content: /*
   *
   *
   *    Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements.
   *    See the NOTICE file distributed with this work for additional information regarding copyright ownership.
   *    The ASF licenses this file to You 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 eu.amidst.core.exponentialfamily;
  
  import eu.amidst.core.distribution.UnivariateDistribution;
  import eu.amidst.core.utils.ArrayVector;
  import eu.amidst.core.utils.Vector;
  import eu.amidst.core.variables.Assignment;
  import eu.amidst.core.variables.Variable;
  import org.apache.commons.math3.special.Gamma;
  
  import java.util.ArrayList;
  import java.util.Random;
  
  /**
   *
   * This class extends the abstract class {@link EF_UnivariateDistribution} and defines a Gamma distribution in exponential family canonical form.
   *
   * <p> For further details about how exponential family models are considered in this toolbox take a look at the following paper:
   * <i>Representation, Inference and Learning of Bayesian Networks as Conjugate Exponential Family Models. Technical Report.</i>
   * (<a href="http://amidst.github.io/toolbox/docs/ce-BNs.pdf">pdf</a>) </p>
   */
  public class EF_Gamma extends EF_UnivariateDistribution {
  
      public static final int LOGX = 0;
      public static final int INVX = 1;
  
      public static double PRECISION_LIMIT = 10;
  
      /**
       * Creates a new EF_Gamma distribution, i.e., Gamma(1,1), for a given {@link Variable} object.
       * @param var1 a {@link Variable} object with a Gamma distribution type.
       */
      public EF_Gamma(Variable var1) {
  
          if (!var1.isGammaParameter())
              throw new IllegalArgumentException("The variable is not a Gamma parameter!");
  
          this.parents = new ArrayList();
  
          this.var = var1;
          this.naturalParameters = this.createZeroNaturalParameters();
          this.momentParameters = this.createZeroMomentParameters();
          double alpha = 1;
          double beta = 1;
          this.naturalParameters.set(0, alpha -1 );
          this.naturalParameters.set(1, -beta);
          this.setNaturalParameters(naturalParameters);
      }
  
  
      /**
       * {@inheritDoc}
       */
      @Override
      public double computeLogBaseMeasure(double val) {
          return 0;
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public SufficientStatistics getSufficientStatistics(double val) {
          SufficientStatistics vec = this.createZeroSufficientStatistics();
          vec.set(LOGX, Math.log(val));
          vec.set(INVX, val);
          return vec;
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public EF_UnivariateDistribution deepCopy(Variable var) {
          EF_Gamma copy = new EF_Gamma(var);
          copy.getNaturalParameters().copy(this.getNaturalParameters());
          copy.getMomentParameters().copy(this.getMomentParameters());
  
          return copy;
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public EF_UnivariateDistribution randomInitialization(Random random) {
  
          double randomVar = random.nextDouble()*2 + 1 + 0.01;
  
          double alpha = 10;
          double beta = randomVar*alpha ;
  
          this.getNaturalParameters().set(0, alpha - 1);
          this.getNaturalParameters().set(1, -beta);
  
          this.fixNumericalInstability();
          this.updateMomentFromNaturalParameters();
  
          return this;
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public <E extends UnivariateDistribution> E toUnivariateDistribution() {
          throw new UnsupportedOperationException("Gamma is not included yet in the Distributions package.");
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public void updateNaturalFromMomentParameters() {
          throw new UnsupportedOperationException("Not Implemented");
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public void updateMomentFromNaturalParameters() {
          double alpha = this.naturalParameters.get(0) + 1;
          double beta = -this.naturalParameters.get(1);
          this.momentParameters.set(0, Gamma.digamma(alpha) - Math.log(beta));
          this.momentParameters.set(1, alpha / beta);
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public SufficientStatistics getSufficientStatistics(Assignment data) {
          return this.getSufficientStatistics(data.getValue(this.var));
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public int sizeOfSufficientStatistics() {
          return 2;
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public double computeLogNormalizer() {
          double alpha = this.naturalParameters.get(0) + 1;
          double beta = -this.naturalParameters.get(1);
          return Gamma.logGamma(alpha) - alpha * Math.log(beta);
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public Vector createZeroVector() {
          return new ArrayVector(2);
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public Vector getExpectedParameters() {
  
          Vector vec = new ArrayVector(1);
          vec.set(0, -(this.naturalParameters.get(0)+1)/this.naturalParameters.get(1));
          return vec;
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public void fixNumericalInstability() {
  
          /*
          double alpha = this.naturalParameters.get(0) + 1;
          double beta = -this.naturalParameters.get(1);
  
  
          if ((alpha/beta)>PRECISION_LIMIT){
              double K = (alpha/beta)/PRECISION_LIMIT;
              double alphaPrime = alpha/K;
              this.naturalParameters.set(0,alphaPrime-1);
          }*/
  
      }
  
      /**
       * {@inheritDoc}
       */
      @Override
      public SufficientStatistics createInitSufficientStatistics() {
  
          ArrayVector vector = new ArrayVector(this.sizeOfSufficientStatistics());
  
          double alpha = 1;
          double beta = 1;
          vector.set(0, Gamma.digamma(alpha) - Math.log(beta));
          vector.set(1, alpha / beta);
  
          return vector;
      }
  }