new document

kNN.py

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+from numpy import *
+import operator
+from os import listdir
+
+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] #dataSet数据0维的维数
+    diffMat = tile(inX, (dataSetSize, 1)) - dataSet #tile函数构造数组
+    sqDiffMat = diffMat**2 #每个元素的平方
+    sqDistances = sqDiffMat.sum(axis=1) #列方向相加 array([2.21, 2.  , 0.  , 0.01])
+    distances = sqDistances**0.5 #开方 
+    sortedDistIndicies = distances.argsort() #排序,按当前排序后的序号生成的列表[2, 3, 1, 0]
+    classCount={}
+    for i in range(k):# [0, 1, 2]
+        voteIlabel = labels[sortedDistIndicies[i]]
+        classCount[voteIlabel] = classCount.get(voteIlabel,0)+ 1 #classCount{"B": 2, "A": 1}
+    sortedClassCount = sorted(classCount.items(),#迭代 [("B", 2), ("A", 1)] 可以用for循环来提取键，值
+                        key = operator.itemgetter(1), reverse=True) #operator.itemgetter(1):使用2,1来做为排序依据
+    return sortedClassCount[0][0]
+
+
+def file2matrix(filename):
+    fr = open(filename) #
+    arrayOLines = fr.readlines() #读取所有行
+    numberOfLines = len(arrayOLines) #行的长度
+    returnMat = zeros((numberOfLines, 3)) #创建一个0数组
+    classLabelVector = []
+    index = 0
+    for line in arrayOLines:
+        line = line.strip() #用于移除字符串头尾指定的字符（默认为空格）
+        listFromLine = line.split('\t')
+        returnMat[index, :] = listFromLine[0:3]
+        if listFromLine[-1] == "largeDoses": #把最后一列的喜欢，魅力，用数值表示
+            classLabelVector.append(3)
+        elif listFromLine[-1] == "smallDoses":
+            classLabelVector.append(2)
+        else:
+            classLabelVector.append(1)
+        index += 1
+    return returnMat, classLabelVector
+
+
+def autoNorm(dataSet):
+    minVals = dataSet.min(0)
+    maxVals = dataSet.max(0)
+    ranges = maxVals - minVals
+    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
+    datingDataMat, datingLabels = file2matrix('datingTestSet.txt')
+    normMat, ranges, minVals = autoNorm(datingDataMat)
+    m = normMat.shape[0]
+    numTestVecs = int(m*hoRatio)
+    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(input("percentage of time spent playing video games?"))
+    ffMiles = float(input("frequent flier miles earned per year?"))
+    iceCream = float(input("liters of ice cream condumed per year?"))
+    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
+    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])
+
+
+def img2vector(filename):
+    returnVect = zeros((1, 1024)) #创建一个1*1024数组
+    fr = open(filename)
+    for i in range(32):
+        lineStr = fr.readline()#读取一行
+        for j in range(32):
+            returnVect[0, 32*i + j] = int(lineStr[j])#平铺
+    return returnVect
+
+
+def handwritingClassTest():
+    hwLabels = []
+    trainingFileList = listdir('trainingDigits') #列出文件夹下所有的文件
+    m = len(trainingFileList)
+    trainingMat = zeros((m, 1024))
+    for i in range(m):
+        fileNameStr = trainingFileList[i] 
+        fileStr = fileNameStr.split('.')[0]
+        classNumStr = int(fileStr.split('_')[0])
+        hwLabels.append(classNumStr)   #
+        trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr) #打开文件并处理
+    testFileList = listdir('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('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)))

readme.txt

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+Git is a version control system.
+Git is free software.