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本篇內容主要講解“怎么使用python k-近鄰算法”，感興趣的朋友不妨來看看。本文介紹的方法操作簡單快捷，實用性強。下面就讓小編來帶大家學習“怎么使用python k-近鄰算法”吧!
它采用測量不同特征值之間的距離方法進行分類
優：精度高、對異常值不敏感、無數據輸入假定
缺：計算復雜度高、空間復雜度高
適用數據范圍：數值型和標稱型
存在一個樣本數據集合（訓練樣品集），并且樣本集中每個數據都存在標簽，即我們知道樣本集中每一個數據與所屬分類的對應關系。
輸入沒有標簽的新數據后，將新數據的每個特征與樣品集中數據對應的特征進行比較，然后算法提取樣本集中特征最相近數據（最鄰近）的分類標簽。
一般來說，只選擇樣本數據集中前k個最相似的數據，這就是k-近鄰算法中K的出處，通常k是不大于20的整數。
最后，選擇k個最相似數據中出現次數最多的分類，作為新數據的分類。
kNN.py
偽代碼
計算已知類別數據集中的點 與 當前點之間的距離；
按照距離遞增次序排序；
選取 與 當前距離最小的k個點；
確定 前k個點所在類別的出現頻率；
返回 前k個點出現頻率最高的類別作為當前點的預測分類。
步驟1用到 歐式距離公式
def classify0(inX, dataSet, labels, k):
#距離計算
dataSetSize = dataSet.shape[0] #numpy庫數組的行數
diffMat = tile(inX, (dataSetSize,1)) - dataSet
#tile復制inX dataSetSize行數倍，以便相減
sqDiffMat = diffMat**2
sqDistances = sqDiffMat.sum(axis=1)
#axis＝0表示按列相加，axis＝1表示按照行的方向相加
#開二次方
distances = sqDistances**0.5
sortedDistIndicies = distances.argsort()
#將元素從小到大排序，提取對應的index,然后輸出返回，如x[3]=-1,y[0]=3
#選擇距離最小的k個點
classCount={} #python對象
for i in range(k):
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 #設置鍵 值 + 1
#排序
sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
#operator.itemgetter(1)返回函數，得classCount K-V的V，對V進行排序，因設置reverse，從左到右，從大到小
return sortedClassCount[0][0]示例使用
def createDataSet(): group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) labels = ['A','A','B','B'] return group, labels group, labels = createDataSet() #最后輸出值 print classify0([0, 0], group, labels, 3) #輸出 B
def file2matrix(filename):
fr = open(filename)
numberOfLines = len(fr.readlines()) #get the number of lines in the file
returnMat = zeros((numberOfLines,3)) #prepare matrix to return
classLabelVector = [] #prepare labels return
fr = open(filename)
index = 0
for line in fr.readlines():
line = line.strip()
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3]
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat,classLabelVector運行結果
>>>datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt')
>>>print datingDataMat
[[ 4.09200000e+04 8.32697600e+00 9.53952000e-01]
[ 1.44880000e+04 7.15346900e+00 1.67390400e+00]
[ 2.60520000e+04 1.44187100e+00 8.05124000e-01]
...,
[ 2.65750000e+04 1.06501020e+01 8.66627000e-01]
[ 4.81110000e+04 9.13452800e+00 7.28045000e-01]
[ 4.37570000e+04 7.88260100e+00 1.33244600e+00]]
>>>print datingLabels[0:20]
[3, 2, 1, 1, 1, 1, 3, 3, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3]import matplotlib import matplotlib.pyplot as plt from numpy import * fig = plt.figure() ax = fig.add_subplot(111)#1行1列1圖 ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2]) #datingDataMat[:, 1] /*返回所有行，第2列*/ plt.show()
#附帶尺寸、顏色參數 ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2] \ , 15.0*array(datingLabels), 15.0*array(datingLabels)) plt.show()
有時發現數據中的不同特征的特征值相差甚遠，會影響計算結果
處理方法
將數值歸一化，如將取值范圍處理為0到1或者-1到之間。公式：
newValue = (oldValue - min) / (max - min)
def autoNorm(dataSet): minVals = dataSet.min(0)#從每列中選出最小值 maxVals = dataSet.max(0)#從每列中選出最大值 ranges = maxVals - minVals#范圍 normDataSet = zeros(shape(dataSet))#行寬和dataSet相同的00矩陣 m = dataSet.shape[0]#dataSet有多少個實例 normDataSet = dataSet - tile(minVals, (m,1)) #tile將數組A重復n次,上例子minVals,重復m次，1表示 #tile(a,(2,1))就是把a先沿x軸（就這樣稱呼吧）復制1倍，即沒有復制，仍然是 [0,1,2]。 再把結果沿y方向復制2倍 normDataSet = normDataSet / tile(ranges, (m,1)) #element wise divide return normDataSet, ranges, minVals
運行結果
>>>normMat, ranges, minVals = kNN.autoNorm(datingDataMat) >>>normMat [[ 0.44832535 0.39805139 0.56233353] [ 0.15873259 0.34195467 0.98724416] [ 0.28542943 0.06892523 0.47449629] ..., [ 0.29115949 0.50910294 0.51079493] [ 0.52711097 0.43665451 0.4290048 ] [ 0.47940793 0.3768091 0.78571804]] >>>ranges [ 9.12730000e+04 2.09193490e+01 1.69436100e+00] >>>minVals [ 0. 0. 0.001156]
機器學習算法一個重要的工作就是評估算法的正確率，通常提供已有數據的90%作為訓練樣品分類器，而使用其余的10%數據去測試分類器，檢測出分類器的正確率。
def datingClassTest():
#取50%的數據進行測試
hoRatio = 0.50 #hold out 10%
#處理數據
datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') #load data setfrom file
#數據歸一化處理
normMat, ranges, minVals = autoNorm(datingDataMat)
m = normMat.shape[0]
#拿來 測試條目 數目
numTestVecs = int(m*hoRatio)
errorCount = 0.0
for i in range(numTestVecs):
#kNN核心算法
classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)
#輸出結果
print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i])
#統計錯誤數
if (classifierResult != datingLabels[i]):
errorCount += 1.0
print "the total error rate is: %f" % (errorCount/float(numTestVecs))
print 'errorCount: '+str(errorCount)運行結果
>>>datingClassTest() ... the classifier came back with: 2, the real answer is: 1 the classifier came back with: 2, the real answer is: 2 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 1, the real answer is: 1 the classifier came back with: 2, the real answer is: 2 the total error rate is: 0.064000 errorCount: 32.0
def classifyPerson(file):
resultList = ['not at all','in small doses', 'in large doses']
percentTats = float(raw_input(\
"percentage of time spent playing video games?"))
ffMiles = float(raw_input("frequent flier miles earned per year?"))
iceCream = float(raw_input("liters of ice cream consumed per year?"))
datingDataMat,datingLabels = file2matrix(file)
normMat, ranges, minVals = autoNorm(datingDataMat)
inArr = array([ffMiles, percentTats, iceCream])
classifierResult = classify0((inArr- \
minVals)/ranges,normMat,datingLabels,3)
print "You will probably like this person: ",\
resultList[classifierResult - 1]
kNN.classifyPerson('..\\datingTestSet2.txt')結果
percentage of time spent playing video games?10 frequent flier miles earned per year?10000 liters of ice cream consumed per year?0.5 You will probably like this person: in small doses
trainingDigits 2000個訓練樣本
testDigitsa 900個測試數據
某一示例：
00000000000001111000000000000000 00000000000011111110000000000000 00000000001111111111000000000000 00000001111111111111100000000000 00000001111111011111100000000000 00000011111110000011110000000000 00000011111110000000111000000000 00000011111110000000111100000000 00000011111110000000011100000000 00000011111110000000011100000000 00000011111100000000011110000000 00000011111100000000001110000000 00000011111100000000001110000000 00000001111110000000000111000000 00000001111110000000000111000000 00000001111110000000000111000000 00000001111110000000000111000000 00000011111110000000001111000000 00000011110110000000001111000000 00000011110000000000011110000000 00000001111000000000001111000000 00000001111000000000011111000000 00000001111000000000111110000000 00000001111000000001111100000000 00000000111000000111111000000000 00000000111100011111110000000000 00000000111111111111110000000000 00000000011111111111110000000000 00000000011111111111100000000000 00000000001111111110000000000000 00000000000111110000000000000000 00000000000011000000000000000000
將把一個32 * 32的二進制圖像矩陣裝換為1 * 1024的向量
def img2vector(filename): returnVect = zeros((1,1024))#創建有1024個元素的列表 fr = open(filename) for i in range(32): lineStr = fr.readline() for j in range(32): returnVect[0,32*i+j] = int(lineStr[j]) return returnVect
運行結果
>>>testVector = kNN.img2vector('testDigits/0_13.txt')
>>>testVector[0, 0:31]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
>>>testVector[0, 32:63]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]import os
def handwritingClassTest():
#準備訓練數據和測試數據
hwLabels = []
trainingFileList = os.listdir('trainingDigits') #load the training set
m = len(trainingFileList)
trainingMat = zeros((m,1024))
for i in range(m):
fileNameStr = trainingFileList[i]
fileStr = fileNameStr.split('.')[0] #take off .txt
classNumStr = int(fileStr.split('_')[0])
hwLabels.append(classNumStr)
trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)
testFileList = os.listdir('testDigits') #iterate through the test set
errorCount = 0.0
mTest = len(testFileList)
#開始測試
for i in range(mTest):
fileNameStr = testFileList[i]
fileStr = fileNameStr.split('.')[0] #take off .txt
classNumStr = int(fileStr.split('_')[0])
vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)
classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)
if (classifierResult != classNumStr): errorCount += 1.0
print "\nthe total number of errors is: %d" % errorCount
print "\nthe total error rate is: %f" % (errorCount/float(mTest))運行結果
>>>handwritingClassTest() ... the classifier came back with: 9, the real answer is: 9 the classifier came back with: 9, the real answer is: 9 the classifier came back with: 9, the real answer is: 9 the classifier came back with: 9, the real answer is: 9 the classifier came back with: 9, the real answer is: 9 the classifier came back with: 9, the real answer is: 9 the classifier came back with: 9, the real answer is: 9 the total number of errors is: 11 the total error rate is: 0.011628
實際使用這個算法時，算法的執行效率并不高
到此，相信大家對“怎么使用python k-近鄰算法”有了更深的了解，不妨來實際操作一番吧！這里是億速云網站，更多相關內容可以進入相關頻道進行查詢，關注我們，繼續學習！
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