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Added example.c, some optimizations to the lib.
This commit is contained in:
Simone Margaritelli
parent
d5a19bf143
commit
a4eaf22c08
3 changed files with 183 additions and 92 deletions
4
Makefile
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4
Makefile
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@ -0,0 +1,4 @@
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all:
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gcc -w *.c -o fsom_example
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clean:
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rm -f *.o fsom_example
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51
example.c
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51
example.c
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@ -0,0 +1,51 @@
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#include "fsom.h"
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#include <alloca.h>
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#include <stdio.h>
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#define VECTORS 10
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#define INPUTS 20
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#define OUT_ROWS 10
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#define OUT_COLS 10
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#define TRAIN_STEPS 30
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int
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main ( int argc, char *argv[] )
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{
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size_t i, j, x, y;
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double step = 0.0;
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double **data = NULL;
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som_network_t *net = NULL;
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data = (double **) alloca ( INPUTS * sizeof ( double* ));
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for ( i=0, step = 0.0; i < INPUTS; ++i, step += 0.1 )
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{
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data[i] = (double *) alloca ( VECTORS * sizeof ( double ));
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for ( j=0; j < VECTORS; ++j )
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{
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data[i][j] = step;
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}
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}
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net = som_network_new ( INPUTS, OUT_ROWS, OUT_COLS );
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if ( !net )
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{
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printf( "ERROR: som_network_new failed.\n" );
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return 1;
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}
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som_init_weights ( net, data, INPUTS );
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som_train ( net, data, VECTORS, TRAIN_STEPS );
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for ( i=0; i < INPUTS; ++i )
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{
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som_set_inputs ( net, data[i] );
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som_get_best_neuron_coordinates ( net, &x, &y );
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// printf ( "best coordinates [%u]: %u,%u\n", i, x, y );
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}
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som_network_destroy ( net );
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}
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220
fsom.c
220
fsom.c
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@ -144,11 +144,11 @@ som_input_layer_new ( size_t neurons_count )
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return NULL;
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}
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for ( i=0; i < neurons_count; i++ )
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for ( i=0; i < neurons_count; ++i )
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{
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if ( !( layer->neurons[i] = som_neuron_new() ))
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{
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for ( j=0; j < i; j++ )
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for ( j=0; j < i; ++j )
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{
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som_neuron_destroy ( layer->neurons[j] );
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layer->neurons[j] = NULL;
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@ -194,11 +194,11 @@ som_output_layer_new ( size_t neurons_rows, size_t neurons_cols )
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return NULL;
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}
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for ( i=0; i < neurons_rows; i++ )
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for ( i=0; i < neurons_rows; ++i )
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{
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if ( !( layer->neurons[i] = ( som_neuron_t** ) malloc ( neurons_cols * sizeof ( som_neuron_t* ))))
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{
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for ( j=0; j < i; j++ )
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for ( j=0; j < i; ++j )
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{
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free ( layer->neurons[j] );
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layer->neurons[j] = NULL;
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@ -210,15 +210,15 @@ som_output_layer_new ( size_t neurons_rows, size_t neurons_cols )
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}
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}
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for ( i=0; i < neurons_rows; i++ )
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for ( i=0; i < neurons_rows; ++i )
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{
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for ( j=0; j < neurons_cols; j++ )
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for ( j=0; j < neurons_cols; ++j )
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{
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if ( !( layer->neurons[i][j] = som_neuron_new() ))
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{
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for ( k=0; k < i; k++ )
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for ( k=0; k < i; ++k )
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{
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for ( l=0; l < j; l++ )
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for ( l=0; l < j; ++l )
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{
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som_neuron_destroy ( layer->neurons[k][l] );
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layer->neurons[k][l] = NULL;
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@ -250,11 +250,11 @@ som_connect_layers ( som_input_layer_t **input_layer, som_output_layer_t **outpu
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j = 0,
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k = 0;
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for ( i=0; i < (*output_layer)->neurons_rows; i++ )
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for ( i=0; i < (*output_layer)->neurons_rows; ++i )
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{
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for ( j=0; j < (*output_layer)->neurons_cols; j++ )
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for ( j=0; j < (*output_layer)->neurons_cols; ++j )
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{
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for ( k=0; k < (*input_layer)->neurons_count; k++ )
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for ( k=0; k < (*input_layer)->neurons_count; ++k )
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{
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if ( !( som_synapsis_new ( (*input_layer)->neurons[k], (*output_layer)->neurons[i][j], 0.0 )))
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{
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@ -322,9 +322,9 @@ som_input_layer_destroy ( som_network_t *net )
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return;
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}
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for ( i=0; i < net->input_layer->neurons_count; i++ )
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for ( i=0; i < net->input_layer->neurons_count; ++i )
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{
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for ( j=0; j < net->input_layer->neurons[i]->synapses_count; j++ )
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for ( j=0; j < net->input_layer->neurons[i]->synapses_count; ++j )
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{
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if ( (int) j < 0 )
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{
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@ -339,7 +339,7 @@ som_input_layer_destroy ( som_network_t *net )
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{
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/* net->input_layer->neurons[i]->synapses[j]->neuron_out->synapses[k]->neuron_in = NULL; */
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for ( k=0; k < net->input_layer->neurons[i]->synapses[j]->neuron_out->synapses_count; k++ )
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for ( k=0; k < net->input_layer->neurons[i]->synapses[j]->neuron_out->synapses_count; ++k )
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{
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if ( net->input_layer->neurons[i]->synapses[j]->neuron_out->synapses[k] )
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{
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@ -386,11 +386,11 @@ som_output_layer_destroy ( som_network_t *net )
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return;
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}
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for ( i=0; i < net->output_layer->neurons_rows; i++ )
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for ( i=0; i < net->output_layer->neurons_rows; ++i )
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{
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for ( j=0; j < net->output_layer->neurons_cols; j++ )
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for ( j=0; j < net->output_layer->neurons_cols; ++j )
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{
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for ( k=0; k < net->output_layer->neurons[i][j]->synapses_count; k++ )
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for ( k=0; k < net->output_layer->neurons[i][j]->synapses_count; ++k )
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{
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if ( net->output_layer->neurons[i][j]->synapses )
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{
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@ -450,7 +450,7 @@ som_set_inputs ( som_network_t *net, double *data )
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{
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size_t i = 0;
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for ( i=0; i < net->input_layer->neurons_count; i++ )
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for ( i=0; i < net->input_layer->neurons_count; ++i )
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{
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net->input_layer->neurons[i]->input = data[i];
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}
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@ -474,13 +474,13 @@ som_get_best_neuron_coordinates ( som_network_t *net, size_t *x, size_t *y )
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double mod = 0.0,
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best_dist = 0.0;
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for ( i=0; i < net->output_layer->neurons_rows; i++ )
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for ( i=0; i < net->output_layer->neurons_rows; ++i )
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{
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for ( j=0; j < net->output_layer->neurons_cols; j++ )
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for ( j=0; j < net->output_layer->neurons_cols; ++j )
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{
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mod = 0.0;
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for ( k=0; k < net->output_layer->neurons[i][j]->synapses_count; k++ )
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for ( k=0; k < net->output_layer->neurons[i][j]->synapses_count; ++k )
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{
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mod += ( net->input_layer->neurons[k]->input - net->output_layer->neurons[i][j]->synapses[k]->weight ) *
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( net->input_layer->neurons[k]->input - net->output_layer->neurons[i][j]->synapses[k]->weight );
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@ -509,12 +509,14 @@ static double
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lambert_W1_function ( double x, int n )
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{
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int j = 0,
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k = 0;
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k = 0;
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double *alphas = NULL,
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*mus = NULL,
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p = 0.0,
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res = 0.0;
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res = 0.0,
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k_plus = 0,
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k_less = 0;
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if ( !( alphas = (double*) alloca ( (n+1) * sizeof ( double ))))
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return 0.0;
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@ -524,27 +526,25 @@ lambert_W1_function ( double x, int n )
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p = - sqrt ( 2 * ( M_E * x + 1 ));
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for ( k=0; k < n; k++ )
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mus[0] = -1;
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mus[1] = 1;
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alphas[0] = 2;
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alphas[1] = -1;
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for ( k=2; k < n; ++k )
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{
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if ( k == 0 )
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alphas[k] = 0.0;
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for ( j=2; j < k; ++j )
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{
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mus[k] = -1;
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alphas[k] = 2;
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} else if ( k == 1 ) {
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mus[k] = 1;
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alphas[k] = -1;
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} else {
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alphas[k] = 0.0;
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for ( j=2; j < k; j++ )
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{
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alphas[k] += mus[j] * mus[k-j+1];
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}
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mus[k] = ((double) ( k - 1 ) / (double) ( k + 1 )) * ( (mus[k-2] / 2.0) + (alphas[k-2] / 4.0) ) - ( alphas[k] / 2.0 ) - ( mus[k-1] / ((double) k + 1 ));
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alphas[k] += mus[j] * mus[ k - j + 1 ];
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}
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k_plus = k + 1;
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k_less = k - 1;
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res += ( mus[k] * pow ( p, (double) k ));
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mus[k] = (k_less / k_plus) * ( (mus[k-2] / 2.0) + (alphas[k-2] / 4.0) ) - ( alphas[k] / 2.0 ) - ( mus[(int)k_less] / k_plus );
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res += ( mus[k] * pow ( p, k ) );
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}
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return res;
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@ -583,6 +583,21 @@ som_learning_rate ( som_network_t* net, size_t t, double M, size_t N )
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return value;
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} /* ----- end of function som_learning_rate ----- */
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/**
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* \brief Get the learning rate of a step of the learning process in function of the current iteration number once T_learning_param is set
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* \param net SOM neural network
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* \param t Iteration number
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* \param M Maximum value for the learning rate (in [0:1])
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* \param N Iteration number after which the function equals the "cutoff" value (0.01), i.e. the learning rate becomes almost meaningless
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* \return Learning rate
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*/
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static double
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som_learning_rate_fast ( som_network_t* net, size_t t, double M, size_t N )
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{
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return M * ( (double) t / net->T_learning_param) * exp ( 1 - ( (double) t / net->T_learning_param ));
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} /* ----- end of function som_learning_rate ----- */
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/**
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* \brief Training iteration for the network given a single input data set
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* \param net SOM neural network
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@ -598,26 +613,28 @@ som_train_iteration ( som_network_t *net, double *data, size_t iter )
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i = 0,
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j = 0,
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k = 0,
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dist = 0;
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dist = 0,
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dist_i;
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double l_rate = 0.0;
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double l_rate = 0.0,
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inv_dist;
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l_rate = som_learning_rate ( net, iter, 0.8, 200 );
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l_rate = som_learning_rate_fast ( net, iter, 0.8, 200 );
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som_set_inputs ( net, data );
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som_get_best_neuron_coordinates ( net, &x, &y );
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for ( i=0; i < net->output_layer->neurons_rows; i++ )
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som_neuron_t *neuron;
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for ( i=0; i < net->output_layer->neurons_rows; ++i )
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{
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for ( j=0; j < net->output_layer->neurons_cols; j++ )
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dist_i = abs( x - i );
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for ( j=0; j < net->output_layer->neurons_cols; ++j )
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{
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dist = abs ( x-i ) + abs ( y-j );
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dist = dist * dist * dist * dist;
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for ( k=0; k < net->input_layer->neurons_count; k++ )
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dist = pow( dist_i + abs ( y - j ), 4 );
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inv_dist = (1.0 / ((double) dist + 1)) * l_rate;
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neuron = net->output_layer->neurons[i][j];
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for ( k=0; k < net->input_layer->neurons_count; ++k )
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{
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net->output_layer->neurons[i][j]->synapses[k]->weight +=
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(( 1.0 / ((double) dist + 1) ) *
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l_rate * ( net->input_layer->neurons[k]->input - net->output_layer->neurons[i][j]->synapses[k]->weight ));
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neuron->synapses[k]->weight += inv_dist * ( net->input_layer->neurons[k]->input - neuron->synapses[k]->weight );
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}
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}
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}
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@ -655,15 +672,15 @@ som_init_weights ( som_network_t *net, double **data, size_t n_data )
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}
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/* Find the couple of data sets with the maximum distance */
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for ( i=0; i < n_data; i++ )
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for ( i=0; i < n_data; ++i )
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{
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for ( j=0; j < n_data; j++ )
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for ( j=0; j < n_data; ++j )
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{
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if ( i != j )
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{
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dist = 0.0;
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for ( k=0; k < net->input_layer->neurons_count; k++ )
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for ( k=0; k < net->input_layer->neurons_count; ++k )
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{
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dist += fabs ( data[i][k] - data[j][k] );
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}
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@ -679,28 +696,32 @@ som_init_weights ( som_network_t *net, double **data, size_t n_data )
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}
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/* Compute the avg_data vector as the vector containing the average values of (data[max_i], data[max_j]) */
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for ( i=0; i < net->input_layer->neurons_count; i++ )
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double *max_i_row = data[max_i],
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*max_j_row = data[max_j];
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for ( i=0; i < net->input_layer->neurons_count; ++i )
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{
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avg_data[i] = fabs ( data[max_i][i] + data[max_j][i] ) / 2.0;
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avg_data[i] = fabs ( max_i_row[i] + max_j_row[i] ) / 2.0;
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}
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/* Initialize the upper-right and bottom-left vertex of the output matrix with these values */
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for ( i=0; i < net->input_layer->neurons_count; i++ )
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som_neuron_t *ur_neuron = net->output_layer->neurons[0][ net->output_layer->neurons_cols - 1 ],
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*bl_neuron = net->output_layer->neurons[ net->output_layer->neurons_rows - 1 ][0];
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for ( i=0; i < net->input_layer->neurons_count; ++i )
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{
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net->output_layer->neurons[0][ net->output_layer->neurons_cols - 1 ]->synapses[i]->weight = data[max_i][i];
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net->output_layer->neurons[ net->output_layer->neurons_rows - 1 ][0]->synapses[i]->weight = data[max_j][i];
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ur_neuron->synapses[i]->weight = max_i_row[i];
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bl_neuron->synapses[i]->weight = max_j_row[i];
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}
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/* Find the vector having the maximum distance from the maximum distance vectors */
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max_dist = DBL_MAX;
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for ( i=0; i < n_data; i++ )
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for ( i=0; i < n_data; ++i )
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{
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if ( i != max_i && i != max_j )
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{
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dist = 0.0;
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for ( k=0; k < net->input_layer->neurons_count; k++ )
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for ( k=0; k < net->input_layer->neurons_count; ++k )
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{
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dist += fabs ( data[i][k] - avg_data[i] );
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@ -712,29 +733,32 @@ som_init_weights ( som_network_t *net, double **data, size_t n_data )
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}
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}
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}
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double *med_i_row = data[medium_i];
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/* Initialize the upper-left corner with the values of this vector */
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for ( i=0; i < net->input_layer->neurons_count; i++ )
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som_neuron_t *ul_neuron = net->output_layer->neurons[0][0];
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for ( i=0; i < net->input_layer->neurons_count; ++i )
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{
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net->output_layer->neurons[0][0]->synapses[i]->weight = data[medium_i][i];
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ul_neuron->synapses[i]->weight = med_i_row[i];
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}
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/* avg_data contains the average values of the 3 vectors computed above */
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for ( i=0; i < net->input_layer->neurons_count; i++ )
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for ( i=0; i < net->input_layer->neurons_count; ++i )
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{
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avg_data[i] = fabs ( data[max_i][i] + data[max_j][i] + data[medium_i][i] ) / 3.0;
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avg_data[i] = fabs ( max_i_row[i] + max_j_row[i] + med_i_row[i] ) / 3.0;
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}
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/* Find the vector having the maximum distance from the 3 vectors above */
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max_dist = DBL_MAX;
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for ( i=0; i < n_data; i++ )
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for ( i=0; i < n_data; ++i )
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{
|
||||
if ( i != max_i && i != max_j && i != medium_i )
|
||||
{
|
||||
dist = 0.0;
|
||||
|
||||
for ( k=0; k < net->input_layer->neurons_count; k++ )
|
||||
for ( k=0; k < net->input_layer->neurons_count; ++k )
|
||||
{
|
||||
dist += fabs ( data[i][k] - avg_data[i] );
|
||||
|
||||
|
|
@ -746,11 +770,14 @@ som_init_weights ( som_network_t *net, double **data, size_t n_data )
|
|||
}
|
||||
}
|
||||
}
|
||||
|
||||
double *med_j_row = data[medium_j];
|
||||
|
||||
/* Initialize the bottom-right corner with the values of this vector */
|
||||
for ( i=0; i < net->input_layer->neurons_count; i++ )
|
||||
som_neuron_t *br_neuron = net->output_layer->neurons[ net->output_layer->neurons_rows - 1 ][ net->output_layer->neurons_cols - 1 ];
|
||||
for ( i=0; i < net->input_layer->neurons_count; ++i )
|
||||
{
|
||||
net->output_layer->neurons[ net->output_layer->neurons_rows - 1 ][ net->output_layer->neurons_cols - 1 ]->synapses[i]->weight = data[medium_j][i];
|
||||
br_neuron->synapses[i]->weight = med_j_row[i];
|
||||
}
|
||||
|
||||
/* Initialize the weights on the 4 edges */
|
||||
|
|
@ -758,19 +785,26 @@ som_init_weights ( som_network_t *net, double **data, size_t n_data )
|
|||
out_cols = net->output_layer->neurons_cols;
|
||||
in_size = net->input_layer->neurons_count;
|
||||
|
||||
for ( j=1; j < out_cols - 1; j++ )
|
||||
som_neuron_t **edges = net->output_layer->neurons[0];
|
||||
size_t last_out_col = out_cols - 1,
|
||||
span_cols;
|
||||
double a, b;
|
||||
for ( j=1; j < out_cols - 1; ++j )
|
||||
{
|
||||
for ( k=0; k < in_size; k++ )
|
||||
span_cols = out_cols - j;
|
||||
a = ( (double)(j - 1) / last_out_col );
|
||||
b = ( (double)span_cols / (double)last_out_col );
|
||||
for ( k=0; k < in_size; ++k )
|
||||
{
|
||||
net->output_layer->neurons[0][j]->synapses[k]->weight =
|
||||
( ((double) j - 1) / ( out_cols - 1 )) * net->output_layer->neurons[0][ out_cols - 1 ]->synapses[k]->weight +
|
||||
( (double) ( out_cols - j ) / ((double) out_cols - 1 )) * net->output_layer->neurons[0][0]->synapses[k]->weight;
|
||||
edges[j]->synapses[k]->weight =
|
||||
a * edges[ last_out_col ]->synapses[k]->weight +
|
||||
b * ul_neuron->synapses[k]->weight;
|
||||
}
|
||||
}
|
||||
|
||||
for ( j=1; j < out_cols - 1; j++ )
|
||||
for ( j=1; j < out_cols - 1; ++j )
|
||||
{
|
||||
for ( k=0; k < in_size; k++ )
|
||||
for ( k=0; k < in_size; ++k )
|
||||
{
|
||||
net->output_layer->neurons[ out_rows - 1 ][j]->synapses[k]->weight =
|
||||
( ((double) j - 1) / ((double) out_cols - 1 )) * net->output_layer->neurons[ out_rows - 1 ][ out_cols - 1 ]->synapses[k]->weight +
|
||||
|
|
@ -778,9 +812,9 @@ som_init_weights ( som_network_t *net, double **data, size_t n_data )
|
|||
}
|
||||
}
|
||||
|
||||
for ( i=1; i < out_rows - 1; i++ )
|
||||
for ( i=1; i < out_rows - 1; ++i )
|
||||
{
|
||||
for ( k=0; k < in_size; k++ )
|
||||
for ( k=0; k < in_size; ++k )
|
||||
{
|
||||
net->output_layer->neurons[i][0]->synapses[k]->weight =
|
||||
( ((double) i - 1) / ((double) out_rows - 1 )) * net->output_layer->neurons[ out_rows-1 ][0]->synapses[k]->weight +
|
||||
|
|
@ -788,9 +822,9 @@ som_init_weights ( som_network_t *net, double **data, size_t n_data )
|
|||
}
|
||||
}
|
||||
|
||||
for ( i=1; i < out_rows - 1; i++ )
|
||||
for ( i=1; i < out_rows - 1; ++i )
|
||||
{
|
||||
for ( k=0; k < in_size; k++ )
|
||||
for ( k=0; k < in_size; ++k )
|
||||
{
|
||||
net->output_layer->neurons[i][ out_cols - 1 ]->synapses[k]->weight =
|
||||
( ((double) i - 1) / ((double) out_rows - 1 )) * net->output_layer->neurons[ out_rows - 1 ][ out_cols - 1 ]->synapses[k]->weight +
|
||||
|
|
@ -799,11 +833,11 @@ som_init_weights ( som_network_t *net, double **data, size_t n_data )
|
|||
}
|
||||
|
||||
/* Initialize the weights in the middle of the matrix */
|
||||
for ( i=1; i < out_rows - 1; i++ )
|
||||
for ( i=1; i < out_rows - 1; ++i )
|
||||
{
|
||||
for ( j=1; j < out_cols - 1; j++ )
|
||||
for ( j=1; j < out_cols - 1; ++j )
|
||||
{
|
||||
for ( k=0; k < in_size; k++ )
|
||||
for ( k=0; k < in_size; ++k )
|
||||
{
|
||||
net->output_layer->neurons[i][j]->synapses[k]->weight =
|
||||
( (((double) j - 1)*((double) i - 1)) / (((double) out_rows - 1)*((double) out_cols - 1))) * net->output_layer->neurons[ out_rows - 1 ][ out_cols - 1 ]->synapses[k]->weight +
|
||||
|
|
@ -830,10 +864,12 @@ som_train ( som_network_t *net, double **data, size_t n_data, size_t iter )
|
|||
k = 0,
|
||||
x = 0,
|
||||
y = 0;
|
||||
|
||||
som_learning_rate( net, iter, 0.8, 200 );
|
||||
|
||||
for ( n=0; n < n_data; n++ )
|
||||
for ( n=0; n < n_data; ++n )
|
||||
{
|
||||
for ( k=1; k <= iter; k++ )
|
||||
for ( k=1; k <= iter; ++k )
|
||||
{
|
||||
som_train_iteration ( net, data[n], k );
|
||||
|
||||
|
|
@ -869,11 +905,11 @@ som_serialize ( som_network_t *net, const char *fname )
|
|||
fwrite ( &(net->output_layer->neurons_rows), sizeof ( size_t ), 1, fp );
|
||||
fwrite ( &(net->output_layer->neurons_cols), sizeof ( size_t ), 1, fp );
|
||||
|
||||
for ( i=0; i < net->output_layer->neurons_rows; i++ )
|
||||
for ( i=0; i < net->output_layer->neurons_rows; ++i )
|
||||
{
|
||||
for ( j=0; j < net->output_layer->neurons_cols; j++ )
|
||||
for ( j=0; j < net->output_layer->neurons_cols; ++j )
|
||||
{
|
||||
for ( k=0; k < net->output_layer->neurons[i][j]->synapses_count; k++ )
|
||||
for ( k=0; k < net->output_layer->neurons[i][j]->synapses_count; ++k )
|
||||
{
|
||||
fwrite ( &(net->output_layer->neurons[i][j]->synapses[k]->weight), sizeof ( double ), 1, fp );
|
||||
}
|
||||
|
|
@ -933,11 +969,11 @@ som_deserialize ( const char* fname )
|
|||
return NULL;
|
||||
}
|
||||
|
||||
for ( i=0; i < output_neurons_rows; i++ )
|
||||
for ( i=0; i < output_neurons_rows; ++i )
|
||||
{
|
||||
for ( j=0; j < output_neurons_cols; j++ )
|
||||
for ( j=0; j < output_neurons_cols; ++j )
|
||||
{
|
||||
for ( k=0; k < input_neurons; k++ )
|
||||
for ( k=0; k < input_neurons; ++k )
|
||||
{
|
||||
fread ( &weight, sizeof ( double ), 1, fp );
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue