Package jline.solvers.ldes.handlers

Class Solver_ssj

java.lang.Object

jline.solvers.ldes.handlers.Solver_ssj

public final class Solver_ssj
extends Object

Top-level Java translation of Solver_ssj.kt. Hosts the public top-level functions (solver_ssj, solver_ssj_transient) as static methods and the package-internal SSJSimulator class as a nested static class.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	Solver_ssj.ConstantGen	Custom random variate generator for deterministic (constant) values.

Method Summary

Modifier and Type	Method	Description
static org.apache.commons.lang3.tuple.Triple<Double,Double,Integer>	computeOBMStatistics(List<Double> observations, int batchSize)	Compute OBM (Overlapping Batch Means) statistics with 50% overlap.
static org.apache.commons.lang3.tuple.Triple<Double,Double,Integer>	computeSpectralStatistics(List<Double> observations, int batchSize)	Overload for default lowFreqFrac = 0.25.
static org.apache.commons.lang3.tuple.Triple<Double,Double,Integer>	computeSpectralStatistics(List<Double> observations, int batchSize, double lowFreqFrac)	Compute Heidelberger-Welch spectral analysis statistics.
static org.apache.commons.lang3.tuple.Triple<Double,Double,Integer>	computeStandardBatchMeansStatistics(List<Double> observations, int batchSize)	Compute standard (non-overlapping) batch means statistics.
static double	getTCriticalValue(double confintLevel, int df)	Get t-distribution critical value for given confidence level and degrees of freedom.
static LDESResult	solver_ssj(NetworkStruct sn, SolverOptions options)
static LDESResult	solver_ssj(NetworkStruct sn, SolverOptions options, Collector stream)	Steady-state queueing network simulation using the SSJ library.
static LDESResult	solver_ssj_transient(NetworkStruct sn, SolverOptions options)
static LDESResult	solver_ssj_transient(NetworkStruct sn, SolverOptions options, Collector stream)	Transient analysis of queueing network using the SSJ library.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Details

solver_ssj

public static LDESResult solver_ssj(NetworkStruct sn,
 SolverOptions options,
 Collector stream)

Steady-state queueing network simulation using the SSJ library.

solver_ssj

public static LDESResult solver_ssj(NetworkStruct sn,
 SolverOptions options)

solver_ssj_transient

public static LDESResult solver_ssj_transient(NetworkStruct sn,
 SolverOptions options,
 Collector stream)

Transient analysis of queueing network using the SSJ library.

solver_ssj_transient

public static LDESResult solver_ssj_transient(NetworkStruct sn,
 SolverOptions options)

computeOBMStatistics

public static org.apache.commons.lang3.tuple.Triple<Double,Double,Integer> computeOBMStatistics(List<Double> observations,
 int batchSize)

Compute OBM (Overlapping Batch Means) statistics with 50% overlap. Exposed as static helper for testing.

Parameters:

observations - List of observations to analyze

batchSize - Size of each batch

Returns:

jline.util.Triple(grandMean, stdError, effectiveDf) or null if insufficient data

getTCriticalValue

public static double getTCriticalValue(double confintLevel,
 int df)

Get t-distribution critical value for given confidence level and degrees of freedom. Exposed as static helper for testing.

Parameters:

confintLevel - Confidence level (e.g., 0.95 for 95% CI)

df - Degrees of freedom

Returns:

Critical value from t-distribution

computeStandardBatchMeansStatistics

public static org.apache.commons.lang3.tuple.Triple<Double,Double,Integer> computeStandardBatchMeansStatistics(List<Double> observations,
 int batchSize)

Compute standard (non-overlapping) batch means statistics. Exposed as static helper for testing comparison with OBM.

Parameters:

observations - List of observations to analyze

batchSize - Size of each batch

Returns:

jline.util.Triple(grandMean, stdError, effectiveDf) or null if insufficient data

computeSpectralStatistics

public static org.apache.commons.lang3.tuple.Triple<Double,Double,Integer> computeSpectralStatistics(List<Double> observations,
 int batchSize,
 double lowFreqFrac)

Compute Heidelberger-Welch spectral analysis statistics. Exposed as static helper for testing. Uses log-periodogram regression at low frequencies to estimate the spectral density at frequency zero, which accounts for autocorrelation between batches.

Parameters:

observations - List of observations to analyze

batchSize - Size of each batch

lowFreqFrac - Fraction of lowest frequencies to use for regression (default 0.25)

Returns:

jline.util.Triple(grandMean, stdError, df) or null if insufficient data or regression fails

computeSpectralStatistics

public static org.apache.commons.lang3.tuple.Triple<Double,Double,Integer> computeSpectralStatistics(List<Double> observations,
 int batchSize)

Overload for default lowFreqFrac = 0.25.