机器学习实战matplotlib画图出错

用Python写个机器学习的线性回归算法的东西，在输出最佳拟合直线时报错
Traceback (most recent call last):
  File "<pyshell#19>", line 1, in <module>
    ax.plot(xCopy[:,1],yHat)
  File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 4137, in plot
    for line in self._get_lines(*args, **kwargs):
  File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 317, in _grab_next_args
    for seg in self._plot_args(remaining, kwargs):
  File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 295, in _plot_args
    x, y = self._xy_from_xy(x, y)
  File "C:\Python27\lib\site-packages\matplotlib\axes.py", line 213, in _xy_from_xy
    bx = self.axes.xaxis.update_units(x)
  File "C:\Python27\lib\site-packages\matplotlib\axis.py", line 1336, in update_units
    converter = munits.registry.get_converter(data)
  File "C:\Python27\lib\site-packages\matplotlib\units.py", line 148, in get_converter
    converter = self.get_converter(xravel[0])
converter = self.get_converter(xravel[0])
  File "C:\Python27\lib\site-packages\numpy\matrixlib\defmatrix.py", line 305, in __getitem__
    out = N.ndarray.__getitem__(self, index)
RuntimeError: maximum recursion depth exceeded

开始以为是递归深度问题，后来加了sys.setrecursionlimit(10000)运行ax.plot(xCopy[:,1],yHat)然后IDE就restart了。。。

import osos.chdir('F:\machinelearninginaction\Ch08')import regressionfrom numpy import *import sysimport matplotlib.pyplot as pltxArr,yArr = regression.loadDataSet('ex0.txt')xMat = mat(xArr)yMat = mat(yArr)ws = regression.standRegres(xArr,yArr)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 * wssys.setrecursionlimit(3000)ax.plot(xCopy[:,1],yHat)plt.show()

shell上运行的代码执行到18行报错

引用的regression算法

from numpy import *def loadDataSet(fileName):      #general function to parse tab -delimited floats    numFeat = len(open(fileName).readline().split('\t')) - 1 #get number of fields     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,labelMatdef standRegres(xArr,yArr):    xMat = mat(xArr); yMat = mat(yArr).T    xTx = xMat.T*xMat    if linalg.det(xTx) == 0.0:        print "This matrix is singular, cannot do inverse"        return    ws = xTx.I * (xMat.T*yMat)    return wsdef lwlr(testPoint,xArr,yArr,k=1.0):    xMat = mat(xArr); yMat = mat(yArr).T    m = shape(xMat)[0]    weights = mat(eye((m)))    for j in range(m):                      #next 2 lines create weights matrix        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"        return    ws = xTx.I * (xMat.T * (weights * yMat))    return testPoint * ws

后面发现是版本问题，用1.3.0就好。