mlearn/mlearnDriver.py at master · shcheng/mlearn · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
#!/usr/bin/python

#
# Test driver for the mlearn module
# by Shih-Ho Cheng (shihho.cheng@gmail.com)
#

from pylab import *
import scipy.stats as st
import mlearn

# Create a binary class data set
nPts  = 200
nCls  = 2
mean1 = array([1., 1.])
sd1   = array([1., 1.])
mean2 = array([3.8, 2.5])
sd2   = array([1., 1.])
g1_x = st.norm.rvs(mean1[0], sd1[0], size=nPts/nCls)
g1_y = st.norm.rvs(mean1[1], sd1[1], size=nPts/nCls)
g1 = concatenate( (g1_x.reshape(len(g1_x),1), g1_y.reshape(len(g1_y),1)), axis=1 )
g2_x = st.norm.rvs(mean2[0], sd2[0], size=nPts/nCls)
g2_y = st.norm.rvs(mean2[1], sd2[1], size=nPts/nCls)
g2 = concatenate( (g2_x.reshape(len(g2_x),1), g2_y.reshape(len(g2_y),1)), axis=1 )
data = concatenate( (g1, g2), axis=0 )
target = concatenate( (zeros(nPts/nCls), ones(nPts/nCls)) ).reshape(nPts, 1)
data = concatenate( (data, target), axis=1 )
shuffle(data)
trainData = data[:100,:]
testData  = data[101:,:]

# Create linear Classifier objects
bayMod = mlearn.Ngbayes(trainData[:,:2], trainData[:,2])
logMod = mlearn.BinLogReg(trainData[:,:2], trainData[:,2])

bayMod.train()
logMod.train()

print
print "Naive (Gaussian) Bayes classifier:"
print bayMod.crossValidate(testData[:,:2], testData[:,2])
print

print "Binary Logistic Regression classifier:"
print logMod.crossValidate(testData[:,:2], testData[:,2])
print

# Changed the class 0 to -1
trainIdx = trainData[:,-1] == 0  # indices where classification = 0
testIdx = testData[:,-1] == 0
trainData[trainIdx,2] = -1
testData[testIdx,2] = -1

# Create an adaBoosted object
bstMod = mlearn.adaBoostStump(trainData[:,:2], trainData[:,2])
weights = ones(len(trainData))/(len(trainData)*1.)
bstMod.bTrain(50, verbose=False)

print "Boosted Stump decision:"
print bstMod.bCrossValidate(testData[:,:2], testData[:,2])
print