SYMBOL INDEX (76 symbols across 13 files)

FILE: AnomalyDetection/AnomalyDetection.py
  function anomalyDetection_example (line 10) | def anomalyDetection_example():
  function display_2d_data (line 40) | def display_2d_data(X,marker):
  function estimateGaussian (line 46) | def estimateGaussian(X):
  function multivariateGaussian (line 56) | def multivariateGaussian(X,mu,Sigma2):
  function visualizeFit (line 67) | def visualizeFit(X,mu,sigma2):
  function selectThreshold (line 83) | def selectThreshold(yval,pval):

FILE: K-Means/K-Means_scikit-learn.py
  function kMenas (line 8) | def kMenas():

FILE: K-Means/K-Menas.py
  function KMeans (line 13) | def KMeans():
  function findClosestCentroids (line 53) | def findClosestCentroids(X,initial_centroids):
  function computerCentroids (line 74) | def computerCentroids(X,idx,K):
  function runKMeans (line 82) | def runKMeans(X,initial_centroids,max_iters,plot_process):
  function plotProcessKMeans (line 101) | def plotProcessKMeans(X,centroids,previous_centroids):
  function kMeansInitCentroids (line 113) | def kMeansInitCentroids(X,K):

FILE: LinearRegression/LinearRegression.py
  function linearRegression (line 9) | def linearRegression(alpha=0.01,num_iters=400):
  function loadtxtAndcsv_data (line 35) | def loadtxtAndcsv_data(fileName,split,dataType):
  function loadnpy_data (line 39) | def loadnpy_data(fileName):
  function featureNormaliza (line 43) | def featureNormaliza(X):
  function plot_X1_X2 (line 57) | def plot_X1_X2(X):
  function gradientDescent (line 63) | def gradientDescent(X,y,theta,alpha,num_iters):
  function computerCost (line 81) | def computerCost(X,y,theta):
  function plotJ (line 89) | def plotJ(J_history,num_iters):
  function testLinearRegression (line 98) | def testLinearRegression():
  function predict (line 104) | def predict(mu,sigma,theta):

FILE: LinearRegression/LinearRegression_scikit-learn.py
  function linearRegression (line 7) | def linearRegression():
  function loadtxtAndcsv_data (line 31) | def loadtxtAndcsv_data(fileName,split,dataType):
  function loadnpy_data (line 35) | def loadnpy_data(fileName):

FILE: LogisticRegression/LogisticRegression.py
  function LogisticRegression (line 10) | def LogisticRegression():
  function loadtxtAndcsv_data (line 42) | def loadtxtAndcsv_data(fileName,split,dataType):
  function loadnpy_data (line 46) | def loadnpy_data(fileName):
  function plot_data (line 50) | def plot_data(X,y):
  function mapFeature (line 61) | def mapFeature(X1,X2):
  function costFunction (line 74) | def costFunction(initial_theta,X,y,inital_lambda):
  function gradient (line 87) | def gradient(initial_theta,X,y,inital_lambda):
  function sigmoid (line 99) | def sigmoid(z):
  function plotDecisionBoundary (line 107) | def plotDecisionBoundary(theta,X,y):
  function predict (line 133) | def predict(X,theta):
  function testLogisticRegression (line 147) | def testLogisticRegression():

FILE: LogisticRegression/LogisticRegression_OneVsAll.py
  function logisticRegression_OneVsAll (line 11) | def logisticRegression_OneVsAll():
  function loadmat_data (line 34) | def loadmat_data(fileName):
  function display_data (line 38) | def display_data(imgData):
  function oneVsAll (line 58) | def oneVsAll(X,y,num_labels,Lambda):
  function costFunction (line 82) | def costFunction(initial_theta,X,y,inital_lambda):
  function gradient (line 95) | def gradient(initial_theta,X,y,inital_lambda):
  function sigmoid (line 107) | def sigmoid(z):
  function predict_oneVsAll (line 114) | def predict_oneVsAll(all_theta,X):

FILE: LogisticRegression/LogisticRegression_OneVsAll_scikit-learn.py
  function logisticRegression_oneVsAll (line 10) | def logisticRegression_oneVsAll():
  function loadmat_data (line 24) | def loadmat_data(fileName):

FILE: LogisticRegression/LogisticRegression_scikit-learn.py
  function logisticRegression (line 9) | def logisticRegression():
  function loadtxtAndcsv_data (line 36) | def loadtxtAndcsv_data(fileName,split,dataType):
  function loadnpy_data (line 40) | def loadnpy_data(fileName):

FILE: NeuralNetwok/NeuralNetwork.py
  function neuralNetwork (line 13) | def neuralNetwork(input_layer_size,hidden_layer_size,out_put_layer):
  function loadmat_data (line 63) | def loadmat_data(fileName):
  function display_data (line 67) | def display_data(imgData):
  function nnCostFunction (line 96) | def nnCostFunction(nn_params,input_layer_size,hidden_layer_size,num_labe...
  function nnGradient (line 133) | def nnGradient(nn_params,input_layer_size,hidden_layer_size,num_labels,X...
  function sigmoid (line 179) | def sigmoid(z):
  function sigmoidGradient (line 186) | def sigmoidGradient(z):
  function randInitializeWeights (line 191) | def randInitializeWeights(L_in,L_out):
  function checkGradient (line 199) | def checkGradient(Lambda = 0):
  function debugInitializeWeights (line 235) | def debugInitializeWeights(fan_in,fan_out):
  function predict (line 242) | def predict(Theta1,Theta2,X):

FILE: PCA/PCA.py
  function PCA_2D (line 12) | def PCA_2D():
  function PCA_faceImage (line 48) | def PCA_faceImage():
  function plot_data_2d (line 75) | def plot_data_2d(X,marker):
  function featureNormalize (line 80) | def featureNormalize(X):
  function projectData (line 94) | def projectData(X_norm,U,K):
  function drawline (line 102) | def drawline(plt,p1,p2,line_type):
  function recoverData (line 108) | def recoverData(Z,U,K):
  function display_imageData (line 115) | def display_imageData(imgData):

FILE: PCA/PCA_scikit-learn.py
  function PCA_2d_example (line 11) | def PCA_2d_example():
  function PCA_face_example (line 44) | def PCA_face_example():
  function plot_data_2d (line 70) | def plot_data_2d(X,marker):
  function display_imageData (line 75) | def display_imageData(imgData):

FILE: SVM/SVM_scikit-learn.py
  function SVM (line 8) | def SVM():
  function plot_data (line 31) | def plot_data(X, y):
  function plot_decisionBoundary (line 44) | def plot_decisionBoundary(X, y, model, class_='linear'):