1.文字檔案資料


等等
2.將其3232的二進位制影象轉換為11024的向量
3.測試演算法

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
'''=================================================
@Project -> File   ：KNN -> kNN
@IDE    ：PyCharm
@Author ：zgq
@Date   ：2021/1/7 14:15
@Desc   ：
=================================================='''
from numpy import
 
 *
import operator #運算子模組
import matplotlib
import matplotlib.pyplot as plt
from os import listdir

def classify0(inX,dataSet,labels,k):
    #inx用於分類的輸入向量
    #訓練樣本集dataset
    #lables標籤
    #k最近鄰數目

    #距離計算
    dataSetSize=dataSet.shape[0] #dataset有幾行
    diffMat=tile(inX,(dataSetSize,1))-dataSet  #輸入向量重複了已有資料集的行數，一起減掉，出來一個新的矩陣，每個數字都記錄當前新樣本該維度與每個樣本差值
 

    sqDiffMat=diffMat**2
    sqDistances=sqDiffMat.sum(axis=1) #所有橫軸元素加和
    distances=sqDistances**0.5  #到此處時 distance為一個一位列陣列，記錄每條樣本與新樣本的距離
    sortedDistIndicies= distances.argsort() #對distance進行升序排序
    classCount={}   #DICT型別
    for i in range(k):  #尋找距離最小的K個點
        voteIlabel = labels[sortedDistIndicies[
 
i]]   #返回距離排序中前K條資料的標籤
        classCount[voteIlabel]=classCount.get(voteIlabel,0)+1
        #classCount.get(voteIlabel,0) 字典獲取vouteIlabel值，沒有的話返回0
        #此處for迴圈將距離最近的K個數據標籤進行統計：每次for迴圈第一步，將第i個標籤記錄到voteIlable中，第二部將該標籤出現後再dict中次數加一
    sortedClassCount=sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)
    return sortedClassCount[0][0]


#將img資料轉換為向量
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  returnVect


#手寫數字識別系統的測試程式碼
def handwritingClassTest():
    hwLabels=[]
    trainingFileList=listdir('trainingDigits')  #listdir可以列出給定目錄的檔名
    m=len(trainingFileList)
    trainingMat=zeros((m,1024))
    for i in range(m):
        fileNameStr=trainingFileList[i] #獲取當前第i個檔名
        fileStr=fileNameStr.split('.')[0]   #先用點來切分，切分為0_0和txt [0_0,txt]取第0項
        classNumStr=int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)    #將所有的標籤按照順序新增到了hwLables中
        trainingMat[i,:]=img2vector('trainingDigits/%s' % fileNameStr)  #順便將每一個檔案都轉為向量存入trainingMat中
    testFileList=listdir('testDigits')  #將測試檔案的名字作為列表給予testFilelist
    errorCount=0.0
    mTest=len(testFileList) #取test的集的總數
    for i in range(mTest):
        fileNameStr=testFileList[i]
        fileStr=fileNameStr.split('.')[0]
        classNumStr=int(fileStr.split('_')[0])
        vectorUnderTest=img2vector('testDigits/%s' % fileNameStr)   #拿出一條測試集資料構成測試向量
        classifierResult=classify0(vectorUnderTest,trainingMat,hwLabels,3)  #此處訓練集樣本和標籤行數是對齊的
        print("the classifier came back with : %d,the real answer is : %d" %(classifierResult,classNumStr))
        if (classifierResult!=classNumStr):
            errorCount=errorCount+1.0
    print("\n the total number of errors is :%d" % errorCount)
    print("\n the total error rate is: %f" % (errorCount/float(mTest)))

handwritingClassTest()

測試結果：

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 : 1,the real answer is : 1
the classifier came back with : 7,the real answer is : 1
the classifier came back with : 1,the real answer is : 1
……
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 6,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
the classifier came back with : 3,the real answer is : 8
the classifier came back with : 8,the real answer is : 8
……

the total number of errors is :10
 the total error rate is: 0.010571