edu.umd.cs.psl.application.learning.weight.em.LatentObjectiveComputer.java Source code

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/*
 * This file is part of the PSL software.
 * Copyright 2011-2015 University of Maryland
 * Copyright 2013-2015 The Regents of the University of California
 *
 * 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 edu.umd.cs.psl.application.learning.weight.em;

import com.google.common.collect.Iterables;

import edu.umd.cs.psl.config.ConfigBundle;
import edu.umd.cs.psl.database.Database;
import edu.umd.cs.psl.model.Model;
import edu.umd.cs.psl.model.kernel.CompatibilityKernel;

public class LatentObjectiveComputer extends HardEM {

    public LatentObjectiveComputer(Model model, Database rvDB, Database observedDB, ConfigBundle config)
            throws ClassNotFoundException, IllegalAccessException, InstantiationException {
        super(model, rvDB, observedDB, config);

        /* Gathers the CompatibilityKernels */
        for (CompatibilityKernel k : Iterables.filter(model.getKernels(), CompatibilityKernel.class))
            if (k.isWeightMutable())
                kernels.add(k);
            else
                immutableKernels.add(k);

        /* Sets up the ground model */
        initGroundModel();
    }

    /**
     * Computes primal objective
     * @return
     */
    public double getObjective() {
        reasoner.changedGroundKernelWeights();
        minimizeKLDivergence();
        computeObservedIncomp();
        computeExpectedIncomp();
        return computeRegularizer() + computeLoss();
    }

}