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()
import sys sys.path+=["G:\\Roliy_ML\\"] import kNN kNN.datingClassTest() #print kNN2.classifyPerson(10000, 10, 0.5)
output:
>>> normMat
array([[ 0.33060119, 0.58918886, 0.69043973],
[ 0.49199139, 0.50262471, 0.13468257],
[ 0.34858782, 0.68886842, 0.59540619],
...,
[ 0.93077422, 0.52696233, 0.58885466],
[ 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
"""
# 2018-05-29-15:00 page27 # -*- coding: UTF-8 -*- #程序说明如下 #标题：page 27 分类器的测试程序/算法 #内容：将数据集的前面部分作为测试集，后面部分作为训练集，输出预测的结果与实际分类对比并计算预测的错误率和错误数 #时间：2018年5月29日 ''' 运行结果： Running /home/yzn/PycharmProjects/kNN_meet_0529_4/main.py line 22,the classifier came back with: 1, the real answer is: 2 line 74,the classifier came back with: 3, the real answer is: 1 line 83,the classifier came back with: 3, the real answer is: 1 line 91,the classifier came back with: 2, the real answer is: 3 line 99,the classifier came back with: 3, the real answer is: 1 预测出错的次数是 5.0 预测集的大小是 100 the total error rate is: 5/100 = 0.050000 ''' import kNN #from numpy import * #import operator kNN.datingClassTest() #运行分类器的测试代码, 没有return()
ax1.scatter(testvector[0], testvector[1], s= 20, c= colormap1[answer], marker = 'x' ) #plot first point
#second point - created, classified and plotted
testvector = [.5, .5]
answer = kNN.classify0(testvector,group, labels, 3)
ax1.scatter(testvector[0], testvector[1], s= 20, c= colormap1[answer], marker = 'x' )
#third point - created, classified and plotted
testvector = [.75, .75]
answer = kNN.classify0(testvector,group, labels, 3)
ax1.scatter(testvector[0], testvector[1], s= 20, c= colormap1[answer], marker = 'x' )
'''Perform K-Nearest Neighbor classification on the datingTestSet2 data set. Do not forget to include the data set in the working directory'''
datingDataMat,datingLabels = kNN.file2matrix('datingTestSet2.txt') # Load data values and labels from the datingTestSet2.txt
datingLabelArray = np.array(datingLabels)
colormap2 = { 1:'red', 2:'blue', 3:'green' } #Define color map with 3 colors
ColoredDatingLabel = []
for things in datingLabelArray: #Get a vector representing the colors
ColoredDatingLabel.append(colormap2[things]) #for each data item
ax2 = FigDating.add_subplot(312, xlim=(0,100000), ylim=(0,25)) #create second sub plot
ax2.scatter(datingDataMat[:,0], datingDataMat[:,1], s= 20, c= ColoredDatingLabel, marker = 'o' ) #Plot a scatter diagram for the data loaded
normMat, ranges, minVals = kNN.autoNorm(datingDataMat) #normalize the data
ax3 = FigDating.add_subplot(313, xlim=(0,1), ylim=(0,1)) #create third sub plot
ax3.scatter(normMat[:,0], normMat[:,1], s = 20, c= ColoredDatingLabel, marker = 'o' ) #Plot normalized data
plt.show()
NumberBad = kNN.datingClassTest(0.1)
def test_acquire(self):
kNN.datingClassTest()
import matplotlib.pyplot as plt
#import Least_squares_fitting as lsf
import numpy as np
from scipy import optimize
k = 0
fig = plt.figure()
ax = fig.add_subplot(111)
plt.xlabel('k')
plt.ylabel('error')
x = np.arange(0, 100, 1.0)
x_list = []
y = []
for i in range(0, 100, 1):
k = k + 1
# print k
s = kNN.datingClassTest(k)
x_list.append(k)
y.append(s)
#print x
#print y
ax.scatter(x, y)
# linear fitting
"""a0,a1 = lsf.linear_regression(x,y)
_X = [0, 100]
_Y = [a0 + a1 * m for m in _X]
plt.plot(x, y, 'ro', _X, _Y, 'b', linewidth=2)
plt.title("y = {} + {}x".format(a0, a1))
plt.show()
"""
def main5():
'''
使用分类器对约会网站数据测试
'''
kNN.datingClassTest()
'datingTestSet2.txt'
) # Load data values and labels from the datingTestSet2.txt
datingLabelArray = np.array(datingLabels)
colormap2 = {1: 'red', 2: 'blue', 3: 'green'} #Define color map with 3 colors
ColoredDatingLabel = []
for things in datingLabelArray: #Get a vector representing the colors
ColoredDatingLabel.append(colormap2[things]) #for each data item
ax2 = FigDating.add_subplot(312, xlim=(0, 100000),
ylim=(0, 25)) #create second sub plot
ax2.scatter(datingDataMat[:, 0],
datingDataMat[:, 1],
s=20,
c=ColoredDatingLabel,
marker='o') #Plot a scatter diagram for the data loaded
normMat, ranges, minVals = kNN.autoNorm(datingDataMat) #normalize the data
ax3 = FigDating.add_subplot(313, xlim=(0, 1),
ylim=(0, 1)) #create third sub plot
ax3.scatter(normMat[:, 0],
normMat[:, 1],
s=20,
c=ColoredDatingLabel,
marker='o') #Plot normalized data
plt.show()
NumberBad = kNN.datingClassTest(0.1)
from numpy import array import kNN reload(kNN) print kNN.datingClassTest() print kNN.classifyPerson()
# plot the data as a scatter plot with color(c) property as per the labelling. ax2.scatter(datingDataMat[:,0], datingDataMat[:,1], s= 20, c= ColoredDatingLabel, marker = 'o' ) # normalize the dataset for 3rd subplot normMat, ranges, minVals = kNN.autoNorm(datingDataMat) # 3rd subplot, specifying the axes limits ax3 = FigDating.add_subplot(313, xlim=(0, 1), ylim=(0, 1)) # plot the normalized data as a scatter plot with color(c) property as per the labelling. ax3.scatter(normMat[:,0], normMat[:,1], s = 20, c= ColoredDatingLabel, marker = 'o' ) plt.show() totalErorr = kNN.datingClassTest(0.1) # lowercase ColoredGroupLabels