/*
 * This file is part of ELKI:
 * Environment for Developing KDD-Applications Supported by Index-Structures
 *
 * Copyright (C) 2022
 * ELKI Development Team
 *
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU Affero General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * This program 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 Affero General Public License for more details.
 *
 * You should have received a copy of the GNU Affero General Public License
 * along with this program. If not, see <http://www.gnu.org/licenses/>.
 */
package elki.math.statistics.distribution.estimator;

import elki.math.statistics.distribution.Distribution;
import elki.utilities.datastructures.arraylike.NumberArrayAdapter;
import elki.math.MeanVariance;
import elki.math.StatisticalMoments;

/**
 * Interface for estimators that only need mean and variance.
 * 
 * These can implicitely (obviously) also handle full statistical moments.
 * 
 * @author Erich Schubert
 * @since 0.6.0
 * 
 * @param <D> Distribution type
 */
public interface MeanVarianceDistributionEstimator<D extends Distribution> extends MOMDistributionEstimator<D> {
  /**
   * Estimate the distribution from mean and variance.
   * 
   * @param mv Mean and variance.
   * @return Distribution
   */
  D estimateFromMeanVariance(MeanVariance mv);

  @Override
  default D estimateFromStatisticalMoments(StatisticalMoments moments) {
    return estimateFromMeanVariance(moments);
  }

  @Override
  default <A> D estimate(A data, NumberArrayAdapter<?, A> adapter) {
    final int size = adapter.size(data);
    MeanVariance mv = new MeanVariance();
    for(int i = 0; i < size; i++) {
      final double val = adapter.getDouble(data, i);
      if(Double.isInfinite(val) || Double.isNaN(val)) {
        continue;
      }
      mv.put(val);
    }
    return estimateFromMeanVariance(mv);
  }
}
