机器学习基础KNN分类算法

#-*-coding:UTF-8-*-

import operator  #运算符模块
from numpy import *  #科学计算包
import matplotlib  #绘图库
import matplotlib.pyplot as plt
from os import listdir      #列出给定目录的文件名

#创造训练集
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

#将文本记录转成numpy的解析函数
def file2matrix(filename):
#得到文件行数
fr = open(filename)
array0Lines = fr.readlines()
numberOfLines = len(array0Lines)
#创建返回的numpy矩阵
returnMat = zeros((numberOfLines, 3))  #选取前三个元素作为特征值
classLabelVector = []
index = 0
for line in array0Lines:
line = line.strip()  #默认去掉line前面的空格和回车
listFromLine = line.split('\t')  #将line按照制表符分割开
returnMat[index, :] = listFromLine[0:3]
#选取最后一列元素存储，必须int否则会当做字符串
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat, classLabelVector

#绘图，使用矩阵的第二列和第三列数据绘制散点图
def show(DataMat, DataLabels):
fig = plt.figure()
ax = fig.add_subplot(111)
#利用分类标记个性化标记散点图上的点
ax.scatter(DataMat[:, 1], DataMat[:, 0], 15.0 * array(DataLabels),
15.0 * array(DataLabels))
plt.show()

#归一化特征值，即将各个特征值化成等权重(都化为[0,1]的值)
def autoNorm(dataSet):
minVals = dataSet.min(0)  #选取每列最小值
maxVals = dataSet.max(0)  #选取每列最大值
ranges = maxVals - minVals  #可能的取值范围
normDataSet = zeros(shape(dataSet))  #新的返回矩阵
m = dataSet.shape[0]
normDataSet = dataSet - tile(minVals, (m, 1))
normDataSet = normDataSet / tile(ranges, (m, 1))  #特征值相除
return normDataSet, ranges, minVals

#分类
def classify0(inX, dataSet, labels, k):
#计算欧式距离 d=sqrt((x1-x2)^2+(y1-y2)^2)
dataSetSize = dataSet.shape[0]
diffMat = tile(inX, (dataSetSize, 1)) - dataSet  #将inX重复shape行，1列
sqDiffMat = diffMat**2
sqDistances = sqDiffMat.sum(axis=1)  #axis=0表示列求和，axis=1表示行求和
distances = sqDistances**0.5

#选取距离最小的k个点
sortedDistIndicies = distances.argsort()  #排序索引
classCount = {}  #建立字典存储每个类的数目
for i in range(k):
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
#排序(逆序)
# key=operator.itemgetter(1)函数表示选取对象第一个域的值进行排序
sortedClassCount = sorted(
classCount.items(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]

#分类器验证函数
def datingClassTest():
hoRatio = 0.10  #选取抽样数据的比例
datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
normMat = autoNorm(datingDataMat)[0]
m = normMat.shape[0]
numTestVecs = int(m * hoRatio)
errorCount = 0.0
for i in range(numTestVecs):
#本来应该随机选取数据，但是这里的数据本身是随机的所以直接选取
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
if numTestVecs != 0:  #考虑到numTestVecs==0时不能除
print("the total error rate is: %f" %
(errorCount / float(numTestVecs)))

#输入数据预测函数
def classifyPerson():
#类别
resultList = ["not at all", "in small doses", "in large doses"]

percentTats = float(input("Percentage of time spent playing video games?"))
ffMiles = float(input("Frequent flier miles earned per year?"))
iceCream = float(input("liters of ice cream 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 like this person:", resultList[classifierResult
- 1])

#将32*32的二进制图像矩阵转化成1*1024的向量
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

#手写数字识别
def handwritingClassTest():
hwLabels = []
trainingFileList = os.listdir('trainingDigits')  #获取目录内容
m = len(trainingFileList)
trainingMat = zeros((m, 1024))
for i in range(m):  #从文件名解析分类数字
fileNameStr = trainingFileList[i]
fileStr = fileNameStr.split('.')[0]
classNumStr = int(fileStr.split('_')[0])
hwLabels.append(classNumStr)
trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr)
testFileList = os.listdir('testDigits')
errorCount = 0.0
mTest = len(testFileList)
for i in range(mTest):
fileNameStr = testFileList[i]
fileStr = fileNameStr.split('.')[0]
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)))

'''
group, labels = createDataSet()
print(classify0([0, 0], group, labels, 3))

datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
show(autoNorm(datingDataMat)[0], datingLabels)

datingClassTest()

classifyPerson()
'''