#####################模型树#####################
def linearSolve(dataSet):   	#模型树的叶节点生成函数
	m,n = shape(dataSet)
	X = mat(ones((m,n))); Y = mat(ones((m,1)))		#建立两个全部元素为1的(m,n)矩阵和(m,1)矩阵
	X[:,1:n] = dataSet[:,0:n-1]; Y = dataSet[:,-1]	#X存放所有的特征，Y存放   
	xTx = X.T*X
	if linalg.det(xTx) == 0.0:
		raise NameError(‘This matrix is singular, cannot do inverse,\n		try increasing the second value of ops‘)
	ws = xTx.I * (X.T * Y)							#求线性回归的回归系数
	return ws,X,Y

def modelLeaf(dataSet):			#建立模型树叶节点函数
    ws,X,Y = linearSolve(dataSet)
    return ws

def modelErr(dataSet):			#模型树平方误差计算函数
    ws,X,Y = linearSolve(dataSet)
    yHat = X * ws
    return sum(power(Y - yHat,2))

# coding:utf-8
# !/usr/bin/env python

import regTrees
import matplotlib.pyplot as plt
from numpy import *

if __name__ == ‘__main__‘:
	myDat = regTrees.loadDataSet(‘exp2.txt‘)
	myMat = mat(myDat)
	myTree = regTrees.createTree(myMat,regTrees.modelLeaf,regTrees.modelErr,(1,10))
	print myTree
	regTrees.plotBestFit(‘exp2.txt‘)