机器学习之K-近邻算法

参考《机器学习实战》

理论定义：K-近邻算法采用测量不同特征值之间的距离方法进行分类。

优点：精度高，、对异常值不敏感、无数据输入假定

缺点：计算复杂度高、空间复杂度高。

适用数据范围：数值型和标称型。

步骤流程：

1.计算已知类别数据集中的点到当前点之间的距离

2.按照距离递增次序排序

3.选取与当前点距离最小的k个点

4.确定前k个点所在类别的出现频率

5.返回前k个点出现频率最高的类别作为当前点的预测分类。

#-*-coding:utf-8-*-
'实现KNN算法'
# 引入模块
from numpy import*
import operator

def createDataSet():
	group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])	#训练数据
	labels = ['A','A','B','B']	#标签
	return group,labels

def classfy0(inx, dataSet, labels, k):	#inx表示输入向量，dataSet表示输入的训练样本集，labels是标签向量，参数k表示用于选择最近邻居的数目

	dataSetSize = dataSet.shape[0]	#dataSet.shap表示数组各维的大小
	diffMat = tile(inx, (dataSetSize,1)) - dataSet	#tilt(A,reps)
	sqDiffMat = diffMat **2		
	sqDistances = sqDiffMat.sum(axis=1)		#矩阵每一行相加 sum(a,axis=0)表所有和
	distances = sqDistances**0.5
	#print distances
	sortedDistIndicies = distances.argsort()	#返回数组值从小到大的索引值
	#print sortedDistIndicies
	
	classCount = {}
	for i in range(k):
		voteIlabel = labels[sortedDistIndicies[i]]
		classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1	#dist.get(key,default="None") 如果字典中不存在此键，则返回default值
	#print classCount
	sortedClassCount = sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)	#operator.itemgetter(1) 按照第二个域进行迭代 reverse逆序
	return sortedClassCount[0][0]
	
if __name__ == '__main__':
	group,labels = createDataSet()
	inputNum = raw_input("please input your number:")	#类型为字符串
	list_inputNum = [int(x) for x in inputNum]	#转为数组
	k = raw_input("please input your k:")
	k = int(k)
	result = classfy0(list_inputNum,group,labels,k)
	print result

运行测试：

#-*-coding:utf-8-*-
'''
Created on Sep 16, 2010
kNN: k Nearest Neighbors

Input:      inX: vector to compare to existing dataset (1xN)
            dataSet: size m data set of known vectors (NxM)
            labels: data set labels (1xM vector)
            k: number of neighbors to use for comparison (should be an odd number)
            
Output:     the most popular class label

@author: pbharrin
'''

# 引入模块
from numpy import*
import operator
from os import listdir
import matplotlib
import matplotlib.pyplot as plt

def createDataSet():
	group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])	#训练数据
	labels = ['A','A','B','B']	#标签
	return group,labels

def classify0(inx, dataSet, labels, k):	#inx表示输入向量，dataSet表示输入的训练样本集，labels是标签向量，参数k表示用于选择最近邻居的数目

	dataSetSize = dataSet.shape[0]	#dataSet.shap表示数组各维的大小
	diffMat = tile(inx, (dataSetSize,1)) - dataSet	#tilt(A,reps)
	sqDiffMat = diffMat **2		
	sqDistances = sqDiffMat.sum(axis=1)		#矩阵每一行相加 sum(a,axis=0)表所有和
	distances = sqDistances**0.5
	#print distances
	sortedDistIndicies = distances.argsort()	#返回数组值从小到大的索引值
	#print sortedDistIndicies
	
	classCount = {}
	for i in range(k):
		voteIlabel = labels[sortedDistIndicies[i]]
		classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1	#dist.get(key,default="None") 如果字典中不存在此键，则返回default值
	#print classCount
	sortedClassCount = sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)	#operator.itemgetter(1) 按照第二个域进行迭代 reverse逆序
	return sortedClassCount[0][0]

def file2matrix(filename):
    fr = open(filename)
    numberOfLines = len(fr.readlines())         #get the number of lines in the file
    returnMat = zeros((numberOfLines,3))        #prepare matrix to return
    classLabelVector = []                       #prepare labels return   
    fr = open(filename)
    index = 0
    for line in fr.readlines():
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index,:] = listFromLine[0:3]
        '''
        if listFromLine[-1] == 'largeDoses':
            listFromLine[-1] = 3
        elif listFromLine[-1] == 'smallDoses':
            listFromLine[-1] = 2
        elif listFromLine[-1] == 'didntLike':
            listFromLine[-1] = 1
		'''
        classLabelVector.append(int(listFromLine[-1]))
        #classLabelVector.append(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))   #element wise divide
    return normDataSet, ranges, minVals

def datingClassTest():
    hoRatio = 0.50      #hold out 10%
    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')       #load data setfrom file
    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[numTestVecs:m,:],datingLabels[numTestVecs:m],3)
        print "the classifier came back with: %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))
    print errorCount

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')           #load the training set
    m = len(trainingFileList)
    trainingMat = zeros((m,1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]     #take off .txt
        classNumStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)
    testFileList = listdir('testDigits')        #iterate through the test set
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]     #take off .txt
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)
        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
        print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)
        if (classifierResult != classNumStr): errorCount += 1.0
    print "\nthe total number of errors is: %d" % errorCount
    print "\nthe total error rate is: %f" % (errorCount/float(mTest))
	
def test():
	group,labels = createDataSet()
	inputNum = raw_input("please input your number:")	#类型为字符串
	list_inputNum = [int(x) for x in inputNum]	#转为数组
	k = raw_input("please input your k:")
	k = int(k)
	result = classify0(list_inputNum,group,labels,k)
	print result

def test1():
	filename = 'datingTestSet.txt'
	datingDataMat,datingLabels = file2matrix(filename)
	print datingDataMat
	print datingLabels[0:20]

def pic_one():
	fig = plt.figure()
	ax = fig.add_subplot(111)
	filename = 'datingTestSet.txt'
	datingDataMat,datingLabels = file2matrix(filename)
	
	ax.scatter(datingDataMat[:,0],datingDataMat[:,1],
	15.0*array(datingLabels),15.0*array(datingLabels))
	#p1=ax.scatter(datingDataMat[:,1],datingDataMat[:,2],marker='x', color='b')
	#p2=ax.scatter(datingDataMat[:,1],datingDataMat[:,2],marker='o', color='c')
	plt.xlabel('x')
	plt.ylabel('y')
	#plt.legend((p1,p2),('aa','bb'))
	plt.show()
	
if __name__ == '__main__':
	#test1()	
	#pic_one()
	datingClassTest()