/*
 * BetaSplittingModel.java
 *
 * Copyright (C) 2002-2006 Alexei Drummond and Andrew Rambaut
 *
 * This file is part of BEAST.
 * See the NOTICE file distributed with this work for additional
 * information regarding copyright ownership and licensing.
 *
 * BEAST 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 2
 * of the License, or (at your option) any later version.
 *
 *  BEAST 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 BEAST; if not, write to the
 * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
 * Boston, MA  02110-1301  USA
 */

package dr.evomodel.speciation;

import dr.evolution.tree.NodeRef;
import dr.evolution.tree.Tree;
import dr.evomodelxml.speciation.BetaSplittingModelParser;
import dr.inference.model.Parameter;
import dr.inference.model.Variable;
import dr.math.GammaFunction;

/**
 * This class contains methods that describe a Beta-splitting branching model (Aldous 1996, 2001).
 *
 * @author Alexei Drummond
 */
public class BetaSplittingModel extends BranchingModel {

    public BetaSplittingModel(Parameter phiParameter, Tree tree) {

        super(BetaSplittingModelParser.BETA_SPLITTING_MODEL);

        this.phiParameter = phiParameter;
        addVariable(phiParameter);
        phiParameter.addBounds(new Parameter.DefaultBounds(Double.POSITIVE_INFINITY, Double.NEGATIVE_INFINITY, 1));
        N = tree.getExternalNodeCount();
        logProbs = new double[N + 1][N + 1];
        storedLogProbs = new double[N + 1][N + 1];
        makeSplitProbs(logProbs);
    }

    /**
     * Returns the phi parameter, which can range from -infinity to +infinity.
     *
     * @return phi phi = log(beta/2.0 + 1.0)
     */
    public double getPhi() {
        return phiParameter.getParameterValue(0);
    }

    /**
     * Sets the phi parameter, which can range from -infinity to +infinity.
     *
     * @param phi = log(beta/2.0 + 1.0)
     */
    public void setPhi(double phi) {

        phiParameter.setParameterValue(0, phi);
    }

    /**
     * Returns the beta parameter, which can range from -2 to positive infinity.
     *
     * @return beta
     */
    public double getBeta() {
        return (Math.exp(getPhi()) - 1) * 2.0;
    }

    /**
     * Sets the beta parameter, which can range from -2 to positive infinity
     *
     * @param beta
     */
    public void setBeta(double beta) {
        if (beta < -2.0) throw new IllegalArgumentException();
        setPhi(Math.log(beta / 2.0 + 1));
    }

    /**
     * Return the probability of this node producing subtrees of the given sizes.
     *
     * @param tree the tree of the node for which the probability will be calculated
     * @param node the node for which the probability will be calculated
     * @return the log of the probability of the split below the given node
     */
    public double logNodeProbability(Tree tree, NodeRef node) {

        if (tree.isExternal(node)) return 0.0;

        int leftChildren = Tree.Utils.getLeafCount(tree, tree.getChild(node, 0));
        int rightChildren = Tree.Utils.getLeafCount(tree, tree.getChild(node, 1));

        // calculate the probability of this pair..

        return logProbs[leftChildren + rightChildren][leftChildren] +
                logProbs[leftChildren + rightChildren][rightChildren];
    }

    // **************************************************************
    // XMLElement IMPLEMENTATION
    // **************************************************************

    protected final void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
        //System.out.println("parameter changed:" + parameter.getParameterName());
        makeSplitProbs(logProbs);
    }

    protected void storeState() {
        //copy the current logProbs into the storedLogProbs
        for (int i = 0; i < logProbs.length; i++) {
            System.arraycopy(logProbs[i], 0, storedLogProbs[i], 0, logProbs[i].length);
        }
    }

    /**
     * Restore the stored state
     */
    protected void restoreState() {

        // swap the logProbs arrays
        double[][] tmp = logProbs;
        logProbs = storedLogProbs;
        storedLogProbs = tmp;
    }

    private void makeSplitProbs(double[][] logProbs) {

        // code modified from original code by Marc Suchard

        double beta = getBeta();
        //double[][] prob = new double[bigN+1][bigN+1];
        logProbs[2][1] = 0;
        logProbs[3][1] = logProbs[3][2] = Math.log(0.5);
        //if( beta < 90 ) {
        double[] logGammaBeta = new double[N];
        double[] logGammaNone = new double[N];
        for (int i = 1; i < N; i++) {
            logGammaBeta[i] = GammaFunction.lnGamma(beta + i + 1.0); //Sfun.gamma(beta + i + 1.0);
            logGammaNone[i] = GammaFunction.lnGamma(i + 1.0); //Sfun.gamma(i + 1.0);
        }
        for (int n = 4; n <= N; n++) {
            double end = (n / 2.0) + 0.5;
            for (int i = 1; i <= end; i++)
                logProbs[n][i] = logProbs[n][n - i] =
                        logGammaBeta[i] + logGammaBeta[n - i] - logGammaNone[i] - logGammaNone[n - i];

            // Normalize
            double sum = 0;
            for (int i = 1; i < n; i++) {
                sum += Math.exp(logProbs[n][i]);
            }
            double logSum = Math.log(sum);
            // divide the probabilities through by the sum
            for (int i = 1; i < n; i++) {
                logProbs[n][i] -= logSum;
                //System.out.println("logProbs[" + n + "][" + i + "]=" + logProbs[n][i] );
            }
        }
        /*} else { // calculate on log scale for numerical stability
              //System.err.println("Log scale calculations");
              double[] logGammaBeta = new double[bigN];
              double[] logGammaNone = new double[bigN];
              for(int i=1; i<bigN; i++) {
                  logGammaBeta[i] = Sfun.logGamma(beta + i + 1.0);
                  logGammaNone[i] = Sfun.logGamma(i + 1.0);
                  //System.err.print(beta+" "+logGammaBeta[i] + " "+logGammaNone[i]);
                  //System.err.println(" "+gammln(beta + i + 1.0));
              }
              double c = logGammaBeta[1];
              logGammaBeta[1] = 0;
              for(int i=2; i<bigN; i++)
                  logGammaBeta[i] -= c;
              for(int n=4; n<=bigN; n++) {
                  double end = (n / 2.0) + 0.5;
                  for(int i=1; i<=end; i++) {
                      prob[n][i] = prob[n][n-i] = Math.exp(logGammaBeta[i] + logGammaBeta[n-i] - logGammaNone[i] - logGammaNone[n-i]);
                      //System.err.println(prob[n][i]);
                  }
                  double sum = 0;
                  for(int i=1; i<n; i++)
                      sum += prob[n][i];
                  for(int i=1; i<n; i++)
                      prob[n][i] /= sum;
              }
          }      */
        //return prob;
    }

    //Protected stuff
    final Parameter phiParameter;

    double[][] logProbs;
    double[][] storedLogProbs;

    final int N;
}
