SYMBOL INDEX (137 symbols across 18 files)

FILE: 02_Python/A_Star.py
  class Node (line 13) | class Node:
    method __init__ (line 14) | def __init__(self, key, i, j):
    method addNeighbor (line 36) | def addNeighbor(self, node):
    method setTraversability (line 39) | def setTraversability(self, isTraversable):
    method isTraversable (line 42) | def isTraversable(self):
    method getKey (line 45) | def getKey(self):
    method getPos (line 48) | def getPos(self):
    method setPriority (line 51) | def setPriority(self, newPriority):
    method getPriority (line 54) | def getPriority(self):
    method setParent (line 57) | def setParent(self, parent):
    method getParent (line 60) | def getParent(self):
    method getDistFromStart (line 63) | def getDistFromStart(self):
    method setDistFromStart (line 66) | def setDistFromStart(self, distFromStart):
    method getNeighbors (line 69) | def getNeighbors(self):
    method setKey (line 72) | def setKey(self, key):
    method __lt__ (line 76) | def __lt__(self, node):
  class Graph (line 79) | class Graph:
    method __init__ (line 80) | def __init__(self):
    method addNode (line 83) | def addNode(self, node):
    method getNode (line 86) | def getNode(self, pos):
    method addEdge (line 90) | def addEdge(self, node1, node2):
  class Map2D (line 94) | class Map2D:
    method __init__ (line 95) | def __init__(self, filePath):
    method printMap (line 141) | def printMap(self):
    method __isValid (line 151) | def __isValid(self, i, j):
    method getStart (line 158) | def getStart(self):
    method getEnd (line 161) | def getEnd(self):
  class AStar (line 164) | class AStar:
    method __init__ (line 165) | def __init__(self, inMap):
    method __heuristic (line 183) | def __heuristic(self, node, targetNode):
    method findPath (line 188) | def findPath(self):
    method getMap (line 235) | def getMap(self):

FILE: 02_Python/Collaborative_Filtering.py
  function compute_similarity (line 96) | def compute_similarity(item1,item2,userRatings):
  function build_similarity_matrix (line 115) | def build_similarity_matrix(userRatings):
  function normalize (line 126) | def normalize(rating):
  function denormalize (line 131) | def denormalize(rating):
  function prediction (line 134) | def prediction(username,item):
  function recommendation (line 145) | def recommendation(username,userRatings):

FILE: 02_Python/DBSCAN.py
  class CustomDBSCAN (line 16) | class CustomDBSCAN():
    method __init__ (line 17) | def __init__(self):
    method neighbour_points (line 22) | def neighbour_points(self, data, pointId, epsilon):
    method fit (line 31) | def fit(self, data, Eps, MinPt):
    method visualize (line 87) | def visualize(self, data, cluster, numberOfClusters):
  function main (line 108) | def main():

FILE: 02_Python/Decision_Trees.py
  class CustomDecisionTree (line 11) | class CustomDecisionTree():
    method __init__ (line 12) | def __init__(self):
    method majorityCnt (line 15) | def majorityCnt(self, classList):
    method calcShannonEnt (line 25) | def calcShannonEnt(self, dataSet):
    method splitDataSet (line 39) | def splitDataSet(self, dataSet, axis, value):
    method chooseBestFeatureToSplit (line 49) | def chooseBestFeatureToSplit(self, dataSet, labels):
    method createTree (line 72) | def createTree(self, dataSet, labels):
  function main (line 95) | def main():

FILE: 02_Python/FP_Growth.py
  class FPGrowth (line 12) | class FPGrowth:
    method __init__ (line 13) | def __init__(self, k =3, tolerance = 0.0001, max_iterations = 500):
  function main (line 17) | def main():

FILE: 02_Python/Genetic_Algorithm.py
  class CustomGeneticAlgorithm (line 7) | class CustomGeneticAlgorithm():
    method server_present (line 9) | def server_present(self, server, time):
    method deployed_to_hourlyplanning (line 17) | def deployed_to_hourlyplanning(self, deployed_hourly_cron_capacity):
    method generate_random_plan (line 40) | def generate_random_plan(self, n_days, n_racks):
    method generate_initial_population (line 54) | def generate_initial_population(self, population_size, n_days=7, n_rac...
    method calculate_fitness (line 62) | def calculate_fitness(self, deployed_hourly_cron_capacity, required_ho...
    method crossover (line 73) | def crossover(self, population, n_offspring):
    method mutate_parent (line 93) | def mutate_parent(self, parent, n_mutations):
    method mutate_gen (line 104) | def mutate_gen(self, population, n_mutations):
    method is_acceptable (line 110) | def is_acceptable(self, parent):
    method select_acceptable (line 113) | def select_acceptable(self, population):
    method select_best (line 118) | def select_best(self, population, required_hourly_cron_capacity, n_best):
    method run (line 139) | def run(self, required_hourly_cron_capacity, n_iterations, n_populatio...
  function main (line 156) | def main():

FILE: 02_Python/K_Means.py
  class K_Means (line 17) | class K_Means:
    method __init__ (line 18) | def __init__(self, k =3, tolerance = 0.0001, max_iterations = 500):
    method fit (line 23) | def fit(self, data):
    method pred (line 63) | def pred(self, data):
  function main (line 68) | def main():

FILE: 02_Python/K_Nearest_Neighbours.py
  class CustomKNN (line 22) | class CustomKNN:
    method __init__ (line 24) | def __init__(self):
    method predict (line 29) | def predict(self, training_data, to_predict, k = 3):
    method test (line 46) | def test(self, test_set, training_set):
  function mod_data (line 58) | def mod_data(df):
  function main (line 76) | def main():

FILE: 02_Python/K_Nearest_Neighbours_In_Parallel.py
  class CustomKNN (line 25) | class CustomKNN:
    method __init__ (line 27) | def __init__(self):
    method predict (line 32) | def predict(self, training_data, to_predict, k = 3):
    method test (line 50) | def test(self, test_set, training_set):
  function mod_data (line 73) | def mod_data(df):
  function main (line 92) | def main():

FILE: 02_Python/Linear_Regression.py
  class CustomLinearRegression (line 23) | class CustomLinearRegression:
    method __init__ (line 25) | def __init__(self):
    method am (line 30) | def am(self, arr):
    method best_fit (line 37) | def best_fit(self, dimOne, dimTwo):
    method y_intercept (line 42) | def y_intercept(self, dimOne ,dimTwo):
    method predict (line 47) | def predict(self, ip):
    method squared_error (line 53) | def squared_error(self, original, model):
    method cod (line 57) | def cod(self, original, model):
  function main (line 63) | def main():

FILE: 02_Python/Logistic_Regression.py
  class CustomLogisticRegression (line 22) | class CustomLogisticRegression:
    method __init__ (line 24) | def __init__(self, x, y, tolerence = 0.00001):
    method cost_fn (line 37) | def cost_fn(self, m):
    method sigmoid_function (line 43) | def sigmoid_function(z):
    method gradients (line 47) | def gradients(self, m):
    method descent (line 54) | def descent(self):
  function main (line 67) | def main():

FILE: 02_Python/MSCRED/cnn_lstm/convlstm-update.py
  function cnn_encoder_layer (line 7) | def cnn_encoder_layer(data, filter_layer, strides):
  function tensor_variable (line 25) | def tensor_variable(shape, name):
  function cnn_encoder (line 37) | def cnn_encoder(data):
  function cnn_lstm_attention_layer (line 66) | def cnn_lstm_attention_layer(input_data, layer_number):
  function cnn_decoder_layer (line 99) | def cnn_decoder_layer(conv_lstm_out_c, filter, output_shape, strides):
  function cnn_decoder (line 119) | def cnn_decoder(lstm1_out, lstm2_out, lstm3_out, lstm4_out):
  function main (line 138) | def main():

FILE: 02_Python/MSCRED/cnn_lstm/convlstm.py
  function cnn_encoder_layer (line 7) | def cnn_encoder_layer(data, filter_layer, strides):
  function tensor_variable (line 25) | def tensor_variable(shape, name):
  function cnn_encoder (line 37) | def cnn_encoder(data):
  function cnn_lstm_attention_layer (line 66) | def cnn_lstm_attention_layer(input_data, layer_number):
  function cnn_decoder_layer (line 99) | def cnn_decoder_layer(conv_lstm_out_c, filter, output_shape, strides):
  function cnn_decoder (line 119) | def cnn_decoder(lstm1_out, lstm2_out, lstm3_out, lstm4_out):
  function main (line 138) | def main():

FILE: 02_Python/MSCRED/cnn_lstm/generation_signature_matrice.py
  class SignatureMatrices (line 15) | class SignatureMatrices:
    method __init__ (line 16) | def __init__(self):
    method signature_matrices_generation (line 27) | def signature_matrices_generation(self, win):
    method generate_train_test (line 50) | def generate_train_test(self, signature_matrices):

FILE: 02_Python/Mean_Shift.py
  class CustomMS (line 18) | class CustomMS:
    method __init__ (line 20) | def __init__(self):
  function main (line 24) | def main():

FILE: 02_Python/Naive_Bayes.py
  class CustomNB (line 20) | class CustomNB:
    method __init__ (line 22) | def __init__(self):
    method create_freq_table (line 25) | def create_freq_table(self, texts, labels=None):
    method train (line 40) | def train(self, freq):
    method predict (line 69) | def predict(self, text, spam_model,non_spam_model):
  function main (line 87) | def main():

FILE: 02_Python/Random_Forest_Classifier.py
  function evaluate_model (line 66) | def evaluate_model(predictions, probs, train_predictions, train_probs):
  function plot_confusion_matrix (line 113) | def plot_confusion_matrix(cm, classes,

FILE: 02_Python/Support_Vector_Machine.py
  class CustomSVM (line 18) | class CustomSVM:
    method __init__ (line 20) | def __init__(self):
    method fit (line 25) | def fit(self, dataset):
    method predict (line 78) | def predict(self, attrs):
    method test (line 85) | def test(self, test_set):
  function main (line 96) | def main():