#!/usr/bin/env python2
# -*- coding:utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt


# 加载数据
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]))
        fr.close()

    return dataMat, labelMat


# sigmoid 激活函数
def sigmoid(intX):
    return 1.0 / (1 + np.exp(-intX))


# 梯度上升算法
def gradAscent(dataMatIn, classLabels):
    dataMatrix = np.mat(dataMatIn)
    labelMat = np.mat(classLabels).transpose()
    m, n = np.shape(dataMatrix)
    alpha = 0.001
    maxCycles = 500
    weights = np.ones((n, 1))
    for k in range(maxCycles):
        h = sigmoid(dataMatrix * weights)
        error = labelMat - h
        weights = weights + alpha * dataMatrix.transpose() * error

    return weights.getA()


def plotBeastFit(weights):
    dataMat, labelMat = loadDataSet()
    dataArr = np.array(dataMat)
    n = np.shape(dataArr)[0]
    xcord1 = []
    xcord2 = []
    ycode1 = []
    ycode2 = []
    for i in range(n):
        if int(labelMat[i]) == 1:
            xcord1.append(dataArr[i, 1])
            ycode1.append(dataArr[i, 2])
        else:
            xcord2.append(dataArr[i, 1])
            ycode2.append(dataArr[i, 2])

    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(xcord1, ycode1, s=20, c='red', marker='s', alpha=0.5)
    ax.scatter(xcord2, ycode2, s=20, c='green', alpha=0.5)
    x = np.arange(-3.0, 3.0, 0.1)
    y = (-weights[0] - weights[1] * x) / weights[2]
    ax.plot(x, y)
    plt.title('BestFit')
    plt.xlabel('X1')
    plt.ylabel('X2')
    plt.show()


# 随机梯度上升算法
def stocGradAscent0(dataMatrix, classLabels):
    m, n = np.shape(dataMatrix)
    alpha = 0.01
    weights = np.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


# 优化后的梯度上升算法
def stocGradAscent1(dataMatrix, classLabels, numIter=150):
    m, n = np.shape(dataMatrix)
    weights = np.ones(n)
    for j in range(numIter):
        dataIndex = range(m)
        for i in range(m):
            #  alpha 每次都需要调整
            alpha = 4 / (1.0 + j + i) + 0.01
            # 随机选取跟新
            rangeIndex = int(np.random.uniform(0, len(dataIndex)))
            h = sigmoid(sum(dataMatrix[rangeIndex] * weights))
            error = classLabels[rangeIndex] - h
            weights = weights + alpha * error * dataMatrix[rangeIndex]

    return weights


if __name__ == '__main__':
    dataMat, labelMat = loadDataSet()
    # weights = gradAscent(dataMat, labelMat)
    # weights = stocGradAscent0(np.array(dataMat), labelMat)
    weights = stocGradAscent1(np.array(dataMat), labelMat)
    plotBeastFit(weights)