SYMBOL INDEX (46 symbols across 11 files)

FILE: homemade/anomaly_detection/gaussian_anomaly_detection.py
  class GaussianAnomalyDetection (line 7) | class GaussianAnomalyDetection:
    method __init__ (line 10) | def __init__(self, data):
    method multivariate_gaussian (line 19) | def multivariate_gaussian(self, data):
    method estimate_gaussian (line 50) | def estimate_gaussian(data):
    method select_threshold (line 64) | def select_threshold(labels, probabilities):

FILE: homemade/k_means/k_means.py
  class KMeans (line 6) | class KMeans:
    method __init__ (line 9) | def __init__(self, data, num_clusters):
    method train (line 18) | def train(self, max_iterations):
    method centroids_init (line 46) | def centroids_init(data, num_clusters):
    method centroids_find_closest (line 66) | def centroids_find_closest(data, centroids):
    method centroids_compute (line 100) | def centroids_compute(data, closest_centroids_ids, num_clusters):

FILE: homemade/linear_regression/linear_regression.py
  class LinearRegression (line 8) | class LinearRegression:
    method __init__ (line 12) | def __init__(self, data, labels, polynomial_degree=0, sinusoid_degree=...
    method train (line 42) | def train(self, alpha, lambda_param=0, num_iterations=500):
    method gradient_descent (line 55) | def gradient_descent(self, alpha, lambda_param, num_iterations):
    method gradient_step (line 78) | def gradient_step(self, alpha, lambda_param):
    method get_cost (line 109) | def get_cost(self, data, labels, lambda_param):
    method cost_function (line 126) | def cost_function(self, data, labels, lambda_param):
    method predict (line 153) | def predict(self, data):
    method hypothesis (line 173) | def hypothesis(data, theta):

FILE: homemade/logistic_regression/logistic_regression.py
  class LogisticRegression (line 9) | class LogisticRegression:
    method __init__ (line 13) | def __init__(self, data, labels, polynomial_degree=0, sinusoid_degree=...
    method train (line 45) | def train(self, lambda_param=0, max_iterations=1000):
    method predict (line 81) | def predict(self, data):
    method gradient_descent (line 103) | def gradient_descent(data, labels, initial_theta, lambda_param, max_it...
    method gradient_step (line 152) | def gradient_step(data, labels, theta, lambda_param):
    method cost_function (line 183) | def cost_function(data, labels, theta, lambda_param):
    method hypothesis (line 214) | def hypothesis(data, theta):

FILE: homemade/neural_network/multilayer_perceptron.py
  class MultilayerPerceptron (line 8) | class MultilayerPerceptron:
    method __init__ (line 12) | def __init__(self, data, labels, layers, epsilon, normalize_data=False):
    method train (line 34) | def train(self, regularization_param=0, max_iterations=1000, alpha=1):
    method predict (line 56) | def predict(self, data):
    method gradient_descent (line 72) | def gradient_descent(
    method gradient_step (line 118) | def gradient_step(data, labels, unrolled_thetas, layers, regularizatio...
    method cost_function (line 145) | def cost_function(data, labels, thetas, layers, regularization_param):
    method feedforward_propagation (line 196) | def feedforward_propagation(data, thetas, layers):
    method back_propagation (line 221) | def back_propagation(data, labels, thetas, layers, regularization_param):
    method thetas_init (line 310) | def thetas_init(layers, epsilon):
    method thetas_unroll (line 338) | def thetas_unroll(thetas):
    method thetas_roll (line 350) | def thetas_roll(unrolled_thetas, layers):

FILE: homemade/utils/features/generate_polynomials.py
  function generate_polynomials (line 7) | def generate_polynomials(dataset, polynomial_degree, normalize_data=False):

FILE: homemade/utils/features/generate_sinusoids.py
  function generate_sinusoids (line 6) | def generate_sinusoids(dataset, sinusoid_degree):

FILE: homemade/utils/features/normalize.py
  function normalize (line 6) | def normalize(features):

FILE: homemade/utils/features/prepare_for_training.py
  function prepare_for_training (line 9) | def prepare_for_training(data, polynomial_degree=0, sinusoid_degree=0, n...

FILE: homemade/utils/hypothesis/sigmoid.py
  function sigmoid (line 6) | def sigmoid(matrix):

FILE: homemade/utils/hypothesis/sigmoid_gradient.py
  function sigmoid_gradient (line 6) | def sigmoid_gradient(matrix):