Python实现kNN
本帖最后由 糖逗 于 2020-11-4 12:54 编辑参考书籍《机器学习实战》
本地测试环境：python3.7
import numpy as np
import operator
class kNN():
def __init__(self, inX, dataSet, labels, k):
self.inX = inX
self.dataSet = dataSet
self.labels = labels
self.k = k
def classify0(self):
dataSetSize = self.dataSet.shape
#https://blog.csdn.net/laobai1015/article/details/85719724
diffMat = np.tile(self.inX, (dataSetSize, 1)) - dataSet
sqDiffMat = diffMat ** 2
sqDistances = sqDiffMat.sum(axis = 1)
distances = sqDistances ** 0.5
sortedDistIndicies = distances.argsort()
classCount = {}
for i in range(self.k):
voteIlabel = self.labels]
#https://blog.csdn.net/weixin_45683963/article/details/103898093
classCount = classCount.get(voteIlabel, 0) + 1
#https://www.runoob.com/python3/python3-func-sorted.html
sortedClassCount = sorted(classCount.items(),
##https://www.cnblogs.com/zhoufankui/p/6274172.html
key = operator.itemgetter(1),
reverse = True)#reverse = True为降序
print(sortedClassCount)
if __name__ == '__main__':
group = np.array([, , , ]) #特征
labels = ['A', 'A', 'B', 'B']#分类
kNN(, group, labels, 3).classify0()#是待分了的特征
import numpy as np
import operator
class kNN():
def __init__(self, filename, k):
self.filename = filename
self.k = k
#读取数据
def file2matrix(filename):
fr = open(filename)
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = np.zeros((numberOfLines, 3))
classLabelVector = []
index = 0
for line in arrayOLines:
line = line.strip()
listFromLine = line.split('\t')
returnMat = listFromLine
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat, classLabelVector
#归一化
def autoNorm(dataSet):
minVals = dataSet.min(0)
maxVals = dataSet.max(0)
ranges = maxVals - minVals
normDataSet = np.zeros(np.shape(dataSet))
m = dataSet.shape
normDataSet = dataSet - np.tile(minVals, (m, 1))
normDataSet = normDataSet / np.tile(ranges, (m, 1))
return normDataSet, ranges, minVals
#kNN
def classify0(self, inX, dataSet, labels):
dataSetSize = dataSet.shape
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(self.k):
voteIlabel = labels]
classCount = classCount.get(voteIlabel, 0) + 1
sortedClassCount = sorted(classCount.items(),
key = operator.itemgetter(1),
reverse = True)#reverse = True为降序
return sortedClassCount
#测试准确率
def datingClassTest(self, hoRatio):
#只取了数据量的10%作为样本
#hoRatio = 0.10
datingDataMat, datingLabels = kNN.file2matrix(self.filename)
normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
m = normMat.shape
numTestVecs = int(m * hoRatio)
errorCount = 0.0
for i in range(numTestVecs):
classifierResult = kNN.classify0(self, normMat, normMat,
datingLabels)
print("the classifier came back with: %d, the real answer is %d"
%(classifierResult, datingLabels))
if(classifierResult != datingLabels):
errorCount += 1
print("the total error rate is: %f" %(errorCount / float(numTestVecs)))
#预测
def classifyPerson(self):
resultList = ["not at all", "in small doses", "in large doses"]
percentTats = float(input("percentage of time spent playing video games?"))#用来获取控制台的输入
ffMiles = float(input("frequent filer miles earned per year?"))
iceCream = float(input("liters of ice cream consumed per year?"))
datingDataMat, datingLabels = kNN.file2matrix(self.filename)
normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
inArr = np.array()
classifierResult = kNN.classify0(self, (inArr - minVals)/ranges, normMat, datingLabels)
print("You will probably like this person:", resultList)#因为实际类别是1,2,3，下标识从0开始的，所以要-1
if __name__ == '__main__':
path = 'datingTestSet2.txt'
test1 = kNN(path, 3)
test1.datingClassTest(0.1)
test1.classifyPerson()
import numpy as np
import operator
import os
class kNN():
def __init__(self, path):
self.path = path
#将矩阵打平，编程一维向量
def img2vector(filname):
returnVect = np.zeros((1, 1024))
fr = open(filname)
for i in range(32):
lineStr = fr.readline()
for j in range(32):
returnVect = int(lineStr)
return returnVect
def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape
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]
classCount = classCount.get(voteIlabel, 0) + 1
sortedClassCount = sorted(classCount.items(),
key = operator.itemgetter(1),
reverse = True)#reverse = True为降序
return sortedClassCount
#在全部的数据上训练，然后在全部的数据上进行检验
def handwritingClassTest(self):
hwLabels = []
#返回文件下的所有文件名
trainingFileList = os.listdir(self.path + "testDigits")
m = len(trainingFileList)
trainingMat = np.zeros((m, 1024))
for i in range(m):
fileNameStr = trainingFileList
fileStr = fileNameStr.split('.')
classNumStr = int(fileStr.split('_'))
hwLabels.append(classNumStr)
trainingMat = kNN.img2vector(self.path + "testDigits/%s" % fileNameStr)
testFileList = os.listdir(self.path + "testDigits")
errorCount = 0
mTest = len(testFileList)
for i in range(mTest):
fileNameStr = testFileList
fileStr = fileNameStr.split('.')
classNumStr = int(fileStr.split('_'))
vectorUnderTest = kNN.img2vector(self.path + "testDigits\%s" % fileNameStr)
classifierResult = kNN.classify0(vectorUnderTest, trainingMat, hwLabels, 3)
print("the classifier came back with:%d, the real answer if :%d" %(classifierResult, classNumStr))
if(classifierResult != classNumStr) :
errorCount += 1
print("\nthe total numner of errors is: %d" % errorCount)
print("\nthe total error rate is %f" %(errorCount / float(mTest)))
if __name__ == '__main__':
path = "C:/.../machinelearninginaction/Ch02/digits/"
kNN(path).handwritingClassTest() 每个框都是独立的个体，可以独立运行{:10_327:} 该书数据代码配套网站：https://www.manning.com/books/machine-learning-in-action
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