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fann.c
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/*
Fast Artificial Neural Network Library (fann)
Copyright (C) 2003-2016 Steffen Nissen ([email protected])
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
*/
#include <stdio.h>
#include <stdlib.h>
#include <stdarg.h>
#include <string.h>
#include <time.h>
#include <math.h>
#include "config.h"
#include "fann.h"
/* #define FANN_NO_SEED */
FANN_EXTERNAL struct fann *FANN_API fann_create_standard(unsigned int num_layers, ...)
{
struct fann *ann;
va_list layer_sizes;
int i;
int status;
int arg;
unsigned int *layers = (unsigned int *) calloc(num_layers, sizeof(unsigned int));
if(layers == NULL)
{
fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
return NULL;
}
va_start(layer_sizes, num_layers);
status = 1;
for(i = 0; i < (int) num_layers; i++)
{
arg = va_arg(layer_sizes, unsigned int);
if(arg < 0 || arg > 1000000)
status = 0;
layers[i] = arg;
}
va_end(layer_sizes);
if(!status)
{
fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
free(layers);
return NULL;
}
ann = fann_create_standard_array(num_layers, layers);
free(layers);
return ann;
}
FANN_EXTERNAL struct fann *FANN_API fann_create_standard_array(unsigned int num_layers,
const unsigned int *layers)
{
return fann_create_sparse_array(1, num_layers, layers);
}
FANN_EXTERNAL struct fann *FANN_API fann_create_sparse(float connection_rate,
unsigned int num_layers, ...)
{
struct fann *ann;
va_list layer_sizes;
int i;
int status;
int arg;
unsigned int *layers = (unsigned int *) calloc(num_layers, sizeof(unsigned int));
if(layers == NULL)
{
fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
return NULL;
}
va_start(layer_sizes, num_layers);
status = 1;
for(i = 0; i < (int) num_layers; i++)
{
arg = va_arg(layer_sizes, unsigned int);
if(arg < 0 || arg > 1000000)
status = 0;
layers[i] = arg;
}
va_end(layer_sizes);
if(!status)
{
fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
free(layers);
return NULL;
}
ann = fann_create_sparse_array(connection_rate, num_layers, layers);
free(layers);
return ann;
}
FANN_EXTERNAL struct fann *FANN_API fann_create_sparse_array(float connection_rate,
unsigned int num_layers,
const unsigned int *layers)
{
struct fann_layer *layer_it, *last_layer, *prev_layer;
struct fann *ann;
struct fann_neuron *neuron_it, *last_neuron, *random_neuron, *bias_neuron;
#ifdef DEBUG
unsigned int prev_layer_size;
#endif
unsigned int num_neurons_in, num_neurons_out, i, j;
unsigned int min_connections, max_connections, num_connections;
unsigned int connections_per_neuron, allocated_connections;
unsigned int random_number, found_connection, tmp_con;
#ifdef FIXEDFANN
unsigned int multiplier;
#endif
if(connection_rate > 1)
{
connection_rate = 1;
}
fann_seed_rand();
/* allocate the general structure */
ann = fann_allocate_structure(num_layers);
if(ann == NULL)
{
fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
return NULL;
}
ann->connection_rate = connection_rate;
#ifdef FIXEDFANN
multiplier = ann->multiplier;
fann_update_stepwise(ann);
#endif
/* determine how many neurons there should be in each layer */
i = 0;
for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++)
{
/* we do not allocate room here, but we make sure that
* last_neuron - first_neuron is the number of neurons */
layer_it->first_neuron = NULL;
layer_it->last_neuron = layer_it->first_neuron + layers[i++] + 1; /* +1 for bias */
ann->total_neurons += (unsigned int)(layer_it->last_neuron - layer_it->first_neuron);
}
ann->num_output = (unsigned int)((ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron - 1);
ann->num_input = (unsigned int)(ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1);
/* allocate room for the actual neurons */
fann_allocate_neurons(ann);
if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
{
fann_destroy(ann);
return NULL;
}
#ifdef DEBUG
printf("creating network with connection rate %f\n", connection_rate);
printf("input\n");
printf(" layer : %d neurons, 1 bias\n",
(int)(ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1));
#endif
num_neurons_in = ann->num_input;
for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer; layer_it++)
{
num_neurons_out = (unsigned int)(layer_it->last_neuron - layer_it->first_neuron - 1);
/*�if all neurons in each layer should be connected to at least one neuron
* in the previous layer, and one neuron in the next layer.
* and the bias node should be connected to the all neurons in the next layer.
* Then this is the minimum amount of neurons */
min_connections = fann_max(num_neurons_in, num_neurons_out); /* not calculating bias */
max_connections = num_neurons_in * num_neurons_out; /* not calculating bias */
num_connections = fann_max(min_connections,
(unsigned int) (0.5 + (connection_rate * max_connections))) +
num_neurons_out;
connections_per_neuron = num_connections / num_neurons_out;
allocated_connections = 0;
/* Now split out the connections on the different neurons */
for(i = 0; i != num_neurons_out; i++)
{
layer_it->first_neuron[i].first_con = ann->total_connections + allocated_connections;
allocated_connections += connections_per_neuron;
layer_it->first_neuron[i].last_con = ann->total_connections + allocated_connections;
layer_it->first_neuron[i].activation_function = FANN_SIGMOID_STEPWISE;
#ifdef FIXEDFANN
layer_it->first_neuron[i].activation_steepness = ann->multiplier / 2;
#else
layer_it->first_neuron[i].activation_steepness = 0.5;
#endif
if(allocated_connections < (num_connections * (i + 1)) / num_neurons_out)
{
layer_it->first_neuron[i].last_con++;
allocated_connections++;
}
}
/* bias neuron also gets stuff */
layer_it->first_neuron[i].first_con = ann->total_connections + allocated_connections;
layer_it->first_neuron[i].last_con = ann->total_connections + allocated_connections;
ann->total_connections += num_connections;
/* used in the next run of the loop */
num_neurons_in = num_neurons_out;
}
fann_allocate_connections(ann);
if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
{
fann_destroy(ann);
return NULL;
}
if(connection_rate >= 1)
{
#ifdef DEBUG
prev_layer_size = ann->num_input + 1;
#endif
prev_layer = ann->first_layer;
last_layer = ann->last_layer;
for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++)
{
last_neuron = layer_it->last_neuron - 1;
for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
{
tmp_con = neuron_it->last_con - 1;
for(i = neuron_it->first_con; i != tmp_con; i++)
{
ann->weights[i] = (fann_type) fann_random_weight();
/* these connections are still initialized for fully connected networks, to allow
* operations to work, that are not optimized for fully connected networks.
*/
ann->connections[i] = prev_layer->first_neuron + (i - neuron_it->first_con);
}
/* bias weight */
ann->weights[tmp_con] = (fann_type) fann_random_bias_weight();
ann->connections[tmp_con] = prev_layer->first_neuron + (tmp_con - neuron_it->first_con);
}
#ifdef DEBUG
prev_layer_size = layer_it->last_neuron - layer_it->first_neuron;
#endif
prev_layer = layer_it;
#ifdef DEBUG
printf(" layer : %d neurons, 1 bias\n", prev_layer_size - 1);
#endif
}
}
else
{
/* make connections for a network, that are not fully connected */
/* generally, what we do is first to connect all the input
* neurons to a output neuron, respecting the number of
* available input neurons for each output neuron. Then
* we go through all the output neurons, and connect the
* rest of the connections to input neurons, that they are
* not allready connected to.
*/
/* All the connections are cleared by calloc, because we want to
* be able to see which connections are allready connected */
for(layer_it = ann->first_layer + 1; layer_it != ann->last_layer; layer_it++)
{
num_neurons_out = (unsigned int)(layer_it->last_neuron - layer_it->first_neuron - 1);
num_neurons_in = (unsigned int)((layer_it - 1)->last_neuron - (layer_it - 1)->first_neuron - 1);
/* first connect the bias neuron */
bias_neuron = (layer_it - 1)->last_neuron - 1;
last_neuron = layer_it->last_neuron - 1;
for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
{
ann->connections[neuron_it->first_con] = bias_neuron;
ann->weights[neuron_it->first_con] = (fann_type) fann_random_bias_weight();
}
/* then connect all neurons in the input layer */
last_neuron = (layer_it - 1)->last_neuron - 1;
for(neuron_it = (layer_it - 1)->first_neuron; neuron_it != last_neuron; neuron_it++)
{
/* random neuron in the output layer that has space
* for more connections */
do
{
random_number = (int) (0.5 + fann_rand(0, num_neurons_out - 1));
random_neuron = layer_it->first_neuron + random_number;
/* checks the last space in the connections array for room */
}
while(ann->connections[random_neuron->last_con - 1]);
/* find an empty space in the connection array and connect */
for(i = random_neuron->first_con; i < random_neuron->last_con; i++)
{
if(ann->connections[i] == NULL)
{
ann->connections[i] = neuron_it;
ann->weights[i] = (fann_type) fann_random_weight();
break;
}
}
}
/* then connect the rest of the unconnected neurons */
last_neuron = layer_it->last_neuron - 1;
for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
{
/* find empty space in the connection array and connect */
for(i = neuron_it->first_con; i < neuron_it->last_con; i++)
{
/* continue if allready connected */
if(ann->connections[i] != NULL)
continue;
do
{
found_connection = 0;
random_number = (int) (0.5 + fann_rand(0, num_neurons_in - 1));
random_neuron = (layer_it - 1)->first_neuron + random_number;
/* check to see if this connection is allready there */
for(j = neuron_it->first_con; j < i; j++)
{
if(random_neuron == ann->connections[j])
{
found_connection = 1;
break;
}
}
}
while(found_connection);
/* we have found a neuron that is not allready
* connected to us, connect it */
ann->connections[i] = random_neuron;
ann->weights[i] = (fann_type) fann_random_weight();
}
}
#ifdef DEBUG
printf(" layer : %d neurons, 1 bias\n", num_neurons_out);
#endif
}
/* TODO it would be nice to have the randomly created
* connections sorted for smoother memory access.
*/
}
#ifdef DEBUG
printf("output\n");
#endif
return ann;
}
FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut(unsigned int num_layers, ...)
{
struct fann *ann;
int i;
int status;
int arg;
va_list layer_sizes;
unsigned int *layers = (unsigned int *) calloc(num_layers, sizeof(unsigned int));
if(layers == NULL)
{
fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
return NULL;
}
va_start(layer_sizes, num_layers);
status = 1;
for(i = 0; i < (int) num_layers; i++)
{
arg = va_arg(layer_sizes, unsigned int);
if(arg < 0 || arg > 1000000)
status = 0;
layers[i] = arg;
}
va_end(layer_sizes);
if(!status)
{
fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
free(layers);
return NULL;
}
ann = fann_create_shortcut_array(num_layers, layers);
free(layers);
return ann;
}
FANN_EXTERNAL struct fann *FANN_API fann_create_shortcut_array(unsigned int num_layers,
const unsigned int *layers)
{
struct fann_layer *layer_it, *layer_it2, *last_layer;
struct fann *ann;
struct fann_neuron *neuron_it, *neuron_it2 = 0;
unsigned int i;
unsigned int num_neurons_in, num_neurons_out;
#ifdef FIXEDFANN
unsigned int multiplier;
#endif
fann_seed_rand();
/* allocate the general structure */
ann = fann_allocate_structure(num_layers);
if(ann == NULL)
{
fann_error(NULL, FANN_E_CANT_ALLOCATE_MEM);
return NULL;
}
ann->connection_rate = 1;
ann->network_type = FANN_NETTYPE_SHORTCUT;
#ifdef FIXEDFANN
multiplier = ann->multiplier;
fann_update_stepwise(ann);
#endif
/* determine how many neurons there should be in each layer */
i = 0;
for(layer_it = ann->first_layer; layer_it != ann->last_layer; layer_it++)
{
/* we do not allocate room here, but we make sure that
* last_neuron - first_neuron is the number of neurons */
layer_it->first_neuron = NULL;
layer_it->last_neuron = layer_it->first_neuron + layers[i++];
if(layer_it == ann->first_layer)
{
/* there is a bias neuron in the first layer */
layer_it->last_neuron++;
}
ann->total_neurons += (unsigned int)(layer_it->last_neuron - layer_it->first_neuron);
}
ann->num_output = (unsigned int)((ann->last_layer - 1)->last_neuron - (ann->last_layer - 1)->first_neuron);
ann->num_input = (unsigned int)(ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1);
/* allocate room for the actual neurons */
fann_allocate_neurons(ann);
if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
{
fann_destroy(ann);
return NULL;
}
#ifdef DEBUG
printf("creating fully shortcut connected network.\n");
printf("input\n");
printf(" layer : %d neurons, 1 bias\n",
(int)(ann->first_layer->last_neuron - ann->first_layer->first_neuron - 1));
#endif
num_neurons_in = ann->num_input;
last_layer = ann->last_layer;
for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++)
{
num_neurons_out = (unsigned int)(layer_it->last_neuron - layer_it->first_neuron);
/* Now split out the connections on the different neurons */
for(i = 0; i != num_neurons_out; i++)
{
layer_it->first_neuron[i].first_con = ann->total_connections;
ann->total_connections += num_neurons_in + 1;
layer_it->first_neuron[i].last_con = ann->total_connections;
layer_it->first_neuron[i].activation_function = FANN_SIGMOID_STEPWISE;
#ifdef FIXEDFANN
layer_it->first_neuron[i].activation_steepness = ann->multiplier / 2;
#else
layer_it->first_neuron[i].activation_steepness = 0.5;
#endif
}
#ifdef DEBUG
printf(" layer : %d neurons, 0 bias\n", num_neurons_out);
#endif
/* used in the next run of the loop */
num_neurons_in += num_neurons_out;
}
fann_allocate_connections(ann);
if(ann->errno_f == FANN_E_CANT_ALLOCATE_MEM)
{
fann_destroy(ann);
return NULL;
}
/* Connections are created from all neurons to all neurons in later layers
*/
num_neurons_in = ann->num_input + 1;
for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++)
{
for(neuron_it = layer_it->first_neuron; neuron_it != layer_it->last_neuron; neuron_it++)
{
i = neuron_it->first_con;
for(layer_it2 = ann->first_layer; layer_it2 != layer_it; layer_it2++)
{
for(neuron_it2 = layer_it2->first_neuron; neuron_it2 != layer_it2->last_neuron;
neuron_it2++)
{
ann->weights[i] = (fann_type) fann_random_weight();
ann->connections[i] = neuron_it2;
i++;
}
}
}
num_neurons_in += (unsigned int)(layer_it->last_neuron - layer_it->first_neuron);
}
#ifdef DEBUG
printf("output\n");
#endif
return ann;
}
FANN_EXTERNAL fann_type *FANN_API fann_run(struct fann * ann, fann_type * input)
{
struct fann_neuron *neuron_it, *last_neuron, *neurons, **neuron_pointers;
unsigned int i, num_connections, num_input, num_output;
fann_type neuron_sum, *output;
fann_type *weights;
struct fann_layer *layer_it, *last_layer;
unsigned int activation_function;
fann_type steepness;
/* store some variabels local for fast access */
struct fann_neuron *first_neuron = ann->first_layer->first_neuron;
#ifdef FIXEDFANN
int multiplier = ann->multiplier;
unsigned int decimal_point = ann->decimal_point;
/* values used for the stepwise linear sigmoid function */
fann_type r1 = 0, r2 = 0, r3 = 0, r4 = 0, r5 = 0, r6 = 0;
fann_type v1 = 0, v2 = 0, v3 = 0, v4 = 0, v5 = 0, v6 = 0;
fann_type last_steepness = 0;
unsigned int last_activation_function = 0;
#else
fann_type max_sum = 0;
#endif
/* first set the input */
num_input = ann->num_input;
for(i = 0; i != num_input; i++)
{
#ifdef FIXEDFANN
if(fann_abs(input[i]) > multiplier)
{
printf
("Warning input number %d is out of range -%d - %d with value %d, integer overflow may occur.\n",
i, multiplier, multiplier, input[i]);
}
#endif
first_neuron[i].value = input[i];
}
/* Set the bias neuron in the input layer */
#ifdef FIXEDFANN
(ann->first_layer->last_neuron - 1)->value = multiplier;
#else
(ann->first_layer->last_neuron - 1)->value = 1;
#endif
last_layer = ann->last_layer;
for(layer_it = ann->first_layer + 1; layer_it != last_layer; layer_it++)
{
last_neuron = layer_it->last_neuron;
for(neuron_it = layer_it->first_neuron; neuron_it != last_neuron; neuron_it++)
{
if(neuron_it->first_con == neuron_it->last_con)
{
/* bias neurons */
#ifdef FIXEDFANN
neuron_it->value = multiplier;
#else
neuron_it->value = 1;
#endif
continue;
}
activation_function = neuron_it->activation_function;
steepness = neuron_it->activation_steepness;
neuron_sum = 0;
num_connections = neuron_it->last_con - neuron_it->first_con;
weights = ann->weights + neuron_it->first_con;
if(ann->connection_rate >= 1)
{
if(ann->network_type == FANN_NETTYPE_SHORTCUT)
{
neurons = ann->first_layer->first_neuron;
}
else
{
neurons = (layer_it - 1)->first_neuron;
}
/* unrolled loop start */
i = num_connections & 3; /* same as modulo 4 */
switch (i)
{
case 3:
neuron_sum += fann_mult(weights[2], neurons[2].value);
case 2:
neuron_sum += fann_mult(weights[1], neurons[1].value);
case 1:
neuron_sum += fann_mult(weights[0], neurons[0].value);
case 0:
break;
}
for(; i != num_connections; i += 4)
{
neuron_sum +=
fann_mult(weights[i], neurons[i].value) +
fann_mult(weights[i + 1], neurons[i + 1].value) +
fann_mult(weights[i + 2], neurons[i + 2].value) +
fann_mult(weights[i + 3], neurons[i + 3].value);
}
/* unrolled loop end */
/*
* for(i = 0;i != num_connections; i++){
* printf("%f += %f*%f, ", neuron_sum, weights[i], neurons[i].value);
* neuron_sum += fann_mult(weights[i], neurons[i].value);
* }
*/
}
else
{
neuron_pointers = ann->connections + neuron_it->first_con;
i = num_connections & 3; /* same as modulo 4 */
switch (i)
{
case 3:
neuron_sum += fann_mult(weights[2], neuron_pointers[2]->value);
case 2:
neuron_sum += fann_mult(weights[1], neuron_pointers[1]->value);
case 1:
neuron_sum += fann_mult(weights[0], neuron_pointers[0]->value);
case 0:
break;
}
for(; i != num_connections; i += 4)
{
neuron_sum +=
fann_mult(weights[i], neuron_pointers[i]->value) +
fann_mult(weights[i + 1], neuron_pointers[i + 1]->value) +
fann_mult(weights[i + 2], neuron_pointers[i + 2]->value) +
fann_mult(weights[i + 3], neuron_pointers[i + 3]->value);
}
}
#ifdef FIXEDFANN
neuron_it->sum = fann_mult(steepness, neuron_sum);
if(activation_function != last_activation_function || steepness != last_steepness)
{
switch (activation_function)
{
case FANN_SIGMOID:
case FANN_SIGMOID_STEPWISE:
r1 = ann->sigmoid_results[0];
r2 = ann->sigmoid_results[1];
r3 = ann->sigmoid_results[2];
r4 = ann->sigmoid_results[3];
r5 = ann->sigmoid_results[4];
r6 = ann->sigmoid_results[5];
v1 = ann->sigmoid_values[0] / steepness;
v2 = ann->sigmoid_values[1] / steepness;
v3 = ann->sigmoid_values[2] / steepness;
v4 = ann->sigmoid_values[3] / steepness;
v5 = ann->sigmoid_values[4] / steepness;
v6 = ann->sigmoid_values[5] / steepness;
break;
case FANN_SIGMOID_SYMMETRIC:
case FANN_SIGMOID_SYMMETRIC_STEPWISE:
r1 = ann->sigmoid_symmetric_results[0];
r2 = ann->sigmoid_symmetric_results[1];
r3 = ann->sigmoid_symmetric_results[2];
r4 = ann->sigmoid_symmetric_results[3];
r5 = ann->sigmoid_symmetric_results[4];
r6 = ann->sigmoid_symmetric_results[5];
v1 = ann->sigmoid_symmetric_values[0] / steepness;
v2 = ann->sigmoid_symmetric_values[1] / steepness;
v3 = ann->sigmoid_symmetric_values[2] / steepness;
v4 = ann->sigmoid_symmetric_values[3] / steepness;
v5 = ann->sigmoid_symmetric_values[4] / steepness;
v6 = ann->sigmoid_symmetric_values[5] / steepness;
break;
case FANN_THRESHOLD:
break;
}
}
switch (activation_function)
{
case FANN_SIGMOID:
case FANN_SIGMOID_STEPWISE:
neuron_it->value =
(fann_type) fann_stepwise(v1, v2, v3, v4, v5, v6, r1, r2, r3, r4, r5, r6, 0,
multiplier, neuron_sum);
break;
case FANN_SIGMOID_SYMMETRIC:
case FANN_SIGMOID_SYMMETRIC_STEPWISE:
neuron_it->value =
(fann_type) fann_stepwise(v1, v2, v3, v4, v5, v6, r1, r2, r3, r4, r5, r6,
-multiplier, multiplier, neuron_sum);
break;
case FANN_THRESHOLD:
neuron_it->value = (fann_type) ((neuron_sum < 0) ? 0 : multiplier);
break;
case FANN_THRESHOLD_SYMMETRIC:
neuron_it->value = (fann_type) ((neuron_sum < 0) ? -multiplier : multiplier);
break;
case FANN_LINEAR:
neuron_it->value = neuron_sum;
break;
case FANN_LINEAR_PIECE:
neuron_it->value = (fann_type)((neuron_sum < 0) ? 0 : (neuron_sum > multiplier) ? multiplier : neuron_sum);
break;
case FANN_LINEAR_PIECE_SYMMETRIC:
neuron_it->value = (fann_type)((neuron_sum < -multiplier) ? -multiplier : (neuron_sum > multiplier) ? multiplier : neuron_sum);
break;
case FANN_ELLIOT:
case FANN_ELLIOT_SYMMETRIC:
case FANN_GAUSSIAN:
case FANN_GAUSSIAN_SYMMETRIC:
case FANN_GAUSSIAN_STEPWISE:
case FANN_SIN_SYMMETRIC:
case FANN_COS_SYMMETRIC:
fann_error((struct fann_error *) ann, FANN_E_CANT_USE_ACTIVATION);
break;
}
last_steepness = steepness;
last_activation_function = activation_function;
#else
neuron_sum = fann_mult(steepness, neuron_sum);
max_sum = 150/steepness;
if(neuron_sum > max_sum)
neuron_sum = max_sum;
else if(neuron_sum < -max_sum)
neuron_sum = -max_sum;
neuron_it->sum = neuron_sum;
fann_activation_switch(activation_function, neuron_sum, neuron_it->value);
#endif
}
}
/* set the output */
output = ann->output;
num_output = ann->num_output;
neurons = (ann->last_layer - 1)->first_neuron;
for(i = 0; i != num_output; i++)
{
output[i] = neurons[i].value;
}
return ann->output;
}
FANN_EXTERNAL void FANN_API fann_destroy(struct fann *ann)
{
if(ann == NULL)
return;
fann_safe_free(ann->weights);
fann_safe_free(ann->connections);
fann_safe_free(ann->first_layer->first_neuron);
fann_safe_free(ann->first_layer);
fann_safe_free(ann->output);
fann_safe_free(ann->train_errors);
fann_safe_free(ann->train_slopes);
fann_safe_free(ann->prev_train_slopes);
fann_safe_free(ann->prev_steps);
fann_safe_free(ann->prev_weights_deltas);
fann_safe_free(ann->errstr);
fann_safe_free(ann->cascade_activation_functions);
fann_safe_free(ann->cascade_activation_steepnesses);
fann_safe_free(ann->cascade_candidate_scores);
#ifndef FIXEDFANN
fann_safe_free( ann->scale_mean_in );
fann_safe_free( ann->scale_deviation_in );
fann_safe_free( ann->scale_new_min_in );
fann_safe_free( ann->scale_factor_in );
fann_safe_free( ann->scale_mean_out );
fann_safe_free( ann->scale_deviation_out );
fann_safe_free( ann->scale_new_min_out );
fann_safe_free( ann->scale_factor_out );
#endif
fann_safe_free(ann);
}
FANN_EXTERNAL void FANN_API fann_randomize_weights(struct fann *ann, fann_type min_weight,
fann_type max_weight)
{
fann_type *last_weight;
fann_type *weights = ann->weights;
last_weight = weights + ann->total_connections;
for(; weights != last_weight; weights++)
{
*weights = (fann_type) (fann_rand(min_weight, max_weight));
}
#ifndef FIXEDFANN
if(ann->prev_train_slopes != NULL)
{
fann_clear_train_arrays(ann);
}
#endif
}
/* deep copy of the fann structure */
FANN_EXTERNAL struct fann* FANN_API fann_copy(struct fann* orig)
{
struct fann* copy;
unsigned int num_layers = (unsigned int)(orig->last_layer - orig->first_layer);
struct fann_layer *orig_layer_it, *copy_layer_it;
unsigned int layer_size;
struct fann_neuron *last_neuron,*orig_neuron_it,*copy_neuron_it;
unsigned int i;
struct fann_neuron *orig_first_neuron,*copy_first_neuron;
unsigned int input_neuron;
copy = fann_allocate_structure(num_layers);
if (copy==NULL) {
fann_error((struct fann_error*)orig, FANN_E_CANT_ALLOCATE_MEM);
return NULL;
}
copy->errno_f = orig->errno_f;
if (orig->errstr)
{
copy->errstr = (char *) malloc(FANN_ERRSTR_MAX);
if (copy->errstr == NULL)
{
fann_destroy(copy);
return NULL;
}
strcpy(copy->errstr,orig->errstr);
}
copy->error_log = orig->error_log;
copy->learning_rate = orig->learning_rate;
copy->learning_momentum = orig->learning_momentum;
copy->connection_rate = orig->connection_rate;
copy->network_type = orig->network_type;
copy->num_MSE = orig->num_MSE;
copy->MSE_value = orig->MSE_value;
copy->num_bit_fail = orig->num_bit_fail;
copy->bit_fail_limit = orig->bit_fail_limit;
copy->train_error_function = orig->train_error_function;
copy->train_stop_function = orig->train_stop_function;
copy->training_algorithm = orig->training_algorithm;
copy->callback = orig->callback;
copy->user_data = orig->user_data;
#ifndef FIXEDFANN
copy->cascade_output_change_fraction = orig->cascade_output_change_fraction;
copy->cascade_output_stagnation_epochs = orig->cascade_output_stagnation_epochs;
copy->cascade_candidate_change_fraction = orig->cascade_candidate_change_fraction;
copy->cascade_candidate_stagnation_epochs = orig->cascade_candidate_stagnation_epochs;
copy->cascade_best_candidate = orig->cascade_best_candidate;
copy->cascade_candidate_limit = orig->cascade_candidate_limit;
copy->cascade_weight_multiplier = orig->cascade_weight_multiplier;
copy->cascade_max_out_epochs = orig->cascade_max_out_epochs;
copy->cascade_max_cand_epochs = orig->cascade_max_cand_epochs;
/* copy cascade activation functions */
copy->cascade_activation_functions_count = orig->cascade_activation_functions_count;
copy->cascade_activation_functions = (enum fann_activationfunc_enum *)realloc(copy->cascade_activation_functions,
copy->cascade_activation_functions_count * sizeof(enum fann_activationfunc_enum));
if(copy->cascade_activation_functions == NULL)
{
fann_error((struct fann_error*)orig, FANN_E_CANT_ALLOCATE_MEM);
fann_destroy(copy);
return NULL;
}
memcpy(copy->cascade_activation_functions,orig->cascade_activation_functions,
copy->cascade_activation_functions_count * sizeof(enum fann_activationfunc_enum));
/* copy cascade activation steepnesses */
copy->cascade_activation_steepnesses_count = orig->cascade_activation_steepnesses_count;
copy->cascade_activation_steepnesses = (fann_type *)realloc(copy->cascade_activation_steepnesses, copy->cascade_activation_steepnesses_count * sizeof(fann_type));
if(copy->cascade_activation_steepnesses == NULL)
{
fann_error((struct fann_error*)orig, FANN_E_CANT_ALLOCATE_MEM);
fann_destroy(copy);
return NULL;
}
memcpy(copy->cascade_activation_steepnesses,orig->cascade_activation_steepnesses,copy->cascade_activation_steepnesses_count * sizeof(fann_type));
copy->cascade_num_candidate_groups = orig->cascade_num_candidate_groups;
/* copy candidate scores, if used */
if (orig->cascade_candidate_scores == NULL)
{
copy->cascade_candidate_scores = NULL;
}
else
{
copy->cascade_candidate_scores =
(fann_type *) malloc(fann_get_cascade_num_candidates(copy) * sizeof(fann_type));
if(copy->cascade_candidate_scores == NULL)
{
fann_error((struct fann_error *) orig, FANN_E_CANT_ALLOCATE_MEM);
fann_destroy(copy);
return NULL;
}
memcpy(copy->cascade_candidate_scores,orig->cascade_candidate_scores,fann_get_cascade_num_candidates(copy) * sizeof(fann_type));
}
#endif /* FIXEDFANN */
copy->quickprop_decay = orig->quickprop_decay;
copy->quickprop_mu = orig->quickprop_mu;
copy->rprop_increase_factor = orig->rprop_increase_factor;
copy->rprop_decrease_factor = orig->rprop_decrease_factor;
copy->rprop_delta_min = orig->rprop_delta_min;
copy->rprop_delta_max = orig->rprop_delta_max;
copy->rprop_delta_zero = orig->rprop_delta_zero;
/* user_data is not deep copied. user should use fann_copy_with_user_data() for that */
copy->user_data = orig->user_data;
#ifdef FIXEDFANN
copy->decimal_point = orig->decimal_point;
copy->multiplier = orig->multiplier;
memcpy(copy->sigmoid_results,orig->sigmoid_results,6*sizeof(fann_type));
memcpy(copy->sigmoid_values,orig->sigmoid_values,6*sizeof(fann_type));
memcpy(copy->sigmoid_symmetric_results,orig->sigmoid_symmetric_results,6*sizeof(fann_type));
memcpy(copy->sigmoid_symmetric_values,orig->sigmoid_symmetric_values,6*sizeof(fann_type));
#endif
/* copy layer sizes, prepare for fann_allocate_neurons */
for (orig_layer_it = orig->first_layer, copy_layer_it = copy->first_layer;
orig_layer_it != orig->last_layer; orig_layer_it++, copy_layer_it++)
{
layer_size = (unsigned int)(orig_layer_it->last_neuron - orig_layer_it->first_neuron);
copy_layer_it->first_neuron = NULL;
copy_layer_it->last_neuron = copy_layer_it->first_neuron + layer_size;
copy->total_neurons += layer_size;
}
copy->num_input = orig->num_input;
copy->num_output = orig->num_output;
/* copy scale parameters, when used */
#ifndef FIXEDFANN
if (orig->scale_mean_in != NULL)