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使用k-近邻算法构建手写识别系统（kNN）

2024-06-11 42
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简介： 使用k-近邻算法构建手写识别系统（kNN）

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 zipfile
 
def 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 returnVect
testVector = 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)) - 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]

测试算法：在训练集测试算法

from os import listdir
def handwritingClassTest():
  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)
  testFileList = listdir('/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/手写识别系统/testDigits/')
  errorCount = 0
  myTest = len(testFileList)
  for i in range(myTest):
    fileNameStr = testFileList[i]
    fileStr = fileNameStr.split('.')[0]
    classNumStr = int(fileStr.split('_')[0])
    vectorUnderTest = img2vector('/content/drive/MyDrive/MachineLearning/机器学习/k-近邻算法/手写识别系统/testDigits/%s' % fileNameStr)
    classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
    print("the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr))
    if (classifierResult != classNumStr):
      errorCount += 1
  print("\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
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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: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
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the classifier came back with: 1, the real answer is: 1
the classifier came back with: 1, the real answer is: 1
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the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
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the classifier came back with: 1, the real answer is: 9
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the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 7, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 9, the real answer is: 9

the total number of errors is: 10

the total error rate is: 0.010571

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
文章标签：
算法
关键词：
算法系统
算法knn
近邻算法
算法近邻
算法构建
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