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使用k-近邻算法构建手写识别系统（kNN）

2024-06-11 20
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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: 1, the real answer is: 1
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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: 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：
算法近邻
算法knn
算法系统
算法构建
近邻算法knn
Want595
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**KNN算法**是一种监督学习的分类算法，适用于解决分类问题。它基于实例学习，无需训练过程，当新样本到来时，通过计算新样本与已有训练样本之间的距离，找到最近的K个邻居，然后根据邻居的类别进行多数表决（或加权表决）来预测新样本的类别。K值的选择、距离度量方式和分类决策规则是KNN的关键要素。KNN简单易懂，但计算复杂度随样本量增加而增加，适用于小规模数据集。在鸢尾花数据集等经典问题上表现良好，同时能处理多分类任务，并可应用于回归和数据预处理中的缺失值填充。
小森ai小小贾
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KNN算法原理及应用（一）
小森ai小小贾
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2天前
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算法
KNN算法原理及应用（二）
不能将所有数据集全部用于训练，为了能够评估模型的泛化能力，可以通过实验测试对学习器的泛化能力进行评估，进而做出选择。因此需要使用一个测试集来测试学习器对新样本的判别能力。
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软件算法开发
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3天前
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算法
基于蝗虫优化的KNN分类特征选择算法的matlab仿真
摘要： - 功能：使用蝗虫优化算法增强KNN分类器的特征选择，提高分类准确性 - 软件版本：MATLAB2022a - 核心算法：通过GOA选择KNN的最优特征以改善性能 - 算法原理： - KNN基于最近邻原则进行分类 - 特征选择能去除冗余，提高效率 - GOA模仿蝗虫行为寻找最佳特征子集，以最大化KNN的验证集准确率 - 运行流程：初始化、评估、更新，直到达到停止标准，输出最佳特征组合
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【机器学习】近邻类模型：KNN算法在数据科学中的实践与探索
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m基于PSO-LSTM粒子群优化长短记忆网络的电力负荷数据预测算法matlab仿真
在MATLAB 2022a中，应用PSO优化的LSTM模型提升了电力负荷预测效果。优化前预测波动大，优化后预测更稳定。PSO借鉴群体智能，寻找LSTM超参数（如学习率、隐藏层大小）的最优组合，以最小化误差。LSTM通过门控机制处理序列数据。代码显示了模型训练、预测及误差可视化过程。经过优化，模型性能得到改善。
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