M-index

A B C D E F G H I J K L M N O P Q R S T U V W X Z

M

M2M - class in jline.io: Model-to-Model transformation class for converting between different queueing network model formats.
M3aCompressor - class in jline.lib.m3a: M3A (Markovian Arrival Process with 3-moment Approximation) tool for MMAP compression.
M3aCompressor.Companion - class in jline.lib.m3a.M3aCompressor
M3aCompressor.CoxianParameters - class in jline.lib.m3a.M3aCompressor
M3aCompressor.ErlangParameters - class in jline.lib.m3a.M3aCompressor
M3aCompressor.HyperExpParameters - class in jline.lib.m3a.M3aCompressor
M3aCompressor.PhaseTypeParameters - class in jline.lib.m3a.M3aCompressor
m3Alloc(java.lang.Integer,java.lang.Integer,java.lang.Integer) - function in jline.lib.empht.EMpht.Companion
M3aUtils - class in jline.lib.m3a: Utility functions for M3A (Markovian Arrival Process with 3-moment Approximation) compression.
M3aUtils.Companion - class in jline.lib.m3a.M3aUtils
M3pp22_fitc_approx_cov - class in jline.api.mam.m3pp: Fits a second-order Marked MMPP for two classes using covariance approximation.
m3pp22_fitc_approx_cov(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,java.lang.Double,java.lang.Double) - function in jline.api.mam.m3pp.M3pp22_fitc_approx_cov.Companion
M3pp22_fitc_approx_cov_multiclass - class in jline.api.mam.m3pp: Fits a M3PP(2,2) given the underlying MMPP(2).
m3pp22_fitc_approx_cov_multiclass(kotlin.Array,kotlin.DoubleArray,java.lang.Double,java.lang.Double) - function in jline.api.mam.m3pp.M3pp22_fitc_approx_cov_multiclass.Companion
m3pp22_interleave_fitc(kotlin.Array,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray,java.lang.Double) - function in jline.api.mam.m3pp.M3pp22_interleave_fitcKt: Fits L pairs of classes into a single MMAP, obtained by lumped superposition of L different M3PP2 processes.
M3pp22_interleave_fitcKt - class in jline.api.mam.m3pp
M3pp22FitCountApproxCovKt - class in jline.api.mam.m3pp: M3Pp22 Fit Count Approx Cov algorithms
M3pp22FitCountApproxCovMulticlassKt - class in jline.api.mam.m3pp: M3Pp22 Fit Count Approx Cov Multiclass algorithms
M3pp22InterleaveFitCountKt - class in jline.api.mam.m3pp: M3Pp22 Interleave Fit Count algorithms
M3pp2m_fitc - class in jline.api.mam.m3pp: Fits a second-order Marked MMPP using exact count statistics.
m3pp2m_fitc(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,java.lang.Double) - function in jline.api.mam.m3pp.M3pp2m_fitc.Companion
m3pp2m_fitc(kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray) - function in jline.api.mam.m3pp.M3pp2m_fitcKt: Simple wrapper function for m3pp2m_fitc
M3pp2m_fitc_approx - class in jline.api.mam.m3pp: Fits a second-order Marked MMPP using approximation with optimization.
m3pp2m_fitc_approx(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,java.lang.Double) - function in jline.api.mam.m3pp.M3pp2m_fitc_approx.Companion
M3pp2m_fitc_approx_ag - class in jline.api.mam.m3pp: Fits a second-order Marked MMPP using auto-gamma approach.
m3pp2m_fitc_approx_ag(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,java.lang.Double) - function in jline.api.mam.m3pp.M3pp2m_fitc_approx_ag.Companion
m3pp2m_fitc_approx_ag_multiclass(kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray) - function in jline.api.mam.m3pp.M3pp2m_fitcKt: Wrapper function for m3pp2m_fitc_approx_ag_multiclass
M3pp2m_fitc_approx_ag_multiclass - class in jline.api.mam.m3pp: Fits a M3PP(2,m) given the underlying MMPP(2).
m3pp2m_fitc_approx_ag_multiclass(kotlin.Array,kotlin.DoubleArray,kotlin.DoubleArray,java.lang.Double) - function in jline.api.mam.m3pp.M3pp2m_fitc_approx_ag_multiclass.Companion
M3pp2m_fitcKt - class in jline.api.mam.m3pp
m3pp2m_interleave(java.util.List) - function in jline.api.mam.m3pp.M3pp2m_interleaveKt: Computes the interleaved MMAP obtained by multiple M3PP(2,m).
M3pp2m_interleaveKt - class in jline.api.mam.m3pp
M3pp2mFitCountApproxAgKt - class in jline.api.mam.m3pp: M3Pp2M Fit Count Approx Ag algorithms
M3pp2mFitCountApproxAgMulticlassKt - class in jline.api.mam.m3pp: M3Pp2M Fit Count Approx Ag Multiclass algorithms
M3pp2mFitCountApproxKt - class in jline.api.mam.m3pp: M3Pp2M Fit Count Approx algorithms
M3pp2mFitCountKt - class in jline.api.mam.m3pp: M3Pp2M Fit Count algorithms
M3pp2mInterleaveKt - class in jline.api.mam.m3pp: M3Pp2M Interleave algorithms
m3pp_interleave_fitc(kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.Array,kotlin.Array,java.lang.Double,java.lang.Double,kotlin.IntArray,java.lang.Boolean) - function in jline.api.mam.m3pp.M3pp_interleave_fitcKt: Fits k second-order M3PPm_j and interleaves them into a M3PPm of order k+1, with m = sum_j=1^k m_j.
m3pp_interleave_fitc_theoretical(jline.util.matrix.MatrixCell,java.lang.Double,java.lang.Double,kotlin.Array) - function in jline.api.mam.m3pp.M3pp_interleave_fitc_theoreticalKt: Interleaves k M3PP to fit a multi-class MMAP using theoretical characteristics.
M3pp_interleave_fitc_theoreticalKt - class in jline.api.mam.m3pp
m3pp_interleave_fitc_trace(kotlin.DoubleArray,kotlin.IntArray,java.lang.Double,java.lang.Double,kotlin.Array) - function in jline.api.mam.m3pp.M3pp_interleave_fitc_traceKt: Interleaves k M3PP to fit a multi-class trace with m classes.
M3pp_interleave_fitc_traceKt - class in jline.api.mam.m3pp
M3pp_interleave_fitcKt - class in jline.api.mam.m3pp
m3pp_rand(java.lang.Integer,java.lang.Integer,java.lang.Long) - function in jline.api.mam.m3pp.M3pp_randKt: Generates a random M3PP (Markovian Multi-class Point Process) with specified order and number of classes.
m3pp_rand_targeted(java.lang.Integer,java.lang.Integer,java.lang.Double,java.lang.Double,java.lang.Long) - function in jline.api.mam.m3pp.M3pp_randKt: Generates a random M3PP with specific characteristics.
M3pp_randKt - class in jline.api.mam.m3pp
m3pp_superpos_fitc(kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.m3pp.M3pp_superpos_fitcKt: Fits k second-order M3PPm_j and superposes them into a M3PPm of order k+1, with m = sum_j=1^k m_j.
m3pp_superpos_fitc_theoretical(jline.util.matrix.MatrixCell,java.lang.Integer,java.lang.Double,java.lang.Double) - function in jline.api.mam.m3pp.M3pp_superpos_fitc_theoreticalKt: Fits k second-order M3PPm_j and superposes them into a M3PPm using theoretical count characteristics of the target process.
M3pp_superpos_fitc_theoreticalKt - class in jline.api.mam.m3pp
m3pp_superpos_fitc_trace(kotlin.DoubleArray,kotlin.IntArray,java.lang.Integer,java.lang.Double,java.lang.Double) - function in jline.api.mam.m3pp.M3pp_superpos_fitc_theoreticalKt: Fits k second-order M3PPm_j and superposes them using trace data to determine theoretical characteristics.
M3pp_superpos_fitcKt - class in jline.api.mam.m3pp
M3ppInterleaveFitCountKt - class in jline.api.mam.m3pp: M3Pp Interleave Fit Count algorithms
M3ppInterleaveFitCountTheoreticalKt - class in jline.api.mam.m3pp: M3Pp Interleave Fit Count Theoretical algorithms
M3ppInterleaveFitCountTraceKt - class in jline.api.mam.m3pp: M3Pp Interleave Fit Count Trace algorithms
M3ppRandKt - class in jline.api.mam.m3pp: M3Pp Rand algorithms
M3ppSuperposFitCountKt - class in jline.api.mam.m3pp: M3Pp Superpos Fit Count algorithms
M3ppSuperposFitCountTheoreticalKt - class in jline.api.mam.m3pp: M3Pp Superpos Fit Count Theoretical algorithms
main(kotlin.Array) - function in jline.bench.AllBench
main(kotlin.Array) - function in jline.bench.QuickBenchTest
main(kotlin.Array) - function in jline.bench.cqn.BenchCQN_FCFS
main(kotlin.Array) - function in jline.bench.cqn.BenchCQN_PS
main(kotlin.Array) - function in jline.bench.cqn.BenchCQN_RM: Main method with command line options
main(kotlin.Array) - function in jline.bench.fj.BenchFJ_Closed
main(kotlin.Array) - function in jline.bench.fj.BenchFJ_FCFS
main(kotlin.Array) - function in jline.bench.fj.BenchFJ_Mixed
main(kotlin.Array) - function in jline.bench.fj.BenchFJ_Nested
main(kotlin.Array) - function in jline.bench.fj.BenchFJ_Open
main(kotlin.Array) - function in jline.bench.fj.BenchFJ_PS
main(kotlin.Array) - function in jline.bench.lqn.BenchLQN_Custom: Main method for testing
main(kotlin.Array) - function in jline.bench.lqn.BenchLQN_Default: Main method for testing
main(kotlin.Array) - function in jline.bench.lqn.BenchLQN_Fluid: Main method for testing
main(kotlin.Array) - function in jline.bench.lqn.BenchLQN_LQNS: Main method for testing
main(kotlin.Array) - function in jline.bench.lqn.BenchLQN_MVA: Main method for testing
main(kotlin.Array) - function in jline.bench.lqn.BenchLQN_NC: Main method for testing
main(kotlin.Array) - function in jline.bench.lqn.BenchLQN_SRVN: Main method for testing
main(kotlin.Array) - function in jline.bench.lqn.SimpleLQNBenchmark: Main method for testing
main(kotlin.Array) - function in jline.cli.LineCLI: Main entry point for the LINE CLI
main(kotlin.Array) - function in jline.cli.LineWebSocketClient: The main method, which serves as the entry point for the program.
main(kotlin.Array) - function in jline.cli.LineWebSocketServer: The main method, which serves as the entry point for the server application.
main(kotlin.Array) - function in jline.examples.NetworkGeneratorExample
main(kotlin.Array) - function in jline.examples.java.AdvancedExamples
main(kotlin.Array) - function in jline.examples.java.AllExamples
main(kotlin.Array) - function in jline.examples.java.BasicExamples
main(kotlin.Array) - function in jline.examples.java.GettingStarted: Main method for testing and demonstrating getting started examples.
main(kotlin.Array) - function in jline.examples.java.advanced.CDFRespTExamples: Main method to run all CDF response time examples.
main(kotlin.Array) - function in jline.examples.java.advanced.CDFRespTModel: Main method for testing and demonstrating response time CDF examples.
main(kotlin.Array) - function in jline.examples.java.advanced.CyclicPollingExamples: Main method to run all cyclic polling examples.
main(kotlin.Array) - function in jline.examples.java.advanced.CyclicPollingModel: Main method for testing and demonstrating cyclic polling examples.
main(kotlin.Array) - function in jline.examples.java.advanced.InitStateExamples: Main method to run all initial state examples.
main(kotlin.Array) - function in jline.examples.java.advanced.LayeredCQExamples: Main method to run all layered CQ examples.
main(kotlin.Array) - function in jline.examples.java.advanced.LoadDependentExamples: Main method to run all load-dependent examples.
main(kotlin.Array) - function in jline.examples.java.advanced.LoadDependentModel: Main method for testing and demonstrating load-dependent examples.
main(kotlin.Array) - function in jline.examples.java.advanced.RandomEnvExamples: Main method to run all random environment examples.
main(kotlin.Array) - function in jline.examples.java.advanced.RandomEnvironmentModel: Main method for testing and demonstrating random environment examples.
main(kotlin.Array) - function in jline.examples.java.advanced.StateDepRoutingExamples: Main method to run all state-dependent routing examples.
main(kotlin.Array) - function in jline.examples.java.advanced.StateDepRoutingModel: Main method for testing and demonstrating state-dependent routing examples.
main(kotlin.Array) - function in jline.examples.java.advanced.StateProbabilitiesExamples: Main method to run all state probability examples.
main(kotlin.Array) - function in jline.examples.java.advanced.StateProbabilitiesModel: Main method for testing and demonstrating state probability examples.
main(kotlin.Array) - function in jline.examples.java.advanced.SwitchoverTimesExamples: Main method to run all switchover time examples.
main(kotlin.Array) - function in jline.examples.java.advanced.SwitchoverTimesModel: Main method for testing and demonstrating switchover times examples.
main(kotlin.Array) - function in jline.examples.java.basic.CacheExamples
main(kotlin.Array) - function in jline.examples.java.basic.CacheModel: Main method for testing and demonstrating cache model examples.
main(kotlin.Array) - function in jline.examples.java.basic.ClassSwitchExamples
main(kotlin.Array) - function in jline.examples.java.basic.ClassSwitchingModel: Main method for testing and demonstrating class switching examples.
main(kotlin.Array) - function in jline.examples.java.basic.ClosedExamples: Main method demonstrating all closed queueing network examples.
main(kotlin.Array) - function in jline.examples.java.basic.ClosedModel: Main method for testing and demonstrating closed model examples.
main(kotlin.Array) - function in jline.examples.java.basic.ForkJoinExamples: Main method demonstrating all fork-join examples.
main(kotlin.Array) - function in jline.examples.java.basic.ForkJoinModel: Main method for testing and demonstrating fork-join model examples.
main(kotlin.Array) - function in jline.examples.java.basic.LayeredExamples: Main method demonstrating selected layered network examples.
main(kotlin.Array) - function in jline.examples.java.basic.LayeredModel: Main method for testing and demonstrating layered model examples.
main(kotlin.Array) - function in jline.examples.java.basic.MixedExamples: Main method demonstrating all mixed queueing network examples.
main(kotlin.Array) - function in jline.examples.java.basic.MixedModel: Main method for testing and demonstrating mixed model examples.
main(kotlin.Array) - function in jline.examples.java.basic.OpenExamples: Main method demonstrating all open queueing network examples.
main(kotlin.Array) - function in jline.examples.java.basic.OpenModel: Main method for testing and demonstrating open model examples.
main(kotlin.Array) - function in jline.examples.java.basic.PrioExamples: Main method demonstrating all priority queueing examples.
main(kotlin.Array) - function in jline.examples.java.basic.PrioModel: Main method for testing and demonstrating priority examples.
main(kotlin.Array) - function in jline.examples.java.basic.StochPetriNetExamples: Main method demonstrating selected Petri net examples.
main(kotlin.Array) - function in jline.examples.java.basic.StochPetriNetModel: Main method for testing and demonstrating stochastic Petri net examples.
main(kotlin.Array) - function in jline.examples.java.models.Gallery: Main method for testing and demonstrating gallery examples.
main(kotlin.Array) - function in jline.io.REPL: Main method to start REPL
main(kotlin.Array) - function in jline.lang.processes.DiscreteSampler
main(kotlin.Array) - function in jline.lang.processes.Geometric
main(kotlin.Array) - function in jline.lang.processes.Poisson
main(kotlin.Array) - function in jline.lang.processes.Zipf
main(kotlin.Array) - function in jline.lib.lti.customromberg
main(kotlin.Array) - function in jline.lib.lti.euler
main(kotlin.Array) - function in jline.lib.lti.gaverstehfest
main() - function in jline.lib.perm.PermExampleKt: Example usage of the permanent computation algorithms.
main(kotlin.Array) - function in kroqbd.ExampleKpcQbd.Companion
mAlloc(java.lang.Integer,java.lang.Integer) - function in jline.lib.empht.EMpht.Companion
MAM - enum entry in jline.lang.constant.SolverType
mama1() - function in jline.lib.kpctoolbox.MAPCatalog: MAMA1 - 2-state MAP model Source: mama1.
mama2() - function in jline.lib.kpctoolbox.MAPCatalog: MAMA2 - 2-state MAP model (variant of MAMA1) Source: mama2.
mama3() - function in jline.lib.kpctoolbox.MAPCatalog: MAMA3 - 2-state MAP model Source: mama3.
mama4() - function in jline.lib.kpctoolbox.MAPCatalog: MAMA4 - 2-state MAP model Source: mama4.
mama5() - function in jline.lib.kpctoolbox.MAPCatalog: MAMA5 - 2-state MAP model Source: mama5.
mamap22_fit_bs_multiclass(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix,jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Object,kotlin.DoubleArray) - function in jline.api.mam.Mamap22_fit_multiclassKt: Fits MAMAP(2,2) with backward moments (B) and sigma characteristics (S) for 2 classes.
mamap22_fit_fs_multiclass(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix,jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Object,kotlin.DoubleArray) - function in jline.api.mam.Mamap22_fit_multiclassKt: Fits MAMAP(2,2) with forward moments (F) and sigma characteristics (S) for 2 classes.
mamap22_fit_gamma_bs(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,jline.util.matrix.Matrix,jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Mamap22_fit_multiclassKt: Fits MAMAP(2,2) with backward selection using gamma auto-correlation.
mamap22_fit_gamma_bs_mmap(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mamap22_fit_multiclassKt: Fits MAMAP(2,2) from an existing MMAP using backward selection with gamma auto-correlation.
mamap22_fit_gamma_bs_trace(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Mamap22_fit_multiclassKt: Fits MAMAP(2,2) from a marked trace using backward selection with gamma auto-correlation.
mamap22_fit_gamma_fs(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,jline.util.matrix.Matrix,jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Mamap22_fit_multiclassKt: Fits MAMAP(2,2) with forward selection using gamma auto-correlation.
mamap22_fit_gamma_fs_mmap(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mamap22_fit_multiclassKt: Fits MAMAP(2,2) from an existing MMAP using forward selection with gamma auto-correlation.
mamap22_fit_gamma_fs_trace(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.mam.Mamap22_fit_gamma_fs_traceKt: Fits a MAMAP(2,2) using forward-start method from trace data.
mamap22_fit_gamma_fs_trace(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Mamap22_fit_multiclassKt: Fits MAMAP(2,2) from a marked trace using forward selection with gamma auto-correlation.
Mamap22_fit_gamma_fs_traceKt - class in jline.api.mam
Mamap22_fit_multiclassKt - class in jline.api.mam
Mamap22FitGammaFsTrace - class in jline.api.mam: MAMAP 22 fit gamma fs trace algorithms
Mamap22FitMulticlass - class in jline.api.mam: MAMAP 22 fit multiclass algorithms
mamap2m_can1_coefficients(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mamap2m_coefficientsKt: Returns the coefficients used in the direct and inverse formulas for fitting a MAMAP(2,m) in first canonical form (gamma 0).
mamap2m_can2_coefficients(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mamap2m_coefficientsKt: Returns the coefficients used in the direct and inverse formulas for fitting a MAMAP(2,m) in second canonical form (gamma < 0).
Mamap2m_coefficientsKt - class in jline.api.mam
Mamap2m_fit - class in jline.api.mam: Fits a MAMAP(2,m) (Markovian Arrival Process with Marked arrivals) that matches the provided characteristics.
mamap2m_fit(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray,jline.util.matrix.Matrix,kotlin.DoubleArray) - function in jline.api.mam.Mamap2m_fit.Companion
mamap2m_fit(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray,jline.util.matrix.Matrix) - function in jline.api.mam.Mamap2m_fit.Companion
mamap2m_fit_fb_multiclass(jline.util.matrix.MatrixCell,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray) - function in jline.api.mam.Mamap2m_fit_fb_multiclassKt: Performs approximate fitting of a MMAP given the underlying MAP, the class probabilities, forward moments, and backward moments.
Mamap2m_fit_fb_multiclassKt - class in jline.api.mam
mamap2m_fit_gamma_fb(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray) - function in jline.api.mam.Mamap2m_fit_gamma_fb_mmapKt: Computes the second-order MAMAPm fitting the given ordinary moments, autocorrelation decay rate, class probabilities, forward moments, and backward moments.
mamap2m_fit_gamma_fb_mmap(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mamap2m_fit_gamma_fb_mmapKt: Fits a second-order acyclic MMAPm to match the characteristics of the input MMAP.
Mamap2m_fit_gamma_fb_mmapKt - class in jline.api.mam
mamap2m_fit_mmap(jline.util.matrix.MatrixCell,kotlin.DoubleArray) - function in jline.api.mam.Mamap2m_fit_mmapKt: Fits a MAPH(2,m) or MAMAP(2,m) that matches the characteristics of the input MMAP.
mamap2m_fit_mmap(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mamap2m_fit_mmapKt: Fits a MAPH(2,m) or MAMAP(2,m) that matches the characteristics of the input MMAP.
Mamap2m_fit_mmapKt - class in jline.api.mam
mamap2m_fit_trace(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.mam.Mamap2m_fit_traceKt: Fits a MAMAP(2,m) to trace data.
Mamap2m_fit_traceKt - class in jline.api.mam
Mamap2mCoefficients - class in jline.api.mam: MAMAP 2m coefficients algorithms
Mamap2mFitFbMulticlassKt - class in jline.api.mam: MAMAP 2m fit fb multiclass algorithms
Mamap2mFitGammaFbMmapKt - class in jline.api.mam: MAMAP 2m fit gamma fb mmap algorithms
Mamap2mFitKt - class in jline.api.mam: MAMAP 2m fit algorithms
Mamap2mFitMmapKt - class in jline.api.mam: MAMAP 2m fit mmap algorithms
Mamap2mFitTraceKt - class in jline.api.mam: MAMAP 2m fit trace algorithms
MAMOptions - class in jline.solvers.mam
MAMResult - class in jline.solvers.mam
MAP - enum entry in jline.lang.constant.ProcessType
MAP - class in jline.lang.processes: A Markovian Arrival Process
map2_fit(java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Map2_fitKt: Fits a 2-phase Markovian Arrival Process (MAP2) using the first three moments.
map2_fit(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Map2_fitKt: Fits a 2-phase Markovian Arrival Process (MAP2) to match the given moments and decay rate of the autocorrelation function.
Map2_fitKt - class in jline.api.mam
Map2FitKt - class in jline.api.mam: Map2 Fit algorithms
map2mmpp(kotlin.Array) - function in jline.api.mam.Map2mmppKt: Convert a MAP to MMPP format by extracting generator matrix Q and rate matrix LAMBDA.
Map2mmppKt - class in jline.api.mam
map_acf(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_acfKt: Computes the autocorrelation function (ACF) for a given MAP using a default lag of 1.
map_acf(jline.util.matrix.Matrix,jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_acfKt: Computes the autocorrelation function (ACF) for a given MAP at multiple lags.
map_acf(jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Integer) - function in jline.api.mam.Map_acfKt: Computes the autocorrelation function (ACF) for a given MAP at a specific lag.
map_acf(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_acfKt: Computes the autocorrelation function (ACF) for a given MAP using a default lag of 1.
map_acf(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix) - function in jline.api.mam.Map_acfKt: Computes the autocorrelation function (ACF) for a given MAP at multiple lags using a MatrixCell.
map_acfc(jline.util.matrix.Matrix,jline.util.matrix.Matrix,kotlin.IntArray,java.lang.Double) - function in jline.api.mam.Map_acfcKt: Computes the autocorrelation function coefficients (ACFC) for a MAP counting process.
map_acfc(jline.util.matrix.MatrixCell,kotlin.IntArray,java.lang.Double) - function in jline.api.mam.Map_acfcKt: Computes the autocorrelation function coefficients (ACFC) for a MAP counting process using a MatrixCell.
Map_acfcKt - class in jline.api.mam
Map_acfKt - class in jline.api.mam
map_block(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.String) - function in jline.api.mam.Map_blockKt: Constructs a MAP(2) or MAP(1) according to given moments and autocorrelation parameters.
Map_blockKt - class in jline.api.mam
map_ccdf_derivative(jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Integer) - function in jline.api.mam.Map_ccdf_derivativeKt: Compute derivative at 0 of a MAP complementary CDF
map_ccdf_derivative(jline.util.matrix.MatrixCell,java.lang.Integer) - function in jline.api.mam.Map_ccdf_derivativeKt: Compute derivative at 0 of a MAP complementary CDFBased on: A.
Map_ccdf_derivativeKt - class in jline.api.mam
map_cdf(jline.util.matrix.Matrix,jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_cdfKt: Computes the cumulative distribution function (CDF) of the inter-arrival times of a Markovian Arrival Process (MAP).
map_cdf(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix) - function in jline.api.mam.Map_cdfKt: Computes the cumulative distribution function (CDF) of the inter-arrival times of a MAP stored in a MatrixCell that contains the MAP's transition matrices.
Map_cdfKt - class in jline.api.mam
map_checkfeasible(jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Double) - function in jline.api.mam.Map_checkfeasibleKt: Check the feasibility of a MAP given separate D0 and D1 matrices.
map_checkfeasible(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_checkfeasibleKt: Check the feasibility of a MAP given separate D0 and D1 matrices.
map_checkfeasible(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Map_checkfeasibleKt: Check the feasibility of a MAP with detailed validation.
Map_checkfeasibleKt - class in jline.api.mam
map_count_mean(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Map_count_meanKt: Computes the mean of the counting process over a specified interval length for a given Markovian Arrival Process (MAP).
map_count_mean(jline.util.matrix.MatrixCell,kotlin.DoubleArray) - function in jline.api.mam.Map_count_meanKt: Computes the mean of the counting process over multiple specified interval lengths for a given Markovian Arrival Process (MAP).
Map_count_meanKt - class in jline.api.mam
map_count_moment(jline.util.matrix.MatrixCell,java.lang.Double,java.lang.Integer) - function in jline.api.mam.Map_count_momentKt: Computes power moments of counts at resolution t for a Markovian Arrival Process (MAP).
map_count_moment(jline.util.matrix.MatrixCell,java.lang.Double,kotlin.IntArray) - function in jline.api.mam.Map_count_momentKt: Computes multiple power moments of counts at resolution t for a MAP.
Map_count_momentKt - class in jline.api.mam
map_count_var(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Map_count_varKt: Computes the variance of the counting process over a specified interval length for a given Markovian Arrival Process (MAP).
map_count_var(jline.util.matrix.MatrixCell,kotlin.DoubleArray) - function in jline.api.mam.Map_count_varKt: Computes the variance of the counting process over multiple specified interval lengths for a given Markovian Arrival Process (MAP).
Map_count_varKt - class in jline.api.mam
map_embedded(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_embeddedKt: Computes the embedded discrete-time Markov chain (DTMC) matrix of a MAP.
map_embedded(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_embeddedKt: Computes the embedded discrete-time Markov chain (DTMC) matrix of a MAP given as a MatrixCell.
Map_embeddedKt - class in jline.api.mam
map_erlang(java.lang.Double,java.lang.Integer) - function in jline.api.mam.Map_erlangKt: Fits an Erlang-k process as a Markovian Arrival Process (MAP).
Map_erlangKt - class in jline.api.mam
map_example1() - function in jline.lib.kpctoolbox.MAPCatalog: MAP Example 1 Source: MAP_example1.
map_exponential(java.lang.Double) - function in jline.api.mam.Map_exponentialKt: Creates a Markovian Arrival Process (MAP) with an exponential inter-arrival time distribution.
Map_exponentialKt - class in jline.api.mam
map_feasblock(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.String) - function in jline.api.mam.Map_feasblockKt: Fits the most similar feasible MAP when exact moment matching fails.
Map_feasblockKt - class in jline.api.mam
map_feastol() - function in jline.api.mam.Map_feastolKt: Returns the feasibility tolerance for MAPs.
Map_feastolKt - class in jline.api.mam
map_gamma(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_gammaKt: Computes the gamma parameter for a MAP, which is the autocorrelation decay rate.
map_gamma(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_gammaKt: Computes the gamma parameter for a MAP using a MatrixCell.
map_gamma2(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_gamma2Kt: Returns the largest non-unit eigenvalue (both real and imaginary parts) of the embedded Discrete-Time Markov Chain (DTMC) of a given Markovian Arrival Process (MAP).
Map_gamma2Kt - class in jline.api.mam
Map_gammaKt - class in jline.api.mam
map_hyperexp(java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Map_hyperexpKt: Fit a two-phase Hyper-exponential renewal process as a MAP
Map_hyperexpKt - class in jline.api.mam
map_idc(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_idcKt: Computes the asymptotic index of dispersion (IDC) for a Markovian Arrival Process (MAP).
map_idc(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_idcKt: Computes the asymptotic index of dispersion (IDC) for a MAP stored in a MatrixCell that contains the MAP's transition matrices.
Map_idcKt - class in jline.api.mam
map_infgen(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_infgenKt: Computes the infinitesimal generator matrix (Q) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).
map_infgen(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_infgenKt: Computes the infinitesimal generator matrix (Q) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).
Map_infgenKt - class in jline.api.mam
map_isfeasible(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_isfeasibleKt: Checks if the provided MAP is feasible using a default tolerance.
map_isfeasible(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Map_isfeasibleKt: Checks if the provided MAP is feasible based on the given tolerance.
Map_isfeasibleKt - class in jline.api.mam
map_joint(jline.util.matrix.Matrix,jline.util.matrix.Matrix,kotlin.IntArray,kotlin.IntArray) - function in jline.api.mam.Map_jointKt: Computes the joint moments of a Markovian Arrival Process (MAP).
map_joint(jline.util.matrix.MatrixCell,kotlin.IntArray,kotlin.IntArray) - function in jline.api.mam.Map_jointKt: Computes the joint moments of a Markovian Arrival Process (MAP).
Map_jointKt - class in jline.api.mam
map_jointpdf_derivative(jline.util.matrix.Matrix,jline.util.matrix.Matrix,kotlin.IntArray) - function in jline.api.mam.Map_jointpdf_derivativeKt: Compute partial derivative at 0 of a MAP's joint PDF
map_jointpdf_derivative(jline.util.matrix.MatrixCell,kotlin.IntArray) - function in jline.api.mam.Map_jointpdf_derivativeKt: Compute partial derivative at 0 of a MAP's joint PDFBased on: A.
Map_jointpdf_derivativeKt - class in jline.api.mam
map_kpc(java.lang.Object,kotlin.Array) - function in jline.api.mam.Map_kpcKt: Kronecker product composition of MAPs.
map_kpc(kotlin.Array,kotlin.Array) - function in jline.api.mam.Map_kpcKt: Convenience function for composing exactly two MAPs.
map_kpc(java.util.List) - function in jline.api.mam.Map_kpcKt: Convenience function for composing a list of MAPs.
Map_kpcKt - class in jline.api.mam
map_kurt(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_kurtKt: Computes the kurtosis of the inter-arrival times in a Markovian Arrival Process (MAP).
Map_kurtKt - class in jline.api.mam
map_lambda(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_lambdaKt: Computes the arrival rate (lambda) of a Markovian Arrival Process (MAP).
map_lambda(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_lambdaKt: Computes the arrival rate (lambda) of a Markovian Arrival Process (MAP) using matrices stored in a MatrixCell.
Map_lambdaKt - class in jline.api.mam
map_largemap() - function in jline.api.mam.Map_largemapKt: Returns the maximum size considered for large MAPs.
Map_largemapKt - class in jline.api.mam
map_mark(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix) - function in jline.api.mam.Map_markKt: Creates a Marked Markovian Arrival Process (MMAP) by marking a given Markovian Arrival Process (MAP) with additional phases based on specified marking probabilities.
Map_markKt - class in jline.api.mam
map_max(jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_maxKt: Computes the MAP that represents the maximum of two independent MAPs.
Map_maxKt - class in jline.api.mam
map_mean(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_meanKt: Computes the mean inter-arrival time of a Markovian Arrival Process (MAP).
map_mean(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_meanKt: Computes the mean inter-arrival time of a Markovian Arrival Process (MAP) using matrices stored in a MatrixCell.
Map_meanKt - class in jline.api.mam
map_mixture(kotlin.DoubleArray,kotlin.Array) - function in jline.api.mam.Map_mixtureKt: Creates a probabilistic mixture of Markovian Arrival Processes (MAPs).
Map_mixtureKt - class in jline.api.mam
map_moment(jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Integer) - function in jline.api.mam.Map_momentKt: Computes the raw moments of the inter-arrival times of a Markovian Arrival Process (MAP).
map_moment(jline.util.matrix.MatrixCell,java.lang.Integer) - function in jline.api.mam.Map_momentKt: Computes the raw moments of the inter-arrival times of a MAP stored in a MatrixCell that contains the MAP's transition matrices.
Map_momentKt - class in jline.api.mam
map_normalize(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_normalizeKt: Sanitizes the (D0, D1) matrices of a Markovian Arrival Process (MAP) by ensuring all elements are non-negative and adjusting diagonal elements.
map_normalize(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_normalizeKt: Sanitizes the (D0, D1) matrices of a Markovian Arrival Process (MAP) stored in a MatrixCell.
Map_normalizeKt - class in jline.api.mam
map_pdf(jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Double) - function in jline.api.mam.Map_pdfKt: Computes the probability density function (PDF) of a MAP at a single time point.
map_pdf(jline.util.matrix.Matrix,jline.util.matrix.Matrix,kotlin.DoubleArray) - function in jline.api.mam.Map_pdfKt: Computes the probability density function (PDF) of a MAP at specified time points.
map_pdf(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Map_pdfKt: Computes the probability density function (PDF) of a MAP at a single time point.
map_pdf(jline.util.matrix.MatrixCell,kotlin.DoubleArray) - function in jline.api.mam.Map_pdfKt: Computes the probability density function (PDF) of a MAP at specified time points.
Map_pdfKt - class in jline.api.mam
map_pie(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_pieKt: Computes the steady-state probability vector of the embedded Discrete Time Markov Chain (DTMC) associated with a Markovian Arrival Process (MAP).
map_pie(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_pieKt: Computes the steady-state probability vector of the embedded DTMC of a MAP stored in a MatrixCell that contains the MAP's transition matrices.
Map_pieKt - class in jline.api.mam
map_piq(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_piqKt: Computes the steady-state vector (pi) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).
map_piq(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_piqKt: Computes the steady-state vector (pi) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).
Map_piqKt - class in jline.api.mam
map_pntiter(kotlin.Array,java.lang.Integer,java.lang.Double,java.lang.Integer) - function in jline.api.mam.Map_pntiterKt: Probability of having exactly na arrivals within time interval t using iterative method.
Map_pntiterKt - class in jline.api.mam
map_pntquad(kotlin.Array,java.lang.Integer,java.lang.Double) - function in jline.api.mam.Map_pntquadKt: Compute MAP point process probabilities using ODE quadrature method.
Map_pntquadKt - class in jline.api.mam
map_prob(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_probKt: Computes the equilibrium distribution of the underlying continuous-time Markov chain for a MAP.
map_prob(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_probKt: Computes the equilibrium distribution of the underlying continuous-time Markov chain for a MAP.
Map_probKt - class in jline.api.mam
map_rand() - function in jline.api.mam.Map_randKt: Generates a random Markovian Arrival Process (MAP) with 2 states.
map_rand(java.lang.Integer) - function in jline.api.mam.Map_randKt: Generates a random Markovian Arrival Process (MAP) with K states.
Map_randKt - class in jline.api.mam
map_randn() - function in jline.api.mam.Map_randnKt: Generates a random Markovian Arrival Process (MAP) with 2 states using normal distribution with mean 1 and standard deviation 2.
map_randn(java.lang.Integer,java.lang.Double,java.lang.Double) - function in jline.api.mam.Map_randnKt: Generates a random Markovian Arrival Process (MAP) with K states using normal distribution.
Map_randnKt - class in jline.api.mam
map_renewal(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_renewalKt: Creates a renewal MAP by removing all correlations from the input MAP.
map_renewal(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_renewalKt: Creates a renewal MAP by removing all correlations from the input MAP.
Map_renewalKt - class in jline.api.mam
map_sample(jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Long,java.util.Random) - function in jline.api.mam.Map_sampleKt: Generates samples of inter-arrival times from a MAP using a specified number of samples and a random generator.
map_sample(jline.util.matrix.MatrixCell,java.lang.Long,java.util.Random) - function in jline.api.mam.Map_sampleKt: Generates samples of inter-arrival times from a MAP using a specified number of samples and a random generator.
Map_sampleKt - class in jline.api.mam
map_scale(jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Double) - function in jline.api.mam.Map_scaleKt: Rescales the mean inter-arrival time of a Markovian Arrival Process (MAP) to a specified new mean.
map_scale(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Map_scaleKt: Rescales the mean inter-arrival time of a MAP stored in a MatrixCell that contains the MAP's transition matrices.
Map_scaleKt - class in jline.api.mam
map_scv(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_scvKt: Computes the squared coefficient of variation (SCV) of the inter-arrival times of a Markovian Arrival Process (MAP).
map_scv(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_scvKt: Computes the squared coefficient of variation (SCV) of the inter-arrival times of a MAP stored in a MatrixCell that contains the MAP's transition matrices.
Map_scvKt - class in jline.api.mam
map_skew(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_skewKt: Computes the skewness of the inter-arrival times for a MAP.
map_skew(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_skewKt: Computes the skewness of the inter-arrival times for a MAP using a MatrixCell.
Map_skewKt - class in jline.api.mam
map_stochcomp(jline.util.matrix.Matrix,jline.util.matrix.Matrix,kotlin.IntArray) - function in jline.api.mam.Map_stochcompKt: Performs stochastic complementation on a MAP by eliminating specified states.
map_stochcomp(jline.util.matrix.MatrixCell,kotlin.IntArray) - function in jline.api.mam.Map_stochcompKt: Performs stochastic complementation on a MAP by eliminating specified states.
Map_stochcompKt - class in jline.api.mam
map_sum(jline.util.matrix.MatrixCell,java.lang.Integer) - function in jline.api.mam.Map_sumKt: Computes the Markovian Arrival Process (MAP) representing the sum of n identical MAPs.
map_sumind(kotlin.Array) - function in jline.api.mam.Map_sumindKt: Computes the Markovian Arrival Process (MAP) representing the sum of n independent MAPs.
Map_sumindKt - class in jline.api.mam
Map_sumKt - class in jline.api.mam
map_super(jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_superKt: Creates a superposition of two Markovian Arrival Processes (MAPs) to form a new MAP.
Map_superKt - class in jline.api.mam
map_timereverse(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_timereverseKt: Computes the time-reversed MAP of a given MAP.
Map_timereverseKt - class in jline.api.mam
map_var(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_varKt: Computes the variance of the inter-arrival times for a MAP.
map_var(jline.util.matrix.MatrixCell) - function in jline.api.mam.Map_varKt: Computes the variance of the inter-arrival times for a MAP using a MatrixCell.
map_varcount(jline.util.matrix.Matrix,jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Map_varcountKt: Computes the variance of the counts in a MAP over multiple time periods.
map_varcount(jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Double) - function in jline.api.mam.Map_varcountKt: Computes the variance of the counts in a MAP over a time period t.
map_varcount(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix) - function in jline.api.mam.Map_varcountKt: Computes the variance of the counts in a MAP over multiple time periods using a MatrixCell.
map_varcount(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Map_varcountKt: Computes the variance of the counts in a MAP over a time period t using a MatrixCell.
Map_varcountKt - class in jline.api.mam
Map_varKt - class in jline.api.mam
MapAcfcKt - class in jline.api.mam: MAP autocorrelation function coefficients algorithms
MapAcfKt - class in jline.api.mam: MAP autocorrelation function algorithms
MapBlockKt - class in jline.api.mam: MAP block matrix construction algorithms
MAPCatalog - class in jline.lib.kpctoolbox: Complete catalog of MAP (Markovian Arrival Process) models migrated from MATLAB Location: /home/gcasale/code/matlab/maps/ This class provides static methods to create all MAP and MMPP2 models from the maps directory including those from .mat files and .m function files.
MapCcdfDerivativeKt - class in jline.api.mam: MAP complementary CDF derivative algorithms
MapCdf - class in jline.api.mam: MAP cumulative distribution function algorithms
MapCheckfeasibleKt - class in jline.api.mam: MAP checkfeasible algorithms
MapCountMeanKt - class in jline.api.mam: MAP counting process mean algorithms
MapCountMoment - class in jline.api.mam: MAP count moment algorithms
MapCountVar - class in jline.api.mam: MAP count var algorithms
mape(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.bench.BenchmarkUtils: Calculate Mean Absolute Percentage Error (MAPE) Wrapper for Utils.
mape(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.util.Utils: Return mean absolute percentage error of approx with respect to exact
MapEmbeddedKt - class in jline.api.mam: MAP embedded algorithms
MapErlangKt - class in jline.api.mam: MAP erlang algorithms
mapeWithNanMean(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.util.Utils: Polymorphic version that returns both MAPE and nanMean.
MapExponentialKt - class in jline.api.mam: MAP exponential algorithms
MapFeasblockKt - class in jline.api.mam: MAP feasblock algorithms
MapFeastolKt - class in jline.api.mam: MAP feastol algorithms
MapGamma2 - class in jline.api.mam: MAP gamma2 algorithms
MapGammaKt - class in jline.api.mam: MAP gamma algorithms
maph2m_fit(java.lang.Double,java.lang.Double,java.lang.Double,jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Maph2m_fitKt: Computes the second-order MAPHm fitting the given ordinary moments (of order up to three), the class probabilities (always fitted exactly) and the backward moments.
maph2m_fit_mmap(jline.util.matrix.MatrixCell) - function in jline.api.mam.Maph2m_fit_mmapKt: Fits an MMAPm with a second-order MAPHm that matches the class probabilities (always fitted exactly) and the backward moments.
Maph2m_fit_mmapKt - class in jline.api.mam
maph2m_fit_multiclass(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Maph2m_fitKt: Fits MAPH(2,m) for multiple classes given underlying APH(2), class probabilities, and backward moments.
maph2m_fit_multiclass(java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,kotlin.DoubleArray) - function in jline.api.mam.Maph2m_fit_multiclassKt: Fits a multi-class MAPH(2,m) model to given multi-class characteristics.
Maph2m_fit_multiclassKt - class in jline.api.mam
maph2m_fit_trace(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.mam.Maph2m_fit_traceKt: Fits a multi-class MAPH(2,m) model to trace data with class labels.
maph2m_fit_trace_timestamps(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.mam.Maph2m_fit_traceKt: Fits a multi-class MAPH(2,m) model to trace data with arrival time stamps and class labels.
Maph2m_fit_traceKt - class in jline.api.mam
maph2m_fitc_approx(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,java.lang.Double) - function in jline.api.mam.Maph2m_fitc_approxKt: Fits a second-order Marked MMPP using approximate count-based characteristics.
Maph2m_fitc_approxKt - class in jline.api.mam
maph2m_fitc_exact(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,java.lang.Double) - function in jline.api.mam.Maph2m_fitc_theoreticalKt: Exact fitting method using all available characteristics
maph2m_fitc_theoretical(jline.util.matrix.MatrixCell,java.lang.String) - function in jline.api.mam.Maph2m_fitc_theoreticalKt: Fits the theoretical characteristics of a MMAP(n,m) with a M3PP(2,m).
Maph2m_fitc_theoreticalKt - class in jline.api.mam
Maph2m_fitKt - class in jline.api.mam
Maph2mFitCountApproxKt - class in jline.api.mam: MAPH 2m fit count approx algorithms
Maph2mFitCountTheoreticalKt - class in jline.api.mam: MAPH 2m fit count theoretical algorithms
Maph2mFitKt - class in jline.api.mam: MAPH 2m fit algorithms
Maph2mFitMmapKt - class in jline.api.mam: MAPH 2m fit mmap algorithms
Maph2mFitMulticlass - class in jline.api.mam: MAPH 2m fit multiclass algorithms
Maph2mFitTraceKt - class in jline.api.mam: MAPH 2m fit trace algorithms
maph2ObjectiveFunction(kotlin.DoubleArray,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,kotlin.DoubleArray,kotlin.DoubleArray,java.lang.Double) - function in jline.api.mam.Maph2m_fitc_approxKt: Objective function for MAPH(2) parameter optimization
MapHyperexpKt - class in jline.api.mam: MAP hyperexp algorithms
MapIdcKt - class in jline.api.mam: MAP idc algorithms
MapInfgenKt - class in jline.api.mam: MAP infgen algorithms
MapIsfeasibleKt - class in jline.api.mam: MAP isfeasible algorithms
MapJointKt - class in jline.api.mam: MAP joint algorithms
MapJointpdfDerivativeKt - class in jline.api.mam: MAP jointpdf derivative algorithms
MapKpcKt - class in jline.api.mam: MAP kpc algorithms
MapKurtKt - class in jline.api.mam: MAP kurt algorithms
MapLambda - class in jline.api.mam: MAP arrival rate computation algorithms.
MapLargemapKt - class in jline.api.mam: MAP largemap algorithms
MapMarkKt - class in jline.api.mam: MAP mark algorithms
MapMaxKt - class in jline.api.mam: MAP max algorithms
MapMean - class in jline.api.mam: MAP mean inter-arrival time computation algorithms.
MapMixture - class in jline.api.mam: MAP mixture algorithms
MapMoment - class in jline.api.mam: MAP moment algorithms
MapNormalizeKt - class in jline.api.mam: MAP normalize algorithms
MapPdfKt - class in jline.api.mam: MAP pdf algorithms
MapPieKt - class in jline.api.mam: MAP pie algorithms
MapPiqKt - class in jline.api.mam: MAP piq algorithms
MapPntiterKt - class in jline.api.mam: MAP pntiter algorithms
MapPntquad - class in jline.api.mam: MAP pntquad algorithms
MapProbKt - class in jline.api.mam: MAP prob algorithms
Mapqn_bnd_lr - class in jline.api.mapqn: Implementation of bnd_linearreduction_new.
Mapqn_bnd_lr_mva - class in jline.api.mapqn: Implementation of bnd_mvaversion.
Mapqn_bnd_lr_mvaKt - class in jline.api.mapqn
Mapqn_bnd_lr_pf - class in jline.api.mapqn: Implementation of bnd_linearreduction_pf.
Mapqn_bnd_lr_pf.PFParameters - class in jline.api.mapqn.Mapqn_bnd_lr_pf: Parameters for the Product Form linear reduction model
Mapqn_bnd_lr_pfKt - class in jline.api.mapqn
Mapqn_bnd_qr - class in jline.api.mapqn: Implementation of bnd_quadraticreduction.
Mapqn_bnd_qr_delay - class in jline.api.mapqn: Implementation of bnd_quadraticreduction_delay.
Mapqn_bnd_qr_delay.QuadraticDelayParameters - class in jline.api.mapqn.Mapqn_bnd_qr_delay: Parameters for the quadratic reduction delay model
Mapqn_bnd_qr_delayKt - class in jline.api.mapqn
Mapqn_bnd_qr_ld - class in jline.api.mapqn: Implementation of bnd_quadraticreduction_ld.
Mapqn_bnd_qr_ld.QuadraticLDParameters - class in jline.api.mapqn.Mapqn_bnd_qr_ld: Parameters for the quadratic reduction load-dependent model
Mapqn_lpmodel - class in jline.api.mapqn: Base class for representing MAPQN Linear Programming models
Mapqn_lpmodel.LinearConstraintBuilder - class in jline.api.mapqn.Mapqn_lpmodel: Helper class for building linear constraints
Mapqn_parameters - class in jline.api.mapqn: Base class for MAPQN model parameters
Mapqn_parameters_factory - class in jline.api.mapqn: Factory class for creating Mapqn_parameters from NetworkStruct.
Mapqn_qr_bounds_bas - class in jline.api.mapqn: Implementation of qrboundsbas_skel.
Mapqn_qr_bounds_bas_parameters - class in jline.api.mapqn: Parameters for QR Bounds BAS (Blocking After Service) model
Mapqn_qr_bounds_rsrd - class in jline.api.mapqn: Implementation of qrboundsrsrd_skel.
Mapqn_qr_bounds_rsrd_parameters - class in jline.api.mapqn: Parameters for QR Bounds RSRD (Repetitive Service Random Destination) model
Mapqn_solution - class in jline.api.mapqn: Solution container for MAPQN linear programs
MapRandKt - class in jline.api.mam: MAP rand algorithms
MapRandnKt - class in jline.api.mam: MAP randn algorithms
MapRenewal - class in jline.api.mam: MAP renewal algorithms
MapSampleKt - class in jline.api.mam: MAP sample algorithms
MapScaleKt - class in jline.api.mam: MAP scale algorithms
MapScvKt - class in jline.api.mam: MAP scv algorithms
mapShrink(jline.util.matrix.MatrixCell,java.lang.Double) - function in kroqbd.QbdUtils: Shrink a MAP by removing insignificant states
MapSkewKt - class in jline.api.mam: MAP skew algorithms
MapStochcompKt - class in jline.api.mam: MAP stochcomp algorithms
MapSumindKt - class in jline.api.mam: MAP sumind algorithms
MapSumKt - class in jline.api.mam: MAP sum algorithms
MapSuperKt - class in jline.api.mam: MAP super algorithms
MapTimereverseKt - class in jline.api.mam: MAP timereverse algorithms
MapVar - class in jline.api.mam: MAP variance computation algorithms.
MapVarcountKt - class in jline.api.mam: MAP varcount algorithms
marginal(jline.lib.perm.PermSolver,kotlin.IntArray,java.lang.Boolean) - function in jline.lib.perm.NetworkNoThink: Compute marginal probability of a state using specified solver.
marginal(jline.lib.perm.PermSolver,kotlin.IntArray,java.lang.Boolean) - function in jline.lib.perm.NetworkThink: Compute marginal probability including think time effects.
Marked - class in jline.lang.processes: An abstract class for marked point processes
MarkedMAP - class in jline.lang.processes: A Marked Markovian Arrival Process
MarkedMarkovProcess - class in jline.lang.processes: A class for continuous time Markov chain where transitions are labeled
MarkedMMPP - class in jline.lang.processes: A Marked Markov-Modulated Poisson Process (M3PP)
MarkovChain - class in jline.lang.processes: A class for a discrete time Markov chain
Markovian - class in jline.lang.processes: An abstract class for a Markovian distribution
MarkovModulated - class in jline.lang.processes: An abstract class for a Markov-modulated point-process
MarkovProcess - class in jline.lang.processes: A class for a continuous time Markov chain
matchRow(java.util.List,kotlin.Array) - function in jline.util.Maths: Finds the index of a matching row in a list.
matchrow(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.util.matrix.Matrix: Returns the index of the row in the matrix that exactly matches the given row vector.
MatFileUtils - class in jline.util: Utility class for saving matrices and workspaces to MATLAB .mat files using the MFL (MATLAB File Library) for Java.
Maths - class in jline.util: Mathematical functions and utilities.
Maths.laplaceApproxComplexReturn - class in jline.util.Maths
Maths.laplaceApproxReturn - class in jline.util.Maths
Maths.simplexQuadResult - class in jline.util.Maths
Matrix - class in jline.util.matrix: A sparse matrix data structure supporting linear algebra functions similar to those available in MATLAB.
matrixAddVector(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.util.matrix.Matrix: Adds a vector to each row or column of the matrix, depending on the vector's orientation.
MatrixCell - class in jline.util.matrix: An ordered collection of Matrix objects that provides indexing and manipulation operations.
MatrixEquation - class in jline.util.matrix: A wrapper class that extends EJML's Equation functionality to work seamlessly with jline.util.matrix.Matrix objects.
matrixExp(jline.util.matrix.Matrix) - function in jline.util.Maths: Adapted from jblas and IHMC Original documentation: Calculate matrix exponential of a square matrix.
MatrixMethodAnalyzer - class in jline.solvers.fluid.analyzers
MatrixMethodODE - class in jline.solvers.fluid.handlers
max(double,double) - function in jline.util.Maths: Returns the max of two numbers.
maxAbsDiff(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.util.matrix.Matrix: Computes the maximum relative absolute difference between corresponding elements of two matrices: max(abs((a - b) / b)).
maxErrorOnSum(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.bench.BenchmarkUtils: Calculate maximum error on sum for queue length metrics Returns max absolute error relative to sum of each column Similar to MATLAB: max(abs(exact-approx))/sum(exact)
maxpos(jline.util.matrix.Matrix) - function in jline.util.Maths: Returns the position of the maximum value in a vector.
maxpos(jline.util.matrix.Matrix,int) - function in jline.util.Maths: Returns the positions of the n largest values in a vector.
MC - enum entry in jline.bench.fj.BenchFJ_Template.FJConfig
MC2 - enum entry in jline.bench.fj.BenchFJ_Template.FJConfig
MC3 - enum entry in jline.bench.fj.BenchFJ_Template.FJConfig
MC4 - enum entry in jline.bench.fj.BenchFJ_Template.FJConfig
mean() - function in jline.lang.processes.Coxian: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Coxian: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Det: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Distribution: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Distribution: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Erlang: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Exp: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Gamma: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.HyperExp: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Lognormal: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Markovian: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Markovian: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.PH: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Pareto: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Uniform: Kotlin-style property alias for getMean()
mean() - function in jline.lang.processes.Weibull: Kotlin-style property alias for getMean()
meanCol() - function in jline.util.matrix.Matrix: Computes the mean of each column.
meanRow() - function in jline.util.matrix.Matrix: Computes the mean of each row.
merge(int,int) - function in jline.lang.processes.GMM: Merges two components into one using moment matching.
method(java.lang.String) - function in jline.solvers.SolverOptions: Sets the solution method/algorithm (builder pattern).
method(java.lang.String) - function in jline.solvers.SolverOptions: Sets the solution method/algorithm (builder pattern).
MethodStepHandler - class in jline.solvers.fluid.handlers
Metric - class in jline.lang: Constants for specifying a Metric
MetricType - class in jline.lang.constant: Constants for specifying a type of metric
mfilename(java.lang.Object) - function in jline.io.InputOutputKt
min(double,double) - function in jline.util.Maths: Returns the min of two numbers.
minpos(jline.util.matrix.Matrix) - function in jline.util.Maths: Returns the position of the minimum value in a vector.
minpos(jline.util.matrix.Matrix,int) - function in jline.util.Maths: Returns the positions of the n smallest values in a vector.
MixedExamples - class in jline.examples.java.basic: Mixed queueing network examples mirroring the Kotlin notebooks in mixedQN.
MixedModel - class in jline.examples.java.basic: Examples of mixed queueing networks
mixture(jline.lang.processes.GMM,jline.lang.processes.GMM,double,double) - function in jline.lang.processes.GMM: Creates a mixture of two GMMs with specified weights.
mmap_backward_moment(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix,java.lang.Integer) - function in jline.api.mam.Mmap_backward_momentKt: Computes the backward moments of an MMAP for specified orders with normalization.
Mmap_backward_momentKt - class in jline.api.mam
Mmap_compress - class in jline.api.mam: Compresses an MMAP using various approximation methods.
mmap_compress(kotlin.Array,java.lang.String) - function in jline.api.mam.Mmap_compress.Companion
mmap_compress(kotlin.Array) - function in jline.api.mam.Mmap_compress.Companion
mmap_count_idc(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Mmap_count_idcKt: Computes the index of dispersion for counts (IDC) for a Markovian Arrival Process with marked arrivals (MMAP) over a time period.
Mmap_count_idcKt - class in jline.api.mam
mmap_count_lambda(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_count_lambdaKt: Computes the arrival rate vector of the counting process for the given Marked MAP (MMAP).
Mmap_count_lambdaKt - class in jline.api.mam
mmap_count_mcov(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Mmap_count_mcovKt: Computes the count covariance between each pair of classes at a given time scale.
mmap_count_mcov(kotlin.Array,java.lang.Double) - function in jline.api.mam.Mmap_count_mcovKt: Computes the count covariance between each pair of classes at a given time scale.
Mmap_count_mcovKt - class in jline.api.mam
mmap_count_mean(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Mmap_count_meanKt: Computes the mean count vector of events of different types in a Markovian Arrival Process with marked arrivals (MMAP) over a time period.
mmap_count_mean_class(jline.util.matrix.MatrixCell,java.lang.Integer,java.lang.Double) - function in jline.api.mam.Maph2m_fitc_theoreticalKt: Helper functions for MMAP analysis - these would be implemented with full MMAP theory
Mmap_count_meanKt - class in jline.api.mam
mmap_count_var(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Mmap_count_varKt: Computes the variance of the count vector of events of different types in a Markovian Arrival Process with marked arrivals (MMAP) over a time period.
mmap_count_var_class(jline.util.matrix.MatrixCell,java.lang.Integer,java.lang.Double) - function in jline.api.mam.Maph2m_fitc_theoreticalKt
mmap_count_var_others(jline.util.matrix.MatrixCell,java.lang.Integer,java.lang.Double) - function in jline.api.mam.Maph2m_fitc_theoreticalKt
Mmap_count_varKt - class in jline.api.mam
mmap_cross_modulate(jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Mmap_modulateKt: Cross-modulation between two MMAPs.
mmap_cross_moment(jline.util.matrix.MatrixCell,java.lang.Integer) - function in jline.api.mam.Mmap_cross_momentKt: Computes the k-th cross-moment matrix for a given MMAP.
Mmap_cross_momentKt - class in jline.api.mam
mmap_embedded(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_embeddedKt: Computes the embedded chain of an MMAP.
Mmap_embeddedKt - class in jline.api.mam
mmap_exponential(jline.util.matrix.Matrix) - function in jline.api.mam.Mmap_exponentialKt: Fits a Markovian Arrival Process with marked arrivals (MMAP) with a single state based on the given arrival rates for each job class.
mmap_exponential(jline.util.matrix.Matrix,java.lang.Integer) - function in jline.api.mam.Mmap_exponentialKt: Fits an order-n Markovian Arrival Process with marked arrivals (MMAP) based on the given arrival rates for each job class.
Mmap_exponentialKt - class in jline.api.mam
mmap_forward_moment(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix,java.lang.Integer) - function in jline.api.mam.Mmap_forward_momentKt: Computes the forward moments of an MMAP for specified orders with normalization.
mmap_forward_moment(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix) - function in jline.api.mam.Mmap_forward_momentKt: Computes the forward moments of an MMAP for specified orders with normalization.
Mmap_forward_momentKt - class in jline.api.mam
mmap_hide(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix) - function in jline.api.mam.Mmap_hideKt: Hides specified types of arrivals in a Markovian Arrival Process with marked arrivals (MMAP).
Mmap_hideKt - class in jline.api.mam
mmap_idc(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_idcKt: Computes the asymptotic index of dispersion for counts (IDC) for a Markovian Arrival Process with marked arrivals (MMAP).
Mmap_idcKt - class in jline.api.mam
mmap_isfeasible(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_isfeasibleKt: Checks the feasibility of a Markovian Arrival Process with marked arrivals (MMAP).
Mmap_isfeasibleKt - class in jline.api.mam
mmap_issym(jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Mmap_issymKt: Checks if an MMAP is symmetric.
Mmap_issymKt - class in jline.api.mam
mmap_lambda(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_lambdaKt: Alias for mmap_count_lambda.
Mmap_lambdaKt - class in jline.api.mam
mmap_maps(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_mapsKt: Extracts K Markovian Arrival Processes (MAPs) from a given MMAP, one for each class.
Mmap_mapsKt - class in jline.api.mam
mmap_mark(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix) - function in jline.api.mam.Mmap_markKt: Converts a Markovian Arrival Process with marked arrivals (MMAP) into a new MMAP with redefined classes based on a given probability matrix.
Mmap_markKt - class in jline.api.mam
mmap_max(jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_maxKt: Computes the element-wise maximum of two MMAPs.
mmap_max_multiple(java.util.List) - function in jline.api.mam.Mmap_maxKt: Computes the element-wise maximum of multiple MMAPs.
Mmap_maxKt - class in jline.api.mam
mmap_mixture(jline.util.matrix.Matrix,java.util.Map) - function in jline.api.mam.Mmap_mixtureKt: Creates a mixture of MMAPs using the given weights (alpha) and MAPs.
mmap_mixture_fit(java.lang.Object,jline.util.matrix.Matrix,jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.mam.Mmap_mixture_fitKt: Fits a mixture of Markovian Arrival Processes (MMAPs) to match the given cross-moments.
mmap_mixture_fit_mmap(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_mixture_fit_mmapKt: Fits a mixture of Markovian Arrival Processes (MMAPs) to match the given moments.
Mmap_mixture_fit_mmapKt - class in jline.api.mam
Mmap_mixture_fitKt - class in jline.api.mam
mmap_mixture_order2(java.util.List,kotlin.DoubleArray) - function in jline.api.mam.Mmap_mixture_order2Kt: Creates a second-order MMAP mixture from a collection of MMAPs.
mmap_mixture_order2_optimal(java.util.List,kotlin.DoubleArray) - function in jline.api.mam.Mmap_mixture_order2Kt: Creates a second-order MMAP mixture with automatic weight selection.
Mmap_mixture_order2Kt - class in jline.api.mam
Mmap_mixtureKt - class in jline.api.mam
mmap_modulate(jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell,java.lang.Double) - function in jline.api.mam.Mmap_modulateKt: Modulates an MMAP by another MMAP, creating a compound arrival process.
mmap_modulate_time_varying(jline.util.matrix.MatrixCell,kotlin.jvm.functions.Function1,java.lang.Double,java.lang.Integer) - function in jline.api.mam.Mmap_modulateKt: Time-varying modulation of an MMAP.
Mmap_modulateKt - class in jline.api.mam
mmap_normalize(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_normalizeKt: Normalizes a Markovian Arrival Process with marked arrivals (MMAP) to ensure feasibility.
Mmap_normalizeKt - class in jline.api.mam
mmap_pc(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_pcKt: Computes the proportion of counts (PC) for each type in a Markovian Arrival Process with marked arrivals (MMAP).
Mmap_pcKt - class in jline.api.mam
Mmap_pie - class in jline.api.mam: Computes the steady-state probability vector for each class in an MMAP.
mmap_pie(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_pie.Companion
mmap_pie(kotlin.Array) - function in jline.api.mam.Mmap_pie.Companion: Overloaded version for Array<Matrix> input
mmap_rand(java.lang.Integer,java.lang.Integer) - function in jline.api.mam.Mmap_randKt: Generates a random MMAP (Marked Markovian Arrival Process) with a given order and number of classes.
Mmap_randKt - class in jline.api.mam
mmap_sample(jline.util.matrix.MatrixCell,java.lang.Long,java.util.Random) - function in jline.api.mam.Mmap_sampleKt: Generates samples of inter-arrival times and event types from a MMAP using a specified number of samples.
Mmap_sampleKt - class in jline.api.mam
mmap_scale(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix) - function in jline.api.mam.Mmap_scaleKt: Overloaded function for backward compatibility
mmap_scale(jline.util.matrix.MatrixCell,jline.util.matrix.Matrix,java.lang.Integer) - function in jline.api.mam.Mmap_scaleKt: Changes the mean inter-arrival time of a Markovian Arrival Process with marked arrivals (MMAP).
Mmap_scaleKt - class in jline.api.mam
mmap_shorten(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_shortenKt: Converts an MMAP representation from M3A format to BUTools format.
Mmap_shortenKt - class in jline.api.mam
mmap_sigma(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_sigmaKt: Computes one-step class transition probabilities for a Marked Markovian Arrival Process (MMAP).
mmap_sigma(kotlin.Array) - function in jline.api.mam.Mmap_sigmaKt: Computes one-step class transition probabilities for a Marked Markovian Arrival Process (MMAP).
mmap_sigma2(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_sigma2Kt: Computes two-step class transition probabilities for a Markovian Arrival Process (MMAP).
mmap_sigma2_cell(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_sigma2Kt: Computes two-step class transition probabilities for a Markovian Arrival Process (MMAP).
Mmap_sigma2Kt - class in jline.api.mam
Mmap_sigmaKt - class in jline.api.mam
mmap_sum(jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_sumKt: Computes the sum of two MMAPs, creating a superposition process.
mmap_sum_multiple(java.util.List) - function in jline.api.mam.Mmap_sumKt: Computes the sum of multiple MMAPs.
mmap_sum_selective(java.util.List,java.util.List) - function in jline.api.mam.Mmap_sumKt: Selective sum - only sum specific classes from each MMAP.
mmap_sum_weighted(java.util.List,kotlin.DoubleArray) - function in jline.api.mam.Mmap_sumKt: Weighted sum of MMAPs with scaling factors.
Mmap_sumKt - class in jline.api.mam
mmap_super(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_superKt: Combines a list of MMAPs into one superposed MMAP.
mmap_super(jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_superKt: Combines two MMAPs into one superposed MMAP using the default option.
mmap_super(jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell,java.lang.String) - function in jline.api.mam.Mmap_superKt: Combines two MMAPs into one superposed MMAP.
mmap_super_safe(java.util.Map,java.lang.Integer,java.lang.String) - function in jline.api.mam.Mmap_super_safeKt
Mmap_super_safeKt - class in jline.api.mam
Mmap_superKt - class in jline.api.mam
mmap_timereverse(jline.util.matrix.MatrixCell) - function in jline.api.mam.Mmap_timereverseKt: Computes the time-reversed version of a Markovian Arrival Process with marked arrivals (MMAP).
Mmap_timereverseKt - class in jline.api.mam
MmapBackwardMomentKt - class in jline.api.mam: MMAP backward moment algorithms
MmapCompressKt - class in jline.api.mam: MMAP compress algorithms
MmapCountIdcKt - class in jline.api.mam: MMAP count idc algorithms
MmapCountLambdaKt - class in jline.api.mam: MMAP count lambda algorithms
MmapCountMcovKt - class in jline.api.mam: MMAP count mcov algorithms
MmapCountMeanKt - class in jline.api.mam: MMAP count mean algorithms
MmapCountVarKt - class in jline.api.mam: MMAP count var algorithms
MmapCrossMomentKt - class in jline.api.mam: MMAP cross moment algorithms
MmapEmbeddedKt - class in jline.api.mam: MMAP embedded algorithms
MmapExponentialKt - class in jline.api.mam: MMAP exponential algorithms
MmapForwardMomentKt - class in jline.api.mam: MMAP forward moment algorithms
MmapHideKt - class in jline.api.mam: MMAP hide algorithms
MmapIdcKt - class in jline.api.mam: MMAP idc algorithms
MmapIsfeasibleKt - class in jline.api.mam: MMAP isfeasible algorithms
MmapIssymKt - class in jline.api.mam: MMAP issym algorithms
MmapLambdaKt - class in jline.api.mam: MMAP lambda algorithms
MmapMapsKt - class in jline.api.mam: MMAP maps algorithms
MmapMark - class in jline.api.mam: MMAP mark algorithms
MmapMaxKt - class in jline.api.mam: MMAP max algorithms
MmapMixtureFitKt - class in jline.api.mam: MMAP mixture fit algorithms
MmapMixtureFitMmapKt - class in jline.api.mam: MMAP mixture fit mmap algorithms
MmapMixtureKt - class in jline.api.mam: MMAP mixture algorithms
MmapMixtureOrder2Kt - class in jline.api.mam: MMAP mixture order2 algorithms
MmapModulateKt - class in jline.api.mam: MMAP modulate algorithms
MmapNormalizeKt - class in jline.api.mam: MMAP normalize algorithms
MmapPcKt - class in jline.api.mam: MMAP pc algorithms
MMAPPH1FCFS(jline.util.matrix.MatrixCell,java.util.Map,java.util.Map,java.lang.Integer,java.lang.Integer,java.lang.Integer,jline.util.matrix.Matrix,java.lang.Boolean,java.lang.Boolean,java.lang.Double,jline.util.matrix.Matrix) - function in jline.lib.butools.MMAPPH1FCFSKt
MMAPPH1FCFSKt - class in jline.lib.butools
MMAPPH1NPPR(jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell,jline.util.matrix.MatrixCell,java.lang.Integer,java.lang.Integer,java.lang.Integer,jline.util.matrix.Matrix,java.lang.Double,java.lang.Integer,jline.util.matrix.Matrix) - function in jline.lib.butools.MMAPPH1NPPRKt
MMAPPH1NPPRKt - class in jline.lib.butools
MmapPieKt - class in jline.api.mam: MMAP pie algorithms
MmapRandKt - class in jline.api.mam: MMAP rand algorithms
MmapSampleKt - class in jline.api.mam: MMAP sample algorithms
MmapScale - class in jline.api.mam: MMAP scale algorithms
MmapShortenKt - class in jline.api.mam: MMAP shorten algorithms
MmapSigma2Kt - class in jline.api.mam: MMAP sigma2 algorithms
MmapSigmaKt - class in jline.api.mam: MMAP sigma algorithms
MmapSum - class in jline.api.mam: MMAP sum algorithms
MmapSuperKt - class in jline.api.mam: MMAP super algorithms
MmapSuperSafeKt - class in jline.api.mam: MMAP super safe algorithms
MmapTimereverse - class in jline.api.mam: MMAP timereverse algorithms
MMPP2 - enum entry in jline.lang.constant.ProcessType
MMPP2 - class in jline.lang.processes: A Markovian-modulated Poisson Process with 2 states
mmpp2_fit(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mmpp2_fitKt: Fits a 2-phase Markovian Arrival Process (MMPP2) to match the given first three moments and a fourth parameter G2.
mmpp2_fit1(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mmpp2_fit1Kt: Fits a 2-phase Markov Modulated Poisson Process (MMPP2) based on the specified parameters.
Mmpp2_fit1Kt - class in jline.api.mam
mmpp2_fit_example() - function in jline.lib.kpctoolbox.MAPCatalog: MMPP2 Fit Example Source: MMPP2_fit_example.
mmpp2_fit_mu00(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mmpp2_fitKt
mmpp2_fit_mu11(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mmpp2_fitKt
mmpp2_fit_q01(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mmpp2_fitKt
mmpp2_fit_q10(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mmpp2_fitKt
Mmpp2_fitc - class in jline.api.mam: Fits a MMPP(2) according to Heffes and Lucantoni, 1986.
mmpp2_fitc(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mmpp2_fitc.Companion
Mmpp2_fitc_approx - class in jline.api.mam: Fits a second-order Marked MMPP using optimization.
mmpp2_fitc_approx(java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double,java.lang.Double) - function in jline.api.mam.Mmpp2_fitc_approx.Companion
Mmpp2_fitKt - class in jline.api.mam
mmpp2_lrd() - function in jline.lib.kpctoolbox.MAPCatalog: MMPP2 LRD (Long Range Dependent) model Source: MMPP2_lrd.
mmpp2_markov_example() - function in jline.lib.kpctoolbox.MAPCatalog: MMPP2 Markov Example Source: MMPP2_markov_example.
mmpp2_noacf() - function in jline.lib.kpctoolbox.MAPCatalog: MMPP2 NoACF (No Autocorrelation Function) model Source: MMPP2_noacf.
mmpp2_simple() - function in jline.lib.kpctoolbox.MAPCatalog: MMPP2 Simple Example Source: MMPP2_simple.
mmpp2_srd() - function in jline.lib.kpctoolbox.MAPCatalog: MMPP2 SRD (Short Range Dependent) model Source: MMPP2_srd.
mmpp2_trace_example() - function in jline.lib.kpctoolbox.MAPCatalog: MMPP2 Trace Example Source: MMPP2_trace_example.
Mmpp2Fit1Kt - class in jline.api.mam: Mmpp2 Fit1 algorithms
Mmpp2FitcKt - class in jline.api.mam: Mmpp2 Fitc algorithms
Mmpp2FitCountApproxKt - class in jline.api.mam: Mmpp2 Fit Count Approx algorithms
Mmpp2FitKt - class in jline.api.mam: Mmpp2 Fit algorithms
mmpp_rand() - function in jline.api.mam.Mmpp_randKt: Generates a random Markov Modulated Poisson Process (MMPP) with 2 states.
mmpp_rand(java.lang.Integer) - function in jline.api.mam.Mmpp_randKt: Generates a random Markov Modulated Poisson Process (MMPP) with K states.
Mmpp_randKt - class in jline.api.mam
MmppRandKt - class in jline.api.mam: Mmpp Rand algorithms
mmt(jline.lang.Network) - function in jline.lang.ModelAdapter: Fork-Join Transform approach with default forkLambda parameter
mmt(jline.lang.Network,jline.util.matrix.Matrix) - function in jline.lang.ModelAdapter: Fork-Join Transform approach to evaluate queueing networks including fork-join systems.
Mode - class in jline.lang: Superclass representing a class of jobs
ModeEvent - class in jline.lang: A mode event occurring in a Network.
Model - class in jline.lang: Class representing a model supported by the library
model() - function in jline.solvers.NetworkSolver
model() - function in jline.solvers.NetworkSolver
ModelAdapter - class in jline.lang: Static class to transform and adapt models, providing functionality for: - Creating tagged job models for response time analysis - Fork-join network transformations (formerly from FJ.
ModelAdapter.TaggedChainResult - class in jline.lang.ModelAdapter: Result of tagging a chain in a model
ModelAnalyzer - class in jline.solvers.auto: Helper class to analyze model characteristics for solver selection
moments() - function in jline.lang.processes.Markovian: Kotlin-style property alias for getMoments()
moments() - function in jline.lang.processes.Markovian: Kotlin-style property alias for getMoments()
moments(org.apache.commons.math3.analysis.UnivariateFunction,java.lang.Integer) - function in jline.lib.lti.cme
MomsFromFactorialMoms(jline.util.matrix.Matrix) - function in jline.lib.butools.MomsFromFactorialMomsKt
MomsFromFactorialMomsKt - class in jline.lib.butools
momsFromHankelMoms(jline.util.matrix.Matrix) - function in jline.lib.butools.MomsFromHankelMomsKt: Returns the raw moments given the Hankel moments.
MomsFromHankelMomsKt - class in jline.lib.butools
momsFromNormMoms(jline.util.matrix.Matrix) - function in jline.lib.butools.MomsFromNormMomsKt: Returns the raw moments given the normalized moments.
MomsFromNormMomsKt - class in jline.lib.butools
momsFromReducedMoms(jline.util.matrix.Matrix) - function in jline.lib.butools.MomsFromReducedMomsKt: Returns the raw moments given the reduced moments.
MomsFromReducedMomsKt - class in jline.lib.butools
mqn_basic() - function in jline.examples.java.basic.MixedExamples: Basic mixed open/closed network (mqn_basic.ipynb).
mqn_basic() - function in jline.examples.java.basic.MixedModel: Simple mixed open/closed network with delay and PS queue.
mqn_multiserver_fcfs() - function in jline.examples.java.basic.MixedExamples: Mixed network with FCFS multi-server queues (mqn_multiserver_fcfs.ipynb).
mqn_multiserver_fcfs() - function in jline.examples.java.basic.MixedModel: Mixed network similar to example 2 but with FCFS scheduling.
mqn_multiserver_ps() - function in jline.examples.java.basic.MixedExamples: Mixed network with PS multi-server queues (mqn_multiserver_ps.ipynb).
mqn_multiserver_ps() - function in jline.examples.java.basic.MixedModel: Complex mixed network with five PS queues and different server counts.
mqn_singleserver_fcfs() - function in jline.examples.java.basic.MixedExamples: Mixed FCFS network with large closed population (mqn_singleserver_fcfs.ipynb).
mqn_singleserver_fcfs() - function in jline.examples.java.basic.MixedModel: Mixed network with large closed class population and APH arrivals.
mqn_singleserver_ps() - function in jline.examples.java.basic.MixedExamples: Mixed PS network with simplified routing (mqn_singleserver_ps.ipynb).
mqn_singleserver_ps() - function in jline.examples.java.basic.MixedModel: Mixed PS network with large closed population and simplified open routing.
ms_additivesymmetricchisquared(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_additivesymmetricchisquaredKt: Additive symmetric chi-squared distance between two probability distributions.
Ms_additivesymmetricchisquaredKt - class in jline.api.measures
ms_anderson_darling(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_anderson_darlingKt: Anderson-Darling distance between two empirical distributions.
Ms_anderson_darlingKt - class in jline.api.measures
ms_avgl1linfty(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_avgl1linftyKt: Average L1 L-infinity distance between two probability distributions.
Ms_avgl1linftyKt - class in jline.api.measures
ms_bhattacharyya(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_bhattacharyyaKt: Bhattacharyya distance between two probability distributions.
Ms_bhattacharyyaKt - class in jline.api.measures
ms_canberra(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_canberraKt: Canberra distance between two probability distributions.
Ms_canberraKt - class in jline.api.measures
ms_chebyshev(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_chebyshevKt: Chebyshev distance between two probability distributions.
Ms_chebyshevKt - class in jline.api.measures
ms_cityblock(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_cityblockKt: City block (Manhattan) distance between two probability distributions.
Ms_cityblockKt - class in jline.api.measures
ms_clark(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_clarkKt: Clark distance between two probability distributions.
Ms_clarkKt - class in jline.api.measures
ms_condentropy(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_condentropyKt: Compute conditional entropy z=H(x|y) of two discrete variables x and y.
Ms_condentropyKt - class in jline.api.measures
ms_cosine(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_cosineKt: Cosine distance between two probability distributions.
Ms_cosineKt - class in jline.api.measures
ms_cramer_von_mises(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_cramer_von_misesKt: Cramér-von Mises distance between two empirical distributions.
Ms_cramer_von_misesKt - class in jline.api.measures
ms_czekanowski(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_czekanowskiKt: Czekanowski distance between two probability distributions.
Ms_czekanowskiKt - class in jline.api.measures
ms_dice(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_diceKt: Dice distance between two probability distributions.
Ms_diceKt - class in jline.api.measures
ms_divergence(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_divergenceKt: Divergence distance between two probability distributions.
Ms_divergenceKt - class in jline.api.measures
ms_entropy(jline.util.matrix.Matrix) - function in jline.api.measures.Ms_entropyKt: Compute entropy z=H(x) of a discrete variable x.
Ms_entropyKt - class in jline.api.measures
ms_euclidean(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_euclideanKt: Euclidean distance between two probability distributions.
Ms_euclideanKt - class in jline.api.measures
ms_fidelity(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_fidelityKt: Fidelity distance between two probability distributions.
Ms_fidelityKt - class in jline.api.measures
ms_gower(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_gowerKt: Gower distance between two probability distributions.
Ms_gowerKt - class in jline.api.measures
ms_harmonicmean(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_harmonicmeanKt: Harmonic mean distance between two probability distributions.
Ms_harmonicmeanKt - class in jline.api.measures
ms_hellinger(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_hellingerKt: Hellinger distance between two probability distributions.
Ms_hellingerKt - class in jline.api.measures
ms_intersection(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_intersectionKt: Intersection distance between two probability distributions.
Ms_intersectionKt - class in jline.api.measures
ms_jaccard(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_jaccardKt: Jaccard distance between two probability distributions.
Ms_jaccardKt - class in jline.api.measures
ms_jeffreys(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_jeffreysKt: Jeffreys divergence between two probability distributions.
Ms_jeffreysKt - class in jline.api.measures
ms_jensendifference(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_jensendifferenceKt: Jensen difference divergence between two probability distributions.
Ms_jensendifferenceKt - class in jline.api.measures
ms_jensenshannon(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_jensenshannonKt: Jensen-Shannon divergence between two probability distributions.
Ms_jensenshannonKt - class in jline.api.measures
ms_jointentropy(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_jointentropyKt: Compute joint entropy z=H(x,y) of two discrete variables x and y.
Ms_jointentropyKt - class in jline.api.measures
ms_kdivergence(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_kdivergenceKt: K-divergence between two probability distributions.
Ms_kdivergenceKt - class in jline.api.measures
ms_kolmogorov_smirnov(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_kolmogorov_smirnovKt: Kolmogorov-Smirnov distance between two empirical distributions.
Ms_kolmogorov_smirnovKt - class in jline.api.measures
ms_kuiper(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_kuiperKt: Kuiper distance between two empirical distributions.
Ms_kuiperKt - class in jline.api.measures
ms_kulczynskid(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_kulczynskidKt: Kulczynski d distance between two probability distributions.
Ms_kulczynskidKt - class in jline.api.measures
ms_kulczynskis(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_kulczynskisKt: Kulczynski s distance between two probability distributions.
Ms_kulczynskisKt - class in jline.api.measures
ms_kullbackleibler(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_kullbackleiblerKt: Kullback-Leibler divergence between two probability distributions.
Ms_kullbackleiblerKt - class in jline.api.measures
ms_kumarhassebrook(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_kumarhassebrookKt: Kumar-Hassebrook distance between two probability distributions.
Ms_kumarhassebrookKt - class in jline.api.measures
ms_kumarjohnson(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_kumarjohnsonKt: Kumar-Johnson distance between two probability distributions.
Ms_kumarjohnsonKt - class in jline.api.measures
ms_lorentzian(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_lorentzianKt: Lorentzian distance between two probability distributions.
Ms_lorentzianKt - class in jline.api.measures
ms_matusita(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_matusitaKt: Matusita distance between two probability distributions.
Ms_matusitaKt - class in jline.api.measures
ms_minkowski(jline.util.matrix.Matrix,jline.util.matrix.Matrix,java.lang.Double) - function in jline.api.measures.Ms_minkowskiKt: Minkowski distance between two probability distributions.
Ms_minkowskiKt - class in jline.api.measures
ms_motyka(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_motykaKt: Motyka distance between two probability distributions.
Ms_motykaKt - class in jline.api.measures
ms_mutinfo(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_mutinfoKt: Compute mutual information I(x,y) of two discrete variables x and y.
Ms_mutinfoKt - class in jline.api.measures
ms_neymanchisquared(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_neymanchisquaredKt: Neyman chi-squared distance between two probability distributions.
Ms_neymanchisquaredKt - class in jline.api.measures
Ms_nmi - class in jline.api.measures: Ms_nmi algorithms
ms_nmi(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_nmiKt: Compute normalized mutual information I(x,y)/sqrt(H(x)*H(y)) of two discrete variables x and y.
Ms_nmiKt - class in jline.api.measures
Ms_nvi - class in jline.api.measures: Ms_nvi algorithms
ms_nvi(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_nviKt: Compute normalized variation information z=(1-I(x,y)/H(x,y)) of two discrete variables x and y.
Ms_nviKt - class in jline.api.measures
ms_pearsonchisquared(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_pearsonchisquaredKt: Pearson chi-squared distance between two probability distributions.
Ms_pearsonchisquaredKt - class in jline.api.measures
ms_probsymmchisquared(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_probsymmchisquaredKt: Probabilistic symmetry chi-squared distance between two probability distributions.
Ms_probsymmchisquaredKt - class in jline.api.measures
ms_product(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_productKt: Product distance between two probability distributions.
Ms_productKt - class in jline.api.measures
ms_relatentropy(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_relatentropyKt: Compute relative entropy (a.k.a KL divergence) z=KL(p(x)||p(y)) of two discrete variables x and y.
Ms_relatentropyKt - class in jline.api.measures
ms_ruzicka(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_ruzickaKt: Ruzicka distance between two probability distributions.
Ms_ruzickaKt - class in jline.api.measures
ms_soergel(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_soergelKt: Soergel distance between two probability distributions.
Ms_soergelKt - class in jline.api.measures
ms_sorensen(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_sorensenKt: Sorensen distance between two probability distributions.
Ms_sorensenKt - class in jline.api.measures
ms_squaredchisquared(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_squaredchisquaredKt: Squared chi-squared distance between two probability distributions.
Ms_squaredchisquaredKt - class in jline.api.measures
ms_squaredchord(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_squaredchordKt: Squared chord distance between two probability distributions.
Ms_squaredchordKt - class in jline.api.measures
ms_squaredeuclidean(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_squaredeuclideanKt: Squared Euclidean distance between two probability distributions.
Ms_squaredeuclideanKt - class in jline.api.measures
ms_taneja(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_tanejaKt: Taneja distance between two probability distributions.
Ms_tanejaKt - class in jline.api.measures
ms_tanimoto(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_tanimotoKt: Tanimoto distance between two probability distributions.
Ms_tanimotoKt - class in jline.api.measures
ms_topsoe(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_topsoeKt: Topsoe distance between two probability distributions.
Ms_topsoeKt - class in jline.api.measures
ms_wasserstein(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_wassersteinKt: Wasserstein distance (Earth Mover's Distance) between two empirical distributions.
Ms_wassersteinKt - class in jline.api.measures
ms_wavehegdes(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.api.measures.Ms_wavehegdesKt: Wave-Hedges distance between two probability distributions.
Ms_wavehegdesKt - class in jline.api.measures
MsAdditivesymmetricchisquaredKt - class in jline.api.measures: Additivesymmetricchisquared metric algorithms
MsAvgl1linftyKt - class in jline.api.measures: Avgl1Linfty metric algorithms
MsBhattacharyyaKt - class in jline.api.measures: Bhattacharyya metric algorithms
MsCanberraKt - class in jline.api.measures: Canberra metric algorithms
MsChebyshevKt - class in jline.api.measures: Chebyshev metric algorithms
MsCityblockKt - class in jline.api.measures: Cityblock metric algorithms
MsClarkKt - class in jline.api.measures: Clark metric algorithms
MsCosineKt - class in jline.api.measures: Cosine metric algorithms
MsCzekanowskiKt - class in jline.api.measures: Czekanowski metric algorithms
MsDiceKt - class in jline.api.measures: Dice metric algorithms
MsDivergenceKt - class in jline.api.measures: Divergence metric algorithms
MsEuclideanKt - class in jline.api.measures: Euclidean distance metric algorithms
MsFidelityKt - class in jline.api.measures: Fidelity metric algorithms
MsGowerKt - class in jline.api.measures: Gower metric algorithms
MsHarmonicmeanKt - class in jline.api.measures: Harmonicmean metric algorithms
MsHellingerKt - class in jline.api.measures: Hellinger metric algorithms
MsIntersectionKt - class in jline.api.measures: Intersection metric algorithms
MsJaccardKt - class in jline.api.measures: Jaccard metric algorithms
MsJeffreysKt - class in jline.api.measures: Jeffreys metric algorithms
MsJensendifferenceKt - class in jline.api.measures: Jensendifference metric algorithms
MsJensenshannonKt - class in jline.api.measures: Jensenshannon metric algorithms
MsKdivergenceKt - class in jline.api.measures: Kdivergence metric algorithms
MsKulczynskidKt - class in jline.api.measures: Kulczynskid metric algorithms
MsKulczynskisKt - class in jline.api.measures: Kulczynskis metric algorithms
MsKullbackleiblerKt - class in jline.api.measures: Kullbackleibler metric algorithms
MsKumarhassebrookKt - class in jline.api.measures: Kumarhassebrook metric algorithms
MsKumarjohnsonKt - class in jline.api.measures: Kumarjohnson metric algorithms
MsLorentzianKt - class in jline.api.measures: Lorentzian metric algorithms
MsMatusitaKt - class in jline.api.measures: Matusita metric algorithms
MsMinkowskiKt - class in jline.api.measures: Minkowski metric algorithms
MsMotykaKt - class in jline.api.measures: Motyka metric algorithms
MsNeymanchisquaredKt - class in jline.api.measures: Neymanchisquared metric algorithms
MsPearsonchisquaredKt - class in jline.api.measures: Pearsonchisquared metric algorithms
MsProductKt - class in jline.api.measures: Product metric algorithms
MsRuzickaKt - class in jline.api.measures: Ruzicka metric algorithms
MsSoergelKt - class in jline.api.measures: Soergel metric algorithms
MsSorensenKt - class in jline.api.measures: Sorensen metric algorithms
MsSquaredchisquaredKt - class in jline.api.measures: Squaredchisquared metric algorithms
MsSquaredchordKt - class in jline.api.measures: Squaredchord metric algorithms
MsSquaredeuclideanKt - class in jline.api.measures: Squaredeuclidean metric algorithms
MsTanejaKt - class in jline.api.measures: Taneja metric algorithms
MsTanimotoKt - class in jline.api.measures: Tanimoto metric algorithms
MsTopsoeKt - class in jline.api.measures: Topsoe metric algorithms
MsWavehegdesKt - class in jline.api.measures: Wavehegdes metric algorithms
mtrace_backward_moment(kotlin.DoubleArray,kotlin.IntArray,java.lang.Integer,java.lang.Integer) - function in jline.api.trace.Mtrace_backward_momentKt: Computes backward moments of a multi-class trace.
mtrace_backward_moment_conditional(kotlin.DoubleArray,kotlin.IntArray,java.lang.Integer,java.lang.Integer) - function in jline.api.trace.Mtrace_backward_momentKt: Computes conditional backward moments given the forward recurrence time.
Mtrace_backward_momentKt - class in jline.api.trace
mtrace_bootstrap(kotlin.DoubleArray,kotlin.IntArray,java.lang.Integer,java.lang.Integer,java.lang.Long) - function in jline.api.trace.Mtrace_bootstrapKt: Performs bootstrap resampling on a multi-class trace to estimate confidence intervals and assess variability of trace statistics.
Mtrace_bootstrapKt - class in jline.api.trace
mtrace_count(kotlin.DoubleArray,kotlin.IntArray,java.lang.Double,java.lang.Integer) - function in jline.api.trace.Mtrace_countKt: Computes count statistics from a multi-class trace over specified time windows.
mtrace_count_multiscale(kotlin.DoubleArray,kotlin.IntArray,kotlin.DoubleArray) - function in jline.api.trace.Mtrace_countKt: Compute multi-scale count statistics
Mtrace_countKt - class in jline.api.trace
mtrace_cov(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.trace.Mtrace_covKt: Computes the covariance matrix for multi-type traces.
Mtrace_covKt - class in jline.api.trace
mtrace_cross_moment(kotlin.DoubleArray,kotlin.IntArray,java.lang.Integer) - function in jline.api.trace.Mtrace_cross_momentKt: Computes the k-th order moment of the inter-arrival time between an event of class i and an event of class j, for all possible pairs of classes.
Mtrace_cross_momentKt - class in jline.api.trace
mtrace_forward_moment(kotlin.DoubleArray,kotlin.IntArray,kotlin.IntArray,java.lang.Integer) - function in jline.api.trace.Mtrace_forward_momentKt: Computes the forward moments of a marked trace.
Mtrace_forward_momentKt - class in jline.api.trace
mtrace_iat2counts(kotlin.DoubleArray,java.lang.Double) - function in jline.api.trace.Mtrace_countKt: Convert inter-arrival times to count process using native implementation equivalent
mtrace_iat2counts(kotlin.DoubleArray,kotlin.IntArray,java.lang.Double) - function in jline.api.trace.Mtrace_iat2countsKt: Computes the per-class counting processes of T, i.e., the counts after "scale" units of time from an arrival.
Mtrace_iat2countsKt - class in jline.api.trace
mtrace_joint(kotlin.DoubleArray,kotlin.IntArray,kotlin.IntArray) - function in jline.api.trace.Mtrace_jointKt: Given a multi-class trace, computes the empirical class-dependent joint moments that estimate E ( X^(a)_j )^i(1) (X^(a)_(j+l) )^i(2) for all classes a.
Mtrace_jointKt - class in jline.api.trace
mtrace_mean(kotlin.DoubleArray,java.lang.Integer,kotlin.IntArray) - function in jline.api.trace.Mtrace_meanKt: Computes the mean of a trace, divided by types.
Mtrace_meanKt - class in jline.api.trace
mtrace_merge(kotlin.DoubleArray,kotlin.DoubleArray) - function in jline.api.trace.Mtrace_mergeKt: Merges two traces in a single marked (multiclass) trace
Mtrace_mergeKt - class in jline.api.trace
mtrace_moment(kotlin.DoubleArray,kotlin.IntArray,kotlin.IntArray,java.lang.Integer,java.lang.Integer) - function in jline.api.trace.Mtrace_momentKt: Computes the empirical class-dependent moments of a multi-class trace.
mtrace_moment_simple(kotlin.DoubleArray,kotlin.IntArray,java.lang.Integer) - function in jline.api.trace.Mtrace_moment_simpleKt: Computes the k-th order moment of the inter-arrival time between an event of class i and an event of class j, for all possible pairs of classes.
Mtrace_moment_simpleKt - class in jline.api.trace
Mtrace_momentKt - class in jline.api.trace
mtrace_pc(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.trace.Mtrace_pcKt: Computes the probabilities of arrival for each class.
Mtrace_pcKt - class in jline.api.trace
mtrace_sigma(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.trace.Mtrace_sigmaKt: Computes the empirical probability of observing a specific 2-element sequence of events, i.e.
mtrace_sigma2(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.trace.Mtrace_sigma2Kt: Computes the empirical probability of observing a specific 3-element sequence of events, i.e.
Mtrace_sigma2Kt - class in jline.api.trace
Mtrace_sigmaKt - class in jline.api.trace
mtrace_split(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.trace.Mtrace_splitKt: Given a multi-class trace with inter-arrivals T and labels L, creates the separate per-class traces.
Mtrace_splitKt - class in jline.api.trace
mtrace_summary(kotlin.DoubleArray,kotlin.IntArray) - function in jline.api.trace.Mtrace_summaryKt: Computes comprehensive summary statistics for a multi-class trace.
Mtrace_summaryKt - class in jline.api.trace
MtraceBackwardMomentKt - class in jline.api.trace: Mtrace Backward Moment algorithms
MtraceBootstrapKt - class in jline.api.trace: Mtrace Bootstrap algorithms
MtraceCountKt - class in jline.api.trace: Mtrace Count algorithms
MtraceCovKt - class in jline.api.trace: Mtrace Cov algorithms
MtraceCrossMomentKt - class in jline.api.trace: Mtrace Cross Moment algorithms
MtraceForwardMomentKt - class in jline.api.trace: Mtrace Forward Moment algorithms
MtraceIat2countsKt - class in jline.api.trace: Mtrace Iat2Counts algorithms
MtraceJointKt - class in jline.api.trace: Mtrace Joint algorithms
MtraceMeanKt - class in jline.api.trace: Mtrace Mean algorithms
MtraceMergeKt - class in jline.api.trace: Mtrace Merge algorithms
MtraceMomentKt - class in jline.api.trace: Mtrace Moment algorithms
MtraceMomentSimpleKt - class in jline.api.trace: Mtrace Moment Simple algorithms
MtracePcKt - class in jline.api.trace: Mtrace Pc algorithms
MtraceSigma2Kt - class in jline.api.trace: Mtrace Sigma2 algorithms
MtraceSigmaKt - class in jline.api.trace: Mtrace Sigma algorithms
MtraceSplitKt - class in jline.api.trace: Mtrace Split algorithms
MtraceSummary - class in jline.api.trace: Data class representing a summary of multi-trace statistics
MtraceSummaryKt - class in jline.api.trace: Mtrace Summary algorithms
mu() - function in jline.lang.processes.Coxian: Kotlin-style property alias for getMu()
mu() - function in jline.lang.processes.Coxian: Kotlin-style property alias for getMu()
mu() - function in jline.lang.processes.Det: Kotlin-style property alias for getMu()
mu() - function in jline.lang.processes.Markovian: Kotlin-style property alias for getMu()
mu() - function in jline.lang.processes.Markovian: Kotlin-style property alias for getMu()
mulByMinusOne() - function in jline.util.matrix.Matrix: Multiplies all elements in the matrix by -1 in-place.
mult(org.ejml.data.DMatrix,org.ejml.data.DMatrix,org.ejml.data.DMatrix) - function in jline.util.matrix.DenseMatrix: Performs matrix multiplication: output = A * B.
mult(jline.util.matrix.Matrix) - function in jline.util.matrix.Matrix: Performs matrix multiplication: this * B
mult(jline.util.matrix.Matrix,jline.util.matrix.Matrix) - function in jline.util.matrix.Matrix: Performs matrix multiplication: this * B
mult(org.ejml.data.DMatrix,org.ejml.data.DMatrix,org.ejml.data.DMatrix) - function in jline.util.matrix.SparseMatrix: Performs matrix multiplication: output = A * B.
multColumnView(jline.util.matrix.ColumnView) - function in jline.util.matrix.Matrix: Efficiently multiplies this row vector with a column view.
multEq(jline.util.matrix.Matrix) - function in jline.util.matrix.Matrix: Replaces this matrix with the result of this * B.
multichoose(double,double) - function in jline.util.Maths
multichoose(int,int) - function in jline.util.Maths: Generates all combinations of n objects taken k at a time with repetition (multichoose).
multiChooseCombinations(int,int) - function in jline.util.Maths: Generate all combinations of distributing n items into k bins
multiChooseCon(jline.util.matrix.Matrix,double) - function in jline.util.Maths
multichooseList(int,int) - function in jline.util.Maths: Generates all combinations of n objects taken k at a time with repetition (multichoose).
multinomialln(jline.util.matrix.Matrix) - function in jline.util.Maths
multiply(java.util.function.UnaryOperator) - function in jline.lib.lti.function_wrapper
multMatrix(org.ejml.data.DMatrix,org.ejml.data.DMatrix) - function in jline.util.matrix.BaseMatrix: Performs matrix multiplication with sparse matrices.
multMatrix(org.ejml.data.DMatrix,org.ejml.data.DMatrix) - function in jline.util.matrix.DenseMatrix: Performs matrix multiplication with sparse matrices.
multMatrix(org.ejml.data.DMatrix,org.ejml.data.DMatrix) - function in jline.util.matrix.SparseMatrix: Performs matrix multiplication with sparse matrices.
multMatrixStatic(org.ejml.data.DMatrix,org.ejml.data.DMatrix,org.ejml.data.DMatrix) - function in jline.util.matrix.DenseMatrix
multMatrixStatic(org.ejml.data.DMatrix,org.ejml.data.DMatrix,org.ejml.data.DMatrix) - function in jline.util.matrix.SparseMatrix
multRowView(jline.util.matrix.RowView) - function in jline.util.matrix.Matrix: Efficiently multiplies a row view with this column vector.
MVA - enum entry in jline.lang.constant.SolverType
MVAOptions - class in jline.solvers.mva: Configuration options for Mean Value Analysis (MVA) solver.
MVAResult - class in jline.solvers.mva: Result container for Mean Value Analysis (MVA) solver computations.
MVARunner - class in jline.solvers.mva.handlers
MVAVersionParameters - class in jline.api.mapqn: Parameters for MVA version models

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