/*******************************************************************************
* 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.Bernoulli;
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.BetaParameters;
import com.analog.lyric.dimple.solvers.gibbs.GibbsBetaEdge;
import com.analog.lyric.dimple.solvers.gibbs.GibbsDiscrete;
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.BetaSampler;
import com.analog.lyric.dimple.solvers.gibbs.samplers.conjugate.IRealConjugateSamplerFactory;
public class CustomBernoulli extends GibbsRealFactor implements IRealConjugateFactor
{
private @Nullable GibbsDiscrete[] _outputVariables;
private int _numParameterEdges;
private int _constantOutputZeroCount;
private int _constantOutputOneCount;
private boolean _hasConstantOutputs;
private static final int NUM_PARAMETERS = 1;
private static final int PARAMETER_INDEX = 0;
public CustomBernoulli(Factor factor, GibbsSolverGraph parent)
{
super(factor, parent);
}
@Override
public @Nullable GibbsSolverEdge<?> createEdge(EdgeState edge)
{
if (edge.getFactorToVariableEdgeNumber() < _numParameterEdges)
{
return new GibbsBetaEdge();
}
return super.createEdge(edge);
}
@Override
public void updateEdgeMessage(EdgeState modelEdge, GibbsSolverEdge<?> solverEdge)
{
final int portNum = modelEdge.getFactorToVariableEdgeNumber();
if (portNum < _numParameterEdges)
{
// Port is the parameter input
// Determine sample alpha and beta parameters
@SuppressWarnings("null")
BetaParameters outputMsg = (BetaParameters)solverEdge.factorToVarMsg;
final GibbsDiscrete[] outputVariables = requireNonNull(_outputVariables);
// Start with the ports to variable outputs
int numZeros = 0;
for (int i = 0; i < outputVariables.length; i++)
{
int outputIndex = outputVariables[i].getCurrentSampleIndex();
if (outputIndex == 0)
numZeros++;
}
int numOnes = outputVariables.length - numZeros;
// Include any constant outputs also
if (_hasConstantOutputs)
{
numZeros += _constantOutputZeroCount;
numOnes += _constantOutputOneCount;
}
outputMsg.setAlphaMinusOne(numOnes);
outputMsg.setBetaMinusOne(numZeros);
}
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 parameter input
availableSamplers.add(BetaSampler.factory); // Parameter inputs have conjugate Beta 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()
{
// Get the factor function and related state
final Factor factor = _model;
FactorFunction factorFunction = factor.getFactorFunction();
Bernoulli specificFactorFunction = (Bernoulli)factorFunction;
boolean hasFactorFunctionConstants = factor.hasConstants();
boolean hasFactorFunctionConstructorConstants = specificFactorFunction.hasConstantParameters();
final int prevNumParameterEdges = _numParameterEdges;
// 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 = NUM_PARAMETERS;
_hasConstantOutputs = false;
if (hasFactorFunctionConstructorConstants)
{
// The factor function has fixed parameter provided in the factor-function constructor
_numParameterEdges = 0;
_hasConstantOutputs = hasFactorFunctionConstants;
}
else if (hasFactorFunctionConstants)
{
// Factor function has constants, figure out which are parameters and which are discrete variables
_numParameterEdges = factor.hasConstantAtIndex(PARAMETER_INDEX) ? 0 : 1;
_hasConstantOutputs = factor.hasConstantAtOrAboveIndex(PARAMETER_INDEX + 1);
}
// Pre-compute statistics associated with any constant output values
_constantOutputZeroCount = 0;
_constantOutputOneCount = 0;
if (_hasConstantOutputs)
{
final List<Value> constantValues = factor.getConstantValues();
int[] constantIndices = factor.getConstantIndices();
for (int i = 0; i < constantIndices.length; i++)
{
if (hasFactorFunctionConstructorConstants || constantIndices[i] >= NUM_PARAMETERS)
{
int outputValue = constantValues.get(i).getInt();
if (outputValue == 0)
_constantOutputZeroCount++;
else
_constantOutputOneCount++;
}
}
}
// 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)
{
if (outputValue == 0)
_constantOutputZeroCount++;
else
_constantOutputOneCount++;
_hasConstantOutputs = true;
}
else
outputVariables[index++] = outputVariable;
}
if (_numParameterEdges != prevNumParameterEdges)
{
removeSiblingEdgeState();
}
}
}