源码如下:
#-*- coding: utf-8 -*-
from numpy import *
import operator
import pdb
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
font = FontProperties(fname='/usr/share/fonts/truetype/droid/DroidSansFallbackFull.ttf',
size=15)
import sys
reload(sys)
sys.setdefaultencoding('utf8')
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):
#pdb.set_trace()
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]
def file2matrix(filename):
# pdb.set_trace()
fr = open(filename)
arrayOfLines = fr.readlines()
numberOfLines = len(arrayOfLines)
returnMat = zeros((numberOfLines, 3))
classLabelVector = []
index = 0
for line in arrayOfLines:
line = line.strip()
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3]
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat, classLabelVector
def conf_zh(font_name):
from pylab import mpl
mpl.rcParams['font.sans-serif'] = [font_name]
mpl.rcParams['axes.unicode_minus'] = False
def drawPlot():
datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title('你好',fontproperties=font)
plt.xlabel(u"每年获取的飞行常客里程数",fontproperties=font)
plt.ylabel(u"玩视频游戏所耗时间百分比", fontproperties=font)
ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1], 15.0 *
array(datingLabels), 1.0 * array(datingLabels))
plt.legend(u"不喜欢", prop=font)
plt.show()
def autoNorm(dataSet):
#pdb.set_trace()
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
def datingClassTest():
hoRatio = 0.10
datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
norMat, ranges, minVals = autoNorm(datingDataMat)
m = norMat.shape[0]
numTestVecs = int(m * hoRatio)
errorCount = 0.0
for i in range(numTestVecs):
classifierResult = classify0(norMat[i, :], norMat[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))
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')
norMat, ranges, minVals = autoNorm(datingDataMat)
inArr = array([ffMiles, percentTats, iceCream])
classifierResult = classify0((inArr - minVals)/ ranges, norMat,
datingLabels, 3)
print "You will probably like this person: ", resultList[classifierResult\
-1]
if __name__ == '__main__':
#group, labels = createDataSet()
#print classify0([0,0], group, labels, 3)
#print group
#print labels
#drawPlot()
#datingClassTest()
classifyPerson()