Model.h

/*
 * Recursive Neural Networks: neural networks for data structures 
 *
 * Copyright (C) 2018 Alessandro Vullo 
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
*/

#ifndef _MODEL_H_
#define _MODEL_H_

#include "Instance.h"
#include "DataSet.h"

#include <string>
#include <stdexcept>

/*
 * Represent a RNN model using an abstract interface
 * to leave the client code unaware of of the various
 * template options
 *
 * A way to combine static/dynamic polymorphism, see:
 * https://stackoverflow.com/questions/1213366/can-template-polymorphism-be-used-in-place-of-oo-polymorphism
 */

class Model {
  
 public:
  
  virtual void propagateStructuredInput(Instance*) = 0;
  virtual void backPropagateError(Instance*) = 0;

  virtual void adjustWeights(float = .0, float = .0, float = .0) = 0;
  virtual void restorePrevWeights() = 0;

  virtual void saveParameters(const char*) = 0;

  virtual void predict(Instance*) = 0;
  virtual void predict(DataSet*) = 0;

  virtual double computeError(Instance*) = 0;
  virtual double computeError(DataSet*) = 0;

  virtual ~Model() {}

  class BadModelCreation: public std::logic_error {
  public:
  BadModelCreation(std::string message): logic_error("Cannot create model: " + message) {}
  };

  static Model* factory(const std::string& = "")
    throw(BadModelCreation);
};


/* class BinaryClassModel: public Model { */
/*   RecursiveNN<TanH, Sigmoid, MGradientDescent>* _rnn; */

/*  public: */
/*   ~BinaryClassModel(); */

/*   void read(const char*); */
/*   void write(const char*); */

/*   void predict(Instance*, std::ostream& = cout); */
/* }; */

/* class MultiClassModel: public Model { */
/*   RecursiveNN<TanH, Linear, MGradientDescent>* _rnn; */

/*  public: */
/*   ~MultiClassModel(); */

/*   void read(const char*); */
/*   void write(const char*); */

/*   void predict(Instance*, std::ostream& = cout); */
/* }; */

/* class RegressionModel: public Model { */
/*   RecursiveNN<TanH, Sigmoid, MGradientDescent>* _rnn; */

/*   void read(const char*); */
/*   void write(const char*); */

/*   void predict(Instance*, std::ostream& = cout); */
/* }; */


#endif // _MODEL_H_