按照书中内容写了一个手写数字识别demo.

尝试自己手写数字并拍了照片,将照片缩小成32*32,然后转成灰度图,再转成二值矩阵.

实际效果不太好,还需要优化.

先贴代码,日后改善.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import operator
from os import listdir
from PIL import Image

def main():
    print(img2vector('./digits/testDigits/0_1.txt')[0, 0:31])
    handwritingClassTest()

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():
    hwLabels = []
    trainingFileList = listdir('./digits/trainingDigits')
    m = len(trainingFileList)
    print(f"file number = {m}")
    trainingMat = np.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(f'./digits/trainingDigits/{filenameStr}')

    testFileList = listdir('./digits/testDigits')
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        filenameStr = testFileList[i]
        fileStr = filenameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2vector(f'./digits/testDigits/{filenameStr}')
        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
        print(f"test={classifierResult}, real num={classNumStr}")
        if classifierResult != classNumStr:
            errorCount += 1
    print(f"total error num={errorCount}")
    print(f"total error rate is: {errorCount / float(mTest)}")

def classify0(inX, trainingDataSet, labels, k):
    dataSetSize = trainingDataSet.shape[0]
    diffMat = np.tile(inX, (dataSetSize, 1)) - trainingDataSet
    # 将inX 平铺(复制)成跟训练集同样的行数
    # 如果inX = [a,b], 训练集有2行,
    # 则结果为 一个矩阵
    # [[a,b],
    # [a,b]]
    # 然后再和训练集做矩阵减法
    sqDiffMat = diffMat ** 2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances ** 0.5
    sortedDistIndices = distances.argsort()
    classCount = {}
    for i in range(k):
        voteILabel = labels[sortedDistIndices[i]]
        classCount[voteILabel] = classCount.get(voteILabel, 0) + 1
    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

def pretreatment(filename):
    ima = Image.open(filename)
    ima = ima.resize((32, 32), Image.ANTIALIAS)
    ima = ima.convert('L')  # 转化为灰度图像
    # ima.show()
    data = ima.getdata()
    im = np.array(ima)  # 转化为二维数组

    for i in range(im.shape[0]):  # 转化为二值矩阵
        for j in range(im.shape[1]):
            if im[i, j] 

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