kNN.py

from numpy import *
import matplotlib
import matplotlib.pyplot as plt
import operator
import os 
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(idx,dataSet,labels,k):   
    dataSetSize = dataSet.shape[0]
    diffMat = tile(idx,(dataSetSize,1)) - dataSet
    sqDiffMat = diffMat**2
    sqDistances =sqDiffMat.sum(axis = 1)
    distances = sqDistances**0.5
    sortDistIndicies = distances.argsort()
    classCount={}

    for i in range(k):
        voteIlabel = labels[sortDistIndicies[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)
    numberOfLines = len(fr.readlines())
    returnMat = zeros((numberOfLines,3))
    classLabelVector =[]
    fr = open(filename)
    index = 0
    for line in fr.readlines():
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index,:] = listFromLine[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat,classLabelVector
def show_data2img(DataMat,DataLabels,datatype1,datatype2):
    fig = plt.figure()
    ax = fig.add_subplot(111)
    ax.scatter(DataMat[:,datatype1],DataMat[:,datatype2],15.0 * array(DataLabels) , 15.0 * array(DataLabels))
    plt.show()

def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = zeros(shape(dataSet))    #shape(dataSet) = 1000 , 3   # 0 , 0 , 0 .....
    m = dataSet.shape[0] 
    normDataSet = dataSet - tile(minVals , (m,1))   #(m,1) = 1000 ,1  tile => copy minVals 1000 times
    normDataSet = normDataSet / tile(ranges ,(m,1))
    return normDataSet , ranges , minVals

def datingClassTest():
    hotRatio = 0.99
    datingDataMat , datingLabels = file2matrix('datingTestSet.txt')
    normMat , ranges ,minVals = autoNorm(datingDataMat)
    m = normMat.shape[0]
    numTestVecs = int(m * hotRatio)
    errorCount = 0.0        
    for i in range(numTestVecs):        
         classfierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)
         
         print ("No.%d the classifier came back with: %d , the real answer is : %d" %(i+1,classfierResult , datingLabels[i]))
         if(classfierResult != datingLabels[i]):
             errorCount += 1.0
    print("the total error rate is : %f" % (errorCount / float(numTestVecs)))
    
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 , datingLabels = file2matrix('datingTestSet.txt')
    normMat , ranges ,minVals = autoNorm(datingDataMat)
    inArr = array([ffMiles , percentTats , iceCream])
    classifierResult = classify0((inArr - minVals) / ranges , normMat , datingLabels , 3)
    print("you will probably like this person",resultList[classifierResult - 1])
    
    
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 = os.listdir('/Users/leo/Desktop/python/trainingDigits')
    m = len(trainingFileList)
    trainingMat = zeros((m,1024))
    for i in range (1,m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = (fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)
    testFileList = os.listdir('/Users/leo/Desktop/python/testDigits')
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(1,mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = (fileStr.split('_')[0])
        vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)
        classifierResult = classify0(vectorUnderTest , trainingMat ,hwLabels ,3)
        print ("the classifier came back with %s the real answer is : %s" %(classifierResult , classNumStr))

        if(classifierResult != classNumStr):
            errorCount +=1.0
    print("\n total number of errors is %d"%errorCount)
    print("\n total error rate is : %f" %(errorCount / float(mTest)))