Linear Regression

# coding=utf-8
from numpy import *
import matplotlib.pyplot as plt
def loadDataSet(fileName):
    numFeat=len(open(fileName).readline().split('\t'))-1
    dataMat=[]
    labelMat=[]
    fr=open(fileName)
    for line in fr.readlines():
        lineArr=[]
        curLine=line.strip().split('\t')
        for i in range(numFeat):
            lineArr.append(float(curLine[i]))
        dataMat.append(lineArr)
        labelMat.append(float(curLine[-1]))
    return dataMat,labelMat
#最小二乘法，ws=（X.T*X).I*X.T*Y
def standRegres(xArr,yArr):
    xMat=mat(xArr)
    yMat=mat(yArr).T
    xTx=xMat.T*xMat
    if linalg.det(xTx)==0.0:#当不能求逆矩阵的时候，就返回
        return
    ws=xTx.I*(xMat.T*yMat)
    return ws
def linear():
    xArr,yArr=loadDataSet("ex0.txt")
    ws=standRegres(xArr,yArr)
    xMat=mat(xArr)
    yMat=mat(yArr)
    yHat=xMat*ws
    fig=plt.figure()
    ax=fig.add_subplot(111)
    ax.scatter(xMat[:,1].flatten().A[0],yMat.T[:,0].flatten().A[0])
    xCopy=xMat.copy()
    xCopy.sort(0)
    yHat=xCopy*ws
    ax.plot(xCopy[:,1],yHat)
    plt.show()
    return

使用最小二乘法来求解ws系数，ws=（X.T*X）.I*X.T*Y