关于KNN的python3实现

　　关于KNN，有幸看到这篇文章，写的很好，这里就不在赘述。直接贴上代码了，有小的改动。（原来是python2版本的，这里改为python3的，主要就是print）

　　环境：win7 32bit + spyder + anaconda3.5

　　一、初阶

# -*- coding: utf-8 -*-
"""
Created on Sun Nov  6 16:09:00 2016

@author: Administrator
"""

#Input:
#	newInput:待测的数据点(1xM)
#	dataSet:已知的数据(NxM)
#	labels:已知数据的标签(1xM)
#	k:选取的最邻近数据点的个数
#
#Output:
#	待测数据点的分类标签
#	

from numpy import *

# creat a dataset which contain 4 samples with 2 class
def createDataSet():
	# creat a matrix: each row as a sample
	group = array([[1.0, 0.9], [1.0, 1.0], [0.1, 0.2], [0.0, 0.1]])
	labels = ['A', 'A', 'B', 'B']
	return group, labels

#classify using KNN
def KNNClassify(newInput, dataSet, labels, k):
	numSamples = dataSet.shape[0]  # row number
	# step1:calculate Euclidean distance
	# tile(A, reps):Constract an array by repeating A reps times
	diff = tile(newInput, (numSamples, 1)) - dataSet
	squreDiff = diff**2
	squreDist = sum(squreDiff, axis=1)  # sum if performed by row
	distance = squreDist ** 0.5

	#step2:sort the distance
	# argsort() returns the indices that would sort an array in a ascending order
	sortedDistIndices = argsort(distance)

	classCount = {}
	for i in range(k):
		# choose the min k distance
		voteLabel = labels[sortedDistIndices[i]]

		#step4:count the times labels occur
		# when the key voteLabel is not in dictionary classCount,
		# get() will return 0
		classCount[voteLabel] = classCount.get(voteLabel, 0) + 1
	#step5:the max vote class will return
	maxCount = 0
	for k, v in classCount.items():
		if v > maxCount:
			maxCount = v
			maxIndex = k

	return maxIndex

# test

dataSet, labels = createDataSet()

testX = array([1.2, 1.0])
k = 3
outputLabel = KNNClassify(testX, dataSet, labels, 3)

print("Your input is:", testX, "and classified to class: ", outputLabel)

testX = array([0.1, 0.3])
k = 3
outputLabel = KNNClassify(testX, dataSet, labels, 3)

print("Your input is:", testX, "and classified to class: ", outputLabel)

　　运行结果：

　　二、进阶

　　用到的手写识别数据库资料在这里下载。关于资料的介绍在上面的博文也已经介绍的很清楚了。

# -*- coding: utf-8 -*-
"""
Created on Sun Nov  6 16:09:00 2016

@author: Administrator
"""

#Input:
#	newInput:待测的数据点(1xM)
#	dataSet:已知的数据(NxM)
#	labels:已知数据的标签(1xM)
#	k:选取的最邻近数据点的个数
#
#Output:
#	待测数据点的分类标签
#	

from numpy import *

#classify using KNN
def KNNClassify(newInput, dataSet, labels, k):
	numSamples = dataSet.shape[0]  # row number
	# step1:calculate Euclidean distance
	# tile(A, reps):Constract an array by repeating A reps times
	diff = tile(newInput, (numSamples, 1)) - dataSet
	squreDiff = diff**2
	squreDist = sum(squreDiff, axis=1)  # sum if performed by row
	distance = squreDist ** 0.5

	#step2:sort the distance
	# argsort() returns the indices that would sort an array in a ascending order
	sortedDistIndices = argsort(distance)

	classCount = {}
	for i in range(k):
		# choose the min k distance
		voteLabel = labels[sortedDistIndices[i]]

		#step4:count the times labels occur
		# when the key voteLabel is not in dictionary classCount,
		# get() will return 0
		classCount[voteLabel] = classCount.get(voteLabel, 0) + 1
	#step5:the max vote class will return
	maxCount = 0
	for k, v in classCount.items():
		if v > maxCount:
			maxCount = v
			maxIndex = k

	return maxIndex

# convert image to vector
def  img2vector(filename):
    rows = 32
    cols = 32
    imgVector = zeros((1, rows * cols))
    fileIn = open(filename)
    for row in range(rows):
        lineStr = fileIn.readline()
        for col in range(cols):
            imgVector[0, row * 32 + col] = int(lineStr[col])  

    return imgVector

# load dataSet
def loadDataSet():
    ## step 1: Getting training set
    print("---Getting training set...")
    dataSetDir = 'F:\\Techonolgoy\\算法学习\\KNN\\进阶\\'
    trainingFileList = os.listdir(dataSetDir + 'trainingDigits') # load the training set
    numSamples = len(trainingFileList)  

    train_x = zeros((numSamples, 1024))
    train_y = []
    for i in range(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: Getting testing set
    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 range(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  

# test hand writing class
def testHandWritingClass():
    ## step 1: load data
    print("step 1: load data...")
    train_x, train_y, test_x, test_y = loadDataSet()  

    ## step 2: training...
    print("step 2: training...")
    pass  

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

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

testHandWritingClass()

　　运行结果：