/*
* GeneralLikelihoodCore.java
*
* Copyright (c) 2002-2015 Alexei Drummond, Andrew Rambaut and Marc Suchard
*
* 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.oldevomodel.treelikelihood;
/**
* GeneralLikelihoodCore - An implementation of LikelihoodCore for any data
*
* @version $Id: GeneralLikelihoodCore.java,v 1.28 2006/08/31 14:57:24 rambaut Exp $
*
* @author Andrew Rambaut
*/
@Deprecated // Switching to BEAGLE
public class GeneralLikelihoodCore extends AbstractLikelihoodCore {
/**
* Constructor
*
* @param stateCount number of states
*/
public GeneralLikelihoodCore(int stateCount) {
super(stateCount);
}
/**
* Calculates partial likelihoods at a node when both children have states.
*/
protected void calculateStatesStatesPruning(int[] states1, double[] matrices1,
int[] states2, double[] matrices2,
double[] partials3)
{
int v = 0;
for (int l = 0; l < matrixCount; l++) {
for (int k = 0; k < patternCount; k++) {
int state1 = states1[k];
int state2 = states2[k];
int w = l * matrixSize;
if (state1 < stateCount && state2 < stateCount) {
for (int i = 0; i < stateCount; i++) {
partials3[v] = matrices1[w + state1] * matrices2[w + state2];
v++;
w += stateCount;
}
} else if (state1 < stateCount) {
// child 2 has a gap or unknown state so treat it as unknown
for (int i = 0; i < stateCount; i++) {
partials3[v] = matrices1[w + state1];
v++;
w += stateCount;
}
} else if (state2 < stateCount) {
// child 2 has a gap or unknown state so treat it as unknown
for (int i = 0; i < stateCount; i++) {
partials3[v] = matrices2[w + state2];
v++;
w += stateCount;
}
} else {
// both children have a gap or unknown state so set partials to 1
for (int j = 0; j < stateCount; j++) {
partials3[v] = 1.0;
v++;
}
}
}
}
}
/**
* Calculates partial likelihoods at a node when one child has states and one has partials.
*/
protected void calculateStatesPartialsPruning( int[] states1, double[] matrices1,
double[] partials2, double[] matrices2,
double[] partials3)
{
double sum, tmp;
int u = 0;
int v = 0;
for (int l = 0; l < matrixCount; l++) {
for (int k = 0; k < patternCount; k++) {
int state1 = states1[k];
int w = l * matrixSize;
if (state1 < stateCount) {
for (int i = 0; i < stateCount; i++) {
tmp = matrices1[w + state1];
sum = 0.0;
for (int j = 0; j < stateCount; j++) {
sum += matrices2[w] * partials2[v + j];
w++;
}
partials3[u] = tmp * sum;
u++;
}
v += stateCount;
} else {
// Child 1 has a gap or unknown state so don't use it
for (int i = 0; i < stateCount; i++) {
sum = 0.0;
for (int j = 0; j < stateCount; j++) {
sum += matrices2[w] * partials2[v + j];
w++;
}
partials3[u] = sum;
u++;
}
v += stateCount;
}
}
}
}
/**
* Calculates partial likelihoods at a node when both children have partials.
*/
protected void calculatePartialsPartialsPruning(double[] partials1, double[] matrices1,
double[] partials2, double[] matrices2,
double[] partials3)
{
double sum1, sum2;
int u = 0;
int v = 0;
for (int l = 0; l < matrixCount; l++) {
for (int k = 0; k < patternCount; k++) {
int w = l * matrixSize;
for (int i = 0; i < stateCount; i++) {
sum1 = sum2 = 0.0;
for (int j = 0; j < stateCount; j++) {
sum1 += matrices1[w] * partials1[v + j];
sum2 += matrices2[w] * partials2[v + j];
w++;
}
partials3[u] = sum1 * sum2;
u++;
}
v += stateCount;
}
}
}
/**
* Calculates partial likelihoods at a node when both children have states.
*/
protected void calculateStatesStatesPruning(int[] states1, double[] matrices1,
int[] states2, double[] matrices2,
double[] partials3, int[] matrixMap)
{
int v = 0;
for (int k = 0; k < patternCount; k++) {
int state1 = states1[k];
int state2 = states2[k];
int w = matrixMap[k] * matrixSize;
if (state1 < stateCount && state2 < stateCount) {
for (int i = 0; i < stateCount; i++) {
partials3[v] = matrices1[w + state1] * matrices2[w + state2];
v++;
w += stateCount;
}
} else if (state1 < stateCount) {
// child 2 has a gap or unknown state so treat it as unknown
for (int i = 0; i < stateCount; i++) {
partials3[v] = matrices1[w + state1];
v++;
w += stateCount;
}
} else if (state2 < stateCount) {
// child 2 has a gap or unknown state so treat it as unknown
for (int i = 0; i < stateCount; i++) {
partials3[v] = matrices2[w + state2];
v++;
w += stateCount;
}
} else {
// both children have a gap or unknown state so set partials to 1
for (int j = 0; j < stateCount; j++) {
partials3[v] = 1.0;
v++;
}
}
}
}
/**
* Calculates partial likelihoods at a node when one child has states and one has partials.
*/
protected void calculateStatesPartialsPruning( int[] states1, double[] matrices1,
double[] partials2, double[] matrices2,
double[] partials3, int[] matrixMap)
{
double sum, tmp;
int u = 0;
int v = 0;
for (int k = 0; k < patternCount; k++) {
int state1 = states1[k];
int w = matrixMap[k] * matrixSize;
if (state1 < stateCount) {
for (int i = 0; i < stateCount; i++) {
tmp = matrices1[w + state1];
sum = 0.0;
for (int j = 0; j < stateCount; j++) {
sum += matrices2[w] * partials2[v + j];
w++;
}
partials3[u] = tmp * sum;
u++;
}
v += stateCount;
} else {
// Child 1 has a gap or unknown state so don't use it
for (int i = 0; i < stateCount; i++) {
sum = 0.0;
for (int j = 0; j < stateCount; j++) {
sum += matrices2[w] * partials2[v + j];
w++;
}
partials3[u] = sum;
u++;
}
v += stateCount;
}
}
}
/**
* Calculates partial likelihoods at a node when both children have partials.
*/
protected void calculatePartialsPartialsPruning(double[] partials1, double[] matrices1,
double[] partials2, double[] matrices2,
double[] partials3, int[] matrixMap)
{
double sum1, sum2;
int u = 0;
int v = 0;
for (int k = 0; k < patternCount; k++) {
int w = matrixMap[k] * matrixSize;
for (int i = 0; i < stateCount; i++) {
sum1 = sum2 = 0.0;
for (int j = 0; j < stateCount; j++) {
sum1 += matrices1[w] * partials1[v + j];
sum2 += matrices2[w] * partials2[v + j];
w++;
}
partials3[u] = sum1 * sum2;
u++;
}
v += stateCount;
}
}
/**
* Integrates partials across categories.
* @param inPartials the array of partials to be integrated
* @param proportions the proportions of sites in each category
* @param outPartials an array into which the partials will go
*/
protected void calculateIntegratePartials(double[] inPartials, double[] proportions, double[] outPartials)
{
int u = 0;
int v = 0;
for (int k = 0; k < patternCount; k++) {
for (int i = 0; i < stateCount; i++) {
outPartials[u] = inPartials[v] * proportions[0];
u++;
v++;
}
}
for (int l = 1; l < matrixCount; l++) {
u = 0;
for (int k = 0; k < patternCount; k++) {
for (int i = 0; i < stateCount; i++) {
outPartials[u] += inPartials[v] * proportions[l];
u++;
v++;
}
}
}
}
/**
* Calculates patten log likelihoods at a node.
* @param partials the partials used to calculate the likelihoods
* @param frequencies an array of state frequencies
* @param outLogLikelihoods an array into which the likelihoods will go
*/
public void calculateLogLikelihoods(double[] partials, double[] frequencies, double[] outLogLikelihoods)
{
int v = 0;
for (int k = 0; k < patternCount; k++) {
double sum = 0.0;
for (int i = 0; i < stateCount; i++) {
sum += frequencies[i] * partials[v];
v++;
}
outLogLikelihoods[k] = Math.log(sum) + getLogScalingFactor(k);
}
}
}