机器学习 MLIA学习笔记（三）之 KNN（二） Dating可能性实例

这是个KNN算法的另一实例，计算Dating的可能性。

 

import numpy as np

import os

import operator

import matplotlib

import matplotlib.pyplot as plt



def classify(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]#lines num; samples num

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet#dataSize*(1*inX)

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)#add as the first dim

    distances = sqDistances**0.5

    #return indicies array from min to max

    #this is an array

    sortedDistanceIndices = distances.argsort()

    #classCount={}

    classCount=dict()   #define a dictionary

    for i in range(k):

        voteIlabel = labels[sortedDistanceIndices[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1#get(key,default=none)

    #return a list like [('C',4),('B',3),('A',2)], not a dict

    #itemgetter(0) is the 1st element

    #default: from min to max

    sortedClassCount = sorted(classCount.iteritems(),

                              key=operator.itemgetter(1), reverse=True)                  

    return sortedClassCount[0][0]



def file2matrix(fileName):

    fileHandler = open(fileName)

    numberOfLines = len(fileHandler.readlines())    #get the number of lines in the file

    returnMat = np.zeros((numberOfLines, 3))           #init a zero return matrix

    classLabelVector = []

    #classLabelVector = list()                       #will be used to record labels

    fileHandler = open(fileName)

    index = 0

    for line in fileHandler.readlines():

        line = line.strip()                         #strip blank characters

        listFromLine = line.split('\t')

        returnMat[index,:] = listFromLine[0:3]

        classLabelVector.append(listFromLine[-1])

        index += 1

    return returnMat, classLabelVector



#normalize data set

def autoNorm(dataSet):

    minVal = dataSet.min(0)

    maxVal = dataSet.max(0)

    ranges = maxVal - minVal

    normDataSet = np.zeros(np.shape(dataSet))

    m = dataSet.shape[0]

    normDataSet = dataSet - np.tile(minVal, (m,1))

    normDataSet = normDataSet/np.tile(ranges, (m,1))

    return normDataSet, ranges, minVal



def showMatrix():

    m,l = file2matrix("datingTestSet.txt")

    m,r,mv = autoNorm(m)

    fig = plt.figure()

    ax = fig.add_subplot(111)

    ax.scatter(m[:,1],m[:,2])

    plt.show()



#calculate the error rate of sample

def calcErrorRate():

    ratio = 0.1         #only use 10% samples to calc the error rate

    matrix,l = file2matrix("datingTestSet.txt")

    matrix,r,mv = autoNorm(matrix)

    m = matrix.shape[0]

    numTestSample = int(m*ratio)

    errorCount = 0

    for i in range(numTestSample):

        classifyResult = classify(matrix[i,:], matrix[numTestSample:m,:],l[numTestSample:m],3)

        print "the classifier came back with: %s, the real answer is: %s" % (classifyResult, l[i])

        if (classifyResult != l[i]):

            errorCount += 1

    print "the total error rate is: %f" %(errorCount/float(numTestSample))

    print errorCount



def classifyPerson():

    percentTats = float(raw_input(\

                "percentage of time spent playing vedio games?"))

    ffMiles = float(raw_input("frequent flier miles earned per year?"))

    iceCream = float(raw_input("liters of ice cream consumed per year?"))

    datingDataMat, datingLabels = file2matrix("datingTestSet.txt")

    normMat, ranges, minVal = autoNorm(datingDataMat)

    inArr = np.array([ffMiles, percentTats, iceCream])

    classifyResult = classify((inArr-minVal)/ranges, normMat, datingLabels,3)

    print "You will probaly like this person: ", classifyResult