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/**
* @file test_gensvm_predict.c
* @author G.J.J. van den Burg
* @date 2016-09-01
* @brief Unit tests for gensvm_predict.c functions
*
* @copyright
Copyright 2016, G.J.J. van den Burg.
This file is part of GenSVM.
GenSVM is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
GenSVM is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with GenSVM. If not, see <http://www.gnu.org/licenses/>.
*/
#include "minunit.h"
#include "gensvm_predict.h"
/**
* This testcase is designed as follows: 12 evenly spaced points are plotted
* on the unit circle in the simplex space. These points are the ones for
* which we want to predict the class. To get these points, we need a Z and a
* V which map to these points. To get this, Z was equal to [1 Q] and V was
* equal to [0; R] where Q and R are from the reduced QR decomposition of the
* 12x2 matrix S which contains the points in simplex space. Here's the
* Matlab/Octave code to generate this data:
*
* n = 12;
* K = 3;
* S = [cos(1/12*pi+1/6*pi*[0:(n-1)])', sin(1/12*pi+1/6*pi*[0:(n-1)])'];
* [Q, R] = qr(S, '0');
* Z = [ones(n, 1), Q];
* V = [zeros(1, K-1); R];
*
*/
char *test_gensvm_predict_labels_dense()
{
int n = 12;
int m = 2;
int K = 3;
struct GenData *data = gensvm_init_data();
struct GenModel *model = gensvm_init_model();
model->n = n;
model->m = m;
model->K = K;
data->n = n;
data->m = m;
data->r = m;
data->K = K;
data->Z = Calloc(double, n*(m+1));
data->y = Calloc(long, n);
matrix_set(data->Z, m+1, 0, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 0, 1, -0.3943375672974065);
matrix_set(data->Z, m+1, 0, 2, -0.1056624327025935);
matrix_set(data->Z, m+1, 1, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 1, 1, -0.2886751345948129);
matrix_set(data->Z, m+1, 1, 2, -0.2886751345948128);
matrix_set(data->Z, m+1, 2, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 2, 1, -0.1056624327025937);
matrix_set(data->Z, m+1, 2, 2, -0.3943375672974063);
matrix_set(data->Z, m+1, 3, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 3, 1, 0.1056624327025935);
matrix_set(data->Z, m+1, 3, 2, -0.3943375672974064);
matrix_set(data->Z, m+1, 4, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 4, 1, 0.2886751345948129);
matrix_set(data->Z, m+1, 4, 2, -0.2886751345948129);
matrix_set(data->Z, m+1, 5, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 5, 1, 0.3943375672974064);
matrix_set(data->Z, m+1, 5, 2, -0.1056624327025937);
matrix_set(data->Z, m+1, 6, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 6, 1, 0.3943375672974065);
matrix_set(data->Z, m+1, 6, 2, 0.1056624327025935);
matrix_set(data->Z, m+1, 7, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 7, 1, 0.2886751345948130);
matrix_set(data->Z, m+1, 7, 2, 0.2886751345948128);
matrix_set(data->Z, m+1, 8, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 8, 1, 0.1056624327025939);
matrix_set(data->Z, m+1, 8, 2, 0.3943375672974063);
matrix_set(data->Z, m+1, 9, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 9, 1, -0.1056624327025934);
matrix_set(data->Z, m+1, 9, 2, 0.3943375672974064);
matrix_set(data->Z, m+1, 10, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 10, 1, -0.2886751345948126);
matrix_set(data->Z, m+1, 10, 2, 0.2886751345948132);
matrix_set(data->Z, m+1, 11, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 11, 1, -0.3943375672974064);
matrix_set(data->Z, m+1, 11, 2, 0.1056624327025939);
gensvm_allocate_model(model);
matrix_set(model->V, K-1, 0, 0, 0.0000000000000000);
matrix_set(model->V, K-1, 0, 1, 0.0000000000000000);
matrix_set(model->V, K-1, 1, 0, -2.4494897427831779);
matrix_set(model->V, K-1, 1, 1, -0.0000000000000002);
matrix_set(model->V, K-1, 2, 0, 0.0000000000000000);
matrix_set(model->V, K-1, 2, 1, -2.4494897427831783);
// start test code
long *predy = Calloc(long, n);
gensvm_predict_labels(data, model, predy);
mu_assert(predy[0] == 2, "Incorrect label at index 0");
mu_assert(predy[1] == 3, "Incorrect label at index 1");
mu_assert(predy[2] == 3, "Incorrect label at index 2");
mu_assert(predy[3] == 3, "Incorrect label at index 3");
mu_assert(predy[4] == 3, "Incorrect label at index 4");
mu_assert(predy[5] == 1, "Incorrect label at index 5");
mu_assert(predy[6] == 1, "Incorrect label at index 6");
mu_assert(predy[7] == 1, "Incorrect label at index 7");
mu_assert(predy[8] == 1, "Incorrect label at index 8");
mu_assert(predy[9] == 2, "Incorrect label at index 9");
mu_assert(predy[10] == 2, "Incorrect label at index 10");
mu_assert(predy[11] == 2, "Incorrect label at index 11");
// end test code
gensvm_free_data(data);
gensvm_free_model(model);
free(predy);
return NULL;
}
char *test_gensvm_predict_labels_sparse()
{
int n = 12;
int m = 2;
int K = 3;
struct GenData *data = gensvm_init_data();
struct GenModel *model = gensvm_init_model();
model->n = n;
model->m = m;
model->K = K;
data->n = n;
data->m = m;
data->r = m;
data->K = K;
data->Z = Calloc(double, n*(m+1));
data->y = Calloc(long, n);
matrix_set(data->Z, m+1, 0, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 0, 1, -0.3943375672974065);
matrix_set(data->Z, m+1, 0, 2, -0.1056624327025935);
matrix_set(data->Z, m+1, 1, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 1, 1, -0.2886751345948129);
matrix_set(data->Z, m+1, 1, 2, -0.2886751345948128);
matrix_set(data->Z, m+1, 2, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 2, 1, -0.1056624327025937);
matrix_set(data->Z, m+1, 2, 2, -0.3943375672974063);
matrix_set(data->Z, m+1, 3, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 3, 1, 0.1056624327025935);
matrix_set(data->Z, m+1, 3, 2, -0.3943375672974064);
matrix_set(data->Z, m+1, 4, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 4, 1, 0.2886751345948129);
matrix_set(data->Z, m+1, 4, 2, -0.2886751345948129);
matrix_set(data->Z, m+1, 5, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 5, 1, 0.3943375672974064);
matrix_set(data->Z, m+1, 5, 2, -0.1056624327025937);
matrix_set(data->Z, m+1, 6, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 6, 1, 0.3943375672974065);
matrix_set(data->Z, m+1, 6, 2, 0.1056624327025935);
matrix_set(data->Z, m+1, 7, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 7, 1, 0.2886751345948130);
matrix_set(data->Z, m+1, 7, 2, 0.2886751345948128);
matrix_set(data->Z, m+1, 8, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 8, 1, 0.1056624327025939);
matrix_set(data->Z, m+1, 8, 2, 0.3943375672974063);
matrix_set(data->Z, m+1, 9, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 9, 1, -0.1056624327025934);
matrix_set(data->Z, m+1, 9, 2, 0.3943375672974064);
matrix_set(data->Z, m+1, 10, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 10, 1, -0.2886751345948126);
matrix_set(data->Z, m+1, 10, 2, 0.2886751345948132);
matrix_set(data->Z, m+1, 11, 0, 1.0000000000000000);
matrix_set(data->Z, m+1, 11, 1, -0.3943375672974064);
matrix_set(data->Z, m+1, 11, 2, 0.1056624327025939);
// convert Z to a sparse matrix to test the sparse functions
data->spZ = gensvm_dense_to_sparse(data->Z, data->n, data->m+1);
free(data->Z);
data->RAW = NULL;
data->Z = NULL;
gensvm_allocate_model(model);
matrix_set(model->V, K-1, 0, 0, 0.0000000000000000);
matrix_set(model->V, K-1, 0, 1, 0.0000000000000000);
matrix_set(model->V, K-1, 1, 0, -2.4494897427831779);
matrix_set(model->V, K-1, 1, 1, -0.0000000000000002);
matrix_set(model->V, K-1, 2, 0, 0.0000000000000000);
matrix_set(model->V, K-1, 2, 1, -2.4494897427831783);
// start test code
long *predy = Calloc(long, n);
gensvm_predict_labels(data, model, predy);
mu_assert(predy[0] == 2, "Incorrect label at index 0");
mu_assert(predy[1] == 3, "Incorrect label at index 1");
mu_assert(predy[2] == 3, "Incorrect label at index 2");
mu_assert(predy[3] == 3, "Incorrect label at index 3");
mu_assert(predy[4] == 3, "Incorrect label at index 4");
mu_assert(predy[5] == 1, "Incorrect label at index 5");
mu_assert(predy[6] == 1, "Incorrect label at index 6");
mu_assert(predy[7] == 1, "Incorrect label at index 7");
mu_assert(predy[8] == 1, "Incorrect label at index 8");
mu_assert(predy[9] == 2, "Incorrect label at index 9");
mu_assert(predy[10] == 2, "Incorrect label at index 10");
mu_assert(predy[11] == 2, "Incorrect label at index 11");
// end test code
gensvm_free_data(data);
gensvm_free_model(model);
free(predy);
return NULL;
}
char *test_gensvm_prediction_perf()
{
int i, n = 8;
struct GenData *data = gensvm_init_data();
data->n = n;
data->y = Calloc(long, n);
data->y[0] = 1;
data->y[1] = 1;
data->y[2] = 1;
data->y[3] = 1;
data->y[4] = 2;
data->y[5] = 2;
data->y[6] = 2;
data->y[7] = 3;
long *y = Calloc(long, n);
for (i=0; i<n; i++)
y[i] = 1;
mu_assert(gensvm_prediction_perf(data, y) == 50.0,
"Incorrect first time.");
for (i=0; i<n; i++)
y[i] = 2;
mu_assert(gensvm_prediction_perf(data, y) == 37.5,
"Incorrect second time.");
for (i=0; i<n; i++)
y[i] = 3;
mu_assert(gensvm_prediction_perf(data, y) == 12.5,
"Incorrect third time.");
free(y);
gensvm_free_data(data);
return NULL;
}
char *all_tests()
{
mu_suite_start();
mu_run_test(test_gensvm_predict_labels_dense);
mu_run_test(test_gensvm_predict_labels_sparse);
mu_run_test(test_gensvm_prediction_perf);
return NULL;
}
RUN_TESTS(all_tests);
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