refactor gensvm_pred to gensvm_predict

1 files changed, 0 insertions, 264 deletions

diff --git a/tests/src/test_gensvm_pred.c b/tests/src/test_gensvm_pred.c
deleted file mode 100644
index 155f0bf..0000000
--- a/tests/src/test_gensvm_pred.c
+++ /dev/null
@@ -1,264 +0,0 @@
-/**
- * @file test_gensvm_pred.c
- * @author Gertjan van den Burg
- * @date September, 2016
- * @brief Unit tests for gensvm_pred.c functions
- */
-#include "minunit.h"
-#include "gensvm_pred.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);