机器学习实战——KNN实现
- 计算已知类别数据集中的点与当前点之间的距离；
- 按照距离递增次序排序；
- 选取与当前点距离最小的k个点；
- 确定前k个点所在类别的出现频率；
- 返回前k个点出现频率最高的类别作为当前点的预测分类。
# -*- coding: utf-8 -*-
from numpy import *
import operator
'''构造数据'''
def createDataSet():
characters=array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
labels=['A','A','B','B']
return characters,labels
def classify(sample,dataSet,labels,k):
dataSetSize=dataSet.shape[0] #统计数据集的个数：4个
'''下面四行计算待分类的点和训练集中的任一点的欧式距离'''
diffMat=tile(sample,(dataSetSize,1))-dataSet #样本与每个数据集中数据的差值构成矩阵
sqDiffMat=diffMat**2 #差值矩阵求平方
sqDistances=sqDiffMat.sum(axis=1) #差值矩阵中每项的两个数字求和
distances=sqDistances**0.5 #开方
sortedDistIndicies=distances.argsort() #按distances中元素进行升序排序后得到的对应下标的列表
'''选择距离最小的k个点'''
classCount={}
for i in range(k):
voteIlabel=labels[sortedDistIndicies[i]]
classCount[voteIlabel]=classCount.get(voteIlabel,0)+1
'''从大到小排序'''
sortedClassCount=sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)
return sortedClassCount[0][0]
def main():
sample=[0,0]
k=3
group,labels=createDataSet()
label=classify(sample,group,labels,k)
print("Classified Label:"+label)
if __name__=='__main__':
main()
KNN在数字识别上的应用： 数据集点这里，准确率为98.94%。
# -*- coding: utf-8 -*-
from numpy import *
import operator
import os
def classify(sample,dataSet,labels,k):
dataSetSize=dataSet.shape[0] #统计数据集的个数：4个
'''下面四行计算待分类的点和训练集中的任一点的欧式距离'''
diffMat=tile(sample,(dataSetSize,1))-dataSet #样本与每个数据集中数据的差值构成矩阵
sqDiffMat=diffMat**2 #差值矩阵求平方
sqDistances=sqDiffMat.sum(axis=1) #差值矩阵中每项的两个数字求和
distances=sqDistances**0.5 #开方
sortedDistIndicies=distances.argsort() #按distances中元素进行升序排序后得到的对应下标的列表
'''选择距离最小的k个点'''
classCount={}
for i in range(k):
voteIlabel=labels[sortedDistIndicies[i]]
classCount[voteIlabel]=classCount.get(voteIlabel,0)+1
'''从大到小排序'''
sortedClassCount=sorted(classCount.items(),key=operator.itemgetter(1),reverse=True)
return sortedClassCount[0][0]
def img2vector(filename):
row = 32
col = 32
imgVector = zeros((1,row*col))
file = open(filename)
for r in range(row):
lineStr = file.readline()
for c in range(col):
imgVector[0,c+r*32] = int(lineStr[c])
return imgVector
#载入数据集
def loadDataSet():
## step 1: Getting training set
dataSetDir = 'C:/Users/Rainey/Desktop/digits/'
trainingFileList = os.listdir(dataSetDir + 'trainingDigits') # load the training set
numSamples = len(trainingFileList)
train_x = zeros((numSamples, 1024))
train_y = []
for i in range(numSamples):
filename = trainingFileList[i]
# get train_x
train_x[i, :] = img2vector(dataSetDir + 'trainingDigits/%s' % filename)
# get label from file name such as "1_18.txt"
label = int(filename.split('_')[0]) # return 1
train_y.append(label)
## step 2: Getting testing set
testingFileList = os.listdir(dataSetDir + 'testDigits') # load the testing set
numSamples = len(testingFileList)
test_x = zeros((numSamples, 1024))
test_y = []
for i in range(numSamples):
filename = testingFileList[i]
# get train_x
test_x[i, :] = img2vector(dataSetDir + 'testDigits/%s' % filename)
# get label from file name such as "1_18.txt"
label = int(filename.split('_')[0]) # return 1
test_y.append(label)
return train_x, train_y, test_x, test_y
# test hand writing class
def testHandWritingClass():
## step 1: load data
print('loading data...')
train_x, train_y, test_x, test_y = loadDataSet()
## step 2: training...
pass
print('training...')
## step 3: testing
print('testing...')
numTestSamples = test_x.shape[0]
matchCount = 0
for i in range(numTestSamples):
predict = classify(test_x[i], train_x, train_y, 3)
if predict == test_y[i]:
matchCount += 1
accuracy = float(matchCount) / numTestSamples
## step 4: show the result
print("step 4: show the result...")
print('The classify accuracy is: %.2f%%' % (accuracy * 100))
testHandWritingClass()
