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Big optimizations, now Lambert function uses static vectors of mus and alphas.

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This commit is contained in:
Simone Margaritelli 2010-10-31 18:06:29 +01:00
parent a4eaf22c08
commit 20b76cb928
4 changed files with 174 additions and 95 deletions
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2
Makefile
View file

@ -1,4 +1,4 @@
all: all:
gcc -w *.c -o fsom_example gcc -w -O3 -pipe -fomit-frame-pointer -ffast-math *.c -o fsom_example
clean: clean:
rm -f *.o fsom_example rm -f *.o fsom_example

68
example.c
View file

@ -1,7 +1,65 @@
/*
* =====================================================================================
*
* Filename: example.c
*
* Description: Examle file to benchmark fsom library.
*
* Version: 0.1
* Revision: none
* Compiler: gcc
*
* Author: BlackLight (http://0x00.ath.cx), <blacklight@autistici.org>
* Contributor: evilsocket (http://www.evilsocket.net), <evilsocket@gmail.com>
* Licence: GNU GPL v.3
* Company: DO WHAT YOU WANT CAUSE A PIRATE IS FREE, YOU ARE A PIRATE!
*
* =====================================================================================
*/
#include "fsom.h" #include "fsom.h"
#include <alloca.h> #include <alloca.h>
#include <stdio.h> #include <stdio.h>
#include <sys/time.h>
static unsigned long prev_uticks = 0;
static unsigned long get_uticks(){
struct timeval ts;
gettimeofday(&ts,0);
return ((ts.tv_sec * 1000000) + ts.tv_usec);
}
static void start_timer(){
prev_uticks = get_uticks();
}
static void print_timing( const char *msg ){
#define MS_DELTA (1000.0)
#define SS_DELTA (MS_DELTA * 1000.0)
#define MM_DELTA (SS_DELTA * 60.0)
#define HH_DELTA (MM_DELTA * 60.0)
double ticks = get_uticks() - prev_uticks;
if( ticks < MS_DELTA ){
printf( "%s\t : %lf us\n", msg, ticks );
}
else if( ticks < SS_DELTA ){
printf( "%s\t : %lf ms\n", msg, ticks / MS_DELTA );
}
else if( ticks < MM_DELTA ){
printf( "%s\t : %lf s\n", msg, ticks / SS_DELTA );
}
else if( ticks < HH_DELTA ){
printf( "%s\t : %lf m\n", msg, ticks / MM_DELTA );
}
else{
printf( "%s\t : %lf h\n", msg, ticks / HH_DELTA );
}
start_timer();
}
#define VECTORS 10 #define VECTORS 10
#define INPUTS 20 #define INPUTS 20
@ -29,6 +87,8 @@ main ( int argc, char *argv[] )
} }
} }
start_timer();
net = som_network_new ( INPUTS, OUT_ROWS, OUT_COLS ); net = som_network_new ( INPUTS, OUT_ROWS, OUT_COLS );
if ( !net ) if ( !net )
@ -37,9 +97,13 @@ main ( int argc, char *argv[] )
return 1; return 1;
} }
print_timing( "Network Creation" );
som_init_weights ( net, data, INPUTS ); som_init_weights ( net, data, INPUTS );
som_train ( net, data, VECTORS, TRAIN_STEPS ); som_train ( net, data, VECTORS, TRAIN_STEPS );
print_timing( "Network Training" );
for ( i=0; i < INPUTS; ++i ) for ( i=0; i < INPUTS; ++i )
{ {
som_set_inputs ( net, data[i] ); som_set_inputs ( net, data[i] );
@ -47,5 +111,9 @@ main ( int argc, char *argv[] )
// printf ( "best coordinates [%u]: %u,%u\n", i, x, y ); // printf ( "best coordinates [%u]: %u,%u\n", i, x, y );
} }
print_timing( "Input Recognition" );
som_network_destroy ( net ); som_network_destroy ( net );
print_timing( "Network Destruction" );
} }

160
fsom.c
View file

@ -11,6 +11,7 @@
* Compiler: gcc * Compiler: gcc
* *
* Author: BlackLight (http://0x00.ath.cx), <blacklight@autistici.org> * Author: BlackLight (http://0x00.ath.cx), <blacklight@autistici.org>
* Contributor: evilsocket (http://www.evilsocket.net), <evilsocket@gmail.com>
* Licence: GNU GPL v.3 * Licence: GNU GPL v.3
* Company: DO WHAT YOU WANT CAUSE A PIRATE IS FREE, YOU ARE A PIRATE! * Company: DO WHAT YOU WANT CAUSE A PIRATE IS FREE, YOU ARE A PIRATE!
* *
@ -81,41 +82,15 @@ som_synapsis_new ( som_neuron_t *input_neuron, som_neuron_t *output_neuron, doub
* \brief Create a new neuron * \brief Create a new neuron
* \return The new neuron * \return The new neuron
*/ */
#define som_neuron_new() ( som_neuron_t *)calloc( 1, sizeof ( som_neuron_t ) )
static som_neuron_t* /* ----- end of macro som_neuron_new ----- */
som_neuron_new ()
{
som_neuron_t *neuron = NULL;
if ( !( neuron = ( som_neuron_t* ) malloc ( sizeof ( som_neuron_t ))))
{
return NULL;
}
neuron->output = 0.0;
neuron->input = 0.0;
neuron->synapses = NULL;
neuron->synapses_count = 0;
return neuron;
} /* ----- end of function som_neuron_new ----- */
/** /**
* \brief Deallocate a neuron * \brief Deallocate a neuron
* \param neuron Neuron to be deallocated * \param neuron Neuron to be deallocated
*/ */
#define som_neuron_destroy( neuron ) if( neuron ){ free(neuron); neuron = NULL; }
static void /* ----- end of macro som_neuron_destroy ----- */
som_neuron_destroy ( som_neuron_t *neuron )
{
if ( !neuron )
{
return;
}
free ( neuron );
neuron = NULL;
} /* ----- end of function som_neuron_destroy ----- */
/** /**
* \brief Create a new input layer * \brief Create a new input layer
@ -151,7 +126,6 @@ som_input_layer_new ( size_t neurons_count )
for ( j=0; j < i; ++j ) for ( j=0; j < i; ++j )
{ {
som_neuron_destroy ( layer->neurons[j] ); som_neuron_destroy ( layer->neurons[j] );
layer->neurons[j] = NULL;
} }
free ( layer->neurons ); free ( layer->neurons );
@ -221,7 +195,6 @@ som_output_layer_new ( size_t neurons_rows, size_t neurons_cols )
for ( l=0; l < j; ++l ) for ( l=0; l < j; ++l )
{ {
som_neuron_destroy ( layer->neurons[k][l] ); som_neuron_destroy ( layer->neurons[k][l] );
layer->neurons[k][l] = NULL;
} }
free ( layer->neurons[k] ); free ( layer->neurons[k] );
@ -279,13 +252,11 @@ som_network_new ( size_t input_neurons, size_t output_neurons_rows, size_t outpu
som_network_t *net = NULL; som_network_t *net = NULL;
srand ( time ( NULL )); srand ( time ( NULL ));
if ( !( net = ( som_network_t* ) malloc ( sizeof ( som_network_t )))) if ( !( net = ( som_network_t* ) calloc ( 1, sizeof ( som_network_t ))))
{ {
return NULL; return NULL;
} }
memset ( net, 0, sizeof ( som_network_t ));
if ( !( net->input_layer = som_input_layer_new ( input_neurons ))) if ( !( net->input_layer = som_input_layer_new ( input_neurons )))
{ {
free ( net ); free ( net );
@ -359,7 +330,6 @@ som_input_layer_destroy ( som_network_t *net )
} }
som_neuron_destroy ( net->input_layer->neurons[i] ); som_neuron_destroy ( net->input_layer->neurons[i] );
net->input_layer->neurons[i] = NULL;
} }
free ( net->input_layer->neurons ); free ( net->input_layer->neurons );
@ -406,7 +376,6 @@ som_output_layer_destroy ( som_network_t *net )
} }
som_neuron_destroy ( net->output_layer->neurons[i][j] ); som_neuron_destroy ( net->output_layer->neurons[i][j] );
net->output_layer->neurons[i][j] = NULL;
} }
free ( net->output_layer->neurons[i] ); free ( net->output_layer->neurons[i] );
@ -472,21 +441,23 @@ som_get_best_neuron_coordinates ( som_network_t *net, size_t *x, size_t *y )
k = 0; k = 0;
double mod = 0.0, double mod = 0.0,
best_dist = 0.0; best_dist = DBL_MAX;
som_neuron_t *neuron;
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 )
{ {
mod = 0.0; mod = 0.0;
neuron = net->output_layer->neurons[i][j];
for ( k=0; k < net->output_layer->neurons[i][j]->synapses_count; ++k ) for ( k=0; k < neuron->synapses_count; ++k )
{ {
mod += ( net->input_layer->neurons[k]->input - net->output_layer->neurons[i][j]->synapses[k]->weight ) * mod += pow( net->input_layer->neurons[k]->input - neuron->synapses[k]->weight, 2 );
( net->input_layer->neurons[k]->input - net->output_layer->neurons[i][j]->synapses[k]->weight );
} }
if (( i == 0 && j == 0 ) || ( mod < best_dist )) if ( mod < best_dist )
{ {
best_dist = mod; best_dist = mod;
*x = i; *x = i;
@ -501,50 +472,44 @@ som_get_best_neuron_coordinates ( som_network_t *net, size_t *x, size_t *y )
/** /**
* \brief Get the n-th approximated step of the analytic continuation of the Lambert W-function of a real number x (see "Numerical Evaluation of the Lambert W Function and Application to Generation of Generalized Gaussian Noise With Exponent 1/2" from Chapeau-Blondeau and Monir, IEEE Transactions on Signal Processing, vol.50, no.9, Sep.2002) * \brief Get the n-th approximated step of the analytic continuation of the Lambert W-function of a real number x (see "Numerical Evaluation of the Lambert W Function and Application to Generation of Generalized Gaussian Noise With Exponent 1/2" from Chapeau-Blondeau and Monir, IEEE Transactions on Signal Processing, vol.50, no.9, Sep.2002)
* \param x Input variable of which we're going to compute W[-1](x) * \param x Input variable of which we're going to compute W[-1](x)
* \param n Number of steps in the series computation
* \return W[-1](x) * \return W[-1](x)
*/ */
static double static double
lambert_W1_function ( double x, int n ) lambert_W1_function ( som_network_t *net, double x )
{ {
int j = 0, int j = 0,
k = 0; k = 0;
double *alphas = NULL, double p = 0.0,
*mus = NULL,
p = 0.0,
res = 0.0, res = 0.0,
k_plus = 0, k_plus = 0,
k_less = 0; k_less = 0;
if ( !( alphas = (double*) alloca ( (n+1) * sizeof ( double ))))
return 0.0;
if ( !( mus = (double*) alloca ( (n+1) * sizeof ( double ))))
return 0.0;
p = - sqrt ( 2 * ( M_E * x + 1 )); p = - sqrt ( 2 * ( M_E * x + 1 ));
mus[0] = -1; net->mus[0] = -1;
mus[1] = 1; net->mus[1] = 1;
alphas[0] = 2; net->alphas[0] = 2;
alphas[1] = -1; net->alphas[1] = -1;
for ( k=2; k < n; ++k ) for ( k=2; k < TAYLOR_LAMBERT_LAST_ELEMENT; ++k )
{ {
alphas[k] = 0.0; net->alphas[k] = 0.0;
for ( j=2; j < k; ++j ) for ( j=2; j < k; ++j )
{ {
alphas[k] += mus[j] * mus[ k - j + 1 ]; net->alphas[k] += net->mus[j] * net->mus[ k - j + 1 ];
} }
k_plus = k + 1; k_plus = k + 1;
k_less = k - 1; k_less = k - 1;
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 ); net->mus[k] = (k_less / k_plus) *
( (net->mus[k-2] / 2.0) + ( net->alphas[k-2] / 4.0) ) -
( net->alphas[k] / 2.0 ) -
( net->mus[(int)k_less] / k_plus );
res += ( mus[k] * pow ( p, k ) ); res += ( net->mus[k] * pow ( p, k ) );
} }
return res; return res;
@ -572,7 +537,7 @@ som_learning_rate ( som_network_t* net, size_t t, double M, size_t N )
{ {
K = ( M * (double) N * M_E ) / 0.01; K = ( M * (double) N * M_E ) / 0.01;
W_arg = -((double) N ) / K; W_arg = -((double) N ) / K;
W = lambert_W1_function ( W_arg, 1000 ); W = lambert_W1_function ( net, W_arg );
T = K * exp ( W ); T = K * exp ( W );
net->T_learning_param = T; net->T_learning_param = T;
} else { } else {
@ -592,10 +557,11 @@ som_learning_rate ( som_network_t* net, size_t t, double M, size_t N )
* \return Learning rate * \return Learning rate
*/ */
static double INLINE double
som_learning_rate_fast ( som_network_t* net, size_t t, double M, size_t N ) som_learning_rate_fast ( som_network_t* net, size_t t, double M, size_t N )
{ {
return M * ( (double) t / net->T_learning_param) * exp ( 1 - ( (double) t / net->T_learning_param )); double inv_lrate = (double) t / net->T_learning_param;
return M * inv_lrate * exp ( 1 - inv_lrate );
} /* ----- end of function som_learning_rate ----- */ } /* ----- end of function som_learning_rate ----- */
/** /**
@ -802,48 +768,80 @@ som_init_weights ( som_network_t *net, double **data, size_t n_data )
} }
} }
size_t last_out_row = out_rows - 1;
som_neuron_t *j_neuron,
*br_edge = net->output_layer->neurons[ last_out_row ][ last_out_col ],
*bl_edge = net->output_layer->neurons[ last_out_row ][0];
for ( j=1; j < out_cols - 1; ++j ) for ( j=1; j < out_cols - 1; ++j )
{ {
j_neuron = net->output_layer->neurons[last_out_row][j];
a = (double)(j - 1) / (double)last_out_col;
b = (double)(out_cols - j) / (double)last_out_col;
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 = j_neuron->synapses[k]->weight = a * br_edge->synapses[k]->weight + b * bl_edge->synapses[k]->weight;
( ((double) j - 1) / ((double) out_cols - 1 )) * net->output_layer->neurons[ out_rows - 1 ][ out_cols - 1 ]->synapses[k]->weight +
( (double) ( out_cols - j ) / ((double) out_cols - 1 )) * net->output_layer->neurons[ out_rows - 1 ][0]->synapses[k]->weight;
} }
} }
som_neuron_t *i_neuron,
*tl_edge = net->output_layer->neurons[0][0];
for ( i=1; i < out_rows - 1; ++i ) for ( i=1; i < out_rows - 1; ++i )
{ {
i_neuron = net->output_layer->neurons[i][0];
a = ( ((double) i - 1) / (double) last_out_row );
b = ( (double) ( out_rows - i ) / (double)last_out_row);
for ( k=0; k < in_size; ++k ) for ( k=0; k < in_size; ++k )
{ {
net->output_layer->neurons[i][0]->synapses[k]->weight = i_neuron->synapses[k]->weight = a * bl_edge->synapses[k]->weight + b * tl_edge->synapses[k]->weight;
( ((double) i - 1) / ((double) out_rows - 1 )) * net->output_layer->neurons[ out_rows-1 ][0]->synapses[k]->weight +
( (double) ( out_rows - i ) / ((double) out_rows - 1 )) * net->output_layer->neurons[0][0]->synapses[k]->weight;
} }
} }
som_neuron_t *tr_edge = net->output_layer->neurons[0][ last_out_col ];
for ( i=1; i < out_rows - 1; ++i ) for ( i=1; i < out_rows - 1; ++i )
{ {
i_neuron = net->output_layer->neurons[i][ last_out_col ];
a = ( ((double) i - 1) / ((double) last_out_row ));
b = ( (double) ( out_rows - i ) / ((double) last_out_row ));
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 = i_neuron->synapses[k]->weight = a * br_edge->synapses[k]->weight + b * tr_edge->synapses[k]->weight;
( ((double) i - 1) / ((double) out_rows - 1 )) * net->output_layer->neurons[ out_rows - 1 ][ out_cols - 1 ]->synapses[k]->weight +
( (double) ( out_rows - i ) / ((double) out_rows - 1 )) * net->output_layer->neurons[0][ out_cols - 1 ]->synapses[k]->weight;
} }
} }
/* Initialize the weights in the middle of the matrix */ /* Initialize the weights in the middle of the matrix */
som_neuron_t *ij_neuron;
double sqr_index = (double) last_out_row * (double) last_out_col,
prev_i,
prev_j,
prev_out_rows,
prev_out_cols,
c,
d;
for ( i=1; i < out_rows - 1; ++i ) for ( i=1; i < out_rows - 1; ++i )
{ {
prev_i = i - 1;
prev_out_rows = out_rows - i;
for ( j=1; j < out_cols - 1; ++j ) for ( j=1; j < out_cols - 1; ++j )
{ {
prev_j = j - 1;
ij_neuron = net->output_layer->neurons[i][j];
prev_out_cols = out_cols - j;
a = ( prev_j * prev_i ) / sqr_index;
b = ( prev_j * prev_out_rows ) / sqr_index;
c = ( prev_out_cols * prev_i ) / sqr_index;
d = ( prev_out_cols * prev_out_rows ) / sqr_index;
for ( k=0; k < in_size; ++k ) for ( k=0; k < in_size; ++k )
{ {
net->output_layer->neurons[i][j]->synapses[k]->weight = ij_neuron->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 + a *
( (((double) j - 1)*(double) (out_rows - i)) / (((double) out_rows - 1)*((double) out_cols - 1))) * net->output_layer->neurons[0][ out_cols - 1 ]->synapses[k]->weight + br_edge->synapses[k]->weight +
( ((double) (out_cols - j)*((double) i - 1)) / (((double) out_rows - 1)*((double) out_cols - 1))) * net->output_layer->neurons[ out_rows - 1 ][0]->synapses[k]->weight + b *
( ((double) (out_cols - j)*(double) (out_rows - i)) / (((double) out_rows - 1)*((double) out_cols - 1))) * net->output_layer->neurons[0][0]->synapses[k]->weight; tr_edge->synapses[k]->weight +
c *
bl_edge->synapses[k]->weight +
d *
tl_edge->synapses[k]->weight;
} }
} }
} }
@ -943,13 +941,11 @@ som_deserialize ( const char* fname )
return NULL; return NULL;
} }
if ( !( net = ( som_network_t* ) malloc ( sizeof ( som_network_t )))) if ( !( net = ( som_network_t* ) calloc ( 1, sizeof ( som_network_t ))))
{ {
return NULL; return NULL;
} }
memset ( net, 0, sizeof ( som_network_t ));
fread ( &(net->serialization_time), sizeof ( time_t ), 1, fp ); fread ( &(net->serialization_time), sizeof ( time_t ), 1, fp );
fread ( &(net->T_learning_param ), sizeof ( double ), 1, fp ); fread ( &(net->T_learning_param ), sizeof ( double ), 1, fp );
fread ( &input_neurons, sizeof ( size_t ), 1, fp ); fread ( &input_neurons, sizeof ( size_t ), 1, fp );

25
fsom.h
View file

@ -11,6 +11,7 @@
* Compiler: gcc * Compiler: gcc
* *
* Author: BlackLight (http://0x00.ath.cx), <blacklight@autistici.org> * Author: BlackLight (http://0x00.ath.cx), <blacklight@autistici.org>
* Contributor: evilsocket (http://www.evilsocket.net), <evilsocket@gmail.com>
* Licence: GNU GPL v.3 * Licence: GNU GPL v.3
* Company: DO WHAT YOU WANT CAUSE A PIRATE IS FREE, YOU ARE A PIRATE! * Company: DO WHAT YOU WANT CAUSE A PIRATE IS FREE, YOU ARE A PIRATE!
* *
@ -23,37 +24,51 @@
#include <stddef.h> #include <stddef.h>
#include <time.h> #include <time.h>
#define TAYLOR_LAMBERT_ELEMENTS 1001
#define TAYLOR_LAMBERT_LAST_ELEMENT 1000
#ifndef INLINE
# define INLINE __inline__ __attribute__((always_inline))
#endif
typedef struct { typedef struct {
double output; double output;
double input; double input;
struct som_synapsis_s **synapses; struct som_synapsis_s **synapses;
size_t synapses_count; size_t synapses_count;
} som_neuron_t; } som_neuron_t
__attribute__ ((aligned));;
typedef struct som_synapsis_s { typedef struct som_synapsis_s {
som_neuron_t *neuron_in; som_neuron_t *neuron_in;
som_neuron_t *neuron_out; som_neuron_t *neuron_out;
double weight; double weight;
} som_synapsis_t; } som_synapsis_t
__attribute__ ((aligned));
typedef struct { typedef struct {
som_neuron_t **neurons; som_neuron_t **neurons;
size_t neurons_count; size_t neurons_count;
} som_input_layer_t; } som_input_layer_t
__attribute__ ((aligned));
typedef struct { typedef struct {
som_neuron_t ***neurons; som_neuron_t ***neurons;
size_t neurons_rows; size_t neurons_rows;
size_t neurons_cols; size_t neurons_cols;
} som_output_layer_t; } som_output_layer_t
__attribute__ ((aligned));
typedef struct { typedef struct {
som_input_layer_t *input_layer; som_input_layer_t *input_layer;
som_output_layer_t *output_layer; som_output_layer_t *output_layer;
double T_learning_param; double T_learning_param;
time_t serialization_time; time_t serialization_time;
} som_network_t; double alphas[TAYLOR_LAMBERT_ELEMENTS];
double mus[TAYLOR_LAMBERT_ELEMENTS];
} som_network_t
__attribute__ ((aligned));
void som_network_destroy ( som_network_t* ); void som_network_destroy ( som_network_t* );
void som_set_inputs ( som_network_t*, double* ); void som_set_inputs ( som_network_t*, double* );