定義將影象轉換為向量函式

# 匯入程式所需要的模組
import numpy as np
import operator
from os import listdir

讀取檔案

def img2vector(filename):
    returnVect = np.zeros((1, 1024))    # 儲存圖片畫素的向量維度是1x1024
    fr = open(filename)
    for i in range(32):
        lineStr = fr.readline()
        for j in range(32):
            returnVect[0, 32*i+j] = int(lineStr[j])    # 圖片尺寸是32x32，將其依次放入向量returnVect中
    return returnVect
 

定義 k 近鄰演算法

def classify0(inX, dataSet, labels, k):    # inX是測試集，dataSet是訓練集，lebels是訓練樣本標籤，k是取的最近鄰個數
    dataSetSize = dataSet.shape[0]    # 訓練樣本個數
    diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet    # np.tile: 重複n次
    sqDiffMat = diffMat**2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances**0.5    # distance是inX與dataSet的歐氏距離
    sortedDistIndicies = distances.argsort()    # 返回排序從小到達的索引位置
    classCount = {}   # 字典儲存k近鄰不同label出現的次數
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1    # 對應label加1，classCount中若無此key，則預設為0
    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)    # operator.itemgetter 獲取物件的哪個維度的資料
    return sortedClassCount[0][0]    # 返回k近鄰中所屬類別最多的哪一類
 

定義手寫數字識別系統函式

def handwritingClassTest():
    # 訓練樣本
    hwLabels = []
    trainingFileList = listdir('./digits/trainingDigits')           #匯入訓練集
    m = len(trainingFileList)
    trainingMat = np.zeros((m, 1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]     # fileNameStr 得到的是每個檔名稱，例如"0_0.txt"
        fileStr = fileNameStr.split('.')[0]     #去掉“.txt”，剩下“0_0”
        classNumStr = int(fileStr.split('_')[0])    # 按下劃線‘_' 劃分“0_0”,取第一個元素為類別標籤
        hwLabels.append(classNumStr)
        trainingMat[i, :] = img2vector('./digits/trainingDigits/%s' % fileNameStr)
    # 測試樣本
    testFileList = listdir('./digits/testDigits')        #iterate through the test set
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]    # fileNameStr 得到的是每個檔名稱，例如"0_0.txt"
        fileStr = fileNameStr.split('.')[0]     #去掉“.txt”，剩下“0_0”
        classNumStr = int(fileStr.split('_')[0])    # 按下劃線‘_' 劃分“0_0”,取第一個元素為類別標籤
        vectorUnderTest = img2vector('./digits/testDigits/%s' % fileNameStr)
        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)    # 呼叫knn函式
        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)))
 

執行例項函式

img2vector('D:/360安全瀏覽器下載/MachineLearningInAction-Camp-master/Week1/Reference Code/digits/testDigits/0_13.txt')

結果為：

array([[0., 0., 0., ..., 0., 0., 0.]])

主函式為：

handwritingClassTest()

結果如下：