/*******************************************************************************
* 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 java.util.HashSet;
import java.util.List;
import java.util.Set;
import org.eclipse.jdt.annotation.Nullable;
import com.analog.lyric.dimple.exceptions.DimpleException;
import com.analog.lyric.dimple.factorfunctions.CategoricalEnergyParameters;
import com.analog.lyric.dimple.factorfunctions.CategoricalUnnormalizedParameters;
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.GibbsDiscrete;
import com.analog.lyric.dimple.solvers.gibbs.GibbsGammaEdge;
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;
import com.analog.lyric.dimple.solvers.gibbs.samplers.conjugate.NegativeExpGammaSampler;
public class CustomCategoricalUnnormalizedOrEnergyParameters extends GibbsRealFactor implements IRealConjugateFactor
{
private @Nullable GibbsDiscrete[] _outputVariables;
private int _parameterDimension;
private int _numParameterEdges;
private @Nullable int[] _constantOutputCounts;
private boolean _hasConstantOutputs;
private boolean _useEnergyParameters;
public CustomCategoricalUnnormalizedOrEnergyParameters(Factor factor, GibbsSolverGraph parent)
{
super(factor, parent);
}
@Override
public @Nullable GibbsSolverEdge<?> createEdge(EdgeState edge)
{
if (edge.getFactorToVariableEdgeNumber() < _numParameterEdges)
{
return new GibbsGammaEdge();
}
return super.createEdge(edge);
}
@SuppressWarnings("null")
@Override
public void updateEdgeMessage(EdgeState modelEdge, GibbsSolverEdge<?> solverEdge)
{
final int portNum = modelEdge.getFactorToVariableEdgeNumber();
if (portNum < _numParameterEdges)
{
// Port is a parameter input
// Determine sample alpha and beta parameters
// NOTE: This case works for either CategoricalUnnormalizedParameters or CategoricalEnergyParameters factor functions
// since the actual parameter value doesn't come into play in determining the message in this direction
GammaParameters outputMsg = (GammaParameters)solverEdge.factorToVarMsg;
// The parameter being updated corresponds to this value
int parameterIndex = _model.siblingNumberToArgIndex(portNum);
// Start with the ports to variable outputs
int count = 0;
for (int i = 0; i < _outputVariables.length; i++)
{
int outputIndex = _outputVariables[i].getCurrentSampleIndex();
if (outputIndex == parameterIndex)
count++;
}
// Include any constant outputs also
if (_hasConstantOutputs)
count += _constantOutputCounts[parameterIndex];
outputMsg.setAlphaMinusOne(count); // Sample alpha
outputMsg.setBeta(0); // Sample beta
}
else
super.updateEdgeMessage(modelEdge, solverEdge);
}
@Override
public Set<IRealConjugateSamplerFactory> getAvailableRealConjugateSamplers(int portNumber)
{
Set<IRealConjugateSamplerFactory> availableSamplers = new HashSet<IRealConjugateSamplerFactory>();
if (isPortParameter(portNumber)) // Conjugate sampler if edge is a parameter input
if (_useEnergyParameters)
availableSamplers.add(NegativeExpGammaSampler.factory); // Parameter inputs have conjugate negative exp-Gamma distribution
else
availableSamplers.add(GammaSampler.factory); // Parameter inputs have conjugate Gamma distribution
return availableSamplers;
}
public boolean isPortParameter(int portNumber)
{
determineConstantsAndEdges(); // Call this here since initialize may not have been called yet
return (portNumber < _numParameterEdges);
}
@Override
public void initialize()
{
super.initialize();
// Determine what parameters are constants or edges, and save the state
determineConstantsAndEdges();
}
private void determineConstantsAndEdges()
{
final int prevNumParameterEdges = _numParameterEdges;
// Get the factor function and related state
final Factor factor = _model;
FactorFunction factorFunction = factor.getFactorFunction();
FactorFunction containedFactorFunction = factorFunction;
boolean hasFactorFunctionConstants = factor.hasConstants();
boolean hasFactorFunctionConstructorConstants;
if (containedFactorFunction instanceof CategoricalUnnormalizedParameters)
{
CategoricalUnnormalizedParameters specificFactorFunction = (CategoricalUnnormalizedParameters)containedFactorFunction;
hasFactorFunctionConstructorConstants = specificFactorFunction.hasConstantParameters();
_parameterDimension = specificFactorFunction.getDimension();
_useEnergyParameters = false;
}
else if (containedFactorFunction instanceof CategoricalEnergyParameters)
{
CategoricalEnergyParameters specificFactorFunction = (CategoricalEnergyParameters)containedFactorFunction;
hasFactorFunctionConstructorConstants = specificFactorFunction.hasConstantParameters();
_parameterDimension = specificFactorFunction.getDimension();
_useEnergyParameters = true;
}
else
throw new DimpleException("Invalid factor function");
// 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();
_numParameterEdges = _parameterDimension;
_hasConstantOutputs = false;
if (hasFactorFunctionConstructorConstants)
{
// The factor function has fixed parameters provided in the factor-function constructor
_numParameterEdges = 0;
_hasConstantOutputs = hasFactorFunctionConstants;
}
else if (hasFactorFunctionConstants)
{
_hasConstantOutputs = factor.hasConstantAtOrAboveIndex(_parameterDimension);
int numConstantParameters = factor.numConstantsInIndexRange(0, _parameterDimension - 1);
_numParameterEdges = _parameterDimension - numConstantParameters;
}
// Pre-compute statistics associated with any constant output values
_constantOutputCounts = null;
if (_hasConstantOutputs)
{
final List<Value> constantValues = factor.getConstantValues();
int[] constantIndices = factor.getConstantIndices();
final int[] constantOutputCounts = _constantOutputCounts = new int[_parameterDimension];
for (int i = 0; i < constantIndices.length; i++)
{
if (hasFactorFunctionConstructorConstants || constantIndices[i] >= _parameterDimension)
{
int outputValue = constantValues.get(i).getInt();
constantOutputCounts[outputValue]++; // Histogram among constant outputs
}
}
}
// 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 GibbsDiscrete[] outputVariables = _outputVariables = new GibbsDiscrete[numVariableOutputs];
for (int edge = _numParameterEdges, index = 0; edge < nEdges; edge++)
{
final GibbsDiscrete outputVariable = (GibbsDiscrete)getSibling(edge);
final int outputValue = outputVariable.getKnownDiscreteIndex();
if (outputValue >= 0)
{
int[] constantOutputCounts = _constantOutputCounts;
if (constantOutputCounts == null)
constantOutputCounts = _constantOutputCounts = new int[_parameterDimension];
constantOutputCounts[outputValue]++; // Histogram among constant outputs
_hasConstantOutputs = true;
}
else
outputVariables[index++] = outputVariable;
}
if (_numParameterEdges != prevNumParameterEdges)
{
removeSiblingEdgeState();
}
}
}