iti.h

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/**
 *
 *   Copyright (c) 2005-2021 by Pierre-Henri WUILLEMIN(@LIP6) & Christophe GONZALES(@AMU)
 *   info_at_agrum_dot_org
 *
 *  This library is free software: you can redistribute it and/or modify
 *  it under the terms of the GNU Lesser General Public License as published by
 *  the Free Software Foundation, either version 3 of the License, or
 *  (at your option) any later version.
 *
 *  This library is distributed in the hope that it will be useful,
 *  but WITHOUT ANY WARRANTY; without even the implied warranty of
 *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *  GNU Lesser General Public License for more details.
 *
 *  You should have received a copy of the GNU Lesser General Public License
 *  along with this library.  If not, see <http://www.gnu.org/licenses/>.
 *
 */


/**
 * @file
 * @brief Headers of the ITI class.
 *
 * @author Jean-Christophe MAGNAN
 */

// =========================================================================
#ifndef GUM_ITI_H
#define GUM_ITI_H
// =========================================================================
#include <agrum/tools/core/multiPriorityQueue.h>
// =========================================================================
#include <agrum/tools/multidim/implementations/multiDimFunctionGraph.h>
// =========================================================================
#include <agrum/FMDP/learning/core/templateStrategy.h>
#include <agrum/FMDP/learning/datastructure/incrementalGraphLearner.h>
// =========================================================================
#include <agrum/tools/variables/discreteVariable.h>
// =========================================================================

namespace gum {

  /**
   * @class ITI
   * @headerfile iti.h <agrum/FMDP/planning/FunctionGraph/iti.h>
   * @brief
   * @ingroup fmdp_group
   *
   * Learn a graphical representation of a function as a decision tree.
   * This learning is done incrementaly.
   * Hence first observation are add to the structure and then
   * the structure is updated.
   * Maintains two graph function : one internal for the learning
   * and a target which is updated on demand.
   *
   */

  template < TESTNAME AttributeSelection, bool isScalar = false >
  class ITI: public IncrementalGraphLearner< AttributeSelection, isScalar > {
    public:
    // ###################################################################
    /// @name Constructor & destructor.
    // ###################################################################
    /// @{

    // ==========================================================================
    /**
     * ITI constructor for functions describing the behaviour of one variable
     * according to a set of other variable such as conditionnal probabilities
     * @param target : the MultiDimFunctionGraph in which we load the structure
     * @param attributeSelectionThreshold : threshold under which a node is not
     * installed (pe-pruning)
     * @param attributeListe : Set of vars on which we rely to explain the
     * behaviour of learned variable
     * @param learnedValue : the variable from which we try to learn the
     * behaviour
     */
    // ==========================================================================
    ITI(MultiDimFunctionGraph< double >* target,
        double                           attributeSelectionThreshold,
        Set< const DiscreteVariable* >   attributeListe,
        const DiscreteVariable*          learnedValue);

    // ==========================================================================
    /**
     * ITI constructeur for real functions. We try to predict the output of a
     * function f given a set of variable
     * @param target : the MultiDimFunctionGraph in which we load the structure
     * @param attributeSelectionThreshold : threshold under which a node is not
     * installed (pe-pruning)
     * @param attributeListe : Set of vars on which we rely to explain the
     * behaviour of learned function
     */
    // ==========================================================================
    ITI(MultiDimFunctionGraph< double >* target,
        double                           attributeSelectionThreshold,
        Set< const DiscreteVariable* >   attributeListe);

    // ==========================================================================
    /// Default destructor
    // ==========================================================================
    ~ITI() {
      GUM_DESTRUCTOR(ITI);
      ;
    }

    /// @}

    // ###################################################################
    /// @name New Observation insertion methods
    // ###################################################################
    /// @{
    public:
    // ==========================================================================
    /**
     * Inserts a new observation
     * @param obs the new observation to learn
     */
    // ==========================================================================
    void addObservation(const Observation* obs);

    protected:
    // ==========================================================================
    /**
     * Will update internal graph's NodeDatabase of given node with the new
     * observation
     * @param newObs
     * @param currentNodeId
     */
    // ==========================================================================
    void updateNodeWithObservation_(const Observation* newObs, NodeId currentNodeId);

    /// @}

    // ###################################################################
    /// @name Graph Structure update methods
    // ###################################################################
    /// @{
    public:
    // ==========================================================================
    /// Updates the internal graph after a new observation has been added
    // ==========================================================================
    void updateGraph();

    protected:
    // ==========================================================================
    /**
     * inserts a new node in internal graph
     * @param nDB : the associated database
     * @param boundVar : the associated variable
     * @return the newly created node's id
     */
    // ==========================================================================
    NodeId insertNode_(NodeDatabase< AttributeSelection, isScalar >* nDB,
                       const DiscreteVariable*                       boundVar);

    // ==========================================================================
    /**
     * Changes the associated variable of a node
     * @param chgedNodeId : the node to change
     * @param desiredVar : its new associated variable
     */
    // ==========================================================================
    void chgNodeBoundVar_(NodeId chgedNodeId, const DiscreteVariable* desiredVar);

    // ==========================================================================
    /**
     * Removes a node from the internal graph
     * @param removedNodeId : the node to remove
     */
    // ==========================================================================
    void removeNode_(NodeId removedNodeId);

    /// @}

    public:
    // ###################################################################
    /// @name Function Graph Updating methods
    // ###################################################################
    /// @{

    // ==========================================================================
    /// Updates target to currently learned graph structure
    // ==========================================================================
    void updateFunctionGraph();

    private:
    // ==========================================================================
    /**
     * Inserts an internal node in the target
     * @param src the source node in internal graph
     * @return the mathcing node id in the target
     */
    // ==========================================================================
    NodeId _insertNodeInFunctionGraph_(NodeId src);

    // ==========================================================================
    /**
     * Insert a terminal node in the target.
     * This function is a dispatcher that will call the right function according
     * to
     * the value of the template isScalar
     * @param src the source node in the learned graph
     * @return the matching node in the target
     */
    // ==========================================================================
    NodeId _insertTerminalNode_(NodeId src) {
      return _insertTerminalNode_(src, Int2Type< isScalar >());
    }

    // ==========================================================================
    /**
     * Insert a terminal node in the target.
     * This function is called if we're learning a real value function.
     * Inserts then a single value in target.
     * @param src the source node in the learned graph
     * @return the matching node in the target
     */
    // ==========================================================================
    NodeId _insertTerminalNode_(NodeId src, Int2Type< true >);

    // ==========================================================================
    /**
     * Insert a terminal node in the target.
     * This function is called if we're learning the behaviour of a variable.
     * Inserts then this variable and the relevant value beneath into target.
     * @param src the source node in the learned graph
     * @return the matching node in the target
     */
    // ==========================================================================
    NodeId _insertTerminalNode_(NodeId src, Int2Type< false >);

    /// @}


    protected:
    // ==========================================================================
    /**
     * @brief insertSetOfVars_
     * @param ret
     */
    // ==========================================================================
    void insertSetOfVars_(MultiDimFunctionGraph< double >* ret) {
      for (SetIteratorSafe< const DiscreteVariable* > varIter = this->setOfVars_.beginSafe();
           varIter != this->setOfVars_.endSafe();
           ++varIter)
        ret->add(**varIter);
    }

    private:
    /// Hashtable indicating if given node has been modified (upon receiving new
    /// exemple or through a transpose)
    /// The aim is not if we have revise the installed variable on that node
    HashTable< NodeId, bool > _staleTable_;

    /// The total number of observation added to this tree
    Idx _nbTotalObservation_;

    /// The threshold above which we consider variables to be dependant
    double _attributeSelectionThreshold_;
  };


} /* namespace gum */

#include <agrum/FMDP/learning/datastructure/iti_tpl.h>

#endif   // GUM_ITI_H