gensvm_init.c

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#include "gensvm_init.h"
#include "gensvm_print.h"

void gensvm_init_V(struct GenModel *from_model,
            struct GenModel *to_model, struct GenData *data)
{
    long i, j, k, jj_start, jj_end, jj;
    double cmin, cmax, value, rnd;
    double *col_min = NULL,
           *col_max = NULL;

    long n = data->n;
    long m = data->m;
    long K = data->K;

    if (from_model == NULL) {
        col_min = Calloc(double, m+1);
        col_max = Calloc(double, m+1);
        for (j=0; j<m+1; j++) {
            col_min[j] = 1.0e100;
            col_max[j] = -1.0e100;
        }

        if (data->Z == NULL) {
            // sparse matrix
            long *visit_count = Calloc(long, m+1);
            for (i=0; i<n; i++) {
                jj_start = data->spZ->ia[i];
                jj_end = data->spZ->ia[i+1];
                for (jj=jj_start; jj<jj_end; jj++) {
                    j = data->spZ->ja[jj];
                    value = data->spZ->values[jj];

                    col_min[j] = minimum(col_min[j], value);
                    col_max[j] = maximum(col_max[j], value);
                    visit_count[j]++;
                }
            }
            // correction in case the minimum or maximum is 0
            for (j=0; j<m+1; j++) {
                if (visit_count[j] < n) {
                    col_min[j] = minimum(col_min[j], 0.0);
                    col_max[j] = maximum(col_max[j], 0.0);
                }
            }
            free(visit_count);
        } else {
            // dense matrix
            for (i=0; i<n; i++) {
                for (j=0; j<m+1; j++) {
                    value = matrix_get(data->Z, m+1, i, j);
                    col_min[j] = minimum(col_min[j], value);
                    col_max[j] = maximum(col_max[j], value);
                }
            }
        }
        for (j=0; j<m+1; j++) {
            cmin = (fabs(col_min[j]) < 1e-10) ? -1 : col_min[j];
            cmax = (fabs(col_max[j]) < 1e-10) ? 1 : col_max[j];
            for (k=0; k<K-1; k++) {
                rnd = ((double) rand()) / ((double) RAND_MAX);
                value = 1.0/cmin + (1.0/cmax - 1.0/cmin)*rnd;
                matrix_set(to_model->V, K-1, j, k, value);
            }
        }
        free(col_min);
        free(col_max);
    } else {
        for (i=0; i<m+1; i++) {
            for (j=0; j<K-1; j++) {
                value = matrix_get(from_model->V, K-1, i, j);
                matrix_set(to_model->V, K-1, i, j, value);
            }
        }
    }
}

void gensvm_initialize_weights(struct GenData *data, struct GenModel *model)
{
    long *groups = NULL;
    long i;

    long n = model->n;
    long K = model->K;

    if (model->weight_idx == 1) {
        for (i=0; i<n; i++)
            model->rho[i] = 1.0;
    }
    else if (model->weight_idx == 2) {
        groups = Calloc(long, K);
        for (i=0; i<n; i++)
            groups[data->y[i]-1]++;
        for (i=0; i<n; i++)
            model->rho[i] = ((double) n)/((double) (
                        groups[data->y[i]-1]*K));
    } else {
        // LCOV_EXCL_START
        err("[GenSVM Error]: Unknown weight specification.\n");
        exit(EXIT_FAILURE);
        // LCOV_EXCL_STOP
    }
    free(groups);
}