diff options
Diffstat (limited to 'tests/src/test_gensvm_optimize.c')
| -rw-r--r-- | tests/src/test_gensvm_optimize.c | 118 |
1 files changed, 116 insertions, 2 deletions
diff --git a/tests/src/test_gensvm_optimize.c b/tests/src/test_gensvm_optimize.c index 86b620d..a91ece7 100644 --- a/tests/src/test_gensvm_optimize.c +++ b/tests/src/test_gensvm_optimize.c @@ -9,9 +9,121 @@ #include "gensvm_optimize.h" #include "gensvm_init.h" +#include "gensvm_debug.h" + +extern FILE *GENSVM_OUTPUT_FILE; +extern FILE *GENSVM_ERROR_FILE; + char *test_gensvm_optimize() { - mu_test_missing(); + struct GenModel *model = gensvm_init_model(); + struct GenModel *seed_model = gensvm_init_model(); + struct GenData *data = gensvm_init_data(); + + int n = 8, + m = 3, + K = 4; + data->n = n; + data->m = m; + data->r = m; + data->K = K; + + model->n = n; + model->m = m; + model->K = K; + + seed_model->n = n; + seed_model->m = m; + seed_model->K = K; + + data->Z = Malloc(double, n*(m+1)); + data->y = Malloc(long, n); + + matrix_set(data->Z, data->m+1, 0, 0, 1.0); + matrix_set(data->Z, data->m+1, 0, 1, 0.8740239771176158); + matrix_set(data->Z, data->m+1, 0, 2, 0.3231542341162253); + matrix_set(data->Z, data->m+1, 0, 3, 0.2533980609669184); + matrix_set(data->Z, data->m+1, 1, 0, 1.0); + matrix_set(data->Z, data->m+1, 1, 1, 0.3433368959379667); + matrix_set(data->Z, data->m+1, 1, 2, 0.2945713387329698); + matrix_set(data->Z, data->m+1, 1, 3, 0.3042498181639990); + matrix_set(data->Z, data->m+1, 2, 0, 1.0); + matrix_set(data->Z, data->m+1, 2, 1, 0.6513609117457242); + matrix_set(data->Z, data->m+1, 2, 2, 0.7738077314847138); + matrix_set(data->Z, data->m+1, 2, 3, 0.4426344045213226); + matrix_set(data->Z, data->m+1, 3, 0, 1.0); + matrix_set(data->Z, data->m+1, 3, 1, 0.7223733317092962); + matrix_set(data->Z, data->m+1, 3, 2, 0.9718611208972370); + matrix_set(data->Z, data->m+1, 3, 3, 0.0796059591969125); + matrix_set(data->Z, data->m+1, 4, 0, 1.0); + matrix_set(data->Z, data->m+1, 4, 1, 0.3014806706103061); + matrix_set(data->Z, data->m+1, 4, 2, 0.1728058294642182); + matrix_set(data->Z, data->m+1, 4, 3, 0.0851401652628196); + matrix_set(data->Z, data->m+1, 5, 0, 1.0); + matrix_set(data->Z, data->m+1, 5, 1, 0.5114600128301799); + matrix_set(data->Z, data->m+1, 5, 2, 0.3319865781913825); + matrix_set(data->Z, data->m+1, 5, 3, 0.3330906711041684); + matrix_set(data->Z, data->m+1, 6, 0, 1.0); + matrix_set(data->Z, data->m+1, 6, 1, 0.5824718351045201); + matrix_set(data->Z, data->m+1, 6, 2, 0.7224023004247955); + matrix_set(data->Z, data->m+1, 6, 3, 0.0937250920308128); + matrix_set(data->Z, data->m+1, 7, 0, 1.0); + matrix_set(data->Z, data->m+1, 7, 1, 0.8228264179835741); + matrix_set(data->Z, data->m+1, 7, 2, 0.4580785175957617); + matrix_set(data->Z, data->m+1, 7, 3, 0.7585636149680212); + + data->y[0] = 2; + data->y[1] = 1; + data->y[2] = 3; + data->y[3] = 2; + data->y[4] = 3; + data->y[5] = 2; + data->y[6] = 4; + data->y[7] = 1; + + model->p = 1.2143; + model->kappa = 0.90298; + model->lambda = 0.00219038; + model->epsilon = 1e-15; + + gensvm_allocate_model(model); + gensvm_allocate_model(seed_model); + matrix_set(seed_model->V, K-1, 0, 0, 0.3294151808829250); + matrix_set(seed_model->V, K-1, 0, 1, 0.8400578887926284); + matrix_set(seed_model->V, K-1, 0, 2, 0.9336268164013294); + matrix_set(seed_model->V, K-1, 1, 0, 0.6047157463292797); + matrix_set(seed_model->V, K-1, 1, 1, 0.1390735925868357); + matrix_set(seed_model->V, K-1, 1, 2, 0.6579825380479839); + matrix_set(seed_model->V, K-1, 2, 0, 0.7628723943431572); + matrix_set(seed_model->V, K-1, 2, 1, 0.3505528063594583); + matrix_set(seed_model->V, K-1, 2, 2, 0.1221488022463632); + matrix_set(seed_model->V, K-1, 3, 0, 0.4561071643209315); + matrix_set(seed_model->V, K-1, 3, 1, 0.0840834388268874); + matrix_set(seed_model->V, K-1, 3, 2, 0.5312457860071739); + + gensvm_init_V(seed_model, model, data); + gensvm_initialize_weights(data, model); + + model->rho[0] = 0.3294151808829250; + model->rho[1] = 0.6047157463292797; + model->rho[2] = 0.7628723943431572; + model->rho[3] = 0.4561071643209315; + model->rho[4] = 0.8400578887926284; + model->rho[5] = 0.1390735925868357; + model->rho[6] = 0.3505528063594583; + model->rho[7] = 0.0840834388268874; + + // start test code // + GENSVM_OUTPUT_FILE = stdout; + gensvm_optimize(model, data); + + gensvm_print_matrix(model->V, model->m+1, model->K-1); + + // end test code // + + gensvm_free_data(data); + gensvm_free_model(model); + return NULL; } @@ -209,6 +321,9 @@ char *test_gensvm_get_loss_2() gensvm_free_model(model); gensvm_free_data(data); + return NULL; +} + char *test_gensvm_calculate_omega() { struct GenModel *model = gensvm_init_model(); @@ -460,7 +575,6 @@ char *test_gensvm_calculate_ab_simple() return NULL; } -char *test_gensvm_get_update() char *test_gensvm_update_B() { struct GenData *data = gensvm_init_data(); |