/*******************************************************************************
* Copyright 2013 Analog Devices, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
********************************************************************************/
package com.analog.lyric.dimple.solvers.gibbs.customFactors;
import static java.util.Objects.*;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import org.eclipse.jdt.annotation.Nullable;
import com.analog.lyric.dimple.factorfunctions.Gamma;
import com.analog.lyric.dimple.factorfunctions.core.FactorFunction;
import com.analog.lyric.dimple.model.core.EdgeState;
import com.analog.lyric.dimple.model.factors.Factor;
import com.analog.lyric.dimple.model.values.Value;
import com.analog.lyric.dimple.model.variables.Variable;
import com.analog.lyric.dimple.solvers.core.parameterizedMessages.GammaParameters;
import com.analog.lyric.dimple.solvers.gibbs.GibbsGammaEdge;
import com.analog.lyric.dimple.solvers.gibbs.GibbsReal;
import com.analog.lyric.dimple.solvers.gibbs.GibbsRealFactor;
import com.analog.lyric.dimple.solvers.gibbs.GibbsSolverEdge;
import com.analog.lyric.dimple.solvers.gibbs.GibbsSolverGraph;
import com.analog.lyric.dimple.solvers.gibbs.samplers.conjugate.GammaSampler;
import com.analog.lyric.dimple.solvers.gibbs.samplers.conjugate.IRealConjugateSamplerFactory;
public class CustomGamma extends GibbsRealFactor implements IRealConjugateFactor
{
private @Nullable GibbsReal[] _outputVariables;
private @Nullable GibbsReal _alphaVariable;
private @Nullable GibbsReal _betaVariable;
private boolean _hasConstantAlpha;
private boolean _hasConstantBeta;
private boolean _hasConstantOutputs;
private int _numParameterEdges;
private int _alphaParameterPort = -1;
private int _betaParameterPort = -1;
private int _constantOutputCount;
private double _constantAlphaMinusOneValue;
private double _constantBetaValue;
private double _constantOutputSum;
private static final int NUM_PARAMETERS = 2;
private static final int ALPHA_PARAMETER_INDEX = 0;
private static final int BETA_PARAMETER_INDEX = 1;
private static final int NO_PORT = -1;
public CustomGamma(Factor factor, GibbsSolverGraph parent)
{
super(factor, parent);
}
@Override
public @Nullable GibbsSolverEdge<?> createEdge(EdgeState edge)
{
if (edge.getFactorToVariableEdgeNumber() != _alphaParameterPort)
{
return new GibbsGammaEdge();
}
return null;
}
@SuppressWarnings("null")
@Override
public void updateEdgeMessage(EdgeState modelEdge, GibbsSolverEdge<?> solverEdge)
{
final int portNum = modelEdge.getFactorToVariableEdgeNumber();
if (portNum == _betaParameterPort)
{
// Port is the beta-parameter input
// Determine sample alpha and beta parameters
GammaParameters outputMsg = (GammaParameters)solverEdge.factorToVarMsg;
// Start with the ports to variable outputs
double sum = 0;
for (int i = 0; i < _outputVariables.length; i++)
sum += _outputVariables[i].getCurrentSample();
int count = _outputVariables.length;
// Include any constant outputs also
if (_hasConstantOutputs)
{
sum += _constantOutputSum;
count += _constantOutputCount;
}
// Get the current alpha value
double alpha = _hasConstantAlpha ? _constantAlphaMinusOneValue + 1 : _alphaVariable.getCurrentSample();
outputMsg.setAlphaMinusOne(count * alpha); // Sample alpha
outputMsg.setBeta(sum); // Sample beta
}
else if (portNum >= _numParameterEdges)
{
// Port is directed output
GammaParameters outputMsg = (GammaParameters)solverEdge.factorToVarMsg;
outputMsg.setAlphaMinusOne(_hasConstantAlpha ? _constantAlphaMinusOneValue : _alphaVariable.getCurrentSample() - 1);
outputMsg.setBeta(_hasConstantBeta ? _constantBetaValue : _betaVariable.getCurrentSample());
}
else
super.updateEdgeMessage(modelEdge, solverEdge);
}
@Override
public Set<IRealConjugateSamplerFactory> getAvailableRealConjugateSamplers(int portNumber)
{
Set<IRealConjugateSamplerFactory> availableSamplers = new HashSet<IRealConjugateSamplerFactory>();
if (!isPortAlphaParameter(portNumber)) // No supported conjugate sampler for alpha parameter
availableSamplers.add(GammaSampler.factory); // Either beta parameter or output, which have Gamma distribution
return availableSamplers;
}
public boolean isPortAlphaParameter(int portNumber)
{
determineConstantsAndEdges(); // Call this here since initialize may not have been called yet
return (portNumber == _alphaParameterPort);
}
@Override
public void initialize()
{
super.initialize();
// Determine what parameters are constants or edges, and save the state
determineConstantsAndEdges();
}
private void determineConstantsAndEdges()
{
// Get the factor function and related state
final Factor factor = _model;
FactorFunction factorFunction = factor.getFactorFunction();
Gamma specificFactorFunction = (Gamma)factorFunction;
boolean hasFactorFunctionConstants = factor.hasConstants();
boolean hasFactorFunctionConstructorConstants = specificFactorFunction.hasConstantParameters();
final int prevAlphaParameterPort = _alphaParameterPort;
// Pre-determine whether or not the parameters are constant; if so save the value; if not save reference to the variable
List<? extends Variable> siblings = factor.getSiblings();
_hasConstantAlpha = false;
_hasConstantBeta = false;
_alphaParameterPort = NO_PORT;
_betaParameterPort = NO_PORT;
_alphaVariable = null;
_betaVariable = null;
_constantAlphaMinusOneValue = 0;
_constantBetaValue = 0;
_numParameterEdges = 0;
if (hasFactorFunctionConstructorConstants)
{
// The factor function has fixed parameters provided in the factor-function constructor
_hasConstantAlpha = true;
_hasConstantBeta = true;
_constantAlphaMinusOneValue = specificFactorFunction.getAlphaMinusOne();
_constantBetaValue = specificFactorFunction.getBeta();
}
else // Variable or constant parameters
{
_hasConstantAlpha = factor.hasConstantAtIndex(ALPHA_PARAMETER_INDEX);
if (_hasConstantAlpha) // Constant mean
_constantAlphaMinusOneValue =
requireNonNull(factor.getConstantValueByIndex(ALPHA_PARAMETER_INDEX)).getDouble() - 1;
else // Variable mean
{
_alphaParameterPort = factor.argIndexToSiblingNumber(ALPHA_PARAMETER_INDEX);
_alphaVariable = (GibbsReal)getSibling(_alphaParameterPort);
_numParameterEdges++;
}
_hasConstantBeta = factor.hasConstantAtIndex(BETA_PARAMETER_INDEX);
if (_hasConstantBeta) // Constant precision
_constantBetaValue =
requireNonNull(factor.getConstantValueByIndex(BETA_PARAMETER_INDEX)).getDouble();
else // Variable precision
{
_betaParameterPort = factor.argIndexToSiblingNumber(BETA_PARAMETER_INDEX);
_betaVariable = (GibbsReal)getSibling(_betaParameterPort);
_numParameterEdges++;
}
}
// Pre-compute statistics associated with any constant output values
_hasConstantOutputs = false;
if (hasFactorFunctionConstants)
{
final List<Value> constantValues = factor.getConstantValues();
int[] constantIndices = factor.getConstantIndices();
_constantOutputCount = 0;
_constantOutputSum = 0;
for (int i = 0; i < constantIndices.length; i++)
{
if (hasFactorFunctionConstructorConstants || constantIndices[i] >= NUM_PARAMETERS)
{
_constantOutputSum += constantValues.get(i).getDouble();
_constantOutputCount++;
}
}
_hasConstantOutputs = true;
}
// Save output variables and add to the statistics any output variables that have fixed values
int numVariableOutputs = 0; // First, determine how many output variables are not fixed
final int nEdges = getSiblingCount();
for (int edge = _numParameterEdges; edge < nEdges; edge++)
if (!(siblings.get(edge).hasFixedValue()))
numVariableOutputs++;
final GibbsReal[] outputVariables = _outputVariables = new GibbsReal[numVariableOutputs];
for (int edge = _numParameterEdges, index = 0; edge < nEdges; edge++)
{
final GibbsReal outputVariable = (GibbsReal)getSibling(edge);
final double knownValue = outputVariable.getKnownReal();
if (knownValue == knownValue) // !NaN
{
_constantOutputSum += knownValue;
_constantOutputCount++;
_hasConstantOutputs = true;
}
else
outputVariables[index++] = outputVariable;
}
if (_alphaParameterPort != prevAlphaParameterPort)
{
removeSiblingEdgeState();
}
}
}