#!/usr/bin/python

 # coding=utf-8

 #########################################

 # kNN: k Nearest Neighbors

 # 参数:        inX: vector to compare to existing dataset (1xN)

 #             dataSet: size m data set of known vectors (NxM)

 #             labels: data set labels (1xM vector)

 #             k: number of neighbors to use for comparison

 # 输出:     多数类

 #########################################

 from numpy import *

 import operator

 import os

 # KNN分类核心方法

 def kNNClassify(newInput, dataSet, labels, k):

     numSamples = dataSet.shape[0]  # shape[0]代表行数

     # # step 1: 计算欧式距离

     # tile(A, reps): 将A重复reps次来构造一个矩阵

     # the following copy numSamples rows for dataSet

     diff = tile(newInput, (numSamples, 1)) - dataSet  # Subtract element-wise

     squaredDiff = diff ** 2  # squared for the subtract

     squaredDist = sum(squaredDiff, axis = 1)   # sum is performed by row

     distance = squaredDist ** 0.5

     # # step 2: 对距离排序

     # argsort()返回排序后的索引

     sortedDistIndices = argsort(distance)

     classCount = {}  # 定义一个空的字典

     for i in xrange(k):

         # # step 3: 选择k个最小距离

         voteLabel = labels[sortedDistIndices[i]]

         # # step 4: 计算类别的出现次数

         # when the key voteLabel is not in dictionary classCount, get()

         # will return 0

         classCount[voteLabel] = classCount.get(voteLabel, 0) + 1

     # # step 5: 返回出现次数最多的类别作为分类结果

     maxCount = 0

     for key, value in classCount.items():

         if value > maxCount:

             maxCount = value

             maxIndex = key

     return maxIndex

 # 将图片转换为向量

 def  img2vector(filename):

     rows = 32

     cols = 32

     imgVector = zeros((1, rows * cols))

     fileIn = open(filename)

     for row in xrange(rows):

         lineStr = fileIn.readline()

         for col in xrange(cols):

             imgVector[0, row * 32 + col] = int(lineStr[col])

     return imgVector

 # 加载数据集

 def loadDataSet():

     # # step 1: 读取训练数据集

     print "---Getting training set..."

     dataSetDir = 'E:/KNNCase/digits/'

     trainingFileList = os.listdir(dataSetDir + 'trainingDigits')  # 加载测试数据

     numSamples = len(trainingFileList)

     train_x = zeros((numSamples, 1024))

     train_y = []

     for i in xrange(numSamples):

         filename = trainingFileList[i]

         # get train_x

         train_x[i, :] = img2vector(dataSetDir + 'trainingDigits/%s' % filename)

         # get label from file name such as "1_18.txt"

         label = int(filename.split('_')[0]) # return 1

         train_y.append(label)

     # # step 2:读取测试数据集

     print "---Getting testing set..."

     testingFileList = os.listdir(dataSetDir + 'testDigits') # load the testing set

     numSamples = len(testingFileList)

     test_x = zeros((numSamples, 1024))

     test_y = []

     for i in xrange(numSamples):

         filename = testingFileList[i]

         # get train_x

         test_x[i, :] = img2vector(dataSetDir + 'testDigits/%s' % filename)

         # get label from file name such as "1_18.txt"

         label = int(filename.split('_')[0]) # return 1

         test_y.append(label)

     return train_x, train_y, test_x, test_y

 # 手写识别主流程

 def testHandWritingClass():

     # # step 1: 加载数据

     print "step 1: load data..."

     train_x, train_y, test_x, test_y = loadDataSet()

     # # step 2: 模型训练.

     print "step 2: training..."

     pass

     # # step 3: 测试

     print "step 3: testing..."

     numTestSamples = test_x.shape[0]

     matchCount = 0

     for i in xrange(numTestSamples):

         predict = kNNClassify(test_x[i], train_x, train_y, 3)

         if predict == test_y[i]:

             matchCount += 1

     accuracy = float(matchCount) / numTestSamples

     # # step 4: 输出结果

     print "step 4: show the result..."

     print 'The classify accuracy is: %.2f%%' % (accuracy * 100)

#!/usr/bin/python

# coding=utf-8

import KNN

KNN.testHandWritingClass()