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
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
|
.. currentmodule:: mlpy
Large Linear Classification from [LIBLINEAR]_
=============================================
Solvers:
* **l2r_lr**: L2-regularized logistic regression (primal)
* **l2r_l2loss_svc_dual**: L2-regularized L2-loss support vector classification (dual)
* **l2r_l2loss_svc**: L2-regularized L2-loss support vector classification (primal)
* **l2r_l1loss_svc_dual**: L2-regularized L1-loss support vector classification (dual)
* **mcsvm_cs**: multi-class support vector classification by Crammer and Singer
* **l1r_l2loss_svc**: L1-regularized L2-loss support vector classification
* **l1r_lr**: L1-regularized logistic regression
* **l2r_lr_dual**: L2-regularized logistic regression (dual)
.. class:: mlpy.LibLinear(solver_type='l2r_lr', C=1, eps=0.01, weight={})
LibLinear is a simple class for solving large-scale regularized
linear classification. It currently supports L2-regularized logistic
regression/L2-loss support vector classification/L1-loss support vector
classification, and L1-regularized L2-loss support vector classification/
logistic regression.
:Parameters:
solver_type : string
solver, can be one of 'l2r_lr', 'l2r_l2loss_svc_dual',
'l2r_l2loss_svc', 'l2r_l1loss_svc_dual', 'mcsvm_cs',
'l1r_l2loss_svc', 'l1r_lr', 'l2r_lr_dual'
C : float
cost of constraints violation
eps : float
stopping criterion
weight : dict
changes the penalty for some classes (if the weight for a
class is not changed, it is set to 1). For example, to
change penalty for classes 1 and 2 to 0.5 and 0.8
respectively set weight={1:0.5, 2:0.8}
.. automethod:: learn(x, y)
.. automethod:: pred(t)
.. automethod:: pred_values(t)
.. automethod:: pred_probability(t)
.. automethod:: w()
.. automethod:: bias()
.. automethod:: nfeature()
.. automethod:: nclasses()
.. automethod:: labels()
.. automethod:: load_model(filename)
.. automethod:: save_model(filename)
Example:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import mlpy
>>> np.random.seed(0)
>>> mean1, cov1, n1 = [1, 5], [[1,1],[1,2]], 200 # 200 samples of class 0
>>> x1 = np.random.multivariate_normal(mean1, cov1, n1)
>>> y1 = np.zeros(n1, dtype=int)
>>> mean2, cov2, n2 = [2.5, 2.5], [[1,0],[0,1]], 300 # 300 samples of class 1
>>> x2 = np.random.multivariate_normal(mean2, cov2, n2)
>>> y2 = np.ones(n2, dtype=int)
>>> mean3, cov3, n3 = [5, 8], [[0.5,0],[0,0.5]], 200 # 200 samples of class 2
>>> x3 = np.random.multivariate_normal(mean3, cov3, n3)
>>> y3 = 2 * np.ones(n3, dtype=int)
>>> x = np.concatenate((x1, x2, x3), axis=0) # concatenate the samples
>>> y = np.concatenate((y1, y2, y3))
>>> svm = mlpy.LibLinear(solver_type='l2r_l2loss_svc_dual', C=0.01)
>>> svm.learn(x, y)
>>> w = svm.w() # w[i]: coefficients for label svm.labels()[i]
>>> w
array([[-0.73225278, 0.33309388],
[ 0.32295557, -0.44097029],
[ 0.23192595, 0.11536679]])
>>> b = svm.bias() # b[i]: bias for label svm.labels()[i]
>>> b
array([-0.21631629, 0.96014472, -1.53933202])
>>> xx = np.arange(np.min(x[:,0]), np.max(x[:,0]), 0.01)
>>> yy1 = (xx* (w[1][0]-w[0][0]) + b[1] - b[0]) / (w[0][1]-w[1][1])
>>> yy2 = (xx* (w[2][0]-w[0][0]) + b[2] - b[0]) / (w[0][1]-w[2][1])
>>> yy3 = (xx* (w[2][0]-w[1][0]) + b[2] - b[1]) / (w[1][1]-w[2][1])
>>> fig = plt.figure(1) # plot
>>> plot1 = plt.plot(x1[:, 0], x1[:, 1], 'ob', x2[:, 0], x2[:, 1], 'or', x3[:, 0], x3[:, 1], 'og')
>>> plot2 = plt.plot(xx, yy1, '--k')
>>> plot3 = plt.plot(xx, yy2, '--k')
>>> plot4 = plt.plot(xx, yy3, '--k')
>>> plt.show()
.. image:: images/liblinear.png
>>> test = [[6,7], [4, 2]] # test points
>>> print svm.pred(test)
array([2, 1])
.. [LIBLINEAR] Machine Learning Group at National Taiwan University. http://www.csie.ntu.edu.tw/~cjlin/liblinear/
|
