优点:精度高,对异常值不敏感,无数据输入假定
缺点:计算复杂度高,空间复杂度高
适用数据范围:数值型和标称型
 
一般流程:
    (1). 收集数据(网络抓取)
    (2).处理数据,将数据处理成结构化的数据格式。
    (3).分析数据
    (4).测试算法(主要是计算模型的出错率)
    (5).使用算法,
 
K-近邻算法采用测量不同特征值之间的距离的方法进行分类
 
工作原理是:存在一个训练样本集,且样本集中每个数据都存在标签(与分类的对应关系)。
当输入没有标签的新数据后,将新数据的每个特征与样本集中的数据对应的特征进行比较,
然后算法提取训练样本集中前k个最相似的数据的分类标签,且k不大于20 。
选择最相似数据中出现次数最多的分类,作为新数据的分类。
 
 
数据归一化的作用,只用在特征数据相差较大且同等重要的条件下
 
aaarticlea/png;base64,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" alt="" name="en-media:image/png:2bdbd1d41ecee133d740c53250e97a8b:none:none" />
 
上面方程中数字差值最大的属性对计算结果的影响最大,仅仅是因为飞行常客里程数远大于其他特征值。然而我们认为这三种特征同样重要,因此作为三个等权重的特征 
 
直接上代码:
from numpy import *
import matplotlib.pyplot as plot
import operator
from os import listdir def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
# 距离计算公式
diffMat = tile(inX, (dataSetSize,1)) - dataSet
sqDiffMat = diffMat**2
sqDistances = sqDiffMat.sum(axis=1)
distances = sqDistances**0.5 # 距离从大到小排序,返回距离的序号
sortedDistIndicies = distances.argsort()
# 声明一个空的字典,用于存放标签
classCount={}
for i in range(k):
# sortedDistIndicies[0]返回的是距离最小的数据样本的序号
# labels[sortedDistIndicies[0]]距离最小的数据样本的标签
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
# 给该字典排序,sortedClassCount[0][0]是K中支持的标签数最大的
sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
print(sortedClassCount[0][0])
return sortedClassCount[0][0] # 创建数据
def createDataSet():
group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
labels = ['A','A','B','B']
return group, labels # 画图
def draw(xs,ys):
fig = plot.figure()
# 将画布分割成1行1列,图像画在从左到右从上到下的第1块
# 设置画布的大小与图像的位置
ax = fig.add_subplot(221)
# ax.scatter(xs, ys)的两个参数分别是所有点的x坐标,所有点的y坐标
ax.scatter(xs,ys)
plot.show() def firstTest():
test1 = (1.0, 1.2)
test2 = (0.0, 0.4)
dataset, labels = createDataSet()
conclusion1 = classify0(test1, dataset, labels, 3)
conclusion2 = classify0(test2, dataset, labels, 3)
print(str(test1) + "分类后的结果是属于" + conclusion1 + "类")
print(str(test2) + "分类后的结果是属于" + conclusion2 + "类")
# 将32*32的矩阵读为1*1024
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 def handwritingClassTest():
hwLabels = []
# 获得训练样本数据集
trainingFileList = listdir('digits/trainingDigits')
# 样本数的个数
m = len(trainingFileList)
# 返回m行1024列的矩阵数据
trainingMat = zeros((m, 1024))
# 文件名下划线_左边的数字是标签
for i in range(m):
fileNameStr = trainingFileList[i]
fileStr = fileNameStr.split(".")[0]
# 分类标签
classNumStr = int(fileStr.split('_')[0])
hwLabels.append(classNumStr)
trainingMat[i, :] = img2vector('digits/trainingDigits/%s' % fileNameStr)
testFileList = listdir('digits/testDigits')
errorCount = 0.0
mTest = len(testFileList)
for i in range(mTest):
fileNameStr = testFileList[i]
fileStr = fileNameStr.split('.')[0] # take off .txt
classNumStr = int(fileStr.split('_')[0])
vectorUnderTest = img2vector('digits/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 += 1.0
print("\nthe total number of errors is: %d" % errorCount)
print("\nthe total error rate is: %f" % (errorCount / float(mTest))) # 主函数调用模块函数
if __name__ == "__main__":
# group,label = createDataSet()
# # group[:, 0] 所有行的第0列
# draw(group[:, 0], group[:, 1])
# # print(group[:, 0])
# firstTest()
handwritingClassTest() 训练数据集合测试集的数据:https://gitee.com/lcl1993213/plist

KNN--用于手写数字识别的更多相关文章

  1. KNN实现手写数字识别

    KNN实现手写数字识别 博客上显示这个没有Jupyter的好看,想看Jupyter Notebook的请戳KNN实现手写数字识别.ipynb 1 - 导入模块 import numpy as np i ...

  2. Softmax用于手写数字识别(Tensorflow实现)-个人理解

    softmax函数的作用   对于分类方面,softmax函数的作用是从样本值计算得到该样本属于各个类别的概率大小.例如手写数字识别,softmax模型从给定的手写体图片像素值得出这张图片为数字0~9 ...

  3. 机器学习(二)-kNN手写数字识别

    一.kNN算法是机器学习的入门算法,其中不涉及训练,主要思想是计算待测点和参照点的距离,选取距离较近的参照点的类别作为待测点的的类别. 1,距离可以是欧式距离,夹角余弦距离等等. 2,k值不能选择太大 ...

  4. 一看就懂的K近邻算法(KNN),K-D树,并实现手写数字识别!

    1. 什么是KNN 1.1 KNN的通俗解释 何谓K近邻算法,即K-Nearest Neighbor algorithm,简称KNN算法,单从名字来猜想,可以简单粗暴的认为是:K个最近的邻居,当K=1 ...

  5. kaggle 实战 (1): PCA + KNN 手写数字识别

    文章目录 加载package read data PCA 降维探索 选择50维度, 拆分数据为训练集,测试机 KNN PCA降维和K值筛选 分析k & 维度 vs 精度 预测 生成提交文件 本 ...

  6. Kaggle竞赛丨入门手写数字识别之KNN、CNN、降维

    引言 这段时间来,看了西瓜书.蓝皮书,各种机器学习算法都有所了解,但在实践方面却缺乏相应的锻炼.于是我决定通过Kaggle这个平台来提升一下自己的应用能力,培养自己的数据分析能力. 我个人的计划是先从 ...

  7. 基于OpenCV的KNN算法实现手写数字识别

    基于OpenCV的KNN算法实现手写数字识别 一.数据预处理 # 导入所需模块 import cv2 import numpy as np import matplotlib.pyplot as pl ...

  8. K近邻实战手写数字识别

    1.导包 import numpy as np import operator from os import listdir from sklearn.neighbors import KNeighb ...

  9. C#中调用Matlab人工神经网络算法实现手写数字识别

    手写数字识别实现 设计技术参数:通过由数字构成的图像,自动实现几个不同数字的识别,设计识别方法,有较高的识别率 关键字:二值化  投影  矩阵  目标定位  Matlab 手写数字图像识别简介: 手写 ...

  10. 利用神经网络算法的C#手写数字识别

    欢迎大家前往云+社区,获取更多腾讯海量技术实践干货哦~ 下载Demo - 2.77 MB (原始地址):handwritten_character_recognition.zip 下载源码 - 70. ...

随机推荐

  1. 《剑指Offer》附加题_用两个队列实现一个栈_C++版

    在<剑指Offer>中,在栈和队列习题中,作者留下来一道题目供读者自己实现,即"用两个队列实现一个栈". 在计算机数据结构中,栈的特点是后进先出,即最后被压入(push ...

  2. Windows下使用nginx搭建反向代理服务器

    反向代理(Reverse Proxy)方式是指以代理服务器来接受internet上的连接请求,然后将请求转发给内部网络上的服务器,并将从服务器上得到的结果返回给internet上请求连接的客户端,此时 ...

  3. JqueryMobile基础之创建页面

    首先简答介绍一下JQueryMobile吧,我觉得用一句话来讲就是可以 "写更少的代码,做更多的事情" : 它可以通过一个灵活及简单的方式来布局网页,且兼容所有移动设备.这也是我自 ...

  4. Linux命令用法

    1.cut http://www.cnblogs.com/dong008259/archive/2011/12/09/2282679.html 2.sed http://www.cnblogs.com ...

  5. 简易RPC框架-上下文

    *:first-child { margin-top: 0 !important; } body>*:last-child { margin-bottom: 0 !important; } /* ...

  6. 【原创】基于禅道的Bug管理操作规范

    1. 禅道简介 禅道是一个基于"敏捷开发"模式的软件开发全生命周期管理软件,在国内的软件开发公司里占据了超过70%的份额,从大公司到小公司,都能适用. 禅道官网:http://ww ...

  7. redis数据库安装及简单的增删改查

    redis下载地址:https://github.com/MSOpenTech/redis/releases. 解压之后,运行 redis-server.exe redis.windows.conf  ...

  8. 最小k个数

    题目 输入n个整数,找出其中最小的K个数.例如输入4,5,1,6,2,7,3,8这8个数字,则最小的4个数字是1,2,3,4,. 思考 方法0: 直接排序然后返回前k个,最好的时间复杂度为 O(nlo ...

  9. 史上最完整的PS快捷键(绝对经典)

    快速恢复默认值 有些不擅长Photoshop的朋友为了调整出满意的效果真是几经周折,结果发现还是原来的默认效果最好,这下傻了眼,后悔不该当初呀!怎么恢复到默认值呀?试着轻轻点按选项栏上的工具图标,然后 ...

  10. VS2015如何连接mySQL数据库

    mySQL数据库           如题,今天给大家简单演示一下VS2015如何连接mySQL数据库.       首先呢,大家需要安装vs2015和mySQL这两个软件,我还安装了一个辅助软件SQ ...