/*
* (c) Copyright Christian P. Fries, Germany. All rights reserved. Contact: email@christian-fries.de.
*
* Created on 09.02.2006
*/
package net.finmath.montecarlo;
import java.util.Arrays;
import java.util.function.DoubleBinaryOperator;
import java.util.function.DoubleUnaryOperator;
import java.util.function.IntToDoubleFunction;
import java.util.stream.DoubleStream;
import java.util.stream.IntStream;
import org.apache.commons.math3.util.FastMath;
import net.finmath.functions.DoubleTernaryOperator;
import net.finmath.stochastic.RandomVariableInterface;
/**
* The class RandomVariable represents a random variable being the evaluation of a stochastic process
* at a certain time within a Monte-Carlo simulation.
* It is thus essentially a vector of doubles - the realizations - together with a double - the time.
* The index of the vector represents path.
* The class may also be used for non-stochastic quantities which may potentially be stochastic
* (e.g. volatility). If only non-stochastic random variables are involved in an operation the class uses
* optimized code.
*
* Accesses performed exclusively through the interface
* <code>RandomVariableInterface</code> is thread safe (and does not mutate the class).
*
* The implementation require Java 8 or better.
*
* @author Christian Fries
* @version 2.0
*/
public class RandomVariable implements RandomVariableInterface {
private static final long serialVersionUID = -1352953450936857742L;
private final double time; // Time (filtration)
// Data model for the stochastic case (otherwise null)
private final double[] realizations; // Realizations
// Data model for the non-stochastic case (if realizations==null)
private final double valueIfNonStochastic;
/**
* Create a random variable from a given other implementation of <code>RandomVariableInterface</code>.
*
* @param value Object implementing <code>RandomVariableInterface</code>.
*/
public RandomVariable(RandomVariableInterface value) {
super();
this.time = value.getFiltrationTime();
this.realizations = value.isDeterministic() ? null : value.getRealizations();
this.valueIfNonStochastic = value.isDeterministic() ? value.get(0) : Double.NaN;
}
/**
* Create a non stochastic random variable, i.e. a constant.
*
* @param value the value, a constant.
*/
public RandomVariable(double value) {
this(-Double.MAX_VALUE, value);
}
/**
* Create a random variable by applying a function to a given other implementation of <code>RandomVariableInterface</code>.
*
* @param value Object implementing <code>RandomVariableInterface</code>.
* @param function A function mapping double to double.
*/
public RandomVariable(RandomVariableInterface value, DoubleUnaryOperator function) {
super();
this.time = value.getFiltrationTime();
this.realizations = value.isDeterministic() ? null : value.getRealizationsStream().map(function).toArray();
this.valueIfNonStochastic = value.isDeterministic() ? function.applyAsDouble(value.get(0)) : Double.NaN;
}
/**
* Create a non stochastic random variable, i.e. a constant.
*
* @param time the filtration time, set to 0.0 if not used.
* @param value the value, a constant.
*/
public RandomVariable(double time, double value) {
super();
this.time = time;
this.realizations = null;
this.valueIfNonStochastic = value;
}
/**
* Create a non stochastic random variable, i.e. a constant.
*
* @param time the filtration time, set to 0.0 if not used.
* @param numberOfPath The number of paths.
* @param value the value, a constant.
*/
public RandomVariable(double time, int numberOfPath, double value) {
super();
this.time = time;
this.realizations = new double[numberOfPath];
java.util.Arrays.fill(this.realizations, value);
this.valueIfNonStochastic = Double.NaN;
}
/**
* Create a stochastic random variable.
*
* Important: The realizations array is not cloned (not defensive copy is made).
*
* @TODO A future version should perform a defensive copy.
*
* @param time the filtration time, set to 0.0 if not used.
* @param realisations the vector of realizations.
*/
public RandomVariable(double time, double[] realisations) {
super();
this.time = time;
this.realizations = realisations;
this.valueIfNonStochastic = Double.NaN;
}
/**
* Create a stochastic random variable.
*
* @param time the filtration time, set to 0.0 if not used.
* @param realizations A map mapping integer (path or state) to double, representing this random variable.
* @param size The size, i.e., number of paths.
*/
public RandomVariable(double time, IntToDoubleFunction realizations, int size) {
super();
this.time = time;
this.realizations = size == 1 ? null : new double[size];//IntStream.range(0,size).parallel().mapToDouble(realisations).toArray();
this.valueIfNonStochastic = size == 1 ? realizations.applyAsDouble(0) : Double.NaN;
if(size > 1) {
IntStream.range(0,size).parallel().forEach(i ->
this.realizations[i] = realizations.applyAsDouble(i)
);
}
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getMutableCopy()
*/
public RandomVariable getMutableCopy() {
return this;
//if(isDeterministic()) return new RandomVariable(time, valueIfNonStochastic);
//else return new RandomVariable(time, realizations.clone());
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#equals(net.finmath.montecarlo.RandomVariable)
*/
@Override
public boolean equals(RandomVariableInterface randomVariable) {
if(this.time != randomVariable.getFiltrationTime()) return false;
if(this.isDeterministic() && randomVariable.isDeterministic()) {
return this.valueIfNonStochastic == randomVariable.get(0);
}
if(this.isDeterministic() != randomVariable.isDeterministic()) return false;
for(int i=0; i<realizations.length; i++) if(realizations[i] != randomVariable.get(i)) return false;
return true;
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getFiltrationTime()
*/
@Override
public double getFiltrationTime() {
return time;
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#get(int)
*/
@Override
public double get(int pathOrState) {
if(isDeterministic()) return valueIfNonStochastic;
else return realizations[pathOrState];
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#size()
*/
@Override
public int size() {
if(isDeterministic()) return 1;
else return realizations.length;
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getMin()
*/
@Override
public double getMin() {
if(isDeterministic()) return valueIfNonStochastic;
return getRealizationsStream().min().getAsDouble();
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getMax()
*/
@Override
public double getMax() {
if(isDeterministic()) return valueIfNonStochastic;
return getRealizationsStream().max().getAsDouble();
}
/**
* @return Sum of all realizations.
*/
public double getSum() {
if(isDeterministic()) return valueIfNonStochastic;
if(size() == 0) return Double.NaN;
return getRealizationsStream().sum();
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getAverage()
*/
@Override
public double getAverage() {
if(isDeterministic()) return valueIfNonStochastic;
if(size() == 0) return Double.NaN;
return getRealizationsStream().sum()/realizations.length;
}
@Override
public double getAverage(RandomVariableInterface probabilities) {
if(isDeterministic()) return valueIfNonStochastic;
if(size() == 0) return Double.NaN;
return this.mult(probabilities).getRealizationsStream().sum();
}
@Override
public double getVariance() {
if(isDeterministic() || size() == 1) return 0.0;
if(size() == 0) return Double.NaN;
double average = getAverage();
/*
* Kahan summation on (realizations[i] - average)^2
*/
double sum = 0.0;
double errorOfSum = 0.0;
for(int i=0; i<realizations.length; i++) {
double value = (realizations[i] - average)*(realizations[i] - average) - errorOfSum;
double newSum = sum + value;
errorOfSum = (newSum - sum) - value;
sum = newSum;
}
return sum/realizations.length;
}
@Override
public double getVariance(RandomVariableInterface probabilities) {
if(isDeterministic()) return 0.0;
if(size() == 0) return Double.NaN;
double average = getAverage(probabilities);
/*
* Kahan summation on (realizations[i] - average)^2 * probabilities.get(i)
*/
double sum = 0.0;
double errorOfSum = 0.0;
for(int i=0; i<realizations.length; i++) {
double value = (realizations[i] - average) * (realizations[i] - average) * probabilities.get(i) - errorOfSum;
double newSum = sum + value;
errorOfSum = (newSum - sum) - value;
sum = newSum;
}
return sum;
}
@Override
public double getSampleVariance() {
if(isDeterministic() || size() == 1) return 0.0;
if(size() == 0) return Double.NaN;
return getVariance() * size()/(size()-1);
}
@Override
public double getStandardDeviation() {
if(isDeterministic()) return 0.0;
if(size() == 0) return Double.NaN;
return Math.sqrt(getVariance());
}
@Override
public double getStandardDeviation(RandomVariableInterface probabilities) {
if(isDeterministic()) return 0.0;
if(size() == 0) return Double.NaN;
return Math.sqrt(getVariance(probabilities));
}
@Override
public double getStandardError() {
if(isDeterministic()) return 0.0;
if(size() == 0) return Double.NaN;
return getStandardDeviation()/Math.sqrt(size());
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getStandardError(net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public double getStandardError(RandomVariableInterface probabilities) {
if(isDeterministic()) return 0.0;
if(size() == 0) return Double.NaN;
return getStandardDeviation(probabilities)/Math.sqrt(size());
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getQuantile()
*/
@Override
public double getQuantile(double quantile) {
if(isDeterministic()) return valueIfNonStochastic;
if(size() == 0) return Double.NaN;
double[] realizationsSorted = realizations.clone();
java.util.Arrays.sort(realizationsSorted);
int indexOfQuantileValue = Math.min(Math.max((int)Math.round((size()+1) * (1-quantile) - 1), 0), size()-1);
return realizationsSorted[indexOfQuantileValue];
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getQuantile(net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public double getQuantile(double quantile, RandomVariableInterface probabilities) {
if(isDeterministic()) return valueIfNonStochastic;
if(size() == 0) return Double.NaN;
throw new RuntimeException("Method not implemented.");
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getConditionalVaR()
*/
@Override
public double getQuantileExpectation(double quantileStart, double quantileEnd) {
if(isDeterministic()) return valueIfNonStochastic;
if(size() == 0) return Double.NaN;
if(quantileStart > quantileEnd) return getQuantileExpectation(quantileEnd, quantileStart);
double[] realizationsSorted = realizations.clone();
java.util.Arrays.sort(realizationsSorted);
int indexOfQuantileValueStart = Math.min(Math.max((int)Math.round((size()+1) * quantileStart - 1), 0), size()-1);
int indexOfQuantileValueEnd = Math.min(Math.max((int)Math.round((size()+1) * quantileEnd - 1), 0), size()-1);
double quantileExpectation = 0.0;
for (int i=indexOfQuantileValueStart; i<=indexOfQuantileValueEnd;i++) {
quantileExpectation += realizationsSorted[i];
}
quantileExpectation /= indexOfQuantileValueEnd-indexOfQuantileValueStart+1;
return quantileExpectation;
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getHistogram()
*/
@Override
public double[] getHistogram(double[] intervalPoints)
{
double[] histogramValues = new double[intervalPoints.length+1];
if(isDeterministic()) {
/*
* If the random variable is deterministic we will return an array
* consisting of 0's and one and only one 1.
*/
java.util.Arrays.fill(histogramValues, 0.0);
for (int intervalIndex=0; intervalIndex<intervalPoints.length; intervalIndex++)
{
if(valueIfNonStochastic > intervalPoints[intervalIndex]) {
histogramValues[intervalIndex] = 1.0;
break;
}
}
histogramValues[intervalPoints.length] = 1.0;
}
else {
/*
* If the random variable is deterministic we will return an array
* representing a density, where the sum of the entries is one.
* There is one exception:
* If the size of the random variable is 0, all entries will be zero.
*/
double[] realizationsSorted = realizations.clone();
java.util.Arrays.sort(realizationsSorted);
int sampleIndex=0;
for (int intervalIndex=0; intervalIndex<intervalPoints.length; intervalIndex++)
{
int sampleCount = 0;
while (sampleIndex < realizationsSorted.length &&
realizationsSorted[sampleIndex] <= intervalPoints[intervalIndex])
{
sampleIndex++;
sampleCount++;
}
histogramValues[intervalIndex] = sampleCount;
}
histogramValues[intervalPoints.length] = realizationsSorted.length-sampleIndex;
// Normalize histogramValues
if(realizationsSorted.length > 0) {
for(int i=0; i<histogramValues.length; i++) histogramValues[i] /= realizationsSorted.length;
}
}
return histogramValues;
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#getHistogram(int,double)
*/
@Override
public double[][] getHistogram(int numberOfPoints, double standardDeviations) {
double[] intervalPoints = new double[numberOfPoints];
double[] anchorPoints = new double[numberOfPoints+1];
double center = getAverage();
double radius = standardDeviations * getStandardDeviation();
double stepSize = (double) (numberOfPoints-1) / 2.0;
for(int i=0; i<numberOfPoints;i++) {
double alpha = (-(double)(numberOfPoints-1) / 2.0 + (double)i) / stepSize;
intervalPoints[i] = center + alpha * radius;
anchorPoints[i] = center + alpha * radius - radius / (2 * stepSize);
}
anchorPoints[numberOfPoints] = center + 1 * radius + radius / (2 * stepSize);
double[][] result = new double[2][];
result[0] = anchorPoints;
result[1] = getHistogram(intervalPoints);
return result;
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#isDeterministic()
*/
@Override
public boolean isDeterministic() {
return realizations == null;
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#expand()
*/
public RandomVariableInterface expand(int numberOfPaths) {
if(isDeterministic()) {
// Expand random variable to a vector of path values
double[] clone = new double[numberOfPaths];
java.util.Arrays.fill(clone,valueIfNonStochastic);
return new RandomVariable(time,clone);
}
return new RandomVariable(time,realizations.clone());
}
@Override
public DoubleStream getRealizationsStream() {
if(isDeterministic()) {
return DoubleStream.generate(() -> {
return valueIfNonStochastic;
});
}
else {
return Arrays.stream(realizations);
}
}
@Override
public double[] getRealizations() {
if(isDeterministic()) {
double[] result = new double[1];
result[0] = get(0);
return result;
}
else {
return realizations.clone();
}
}
public IntToDoubleFunction getOperator() {
if(isDeterministic()) {
return i -> valueIfNonStochastic;
}
else {
return i -> realizations[i];
}
}
/**
* Returns the realizations as double array. If the random variable is deterministic, then it is expanded
* to the given number of paths.
*
* @param numberOfPaths Number of paths.
* @return The realization as double array.
*/
@Override
public double[] getRealizations(int numberOfPaths) {
if(isDeterministic()) {
// Expand random variable to a vector of path values
double[] v = new double[numberOfPaths];
java.util.Arrays.fill(v,valueIfNonStochastic);
return v;
}
if(!isDeterministic() && realizations.length != numberOfPaths) throw new RuntimeException("Inconsistent number of paths.");
return realizations.clone();
}
@Override
public RandomVariableInterface cache() {
return this;
}
@Override
public RandomVariableInterface apply(DoubleUnaryOperator operator) {
if(isDeterministic()) {
return new RandomVariable(time, operator.applyAsDouble(valueIfNonStochastic));
}
else
{
// Still faster than a parallel stream (2014.04)
double[] result = new double[this.realizations.length];
for(int i=0; i<result.length; i++) result[i] = operator.applyAsDouble(realizations[i]);
return new RandomVariable(time, result);
}
}
@Override
public RandomVariableInterface apply(DoubleBinaryOperator operator, RandomVariableInterface argument) {
double newTime = Math.max(time, argument.getFiltrationTime());
int newSize = Math.max(this.size(), argument.size());
IntToDoubleFunction argument0Operator = this.getOperator();
IntToDoubleFunction argument1Operator = argument.getOperator();
IntToDoubleFunction result = i -> operator.applyAsDouble(argument0Operator.applyAsDouble(i), argument1Operator.applyAsDouble(i));
return new RandomVariable(newTime, result, newSize);
}
@Override
public RandomVariableInterface apply(DoubleTernaryOperator operator, RandomVariableInterface argument1, RandomVariableInterface argument2) {
double newTime = Math.max(time, argument1.getFiltrationTime());
newTime = Math.max(newTime, argument2.getFiltrationTime());
int newSize = Math.max(Math.max(this.size(), argument1.size()), argument2.size());
IntToDoubleFunction argument0Operator = this.getOperator();
IntToDoubleFunction argument1Operator = argument1.getOperator();
IntToDoubleFunction argument2Operator = argument2.getOperator();
IntToDoubleFunction result = i -> operator.applyAsDouble(argument0Operator.applyAsDouble(i), argument1Operator.applyAsDouble(i), argument2Operator.applyAsDouble(i));
return new RandomVariable(newTime, result, newSize);
}
public RandomVariableInterface apply(DoubleBinaryOperator operatorOuter, DoubleBinaryOperator operatorInner, RandomVariableInterface argument1, RandomVariableInterface argument2)
{
return apply((x,y,z) -> operatorOuter.applyAsDouble(x,operatorInner.applyAsDouble(y,z)), argument1, argument2);
}
@Override
public RandomVariableInterface cap(double cap) {
return apply(x -> Math.min(x, cap));
}
@Override
public RandomVariableInterface floor(double floor) {
return apply(x -> Math.max(x, floor));
}
@Override
public RandomVariableInterface add(double value) {
return apply(x -> x + value);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#sub(double)
*/
@Override
public RandomVariableInterface sub(double value) {
return apply(x -> x - value);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#mult(double)
*/
@Override
public RandomVariableInterface mult(double value) {
return apply(x -> x * value);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#div(double)
*/
@Override
public RandomVariableInterface div(double value) {
return apply(x -> x / value);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#pow(double)
*/
@Override
public RandomVariableInterface pow(double exponent) {
return apply(x -> FastMath.pow(x, exponent));
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#squared()
*/
@Override
public RandomVariableInterface squared() {
return apply(x -> x * x);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#sqrt()
*/
@Override
public RandomVariableInterface sqrt() {
return apply(FastMath::sqrt);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#exp()
*/
@Override
public RandomVariableInterface exp() {
return apply(FastMath::exp);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#log()
*/
@Override
public RandomVariableInterface log() {
return apply(FastMath::log);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#sin()
*/
@Override
public RandomVariableInterface sin() {
return apply(FastMath::sin);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#cos()
*/
@Override
public RandomVariableInterface cos() {
return apply(FastMath::cos);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#add(net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface add(RandomVariableInterface randomVariable) {
return apply((x, y) -> x + y, randomVariable);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#sub(net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface sub(RandomVariableInterface randomVariable) {
return apply((x, y) -> x - y, randomVariable);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#mult(net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface mult(RandomVariableInterface randomVariable) {
return apply((x, y) -> x * y, randomVariable);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#div(net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface div(RandomVariableInterface randomVariable) {
return apply((x, y) -> x / y, randomVariable);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#cap(net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface cap(RandomVariableInterface cap) {
return apply(FastMath::min, cap);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#floor(net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface floor(RandomVariableInterface floor) {
return apply(FastMath::max, floor);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#accrue(net.finmath.stochastic.RandomVariableInterface, double)
*/
@Override
public RandomVariableInterface accrue(RandomVariableInterface rate, double periodLength) {
return apply((x, y) -> x * (1.0 + y * periodLength), rate);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#discount(net.finmath.stochastic.RandomVariableInterface, double)
*/
@Override
public RandomVariableInterface discount(RandomVariableInterface rate, double periodLength) {
return apply((x, y) -> x / (1.0 + y * periodLength), rate);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#barrier(net.finmath.stochastic.RandomVariableInterface, net.finmath.stochastic.RandomVariableInterface, net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface barrier(RandomVariableInterface trigger, RandomVariableInterface valueIfTriggerNonNegative, RandomVariableInterface valueIfTriggerNegative) {
return trigger.apply(( x, y, z) -> (x >= 0 ? y : z), valueIfTriggerNonNegative, valueIfTriggerNegative);
}
@Override
public RandomVariableInterface barrier(RandomVariableInterface trigger, RandomVariableInterface valueIfTriggerNonNegative, double valueIfTriggerNegative) {
return this.barrier(trigger, valueIfTriggerNonNegative, new RandomVariable(valueIfTriggerNonNegative.getFiltrationTime(), valueIfTriggerNegative));
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#invert()
*/
@Override
public RandomVariableInterface invert() {
return apply(x -> 1.0 / x);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#abs()
*/
@Override
public RandomVariableInterface abs() {
return apply(Math::abs);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#addProduct(net.finmath.stochastic.RandomVariableInterface, double)
*/
@Override
public RandomVariableInterface addProduct(RandomVariableInterface factor1, double factor2) {
return apply((x, y) -> x + y * factor2, factor1);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#addProduct(net.finmath.stochastic.RandomVariableInterface, net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface addProduct(RandomVariableInterface factor1, RandomVariableInterface factor2) {
return apply((x,y) -> x + y, (x, y) -> x * y, factor1, factor2);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#addRatio(net.finmath.stochastic.RandomVariableInterface, net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface addRatio(RandomVariableInterface numerator, RandomVariableInterface denominator) {
return apply((x, y) -> x + y, (x, y) -> x / y, numerator, denominator);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#subRatio(net.finmath.stochastic.RandomVariableInterface, net.finmath.stochastic.RandomVariableInterface)
*/
@Override
public RandomVariableInterface subRatio(RandomVariableInterface numerator, RandomVariableInterface denominator) {
return apply((x,y) -> x - y, (x, y) -> x / y, numerator, denominator);
}
/* (non-Javadoc)
* @see net.finmath.stochastic.RandomVariableInterface#isNaN()
*/
@Override
public RandomVariableInterface isNaN() {
if(isDeterministic()) {
return new RandomVariable(time, Double.isNaN(valueIfNonStochastic) ? 1.0 : 0.0);
}
else {
double[] newRealizations = new double[size()];
for(int i=0; i<newRealizations.length; i++) newRealizations[i] = Double.isNaN(get(i)) ? 1.0 : 0.0;
return new RandomVariable(time, newRealizations);
}
}
@Override
public String toString() {
return super.toString()
+ "\n" + "time: " + time
+ "\n" + "realizations: " +
(isDeterministic() ? valueIfNonStochastic : Arrays.toString(realizations));
}
}