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/*******************************************************************************
 *  Copyright (C) 2007, 2015:
 *  
 *    - Ferdinando Villa 
 *    - integratedmodelling.org
 *    - any other authors listed in @author annotations
 *
 *    All rights reserved. This file is part of the k.LAB software suite,
 *    meant to enable modular, collaborative, integrated 
 *    development of interoperable data and model components. For
 *    details, see http://integratedmodelling.org.
 *    
 *    This program is free software; you can redistribute it and/or
 *    modify it under the terms of the Affero General Public License 
 *    Version 3 or any later version.
 *
 *    This program 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
 *    Affero General Public License for more details.
 *  
 *     You should have received a copy of the Affero General Public License
 *     along with this program; if not, write to the Free Software
 *     Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA  02111-1307, USA.
 *     The license is also available at: https://www.gnu.org/licenses/agpl.html
 *******************************************************************************/
package org.integratedmodelling.engine.modelling.bayes.gn;

import java.io.File;

import org.integratedmodelling.api.knowledge.IConcept;
import org.integratedmodelling.engine.modelling.bayes.IBayesianInference;
import org.integratedmodelling.engine.modelling.bayes.IBayesianNetwork;
import org.integratedmodelling.exceptions.KlabException;
import org.integratedmodelling.exceptions.KlabIOException;
import org.integratedmodelling.exceptions.KlabValidationException;

import smile.Network;
import smile.SMILEException;
import smile.learning.DataMatch;
import smile.learning.DataSet;
import smile.learning.EM;

public class GenieBayesianNetwork implements IBayesianNetwork {

    /*
     * we keep one network as "prototype" and we use it for the first
     * inference object.
     */
    Network prototype;
    IConcept observable;
    boolean used = false;
    String input = null;

    public GenieBayesianNetwork(File in) throws KlabIOException {

        prototype = new Network();

        try {
            this.prototype.readFile(this.input = in.toString());
        } catch (Exception e) {
            throw new KlabIOException("GENIE import: reading " + in + ": " + e.getMessage());
        }
    }

    public GenieBayesianNetwork(Network network, IConcept observable, String input) {
        this.prototype = network;
        this.observable = observable;
        this.input = input;
    }

    @Override
    public IBayesianInference getInference() {
        // TODO Auto-generated method stub
        if (!used) {
            return new GenieBayesianInference(prototype);
        }

        Network net = new Network();

        /*
         * one has been succesfully read already, don't capture exceptions.
         */
        net.readFile(this.input);
        return new GenieBayesianInference(net);

    }

    @Override
    public int getNodeCount() {
        return prototype.getNodeCount();
    }

    @Override
    public String[] getAllNodeIds() {
        return prototype.getAllNodeIds();
    }

    @Override
    public int getOutcomeCount(String nodeId) {
        return prototype.getOutcomeCount(nodeId);
    }

    @Override
    public String getOutcomeId(String nodeId, int outcomeIndex) {
        return prototype.getOutcomeId(nodeId, outcomeIndex);
    }

    @Override
    public String[] getParentIds(String nodeId) {
        return prototype.getParentIds(nodeId);
    }

    @Override
    public String[] getChildIds(String nodeId) {
        return prototype.getChildIds(nodeId);
    }

    @Override
    public String[] getOutcomeIds(String nodeId) {
        return prototype.getOutcomeIds(nodeId);
    }

    @Override
    public String getName() {
        return prototype.getName();
    }

    @Override
    public IBayesianNetwork train(File observations, String method) throws KlabException {

        Network network = new Network();
        try {
            network.readFile(this.input);
        } catch (Exception e) {
            throw new KlabIOException("GENIE import: reading " + input + ": " + e.getMessage());
        }

        DataSet dset = new DataSet();
        dset.readFile(observations.toString(), "*");
        dset.matchNetwork(network);

        DataMatch[] dm = new DataMatch[dset.getVariableCount()];

        for (int i = 0; i < dset.getVariableCount(); i++) {
            String nodeId = dset.getVariableId(i);
            int node = network.getNode(nodeId);

            // TODO check this bizarre slice parameter
            dm[i] = new DataMatch(i, node, 0);
        }

        try {
            if (method.equals("EM")) {
                EM em = new EM();
                em.learn(dset, network, dm);
            } // TODO remaining methods
        } catch (SMILEException e) {
            throw new KlabValidationException(e);
        }

        return new GenieBayesianNetwork(network, observable, input);
    }

    @Override
    public void write(File modelFile) throws KlabIOException {
        try {
            this.prototype.writeFile(modelFile.toString());
        } catch (SMILEException e) {
            throw new KlabIOException(e);
        }
    }

    @Override
    public boolean isLeaf(String nodeId) {
        // TODO Auto-generated method stub
        String[] ids = getParentIds(nodeId);
        return ids == null || ids.length == 0;
    }

}