AI-NeuralNet-Simple
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/*
* Load a Perl array of array (a matrix) with "rows" rows and "columns" columns
* into the pre-allocated C array of arrays.
*
* The "hold" argument is an holding array and the Perl array of array which
* we expect is at index "idx" within that holding array.
*/
void c_load_axa(AV *hold, int idx, void *arena, int rows, int columns)
{
SV **sav;
SV *rv;
AV *av;
int i;
double **array = arena;
sav = av_fetch(hold, idx, 0);
if (sav == NULL)
croak("serialized item %d is not defined", idx);
rv = *sav;
if (!is_array_ref(rv))
croak("serialized item %d is not an array reference", idx);
av = get_array(rv); /* This is an array of array refs */
for (i = 0; i < rows; i++) {
double *row = array[i];
int j;
AV *subav;
sav = av_fetch(av, i, 0);
if (sav == NULL)
croak("serialized item %d has undefined row %d", idx, i);
rv = *sav;
if (!is_array_ref(rv))
croak("row %d of serialized item %d is not an array ref", i, idx);
subav = get_array(rv);
for (j = 0; j < columns; j++)
row[j] = get_float_element(subav, j);
}
}
/*
* Create new network from a retrieved data structure, such as the one
* produced by c_export_network().
*/
int c_import_network(SV *rv)
{
NEURAL_NETWORK *n;
int handle;
SV **sav;
AV *av;
int i = 0;
/*
* Unfortunately, since those data come from the outside, we need
* to validate most of the structural information to make sure
* we're not fed garbage or something we cannot process, like a
* newer version of the serialized data. This makes the code heavy.
* --RAM
*/
if (!is_array_ref(rv))
croak("c_import_network() not given an array reference");
av = get_array(rv);
/* Check version number */
sav = av_fetch(av, i++, 0);
if (sav == NULL || SvIVx(*sav) != EXPORT_VERSION)
croak("c_import_network() given unknown version %d",
sav == NULL ? 0 : SvIVx(*sav));
/* Check length -- at version 1, length is fixed to 13 */
if (av_len(av) + 1 != EXPORTED_ITEMS)
croak("c_import_network() not given a %d-item array reference",
EXPORTED_ITEMS);
handle = c_new_handle();
n = c_get_network(handle);
sav = av_fetch(av, i++, 0);
if (sav == NULL)
croak("undefined input size (item %d)", i - 1);
n->size.input = SvIVx(*sav);
sav = av_fetch(av, i++, 0);
if (sav == NULL)
croak("undefined hidden size (item %d), i - 1");
n->size.hidden = SvIVx(*sav);
sav = av_fetch(av, i++, 0);
if (sav == NULL)
croak("undefined output size (item %d)", i - 1);
n->size.output = SvIVx(*sav);
if (!c_create_network(n))
return -1;
sav = av_fetch(av, i++, 0);
if (sav == NULL)
croak("undefined learn_rate (item %d)", i - 1);
n->learn_rate = SvNVx(*sav);
sav = av_fetch(av, i++, 0);
if (sav == NULL)
croak("undefined delta (item %d)", i - 1);
n->delta = SvNVx(*sav);
sav = av_fetch(av, i++, 0);
if (sav == NULL)
croak("undefined use_bipolar (item %d)", i - 1);
n->use_bipolar = SvIVx(*sav);
c_load_axa(av, i++, n->weight.input_to_hidden,
n->size.input + 1, n->size.hidden + 1);
c_load_axa(av, i++, n->weight.hidden_to_output,
n->size.hidden + 1, n->size.output);
( run in 0.842 second using v1.01-cache-2.11-cpan-d59ab9ce9b0 )