mirror of
https://github.com/codeplea/genann
synced 2024-11-21 22:11:34 +03:00
110 lines
3.1 KiB
C
110 lines
3.1 KiB
C
/*
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* GENANN - Minimal C Artificial Neural Network
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*
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* Copyright (c) 2015, 2016 Lewis Van Winkle
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*
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* http://CodePlea.com
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*
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* This software is provided 'as-is', without any express or implied
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* warranty. In no event will the authors be held liable for any damages
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* arising from the use of this software.
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*
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* Permission is granted to anyone to use this software for any purpose,
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* including commercial applications, and to alter it and redistribute it
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* freely, subject to the following restrictions:
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*
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* 1. The origin of this software must not be misrepresented; you must not
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* claim that you wrote the original software. If you use this software
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* in a product, an acknowledgement in the product documentation would be
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* appreciated but is not required.
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* 2. Altered source versions must be plainly marked as such, and must not be
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* misrepresented as being the original software.
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* 3. This notice may not be removed or altered from any source distribution.
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*
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*/
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#ifndef __GENANN_H__
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#define __GENANN_H__
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#include <stdio.h>
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#ifdef __cplusplus
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extern "C" {
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#endif
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#ifndef GENANN_RANDOM
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/* We use the following for uniform random numbers between 0 and 1.
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* If you have a better function, redefine this macro. */
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#define GENANN_RANDOM() (((double)rand())/RAND_MAX)
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#endif
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typedef double (*genann_actfun)(double a);
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typedef struct genann {
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/* How many inputs, outputs, and hidden neurons. */
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int inputs, hidden_layers, hidden, outputs;
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/* Which activation function to use for hidden neurons. Default: gennann_act_sigmoid_cached*/
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genann_actfun activation_hidden;
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/* Which activation function to use for output. Default: gennann_act_sigmoid_cached*/
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genann_actfun activation_output;
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/* Total number of weights, and size of weights buffer. */
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int total_weights;
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/* Total number of neurons + inputs and size of output buffer. */
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int total_neurons;
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/* All weights (total_weights long). */
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double *weight;
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/* Stores input array and output of each neuron (total_neurons long). */
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double *output;
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/* Stores delta of each hidden and output neuron (total_neurons - inputs long). */
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double *delta;
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} genann;
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/* Creates and returns a new ann. */
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genann *genann_init(int inputs, int hidden_layers, int hidden, int outputs);
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/* Creates ANN from file saved with genann_write. */
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genann *genann_read(FILE *in);
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/* Sets weights randomly. Called by init. */
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void genann_randomize(genann *ann);
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/* Returns a new copy of ann. */
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genann *genann_copy(genann const *ann);
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/* Frees the memory used by an ann. */
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void genann_free(genann *ann);
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/* Runs the feedforward algorithm to calculate the ann's output. */
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double const *genann_run(genann const *ann, double const *inputs);
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/* Does a single backprop update. */
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void genann_train(genann const *ann, double const *inputs, double const *desired_outputs, double learning_rate);
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/* Saves the ann. */
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void genann_write(genann const *ann, FILE *out);
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double genann_act_sigmoid(double a);
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double genann_act_sigmoid_cached(double a);
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double genann_act_threshold(double a);
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#ifdef __cplusplus
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}
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#endif
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#endif /*__GENANN_H__*/
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