blacklight
/
fsom
mirror of https://github.com/BlackLight/fsom.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
fsom/fsom.c

989 lines
25 KiB
C
Raw Blame History

/*
* =====================================================================================
*
* Filename: fsom.c
*
* Description: Manage a self-organizing map (SOM) as a neural network
*
* Version: 0.1
* Created: 15/10/2010 13:53:31
* 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 <alloca.h>
#include <float.h>
#include <limits.h>
#include <math.h>
#include <memory.h>
#include <stdio.h>
#include <stdlib.h>
#ifndef M_E
#define M_E 2.7182818284590452354
#endif
/**
* \brief Create a new synapsis between two neurons
* \param input_neuron Input neuron for the synapsis
* \param output_neuron Output neuron for the synapsis
* \param weight Weight of the synapsis (set it to 0 for a random value between 0 and 1)
* \return A pointer representing the new synapsis
*/
static som_synapsis_t*
som_synapsis_new ( som_neuron_t *input_neuron, som_neuron_t *output_neuron, double weight )
{
som_synapsis_t *synapsis = NULL;
if ( !( synapsis = ( som_synapsis_t* ) malloc ( sizeof ( som_synapsis_t ))))
{
return NULL;
}
synapsis->neuron_in = input_neuron;
synapsis->neuron_out = output_neuron;
if ( weight == 0.0 )
{
synapsis->weight = (double) rand() / (double) UINT_MAX;
} else {
synapsis->weight = weight;
}
if ( !( input_neuron->synapses = ( som_synapsis_t** ) realloc ( input_neuron->synapses, (++( input_neuron->synapses_count )) * sizeof ( som_synapsis_t ))))
{
free ( synapsis );
return NULL;
}
if ( !( output_neuron->synapses = ( som_synapsis_t** ) realloc ( output_neuron->synapses, (++( output_neuron->synapses_count )) * sizeof ( som_synapsis_t ))))
{
free ( synapsis );
return NULL;
}
input_neuron->synapses[ input_neuron->synapses_count - 1 ] = synapsis;
output_neuron->synapses[ output_neuron->synapses_count - 1 ] = synapsis;
return synapsis;
} /* ----- end of function som_synapsis_new ----- */
/**
* \brief Create a new neuron
* \return The new neuron
*/
#define som_neuron_new() ( som_neuron_t *)calloc( 1, sizeof ( som_neuron_t ) )
/* ----- end of macro som_neuron_new ----- */
/**
* \brief Deallocate a neuron
* \param neuron Neuron to be deallocated
*/
#define som_neuron_destroy( neuron ) if( neuron ){ free(neuron); neuron = NULL; }
/* ----- end of macro som_neuron_destroy ----- */
/**
* \brief Create a new input layer
* \param neurons_count Number of neurons in the new input layer
* \return The new layer
*/
static som_input_layer_t*
som_input_layer_new ( size_t neurons_count )
{
size_t i = 0,
j = 0;
som_input_layer_t *layer = NULL;
if ( !( layer = ( som_input_layer_t* ) malloc ( sizeof ( som_input_layer_t ))))
{
return NULL;
}
layer->neurons_count = neurons_count;
if ( !( layer->neurons = ( som_neuron_t** ) malloc ( neurons_count * sizeof ( som_neuron_t* ))))
{
free ( layer );
return NULL;
}
for ( i=0; i < neurons_count; ++i )
{
if ( !( layer->neurons[i] = som_neuron_new() ))
{
for ( j=0; j < i; ++j )
{
som_neuron_destroy ( layer->neurons[j] );
}
free ( layer->neurons );
free ( layer );
return NULL;
}
}
return layer;
} /* ----- end of function som_input_layer_new ----- */
/**
* \brief Create a new output layer
* \param neurons_rows Number of rows in the matrix of output neurons
* \param neurons_cols Number of cols in the matrix of output neurons
* \return The new layer
*/
static som_output_layer_t*
som_output_layer_new ( size_t neurons_rows, size_t neurons_cols )
{
size_t i = 0,
j = 0,
k = 0,
l = 0;
som_output_layer_t *layer = NULL;
if ( !( layer = ( som_output_layer_t* ) malloc ( sizeof ( som_output_layer_t ))))
{
return NULL;
}
layer->neurons_rows = neurons_rows;
layer->neurons_cols = neurons_cols;
if ( !( layer->neurons = ( som_neuron_t*** ) malloc ( neurons_rows * neurons_cols * sizeof ( som_neuron_t** ))))
{
free ( layer );
return NULL;
}
for ( i=0; i < neurons_rows; ++i )
{
if ( !( layer->neurons[i] = ( som_neuron_t** ) malloc ( neurons_cols * sizeof ( som_neuron_t* ))))
{
for ( j=0; j < i; ++j )
{
free ( layer->neurons[j] );
layer->neurons[j] = NULL;
}
free ( layer->neurons );
free ( layer );
return NULL;
}
}
for ( i=0; i < neurons_rows; ++i )
{
for ( j=0; j < neurons_cols; ++j )
{
if ( !( layer->neurons[i][j] = som_neuron_new() ))
{
for ( k=0; k < i; ++k )
{
for ( l=0; l < j; ++l )
{
som_neuron_destroy ( layer->neurons[k][l] );
}
free ( layer->neurons[k] );
layer->neurons[k] = NULL;
}
free ( layer->neurons );
return NULL;
}
}
}
return layer;
} /* ----- end of function som_output_layer_new ----- */
/**
* \brief Connect two layers of a neural SOM
* \param input_layer Reference to the input layer
* \param output_layer Reference to the output layer
*/
static void
som_connect_layers ( som_input_layer_t **input_layer, som_output_layer_t **output_layer )
{
size_t i = 0,
j = 0,
k = 0;
for ( i=0; i < (*output_layer)->neurons_rows; ++i )
{
for ( j=0; j < (*output_layer)->neurons_cols; ++j )
{
for ( k=0; k < (*input_layer)->neurons_count; ++k )
{
if ( !( som_synapsis_new ( (*input_layer)->neurons[k], (*output_layer)->neurons[i][j], 0.0 )))
{
return;
}
}
}
}
} /* ----- end of function som_connect_layers ----- */
/**
* \brief Initialize a new SOM neural network
* \param input_neurons Number of neurons in the input layer
* \param output_neurons_rows Number of rows of neurons in the output layer
* \param output_neurons_cols Number of cols of neurons in the output layer
* \return The new SOM neural network
*/
som_network_t*
som_network_new ( size_t input_neurons, size_t output_neurons_rows, size_t output_neurons_cols )
{
som_network_t *net = NULL;
srand ( time ( NULL ));
if ( !( net = ( som_network_t* ) calloc ( 1, sizeof ( som_network_t ))))
{
return NULL;
}
if ( !( net->input_layer = som_input_layer_new ( input_neurons )))
{
free ( net );
return NULL;
}
if ( !( net->output_layer = som_output_layer_new ( output_neurons_rows, output_neurons_cols )))
{
free ( net->input_layer );
free ( net );
return NULL;
}
net->T_learning_param = 0.0;
net->serialization_time = ( time_t ) 0;
som_connect_layers ( &( net->input_layer ), &( net->output_layer ));
return net;
} /* ----- end of function som_network_new ----- */
/**
* \brief Deallocate an input layer
* \param net Network whose input layer should be deallocated
*/
static void
som_input_layer_destroy ( som_network_t *net )
{
size_t i = 0,
j = 0,
k = 0;
if ( !( net->input_layer ))
{
return;
}
for ( i=0; i < net->input_layer->neurons_count; ++i )
{
for ( j=0; j < net->input_layer->neurons[i]->synapses_count; ++j )
{
if ( (int) j < 0 )
{
break;
}
if ( net->input_layer->neurons[i]->synapses )
{
if ( net->input_layer->neurons[i]->synapses[j] )
{
if ( net->input_layer->neurons[i]->synapses[j]->neuron_out )
{
/* net->input_layer->neurons[i]->synapses[j]->neuron_out->synapses[k]->neuron_in = NULL; */
for ( k=0; k < net->input_layer->neurons[i]->synapses[j]->neuron_out->synapses_count; ++k )
{
if ( net->input_layer->neurons[i]->synapses[j]->neuron_out->synapses[k] )
{
net->input_layer->neurons[i]->synapses[j]->neuron_out->synapses[k]->neuron_in = NULL;
net->input_layer->neurons[i]->synapses[j]->neuron_out->synapses[k] = NULL;
}
}
}
free ( net->input_layer->neurons[i]->synapses[j] );
net->input_layer->neurons[i]->synapses[j] = NULL;
}
free ( net->input_layer->neurons[i]->synapses );
net->input_layer->neurons[i]->synapses = NULL;
}
}
som_neuron_destroy ( net->input_layer->neurons[i] );
}
free ( net->input_layer->neurons );
net->input_layer->neurons = NULL;
free ( net->input_layer );
net->input_layer = NULL;
} /* ----- end of function som_input_layer_destroy ----- */
/**
* \brief Deallocate an output layer
* \param net Network whose output layer should be deallocated
*/
static void
som_output_layer_destroy ( som_network_t *net )
{
size_t i = 0,
j = 0,
k = 0;
if ( !( net->output_layer ))
{
return;
}
for ( i=0; i < net->output_layer->neurons_rows; ++i )
{
for ( j=0; j < net->output_layer->neurons_cols; ++j )
{
for ( k=0; k < net->output_layer->neurons[i][j]->synapses_count; ++k )
{
if ( net->output_layer->neurons[i][j]->synapses )
{
if ( net->output_layer->neurons[i][j]->synapses[k] )
{
free ( net->output_layer->neurons[i][j]->synapses[k] );
net->output_layer->neurons[i][j]->synapses[k] = NULL;
}
free ( net->output_layer->neurons[i][j]->synapses );
net->output_layer->neurons[i][j]->synapses = NULL;
}
}
som_neuron_destroy ( net->output_layer->neurons[i][j] );
}
free ( net->output_layer->neurons[i] );
net->output_layer->neurons[i] = NULL;
}
free ( net->output_layer->neurons );
net->output_layer->neurons = NULL;
free ( net->output_layer );
net->output_layer = NULL;
} /* ----- end of function som_output_layer_destroy ----- */
/**
* \brief Deallocate a SOM neural network
* \param net Network to be deallocated
*/
void
som_network_destroy ( som_network_t *net )
{
if ( !net )
{
return;
}
som_input_layer_destroy ( net );
som_output_layer_destroy ( net );
free ( net );
net = NULL;
} /* ----- end of function som_network_destroy ----- */
/**
* \brief Set a vector as input for the network
* \param net SOM neural network
* \param data Vector to be passed as input for the network
*/
void
som_set_inputs ( som_network_t *net, double *data )
{
size_t i = 0;
for ( i=0; i < net->input_layer->neurons_count; ++i )
{
net->input_layer->neurons[i]->input = data[i];
}
} /* ----- end of function som_set_inputs ----- */
/**
* \brief Get the coordinates of the output neuron closest to the current input data
* \param net SOM neural network
* \param x Reference to the X coordinate of the best output neuron
* \param y Reference to the Y coordinate of the best output neuron
* \return The value of the module ||X-W|| (squared euclidean distance) for the best neuron
*/
double
som_get_best_neuron_coordinates ( som_network_t *net, size_t *x, size_t *y )
{
size_t i = 0,
j = 0,
k = 0;
double mod = 0.0,
best_dist = DBL_MAX;
som_neuron_t *neuron;
for ( i=0; i < net->output_layer->neurons_rows; ++i )
{
for ( j=0; j < net->output_layer->neurons_cols; ++j )
{
mod = 0.0;
neuron = net->output_layer->neurons[i][j];
for ( k=0; k < neuron->synapses_count; ++k )
{
mod += pow( net->input_layer->neurons[k]->input - neuron->synapses[k]->weight, 2 );
}
if ( mod < best_dist )
{
best_dist = mod;
*x = i;
*y = j;
}
}
}
return mod;
} /* ----- end of function som_get_best_neuron_coordinates ----- */
/**
* \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)
* \return W[-1](x)
*/
static double
lambert_W1_function ( som_network_t *net, double x )
{
int j = 0,
k = 0;
double p = 0.0,
res = 0.0,
k_plus = 0,
k_less = 0;
p = - sqrt ( 2 * ( M_E * x + 1 ));
net->mus[0] = -1;
net->mus[1] = 1;
net->alphas[0] = 2;
net->alphas[1] = -1;
for ( k=2; k < TAYLOR_LAMBERT_LAST_ELEMENT; ++k )
{
net->alphas[k] = 0.0;
for ( j=2; j < k; ++j )
{
net->alphas[k] += net->mus[j] * net->mus[ k - j + 1 ];
}
k_plus = k + 1;
k_less = k - 1;
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 += ( net->mus[k] * pow ( p, k ) );
}
return res;
} /* ----- end of function lambert_W1_function ----- */
/**
* \brief Get the learning rate of a step of the learning process in function of the current iteration number
* \param net SOM neural network
* \param t Iteration number
* \param M Maximum value for the learning rate (in [0:1])
* \param N Iteration number after which the function equals the "cutoff" value (0.01), i.e. the learning rate becomes almost meaningless
* \return Learning rate
*/
static double
som_learning_rate ( som_network_t* net, size_t t, double M, size_t N )
{
double value = 0.0,
T = 0.0,
K = 0.0,
W = 0.0,
W_arg = 0.0;
if ( net->T_learning_param == 0.0 )
{
K = ( M * (double) N * M_E ) / 0.01;
W_arg = -((double) N ) / K;
W = lambert_W1_function ( net, W_arg );
T = K * exp ( W );
net->T_learning_param = T;
} else {
T = net->T_learning_param;
}
value = M * ( (double) t / T) * exp ( 1 - ( (double) t / T ));
return value;
} /* ----- end of function som_learning_rate ----- */
/**
* \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
* \param net SOM neural network
* \param t Iteration number
* \param M Maximum value for the learning rate (in [0:1])
* \param N Iteration number after which the function equals the "cutoff" value (0.01), i.e. the learning rate becomes almost meaningless
* \return Learning rate
*/
INLINE double
som_learning_rate_fast ( som_network_t* net, size_t t, double M, size_t N )
{
double inv_lrate = (double) t / net->T_learning_param;
return M * inv_lrate * exp ( 1 - inv_lrate );
} /* ----- end of function som_learning_rate ----- */
/**
* \brief Training iteration for the network given a single input data set
* \param net SOM neural network
* \param data Input data
* \param iter Iteration number
*/
static void
som_train_iteration ( som_network_t *net, double *data, size_t iter )
{
size_t x = 0,
y = 0,
i = 0,
j = 0,
k = 0,
dist = 0,
dist_i;
double l_rate = 0.0,
inv_dist;
l_rate = som_learning_rate_fast ( net, iter, 0.8, 200 );
som_set_inputs ( net, data );
som_get_best_neuron_coordinates ( net, &x, &y );
som_neuron_t *neuron;
for ( i=0; i < net->output_layer->neurons_rows; ++i )
{
dist_i = abs( x - i );
for ( j=0; j < net->output_layer->neurons_cols; ++j )
{
dist = pow( dist_i + abs ( y - j ), 4 );
inv_dist = (1.0 / ((double) dist + 1)) * l_rate;
neuron = net->output_layer->neurons[i][j];
for ( k=0; k < net->input_layer->neurons_count; ++k )
{
neuron->synapses[k]->weight += inv_dist * ( net->input_layer->neurons[k]->input - neuron->synapses[k]->weight );
}
}
}
} /* ----- end of function som_train_loop ----- */
/**
* \brief Initialize the synaptical weights of the network using the algorithm proposed in "Improving the Self-Organization Feature Map Algorithm Using an Efficient Initialization Scheme", by Su, Liu and Chang, on "Tamkang Journal of Science and Engineering", vol.5, no.1, pp.35-48, 2002
* \param net SOM neural network
* \param data Input data set
* \param n_data Number of vectors in the input set
*/
void
som_init_weights ( som_network_t *net, double **data, size_t n_data )
{
size_t i = 0,
j = 0,
k = 0,
out_rows = 0,
out_cols = 0,
in_size = 0,
max_i = 0,
max_j = 0,
medium_i = 0,
medium_j = 0;
double dist = 0.0,
max_dist = 0.0;
double *avg_data = NULL;
if ( !( avg_data = (double*) alloca ( net->input_layer->neurons_count * sizeof ( double ))))
{
return;
}
/* Find the couple of data sets with the maximum distance */
for ( i=0; i < n_data; ++i )
{
for ( j=0; j < n_data; ++j )
{
if ( i != j )
{
dist = 0.0;
for ( k=0; k < net->input_layer->neurons_count; ++k )
{
dist += fabs ( data[i][k] - data[j][k] );
}
if ( dist > max_dist )
{
max_dist = dist;
max_i = i;
max_j = j;
}
}
}
}
/* Compute the avg_data vector as the vector containing the average values of (data[max_i], data[max_j]) */
double *max_i_row = data[max_i],
*max_j_row = data[max_j];
for ( i=0; i < net->input_layer->neurons_count; ++i )
{
avg_data[i] = fabs ( max_i_row[i] + max_j_row[i] ) / 2.0;
}
/* Initialize the upper-right and bottom-left vertex of the output matrix with these values */
som_neuron_t *ur_neuron = net->output_layer->neurons[0][ net->output_layer->neurons_cols - 1 ],
*bl_neuron = net->output_layer->neurons[ net->output_layer->neurons_rows - 1 ][0];
for ( i=0; i < net->input_layer->neurons_count; ++i )
{
ur_neuron->synapses[i]->weight = max_i_row[i];
bl_neuron->synapses[i]->weight = max_j_row[i];
}
/* Find the vector having the maximum distance from the maximum distance vectors */
max_dist = DBL_MAX;
for ( i=0; i < n_data; ++i )
{
if ( i != max_i && i != max_j )
{
dist = 0.0;
for ( k=0; k < net->input_layer->neurons_count; ++k )
{
dist += fabs ( data[i][k] - avg_data[i] );
if ( dist < max_dist )
{
max_dist = dist;
medium_i = i;
}
}
}
}
double *med_i_row = data[medium_i];
/* Initialize the upper-left corner with the values of this vector */
som_neuron_t *ul_neuron = net->output_layer->neurons[0][0];
for ( i=0; i < net->input_layer->neurons_count; ++i )
{
ul_neuron->synapses[i]->weight = med_i_row[i];
}
/* avg_data contains the average values of the 3 vectors computed above */
for ( i=0; i < net->input_layer->neurons_count; ++i )
{
avg_data[i] = fabs ( max_i_row[i] + max_j_row[i] + med_i_row[i] ) / 3.0;
}
/* Find the vector having the maximum distance from the 3 vectors above */
max_dist = DBL_MAX;
for ( i=0; i < n_data; ++i )
{
if ( i != max_i && i != max_j && i != medium_i )
{
dist = 0.0;
for ( k=0; k < net->input_layer->neurons_count; ++k )
{
dist += fabs ( data[i][k] - avg_data[i] );
if ( dist < max_dist )
{
max_dist = dist;
medium_j = i;
}
}
}
}
double *med_j_row = data[medium_j];
/* Initialize the bottom-right corner with the values of this vector */
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 )
{
br_neuron->synapses[i]->weight = med_j_row[i];
}
/* Initialize the weights on the 4 edges */
out_rows = net->output_layer->neurons_rows;
out_cols = net->output_layer->neurons_cols;
in_size = net->input_layer->neurons_count;
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 )
{
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 )
{
edges[j]->synapses[k]->weight =
a * edges[ last_out_col ]->synapses[k]->weight +
b * ul_neuron->synapses[k]->weight;
}
}
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 )
{
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 )
{
j_neuron->synapses[k]->weight = a * br_edge->synapses[k]->weight + b * bl_edge->synapses[k]->weight;
}
}
som_neuron_t *i_neuron,
*tl_edge = net->output_layer->neurons[0][0];
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 )
{
i_neuron->synapses[k]->weight = a * bl_edge->synapses[k]->weight + b * tl_edge->synapses[k]->weight;
}
}
som_neuron_t *tr_edge = net->output_layer->neurons[0][ last_out_col ];
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 )
{
i_neuron->synapses[k]->weight = a * br_edge->synapses[k]->weight + b * tr_edge->synapses[k]->weight;
}
}
/* 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 )
{
prev_i = i - 1;
prev_out_rows = out_rows - i;
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 )
{
ij_neuron->synapses[k]->weight =
a *
br_edge->synapses[k]->weight +
b *
tr_edge->synapses[k]->weight +
c *
bl_edge->synapses[k]->weight +
d *
tl_edge->synapses[k]->weight;
}
}
}
} /* ----- end of function som_init_weights ----- */
/**
* \brief Train the self-organizing map through a data set
* \param net SOM neural network
* \param data Data set (set of input vectors)
* \param n_data Number of input vectors in data
* \param iter Number of iterations
*/
void
som_train ( som_network_t *net, double **data, size_t n_data, size_t iter )
{
size_t n = 0,
k = 0,
x = 0,
y = 0;
som_learning_rate( net, iter, 0.8, 200 );
for ( n=0; n < n_data; ++n )
{
for ( k=1; k <= iter; ++k )
{
som_train_iteration ( net, data[n], k );
if ( som_get_best_neuron_coordinates ( net, &x, &y ) == 0.0 )
break;
}
}
} /* ----- end of function som_train ----- */
/**
* \brief Serialize a neural network on a binary file
* \param net SOM network to be serialized
* \param fname Output file name
*/
void
som_serialize ( som_network_t *net, const char *fname )
{
FILE *fp = NULL;
size_t i = 0,
j = 0,
k = 0;
if ( !( fp = fopen ( fname, "w" )))
{
return;
}
net->serialization_time = time ( NULL );
fwrite ( &(net->serialization_time), sizeof ( time_t ), 1, fp );
fwrite ( &(net->T_learning_param), sizeof ( double ), 1, fp );
fwrite ( &(net->input_layer->neurons_count), sizeof ( size_t ), 1, fp );
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 ( j=0; j < net->output_layer->neurons_cols; ++j )
{
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 );
}
}
}
fclose ( fp );
} /* ----- end of function som_serialize ----- */
/**
* \brief Initialize a SOM neural network from a serialized one on a file
* \param fname Binary file containing the network
* \return The initialized network in case of success, NULL otherwise
*/
som_network_t*
som_deserialize ( const char* fname )
{
som_network_t *net = NULL;
FILE *fp = NULL;
double weight = 0.0;
size_t i = 0,
j = 0,
k = 0,
input_neurons = 0,
output_neurons_rows = 0,
output_neurons_cols = 0;
if ( !( fp = fopen ( fname, "r" )))
{
return NULL;
}
if ( !( net = ( som_network_t* ) calloc ( 1, sizeof ( som_network_t ))))
{
return NULL;
}
fread ( &(net->serialization_time), sizeof ( time_t ), 1, fp );
fread ( &(net->T_learning_param ), sizeof ( double ), 1, fp );
fread ( &input_neurons, sizeof ( size_t ), 1, fp );
fread ( &output_neurons_rows, sizeof ( size_t ), 1, fp );
fread ( &output_neurons_cols, sizeof ( size_t ), 1, fp );
if ( !( net->input_layer = som_input_layer_new ( input_neurons )))
{
free ( net );
return NULL;
}
if ( !( net->output_layer = som_output_layer_new ( output_neurons_rows, output_neurons_cols )))
{
free ( net->input_layer );
free ( net );
return NULL;
}
for ( i=0; i < output_neurons_rows; ++i )
{
for ( j=0; j < output_neurons_cols; ++j )
{
for ( k=0; k < input_neurons; ++k )
{
fread ( &weight, sizeof ( double ), 1, fp );
if ( !( som_synapsis_new ( net->input_layer->neurons[k], net->output_layer->neurons[i][j], weight )))
{
som_input_layer_destroy ( net );
som_output_layer_destroy ( net );
return NULL;
}
}
}
}
return net;
} /* ----- end of function som_deserialize ----- */
Reference in New Issue View Git Blame Copy Permalink