KNN最近邻算法numpy版本——深度学习

#!python
from numpy import *
import operator
from os import listdir
 
def createDataSet():
    group = array([[1.0,1.1],[1.0,1.0],[0.1,0.1],[0.2,0.1]])
    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.items(),key=\
                              operator.itemgetter(1),reverse=True)
    return sortedClassCount[0][0];
 
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 = lie.split('\t')
        returnMat[index,:] = listFromLIne[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat,classLabelVector
 
def autoNorm(dataSet):
    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 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[numTestVec:m,:],\
                                     datingLabels[numTestVecs:m],3)
        print("the classifier came backwith: %d,the real answer is:%d"\
              %(classifierResult,datingLabels[i]))
        if(classifierResult != datingLabels[i]):
              errorCount += 1.0
        print("the total error rate is:%f" %(errorCount/float(numTestVecs)))
 
def img2vector(filename):
    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
 
def handwritingClassTest():
    hwLabels = []
    trainingFileList = listdir('trainingDigits')
    m = len(trainingFileList)
    trainingMat =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 = 0.0
    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 cameback with:%d,the real answer is:%d"\
              %(classifierResult,classNumStr))
        if(classifierResult != classNumStr): errorCount += 1.0
    print ("\n the total number of errors is:%d" %errorCount)
    print("\nthe total error rate is:%f" %(errorCount/float(mTest)))