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package org.opentrafficsim.kpi.sampling.indicator;

import org.djunits.unit.Unit;
import org.djunits.value.vdouble.scalar.base.AbstractDoubleScalarRel;
import org.djutils.exceptions.Throw;

/**
 * 

* Copyright (c) 2013-2023 Delft University of Technology, PO Box 5, 2600 AA, Delft, the Netherlands. All rights reserved.
* BSD-style license. See OpenTrafficSim License. *

* @author Alexander Verbraeck * @author Peter Knoppers * @author Wouter Schakel * @param unit of the value * @param class of the value * @param weight class */ public class Persistent, T extends AbstractDoubleScalarRel, W extends Number> { /** Unit. */ private final U displayUnit; /** Sum of values. */ private T sum = null; /** Min value. */ private T min = null; /** Max value. */ private T max = null; /** Mean value. */ private double mean = Double.NaN; /** Variance sum. */ private double varianceSum = Double.NaN; /** Sum of weights. */ private double weightSum = 0.0; /** Number of measurements. */ private long n = 0; /** Semaphore. */ private Object semaphore = new Object(); /** * Constructor. * @param displayUnit U; unit used for displaying the persistent values, minimum, maximum, mean, etc. */ public Persistent(final U displayUnit) { this.displayUnit = displayUnit; } /** * Creates a copy. * @return a copy */ public Persistent copy() { Persistent persistent = new Persistent<>(this.displayUnit); persistent.sum = this.sum; persistent.min = this.min; persistent.max = this.max; persistent.mean = this.mean; persistent.varianceSum = this.varianceSum; persistent.weightSum = this.weightSum; persistent.n = this.n; return persistent; } /** * Adds a value with given weight to the persistent. * @param value T; the value * @param weight W; the weight */ public void addValue(final T value, final W weight) { Throw.whenNull(value, "Value may not be null."); Throw.whenNull(weight, "Weight may not be null."); Throw.when(weight.doubleValue() < 0.0, IllegalArgumentException.class, "Weight may not be negative."); synchronized (this.semaphore) { this.n++; this.min = this.min == null || value.lt(this.min) ? value : this.min; this.max = this.max == null || value.gt(this.max) ? value : this.max; this.sum = this.sum != null ? this.sum.plus(value) : value; // see Knuth's The Art Of Computer Programming Volume II: Seminumerical Algorithms double w = weight.doubleValue(); if (this.n == 1) { this.mean = value.si; this.weightSum = w; this.varianceSum = 0.0; } else { if (w > 0.0) { double newWeightSum = this.weightSum + w; double newMean = ((this.mean * this.weightSum) + (value.si * w)) / newWeightSum; this.varianceSum += (value.si - this.mean) * (value.si - newMean) * w; this.mean = newMean; this.weightSum = newWeightSum; } } } } /** * @param alpha double; confidence level * @return both-side confidence interval */ public ConfidenceInterval getConfidenceInterval(final double alpha) { return getConfidenceInterval(alpha, IntervalSide.BOTH); } /** * @param alpha double; confidence level * @param side IntervalSide; side of confidence interval * @return confidence interval */ public ConfidenceInterval getConfidenceInterval(final double alpha, final IntervalSide side) { Throw.whenNull(side, "Interval side may not be null."); if (alpha < 0 || alpha > 1) { throw new IllegalArgumentException("1 >= confidenceLevel >= 0"); } synchronized (this.semaphore) { if (Double.isNaN(this.mean) || Double.isNaN(this.getStDev().si)) { return null; } double level = 1 - alpha; if (side == IntervalSide.BOTH) { level = 1 - alpha / 2.0; } double z = getInverseCumulativeProbability(level); double confidence = z * Math.sqrt(this.getVariance() / this.n); double[] result = {this.mean - confidence, this.mean + confidence}; if (side == IntervalSide.LEFT) { result[1] = this.mean; } if (side == IntervalSide.RIGHT) { result[0] = this.mean; } result[0] = Math.max(result[0], this.min.si); result[1] = Math.min(result[1], this.max.si); return new ConfidenceInterval<>(instantiate(result[0]), instantiate(result[1])); } } /** * @return min value */ public T getMin() { return this.min; } /** * @return max value */ public T getMax() { return this.max; } /** * @return number of measurments */ public long getN() { return this.n; } /** * @return mean value */ public T getMean() { return instantiate(this.mean); } /** * @return variance in si */ public double getVariance() { synchronized (this.semaphore) { if (this.n > 1) { return this.n * this.varianceSum / (this.weightSum * (this.n - 1)); } return Double.NaN; } } /** * @return standard deviation */ public T getStDev() { synchronized (this.semaphore) { if (this.n > 1) { return instantiate(Math.sqrt(this.n * this.varianceSum / (this.weightSum * (this.n - 1)))); } return instantiate(Double.NaN); } } /** * @param valueSI double; si value * @return instantiate typed value from si value */ private T instantiate(final double valueSI) { return this.min.instantiateRel(valueSI, this.displayUnit.getStandardUnit()); } /** * @return sum */ public T getSum() { return this.sum; } /** * returns the x-value of the given cumulativePropability. * @param cumulativeProbability double; reflects cum prob * @return double the inverse cumulative probability */ private double getInverseCumulativeProbability(final double cumulativeProbability) { if (cumulativeProbability < 0 || cumulativeProbability > 1) { throw new IllegalArgumentException("1 prob) { located = true; } i++; } return (0 + i / 100.0); } /** * CUMULATIVE_NORMAL_PROPABILITIES represents the NORMAL DISTRIBUTION FUNCTION TABLE. In order to keep this table as fast as * possible no x values are stored. The range of the table is {0.00,0.01,0.02,...,10.00} */ private static final double[] CUMULATIVE_NORMAL_PROPABILITIES = {0.5000000000000000, 0.5039873616189113, 0.5079763193203305, 0.5119644795160448, 0.5159514436524734, 0.5199368135347197, 0.5239201914458871, 0.5279011802661332, 0.5318793835914418, 0.5358544058520341, 0.5398258524303582, 0.5437933297786074, 0.5477564455357087, 0.5517148086437129, 0.5556680294635363, 0.5596157198900099, 0.5635574934661438, 0.567492965496589, 0.5714217531602216, 0.5753434756217956, 0.5792577541426178, 0.5831642121901748, 0.5870624755466856, 0.5909521724164968, 0.5948329335322977, 0.5987043922600851, 0.6025661847028365, 0.6064179498028396, 0.6102593294426336, 0.6140899685445192, 0.6179095151685767, 0.6217176206091617, 0.6255139394898266, 0.6292981298566381, 0.6330698532698229, 0.6368287748937326, 0.6405745635850643, 0.644306891979308, 0.6480254365753887, 0.6517298778184553, 0.6554199001807951, 0.6590951922408244, 0.6627554467601383, 0.6664003607585898, 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