机器学习实战第二章 k-近邻算法
2.1 KNN算法概述
KNN原理
1 计算需预测的数据与已知数据集中数据之间的距离
2 对距离进行排序
3 取前k个距离,并找到对应的label
4 对前k个距离对应的label进行计数,数最多的即为这个这个需被预测数据的label
2.1.1 导入数据
import numpy as np
import operator
def createDataSet():
group = np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
labels = ['A','A','B','B']
return group,labels
2.1.2实施KNN算法
def classify0(inX,dataSet,labels,k):
dataSetSize = dataSet.shape[0] #此处为行数 ex : (4,2) 4行 2列
diffMat = np.tile(inX,(dataSetSize,1)) - dataSet #在列方向上重复inX dataSetSize行,1列
sqDiffMat = diffMat** 2 #两矩阵内的值的差,的平方 ex : (Xa0 - Xb0)**2
sqDistances = sqDiffMat.sum(axis=1) # axis = 1 矩阵的每一行里的值相加 axis = 0 矩阵的每一列里的值相加 (Xa0 - Xb0)**2 + (Xa1 - Xb1)**2 + ...
distances = sqDistances ** 0.5
sortedDistIndices = distances.argsort() # 把算出来的距离从小到大排序,新矩阵数值大小的排名,值为原矩阵数值得indice
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndices[i]] #把前k个距离对应的label找出来
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 # 对label计数
sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1),reverse=True) #把label计数的字典排序 从高到低
return sortedClassCount[0][0] #返回第一个
group,labels = createDataSet()
# 预测[0,0] 属于哪一类
classify0([0,0],group,labels,3)
'B'
2.2 示例:使用KNN算法提高约会网站的配对效果
数据源名称:datingTestSet.txt,datingTestSet2.txt
数据源地址:
https://github.com/pbharrin/machinelearninginaction/blob/master/Ch02/datingTestSet.txt
https://github.com/pbharrin/machinelearninginaction/blob/master/Ch02/datingTestSet2.txt
2.2.1 准备数据:从文本文件中解析数据
def file2matrix(filename):
label = ['didntLike','smallDoses','largeDoses']
fr = open(filename)
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = np.zeros((numberOfLines,3)) # 形成numberOfLines行,3列的数组,数组内每个值都为0
classLabelVector = []
index = 0
for line in arrayOLines :
line = line.strip() #移除字符串头尾指定的字符(默认为空格或换行符)或字符序列
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3] #[0:3] 取0,1,2列。把分割好的line放入listFromline数组里
classLabelVector.append(int(label.index(listFromLine[-1]))+1)
index += 1
return returnMat,classLabelVector
测试
datingDataMat, datingLabels = file2matrix(filename='datingTestSet.txt')
datingLabels[0:20]
[3, 2, 1, 1, 1, 1, 3, 3, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3]
2.2.2 分析数据:使用Matplotlib创建散点图
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111) #若参数为349,含义为:将画布分割成3行4列,图像画在从左到右从上到下的第9块,如下图:
ax.scatter(datingDataMat[:,1],datingDataMat[:,2]) #取了datingDataMat第1列,和第2列
plt.show()
用颜色区分点
用第一列,第二列展示结果,用第一列第二列在第三列中对应的标签值作为第三维度来区分点
fig = plt.figure()
ax = fig.add_subplot(221) #若参数为349,含义为:将画布分割成3行4列,图像画在从左到右从上到下的第9块,如下图:
# 不好的散点图:无颜色区分
# scatter参数内容:datingDataMat第一列,第二列在坐标系形成一个点
# ax.scatter(datingDataMat[:,1],datingDataMat[:,2]) #取了datingDataMat第1列,和第2列
ax.scatter(datingDataMat[:,1],datingDataMat[:,2], 15.0*np.array(datingLabels), 15.0*np.array(datingLabels)) #取了datingDataMat第1列,和第2列
ax.set_title('result of column 1,2')
Text(0.5,1,'result of column 1,2')
用第0列,第1列展示结果,用第0列第1列在第三列中对应的标签值作为第三维度来区分点
# 带颜色的散点图
# scatter参数内容:datingDataMat第一列,第二列在坐标系形成一个点
# 15.0*np.array(datingLabels)是第一列第二列中的值在第三列中的label值(ex : 1,2,3)
# 在scatter中对于第一列第二列的数据,都加上第三列的值即多一个维度,scatter会用颜色区分
ax2 = fig.add_subplot(222)
ax2.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*np.array(datingLabels),15.0*np.array(datingLabels))
ax2.set_title('result of column 0,1')
Text(0.5,1,'result of column 0,1')
plt.show()
用第0列、第1列展示的数据比第1列、第2列展示的数据更好,因为颜色在图中更集中。
2.2.3 准备数据:归一化数值
把任意特征取值转化为0到1之间:newValue = (oldValue - min)/(max - min)
min, max分别为数据集中的最大特征值和最小特征值
# 根据公式建立归一化函数
def autoNorm(dataSet):
minVals = dataSet.min(0) #min(0):列内最小 min(1):行内最小
maxVals = dataSet.max(0) #max(0):列内最大 max(1):行内最大
ranges = maxVals - minVals # maxvalue - minvalue
normDataSet = np.zeros(list(dataSet.shape))
m = dataSet.shape[0]
normDataSet = dataSet - np.tile(minVals,(m,1)) # oldvalue - minvalue
normDataSet = normDataSet/np.tile(ranges,(m,1)) # oldvalue - minvalue/maxvalue - minvalue /对应元素相处 np.linalg.solve()矩阵相除
return normDataSet,ranges,minVals
datingDataMat.shape
(1000, 3)
normMat,ranges,minVals = autoNorm(datingDataMat)
normMat
array([[0.44832535, 0.39805139, 0.56233353],
[0.15873259, 0.34195467, 0.98724416],
[0.28542943, 0.06892523, 0.47449629],
...,
[0.29115949, 0.50910294, 0.51079493],
[0.52711097, 0.43665451, 0.4290048 ],
[0.47940793, 0.3768091 , 0.78571804]])
ranges
array([9.1273000e+04, 2.0919349e+01, 1.6943610e+00])
minVals
array([0. , 0. , 0.001156])
2.2.4 测试算法:作为完整的程序验证分类器
把任意特征取值转化为0到1之间:newValue = (oldValue - min)/(max - min)
min, max分别为数据集中的最大特征值和最小特征值
def datingClassTest():
hoRatio = 0.3 #要取出的训练集数据个数占总dataset的百分比
datingDataMat, datingLabels = file2matrix('datingTestSet.txt')
normMat, ranges, minval = autoNorm(datingDataMat) #归一化数据
m = normMat.shape[0] # 数据总行数
numTestVecs = int(m*hoRatio) #测试集数据个数
errorCount = 0.0
for i in range(numTestVecs):
classifierResult = classify0(normMat[i,:],normMat[numTestVecs:,:],datingLabels[numTestVecs:],5)
print("the classifier came back with : " + str(classifierResult) + ", the real anwser is : " + str(datingLabels[i]))
if classifierResult != datingLabels[i]:
errorCount += 1
errorCount = errorCount/numTestVecs
print("the error rate is : " + str(errorCount))
datingClassTest()
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
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the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
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the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
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the classifier came back with : 2, the real anwser is : 2
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the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 2, the real anwser is : 2
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 1, the real anwser is : 1
the classifier came back with : 3, the real anwser is : 3
the classifier came back with : 2, the real anwser is : 2
the error rate is : 0.07
如果改变k和测试集数据数量,会对错误率有影响
2.2.5 使用算法:构建完整可用系统
基于之前的datingClassTest,添加输入功能,使用户可以输入值,得到预测结果。
def datingClassTest():
hoRatio = 0.1 #要取出的训练集数据个数占总dataset的百分比
datingDataMat, datingLabels = file2matrix('datingTestSet.txt')
normMat, ranges, minval = autoNorm(datingDataMat) #归一化数据
m = normMat.shape[0] # 数据总行数
numTestVecs = int(m*hoRatio) #测试集数据个数
errorCount = 0.0
percenTats = float(input("percentage of time spent playing video games ?"))
ffMiles = float(input("frequent filier miles eraned per year ?"))
iceCream = float(input("liters of ice cream consumed per year ?"))
inX = np.array([ffMiles,percenTats,iceCream])
inXNorm = (inX - minval)/ranges
classifierResult = classify0(inXNorm,normMat[numTestVecs:,:],datingLabels[numTestVecs:],3)
label = ['didntLike','smallDoses','largeDoses']
print("the classifier came back with : " + label[classifierResult-1])
datingClassTest()
percentage of time spent playing video games ?10
frequent filier miles eraned per year ?10000
liters of ice cream consumed per year ?0.5
the classifier came back with : smallDoses
2.3 示例:使用手写识别系统
数据源名称:digits.zip
数据源地址:
https://github.com/pbharrin/machinelearninginaction/tree/master/Ch02
2.3.1 准备数据:将图像转换为测试向量
# 把图像数据 32行32列 转换为 1行1024列
def img2vector(filename):
returnVect = np.zeros((1,1024))
fr = open(filename)
for i in range(32):
lineStr = fr.readline() #运行一次读一行,再运行读下一行
for j in range(32): #读每一行中的每一个数
returnVect[0,32*i+j] = int(lineStr[j])
return returnVect
#测试
res = img2vector('digits/trainingDigits/0_0.txt')
res2 = img2vector('digits/trainingDigits/0_1.txt')
2.3.2 测试算法:使用k-临近算法识别手写数字
数据理解:
文件内的值表示数字,即特征
文件标题的第一个数字,即为正确答案
自己写的版本
def handWritingClassTest():
import os
lst_doc_train = os.listdir('digits/trainingDigits/')
lst_doc_test = os.listdir('digits/testDigits/')
#整理features集
featureTrain = []
featureTest = []
check = []
for i in lst_doc_train:
check.append(i)
trainVector = img2vector('digits/trainingDigits/' + i)
featureTrain.append(list(trainVector[0]))
featureTrain = np.array(featureTrain)
#整理label集
labelTrain = []
labelTest = []
for i in lst_doc_train:
i = int(i.split('_')[0])
labelTrain.append(i)
for j in lst_doc_test:
j = int(j.split('_')[0])
labelTest.append(j)
#预测
error = 0
for i in lst_doc_test:
returnVect = img2vector('digits/testDigits/'+i)
returnVect = np.array(returnVect[0])
classifierResult = classify0(inX=returnVect,dataSet=featureTrain,labels=labelTrain,k=3)
print("the predict result is : " + str(classifierResult) + ", " + "the real answer is : " + str(labelTest[lst_doc_test.index(i)]))
if labelTest[lst_doc_test.index(i)] != classifierResult:
error += 1
errorRate = error/len(lst_doc_test)
print("error rate : " + str(errorRate))
handWritingClassTest()
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the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
the predict result is : 4, the real answer is : 4
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the predict result is : 5, the real answer is : 5
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the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 3, the real answer is : 5
the predict result is : 6, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
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the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
the predict result is : 5, the real answer is : 5
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the predict result is : 6, the real answer is : 6
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the predict result is : 1, the real answer is : 8
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the predict result is : 8, the real answer is : 8
the predict result is : 8, the real answer is : 8
the predict result is : 8, the real answer is : 8
the predict result is : 8, the real answer is : 8
the predict result is : 8, the real answer is : 8
the predict result is : 8, the real answer is : 8
the predict result is : 8, the real answer is : 8
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the predict result is : 8, the real answer is : 8
the predict result is : 8, the real answer is : 8
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
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the predict result is : 9, the real answer is : 9
the predict result is : 1, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
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the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
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the predict result is : 9, the real answer is : 9
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the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 7, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
the predict result is : 9, the real answer is : 9
error rate : 0.010570824524312896
书上的版本
def handWritingClassTest2():
import os
hwLabels = []
trainingFileList = os.listdir('digits/trainingDigits/')
m = len(trainingFileList)
trainingMat = np.zeros((m,1024))
for i in range(m):
fileNamestr = trainingFileList[i]
fileStr = fileNamestr.split('.')[0]
classNumStr = int(fileStr.split('_')[0])
hwLabels.append(classNumStr)
trainingMat[i,:] = img2vector('digits/trainingDigits/' + fileNamestr)
testFileList = os.listdir('digits/testDigits/')
errorCount = 0.0
mTest = len(testFileList)
for i in range(mTest):
fileNamestr = testFileList[i]
fileStr = fileNamestr.split('.')[0]
classNumStr = int(fileStr.split('_')[0])
vectorUnderTest = img2vector('digits/testDigits/' + fileNamestr)
classifierResult = classify0(inX=vectorUnderTest,dataSet=trainingMat,labels=hwLabels,k=3)
print("the classifier came back with : " + str(classifierResult) + ', the real value is : ' + str(classNumStr))
if (classifierResult != classNumStr):
errorCount += 1
print("the error rate is : " + str(errorCount/mTest))
handWritingClassTest2()
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
the classifier came back with : 0, the real value is : 0
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the classifier came back with : 9, the real value is : 9
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the classifier came back with : 9, the real value is : 9
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the classifier came back with : 9, the real value is : 9
the classifier came back with : 9, the real value is : 9
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the classifier came back with : 7, the real value is : 9
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the classifier came back with : 9, the real value is : 9
the classifier came back with : 9, the real value is : 9
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the classifier came back with : 9, the real value is : 9
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the classifier came back with : 9, the real value is : 9
the classifier came back with : 9, the real value is : 9
the classifier came back with : 9, the real value is : 9
the classifier came back with : 9, the real value is : 9
the classifier came back with : 9, the real value is : 9
the error rate is : 0.010570824524312896