机器学习实战 p21
源代码：
def file2matrix(filename):
fr = open(filename)
numberOfLines = len(fr.readlines()) #get the number of lines in the file
returnMat = zeros((numberOfLines,3)) #prepare matrix to return
classLabelVector = [] #prepare labels return
fr = open(filename)
index = 0
for line in fr.readlines():
line = line.strip()
listFromLine = line.split(‘\t‘)
returnMat[index,:] = listFromLine[0:3]
classLabelVector.append(int(listFromLine[-1])) 此句报错
index += 1
return returnMat,classLabelVector
报错如下：
>>> mat,label = kNN.file2matrix(‘datingTestSet.txt‘)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "kNN.py", line 50, in file2matrix
classLabelVector.append(int(listFromLine[-1]))
ValueError: could not convert string to int: largeDoses
解决方法：
listFromLine[-1]的值形似如下格式，带有回车换行符
largeDoses\r\n
smallDoses\r\n
didntLike\r\n
didntLike\r\n
didntLike\r\n
要将字母字符串转换为int类型是不可能的。
作者定义largeDoses 为3，smallDoses 为2，didntLike为1
于是笔者增加了一个字典类型
d = {‘didntLike‘: 1, ‘smallDoses‘: 2, ‘largeDoses‘: 3}
通过d[listFromLine[-1]]得到对应的label
更改后的代码如下：
rf.py
from numpy import *
import operator
from os import listdir
def rf(filename):
fr = open(filename)
numberOfLines = len(fr.readlines()) #get the number of lines in the file
returnMat = zeros((numberOfLines,3)) #prepare matrix to return
d = {‘didntLike‘: 1, ‘smallDoses‘: 2, ‘largeDoses‘: 3}
classLabelVector = []
index = 0
fr = open(filename)
for line in fr.readlines():
listFromLine = line.split(‘\t‘)
returnMat[index,:] = listFromLine[0:3]
listFromLine[-1] = listFromLine[-1][0:-2] #去除尾端的回车换行符
classLabelVector.append(d[listFromLine[-1]]) #取到字典中对应的label值
index += 1
return returnMat,classLabelVector
画图：
import rf
mat,label = rf.rf(‘datingTestSet.txt‘)
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
>>> ax1 = fig.add_subplot(2, 2, 1)
>>> ax1.scatter(mat[:,0],mat[:,1])
>>> ax2 = fig.add_subplot(2, 2, 2)
>>> ax2.scatter(mat[:,1],mat[:,2])
from numpy import array #需要自己导入array，否则会报错
>>> ax3 = fig.add_subplot(2, 2, 3)
>>> ax3.scatter(mat[:,0],mat[:,1],15.0*array(label),15.0*array(label))
ax4 = fig.add_subplot(2, 2, 4)
ax4.scatter(mat[:,1],mat[:,2],15.0*array(label),15.0*array(label))
plt.show()