算法介绍

之前已经介绍过,简单来说，K-NN可以看成：有那么一堆你已经知道分类的数据，然后当一个新数据进入的时候，就开始跟训练数据里的每个点求距离，然后挑离这个训练数据最近的K个点看看这几个点属于什么类型，然后用少数服从多数的原则，给新数据归类。

算法步骤:

步骤：

1. step.1—初始化距离为最大值

2. step.2—计算未知样本和每个训练样本的距离dist

3. step.3—得到目前K个最临近样本中的最大距离maxdist

4. step.4—如果dist小于maxdist，则将该训练样本作为K-最近邻样本

5. step.5—重复步骤2、3、4，直到未知样本和所有训练样本的距离都算完

6. step.6—统计K-最近邻样本中每个类标号出现的次数

7. step.7—选择出现频率最大的类标号作为未知样本的类标号

案例分析

首先一个CNN算法

from numpy import *  import operator  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(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.iteritems(), key=operator.itemgetter(1),reverse=True) return sortedClassCount[0][0]

测试算法:

#! /usr/bin/env python# -*- coding: utf-8 -*-from numpy import *from os import listdir import KNN from numpy.core import multiarray def img2vector(filename): '图像文件转换成矩阵' returnVect = zeros((1,1024)) fr = open(filename) for i in range(32): #将32行合并成一行 lineStr = fr.readline() for j in range(32): returnVect[0,32*i+j] = int(lineStr[j]) return returnVect #一个样本最终成为一个1*1024的向量 def handwritingClassTest(): '手写识别测试函数，调用了KNN模块的KNN分类器函数' 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 = KNN.classify0(vectorUnderTest, trainingMat, hwLabels, 3) print "in #%d, the classifier came back with: %d, the real answer is: %d" % (i, 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)) handwritingClassTest()

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源码下载地址:

http://www.woaipu.com/shops/zuzhuan/61406

转载于:https://www.cnblogs.com/sy646et/p/7194115.html

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