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Diffstat (limited to 'test/monniaux/genann/genann.h')
-rw-r--r-- | test/monniaux/genann/genann.h | 109 |
1 files changed, 109 insertions, 0 deletions
diff --git a/test/monniaux/genann/genann.h b/test/monniaux/genann/genann.h new file mode 100644 index 00000000..7eeb1cdc --- /dev/null +++ b/test/monniaux/genann/genann.h @@ -0,0 +1,109 @@ +/* + * 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*/ |