kNN

【准备数据】将图像转换为向量

import numpy as np import os #将单个图片的数据转化为行向量 def img2Vect(filename): returnVect = np.zeros((1,1024)) fr = open(filename) for i in range(32): lineStr = fr.readline() for j in range(32): returnVect[0,j+i*32] = int(lineStr[j]) return returnVect img2Vect('trainingDigits\\0_0.txt')[0,0:31] #将文件夹中所有的数据转化为数据矩阵以及对应的结果集 def imgs2Mat(pathname): dataFileList = os.listdir(pathname) m = len(dataFileList) dataSet = np.zeros((m,1024)) labels = [] for i in range(m): filename = dataFileList[i] classNum = int((filename.split('.')[0]).split('_')[0]) labels.append(classNum) vector = img2Vect(pathname+'//'+filename) dataSet[i,:] = vector[:] return dataSet,labels dataSet,labels = imgs2Mat('trainingDigits')

【实施算法】k-近邻算法实现

import operator def classify0(dataVect,dataSet,labels,k): dataSetSize = dataSet.shape[0] diffMat = np.tile(dataVect,(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.items(),key=operator.itemgetter(1),reverse=True) return sortedClassCount[0][0] #dataVect = img2Vect('testDigits\\7_18.txt') #classify0(dataVect,dataSet,labels,5)

【测试算法】测试错误率函数

def handwritingClassTest(): #hoRatio = 0.1 dataSet,labels = imgs2Mat('trainingDigits') testDataSet,testLabels = imgs2Mat('testDigits') errorCount = 0.0 m = testDataSet.shape[0] for i in range(m): dataVect = testDataSet[i,:] classifierResult = classify0(dataVect,dataSet,labels,5) #print("the classifier came back with: %d, the real answer is: %d" % (classifierResult,testLabels[i])) if classifierResult != testLabels[i]: errorCount += 1.0 print("the total number of errors is: %d" % errorCount) print("the total error rate is: %f" % (errorCount/float(m))) handwritingClassTest() the total number of errors is: 17 the total error rate is: 0.017970

【使用算法】输入图像并输出预测结果

def classifyHandwriting(filename): dataVect = img2Vect(filename) dataSet,labels = imgs2Mat('trainingDigits') classifierResult = classify0(dataVect,dataSet,labels,5) return classifierResult classifyHandwriting('dataImg.txt') 8