def main():
import numpy
import matplotlib
import matplotlib.pyplot as plt
import kNN
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
datefmt='%a, %d %b %Y %H:%M:%S',
filename='./test.log',
filemode='w')
# group,labels = kNN.createDataSet()
# print kNN.classify0([0, 0], group, labels, 3)
# datingDataMat,datingLabels=kNN.file2matrix('datingTestSet2.txt')
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels))
# fig = plt.figure()
# ax = fig.add_subplot(111)
# ax.scatter(datingDataMat[:,0],datingDataMat[:,1],15.0*numpy.array(datingLabels),15.0*numpy.array(datingLabels))
# plt.show()
#kNN.classifyPerson()
# testVector = kNN.img2vector('testDigits/0_13.txt')
# print testVector[0, 0:31]
# print testVector[0, 32:63]
kNN.handwritingClassTest()
def main():
# 2.1
# print(classify0([1.2,1.2],group,labels,4))
# 2.2
# matplot()
# datingClassTest()
#2.3
# print(listdir('machinelearninginaction\Ch02\digits\\trainingDigits'))
kNN.handwritingClassTest()
def main():
# group, labels = kNN.createDataSet()
# print kNN.classify0([0,0], group, labels, 3)
# datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt')
# #print datingLabels
# #print datingDataMat
# # showData(datingDataMat, datingLabels)
# normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
# print normMat
# print ranges
# print minVals
# kNN.datingClassTest()
# kNN.classifyPerson()
# def showData(datingDataMat, datingLabels):
# fig = plt.figure()
# ax = fig.add_subplot(111)
# # x,y color marker
# ax.scatter(datingDataMat[:,0], datingDataMat[:,1], 15*array(datingLabels),
# 15.0*array(datingLabels))
# plt.show()
# returnVect = kNN.img2vector('digits/testDigits/0_0.txt')
kNN.handwritingClassTest()
# -*- coding: utf-8 -*-
# os.chdir('D:\www\IdeaProject\MLiA_SourceCode\machinelearninginaction')
# print os.getcwd()
from numpy.ma import zeros, array
if __name__ == '__main__':
print 'hello'
# from numpy import *
# import operator
import kNN
# group ,lables = kNN.createDataSet()
# print kNN.classify0([0,0],group,lables,3)
# dataMat ,dataLables = kNN.file2matrix('datingTestSet2.txt')
# print dataMat
# import matplotlib
# import matplotlib.pyplot as plt
# fig = plt.figure()
# ax = fig.add_subplot(211)
# ax.scatter(dataMat[:,1],dataMat[:,2],15.0*array(dataLables),15.0*array(dataLables))
# ax = fig.add_subplot(212)
# ax.scatter(dataMat[:,0],dataMat[:,1],15.0*array(dataLables),15.0*array(dataLables))
# plt.show()
# # kNN.datingClassTest()
kNN.handwritingClassTest()
import kNN kNN.handwritingClassTest()
[ 0.76626481, 0.44109859, 0.88192528],
[ 0.0975718 , 0.02096883, 0.02443895]])
>>> ranges
array([ 8.78430000e+04, 2.02823930e+01, 1.69197100e+00])
>>> minVals
array([ 0. , 0. , 0.001818])
"""
KNN.datingClassTest()
"""
output:
the total error rate is: 0.080000
16.0
"""
KNN.classifyPerson()
"""
output:
percentage of time spent playing video games?4
frequent flier miles earned per year?5569
liters of ice cream consumed per year?1.213192
You will probably like this person: in small doses
"""
KNN.handwritingClassTest()
"""
output:
the total number of errors is: 10
the total error rate is: 0.010571
"""
def test_handwriting(self):
kNN.handwritingClassTest()
def digitTests():
kNN.handwritingClassTest()
def main7():
'''
手写数字识别系统
'''
testVector = kNN.img2vector('testDigits/0_0.txt')
kNN.handwritingClassTest()
result = kNN.classify0([0, 0.1], group, label, 3)
print("the KNN classify result is %s" % result)
#test knn classify simple end
#test dating classify
###datingDatMat,datingLabels = kNN.file2matrix('datingTestSet.txt')
####print("datingDatMat is %s" % datingDatMat)
####show the seconde,third column of the matrix start
###fig = plt.figure();
###ax = fig.add_subplot(111)
####ax.scatter(datingDatMat[:,1],datingDatMat[:,2]) #no color
###ax.scatter(datingDatMat[:,0],datingDatMat[:,1],15.0 * array(datingLabels),15.0 * array(datingLabels)) #has color
###plt.show()
#show the seconde,third column of the matrix start
###数值归一化处理
###datingDatMat,datingLabels = kNN.file2matrix('datingTestSet.txt')
###normMat,ranges,minVals = kNN.autoNorm(datingDatMat);
###约会网站测试调用
# kNN.datingClassTest()
#kNN.classifyPersion()
#手写数字识别系统测试条用
'''
time_start = time.time()
kNN.handwritingClassTest()
time_end = time.time()
print ("the program spend %d s" % (time_end - time_start))
'''
import kNN
import matplotlib
import matplotlib.pyplot as plt
from numpy import *
#group,labels = kNN.createDataSet()
#kNN.classify0([0,0], group,labels,3)
datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt')
"""
fig = plt.figure() #表示绘制一个图
ax = fig.add_subplot(111) #将画布分割成1行1列，图像画在从左到右从上到下的第1块
ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels)) #scatter生成散点图函数。使用datingDataMat矩阵的第二、三列数据
#datingDataMat[:,1]意思是所有行的第2列(从0开始)
#后面第一个数字参数对应左边两种颜色的点的半径大小，第二个数字试了下没什么变化
plt.show()
"""
normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
#print kNN.datingClassTest()
#print kNN.classifyPerson()
#testVector = kNN.img2vector('testDigits/0_13.txt')
#print testVector[0,32:63]
print kNN.handwritingClassTest()
import kNN print(kNN.handwritingClassTest())
