import matplotlib.pyplot as plt
from numpy import *
import kNN
def paintDataSet():
datingDataMat, datingLabels = kNN.file2matrix('datingTestSet.txt')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:, 1], datingDataMat[:, 2], 20.0*array(datingLabels), 20.0*array(datingLabels))
plt.show()
if __name__ == '__main__':
kNN.classifyPerson()
datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt')
print(datingDataMat)
print(datingLabels[0:20])
# scatter plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1],
15.0 * np.array(datingLabels), 15.0 * np.array(datingLabels))
plt.show()
# normalization
normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
print(normMat)
print(ranges)
print(minVals)
# test error rate
kNN.datingClassTest()
# predict
kNN.classifyPerson()
# handwriting nums recognition
# load daata
testVector = kNN.img2vector('dataset/testDigits/0_13.txt')
print(testVector[0, 0:31])
# handwriting class test
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
"""
'''
datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt')
print(datingDataMat)
print(datingLabels)
fig = plt.figure()
# 图片位置
ax = fig.add_subplot(111)
# scatter(x,y,大小,颜色)
ax.scatter(datingDataMat[:,0],datingDataMat[:,1],
20.0*array(datingLabels),15.0*array(datingLabels))
plt.show()
'''
'''
datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt')
normMat,ranges,minVals = kNN.autoNorm(datingDataMat)
# print(normMat)
# kNN.datingClassTest()
kNN.classifyPerson()
'''
'''
testVector = kNN.img2Vector('digits/testDigitsnumFeatures/0_13.txt')
print("--------")
print(testVector[0,0:31])
print(testVector[0,32:63])
'''
def test_classifyPerson(self):
kNN.classifyPerson()
def main6():
'''
约会网站测试函数
'''
kNN.classifyPerson()
from numpy import array import kNN reload(kNN) print kNN.datingClassTest() print kNN.classifyPerson()