/*
    * 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
    *
    *      https://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.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  package org.hipparchus.stat.descriptive;
  
  import java.io.Serializable;
  import java.util.Arrays;
  import java.util.function.DoubleConsumer;
  
  import org.hipparchus.exception.LocalizedCoreFormats;
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.exception.MathIllegalStateException;
  import org.hipparchus.stat.descriptive.moment.GeometricMean;
  import org.hipparchus.stat.descriptive.moment.Kurtosis;
  import org.hipparchus.stat.descriptive.moment.Mean;
  import org.hipparchus.stat.descriptive.moment.Skewness;
  import org.hipparchus.stat.descriptive.moment.Variance;
  import org.hipparchus.stat.descriptive.rank.Max;
  import org.hipparchus.stat.descriptive.rank.Min;
  import org.hipparchus.stat.descriptive.rank.Percentile;
  import org.hipparchus.stat.descriptive.summary.Sum;
  import org.hipparchus.stat.descriptive.summary.SumOfSquares;
  import org.hipparchus.util.FastMath;
  import org.hipparchus.util.MathUtils;
  import org.hipparchus.util.ResizableDoubleArray;
  
  
  /**
   * Maintains a dataset of values of a single variable and computes descriptive
   * statistics based on stored data.
   * <p>
   * The {@link #getWindowSize() windowSize} property sets a limit on the number
   * of values that can be stored in the dataset. The default value, INFINITE_WINDOW,
   * puts no limit on the size of the dataset. This value should be used with
   * caution, as the backing store will grow without bound in this case.
   * <p>
   * For very large datasets, {@link StreamingStatistics}, which does not store
   * the dataset, should be used instead of this class. If <code>windowSize</code>
   * is not INFINITE_WINDOW and more values are added than can be stored in the
   * dataset, new values are added in a "rolling" manner, with new values replacing
   * the "oldest" values in the dataset.
   * <p>
   * Note: this class is not threadsafe.
   */
  public class DescriptiveStatistics
      implements StatisticalSummary, DoubleConsumer, Serializable {
  
      /**
       * Represents an infinite window size.  When the {@link #getWindowSize()}
       * returns this value, there is no limit to the number of data values
       * that can be stored in the dataset.
       */
      protected static final int INFINITE_WINDOW = -1;
  
      /** Serialization UID */
      private static final long serialVersionUID = 20160411L;
  
      /** The statistic used to calculate the population variance - fixed. */
      private static final UnivariateStatistic POPULATION_VARIANCE = new Variance(false);
  
      /** Maximum statistic implementation. */
      private final UnivariateStatistic          maxImpl;
      /** Minimum statistic implementation. */
      private final UnivariateStatistic          minImpl;
      /** Sum statistic implementation. */
      private final UnivariateStatistic          sumImpl;
      /** Sum of squares statistic implementation. */
      private final UnivariateStatistic          sumOfSquaresImpl;
      /** Mean statistic implementation. */
      private final UnivariateStatistic          meanImpl;
      /** Variance statistic implementation. */
      private final UnivariateStatistic          varianceImpl;
      /** Geometric mean statistic implementation. */
      private final UnivariateStatistic          geometricMeanImpl;
      /** Kurtosis statistic implementation. */
      private final UnivariateStatistic          kurtosisImpl;
      /** Skewness statistic implementation. */
      private final UnivariateStatistic          skewnessImpl;
      /** Percentile statistic implementation. */
      private final Percentile                   percentileImpl;
  
     /** holds the window size. */
     private int windowSize;
 
     /** Stored data values. */
     private final ResizableDoubleArray eDA;
 
     /**
      * Construct a DescriptiveStatistics instance with an infinite window.
      */
     public DescriptiveStatistics() {
         this(INFINITE_WINDOW);
     }
 
     /**
      * Construct a DescriptiveStatistics instance with the specified window.
      *
      * @param size the window size.
      * @throws MathIllegalArgumentException if window size is less than 1 but
      * not equal to {@link #INFINITE_WINDOW}
      */
     public DescriptiveStatistics(int size) throws MathIllegalArgumentException {
         this(size, false, null);
     }
 
     /**
      * Construct a DescriptiveStatistics instance with an infinite window
      * and the initial data values in double[] initialDoubleArray.
      *
      * @param initialDoubleArray the initial double[].
      * @throws org.hipparchus.exception.NullArgumentException if the input array is null
      */
     public DescriptiveStatistics(double[] initialDoubleArray) {
         this(INFINITE_WINDOW, true, initialDoubleArray);
     }
 
     /**
      * Copy constructor.
      * <p>
      * Construct a new DescriptiveStatistics instance that
      * is a copy of original.
      *
      * @param original DescriptiveStatistics instance to copy
      * @throws org.hipparchus.exception.NullArgumentException if original is null
      */
     protected DescriptiveStatistics(DescriptiveStatistics original) {
         MathUtils.checkNotNull(original);
 
         // Copy data and window size
         this.windowSize = original.windowSize;
         this.eDA        = original.eDA.copy();
 
         // Copy implementations
         this.maxImpl           = original.maxImpl.copy();
         this.minImpl           = original.minImpl.copy();
         this.meanImpl          = original.meanImpl.copy();
         this.sumImpl           = original.sumImpl.copy();
         this.sumOfSquaresImpl  = original.sumOfSquaresImpl.copy();
         this.varianceImpl      = original.varianceImpl.copy();
         this.geometricMeanImpl = original.geometricMeanImpl.copy();
         this.kurtosisImpl      = original.kurtosisImpl.copy();
         this.skewnessImpl      = original.skewnessImpl.copy();
         this.percentileImpl    = original.percentileImpl.copy();
     }
 
     /**
      * Construct a DescriptiveStatistics instance with the specified window.
      *
      * @param windowSize the window size
      * @param hasInitialValues if initial values have been provided
      * @param initialValues the initial values
      * @throws org.hipparchus.exception.NullArgumentException if initialValues is null
      * @throws MathIllegalArgumentException if window size is less than 1 but
      * not equal to {@link #INFINITE_WINDOW}
      */
     DescriptiveStatistics(int windowSize, boolean hasInitialValues, double[] initialValues) {
         if (windowSize < 1 && windowSize != INFINITE_WINDOW) {
             throw new MathIllegalArgumentException(
                     LocalizedCoreFormats.NOT_POSITIVE_WINDOW_SIZE, windowSize);
         }
 
         if (hasInitialValues) {
             MathUtils.checkNotNull(initialValues, LocalizedCoreFormats.INPUT_ARRAY);
         }
 
         this.windowSize     = windowSize;
         int initialCapacity = this.windowSize < 0 ? 100 : this.windowSize;
         this.eDA            = hasInitialValues ?
             new ResizableDoubleArray(initialValues) :
             new ResizableDoubleArray(initialCapacity);
 
         maxImpl           = new Max();
         minImpl           = new Min();
         sumImpl           = new Sum();
         sumOfSquaresImpl  = new SumOfSquares();
         meanImpl          = new Mean();
         varianceImpl      = new Variance();
         geometricMeanImpl = new GeometricMean();
         kurtosisImpl      = new Kurtosis();
         skewnessImpl      = new Skewness();
         percentileImpl    = new Percentile();
     }
 
     /**
      * Returns a copy of this DescriptiveStatistics instance with the same internal state.
      *
      * @return a copy of this
      */
     public DescriptiveStatistics copy() {
         return new DescriptiveStatistics(this);
     }
 
     /**
      * Adds the value to the dataset. If the dataset is at the maximum size
      * (i.e., the number of stored elements equals the currently configured
      * windowSize), the first (oldest) element in the dataset is discarded
      * to make room for the new value.
      *
      * @param v the value to be added
      */
     public void addValue(double v) {
         if (windowSize != INFINITE_WINDOW) {
             if (getN() == windowSize) {
                 eDA.addElementRolling(v);
             } else if (getN() < windowSize) {
                 eDA.addElement(v);
             }
         } else {
             eDA.addElement(v);
         }
     }
 
     /** {@inheritDoc} */
     @Override
     public void accept(double v) {
         addValue(v);
     }
 
     /**
      * Resets all statistics and storage.
      */
     public void clear() {
         eDA.clear();
     }
 
     /**
      * Removes the most recent value from the dataset.
      *
      * @throws MathIllegalStateException if there are no elements stored
      */
     public void removeMostRecentValue() throws MathIllegalStateException {
         try {
             eDA.discardMostRecentElements(1);
         } catch (MathIllegalArgumentException ex) {
             throw new MathIllegalStateException(ex, LocalizedCoreFormats.NO_DATA);
         }
     }
 
     /**
      * Replaces the most recently stored value with the given value.
      * There must be at least one element stored to call this method.
      *
      * @param v the value to replace the most recent stored value
      * @return replaced value
      * @throws MathIllegalStateException if there are no elements stored
      */
     public double replaceMostRecentValue(double v) throws MathIllegalStateException {
         return eDA.substituteMostRecentElement(v);
     }
 
     /**
      * Apply the given statistic to the data associated with this set of statistics.
      * @param stat the statistic to apply
      * @return the computed value of the statistic.
      */
     public double apply(UnivariateStatistic stat) {
         // No try-catch or advertised exception here because arguments
         // are guaranteed valid.
         return eDA.compute(stat);
     }
 
     /** {@inheritDoc} */
     @Override
     public double getMean() {
         return apply(meanImpl);
     }
 
     /**
      * Returns the geometric mean of the available values.
      * <p>
      * See {@link GeometricMean} for details on the computing algorithm.
      *
      * @see <a href="http://www.xycoon.com/geometric_mean.htm">
      * Geometric mean</a>
      *
      * @return The geometricMean, Double.NaN if no values have been added,
      * or if any negative values have been added.
      */
     public double getGeometricMean() {
         return apply(geometricMeanImpl);
     }
 
     /**
      * Returns the standard deviation of the available values.
      * @return The standard deviation, Double.NaN if no values have been added
      * or 0.0 for a single value set.
      */
     @Override
     public double getStandardDeviation() {
         double stdDev = Double.NaN;
         if (getN() > 0) {
             if (getN() > 1) {
                 stdDev = FastMath.sqrt(getVariance());
             } else {
                 stdDev = 0.0;
             }
         }
         return stdDev;
     }
 
     /**
      * Returns the quadratic mean of the available values.
      *
      * @see <a href="http://mathworld.wolfram.com/Root-Mean-Square.html">
      * Root Mean Square</a>
      *
      * @return The quadratic mean or {@code Double.NaN} if no values
      * have been added.
      */
     public double getQuadraticMean() {
         final long n = getN();
         return n > 0 ? FastMath.sqrt(getSumOfSquares() / n) : Double.NaN;
     }
 
     /** {@inheritDoc} */
     @Override
     public double getVariance() {
         return apply(varianceImpl);
     }
 
     /**
      * Returns the population variance of the available values.
      *
      * @see <a href="http://en.wikibooks.org/wiki/Statistics/Summary/Variance">
      * Population variance</a>
      *
      * @return The population variance, Double.NaN if no values have been added,
      * or 0.0 for a single value set.
      */
     public double getPopulationVariance() {
         return apply(POPULATION_VARIANCE);
     }
 
     /**
      * Returns the skewness of the available values. Skewness is a
      * measure of the asymmetry of a given distribution.
      *
      * @return The skewness, Double.NaN if less than 3 values have been added.
      */
     public double getSkewness() {
         return apply(skewnessImpl);
     }
 
     /**
      * Returns the Kurtosis of the available values. Kurtosis is a
      * measure of the "peakedness" of a distribution.
      *
      * @return The kurtosis, Double.NaN if less than 4 values have been added.
      */
     public double getKurtosis() {
         return apply(kurtosisImpl);
     }
 
     /** {@inheritDoc} */
     @Override
     public double getMax() {
         return apply(maxImpl);
     }
 
     /** {@inheritDoc} */
     @Override
     public double getMin() {
         return apply(minImpl);
     }
 
     /** {@inheritDoc} */
     @Override
     public double getSum() {
         return apply(sumImpl);
     }
 
     /**
      * Returns the sum of the squares of the available values.
      * @return The sum of the squares or Double.NaN if no
      * values have been added.
      */
     public double getSumOfSquares() {
         return apply(sumOfSquaresImpl);
     }
 
     /**
      * Returns an estimate for the pth percentile of the stored values.
      * <p>
      * The implementation provided here follows the first estimation procedure presented
      * <a href="http://www.itl.nist.gov/div898/handbook/prc/section2/prc252.htm">here.</a>
      * </p><p>
      * <strong>Preconditions</strong>:<ul>
      * <li><code>0 &lt; p &le; 100</code> (otherwise an
      * <code>MathIllegalArgumentException</code> is thrown)</li>
      * <li>at least one value must be stored (returns <code>Double.NaN
      *     </code> otherwise)</li>
      * </ul>
      *
      * @param p the requested percentile (scaled from 0 - 100)
      * @return An estimate for the pth percentile of the stored data
      * @throws MathIllegalArgumentException if p is not a valid quantile
      */
     public double getPercentile(final double p)
         throws MathIllegalArgumentException {
 
         percentileImpl.setQuantile(p);
         return apply(percentileImpl);
     }
 
     /** {@inheritDoc} */
     @Override
     public long getN() {
         return eDA.getNumElements();
     }
 
     /**
      * Returns the maximum number of values that can be stored in the
      * dataset, or INFINITE_WINDOW (-1) if there is no limit.
      *
      * @return The current window size or -1 if its Infinite.
      */
     public int getWindowSize() {
         return windowSize;
     }
 
     /**
      * WindowSize controls the number of values that contribute to the
      * reported statistics.  For example, if windowSize is set to 3 and the
      * values {1,2,3,4,5} have been added <strong> in that order</strong> then
      * the <i>available values</i> are {3,4,5} and all reported statistics will
      * be based on these values. If {@code windowSize} is decreased as a result
      * of this call and there are more than the new value of elements in the
      * current dataset, values from the front of the array are discarded to
      * reduce the dataset to {@code windowSize} elements.
      *
      * @param windowSize sets the size of the window.
      * @throws MathIllegalArgumentException if window size is less than 1 but
      * not equal to {@link #INFINITE_WINDOW}
      */
     public void setWindowSize(int windowSize)
         throws MathIllegalArgumentException {
 
         if (windowSize < 1 && windowSize != INFINITE_WINDOW) {
             throw new MathIllegalArgumentException(
                     LocalizedCoreFormats.NOT_POSITIVE_WINDOW_SIZE, windowSize);
         }
 
         this.windowSize = windowSize;
 
         // We need to check to see if we need to discard elements
         // from the front of the array.  If the windowSize is less than
         // the current number of elements.
         if (windowSize != INFINITE_WINDOW && windowSize < eDA.getNumElements()) {
             eDA.discardFrontElements(eDA.getNumElements() - windowSize);
         }
     }
 
     /**
      * Returns the current set of values in an array of double primitives.
      * The order of addition is preserved.  The returned array is a fresh
      * copy of the underlying data -- i.e., it is not a reference to the
      * stored data.
      *
      * @return the current set of numbers in the order in which they
      * were added to this set
      */
     public double[] getValues() {
         return eDA.getElements();
     }
 
     /**
      * Returns the current set of values in an array of double primitives,
      * sorted in ascending order.  The returned array is a fresh
      * copy of the underlying data -- i.e., it is not a reference to the
      * stored data.
      * @return returns the current set of
      * numbers sorted in ascending order
      */
     public double[] getSortedValues() {
         double[] sort = getValues();
         Arrays.sort(sort);
         return sort;
     }
 
     /**
      * Returns the element at the specified index
      * @param index The Index of the element
      * @return return the element at the specified index
      */
     public double getElement(int index) {
         return eDA.getElement(index);
     }
 
     /**
      * Generates a text report displaying univariate statistics from values
      * that have been added.  Each statistic is displayed on a separate line.
      *
      * @return String with line feeds displaying statistics
      */
     @Override
     public String toString() {
         final StringBuilder outBuffer = new StringBuilder(100);
         final String endl = "\n";
         outBuffer.append("DescriptiveStatistics:").append(endl).
                   append("n: ").append(getN()).append(endl).
                   append("min: ").append(getMin()).append(endl).
                   append("max: ").append(getMax()).append(endl).
                   append("mean: ").append(getMean()).append(endl).
                   append("std dev: ").append(getStandardDeviation()).append(endl);
         try {
             // No catch for MIAE because actual parameter is valid below
             outBuffer.append("median: ").append(getPercentile(50)).append(endl);
         } catch (MathIllegalStateException ex) {
             outBuffer.append("median: unavailable").append(endl);
         }
         outBuffer.append("skewness: ").append(getSkewness()).append(endl).
                   append("kurtosis: ").append(getKurtosis()).append(endl);
         return outBuffer.toString();
     }
 
 }