// ******************************************************************************
   //
   // Title:       Force Field X.
   // Description: Force Field X - Software for Molecular Biophysics.
   // Copyright:   Copyright (c) Michael J. Schnieders 2001-2024.
   //
   // This file is part of Force Field X.
   //
   // Force Field X is free software; you can redistribute it and/or modify it
  // under the terms of the GNU General Public License version 3 as published by
  // the Free Software Foundation.
  //
  // Force Field X 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 General Public License for more
  // details.
  //
  // You should have received a copy of the GNU General Public License along with
  // Force Field X; if not, write to the Free Software Foundation, Inc., 59 Temple
  // Place, Suite 330, Boston, MA 02111-1307 USA
  //
  // Linking this library statically or dynamically with other modules is making a
  // combined work based on this library. Thus, the terms and conditions of the
  // GNU General Public License cover the whole combination.
  //
  // As a special exception, the copyright holders of this library give you
  // permission to link this library with independent modules to produce an
  // executable, regardless of the license terms of these independent modules, and
  // to copy and distribute the resulting executable under terms of your choice,
  // provided that you also meet, for each linked independent module, the terms
  // and conditions of the license of that module. An independent module is a
  // module which is not derived from or based on this library. If you modify this
  // library, you may extend this exception to your version of the library, but
  // you are not obligated to do so. If you do not wish to do so, delete this
  // exception statement from your version.
  //
  // ******************************************************************************
  package ffx.numerics.math;
  
  import static java.lang.Double.isFinite;
  import static java.lang.Double.isNaN;
  import static java.lang.String.format;
  import static org.apache.commons.math3.util.FastMath.max;
  import static org.apache.commons.math3.util.FastMath.min;
  import static org.apache.commons.math3.util.FastMath.sqrt;
  
  /**
   * The RunningStatistics class uses online, stable algorithms to calculate summary statistics from a
   * source of doubles, including mean, variance, standard deviation, max, min, sum, and count.
   *
   * <p>This is intended for accuracy and numerical stability, not necessarily for performance (e.g.
   * using Kahan summation).
   *
   * <p>This is effectively a dynamic version of SummaryStatistics.
   *
   * @author Michael J. Schnieders
   * @author Jacob M. Litman
   * @since 1.0
   */
  public class RunningStatistics {
  
    // Weight-sensitive values.
    private double mean = 0;
    private double var = 0;
    private double weight = 0;
    // Weight-insensitive values.
    private double min = Double.MAX_VALUE;
    private double max = Double.MIN_VALUE;
    private long count = 0;
    private double sum = 0;
    private long dof = -1;
    private double comp = 0;
  
    /** Constructs new running statistics accumulator. */
    public RunningStatistics() {
      // Empty constructor; all variables are initialized at definition.
    }
  
    /**
     * Add a value and update key variables.
     *
     * @param val Value to add.
     */
    public void addValue(double val) {
      addValue(val, 1.0);
    }
  
    /**
     * Add a value and update key variables.
     *
     * @param val Value to add.
     * @param weight Weight to give the value.
     */
    public void addValue(double val, double weight) {
      assert isFinite(val);
      assert isFinite(weight);
      assert weight > 0.0;
      ++count;
      ++dof;
     double priorMean = mean;
     this.weight += weight;
     double y = val - comp;
     double t = sum + y;
     comp = (t - sum) - y;
     sum = t;
 
     min = min(min, val);
     max = max(max, val);
     double invCount = 1.0 / this.weight;
     mean += ((val - mean) * invCount);
     var += ((val - priorMean) * (val - mean)) * weight;
     if (isNaN(var)) {
       throw new IllegalArgumentException(
           format(" Val %.5f w/ wt %.3f resulted in NaN varAcc; current state %s",
               val, weight, new SummaryStatistics(this)));
     }
   }
 
   public void reset() {
     // Weight-sensitive values.
     mean = 0;
     var = 0;
     weight = 0;
     // Weight-insensitive values.
     min = Double.MAX_VALUE;
     max = Double.MIN_VALUE;
     count = 0;
     sum = 0;
     dof = -1;
     comp = 0;
   }
 
   /**
    * Get the count.
    *
    * @return Returns the count.
    */
   public long getCount() {
     return count;
   }
 
   /**
    * Get the DOF.
    *
    * @return Returns DOF.
    */
   public long getDOF() {
     return dof;
   }
 
   /**
    * Get the max.
    *
    * @return Returns the max.
    */
   public double getMax() {
     return max;
   }
 
   /**
    * Gets the mean as of the last value added.
    *
    * @return Current running mean.
    */
   public double getMean() {
     return mean;
   }
 
   /**
    * Get the min.
    *
    * @return Returns the min.
    */
   public double getMin() {
     return min;
   }
 
   /**
    * Get the population standard deviations.
    *
    * @return The population standard deviation.
    */
   public double getPopulationStandardDeviation() {
     return sqrt(getPopulationVariance());
   }
 
   /**
    * Get the population variance.
    *
    * @return Returns the population variance.
    */
   public double getPopulationVariance() {
     return var / ((double) count);
   }
 
   /**
    * Get the standard deviation.
    *
    * @return Returns the standard deviation.
    */
   public double getStandardDeviation() {
     return sqrt(getVariance());
   }
 
   /**
    * Get the sum.
    *
    * @return Returns the sum.
    */
   public double getSum() {
     return sum;
   }
 
   /**
    * Get the variance.
    *
    * @return Returns the variance.
    */
   public double getVariance() {
     return var / ((double) dof);
   }
 
   /**
    * Get the weight.
    *
    * @return Returns the weight.
    */
   public double getWeight() {
     return weight;
   }
 
   /**
    * Describe the Summary Statistics.
    *
    * @return Return the description.
    */
   public String describe() {
     return format(" Mean: %12.6f +/-%12.6f, Min/Max: %12.6f/%12.6f", mean,
         getStandardDeviation(), min, max);
   }
 
 }