算法实现
def classify0(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0] #距离度量 度量公式为欧⽒距离 diffMat = np.tile(inX, (dataSetSize,1))-dataSet sqDiffMat = diffMat**2 sqDistances = sqDiffMat.sum(axis=1) distances = sqDistances**0.5 #将距离排序：从⼩到⼤ sortedDistIndicies = distances.argsort() #选取前K个最短距离， 选取这K个中最多的分类类别 classCount={} for i in range(k): voteIlabel = labels[sortedDistIndicies[i]] classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1),reverse=True) return sortedClassCount[0][0]
def img2vector(filename): returnVect = np.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(): # 1. 导⼊训练数据 hwLabels = [] trainingFileList = os.listdir('2.KNN/trainingDigits') # load the training set m = len(trainingFileList) trainingMat = np.zeros((m, 1024)) # hwLabels存储0～9对应的index位置， trainingMat存放的每个位置对应的图⽚向量 for i in range(m): fileNameStr = trainingFileList[i] fileStr = fileNameStr.split('.')[0] # take off .txt classNumStr = int(fileStr.split('_')[0]) hwLabels.append(classNumStr) # 将 32*32的矩阵->1*1024的矩阵 trainingMat[i, :] = img2vector('2.KNN/trainingDigits/%s' % fileNameStr) # 2. 导⼊测试数据 testFileList = os.listdir('2.KNN/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('2.KNN/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 (" the total number of errors is: %d" % errorCount) print (" the total error rate is: %f" % (errorCount / float(mTest)))
import os import numpy as np import operator handwritingClassTest()
优点：⾼的准确率，对于异常值不敏感
缺点：空间。时间复杂度太⾼了