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今天学习了《机器学习实战》这本书介绍的第一个机器学习算法—k近邻算法。争取理解代码每一步。
code and analysis
'''
Created on Sep 16, 2010
kNN: k Nearest Neighbors
Input: inX: vector to compare to existing dataset (1xN)
dataSet: size m data set of known vectors (NxM)
labels: data set labels (1xM vector)
k: number of neighbors to use for comparison (should be an odd number)
Output: the most popular class label
@author: pbharrin
'''
from numpy import *
import operator
from os import listdir
def classify0(inX, dataSet, labels, k):
"""
inX 是输入的测试样本,当前的点，是一个[x, y] 大小1*N样式的
dataset 是训练样本集，大小N*M样式的
labels 是训练样本标签，大小1*M样式的
k 是top k最相近的.
tile属于numpy模块下边的函数
tile（A, reps）返回一个shape=reps的矩阵，矩阵的每个元素是A
比如 A=[0,1,2] 那么，tile(A, 2)= [0, 1, 2, 0, 1, 2]
tile(A,(2,2)) = [[0, 1, 2, 0, 1, 2],
[0, 1, 2, 0, 1, 2]]
"""
dataSetSize = dataSet.shape[0]
diffMat = 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):
'''
range(k)等价range(0,k)
index = sortedDistIndicies[i]是第i个最相近的样本下标
voteIlabel = labels[index]是样本index对应的分类结果('A' or 'B')
classCount.get(voteIlabel, 0)返回voteIlabel的值，如果不存在，则返回0
然后将票数增1
把分类结果进行排序，然后返回得票数最多的分类结果
'''
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
"""
sorted(iterable[, cmp[, key[, reverse]]])
iterable -- 可迭代对象。
cmp -- 比较的函数，这个具有两个参数，参数的值都是从可迭代对象中取出，此函数必须遵守的规则为，大于则返回1，小于则返回-1，等于则返回0。
key -- 主要是用来进行比较的元素，只有一个参数，具体的函数的参数就是取自于可迭代对象中，指定可迭代对象中的一个元素来进行排序。
reverse -- 排序规则，reverse = True 降序 ， reverse = False 升序（默认）。
dict.get(key, default=None)
key -- 字典中要查找的键。
default -- 如果指定键的值不存在时，返回该默认值值。
返回指定键的值，如果值不在字典中返回默认值 None。
"""
return sortedClassCount[0][0]
def createDataSet():
"""
函数作用：构建一组训练数据（训练样本），共4个样本
同时给出了这4个样本的标签，及labels
"""
group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
labels = ['A','A','B','B']
return group, labels
def file2matrix(filename):
"""
从文件中读入训练数据，并存储为矩阵
str.strip([chars]);
chars -- 移除字符串头尾指定的字符序列。
返回移除字符串头尾指定的字符序列生成的新字符串。
此函数中： line.strip()去除首尾空格 ,回车
例如 str = "Line1-abcdef \nLine2-abc \nLine4-abcd";
line.split('\t')按tab键分割字符，之后得到
['Line1-abcdef', 'Line2-abc', 'Line4-abcd']
"""
fr = open(filename)
numberOfLines = len(fr.readlines()) #get the number of lines in the file
returnMat = zeros((numberOfLines,3)) #prepare matrix to return
classLabelVector = [] #prepare labels return
index = 0
for line in fr.readlines():
line = line.strip()
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3] # 表示0，1，2 column 见下面举例图
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat,classLabelVector
def autoNorm(dataSet):
minVals = dataSet.min(0)
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)) #element wise divide
return normDataSet, ranges, minVals
def datingClassTest():
hoRatio = 0.50 #hold out 10%
datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') #load data setfrom file
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: %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)))
print (errorCount)
def img2vector(filename):
returnVect = zeros((1,1024))
fr = open(filename)
for i in range(32):
lineStr = fr.readline()
for j in range(32):
returnVect[0,32*i+j] = int(lineStr[j])
return returnVect
'''
os.listdir(path),path -- 需要列出的目录路径,返回指定路径下的文件和文件夹列表。
'''
def handwritingClassTest():
hwLabels = []
trainingFileList = listdir('trainingDigits') #load the training set
m = len(trainingFileList) #number
trainingMat = zeros((m,1024))
for i in range(m): #遍历
fileNameStr = trainingFileList[i]
fileStr = fileNameStr.split('.')[0] #take off .txt
classNumStr = int(fileStr.split('_')[0])
hwLabels.append(classNumStr)
trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)#把 列向量合并成矩阵
testFileList = listdir('testDigits') #iterate through the test set
errorCount = 0.0
mTest = len(testFileList)
for i in range(mTest):
fileNameStr = testFileList[i]
fileStr = fileNameStr.split('.')[0] #take off .txt
classNumStr = int(fileStr.split('_')[0])
vectorUnderTest = img2vector('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)))Simple classification of kNN algorithm in python
切换到kNN.py目录下import kNN
note：
Python3.5中：iteritems变为items
Parsing data from a text file
先看一下数据：
运行代码：
>>> import kNN
>>> datingDataMat,datingLabels=kNN.file2matrix('datingTestSet2.txt')
# 表示0，1，2 column 见下面举例图note：
Hand writing kNN Test
import kNN
kNN.handwritingClassTest()