python机器学习实战1：实现k-近邻算法

首先分享一下链接：http://pan.baidu.com/s/1jIsS8HC 密码：rfew，

里面有kNN当中使用的数据集。这个系列的教程可能更注重机器学习的算法，没有使用深度框架，主要是从低层的一些函数进行编程。一方面可以加深对机器学习的理解，另外一方面增加python的编程能力，能够更好的学会处理自己的数据。

#coding:utf-8
#首先先导入相关的数据库，这里使用的主要是Numpy和matplotlib，里面函数的定义LZ都已经给出了，具体想要如何调用函数就看小伙伴们的需要了
from numpy import *
import operator
import matplotlib
import matplotlib.pyplot as plt
import os

#首先基本的学会创建一个数据集
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

#主要的就是这个分类器啦！kNN分类器，有四个参数，输入，训练数据，标签，k类，这主要就是计算对应的欧氏距离
def classify0(inX, dataSet, labels, k):
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):
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
sortedClassCount = sorted(classCount.iteritems(), key = operator.itemgetter(1), reverse = True)
return sortedClassCount[0][0]

#在这个代码中使用的数据集是要转化成矩阵的形式，所以这个函数就是构造对应的矩阵
def file2matrix(filename):
fr = open(filename)
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = zeros((numberOfLines, 3))
classLabelVector = []
index = 0
for line in arrayOLines:
line = line.strip()
listFromLine = line.split('\t')
returnMat[index, :] = listFromLine[0: 3]
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat,classLabelVector

#这个函数主要就是画图的啦，如果使用可以看到训练集的数据分布哦
def create_fig():
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDat
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aMat[:, 1], datingDataMat[:, 2], 15.0 * array(datingLabels), 15.0 * array(datingLabels))
plt.show()

#对数据进行预处理，为什么要进行归一化，因为如果一个数据集是体重和身高，如果不对数据进行归一化，体重的均值在60kg,身高在1.75m，那么明显可以看出体重的权重会更大些，所以在特征权重均等的情况下，应该对原始数据进行归一化
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))
return normDataSet, ranges, minVals

#测试一下咯
def datingClassTest():
hoRatio = 0.50
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):
classfierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :],\
datingLabels[numTestVecs:m], 3)
print "the classfier came back with: %d, the real answer is: %d"\
%(classfierResult, datingLabels[i])
if (classfierResult != datingLabels[i]): errorCount += 1.0
print "the total error rate is %f" %(errorCount / float(numTestVecs))

#这个函数主要就是针对输入来判断到底是哪一个类别的
def classifyPerson():
resultList = ['not at all', 'in small doses', 'in large doses']
percentTats = float(raw_input("percentage of time spent playing video games?"))
ffMiles = float(raw_input("frequent flier miles earned per year?"))
iceCream = float(raw_input("liters of ice cream consumed per year?"))
datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
normMat, ranges, minVals = autoNorm(datingDataMat)
inArr = array([ffMiles, percentTats, iceCream])
classfierResult = classify0((inArr - minVals) / ranges, normMat, datingLabels, 3)
print "you will probably like this person: ", resultList[classfierResult - 1]

#要把图片转化成向量，怎么转化呢？
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

#定义手写数字测试的函数
def handwritingClassTest():
hwLabels = []
trainingFileList = os.listdir('./digits/trainingDigits')           #load the training set
m = len(trainingFileList)
trainingMat = 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('./digits/trainingDigits/%s' % fileNameStr)
testFileList = os.listdir('./digits/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('./digits/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))

# group, labels = createDataSet()
# print classify0([0, 0], group, labels, 3)
# datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
# normMat, ranges, minVals = autoNorm(datingDataMat)
# # print datingDataMat[:, 1], datingLabels
# print normMat, ranges, minVals
# create_fig()

# classifyPerson()
# testVector = img2vector('./digits/testDigits/0_13.txt')
# print testVector
handwritingClassTest()
# fr = open('./digits/testDigits/0_13.txt')

哈哈，还不错哦，我们还学会了怎么批量读取文件O(∩_∩)O