptP8ApHZg8wWMUe3w51aL/dZVpGQkJCQ+J9CEARXoApw7aPnBwuCcFMQhJuJiYn5zr2Z+AxP62JYaFAvfT3mOUpRpI6e6qXPBz1GLhNoWEm3Eo+T50NQKkXaqTgwlp6exfFjQTRuXF5t2/DcnFy2rj6PR3lHGrTIP7OcH1mZ2cwbvRUrW3PGL+mNXJ7/x/mKn3Zx5ehdBv/amR5j/DQS0i9fJDGx/QJWjt9O2aqlWHJ2Mov8J9GwS00MFOqrQ7Ozckh49pKw6xHcuxjG8/BY0lML9oT+kHLVSzNh3WA2B8+j44hmnN4RwLDaU7h7QTOh6VWrDHN2jyItNYPJPZbzLiV/e7r2/etRv3VlNv12nIiQ5xrFBrApakHXfnW4fCaUiAexKscKgkC37rWIiUniWmDBjhcVyjpSyqUox87q5jltY2FK1TIlOHcvQqf5f9uPvT3WxsYEaOgZW9PehbDkeFKyM7Veq7S5GwCPUzXzttYUQRDystPZn8B6Lx8kMS0hISEhoRWCIJgDe4HRoij+V69lURTXiKJYXRTF6nb52NgpRZH7SXFUttXs8NetFy9QyGR4F9O+njQ/roQ+pZKbA1b5uD5owrkrD6lQ1gFHFWUN586GkZ6eRdv2VdTGu+gfSlxMEj0GqW/1/SE7lvnz7HECo+d0w9o2fwu9s3uuc2J7AN2+bU7HwY01ihtw5DbDak/hwa0njFk+gJn7v6dstVIFjk9LSefivuvMG7iKYbUm81XJEbQp8jV9yn3P6EbTGO83m4GVJ9Ch2GA6Fh/MwCoTmNzxNw6uPEV8dP6ZYwBbhyIMntmdJWcnY2JuzE/tf2P91N1kZ6kvB/CoXJIpGwYTF/2KpWO351vKIQgCI2d2wcrGjEXjdxVorZcfHXrWxNTMiN0bL6sdW6eOOzY2Zhw/dq/AMYIg0LS+JyEPY4lL0K29dt0KbkS8eEVCsmZZfFXIBIGqDo7cjn2h0fhKto6IQPBr1RcX+WFraIOZ3JToNM0vaDTGoDTkRiKK6rtXFhZJTEtISEhIaIwgCAryhPQ2URT3aTs/MuUVqdmZVLJR3wkQ4FbsC7zsi2FkUPjz8q9T0giLTqB2eVed5kfHvCY8MoFGdcqpHHfs2F1KlixK+fKqK2BEUeTPjZdxcbOjVgPNy05iIhPZs+YcTTpVp1r9/A99PQuPY9mEHXjVLE2f8W3UxszKzGbp6M1M67Wc4iWLsvziVFr0qZdvJvt1XDJH1p1lUoff+MplBDP7LOeGfxB2TjbUaV+dPpM7Mfr3r5m+9wfmHJnA+HVDGDi9G8371MfNy5nYp4msGLuVvp7fM9x3Mptn7OPxvfwPvJXxLsnyC1Np2bcefy4+zvfNZxEbmaD29XjVLEO/H9ty8dBtjm6+lO8YCytTRkzrzJPQGI5tD1Ab8z3mFia07lKdi/4hxD5/rXKsgYGcFi0rERgYwcuXKQWOa1Qn7+d4Xk2r9ILwLZ9XmhEQ+lSn+R9T1cGRJ0lJvE5X31q84l/v5SAdxLQgCLiYOuu/zAMQDEqBmA5K9c12Covk5iEhISEhoRFCnrL6AwgTRXGhLjGCXuV94FbUQExn5uRwPz6ePt6VdVnqbwSG5Qk2X0/d6q/PB+S1Pm7oW7DwjYxMJCz0BcOGN1FbUnHr6mOePIrj+186aJWVXjfrEAojAwb+2Dbff89Iy2LW4D8wNDZkwsqvkauo7Ya87PK0Xsu5eyGMrt/50XdyRxT5uE+8ik1m52+HOL7+PNlZOTiWLka7oc3wbVOV8jXLqF3nQ56Hx3L16B2uHr3NjrkH2Tb7AFUaVaDflM54fnSw09jMiO+W9qda04os/nYj3zefxcx931OqourSny7Dm3L/ajirp+ylXFU3ylT8e41+7RYVqVLHg62LT9KofVWV/twf0rFnLQ5sD2TvlgBG/qT6YqWlXyV2bL/KqZP36dkrf5s8J4cieJQqxtkrD+neQXv3CXfHohS1MuNqWBQdantpPf9jqjnmvT/vxMbSpFTBdyYAbIxMcTaz5r4OYhrAxcyZcwkXUYpKZIIec7zyv/adEwlyzW0QdUHKTEt8EbZtA1dXkMnyHrdt+9I7kpCQ0IA6QB+gsSAId//6Uu0R9hFBr2MxNVBQxrKo2rGhiQlk5eZS1UGzLLY6AsKisDY3wdO5mE7zz155SEXPEhSzK7gO+vixexgYyGjWXL2g2b3pMkXtLWncqqLGe7hz+RGBp0PoPqIpRezy989d9fNunj54wfjl/SjqoNpl482rFCa0nU/Q5YeMW/MNA6d1/ZuQTk54y+oft9Pf6weOrjtHs971WH1jFuvvzWPw7B5UrFNWKyEN4OTuQNfRrVjoP5kdT5YxeHYPntyPZnSjafzcaQHhd/5eQ1u3XTUW+U9ErpAzrvVcgtVkcWUyGWOX9sPK1pxZg9flWz8tCALfTG7Hu7fpbF18UuP929pb0qS1NycP3iH5tWrXCycnG7wru3D82D2V7iGN65YlLDyW2HjtSz0EQaC2Z0kCH0SRqyx8WUOlYsWRC4LGpR4VbRz+c6GsLS6mzmQps4jPUH/HQSsM8uqxydGtQ6M2SGJa4rOzbVteF8KoKBDFvMfBgyVBLSHxT0cUxcuiKAqiKFYSRbHyX1/HtIlx/3UsXkWKI9cgE3srNu/DuZpj4bNKoihyNSwK33Ilkcm09+KNjnnN46eJNK5TsJdubq6SM2dC8PV1xyqfxiEfEh76grvXI+nYqxYKDQ71QZ7DxpoZByjuYkuHAQ3yHXP+wE1Obg+g26gWVGuk2rUw+eVbxvnNISoshqnbR9Kkm+/f1ts5/zD9vH7gwO8nadC5Jn/cnct3ywbgWt5JL90oAaztLOk8yo9NIQv4+teuhF6PYGTdqczos5zkxP8qycfZw4GFJydSxN6KiR0XcEdNK24rW3N+XDmA+GevWT5hZ75j3Mo54tfTlyNbA3imhR1fl351yM7K5dCua2rHtmxZiRcvkgkJjilwzPtSj3N/3QHRFl9PV96mZRISpflrKAgThYLy9vbcelHwfj/E29aRmLQ3vMpQb6f3MS6meXcM9O/oYQeCOWLuJ/Cx/nipT76ChMRHTJr03+28Ie//J036MvuRkJD4PChFkYdvEvAsollmODg+nuLm5tib5X/AThuiEpJ4nZJGdQ8nneYH3sr7QK6rwls6LPQFyUlpNGiouqYa4PSRuxgaGeDXUX1Dl/dc9Q/m6cM4+o1thaHR3wV46ps0Vv+8h7JVStJnXGuVsTLSMvml21Liol4yc9/31Gz536U0WZnZzBmwkg2/7KZa04qsuTWHsWsG41CI1uPqMDE3ptvYtmwOXUjviR0IPHqHMU2m8+LJf4tDe2dbfjvxI46l7JneezlPglWLMK+aZej5vR/n99/k1vn8XUF6j26JwtCA3as0t8pzdi1KNd/SnD6iOuMMUKeuBwYGMgICwgsc41jMmjKudly7rZuzhU/ZPFF6J0IzAayOSsWKE5qYiFIDL25P67zfiwfJ2meXHU0cEBCISdcts10QgiCAvCTk6L8e+2MkMS3x2SnIbUdDFx6NkUpJJCT+Wbx494a0nGzcLTXzKH7wMhHPfBxBdCHoSd4HtXcp3bLcN+4+xaWEDcXtrQocExgYgUwmUMNHdY1pbq6Si/4h1KjjjpmFZq4ioiiye9VZirvYUs8vf1u+rfOP8vZ1KiPndFdZdpGbq2TeN2t5eCuS8WsHU/GjbHtq8jsmtZ/PhT3XGDSzOz9v+xZnD+1KbURRJCn+DfFRieTmaO6UAWBmaUKfSZ2Ye+xHUpJSGdN4Og9v/Xd20bqoJdN3j8HE3JgpXReTGKP6IGDXEc0oUcqelZP+zNcRxNrWnBZf+XDu4G0SY5M13mvDFhWJf5HMQxUZZwAzMyMqVnTmWqBq+7oalV0JCntOeob2DVxsLExxLmpFUKR+RKlnUTtSs7KIeftW7VgPq7z3afjbgh1aCsJQpsDW0Ia4jMJn1P+GvATk6ufiQhWSmJb47BRkF6snG1lAKiWRkPgn8v6D1t1Kfb10Zk4OT5KS8CyqHzF9/2kc5saGuBWz0XpudnYud0OeU91b9cHFa4GP8fJywtxctUC+fzuK1y9TadhS81rp4OtPeHg3ms6DGuYrlOOiX3J08yVa9qpDmUqq/5hun3eIgCO3GTK7O3Xb/XdmPOH5K75vOoPQwHAmrB/YlYjIAAAgAElEQVRK19Gt1JZzpKdmcGrzRZaN2sDk9vMYVGkc7awH0M15GH3cv6O1RT/6uI9iXLMZ/DZoFX/+dpj4qPw9yD+kQi13Fp2ZgpGpIeNazuL6if+2l7NzsmH67tGkpaQzvffv5GQXbJtnaKRg8LTOxDxJ4OSO/J07Og1qiFIpcmD9RbV7e0/tRuVQKOScP6neI9qnVmmePn1JfFzBNdE1KruSk6PkbrBuVnEV3Ry4/1RPYtou730aVoBf/IcUNTbD2tCE8Dfqx+ZHceNin1RMa9PpUhckMf0P5H89ozpzJph+VE5oapr3vL6QSkkkJP55hL/RXExHvH5NjlKpt8x0cFQcFVyL61QvHfoolozMbKqpEKmJiW958iSBmrUKLgN5z4WT9zE2McSnrrvGe9i9+ixWtuY065q/08PW344hk8vo+b2fyjjBVx+xY95hmnT3pcOwZv/1b5HBzxjTaBqJMa+ZeWAcjbvl7zwBeZnn4CsPWPDNaro5D+O3Qas4t/MKr2OTcS7rSJshTRm+qB9jVn1D9/HtqFC7LDnZOdw+E8y6iTvo6zGaCX6zOLvjCpkq2mg7eziw+OwUnN0dmPrVIk5uuvBf/16qogvf//41j25HsmXWQZWvvUbjCnjVLM2ORSfISPv7msWcbKjfpjLHd1wl5U1aPhH+jpmFMTXqunPhZIjKLocAtf763bh2reADcRU9S2BgION2sG63ar1ci5P45h3xSQXb8GlK2aJ2CEDYS/UCWRAE3K2K/uc9ri3FTfLEtL5FryB3AjJBqZ9W6wXxRa3xBEFYD7T5f+ydd3gU5fqG79mWTe+9B0Ig9I50UClWLNiwHdux67GXox49dkUsHCtWFBW70kREmnQIEAgQSkJ678lmk92d3x+7G5Ls7uwk2Yg/nfu6uAI738x+u9lhn3nm/Z4XKBNFsedZLn8B7I6qXQjaHVWAefNO3bw8if11PPqotbQjIcEqpD35+v6oUhIFBQX5HK2rIFzvS6DO2+1YuxvmCWfa0NLKkcJy/jGj65FjALv35yEIMGyg6/bn9g5348ZJd1Y0tZrZuCaL06amoffWyXr+nENF7PjtIFffMxsvveM+Jw4Xsfbr7Vx0y+mESjSTaahp4sUb3yMyIYzbXr6yw7YjGbk8ePbz6H28mL/6UZexc2aTme//9zPL3llD4dESvP30TL3kNGZeM4X00/rJWpRYklvOL4s3sHrxBp6/5n/4Bvow/fIJzHvkAkKczD8kKoiXfn6Ep+e9wSu3vk9jnYEL7zjZPn3i+aOYedUkli5YwYhp6QydPMDp8wqCwDUPncf9Fyzgpw/XM/e2Mx3GzL1pGut+2M3yTzdz2W1nuH0tAFNmDmbzb4c4kJHHkFFJLsfFx4cQHRPE1q1HOe/8EU7H6L20DOwXQ0Zm976sBidZy3Eyc0uIDHae9iIXH62WxKAgWc40QJ+AUFblH0YUxS4vTo3WR2EwG6gz1RGodV1K1WXUtjUS5kJQu7+I7y6n2pn+CJjlbtDfib+LozpvHuTmgsVi/enpC4U/opREQUGhaxyvqyQlIFTW2MOVFXip1SQGSUe7yeFIYQVmi0h6Qve6KO7LKqBPYjgB/q4vAnbuzCEiMoDEJOkv7MzdJ6ivNTD5TPn+0U+fbMLLW8c5Vzl3ipe+sRq9r5dTcdiej5/+loqiah58/yZ82r2WxjoD/73idfwCfXj1t8dcCun66gYeOfcF3rn/U4IjA7lv0c18kfcm97xzEwPHp8kWUFFJ4Vz12EV8fGgBL/z8KOPOHsGqD37j1rGPcHinc9fWx9+bp765h0kXjObdhz8n8/eOiRe3vHAFMX0iePWOjyS7JA4a15dR09P5+s01tBhbHbanpMcycnIayxZvcus02xk3uR9eei2/r5VOFhEEgbFj+7An4wQmiTry4YPjOZJTRpOEY++KfrFhaNQqsvI8UzLRPyycw5Xy3OY+AWHUtBiobnHf6KUzkXrrAkaPx+PZ86V7uW76lIppURQ3ANKrBv5mKI6qZ/gjSkkUFBS6RmFjLfG+8sTxiZoaEoOCZEXoueNYsfUWb2qsPCHfHlEUyT5eyoBU1wvwRFEk60Ahgwe5j4vL2HYMtUbF8LHJsp6/tcXExhV7GT9jkNOGInVVDWxclsEZc8cQEOI69aTgaAkrPlzPWddOof+oju75oke/oCy/kgc/vIWIeOcXA/mHi7hzwuNkbjjIve/dxCu/PcGMqyfj7aY+XAqVSsXwaQN58KNbWbjlabReWu6d/hTrlm5xOl6j1XDv2zcSmRjG/Jvfo7nR2LZN7+vFLS9cQXFuOcs/+E3yeefcOJ26qgY2r3Te4nvG3LFUltaRKVGO0R69t45BwxPYvc19BNugQXEYjSaOHXMtGvv3jcZiETma23VhqdNqSAgP4nixZ8oakoODKayrwyQjuzrO1+ooFzR2PSc7VGddy1DVUt3lfSVR29JnLN0rP5HLqXam3SIIwk2CIOwUBGFnucxbDf+fURxVzzBvHrz7LiQmgiBYf7777l+nVEZB4f8bRrOJsuaGti9cd5yoqSHJA640QE5JFV5aNdEhrputuKK0vI66+mb69XEd51dWWkdlZQPpA93H7mVsz6H/oDi8fbxkPf++rcdoqDUw+WznXSB//Wo7rUYTs6+aKHmcj578Bq2XhnkPndfh8V2/7mfFB79x4R2zGDjOeQ33rjWZ3DXpcRpqGnlx9aPMvGaqrLl3heTBCbzx+3/pNzKFZ698g4//8xUWJwLO20/PPW/dQPHxMj584qsO20aePohhUwaw5IWfaJSoeR4+OY2ohFBWLt7kdPuY09Px0mvZvGqf7PkPG5NC3vFyKsula5UHDrJ+RrIOuHZK+6VYBWC2hOCWIiU6lJxSz/iUiUFBmCwWiurdJ3rE2S6Ui7ohpkN0wUAviGkhENAiWjzseHfiTy+mRVF8VxTFUaIojgr30EKUPzOKo+o5eruUREFBQT5FTdYv4xgZYtoiipyorSEh0DNi+nhxJUmRId1yubNtGcepKa7zlQ9kWYVR+sBYyWM11Bs4erCIoaPludIAm1dnovfRMWKyY7MYURRZsXgT/UcmkzzA9XNnbTvKph93Mfeu2QS3i/ZrrDOw4NZFxPWL5prHL3K6749vrebRc18gLDaUNzY/zaAJ7jO0u0tQeADPr3qEmddM4bNnv+Ppy1+nucnoMG7o5AGcf8uZfP/WajI3HWp7XBAErn/qEuqqGvhygeteQiqVilnzJrBv8xEKnDRp0Xtb3+/Nv+x3KuidMcz2O927QzojOiIigLAwf0kxHRbiR3CgT9tnr6ukRIVQUF6LUSLdRC5JtnPwRI37uMBYX+vFanecaW+1N3qVl8fFtCCoQBWqONN/NxRHVUFB4a+I3a2S40yXNjTQYjZ7TkyXVJES1fUSD4Ds42WoVQJ9E12bOVkHCtHrtfTpI93QZN/OXCwWkeFucqjtWCwWtv6yn1FTBqDz0jps37/1KAXHSjnrqgkujyGKIoseW0pwZCAX3jajw7b3HvmcyqJq7nvnRrycLIZcvuhXFt71EWNmDePVDf8hKqn3DS2dl5Z73r2Jm16cx+/f7+C5Kxc6rV2+7slLiE6JYP7NizqUe6QOS2TaJeP4/q1fKC9w7c6eedk41BoVKz/93en28TMGU1lSy5F98hp+9OkfjZ+/nozt7ks90gfGkpXlWkwLgkBqSgRHuimmk6NCrRekZT0XpvY1C7kyxHSAVo+f1ovCbohpQRAI0QVTZfSwMw2gDgdz71Y2KGL6T4jiqCooKPzVsLtVsT7uxXR+rXVsYlDPV/UbjK0UV9WREt31fGmwOtOJcaF4ORGzdrKyCklLi0atlv5K3bM9By+9lv5D5HVhPLwnj6qyOsbPdL5YceWnv+Mb4M2kc113Udy6Yg9Z245y1cPnd6hv3rMui5UfruOiO2czwElXx30bDvLGHR8yauZQnvjqXx0WLPY2giBw8d1nc+uCq9mybBcfPfalwxi9rxf3vnUDxTllfPTk1x22XfPvCxEtIouf+97lc4REBHLarKGs+XKr04WIY09PR6VWsXm1+/xoALVaxdDRyeyRUTednh5LSUktlZUNLsf0S4kkJ78So8RiSlfYP+vHi3te6hHu64uXWt12TkohCAKxPgHdEtMAIboQKlt6YRmdKhz+ymUegiB8DmwB0gRBKBAE4fpTOR8FBQUFhd6hzGAVDhHe7uO6ihusdacx/l2vce5MUaXNEQ/rnsudV1BFUoLrhA6LRSQ3p5y+qe5bpGcfKCRtYCw6nbxU2n22bnmjpjiWVphNZrav2c/Ec4aj93EdsffzpxsJiw1m5lWTOjz+6XPfER4XwtWPXeiwT2uLiddue5+opHAe/ewOyW6Kvcn5t85k9nXT+OqVZRzf57gKf/DE/sy6ZgrLFq2lquSkcxqVGMaZ8yay/pvtTstE7Jx+8RjqqhvJ2uEogP2DfOk/PJF9W+UtQgQYOCyBspJaaqpci2SAfv2sqTI5x10LvOSEMMxmC0Wl8rsx2okPt37WCyu7J2rboxIEov39KWmQfk12onwCKGuWN7YzgbpA6kw9z8d2QBUCll5wvNs/Ra8e3Q2iKF4uimK0KIpaURTjRFF8/1TOR+HPwV+9aY2CwqlAEISVp/L5K42NBGj16NTuhVmFLR80vPMCkm5QUm39Yo8Kdp104QqT2UJJeR2xEtnN5WV1GI0mEhKky0gsFgs5R8tI6ededNs5lHGCuJQIpykeh3bn0lhnYOQ055nKYO1MuOvX/Uw4d2QHQXxoxzEyNx3mwjtmoXOSW/39wlXkHy7ilvlX4xvQ9d9BVUk167/awv/u/IAXrnmDZe/8woms/G415Lj+2cvxC/Zl4d0fOd3/knvOxtRi4oe3f+nw+KQ5ozAaWtj1636Xxx46sR8arZpd6w463T5geCJHDxTQYpTnDqfYRPLxbOnyjPgEq3Ocn+/ahbV/5ookuiW6wlunJdBX75HGLWB1pyuaGmWNDfHyobJZ3tjO+Gl8aWjtnhCXRPAHsRdEejtOadMWBYXO/B2a1igo9BaCIDjvBAEC4DwO4g+isrmJUL08YVbe2IhOrcbfS17ihRSlNdYv0e40sCirqMNsthAT6brcJC/PGkHmTkwXF1TTbGghWYaDDdZa50MZJxg11blY3rXuICqVwLCJrhcE7lyzn1ajiQmdykCWv78Wbz89s6+d6rBPZVE1nz79LWPPGs7Ys4bLmitAxtpM1i7ZxP5NBynItraz1vt44e2vZ81ia3vuwDB/Bk3sz8gZwzjrhtNlOd4BIX5c9/RlvHrLIn77YjPTL+9YHx7bN4qxZw3j5483cNWjF6DRWmXN4An98A/2ZfOy3Q6v3463r5700SnsXpfF9f+e47C9//BEvnlvHcezCuk/XLqVPND2u805UsoIieY9wcG++Pp6tX12nBHTJqa77kwDRAT5eU5M+/hyuELeAr5QvQ+VxqZuNW7x0/jRbGnGZDGhUXlOngqqQETRgCi2IAjyGiV1FUVMK/ypkGpao4hpBQW37ADWYxXPnfHMar5uUmlsJNRLnpiuaGoizMeny1/GziiprkclCIQFOrq77rC7gjESznR+vlUQxcVLi+mcI1a3MrmfvMYxZYXV1FQ2kDbMeS7q7vUHSRuRhH+Q6/f09592ERjqx8DTTkbeGRqa2fDtdqZePM5pRvSiRz7H1GLilvlXy5pni7GVRQ9+ynevr8A/2JeBE/tz1g1nMHjyAPoOT0atUVN0rITMjYfI3JhF5oaD/P79Dn79bAMPf3oXkRILO+3MvHYqKxat5b2HlzDunBEO9dszr57M1uUZ7FyTybjZ1gsAjVbD2NnD2Lo8g9YWE1oXpTUjp6bz4bM/UFVWS0hEx4smu4A+vOeELDEdFOJLSJhf2+/aFYIgEB8fQoGEMx0U4I23XtutMg+AqGD/trsyPSXMx4dNMp3pUC9fjGYTjaYW/LRduxj201jP0QZTA0E6D/53JdjKxSx1vdYFsUtlHoIgdP1/IwWFLqA0rVFQ6BEHgX+Kojit8x+gd7Oh3FBlbCKkC850uI9nvm7KqhsIDfBBK6O8pDN2ISNV5pGfX4WvrxfBwdKvLedIKSqVQGKKvESMgxknAJyKuKaGZo7szWPYRMe4PDstxla2/7yXcWcN77AwctP3O2huNHLmlY651JmbDvHrkk3MvfccYiRyte3kHSrkjnEP893rK5hzx2y+KHyX//7wEHPvO4/+Y1LRaDXWRWl9o5n1j2nc/8FtfHJ0IQ9/eic5mXn8c9h9rPvSeZpGe9RqFbe9di2VRdUsedZxUeGYmUMJCg9g9eKNHR6fcO4IGmqb2LfxkMM+doZOsr6HmVuOOmwLiwoiLDqIQ3tOuJ2jneTUSLdiGiA+PrTtQswZgiAQExXUrTIPgMggf8pqPFfmUWc0YjS5L3cJsV0wVxld53y7wl9jvXtUb+pemYhLVLaLJLHnNeQun0LOIEEQxguCkIX1P2oEQRgqCMKbvTYrhb8tStMaBYUe8R9c/79+xx84DweqmpsI1skT05WGJkI9UC8NUFbbQERQ1+ulAUrK6lCrVYRJdBYsKqwmLj7ErYt+4lgZ0XEh6J1E0DnjyL58dF4aktMcOy9m7zmBxWxh4FjHFA47WVuP0lTfzPhzOpZqbPh2O9EpEQw8rZ/DPp889TVhcSFc9uD5budXll/BPZMfo7Kwiv/++BC3vXad0/prZ0y/YhJvZ7xEwoBYnrn8VdYv3ex2nwFj+jLzmil8+/oK6io7ikSNVsO0S05j24qMDgsOR0wbiJe3jh2/uG6+0ndQHF7eOg46WYQIkDY0niP7CmS9LoCkvpGcOF7uNp86Lj6E8vJ6jE6SROzERAZSXNZNMR3sR01js0eypsNs52KlwX2bcHspV3fEtN2ZbvS4mLY7071XNy3XmV4AzAQqAURR3AtM7q1JKfx9UZrWKCh0H1EUvxZF8bCLbW2WniAI1/xxs7LSaGrBXyfvtm9DSwv+Xp6pbaxraibQt3uxbjW1TQT66yUj76qqGgkNdS/WK8rqCY+Sn05SWlBJZFyI07riIlsKREKq65KR4/ut+chpI09mWouiyMEdRxk6aYCD+K8urSVzw0FmXTMVvZvujKZWE89ctoCW5lYWbPwv485xHc3niujkSOave5L00/rxyo1vU5Bd5Hafc285E1OrmS3LdjlsGzplAKZWM8f2nnSRvbx1JA+M4/h+12JYrVETmxJOUa7zHOLoxDBKi6pkN28JjwqgtcVEfa208AyxXaDVVLsWnUEBPtTWdV2UAgT4WEt46iXSTOTip7Oeiw0t7o/lp7GObWxt6fLzeKmtn7tmS3OX95VEsJ3/ovuLge4iu8xDFMXOyeVmD89FQUFpWqOg8Mdw1x/5ZGaLhWazCV+NPIHc2NKCr9YzYrreYMTfu3sLGevqDQS4yVeurW0iSKJu2U5VeT2h4fIXQZYVVhMRG+x0W3FeJRqdhpAo1wsjc7MKCAoPICj8pIAvyS2nvqqRfiMdOzDuWpOJxSJy2nnuhfH7Dy8ha0s29y66hfg06a6PUmh1Wh794l9odBqemjsfo0FarKUOTyYsNoSdPzs6zf1GWC8aDu/q6DAnpceRe6BAMkkkOjGckhPOq6AiY0MwtZipqZBXfxxqe7/dtRUPspUF1Ui0PQ8I8Ka2vrlbKSj+tguiejfvqRzs52Jji3uB7GMfa+qGmFZZ52w0d31fN0e2/hA9LNLbIVdM5wuCMB4QBUHQCYJwH7aSDwUFT6M0rVFQ6HV6vrKvCzSZrbeyfWSK6YaWljY3rKfUN3VfTNfWNxMoIaYtFpGamiaCnETXtUcURaoq6gkJky+mSyXEdMmJCqLiQyUd85wDBSSldxS6dqHZ3q22s3d9Fv7BvvQZKr3QrjinlG9fXcbZN57BlEvGu3sZbomID+OBj28nJzOPVR/8JjlWEASGTU1nz/osB4EZGh1EWGwI2bs6tvNOGhhLXVUDVaWuyyWiEkMpya906j7bfwelhfKaiYSEWR3nKjfiO9h2AVZT7bqkIdBfj9lsocnQdXFp/8x7REy3OdOuS1Ls2M/xJpP7sZ3xUln3NVp6PucOCPaFtqdeTN8M3AbEAgVYI5Zu661JKSgoKCj0Kl23unqAwWQX0667CNoxWSwYzea2L/CeIIoi9QYjfjLrlDtT12DA30nihZ2GhmbMZotbZ7q+1kBrq5kQmc50c5ORuqpGImKdd20szq0gKtF1eojZbCHvUBHJg+I7PH5453F0ei1JAx07MO5df4DBkwagUknLgm8XLEelVnHl4xfLeCXyGDN7OAPGpfLNgmWYzdI3vYdOTae2vI7cA46lG2kjkx2c6eR062t1Nt5OdGI4rUYTlU4W+9nFdFmhvKYf9rsP7pzpQNtnprpGwpn2s17I1dV3XQT62cW0B8s85JRu+LaJ6e6UeVj3bbF42JluK/M4xWJaFMUKURTniaIYKYpihCiKV4qi6HoZqoKCgoLCn5k/1Jm23/KVU+Zhv5XsiTIPY6sZk9nSdsu7q9S5caZrbELIXZJHVYVVWMl1psuKrCkizpxpURQpyasgOtF1xFdJbjlGQwvJnZzp7N059BmS0JbFbKf0RDklOeUMnZouOa/66gZWfbCWaVdMJCxWOgqwKwiCwNz7zqf4eCm/f7ddcuzQKQMBq5PemX4jUig6Vkp9O7fXfuGQm+VaTEfZ3sviXMdSj/AYm5gukCem7b9j++/cFcHB1rsZUjXTAf7WC7m6+q7X+tqd6YZmTzjT1otgWWUetgvm7jnT9jIPTzvT9jIPDx+3HXLTPD4WBCGo3b+DBUH4oNdmpaCgoKDQm7jPI/MgLWZrooCc7odG21gvD7SwbrbdltZr3TviTvdvbsXb2/W+Btvtd2+Jdt4AjQ3WL3H/ANcud3sabHW0gcGO5SNmk4XGOgNBYa4XM1YW28R4fEfBXV5QSUwfx0WL9gWNKYOlY5OO7ztBc5OR6ZdPkhzXHcafPwovbx1ZW7Ilx0UlheMT4E3xccf4udi+1ji/8oKTXl9gqD9ePjoqJZqfhERY38s6J23Aff31aHUa6mrkJUx46bVodRoaG6RdUL1eiyCAQaIMw8d2R6WpuevC1NuWq93c0vM0Dy+19VhGk/ulcjpbsxX7edwVNIJ131ax53PugGA7h0VP12KfRG7TliGiKLZ9EkVRrBYEQX5rJAUFBQWFPxRBEM4GBgJtCk4UxadsP2//I+disdW3qgT3/o19rNoDDVvajqXq3rHMFgtqibIHs9laY6txc5FgsokQOR3/AEyt1vEaneN4ky3qTOvl+uu7xSa+dJ0uBIxNLeh9HV36Zpvw8/aXFvu15XUAhEjkbncXtVpNUEQgtRV1bsd6++lpbnQUoV42N7alk/j00uscHmuPRmt9n+3vu7PtZpO8NA8AjUaFuVV6vCAIqNUqzGbXFVf2khu5SSLO9jV3Y1+HY9nORYuMhZDqLox1fB77uebpKrQutVTp1WdQCYLQdr9JEIQQlO6JCgoKCn9KBEF4G7gUa7a0AMwF3Ldw6yUsti9HlYzqEovte9QT3Q/tQkLVbTEtSi7ys4tpqTFAmxDTaOR95ba22MS01vFr1r7NVUc/gFZbdrHOq5OYNrTg5aR+3GAX077SYrqmzCp0gyLkR/x1haCIAGpk5Cp7++rb5tweL9sdguZOdcI6vVZaTNvey1YXmcwarRpTFxxetUbd9tmQHKdWSY5Tq23C1NIdYdr9fV0eS4bItY8VuyGIBbovxKWxn3c9v7BwhVxBPB/YLAjC17Z/zwWU5F8FBQWFPyfjRVEcIgjCPlEUnxQEYT7w7amazElnWoaYtgtgTzjTFrsz3T1nymy2SArxroppd+NOjreLaUdnutVWuuLMtbZjtJWf6PQnxbQoihibjM7FdKNNTEsstoSTznRAqPxUkq4QGB5AtUQ5hh29n5ekmDZ2Sr/QemlpNboWw/YLE1djNFp1290FOajVKlnj3Yppu7ssQ5g77us5YXpSmLufh9ADZ1rogRCXpvfFtNwFiJ8AFwGlQBlwoSiKi3ttVgoKCgoKPcG+YqlJEIQYoBVwDBf+gxC7Iqaxl3n0/Nas2SL/eZ3OxWKR5Uyr1NLHtwsrjcwyD7tD6kxMm2Q40y1OnGlTqxmLRWwTnO1pc6bdiOnqslr8Q/ycOuaeICgisM39lsJlmYetA2NLJzHt5e3GmW4r85AQ0y5KQJyO16hklYWo1Sosks60vVSjG860B8s8TpZuyHxuQei2iBcQupWrLY/eCzGSPCMEQQgQRbHOVtZRAixpty1EFEV5wYsKCgoKCn8ky2yLxl8CdmP9Fll0qiYj5/Zw21jbF6kHjGnsX57dKRkRRRFRlJ6HxSxPrNsdckFmuUnbcZ2MPyngJUR+q2ONtr30w5mgb7UJTbUT8d6epromp862p/Dy9qJRoomJHY1Og8FJXJx9/p0dZrVa7VIoW7fbhKeLOmeVSp44bhuvVskSsYIgSAploQuOsMMc7C6vJ4RpF8o8wFpX1pVzvjOed6bt51HviWl3l/528bwL2Nnuj/3fCgoKCgp/Pl4URbFGFMVvsNZK9weePlWT0apsIsciIw3AlhzQ4iZvWNbz2oRji6nr6QCCIKDTaTBKlAfobQv8mo3SaQv2tA9Dk7w0AR+bQ9zoRDD6BVpj+Bok8okDbfFs1e3qj7399Hj76akocox4i0iwpn6U5DhvqW2n36g+VBRWOU3S8ASZG7PoN7qP23ElOWVEJjhGA1baXltIdMcFktXltW3viTPqbQLez0XEYWO9Ad8A+S3pDY1GfGTEMRqNrej1rtNijLbPld6r62k0RtvFg5cH7iK02s5FOWk8FlHELIroVF1P47GIFkRE1ELPk3w6Yv+/pPeW+kmKaVEUzxGsl0ZTRFFMafcnWRRFxxZKCkm12BEAACAASURBVAoKCgp/BrbY/yKKolEUxdr2j/3ReNnislpkiGkv2xe20QNiWm/Lx+1uPJi3XotBojygLbqsUVok+/m7FsfOCAyxRuLVOemO5xfojVqjolYixzjc1uylvXAWBIGopHBKch0Fc8IAax513qFCyXmNOWsEANtXZrh5BV2nLK+c3P35jJk9QnKc0dBCSU458f1jHLYV51oj/qKTI9oeM7WaqC6tIzzOeQMcoO29DHJSC242mWmoNRDgJkvcjsViobHBiK+bZBSTyYzRaMJHIlbR/tnzlhDcrrB/5u3nQE+wn4teMsS0PQbTHqfXFUy2SDytysOiV7Sdw0LP3wtXuC1KE633CL7rtRkoKCgoKHgEQRCiBEEYCXgLgjBcEIQRtj9TAXlqoBfQtQlk96LWS2PPtO151qy+h1m7PnqdpOvsY4uZa3TTZc4urBpkiml/W3e8eidiWhAEAkP9qal03a46zNbspbxTC2xXYjquXzTgXkzHpUYT0zeK7St3S7+AbrB95R4AxpwlnbpbkF2MKIok9I912FaSW4FaoyasXefIyuIaRFEkLEZCTNvey8BQP4dt9bXW5QcBTjK/nWFobEEUxbYLKFc02e5SSDnYhmZbjrm+66U1za02V1uitl72sUz27Hf3xzJabJny3XCmW2372vOmPYf9HO49MS13xlsFQRgtiuKOXpuJgoKCgkJPmQlcC8QBr7R7vA545FRMCE66VHJKN9qcaQ+Iaa1ajUalamve0lW89dq2xizOsLuKTU4Ww7XnpDMtr5PdSWfaeSlHUJgfNRLOdGCYP1qdhopOLbCjk8LZvXY/oih2qCP39tUTmRhG/qEit3MbM3s4K95bg9FgbMt19gTbV+4mKincqUhuj13wOxfTZUQkhHZYNGp358OddJO0Y38vg5yUgtTbmrXIFdP2Cyb3Ytr6mfF1kvttp82Zlmgc5Io2Z7qbDYvaYz8X5TjTJ13s7jvTHhfTfwZn2sY0rIL6mCAI+wRByBQEYV+vzUpBQUFBocuIovixKIrTgGtFUZzW7s/5oiiesmg8u0tld62k0KhUqATBI2UeYHXmmiUWn0nh7a2V7D5ndxWlBDeAr1/XnGkvb521654TZxqsXf2kxLRKpSIsJtjRmU6OwNjU4jQxI75/rFtnGqylHi3Nrez6xXMSwNDYTMaaTEbPHuF2sWj+oSJUKoG4fo6dHItzyolOiujwmP2Cor1b3Rn7e+nMma6tsv4O/IO6JqbdlXnYnWmp7pk9cqZbPOdMt5VuyHCm27qddsOZNll6qczjD3Cm5Yrp2UAKMB04FzjH9lNBQUFB4c/H74IgvC8IwkoAQRDSBUG4/lRNxkdjc3Bb3S/AEwQBH62WxhbPtP711euod1OG4Qp/Xz21da7dZK1WjY+Pjuoq6VbTGq0a/0BvKspcC+D2CIJAWHQgpQXOA7Mi4kIozimXTGqIS43i+P78Do8lD4wH4OD2ow7j+4/uQ05mHhWF0iFdQ6ekEx4Xyvzr3+LgtiPuXopb6qsbeGT2MxgNLUy/fILb8TtW7yVlSCK6TgKzudFIzv58ktI7OtbHMvNQa9REJTouWLRTfKKCwFA/p0klZbbfQVh0oJyXQ1W59Xcc7ESYt6fGdtchQGJhY63d5ZaxmLEz9bY25X7dEOKdabAJc18ZLnejyXYBoOm6cG2x2PLRVZ674wGAaLuIFTx83HZIimlBEPSCINwN3A/MAgpFUTxh/9Nrs1JQUFBQ6AkfAj8D9lVa2cDdp2oyXmoNerWG2lZ5zmyw3pvqZnklEe4ICfChst593JozwkL9qaiSFsCRkYGUlrrPRo5NCKUoX36abFK/aHKzS5xu6zs4nrrqRiqKXDc4GTKxP3mHijokeqSP64tvoA9bljvWPJ8xbxIAP7y5WnJeOr2Ol9Y+gW+gDw+c/iQ7VnV/MWJFYSX3TH6cw9uP8ujndzNo4gDJ8Qe2ZHN4xzFmXDPFYdvutftpNbYydnbHmut9Gw+RNioZvYQgPZqZT9/B8U635WaXoNGpiU0Kl/GKoDCvEoDYBNdOOEBJifX3EhXlWqSXV9QTHOiD1k1koTMq66yf+ZCAni+VqLGdi0F694kmtS3WczxIJz/9xE6j2TpnH3XX95VEtF3sCvLuLnQHd870x8AoIBOrOz2/12aioKCgoOApwkRRXIqt5ZcoiiZO5kOdEgJ1eupa5InpIG89Nc3yxroj1N+Xqm6K6fAQP6prmyS72UVEBlBa6r4Fdkx8CEU2oSWHxH5RFOaU0+Ikmq/PoDgAjmbmudx/yKT+AOzdcKjtMY1Ww+gzh7B91V6H7OKYPpFMunAsP739Cw010k57bN9oXt30X2L7RfPYeS/wy+L1sl+XnbxDhdw14d+U5VXwzIpHmHLJeLf7fPHCDwSE+jHzWkcxvXVFBr6BPgya0K/tscY6A9kZuQy1vRfOaGlu5cShItdi+nAx8SkRThvoOKMwrxIfXy8C3dRYl5bWolIJhIe7juwrr2ogzI3D7Qr7Zz7Yr+diutpgPReDvaVLV+CkmA7QuR/bGYPJKtp9NB4W0xbbYl1V995LObgT0+miKF4piuI7wMXApF6biYKCgoKCp2gUBCEUW5cCQRDGAe4VXy8SoNNT0yLPbQ7y0lNj8JSY9qGyTlocuiI8zB9RhEoXtctgdRblOdMhlJfWtXUndEdS/2gsZgv5xxwznZPTY1GpBI5m5jvZ00rfoQn4BHizd+OhDo+PPWsYNeV1HN553GGfS+8/j6Z6Az+9/Yvb+YVEBTN/3ZMMnjyAF69ZyFcv/yjjVVnJ2prN3RP/TauxlfnrnmT49MFu98nJzGPbigzm3D4Lb9+OQs1isbB91V5GnzmkQ3fG/VuysZgtDJ3k2vHOPVSE2WShzxDXYjopLVrmK4PC/CpiEkLc1n6XltYSFuYv2RWzorKeiG62bq+sayLIV49GZgt7KezOdKCXfDEd2A0x3WRzpr3VHg4e+hM4021nvc3ZUFBQUFD483MP8COQIgjC78AnwB2nckKBWn3bF607rM6058o8qusNbV0Iu0J4iNXJKqt0XeoRGRlIQ0MzDQ3Sry0mIRRRFCkqcGya4gy7gDtxuNhhm97Hi/jUKLL3uK62VGvUDB7fj32dxPToGUNRqVVsXeFYntF3eBKjZw3l2zdW0Syjztw3wIdnlj/C5Lmn8e4Di3nqkvls/HYbjXWOdwJMrSYObD7Mooc+5YHTn8Q/2JdXNz1N3+Hyutx/8dKPePvpOf/WGQ7bsnflUF1Wy9izhnV4fN+GQ2h1GgaMcd0I5sheq7ufOiTBYVtjnYHyopouiemivEpi40PdjistqSUiMkByTHllz5zpEH/PiNLq5mZ8tFpZCxDrWnsipq3nvK/HyzxsznQviml378xQQRDsl9wC1uzSOtvfRVEUpT8JCgoKCgqngiys/QGagHrge6x106eMIC9v8htc1/i2J8TbmyoPOdNhAT6YLBZqGg1dFhfhNlewTCI5w17zWlxcQ2qqY8KEnbgEq8DKz6kgqU+Ey3F2YpPC0ejUHDtQyPQLRjlsHzAymY3LMjC1ml2WIAydPIBtq/ZSdLyMmBTrc/oH+zJoQhobv9vO1Y9d1CFGDuCyB87n3ulPsfTln7j68YvdzlPnpeWRJXcRkxLJsnd+YePXW63zT42m7/AkmhuNVJfWkpOZR6uxFbVGzaiZQ7l30S0ERwa5ObqVQzuOsn7pFi68+yz8gx3F5fpvtqFSqxh15pC2x0RRZOev++k/po9kC/T9244SFOZPpBMBfPygNSpQrpg2NrdSWlTDtFnunfaSklqGDHXuhoO1q2ZtvaHbznRFXSOhAZ4Rj9UGA0F6eeK42tiEShDw13ZdTDearA6yt8bTzrRdTJ+iMg9RFNWiKAbY/viLoqhp93dFSCtI8tlnkJQEKpX152efneoZKSj8bfgEawvxZ4E3gFRg8amcULjej/Jm141G2hPp60d9i9EjiR7RIVaxW1TpvhSjM3HRQahUAnkFrmudk5KtC9OOHyuTPFZS3wg0GjWH9xfIem6NVk36iCT2bHFM3gAYO2MwjXUGMjYccrodYNKcUahUAqs/3djh8XNumE7h0VLWdHocYPDE/ky/fAJLnv2OfRsOypqrWq3m+ufm8VXpIl5e+x+ufeoykgcncHjHMUpzy/EN9GHO7bP495f38HXZ+zz908OyhXRDTSPPXvkGYXGhXP7g+Q7by/IrWPber0y9eBwBISfF0uFdxzlxsJBpF491eWyjoYVtqzMZO2Ow07KMPZuPoFIJDBiZJGuuRw4WYbGIpKZLZ2XX1RkoK6tr++w4I8+WqhIvEeknRWFFLbFh8hJI3FHa0EC0nzxRX25oJMzLF5WbMhdn1LXWo1Pp0Hs4zUO0VAPaU+pMKyh0i88+g5tugibb3b4TJ6z/Bpg379TNS0Hhb0KaKIpD2/37N0EQ9p6y2QCR3v5UGQ0YzSa3DR2i/a1f3MUN9fQNcX/LXIqEcKugyK+oYVCSa+fYGV5eWmIiA8mRWDgYFxeCXq/l6NFSZkocS+elJXVANFn7XNc5d2bEpDQ+emkF1eX1BHdaqDZi6gD8Ar1Z/8MuRp8+0On+4bEhjJ4xhJ8Xb+SKB85DZ2tLPfnCMXz3v5/58D9fM/GCMfh2ime7c+F1HN5xjOeuXsjbO58jMEyed6bRahg6dSBDpzqfT1cRRZEFN79HWV4lr/z2uFNX+v1/LwXgH092dNGXvb8Ova8XUyXE9PZf92NoNDJlzkin23dvPEy/oQn4B8pzSg/afrfpQ+Mkxx07aq2DT+0b6XJMTl4FAMnxriP9XNHY3EJlfRNxHhLTRfV1DI6Ud+6UGOqJ9Omem17bWkugNsBtvXmXsVSBKtjzx21HzyvTFRSc8OijJ4W0naYm6+MKCgq9ToZt0SEAgiCMBX4/hfMhyvYFW2Zw707H+FvFW3G9vFxmKWLDrA5ofrm8EpPOJMWHkZNf4XK7Wq0iOSWco0ccFwp2ZsDQeLIPFNEqs4nMiElpAGT87liho/PSMv6sYWxeuRejRNOY828+g+qyOn79YnPbY4IgcMtLV1JdVssXLzkuHPTx9+bfS+6krqKel294RzLPujdZ/t6vbPx2O/946hLSx/Vz2H5gSzbrvt7KxXefRUQ70VleUMVvS7cy48qJ+Pi7rr9d//0ugsP9GTLe8dj1tU1k781r+x3IIWtvPjHxIQSFSJcTHLGJ6b4SZUE5+ZVoNCriouU5+O0pqLCuNU4I7/q+nRFFkeKGBmL85QnkUkM9kd7dFdN1BGo9cwHQAUsVqLrn8MtFEdMKvUKei8QmV48rKCj0nHbdaccCmwVByBUEIQfYAkw+lXOzf8GWGtwLZPsXd2Fd10szOqPXaYgM8uuBmA4lv6ia1lbX8XipfSM5dqzM7SLH9CHxtLaYOHbYeX50Z/oMjCUg2JfdGw873T51zigMDc3sXJvl8hjDp6aTOiyRr15bidl8Mg4vbWQKZ1wxge8W/kzRcccLgT7DkrjpxXlsW5HBooc//8MF9a41mbx172JGzRjC3HvPcdhusVh46/5PCYsJ5tJ7Om7/ZuEqAC663fW9gsZ6A9vX7GfSeSMc6sYB9m4+gsUiyhbToihycF8BA1ykgrTn6JFSwsL8CQpy7Xjn5lUQHxMimfbhCvtnPd4DYrrSYKDFbG67W+SOUkM9Ud7dq02uaa0lSNsLFcSKmFb4/0qC48JoyccVFBQ8gr077SwgGZgCTLX9/exTNy2ItH3BlsgQ05F+fqgEgSIPONMAceFBFJR3LxkwOSEMs9lCQbHrFI6+qVE0NhoplhgDkG5bcJa1V16ph0qlYtiEVDI2ZTsVs0PGpxIU5s+673e6PIYgCFx6z9kUHS9j43c7Omy77qlLUGvVvOtCLJ93ywzO+ecZfPXKMhbe9RGtLX9MqNeGb7bx+JyXiO8XzYMf3YpK5ShVflm8kSMZuVz/30vR+56ssa2trGflxxuYNncskQmuSyS2rNxHq9HElPMdF3cCZGzKxtvPi7Sh8r60Sgqrqa5saPsdS3HkSAl9U12XeIC1zCNZRiqIM+xi2hNlHvYLWjnOtMHUSm1L85/Ume5ZuZg7FDGt0Cs88wz4dLro9vGxPq6goNA7tO9Q6+zPqZxbjI/VcSpsdC9qNSoVMf7+5NZ0z03uTFJEMMdLKrvlrvZJtC4SO3zMtZuc1t+a9nBgf6HksUIjAoiODyFjm2PGsytGTx1AVVkdWbtyHbapNWqmXjCKrT/vo7zQtZAff+4IktJjef+Jr2iqPxk5GBodzJUPz2HLst0se+9Xh/0EQeCO1//Bxf86m5/e/oW7Jz9B/uEi2XPvKoaGZhbc/B5PX/4afYYl8eIv/3Zar513qJC3HviMQRPSmHbpaR22LXpsKaYWM5f86yyXzyOKIss+Wk90UhgDRjlG85lazWz+OZMRE9NkN2ux/04HjUiUHFdXZyA/r5I0iYSQ+oZmikpr6SOz62JnjpdUEhboi593zxfy5dXaXO5A9yLXfm5H+3TdXW6xtNBgaiBI13M3vT2iKIKlQhHTCv8/mTcP3n0XEhNBEKw/331XWXyooPB3xV+nJ8TLh9x6eTnLfUNCOVYlv/22FP3iwqlrMlLspjW4M5LiQ/H10bFfQkSmpEQQEOBNRob765Vxk/uRse04Bhk5zgATZg1B76Nj1RdbnW6fc+M0LBaRb99xFMN2VCoVd752DRWF1Xz89Hcdtl1012zGzBzK2w98Rta2Iw77CoLATS/M4/Gl/6Ikt5xbxz7K8kW/erzs4/DOY9w65hFWfbiOS+8/j/lrH++QzmGnsc7Ak5e/jpe3joc/uqXDorLdvx3gl89+Z+5ds0hIi3H5XPs2H+FwxgkuvuUMp4vStq09QE1lA2dc6Ny1dsaWdYeIjgsmMUVaAO/dk4cownAJ0X3A9lkb1F86FcQVh/PLSYvtnhDvzNGqSlSCQHJQsNuxJxqs53aSf9dLKsqN1nUJ4V5dX3ApiVgHogFB3bXFx11FEdMKvca8eZCbCxaL9acipBUU/t4k+YdwokGeQE4NDeFYdRXmTm2vu0P/eGvG8uEC6fg6Z6jVKtL7xbD/kGsxrVIJDBueSMbuXLcic9yU/rS2mNi9VZ477e3rxfQLRrFhWQZ1TjoxRsaHMu2CUaz89Hfqqlwv7kwf05dzb5zOj+/8ysEdx9rNXcUD799MeFwoT89bSEWR89/PxDmjeXf3C6Sflsprt77P3ZOf4LcvNveo9EMURQ5uO8JzVy/k7sn/wdjcwourH+X6Zy5Dq3NMfDGbLcz/57sUHSvlkU9uIyzmpGhrbjLy+l0fE9s3kiseOE/yeZe+sZrgcH/OuGSc0+3LF28mPCaI0dNcd05sj6HJSMb2HMZNSXObGLF7dy56vZb+/V2L/f2HilCrBPr37boANLaayCmpavvM95QjlVUkBgXJatiSW2/97CT5uxfenTkppj3sIJttTY/U8hvvdAdFTCsoKCgoyEYQhFmCIBwWBOGoIAgPdWXfRL9gTnTBmW4xm8mv63kX9NTYMFSCwKH8rotpgMH9Yzh+opyGRtdu8ogRiZSX11NQIH2xMGhYAn4B3mxZ7zofujPnXDmeFqOJX77e4XT73NvPxGho4acP10se59rHLyQ0JohX7+hY/+wf7MtjS+7E0GDg/lnPUV7o/DWExgTz3PKHuHPhddRW1vPc1Qu5qu+dLP7vN1SVyC/JaTG28sviDdwx/jHumvQE25ZncN4tZ/LOrucZOiXd6T5mk5mXbniH33/cxU3PXc7QyR2F7uJnf6DkRAV3vXZtWwSgM47szWP3+oPMuWm603EFx8rI+D2b2Zefhlrm4r9dW47R2mLitKn93Y7N2J3LkKHxaCXKRzIPFdInKQIfiWYzrjhWXInJYiEtznPOdGqIPKf5REM1gTo9wV5db7pS3mwV0xFenpl3G3YxrVLEtIKCgoLCnwBBENTA/4DZQDpwuSAIztWPE5L8gykx1NNkct+MJdWWL320suelHt46LYmRwRwqKO/W/oP6xyKKkJXt2p0ePiIJgIzd0qUeGq2aMRNT2b4hu0O6hhTJ/WMYNDqF5Z/9jsWJU5+YFsO4mYP54f31GBpdd4708ffmjleu4sTBQpa+uqLDtj5DEnjmh/upKavl/lnPuhTUKpWKc246gw/2z+fpHx+gz7BEFv/3G67scwcPznqG1+/4gK9fXc6Wn3ZxIquA4uOlZKzdz7L3fuXdhz7jiYvmMy/lDl66/m0MDc3c/vo/WJK7kFvmX+00RxqsQvrFG97ht6Vb+Md/5nLBbR1TOo5k5PLd/35m9rVTGDJROn3jq/+txsdfz9lXT3K6ffmSzWi0amZd6ty1dsaWdYfwC/BmoJvFiuXl9eTnV7V9VpxhMls4eKSYwQO6V+Jhv2BMi+u5M91iNpNbUyM76z23vookv+6lZpQby9EKWs8vQLTYkmp6ucxDadqioKCgoCCXMcBRURSPAwiC8AVwPtb25W5JttVS5tRXMTBY+sutj80NO1xZwRl9+nR/xjb6x0Ww66i87oOdGZgWg0olsC+rkDHDHResAcTGBhMREcCuXTmcd/4IyeONm5LG2hX72J9xgqFOFsA54+yrJvDCnYvZvvYg485wbIpyye0zuOfc+fz0wQYuuWOGy+OMnTWMKReN4YuXljFgdB9GTDt5rPSxqTz74wM8cv5L3HP6f3n887tIHZ7k9DgqlYoxs4YxZtYwCo8U89O7aziwOZv1X22l3kW5iU6vJTolkmFT05n1j2mMOH2Q27KI+upGXrzhbbav2st1T13CpZ1i8qrLann+hncIigjg+ifnSh4r91ARm5btYe5tZzo0qgFoqDPwy1fbmTBriEOTHFe0tprYtjGbMRNT3S5W3LUrB4AREmL6aE4ZhuZWBknUfEtxKL8MX73OI0keuTXVmCwW+sp0po/XVzEm3H2aiTNKjeVE6MM93lhFNBcDalB52PHuhCKmFRQUFBTkEgu0z3UrwJpp3YYgCDcBNwEkdMrCTAu0umWHasrciml/Ly8SAwPZX+a+GYochqZEs3LnoW61Wfbx1jEwLYbNu45xw7yJTscIgsBp4/uyauU+DIYWvCVu0Y+d1A8fXy9W/5AhW0xPnDWETxJDWfzKSsZMH+AQFzdgVAqnzRzCkldWMP6socT1cR29dtvLV3LiUBFPXv4GT355F8OmnCyZGDCmLy+ueIgnL3uNf53+X+549RpmXi0dUR6bGs3NL13V9u+6qgaKjpVQdLQUQSUQHBlITJ8owmKDncbcueJ4Zh5PXf465QWV3P7qNZx74+kdtteU1/HQeS9TUVTNM9/eg59EbrPZZGbBvz7FP8iHC28+3emYb99bR2N9Mxf/c5rsOW5dd5j6WgPTZg12O3bTxsNERATQp49r13jzjmOoVAIjh0qngrhi99FCBidFoVL1XJRmllrPvYER0jF+ADVGA8VNdaQFdc8RLzIUE+fdPTdeEnMeqGOw3lTrPZQyDwUFBQUFuTj7hu6w4k4UxXdFURwliuKo8PCOblCyfwjeai1Z1fIE8uDIKPaVekZMj+pndcx2ZMtv592eiWP6cuR4GaXlrhvJTJ7SH6PRxPZtx1yOAdB765g6azAb12TRWO+6LKM9Gq2aK++exfGDRWxc4bwz/O0vXIbOW8fLd36C2eS6yUxAiB/P/3gf0cnhPHHJq+x9aD48+CAsWgT19aQOT2bhpqcYeFoqr9yyiCcve43Co/IazdiP3390X6ZfPoFpl45n2NSBRMSHyhbSTfUGPnrya+6a+iQtza289PMjjkK6oo6HznuJktxynlp6N4NOc+xi2J4vX19N9p4T3P7iZQSGOpaT1FTU890H65l8zjD6DpRuB96eVd/vJiwigJHj+0qOa2hoZueOHCZNll6kuGn7UQalxRAss4V5e6rqmzhWXMnotO65w53ZV1qCr1ZLSrD7BYVZNdbzND3IvfDuTIulldLmMuJ8uufGS2I6AeruXZh0BUVMKygoKCjIpQBo/00dB8gOHlarVPQPiuCgTDE9JCqK4vp6yhsdUyy6SkpUCKH+PuzsgZgGq9hxxeDB8QQF+7B+nfvFhWddNBJjcyurf8qQPYep5w0nKS2axfNXORXLIZGB3PbcpRzencvShb9IHisoLIDnH51MpKGCx9/cS+b8j+HuuyE2FjZtIig8gGd/uJ/rnpxLxm8HuHHkw7x1/6eSiSE9xWy2sOLD37huyAN8/uKPjD9nJAs3PUn62NQO42or63n4/JcpOl7Gk1/exdBJ0gv/juzNY8mCFUy9YBSTznFegvPlW7/SYjRx1b9myZ5vYV4lu7YcY+ac4U67KLZn65ajtLaamSKxSLGkrJYjOWVMHCstzF1h/2yPTvWQmC4pZVBEJGoZF0H2c3pAcNfFdLGhGBGRWG/PimlRFMF8AjSKmFZQUFBQ+POwA0gVBCFZEAQdcBnwY1cOMCA4gqyaUlk5xUMirV/M+0rlu6KuEASBkf3i2J6d362M5ITYEBJiQyTFtFqtYvKkNLZuPUqTmxzp1AExDBgSz49fbHe6qNAZKpWKa+6dTWFuOWu+cZ7sMeX8kUyZM5LP5i/naKbEhUN9PUFXXMQLDSuIsDTwmM+Z7G/2g/p6OOssaGhArVFz6X3n8sHeF5l59WR+fPsX/jHkfr59YxUtxlZZc5aDKIrsWpPJbac9xmu3f0h0SgSvrXuChz++lZCojk086qoaePj8lyk8Wsp/vrizQ4mKM1qaW3n5zo8JCvPn1mcvdTqmrLCaZZ/+zpkXjSYuRX6ZwrKlO1CpBc6+yH0e9dq1WYSH+zNAYmHhpu3WOxoTRndPTO/ILsBXr2NAQtcFbWdazGYOVpQzJEresbKqS4n09iNM79vl5yowWK/HYz1d5iFWg1iPoDjTCgoKCgp/FkRRNAG3Az8D9BSZcwAAIABJREFUB4Gloige6Mox0oOiqG81UiCjE+KgiEhUguARMQ1Wx668tpG8su51Vpw4pi8Z+/Opb3BdmnH6GYMwGk1s2pjt9njnXzaWovwqdm2WLgtpz9gzBpI2LIFPX1vtUtDe9uylBIb68/IdH2M0uEhO+fJLsFgIFpt5vmkVYZZGHvM5k82aBGtzgC+/bBsaEhXEXW/8gze3PE2/4cm889ASLku+g5dvepeN3++goabrdw5MrSYyNx3ig8eXct1Q66JHQ2Mzjy6+nVfW/Jv+ox0XnRYdK+Wh814iP7uYJ5bc0WHxpCs+fv5H8rJL+NcrV+LvoqZ6yeurAbjiTtcLNzvTbGjh5x8ymHR6OqER0h3/amub2Lkjh+mnD5SsZf59x1ES46wXbd1h55F8hveJRePGJZfDoYpyWsxmhkTKS8HIqikl3c06CFcUGopQC2qi9T2/COiAyZas8weIaWUBooKCgoKCbERRXAGscDvQBQNDrF+4e6uKiPeTbh3so9XSLzSUXUXF3X26Doztb10Q+XtWLomRXW8sMXlcKku+286GbUc4+3TnC84GDoolJiaI5cv3MGOm9KK0iWcMIHSBP59/sIFRE/rKSjIQBIHrHjyHBy9/kyWvr+ba+892GOMf7Mu/XpnHY/Pe5LX7lnD/wmscj33kCNjKZ0JFAy80reIJnzN4yud0prQe55bMbDr/dpIHxfPsj/ezd/1B1izZxOZlu/nls02o1CoGjOlDfFoMsX2iCI4IIDgykODIILQ6NdVldVSX1lJVWkNNWR352cXsWXeApvpmVGoVQycP4NJ7z2H6ZePReTlmP5tNZr7932oWP/s9Wi8N//n8TkaePsjte7Xu+518+85azrl2MiOnOU9wzNqVw+qvtjPnuslExMr/TPy0dDuNDc2cf7n7CL1VK/dhNls4c4brOVfVNJKRmcdlF4yRPYf2FFXWkltazYUT3C+ElENGsfWcGxblPp+5sbWFY3UVzIiTrlt3RX5TAVH6SDQqD0tSkzU95Y8o81DE9F+Qzz6DRx+FvDxISIBnnvlrdR/8q78+BYW/MgOCIvBWa9ldUcA5Ce4jqsfFxfPF/kxazGZ06p6tyI8PDyI5KoT1+45xxbThXd4/vV80cTHBrFp7wKWYFgSB8+eM5K03fyU7u4R+/Vy7dVqthstvmMzC55aze+sxRp4m7/b+kHF9OXPuGJa+tZYRk9IYMs5xv1HTB3LNQ+fy8fM/kZgWzaV3dsxmJjUVfH07COoFjcv5SjeYz72GkrG0mpvHb2Ha3HEdhLggCAybms6wqemYWk0c3H6MXWsyydp6hHVfbaVZorENWBdShseHMuXicYw8YzDDp6bjF+S6NOD4/nwW3P4hRzJyOe3s4dw+/0pCo92L3sN7TrDgX58yaGwfbnryIqdjGusMvHj3Z0TEBjPvrplOxzijqdHI0o9+Z+T4vqQPla5PNpstfP/9LoYOSyA52XU82+r1WZgtIrOmunfbnbFun/XuxpTBPY+RBNhakE9cQACxAdKuO8CeykLMosiIMPkLN+2IosjxhhyGBHnmIqDDsU3ZgE5xphW6zmefwU03QVOT9d8nTlj/DX8NwflXf30KCn91tCo1Q0Kj2VUuL/N5bFwcH+3JYF9pCaNiel5TOXVIHz5Zs5O6pmYCfPRd2lcQBGZNHciiJZsoKq0hJtK5sz5r9hA+/GAD33+3kwcePMfpGDsz54xg6Yeb+PjNtYwY10d2zu7Nj8/hwI7jvHTPEt5ceR/+TtIfLr1zJicOF/PRcz/iH+TDWe0blVx6KdxzT4fxWixc0bKXCV7lLEj7Jy/e+B6/fbWNm569lPhUR4dSo9UweEIagyecbJRiaGimuqzW5kTX0mpsJSQyyOZUB+If7CvrNTbWNvHV66v46tWV+Af78shHtzBpzihZ++YcLOTxK/5HcHgAjy660WlrcoCFj39DeXENLy+9HV9/+Z+F7z/fSl1NE1ff4j5Cb8vmI5SV1nHbbWdIjlv12wEGpEaRFN+9dtrr9h0nJSqEhAjpuz1ysIgi2wsKmJ4iT5jvrihAAEaEdv38rGqpps5UT4pvUpf3dYvpCGj69HosHig10385Hn30pNC009RkffyvwF/99Sko/B0YGRbHwZpSGlvdd0IcExuHAGwr6F7Dlc5MHdIHs0Vk0/6cbu0/c6rVTf95nes+NX5+embMHMzaX7OorpauJ9bpNFxx4xQO7y9k2wb3ddZ2fPz0PPDqlVSX1/H6w185XVQpCAL3vHoVY84YxBsPfsHPSzaf3OjvDytWWH/62pxhX1/w9ydxxZfMX/Nvbn7+cvZtOsRNo//NU/MWcmin+9pubz89MSmRDDytH5PmjGb6peMZNjWdxAGxBIT4uRXDlcXVLHpsKVcNvI8vXl7GlIvG8O72p5l8wWhZQvrE4WIenvs6Wi8Nzy69g6Aw581X1n63k3U/7GbenTMYINFEpTMN9Qa++WQz4yan0X+Qeyf22292EhkZyGnjU12OOXK8lGO55cySUQPujNrGZnYfLWDqEM+40tmVFVQ3NzMuTp7TvKuigH6B4fjrunZxCpDTmAtAsl9Sl/d1i+kwaLpXetJVFDH9FyMvr2uP/3/jr/76FBT+DowIi8Msiuyrcp+qF+ztTf+wcLYWdC/SrjODEqMI9fdhfebxbu0fFRHIiMEJ/PzbAclUkAsuHEVrq5nly/a4PeaZ5w4jOi6YT95aKzvZAyBtaAJX3zubTSv3svqr7U7HaHUaHn3vBkZOHcBr9y3hl6VbT26cOBGKiuC11+Chh6w/i4pg4kTUahVzbjmTj/e9yGX3nc2+TYe5+/RnuP/sF9i+el+3ElGkyD9SzILbP+TaIQ/y7cKfGT1jCG+sf4IH3r2RgBDnbcYdj1HCw3NfQ6VW8fzXdxGT5LysouhEBQsf/4ZBo1O41I1j3JlvFm+hob6Zq29170ofO1rK3r15zLlgpGR03srfDqDVqDl9onQyiSt+P5CD2SJ6TEzbL1zHxbmP2DNbLGRUFnWrxAOsYlotqEnw6d7+rhAtNWApQ9BKt5f3FEqZx1+MhARr6YOzx/8K/NVfn4LC34ERYdbbwTvLCzgtMsnt+LFxcXyxPxOjyYSXpmdfWyqVwOTBKazenY2x1YSXtuvHmz19IM+8tpI9BwoYPsi54EhICGX06GR+/GE3l1w6Fp2LUgOwNWT55zReeuxbNqw+wFQZ3fTsXHzTNHZvzOat/3xH6qA4UtIdb7Xr9Foe++Amnrz2HRbc/SkthlbOvsZW8uHnB9df7/L4QeEBXPPvC5l712xWfbKRb//3M4/PfZWg8AAGjktl0PhUBp3Wj5TB8ag18m6ni6JIaV4l+7dkc2BzNvu3HCE/uxidXsvMqydx0e0ziU7uWie9o5n5PH7lm4givPD1XS47QDYbWnj+zsWoBIH7F8xzmw/dnqqKer5fspVJZ6TTJ839wryvv96BXq9l9llDXI5paTXxy4aDjB/dh0AnLc7lsHbvUcICfBiY2L00jc5syZdfL51dW05Dq5GR3RTTxxtyifWOQady3TG0W5hsd3n+IGdaEdN/MZ55pmNNMYCPj/XxvwJ/9denoPB3IFDnTXpQJFtKc7ljkPP23O2ZlJjIR3sy2F5YwKTEpB4//4wR/fhu8342ZB7nzBFd/7KdOj6NNz74jW+W73YppgEuvWwc9937OcuX7eGCC6WziKfNHsx3n23hvQWrGTu5H94+XrLmolKpeGDBPO48fwH/ufF9Xv3+bkLCHUWQl7eOxz/8J8/9830WPvQFxSfK+cejc2SLSR9/by68bQbn3jidTT/sZOeaTPZvPsLvP+0CwNvPi4j4MCITQgkI9Sfw/9g776iorq8NP0PvXUS6SFUsKFY0Kvbeu8bE3lKMLTHGkth7jC32hr333rsgKioqKIqi0nuHud8f/Ez8DMKdYVDU+6w1i4jn7HMukpn37rvPu80MMDI3QENLg6TYFJJik0mMTSExNpnIZzHEvIwHQN9Yl/I1XWjauy5Nevpgks/eC+PaiSBmDlmDoYk+f2wehv17Dn3K5XLmjdpCaNALJv79rULuHQCrFp4gKzOHb0bk3478bSIi4jh54i7tO1TD0PD9Ivn0xQckJKbRtun7BXdBJKdncuFuGB19KqqkhXhWbi6Xwp/R1l1clvxy5FMAaloqfsgvV8glNOUxPha1FZ5bKNlBeV81C3d9UQWSmP7MeHMI73N1u/jcr09C4kvBx6os6x5dJzU7C33NgrNSte3s0NHQ4HTYE5WI6epudlgY63PoerBSYlpHW5M2TSuzZc91XkUmUqa0cb7jqng5ULmyPZv9LtOyVWW087F9e4O6uhojfmnFyG9Ws2bRSYb//F/Lu/dhZmnE5FX9Gd1lMZP7r2LGpqHo55Pl1NHTYuLaQSybsINdy07xIOApYxb3pbQCh940tTRo2KUWDbvkWcJFR8Rx98ojHt8OJ+zeC+JeJ/L0fgSJsclkpuXVxKtrqGNkboCxuQHG5oZUrOuGR41yeNZ2xcHDRqHs8NtkZWSzdvo+9q48QzlPO6ZsHIK5Vf4H8ARBYPX0A1w8cpsB49tSq4liIivgSiinDt2me/962DpYFDp+44ZLaGqq06Pn+4WiIAhsPxCAo5051as4KrSfN5wMDCErJ5dWNZQrEXmX6xEvSM3OxrdsWVHjL0aG4WRohrW+4jdB4WnPyZBn4mb4/npyZRGy74CaDTI15Ty7FUUS058hvXp93uLyc78+CYkvgbpWZVn54CrXo8NpaF2wJZyOhia17ew4ExbGxPqCaMeL96GupkYLb3e2nAkkPiUdUwPFH693bOHF1j3X2XXoJiP65V8/K5PJ6PtNPX4a6cfBA4F06lywh7BHJTva9aj5v1KCClTydhS9H+cKtoxf/DV/DFnLb9+uZOr6QegZ/PdAmLqGOiNmdqd8dScW/7yNYY2m892sHjToUHgXv/woZWNGw861aNj5v37LGWmZ5GTlom+sW+R/s3d59vAls4atI+x+BG361af/hPZo6+Z/UyYIAuvnHmH36nO0+bouHQfUV2it1JQMFvy+H/uypeg1sPC5z5/HcurkPTp2qo5ZAfXet++/IORJFGOGNVX653P4ejAOlqZUcFBNw5MzYWFoq6tTx67w2snM3ByuR4XTxamyUms9TAoBKBYxTXYQaKrebu99SAcQJSQkJCQ+ON4WtmipqXPptThXjYaOToQnJvIkPl4l67eq4UGOXM6Jm+IdNN7G0sKQhj7u7D9+u8COiJWr2OPl5cDmzVdIf183wrf4dkQjytiZMX/KXjJEjH+bGr7l+fmvPjy8Hc6k/qvIKKCluW+nGiw99QuO7tbMGraWOSPWkZqUrtB6haGjp42BiZ5KhbQgCOxfc5bvm88mPiqJKZuGMmxa1/cKaYDNfx1n29KTNO9eiyGT2iu8n5ULjhMblcSoKe3zbSrzLhvWXURLS4Nu3Qtu6LJ5z3WMjXRpWr9wv/X8eBWXhH/IC1rWcFfZz/hM2BNq2dmhq1n4dd6MeUFGbg51rcRlsd/lYXIIFtoWmGmrNnssyOMg9wUySUxLSEhISHzO6GhoUr2UHRcjRYrp/z12Ph2mnAvHu7jaWOBsbc6h68FKx+jZoTrpGdnsORJY4Lh+A+qTEJ/G9m3XCo2po6vFqEntePUintV/nlB4Tz7NKjF2YS/u+4cxeeCaAgW5lb0Fs3f/SO8xrTi7N4DhjWdw6dAthRxFPiRhwRH81nMJy37dQaU6Liw9PZ4ahXRC3L7sFJsWHKNxp+p8N60zamqKyZ6Aq485sjuATl/Xwb1i4YfsHj18xenT9+nQ0Rszs/c3o3n8NJor/k/o3KoqOiIEen4c8X8AQMvq7krNf5cn8fE8TUigYVknUeMvvX6KukymVL20IAg8TA4ppqz03byvkpiW+ND4+YGjI6ip5X318/vYO5KQkPjcqVfGiZDEGF6mJhY61sbIiPKlSnE0JEQla8tkMlrXKM+dsFc8fhmjVAwXp9LUqlqWbfv9C8xOly9vQ/367mzdcpWXEYVn1itWc6R9z1rs33adK2cfKLyv+q29+GlOD4KuhvJLr2UkxqW8d6y6hjq9fmrJ3L0j0dLWYOqAlQzznc6Z3TfIzclVeO3i4OGtZ/z+zd8M853Off8whk7rwu+bhmFawGFFuVzOqun7WTv7EA3aevHjrG4KC+m4mGTmTNiNfdlSfD2kcCu83Fw5fy48homJHt0LaTO+ZusldHU06dhS8U6cAHK5wL4r9/AqZ42tRdEbtQAcCcl7StNIpJg+9+oxXuY2GGqKOyz7Ni/TX5Gck4x7cdRLZ90C1D7Y4UOQxLQE/3YVfPYMBOHfroKSoJaQkChOGlnnfZCejBAnkNu4uRP4+hXhiQkqWb9t7Qpoa6qz+WzBmeWCGNi7HknJGWzek7/P8xuGDm+MhoYaCxceFeXR3P/7xrh4WDN34h5evYhTeF+NOnozfklfHt+LYFSnvwh7ULCnt4e3E8vO/Mq4pd8CMHv4OgbW+51D6y+QqWC5iSoQBIE7lx/xa/fF/NhiNkHXQuk9uiXrr/9O234NCixrSE3OYMaIjexaeZY2fXwYPV8xCzyA3JxcZvyyk7TUTH6d3UVUeceB/Td58OAVw4Y3xiCfevU33H0QwfmrIfToUAOjApw+CuLivTDCoxLo8pVy9crvIggC+x88oJq1tShLvIjURO4nRNLIRjnruaDEewB4GivXqKZAsv1Bwx2Zmjh/clUgiWkJpbsKStlsCQmJouBkZI6ToRknI8TVLbd2zWvAsP+B4tna/DA10KVVDQ8OXQ8mPkW5emFXp9I0rufBjgMBxBSQAS5VypD+AxoQ4P+UkyfvFRpXS1uTCXO6AjB17HayMrMV3ptP80pM3zSEtNQMRnb4872NXd6grqFOgw7eLD09nt/WDMLASI/FP2+lR6VfmPvdeq6fuktOdvFlqwVB4PHd56yZtpdvakxkXKc/eXLvBf0mtGf9jT/oNaoVhqbvL50AeHI/gh/aLuDy8SD6/9KGoVM6KuUWsn7ZGe74P+W78a1xdC78cF90dBKrVp7D27ssvo3eXwMtCALLN57H1FiPbm2VO/QJ4Hf6JqVNDGjspZrM7oOYGELiYmnrJq5k5M3/s01slFs/KPEeZXSssNBWrn36+xCEbMi+DVrK/2yVQRLTEkp1FVQkmy2JbgkJiffR2MaVa1HhJGW9v0ziDTZGRtSwsWHfgwcq68DXs2FVMrNz2XXxjtIxBvaqS65czrptlwsc16atFx7lrVm25CSJiWkFjgWwsjFl9O8dCA1+xaJpB5W6Zs/qTiw5NBr3qo4sGLuV+WO2FHqwUU1NjTotKvPn0bHM2TuSr9pW5dqJICb1XkbPyr/w5+jNXDp0i+iI+CL/O6QmpXPr4kM2zT3E4K/+YESTmexadgp7Fyt++rMP667/TpfhTfJ1JnkbQRA4uvUqIzv+SUZ6JrM2D6PzoIZKHcy7cOIe29ZcoEXHajRpU0XUnL8WnUAul/PDyGYFrnk1IIzb917wbbc66BVwaLIgHkVEc/3Rc7rVr4KmurhGOYWx70EwGmpqtHIV1zHwZEQI5YzMKWukuBjOlmfzIPkRnsbKHbwsOPh9ENKRfWAxLVnjSSjVVbCgbPbbtnVvRPebsW9EN0j2dhISEtDE1pUVD65y9uVj2joW/si3rZsHE06f5F50FJ6WRbcDK1fGnDoeDmw7f5u+jb3RFNnF722srUxo16wKe48E0rWtN/Y2+bsTqKur8dOoFgwZtJa/l59m7LjWhcau3cCdPkMasHH5WaztzeiZj61bcjJs2wYhIeDiAt26gaHhv39vWsqQaRsGs3nRcbb8dYKQoOeMX9IXu/d0CXyDTCbDs6YznjWdGT6jGzfPBXNubwBn99zgqN+lf2K7VHHAtbI9NuVKY2iij6GJHgYmehia6KOhqUZKQjrJCakkJ6SRkpBG9Mt4Qm4/49GtcF48jvxnrYq1nWk/sCE+rbwwNhf/iD4jLZPFE3Zxao8/Xj6ujF3YCxMLw8In5sODoBfM/m035SvbMWxsC1FzLl54yKWLjxg4qCHW1u9vBJObK2f5xnPYljGhjZJNWgA2nw5ER0uDjj6qOWAnFwQOPHpIXXsHzHQLLztJzErnelQ4/d1rKrXeo+RQsuRZVCyuEg8AzWqqj10AkpiWUKqroNhstljRLSEh8WVSxdyG0roGHHkRLEpMt3BxYcrZ0+wNDlaJmIa87PSIpXs44v+AtrWU+4Dv26UWh08FsXzDOab/0uG945ycLOnarSZbNl/Bt1EFvL0LtxXrNagBL5/HsX7JaUpZGtOk7b/Z0osXoWVLkMshNRX09eGnn+DwYaj7VnNJdXU1+oxsTvlqZZk9chPftVlAlyEN6TSgAToiui1qamlQs0lFajapSFZmNmH3I3h069k/rxsn7ymUpTa3Msa1sgO+nWvgWsUBl0p2GBXgyZwfgiBw6egd1sw6yOvwOHqPbEb34U2UbgLz4lkMk37cjJmFIZPm9xBVJ52UlM6iRccpV86Szl2qFzj22Nn7PHkWw+TRbdBQ4qYNICYxlcP+D2hfuwLG+gVn68Vy/cULXiUnM9an8G6kACdehJAjyGluKy6L/S53Eu+iLlPH3Ui5+QUhZPmDugMy9VIqj10QH1VMy2Sy5sCfgDqwShCEmR9zP18qynQVFJvNVqaEREJC4stBTSajhZ0Hm0NvkpiVjrFWwZkxU11dGjmVY0/wfcb41EVbo+gfY3XKO+BiY8GaYzdoVcMDdQVdHwBMTfTp07kWKzZd4GrAE2pVe78jQp+vfbh06RGzZx5kxar+mJjoFRhbJpPx48R2xEanMH/KXrR0NKjf1JPk5DwhnZz879jU1LyvLVvCy5dg8I4+rfaVG0sOjWL5lL1sWnCMw35X6DOyOU06V0ddpMDT0tbEzcsRNy/Hf76XlpJBzMuE/5eBTk5IJSc7F8P/ZanzstV6mJQyxMwy/66RYrkfEMaq6QcIvvkUB1crZvgNoXJt5euHX0fEM27QegQB/virFyYF2Nq9QRAE5s89QkJ8GlOndi5QICenZLBswzkquJWhYR3lReS6k/7I5XJ6+6ou87rtXhAGWlo0KVdw86Q37Ht2F3t9EyqalVF4LUEQCIgLpLyRO7rqqrkZ+Dd2DmRdA52WKo0rho9WMy2TydSBJUALoDzQQyaTFUMBjYQYevWCp0/zshtPnxaeNZ42LS97/Tb5ZbPfVypSUAmJhITEl0V7R0+y5LkcChfn+dyjYkXiMzI4/jhUJevLZDIGtajFs6h4jvk/VDpOt3Z5JR7zV5wkPeP9dcna2ppMmNCO5OQMpv6+l9zcwn2dtbQ0mLKwBx6V7Zj16y4un3nAtm1579n5IZfnlX7kh4WVCROWfcP8Xd9jZWfGn79sZ3ireVw7pVh2+W30DHSwd7WiQo1y1GpakcZda9JhkC9dhjeheS8ffFpVobKPK04VbIskpF88iWLq0LWM6vwXkS/i+HFmV5YcGlUkIR31OpGxg9aRkZHNjGVfY19WXFZz544bXLjwkP4D6uPqVrCwXL7hHEnJ6Ywa3AQ1NeUarMQmpbLrwh1aVHfH3lI1dnjx6ekcCQmhg4cHeiIatbxKS+JK5FPaOXoqVY/+Ij2CyMwovM2qKrPdgskOAiEFmbaP6mMXwsc8gFgDCBUE4YkgCFnAVqDdR9yPhAL06gUrVoCDA8hkeV9XrPivCBcruiUkJL5cPE2tcDYyZ+/Tu6LG+9g7YGtkxNagIJXtwbeyM87W5qw8eo1cJZuWaGlqMGZYU15FJrLS72KBY8s5l+aHkc0IDHzG2jXnRcXX0dXij0W9KOdWhunjthNwOeSfTPS7pKZCaCH3Gh5VHZm74zsmLPuGnKxcJg9YzbAWczi8+YrC3ReLE0EQCLz4iMkDVjOo8SxuXnhEn5HNWX3mF5p1qyU6o54fsVFJ/Dx4PclJ6cxY2odyblai5t25Hc6Kv09Tt54rXbsVXDsccOcZ+4/foWubarg4KV+atP5kAFk5uQxorlytcn7sCQ4mKzeXHhXF1XDvf3YPAWjnqJyH8424m8iQUc1U3MFOhci6BMhAq2CP7+LgY4ppG+D5W39+8b/v/T9kMtkgmUzmL5PJ/KOjoz/Y5iQKR0w2W6zolpCQ+HKRyWS0d6xIQMwLwlMKb2qiJpPRzbMiV148V1l7cTW1vOz008h4jivZYhygSgU7OrSows6DAdx9EFHg2ObNK9G6jRdbNl/h4kVxa+ob6DBtSW8cylkSdnsLDqXzv6HQ1wdnEU/tZTIZPs0rsfz42H8am/z16w761J7Cqun7iVTC41pVpKdmcnDjJQY3nc34Pst5cOsZ3Yc3ZvWZ8fT8vqmoWu+CiHgWy8hvVxMXk8zUv3rjWuE/EiRfYmKS+X3KXqxtTBk7rnWBGdr0jCxmLTmGrbUpA3qKq0nOj7jkNHacv00LbzccLN9/yFERBEFg6907eFmVwd2i8Gy8IAjsfXoXL3Mbyhoq1wLcPy4AV0NnjDWLVuaTH0LmZdCogExNNT8fRfiYYjq/377/PF8SBGGFIAjegiB4lyr1YQvKJVSDoiUkEhISXx7tHCogA/aJzE53reCJhpoa2+4qb2n3Lo2quFCujDkrjyifnQYY8nV9LC2MmLn4KJlZOQWOHT6iMW7uZZg98yDPn8eKim9krMesFX1x97TF1WoXdhb/bVOuppbn6iEWDU11mnWtyeJDo5izbQRePq7sWXOefvWnMabbYnauOMPTh69UZkn4PuKikzi125/pw9fTq+ZklkzchY6uJqPm9mDDpd/4elQLTEsp59TxNiH3XzLy29VkpGUxe8U3VKgirvYwOzuXP6bsJT09iylTOqKvX7CgX7npIq8iExk3vBnaSrYNB9hw0j8vK91CdVnpGy8jCI2Lo3vgZZDMAAAgAElEQVRFca4gwQlRPEqMpoOSWenXGZE8T4/A21T1JR6CPAWyb4F2HZXHFsPHFNMvALu3/mwLFNyiSeI/FJeH84f0hpZ8qCUkJKz1jall6cCusCDkIgRbKX19GjuVY8e9e6RnK97QJD/ystM1CXsdx8Fr4uq380NPV4uxw5ry7EUcyzecK3CslpYGkyZ3QENDjV/H7xDlPw1gYKjLrL/74FbJDQ/bw1R0PISMXPT182zxDh/+7+FDMchkMjxrODF+SV/Wnf+VHt81IS05g9UzDjC0+Rz61J7CrB82csjvMnevPyEp/j11JiLIzMji8f0IzuwLYNX0/QxvOZdeNSYzd9Rm7t54Qv02Xszf9T1/7htJ407VRblriOH8iXuM6r8GbW0N5q3tLzojLQgCC+Yf4e7dF4wa0xLHQmqrrweGseNgAB1belGlgl2BYwvidXwy287dprm3G46llcsI58em27cx1NL+pxlSYex4chstNXVa2nsotd712DzbumKpl866BuQg0/rw9dLwcd08bgAuMpmsLBABdAd6fsT9fHIUl4fzh/SGlnyoJSQk3tDd2YsfLu/l4uswvirzfjeMN3zrVZWjoSHsDr5Pr0qqaavcpKorfmcCWbTvIr5VnDHUVa6UoIZXWbq0rsaOgwF4V3LAp8b7ay5Klzbm9z86M3rUZiZO2MWceT3Q0ir841lbR5OFa7uxfO4J9m+9jKtvNL4duvL1N3pKCel3KWVtSu8fm9P7x+ZEv4zn5oWH3LzwiDtXH3N2/78t2E3MDbB3KU0pa1P0DHTyXoba6BnooK6uRnpqJmkpGaSlZJKWnEF8TDLPH0fyOjzun0y3hqY6FbzL8u3YVlSt54ZTeWvUlHBVKQi5XM6mv8/it+IcHpXsmDS/O6YK+Flv3HCJY0eD+LpvXXx9C/ZLiEtIZdqfhylrb8HQvv/1BleEBbvzauqHt1GdUIxISuJIyCP6V62GroiDh2k5Wex5GkRzO3dMtQt2n8kPQRC4FHMVV0MXlXc9BBAyT4PMALQ+rL/0G2TF/cimwMVlspbAQvKs8dYIglDgsTRvb2/B39//g+ztU8DRMX97OgeHvHKKkhb3Y68lIfExkclkAYIgfNi2XB8ZRd+zs3Jz8dn/F9UsbFler3Oh4wVBoMPWzSRmZnLy62+UsrTLj/vhkfSevZleDasyqpPyQigrO4chY/2Iiklm7cK+lDIvuDzh3Nlgfp+yl5q1yjHl905oaoo/WHdi/y3+nLofU3MDxk3vhKeXg9L7Lgy5XE70ywTCQyMJD4kkPPQ14SGRxEUl/SOa5fk4lOga5AlsQ2M97MpZYu9SGntnKxxcrbB2tEBTxA2EssTFJDNv0l78L4fSpG0Vvv+1jagbljfs3RPAX4uO06SpJ+N+LrhOWi4XGPPHTm7de8HKOb1xclC+RNX/0XMG/rmTIa1qM7il6g7W/XHuLBtuBXL22/7YGBkVOn7Hk9v8fP0QW3x7U8NScTuup6nh/Hb3d7517INv6aLdXLyLIMgRouuBljdqJn+qNLbY9+2P6jMtCMJh4PDH3MOnTHF5OH9Ib2jJh1pCQuINWurqdHGqzKoHV3mVlkQZvYI/5GUyGYO8qzPi0EGOhYbS0tVVJfsob1+aDnU82Xr2Fh18PHGyUi6TpqWpweTRbRgwagO/zz/Egt+7olFAQ5H6DTz4MSmdhQuOMX3afib81k50A5Imbatg52TBzF92MmbAWrr3q0evQQ3QUECQi0VNTY3StmaUtjWjeoP/PvIXBIHMjGzSUjLIzc5Fz1AXXX0tlWeaxXL1/EPmT95Helomw39uSZuuNRSydTty+DZ/LTpOHR8XRo9pWejcrftucD3wKT8NblwkIZ2TK2f2jrNYmxvRt7Hq7sPj09PZGnSHtu7uooQ0wJbQQJyNzKleSrlylcsxV1CXqVPDvBgyx9l3QB6NTLuR6mOL5GPWTEsUkeLycP6Q3tCSD7WEhMTbdC9XhVxBYMeT26LGNyvnjKOJCcv9r6v0cNzwNj7oamsye8fZIsW1tzHjp8GNuXXvOX9vLNwCr03bqgwd1ojz5x4wZ9Yh5HLxa7t72rJkyxAatarM5lXnGdV/DS+ff3g3DplMho6uFmaljChlbYq+oc5HEdKZGdksnnmIST9sxszCgL/8BtO2W02FhPTpU/eYN/cw3t5l+W1i+0I7F94MCmfFxvPUr+1K++ZFs3/beeEOIS9jGNWxPjoqzNpvuH2L9JwcBlUruGPjG+7GveZ23Et6OFdVyltaLsi5EnudyiYVMdBQQf3ROwiZJwF10FZtxlsRJDH9CVNcHs4f0hta8qGWkJB4G3sDU+pZObHt8S2y5bmFjldXU2Owd3XuRkVxMTyfmjElMTPUY1jrOlx7EM7JwJAixWre0JMOLaqwde8Njp+7X+j4zl1q0K//V5w4cZd5cw+LauryBn0DHUb/3oFfZnbheVgMw7ov48D26+TmFP6z/Jy4G/iMET3/5sC263TsVZtFGwfiWM5SoRhnzwQzY/oBKlWyZ8ofnQotC3n5OoGJc/Zja23G+O9bKCU83xCblMrSg5ep5W5Pw8rllI7zLqlZWWy4FUgjJyfcLCxEzdkcehMddQ2lXTzuJQWTkJ1IHXPV+z8LggAZJ0GrOjI11dvtiUUS058wxeXh/CG9oSUfagkJiXfp41KN1+nJHHn+QNT49u4elDE0ZMGVyyrNTneuVwl3O0tm7zhDUlpGkWJ918+XKhXsmPnXUQLvPi90fK/ePnzdty5Hj9xh+rT9ZGcrJoYbNPNk+fahuHnasnjGIYZ0XcaVsw+K3druYxMeFs2Un7Ywqt8a0tOzmL60D4NHN1fYCeTI4dtMm7qPCp62TJ3eGR2dgucnJacz5o9dyOUC039pj56uVlEug1nbz5CRncPYLg2LJMrfZd2tQOIzMhhWvYao8bEZqex9dpe2Dp4Ya+kqtea5qIvoq+vhZSquMYxC5DyC3CfIdJqpPrYCSGL6E6e4PJw/pDe05EMtISHxNg2tnSlnZM6K4CuixJ+2hgbf1ajJrdevOR0WprJ9aKirMalXE+JT0pm/W1yXwvehqanO9F/aY21lwvgZe3gqwlO67zf1GDzEl7Nnghn/83aSk9MVWtOyjAkzl3/NpPndkcvlTB65hdH91/Ig6IWyl1FiiYtJZtG0AwzuspRb18PoO9yXVbtHUK22iM41byEIAhvWX2TunMNUq+bIzFnd0CukOUxmVg6/TN/Dq8hEZvzSAXubotnXnb4VyonAEAa3qEVZK9VZ4SVlZLAywB/fsk54lbEWNWdDiD9ZuTn0dxMnvt8lOTuZgPhAfCxqo6VWtBuM/BAyDgNqoC2JaQkJCQmJzxDhv324RKEmkzHIvRbBCVFceC1OHHcqXwF7Y2MWXLkkyqdaLO52lnzTpDr7rtzjanDRykgMDXSY81sntDTVGfP7TmLjUwqd07VbTcaOa8WdO+F8P2IjLyMU6/gok8mo09CDv3cM57vxrXnxLIYfvl7J1DHbCAn+9Fs7xMemsG7JKb5tu4ije2/SurM3a/d/T88B9dFRMDuclZXDjGn7Wb/uAk2bVeT3qYVnpOVygemLjnAnOIJff2xJ5Qq2RbkcktIymLHtFG62pfi6iWoP660ODCApM5ORtcU1NknNzmJjSACNbVxxNhZXEvIuF2OukCPk0MDyK6XmF0Reicdh0KqFTF25/akKSUx/QpTk5iYleW9Q8vcnIfE5Ep76nIh05QRbWwdPrHQNWR58WdR4TXV1fqhVm/vR0RwNLVqN87sMbFGTsqXNmLzpeJHLPcqUNmb2hE4kJKUzbupu0tKzCp3TrHkl5sztQXxCKsOHrefObcXtjjQ01WndpTpr9/9A78ENCLjymBE9/2bMwLVcPfcQeRE6Pn4Mnj6OYv7kvfRpMZ+tqy9Q3ceZlTtHMPznVpiYKX7ILT4+lVE/bebUqfsMGNiAseNaFVojLQgCyzac4/TFBwztW59Gdd2VvZx/mLHtNAkpGUzq1QRNddU5scSlp7Pm5k1auLhQwVJc7fi2J7dIzMpgsEdtpdYUBIHTUedwNiiHnZ64xjgKkXMfcp8h02mp+tgKIonpT4Q3zU2ePQNB+Le5SUkQhSV5b1Dy9ych8bkiIHA6suAOgO9DS12d/u41uRYVzq3YCFFz2rq5U87UjD+vXClSO/B30dbU4I++zYhNSmPmtjNFjufmbMWUMW0IDYti7NRdZGQW3sGxUmV7lizti7GxLmNGb+HYUeXaqOvpa9NnSEM2HfmJgSOb8upFPJN+3MzAjovZt+UqSQniOjB+DLKzc7h+4RHjh29kcOclnD12l2btq7JqzwgmzOmGjYNyFoZhYdGMGLaex6GRTJrcgR49a4uqU96w4ypb996gY0sverQX54xREEf9H3LU/yGDWtbCw750keO9zQr/G6RlZ/NjLXFZ6azcXFY/uEZNS3u8LJQTwveTHvA6I5JGlg2Uml8YeSUeGqDTtFjiK8JHbdqiKF9y05aS3NykJO8NSv7+JL4MvsSmLQ4VHYVma9vwZ5XZ6GoofngpNTuLegcWU9XCllVfdRU153DII0YcOsjMJk3pWkE594H3seLIVZYdvMKU3k1pW7tCkeOdvBDMHwsOUdHDhtkTOok6tJacnM7vU/ZyM+AprVpVYcgw30JregsiJzuXi6fus2vjZUKCXyFTk1GpmgM+vh7UaeiBhaU4H+LiIiM9C//LoVw6Hcy1849ITcnA1NyAtt1r0KqTN8am+krHFgSBw4dvs3TxSfT1tfljWmfc3MqImrd262XWbrtMswblGf99S9TUinZI8Hl0Aj1nbaaslRlrRhbsR64oL5OTabRuDS1cXJnfvIWoOVtCA5ngf4Q19btRv4xybiILHi3mUXIof3rNQUtNNa3g35DXqKUhaLqiZrpSpbHf5pNo2iIhnpLc3KQk7w1K/v4kJD5XjDQNSc9N52z0BVqUUTx7pK+pxQC3mswLOkdgTISoDFkLZxe8rMow79IlWji7YKitvNB8l/7NauD/6AUztp2mvENpnK2LVqfZuF5ew5OpCw7x48TtzJvUGUMDnQLnGBrqMnNWN9asPse2rVe5efMpY8e1olJl5cz5NTTVadC8IvWbefL44WsunLzHpdPBLJl5mCUzD+PmaYNXTSfKV7bHo5ItRsaKt5JWhKysHEKDX3H/djhBN58ReO0JmRnZGBrrUsfXnbq+5alau5xC3QvzIzY2hXlzD3Pt6mOqeDnwy/g2WFgU3KES8oT00vXn2Lr3Bi18PRk3vFmRhXRmdg7jVh9CBsz8toVKhTTArIvnEYBRdcS1I8/MzWHJ/UtUMbfmKysnpdZ8nRFJYPxt2lq3UrmQBiDrKshfIdMZq/rYSiBlpj8RSnJ2tSTvDUr+/iS+DL7EzLS3t7fQfkMXYjPjmFtlOuoyxWtAU7OzaHBwCeVNrFjfsIeoObdfv6bj1s30r1qN8V+ptpFDTGIqPWZuwkBXm01je6KvU3SHggvXQpg05wAOtmbMn9wFUxNx2dagoOfMmnmQ168S6NS5Bv0H1C+yyHxD+JNoLp0J5srZB4QEv/qnPbh92VKUr2yHo4slZWzNsLY1w8rGRGHrOblcTkxkEi9fxPPqeRzPn8YQfOc5Ifdf/mMDWMbWlOo+Lvg0Kk9FL3vUC2mYIpbTp++zaOExMjNzGDioAe07eIsSxHK5wIIVJ9l79BYdW3rxw4BGRRbSANO3nmLHhTssGNyWBpVU5ykNcO3Fc3rs3MF3NWuJPni44ZE/U24eZ32DHtS1KqvUuuvC/DgXfYEFVWZhoqV6/2d5whjIPI3M8jIymepumN9Fykx/Zkybllfnm/ZWOVtJaW5SkvcGJX9/EhKfM82tmvJnyBL8425S01zxulJ9TS0GedRm5q3T3IgKp7pl4RnYylZWdPX0ZN2tQLpU8MTFXLla2vywMNZnxrctGbxoF39sPsmMb4vWnAOgXk0XZv7agfEz9vLdhK3Mm9SF0qUKL6+oWNGOlav6s2L5aXbuuM71648Z93Nr3N3F2Z4VhL1TKeydStGj/1dkpGfx6N5L7t8O597t51w6E8zRvTf/GSuTyTC3NMS8lCE6ulro6mqho6eFrp4WamoyMtKzyUjPIj0t75WclE5kRPz/887W1FTH2cOadj1q/pMFNxORKVaExMQ0Fv15nLNngvHwsGbcz62xsxf3u5GTk8usJcc4euYePTvUYMjXX6nE//mI/wN2XLhD38bVVC6kc+RyJp05jY2hEUO8xf2/l5GTzbL7l6leyg6f0o5KrZucncKFmEvUsahZLEJakKdA5nHQaVesQloRJDH9ifDGe/nXX/PKE+zt88RgSfBkLsl7g5K/PwmJz5mqppUprW3J4VfHqWHmrZQA6e1cjdUPrrEg6Dx+vr1ExRhdpy5HQkKYcvYMGzt2UmnjC29XO4a1rsPiA5eo6mxD168qFzlmDa+yzJvUhXHTdjFknB+zJnTE1anwQ2i6ulr8MLI5PnVdmTP7MCOGradlqyr0H1AfYxWVZOjoalHJ25FK3o5AXqlDYnwqL5/H8+pFHK9exPHyeTyJ8amkp2cRHZlIRno26WlZ5ObK0f2fsNbR1UJHVxMLS0Nq13ejjK1pXnbbzoxSpY1Ulnl+l9xcOQf2B7Ju7XnS07Po178+3XvUQl1kOUVKaiYTZ+/jxu1n9O/hQ9+u4g4oFkbY6zimbj5JFSdrhrcVV4KhCJtu3+JRbCzLWrdBV1PckwO/0JtEZaSwsE47pa/xdNRZsuRZNLcqpoOBGcdASEem26F44iuBVOYhISEh8QH4Uss8/P39ORl5hvVP/ZjgMRY3I1elYq17dIM/bp5gQ4Me+Ih89Lzh9i0mnznNXy1b0crVTal134dcLvD9sr1cexDOsu874e1SNH/hNzx5Fs2YP3aRnJLBhB9b8VUtF9FzU1Iy2LD+Int2+6Ovr03vPj60bVdVZaUfnyL+N57w9/IzPHkShZeXAyO+a4Jj2VKi50e8imf8jL08i4hjzLCmtGpUUSX7SkzNoO/crSSlZbDl516UNlVtFj46NZVG69fiZVWGdR06ihLGeSVVS3E3sWRjw55KrZslz+anWz9jr2fLWPeRSsUoDHlsL5DHILM4qtKb5PwQ+74tWeOpCDE+xiXd61js/kr6dYjlc7kOCYmSTj2LOhhqGLL/5WGlY/Qo54WNnjHTb50iR6TtXa+KlfC0tGTK2TMkZCjWPbAw1NRkTP+2BXalTPjp7/08fhmjkrhODqX4e3ZvHO0s+HXmXlZtvohcLi7pZWCgw7DhjVm5qj+ublYsW3qKb/uu4MTxIHJzPy0f6aLyIPglY0ZvYdzYbaSnZzFxcgfmzOuhkJC+FhjGwDGbiI5LYe7EzioT0pnZOYz8ex8v45KYN7CNyoU0wOSzp8nKzWVSQ1/RgnPZ/cvEZaYxqpLy5wzOR18kMTuRVmWaKx2jIIScUMi+gUxX3A3CB0MQhE/mVa1aNaEksmmTIOjpCUKei3HeS08v7/uKjPmYiN1fSb8OsXwu1yHx6QD4CyXgffRDvt5+z94fcUjofbW/8Dj5idI/w0PP7gtOW6YJmx75i55zLzJScF44Xxhz7KjS6xZEREyC0Pjn5ULzX1cKkfHJKoubkZktzFh0RKjbbrYw+vedQlJyusIxblx/LAzsv0rwbTBd6Nl9ibBn9w0hLS1TZXssacjlcuHq1VBh5I+bBN8G04V2becLO3dcEzIzsxWKk5srFzbsuCLUaz9b6PvDWiHiVbzK9pibKxdGrdgvVBk2Xzge8FBlcd/mWEiIUHbBPGHxtaui5zxNihPct80Ufrq8T+l1s3OzhR9ujhEm350uyOVypeMURG7iZCH3VXlBnhtbLPHfRez7tlTmoQLEuEWUdEcJsfsr6dchls/lOiQ+Hb7kMg+A9Jx0Rt4ah5uRKyNdRygVTxAEep3242FiNKdaDcFEW5x39eyLF1juf4MNHTtR195BqbUL4sHzKPov2I6thQmrR3bBQFc1h6IEQWDfsdv8ueoU5qYGTPixJVUq2CkUQy4XuHI5hG1br3LvXgRGRrq071CN9h2qqaym+mOTk5PLmTPBbNt6lbAn0VhYGNKpS3VataqCvr5i/xYxcSlMX3SEG7ee0rieB2OHN0VXBY4tb5i78yx+ZwIZ1ak+vX2rqizuG5IyMmi6cT3munrs7dFTdBfFwRd2cjkyjJOthlBaV7lM+bmoi6wKW8dotx+obKKaLP7bCPIUhOh6oN0YNZM5Ko+fH2LftyUxrQLU1PJym+8ik8Gbp5FixnxMxO6vpF+HWD6X65D4dPjSxTTA7hf72ROxn6meE3HQV84X+UFCFG2Oraa3czUmVRN3wCkjJ5uWmzaSKxc43LsP+lqqE0dvuHz/KT8s20c1V1v+GtoeTRUepgsOecXv8w8S8TqB3p1q0a97HTSUiH836AXbtl7l8uUQNDXVqVvPlebNK+FV1VH0YbySxLNnMRw7GsTJE3eJjU3B0dGCbt1r0dC3PJqaiv98Ll4PZebio2RkZPNd/4a0bVpZpaUEm07fZN6uc/Rq6MXozg1UFvdtxp88wfZ7d9ndvQeVSluJmnPxdRh9z25hdKUGDC0vzj7vXXLkOYy78xv6GnpMqTChWEowhDQ/hKQpyMx2INMq+qFfMUg10x8Q+/d8Jrz9fTFjPiZi91fSr0Msn8t1SEh8SjSzaoyeui67I/YrHcPdxJIe5bzwCw3gYUKUqDk6GprMbNKUF0mJzLp4Qem1C6JOeUd+69mYaw/CmbLpuOg6ZzF4uJRh9fy+tPStyMadVxn2y2aev4xXOI5nRVv+mNaZNesG0rJVZfxvhDFu7DZ69VjK6lXnCA+PVdmei4ukpHQO7A9kxLD19PtmJTu2X8PNrQzTpndh1ZoBNG1WUWEhnZGZzdzlx/ll+h4szQ1ZNe9r2jWrolJBeNT/IfN2naOxlws/dVSt9/kbzj0NY+vdIAZUrSZaSGfLc5l68wT2+ib0c6uh9NqXYq4SlRlNB5u2xSOkBQEhzQ80PEGzksrjF5Uv94ivChHjY1zSvY7F7q+kX4dYPpfrkJD4lNDX0KNFmabserGP0OTHOBsq56s7suJXHAy/z+SAY2z27S3qw7uGjS39q1Zj1c0A6juWpZGTcp3dCqJt7QpEJaaw5MBl1NXVmNirCepqqslZ6elq8fN3zalZrSxzlh7n2x/X8W33OnRt462weHRwsOD7H5oxZGgjrlwO4ejRO2zdcoXNfpextjbB27ss1ao7UaWKPQaFdGQsbnJycnkQ/Ap//ycEBDzlQfBL5HIBR0cLBg/1pXFjT8zMlG8pfi0wjAV/nyTidQLd21dnYK+6aGmqVhod9X/IhPVH8Cpnw9S+zVXS6OVdolNTGXv8OK7m5qKbswCsf+RPSFIMy+t2RltduevOkmezJ2I/TvqOVDEpJqGbdQVyQpEZzShZBw//hySmVYAYH+OS7nUsdn8l/TrE8rlch4TEp0ZzqyacjDzD1uc7+dVjrFIfjKbaeoyt3JBfbxxhV9gdOjuJe+Q7qo4PV54/Z+zxoxzs1Ycyhqp3UejfrAa5uXKWH75Kdo6c379uptL20A3ruOHpZs38v0+yfMN5Dp4M4vt+vtT2VvzmQEtLg/oNPKjfwIOYmGQuXwrh2rXHHD9+l/37A1FTk+HhYU35Cja4uFjh7FIaW1uzYi0JiY1NIeTRa0JCXvPw4Svu3H5Oamomamoy3NzK0LNXHXx8XHBxtSqSqHrxKp7Fa85w6cZjbMuYsOiPbnhVVP2jyQPX7jN543GqlLNm0dD2aKtYqAPIBYExx4+SlJnB+o4d0dYQt8aLlAQWBp3H19qZxjbiLRjf5UTkaWKz4hjk1K/YhK6QugbULEC3dbHELypSzbTEB8PPTxKvEl8uUs30v5yOPMfapxsZ6TqCqqZVlIotFwR6nNpIaFIsx1sOwlxHXHbySXw8bTdvwtPSEr9OXVSWOX6X1ceus3j/JRp7uTD92xaiD4IpwtWAJyxafZrnL+OpVc2J7/o1xN7GrMhxs7NzCb4fgb9/GDdvPiU0JPKfboU6OpqUK2eJnZ05lqWNsLQ0+ueriYke2tqaaGqq5yuqcnJyyczMISUlg+ioZKKiEomMSiIqMolXrxIIDYkkPj4VyDu/YmNrRuXK9nh7l8WrqgOGhuIOnBZEWnoWG3ZeZfs+fzQ01OjbtTZd2lRTeTYaYPelIKZuOUl1FzsWDmmHroIt18Wywv8GMy9eYKpvY3pWEpcZFgSBAee3cz06nGMtBmGtr1ynwpScFEbdGo+LYTlGu/2gVIzCELJDEGJbITP4EZnBsGJZ431IBxAlRPGhBK6fX/5lFStWSIJa4svgUxfTMplsDtAGyAIeA98KgpBQ0Jz3vWfnCrn8cmcSMmB6pSmoy5QTmiGJ0bQ5tpqWdh7Mr91O9Lw9wfcZdewoP9SqzQ+1aiu1thg2ngpg/u7zOBo74ZzTCjdXDbp1A1UmxLOzc9l16CZrt10mKzuHji296N2xJqYmypc+vEtOTi7h4bH/yxhHEhoaycuX8cTFprz3ILe2tiY6OpqoqcnIzMwhIyP7vV7XhoY6lC5tjFM5S1xcSuPiakW5cpbo6amuVXROTi5Hztxj9eaLxMan0rxhBQb3+QoLMwOVrfE2287dYub2M9Qp78i8gW3QKabGObdevaLrjm00KVeOxS1bi84MHwq/z/eX9/KrV+Mi1Upvfrado69PMK3iZOz0bJSOUxDyxPGQfhCZ5TlkaqbFssb7kMS0RKF8SIErWdFJfOl8BmK6KXBaEIQcmUw2C0AQhHEFzSnoPTsgLpCFIUv4tmwffC2VP5C1MOg8f927yMp6XfBV4FH16GNH2fsgGL9Onalpq5jdnFguXoTuP96iVM0zJD1zJOpia2RyTQ4fhrp1VbtWbKv3/aYAACAASURBVHwKKzZd4OiZe2hpatCxpRc9OlTHxKj47O+ys3OJiUkmKjKJqKhEkpIyyMzMJjMzm4yMHDIz8wS0jo7mP+JaW1sTPT0tSlkaYmlpjKWloUpF87vk5Mo5dvYe67df4VVkIh4uZfhhgC8V3KyLbc0NJwNYsOc89Ss6Mbt/q2LJekOeDV7rzZsQBDjUqzdGOuLq22MzUml+ZCXWekbsavINGko+nYnOjGHs7QnUsajFQKdvlIpRGEJuNEJ0A9DriprRpGJZoyAkMS1RKB9S4EpWdBJfOp+6mH4bmUzWAegsCEKBt90FvWcLgsDU4NlEZkQyp/J0dNWVO+iWlZtL++NrSMhK53DzgaK9p1OzsmizeRPp2dkc6NUHCz3Vis7kZLCxyftq5hGEXf1TpEWXJuxwW3Q19Hn5EgyKISkaHhHHum2XOXkhGB1tTdo3r0LXtt7FloEtqWRl53Ds7H38dl0j4nUCbuVK06+HD7WrORVbXW+uXM6C3efxOxNIEy8Xpn3TQqUWiW8jCAIjDh/keGgo27p0o6q1+JuD7y/v4fiLh+xr2g83E0ul97A0dCUB8YHMqTQVM+2ilxflhzx5PqT+jcziODIN1XvEF4ZkjSdRKOHhin2/KEhWdBISnxX9gCP5/YVMJhskk8n8ZTKZf3R09HsDyGQyeth3ITE7if0RB5XeiJa6OrNrtiE2I43f/I8iNkGkr6XF4patSczMZOiB/WTm5Ci9h/zYtu3fREFccEWeHmuNjlkMrp23oGEczbZtKl3uH+xtzJj4U2s2LOqHT3Vntu33p8ugv5k4ez/+t5+q1LKvJBIeEceSdWfp2H85s5ccw0Bfm5njO7Bybh/qeJcrNiGdkp7Jj8v343cmkB4NqjCjX8tiE9IAf127ypGQEMb41FVISO97epdD4cF8V6FekYT0o+QQrsReo7lVk2IT0oI8GdL8QLvpRxHSiiCJ6S+YDylwp03LKyF5G8mKTkKiZCGTyU7KZLK7+bzavTXmVyAH8MsvhiAIKwRB8BYEwbtUqVIFruds4ERdizoceX2CV+mvld63p5kVP1b8isPPg9nz9K7oeeUtLZnbtDkBr14y/tQJ0UJcDCEhkJr6758Tw5wJ3dMVZAK2LbZxKThUZWvlh6OdOZNGtcZvSX86tvQiIOgZIyftoMfQlWzadY24hNTCg3wiZGXncPJCMN9P2Eqv4avZsd+fKuVtWTClCyvn9sGnhnOx2qm9iEngm3nbuBL8lPHdfRnbpWGxHWwFOPToIQuvXqGjR3kGVhP/sOtFSgITA45RzcKWIR7KnxWQC3I2PN2CqaYpbaxbKh2nUNLWg5CMzGBo8a2hIiRrvC+YD+m1LFnRSUiUfARBaFzQ38tksr5Aa6CRoCLl2c2uEwHxgax/6sc495+UFj2D3Gtx/tVjpgQco3opO+wMTETNa+nqysj4Oiy4chlnM3OGVlf+MNbbuLiAvv7/F9TpMaV5tLMHzq0OEKh5gDXHffi2SfViFXq2ZUz5rp8vg3p/xfkrj9h//A5/bzzPKr8LVPG0o0EdN76q5YKZCg8sfggyM7O5fuspZy8/4vKNx6SkZVKmtDGDetejha/nBytruRn6glErDyKXy1k6oiM13Ir3cevt168Zfewo1aytmdaosejfnVy5nNHXDoAgMK9W2yKJ/ZORZ3iWFs5w50HoqBdPvbsgT0JIXQvajZFpli+WNVSJVDP9hSPZ1UlIfBg+9ZppmUzWHJgP1BcE4f31G28h9j37xOvTbHi2meHOg6hlrryYjUhNpNXRVbgYl2KLb2/RB6sEQeCHI4c5+Oghy1q3oZmz8p67b3i7ZvpdjExyGDjrOKduP8S3ijOTezfFULf4DuG9S3hEHEfP3OPs5Yc8fxmPTAaVytvSoLYr3pUdcbA1K5GNMeITUrl59znnr4Zwxf8x6RnZGBroUK+mM43reVCtkkOxNETJD0EQ2HT6Jov2XsS2lDELh7TDwbJ4nSZepyTTfstmNNXU2dOjp0J1/kvvXWJe0Dnm1mxDh7IVld5DQlYCY+/8Rjn9sox1H1l8vtIpfyGk/IXMfO9HFdPSAUQJCQmJEsRnIKZDAW3gTc/pq4IgDClojtj3bLkgZ/K9acRnJTC70lR0NZT3E97/9B4jr+7jR8+v+M5TvGVGRk42PXbs4FFsDNu7dqeCpfL1pG+4eBFatsyrnU5NzctUq6nB4cPg4/OvGCtjZsjsAa1xtyv6moogCAJh4TGcvfyIc1ce8SQ8BgAzU328PO2o6mlPFU87bMuYfjCR+jbxCancCY7gZlA4gXefE/a//ZkY6/FVTWca1HHDy9MOjWKsTc6P5LQMJm06zpnbj/Gt7MzkPsV/M5SenU23HdsIi49nR7fuuFsUXEL1NnfiXtHlxHqa2bnxZ+32RRLAS0NX4B93k+mVpmClU1rpOAUhyJMQohuCVi3UTJcUyxpikcS0hISERAniUxfTyqDIe/aTlKdMvjcNX8v6fFO2d5HWHXllHwfD77O+QQ/qlHYUPS8qNYUOWzYjFwR2duuBjZFRkfYBkJKSdxgxNBScnaFbt//v4nHrcQTj1hwmPiWd79vVpWcDr48iXAEiXsVz8+5zAoPCuRkUTuz/Gqjo6mjiaGdOWXuLf17WpU2wMNNHV0erSGvm5OQSG59KZEwST5/HEhYe888rLiGvBlFHW5OKHjZUrWiPV0U73MtZFWsXxoIIDI3g1/VHiU5I4YcO9ejV0KvYs/g5cjnDDx7g5JPHrGjbnkZO4rtdxmem0eH4OrLluRxuMQBjLeVvVO8k3GXOw4V0sGlDR1vxvu6KIk9eCKlLP3pWGiQxncehQ5CTAzVqQJkyxbcxCQkJiUKQxHThbHq2lWOvTzLeYwweRm5Kr5uSnUnnE+uJzUxlb9N+2CjQ3S04OpruO7ZjrqfLti7dKKVf/LXEcclp/O53gnNBT6jt4cCkXk0obar6VueKIAgCz1/Gc/fBS0LCIv8jcN+gr6eFhZkB5qYGGBnqoK2lgZamBtraGmhraSCTycjKziEzK4fMzByysnNJS8skNj6VmLgUEpLS/p9t6v8T7nYWlHcrg4dzGTQ1P2z2+V2ysnNYefQaa47dwMbciGnftKBi2eLXFW9ahe8JDmZyg4Z8XcVL9NxcuZx+57ZxPTqcLY16U8Vc+aYq6Tnp/BI0CW01baZWnIimWvF0cxRyoxFiGoN2Q9RMFhbLGoogiWmAOnXgypW8/7a1hZo18141akC1asVj8ikhISGRD5KYLpyM3Ex+DZqMDBnTKk5CuwiHm8KS4+hwfC32BqZsb9QHHQ3xH/4BL1/Sd88u7IyN2dK5CyY6RW9jXRiCILDrYhDzdp1DXV2NH9rXpZNPpY+WpX4fCUlphIXHEhmdRGx8CjFx/3vFppCcmvmPcM7KzCErO4dcuZAnsLXyxLW2lgY62ppYmBlgYaafJ8TNDLA0N8Te1gyrUsYl7prvhL1iyqbjPHkdR9ta5RnbpSH6RczIi0EQBCaeOY3fndv8VLsOI2rWUmj+7Ntn+Dv4CjOqt6RruSpF2su6sE2cjjrHxPI/42xYrkixCkKeNBnStiOzOFIi7PAkMZ2VBUZGkJmZ/9+rqUGFCv+K65o1oXx50JAMTiQkJFSPJKbFcT/xATMezKWFVVN6OnQt0vqnI0IYeGEHHRwrMqem+FbLAJfCw+m/bw/uFhZs7NgZQ+0Pc0DweXQCUzef5Pqj51R1tmFizyY4lP6wLZQl8kjLyGLJwctsORuIpbEBv/ZoRD1P8SUWRUEQBGZdvMCKAH8GVfNmXN16Cv3+Hg4P5rvLe+hRzoup1VsUaS/BSQ+YHjyX5lZN6OXQrUixCkLIeYYQ0wJ0u6JmPLnY1lEEqWlLUND7hTTknQgJCoJVq/L84SpXBmNj+OorGDMGduzIs7j4hG42JCQkJD51yhu742tZn6OvTxCa8qRIsXxtXPjRsx57ngaxIUQxUe9jb8+SVq25Hx3NwP17Sc/OLtJexGJXyoTl33diYq8mPIqIoev0jaw5fp3s3NwPsr5EHleDn9Fl+kY2nwmkS73K7Jzw9QcT0gBLrl9jRYA/vStVVlhIP0yIYtz1g3iZ2/Bb1SZF2kdGbiarnqzHUrsUnW3bFylWYQgpC0GmicxgeLGuUxx8vmL62jXF56SlwYULMHcudO2a11e7TBlo1y7PM+7kSUhIUP1eJSQkJCT+obtdZ8y0TPn78WoycgtIiohgeIW6NLZxYXrgKa5FPVNobiOncsxr1pwbEREMPaj6LonvQyaT0aGOJ7t/+5q6nmX5a98luk/fxIW7YSptLCPxX55FxTNq5QGGLt6NloY6a0Z25Zduvhh8QOvC1TcDmH/lMh09yjO5oa9CQjohM52hF3ehr6HFkrod0VYv2tP2reE7icqMZoBT3yKVXRWGkH0XMg6B3jfI1MU7lZQUPl8xXbEiDBkCXl6gXoSDC5GRsH8/TJgATZqAqSl4eEDfvrB0Kfj755WU5IOfHzg65lWUODrm/flT53O8JgkJiZKFroYug5z6EZkRhd+zrUWKpSaTMbdmGxwMTRl2cTePk2IUmt/GzZ0ZTZpy/tkz+u3bQ8p73u+Lg1LGBswb2Ib5g9qSnSvn+2V7GfTnTu49U75bpET+xCalMmPbaTr/sYEr958xtHVttv7SGy9n5Q/tKYogCCy5fo1p58/RwsWFmU2aoqaAkM7MzWHYpV28SktisU9HSusW7RBrQFwgp6LO0NyqCR5G7kWKVRCCICAkTQU1M2T6A4ptneLk862Zfpu0NAgMhOvX8zLW169DWJjqNqatnSfa36q/9rvixKDBsv90F1yx4tNtiuLnl3/HxE/5miQkPhRSzbTibH++mwMvDzPCeTA1zasXaS/PkuPpcnI9Ouqa7Gjy9f+1d99hcZZZH8e/Nwxt6DUQWgqkx3SSGFuiibH3tazdXV97WfvqrutaV91d3eKqa83ajS1qLImmq6kQAyEhpEAgJBDa0IZp9/vHgEJMgWEKDOdzXVwQQp7nPAkZfnPPee7T7aDxUeFm7v76K0YnJfHq2ecSG+b5mxI7strtfLhyEy8s/IHaxhZmT8jmxjNmSD91DzW0tPLWtxuY9816Wq02zjvmKK49ZSrxUd6dCOnQmseWL+OV3A2cPWIkf5k9h6BuLAQ6tObW7z5m4e5C/jbtTM4aNKZH9dS01nB//kPEB8fz4Oj7PLZ7B4Bu+Rhdfzcq6jGU8XyPnccVcgPikVRWwtq1P4frNWugttY9xwZqAuL5wZHDGn5+qyaBzEzYtcttp/GqQYOg5CCvkvblaxLCWyRMd5/NYeORzU9SYa7gkbEPkhiS0KN6NtVUcMm3b5AZEcvbsy4lMji0W39+8fbt3LTwMzKio3n9nPNIifT+9nVNZgvzFq9j3jfrsVjtnDQxm6vn5DA8re+9NO5LtY0tvLUkl3eW5dHY0sqJ47O4+cxjfPLkxOZwcN+ir/mgcDNXjp/AA8ef0K0Vaa01j+Qu5rWitdw7fha/HdG9XT8O5NAOHi98mp1NJTw85g+khCX36HiHox2N6P0nQ2AKKu49lOpdDRMSprtLa+eu+u3hevVqyMs7ZAuHK7YzhNVM5ZK/t+0eMn48eHl1o6sONmb8sssOfj+mUs77OYUQhyZh2jWV5ioeyP8zqWEp3D/ybgwBPesBXVGxg98sf48piem8fPyF3e4pXV22m98u+ISokBDmnXs+Q2J9szJcbWrizSW5vLd8I01mC8eNGcw1c6dylBf2Pu7LKusamffNej5Y+SNmi41Z47O45uQcRmV4ZprfkbTabNyy8HMW7djObdOmc/PUad0eAvNi4Q/8ZeO3XDVsCvdPOKnHQ2Q+Lv+UD8o+4bdDruK4xBk9OtaROEx/geaXUXHvo4LHefRcrpAw7Q6trbBxY+f2kKIi9x3fYHDuItK+NV9ODgwf7mxI9qCDBeWObRqHaucIC4Pq6l8eT1amhTgyCdOu+6F6Df8ufpEzB57KBenn9vh4H+3cxJ2rP+X0jFH8ffpZ3VoFBMiv3MdVH30IwKvnnMuYJN8EMQBTs5l3luXx1pJc6pvMTMxK5cLjxjFzXBZBXh6z3VtprSko2cd7yzfy5fqtOBwO5k4ewVVzpjA0Jd5ndTW0tnLdpwv4vmx3tweytPt4Vz53/LCA0zJG8sz0s7v9vXygooZtPLL5SabF53D90N94dLqjthWj958JYWcTEP2Yx87TExKm3aw9gJpKajktaS13HreGcebVzpBdVeW281jColijp7DcPJWdiTnMfXAq593ovpdYutL3fKh2jvh4aGmRnmkhXCFhumf+u+M1VlSt4s7ht3JUTM/6QQFeKPyeJzcu4bLsSTw4cU63Q8OO2lou/3A+dWYzT588l7lZ2T2uqSeazRY+/C6fd5bmUl5tIjYijDOmjuLso8cwODnOp7X5iqnZzMI1W/jou3yKyqswhgRx+tRRXHbiRNISYnxaW0ldHdcu+ISddbU8Oedkzh4xstvHWLKnmOtWzGdyYjqvuPAqy4EarA38If9hAlQAj455kDCD514511qja68AawEq4WtUoO+e1ByOhGk3OmwAvUQ7k2fH1ev1652p002a4tMJn9lhuMzEiS5Pb+xK33NAwKHbOf73v8OvagshDk7CdM+Y7a38ueAxaiy1PDTmAQaEJvXoeFprHs/7lpe3rubq4Tn8fvyJ3Q7U+xobue7TBWzct5dbpk7jlmnTe7wy2FMOh+a7wl189F0+y3/cgc3hYNyQFOZMHM5JE7JJivHvyb8tFisr83eyOHcbyzZtp9VqZ2RGEuccPYZTJo/w6hZ3h7KipIRbFn6GUop/nno6MzIyun2MZRXbuW7FfIZFJ/LGzEu63f9/IJvDxpNbn6G4oZgHRt3LkIhBPTrekfx80+GfUMZLPHqunpAw7UbdvvHOaoWCgs7915s3u28ATEAAjBnTuT1k9OgubQF4uKDc3vcsNxoK4X4Spnuu0lzFgwWPEB0UzYOjf09YYM8ChNaah3MX8XrROpf7TVttNh74ZjEfFG5mztChPH3yKUQEe37UdFfsr2/i09Wb+WLtFrbtcW4JOH7IQGZPzGbW+GySY71/A6UntLRaWVmwk0UbilhRsBOzxUZcpJHZE7I5++gxjEjv2RMvd9Fa80ruBh5fsZysuDhePPMsMqK7v0K+dM92rl85n+zoBOadcAkxIT1fQZ636y0W7fuW/xtyDcckTu/x8Q5HO2rRVXPBkImKe6fX3XTYkYRpN+pKAD2ihgbnntQdV7DLy91XZHg4TJrUeTx6WpqzyA66EpRlCzwh3E/CtHvk12/myS1/Z1LseG7Ovp6AHv4g7rgTwpXDpvCAC4Faa82rubk8tmIZWXFxvHDGWWTG+LaN4EC79tWwaMM2FuUWsa3cGayHpsQzbWQm00ZkMCkrjbAQz21/5k4Oh6Zw9z5+2FLKD4UlbNxZgdVmJy7SyInjs5g9cRgTs1IJ9PD9R93R+UlXFn89eS7hLjzpWrKnmBtWfkB2dCLzTrjYLUF6edVK/rvjNY+PC2/nqLsHzAtQ8R+jgoZ7/Hw9IWHajTy2Ulte3ilcNy5dS4Ru7MEBD5Cc3Hn1esoU3vwsuktB+Ug3KQohukfCtPt8WbGIN0vf5dzUszgn7YweH09rzaO5i3m1aC1XDJvMHybMdunGq5WlJdz8+Wdo4Kk5JzN7aFaPa/OEXftqWPrjdn7YUkpucTkWm50gQyDjBqcwbshARmcmM2bQABKje0dLSLPZQuHuSgpK9vLjzgrWbyujrskMwLDURKaNyODYMYOZ0MsCdLuSujpuXvgZ+ZWV3Nq2Y4cr7UCeCNLFDdt5tPAphkdmc9eI2whUnr1pVbeuQNdeA+HXExB5u0fP5Q4Spt3IWyu1b/3Pzt+u3cJR5tXksIaprGYsmzBgd99JRoxge+JUXi3I4cuaqdRnjOVPjwVLUBbCwyRMu4/Wmhd3vMrK/d9xa/aNTI7r/i4IBzvmY3nf8MrWNVyWPYk/Tuze9Ll2pfV13PT55+RX7uPqCRO5a8YxhBh6dmOYJ5ktNnK3l/NDYQlrinazrbwKu8OZCwbERDAiPYns1ATSE2PISIolIzGG2Igwj+zy0NJqpbSqjtLKWnbvr6NkXy2bS/exo6IGR1tWSYmLYlJ2KtNHZDJ1RIbXh6t018JtRdy76GvnJM45czlp6FCXjvNN+TZuWvUhw6ITmTfzYqKDex6kayy1PJj/CMEBwTw05n4iDJ598uTcU/p0UKGohE9Qyvf960ciYdrNurKdnDtWcjseJyAAgu3NTGTDT6NfprKawexy23UREuK8obHjCvaQIb9oD5GVaiF6RsK0e1kcVh7d/CRlLeX8YdQ9DArP7PExtdY8sfFbXtqymjMzR/NEzmku7ZDQarPx+IrlzNuYx6jERJ455VSy4nrnbgUHarFY2VpWRcGuveSX7GXr7ipKq2p/CtgAEaHBJMVEEBdpJC7SSGykkbjIMCJCgwk2GAg2BBIUFEiwIZAApbDY7Fhsdqw2OxarnRaLldrGFmoamqltaKamsYX99Y3sNzV3qiUhysiw1ETGDEpmzKBkRmcmExdp9PZfiUuaLBYeWrqE+ZsLGJ+czD9OOY206GiXjvX+jo3cv3Yho2OTee2Ei9wSpM32Vh7Z/Bf2mffxx9H3kW5M6/Exj8RRfz+0fICKexsV3PMnwN4gYdqLPLVyfahe7SQq2fepc2rjVw87V7FjqXP9RAeKj+8Urt8vyeHKO+Klh1qIHpAw7X51lnr+VPAoNm3nj6PuJSm051MAtdY8X/g9T/+4lGlJmfznmPOIcnGnhG92bOeeRV/TbLXy+2OP59dHHeXRfXs9xWqzs6fGRGllHbur6iitqmN/fSM1DS3UNjZT09CMqbm1W8cMCw4iLjKM2AhnEI+LCictIZqMxBgyEmNIS4whPLR33MjZXXkVFdz+1ReU1tVxQ85Ubpk6rVujwdtprflnwUqezV/BscmD+deMc4kI6vlqrs1h45lt/+bHunx+N/xmxscc1eNjHok2L0HX/R+EX0tA5J0eP5+7SJj2Ik/1VHfluM6v0WSz7aeV6xzWMJ48QnDf9MZihrKaqawhh9VMJY/xJGeGyu4eQnSRhGnPKG/ew8ObnyAyKJI/jLqXqCD37FDx0c5N3Lvmc4ZExfPK8ReSYoxy6TiVTY3c9dVXrCgt4ej0DB4/aTbpLq5Q9mZWm53mVisWmw2rzU6r1bkarbUmyOBcpQ4JMhBkCCQ02EBYcN+42bE7zDYrz3z/PS9tWM+AiAj+fvIp5KS5tuJrddj547oveW/HRs4dNJbHck4lKKDn/cxaa/6741VW7P+OqwZfxqyk43t8zCOe01HjbO8IiEfFf4BSfedJkoRpL3LLbh8H0ZUV70N9zUv/buXiURs7b8+3bZvrxRzAioGNjGPyDR12Dxk2zOPTG4XoqyRMe05RwzaeKPwb6cY07ht5J6GB7unFXLV3Jzes/ICIoBBeOf5Chse4tsWaQ2ve2bSJJ1Yux+5wcPcxx3LZuPE+35NauM/a8nLuWfQVu+rquHjMWO459jiiQlz7PmyyWrj5u49YVrGdm0bP4LYxx7ntFY33d3/Igj0LOSf1TM5NO9MtxzwcrTW67hZo/dYZpINGePyc7iRh2ovcsTJ9qJ7krvQqd7mfuaYG1q7tvD2fG6c3Eh0NU6Z07r9Odt/0RiH6sv4ZpsfrtWtzvdLasL4ml2e3PcdRMWO4LftGDAHuuemvsHYf1yx/lyabledmnMuM5MEuH6vcZOKBbxazrGQXkwYO5C8nzWFIXP+cTugvmiwWnv5uJfPy8kiNiuLxk+a4NISlXWVLI79d/h6b6/bx50lzuTjLfb3Fi/Z+y7ySt5iZdBxXDbrMK/8vdcsCdP2dqIg7URHXevx87iZh2osOtjqslHO1OjPzyDfr+WxfZ62daX/Nmp8D9vr1YDa77xwZGZ3D9aRJzj2xhehn+mWYHmfUa7+fhzKe75XzfVu5jFd3/o8Z8dO4dujVPd6Dut2epnquXv4uO0zV3Dv+RK4aNsXlIKK15sPCzTy8bClmm41rJk7i+ik5vWbQi+garTULtm7hyVUr2dvQwBXjJ3DH0TNc2ju63fr9Zdy08kMaba08O/1sZqW6b0T99/tX85/tLzEhdhy3ZF/v8S3wALStFF19NhiGoeLeRHnhnO4mYdrL2leHS0p+DtLtjhSMe9XEQasV8vM7r157Ynpjx+Eyo0Z1aXqjEH1ZvwzT4xP0mi9TUPHzvTac4ZPyz5hf9jGzko7nykGXum31rcHayt2rP+XrsiJOyxjJ41NOIzzI9eBU2dTIEytW8PGWQhKMRu44egbnjxrdK/dJFp3lVuzh4WVLydu7l9GJSTw4cyaTB6a6fDytNW8Ur+fR3MWkGKP5zzHnMcLFlqKD2VCbx7NFzzEsMou7RtxGcIDnn7hp3YquvhDs5aiEj1GBrv/9+JKEaR9xJRh7qufabUwm54p1x/7rPXvcd/zwcJg8ufMK9kGmNwrRl/XLMD15vF6zMBxUBCr+Q1SA51+V0lrz3u4P+aziC04aMJPLMy9xW6DWWvNC4ff8ddMyhkbF859jzmdwZM/aNDbureDhZUvZUFHByIREHjj+eKanu94mIDyn3GTiqVUrWbB1C4nGcO6acQznjhrVo973FpuVB9Z9wce78pk5MIu/TTvT5d1jDmZDbR7/2PYfMo0Z3DvyDsIC3Xfsw3GY/gTNb6FinkeFzvLKOT1BwrSPuBKMe9XKdFeVl3cO1+vWQaMbpzempPxieiNRrt1NL0Rv0D/D9GS9dtW/0bVXQOjpqOinvNOnqTXv7J7Pwoqv3B6owXlj4q3ffYxVO3hq6unMSevZqrvWms+Linhi5XL2NDRw/KBB/G76DMYOGOCmikVPVDU18fy6tbz540YU1ML22AAAIABJREFU8JtJk7lu8pQetXQAlDTUctOqDyms28etY47lxtHHuPWm1J+DdDp3j/gd4Qbv7NGtWz5H198OxmsIiLrHK+f0FAnTPuJKMPZZz7Q72e1QWNi5PWTTJufn3UEpGDHi53CdkwNHHQVB/re9kvBP/TVMr1u3Dt34HLrxGVTUwyjjhV45d8dAPXvALC7LvNitgbq8qZ4bV33IppoKLsuexH3jT3RpwEtHZpuV1/PyeGHdWurMZuYMHcpt049mRELP988W3VdnbuHFdet4PS+XVrudc0eO4tZp00l1w8LOgpIC/rD2CwJUAH+bfiYzB7p39Hxu7Uae3fac94O0bRe6+hwwDEfF/Q+l+vbPaAnTPuJqMPbLCYNNTZCb23kF+2DPNFwVGgoTJnTuvx48WNpDRK/Ur8O0dqBrrwHLWlT8e6igUV45v9aat0vf54u9XzN7wCwuzbzIbTclArTabTy1cQmvFq1lZEwSz0w/m6zohB4ft6G1lVdzN/DShvU0WSycNmw410/JYWSihGpvqG5uZt7GPF7N3UCTxcLpw4dz69Tpbtl5pdHaysMbFjF/549MSkjj79PPIjXcvfuO+2xFWpvR1b8C+17nuPDAFK+c15MkTPuQXwZjd9m3r/PuIWvWQH29+46fkPDzyvXUqc72kPi+McZX+Lf+HKYBtL0aXX0WqGDnfrMBsV6poWOgPj7xGK4efLlbAzXAkj3F3L36U5psVu4eN5PLsye75eX6OnMLL61fz2t5uTRbrUxPS+eqCROZOXiw3KjoAVv2V/Fabi4fbynEYrczZ2gWt02f7rZXBtZV7eauHz6lrLme60cezS1jjsXg5n/H1dVr+c/2l3wQpDW6/m4wf4KKfREVcoJXzutpEqZF3+BwQHFx59XrvDznriLukpXVuf96/HjnqrYQXtTfwzSAtmxE11wCwZNQsS977SVgrTUfln/Cx+WfMSl2Ajdk/dbtOxpUtTRy39qFLNlTzNEDBvHolFPIiHDPE4Z6s5l38zfx+sY8KhoayIyO5orxEzhv1GgiXRwMIpzsDgfLSnbxyoYNfLe7lFCDgXNHjuLKCRPIinPPQkyLzcqz+St4eetqUo3RPD3tDCYnprvl2B0t2vst/yt5m2GRWdwx7BbCDGFuP8eh6KaX0Q1/QUXcgoq4yWvn9bReHaaVUhcAfwJGAjla6y4lZAnT/URrqzNQd1y9duP0RoKCYNy4zu0h2dkyvVF4lIRpJ93yEbr+HjBeQkDUn7xaz1d7F/NmybsMi8zi9mE3u33VTmvNO9vzeCLvG+xac/vY47hy2BS3rSLbHA6+Kt7Gq7kb2FBRQZjBwKnDhvOr0WOYPHCgV27u9Bdl9fW8v7mA+ZsLqGhoIDkigsvGjeeiMWOJDXNfCP1+3y5+v/YLShtr+dWQ8dw/4UQigtz7BEhrzfyyj1mw53Mmxo7nxqxrvbL93U/nb12Krv0/CJ2Liv47ys2v/PhSbw/TIwEH8AJwp4RpcUTt0xs7rmDv3+++47dPb+wYsOVOeuFGEqZ/5mh4EppeQkX9CWW8xKs1/VC9hue3v0xKaDJ3jbiNuGD3t5vsaTLx4Pov+XZPMUfFpfBEzmkujyI/lI179/Ju/iY+3bqFJquVIbGxXDB6DOeMHElSeIRbz+UvzDYri7fv4L2CfFaVOu/fOSYjk1+NGcOcoVkEuXHegcli5om8b3l3Rx4ZEbE8OuUUjh4wyG3Hb2fXdl7Z+T+WV63khMTjuHLwr70ykKWdthU7+6QDM1Dxb6OU91bDvaFXh+mfTq7UUvpxmJbe6h5on97YMVxv2OD+6Y0dw/XEiTK9UbhMwvTPtLaj666H1hWo2FdRIdO8WldBfSHPFP2bcIORu0bcRmrYQLefQ2vNZ6Wb+fOGRZgsZv5v5HRuGHU0oQb3trY0WSws3FbEewX5rN+zBwVMTUvj1OzhnJyVRWI/f8xqtdlYXrKLz4uK+GbHdpqsVlIjozh/9GjOHzXaLTtzdKS15quyrTy0/mv2tzZxzfCp3DrmWMLc/O8O0Gpv5V/FL5BX9yNnp57BualnevXVCe2oRVdfALrZeR+EH9xweCC/CdNKqWuBawEyMjImlbhzNwgf8ovt8Hobq9W5HV/HGxwLC903vTEw0Dm9sWP/tUxvFF0kYboz7Wh0/iB2VKPi30UZBnu1tl1NpTy99RlsDhs3Z1/P6OiRHjlPbWszj+Qu5uNd+Qw0RnHXuJmckTHKI6FnR00NC7Zu4fOiIrbX1hCgFJMHDmTW4CGcMHgw2XHx/aIVpKqpiVWlJXy7cydLdu6gyWolJjSUuVnZnDZsONPT0926n3O7zbX7eCx3Md9XljAyJonHc05jbJxnAmadpY5nip5jR9NOLh90CScNmOmR8xyK1hZ07W/Ash4V9wYqeIJXz+8tPg/TSqnFQPJBfut+rfUnbV+zlH66Mt0nB7X0RSaTc6BMe7hevRoqKtx3/PbpjR1XsFNTZXs+8QsSpn9J20qdgTog0rllXkDPtx7rjkpzFX8r+icVLXv5deaFzB4wy2Nhc3VlCY/mLqagdh8T4lO5f8JJTEjwzIhlrTVF1dUs3FbE19uLKdq/Hw0MCA9nRkYmx2RkMCMj029WrVusVtbtKWdFSQkrS0vY0tYCGB8WxuyhWczNymZ6erpb2zg6qmpp5G+blvH+jo1EB4dx65hjuSRrott36mi3vXEHzxY9R7O9heuG/obJcd4Nss6dO+4B88fOQUxhZ3n1/N7k8zDdFf05TPf6EeL+rKys882Na9c698R2l5SUzuF68mSZ3igkTB+CtuSiay6HoGGo2HleGTneUYvdzPPbX2JDbR7HJc7gykGXEhTgmV1G7A4HH+3axNM/LqXK3MQZGaO4fezxZEZ6dpvAcpOJlaUlrCwp4bvdpdS2tcNlREczISWFCckpTEgZyIiEBI8FTnfRWrPbVE9uRQW5FRVsqKhgy/4qbA4HwYGBTEoZ6HzCkJnB6MQkj24h2GS18GrRGl4s/AGLw8Zl2ZO5afQMooM91ze8omoVr+78HzHBMdw+7CbSjWkeO9ehOBqehqYX/W7njoORMN3Lycp0L9I+vbFj/3V+vnunN44c2bk9ZOxYmd7Yz0iYPjRt/gZddyMEz0DFPu/1qWkO7eCj8k/5uPxTsiKGcGv2DcQEx3jsfE1WCy8Ufs/LW1djddg5f/A4bho9g4FuHt5xMA6tKais5Iey3Wyo2ENuRQWVbYsJoQYDWXHxDI+PZ3hCAsPiE8iOjyc5IsIn7SEms5ltNTUUVe93vu2vZmv1fmpaWgAwBgUxbkAyE1JSmJKaypTUNIxeeFw126y8WbyB5wu/p6a1mZNSh3Hv+FkMjvTcKyt2beft0vf5au9iRkWN4Kas64gM8v6NprppHrrhEQi7CBX1kN+3DfXqMK2UOgf4J5AI1AF5WuuTj/Tn/ClMS890L9fU5LyhseMKtrunN06c2Hk8ukxv9GsSpg9PN7+HNj0AoWeiop/0yfZaa2vW88L2VwgLDOWW7BvIjhzq0fNVtjTy3OZVvLM9F4XioqETuGHU0SSGeS8kaa3Z09BAbkUFeXsrKKqupqh6/08BGyAkMJCBUVGkRkaRFhVFalQUCUYjsaFhxISFOt+HhhIRHExwYOAhV4MdWmO12zHbbNSaW6hrMTvfm81Ut7Swx2Si3GSivMFEmcmEqbX1pz8bHhREdnw82fHxjE1KZmJKCsMSEjzWSnEwFrud93fk8e/Nq9jX0siMAYO4fezxHmvXaWeyNvBc8YsUmAo5OfkkLs64wKs7drTTLZ+h6++AkJNQMf9A+aAGb+vVYdpV/hSmQXbz6HPapze2h2tPTW/sGLDdML5W9A4Spo9MNz6HbnwGjFejIu/xyarX7uYynin6N9WWGi5MP4+5ybM9Xkd5Uz3/KljFBzs3EhQQyIVDx3PVsBzSIzy3On4ktS0tbKt2rgSXmeopNzU43zc0UN1xFeggApQiKCCAoMBAApTCardjdTiwHaGHMTwoiNS2sN4e3Ie2rZQPjIryyE2DXdFktTB/50Ze3rKG8uZ6JiWk8buxxzNtQKbHz13UsI1/bXuRRlsDVw6+lOMSj/H4OQ9Gt65C114LQeNQca+iVP8YFiRh2odcDckSrvsYh8M5TKZje8jGje6f3tix/3r8eJCJZ32ShOkj01qjG/4MzW+iIm5HRVzvweoOrcnWzEs7XmVdbS7jYsZy7ZCriQqK9Ph5dzbU8FzBKhaUFOBAMzdtOFcPn+rxlc/uarFaqWlpodbcQm2LmTpzC7VmM80WC1aHA6vdjqXtTaMJDgwkKCCQoLb3IYbADqvaocSEhhEXFkZUSEivahuoaDbxv23rebs4F5PVzMSENG4aPYPjkod4vE6HdrBgz+d8WLaAxJAEbs6+nkHhGR4956FoSy669ioITEPFvYUK6D/3AEmY9hFX2zek7cNPmM3OQN0xYBcXu+/4QUHOQN1xBVumN/YJEqa7RmsHuv5uMC9ARf4eFX6lZ4o7Yh2axfuW8Hbpe4Qbwrlu6G88tn3egfY2NzBv27qfQtyE+FQuz57MyenDCQk0eKWG/kprzYbqct7atoHPSjfjQHNy2nCuHp7DxATv3OxXY6nl+eKXKGzYyvT4qVw16FKvjgbvSFsL0DVXQECsM0gHJvqkDl+RMO0jrt5YKDck+rHqaueOIR1bRNw5vTEmpvP0xpwcmd7YC0mY7jqtbei626H1K1TkA6jwyz1QXdeUNO3mueIXqDDv45SUOZyXdjbBHtrt40Dt7QWvbV1LaVMdscFhnDt4LBcNncCQqHiv1NBf1Fta+HhXPu9sz6OovopwQzAXDBnHlcOmeLXdZk31Ol7d9QZWh5UrBl3CMQlH+2y1Xls3O4O0ikDFv4EK7F2vkHiDhGkfcXXLO9kqrx/RGnbu7Byu3T29MTPzl9MbjUb3HV90m4Tp7tHaiq67FVoXo6IeRBl99xKd2d7KmyXvsrRqOalhA7lu6G+8+pK7Q2u+27eLd7bnsqisCJt2MDUxg/OHjGN22jAig6T1yxV2h4PVVaV8uHMTC3cX0mq3MTYuhYuHTuD0jFGEBwV7rZZGWyPzdr3N99WrGRyeyfVDf0tK2MFGdXiHtha2BWkjKu5/KEO6z2rxJQnTPiIr08Il7dMbO7aHbNni3umNY8d2bg8ZOVKmN3qRhOnu09rSFqi/QUX9GWW8yI3VdV9e3Y+8vON1GmyNnJ16OmcMPNXruypUtTTywc4feXd7HqVNdYQEGjhxYDZnZo7m+JShBMv/6cPSWpNfu5cFJQV8VrKZSnMjEYZgzhw0mguHTGBMnPcD7Ma6Tby843VMtgbOGng6Zww8BUOA79p5tHUruuYyUKHO6YYG3/Rq9wYSpn1EeqaF29TX/3J649697jt+RIRzoEzHgJ3m/QEA/YWEadc4A/VN0LrUua+t8WI3Veca5wriW3xfvYbB4ZlcM/hKMsO9v2rX3tv7aUkBn5dupqa1haigUGalZjFzYBbHJQ8hKjjU63X1RlaHnfVVZSypKGZx+TZ2NdQQFBDA8SlZnJU5mpkDswgzeH/f/yZbE2+Xvs+yqpVtr3hcw6Bwz+8QcjjaWui82ZCgtiDt23p8TcK0D8luHsIjtIby8s6r1+vWuXd648CBncO1TG90GwnTrnMG6puhdQkq8l5U+NVuqK5n1lSv4/Vdb9Joa2JO8omcm3YWYYG+Ca9Wh51Ve3fxWelmluwpps7SQqBSTE5I54SBWRyfMoRh0Ym9aqcMT6tqaWTF3p0s2VPMir07aLC2EhQQQE5iBqdmjOSU9BEenVR4OFprVu3/nrdK36fJ1sSpKXM4J+0sr/XiH7Iuy0Z07W9AhaHi5qEMg3xaT28gYVqI/sBuh82bO/dfb9rkvkb79umNHfuvx4yR6Y0ukDDdM85AfSe0fgnh16EibqexUfHuu84dKrOz4cILIdLzO9j9pNHWyPu7P2JJ5XJigqL5deaF5MRN9mlotTkc5FWXs3TPdpZUFLOlrhKAuJAwchIzyEnKYGpSJsOiE322b7Mn7G1uYE1lKaurSlhdWcrOhhoAEkPDOSEli5kDh3J08mCf95fvbi7n9V1vsLVhG1kRQ7hy0KVk+mjLu4506yrnFNKAeFTsa/22R/pAEqaF6K/apzd2XMEuLXXf8cPCnDc0dlzBHjRIpjcegb+EaaXUncBTQKLW+rDb0rj7MVtrO9r0ILS8x976ixg19UFstkCamiA83Hkj98KFcIyX51oUN+7gtZ1vUNJcyuiokVwx6Nc+vXmsoz1NJlbt28nqylLWVJZS3uwcNBUdHMrY2BTGxqUwNi6ZsXEDSTFG9onV6waLmU21e9lUU0F+TQWbavayu6kOgIigEKYkppOTmMH0AZmMjk3uFU8azHYzH5V/ypcVizAawrgw/XyOS5xBgA8mfR5Im79E190BhiGo2JdRgUm+LqnXkDAthPjZ3r0/T21cvdq5VZ87pzcmJv5yemNsrPuO7wf8IUwrpdKBl4ARwCRvh2lwvkTeWvNXgq0v8v6CU7j85qewWH7edSEyEvbscd4S4E0O7eCbfUuZX/YRrQ4LJw2YyTmpZxBuCPduIUdQ1ljH6qpS1lWVkV9TQVF9FTbtfCUrLsRIdnQCQ6PiGRIZT1aU8+MBYZGHHBHuKVpr6iwt7GioYYepmmLT/rb31ZQ21v70denhMYyNS2ZcfCpTkzIYFTPA67UejkM7WF61ivllH1NvreeExGP5Vfq5RHphCFBX6OZ30aY/QtAEVOwLqIBoX5fUq0iYFkIcmsMBRUWdA7a7pzdmZ3cO2P18eqOfhOn5wMPAJ8BkX4RpgJdegu2bXubR+/7ComUzOO/qf9HU7Ayt4eHw7LNwzTVuP22X1Fvrmb/7E5ZVrSDcYOTs1DM4MekEn+7OcDhmm5Ut9ZXk1+wlv2Yvxab9bDdVY7L+vFWnQQWQFBZBsjGKFGMkA8IiSQgNJzo4lOjgUKKCQokKDiXcEIwhIMA57bDtvUJhdbSPE7djddhpddgxWczUW8yYrGZMFjO1rS3sbWmgotnE3mYTFc0NtNh/fjwKDghkcGQcQ6PiGRkzgLFxKYyJSyY2pPdu+Zlfv5m3S9+ntHk3WRFD+XXmhWRFDPF1WYDzyQpNL6Ib/wrBx6Ji/okK6L1/l74iYRq5oU+IbjGbIS+vc3vI9u3uO3779MaO/ddZWf1memNfD9NKqTOBE7XWtyqldnGIMK2Uuha4FiAjI2NSycH2/Oyhe+6BJ5+EKy/8gBeefoAfC4dz1uXPs2evs7Xi3nvh8cfdftpuKW3ezVsl71FgKiQxJIHz085hWvyUXvGy/pForalubWaHqZrtpmrKm+upaDa1vTVQ2dKA2W5z6zkVkBQWQYoxqtNbe4BONUb3qhXnw9nZtIv3d3/EpvoCEkISuCj9PJ/30nektRVtegRa3obQ01HRT6CU9/bU7kv6fZiWreaEcIPq6p9Xr9sDdnW1+44fE/NzW0h7yE7yz369vhCmlVKLgYM1+94P/B6Yo7WuP1yY7siTK9O33ea8PeCUWUt56/nbaWgM5+wrn2fr9jE+XZnuSGvNpvoC3t39AaXNuxkYmsxZqWf0mVB9KFprmm3Wn1aVTRYz9VYzzTYrNocDq8OOzWHH4rCjgaCAAAwqkKCAQIIDAgkODCQyKITo4DDnynZwCBGGkD4Tlg9lZ9MuPir7lNy6jUQYwjlz4GmcNGAmQT7epaMj7TA59263rILw36Ii7kD14e9FT+v3YVqGoAjhAe3TGzuuXm/YAK2t7jvHoEGdw7WfTG/sC2H6UJRSY4FvgPbliTRgD5CjtT7k5ueeCtMNDZCa6nwPMGbEVj6Zdx2J8TVcd/eTvPDayV7vmT4ch3awtmYDH5d/SllLOSmhyZyVejrT43P6dKgWTjsad/JR+afk1f1IeKCRuSlzmDNgFkZD73rc0rZSdO21YN/dNgTpPF+X1Ov1+zAt47mF8BKLxbkdX8ft+QoL3Xf89umNHdtDRozoc9Mb+3KYPpCvV6YBVq6EU091Pp43NcGgjP28/fwN5EzIQ0XcAeHX9pqX1ds5tIN1baF6d0s5yaEDODXlZGYkTPf5HsOie7TWFJq28nnFl/xYn0+EIZxTkucwe8Aswgy+2b/6cLRlLbr2RgBU7L9QwTk+rqhv6PdhWlamhfCh9umNHVew3Tm9MTLyl9MbU1Pdd3wPkDDtfo2N8O67UFzsbL+/8FetGO33gfkzCD0HFf1nlOp9N706tIP1tbksKP+cXc2lRBkimZ18IicmnUBkUC9aUhe/YNd21lSvY2HFVz/9252cPJvZybN8NrTnSHTzB84dOwLTULEv9vupht3R78O0Kz3TcsOiEB6iNZSV/XJ6Y8f/oD2VmvrL6Y3enOBxBP4UprvKFzswOXcpeA7d+CwYxqBi/oEypHm1hq5qX91cuPcrNtZtIjggmGMSpjMr6QSfjCgXh1ZvNbGiahWL9y2h2lJDSmgyp6bM4ehe/KqC1ha06TFoeQuCj0bFPCtb33VTvw/T0L1wLDcsCuFlNpuzHaRjwM7Pd+/0xlGjOgfssWPB4JstyiRMe5c2f4OuvxsIQMU8hQo5wSd1dFVZczlf7F3E9/t/wKptDAkfxMyk45gWn0NoL13x9HcO7aCgvpAlVcvZUJuHXdsZGTmcuSmzGR9zVK/ud9e2MnTdLWDLh/DfoCJ+h1K9c3vG3kzCdDdJW4gQvUBTE6xf37n/2hPTGzv2X2dmemV6o4Rp79O2UnTdTWDbAuHXoyJuQane3WvfYG1k1f7vWVq1gvKWPYQGhDAtfirHJh5NdsTQXtcH7o+qWvfz3f4fWFa1kqrW/UQYIjgmYTonJB5LqnGgr8s7It26FF13F2BHRf8FFTrb1yX1WRKmu0luWBSil6qocE5sbA/Xa9aAyeS+4ycldd6eb8oUj0xvlDDtG1qb0aY/Q8t8CJ6Oin6qT4xL1lqzrXE7SyuXs7pmHRaHhYSQBI6Oz2F6/FTSjL37HoG+xmRtYE3NOr7fv5qixmIARkaNYGbScUyOndCrtrc7FK1t6MZ/QtN/wDDCOYhF+qN7RMJ0N8nKtBB9RPv0xo7tIRs3OttG3GXYsM7tIePG9Xh6o4Rp39LN852hOsDoXK0LOd7XJXVZi93M+poNfFe9mvz6zWg06cY0psVNYVLsBAaGpciKtQtM1gZyazeytnY9+fWbsWs7qWEDOTp+GtPip5AUmujrErtM28rQ9XeANRfCzkdF/RGlpD2opyRMd5P0TAvRh5nNkJvbuT3EndMbg4Od0xs7Buzs7G61h0iY9j1tK0bX3Q62rWC8EhV5Z5+b/FZvrWd19Tq+r15NceMOAJJDBzAxdjwTY8aRFTmUwF7eyuJLe837yK3dyPraXIoaitFo4oPjmBafw9EJU0kPS+tzT0y0+Qt0/QOAdu4fHXa6r0vyGxKmXSC7eQjhRzpOb2wP2O6c3njSSbBoUZe/XMJ076B1K7rhCWh+EwzDUdFPo4KG+7osl9RYatlQm8eG2jw2m7Zg13aMgWGMiBrO6KiRjIoaQWrYwD4XDt2pzlJPoWkLBaZCNpu2UNXq3M0x3ZjGpNgJTIqdQKYxvU/+HWlHA9r0MJg/hqBxqOi/ogwZvi7Lr0iYFkKIjrSGHTs6h+ueTG+85RZ49tkuf7mE6d5Fm5egTb8HhwkVcQuEX9OndztotjWzqX4z+fUFFJgKfwqN0UHRjIwaTlbEELIihpBpzMAQ0Hev83C01uxrraS4YQfFjdvZ0lBEecseAIyBYYyMGsHoqJGMixnbp1o4Dka3rnKuRjv2Qvh1qIgbUKr393X3NV193PbP/1FCCHEgpWDoUOfbxRc7P9c+vbFj//WWLV07Xo5MEOvLVOhMCP4cXf8guvGvYP4Kop9ABQ3zdWkuMRqMTI2fzNR458/9SnMVm01b2GwqZItpGz9UrwEgSBkYFJ7J0IghZBjTyDCmMzAspU/cYNeRQzvY31pNafNuSpvL2Nm0i+LGHTTaGgEIDQghK2IoMxKmMTpqJIPCM3v1VnZdpR0NzldWWt6HwMGouLdRweN9XVa/JyvTQgjRUX29c/eQ9nC9ejXs2/fLr9u2zTl2r4tkZbr30uYv0KaHwNGAirgRwn/rd6t8Na01FDfuoLhxB9sbd7CzqQSrtgIQQAApYcmkG9NIDh3AgJAkBoQ63yINET5tgTDbW6lsrWSfuf2tirKWcsqayzA7nK8qKRQpoclkRQ79aQU+NWygX4TnjnTrMnT9H8BR6XwlJeJmucnQw6TNQwgh3EFr2L27c3vIzp3O7X/kBsTD6kuP2dpR09Z/+jkYRqKiH0UFjfF1WR5j13b2mSvZ3VxGaXMZu5vL2N1STnVrNZqfc0FYYBhxwbHEBEUTExxDbNv7CEMExsAw55vBSFhgGCEBwRiUgcCAQAJVIAEEoJTCoR3YtB27tmPXNqwOG832ZlpsLTTbW2i2N9Nka6bOWk+dpZ46ax11ljpqrXXUWztvgxlpiCA1bCDpxjQyjGmkG9NIDUslNLD3jY13F+f35l/A/BEYslHRj6OCjvJ1Wf2ChGkhhPAUrbs96EXCdN+gzYvQpj+BoxqMl6AibkMFRPm6LK+xOqzsb612rgK3rQjXWuraAm49tdY67Nre5eMpVKdwfqSvjQqK7BDcY0gMSfhplTwpJBGjwejqpfU5WjugZT664WnQjc5XTCJu7HM70PRl0jMthBCe0gfv/Bddo0JnQ/BUdOPfofkttHkhRNwNYWej/Kxt4GCCAoJICUsmJSz5oL+vtabR1kijrYkWewtNP60wN9PqsLStPv/8pjUEKudKdWBAIAYViEEZMAaGEWYIwxho/Gl1O8oQ6bc3R3aXtm5yth5Zf4SgSaioh/psP39/IN+1QgghRAcqIAoV9SA67AK06SG06V5oeRei/uD6lo6/AAAKhklEQVTXrR9doZQiMiiSyKBIX5fil7SjBt3wjPP7LSABFf0UhJ7ZJ7fu60/8/2m2EEII4QIVNMq5W0LUE2AvRVefi6PuLrS9wtelCT+jdSu68b/oqpOcO3UYr0AlfIUKO0uCdB8gK9NCCCHEISgVAMZzIXQOuul5aHoNbf4SHX41Kvy3qIAIX5co+jCtNZg/Rzf8FRzlEDILFXkXyjDU16WJbpCVaSGEEOIIVEAEAZF3ohK/gtDZ0PQf9P456Oa30dri6/JEH6Qta9A1v0LX/w4ColCxrxMQ+7wE6T5IwrQQQgjRRSowlYCYv6Hi3ofADLTpQXTVyejmdyVUiy7RlrU4ai5D11wK9gpU1BOo+A9RIdN9XZpwkYRpIYQQoptU8DhnP3XsSxAYjzb9Ab1/Lrr5PXTbMBQhOnKG6MvRNb8G2w5U5P2oxMUo47koFejr8kQPSM+0EEII4QKlFIQcB8HHgmU5uvEfaNMD0PgfCL8KjBegVJivyxQ+pLVu+974L1jXOHfoiPw9GC+S6YV+RMK0EEII0QPOUH08BB8HlmXoxufRDY9A478g/DIwXooKiPV1mcKLtLaB+Qt003/BtgUCkttC9IXyBMsPSZgWQggh3MAZqk9AhZyAtqxHN/0X3fhPaHoJHXYByngZypDp6zKFB2lHI7R8iG5+DexlEDgUFf0EhJ4ukwv9mIRpIYQQws1U8CRU8CS0tQjd9BI0v41ufgMSl6ICDz5dUPR9uuZysOVD0ERU5P0QMrNfTM7s75TW2tc1dJlSqgoo8XUdXZAA7Pd1EV7Un663P10ryPW6U6bWOtFDx+6VvPiY7W/fp/50Pf50LeBf1+NP1wKeuZ4uPW73qTDdVyil1mmtJ/u6Dm/pT9fbn64V5HpF3+Bv/27+dD3+dC3gX9fjT9cCvr0eee1BCCGEEEIIF0mYFkIIIYQQwkUSpj3jRV8X4GX96Xr707WCXK/oG/zt382frsefrgX863r86VrAh9cjPdNCCCGEEEK4SFamhRBCCCGEcJGEaSGEEEIIIVwkYdpDlFIXKKUKlFIOpZTfbD3TkVJqrlJqq1KqWCl1r6/r8SSl1CtKqUqlVL6va/EGpVS6UmqJUqqw7fv4Vl/X5ClKqVCl1Bql1Ma2a33I1zUJ1yml7lRKaaVUgq9rcZVS6iml1Bal1I9KqY+UUjG+rskV/vIzwl8fD5VSgUqpXKXUZ76upSeUUjFKqflt/2cKlVLTvV2DhGnPyQfOBZb7uhBPUEoFAv8GTgFGARcrpUb5tiqPeg2Y6+sivMgG3KG1HglMA27043/fVmCW1nocMB6Yq5Sa5uOahAuUUunAbKDU17X00CJgjNb6KKAIuM/H9XSbn/2M8NfHw1uBQl8X4QbPAl9qrUcA4/DBNUmY9hCtdaHWequv6/CgHKBYa71Da20B3gHO8nFNHqO1Xg7U+LoOb9FaV2itN7R93IDzwSnVt1V5hnZqbPtlUNub3JndN/0duJs+/u+ntf5aa21r++UPQJov63GR3/yM8MfHQ6VUGnAa8JKva+kJpVQUcBzwMoDW2qK1rvN2HRKmhatSgd0dfl1GH39wEQenlBoETABW+7YSz2l7uTMPqAQWaa399lr9lVLqTKBca73R17W42dXAF74uwgV++TPCjx4Pn8H5xNPh60J6aAhQBbza1rLyklIq3NtFGLx9Qn+ilFoMJB/kt+7XWn/i7Xq8TB3kc316NUj8klIqAvgAuE1rbfJ1PZ6itbYD49t6Uz9SSo3RWveL/vi+5HCPucDvgTnerch1Xfn5oZS6H2eLwZverM1N/O5nhL88HiqlTgcqtdbrlVIn+LqeHjIAE4GbtdarlVLPAvcCf/B2EcJFWuuTfF2DD5UB6R1+nQbs8VEtwgOUUkE4f3C8qbX+0Nf1eIPWuk4ptRRnf7yE6V7mUI+5SqmxwGBgo1IKnI9HG5RSOVrrvV4sscuO9PNDKXUFcDpwou6bAyH86meEnz0ezgDOVEqdCoQCUUqpN7TWl/q4LleUAWUdXk2cjzNMe5W0eQhXrQWylVKDlVLBwEXAAh/XJNxEORPJy0Ch1vpvvq7Hk5RSie27JSilwoCTgC2+rUp0h9Z6k9Y6SWs9SGs9COcP2Im9NUgfiVJqLnAPcKbWutnX9bjIb35G+Nvjodb6Pq11Wtv/lYuAb/tokKbt//hupdTwtk+dCGz2dh0Spj1EKXWOUqoMmA58rpT6ytc1uVPbzTE3AV/hvBnjPa11gW+r8hyl1NvA98BwpVSZUuoaX9fkYTOAy4BZSqm8trdTfV2Uh6QAS5RSP+IMAIu01n16qyjR5/0LiAQWtf3fe97XBXWXn/2M6E+Ph33RzcCbbY/h44HHvF2AjBMXQgghhBDCRbIyLYQQQgghhIskTAshhBBCCOEiCdNCCCGEEEK4SMK0EEIIIYQQLpIwLYQQQgghhIskTIt+oW2rQq2UGuHrWoQQQgjhPyRMi/7iYmAlzg3qO1FKBXq/HCGE6N+UUoOUUl2eNKqUulIpNdDFc8UppRYppba1vY915ThCHIyEaeH3lFIRODfdv4a2MK2UOkEptUQp9Rawqe1zlyql1rRtyP9Ce8hWSv1HKbVOKVWglHrIV9chhBD93JWAS2Ea54jpb7TW2cA3+GDktPBfEqZFf3A28KXWugioUUpNbPt8DnC/1nqUUmokcCEwQ2s9HrADv277uvu11pOBo4DjlVJHebl+IYTwVwal1OtKqR+VUvOVUkal1CSl1DKl1Hql1FdKqRSl1PnAZJyT7vKUUmFKqT8qpdYqpfKVUi+2jf0+lLOA19s+fh3nzwUh3ELCtOgPLgbeafv4nbZfA6zRWu9s+/hEYBKwVimV1/brIW2/9yul1AYgFxgNjPJK1UII4f+GAy9qrY8CTMCNwD+B87XWk4BXgEe11vOBdcCvtdbjtdYtwL+01lO01mOAMOD0w5xngNa6AqDtfZLnLkn0NwZfFyCEJyml4oFZwBillAYCAQ0sBJo6finwutb6vgP+/GDgTmCK1rpWKfUaEOqN2oUQoh/YrbVe1fbxG8DvgTHAoraF5kCg4hB/dqZS6m7ACMQBBcCnni1XiF+SlWnh784H5mmtM7XWg7TW6cBO4JgDvu4b4HylVBL8dLNKJhCFM3TXK6UGAKd4sXYhhPB3+oBfNwAFbavP47XWY7XWcw78Q0qpUOA5nCvYY4H/cviFjn1KqZS2P5sCVLqnfCEkTAv/dzHw0QGf+wC4pOMntNabgQeAr5VSPwKLgBSt9Uac7R0FOF9uXIUQQgh3yVBKTW/7+GLgByCx/XNKqSCl1Oi2328AIts+bg/O+9tuMj//COdZAFzR9vEVwCfuKF4IAKX1gU8KhRBCCCE8Syk1CGfL3XLgaGAbcBkwDPgHEI2zHfUZrfV/lVLnAY8BLcB04H6cOzTtAnYDJVrrPx3iXPHAe0AGUApcoLWu8cyVif5GwrQQQgghhBAukjYPIYQQQgghXCS7eQghhBDCLyil/o1zSFdHz2qtX/VFPaJ/kDYPIYQQQgghXCRtHkIIIYQQQrhIwrQQQgghhBAukjAthBBCCCGEiyRMCyGEEEII4aL/B7orEJrgk9+xAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-0.51404863 0.23106267]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-0.3077799 0.48142069]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-0.1842792 0.63131929]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-0.11033476 0.72106912]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-0.0660615 0.77480566]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"\\n\",\n \"Linear Regression with betas [-0.03955346 0.8069797 ]\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"betas_ = betas[range(0, iters, 10), :-1]\\n\",\n \"for i, beta in enumerate(betas_):\\n\",\n \" print('\\\\n\\\\nLinear Regression with betas ', beta)\\n\",\n \" f, (ax1, ax2) = plt.subplots(1,2, figsize=(12, 6))\\n\",\n \" ax2.contour(beta0, beta1, cost_func.T.values, contour_levels)\\n\",\n \" ax2.set_xlabel('beta_0')\\n\",\n \" ax2.set_ylabel('beta_1')\\n\",\n \" ax2.scatter(beta[0], beta[1], c='r', s=50)\\n\",\n \" \\n\",\n \" if i > 0:\\n\",\n \" for beta_ in betas_[:i]:\\n\",\n \" ax2.scatter(beta_[0], beta_[1], c='b', s=50)\\n\",\n \"\\n\",\n \" # scatter plot\\n\",\n \" ax1.scatter(X, y,c='b')\\n\",\n \"\\n\",\n \" # Plot the linear regression\\n\",\n \" x = np.c_[np.ones(2), [X.min(), X.max()]]\\n\",\n \" ax1.plot(x[:, 1], lr_h(beta, x), 'r', lw=5)\\n\",\n \" ax1.set_xlabel('Area')\\n\",\n \" ax1.set_ylabel('Price')\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Estimated Betas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-0.02492854, 0.82473065])\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"betas[-1, :-1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Normal equations (aka OLS)\\n\",\n \"\\n\",\n \"## $$ \\\\beta = (X^T X)^{-1} X^T Y $$\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"beta = np.dot(np.linalg.inv(np.dot(X_.T, X_)),np.dot(X_.T, y))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-6.21016827e-17, 8.54987593e-01])\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"beta\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Estimating the regression using sklearn\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Using OLS\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# import\\n\",\n \"from sklearn.linear_model import LinearRegression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Initialize\\n\",\n \"linreg = LinearRegression(fit_intercept=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression(copy_X=True, fit_intercept=False, n_jobs=1, normalize=False)\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Fit\\n\",\n \"linreg.fit(X_, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-8.88030989e-17, 8.54987593e-01])\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"linreg.coef_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Using (Stochastic) Gradient Descent*\\n\",\n \"\\n\",\n \"*Differs from normal gradient descent by updating the weights with each example. This converges faster for large datasets\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# import\\n\",\n \"from sklearn.linear_model import SGDRegressor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Initialize\\n\",\n \"linreg2 = SGDRegressor(fit_intercept=False, max_iter=100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,\\n\",\n \" fit_intercept=False, l1_ratio=0.15, learning_rate='invscaling',\\n\",\n \" loss='squared_loss', max_iter=100, n_iter=None, penalty='l2',\\n\",\n \" power_t=0.25, random_state=None, shuffle=True, tol=None, verbose=0,\\n\",\n \" warm_start=False)\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Fit\\n\",\n \"linreg2.fit(X_,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-2.66537282e-04, 8.53785614e-01])\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"linreg2.coef_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Comparing OLS and GD\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"|Gradient Descent|Normal Equation|\\n\",\n \"| :------------- | :------------- |\\n\",\n \"| Need to choose $\\\\alpha$| No need to choose $\\\\alpha$|\\n\",\n \"|Needs many iterations | Don't need to iterate|\\n\",\n \"|Works weel even when $k$ is large |Slow if $k$ is very large |\\n\",\n \"||Need to compute $(X^TX)^{-1}$|\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Linear regression with multiple variables\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Lets create a new freature $ area^2 $\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
areabedroompricearea2
0210433999004426816
1160033299002560000
2240033690005760000
3141622320002005056
4300045399009000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" area bedroom price area2\\n\",\n \"0 2104 3 399900 4426816\\n\",\n \"1 1600 3 329900 2560000\\n\",\n \"2 2400 3 369000 5760000\\n\",\n \"3 1416 2 232000 2005056\\n\",\n \"4 3000 4 539900 9000000\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['area2'] = data['area'] ** 2\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Notation review\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" * n = n_samples = number of examples\\n\",\n \" * k = number of features\\n\",\n \" * y = price\\n\",\n \" * $x^{(i)}$ = features of the $i$ example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"area 2400\\n\",\n \"area2 5760000\\n\",\n \"Name: 2, dtype: int64\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"i = 2\\n\",\n \"data.loc[2, ['area', 'area2']]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* $x_j^{(i)}$ = value of the $j$ feature of the $i$ example\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5760000\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"i = 2\\n\",\n \"j = 2\\n\",\n \"data.loc[2, 'area2']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Hypothesis:\\n\",\n \"\\n\",\n \"* Previously:\\n\",\n \"\\n\",\n \"### $$ h_\\\\beta(x) = \\\\beta_0 + \\\\beta_1 x_1 $$\\n\",\n \"\\n\",\n \"where $x_1$ = area\\n\",\n \"\\n\",\n \"* New:\\n\",\n \"\\n\",\n \"### $$ h_\\\\beta(x) = \\\\beta_0 + \\\\beta_1 x_1 + \\\\beta_2 x_2$$\\n\",\n \"where $x_2$ = $area^2$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Create new matrix X and scale\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 2104, 4426816],\\n\",\n \" [ 1600, 2560000],\\n\",\n \" [ 2400, 5760000],\\n\",\n \" [ 1416, 2005056],\\n\",\n \" [ 3000, 9000000]])\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X = data[['area', 'area2']].values\\n\",\n \"X[0:5]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([2.00068085e+03, 4.62083843e+06]),\\n\",\n \" array([7.86202619e+02, 4.05394589e+06]))\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"ss = StandardScaler(with_mean=True, with_std=True)\\n\",\n \"ss.fit(X.astype(np.float))\\n\",\n \"X = ss.transform(X.astype(np.float))\\n\",\n \"ss.mean_, ss.scale_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0.13141542, -0.04786014],\\n\",\n \" [-0.5096407 , -0.50835371],\\n\",\n \" [ 0.5079087 , 0.28100069],\\n\",\n \" [-0.74367706, -0.64524355],\\n\",\n \" [ 1.27107075, 1.08022201]])\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X[0:5]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 1. , 0.13141542, -0.04786014],\\n\",\n \" [ 1. , -0.5096407 , -0.50835371],\\n\",\n \" [ 1. , 0.5079087 , 0.28100069],\\n\",\n \" [ 1. , -0.74367706, -0.64524355],\\n\",\n \" [ 1. , 1.27107075, 1.08022201]])\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_ = np.c_[np.ones(n_samples), X]\\n\",\n \"X_[0:5]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Cost function\\n\",\n \"\\n\",\n \"The goal became to estimate the parameters $\\\\beta$ that minimize the sum of squared residuals\\n\",\n \"\\n\",\n \"### $$J(\\\\beta)=\\\\frac{1}{2n}\\\\sum_{i=1}^n (h_\\\\beta(x^{(i)})-y_i)^2$$\\n\",\n \"\\n\",\n \"\\n\",\n \"### $$h_\\\\beta(x^{(i)}) = \\\\sum_{j=0}^k \\\\beta_j x_j^{(i)}$$\\n\",\n \"\\n\",\n \"\\n\",\n \"### $$J(\\\\beta)=\\\\frac{1}{2n}\\\\sum_{i=1}^n \\\\left( \\\\left( \\\\sum_{j=0}^k \\\\beta_j x_j^{(i)}\\\\right) -y_i \\\\right)^2$$\\n\",\n \"\\n\",\n \"Note that $x^0$ is refering to the column of ones\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Gradient descent algorithm\\n\",\n \"\\n\",\n \"Repeat until convergence{\\n\",\n \"\\n\",\n \"### $$ \\\\beta_j := \\\\beta_j - \\\\alpha \\\\frac{\\\\partial }{\\\\partial \\\\beta_j} J(\\\\beta)$$\\n\",\n \"\\n\",\n \"\\n\",\n \"}\\n\",\n \"\\n\",\n \"while simuntaneously update j=0..k\\n\",\n \"\\n\",\n \"$\\\\alpha$ is refered as the learning rate\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 9.44870659e-17, -8.54987593e-01, -8.33162685e-01])\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"beta_ini = np.array([0., 0., 0.])\\n\",\n \"\\n\",\n \"# gradient calculation\\n\",\n \"def gradient(beta, x, y):\\n\",\n \" return 1 / x.shape[0] * np.dot((lr_h(beta, x) - y).T, x)\\n\",\n \"\\n\",\n \"gradient(beta_ini, X_, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"beta_ini = np.array([0., 0., 0.])\\n\",\n \"alpha = 0.005\\n\",\n \"iters = 100\\n\",\n \"betas = gradient_descent(X_, y, beta_ini, alpha, iters)\\n\",\n \"\\n\",\n \"# Print iteration vs J\\n\",\n \"plt.plot(range(iters), betas[:, -1])\\n\",\n \"plt.xlabel('iteration')\\n\",\n \"plt.ylabel('J(beta)');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Aparently the cost function is not converging \\n\",\n \"\\n\",\n \"Lets change alpha and increase the number of iterations\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"beta_ini = np.array([0., 0., 0.])\\n\",\n \"alpha = 0.5\\n\",\n \"iters = 1000\\n\",\n \"betas = gradient_descent(X_, y, beta_ini, alpha, iters)\\n\",\n \"\\n\",\n \"# Print iteration vs J\\n\",\n \"plt.plot(range(1,iters), betas[1:, -1])\\n\",\n \"plt.xlabel('iteration')\\n\",\n \"plt.ylabel('J(beta)');\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"betas using gradient descent\\n\",\n \" [-9.44870659e-17 8.91147493e-01 -3.70307030e-02]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('betas using gradient descent\\\\n', betas[-1, :-1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Using the normal equations\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-8.69526697e-17, 8.91150925e-01, -3.70341353e-02])\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"betas_ols = np.dot(np.linalg.inv(np.dot(X_.T, X_)),np.dot(X_.T, y))\\n\",\n \"betas_ols\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Difference\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 7.53439626e-18, 3.43234287e-06, -3.43234287e-06])\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"betas_ols - betas[-1, :-1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Making predictions\\n\",\n \"\\n\",\n \"Predict the price when the area is 3000\\n\",\n \"\\n\",\n \"Note: remeber the matrix X is scaled\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1. , 1.27107075, 1.08022201]])\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = np.array([3000., 3000.**2])\\n\",\n \"\\n\",\n \"# scale\\n\",\n \"x_scaled = ss.transform(x.reshape(1, -1))\\n\",\n \"x_ = np.c_[1, x_scaled]\\n\",\n \"x_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1.09271078])\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_pred = lr_h(betas_ols, x_)\\n\",\n \"y_pred\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([475583.75451797])\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_pred = y_pred * y_std + y_mean\\n\",\n \"y_pred\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Using sklearn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import SGDRegressor\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"\\n\",\n \"clf1 = LinearRegression()\\n\",\n \"clf2 = SGDRegressor(max_iter=10000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When using sklearn there is no need to create the intercept\\n\",\n \"\\n\",\n \"Also sklearn works with pandas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"clf1.fit(data[['area', 'area2']], data[' price'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,\\n\",\n \" fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',\\n\",\n \" loss='squared_loss', max_iter=10000, n_iter=None, penalty='l2',\\n\",\n \" power_t=0.25, random_state=None, shuffle=True, tol=None, verbose=0,\\n\",\n \" warm_start=False)\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"clf2.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Making predictions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([475583.75451797])\"\n ]\n },\n \"execution_count\": 57,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"clf1.predict(x.reshape(1, -1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([475469.3288585])\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"clf2.predict(x_scaled.reshape(1, -1)) * y_std + y_mean\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation metrics for regression problems\\n\",\n \"\\n\",\n \"Evaluation metrics for classification problems, such as **accuracy**, are not useful for regression problems. We need evaluation metrics designed for comparing **continuous values**.\\n\",\n \"\\n\",\n \"Here are three common evaluation metrics for regression problems:\\n\",\n \"\\n\",\n \"**Mean Absolute Error** (MAE) is the mean of the absolute value of the errors:\\n\",\n \"\\n\",\n \"$$\\\\frac 1n\\\\sum_{i=1}^n|y_i-\\\\hat{y}_i|$$\\n\",\n \"\\n\",\n \"**Mean Squared Error** (MSE) is the mean of the squared errors:\\n\",\n \"\\n\",\n \"$$\\\\frac 1n\\\\sum_{i=1}^n(y_i-\\\\hat{y}_i)^2$$\\n\",\n \"\\n\",\n \"**Root Mean Squared Error** (RMSE) is the square root of the mean of the squared errors:\\n\",\n \"\\n\",\n \"$$\\\\sqrt{\\\\frac 1n\\\\sum_{i=1}^n(y_i-\\\\hat{y}_i)^2}$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"y_pred = clf1.predict(data[['area', 'area2']])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"MAE: 51990.96151069319\\n\",\n \"MSE: 4115290102.0599403\\n\",\n \"RMSE: 64150.526903993086\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn import metrics\\n\",\n \"import numpy as np\\n\",\n \"print('MAE:', metrics.mean_absolute_error(data[' price'], y_pred))\\n\",\n \"print('MSE:', metrics.mean_squared_error(data[' price'], y_pred))\\n\",\n \"print('RMSE:', np.sqrt(metrics.mean_squared_error(data[' price'], y_pred)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Comparing these metrics:\\n\",\n \"\\n\",\n \"- **MAE** is the easiest to understand, because it's the average error.\\n\",\n \"- **MSE** is more popular than MAE, because MSE \\\"punishes\\\" larger errors, which tends to be useful in the real world.\\n\",\n \"- **RMSE** is even more popular than MSE, because RMSE is interpretable in the \\\"y\\\" units.\\n\",\n \"\\n\",\n \"All of these are **loss functions**, because we want to minimize them.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Comparing linear regression with other models\\n\",\n \"\\n\",\n \"Advantages of linear regression:\\n\",\n \"\\n\",\n \"- Simple to explain\\n\",\n \"- Highly interpretable\\n\",\n \"- Model training and prediction are fast\\n\",\n \"- No tuning is required (excluding regularization)\\n\",\n \"- Features don't need scaling\\n\",\n \"- Can perform well with a small number of observations\\n\",\n \"- Well-understood\\n\",\n \"\\n\",\n \"Disadvantages of linear regression:\\n\",\n \"\\n\",\n \"- Presumes a linear relationship between the features and the response\\n\",\n \"- Performance is (generally) not competitive with the best supervised learning methods due to high bias\\n\",\n \"- Can't automatically learn feature interactions\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/04-logistic_regression.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 04 - Logistic Regression\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Kevin Markham](https://github.com/justmarkham)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Review: Predicting a Continuous Response\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
rinamgalsikcabafeglass_type
id
221.5196614.773.750.2972.020.039.000.00.001
1851.5111517.380.000.3475.410.006.650.00.006
401.5221314.213.820.4771.770.119.570.00.001
391.5221314.213.820.4771.770.119.570.00.001
511.5232013.723.720.5171.750.0910.060.00.161
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ri na mg al si k ca ba fe glass_type\\n\",\n \"id \\n\",\n \"22 1.51966 14.77 3.75 0.29 72.02 0.03 9.00 0.0 0.00 1\\n\",\n \"185 1.51115 17.38 0.00 0.34 75.41 0.00 6.65 0.0 0.00 6\\n\",\n \"40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1\\n\",\n \"39 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1\\n\",\n \"51 1.52320 13.72 3.72 0.51 71.75 0.09 10.06 0.0 0.16 1\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/glass.csv.zip', 'r') as z:\\n\",\n \" f = z.open('glass.csv')\\n\",\n \" glass = pd.read_csv(f, sep=',', index_col=0)\\n\",\n \"glass.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Question:** Pretend that we want to predict **ri**, and our only feature is **al**. How could we do it using machine learning?\\n\",\n \"\\n\",\n \"**Answer:** We could frame it as a regression problem, and use a linear regression model with **al** as the only feature and **ri** as the response.\\n\",\n \"\\n\",\n \"**Question:** How would we **visualize** this model?\\n\",\n \"\\n\",\n \"**Answer:** Create a scatter plot with **al** on the x-axis and **ri** on the y-axis, and draw the line of best fit.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# scatter plot using Pandas\\n\",\n \"glass.plot(kind='scatter', x='al', y='ri')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'ri')\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# equivalent scatter plot using Matplotlib\\n\",\n \"plt.scatter(glass.al, glass.ri)\\n\",\n \"plt.xlabel('al')\\n\",\n \"plt.ylabel('ri')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# fit a linear regression model\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"linreg = LinearRegression()\\n\",\n \"feature_cols = ['al']\\n\",\n \"X = glass[feature_cols]\\n\",\n \"y = glass.ri\\n\",\n \"linreg.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
rinamgalsikcabafeglass_typeri_pred
id
221.5196614.773.750.2972.020.039.000.00.0011.521227
1851.5111517.380.000.3475.410.006.650.00.0061.521103
401.5221314.213.820.4771.770.119.570.00.0011.520781
391.5221314.213.820.4771.770.119.570.00.0011.520781
511.5232013.723.720.5171.750.0910.060.00.1611.520682
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ri na mg al si k ca ba fe glass_type \\\\\\n\",\n \"id \\n\",\n \"22 1.51966 14.77 3.75 0.29 72.02 0.03 9.00 0.0 0.00 1 \\n\",\n \"185 1.51115 17.38 0.00 0.34 75.41 0.00 6.65 0.0 0.00 6 \\n\",\n \"40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \\n\",\n \"39 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \\n\",\n \"51 1.52320 13.72 3.72 0.51 71.75 0.09 10.06 0.0 0.16 1 \\n\",\n \"\\n\",\n \" ri_pred \\n\",\n \"id \\n\",\n \"22 1.521227 \\n\",\n \"185 1.521103 \\n\",\n \"40 1.520781 \\n\",\n \"39 1.520781 \\n\",\n \"51 1.520682 \"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# make predictions for all values of X\\n\",\n \"glass['ri_pred'] = linreg.predict(X)\\n\",\n \"glass.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'ri')\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# put the plots together\\n\",\n \"plt.scatter(glass.al, glass.ri)\\n\",\n \"plt.plot(glass.al, glass.ri_pred, color='red')\\n\",\n \"plt.xlabel('al')\\n\",\n \"plt.ylabel('ri')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Refresher: interpreting linear regression coefficients\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Linear regression equation: $y = \\\\beta_0 + \\\\beta_1x$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1.51699012])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute prediction for al=2 using the equation\\n\",\n \"linreg.intercept_ + linreg.coef_ * 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1.51699012])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute prediction for al=2 using the predict method\\n\",\n \"linreg.predict(2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['al'] [-0.00247761]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# examine coefficient for al\\n\",\n \"print(feature_cols, linreg.coef_)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Interpretation:** A 1 unit increase in 'al' is associated with a 0.0025 unit decrease in 'ri'.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.5145125136125304\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# increasing al by 1 (so that al=3) decreases ri by 0.0025\\n\",\n \"1.51699012 - 0.0024776063874696243\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1.51451251])\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute prediction for al=3 using the predict method\\n\",\n \"linreg.predict(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Predicting a Categorical Response\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1 70\\n\",\n \"2 76\\n\",\n \"3 17\\n\",\n \"5 13\\n\",\n \"6 9\\n\",\n \"7 29\\n\",\n \"Name: glass_type, dtype: int64\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# examine glass_type\\n\",\n \"glass.glass_type.value_counts().sort_index()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
rinamgalsikcabafeglass_typeri_predhousehold
id
221.5196614.773.750.2972.020.039.000.00.0011.5212270
1851.5111517.380.000.3475.410.006.650.00.0061.5211031
401.5221314.213.820.4771.770.119.570.00.0011.5207810
391.5221314.213.820.4771.770.119.570.00.0011.5207810
511.5232013.723.720.5171.750.0910.060.00.1611.5206820
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ri na mg al si k ca ba fe glass_type \\\\\\n\",\n \"id \\n\",\n \"22 1.51966 14.77 3.75 0.29 72.02 0.03 9.00 0.0 0.00 1 \\n\",\n \"185 1.51115 17.38 0.00 0.34 75.41 0.00 6.65 0.0 0.00 6 \\n\",\n \"40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \\n\",\n \"39 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \\n\",\n \"51 1.52320 13.72 3.72 0.51 71.75 0.09 10.06 0.0 0.16 1 \\n\",\n \"\\n\",\n \" ri_pred household \\n\",\n \"id \\n\",\n \"22 1.521227 0 \\n\",\n \"185 1.521103 1 \\n\",\n \"40 1.520781 0 \\n\",\n \"39 1.520781 0 \\n\",\n \"51 1.520682 0 \"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# types 1, 2, 3 are window glass\\n\",\n \"# types 5, 6, 7 are household glass\\n\",\n \"glass['household'] = glass.glass_type.map({1:0, 2:0, 3:0, 5:1, 6:1, 7:1})\\n\",\n \"glass.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's change our task, so that we're predicting **household** using **al**. Let's visualize the relationship to figure out how to do this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'household')\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(glass.al, glass.household)\\n\",\n \"plt.xlabel('al')\\n\",\n \"plt.ylabel('household')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's draw a **regression line**, like we did before:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# fit a linear regression model and store the predictions\\n\",\n \"feature_cols = ['al']\\n\",\n \"X = glass[feature_cols]\\n\",\n \"y = glass.household\\n\",\n \"linreg.fit(X, y)\\n\",\n \"glass['household_pred'] = linreg.predict(X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'household')\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# scatter plot that includes the regression line\\n\",\n \"plt.scatter(glass.al, glass.household)\\n\",\n \"plt.plot(glass.al, glass.household_pred, color='red')\\n\",\n \"plt.xlabel('al')\\n\",\n \"plt.ylabel('household')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If **al=3**, what class do we predict for household? **1**\\n\",\n \"\\n\",\n \"If **al=1.5**, what class do we predict for household? **0**\\n\",\n \"\\n\",\n \"We predict the 0 class for **lower** values of al, and the 1 class for **higher** values of al. What's our cutoff value? Around **al=2**, because that's where the linear regression line crosses the midpoint between predicting class 0 and class 1.\\n\",\n \"\\n\",\n \"Therefore, we'll say that if **household_pred >= 0.5**, we predict a class of **1**, else we predict a class of **0**.\\n\",\n \"\\n\",\n \"## $$h_\\\\beta(x) = \\\\beta_0 + \\\\beta_1x_1 + \\\\beta_2x_2 + ... + \\\\beta_nx_n$$\\n\",\n \"\\n\",\n \"- $h_\\\\beta(x)$ is the response\\n\",\n \"- $\\\\beta_0$ is the intercept\\n\",\n \"- $\\\\beta_1$ is the coefficient for $x_1$ (the first feature)\\n\",\n \"- $\\\\beta_n$ is the coefficient for $x_n$ (the nth feature)\\n\",\n \"\\n\",\n \"### if $h_\\\\beta(x)\\\\le 0.5$ then $\\\\hat y = 0$ \\n\",\n \"\\n\",\n \"### if $h_\\\\beta(x)> 0.5$ then $\\\\hat y = 1$ \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array(['small', 'big', 'small'], dtype=' 10, 'big', 'small')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
rinamgalsikcabafeglass_typeri_predhouseholdhousehold_predhousehold_pred_class
id
221.5196614.773.750.2972.020.039.000.00.0011.5212270-0.3404950
1851.5111517.380.000.3475.410.006.650.00.0061.5211031-0.3154360
401.5221314.213.820.4771.770.119.570.00.0011.5207810-0.2502830
391.5221314.213.820.4771.770.119.570.00.0011.5207810-0.2502830
511.5232013.723.720.5171.750.0910.060.00.1611.5206820-0.2302360
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" ri na mg al si k ca ba fe glass_type \\\\\\n\",\n \"id \\n\",\n \"22 1.51966 14.77 3.75 0.29 72.02 0.03 9.00 0.0 0.00 1 \\n\",\n \"185 1.51115 17.38 0.00 0.34 75.41 0.00 6.65 0.0 0.00 6 \\n\",\n \"40 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \\n\",\n \"39 1.52213 14.21 3.82 0.47 71.77 0.11 9.57 0.0 0.00 1 \\n\",\n \"51 1.52320 13.72 3.72 0.51 71.75 0.09 10.06 0.0 0.16 1 \\n\",\n \"\\n\",\n \" ri_pred household household_pred household_pred_class \\n\",\n \"id \\n\",\n \"22 1.521227 0 -0.340495 0 \\n\",\n \"185 1.521103 1 -0.315436 0 \\n\",\n \"40 1.520781 0 -0.250283 0 \\n\",\n \"39 1.520781 0 -0.250283 0 \\n\",\n \"51 1.520682 0 -0.230236 0 \"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# transform household_pred to 1 or 0\\n\",\n \"glass['household_pred_class'] = np.where(glass.household_pred >= 0.5, 1, 0)\\n\",\n \"glass.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'household')\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the class predictions\\n\",\n \"plt.scatter(glass.al, glass.household)\\n\",\n \"plt.plot(glass.al, glass.household_pred_class, color='red')\\n\",\n \"plt.xlabel('al')\\n\",\n \"plt.ylabel('household')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"$h_\\\\beta(x)$ can be lower 0 or higher than 1, which is countra intuitive\\n\",\n \"\\n\",\n \"## Using Logistic Regression Instead\\n\",\n \"\\n\",\n \"Logistic regression can do what we just did:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# fit a logistic regression model and store the class predictions\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"logreg = LogisticRegression(C=1e9)\\n\",\n \"feature_cols = ['al']\\n\",\n \"X = glass[feature_cols]\\n\",\n \"y = glass.household\\n\",\n \"logreg.fit(X, y)\\n\",\n \"glass['household_pred_class'] = logreg.predict(X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'household')\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the class predictions\\n\",\n \"plt.scatter(glass.al, glass.household)\\n\",\n \"plt.plot(glass.al, glass.household_pred_class, color='red')\\n\",\n \"plt.xlabel('al')\\n\",\n \"plt.ylabel('household')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"What if we wanted the **predicted probabilities** instead of just the **class predictions**, to understand how confident we are in a given prediction?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# store the predicted probabilites of class 1\\n\",\n \"glass['household_pred_prob'] = logreg.predict_proba(X)[:, 1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'household')\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the predicted probabilities\\n\",\n \"plt.scatter(glass.al, glass.household)\\n\",\n \"plt.plot(glass.al, glass.household_pred_prob, color='red')\\n\",\n \"plt.xlabel('al')\\n\",\n \"plt.ylabel('household')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[0.97161726 0.02838274]]\\n\",\n \"[[0.34361555 0.65638445]]\\n\",\n \"[[0.00794192 0.99205808]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# examine some example predictions\\n\",\n \"print(logreg.predict_proba(1))\\n\",\n \"print(logreg.predict_proba(2))\\n\",\n \"print(logreg.predict_proba(3))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first column indicates the predicted probability of **class 0**, and the second column indicates the predicted probability of **class 1**.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Probability, odds, e, log, log-odds\\n\",\n \"\\n\",\n \"$$probability = \\\\frac {one\\\\ outcome} {all\\\\ outcomes}$$\\n\",\n \"\\n\",\n \"$$odds = \\\\frac {one\\\\ outcome} {all\\\\ other\\\\ outcomes}$$\\n\",\n \"\\n\",\n \"Examples:\\n\",\n \"\\n\",\n \"- Dice roll of 1: probability = 1/6, odds = 1/5\\n\",\n \"- Even dice roll: probability = 3/6, odds = 3/3 = 1\\n\",\n \"- Dice roll less than 5: probability = 4/6, odds = 4/2 = 2\\n\",\n \"\\n\",\n \"$$odds = \\\\frac {probability} {1 - probability}$$\\n\",\n \"\\n\",\n \"$$probability = \\\\frac {odds} {1 + odds}$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
probabilityodds
00.100.111111
10.200.250000
20.250.333333
30.501.000000
40.601.500000
50.804.000000
60.909.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" probability odds\\n\",\n \"0 0.10 0.111111\\n\",\n \"1 0.20 0.250000\\n\",\n \"2 0.25 0.333333\\n\",\n \"3 0.50 1.000000\\n\",\n \"4 0.60 1.500000\\n\",\n \"5 0.80 4.000000\\n\",\n \"6 0.90 9.000000\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# create a table of probability versus odds\\n\",\n \"table = pd.DataFrame({'probability':[0.1, 0.2, 0.25, 0.5, 0.6, 0.8, 0.9]})\\n\",\n \"table['odds'] = table.probability/(1 - table.probability)\\n\",\n \"table\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"What is **e**? It is the base rate of growth shared by all continually growing processes:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2.718281828459045\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# exponential function: e^1\\n\",\n \"np.exp(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"What is a **(natural) log**? It gives you the time needed to reach a certain level of growth:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.999896315728952\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# time needed to grow 1 unit to 2.718 units\\n\",\n \"np.log(2.718)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is also the **inverse** of the exponential function:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5.0\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.log(np.exp(5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
probabilityoddslogodds
00.100.111111-2.197225
10.200.250000-1.386294
20.250.333333-1.098612
30.501.0000000.000000
40.601.5000000.405465
50.804.0000001.386294
60.909.0000002.197225
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" probability odds logodds\\n\",\n \"0 0.10 0.111111 -2.197225\\n\",\n \"1 0.20 0.250000 -1.386294\\n\",\n \"2 0.25 0.333333 -1.098612\\n\",\n \"3 0.50 1.000000 0.000000\\n\",\n \"4 0.60 1.500000 0.405465\\n\",\n \"5 0.80 4.000000 1.386294\\n\",\n \"6 0.90 9.000000 2.197225\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# add log-odds to the table\\n\",\n \"table['logodds'] = np.log(table.odds)\\n\",\n \"table\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What is Logistic Regression?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Linear regression:** continuous response is modeled as a linear combination of the features:\\n\",\n \"\\n\",\n \"$$y = \\\\beta_0 + \\\\beta_1x$$\\n\",\n \"\\n\",\n \"**Logistic regression:** log-odds of a categorical response being \\\"true\\\" (1) is modeled as a linear combination of the features:\\n\",\n \"\\n\",\n \"$$\\\\log \\\\left({p\\\\over 1-p}\\\\right) = \\\\beta_0 + \\\\beta_1x$$\\n\",\n \"\\n\",\n \"This is called the **logit function**.\\n\",\n \"\\n\",\n \"Probability is sometimes written as pi:\\n\",\n \"\\n\",\n \"$$\\\\log \\\\left({\\\\pi\\\\over 1-\\\\pi}\\\\right) = \\\\beta_0 + \\\\beta_1x$$\\n\",\n \"\\n\",\n \"The equation can be rearranged into the **logistic function**:\\n\",\n \"\\n\",\n \"$$\\\\pi = \\\\frac{e^{\\\\beta_0 + \\\\beta_1x}} {1 + e^{\\\\beta_0 + \\\\beta_1x}}$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In other words:\\n\",\n \"\\n\",\n \"- Logistic regression outputs the **probabilities of a specific class**\\n\",\n \"- Those probabilities can be converted into **class predictions**\\n\",\n \"\\n\",\n \"The **logistic function** has some nice properties:\\n\",\n \"\\n\",\n \"- Takes on an \\\"s\\\" shape\\n\",\n \"- Output is bounded by 0 and 1\\n\",\n \"\\n\",\n \"We have covered how this works for **binary classification problems** (two response classes). But what about **multi-class classification problems** (more than two response classes)?\\n\",\n \"\\n\",\n \"- Most common solution for classification models is **\\\"one-vs-all\\\"** (also known as **\\\"one-vs-rest\\\"**): decompose the problem into multiple binary classification problems\\n\",\n \"- **Multinomial logistic regression** can solve this as a single problem\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Part 6: Interpreting Logistic Regression Coefficients\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'household')\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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wzoago6jQioYUqCu6+CDjQzLoD5u7rQzxsJZA6LnB/YFWaeV529yrgfTNbRlAkXq33/NOAaQAlJSUNdnyLZI3Jk4MB7773vaiTiDRLU3sf/biB2wFw99sbefirwBAzGwyUAmOA+nsWzQLGAg+aWS+CzUkrQiUXyVZvvgl//CP87Gc6LkFyTlMthR1bumB3rzazy4B5BLukPuDub5nZJGChu89O3Hecmb0N1ADj3X1tS59TJCvcfHMw4J3GOJIcZO65tTWmpKTEFy5cGHUMkfSWL4e99oIrr4Rbb406jUiSmS1y9yZPCB5q7yMz629mvzez1Wb2mZk9ZWb9Wx9TJM9MmQLxOFxxRdRJRFok7C6pvwVmA7sS7Fb6TOI2EamzahU8+CCcdx7sskvUaURaJGxR6O3uv3X36sTlQaB3BnOJ5J477oDq6uAkOiI5KmxR+NzMxplZLHEZB6hDWKTOF1/AvffCmDGw++5RpxFpsbBF4XzgTOBT4BPgdJo+eE2k4/jlL+HLL+Gaa6JOItIqYQ9e+wg4JcNZRHLTpk1w111w8smw//5RpxFplVBFwcx6EwxeNyj1Me6u1oLIfffB2rVw7bVRJxFptbBjH/0B+BvwHMFBZiICUFkZnFHtqKPg8MOjTiPSamGLQld3vzqjSURy0SOPQGkpPPBA1ElE2kTYjuY5ZnZiRpOI5JqaGrjlFjj4YDj22KjTiLSJpgbE20gw3LUB/21mlUBlYtrdvXvmI4pkqaefhnffhSeeAEs3UrxI7mnqzGstHhBPJK+5B8Nj77knjB4ddRqRNhN27CNLHLz2P4npAWZ2SGajiWSxefNg8eLguIRYLOo0Im0mbJ/Cr4DD2Xo+hC+BezKSSCQXTJ4M/fvDOedEnUSkTYXd++hQdz/YzBYDuPs6MyvMYC6R7PX3v8OLL8Kdd0KhPgaSX8K2FKrMLEbiHMuJg9lqM5ZKJJvVnWrzwgujTiLS5sIWhbuA3wN9zOxG4CXgpoylEslWb74Jc+bA5ZfrVJuSl8KOffQ7M1sE/CfB7qinuvs7GU0mko10qk3Jc2H3PtoDeN/d7wGWAseaWXFGk4lkm/feg8cfh0sugZ12ijqNSEaE3Xz0FFBjZl8FpgODgf/NWCqRbKRTbUoHELYo1Lp7NXAaMNXdrwB0vkHpOD75BH77W51qU/Jec/Y+Ggt8B5iTuC2emUgiWei224KxjnSqTclzYYvCeQQHr93o7u+b2WDg0czFEskia9cGp9ocO1an2pS8F3bvo7eBH6VMvw/cnKlQIlll6tTg7Go6iY50AGHPvPY+iQPXUrm7fjZJftuwAe6+Oxj0bp99ok4jknFhh7koSbneBTgD6Nn2cUSyzK9+BWVlcN11UScRaReh+hTcfW3KpdTd7wS+meFsItEqL4fbb4eRI2HYsKjTiLSLsJuPDk6Z7ETQctC5FiS/3X8/rFmjVoJ0KGE3H92Wcr0a+AA4s83TiGSLykq49VY48sjgItJBhN376OhMBxHJKo88AitXwvTpUScRaVdhxz7qYWa3m9nCxOU2M+sR4nHHm9kyM1tuZtc0Mt/pZuZmVtLQPCLtpro6GPhu2DA47rio04i0q7AHrz0AbCTYZHQmsAH4bWMPSJx/4R7gBGAfYKyZbbdPn5ntSHAMxCvhY4tk0BNPwPLlQV+CWdRpRNpV2KKwh7tPcPcVictPgaaOUTgEWJ6YvxJ4DBiVZr6fAbcCm0OnFsmU2lq46abgmIRR6d6uIvktbFGoMLNv1E2Y2XCgoonH9AM+TplembgtycyGAgPcfQ6NMLOL6jZdrVmzJmRkkRZ45hlYujQ4erlT2I+HSP4Iu/fRJcBDKf0I64DvNvGYdO3u5FHRZtYJuAM4t6knd/dpwDSAkpKS7Y6sFmkT7nDjjcH4RmPGRJ1GJBJhi8I7BJt49gCKgfXAqcCbjTxmJTAgZbo/sCplekdgP+AvFmy37QvMNrNT3H1hyFwibWfuXHj1VbjvPigI+9EQyS9h3/l/AMqA14DSkI95FRiSGFG1FBgDnF13p7uvB3rVTZvZX4CrVBAkEu4wcSIMHgzfbaoRLJK/whaF/u5+fHMW7O7VZnYZMA+IAQ+4+1tmNglY6O6zm5lVJHPmzIGFC4OjmOM6VYh0XObe9CZ6M5sG3O3uSzIfqXElJSW+cKEaE9KG3KGkJBj47l//UlGQvGRmi9y9yWPBGm0pmNkSgs7hAuA8M1sBbCHoRHZ3P6AtwopEavZseO01ePBBFQTp8JrafHRSu6QQiUptLUyYAEOGwDnnRJ1GJHKNFgV3/7C9gohEYtYseOMNePhh7XEkQviD10TyT21tsMfRnnsG518WkdB7H4nkn6efhiVL4NFH1UoQSVBLQTqm6mq4/nrYe28dvSySQj+PpGN66CFYtixoLcRiUacRyRpqKUjHU1ER7HF02GFw6qlRpxHJKmopSMfzy19CaSn87nc6X4JIPWopSMdSVgaTJ8MJJ8BRR0WdRiTrqChIx3LLLVsLg4hsR0VBOo5Vq2DqVDj7bDjwwKjTiGQlFQXpOK6/PtgVddKkqJOIZC0VBekYFi0KBry7/PLgzGoikpaKguQ/d7jiCujVC667Luo0IllNu6RK/nvySfjb3+A3v4EePZqeX6QDU0tB8tvmzTB+PBxwAFxwQdRpRLKeWgqS3+64Az78EF54QcNZiISgloLkr1Wr4KabgqEsjj466jQiOUFFQfLXFVdAVRX84hdRJxHJGSoKkp/mzYOZM4O9jfbYI+o0IjlDRUHyT0UF/OAHwRnVfvKTqNOI5BR1NEv+mTwZ3nsPnn8eOneOOo1ITlFLQfLLsmXBoHfnnAPf/GbUaURyjoqC5A93uPRSKCqC226LOo1ITtLmI8kf06YFxyPcey985StRpxHJSWopSH54/3248ko45hi46KKo04jkLBUFyX21tcEQFp06wfTpOsWmSCto85Hkvl//GubPh/vug912izqNSE7LaEvBzI43s2VmttzMrklz/4/N7G0ze9PMnjczfaKleVasCI5FGDlSA96JtIGMFQUziwH3ACcA+wBjzWyferMtBkrc/QDgSeDWTOWRPFRdDd/5DsTj2mwk0kYy2VI4BFju7ivcvRJ4DBiVOoO7z3f38sTky0D/DOaRfDNhAixYEGw+6q+3jkhbyGRR6Ad8nDK9MnFbQy4A/pTBPJJPnn02OHL5ggtg7Nio04jkjUx2NKdry3vaGc3GASXAUQ3cfxFwEcDAgQPbKp/kqk8/hXHj4Gtfg7vuijqNSF7JZEthJTAgZbo/sKr+TGZ2DHAdcIq7b0m3IHef5u4l7l7Su3fvjISVHFFbC9/+NmzcGIyC2rVr1IlE8komi8KrwBAzG2xmhcAYYHbqDGY2FPgNQUFYncEski8mTYLnngtaCPvuG3UakbyTsaLg7tXAZcA84B1gpru/ZWaTzOyUxGxTgB2AJ8zsdTOb3cDiROCpp+CnP4Xvfle7n4pkiLmn3cyftUpKSnzhwoVRx5D29sYbcMQRcMABwYFqXbpEnUgkp5jZIncvaWo+DXMh2W/1ajjlFOjZE37/exUEkQzSMBeS3Sor4VvfCgrDSy9B375RJxLJayoKkr1qaoI9jV56CWbMgGHDok4kkve0+Uiykztcdlmw2+mUKTBmTNSJRDoEFQXJThMmBCfLufpquOqqqNOIdBgqCpJ9pk6Fn/0s2O108uSo04h0KCoKkl3uuQcuvxxGjw5aChr5VKRdqShI9rjzzqAfYdSooGO5QPtBiLQ3FQXJDrfeCldcEex++sQT0Llz1IlEOiQVBYlWbW3QkXz11cEeRjNmBCfNEZFIqH0u0dm8OehM/t//DTYbTZ0KnfQ7RSRKKgoSjU8/DTqTX34ZbroJrrlGncoiWUBFQdrf4sVBZ/LatcHIp6edFnUiEUlQW13ajztMmwaHHx5cf+klFQSRLKOiIO1j48ZgHKPvfx9GjIDXXoOhQ6NOJSL1qChI5v31r8F5EGbMgJ//HObOBZ1WVSQrqShI5mzaBD/+MRx9NMRi8OKLcN112sNIJIvp0yltzx2efhq+9jW44w64+GJ4/XUYPjzqZCLSBBUFaVvLl8OJJwZHJhcXB62DX/0Kdtgh6mQiEoKKgrSNTz4JDkDbZx9YsCBoIbz2Ghx5ZNTJRKQZdJyCtM4XXwTjFt11V3DqzAsvhBtugF13jTqZiLSAioK0zPvvB6Oa3n8/lJfD2WfDxInw1a9GnUxEWkFFQcJzDzYN3X03PPlksBfR2WfD+PGw335RpxORNqCiIE377DN4+OGgVbBsGXTvHoxs+sMfQv/+UacTkTakoiDpffEFzJoFM2fC889DdXWwS+k118AZZ0C3blEnFJEMUFGQgDssXQrPPgvz5sELLwSFYPfd4cor4dxzYe+9o04pIhmmotCRffIJPPdcUAieey6YBthrr+BI5DPPhIMP1pDWIh2IikJHUVYWHDewaNHWv+++G9y3885wzDFw3HFw7LEwYEC0WUUkMioK+aa2FkpL4V//2vrlv2gRrFixdZ6BA4MWwPnnB4XgoIM0HpGIACoKuccd1q2DDz+Ejz7a9u+77waX8vKt8w8eDMOGBQeVDRsWDFetEUpFpAEZLQpmdjwwFYgB09395nr3dwYeBoYBa4Gz3P2DTGSZtbiUKfOWsaqsgl2Lixg/ci9OHdqvWcu4ftYSZrzyMTXuxMw4bPed+GBtxTbLBJg4+y3KKqoA2KlrnAkn75t8rlmLS7e5H3d2idcyao8d+cdrK2D9enpWbKBv+TqKN62n16Yydi4vo9emMvp8uY5+G9fQrbJim1xbCgop7d6bD4v7smKfY1nRsx8revbn7T6Dqe5RzI2j99/mtdZ/HWMPHUDJbj23zZUHCmNGZY1vd/tOXeOsK68iZkaNb73fgK6FMTZV1iTvKy6KU1ldQ3lVbfKxdf/P+v/LTga1Dv1a+P6qe4+WllUkn7/ub0uXKdJc5r79h6ZNFmwWA/4NHAusBF4Fxrr72ynzXAoc4O4Xm9kYYLS7n9XYcktKSnzhwoXNyjJrcSnXPr2Eiqqa5G1F8RiTT9u/8Q+ZO9TUQFUVk2a9wVMvf0C8tpp4TTVdqivpUr2FLlWVdKmupHN1Jd1qKyms2kJh4rYu1VvYYUsFPSo3MaJvZ+JfbuSj9z+h25ZydtyyiR0Tf+O1NQ1G+LKwiLVde/B512I+71ZMafc+lPboQ2n33pR278Oq7r1Z27VHo53BsU7GbWccyKlD+3H9rCU8+vJH281jQGbeCfknHjPO+voAHv/nx1TVpl9rod5fKdK9R1u7TJFUZrbI3UuanC+DReFwYKK7j0xMXwvg7pNT5pmXmOcfZlYAfAr09kZCtaQoDL/5BUrLKrj0HzM57t2XKaitoaCmmiKrZbcd41BVFVyqq7der7u0Uo11YmPnrlR06caXXbrxRUERGzt3ZWPnbnzZuWvy+sbO3diQuL6uqDufdyvm86492Bzv0uoMEPx6XXDNN9nj2rnb/DqWlqnfykinbp2HUfcebUpzlimSKmxRyOTmo37AxynTK4FDG5rH3avNbD2wM/B56kxmdhFwEcDAgQObHWRV4sO2JRZnfZcdqOoUozpWQHWnAnYbNhDi8eBSULD1euqloIAb/2851bEYVZ0KqIoVsLmgkM0FndlSUMiWgsJgOl53W5zNBZ3ZXFBIebwLmFH3Oz6qr+O6daCC0DbCrMdVIb7kmztvc5Yp0hKZLArptmfU/ySFmQd3nwZMg6Cl0NwguxYXUVpWwf2HjOb+Q0Ynb+9XXMRJIX91PbChdb+wdy0uAgj1azAT6p4/zC9caVqY9Vi3zsOoe4+GmU8kkzK5H+JKIHWH9/7AqobmSWw+6gF80dZBxo/ci6J4bJvbiuKxZMdwGGMPbXrf/Xgno1OaMhePGeNH7sX4kXsRTzdDhsU6WfK1NvQ6dHhaePFY0Dnf2P+yue+vdO/R1i5TpCUyWRReBYaY2WAzKwTGALPrzTMb+G7i+unAC431J7TUqUP7Mfm0/elXXIQRtBCa22H381P3Z9xhA4klOnRjZgzfo+c2y5xyxoHcfuZBFBfFk4/bqWucKacHnbynDu3HlDMO3Ob+unnGHTZwm9vjDfw3zwQbAAAFZUlEQVRnusY7bXNfJ4Mhfbo1+KXerTCW7GRu6HWMO2wgd5x10Ha5cl1hLP1a2alr8Dpj9TrnjWB9pd5XXBSna8oKr/t//vzU/bf7X9bViJa8v1Lfo6nPX/e3JcsUaYmMdTQDmNmJwJ0Eu6Q+4O43mtkkYKG7zzazLsAjwFCCFsIYd1/R8BJb1tEsItLRZUNHM+4+F5hb77YbUq5vBs7IZAYREQlPYxuIiEiSioKIiCSpKIiISJKKgoiIJKkoiIhIkoqCiIgkqSiIiEhSRg9eywQzWwN8GHWOFL2oN4BfDlH2aORydsjt/B05+27u3uQZtnKuKGQbM1sY5ijBbKTs0cjl7JDb+ZW9adp8JCIiSSoKIiKSpKLQetOiDtAKyh6NXM4OuZ1f2ZugPgUREUlSS0FERJJUFEIys+PNbJmZLTeza9Lcf66ZrTGz1xOXC6PIWZ+ZPWBmq81saQP3m5ndlXhdb5rZwe2dsSEhso8ws/Up6/yGdPNFwcwGmNl8M3vHzN4ys/9KM09WrvuQ2bN53Xcxs3+a2RuJ/D9NM09nM3s8se5fMbNB7Z90eyGzZ/a7xt11aeJCcJKg94DdgULgDWCfevOcC/wy6qxpsv8HcDCwtIH7TwT+RHDiscOAV6LO3IzsI4A5UedsINsuwMGJ6zsC/07znsnKdR8yezavewN2SFyPA68Ah9Wb51Lg3sT1McDjUeduRvaMfteopRDOIcByd1/h7pXAY8CoiDOF4u4v0vh5r0cBD3vgZaDYzHZpn3SNC5E9a7n7J+7+WuL6RuAdoP65NLNy3YfMnrUS6/PLxGQ8canfeToKeChx/UngP80s8lOVh8yeUSoK4fQDPk6ZXkn6D8m3EpsBnjSzAe0TrdXCvrZsdXiiqf0nM9s36jDpJDZNDCX41Zcq69d9I9khi9e9mcXM7HVgNfCsuze47t29GlgP7Ny+KdMLkR0y+F2johBOul8Q9av3M8Agdz8AeI6tv0KyXZjXlq1eIzh0/0DgbmBWxHm2Y2Y7AE8Bl7v7hvp3p3lI1qz7JrJn9bp39xp3PwjoDxxiZvvVmyVr132I7Bn9rlFRCGclkFqN+wOrUmdw97XuviUxeR8wrJ2ytVaTry1bufuGuqa2B+cDj5tZr4hjJZlZnOBL9Xfu/nSaWbJ23TeVPdvXfR13LwP+Ahxf767kujezAqAHWbapsqHsmf6uUVEI51VgiJkNNrNCgo6p2akz1NsWfArBdthcMBv4TmJPmMOA9e7+SdShwjCzvnXbgc3sEIL389poUwUSue4H3nH32xuYLSvXfZjsWb7ue5tZceJ6EXAM8K96s80Gvpu4fjrwgid6caMUJnumv2sK2nJh+crdq83sMmAewZ5ID7j7W2Y2CVjo7rOBH5nZKUA1wS+OcyMLnMLMZhDsKdLLzFYCEwg6r3D3e4G5BHvBLAfKgfOiSbq9ENlPBy4xs2qgAhiTDR/shOHAt4Elie3DAP8NDISsX/dhsmfzut8FeMjMYgTFaqa7z6n3eb0feMTMlhN8XsdEF3cbYbJn9LtGRzSLiEiSNh+JiEiSioKIiCSpKIiISJKKgoiIJKkoiIhIkoqCSAaY2QfZeDCXSFNUFEREJElFQaSVzGyWmS1KjH9/UdR5RFpDRzSLtN757v5FYliCV83sqagDibSUioJI6/3IzEYnrg8AhkQZRqQ1VBREWsHMRhAMWna4u5eb2V+ALpGGEmkF9SmItE4PYF2iIOxNcFpNkZyloiDSOn8GCszsTeBnwMsR5xFpFY2SKiIiSWopiIhIkoqCiIgkqSiIiEiSioKIiCSpKIiISJKKgoiIJKkoiIhIkoqCiIgk/T88gTJFdrKNdAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot the predicted probabilities again\\n\",\n \"plt.scatter(glass.al, glass.household)\\n\",\n \"plt.plot(glass.al, glass.household_pred_prob, color='red')\\n\",\n \"plt.xlabel('al')\\n\",\n \"plt.ylabel('household')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.64722323])\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute predicted log-odds for al=2 using the equation\\n\",\n \"logodds = logreg.intercept_ + logreg.coef_[0] * 2\\n\",\n \"logodds\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1.91022919])\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# convert log-odds to odds\\n\",\n \"odds = np.exp(logodds)\\n\",\n \"odds\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.65638445])\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# convert odds to probability\\n\",\n \"prob = odds/(1 + odds)\\n\",\n \"prob\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.65638445])\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute predicted probability for al=2 using the predict_proba method\\n\",\n \"logreg.predict_proba(2)[:, 1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(['al'], array([4.18040386]))\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# examine the coefficient for al\\n\",\n \"feature_cols, logreg.coef_[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Interpretation:** A 1 unit increase in 'al' is associated with a 4.18 unit increase in the log-odds of 'household'.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.9920580839167457\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# increasing al by 1 (so that al=3) increases the log-odds by 4.18\\n\",\n \"logodds = 0.64722323 + 4.1804038614510901\\n\",\n \"odds = np.exp(logodds)\\n\",\n \"prob = odds/(1 + odds)\\n\",\n \"prob\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.99205808])\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute predicted probability for al=3 using the predict_proba method\\n\",\n \"logreg.predict_proba(3)[:, 1]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Bottom line:** Positive coefficients increase the log-odds of the response (and thus increase the probability), and negative coefficients decrease the log-odds of the response (and thus decrease the probability).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-7.71358449])\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# examine the intercept\\n\",\n \"logreg.intercept_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Interpretation:** For an 'al' value of 0, the log-odds of 'household' is -7.71.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.00044652])\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# convert log-odds to probability\\n\",\n \"logodds = logreg.intercept_\\n\",\n \"odds = np.exp(logodds)\\n\",\n \"prob = odds/(1 + odds)\\n\",\n \"prob\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"That makes sense from the plot above, because the probability of household=1 should be very low for such a low 'al' value.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Logistic regression beta values](images/logistic_betas.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Changing the $\\\\beta_0$ value shifts the curve **horizontally**, whereas changing the $\\\\beta_1$ value changes the **slope** of the curve.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Comparing Logistic Regression with Other Models\\n\",\n \"\\n\",\n \"Advantages of logistic regression:\\n\",\n \"\\n\",\n \"- Highly interpretable (if you remember how)\\n\",\n \"- Model training and prediction are fast\\n\",\n \"- No tuning is required (excluding regularization)\\n\",\n \"- Features don't need scaling\\n\",\n \"- Can perform well with a small number of observations\\n\",\n \"- Outputs well-calibrated predicted probabilities\\n\",\n \"\\n\",\n \"Disadvantages of logistic regression:\\n\",\n \"\\n\",\n \"- Presumes a linear relationship between the features and the log-odds of the response\\n\",\n \"- Performance is (generally) not competitive with the best supervised learning methods\\n\",\n \"- Can't automatically learn feature interactions\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/05-data_preparation_evaluation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 05 - Data Preparation and Advanced Model Evaluation\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Kevin Markham](https://github.com/justmarkham)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Handling missing values\\n\",\n \"\\n\",\n \"scikit-learn models expect that all values are **numeric** and **hold meaning**. Thus, missing values are not allowed by scikit-learn.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Survived Pclass \\\\\\n\",\n \"PassengerId \\n\",\n \"1 0 3 \\n\",\n \"2 1 1 \\n\",\n \"3 1 3 \\n\",\n \"4 1 1 \\n\",\n \"5 0 3 \\n\",\n \"\\n\",\n \" Name Sex Age \\\\\\n\",\n \"PassengerId \\n\",\n \"1 Braund, Mr. Owen Harris male 22.0 \\n\",\n \"2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \\n\",\n \"3 Heikkinen, Miss. Laina female 26.0 \\n\",\n \"4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \\n\",\n \"5 Allen, Mr. William Henry male 35.0 \\n\",\n \"\\n\",\n \" SibSp Parch Ticket Fare Cabin Embarked \\n\",\n \"PassengerId \\n\",\n \"1 1 0 A/5 21171 7.2500 NaN S \\n\",\n \"2 1 0 PC 17599 71.2833 C85 C \\n\",\n \"3 0 0 STON/O2. 3101282 7.9250 NaN S \\n\",\n \"4 1 0 113803 53.1000 C123 S \\n\",\n \"5 0 0 373450 8.0500 NaN S \"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/titanic.csv.zip', 'r') as z:\\n\",\n \" f = z.open('titanic.csv')\\n\",\n \" titanic = pd.read_csv(f, sep=',', index_col=0)\\n\",\n \"titanic.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Survived 0\\n\",\n \"Pclass 0\\n\",\n \"Name 0\\n\",\n \"Sex 0\\n\",\n \"Age 177\\n\",\n \"SibSp 0\\n\",\n \"Parch 0\\n\",\n \"Ticket 0\\n\",\n \"Fare 0\\n\",\n \"Cabin 687\\n\",\n \"Embarked 2\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# check for missing values\\n\",\n \"titanic.isnull().sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"One possible strategy is to **drop missing values**:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(183, 11)\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# drop rows with any missing values\\n\",\n \"titanic.dropna().shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(714, 11)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# drop rows where Age is missing\\n\",\n \"titanic[titanic.Age.notnull()].shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sometimes a better strategy is to **impute missing values**:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"29.69911764705882\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# mean Age\\n\",\n \"titanic.Age.mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"28.0\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# median Age\\n\",\n \"titanic.Age.median()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 24.0\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# most frequent Age\\n\",\n \"titanic.Age.mode()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# fill missing values for Age with the median age\\n\",\n \"titanic.Age.fillna(titanic.Age.median(), inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Another strategy would be to build a **KNN model** just to impute missing values. How would we do that?\\n\",\n \"\\n\",\n \"If values are missing from a categorical feature, we could treat the missing values as **another category**. Why might that make sense?\\n\",\n \"\\n\",\n \"How do we **choose** between all of these strategies?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Handling categorical features\\n\",\n \"\\n\",\n \"How do we include a categorical feature in our model?\\n\",\n \"\\n\",\n \"- **Ordered categories:** transform them to sensible numeric values (example: small=1, medium=2, large=3)\\n\",\n \"- **Unordered categories:** use dummy encoding (0/1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
PassengerId
103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
503Allen, Mr. William Henrymale35.0003734508.0500NaNS
603Moran, Mr. Jamesmale28.0003308778.4583NaNQ
701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
803Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNS
913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNS
1012Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNC
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Survived Pclass \\\\\\n\",\n \"PassengerId \\n\",\n \"1 0 3 \\n\",\n \"2 1 1 \\n\",\n \"3 1 3 \\n\",\n \"4 1 1 \\n\",\n \"5 0 3 \\n\",\n \"6 0 3 \\n\",\n \"7 0 1 \\n\",\n \"8 0 3 \\n\",\n \"9 1 3 \\n\",\n \"10 1 2 \\n\",\n \"\\n\",\n \" Name Sex Age \\\\\\n\",\n \"PassengerId \\n\",\n \"1 Braund, Mr. Owen Harris male 22.0 \\n\",\n \"2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \\n\",\n \"3 Heikkinen, Miss. Laina female 26.0 \\n\",\n \"4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \\n\",\n \"5 Allen, Mr. William Henry male 35.0 \\n\",\n \"6 Moran, Mr. James male 28.0 \\n\",\n \"7 McCarthy, Mr. Timothy J male 54.0 \\n\",\n \"8 Palsson, Master. Gosta Leonard male 2.0 \\n\",\n \"9 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 \\n\",\n \"10 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 \\n\",\n \"\\n\",\n \" SibSp Parch Ticket Fare Cabin Embarked \\n\",\n \"PassengerId \\n\",\n \"1 1 0 A/5 21171 7.2500 NaN S \\n\",\n \"2 1 0 PC 17599 71.2833 C85 C \\n\",\n \"3 0 0 STON/O2. 3101282 7.9250 NaN S \\n\",\n \"4 1 0 113803 53.1000 C123 S \\n\",\n \"5 0 0 373450 8.0500 NaN S \\n\",\n \"6 0 0 330877 8.4583 NaN Q \\n\",\n \"7 0 0 17463 51.8625 E46 S \\n\",\n \"8 3 1 349909 21.0750 NaN S \\n\",\n \"9 0 2 347742 11.1333 NaN S \\n\",\n \"10 1 0 237736 30.0708 NaN C \"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.head(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# encode Sex_Female feature\\n\",\n \"titanic['Sex_Female'] = titanic.Sex.map({'male':0, 'female':1})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create a DataFrame of dummy variables for Embarked\\n\",\n \"embarked_dummies = pd.get_dummies(titanic.Embarked, prefix='Embarked')\\n\",\n \"embarked_dummies.drop(embarked_dummies.columns[0], axis=1, inplace=True)\\n\",\n \"\\n\",\n \"# concatenate the original DataFrame and the dummy DataFrame\\n\",\n \"titanic = pd.concat([titanic, embarked_dummies], axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedSex_FemaleEmbarked_QEmbarked_S
PassengerId
103Braund, Mr. Owen Harrismale22.010A/5 211717.25NaNS001
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Survived Pclass Name Sex Age SibSp \\\\\\n\",\n \"PassengerId \\n\",\n \"1 0 3 Braund, Mr. Owen Harris male 22.0 1 \\n\",\n \"\\n\",\n \" Parch Ticket Fare Cabin Embarked Sex_Female Embarked_Q \\\\\\n\",\n \"PassengerId \\n\",\n \"1 0 A/5 21171 7.25 NaN S 0 0 \\n\",\n \"\\n\",\n \" Embarked_S \\n\",\n \"PassengerId \\n\",\n \"1 1 \"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.head(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- How do we **interpret** the encoding for Embarked?\\n\",\n \"- Why didn't we just encode Embarked using a **single feature** (C=0, Q=1, S=2)?\\n\",\n \"- Does it matter which category we choose to define as the **baseline**?\\n\",\n \"- Why do we only need **two dummy variables** for Embarked?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7937219730941704\\n\"\n ]\n }\n ],\n \"source\": [\n \"# define X and y\\n\",\n \"feature_cols = ['Pclass', 'Parch', 'Age', 'Sex_Female', 'Embarked_Q', 'Embarked_S']\\n\",\n \"X = titanic[feature_cols]\\n\",\n \"y = titanic.Survived\\n\",\n \"\\n\",\n \"# train/test split\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\\n\",\n \"\\n\",\n \"# train a logistic regression model\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"logreg = LogisticRegression(C=1e9)\\n\",\n \"logreg.fit(X_train, y_train)\\n\",\n \"\\n\",\n \"# make predictions for testing set\\n\",\n \"y_pred_class = logreg.predict(X_test)\\n\",\n \"\\n\",\n \"# calculate testing accuracy\\n\",\n \"from sklearn import metrics\\n\",\n \"print(metrics.accuracy_score(y_test, y_pred_class))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# ROC curves and AUC\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# predict probability of survival\\n\",\n \"y_pred_prob = logreg.predict_proba(X_test)[:, 1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.rcParams['figure.figsize'] = (8, 6)\\n\",\n \"plt.rcParams['font.size'] = 14\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'True Positive Rate (Sensitivity)')\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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w5s7sCWyJia0Q8JWkzcDwwEsiVKCQdB/wAqAWujYhvNbLPKcBXSbq2ZkXER1v8Kcpg5aub+cqUp9j4+la2bK3ngfkrOXRYX37+8XH06+FLNmbWfmV1Pd0F7AkgaU9gOjAWuFjS15srWFItSbfV8elxp0ka22Cf0cAXgaMiYl/gs635EOXw5JK13PHESzy/agOvvLaFjx42lBvPPcxJwszavayup34R8Uz6/hPAzRHxKUm7ADOBy5opewKwIB2WHEk3kwxPXjrf9n8C10TEGoCIWN6Kz7DT3PHES6zesLnRbfOWrQfgqlMO4qAhfcoZlplZRWUliih5fwxwFUBEbJaU566nQcDikuUlwGEN9tkLQNJDJN1TX42IPzcsSNJEYCLA0KFDc5y65Za+spFP//rRzH061Yjd3IIwsw4mK1E8JelbwIskX+h/AZDUG8hzm09j+0SD5U7AaOBoYDDwgKT9IuKVNxwUMQmYBDBu3LiGZewUdVuTYv/rxH2bvDjdtXMtPXfJ8+iJmVn7kfWtdy5wETAGOC4iNqTr9wP+J0fZS4AhJcuDgaWN7DMtIrYAz0maR5I4ZuQovxA9d+lE/567VOr0ZmZVp8lEkSaGrzWy/iHgoRxlzwBGSxpB0io5FWh4R9PvgdOAyelMensBC/OFbmZm5dDkXU+Sfi/peEk7JBNJwyRdLunspo6PiDrgApK7p54Gbk1vs71C0onpbncBqyTNAe4BLomIVW/mA7VWfRTSo2Vm1uZldT19GvgccI2kZcAKoCvJcxQvkNyt9LuswiNiKjC1wbrLS94HcHH6qqjZ6TDhw/t3r3AkZmbVJavr6UXSL3FJo4CBwEZgXkSsL1N8ZXPvvBX07taZg4b0rXQoZmZVJdctPBGxAFhQcCwVU18f3PfMCt4+uj+1NR63ycysVJ5BAdu9OS+tY8X6zRy99+6VDsXMrOo4UQD3PbMCgHfuNaDCkZiZVZ9ciUJSl/Q6Rbt077zl7DeoFwN29fMTZmYNNZsoJL0feBL4a7p8kKTbiw6sXNa+toVHnl/D0Xu528nMrDF5WhRXkIzR9ApARDwOtJvWxQMLVlAfcPTe7nYyM2tMnruetkTEK3rjLG5t+um0F1/ZyLfunMvrdVuZv+zV9LZYjwhrZtaYPIni6XRyoZp0OI7/A0wrNqxiTXt2FX+ctZSR/XvQpVMNE98xkk61vq5vZtaYPIniAuByoB64jWTYjS8WGVS5TD5rAkN385PYZmZZ8iSK90XEF4AvbFsh6WSSpGFmZu1cnv6WLzeyrrnZ7czMrJ1oskUh6X3AccAgSaXzT/Qi6YYyM7MOIKvraTkwG9gEPFWyfj1waZFBmZlZ9cgaPfYx4DFJN0bEpjLGZGZmVSTPxexBkr4OjCWZjwKAiNirsKjMzKxq5EkUk0mmRP0ucDxwFm30GsVtjy5h/vJXmfdyu5tOw8ysMHkSRfeIuEvSdyPiWeDLkh4oOrAiXPq7J6mrr6dTTQ0De3elb4/OlQ7JzKzq5UkUm5WM3/GspPOAF4E2OYJefQTnH70nl7xvTKVDMTNrM/IkiouAnsBngK8DvYGziwzKzMyqR7OJIiL+mb5dD3wMQNLgIoMyM7PqkZkoJI0HBgEPRsRKSfuSDOVxDNAmksUN057n23fOJSKoqw9q5DmxzcxaIuvJ7G8CHwJmkVzAvp1k5NhvA+eVJ7w3b87SdWytD04/bCg1NeLkQ9pEfjMzqxpZLYqTgAMjYqOkfsDSdHleeUJrvZfWbuQHf5vP+s11PLlkLT27duLLHxhb6bDMzNqkrESxKSI2AkTEaklz20KSmLN0HWdPnsHajVvYo09XOteKo0a9pdJhmZm1WVmJYqSkbUOJCxheskxEnFxoZK3wjwUrmXjDI/TcpRO3fepI9hnYq9IhmZm1eVmJ4kMNlq8uMpCd4Zt3zqVfjy7c8snDGdi7W6XDMTNrF7IGBby7nIHsDJvrtrLvHr2cJMzMdiJPFG1mZpmcKMzMLFPuRCFplyIDMTOz6tRsopA0QdKTwPx0+UBJPyo8MjMzqwp5WhQ/BD4ArAKIiFnAu4oMyszMqkeeRFETEc83WLe1iGDMzKz65BlmfLGkCUBIqgUuBJ4pNiwzM6sWeVoU5wMXA0OBZcDh6bpmSTpO0jxJCyRdmrHfhyWFpHF5yjUzs/LJ06Koi4hTW1pw2vq4BngPsASYIWlKRMxpsN+uJJMi/XPHUszMrNLyJIoZkuYBtwC3RcT6nGVPABZExEIASTeTjEg7p8F+/w1cCXw+Z7lv8MD8FVx0y+PU1QfrNm5h1O49W1OMmZk1odmup4jYE/gacCjwpKTfS8rTwhgELC5ZXpKu+xdJBwNDIuJPWQVJmihppqSZK1aseMO2eS+vZ+Wrr3P8fgP52OHDOPuoETlCMzOzvPK0KIiIfwD/kPRV4PvAjcDNzRzW2FRy8a+NUg3wPeDMHOefBEwCGDduXDS2z5dOGMOuXTs3V5SZmbVQngfueko6XdIfgenACuDIHGUvAYaULA8mmfxom12B/YB7JS0iuUg+xRe0zcyqS54WxWzgj8CVEfFAC8qeAYyWNAJ4ETgV+Oi2jRGxFui/bVnSvcDnI2JmC85hZmYFy5MoRkZEfUsLjog6SRcAdwG1wHUR8ZSkK4CZETGlpWWamVn5NZkoJF0VEZ8Dfidph+sCeWa4i4ipwNQG6y5vYt+jm422xOt19Wyq28qmLX5I3MysSFktilvSf6tuZrvX6+o54pt3s2rD6/9aV1vT2LVzMzN7s7JmuJuevt0nIt6QLNIupYrNgLepbiurNrzOu/d5C4eP7MegPt3o3iXXDVxmZtZCeYbwOLuRdefs7EBa4/CR/Tj37SM5fv+BlQ7FzKzdyrpG8RGSO5VGSLqtZNOuwCtFB2ZmZtUhq79mOskcFINJxmzaZj3wWJFBmZlZ9ci6RvEc8Bzwt/KFY2Zm1Sar6+m+iHinpDWUDL1BMjRHRES/wqMzM7OKy+p62jbdaf+MfczMrJ1r8q6nkqexhwC1EbEVOAL4JNCjDLGZmVkVyHN77O9JpkHdE/glsA/w60KjMjOzqpEnUdRHxBbgZOD7EXEhDeaVMDOz9itPoqiT9B/Ax4BtEwx54gczsw4i75PZ7yIZZnxhOmz4TcWGZWZm1aLZAZIiYrakzwCjJI0hmQf768WHZmZm1aDZRCHp7cANJJMPCXirpI9FxENFB2dmZpWXZ8jV7wEnRMQcAEn7kCQOT1lqZtYB5LlG0WVbkgCIiKeBLsWFZGZm1SRPi+JRST8jaUUAnI4HBTQz6zDyJIrzgM8A/5fkGsX9wI+KDMrMzKpHZqKQtD+wJ3B7RFxZnpDMzKyaNHmNQtKXSIbvOB34q6TGZrozM7N2LqtFcTpwQERskDQAmApcV56wzMysWmTd9bQ5IjYARMSKZvY1M7N2KqtFMbJkrmwBe5bOnR0RJxcamZmZVYWsRPGhBstXFxmImZlVp6w5s+8uZyBmZladfN3BzMwyOVGYmVmm3IlC0i5FBmJmZtWp2UQhaYKkJ4H56fKBkjyEh5lZB5GnRfFD4APAKoCImEUy452ZmXUAeRJFTUQ832Dd1iKCMTOz6pNn9NjFkiYAIakWuBB4ptiwzMysWuRpUZwPXAwMBZYBh6frzMysA2g2UUTE8og4NSL6p69TI2JlnsIlHSdpnqQFki5tZPvFkuZIekLS3ZKGteZDmJlZcZrtepL0cyAaro+Iic0cVwtcA7wHWALMkDSldFpVkpnyxkXEa5LOB64EPtKC+M3MrGB5rlH8reR9V+DfgcU5jpsALIiIhQCSbgZOAkrn376nZP9pwBk5yjUzszJqNlFExC2ly5JuAP6ao+xBvDGhLAEOy9j/HODOxjZImghMBBg6dGiOU5uZ2c7SmiE8RgB5riWokXU7dGEBSDoDGAd8p7HtETEpIsZFxLgBAwbkDtTMzN68PNco1rD9C74GWA3scGG6EUuAISXLg4GljZT/buAy4J0RsTlHuWZmVkaZiUKSgAOBF9NV9RHRaKugETOA0ZJGpMefCny0QfkHAz8DjouI5S0J3MzMyiOz6ylNCrdHxNb0lTdJEBF1wAXAXcDTwK0R8ZSkKySdmO72HaAn8BtJj0ua0rqPYWZmRclz19N0SYdExKMtLTwipgJTG6y7vOT9u1tappmZlVeTiUJSp7RV8DbgPyU9C2wguUgdEXFImWJ8g6WvbOSbU+dW4tRmZh1SVotiOnAI8MEyxZLLqg2vM/XJl+jfswtj3tqr0uGYmbV7WYlCABHxbJliyaVrp1pmfeW9lQ7DzKzDyEoUAyRd3NTGiPifAuIxM7Mqk5UoaknuSGrswTkzM+sgshLFSxFxRdkiMTOzqpT1HIVbEmZmlpkoji1bFGZmVrWaTBQRsbqcgZiZWXVqzeixZmbWgThRmJlZJicKMzPL5ERhZmaZnCjMzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZJicKMzPL5ERhZmaZnCjMzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZJicKMzPL5ERhZmaZnCjMzCyTE4WZmWVyojAzs0xOFGZmlsmJwszMMjlRmJlZpkIThaTjJM2TtEDSpY1s30XSLen2f0oaXmQ8ZmbWcoUlCkm1wDXA8cBY4DRJYxvsdg6wJiJGAd8Dvt1cuYP6dtvZoZqZWYYiWxQTgAURsTAiXgduBk5qsM9JwPXp+98Cx0pSVqHdu9Tu9EDNzKxpnQosexCwuGR5CXBYU/tERJ2ktcBuwMrSnSRNBCami5slzS4k4ranPw3qqgNzXWznutjOdbHd3q09sMhE0VjLIFqxDxExCZgEIGlmRIx78+G1fa6L7VwX27kutnNdbCdpZmuPLbLraQkwpGR5MLC0qX0kdQJ6A6sLjMnMzFqoyEQxAxgtaYSkLsCpwJQG+0wBPpG+/zDw94jYoUVhZmaVU1jXU3rN4QLgLqAWuC4inpJ0BTAzIqYA/wvcIGkBSUvi1BxFTyoq5jbIdbGd62I718V2rovtWl0X8h/wZmaWxU9mm5lZJicKMzPLVLWJwsN/bJejLi6WNEfSE5LuljSsEnGWQ3N1UbLfhyWFpHZ7a2SeupB0Svq78ZSkX5c7xnLJ8X9kqKR7JD2W/j85oRJxFk3SdZKWN/WsmRI/TOvpCUmH5Co4IqruRXLx+1lgJNAFmAWMbbDPp4Cfpu9PBW6pdNwVrIt3Ad3T9+d35LpI99sVuB+YBoyrdNwV/L0YDTwG9E2Xd6903BWsi0nA+en7scCiSsddUF28AzgEmN3E9hOAO0meYTsc+Geecqu1RVHI8B9tVLN1ERH3RMRr6eI0kmdW2qM8vxcA/w1cCWwqZ3Bllqcu/hO4JiLWAETE8jLHWC556iKAXun73uz4TFe7EBH3k/0s2knALyMxDegjaWBz5VZromhs+I9BTe0TEXXAtuE/2ps8dVHqHJK/GNqjZutC0sHAkIj4UzkDq4A8vxd7AXtJekjSNEnHlS268spTF18FzpC0BJgKXFie0KpOS79PgGKH8HgzdtrwH+1A7s8p6QxgHPDOQiOqnMy6kFRDMgrxmeUKqILy/F50Iul+OpqklfmApP0i4pWCYyu3PHVxGjA5Iq6SdATJ81v7RUR98eFVlVZ9b1Zri8LDf2yXpy6Q9G7gMuDEiNhcptjKrbm62BXYD7hX0iKSPtgp7fSCdt7/I3+IiC0R8RwwjyRxtDd56uIc4FaAiHgY6EoyYGBHk+v7pKFqTRQe/mO7Zusi7W75GUmSaK/90NBMXUTE2ojoHxHDI2I4yfWaEyOi1YOhVbE8/0d+T3KjA5L6k3RFLSxrlOWRpy5eAI4FkLQPSaJYUdYoq8MU4OPp3U+HA2sj4qXmDqrKrqcobviPNidnXXwH6An8Jr2e/0JEnFixoAuSsy46hJx1cRfwXklzgK3AJRGxqnJRFyNnXXwO+Lmki0i6Ws5sj39YSrqJpKuxf3o95itAZ4CI+CnJ9ZkTgAXAa8BZucpth3VlZmY7UbV2PZmZWZVwojAzs0xOFGZmlsmJwszMMjlRmJlZJieKDkrSVkmPl7yGZ+w7vKnRKFt4znvTET5npcNK7N2KMs72a0i3AAAGAElEQVST9PH0/ZmS9ijZdq2ksTs5zhmSDspxzGcldW/Fub4v6R3p+wvSUT0jfe6hpWXtncb+uKSnJe3U2d0knbhtZFZJA5SM2vyYpLdLmiqpT8axTf7cMo75m6S+O+8TWKtVerRDvyrzAl5twb7DaWI0yhae817S0VyBicCUnVXeTq6b0jjPAv6a45hFQP8WnqcfMK1k+eC0rltcVnr8XcBJJcv7F/j7cypwfZE/N5IHai8r6jP4lf/lFoX9S9pyeEDSo+nryEb22VfS9PSv1ickjU7Xn1Gy/meSaps53f3AqPTYY9O/TJ9UMp7+Lun6b2n7PBvfTdd9VdLnJX2YZFyrG9Nzdkv/mh4n6XxJV5bEfKakH7UyzocpGTRN0k8kzVQyv8N/pes+A+wB3CPpnnTdeyU9nNbjbyT1bKTsDwN/3rYQEY9FxKJm4skykGSIhm3lPZnGcqakP0j6c9pS+krJ52m0PpTM7/Bo2qq6u6Scq9MW1pXACSV1v2hbK0jSx9Of2SxJN6Trmvq5vV/S7SXxvEfSbeniFJIxmqzSKp2p/KrMi+RJ3cfT1+3puu5A1/T9aJKnWqGkRQH8CDg9fd8F6AbsA/wR6Jyu/zHw8UbOeS/b/1K/BLiFZCiFxcBe6fpfAp8l+Wt7HtsfCu2T/vtV4PMNyytdBgaQDDu9bf2dwNtaGedngW+UbOuX/lub7ndAuryItBVAMobQ/UCPdPkLwOWNnOd64N8aWf+vslr4Mz2LZBTlO4GLSursTOAlktGVuwGz03pqtD7S+lsMjGjwmc8Erm74vjRmYN/059a/wbGN/txIBqmbCwxIl39dWifAfGC3Sv9/6eivqhzCw8piY0Q07HvvDGz7i3ErydhADT0MXCZpMHBbRMyXdCxwKDBDyRAi3YCmxpy6UdJGki+WC4G9geci4pl0+/XAp4GrSeaTuFbSHUDuYcMjYoWkhUrGspmfnuOhtNyWxNmDJCGUzgJ2iqSJJMPfDCSZBOeJBscenq5/KD1PF5J6a2ggO3G8oYj4haS7gONI5h34pKQD081/jXT4jvQv9rcBdTReH4cD90cykCAR0ZLBNo8BfhsRK/McGxGRtjrOkPQL4AiSZLXNcpLWWrsbeqQtcaKwUhcBy4ADSW502GHin4j4taR/Au8H7pJ0LslfhddHxBdznOP0KBmkT1Kjc4hEMn7PBJKB3E4FLiD5EsrrFuAUkr9Wb0+/kFoUJ8lMad8CrgFOljQC+DwwPiLWSJpM0iJqSCRfzM11m2xs4vgmpV+mBwNLI2KH6TwjYilwHXCdkhsQ9tu2qeGuNPFzk3RiI/vnDrEVx/6CpGWzCfhNJPPLbNOVpJ6sgnyNwkr1Bl6KZIz+j5H8Nf0GkkYCCyPihyR9yAcAdwMflrR7uk8/5Z+3ey4wXNKodPljwH1pn37viJhK0v3T2J1H60mGFm/MbcAHSfq4b0nXtSjOiNgCfBk4XMmIo72ADcBaSW8Bjm8ilmnAUds+k6TukhprnT1Nep0mr4g4KyIOaixJpNcVOqfv30rS1fRiuvk96eftRlIvD9F0fTwMvDNNjEjq14IQ7yZpde2Wcewbfm5pcltKUteTSz6PgLeStD6tgpworNSPgU9ImkbS7bShkX0+AsyW9DgwhmRaxTkk/8n/IukJ4K8k3SrNiohNJH3rv5H0JFAP/JTki+RPaXn3kbR2GpoM/HTbBdUG5a4B5gDDImJ6uq7FcUbERuAqkv71WSRzUD9F8lf7QyW7TgLulHRPRKwg6cO/KT3PNJK6augOkpE+geSiuJIRPwcDT0i6Niu2RryX5Gczi+QOqEsi4uV024PADSTXpH4XETObqo80/onAbWlZtzQ8UVMi4ing6yTJfhbwP43sNpkdf243AovTmLY5lOSusLqGBVh5efRYswqS9CDwgShw1jlJZ5JcPL6gqHO8WZKuBh6LiP8tWfcDkluo765cZAZuUZhV2ueAoZUOopIkPULShfmrBptmO0lUB7cozMwsk1sUZmaWyYnCzMwyOVGYmVkmJwozM8vkRGFmZpn+PxvEdstpBSQuAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot ROC curve\\n\",\n \"fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_prob)\\n\",\n \"plt.plot(fpr, tpr)\\n\",\n \"plt.xlim([0.0, 1.0])\\n\",\n \"plt.ylim([0.0, 1.0])\\n\",\n \"plt.xlabel('False Positive Rate (1 - Specificity)')\\n\",\n \"plt.ylabel('True Positive Rate (Sensitivity)')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.8386924342105262\\n\"\n ]\n }\n ],\n \"source\": [\n \"# calculate AUC\\n\",\n \"print(metrics.roc_auc_score(y_test, y_pred_prob))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Besides allowing you to calculate AUC, seeing the ROC curve can help you to choose a threshold that **balances sensitivity and specificity** in a way that makes sense for the particular context.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([,\\n\",\n \" ],\\n\",\n \" dtype=object)\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# histogram of predicted probabilities grouped by actual response value\\n\",\n \"df = pd.DataFrame({'probability':y_pred_prob, 'actual':y_test})\\n\",\n \"df.hist(column='probability', by='actual', sharex=True, sharey=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# ROC curve using y_pred_class - WRONG!\\n\",\n \"fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_class)\\n\",\n \"plt.plot(fpr, tpr)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7809621710526315\\n\"\n ]\n }\n ],\n \"source\": [\n \"# AUC using y_pred_class - WRONG!\\n\",\n \"print(metrics.roc_auc_score(y_test, y_pred_class))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you use **y_pred_class**, it will interpret the zeros and ones as predicted probabilities of 0% and 100%.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Cross-validation\\n\",\n \"\\n\",\n \"## Review of model evaluation procedures\\n\",\n \"\\n\",\n \"**Motivation:** Need a way to choose between machine learning models\\n\",\n \"\\n\",\n \"- Goal is to estimate likely performance of a model on **out-of-sample data**\\n\",\n \"\\n\",\n \"**Initial idea:** Train and test on the same data\\n\",\n \"\\n\",\n \"- But, maximizing **training accuracy** rewards overly complex models which **overfit** the training data\\n\",\n \"\\n\",\n \"**Alternative idea:** Train/test split\\n\",\n \"\\n\",\n \"- Split the dataset into two pieces, so that the model can be trained and tested on **different data**\\n\",\n \"- **Testing accuracy** is a better estimate than training accuracy of out-of-sample performance\\n\",\n \"- But, it provides a **high variance** estimate since changing which observations happen to be in the testing set can significantly change testing accuracy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn import metrics\\n\",\n \"\\n\",\n \"# define X and y\\n\",\n \"feature_cols = ['Pclass', 'Parch', 'Age', 'Sex_Female', 'Embarked_Q', 'Embarked_S']\\n\",\n \"X = titanic[feature_cols]\\n\",\n \"y = titanic.Survived\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7937219730941704\\n\"\n ]\n }\n ],\n \"source\": [\n \"# train/test split\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\\n\",\n \"\\n\",\n \"# train a logistic regression model\\n\",\n \"logreg = LogisticRegression(C=1e9)\\n\",\n \"logreg.fit(X_train, y_train)\\n\",\n \"\\n\",\n \"# make predictions for testing set\\n\",\n \"y_pred_class = logreg.predict(X_test)\\n\",\n \"\\n\",\n \"# calculate testing accuracy\\n\",\n \"print(metrics.accuracy_score(y_test, y_pred_class))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7802690582959642\\n\"\n ]\n }\n ],\n \"source\": [\n \"# train/test split\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=2)\\n\",\n \"\\n\",\n \"# train a logistic regression model\\n\",\n \"logreg = LogisticRegression(C=1e9)\\n\",\n \"logreg.fit(X_train, y_train)\\n\",\n \"\\n\",\n \"# make predictions for testing set\\n\",\n \"y_pred_class = logreg.predict(X_test)\\n\",\n \"\\n\",\n \"# calculate testing accuracy\\n\",\n \"print(metrics.accuracy_score(y_test, y_pred_class))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.7847533632286996\\n\"\n ]\n }\n ],\n \"source\": [\n \"# train/test split\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=3)\\n\",\n \"\\n\",\n \"# train a logistic regression model\\n\",\n \"logreg = LogisticRegression(C=1e9)\\n\",\n \"logreg.fit(X_train, y_train)\\n\",\n \"\\n\",\n \"# make predictions for testing set\\n\",\n \"y_pred_class = logreg.predict(X_test)\\n\",\n \"\\n\",\n \"# calculate testing accuracy\\n\",\n \"print(metrics.accuracy_score(y_test, y_pred_class))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"train test spliting create bias due to the intrinsic randomness in the sets selection\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# K-fold cross-validation\\n\",\n \"\\n\",\n \"1. Split the dataset into K **equal** partitions (or \\\"folds\\\").\\n\",\n \"2. Use fold 1 as the **testing set** and the union of the other folds as the **training set**.\\n\",\n \"3. Calculate **testing accuracy**.\\n\",\n \"4. Repeat steps 2 and 3 K times, using a **different fold** as the testing set each time.\\n\",\n \"5. Use the **average testing accuracy** as the estimate of out-of-sample accuracy.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Diagram of **5-fold cross-validation:**\\n\",\n \"\\n\",\n \"![5-fold cross-validation](https://raw.githubusercontent.com/justmarkham/DAT8/master/notebooks/images/cross_validation_diagram.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Iteration Training set observations Testing set observations\\n\",\n \" 1 [ 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [0 1 2 3 4] \\n\",\n \" 2 [ 0 1 2 3 4 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24] [5 6 7 8 9] \\n\",\n \" 3 [ 0 1 2 3 4 5 6 7 8 9 15 16 17 18 19 20 21 22 23 24] [10 11 12 13 14] \\n\",\n \" 4 [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 20 21 22 23 24] [15 16 17 18 19] \\n\",\n \" 5 [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19] [20 21 22 23 24] \\n\"\n ]\n }\n ],\n \"source\": [\n \"# simulate splitting a dataset of 25 observations into 5 folds\\n\",\n \"from sklearn.cross_validation import KFold\\n\",\n \"kf = KFold(25, n_folds=5, shuffle=False)\\n\",\n \"\\n\",\n \"# print the contents of each training and testing set\\n\",\n \"print('{} {:^61} {}'.format('Iteration', 'Training set observations', 'Testing set observations'))\\n\",\n \"for iteration, data in enumerate(kf, start=1):\\n\",\n \" print('{:^9} {} {:^25}'.format(str(iteration), str(data[0]), str(data[1])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- Dataset contains **25 observations** (numbered 0 through 24)\\n\",\n \"- 5-fold cross-validation, thus it runs for **5 iterations**\\n\",\n \"- For each iteration, every observation is either in the training set or the testing set, **but not both**\\n\",\n \"- Every observation is in the testing set **exactly once**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Create k-folds\\n\",\n \"kf = KFold(X.shape[0], n_folds=10, random_state=0)\\n\",\n \"\\n\",\n \"results = []\\n\",\n \"\\n\",\n \"for train_index, test_index in kf:\\n\",\n \" X_train, X_test = X.iloc[train_index], X.iloc[test_index]\\n\",\n \" y_train, y_test = y.iloc[train_index], y.iloc[test_index]\\n\",\n \"\\n\",\n \" # train a logistic regression model\\n\",\n \" logreg = LogisticRegression(C=1e9)\\n\",\n \" logreg.fit(X_train, y_train)\\n\",\n \"\\n\",\n \" # make predictions for testing set\\n\",\n \" y_pred_class = logreg.predict(X_test)\\n\",\n \"\\n\",\n \" # calculate testing accuracy\\n\",\n \" results.append(metrics.accuracy_score(y_test, y_pred_class))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"count 10.000000\\n\",\n \"mean 0.794644\\n\",\n \"std 0.030631\\n\",\n \"min 0.764045\\n\",\n \"25% 0.768820\\n\",\n \"50% 0.780899\\n\",\n \"75% 0.820225\\n\",\n \"max 0.842697\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.Series(results).describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.cross_validation import cross_val_score\\n\",\n \"\\n\",\n \"logreg = LogisticRegression(C=1e9)\\n\",\n \"\\n\",\n \"results = cross_val_score(logreg, X, y, cv=10, scoring='accuracy')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"count 10.000000\\n\",\n \"mean 0.794665\\n\",\n \"std 0.019263\\n\",\n \"min 0.775281\\n\",\n \"25% 0.779963\\n\",\n \"50% 0.786517\\n\",\n \"75% 0.806742\\n\",\n \"max 0.829545\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.Series(results).describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Comparing cross-validation to train/test split\\n\",\n \"\\n\",\n \"Advantages of **cross-validation:**\\n\",\n \"\\n\",\n \"- More accurate estimate of out-of-sample accuracy\\n\",\n \"- More \\\"efficient\\\" use of data (every observation is used for both training and testing)\\n\",\n \"\\n\",\n \"Advantages of **train/test split:**\\n\",\n \"\\n\",\n \"- Runs K times faster than K-fold cross-validation\\n\",\n \"- Simpler to examine the detailed results of the testing process\\n\",\n \"\\n\",\n \"## Cross-validation recommendations\\n\",\n \"\\n\",\n \"1. K can be any number, but **K=10** is generally recommended\\n\",\n \"2. For classification problems, **stratified sampling** is recommended for creating the folds\\n\",\n \" - Each response class should be represented with equal proportions in each of the K folds\\n\",\n \" - scikit-learn's `cross_val_score` function does this by default\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Improvements to cross-validation\\n\",\n \"\\n\",\n \"**Repeated cross-validation**\\n\",\n \"\\n\",\n \"- Repeat cross-validation multiple times (with **different random splits** of the data) and average the results\\n\",\n \"- More reliable estimate of out-of-sample performance by **reducing the variance** associated with a single trial of cross-validation\\n\",\n \"\\n\",\n \"**Creating a hold-out set**\\n\",\n \"\\n\",\n \"- \\\"Hold out\\\" a portion of the data **before** beginning the model building process\\n\",\n \"- Locate the best model using cross-validation on the remaining data, and test it **using the hold-out set**\\n\",\n \"- More reliable estimate of out-of-sample performance since hold-out set is **truly out-of-sample**\\n\",\n \"\\n\",\n \"**Feature engineering and selection within cross-validation iterations**\\n\",\n \"\\n\",\n \"- Normally, feature engineering and selection occurs **before** cross-validation\\n\",\n \"- Instead, perform all feature engineering and selection **within each cross-validation iteration**\\n\",\n \"- More reliable estimate of out-of-sample performance since it **better mimics** the application of the model to out-of-sample data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Overfitting, Underfitting and Model Selection\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that we've gone over the basics of validation, and cross-validation, it's time to go into even more depth regarding model selection.\\n\",\n \"\\n\",\n \"The issues associated with validation and \\n\",\n \"cross-validation are some of the most important\\n\",\n \"aspects of the practice of machine learning. Selecting the optimal model\\n\",\n \"for your data is vital, and is a piece of the problem that is not often\\n\",\n \"appreciated by machine learning practitioners.\\n\",\n \"\\n\",\n \"Of core importance is the following question:\\n\",\n \"\\n\",\n \"**If our estimator is underperforming, how should we move forward?**\\n\",\n \"\\n\",\n \"- Use simpler or more complicated model?\\n\",\n \"- Add more features to each observed data point?\\n\",\n \"- Add more training samples?\\n\",\n \"\\n\",\n \"The answer is often counter-intuitive. In particular, **Sometimes using a\\n\",\n \"more complicated model will give _worse_ results.** Also, **Sometimes adding\\n\",\n \"training data will not improve your results.** The ability to determine\\n\",\n \"what steps will improve your model is what separates the successful machine\\n\",\n \"learning practitioners from the unsuccessful.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Illustration of the Bias-Variance Tradeoff\\n\",\n \"\\n\",\n \"For this section, we'll work with a simple 1D regression problem. This will help us to\\n\",\n \"easily visualize the data and the model, and the results generalize easily to higher-dimensional\\n\",\n \"datasets. We'll explore a simple **linear regression** problem.\\n\",\n \"This can be accomplished within scikit-learn with the `sklearn.linear_model` module.\\n\",\n \"\\n\",\n \"We'll create a simple nonlinear function that we'd like to fit\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"def test_func(x, err=0.5):\\n\",\n \" y = 10 - 1. / (x + 0.1)\\n\",\n \" if err > 0:\\n\",\n \" y = np.random.normal(y, err)\\n\",\n \" return y\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's create a realization of this dataset:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def make_data(N=40, error=1.0, random_seed=1):\\n\",\n \" # randomly sample the data\\n\",\n \" np.random.seed(1)\\n\",\n \" X = np.random.random(N)[:, np.newaxis]\\n\",\n \" y = test_func(X.ravel(), error)\\n\",\n \" \\n\",\n \" return X, y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X, y = make_data(40, error=1)\\n\",\n \"plt.scatter(X.ravel(), y);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now say we want to perform a regression on this data. Let's use the built-in linear regression function to compute a fit:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X_test = np.linspace(-0.1, 1.1, 500)[:, None]\\n\",\n \"\\n\",\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"model = LinearRegression()\\n\",\n \"model.fit(X, y)\\n\",\n \"y_test = model.predict(X_test)\\n\",\n \"\\n\",\n \"plt.scatter(X.ravel(), y)\\n\",\n \"plt.plot(X_test.ravel(), y_test,c='r')\\n\",\n \"plt.title(\\\"mean squared error: {0:.3g}\\\".format(mean_squared_error(model.predict(X), y)));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We have fit a straight line to the data, but clearly this model is not a good choice. We say that this model is **biased**, or that it **under-fits** the data.\\n\",\n \"\\n\",\n \"Let's try to improve this by creating a more complicated model. We can do this by adding degrees of freedom, and computing a polynomial regression over the inputs. Scikit-learn makes this easy with the ``PolynomialFeatures`` preprocessor, which can be pipelined with a linear regression.\\n\",\n \"\\n\",\n \"Let's make a convenience routine to do this:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.preprocessing import PolynomialFeatures\\n\",\n \"from sklearn.linear_model import LinearRegression\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we'll use this to fit a quadratic curve to the data.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"X_poly = PolynomialFeatures(degree=2).fit_transform(X)\\n\",\n \"X_test_poly = PolynomialFeatures(degree=2).fit_transform(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"model = LinearRegression()\\n\",\n \"model.fit(X_poly, y)\\n\",\n \"y_test = model.predict(X_test_poly)\\n\",\n \"\\n\",\n \"plt.scatter(X.ravel(), y)\\n\",\n \"plt.plot(X_test.ravel(), y_test,c='r')\\n\",\n \"plt.title(\\\"mean squared error: {0:.3g}\\\".format(mean_squared_error(model.predict(X_poly), y)));\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This reduces the mean squared error, and makes a much better fit. What happens if we use an even higher-degree polynomial?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X_poly = PolynomialFeatures(degree=30).fit_transform(X)\\n\",\n \"X_test_poly = PolynomialFeatures(degree=30).fit_transform(X_test)\\n\",\n \"\\n\",\n \"model = LinearRegression()\\n\",\n \"model.fit(X_poly, y)\\n\",\n \"y_test = model.predict(X_test_poly)\\n\",\n \"\\n\",\n \"plt.scatter(X.ravel(), y)\\n\",\n \"plt.plot(X_test.ravel(), y_test,c='r')\\n\",\n \"plt.title(\\\"mean squared error: {0:.3g}\\\".format(mean_squared_error(model.predict(X_poly), y)))\\n\",\n \"plt.ylim(-4, 14);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When we increase the degree to this extent, it's clear that the resulting fit is no longer reflecting the true underlying distribution, but is more sensitive to the noise in the training data. For this reason, we call it a **high-variance model**, and we say that it **over-fits** the data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just for fun, let's use IPython's interact capability (only in IPython 2.0+) to explore this interactively:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from ipywidgets import interact\\n\",\n \"\\n\",\n \"def plot_fit(degree=1, Npts=50):\\n\",\n \" X, y = make_data(Npts, error=1)\\n\",\n \" X_test = np.linspace(-0.1, 1.1, 500)[:, None]\\n\",\n \" \\n\",\n \" X_poly = PolynomialFeatures(degree=degree).fit_transform(X)\\n\",\n \" X_test_poly = PolynomialFeatures(degree=degree).fit_transform(X_test)\\n\",\n \"\\n\",\n \" model = LinearRegression()\\n\",\n \" model.fit(X_poly, y)\\n\",\n \" y_test = model.predict(X_test_poly)\\n\",\n \"\\n\",\n \" plt.scatter(X.ravel(), y)\\n\",\n \" plt.plot(X_test.ravel(), y_test)\\n\",\n \" plt.title(\\\"mean squared error: {0:.3g}\\\".format(mean_squared_error(model.predict(X_poly), y)))\\n\",\n \" plt.ylim(-4, 14)\\n\",\n \"\\n\",\n \" \\n\",\n \"interact(plot_fit, degree=[1, 40], Npts=[2, 100]);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Detecting Over-fitting with Validation Curves\\n\",\n \"\\n\",\n \"Clearly, computing the error on the training data is not enough (we saw this previously). As above, we can use **cross-validation** to get a better handle on how the model fit is working.\\n\",\n \"\\n\",\n \"Let's do this here, again using the ``validation_curve`` utility. To make things more clear, we'll use a slightly larger dataset:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"X, y = make_data(120, error=1.0)\\n\",\n \"plt.scatter(X, y);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import validation_curve\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def rms_error(model, X, y):\\n\",\n \" y_pred = model.predict(X)\\n\",\n \" return np.sqrt(np.mean((y - y_pred) ** 2))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.pipeline import make_pipeline\\n\",\n \"\\n\",\n \"def PolynomialRegression(degree=2, **kwargs):\\n\",\n \" return make_pipeline(PolynomialFeatures(degree),\\n\",\n \" LinearRegression(**kwargs))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"degree = np.arange(0, 18)\\n\",\n \"val_train, val_test = validation_curve(PolynomialRegression(), X, y,\\n\",\n \" 'polynomialfeatures__degree', degree, cv=7,\\n\",\n \" scoring=rms_error)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's plot the validation curves:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def plot_with_err(x, data, **kwargs):\\n\",\n \" mu, std = data.mean(1), data.std(1)\\n\",\n \" lines = plt.plot(x, mu, '-', **kwargs)\\n\",\n \" plt.fill_between(x, mu - std, mu + std, edgecolor='none',\\n\",\n \" facecolor=lines[0].get_color(), alpha=0.2)\\n\",\n \"\\n\",\n \"plot_with_err(degree, val_train, label='training scores')\\n\",\n \"plot_with_err(degree, val_test, label='validation scores')\\n\",\n \"plt.xlabel('degree'); plt.ylabel('rms error')\\n\",\n \"plt.legend();\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice the trend here, which is common for this type of plot.\\n\",\n \"\\n\",\n \"1. For a small model complexity, the training error and validation error are very similar. This indicates that the model is **under-fitting** the data: it doesn't have enough complexity to represent the data. Another way of putting it is that this is a **high-bias** model.\\n\",\n \"\\n\",\n \"2. As the model complexity grows, the training and validation scores diverge. This indicates that the model is **over-fitting** the data: it has so much flexibility, that it fits the noise rather than the underlying trend. Another way of putting it is that this is a **high-variance** model.\\n\",\n \"\\n\",\n \"3. Note that the training score (nearly) always improves with model complexity. This is because a more complicated model can fit the noise better, so the model improves. The validation data generally has a sweet spot, which here is around 5 terms.\\n\",\n \"\\n\",\n \"Here's our best-fit model according to the cross-validation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"model = PolynomialRegression(4).fit(X, y)\\n\",\n \"plt.scatter(X, y)\\n\",\n \"plt.plot(X_test, model.predict(X_test),c='r');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Detecting Data Sufficiency with Learning Curves\\n\",\n \"\\n\",\n \"As you might guess, the exact turning-point of the tradeoff between bias and variance is highly dependent on the number of training points used. Here we'll illustrate the use of *learning curves*, which display this property.\\n\",\n \"\\n\",\n \"The idea is to plot the mean-squared-error for the training and test set as a function of *Number of Training Points*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.learning_curve import learning_curve\\n\",\n \"\\n\",\n \"def plot_learning_curve(degree=3):\\n\",\n \" train_sizes = np.linspace(0.05, 1, 20)\\n\",\n \" N_train, val_train, val_test = learning_curve(PolynomialRegression(degree),\\n\",\n \" X, y, train_sizes, cv=5,\\n\",\n \" scoring=rms_error)\\n\",\n \" plot_with_err(N_train, val_train, label='training scores')\\n\",\n \" plot_with_err(N_train, val_test, label='validation scores')\\n\",\n \" plt.xlabel('Training Set Size'); plt.ylabel('rms error')\\n\",\n \" plt.ylim(0, 3)\\n\",\n \" plt.xlim(5, 80)\\n\",\n \" plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's see what the learning curves look like for a linear model:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Fu60uqk1rcCJEfAW/0FhxEZDKwTVVVRI7B1Wh29bnjlu3Qsg2qJ+W7yH1r22MBwowKsViMGTNmFLsYZgQo5F1MxwN/A/xZRNLDAf8ZOAhAVW8DzgeuEJEk0AYs0f6qNO0NcM9nYNZfwYLPQO0BBfsBuvETbuBcec3wHM8YY4qsoE1MhbB4zixdfv0H4a1fg5+CQz8MCz8DYw4q/MErxkL9wYU/jjHG5FlJNTEVTCQKJ1wDi/4GXr0X3ngU1j4Fh5zi1o09pHDHtgyvxpj9yMgLEGmV4+CDV8KCT8Of74PXHoa3n4XpJ8Kiz8H4mfk/pqagoxEq6vO/b2OMKTEjN0CkVYyBYy6FeUtgzYOw5gHY+Ds46DgXKCYemd/jte2xAGGM2S+MnraSeC0svgQ+fS8s/jxsex0euRJ+/RV4b3X+jtPe5Po+jDFmlBs9ASKtrNr1RXz6Hjj2cti9Hn75Jfjll2HLCtjnTnmFNnuQkDFm9Bt9ASItVgnzl8BFd8MHr4bGLfDrv4dHr4Z3/rhvgcJSbxhj9gOjN0CkReMw93xYchec8L9g70544jp45sah77OzGZKd+SujMcaUoNEfINKi5XDkOXDhz2HehfD2c/v2sHarRRhjRrn9J0CkRWJw1FKIj4EVPxn6fixAGGNGuf0vQADEKlz/xJblsO21oe0j2QaJng8vN8aY0WL/DBDgmpvidbDix0Pfh9UijDGj2P4bIGIVri9i88uwPfcpqANkAcIYM4rtvwECYPYnoLx26H0RqU7oaMlvmYwxpkTs3wEiVulqEe++BNvfGNo+rBZhjBml9u8AATD7XFeLWDnEWkR7Qx5GZxtjTOmxAFFWCXMvcKOrd7w5+M/7SZcG3BhjRhkLEABzznNPilvx06F93pqZjDGjkAUIgLKqoBbxB9j5l8F/vsMyvBpjRh8LEGlzznOZYIdyR5P61sxkjBl1LECklVW7pH6bfg871w7+89bMZIwZZSxAZJvzSdfctHIIfREdzZBK5L9MxhhTJBYgspXXwJzz3SNLd60f5IftQULGmNHFAkSuuedDrGpo4yLadue/PMYYUyQWIHKV17gO6w3Pw+63B/fZRCsk2gtTLmOMGWbRYhegJM29ANY86Poizvjq4D67ay1IVtztMco6ZNR16EjsnHXiwfhZ7sFHxhgzDKwGESZeC7PPg7f/B3ZvGNxn/aRL4pd++YmcV7LnS1MhL7/7y0+6svh+YX5mY4zJYQGiN/MugFh8aHc0FUqyDRo2FrsUxpj9hAWI3sTrXCK/t5+DPRuLXZou7Y3Q/H6xS2GM2Q8ULECIyIEi8qyIvCEir4nIl0O2ERH5roisE5HVIrKoUOUZknmfcm3+K39W7JJ01/yejdw2xhRcIWsQSeDvVfUI4DjgKhE5Mmebs4GZwetS4NYClmfw4mNcLWL9b6FhU7FL092eTXbHlDGmoAoWIFT1PVVdGcw3A28AU3M2Owf4qTp/BMaIyJRClWlI5l1YmrUITcGeDZYk0BhTMMPSByEi04GFwEs5b00F3s1a3kzPIIKIXCoiy0Vk+Y5dw5zzqGIMHHlOUIt4t//th1OyvbT6R4wxo0rBA4SIVAMPAteoalPu2yEf6TEoQFWXqepiVV08YVx9IYrZt3kXgheDV0qsFgEu1XjTe8UuhTFmFCpogBCRGC443KWqD4Vsshk4MGt5GrC1kGUaksqxrhax7mlo3Fzs0vTU8r5lkzXG5F0h72IS4EfAG6r6rV42exT4XHA303FAo6qW5uXw/AvBi5ZmLQKg4R1ItBW7FMaYUaSQNYjjgb8BThORVcHrIyJyuYhcHmzzGPA2sA64HbiygOXZN5Xj4IiPw9rfQNOWYpemJ/WDkdbWaW2MyY+C5WJS1RcI72PI3kaBqwpVhrxbcBG88Sis/Dmc8o/FLk1PqQ7XaT32EJA+f/XGGNOvkTeS2itifsHKcXDEx2Dtk9BUel0lQNBpXaJlM8aMKCMvQETKoHYa/VROCmf+ReBF4JWfF+f4A7F3O7TasymMMftm5AUIgOoJQTNKZPiPXTUeDv8o/OVJl/KiVDW+C52txS6FMWYEG5kBAlxK7vGzIFKE5yPMv8g9n+GVu4b/2AOlvhtpnUoWuyTGmBFq5AYIcOm4x8+CsurhPW71RDj8r+Gtx0s7s2qq03Vahz6QyBhj+jayAwRAJArjDoWKscN73AWfdncKrSrhWgRAZ3Np3pZrjCl5Iz9AgDtR1x8MtT3SOBVOdi2iZdvwHXco9u6wTmtjzKCNjgCRVj0R6md0fyZ0IS34tJuu+u/hOd6+aHgHOvcWuxTGmBGkzzOpiERE5P8OV2HyomJM0HldVvhjVU+Cw86GNx+Dlu2FP94+UTfSOpUodkGMMSNEnwFCVVPAUUFepZEjVuGCRKyq8Mda8BlAR0Ytwk9Yp7UxZsAG0hbzCvALEfkbETkv/Sp0wfZZJBZ0Xhc4PXjNZJh1Frz5a9fWX+o6W9wYCWOM6cdAAsRYYBdwGvCx4PXRQhYqbzwP6qdDTYEfUrfwM27cwaq7C3ucfGndBXt3FrsUxpgS129iI1W9ZDgKUlA1k91jQxvecSfyvO9/Csz6K3jzl67jump8/o+Rb42bIRqH8mEeQ2KMGTH6rUGIyDQReVhEtovINhF5UESmDUfh8qqi3jU5ebHC7H/hZ12q7Vd7qUWoD37SDV5LtkOi1TX3tDdBe4O7DXXvTtfZ3fy+S7jXuNkFtT0boWFTnoObuv1ap7UxphcDSY16J/DfwAXB8meDdR8uVKEKpqwKJhwGu992J+h8qj0AZv4VrHnI9Ueo3/2VD1OPgtOvh3hdfvbnJ9ydTbUHuASE4rn8Vl7E0oUbYxDt544WEVmlqgv6WzdcFi9erMuXL9+3nfg+7F7vruDzqXUXrL4f8N2JVqTrpBs6H/LyPCBnKh40b4Pld7jHn555M4yfmd+y9yAhQSNdxt7WR93NAV7UvSzIGFMyRGSFqi4ezGcGUoPYKSKfBdJtJxfhOq1HLs9zA+p2vuWafPKlchwcd3n/2w3VAQvhN/8Kv7gKTvwKzDqzcMdCXZPYvvCirkkvEnOBJDMftWBizAgwkADxt8D3gW8DCvwhWDeyRaIuSOxaW5iO60KYeDictwyevhGe+/9cgDvuiuI+RKkvftK9kgN4VnZfwUQ83FeP7mM4VN36zLqwee2+be58RhCgMoFqX5YFomVuHI43upIVmP1Ln2cWEYkAn1TVjw9TeYZXWSWMOch11o4UFfXw19+El/4L/nw/7FoHp9/gmp5GssEEkxFDIFbp+r7KqlzW4UiJBnNjQgxkJPU5w1SW4qioh+rJxS7F4HhR+OBVcNr/ge1vwkOXwrbXil0q04NCYq97wt+eDbDtz7DtddizCfbugkR7sQtoTJ8GcjnzexH5PnAvkMn2pqorC1aq4VY7xV25tjcWuySDc+gZbiDgU/8Kv7wGjv8yHDEyxjDut1Id0NYBbUF2XS/aVbtI1zasP8aUiIHcxfRsyGpV1dMKU6S+5eUupjC+Dzv/MjKbONqb4Ldfg80vu8ehHv+l4UlWaPJPvK5AEavs6sAXD5CuPg7xuuatn8MMQN7vYhIRD7hVVe/bp5KNBJ7nnnO98619v3tnuMVr4axvwPI7YdXP3S28Z9zo0p+bkUV9d/v1UG7Bzg4ifQWU3BpKdsf+Pi9n3cKdfQt07u3RmWXJWc5+3wJfsfUZIFTVF5GrgdEfIMDdeVI/w3X89rjLpcR5ETjmCzBhFjz3dXj4MjjjqzBlfrFLZoZL+m68EfbV7Z10DxxhQltAQtb1uV3YuKSsQJv96rGt9DIfbJu+m079rvnQdfS+HcH6Huuy99XHe93u7hucgfRB/EZEvkLPPojR+Yiy8mqoOxAa3yl2SYZmxknuzqyn/hV+9b/hg1fC7POsXduMQAqaglSq2AXZbw10HATAVVnrFDgk/8UpEVXjXCqO1hGa8bR+Opx7Kzz7dfjD92DHW3Di/3bJ+YwxZoAGks11xnAUpOTUTYNkB3Q2F7skQ1NWDWd+DV75ueub2P22Wy506nNjzKgxkGyulSLyf0RkWbA8U0T6vZdSRO4IMsCu6eX9U0SkUURWBa/rB1/8AhJxV+KR8mKXZOjEg0Wfg7O+Ds3vwUOXweYC3AFmjBmVBnKbwJ1AJ/ChYHkzcPMAPvdj4Kx+tvmdqi4IXjcNYJ/DKxKFsTPcXRUj2UHHwbn/5XJFPf4P7sFG9thRY0w/BhIgPqCq/wEkAFS1jUzimd6p6vPAyO/IjlVA/cHFLsW+q5sGn7gFZpwMf/oveObG/Kc8N8aMKgMJEJ0iUkFwT5iIfADoyNPxPygir4rI4yIyu7eNRORSEVkuIst37CjCc5/jdVBzwPAfN99ile55EsdeDhueh0eudA8lMsaYEAO5i+kG4AngQBG5CzgeWJqHY68EDlbVFhH5CPAIEPqQA1VdBiwDN5I6D8cevJpJbpR1256iHD5vRGD+Evc8iadvdP0S8y5wI6/T95qnB1QhQV1Rer6XncVUPLddWTVUjnePXK0c57KxGmNGrIHcxfQbEVkJHIc7DXxZVff5/k9Vbcqaf0xEfiAi4/Ox74KpO8jd2TQammamHhWkDv8qrPhxYY4RHxMEi6ygUTXB3UZcOd7Nx2t7HwRljCmqAeUeVtVdwK/zeWARmQxsU1UVkWNwzV2l/SCidDqOHW+5x3WOdDWT4RO3ugRymRGXuGnuCE/8rvfCnq2gCh1NLktp6w433bvTjSXZu9OlMGlr6PpcmhcNAkd2IBnvgkjVBDdfMcbVcCIxCybGDKOCJacXkbuBU4DxIrIZ11QVA1DV24DzgStEJAm0AUu0v8yBpSASc3c27VzLqMhpIJK/AXTVE2Hcob2/7yehdTfs3eEez7p3Z/f5PRtcwsG+amiZJ9HFXGoUryyYxnpOI2VdgSVn6ntl+F4UJSutAlnpEiCTPkHJei9oXtPc7chqehMPr7ySaEU9UlHnRudbYDMjUMEChKpe1M/738c9qW7kKaty6SwaNhW7JCOLF3VBpL8kgp3BKPbWXS6AtDdCKhG8OrOmnTnLbqrtzWiqA5JZn/Hd+5LqRFxYGNAdGvmg4qFltWi8DuJ1SEUdEh+DBMuhr1iFpUcxRddvgAjuWtqsqh0icgowD/ipqjYUunAlrXIsJNrcw2BMfpVVQtlBLgjnUIWE75NMKSlfM/Nu2SeRUlJ9VUTV5fcRP+FeOQnSJCcxWmYZQH23faa5Lb2s3ZrhRH28ZCuRziYiHU1u2tmIl17etYlI55+JdDYhvT3u1osF/TMRupr8oM8mPnLe77W5UIMkeNnPBY+5cT/Z08yzw7OWI7G+PyeRnMewStdseib3Ma2Zt7K3z35PMr9/90qBn+pazsznrM+8l8rZLme95vz+Mr9buub7euRtt/ezP0NW8r6sDLU9stdmbeOFbZv1exlmA6lBPAgsFpFDgR8BjwL/DXykkAUbEeqmQrLdtb3jvi9JX4l6Yhd/fVCFlCq+Kr4PqkoKRX1I+YpC8J472atCMjWAk/9AiIBEUS+KUpGXn2fIVPESe4MA4l5eRxPRziZiiWaiiSYEH0EQT9xUsudds5cnbn3m5+txl1nOHWkIEJwkUwnX9Jee+glIBVM/6Zr7OpK9b5OelsJz3fs8EfdzgnY7CCZZf7zdTs7ZwU5C3s/9THBx4ftZwSgscGUFvtxApqmiDmodSIDwVTUpIucC31HV74nIK4Uu2EiQTPm0Vkyls7GZ9rZW2hOpdEZ8ohGPWMQjFhFiEY9oRCiLeMQ8b9SluU+klPZEikTKdyf+9EneJzjBK76C77ugMAp6bvJDBL+sGr+smgT7Ns5GgIgnRDwXLCIieB5EpGtd1BPEgwiCF2wb9fJ0JeOnguCRzryaW8shZ76PK+8e2wTrup3gc662M8+7GD2SvtKRTJFKuR8vElwMRLz0dJA7vGHRoMswkACREJGLgIuBjwXr9ssb3NsTKVo7U+ztSNKWSNGRcFdN4k2hPLk+s50CiZRPIhV+VRX1hGjEoywixKIuaMSCgDLo//Qi6Ez5tHX6tCXc7yGZslN+sSnuhJL0B/d/IeBONhF30ol6gicQ8TwXcETwIl2BJiK9fEfA8oRtAAAbu0lEQVS9iHuZIUkEwaAz4dOR9GlP9v93lf6/8zwhIuB5XvB/J8F6iIhbN9SL0oEEiEuAy4F/U9UNIjID+PnQDjdypHyltTNJa2cqeCXxe6lFazROZ81BlDVtZCB3Nrk/5BTtIXfKRsTVOGJRNy0Lah+xiJe/q71B6kj6tHWmaEukaE+kBn0SMqVLcbW8VHLg/6fZtZX0FW00q/bi9qvdKgeqYeu61ya7PTcns65rGwFEXAAT6VoWIWhmwzW90bWM4E6QQRNbZjsJtkOGvUafSCntyRSdSZ+OhE9Hcmh/U5n/u5S6PEjkv5lvIAPlXge+lLW8AfhG3ktSZO2JFG2dKfZ2JmnrTNGeGNwv2y+rIVk5mWjre/tUDvfHmqI95Kmn6T/MqCd4nruSc8ueu8KLuCuGSESIytD7QdoTfiYYtHWm9r3d34wqQ62tlDJPuq7GPdzfTsTrCj5eJjB1zXteOsBIJvBEcv7usoNBe8KnIzGwv6ekr2xp9tnY6NPUqdSUCdUxcdMyoaYMqmIuKBfSQO5i+ijwNeDgYHsBVFVrC1qyAkr5SlsiRWtHVw0hlYcve7JyAioeoikguFce6NZRiKA97ujIur8+rPMr2I+X2Euko4FkooWBXC144vpC0k0HmeASNClEg2nKVxcUOlO0JZKMor/7QUmklLYktCWVtqTSmlDak9CaVNoS7r3WpNKeUFqTBOvcfHuwfVswH/Ggpkyoirk/6OrMFDfNXhfMV5UJlVG6OpzNsPHV3RiRjy9/ugajaL+781V5v0XZ2JhiY6Ofmb7b7JMcwDVqVYxM0EgHkK4g0jWtKRvad2ogTUzfAc4D/jwiBrLl8INg0BZcDWf3HRRCqmJc4fYdKScVHwt+gkhHI9GOBiTZ+6AyX6FzIN+yUcBXpS0BLQmlpTN4JZTmTmVvZl3P99Mn/rakMtCvhQAVMaiICpVRycxPqPSoiLr5hE/muNv2+qwPjtfazwB8T9yVYTqQVMWEypi4Y8WgMuaOWRnrezkedSepkSrlK9tb3e/u/b0+O1rdDQ5RT4h5EPWCvjyPYFmCdQxom9zlfP6u0k0/3dapsrNNM0FgQ4PPpqYU7zT6tGc9UXVSpTB9TIRjDogyvc5jel2EMXFhb6f7LrekpwmluUNpTpD5Pjd1Kpua/Mw2+TjNDSRAvAusGQnBQVW7BYK2zhQdSX/0PfrAi5GqGE+qYjyS6iDS0UikowFJtRe7ZPsskVIaO5WmDqWhXWnscF/8luBE39ypmT+WzIk/oexN9H/xVxWj21X7AdVecPINTvY58/EoQQDoOkFXRIXyyNCv8lO+K2t22bMD1t5OpTmh7O2E5k5XQ3l/r5+pnexN6ICuLNNBrCt4uNpJer6mTBhTLoyJC3XBdEy5x5i4+1kLLeUru9rcz+aCgJt/v8Ut72jr/+o7n6IelHm4uw49KItAzBNiESgLpm69ez8WkWD73rfrTCnvNPlsaPTZ1Jhib9bFwdi4ML3O4yMfKHOBYIzHwbURKmPhv/vxg7wjW1XpSNEVUDqVT/77EH4vA9jmH4DHROR/yErzrarfGvzh8kdVM23lLhgkaU+MwmDQD42Uk6ycSLJyIpJsJ9LRQKSjEfHzlZF9H8qmrvmlsV1p7PTdtKO3l09DR99X2OURujXHjK3wOKhWqCqDmmBddgDIbuKpjFLw9tqBiHhCbTnUlg+9LJ2pdBOYCxhtCaU16QJP17yrUbUmlL1BE1lrAna3+5ng1BbSzwUQj9AVNOJe90BS7tbVx918XbkQi/T8WXxVdrdrcMIPTv7pYNDis71Vyb5JR4BxFcLkKo85E6JMrnbzk6o8Jld5TKh0d+qkFBI+JP10P4ibT2TNJ33tc5uED6lgOZHq2r4z1bWuM5i69e79lk7NWq8524dfoNSUwfS6CKcdHGN6XSSoFXjUlhe2Z1zEXeDEo8L4yqHvZyAB4t+AFiAOlA39UPmRSPms297ixhzsZ8GgPxqNk4xOJlk1GUm2ZQWLzoIe970Wn6c3JninKdXtpN/U0Xs1N+bBmHKhNjjJTK6KUBecjGqDE1H2tComlIWciPZHZRH3u6jbx6fhtifd/1NDu9LQ4QdTzZr67G7zeXuPW+7t/7IqRiaQlEfINA3lbj82Lkyq8jh8XISTD/KYUu0CwKQqYWKlN6D/36i4q32ntL4Pqayg0elDRNx3fCC1zeyxU2WR4Db44E5GTwRfXeaA9ODS9HwqWJ8epKvBcnrA6b4aSIAYq6pn5uFYeZFMKW2dqf433M9ptIJktIJk1ZRM53akoxG0l8vGQepMKX/YkuTx9Z2s3JZCgCnV7iQxqcpjVr1QF1x1dr28zHyFdcYWXTwqxKPCpCqAvscwqLoaSENHVjBJB5IgmDS0uxrjIWM8PjQ1yuQqj8nVLgBMqvQoH4amq2KKeEKFBxV9BC43YDaSGUAbiwoxz93O3vefg/T3X9SD70MKN3DVH2J73UACxNMicqaqPjWkI+SZVRoGz49V4ceqSFQdgJdoccGis8kN4x+kDQ0pnng7wW82JmjuVCZVChfPKefMGTEmVo2AUX5mSERcU15VmTC1Znj+nwWIxyJUlEUoj3lEXGYs0tfGXWMl0jNdn82Mqcgs97yiVrpvFDYmo9t2ucfotr32WO9J9ywK/QeB/Erfhss+NK1Kf33PItIMVOH6HxIU+TbX+JSZeua/3MmcA+o48oBaDptUQzxmIzgHTdUlj+tsxEt2oFnpCjTIaaNBGuvWJPzPhhaefKuRt3a0EfWED06v48wjJjB/Wi2eF8nc3iupIAme39ljvhADeczo4QlUxKJUlEWIxzzi0choy55RVDJ10QpVXTyYz/RZgxDXBjBbVd/Zp5LlUW1FjJaOJHf/6R0Ud6vaoROrmX1ALbMPqOOIKbVUlxcsi/noIYJfXodfXhf6tqry1rZmnnp9G79bu4P2hM+BYyv5/AkzOPWwidRVdGVb6bqAi6KRPhrG/VSvwSOdXXU01BHTg65Ij+yVrtZySf+bNRI4vb5bvr0s6dvz/ay8VhokO8wedTzSRCNCRSxCPBrUEqLFrIFKLxlWs54REvqK9PG+9Mw2q5qznJuRNiRDbRETIQ6kBrFCVY8apvL0a/a8hXrPY8/R0pHkzfeaeG1rE69tbWTt9haSvhtmNmN8FUceUJupZdRXFr1vfcRobEvw3Fvbeer1bbyzu5V4zOPEmRM484hJHDa5prD9BqpdabhTQfBIBw71ET+J+KmgH2V4T4sCweBC12TQY9BhMKI9/5l8JfxElVnvBlf6WS+l+zpVzwUYBB8vK71GkkQyiZ9Kkkwm8VPuhCTBSUmCk5RkTlL7dqIqi3rEox6VZVHisUhw91NfJ2av+3vdMtNKzjr6eC9s6uVsX+JVlUxK8r6mfp/bSNW4/NYgAn8UkaNV9eXB/kwFEfw/VpdHWTx9LIunjwVcqoy125pZEwSM37y+jV+tdmkvpo6pyNQwZh9Qy8SacusgzeKrsnpzI0+9/j4vrt9F0lcOm1TD1aceyokzx1NZNkw1MhE0UoZGyvpPBxkEjK6pCx7uZJZywSR4PoBoEvH90MASEQlO+C51STRIUpceeZ5JZZKPi9v08xW8KJnnMXR7RXouD/AOmAiD7sPsQdWlz0imlGTwnI2E75MK1iWSKVJBMEmlUu5ZFpoKfrddgSV9Ui8vi1JZHqOivIyq8hjRSJSewcD+DgekSEFsIDWI14FZwCZgL119EPMKX7yejjpqsd7z2LP95kpKpnzW79jLa1sbWbO1kdffa2Jvh+uUHV9dzvRxldTGY1THo9TEo1SXu1dNPNZtuao8WhL3z+eDr0pDa4Ltze3saO5gR3MH25o7WL5xN9ubO6gpj3Lq4RP58BGTmD6+atjLJ0ImMWHMc3d4ZF+VS7ppJrvJJki6lknUJt33l93EIyJBwAiCRuaKK1vI30Po30g/26WfQZD90J1RpLdggkJleZTKWARvlPzdjBZBa9CgahADCRAHh61X1U2DOVC+LF68WF9++WW2NbkT3ED5qmza1RoEjCbea2ijpSNJc7tLWd2XqvIINeUumLggkhVMgkCSHWgqy1xHW0UsMqzBJZHyMyf+Hc0dbG9uZ3tzBztautblJlirKo8wc2INZx45iWNnjKOsQO3AnkcmpXnUE8qibtqV7tzd+22MKYyCBIhSs3jxYl2+fDkAezuSbN7Tts/5hpIpn5aOpHu1J2kOAkdLRyKznD11gSVBS0f/ie3iMY/K4M6MyswrezmaWV8RC6ZZ69LvRz1hb2eKHemTfnNHZpoOBntyhiELUF9VxsSacibWlDOhppwJNXE3X13OxNryvDUfRTyhqjwSDPaRoAYQBIOIZ1eTxhTZUALEiK73VpVHmTmxmvea2tndMvTRwtGIx5jKMsYMsjPbVzdoryUTUFzgaAseLNQWPEcinTE2vb6xsa37cyYGEKM96TmUPxaR4EQfZ/H0sW4+EwzijKsuI1bgq3LPgwnV5YyvLrcgYMwoM6IDBIDnCVPHVFAbj7J5T9uwPt3ME6Eq6KeYNMRRIaqaeSBPOmC0ZgJMMhNo2hIpauJRJtbEg5pAOWMqYkXrbBeBsUHtxJqGjBmdRnyASKuJx5g1KcrWhjYa+supXEJEhHgsQjwWoX74+4WHZExljEm18YL1VxhjSsOoCRDg2sEPHFtJbTzBloa2vDwEyHSpiUeZXBe3kevG7CdGVYBIq6uMUVkeYcueNprDnt1pBqWiLMKUujhVNkLdmP3KqP2Lj0U8po+vYvfeTrY2tFlq8CEoj3lMqo13S6thjNl/jNoAkTa2qoyq8gib97TR2mFpwgciFhUm1sSpryxeJ7gxpvgK1ssoIneIyHYRWdPL+yIi3xWRdSKyWkQWFaos5dEIH5hQzeS6uI3s74PnweS6OLMm1jC2qsyCgzH7uULehvJj4Kw+3j8bmBm8LgVuLWBZAJhQU86hE6upKLO7b7KJuN/N4ZNrmVBj4xmMMU7BmphU9XkRmd7HJucAP1U3lPuPIjJGRKao6nuFKhO4B5B8YEI1Da0JGtrcSOn9VTzmUVsRY2xV4QfUGWNGnmL2QUwF3s1a3hys6xEgRORSXC2Dgw46aJ8PLCLUV5VRX1VGIuW7YNHa2W8CwNGgosyjNh6jtiJmt6saY/pUzAAR1o4Req+Rqi4DloHLxZTPQsQiXmZkcnsixZ7WThpaE8M6IrvQKsoi1FZEqauIUR61oGCMGZhiBojNwIFZy9OArUUqC+Can6bUVTClroLm9gQNrQka2xIj8hbZyvIItfEYdRUxG/FsjBmSYgaIR4GrReQe4FigsdD9D4PhngsRY6qvNLUn2NNa+v0VleUR6ipcULA+BWPMvipYgBCRu4FTgPEishm4geA5Yap6G/AY8BFgHdAKXFKosuwLz5NMptdS668QgcoyFxRqLSgYY/KskHcxXdTP+wpcVajjF0Ip9FeIuDTndRUxauNRy6RqjCmYUT+SulDy0V8h0vVYTM8LpplnqAte8DhNT9xjM6vLo9RWxEbNI1CNMaXNAkQeZPdX7O10/RTuBO9O7tB1ks+c9O0kb4wpcRYg8sjzhJq4JbYzxowO1oBtjDEmlAUIY4wxoSxAGGOMCWUBwhhjTCgLEMYYY0JZgDDGGBPKAoQxxphQFiCMMcaEsgBhjDEmlAUIY4wxoSxAGGOMCWUBwhhjTCgLEMYYY0JZgDDGGBPKAoQxxphQFiCMMcaEsgBhjDEmlAUIY4wxoSxAGGOMCWUBwhhjTCgLEMYYY0JZgDDGGBPKAoQxxphQFiCMMcaEKmiAEJGzROQtEVknIteFvL9URHaIyKrg9YVClscYY8zARQu1YxGJALcAHwY2Ay+LyKOq+nrOpveq6tWFKocxxpihKWQN4hhgnaq+raqdwD3AOQU8njHGmDwqZICYCrybtbw5WJfrkyKyWkQeEJEDC1geY4wxg1DIACEh6zRn+ZfAdFWdBzwN/CR0RyKXishyEVm+Y8eOPBfTGGNMmEIGiM1Ado1gGrA1ewNV3aWqHcHi7cBRYTtS1WWqulhVF0+YMKEghTXGGNNdIQPEy8BMEZkhImXAEuDR7A1EZErW4seBNwpYHmOMMYNQsLuYVDUpIlcDTwIR4A5VfU1EbgKWq+qjwJdE5ONAEtgNLC1UeYwxxgyOqOZ2C5S2xYsX6/Lly4tdDGOMGVFEZIWqLh7MZ2wktTHGmFAWIIwxxoSyAGGMMSaUBQhjjDGhLEAYY4wJZQHCGGNMKAsQxhhjQlmAMMYYE8oChDHGmFAWIIwxxoSyAGGMMSaUBQhjjDGhLEAYY4wJZQHCGGNMKAsQxhhjQlmAMMYYE8oChDHGmFAWIIwxxoSyAGGMMSaUBQhjjDGhLEAYY4wJZQHCGGNMKAsQxhhjQlmAMMYYE8oChDHGmFAWIIwxxoSyAGGMMSaUBQhjjDGhChogROQsEXlLRNaJyHUh75eLyL3B+y+JyPRClscYY8zAFSxAiEgEuAU4GzgSuEhEjszZ7PPAHlU9FPg28O+FKo8xxpjBKWQN4hhgnaq+raqdwD3AOTnbnAP8JJh/ADhdRKSAZTLGGDNA0QLueyrwbtbyZuDY3rZR1aSINALjgJ3ZG4nIpcClwWKLiLxVkBLD+Nxjl7iRVl4YeWUeaeUFK/NwGGnlBThssB8oZIAIqwnoELZBVZcBy/JRqL6IyHJVXVzo4+TLSCsvjLwyj7TygpV5OIy08oIr82A/U8gmps3AgVnL04CtvW0jIlGgDthdwDIZY4wZoEIGiJeBmSIyQ0TKgCXAoznbPApcHMyfD/xWVXvUIIwxxgy/gjUxBX0KVwNPAhHgDlV9TURuApar6qPAj4Cficg6XM1hSaHKM0AFb8bKs5FWXhh5ZR5p5QUr83AYaeWFIZRZ7ILdGGNMGBtJbYwxJpQFCGOMMaH2ywAhIneIyHYRWZO1bqyI/EZE1gbT+mKWMZeIHCgiz4rIGyLymoh8OVhfkuUWkbiI/ElEXg3Ke2OwfkaQVmVtkGalrNhlzSUiERF5RUR+FSyXdJlFZKOI/FlEVqVvZSzV7wWAiIwRkQdE5M3g+/zBEi/vYcHvNv1qEpFrSrzM/yv4u1sjIncHf4+D/h7vlwEC+DFwVs6664BnVHUm8EywXEqSwN+r6hHAccBVQeqSUi13B3Caqs4HFgBnichxuHQq3w7KuweXbqXUfBl4I2t5JJT5VFVdkHVvfql+LwD+H/CEqh4OzMf9rku2vKr6VvC7XQAcBbQCD1OiZRaRqcCXgMWqOgd3k9AShvI9VtX98gVMB9ZkLb8FTAnmpwBvFbuM/ZT/F8CHR0K5gUpgJW4k/U4gGqz/IPBkscuXU9ZpuD/204Bf4QZzlnqZNwLjc9aV5PcCqAU2ENwgU+rlDSn/mcDvS7nMdGWoGIu7U/VXwF8N5Xu8v9YgwkxS1fcAgunEIpenV0HW24XAS5RwuYOmmlXAduA3wHqgQVWTwSabcV/mUvId4B8AP1geR+mXWYGnRGRFkJYGSvd7cQiwA7gzaMb7oYhUUbrlzbUEuDuYL8kyq+oW4JvAO8B7QCOwgiF8jy1AjDAiUg08CFyjqk3FLk9fVDWlrlo+DZe88YiwzYa3VL0TkY8C21V1RfbqkE1LpsyB41V1ES5z8lUiclKxC9SHKLAIuFVVFwJ7KZGmmf4EbfYfB+4vdln6EvSFnAPMAA4AqnDfjVz9fo8tQHTZJiJTAILp9iKXpwcRieGCw12q+lCwuuTLraoNwHO4vpMxQVoVCE+/UkzHAx8XkY247MOn4WoUpVxmVHVrMN2Oaxs/htL9XmwGNqvqS8HyA7iAUarlzXY2sFJVtwXLpVrmM4ANqrpDVRPAQ8CHGML32AJEl+y0Hxfj2vhLhogIbuT5G6r6ray3SrLcIjJBRMYE8xW4L+0bwLO4tCpQQuUFUNV/UtVpqjod15TwW1X9DCVcZhGpEpGa9DyujXwNJfq9UNX3gXdFJJ1Z9HTgdUq0vDkuoqt5CUq3zO8Ax4lIZXDeSP+OB/89LnaHSpE6ce7Gtc0lcFc0n8e1NT8DrA2mY4tdzpwyn4CrEq4GVgWvj5RquYF5wCtBedcA1wfrDwH+BKzDVdXLi13WXsp/CvCrUi9zULZXg9drwL8E60vyexGUbQGwPPhuPALUl3J5gzJXAruAuqx1JVtm4EbgzeBv72dA+VC+x5ZqwxhjTChrYjLGGBPKAoQxxphQFiCMMcaEsgBhjDEmlAUIY4wxoSxAmBFBRMZlZdN8X0S2ZC0PKLuqiNyZdf99b9tcJSKfyVOZzwnK96qIvC4iX+hn+9OChIZh700Rkcey9vVosP5AEbk3H+U1Jpfd5mpGHBH5KtCiqt/MWS+477Qf+sFhJCLluKR0i1V1a7B8sKr+pY/P3AzsVNXvhLz3I9wo3luC5XmqurpAxTcGsBqEGeFE5NAg5/1tuIyxU0RkmYgsD/LhX5+17QsiskBEoiLSICLfCK7IXxSRicE2N4vINVnbf0Pccy3eEpEPBeurROTB4LN3B8dakFO0Olwep90AqtqRDg4iMklEHgo+9ycROU5EPgB8Abg2qHV8KGd/U3CDOgn2tzrr518VzN+ZVavaKSL/Eqy/LjjO6uzfhzH9sQBhRoMjgR+p6kJ1mSyvU/dchPnAh4PnZuSqA/5H3fMqXgT+tpd9i6oeA1wLpE+ufwe8H3z2G7jMut2oy4v0JLBJRP5bRC4SkfTf23eB/wjK+Cngh6q6Hvgh8H/VPXvgDzm7/D7wExH5rYj8czoHUM4xL1GXHPFcXGrnn4rIR4CDcKnWFwAfCgk+xoSyAGFGg/Wq+nLW8kUishJXozgCF0Bytanq48H8CtzzQcI8FLLNCbhkfqhqOsVFD6q6FPfMjuW4jKXLgrfOAG4LrvwfAeqDfFW9UtXHgA/g8nEdCbwiIuNytwv2cz9whaq+i8vNdDYu7clK4FBgVl/HMiYt2v8mxpS8vekZEZmJeyLcMaraICI/B+Ihn+nMmk/R+99CR8g2YSnAQwVNQatF5L9xyQq/EHz+GFXNLgOuC6XPfe0C7gLuEpEncIEqNzjdDtyjqs9mlfVmVf3RQMtsTJrVIMxoUws0A01BM8xfFeAYL+CahhCRuYTUUESkVro/l2EBsCmYfxq4KmvbdP9FM1ATdkAROT1dyxCRWlyu/3dytvkyEMvpvH8S+HyQ6RURmSYi4wf4c5r9nNUgzGizEpfaeA3wNvD7Ahzje7j2/dXB8dbgntqVTYB/EpHbgTagha5+jquAW0XkEtzf4LPBul8A94vIecBVOf0QRwPfF5EE7sLuVlV9RUQOzdrmK0BrutMa+L6q/lBEDgf+GNRQmoFP4/oojOmT3eZqzCCJe+hKVFXbgyatp4CZ2vU4R2NGBatBGDN41cAzQaAQ4DILDmY0shqEMcaYUNZJbYwxJpQFCGOMMaEsQBhjjAllAcIYY0woCxDGGGNC/f/x1kWgzUS9WAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_learning_curve(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This shows a typical learning curve: for very few training points, there is a large separation between the training and test error, which indicates **over-fitting**. Given the same model, for a large number of training points, the training and testing errors converge, which indicates potential **under-fitting**.\\n\",\n \"\\n\",\n \"As you add more data points, the training error will never increase, and the testing error will never decrease (why do you think this is?)\\n\",\n \"\\n\",\n \"It is easy to see that, in this plot, if you'd like to reduce the MSE down to the nominal value of 1.0 (which is the magnitude of the scatter we put in when constructing the data), then adding more samples will *never* get you there. For $d=1$, the two curves have converged and cannot move lower. What about for a larger value of $d$?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_learning_curve(3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we see that by adding more model complexity, we've managed to lower the level of convergence to an rms error of 1.0!\\n\",\n \"\\n\",\n \"What if we get even more complex?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_learning_curve(10)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For an even more complex model, we still converge, but the convergence only happens for *large* amounts of training data.\\n\",\n \"\\n\",\n \"So we see the following:\\n\",\n \"\\n\",\n \"- you can **cause the lines to converge** by adding more points or by simplifying the model.\\n\",\n \"- you can **bring the convergence error down** only by increasing the complexity of the model.\\n\",\n \"\\n\",\n \"Thus these curves can give you hints about how you might improve a sub-optimal model. If the curves are already close together, you need more model complexity. If the curves are far apart, you might also improve the model by adding more data.\\n\",\n \"\\n\",\n \"To make this more concrete, imagine some telescope data in which the results are not robust enough. You must think about whether to spend your valuable telescope time observing *more objects* to get a larger training set, or *more attributes of each object* in order to improve the model. The answer to this question has real consequences, and can be addressed using these metrics.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Recall, Precision and F1-Score\\n\",\n \"\\n\",\n \"Intuitively, [precision](http://en.wikipedia.org/wiki/Precision_and_recall#Precision) is the ability\\n\",\n \"of the classifier not to label as positive a sample that is negative, and\\n\",\n \"[recall](http://en.wikipedia.org/wiki/Precision_and_recall#Recall) is the\\n\",\n \"ability of the classifier to find all the positive samples.\\n\",\n \"\\n\",\n \"The [F-measure](http://en.wikipedia.org/wiki/F1_score>)\\n\",\n \"($F_\\\\beta$ and $F_1$ measures) can be interpreted as a weighted\\n\",\n \"harmonic mean of the precision and recall. A\\n\",\n \"$F_\\\\beta$ measure reaches its best value at 1 and its worst score at 0.\\n\",\n \"With $\\\\beta = 1$, $F_\\\\beta$ and\\n\",\n \"$F_1$ are equivalent, and the recall and the precision are equally important.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/titanic.csv.zip', 'r') as z:\\n\",\n \" f = z.open('titanic.csv')\\n\",\n \" titanic = pd.read_csv(f, sep=',', index_col=0)\\n\",\n \"titanic.head()\\n\",\n \"\\n\",\n \"# fill missing values for Age with the median age\\n\",\n \"titanic.Age.fillna(titanic.Age.median(), inplace=True)\\n\",\n \"\\n\",\n \"# define X and y\\n\",\n \"feature_cols = ['Pclass', 'Parch', 'Age']\\n\",\n \"X = titanic[feature_cols]\\n\",\n \"y = titanic.Survived\\n\",\n \"\\n\",\n \"# train/test split\\n\",\n \"from sklearn.cross_validation import train_test_split\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\\n\",\n \"\\n\",\n \"# train a logistic regression model\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"logreg = LogisticRegression(C=1e9)\\n\",\n \"logreg.fit(X_train, y_train)\\n\",\n \"\\n\",\n \"# make predictions for testing set\\n\",\n \"y_pred_class = logreg.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[107, 21],\\n\",\n \" [ 52, 43]])\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"confusion_matrix(y_test, y_pred_class)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![f1score](images/Precisionrecall.svg.png)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"precision_score 0.671875\\n\",\n \"recall_score 0.45263157894736844\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import precision_score, recall_score, f1_score\\n\",\n \"print('precision_score ', precision_score(y_test, y_pred_class))\\n\",\n \"print('recall_score ', recall_score(y_test, y_pred_class))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### F1Score\\n\",\n \"\\n\",\n \"The traditional F-measure or balanced F-score (F1 score) is the harmonic mean of precision and recall:\\n\",\n \"\\n\",\n \"$$F_1 = 2 \\\\cdot \\\\frac{\\\\mathrm{precision} \\\\cdot \\\\mathrm{recall}}{\\\\mathrm{precision} + \\\\mathrm{recall}}.$$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"f1_score 0.5408805031446541\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('f1_score ', f1_score(y_test, y_pred_class))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Summary\\n\",\n \"\\n\",\n \"We've gone over several useful tools for model validation\\n\",\n \"\\n\",\n \"- The **Training Score** shows how well a model fits the data it was trained on. This is not a good indication of model effectiveness\\n\",\n \"- The **Validation Score** shows how well a model fits hold-out data. The most effective method is some form of cross-validation, where multiple hold-out sets are used.\\n\",\n \"- **Validation Curves** are a plot of validation score and training score as a function of **model complexity**:\\n\",\n \" + when the two curves are close, it indicates *underfitting*\\n\",\n \" + when the two curves are separated, it indicates *overfitting*\\n\",\n \" + the \\\"sweet spot\\\" is in the middle\\n\",\n \"- **Learning Curves** are a plot of the validation score and training score as a function of **Number of training samples**\\n\",\n \" + when the curves are close, it indicates *underfitting*, and adding more data will not generally improve the estimator.\\n\",\n \" + when the curves are far apart, it indicates *overfitting*, and adding more data may increase the effectiveness of the model.\\n\",\n \" \\n\",\n \"These tools are powerful means of evaluating your model on your data.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n },\n \"name\": \"_merged\"\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/06_Unbalanced_Datasets.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 06 - Unbalanced Datasets\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Kevin Markham](https://github.com/justmarkham)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Background\\n\",\n \"\\n\",\n \"Unbalanced classification problems cause problems to many learning algorithms. These problems are characterized by the uneven proportion of cases that are available for each class of the problem.\\n\",\n \"\\n\",\n \"Most classification algorithms will only perform optimally when the number of samples of each class is roughly the same. Highly skewed datasets, where the minority heavily outnumbered by one or more classes, haven proven to be a challenge while at the same time becoming more and more common.\\n\",\n \"\\n\",\n \"One way of addresing this issue is by resampling the dataset as to offset this imbalance with the hope of arriving and a more robust and fair decision boundary than you would otherwise.\\n\",\n \"\\n\",\n \"Resampling techniques are divided in two categories: \\n\",\n \"* Under-sampling the majority class(es)\\n\",\n \"* Over-sampling the minority class\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"import matplotlib as mpl\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"from sklearn.datasets import make_classification\\n\",\n \"from sklearn.decomposition import PCA\\n\",\n \"plt.style.use('ggplot')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"\\n\",\n \"# Generate some data\\n\",\n \"X, y = make_classification(n_classes=2, class_sep=2, weights=[0.9, 0.1],\\n\",\n \" n_informative=3, n_redundant=1, flip_y=0,\\n\",\n \" n_features=20, n_clusters_per_class=1,\\n\",\n \" n_samples=1000, random_state=10)\\n\",\n \"\\n\",\n \"# Instanciate a PCA object for the sake of easy visualisation\\n\",\n \"pca = PCA(n_components = 2)\\n\",\n \"\\n\",\n \"# Fit and transform x to visualise inside a 2D feature space\\n\",\n \"x_vis = pca.fit_transform(X)\\n\",\n \"\\n\",\n \"# Plot the original data\\n\",\n \"\\n\",\n \"def plot_two_classes(X, y, subplot=False, size=(10, 10)):\\n\",\n \" # Plot the two classes\\n\",\n \" if subplot == False:\\n\",\n \" fig, subplot = plt.subplots(nrows=1, ncols=1, figsize=size)\\n\",\n \" \\n\",\n \" subplot.scatter(X[y==0, 0], X[y==0, 1], label=\\\"Class #0\\\", \\n\",\n \" alpha=0.5, s=70)\\n\",\n \" subplot.scatter(X[y==1, 0], X[y==1, 1], label=\\\"Class #1\\\", \\n\",\n \" alpha=0.5, s=70)\\n\",\n \" subplot.legend()\\n\",\n \" return subplot\\n\",\n \"\\n\",\n \"plot_two_classes(x_vis, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.1\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y.mean()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 1: Under-sampling\\n\",\n \"\\n\",\n \"## Random - Under-sampling\\n\",\n \"Randomly select a percentage of the negative class such that the resulting dataset is balanced\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1000\\n\"\n ]\n }\n ],\n \"source\": [\n \"n_samples = y.shape[0]\\n\",\n \"print(n_samples)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"900\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"n_samples_0 = (y == 0).sum()\\n\",\n \"n_samples_0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"100\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"n_samples_1 = (y == 1).sum()\\n\",\n \"n_samples_1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.1\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"n_samples_1 / n_samples\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"How many negatives cases should I select if I want a new dataset with 50% of positives?\\n\",\n \"\\n\",\n \"0.5 = n_samples_1 / (n_samples_1 + n_samples_0_new)\\n\",\n \"\\n\",\n \"(n_samples_1 + n_samples_0_new) = n_samples_1 / 0.5\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"100.0\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"n_samples_0_new = n_samples_1 / 0.5 - n_samples_1\\n\",\n \"n_samples_0_new\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.1111111111111111\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"n_samples_0_new_per = n_samples_0_new / n_samples_0\\n\",\n \"n_samples_0_new_per\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create a filter to select `n_samples_0_new_per` from the negative class\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Select all negatives\\n\",\n \"filter_ = y == 0\\n\",\n \"\\n\",\n \"# Random sample\\n\",\n \"np.random.seed(42)\\n\",\n \"rand_1 = np.random.binomial(n=1, p=n_samples_0_new_per, size=n_samples)\\n\",\n \"\\n\",\n \"# Combine\\n\",\n \"filter_ = filter_ & rand_1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"108\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"filter_.sum()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Also select all the positives\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"filter_ = filter_ | (y == 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"filter_ = filter_.astype(bool)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"plot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_two_classes(x_vis[filter_], y[filter_])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Convert into a function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def UnderSampling(X, y, target_percentage=0.5, seed=None):\\n\",\n \" # Assuming minority class is the positive\\n\",\n \" n_samples = y.shape[0]\\n\",\n \" n_samples_0 = (y == 0).sum()\\n\",\n \" n_samples_1 = (y == 1).sum()\\n\",\n \"\\n\",\n \" n_samples_0_new = n_samples_1 / target_percentage - n_samples_1\\n\",\n \" n_samples_0_new_per = n_samples_0_new / n_samples_0\\n\",\n \"\\n\",\n \" filter_ = y == 0\\n\",\n \"\\n\",\n \" np.random.seed(seed)\\n\",\n \" rand_1 = np.random.binomial(n=1, p=n_samples_0_new_per, size=n_samples)\\n\",\n \" \\n\",\n \" filter_ = filter_ & rand_1\\n\",\n \" filter_ = filter_ | (y == 1)\\n\",\n \" filter_ = filter_.astype(bool)\\n\",\n \" \\n\",\n \" return X[filter_], y[filter_]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.1\\n\",\n \"y.shape = 1000 y.mean() = 0.1\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.2\\n\",\n \"y.shape = 510 y.mean() = 0.19607843137254902\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.3\\n\",\n \"y.shape = 343 y.mean() = 0.2915451895043732\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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s/Z6wXjLEHkbLWpqL17t8oYuJj62Sw2eeviBTNMBWOpjymJ+T7SBdorVKS3QBZxXrBGHsRx/vSOJ5uep/taY73pdt6vi03N0ZHR48CfwCMAAHw+Xw+/7mtHncnoZIpibhWwzpksOnnL48Uu/tgalwI0DJhfhYGR+R728HoHWh+nI2Q1QMn0BfPxisqY+wpnBnOcnvRZrrY2OCwPc1AzuLMcLat52tHxOcBv5LP5x8BfhD4hdHR0Xvrr2er4uFNPn9FpDgw1Ch+jiK9TAckU5hDhzZ3fXVQJ8+g+gbkecuOE+sFY+wWTEPxwkO9PH4oR9I00BqSpsHjh3Jtl7JAGyK+fD4/BoyF/18aHR19E7gPuLTVY+8Utioe3uzzl0eKShnoYw9JM2NhFjSQSMJHnoexm/i33xXLq0gUneuEEyc3RFYb8RaMEWMnYRqKxw/l2ipbWQ1t1fGNjo4+CDwBfLudx91urEUGPPyo1OLW0PdtmkyapJ1KGTA4gu7qRZ16AuPRx6XzOxa6uqi6L61Bb/yTMF5RGeOgQzXIJraA0dHRDuAbwL/M5/N/3OT+F4EXAfL5/PscZ42a2A7Csiy8Jt1SAO37lF7+CsHMJCpVK65qu4LRP0z2hZ9EmZuPkOT4f0wwM7Hm8e1zr+Oc+zvMdIYgaGzra7tC8vEPknrsA5u+ju3AWu/rXkN8rduD3bjWZDIJrD/o1BbiGx0dTQBfA/48n8//Hy08Rd+9e3fL520HBgYGmJ6ebnpfcOns2o2AMCLbCuonPqJIcXlEGbz8ZXAdstkspVJp5UESSYwXfmpL19FurPW+7jXE17o96O3r59WLN3dk9jbC4cOHoQXia0dXVwH/BnizRdK7d9CKPm+LxNdK2qkdW6Y1VkOsvYuxx+AHmj8+N8aNqeKOzN5uFO2o8T0FfAY4Pzo6eja87X/N5/MvteHYu4q9QjhbldvEiLHTOD9RYqoQ7Njs7UbRjq7uX9NCaHkvQiVTaKdS67JG3dTuPhgYQiXbK6pcFWEThOxKLVOkvYsdV2LsJVybrZBKpSm5K++LZm/vaeK7F1FPEgXLJPD8piShj74H/tMfS7QV2cn7PkyOwZ0b6KPvwf/TL207yVTlMpVC4+sI5TI8/GjsuBJjT8H2Aqw1EpF2z95uFAduZG3FfOwqTic68OHWNZidhNkp+SoWREKyMAeFAvjulpxSWoUyTNQzL5B8/IOi69MaEknUqSdQz7wAb1+KHVdi7Cns9OztRnHgIr71nFS4fB5OnkG/+hKcfwN6+qFUgEpZiK+4BOksdHbB4jwMHV7x/GZ7c7cKZZikHvsAxn3HVr6mHWjCxNgadtJdeC/geF+at+ebR3W2p3lkcIfKRKvgwBFfK51aDejZmVrlMtchXyD1PtOUSHHZUiGVSqHffZsgPM9O1dr2ShMmRnPstLvwXsCZ4SxzvsuNyfKOzN5uFAcv1W3FSSUix9XEyXZZ/l12v9YBvHVx04amm0XsuLK30Yq78H6DaSh+8gcO7djs7UZx4CK+VqQh1Qiq3i2legAFgRYjgb7BxudOTwrhrZFGb0caHDuu7G204i68mx3O7cJOzt5uFAcu4mvFSaUaQS13SwH5PghkVeTAUOMBZqagfxkZhthOd+PYcWVvY6fdhWOsj4MX8bXgpKKhGkE1uKX4PmRy0D8Mw4fFUKDu+SST0D+InhoXL71I99fTBwPDqG2qtcWOK3sbKcvA8Vcnt3Z0OA9a82SrOHjEt5wkQuunBieWd6/Anetox5EIbmAINThS0809/Ty8fWkFyehsFq68Ke7JlinNkchKfmkR/fCpbX1dsePK3sTxvjTnxopN0912dDgPYvNkqzhwxAeNJNExMEBlenrF2kV97GGxfp+ZgqUF9MOnGiOoJiTj37gmcpfkslqbZUJxEdTBqyzE2H534XavZjwI0eOBJL5mWK7vU0qJ7XsU6T348PpOLNoX2Uu5XJv00EBhEXwPLr5BgN70vt61pkxi7F1E7sLLyeSRwfaQSTubJwcleoyJL0I7RMCuC1FNcH4WFhekNgiQTkNhUWZ/Wxwlq0ahM1OwNI9TKqArZTj/XfT3voX6b/97DKv5NcfYW9jODqftBawl49xI82SnF3vvFmLiC6HtCsxMNm9KKNWSCDiSyuiBYVhaBB3IiJlhSORXKsH1K3DsIRklu3QOZRirip315fPomWm4e0OiyExYC1LAu2+j/+MX0f/wn8SR3wFHO5snB0V6ExMf0VzudSgsNW9KHHtofZEw1FxUFmelweHYQnpyEsiGafD0pEhhXv3/0CNHVjUW4MZVWJqTcTlrGbklU3DnBsGb56S7HLuyHFi0s3nSzuhxLyMmPqS+h5Vcaa4V7bcduw0//PF1j1OVylx/S54buVvrAKwEZHMigI7S3+Jio6RGB7A4h77+Nvr62/K4aDa4+ZXDX61NnjH57X+0s3myE9KbvYB9R3yb8qW7cRUO3wd2qbExEcF1GkTAa57jmReE+BbnqH50ZjpqpKeR2t/0JAQB+solSan7B+Hdq7XzL8wKES8tSsTX07/yuitl8IOdnxSJsafQzubJsd4Uf3l1niXHxw80pqHoy1gMdSRwfXbdXKBd2FfEt1yS0moEJCNqxkqxsmnKWFr/YPV5651DPfMC6r4HYOgQurtP0uX6Du/8jEyC6EA6wFFKPXZbHmeFPxLfFzKcGJOmSakAyd7aRUfTJLErSwxqzZMzw9kqAV6YKPJfbiwC0Js2ySTMNWUpfqC5veCw6AS4vpCer2G84DJb8vjA0Y5dNxdoF/YX8bVgOdUsAoqaEtFqRwZHGh+QqJFLS7ZWUa1vYFiiu9lpcG2wK0Ji2RwkwtQXhPDmZsRhOXKBMU2pA47dgvmwzkdIfJ4PmayQZ3ff6m9I7Mqyq9hpPVy9FCVhwtXZCmVX0tbZksGJ/syaspTzEyXmyh6PDGaYLLjMlj38QJM0DTpSBke7UvtCygL7jPg2LUnZyJB/C+dQH/+k1PpmJsM7wgd4LqDFJKFSljTWsqRR4dpQUUJ8ng9DgxKFnn4CLp4VAtWAYcLQIHT2wMSdlfPC9YhdWXYN7dTD1ROokSwSOJWmBFovRRlfcii7QfX+khswWXAZ6UysKkup7+iOdCYY6Uw03P/OnM0Thzu28rbsGeyPSmWIliynmmAjQ/6tnCNyTKazW0iuowsGh4WIrISQnl0Jya4sAudSSZydpyZE7ByaHSjDgtNPwiOPkXzfh1AnHkEdvh91+kl45hPgrLITODRciLE7aJcVVUSgZ8eKOH6AUUegL1+Zww9q62HriWu27DWQomkoZste9RquzVZWnOsgmSnsq4hvs9vINjLk3+o5lGGi0BCSpp4cg5vvSKqrda3xEQRCdCrU+iklpPj636C7eyQizHTAR56n46PPU6nbqaoDHz1xd03DhXYjXmrUGtqlh9uIoLheiuIHdb9j1N0WohmJHZSOLuwz4tuKL13LQ/6rnENrLQ2KdKa6gEjfuQmDoQD67k1JU4O6iFEHQnjKBENBOifyGR2EgucipDKgCzB+C73c8XmHXVk22zw6iNiIHm6tWuBGCLSeuKLGRD3qI8BmJLbdZgp7CfuK+Ko6uplJWJqvTWEA3PcAPLx1Q87ltlZaB9KVfes8OI40LLI59OH7xeCgIAJoSkVpkpSKNWJDA+EfgDbke62FILUWIjt6TJokc7M4F96AZTs3dtKVZbPNo4OIVqOn9WqBZddfsxZYT6D1xNWXsRgvuNXn+oFmsCMRPqc5iW23mcJewv6JXQkjoKefl1R0ZkqkIpYp9bJEEr7xF1u2f4/qd+rUE2jLgnfelojL88ScVGup2V19U2p4hQVxedFa6nlKNXFpUfLlhfU6w5Br7ulHDY6glEKlUrjXLm/p2reMVppHMQAhIcfTTe+zPc3xPiGe9VLZuUrz39dAw/iSy9szFf79uSm+cnGGQGt6MxZlN5BlgBWf8SWHyYKLF2gGc9aaJBbpAfeqXXw70ZaIb3R09IvAjwGT+Xz+dDuOuWm8fQmS6ab1rXZFJVGUFQD65nWpw1lW/QOEdG0bUikhYWVIV1drqetVH6tqtb3ADzu8rkSGnd2NJ7Z3V54SLzVqHa1GT8tT2UDrBilJxdeU3ARHulJEvBNouDJTZskOONKVqEaJ3x8v0ZM2qXgB0yWP3oxB2ZUnGcD3x4sc7U5hGfDVN2ebdob3sl18O9GuVPf/Bv4v4A/adLzNYydXLd64KiNlTU9mSOTZ0wtdvTA9LsSmFJgGRGlQ1OgwDCHPRAJQUus7umyVZGp35SmbbR4dRLQ6TVFfCwy05sqMaO8MBUUnoOz6LFU8rs3YvHcgzXsyorEr2AGdKYOBnMX4klMlyiXHpyNhcnokixEeODrukh3gBXJt+9FqaiNoC/Hl8/nXRkdHH2zHsbaKnYxKtGNLDVGp2lxu7V451/SE7ONFQa5LOriBL51brcJ6n5bbXFeeY1qQSjfs79C2TeL4SVaKEHYQB3ip0WbEyK1ET/W1wMmCWyW92bKHG2hMpRjIWizaPjfmbSoUcGyP+7oSDOQsrs3aNb2eUlQ8Tdl1uTJT4aH+NIZS1eOmLJG0RPq8/WY1tRHsWHNjdHT0ReBFgHw+z8DAwLacp9Dbtza5JVN01J3bsqxNX0uhtw8nkyHo6CQoLIECXSpJbS9qqvg+Rkc3JBJopdCGIUTnueFjjLDEZ8i0hidEqBIJrLs3SZx4BFwH48j9GEqRfu1lIc1UmsTxkyRPP4labQ1mm6GfeobS4hzBzAQqVSuOa7uCcfRBsk89U72WrbyvO431rtUPNH98bozJQkA6lcYKA9u35gNmPIcHezO8PVWk4gWkLYOTwx08eaSnpSjqyWMmr9+YJ5UwKMx7pFNJliougTYwFHSmLZJJi/6EpuT4OF7AgquxEgZLCz6uNkinan/GSvkoBa42WHAN7uvJVI8bIZut1feywFjF4GPb8LPay78DO0Z8+Xz+88Dnw2/1dJ0erZ0IBg6tHZWceqJBCzcwMMDya2lVqxYMHEInUmAmJOqbn611bCPNlOsRTI1Lyus4spPXtETIHAS1ml8iKWNoAHYFrQzchXncu7fhAx+Gu7fIfu9blKMUuVxGf/PrqMsXpdmyQzIS/fd+uPreRPIZjp1EnTxDeW6u+rhm7+texXrXenasyM0p6ZaW3Nrtgda8enmWzrTJ/d3ChhXgG3NLXLw1tWoKWR89ll2fd+dtEoaiVHZRhsFSxcUPNAlTkTI0jiOprOtrcimLcsWjbMJkwamaCERZjtby+6EDn7H5Ir1JTbliVzV9plKUSo3i6aJmW35Wu/E7cPjw4ZYet6/kLNDaFrW1sBGtmjp5Bn33Bpx7PazfAahaLS+RgFynNCrKZSG4IADC7q0y5P9Rl9d1hRSzHWKM8NApIURloOfnUL09MuERvdZdkJEcxKVGq2npJgsubqAp2I2ylbVSyOXyFdNQHOtNc3vRZsEJ6E7KeTqSJrmkiVJQsGumAdHx/UCjAdfXFJ2AXNKg6ARUPPngnSq6ZCxFoGtmA/WSlsbr3Vfijpaw/4hvi6LeVrVq1ajQrkiEtzAfCpJN0eGls7J0SCn5t1yU1ZRBSup8WgsxBoE0NZQht3f1iBYwiuwce2cbNjFWoJkYOdBwY96h6PoiXoeqfZOhGsXF9RHejXmb6aLLYC56rMJQVCNGpcEyFV6gKTo+ZS+g4PigoSNlorWmK22SMJRUSJSi7AXYfoDtabKWlE5cX1NypanRkza5u+TRlTIYWkZ8+02Y3CraJWf5Q+CjwMDo6Oht4Dfy+fy/acexN4MtRSUtkIw+eaa2C+PuTel0JsJfKMMIIzsPXBWSXCAaPaMiqWE6JWTp+/L4VBoJF8P0eG5atMxXLokJQXfv2oRdV9OMR8raj+Vi5EhOsmh7GEqhlMLXmvGCw6LtcaI/g6GEMBsdUxQ3F2yKjs94wSE5rXi4P81wZxJDKY50pbg+V6EjaXB11sYPNIZSomlXErFNLDk83JdgpDOFF2jGllxsNyBpGXSlJEoEWLJ9LENRcgP6MhYP9adJmor6zHs/CpNbRbu6uv+wHcfZDNr9h95KVziKClmaF9mJGTYsfE+4S2tAg+lJ6ooyK/iRAAAgAElEQVSqRX+VMvT2SyPD9SCXk1RYAZWK1AE9T4h0ckyOuTiPPvog3vgdmfmNvAK7+2TnbzJdey/ikbK2Y/koV9QlNQ1JOTsS8p6aIdFELigpy6gKlBOmErKseGGUp3ACzTtzNotOUO3AHutJk7TECcVHfmVSloFlKCS51SiECE8OZjANxZ1Fh8FcLZLzAyG06JhJ0+AnHunbti1v9yLu6VR3O/7QW9KqhVGhnp8V0pufBVSdNi/8N3CFoLSGjHR2q2NogyOyeKhUFLIrFaTTq5Q8x7TCLq8vfn6z0/i5jppsxg8NTOdm0T/6k3LaeKRsW7BcjDxbFvIKAk3FC1BA2QvIWAa5pMls2aM3Y/HIYLpaHxxfqpFl9CM0UNi+plwlyyTphEGgNe/tT3NzQVJptMb1Nd1pk95cirmKx3CnCJ3tUAUwU3RJWYqulMlgR6KaRoNEngdFmNwq7m3i244/9Ba0avrKRSFZ3xfickPCUtS6uVHq6jjSrQ1TVibuyDjb7LQQWyot6W6pKLXBhAXJNKQzNbv6clHuzzXxQgun3YAaIetgpZN0dx/63bfjWuAmsFyM7PoB87ZP2lKYhomntUwqOj62H9Cdsqop5KXJEkrVbKIylkHB8evExRK93Zi3mSy6mArenXNAaTKWQSZh0p81mCl5VMIROC8IqoLkrpTBcGeSIGxeWKbRQHpwMJsX6+GeJr7tKPq31BW+cVWiQtMUeYrvAVo6sJ4X2kwhDYtUCk49AUOHJMIzLSE1yxRS0kgEaBnVEt8KhDVEc+QI3sTdGpkNDcr6y5vvwKknQ69ALeep3x0SRYdLC+gf9eN0dxOoj5juLjnoBTuM3qSrWvYCFJL6ZpNGVcoS1Qe9IKg+ruLJLG3CVCRNiSBtT+MGGjT4IZEW3QBfg+0H9GctCnZAyfbxfI1SmiNdSYY6EkwWXMZDaUt99AgHt3mxHu5p4tuOKY1WusLB/e+B//KKjKstLQnRGYZ8JRISuUX/Vwquvw13bkg9MJOFzq6GKQdt2yJTyaxSZA7tq6yRwzhdPau+TpVMoe/ebL6O0jLBcdBxuttWKKXoSJl0pOT99gNNfzZRrZsd70vzvbsF5ioiPjaUIpMwcMIUt+QIARrh451Qv+f4mkBD2Q0ou4STFwbJhBgcDNTV9IY6EizaHqUwlZbpjOSBbl6sh3ua+LZrdnStrrAOfLh7S3bwJpJhHc4BPxw7S6QgYQpZ+X4oW0kKGbmOjK8tLUhw19MvzYlUSiY6okhuxUmVNEHWe50PnIDz321+DM+HoZFY+rIJ+IHm3HiRv7m5xGTBYWzJwdeQMcE0DSqeJtDSdOhKW/SkaqnlmeEs37q1hGqI5hV+EEjDQmssQ7qtZVeiwUzCwAs0dpjaJk2FG2isQKY3pkvQl01UO7SGghP99XsyApLmwW5erId7mvh2Y3ZUXz4vy38eOil1NNeB8bthQ0OJJq+zW4ixUpZU13VqBqPKAN+SfRuOA+O30akMOC4UixIldnTW3HM9X2p7g4eaX0/d61Qnz6D/4k9WrsiMlhMNDMcOKhuEH2i+9tYsr98uyOysIfIVgoCZcoBpCFEZSup3lqG4seDgeAGXpspcm61we6FC0gTbgwAdylSk9lZwfQItC32UUgRaNHiWoXCVpl4a3ZE0GehMU7Hdauc4gqFqezKSpsHfP9VkHWmMKu5p4tvqlMamUF9XHBxBDwyD8V3p7Jqhk3JHp6Su6YyQXtTlNSxJgT1PzAtMS9LkZEp26/aMwOyk7N7o7hUS7eiG95wEUxGUiugrb8LMRM2u/r4H0T/+KXk/DBMePh1uZptdWQtUqmFjXIz1cX6ixFtT5SrpAWQsg1lXZrFNpcglGlNdy1B84bsTLNk+BcdnbMnFMBSZhCJpyYSFHzY10qaBZaqqXi9pKlxfy2eoIQ2zXEImODIJg4FcEtvSTJXcFcuAYPWa3k5vfNvruLeJbxut11fTB2q73LiDQyn0qcfhO9+EpYVaVGeakE6Dm6Tq1BKl5V7o0BIEQn5uqN0zFDz4kHR8+wZkP2+oSQw8B+df/UsYvyOPU4YQ68IMfOFzBC/+MoaVRD34ELpYRC1fkcn+d1DZDlybrbDk1FyQtRY1naS34OsAVfHIJQ0CDdmEQcJQnBsr0pOxMA2p3wVas2BrgoqPgSaTtMhYBmlLGhJRtzdhKLxA5oABkuFzs5ZBJmFwqDtNqRSwUPGxPd2SU3I7N77tF9zTxAfbMzu6pj5w/BY6k5OozPeFhOZmQzspU+pxyZSkuZWS6PVmJiWF9b3asiFNzZLKNCRKLJehs1si1UQS44Wfql3Uf34J5blSp1uOuUn0K38GL/z93YmC9zGi6QuU1OMiuygrJCRfC4nMlf3qFMbrdworIsSpoivDOoHGD8DTPgXbJ2FAf84iYRrYvszkZhMKxw+jRwVJy+BYb4qRcMLDUIpTw1ke6s+0JEjeyMKig4J7nvi2A6vpA0kmZfH3xF1JRdEwdlcIzgr1d7kOWRBkWUKIpWK4QU1LhOZ5jdq7ZFKIUimpy83PSONjfha/LtLkre/L9bguK5BMwVvfF+Lb4QVE9wrWSvXWQsoyqkP+RSfADTQGEjWZKPnMSpp0pyUdFQPRJnsyFLjLrOg14Abyn+GcRdJUzJU9UpZBNmGwUHYxDYOMpZiv+BjKJZuVpscjh7ItC5LbtfFtP+HAEN+qqetTz6x88Gr6wOkJ6draFSGwhTkoLdXmblGyPzcyJjVUTcJSKggBRk7LgdRxZKQNmJ2SyQ+nIoYH3T2NkebEXXSzaC+CU+tuH0QHlbWwXqr3mTU84473pbk2U2am7FH2AozwEythyNRFImxCzZa98Mun4gX0pE3R4jk+0yUPL3RTgejXQ1Jl04CJokdHyuJ4b5rBowmeP97Df7oyz9/dKeD6ASok3vGCy5K3yOPDqQ1JVDay8e2g4EAQ36qp64U3WHzzLH4mC65bV8eroIwmavf5WYnKuntqXdhoX0a0Z2N+VmQqCUsWifsedHZJ5OWHOzUsS4hKB2HzIxHO7rpCfqUCJFNorauLhrTW6GIhNDRogmTctFgN66V6b9ye59gqPHJmOMvNhQqv3ypUTQNAmhrogEooPK5OEgY2jh8wX9ZUQgFyRHKKcOJQQS5p4gXy3CAktfu6Ujx/vIdLU2Xmyh6PDGYa9m8kTYPudIKjXakN1eQO0r7cVrGnia9dBgTNUletNdy9iVsphZ53yN7a898Fz0M/+gMrz+H7NTOBRAqyGSjr2sSF1pLaVipCfMqQfRtHj8OJR0T+cucGLM7LOBpK0uRId+c5YmqaTMlt0xNSIwQYHBFD02bE59hw+v0tvx8HDVdmysyWawQSmXcOdSRIWYrLEwWOHcus+vyjXSne7bC5tWhT8UR/ZypFR9LA9nW10SETi5rBrMVEwaPi+XSlzQbjHY0s/vFCIkua8rk5mE2QtoyqBCZKTSOJSoRsNss7cxWeONxkfHEVHKR9ua1izxJfWw0ImqWu0xNQKqLLBYm+BobkdoVMZHz/O+jHPoCqXwVpmrWU0jKF2KxEGLVRG1ULfGlmaC1OLK4DHR2ogWH0oSNS+0sk4eY1mPZkFtcPraxynSJtiRydI+I7/l7U4gJ6fk4eFw15Ghbc9wDquf96C+/2/oUfaC5OlCh7tb0UUdq4YPsc70vxzkyRr5RKTWt/UYo8mEvw2EiOsYLLbMmj5Pp4gY42BWAoSJqEzQeDpCmpsOdrEqEmT/zzwFLgBpqkKUTZkTDD8TZVrUG2MzU9SPtyW8XeJb42GhA0HW2bnwWngnYdIabZqRqZpNJSm7t7G+67v/acXCfoJanTgTQrfF8Iz3FC0bGqkWBPPxw+Co6DevBhjPB6deATvHkOzn1bnp/JybEqZfl+flbIL9rbAaAMjJFDBBNjML9YmwpJpmB6HP2v/jf8zh44fBT14EMHupFRj/MTJZy6DmuEyELqe3dLaMNk3KIaDV6dLXNz3uZod7IhRR7IJbgyW6HgCOlVzXiQX51AQyUk2LRlYBiatGXQl7WYWHKxgyCcs5XfkUDLeFraMujLyJ9iRL7tTE1b3fh2kLBnia+dBgRNR9t8HypldKUi/4+EvUEgzYXAh5vX0Levg5WE3j5472kwEkJYkeeea8tkhA4k+iJcF9nTD6efkF/yZderDBOlDHR3nzy+VBTSsytiTeVacj31c7ljt8QDMJWBww/A/IzUBCslmRyZm5FruvBddGc3enAEfuTHMB557EAQ4Gpd2yvTZQazieoQfz3KbsB0yWWoI40f1t68IODmvMu1mQrZhKIzlaimxdNFj46EwRy1ml74tGo2G+gaeaUNRX/O4sxwDm8o4HtjJcaXnPDzVdGRNElbBrlkzRk5uu7VUtOKG/Devo2nprEtVSP2LPG11YCg2WibaQrR+F7dNIOuRV2+D6oM9x0VYkOJIUFfP5x6HP76P4PvisV8mnCFpJb09wGp6TVc//LrvXEV+gbEoirw5XnJpPzfc4XIwr262rbFlTmVEnIrFmqkZ9uhw7MjabdhQmFRiP61v0CP3933BqRrdW3fna9woi/NQsVjquTi+Lq6yjiydO9KmyyUHMqeT9EJCLSY5ZRdyCatqrOy64vtk1IK09AEYUAe9bd8DaaSRXldpgHIekgAyzB43+Ecl6cM7iw6oQTGarCrj+pta6WmD/SlODO8cmIjxsawZ4mvnQYETUW9PX3wbmgTFdnGu25tIZBSoMPfbMuU1HdpXm7v6KjNvdbPxFa973pXkvay69VVIlS1sAEVpry2yFoun0NffVPEzYUCDA1XI1WcSkh6Ueiha1vbDCWPmxqTkbp97siyVtfW8TSToXi4BnnDpf4G00UX2xO7p6hJ4QXSgNDhsp6SG1B2A3JJUwTIgTQpNBAQqpM0aEPIbyBr4QaajmTt98NQivf0psklTYZyCTKJWspaX29bKzV95tQh5mZntu29PCjYs8TXTgOCpqLeQ0fhnbdRrh2aeSORlgaxjbfC1DWEZcL8LGpwBP3WBThxUhok0UxsNifavo4u0fcN1UwFml2vSqbQi3MS9ZUKQmaR1iEILeutZG2p+MRd/NtlGL5Prj8Ila91HcNqCIIOtYPFA7GMaC2B7mDO4vqcTSZh0J1u/HUv2B5BqLVLGgo3CKq68ih9XbJ9utIyemb7Gu34eOHERj2iH4PW0J+xeKAnRX82wZHuJNfn7DryyvLoYIZLU2WuzJS5NS/W810pk1xCrOoj8muWmh7Eetx2YM8SX7tHr5qJen0dYJz/Lv5c3SdoMgkOoYnoMtKNmg2OIxHd4Ei166p1UDMArYOuVKQb/e7b+Fcu1rSCR98DZ/9OCDXXUXNXnpkSUkula+YGIBGjbUsK7HtUI8Vm7qV+IESaCf9o9rkjy1pd0KGOBG9NV8gsyw69UJNX9jRO4IXLfKRvZUrzV+yhdK3pkTRhfMmRx4XH0XVfuYQikzA5MZDhicMdVQJ7son05MxwllsLNn3ZBIe65OI9rQ/0/OxOYu8S3w6MXqlj7yVJQHl8TGzaJ8drd0YrIusR6e2aiIWVMtDHHhKt3tICaI1OJMFdhEQS5Xny1xRJcnr7hOyKhcZ0uVQQgXMiScNfc2c3eDOhdZUFXui2XI/qMgejtsENGtLs/biFba0uqKEUfWlFyQuYKrr4WggvZSo6LE0pHJ2uNicCGbcGSJngegG2aQABixUfp66TC0KARjioo7XM1o402V27HPH87O5izxIf7MDo1cOPot88K1EWSLrqhs0O1wlFxiE8H4YGJYLrGRDyUDRuO1MGuqsX9aFnxRvvz78qgmUF2jTDLq2CxTm4ekmI1Qzdmv1A5pcgFEIX5DqKBVkwnu3A8DyCwqJEg5XS8lfTCMOAVKYhzd6vW9jW6oKWHZ/JUoCvNZlwG5rjB5TcgBIhaQGo2mSFDpsUGqp2UhMFjyUnWBHpBeE3JtLcGMxZeMH6kVs8P7u7OHizKiF04MM3/lxqiP2DEnWls9KpNS0YPlTbmhYZeXZ0S10vk5EoyvNr+yyuX0FXKqi+QXj4UYK/+hp86+swNyVR4OQ4XPq+dHE9T6I635PIEgUPPSJpsutKt9mxJV2dmYTJu4DG6B8UofXQYSHDZOgAXS+yRsn1d3RBItFQFlhPG6kvn9+Jt77tODOcpT9nVR2LI5TdgMvTZSqeNCaKro8TOqAYSuGGKWvaEsFxqn7vrIKSE2B7ssPC8wOINONKiNFSYcSH/PoYCvqzkSylFrk1w3oi5IM4P7uTaNdC8R8FPod88H0hn8//7+047nYiIgGjt0e866q1Oi3RTyotxFRYhN5uESKHJgMqlQrT2rC5YSIC5s5u1DMvhOLk1yVqi9JNx5YvzxWfvlyn5FXvPSVC6Te+VduvG+jaYnKtQZdlx+7AMLzvw6HnnxLDUbSQpeuGtT+kM9w/CP3DqPoobhuWM20XNmKc2awLaiq4u2Rze1F0c14gLawgkMaEFR4jALozCWzPk25uxa9KNJWiOoERaIUK/eMjD4rG64VkoqbHg7Ujt3h+dnexZeIbHR01gd8FngNuA6+Pjo7+v/l8/tJWj72tWIUElFJCcnV+eFFdTP/Fn0hqWJ/eNhh+aiGZ7/y1EGG0UxfCnbkhmS0uAkqI8K2LkrYWC2JmoJD/R42NKPeqlCX1/ZH/Cv7g98LxOCVkaSWkXpjOSCSogPtPoE490ZC6bsdypu3AZowz67ugfqD5/e9MMF8Owl0YCitc/u2Hb2ek1TOUYrAjyXxRs1DxqvaI2YREgUMdSSYLDgU/wDRExpJQNQlLvePKgz3JhrWOsHrkFs/P7i7a8bHy94Cr+Xz+nXw+7wD/AfjxNhx3W6HX+yMP76/WxS5+rzanW5/e1ndeo2NOT4apc6YmO/E8ERl7LhQXpTubTAlJlYrgVqTBYphhmpqqNTeCADq6sB77AOq//GU4XudD36A8Ntrwls1Vq/TNOt9qPe3jJpcztRutFP7Xe/7NBZukpapEpJAozzJkpaOpJMWV8V3ZlJYMU95sQuzgs2FNUKnQf88Q/z2tFKahSJgKK0x7M5bBycGVM6+rRW6rpecHeX52J9GOVPc+4Fbd97eBD7bhuNuKVgXS9XUxbZo1rZxlSk1uagIdGQoAwctfFpODdFaiL9sWogtCggzCGVunIuQH4bhbuGfXdaV2l12WHg2NoMduo9M5SWOnxiWi7OmXlLpcEgLNdsCZ9zemuBF2YTnTZnBlulz1t2vmprJe4f/abKUqCs8lDBZtv4EAo0YGQHfSlPodEmlGRJUwFLmk/D9jGVTcAMfXjHQkqHg6nPCQpUFJS6LN5VHoWpFbPD+7u2gH8TX7Ca0Ql42Ojr4IvAiQz+cZWMP8cSPQvo9z4Q3ca5elKZBKkzh+kuTpJ1HN1iyGsH/gfTjn/g7DMMhms+hA40+OEcxNo+0K5tFjZO5cx528C70yM+sNHcJ75y2JFgMRfen5GczBIUBhjtyHlbCoKEWwMIs5MIzOZvHtijzecajOSyVSoAPU4jw68FG5TnRxCbSHYTb+sQRaYyUSuFffJNHTizat8O9aoZIWJHuhpxetNekPPUvm+R/HffPcivck8YM/THlxjmBmAlVnb6XtCsbRB8k+9cya79lGYFnWqj9jP9C8cXueyxMFKl5A2jI4OdzBk0fkfb4yP07R0ViWVU1JpiuasvZ5ZLgTpVjz2OPlaebtQLR6hpgFaK2ru2sBsgmTQ11p3n9/N3cXHOZKDv2BoiNlUXR9LKVIhCTYY1nYASjHpzOToi/svnt+QNJUdKQS3NedJlM3pVFxAx7oS/HMqUNrktjwEHys5Xd17fd1r2EvX2s7iO82cLTu+yPA3eUPyufznwc+H36rp6ent3zi5fIMAMpl9De/jrp8sXnUEz330P3oyxfJlAuUPD8UH4cpVCaDl85gf/PrcOddOPaw3D49KY+JNHJh99VTCkbuw+vsximV0IeOwtsX8OZmxMQgkQjnaT3RPFiJau1Pey4kkmjDrO7fDeqL3p4HpoG3sIBpJXA9L1xWpMErg5ENlxaZ0NVL8f1PUfjK/yPd43KhNkZ38RxcOo965kfh7UsN2kiOnUSdPEN5bm7LP5MIAwMDNPsZL6/fAVSAb8wtcfHWFIc7kxTKtli1LyuPLRRd3p0MuL8n1fTYjhfwxTcmOHu3SMUNQusnRcrQor/TtTndx0Yy/I/vHyFpGdVrPTtW5NxYEctMNBiAmobiWE+S9/SmmCh6TBZcQDPSkeSp+zs5PZSt+uhFkdt7+9KcGU60fbxstfd1L2I3rvXw4cMtPa4dxPc68NDo6Ogx4A7wKeCn23DcdbEV66pIIJ0cu0npL/9MCC2VFONQgKuXhTQiS/juHmkw9A1ISmmXpV5nmEJkC3Pw7dfQnidEExkfaIT87DLVpQwqKsQZIj0ZOizmpH1DIn/RhIOf4exuJgMdXRiGkugRQhGzuMBUGyyJJPryBekou05NGB1ZXZ37O/ShI5inn9y17u169bvrczaDOYvxgtu0gTFVcnnmPd0rjusHmi++McnVGZuMZYSNDC0dXsMgZUJnSpb6PDyQ4n9438iK49ebA9QbgEZ1t7WmKWLnk3sLWya+fD7vjY6O/k/AnyPCji/m8/mLW76yVrAFeUbUqXWnxySSi0hvaVEIzgqdc9MZGL8tBNjdK7dFI2ZeOKO7MCdRWK5DSC3wpe5WlaWE7ivplLQD7YrM41qW/Fsuhnb0FWlWKCXPGz4i9cHObhgYxlhagFvv1gjNNOHODfT8bG3++K3zMtVhLfvRWqaQ4Xf+Gk4/2a6fwIaxvnDX4eRghgXbp+wGGEpRdHzK4bazhKG4OlPiyrTsuo1kLlrrakMj2lgGhti7+xpfK8pewKNDWf7xE0NNCay+7rbeHG2Mextt0fHl8/mXgJfacayNYLPyjPoUmd7emqX8zXfkOX11dYlsTm6zy1BK1GZqPV+aEJUw8qteR2RtFQjhWWmxkyqXQ9++UO8f6NAHMCGkCvCe98Kxh2QCJJxJ1kvz1f0f5tChWrptmjVPvkRKBNa9fXDprJBeT//K6qtliiB6F7GeuzBIJ/ah/jTjSy5XZis4oblnLmHgBpq/vVWkK2Vwoj+D4we8cbfAm1Nl5ssuOtyAlrHE1r3iBSRNsYd670CGf/L+4TWJyzRUPEd7ALCnR9bWw2atq1akyFG31rHDaK1YIzilpItaDB1Uch2S3iZNOf7MtKSlhklVTBwENdKxw0aI74Xau3DLmgplLpF7ZS4HHV1Vq/tqul5agl4hYmUoiITTd26EpJeQXbvRyJxpyO2lQu017CHUC3cDzYpammlIYyCdMDCUojtlYobuxIsVDyu0aC+5MlEx1JHg5oIte2tDR2OtxWsvYSoGsomQaDUP9KZbIqx4jnb/496Whz9wAm03Jz5t27KPthmWp8g9fbVoTBlCcBE8X6Kn+x6Arm54+LTU5RxHoixoFBl74d5bZUhDI/BlCiQVLrMJwjG3ZFpG4BIpOW7/sKTMdVCplHBj3WtUSklNL9cpOr7jJ1GDI7XdIJlQy1dpdImpvpZot8gu4XhfGidcznNlpsx4wcEPXQIcPyBjKSaKLmU3YLbsVYnKDzSehs6UpPmmoZgtS6Oh7AYkzHASI2waGUrh+pqiE7mFKo636FzcyhxtjHsb9zTxqZNnUH0DQnJ1WM+6aoV4eWC4RmJQm5rwfGksDAzJV65LxtnKpbAGqCTiMhNChn5Qs7G3zJo1r1K1xeGJpDRDlJLIL5utpaX1OzYi9PQ2fY04du3akFE7PTUeEnBFItRioSYsilLz93+4tTd3mxAJd28v2pTdoIHYMgmDI90phnMJutNhBI3GDB1PetNmQ2nDD3SVHNOWqjYzCo5PMVwGVPYCcS7uTbYsCo7naPc/7u1Ud5PWVctTZKWUzN76vhgCKEPS377BWgpp2/DMJ6Q5kErWZCKDw7KjI2omROsqy8Uwra27hsjd2TAl7e0fBnRNFN1EQ6dSGVT0GqfHxJYqmZJGRm+fXJvWNTlOOiPEWirA+B0h2FyHGKOeeR/GI4+16+3fFKIGwvU5m4Lp42upvw12JEILdkU6oQDF44c6GuZZZ8tegwGoGY6haYTwAi3TGNG+WsfXeIHPk4dz/OMn1q7t1SOeo93/uKeJDzZpXdVkgkEphT5xEtCQ7UDVbVerRpCPPkZw7U1U2PzQOpDRtbkZKJZrhgQRwaXCpoPnSce2amkf2nw44aIjyxTpytBgw2VG0xTRa8z1PkPpb14Vkvc8eOsiun9QjlcuyZDp3Ix8AIS7gqtNlPlZuPx9glIBlcpsiwdfZCxwZabMZGWaqcUyXSmTo11JHhrIVDuipqHoz1gMZFf/9bPDDmz9PGtfpiZz8XyNacFM2aMcuq50JAwySYOKp0mEb3HGMvjgkU6SGyCreI52/+OeJ77NoN7dmWxd+uM48NgHYOQo3HqnaQQZRYsNjssjR2QZUKkoabLvyZ6M3kEhn9lpSYMTiarDC6m0RIk6PG+uU1LuEMvT9cBzWPzC76CvXpZo0qnI8ybH5BwDw3L+UlGOn8lQHdythDZX05NynsGRBg8+YMvmpJEwebrocmPBxtUGOgiYKPgs2R5Ljt/QEW0lqlq+dGeoI8GC7bNk+1S8ADBJmYqFisZQBj7g+JqBrIVSEg2OdIj1ezMX5NUQ76Hd/ziYxFefIteljw0p8uknG9yKg9A2XqNkGdDSXE3vB9L57e6TDmtnjxDQZDjAYhhSi4vmbwtLIelpMS5Qhgikr7+N7uiS3bh116IDH/0fv4h367qYmLquHDNyeymHtlWpVLg8yaLaVnZdSaXNjAipF2RJedWD79I51MSdpuak/t3bXHj0ad6Zc9a1hoo6ofMVj7IbkE5ZuIFfXdSzUJF52UgLB5rz4yWgcRbXUIqKK6NgX31zltuZKHEAACAASURBVLLrs+T4LDnQmzY5NZRlvOBwd9FBKeiyTBZtsZQylchdik5AJmGQDW2iNlqTi+do9z8OJPFBLUXuGPgYlSZjNau5FVOphPby8401ucisdGBY0uaODtTRYxJVXn+7Jm/xfNEJPngc3r0WOi+bqKHDMBRGeqlsQ7SlL58XobLn1EgPQvIjJLyEdIr9cBVm9brqNIZR2hu9B6kU+jt/jc51rZh+CZIpXp4ymbn4LunhkXWtoaJOaH0nNkLUgR3pTIgweMFmoeKHshSNDjTXZiu8NV2mM2lS8QOOdCU50p3CNBQDWXEx6cnI9MRX35xlIFtn765h0RaRs6EVtqc51psMvfE0M2Wfr1ycWZe8l19zPI2xfxFXaVfBquNw6bREb6YlTQrxJ5eGg+/BWxfQVy6JuejTz6NOPVHb0WGaEhEee0j0f1HEWEegTd2Qb1yVayqXaqRXfYKSel+5KCl1uRw2bsIuQOQKUy7K19wMemq8Zqc1M9l0+uWC38GsmSG12CixWc0aKoqq/KDJ8qO622/N28yWPDIJESAPdySYr/iU3AAvACeIpC4VXru+yMWJIuNLDgmT6nmXR3D9WVnVOJiTBkl/zgzHzTRvTpYphTXAevJ++crcqtcaY//jwEZ862KZ1k9r3bhOcmFWJi36ByVyq0pckNRydgq+8RfSkYUVzRS9MCuEF+7yqMfycTvt2KHIennKFmoHAz/sKoebXh07XJSeCp8TRjaWJQQ9OQ5LC9LJXgVXvSyJ8hK6Ukb7Hsq0RO84MNTUGiqq2ZmGWrF6EWprERedoDoNYShxmOkKRcpaw60Fm6Qp0xuu1izaAW7gsmD7PNSf5tpsZUV9cKgjwaLtUQrlMdES79sLsg3vSFfjh1csRI4RR3yroF7rV5WLTI6Hc7dI13T8Nlz4Xo30Ing+9A9WI7emekPfb0iPV6Du/CqZgp6+lV5f1SkRMSugrz90YI5s64NweiWcGc5kpc4YeQnevd1U0Ky1xp6fFx0goSt14KMnx+D6Vbl/WdQVCZP7MtaKSMoP5Hbb03SlGpsl9alx0fFxw7WPAAYyX2sainI4qWF7QfVcEQwFJ/ozjHTIB0tv2iJpGmSTJicHMzTLaGMh8sFGTHyroMGteHpiJbl1dkmHdGFO5ngj1Imeo8hNGaZYZJ16Am1ZQiDzs/I834PpiUYnZ0AnEgSXzhK8/GX0nZuyCS6RlLS2eq5wN2IqLfW9+x6AD/6wdKYHR+TY6YxMc3T3Qm9/48pKz4P3f3jl9Mv0BCknfE3pTO09sSx0uQjTkyu0bJEwuSdtyT7aOnPPbMKgO20ykLM42tWYVteTZDnclaHROL4sByrYPlPhJMdM2at2e5e7FxsKejMWP3R/J5/9ocP8/VP99GespqQXIRYiH1zEqe5qqNf6zc+ujOiGwo1nkTNKrmOF6BmoRm7KMOHkGRi7he7ukzQ5iiDrUk+lDHTYpdUXvifHMA1wbIxMFr9SqpmZamQUrndQIrmwscLQIRg6hL78fdTJH1iZpptm9fqNRx5Dj99tXNw+P8sJpTjb8QDJTGMqqCwLe36WRx891nB7fSe0I2UyWYGpxTLdWZMjdTq+8xOlBo2cpMZCYH6gySUMCk4QGShXZY8FR9xajj2aarnrGguRY6yGmPhWQYPWL3JvgcaIThnozm4IfNR7m4/HNSzzrrexHxgWC6wokiyX4Opl8fObmRBSy8k+3arRqFLQPyQdXMsUD79sh0RyEek1ObdSSiLAhsVIyKLzZtMvwKluza2uDma0Qaouyba1oj+oNNWy1XdCVzOhXK6RE1GyRJxJ0yBhQMENqv4NibqQTSlV/TG00nWNhcgxVkNMfKsgIgT95jl46/uyRyPy13Nt6d5GY2qrdAdX7LGoa5hUx+SiSGxpRghucKQ231ssCBH19IMlolx6B1CH78d44acILp1FXzwLSUvS5YW6iC7TAQ+fRheLq+7Y4PhJgktnG4XLD52CRBLLc3lBT3PB7+Can8XWBikV8IhV4nTa2bSWbXm0NpRLUHR9EoZiOJfg0lSZbMLE9gK0FuJTCrKWybHeFO/M2TzRohg5FiLHWA0x8a0DNX4HPTAChgWFBamrRWalPX0yPZFIyjLxdP0ei5VGCcv9A6NITIMcJ+oKRzDqLKayHWi7DNcuo2emCAB9/3ugqwfOvy6Pi9Jx2wFdEAK0S+jrb8ntpinX3NkjjZC7t9DzsyuEyzg2OpnCTKV4zCrwmCVNDq0DaYhks/h/+qU1Jzz8QPzrVtuLWx+tVUfdpstcni4TaGmG5JJGdQIjkzAY6dyYGDkWIsdYDTHxrYEoNeXQEYnMol25CiGapUVJPQ8dEZfkSEqyilHCqv6BUQ3RMGBxIZSo6HAnYkKMDxybIPAl/4vclC+dA6csTZZSobF+1z8IF74rkV//oMhvfF+aJOksDN0Hb11obtuv5XXo8HsISe/KmxKJHj7aSJTh6Fv0Wv1A88fnxrgxVWxpL24DESq4vWCHhgTizFJvYJA0N1aXi4XIMZohJr61UJea6kxWor1KWartpimztw8eFzIZu4U+9vDac66rrXf0/ZpRabEopBcRpOOE+0DEXp0ggMKSWFANDMtUSP8g6r7G1ZB6ahwcF1QRdeQB9MCQTJwszMLNa3D9LTh0FF3fiAmh0mmZPHnw4Vrdb25WbPEPHalGrb6GC2Y/VycTOH91ifShwxzvSxNozVRBb8rI86H+DCUnYKRzZXoe1+VitAsx8a2BhtQ08Gu7NiIEGt69Kpo4U7zitGPDa3+O/s9/BkcfbHRCqWuYLI+0CHyZBAn8UCuoqpvY8EK9XjIpspV0JuwELwK6On/bgCiK9P1GQwXLFCHzwtyKbnI9lOti1LneBC9/uSFa9TW87A4wEyRJWRq1MIczNFKN6k7d14vvrnxP19uLG9flYuwE4n7+GmjQ8jXbN2uXG8bOqkLnuRkxIpiZqqaD+tWX5Zihno9EUtYdWolwy9qCLAl3KoASEjTMmimqDsKNbGHzI+oEV8rNDUyj20xTIr16Q4UIkZB5uskejmW2/cvNWy/4HcwGSVIqJGdf9IUpS7Fo+4wtrC4OXqtOF9XlHj+UI2kaaC3d3scP5eJdFzHahjjiWwv1qWl3nxBTRIBeHbFEur7lQudlTihcPo/x6ONV/0Ad+PDqS+hsLjyOCyjZ4QFS89MhASoD9f+3d+4xct3Xff/cmdmZfXG5j+GSXL4lMiJFSjRlV7BjuaklxY4c2YYf+aUtoiYRCsJAmj7coKkrtClQpAjgoqmRBEgEFwUSC0h/9QMCmqqRDLiOAseWbIkSRYoSSYkPiY/lvrjvedz76x/n3nntzD44szuzmvMBFuTs3Llzdkh+eX6/3znf09ODK+3VTcRBRrwuJR6XeR+xOFwMR2XGPKn76+6Rr3zoBViRMS45jWbp/uRFv5ukV/LG8eJfpVQ8xvh8joFk9b9eK9XP6b6cst5oxlcDF4RLxBvvyQb+1LhkNfl8sdWss6u87Szqxhi/JVnU6I3QEMAV+29L3yOq6+vslEOSREdxJkc+X/TV6+ySpXS1splEB+zat9SavrtXRlNGfbsgS/O5WTEz2LknNEn1yzLGmrb9FfNNMq74V8fl83JaHDLYlWCxchp49Lq8W/XsC0VZLzTjgzLfPRe5F0+Oy697DxRr7byYZFc7d8PgoCxrw5m3gIxuLLWNgsJenDtwCK9y1kepEcLWAbh8UV4bHYo4B7lQtFKppYld3octW/F+9Ul4+2yZ/T777xaRy4V+f67i1V7JxLaZ2/L8Mrb93uH7cO9fwV04CwuzJLsCsrGkfEYD28p6fod7O8h5MutC9+mUVqTthc/5S3333PWrMq+ip1fm3JZ0PbhMBu/oCWL3fqhQQOx5npyi+n5R9FwgpSTRXtzYTRjZW/7elXOBEx2yxxd9zyEHEfk8dHUTH95BPpcrlq30boVPfIpYIimHECX2+8Fz38YdOiLvm8vIQUg8XuwGuT2JN7wT1zeA97GH5SBjJbxiq9zB7DinOneRjL5fQs53fOn+nczMzGj9nNKS1CV8xphfAf4jcAR40Fr700YEtZFk33hlqe/e1IScoEYb/yX7X6WWUWVtbVMTsnScl2JfEh1Fx+VEXJa/H3u47L3L9s2mJ2H7iDgpR5lhzJNMcFGGkSfvuQ9/UQ4NSueAVKMgqtt2SClL6akuyGnvCtPooFhcfOHNd1i81U3n0Ie5e3ieI7FZrub7GA+SJAuf0/ZCVveRvQNMTgS6T6e0JPVmfG8AXwT+rAGxNIXcxXPV6+o8qm78A+XGA2Gfq7tyUeZ3zM1KhuaQrBFPTAaSKZwLcIFfXEaWHp5Ehw/bd4azM+blfn1bYfd+cOB1dol4rXGSnOfFwva40WIhczKJd/TEsveI5miMz+dJTk4SSyTIAqfyfVyNdfKpxBhvBr1c9LrJTI2T2rFTszplU1CX8Flr3wQwxjQmmmaQqVJ2EY8XRz5WKxUpNR4IfNzli2I8OjMte2WJhNzXhSagsRQMpOHsa7gb1wpdDmUZYzweCm548to/JMvsaNnbkaT3i09UtckviyXaq3z/isQ0VOIWEy7ZS5fryxHN0UglPAI/j8Nj1CWZDDp4M9/D5XwXDyUn+XxylDiO2NFfWOnTVpSWYMP2+IwxJ4GTANZa0un0Rr31ssx3ddNdss/mAkcumSR/7aq0zSYSdExPER/eiRfzcJlFkvd/mFQ6TZDNcvsPf49gbJQACFwge3yZDODwtvbjeR5BLo93exLv9ZdwuSzeyz+k8+cfoftxg/flJ8i+8QoLf5vHv3oJL5UiNpAuvB9QeM9EIlHzc3O+z/xz3yEYH8VLdeL2HiCXXcSN3cTLLNBx8AheLIbLLBLbs5/uj38Sr1ptYgnX312gv0+WqoudXby92ME8MeJxSALjdHHG87hJH5/tX2BrSWzLxdpqaKzrQyvHuqLwGWO+D+yo8tRT1tpnV/tG1tqngafDh66aZVEz2HLg55j50Q/xUsmSwdxz4Hm4bBZSnWSuXpJl4shevKFhFnfuxRsbw3/uO3DjmuwHRgcbfjhTlwA3N4tLdsLCLG5hHhd2ffhTk8y9+Dxzb5zCO/lVYrsO4H7lSdwPnit0dbjoQGT8FiSTzIedFdM791ZdmgZnT+GuXpFl87zMw3C79hXukbvyLt6ufXDgMN7h+1iYnFxyj0omp2cL5yzXE1uYyc2TiDmiQpUsEPNyXMsHvLR9hBMlf6a1bKlaEY11fWhGrCMjI6u6bkXhs9Y+Wnc0LUzy2AN4587IcnN6MixATkiZSuBLDV0QyL7elq3SeREJz1uvFwcJOSfX5nLFQuR8LhzsHSt3Po4s4SdGcd/6M4LtO8Mymg7o7cMFeTh/Vvp0B4fkXudeZ/bsqwSpHnj4M8SOHC8XwIoZIVDhw9eRJPbYl9f02ZQaeU52bCHRkZOMNTy5juNw+TydXT1c7BnhxJrurijNo+3LWby42MJz7jTu+e8Vp55VOikD4MrFJpspClrkiNyRhGQ+rJtz4XjH8B65bCiKSCaXz0kN3eCQiFQ+j8vMyX137YdkR9lprNfRAbPT8OILZXuFUKU0ppLKGsIqVNYz3sUgr3XvJjm8XQYI9Q/gzUubXD4ISCd8vKGdkB4mqy7uyiai3nKWLwB/BGwD/soYc8pa++mGRLaBRDN2/fNn1iYepeUonV1yohuLSftWPiu/DwIRQUfx2kRS7pWVlrLCvI3o1PXGNejdInZT2Uw4QS0kEYe5mUILnBceUNS0vCqNdRmqzRE+5ia5OgbjczPEe3bh40FPD35XN90dMbYPdRY+r7XaRSlKM6n3VPd7wPcaFEvTWbN43HM/vPIjWe5294pI5XLhqe6CZHq5sJnWk35bYqGd1eJ8MRO8dVMyuajOzvOkpGVutmgeWirIvr9kBGWl5VXZnI1sBnbuITh7qmb5SrU5wnEPHuue5I2FeWZTW3gvtoVk3CvzxwO1i1I2H/rfdCkV/ailuEwG9h0s+573i5+Vdq1sRur++oekaHlxQcRtaJtkbiD7eg6ZiBYti10g11+7Uu6eEiuxpMrlpLd2/Bb+zeuyRF6cL5iFFmIpGWFZNg4zm5XC6oHBgkuMC6qU6FTZIwQRv+PdGb7i3uIT+/u4e7CLHVuSZaKnbWjKZkOFr4Sq82+p3bgfSyTxTn4VHvi47O2BCN6eA/DJz+Cd+Cg89Kg4HicSImj5PODEOCAI5ABlfLRocApSxwcikNlFmffhQkdm3xcxfPc8rqMoVKUjLJkaD81Lk2JjFfrtRS4x7tzpJT97pe1UJfFcRu2ilA8MbX+4UUrViWMrdEnEEkl47EvyxVLDTi8Wx+3eL/t3C7PhLNxAxC2RCId/u5LBQoOSFSY6YGYqLGqW/59cEIiwbukT55VK89BwrzK4fEEKpqv9jJVL5Oj7q1jmq12U8kFBha+CSDwqhWG1RKerZXtss9MidgNpWeKGNlNMjReGFRHzpP5uYV6WpoPbJNOLJ0J3Zo9Yzxb8ZEpqBXu2QJArm5JWKJ0582pxJGX/4NLRk9Wyu1q2+FT351OUzYwKX4PxkikRoXfPF01Ju3tEbGanZanbE7q0xBPgZUWkpm/L0rarW/YKXVBwZSG9XYQvFsMPAhgahKE0vP0mbn6heCDx9hnJBOfnxNygMKw8tMWKxK/KCW8tW/zVGBkoymZDha/R7DsIf/PX5U7MnieZ1+yMCNxcuOSNxaW4eG4WJvOyrM0sSI+tC6TfN5GArQN420dIdneTj7oyRq9L6Uvg43xfTonzeRkIFMvIe23pK7fFivp0q2Rvd7LMV5TNigpfnSw1Me0Q4aqsB/QDGEzD1n4Rus6+kjm4i7LnNz8n4pXNQColmWIuB5fO44ZLrOGdg3fOQTx0bPaQNru8L4K6dVC6UCJr+UQcpibEe2+Z7C1a5rvD9xV/pvNn5Ndak+MUZROiwrdKKgXOS6Zwe+6CG1dhsmQodz4vTsl+Xur7or22sBMksqQn3V16cxE8DxG7ZEqud8g+YCYjr4smvF1/T8ac9fcV7xG44gDyhXCpOzRctKGCFW2oCj9nRSFzrfm5irJZUeFbBbXEgBeflyzt4OHyF6Q65dCif0jcm0vvlR6Gd94qZmMgBxL5bHFWbxBIVphZCA0PnMzt3b1fDjA6u2B4e7ErZH5OlrpBIPfy87Clr8w5mo7kqlyWqxUyA2UDk7w7PPhRlFZB6/hWQS0xYCEsQRm7Wbw2aj+bHIcLZ3EXzoYDh8LvZ3Nw6F6pr4tsobp7ZNnaGdbvZRZF+AInX719EO8gtnUA79NfgIEhOQDJ5+XUeH62OOUsKmxeXCi8Z7Xi65rUKGQGqg5MUpTNiGZ8q6GWGPh+Yf+MbTuKg7uzmXD0YzgfIxrcPbIPb2gYd+9xOPt6IRt0LoCXXpQsMRpWFPX5dnRIO1wiTjB+E3futCyz08Nw/aq8JsoU/fCAJJ6Qg5NbN3A5mQznOjqkvm+FvbpGmB0oSqujwrcKaopB5JocuTQXBncnJCNbnBcBiiMCFdlaAe7GtULpSMEa/tIFWUInU3I4Eg0G8n0Y2oaX6pSMa99BOHMqHA+ZD5fEQFePiGXvFont0gXo6xchPH8GF37PvX+F2CO/XFX86jU7UJTNgArfKqgpBtGQ8ah17PZEcfnq+7BrX8UeX4mtVWXpyMhemcp2+SJ0lPyx5H2ZrRuNb8xmijV3F86IMIIIru+LMI3slSXvxJicJnslNvpTE/DaSwQ7dxM/9sDSn0kLmZU2QIVvNdQSg/QwTE5IFwUUM7/SIeOllJoKVOkQcYFP8Od/AhOjxRGSlb6AyVSh5s5dfSc0LA2X1j29sl94K3RujseKBygRibiI+E//FqoInxYyK+2ACt8qqCUGZHNw/EEY2Q1X3ikOAh/etrRNDFa3TNy+S0ZMlr7UOdzYDTLTk7gt/dIPvO8gDO+E9y9D/0D5PRJxWJyTQ5FqJOJijFDtZ9VCZqUNUOFbBasSg6MPENSxTIxKZpi5LQK5IB0ajF6XvbpUCm8wDdu2l9TVXZUsr3ReLkjGucIgoZV+3nr6lRWl1VHhWyWrEYPKzLBgVBAODHI9PQThdZWZk5TMjIWOLDk5sJjJyGGF70MigT85AVfexXnIQUY2C/vulvkgUaFytDzOhb3B1cj7MLKzcR+OomwyVPgaSNmA8Uvn4e03RJyi2bb5fM0OCHfpvCxbox7fnl75unVT9uTGb+G6e4o29jPT4tV36Tw8+ImlhdIuWFooDfI4mYSPPLRBn4qitB4qfHVQrY0tqpMDcHNzSyef1eqAuHZVRC8eK+nacGJN5echFsOL9eL80Kw0FtrYz81IGc22HeVWWJlF6OmTDDAWL2aDA/1w8DCxI8c36mNSlJZDhe8OWamn1WXmIZnA3bpRXIbGYsiphcNduVhWUMzsbRG9qYliETOELs0BBK5g0FwgmRKBG7+FS28vWmGBnO7uvxuuX5Nav7378ZKdKxYwK0o7oMJ3h6zY0zo1LtlYdPDgnOz1ZbOy1OwfLG/+79kidXf5EtED+X3eSUFzLltsTQMRxP5B2dMrtZvfOlgsgdm1Vw5WDh1bVa+uorQDKnx3ygo9re7m+2IfH+2vzc+JqMXDVrbFheK1E7fkWj+/9GaxeCFTdLlcUfgKNvRb8XaFxqZrtJtXlHZFhe8OqdXG5lwge25TE+KUEo+L+cBCyTjJCrxUCjfvyZ5cdjEcPBQ6rfT0RqvjkheE7WypTskU9x3ErXUmsKK0MSp8d0i1NraCScHCgmRwkT/e/CwsLooAOieZWuTEEtE/CNPTMOsVffdA9ucSHZDP4cU8XJTVRaezBw/LHuHlC9pjqyirpC7hM8Z8HfgskAUuAr9prZ1qRGAtT7Vi5cikAORwobM7FL1wdGQ+J6YB3b1LCoy9ZCfu4c/Aiy+EJ7nhYUgyJb3Atycl01uYl9eP7ISPPETsyHGZ5LZkoHiYed6eECPTkeUHiitKO1GvH98LwDFr7f3A28DX6g+ptXGBT3D2VFh3964cckR+e7cnZEna1S1GAb4v2dvQNvnqSMpj35cML7pn6JcXO3Ic79BR2Hs33HNUXF4iq6qhYboeehSOfAjvxMeIPfFbxI8+UBCx8oHiQXGgeCYrxc79Q8sPFFeUNqKujM9a+3zJwx8DX64vnNamsoTFHfi5YmfGzG2puxveUTQnmJ0pn7SWWZS9tp6+wjWlzf9lBdAv/Y3YWiVTxRGRMa9mHeCKr/U8UBdlRQEau8f3JPA/az1pjDkJnASw1pJOVz+B3Aic75N94xVyF88xl83SmUzScfdhkscewFumxzXz2stkM/N4AyWmAD13wb67cJlFgtkZ4kPbiu9z73H80esEk2OyV7d7P8n7PiwnsrkspFLV33d4O7Oj18gnEvLa6SmYnyUY2kZXegdedzeMXac3/ejSIIe3Mzt2HXbvrf5DLPfaBpJIJJr6Z7wWNNb1oZVjXVH4jDHfB3ZUeeopa+2z4TVPAXngmVr3sdY+DTwdPnRjY2Nrj7YBVGZt3d3dzE9N4n70//DOnRGj0Bp7YMHrPwsnmc1Xv/fCAkxOle/79fVDX79kdkdPEFRkWosAk5NLYgxO/aTcfCCfw127Sn70Jhw4hDc3x2KNz9CfnFj+hHeZ1zaKdDpNs/6M14rGuj40I9aRkZFVXbei8Flrl00NjDG/DjwOPGKtrewtaDnqGaazoi17/wDeloG6vezcudOyt1fhpeclEsUZuSM1MjrURVlRVqKuww1jzC8Bvwt8zlpbPQ1qNeoYpuOtIBheqksyxqMn5CDDOehIyljHtYxlvHxBDkP8KocQ8ThcfRd38xr+s88QPPdtOWwpPbDYdxCXqS58axo8pCgfUOrd4/tjIAW8YIwB+LG19it1R7WO1DVMZxV+e43wsnPZjLg7z9wuW+46hwwrBxgYrDnzVl2UFWV56j3V3XSpQz3LwI0SlChGd+BQsRbP93EL89CRgv7+ohU9iNff+TNiRT8wVBx2vmNEnKHVRVlRymi/zo06XJI3zJa9NMaSoeCxyxfwvZhMcItidq7oypJKiktzLgtvviZi/OkvqNApSgVtJ3z1Zm0bYcteO8bF8olrIAcdUa1gyZ7gag5rFKVdaT/hq8zaHHIA0YRl4HJGpl6VzDK+5wD5zq7yZe7URPH0t7INTl1ZFKUqbSd8UJ619abT617TVo2VjEy9Tz4m/nklotX1/rtkfvRD8dyL8H1xb8n74stX+T6ZBYKzp6qLqy6BlTal3l5d5Q5ZqZ7QnTu95DXJYw8U+nELxONLh45H7+ECuHpJxDSXLRNX7dlV2hkVvmZxB/WEXjy+tE4wPSyHHQcOlS+BAa69J8v4NYirorQDbbnUbQXutJ6w8nBFlszPyUFGxWGNzNo4UP0+uv+ntDGa8TWJlbpAVttW5sWqZIFhtwh79qsrs6JUQTO+ZlFHPWEltUpsAnVlVpSqaMbXJEqNQ0tpaBeI9uwqSlVU+JrEckvUNRkaLPceGyGuirIJ0aVuE1nvLpANa7FTlE2GCt8HnI1osVOUzYYKXxNZtmVNszFFWTd0j69JFFrWtKtCUTYcFb4mcScta4qiNAZd6jaL1bSsNXBfTpfVilJEM74m4VbqmmhgV4UuqxWlHBW+JtGolrXVoMtqRSlHha9ZbGBXhbv0NkxP4C6cxb11Wn69dQPnghUnyynKBxHd42sSa7XAd4FP5rWX8V97Ga5dhdlp6N0KI3vw9h+quVfnAh/eOgOLJcPJfR9Gb8DMbdyBQ3hqVqC0GSp8TWItXRXRHl1mYQYuvFUcOXnrBsxO42ZnysZLluLOnRajgorh5CTicp+x0WWHkyvKBxEVviay2q6KaI8uyMyVZ26JuAwampnCeV71wUKXL8BgGi6/A7kMuAC8GHR2QXcPjN+Cjz28Tj+horQm0EThywAAB1JJREFUdQmfMeY/AZ8HAmAU+A1r7bVGBKaUEJa+BDeuLBkoRCIOUxN423ZULYFxmQWYmRbR832IxcQQYX5WRHT7LjUrUNqOeg83vm6tvd9a+yHgfwP/oQExKRUUSl/yueoXRGMlq+3VTU1CZkEGEfX0QmRMGovLyXH/kNbxKW1HXRmftXa65GEPMqxRaTBeMhXu03WInXwlURZYrQTGQ/5UPET4enqLz+V9PddX2pK69/iMMb8P/BPgNvDJuiNSlhK6NccGhuC9y+XL3bwPw9tquzZvHYTJieLQ8dLXdXVD/+D6x68oLYbn3PJJmjHm+8COKk89Za19tuS6rwGd1trfq3Gfk8BJAGvth7PZZSzRN5BEIkG+WhbVQjjfZ/657xJM3CR36SJufg4vkcDl83jdvXTsOUBs2w66H/siXsUe4Ox3/wK3uIg/ep1gckwyxkSC2ECa+PBOvM5Oer/4RMNj3gyfa4TGuj40I9ZkMgmyvlmWFYVvtRhj9gF/Za09torL3bVrrXEGkk6nGWvCQPG14gKfvutXmFpjHV9w9tTysz2OnpDB5Q1ms3yuoLGuF82IdWRkBFYhfPWe6h6y1p4PH34OOFfP/ZTaeLE4qeN/j/iu6uMia75ujYXSitIO1LvH9wfGmHuQcpbLwFfqD0lpJGo/ryhLqfdU90uNCkRZP9R+XlHK0WIGRVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDhU+RVHaDh0o3iBc4ONCs0+XzchktH0H1exTUVoQzfgagAt83A/+D+7Mq5DL4nke5LK4M6/ifvAcLvCbHaKiKCWo8DUAd+40bmK8bKYFgJdK4SZuSSaoKErLoMLXCC5fqDrFDET8uHxhgwNSFGU5VPgagMtmlr9gpecVRdlQVPgagJdMLX/BSs8rirKhqPA1gn0HcZls1adcJgP7Dm5wQIqiLIcKXwPwDt+HN5gWkStBh3YrSmuidXwNQId2K8rmoiHCZ4z5HeDrwDZr7Vgj7rnZ0KHdirJ5qHupa4zZA/wicKX+cBRFUdafRuzx/SHwbwDXgHspiqKsO3UtdY0xnwPet9a+ZoxZ6dqTwEkAay3pdLqet24YiUSiZWJZCY11fdBY14dWjnVF4TPGfB/YUeWpp4B/B3xqNW9krX0aeDp86MbGWmMrMJ1O0yqxrITGuj5orOtDM2IdGRlZ1XUrCp+19tFq3zfG3AccAKJsbzfwijHmQWvtjdWHqiiKsrHc8VLXWnsaGI4eG2MuAR9p11NdRVE2D1rArChK2+E515TDWD0BVhRlvfBWuqBZGZ/XKl/GmJ81OwaNVWPVWBv6tSK61FUUpe1Q4VMUpe1Q4SvWFm4GNNb1QWNdH1o21mYdbiiKojQNzfgURWk7VPgURWk71IgUMMZ8HfgskAUuAr9prZ1qblTlGGN+CfgGEAe+aa39gyaHVJXQpuzPkf7uAHjaWvuN5ka1PMaYOPBTxHDj8WbHUwtjTD/wTeAYUgv7pLX275obVXWMMf8K+KdInKeRf1OLzY2qiGZ8wgvAMWvt/cDbwNeaHE8Z4T/MPwEeA+4F/pEx5t7mRlWTPPCvrbVHgI8Cv9XCsUb8C+DNZgexCr4B/F9r7WHgOC0aszFmF/DPkRbWY8h/1v+wuVGVoxkfYK19vuThj4EvNyuWGjwIXLDWvgNgjPlL4PPA2aZGVQVr7XXgevj7GWPMm8AuWjBWAGPMbuCXgd8HvtrkcGpijOkD/j7wGwDW2iyyQmlVEkCXMSYHdAPXmhxPGZrxLeVJ4LlmB1HBLuBqyeP3wu+1NMaY/cAJ4CdNDmU5/htipBs0O5AVuAu4BfwPY8yrxphvGmN6mh1UNay17wP/BXFlvw7crkgumk7bZHzL+Qpaa58Nr3kKWao9s5GxrYJqbTgtXYdkjOkFvgP8S2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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.4\\n\",\n \"y.shape = 249 y.mean() = 0.40160642570281124\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for target_percentage in [0.1, 0.2, 0.3, 0.4, 0.5]:\\n\",\n \" X_u, y_u = UnderSampling(x_vis, y, target_percentage, 1)\\n\",\n \" print('Target percentage', target_percentage)\\n\",\n \" print('y.shape = ',y_u.shape[0], 'y.mean() = ', y_u.mean())\\n\",\n \" plot_two_classes(X_u, y_u, size=(5, 5))\\n\",\n \" plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Comparing Under-Sampling with other models\\n\",\n \"\\n\",\n \"**Advantages of Under-Sampling:**\\n\",\n \"\\n\",\n \"- Fast to estimate\\n\",\n \"- Easy to understand\\n\",\n \"\\n\",\n \"**Disadvantages of Under-Sampling:**\\n\",\n \"\\n\",\n \"- Information of the negative class is lost\\n\",\n \"- Variance due to randomnes \\n\",\n \"- Need to define the target percentage\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## TomekLinks\\n\",\n \"\\n\",\n \"Identify and remove majority samples that form a Tomek link with minority samples.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Find the nearest neighbour of every point\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.neighbors import NearestNeighbors\\n\",\n \"\\n\",\n \"nn = NearestNeighbors(n_neighbors=2)\\n\",\n \"nn.fit(x_vis)\\n\",\n \"nns = nn.kneighbors(x_vis, return_distance=False)[:, 1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([506, 633, 161, 610, 221, 268, 827, 529, 591, 561])\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"nns[0:10]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Find if tomek. Use the target vector and the first neighbour of every sample\\n\",\n \"point and looks for Tomek pairs. Returning a boolean vector with True\\n\",\n \"for majority Tomek links.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Initialize the boolean result as false, and also a counter\\n\",\n \"links = np.zeros(len(y), dtype=bool)\\n\",\n \"\\n\",\n \"# Loop through each sample of the majority class then we\\n\",\n \"# look at its first neighbour. If its closest neighbour also has the\\n\",\n \"# current sample as its closest neighbour, the two form a Tomek link.\\n\",\n \"for ind, ele in enumerate(y):\\n\",\n \"\\n\",\n \" if ele == 1 | links[ind] == True: # Keep all from the minority class\\n\",\n \" continue\\n\",\n \"\\n\",\n \" if y[nns[ind]] == 1:\\n\",\n \"\\n\",\n \" # If they form a tomek link, put a True marker on this\\n\",\n \" # sample, and increase counter by one.\\n\",\n \" if nns[nns[ind]] == ind:\\n\",\n \" links[ind] = True\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"links.sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"filter_ = np.logical_not(links)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"y.shape = 998 y.mean() = 0.10020040080160321\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print('y.shape = ',y[filter_].shape[0], 'y.mean() = ', y[filter_].mean())\\n\",\n \"plot_two_classes(x_vis[filter_], y[filter_])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Comparing TomekLinks with other models\\n\",\n \"\\n\",\n \"**Advantages of TomekLinks:**\\n\",\n \"\\n\",\n \"- Creates a linearly-separable space\\n\",\n \"\\n\",\n \"**Disadvantages of TomekLinks:**\\n\",\n \"\\n\",\n \"- Still a highly imbalance dataset\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Condensed Nearest Neighbour\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The goal is to eliminate examples from\\n\",\n \"the majority class that are much further\\n\",\n \"away from the border\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Import the K-NN classifier\\n\",\n \"from sklearn.neighbors import KNeighborsClassifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Number of samples to extract in order to build the set S.\\n\",\n \"n_seeds_S = 51\\n\",\n \"\\n\",\n \"# Size of the neighbourhood to consider to compute the\\n\",\n \"# average distance to the minority point samples.\\n\",\n \"size_ngh = 100\\n\",\n \"\\n\",\n \"# Randomly get one sample from the majority class\\n\",\n \"np.random.seed(42)\\n\",\n \"maj_sample = np.random.choice(x_vis[y == 0].shape[0], n_seeds_S)\\n\",\n \"maj_sample = x_vis[y == 0][maj_sample]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Create the set C\\n\",\n \"# Select all positive and the randomly selected negatives\\n\",\n \"C_x = np.append(x_vis[y == 1], maj_sample, axis=0)\\n\",\n \"C_y = np.append(y[y == 1], [0] * n_seeds_S)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Create the set S\\n\",\n \"S_x = x_vis[y == 0]\\n\",\n \"S_y = y[y == 0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"knn = KNeighborsClassifier(n_neighbors=size_ngh)\\n\",\n \"\\n\",\n \"# Fit C into the knn\\n\",\n \"knn.fit(C_x, C_y)\\n\",\n \"\\n\",\n \"# Classify on S\\n\",\n \"pred_S_y = knn.predict(S_x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Find the misclassified S_y\\n\",\n \"idx_tmp = np.nonzero(y == 0)[0][np.nonzero(pred_S_y != S_y)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"filter_ = np.nonzero(y == 1)[0]\\n\",\n \"filter_ = np.concatenate((filter_, idx_tmp), axis=0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"y.shape = 178 y.mean() = 0.5617977528089888\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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gKdfdfMpm8Kek/SfrFMK8LAADQ6uoOVYlEYmBlhUqJRKJb0rslXaz3ugAAAO2k7kZ1SUOS/veVvipHUjKZTH4zhOsCAAC0jTB2//2TpPtCuBcAAIC2xUR1AACAEBCqAAAAQkCoAgAACAGhCgAAIASEKgAAgBAQqgAAAEJAqAIAAAgBoQoAACAEhCoAAIAQEKoAAABCQKgCAAAIAaEKAAAgBIQqAACAEBCqAAAAQkCoAgAACAGhCgAAIASEKgAAgBAQqgAAAEJAqAIAAAgBoQoAACAEhCoAAIAQEKoAAABCQKgCAAAIAaEKAAAgBIQqAACAEBCqAAAAQkCoAgAACAGhCgAAIASEKgAAgBAQqgAAAEJAqAIAAAhBpNk3gNrYwJe9OCpdvSyb8WRicenosMyJERnHbfbtAQCw47BS1YZs4Mu++Lzs+ZekbEbGGCmbkT3/kuyLZ2QDv9m3CADAjkOoakP24qhsakYmHi/5uonHZVM38itYAABgWxGq2tHVyzLxWNmHTDwuXb28zTcEAAAIVW3IZrzNn7DV4wAAIHSEqjZkYvHNn7DV4wAAIHSEqnZ0dFjWy5R9yHqedHR4m28IAAAQqtqQOTEi09efD1BFrOfJ9A3InBhp0p0BALBzMaeqDRnHlR4+La3MqVLGk2JxmeGTzKkCAKBJCFUtaqvhnsZxZU6ekk6eavatAgAAUf5rSQz3BACg/RCqWhDDPQEAaD+U/1pRJcM9KfsBAHYgP7AanVzSldSyvFygeMTRsb4ujRzqkeuYpt4bK1UtiOGeAACs5wdWz788q7Pji8r4gYyRMn6gs+OLOnNpVn5gm3p/hKoWxHBPAADWG51cUmopp3ikdEUqHjGaXsxpdHKpSXeWR6hqRSvDPa21sjcmZC9dyPdZXboge/2a7B0/0ew7BABg211JLSsWKV/ii0eMrqSWt/mOShGqWpA5MSLt3y9dviBNTUiBLxlJmYy0cEu6PsYOQADAjuPlgroebzRCVQsyjisNvlnq2SMVGtZdVzo4KB0/Id2cYQcgAGDHiUc2jy1bPd5o7P5rUebaK9Kb3lz+QXYAAgB2oGN9XTo3vli2BOjlrO4a6GrCXd3GSlWLYgcgAAClRg716EBvRF6udJefl7Pq741o5FBPk+4sj1DVotgBCABAKdcxOn18v04N9SrmOrJWirmOTg316vTx/U2fU0X5r1UdHZY9f7bsEFDreTLDJ5twUwAANJfrGJ0a6tWpod5m38o6rFS1KHNiRKavX9YrLfNZz5PpG8jvEAQAAC2DlaoWZRxXevi0dHE035Se8aRYXGb4ZD5wOW6zbxEAABQhVLUw47gyJ0+xyw8AgDZA+Q8AACAEhCoAAIAQ1F3+SyQSb5b07yUNSgokfTGZTD5b73UBAADaSRgrVTlJn0gmk3dJeoekjyUSCfb7AwCAHaXuUJVMJseTyeQ/rvz/W5J+JOlN9V4XAACgnYS6+y+RSLxF0n2SvlvmsaclPS1JyWRS/f39Yb60IpFI6NdE8/G5diY+187DZ9qZ+FyrY6y1Wz+rAolEYpek/yzpf0omk3+xxdPt9evXQ3ndgv7+fk1PT4d6TTQfn2tn4nPtPHymnYnPNe/w4cOStOUZOKHs/kskElFJz0n6agWBCgAAoOPUHaoSiYSR9L9J+lEymfy39d8SAABA+wmjp+pBSU9JGk0kEmdXvvY/JJPJ50O4NgAAQFuoO1Qlk8n/TxXUGQEAADoZE9UBAABCwIHKAACgYn5gNTq5pCupZXm5QPGIo2N9XRo51CPX2dmFK0IVAACoiB9YPf/yrGaWcopHjIyRMn6gs+OLGpv3dPr4/h0drCj/AQCAioxOLim1EqiKxSNG04s5jU4uNenOWgOhCgAAVORKalmxSPmVqHjE6EpqeZvvqLUQqgAAQEW8XFDX452OUAUAACoSj2weG7Z6vNPt7HcPAAAqdqyvS5lc+TODvZzVsb6ubb6j1kKoAgAAFRk51KMDvRF5a4KVl7Pq741o5FBPk+6sNTBSAQAAVMR1jE4f379uTtVdA8ypkghVAACgCq5jdGqoV6eGept9Ky2H8h8AAEAICFUAAAAhIFQBAACEgJ4qAGgxHFgLtCdCFQC0EA6sBdoX5T8AaCEcWAu0L0IVALQQDqwF2hehCgBaCAfWAu2LUAUALYQDa4H2RaN6G7GBL3txVLp6WTbjycTi0tFhmRMjMo7b7NsDEIJjfV06N75YtgTo5azuGtjZB9YCrYz/5GkTNvBlX3xe9vxLUjYjY4yUzcief0n2xTOygd/sWwQQAg6sBdoXoapN2IujsqkZmXi85OsmHpdN3civYAFoe4UDa08N9SrmOrJWirmOTg31Mk4BaHGU/9rF1csy8VjZh0w8Ll29LJ08tc03BaAROLAWaE+EqjZhM16+5LeRjLd9NwMA24Tp8mgnlP/ahInFN3/CVo8DQJspTJc/O76ojB+UTJc/c2lWfmC3vgiwjQhV7eLosKyXKfuQ9Tzp6PA23xAANBbT5dFuCFVtwpwYkenrzweoItbzZPoGZE6MNOnOAKAxmC6PdkNPVZswjis9fFpamVOljCfF4jLDJ5lTBaAjebl8yW+zx4FWQqhqI8ZxZU6eYpcfgB0hHnGU8TcOTu06XZ7m+87Vnr8jAQAd71hflzK58s3oXs7qWF/7TZen+b6zEaoAAC2pE6fL03zf2QhVAICW1InT5Wm+72z0VAEAWlanTZen+b6zsVIFAMA22aq5vl2b75HHpwcAwDbpxOZ73EaoAgBgm3Ri8z1uo6cKAIBtUmi+Xzun6q4B5lR1AkIVAADbqNOa73Eb5T8AAIAQEKoAAABCQPkPAIAG4Zy/nYVQBQBoK+0SVArn/M2sHEtTfM7f2LzXtlPhsTFCFQCgbVQaVFoheFVyzh/N6p2FnioAQNuoJKgUgtfZ8UVl/KAkeJ25NCs/KD98M2yc87fzEKoAAG2jkqBSSfDaDlud48c5f52H8h8ANEErlKfaUSUHElcSvLaj7BaPOMr4GwcnzvnrPIQqANhmndDAvDYU7t+T1lBX0PBQWElQqSR4bYdjfV06N75YNuB5Oau7Bjjnr9MQkwFgm7VKeapWzexZquRA4q1WgLZrhYhz/nYeQhUAbLN2b2BuZiisJKhUEry2Q+Gcv1NDvYq5jqyVYq6jU0O9bbEaiepR/kPdbODLXhyVrl6WzXgysbh0dFjmxIiM4zb79oCW0yrlqVo1s2epkgOJRw71aGze0/RiafBrxgoR5/ztLIQq1MUGvuyLz8umpmXicRljpGxG9vxL0viY9PBpghWwRrs3MDc7FG4VVCoJXkAjEKpQF3txVDY1IxOPl3zdxOOyqRvSxVGZk6eadHdAa2r3BuZ2CIWsEKEZCFWoz9XLMvFY2YdMPC5dvSwRqoAS21measTohnYPhUCjNP8/J9DWbMbb/AlbPQ7sQNvVwNyoXXrsagPKY6UKdTGxuJTNbPyEWHzjx4AdbDvKU406e65cz1IhFNKzhJ2MUIX6HB2WPX+2bAnQep7M8Mkm3BQAqbG79NaGwv7+fk1PT9d8r0AnIFShLubEiDQ+Jpu6UdKsbj1Ppm8g/ziApmj2Lr1ywuzx4qgftJpQQlUikfhjSb8kaSqZTN4TxjXRHozjSg+fllbmVCnjSbG4zPBJ5lQBTdZqu/TCPJ6nnmsRxtAoYa1UfUXS/yrp34d0PbQR47j5sQns8gNaSqvt0guzx6vWa3XCuYtoXaH8Z0oymfyWpFQY1wIAhKOSXXp+YHV2fFHPnZ/R187d0HPnZ3R2fLEh5/eFeTxPrddq93MX0dq2racqkUg8LelpSUomk+rv7w/1+pFIJPRrovn4XDsTn+v2eaq/X/84dlMXJxfk+YHirqMTh3bp/iP7JEl/cW5cUwuBuuJdiqy0Rf74ZqBZP6v3/dRQxas2lXymTmxRPRtcLrBW1+eWdebVtJZzgboit++z3D1sdi1JslLZ+xl/Na19e8qvhvVIGl929G5+b67iz2p1ti1UJZPJL0r64sovbdi7RNh50pn4XDsTn+v2urNHuvPO7qKv+JpN5VekXr+RLw8uZUu/5+pUWv/vDzNyjKmo96iSzzTILGu5TI9XYK0uzSwr41v1xfIrZMuS/vPsLZ2/dqNsSW6jaxXEXKfs/czOL2zavL9oxe/NIvxZzTt8+HBFz2P3H4COR2NyeZuV0KKu9PzLszqyJx5a79FGPV5TC1nd8gId2VM6mmWz/qha+8VarXkfnYXfPQA6WhhTxbez72g7bTZSYWohq3nPD7X3aKMerxuLOe2JOzq4K7ruezbqj6p1qvuxvi5lcuU/Ny9ndayPI3ZQu1BCVSKR+FNJ/yDpJxOJxFgikfhoGNcFgHrV25jcqKNeWsFmqzKpdE5xt/zja4OOH1h97/XZLUPnRsfz9PdGNXygWxstfJULf7Ue9cMRO2ikUMp/yWTyV8O4DgCErd6p4o066qUVbFpC862GyqwcrT6+EnQKoXPJRhWsCZ3lyoTljud57vxMTSW5Wo76KXfETjzi6K4BysGoHz1VADpavVPFG3nUS7ONHOrR2Lyn6cXS0OjlrPbE3bLluIJC0CmEzn174iXN7tWEzu2ep7Ud5y5iZyJUAeho9TYmd/JRL5ut2tw72KN/mljaMuiEETo3CnfpbKCMb3VpJq0LU0tsMEDLI1QB6Gj1roK02m6xsCeCb7Rq4wdWb9zKlF3FKu49CiN0lgt3UccoszJXKxdYJp+jLbD7D0BHq7cxudV2i23XRPBKG8G3CpWVhs5CuPuVuw/oQ/cO6Hh/t7ojrrqipd/P5HO0MlaqAHS0WhqTi8tr6ayva/Oeoq7RkT3x1R1qzdottp09XpX0HhVWAsv9FOrph+rkXjZ0LkIVgI5XTWPy2vKa6xjduT+usbmMXp1d1p37utQVbdxusa36pbazx6uS3q1CP9RitvR16w2drdjLBmyFUAUARcqV1xxjdMe+uLyc1d2Hehq2QrI20FlJr9/09IPrC/rLC0Z3H+rRTDqnvm5XzgaJI6wer0p7tworga8vR/SPr06FNqKg1XrZgEoQqgCgSDPLTsWBLrDSpZm00tlArmOUzgUam/OUCaymbmV04mD3umAV5viBauZzuY7R2+/Yrzt7/FBeW9r+MQtAGIj6AFBkq7JSI8tOxYFuaiG7GqikfHBJpXM6sicua4zG5jJr7ivcHq9KwmUjMfkc7YiVKgAo0syyU3EfUSqdW1c6862VY6S7BrqVSucUc52GTQRvdk8Tk8/RjghVAFCkmWWn4kDnB1ZacwvuSspxjHSgO6JfufvAttzLRo/XoprBpUw+R7shVAFAkcJutqmFrOaWfaXSudWAc8feuE4OdDfstYsDnesY+fZ26csPrAaKjo1p1IpZIfRcv5XR2JynmGvU1x3RwV3R1R6uWsNl2INLgVZDTxUAFHEdo/cc26eMH+jGYla+DeQ60kBPRHHX6G+u3MyHrAYo7iPq646svo4fWHVHndWz+KoZOuoHVmfHF/Xc+Rl97dwNPXd+RmfHF8u+h0LoOTu+qL7uiHqijjK+1cRCVpdmlhVYW1dP03YNLgWahZUqAFjjwo20uiOuRgbXB4dKDwmuRXEf0aXptOa8nDI5q8Fd0dWVompCTbUrQ2tDz/CBbk0tZJVK57SUDZRK+/r5o3tq7mlioCc6HaEKANZo5l/+xX1E71vTfxRzq2vU3mxlaGohq7/80Ywks9rbdP1WRn3dt/9acIw0uDuqwd35FbLCMTW1anbzO9BohCoAWKPZf/mXa+Y+ebCn6hWijcJhYKXX5zxlZqxGBntWV7CuzXlKLWU1fKBb5V6m3vfNQE90OkIVAKzRzL/8a23mLhfErt70dGhXdF1Auj0Da837co2WsoGmFrKrq1Mlj9f5vhnoiU7HfxYAwBrH+rqUyZVvRq+mSbwWtTRzFzeYZ/xgNYhNL2Z1eSattT3phRlY7prluELpL5XOlXy9sLJ1/VZmy2b3zTDQE52OlSoAO0I185EKYxWmF0vDzXb85V9LP9dGQWygN6Kx+ey6lSc/sPJt6YgGSTq4K6o5z9dS0eHIgZV+NJWWkdWRPbG6xiAw0BOdjlDVYmzgy14cla5els14MrHcBzjrAAAgAElEQVS4dHRY5sSIjOM2+/aAtlRtSa3wl/+5iUX93eu3NLWQPxLm4K6Y7i2zIzBMtfRzbRTECiHpxtKacp6RuiO3RzQUOMbo+IEupdL+6rT2VDqnPXFHb9obKzlrsNwZgJVgoCc6GaGqhdjAl33xednUtEw8LmOMlM3Inn9JGh+THj5NsAIqVLwydXV2WdPpnAZ6CqMJ8s/ZKhi8MZ/R7pir/oO3g9S5iSW9cSvTsEGVtfRzbRTECiFp8la25Eibtx3u1a1lf92BzJKU9aWfP7pn9efx3PmZDe+HMQhAKUJVC7EXR2VTMzLxeMnXTTwum7ohXRyVOXmqSXcHtI+1K1Ozy/keoYmFjOa9XMnutmpLarWu0FRafqylmXuzIOYYo6P7u0qOtPEDqzOXZisqbzZ7JyTQTmhUbyVXL8vEY6u/tDaQvTEhe/mC9NrLsn/zDQUXzsoGfhNvEmh9awNRoaHadW7vbitWTUlNuh3EKrVRI/nZ8UWduTRb0vBdSzN3tY31hfLmqaFexVxH1t6eQbV2BW6rHX+MQQBuY6WqhdiMly/5KR+o9OolKZ2WIislvwylQKASawNR/hy92/8/lc6V9BhVU1IrfrxShZAXdaWJW5nV8wRdx2hywdXh3THdf3jX6v1V28xdS2N9pb1NnTgGoZpNC0A1CFUtxMTiUjbfEKvpKWm5KFBJkutSCgQqsDYQ9XVHNLGQXf0Ls3hlqJaSWuHxSl1JLSviSpdmllfmQxnJ5INeKp3T8y/P6t7B3tX7q7aZu5G76pq5E7IRONQZjUSoaiVHh2XPn82XAOdSklsUqHK+dHBAUr7HSlcvS4QqoKy1gaiwC64QaAp/aXo5q74eV9ZaPXd+pmTV4s79cY1OLIWyQuPlgqKBm6XXcx2jec+v+zzBRu2q67QxCGH3ygHFCFUtxJwYkcbH8itRflHfVM6Xunuk/kO3v5bxtv8GgTaxtmRV2AU3tZDVjcWc+noiirmOfrI/rrG5jM5NLK1btejrjmh/j6vUkl/3Ck084qwO3Cz7uOuUbZZvlTJVJ41B4FBnNBKhqoUYx5UePi1dHJV94zUpk8mvVh0ckPoPrfZbSZJi8Q2vA+x05UpWjjHa3x3V8f7u1RLP2fFFzabLr1qk0jndO9ijO/aauldojvV16b+M3drweJmBXdF1PVqFMtWNxazmlv3VPqwfXF/Qd67F9ZH7DypGk3jV2M2IRiJUtRjjuDInTymQbpcC17CeJzN8cvtvDmgTlZastlq1eGXW06/cfaDulYuRQz36ZtzVLc8vCVZ+YNUTzQ/hXNujNTq5pOnFrF6f84r6sPKPXZpZ1pdfmtS/eNtg25Xfmo1DndFIhKoWVVwKLJ5bZT1Ppm8g/zjQZK1SniqnkpLVdq1auI7RE2/dr7+5clMLXrC6829gV34Yada3uvtgaY/WldSybi77Zfuw4hGjq7MZ+n9q0Im7GdE6CFUtqrgUqKuX8z1UsbjM8EmOrEFL6IRdVNu5avFTg71641am4l10hSNiNvwZGrst/T+tHJxr0Wm7GdFaCFUtrFAKZJcfWlEn7KKqZdWi1pBR7S66eMTJj37Y4JKuMQ3v/+mE4LxWp+1mRGshVO1gHN6MenTCLqpyqxaBlcbmPWV9q4iTf5+F0CSprpBRXJIsDmcXppbWhbNjfV36wfWFstfJ+YHciKOXZ5b1tXM3GrZ61AnBuZxO2s2I1kJH3g61enjz+ZekbKbk8Gb74hmOwsGWtlolaYddVGuPawkC6ZXZZclKd+6Py3VMyXEy/zSxuGXIqEQlx9aMHOrRHXvj646fyfmBFrKBlrKB9sbdTY+8qVeYR/UAOwErVS2skStJHN6MenXKLqriVYuz44vKBXZdkCiEpldnPfX3RBRYaWohW3LcTF93RJem0xWtflS6AvSR+w/qyy9N6upsRjJWrjFyI458K+2Juzq4K7rh94aB8QNAdQhVLWp1JSk1LROPl6wkhXL235rDm4sxsR2VaOVdVLX2PW29MpPJh6eZdMmYA99aTSxkNOfl9L6VkLWZSkunsYijf/G2wZL38vLMso7sye8aXPsyYZddOyU4A9uFUNWiGr2SVHx4c1lMbMcm/MDKWqtr857mPV9xN79Skx8PoKbuoqqnuXqrlRlJmx43k8nZilaKqlkBWtv/87VzN7Zt9aiVgzPQivjPjFZVyUpSHcxWE9mZ2I4NFELLuYkl3bm/S0O7YpKMxheyenXW008N9jR1V1glpbUCP7A6O76o587P6GvnbujlmWVN3MoosOX7kg7uiunGUrbse/MDq4HeSEV9Rlut8Gz2eD3fW62RQz060BuRt6avi/EDQHmsVLWohq8kFR/evPa1mdiOTawNLYO7oxrcne/t8XJWjjFbBqpCmGnE7KNKS2vlVrT2xl29MZ/R3HJOe+KuZpf91Z6pXTFXjx7bq29enFU6V7pS5QdW3SuT0StZKapnBWg7V48YPwBUh1DVokwsLmUzGz+hzpUkJrajVvWOUvADq784N66rNxYbMvuo0tJauRWtg7uimvNyujbnqTviaE9XRDJGGd/qVibQ9fms7hro1vVbmXyTus03jxcmozvGKOZuvVJUzwDK7R5eyfgBoHKEqlbV4JUkJrajVvXuCBudXNKNhUBR12jiVukOuslFR2/aHdN9h3fVfH+VNleXC4eOye+q64o4yvqSrEqOk0mlc9odd9TXHdXg7vV/NitdKapnBYjVI6B1Eapa1HasJDGxvfM14oiReneEXUktKxrr0oXx9TvoUks5/ceXZ/VTg73rymuVvo9Ky2MbhcPZtK+9XRFJdt2qTzxiFFjpQG+k7pWielaAWD0CWhON6i3KOK7Mw6dl7r5PisYka6VoTObu+/JfZyUJW6hkwGQtjvV1rRtIWeDlrI71bb5S4+UCjc8vb7iD7pbnr2smr+Z9VNpcvVH4K1zP3WA5LuvbkoGh1kox19Gpod62PLYFQHhYqWphrCShHo06YqTenp54xNH0zUxJ+LDWajETKJ0LJFn95Y9Sq69V7fuotDxWbkUrsNJSLtCC56sn6ujCVHp1VEThduMRh5UiAGURqoAO1aiz+YpDy6XptMbmM5rzfO2JOeqNORqdXNq0vHisr0v/OJFe/bW1Vql0TtnASlbaFXOVyd1uXE9ng6rfRyWhZ204DKx0aSYtL5tfDeuNOatDPee9nIYPdCvrM5sJwMYo/wEdqpFn87mO0cihHnVFHe3vjugn+7s0tCem3MqohM3KiyOHerS3K7r6+GImWA1UUdeoN5ZfCSqsRF27ufn4kFrfx9pz/6YWssrkrIYPxHV4d1SF23cdo6VsoLF5j9lMADbFStUO0chzBNGaGn3ESK3lRdcx+pVTQ3ruB6/rVsZXOufLNUbd0fxKV2Clvu7I6rXmM4GGNrmPet5H8YrWc+dnVudtDVp7+2w/axVzjXqiLj1TADZFqNoBGn6OIFpSo4dE1lNe/Ok379ePxqY1vZgfp1DYhucHVj0rQzQL9sZdZXLrDzkO633cvtbt3YCOMRrcHSsZm2BXxiu0qkbs9ARQHcp/O8BW5wjai6NNujM0UqOPGKmnvFhSeou4+XlQxmhwV0zDB7pLDgo+sifW0PdRmO7+8syyRieWdGEqrYlbWa2tXrby4cGN2ukJoDqsVO0ElZwjyA7DjtPoIZHVlBfXrqLs35PWUFewGog2XVEb6lndBRj2+/ADq//441n9aHpJN9M5zXs5uY5RKp3V3HJOx/vzAa/VDw9u1E5PANUhVO0ADT9HEC2rkVv/Ky0vljtjr7CK8vrcst60O6Zr857mPV9x11kdYZD1b69ENep9/NPEor73xoIyfqDuqFE6Z5QNrNJZqzduZbSny1Vfd7TlG9QbtdMTQHUIVTtAo88RxM5SWHW6NJ3WqzeXlclZDfRGVs++W1uW22gVJepK/+XagnZ3ubpzf3y1MXxiIaPFrK8n3rp/3WT1sO69sOL1/esLyuQC7Y67MsaorzuyOi/LD6zG5jN65Cf2tXxfUr1HBwEIB6FqB7Bv/gnp2y9I6VuS70uuK+3tk/oPSpls3ecIYudYu+p0/EC3phayurGU1dyyr7sP9ayW6wohZKNVlKmFrLKB1YIXrGsM93JWxph1QaaeZuy1924lzSzlxyik0jn1RB31RF31xhztiuc3brjGrFvhacWG8Ebv9ARQGf6kdTgb+NLENWnpluStrFb5vjQ1IV26KO07EMo5gtgZRieXNLOY1Ww6qwtTSzo/uahUOquBnoiO7ovr+IFunRoqXV3aaJUklc73L5Vroi6UrIrV24xdvGK2Ouhzpfk9sFLGt1rI+Eqlc7K2cK3Sa7ZqQ3i9RwcBCAehqsPZi6PS7Kw0fJd0cFBy8jutFItJu3ZLh48wTgEVuzST1mtzniYWsvKtJGPkW2liIaurc54uzaTXfc+WZ+xtsLqzNoxV0oy9meIVs6mFrNLZQHHXkZVkJOUCK8cYZf38kTl+YHVwV+kGj3rvoVEavdMTQGUo/3W64p1/A4P5f4q9/op09/3bf19oeeXKXP80sSjPt4qUOQg5nQ3KTj/fqKHddYwyvtVA0UyqYmvDWL3N2MV9R4VVsr1drm4sBbJWq4tSjjFayvraG3f14B27Q72HRmn0Tk8AlSFUdTh2/qEWG+3Ym1jIyjFSX3d0XWO06+Snn6+10QHMu2KuFjJByaDPgnIjDNJZXzcWV6acB1auY4oOOzZbNmMX9x3lB45KvTFXXi7QUjaQb62sJMdIcdfR29+8S/cOlgakVm4I55BnoPkIVR2OnX+oRmF16ttX5zU25ynmlgaX7ojRvBdoMeOvNnMXf+/envWl5HKrKDHX0WPD+zQ2l1EqXRq2ypWs/MDqtZuebnl+ftWlqOw45/k6fqBLXZHNy9jFK2auY+RbK2Okvp6oop6viGPUE82fO3hwV1RPvLVv3QoPDeEANhNKqEokEr8o6VlJrqQvJZPJ/zmM6yIER4dlz58tO/zTeh47/xqsFXeKbaR4dWpqIbsSPEqDy56uiDw/q6VsUBKqCsfLHNlTfsjs2lWU/v5+TU9P697B9T+fciWr0cklRd31P69C2XFsLqPHj+/f9P0Vr5j1dUc0sZCR6xgFNh/ijh/oWh0JsbbZvqDRR/8AaG91h6pEIuFK+rykxySNSfpeIpH4v5LJ5IV6r436mRMj0viYbOpGyTE11vNk+gbY+ddAmw29HJv3Wu5w3uIm7EJ5TLodXKYWsjrQHVHGt1rOBnKNWS3DDeyKam+Xq+P93VW9ZqUlqyupZR3ZE9dyNl+qW/tzywZ2y2bs4hWzS9NpzXk5ZXJWg7uiG87YWmujUiYN4QCkcFaqfkbS5WQy+YokJRKJ/1PSP5dEqGoBxnGlh09LF0fzx9FkPCkWlxk+KXNihJ1/DdRuR4cUN2EXymMF+aNbcjox0K25lVLZyYO3A1SjQ4WXC+QYaXhlLlZxX9XArqgO9kYrCqjFIe59a1YRY+7Wjd00hAPYTBih6k2SrhX9ekzSz659UiKReFrS05KUTCbV398fwkvfFolEQr9mRzl4SNK7m30XVWvnz3X81bT27SkfmnokjS87encLvTcntqielUwwtN/R9ZvLcotLblba1durn4x2aX93VFaS5+fHEpw4tEv3H9lXcaio9nPdvye92su0q1f6iTWPx1ynpt8nhw7W9qei1u/rZO38ZxUb43OtThihqty/RddNoUsmk1+U9MXC49PT0yG89G2FHg10lnb+XGfnFzbdKbZo1VLvLcgsa3kluOyNSDeMryXvdqnNNdLs/KL6eyN67OjuNQHK12xqpuLXqvZzHeoKNu1lOjXU21I/y52onf+sYmN8rnmHDx+u6HlhbFUZk/Tmol8fkXQ9hOsCbW2rnWCttlOseCp3odQ2uCsm1xjlVgZhnhrqbUovGMMtAbSDMFaqvifpeCKRuFPSG5I+KOlDIVwXaGvttlNsbRO2Y6TB3VHt746ovzfS1MZ6epkAtIO6Q1UymcwlEonflPTXyo9U+ONkMnm+7jsD2lRhjMKlmbRenV1WJrAa6CnsMGuN1ZWNRj2859g+XbiRbsngwnBLAK0ulDlVyWTyeUnPh3EtYLuFOUtq7RiF4/1dmlrI6sZiVnNeTncP9OiuoZ6mhpRKRj0QXACgekxU30Fs4OcPWL56OX98TSwuHR3e0aMVwp4ltXaMgmOMBnfHNLg7Ji9ndby/u+mBpd1GPQBAu2itTlk0jA182Reflz3/kpTN5M8DzGZkz78k++IZ2cBv9i02RSUBoxqVHLjbbJdm0kqls7owtaTRiUVdmFrSxK2MAmtb5h4BoB2xUtVhNlqNskEgpWZKpqpLkonHZVM3pIujMidPNemum6eSEFTNqk0rH7gr5Vfmzk8uKZ0LNjxDr9n3CADtilDVhjYKTnrrSek//7VsalomHi9ZjdLEmHTHnWWvZ+Lx/LT1TUJVp5YOww5BrX7g7ujkkjIrk8iLFR9Fc8e+1tqVCADtgvJfm9m0jPdnf7waqIqZeFxamJempza+cMar7TXbvHQY9iyp4llPa3k5q2N9zQ0sV1LLGuiJ5s/2W8N1jG4s5pp+jwDQrlipajP24qjsRmW8V1+WDgxIA4PrvzEWl+ZS5R8rPF7La7Z56TDsWVKVHrhb6Y7DMHcm5u8j0MFdUc17uXUHE/uBVXfUYZAmANSIUNVurl6WicfKP2Yk3dwgOO3rk6Ymyn6b9TyZ4ZM1vWYlpcNWVmkIqlQlQyor3XEY9s5E6XZ5cqODiY/siTV9HlU1wg6dAFAPQlWbsRkvX34rx3Wl3AaluP5D0tJSPkAVrThZz5PpG5A5MVLba0qblg5bXSMmdW81pLLSkQb1jj4oFzgkq+VsoK6oo8HdUQ3ujq4+vzDyoV00InQCQD0IVW3GxOJSNlP+wb190syN8o9lMtIj/0zGOPmVpYwnxeIywye3bDbf9DWlTUuH7WC7J3VXuuOwnp2JGwWO5WygycWsDvZG1R293S/WClPeq8W8LQCthlDVbo4Oy54/W74ct3uf1NWz8WrUXffmw1O1pbpNXnPL0iHWqXTHYT07EzcKHF1RR4d6o9rT5UoyLXcUTTXCHocBAPUiVLUZc2JEGh+TTd1YH5wOHJQeeo/08oWqV6Nqfs0tSoftqNF9OpWOXahnPMNmgaMr6kgy+pW7D1R2wy2q1WeCAdh5CFVtxjiu9PBpaWVmVNngdPJUqI3jFb1mh9iOPp1KdxzWszNxJwSOVp8JBmDnIVS1IeO4+REG27jjrhmv2Qzb0adT6Y7DenYm7oTAEfY4DACoV/v/mxUI0Xac3VfYcXhqqFcx15G1Usx1dGqot2QlrNLnldPqQ0jDMHKoRwd6I/LWvM92bLoH0BlYqWpznXp8TLNsV9ms0h2Hte5MLF7lirpanUnl+VZ74q7uHexZnU/VrhoxDgMA6kGoamOrx8eUO+tvfEx6+DTBqkqdUjYrBI5zE4t6/uVZzXu+4q6joV1RHdwV1bmJJb1xK9P2s5y2exwGAGymPf6GQFlbHR9jL4426c7aVyeVzVzHyDFGb94T131DvTp5sFuDu6NyTGmPGAAgHISqdlbJ8TGoSqf16WxHjxgAII/yXxvr5ONjim3n+W7t1KdTyc9lJ4xWAIBWQahqY+WOj7HWStOT+YOVJQVn/rztGteLw4KiC/rR9VlFXaMje+JytuF8t3bo06l0nlan9IgBQDvg36jt7OiwrHc7VFlrpVcvSVMT+bP+9vWtNq7bF8/IBhscttxCCmHh7PiiMn6gyfll3fJ8pZZyujyTVrBSldvpPUGVzNOSOqtHDABaHaGqjZkTIzJ9/bLeSplvelJKr4SM7m6p/2D+eU1uXLeBr+DCWQVn/lz+X31VwZk/V3DhbNmQtzYszCxl5TpGrmO0lA00tZBdfe5O7gmqtFeq03rEAKCVUf5rY+uOj7mZkuIxaW+f1H9QxtzOzKuN65tMRG9E71K1Yx/WhoWsfzsMuI5RKp3T4O7o6tfq6QlqZK9Wo/vAKu2VaqceMQBod4SqNld8fIxfR+N6o86822rsgy6O5u9/xdqwEHWNcrnS+yxWa09QI8/4247zA6vplQqrR2w7NwwAQDui/NdBTCy++RM2ebzSHp2qVTn2YW1IOtATLQlSxX9519MT1LD32+BrF2x3r9TaXrfioHjm0uy6sAsAOxGhqpOsaVwvZj1POjq84bc2ap6R3Wqsw5rH14aFob1d6o468gMrP7Dq684vrtbbE1TL+/UDq7Pji3ru/Iy+du6Gnjs/o7PjiyWBwg+svn11XpdTaY1OLOrC1JImbmUUWLvptau13b1S2xEUAaDdEao6yLrG9RXW82T6BmROjGz4vVv1JtXau1Tt6tnasOAYo+MHutTXHdHuuKuDvdGKDxXeTLXvt5KVmsJzrs158q0kY+RbaWIhq0szy6vBKozZUPUctlwLhogCwNboqeog6xrXM54Ui8sMn9xyTlXD5hkdHZY9f7ZsCdB6nszwyZKvrW2stpK6Iq4eP74/1N6dat9vpSs1qaWc4q4j35aWLNMrOxcHd8dCmw21nfO0GCIKAFsjVHWY4sb1StjAl704qp+4PK6zXrfirpOfb9V/cLXp3ctZ3TVQW4+OOTEijY/Jpm6UNKtvtnpWHBb6+/s1PT1d02tv5lhfl86NL5ZdfSn3fitdqYlFjPq6I5pYyJQEwMLOxf3d0Zp/ls3EEFEA2Br/JtzBVscdnH9J92hWB5yMPD+QnRqXXr0sa23dPTrGcWUePi1z931SNCZZK0VjMnffl/96k6a8V9uTVEm5sPCcg7ui6lnpAyuW8dt3NhRDRAFga6xU7WDF4w5cSaej0/qhv0tXTI+89ILiM5M6eddP1F12q3b1bDtUO7+p0pWajB/IMdLwgW5NLWSVSufkB1auYzS0N9aQfqftMHKoR2PznqYXS0ugDBEFgNsIVTvZmnEHrpHujSzo3siCFJfkzcsZ2ri5vd1V05NUabmw8BzHSIO7o6uDSr2c1amh3rYMVBJDRAGgEoSqHczWMSx0p6l0paaTV3Pa4aBpAGgmeqp2sHqGhe40lYwwKH5OxBhN3Mrqx9PLSi1llc4GGp1cYkgmAHQwVqp2sirHHex0lazUuI7RyKEeXZvztL87slr+y60MDg3rmBoAQOshVO1QNvBlbSBNjMkuzEnx+OpBzMpktxwWio1VMtOKEhoAdB5C1Q5QmEWlq5fzx8ZEY9LsTP5/77hTmp6UbqakqXFpaVF6+AmZk/c2bdxBu6tkphWhCgA6D6Gqw63OokpNy8TjMsbIjl+TJt6QendJdx6XGRiUBgbzz/c8GcchUNWB6eMAsDMRqtrM2lUnE4tLR4c3PIameBbVqpspKRaT0mlpemo1UEnKP+/q5ZaaKdVumD4OADsT/3ZvI8UT0JXN5MchZDOy51+SffGMbOCv/6Y1s6gkSf7K8yKuNJda/z2MUqgL08cBYGciVLWRsqtOyq8u2dSN/ArW2u8pF5DcohUtv0wQY5RCXao9AgcA0Bko/7WTcqtOKzYq25lYXMpmSp+8r0+amsivVLmlJcNqRilUW4oMix/YdZO9j/W1zmRvpo8DwM5EqGqyaoJJTRPQy82i6j8k3ZqXFuelwSO3r+95FY9SKNcAXyhFanxMatBhyX5g9fzLs5pZGVlgTP68vVabAcX0cQDYeSj/NVG1PVK1TEA3J0Zk+vplvduByxgjHb5DestbpaEjkrVSNCZz930yFYahWkqRYahkBhQAAM3ASlUTbRVMdHFUpricV8MEdOO40sOnpZXVMGU8KRaXGT5ZX5muylJkWCU7ZkABAFoVoaqZrl6WYhHZGxP5XXi+n+9xKkw2XxNMzIkRaXxMNnWjJIhtVbYzjpsPZyGOSaimFBlmyY4ZUACAVkX5r4msl5ZevZRvGi/swvP9/K9fvZR/vIhx3Hx57u778tPQayjbhaWaUmSYJbutZjwxAwoA0CysVDXTzVlpOZ3fhVcs4uYHc96cXfctjVh1qkkVpcgwS3bH+rp0bnyx7PW8nNVdA8yAAgA0B/9Z30xGUvkZkbcfb1HlGuCl8qXIrUpy1ZTsmAEFAGhVhKpm2tsndfdIuTUDOHN+/uv7+ppzXxWophQZZsmuMAPq1FCvYq4ja6WY6+jUUG/LjFMAAOxMlP+ayMS7ZO88Lk1P5s/jKzSqHxyQ+g9t3bfUZJWWIsMu2TEDCgDQighVzXR0WDp/VmZgsORQYylfRtPxu5t0Y+EaOdSjsXlP04ulzeqU7AAAnYTyXxNV05fUzijZAQB2AlaqmqhhgzlbECU7AECnI1Q1WcuMSAAAAHUhVHWgag5pbofXAQCgHdBT1WGqPaS51V8HAIB2QajqMFsd0mwvjrbV6wAA0C7qKv8lEokPSPofJd0l6WeSyeT3w7gp1OHq5bJHx0j5wLP2kOaWfx0AANpEvT1VP5T0Pkl/GMK9dKzt7D2yGS9fittIxtv4sTX8wGp0cklXUsvycoHiEUfH+rryc6VCfB0AADpBXaEqmUz+SJISiUQ4d9OBVnuPUtMy8XhJ75HGx6Q1R7rUy8TiUjaz8RMqnNLuB1bPvzyrmaX8wE5jpIwf6Oz4osbmPT0ejcvN1f86AAB0CnqqGmzbe4+ODst65cOO9bz8FPcKjE4uKbVUOgFdkuIRo+nFnH7YdzyU1wEAoFNsuVKVSCT+H0mDZR76TDKZ/KtKXyiRSDwt6WlJSiaT6u/vr/gmKxGJREK/ZhgWpsel/fvKP9jTI02Pa1f/u0N7Pfvgw1qan1UwMykTv32mnvWW5bz5Lep58GEZd+uVsfFX09q3p/ygzh5JE85xvVOpul9nK636uaI+fK6dh8+0M/G5VmfLUJVMJkP5Gz+ZTH5R0hdXfmmnp6fDuOyq/v5+hX3NMPizqc17jxYXtVzmvuvpw7I/8zdSxcoAABrXSURBVAur31uY0q47T8icGFF6drai15idX9BGtx1YaWohq/9jzz3yFvoVn5/WMTuve7qzcsu8Tj1a9XNFffhcOw+faWfic807fPhwRc9j+GeD1dLjVG8fViVT2rd6jdjBn1XWrv++wEqXZtLK5KwGd0flHBxU9uCgzuWs3uiN6PTx/XI4yw8AsAPV1VOVSCTem0gkxiQ9IOk/JhKJvw7ntjpIDT1O29GHtdVrHFu8rkxufaqaWshqwQs00Fuaxwu9VqOTS3XfGwAA7aje3X/fkPSNkO6lI5kTI9L4mGzqRkmAsZ4n0zeQf3yt7ZgBtcVr3JO6pDeOvVnTi6XN6jeWstoVd3RwV3Td98UjRldSyxyaDADYkdj912DGcWUePi1z931SNCZZK0VjMnffl/96mTKe3WrGUwgzoLZ6DTfr6fTx/To11KuY68haKeY66u+O6PiBLjkbNFx5uaDuewMAoB3RU7UNNupxsoGv4MLZdY3iikalXG7jC4YwA6qSXi/XMTo11Fuy8vTc+Rll/I2DUzxCTgcA7EyEqiZZbRSfuSHduindTMn6vjT6A6m7V/bQYZmurvXf53kywyfrv4Gjw7Lnz5YtAW72Gsf6unRufFGxyPqVKi9nddfA+nsGAGAnYFmhSfKN4tPS9delqQkp8CWj/D9zKemVl2WXl0u/Z7M+rCqZEyMyff35ZvkqXmPkUI8O9EbkrWli93JW/b2R/BE2AADsQKxUNcvVy9L8rJRekiJr+qri8fzsgt17JdnVWVNm+GRo5wUax5UePi2tmWe11Wu4jtHp4/vXnQl410D+TECXcQoAgB2KUNUkNuNJN1PrA1WBY2Rk5Zx+f32vs8UQ0a3mWZVTrtcKAICdjvJfk5hYXPL9jZ/gunXv8lvt2zr/kpTNlAz4tC+ekQ02eX0AAFAVQlWzbHbgcM6X9vbVvctv2w9zBgBgB6P81yBblt1OjMi+9B3ptUtSrGgHXs6Xurul3fs2D16V2I4hogAAQBIrVQ1RSdnNOK7Mf/0R6S3HpcJGOteVDg5Kh4/KHDhY9y6/7RgiCgAA8lipaoCtym66OCpz8pScSEz2V//b1RWtwg684hWtetRymDMAAKgNoaoRqii71boDryI1DvgEAADVo/zXAK1Sdqt1wCcAAKgeoaoBzFZltW0qu9VymDMAAKgN5b9GaKGyW0PLiwAAYBUrVQ1A2Q0AgJ2HlaoGqPVcPQAA0L4IVQ1C2Q0AgJ2F8h8AAEAICFUAAAAhIFQBAACEgJ6qJtjqsGUAANB+CFXbbPWw5dS0TDxectiyxsekbRrKSbADACBclP+22VaHLduLo42/h0KwO/+SlM2UBDv74hnZwG/4PQAA0GkIVdutksOWG6wVgh0AAJ2GULXNWuKw5RYIdgAAdBp6qkJSaY+SicWlbGbjC23DYcs24+VLfhvZjmAHAECHYaUqBFX1KB0dlvXKhyrredLR4Ybfr9kquG1DsAMAoNMQqkJQTY9SSxy23ALBDgCATkOoCkMVPUrGcWUePi1z931SNJZf5UpNS0u3ZG/dlP3rbyi4cLahO/BaItgBANBhCFUhqLb53DiunJOnZB5/r8zufVLvbpn9/TKOsy2jDdYGO1krRWMyd9+X/zpzqgAAqBqN6iHYqPncWitNT0q35uT/1VfXNa9vVTbUxVGZk6cac8+Om792g64PAMBOQ6iqkw18WRmp0DflutK+PunAgPTaFWlxXho8UnZyekVlQ0IPAABtgVBVh8KuP92ak2IxKZ2WjKSpiXxwks0/8eZMfuXJdaW9ffkVrIujjDYAAKCDEKrqsFq+6+qSvfO4ND0lzaXyD96ckSIRadceKQjyX/P9fOC6NSe7a1dLzKwCAADhoFG9RjbwZb/3Len1y/mRCZcv5h84dpfMT45I3T1SLpcPVsUibn5F6/o1RhsAANBBCFU1WC37Xb8mBX6+5BesrEK9eilf3tusdBdxpYV5RhsAANBBKP/VoFD2UzyeL+kVRFwpvZTf8ReNSctzUuqGFFjJMVK8W+rplfxA2r83P7rg4dP5Jverl/NBLBaXGT657ngbAADQ2ghVtVjZtWf39kk3JvIN6AURV5qdyQckY6ScLzlOPlgtLkjLaenQm6TDb5bEaAMAADoF5b8arA777D8odXXng1OxuVmpuzf/eO+ufLiS8uErFpe6u2Xecnxb7xkAADQWK1U1KOzaM8Yp3fXn+/ngFI9L99wvvXZZkpMPVgU5X8pm6ZcCAKDDEKpqcXRY9vxZmXhMxjjSwGD+H63s2pubzU9MLxe4+gakAwP0SwEA0GEo/9Vgq117q/1SxpH6D0l7+yTHza9S3UxJc7MNPTAZAABsP0JVlWzg53f/eUvSXEr2lR/LTk3IRqK3DyR+y3FZL5MfrfDqpfyohcLohUxGWlpq6IHJAABg+1H+q0JhPpVNTefP5js4JB0cyq9Qxbtvj0E4MSKNj8leOp8fsRBx8yfWLMxLfk5ynP+/vXsPkfM67zj+OzO7M5YsK7K8KwnZQpa7kjcrybbStA0NbaqkON40dUIbDm3pJQnFGHq/0NYY2j9KSiCl1NBSCC6FEkN6SAOBpm7sgDGlNBe7lrNIXtuyZUmri7WrtVf3md2Z0z/Ojvai3dnLe2bemff9fmCQdnd29oxfy/r5nOd9HvmX/0e+WlHhk5/lKBAAgAxgp2oNbo6lKS8cH2PKZfnJ8dBZXbNtEg4PhzsDy6UQqKbeC79+4M7Qs8pIGnmJHSsAADKCULUWs/2plmLK5dDAs/FxoSjdeZfMwJB0V3+YAbh581x7BUkyWhDGAABA9+L4bw185YZ08UIoNm/czbdlq9S3XcaYW0bT3ByY/P5kOAJcrFicC2M0/wQAoKuxU7VKvl6TTp9YWHS+eN5faeGx4M2BybUljvdmauGuQKn5nEAAANAVCFWr5EdHpGKvdP2qdHE8zPe7OC5VrkvXrkrnxqTdAwu+p9F64RYzNWnDhtBxXbo1jAEAgK7D8d8q+XfekCrXwq5SbXaen/fStdl5fpvuuKVLemNgsq9WpJGXwu5WowFo37bQkb1SCXVXAACgqxGqVuvs6bArddsG6fJU+L0k9Zak8gZpw8YlWyOYQlGFT35WvhTuEJx/52CjWSgjawAA6H6EqtW6ckm6NCVNT0s9veEhSfV66D119fKy39rYsdLoSChKr1akUllmYGiutxUAAOhqhKrVqtVCN/TiogBUKMwdCTZhCkWZoYe4yw8AgIyiUH21ikWpVAo7U/PV66HQfHHYAgAAucJO1Wpt2izduCbduBHqqeo+dEbfsEkq3xa+DgAAcotQtVo7d4W6Km+k2zfNfb7RHmHnrvTWBgAAUkeoWiVz7z75K1ely+9JU/M6qm/tl+7YEr5er4V+ViePy1croaP67gGK0QEAyIFEocpa+xVJvyipKuktSV9wzr0fY2GdxgwelM6NyRvJ9O+4+flGWwTtG5J/4T/lJydkyuUwtma6Kn/0ldAY9PAwwQoAgAxLWqj+vKQDzrkHJL0h6YnkS+pMplCUOTwss/9Q6E3lvdRbktl/SObwsPTGMfnJiwv6UElh0DJDkwEAyL5EO1XOuefmffg9SZ9LtpzO1qwtgj95XKZcWvr7GJoMAEDmxayp+qKkf4v4el3FVyvhyK/xsfdhPuD7c/VXdeqrAADIrBVDlbX2u5J2LPGlJ51z35p9zpOSZiQ90+R1HpP0mCQ559TXt8Sg4QR6enqiv+ZaXLlza2gCKsnXvaaPH5O/dlWmp0cqFqRij3pPjKpw6T1tHP4lGfparUrRGN1x5oSm3xqVKjek8m3q/bFBlQ58iH+GXSztP6+Ij2uaTVzXtTHe+0QvYK39LUmPS/qEc+7aKr/Nnz17NtHPXayvr08TExNRX3Mt6seOyB89IlMuyY+fly6cl3pm/9KfqUnbdsj07wiF7fsPqcBR4Ip8vaaNP3hRV06fXHpmIsX/XSvtP6+Ij2uaTVzXYOfOnZJkVnpeokJ1a+0jkv5c0qNrCFSZZAYPymztk69UwpHf/EC1YYPUty08r1FfhRX50RHVFw2hlij+BwB0pqQ1Vf8gqSzpeWutJH3POfd44lV1oflDk/2pt8InG32s+rbJmHn5dfaYcL1y0w/r5PHw3mZuzesU/wMAOk3Su/8GYi0kCxp3B9ZPHpemq8s/sVRe/msr8PVabvph+WpF6m3yr2jCcAoAQEwMVG6F3QPylaVDla9UpN3rz6J+dCQ3/bDMSuEzQTgFACA2QlULLKivmudmgfXgwfW/+Gr6YWXF7oGwW7WEpOEUAIDYCFUtYApF6WMPS5s2y7/9uvzoj+Tffl3atFn62MOJjueWCxk3ZehIzAweVGHrttaEUwAAIiNUtYCv16QXvyNduSRz3/0ygw/I3He/dOWS9OJz4evrlKcjMVMohp5ey4wGykrtGAAgG2J2VMesleqeNDoSxt2sx+6Bm/2wbvm5lYrMwND6XrdDmWIx9PTiLj8AQIcjVLVCC+cAmsGD0rkx+UX9m2IdieWmXQMAAJERqiLz9Zr8mVPS1NzMP23ZKvVtn5sNmKDuaX4/LJ08Hl6rVJYZGEocfPLUrgEAgNgIVRE1Qokmx0MzeyOpXgsjay5fkt+zNwSVhHVPjX5YsY/EWnpsCQBAxlGoHlEjlOiu/rBL1dBTlK5fkybe7exWAHlq1wAAQGTsVDWx5vqi2VDi+7ZJl6ek69fnZgD2FKWL4zL37uvYVgC+Wpk7olxKhto1AAAQG6FqGeupL2qEEmMK8nv2ShMXFtZWbb5zXa0A2lU8bkrllo3XAQAg63IfqpYLLL5el9ZYXzQ/lBhTkPp3hEdDb2l9gapdxeM5a9cAAEBMua6puhlYjr4iTVcXBpYXvi2Vls6cy9YXtWDmXztn/bV0vA4AABmX71DVJLDoyqVwfLecJeqLWhJK2lg8bgrFcDxJB3MAANYs38d/TQKLSuVQDzX/+G7x1xdpRQ+pdhePt6pdAwAAWZfrUNU0sGzZGvpLLfV9TeqLYocSiscBAOgOuT7+azqcuG+7tGlz+vVFLajTAgAA8eV6p6rZ3W6qVqWPfyrcxRfhKG+9bRFaPesPAADEketQtWJg+eCDIfAkPMpL0hahlbP+AABAPPkOVW0KLEln6lE8DgBA58t1qJLaFFhW0xZhlT+/Xd3VAQDA2uQ+VK0kRoiJ1Rahrd3VAQDAmuT67r+VNOu47l94Vr5eW9XrNL3LUFp1W4R2dlcHAABrQ6hqIlqIidUWoY3d1QEAwNoQqppJGGJ8vab6sSPy77wpnTkRQtr4eXlfD19fY1sEv9IxYeTu6gAAYPWoqWoiSS3U4vonv2efNPGudHFcujwlv2//mu8ypLs6AACdi52qJpLUQi0+OjTGyPTvCLtSd98rc+8+FYYeWlthOd3VAQDoWISqZpKEmBbUP5nBgzJb+9IfnQMAAG5BqGoiSYhpRf2TKRRlDg/L7D8k9ZYk76Xeksz+Q+HztFMAACA11FQ1kaTjeqvqn+iuDgBAZyJUrWDdIabJsGZfqcgMDEVaIQAA6AQc/7UI9U8AAOQLO1Ut0q5hzQAAoDPkMlS1aygx9U8AAORH7o7/Ys3zAwAAmC9/oYqhxAAAoAVyF6oYSgwAAFohdzVVSeb5dbN21ZEBAJBXudupSjLPr1tRRwYAQOvlLlTlcSgxdWQAALRe7kJVLptyUkcGAEDL5a6mqpOacrarzimvdWQAALRT7kKV1BlNOW/WOU1OyJTLC+qcdG5MOjwcLVi1argzAACYk7vjv07R1jqnHNaRAQDQbrncqeoIs3VO3teliQvS1KRUq0nFovSBrfLvvBFtJ80MHpTOjclPji8IcZmuIwMAoM0IVSnx1YokL514U7p+XeqZPeqr1aQL56XLU/KP1KIcAXZSHRkAAFlFqEqJKZXlz56SbswLVA09RalalR8dCbVfMX5eB9SRAQCQZdRUpWX3gHRxPBz3LTZTk+7qp9UBAABdhFCVEjN4UCqVQoCab6Ymbdgo9W2n1QEAAF2EUJUSUyhK+w5I23ZIhaLkFX7dtkPasze0WKDVAQAAXYOaqjab3/BTZ09Lk+PhqK9vm4yZy7i+UpEZGEpxpQAAYC0IVW20uOGn+rdLVy5J58fC3X579sqYAq0OAADoQpkLVe0a/bKutS1q+GmMkd+zV5p4NxStj78r3b2bVgcAAHShTIWqZqNf/JlT0s57ZE69nV7YWmKwsTFG6t8RHr0lFYY/1561AACAqDJVqL7c6BeVeqVXvy/99/PSdHVh2HrhWfl6bekXjL2+le7m424/AAC6VqZC1VI7QZLCGJiZaenq5QWfbsmcvSbMSnfzcbcfAABdK1OhatmdoKnJ0GSzduuOlCmX29dkc3awsfd1+fHz8sePyb8+En49c0p+133tWQcAAIguU6Fq2Z2gRphaqnu51LZjNzN4UNqyVXrztTDfr7GuSlW6dkU6f7ptR5EAACCuRIXq1tq/lvQZSXVJFyR93jl3NsbC1mX3gPzRI7ceARaLIbhs61/6+9p07GYKRWnnPdJbr4WjyFotrG1bf+ig/t5k1Hl/AACgfZLuVH3FOfeAc+4hSf8h6S8jrGndzOBBma198pVFO08bNoXg1Lf9lu/xlUqYw9cm5tTbMjt3yeydbZuwd0imf4eMMe09igQAAFElClXOuUvzPrxdYdhKakyhKHN4WGb/Iam3JHkffv2Zh6UHPyxVqwuen0aTTe4ABAAgmxL3qbLWfknSb0qaknQ48YoSMoViOD5bdITmhx6cGw9TrUilcipNNk2pLE1Xl38CdwACANCVjPfNN5estd+VtGOJLz3pnPvWvOc9Iek259xfLfM6j0l6TJKccz9erTYJFuvQ09OjmZmZqK/ZCpVXf6jqqz9YsqjeV26o9NBPqfzgT6Swss7ULdcVa8N1zR6uaTZxXYNSqSRJZqXnrRiqVstau1vSt51zB1bxdH/2bNx69r6+Pk1MTER9zVYIXd+flZ8cX9Ck9OZR5OFhxtPM0y3XFWvDdc0ermk2cV2DnTt3SqsIVUnv/tvrnHtz9sNHJY0meb1W6LRZgKZQlA4PSx1wFAkAAOJJWlP1ZWvt/QotFU5Kejz5kuJpNgtQ58aklHaFlqv7AgAA3StRqHLO/XKshbTCcrMAG+NpRE8oAAAQSaY6qt9iuVmAavN4GgAAkHmZDlX0hAIAAO2S6VC17CzABnpCAQCASDIdqrR7QL6ydD+sdo+nAQAA2ZbpULXcLMA0xtMAAIBsSzymppPREwoAALRLpkOVRE8oAADQHpk+/gMAAGgXQhUAAEAEhCoAAIAIMl9TNV+nDVdOKmvvBwCAbpabnaqbw5WPviJNVxcMV/YvPCtfr6W9xDXJ2vsBAKDb5SdUrTBc2Y+OpLSy9cna+wEAoNvlJlRlbrhy1t4PAABdLjehKmvDlbP2fgAA6Ha5CVVZG66ctfcDAEC3y02oytxw5ay9HwAAulxuQlXWhitn7f0AANDtctOnKmvDlbP2fgAA6Ha5CVVS9oYrZ+39AADQzXIVqpqhOzkAAEgiNzVVzdCdHAAAJMVOlVbuTq7RkXDMFuNnsSMGAEAmsVMlta07OTtiAABkF6FK7etOzrw+AACyi1ClNnYnZ14fAACZRaiS2tadnHl9AABkF6FK7etOzrw+AACyi7v/FKc7+aru6ts9IH/0yJJHgL5SkRkYiv3WAABAmxCqZiXpTn7zrr7JCZlyecFdfTo3Jh0eDq8/eFA6NyY/Ob6gWJ15fQAAdD+O/yJY7V19plCUOTwss/+Q1FuSvJd6SzL7D4XP06cKAICuxU5VDKu5q292B4x5fQAAZBM7VRFwVx8AACBURcBdfQAAgFAVQ5v6XAEAgM5FqIqgXX2uAABA56JQPYIYfa4AAEB3I1RFwl19AADkG8d/AAAAERCqAAAAIiBUAQAARECoAgAAiIBQBQAAEAGhCgAAIAJCFQAAQASEKgAAgAgIVQAAABEQqgAAACIgVAEAAERAqAIAAIiAUAUAABABoQoAACACQhUAAEAEhCoAAIAICFUAAAAREKoAAAAiIFQBAABEYLz3afzcVH4oAADAOpmVnpDWTpWJ/bDWvtyK1+WR7oPrms0H1zV7D65pNh9c1wWPFXH8BwAAEAGhCgAAIIIshaqvpr0AtATXNZu4rtnDNc0mrusapFWoDgAAkClZ2qkCAABITU/aC4jBWvuIpKckFSU97Zz7cspLQkLW2l2S/lXSDkl1SV91zj2V7qoQg7W2KOklSWecc59Oez1Izlq7RdLTkg4otMz5onPuf9NdFZKy1v6RpN9WuKYjkr7gnLuR7qo6W9fvVM3+B/ofJQ1LGpL0q9baoXRXhQhmJP2Jc+6Dkj4i6Xe4rpnxB5JeS3sRiOopSf/lnBuU9KC4vl3PWnu3pN+X9GHn3AGFTYtfSXdVnS8LO1U/Kem4c+5tSbLWfl3SZyQdS3VVSMQ5d07SudnfX7bWvibpbnFdu5q19h5JvyDpS5L+OOXlIAJr7WZJPyvp85LknKtKqqa5JkTTI2mDtXZa0kZJZ1NeT8fr+p0qhb9oT8/7eGz2c8gIa+29kg5J+n7KS0Fyfy/pzxSOdJEN90kal/Qv1tpXrLVPW2tvT3tRSMY5d0bS30o6pfA/uFPOuefSXVXny0KoWqrLKbc0ZoS1dpOkf5f0h865S2mvB+tnrf20pAvOuZfTXgui6pH0IUn/5Jw7JOmqpL9Id0lIylp7p8Kpzx5JOyXdbq399XRX1fmyEKrGJO2a9/E9YosyE6y1vQqB6hnn3DfTXg8S+6ikR62170j6uqSPW2u/lu6SEMGYpDHnXGMn+RsKIQvd7eclnXDOjTvnpiV9U9JPp7ymjpeFmqofStprrd0j6YxCId2vpbskJGWtNZL+WdJrzrm/S3s9SM4594SkJyTJWvtzkv7UOcf/+XY559x5a+1pa+39zrnXJX1C1D5mwSlJH7HWbpR0XeG6vpTukjpf1+9UOedmJP2upO8o3HHinHNH010VIviopN9Q2M04Mvv4VNqLArCk35P0jLX2R5IekvQ3Ka8HCc3uPH5D0v8ptFMoiO7qK6KjOgAAQARdv1MFAADQCQhVAAAAERCqAAAAIiBUAQAARECoAgAAiIBQBQAAEAGhCgAAIAJCFQAAQAT/DxKS7kSii97mAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"print('y.shape = ',y[filter_].shape[0], 'y.mean() = ', y[filter_].mean())\\n\",\n \"plot_two_classes(x_vis[filter_], y[filter_])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Import the K-NN classifier\\n\",\n \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n \"def CondensedNearestNeighbor(X, y, n_seeds_S=1, size_ngh=1, seed=None):\\n\",\n \" # Randomly get one sample from the majority class\\n\",\n \" np.random.seed(seed)\\n\",\n \" maj_sample = np.random.choice(X[y == 0].shape[0], n_seeds_S)\\n\",\n \" maj_sample = X[y == 0][maj_sample]\\n\",\n \" # Create the set C\\n\",\n \" # Select all positive and the randomly selected negatives\\n\",\n \" C_x = np.append(X[y == 1], maj_sample, axis=0)\\n\",\n \" C_y = np.append(y[y == 1], [0] * n_seeds_S)\\n\",\n \" # Create the set S\\n\",\n \" S_x = X[y == 0]\\n\",\n \" S_y = y[y == 0]\\n\",\n \" knn = KNeighborsClassifier(n_neighbors=size_ngh)\\n\",\n \"\\n\",\n \" # Fit C into the knn\\n\",\n \" knn.fit(C_x, C_y)\\n\",\n \"\\n\",\n \" # Classify on S\\n\",\n \" pred_S_y = knn.predict(S_x)\\n\",\n \" # Find the misclassified S_y\\n\",\n \" idx_tmp = np.nonzero(y == 0)[0][np.nonzero(pred_S_y != S_y)]\\n\",\n \"\\n\",\n \" filter_ = np.nonzero(y == 1)[0]\\n\",\n \" filter_ = np.concatenate((filter_, idx_tmp), axis=0)\\n\",\n \"\\n\",\n \" return X[filter_], y[filter_]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"n_seeds_S 1 size_ngh 1\\n\",\n \"y.shape = 386 y.mean() = 0.25906735751295334\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"n_seeds_S 100 size_ngh 100\\n\",\n \"y.shape = 100 y.mean() = 1.0\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for n_seeds_S, size_ngh in [(1, 1), (100, 100), (50, 50), (100, 50), (50, 100)]:\\n\",\n \" X_u, y_u = CondensedNearestNeighbor(x_vis, y, n_seeds_S, size_ngh, 1)\\n\",\n \" print('n_seeds_S ', n_seeds_S, 'size_ngh ', size_ngh)\\n\",\n \" print('y.shape = ',y_u.shape[0], 'y.mean() = ', y_u.mean())\\n\",\n \" plot_two_classes(X_u, y_u, size=(5, 5))\\n\",\n \" plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Comparing CondensedNearestNeighbor with other models\\n\",\n \"\\n\",\n \"**Advantages of CondensedNearestNeighbor:**\\n\",\n \"\\n\",\n \"- Create Condensed space\\n\",\n \"\\n\",\n \"**Disadvantages of CondensedNearestNeighbor:**\\n\",\n \"\\n\",\n \"- Variance due to randomnes \\n\",\n \"- Need to define the two parameters\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Other Under-sampling methods\\n\",\n \"\\n\",\n \"* Under-sampling with Cluster Centroids\\n\",\n \"* NearMiss-(1 & 2 & 3)\\n\",\n \"* One-Sided Selection\\n\",\n \"* Neighboorhood Cleaning Rule\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 2: Over-Sampling\\n\",\n \"\\n\",\n \"## Random over-sampling\\n\",\n \"\\n\",\n \"Replicate the minority class examples to increase\\n\",\n \"their relevance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import random\\n\",\n \"def OverSampling(X, y, target_percentage=0.5, seed=None):\\n\",\n \" # Assuming minority class is the positive\\n\",\n \" n_samples = y.shape[0]\\n\",\n \" n_samples_0 = (y == 0).sum()\\n\",\n \" n_samples_1 = (y == 1).sum()\\n\",\n \"\\n\",\n \" n_samples_1_new = -target_percentage * n_samples_0 / (target_percentage- 1)\\n\",\n \"\\n\",\n \" np.random.seed(seed)\\n\",\n \" filter_ = np.random.choice(X[y == 1].shape[0], int(n_samples_1_new))\\n\",\n \" # filter_ is within the positives, change to be of all\\n\",\n \" filter_ = np.nonzero(y == 1)[0][filter_]\\n\",\n \" \\n\",\n \" filter_ = np.concatenate((filter_, np.nonzero(y == 0)[0]), axis=0)\\n\",\n \" \\n\",\n \" return X[filter_], y[filter_]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.1\\n\",\n \"y.shape = 1000 y.mean() = 0.1\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.3\\n\",\n \"y.shape = 1285 y.mean() = 0.29961089494163423\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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z/M01obYPxMYk+rSKish0Ih+FN92LuuwXnG4/ka4f+dIeVy2GPDEEuB4Yh5gaxGGzZgdrQPacZ4oXCcu7olSM414XBUpxrvbO6De3qxuPxUeDfgDc18rjLDeV+fUoZqK5NQkTNLUJGzS1Mc490HNh6Fdz4CunoGqYUBQxTfr/xlRg33CjHLO44e9MdOpOSJohhyLEtE1IpmBgNpjwCBJgFGtHV7QJy8Xh8tKenJ4bklA/O+8yWMar69ZkmRGPSoJiaFImKdkEZcnmsGXX1dag9B9BbdpTUHWtOeowlwDTRE2NyLNcVYgUhv7GEEG8w5REgQF1oRI1vM/B3Xp3PAOLxePybDTjuskLJWNqlc5AYgg1d0LmxIGdp6xCzATRMTnjdWwVaw8QYxJrhur11GSCUTHp4xo+4rvyEQtIF9uFfH0x5BAhQFxrR1f0P4OYGnMuyRflYGl0bYXIcrlyUaYudu4X8WtskLW1tk5R0YgxsW6K+aBO0rkcffwH23zLjYxZ3pPWlsyJ6Ni1JjV0Xhgck3Y3GpLMMwZRHgAB1YlVPbjQK5WNpShmwczds2iZR1mA/hMKo/bfA/pth8zYhPtOSBseGbujaBGMj8PQ/163BU4aJsfcm1N1vh53XoVpaRfzsN6S0F1mmkuh0OpjyCBCgTqzqWd2G4VyvmIUOXslPY2Caktpevx8VjmAcfCcAzsmjEom1ri8VKoPU4ybH0bPU4Kk9B9A//6H0qsKRQldXu9IJRovIOdjPESBAXQiIrw7oTBrO9kr9zjKFgFzHEyaPo3cWIi0VjqC9ZkRFhAtWU/WapCrDhPYOLO3gWCGRzGQzIo5uXSfd5PaOZSNlCRBguSMgvnowliiQXjEsUy4fTRQu27ELDv94+m1BtHrdXTJ6VqedVR65HNamrWTXtVc+R2/lX4AAAWZGQHwVUB6JceGMNBdaWqdr86BkQE/tOYD+X+uk+VEuUI41QedGCEfqtrPKX74YTjMBAqwRrFrim+uujUqRmA5HpImQyMhEhk9+tiNi5bZCFKYME+74ZfjeUzA1UagHdov0hWwWdu+rz86quA64Yxf67InK59wgp5kAAdYKViXxzTqNLL5vpUjMtITwJidElBxrEjLr6ILOblS41AnWuOFG9JW+6usp9xzAPXl0Viapas8BjPER9IWzweLxAAHmidVJfLNMI0vgRWIlnnoTY1LLizVBcwtq9z55HO1C30V0U5PM1hZHlTO4wlSd/vBRvnzIMGk6eC9Tzz7dUKeZAAHWIlYl8c06jSxCfg7XX/lomTIlkc1I3S6TRu+6QW5z8kVJe7dsL93gduk8bNmGOn969suHqJ66KlN0fcFYWoAA88OqJL757NpQ4Qi673xpF1chmr3EsMzg/vxHYFmyJOi6vWU7MkLw/I/g1IvTCLHa8iE/MtVay21yWXTIwj3XGywWDxBgAbAqiW+2aWQJduwSB+XijqxGUl47J7W+dd6IWCYLZ08VRtZARtbsLAz0oYubG20dQmzly4defB793L9LWp0YEvflbVeLGUEZYTYac20ABQiw0rE6R9Z27EJnKhPfTAt81J4DMg1hF42VJSelGxsOywia48iPr+Mb6i/cdjQhfnsT4yJyLhY7951Hnz1Z+nhXLkFTq4iQW9eL68pgP5w5KesmF2ix+HJath4gwGJjdUZ8FdJIqK8DqgwTrtsnUdaYN56WzYiGzzcW9acyfPIbTcgsLkgjJJeTy7UWi6pMClwNo8Pg2Og33YsyzJImTMm0h+Xt3uh9Ea3kcfSls2TWr0dvvqoh0di8GkABAqxwrMqITxmmuBHvu1lSR63FRGDfzVVdiv3lQe6hx6DvAgwPwvoOuG5/wWBUKYkE13fIj28H5RRFR379MBIVQpyaFNLzMXilEFGd64WwJTPAA1ckchweFNnMxKjs8MhbTmXJHv5R46KxWazFDBBgtWFVRnxAXZ53PmayncL0zD99wXJnt9xxYkwckMNFBGJacltUwS3Zh+uKoehPn0WfOSHEmE5JpKcA7Xn3jY3IbaNFy6ZNExWJzjsa8+t6+thhbymSKSTe2S2uMz4Cb78Aqxirlvhmg0q2U3rnbmlUDA8KOWige1MJQeidu6HvIjQ1CWGFI3DN9ZIiX74oDQofrus1XMJSMxwdlhQ4k4VoFJpbITVVcFjWLji23Nef8WVmOU7N51m8Hc6H4xS2wO3cXSC/YAQuwCJiJJnhY4fOkkgXsqOOqOKhg1fT3tT492JAfFAx7VPKkLpd1ya0FUJFYlIzLImKcqjr9pekz+6xw+gXfl6I5nzredME15TfpybBUOR3oqRTQqzhcMG41P/7F8/45h93btFYSU2xrUMIzzILuzuGBuT5BiNwARYRI8kM9z9xBrfs8kRac/8TZ3j47TsbTn6rssY3W5QvDyqHymXrrhmqPQdQG7pE9OxZ07OhS+6jlKS+hgEoz0relmZIclIIL9YEjn95FnJeY6UYc43GiqdStJZ0fuCKRLWZFIwmghG4AIuOjx06O430fLje9Y1GEPHhiZazaYl4xoqMRv3aVzg6Y82wRBOXSUv0NpKA5map+8WaJLLLZkQao11P7mKCqbxIcAoyGWhpkYjPNOV+g/0wMY7d2YW+fAk6uqQJM0vN3bSplPVtha7z1KQQ8JvuRd1wY6DjC7BoKE5v53L9XLDqia8eka7efg38yz9JhGUVSVUGrsBIAv2me2d+jGJTBMOrEdrHJZvddQP0HoORITmu1pL+Gnh1PCUrJl0Hsm7B/cVvbpgG9F8iNzoM7Z3SfKnTdKEYFadSmlsKG9u0tyozIL0AqxyrOtWtW6SrqOyzl79uhsepoIlTyoDdeyTlHR0mn9qGIkJyftqLEvKzsxJxuTZk0pLORiJym+RUoXnS0gqnXkSfOAIXTqFfegH32PP1vSA7dklaW80kdUNXIGMJsCawqiO+ekS6dG8UM4FdewpuLGUeeur8afQNN1aNHMubI1q7pWlzJgUd3UJgYwmwNXlG1UXVDZU/gNcQsURDqJSIp8fHJO0tjkpHE7LAaO/M6anacwD97SekkTHNJNWT6QQylgCLjI6oqpnOdkRniDzmgFVNfPWJdH+pYGrgdXHLoTMpqOHvpzOpQtqsXa+GVkQu2SxcuVBoUgxeEXLzI07TEnILhaS2pxANYCZdIM/kFNq1K9vf17nAqOJUSrGvoDKkeRMgwCLioYNXV+zqgqSkDx28uuGPOW/i6+np2Q78PbAJacJ8IR6Pf26+x20E6nVpmdHUYHQEnXMgHJ62aU0PD0AkBhs65bZDA9LEKCYo05SfdFpWTzY1y1RGaqpAPqYJVkjOKeSluOmU1N9ME5JJjHXrqDizEa5f26euvg49lURVJPhAxhJg8dHeFOHht+9ccTo+G/h4PB7/WU9PTyvw056enqfi8fixBhx7XqjbpWUGbzxZ6xgq9ejzzQdGhkG76JZWiSLLN6wViY8ZuALjI7Brj9zm0nnytT8rJJGf5cq/IFGhaUKsRW6WSkq06LpCjLEWcYrZ2FV3ijqfOeYAARYK7U0RvvyO6xft8eZNfPF4/DJw2fv/RE9Pz4vAVmDJia9es89aZEB7hzQnnv9xYXzNL8b54uRQWAxKoXRut1x8PDEOqWRhMmR8FHI2kBMSjsZkCiQ5JbfNZWVut22DLA13HW8aRMn878QoZNNww8vq1vb5dli13KEDBFjtUFo3TiPT09NzNfAMsD8ej4+XXfcA8ABAPB5/eTZbIxJrELTjkDz0T7jD/ahIYS+GzqQxNmyk6eC9hCIRbNtGOw7ZF35G7tRx0dJFIlg7r8O+cIbsj76LMzQgXeJU0ktPLVSsSY6XzWC0rsO65nrs3uPSpVWG1APXt2N2dGF2bwbAHRnC3LwNMhmcxCDu1CQ6NYVOJVGWhdYaNzGETqcwNnQTueVV5E68QK73xUJarAGlUKEQWhkY69sI7d6L1dEJkSiha/cQ3n8Lqtpu30WAZVnYtr1kjz8bBOe6MFiKcw3L3PyM3ZCGEV9PT08L8F3gj+Px+D/NcHPd19fXkMedduDy1ZChkIiEtSvR0eiIvCzrO1CRKOtf9nLGq1g9uccOo48ehvEEvPgf0mwoTp3DXjPCDMnjXL8PrlyS9DcS9TqyFCK/LVeh9t8i9vH+uT59SOqEE6OSJo+PiZh4fTvsv1nsq374bzLKlklLEyRP4lqaKErBHb9cmCH209Y69X0Lgc7OToaGhma+4TJAcK4Lg2hLG5/6/56ndziD7bhYpsGuDRE+cetWmiML01fdsmUL1EF8DXn0np6eEPA48EgdpDdvVBMlc91e+O6/lnZfbRudmRISsiy40idrH/svo02T1JWLuNt3Ytzx5ukk4Y94beiUKC6XoeQ1TWcK0xWGKXZWobAQUzYrY2hNzSJnSQxK97WlBRcKaWVx2tm5EX3mJdi+U2Q0fmPGsYXcYl4anEp6kx9eqh1rKpkhDjz1Aiw1pjI29z/xUyYzOUylUErhuJpjAyk++M3TfP7N1ywY+dWDRnR1FfDfgRfj8fhD8z+l2siLkocH81GSdhyxi481w8bNqGI7Jzwi6D0mjYFQuKQ54QwPQGIId/N2zH23lD6W3xUeHoKWdRJx+QYCvujZsoTk2jqkSxtrkv8np+T2mbTM3ja3QDSGsu3p+zeKRuGcbzwiu3y1ix7slygwlQK05+vnQvM6OUGfAHNZcWsu6mDPx8UlQID54s+fvcRU1sYsU1WYSpHMufz5s5f4wzt2LNHZNSbiuxV4D3Ckp6fnsHfZ78fj8W814NjToI8fQQ8PQd+5Uq2cAs6fEiI88PLpMpbBy5JC+l56HpRlQSoNz/07lBFfvis8lhBb+PFRb+TMm7PVrkxbOLZIT6YmhRSjMZnYwPu/X2tzRamUt5N/8XmJ1Ioj19Fh9Pp2ONtbeH5NLdINdr1xNt/wQLvy/0hUxNflEpVAjBxgidA7nME0FNqdXkozlaJ3eGnfm43o6v47deTUDcO5XpgYma6VA7F6mhirTAKpqenH0hp3ckI6p2OJ6YP/O3aJxdT4mKSrvrYu/3gGoCWiGxuRtDM5KbdJp2UMLZ2SqM+Vhog2DGjfIONh3/ln9KZtJaJokkk4d1rqh5b35+nYIMQHQni5nJggaC0ymA1dMDIsXeUSYXI32nWCTm2ARYftuBhm9YlYuwIhLiZW3OSGzmama+V8KEPIoHgHRv6OFOZxNVLnG03g+CNjobAc++jP0X3nYNN2OHcSjvxYjudPNBielKS4/ubDMqUJ4WQk1TUMb/eGktsrV2p92aykuqZVKp/RLoQsGB6Q5xKNetFjs6TKtu0tOrLBjEH3Frk9yAyuv+sDxOA0nUI/fahuE4MAARoFyzSqWk0BWMbixUoVH39JH30OUOGI1PQqIRqTulel66Mxz9kY0eUlpyQKM3zHYy0C5auvFc3eyRclqmpuEUKZmhDiscIyTlY8cgZyXDtbIF+0HDfr/fkNw4tIxyUKzGUhEpVJED/99kfdolF5LJD0eWoCXBcVjYmXXjQGN79KHuPMSalBuk6B9Pwu8uZthZnkPQeCVZJLiFTW4Us/6+foQIqc7RKyDPZ1x3jfLRuJhVff679rQ4QXB9MVXVAcrbl+Q7TCNYuHFePO4i8D0gN9Mqg/POjV1IpC5nC0sPO2+L6ZDOzaC+vaxXwzlyvsxfDrZevahDRPvQTZHKQmvcjSkl26LV5Dwc4Jyfhkibd1TanSGpzWXg3Ot5F3RB+Yy0A66T8pmeY4c1IaL376bhiSwnZ0efO7cjydnJTnnBiSZo7WsHO3EGqz5+FnmGKR7+36VZEI+uzJYJXkEiKVdfjUt8/x3KUpbEeaULajee7SFJ966hyp7Op7/T9x61aawxZOmVzO0ZqmkMEnbt26RGcmWBHEV2Iv1bZBSMr2XItHE0I44+MSGWHITO3IkJc6ek7J7/oNuPEXhFRMT9enEIJp9lZHWiYMe+4n/u7cEngLhPydGHJ2hYaH76lHpfqFFyG6Xk1QayHVTErS3mP/IUQ7NSkEns1C/6WCri+dKjQ0TEui1hd+Lilt+wa48Rcketu9F9W1qbS503ehpktNo3f2BijFl37Wz2jGIVSW3oUMxWhaIsHVhuaIxVfkWpfxAAAgAElEQVTf83L2dsekyQGYhmJvd2zJpSywQlLdcnspvf9m+dD7O2wH+4W4WlpF0rJzN2SzqJa2EhGvvvMe3HMnhWCyWXBynnTE23/b1Fyo2RXvzk1OCeFVJDT/JDXoOr65tSuCalcLWaeSXvqclehualKE1qZVcGrWnpRFazFEsCw5l8lx6TZftx9l56o/5uQYqru05llsnaXPn8I91xukvguEowOpaaTnI2Qojg6kKl630tEStZZUslILKyLiK7eXUoYJB14uFkut62R+dn07dG8uTfHKohllmLBlu5BKcytEokKmWgvhjHqprb87d1271ORGhuT6WuQyG7gOODlvpSRCerYt8pPkpKTE4GkN/RqhknNraZWaoLfLQ6FRV+9GZyqPAOpMppCm+5f51lkDVwqRbZD6Lhhydq0yP+Sc2tcHaDxWRsRXwV7K98/TowmZXNg93U6poohXmUJira3g5NBuUfMhmxFxcCgsNlNnvCH+fD1v2iNQMwqsBVdDyJT7+ysl/ZG0UMhroHi3DYXACmNEo7jZNOD5+pkmZDMzOq7o5uZCswSmW2d50W0w8bEwCFkGtlP9fRKqIfuoFxPpHJ9++gIXRrM4rsQC29vCfOb27bRGQ/M+/mrDioj4VC3nEV+zVg3lIl7tSrTnuNDWgdHSWmhMRKLQ1i5OJz/9oSwU9++Tl62owr+GUd2yfkbowr4NDUSbhHB9O3qtvcaGW/g/eNMbFKLScESmP2ptgbv6utKIsFgOZDsyaeI/q7xBa4BGYV93jFwV3VrO1ezrjlW8rl5MpHO8/xunOJPI5pVWroYziSzv/8YpJtINylRWEVZExFfLXgoQAqiGctLMZaUG6NnMG81hnFizpMoT49Jx3Xu9pLcjQ4XISxmAW7CFz3dt5/G8HBeUl1b6S8kdW6yqtHfutu1tY/MI1lAFq/jWNplRhtpb4MojQr9pU2lnLwQTHw3G+27ZSO/wOUbTpQ2OnKtpi5q875aNNe49Mz799AUytrequQiGgowt1z908Jq6j7cWpDcrgvhq+uVt3SGOxRVQyVE4P4bm2cyHm5qwk0nR02VSHlFqGU8DObbWsgTI9vR5+Y7uPFJdKESSyhAJTTgsZDY5IbW85hYh5EtnYWwUF8+w1I86+86hW1pLTQ8qYJoZgn87f6dIedQ61729ASoiFjb507t2FMjEcQmZBjdubmoImVwYzU4jPR+GkuvrhS+98bvQxdKb3uFz/OldO1YF+a0M4qthnimOLN+u31G4WvQ4lhAOa+uQGljOEyODRFtmSAgjmynU+xpR99MalBZ9n2EI6cRskbS4LvT3yTFDlkRqtiORaCoJr78bZefqWjNZHBG6dRq0BmgcYmGTD71qy4Ic23FrV1xmMx1Wj/RmoZ7HYmJFEB/UTuX0LByFq0WPpDOSUo4OizzGsQtaOx/NLUJOqaR/Ut4JFHflZhkBak/X53pEa+fglldBYhh6XxRJi9fRVeGoLCv353V/+n30L7xu1k2JwH5+dcE0apPbbKbD1or0ZsUQXy3UrG9VuG1J9KhBWyHA22/hi5MNQ8hFZ0Vnp0B24Hr275Go3Me2wdFVor864T+unYPBATh6GPbfDCe11P6UAYaBTqfk3GxvKfngFfjB02jPCUaf661LjxfYz68ubG8LcyZROd11Nexor39zXs52ay7oWi3Sm1VBfLNFMVG2dHaSfOZ/os+dhrGhwpSEUyQcdjMS+eVt572taNlsYTxtziejCkQLUku80iduL44NGBDyrstlvSkRr9YIMDYqAutITEwJsmmYZeobYGXjM7dv5/3fODWtweFqiFhyfb1YDOnNcsCqI75q7sw1I5lzvbB5M/SdFZMAwwLDKURyCtH9oWSqwvBcWHIN6H5qLRxmIqRmWQUvP609Es4J8eYJVhf+cT1ydmwhz6EBVNemQI+3htAaDfHFt16b1/G5WghwR/vsdHyprIxeDiWl668QqWlL2MBQBjlXc+PmpgV8JouHVUV8+ZneKou/q0VAOpsRh5OmlkJkl0vLlYYhEdnEuHRUrZDMxzbUVceb49VIWu0jHBFfP8cFOy1RZrENlu8C7df81rVJk6ZrU+DAvMbQGg3x0MFrSqQoo0mbDzx5mrBp0N1sEQtZHNgY4y3XdxC2SiM3v5s7lXVlYMi7POvAaMqlOazpaArNW3qzXLC6iK9sptfHTMV/FY6gxxISbVktntOJFtMDx5EIzwqJS8voiGdB3+iT9w7om48qBevbCjs2/F2609Jq3/DA8GaNi8bNAj3ekmKx9XDFUhRTacazLq6GVM5hKutwTbvi++cnOTmc5mOv2VJCfn43N2KZhEyXyaxLzvsudoHmsLFqpCywyoivfKa3GDUjoB274PCPSx2dc1lvMkPcXgh53nyJoYL1VMOhvLE1Q4is7yJEIhLNZdOl/n8+HAeclAiRKe1C61AI99jhwINvCdBIPVzWdnnyRIIj/Skc1YepnYqRW7EUZSrryBSHd53twuWJLFvXRxicsnnyRIJ37OvM37e4m2sog3WRslqeUquG9GCFjKzVC10W4Wit0YNX0CePSTR47LB4+pURiNpzQAb57aLLi5sIdk6s6xOD8v85j6nN+AxkciSTKnj7FZNsrcdNJcV4YGJMnnMqBSOJwINvidAoK6qs7fLQs318//wkWcfFUJB1XL5/fpKHvt9HtsgAoZi8cm5pNUYBUzm5bcRSHOkvlaWsNSOFVRXx5acyENITR+NkYauaomK9Txkm3PHL8L2nCumtZcm8j/JEUuFwoZHgGxssROSnveOnpqTGN5n1yNbw3KKd6o/puDA1JTPG5gBcs3ta2k84hH7pBfTF09C2YdZR4JyaR2sQjdLDPXkiwVDSJmKVHitiqWmRW7EUpdJbpPiyrF36xbdWurk+VhXxlUxlDPUXSE9rGQOzLDj7EvrMS+hsBuONb8vf1bjhRvSVvryoV6eTcgzXlaaCv3PDsqTLCguU7oJnpSyP7RQZm85EtEqJrdX1+2X6ZGJMbOr9o/p2VKkUJCdR7Z11NX/y959j82gtYjZ6uLFUlk9++xz9kw4ukoZtbDF58O4dHOlPEbYqH8eP3N6xT34vJq9Kb5Xi0wmXLera1x3juUtTFcl6NXVzfawqGld7DqA6OtHpNFw6Jx3OwX75/9SEaN20FnL47iHc//Z/MvnY30sdDEodTkDqZi3rxdzURzjC7Fu6s7y9b4DgS1R8JxVdJd3w9/uGI6AMVNcm0TOMJkpvV2xHVdQEqdeJeabmUeDkXEDIqv3R8iOosVSW+79+msuTTn5dswYuTzrc//XTTM3grFIcuRW7wISM0v6bBpo9PWjG1hzYWOoI875bNtIWNae5yDTKSGG5YcVHfOWpF5Yl862T40IItpeuKkPG0VCeCagBqRT2xbOyuPvyRdTtBzE8Ua+TSZXutgURNzsTYn46MVb/SZbLUOqF45ZudKs6l6Sk86ylB6d7j0k9UoNu64DObtnfW2xHVWblVZf8Za7NozWIeiOoT377HLY7/atRIQ2Js2NZdndOt61yXZfzY1lSOc27/vE4lmlwTVuI1rDBeMbBdkWGWoxk1s2n2OdG0jx7fjyv81toI4XlhoYQX09Pz8PAm4GBeDy+vxHHrAeVUi995ZLsoA2FxdkkMVSI+f0Z20jUi5JM3JEh1I5d6OEB9L9+HYUWAj3bKw2PltbCIvFsRn73HZvrbRAULyGq+8kViZTxp0Oq3N/vPts5ifocR6LbVFJclifG0Dt3l9pRdXdNP84M8pdKhrCzuf9aQr1WVP2TTgnplf+Fsy6cH02zbV0EwzuO67q8NJzB0WApMeV1XM3x4QwRQxobuQrJQdbr8hqeBaTv1/fFt16bJ7/VYEBQDxoV8f0P4K+Av2/Q8epCxdRrNCGNiIwldT3tbVMDrynhNQ+avekI25ZGSN95+eDuOSAE2roerlyS2127B6UM9AkvlRsakMgyp6unnz7yu3znUA9URsHzT6lCg6PYAt/w6o9+t7l9g1weiXoWVkjUOjQgUV4mW9mDD2a0oypuHlXEKrazmq3Dcb0RVNG7s6o0dCKreXEoTcSAXd0m58eyOFrqVKaClK3z9814320bYiaG994bTtolO279x5urX99qQEOILx6PP9PT03N1I441K1RKvRxH/rItrTLvqlXRO8sjkVCoMBZmWaWNEB+dG2VaY2pcSKNrU4E4TNOLrGyYKejLR2qeY7O/ia2eaNEnO43sB4lG0aYlj59Kir4vly2YpUY8M4WcLWLmq6+VNZyjCfnZ0C01vs3bpkVuddlRrVE7K9/h2J+F9R2OTyWy/Nrjp0pu2xFVPHTwatqbInVFUAb1a+EzLhy7MikDPkpIL1PlezeRcujwyK+Wd9Bs/fpWCxatxtfT0/MA8ABAPB6ns7NzhnvMjEnLhFBptykbi+XraXpDN4QsnMsXZQzHtFChEGbXJsk8bRtrQ5eQQywKpkW4qXA8vfdGnIHLuOMjhGNN2Ft3QHIKp7kZp/8SOpOqvLy8IjxxsXbk3VaPLMr0JzUUmKZMmExNoGJNaH9PRnMLxro2nJEhlBWCbAYj2kT4hgMYpilfADuuAQ3NPf+F5KF/wh3uR0UKC511Jo2x/Wqabr0dVcPGX996O8nxkbrub1lWQ/7Gi4GZzvWTX/0ZWUf+HD7caoST1tz/xBn+6b+8kq51M0fAW9dHuDCWqZsA/du42vPFrXG78YxDR/P0L6nyaoWGBflbLef3wKIRXzwe/wLwBe9XPTQ0NO9jurYzLfXSTS1S17I8B5WrrgXHFUGvGUYrsO1c3nY93LmJ3MXzQmDdm8SNuWj1oj+yZm+9Gm7/ZTE9PXkUjDkscPH3Z5gWUEezw/Y8AS2ZH3ZtCTm0q70F5Wm06+K86jY4dSIvSnZsh9TFc9Ld9REKkx4ZQf/C6/PNIN+Oip17UHsOkBoZmfkp1Hn/zs5OGvE3XgzMdK6nh5PSbS0imlpfdy5w/z88x5ffcX3F64vTZl83PFt5cD0kmXMhU2H7XnnVxVAsyN9qKd4DW7bUV6Nc2V3dSqlXcYq6aZvU5nbulvGv5laxfcpmoXuT2K77hedYDDq7S7VuRamvPvpz1OWL6NfdKdMVJ16QlHLWUPU3RQyzkJ67rqyhNCyR49h2od536iVxjvYJ3zLzZgVQmobO145qLdpZzeRwXAmJdGVqKk+bDSUG3As1R1OexZY/jdn69a0WrGjiq+QkrJRCb7lKIsH2DsjlUOEo3PamvLPwtIjlwCtgYlxIcvBK6epFrwOqIhH08AA8+nciZeneJO+i2chaoNCwqAe+js+XwrguGF7qq3WB/M6fAucqkfCYXh0xnUL7Gq+tO8SiP8CcUO5wPBsrd5C53f/3h5d45kKy5HJXixuZUvJBtHX9iwsMNV2uUg/Mefr1rRY0Ss7yD8AbgM6enp6LwKfj8fh/b8Sxa0EZJvq2u+GpJ9En/kMiuXAYrn8Z6q63YFhVvsn2HJA317lez2nFhVwGjZ6+etHrgGqt4cJpcT0GaS7YWWa1Y8P2nSLLZnBrofhTltfq+fDsrPyO7vo26L9UaG5YpmygC4UlRQ8mK+aEcofj2aSlqazDxw+d5tJk5ZjOAUxPqmnWSWYhYNeGCMeHMnU3Rl5/VYyL4zYXx3Nz9utbTWhUV/fdjTjObKFdB777rzA5jrqmUE/Rk+NVP+jl2j9CTbKwJxSWKNH1jUHN/BYyQNLf/j4hmXRaSG+2EhVDFVXF52B0kN/Dq0sF0dmMSG9CYUmFw6Z48hV1WQNj0rmjmsNxLXRE5YZf+ll/VdLz4SAfRIVEgK0hGK0xsGF5Pa+YBcka1ZbiU/3463bUd+JrBCt6ZG0uI1TV7kMkImlrOpk3NJC9Fpfh8I/gzEsSDU5NiPPyXHR5blm0VhfKb6crdJIVZDIi38mk5NyG+tEnjqB7j0n6Hg4Fi8LnCN/heGdHuG7ie+jg1QB1mxHYXpfWoTbpAaRdOJnIkLSrf32WO7MEKMWKrvHNaYSqwn1KnFzcomU+vS+St3/PzSHCK4fvrgLzEzaXk6FhFHZ3ZLOIwYEjMh3twpXLcPkCet/N8zn7NQ3f4RjgrY8cn/H27U3yxTqT3dNs4RdWHF30/Vzj9hrY1BKUN8qxoolvLiNUec++oX4YTZA1DIn0bFtG1MwmaXgMDwp5ZLNeCux6FlXzeCMXd3O19hZ7zzCOVhNKKu+mJTW9SaewC0SHIOw1QpT3fH/wNI5SqEgssJKaB0JKhnZqXZ//v2XQyJ6t/7AGknaHPL+JbCUbKiQtfvDuIM0tx4omvjmNUIXC8NLRwqSGaYj3ne01CTZ0w87dMu+b89dNFrfbGmhFVRz91dXK84o7eWNSCmNsIOfqOgWb+lxWplTSqcJazOFB6NoUWEnNEiU28jP8rdqiBebb1x3j8sQMuescEDbJj6RZJuDoEvJTSKT34N07WB9be3KVmbCiiW9OI1TKkDpduOg+rhaiyAqJKqXQ0ZgQyNRkYdfuQqHudLesE2x40Z7rekYKfqXba34kpzyhmJcKOzYcPYyORiEcQcea876EAflVx0Q6xwe/eYZJbxFPLShgNKOZytj8S+8oA5ONJz2QJUDRok+vZar8h9k0FF/5lesW5HFXC1Y08VXS8YFHeh1ded1eCbQjxgPFAmWlJCpCw8BlCb4GPWtw1ynMzNY9nrZAMLzBJp+oQxE5P8eWmeRiUvQ1CzmvFRkKy2yvb6k/MSbPaWQINzGI+s/3V5f/rGFkbZePHTrLRMat2dgo9gp1XfjdQ6fon9INFyYrRPZSbVzN0ZrrN0QrXxkgj5VNfIYJtx+EMkGy2rW3Yv1Ku45McNi2dD8nMjix5sJoWCQq6WHfeYmW5E5SizNNcNI0NNWdLYrTXNf1OtB+RIf8i187RM7ZNzW1bW+63i3oD7Urr9nZl9Bfexj97vcHkV8ZnjyRIJFy6u7m+n+evqnGv08U4rrcHFIkUi6O1phFNW5Ha5pCBp+4deu0+xYvLMrYsk2t2qrJtYAVTXxQ/wiVr98jMSjvoGZvjWRyUtLFUEjSWr+eZ5qFpoa/Z6O4K7sUqGRmqrVEsSh5DigZy/NH4/K+fq5o/ICCwMFLf8MRuHQOHej8pqF8KU+1SMv2bKJCRnXHlPnCANZFLbSr6W5WbFkXpnc4g+1qLENx/YYon7h1K82R0o+1v7Bo0NvdoZTKLyyqtGpyLWDFE1+98PV7bOiS6Qt/OiOb9cjM9GaHQtIVdb19ur4Xnhxlic4eCtMeRYuOoKjhoiXiC4Xkk+eTpNfYzdcHraI/udZgFqn2V7GDsuNqjvQnOZVIk7FdIpbBtR1RDmysvUsiYzv5IZuZpipCBuxsD3N8uPE2T2EDWiOmkJbr8oqtzXWbhs5mYdFawdohvrMnYTwBI8MwNirEFmtC+5FcalLkLG0dMqGRBSEbb8etUrK4x7/vgu3WrfoMvDnfIn9B7xTzfKxdr1bp+/hpmYD3ydEyyd/ZX0Lud75NMy//WW2b1BxX862XRhjORzyyovHw5Skujmd4Tw3rpIhl0hwymMi6Nb/2FOKGcmF8bs0M31QUoKPJJJNzSgwGsi6MpR1aIsx6B8ZsFhatFayJ+Fa7Drz0griXaFfMC2JNkEyiU8minRauyD0y6cJ2M8v0UlzPFMA0PWfjJdDDu0XLGfw9IlA4/1yu4DJdvIAoHJYff7OXwrssKmRuO0L44UhhpG8V7eM90p8kUSXiGZqy+dnF0ar3PbAxxoYmi5kyQY3M8Dp1OhhMc0kB9nZF+Lt37OIvDu5EGQYhVfoBtTUksw5/ePvWWe3AyNi1/2blqybXApY18WnXwT12GPfQYzjfeAT30GMVF4LPeJzjRySlLe7iNrdAZ7c0NMZGZP52NCEOJ64rP7mc1MWiMRlps3NCeq3rl8hm3W9ueGmv8rX7TJ8EUQqiUSH4UFh2cIQi8nxjTXK7bEakPZPjQm5XXbMqN6mdSqRrRjzH+yer3vdNu9rIOrruD0q6zrdmJXocy2oMpfjzZy+RzLmETEXUUjQV/RiG4nM/vFLn2QgiVm2SLF81uRawbFPdhu5wPdc7vbangZEh+dA7dqET6ni28KYJeN1SV8viIteVVZNDnu+dbc1te9p8UfwZNozC7l+thZBDIYn2DEMIr0miWzJpIfyhK/LcYk0eqUdhchL6LqJzmVW3SW0qk+Nnl5MMTtk4rotpGHQ1W9yypRmt4ckXrvDIczlsx8UyDXZtiPCJW7cSMg3+8odXCJuK1oiJnXYqLvFpBAxgNC0i6d7hTEm3thimoegdnt1SpwMbY/zg/GRF8s/Ympdvmb7FbbVj+RLfDJHHbJxGdDYj0d3EWEG/l5yU6Q3HI7lYk9TH/I1ojivzPnZO9nNsuUoiw9ERSXkjUdHILQbx+ZGdP9rmukw3R7KEEJuahczyZJfyJlO8Tu/OLQUfv2hTwbqqs1vWb46NiNdgNaywTWqprMP/Oj1O2nYxlHQ0Xa25PJHlqV6bVM7FBSKmym8rOzaQ4oPfPM0bd7UxlLSJhkyiIZP2qM2JxMIIkkMmhAzF0YEUtlN7Gbk9S0PAt1zfwcnhNINTpel+xtZ0NVu85fqOOZ/3SsWyJb5G7nD1R9v0zt0FS3mf5PLee7bIWpRbMCTIZSGHkODUpLg0Q2HheNjbbrbQda888c0QbmhX1mn6TYxwRH783SBh73Vb3y7PKRyBnbsLH7JIBD05Vpv4lukmtWpb0La1hMi5ehqRGErlGxYhA7KeHZkvV8k4Lv/4gixkN4Br2y3Ojs7tS87yfPaq0ZUJtHo1u5wXddaqFVr1igo9hC2Dj71mS17Hl7UdwpbJy7cEOr5lh4bucC0ebevaJLOqtjfTOjmGkIp3W1/MXIxcVkS/Tk5SR8MoEGc47HVSFxCuXzqvAccmXwPMeUsLtS4YrfpEODUpdcqWVol4h/rzFvUAtKxDZ7IrapNatS1oZxJZTiWydEQN0rYmWUWE53rSzWoaPRc4OTK/yN70zql8zWPYFNLz3+sh02D7eotjA6mK6a7jaq7rmv1kRtgyeMe+zjXXva2GZUv1aqbIYhaRh9pzANXRic4UkaVpes0LsxD1+UP+xfCvN0whxVRSIkPHI5NF6XIWz+eaRY2N4pt45+N3etESjfrmpeGIEJ7rSBNHex3r0UThEL7O78oF6eKeFC8/rXXtMcAlxqefvlDRJNT/fTTtViU9mL2V/FxRfH4m0NlksS5i5Ukv52r2dcf4xK1baQoZOGVfwI7WNIetipMZAWaHZUt87NiFrrAhCiTyYMeuug+lDBN1+0HUvpslTdXa6+hGUM2tBdeTSiRWQjDe+FcmBevWSy2t7k9Ng+Qv/jRGJQ2hrmCmoLwdHNFYQaDtGxhAfv5Yaw29x6QGetVO6N4spzxwBc6fgb03ymu4DHV8F0azNUfKZupHLKYa0z/NprDCdhyGkjaD3s9o2uGFy5PYrsvn33wNe7tjmIYSQ3BDsbc7xlff8/JpkxkBZo9l+wrOyYCg1vHKRtu06+B+7jOooSvocFTIIFWhcO1642C+dZSBF/V5S73tOovdprlEHWCvNuh/4kIhIbtkUjq8frR7+aKIo7fIZjq/JADea66MZUl6MLctaOWotaN2rjCB7etM+iadkj0Xf/D6LfyPnw7yzIWpktsr4ErS5f6vn+bht13DH94x3UevJWqRrq6+CVAnli/xzdKAYE7Hv/4AVudGsmdekgjITwunkZnfScUjCiWkN5s641KQHniE60haHwpLhBprkrrlYD+0tKJHhqR2uWkzDA2g/X3CZlHHdxnLWMq3oJVcx8KtbqyFqAl/9eYddLVUloqcHEkD0/MABdgufPLb5/ibt+5e2JNcw1i2xAf1GxDMGTuuRfVfkn270SZIN0tXNE98FcKIYlJc1JG1OUIZ0lbM5aQ2mcsCYZnR3bJNTFezObh4RuQsxas1HUdS3Ykx9NX1lxYWG+Vb0IqxgC6KVdHVZJFzNX/03cv86V07Kk5Z9E86Nfdl9M+woCjA/LB8a3wLDLGouoA7MSZTHQohP9++SW41vZ6mdenExGJhtrmcX9szTUlpw2FJc0NhOVZLi0ShLx2FC6dkPjkxWLJEHZDfUynRLy5TfOb27USs6VFfLQnJQsH/QIUMlRckV8JyqjuuRTRqr+6bgM8hmcWX4vH4nzbiuAsJffwIjI4Q2nOA3IWzou3zZ3FDEYl2fIlIOdwGFJVmfcKz+Cj4hgrhsFjpW1ZhMiWTEUOGULiQvjuOyF6SU9C6rvJzW+SnW8tNxSwL7fwtaL6Oz5enLAV5rC8SG/iC5ErwnBOrYgkmwdcU5h3x9fT0mMDngYPAXuDdPT09y0/sVQ5PIK0MA+XvoN3QLYTR0ioTEC2tUg+rVE9crmmuMmSqZF2bzBRn0mK8AJ7ZakQiQKvsO8+34p+YKL3cX6retnjqft9N5fDlKbKOW+KmcujkSEVxr78F7dF37+Er79xdt3Foo2GZpa9rzqkc221sMasSn/auD7BwaESq+wtAbzwePx2Px7PAPwJvbcBxFxS6UmNiLOEN8HsuxeGIpL8rJfHwJzVyORnPizV5TYutsP0aIUS01PrKYRhC9iGr4ENomDLFsXM3Krx4duYzuakc6U/WvP+Xfta/YGagtdARmc62IbPyR+zBu3fIYvCyyzXBZrTFQCNS3a3AhaLfLwK/2IDj1oW5esdV3NDmOPLhz2ZAeylKLrd8ozsffs0x7DnIhMPy/3QKtu6Azm6UMtChsDQr1q2bfgzLq/3FmlC7SwN2ncnA7sWT/B8bmOLpM6OMpd28j+r6qMEbdq4nYhmcSqS5aXNzxftOZWyeOT2+aOdaDNMsfb/lXM2Nmysbna6PhXn4bdfwyW+fo3/SyT/PYDPa4qARxFcpqZjGFD09PQ8ADwDE43E6a5g/1gvtOCQPPVfLrp4AACAASURBVI47PICKRCVaAfSZ4xjjIzQdvBflvRm145B94WfkTh2HTBp7fASSU6imnTQ1NaFdTSaXQ09NoB0HbVko10E7FUbYlhOU8maMFSoWQ1nrUW3tkJxCZ9KY46OQnMRo78TcuYv0lYsyrF+U6mrbRm2SaQCdSWGOj+CODOc72OZV19L6qtdjhOv/MFqWVfVvPDie4X1f+znDSTv/gd/QZPGl/3wzYcvg8WPHRZtH4c01mnZ58sQIPTdtxQwZFY89mba5/4mfVtwxWwkmEA0pkjk975i+LWpiFEV3OcelsznCp+7eV9U7rxN47Dfqc1H2Uet1XW5YzufaCOK7CGwv+n0b0Fd+o3g8/gXgC96vemhoaN4P7B47jL5wXuZKk6Xpj75wlqlnn8bYe9M0iytA1kdeOIc7PkZuxzVw9pR0LzOir0IpdDIJ2fS8z3PuqKNEr3W+E60nJ9CWJS7TjiMTdZmMpLED/aLb23oVruOKD5+v1WvbIMcaGoCJMeyBK16tcx20byDnuGSe+OqsJjc6Ozup9DceSWa4/4kzJV1NDQwlbe798k/Y1qKw3crP3Hbh0LHLvGXPhorH/v1vn2Y4WZ+gvCUEX3jrLpojVv5cHz86xA/OT2Iol7OjWTJFihITCAHl74Yd60w+fcd2vnokITt3HZeQaXDL5ibed8tGpsZHmKJxqPa6Lkcsxblu2VLfF0kjiO8nwO6enp6dwCXgXcCvNuC4M6NOB5dKFldKGejdezASQx7pJWF9G2SSMgoQCkkHdElRbxziSWx8obJvjY+S8bpoTJ5X/yVo24+6ZpdYe4XD4lF45iUhfKWkprm+S45lWSJeRqFPHkVfOA3tG+ZlRf+xQ2erSjlc4PykrtrxVMBY2uXajun1xrFUlqOD9e26CBsF0itGsX3Trg0F4bFv31RrKU+9+y8CLA/Mu7kRj8dt4LeBfwVelIviR+d73HpQsUFRDP/6CgSptQtDA7iT40IIEa/GtX6DJ+kwvD225Sh2P15gWKGZb+PDdUWPaHv2876haibj6RQNiQxHR1C3H4S9N8L503DutFzesk5S5nRKhMymp98b7IczJyWKTAzM24o+kZ6ZzMtVkrroxwWeODbIP/7HIF99fpDHjw7z4wvjvP/rp+t6/IgBX3jrzorzrr5902uuakG5Dr1DKY4OpOhNpPnppUk+9dRZJtIL48cXYHHREB1fPB7/FvCtRhxrNqjYoCiG5+BSbnGltSsf5lQKYlFvKbcjqZ5pinSjuaVgz14sYlZeErYYdb9654DzqKQ5dMR01bK8vRu2TMQoA71pu2yU8xelp5JC9rmc3Ke5RXYMhyIiZC5aqD4XQ9h6kX+JKz8jXhxMc2Ykyy9d24aTtfnLHw7X1cW1FPzdO3bX3FcRtgzuvnY9j74wRM4tLAr3ba7e/41TfPGt19IancWXUoBlh5U9uVGng8s0i6uhgdLRrGJ5h50rOJcY3sIeRdGn0V0E0mtUNFm0h8N1hdD9LwA/Ci4is7z4zfcbBHkt8q9TKWHkywkNhq0LnhCVYCpF2tb87PIkP+0TrV89OLApVteSnlo2Vxlbrg+wsrGiia+izx4VHFzKCXIskf8Qu7mc1LUGB+RDnk7D+Kgn9o3lO6aLNqmhKpX1Z0KVP6Phj61Znjef69XsisoExWQWiRVsrcrJ3XbEsKAcs7Si74jW9zpWc0uJek/VVDA4ZTM4ZWPU+bep18euls2VoeT6ACsbK5v4KvnshcKofTeXdCCnEaQf5eRs9JSXBlqm/Ot61vOjCUmVI56B50y27w17UrMlWFU9QPRXZCrDs5oPwyteK/fyo+B1bTAxLnO6qSmvJpiR87A9XaPtiO2+R5olmKUV/UMHr675pmuPKNZFDFrKMkkDqc8ZRYzkuBqnzr/LxphRt4/dTAHkYhmXBlg4LGt3lnpQj4PLNIsrvxMZMTFCIVy0SDrCESni+82BSBQ2boGBy55bsyr48y0UEbqzPa6vhKtynU/ykWZ42SvEYevQY+hL52F4QEgum/E6wYaspMyk5fnGYrB5K6TSsGWr+PQVH30OVvTtTREefvtO/rcnzky/LqKwTBMLsMImrVGwnQLLJHNOSSBqGgq0ImvXfs0M4M8PXl33OdayuYLpKXCAlYcVQ3xzndDwUUyQrreDgwu9qJAlH3KFFPMjUVm07aebypCOpy8PSU1JlzT/gVwGX/8z1Rx9Mj16GJ57Fh2JwLp2IbzRYYmWDaOwdc4KwcbNcOdbMPbdLN3bxKBEv/5DVjCEzdouX32+n68fHyt5VSIKPv+fCt507U0ROmMmtXaq5ByXmzY389ylKUIe01iGIutoFJBzNDiaXB0vf2dMzWoSoqbNlRYz0QArGyuC+Bq1Y9cnT332JFw8DRfOkTNNrxZmSMTX1iHv7sSAt3jIlfTPdQvylryEw//U+enmAkaCNeF3A6qwgAbSSTh1XPYLm6bIVNJJIfWRYXluMX81ZYsQ4L/9C3hlg0qGsFy3V17Pc71k0hn+JLmL52mlvIKS0fC+b5zjS28tkF/IMkqiuWI4rstI2uV/niodPVtngaEMMjkX39Z1poqoAoZSs/ty+szt20uWF/lwNUQsuT7AysbKIL4G7NgtJk9xIvE+MrmMfHKamoTcLpwRYjBNSfkMoyDu9cllGsF4dbRITATDs05XG4BaUZ9CCld2UmZ1DSXklpqSaC8SkQbIhq7CfSyT5GSSzz75Ir3ZMLYTxTJfll+23RRSJV9G/2x38zwtFKhoerj02988x9fetYeJdI6JdI7JIrWOiczjAiTSlV+/cRs2hF0cU8qOvt6v2jP3z8AF/vA7F8jYDhHL5MDG2msVK9lc+bbxn7l9eyBlWQVYEcTXiB27xeSpB6+IXGN9OyqdEhFu1Fsons0KEdiOFwWGpdnhN0aquG2IgDgjBJj3MV2K6A/yEaiGgubQowcnJy3TXE6eXzYr6X209HklsfjQlreRnATT0tOWbf/V7iSxoi+jF+xWZpLhpJ3CKsh0mRO/Q3XCK4Y2FK6tscoisQqvABSetWdvpcg6Lt8/P8nJ4XTNSQzf5irA6sSK6OrWPaFRC8XkOZqQLm5TMyoU8lLBlMhZfAGvdguRoV8Dg1LdWzkcW358wqtk/7QoKCO7Yriupwx2C/q+XGaaWPqz619L0ghRXkAwlSKZc/m/j6VLvowyGEyft5gOXyNnKqYdux4k0nrGrmv5GZT/FSKWYnDK5skTifK7BFgjWBERX70TGj4qNUL0pfPQtVHqg46fJymMjk7csVFJUbXnrGyF5CeXE0LIZj3LeersZXi1PuW5Odu5JYz+KqHIk93fvm0XhWC2Q2+0G0MptOuCnUOh8rVQUyl67VK7pQgutTvMgmKN3Jwki0zvuhrUtnLf2T79bR6xFEf6U8GC7TWKFRHxzWbHbr6Wd/TnkMvmGyEkBuHMSdkfWyTaVUpJN7drkzgWx5qFSDNpj/AoRE/1fkiVksfwoyq/gbLcoF1v0bhV4CvThK6N2MrMfxHIVVpKAh5B2mVvnf3WBDO9QFFzZo1cPdjeFi4lPjX9jayQb/Xd7RbhUOWaXNYOFvqsVSzDT+N01D2hQfVGCBu6YGochvqlc1v8pvenEjZ0F1K+klpdjdSxEsLhAoEaSn6PNi0i+ZVHXTWisA1dsL4dIjHU9QekW6sUlr9qs0hyIgGapMeWoUq+jN4cGuRGxijpdJfhr968o2qJtF50RFXF5UI++cUs+Mo7ruWJ+/ZwYHNzVdIDCJcvVgqwZrAyiK/OCQ2geiOks1vWSA4PQudGsWW3HbRtF6YStu2E1jZPvOzX9Yq6uHVZMHlOyFZIyM80xQU5Gl2Ebq8qcpUpIp6Ky4MMOcfmFol0N29HWxZ64DKcPsGuqT5cVwTQ2nXRWucTWcd12NXdUvJlFDbg95vP8jbOo8oSz4giL2Upj9Zmi//rrm35ruvOjnBhvFjBzo5wiYHAgY0xslVm3zK25sDGyjtvA6x+KL007sK6r2+aV2lD4HzjkarCWK1dGOxHbd2BzqZhNEEkGiMTiaIiMUmpd10P/+2PCzo+14bJCS9Hq+O18pf9RCKFpklrK4wkivR/CwjDKDQvqp+kl94q71yjcN1++dey4PCPSdqaD93wPqbMKKbSeQG3Yxg04/D5d+4TSUuZvs8XlXd1b6xoQul3dX2NXLWZ3EowgZdtjvGHd+yo6/ZZ2+Wh7/cxOFW6v6PcXy8w91wYLKER6YyzNSuiuTEb1GqEKGXA1h0YB9+Zv2x9ezuDzz4tjZCTR1HnetGd3bB7j9zgzEk42yvND8epw5lFFzzx7JwQSSrdeNJTSiJQp0wX4hal5HlRc1kXwTLJ7wfWSsbyXBvOviQ3c2yaDMVfnniYz171ZnqbNmMbJhaaPe44v9sxRHPkRnmIWS58L9fIGbrQmKjU6zBVwRzHMhS9w/WbIvj+ek+eSHCkP0XWdghbJi/fUlvHF2D1Y1URn3YdNEqmDBSSZq7vKCzbKZst1a7s7NAXzpVMhJBOwcnj0NYmY1xFRf2ZT8ITM7ueDrC5VbztFgLlZGoUtzuLJznK6MT1Os7RmKS511wnUx3hiFh2eYTaZNj8H+e/7h1OQUcnTE0C10lkXWNsMGu7PH50iCP9qWnC4XKN3EQ6lyfCbFGganolRlMhe0IAe5Z5ctgyeMe+zqB7G6AEq4b4/G4uE2PyAU55OzgGLsOVi2hLJhR0czMu0jDRx4/gJganN0I2b4PeF+H8Gc/Oyak9ElYOhWwti8Xkl9wCuPYqo3A+oZCQfFMLjI3IZbX0hqZVSGv9Zk7J7bUc0xc5g5Blwktb2jaIGDiT5ZuHr/DCCzaZ9Z1EQkJub9rVxkOHTnBpdIqIpWYUDhcT4a89+lLFvbk+rMAhIEADsGpi/Xw3NxqFnbtlH6xhChGOjsiHfOdulG0XbNPP/v/tnXlwHNl93z+vu+fA4AZBkASv5S654nK5pxRFinxkda+0kiNZ6ZJTTnwlW65KFMexK46y5cRVKaeckhNbZbvKUTlOKrGSuKN4I5ftzUqKbZUcW/dyL2pv7i5vEMSNubtf/vi9np4BZgYDYsABOO9TBQGDGXS/4Qo//M7v76X1IqWYFpfjd4n3F4ZJsSDWt2uF45hmZ1f28q6uirZfPEvbLeqv5Xly/VRdQUevCXfXFjvCajKl4riJ8jRI4k0btZrBoeReUSh/UPbuF0MWwa8Vb+OvmKRUCVGFfM24ycrEYtO9uBs1Dh/fkyFs8Qcm1JrjezYng2WxNOOWMXz11VylFGrvfmlbGR4TI6ioySrVZnwvNVfSlX0cV2WWFcRweJ54Sms9jnqRUsfkzsolCY3LRTPJYX6R3S61T9TL4MeqMfGUSNw/GBOfqT7cjXsT4zzkwnVJCYShzBtDolazZ68YwZExqVKPiRjpH1X2MhulySiNqlNszniK2XyVxULz1EDcONwKmQN21hm/UGtyKadjMVGLpR23jOFrOtYWj6bButBPZTKwsn7xdG0fx8wVMSJRJEYlMoUNL9XoQem4sdlo9VXjLWdOku+r5du66PXpKGmQTmfESDmmlSa+T9xIXe8hKif5/sCAGOblJWnnyQ7I+xseTfocYxFSL9UgRvpcdZiM0o3nSR6wUmqdE23XODyY8fitR27n1NQArqPQiO7eqakBfuuR2zsWE7VY2rHr/19UG0879xK6XE6WBU3uS0bToLm3NTS63mDG+zhAvKD4a8+DSlXC31pFlCQ01DT26dVLVzkKQtUiV3iDc1vxaJ1y5L7FAkzslWtdeF0WKWmdnDe+Vzojt8xkpRDiOFAuSfFn+qi8fmwcLl+ElUUYH4Hpw/KHYHyi5jWXcBrNeJ1IqaMUUZt86EaNw4MZr+OWFYvlRtjVhq9Bamp4VHbEKuSXdHnJDHVG4rVM7F1/genDqLEJ9JlvwuxlWFiQ8FYZ2aaREQlZw6rJ7VWNB+gm4aIiCSfXtpaAqbJGScXV9ZLX1QzoDfZSauONZnMSXl+9KLk+Zfr04hA39nYVYtiGRmDqgIgyFPKQysgi8uFRuWaljDp4pKFiG509g37uKfTyPCzOkRmZouSm5D6OhxocrB1rMOWw2kIhtFTVvHXaNg5besuuDnUbxtPiUK0aSnhbyANGdqnJvghdKsGRO8SQXbssEx3xKkmA/LIY0HJJjMvAoBiVVFoMRNYsInLcFsZrjTBSbISqlboROG5cvCCKxChnMnKNsJrs1Y0VZYZHxIDHnqFGzjo0bJqXB+DAYXj790kD98oSqlppvjv3zlMwewWuXIAw5HTpMmWtTD6zArnEmA2lXaaGMpTWdCfHjcMfeUuTpUUWy01kSx6f7/t/G/gl4C7g7UEQfLsbh+qYhoKGgz52QkLVRVM1dB247YSMt9WFYvGMr1ZQffn5ZL+GiuXXq8ZLMuFsHCYrBcqDfQel9+3rfy55xHWjaE1yedsxIaONqoqXEiNWKYvxK5fkDK5LrRodq8XEhYiRMZjaK8Ufx2sp9Lo6c03ESPOK6ugjeEMhx0sz/Mz8/+Pl9BTXMiNkXAfyBRgcpFTV7BtO80sfOs3nv/GKbRy27Ei2Guo+B3wc+A9dOMumWbsoXClHVFb27jcv0KiPfHLdWJU6fkqEDZ58nGhlSfToYsPoeonh0BFoEybu2SuGZiAnn994VYxMKm1kncprjNtNGgVcWZYm6bj4Qp0nmV8Vgz44bCZPoiQ0P34SyhX5A1DKN51vzlfhU9GDIkYKKBSh63E2N83P5j7Or2ee5yva4bnqMKVinszIcM24DWU92zhs2bFsyfAFQfA9AN/3u3OaTdKJTl+7LWxRHKbVN8ymUlDE5PBIwtJqVfJZ+dWkSbhYJzOvMXm8NjO9W8nntUSbAowxymFVjFs8uqZNy0p2QBqSw6rs2nj2O7D/EHpwEC5dQMdahSDSXbNX+dXoNPkMOCpCK4VWiqIRHC3h8GOl+zhAkV/JvMhISuO8+21dfm8Wy/awu2OOTej0NUPFKir1vXkKkw+LklG1uMCRGxaPsFRIVJqj0FRw9cbqK9slCBFWGltWIp20usS9enGvn+dJcSM3iJo6gKpWG7QKtda1dp5X0ntry7q11hS1CZ3BfHa4zAA/VbqXJWxjsWX3sKHH5/v+V4D9TZ56LAiCL3Z6I9/3HwUeBQiCgMnJyY4P2Qr9rofIL80TXb+KymST75eKOIdvI/euh1BtmoZL976VwtVLONlZdCEPStU+i2yTC5ksKp2GhXlUJkNUzJup+rD9WNjNxvNEPNX1TDXbtM5EYa1SS6UMjoMThZBfJZvNohyH6vQhwotv4i4vAhCGFdRAlqrjmuVxikIsFL+mHVGhqOLwWOU0v1/339TzvK78N74Z2LNuDzv5rBsaviAI3tuNGwVB8Dngc+ah7pZcjX77D6yXRjp2EnXyHgrz8+1/9sARBu68m9LsVSlqFAomVFTJqFc6i86vQlhFxxVZxc2RmNoMxbppCMcFJ16HqaXSXa3UmqqjpUVYXiL/rb+Aux+QKnUmS/XS+aRpO4rwoipVx0umQ1pF8CgulWiQILLySduDPWt7jCzVhuzqPj6gbQ6vk58d/PAnWB0Zh299DZ7/rhQz3BSMjiXKKquLxpjEwgDGEOxE4lzk0LCZGa5Q0+eLG6jR8h7n5+C5p+Cet8p888wVuHxeehk1HB+5ytncwSQf0rLXWu2EteoWS8dstZ3lY8BvAHuBP/Z9/0wQBB/oysluEsp1cU8/CKcfbCpiql85K4YgLoAU8+IVNSzDXmMR3Hi5RC/MgU4qvPHWN9cz6zJVMkaXSokRXFqUueTJKfEaszkpkgA/t/SXfCrzUVZ1pq6lp/Y/DezQPwMWS1O2WtV9HHi8S2fpOU2rxPHgfn5FPCjXg6hsjEmVdcZNmSJC2Llg5papVZNJwtKwahacl6Bs8pZOWvJ/6WxSDFFaehHj1pzcoHh+nkuOKr9x7Q/5d6Pfx1PZQyKMV2fi6lu09w/Z/RWW3cPurup2m2ZVYlf27+K4kj9MZ5KQNzYeriPGLm5X6WTPb9cwxshxkqKMG8vP69pKSLI56elDNVaflZOoyRw41LCPBCBHlV9c/HP+85UAz1Q26s2fBjwH/u377WytZfdgDV8dTbe5jU6IF7jvIBy5XYxgOisG0TXG0PXqvr6Jnk9tjaVOjB4kEvmxnJY2wqKxXqBn5LV0JO/nwGE4fJvIeSnVqGeoAcdlZGKU3/3Y7RwYchsaWg4Mufzu37qd0YEmC54slh3Kri9udBPluPDQwyJd/8Yr6FIhKWrEEvTTR0QA4dKbUkAYHZcc4OKCUULpwcF1BCol7TeRKWKkTH/i6Li8h1hcIVZUHpmQGebpo6jTD0pV3IT5SqnGCRiAVJrR3AC//UMnevAGLZbuYj2+NSjHxTl1P+oDH0MNj0mz76n7Yd8BcXGuXRUDs29axtiUSiTaBwYbhUlvBrHIQSxaENb17eVXoViE/QdrAqLyJhGPbvooas+UjO9tsRncYtlNWI+vBesWk9d5QLpUgvwyanxS9tBevybFAdj+/r54b248XeLUhbhRmIzOgVSilxdkg9o7H5KdGQsi4KCmG2WnOHkPXL6AXrODpNnSdotlt2M9vla0WkyOUW/WxgCee0lC32olEf3czl3FsYenQ/HyUhlpQI69TM/kG0MzRud68vWz3zXV2xLsmVq3Ha1+abv2UuiZK+jXXoTFOXQpL38IdlrTtsVyg1iPrwVrlV/iwX0W5pIFRMsLskworGyvsVtLveCp45hZY514eq6b9O+lMzJbPHsFUkekYjs+gX7+Kbh8AR56uMH4ied3Hj06jpoyOT6zoGnt6y2W3Yr1+FpQv32tfnBfZmARY3Pt6hppdxok2OVxfb6vy7k/5YgRDk2IW79LN52RosbKsszwhiHs3Seb5pRTW7ikX3i24ZLrQvz4Vi1eb7HsRqzha0V9sj+e5Y13RVRDaXNZWTY5N7fuw2k0dsq0kAzkpIrazXYXbXZtFIwsFYjhK5XkI+XJmVOm5WbNciWVyUg1t56NQvy1r7dYdiE21G2Bqkv2N2xri7eOTU5JXs+LJzhoHu4qBdms2XurZGER8Tibqpt228LejcioRaOTHKORj6dqZLNSaRFPvXoJPb5Hzj06IZXp+sutCfHXcVObsy2W7cEavhbU9/TpN1+Vb7quLC2anBKpey8l6s2eJ4alVDQ6fWvmdotFMUDZ2OOLEkmrOF9Wn7erraakde6wVsAgaVaO6hSjqxVYrpjXOknbTdVo941NSOieX0VHYZLn60Dc1WLZ7dhQtw21nr5T96Peco9I1u/dn+zvGBtrzOlp3bg8qDYP65jeuWnZf5vOUFtPGePUXSedEWPqpZr0BKrEe0QlY3JrjVVNhl5LW0sxL0YvrOvxA0ilG/N2tp/P0gdYw9cJrYzB9FEJYx2zza1SqXtSJT13rsmz5ZdN6Dsg28/qFU+8lMzSup58HhmT16XSxitU8tlzk7lgSPr5oLUwaq0Fxii3lEqwuip5xwOHGvJ2Tcf2sP18llsLG+p2gGrR3MvIOIxPyeLtwqqEurWlRU5SdXWQ52ZNWFytiBHbNy0GrFhIpOLL5UQzL52BkVEpXiwtSDgcqSS3qJFWmhpt8oRaSxicSsvXrmMqvKohb7d2bG/tgibbymK5FbCGrwPaGQM9kIWnvgnz16gVFxxXPDgwOzuMcUqbnbxz18QgVsrJKNnivBhHp64HLztA4jkaUYRU2rSwxIvOnbplSRsUSJSScw0MgOMkRYw1ebutiLtaLLsBa/g6JG7u1SACBuUSnHsBvvYVCR9dT/Jn2nzEMvhxlTVWQHYcmDognl2pCFcvQykP1bg6a7akUZJrrK5QWwJeLiXLwR1HjKrjAPF9O3gj1QqUHBgbBEwIe/zUdv2zWSw7Emv4OkRHIfrP/gQ9N4vKZFBKoV94TgRKXdfo4LliwCDppwMzXeHJrG86K57c0DC89pJ4erGqsY4aix4a8eq0lobpePyssJrk6xxHpKVi9Zh2EyRam3WaEQwO27ydpW+xhq9Dmk40zF4Vg1cqmm+opBVFayCSEFWZSmyxIAZKa3kcb3RLpZM8W22nh1lSHlduCwUpegzkoGLC6Mh4iakUFDvY2Rvn57QWGa1T96Huus/m7Sx9hzV8nbJmokHrSMLQeIdFFCUzs0qZvFvspVVMj18pydWZzW1A3W7e2tUbv9ZavpUyBi9uevY805qSb+wDXEssWOql5QcdF4ZHZHTNGj1LH2LbWTpEr51YmJ2Rz0oZRRSzrNtxAVOM8LzESMUhbKUsnl6pYPrqqu2NVj2xZ+mlEoPruOIZtvP24nvH+0DGJsRI2/EzS59iPb5OSaXQly/AolFnmb+eLPGO++sGBpPXLy9KaFop1+2qbdFn13Yxed0Gt7gSnEonzcjpjKka112jvrcvvkY6LT2HXkqaqF3Xjp9Z+pa+Nnw6Cik9/S2iZ74jM6rpzDqduvh1zM/BlYtiQECMkOsmBYjsgHmxFm8uMtMU1WoSCm8aJeFtpSJ9d2CUVupWRiqzPMhxkTA7FiGlTp05lDA3NySFkDCCPRN2/MzSt/St4YurtOXiKoSR9LRVyk115/QLz4qRGBxOVFocBbhSbFCOKKEUC2KYXE8MZGTEQHXdjt36ETStAaduJndNuOo6ZrzNvCaK5OtsLtmtkU5DuWi8wGoS8q5d/j0ymhi9gQEYHrPjZ5a+ZasLxT8DfAQoA68CPxEEwUI3Drbd1Kq042NSHDDEunO88Kw08YIpbGTQx04kYqSZATGCwyOyl8NLiQR9KiUGJwqhHCVV3toNnMRLUyTjZ5ERMSjkTW7QrIzMmJG4lWUjNe9KD+DIaJJfdIxggWfaXeJ9v/GOXYW03VTKcOxOGBlPdm1YLH3IVosbXwZOB0FwL/AS8OmtH+kmsQndubiwoZQSkYITX291lAAAFxJJREFUp+CBd8haxsyA6NwV8lK1jauvw6NiDGtFDtW4iKh+HaXrQiYDh46JQYsFCDRisMqlxteHZhpk6oC8fmAgqfzGBjP+cI3x9FLSS+imUKcfFJn5bajo6igkOnuGlT/4r4Rf/DzRE18gOnvGytZbdhRb8viCIPhS3cOvA5/Y2nFuHpvRnauXamqUoK+KsVtdljBSIYUDkLA3NG0q8ahYtSLGyVGSb5vaLw3MxYLpzyuLSvKVyxK+xjp/2QGgnAgZRJGpzo6LJ5gbNnlAE26vHZ0bn5SG6WoIK0vbNnNb3+TN+Hjb9IHF0ku6meP7SeD3Wz3p+/6jwKMAQRAwOTnZxVt3jg5DSs98m5XvPYNeXqAAOIPDeIeP4e6bRjnJ/OqQOWPp3rdSfvqb4KWpvHIWnV9FeR64abTjEOkId2gYnckSzV5FV8oox0EPDkG5jC7mJQenFGSyuOOTqKFh6TIZGSPKDqAGh3HMAnDngb9O9c1XCa9cFONXMY3M2shLmbYYd+Yy6be+k2jmCuVXX0QpRTQ/K4UVs0Rc5QZxxybE0Uyl0KU8Q5ffJHPfX0OHIeXnvkvl1RckN5nJkrrjJOnTD6JuQCm69PS3KJfyqPFxHMchl8vJE7kcurhC2tx3p+F5Xs/+/7hZ7Fm7g9IbdPv7vv8VYH+Tpx4LguCL5jWPAW8DPh4EQScTo/rSpUubPeuW0VFI9H//CJ7+pkwulPI4nkdUqUoIuf+g5MDKZdTdD+CYHJ94Mk+gX37etLGsUWOuVsXjKhdhftYIgurE00tnJBd4/zvEC3vxGcnTpdPwlnshqqLqWlq01vCNr4oiS1zYCCPxMOO8Hkbe6uARkce6+34RR/3S41J9bnjjZuQtMyCNy6ceQH3gYw0jeLWXxmNsN+CdRU98oeYZ53I58nW5UwBSaZyHd15QMDk5yezsbK+P0RH2rO2Znp6GDpbbbOjxBUHw3nbP+77/Y8AjwHs6NHo9Q7/wLLzygoScw8MQVtBRJLmwMJS9s9kc6sTdDYn/WJ1Fn38NMumklcWoMTM7I2IDc7OJvFScoyuXxFgNDssgx8M/DA//cMO5wi9+vrHaO3vVCBq4cq2wCsrs84jMWsk4N3jtqhRn0ikxbG+5V2Z5l5eSRecgXuDKMngeulQQZek2S4Uaijud/vta2XrLLmGrVd0PAr8A/GAQBPmNXt9z3ngFCsuJAOjYBE65RLiyLB5RtQLZgabejnJcGN+DmljvuuvJfZLDCquSu6uYiYzIVFpzQzA0gmoQKq27djojBZQ4d3jtiskHxn14GjzjPYYqyf2tLIk3mckkOzaKRVgwKy+jCEqVpK0l3ry2MN9RcUefvEf+WBg1mlZ9jvXvw8rWW3YDW83x/SaQAb7s+z7A14Mg+Oktn2qb0OVS45SEUjhDw4SZrHkBqPE9LUO8Vr/YSin0QM7s1HBApcHJiAeWGxSjtDQPB4/KbaKwwaAwdx3OvybVYM8TL8/zxEOqVmnw3ONiSTaXzOAuL8G5l9HHTqCyWXQmAwurxnCqZFRNKQmfp49s6J3pUgHWqNFsWKg4ehz9/JmmBtXKX1l2Elut6u6qDliVzqDrl/SsJW4ZaUWbX2zKJThwJGk9ya9IhTVWTlYuemKK8LnvwpXzMD+XyFuFFfHeHAcmJsX7jBDjVq2b5Y1CCXkHsibPV3fuuVlYWkBnc3KtMBSjW7/LI54eKZdh/jp6biYJ20cnakuUAFiYR1fCTYXCDZvp4sIGVrbesvPoL5GCo8dhYLi54auGkodrM83Qbh8FQ6Nwx51S7Ji7JsothVXxEEMzpzt/Hb72JXj625Dy0NeuoF8+C698T4xdvCc3Y8bfBofgwEHJI04fxhkdl++NjAE6WRq0vCjXXpiX71eNmGms7wdy/cEhmNgD5181UybGew1D2bh27mW0juT9KDa9X1c5rqQJ7n4gEWFNpVF3P7BtfYMWy43QVyNr6uQ96ItvSlW3Um6szqbTcPxkW6+krQT9qfvg7DMyxZGahcqymc4w4ejYuBib5QVpbXnuKaPo4ibjaqUylGbF2MUGE8QQZjJEVy8l11xZorZWMg6FK0XJEcZFEeVI+B33FoKExUrJkqFYH9AzIguFAly6gLrzNDqWwGpFi0JFLFs/NPleiruk+mjpP/rL8Dkuzns+THTgEHz7L+D6jBif6UPwtu/D6UCUs9U+Ch2F6CuX0OdehJERqBqZeB3J59yQGJzFVchGYnD27DWeYV4MXdx2ks/DqGlOXpyH8T1izAbMjG65SG0YNwyT/R7KrdvvkZGXlAqJ4auaXR3jkxJi14/gxXO/uRzqoYfhycdtocJyy9JXhg/EcLmnH4TTDwIw0UGv0dpiRLPqZq3l5dyLUsiIBQUGhpICR0w8XbEwZ1ZS6qQQEWmRvhockt6/cgkmD8DgMJmJSUrnX5fnUxlpZi4WTXuLA24qGZkb2yNTJcWCGNd4B4jZ4qZ1JPm8vfvlo/ZmNcpx0bZQYbmF6TvDt1ma7dpoVd1Ujos6eBSmDkgHSbNcYm5Qws14P0ZcfIilq+K9GvlVCXEHR2BhFsYnoKglxI3bbzxTBY4iUJ70GGZzUlhID0DetLhkTLhbDU2jdUnyeUMj4lHGBY6xCZk/hpYrNW2hwnIr0F/FjRug6a4NkuqmfuHZxh+Il4+PTqw3fNUQpo+IwYo9Qkhk5FMp0w5jdt1O7YdjJ2qGKrx6EUbHpG8PjPdmRAjG9wBKwuaFeZnNjeXmXVdC5fh6KJnueOM1UylGPl+5INXeKGwsVMS7eG2hwnKLYD2+jehExaUu31fzlHQk1dZCQQoH1VBydCPjcPcDUiCplo3UlAInZSSttHhng0MoE4JK/2GEGsiKxzY8Ku0ysZdYLIhQguvJ9dI5uHoRiiU4ckwKKmEoIfLIuOQB4ybomLiqnUqjTauK3a9ruVWxHt8GrNu1sZY1z9c8pdMPwp13i9pKKi15tDvvFkmoT/4U7Js2vXP7RD0l5dWEBcgN1aZLdKkk4W59r3FuMPEaK2UxeEMj8pyXktzgvoNytvPnEs8zDGXErZgX4xpf0028QZXN2l0cllse6/FtwI2MYXXiKYXv/hB87cuJpFWlIgZoaEQM1J69tXya9lx441V0XLFVSvJxK8tytnRKdPkOH6s1IetrVwC9vu3EM0Kmxhg3LVLYmVrLLY41fBuxTdVN5677pP3FFA9qOn/Xr0kT9IFDqNvulND5ycfRx07gLi9SnbmcqC0fvUMEElLe+nMszklonV9Zf3PXlZaZw8eaH862qlhucazh24Dtqm6ubYZW5ZIUPt757vXLjo4eh+fP4O0/SHlkXPKHszNi3K7PwNCweHj1I2fxyFqlIvk7r64Ykc6YNpmpdeeyrSqWfsAavg1Ya6B0qSBVU4VMNzz5OLqNYslG1+6keFAzvsUVMXrnXpaiCcj4muvJyNnyoggVKEe8ulJZqrhKJU3KrgtHbpd2mXIlqRBjW1Us/YM1fB0QGyh98h5RLKlUE+9vm6XV4+ZpXSoQzl1PRuVGxqWFZc9eeP1VaWMpFMQT3LtfGqf1quTxlGpoUtalErzzITGQdaN33HFSpueefJyoAxkqi2W3Yg3fJtiop2+z4p0bTYSsbZ72DhykNHNZJjQ8Lylk1I+eLcxJyPz976+pwDT16uLxvDqV6ViGik5lqCyWXYptZ9kMm9jMthE1o/b8U1ApNxga/WdPJJ7eWkMbhomgwOyM3Ltu+5s6fhfOw5/APf0gzrsfqTUgax2h52dhdRm9PI9+8vGG7WebbtS2WHYx1vBtgs329LW9VjtDc32G6Mn/LfszXn8R/cpZkbCKJedBjN/i3PoL11VklePinLof9YGPoYZGITeMmpgUz22Nke2mUbdYdjo21N0EXZVWb2FotI7g0hvSawdm0ZDo5VVKBVFtuXZVDN+akbhWFVn9wrPo67OwPI9enGsQH9Vay/4Nuy/D0kdYw7cZutjT19LQzM4kqiuxVL6SjWp61YN0VvrzCnkRJai/f4uKrH79Jbj0ejI+B4n46PIiemjI7suw9BU21N0E7RSYN9sGoloZkoU5WFoUw5bOJFvbVleIluZFTeXYCanoTkx1Jh5w8U2Rpl+cS5qkV1dEEKFQgEvnE3GFJuhSqa0ytcWy27Ae3yZop8C86ZaPVt7j8qKEuUPD0oBcLpk1kYpodcX03xmp++9/X1KdbYGOQnjzVVkYroxas9Yy0VEumdG3JStDZekrrOHbJN1SLGllaCgWjBLyoDxOZ+R7K2Z7p+OICGm5CH/434n+9E/g3R9qqR4t1VhHpK/qI2vliErL8jIcnequUbdYdjjW8PUQvX8aLryGfvNV+caeKVFrqZqQs6bQTLJVLYxg9oosM/c8ESb92pfRVy4177V74xXR8CusyHwuiNFzPSlwRCFMG/FRK0Nl6ROs4esBcQ8fc7Oo8UmRpcLk0pYWxKDNzYpH5jiyFBwkRHUdo9C8ItJSniu9eS0aqHWpYKTulSiyVCtynfjag0Oo207c3H8Ai6XHbMnw+b7/r4EfQrbAzgA/HgTBpW4c7FambQ9fOiN7NzzPbEszm9a8NIQV0dtzHDFm8RKhMGwqigrIXLHnSvhcqYjxiwlDQNn8naXv2GpV9zNBENwbBMH9wB8B/7ILZ7r1adMszIFDsgktlZY53Ml9IkzqeSa/Z35O6+Rn4qbmZr12CtBKlg8NDiVLj5SSPOL0YZu/s/QdW/L4giBYqns4iKTQLRvQrllYKYU+cpvk92ZnEnmpahWVSqGLhWSF5Nw18QSP3iGPm7XIjE7IvG4hX5O0BxIp/PGJ7r9Bi2WHs+Ucn+/7vwz8PWAReKjN6x4FHgUIgoDJycmt3roreJ5308+yMj7RfhIinSH1tndRfvqbqHQGHUWUXz5L9dwrEq4q0weoNSoMcUtFUkqRvvetZNa8l5WJPejcIOHMZaL5Wdm14Xk445O4UwdQ2SxD2/D+e/HveqPYs24PO/msSuv2Tprv+18B9jd56rEgCL5Y97pPA9kgCP5VB/fVly7tjFTgZAd7dbtNdPZM+wmQux9AnbxH5mhjheaZS6gLb4h6CogXmB2QMLhSgtvuxPmRf7AubO3kXs42VHF78e96o9izbg+9OOv09DQ0Nm41ZUOPLwiC93Z4z/8G/DHQieHrazppFl7XV7e4gLdnL5VDt8mLl+r24e45BOMTTXN1tjHZYlnPVqu6J4IgeNk8/CjwwtaPdOvTabNwfV9dWC6RHhykGvfiTR1ovGjc73eD97JY+omt5vh+xff9tyDtLG8AP731I/UHm20WbjnbG9PmeduYbLE0stWq7g936yCWDTh6HP36i8k2tvodGoPD8P3vu6HLbqQCbbHcitjJjZvIVoyMOnkPzuJ1+Ms/lSVBnisp3HIZ9DJcuoDeQLCg6XnqpO2t3LylX7CyVDeJTqTm26EcF/fQbZAbSXT4XBem9sOJk7BwfdPy8FZu3tKvWI/vJtGNRUXVcy+hDh5u/mSrkbV2dCI3b/OCllsQ6/HdLLqx06JUbP/8JuXhu7lDxGLZTViPr4u0y+F1ZadFJpssEsfs55idEWXlMIRUmujsmY4LE1Zu3tKvWMPXJTYsFKRSMi7Wig6MTOqOk+i//CoqI+siOfdyskejGsLEaNPCRCuDrI/cDmef6coOEYtlN2FD3S6xYQ5PuVveaZE+/WCy82N2RqSpYqM3kIPJfesKE+2KKly6AOPjXdkhYrHsJqzH1y02yOFpHYnRajE6xp2niM6eadvqolwXZaYw9JceF70+x4Upka+KQ+n6wkRbg7xwHe66DzV91E51WPoKa/i6xEY5PFUpoz748aajY9x5Cr76ZEf9dPEURvjy8y3vp7WGi28SPfEF9Nkzsl9odAImp1AqcfJVJgPnX8N5+BO2emvpK6zh6xKdFApajY5FZ89sutWl1f201pL7K5ekxy8MpdE53qF77ESD8bOVW0s/YnN83WIre2lvpNWl1f1mr8Lqsqg3Q6LO7LlSCJmdaXy9rdxa+hBr+LrEVpaN30g/Xav7cf2aqCxPTsnjsQkpfoAYv8W5hrPZReGWfsQavi6hHCk8qLsfkL0YWkMqLaKiG8y83ojySqv7MTEJ9eHs5D6p+MbGL5TPtnJr6Wdsjq+L3LD809Hj7VWSW/TTNbtf9MQXGnJ/Sin0sROJoguIQbaVW0sfYw1fl9iq8krXVJKbGFGlFOzdjx4Z3zapeYtlN2FD3S7QDeWVGw2T111rC7lGi6VfsB5fF+iG8kq3VJKt1LzFsjHW8HWDHSbvZKXmLZb22FC3C1h5J4tld2ENXxfYyiIgi8Vy87GGrxtsZWrDYrHcdKzh6wK2kmqx7C5scaML2EqqxbK76Irh833/54HPAHuDIJjtxjV3G7aSarHsHrYc6vq+fxh4H/Dm1o9jsVgs2083cny/BvwzQHfhWhaLxbLtbCnU9X3/o8DFIAie9n1/o9c+CjwKEAQBk5OTW7l11/A8b8ecZSPsWbcHe9btYSefdUPD5/v+V4D9TZ56DPgXwPs7uVEQBJ8DPmce6tnZnZEKnJycZKecZSPsWbcHe9btoRdnnZ6e7uh1Gxq+IAje2+z7vu/fAxwDYm/vEPBd3/ffHgTBlc6ParFYLDeXGw51gyB4FpiKH/u+/zrwtn6t6loslt2DbWC2WCx9h9K6J8VYWwG2WCzbRes9r4ZeeXxqp3z4vv+dXp/BntWe1Z61qx8bYkNdi8XSd1jDZ7FY+g5r+JLewt2APev2YM+6PezYs/aquGGxWCw9w3p8Foul77CGz2Kx9B1WiBTwff8zwEeAMvAq8BNBECz09lSN+L7/QeCzgAv8ThAEv9LjIzXFyJT9F2S+OwI+FwTBZ3t7qtb4vu8C30bENh7p9Xna4fv+GPA7wGmkF/YngyD4q96eqjm+7/8s8PeRcz6L/E4Ve3uqBOvxCV8GTgdBcC/wEvDpHp+nAfPL+VvAw8Ap4Ed83z/V21O1pAr8XBAEdwHvAP7hDj4rwM8A3+v1ITrks8D/CYLgJHAfO/Tcvu8fBP4xMsJ6Gvlj/cnenqoR6/EBQRB8qe7h14FP9OosLXg78EoQBK8B+L7/P4AfAs729FRNCILgMnDZfL3s+/73gIPswLP6vn8I+DDwy8A/7fFx2uL7/gjwA8CPAwRBUEYilJ2KBwz4vl8BcsClHp+nAevxrecngSd6fYg1HATO1z2+YL63o/F9/zbgAeAbPT5KK34dEdGNen2QDrgduAb8J9/3n/J9/3d83x/s9aGaEQTBReBXEVX2y8DiGuei5/SNx9dOVzAIgi+a1zyGhGqfv5ln64BmYzg7ug/J9/0h4H8B/yQIgqVen2ctvu8/AswEQfAd3/f/Zq/P0wEe8CDwqSAIvuH7/meBfw78Ym+PtR7f98eRiOQYsAD8T9/3fzQIgt/r7ckS+sbwtdIVjPF9/8eAR4D3BEGw04zKBeBw3eND7LDQoR7f91OI0ft8EAR/0OvztOBdwEd93/8QkAVGfN//vSAIfrTH52rFBeBCEASx9/wFxPDtRN4LnAuC4BqA7/t/APwNwBq+nYSpmP4C8INBEOR7fZ4mfAs44fv+MeAikij+O709UnN831fAfwS+FwTBv+/1eVoRBMGnMUUs4/H9/A42egRBcMX3/fO+778lCIIXgfewA/OmhjeBd/i+nwMKyFm/3dsjNWJzfMJvAsPAl33fP+P7/m/3+kD1BEFQBf4R8CRSyQuCIHi+t6dqybuAvwu82/xbnjFelWXrfAr4vO/7zwD3A/+mx+dpivFKvwB8F2llcdhh42t2ZM1isfQd1uOzWCx9hzV8Foul77CGz2Kx9B3W8Fkslr7DGj6LxdJ3WMNnsVj6Dmv4LBZL3/H/AaOKNRcBh2bvAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.4\\n\",\n \"y.shape = 1500 y.mean() = 0.4\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.5\\n\",\n \"y.shape = 1800 y.mean() = 0.5\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for target_percentage in [0.1, 0.2, 0.3, 0.4, 0.5]:\\n\",\n \" X_u, y_u = OverSampling(x_vis, y, target_percentage, 1)\\n\",\n \" print('Target percentage', target_percentage)\\n\",\n \" print('y.shape = ',y_u.shape[0], 'y.mean() = ', y_u.mean())\\n\",\n \" plot_two_classes(X_u, y_u, size=(5, 5))\\n\",\n \" plt.show() \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Comparing Over-Sampling with other models\\n\",\n \"\\n\",\n \"**Advantages of Over-Sampling :**\\n\",\n \"\\n\",\n \"- Fast to estimate\\n\",\n \"- Easy to understant\\n\",\n \"- No information of the majority class is lost\\n\",\n \"\\n\",\n \"**Disadvantages of Over-Sampling :**\\n\",\n \"\\n\",\n \"- No new information is added\\n\",\n \"- Hurts the generalization capacity\\n\",\n \"- Variance due to randomnes \\n\",\n \"- Need to define the target percentage\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## SMOTE\\n\",\n \"\\n\",\n \"SMOTE (Chawla et. al. 2002) is a well-known algorithm to fight this problem. The general idea of this method is to artificially generate new examples of the minority class using the nearest neighbors of these cases. Furthermore, the majority class examples are also under-sampled, leading to a more balanced dataset.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Number of nearest neighbours to used to construct synthetic samples.\\n\",\n \"k = 5\\n\",\n \"\\n\",\n \"from sklearn.neighbors import NearestNeighbors\\n\",\n \"nearest_neighbour_ = NearestNeighbors(n_neighbors=k + 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\\n\",\n \" metric_params=None, n_jobs=1, n_neighbors=6, p=2, radius=1.0)\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Look for k-th nearest neighbours, excluding, of course, the\\n\",\n \"# point itself.#\\n\",\n \"nearest_neighbour_.fit(x_vis[y==1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Matrix with k-th nearest neighbours indexes for each minority\\n\",\n \"# element.#\\n\",\n \"nns = nearest_neighbour_.kneighbors(x_vis[y==1], \\n\",\n \" return_distance=False)[:, 1:]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Example of the nns of two cases\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def base_smote_plot(sel, nns):\\n\",\n \" fig, subplot = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))\\n\",\n \" \\n\",\n \" for i in range(len(sel)):\\n\",\n \" # plot the select sample\\n\",\n \" subplot.scatter(x_vis[y==1, 0][sel[i]], x_vis[y==1, 1][sel[i]],\\n\",\n \" alpha=1., s=70)\\n\",\n \" # plot the neighbors\\n\",\n \" subplot.scatter(x_vis[y==1, 0][nns[sel[i]]], \\n\",\n \" x_vis[y==1, 1][nns[sel[i]]],\\n\",\n \" alpha=0.5, s=70) \\n\",\n \" \\n\",\n \" # plot the lines\\n\",\n \" for nn in nns[sel[i]]:\\n\",\n \" plt.plot([x_vis[y==1, 0][sel[i]], x_vis[y==1, 0][nn]], \\n\",\n \" [x_vis[y==1, 1][sel[i]], x_vis[y==1, 1][nn]],\\n\",\n \" 'r--') \\n\",\n \" \\n\",\n \" xlim = subplot.get_xlim()\\n\",\n \" ylim = subplot.get_ylim()\\n\",\n \" subplot.scatter(x_vis[y==1, 0], x_vis[y==1, 1], alpha=0.1, s=70)\\n\",\n \" subplot.set_xlim(xlim)\\n\",\n \" subplot.set_ylim(ylim)\\n\",\n \" return subplot\\n\",\n \"base_smote_plot([12],nns)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([4.13147735, 0.21176073])\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create syntetic sample for 12\\n\",\n \"\\n\",\n \"# Select one random neighbor\\n\",\n \"np.random.seed(3)\\n\",\n \"nn_ = np.random.choice(nns[12])\\n\",\n \"\\n\",\n \"x_vis[y==1][nn_]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([4.02071396, 0.14216852])\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Take a step of random size (0,1) in the direction of the\\n\",\n \"# n nearest neighbours\\n\",\n \"np.random.seed(5)\\n\",\n \"step = np.random.uniform()\\n\",\n \"\\n\",\n \"# Construct synthetic sample\\n\",\n \"new = x_vis[y==1][12] - step * (x_vis[y==1][12] - x_vis[y==1][nn_])\\n\",\n \"new\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_ = base_smote_plot([12],nns)\\n\",\n \"plot_.scatter(new[0], new[1], alpha=1., s=400, marker=\\\"*\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Select one random neighbor\\n\",\n \"np.random.seed(5)\\n\",\n \"nn_2 = np.random.choice(nns[12])\\n\",\n \"np.random.seed(1)\\n\",\n \"step = np.random.uniform()\\n\",\n \"# Construct synthetic sample\\n\",\n \"new2 = x_vis[y==1][12] - step * (x_vis[y==1][12] - x_vis[y==1][nn_2])\\n\",\n \"\\n\",\n \"plot_ = base_smote_plot([12],nns)\\n\",\n \"plot_.scatter(new[0], new[1], alpha=1., s=400, marker=\\\"*\\\")\\n\",\n \"plot_.scatter(new2[0], new2[1], alpha=1., s=400, marker=\\\"*\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Apply to all cases\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Estimate number of synthetic cases to create\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"800\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"target_percentage = 0.5\\n\",\n \"n_samples_1_new = -target_percentage * n_samples_0 / (target_percentage- 1) - n_samples_1\\n\",\n \"n_samples_1_new = int(n_samples_1_new)\\n\",\n \"n_samples_1_new\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# A matrix to store the synthetic samples\\n\",\n \"new = np.zeros((n_samples_1_new, x_vis.shape[1]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Select examples to use as base\\n\",\n \"np.random.seed(34)\\n\",\n \"sel_ = np.random.choice(y[y==1].shape[0], n_samples_1_new)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Define random seeds (2 per example)\\n\",\n \"np.random.seed(64)\\n\",\n \"nn__ = np.random.choice(k, n_samples_1_new)\\n\",\n \"np.random.seed(65)\\n\",\n \"steps = np.random.uniform(size=n_samples_1_new)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# For each selected examples create one synthetic case\\n\",\n \"for i, sel in enumerate(sel_):\\n\",\n \" # Select neighbor\\n\",\n \" nn_ = nn__[i]\\n\",\n \" step = steps[i]\\n\",\n \" new[i, :] = x_vis[y==1][sel] - step * (x_vis[y==1][sel] - x_vis[y==1][nn_])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[4.99956657, 0.45629856],\\n\",\n \" [4.9262738 , 0.41983964],\\n\",\n \" [4.64508466, 0.25702488],\\n\",\n \" ...,\\n\",\n \" [5.58945487, 0.51102965],\\n\",\n \" [5.47529954, 0.39266754],\\n\",\n \" [5.09802765, 0.36978454]])\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_ = plot_two_classes(x_vis, y, size=(10, 10))\\n\",\n \"plot_.scatter(new[:, 0], new[:, 1], alpha=0.5, s=150, marker='*')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def SMOTE(X, y, target_percentage=0.5, k=5, seed=None):\\n\",\n \" \\n\",\n \" # New samples\\n\",\n \" n_samples_1_new = int(-target_percentage * n_samples_0 / (target_percentage- 1) - n_samples_1)\\n\",\n \" \\n\",\n \" # A matrix to store the synthetic samples\\n\",\n \" new = np.zeros((n_samples_1_new, x_vis.shape[1]))\\n\",\n \" \\n\",\n \" # Create seeds\\n\",\n \" np.random.seed(seed)\\n\",\n \" seeds = np.random.randint(1, 1000000, 3)\\n\",\n \" \\n\",\n \" # Select examples to use as base\\n\",\n \" np.random.seed(seeds[0])\\n\",\n \" sel_ = np.random.choice(y[y==1].shape[0], n_samples_1_new)\\n\",\n \" \\n\",\n \" # Define random seeds (2 per example)\\n\",\n \" np.random.seed(seeds[1])\\n\",\n \" nn__ = np.random.choice(k, n_samples_1_new)\\n\",\n \" np.random.seed(seeds[2])\\n\",\n \" steps = np.random.uniform(size=n_samples_1_new) \\n\",\n \"\\n\",\n \" # For each selected examples create one synthetic case\\n\",\n \" for i, sel in enumerate(sel_):\\n\",\n \" # Select neighbor\\n\",\n \" nn_ = nn__[i]\\n\",\n \" step = steps[i]\\n\",\n \" # Create new sample\\n\",\n \" new[i, :] = X[y==1][sel] - step * (X[y==1][sel] - X[y==1][nn_])\\n\",\n \" \\n\",\n \" X = np.vstack((X, new))\\n\",\n \" y = np.append(y, np.ones(n_samples_1_new))\\n\",\n \" \\n\",\n \" return X, y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.25 k 5\\n\",\n \"y.shape = 1200 y.mean() = 0.25\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.25 k 15\\n\",\n \"y.shape = 1200 y.mean() = 0.25\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.5 k 5\\n\",\n \"y.shape = 1800 y.mean() = 0.5\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Target percentage 0.5 k 15\\n\",\n \"y.shape = 1800 y.mean() = 0.5\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"for target_percentage in [0.25, 0.5]:\\n\",\n \" for k in [5, 15]:\\n\",\n \" X_u, y_u = SMOTE(x_vis, y, target_percentage, k, seed=3)\\n\",\n \" print('Target percentage', target_percentage, 'k ', k)\\n\",\n \" print('y.shape = ',y_u.shape[0], 'y.mean() = ', y_u.mean())\\n\",\n \" plot_two_classes(X_u, y_u, size=(5, 5))\\n\",\n \" plt.show() \\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Comparing SMOTE with other models\\n\",\n \"\\n\",\n \"**Advantages of SMOTE :**\\n\",\n \"\\n\",\n \"- Allow generalization\\n\",\n \"- Add new information\\n\",\n \"- No information of the majority class is lost\\n\",\n \"\\n\",\n \"**Disadvantages of SMOTE :**\\n\",\n \"\\n\",\n \"- **Variance due to randomnes** \\n\",\n \"- Need to define the target percentage, and k\\n\",\n \"- No synthetic example is created outside the convex-hull\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Other Over-sampling techniques\\n\",\n \"\\n\",\n \"* bSMOTE(1&2) - Borderline SMOTE of types 1 and 2\\n\",\n \"* SVM_SMOTE - Support Vectors SMOTE\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/07_decision_trees.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 07 - Decision Trees\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Kevin Markham](https://github.com/justmarkham)\\n\",\n \"\\n\",\n \"*Adapted from Chapter 8 of [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)*\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Why are we learning about decision trees?\\n\",\n \"\\n\",\n \"- Can be applied to both regression and classification problems\\n\",\n \"- Many useful properties\\n\",\n \"- Very popular\\n\",\n \"- Basis for more sophisticated models\\n\",\n \"- Have a different way of \\\"thinking\\\" than the other models we have studied\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Lesson objectives\\n\",\n \"\\n\",\n \"Students will be able to:\\n\",\n \"\\n\",\n \"- Explain how a decision tree is created\\n\",\n \"- Build a decision tree model in scikit-learn\\n\",\n \"- Tune a decision tree model and explain how tuning impacts the model\\n\",\n \"- Interpret a tree diagram\\n\",\n \"- Describe the key differences between regression and classification trees\\n\",\n \"- Decide whether a decision tree is an appropriate model for a given problem\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 1: Regression trees\\n\",\n \"\\n\",\n \"Major League Baseball player data from 1986-87:\\n\",\n \"\\n\",\n \"- **Years** (x-axis): number of years playing in the major leagues\\n\",\n \"- **Hits** (y-axis): number of hits in the previous year\\n\",\n \"- **Salary** (color): low salary is blue/green, high salary is red/yellow\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Salary data](images/salary_color.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Group exercise:\\n\",\n \"\\n\",\n \"- The data above is our **training data**.\\n\",\n \"- We want to build a model that predicts the Salary of **future players** based on Years and Hits.\\n\",\n \"- We are going to \\\"segment\\\" the feature space into regions, and then use the **mean Salary in each region** as the predicted Salary for future players.\\n\",\n \"- Intuitively, you want to **maximize** the similarity (or \\\"homogeneity\\\") within a given region, and **minimize** the similarity between different regions.\\n\",\n \"\\n\",\n \"Rules for segmenting:\\n\",\n \"\\n\",\n \"- You can only use **straight lines**, drawn one at a time.\\n\",\n \"- Your line must either be **vertical or horizontal**.\\n\",\n \"- Your line **stops** when it hits an existing line.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Salary regions](images/salary_regions.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Above are the regions created by a computer:\\n\",\n \"\\n\",\n \"- $R_1$: players with **less than 5 years** of experience, mean Salary of **\\\\$166,000 **\\n\",\n \"- $R_2$: players with **5 or more years** of experience and **less than 118 hits**, mean Salary of **\\\\$403,000 **\\n\",\n \"- $R_3$: players with **5 or more years** of experience and **118 hits or more**, mean Salary of **\\\\$846,000 **\\n\",\n \"\\n\",\n \"**Note:** Years and Hits are both integers, but the convention is to use the **midpoint** between adjacent values to label a split.\\n\",\n \"\\n\",\n \"These regions are used to make predictions on **out-of-sample data**. Thus, there are only three possible predictions! (Is this different from how **linear regression** makes predictions?)\\n\",\n \"\\n\",\n \"Below is the equivalent regression tree:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Salary tree](images/salary_tree.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first split is **Years < 4.5**, thus that split goes at the top of the tree. When a splitting rule is **True**, you follow the left branch. When a splitting rule is **False**, you follow the right branch.\\n\",\n \"\\n\",\n \"For players in the **left branch**, the mean Salary is \\\\$166,000, thus you label it with that value. (Salary has been divided by 1000 and log-transformed to 5.11.)\\n\",\n \"\\n\",\n \"For players in the **right branch**, there is a further split on **Hits < 117.5**, dividing players into two more Salary regions: \\\\$403,000 (transformed to 6.00), and \\\\$846,000 (transformed to 6.74).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Salary tree annotated](images/salary_tree_annotated.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**What does this tree tell you about your data?**\\n\",\n \"\\n\",\n \"- Years is the most important factor determining Salary, with a lower number of Years corresponding to a lower Salary.\\n\",\n \"- For a player with a lower number of Years, Hits is not an important factor determining Salary.\\n\",\n \"- For a player with a higher number of Years, Hits is an important factor determining Salary, with a greater number of Hits corresponding to a higher Salary.\\n\",\n \"\\n\",\n \"**Question:** What do you like and dislike about decision trees so far?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Building a regression tree by hand\\n\",\n \"\\n\",\n \"Your **training data** is a tiny dataset of [used vehicle sale prices](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/vehicles_train.csv). Your goal is to **predict price** for testing data.\\n\",\n \"\\n\",\n \"1. Read the data into a Pandas DataFrame.\\n\",\n \"2. Explore the data by sorting, plotting, or split-apply-combine (aka `group_by`).\\n\",\n \"3. Decide which feature is the most important predictor, and use that to create your first splitting rule.\\n\",\n \" - Only binary splits are allowed.\\n\",\n \"4. After making your first split, split your DataFrame into two parts, and then explore each part to figure out what other splits to make.\\n\",\n \"5. Stop making splits once you are convinced that it strikes a good balance between underfitting and overfitting.\\n\",\n \" - Your goal is to build a model that generalizes well.\\n\",\n \" - You are allowed to split on the same variable multiple times!\\n\",\n \"6. Draw your tree, labeling the leaves with the mean price for the observations in that region.\\n\",\n \" - Make sure nothing is backwards: You follow the **left branch** if the rule is true, and the **right branch** if the rule is false.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## How does a computer build a regression tree?\\n\",\n \"\\n\",\n \"**Ideal approach:** Consider every possible partition of the feature space (computationally infeasible)\\n\",\n \"\\n\",\n \"**\\\"Good enough\\\" approach:** recursive binary splitting\\n\",\n \"\\n\",\n \"1. Begin at the top of the tree.\\n\",\n \"2. For **every feature**, examine **every possible cutpoint**, and choose the feature and cutpoint such that the resulting tree has the lowest possible mean squared error (MSE). Make that split.\\n\",\n \"3. Examine the two resulting regions, and again make a **single split** (in one of the regions) to minimize the MSE.\\n\",\n \"4. Keep repeating step 3 until a **stopping criterion** is met:\\n\",\n \" - maximum tree depth (maximum number of splits required to arrive at a leaf)\\n\",\n \" - minimum number of observations in a leaf\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Demo: Choosing the ideal cutpoint for a given feature\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# vehicle data\\n\",\n \"import pandas as pd\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/vehicles_train.csv.zip', 'r') as z:\\n\",\n \" f = z.open('vehicles_train.csv')\\n\",\n \" train = pd.io.parsers.read_table(f, index_col=False, sep=',')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
priceyearmilesdoorsvtypeprediction
0220002012130002car6571.428571
1140002010300002car6571.428571
2130002010735004car6571.428571
395002009780004car6571.428571
490002007470004car6571.428571
5400020061240002car6571.428571
6300020041770004car6571.428571
7200020042090004truck6571.428571
8300020031380002car6571.428571
9190020031600004car6571.428571
10250020031900002truck6571.428571
1150002001620004car6571.428571
12180019991630002truck6571.428571
13130019971380004car6571.428571
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" price year miles doors vtype prediction\\n\",\n \"0 22000 2012 13000 2 car 6571.428571\\n\",\n \"1 14000 2010 30000 2 car 6571.428571\\n\",\n \"2 13000 2010 73500 4 car 6571.428571\\n\",\n \"3 9500 2009 78000 4 car 6571.428571\\n\",\n \"4 9000 2007 47000 4 car 6571.428571\\n\",\n \"5 4000 2006 124000 2 car 6571.428571\\n\",\n \"6 3000 2004 177000 4 car 6571.428571\\n\",\n \"7 2000 2004 209000 4 truck 6571.428571\\n\",\n \"8 3000 2003 138000 2 car 6571.428571\\n\",\n \"9 1900 2003 160000 4 car 6571.428571\\n\",\n \"10 2500 2003 190000 2 truck 6571.428571\\n\",\n \"11 5000 2001 62000 4 car 6571.428571\\n\",\n \"12 1800 1999 163000 2 truck 6571.428571\\n\",\n \"13 1300 1997 138000 4 car 6571.428571\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# before splitting anything, just predict the mean of the entire dataset\\n\",\n \"train['prediction'] = train.price.mean()\\n\",\n \"train\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5936.981985995983\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"year = 0\\n\",\n \"train['pred'] = train.loc[train.year=year, 'pred'] = train.loc[train.year>=year, 'price'].mean()\\n\",\n \"\\n\",\n \"(((train['price'] - train['pred'])**2).mean()) ** 0.5\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"train_izq = train.loc[train.year<0].copy()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([], dtype=int64)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train_izq.year.unique()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def error_año(train, year):\\n\",\n \" train['pred'] = train.loc[train.year=year, 'pred'] = train.loc[train.year>=year, 'price'].mean()\\n\",\n \" return round(((((train['price'] - train['pred'])**2).mean()) ** 0.5), 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def error_miles(train, miles):\\n\",\n \" train['pred'] = train.loc[train.miles=miles, 'pred'] = train.loc[train.miles>=miles, 'price'].mean()\\n\",\n \" return round(((((train['price'] - train['pred'])**2).mean()) ** 0.5), 2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Recap:** Before every split, this process is repeated for every feature, and the feature and cutpoint that produces the lowest MSE is chosen.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Building a regression tree in scikit-learn\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# encode car as 0 and truck as 1\\n\",\n \"train['vtype'] = train.vtype.map({'car':0, 'truck':1})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# define X and y\\n\",\n \"feature_cols = ['year', 'miles', 'doors', 'vtype']\\n\",\n \"X = train[feature_cols]\\n\",\n \"y = train.price\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\\n\",\n \" max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n \" min_impurity_split=None, min_samples_leaf=1,\\n\",\n \" min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n \" presort=False, random_state=1, splitter='best')\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# instantiate a DecisionTreeRegressor (with random_state=1)\\n\",\n \"from sklearn.tree import DecisionTreeRegressor\\n\",\n \"treereg = DecisionTreeRegressor(random_state=1)\\n\",\n \"treereg\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3107.1428571428573\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# use leave-one-out cross-validation (LOOCV) to estimate the RMSE for this model\\n\",\n \"import numpy as np\\n\",\n \"from sklearn.model_selection import cross_val_score\\n\",\n \"scores = cross_val_score(treereg, X, y, cv=14, scoring='neg_mean_squared_error')\\n\",\n \"np.mean(np.sqrt(-scores))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What happens when we grow a tree too deep?\\n\",\n \"\\n\",\n \"- Left: Regression tree for Salary **grown deeper**\\n\",\n \"- Right: Comparison of the **training, testing, and cross-validation errors** for trees with different numbers of leaves\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Salary tree grown deep](images/salary_tree_deep.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The **training error** continues to go down as the tree size increases (due to overfitting), but the lowest **cross-validation error** occurs for a tree with 3 leaves.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Tuning a regression tree\\n\",\n \"\\n\",\n \"Let's try to reduce the RMSE by tuning the **max_depth** parameter:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"4050.1443001443\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# try different values one-by-one\\n\",\n \"treereg = DecisionTreeRegressor(max_depth=1, random_state=1)\\n\",\n \"scores = cross_val_score(treereg, X, y, cv=14, scoring='neg_mean_squared_error')\\n\",\n \"np.mean(np.sqrt(-scores))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Or, we could write a loop to try a range of values:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# list of values to try\\n\",\n \"max_depth_range = range(1, 8)\\n\",\n \"\\n\",\n \"# list to store the average RMSE for each value of max_depth\\n\",\n \"RMSE_scores = []\\n\",\n \"\\n\",\n \"# use LOOCV with each value of max_depth\\n\",\n \"for depth in max_depth_range:\\n\",\n \" treereg = DecisionTreeRegressor(max_depth=depth, random_state=1)\\n\",\n \" MSE_scores = cross_val_score(treereg, X, y, cv=14, scoring='neg_mean_squared_error')\\n\",\n \" RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'RMSE (lower is better)')\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"# plot max_depth (x-axis) versus RMSE (y-axis)\\n\",\n \"plt.plot(max_depth_range, RMSE_scores)\\n\",\n \"plt.xlabel('max_depth')\\n\",\n \"plt.ylabel('RMSE (lower is better)')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DecisionTreeRegressor(criterion='mse', max_depth=3, max_features=None,\\n\",\n \" max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n \" min_impurity_split=None, min_samples_leaf=1,\\n\",\n \" min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n \" presort=False, random_state=1, splitter='best')\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# max_depth=3 was best, so fit a tree using that parameter\\n\",\n \"treereg = DecisionTreeRegressor(max_depth=3, random_state=1)\\n\",\n \"treereg.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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featureimportance
0year0.798744
1miles0.201256
2doors0.000000
3vtype0.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" feature importance\\n\",\n \"0 year 0.798744\\n\",\n \"1 miles 0.201256\\n\",\n \"2 doors 0.000000\\n\",\n \"3 vtype 0.000000\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# \\\"Gini importance\\\" of each feature: the (normalized) total reduction of error brought by that feature\\n\",\n \"pd.DataFrame({'feature':feature_cols, 'importance':treereg.feature_importances_})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Creating a tree diagram\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create a Graphviz file\\n\",\n \"from sklearn.tree import export_graphviz\\n\",\n \"export_graphviz(treereg, out_file='tree_vehicles.dot', feature_names=feature_cols)\\n\",\n \"\\n\",\n \"# At the command line, run this to convert to PNG:\\n\",\n \"# dot -Tpng tree_vehicles.dot -o tree_vehicles.png\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Tree for vehicle data](images/tree_vehicles.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Reading the internal nodes:\\n\",\n \"\\n\",\n \"- **samples:** number of observations in that node before splitting\\n\",\n \"- **mse:** MSE calculated by comparing the actual response values in that node against the mean response value in that node\\n\",\n \"- **rule:** rule used to split that node (go left if true, go right if false)\\n\",\n \"\\n\",\n \"Reading the leaves:\\n\",\n \"\\n\",\n \"- **samples:** number of observations in that node\\n\",\n \"- **value:** mean response value in that node\\n\",\n \"- **mse:** MSE calculated by comparing the actual response values in that node against \\\"value\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Making predictions for the testing data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
priceyearmilesdoorsvtype
03000200313000041
1600020058250040
21200020106000020
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" price year miles doors vtype\\n\",\n \"0 3000 2003 130000 4 1\\n\",\n \"1 6000 2005 82500 4 0\\n\",\n \"2 12000 2010 60000 2 0\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# read the testing data\\n\",\n \"with zipfile.ZipFile('../datasets/vehicles_test.csv.zip', 'r') as z:\\n\",\n \" f = z.open('vehicles_test.csv')\\n\",\n \" test = pd.io.parsers.read_table(f, index_col=False, sep=',')\\n\",\n \"\\n\",\n \"test['vtype'] = test.vtype.map({'car':0, 'truck':1})\\n\",\n \"test\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Question:** Using the tree diagram above, what predictions will the model make for each observation?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 4000., 5000., 13500.])\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# use fitted model to make predictions on testing data\\n\",\n \"X_test = test[feature_cols]\\n\",\n \"y_test = test.price\\n\",\n \"y_pred = treereg.predict(X_test)\\n\",\n \"y_pred\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1190.2380714238084\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# calculate RMSE\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"np.sqrt(mean_squared_error(y_test, y_pred))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 2: Classification trees\\n\",\n \"\\n\",\n \"**Example:** Predict whether Barack Obama or Hillary Clinton will win the Democratic primary in a particular county in 2008:\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Obama-Clinton decision tree](images/obama_clinton_tree.jpg)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Questions:**\\n\",\n \"\\n\",\n \"- What are the observations? How many observations are there?\\n\",\n \"- What is the response variable?\\n\",\n \"- What are the features?\\n\",\n \"- What is the most predictive feature?\\n\",\n \"- Why does the tree split on high school graduation rate twice in a row?\\n\",\n \"- What is the class prediction for the following county: 15% African-American, 90% high school graduation rate, located in the South, high poverty, high population density?\\n\",\n \"- What is the predicted probability for that same county?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Comparing regression trees and classification trees\\n\",\n \"\\n\",\n \"|regression trees|classification trees|\\n\",\n \"|---|---|\\n\",\n \"|predict a continuous response|predict a categorical response|\\n\",\n \"|predict using mean response of each leaf|predict using most commonly occuring class of each leaf|\\n\",\n \"|splits are chosen to minimize MSE|splits are chosen to minimize Gini index (discussed below)|\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Splitting criteria for classification trees\\n\",\n \"\\n\",\n \"Common options for the splitting criteria:\\n\",\n \"\\n\",\n \"- **classification error rate:** fraction of training observations in a region that don't belong to the most common class\\n\",\n \"- **Gini index:** measure of total variance across classes in a region\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Example of classification error rate\\n\",\n \"\\n\",\n \"Pretend we are predicting whether someone buys an iPhone or an Android:\\n\",\n \"\\n\",\n \"- At a particular node, there are **25 observations** (phone buyers), of whom **10 bought iPhones and 15 bought Androids**.\\n\",\n \"- Since the majority class is **Android**, that's our prediction for all 25 observations, and thus the classification error rate is **10/25 = 40%**.\\n\",\n \"\\n\",\n \"Our goal in making splits is to **reduce the classification error rate**. Let's try splitting on gender:\\n\",\n \"\\n\",\n \"- **Males:** 2 iPhones and 12 Androids, thus the predicted class is Android\\n\",\n \"- **Females:** 8 iPhones and 3 Androids, thus the predicted class is iPhone\\n\",\n \"- Classification error rate after this split would be **5/25 = 20%**\\n\",\n \"\\n\",\n \"Compare that with a split on age:\\n\",\n \"\\n\",\n \"- **30 or younger:** 4 iPhones and 8 Androids, thus the predicted class is Android\\n\",\n \"- **31 or older:** 6 iPhones and 7 Androids, thus the predicted class is Android\\n\",\n \"- Classification error rate after this split would be **10/25 = 40%**\\n\",\n \"\\n\",\n \"The decision tree algorithm will try **every possible split across all features**, and choose the split that **reduces the error rate the most.**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Example of Gini index\\n\",\n \"\\n\",\n \"Calculate the Gini index before making a split:\\n\",\n \"\\n\",\n \"$$1 - \\\\left(\\\\frac {iPhone} {Total}\\\\right)^2 - \\\\left(\\\\frac {Android} {Total}\\\\right)^2 = 1 - \\\\left(\\\\frac {10} {25}\\\\right)^2 - \\\\left(\\\\frac {15} {25}\\\\right)^2 = 0.48$$\\n\",\n \"\\n\",\n \"- The **maximum value** of the Gini index is 0.5, and occurs when the classes are perfectly balanced in a node.\\n\",\n \"- The **minimum value** of the Gini index is 0, and occurs when there is only one class represented in a node.\\n\",\n \"- A node with a lower Gini index is said to be more \\\"pure\\\".\\n\",\n \"\\n\",\n \"Evaluating the split on **gender** using Gini index:\\n\",\n \"\\n\",\n \"$$\\\\text{Males: } 1 - \\\\left(\\\\frac {2} {14}\\\\right)^2 - \\\\left(\\\\frac {12} {14}\\\\right)^2 = 0.24$$\\n\",\n \"$$\\\\text{Females: } 1 - \\\\left(\\\\frac {8} {11}\\\\right)^2 - \\\\left(\\\\frac {3} {11}\\\\right)^2 = 0.40$$\\n\",\n \"$$\\\\text{Weighted Average: } 0.24 \\\\left(\\\\frac {14} {25}\\\\right) + 0.40 \\\\left(\\\\frac {11} {25}\\\\right) = 0.31$$\\n\",\n \"\\n\",\n \"Evaluating the split on **age** using Gini index:\\n\",\n \"\\n\",\n \"$$\\\\text{30 or younger: } 1 - \\\\left(\\\\frac {4} {12}\\\\right)^2 - \\\\left(\\\\frac {8} {12}\\\\right)^2 = 0.44$$\\n\",\n \"$$\\\\text{31 or older: } 1 - \\\\left(\\\\frac {6} {13}\\\\right)^2 - \\\\left(\\\\frac {7} {13}\\\\right)^2 = 0.50$$\\n\",\n \"$$\\\\text{Weighted Average: } 0.44 \\\\left(\\\\frac {12} {25}\\\\right) + 0.50 \\\\left(\\\\frac {13} {25}\\\\right) = 0.47$$\\n\",\n \"\\n\",\n \"Again, the decision tree algorithm will try **every possible split**, and will choose the split that **reduces the Gini index (and thus increases the \\\"node purity\\\") the most.**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Comparing classification error rate and Gini index\\n\",\n \"\\n\",\n \"- Gini index is generally preferred because it will make splits that **increase node purity**, even if that split does not change the classification error rate.\\n\",\n \"- Node purity is important because we're interested in the **class proportions** in each region, since that's how we calculate the **predicted probability** of each class.\\n\",\n \"- scikit-learn's default splitting criteria for classification trees is Gini index.\\n\",\n \"\\n\",\n \"Note: There is another common splitting criteria called **cross-entropy**. It's numerically similar to Gini index, but slower to compute, thus it's not as popular as Gini index.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Building a classification tree in scikit-learn\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll build a classification tree using the Titanic data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedEmbarked_QEmbarked_S
PassengerId
103Braund, Mr. Owen Harris122.010A/5 211717.2500NaNS01
211Cumings, Mrs. John Bradley (Florence Briggs Th...038.010PC 1759971.2833C85C00
313Heikkinen, Miss. Laina026.000STON/O2. 31012827.9250NaNS01
411Futrelle, Mrs. Jacques Heath (Lily May Peel)035.01011380353.1000C123S01
503Allen, Mr. William Henry135.0003734508.0500NaNS01
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Survived Pclass \\\\\\n\",\n \"PassengerId \\n\",\n \"1 0 3 \\n\",\n \"2 1 1 \\n\",\n \"3 1 3 \\n\",\n \"4 1 1 \\n\",\n \"5 0 3 \\n\",\n \"\\n\",\n \" Name Sex Age \\\\\\n\",\n \"PassengerId \\n\",\n \"1 Braund, Mr. Owen Harris 1 22.0 \\n\",\n \"2 Cumings, Mrs. John Bradley (Florence Briggs Th... 0 38.0 \\n\",\n \"3 Heikkinen, Miss. Laina 0 26.0 \\n\",\n \"4 Futrelle, Mrs. Jacques Heath (Lily May Peel) 0 35.0 \\n\",\n \"5 Allen, Mr. William Henry 1 35.0 \\n\",\n \"\\n\",\n \" SibSp Parch Ticket Fare Cabin Embarked \\\\\\n\",\n \"PassengerId \\n\",\n \"1 1 0 A/5 21171 7.2500 NaN S \\n\",\n \"2 1 0 PC 17599 71.2833 C85 C \\n\",\n \"3 0 0 STON/O2. 3101282 7.9250 NaN S \\n\",\n \"4 1 0 113803 53.1000 C123 S \\n\",\n \"5 0 0 373450 8.0500 NaN S \\n\",\n \"\\n\",\n \" Embarked_Q Embarked_S \\n\",\n \"PassengerId \\n\",\n \"1 0 1 \\n\",\n \"2 0 0 \\n\",\n \"3 0 1 \\n\",\n \"4 0 1 \\n\",\n \"5 0 1 \"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# read in the data\\n\",\n \"with zipfile.ZipFile('../datasets/titanic.csv.zip', 'r') as z:\\n\",\n \" f = z.open('titanic.csv')\\n\",\n \" titanic = pd.read_csv(f, sep=',', index_col=0)\\n\",\n \"\\n\",\n \"# encode female as 0 and male as 1\\n\",\n \"titanic['Sex'] = titanic.Sex.map({'female':0, 'male':1})\\n\",\n \"\\n\",\n \"# fill in the missing values for age with the median age\\n\",\n \"titanic.Age.fillna(titanic.Age.median(), inplace=True)\\n\",\n \"\\n\",\n \"# create a DataFrame of dummy variables for Embarked\\n\",\n \"embarked_dummies = pd.get_dummies(titanic.Embarked, prefix='Embarked')\\n\",\n \"embarked_dummies.drop(embarked_dummies.columns[0], axis=1, inplace=True)\\n\",\n \"\\n\",\n \"# concatenate the original DataFrame and the dummy DataFrame\\n\",\n \"titanic = pd.concat([titanic, embarked_dummies], axis=1)\\n\",\n \"\\n\",\n \"# print the updated DataFrame\\n\",\n \"titanic.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- **Survived:** 0=died, 1=survived (response variable)\\n\",\n \"- **Pclass:** 1=first class, 2=second class, 3=third class\\n\",\n \" - What will happen if the tree splits on this feature?\\n\",\n \"- **Sex:** 0=female, 1=male\\n\",\n \"- **Age:** numeric value\\n\",\n \"- **Embarked:** C or Q or S\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# define X and y\\n\",\n \"feature_cols = ['Pclass', 'Sex', 'Age', 'Embarked_Q', 'Embarked_S']\\n\",\n \"X = titanic[feature_cols]\\n\",\n \"y = titanic.Survived\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\\n\",\n \" max_features=None, max_leaf_nodes=None,\\n\",\n \" min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, presort=False, random_state=1,\\n\",\n \" splitter='best')\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# fit a classification tree with max_depth=3 on all data\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"treeclf = DecisionTreeClassifier(max_depth=3, random_state=1)\\n\",\n \"treeclf.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create a Graphviz file\\n\",\n \"export_graphviz(treeclf, out_file='tree_titanic.dot', feature_names=feature_cols)\\n\",\n \"\\n\",\n \"# At the command line, run this to convert to PNG:\\n\",\n \"# dot -Tpng tree_titanic.dot -o tree_titanic.png\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Tree for Titanic data](images/tree_titanic.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Notice the split in the bottom right: the **same class** is predicted in both of its leaves. That split didn't affect the **classification error rate**, though it did increase the **node purity**, which is important because it increases the accuracy of our predicted probabilities.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
featureimportance
0Pclass0.242664
1Sex0.655584
2Age0.064494
3Embarked_Q0.000000
4Embarked_S0.037258
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" feature importance\\n\",\n \"0 Pclass 0.242664\\n\",\n \"1 Sex 0.655584\\n\",\n \"2 Age 0.064494\\n\",\n \"3 Embarked_Q 0.000000\\n\",\n \"4 Embarked_S 0.037258\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute the feature importances\\n\",\n \"pd.DataFrame({'feature':feature_cols, 'importance':treeclf.feature_importances_})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 3: Comparing decision trees with other models\\n\",\n \"\\n\",\n \"**Advantages of decision trees:**\\n\",\n \"\\n\",\n \"- Can be used for regression or classification\\n\",\n \"- Can be displayed graphically\\n\",\n \"- Highly interpretable\\n\",\n \"- Can be specified as a series of rules, and more closely approximate human decision-making than other models\\n\",\n \"- Prediction is fast\\n\",\n \"- Features don't need scaling\\n\",\n \"- Automatically learns feature interactions\\n\",\n \"- Tends to ignore irrelevant features\\n\",\n \"- Non-parametric (will outperform linear models if relationship between features and response is highly non-linear)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Trees versus linear models](images/tree_vs_linear.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Disadvantages of decision trees:**\\n\",\n \"\\n\",\n \"- Performance is (generally) not competitive with the best supervised learning methods\\n\",\n \"- Can easily overfit the training data (tuning is required)\\n\",\n \"- Small variations in the data can result in a completely different tree (high variance)\\n\",\n \"- Recursive binary splitting makes \\\"locally optimal\\\" decisions that may not result in a globally optimal tree\\n\",\n \"- Doesn't tend to work well if the classes are highly unbalanced\\n\",\n \"- Doesn't tend to work well with very small datasets\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/08_EnsembleMethods_Bagging.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 08 - Ensemble Methods - Bagging\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). Special thanks goes to [Kevin Markham](https://github.com/justmarkham)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Why are we learning about ensembling?\\n\",\n \"\\n\",\n \"- Very popular method for improving the predictive performance of machine learning models\\n\",\n \"- Provides a foundation for understanding more sophisticated models\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Lesson objectives\\n\",\n \"\\n\",\n \"Students will be able to:\\n\",\n \"\\n\",\n \"- Define ensembling and its requirements\\n\",\n \"- Identify the two basic methods of ensembling\\n\",\n \"- Decide whether manual ensembling is a useful approach for a given problem\\n\",\n \"- Explain bagging and how it can be applied to decision trees\\n\",\n \"- Explain how out-of-bag error and feature importances are calculated from bagged trees\\n\",\n \"- Explain the difference between bagged trees and Random Forests\\n\",\n \"- Build and tune a Random Forest model in scikit-learn\\n\",\n \"- Decide whether a decision tree or a Random Forest is a better model for a given problem\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 1: Introduction\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Ensemble learning is a widely studied topic in the machine learning community. The main idea behind \\n\",\n \"the ensemble methodology is to combine several individual base classifiers in order to have a \\n\",\n \"classifier that outperforms each of them.\\n\",\n \"\\n\",\n \"Nowadays, ensemble methods are one \\n\",\n \"of the most popular and well studied machine learning techniques, and it can be \\n\",\n \"noted that since 2009 all the first-place and second-place winners of the KDD-Cup https://www.sigkdd.org/kddcup/ used ensemble methods. The core \\n\",\n \"principle in ensemble learning, is to induce random perturbations into the learning procedure in \\n\",\n \"order to produce several different base classifiers from a single training set, then combining the \\n\",\n \"base classifiers in order to make the final prediction. In order to induce the random permutations \\n\",\n \"and therefore create the different base classifiers, several methods have been proposed, in \\n\",\n \"particular: \\n\",\n \"* bagging\\n\",\n \"* pasting\\n\",\n \"* random forests \\n\",\n \"* random patches \\n\",\n \"\\n\",\n \"Finally, after the base classifiers \\n\",\n \"are trained, they are typically combined using either:\\n\",\n \"* majority voting\\n\",\n \"* weighted voting \\n\",\n \"* stacking\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are three main reasons regarding why ensemble \\n\",\n \"methods perform better than single models: statistical, computational and representational . First, from a statistical point of view, when the learning set is too \\n\",\n \"small, an algorithm can find several good models within the search space, that arise to the same \\n\",\n \"performance on the training set $\\\\mathcal{S}$. Nevertheless, without a validation set, there is \\n\",\n \"a risk of choosing the wrong model. The second reason is computational; in general, algorithms \\n\",\n \"rely on some local search optimization and may get stuck in a local optima. Then, an ensemble may \\n\",\n \"solve this by focusing different algorithms to different spaces across the training set. The last \\n\",\n \"reason is representational. In most cases, for a learning set of finite size, the true function \\n\",\n \"$f$ cannot be represented by any of the candidate models. By combining several models in an \\n\",\n \"ensemble, it may be possible to obtain a model with a larger coverage across the space of \\n\",\n \"representable functions.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![](images/ch9_fig1.png)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example\\n\",\n \"\\n\",\n \"Let's pretend that instead of building a single model to solve a binary classification problem, you created **five independent models**, and each model was correct about 70% of the time. If you combined these models into an \\\"ensemble\\\" and used their majority vote as a prediction, how often would the ensemble be correct?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1]\\n\",\n \"[1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 0 0 0]\\n\",\n \"[1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1]\\n\",\n \"[1 1 0 0 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 0]\\n\",\n \"[0 0 1 0 0 0 1 0 1 0 0 0 1 1 1 1 1 1 1 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"# set a seed for reproducibility\\n\",\n \"np.random.seed(1234)\\n\",\n \"\\n\",\n \"# generate 1000 random numbers (between 0 and 1) for each model, representing 1000 observations\\n\",\n \"mod1 = np.random.rand(1000)\\n\",\n \"mod2 = np.random.rand(1000)\\n\",\n \"mod3 = np.random.rand(1000)\\n\",\n \"mod4 = np.random.rand(1000)\\n\",\n \"mod5 = np.random.rand(1000)\\n\",\n \"\\n\",\n \"# each model independently predicts 1 (the \\\"correct response\\\") if random number was at least 0.3\\n\",\n \"preds1 = np.where(mod1 > 0.3, 1, 0)\\n\",\n \"preds2 = np.where(mod2 > 0.3, 1, 0)\\n\",\n \"preds3 = np.where(mod3 > 0.3, 1, 0)\\n\",\n \"preds4 = np.where(mod4 > 0.3, 1, 0)\\n\",\n \"preds5 = np.where(mod5 > 0.3, 1, 0)\\n\",\n \"\\n\",\n \"# print the first 20 predictions from each model\\n\",\n \"print(preds1[:20])\\n\",\n \"print(preds2[:20])\\n\",\n \"print(preds3[:20])\\n\",\n \"print(preds4[:20])\\n\",\n \"print(preds5[:20])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# average the predictions and then round to 0 or 1\\n\",\n \"ensemble_preds = np.round((preds1 + preds2 + preds3 + preds4 + preds5)/5.0).astype(int)\\n\",\n \"\\n\",\n \"# print the ensemble's first 20 predictions\\n\",\n \"print(ensemble_preds[:20])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.713\\n\",\n \"0.665\\n\",\n \"0.717\\n\",\n \"0.712\\n\",\n \"0.687\\n\"\n ]\n }\n ],\n \"source\": [\n \"# how accurate was each individual model?\\n\",\n \"print(preds1.mean())\\n\",\n \"print(preds2.mean())\\n\",\n \"print(preds3.mean())\\n\",\n \"print(preds4.mean())\\n\",\n \"print(preds5.mean())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.841\\n\"\n ]\n }\n ],\n \"source\": [\n \"# how accurate was the ensemble?\\n\",\n \"print(ensemble_preds.mean())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Note:** As you add more models to the voting process, the probability of error decreases, which is known as [Condorcet's Jury Theorem](http://en.wikipedia.org/wiki/Condorcet%27s_jury_theorem).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## What is ensembling?\\n\",\n \"\\n\",\n \"**Ensemble learning (or \\\"ensembling\\\")** is the process of combining several predictive models in order to produce a combined model that is more accurate than any individual model.\\n\",\n \"\\n\",\n \"- **Regression:** take the average of the predictions\\n\",\n \"- **Classification:** take a vote and use the most common prediction, or take the average of the predicted probabilities\\n\",\n \"\\n\",\n \"For ensembling to work well, the models must have the following characteristics:\\n\",\n \"\\n\",\n \"- **Accurate:** they outperform the null model\\n\",\n \"- **Independent:** their predictions are generated using different processes\\n\",\n \"\\n\",\n \"**The big idea:** If you have a collection of individually imperfect (and independent) models, the \\\"one-off\\\" mistakes made by each model are probably not going to be made by the rest of the models, and thus the mistakes will be discarded when averaging the models.\\n\",\n \"\\n\",\n \"There are two basic **methods for ensembling:**\\n\",\n \"\\n\",\n \"- Manually ensemble your individual models\\n\",\n \"- Use a model that ensembles for you\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Theoretical performance of an ensemble\\n\",\n \" If we assume that each one of the $T$ base classifiers has a probability $\\\\rho$ of \\n\",\n \" being correct, the probability of an ensemble making the correct decision, assuming independence, \\n\",\n \" denoted by $P_c$, can be calculated using the binomial distribution\\n\",\n \"\\n\",\n \"$$P_c = \\\\sum_{j>T/2}^{T} {{T}\\\\choose{j}} \\\\rho^j(1-\\\\rho)^{T-j}.$$\\n\",\n \"\\n\",\n \" Furthermore, as shown, if $T\\\\ge3$ then:\\n\",\n \"\\n\",\n \"$$\\n\",\n \" \\\\lim_{T \\\\to \\\\infty} P_c= \\\\begin{cases} \\n\",\n \" 1 &\\\\mbox{if } \\\\rho>0.5 \\\\\\\\ \\n\",\n \" 0 &\\\\mbox{if } \\\\rho<0.5 \\\\\\\\ \\n\",\n \" 0.5 &\\\\mbox{if } \\\\rho=0.5 ,\\n\",\n \" \\\\end{cases}\\n\",\n \"$$\\n\",\n \"\\tleading to the conclusion that \\n\",\n \"$$\\n\",\n \" \\\\rho \\\\ge 0.5 \\\\quad \\\\text{and} \\\\quad T\\\\ge3 \\\\quad \\\\Rightarrow \\\\quad P_c\\\\ge \\\\rho.\\n\",\n \"$$\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"# Part 2: Manual ensembling\\n\",\n \"\\n\",\n \"What makes a good manual ensemble?\\n\",\n \"\\n\",\n \"- Different types of **models**\\n\",\n \"- Different combinations of **features**\\n\",\n \"- Different **tuning parameters**\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Machine learning flowchart](https://raw.githubusercontent.com/justmarkham/DAT8/master/notebooks/images/crowdflower_ensembling.jpg)\\n\",\n \"\\n\",\n \"*Machine learning flowchart created by the [winner](https://github.com/ChenglongChen/Kaggle_CrowdFlower) of Kaggle's [CrowdFlower competition](https://www.kaggle.com/c/crowdflower-search-relevance)*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# read in and prepare the vehicle training data\\n\",\n \"import zipfile\\n\",\n \"import pandas as pd\\n\",\n \"with zipfile.ZipFile('../datasets/vehicles_train.csv.zip', 'r') as z:\\n\",\n \" f = z.open('vehicles_train.csv')\\n\",\n \" train = pd.io.parsers.read_table(f, index_col=False, sep=',')\\n\",\n \"with zipfile.ZipFile('../datasets/vehicles_test.csv.zip', 'r') as z:\\n\",\n \" f = z.open('vehicles_test.csv')\\n\",\n \" test = pd.io.parsers.read_table(f, index_col=False, sep=',')\\n\",\n \"\\n\",\n \"train['vtype'] = train.vtype.map({'car':0, 'truck':1})\\n\",\n \"# read in and prepare the vehicle testing data\\n\",\n \"test['vtype'] = test.vtype.map({'car':0, 'truck':1})\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
priceyearmilesdoorsvtype
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\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" price year miles doors vtype\\n\",\n \"0 22000 2012 13000 2 0\\n\",\n \"1 14000 2010 30000 2 0\\n\",\n \"2 13000 2010 73500 4 0\\n\",\n \"3 9500 2009 78000 4 0\\n\",\n \"4 9000 2007 47000 4 0\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Train different models\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"from sklearn.tree import DecisionTreeRegressor\\n\",\n \"from sklearn.naive_bayes import GaussianNB\\n\",\n \"from sklearn.neighbors import KNeighborsRegressor\\n\",\n \"\\n\",\n \"models = {'lr': LinearRegression(),\\n\",\n \" 'dt': DecisionTreeRegressor(),\\n\",\n \" 'nb': GaussianNB(),\\n\",\n \" 'nn': KNeighborsRegressor()}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Train all the models\\n\",\n \"X_train = train.iloc[:, 1:]\\n\",\n \"X_test = test.iloc[:, 1:]\\n\",\n \"y_train = train.price\\n\",\n \"y_test = test.price\\n\",\n \"\\n\",\n \"for model in models.keys():\\n\",\n \" models[model].fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# predict test for each model\\n\",\n \"y_pred = pd.DataFrame(index=test.index, columns=models.keys())\\n\",\n \"for model in models.keys():\\n\",\n \" y_pred[model] = models[model].predict(X_test)\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"lr 2138.3579028745116\\n\",\n \"dt 1414.213562373095\\n\",\n \"nb 5477.2255750516615\\n\",\n \"nn 1671.3268182295567\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Evaluate each model\\n\",\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"\\n\",\n \"for model in models.keys():\\n\",\n \" print(model,np.sqrt(mean_squared_error(y_pred[model], y_test)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Evaluate the error of the mean of the predictions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1193.164765760328\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.sqrt(mean_squared_error(y_pred.mean(axis=1), y_test))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Comparing manual ensembling with a single model approach\\n\",\n \"\\n\",\n \"**Advantages of manual ensembling:**\\n\",\n \"\\n\",\n \"- Increases predictive accuracy\\n\",\n \"- Easy to get started\\n\",\n \"\\n\",\n \"**Disadvantages of manual ensembling:**\\n\",\n \"\\n\",\n \"- Decreases interpretability\\n\",\n \"- Takes longer to train\\n\",\n \"- Takes longer to predict\\n\",\n \"- More complex to automate and maintain\\n\",\n \"- Small gains in accuracy may not be worth the added complexity\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 3: Bagging\\n\",\n \"\\n\",\n \"The primary weakness of **decision trees** is that they don't tend to have the best predictive accuracy. This is partially due to **high variance**, meaning that different splits in the training data can lead to very different trees.\\n\",\n \"\\n\",\n \"**Bagging** is a general purpose procedure for reducing the variance of a machine learning method, but is particularly useful for decision trees. Bagging is short for **bootstrap aggregation**, meaning the aggregation of bootstrap samples.\\n\",\n \"\\n\",\n \"What is a **bootstrap sample**? A random sample with replacement:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20]\\n\",\n \"[ 6 12 13 9 10 12 6 16 1 17 2 13 8 14 7 19 6 19 12 11]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# set a seed for reproducibility\\n\",\n \"np.random.seed(1)\\n\",\n \"\\n\",\n \"# create an array of 1 through 20\\n\",\n \"nums = np.arange(1, 21)\\n\",\n \"print(nums)\\n\",\n \"\\n\",\n \"# sample that array 20 times with replacement\\n\",\n \"print(np.random.choice(a=nums, size=20, replace=True))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**How does bagging work (for decision trees)?**\\n\",\n \"\\n\",\n \"1. Grow B trees using B bootstrap samples from the training data.\\n\",\n \"2. Train each tree on its bootstrap sample and make predictions.\\n\",\n \"3. Combine the predictions:\\n\",\n \" - Average the predictions for **regression trees**\\n\",\n \" - Take a vote for **classification trees**\\n\",\n \"\\n\",\n \"Notes:\\n\",\n \"\\n\",\n \"- **Each bootstrap sample** should be the same size as the original training set.\\n\",\n \"- **B** should be a large enough value that the error seems to have \\\"stabilized\\\".\\n\",\n \"- The trees are **grown deep** so that they have low bias/high variance.\\n\",\n \"\\n\",\n \"Bagging increases predictive accuracy by **reducing the variance**, similar to how cross-validation reduces the variance associated with train/test split (for estimating out-of-sample error) by splitting many times an averaging the results.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[array([13, 2, 12, 2, 6, 1, 3, 10, 11, 9, 6, 1, 0, 1]),\\n\",\n \" array([ 9, 0, 0, 9, 3, 13, 4, 0, 0, 4, 1, 7, 3, 2]),\\n\",\n \" array([ 4, 7, 2, 4, 8, 13, 0, 7, 9, 3, 12, 12, 4, 6]),\\n\",\n \" array([ 1, 5, 6, 11, 2, 1, 12, 8, 3, 10, 5, 0, 11, 2]),\\n\",\n \" array([10, 10, 6, 13, 2, 4, 11, 11, 13, 12, 4, 6, 13, 3]),\\n\",\n \" array([10, 0, 6, 4, 7, 11, 6, 7, 1, 11, 10, 5, 7, 9]),\\n\",\n \" array([ 2, 4, 8, 1, 12, 2, 1, 1, 3, 12, 5, 9, 0, 8]),\\n\",\n \" array([11, 1, 6, 3, 3, 11, 5, 9, 7, 9, 2, 3, 11, 3]),\\n\",\n \" array([ 3, 8, 6, 9, 7, 6, 3, 9, 6, 12, 6, 11, 6, 1]),\\n\",\n \" array([13, 10, 3, 4, 3, 1, 13, 0, 5, 8, 13, 6, 11, 8])]\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# set a seed for reproducibility\\n\",\n \"np.random.seed(123)\\n\",\n \"\\n\",\n \"n_samples = train.shape[0]\\n\",\n \"n_B = 10\\n\",\n \"\\n\",\n \"# create ten bootstrap samples (will be used to select rows from the DataFrame)\\n\",\n \"samples = [np.random.choice(a=n_samples, size=n_samples, replace=True) for _ in range(1, n_B +1 )]\\n\",\n \"samples\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" price year miles doors vtype\\n\",\n \"13 1300 1997 138000 4 0\\n\",\n \"2 13000 2010 73500 4 0\\n\",\n \"12 1800 1999 163000 2 1\\n\",\n \"2 13000 2010 73500 4 0\\n\",\n \"6 3000 2004 177000 4 0\\n\",\n \"1 14000 2010 30000 2 0\\n\",\n \"3 9500 2009 78000 4 0\\n\",\n \"10 2500 2003 190000 2 1\\n\",\n \"11 5000 2001 62000 4 0\\n\",\n \"9 1900 2003 160000 4 0\\n\",\n \"6 3000 2004 177000 4 0\\n\",\n \"1 14000 2010 30000 2 0\\n\",\n \"0 22000 2012 13000 2 0\\n\",\n \"1 14000 2010 30000 2 0\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# show the rows for the first decision tree\\n\",\n \"train.iloc[samples[0], :]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" Build one tree for each sample\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.tree import DecisionTreeRegressor\\n\",\n \"\\n\",\n \"# grow each tree deep\\n\",\n \"treereg = DecisionTreeRegressor(max_depth=None, random_state=123)\\n\",\n \"\\n\",\n \"# DataFrame for storing predicted price from each tree\\n\",\n \"y_pred = pd.DataFrame(index=test.index, columns=[list(range(n_B))])\\n\",\n \"\\n\",\n \"# grow one tree for each bootstrap sample and make predictions on testing data\\n\",\n \"for i, sample in enumerate(samples):\\n\",\n \" X_train = train.iloc[sample, 1:]\\n\",\n \" y_train = train.iloc[sample, 0]\\n\",\n \" treereg.fit(X_train, y_train)\\n\",\n \" y_pred[i] = treereg.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" 0 1 2 3 4 5 6 7 \\\\\\n\",\n \"0 1300.0 1300.0 3000.0 4000.0 1300.0 4000.0 4000.0 4000.0 \\n\",\n \"1 5000.0 1300.0 3000.0 5000.0 5000.0 5000.0 4000.0 5000.0 \\n\",\n \"2 14000.0 13000.0 13000.0 13000.0 13000.0 14000.0 13000.0 13000.0 \\n\",\n \"\\n\",\n \" 8 9 \\n\",\n \"0 3000.0 4000.0 \\n\",\n \"1 5000.0 5000.0 \\n\",\n \"2 9500.0 9000.0 \"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_pred\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Results of each tree\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0 1621.7274740226856\\n\",\n \"1 2942.7877939124323\\n\",\n \"2 1825.7418583505537\\n\",\n \"3 1000.0\\n\",\n \"4 1276.7145334803704\\n\",\n \"5 1414.213562373095\\n\",\n \"6 1414.213562373095\\n\",\n \"7 1000.0\\n\",\n \"8 1554.5631755148024\\n\",\n \"9 1914.854215512676\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i in range(n_B):\\n\",\n \" print(i, np.sqrt(mean_squared_error(y_pred[i], y_test)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Results of the ensemble\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 2990.0\\n\",\n \"1 4330.0\\n\",\n \"2 12450.0\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y_pred.mean(axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"998.5823284370031\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.sqrt(mean_squared_error(y_test, y_pred.mean(axis=1)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Bagged decision trees in scikit-learn (with B=500)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# define the training and testing sets\\n\",\n \"X_train = train.iloc[:, 1:]\\n\",\n \"y_train = train.iloc[:, 0]\\n\",\n \"X_test = test.iloc[:, 1:]\\n\",\n \"y_test = test.iloc[:, 0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# instruct BaggingRegressor to use DecisionTreeRegressor as the \\\"base estimator\\\"\\n\",\n \"from sklearn.ensemble import BaggingRegressor\\n\",\n \"bagreg = BaggingRegressor(DecisionTreeRegressor(), n_estimators=500, \\n\",\n \" bootstrap=True, oob_score=True, random_state=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 3344.2, 5395. , 12902. ])\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# fit and predict\\n\",\n \"bagreg.fit(X_train, y_train)\\n\",\n \"y_pred = bagreg.predict(X_test)\\n\",\n \"y_pred\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"657.8000304043775\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# calculate RMSE\\n\",\n \"np.sqrt(mean_squared_error(y_test, y_pred))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Estimating out-of-sample error\\n\",\n \"\\n\",\n \"For bagged models, out-of-sample error can be estimated without using **train/test split** or **cross-validation**!\\n\",\n \"\\n\",\n \"On average, each bagged tree uses about **two-thirds** of the observations. For each tree, the **remaining observations** are called \\\"out-of-bag\\\" observations.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([13, 2, 12, 2, 6, 1, 3, 10, 11, 9, 6, 1, 0, 1])\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# show the first bootstrap sample\\n\",\n \"samples[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{0, 1, 2, 3, 6, 9, 10, 11, 12, 13}\\n\",\n \"{0, 1, 2, 3, 4, 7, 9, 13}\\n\",\n \"{0, 2, 3, 4, 6, 7, 8, 9, 12, 13}\\n\",\n \"{0, 1, 2, 3, 5, 6, 8, 10, 11, 12}\\n\",\n \"{2, 3, 4, 6, 10, 11, 12, 13}\\n\",\n \"{0, 1, 4, 5, 6, 7, 9, 10, 11}\\n\",\n \"{0, 1, 2, 3, 4, 5, 8, 9, 12}\\n\",\n \"{1, 2, 3, 5, 6, 7, 9, 11}\\n\",\n \"{1, 3, 6, 7, 8, 9, 11, 12}\\n\",\n \"{0, 1, 3, 4, 5, 6, 8, 10, 11, 13}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# show the \\\"in-bag\\\" observations for each sample\\n\",\n \"for sample in samples:\\n\",\n \" print(set(sample))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[4, 5, 7, 8]\\n\",\n \"[5, 6, 8, 10, 11, 12]\\n\",\n \"[1, 5, 10, 11]\\n\",\n \"[4, 7, 9, 13]\\n\",\n \"[0, 1, 5, 7, 8, 9]\\n\",\n \"[2, 3, 8, 12, 13]\\n\",\n \"[6, 7, 10, 11, 13]\\n\",\n \"[0, 4, 8, 10, 12, 13]\\n\",\n \"[0, 2, 4, 5, 10, 13]\\n\",\n \"[2, 7, 9, 12]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# show the \\\"out-of-bag\\\" observations for each sample\\n\",\n \"for sample in samples:\\n\",\n \" print(sorted(set(range(n_samples)) - set(sample)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"How to calculate **\\\"out-of-bag error\\\":**\\n\",\n \"\\n\",\n \"1. For every observation in the training data, predict its response value using **only** the trees in which that observation was out-of-bag. Average those predictions (for regression) or take a vote (for classification).\\n\",\n \"2. Compare all predictions to the actual response values in order to compute the out-of-bag error.\\n\",\n \"\\n\",\n \"When B is sufficiently large, the **out-of-bag error** is an accurate estimate of **out-of-sample error**.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.7986955133989982\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute the out-of-bag R-squared score (not MSE, unfortunately!) for B=500\\n\",\n \"bagreg.oob_score_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Estimating feature importance\\n\",\n \"\\n\",\n \"Bagging increases **predictive accuracy**, but decreases **model interpretability** because it's no longer possible to visualize the tree to understand the importance of each feature.\\n\",\n \"\\n\",\n \"However, we can still obtain an overall summary of **feature importance** from bagged models:\\n\",\n \"\\n\",\n \"- **Bagged regression trees:** calculate the total amount that **MSE** is decreased due to splits over a given feature, averaged over all trees\\n\",\n \"- **Bagged classification trees:** calculate the total amount that **Gini index** is decreased due to splits over a given feature, averaged over all trees\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 4: Random Forests\\n\",\n \"\\n\",\n \"Random Forests is a **slight variation of bagged trees** that has even better performance:\\n\",\n \"\\n\",\n \"- Exactly like bagging, we create an ensemble of decision trees using bootstrapped samples of the training set.\\n\",\n \"- However, when building each tree, each time a split is considered, a **random sample of m features** is chosen as split candidates from the **full set of p features**. The split is only allowed to use **one of those m features**.\\n\",\n \" - A new random sample of features is chosen for **every single tree at every single split**.\\n\",\n \" - For **classification**, m is typically chosen to be the square root of p.\\n\",\n \" - For **regression**, m is typically chosen to be somewhere between p/3 and p.\\n\",\n \"\\n\",\n \"What's the point?\\n\",\n \"\\n\",\n \"- Suppose there is **one very strong feature** in the data set. When using bagged trees, most of the trees will use that feature as the top split, resulting in an ensemble of similar trees that are **highly correlated**.\\n\",\n \"- Averaging highly correlated quantities does not significantly reduce variance (which is the entire goal of bagging).\\n\",\n \"- By randomly leaving out candidate features from each split, **Random Forests \\\"decorrelates\\\" the trees**, such that the averaging process can reduce the variance of the resulting model.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Part 5: Building and tuning decision trees and Random Forests\\n\",\n \"\\n\",\n \"- Major League Baseball player data from 1986-87: [data](https://github.com/justmarkham/DAT8/blob/master/data/hitters.csv), [data dictionary](https://cran.r-project.org/web/packages/ISLR/ISLR.pdf) (page 7)\\n\",\n \"- Each observation represents a player\\n\",\n \"- **Goal:** Predict player salary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" AtBat Hits HmRun Runs RBI Walks Years CAtBat CHits CHmRun CRuns \\\\\\n\",\n \"1 315 81 7 24 38 39 14 3449 835 69 321 \\n\",\n \"2 479 130 18 66 72 76 3 1624 457 63 224 \\n\",\n \"3 496 141 20 65 78 37 11 5628 1575 225 828 \\n\",\n \"4 321 87 10 39 42 30 2 396 101 12 48 \\n\",\n \"5 594 169 4 74 51 35 11 4408 1133 19 501 \\n\",\n \"\\n\",\n \" CRBI CWalks League Division PutOuts Assists Errors Salary NewLeague \\n\",\n \"1 414 375 N W 632 43 10 475.0 N \\n\",\n \"2 266 263 A W 880 82 14 480.0 A \\n\",\n \"3 838 354 N E 200 11 3 500.0 N \\n\",\n \"4 46 33 N E 805 40 4 91.5 N \\n\",\n \"5 336 194 A W 282 421 25 750.0 A \"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# read in the data\\n\",\n \"with zipfile.ZipFile('../datasets/hitters.csv.zip', 'r') as z:\\n\",\n \" f = z.open('hitters.csv')\\n\",\n \" hitters = pd.read_csv(f, sep=',', index_col=False)\\n\",\n \"\\n\",\n \"# remove rows with missing values\\n\",\n \"hitters.dropna(inplace=True)\\n\",\n \"hitters.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" AtBat Hits HmRun Runs RBI Walks Years CAtBat CHits CHmRun CRuns \\\\\\n\",\n \"1 315 81 7 24 38 39 14 3449 835 69 321 \\n\",\n \"2 479 130 18 66 72 76 3 1624 457 63 224 \\n\",\n \"3 496 141 20 65 78 37 11 5628 1575 225 828 \\n\",\n \"4 321 87 10 39 42 30 2 396 101 12 48 \\n\",\n \"5 594 169 4 74 51 35 11 4408 1133 19 501 \\n\",\n \"\\n\",\n \" CRBI CWalks League Division PutOuts Assists Errors Salary NewLeague \\n\",\n \"1 414 375 0 0 632 43 10 475.0 0 \\n\",\n \"2 266 263 1 0 880 82 14 480.0 1 \\n\",\n \"3 838 354 0 1 200 11 3 500.0 0 \\n\",\n \"4 46 33 0 1 805 40 4 91.5 0 \\n\",\n \"5 336 194 1 0 282 421 25 750.0 1 \"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# encode categorical variables as integers\\n\",\n \"hitters['League'] = pd.factorize(hitters.League)[0]\\n\",\n \"hitters['Division'] = pd.factorize(hitters.Division)[0]\\n\",\n \"hitters['NewLeague'] = pd.factorize(hitters.NewLeague)[0]\\n\",\n \"hitters.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# allow plots to appear in the notebook\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('fivethirtyeight')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# scatter plot of Years versus Hits colored by Salary\\n\",\n \"hitters.plot(kind='scatter', x='Years', y='Hits', c='Salary', colormap='jet', xlim=(0, 25), ylim=(0, 250))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['AtBat', 'Hits', 'HmRun', 'Runs', 'RBI', 'Walks', 'Years', 'League',\\n\",\n \" 'Division', 'PutOuts', 'Assists', 'Errors', 'NewLeague'],\\n\",\n \" dtype='object')\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# define features: exclude career statistics (which start with \\\"C\\\") and the response (Salary)\\n\",\n \"feature_cols = hitters.columns[hitters.columns.str.startswith('C') == False].drop('Salary')\\n\",\n \"feature_cols\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# define X and y\\n\",\n \"X = hitters[feature_cols]\\n\",\n \"y = hitters.Salary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Predicting salary with a decision tree\\n\",\n \"\\n\",\n \"Find the best **max_depth** for a decision tree using cross-validation:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# list of values to try for max_depth\\n\",\n \"max_depth_range = range(1, 21)\\n\",\n \"\\n\",\n \"# list to store the average RMSE for each value of max_depth\\n\",\n \"RMSE_scores = []\\n\",\n \"\\n\",\n \"# use 10-fold cross-validation with each value of max_depth\\n\",\n \"from sklearn.model_selection import cross_val_score\\n\",\n \"for depth in max_depth_range:\\n\",\n \" treereg = DecisionTreeRegressor(max_depth=depth, random_state=1)\\n\",\n \" MSE_scores = cross_val_score(treereg, X, y, cv=10, scoring='neg_mean_squared_error')\\n\",\n \" RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'RMSE (lower is better)')\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot max_depth (x-axis) versus RMSE (y-axis)\\n\",\n \"plt.plot(max_depth_range, RMSE_scores)\\n\",\n \"plt.xlabel('max_depth')\\n\",\n \"plt.ylabel('RMSE (lower is better)')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(340.034168704752, 2)\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# show the best RMSE and the corresponding max_depth\\n\",\n \"sorted(zip(RMSE_scores, max_depth_range))[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"DecisionTreeRegressor(criterion='mse', max_depth=2, max_features=None,\\n\",\n \" max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n \" min_impurity_split=None, min_samples_leaf=1,\\n\",\n \" min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n \" presort=False, random_state=1, splitter='best')\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# max_depth=2 was best, so fit a tree using that parameter\\n\",\n \"treereg = DecisionTreeRegressor(max_depth=2, random_state=1)\\n\",\n \"treereg.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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featureimportance
0AtBat0.000000
2HmRun0.000000
3Runs0.000000
4RBI0.000000
5Walks0.000000
7League0.000000
8Division0.000000
9PutOuts0.000000
10Assists0.000000
11Errors0.000000
12NewLeague0.000000
6Years0.488391
1Hits0.511609
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" feature importance\\n\",\n \"0 AtBat 0.000000\\n\",\n \"2 HmRun 0.000000\\n\",\n \"3 Runs 0.000000\\n\",\n \"4 RBI 0.000000\\n\",\n \"5 Walks 0.000000\\n\",\n \"7 League 0.000000\\n\",\n \"8 Division 0.000000\\n\",\n \"9 PutOuts 0.000000\\n\",\n \"10 Assists 0.000000\\n\",\n \"11 Errors 0.000000\\n\",\n \"12 NewLeague 0.000000\\n\",\n \"6 Years 0.488391\\n\",\n \"1 Hits 0.511609\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute feature importances\\n\",\n \"pd.DataFrame({'feature':feature_cols, 'importance':treereg.feature_importances_}).sort_values('importance')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Predicting salary with a Random Forest\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n \" max_features='auto', max_leaf_nodes=None,\\n\",\n \" min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\\n\",\n \" oob_score=False, random_state=None, verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.ensemble import RandomForestRegressor\\n\",\n \"rfreg = RandomForestRegressor()\\n\",\n \"rfreg\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tuning n_estimators\\n\",\n \"\\n\",\n \"One important tuning parameter is **n_estimators**, which is the number of trees that should be grown. It should be a large enough value that the error seems to have \\\"stabilized\\\".\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# list of values to try for n_estimators\\n\",\n \"estimator_range = range(10, 310, 10)\\n\",\n \"\\n\",\n \"# list to store the average RMSE for each value of n_estimators\\n\",\n \"RMSE_scores = []\\n\",\n \"\\n\",\n \"# use 5-fold cross-validation with each value of n_estimators (WARNING: SLOW!)\\n\",\n \"for estimator in estimator_range:\\n\",\n \" rfreg = RandomForestRegressor(n_estimators=estimator, random_state=1, n_jobs=-1)\\n\",\n \" MSE_scores = cross_val_score(rfreg, X, y, cv=5, scoring='neg_mean_squared_error')\\n\",\n \" RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'RMSE (lower is better)')\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot n_estimators (x-axis) versus RMSE (y-axis)\\n\",\n \"plt.plot(estimator_range, RMSE_scores)\\n\",\n \"plt.xlabel('n_estimators')\\n\",\n \"plt.ylabel('RMSE (lower is better)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Tuning max_features\\n\",\n \"\\n\",\n \"The other important tuning parameter is **max_features**, which is the number of features that should be considered at each split.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# list of values to try for max_features\\n\",\n \"feature_range = range(1, len(feature_cols)+1)\\n\",\n \"\\n\",\n \"# list to store the average RMSE for each value of max_features\\n\",\n \"RMSE_scores = []\\n\",\n \"\\n\",\n \"# use 10-fold cross-validation with each value of max_features (WARNING: SLOW!)\\n\",\n \"for feature in feature_range:\\n\",\n \" rfreg = RandomForestRegressor(n_estimators=150, max_features=feature, random_state=1, n_jobs=-1)\\n\",\n \" MSE_scores = cross_val_score(rfreg, X, y, cv=10, scoring='neg_mean_squared_error')\\n\",\n \" RMSE_scores.append(np.mean(np.sqrt(-MSE_scores)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0,0.5,'RMSE (lower is better)')\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# plot max_features (x-axis) versus RMSE (y-axis)\\n\",\n \"plt.plot(feature_range, RMSE_scores)\\n\",\n \"plt.xlabel('max_features')\\n\",\n \"plt.ylabel('RMSE (lower is better)')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(290.0078511328435, 10)\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# show the best RMSE and the corresponding max_features\\n\",\n \"sorted(zip(RMSE_scores, feature_range))[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Fitting a Random Forest with the best parameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n \" max_features=8, max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n \" min_impurity_split=None, min_samples_leaf=1,\\n\",\n \" min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n \" n_estimators=150, n_jobs=1, oob_score=True, random_state=1,\\n\",\n \" verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# max_features=8 is best and n_estimators=150 is sufficiently large\\n\",\n \"rfreg = RandomForestRegressor(n_estimators=150, max_features=8, oob_score=True, random_state=1)\\n\",\n \"rfreg.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
featureimportance
7League0.003603
12NewLeague0.004290
8Division0.005477
10Assists0.023842
11Errors0.028618
2HmRun0.044607
9PutOuts0.060063
3Runs0.071800
0AtBat0.094592
4RBI0.130965
5Walks0.139899
1Hits0.145264
6Years0.246980
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" feature importance\\n\",\n \"7 League 0.003603\\n\",\n \"12 NewLeague 0.004290\\n\",\n \"8 Division 0.005477\\n\",\n \"10 Assists 0.023842\\n\",\n \"11 Errors 0.028618\\n\",\n \"2 HmRun 0.044607\\n\",\n \"9 PutOuts 0.060063\\n\",\n \"3 Runs 0.071800\\n\",\n \"0 AtBat 0.094592\\n\",\n \"4 RBI 0.130965\\n\",\n \"5 Walks 0.139899\\n\",\n \"1 Hits 0.145264\\n\",\n \"6 Years 0.246980\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute feature importances\\n\",\n \"pd.DataFrame({'feature':feature_cols, 'importance':rfreg.feature_importances_}).sort_values('importance')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.5274187002769267\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# compute the out-of-bag R-squared score\\n\",\n \"rfreg.oob_score_\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Reducing X to its most important features\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(263, 13)\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# check the shape of X\\n\",\n \"X.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\\n\",\n \" max_features=8, max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n \" min_impurity_split=None, min_samples_leaf=1,\\n\",\n \" min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n \" n_estimators=150, n_jobs=1, oob_score=True, random_state=1,\\n\",\n \" verbose=0, warm_start=False)\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"rfreg\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(263, 4)\\n\",\n \"(263, 5)\\n\",\n \"(263, 7)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# set a threshold for which features to include\\n\",\n \"from sklearn.feature_selection import SelectFromModel\\n\",\n \"print(SelectFromModel(rfreg, threshold=0.1, prefit=True).transform(X).shape)\\n\",\n \"print(SelectFromModel(rfreg, threshold='mean', prefit=True).transform(X).shape)\\n\",\n \"print(SelectFromModel(rfreg, threshold='median', prefit=True).transform(X).shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# create a new feature matrix that only includes important features\\n\",\n \"X_important = SelectFromModel(rfreg, threshold='mean', prefit=True).transform(X)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"284.35550515135395\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# check the RMSE for a Random Forest that only includes important features\\n\",\n \"rfreg = RandomForestRegressor(n_estimators=150, max_features=3, random_state=1)\\n\",\n \"scores = cross_val_score(rfreg, X_important, y, cv=10, scoring='neg_mean_squared_error')\\n\",\n \"np.mean(np.sqrt(-scores))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Comparing Random Forests with decision trees\\n\",\n \"\\n\",\n \"**Advantages of Random Forests:**\\n\",\n \"\\n\",\n \"- Performance is competitive with the best supervised learning methods\\n\",\n \"- Provides a more reliable estimate of feature importance\\n\",\n \"- Allows you to estimate out-of-sample error without using train/test split or cross-validation\\n\",\n \"\\n\",\n \"**Disadvantages of Random Forests:**\\n\",\n \"\\n\",\n \"- Less interpretable\\n\",\n \"- Slower to train\\n\",\n \"- Slower to predict\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![Machine learning flowchart](images/driver_ensembling.png)\\n\",\n \"\\n\",\n \"*Machine learning flowchart created by the [second place finisher](http://blog.kaggle.com/2015/04/20/axa-winners-interview-learning-telematic-fingerprints-from-gps-data/) of Kaggle's [Driver Telematics competition](https://www.kaggle.com/c/axa-driver-telematics-analysis)*\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/09_StatisticalInference.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 09 - Statistical Inference \\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## How we do Statiscal Inference in Machine Learning?\\n\",\n \"\\n\",\n \"It's usually acepted that Machine Learning algorithms have a huge power to predict and describe unknown data based on observed data. However, Machine Learning algorithms is not generally concern about the statistical inference for example as significance of predictions or estimated parameters. This focus it's usually true for traditional quantitative areas like econometrics, psicometrics that use significance as a evaluation metrics of models.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The following data is a sample of demographic and bank information of certain group of clients.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
IncomeLimitRatingCardsAgeEducationGenderStudentMarriedEthnicityBalance
114.891360628323411MaleNoYesCaucasian333
2106.025664548338215FemaleYesYesAsian903
3104.593707551447111MaleNoNoAsian580
4148.924950468133611FemaleNoNoAsian964
555.882489735726816MaleNoYesCaucasian331
680.180804756947710MaleNoNoCaucasian1151
720.996338825923712FemaleNoNoAfrican American203
871.40871145122879MaleNoNoAsian872
915.125330026656613FemaleNoNoCaucasian279
1071.061681949134119FemaleYesYesAfrican American1350
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Income Limit Rating Cards Age Education Gender Student Married \\\\\\n\",\n \"1 14.891 3606 283 2 34 11 Male No Yes \\n\",\n \"2 106.025 6645 483 3 82 15 Female Yes Yes \\n\",\n \"3 104.593 7075 514 4 71 11 Male No No \\n\",\n \"4 148.924 9504 681 3 36 11 Female No No \\n\",\n \"5 55.882 4897 357 2 68 16 Male No Yes \\n\",\n \"6 80.180 8047 569 4 77 10 Male No No \\n\",\n \"7 20.996 3388 259 2 37 12 Female No No \\n\",\n \"8 71.408 7114 512 2 87 9 Male No No \\n\",\n \"9 15.125 3300 266 5 66 13 Female No No \\n\",\n \"10 71.061 6819 491 3 41 19 Female Yes Yes \\n\",\n \"\\n\",\n \" Ethnicity Balance \\n\",\n \"1 Caucasian 333 \\n\",\n \"2 Asian 903 \\n\",\n \"3 Asian 580 \\n\",\n \"4 Asian 964 \\n\",\n \"5 Caucasian 331 \\n\",\n \"6 Caucasian 1151 \\n\",\n \"7 African American 203 \\n\",\n \"8 Asian 872 \\n\",\n \"9 Caucasian 279 \\n\",\n \"10 African American 1350 \"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Credit.csv', index_col=0)\\n\",\n \"data.head(10)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Usually, this kind of data it's commonly used to create scoring models. With the tools already studied, we could achieve this task easily. However this time, we would like to know which variables are important to explain Balance account of a given client?. In other words, we would like to know if it is a statistical relation between Balance and the other variables. For now, take Gender to test this hypothesis. \\n\",\n \"\\n\",\n \"\\n\",\n \"**Question:**\\n\",\n \"Is Gender **statistically** relevant to explain Balance account of a client? \\n\",\n \"To answer this question we could find if there are a difference in Balance account between Males and Females.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But, first analyze data visually to get a sense of data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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kRpGmDfnDf6+Tr202clzdNvOunnPiEt+B0aZOx7e7uCV9dK/F4IABpA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\\n4jkvXAA9gBudNcaB4Asm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D9bZJI/1xP/XtndAweT/SWuhxmUnUkgbzKM9P+h5NI6hnvQZtbRbg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\\nDMMwDMMwDMMwDMMwLcv/D3S4Vn8dYTpKAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"%matplotlib inline\\n\",\n \"sns.pairplot(data,hue='Gender',palette=\\\"husl\\\",markers=\\\"+\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It seems that Balance account distribution doesn't change across Gender. But, if we calculate the mean value of the Balance by Male and Female?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"\\n\",\n \"# splitting data \\n\",\n \"male_= data[data.Gender==' Male'].Balance\\n\",\n \"female_ = data[data.Gender=='Female'].Balance\\n\",\n \"\\n\",\n \"fig = plt.figure(figsize=(14,7))\\n\",\n \"n, bins, patches = plt.hist(male_, bins =50, facecolor='blue', alpha=0.5,label='Male')\\n\",\n \"n, bins, patches = plt.hist(female_, bins =50,facecolor='red', alpha=0.5,label='Female')\\n\",\n \"plt.axvline(male_.mean(),linestyle='--',color='blue',)\\n\",\n \"plt.axvline(female_.mean(),linestyle='--',color='red',)\\n\",\n \"plt.xlabel('Balance')\\n\",\n \"plt.legend();\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
IncomeLimitRatingCardsAgeEducationBalance
Gender
Male45.6103164713.165803353.5181352.98963755.59585513.466321509.803109
Female44.8539284756.516908356.2657002.92753655.73430013.434783529.536232
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Income Limit Rating Cards Age Education \\\\\\n\",\n \"Gender \\n\",\n \" Male 45.610316 4713.165803 353.518135 2.989637 55.595855 13.466321 \\n\",\n \"Female 44.853928 4756.516908 356.265700 2.927536 55.734300 13.434783 \\n\",\n \"\\n\",\n \" Balance \\n\",\n \"Gender \\n\",\n \" Male 509.803109 \\n\",\n \"Female 529.536232 \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Gender_differences = data.groupby('Gender').mean()\\n\",\n \"Gender_differences\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The mean difference in Balance by Gender is : -19.73312307576782\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('The mean difference in Balance by Gender is : '+ str(Gender_differences.loc[' Male','Balance']-Gender_differences.loc['Female','Balance']))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"So, we got it?, is this difference between Male and Female Balance enough to answer the initial question?\\n\",\n \"\\n\",\n \"**Short Answer:** No!\\n\",\n \"\\n\",\n \"**Long Answer:** No, we calculate a mean difference, but we haven't checked yet whether this value is statistically significant\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Hypothesis Testing\\n\",\n \"\\n\",\n \"To check the statistical significance of the mean difference estimated above we can postulate the following hypothesis:\\n\",\n \"\\n\",\n \" Ho: There is no difference in Balance account between Male and Female \\n\",\n \" \\n\",\n \" Ha: There is a statistical difference in Balance account between Male and Female\\n\",\n \" \\n\",\n \"We want to calculate the p-value of our estimation to compare with a accepted threshold of significance choosen by us:\\n\",\n \"\\n\",\n \"$\\\\alpha = (1\\\\%,5\\\\%,10\\\\%)$ \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### How we calculate the P-value?\\n\",\n \"\\n\",\n \"1. We can use the traditional method of statistics: assume a distribution for the data, calculate a statistcs like t distribution. \\n\",\n \"\\n\",\n \"2. Using the data and some sampling techniques we can computing the empirical distrubutin of data, and to check what is the probability asocieated with our estimation (P-value). \\n\",\n \"\\n\",\n \"\\n\",\n \"As we know traditional method (1), lets do the uncommon approach. We are going to see that this method can be implemented easily and have a huge power in more complicated tasks.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Data Sampling: Shuffling Algorithm \\n\",\n \"\\n\",\n \"The Shuffling algorithm is a sampling technique commonly used to simulate empirical distributions from the data.\\n\",\n \"\\n\",\n \"The basic idea is to simulate the distribution by shuffling the labels (Male and Female) repeatedly and computing a desired statistic. In our case, the choosen statistic is the mean difference.\\n\",\n \"\\n\",\n \"If the labels (Male and Female) really don't matter to explain Balance, then switching them randomly sould not change the result we got. \\n\",\n \"\\n\",\n \"**Steps:**\\n\",\n \"\\n\",\n \"1. Shuffle labels in the data. \\n\",\n \"2. Rearrange \\n\",\n \"3. Compunte the statistics: mean by Gender.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Building features and target variable\\n\",\n \"X = data.Gender.map({' Male': 1, 'Female':0})\\n\",\n \"Y = data.Balance\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First calculate the statistics (mean difference) in the data. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"The difference in Balance by Gender (in the data) is: 19.73312307576782\\n\"\n ]\n }\n ],\n \"source\": [\n \"original_difference = female_.mean() - male_.mean()\\n\",\n \"print('The difference in Balance by Gender (in the data) is: '+ str(original_difference))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
GenderBalance
11333
20903
31580
40964
51331
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Gender Balance\\n\",\n \"1 1 333\\n\",\n \"2 0 903\\n\",\n \"3 1 580\\n\",\n \"4 0 964\\n\",\n \"5 1 331\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create a Data Frame with desiered variables \\n\",\n \"dataframe = pd.DataFrame(X)\\n\",\n \"dataframe['Balance'] = Y\\n\",\n \"dataframe.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Step 1 & 2 \\n\",\n \"def shuffle_data(frame):\\n\",\n \" vec = np.zeros(frame.Gender.count())#.astype(float)\\n\",\n \" vec[np.random.choice(frame.Gender.count(),int(sum(frame.Gender)),replace=False)] = 1\\n\",\n \" frame['Gender'] = vec \\n\",\n \" return frame\\n\",\n \"# Step 3\\n\",\n \"def mean_difference(frame):\\n\",\n \" return frame.groupby('Gender').mean().loc[0,'Balance'] - frame.groupby('Gender').mean().loc[1,'Balance']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"\\n\",\n \"def simulate_distribution(frame, N=100):\\n\",\n \" a = []\\n\",\n \" for i in range(N):\\n\",\n \" a.append(mean_difference(shuffle_data(dataframe)))\\n\",\n \" return a\\n\",\n \"\\n\",\n \"def plot_distribution(dist,data,color='blue',bins=bins,orig=True):\\n\",\n \" fig = plt.figure(figsize=(10,6))\\n\",\n \" n, bins, patches = plt.hist(dist, bins = bins, normed=1.0, facecolor=color, alpha=0.5)\\n\",\n \" values, base = np.histogram(dist, bins = bins)\\n\",\n \" if orig:\\n\",\n \" plt.axvline(np.mean(data), color=color, linestyle='dashed', linewidth=2,label='Original data')\\n\",\n \" plt.legend()\\n\",\n \" plt.title('Mean difference')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"## Simulation\\n\",\n \"N = 1000\\n\",\n \"distribution = simulate_distribution(dataframe,N)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_distribution(distribution,original_difference,'blue',100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Calculating P-Value\\n\",\n \"def pvalue(dist,estimation):\\n\",\n \" return float(sum(np.array(dist)>estimation))/len(dist)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.355\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"p_value = pvalue(distribution,original_difference)\\n\",\n \"p_value\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Conclusion\\n\",\n \"\\n\",\n \"As we get the calculated P-value for the mean difference of our data is 0.316, so for any of the proposed significance thresholds we cann't reject the null hypothesis. So there is not statistical evidence to show that balance account is different between Male and Female. \"\n ]\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/10_CostSensitiveClassification.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 10 - Cost-Sensitive Classification\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"
\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Install costcla\\n\",\n \"\\n\",\n \"```\\n\",\n \"pip install costcla\\n\",\n \"```\\n\",\n \"\\n\",\n \"see https://github.com/albahnsen/CostSensitiveClassification\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" ## Credit Scoring\\n\",\n \" \\n\",\n \" In order to mitigate the impact of credit risk and make more objective and accurate decisions, \\n\",\n \" financial institutions use credit scores to predict and control their losses.\\n\",\n \" The objective in credit scoring is to classify which potential customers are likely to default a \\n\",\n \" contracted financial obligation based on the customer's past financial experience, and with that \\n\",\n \" information decide whether to approve or decline a loan [1]. This tool has \\n\",\n \" become a standard practice among financial institutions around the world in order to predict \\n\",\n \" and control their loans portfolios. When constructing credit scores, it is a common practice to \\n\",\n \" use standard cost-insensitive binary classification algorithms such as logistic regression, \\n\",\n \" neural networks, discriminant analysis, genetic programing, decision trees, among \\n\",\n \" others [2,3]. \\n\",\n \" \\n\",\n \" Formally, a credit score is a statistical model that allows the estimation of the probability \\n\",\n \" $\\\\hat p_i=P(y_i=1|\\\\mathbf{x}_i)$ of a customer $i$ defaulting a contracted debt. Additionally, \\n\",\n \"since the objective of credit scoring is to estimate a classifier $c_i$ to decide whether or not to grant a \\n\",\n \"loan to a customer $i$, a threshold $t$ is defined such that if $\\\\hat p_i < t $, then the loan is \\n\",\n \" granted, i.e., $c_i(t)=0$, and denied otherwise, i.e., $c_i(t)=1$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Example: Kaggle Credit Competition\\n\",\n \"\\n\",\n \"Improve on the state of the art in credit scoring by predicting the probability that somebody will experience financial distress in the next two years.\\n\",\n \"\\n\",\n \"https://www.kaggle.com/c/GiveMeSomeCredit\\n\",\n \"\\n\",\n \"### Load dataset and show basic statistics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\ProgramData\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\\n\",\n \" \\\"This module will be removed in 0.20.\\\", DeprecationWarning)\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dict_keys(['data', 'target', 'cost_mat', 'target_names', 'DESCR', 'feature_names', 'name'])\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"from costcla.datasets import load_creditscoring1\\n\",\n \"data = load_creditscoring1()\\n\",\n \"\\n\",\n \"# Elements of the data file\\n\",\n \"print(data.keys())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RevolvingUtilizationOfUnsecuredLinesageNumberOfTime30-59DaysPastDueNotWorseDebtRatioMonthlyIncomeNumberOfOpenCreditLinesAndLoansNumberOfTimes90DaysLateNumberRealEstateLoansOrLinesNumberOfTime60-89DaysPastDueNotWorseNumberOfDependents
00.76612745.02.00.8029829120.013.00.06.00.02.0
10.95715140.00.00.1218762600.04.00.00.00.01.0
20.65818038.01.00.0851133042.02.01.00.00.00.0
30.23381030.00.00.0360503300.05.00.00.00.00.0
40.90723949.01.00.02492663588.07.00.01.00.00.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" RevolvingUtilizationOfUnsecuredLines age \\\\\\n\",\n \"0 0.766127 45.0 \\n\",\n \"1 0.957151 40.0 \\n\",\n \"2 0.658180 38.0 \\n\",\n \"3 0.233810 30.0 \\n\",\n \"4 0.907239 49.0 \\n\",\n \"\\n\",\n \" NumberOfTime30-59DaysPastDueNotWorse DebtRatio MonthlyIncome \\\\\\n\",\n \"0 2.0 0.802982 9120.0 \\n\",\n \"1 0.0 0.121876 2600.0 \\n\",\n \"2 1.0 0.085113 3042.0 \\n\",\n \"3 0.0 0.036050 3300.0 \\n\",\n \"4 1.0 0.024926 63588.0 \\n\",\n \"\\n\",\n \" NumberOfOpenCreditLinesAndLoans NumberOfTimes90DaysLate \\\\\\n\",\n \"0 13.0 0.0 \\n\",\n \"1 4.0 0.0 \\n\",\n \"2 2.0 1.0 \\n\",\n \"3 5.0 0.0 \\n\",\n \"4 7.0 0.0 \\n\",\n \"\\n\",\n \" NumberRealEstateLoansOrLines NumberOfTime60-89DaysPastDueNotWorse \\\\\\n\",\n \"0 6.0 0.0 \\n\",\n \"1 0.0 0.0 \\n\",\n \"2 0.0 0.0 \\n\",\n \"3 0.0 0.0 \\n\",\n \"4 1.0 0.0 \\n\",\n \"\\n\",\n \" NumberOfDependents \\n\",\n \"0 2.0 \\n\",\n \"1 1.0 \\n\",\n \"2 0.0 \\n\",\n \"3 0.0 \\n\",\n \"4 0.0 \"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.DataFrame(data.data, columns=data.feature_names).head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" Frequency Percentage\\n\",\n \"Negative (Good Customers) 105299 0.932551\\n\",\n \"Positive (Bad Customers) 7616 0.067449\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Class label\\n\",\n \"target = pd.DataFrame(pd.Series(data.target).value_counts(), columns=('Frequency',))\\n\",\n \"target['Percentage'] = target['Frequency'] / target['Frequency'].sum()\\n\",\n \"target.index = ['Negative (Good Customers)', 'Positive (Bad Customers)']\\n\",\n \"print(target)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Full description of the dataset\\n\",\n \"# print data.DESCR\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Features
0RevolvingUtilizationOfUnsecuredLines
1age
2NumberOfTime30-59DaysPastDueNotWorse
3DebtRatio
4MonthlyIncome
5NumberOfOpenCreditLinesAndLoans
6NumberOfTimes90DaysLate
7NumberRealEstateLoansOrLines
8NumberOfTime60-89DaysPastDueNotWorse
9NumberOfDependents
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Features\\n\",\n \"0 RevolvingUtilizationOfUnsecuredLines\\n\",\n \"1 age\\n\",\n \"2 NumberOfTime30-59DaysPastDueNotWorse\\n\",\n \"3 DebtRatio\\n\",\n \"4 MonthlyIncome\\n\",\n \"5 NumberOfOpenCreditLinesAndLoans\\n\",\n \"6 NumberOfTimes90DaysLate\\n\",\n \"7 NumberRealEstateLoansOrLines\\n\",\n \"8 NumberOfTime60-89DaysPastDueNotWorse\\n\",\n \"9 NumberOfDependents\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Number of features\\n\",\n \"pd.DataFrame(data.feature_names, columns=('Features',))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Credit scoring as a standard classification problem\\n\",\n \"\\n\",\n \"Using three classifiers, a model is learned to classify customers in good and bad\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Load classifiers and split dataset in training and testing\\n\",\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"X_train, X_test, y_train, y_test, cost_mat_train, cost_mat_test = \\\\\\n\",\n \"train_test_split(data.data, data.target, data.cost_mat)\\n\",\n \"\\n\",\n \"# Fit the classifiers using the training dataset\\n\",\n \"classifiers = {\\\"RF\\\": {\\\"f\\\": RandomForestClassifier()},\\n\",\n \" \\\"DT\\\": {\\\"f\\\": DecisionTreeClassifier()},\\n\",\n \" \\\"LR\\\": {\\\"f\\\": LogisticRegression()}}\\n\",\n \"\\n\",\n \"for model in classifiers.keys():\\n\",\n \" # Fit\\n\",\n \" classifiers[model][\\\"f\\\"].fit(X_train, y_train)\\n\",\n \" # Predict\\n\",\n \" classifiers[model][\\\"c\\\"] = classifiers[model][\\\"f\\\"].predict(X_test)\\n\",\n \" classifiers[model][\\\"p\\\"] = classifiers[model][\\\"f\\\"].predict_proba(X_test)\\n\",\n \" classifiers[model][\\\"p_train\\\"] = classifiers[model][\\\"f\\\"].predict_proba(X_train)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" After the classifier $c_i$ is estimated, there is a need to evaluate its performance. In \\n\",\n \" practice, many statistical evaluation measures are used to assess the performance of a credit \\n\",\n \" scoring model. Measures such as the area under the receiver operating characteristic curve (AUC),\\n\",\n \" Brier score, Kolmogorov-Smirnoff (K-S) statistic, $F_1$-Score, and misclassification are among \\n\",\n \" the most common [4]. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
f1prerecacc
RF0.2215160.4843750.1435920.930532
DT0.2445540.2355750.2542460.891884
LR0.0221890.5500000.0113230.931312
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" f1 pre rec acc\\n\",\n \"RF 0.221516 0.484375 0.143592 0.930532\\n\",\n \"DT 0.244554 0.235575 0.254246 0.891884\\n\",\n \"LR 0.022189 0.550000 0.011323 0.931312\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Evaluate the performance\\n\",\n \"from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\\n\",\n \"measures = {\\\"f1\\\": f1_score, \\\"pre\\\": precision_score, \\n\",\n \" \\\"rec\\\": recall_score, \\\"acc\\\": accuracy_score}\\n\",\n \"results = pd.DataFrame(columns=measures.keys())\\n\",\n \"\\n\",\n \"# Evaluate each model in classifiers\\n\",\n \"for model in classifiers.keys():\\n\",\n \" results.loc[model] = [measures[measure](y_test, classifiers[model][\\\"c\\\"]) for measure in measures.keys()]\\n\",\n \"\\n\",\n \"results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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RdgXZkHm2FMeb8HagM8UBsgFArR1dV1h49a7iSFRhERkUUqkbI42JZu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\\nomkEAACAI5pGAAAAOKJpBAAAgKO/AYrvNRReHYTEAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot the results\\n\",\n \"%matplotlib inline\\n\",\n \"from IPython.core.pylabtools import figsize\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use('ggplot')\\n\",\n \"\\n\",\n \"figsize(10, 5)\\n\",\n \"ax = plt.subplot(111)\\n\",\n \"\\n\",\n \"ind = np.arange(results.shape[0])\\n\",\n \"width = 0.2\\n\",\n \"l = ax.plot(ind, results, \\\"-o\\\")\\n\",\n \"plt.legend(iter(l), results.columns.tolist(), loc='center left', bbox_to_anchor=(1, 0.5))\\n\",\n \"ax.set_xlim([-0.25, ind[-1]+.25])\\n\",\n \"ax.set_xticks(ind)\\n\",\n \"ax.set_xticklabels(results.index)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" Nevertheless, none of these measures takes into account the \\n\",\n \" business and economical realities that take place in credit scoring. Costs that the financial \\n\",\n \" institution had incurred to acquire customers, or the expected profit due to a particular client, \\n\",\n \" are not considered in the evaluation of the different models. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Financial Evaluation of a Credit Scorecard\\n\",\n \"\\n\",\n \" Typically, a credit risk model is evaluated using standard cost-insensitive measures.\\n\",\n \" However, in practice, the cost associated with approving \\n\",\n \" what is known as a bad customer, i.e., a customer who default his credit loan, is quite \\n\",\n \" different from the cost associated with declining a good customer, i.e., a customer who \\n\",\n \" successfully repay his credit loan. Furthermore, the costs are not constant among customers. \\n\",\n \" This is because loans have different credit line amounts, terms, and even interest rates. Some \\n\",\n \" authors have proposed methods that include the misclassification costs in the credit scoring \\n\",\n \" context [4,5,6,7].\\n\",\n \" \\n\",\n \" In order to take into account the varying costs that each example carries, we proposed in \\n\",\n \" [8], a cost matrix with example-dependent misclassification costs as \\n\",\n \" given in the following table.\\n\",\n \" \\n\",\n \" \\n\",\n \"| \\t| Actual Positive ($y_i=1$) \\t| Actual Negative \\t($y_i=0$)|\\n\",\n \"|---\\t|:-:\\t|:-:\\t|\\n\",\n \"| Predicted Positive ($c_i=1$)\\t| $C_{TP_i}=0$\\t| $C_{FP_i}=r_i+C^a_{FP}$ \\t|\\n\",\n \"| Predicted Negative ($c_i=0$) \\t| $C_{FN_i}=Cl_i \\\\cdot L_{gd}$\\t| $C_{TN_i}=0$\\t|\\n\",\n \" \\n\",\n \" First, we assume that the costs of a correct \\n\",\n \" classification, $C_{TP_i}$ and $C_{TN_i}$, are zero for every customer $i$. We define $C_{FN_i}$ \\n\",\n \" to be the losses if the customer $i$ defaults to be proportional to his credit line $Cl_i$. We \\n\",\n \" define the cost of a false positive per customer $C_{FP_i}$ as the sum of two real financial \\n\",\n \" costs $r_i$ and $C^a_{FP}$, where $r_i$ is the loss in profit by rejecting what would have been a \\n\",\n \" good customer. \\n\",\n \" \\n\",\n \" The profit per customer $r_i$ is calculated as the present value of the difference between the \\n\",\n \" financial institution gains and expenses, given the credit line $Cl_i$, the term $l_i$ and the \\n\",\n \" financial institution lending rate $int_{r_i}$ for customer $i$, and the financial institution \\n\",\n \" of cost funds $int_{cf}$.\\n\",\n \"\\n\",\n \" $$ r_i= PV(A(Cl_i,int_{r_i},l_i),int_{cf},l_i)-Cl_i,$$\\n\",\n \" \\n\",\n \" with $A$ being the customer monthly payment and $PV$ the present value of the monthly payments,\\n\",\n \" which are calculated using the time value of money equations [9],\\n\",\n \" \\n\",\n \" $$ A(Cl_i,int_{r_i},l_i) = Cl_i \\\\frac{int_{r_i}(1+int_{r_i})^{l_i}}{(1+int_{r_i})^{l_i}-1},$$\\n\",\n \" \\n\",\n \" $$ PV(A,int_{cf},l_i) = \\\\frac{A}{int_{cf}} \\\\left(1-\\\\frac{1}{(1+int_{cf})^{l_i}} \\\\right).$$\\n\",\n \" \\n\",\n \" The second term $C^a_{FP}$, is related to the assumption that the financial institution will not \\n\",\n \" keep the money of the declined customer idle. It will instead give a loan to an alternative \\n\",\n \" customer [10]. Since no further information is known about the alternative customer, \\n\",\n \" it is assumed to have an average credit line $\\\\overline{Cl}$ and an average profit $\\\\overline{r}$.\\n\",\n \" Given that, \\n\",\n \" \\n\",\n \" $$ C^a_{FP}=- \\\\overline{r} \\\\cdot \\\\pi_0+\\\\overline{Cl}\\\\cdot L_{gd} \\\\cdot \\\\pi_1,$$\\n\",\n \"\\n\",\n \" in other words minus the profit of an average alternative customer plus the expected loss, \\n\",\n \" taking into account that the alternative customer will pay his debt with a probability equal to \\n\",\n \" the prior negative rate, and similarly will default with probability equal to the prior positive \\n\",\n \" rate.\\n\",\n \" \\n\",\n \" One key parameter of our model is the credit limit. There exists several strategies to calculate \\n\",\n \" the $Cl_i$ depending on the type of loans, the state of the economy, the current portfolio, \\n\",\n \" among others [1,9]. Nevertheless, given the lack of information \\n\",\n \" regarding the specific business environments of the considered datasets, we simply define \\n\",\n \" $Cl_i$ as\\n\",\n \"\\n\",\n \"$$ Cl_i = \\\\min \\\\bigg\\\\{ q \\\\cdot Inc_i, Cl_{max}, Cl_{max}(debt_i) \\\\bigg\\\\},$$\\n\",\n \" \\n\",\n \" where $Inc_i$ and $debt_i$ are the monthly income and debt ratio of the customer $i$, \\n\",\n \" respectively, $q$ is a parameter that defines the maximum $Cl_i$ in times $Inc_i$, and \\n\",\n \" $Cl_{max}$ the maximum overall credit line. Lastly, the maximum credit line given the current \\n\",\n \" debt is calculated as the maximum credit limit such that the current debt ratio plus the new \\n\",\n \" monthly payment does not surpass the customer monthly income. It is calculated as\\n\",\n \" \\n\",\n \" $$ Cl_{max}(debt_i)=PV\\\\left(Inc_i \\\\cdot P_{m}(debt_i),int_{r_i},l_i\\\\right),$$\\n\",\n \" and\\n\",\n \" $$ P_{m}(debt_i)=\\\\min \\\\left\\\\{ \\\\frac{A(q \\\\cdot Inc_i,int_{r_i},l_i)}{Inc_i},\\\\left(1-debt_i \\\\right) \\\\right\\\\}.$$\\n\",\n \" \\n\",\n \" \\n\",\n \"### Financial savings\\n\",\n \"\\n\",\n \" Let $\\\\mathcal{S}$ be a set of $N$ examples $i$, $N=\\\\vert S \\\\vert$, where each example is \\n\",\n \" represented by the augmented feature vector \\n\",\n \" $\\\\mathbf{x}_i^*=[\\\\mathbf{x}_i, C_{TP_i},C_{FP_i},C_{FN_i},C_{TN_i}]$ \\n\",\n \" and labeled using the class label $y_i \\\\in \\\\{0,1\\\\}$. \\n\",\n \" A classifier $f$ which generates the predicted label $c_i$ for each element $i$ is trained \\n\",\n \" using the set $\\\\mathcal{S}$. Then the cost of using $f$ on $\\\\mathcal{S}$ is calculated by\\n\",\n \" \\n\",\n \" $$ Cost(f(\\\\mathcal{S})) = \\\\sum_{i=1}^N Cost(f(\\\\mathbf{x}_i^*)),$$\\n\",\n \" \\n\",\n \" where\\n\",\n \" \\n\",\n \" $$ Cost(f(\\\\mathbf{x}_i^*)) = y_i(c_i C_{TP_i} + (1-c_i)C_{FN_i}) + (1-y_i)(c_i C_{FP_i} + (1-c_i)C_{TN_i}).$$\\n\",\n \" \\n\",\n \"\\n\",\n \" However, the total cost may not be easy to interpret. We proposed an approach in [8], where the savings of using an algorithm are defined as the cost of the algorithm versus the cost of using no algorithm at all. To do that, the cost of the costless class is defined as \\n\",\n \" \\n\",\n \" $$ Cost_l(\\\\mathcal{S}) = \\\\min \\\\{Cost(f_0(\\\\mathcal{S})), Cost(f_1(\\\\mathcal{S}))\\\\},$$\\n\",\n \" \\n\",\n \" where \\n\",\n \" \\n\",\n \" $$ f_a(\\\\mathcal{S}) = \\\\mathbf{a}, \\\\text{ with } a\\\\in \\\\{0,1\\\\}.$$\\n\",\n \" \\n\",\n \"\\n\",\n \" The cost improvement can be expressed as the cost savings as compared with $Cost_l(\\\\mathcal{S})$. \\n\",\n \" \\n\",\n \" $$ Savings(f(\\\\mathcal{S})) = \\\\frac{ Cost_l(\\\\mathcal{S}) - Cost(f(\\\\mathcal{S}))} {Cost_l(\\\\mathcal{S})}.$$\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" ### Parameters for the Kaggle Credit Database\\n\",\n \"\\n\",\n \" As this database contain information regarding the features, and more importantly about the income of each example, from which an estimated credit limit $Cl_i$ can be calculated.\\n\",\n \"Since no specific information regarding the datasets is provided, we assume that they belong to an\\n\",\n \"average Portuguese financial institution. This enabled us to find the different \\n\",\n \"parameters needed to calculate the cost measure. \\n\",\n \"\\n\",\n \"| Parameter \\t| Value |\\n\",\n \"|---\\t|:-:\\t|\\n\",\n \"|Interest rate ($int_r$) | 4.79% |\\n\",\n \"| Cost of funds ($int_{cf}$) | 2.94% |\\n\",\n \"| Term ($l$) in months | 24 |\\n\",\n \"| Loss given default ($L_{gd}$) | 75% |\\n\",\n \"| Times income ($q$) | 3 |\\n\",\n \"| Maximum credit line ($Cl_{max}$) | 25,000|\\n\",\n \"\\n\",\n \"In particular, we obtain the average interest rates in Europe during 2013 from the European Central Bank [11]. Additionally, we use a fixed loan term $l$ to two years,\\n\",\n \"considering that in the Kaggle Credit dataset the class was constructed to predict two years of credit behavior.\\n\",\n \"Moreover, we set the loss given default $L_{gd}$ using information from \\n\",\n \"the Basel II standard, $q$ to 3 since it is the average personal loan requests related to monthly income, and the maximum credit limit $Cl_{max}$ to 25,000 Euros.\\n\",\n \"\\n\",\n \"### Calculation of the savings of the models\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 1023.73054104 18750. 0. 0. ]\\n\",\n \" [ 717.25781516 6749.25 0. 0. ]\\n\",\n \" [ 866.65393177 12599.25 0. 0. ]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# The cost matrix is already calculated for the dataset\\n\",\n \"# cost_mat[C_FP,C_FN,C_TP,C_TN]\\n\",\n \"print(data.cost_mat[[10, 17, 50]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" f1 pre rec acc sav\\n\",\n \"RF 0.221516 0.484375 0.143592 0.930532 0.126101\\n\",\n \"DT 0.244554 0.235575 0.254246 0.891884 0.177077\\n\",\n \"LR 0.022189 0.550000 0.011323 0.931312 0.005574\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Calculation of the cost and savings\\n\",\n \"from costcla.metrics import savings_score, cost_loss \\n\",\n \"\\n\",\n \"# Evaluate the savings for each model\\n\",\n \"results[\\\"sav\\\"] = np.zeros(results.shape[0])\\n\",\n \"for model in classifiers.keys():\\n\",\n \" results[\\\"sav\\\"].loc[model] = savings_score(y_test, classifiers[model][\\\"c\\\"], cost_mat_test)\\n\",\n \"\\n\",\n \"# TODO: plot results\\n\",\n \"print(results)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n 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WPHqri4uB92AwACx7e/7tT45Gj95oNKfVJ9wug4AOC3ehz6r6urk8Ph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\\nSUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot the results\\n\",\n \"\\n\",\n \"figsize(10, 5)\\n\",\n \"ax = plt.subplot(111)\\n\",\n \"l = ax.plot(ind, results[\\\"f1\\\"], \\\"-o\\\", label='F1Score', c='C1')\\n\",\n \"b = ax.bar(ind, results['sav'], 0.6, label='Savings')\\n\",\n \"plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\\n\",\n \"ax.set_xlim([-0.5, ind[-1]+.5])\\n\",\n \"ax.set_xticks(ind)\\n\",\n \"ax.set_xticklabels(results.index)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There are significant differences in the \\n\",\n \"results when evaluating a model using a traditional cost-insensitive measure such as the \\n\",\n \"accuracy or F1Score, than when using the savings, leading to the conclusion of the \\n\",\n \"importance of using the real practical financial costs of each context.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Bayes minimum risk\\n\",\n \"\\n\",\n \"As these methods (RF, LR and DT) are not performing well we then move to use cost-sensitive methods. The first model we used is the Bayes minimum risk model (BMR) [8].\\n\",\n \"As defined in [12], the BMR classifier is a decision model based on quantifying \\n\",\n \"tradeoffs between various decisions using probabilities and the costs that accompany such decisions. \\n\",\n \"This is done in a way that for each example the expected losses are minimized. In what follows, we \\n\",\n \"consider the probability estimates $\\\\hat p_i$ as known, regardless of the algorithm used to \\n\",\n \"calculate them. The risk that accompanies each decision is calculated using the cost matrix described above.\\n\",\n \"In the specific framework of binary classification, the risk of predicting the example $i$ as negative is \\n\",\n \"\\n\",\n \"$$ R(c_i=0|\\\\mathbf{x}_i)=C_{TN_i}(1-\\\\hat p_i)+C_{FN_i} \\\\cdot \\\\hat p_i, $$\\n\",\n \"and\\n\",\n \"$$ R(c_i=1|\\\\mathbf{x}_i)=C_{TP_i} \\\\cdot \\\\hat p_i + C_{FP_i}(1- \\\\hat p_i), $$\\n\",\n \"\\n\",\n \"is the risk when predicting the example as positive, where $\\\\hat p_i$ is the estimated positive \\n\",\n \"probability for example $i$. Subsequently, if \\n\",\n \"\\n\",\n \"$$ R(c_i=0|\\\\mathbf{x}_i) \\\\le R(c_i=1|\\\\mathbf{x}_i), $$\\n\",\n \"\\n\",\n \"then the example $i$ is classified as negative. This means that the risk associated with the \\n\",\n \"decision $c_i$ is lower than the risk associated with classifying it as positive. \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
f1prerecaccsav
RF0.2215160.4843750.1435920.9305320.126101
DT0.2445540.2355750.2542460.8918840.177077
LR0.0221890.5500000.0113230.9313120.005574
RF-BMR0.2778420.1794560.6150280.7799430.399326
DT-BMR0.1300960.0766340.4302620.6039530.077424
LR-BMR0.1700020.1000820.5640760.6208860.188954
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" f1 pre rec acc sav\\n\",\n \"RF 0.221516 0.484375 0.143592 0.930532 0.126101\\n\",\n \"DT 0.244554 0.235575 0.254246 0.891884 0.177077\\n\",\n \"LR 0.022189 0.550000 0.011323 0.931312 0.005574\\n\",\n \"RF-BMR 0.277842 0.179456 0.615028 0.779943 0.399326\\n\",\n \"DT-BMR 0.130096 0.076634 0.430262 0.603953 0.077424\\n\",\n \"LR-BMR 0.170002 0.100082 0.564076 0.620886 0.188954\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from costcla.models import BayesMinimumRiskClassifier\\n\",\n \"ci_models = list(classifiers.keys())\\n\",\n \"\\n\",\n \"for model in ci_models:\\n\",\n \" classifiers[model+\\\"-BMR\\\"] = {\\\"f\\\": BayesMinimumRiskClassifier()}\\n\",\n \" # Fit\\n\",\n \" classifiers[model+\\\"-BMR\\\"][\\\"f\\\"].fit(y_test, classifiers[model][\\\"p\\\"]) \\n\",\n \" # Calibration must be made in a validation set\\n\",\n \" # Predict\\n\",\n \" classifiers[model+\\\"-BMR\\\"][\\\"c\\\"] = classifiers[model+\\\"-BMR\\\"][\\\"f\\\"].predict(classifiers[model][\\\"p\\\"], cost_mat_test)\\n\",\n \" # Evaluate\\n\",\n \" results.loc[model+\\\"-BMR\\\"] = 0\\n\",\n \" results.loc[model+\\\"-BMR\\\", measures.keys()] = \\\\\\n\",\n \" [measures[measure](y_test, classifiers[model+\\\"-BMR\\\"][\\\"c\\\"]) for measure in measures.keys()]\\n\",\n \" results[\\\"sav\\\"].loc[model+\\\"-BMR\\\"] = savings_score(y_test, classifiers[model+\\\"-BMR\\\"][\\\"c\\\"], cost_mat_test)\\n\",\n \" \\n\",\n \"results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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TkwmUwwGo1QKpVITExERkZGi2WGDRsGtVoNABgwYABsNlv3VEtE5CP2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\\nyC0xUSUiIiIit8RElYiIiIjc0v8Ae0iHF1042JQAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot the results\\n\",\n \"ind = np.arange(results.shape[0])\\n\",\n \"figsize(10, 5)\\n\",\n \"ax = plt.subplot(111)\\n\",\n \"l = ax.plot(ind, results[\\\"f1\\\"], \\\"-o\\\", label='F1Score', c='C1')\\n\",\n \"b = ax.bar(ind, results['sav'], 0.6, label='Savings')\\n\",\n \"plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\\n\",\n \"ax.set_xlim([-0.5, ind[-1]+.5])\\n\",\n \"ax.set_xticks(ind)\\n\",\n \"ax.set_xticklabels(results.index)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Cost-sensitive decision trees\\n\",\n \"\\n\",\n \"The next algorithm that is evaluated is the cost-sensitive decision trees algorithm [13].\\n\",\n \"\\n\",\n \"Decision trees are one of the most widely used machine learning algorithms [14]. \\n\",\n \"The technique is considered to be white box, in the sense that is easy to interpret, and has a \\n\",\n \"very low computational cost, while maintaining a good performance as compared with more complex \\n\",\n \"techniques [15]. There are two types of decision tree depending on the objective of \\n\",\n \"the model. They work either for classification or regression. \\n\",\n \"\\n\",\n \"- Construction of classification trees\\n\",\n \"\\n\",\n \"Classification trees is one of the most common types of decision tree, in which the objective \\n\",\n \"is to find the $Tree$ that best discriminates between classes. In general the decision tree \\n\",\n \"represents a set of splitting rules organized in levels in a flowchart structure.\\n\",\n \"\\n\",\n \"- Splitting criteria\\n\",\n \"\\n\",\n \"In the $Tree$, each rule is shown as a node, and it is represented as $(\\\\mathbf{x}^j,l^j)$, meaning \\n\",\n \"that the set $\\\\mathcal{S}$ is split in two sets $\\\\mathcal{S}^l$ and $\\\\mathcal{S}^r$ according to \\n\",\n \"$\\\\mathbf{x}^j$ and $l^j$:\\n\",\n \"\\n\",\n \"$$ \\\\mathcal{S}^l = \\\\{\\\\mathbf{x}_i^* \\\\vert \\\\mathbf{x}_i^* \\\\in \\\\mathcal{S} \\\\wedge x^j_i \\\\le l^j \\\\}, \\\\quad and \\\\quad\\n\",\n \" \\\\mathcal{S}^r = \\\\{\\\\mathbf{x}_i^* \\\\vert \\\\mathbf{x}_i^* \\\\in \\\\mathcal{S} \\\\wedge x^j_i > l^j \\\\}, $$\\n\",\n \"\\n\",\n \"where $\\\\mathbf{x}^j$ is the $j^{th}$ feature represented in the vector \\n\",\n \"$\\\\mathbf{x}^j=[x_1^j,x_2^j,...,x_N^j]$, and $l^j$ is a value such that $min(\\\\mathbf{x}^j) \\\\le l^j < \\n\",\n \"max(\\\\mathbf{x}^j)$. Moreover, $\\\\mathcal{S} = \\\\mathcal{S}^l \\\\cup \\\\mathcal{S}^r$.\\n\",\n \"\\n\",\n \"After the training set have been split, the percentage of positives in the different sets is calculated. First, the number of positives in each set is estimated by\\n\",\n \"\\n\",\n \"$$ \\\\mathcal{S}_1 = \\\\{\\\\mathbf{x}_i^* \\\\vert \\\\mathbf{x}_i^* \\\\in \\\\mathcal{S} \\\\wedge y_i =1 \\\\}, $$\\n\",\n \"\\n\",\n \"and the percentage of positives is calculates as $\\\\pi_1=\\\\vert \\\\mathcal{S}_1 \\\\vert / \\\\vert \\\\mathcal{S} \\\\vert.$\\n\",\n \"\\n\",\n \"Then, the impurity of each leaf is calculated using either a misclassification error, \\n\",\n \"entropy or Gini measures:\\n\",\n \"\\n\",\n \"\\n\",\n \"a) Misclassification: \\n\",\n \" $I_m(\\\\pi_1)=1-max(\\\\pi_1,1-\\\\pi_1)$ \\n\",\n \"\\n\",\n \"b) Entropy: $I_e(\\\\pi_1)=-\\\\pi_1\\\\log \\\\pi_1 -(1-\\\\pi_1) \\\\log (1-\\\\pi_1)$ \\n\",\n \"\\n\",\n \"c) Gini: \\n\",\n \" $I_g(\\\\pi_1)=2\\\\pi_1(1-\\\\pi_1)$\\n\",\n \"\\n\",\n \"Finally the gain of the splitting criteria using the rule $(\\\\mathbf{x}^j,l^j)$ is calculated as the \\n\",\n \"impurity of $\\\\mathcal{S}$ minus the weighted impurity of each leaf:\\n\",\n \"\\n\",\n \"$$ Gain(\\\\mathbf{x}^j,l^j)=I(\\\\pi_1)-\\\\frac{\\\\vert \\\\mathcal{S}^l \\\\vert}{\\\\vert \\\\mathcal{S} \\n\",\n \"\\\\vert}I(\\\\pi^l_1) -\\\\frac{\\\\vert \\\\mathcal{S}^r \\\\vert}{\\\\vert \\\\mathcal{S} \\\\vert}I(\\\\pi^r_1), $$\\n\",\n \"\\n\",\n \"where $I(\\\\pi_1)$ can be either of the impurity measures $I_e(\\\\pi_1)$ or $I_g(\\\\pi_1)$.\\n\",\n \"\\n\",\n \"Subsequently, the gain of all possible splitting rules is calculated. The rule with maximal \\n\",\n \"gain is selected\\n\",\n \"\\n\",\n \"$$ (best_x, best_l) = argmax_{(\\\\mathbf{x}^j,l^j)}Gain(\\\\mathbf{x}^j,l^j), $$\\n\",\n \"\\n\",\n \"and the set $\\\\mathcal{S}$ is split into $\\\\mathcal{S}^l$ and $\\\\mathcal{S}^r$ according to that rule. \\n\",\n \"\\n\",\n \"\\n\",\n \"- Tree growing\\n\",\n \"\\n\",\n \"In order to grow a tree typical algorithms use a top-down induction using a greedy search in each\\n\",\n \"iteration [16]. In each iteration, the algorithms evaluates all possible splitting \\n\",\n \"rules and pick the one that maximizes the splitting criteria. After the selection of a splitting \\n\",\n \"rule, each leaf is further selected and it is subdivides into smaller leafs, until one of the \\n\",\n \"stopping criteria is meet. \\n\",\n \"\\n\",\n \"- Cost-sensitive impurity measures\\n\",\n \"\\n\",\n \"Standard impurity measures such as misclassification, entropy or Gini, take into account the \\n\",\n \"distribution of classes of each leaf to evaluate the predictive power of a splitting rule,\\n\",\n \"leading to an impurity measure that is based on minimizing the misclassification rate. However, \\n\",\n \"as has been previously shown [8], minimizing misclassification does not \\n\",\n \"lead to the same results than minimizing cost. Instead, we are interested in measuring how good \\n\",\n \"is a splitting rule in terms of cost not only accuracy. For doing that, we propose a new \\n\",\n \"example-dependent cost based impurity measure that takes into account the cost matrix of each \\n\",\n \"example.\\n\",\n \"\\n\",\n \"We define a new cost-based impurity measure taking into account the costs when all the examples\\n\",\n \"in a leaf are classified both as negative using $f_0$ and positive using $f_1$\\n\",\n \"\\n\",\n \"$$ I_c(\\\\mathcal{S}) = \\\\min \\\\bigg\\\\{ Cost(f_0(\\\\mathcal{S})), Cost(f_1(\\\\mathcal{S})) \\\\bigg\\\\}. $$\\n\",\n \"\\n\",\n \"The objective of this measure is to evaluate the lowest expected cost of a splitting rule.\\n\",\n \"Following the same logic, the classification of each set is calculated as the prediction that \\n\",\n \"leads to the lowest cost\\n\",\n \"\\n\",\n \"$$\\n\",\n \"f(\\\\mathcal{S}) = \\n\",\n \"\\\\begin{cases}\\n\",\n \" \\\\phantom{-}0 \\\\phantom{-} \\\\mbox{if} \\\\phantom{-} Cost(f_0(\\\\mathcal{S})) \\\\le \\n\",\n \"Cost(f_1(\\\\mathcal{S}))\\\\\\\\\\n\",\n \" \\\\phantom{-}1 \\\\phantom{-}\\\\mbox{otherwise}\\n\",\n \"\\\\end{cases}\\n\",\n \"$$\\n\",\n \"\\n\",\n \"Finally, using the cost-based impurity, the splitting criteria cost based gain of using the \\n\",\n \"splitting rule $(\\\\mathbf{x}^j,l^j)$ is calculated. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
f1prerecaccsav
RF0.2215160.4843750.1435920.9305320.126101
DT0.2445540.2355750.2542460.8918840.177077
LR0.0221890.5500000.0113230.9313120.005574
RF-BMR0.2778420.1794560.6150280.7799430.399326
DT-BMR0.1300960.0766340.4302620.6039530.077424
LR-BMR0.1700020.1000820.5640760.6208860.188954
CSDT0.2764880.1671330.7997940.7118920.480551
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" f1 pre rec acc sav\\n\",\n \"RF 0.221516 0.484375 0.143592 0.930532 0.126101\\n\",\n \"DT 0.244554 0.235575 0.254246 0.891884 0.177077\\n\",\n \"LR 0.022189 0.550000 0.011323 0.931312 0.005574\\n\",\n \"RF-BMR 0.277842 0.179456 0.615028 0.779943 0.399326\\n\",\n \"DT-BMR 0.130096 0.076634 0.430262 0.603953 0.077424\\n\",\n \"LR-BMR 0.170002 0.100082 0.564076 0.620886 0.188954\\n\",\n \"CSDT 0.276488 0.167133 0.799794 0.711892 0.480551\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from costcla.models import CostSensitiveDecisionTreeClassifier\\n\",\n \"\\n\",\n \"classifiers[\\\"CSDT\\\"] = {\\\"f\\\": CostSensitiveDecisionTreeClassifier()}\\n\",\n \"# Fit\\n\",\n \"classifiers[\\\"CSDT\\\"][\\\"f\\\"].fit(X_train, y_train, cost_mat_train)\\n\",\n \"# Predict\\n\",\n \"classifiers[\\\"CSDT\\\"][\\\"c\\\"] = classifiers[\\\"CSDT\\\"][\\\"f\\\"].predict(X_test)\\n\",\n \"# Evaluate\\n\",\n \"results.loc[\\\"CSDT\\\"] = 0\\n\",\n \"results.loc[\\\"CSDT\\\", measures.keys()] = \\\\\\n\",\n \"[measures[measure](y_test, classifiers[\\\"CSDT\\\"][\\\"c\\\"]) for measure in measures.keys()]\\n\",\n \"results[\\\"sav\\\"].loc[\\\"CSDT\\\"] = savings_score(y_test, classifiers[\\\"CSDT\\\"][\\\"c\\\"], cost_mat_test)\\n\",\n \" \\n\",\n \"results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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DEAakKyVqtt0etgICUit2T0UmNL9F1Iyj+OvkXnb7vdraH0s9qZUoZS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\\nAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Plot the results\\n\",\n \"ind = np.arange(results.shape[0])\\n\",\n \"figsize(10, 5)\\n\",\n \"ax = plt.subplot(111)\\n\",\n \"l = ax.plot(ind, results[\\\"f1\\\"], \\\"-o\\\", label='F1Score', c='C1')\\n\",\n \"b = ax.bar(ind, results['sav'], 0.6, label='Savings')\\n\",\n \"plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\\n\",\n \"ax.set_xlim([-0.5, ind[-1]+.5])\\n\",\n \"ax.set_xticks(ind)\\n\",\n \"ax.set_xticklabels(results.index)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Conclusions\\n\",\n \"\\n\",\n \"CostCla is a easy to use Python library for example-dependent cost-sensitive \\n\",\n \"classification problems. It includes many example-dependent cost-sensitive algorithms. Since \\n\",\n \"it is part of the scientific Python ecosystem, it can be easily integrated with other \\n\",\n \"machine learning libraries. Future work includes adding more cost-sensitive databases and \\n\",\n \"algorithms, and support for Python $\\\\ge$ 3.4. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## References\\n\",\n \"1. Anderson, R. (2007). The Credit Scoring Toolkit : Theory and Practice for Retail Credit Risk Management and Decision Automation. Oxford University Press.\\n\",\n \"2. Hand, D. J., & Henley, W. E. (1997). Statistical Classification Methods in Consumer Credit Scoring: A Review. Journal of the Royal Statstical Society. Series A (Statistics in Society), 160(3), 523–541.\\n\",\n \"3. Correa Bahnsen, A., & Gonzalez Montoya, A. (2011). Evolutionary Algorithms for Selecting the Architecture of a MLP Neural Network: A Credit Scoring Case. In IEEE 11th International Conference on Data Mining Workshops (pp. 725–732). Ieee. http://doi.org/10.1109/ICDMW.2011.80\\n\",\n \"4. Beling, P., Covaliu, Z., & Oliver, R. M. (2005). Optimal scoring cutoff policies and efficient frontiers. Journal of the Operational Research Society, 56(9), 1016–1029. http://doi.org/10.1057/palgrave.jors.2602021\\n\",\n \"5. Verbraken, T., Bravo, C., Weber, R., & Baesens, B. (2014). Development and application of consumer credit scoring models using profit-based classification measures. European Journal of Operational Research, 238(2), 505–513. http://doi.org/10.1016/j.ejor.2014.04.001\\n\",\n \"6. Alejo, R., & Garc, V. (2013). Making Accurate Credit Risk Predictions with Cost-Sensitive MLP Neural Networks. In J. Casillas, F. J. Martínez-López, R. Vicari, & F. De la Prieta (Eds.), Advances in Intelligent Systems and Computing (Vol. 220, pp. 1–8). Heidelberg: Springer International Publishing. http://doi.org/10.1007/978-3-319-00569-0\\n\",\n \"7. Oliver, R., & Thomas, L. (2009). Optimal score cutoffs and pricing in regulatory capital in retail credit portfolios (No. CRR-09-01). Southampton. Retrieved from http://eprints.soton.ac.uk/71321/\\n\",\n \"8. Correa Bahnsen, A., Aouada, D., & Ottersten, B. (2014). Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring. In 2014 13th International Conference on Machine Learning and Applications (pp. 263–269). Detroit, USA: IEEE. http://doi.org/10.1109/ICMLA.2014.48\\n\",\n \"9. Lawrence, D., & Solomon, A. (2012). Managing a Consumer Lending Business. Solomon Lawrence Partners. \\n\",\n \"10. Nayak, G. N., & Turvey, C. G. (1997). Credit Risk Assessment and the Opportunity Costs of Loan Misclassification. Canadian Journal of Agricultural Economics, 45(3), 285–299. http://doi.org/10.1111/j.1744-7976.1997.tb00209.x\\n\",\n \"11. ECB. (2014). European Central Bank. Retrieved from http://www.ecb.europa.eu/stats\\n\",\n \"12. Jayanta K., G., Mohan, D., & Tapas, S. (2006). Bayesian Inference and Decision Theory. In An Introduction to Bayesian Analysis (Vol. 13, pp. 26–63). Springer New York. http://doi.org/10.1007/978-0-387-35433-0_2\\n\",\n \"13. Correa Bahnsen, A., Aouada, D., & Ottersten, B. (2015). Example-Dependent Cost-Sensitive Decision Trees. Expert Systems with Applications, in press. http://doi.org/10.1016/j.eswa.2015.04.042\\n\",\n \"14. Maimon, L. R. O. (2008). Data Mining with Decision Trees: Theory and Applications. World Scientific Publishing Company.\\n\",\n \"15. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Stanford, CA: Springer.\\n\",\n \"16. Rokach, L., & Maimon, O. (2010). Decision Trees. In L. Rokach & O. Maimon (Eds.), Data Mining and Knowledge Discovery Handbook (2nd ed., pp. 149–174). Springer US. http://doi.org/10.1007/978-0-387-09823-4_9\\n\",\n \"17. \\\\citep{Rokach2009}\\n\",\n \"18. \\\\citep{Zhou2012}\\n\",\n \"19. \\\\citep{Louppe2012}\\n\",\n \"20. Correa Bahnsen, A., Aouada, D., & Ottersten, B. (2015). Ensembles of Example-Dependent Cost-Sensitive Decision Trees. http://arxiv.org/abs/1505.04637\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/11_Model_Deployment.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# 07 - Model Deployment\\n\",\n \"\\n\",\n \"by [Alejandro Correa Bahnsen](http://www.albahnsen.com/) & [Iván Torroledo](http://www.ivantorroledo.com/)\\n\",\n \"\\n\",\n \"version 1.2, Feb 2018\\n\",\n \"\\n\",\n \"## Part of the class [Machine Learning for Risk Management](https://github.com/albahnsen/ML_RiskManagement)\\n\",\n \"\\n\",\n \"This notebook is licensed under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Agenda:\\n\",\n \"\\n\",\n \"1. Creating and saving a model\\n\",\n \"2. Running the model in batch\\n\",\n \"3. Exposing the model as an API\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Part 1: Phishing Detection\\n\",\n \"\\n\",\n \"Phishing, by definition, is the act of defrauding an online user in order to obtain personal information by posing as a trustworthy institution or entity. Users usually have a hard time differentiating between legitimate and malicious sites because they are made to look exactly the same. Therefore, there is a need to create better tools to combat attackers.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import zipfile\\n\",\n \"with zipfile.ZipFile('../datasets/model_deployment/phishing.csv.zip', 'r') as z:\\n\",\n \" f = z.open('phishing.csv')\\n\",\n \" data = pd.read_csv(f, index_col=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
urlphishing
0http://www.subalipack.com/contact/images/sampl...1
1http://fasc.maximecapellot-gypsyjazz-ensemble....1
2http://theotheragency.com/confirmer/confirmer-...1
3http://aaalandscaping.com/components/com_smart...1
4http://paypal.com.confirm-key-21107316126168.s...1
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" url phishing\\n\",\n \"0 http://www.subalipack.com/contact/images/sampl... 1\\n\",\n \"1 http://fasc.maximecapellot-gypsyjazz-ensemble.... 1\\n\",\n \"2 http://theotheragency.com/confirmer/confirmer-... 1\\n\",\n \"3 http://aaalandscaping.com/components/com_smart... 1\\n\",\n \"4 http://paypal.com.confirm-key-21107316126168.s... 1\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
urlphishing
39995http://www.diaperswappers.com/forum/member.php...0
39996http://posting.bohemian.com/northbay/Tools/Ema...0
39997http://www.tripadvisor.jp/Hotel_Review-g303832...0
39998http://www.baylor.edu/content/services/downloa...0
39999http://www.phinfever.com/forums/viewtopic.php?...0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" url phishing\\n\",\n \"39995 http://www.diaperswappers.com/forum/member.php... 0\\n\",\n \"39996 http://posting.bohemian.com/northbay/Tools/Ema... 0\\n\",\n \"39997 http://www.tripadvisor.jp/Hotel_Review-g303832... 0\\n\",\n \"39998 http://www.baylor.edu/content/services/downloa... 0\\n\",\n \"39999 http://www.phinfever.com/forums/viewtopic.php?... 0\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.tail()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1 20000\\n\",\n \"0 20000\\n\",\n \"Name: phishing, dtype: int64\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.phishing.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Creating features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['http://dothan.com.co/gold/austspark/index.htm\\\\n',\\n\",\n \" 'http://78.142.63.63/%7Enetsysco/process/fc1d9c7ea4773b7ff90925c2902cb5f2\\\\n',\\n\",\n \" 'http://verify95.5gbfree.com/coverme2010/\\\\n',\\n\",\n \" 'http://www.racom.com/uploads/productscat/bookmark/ii.php?.rand=13vqcr8bp0gud&cbcxt=mai&email=abuse@tradinghouse.ca\\\\n',\\n\",\n \" 'http://www.cleanenergytci.com/components/update.logon.l3an7lofamerica/2342343234532534546347677898765432876543345687656543876/\\\\n',\\n\",\n \" 'http://209.148.89.163/-/santander.co.uk/weblegn/AccountLogin.php\\\\n',\\n\",\n \" 'http://senevi.com/confirmation/\\\\n',\\n\",\n \" 'http://www.hellenkeller.cl/tmp/new/noticias/Modulo_de_Atualizacao_Bradesco/index2.php?id=PSO1AM04L3Q6PSBNVJ82QUCO0L5GBSY2KM2U9BYUEO14HCRDVZEMTRB3DGJO9HPT4ROC4M8HA8LRJD5FCJ27AD0NTSC3A3VDUJQX6XFG519OED4RW6Y8J8VC19EAAAO5UF21CHGHIP7W4AO1GM8ZU4BUBQ6L2UQVARVM\\\\n',\\n\",\n \" 'http://internet-sicherheit.co/de/konflikt/src%3Dde/AZ00276ZZ75/we%3Dhs_0_2/sicherheit/konto_verifizieren/verifizierung.php\\\\n',\\n\",\n \" 'http://alen.co/docs/cleaner\\\\n',\\n\",\n \" 'http://rattanhouse.co/Atualizacao_Bradesco/cadastro2013.php?2MAS2XACUJPI3U8D9ZDDG2G9YJICVABQ3K73KWDKYK0NA0AWWWCOUEDUJRXHRKPNMUYLDV89RA6OCG2MQUS0TAUXX9IOGJUEIXPDS5B0RM18OF1H860UAMJOY6ICUR81VSEKKJFPBYNLYGUXBGJ1HEHKOMLTM01P658M\\\\n',\\n\",\n \" 'http://steamcommunily.co/p.php?login=true\\\\n',\\n\",\n \" 'http://www.nyyg.com/Bradesco/5W9SQ394.html\\\\n',\\n\",\n \" 'http://wp.tipografiacentral.com.co/sparkde/index.html\\\\n',\\n\",\n \" 'http://www.entrerev.com/component/.secure.wpa/.www.paypal.com.returnUrl=/cgi-bin/5RF3S6y0K349/PayPal.co.uk/dispute_centre/sotmks/npsw&st.payment.decline.centre/ipoi/secure-codes.paypal.account4738154login.complete-infrmations.login.accountSecure26/securities/\\\\n',\\n\",\n \" 'http://x.co/SecurCent\\\\n',\\n\",\n \" 'http://dejatequerer.co/united.com/index.html\\\\n',\\n\",\n \" 'http://www.speakeasymovies.com/components/com_wrapper/.amazon.co.uk/\\\\n',\\n\",\n \" 'http://www.culturaespanola.com.br/bt/www.paypal.com/paypal.com.com/index-new.php\\\\n',\\n\",\n \" 'http://www.agroassistance.com/components/com_content/c05354aa285b6a932a57086ba13762a1/\\\\n',\\n\",\n \" 'http://www.estranetsrl.com.ar/bbvacambios.html\\\\n',\\n\",\n \" 'http://osfsw.cba.pl/content/classic/html/ibpf/bradesco/?UOREEIYGQTERIRVSJTUHMVMZJWWYSVNYQOFSPWVFTEJEEKMJWHFERRYTFRWPSYYWGFIGJUPLZMZLTNSKOGMQQSHSXPLMXILVSM\\\\n',\\n\",\n \" 'http://bitcrush.co/~geetha5/natwest/natwest/ibcarregister-natwst.html\\\\n',\\n\",\n \" 'http://cannot-hide-from-PhishTank.zenith-services.com/controllare/auth/\\\\n',\\n\",\n \" 'http://nova.pymesonline.co/fr.php\\\\n',\\n\",\n \" 'http://comococino.com/wp-content/uploads/2013/01/paypal.com/us/cgi-bin/webscr.htm?\\\\n',\\n\",\n \" 'http://www.fundacionchwinqlal.com.gt/imgs/Notas/img/_New/Agencias_Bradesco/Public_201133.php?KSR6YOU359CY1USIRMSBI8CFJF7TVREFJ6KIUFKZNXXNRP7JBYVU79APNGJI8YYR5I0YXUXLRU0JKF4WEYQL81BUGVDOTBFXUPVSKSEBNNU84X4IWT54UFYABCY5OE3J5XBOQQ1EDVMHTPZPJ4TEJSOU5NZS32B8ZNWQ\\\\n',\\n\",\n \" 'http://flightripe.com/confirmation/update/billing/9a523c6017caa3406af9d5c2c0cb1854/\\\\n',\\n\",\n \" 'http://accademiazerootto.it/templates/zerootto-new/html/com_content/category/bompreco.php\\\\n',\\n\",\n \" 'http://santanderseguranca.zapto.org/Clientesx/\\\\n',\\n\",\n \" 'http://www.muttico.com/components/com_media/p3rs0na4l/53f8b14c76c890e1806b8f9d97f12f80/\\\\n',\\n\",\n \" 'http://us.fxlhtvf.ml/login/en/login.html.asp?refhttp:%2F%2Futddirect.com%2Fcomponents%2Fcom_content%2Fviews%2Fcategories%2Fmenu.html\\\\n',\\n\",\n \" 'http://conferencistainternacional.com.co/urruirrhyttjk/Index.htm\\\\n',\\n\",\n \" 'http://www.creativesovereign.com/components/com_newsfeeds/views/.../perfil/\\\\n',\\n\",\n \" 'http://villamarina.com.co/administrator/servers/BankofAmerica/security-update/SecMeasure/account-overview.cgi/presentation/jskeys/sas/signonScreen.do/\\\\n',\\n\",\n \" 'http://www.vipturismolondres.com/com.br/?atendimento=Cliente&/LgSgkszm64/B8aNzHa8Aj.php\\\\n',\\n\",\n \" 'http://www.enoxia.fr/components/com_content/tamfidelidade01.php\\\\n',\\n\",\n \" 'http://gobbva.com/bb/empresa/index.php?tarjeta=\\\\n',\\n\",\n \" 'http://paypal-com-confim.sharmikelectric.com/s4575234bf5055889415\\\\n',\\n\",\n \" 'http://paypal.com.au.au.webapps.mpp.homes.konyadosemeciler.com/confirm/login.australia/au/webapps/mpp/home/initthi.php?cmd=SignIn&co_partnerId=2&pUserId=&siteid=0&pageType=&pa1=&i1=&bshowgif=&UsingSSL=&ru=&pp=&pa2=&errmsg=&runame=%5C%5C%5C%5C\\\\n',\\n\",\n \" 'http://www.bbvabancocontinental.ya.st\\\\n',\\n\",\n \" 'http://www.giannielectric.com/company/components/com_poll/assets/a/a5643cded2383f7568719482a943e1a5\\\\n',\\n\",\n \" 'http://cooperativasanjose.com.co/plugins/josetta_ext/k2category/section/first.php\\\\n',\\n\",\n \" 'http://appleid-apple-com-confirm-oyns-uattw6w61x3oka3pq.scientificcollectables.com/3c43e3d92e0b8a48f09f5fbb25d008a9/index1.php?cmd=https://connect.paypal.com/WebObjects/iTunesConnect.woa?login-processing=t&login_access=13409884065d3a174c294a9bf21bf71c23a3\\\\n',\\n\",\n \" 'http://consultoriojuridico.co/pp/www.paypal.com/\\\\n',\\n\",\n \" 'http://lovetodo.in.th/administrator/components/com_content/models/key/\\\\n',\\n\",\n \" 'http://lnk.co/io6u45y45?erydh?mario.Carelli@poste.it\\\\n',\\n\",\n \" 'http://www2.bancobbvacontnental.com/Centroll/informe/03/14/datitarlz/WUJFQ0VSUkFATVVOSVpMQVcuQ09N\\\\n',\\n\",\n \" 'http://lfcintl.com/components/com_user/zzxc/bpd.com.do/app/do/personas/289302294350311363178310441412402464323394411438376403437407/banco.popular.php?Personal\\\\n',\\n\",\n \" 'http://procuraduria.videoteca.com.co/update/apple.com/.cgi-bin/WebObjects/MyAppleIdwoa/wa/sign_in.html?appId=4129.returnURL=DaHR0cDovL3N0b3JlLmFwcGxlLmNvbS91c3wxYW9zZmU4OGZjNWIyNThhYWVhOTM5MzVjZjI2NTk1OGE3MWUwY2Y0MmI2OA%26r%3DSDHCD9JUYKX777H9KT\\\\n']\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.url[data.phishing==1].sample(50, random_state=1).tolist()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Contain any of the following:\\n\",\n \"* https\\n\",\n \"* login\\n\",\n \"* .php\\n\",\n \"* .html\\n\",\n \"* @\\n\",\n \"* sign\\n\",\n \"* ?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"keywords = ['https', 'login', '.php', '.html', '@', 'sign']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"for keyword in keywords:\\n\",\n \" data['keyword_' + keyword] = data.url.str.contains(keyword).astype(int)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"* Lenght of the url\\n\",\n \"* Lenght of domain\\n\",\n \"* is IP?\\n\",\n \"* Number of .com\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"data['lenght'] = data.url.str.len() - 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"domain = data.url.str.split('/', expand=True).iloc[:, 2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"data['lenght_domain'] = domain.str.len()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 www.subalipack.com\\n\",\n \"1 fasc.maximecapellot-gypsyjazz-ensemble.nl\\n\",\n \"2 theotheragency.com\\n\",\n \"3 aaalandscaping.com\\n\",\n \"4 paypal.com.confirm-key-21107316126168.securepp...\\n\",\n \"5 lcthomasdeiriarte.edu.co\\n\",\n \"6 livetoshare.org\\n\",\n \"7 www.i-m.co\\n\",\n \"8 manuelfernando.co\\n\",\n \"9 www.bladesmithnews.com\\n\",\n \"10 www.rasbaek.com\\n\",\n \"11 199.231.190.160\\n\",\n \"Name: 2, dtype: object\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"domain.head(12)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"data['isIP'] = (domain.str.replace('.', '') * 1).str.isnumeric().astype(int)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"data['count_com'] = data.url.str.count('com')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
urlphishingkeyword_httpskeyword_loginkeyword_.phpkeyword_.htmlkeyword_@keyword_signlenghtlenght_domainisIPcount_com
28607http://pennstatehershey.org/web/ibd/home/event...0000000802000
3689http://guiadesanborja.com/multiprinter/muestra...1011000811801
6405http://paranaibaweb.com/faleconosco/accounting...1000100651601
35355http://courts.delaware.gov/Jury%20Services/Hel...0000000941900
16520http://erpa.co/tmp/getproductrequest.htm\\\\n100000039700
16196http://pulapulapipoca.com/components/com_media...10110002391804
3810http://www.dag.or.kr/zboard/icon/visa/img/Atua...1000000621300
3005http://www.amazingdressup.com/wp-content/theme...1000100942201
9003http://web.indosuksesfutures.com/content_file/...1000000802501
34704http://www.nutritionaltree.com/subcat.aspx?cid...0000000692301
12561http://www.formation-continue-loiret.fr/compon...10000001223205
10885http://191.91.128.205/httpss/bancolombiaa.olb....11011004511412
2633http://www.sternies-hp.de/components/com_conte...1000000851802
22253http://www.silive.com/northshore/index.ssf/200...0000100851401
4720http://www.dineo.co.za/components/com_content/...10010001721503
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" url phishing \\\\\\n\",\n \"28607 http://pennstatehershey.org/web/ibd/home/event... 0 \\n\",\n \"3689 http://guiadesanborja.com/multiprinter/muestra... 1 \\n\",\n \"6405 http://paranaibaweb.com/faleconosco/accounting... 1 \\n\",\n \"35355 http://courts.delaware.gov/Jury%20Services/Hel... 0 \\n\",\n \"16520 http://erpa.co/tmp/getproductrequest.htm\\\\n 1 \\n\",\n \"16196 http://pulapulapipoca.com/components/com_media... 1 \\n\",\n \"3810 http://www.dag.or.kr/zboard/icon/visa/img/Atua... 1 \\n\",\n \"3005 http://www.amazingdressup.com/wp-content/theme... 1 \\n\",\n \"9003 http://web.indosuksesfutures.com/content_file/... 1 \\n\",\n \"34704 http://www.nutritionaltree.com/subcat.aspx?cid... 0 \\n\",\n \"12561 http://www.formation-continue-loiret.fr/compon... 1 \\n\",\n \"10885 http://191.91.128.205/httpss/bancolombiaa.olb.... 1 \\n\",\n \"2633 http://www.sternies-hp.de/components/com_conte... 1 \\n\",\n \"22253 http://www.silive.com/northshore/index.ssf/200... 0 \\n\",\n \"4720 http://www.dineo.co.za/components/com_content/... 1 \\n\",\n \"\\n\",\n \" keyword_https keyword_login keyword_.php keyword_.html keyword_@ \\\\\\n\",\n \"28607 0 0 0 0 0 \\n\",\n \"3689 0 1 1 0 0 \\n\",\n \"6405 0 0 0 1 0 \\n\",\n \"35355 0 0 0 0 0 \\n\",\n \"16520 0 0 0 0 0 \\n\",\n \"16196 0 1 1 0 0 \\n\",\n \"3810 0 0 0 0 0 \\n\",\n \"3005 0 0 0 1 0 \\n\",\n \"9003 0 0 0 0 0 \\n\",\n \"34704 0 0 0 0 0 \\n\",\n \"12561 0 0 0 0 0 \\n\",\n \"10885 1 0 1 1 0 \\n\",\n \"2633 0 0 0 0 0 \\n\",\n \"22253 0 0 0 1 0 \\n\",\n \"4720 0 0 1 0 0 \\n\",\n \"\\n\",\n \" keyword_sign lenght lenght_domain isIP count_com \\n\",\n \"28607 0 80 20 0 0 \\n\",\n \"3689 0 81 18 0 1 \\n\",\n \"6405 0 65 16 0 1 \\n\",\n \"35355 0 94 19 0 0 \\n\",\n \"16520 0 39 7 0 0 \\n\",\n \"16196 0 239 18 0 4 \\n\",\n \"3810 0 62 13 0 0 \\n\",\n \"3005 0 94 22 0 1 \\n\",\n \"9003 0 80 25 0 1 \\n\",\n \"34704 0 69 23 0 1 \\n\",\n \"12561 0 122 32 0 5 \\n\",\n \"10885 0 451 14 1 2 \\n\",\n \"2633 0 85 18 0 2 \\n\",\n \"22253 0 85 14 0 1 \\n\",\n \"4720 0 172 15 0 3 \"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.sample(15, random_state=4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Create Model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"X = data.drop(['url', 'phishing'], axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"y = data.phishing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.ensemble import RandomForestClassifier\\n\",\n \"from sklearn.model_selection import cross_val_score\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"clf = RandomForestClassifier(n_jobs=-1, n_estimators=100)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0.80875, 0.80825, 0.804 , 0.79025, 0.80475, 0.81125, 0.80475,\\n\",\n \" 0.80675, 0.8045 , 0.78925])\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"cross_val_score(clf, X, y, cv=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\\n\",\n \" max_depth=None, max_features='auto', max_leaf_nodes=None,\\n\",\n \" min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n \" min_samples_leaf=1, min_samples_split=2,\\n\",\n \" min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\\n\",\n \" oob_score=False, random_state=None, verbose=0,\\n\",\n \" warm_start=False)\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"clf.fit(X, y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Save model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.externals import joblib\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['../datasets/model_deployment/07_phishing_clf.pkl']\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"joblib.dump(clf, '../datasets/model_deployment/07_phishing_clf.pkl', compress=3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Part 2: Model in batch\\n\",\n \"\\n\",\n \"See m07_model_deployment.py\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from m07_model_deployment import predict_proba\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.6824999999999997\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predict_proba('http://www.vipturismolondres.com/com.br/?atendimento=Cliente&/LgSgkszm64/B8aNzHa8Aj.php')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Part 3: API\\n\",\n \"\\n\",\n \"Flask is considered more Pythonic than Django because Flask web application code is in most cases more explicit. Flask is easy to get started with as a beginner because there is little boilerplate code for getting a simple app up and running.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"First we need to install some libraries \\n\",\n \"\\n\",\n \"```\\n\",\n \"pip install flask-restplus\\n\",\n \"```\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Load Flask\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from flask import Flask\\n\",\n \"from flask_restplus import Api, Resource, fields\\n\",\n \"from sklearn.externals import joblib\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create api\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"app = Flask(__name__)\\n\",\n \"\\n\",\n \"api = Api(\\n\",\n \" app, \\n\",\n \" version='1.0', \\n\",\n \" title='Phishing Prediction API',\\n\",\n \" description='Phishing Prediction API')\\n\",\n \"\\n\",\n \"ns = api.namespace('predict', \\n\",\n \" description='Phishing Classifier')\\n\",\n \" \\n\",\n \"parser = api.parser()\\n\",\n \"\\n\",\n \"parser.add_argument(\\n\",\n \" 'URL', \\n\",\n \" type=str, \\n\",\n \" required=True, \\n\",\n \" help='URL to be analyzed', \\n\",\n \" location='args')\\n\",\n \"\\n\",\n \"resource_fields = api.model('Resource', {\\n\",\n \" 'result': fields.String,\\n\",\n \"})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Load model and create function that predicts an URL\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"clf = joblib.load('../datasets/model_deployment/07_phishing_clf.pkl') \\n\",\n \"\\n\",\n \"@ns.route('/')\\n\",\n \"class PhishingApi(Resource):\\n\",\n \"\\n\",\n \" @api.doc(parser=parser)\\n\",\n \" @api.marshal_with(resource_fields)\\n\",\n \" def get(self):\\n\",\n \" args = parser.parse_args()\\n\",\n \" result = self.predict_proba(args)\\n\",\n \"\\n\",\n \" return result, 200\\n\",\n \"\\n\",\n \" def predict_proba(self, args):\\n\",\n \" url = args['URL']\\n\",\n \" \\n\",\n \" url_ = pd.DataFrame([url], columns=['url'])\\n\",\n \" \\n\",\n \" # Create features\\n\",\n \" keywords = ['https', 'login', '.php', '.html', '@', 'sign']\\n\",\n \" for keyword in keywords:\\n\",\n \" url_['keyword_' + keyword] = url_.url.str.contains(keyword).astype(int)\\n\",\n \" \\n\",\n \" url_['lenght'] = url_.url.str.len() - 2\\n\",\n \" domain = url_.url.str.split('/', expand=True).iloc[:, 2]\\n\",\n \" url_['lenght_domain'] = domain.str.len()\\n\",\n \" url_['isIP'] = (url_.url.str.replace('.', '') * 1).str.isnumeric().astype(int)\\n\",\n \" url_['count_com'] = url_.url.str.count('com')\\n\",\n \"\\n\",\n \" # Make prediction\\n\",\n \" p1 = clf.predict_proba(url_.drop('url', axis=1))[0,1]\\n\",\n \"\\n\",\n \" print('url=', url,'| p1=', p1)\\n\",\n \"\\n\",\n \" return {\\n\",\n \" \\\"result\\\": p1\\n\",\n \" }\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Run API\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \" * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)\\n\",\n \"127.0.0.1 - - [07/Feb/2018 15:56:34] \\\"GET /predict/?URL=http://consultoriojuridico.co/pp/www.paypal.com/ HTTP/1.1\\\" 200 -\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"url= http://consultoriojuridico.co/pp/www.paypal.com/ | p1= 0.32507780944545644\\n\"\n ]\n }\n ],\n \"source\": [\n \"app.run(debug=True, use_reloader=False, host='0.0.0.0', port=5000)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Check using \\n\",\n \"\\n\",\n \"* http://localhost:5000/predict/?URL=http://consultoriojuridico.co/pp/www.paypal.com/\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n" }, { "path": "notebooks/m07_model_deployment.py", "content": "#!/usr/bin/python\n\nimport pandas as pd\nfrom sklearn.externals import joblib\nimport sys\n\n\ndef predict_proba(url):\n\n clf = joblib.load('../datasets/model_deployment/07_phishing_clf.pkl') \n\n url_ = pd.DataFrame([url], columns=['url'])\n \n # Create features\n keywords = ['https', 'login', '.php', '.html', '@', 'sign']\n for keyword in keywords:\n url_['keyword_' + keyword] = url_.url.str.contains(keyword).astype(int)\n\n url_['lenght'] = url_.url.str.len() - 2\n domain = url_.url.str.split('/', expand=True).iloc[:, 2]\n url_['lenght_domain'] = domain.str.len()\n url_['isIP'] = (url_.url.str.replace('.', '') * 1).str.isnumeric().astype(int)\n url_['count_com'] = url_.url.str.count('com')\n\n # Make prediction\n p1 = clf.predict_proba(url_.drop('url', axis=1))[0,1]\n\n return p1\n\n\nif __name__ == \"__main__\":\n \n if len(sys.argv) == 1:\n print('Please add an URL')\n \n else:\n\n url = sys.argv[1]\n\n p1 = predict_proba(url)\n \n print(url)\n print('Probability of Phishing: ', p1)\n \n" }, { "path": "notebooks/tree_titanic.dot", "content": "digraph Tree {\nnode [shape=box] ;\n0 [label=\"Sex <= 0.5\\ngini = 0.473\\nsamples = 891\\nvalue = [549, 342]\"] ;\n1 [label=\"Pclass <= 2.5\\ngini = 0.383\\nsamples = 314\\nvalue = [81, 233]\"] ;\n0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n2 [label=\"Age <= 2.5\\ngini = 0.1\\nsamples = 170\\nvalue = [9, 161]\"] ;\n1 -> 2 ;\n3 [label=\"gini = 0.5\\nsamples = 2\\nvalue = [1, 1]\"] ;\n2 -> 3 ;\n4 [label=\"gini = 0.091\\nsamples = 168\\nvalue = [8, 160]\"] ;\n2 -> 4 ;\n5 [label=\"Embarked_S <= 0.5\\ngini = 0.5\\nsamples = 144\\nvalue = [72, 72]\"] ;\n1 -> 5 ;\n6 [label=\"gini = 0.423\\nsamples = 56\\nvalue = [17, 39]\"] ;\n5 -> 6 ;\n7 [label=\"gini = 0.469\\nsamples = 88\\nvalue = [55, 33]\"] ;\n5 -> 7 ;\n8 [label=\"Age <= 6.5\\ngini = 0.306\\nsamples = 577\\nvalue = [468, 109]\"] ;\n0 -> 8 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n9 [label=\"Pclass <= 2.5\\ngini = 0.444\\nsamples = 24\\nvalue = [8, 16]\"] ;\n8 -> 9 ;\n10 [label=\"gini = 0.0\\nsamples = 10\\nvalue = [0, 10]\"] ;\n9 -> 10 ;\n11 [label=\"gini = 0.49\\nsamples = 14\\nvalue = [8, 6]\"] ;\n9 -> 11 ;\n12 [label=\"Pclass <= 1.5\\ngini = 0.28\\nsamples = 553\\nvalue = [460, 93]\"] ;\n8 -> 12 ;\n13 [label=\"gini = 0.46\\nsamples = 120\\nvalue = [77, 43]\"] ;\n12 -> 13 ;\n14 [label=\"gini = 0.204\\nsamples = 433\\nvalue = [383, 50]\"] ;\n12 -> 14 ;\n}" }, { "path": "notebooks/tree_vehicles.dot", "content": "digraph Tree {\nnode [shape=box] ;\n0 [label=\"year <= 2006.5\\nmse = 35247755.102\\nsamples = 14\\nvalue = 6571.429\"] ;\n1 [label=\"miles <= 131000.0\\nmse = 1232839.506\\nsamples = 9\\nvalue = 2722.222\"] ;\n0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n2 [label=\"miles <= 93000.0\\nmse = 250000.0\\nsamples = 2\\nvalue = 4500.0\"] ;\n1 -> 2 ;\n3 [label=\"mse = 0.0\\nsamples = 1\\nvalue = 5000.0\"] ;\n2 -> 3 ;\n4 [label=\"mse = 0.0\\nsamples = 1\\nvalue = 4000.0\"] ;\n2 -> 4 ;\n5 [label=\"year <= 2001.0\\nmse = 352653.061\\nsamples = 7\\nvalue = 2214.286\"] ;\n1 -> 5 ;\n6 [label=\"mse = 62500.0\\nsamples = 2\\nvalue = 1550.0\"] ;\n5 -> 6 ;\n7 [label=\"mse = 221600.0\\nsamples = 5\\nvalue = 2480.0\"] ;\n5 -> 7 ;\n8 [label=\"miles <= 21500.0\\nmse = 21800000.0\\nsamples = 5\\nvalue = 13500.0\"] ;\n0 -> 8 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n9 [label=\"mse = 0.0\\nsamples = 1\\nvalue = 22000.0\"] ;\n8 -> 9 ;\n10 [label=\"year <= 2009.5\\nmse = 4671875.0\\nsamples = 4\\nvalue = 11375.0\"] ;\n8 -> 10 ;\n11 [label=\"mse = 62500.0\\nsamples = 2\\nvalue = 9250.0\"] ;\n10 -> 11 ;\n12 [label=\"mse = 250000.0\\nsamples = 2\\nvalue = 13500.0\"] ;\n10 -> 12 ;\n}" } ]