2.1节
1.切换到工作目录
2.在工作目录下新建一个python脚本文件kNN.py，内容如下:
from numpy import * import operator def createDataSet(): group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) labels = ['A','A','B','B'] return group, labels def classify0(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0] diffMat = tile(inX, (dataSetSize, 1)) - dataSet sqDiffMat = diffMat**2 sqDistances = sqDiffMat.sum(axis=1) distances = sqDistances**0.5 sortedDistIndicies = distances.argsort() classCount={} for i in range(k): voteILabel = labels[sortedDistIndicies[i]] classCount[voteILabel] = classCount.get(voteILabel, 0) + 1 sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True) return sortedClassCount[0][0]
然后进入Python REPL:
F:studioMachineLearningInActionch02>python Python 2.7.10 |Anaconda 2.3.0 (64-bit)| (default, May 28 2015, 16:44:52) [MSC v.1500 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://binstar.org >>> from numpy import * >>> import operator >>> import kNN >>> group, labels = kNN.createDataSet() >>> group array([[ 1. , 1.1], [ 1. , 1. ], [ 0. , 0. ], [ 0. , 0.1]]) >>> labels ['A', 'A', 'B', 'B'] >>> kNN.classify0([0,0], group, labels, 3) 'B' >>>
2.2节
修改kNN.py文件，增加如下内容：
def getLabelID(labelName): if (labelName == "largeDoses"): return 3 elif (labelName == "smallDoses"): return 2 else: return 1 def file2matrix(filename): fr = open(filename) arrayOLines = fr.readlines() numberOfLines = len(arrayOLines) returnMat = zeros((numberOfLines, 3)) classLabelVector = [] index = 0 for line in arrayOLines: line = line.strip() listFromLine = line.split(' ') returnMat[index,:] = listFromLine[0:3] classLabelVector.append(getLabelID(listFromLine[-1])) index += 1 return returnMat,classLabelVector
注意这里和原书的代码不一样，这里我定义了一个从labelName到labelID的函数，由于测试数据中的label是名称。
输入如下脚本
>>> from numpy import * >>> import operator >>> import kNN >>> datingDataMat,datingLabels = kNN.file2matrix('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] >>> import matplotlib >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> ax.scatter(datingDataMat[:,1],datingDataMat[:,2]) <matplotlib.collections.PathCollection object at 0x0000000002E19278> >>> plt.show()
注意最后一句plt.show(),如果没有这一句是没有显示的。
>>> ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15*array(datingLabels), 15*array(datingLabels)) <matplotlib.collections.PathCollection object at 0x00000000035A9908> >>> plt.show() >>>
from numpy import * import operator import kNN datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt') import matplotlib import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(datingDataMat[:,0],datingDataMat[:,1],15*array(datingLabels), 15*array(datingLabels)) plt.show()
接着对数据进行规范化：
reload(kNN) normMat, ranges, minVals = kNN.autoNorm(datingDataMat) normMat
ranges
minVals
结果如下:
F:studioMachineLearningInActionch02>python Python 2.7.10 |Anaconda 2.3.0 (64-bit)| (default, May 28 2015, 16:44:52) [MSC v.1500 64 bit (AMD64)] on win32 Type "help", "copyright", "credits" or "license" for more information. Anaconda is brought to you by Continuum Analytics. Please check out: http://continuum.io/thanks and https://binstar.org >>> from numpy import * >>> import operator >>> import kNN >>> datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt') >>> normMat, ranges, minVals = kNN.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([ 9.12730000e+04, 2.09193490e+01, 1.69436100e+00]) >>> minVals array([ 0. , 0. , 0.001156]) >>>
测试算法：向kNN.py中增加如下内容:
def datingClassTest(): hoRatio = 0.10 datingDataMat, datingLabels = file2matrix('datingTestSet.txt') normMat, ranges, minVals = autoNorm(datingDataMat) m = normMat.shape[0] numTestVecs = int(m*hoRatio) errorCount = 0.0 for i in range(numTestVecs): classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3) print "the classifier came back with: %d, the real answer is: %d" %(classifierResult, datingLabels[i]) if (classifierResult != datingLabels[i]): errorCount += 1.0 print "ERROR: the classifier came back with: %d, the real answer is: %d" %(classifierResult, datingLabels[i]) print "the total error rate is: %f" %(errorCount/float(numTestVecs))
在Python REPL中运行脚本
>>> reload(kNN) <module 'kNN' from 'kNN.py'> >>> kNN.datingClassTest() the classifier came back with: 3, the real answer is: 3 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, 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classifier came back with: 1, the real answer is: 1 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 3, the real answer is: 1 ERROR: the classifier came back with: 3, the real answer is: 1 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 3, the real answer is: 1 ERROR: the classifier came back with: 3, the real answer is: 1 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 2, the real answer is: 3 ERROR: the classifier came back with: 2, the real answer is: 3 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 3, the real answer is: 3 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 3, the real answer is: 1 ERROR: the classifier came back with: 3, the real answer is: 1 the total error rate is: 0.050000