void
som_train_incremental(struct som *ann,
struct matrix *x,
unsigned int epochs)
{
clann_size_type s, *mess = malloc(sizeof(clann_size_type) * x->rows);
clann_real_type *sample, *winner = NULL;
/*
* Index vector used to shuffle the input presentation sequence
*/
for (s = 0; s < x->rows; s++)
mess[s] = s;
while (ann->epoch <= epochs)
{
clann_shuffle((clann_int_type *) mess, x->rows);
som_adjust_width(ann);
som_adjust_learning_rate(ann);
/*
* For each input sample `s'
*/
for (s = 0; s < x->rows; s++)
{
sample = matrix_value(x, mess[s], 0);
som_find_winner_neuron(ann, sample, &winner);
som_incremental_adjust_of_weights(ann, sample, winner);
}
#if CLANN_VERBOSE
printf("N. [SOM] Width: "CLANN_PRINTF", Rate: "CLANN_PRINTF" (%d).\n",
ann->actual_width,
ann->actual_learning_rate,
ann->epoch);
#endif
ann->epoch += ann->step;
}
free((void *) mess);
}
clann_void_type
kmeans_train(struct kmeans *ann,
const clann_matrix_type *x,
clann_real_type learning_rate)
{
clann_size_type s, i, *mess = malloc(sizeof(clann_size_type) * x->rows);
clann_real_type e, distance, minimun, *sample, *winner = NULL;
ann->old_centers = clann_matrix_copy_new(&ann->centers);
/*
* Index vector used to shuffle the input presentation sequence
*/
for (s = 0; s < x->rows; s++)
mess[s] = s;
do
{
/*
* For each input in the shuffle list
*/
clann_shuffle((clann_int_type *) mess, x->rows);
e = 0;
for (s = 0; s < x->rows; s++)
{
sample = clann_matrix_value(x, mess[s], 0);
/*
* Find the center most closer to current input sample
*/
minimun = metric_euclidean(sample,
clann_matrix_value(&ann->centers, 0, 0),
ann->center_size);
winner = clann_matrix_value(&ann->centers, 0, 0);
for (i = 1; i < ann->n_centers; i++)
{
distance = metric_euclidean(sample,
clann_matrix_value(&ann->centers, i, 0),
ann->center_size);
if (distance < minimun)
{
minimun = distance;
winner = clann_matrix_value(&ann->centers, i, 0);
}
}
/*
* Adjust winning center positions
*/
for (i = 0; i < ann->center_size; i++)
winner[i] += learning_rate * (sample[i] - winner[i]);
}
/*
* Compute the mean of changes
*/
for (i = 0; i < ann->n_centers; i++)
{
e += metric_euclidean(clann_matrix_value(&ann->centers, i, 0),
clann_matrix_value(ann->old_centers, i, 0),
ann->center_size);
}
e = e / ann->n_centers;
#if CLANN_VERBOSE
printf("N. [KMEANS] Mean centers' update: " CLANN_PRINTF ".\n", e);
#endif
clann_matrix_copy(&ann->centers, ann->old_centers);
}
while (e > ann->noticeable_change_rate);
free((clann_void_type *) ann->old_centers);
ann->old_centers = NULL;
}
