/*******************************************************************************
* Copyright 2015 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.sampledfactor;
import java.util.List;
import com.analog.lyric.dimple.factorfunctions.MultivariateNormal;
import com.analog.lyric.dimple.model.variables.RealJoint;
import com.analog.lyric.dimple.solvers.core.parameterizedMessages.MultivariateNormalParameters;
import com.analog.lyric.dimple.solvers.gibbs.GibbsOptions;
import com.analog.lyric.dimple.solvers.gibbs.GibbsRealJoint;
import com.analog.lyric.dimple.solvers.sumproduct.SumProductMultivariateNormalEdge;
import com.analog.lyric.util.misc.Internal;
/**
*
* @since 0.08
* @author Christopher Barber
* @category internal
*/
@Internal
class SumProductSampledMultivariateNormalEdge extends SumProductMultivariateNormalEdge
implements ISumProductSampledEdge<MultivariateNormalParameters>
{
private final GibbsRealJoint _svar;
SumProductSampledMultivariateNormalEdge(GibbsRealJoint svar)
{
super(svar.getModelObject());
_svar = svar;
setVariableInputUniform();
}
@Override
public void setFactorToVarDirection()
{
_svar.setOption(GibbsOptions.saveAllSamples, true);
setVariableInputUniform();
}
@Override
public void setVarToFactorDirection()
{
_svar.setOption(GibbsOptions.saveAllSamples, false);
RealJoint var = _svar.getModelObject();
if (!var.hasFixedValue()) // Only set the input if there isn't already a fixed value
{
MultivariateNormalParameters inputMessage = varToFactorMsg;
if (inputMessage.isNull())
{
var.setPrior(null); // If zero precision, then set the input to null to avoid numerical issues
}
else
{
var.setPrior(new MultivariateNormal(inputMessage));
}
}
}
@Override
public void setFactorToVarMsgFromSamples()
{
// Get the raw sample array to avoid making a copy; this is unsafe, so be careful not to modify it
@SuppressWarnings("null")
List<double[]> sampleValues = _svar._getSampleArrayUnsafe();
@SuppressWarnings("null")
int numSamples = sampleValues.size();
int dimension = sampleValues.get(0).length;
// For all sample values, compute the mean
double[] mean = new double[dimension];
for (int sample = 0; sample < numSamples; sample++)
{
double[] tmp = sampleValues.get(sample);
for (int i = 0; i < dimension; i++)
mean[i] += tmp[i];
}
for (int i = 0; i < dimension; i++)
mean[i] /= numSamples;
// For all sample values, compute the covariance matrix
double[] diff = new double[dimension];
double[][] covariance = new double[dimension][dimension];
for (int sample = 0; sample < numSamples; sample++)
{
double[] tmp = sampleValues.get(sample);
for (int i = 0; i < dimension; i++)
diff[i] = tmp[i] - mean[i];
for (int row = 0; row < dimension; row++)
{
double[] covarianceRow = covariance[row];
for (int col = row; col < dimension; col++) // Compute only the upper triangular half for now
covarianceRow[col] += diff[row] * diff[col];
}
}
double numSamplesMinusOne = numSamples - 1;
for (int row = 0; row < dimension; row++)
{
for (int col = row; col < dimension; col++)
{
double value = covariance[row][col] / numSamplesMinusOne;
covariance[row][col] = value;
covariance[col][row] = value; // Fill in lower triangular half
}
}
factorToVarMsg.setMeanAndCovariance(mean, covariance);
}
private final void setVariableInputUniform()
{
RealJoint var = _svar.getModelObject();
if (!var.hasFixedValue()) // Only set the input if there isn't already a fixed value
{
var.setPrior(null);
}
}
}