機器學習實戰-第二章代碼+註釋-KNN
阿新 • • 發佈：2018-06-20
rep sdn odi als cti 元素 集合 pre recv
#-*- coding:utf-8 -*- #https://blog.csdn.net/fenfenmiao/article/details/52165472 from numpy import * #科學計算包 import operator #運算符模塊 import matplotlib import matplotlib.pyplot as plt #matplotlib.pyplot是一些命令行風格函數的集合 from os import listdir #列出給定目錄的文件名 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 def classify0(inX, dataSet, labels, k) : dataSetSize = dataSet.shape[0] #查看矩陣或者數組的維數 c.shape[1] 為第一維的長度，c.shape[0] 為第二維的長度 此處為4 #(dataSetSize, 1)使數組重復完是四行一樣的 而不是在1行中。 diffMat = tile(inX, (dataSetSize, 1)) - dataSet #numpy.tile(A,reps) tile共有2個參數，A指待輸入數組，reps則決定A重復的次數。整個函數用於重復數組A來構建新的數組。sqDiffMat = diffMat ** 2 #冪 （x1 - x2）的冪 sqDistance = sqDiffMat.sum(axis = 1) #每行相加 橫著相加 distances = sqDistance ** 0.5 #開根號 sortedDistIndicies = distances.argsort() #argsort是排序，將元素按照由小到大的順序返回下標 classCount = {} #dict字典數據類型，字典是Python中唯一內建的映射類型 for i in range(k) : voteIlabel= labels[sortedDistIndicies[i]] #get是取字典裏的元素，如果之前這個voteIlabel是有的，那麽就返回字典裏這個voteIlabel裏的值，如果沒有就返回0（後面寫的），這行代碼的意思就是算離目標點距離最近的k個點的類別，這個點是哪個類別哪個類別就加1 classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1 #key=operator.itemgetter(1)的意思是按照字典裏的第一個排序，{A:1,B:2},要按照第1個（AB是第0個），即‘1’‘2’排序。reverse=True是降序排序 sortedClassCount = sorted(classCount.iteritems(), key = operator.itemgetter(0), reverse = True) return sortedClassCount[0][0] def file2matrix(filename): fr = open(filename) # 一次讀取整個文本數據，並且自動將文件內容分析成一個行的列表，比readline（）快 ，後面的img2vector就是使用的readline（），因為要逐行逐個讀取，可以對比一下 arrayOLines = fr.readlines() numberOfLines = len(arrayOLines) #返回來一個給定形狀和類型的用0填充的數組; returnMat = zeros((numberOfLines, 3)) #文件有幾行就是幾行，設置為3列（可調） classLabelVector = [] index = 0 for line in arrayOLines : line = line.strip() #去掉回車符 listFromLine = line.split(‘\t‘) #分成了4列數據，得到了4個列表 #將數據前三列提取出來,存放到returnMat的NumPy矩陣中,也就是特征矩陣 returnMat[index, :] = listFromLine[0 : 3] #前3個列表元素是愛倫要的特征，取出來去填充returnMat #classLabelVector.append(int(listFromLine[-1])) if listFromLine[-1] == ‘didntLike‘: classLabelVector.append(1) elif listFromLine[-1] == ‘smallDoses‘: classLabelVector.append(2) elif listFromLine[-1] == ‘largeDoses‘: classLabelVector.append(3) index += 1 return returnMat, classLabelVector #歸一化 def autoNorm(dataSet) : #min(0)返回該矩陣中每一列的最小值 #min(1)返回該矩陣中每一行的最小值 #max(0)返回該矩陣中每一列的最大值 #max(1)返回該矩陣中每一行的最大值 minVals = dataSet.min(0) maxVals = dataSet.max(0) ranges = maxVals - minVals #得到數據集的行數 shape方法用來得到矩陣或數組的維數 normDataSet = zeros(shape(dataSet)) m = dataSet.shape[0] normDataSet = dataSet - tile(minVals, (m, 1)) normDataSet = normDataSet / tile(ranges, (m, 1)) return normDataSet, ranges, minVals def datingClassTest() : hoRatio = 0.10 datingDataMatm, datingLabels = file2matrix(‘F:\jxq\Desktop\datingTestSet.txt‘) normMat, ranges, minVals = autoNorm(datingDataMat) #歸一化 m = normMat.shape[0] #二維數組維度大小 numTestVecs = int(m * hoRatio) #訓練樣本從第m * hoRatio 開始 errorCount = 0.0 for i in range(numTestVecs): classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs : m], 3) print("the classifier came back with : %d, the real answer is : %d" % (classifierResult, datingLabels[i])) if (classifierResult != datingLabels[i]) : errorCount += 1.0 print("the total error rate is : %f" % (errorCount / float(numTestVecs))) def classifyPerson() : 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, datingLables = file2matrix(‘F:\jxq\Desktop\datingTestSet.txt‘) normMat, ranges, minVals = autoNorm(datingDataMat) inArr = array([ffMiles, percentTats, iceCream]) classifierResult = classify0((inArr-minVals)/ranges, normMat, datingLables, 3) print ("You will probably like this person:", resultList[classifierResult - 1]) #索引從0開始，索引減去1才能索引到對應的resultList def img2vector(filename) : returnVect = zeros((1, 1024)) # 用於保存1x1024的向量 fr = open(filename) for i in range(32) : lineStr = fr.readline() for j in range(32) : returnVect[0, 32*i+j] = int(lineStr[j]) # 字符需要強制類型轉換成整數 return returnVect def handwritingClassTest() : hwLabels = [] #獲取目錄內容 trainingFileList = listdir(‘F:\\jxq\\Documents\\Tencent Files\\834810071\\FileRecv\\machinelearninginaction-master\\machinelearninginaction-master\\Ch02\\digits\\trainingDigits‘) m = len(trainingFileList) trainingMat = zeros((m, 1024)) for i in range(m) : fileNameStr = trainingFileList[i] fileStr = fileNameStr.split(‘.‘)[0] #無後綴文件名 classNumStr = int(fileStr.split(‘_‘)[0]) #獲取文件內的數字 hwLabels.append(classNumStr) #圖片轉換為向量 trainingMat[i, :] = img2vector(‘F:\\jxq\\Documents\\Tencent Files\\834810071\\FileRecv\\machinelearninginaction-master\\machinelearninginaction-master\\Ch02\\digits\\trainingDigits\\%s‘ %fileNameStr) testFileList = listdir(‘F:\\jxq\\Documents\\Tencent Files\\834810071\\FileRecv\\machinelearninginaction-master\\machinelearninginaction-master\\Ch02\\digits\\testDigits‘) errorCount = 0.0 mTest = len(testFileList) for i in range(mTest): fileNameStr = testFileList[i] fileStr = fileNameStr.split(‘.‘)[0] classNumStr = int(fileStr.split(‘_‘)[0]) vectorUnderTest = img2vector(‘F:\\jxq\\Documents\\Tencent Files\\834810071\\FileRecv\\machinelearninginaction-master\\machinelearninginaction-master\\Ch02\\digits\\testDigits\\%s‘ %fileNameStr) classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3) #分類 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 error is: %d" %errorCount) print("\nthe total error rate is: %f" %(errorCount/float(mTest))) if __name__ == ‘__main__‘: #filename = ‘F:\jxq\Desktop\datingTestSet.txt‘ #datingDataMat, datingLabels = file2matrix(filename) ‘‘‘ print(datingDataMat) print(datingLabels) fig = plt.figure() #figure()來創建多個圖 ax = fig.add_subplot(111) #參數349的意思是：將畫布分割成3行4列，圖像畫在從左到右從上到下的第9塊 #後兩個是顏色 ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2], 15.0*array(datingLabels), 15.0*array(datingLabels)) #繪制散點圖 X[:,0]就是取所有行的第0個數據, X[:,1] 就是取所有行的第1個數據 ‘‘‘ ‘‘‘ normMat, range, minVals = autoNorm(datingDataMat) print(normMat) print(range) print(minVals) ‘‘‘ #datingClassTest() #plt.show() #classifyPerson() #testVector = img2vector(‘F:\\jxq\\Documents\\Tencent Files\\834810071\\FileRecv\\machinelearninginaction-master\\machinelearninginaction-master\\Ch02\\digits\\testDigits\\0_13.txt‘) #print(testVector[0, 0:31]) #print(testVector[0, 32:63]) handwritingClassTest()
機器學習實戰-第二章代碼+註釋-KNN