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详细错误提示：
Traceback (most recent call last):
File "D:\Nicole's code\test\chapter07\rbfNettest.py", line 52, in <module>
plotscatter(xMat[:,1],yMat,yHat,plt) # 绘制图形
File "D:\Nicole's code\test\chapter07\rbfNettest.py", line 21, in plotscatter
ax.scatter(Xmat,Ymat,c='blue',marker='o') # 绘制散点图
File "C:\Python27\lib\site-packages\matplotlib\__init__.py", line 1892, in inner
return func(ax, *args, **kwargs)
File "C:\Python27\lib\site-packages\matplotlib\axes\_axes.py", line 3995, in scatter
cbook.delete_masked_points(*maskargs)
File "C:\Python27\lib\site-packages\matplotlib\cbook.py", line 1845, in delete_masked_points
raise ValueError("Masked arrays must be 1-D")
ValueError: Masked arrays must be 1-D
源程序代码：
# -*- coding: utf-8 -*-
from numpy import *
import sys
import os
import matplotlib.pyplot as plt
def loadDataSet(fileName):
numFeat = len(open(fileName).readline().split('\t')) - 1
X= []; Y = []
fr = open(fileName)
for line in fr.readlines():
curLine = line.strip().split('\t')
X.append([float(curLine[i]) for i in xrange(numFeat) ])
Y.append(float(curLine[-1]))
return X,Y
#绘制图形
def plotscatter(Xmat,Ymat,yHat,plt):
fig = plt.figure()
ax = fig.add_subplot(111) # 绘制图形位置
ax.scatter(Xmat,Ymat,c='blue',marker='o') # 绘制散点图
plt.plot(Xmat,yHat,'r') # 绘制散点图
plt.show()
# 数据矩阵,分类标签
xArr,yArr = loadDataSet("nolinear.txt")
# 局部加权线性回归算法：回归线矩阵
# RBF函数的平滑系数
miu= 0.02
k = 0.03
# 数据集坐标数组转换为矩阵
xMat = mat(xArr); yMat = mat(yArr).T
testArr = xArr # 测试数组
m,n = shape(xArr) # xArr的行数
yHat = zeros(m) # yHat是y的预测值,yHat的数据是y的回归线矩阵
for i in xrange(m):
weights = mat(eye(m))
for j in xrange(m):
diffMat = testArr[i] - xMat[j,:]
# 利用高斯核函数计算权重矩阵,计算后的权重是一个对角阵
weights[j,j] = exp(diffMat*diffMat.T/(-miu*k**2))
xTx = xMat.T * (weights * xMat) # 矩阵左乘自身的转置
if linalg.det(xTx) != 0.0:
ws = xTx.I * (xMat.T * (weights * yMat))
yHat[i] = testArr[i] * ws # 计算回归线坐标矩阵
else:
print "This matrix is singular, cannot do inverse"
sys.exit(0) # 退出程序
plotscatter(xMat[:,1],yMat,yHat,plt) # 绘制图形
# 计算相关系数:
print corrcoef(yHat,yMat.T)
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