SYMBOL INDEX (82 symbols across 19 files)

FILE: classification_and_regression_trees/compare.py
  function get_corrcoef (line 7) | def get_corrcoef(X, Y):

FILE: classification_and_regression_trees/model_tree.py
  function linear_regression (line 6) | def linear_regression(dataset):
  function fleaf (line 21) | def fleaf(dataset):
  function ferr (line 27) | def ferr(dataset):
  function get_nodes_edges (line 34) | def get_nodes_edges(tree, root_node=None):
  function dotify (line 67) | def dotify(tree):
  function tree_predict (line 83) | def tree_predict(data, tree):

FILE: classification_and_regression_trees/prune.py
  function not_tree (line 6) | def not_tree(tree):
  function collapse (line 11) | def collapse(tree):
  function postprune (line 19) | def postprune(tree, test_data):

FILE: classification_and_regression_trees/regression_tree.py
  function load_data (line 14) | def load_data(filename):
  function split_dataset (line 24) | def split_dataset(dataset, feat_idx, value):
  function create_tree (line 35) | def create_tree(dataset, fleaf, ferr, opt=None):
  function fleaf (line 67) | def fleaf(dataset):
  function ferr (line 73) | def ferr(dataset):
  function choose_best_feature (line 80) | def choose_best_feature(dataset, fleaf, ferr, opt):
  function get_nodes_edges (line 126) | def get_nodes_edges(tree, root_node=None):
  function dotify (line 159) | def dotify(tree):
  function tree_predict (line 175) | def tree_predict(data, tree):

FILE: decision_tree/sms_tree.py
  function get_doc_vector (line 19) | def get_doc_vector(words, vocabulary):
  function parse_line (line 40) | def parse_line(line):
  function parse_file (line 48) | def parse_file(filename):

FILE: decision_tree/trees.py
  class DecisionTreeClassifier (line 13) | class DecisionTreeClassifier(object):
    method split_dataset (line 18) | def split_dataset(dataset, classes, feat_idx):
    method get_shanno_entropy (line 36) | def get_shanno_entropy(self, values):
    method choose_best_split_feature (line 45) | def choose_best_split_feature(self, dataset, classes):
    method get_majority (line 67) | def get_majority(classes):
    method create_tree (line 76) | def create_tree(self, dataset, classes, feat_names):
    method get_nodes_edges (line 113) | def get_nodes_edges(self, tree=None, root_node=None):
    method dotify (line 146) | def dotify(self, tree=None):
    method classify (line 165) | def classify(self, data_vect, feat_names=None, tree=None):
    method dump_tree (line 184) | def dump_tree(self, filename, tree=None):
    method load_tree (line 193) | def load_tree(self, filename):

FILE: linear_regression/lasso_regression.py
  function lasso_regression (line 13) | def lasso_regression(X, y, lambd=0.2, threshold=0.1):
  function lasso_traj (line 54) | def lasso_traj(X, y, ntest=30):

FILE: linear_regression/local_weighted_linear_regression.py
  function lwlr (line 11) | def lwlr(x, X, Y, k):

FILE: linear_regression/ridge_regression.py
  function ridge_regression (line 11) | def ridge_regression(X, y, lambd=0.2):
  function ridge_traj (line 20) | def ridge_traj(X, y, ntest=30):

FILE: linear_regression/stage_wise_regression.py
  function stagewise_regression (line 10) | def stagewise_regression(X, y, eps=0.01, niter=100):

FILE: linear_regression/standard_linear_regression.py
  function load_data (line 8) | def load_data(filename):
  function standarize (line 21) | def standarize(X):
  function std_linreg (line 28) | def std_linreg(X, Y):
  function get_corrcoef (line 35) | def get_corrcoef(X, Y):

FILE: logistic_regression/logreg_grad_ascent.py
  class LogisticRegressionClassifier (line 10) | class LogisticRegressionClassifier(object):
    method sigmoid (line 15) | def sigmoid(x):
    method gradient_ascent (line 20) | def gradient_ascent(self, dataset, labels, max_iter=10000):
    method classify (line 44) | def classify(self, data, w=None):
  function load_data (line 54) | def load_data(filename):
  function snapshot (line 67) | def snapshot(w, dataset, labels, pic_name):

FILE: logistic_regression/logreg_stoch_grad_ascent.py
  class LogisticRegressionClassifier (line 12) | class LogisticRegressionClassifier(BaseClassifer):
    method stoch_gradient_ascent (line 14) | def stoch_gradient_ascent(self, dataset, labels, max_iter=150):

FILE: logistic_regression/sms.py
  function get_doc_vector (line 18) | def get_doc_vector(words, vocabulary):
  function parse_line (line 39) | def parse_line(line):
  function parse_file (line 47) | def parse_file(filename):

FILE: naive_bayes/bayes.py
  class NaiveBayesClassifier (line 8) | class NaiveBayesClassifier(object):
    method train (line 12) | def train(self, dataset, classes):
    method classify (line 49) | def classify(self, doc_vect, cond_probs, cls_probs):

FILE: naive_bayes/sms.py
  function get_doc_vector (line 18) | def get_doc_vector(words, vocabulary):
  function parse_line (line 39) | def parse_line(line):
  function parse_file (line 47) | def parse_file(filename):

FILE: support_vector_machine/svm_ga.py
  function load_data (line 22) | def load_data(filename):
  function get_w (line 31) | def get_w(alphas, dataset, labels):
  function fitness (line 59) | def fitness(indv):

FILE: support_vector_machine/svm_platt_smo.py
  class SVMUtil (line 10) | class SVMUtil(object):
    method __init__ (line 14) | def __init__(self, dataset, labels, C, tolerance=0.001):
    method f (line 24) | def f(self, x):
    method get_error (line 38) | def get_error(self, i):
    method update_errors (line 45) | def update_errors(self):
    method meet_kkt (line 50) | def meet_kkt(self, i):
  function load_data (line 61) | def load_data(filename):
  function clip (line 70) | def clip(alpha, L, H):
  function select_j_rand (line 80) | def select_j_rand(i, m):
  function select_j (line 87) | def select_j(i, svm_util):
  function get_w (line 107) | def get_w(alphas, dataset, labels):
  function take_step (line 116) | def take_step(i, j, svm_util):
  function examine_example (line 169) | def examine_example(i, svm_util):
  function platt_smo (line 184) | def platt_smo(dataset, labels, C, max_iter):

FILE: support_vector_machine/svm_simple_smo.py
  function load_data (line 10) | def load_data(filename):
  function clip (line 19) | def clip(alpha, L, H):
  function select_j (line 29) | def select_j(i, m):
  function get_w (line 36) | def get_w(alphas, dataset, labels):
  function simple_smo (line 45) | def simple_smo(dataset, labels, C, max_iter):