from numpy import *

def loadDataSet():
    dataMat = []
    labelMat = []
    fr = open('testSet.txt')

    for line in fr.readlines():
        lineArr = line.strip().split()
        dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])
        labelMat.append(int(lineArr[2]))

    return dataMat,labelMat

def sigmoid(inx) :
    return 1.0 / (1 + exp(-inx))

def gradAscent(dataMatIn, classLabels) :
    dataMatrix = mat(dataMatIn)
    labelMat = mat(classLabels).transpose()
    m, n = shape(dataMatrix)

    alpha = 0.001
    maxCycles = 500
    weights = ones((n, 1))

    for k in range(maxCycles) :
        h = sigmoid(dataMatrix * weights)
        error = (labelMat - h)
        weights = weights + alpha * dataMatrix.transpose() * error
    
    return weights

def plotBestFit(weights) :
    import matplotlib.pyplot as plt

    dataMat, labelMat = loadDataSet()
    dataArr = array(dataMat)
    n = shape(dataArr)[0]
    xcord1 = []
    ycord1 = []

    xcord2 = []
    ycord2 = []

    for i in range(n) :
        if int(labelMat[i]) == 1 :
            xcord1.append(dataArr[i, 1])
            ycord1.append(dataArr[i, 2])
        else :
            xcord2.append(dataArr[i, 1])
            ycord2.append(dataArr[i, 2])
          
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(xcord1, ycord1, s=30, c='red', marker='s')
    ax.scatter(xcord2, ycord2, s=30, c='green')

    x = arange(-3.0, 3.0, 0.1)
    y = (-weights[0] - weights[1] * x) / weights[2]
    ax.plot(x, y)
    plt.xlabel('X1')
    plt.ylabel('X2')
    plt.show()

def stocGradAscent0(dataMatrix, classLabels) :
    m, n = shape(dataMatrix)
    alpha = 0.01
    weights = ones(n)

    for i in range(m) :
        h = sigmoid(sum(dataMatrix[i] * weights))
        error = classLabels[i] - h
        weights = weights + alpha * error * dataMatrix[i]
    
    return weights

def stocGradAscent1(dataMatrix, classLabels, numIter=150) :
    m, n = shape(dataMatrix)
    weights = ones(n)

    for j in range(numIter) :
        dataIndex = range(m)
        for i in range(m) :
            alpha = 4 / (1.0 + j + i) + 0.01
            randIndex = int(random.uniform(0, len(dataIndex)))
            h = sigmoid(sum(dataMatrix[randIndex] * weights))
            error = classLabels[randIndex] - h
            weights = weights + alpha * error * dataMatrix[randIndex]
            del(dataIndex[randIndex])
    
    return weights
