手写识别系统（kNN）
目录
谷歌笔记本（可选）
准备数据：将图像转换为测试向量
编写算法：k-近邻算法
测试算法：在训练集测试算法
使用算法：使用k-近邻算法预测数字
谷歌笔记本（可选）
from google.colab import drive
drive.mount("/content/drive")Mounted at /content/drive
准备数据：将图像转换为测试向量
import zipfiledef unzip_data(zip_file_path, extract_to_path):with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:zip_ref.extractall(extract_to_path)# 用法示例
zip_file_path = '/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/手写识别系统/digits.zip'
extract_to_path = '/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/手写识别系统/'unzip_data(zip_file_path, extract_to_path)from numpy import *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 returnVecttestVector = img2vector('/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/手写识别系统/testDigits/0_13.txt')
testVector.shape(1, 1024)
编写算法：编写k-近邻算法
import operator
def classify0(inX, dataSet, labels, k):dataSetSize = dataSet.shape[0]diffMat = tile(inX, (dataSetSize, 1)) - dataSetsqDiffMat = diffMat ** 2sqDistances = sqDiffMat.sum(axis=1)distances = sqDistances**0.5sortedDistIndicies = distances.argsort()classCount = {}for i in range(k):voteIlabel = labels[sortedDistIndicies[i]]classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)return sortedClassCount[0][0]测试算法：在训练集测试算法
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answer is: %d" % (classifierResult, classNumStr))if (classifierResult != classNumStr):errorCount += 1print("\nthe total number of errors is: %d" % errorCount)print("\nthe total error rate is: %f" % (errorCount/float(myTest)))handwritingClassTest()the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back with: 0, the real answer is: 0 the classifier came back 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使用算法：使用k-近邻算法预测数字
def handwritingPredict(vectorUnderTest):hwLabels = []trainingFileList = listdir('/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/手写识别系统/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('/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/手写识别系统/trainingDigits/%s' % fileNameStr)classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)print("the classifier came back with: %d" % (classifierResult))testVector[0]array([0., 0., 0., ..., 0., 0., 0.])
handwritingPredict(testVector[0])the classifier came back with: 0