add gmrf2 class,parser

src/dr/app/beast/development_parsers.properties

@@ -76,6 +76,7 @@ dr.evomodelxml.branchratemodel.TimeVaryingBranchRateModelParser
 # GMRF
 dr.evomodel.coalescent.GMRFDensityComponent
 dr.inferencexml.distribution.GaussianMarkovRandomFieldParser
+dr.inferencexml.distribution.GaussianMarkovRandomFieldParser2
 
 # GAUSSIAN PROCESS
 dr.evomodelxml.coalescent.GPSkytrackAnalysisParser

src/dr/inference/distribution/GaussianMarkovRandomFieldModel2.java

@@ -0,0 +1,167 @@
+/*
+ * MultivariateNormalDistributionModel.java
+ *
+ * Copyright (c) 2002-2020 Alexei Drummond, Andrew Rambaut and Marc Suchard
+ *
+ * This file is part of BEAST.
+ * See the NOTICE file distributed with this work for additional
+ * information regarding copyright ownership and licensing.
+ *
+ * BEAST is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License as
+ * published by the Free Software Foundation; either version 2
+ * of the License, or (at your option) any later version.
+ *
+ *  BEAST is distributed in the hope that it will be useful,
+ *  but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ *  GNU Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with BEAST; if not, write to the
+ * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
+ * Boston, MA  02110-1301  USA
+ */
+
+package dr.inference.distribution;
+
+import dr.inference.hmc.GradientWrtParameterProvider;
+import dr.inference.hmc.HessianWrtParameterProvider;
+import dr.inference.model.*;
+import dr.inferencexml.distribution.MultivariateNormalDistributionModelParser;
+import dr.math.distributions.GaussianMarkovRandomField2;
+import dr.math.distributions.GaussianProcessRandomGenerator;
+
+
+/**
+ * A class that acts as a model for gaussian random walk
+ *
+ * @author Marc Suchard
+ * Pratyusa Datta
+ */
+
+public class GaussianMarkovRandomFieldModel2 extends AbstractModelLikelihood implements
+        GradientWrtParameterProvider, HessianWrtParameterProvider {
+
+    public GaussianMarkovRandomFieldModel2(Parameter coefficients,
+                                          GaussianMarkovRandomField2 distribution) {
+        super(MultivariateNormalDistributionModelParser.NORMAL_DISTRIBUTION_MODEL);
+
+        this.coefficients = coefficients;
+        this.distribution = distribution;
+        this.dim = coefficients.getDimension();
+
+        addModel(distribution);
+        addVariable(coefficients);
+    }
+
+
+    public Parameter getincrementPrecision() { return distribution.getincrementPrecision(); }
+
+    public Parameter getstart() { return distribution.getstart(); }
+    public double[][] getScaleMatrix() {
+        return distribution.getScaleMatrix();
+    }
+
+
+    public double[] getMean() {
+        return distribution.getMean();
+    }
+
+    public String getType() {
+        return distribution.getType();
+    }
+
+    // *****************************************************************
+    // Interface Model
+    // *****************************************************************
+    @Override
+    public Likelihood getLikelihood() {
+        return this;
+    }
+
+    @Override
+    public Model getModel() {
+        return this;
+    }
+
+    @Override
+    public Parameter getParameter() {
+        return coefficients;
+    }
+
+    @Override
+    public final void makeDirty() {
+        // Do nothing
+    }
+
+    @Override
+    public final void handleModelChangedEvent(Model model, Object object, int index) {
+        // no intermediates need to be recalculated...
+    }
+
+    @Override
+    public final void handleVariableChangedEvent(Variable variable, int index, Parameter.ChangeType type) {
+        // no intermediates need to be recalculated...
+    }
+
+    @Override
+    public void storeState() {
+        // Do nothing
+    }
+
+    @Override
+    public void restoreState() {
+        // Do nothing
+    }
+
+    @Override
+    public void acceptState() {
+    } // no additional state needs accepting
+
+
+    @Override
+    public int getDimension() {
+        return dim;
+    }
+
+
+    // *****************************************************************
+    // Interface DensityModel
+    // *****************************************************************
+
+    public Parameter getIncrementPrecision() { return distribution.getincrementPrecision(); }
+
+    public Parameter getStart() { return distribution.getstart(); }
+
+    public double getLogLikelihood() {
+        return distribution.logPdf(coefficients.getParameterValues());
+    }
+
+    public double[] getGradientLogDensity() {
+        return distribution.gradLogPdf(coefficients.getParameterValues());
+    }
+
+
+    // **************************************************************
+    // Private instance variables and functions
+    // **************************************************************
+
+
+
+
+    private final Parameter coefficients;
+    private final GaussianMarkovRandomField2 distribution;
+    private final int dim;
+
+
+    @Override
+    public double[] getDiagonalHessianLogDensity() {
+        return new double[0];
+    }
+
+    @Override
+    public double[][] getHessianLogDensity() {
+        return new double[0][];
+    }
+}

src/dr/inferencexml/distribution/GaussianMarkovRandomFieldParser2.java

@@ -0,0 +1,89 @@
+/*
+ * MultivariateNormalDistributionModelParser.java
+ *
+ * Copyright (c) 2002-2015 Alexei Drummond, Andrew Rambaut and Marc Suchard
+ *
+ * This file is part of BEAST.
+ * See the NOTICE file distributed with this work for additional
+ * information regarding copyright ownership and licensing.
+ *
+ * BEAST is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License as
+ * published by the Free Software Foundation; either version 2
+ * of the License, or (at your option) any later version.
+ *
+ *  BEAST is distributed in the hope that it will be useful,
+ *  but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ *  GNU Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with BEAST; if not, write to the
+ * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
+ * Boston, MA  02110-1301  USA
+ */
+
+package dr.inferencexml.distribution;
+
+import dr.inference.distribution.GaussianMarkovRandomFieldModel2;
+import dr.inference.model.Model;
+import dr.inference.model.Parameter;
+import dr.inference.model.Variable;
+import dr.math.distributions.GaussianMarkovRandomField2;
+import dr.xml.*;
+
+public class GaussianMarkovRandomFieldParser2 extends AbstractXMLObjectParser {
+
+    public static final String NORMAL_DISTRIBUTION_MODEL = "gaussianMarkovRandomField2";
+    private static final String DIMENSION = "dim";
+    private static final String PRECISION = "precision";
+    private static final String START = "start";
+
+    public String getParserName() {
+
+        return NORMAL_DISTRIBUTION_MODEL;
+    }
+
+    public Object parseXMLObject(XMLObject xo) throws XMLParseException {
+
+        Parameter coefficients = (Parameter) xo.getChild(Parameter.class);
+
+        int dim = coefficients.getDimension();
+
+        XMLObject cxo = xo.getChild(PRECISION);
+        Parameter incrementPrecision = (Parameter) cxo.getChild(Parameter.class);
+
+        if (incrementPrecision.getParameterValue(0) <= 0.0) {
+            throw new XMLParseException("Scale must be > 0.0");
+        }
+
+        cxo = xo.getChild(START);
+        Parameter start = (Parameter) cxo.getChild(Parameter.class);
+
+
+
+        return new GaussianMarkovRandomFieldModel2(coefficients, new GaussianMarkovRandomField2(dim, incrementPrecision, start));
+    }
+
+    public XMLSyntaxRule[] getSyntaxRules() {
+        return rules;
+    }
+
+    private final XMLSyntaxRule[] rules = {
+//            AttributeRule.newIntegerRule(DIMENSION),
+            new ElementRule(PRECISION,
+                    new XMLSyntaxRule[]{new ElementRule(Parameter.class)}),
+            new ElementRule(START,
+                    new XMLSyntaxRule[]{new ElementRule(Parameter.class)}),
+    };
+
+    public String getParserDescription() {
+        return "Describes a normal distribution with a given mean and precision " +
+                "that can be used in a distributionLikelihood element";
+    }
+
+    public Class getReturnType() {
+        return GaussianMarkovRandomFieldModel2.class;
+    }
+
+}