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