KNN算法——python实现

二、Python实现

       对于机器学习而已，Python需要额外安装三件宝，分别是Numpy，scipy和Matplotlib。前两者用于数值计算，后者用于画图。安装很简单，直接到各自的官网下载回来安装即可。安装程序会自动搜索我们的python版本和目录，然后安装到python支持的搜索路径下。反正就python和这三个插件都默认安装就没问题了。

       另外，如果我们需要添加我们的脚本目录进Python的目录（这样Python的命令行就可以直接import），可以在系统环境变量中添加：PYTHONPATH环境变量，值为我们的路径，例如：E:PythonMachine Learning in Action

 

2.1、kNN基础实践

       一般实现一个算法后，我们需要先用一个很小的数据库来测试它的正确性，否则一下子给个大数据给它，它也很难消化，而且还不利于我们分析代码的有效性。

      首先，我们新建一个kNN.py脚本文件，文件里面包含两个函数，一个用来生成小数据库，一个实现kNN分类算法。代码如下：

 

#########################################
# kNN: k Nearest Neighbors

# Input:      newInput: 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

# create a dataset which contains 4 samples with 2 classes
def createDataSet():
    # create 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'] # four samples and two classes
    return group, labels

# classify using kNN
def kNNClassify(newInput, dataSet, labels, k):
    numSamples = dataSet.shape[0] # shape[0] stands for the num of row

    ## step 1: calculate Euclidean distance
    # 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: sort the distance
    # argsort() returns the indices that would sort an array in a ascending order
    sortedDistIndices = argsort(distance)

    classCount = {} # define a dictionary (can be append element)
    for i in xrange(k):
        ## step 3: choose the min k distance
        voteLabel = labels[sortedDistIndices[i]]

        ## step 4: 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

    ## step 5: the max voted class will return
    maxCount = 0
    for key, value in classCount.items():
        if value > maxCount:
            maxCount = value
            maxIndex = key

    return maxIndex

 

       然后我们在命令行中这样测试即可：

 

import kNN
from numpy import *

dataSet, labels = kNN.createDataSet()

testX = array([1.2, 1.0])
k = 3
outputLabel = kNN.kNNClassify(testX, dataSet, labels, 3)
print "Your input is:", testX, "and classified to class: ", outputLabel

testX = array([0.1, 0.3])
outputLabel = kNN.kNNClassify(testX, dataSet, labels, 3)
print "Your input is:", testX, "and classified to class: ", outputLabel

 

       这时候会输出：

 

Your input is: [ 1.2  1.0] and classified to class:  A
Your input is: [ 0.1  0.3] and classified to class:  B

 

2.2、kNN进阶

       这里我们用kNN来分类一个大点的数据库，包括数据维度比较大和样本数比较多的数据库。这里我们用到一个手写数字的数据库，可以到这里下载。这个数据库包括数字0-9的手写体。每个数字大约有200个样本。每个样本保持在一个txt文件中。手写体图像本身的大小是32x32的二值图，转换到txt文件保存后，内容也是32x32个数字，0或者1，如下：

 

       数据库解压后有两个目录：目录trainingDigits存放的是大约2000个训练数据，testDigits存放大约900个测试数据。

        这里我们还是新建一个kNN.py脚本文件，文件里面包含四个函数，一个用来生成将每个样本的txt文件转换为对应的一个向量，一个用来加载整个数据库，一个实现kNN分类算法。最后就是实现这个加载，测试的函数。

 

#########################################
# kNN: k Nearest Neighbors

# 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


# classify using kNN
def kNNClassify(newInput, dataSet, labels, k):
    numSamples = dataSet.shape[0] # shape[0] stands for the num of row

    ## step 1: calculate Euclidean distance
    # 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: sort the distance
    # argsort() returns the indices that would sort an array in a ascending order
    sortedDistIndices = argsort(distance)

    classCount = {} # define a dictionary (can be append element)
    for i in xrange(k):
        ## step 3: choose the min k distance
        voteLabel = labels[sortedDistIndices[i]]

        ## step 4: 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

    ## step 5: the max voted class will return
    maxCount = 0
    for key, value in classCount.items():
        if value > maxCount:
            maxCount = value
            maxIndex = key

    return maxIndex

# convert image to vector
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

# load dataSet
def loadDataSet():
    ## step 1: Getting training set
    print "---Getting training set..."
    dataSetDir = 'E:/Python/Machine Learning in Action/'
    trainingFileList = os.listdir(dataSetDir + 'trainingDigits') # load the training set
    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: 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 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

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

 

       测试非常简单，只需要在命令行中输入：

 

import kNN
kNN.testHandWritingClass()

 

       输出结果如下：

 

step 1: load data...
---Getting training set...
---Getting testing set...
step 2: training...
step 3: testing...
step 4: show the result...
The classify accuracy is: 98.84%