test_gensvm_optimize.c

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#include "minunit.h"
#include "gensvm_optimize.h"
#include "gensvm_init.h"

char *test_gensvm_optimize()
{
    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] = 0.3607870295944514;
    model->rho[1] = 0.2049421299461539;
    model->rho[2] = 0.0601488725348535;
    model->rho[3] = 0.4504181439770731;
    model->rho[4] = 0.0925063643277065;
    model->rho[5] = 0.2634120202183680;
    model->rho[6] = 0.8675978657103286;
    model->rho[7] = 0.1633697022472280;

    // start test code //
    gensvm_optimize(model, data);

    double eps = 1e-7;
    mu_assert(fabs(matrix_get(model->V, model->K-1, 0, 0) -
                -0.3268931274065331) < eps,
            "Incorrect model->V at 0, 0");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 0, 1) -
                0.1117992620472728) < eps,
            "Incorrect model->V at 0, 1");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 0, 2) -
                0.1988823609241294) < eps,
            "Incorrect model->V at 0, 2");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 1, 0) -
                1.2997452108481067) < eps,
            "Incorrect model->V at 1, 0");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 1, 1) -
                -0.7171806413563449) < eps,
            "Incorrect model->V at 1, 1");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 1, 2) -
                -0.4657948105281003) < eps,
            "Incorrect model->V at 1, 2");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 2, 0) -
                0.4408949033586493) < eps,
            "Incorrect model->V at 2, 0");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 2, 1) -
                0.0257888242538633) < eps,
            "Incorrect model->V at 2, 1");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 2, 2) -
                1.1285833836998647) < eps,
            "Incorrect model->V at 2, 2");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 3, 0) -
                -1.1983357619969028) < eps,
            "Incorrect model->V at 3, 0");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 3, 1) -
                -0.4872684816635944) < eps,
            "Incorrect model->V at 3, 1");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 3, 2) -
                -1.3711836483504121) < eps,
            "Incorrect model->V at 3, 2");

    // end test code //

    gensvm_free_data(data);
    gensvm_free_model(model);
    gensvm_free_model(seed_model);

    return NULL;
}

char *test_gensvm_get_loss_1()
{
    struct GenModel *model = gensvm_init_model();
    struct GenData *data = gensvm_init_data();

    int n = 8,
        m = 3,
        K = 3;
    model->n = n;
    model->m = m;
    model->K = K;
    struct GenWork *work = gensvm_init_work(model);

    // initialize the data
    data->n = n;
    data->K = K;
    data->m = m;


    data->y = Calloc(long, data->n);
    data->y[0] = 2;
    data->y[1] = 1;
    data->y[2] = 3;
    data->y[3] = 2;
    data->y[4] = 3;
    data->y[5] = 3;
    data->y[6] = 1;
    data->y[7] = 2;

    data->Z = Calloc(double, (data->n)*(data->m+1));
    matrix_set(data->Z, data->m+1, 0, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 0, 1, 0.6112542725178001);
    matrix_set(data->Z, data->m+1, 0, 2, -0.7672096202890778);
    matrix_set(data->Z, data->m+1, 0, 3, -0.2600867145849611);
    matrix_set(data->Z, data->m+1, 1, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 1, 1, 0.5881180210361963);
    matrix_set(data->Z, data->m+1, 1, 2, -0.5419496202623567);
    matrix_set(data->Z, data->m+1, 1, 3, 0.7079932865564023);
    matrix_set(data->Z, data->m+1, 2, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 2, 1, -0.9411484777876639);
    matrix_set(data->Z, data->m+1, 2, 2, -0.0251648291772256);
    matrix_set(data->Z, data->m+1, 2, 3, 0.5335722872738475);
    matrix_set(data->Z, data->m+1, 3, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 3, 1, -0.6506872332924795);
    matrix_set(data->Z, data->m+1, 3, 2, -0.6277901989029552);
    matrix_set(data->Z, data->m+1, 3, 3, -0.1196037902922388);
    matrix_set(data->Z, data->m+1, 4, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 4, 1, -0.9955402476800429);
    matrix_set(data->Z, data->m+1, 4, 2, -0.9514564047869466);
    matrix_set(data->Z, data->m+1, 4, 3, -0.1093968234456487);
    matrix_set(data->Z, data->m+1, 5, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 5, 1, 0.3277661334163890);
    matrix_set(data->Z, data->m+1, 5, 2, 0.8271472175263959);
    matrix_set(data->Z, data->m+1, 5, 3, 0.6938788574898458);
    matrix_set(data->Z, data->m+1, 6, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 6, 1, -0.8459013990907077);
    matrix_set(data->Z, data->m+1, 6, 2, -0.2453035880572786);
    matrix_set(data->Z, data->m+1, 6, 3, 0.0078257345629504);
    matrix_set(data->Z, data->m+1, 7, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 7, 1, -0.4629532094536982);
    matrix_set(data->Z, data->m+1, 7, 2, 0.2935215202707828);
    matrix_set(data->Z, data->m+1, 7, 3, 0.0540516162042732);

    // initialize the model
    model->weight_idx = 1;
    model->kappa = 0.5;
    model->p = 1.5;
    model->lambda = 0.123;

    gensvm_allocate_model(model);
    gensvm_initialize_weights(data, model);
    gensvm_simplex(model);
    gensvm_simplex_diff(model);

    matrix_set(model->V, model->K-1, 0, 0, 0.6019309459245683);
    matrix_set(model->V, model->K-1, 0, 1, 0.0063825200426701);
    matrix_set(model->V, model->K-1, 1, 0, -0.9130102529085783);
    matrix_set(model->V, model->K-1, 1, 1, -0.8230766493212237);
    matrix_set(model->V, model->K-1, 2, 0, 0.5727079522160434);
    matrix_set(model->V, model->K-1, 2, 1, 0.6466468145039965);
    matrix_set(model->V, model->K-1, 3, 0, -0.8065680884346328);
    matrix_set(model->V, model->K-1, 3, 1, 0.5912336906588613);

    // start test code //
    double loss = gensvm_get_loss(model, data, work);

    mu_assert(fabs(loss - 0.903071383013108) < 1e-14,
            "Incorrect value of the loss");

    // end test code //

    gensvm_free_model(model);
    gensvm_free_data(data);
    gensvm_free_work(work);


    return NULL;
}

char *test_gensvm_get_loss_2()
{
    struct GenModel *model = gensvm_init_model();
    struct GenData *data = gensvm_init_data();

    int n = 8,
        m = 3,
        K = 3;
    model->n = n;
    model->m = m;
    model->K = K;
    struct GenWork *work = gensvm_init_work(model);

    // initialize the data
    data->n = n;
    data->K = K;
    data->m = m;

    data->y = Calloc(long, data->n);
    data->y[0] = 2;
    data->y[1] = 1;
    data->y[2] = 3;
    data->y[3] = 2;
    data->y[4] = 3;
    data->y[5] = 3;
    data->y[6] = 1;
    data->y[7] = 2;

    data->Z = Calloc(double, (data->n)*(data->m+1));
    matrix_set(data->Z, data->m+1, 0, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 0, 1, 0.6112542725178001);
    matrix_set(data->Z, data->m+1, 0, 2, -0.7672096202890778);
    matrix_set(data->Z, data->m+1, 0, 3, -0.2600867145849611);
    matrix_set(data->Z, data->m+1, 1, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 1, 1, 0.5881180210361963);
    matrix_set(data->Z, data->m+1, 1, 2, -0.5419496202623567);
    matrix_set(data->Z, data->m+1, 1, 3, 0.7079932865564023);
    matrix_set(data->Z, data->m+1, 2, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 2, 1, -0.9411484777876639);
    matrix_set(data->Z, data->m+1, 2, 2, -0.0251648291772256);
    matrix_set(data->Z, data->m+1, 2, 3, 0.5335722872738475);
    matrix_set(data->Z, data->m+1, 3, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 3, 1, -0.6506872332924795);
    matrix_set(data->Z, data->m+1, 3, 2, -0.6277901989029552);
    matrix_set(data->Z, data->m+1, 3, 3, -0.1196037902922388);
    matrix_set(data->Z, data->m+1, 4, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 4, 1, -0.9955402476800429);
    matrix_set(data->Z, data->m+1, 4, 2, -0.9514564047869466);
    matrix_set(data->Z, data->m+1, 4, 3, -0.1093968234456487);
    matrix_set(data->Z, data->m+1, 5, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 5, 1, 0.3277661334163890);
    matrix_set(data->Z, data->m+1, 5, 2, 0.8271472175263959);
    matrix_set(data->Z, data->m+1, 5, 3, 0.6938788574898458);
    matrix_set(data->Z, data->m+1, 6, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 6, 1, -0.8459013990907077);
    matrix_set(data->Z, data->m+1, 6, 2, -0.2453035880572786);
    matrix_set(data->Z, data->m+1, 6, 3, 0.0078257345629504);
    matrix_set(data->Z, data->m+1, 7, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 7, 1, -0.4629532094536982);
    matrix_set(data->Z, data->m+1, 7, 2, 0.2935215202707828);
    matrix_set(data->Z, data->m+1, 7, 3, 0.0540516162042732);

    // initialize the model
    model->weight_idx = 2;
    model->kappa = 0.5;
    model->p = 1.5;
    model->lambda = 0.123;

    gensvm_allocate_model(model);
    gensvm_initialize_weights(data, model);
    gensvm_simplex(model);
    gensvm_simplex_diff(model);

    matrix_set(model->V, model->K-1, 0, 0, 0.6019309459245683);
    matrix_set(model->V, model->K-1, 0, 1, 0.0063825200426701);
    matrix_set(model->V, model->K-1, 1, 0, -0.9130102529085783);
    matrix_set(model->V, model->K-1, 1, 1, -0.8230766493212237);
    matrix_set(model->V, model->K-1, 2, 0, 0.5727079522160434);
    matrix_set(model->V, model->K-1, 2, 1, 0.6466468145039965);
    matrix_set(model->V, model->K-1, 3, 0, -0.8065680884346328);
    matrix_set(model->V, model->K-1, 3, 1, 0.5912336906588613);

    // start test code //
    double loss = gensvm_get_loss(model, data, work);

    mu_assert(fabs(loss - 0.972847045993281) < 1e-14,
            "Incorrect value of the loss");
    // end test code //

    gensvm_free_model(model);
    gensvm_free_data(data);
    gensvm_free_work(work);

    return NULL;
}

char *test_gensvm_calculate_errors()
{
    struct GenModel *model = gensvm_init_model();
    struct GenData *data = gensvm_init_data();

    int n = 8,
        m = 3,
        K = 3;

    // initialize the data
    data->n = n;
    data->K = K;
    data->m = m;

    data->y = Calloc(long, data->n);
    data->y[0] = 2;
    data->y[1] = 1;
    data->y[2] = 3;
    data->y[3] = 2;
    data->y[4] = 3;
    data->y[5] = 3;
    data->y[6] = 1;
    data->y[7] = 2;

    data->Z = Calloc(double, (data->n)*(data->m+1));
    matrix_set(data->Z, data->m+1, 0, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 0, 1, 0.6112542725178001);
    matrix_set(data->Z, data->m+1, 0, 2, -0.7672096202890778);
    matrix_set(data->Z, data->m+1, 0, 3, -0.2600867145849611);
    matrix_set(data->Z, data->m+1, 1, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 1, 1, 0.5881180210361963);
    matrix_set(data->Z, data->m+1, 1, 2, -0.5419496202623567);
    matrix_set(data->Z, data->m+1, 1, 3, 0.7079932865564023);
    matrix_set(data->Z, data->m+1, 2, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 2, 1, -0.9411484777876639);
    matrix_set(data->Z, data->m+1, 2, 2, -0.0251648291772256);
    matrix_set(data->Z, data->m+1, 2, 3, 0.5335722872738475);
    matrix_set(data->Z, data->m+1, 3, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 3, 1, -0.6506872332924795);
    matrix_set(data->Z, data->m+1, 3, 2, -0.6277901989029552);
    matrix_set(data->Z, data->m+1, 3, 3, -0.1196037902922388);
    matrix_set(data->Z, data->m+1, 4, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 4, 1, -0.9955402476800429);
    matrix_set(data->Z, data->m+1, 4, 2, -0.9514564047869466);
    matrix_set(data->Z, data->m+1, 4, 3, -0.1093968234456487);
    matrix_set(data->Z, data->m+1, 5, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 5, 1, 0.3277661334163890);
    matrix_set(data->Z, data->m+1, 5, 2, 0.8271472175263959);
    matrix_set(data->Z, data->m+1, 5, 3, 0.6938788574898458);
    matrix_set(data->Z, data->m+1, 6, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 6, 1, -0.8459013990907077);
    matrix_set(data->Z, data->m+1, 6, 2, -0.2453035880572786);
    matrix_set(data->Z, data->m+1, 6, 3, 0.0078257345629504);
    matrix_set(data->Z, data->m+1, 7, 0, 1.0000000000000000);
    matrix_set(data->Z, data->m+1, 7, 1, -0.4629532094536982);
    matrix_set(data->Z, data->m+1, 7, 2, 0.2935215202707828);
    matrix_set(data->Z, data->m+1, 7, 3, 0.0540516162042732);

    // initialize the model
    model->n = n;
    model->m = m;
    model->K = K;
    gensvm_allocate_model(model);
    gensvm_simplex(model);
    gensvm_simplex_diff(model);

    matrix_set(model->V, model->K-1, 0, 0, 0.6019309459245683);
    matrix_set(model->V, model->K-1, 0, 1, 0.0063825200426701);
    matrix_set(model->V, model->K-1, 1, 0, -0.9130102529085783);
    matrix_set(model->V, model->K-1, 1, 1, -0.8230766493212237);
    matrix_set(model->V, model->K-1, 2, 0, 0.5727079522160434);
    matrix_set(model->V, model->K-1, 2, 1, 0.6466468145039965);
    matrix_set(model->V, model->K-1, 3, 0, -0.8065680884346328);
    matrix_set(model->V, model->K-1, 3, 1, 0.5912336906588613);

    // start test code //
    double *ZV = Calloc(double, (data->n)*(data->K-1));
    gensvm_calculate_errors(model, data, ZV);

    // test ZV values
    mu_assert(fabs(matrix_get(ZV, data->K-1, 0, 0) -
                -0.1857598783645273) < 1e-14,
            "Incorrect value of ZV at 0, 0");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 0, 1) -
                -1.1466122836367203) < 1e-14,
            "Incorrect value of ZV at 0, 1");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 1, 0) -
                -0.8164504861888491) < 1e-14,
            "Incorrect value of ZV at 1, 0");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 1, 1) -
                -0.4095442019090963) < 1e-14,
            "Incorrect value of ZV at 1, 1");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 2, 0) -
                1.0164346780798894) < 1e-14,
            "Incorrect value of ZV at 2, 0");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 2, 1) -
                1.0802130116671276) < 1e-14,
            "Incorrect value of ZV at 2, 1");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 3, 0) -
                0.9329432226278516) < 1e-14,
            "Incorrect value of ZV at 3, 0");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 3, 1) -
                0.0652756651284464) < 1e-14,
            "Incorrect value of ZV at 3, 1");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 4, 0) -
                1.0541987367985999) < 1e-14,
            "Incorrect value of ZV at 4, 0");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 4, 1) -
                0.1458531104005502) < 1e-14,
            "Incorrect value of ZV at 4, 1");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 5, 0) -
                0.2167303509991526) < 1e-14,
            "Incorrect value of ZV at 5, 0");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 5, 1) -
                0.6817225403124993) < 1e-14,
            "Incorrect value of ZV at 5, 1");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 6, 0) -
                1.2274482928895278) < 1e-14,
            "Incorrect value of ZV at 6, 0");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 6, 1) -
                0.5486262633865150) < 1e-14,
            "Incorrect value of ZV at 6, 1");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 7, 0) -
                1.1491177728196642) < 1e-14,
            "Incorrect value of ZV at 7, 0");
    mu_assert(fabs(matrix_get(ZV, data->K-1, 7, 1) -
                0.6091903890783275) < 1e-14,
            "Incorrect value of ZV at 7, 1");

    // test Q values
    mu_assert(fabs(matrix_get(model->Q, data->K, 0, 0) -
                -0.1857598783645273) < 1e-14,
            "Incorrect value of Q at 0, 0");
    mu_assert(fabs(matrix_get(model->Q, data->K, 0, 1) -
                0.0000000000000000) < 1e-14,
            "Incorrect value of Q at 0, 1");
    mu_assert(fabs(matrix_get(model->Q, data->K, 0, 2) -
                0.9001154267384245) < 1e-14,
            "Incorrect value of Q at 0, 2");
    mu_assert(fabs(matrix_get(model->Q, data->K, 1, 0) -
                0.0000000000000000) < 1e-14,
            "Incorrect value of Q at 1, 0");
    mu_assert(fabs(matrix_get(model->Q, data->K, 1, 1) -
                0.8164504861888491) < 1e-14,
            "Incorrect value of Q at 1, 1");
    mu_assert(fabs(matrix_get(model->Q, data->K, 1, 2) -
                0.7629009259203254) < 1e-14,
            "Incorrect value of Q at 1, 2");
    mu_assert(fabs(matrix_get(model->Q, data->K, 2, 0) -
                1.4437092486421736) < 1e-14,
            "Incorrect value of Q at 2, 0");
    mu_assert(fabs(matrix_get(model->Q, data->K, 2, 1) -
                0.4272745705622841) < 1e-14,
            "Incorrect value of Q at 2, 1");
    mu_assert(fabs(matrix_get(model->Q, data->K, 2, 2) -
                0.0000000000000000) < 1e-14,
            "Incorrect value of Q at 2, 2");
    mu_assert(fabs(matrix_get(model->Q, data->K, 3, 0) -
                0.9329432226278516) < 1e-14,
            "Incorrect value of Q at 3, 0");
    mu_assert(fabs(matrix_get(model->Q, data->K, 3, 1) -
                0.0000000000000000) < 1e-14,
            "Incorrect value of Q at 3, 1");
    mu_assert(fabs(matrix_get(model->Q, data->K, 3, 2) -
                0.4099412270637651) < 1e-14,
            "Incorrect value of Q at 3, 2");
    mu_assert(fabs(matrix_get(model->Q, data->K, 4, 0) -
                0.6534118672271528) < 1e-14,
            "Incorrect value of Q at 4, 0");
    mu_assert(fabs(matrix_get(model->Q, data->K, 4, 1) -
                -0.4007868695714470) < 1e-14,
            "Incorrect value of Q at 4, 1");
    mu_assert(fabs(matrix_get(model->Q, data->K, 4, 2) -
                0.0000000000000000) < 1e-14,
            "Incorrect value of Q at 4, 2");
    mu_assert(fabs(matrix_get(model->Q, data->K, 5, 0) -
                0.6987542137426619) < 1e-14,
            "Incorrect value of Q at 5, 0");
    mu_assert(fabs(matrix_get(model->Q, data->K, 5, 1) -
                0.4820238627435093) < 1e-14,
            "Incorrect value of Q at 5, 1");
    mu_assert(fabs(matrix_get(model->Q, data->K, 5, 2) -
                0.0000000000000000) < 1e-14,
            "Incorrect value of Q at 5, 2");
    mu_assert(fabs(matrix_get(model->Q, data->K, 6, 0) -
                0.0000000000000000) < 1e-14,
            "Incorrect value of Q at 6, 0");
    mu_assert(fabs(matrix_get(model->Q, data->K, 6, 1) -
                -1.2274482928895278) < 1e-14,
            "Incorrect value of Q at 6, 1");
    mu_assert(fabs(matrix_get(model->Q, data->K, 6, 2) -
                -1.0888484277208184) < 1e-14,
            "Incorrect value of Q at 6, 2");
    mu_assert(fabs(matrix_get(model->Q, data->K, 7, 0) -
                1.1491177728196642) < 1e-14,
            "Incorrect value of Q at 7, 0");
    mu_assert(fabs(matrix_get(model->Q, data->K, 7, 1) -
                0.0000000000000000) < 1e-14,
            "Incorrect value of Q at 7, 1");
    mu_assert(fabs(matrix_get(model->Q, data->K, 7, 2) -
                0.0469845337266742) < 1e-14,
            "Incorrect value of Q at 7, 2");

    free(ZV);
    // end test code //

    gensvm_free_model(model);
    gensvm_free_data(data);

    return NULL;
}

char *test_gensvm_calculate_huber()
{
    struct GenModel *model = gensvm_init_model();
    model->n = 5;
    model->m = 3;
    model->K = 3;
    model->kappa = 0.5;

    gensvm_allocate_model(model);

    matrix_set(model->Q, model->K, 0, 0, -0.3386242674244120);
    matrix_set(model->Q, model->K, 0, 1, 1.0828252163937386);
    matrix_set(model->Q, model->K, 0, 2, 0.9734009993634181);
    matrix_set(model->Q, model->K, 1, 0, 1.3744927461858576);
    matrix_set(model->Q, model->K, 1, 1, 1.8086820272988162);
    matrix_set(model->Q, model->K, 1, 2, 0.9587412628706828);
    matrix_set(model->Q, model->K, 2, 0, -0.0530412768492290);
    matrix_set(model->Q, model->K, 2, 1, 0.4026826962708268);
    matrix_set(model->Q, model->K, 2, 2, -1.9705914880746673);
    matrix_set(model->Q, model->K, 3, 0, -0.8749982403551375);
    matrix_set(model->Q, model->K, 3, 1, 1.3981525936474806);
    matrix_set(model->Q, model->K, 3, 2, 0.5845478158465323);
    matrix_set(model->Q, model->K, 4, 0, 0.9594104113136890);
    matrix_set(model->Q, model->K, 4, 1, -0.7058945833639207);
    matrix_set(model->Q, model->K, 4, 2, -1.8413342248272893);

    // start test code //
    gensvm_calculate_huber(model);

    mu_assert(fabs(matrix_get(model->H, model->K, 0, 0) -
                0.5973049764458478) < 1e-14,
            "Incorrect H at 0, 0");
    mu_assert(fabs(matrix_get(model->H, model->K, 0, 1) -
                0.0000000000000000) < 1e-14,
            "Incorrect H at 0, 1");
    mu_assert(fabs(matrix_get(model->H, model->K, 0, 2) -
                0.0002358356116216) < 1e-14,
            "Incorrect H at 0, 2");
    mu_assert(fabs(matrix_get(model->H, model->K, 1, 0) -
                0.0000000000000000) < 1e-14,
            "Incorrect H at 1, 0");
    mu_assert(fabs(matrix_get(model->H, model->K, 1, 1) -
                0.0000000000000000) < 1e-14,
            "Incorrect H at 1, 1");
    mu_assert(fabs(matrix_get(model->H, model->K, 1, 2) -
                0.0005674277965020) < 1e-14,
            "Incorrect H at 1, 2");
    mu_assert(fabs(matrix_get(model->H, model->K, 2, 0) -
                0.3696319769160848) < 1e-14,
            "Incorrect H at 2, 0");
    mu_assert(fabs(matrix_get(model->H, model->K, 2, 1) -
                0.1189293204447631) < 1e-14,
            "Incorrect H at 2, 1");
    mu_assert(fabs(matrix_get(model->H, model->K, 2, 2) -
                2.2205914880746676) < 1e-14,
            "Incorrect H at 2, 2");
    mu_assert(fabs(matrix_get(model->H, model->K, 3, 0) -
                1.1249982403551375) < 1e-14,
            "Incorrect H at 3, 0");
    mu_assert(fabs(matrix_get(model->H, model->K, 3, 1) -
                0.0000000000000000) < 1e-14,
            "Incorrect H at 3, 1");
    mu_assert(fabs(matrix_get(model->H, model->K, 3, 2) -
                0.0575335057726290) < 1e-14,
            "Incorrect H at 3, 2");
    mu_assert(fabs(matrix_get(model->H, model->K, 4, 0) -
                0.0005491715699080) < 1e-14,
            "Incorrect H at 4, 0");
    mu_assert(fabs(matrix_get(model->H, model->K, 4, 1) -
                0.9558945833639207) < 1e-14,
            "Incorrect H at 4, 1");
    mu_assert(fabs(matrix_get(model->H, model->K, 4, 2) -
                2.0913342248272890) < 1e-14,
            "Incorrect H at 4, 2");

    // start end code //

    gensvm_free_model(model);

    return NULL;
}

char *test_gensvm_step_doubling()
{
    struct GenModel *model = gensvm_init_model();
    model->n = 5;
    model->m = 3;
    model->K = 3;

    gensvm_allocate_model(model);

    // start test code //

    matrix_set(model->V, model->K-1, 0, 0, 0.4534886648299394);
    matrix_set(model->V, model->K-1, 0, 1, 0.0744278734246461);
    matrix_set(model->V, model->K-1, 1, 0, 0.3119251404109698);
    matrix_set(model->V, model->K-1, 1, 1, 0.4656439597720683);
    matrix_set(model->V, model->K-1, 2, 0, 0.2011718791962903);
    matrix_set(model->V, model->K-1, 2, 1, 0.6500799120482493);
    matrix_set(model->V, model->K-1, 3, 0, 0.4203721613186416);
    matrix_set(model->V, model->K-1, 3, 1, 0.3322561487796912);

    matrix_set(model->Vbar, model->K-1, 0, 0, 0.4716285304362394);
    matrix_set(model->Vbar, model->K-1, 0, 1, 0.9580963971307287);
    matrix_set(model->Vbar, model->K-1, 1, 0, 0.4665492786460124);
    matrix_set(model->Vbar, model->K-1, 1, 1, 0.7584128769293659);
    matrix_set(model->Vbar, model->K-1, 2, 0, 0.0694310200377222);
    matrix_set(model->Vbar, model->K-1, 2, 1, 0.9819492320913891);
    matrix_set(model->Vbar, model->K-1, 3, 0, 0.9308026068356582);
    matrix_set(model->Vbar, model->K-1, 3, 1, 0.1323286413371241);

    gensvm_step_doubling(model);

    mu_assert(fabs(matrix_get(model->V, model->K-1, 0, 0) -
                0.4353487992236394) < 1e-14,
            "Incorrect V at 0, 0");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 0, 1) -
                -0.8092406502814364) < 1e-14,
            "Incorrect V at 0, 1");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 1, 0) -
                0.1573010021759272) < 1e-14,
            "Incorrect V at 1, 0");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 1, 1) -
                0.1728750426147708) < 1e-14,
            "Incorrect V at 1, 1");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 2, 0) -
                0.3329127383548583) < 1e-14,
            "Incorrect V at 2, 0");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 2, 1) -
                0.3182105920051096) < 1e-14,
            "Incorrect V at 2, 1");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 3, 0) -
                -0.0900582841983750) < 1e-14,
            "Incorrect V at 3, 0");
    mu_assert(fabs(matrix_get(model->V, model->K-1, 3, 1) -
                0.5321836562222582) < 1e-14,
            "Incorrect V at 3, 1");

    // end test code //

    gensvm_free_model(model);

    return NULL;
}

char *all_tests()
{
    mu_suite_start();

    mu_run_test(test_gensvm_calculate_errors);
    mu_run_test(test_gensvm_get_loss_1);
    mu_run_test(test_gensvm_get_loss_2);
    mu_run_test(test_gensvm_calculate_huber);
    mu_run_test(test_gensvm_step_doubling);

    mu_run_test(test_gensvm_optimize);

    return NULL;
}

RUN_TESTS(all_tests);