Machine Learning in Action Chapter2

python 3.6

此代码取自书中。

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@file:KNN.py
@time:2019/1/1 14:10
@author:Victor
@site:https://blog.youkuaiyun.com/sumup
"""

from numpy import *
import operator
import matplotlib
import matplotlib.pyplot as plt

# 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]
	
	##输出dataSet的行数
	print(dataSet.shape[1])##2列
	print(dataSet.shape[0])##4行
	
	##构造(dataSetSize,1)规摸的数组，并inX数组与dataSet相减
	# [[-1.  -1.1]
	# [-1.  -1. ]
	# [ 0.   0. ]
	# [ 0.  -0.1]]
	diffMat = tile(inX, (dataSetSize,1)) - dataSet
	print(diffMat)
	
	##矩阵平方
	# [[1.   1.21]
	# [1.   1.  ]
	# [0.   0.  ]
	# [0.   0.01]]
	sqDiffMat = diffMat**2
	print(sqDiffMat)
	
	##矩阵每行求和
	# [2.21 2.   0.   0.01]
	sqDistances = sqDiffMat.sum(axis=1)
	print(sqDistances)
	# print(sqDistances.shape[0]) ##4
	
	##每行的和开方
	# [1.48660687 1.41421356 0.         0.1       ]
	distances = sqDistances**0.5
	print(distances)
	##从小到大排序，输出下标
	# [2 3 1 0]
	sortedDistIndicies = distances.argsort()
	print(sortedDistIndicies)
	
	classCount = {}
	for i in range(k):
		voteIlabel = labels[sortedDistIndicies[i]]
		print(voteIlabel)
		##在classCount中查找voteIlabel，若没，赋值0，并+1
		classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
	
	# print(classCount.items())  ##dict_items([('B', 2), ('A', 1)])
	##key 为classCount的value
	sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1), reverse=True)
	
	return sortedClassCount[0][0]
	
##Input filename
##Output returnMat classLabelVector
def file2matrix(filename):
	fr = open(filename)
	arrayOLines = fr.readlines()
	numberOfLines = len(arrayOLines)
	##创建(numberOfLines,3)矩阵，并初始化0
	returnMat = zeros((numberOfLines,3))
	classLabelVector = []
	index = 0
	for line in arrayOLines:
		##默认删除空白符（包括'\n', '\r',  '\t',  ' ')
		line = line.strip()
		##根据‘\t’划分开
		listFromLine = line.split('\t')
		
		returnMat[index,:] = listFromLine[0:3]
		# print(returnMat[index,:])
		classLabelVector.append(int(listFromLine[-1]))
		index += 1
	return returnMat,classLabelVector

##newvalue = (oldValue-min)/(max-min)	
def autoNorm(dataSet):
	##返回矩阵中每一列的最小值
	minVals = dataSet.min(0)
	##返回矩阵中每一列的最大值
	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():
	#10%用于求结果
	hoRatio = 0.10
	datingDataMat,datingLabels = file2matrix('datingTestSet2.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 classfier 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 does','in large does']
	percentTats = float(input("percentage of time spent playing video games?"))
	ffMiles = float(input("frequent flier miles earned per year"))
	iceCream = float(input("liters of iceam consumed 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 link this person: ",resultList[classifierResult-1])
	
# group,labels = createDataSet()

# print(group)
# print(labels)
# print(classify0([0,0],group,labels,3))
# datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')

# fig = plt.figure()            
#add_subplot(mnp)#添加子轴、图。subplot（m,n,p）或者subplot（mnp）此函数最常用：
#subplot是将多个图画到一个平面上的工具。其中，m表示是图排成m行，n表示图排成n列，也就是整个figure中有n个图是排成一行的，一共m行，如果第一个数字是2就是表示2行图。
#p是指你现在要把曲线画到figure中哪个图上，最后一个如果是1表示是从左到右第一个位置。
# ax = fig.add_subplot(111)
# ax.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*array(datingLabels),15.0*array(datingLabels))
# print(autoNorm(datingDataMat))
# plt.show()
# datingClassTest()
classifyPerson()