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/**
* @file gensvm_init.c
* @author Gertjan van den Burg
* @date January 7, 2014
* @brief Functions for initializing model and data structures
*
* @details
* This file contains functions for initializing a GenModel instance
* and a GenData instance. In addition, default values for these
* structures are defined here (and only here). Functions for allocating
* memory for the model structure and freeing of the model and data structures
* are also included.
*
*/
#include "gensvm_init.h"
#include "gensvm_print.h"
inline double rnd() { return (double) rand()/0x7FFFFFFF; }
/**
* @brief seed the matrix V from an existing model or using rand
*
* @details
* The matrix V must be seeded before the main_loop() can start.
* This can be done by either seeding it with random numbers or
* using the solution from a previous model on the same dataset
* as initial seed. The latter option usually allows for a
* significant improvement in the number of iterations necessary
* because the seeded model V is closer to the optimal V.
*
* @param[in] from_model GenModel from which to copy V
* @param[in,out] to_model GenModel to which V will be copied
*/
void gensvm_init_V(struct GenModel *from_model,
struct GenModel *to_model, struct GenData *data)
{
long i, j, k;
double cmin, cmax, value;
long n = data->n;
long m = data->m;
long K = data->K;
if (from_model == NULL) {
for (i=0; i<m+1; i++) {
cmin = 1e100;
cmax = -1e100;
for (k=0; k<n; k++) {
value = matrix_get(data->Z, m+1, k, i);
cmin = minimum(cmin, value);
cmax = maximum(cmax, value);
}
for (j=0; j<K-1; j++) {
cmin = (abs(cmin) < 1e-10) ? -1 : cmin;
cmax = (abs(cmax) < 1e-10) ? 1 : cmax;
value = 1.0/cmin + (1.0/cmax - 1.0/cmin)*rnd();
matrix_set(to_model->V, K-1, i, j, value);
}
}
} 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);
}
}
}
/**
* @brief Initialize instance weights
*
* @details
* Instance weights can for instance be used to add additional weights to
* instances of certain classes. Two default weight possibilities are
* implemented here. The first is unit weights, where each instance gets
* weight 1.
*
* The second are group size correction weights, which are calculated as
* @f[
* \rho_i = \frac{n}{Kn_k} ,
* @f]
* where @f$ n_k @f$ is the number of instances in group @f$ k @f$ and
* @f$ y_i = k @f$.
*
* @param[in] data GenData with the dataset
* @param[in,out] model GenModel with the weight specification. On
* exit GenModel::rho contains the instance
* weights.
*/
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);
}
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