Similar to ArrayOfComparableChains, except that instead of working with \(n\) clones of a MarkovChain, we use a single MarkovChainDouble object for all the chains. More...
Public Member Functions | |
| ArrayOfDoubleChains (MarkovChainDouble baseChain, PointSetRandomization rand) | |
Creates a virtual array for the chain baseChain. More... | |
| ArrayOfDoubleChains (MarkovChainDouble baseChain) | |
| Same as ArrayOfDoubleChains(baseChain, new RandomShift(new MRG32k3a())). | |
| void | makeCopies (int n) |
Creates the vector of states for n copies of the base chain. | |
| void | setStatesDouble (double[] S) |
Sets the states of the n copies of the base chain to S. | |
| double [] | getStatesDouble () |
Returns the array containing the states of the n chains. | |
| void | initStatesDouble () |
Initializes the states of the n copies of the base chain. | |
| boolean | simulOneStepArrayRQMC (int step, PointSet p) |
Simulate one step for the n copies of the base chain, assuming that we are at step step. More... | |
| double | simulArrayRQMC (PointSet p, int numSteps) |
Simulates the \(n\) copies of the chain, numSteps steps for each copy, using point set p, where \(n\) is the current number of copies of the chain and is assumed to equal the number of points in p. More... | |
| double | calcMeanPerf () |
| Computes and returns the mean performance of the \(n\) chains. | |
| void | sortChains () |
| Sorts the arrays containing the states of the \(n\) chains. | |
| String | toString () |
| Creates a String with the states. | |
 Public Member Functions inherited from ArrayOfComparableChains< T extends MarkovChainComparable > | |
| ArrayOfComparableChains (T baseChain) | |
Creates an array of the comparable chain baseChain. More... | |
| ArrayOfComparableChains (T baseChain, PointSetRandomization rand, MultiDimSort sort) | |
Creates an array of the comparable chain baseChain. More... | |
| void | makeCopies (int n) |
Creates n copies (clones) of the chain baseChain and puts them in an array, ready for the array RQMC simulation. | |
| void | initialStates () |
Initializes the n copies (clones) of the chain baseChain to their initial state by calling initialState() on each chain. | |
| int | getN () |
Returns the number n of chains. | |
| T [] | getChains () |
Returns the underlying array of n MarkovChainComparable. | |
| void | setRandomization (PointSetRandomization rand) |
Sets the internal umontreal.ssj.hups.PointSetRandomization to rand. | |
| PointSetRandomization | getRandomization () |
| Returns the internal umontreal.ssj.hups.PointSetRandomization. | |
| void | setSort (MultiDimSort sort) |
Sets the internal umontreal.ssj.util.MultiDimSort to sort. | |
| MultiDimSort | getSort () |
| Returns the saved umontreal.ssj.util.MultiDimSort. | |
| int | simulOneStepArrayRQMC (PointSet p, PointSetRandomization rand, MultiDimSort sort, int sortCoordPts) |
Randomized the point set p and Simulates the \(n\) copies of the chain, one step for each copy, using umontreal.ssj.hups.PointSet p, where \(n\) is the current number of copies (clones) of the chain and is assumed to equal the number of points in p. More... | |
| int | simulOneStepArrayRQMC (PointSet p) |
This version uses the preselected randomization and sort, with sortCoordPts = 0. | |
| double | simulArrayRQMC (PointSet p, PointSetRandomization rand, MultiDimSort sort, int sortCoordPts, int numSteps) |
Simulates the \(n\) copies of the chain, numSteps steps for each copy, using umontreal.ssj.hups.PointSet p, where \(n\) is the current number of copies (clones) of the chain and is assumed to equal the number of points in p. More... | |
| double | simulArrayRQMC (PointSet p, PointSetRandomization rand, MultiDimSort sort, int numSteps) |
This version assumes that sortCoordPts = 0, so that there is no need to sort the points at each step. | |
| double | simulArrayRQMC (PointSet p, int numSteps) |
This version assumes that sortCoordPts = 0 and uses the preset randomization and sort for the chains. | |
| double [] | getPerformances () |
| Returns the vector for performances for the \(n\) chains. | |
| double | calcMeanPerf () |
| Computes and returns the mean performance of the \(n\) chains. | |
| void | simulReplicatesArrayRQMC (PointSet p, PointSetRandomization rand, MultiDimSort sort, int sortCoordPts, int numSteps, int m, Tally statReps) |
Performs m independent replications of an array-RQMC simulation as in simulArrayRQMC. More... | |
| String | simulReplicatesArrayRQMCFormat (PointSet p, PointSetRandomization rand, MultiDimSort sort, int sortCoordPts, int numSteps, int m, Tally statReps) |
Performs m independent replications of an array-RQMC simulation as in simulFormatArrayRQMC. More... | |
| String | varianceImprovementFormat (double varRQMC, double varMC) |
Returns a string that reports the the ratio of MC variance per run varMC over the RQMC variance per run varRQMC. | |
| String | testVarianceRateFormat (PointSet[] pointSets, PointSetRandomization rand, MultiDimSort sort, int sortCoordPts, int numSteps, int m, double varMC, String filenamePlot, String methodLabel) |
| Performs an experiment to estimate the convergence rate of the RQMC variance as a function of \(n\), by invoking #simulReplicatesArrayRQMC(r) epeatedly with a given array of point sets of different sizes \(n\). More... | |
| void | sortNotStoppedChains (MultiDimSort sort) |
| Sorts the chains that have not stopped yet using the stored umontreal.ssj.util.MultiDimSort. More... | |
| void | sortChains () |
| Sorts the chains using the stored umontreal.ssj.util.MultiDimSort. | |
| String | formatTime (double time) |
| double | slope (double[] x, double[] y, int n) |
Protected Attributes | |
| MarkovChainDouble | baseChain |
| double [] | state |
| double [] | perfState |
| Protected Attributes inherited from ArrayOfComparableChains< T extends MarkovChainComparable > | |
| T | baseChain |
| int | n |
| T [] | chains |
| double [] | performances |
| PointSetRandomization | randomization |
| MultiDimSort< T > | savedSort |
| int | sortCoordPts = 0 |
Detailed Description
Similar to ArrayOfComparableChains, except that instead of working with \(n\) clones of a MarkovChain, we use a single MarkovChainDouble object for all the chains.
The states of the chains are maintained in an array of real numbers (double) and the MarkovChainDouble.nextStepDouble method is used to advance each chain by one step. The performance measure is assumed to be additive over all steps of all copies of the chain. The sum is cumulated in a single accumulator for all copies of the chain, updated at each step of each copy.
Constructor & Destructor Documentation
◆ ArrayOfDoubleChains()
| ArrayOfDoubleChains | ( | MarkovChainDouble | baseChain, |
| PointSetRandomization | rand | ||
| ) |
Creates a virtual array for the chain baseChain.
The method makeCopies must be called to make the copies.
Member Function Documentation
◆ simulArrayRQMC()
| double simulArrayRQMC | ( | PointSet | p, |
| int | numSteps | ||
| ) |
Simulates the \(n\) copies of the chain, numSteps steps for each copy, using point set p, where \(n\) is the current number of copies of the chain and is assumed to equal the number of points in p.
At each step, the points are randomized using the stored umontreal.ssj.hups.PointSetRandomization. The dimension of p must be at least as large as the number of uniforms required to simulate one step of the chain. Returns the average performance per run.
◆ simulOneStepArrayRQMC()
| boolean simulOneStepArrayRQMC | ( | int | step, |
| PointSet | p | ||
| ) |
Simulate one step for the n copies of the base chain, assuming that we are at step step.
The points are randomized before the simulation using the stored umontreal.ssj.hups.PointSetRandomization. The dimension of p must be at least as large as the number of uniforms required to simulate one step of the chain. Returns true if and only if all chains have stopped. Compute and stores the performances.
The documentation for this class was generated from the following file:
- ArrayOfDoubleChains.java
