SYMBOL INDEX (89 symbols across 18 files)

FILE: AdaBoost/AdaBoost.py
  function loadData (line 20) | def loadData(fileName):
  function calc_e_Gx (line 52) | def calc_e_Gx(trainDataArr, trainLabelArr, n, div, rule, D):
  function createSigleBoostingTree (line 95) | def createSigleBoostingTree(trainDataArr, trainLabelArr, D):
  function createBosstingTree (line 137) | def createBosstingTree(trainDataList, trainLabelList, treeNum = 50):
  function predict (line 199) | def predict(x, div, rule, feature):
  function model_test (line 216) | def model_test(testDataList, testLabelList, tree):

FILE: Clustering/Hierachical_Clustering/Hierachical_Clustering.py
  function load_data (line 22) | def load_data(file):
  function Normalize (line 49) | def Normalize(Xarray):
  function cal_distance (line 67) | def cal_distance(xi, xj):
  function Distances (line 85) | def Distances(Xarray):
  function cal_groupdist (line 103) | def cal_groupdist(g1, g2, group_dict, dists):
  function Clustering (line 125) | def Clustering(Xarray, k, dists):
  function Adjusted_Rand_Index (line 163) | def Adjusted_Rand_Index(group_dict, Ylist, k):

FILE: Clustering/K-means_Clustering/K-means_Clustering.py
  function load_data (line 24) | def load_data(file):
  function Normalize (line 51) | def Normalize(Xarray):
  function cal_distance (line 69) | def cal_distance(xi, xj):
  function cal_groupcenter (line 87) | def cal_groupcenter(group, Xarray):
  function Adjusted_Rand_Index (line 106) | def Adjusted_Rand_Index(group_dict, Ylist, k):
  function Kmeans (line 151) | def Kmeans(Xarray, k, iters):

FILE: DecisionTree/DecisionTree.py
  function loadData (line 19) | def loadData(fileName):
  function majorClass (line 45) | def majorClass(labelArr):
  function calc_H_D (line 68) | def calc_H_D(trainLabelArr):
  function calcH_D_A (line 101) | def calcH_D_A(trainDataArr_DevFeature, trainLabelArr):
  function calcBestFeature (line 123) | def calcBestFeature(trainDataList, trainLabelList):
  function getSubDataArr (line 166) | def getSubDataArr(trainDataArr, trainLabelArr, A, a):
  function createTree (line 190) | def createTree(*dataSet):
  function predict (line 244) | def predict(testDataList, tree):
  function model_test (line 279) | def model_test(testDataList, testLabelList, tree):

FILE: EM/EM.py
  function loadData (line 26) | def loadData(mu0, sigma0, mu1, sigma1, alpha0, alpha1):
  function calcGauss (line 61) | def calcGauss(dataSetArr, mu, sigmod):
  function E_step (line 82) | def E_step(dataSetArr, alpha0, mu0, sigmod0, alpha1, mu1, sigmod1):
  function M_step (line 110) | def M_step(muo, mu1, gamma0, gamma1, dataSetArr):
  function EM_Train (line 126) | def EM_Train(dataSetList, iter = 500):

FILE: HMM/HMM.py
  function trainParameter (line 24) | def trainParameter(fileName):
  function loadArticle (line 145) | def loadArticle(fileName):
  function participle (line 164) | def participle(artical, PI, A, B):

FILE: KNN/KNN.py
  function loadData (line 23) | def loadData(fileName):
  function calcDist (line 49) | def calcDist(x1, x2):
  function getClosest (line 66) | def getClosest(trainDataMat, trainLabelMat, x, topK):
  function model_test (line 123) | def model_test(trainDataArr, trainLabelArr, testDataArr, testLabelArr, t...

FILE: LDA/LDA.py
  function load_data (line 30) | def load_data(file, K):
  function do_lda (line 86) | def do_lda(text, words, alpha, beta, K, iters):

FILE: LSA/LSA.py
  function load_data (line 30) | def load_data(file):
  function frequency_counter (line 58) | def frequency_counter(text, words):
  function do_lsa (line 78) | def do_lsa(X, k, words):

FILE: Logistic_and_maximum_entropy_models/logisticRegression.py
  function loadData (line 21) | def loadData(fileName):
  function predict (line 53) | def predict(w, x):
  function logisticRegression (line 71) | def logisticRegression(trainDataList, trainLabelList, iter = 200):
  function model_test (line 117) | def model_test(testDataList, testLabelList, w):

FILE: Logistic_and_maximum_entropy_models/maxEntropy.py
  function loadData (line 25) | def loadData(fileName):
  class maxEnt (line 51) | class maxEnt:
    method __init__ (line 55) | def __init__(self, trainDataList, trainLabelList, testDataList, testLa...
    method calcEpxy (line 73) | def calcEpxy(self):
    method calcEp_xy (line 102) | def calcEp_xy(self):
    method createSearchDict (line 125) | def createSearchDict(self):
    method calc_fixy (line 162) | def calc_fixy(self):
    method calcPwy_x (line 182) | def calcPwy_x(self, X, y):
    method maxEntropyTrain (line 218) | def maxEntropyTrain(self, iter = 500):
    method predict (line 242) | def predict(self, X):
    method test (line 259) | def test(self):

FILE: NaiveBayes/NaiveBayes.py
  function loadData (line 19) | def loadData(fileName):
  function NaiveBayes (line 45) | def NaiveBayes(Py, Px_y, x):
  function model_test (line 76) | def model_test(Py, Px_y, testDataArr, testLabelArr):
  function getAllProbability (line 99) | def getAllProbability(trainDataArr, trainLabelArr):

FILE: PCA/PCA.py
  function load_data (line 22) | def load_data(file):
  function Normalize (line 39) | def Normalize(X):
  function cal_V (line 58) | def cal_V(X):
  function do_pca (line 86) | def do_pca(X, k):

FILE: PLSA/PLSA.py
  function load_data (line 30) | def load_data(file):
  function frequency_counter (line 58) | def frequency_counter(text, words):
  function do_plsa (line 90) | def do_plsa(X, K, words, iters = 10):

FILE: Page_Rank/Page_Rank.py
  function iter_method (line 38) | def iter_method(n, d, M, R0, eps):
  function power_method (line 53) | def power_method(n, d, M, R0, eps):

FILE: SVM/SVM.py
  function loadData (line 21) | def loadData(fileName):
  class SVM (line 50) | class SVM:
    method __init__ (line 54) | def __init__(self, trainDataList, trainLabelList, sigma = 10, C = 200,...
    method calcKernel (line 84) | def calcKernel(self):
    method isSatisfyKKT (line 120) | def isSatisfyKKT(self, i):
    method calc_gxi (line 147) | def calc_gxi(self, i):
    method calcEi (line 176) | def calcEi(self, i):
    method getAlphaJ (line 188) | def getAlphaJ(self, E1, i):
    method train (line 244) | def train(self, iter = 100):
    method calcSinglKernel (line 342) | def calcSinglKernel(self, x1, x2):
    method predict (line 356) | def predict(self, x):
    method test (line 381) | def test(self, testDataList, testLabelList):

FILE: perceptron/perceptron_dichotomy.py
  function loadData (line 19) | def loadData(fileName):
  function perceptron (line 48) | def perceptron(dataArr, labelArr, iter=50):
  function model_test (line 102) | def model_test(dataArr, labelArr, w, b):

FILE: transMnist/transMnist.py
  function convert (line 11) | def convert(imgf, labelf, outf, n):