import numpy as np

import operator as op

from os import listdir

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1

    sortedClassCount = sorted(classCount.items(), key=op.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

def createDataSet():

    group = np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])

    labels = ['A','A','B','B']

    return group, labels

data,labels = createDataSet()

print(data)

print(labels)

test = np.array([[0,0.5]])

result = classify0(test,data,labels,3)

print(result)


import numpy as np

import operator as op

from os import listdir

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1

    sortedClassCount = sorted(classCount.items(), key=op.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

def file2matrix(filename):

    fr = open(filename)

    returnMat = []

    classLabelVector = []                       #prepare labels return

    for line in fr.readlines():

        line = line.strip()

        listFromLine = line.split('\t')

        returnMat.append([float(listFromLine[0]),float(listFromLine[1]),float(listFromLine[2])])

        classLabelVector.append(int(listFromLine[-1]))

    return np.array(returnMat),np.array(classLabelVector)

trainData,trainLabel = file2matrix("D:\\LearningResource\\machinelearninginaction\\Ch02\\datingTestSet2.txt")

print(trainData[0:4])

print(trainLabel[0:4])

def autoNorm(dataSet):

    minVals = dataSet.min(0)

    maxVals = dataSet.max(0)

    ranges = maxVals - minVals

    normDataSet = np.zeros(np.shape(dataSet))

    m = dataSet.shape[0]

    normDataSet = dataSet - np.tile(minVals, (m,1))

    normDataSet = normDataSet/np.tile(ranges, (m,1))   #element wise divide

    return normDataSet, ranges, minVals

normDataSet, ranges, minVals = autoNorm(trainData)

print(ranges)

print(minVals)

print(normDataSet[0:4])

print(trainLabel[0:4])

testData = np.array([[0.5,0.3,0.5]])

result = classify0(testData, normDataSet, trainLabel, 5)

print(result)

import numpy as np

import operator as op

from os import listdir

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1

    sortedClassCount = sorted(classCount.items(), key=op.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

def file2matrix(filename):

    fr = open(filename)

    returnMat = []

    classLabelVector = []                       #prepare labels return

    for line in fr.readlines():

        line = line.strip()

        listFromLine = line.split('\t')

        returnMat.append([float(listFromLine[0]),float(listFromLine[1]),float(listFromLine[2])])

        classLabelVector.append(listFromLine[-1])

    return np.array(returnMat),np.array(classLabelVector)

def autoNorm(dataSet):

    minVals = dataSet.min(0)

    maxVals = dataSet.max(0)

    ranges = maxVals - minVals

    normDataSet = np.zeros(np.shape(dataSet))

    m = dataSet.shape[0]

    normDataSet = dataSet - np.tile(minVals, (m,1))

    normDataSet = normDataSet/np.tile(ranges, (m,1))   #element wise divide

    return normDataSet, ranges, minVals

normDataSet, ranges, minVals = autoNorm(trainData)

def datingClassTest():

    hoRatio = 0.10      #hold out 10%

    datingDataMat,datingLabels = file2matrix("D:\\LearningResource\\machinelearninginaction\\Ch02\\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: %s, the real answer is: %s') % (classifierResult, datingLabels[i]))

        if (classifierResult != datingLabels[i]):

            errorCount += 1.0

    print(('the total error rate is: %f') % (errorCount/float(numTestVecs)))

    print(errorCount)

datingClassTest()
import numpy as np

import operator as op

from os import listdir

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1

    sortedClassCount = sorted(classCount.items(), key=op.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

def file2matrix(filename):

    fr = open(filename)

    returnMat = []

    classLabelVector = []                       #prepare labels return

    for line in fr.readlines():

        line = line.strip()

        listFromLine = line.split('\t')

        returnMat.append([float(listFromLine[0]),float(listFromLine[1]),float(listFromLine[2])])

        classLabelVector.append(listFromLine[-1])

    return np.array(returnMat),np.array(classLabelVector)

def autoNorm(dataSet):

    minVals = dataSet.min(0)

    maxVals = dataSet.max(0)

    ranges = maxVals - minVals

    normDataSet = np.zeros(np.shape(dataSet))

    m = dataSet.shape[0]

    normDataSet = dataSet - np.tile(minVals, (m,1))

    normDataSet = normDataSet/np.tile(ranges, (m,1))   #element wise divide

    return normDataSet, ranges, minVals

normDataSet, ranges, minVals = autoNorm(trainData)

def datingClassTest():

    hoRatio = 0.10      #hold out 10%

    datingDataMat,datingLabels = file2matrix("D:\\LearningResource\\machinelearninginaction\\Ch02\\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: %s, the real answer is: %s') % (classifierResult, datingLabels[i]))

        if (classifierResult != datingLabels[i]):

            errorCount += 1.0

    print(('the total error rate is: %f') % (errorCount/float(numTestVecs)))

    print(errorCount)

datingClassTest()

................................................

import numpy as np

import operator as op

from os import listdir

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1

    sortedClassCount = sorted(classCount.items(), key=op.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

def file2matrix(filename):

    fr = open(filename)

    returnMat = []

    classLabelVector = []                       #prepare labels return

    for line in fr.readlines():

        line = line.strip()

        listFromLine = line.split('\t')

        returnMat.append([float(listFromLine[0]),float(listFromLine[1]),float(listFromLine[2])])

        classLabelVector.append(int(listFromLine[-1]))

    return np.array(returnMat),np.array(classLabelVector)

def autoNorm(dataSet):

    minVals = dataSet.min(0)

    maxVals = dataSet.max(0)

    ranges = maxVals - minVals

    normDataSet = np.zeros(np.shape(dataSet))

    m = dataSet.shape[0]

    normDataSet = dataSet - np.tile(minVals, (m,1))

    normDataSet = normDataSet/np.tile(ranges, (m,1))   #element wise divide

    return normDataSet, ranges, minVals

def classifyPerson():

    resultList = ["not at all", "in samll doses", "in large doses"]

    percentTats = float(input("percentage of time spent playing video game?"))

    ffMiles = float(input("frequent flier miles earned per year?"))

    iceCream = float(input("liters of ice cream consumed per year?"))

    testData = np.array([percentTats,ffMiles,iceCream])

    trainData,trainLabel = file2matrix("D:\\LearningResource\\machinelearninginaction\\Ch02\\datingTestSet2.txt")

    normDataSet, ranges, minVals = autoNorm(trainData)

    result = classify0((testData-minVals)/ranges, normDataSet, trainLabel, 3)

    print("You will probably like this person: ",resultList[result-1])

classifyPerson()
import numpy as np

import operator as op

from os import listdir

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1

    sortedClassCount = sorted(classCount.items(), key=op.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

def file2matrix(filename):

    fr = open(filename)

    returnMat = []

    classLabelVector = []                       #prepare labels return

    for line in fr.readlines():

        line = line.strip()

        listFromLine = line.split('\t')

        returnMat.append([float(listFromLine[0]),float(listFromLine[1]),float(listFromLine[2])])

        classLabelVector.append(int(listFromLine[-1]))

    return np.array(returnMat),np.array(classLabelVector)

def autoNorm(dataSet):

    minVals = dataSet.min(0)

    maxVals = dataSet.max(0)

    ranges = maxVals - minVals

    normDataSet = np.zeros(np.shape(dataSet))

    m = dataSet.shape[0]

    normDataSet = dataSet - np.tile(minVals, (m,1))

    normDataSet = normDataSet/np.tile(ranges, (m,1))   #element wise divide

    return normDataSet, ranges, minVals

def classifyPerson():

    resultList = ["not at all", "in samll doses", "in large doses"]

    percentTats = float(input("percentage of time spent playing video game?"))

    ffMiles = float(input("frequent flier miles earned per year?"))

    iceCream = float(input("liters of ice cream consumed per year?"))

    testData = np.array([percentTats,ffMiles,iceCream])

    trainData,trainLabel = file2matrix("D:\\LearningResource\\machinelearninginaction\\Ch02\\datingTestSet2.txt")

    normDataSet, ranges, minVals = autoNorm(trainData)

    result = classify0((testData-minVals)/ranges, normDataSet, trainLabel, 3)

    print("You will probably like this person: ",resultList[result-1])

classifyPerson()

import numpy as np

import operator as op

from os import listdir

def classify0(inX, dataSet, labels, k):

    dataSetSize = dataSet.shape[0]

    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet

    sqDiffMat = diffMat**2

    sqDistances = sqDiffMat.sum(axis=1)

    distances = sqDistances**0.5

    sortedDistIndicies = distances.argsort()

    classCount={}

    for i in range(k):

        voteIlabel = labels[sortedDistIndicies[i]]

        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1

    sortedClassCount = sorted(classCount.items(), key=op.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]

def img2vector(filename):

    returnVect = []

    fr = open(filename)

    for i in range(32):

        lineStr = fr.readline()

        for j in range(32):

            returnVect.append(int(lineStr[j]))

    return np.array([returnVect])

def handwritingClassTest():

    hwLabels = []

    trainingFileList = listdir('D:\\LearningResource\\machinelearninginaction\\Ch02\\trainingDigits')           #load the training set

    m = len(trainingFileList)

    trainingMat = np.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('D:\\LearningResource\\machinelearninginaction\\Ch02\\trainingDigits\\%s' % fileNameStr)

    testFileList = listdir('D:\\LearningResource\\machinelearninginaction\\Ch02\\testDigits')        #iterate through the test set

    mTest = len(testFileList)

    errorCount = 0.0

    for i in range(mTest):

        fileNameStr = testFileList[i]

        fileStr = fileNameStr.split('.')[0]     #take off .txt

        classNumStr = int(fileStr.split('_')[0])

        vectorUnderTest = img2vector('D:\\LearningResource\\machinelearninginaction\\Ch02\\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)))

handwritingClassTest()

.......................................

吴裕雄 python 机器学习-KNN算法（1）的更多相关文章

  1. 吴裕雄 python 机器学习——KNN回归KNeighborsRegressor模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import neighbors, datasets from skle ...

  2. 吴裕雄 python 机器学习——KNN分类KNeighborsClassifier模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import neighbors, datasets from skle ...

  3. 吴裕雄 python 机器学习-KNN（2）

    import matplotlib import numpy as np import matplotlib.pyplot as plt from matplotlib.patches import ...

  4. 吴裕雄 python 机器学习——半监督学习标准迭代式标记传播算法LabelPropagation模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import metrics from sklearn import d ...

  5. 吴裕雄 python 机器学习——集成学习AdaBoost算法回归模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,ensemble from sklear ...

  6. 吴裕雄 python 机器学习——集成学习AdaBoost算法分类模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,ensemble from sklear ...

  7. 吴裕雄 python 机器学习——人工神经网络感知机学习算法的应用

    import numpy as np from matplotlib import pyplot as plt from sklearn import neighbors, datasets from ...

  8. 吴裕雄 python 机器学习——半监督学习LabelSpreading模型

    import numpy as np import matplotlib.pyplot as plt from sklearn import metrics from sklearn import d ...

  9. 吴裕雄 python 机器学习——人工神经网络与原始感知机模型

    import numpy as np from matplotlib import pyplot as plt from mpl_toolkits.mplot3d import Axes3D from ...

随机推荐

  1. CS229 6.12 Neurons Networks from self-taught learning to deep network

    self-taught learning 在特征提取方面完全是用的无监督的方法,对于有标记的数据,可以结合有监督学习来对上述方法得到的参数进行微调,从而得到一个更加准确的参数a. 在self-taug ...

  2. 分布式系统的Raft算法

    好东西~~ 英文动画演示Raft 过去, Paxos一直是分布式协议的标准,但是Paxos难于理解,更难以实现,Google的分布式锁系统Chubby作为Paxos实现曾经遭遇到很多坑. 来自Stan ...

  3. Wsgi的web框架实例

    建立server服务端: from wsgiref.simple_server import make_server import time def f1(request): return [b'&l ...

  4. python函数的创建和函数参数

    [1]#函数的作用:1.减少重复代码 2.方便修改,更容易扩展3.保持代码的一致性 [2]#函数简单的定义规则: 函数代码块以def关键词开头,后接函数标识符名称和圆括号(),任何传入参数和自变量必须 ...

  5. es6(8)--对象

    //对象 { //简洁表示法 let o = 1; let k = 2; let es5 = { o:o, k:k }; let es6 = { o, k }; console.log(es5); c ...

  6. Python的字典类型

    Python的字典类型为dict,用{}来表示,字典存放键值对数据,每个键值对用:号分隔,每个键值对之间用,号分隔,其基本格式如下: d = {key1 : value1, key2 : value2 ...

  7. 虚拟机安装 gentoo 的时候，通过 filezilla 上传 stage3 文件

    最近需要在 虚拟机里面安装gentoo,但因为虚拟机里面自动下载的 stage3 太慢了,所以也在寻找解决办法,最终发现 filezilla 是个好办法. 主要参考 https://www.linux ...

  8. RPC通信原理

    什么是 RPCRPC(Remote Procedure Call Protocol)远程过程调用协议.通俗的描述是:客户端在不知道调用细节的情况下,调用存在于远程计算上的某个过程或函数,就像调用本地应 ...

  9. pom格式

    参考: https://www.jianshu.com/p/0e3a1f9c9ce7 https://blog.csdn.net/u012152619/article/details/51485297 ...

  10. 浮动ip cz