K-鄰近均值演算法

程式碼來自Peter Harrington《機器學習實戰》
相關資料集見www.manning.com/MachineLearninginAction
kNN.py

from numpy import *
import operator

class k_algrithom:
    def __init__(self):
        self.inx = None
        self.dataset = None
        self.labels = None
        self.k = None

   # def createDataset(self):
    #     group = array([1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1])
    #     labaels = ['A', 'A', 'B', 'B']
    #     return group, labaels

    def classify0(self,inx, dataset, labels, k):
        # inx:輸入向量，dataset:輸入樣本，labels:標籤向量,k:選擇最鄰近的數目
        dataSetSize = dataset.shape[0]
        diffMat = tile(inx, (dataSetSize, 1)) - dataset
        sqDiffMat = diffMat ** 2
        sqDistance = sqDiffMat.sum(axis=1)
        distance = sqDistance ** 0.5
        sortedDistIndices = distance.argsort()
        classcount = {}
        for i in range(k):
            votelabel = labels[sortedDistIndices[i]]
            classcount[votelabel] = classcount.get(votelabel, 0) + 1
        sortedclasscount = sorted(classcount.items(), key=operator.itemgetter(1),
                                  reverse=True)
        return sortedclasscount[0][0]

    def file2matrix(self,filename):#將文字記錄轉化為numpy的解析程式
    # 得到檔案行數
        fr = open(filename)
        arrayOlines = fr.readlines()
        numberOflines = len(arrayOlines)
        returnMat = zeros((numberOflines,3))
        classLabelVector = []
        index = 0
        # 解析檔案資料得到列表
        for line in arrayOlines:
            line = line.strip()
            listFromLine = line.split('\t')
            returnMat[index,:] = listFromLine[0:3]
            classLabelVector.append(int(listFromLine[-1]))
            index += 1
        return returnMat, classLabelVector
    def autonorm(self, dataset):# 歸一化特徵值
        minvals = dataset.min(0)
        maxvals = dataset.max(0)
        ranges = maxvals - minvals
        normDataset = zeros(shape(dataset))
        m = dataset.shape[0]
        normDataset = dataset - tile(minvals, (m, 1))
        normDataset = normDataset / tile(ranges, (m, 1))
        return normDataset, ranges, minvals
    def imgvector(self, filename):# 將影像轉化為向量
        returnVect = zeros((1, 1024))
        fr = open(filename)
        for i in range(32):
            lineStr = fr.readline()
            for j in range(32):
                returnVect[0, 32*i + j] = int(lineStr[j])
        return returnVect

running.py

from kmean.kNN import *
import matplotlib.pyplot as plt
import os

datingDataMat, datingLabels = k_algrithom().file2matrix('datingTestSet2.txt')
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2], 15.0 * array(datingLabels), 15.0 * array(datingLabels))
# plt.show()

# normMat, ranges, minVals = k_algrithom().autonorm(datingDataMat)
# print(normMat, ranges, minVals)


def datingClassTest():  # 分類器對約會網站的測試程式碼
    hoRitio = 0.1
    datingDataMat, datingLabels = k_algrithom().file2matrix('datingTestSet2.txt')
    normMat, ranges, minvals = k_algrithom().autonorm(datingDataMat)
    m = normMat.shape[0]
    numTestVecs = int(m*hoRitio)
    errorCount = 0.0
    for i in range(numTestVecs):
        classfireResult = k_algrithom().classify0(normMat[i, :], normMat[numTestVecs:m, :],\
                                                datingLabels[numTestVecs:m], 3)
        print('the classfier came back with: %d, the real answer is :%d'\
              % (classfireResult, datingLabels[i]))
        if (classfireResult != datingLabels[i]): errorCount += 1.0
    print('the total error rate is: %f'% (errorCount/float(numTestVecs)))

def handwritingClassTest():# 實現手寫輸入識別
    hwLabels = []
    trainingFileList = os.listdir('trainingDigits')
    m = len(trainingFileList)
    trainingMat = zeros((m, 1024))
    for i in range(m):
        filenameStr = trainingFileList[i]
        fileStr = filenameStr.split('.')[0]
        classNumberStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumberStr)
        trainingMat[i,:] = k_algrithom().imgvector('trainingDigits/%s' % filenameStr)
    testfileList = os.listdir('testDigits')
    errorcount = 0.0
    mTest = len(testfileList)
    for i in range(mTest):
        filenameStr = testfileList[i]
        fileStr2 = filenameStr.split('.')[0]
        classNumberStr = int(fileStr.split('_')[0])
        vectorUnderTest = k_algrithom().imgvector('testDigits/%s' % filenameStr)
        classfireResult = k_algrithom().classify0(vectorUnderTest, \
                                                  trainingMat, hwLabels, 3)
        print('the classfier came back with: %d, the real answer is :%d' \
              % (classfireResult, datingLabels[i]))
        if (classfireResult != datingLabels[i]): errorcount += 1.0
    print('\nthe total number of errors is: %d' % errorcount)
    print('\nthe total error rate is: %f' % (errorcount / float(mTest)))

handwritingClassTest()