# -*- coding: utf-8 -*-
"""
Created on Sat Jun  9 08:18:10 2018

@author: [email protected]
"""
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animation as amat

def loadDataSet(fileName):
    """从文本文件加载数据.

    文件内容格式.

    Args:
        path: 传入文件名称.

    Returns:
        返回一个参数矩阵x和一个结论列向量y. 
    
    """
    dataX=[];dataY=[]
    fr=open(fileName)
    for line in fr.readlines():
        dataArr= []
        for item in  line.strip().split('\t'):
            dataArr.append(float(item))
       
        dataX.append( dataArr[0:-1])
        dataY.append( dataArr[-1])
    
    print(dataX)
    print(dataY)
    return np.array( dataX),np.array(dataY)

def dataNomalize(dataX,dataY):
    """对数据进行归一化处理.

    文件内容格式.

    Args:
        matriX: 参数组成的矩阵x，每行为一组样本个例参数.
        vertY: 结论值组成的向量y.

    Returns:
        返回归一化后的一个参数矩阵x和一个结论列向量y
    
    """
    m,n=np.shape(dataX)
    colMaxMin=[dataX.max(axis=0),dataX.min(axis=0)]

    for i in range(m):
        for j in range(n):
            #print (i,j,dataX[i][j])
            dataX[i][j]= (dataX[i][j]-colMaxMin[1][j])/colMaxMin[0][j]

    minY=dataY.min();maxY=dataY.max()

    for i in range(len(dataY)):
        dataY[i]=(dataY[i]-minY)/maxY

    print(dataX)
    print(dataY)
    return dataX,dataY

def computeCost(x,y,theta):
    m = y.shape[0]
#     J = (np.sum((X.dot(theta) - y)**2)) / (2*m)
    C = x.dot(theta) - y
    J2 = (C.T.dot(C))/ (2*m)
    return J2

def batchGradientDescent(dataX,dataY):
    matX=np.mat(dataX)
    matY=np.mat(dataY).transpose()
    m,n=np.shape(matX)
    alpha=0.001
    theta=np.ones((n,1))
    maxTimes=500
    for k in range(maxTimes):
        theta=theta-(alpha/m)*(np.dot(matX.T,np.dot(matX,theta)-matY))
        cost=computeCost(matX,matY,theta)
        print(theta,cost)
    return theta

def stochastieGradientDeccent():
    return 3

def miniBatchGradientDescent():
    return 4

if __name__ == '__main__':
    dataX,dataY=loadDataSet('house_prize.txt')
    dataX,dataY=dataNomalize(dataX,dataY)
    batchGradientDescent(dataX,dataY)