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/*
* GENANN - Minimal C Artificial Neural Network
*
* Copyright (c) 2015-2018 Lewis Van Winkle
*
* http://CodePlea.com
*
* This software is provided 'as-is', without any express or implied
* warranty. In no event will the authors be held liable for any damages
* arising from the use of this software.
*
* Permission is granted to anyone to use this software for any purpose,
* including commercial applications, and to alter it and redistribute it
* freely, subject to the following restrictions:
*
* 1. The origin of this software must not be misrepresented; you must not
* claim that you wrote the original software. If you use this software
* in a product, an acknowledgement in the product documentation would be
* appreciated but is not required.
* 2. Altered source versions must be plainly marked as such, and must not be
* misrepresented as being the original software.
* 3. This notice may not be removed or altered from any source distribution.
*
*/
#ifndef GENANN_H
#define GENANN_H
#include <stdio.h>
#ifdef __cplusplus
extern "C" {
#endif
#ifndef GENANN_RANDOM
/* We use the following for uniform random numbers between 0 and 1.
* If you have a better function, redefine this macro. */
#define GENANN_RANDOM() (((double)rand())/RAND_MAX)
#endif
struct genann;
typedef double (*genann_actfun)(const struct genann *ann, double a);
typedef struct genann {
/* How many inputs, outputs, and hidden neurons. */
int inputs, hidden_layers, hidden, outputs;
/* Which activation function to use for hidden neurons. Default: gennann_act_sigmoid_cached*/
genann_actfun activation_hidden;
/* Which activation function to use for output. Default: gennann_act_sigmoid_cached*/
genann_actfun activation_output;
/* Total number of weights, and size of weights buffer. */
int total_weights;
/* Total number of neurons + inputs and size of output buffer. */
int total_neurons;
/* All weights (total_weights long). */
double *weight;
/* Stores input array and output of each neuron (total_neurons long). */
double *output;
/* Stores delta of each hidden and output neuron (total_neurons - inputs long). */
double *delta;
} genann;
/* Creates and returns a new ann. */
genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
/* Creates ANN from file saved with genann_write. */
genann *genann_read(FILE *in);
/* Sets weights randomly. Called by init. */
void genann_randomize(genann *ann);
/* Returns a new copy of ann. */
genann *genann_copy(genann const *ann);
/* Frees the memory used by an ann. */
void genann_free(genann *ann);
/* Runs the feedforward algorithm to calculate the ann's output. */
double const *genann_run(genann const *ann, double const *inputs);
/* Does a single backprop update. */
void genann_train(genann const *ann, double const *inputs, double const *desired_outputs, double learning_rate);
/* Saves the ann. */
void genann_write(genann const *ann, FILE *out);
void genann_init_sigmoid_lookup(const genann *ann);
#ifndef VERIMAG
double genann_act_sigmoid(const genann *ann, double a);
double genann_act_sigmoid_cached(const genann *ann, double a);
double genann_act_threshold(const genann *ann, double a);
double genann_act_linear(const genann *ann, double a);
#endif
#ifdef __cplusplus
}
#endif
#endif /*GENANN_H*/
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