Machine Learning in action --regression(已勘误)
最近在自学机器学习，应导师要求，先把《Machine Learning with R》动手刷了一遍，感觉R真不能算是一门计算机语言，感觉也就是一个功能复杂的计算器。所以这次就决定使用经典教材《Machine Learning in action》。因为开学得换work station ，怕到时候代码又丢了，所以就索性开个博客，把代码上传上来。
因为书上的原代码有很多错误，并且网上的许多博客的代码也是没有改正的，这次我把修正过的代码po上来
version：python3.5
talk is cheap show me the code
函数定义代码
#coding=utf-8
from numpy import *
import matplotlib.pyplot as plt
def loadDataSet(fileName):
fr = open(fileName)
numFeat = len(fr.readline().split('\t'))
dataMat = [] ; labelMat = []
for line in fr.readlines():
lineArr = []
curLine = line.strip().split('\t')
for i in range(numFeat - 1):
lineArr.append(float(curLine[i]))
dataMat.append(lineArr)
labelMat.append(float(curLine[-1]))
return dataMat, labelMat
def standRegres(xArr, yArr):
xMat = mat(xArr) ; yMat = mat(yArr).T
xTx = xMat.T * xMat
if linalg.det(xTx) == 0:
print("This matrix is singular, can not do inverse")
ws = xTx.I * (xMat.T * yMat)
return ws
def lwlr(testPoint, xArr, yArr, k = 1.0):
xMat = mat(xArr) ; yMat = mat(yArr).T
m = shape(xMat)[0]
weights = mat(eye((m)))
#创建权重对角矩阵
for j in range(m):
diffMat = testPoint - xMat[j, :]
weights[j, j] = exp(diffMat * diffMat.T / (-2.0 *k **2))
xTx = xMat.T * (weights * xMat)
if linalg.det(xTx) == 0.0:
print("this matrix is singular, cannot do inverse")
ws = xTx.I * (xMat.T * (weights * yMat))
return testPoint * ws
def lwlrTest(testArr, xArr, yArr, k = 1.0):
m = shape(testArr)[0]
yHat = zeros(m)
for i in range(m):
yHat[i] = lwlr(testArr[i], xArr, yArr, k)
return yHat
def rssError(yArr, yHatArr):
return ((yArr - yHatArr)**2).sum()
def ridgeRegres(xMat, yMat, lam = 0.2):
xTx = xMat.T * xMat
denom = xTx + eye(shape(xMat)[1]) * lam
if linalg.det(denom) == 0:
print("This Matrix is singular, cannot do inverse")
return
ws = denom.I * (xMat.T * yMat)
return ws
def ridgeTest(xArr, yArr):
xMat = mat(xArr) ; yMat = mat(yArr).T
yMean = mean(yMat, 0)#对列求均值
#数据标准化
yMat = yMat -yMean
xMeans = mean(xMat, 0) #对 列 求均值
xVar = var(xMat, 0)#对列求方差
xMat = (xMat - xMeans) / xVar
numTestPts = 30
wMat = zeros((numTestPts, shape(xMat)[1]))
for i in range(numTestPts):
ws = ridgeRegres(xMat, yMat, exp(i-10))
wMat[i,:] = ws.T
return wMat
def regularize(xMat):
inMat = xMat.copy()
inMeans = mean(inMat, 0)
inVar = var(inMat, 0)
inMat = (inMat - inMeans)/inVar
return inMat
def stageWise(xArr, yArr, eps = 0.01, numIt = 100):
xMat = mat(xArr) ; yMat = mat(yArr).T
yMean = mean(yMat, 0)
yMat = yMat -yMean
xMat = regularize(xMat)
m, n = shape(xMat)
returnMat = zeros((numIt, n))
ws = zeros((n, 1)) ; wsTest = ws.copy() ; wsMax = ws.copy()
for i in range(numIt):
print("ws.T: ",ws.T)
lowestError = inf
for j in range(n):
for sign in [-1, 1]:
wsTest = ws.copy()
wsTest[j] += eps * sign
yTest = xMat * wsTest
rssE = rssError(yMat.A, yTest.A)
if rssE < lowestError :
lowestError = rssE
wsMax = wsTest
ws = ws.copy()
returnMat[i, :] = ws.T
return returnMat
上面代码块只是定义了主要的函数，离运行还差一点。由于书原文中，采用了使用 iPython 命令行的运行方式，但是博主比较懒，所以干脆舍弃掉原来的方式。
废话不多少，直接上代码
实验1
if __name__=="__main__":
xArr, yArr = loadDataSet('ex0.txt')
ws = standRegres(xArr,yArr)
print(ws)
xMat = mat(xArr)
yMat = mat(yArr)
yHat = xMat * ws实验2 ：
if __name__ == "__main__":
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()
实验3 ：
if __name__ == "__main__":
xArr, yArr = loadDataSet('ex0.txt')
print("actual yArr[0]:",yArr[0])
lwlr(xArr[0], xArr, yArr, 1.0)
print(lwlr(xArr[0], xArr, yArr, 1.0))
实验4 ：
if __name__ == "__main__":
xArr, yArr = loadDataSet('ex0.txt')
print(lwlrTest(xArr, xArr, yArr, 0.003 ))实验5 ：
if __name__ == "__main__":
xArr, yArr = loadDataSet('ex0.txt')
xMat = mat(xArr)
print("xMat: ",xMat)
yMat = mat(yArr)
yHat = lwlrTest(xArr, xArr, yArr, 0.01 )
srtInd = xMat[:, 1].argsort(0) #返回的是数组值从小到大的索引值, 按列排序
print("srtInd: ",srtInd)
xSort = xMat[srtInd][:, 0, :] #从小到大 排序
print("xSort: ",xSort)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(xSort[:,1], yHat[srtInd])
ax.scatter(xMat[:,1].flatten().A[0], mat(yArr).T.flatten().A[0], s=2, c = 'red')
plt.show()
实验6 ：
if __name__ == "__main__":
abX, abY = loadDataSet('abalone.txt')
ridgeWeights = ridgeTest(abX, abY)
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(ridgeWeights)
plt.show()
实验7 ：
if __name__ == "__main__":
xArr, yArr = loadDataSet('abalone.txt')
#stageWise(xArr, yArr, 0.001, 5000)
print(stageWise(xArr, yArr, 0.001,5000))更多请戳github
https://github.com/Edgis/Machine-learning-in-action/blob/master/regression.py
