text: /*
   * 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);
  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);
  
  
  #ifdef __cplusplus
  }
  #endif
  
  #endif /*GENANN_H*/
  