/*******************************************************************************
* Copyright 2012 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.sumproduct.customFactors;
import static java.util.Objects.*;
import com.analog.lyric.collect.ArrayUtil;
import com.analog.lyric.dimple.factorfunctions.LinearEquation;
import com.analog.lyric.dimple.factorfunctions.core.FactorFunction;
import com.analog.lyric.dimple.model.factors.Factor;
import com.analog.lyric.dimple.model.variables.VariablePredicates;
import com.analog.lyric.dimple.solvers.core.parameterizedMessages.NormalParameters;
import com.analog.lyric.dimple.solvers.sumproduct.SumProductSolverGraph;
import com.google.common.collect.Iterables;
public class CustomGaussianLinearEquation extends GaussianFactorBase
{
private double[] _weightVector = ArrayUtil.EMPTY_DOUBLE_ARRAY;
private double _initialWeightedSum;
public CustomGaussianLinearEquation(Factor factor, SumProductSolverGraph parent)
{
super(factor, parent);
assertUnboundedReal(factor);
}
@Override
public void doUpdateEdge(int outPortNum)
{
double mean;
double variance;
if (_weightVector[outPortNum] == 0)
{
mean = 0;
variance = Double.POSITIVE_INFINITY;
}
else
{
mean = _initialWeightedSum;
variance = 0;
for (int i = 0, n = getSiblingCount(); i < n; i++)
{
if (i != outPortNum)
{
double constantsi = _weightVector[i];
NormalParameters msg = getSiblingEdgeState(i).varToFactorMsg;
mean -= msg.getMean() * constantsi;
variance += msg.getVariance() * constantsi * constantsi;
}
}
double constantsout = _weightVector[outPortNum];
mean /= constantsout;
variance /= (constantsout*constantsout);
}
NormalParameters msg = getSiblingEdgeState(outPortNum).factorToVarMsg;
msg.setMean(mean);
msg.setVariance(variance);
}
@Override
public void initialize()
{
super.initialize();
// Pre-compute for any constant edges
final Factor factor = _model;
FactorFunction factorFunction = factor.getFactorFunction();
LinearEquation specificFactorFunction = (LinearEquation)factorFunction;
double[] specifiedWeightVector = specificFactorFunction.getWeightArray();
double[] extendedWeightVector = new double[specifiedWeightVector.length + 1];
System.arraycopy(specifiedWeightVector, 0, extendedWeightVector, 1, specifiedWeightVector.length);
extendedWeightVector[0] = -1; // Treat output as another variable with constant -1, so append to beginning of weight vector
int extendedWeigthVectorLength = extendedWeightVector.length;
// Account for constant and variable inputs; pre-compute a weighted sum for all constant inputs
_initialWeightedSum = 0;
_weightVector = new double[extendedWeigthVectorLength - factor.getConstantCount()];
for (int index = 0, edge = 0; index < extendedWeigthVectorLength; index++)
{
if (factor.hasConstantAtIndex(index))
{
// Constant in this position, so subtract off the initial weighted sum (move to the other side of the equation)
_initialWeightedSum -=
extendedWeightVector[index] *
requireNonNull(factor.getConstantValueByIndex(index)).getDouble();
}
else
{
// Variable in this position, so include in the weight vector
_weightVector[edge++] = extendedWeightVector[index];
}
}
}
/**
* Utility to indicate whether or not a factor is compatible with the requirements of this custom factor
* @deprecated as of release 0.08
*/
@Deprecated
public static boolean isFactorCompatible(Factor factor)
{
return Iterables.all(factor.getSiblings(), VariablePredicates.isUnboundedReal());
}
}