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finmath lib is a Mathematical Finance Library in Java.
It provides algorithms and methodologies related to mathematical finance.
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/*
* (c) Copyright Christian P. Fries, Germany. Contact: [email protected].
*
* Created on 17.06.2017
*/
package net.finmath.montecarlo.automaticdifferentiation.forward;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.TreeMap;
import java.util.concurrent.atomic.AtomicLong;
import java.util.function.DoubleBinaryOperator;
import java.util.function.DoubleUnaryOperator;
import java.util.function.Function;
import java.util.function.IntToDoubleFunction;
import java.util.stream.Collectors;
import java.util.stream.DoubleStream;
import net.finmath.functions.DoubleTernaryOperator;
import net.finmath.montecarlo.RandomVariableFromDoubleArray;
import net.finmath.montecarlo.automaticdifferentiation.RandomVariableDifferentiable;
import net.finmath.stochastic.ConditionalExpectationEstimator;
import net.finmath.stochastic.RandomVariable;
import net.finmath.stochastic.Scalar;
/**
* Implementation of RandomVariableDifferentiable using
* the forward algorithmic differentiation (AD).
*
* This class implements the optimized stochastic AD as it is described in
* ssrn.com/abstract=2995695.
* For details see http://christianfries.com/finmath/stochasticautodiff/.
*
* @author Christian Fries
* @author Stefan Sedlmair
* @version 1.1
*/
public class RandomVariableDifferentiableAD implements RandomVariableDifferentiable {
private static final long serialVersionUID = 2459373647785530657L;
private static final int typePriorityDefault = 3;
private final int typePriority;
private static AtomicLong indexOfNextRandomVariable = new AtomicLong(0);
private enum OperatorType {
ADD, MULT, DIV, SUB, SQUARED, SQRT, LOG, SIN, COS, EXP, INVERT, CAP, FLOOR, ABS,
ADDPRODUCT, ADDRATIO, SUBRATIO, BARRIER, DISCOUNT, ACCRUE, POW, MIN, MAX, AVERAGE, VARIANCE,
STDEV, STDERROR, SVARIANCE, AVERAGE2, VARIANCE2,
STDEV2, STDERROR2, CONDITIONAL_EXPECTATION
}
/**
* A node in the operator tree. It
* stores an id (the index m), the operator (the function f_m), and the arguments.
* It also stores reference to the argument values, if required.
*
* @author Christian Fries
*/
private static class OperatorTreeNode {
private final Long id;
private final OperatorType operatorType;
private final List arguments;
private final List argumentValues;
private final Object operator;
private static final RandomVariable zero = new Scalar(0.0);
private static final RandomVariable one = new Scalar(1.0);
private static final RandomVariable minusOne = new Scalar(-1.0);
OperatorTreeNode(final OperatorType operatorType, final List arguments, final Object operator) {
this(operatorType,
arguments != null ? arguments.stream().map(new Function() {
@Override
public OperatorTreeNode apply(final RandomVariable x) {
return (x != null && x instanceof RandomVariableDifferentiableAD) ? ((RandomVariableDifferentiableAD)x).getOperatorTreeNode(): null;
}
}).collect(Collectors.toList()) : null,
arguments != null ? arguments.stream().map(new Function() {
@Override
public RandomVariable apply(final RandomVariable x) {
return (x != null && x instanceof RandomVariableDifferentiableAD) ? ((RandomVariableDifferentiableAD)x).getValues() : x;
}
}).collect(Collectors.toList()) : null,
operator
);
}
OperatorTreeNode(final OperatorType operatorType, final List arguments, List argumentValues, final Object operator) {
super();
id = indexOfNextRandomVariable.getAndIncrement();
this.operatorType = operatorType;
this.arguments = arguments;
this.operator = operator;
// This is the simple modification which reduces memory requirements.
if(operatorType != null && (operatorType.equals(OperatorType.ADD) || operatorType.equals(OperatorType.SUB))) {
// Addition does not need to retain arguments
argumentValues = null;
}
else if(operatorType != null && operatorType.equals(OperatorType.AVERAGE)) {
// Average does not need to retain arguments
argumentValues = null;
}
else if(operatorType != null && operatorType.equals(OperatorType.MULT)) {
// Product only needs to retain factors on differentiables
if(arguments.get(0) == null) {
argumentValues.set(1, null);
}
if(arguments.get(1) == null) {
argumentValues.set(0, null);
}
}
else if(operatorType != null && operatorType.equals(OperatorType.DIV)) {
// Division only needs to retain numerator if denominator is differentiable
if(arguments.get(1) == null) {
argumentValues.set(0, null);
}
}
else if(operatorType != null && operatorType.equals(OperatorType.ADDPRODUCT)) {
// Addition does not need to retain arguments
argumentValues.set(0, null);
// Addition of product only needs to retain factors on differentiables
if(arguments.get(1) == null) {
argumentValues.set(2, null);
}
if(arguments.get(2) == null) {
argumentValues.set(1, null);
}
}
else if(operatorType != null && operatorType.equals(OperatorType.ACCRUE)) {
// Addition of product only needs to retain factors on differentiables
if(arguments.get(1) == null && arguments.get(2) == null) {
argumentValues.set(0, null);
}
if(arguments.get(0) == null && arguments.get(1) == null) {
argumentValues.set(1, null);
}
if(arguments.get(0) == null && arguments.get(2) == null) {
argumentValues.set(2, null);
}
}
else if(operatorType != null && operatorType.equals(OperatorType.BARRIER)) {
if(arguments.get(0) == null) {
argumentValues.set(1, null);
argumentValues.set(2, null);
}
}
this.argumentValues = argumentValues;
}
private void propagateDerivativesFromResultToArgument(final Map derivatives) {
if(arguments == null) {
return;
}
for(int argumentIndex = 0; argumentIndex < arguments.size(); argumentIndex++) {
final OperatorTreeNode argument = arguments.get(argumentIndex);
if(argument != null) {
final Long argumentID = argument.id;
final RandomVariable partialDerivative = getPartialDerivative(argument, argumentIndex);
RandomVariable derivative = derivatives.get(id);
RandomVariable argumentDerivative = derivatives.get(argumentID);
// Implementation of AVERAGE (see https://ssrn.com/abstract=2995695 for details).
if(operatorType == OperatorType.AVERAGE) {
derivative = derivative.average();
}
// Implementation of CONDITIONAL_EXPECTATION (see https://ssrn.com/abstract=2995695 for details).
if(operatorType == OperatorType.CONDITIONAL_EXPECTATION) {
final ConditionalExpectationEstimator estimator = (ConditionalExpectationEstimator)operator;
derivative = estimator.getConditionalExpectation(derivative);
}
if(argumentDerivative == null) {
argumentDerivative = derivative.mult(partialDerivative);
}
else {
argumentDerivative = argumentDerivative.addProduct(partialDerivative, derivative);
}
derivatives.put(argumentID, argumentDerivative);
}
}
}
private RandomVariable getPartialDerivative(final OperatorTreeNode differential, final int differentialIndex) {
if(!arguments.contains(differential)) {
return zero;
}
final RandomVariable X = arguments.size() > 0 && argumentValues != null ? argumentValues.get(0) : null;
final RandomVariable Y = arguments.size() > 1 && argumentValues != null ? argumentValues.get(1) : null;
final RandomVariable Z = arguments.size() > 2 && argumentValues != null ? argumentValues.get(2) : null;
RandomVariable derivative = null;
switch(operatorType) {
/* functions with one argument */
case SQUARED:
derivative = X.mult(2.0);
break;
case SQRT:
derivative = X.sqrt().invert().mult(0.5);
break;
case EXP:
derivative = X.exp();
break;
case LOG:
derivative = X.invert();
break;
case SIN:
derivative = X.cos();
break;
case COS:
derivative = X.sin().mult(-1.0);
break;
case INVERT:
derivative = X.invert().squared().mult(-1);
break;
case AVERAGE:
derivative = one;
break;
case CONDITIONAL_EXPECTATION:
derivative = one;
break;
case VARIANCE:
derivative = X.sub(X.getAverage()*(2.0*X.size()-1.0)/X.size()).mult(2.0/X.size());
break;
case STDEV:
derivative = X.sub(X.getAverage()*(2.0*X.size()-1.0)/X.size()).mult(2.0/X.size()).mult(0.5).div(Math.sqrt(X.getVariance()));
break;
case MIN:
final double min = X.getMin();
derivative = X.apply(new DoubleUnaryOperator() {
@Override
public double applyAsDouble(final double x) {
return (x == min) ? 1.0 : 0.0;
}
});
break;
case MAX:
final double max = X.getMax();
derivative = X.apply(new DoubleUnaryOperator() {
@Override
public double applyAsDouble(final double x) {
return (x == max) ? 1.0 : 0.0;
}
});
break;
case ABS:
derivative = X.choose(one, minusOne);
break;
case STDERROR:
derivative = X.sub(X.getAverage()*(2.0*X.size()-1.0)/X.size()).mult(2.0/X.size()).mult(0.5).div(Math.sqrt(X.getVariance() * X.size()));
break;
case SVARIANCE:
derivative = X.sub(X.getAverage()*(2.0*X.size()-1.0)/X.size()).mult(2.0/(X.size()-1));
break;
case ADD:
derivative = one;
break;
case SUB:
derivative = differentialIndex == 0 ? one : minusOne;
break;
case MULT:
derivative = differentialIndex == 0 ? Y : X;
break;
case DIV:
derivative = differentialIndex == 0 ? Y.invert() : X.div(Y.squared()).mult(-1);
break;
case CAP:
if(differentialIndex == 0) {
derivative = X.sub(Y).choose(zero, one);
}
else {
derivative = X.sub(Y).choose(one, zero);
}
break;
case FLOOR:
if(differentialIndex == 0) {
derivative = X.sub(Y).choose(one, zero);
}
else {
derivative = X.sub(Y).choose(zero, one);
}
break;
case AVERAGE2:
derivative = differentialIndex == 0 ? Y : X;
break;
case VARIANCE2:
derivative = differentialIndex == 0 ? Y.mult(2.0).mult(X.mult(Y.add(X.getAverage(Y)*(X.size()-1)).sub(X.getAverage(Y)))) :
X.mult(2.0).mult(Y.mult(X.add(Y.getAverage(X)*(X.size()-1)).sub(Y.getAverage(X))));
break;
case STDEV2:
derivative = differentialIndex == 0 ? Y.mult(2.0).mult(X.mult(Y.add(X.getAverage(Y)*(X.size()-1)).sub(X.getAverage(Y)))).div(Math.sqrt(X.getVariance(Y))) :
X.mult(2.0).mult(Y.mult(X.add(Y.getAverage(X)*(X.size()-1)).sub(Y.getAverage(X)))).div(Math.sqrt(Y.getVariance(X)));
break;
case STDERROR2:
derivative = differentialIndex == 0 ? Y.mult(2.0).mult(X.mult(Y.add(X.getAverage(Y)*(X.size()-1)).sub(X.getAverage(Y)))).div(Math.sqrt(X.getVariance(Y) * X.size())) :
X.mult(2.0).mult(Y.mult(X.add(Y.getAverage(X)*(X.size()-1)).sub(Y.getAverage(X)))).div(Math.sqrt(Y.getVariance(X) * Y.size()));
break;
case POW:
// second argument will always be deterministic and constant.
// @TODO Optimize this part by making use of Y being scalar.
derivative = (differentialIndex == 0) ? X.pow(Y.getAverage() - 1.0).mult(Y) : zero;
break;
case ADDPRODUCT:
if(differentialIndex == 0) {
derivative = one;
} else if(differentialIndex == 1) {
derivative = Z;
} else {
derivative = Y;
}
break;
case ADDRATIO:
if(differentialIndex == 0) {
derivative = one;
} else if(differentialIndex == 1) {
derivative = Z.invert();
} else {
derivative = Y.div(Z.squared()).mult(-1.0);
}
break;
case SUBRATIO:
if(differentialIndex == 0) {
derivative = one;
} else if(differentialIndex == 1) {
derivative = Z.invert().mult(-1.0);
} else {
derivative = Y.div(Z.squared());
}
break;
case ACCRUE:
if(differentialIndex == 0) {
derivative = Y.mult(Z).add(1.0);
} else if(differentialIndex == 1) {
derivative = X.mult(Z);
} else {
derivative = X.mult(Y);
}
break;
case DISCOUNT:
if(differentialIndex == 0) {
derivative = Y.mult(Z).add(1.0).invert();
} else if(differentialIndex == 1) {
derivative = X.mult(Z).div(Y.mult(Z).add(1.0).squared()).mult(-1.0);
} else {
derivative = X.mult(Y).div(Y.mult(Z).add(1.0).squared()).mult(-1.0);
}
break;
case BARRIER:
if(differentialIndex == 0) {
/*
* Approximation via local finite difference
* (see https://ssrn.com/abstract=2995695 for details).
*/
derivative = Y.sub(Z);
final double epsilon = 0.2*X.getStandardDeviation();
derivative = derivative.mult(X.add(epsilon/2).choose(new RandomVariableFromDoubleArray(1.0), new RandomVariableFromDoubleArray(0.0)));
derivative = derivative.mult(X.sub(epsilon/2).choose(new RandomVariableFromDoubleArray(0.0), new RandomVariableFromDoubleArray(1.0)));
derivative = derivative.div(epsilon);
} else if(differentialIndex == 1) {
derivative = X.choose(one, zero);
} else {
derivative = X.choose(zero, one);
}
break;
default:
throw new IllegalArgumentException("Operation " + operatorType.name() + " not supported in differentiation.");
}
return derivative;
}
}
/*
* Data model. We maintain the underlying values and a link to the node in the operator tree.
*/
private RandomVariable values;
private final OperatorTreeNode operatorTreeNode;
public static RandomVariableDifferentiableAD of(final double value) {
return new RandomVariableDifferentiableAD(value);
}
public static RandomVariableDifferentiableAD of(final RandomVariable randomVariable) {
return new RandomVariableDifferentiableAD(randomVariable);
}
public RandomVariableDifferentiableAD(final double value) {
this(new RandomVariableFromDoubleArray(value), null, null);
}
public RandomVariableDifferentiableAD(final double time, final double[] realisations) {
this(new RandomVariableFromDoubleArray(time, realisations), null, null);
}
public RandomVariableDifferentiableAD(final RandomVariable randomVariable) {
this(randomVariable, null, null);
}
private RandomVariableDifferentiableAD(final RandomVariable values, final List arguments, final OperatorType operator) {
this(values, arguments, null, operator);
}
public RandomVariableDifferentiableAD(final RandomVariable values, final List arguments, final ConditionalExpectationEstimator estimator, final OperatorType operator) {
this(values, arguments, estimator, operator, typePriorityDefault);
}
public RandomVariableDifferentiableAD(final RandomVariable values, final List arguments, final ConditionalExpectationEstimator estimator, final OperatorType operator, final int methodArgumentTypePriority) {
super();
this.values = values;
operatorTreeNode = new OperatorTreeNode(operator, arguments, estimator);
typePriority = methodArgumentTypePriority;
}
public OperatorTreeNode getOperatorTreeNode() {
return operatorTreeNode;
}
/**
* Returns the underlying values.
*
* @return The underling values.
*/
@Override
public RandomVariable getValues(){
return values;
}
@Override
public Long getID(){
return getOperatorTreeNode().id;
}
/**
* Returns the gradient of this random variable with respect to all its leaf nodes.
* The method calculated the map \( v \mapsto \frac{d u}{d v} \) where \( u \) denotes this.
*
* Performs a backward automatic differentiation.
*
* @return The gradient map.
*/
@Override
public Map getGradient(final Set independentIDs) {
// The map maintaining the derivatives id -> derivative
final Map derivatives = new HashMap<>();
// Put derivative of this node w.r.t. itself
derivatives.put(getID(), new RandomVariableFromDoubleArray(1.0));
// The set maintaining the independents. Note: TreeMap is maintaining a sort on the keys.
final TreeMap independents = new TreeMap<>();
independents.put(getID(), getOperatorTreeNode());
while(independents.size() > 0) {
// Process node with the highest id in independents
final Map.Entry independentEntry = independents.lastEntry();
final Long id = independentEntry.getKey();
final OperatorTreeNode independent = independentEntry.getValue();
// Get arguments of this node and propagate derivative to arguments
final List arguments = independent.arguments;
if(arguments != null && arguments.size() > 0) {
independent.propagateDerivativesFromResultToArgument(derivatives);
// Add all non constant arguments to the list of independents
for(final OperatorTreeNode argument : arguments) {
if(argument != null) {
final Long argumentId = argument.id;
independents.put(argumentId, argument);
}
}
// Remove id from derivatives - keep only leaf nodes.
derivatives.remove(id);
}
// Done with processing. Remove from map.
independents.remove(id);
}
return derivatives;
}
@Override
public Map getTangents(final Set dependentIDs) {
throw new UnsupportedOperationException();
}
/*
* The following methods are end points since they return double values.
* You cannot differentiate these results.
*/
@Override
public boolean equals(final RandomVariable randomVariable) {
return getValues().equals(randomVariable);
}
@Override
public double getFiltrationTime() {
return getValues().getFiltrationTime();
}
@Override
public int getTypePriority() {
return typePriority;
}
@Override
public double get(final int pathOrState) {
return getValues().get(pathOrState);
}
@Override
public int size() {
return getValues().size();
}
@Override
public boolean isDeterministic() {
return getValues().isDeterministic();
}
@Override
public double[] getRealizations() {
return getValues().getRealizations();
}
@Override
public Double doubleValue() {
return getValues().doubleValue();
}
@Override
public double getMin() {
return getValues().getMin();
}
@Override
public double getMax() {
return getValues().getMax();
}
@Override
public double getAverage() {
return getValues().getAverage();
}
@Override
public double getAverage(final RandomVariable probabilities) {
return getValues().getAverage(probabilities);
}
@Override
public double getVariance() {
return getValues().getVariance();
}
@Override
public double getVariance(final RandomVariable probabilities) {
return getValues().getVariance(probabilities);
}
@Override
public double getSampleVariance() {
return getValues().getSampleVariance();
}
@Override
public double getStandardDeviation() {
return getValues().getStandardDeviation();
}
@Override
public double getStandardDeviation(final RandomVariable probabilities) {
return getValues().getStandardDeviation(probabilities);
}
@Override
public double getStandardError() {
return getValues().getStandardError();
}
@Override
public double getStandardError(final RandomVariable probabilities) {
return getValues().getStandardError(probabilities);
}
@Override
public double getQuantile(final double quantile) {
return getValues().getQuantile(quantile);
}
@Override
public double getQuantile(final double quantile, final RandomVariable probabilities) {
return getValues().getQuantile(quantile, probabilities);
}
@Override
public double getQuantileExpectation(final double quantileStart, final double quantileEnd) {
return getValues().getQuantileExpectation(quantileStart, quantileEnd);
}
@Override
public double[] getHistogram(final double[] intervalPoints) {
return getValues().getHistogram(intervalPoints);
}
@Override
public double[][] getHistogram(final int numberOfPoints, final double standardDeviations) {
return getValues().getHistogram(numberOfPoints, standardDeviations);
}
/*
* The following methods are operations with are differentiable.
*/
@Override
public RandomVariable cache() {
values = values.cache();
return this;
}
@Override
public RandomVariable cap(final double cap) {
return new RandomVariableDifferentiableAD(
getValues().cap(cap),
Arrays.asList(this, new RandomVariableFromDoubleArray(cap)),
OperatorType.CAP);
}
@Override
public RandomVariable floor(final double floor) {
return new RandomVariableDifferentiableAD(
getValues().floor(floor),
Arrays.asList(this, new RandomVariableFromDoubleArray(floor)),
OperatorType.FLOOR);
}
@Override
public RandomVariable add(final double value) {
return new RandomVariableDifferentiableAD(
getValues().add(value),
Arrays.asList(this, new RandomVariableFromDoubleArray(value)),
OperatorType.ADD);
}
@Override
public RandomVariable sub(final double value) {
return new RandomVariableDifferentiableAD(
getValues().sub(value),
Arrays.asList(this, new RandomVariableFromDoubleArray(value)),
OperatorType.SUB);
}
@Override
public RandomVariable mult(final double value) {
return new RandomVariableDifferentiableAD(
getValues().mult(value),
Arrays.asList(this, new RandomVariableFromDoubleArray(value)),
OperatorType.MULT);
}
@Override
public RandomVariable div(final double value) {
return new RandomVariableDifferentiableAD(
getValues().div(value),
Arrays.asList(this, new RandomVariableFromDoubleArray(value)),
OperatorType.DIV);
}
@Override
public RandomVariable pow(final double exponent) {
return new RandomVariableDifferentiableAD(
getValues().pow(exponent),
Arrays.asList(this, new RandomVariableFromDoubleArray(exponent)),
OperatorType.POW);
}
@Override
public RandomVariable average() {
return new RandomVariableDifferentiableAD(
getValues().average(),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.AVERAGE);
}
@Override
public RandomVariable getConditionalExpectation(final ConditionalExpectationEstimator estimator) {
return new RandomVariableDifferentiableAD(
getValues().average(),
Arrays.asList(new RandomVariable[]{ this }),
estimator,
OperatorType.CONDITIONAL_EXPECTATION);
}
@Override
public RandomVariable squared() {
return new RandomVariableDifferentiableAD(
getValues().squared(),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.SQUARED);
}
@Override
public RandomVariable sqrt() {
return new RandomVariableDifferentiableAD(
getValues().sqrt(),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.SQRT);
}
@Override
public RandomVariable exp() {
return new RandomVariableDifferentiableAD(
getValues().exp(),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.EXP);
}
@Override
public RandomVariable log() {
return new RandomVariableDifferentiableAD(
getValues().log(),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.LOG);
}
@Override
public RandomVariable sin() {
return new RandomVariableDifferentiableAD(
getValues().sin(),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.SIN);
}
@Override
public RandomVariable cos() {
return new RandomVariableDifferentiableAD(
getValues().cos(),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.COS);
}
/*
* Binary operators: checking for return type priority.
*/
@Override
public RandomVariable add(final RandomVariable randomVariable) {
if(randomVariable.getTypePriority() > this.getTypePriority()) {
// Check type priority
return randomVariable.add(this);
}
return new RandomVariableDifferentiableAD(
getValues().add(randomVariable.getValues()),
Arrays.asList(this, randomVariable),
OperatorType.ADD);
}
@Override
public RandomVariable sub(final RandomVariable randomVariable) {
if(randomVariable.getTypePriority() > this.getTypePriority()) {
// Check type priority
return randomVariable.bus(this);
}
return new RandomVariableDifferentiableAD(
getValues().sub(randomVariable.getValues()),
Arrays.asList(this, randomVariable),
OperatorType.SUB);
}
@Override
public RandomVariable bus(final RandomVariable randomVariable) {
if(randomVariable.getTypePriority() > this.getTypePriority()) {
// Check type priority
return randomVariable.sub(this);
}
return new RandomVariableDifferentiableAD(
getValues().bus(randomVariable.getValues()),
Arrays.asList(randomVariable, this), // SUB with swapped arguments
OperatorType.SUB);
}
@Override
public RandomVariable mult(final RandomVariable randomVariable) {
if(randomVariable.getTypePriority() > this.getTypePriority()) {
// Check type priority
return randomVariable.mult(this);
}
return new RandomVariableDifferentiableAD(
getValues().mult(randomVariable.getValues()),
Arrays.asList(this, randomVariable),
OperatorType.MULT);
}
@Override
public RandomVariable div(final RandomVariable randomVariable) {
if(randomVariable.getTypePriority() > this.getTypePriority()) {
// Check type priority
return randomVariable.vid(this);
}
return new RandomVariableDifferentiableAD(
getValues().div(randomVariable.getValues()),
Arrays.asList(this, randomVariable),
OperatorType.DIV);
}
@Override
public RandomVariable vid(final RandomVariable randomVariable) {
if(randomVariable.getTypePriority() > this.getTypePriority()) {
// Check type priority
return randomVariable.div(this);
}
return new RandomVariableDifferentiableAD(
getValues().vid(randomVariable.getValues()),
Arrays.asList(randomVariable, this), // DIV with swapped arguments
OperatorType.DIV);
}
@Override
public RandomVariable cap(final RandomVariable randomVariable) {
if(randomVariable.getTypePriority() > this.getTypePriority()) {
// Check type priority
return randomVariable.cap(this);
}
return new RandomVariableDifferentiableAD(
getValues().cap(randomVariable.getValues()),
Arrays.asList(this, randomVariable),
OperatorType.CAP);
}
@Override
public RandomVariable floor(final RandomVariable floor) {
if(floor.getTypePriority() > this.getTypePriority()) {
// Check type priority
return floor.floor(this);
}
return new RandomVariableDifferentiableAD(
getValues().floor(floor.getValues()),
Arrays.asList(this, floor),
OperatorType.FLOOR);
}
@Override
public RandomVariable accrue(final RandomVariable rate, final double periodLength) {
if(rate.getTypePriority() > this.getTypePriority()) {
// Check type priority
return rate.mult(periodLength).add(1.0).mult(this);
}
return new RandomVariableDifferentiableAD(
getValues().accrue(rate.getValues(), periodLength),
Arrays.asList(this, rate, new RandomVariableFromDoubleArray(periodLength)),
OperatorType.ACCRUE);
}
@Override
public RandomVariable discount(final RandomVariable rate, final double periodLength) {
if(rate.getTypePriority() > this.getTypePriority()) {
// Check type priority
return rate.mult(periodLength).add(1.0).invert().mult(this);
}
return new RandomVariableDifferentiableAD(
getValues().discount(rate.getValues(), periodLength),
Arrays.asList(this, rate, new RandomVariableFromDoubleArray(periodLength)),
OperatorType.DISCOUNT);
}
/*
* Ternary operators: checking for return type priority.
* @TODO add checking for return type priority.
*/
@Override
public RandomVariable choose(final RandomVariable valueIfTriggerNonNegative, final RandomVariable valueIfTriggerNegative) {
return new RandomVariableDifferentiableAD(
getValues().choose(valueIfTriggerNonNegative.getValues(), valueIfTriggerNegative.getValues()),
Arrays.asList(this, valueIfTriggerNonNegative, valueIfTriggerNegative),
OperatorType.BARRIER);
}
@Override
public RandomVariable invert() {
return new RandomVariableDifferentiableAD(
getValues().invert(),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.INVERT);
}
@Override
public RandomVariable abs() {
return new RandomVariableDifferentiableAD(
getValues().abs(),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.ABS);
}
@Override
public RandomVariable addProduct(final RandomVariable factor1, final double factor2) {
if(factor1.getTypePriority() > this.getTypePriority()) {
// Check type priority
return factor1.mult(factor2).add(this);
}
return new RandomVariableDifferentiableAD(
getValues().addProduct(factor1.getValues(), factor2),
Arrays.asList(this, factor1, new RandomVariableFromDoubleArray(factor2)),
OperatorType.ADDPRODUCT);
}
@Override
public RandomVariable addProduct(final RandomVariable factor1, final RandomVariable factor2) {
if(factor1.getTypePriority() > this.getTypePriority() || factor2.getTypePriority() > this.getTypePriority()) {
// Check type priority
return factor1.mult(factor2).add(this);
}
return new RandomVariableDifferentiableAD(
getValues().addProduct(factor1.getValues(), factor2.getValues()),
Arrays.asList(this, factor1, factor2),
OperatorType.ADDPRODUCT);
}
@Override
public RandomVariable addRatio(final RandomVariable numerator, final RandomVariable denominator) {
if(numerator.getTypePriority() > this.getTypePriority() || denominator.getTypePriority() > this.getTypePriority()) {
// Check type priority
return numerator.div(denominator).add(this);
}
return new RandomVariableDifferentiableAD(
getValues().addRatio(numerator.getValues(), denominator.getValues()),
Arrays.asList(this, numerator, denominator),
OperatorType.ADDRATIO);
}
@Override
public RandomVariable subRatio(final RandomVariable numerator, final RandomVariable denominator) {
if(numerator.getTypePriority() > this.getTypePriority() || denominator.getTypePriority() > this.getTypePriority()) {
// Check type priority
return numerator.div(denominator).mult(-1).add(this);
}
return new RandomVariableDifferentiableAD(
getValues().subRatio(numerator.getValues(), denominator.getValues()),
Arrays.asList(this, numerator, denominator),
OperatorType.SUBRATIO);
}
/*
* The following methods are end points, the result is not differentiable.
*/
@Override
public RandomVariable isNaN() {
return getValues().isNaN();
}
@Override
public IntToDoubleFunction getOperator() {
return getValues().getOperator();
}
@Override
public DoubleStream getRealizationsStream() {
return getValues().getRealizationsStream();
}
@Override
public RandomVariable apply(final DoubleUnaryOperator operator) {
throw new UnsupportedOperationException("Applying functions is not supported.");
}
@Override
public RandomVariable apply(final DoubleBinaryOperator operator, final RandomVariable argument) {
throw new UnsupportedOperationException("Applying functions is not supported.");
}
@Override
public RandomVariable apply(final DoubleTernaryOperator operator, final RandomVariable argument1, final RandomVariable argument2) {
throw new UnsupportedOperationException("Applying functions is not supported.");
}
public RandomVariable getVarianceAsRandomVariableAAD(){
/*returns deterministic AAD random variable */
return new RandomVariableDifferentiableAD(
new RandomVariableFromDoubleArray(getVariance()),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.VARIANCE);
}
public RandomVariable getSampleVarianceAsRandomVariableAAD() {
/*returns deterministic AAD random variable */
return new RandomVariableDifferentiableAD(
new RandomVariableFromDoubleArray(getSampleVariance()),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.SVARIANCE);
}
public RandomVariable getStandardDeviationAsRandomVariableAAD(){
/*returns deterministic AAD random variable */
return new RandomVariableDifferentiableAD(
new RandomVariableFromDoubleArray(getStandardDeviation()),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.STDEV);
}
public RandomVariable getStandardErrorAsRandomVariableAAD(){
/*returns deterministic AAD random variable */
return new RandomVariableDifferentiableAD(
new RandomVariableFromDoubleArray(getStandardError()),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.STDERROR);
}
public RandomVariable getMinAsRandomVariableAAD(){
/*returns deterministic AAD random variable */
return new RandomVariableDifferentiableAD(
new RandomVariableFromDoubleArray(getMin()),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.MIN);
}
public RandomVariable getMaxAsRandomVariableAAD(){
/*returns deterministic AAD random variable */
return new RandomVariableDifferentiableAD(
new RandomVariableFromDoubleArray(getMax()),
Arrays.asList(new RandomVariable[]{ this }),
OperatorType.MAX);
}
@Override
public Map getTangents() {
// TODO Auto-generated method stub
return null;
}
}