#!/usr/bin/env python3
# -*- coding:UTF-8 -*-
import matplotlib.pyplot as plt
import numpy as np

"""
函数说明:梯度上升算法测试函数

求函数f(x) = -x^2 + 4x的极大值

Parameters:
	无
Returns:
	无
Author:
	Jack Cui
Blog:
	http://blog.csdn.net/c406495762
Zhihu:
	https://www.zhihu.com/people/Jack--Cui/
Modify:
	2017-08-28
"""
def Gradient_Ascent_test():
	def f_prime(x_old):									#f(x)的导数
		return -2 * x_old + 4
	x_old = -1											#初始值，给一个小于x_new的值
	x_new = 0											#梯度上升算法初始值，即从(0,0)开始
	alpha = 0.01										#步长，也就是学习速率，控制更新的幅度
	presision = 0.00000001								#精度，也就是更新阈值
	while abs(x_new - x_old) > presision:
		x_old = x_new
		x_new = x_old + alpha * f_prime(x_old)			#上面提到的公式
	print(x_new)										#打印最终求解的极值近似值

"""
函数说明:加载数据

Parameters:
	无
Returns:
	dataMat - 数据列表
	labelMat - 标签列表
Author:
	Jack Cui
Blog:
	http://blog.csdn.net/c406495762
Zhihu:
	https://www.zhihu.com/people/Jack--Cui/
Modify:
	2017-08-28
"""
def loadDataSet():
	dataMat = []														#创建数据列表
	labelMat = []														#创建标签列表
	fr = open('testSet.txt')											#打开文件
	for line in fr.readlines():											#逐行读取
		lineArr = line.strip().split()									#去回车，放入列表
		dataMat.append([1.0, float(lineArr[0]), float(lineArr[1])])		#添加数据
		labelMat.append(int(lineArr[2]))								#添加标签
	fr.close()															#关闭文件
	return dataMat, labelMat											#返回

"""
函数说明:sigmoid函数

Parameters:
	inX - 数据
Returns:
	sigmoid函数
Author:
	Jack Cui
Blog:
	http://blog.csdn.net/c406495762
Zhihu:
	https://www.zhihu.com/people/Jack--Cui/
Modify:
	2017-08-28
"""
def sigmoid(inX):
	return 1.0 / (1 + np.exp(-inX))

"""
函数说明:梯度上升算法

Parameters:
	dataMatIn - 数据集
	classLabels - 数据标签
Returns:
	weights.getA() - 求得的权重数组(最优参数)
Author:
	Jack Cui
Blog:
	http://blog.csdn.net/c406495762
Zhihu:
	https://www.zhihu.com/people/Jack--Cui/
Modify:
	2017-08-28
"""
def gradAscent(dataMatIn, classLabels):
	dataMatrix = np.mat(dataMatIn)										#转换成numpy的mat
	labelMat = np.mat(classLabels).transpose()							#转换成numpy的mat,并进行转置
	m, n = np.shape(dataMatrix)											#返回dataMatrix的大小。m为行数,n为列数。
	alpha = 0.001														#移动步长,也就是学习速率,控制更新的幅度。
	maxCycles = 500														#最大迭代次数
	weights = np.ones((n,1))
	for k in range(maxCycles):
		h = sigmoid(dataMatrix * weights)								#梯度上升矢量化公式
		error = labelMat - h
		weights = weights + alpha * dataMatrix.transpose() * error
	return weights.getA()												#将矩阵转换为数组，返回权重数组

"""
函数说明:绘制数据集

Parameters:
	无
Returns:
	无
Author:
	Jack Cui
Blog:
	http://blog.csdn.net/c406495762
Zhihu:
	https://www.zhihu.com/people/Jack--Cui/
Modify:
	2017-08-30
"""
def plotDataSet():
	dataMat, labelMat = loadDataSet()									#加载数据集
	dataArr = np.array(dataMat)											#转换成numpy的array数组
	n = np.shape(dataMat)[0]											#数据个数
	xcord1 = []; ycord1 = []											#正样本
	xcord2 = []; ycord2 = []											#负样本
	for i in range(n):													#根据数据集标签进行分类
		if int(labelMat[i]) == 1:
			xcord1.append(dataArr[i,1]); ycord1.append(dataArr[i,2])	#1为正样本
		else:
			xcord2.append(dataArr[i,1]); ycord2.append(dataArr[i,2])	#0为负样本
	fig = plt.figure()
	ax = fig.add_subplot(111)											#添加subplot
	ax.scatter(xcord1, ycord1, s = 20, c = 'red', marker = 's',alpha=.5)#绘制正样本
	ax.scatter(xcord2, ycord2, s = 20, c = 'green',alpha=.5)			#绘制负样本
	plt.title('DataSet')												#绘制title
	plt.xlabel('X1'); plt.ylabel('X2')									#绘制label
	plt.show()															#显示

"""
函数说明:绘制数据集

Parameters:
	weights - 权重参数数组
Returns:
	无
Author:
	Jack Cui
Blog:
	http://blog.csdn.net/c406495762
Zhihu:
	https://www.zhihu.com/people/Jack--Cui/
Modify:
	2017-08-30
"""
def plotBestFit(weights):
	dataMat, labelMat = loadDataSet()									#加载数据集
	dataArr = np.array(dataMat)											#转换成numpy的array数组
	n = np.shape(dataMat)[0]											#数据个数
	xcord1 = []; ycord1 = []											#正样本
	xcord2 = []; ycord2 = []											#负样本
	for i in range(n):													#根据数据集标签进行分类
		if int(labelMat[i]) == 1:
			xcord1.append(dataArr[i,1]); ycord1.append(dataArr[i,2])	#1为正样本
		else:
			xcord2.append(dataArr[i,1]); ycord2.append(dataArr[i,2])	#0为负样本
	fig = plt.figure()
	ax = fig.add_subplot(111)											#添加subplot
	ax.scatter(xcord1, ycord1, s = 20, c = 'red', marker = 's',alpha=.5)#绘制正样本
	ax.scatter(xcord2, ycord2, s = 20, c = 'green',alpha=.5)			#绘制负样本
	x = np.arange(-3.0, 3.0, 0.1)
	y = (-weights[0] - weights[1] * x) / weights[2]
	ax.plot(x, y)
	plt.title('BestFit')												#绘制title
	plt.xlabel('X1'); plt.ylabel('X2')									#绘制label
	plt.show()

if __name__ == '__main__':
	dataMat, labelMat = loadDataSet()
	weights = gradAscent(dataMat, labelMat)
	plotBestFit(weights)