内容

• k近邻分类算法
• 从文本文件中解析和导入数据
• 使用Matplotlib创建扩展图
• 归一化数据

代码

kNN.py

#!/usr/bin/env python
# _*_ coding:utf-8 _*_
import numpy as np
import operator
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=14)
from os import listdir


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

def classify0(inX, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    diffMat = np.tile(inX,(dataSetSize,1)) - dataSet
    sqdiffMat = diffMat**2
    sqDistances = sqdiffMat.sum(axis=1)
    Distances = sqDistances**.5
    SortedIndices = Distances.argsort()
    classCount = {}
    for i in range(k):
        label = labels[SortedIndices[i]]
        classCount[label] = classCount.get(label,0) + 1
    sortedClassCount = sorted(classCount.items(),key = operator.itemgetter(1),reverse=True)
    return sortedClassCount[0][0]

def file2matrix(filename):
    '''parse file'''
    fr = open(filename)
    arrayOfLines = fr.readlines()
    numberOfLines = len(arrayOfLines)
    returnMat = np.zeros((numberOfLines,3))
    classLabelVector = []
    index = 0
    for line in arrayOfLines:
        line = line.strip()
        lineFromLine = line.split('\t')
        returnMat[index,:] = lineFromLine[0:3]
        classLabelVector.append(int(lineFromLine[-1]))
        index += 1
    return returnMat, classLabelVector

datingDataMat, dataLabels = file2matrix('datingTestSet2.txt')
dataLabels = np.array(dataLabels)

fig = plt.figure()
ax1 = fig.add_subplot(1,2,1)
ax1.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*dataLabels,15.0*dataLabels)
ax2 = fig.add_subplot(1,2,2)
ind1 = np.where(dataLabels == 1)
ax2.scatter(datingDataMat[ind1,0],datingDataMat[ind1,1],s=20,c='r',marker='*',label=1)
ind2 = np.where(dataLabels == 2)
ax2.scatter(datingDataMat[ind2,0],datingDataMat[ind2,1],s=10,c='b',marker='o',label=2)
ind3 = np.where(dataLabels == 3)
ax2.scatter(datingDataMat[ind3,0],datingDataMat[ind3,1],s=30,c='g',marker='+',label=3)
ax2.set_xlabel('玩视频游戏所耗时间百分比',fontproperties=font)
ax2.set_ylabel('每周消费的冰淇淋公升数',fontproperties=font)
plt.legend()
plt.show()

def autoNorm(dataset):
    minVals = dataset.min(0)
    maxVals = dataset.max(0)
    ranges = maxVals - minVals
    r = dataset.shape[0]
    normDataSet = (dataset - minVals) / ranges
    return normDataSet, ranges, minVals

normMat, ranges, minVals = autoNorm(datingDataMat)

def datingClassTest():
    testRatio = 0.1
    datingDataMat, dataLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    r = normMat.shape[0]
    numTestVecs = int(r*testRatio)
    errorCount = 0
    for i in range(numTestVecs):
        classifierResult = classify0(normMat[i,:],normMat[numTestVecs:,:],dataLabels[numTestVecs:r],3)
        print('The classifier came back with: %d,the real answer is: %d'%(classifierResult,dataLabels[i]))
        if classifierResult != dataLabels[i]:
            errorCount += 1
    print('the total error rate is: %f'%(errorCount/numTestVecs))

datingClassTest()

def classifyPerson():
    resultList = ['not at all','in small doses','in large doses']
    percentTats = float(input('percentage of time spent playing video games?'))
    ffMiles = float(input('frequent flier miles earned per year?'))
    iceCream = float(input('liters of ice cream consumed per year?'))
    datingDataMat, dataLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    inArr = np.array([ffMiles,percentTats,iceCream])
    classifierResult = classify0((inArr - minVals)/ranges,normMat,dataLabels,3)
    print('You will probably like this person: %s'%resultList[classifierResult - 1])

classifyPerson()

def img2vector(filename):
    '''parse img'''
    returnVect = np.zeros((1,1024))
    try:
        fr = open(filename)
        for i in range(32):
            lineStr = fr.readline()
            for j in range(32):
                returnVect[0,32*i+j] = int(lineStr[j])
    finally:
        fr.close()
    return returnVect

testVct = img2vector('testDigits/0_13.txt')
print(testVct[0,0:31])

def handwritingClassTest():
    hwLabels = []
    trainingFileList = listdir('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('trainingDigits/%s'%(fileNameStr))
    testFileList = listdir('testDigits')
    errorCount = 0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2vector('testDigits/%s'%(fileNameStr))
        classifierResult = classify0(vectorUnderTest,trainingMat,hwLabels,3)
        print('the classifier came back with: %d, the real answer is: %d'%(classifierResult,classNumStr))
        if classifierResult != classNumStr:
            errorCount += 1
    print('\nthe total number of errors is: %d'%errorCount)
    print('\nthe total error rate is: %f'%(errorCount/mTest))

handwritingClassTest()