后端程序员之路 13、使用KNN进行数字识别

尝试一些用KNN来做数字识别，测试数据来自：
MNIST handwritten digit database, Yann LeCun, Corinna Cortes and Chris Burges
http://yann.lecun.com/exdb/mnist/

1、数据
将位图转为向量（数组），k尝试取值3-15，距离计算采用欧式距离。
d(x,y)=\sqrt{\sum_{i=1}^{n}(x_i-y_i)^2}

2、测试
调整k的取值和基础样本数量，测试得出k取值对识别正确率的影响，以及分类识别的耗时。

如何用python解析mnist图片 - 海上扬凡的博客 - 博客频道 - CSDN.NET
http://blog.csdn.net/u014046170/article/details/47445919

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 08 14:38:15 2017

@author: zapline<278998871@qq.com>
"""

import struct
import os
import numpy

def read_file_data(filename):
    f = open(filename, 'rb')
    buf = f.read()
    f.close()
    return buf

def loadImageDataSet(filename):
    index = 0
    buf = read_file_data(filename)
    magic, images, rows, columns = struct.unpack_from('>IIII' , buf , index)
    index += struct.calcsize('>IIII')
    data = numpy.zeros((images, rows * columns))
    for i in xrange(images):
        imgVector = numpy.zeros((1, rows * columns)) 
        for x in xrange(rows):
            for y in xrange(columns):
                imgVector[0, x * columns + y] = int(struct.unpack_from('>B', buf, index)[0])
                index += struct.calcsize('>B')
        data[i, :] = imgVector
    return data

def loadLableDataSet(filename):
    index = 0
    buf = read_file_data(filename)
    magic, images = struct.unpack_from('>II' , buf , index)
    index += struct.calcsize('>II')
    data = []
    for i in xrange(images):
        lable = int(struct.unpack_from('>B', buf, index)[0])
        index += struct.calcsize('>B')
        data.append(lable)
    return data

def loadDataSet():
    path = "D:\\kingsoft\\ml\\dataset\\"
    trainingImageFile = path + "train-images.idx3-ubyte"
    trainingLableFile = path + "train-labels.idx1-ubyte"
    testingImageFile = path + "t10k-images.idx3-ubyte"
    testingLableFile = path + "t10k-labels.idx1-ubyte"
    train_x = loadImageDataSet(trainingImageFile)
    train_y = loadLableDataSet(trainingLableFile)
    test_x = loadImageDataSet(testingImageFile)
    test_y = loadLableDataSet(testingLableFile)
    return train_x, train_y, test_x, test_y

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 08 14:35:55 2017

@author: zapline<278998871@qq.com>
"""

import numpy

def kNNClassify(newInput, dataSet, labels, k):
    numSamples = dataSet.shape[0]
    diff = numpy.tile(newInput, (numSamples, 1)) - dataSet
    squaredDiff = diff ** 2
    squaredDist = numpy.sum(squaredDiff, axis = 1)
    distance = squaredDist ** 0.5
    sortedDistIndices = numpy.argsort(distance)

classCount = {}
    for i in xrange(k):
        voteLabel = labels[sortedDistIndices[i]]
        classCount[voteLabel] = classCount.get(voteLabel, 0) + 1

maxCount = 0
    for key, value in classCount.items():
        if value > maxCount:
            maxCount = value
            maxIndex = key
    return maxIndex

# -*- coding: utf-8 -*-
"""
Created on Wed Mar 08 14:39:21 2017

@author: zapline<278998871@qq.com>
"""

import dataset
import knn

def testHandWritingClass():
    print "step 1: load data..."
    train_x, train_y, test_x, test_y = dataset.loadDataSet()

print "step 2: training..."
    pass

print "step 3: testing..."
    numTestSamples = test_x.shape[0]
    matchCount = 0
    for i in xrange(numTestSamples):
        predict = knn.kNNClassify(test_x[i], train_x, train_y, 3)
        if predict == test_y[i]:
            matchCount += 1
    accuracy = float(matchCount) / numTestSamples

print "step 4: show the result..."
    print 'The classify accuracy is: %.2f%%' % (accuracy * 100)
 
testHandWritingClass()
print "game over"

总结：上述代码跑起来比较慢，但是在train数据够多的情况下，准确率不错