# -*- coding: utf-8 -*-
"""
Input: 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
Output: the most popular class label
"""
from numpy import *
import operator
import os
from data_util import DataUtils
import datetime
import time
import matplotlib.pyplot as plt
import numpy as np
trainfile_X = 'train-images.idx3-ubyte'
trainfile_y = 'train-labels.idx1-ubyte'
testfile_X = 't10k-images.idx3-ubyte'
testfile_y = 't10k-labels.idx1-ubyte'
# 定义kNN分类函数
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): Construct an array by repeating A reps times
# 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: 计算距离
# argsort() returns the indices that would sort an array in a ascending order
sortedDistIndices = argsort(distance)
classCount = {} # 定义一个字典用于放入元素
for i in xrange(k):
## step 3: 选择k值对应的距离
voteLabel = labels[sortedDistIndices[i]]
## step 4: 记录标签出现的次数
# when the key voteLabel is not in dictionary classCount, get()
# will return 0
classCount[voteLabel] = classCount.get(voteLabel, 0) + 1
## step 5: 返回对应标签最多的类别
maxCount = 0
for key, value in classCount.items():
if value > maxCount:
maxCount = value
maxIndex = key
return maxIndex
# 将图片转化为向量形式
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
# 测试HandWriting类
def testHandWritingClass():
## step 1: 加载数据
ISOTIMEFORMAT='%Y-%m-%d %X'
load_time=time.strftime(ISOTIMEFORMAT, time.localtime())
print "step 1: loading data...",
print"load_time:%s"%load_time
train_x=DataUtils(filename=trainfile_X).getImage()
train_y=DataUtils(filename=trainfile_y).getLabel()
test_x=DataUtils(testfile_X).getImage()
test_y=DataUtils(testfile_y).getLabel()
## step 2: 训练
train_time=time.strftime(ISOTIMEFORMAT, time.localtime())
print "step 2: training...",
print "train_time:%s"%train_time
pass
## step 3: 测试
test_time=time.strftime(ISOTIMEFORMAT, time.localtime())
print "step 3: testing...",
print "test_time:%s"%test_time
numTestSamples = test_x.shape[0]
matchCount = 0
result=[]
for m in range(1,121):
for i in xrange(numTestSamples):
predict = kNNClassify(test_x[i], train_x, train_y, m)
if predict == test_y[i]:
matchCount += 1
error = 1 - float(matchCount) / numTestSamples
matchCount=0
result_time=time.strftime(ISOTIMEFORMAT, time.localtime())
print m,"result_time:%s"%result_time
print 'The classify error is: %.4f' %error
result.append(error)
##step4:出图
x = range(1,121)
y = result
plt.plot(x, y)
plt.show()
if __name__ == '__main__':
testHandWritingClass()
