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()

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

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