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A Java's Collaborative Filtering library to carry out experiments in research of Collaborative Filtering based Recommender Systems. The library has been designed from researchers to researchers.
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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.ode;
import org.apache.commons.math3.Field;
import org.apache.commons.math3.RealFieldElement;
import org.apache.commons.math3.exception.DimensionMismatchException;
import org.apache.commons.math3.exception.MathIllegalStateException;
import org.apache.commons.math3.exception.MaxCountExceededException;
import org.apache.commons.math3.exception.NoBracketingException;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.exception.util.LocalizedFormats;
import org.apache.commons.math3.linear.Array2DRowFieldMatrix;
import org.apache.commons.math3.ode.nonstiff.AdaptiveStepsizeFieldIntegrator;
import org.apache.commons.math3.ode.nonstiff.DormandPrince853FieldIntegrator;
import org.apache.commons.math3.ode.sampling.FieldStepHandler;
import org.apache.commons.math3.ode.sampling.FieldStepInterpolator;
import org.apache.commons.math3.util.FastMath;
import org.apache.commons.math3.util.MathArrays;
import org.apache.commons.math3.util.MathUtils;
/**
* This class is the base class for multistep integrators for Ordinary
* Differential Equations.
* We define scaled derivatives si(n) at step n as:
*
* s1(n) = h y'n for first derivative
* s2(n) = h2/2 y''n for second derivative
* s3(n) = h3/6 y'''n for third derivative
* ...
* sk(n) = hk/k! y(k)n for kth derivative
*
* Rather than storing several previous steps separately, this implementation uses
* the Nordsieck vector with higher degrees scaled derivatives all taken at the same
* step (yn, s1(n) and rn) where rn is defined as:
*
* rn = [ s2(n), s3(n) ... sk(n) ]T
*
* (we omit the k index in the notation for clarity)
*
* Multistep integrators with Nordsieck representation are highly sensitive to
* large step changes because when the step is multiplied by factor a, the
* kth component of the Nordsieck vector is multiplied by ak
* and the last components are the least accurate ones. The default max growth
* factor is therefore set to a quite low value: 21/order.
*
*
* @see org.apache.commons.math3.ode.nonstiff.AdamsBashforthFieldIntegrator
* @see org.apache.commons.math3.ode.nonstiff.AdamsMoultonFieldIntegrator
* @param the type of the field elements
* @since 3.6
*/
public abstract class MultistepFieldIntegrator>
extends AdaptiveStepsizeFieldIntegrator {
/** First scaled derivative (h y'). */
protected T[] scaled;
/** Nordsieck matrix of the higher scaled derivatives.
* (h2/2 y'', h3/6 y''' ..., hk/k! y(k))
*/
protected Array2DRowFieldMatrix nordsieck;
/** Starter integrator. */
private FirstOrderFieldIntegrator starter;
/** Number of steps of the multistep method (excluding the one being computed). */
private final int nSteps;
/** Stepsize control exponent. */
private double exp;
/** Safety factor for stepsize control. */
private double safety;
/** Minimal reduction factor for stepsize control. */
private double minReduction;
/** Maximal growth factor for stepsize control. */
private double maxGrowth;
/**
* Build a multistep integrator with the given stepsize bounds.
* The default starter integrator is set to the {@link
* DormandPrince853FieldIntegrator Dormand-Prince 8(5,3)} integrator with
* some defaults settings.
*
* The default max growth factor is set to a quite low value: 21/order.
*
* @param field field to which the time and state vector elements belong
* @param name name of the method
* @param nSteps number of steps of the multistep method
* (excluding the one being computed)
* @param order order of the method
* @param minStep minimal step (must be positive even for backward
* integration), the last step can be smaller than this
* @param maxStep maximal step (must be positive even for backward
* integration)
* @param scalAbsoluteTolerance allowed absolute error
* @param scalRelativeTolerance allowed relative error
* @exception NumberIsTooSmallException if number of steps is smaller than 2
*/
protected MultistepFieldIntegrator(final Field field, final String name,
final int nSteps, final int order,
final double minStep, final double maxStep,
final double scalAbsoluteTolerance,
final double scalRelativeTolerance)
throws NumberIsTooSmallException {
super(field, name, minStep, maxStep, scalAbsoluteTolerance, scalRelativeTolerance);
if (nSteps < 2) {
throw new NumberIsTooSmallException(
LocalizedFormats.INTEGRATION_METHOD_NEEDS_AT_LEAST_TWO_PREVIOUS_POINTS,
nSteps, 2, true);
}
starter = new DormandPrince853FieldIntegrator(field, minStep, maxStep,
scalAbsoluteTolerance,
scalRelativeTolerance);
this.nSteps = nSteps;
exp = -1.0 / order;
// set the default values of the algorithm control parameters
setSafety(0.9);
setMinReduction(0.2);
setMaxGrowth(FastMath.pow(2.0, -exp));
}
/**
* Build a multistep integrator with the given stepsize bounds.
* The default starter integrator is set to the {@link
* DormandPrince853FieldIntegrator Dormand-Prince 8(5,3)} integrator with
* some defaults settings.
*
* The default max growth factor is set to a quite low value: 21/order.
*
* @param field field to which the time and state vector elements belong
* @param name name of the method
* @param nSteps number of steps of the multistep method
* (excluding the one being computed)
* @param order order of the method
* @param minStep minimal step (must be positive even for backward
* integration), the last step can be smaller than this
* @param maxStep maximal step (must be positive even for backward
* integration)
* @param vecAbsoluteTolerance allowed absolute error
* @param vecRelativeTolerance allowed relative error
*/
protected MultistepFieldIntegrator(final Field field, final String name, final int nSteps,
final int order,
final double minStep, final double maxStep,
final double[] vecAbsoluteTolerance,
final double[] vecRelativeTolerance) {
super(field, name, minStep, maxStep, vecAbsoluteTolerance, vecRelativeTolerance);
starter = new DormandPrince853FieldIntegrator(field, minStep, maxStep,
vecAbsoluteTolerance,
vecRelativeTolerance);
this.nSteps = nSteps;
exp = -1.0 / order;
// set the default values of the algorithm control parameters
setSafety(0.9);
setMinReduction(0.2);
setMaxGrowth(FastMath.pow(2.0, -exp));
}
/**
* Get the starter integrator.
* @return starter integrator
*/
public FirstOrderFieldIntegrator getStarterIntegrator() {
return starter;
}
/**
* Set the starter integrator.
* The various step and event handlers for this starter integrator
* will be managed automatically by the multi-step integrator. Any
* user configuration for these elements will be cleared before use.
* @param starterIntegrator starter integrator
*/
public void setStarterIntegrator(FirstOrderFieldIntegrator starterIntegrator) {
this.starter = starterIntegrator;
}
/** Start the integration.
* This method computes one step using the underlying starter integrator,
* and initializes the Nordsieck vector at step start. The starter integrator
* purpose is only to establish initial conditions, it does not really change
* time by itself. The top level multistep integrator remains in charge of
* handling time propagation and events handling as it will starts its own
* computation right from the beginning. In a sense, the starter integrator
* can be seen as a dummy one and so it will never trigger any user event nor
* call any user step handler.
* @param equations complete set of differential equations to integrate
* @param initialState initial state (time, primary and secondary state vectors)
* @param t target time for the integration
* (can be set to a value smaller than t0 for backward integration)
* @exception DimensionMismatchException if arrays dimension do not match equations settings
* @exception NumberIsTooSmallException if integration step is too small
* @exception MaxCountExceededException if the number of functions evaluations is exceeded
* @exception NoBracketingException if the location of an event cannot be bracketed
*/
protected void start(final FieldExpandableODE equations, final FieldODEState initialState, final T t)
throws DimensionMismatchException, NumberIsTooSmallException,
MaxCountExceededException, NoBracketingException {
// make sure NO user event nor user step handler is triggered,
// this is the task of the top level integrator, not the task
// of the starter integrator
starter.clearEventHandlers();
starter.clearStepHandlers();
// set up one specific step handler to extract initial Nordsieck vector
starter.addStepHandler(new FieldNordsieckInitializer(equations.getMapper(), (nSteps + 3) / 2));
// start integration, expecting a InitializationCompletedMarkerException
try {
starter.integrate(equations, initialState, t);
// we should not reach this step
throw new MathIllegalStateException(LocalizedFormats.MULTISTEP_STARTER_STOPPED_EARLY);
} catch (InitializationCompletedMarkerException icme) { // NOPMD
// this is the expected nominal interruption of the start integrator
// count the evaluations used by the starter
getEvaluationsCounter().increment(starter.getEvaluations());
}
// remove the specific step handler
starter.clearStepHandlers();
}
/** Initialize the high order scaled derivatives at step start.
* @param h step size to use for scaling
* @param t first steps times
* @param y first steps states
* @param yDot first steps derivatives
* @return Nordieck vector at first step (h2/2 y''n,
* h3/6 y'''n ... hk/k! y(k)n)
*/
protected abstract Array2DRowFieldMatrix initializeHighOrderDerivatives(final T h, final T[] t,
final T[][] y,
final T[][] yDot);
/** Get the minimal reduction factor for stepsize control.
* @return minimal reduction factor
*/
public double getMinReduction() {
return minReduction;
}
/** Set the minimal reduction factor for stepsize control.
* @param minReduction minimal reduction factor
*/
public void setMinReduction(final double minReduction) {
this.minReduction = minReduction;
}
/** Get the maximal growth factor for stepsize control.
* @return maximal growth factor
*/
public double getMaxGrowth() {
return maxGrowth;
}
/** Set the maximal growth factor for stepsize control.
* @param maxGrowth maximal growth factor
*/
public void setMaxGrowth(final double maxGrowth) {
this.maxGrowth = maxGrowth;
}
/** Get the safety factor for stepsize control.
* @return safety factor
*/
public double getSafety() {
return safety;
}
/** Set the safety factor for stepsize control.
* @param safety safety factor
*/
public void setSafety(final double safety) {
this.safety = safety;
}
/** Get the number of steps of the multistep method (excluding the one being computed).
* @return number of steps of the multistep method (excluding the one being computed)
*/
public int getNSteps() {
return nSteps;
}
/** Rescale the instance.
* Since the scaled and Nordsieck arrays are shared with the caller,
* this method has the side effect of rescaling this arrays in the caller too.
* @param newStepSize new step size to use in the scaled and Nordsieck arrays
*/
protected void rescale(final T newStepSize) {
final T ratio = newStepSize.divide(getStepSize());
for (int i = 0; i < scaled.length; ++i) {
scaled[i] = scaled[i].multiply(ratio);
}
final T[][] nData = nordsieck.getDataRef();
T power = ratio;
for (int i = 0; i < nData.length; ++i) {
power = power.multiply(ratio);
final T[] nDataI = nData[i];
for (int j = 0; j < nDataI.length; ++j) {
nDataI[j] = nDataI[j].multiply(power);
}
}
setStepSize(newStepSize);
}
/** Compute step grow/shrink factor according to normalized error.
* @param error normalized error of the current step
* @return grow/shrink factor for next step
*/
protected T computeStepGrowShrinkFactor(final T error) {
return MathUtils.min(error.getField().getZero().add(maxGrowth),
MathUtils.max(error.getField().getZero().add(minReduction),
error.pow(exp).multiply(safety)));
}
/** Specialized step handler storing the first step.
*/
private class FieldNordsieckInitializer implements FieldStepHandler {
/** Equation mapper. */
private final FieldEquationsMapper mapper;
/** Steps counter. */
private int count;
/** Saved start. */
private FieldODEStateAndDerivative savedStart;
/** First steps times. */
private final T[] t;
/** First steps states. */
private final T[][] y;
/** First steps derivatives. */
private final T[][] yDot;
/** Simple constructor.
* @param mapper equation mapper
* @param nbStartPoints number of start points (including the initial point)
*/
FieldNordsieckInitializer(final FieldEquationsMapper mapper, final int nbStartPoints) {
this.mapper = mapper;
this.count = 0;
this.t = MathArrays.buildArray(getField(), nbStartPoints);
this.y = MathArrays.buildArray(getField(), nbStartPoints, -1);
this.yDot = MathArrays.buildArray(getField(), nbStartPoints, -1);
}
/** {@inheritDoc} */
public void handleStep(FieldStepInterpolator interpolator, boolean isLast)
throws MaxCountExceededException {
if (count == 0) {
// first step, we need to store also the point at the beginning of the step
final FieldODEStateAndDerivative prev = interpolator.getPreviousState();
savedStart = prev;
t[count] = prev.getTime();
y[count] = mapper.mapState(prev);
yDot[count] = mapper.mapDerivative(prev);
}
// store the point at the end of the step
++count;
final FieldODEStateAndDerivative curr = interpolator.getCurrentState();
t[count] = curr.getTime();
y[count] = mapper.mapState(curr);
yDot[count] = mapper.mapDerivative(curr);
if (count == t.length - 1) {
// this was the last point we needed, we can compute the derivatives
setStepSize(t[t.length - 1].subtract(t[0]).divide(t.length - 1));
// first scaled derivative
scaled = MathArrays.buildArray(getField(), yDot[0].length);
for (int j = 0; j < scaled.length; ++j) {
scaled[j] = yDot[0][j].multiply(getStepSize());
}
// higher order derivatives
nordsieck = initializeHighOrderDerivatives(getStepSize(), t, y, yDot);
// stop the integrator now that all needed steps have been handled
setStepStart(savedStart);
throw new InitializationCompletedMarkerException();
}
}
/** {@inheritDoc} */
public void init(final FieldODEStateAndDerivative initialState, T finalTime) {
// nothing to do
}
}
/** Marker exception used ONLY to stop the starter integrator after first step. */
private static class InitializationCompletedMarkerException
extends RuntimeException {
/** Serializable version identifier. */
private static final long serialVersionUID = -1914085471038046418L;
/** Simple constructor. */
InitializationCompletedMarkerException() {
super((Throwable) null);
}
}
}
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