Map_sampleKt

All Implemented Interfaces:


public final class Map_sampleKt

Nested Class Summary

Nested Classes
Modifier and Type	Class	Description

Field Summary

Fields
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Constructor Summary

Constructors
Constructor	Description

Enum Constant Summary

Enum Constants
Enum Constant	Description

Method Summary

Modifier and Type	Method	Description
`final static DoubleArray`	`map_sample(MatrixCell MAP, Long n, Random random)`	Generates samples of inter-arrival times from a MAP using a specified number of samples and a random generator.
`final static DoubleArray`	`map_sample(Matrix D0, Matrix D1, Long n, Random random)`	Generates samples of inter-arrival times from a MAP using a specified number of samples and a random generator.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
- Method Detail
  - map_sample
```
 final static DoubleArray map_sample(MatrixCell MAP, Long n, Random random)
```
    Generates samples of inter-arrival times from a MAP using a specified number of samples and a random generator.
    This method generates n samples of inter-arrival times from the MAP defined by matrices D0 and D1. If the MAP has a single phase, the samples are generated from an exponential distribution. For multi-phase MAPs, the sampling accounts for transitions between states/phases.
    
    Parameters:
    
    MAP - the MatrixCell representing the MAP, containing the D0 and D1 matrices
    
    n - the number of samples to generate
    
    random - the random number generator to use
    
    Returns:
    
    an array of doubles containing the generated samples
  - map_sample
```
 final static DoubleArray map_sample(Matrix D0, Matrix D1, Long n, Random random)
```
    Generates samples of inter-arrival times from a MAP using a specified number of samples and a random generator.
    
    Parameters:
    
    D0 - the hidden transition matrix of the MAP
    
    D1 - the visible transition matrix of the MAP
    
    n - the number of samples to generate
    
    random - the random number generator to use
    
    Returns:
    
    an array of doubles containing the generated samples