Line data Source code
1 : /*
2 : * File: TestLogLogisticLoss.cpp
3 : * Author: Pantelis Sopasakis
4 : *
5 : * Created on Oct 29, 2015, 7:38:05 PM
6 : *
7 : * ForBES is free software: you can redistribute it and/or modify
8 : * it under the terms of the GNU Lesser General Public License as published by
9 : * the Free Software Foundation, either version 3 of the License, or
10 : * (at your option) any later version.
11 : *
12 : * ForBES is distributed in the hope that it will be useful,
13 : * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 : * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
15 : * GNU Lesser General Public License for more details.
16 : *
17 : * You should have received a copy of the GNU Lesser General Public License
18 : * along with ForBES. If not, see <http://www.gnu.org/licenses/>.
19 : */
20 :
21 : #include "TestLogLogisticLoss.h"
22 : #include "LogLogisticLoss.h"
23 :
24 : const static double DATA_X[] = {
25 : 0.537667139546100,
26 : 1.833885014595086,
27 : -2.258846861003648,
28 : 0.862173320368121,
29 : 0.318765239858981,
30 : -1.307688296305273,
31 : -0.433592022305684,
32 : 0.342624466538650,
33 : 3.578396939725760,
34 : 2.769437029884877
35 : };
36 :
37 1 : CPPUNIT_TEST_SUITE_REGISTRATION(TestLogLogisticLoss);
38 :
39 1 : TestLogLogisticLoss::TestLogLogisticLoss() {
40 1 : }
41 :
42 2 : TestLogLogisticLoss::~TestLogLogisticLoss() {
43 2 : }
44 :
45 1 : void TestLogLogisticLoss::setUp() {
46 1 : }
47 :
48 1 : void TestLogLogisticLoss::tearDown() {
49 1 : }
50 :
51 1 : void TestLogLogisticLoss::testCall() {
52 1 : const double mu = 1.5;
53 1 : Function *logLogisticLoss = new LogLogisticLoss(mu);
54 1 : const size_t n = 10;
55 1 : const double * xdata = DATA_X;
56 : const double ddata[n] = {
57 : -0.013569096140251,
58 : -0.333434615170239,
59 : -0.431122888787024,
60 : 0.407923343445659,
61 : 0.404836067818807,
62 : -0.257685527804887,
63 : -0.057441551301617,
64 : -0.101942255039910,
65 : -0.226500118101661,
66 : 0.490999700556483
67 1 : };
68 :
69 1 : Matrix x(n, 1, xdata);
70 2 : Matrix d(n, 1, ddata);
71 :
72 : double f;
73 2 : Matrix grad(n, 1);
74 2 : Matrix Hd(n, 1);
75 :
76 1 : const double tol = 1e-12;
77 1 : const double f_expected = 10.455339600285200;
78 :
79 : const double grad_expected_data[n] = {
80 : -0.553095647613005,
81 : -0.206664161288562,
82 : -1.358116380141062,
83 : -0.445328215242575,
84 : -0.631465046697517,
85 : -1.180689100643326,
86 : -0.910096624790372,
87 : -0.622758155942074,
88 : -0.040743067205105,
89 : -0.088497390570449
90 1 : };
91 :
92 : const double Hd_expected_data[n] = {
93 : -0.004737683170800,
94 : -0.059414973349659,
95 : -0.055383330523630,
96 : 0.127727625721715,
97 : 0.148021416496942,
98 : -0.064766147765657,
99 : -0.020559061512420,
100 : -0.037128015811522,
101 : -0.008977649776764,
102 : 0.040888588516473,
103 1 : };
104 :
105 2 : Matrix grad_expected(n, 1, grad_expected_data);
106 2 : Matrix Hd_expected(n, 1, Hd_expected_data);
107 :
108 1 : _ASSERT(logLogisticLoss->category().defines_f());
109 1 : int status = logLogisticLoss->call(x, f, grad);
110 1 : _ASSERT_EQ(ForBESUtils::STATUS_OK, status);
111 1 : _ASSERT_NUM_EQ(f_expected, f, tol);
112 1 : _ASSERT_EQ(grad_expected, grad);
113 :
114 1 : status = logLogisticLoss->hessianProduct(x, d, Hd);
115 1 : _ASSERT_EQ(ForBESUtils::STATUS_OK, status);
116 1 : _ASSERT_EQ(Hd_expected, Hd);
117 :
118 : double f2;
119 1 : status = logLogisticLoss->call(x, f2);
120 1 : _ASSERT_EQ(ForBESUtils::STATUS_OK, status);
121 1 : _ASSERT_NUM_EQ(f, f2, tol);
122 :
123 1 : x.set(1, 0, 40);
124 1 : x.set(2, 0, 34);
125 :
126 : double f3;
127 1 : status = logLogisticLoss->call(x, f3);
128 1 : _ASSERT_EQ(ForBESUtils::STATUS_OK, status);
129 1 : _ASSERT_NUM_EQ(6.695659275314881, f3, tol);
130 :
131 : double f4;
132 1 : x.set(0, 0, -70);
133 1 : status = logLogisticLoss->call(x, f4);
134 1 : _ASSERT_EQ(ForBESUtils::STATUS_OK, status);
135 1 : _ASSERT_NUM_EQ(1.110056258258362e+02, f4, tol);
136 :
137 2 : delete logLogisticLoss;
138 :
139 4 : }
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