Unit tests and corresponding data

1 files changed, 165 insertions, 0 deletions

diff --git a/tests/src/test_gensvm_pred.c b/tests/src/test_gensvm_pred.c
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+++ b/tests/src/test_gensvm_pred.c
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+/**
+ * @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()
+{
+	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_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);
+	mu_run_test(test_gensvm_prediction_perf);
+
+	return NULL;
+}
+
+RUN_TESTS(all_tests);