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/*
 * 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 org.apache.commons.math3.stat.descriptive.rank;

import java.io.IOException;
import java.io.ObjectInputStream;
import java.io.Serializable;
import java.text.DecimalFormat;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.Collections;
import java.util.List;

import org.apache.commons.math3.analysis.UnivariateFunction;
import org.apache.commons.math3.analysis.interpolation.LinearInterpolator;
import org.apache.commons.math3.analysis.interpolation.NevilleInterpolator;
import org.apache.commons.math3.analysis.interpolation.UnivariateInterpolator;
import org.apache.commons.math3.exception.InsufficientDataException;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.stat.descriptive.AbstractStorelessUnivariateStatistic;
import org.apache.commons.math3.stat.descriptive.StorelessUnivariateStatistic;
import org.apache.commons.math3.util.MathArrays;
import org.apache.commons.math3.util.MathUtils;
import org.apache.commons.math3.util.Precision;

/**
 * A {@link StorelessUnivariateStatistic} estimating percentiles using the
 * P2
 * Algorithm as explained by Raj
 * Jain and Imrich Chlamtac in
 * P2 Algorithm
 * for Dynamic Calculation of Quantiles and Histogram Without Storing
 * Observations.
 * 

* Note: This implementation is not synchronized and produces an approximate * result. For small samples, where data can be stored and processed in memory, * {@link Percentile} should be used.

* */ public class PSquarePercentile extends AbstractStorelessUnivariateStatistic implements StorelessUnivariateStatistic, Serializable { /** * The maximum array size used for psquare algorithm */ private static final int PSQUARE_CONSTANT = 5; /** * A Default quantile needed in case if user prefers to use default no * argument constructor. */ private static final double DEFAULT_QUANTILE_DESIRED = 50d; /** * Serial ID */ private static final long serialVersionUID = 2283912083175715479L; /** * A decimal formatter for print convenience */ private static final DecimalFormat DECIMAL_FORMAT = new DecimalFormat( "00.00"); /** * Initial list of 5 numbers corresponding to 5 markers. NOTE:watch * out for the add methods that are overloaded */ private final List initialFive = new FixedCapacityList( PSQUARE_CONSTANT); /** * The quantile needed should be in range of 0-1. The constructor * {@link #PSquarePercentile(double)} ensures that passed in percentile is * divided by 100. */ private final double quantile; /** * lastObservation is the last observation value/input sample. No need to * serialize */ private transient double lastObservation; /** * Markers is the marker collection object which comes to effect * only after 5 values are inserted */ private PSquareMarkers markers = null; /** * Computed p value (i,e percentile value of data set hither to received) */ private double pValue = Double.NaN; /** * Counter to count the values/observations accepted into this data set */ private long countOfObservations; /** * Constructs a PSquarePercentile with the specific percentile value. * @param p the percentile * @throws OutOfRangeException if p is not greater than 0 and less * than or equal to 100 */ public PSquarePercentile(final double p) { if (p > 100 || p < 0) { throw new OutOfRangeException(LocalizedFormats.OUT_OF_RANGE, p, 0, 100); } this.quantile = p / 100d;// always set it within (0,1] } /** * Default constructor that assumes a {@link #DEFAULT_QUANTILE_DESIRED * default quantile} needed */ PSquarePercentile() { this(DEFAULT_QUANTILE_DESIRED); } /** * {@inheritDoc} */ @Override public int hashCode() { double result = getResult(); result = Double.isNaN(result) ? 37 : result; final double markersHash = markers == null ? 0 : markers.hashCode(); final double[] toHash = {result, quantile, markersHash, countOfObservations}; return Arrays.hashCode(toHash); } /** * Returns true iff {@code o} is a {@code PSquarePercentile} returning the * same values as this for {@code getResult()} and {@code getN()} and also * having equal markers * * @param o object to compare * @return true if {@code o} is a {@code PSquarePercentile} with * equivalent internal state */ @Override public boolean equals(Object o) { boolean result = false; if (this == o) { result = true; } else if (o != null && o instanceof PSquarePercentile) { PSquarePercentile that = (PSquarePercentile) o; boolean isNotNull = markers != null && that.markers != null; boolean isNull = markers == null && that.markers == null; result = isNotNull ? markers.equals(that.markers) : isNull; // markers as in the case of first // five observations result = result && getN() == that.getN(); } return result; } /** * {@inheritDoc}The internal state updated due to the new value in this * context is basically of the marker positions and computation of the * approximate quantile. * * @param observation the observation currently being added. */ @Override public void increment(final double observation) { // Increment counter countOfObservations++; // Store last observation this.lastObservation = observation; // 0. Use Brute force for <5 if (markers == null) { if (initialFive.add(observation)) { Collections.sort(initialFive); pValue = initialFive .get((int) (quantile * (initialFive.size() - 1))); return; } // 1. Initialize once after 5th observation markers = newMarkers(initialFive, quantile); } // 2. process a Data Point and return pValue pValue = markers.processDataPoint(observation); } /** * Returns a string containing the last observation, the current estimate * of the quantile and all markers. * * @return string representation of state data */ @Override public String toString() { if (markers == null) { return String.format("obs=%s pValue=%s", DECIMAL_FORMAT.format(lastObservation), DECIMAL_FORMAT.format(pValue)); } else { return String.format("obs=%s markers=%s", DECIMAL_FORMAT.format(lastObservation), markers.toString()); } } /** * {@inheritDoc} */ public long getN() { return countOfObservations; } /** * {@inheritDoc} */ @Override public StorelessUnivariateStatistic copy() { // multiply quantile by 100 now as anyway constructor divides it by 100 PSquarePercentile copy = new PSquarePercentile(100d * quantile); if (markers != null) { copy.markers = (PSquareMarkers) markers.clone(); } copy.countOfObservations = countOfObservations; copy.pValue = pValue; copy.initialFive.clear(); copy.initialFive.addAll(initialFive); return copy; } /** * Returns the quantile estimated by this statistic in the range [0.0-1.0] * * @return quantile estimated by {@link #getResult()} */ public double quantile() { return quantile; } /** * {@inheritDoc}. This basically clears all the markers, the * initialFive list and sets countOfObservations to 0. */ @Override public void clear() { markers = null; initialFive.clear(); countOfObservations = 0L; pValue = Double.NaN; } /** * {@inheritDoc} */ @Override public double getResult() { if (Double.compare(quantile, 1d) == 0) { pValue = maximum(); } else if (Double.compare(quantile, 0d) == 0) { pValue = minimum(); } return pValue; } /** * @return maximum in the data set added to this statistic */ private double maximum() { double val = Double.NaN; if (markers != null) { val = markers.height(PSQUARE_CONSTANT); } else if (!initialFive.isEmpty()) { val = initialFive.get(initialFive.size() - 1); } return val; } /** * @return minimum in the data set added to this statistic */ private double minimum() { double val = Double.NaN; if (markers != null) { val = markers.height(1); } else if (!initialFive.isEmpty()) { val = initialFive.get(0); } return val; } /** * Markers is an encapsulation of the five markers/buckets as indicated in * the original works. */ private static class Markers implements PSquareMarkers, Serializable { /** * Serial version id */ private static final long serialVersionUID = 1L; /** Low marker index */ private static final int LOW = 2; /** High marker index */ private static final int HIGH = 4; /** * Array of 5+1 Markers (The first marker is dummy just so we * can match the rest of indexes [1-5] indicated in the original works * which follows unit based index) */ private final Marker[] markerArray; /** * Kth cell belonging to [1-5] of the markerArray. No need for * this to be serialized */ private transient int k = -1; /** * Constructor * * @param theMarkerArray marker array to be used */ private Markers(final Marker[] theMarkerArray) { MathUtils.checkNotNull(theMarkerArray); markerArray = theMarkerArray; for (int i = 1; i < PSQUARE_CONSTANT; i++) { markerArray[i].previous(markerArray[i - 1]) .next(markerArray[i + 1]).index(i); } markerArray[0].previous(markerArray[0]).next(markerArray[1]) .index(0); markerArray[5].previous(markerArray[4]).next(markerArray[5]) .index(5); } /** * Constructor * * @param initialFive elements required to build Marker * @param p quantile required to be computed */ private Markers(final List initialFive, final double p) { this(createMarkerArray(initialFive, p)); } /** * Creates a marker array using initial five elements and a quantile * * @param initialFive list of initial five elements * @param p the pth quantile * @return Marker array */ private static Marker[] createMarkerArray( final List initialFive, final double p) { final int countObserved = initialFive == null ? -1 : initialFive.size(); if (countObserved < PSQUARE_CONSTANT) { throw new InsufficientDataException( LocalizedFormats.INSUFFICIENT_OBSERVED_POINTS_IN_SAMPLE, countObserved, PSQUARE_CONSTANT); } Collections.sort(initialFive); return new Marker[] { new Marker(),// Null Marker new Marker(initialFive.get(0), 1, 0, 1), new Marker(initialFive.get(1), 1 + 2 * p, p / 2, 2), new Marker(initialFive.get(2), 1 + 4 * p, p, 3), new Marker(initialFive.get(3), 3 + 2 * p, (1 + p) / 2, 4), new Marker(initialFive.get(4), 5, 1, 5) }; } /** * {@inheritDoc} */ @Override public int hashCode() { return Arrays.deepHashCode(markerArray); } /** * {@inheritDoc}.This equals method basically checks for marker array to * be deep equals. * * @param o is the other object * @return true if the object compares with this object are equivalent */ @Override public boolean equals(Object o) { boolean result = false; if (this == o) { result = true; } else if (o != null && o instanceof Markers) { Markers that = (Markers) o; result = Arrays.deepEquals(markerArray, that.markerArray); } return result; } /** * Process a data point * * @param inputDataPoint is the data point passed * @return computed percentile */ public double processDataPoint(final double inputDataPoint) { // 1. Find cell and update minima and maxima final int kthCell = findCellAndUpdateMinMax(inputDataPoint); // 2. Increment positions incrementPositions(1, kthCell + 1, 5); // 2a. Update desired position with increments updateDesiredPositions(); // 3. Adjust heights of m[2-4] if necessary adjustHeightsOfMarkers(); // 4. Return percentile return getPercentileValue(); } /** * Returns the percentile computed thus far. * * @return height of mid point marker */ public double getPercentileValue() { return height(3); } /** * Finds the cell where the input observation / value fits. * * @param observation the input value to be checked for * @return kth cell (of the markers ranging from 1-5) where observed * sample fits */ private int findCellAndUpdateMinMax(final double observation) { k = -1; if (observation < height(1)) { markerArray[1].markerHeight = observation; k = 1; } else if (observation < height(2)) { k = 1; } else if (observation < height(3)) { k = 2; } else if (observation < height(4)) { k = 3; } else if (observation <= height(5)) { k = 4; } else { markerArray[5].markerHeight = observation; k = 4; } return k; } /** * Adjust marker heights by setting quantile estimates to middle markers. */ private void adjustHeightsOfMarkers() { for (int i = LOW; i <= HIGH; i++) { estimate(i); } } /** * {@inheritDoc} */ public double estimate(final int index) { if (index < LOW || index > HIGH) { throw new OutOfRangeException(index, LOW, HIGH); } return markerArray[index].estimate(); } /** * Increment positions by d. Refer to algorithm paper for the * definition of d. * * @param d The increment value for the position * @param startIndex start index of the marker array * @param endIndex end index of the marker array */ private void incrementPositions(final int d, final int startIndex, final int endIndex) { for (int i = startIndex; i <= endIndex; i++) { markerArray[i].incrementPosition(d); } } /** * Desired positions incremented by bucket width. The bucket width is * basically the desired increments. */ private void updateDesiredPositions() { for (int i = 1; i < markerArray.length; i++) { markerArray[i].updateDesiredPosition(); } } /** * Sets previous and next markers after default read is done. * * @param anInputStream the input stream to be deserialized * @throws ClassNotFoundException thrown when a desired class not found * @throws IOException thrown due to any io errors */ private void readObject(ObjectInputStream anInputStream) throws ClassNotFoundException, IOException { // always perform the default de-serialization first anInputStream.defaultReadObject(); // Build links for (int i = 1; i < PSQUARE_CONSTANT; i++) { markerArray[i].previous(markerArray[i - 1]) .next(markerArray[i + 1]).index(i); } markerArray[0].previous(markerArray[0]).next(markerArray[1]) .index(0); markerArray[5].previous(markerArray[4]).next(markerArray[5]) .index(5); } /** * Return marker height given index * * @param markerIndex index of marker within (1,6) * @return marker height */ public double height(final int markerIndex) { if (markerIndex >= markerArray.length || markerIndex <= 0) { throw new OutOfRangeException(markerIndex, 1, markerArray.length); } return markerArray[markerIndex].markerHeight; } /** * {@inheritDoc}.Clone Markers * * @return cloned object */ @Override public Object clone() { return new Markers(new Marker[] { new Marker(), (Marker) markerArray[1].clone(), (Marker) markerArray[2].clone(), (Marker) markerArray[3].clone(), (Marker) markerArray[4].clone(), (Marker) markerArray[5].clone() }); } /** * Returns string representation of the Marker array. * * @return Markers as a string */ @Override public String toString() { return String.format("m1=[%s],m2=[%s],m3=[%s],m4=[%s],m5=[%s]", markerArray[1].toString(), markerArray[2].toString(), markerArray[3].toString(), markerArray[4].toString(), markerArray[5].toString()); } } /** * The class modeling the attributes of the marker of the P-square algorithm */ private static class Marker implements Serializable, Cloneable { /** * Serial Version ID */ private static final long serialVersionUID = -3575879478288538431L; /** * The marker index which is just a serial number for the marker in the * marker array of 5+1. */ private int index; /** * The integral marker position. Refer to the variable n in the original * works. */ private double intMarkerPosition; /** * Desired marker position. Refer to the variable n' in the original * works. */ private double desiredMarkerPosition; /** * Marker height or the quantile. Refer to the variable q in the * original works. */ private double markerHeight; /** * Desired marker increment. Refer to the variable dn' in the original * works. */ private double desiredMarkerIncrement; /** * Next and previous markers for easy linked navigation in loops. this * is not serialized as they can be rebuilt during deserialization. */ private transient Marker next; /** * The previous marker links */ private transient Marker previous; /** * Nonlinear interpolator */ private final UnivariateInterpolator nonLinear = new NevilleInterpolator(); /** * Linear interpolator which is not serializable */ private transient UnivariateInterpolator linear = new LinearInterpolator(); /** * Default constructor */ private Marker() { this.next = this.previous = this; } /** * Constructor of the marker with parameters * * @param heightOfMarker represent the quantile value * @param makerPositionDesired represent the desired marker position * @param markerPositionIncrement represent increments for position * @param markerPositionNumber represent the position number of marker */ private Marker(double heightOfMarker, double makerPositionDesired, double markerPositionIncrement, double markerPositionNumber) { this(); this.markerHeight = heightOfMarker; this.desiredMarkerPosition = makerPositionDesired; this.desiredMarkerIncrement = markerPositionIncrement; this.intMarkerPosition = markerPositionNumber; } /** * Sets the previous marker. * * @param previousMarker the previous marker to the current marker in * the array of markers * @return this instance */ private Marker previous(final Marker previousMarker) { MathUtils.checkNotNull(previousMarker); this.previous = previousMarker; return this; } /** * Sets the next marker. * * @param nextMarker the next marker to the current marker in the array * of markers * @return this instance */ private Marker next(final Marker nextMarker) { MathUtils.checkNotNull(nextMarker); this.next = nextMarker; return this; } /** * Sets the index of the marker. * * @param indexOfMarker the array index of the marker in marker array * @return this instance */ private Marker index(final int indexOfMarker) { this.index = indexOfMarker; return this; } /** * Update desired Position with increment. */ private void updateDesiredPosition() { desiredMarkerPosition += desiredMarkerIncrement; } /** * Increment Position by d. * * @param d a delta value to increment */ private void incrementPosition(final int d) { intMarkerPosition += d; } /** * Difference between desired and actual position * * @return difference between desired and actual position */ private double difference() { return desiredMarkerPosition - intMarkerPosition; } /** * Estimate the quantile for the current marker. * * @return estimated quantile */ private double estimate() { final double di = difference(); final boolean isNextHigher = next.intMarkerPosition - intMarkerPosition > 1; final boolean isPreviousLower = previous.intMarkerPosition - intMarkerPosition < -1; if (di >= 1 && isNextHigher || di <= -1 && isPreviousLower) { final int d = di >= 0 ? 1 : -1; final double[] xval = new double[] { previous.intMarkerPosition, intMarkerPosition, next.intMarkerPosition }; final double[] yval = new double[] { previous.markerHeight, markerHeight, next.markerHeight }; final double xD = intMarkerPosition + d; UnivariateFunction univariateFunction = nonLinear.interpolate(xval, yval); markerHeight = univariateFunction.value(xD); // If parabolic estimate is bad then turn linear if (isEstimateBad(yval, markerHeight)) { int delta = xD - xval[1] > 0 ? 1 : -1; final double[] xBad = new double[] { xval[1], xval[1 + delta] }; final double[] yBad = new double[] { yval[1], yval[1 + delta] }; MathArrays.sortInPlace(xBad, yBad);// since d can be +/- 1 univariateFunction = linear.interpolate(xBad, yBad); markerHeight = univariateFunction.value(xD); } incrementPosition(d); } return markerHeight; } /** * Check if parabolic/nonlinear estimate is bad by checking if the * ordinate found is beyond the y[0] and y[2]. * * @param y the array to get the bounds * @param yD the estimate * @return true if yD is a bad estimate */ private boolean isEstimateBad(final double[] y, final double yD) { return yD <= y[0] || yD >= y[2]; } /** * {@inheritDoc}This equals method checks for marker attributes and * as well checks if navigation pointers (next and previous) are the same * between this and passed in object * * @param o Other object * @return true if this equals passed in other object o */ @Override public boolean equals(Object o) { boolean result = false; if (this == o) { result = true; } else if (o != null && o instanceof Marker) { Marker that = (Marker) o; result = Double.compare(markerHeight, that.markerHeight) == 0; result = result && Double.compare(intMarkerPosition, that.intMarkerPosition) == 0; result = result && Double.compare(desiredMarkerPosition, that.desiredMarkerPosition) == 0; result = result && Double.compare(desiredMarkerIncrement, that.desiredMarkerIncrement) == 0; result = result && next.index == that.next.index; result = result && previous.index == that.previous.index; } return result; } /** {@inheritDoc} */ @Override public int hashCode() { return Arrays.hashCode(new double[] {markerHeight, intMarkerPosition, desiredMarkerIncrement, desiredMarkerPosition, previous.index, next.index}); } /** * Read Object to deserialize. * * @param anInstream Stream Object data * @throws IOException thrown for IO Errors * @throws ClassNotFoundException thrown for class not being found */ private void readObject(ObjectInputStream anInstream) throws ClassNotFoundException, IOException { anInstream.defaultReadObject(); previous=next=this; linear = new LinearInterpolator(); } /** * Clone this instance. * * @return cloned marker */ @Override public Object clone() { return new Marker(markerHeight, desiredMarkerPosition, desiredMarkerIncrement, intMarkerPosition); } /** * {@inheritDoc} */ @Override public String toString() { return String.format( "index=%.0f,n=%.0f,np=%.2f,q=%.2f,dn=%.2f,prev=%d,next=%d", (double) index, Precision.round(intMarkerPosition, 0), Precision.round(desiredMarkerPosition, 2), Precision.round(markerHeight, 2), Precision.round(desiredMarkerIncrement, 2), previous.index, next.index); } } /** * A simple fixed capacity list that has an upper bound to growth. * Once its capacity is reached, {@code add} is a no-op, returning * {@code false}. * * @param */ private static class FixedCapacityList extends ArrayList implements Serializable { /** * Serialization Version Id */ private static final long serialVersionUID = 2283952083075725479L; /** * Capacity of the list */ private final int capacity; /** * This constructor constructs the list with given capacity and as well * as stores the capacity * * @param fixedCapacity the capacity to be fixed for this list */ FixedCapacityList(final int fixedCapacity) { super(fixedCapacity); this.capacity = fixedCapacity; } /** * {@inheritDoc} In addition it checks if the {@link #size()} returns a * size that is within capacity and if true it adds; otherwise the list * contents are unchanged and {@code false} is returned. * * @return true if addition is successful and false otherwise */ @Override public boolean add(final E e) { return size() < capacity ? super.add(e) : false; } /** * {@inheritDoc} In addition it checks if the sum of Collection size and * this instance's {@link #size()} returns a value that is within * capacity and if true it adds the collection; otherwise the list * contents are unchanged and {@code false} is returned. * * @return true if addition is successful and false otherwise */ @Override public boolean addAll(Collection collection) { boolean isCollectionLess = collection != null && collection.size() + size() <= capacity; return isCollectionLess ? super.addAll(collection) : false; } } /** * A creation method to build Markers * * @param initialFive list of initial five elements * @param p the quantile desired * @return an instance of PSquareMarkers */ public static PSquareMarkers newMarkers(final List initialFive, final double p) { return new Markers(initialFive, p); } /** * An interface that encapsulates abstractions of the * P-square algorithm markers as is explained in the original works. This * interface is exposed with protected access to help in testability. */ protected interface PSquareMarkers extends Cloneable { /** * Returns Percentile value computed thus far. * * @return percentile */ double getPercentileValue(); /** * A clone function to clone the current instance. It's created as an * interface method as well for convenience though Cloneable is just a * marker interface. * * @return clone of this instance */ Object clone(); /** * Returns the marker height (or percentile) of a given marker index. * * @param markerIndex is the index of marker in the marker array * @return percentile value of the marker index passed * @throws OutOfRangeException in case the index is not within [1-5] */ double height(final int markerIndex); /** * Process a data point by moving the marker heights based on estimator. * * @param inputDataPoint is the data point passed * @return computed percentile */ double processDataPoint(final double inputDataPoint); /** * An Estimate of the percentile value of a given Marker * * @param index the marker's index in the array of markers * @return percentile estimate * @throws OutOfRangeException in case if index is not within [1-5] */ double estimate(final int index); } }




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