KNN算法——python實現

二、Python實現

       對於機器學習而已，Python需要額外安裝三件寶，分別是Numpy，scipy和Matplotlib。前兩者用於數值計算，后者用於畫圖。安裝很簡單，直接到各自的官網下載回來安裝即可。安裝程序會自動搜索我們的python版本和目錄，然后安裝到python支持的搜索路徑下。反正就python和這三個插件都默認安裝就沒問題了。

       另外，如果我們需要添加我們的腳本目錄進Python的目錄（這樣Python的命令行就可以直接import），可以在系統環境變量中添加：PYTHONPATH環境變量，值為我們的路徑，例如：E:\Python\Machine Learning in Action

 

2.1、kNN基礎實踐

       一般實現一個算法后，我們需要先用一個很小的數據庫來測試它的正確性，否則一下子給個大數據給它，它也很難消化，而且還不利於我們分析代碼的有效性。

      首先，我們新建一個kNN.py腳本文件，文件里面包含兩個函數，一個用來生成小數據庫，一個實現kNN分類算法。代碼如下：

 

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

 

 

       然后我們在命令行中這樣測試即可：

 

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

 

 

       這時候會輸出：

 

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進階

       這里我們用kNN來分類一個大點的數據庫，包括數據維度比較大和樣本數比較多的數據庫。這里我們用到一個手寫數字的數據庫，可以到這里下載。這個數據庫包括數字0-9的手寫體。每個數字大約有200個樣本。每個樣本保持在一個txt文件中。手寫體圖像本身的大小是32x32的二值圖，轉換到txt文件保存后，內容也是32x32個數字，0或者1，如下：

 

       數據庫解壓后有兩個目錄：目錄trainingDigits存放的是大約2000個訓練數據，testDigits存放大約900個測試數據。

        這里我們還是新建一個kNN.py腳本文件，文件里面包含四個函數，一個用來生成將每個樣本的txt文件轉換為對應的一個向量，一個用來加載整個數據庫，一個實現kNN分類算法。最后就是實現這個加載，測試的函數。

 

#########################################
# 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)

 

 

       測試非常簡單，只需要在命令行中輸入：

 

import kNN
kNN.testHandWritingClass()

 

 

       輸出結果如下：

 

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%