Uses of Class
jline.util.matrix.Matrix
Packages that use Matrix
Package
Description
Age of Information (AoI) analysis algorithms.
Cache modeling algorithms and performance analysis methods.
APIs for stochastic models of loss networks and blocking systems.
Layered Stochastic Network (LSN) algorithms.
Matrix Analytic Methods (MAM) for structured Markov chains.
Marked Markov-Modulated Poisson Process (M3PP) manipulation and fitting.
MAP queueing network analysis algorithms.
Markov Chain analysis algorithms.
Decision-diagram state-space storage and Miner-Ciardo-Donatelli aggregation.
Moment conversion algorithms for discrete distributions.
Non-Product Form Queueing Network algorithms.
Product-Form Queueing Network (PFQN) analysis algorithms.
Load-dependent Product Form Queueing Network algorithms.
Mean Value Analysis algorithms for Product Form Queueing Networks.
Normalizing constant algorithms for Product Form Queueing Networks.
Queueing system analysis algorithms.
Simulation output analysis.
Stochastic network analysis utilities.
Stochastic network calculus (SNC).
Stochastic Petri net analysis.
Trace analysis algorithms for empirical data.
Workflow analysis algorithms.
Benchmarking utilities and performance test models for LINE solvers.
Comprehensive examples and tutorials for LINE queueing network modeling.
Basic queueing network examples demonstrating fundamental concepts.
Input/output from the command line or XML files.
TikZ visualization package for LINE queueing networks.
Abstractions to declare basic elements of a model.
This package contains the classes used the specify LayeredNetwork objects
Node parameter specifications and configuration classes.
This package contains the classes used the specify Network objects
This package contains processes and statistical distributions used to specify arrival rates, service rates, and item popularities
Reward function types and utilities for CTMC analysis.
Classes that model the individual sections that form a Network node
Classes that model the state of a network and its individual nodes
Port of BuTools library for phase-type distributions and MAP processes.
Package containing Discrete Phase-Type (DPH) distribution functions.
Package containing trace fitting utility functions.
Package containing Matrix-Analytic Methods (MAM) solvers.
Package containing Markovian Arrival Process (MAP) functions.
Package containing Markov chain validation and solving functions.
Package containing phase-type distribution functions.
Package containing representation transformation functions.
Package containing trace analysis functions.
Port of the KPC-Toolbox library for Markovian process fitting and manipulation.
Port of M3A library for matrix-based moment matching approximation algorithms.
Port of permanent computation algorithms for matrix permanents.
Port of SMC library for structured Markov chain analysis algorithms.
Solver superclasses and related data structures.
Automatic solver selection and configuration for queueing networks.
This package provides an implementation of SolverCTMC.
Analyzers for SolverCTMC.
Handlers for SolverCTMC.
This package provides an implementation of SolverENV (ENV).
This package provides an implementation of SolverFluid (FLD).
Analyzers for SolverFluid.
Handlers for SolverFluid.
LDES solver using SSJ library.
This package provides an implementation of SolverLN.
This package provides an implementation of SolverMAM.
Handlers for SolverMAM.
This package provides an implementation of SolverMVA.
Handlers for SolverMVA.
This package provides an implementation of SolverNC.
Analyzers for SolverNC.
Handlers for SolverNC.
This package provides an implementation of SolverSSA.
Analyzers for SolverSSA.
Handlers for SolverSSA.
This package provides an implementation of SolverJMT.
Handlers for SolverJMT.
This package provides an implementation of SolverQNS.
Fundamental data structures and utilities
Graph theory utilities and algorithms for network analysis.
Matrix operations and linear algebra utilities for queueing analysis.
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Uses of Matrix in jline.api.aoi
Methods in jline.api.aoi that return MatrixModifier and TypeMethodDescriptionAoi_dist2phResult.component1()Aoi_dist2phResult.component2()Aoi_dist2phResult.getAlpha()AoiMfqResult.getAoiA()AoiMfqResult.getAoiG()AoiMfqResult.getAoiH()AoiMfqResult.getPaoiA()AoiMfqResult.getPaoiG()AoiMfqResult.getPaoiH()AoiParams.getS()AoiParams.getSigma()Aoi_dist2phResult.getT()AoiParams.getT()AoiParams.getTau()Methods in jline.api.aoi with parameters of type MatrixModifier and TypeMethodDescriptionstatic LstFunctionAoi_lst.aoi_lst_ph(Matrix alpha, Matrix T) LST for phase-type distribution: H*(s) = alpha * (s*I - T)^{-1} * (-T * e)static AoiMfqResultAoi_solve_bufferless.aoi_solve_bufferless(Matrix tau, Matrix T, Matrix sigma, Matrix S, double p) Solve bufferless (PH/PH/1/1 or PH/PH/1/1*) AoI system using MFQ.static AoiMfqResultAoi_solve_singlebuffer.aoi_solve_singlebuffer(double lambda, Matrix sigma, Matrix S, double r) Constructors in jline.api.aoi with parameters of type MatrixModifierConstructorDescriptionAoi_dist2phResult(Matrix alpha, Matrix T) AoiMfqResult(Matrix aoiG, Matrix aoiA, Matrix aoiH, double aoiMean, double aoiVar, Matrix paoiG, Matrix paoiA, Matrix paoiH, double paoiMean, double paoiVar, String systemType, double preemption) AoiParams(Matrix tau, Matrix T, Matrix sigma, Matrix S, double p, double lambda, double r, String systemType, String arrivalType) -
Uses of Matrix in jline.api.cache
Fields in jline.api.cache declared as MatrixModifier and TypeFieldDescriptionfinal MatrixCacheMissFpiResult.MIfinal MatrixCacheMissFpiResult.MUfinal MatrixCache_miss.CacheMissResult.perItemMissProbfinal MatrixCache_miss.CacheMissResult.perItemMissRatefinal MatrixCache_miss.CacheMissResult.perUserMissRatefinal MatrixCacheMissFpiResult.pi0final MatrixCacheRrmMeanfieldResult.x(n x (1+h)) occupancy; column 0 is the probability of being out of cache.Methods in jline.api.cache that return MatrixModifier and TypeMethodDescriptionstatic MatrixCache_cost.cache_cost(Matrix gamma, Matrix m, Matrix sigma, Matrix k) Computes K_j = sum_i sigma_i pi_ij, the expected storage cost of the items resident in each list at steady state, as defined in Casale-Gast, IEEE/ACM Trans.static MatrixComputes the mean per-list storage cost from a precomputed occupancy matrix.static MatrixCache_erec.cache_erec(Matrix gamma, Matrix m) Computes the cache miss rate using an exact recursive method.static MatrixCache_erec.cache_erec(Matrix gamma, Matrix m, Matrix sigma, Matrix k) Computes the normalizing constant under per-list storage cost caps, E(m,k) = E_i(m,k) + sum_j m_j gamma_ij E_i(m-1_j, k-sigma_i 1_j), with the extra boundary E(m,k)=0 whenever some residual cap is negative.static MatrixCache_erec.cache_erec_aux(Matrix gamma, Matrix m, int k) Auxiliary method for computing the cache miss rate using an exact recursive method.static MatrixCache_prob_erec.cache_prob_erec(Matrix gamma, Matrix m) Computes the cache state probabilities using an exact recursive method.static MatrixCache_prob_erec.cache_prob_erec(Matrix gamma, Matrix m, Matrix sigma, Matrix k) Computes the cache state probabilities under per-list storage cost caps, pi_ij = m_j gamma_ij E_i(m-1_j, k-sigma_i 1_j) / E(m,k).static MatrixCache_prob_fpi.cache_prob_fpi(Matrix gamma, Matrix m) Estimate asymptotic values of the cache state probabilities at steady-state.static MatrixCache_prob_is.cache_prob_is(Matrix gamma, Matrix m) static MatrixCache_prob_is.cache_prob_is(Matrix gamma, Matrix m, int samples) Computes cache hit probabilities using Monte Carlo importance sampling.static MatrixCache_prob_is.cache_prob_is(Matrix gamma, Matrix m, int samples, Matrix sigma, Matrix k) Computes cache hit probabilities by importance sampling, discarding states that breach a per-list storage cost cap (the feasibility indicator of Casale-Gast, IEEE/ACM Trans.static MatrixCache_prob_rayint.cache_prob_rayint(Matrix gamma, Matrix m) Computes the cache state probabilities using the ray method.static MatrixCache_prob_spm.cache_prob_spm(Matrix gamma, Matrix m) Computes the cache state probabilities using the ray method.static MatrixCache_rrm_meanfield_ode.cache_rrm_meanfield_ode(Matrix x, Matrix lambda, Matrix m, int n, int h) ODE system for RRM (Random Replacement Model) mean field equations.static MatrixCache_t_hlru.cache_t_hlru(Matrix gamma, Matrix m) Characteristic time of each list of an h-LRU cache, solved by per-list bisection with Gauss-Seidel sweeps.static MatrixCache_t_lrum_map.cache_t_lrum_map(MatrixCell[] D0, MatrixCell[] D1, Matrix m) static MatrixCache_ttl_hlru.cache_ttl_hlru(Matrix[] lambda, Matrix m) Steady-state list occupancy probabilities for an h-LRU cache.static MatrixCache_ttl_lrua.cache_ttl_lrua(Matrix[] lambda, Matrix[][] R, Matrix m) Solve LRU caches with access graphs using the TTL tree approximation.static MatrixCache_ttl_lrum_map.cache_ttl_lrum_map_pij(MatrixCell[] D0Matrix, MatrixCell[] D1Matrix, Matrix m) Per-item request-weighted probabilities under the LRU(m)-MAP TTL approximation.static MatrixCache_ttl_tree.cache_ttl_tree(Matrix[] lambda, Matrix[][] R, Matrix m) static MatrixCache_ttl_tree.cache_ttl_tree(Matrix[] lambda, Matrix[][] R, Matrix m, Long seed) static MatrixCache_xi_bvh.cache_xi_bvh(Matrix gamma, Matrix m) Computes the cache xi terms using the iterative method used in Gast-van Houdt, SIGMETRICS 2015.static MatrixCache_xi_iter.cache_xi_iter(Matrix gamma, Matrix m) Computes the cache xi terms using the iterative method (Gast-van Houdt, SIGMETRICS 2015).CacheMissFpiResult.getMI()CacheMissFpiResult.getMU()CacheMissFpiResult.getPi0()CacheRrmMeanfieldResult.getX()static MatrixCache_t_lrum_map.lrummapTime(double[] x, MatrixCell[] D0Matrix, MatrixCell[] D1Matrix, Matrix m, int n, int h) Capacity residuals m_l - E[occupancy of list l] for times x.Methods in jline.api.cache that return types with arguments of type MatrixModifier and TypeMethodDescriptionCache_mva_miss.cache_mva_miss(Matrix p, Matrix m, Matrix R) Compute cache miss probabilities using Mean Value Analysis approach.Methods in jline.api.cache with parameters of type MatrixModifier and TypeMethodDescriptionstatic MatrixCache_cost.cache_cost(Matrix gamma, Matrix m, Matrix sigma, Matrix k) Computes K_j = sum_i sigma_i pi_ij, the expected storage cost of the items resident in each list at steady state, as defined in Casale-Gast, IEEE/ACM Trans.static MatrixComputes the mean per-list storage cost from a precomputed occupancy matrix.static List<Cache_cost_pathcheck.BlockedPair>Cache_cost_pathcheck.cache_cost_pathcheck(Matrix gamma, Matrix sigma, Matrix k, int[] parent) Detects promotion paths blocked by storage cost caps.static MatrixCache_erec.cache_erec(Matrix gamma, Matrix m) Computes the cache miss rate using an exact recursive method.static MatrixCache_erec.cache_erec(Matrix gamma, Matrix m, Matrix sigma, Matrix k) Computes the normalizing constant under per-list storage cost caps, E(m,k) = E_i(m,k) + sum_j m_j gamma_ij E_i(m-1_j, k-sigma_i 1_j), with the extra boundary E(m,k)=0 whenever some residual cap is negative.static MatrixCache_erec.cache_erec_aux(Matrix gamma, Matrix m, int k) Auxiliary method for computing the cache miss rate using an exact recursive method.static Ret.cacheGammaCache_gamma_lp.cache_gamma_lp(Matrix[] lambda, Matrix[][] R) Computes access factors for the cache.static Ret.cacheIsstatic Ret.cacheIsEstimate the normalizing constant of the cache steady state distribution using Monte Carlo importance sampling.static Ret.cacheIsEstimate the (cost-capped) normalizing constant with the feasibility indicator I{S in O} of Casale-Gast, IEEE/ACM Trans.static Cache_miss.CacheMissResultCache_miss.cache_miss(Matrix gamma, Matrix m) static Cache_miss.CacheMissResultCache_miss.cache_miss(Matrix gamma, Matrix m, Matrix lambda) static Cache_miss.CacheMissResultComputes cache miss metrics, optionally under per-list storage cost caps.static doubleCache_miss_asy.cache_miss_asy(Matrix gamma, Matrix m) static doubleCache_miss_asy.cache_miss_asy(Matrix gamma, Matrix m, int maxIter, double tolerance) Compute cache miss rates using asymptotic approximation (Fixed Point Iteration method).static CacheMissFpiResultCache_miss_fifo_rmf.cache_miss_fifo_rmf(Matrix gamma, Matrix m, Matrix[] lambdaCache) Steady-state FIFO(m) miss rates via the position-resolved mean field.static CacheMissFpiResultCache_miss_fifo_rmf.cache_miss_fifo_rmf(Matrix gamma, Matrix m, Matrix[] lambdaCache, Matrix[][] accost) FIFO(m) miss rates honouring a custom access graph.static CacheMissRmfTranResultCache_miss_fifo_rmf.cache_miss_fifo_rmf_tran(Matrix gamma, Matrix m, Matrix[] lambdaCache, double time, int nPoints, double[] xinit) Transient FIFO(m) cache trajectory via the position-resolved mean field.static CacheMissRmfTranResultCache_miss_fifo_rmf.cache_miss_fifo_rmf_tran(Matrix gamma, Matrix m, Matrix[] lambdaCache, double time, int nPoints, double[] xinit, Matrix[][] accost) Transient FIFO(m) cache trajectory honouring a custom access graph.static CacheMissFpiResultCache_miss_fpi.cache_miss_fpi(Matrix gamma, Matrix m) static CacheMissFpiResultCache_miss_fpi.cache_miss_fpi(Matrix gamma, Matrix m, MatrixCell lambda) Compute cache miss rates using Fixed Point Iteration (FPI) method.static Ret.cacheMissSpmCache_miss_is.cache_miss_is(Matrix gamma, Matrix m, MatrixCell lambda) static Ret.cacheMissSpmCache_miss_is.cache_miss_is(Matrix gamma, Matrix m, MatrixCell lambda, int samples) Computes cache miss rates using Monte Carlo importance sampling.static Ret.cacheMissSpmCache_miss_is.cache_miss_is(Matrix gamma, Matrix m, MatrixCell lambda, int samples, Matrix sigma, Matrix cap) Computes cache miss rates by importance sampling, optionally under per-list storage cost caps.static Ret.cacheMissSpmCache_miss_rayint.cache_miss_rayint(Matrix gamma, Matrix m, MatrixCell lambda) Estimates the cache miss rate and related metrics using the ray method for PDEs.static CacheMissFpiResultCache_miss_rmf.cache_miss_rmf(Matrix gamma, Matrix m, Matrix[] lambdaCache) Compute cache miss rates using the refined mean-field method.static CacheMissFpiResultCache_miss_rmf.cache_miss_rmf(Matrix gamma, Matrix m, Matrix[] lambdaCache, Matrix[][] accost) Compute cache miss rates, honouring a custom access graph.static CacheMissRmfTranResultCache_miss_rmf.cache_miss_rmf_tran(Matrix gamma, Matrix m, Matrix[] lambdaCache, double time, int nPoints, double[] xinit) Transient refined mean-field cache trajectory.static CacheMissFpiResultCache_miss_sfifo_rmf.cache_miss_sfifo_rmf(Matrix gamma, Matrix m, Matrix[] lambdaCache) Compute strict FIFO(m) cache miss rates via the position-resolved mean field.static CacheMissFpiResultCache_miss_sfifo_rmf.cache_miss_sfifo_rmf(Matrix gamma, Matrix m, Matrix[] lambdaCache, Matrix[][] accost) Strict FIFO(m) miss rates honouring a custom access graph.static CacheMissRmfTranResultCache_miss_sfifo_rmf.cache_miss_sfifo_rmf_tran(Matrix gamma, Matrix m, Matrix[] lambdaCache, double time, int nPoints, double[] xinit) Transient strict FIFO(m) cache trajectory via the position-resolved mean field.static CacheMissRmfTranResultCache_miss_sfifo_rmf.cache_miss_sfifo_rmf_tran(Matrix gamma, Matrix m, Matrix[] lambdaCache, double time, int nPoints, double[] xinit, Matrix[][] accost) Transient strict FIFO(m) cache trajectory honouring a custom access graph.static Ret.cacheMissSpmCache_miss_spm.cache_miss_spm(Matrix gamma, Matrix m, MatrixCell lambda) static Ret.cacheMVAExact recursive solution of the caching model.Cache_mva_miss.cache_mva_miss(Matrix p, Matrix m, Matrix R) Compute cache miss probabilities using Mean Value Analysis approach.Finds the parent of a given list index.static MatrixCache_prob_erec.cache_prob_erec(Matrix gamma, Matrix m) Computes the cache state probabilities using an exact recursive method.static MatrixCache_prob_erec.cache_prob_erec(Matrix gamma, Matrix m, Matrix sigma, Matrix k) Computes the cache state probabilities under per-list storage cost caps, pi_ij = m_j gamma_ij E_i(m-1_j, k-sigma_i 1_j) / E(m,k).static MatrixCache_prob_fpi.cache_prob_fpi(Matrix gamma, Matrix m) Estimate asymptotic values of the cache state probabilities at steady-state.static MatrixCache_prob_is.cache_prob_is(Matrix gamma, Matrix m) static MatrixCache_prob_is.cache_prob_is(Matrix gamma, Matrix m, int samples) Computes cache hit probabilities using Monte Carlo importance sampling.static MatrixCache_prob_is.cache_prob_is(Matrix gamma, Matrix m, int samples, Matrix sigma, Matrix k) Computes cache hit probabilities by importance sampling, discarding states that breach a per-list storage cost cap (the feasibility indicator of Casale-Gast, IEEE/ACM Trans.static MatrixCache_prob_rayint.cache_prob_rayint(Matrix gamma, Matrix m) Computes the cache state probabilities using the ray method.static MatrixCache_prob_spm.cache_prob_spm(Matrix gamma, Matrix m) Computes the cache state probabilities using the ray method.static Ret.cacheSpmCache_rayint.cache_rayint(Matrix gamma, Matrix m) Approximate the normalizing constant of the cache steady state distribution using the ray method.static CacheRrmMeanfieldResultCache_rrm_meanfield.cache_rrm_meanfield(Matrix lambda, Matrix m) Steady state of the RANDOM(m) mean field with the reference horizon.static CacheRrmMeanfieldResultCache_rrm_meanfield.cache_rrm_meanfield(Matrix lambda, Matrix m, double tmax) Steady state of the RANDOM(m) mean field.static MatrixCache_rrm_meanfield_ode.cache_rrm_meanfield_ode(Matrix x, Matrix lambda, Matrix m, int n, int h) ODE system for RRM (Random Replacement Model) mean field equations.static Ret.cacheSpmApproximate the normalizing constant of the cache steady state distribution using the SPM method.static MatrixCache_t_hlru.cache_t_hlru(Matrix gamma, Matrix m) Characteristic time of each list of an h-LRU cache, solved by per-list bisection with Gauss-Seidel sweeps.static MatrixCache_t_lrum_map.cache_t_lrum_map(MatrixCell[] D0, MatrixCell[] D1, Matrix m) static MatrixCache_ttl_hlru.cache_ttl_hlru(Matrix[] lambda, Matrix m) Steady-state list occupancy probabilities for an h-LRU cache.static MatrixCache_ttl_lrua.cache_ttl_lrua(Matrix[] lambda, Matrix[][] R, Matrix m) Solve LRU caches with access graphs using the TTL tree approximation.static doubleCache_ttl_lrum_map.cache_ttl_lrum_map(MatrixCell[] D0Matrix, MatrixCell[] D1Matrix, Matrix m) Aggregate request-weighted hit rate under the LRU(m)-MAP TTL approximation.static MatrixCache_ttl_lrum_map.cache_ttl_lrum_map_pij(MatrixCell[] D0Matrix, MatrixCell[] D1Matrix, Matrix m) Per-item request-weighted probabilities under the LRU(m)-MAP TTL approximation.static MatrixCache_ttl_tree.cache_ttl_tree(Matrix[] lambda, Matrix[][] R, Matrix m) static MatrixCache_ttl_tree.cache_ttl_tree(Matrix[] lambda, Matrix[][] R, Matrix m, Long seed) static MatrixCache_xi_bvh.cache_xi_bvh(Matrix gamma, Matrix m) Computes the cache xi terms using the iterative method used in Gast-van Houdt, SIGMETRICS 2015.static Ret.cacheXiFpCache_xi_fp.cache_xi_fp(Matrix gamma, Matrix m, Matrix xi) static MatrixCache_xi_iter.cache_xi_iter(Matrix gamma, Matrix m) Computes the cache xi terms using the iterative method (Gast-van Houdt, SIGMETRICS 2015).static MatrixCache_t_lrum_map.lrummapTime(double[] x, MatrixCell[] D0Matrix, MatrixCell[] D1Matrix, Matrix m, int n, int h) Capacity residuals m_l - E[occupancy of list l] for times x.Constructors in jline.api.cache with parameters of type MatrixModifierConstructorDescriptionCacheMissFpiResult(double M, Matrix MU, Matrix MI, Matrix pi0) CacheMissResult(double globalMissRate, Matrix perUserMissRate, Matrix perItemMissRate, Matrix perItemMissProb) CacheRrmMeanfieldResult(Matrix x, double missrate, double missratio) -
Uses of Matrix in jline.api.da
Fields in jline.api.da declared as MatrixModifier and TypeFieldDescriptionDa_cacheqn_retrieval.Result.delayedprobDa_cacheqn_retrieval.Result.hitprobDa_cacheqn_retrieval.Result.missprobMethods in jline.api.da with parameters of type MatrixModifier and TypeMethodDescriptionstatic doubleDa_traffic_superpos.da_traffic_superpos(Matrix lambda, Matrix a2) -
Uses of Matrix in jline.api.fes
Fields in jline.api.fes declared as MatrixModifier and TypeFieldDescriptionfinal MatrixFESDeaggInfo.cutoffsfinal MatrixFESDeaggInfo.stochCompComplementfinal MatrixFESDeaggInfo.stochCompSubsetFields in jline.api.fes with type parameters of type MatrixModifier and TypeFieldDescriptionFESAggregator.ConditionalMetrics.QNIndexed by the linearized population state; each (M_sub x K).FESDeaggInfo.throughputTableFESAggregator.ConditionalMetrics.UNIndexed by the linearized population state; each (M_sub x K).Methods in jline.api.fes that return MatrixModifier and TypeMethodDescriptionstatic MatrixFes_map_euler.fes_map_euler(Matrix v, Matrix T0, double dt, double tol, int iterMax) Approximates v*(-T0)^-1 by quadrature.FESDeaggInfo.getCutoffs()FESOptions.getCutoffs()Per-class population cutoffs (default: null, uses total jobs per class).FESDeaggInfo.getStochCompComplement()FESDeaggInfo.getStochCompSubset()Methods in jline.api.fes that return types with arguments of type MatrixMethods in jline.api.fes with parameters of type MatrixModifier and TypeMethodDescriptionFESAggregator.computeConditionalMetrics(Network isolatedModel, Matrix cutoffs) Deprecated.Per-station metrics of the ISOLATED subnetwork at every population state.static MatrixFes_map_euler.fes_map_euler(Matrix v, Matrix T0, double dt, double tol, int iterMax) Approximates v*(-T0)^-1 by quadrature.static FesMapMomentsResultFes_map_moments.fes_map_moments(Matrix T0, Matrix T1, String method) Computes the descriptors of an inter-departure MAP.static FesMapMomentsResultFes_map_moments.fes_map_moments(Matrix T0, Matrix T1, String method, double stepSafety) Computes the descriptors of an inter-departure MAP.static FESOptionsFESOptions.withCutoffs(Matrix cutoffs) Create options with specified cutoffs.Constructors in jline.api.fes with parameters of type MatrixModifierConstructorDescriptionFesBetaFunction(List<Matrix> scalingTables, Matrix cutoffs) Creates a per-class FES rate function.FESDeaggInfo(Network originalModel, List<Station> stationSubset, int[] subsetIndices, int[] complementIndices, List<Matrix> throughputTable, Matrix cutoffs, Matrix stochCompSubset, Matrix stochCompComplement, Network isolatedModel, int fesNodeIdx) FESOptions(String solver, Matrix cutoffs, boolean verbose) Constructor parameters in jline.api.fes with type arguments of type MatrixModifierConstructorDescriptionFesBetaFunction(List<Matrix> scalingTables, Matrix cutoffs) Creates a per-class FES rate function. -
Uses of Matrix in jline.api.fj
Methods in jline.api.fj with parameters of type MatrixModifier and TypeMethodDescriptionstatic FJArrivalFJConvert.convertToFJArrival(Matrix D0, Matrix D1) Convert LINE MAP to FJ arrival formatstatic FJServiceFJConvert.convertToFJService(Matrix S, Matrix s, Matrix tau) Convert LINE PH to FJ service formatstatic doubleFJ_ordstat_exp.fj_ordstat_exp(Matrix ri, int k) -
Uses of Matrix in jline.api.infer
Fields in jline.api.infer declared as MatrixModifier and TypeFieldDescriptionInferLqnOptions.a0InferLqnResult.a0InferLqnResult.ahatInferLqnOptions.aTrueInferLqnResult.efinal MatrixInferLqn.JacobianResult.Hfinal MatrixInferLqn.JacobianResult.h0InferLqnResult.PInferLqnOptions.P0InferLqnResult.P0InferLqnOptions.QInferLqnResult.QInferLqnOptions.RInferLqnResult.RInferLqnResult.zpredFields in jline.api.infer with type parameters of type MatrixMethods in jline.api.infer that return MatrixModifier and TypeMethodDescriptionstatic MatrixInferLqn.getObs(LayeredNetworkAvgTable table, List<ObsSpec> obsSpec) Extract the observation vector selected by obsSpec from a solved table.static MatrixInferLqn.getParams(LayeredNetwork model, List<ParamSpec> paramSpec) Read the current values of the parameters named in paramSpec.Methods in jline.api.infer with parameters of type MatrixModifier and TypeMethodDescriptionstatic InferLqnResultInferLqn.ekf(Function<Matrix, Matrix> hfun, Matrix a0, Matrix P0, Matrix Z, Matrix Q, Matrix R, InferLqnOptions opt) Extended Kalman Filter tracking a hidden parameter vector across Z.static InferLqnResultInferLqn.inferLqn(LayeredNetwork model, List<ParamSpec> paramSpec, List<ObsSpec> obsSpec, Matrix Z, InferLqnOptions opt) Identify hidden LQN parameters from measured performance data.static InferLqn.JacobianResultForward finite-difference sensitivity matrix of h at a.static voidInferLqn.setParams(LayeredNetwork model, List<ParamSpec> paramSpec, Matrix a) Apply a parameter vector to a LayeredNetwork in place.Method parameters in jline.api.infer with type arguments of type MatrixModifier and TypeMethodDescriptionstatic InferLqnResultInferLqn.ekf(Function<Matrix, Matrix> hfun, Matrix a0, Matrix P0, Matrix Z, Matrix Q, Matrix R, InferLqnOptions opt) Extended Kalman Filter tracking a hidden parameter vector across Z.static InferLqnResultInferLqn.ekf(Function<Matrix, Matrix> hfun, Matrix a0, Matrix P0, Matrix Z, Matrix Q, Matrix R, InferLqnOptions opt) Extended Kalman Filter tracking a hidden parameter vector across Z.static InferLqn.JacobianResultForward finite-difference sensitivity matrix of h at a.static InferLqn.JacobianResultForward finite-difference sensitivity matrix of h at a.Constructors in jline.api.infer with parameters of type Matrix -
Uses of Matrix in jline.api.lossn
Methods in jline.api.lossn with parameters of type MatrixModifier and TypeMethodDescriptionstatic Ret.lossnErlangFPLossn_erlangfp.lossn_erlangfp(Matrix nuVec, Matrix Amat, Matrix cVec) Erlang fixed point approximation for loss networks.static Ret.lossnManjunathLossn_manjunath.lossn_manjunath(Matrix nuVec, Matrix Amat, Matrix cVec) Exact normalization constant, carried load and blocking of a loss network.static Ret.lossnManjunathLossn_manjunath.lossn_manjunath(Matrix nuVec, Matrix Amat, Matrix cVec, long maxLiveStates) Exact normalization constant, carried load and blocking of a loss network.static Ret.lossnMCILossn_mci.lossn_mci(Matrix nuVec, Matrix Amat, Matrix cVec, int samples, Matrix gammaVec, long seed, double alpha) Monte Carlo importance-sampling summation for loss networks.static Lossn_rec.LossnRecResultExact loss-network analysis by MDD-rec. -
Uses of Matrix in jline.api.lqn
Methods in jline.api.lqn that return types with arguments of type MatrixModifier and TypeMethodDescriptionComposed law of a workflow in which the branches of an AND fork are SERIAL rather than concurrent, that is, the total work the branches request rather than the elapsed time until the last of them finishes.Composed law of a workflow in which the branches of an AND fork are SERIAL rather than concurrent, that is, the total work the branches request rather than the elapsed time until the last of them finishes.Methods in jline.api.lqn with parameters of type Matrix -
Uses of Matrix in jline.api.lsn
Methods in jline.api.lsn that return MatrixModifier and TypeMethodDescriptionstatic MatrixLsnMaxMultiplicity.lsnMaxMultiplicity(LayeredNetworkStruct lsn) Computes the maximum multiplicity that can be sustained by each task. -
Uses of Matrix in jline.api.mam
Fields in jline.api.mam declared as MatrixModifier and TypeFieldDescriptionfinal MatrixQbd_bmapbmap1.QbdBmapResult.A_1final MatrixQbd_bmapbmap1.QbdBmapResult.A0final MatrixQbd_rg.QbdRgResult.Bfinal MatrixQbd_bmapbmap1.QbdBmapResult.B0final MatrixQbd_rg.QbdRgResult.Ffinal MatrixMamap2m_coefficients.Mamap2mCoefficients.firstfinal MatrixQbd_rg.QbdCrResult.Gfinal MatrixQbd_rg.QbdRgResult.Gfinal MatrixQbd_rg.QbdRgResult.Lfinal MatrixMg1_dt_queue.Result.qlfinal MatrixQbd_rg.QbdCrResult.Rfinal MatrixQbd_rg.QbdRgResult.Rfinal MatrixMamap2m_coefficients.Mamap2mCoefficients.secondfinal MatrixMamap2m_coefficients.Mamap2mCoefficients.thirdfinal MatrixQbd_rg.QbdCrResult.Ufinal MatrixQbd_rg.QbdRgResult.UMap_m1ps.SojournResult.Wbar1 x numel(x), Pr[W > x].Map_m1ps.SojournResult.WbarN(N+1) x numel(x), the CCDF conditional on the arrival finding n customers already in the system, n = 0..N.Fields in jline.api.mam with type parameters of type MatrixModifier and TypeFieldDescriptionQbd_bmapbmap1.QbdBmapResult.A1Qbd_bmapbmap1.QbdBmapResult.B1LdqbdMphc.Blocks.Q0LdqbdMphc.Blocks.Q1LdqbdMphc.Blocks.Q2Methods in jline.api.mam that return MatrixModifier and TypeMethodDescriptionQbd_rg.QbdRgResult.component1()LdqbdResult.component2()Qbd_rg.QbdRgResult.component2()Qbd_rg.QbdRgResult.component3()Qbd_rg.QbdRgResult.component4()Qbd_rg.QbdRgResult.component5()Qbd_rg.QbdRgResult.component6()static MatrixDmap_pie.dmap_pie(MatrixCell DMAP) static MatrixComputes the stationary vector at arrival epochs of a discrete-time MAP.static MatrixDph_to_dmap.dmap_to_dph_alpha(Matrix D0, Matrix D1) Discrete phase-type law underlying a renewal DMAP: alpha is the normalized pivot row of D1 and A is D0.QbdMapMap1Result.getA_1()QbdMapMap1Result.getA0()QbdMapMap1Result.getA1()QbdRapRap1Result.getB()QbdMapMap1Result.getEta()QbdRapRap1Result.getEta()QbdRapRap1Result.getF()QbdMapMap1Result.getG()QbdRapRap1Result.getG()QbdRapResult.getG()Matrix G solving A0*G^2 + A1*G + A2 = 0.QbdRapRap1Result.getL()QbdRapResult.getLevelProb()Row vector of marginal level probabilities, levels 0 to numLevels.LdqbdResult.getPi()QbdRapResult.getPi0()Level-0 vector pi_0, the boundary vector of Theorem 7.QbdMapMap1Result.getPqueue()QbdRapRap1Result.getPqueue()QbdRapResult.getPqueue()(numLevels+1) x m matrix whose n-th row is the level vector pi_n.QbdMapMap1Result.getR()QbdRapRap1Result.getR()QbdRapResult.getR()Rate matrix R = A0*inv(-U).QbdMapMap1Result.getU()QbdRapResult.getU()Matrix U = A1 + A0*G.static Matrix[]Mamap2m_fit.mamap2m_fit(double M1, double M2, double M3, double GAMMA, double[] P, double[] F, double[] B, Matrix S) static Matrix[]Mamap2m_fit.mamap2m_fit(double M1, double M2, double M3, double GAMMA, double[] P, double[] F, double[] B, Matrix S, double[] fbsWeights) Fits a MAPH(2,m) or MAMAP(2,m) matching the inter-arrival moments and decay rate, the class probabilities (always exactly) and, as far as the form allows, the forward moments, backward moments and class transition probabilities.static MatrixMap_acf.map_acf(MatrixCell MAP) static MatrixMap_acf.map_acf(MatrixCell MAP, Matrix lags) static Matrixstatic Matrixstatic MatrixComputes the autocorrelation function (ACF) for a given MAP at multiple lags.static Matrix[]Map_block.map_block(double E1, double E2, double E3, double G2) static Matrix[]Constructs a MAP(2) or MAP(1) according to given moments and autocorrelation parameters.static MatrixMap_cdf.map_cdf(MatrixCell MAP, Matrix points) CDF of MAP inter-arrival times when MAP is a MatrixCell.static MatrixComputes the cumulative distribution function (CDF) of the inter-arrival times of a Markovian Arrival Process (MAP).static MatrixMap_m1ps.map_compute_R(Matrix C, Matrix D, double mu) Minimal nonnegative solution R of D + R(C - mu I) + mu R^2 = 0, by the fixed point R <- -D (C - mu I + mu R)^-1 started at R = 0.static MatrixMap_embedded.map_embedded(MatrixCell MAP) Computes the embedded discrete-time Markov chain (DTMC) matrix of a MAP given as a MatrixCell.static MatrixMap_embedded.map_embedded(Matrix D0, Matrix D1) Computes the embedded discrete-time Markov chain (DTMC) matrix of a MAP.static Matrix[]Map_feasblock.map_feasblock(double E1, double E2, double E3, double G2) static Matrix[]Map_feasblock.map_feasblock(double E1, double E2, double E3, double G2, String OPT) Fits the most similar feasible MAP when exact moment matching fails.static MatrixMap_infgen.map_infgen(MatrixCell MAP) Computes the infinitesimal generator matrix (Q) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).static MatrixMap_infgen.map_infgen(Matrix D0, Matrix D1) Computes the infinitesimal generator matrix (Q) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).static Matrix[]Convenience function for composing a list of MAPs.static Matrix[]Convenience function for composing exactly two MAPs.static MatrixMap_pie.map_pie(MatrixCell MAP) Computes the steady-state probability vector of the embedded DTMC of a MAP stored in a MatrixCell that contains the MAP's transition matrices.static MatrixComputes the steady-state probability vector of the embedded Discrete Time Markov Chain (DTMC) associated with a Markovian Arrival Process (MAP).static MatrixMap_piq.map_piq(MatrixCell MAP) Computes the steady-state vector (pi) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).static MatrixComputes the steady-state vector (pi) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).static MatrixMap_pntiter.map_pntiter(Matrix[] MAP, int na, double t) static MatrixMap_pntiter.map_pntiter(Matrix[] MAP, int na, double t, Integer M) Probability of having exactly na arrivals within time interval t using iterative method.static MatrixMap_prob.map_prob(MatrixCell MAP) Computes the equilibrium distribution of the underlying continuous-time Markov chain for a MAP.static MatrixComputes the equilibrium distribution of the underlying continuous-time Markov chain for a MAP.static MatrixMap_varcount.map_varcount(MatrixCell MAP, Matrix t) static MatrixMap_varcount.map_varcount(Matrix D0, Matrix D1, Matrix t) Variance of the counts in a MAP over multiple time periods.static MatrixMe_sample.me_alpha(MatrixCell ME) Recovers the initial vector alpha of an ME process stored as a MatrixCell {D0 = A, D1 = -A*e*alpha}.static MatrixMe_pie.me_pie(MatrixCell ME) Computes the stationary initial probability for an ME/RAP distribution using matrices stored in a MatrixCell.static MatrixComputes the stationary initial probability for an ME/RAP distribution.static Matrix[]Mfq_fluflu_sojourn.mfq_fluflu_sojourn(Matrix Qin, Matrix Rin, Matrix Qout, Matrix Rout, boolean srv0stop, boolean transToPH) Sojourn-time distribution of a fluid queue with fluid-modulated service, as {alpha, A}.static MatrixMfq_ld_distr.mfq_ld_distr(LevelDependentFluidSolution sol, double[] T, String what, double[] points) Stationary pdf/pdfd/cdf/cdfm at the requested points.static Matrix[]Mfq_multiregime.mfq_multiregime(List<Matrix> Q, List<double[]> R, List<Matrix> Qt, List<double[]> Rt, double[] T, double[] pdfpoints, double[] cdfpoints) Multi-regime feedback fluid queue; returns {pdf, pdfd, cdf, cdfm}.static Matrix[]Mfq_sojourn.mfq_sojourn(Matrix Q, Matrix Rin, Matrix Rout, Matrix Q0, boolean transToPH) Sojourn-time distribution as {alpha, A} (ME if transToPH=false, PH otherwise).static MatrixMmap_backward_moment.mmap_backward_moment(MatrixCell MMAP, Matrix ORDERS) static MatrixMmap_backward_moment.mmap_backward_moment(MatrixCell MMAP, Matrix ORDERS, int NORM) Computes the backward moments of an MMAP for specified orders with normalization.static Matrix[]Mmap_compress.mmap_compress(Matrix[] mmap) static Matrix[]Mmap_compress.mmap_compress(Matrix[] mmap, String method) static MatrixMmap_count_idc.mmap_count_idc(MatrixCell MMAP, double t) Computes the index of dispersion for counts (IDC) for a Markovian Arrival Process with marked arrivals (MMAP) over a time period.static MatrixMmap_count_lambda.mmap_count_lambda(MatrixCell mmap) Computes the arrival rate vector of the counting process for the given Marked MAP (MMAP).static MatrixMmap_count_mcov.mmap_count_mcov(Matrix[] mmap, double t) Array-based overload.static MatrixMmap_count_mcov.mmap_count_mcov(MatrixCell MMAP, double t) Computes the count covariance between each pair of classes at a given time scale.static MatrixMmap_count_mean.mmap_count_mean(MatrixCell MMAP, double t) Computes the mean count vector of events of different types in a Markovian Arrival Process with marked arrivals (MMAP) over a time period.static MatrixMmap_count_moment.mmap_count_moment(MatrixCell MMAP, double t, int[] orders) Per-class power moments of counts, at resolution t, of a Marked MAP.static MatrixMmap_count_var.mmap_count_var(MatrixCell MMAP, double t) Computes the variance of the count vector of events of different types in a MMAP over a time period.static MatrixMmap_cross_moment.mmap_cross_moment(MatrixCell mmap, int k) Computes the k-th cross-moment matrix for a given MMAP.static MatrixMmap_embedded.mmap_embedded(MatrixCell mmap) Computes the embedded chain of an MMAP.static MatrixMmap_forward_moment.mmap_forward_moment(MatrixCell MMAP, Matrix ORDERS) static MatrixMmap_forward_moment.mmap_forward_moment(MatrixCell MMAP, Matrix ORDERS, int NORM) Computes the forward moments of an MMAP for specified orders with normalization.static MatrixMmap_idc.mmap_idc(MatrixCell MMAP) Computes the asymptotic index of dispersion for counts (IDC) for a Markovian Arrival Process with marked arrivals (MMAP).static MatrixMmap_lambda.mmap_lambda(MatrixCell MMAP) Alias for mmap_count_lambda.static MatrixMmap_pc.mmap_pc(MatrixCell MMAP) Computes the proportion of counts (PC) for each type in a Markovian Arrival Process with marked arrivals (MMAP).static Matrixstatic MatrixMmap_pie.mmap_pie(MatrixCell mmap) static MatrixMmap_sigma.mmap_sigma(Matrix[] mmap) Computes one-step class transition probabilities for an MMAP given as Matrix[].static MatrixMmap_sigma.mmap_sigma(MatrixCell MMAP) Computes one-step class transition probabilities for a Marked Markovian Arrival Process (MMAP).static Matrix[]Mmpp2_fit_count.mmpp2_fit_count(double mu, double bt1, double bt2, double binf, double m3t2, double t1, double t2) static Matrix[]Mmpp2_fit_count_approx.mmpp2_fit_count_approx(double a, double bt1, double bt2, double binf, double m3t2, double t1, double t2) static Matrix[]Mmpp2_fitc.mmpp2_fitc(double mu, double bt1, double bt2, double binf, double m3t2, double t1, double t2) Fits a MMPP(2) according to [Heffes and Lucantoni, 1986].static Matrix[]Mmpp2_fitc_approx.mmpp2_fitc_approx(double a, double bt1, double bt2, double binf, double m3t2, double t1, double t2) Fits a second-order Marked MMPP using optimization.static Matrixstatic MatrixCompute R matrix using successive substitutions method for QBD processes.static MatrixQbd_R_logred.qbd_R_logred(Matrix B, Matrix L, Matrix F) static MatrixQbd_R_logred.qbd_R_logred(Matrix B, Matrix L, Matrix F, int iterMax) Compute R matrix using logarithmic reduction method for QBD processes.static MatrixSolves A0*G^2 + A1*G + A2 = 0 for the matrix G, using the exact rank-one closed form when A2 has rank one and otherwise functional iteration followed by Newton's method.static MatrixRandp.randp(double[] P, int rows) static MatrixRandp.randp(double[] P, int rows, int cols) Pick random values with relative probability.static Matrixstatic MatrixMethods in jline.api.mam that return types with arguments of type MatrixModifier and TypeMethodDescriptionAph_bernstein.aph_bernstein(DoubleUnaryOperator f, int order) Fits an Acyclic Phase-type distribution using Bernstein's approximation.Aph_bernstein.aph_bernstein(DoubleUnaryOperator f, int order) Fits an Acyclic Phase-type distribution using Bernstein's approximation.Aph_convseq.aph_convseq(List<Pair<Matrix, Matrix>> aphParams) Performs sequential convolution of multiple APH distributions.Aph_convseq.aph_convseq(List<Pair<Matrix, Matrix>> aphParams) Performs sequential convolution of multiple APH distributions.Aph_simplify.aph_simplify(Matrix a1, Matrix T1, Matrix a2, Matrix T2, double p1, double p2, int pattern) Simplifies and combines two APH distributions using different structural patterns.Aph_simplify.aph_simplify(Matrix a1, Matrix T1, Matrix a2, Matrix T2, double p1, double p2, int pattern) Simplifies and combines two APH distributions using different structural patterns.LdqbdResult.component1()Dph_from_dist.dph_from_dist(ProcessType procType, double meanSlots, double scv) Dph_from_dist.dph_from_dist(ProcessType procType, double meanSlots, double scv) LdqbdResult.getPiCells()Per-level stationary vectors, still resolved by phase.LdqbdResult.getR()Map_pntquad.map_pntquad(Matrix[] MAP, int na, double t) Compute MAP point process probabilities using ODE quadrature method.Map_pntquad.map_pntquad(Matrix[] MAP, int na, double t) Compute MAP point process probabilities using ODE quadrature method.Convert a MAP to MMPP format by extracting generator matrix Q and rate matrix LAMBDA.Convert a MAP to MMPP format by extracting generator matrix Q and rate matrix LAMBDA.static Triple<Matrix,Matrix, MatrixCell> Map2ph.map2ph(MatrixCell MAP) Converts a MAP to a Phase-Type (PH) distribution.static Triple<Matrix,Matrix, MatrixCell> Map2ph.map2ph(MatrixCell MAP) Converts a MAP to a Phase-Type (PH) distribution.static Pair<MatrixCell,Matrix> Maph2m_fit.maph2m_fit_multiclass(MatrixCell aph, Matrix P, Matrix B) Convenience overload with unit class weights.static Pair<MatrixCell,Matrix> Maph2m_fit.maph2m_fit_multiclass(MatrixCell aph, Matrix P, Matrix B, double[] classWeights) Marks a canonical acyclic APH(2) so that the class probabilities are matched exactly and the backward moments as closely as possible.Methods in jline.api.mam with parameters of type MatrixModifier and TypeMethodDescriptionAph_simplify.aph_simplify(Matrix a1, Matrix T1, Matrix a2, Matrix T2, double p1, double p2, int pattern) Simplifies and combines two APH distributions using different structural patterns.static BmapSample[]Map_sample.bmap_sample(Matrix D0, Matrix D1_total, Matrix[] Dk, long n, Random random) Generates samples from a BMAP using D0, D1_total, and individual Dk matrices.static org.apache.commons.math3.linear.FieldMatrix<org.apache.commons.math3.complex.Complex>Wrap a real jline Matrix as a dense complex FieldMatrix.static doubleComputes the squared L2 distance between lag-L joint PMFs of two D-MAPs.static doublestatic doubleDmap_dist.dmap_dist_acf(Matrix D0A, Matrix D1A, Matrix D0B, Matrix D1B) Computes the squared L2 distance between autocorrelation functions of two D-MAPs.static doublestatic doubleDmap_dist.dmap_geo_mul_sum(Matrix D0A, Matrix D1A, Matrix D0B, Matrix D1B, int L, Matrix alA, Matrix alB) Computes the joint PMF inner product of two D-MAPs via recursive discrete Lyapunov equations.static doubleDmap_dist.dmap_geo_mul_sum_acf(Matrix D0A, Matrix D1A, Matrix D0B, Matrix D1B, Matrix alA, Matrix alB) Computes the geometric sum for the discrete autocorrelation distance of two D-MAPs.static booleanDph_to_dmap.dmap_is_renewal(Matrix D0, Matrix D1) True when the DMAP renews at every event, i.e.static doubleDmap_moment.dmap_moment(Matrix D0, Matrix D1, int order) Computes the k-th raw moment of the inter-arrival time of a discrete MAP.static MatrixComputes the stationary vector at arrival epochs of a discrete-time MAP.static double[]Dmap_sample.dmap_sample(Matrix D0, Matrix D1, int n, Random random) Generates samples of inter-arrival times from a discrete-time MAP.static MatrixDph_to_dmap.dmap_to_dph_alpha(Matrix D0, Matrix D1) Discrete phase-type law underlying a renewal DMAP: alpha is the normalized pivot row of D1 and A is D0.static MatrixCellDph_to_dmap.dph_to_dmap(Matrix alpha, Matrix A) Builds the renewal DMAP {A, a*alpha} of the DPH (alpha, A).static LdqbdMphc.BlocksLdqbdMphc.ldqbd_mphc(Matrix D0, Matrix D1, Matrix alpha, double c, double[] arrRate, double[] sf) Block-tridiagonal generator of an M/PH/c queue with level-dependent arrivals.static booleanMam_is_renewal_map.mam_is_renewal_map(Matrix D0, Matrix D1) static MatrixCellMamap22_fit_multiclass.mamap22_fit_bs_multiclass(MatrixCell amap, Matrix P, Matrix B, Matrix S) static MatrixCellMamap22_fit_multiclass.mamap22_fit_bs_multiclass(MatrixCell amap, Matrix P, Matrix B, Matrix S, Object options, double[] weights) static MatrixCellMamap22_fit_multiclass.mamap22_fit_fs_multiclass(MatrixCell amap, Matrix P, Matrix F, Matrix S) static MatrixCellMamap22_fit_multiclass.mamap22_fit_fs_multiclass(MatrixCell amap, Matrix P, Matrix F, Matrix S, Object options, double[] weights) static MatrixCellMamap22_fit_multiclass.mamap22_fit_gamma_bs(double M1, double M2, double M3, double GAMMA, Matrix P, Matrix B, Matrix S) static MatrixCellMamap22_fit_multiclass.mamap22_fit_gamma_bs_trace(Matrix T, Matrix A) static MatrixCellMamap22_fit_multiclass.mamap22_fit_gamma_fs(double M1, double M2, double M3, double GAMMA, Matrix P, Matrix F, Matrix S) static MatrixCellMamap22_fit_multiclass.mamap22_fit_gamma_fs_trace(Matrix T, Matrix A) static Matrix[]Mamap2m_fit.mamap2m_fit(double M1, double M2, double M3, double GAMMA, double[] P, double[] F, double[] B, Matrix S) static Matrix[]Mamap2m_fit.mamap2m_fit(double M1, double M2, double M3, double GAMMA, double[] P, double[] F, double[] B, Matrix S, double[] fbsWeights) Fits a MAPH(2,m) or MAMAP(2,m) matching the inter-arrival moments and decay rate, the class probabilities (always exactly) and, as far as the form allows, the forward moments, backward moments and class transition probabilities.static MatrixCellMamap2m_fit_gamma_fb_trace.mamap2m_fit_gamma_fb_trace(Matrix T, Matrix A) Performs approximate fitting of a marked trace from Matrix inputs.static MatrixMap_acf.map_acf(MatrixCell MAP, Matrix lags) static Matrixstatic Matrixstatic MatrixComputes the autocorrelation function (ACF) for a given MAP at multiple lags.static double[]Computes the autocorrelation function coefficients (ACFC) for a MAP counting process.static doubleMap_ccdf_derivative.map_ccdf_derivative(Matrix D0, Matrix D1, int i) Compute derivative at 0 of a MAP complementary CDF.static MatrixMap_cdf.map_cdf(MatrixCell MAP, Matrix points) CDF of MAP inter-arrival times when MAP is a MatrixCell.static MatrixComputes the cumulative distribution function (CDF) of the inter-arrival times of a Markovian Arrival Process (MAP).static booleanMap_checkfeasible.map_checkfeasible(Matrix D0, Matrix D1) static booleanMap_checkfeasible.map_checkfeasible(Matrix D0, Matrix D1, double TOL) Check the feasibility of a MAP given separate D0 and D1 matrices.static MatrixMap_m1ps.map_compute_R(Matrix C, Matrix D, double mu) Minimal nonnegative solution R of D + R(C - mu I) + mu R^2 = 0, by the fixed point R <- -D (C - mu I + mu R)^-1 started at R = 0.static doubleComputes the squared L2 distance between lag-L joint densities of two MAPs.static doubleComputes the squared L2 distance between lag-L joint densities of two MAPs.static doubleMap_dist_acf.map_dist_acf(Matrix A0, Matrix A1, Matrix B0, Matrix B1) Computes the squared L2 distance between autocorrelation functions of two MAPs.static doubleComputes the squared L2 distance between autocorrelation functions of two MAPs.static MatrixMap_embedded.map_embedded(Matrix D0, Matrix D1) Computes the embedded discrete-time Markov chain (DTMC) matrix of a MAP.static doubleMap_exp_mul_int.map_exp_mul_int(Matrix A0, Matrix A1, Matrix B0, Matrix B1, int L, Matrix alA, Matrix alB) Computes the inner product of lag-L joint densities of two MAPs.static doubleEstimates the autocorrelation decay rate of a MAP.static doubleEstimates the autocorrelation decay rate of a MAP.static doubleComputes the geometric sum needed for the autocorrelation distance.static doubleComputes the asymptotic index of dispersion (IDC) for a Markovian Arrival Process (MAP).static MatrixMap_infgen.map_infgen(Matrix D0, Matrix D1) Computes the infinitesimal generator matrix (Q) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).static doubleComputes the joint moments of a Markovian Arrival Process (MAP).static doubleMap_jointpdf_derivative.map_jointpdf_derivative(Matrix D0, Matrix D1, int[] iset) Compute partial derivative at 0 of a MAP's joint PDF.static Matrix[]Convenience function for composing exactly two MAPs.static doubleMap_lambda.map_lambda(Matrix D0, Matrix D1) Computes the arrival rate (lambda) of a Markovian Arrival Process (MAP).static double[][][]Map_m1ps.map_m1ps_h_recursive(Matrix C, Matrix D, double mu, int N, int K) The h_{n,k} vectors of Theorem 1, returned as h[n][k], each M x 1:static Map_m1ps.SojournResultMap_m1ps.map_m1ps_sojourn(Matrix C, Matrix D, double mu, double[] x) map_m1ps_sojourn at the reference defaults epsilon = 1e-11, epsilon' = 1e-10.static Map_m1ps.SojournResultMap_m1ps.map_m1ps_sojourn(Matrix C, Matrix D, double mu, double[] x, double epsilon, double epsilonPrime) static MatrixCellMap_mark.map_mark(MatrixCell MAP, Matrix prob) Creates a Marked Markovian Arrival Process (MMAP) by marking a given MAP with additional phases based on specified marking probabilities.static doubleComputes the mean inter-arrival time of a Markovian Arrival Process (MAP).static doubleMap_moment.map_moment(Matrix D0, Matrix D1, int order) Computes the raw moments of the inter-arrival times of a MAP.static MatrixCellMap_normalize.map_normalize(Matrix D0, Matrix D1) Sanitizes the (D0, D1) matrices of a Markovian Arrival Process (MAP).static doublestatic double[]static MatrixComputes the steady-state probability vector of the embedded Discrete Time Markov Chain (DTMC) associated with a Markovian Arrival Process (MAP).static MatrixComputes the steady-state vector (pi) of the Continuous-Time Markov Chain (CTMC) underlying a Markovian Arrival Process (MAP).static MatrixMap_pntiter.map_pntiter(Matrix[] MAP, int na, double t) static MatrixMap_pntiter.map_pntiter(Matrix[] MAP, int na, double t, Integer M) Probability of having exactly na arrivals within time interval t using iterative method.Map_pntquad.map_pntquad(Matrix[] MAP, int na, double t) Compute MAP point process probabilities using ODE quadrature method.static MatrixComputes the equilibrium distribution of the underlying continuous-time Markov chain for a MAP.static MatrixCellMap_renewal.map_renewal(Matrix D0, Matrix D1) Creates a renewal MAP by removing all correlations from the input MAP.static double[]Map_sample.map_sample(Matrix D0, Matrix D1, long n, Random random) Generates samples of inter-arrival times from a MAP using a specified number of samples and a random generator.static MatrixCellRescales the mean inter-arrival time of a Markovian Arrival Process (MAP) to a specified new mean.static doubleComputes the squared coefficient of variation (SCV) of the inter-arrival times of a Markovian Arrival Process (MAP).static doubleComputes the skewness of the inter-arrival times for a MAP.static MatrixCellMap_stochcomp.map_stochcomp(Matrix D0, Matrix D1, int[] retainIdx) Performs stochastic complementation on a MAP by eliminating specified states.static doubleComputes the variance of the inter-arrival times for a MAP.static MatrixMap_varcount.map_varcount(MatrixCell MAP, Matrix t) static doubleMap_varcount.map_varcount(Matrix D0, Matrix D1, double t) Variance of the counts in a MAP over a time period t.static MatrixMap_varcount.map_varcount(Matrix D0, Matrix D1, Matrix t) Variance of the counts in a MAP over multiple time periods.Convert a MAP to MMPP format by extracting generator matrix Q and rate matrix LAMBDA.static MatrixCellMaph2m_fit.maph2m_fit(double M1, double M2, double M3, Matrix P, Matrix B) Computes the second-order MAPH[m] fitting the first three moments, the class probabilities (always fitted exactly) and the first-order backward moments.static Pair<MatrixCell,Matrix> Maph2m_fit.maph2m_fit_multiclass(MatrixCell aph, Matrix P, Matrix B) Convenience overload with unit class weights.static Pair<MatrixCell,Matrix> Maph2m_fit.maph2m_fit_multiclass(MatrixCell aph, Matrix P, Matrix B, double[] classWeights) Marks a canonical acyclic APH(2) so that the class probabilities are matched exactly and the backward moments as closely as possible.static doubleComputes the mean of a Matrix Exponential (ME) distribution.static MatrixComputes the stationary initial probability for an ME/RAP distribution.static double[]Generates random samples from a Matrix Exponential (ME) distribution by numerical inversion of its exact cumulative distribution function.static doubleComputes the squared coefficient of variation (SCV) of a Matrix Exponential (ME) distribution.static doubleComputes the variance of a Matrix Exponential (ME) distribution.static Matrix[]Mfq_fluflu_sojourn.mfq_fluflu_sojourn(Matrix Qin, Matrix Rin, Matrix Qout, Matrix Rout, boolean srv0stop, boolean transToPH) Sojourn-time distribution of a fluid queue with fluid-modulated service, as {alpha, A}.static List<double[]>Mfq_prio_queue.mfq_prio_queue(Matrix Q, Matrix R, double d, int[] classes, double prec, int erlMaxOrder, Object... measures) Performance measures of a continuous-time fluid priority queue (delegates to BUTools FluidPrioQueue).static Matrix[]Mfq_sojourn.mfq_sojourn(Matrix Q, Matrix Rin, Matrix Rout, Matrix Q0, boolean transToPH) Sojourn-time distribution as {alpha, A} (ME if transToPH=false, PH otherwise).static MatrixMmap_backward_moment.mmap_backward_moment(MatrixCell MMAP, Matrix ORDERS) static MatrixMmap_backward_moment.mmap_backward_moment(MatrixCell MMAP, Matrix ORDERS, int NORM) Computes the backward moments of an MMAP for specified orders with normalization.static Matrix[]Mmap_compress.mmap_compress(Matrix[] mmap) static Matrix[]Mmap_compress.mmap_compress(Matrix[] mmap, String method) static MatrixMmap_count_mcov.mmap_count_mcov(Matrix[] mmap, double t) Array-based overload.static MatrixCellMmap_exponential.mmap_exponential(Matrix lambda) Fits a single-state MMAP based on the given arrival rates for each job class.static MatrixCellMmap_exponential.mmap_exponential(Matrix lambda, int n) Fits an order-n MMAP based on the given arrival rates for each job class.static MatrixMmap_forward_moment.mmap_forward_moment(MatrixCell MMAP, Matrix ORDERS) static MatrixMmap_forward_moment.mmap_forward_moment(MatrixCell MMAP, Matrix ORDERS, int NORM) Computes the forward moments of an MMAP for specified orders with normalization.static MatrixCellMmap_hide.mmap_hide(MatrixCell MMAP, Matrix types) Hides specified types of arrivals in a Markovian Arrival Process with marked arrivals (MMAP).static MatrixCellMmap_mark.mmap_mark(MatrixCell MMAP, Matrix prob) Converts a Markovian Arrival Process with marked arrivals (MMAP) into a new MMAP with redefined classes based on a given probability matrix.static MatrixCellMmap_mixture.mmap_mixture(Matrix alpha, Map<Integer, MatrixCell> MAPs) Creates a mixture of MMAPs using the given weights (alpha) and MAPs.static Ret.mamMMAPMixtureFitMmap_mixture_fit.mmap_mixture_fit(Object P2, Matrix M1, Matrix M2, Matrix M3) Fits a mixture of Markovian Arrival Processes (MMAPs) to match the given cross-moments.static Ret.mamMMAPMixtureFitMmap_mixture_fit_trace.mmap_mixture_fit_trace(Matrix T, Matrix A) Fits a MMAP with m classes using a mixture of m^2 PH-distributions from Matrix inputs.static Matrixstatic MatrixCellMmap_scale.mmap_scale(MatrixCell MMAP, Matrix M) Overloaded function for backward compatibility.static MatrixCellMmap_scale.mmap_scale(MatrixCell MMAP, Matrix M, int maxIter) Changes the mean inter-arrival time of a Markovian Arrival Process with marked arrivals (MMAP).static MatrixMmap_sigma.mmap_sigma(Matrix[] mmap) Computes one-step class transition probabilities for an MMAP given as Matrix[].static Mmap3k_fit.ResultMmap3k_fit.mmap3k_fit(Matrix D0, Matrix D1, double[] P, double[] F, double[] B, double[] B2) Marks a given MAP so that the per-class characteristics are matched.static Qbd_bmapbmap1.QbdBmapResultQbd_bmapbmap1.qbd_bmapbmap1(MatrixCell MAPa, Matrix pbatcha, MatrixCell MAPs) Set up QBD matrices for BMAP/BMAP/1 queue analysis.static double[]Qbd_depproc_jointmom.qbd_depproc_jointmom(MatrixCell MAPa, MatrixCell MAPs, Matrix iset) Compute joint moments of consecutive inter-departure times.static Matrixstatic MatrixCompute R matrix using successive substitutions method for QBD processes.static MatrixQbd_R_logred.qbd_R_logred(Matrix B, Matrix L, Matrix F) static MatrixQbd_R_logred.qbd_R_logred(Matrix B, Matrix L, Matrix F, int iterMax) Compute R matrix using logarithmic reduction method for QBD processes.static QbdRapResultstatic QbdRapResultstatic QbdRapResultEquilibrium analysis of a QBD with RAP components.static MatrixSolves A0*G^2 + A1*G + A2 = 0 for the matrix G, using the exact rank-one closed form when A2 has rank one and otherwise functional iteration followed by Newton's method.static intstatic Matrixstatic Matrixstatic double[]Rap_sample.rap_sample(Matrix H0, Matrix H1, long n, Random random) Generates a correlated sample sequence from a Rational Arrival Process given its two matrices.Method parameters in jline.api.mam with type arguments of type MatrixModifier and TypeMethodDescriptionAph_convseq.aph_convseq(List<Pair<Matrix, Matrix>> aphParams) Performs sequential convolution of multiple APH distributions.Aph_convseq.aph_convseq(List<Pair<Matrix, Matrix>> aphParams) Performs sequential convolution of multiple APH distributions.static LdqbdResultstatic LdqbdResultstatic LevelDependentFluidSolutionMfq_ld_solve.mfq_ld_solve(List<Matrix> Q, List<Matrix> R, List<Matrix> S, double[] T, double[] boundaryL, double[] boundaryU, List<Matrix> Qt, double prec) Matrix-exponential solution of a multi-regime first/second-order fluid queue.static Matrix[]Mfq_multiregime.mfq_multiregime(List<Matrix> Q, List<double[]> R, List<Matrix> Qt, List<double[]> Rt, double[] T, double[] pdfpoints, double[] cdfpoints) Multi-regime feedback fluid queue; returns {pdf, pdfd, cdf, cdfm}.Constructors in jline.api.mam with parameters of type MatrixModifierConstructorDescriptionLdqbdResult(List<Matrix> R, Matrix pi) Mamap2mCoefficients(Matrix first, Matrix second, Matrix third) MapSampler(Matrix D0, Matrix D1) Builds the inversion table for the ME representation (alpha, A).QbdCrResult(Matrix G, Matrix R, Matrix U) QbdMapMap1Result(double XN, double QN, double UN, Matrix pqueue, Matrix R, Matrix eta, Matrix G, Matrix A_1, Matrix A0, Matrix A1, Matrix U, MatrixCell MAPs) QbdRapRap1Result(double XN, double QN, double UN, Matrix pqueue, Matrix R, Matrix eta, Matrix G, Matrix B, Matrix L, Matrix F) QbdRapResult(Matrix levelProb, double QN, Matrix R, Matrix G, Matrix U, double spr, Matrix pqueue, Matrix pi0) RapSampler(Matrix D0, Matrix D1) Builds the cached grid of matrix exponentials of H0 and initialises the conditional vector to the embedded stationary vector.Result(double QN, double UN, double TN, Matrix ql, MatrixCell dep) Constructor parameters in jline.api.mam with type arguments of type Matrix -
Uses of Matrix in jline.api.mam.m3pp
Methods in jline.api.mam.m3pp that return MatrixModifier and TypeMethodDescriptionstatic Matrix[]M3pp22_fitc_approx_cov.m3pp22_fitc_approx_cov(double a, double bt1, double bt2, double binf, double m3t2, double t1, double t2, double[] ai, double st3, double t3) Fits a second-order Marked MMPP for two classes using covariance approximation.static Matrix[]M3pp22_fitc_approx_cov_multiclass.m3pp22_fitc_approx_cov_multiclass(Matrix[] mmpp, double[] ai, double st3, double t3) Fits a M3PP(2,2) given the underlying MMPP(2).static Matrix[]M3pp2m_fitc.m3pp2m_fitc(double a, double bt1, double bt2, double binf, double m3t2, double t1, double t2, double[] ai, double[] dvt3, double t3) Fits a second-order Marked MMPP using exact count statistics.static Matrix[]M3pp2m_fitc_approx.m3pp2m_fitc_approx(double a, double bt1, double bt2, double binf, double m3t2, double t1, double t2, double[] ai, double[] dvt3, double t3) static Matrix[]M3pp2m_fitc_approx_ag.m3pp2m_fitc_approx_ag(double a, double bt1, double bt2, double binf, double m3t2, double t1, double t2, double[] ai, double[] gt3, double t3) Fits a second-order Marked MMPP using auto-gamma approach.static Matrix[]M3pp2m_fitc_approx_ag_multiclass.m3pp2m_fitc_approx_ag_multiclass(Matrix[] mmpp, double[] ai, double[] gt3, double t3) static Matrix[]M3pp2m_fitc_theoretical.m3pp2m_fitc_theoretical(MatrixCell mmap) static Matrix[]M3pp2m_fitc_theoretical.m3pp2m_fitc_theoretical(MatrixCell mmap, String method) static Matrix[]M3pp2m_fitc_theoretical.m3pp2m_fitc_theoretical(MatrixCell mmap, String method, double t) static Matrix[]M3pp2m_fitc_theoretical.m3pp2m_fitc_theoretical(MatrixCell mmap, String method, double t, double tinf) Fits the theoretical characteristics of a MMAP(n,m) with a M3PP(2,m).static Matrix[]M3pp2m_fitc_trace.m3pp2m_fitc_trace(double[] T, int[] A) static Matrix[]M3pp2m_fitc_trace.m3pp2m_fitc_trace(double[] T, int[] A, String method) static Matrix[]M3pp2m_fitc_trace.m3pp2m_fitc_trace(double[] T, int[] A, String method, Double t1, Double tinf) Fits a M3PP(2,m) from trace data using counting process characteristics.static Matrix[]M3pp2m_fitc_trace.m3pp2m_fitc_trace(Matrix T, Matrix A) static Matrix[]M3pp2m_fitc_trace.m3pp2m_fitc_trace(Matrix T, Matrix A, String method) static Matrix[]Fits a M3PP(2,m) from trace data using Matrix inputs.Methods in jline.api.mam.m3pp with parameters of type MatrixModifier and TypeMethodDescriptionM3pp_superpos_fitc_trace.m3pp_superpos_fitc_trace(Matrix T, Matrix A) M3pp_superpos_fitc_trace.m3pp_superpos_fitc_trace(Matrix T, Matrix A, Double t, Double tinf) Matrix-argument overload (T and A as column matrices).static Matrix[]M3pp22_fitc_approx_cov_multiclass.m3pp22_fitc_approx_cov_multiclass(Matrix[] mmpp, double[] ai, double st3, double t3) Fits a M3PP(2,2) given the underlying MMPP(2).static Matrix[]M3pp2m_fitc_approx_ag_multiclass.m3pp2m_fitc_approx_ag_multiclass(Matrix[] mmpp, double[] ai, double[] gt3, double t3) static Matrix[]M3pp2m_fitc_trace.m3pp2m_fitc_trace(Matrix T, Matrix A) static Matrix[]M3pp2m_fitc_trace.m3pp2m_fitc_trace(Matrix T, Matrix A, String method) static Matrix[]Fits a M3PP(2,m) from trace data using Matrix inputs. -
Uses of Matrix in jline.api.map
Methods in jline.api.map with parameters of type MatrixModifier and TypeMethodDescriptionstatic double[]MAPM1PSCdfRespT.computeCdf(Matrix C, Matrix D, double mu, double[] x) static double[]MAPM1PSCdfRespT.computeCdf(Matrix C, Matrix D, double mu, double[] x, double epsilon) static double[]MAPM1PSCdfRespT.computeCdf(Matrix C, Matrix D, double mu, double[] x, double epsilon, double epsilonPrime) -
Uses of Matrix in jline.api.mapqn
Fields in jline.api.mapqn declared as MatrixModifier and TypeFieldDescriptionfinal MatrixMapqn_qr_bounds_bas_parameters.BBfinal MatrixMapqn_qr_bounds_bas_parameters.MMfinal MatrixMapqn_qr_bounds_bas_parameters.MM1final Matrix[]LinearReductionParameters.mufinal Matrix[]Mapqn_qr_bounds_bas_parameters.mufinal Matrix[]Mapqn_qr_bounds_rsrd_parameters.mufinal MatrixMVAVersionParameters.muMAPfinal MatrixLinearReductionParameters.rfinal MatrixMapqn_qr_bounds_bas_parameters.rfinal MatrixMapqn_qr_bounds_rsrd_parameters.rfinal MatrixMVAVersionParameters.rfinal Matrix[]LinearReductionParameters.vfinal Matrix[]Mapqn_qr_bounds_bas_parameters.vfinal Matrix[]Mapqn_qr_bounds_rsrd_parameters.vfinal MatrixMVAVersionParameters.vMethods in jline.api.mapqn that return MatrixMethods in jline.api.mapqn with parameters of type MatrixConstructors in jline.api.mapqn with parameters of type MatrixModifierConstructorDescriptionLinearReductionParameters(int M, int N, int[] K, Matrix[] mu, Matrix r, Matrix[] v) Mapqn_qr_bounds_bas_parameters(int M, int N, int MR, int f, int[] K, int[] F, Matrix MM, Matrix MM1, int[] ZZ, Matrix BB, Matrix[] mu, Matrix[] v, Matrix r) Mapqn_qr_bounds_rsrd_parameters(int M, int N, int[] F, int[] K, Matrix[] mu, Matrix[] v, double[][] alpha, Matrix r) MVAVersionParameters(int M, int N, int K, double[] muM, Matrix muMAP, Matrix r, Matrix v) -
Uses of Matrix in jline.api.mc
Fields in jline.api.mc declared as MatrixModifier and TypeFieldDescriptionfinal MatrixCtmcPassagePh.alphapi0 restricted to the non-target block, UNNORMALIZED.Ctmc_transient_sens.Result.dpilength(t) x n, its derivative with respect to theta.final MatrixPassageMomentsResult.mall(nstates x nmax): row i holds the moments for a passage STARTED IN STATE i, zero on target states and infinite where the target cannot be reached.final MatrixCtmc_multi.CtmcMultiResult.pfinal MatrixDtmc_stochcomp.DtmcStochCompResult.P11final MatrixDtmc_stochcomp.DtmcStochCompResult.P12final MatrixDtmc_stochcomp.DtmcStochCompResult.P21final MatrixDtmc_stochcomp.DtmcStochCompResult.P22final MatrixCtmc_solve_reducible.CtmcSolveReducibleResult.piCtmc_transient_sens.Result.pilength(t) x n, the distribution.final MatrixDtmc_solve_reducible.DtmcSolveReducibleResult.pifinal MatrixDtmc_uniformization.DtmcUniformizationResult.pifinal MatrixCtmc_solve_reducible.CtmcSolveReducibleResult.pi0final MatrixDtmc_solve_reducible.DtmcSolveReducibleResult.pi0final MatrixDtmc_solve_reducible.DtmcSolveReducibleResult.pilfinal MatrixCtmc_fau.CtmcFauResult.pitDefective distribution at time t, a componentwise lower bound on pi(t).final MatrixDtmc_solve_reducible.DtmcSolveReducibleResult.Plfinal MatrixCtmcPassagePh.SSub-generator Q(A,A).final MatrixDtmc_stochcomp.DtmcStochCompResult.Sfinal MatrixCtmcPassagePh.s0Exit vector -S*1, as a column.final MatrixCtmcSsgReachabilityResult.stateSpacefinal MatrixCtmcSsgReachabilityResult.stateSpaceAggrfinal MatrixCtmcSsgReachabilityResult.stateSpaceHashedfinal MatrixCtmc_bicgstab.BicgstabResult.xfinal MatrixCtmc_gmres.GmresResult.xFields in jline.api.mc with type parameters of type MatrixModifier and TypeFieldDescriptionfinal Map<StatefulNode,Matrix> CtmcSsgReachabilityResult.nodeStateSpaceCtmc_solve_reducible.CtmcSolveReducibleResult.pisDtmc_solve_reducible.DtmcSolveReducibleResult.pisMethods in jline.api.mc that return MatrixModifier and TypeMethodDescriptionDtmc_uniformization.DtmcUniformizationResult.component1()static MatrixCtmc_bicgstab.ctmc_bicgstab(Matrix A, Matrix B, double tol, int maxit) Solve A*X = B for every column of B, reusing one incomplete factorization across all of them and starting each column from the previous solution.static MatrixCtmc_foxglynn.ctmc_foxglynn(Matrix pi0, Matrix Q, double t) Return the transient probability distribution of the CTMC by Fox-Glynn uniformization.static MatrixCtmc_foxglynn.ctmc_foxglynn(Matrix pi0, Matrix Q, double t, double tol, int maxiter) Return the transient probability distribution of the CTMC by Fox-Glynn uniformization.static MatrixCtmc_gmres.ctmc_gmres(Matrix A, Matrix B, double tol, int restart, int maxit) Solve A*X = B for every column of B, reusing one incomplete factorization across all of them and starting each column from the previous solution.static MatrixCtmc_hitting_time.ctmc_hitting_time(Matrix Q, int[] targetStates) static MatrixCtmc_makeinfgen.ctmc_makeinfgen(Matrix Q) Converts a matrix into a valid infinitesimal generator for a CTMC.static MatrixCtmc_rand.ctmc_rand(int length) Form a random infinitesimal generator of a CTMCstatic MatrixSensitivity of the steady-state distribution of a CTMC to a scalar parameter theta, computing pi from Q if it is not supplied.static MatrixSensitivity of the steady-state distribution of a CTMC to a scalar parameter theta, given the generator Q, its derivative dQ = dQ/dtheta, and the steady-state vector pi.static MatrixCtmc_solve.ctmc_solve(Matrix Q) Return the steady-state probability of a CTMC, choosing the solution method by size alone.static MatrixCtmc_solve.ctmc_solve(Matrix Q, SolverOptions options) Return the steady-state probability of a CTMC.static MatrixCtmc_stmonotone.ctmc_stmonotone(Matrix Q) Computes the stochastically monotone upper bound for a CTMC.static MatrixCtmc_timereverse.ctmc_timereverse(Matrix Q) Compute the infinitesimal generator of the time-reserved CTMCstatic MatrixCtmc_uniformization.ctmc_uniformization(Matrix pi0, Matrix Q, double t) Return the transient probability distribution of the CTMC via the uniformization method.static MatrixCtmc_uniformization.ctmc_uniformization(Matrix pi0, Matrix Q, double t, double tol, int maxiter) static MatrixDtmc_hitting_time.dtmc_hitting_time(Matrix P, int[] targetStates) Mean number of steps to reach any target state from each state of a DTMC.static MatrixDtmc_makestochastic.dtmc_makestochastic(Matrix P) Normalize a given non-negative matrix into a DTMC.static MatrixDtmc_rand.dtmc_rand(int length) Form a random infinitesimal generator of a DTMCstatic MatrixDtmc_solve.dtmc_solve(Matrix P) Returns the steady-state solution of a DTMC.static MatrixCtmc_stmonotone.dtmc_stmonotone(Matrix P) Implementation of the dtmc_stmonotone algorithm.static MatrixDtmc_stochcomp.dtmc_stochcomp(Matrix P, List<Integer> I) Returns the stochastic complement of a DTMC.static MatrixDtmc_timereverse.dtmc_timereverse(Matrix P) Compute the infinitesimal generator of the time-reversed DTMC.static MatrixDtmc_transient.dtmc_transient(Matrix P, Matrix pi0, int steps) Transient distribution of a DTMC, i.e.Ctmc_solve_reducible_blkdecompResult.getPi()Ctmc_solve_reducible_blkdecompResult.getPi0()CtmcSsgReachabilityResult.getStateSpace()CtmcSsgReachabilityResult.getStateSpaceAggr()CtmcSsgReachabilityResult.getStateSpaceHashed()Methods in jline.api.mc that return types with arguments of type MatrixModifier and TypeMethodDescriptionCtmc_courtois.ctmc_courtois(Matrix Q, List<List<Integer>> MS) Courtois decomposition for nearly completely decomposable CTMCsKoury-McAllister-Stewart aggregation-disaggregation method for CTMCsCtmc_randomization.ctmc_randomization(Matrix Q) Ctmc_randomization.ctmc_randomization(Matrix Q, Double q) Convert a CTMC to a DTMC using randomization techniqueCtmc_solve_reducible.ctmc_solve_reducible(Matrix Q) Ctmc_solve_reducible.ctmc_solve_reducible(Matrix Q, Matrix pi0) Solve reducible CTMCs by converting to DTMC via randomization.Ctmc_solve_reducible_blkdecomp.ctmc_solve_reducible_blkdecomp(Matrix Q) Ctmc_solve_reducible_blkdecomp.ctmc_solve_reducible_blkdecomp(Matrix Q, Matrix pin) Ctmc_solve_reducible_blkdecomp.ctmc_solve_reducible_blkdecomp(Matrix Q, Matrix pin, Map<String, Object> options) Ctmc_takahashi.ctmc_takahashi(Matrix Q, List<List<Integer>> MS, int numSteps) Takahashi's aggregation-disaggregation method for CTMCsCtmc_timeaverage.ctmc_timeaverage(Matrix pi0, Matrix Q, double t) Time-averaged transient distribution of a CTMC over [0,t].Ctmc_timeaverage.ctmc_timeaverage(Matrix pi0, Matrix Q, double t) Time-averaged transient distribution of a CTMC over [0,t].Ctmc_timeaverage.ctmc_timeaverage(Matrix pi0, Matrix Q, double t, double tol, int maxiter) Ctmc_timeaverage.ctmc_timeaverage(Matrix pi0, Matrix Q, double t, double tol, int maxiter) Dtmc_solve_reducible.dtmc_solve_reducible(Matrix P) Dtmc_solve_reducible.dtmc_solve_reducible(Matrix P, Matrix pin) Estimate limiting distribution for a DTMC that may have reducible components.CtmcSsgReachabilityResult.getNodeStateSpace()Ctmc_solve_reducible_blkdecompResult.getPis()Methods in jline.api.mc with parameters of type MatrixModifier and TypeMethodDescriptionstatic Ctmc_bicgstab.BicgstabResultCtmc_bicgstab.ctmc_bicgstab(Matrix A, Matrix b) Solve A*x = b by preconditioned BiCGSTAB with the default tolerance and iteration cap, starting from a uniform initial guess.static MatrixCtmc_bicgstab.ctmc_bicgstab(Matrix A, Matrix B, double tol, int maxit) Solve A*X = B for every column of B, reusing one incomplete factorization across all of them and starting each column from the previous solution.static Ctmc_bicgstab.BicgstabResultCtmc_bicgstab.ctmc_bicgstab(Matrix A, Matrix b, double tol, int maxit, Matrix x0) Solve A*x = b by preconditioned BiCGSTAB.Ctmc_courtois.ctmc_courtois(Matrix Q, List<List<Integer>> MS) Courtois decomposition for nearly completely decomposable CTMCsstatic Ctmc_fau.CtmcFauResultReturn the transient distribution of the CTMC at time t by fast adaptive uniformization, at the default tolerances.static Ctmc_fau.CtmcFauResultReturn the transient distribution of the CTMC at time t by fast adaptive uniformization.static MatrixCtmc_foxglynn.ctmc_foxglynn(Matrix pi0, Matrix Q, double t) Return the transient probability distribution of the CTMC by Fox-Glynn uniformization.static MatrixCtmc_foxglynn.ctmc_foxglynn(Matrix pi0, Matrix Q, double t, double tol, int maxiter) Return the transient probability distribution of the CTMC by Fox-Glynn uniformization.static Ctmc_gmres.GmresResultCtmc_gmres.ctmc_gmres(Matrix A, Matrix b) Solve A*x = b by restarted GMRES with the default tolerance, restart and iteration cap, starting from a uniform initial guess.static MatrixCtmc_gmres.ctmc_gmres(Matrix A, Matrix B, double tol, int restart, int maxit) Solve A*X = B for every column of B, reusing one incomplete factorization across all of them and starting each column from the previous solution.static Ctmc_gmres.GmresResultCtmc_gmres.ctmc_gmres(Matrix A, Matrix b, double tol, int restart, int maxit, Matrix x0) Solve A*x = b by restarted GMRES.static MatrixCtmc_hitting_time.ctmc_hitting_time(Matrix Q, int[] targetStates) static booleanCtmc_isfeasible.ctmc_isfeasible(Matrix Q) Checks that Q is a valid infinitesimal generator, with the default tolerance of 1e-10.static booleanCtmc_isfeasible.ctmc_isfeasible(Matrix Q, double tolerance) Checks that Q is a valid infinitesimal generator: square, non-negative off-diagonal entries, non-positive diagonal, and zero row sums, each up to the given tolerance.Koury-McAllister-Stewart aggregation-disaggregation method for CTMCsstatic MatrixCtmc_makeinfgen.ctmc_makeinfgen(Matrix Q) Converts a matrix into a valid infinitesimal generator for a CTMC.static Ctmc_multi.CtmcMultiResultMulti-level aggregation method for CTMCs.static org.apache.commons.math3.complex.ComplexCtmc_passage_lst.ctmc_passage_lst(Matrix Q, Matrix pi0, int[] target, org.apache.commons.math3.complex.Complex s) L(s) at a single (possibly complex) point.static org.apache.commons.math3.complex.Complex[]Ctmc_passage_lst.ctmc_passage_lst(Matrix Q, Matrix pi0, int[] target, org.apache.commons.math3.complex.Complex[] s) L(s) on a vector of points.static PassageMomentsResultCtmc_passage_moments.ctmc_passage_moments(Matrix Q, Matrix pi0, int[] target, int nmax) static CtmcPassagePhCtmc_passage_ph.ctmc_passage_ph(Matrix Q, Matrix pi0, int[] target) static PassageCurveCtmc_passage_time.ctmc_passage_time(Matrix Q, Matrix pi0, int[] target, double[] tset) static PassageCurveCtmc_passage_time.ctmc_passage_time(Matrix Q, Matrix pi0, int[] target, double[] tset, String method, String ltiMethod) static SolverCTMC.StochCompResultCtmc_pseudostochcomp.ctmc_pseudostochcomp(Matrix Q, List<Double> I_list) Ctmc_randomization.ctmc_randomization(Matrix Q) Ctmc_randomization.ctmc_randomization(Matrix Q, Double q) Convert a CTMC to a DTMC using randomization techniquestatic Object[]Ctmc_relsolve.ctmc_relsolve(Matrix Q) static Object[]Ctmc_relsolve.ctmc_relsolve(Matrix Q, int refstate) static Object[]Ctmc_relsolve.ctmc_relsolve(Matrix Q, int refstate, Map<String, Object> options) Equilibrium distribution of a continuous-time Markov chain re-normalized with respect to the probability of a reference state.Ctmc_saddlepoint.ctmc_saddlepoint(Matrix D0, Matrix D1, double[] t, int[] k, String method, double[] pi0) Pr{N(t)=k} over arrays of horizons and counts.static doubleCtmc_saddlepoint.ctmc_saddlepoint(Matrix D0, Matrix D1, double t, int k) Pr{N(t)=k} at a single (t,k), with the default method.static doubleCtmc_saddlepoint.ctmc_saddlepoint(Matrix D0, Matrix D1, double t, int k, String method) Pr{N(t)=k} at a single (t,k).static MatrixSensitivity of the steady-state distribution of a CTMC to a scalar parameter theta, computing pi from Q if it is not supplied.static MatrixSensitivity of the steady-state distribution of a CTMC to a scalar parameter theta, given the generator Q, its derivative dQ = dQ/dtheta, and the steady-state vector pi.static Ret.ctmcSimulationCtmc_simulate.ctmc_simulate(Matrix Q, double[] pi0, int n) static Ret.ctmcSimulationCtmc_simulate.ctmc_simulate(Matrix Q, double[] pi0, int n, Random random) static MatrixCtmc_solve.ctmc_solve(Matrix Q) Return the steady-state probability of a CTMC, choosing the solution method by size alone.static MatrixCtmc_solve.ctmc_solve(Matrix Q, SolverOptions options) Return the steady-state probability of a CTMC.Ctmc_solve_reducible.ctmc_solve_reducible(Matrix Q) Ctmc_solve_reducible.ctmc_solve_reducible(Matrix Q, Matrix pi0) Solve reducible CTMCs by converting to DTMC via randomization.Ctmc_solve_reducible_blkdecomp.ctmc_solve_reducible_blkdecomp(Matrix Q) Ctmc_solve_reducible_blkdecomp.ctmc_solve_reducible_blkdecomp(Matrix Q, Matrix pin) Ctmc_solve_reducible_blkdecomp.ctmc_solve_reducible_blkdecomp(Matrix Q, Matrix pin, Map<String, Object> options) Ctmc_solve_reducible_blkdecomp.ctmc_solve_reducible_blkdecomp_full(Matrix Q) Ctmc_solve_reducible_blkdecomp.ctmc_solve_reducible_blkdecomp_full(Matrix Q, Matrix pin) Ctmc_solve_reducible_blkdecomp.ctmc_solve_reducible_blkdecomp_full(Matrix Q, Matrix pin, Map<String, Object> options) Ctmc_solve_reducible.ctmc_solve_reducible_full(Matrix Q) Ctmc_solve_reducible.ctmc_solve_reducible_full(Matrix Q, Matrix pi0) Alternative signature that returns additional information.static MatrixCtmc_stmonotone.ctmc_stmonotone(Matrix Q) Computes the stochastically monotone upper bound for a CTMC.static SolverCTMC.StochCompResultCtmc_stochcomp.ctmc_stochcomp(Matrix Q, List<Double> I_list) Ctmc_takahashi.ctmc_takahashi(Matrix Q, List<List<Integer>> MS, int numSteps) Takahashi's aggregation-disaggregation method for CTMCsstatic booleanCtmc_testpf_kolmogorov.ctmc_testpf_kolmogorov(Matrix Q) Test if a CTMC has product form using Kolmogorov's criteria.Ctmc_timeaverage.ctmc_timeaverage(Matrix pi0, Matrix Q, double t) Time-averaged transient distribution of a CTMC over [0,t].Ctmc_timeaverage.ctmc_timeaverage(Matrix pi0, Matrix Q, double t, double tol, int maxiter) static MatrixCtmc_timereverse.ctmc_timereverse(Matrix Q) Compute the infinitesimal generator of the time-reserved CTMCCtmc_transient.ctmc_transient(Matrix Q, double t1) Ctmc_transient.ctmc_transient(Matrix Q, Matrix pi0, double t1) Ctmc_transient.ctmc_transient(Matrix Q, Matrix pi0, double t0, double t1) Ctmc_transient.ctmc_transient(Matrix Q, Matrix pi0, double t0, double t1, Double timestep) static Ctmc_transient_sens.ResultCtmc_transient_sens.ctmc_transient_sens(Matrix Q, Matrix dQ, double t1) From the uniform initial distribution over [0, t1].static Ctmc_transient_sens.ResultCtmc_transient_sens.ctmc_transient_sens(Matrix Q, Matrix dQ, Matrix pi0, double t1) From PI0 over [0, t1].static Ctmc_transient_sens.ResultCtmc_transient_sens.ctmc_transient_sens(Matrix Q, Matrix dQ, Matrix pi0, double t0, double t1) static MatrixCtmc_uniformization.ctmc_uniformization(Matrix pi0, Matrix Q, double t) Return the transient probability distribution of the CTMC via the uniformization method.static MatrixCtmc_uniformization.ctmc_uniformization(Matrix pi0, Matrix Q, double t, double tol, int maxiter) static MatrixDtmc_hitting_time.dtmc_hitting_time(Matrix P, int[] targetStates) Mean number of steps to reach any target state from each state of a DTMC.static intDtmc_isfeasible.dtmc_isfeasible(Matrix P) Check if a matrix represents a feasible DTMC transition matrixstatic MatrixDtmc_makestochastic.dtmc_makestochastic(Matrix P) Normalize a given non-negative matrix into a DTMC.static int[]Dtmc_simulate.dtmc_simulate(Matrix P, Matrix pi0, int n) Simulate a discrete-time Markov chain trajectory.static MatrixDtmc_solve.dtmc_solve(Matrix P) Returns the steady-state solution of a DTMC.Dtmc_solve_reducible.dtmc_solve_reducible(Matrix P) Dtmc_solve_reducible.dtmc_solve_reducible(Matrix P, Matrix pin) Estimate limiting distribution for a DTMC that may have reducible components.Dtmc_solve_reducible.dtmc_solve_reducible_full(Matrix P) Dtmc_solve_reducible.dtmc_solve_reducible_full(Matrix P, Matrix pin) Full version that returns all computed values.static MatrixCtmc_stmonotone.dtmc_stmonotone(Matrix P) Implementation of the dtmc_stmonotone algorithm.static MatrixDtmc_stochcomp.dtmc_stochcomp(Matrix P, List<Integer> I) Returns the stochastic complement of a DTMC.Dtmc_stochcomp.dtmc_stochcomp_full(Matrix P, List<Integer> I) Stochastic complement of a DTMC together with the four blocks of the transition matrix partitioned by the kept states and their complement.static MatrixDtmc_timereverse.dtmc_timereverse(Matrix P) Compute the infinitesimal generator of the time-reversed DTMC.static MatrixDtmc_transient.dtmc_transient(Matrix P, Matrix pi0, int steps) Transient distribution of a DTMC, i.e.Dtmc_uniformization.dtmc_uniformization(Matrix pi0, Matrix P) Dtmc_uniformization.dtmc_uniformization(Matrix pi0, Matrix P, double t) Dtmc_uniformization.dtmc_uniformization(Matrix pi0, Matrix P, double t, double tol) Dtmc_uniformization.dtmc_uniformization(Matrix pi0, Matrix P, double t, double tol, int maxiter) Compute the transient probability distribution of a DTMC using uniformization.static org.apache.commons.math3.complex.ComplexSmp_passage_lst.smp_passage_lst(Matrix P, List<UnaryOperator<org.apache.commons.math3.complex.Complex>> hlst, Matrix pi0, int[] target, org.apache.commons.math3.complex.Complex s) static PassageMomentsResultSmp_passage_moments.smp_passage_moments(Matrix P, Matrix hmom, Matrix pi0, int[] target, int nmax) static PassageCurveSmp_passage_time.smp_passage_time(Matrix P, List<UnaryOperator<org.apache.commons.math3.complex.Complex>> hlst, Matrix pi0, int[] target, double[] tset) static PassageCurveSmp_passage_time.smp_passage_time(Matrix P, List<UnaryOperator<org.apache.commons.math3.complex.Complex>> hlst, Matrix pi0, int[] target, double[] tset, String ltiMethod) Constructors in jline.api.mc with parameters of type MatrixModifierConstructorDescriptionCtmc_solve_reducible_blkdecompResult(Matrix pi, List<Matrix> pis, Matrix pi0, List<List<Integer>> scc, List<Boolean> isrec) CtmcMultiResult(Matrix p, double eps, double epsMAX) CtmcPassagePh(Matrix alpha, Matrix S, Matrix s0, int[] keep, double atom) CtmcSolveReducibleResult(Matrix pi, List<Matrix> pis, Matrix pi0, List<List<Integer>> scc, List<Boolean> isrec) CtmcSsgReachabilityResult(Matrix stateSpace, Matrix stateSpaceAggr, Matrix stateSpaceHashed, Map<StatefulNode, Matrix> nodeStateSpace, NetworkStruct sn) DtmcSolveReducibleResult(Matrix pi, List<Matrix> pis, Matrix pi0, List<List<Integer>> scc, List<Boolean> isrec, Matrix Pl, Matrix pil) DtmcUniformizationResult(Matrix pi, int kmax) PassageMomentsResult(Matrix mall, double[] m) Constructor parameters in jline.api.mc with type arguments of type MatrixModifierConstructorDescriptionCtmc_solve_reducible_blkdecompResult(Matrix pi, List<Matrix> pis, Matrix pi0, List<List<Integer>> scc, List<Boolean> isrec) CtmcSolveReducibleResult(Matrix pi, List<Matrix> pis, Matrix pi0, List<List<Integer>> scc, List<Boolean> isrec) CtmcSsgReachabilityResult(Matrix stateSpace, Matrix stateSpaceAggr, Matrix stateSpaceHashed, Map<StatefulNode, Matrix> nodeStateSpace, NetworkStruct sn) DtmcSolveReducibleResult(Matrix pi, List<Matrix> pis, Matrix pi0, List<List<Integer>> scc, List<Boolean> isrec, Matrix Pl, Matrix pil) -
Uses of Matrix in jline.api.mdd
Fields in jline.api.mdd declared as MatrixModifier and TypeFieldDescriptionfinal MatrixMddClosedQnResult.piStationary distribution over the reachable states, MDD index order.final MatrixMddClosedQnResult.QCTMC generator, rows aligned toMDD.index(int[])order.Methods in jline.api.mdd with parameters of type MatrixModifier and TypeMethodDescriptionstatic MddClosedQnResultMdd_closedqn.mdd_closedqn(double[] mu, Matrix P, double[] servers, int N) static MddClosedQnResultMdd_closedqn.mdd_closedqn(double[] mu, Matrix P, double[] servers, int N, MDD reuse) Constructors in jline.api.mdd with parameters of type MatrixModifierConstructorDescriptionMddClosedQnResult(MDD mdd, Matrix Q, Matrix pi, int[][] states, double[] QLen, double[] U, double[] X, MddStats stats, double timeReach, double timeGen, double timeSolve, double timeMetrics) -
Uses of Matrix in jline.api.moment
Methods in jline.api.moment that return MatrixModifier and TypeMethodDescriptionstatic MatrixMoment_binomial_from_factorial.moment_binomial_from_factorial(Matrix f) Binomial moments from factorial moments.static MatrixMoment_binomial_from_negbinomial.moment_binomial_from_negbinomial(Matrix bm) Converts negative-binomial moments into binomial moments.static MatrixMoment_binomial_from_tail.moment_binomial_from_tail(Matrix t) Converts survival probabilities into binomial moments.static MatrixMoment_binotrans.moment_binotrans(Matrix x) Binomial transform of a sequence.static MatrixMoment_binotransinv.moment_binotransinv(Matrix y) Inverse binomial transform of a sequence.static MatrixMoment_central_from_raw.moment_central_from_raw(Matrix m) Converts power (raw) moments into central moments.static MatrixMoment_cumulant_from_raw.moment_cumulant_from_raw(Matrix m) Converts power (raw) moments into cumulants.static MatrixMoment_factcumulant_from_factorial.moment_factcumulant_from_factorial(Matrix f) Converts factorial moments into factorial cumulants.static MatrixMoment_factorial_from_binomial.moment_factorial_from_binomial(Matrix b) Factorial moments from binomial moments.static MatrixMoment_factorial_from_factcumulant.moment_factorial_from_factcumulant(Matrix kappa) Converts factorial cumulants into factorial moments.static MatrixMoment_factorial_from_raw.moment_factorial_from_raw(Matrix m) Converts power (raw) moments into factorial moments.static MatrixMoment_factorial_from_upfactorial.moment_factorial_from_upfactorial(Matrix fp) Converts upward-factorial moments into factorial moments.static MatrixMoment_housematrix.moment_housematrix(String edge, int n) Returns the conversion matrix of one edge of the house of moments.static MatrixMoment_joint_aggregate.moment_joint_aggregate(double[] F, int[] dims) Factorial moments of the total count.static MatrixMoment_lah.moment_lah(int n) Triangle of the Lah numbers L(i,j).static MatrixMoment_negbinomial_from_binomial.moment_negbinomial_from_binomial(Matrix b) Converts binomial moments into negative-binomial moments.static MatrixMoment_negbinomial_from_upfactorial.moment_negbinomial_from_upfactorial(Matrix fp) Negative-binomial moments from upward-factorial moments.static MatrixMoment_raw_from_central.moment_raw_from_central(Matrix mc, double m1) Converts central moments into power (raw) moments.static MatrixMoment_raw_from_cumulant.moment_raw_from_cumulant(Matrix kappa) Converts cumulants into power (raw) moments.static MatrixMoment_raw_from_factorial.moment_raw_from_factorial(Matrix f) Converts factorial moments into power (raw) moments.static MatrixMoment_raw_from_upfactorial.moment_raw_from_upfactorial(Matrix fp) Converts upward-factorial moments into power (raw) moments.static MatrixMoment_stirling1.moment_stirling1(int n) Triangle of the signed Stirling numbers of the first kind s(i,j).static MatrixMoment_stirling2.moment_stirling2(int n) Triangle of the Stirling numbers of the second kind S(i,j).static MatrixMoment_stirlingcycle.moment_stirlingcycle(int n) Triangle of the Stirling cycle numbers sigma(i,j).static MatrixMoment_tail_from_binomial.moment_tail_from_binomial(Matrix b) Converts binomial moments into survival probabilities.static MatrixMoment_upfactorial_from_factorial.moment_upfactorial_from_factorial(Matrix f) Converts factorial moments into upward-factorial moments.static MatrixMoment_upfactorial_from_negbinomial.moment_upfactorial_from_negbinomial(Matrix bm) Upward-factorial moments from negative-binomial moments.static MatrixMoment_upfactorial_from_raw.moment_upfactorial_from_raw(Matrix m) Converts power (raw) moments into upward-factorial moments.Methods in jline.api.moment with parameters of type MatrixModifier and TypeMethodDescriptionstatic MatrixMoment_binomial_from_factorial.moment_binomial_from_factorial(Matrix f) Binomial moments from factorial moments.static MatrixMoment_binomial_from_negbinomial.moment_binomial_from_negbinomial(Matrix bm) Converts negative-binomial moments into binomial moments.static MatrixMoment_binomial_from_tail.moment_binomial_from_tail(Matrix t) Converts survival probabilities into binomial moments.static MatrixMoment_binotrans.moment_binotrans(Matrix x) Binomial transform of a sequence.static MatrixMoment_binotransinv.moment_binotransinv(Matrix y) Inverse binomial transform of a sequence.static MatrixMoment_central_from_raw.moment_central_from_raw(Matrix m) Converts power (raw) moments into central moments.static MatrixMoment_cumulant_from_raw.moment_cumulant_from_raw(Matrix m) Converts power (raw) moments into cumulants.static MatrixMoment_factcumulant_from_factorial.moment_factcumulant_from_factorial(Matrix f) Converts factorial moments into factorial cumulants.static MatrixMoment_factorial_from_binomial.moment_factorial_from_binomial(Matrix b) Factorial moments from binomial moments.static MatrixMoment_factorial_from_factcumulant.moment_factorial_from_factcumulant(Matrix kappa) Converts factorial cumulants into factorial moments.static MatrixMoment_factorial_from_raw.moment_factorial_from_raw(Matrix m) Converts power (raw) moments into factorial moments.static MatrixMoment_factorial_from_upfactorial.moment_factorial_from_upfactorial(Matrix fp) Converts upward-factorial moments into factorial moments.static double[]Moment_joint_marking.moment_joint_marking(Matrix f, double[] p, int[] dims) Joint factorial moments of the per-class counts.static MatrixMoment_negbinomial_from_binomial.moment_negbinomial_from_binomial(Matrix b) Converts binomial moments into negative-binomial moments.static MatrixMoment_negbinomial_from_upfactorial.moment_negbinomial_from_upfactorial(Matrix fp) Negative-binomial moments from upward-factorial moments.static MatrixMoment_raw_from_central.moment_raw_from_central(Matrix mc, double m1) Converts central moments into power (raw) moments.static MatrixMoment_raw_from_cumulant.moment_raw_from_cumulant(Matrix kappa) Converts cumulants into power (raw) moments.static MatrixMoment_raw_from_factorial.moment_raw_from_factorial(Matrix f) Converts factorial moments into power (raw) moments.static MatrixMoment_raw_from_upfactorial.moment_raw_from_upfactorial(Matrix fp) Converts upward-factorial moments into power (raw) moments.static MatrixMoment_tail_from_binomial.moment_tail_from_binomial(Matrix b) Converts binomial moments into survival probabilities.static double[]Moment_tensortrans.moment_tensortrans(double[] a, int[] dims, Matrix T, int mode) Applies a conversion matrix along one dimension of a joint moment array.static MatrixMoment_upfactorial_from_factorial.moment_upfactorial_from_factorial(Matrix f) Converts factorial moments into upward-factorial moments.static MatrixMoment_upfactorial_from_negbinomial.moment_upfactorial_from_negbinomial(Matrix bm) Upward-factorial moments from negative-binomial moments.static MatrixMoment_upfactorial_from_raw.moment_upfactorial_from_raw(Matrix m) Converts power (raw) moments into upward-factorial moments. -
Uses of Matrix in jline.api.nc
Methods in jline.api.nc that return MatrixModifier and TypeMethodDescriptionMeCqnResult.getCa()MeOqnBlkResult.getCa()Interarrival scv of the offered flow at each station.MeOqnResult.getCa()MeCqnResult.getCd()MeOqnBlkResult.getCd()Interdeparture scv at each station.MeOqnResult.getCd()MeCqnResult.getL()MeOqnResult.getL()MeCqnResult.getLambda()MeOqnBlkResult.getLambda()Offered arrival rate at each station, re-attempts included.MeOqnResult.getLambda()MeOqnBlkResult.getPBa()Probability that an arrival finds the station full.MeOqnBlkResult.getQ()Mean number of jobs at each station, the jobs held blocked included.MeCqnResult.getRho()MeOqnResult.getRho()MeOqnBlkResult.getT()Carried throughput at each station.MeOqnBlkResult.getU()Utilization at each station, a blocked server not counted as busy.MeCqnResult.getW()MeOqnBlkResult.getW()Mean response time at each station.MeOqnResult.getW()MeCqnResult.getX()Methods in jline.api.nc with parameters of type MatrixModifier and TypeMethodDescriptionstatic MeCqnResultstatic MeCqnResultMe_cqn.me_cqn(int M, int R, Matrix N, Matrix mu, Matrix Cs, Matrix[][] P, Matrix c, int[] refstat, boolean[] insens, MeOqnOptions options) Maximum Entropy algorithm for Closed Queueing Networks with discipline-aware building blocks: insens[i] marks a station with an insensitive scheduling discipline (PS, LCFS-PR), solved with the product-form mean queue length instead of the FCFS GE formula.static MeCqnResultMe_cqn.me_cqn(int M, int R, Matrix N, Matrix mu, Matrix Cs, Matrix[][] P, Matrix c, int[] refstat, MeOqnOptions options) Maximum Entropy algorithm for Closed Queueing Networks.static MeCqnResultMe_mqn.me_mqn(int M, int R, boolean[] openClasses, Matrix lambda0, Matrix Ca0, Matrix N, Matrix mu, Matrix Cs, Matrix[][] P, Matrix c, int[] refstat, boolean[] insens, MeOqnOptions options) Maximum Entropy algorithm for Mixed Queueing Networks with discipline-aware building blocks (insens[i] true for PS/LCFS-PR).static MeCqnResultMe_mqn.me_mqn(int M, int R, boolean[] openClasses, Matrix lambda0, Matrix Ca0, Matrix N, Matrix mu, Matrix Cs, Matrix[][] P, Matrix c, int[] refstat, MeOqnOptions options) Maximum Entropy algorithm for Mixed Queueing Networks.static MeOqnResultstatic MeOqnResultMe_oqn.me_oqn(int M, int R, Matrix lambda0, Matrix Ca0, Matrix mu, Matrix Cs, Matrix[][] P, MeOqnOptions options) static MeOqnResultMe_oqn.me_oqn(int M, int R, Matrix lambda0, Matrix Ca0, Matrix mu, Matrix Cs, Matrix[][] P, Matrix c, boolean[] insens, MeOqnOptions options) Maximum Entropy algorithm for Open Queueing Networks with discipline-aware building blocks: insens[i] marks a station with an insensitive scheduling discipline (PS, LCFS-PR), solved with the product-form mean queue length L_r = rho_r/(1-rho) instead of the FCFS GE formula.static MeOqnResultMe_oqn.me_oqn(int M, int R, Matrix lambda0, Matrix Ca0, Matrix mu, Matrix Cs, Matrix[][] P, Matrix c, MeOqnOptions options) Maximum Entropy algorithm for Open Queueing Networks.static MeOqnBlkResultMe_oqn_blk.me_oqn_blk(int M, Matrix lambda0, Matrix Ca0, Matrix mu, Matrix Cs, Matrix P, Matrix c, Matrix N, int[] blockrule) static MeOqnBlkResultMe_oqn_blk.me_oqn_blk(int M, Matrix lambda0, Matrix Ca0, Matrix mu, Matrix Cs, Matrix P, Matrix c, Matrix N, int[] blockrule, MeOqnOptions options, double damping) Maximum Entropy algorithm for a single-class open network with finite buffers.Constructors in jline.api.nc with parameters of type Matrix -
Uses of Matrix in jline.api.npfqn
Fields in jline.api.npfqn declared as MatrixModifier and TypeFieldDescriptionNpfqn_traffic_idc.c2aijNpfqn_traffic_idc.c2alphaNpfqn_traffic_idc.lam_jiNpfqn_traffic_idc.PNpfqn_dps_morrison.Result.QMean number of class-k jobs at the DPS station, 1 x K.Npfqn_dps_morrison.Result.QleadLeading-order (one-term) queue lengths, 1 x K.Npfqn_dps_morrison.Result.RMean class-k sojourn time per visit to the DPS station, 1 x K.Npfqn_dps_morrison.Result.RleadLeading-order (one-term) sojourn times, 1 x K.Npfqn_dps_morrison.Result.sigmaThe vector sigma of eq.Npfqn_dps_morrison.Result.XPer-class throughput, 1 x K.Npfqn_traffic_idc.XiMatrix[]Npfqn_traffic_idc.zetaAllMethods in jline.api.npfqn that return MatrixModifier and TypeMethodDescriptionstatic MatrixNpfqn_traffic_split_rr.npfqn_traffic_split_rr(NetworkStruct sn) Deterministic (round-robin) split degree of the departure stream of each station-class.Methods in jline.api.npfqn with parameters of type MatrixModifier and TypeMethodDescriptionstatic Npfqn_bnd_bpt.ResultNpfqn_bnd_bpt.npfqn_bnd_bpt(double[] lambda0, double[] mu, Matrix P, int[] stationOf, double[] c) static Npfqn_dps_morrison.ResultNpfqn_dps_morrison.npfqn_dps_morrison(Matrix N, Matrix Z, Matrix S, Matrix w) Evaluates Morrison's two-term approximation.Npfqn_feedback_elim.npfqn_feedback_elim(Matrix P, double[] rho, double[] cs2, double[] lambda, boolean immediateOnly) static Ret.npfqnNonexpApproxNpfqn_nonexp_approx.npfqn_nonexp_approx(String method, NetworkStruct sn, Matrix ST, Matrix V, Matrix SCV, Matrix Tin, Matrix Uin, Matrix gamma, Matrix nservers) Approximates non-product-form queueing networks using the specified method.static Npfqn_traffic_idcNpfqn_traffic_idc.npfqn_traffic_idc(double[] lambda0, Matrix P, double[] c2a0, IdcFunction a0IdcFun, double[] mu, double[] cs2, Npfqn_traffic_idc.PerQueueIdc sIdcFun, boolean useAlpha, boolean useBeta) Traffic variability equations for the RQNA.static MatrixCellNpfqn_traffic_merge_cs.npfqn_traffic_merge_cs(Map<Integer, MatrixCell> MMAPs, Matrix prob) static MatrixCellNpfqn_traffic_merge_cs.npfqn_traffic_merge_cs(Map<Integer, MatrixCell> MMAPs, Matrix prob, String config) Merges MMAP traffic flows with class switching.static double[][]Npfqn_traffic_rqt.npfqn_traffic_rqt(double[] lambda0, double[] Gamma0, double[] alpha0, Matrix F) Effective arrival process perceived at each node of a single-class open queueing network under the Robust Queueing Theory calculus.static Map<Integer,MatrixCell> Npfqn_traffic_split_cs.npfqn_traffic_split_cs(MatrixCell MMAP, Matrix P) Splits MMAP traffic flows with class switching. -
Uses of Matrix in jline.api.pfqn
Fields in jline.api.pfqn declared as MatrixModifier and TypeFieldDescriptionPfqn_marie.MultiResult.CPfqn_marie.Result.CPfqn_marie.Result.mufinal MatrixPfqn_cftp.PfqnCftpReturn.QEmpirical mean queue length per station (1 x M).Pfqn_marie.MultiResult.QPfqn_marie.Result.QPfqn_sdr.Result.QMean queue lengths, centers by chains.Pfqn_sdr.Result.RMean response times at the center, Q elementwise over X.final MatrixPfqn_cftp.PfqnCftpReturn.TPer-sample coalescence horizon ('cftp') or mixing steps ('approx') (nsamples x 1).Pfqn_marie.MultiResult.UPfqn_marie.Result.UPfqn_sdr.Result.UMean number in service, X elementwise times S.final MatrixPfqn_mwrbb.Result.Wlo(K x C) per-visit residence time consistent with the lower bound.final MatrixPfqn_cftp.PfqnCftpReturn.XSampled states, one per row (nsamples x M), each row sums to N.Pfqn_marie.MultiResult.XPfqn_sdr.Result.XPer-center chain throughputs.final MatrixPfqn_mwrbb.Result.Xlo(1 x C) lower bound on class throughput (Theorem 2).final MatrixPfqn_mwrbb.Result.Xup(1 x C) upper bound on class throughput (eqs.Methods in jline.api.pfqn that return MatrixModifier and TypeMethodDescriptionPfqnUniqueResult.getGamma_unique()PfqnUniqueResult.getL_unique()PfqnUniqueResult.getMi()PfqnUniqueResult.getMu_unique()static MatrixPfqn_replicas.pfqn_combine_mi(Matrix mi, int[] mapping, int M_unique) Combine user-provided multiplicity vector with detected replica multiplicity.static MatrixPfqn_sdr.pfqn_sdrvisits(StateDepRouting sdr, List<Matrix> P) Coefficients xi of Section 3.2.Methods in jline.api.pfqn that return types with arguments of type MatrixModifier and TypeMethodDescriptionPfqn_replicas.pfqn_expand(Matrix QN, Matrix UN, Matrix CN, int[] mapping) Expand per-station metrics from reduced model to original dimensions.Pfqn_replicas.pfqn_expand(Matrix QN, Matrix UN, Matrix CN, int[] mapping) Expand per-station metrics from reduced model to original dimensions.Pfqn_replicas.pfqn_expand(Matrix QN, Matrix UN, Matrix CN, int[] mapping) Expand per-station metrics from reduced model to original dimensions.Methods in jline.api.pfqn with parameters of type MatrixModifier and TypeMethodDescriptionstatic double[]Pfqn_busyp.pfqn_busyp(Matrix alpha, Pfqn_busyp.RateFunction mu, Matrix P, double N, int[] subnet, int[] n, Matrix gamma) Rate-function form with the default tolerance.static double[]Pfqn_busyp.pfqn_busyp(Matrix alpha, Pfqn_busyp.RateFunction mu, Matrix P, double N, int[] subnet, int[] n, Matrix gamma, double tol) Rate-function form, the one the MATLAB and C++ ports take.static double[]Pfqn_busyp.pfqn_busyp(Matrix alpha, Matrix mu, Matrix P, double N, int[] subnet, int[] n) Closed-network form with the default tolerance.static double[]Pfqn_busyp.pfqn_busyp(Matrix alpha, Matrix mu, Matrix P, double N, int[] subnet, int[] n, Matrix gamma, double tol) Mean busy period of order n for the subnetwork, that is the time from the instant a job entering the subnetwork finds n-1 jobs in it up to the next instant when fewer than n jobs remain in it.static doublePfqn_busyp.pfqn_busyp(Matrix alpha, Matrix mu, Matrix P, double N, int[] subnet, int n, Matrix gamma) Single-order form.static double[]Pfqn_busyp_clw.pfqn_busyp_clw(Matrix alpha, Matrix mu, Matrix[] P, double[] N, int[] subnet, int[] n, Matrix gamma, boolean[] isdelay, String method) Mean busy period of order n for the subnetwork, via NC point evaluations.static double[]Pfqn_busyp_clw.pfqn_busyp_clw(Matrix alpha, Matrix mu, Matrix P, double[] N, int[] subnet, int[] n) Closed-network form with a shared routing matrix and the CLW default.static double[]Pfqn_busyp_multiclass.pfqn_busyp_multiclass(Matrix alpha, Matrix mu, Matrix[] P, double[] N, int[] subnet, int[] n, Matrix gamma, Matrix phi, double tol, int jobclass) Mean busy period of order n for the subnetwork, multichain.static double[]Pfqn_busyp_multiclass.pfqn_busyp_multiclass(Matrix alpha, Matrix mu, Matrix P, double[] N, int[] subnet, int[] n) Closed-network form with the default tolerance and a shared routing matrix.static Pfqn_cftp.PfqnCftpReturnExact (perfect) stationary state sampling with default single servers, one sample, and the 'cftp' method.static Pfqn_cftp.PfqnCftpReturnExact (perfect) or approximate stationary state sampling for closed single-class multiserver product-form networks via monotone Coupling From The Past.static MatrixPfqn_replicas.pfqn_combine_mi(Matrix mi, int[] mapping, int M_unique) Combine user-provided multiplicity vector with detected replica multiplicity.Pfqn_replicas.pfqn_expand(Matrix QN, Matrix UN, Matrix CN, int[] mapping) Expand per-station metrics from reduced model to original dimensions.static Ret.pfqnHarelBoundsPfqn_harel_bounds.pfqn_harel_bounds(Matrix rho, int N) static Ret.pfqnHarelBoundsPfqn_harel_bounds.pfqn_harel_bounds(Matrix rho, int N, double Z) static Ret.pfqnHarelBoundsPfqn_harel_bounds.pfqn_harel_bounds(Matrix rho, int N, double Z, Integer maxUB) static doublePfqn_harel_bounds.pfqn_harel_lb(Matrix rho, int N) static doublePfqn_harel_bounds.pfqn_harel_lb(Matrix rho, int N, double Z) static doublePfqn_harel_bounds.pfqn_harel_ub(Matrix rho, int N, int n) static doublePfqn_harel_bounds.pfqn_harel_ub(Matrix rho, int N, int n, double Z) static doublePfqn_joint.pfqn_joint(Matrix n, Matrix L, Matrix N) static doublePfqn_joint.pfqn_joint(Matrix n, Matrix L, Matrix N, Matrix Z) static doubleCompute the joint queue-length probability for vector nstatic Pfqn_jointmarg.Ret_jointmargPfqn_jointmarg.pfqn_jointmarg(Matrix n, Matrix L, Matrix N, int[] infset) static Pfqn_jointmarg.Ret_jointmargPfqn_jointmarg.pfqn_jointmarg(Matrix n, Matrix L, Matrix N, int[] infset, Double lGN) static Pfqn_jointmarg.Ret_jointmargPfqn_jointmarg.pfqn_jointmarg(Matrix n, Matrix L, Matrix N, int[] infset, Double lGN, String engine) Joint probability of the per-station total queue lengths.static Pfqn_marie.ResultPfqn_marie.pfqn_marie(Matrix L, double N, double Z, Matrix scv) static Pfqn_marie.ResultPfqn_marie.pfqn_marie(Matrix L, double N, double Z, Matrix scv, double tol, int maxiter, Matrix nservers) static Pfqn_marie.MultiResultPfqn_marie.pfqn_marie_multi(Matrix L, Matrix N, Matrix Z, Matrix scv) static Pfqn_marie.MultiResultPfqn_marie.pfqn_marie_multi(Matrix L, Matrix N, Matrix Z, Matrix scv, double tol, int maxiter) Marie's method for multiclass FCFS Coxian closed networks.static Pfqn_mwrbb.ResultPfqn_mwrbb.pfqn_mwrbb(Matrix V, Matrix S, Matrix N, Matrix Z) FIFO, equal-priority convenience overload.static Pfqn_mwrbb.Resultstatic Pfqn_sdr.ResultExact product form of eq.static Pfqn_sdr.ResultPfqn_sdr.pfqn_sdrmva(Matrix S, Matrix xi, Matrix N, StateDepRouting sdr, Matrix alpha) Section 4 mean value analysis and convolution.static PfqnUniqueResultPfqn_replicas.pfqn_unique(Matrix L) static PfqnUniqueResultPfqn_replicas.pfqn_unique(Matrix L, Matrix mu) static PfqnUniqueResultPfqn_replicas.pfqn_unique(Matrix L, Matrix mu, Matrix gamma) Consolidate replicated stations into unique stations with multiplicity.Method parameters in jline.api.pfqn with type arguments of type MatrixModifier and TypeMethodDescriptionstatic MatrixPfqn_sdr.pfqn_sdrvisits(StateDepRouting sdr, List<Matrix> P) Coefficients xi of Section 3.2.Constructors in jline.api.pfqn with parameters of type MatrixModifierConstructorDescriptionMultiResult(Matrix X, Matrix Q, Matrix U, Matrix C, int iter) PfqnCftpReturn(Matrix Q, Matrix X, Matrix T) PfqnUniqueResult(Matrix L_unique, Matrix mu_unique, Matrix gamma_unique, Matrix mi, int[] mapping) -
Uses of Matrix in jline.api.pfqn.lcfs
Fields in jline.api.pfqn.lcfs declared as MatrixMethods in jline.api.pfqn.lcfs that return MatrixModifier and TypeMethodDescriptionLcfsqnMvaResult.getB()LcfsqnMvaResult.getQ()LcfsqnMvaResult.getT()LcfsqnMvaResult.getU()Methods in jline.api.pfqn.lcfs with parameters of type MatrixModifier and TypeMethodDescriptionstatic LcfsqnCaResultPfqn_lcfsqn_ca.pfqn_lcfsqn_ca(Matrix alpha, Matrix beta) static LcfsqnCaResultPfqn_lcfsqn_ca.pfqn_lcfsqn_ca(Matrix alpha, Matrix beta, Matrix N) Convolution algorithm for multiclass LCFS queueing networks.static LcfsqnMvaResultPfqn_lcfsqn_mva.pfqn_lcfsqn_mva(Matrix alpha, Matrix beta) static LcfsqnMvaResultPfqn_lcfsqn_mva.pfqn_lcfsqn_mva(Matrix alpha, Matrix beta, Matrix N) Pfqn_lcfsqn_nc.pfqn_lcfsqn_nc(Matrix alpha, Matrix beta, Matrix N) Normalizing constant for multiclass LCFS queueing networks.Constructors in jline.api.pfqn.lcfs with parameters of type MatrixModifierConstructorDescriptionLcfsqnMvaResult(Matrix T, Matrix Q, Matrix U, Matrix B) LcfsqnNcResult(double G, Matrix[] Ax) -
Uses of Matrix in jline.api.pfqn.ld
Fields in jline.api.pfqn.ld declared as MatrixModifier and TypeFieldDescriptionfinal MatrixPfqn_qdlin.Result.C(1 x R) per-class cycle times, think time included.final MatrixPfqn_qdamva.Result.Q(M x R) mean queue lengths.final MatrixPfqn_qdlin.Result.Q(M x R) mean queue lengths at the queueing stations.final MatrixPfqn_qdamva.Result.R(M x R) per-class residence times, Q = X .* R.final MatrixPfqn_qdlin.Result.R(M x R) per-class residence times.final MatrixPfqn_qdamva.Result.U(M x R) per-class utilizations, carrying the g scaling.final MatrixPfqn_qdlin.Result.U(M x R) per-class utilizations.final MatrixPfqn_qdamva.Result.X(1 x R) per-class throughputs.final MatrixPfqn_qdlin.Result.X(1 x R) per-class throughputs.Methods in jline.api.pfqn.ld that return MatrixModifier and TypeMethodDescriptionstatic MatrixLjd.ljd_delinearize(int idx, Matrix cutoffs) Inverse ofLjd.ljd_linearize(jline.util.matrix.Matrix, jline.util.matrix.Matrix): the population vector behind an index.static MatrixPfqn_cdfun.pfqn_cdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> cdscaling, int M) Evaluate class-dependent (CD) scaling function for the first class.static MatrixPfqn_cdfun.pfqn_cdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> cdscaling, int M, int classIdx) Evaluate class-dependent (CD) scaling function.static MatrixPfqn_jdfun.pfqn_jdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> jdscaling, int M) Evaluate joint-dependent (JD) scaling function for the first class.static MatrixPfqn_jdfun.pfqn_jdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> jdscaling, int M, int classIdx) Evaluate joint-dependent (JD) scaling function.static MatrixPfqn_lldfun.pfqn_lldfun(Matrix n, Matrix lldscaling, Matrix nservers) Evaluate limited-load dependent (LLD) function.static MatrixPfqn_mu_ms.pfqn_mu_ms(int N, int m, int c) static MatrixPfqn_mushift.pfqn_mushift(Matrix mu, int k) Shifts a load-dependent scaling vector by one positionMethods in jline.api.pfqn.ld with parameters of type MatrixModifier and TypeMethodDescriptionstatic Ret.pfqnNcPfqn_ncld.compute_norm_const_ld(Matrix L, Matrix N, Matrix Z, Matrix mu, SolverOptions options) static MatrixLjd.ljd_delinearize(int idx, Matrix cutoffs) Inverse ofLjd.ljd_linearize(jline.util.matrix.Matrix, jline.util.matrix.Matrix): the population vector behind an index.static intLjd.ljd_linearize(Matrix nvec, Matrix cutoffs) Convert per-class population vector to linearized index (0-indexed).static Ret.pfqnABstatic MatrixPfqn_cdfun.pfqn_cdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> cdscaling, int M) Evaluate class-dependent (CD) scaling function for the first class.static MatrixPfqn_cdfun.pfqn_cdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> cdscaling, int M, int classIdx) Evaluate class-dependent (CD) scaling function.static Ret.pfqnComomrmLdPfqn_comomrm_ld.pfqn_comomrm_ld(Matrix Lin, Matrix Nin, Matrix Zin, Matrix muIn, SolverOptions options) Run the COMOM normalizing constant solution method on a repairman model.static double[]Pfqn_conv.pfqn_conv(Matrix L, int[] N, double[] Z, List<SerializableFunction<Matrix, Matrix>> cdscaling) Multichain convolution for networks with class-dependent service rates.static Ret.pfqnDACDAC with default think times (zero) and default rates (single-server fixed rate).static Ret.pfqnDACDAC (Distribution Analysis by Chain) for joint queue-length distributions.static Pfqn_explicit_ld.ResultPfqn_explicit_ld.pfqn_explicit_ld(Matrix L, Matrix N, Matrix mu) Overload with the documented defaults: machine precision, "auto", no budget.static Pfqn_explicit_ld.ResultPfqn_explicit_ld.pfqn_explicit_ld(Matrix L, Matrix N, Matrix mu, double tol, String method, double maxloss) Explicit closed-form normalizing constant of a multiclass limited load-dependent network.static Ret.pfqnFncCompute scaling factor of a load-dependent functional server use to calculate the meanstatic Ret.pfqnFncCompute scaling factor of a load-dependent functional server use to calculate the mean instantiated with scaling constant c.static Ret.pfqnNcPfqn_gld.pfqn_gld(Matrix L, Matrix N, Matrix mu, SolverOptions options) Compute the normalizing constant of a single-class load-dependent closed queueing network modelstatic Ret.pfqnNcComplexPfqn_gld_complex.pfqn_gld_complex(ComplexMatrix L, Matrix N, Matrix mu, SolverOptions options) Compute the normalizing constant of a single-class load-dependent closed queueing network model with complex demands.static Ret.pfqnNcSymPfqn_gld_sym.pfqn_gld_sym(SymMatrix L, Matrix N, SymMatrix mu) Normalizing constant of a load-dependent closed network, symbolically.static Ret.pfqnNcPfqn_gldsingle.pfqn_gldsingle(Matrix L, Matrix N, Matrix mu, SolverOptions options) Auxiliary function used by pfqn_gld to compute the normalizing constant in a single-class load-dependent model.static Ret.pfqnNcComplexPfqn_gldsingle_complex.pfqn_gldsingle_complex(ComplexMatrix L, Matrix N, Matrix mu, SolverOptions options) Auxiliary function used by pfqn_gld to compute the normalizing constant in a single-class load-dependent model with complex demands.static Ret.pfqnNcSymPfqn_gldsingle_sym.pfqn_gldsingle_sym(SymMatrix L, Matrix N, SymMatrix mu) Normalizing constant of a single-class load-dependent model, symbolically.static MatrixPfqn_jdfun.pfqn_jdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> jdscaling, int M) Evaluate joint-dependent (JD) scaling function for the first class.static MatrixPfqn_jdfun.pfqn_jdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> jdscaling, int M, int classIdx) Evaluate joint-dependent (JD) scaling function.static Ret.pfqnLDMXECPfqn_ldmx_ec.pfqn_ldmx_ec(Matrix lambda, Matrix D, Matrix mu) Auxiliary function used by pfqn_mvaldmx to compute the EC terms.static Ret.pfqnNcPfqn_lld.pfqn_lld(Matrix L, Matrix N, Matrix mu, SolverOptions options) Normalizing constant of a multiclass limited load-dependent closed model.static MatrixPfqn_lldfun.pfqn_lldfun(Matrix n, Matrix lldscaling, Matrix nservers) Evaluate limited-load dependent (LLD) function.static Ret.pfqnNcPfqn_lldsingle.pfqn_lldsingle(Matrix L, Matrix N, Matrix mu, SolverOptions options) Auxiliary function used by Pfqn_ncld and Pfqn_nre to compute the normalizing constant in a single-class limited load-dependent model.static doublePfqn_mu_ms.pfqn_mu_ms_gnaux(int n, int m, int c, Matrix g) static MatrixPfqn_mushift.pfqn_mushift(Matrix mu, int k) Shifts a load-dependent scaling vector by one positionstatic Ret.pfqnMVACLDPfqn_mvacld.pfqn_mvacld(Matrix L, Matrix N, Matrix mu) MVAC for a load-dependent closed network with no infinite-server center.static Ret.pfqnMVACLDPfqn_mvacld.pfqn_mvacld(Matrix L, Matrix N, Matrix Z, Matrix mu) MVAC (Mean Value Analysis by Chain) for a closed multichain product-form queueing network that may contain queue-length dependent (QLD) service centers.static Ret.pfqnMVALDPfqn_mvald.pfqn_mvald(Matrix L, Matrix N, Matrix Z, Matrix mu) static Ret.pfqnMVALDPfqn_mvald.pfqn_mvald(Matrix L, Matrix N, Matrix Z, Matrix mu, boolean stabilize) static Ret.pfqnMVAWrapper for pfqn_mvaldmx that adjusts utilizations to account for multiservers.static Ret.pfqnMVALDMXMVA method for mixed queueing networks with load-dependent nodes.static Ret.pfqnNcstatic Ret.pfqnNcldmxPfqn_ncldmx.pfqn_ncldmx(Matrix lambda, Matrix D, Matrix N, Matrix Z, Matrix mu, Matrix S, SolverOptions options) Normalizing constant and mean measures for mixed open/closed networks with limited load dependence.static Ret.pfqnOifncPfqn_oi_fnc.pfqn_oi_fnc(Matrix Phi, int[] N) Default target f(n) = sum(n) (total occupancy).static Ret.pfqnOifncPfqn_oi_fnc.pfqn_oi_fnc(Matrix Phi, int[] N, ToDoubleFunction<int[]> f) OI functional-server balance/rate for a target queue-dependent function.static Ret.pfqnNcPfqn_panaceald.pfqn_panaceald(Matrix L, Matrix N, Matrix Z, Matrix mu) static Ret.pfqnNcPfqn_panaceald.pfqn_panaceald(Matrix L, Matrix N, Matrix Z, Matrix mu, int terms) Compute the load-dependent PANACEA approximationstatic Ret.pfqnProcomom2Pfqn_procomom2.pfqn_procomom2(Matrix L, Matrix N, Matrix Z) static Ret.pfqnProcomom2Pfqn_procomom2.pfqn_procomom2(Matrix L, Matrix N, Matrix Z, Matrix mu) static Ret.pfqnProcomom2Compute marginal state probabilities for the queue in a model consisting of a queueing station and a delay station only.static Pfqn_qdamva.ResultPfqn_qdamva.pfqn_qdamva(Matrix L, Matrix N, Matrix Z, Matrix mu) QD-AMVA with the reference's default tolerance and iteration cap.static Pfqn_qdamva.ResultPfqn_qdamva.pfqn_qdamva(Matrix L, Matrix N, Matrix Z, Matrix mu, Matrix Q0, double tol, int maxiter) QD-AMVA.static Pfqn_qdlin.ResultQD-LIN with the reference's default tolerances and iteration cap.static Pfqn_qdlin.ResultPfqn_qdlin.pfqn_qdlin(Matrix L, Matrix N, Matrix Z, Matrix mu, Matrix nservers, double tol, int maxiter, double wtol) QD-LIN.static Ret.pfqnSchmidtPfqn_schmidt.pfqn_schmidt(Matrix D, Matrix N, Matrix S, List<SchedStrategy> sched) static doublePfqn_xia.pfqn_xia(Matrix L, int N, Matrix s, SolverOptions options) Method parameters in jline.api.pfqn.ld with type arguments of type MatrixModifier and TypeMethodDescriptionstatic doubleCdPeakScaling.cd_peak_scaling(SerializableFunction<Matrix, Matrix> beta, int[] NK, int K) Peak of the class-dependence function over the reachable population lattice 0 <= n[r] <= NK[r].static doubleCdPeakScaling.cd_peak_scaling(SerializableFunction<Matrix, Matrix> beta, int[] NK, int K) Peak of the class-dependence function over the reachable population lattice 0 <= n[r] <= NK[r].static MatrixPfqn_cdfun.pfqn_cdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> cdscaling, int M) Evaluate class-dependent (CD) scaling function for the first class.static MatrixPfqn_cdfun.pfqn_cdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> cdscaling, int M) Evaluate class-dependent (CD) scaling function for the first class.static MatrixPfqn_cdfun.pfqn_cdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> cdscaling, int M, int classIdx) Evaluate class-dependent (CD) scaling function.static MatrixPfqn_cdfun.pfqn_cdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> cdscaling, int M, int classIdx) Evaluate class-dependent (CD) scaling function.static double[]Pfqn_conv.pfqn_conv(Matrix L, int[] N, double[] Z, List<SerializableFunction<Matrix, Matrix>> cdscaling) Multichain convolution for networks with class-dependent service rates.static double[]Pfqn_conv.pfqn_conv(Matrix L, int[] N, double[] Z, List<SerializableFunction<Matrix, Matrix>> cdscaling) Multichain convolution for networks with class-dependent service rates.static MatrixPfqn_jdfun.pfqn_jdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> jdscaling, int M) Evaluate joint-dependent (JD) scaling function for the first class.static MatrixPfqn_jdfun.pfqn_jdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> jdscaling, int M) Evaluate joint-dependent (JD) scaling function for the first class.static MatrixPfqn_jdfun.pfqn_jdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> jdscaling, int M, int classIdx) Evaluate joint-dependent (JD) scaling function.static MatrixPfqn_jdfun.pfqn_jdfun(Matrix nvec, List<SerializableFunction<Matrix, Matrix>> jdscaling, int M, int classIdx) Evaluate joint-dependent (JD) scaling function.Constructors in jline.api.pfqn.ld with parameters of type Matrix -
Uses of Matrix in jline.api.pfqn.mva
Fields in jline.api.pfqn.mva declared as MatrixModifier and TypeFieldDescriptionfinal MatrixPfqn_mvasjn.Result.CResidence times (M x R).Matrix[][]Pfqn_momlin.MomlinResult.dQPfqn_looping.Result.QMean queue lengths on the pessimistic side.Pfqn_momlin.MomlinResult.Qfinal MatrixPfqn_mva_interval.Result.QMean queue-length intervals per station (M x 2).final MatrixPfqn_mvasjn.Result.QMean queue lengths (M x R).final MatrixPfqn_nintmva.Result.QMean queue lengths (M x 1).final MatrixPfqn_sqni.PfqnSqniResult.Qfinal MatrixPfqn_tay.Result.QMean queue lengths (M x R).final Matrix[]Pfqn_tay.Result.QarrArrival-instant queue lengths, indexed [r][m][k]: the class-k queue length at station m as seen by an arriving class-r job.Matrix[][]Pfqn_momlin.MomlinResult.QCovfinal MatrixPfqn_mva_interval.Result.QtotInterval of the total number of jobs at the stations (1 x 2).Pfqn_momlin.MomlinResult.QVarPfqn_looping.Result.RResidence times.Pfqn_momlin.MomlinResult.Rfinal MatrixPfqn_mva_interval.Result.RResidence-time intervals per station (M x 2).final MatrixPfqn_nintmva.Result.RResidence times (M x 1).final MatrixPfqn_tay.Result.RResidence times (M x R).final MatrixPfqn_mva_interval.Result.RtotTotal response-time interval (1 x 2).Pfqn_momlin.MomlinResult.Ufinal MatrixPfqn_mva_interval.Result.UUtilization enclosures per station (M x 2).final MatrixPfqn_mvasjn.Result.UUtilizations (M x R).final MatrixPfqn_nintmva.Result.UUtilizations (M x 1).final MatrixPfqn_sqni.PfqnSqniResult.Ufinal MatrixPfqn_tay.Result.UUtilizations (M x R).Pfqn_momlin.MomlinResult.Xfinal MatrixPfqn_mva_interval.Result.XThroughput interval (1 x 2).final MatrixPfqn_mvasjn.Result.XThroughputs (1 x R).final MatrixPfqn_sqni.PfqnSqniResult.Xfinal MatrixPfqn_tay.Result.XPer-class throughputs (1 x R).Pfqn_looping.Result.XloPessimistic (lower) throughput bound, one entry per class.Pfqn_looping.Result.XupOptimistic (upper) throughput bound, one entry per class.Methods in jline.api.pfqn.mva that return MatrixModifier and TypeMethodDescriptionstatic Matrix[]Multiclass composite upper bound and its BJB lower seed.Methods in jline.api.pfqn.mva with parameters of type MatrixModifier and TypeMethodDescriptionstatic Ret.pfqnAMVAMSPfqn_ab_amva.ab_amva(Matrix serviceTimes, Matrix N, Matrix V, Matrix nservers, List<SchedStrategy> schedStrategies, boolean fcfsSchmidt, String marginalProbMethod) static Ret.pfqnAMVAPfqn_ab_amva.ab_linearizer(int K, int M, Matrix population, Matrix nservers, List<SchedStrategy> type, Matrix v, Matrix s, boolean fcfsSchmidt, String marginalProbMethod) Pfqn_ab_amva.findMarginalProbs(double avgJobs, int numServers, Matrix population, int classIdx, String marginalProbMethod) static Ret.pfqnAMVAPfqn_ab_amva.pfqn_ab_core(int K, int M, Matrix population, Matrix nservers, List<SchedStrategy> type, Matrix v, Matrix s, int maxiter, double[][][] D, Matrix lIn, boolean fcfsSchmidt, String marginalProbMethod) static Pfqn_mvasjn.ResultPfqn_amvasjn.pfqn_amvasjn(Matrix L, Matrix N, Matrix Z, Matrix scv, int[] sjnset, Matrix V, SjnOptions options) Mean value analysis with shortest-job-next stations, through a Schweitzer fixed point.static Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic double[]Iterative balanced job bounds BJB(k).static Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAPfqn_bs.pfqn_bs(Matrix L, Matrix N, Matrix Z, double tol, int maxiter, Matrix QN0, SchedStrategy[] type) static Ret.pfqnAMVAPfqn_bs.pfqn_bs(Matrix L, Matrix N, Matrix Z, double tol, int maxiter, Matrix QN0, SchedStrategy[] type, Matrix weight) Bard-Schweitzer approximate mean value analysis algorithm with optional weighted priorities.static Ret.pfqnAMVABard-Schweitzer approximate mean value analysis algorithm with weighted priorities.static double[]Level-`level` convolutional bound hierarchy on throughput.static Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAPfqn_chow.pfqn_chow(Matrix L, Matrix N, Matrix Z, double tol, int maxiter, Matrix QN0, SchedStrategy[] type) static Ret.pfqnAMVAPfqn_chow.pfqn_chow(Matrix L, Matrix N, Matrix Z, double tol, int maxiter, Matrix QN0, SchedStrategy[] type, String variant) static Ret.pfqnAMVAPfqn_clust.pfqn_clust(Matrix L, Matrix N, Matrix Z) static Ret.pfqnAMVAPfqn_clust.pfqn_clust(Matrix L, Matrix N, Matrix Z, double tol, int maxiter) static Ret.pfqnAMVAPfqn_clust.pfqn_clust(Matrix L, Matrix N, Matrix Zin, List<int[]> subnets, List<int[]> localclasses, String inner, double tol, int maxiter) static doublePfqn_cntol.pfqn_cntol(Matrix N) Termination cutoff at the given population vector.static Ret.pfqnAMVAMSPfqn_conwayms.pfqn_conwayms(Matrix L, Matrix N, Matrix Z) static Ret.pfqnAMVAMSPfqn_conwayms.pfqn_conwayms(Matrix L, Matrix N, Matrix Z, int[] nservers) static Ret.pfqnAMVAMSPfqn_conwayms.pfqn_conwayms(Matrix L, Matrix N, Matrix Z, int[] nservers, SchedStrategy[] type, double tol, int maxiter) static Ret.pfqnAMVAMSPfqn_conwayms.pfqn_conwayms(Matrix L, Matrix N, Matrix Z, int[] nservers, SchedStrategy[] type, double tol, int maxiter, Matrix QN0) static Ret.LinearizerResultPfqn_conwayms.pfqn_conwayms_core(Matrix L, int M, int R, Matrix N_1, Matrix Z, int[] nservers, Matrix Q, Matrix P, Matrix PB, MatrixCell Delta, SchedStrategy[] type, double tol, int maxiter) static Ret.pfqnEstimatePfqn_conwayms.pfqn_conwayms_estimate(int M, int R, Matrix N_1, int[] nservers, Matrix Q, Matrix P, Matrix PB, MatrixCell Delta, Matrix W) static Ret.LinearizerResultPfqn_conwayms.pfqn_conwayms_forwardmva(Matrix L, int M, int R, Matrix N_1, Matrix Z, int[] nservers, SchedStrategy[] type, MatrixCell Q_1, MatrixCell P_1, Matrix PB_1, Matrix T_1) static Ret.pfqnAMVAPfqn_dmlin.pfqn_dmlin(Matrix L, Matrix N) static Ret.pfqnAMVAPfqn_dmlin.pfqn_dmlin(Matrix L, Matrix N, Matrix Z) static Ret.pfqnAMVAPfqn_dmlin.pfqn_dmlin(Matrix L, Matrix N, Matrix Z, double tol, int maxiter) static Ret.pfqnAMVAPfqn_dmlin.pfqn_dmlin(Matrix L, Matrix N, Matrix Z, double tol, int maxiter, Matrix QN0) static Ret.pfqnAMVAPfqn_dmlin.pfqn_dmlin(Matrix L, Matrix N, Matrix Zin, double tol, int maxiter, Matrix QN0, int npasses) static Ret.pfqnAMVAPfqn_egflinearizer.pfqn_egflinearizer(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter, Matrix alpha) static Ret.pfqnAMVAPfqn_egflinearizer.pfqn_egflinearizer(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter, Matrix alpha, Matrix QN0) static Ret.pfqnAMVAPfqn_egflinearizer.pfqn_egflinearizer(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter, Matrix alpha, Matrix QN0, int npasses) static Ret.pfqnAMVAPfqn_gflinearizer.pfqn_gflinearizer(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter, double alpha) General-form linearizer algorithmstatic Ret.pfqnAMVAPfqn_gflinearizer.pfqn_gflinearizer(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter, double alpha, Matrix QN0) static Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAPfqn_lcp.pfqn_lcp(Matrix L, Matrix N, Matrix Z, double tol, int maxiter, Matrix QN0, SchedStrategy[] type) static double[]Pfqn_ldbcmp.pfqn_ldbcmp(Matrix L, double N, double Z, Matrix c) Lower throughput bound (and response-time upper bound) for LD-BCMP closed networks.static Ret.pfqnAMVAPfqn_linearizer.pfqn_linearizer(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type) static Ret.pfqnAMVAPfqn_linearizer.pfqn_linearizer(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol) static Ret.pfqnAMVAPfqn_linearizer.pfqn_linearizer(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter) Linearizer approximate mean value analysis algorithmstatic Ret.pfqnAMVAPfqn_linearizer.pfqn_linearizer(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter, Matrix QN0) static Ret.pfqnAMVAMSPfqn_linearizerms.pfqn_linearizerms(Matrix L, Matrix N, Matrix nservers) static Ret.pfqnAMVAMSPfqn_linearizerms.pfqn_linearizerms(Matrix L, Matrix N, Matrix Z, Matrix nservers) static Ret.pfqnAMVAMSPfqn_linearizerms.pfqn_linearizerms(Matrix L, Matrix N, Matrix Z, Matrix nservers, List<SchedStrategy> type) static Ret.pfqnAMVAMSPfqn_linearizerms.pfqn_linearizerms(Matrix L, Matrix N, Matrix Z, Matrix nservers, List<SchedStrategy> type, double tol, int maxiter) static Ret.pfqnAMVAMSPfqn_linearizerms.pfqn_linearizerms(Matrix L, Matrix N, Matrix Z, Matrix nservers, List<SchedStrategy> type, double tol, int maxiter, Matrix QN0) static Ret.pfqnAMVAPfqn_linearizermx.pfqn_linearizermx(Matrix lambda, Matrix L, Matrix N, Matrix Z, Matrix nservers, SchedStrategy[] type, double tol, int maxiter, String method) Linearizer method for mixed models with multi-server stations.static Ret.pfqnAMVAPfqn_linearizermx.pfqn_linearizermx(Matrix lambda, Matrix L, Matrix N, Matrix Z, Matrix nservers, SchedStrategy[] type, double tol, int maxiter, String method, Matrix QN0) static Ret.pfqnAMVAPfqn_linearizerpp.pfqn_linearizerpp(Matrix L, Matrix N, int level) static Ret.pfqnAMVAPfqn_linearizerpp.pfqn_linearizerpp(Matrix L, Matrix N, Matrix Z, int level) static Ret.pfqnAMVAPfqn_linearizerpp.pfqn_linearizerpp(Matrix L, Matrix N, Matrix Z, int level, double tol) static Ret.pfqnAMVAPfqn_linearizerpp.pfqn_linearizerpp(Matrix L, Matrix N, Matrix Z, int level, double tol, int maxiter) static Ret.pfqnAMVAPfqn_linearizerpp.pfqn_linearizerpp(Matrix L, Matrix N, Matrix Z, int level, double tol, int maxiter, int flag) static Pfqn_looping.ResultPfqn_looping.pfqn_looping(Matrix L, Matrix N, Matrix Z) static Pfqn_looping.ResultPfqn_looping.pfqn_looping(Matrix L, Matrix N, Matrix Zin, double tol, int maxiter) static Matrix[]Multiclass composite upper bound and its BJB lower seed.static Pfqn_momlin.MomlinResultPfqn_momlin.pfqn_momlin(Matrix L, Matrix N, Matrix Z) static Pfqn_momlin.MomlinResultPfqn_momlin.pfqn_momlin(Matrix L, Matrix N, Matrix Z, double tol, int maxiter) Approximate first and second queue-length moments of a closed product-form network.static Ret.pfqnMVAstatic Ret.pfqnMVAstatic Ret.pfqnMVAMean Value Analysis (MVA) Algorithm for closed Product-Form Queueing Networks.static Ret.pfqnMVAMean Value Analysis with the interlocked-flow correction of Franks (1999), Eq.static Pfqn_mva_interval.ResultPfqn_mva_interval.pfqn_mva_interval(Matrix L, double N, double Z) Convenience overload for a thin population and think time.static Pfqn_mva_interval.ResultPfqn_mva_interval.pfqn_mva_interval(Matrix L, Matrix N, Matrix Z) Exact hull of single-class MVA over an input box.static Ret.pfqnMVACMVAC algorithm for a closed product-form network with no infinite-server center.static Ret.pfqnMVACMVAC (Mean Value Analysis by Chain) algorithm for closed multichain product-form queueing networks composed of single-server fixed-rate (SSFR) queues and infinite-server (IS) centers.static Ret.pfqnMVAstatic Ret.pfqnMVAGeneral purpose script to handle mixed Query Networks with multiserver nodes.static Ret.pfqnMVAPfqn_mvams_ilock.pfqn_mvams_ilock(Matrix lambda, Matrix L, Matrix N, Matrix Z, Matrix mi, Matrix S, Matrix IL) AsPfqn_mvams.pfqn_mvams(jline.util.matrix.Matrix, jline.util.matrix.Matrix, jline.util.matrix.Matrix, jline.util.matrix.Matrix, jline.util.matrix.Matrix, jline.util.matrix.Matrix), but carrying the interlock matrix IL through toPfqn_mva_ilock.static Ret.pfqnMVAPfqn_mvamx.pfqn_mvamx(Matrix lambda, Matrix D, Matrix N, Matrix mi) static Ret.pfqnMVAMean Value Analysis (MVA) method for open and mixed queueing networks with no multi-server nodes.static Pfqn_mvasjn.ResultPfqn_mvasjn.pfqn_mvasjn(Matrix L, Matrix N, Matrix Z, Matrix scv, int[] sjnset, Matrix V, SjnOptions options) Mean value analysis with shortest-job-next stations, over the whole population lattice.static Pfqn_nintmva.ResultPfqn_nintmva.pfqn_nintmva(Matrix L, double N, double Z) MVA recursion from the fractional base.static Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic double[]Level-`level` Performance Bound Hierarchy throughput bounds.static double[]Iterative proportional bounds PB(k).static Ret.pfqnAMVAstatic Ret.pfqnAMVAstatic Ret.pfqnAMVAPfqn_qsa.pfqn_qsa(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter) static Ret.pfqnAMVAPfqn_qsa.pfqn_qsa(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter, int levels, Matrix QN0) static doublePfqn_qzgblow.pfqn_qzgblow(Matrix L, double N, double Z, int i) Computes the lower Geometric Bound (GB) for the queue length of the given closed single-class queueing networksstatic doublePfqn_qzgbup.pfqn_qzgbup(Matrix L, double N, double Z, int i) Computes the upper Geometric Bound (GB) for the queue length of the given closed single-class queueing networks.static Ret.pfqnAMVAPfqn_scat.pfqn_scat(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type) static Ret.pfqnAMVAPfqn_scat.pfqn_scat(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter) static Ret.pfqnAMVAPfqn_scat.pfqn_scat(Matrix L, Matrix N, Matrix Z, SchedStrategy[] type, double tol, int maxiter, Matrix QN0) static double[]Bracket on the total throughput and the per-device utilizations of the UNKNOWN multiclass system whose single-class counterpart has demand vector L at population N.static Ret.pfqnAMVASchmidtPfqn_schmidt_amva.pfqn_schmidt(Matrix rates, Matrix N, Matrix S, Matrix v, List<SchedStrategy> sched) static Ret.pfqnAMVASchmidtPfqn_schmidt_amva.pfqn_schmidt_ext(Matrix rates, Matrix N, Matrix S, Matrix v, List<SchedStrategy> sched) static double[]Level-`level` successively-improving throughput/cycle-time bounds.static Pfqn_sqni.PfqnSqniResultstatic double[]SSD multiserver throughput bounds.static Pfqn_tay.ResultConvenience overload with the default tolerance and iteration budget.static Pfqn_tay.ResultTay's arrival-instant AMVA.static doublePfqn_xzabalow.pfqn_xzabalow(Matrix L, double N, double Z) Computes the lower ABA for the throughput of the given closed single-class queueing networksstatic doublePfqn_xzabaup.pfqn_xzabaup(Matrix L, double N, double Z) Computes the upper ABA for the throughput of the given closed single-class queueing networksstatic doublePfqn_xzgsblow.pfqn_xzgsblow(Matrix L, double N, double Z) Computes the lower Geometric Square-Root Bound (GSB) for the throughput of the given closed single-class queueing networks.static doublePfqn_xzgsbup.pfqn_xzgsbup(Matrix L, double N, double Z) Computes the upper Geometric Square-Root Bound (GSB) for the throughput of the given closed single-class queueing networks.Constructors in jline.api.pfqn.mva with parameters of type Matrix -
Uses of Matrix in jline.api.pfqn.nc
Fields in jline.api.pfqn.nc declared as MatrixModifier and TypeFieldDescriptionfinal MatrixPfqn_nre.Result.vsadThe tilt actually used (1xd), null when no saddle point was solved.Methods in jline.api.pfqn.nc that return MatrixModifier and TypeMethodDescriptionstatic MatrixPfqn_le_hessian.pfqn_le_hessian(Matrix L, Matrix N, Matrix u0) Auxiliary function to compute the Hessian used in the logistic expansion method.static Matrixstatic Matrix[][]Pfqn_stdf.pfqn_stdf(Matrix L, Matrix N, Matrix Z, Matrix S, Matrix fcfsNodes, Matrix rates, Matrix tset) Sojourn time distribution function at multiserver FCFS nodes (McKenna 1987 JACM).static Matrix[][]Pfqn_stdf_heur.pfqn_stdf_heur(Matrix L, Matrix N, Matrix Z, Matrix S, Matrix fcfsNodes, Matrix rates, Matrix tset) Heuristic sojourn time distribution analysis at multiserver FCFS nodes.Methods in jline.api.pfqn.nc that return types with arguments of type MatrixModifier and TypeMethodDescriptionstatic SerializableFunction<Matrix,ComplexMatrix> Pfqn_nrl.infradius_h(Matrix L, Matrix N, Matrix alpha) static SerializableFunction<Matrix,ComplexMatrix> Pfqn_nrp.infradius_hnorm(Matrix L, Matrix N, Matrix alpha) Methods in jline.api.pfqn.nc with parameters of type MatrixModifier and TypeMethodDescriptionstatic Ret.pfqnNcXQPfqn_nc.compute_norm_const(Matrix L, Matrix N, Matrix Z, SolverOptions options) static Ret.pfqnNcXQPfqn_nc.compute_norm_const(Matrix L, Matrix N, Matrix Z, SolverOptions options, boolean wantXQ) Auxiliary routine that computes lG after the initial filtering of L, N and Z.static double[]Induced demands theta_k(t) = sum_r t_r L(k,r).static SerializableFunction<Matrix,ComplexMatrix> Pfqn_nrl.infradius_h(Matrix L, Matrix N, Matrix alpha) static SerializableFunction<Matrix,ComplexMatrix> Pfqn_nrp.infradius_hnorm(Matrix L, Matrix N, Matrix alpha) static int[][]Pas_swap2order.pas_swap2order(Matrix G1, Matrix G2, SerializableFunction<Matrix, Double> svc1, SerializableFunction<Matrix, Double> svc2, int[] N0) static Ret.pfqnNcstatic Ret.pfqnNcstatic Ret.pfqnNcstatic Ret.pfqnNcBirman-Kogan saddle point normalizing constant with bottleneck detection.static Ret.pfqnBkLcBirman-Kogan load concealment algorithm (Algorithm 2).static Ret.pfqnNcstatic Ret.pfqnNcKnessl-Tier expansion corrected for the Stirling remainder that steepest descent drops in each class direction.static Ret.pfqnNcBirman-Kogan uniform (van der Waerden) expansion for a single chain.static Ret.pfqnNcstatic Ret.pfqnNcLogistic expansion with an additive (1-log(2*pi)/2) bias correction per Gaussian direction the branch actually takes.static Ret.pfqnNcstatic Ret.pfqnNcstatic Ret.pfqnNcstatic Ret.pfqnNcstatic Ret.pfqnNcstatic Ret.pfqnNcstatic Ret.pfqnNcPfqn_clw.pfqn_clw(Matrix L, Matrix N, Matrix Z, Matrix m, Matrix lpar, Matrix gampar, Pfqn_clw.Options opt) static Ret.pfqnNcPfqn_clw_lld.pfqn_clw_lld(Matrix L, Matrix N) static Ret.pfqnNcPfqn_clw_lld.pfqn_clw_lld(Matrix L, Matrix N, Matrix Z) static Ret.pfqnNcPfqn_clw_lld.pfqn_clw_lld(Matrix L, Matrix N, Matrix Z, Matrix mu) static Ret.pfqnNcstatic doublePfqn_comom.pfqn_comom(Matrix L, Matrix N, Matrix Z, double atol) static Ret.pfqnComomrmPfqn_comomrm.pfqn_comomrm(Matrix L, Matrix N, Matrix Z, Integer m, double atol) Compute the normalizing constant of a repairmen model using COMOMstatic Ret.pfqnComomrmMsPfqn_comomrm_ms.pfqn_comomrm_ms(Matrix L, Matrix N, Matrix Z, int m) Four-argument form.static Ret.pfqnComomrmMsPfqn_comomrm_ms.pfqn_comomrm_ms(Matrix L, Matrix N, Matrix Z, int m, int S) Compute the normalizing constant of a multiserver repairman model using CoMoM.static doublePfqn_comomrm_orig.pfqn_comomrm_orig(Matrix L, Matrix N, Matrix Z) static doublePfqn_comomrm_orig.pfqn_comomrm_orig(Matrix L, Matrix N, Matrix Z, double atol) static Ret.pfqnNcCubature method to compute the normalizing constant of a load-independent closed queueing network modelstatic Ret.pfqnNcCubature method to compute the normalizing constant of a load-independent closed queueing network modelstatic doublePfqn_cub.pfqn_cub_evals(int M, int order, Matrix Z) Number of integrand evaluations pfqn_cub performs at this order.static Pfqn_dnc.ResultNormalizing constant and throughput at a real-valued population.static Pfqn_explicit.ResultPfqn_explicit.pfqn_explicit(Matrix L, Matrix N) Overload with the documented defaults: machine precision, 'auto', no budget.static Pfqn_explicit.ResultPfqn_explicit.pfqn_explicit(Matrix L, Matrix N, double tol, String method, double maxloss) Explicit closed-form normalizing constant of a multiclass closed network.static Ret.pfqnNcPfqn_gerasimov.pfqn_gerasimov(Matrix L, Matrix N) Delay-free case.static Ret.pfqnNcPfqn_gerasimov.pfqn_gerasimov(Matrix L, Matrix N, Matrix Z) Defaults: tol = 1e-12, maxterms = 200000.static Ret.pfqnNcPfqn_gerasimov.pfqn_gerasimov(Matrix L, Matrix N, Matrix Z, double tol, int maxterms) Exact normalizing constant of a closed multiclass product-form network by ITERATED RESIDUES of its rational generating function, one class at a time.static doublePfqn_grnmol.pfqn_grnmol(Matrix L, Matrix N) Compute the normalizing constant using Grundmann-Moeller quadraturestatic Ret.pfqnNcPfqn_is.pfqn_is(Matrix L, Matrix N, Matrix Z, SolverOptions options) Importance-sampling estimate of the load-independent normalizing constant.static Ret.pfqnNcKnessl-Tier asymptotic expansion of the normalizing constant using the ray method.static doubleCompute the Laplace approximation for the log normalizing constant.static Ret.pfqnNcPfqn_ld_is.pfqn_ld_is(Matrix L, Matrix N, Matrix Z, Matrix mu, SolverOptions options) Importance-sampling estimate of the load-dependent normalizing constant.static Ret.pfqnNcstatic Ret.pfqnNcLogistic expansion method to compute the normalizing constant.static Ret.pfqnLeFpiPfqn_le_fpi.pfqn_le_fpi(Matrix L, Matrix N) Fixed-point iteration used in the logistic expansion method.static Ret.pfqnLeFpiZPfqn_le_fpiZ.pfqn_le_fpiZ(Matrix L, Matrix N, Matrix Z) Fixed-point iteration used in the logistic expansion method in models with delays.static MatrixPfqn_le_hessian.pfqn_le_hessian(Matrix L, Matrix N, Matrix u0) Auxiliary function to compute the Hessian used in the logistic expansion method.static Ret.pfqnNcstatic Ret.pfqnNcThe corrected logistic expansion (pfqn_ble) and the corrected Knessl-Tier expansion (pfqn_bkt) are ONE estimator, evaluated in M-1 and in R dimensions: with a think time their stationary points are one point in dual coordinates (xi_r = N_r/(Z_r + v u'L_r), the class throughputs of the LE fixed point, and v u_k = 1/(1-U_k), the M/M/1 factor of the KT saddle) and Sylvester's identity exchanges the R x R Hessian determinant for the M x M one, after which every 2 pi cancels.static Ret.pfqnNcstatic Ret.pfqnNcstatic Ret.pfqnNcstatic Ret.pfqnNcLogistic sampling method to compute the normalizing constant.static Ret.pfqnManjunathPfqn_manjunath.pfqn_manjunath(Matrix L, Matrix N) Exact normalizing constant of an unconstrained closed network.static Ret.pfqnManjunathPfqn_manjunath.pfqn_manjunath(Matrix L, Matrix N, Matrix Z) Exact normalizing constant of an unconstrained closed network with delay.static Ret.pfqnManjunathExact normalizing constant of a constrained closed product-form network.static Ret.pfqnManjunathPfqn_manjunath.pfqn_manjunath(Matrix L, Matrix N, Matrix Z, Matrix A, Matrix b, String sense, boolean stats) As above, additionally returning the per-class decomposition whenstatsis set.static Ret.pfqnNcMonte Carlo integration for the normalizing constant.static Ret.pfqnMcmcPfqn_mcmc.pfqn_mcmc(Matrix L, Matrix N, Matrix Z, SolverOptions options) Single-server form: every queueing station serves one job at a time.static Ret.pfqnMcmcMultiserver form, the paper's own selling point (its Tables IV and V).static Ret.pfqnNcPfqn_mmint2.pfqn_mmint2(Matrix L, Matrix N, Matrix Z) static Ret.pfqnNcPfqn_mmint2_gausslaguerre.pfqn_mmint2_gausslaguerre(Matrix L, Matrix N, Matrix Z) static Ret.pfqnNcPfqn_mmint2_gausslaguerre.pfqn_mmint2_gausslaguerre(Matrix L, Matrix N, Matrix Z, int m) static Ret.pfqnNcPfqn_mmint2_gausslegendre.pfqn_mmint2_gausslegendre(Matrix L, Matrix N, Matrix Z, Integer m) Compute the normalizing constant of a repairmen model using Gauss-Legendre integration.static Ret.pfqnNcPfqn_mmsample2.pfqn_mmsample2(Matrix L, Matrix N, Matrix Z, int samples) static Ret.pfqnNcXQNormalizing constant of a closed or mixed product-form network, with the mean values a few methods produce as a by-product.static Ret.pfqnNcXQstatic Ret.pfqnNcSanitizePfqn_nc_sanitize.pfqn_nc_sanitize(Matrix lambda, Matrix L, Matrix N, Matrix Z, double atol) Sanitizes product-form model parameters to avoid degeneracies.static Matrixstatic doubleLogarithm of the normalizing constant of a limited load-dependent model.static Pfqn_nre.ResultPfqn_nre.pfqn_nre_full(Matrix Lin, Matrix N, Matrix Z, Matrix alphaIn, SolverOptions options, Matrix vfix) The full form of the reference's four outputs, named alike in the native python and C++ ports: the constant, the saddlepoint term alone and the tilt used.static doublestatic doublestatic Ret.pfqnNcPfqn_panacea.pfqn_panacea(Matrix L, Matrix N, Matrix Z) static Ret.pfqnNcPfqn_panacea.pfqn_panacea(Matrix L, Matrix N, Matrix Z, SolverOptions options) Compute the PANACEA approximationstatic doublePermanent of the square matrix A.static doublePermanent of the matrix whose column j is column j of A repeated m[j] times, so that sum(m) equals the number of rows of A.static doublePfqn_pff_delay.pfqn_pff_delay(Matrix Z, Matrix n) Compute the product-form factor relatively to a Delay station.static Ret.pfqnProcomomPfqn_procomom.pfqn_procomom(Matrix L, Matrix N) static Ret.pfqnProcomomPfqn_procomom.pfqn_procomom(Matrix L, Matrix N, Matrix Z) static Ret.pfqnProcomomPfqn_procomom.pfqn_procomom(Matrix L, Matrix N, Matrix Z, double atol) static Ret.pfqnNcXQPfqn_propfair.pfqn_propfair(Matrix L, Matrix N, Matrix Z) Compute the proportionally fair allocation approximation.static Ret.pfqnQlenMomentsPfqn_qlen_joint_moments.pfqn_qlen_joint_moments(Matrix L, Matrix N, Matrix Z, int[][] pairs) Joint queue-length moments with the default options and no injected source of normalizing constants.static Ret.pfqnQlenMomentsPfqn_qlen_joint_moments.pfqn_qlen_joint_moments(Matrix L, Matrix N, Matrix Z, int[][] pairs, String route, BiFunction<Matrix, Matrix, Matrix> lgSource, SolverOptions options) Joint queue-length moments of a closed product-form network.static Ret.pfqnRdstatic Ret.pfqnNcPfqn_recal.pfqn_recal(Matrix L, Matrix N) RECAL method to compute the normalizing constant of a load-independent closed queueing network model.static Ret.pfqnNcPfqn_recal.pfqn_recal(Matrix L, Matrix N, Matrix Z) static Ret.pfqnNcPfqn_recal.pfqn_recal(Matrix L, Matrix N, Matrix Z, Matrix m0) static Pfqn_rgf.ResultExact normalizing constant by convolution of per-node generating-function sequences.static Pfqn_rgfmc.ResultPfqn_rgfmc.pfqn_rgfmc(Matrix L, double[] N, double[] Z) Exact multiclass normalizing constant by recursion on generating functions.static Pfqn_rgfmc.ResultPfqn_rgfmc.pfqn_rgfmc(Matrix L, double[] N, double[] Z, double tol, int maxterms, double maxcancel) static Matrix[][]Pfqn_stdf.pfqn_stdf(Matrix L, Matrix N, Matrix Z, Matrix S, Matrix fcfsNodes, Matrix rates, Matrix tset) Sojourn time distribution function at multiserver FCFS nodes (McKenna 1987 JACM).static Matrix[][]Pfqn_stdf_heur.pfqn_stdf_heur(Matrix L, Matrix N, Matrix Z, Matrix S, Matrix fcfsNodes, Matrix rates, Matrix tset) Heuristic sojourn time distribution analysis at multiserver FCFS nodes.static StringThe side the estimator is computed on: "kt" when R <= M or a class self-loops (one nonzero demand and no think time, which pfqn_kt extracts exactly), "le" otherwise.Method parameters in jline.api.pfqn.nc with type arguments of type MatrixModifier and TypeMethodDescriptionstatic int[][]Pas_swap2order.pas_swap2order(Matrix G1, Matrix G2, SerializableFunction<Matrix, Double> svc1, SerializableFunction<Matrix, Double> svc2, int[] N0) static Ret.pfqnQlenMomentsPfqn_qlen_joint_moments.pfqn_qlen_joint_moments(Matrix L, Matrix N, Matrix Z, int[][] pairs, String route, BiFunction<Matrix, Matrix, Matrix> lgSource, SolverOptions options) Joint queue-length moments of a closed product-form network.static Ret.pfqnQlenMomentsPfqn_qlen_joint_moments.pfqn_qlen_joint_moments(Matrix L, Matrix N, Matrix Z, int[][] pairs, String route, BiFunction<Matrix, Matrix, Matrix> lgSource, SolverOptions options) Joint queue-length moments of a closed product-form network.static Ret.pfqnQlenMomentsPfqn_qlen_joint_moments.pfqn_qlen_joint_moments(Matrix L, Matrix N, Matrix Z, int[][] pairs, String route, BiFunction<Matrix, Matrix, Matrix> lgSource, SolverOptions options) Joint queue-length moments of a closed product-form network. -
Uses of Matrix in jline.api.pfqn.sens
Methods in jline.api.pfqn.sens that return MatrixModifier and TypeMethodDescriptionstatic MatrixPfqn_sens_mom.perClassGroups(int R) The class-to-group map that puts one class per group,1..R, which yields genuinely per-class moments (Akyildiz-Strelen Theorem 1 with T = {r}).Methods in jline.api.pfqn.sens with parameters of type MatrixModifier and TypeMethodDescriptionstatic Pfqn_hst.ResultConvenience overload analysing the bottleneck station.static Pfqn_hst.ResultHST robustness certificate.static Ret.pfqnSensstatic Ret.pfqnSensExact derivatives of {X, Q, U, R} w.r.t.static Ret.pfqnSensLdmxEcPfqn_sens_ldmx_ec.pfqn_sens_ldmx_ec(Matrix lambda, Matrix D, Matrix mu) Computes the effective capacity terms EC, E and Eprime of the mixed load-dependent MVA of Bruell-Balbo-Afshari, exactly asPfqn_ldmx_ecdoes, and additionally their exact analytic derivatives with respect to the open-class load Lo(i) of each station.static Ret.pfqnSensLinearizerPfqn_sens_linearizer.pfqn_sens_linearizer(Matrix L, Matrix N) static Ret.pfqnSensLinearizerPfqn_sens_linearizer.pfqn_sens_linearizer(Matrix L, Matrix N, Matrix Z) Uses the termination test of the reference and 200 CORE iterations.static Ret.pfqnSensLinearizerPfqn_sens_linearizer.pfqn_sens_linearizer(Matrix L, Matrix N, Matrix Z, double tol, int maxiter) Approximate moments E[Q_i], Var[Q_i], Cov[Q_i,Q_j], E[Q_i^2] and E[Q_i^3] of the per-station total queue lengths of a closed product-form queueing network, by the LINEARIZER-2 / LINEARIZER-3 algorithms of the reference (Section 5).static Ret.pfqnSensMomPfqn_sens_mom.pfqn_sens_mom(Matrix L, Matrix N) static Ret.pfqnSensMomPfqn_sens_mom.pfqn_sens_mom(Matrix L, Matrix N, Matrix Z) static Ret.pfqnSensMomPfqn_sens_mom.pfqn_sens_mom(Matrix L, Matrix N, Matrix Z, Matrix mi) The per-station totals, i.e.static Ret.pfqnSensMomExact moments E[Q_i], E[Q_i^2], E[Q_i^3] and the covariances Cov[Q_i,Q_j] of the TOTAL queue lengths Q_i = sum_r n(i,r) of a closed product-form (BCMP) queueing network.static Ret.pfqnSensMvaPfqn_sens_mva.pfqn_sens_mva(Matrix L, Matrix N) static Ret.pfqnSensMvaPfqn_sens_mva.pfqn_sens_mva(Matrix L, Matrix N, Matrix Z) static Ret.pfqnSensMvaPfqn_sens_mva.pfqn_sens_mva(Matrix L, Matrix N, Matrix Z, Matrix mi) Exact second moments (variances and per-station covariances) of the queue lengths of a closed product-form (BCMP) queueing network.static Ret.pfqnSensMvaldmxPfqn_sens_mvaldmx.pfqn_sens_mvaldmx(Matrix lambda, Matrix D, Matrix N, Matrix Z) static Ret.pfqnSensMvaldmxPfqn_sens_mvaldmx.pfqn_sens_mvaldmx(Matrix lambda, Matrix D, Matrix N, Matrix Z, Matrix mu, Matrix S) Exact second moments (variances and covariances) of the queue lengths of a mixed open/closed product-form queueing network with limited load-dependent service rates.static Ret.pfqnSensResptPfqn_sens_respt.pfqn_sens_respt(Matrix S, Matrix V, Matrix N) static Ret.pfqnSensResptPfqn_sens_respt.pfqn_sens_respt(Matrix S, Matrix V, Matrix N, Matrix Z) static Ret.pfqnSensResptstatic Ret.pfqnSensResptExact raw moments E[W_(i,l)^t], t = 1..tmax, of the sojourn time of a class-l job at an FCFS b-server center i of a closed product-form queueing network, together with the variance of that sojourn time. -
Uses of Matrix in jline.api.qsys
Fields in jline.api.qsys declared as MatrixModifier and TypeFieldDescriptionfinal MatrixQsysBmapM1Result.AA = A0 + A1 + sum_k Bk[k], the phase process of the randomized chain.final MatrixQsysBmapM1Result.A0Randomized block A0 = (mu/q)I: a service completion, level down by one.final MatrixQsysBmapM1Result.A1Randomized block A1 = (1/q)(D0 - mu*I) + I: level unchanged.final MatrixQsysBmapM1Result.alphaStationary vector of A.final MatrixQsysBmapM1Result.B0Boundary local block B0 = (1/q)D0 + I, used at level 0 where no service can complete.final Matrix[]QsysBmapM1Result.BkRandomized blocks Bk[k] = (1/q)D_k, raising the level by k; index 1..K.final MatrixQsysBmapM1Result.GMinimal non-negative solution of G = A0 + A1*G + sum_k Bk[k]*G^(k+1).QsysMapG1kPerflowResult.lambda1 x N, arrival rate of flow n [pkts/s].QsysMmapG1kResult.lambda1 x R, arrival rate of class k [pkts/s].final MatrixQsysBmapM1Result.levelProbLevel probabilities pi_n as rows (level 0 first).QsysMapG1kPerflowResult.lossRatio1 x N, loss ratio of flow n, in [0,1].QsysMmapG1kResult.lossRatio1 x R, loss ratio of class k, in [0,1].QsysMapG1kPerflowResult.p01 x N, P(buffer empty) in the n-th model.QsysMapG1kResult.p0vec1 x M, P(level = 0, phase j), summing to p0.final MatrixQsysServiceLaw.phAlphaPH initial probability row, 1 x p.final MatrixQsysServiceLaw.phTPH subgenerator, p x p.final MatrixQsysRetrialResult.piQsysMapG1kPerflowResult.pK1 x N, P(buffer full) in the n-th model.QsysMapG1kResult.pKvec1 x M, P(level = K, phase j), summing to pK.QsysMmapG1kResult.pKvec1 x M, P(buffer full, phase j).QsysMapG1kResult.plevel1 x (K+1), P(level = l), l = 0..K.QsysMmapG1kResult.plevel1 x (K+1), P(level = l).QsysMapG1kResult.sigmaStationary law of the embedded chain, 1 x K*M.final MatrixQsysBmapM1Result.thetaStationary vector of the BMAP phase process, generator sum_k D_k.QsysMapG1kPerflowResult.throughput1 x N, throughput of flow n [pkts/s].QsysMmapG1kResult.throughput1 x R, throughput of class k [pkts/s].Methods in jline.api.qsys that return MatrixModifier and TypeMethodDescriptionQsysMmapGk1Result.getIdleVector()QsysMmapGk1Result.getLambdas()QsysMmapGk1Result.getMeanSojournTime()QsysMmapGk1Result.getMeanWaitingTime()QsysRetrialResult.getPi()QsysMapDcResult.getQueueLengthDist()QsysMapPhcResult.getQueueLengthDist()QsysMapPhResult.getQueueLengthDist()QsysMapPhResult.getQueueLengthMoments()QsysMapPhResult.getSojournTimeMoments()QsysMmapGk1Result.getWaitCDF()QsysMapPhcResult.getWaitingTimeCCDF()QsysMapDcResult.getWaitingTimeDist()QsysMapPhcResult.getWaitingTimeMoments()QsysMapPhcResult.getWaitingTimePoints()QsysMmapGk1Result.getWaitMoments()QsysMmapGk1Result.getWaitPoints()static MatrixQsys_mm1_dps.qsys_mm1_dps(Matrix lambda, Matrix mu, Matrix w) Solves the M/M/1-DPS queue exactly on a truncated state space.Methods in jline.api.qsys with parameters of type MatrixModifier and TypeMethodDescriptionstatic QsysServiceLawPhase type with initial row ALPHA and subgenerator T.static QsysBmapM1ResultQsys_bmapm1.qsys_bmapm1(Matrix[] D, double mu) Analyzes a BMAP/M/1 queue with the default uniformization constant and adaptive level truncation.static QsysBmapM1ResultQsys_bmapm1.qsys_bmapm1(Matrix[] D, double mu, double uniformization, int maxIter, double tolerance, int maxLevel, double tailTolerance) Analyzes a BMAP/M/1 queue.static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int R) static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int[] R) static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int[] R, int maxLevel) static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int[] R, int maxLevel, double tolerance, boolean verbose) static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int[] R, int maxLevel, double tolerance, boolean verbose, double tailTolerance, double maxDim, int maxBlockSize) Analyzes a BMAP/PH/N/N bufferless retrial queue with admission control.static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int[] R, int maxLevel, double tolerance, boolean verbose, double tailTolerance, double maxDim, int maxBlockSize, int retrialPolicy) As above, with an explicit retrial policy.static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int R, int maxLevel) static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int R, int maxLevel, double tolerance, boolean verbose) static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int R, int maxLevel, double tolerance, boolean verbose, double tailTolerance, double maxDim, int maxBlockSize) static QsysRetrialResultQsys_bmapphnn_retrial.qsys_bmapphnn_retrial(Matrix[] D, Matrix beta, Matrix S, int N, double alpha, double gamma, double p, int R, int maxLevel, double tolerance, boolean verbose, double tailTolerance, double maxDim, int maxBlockSize, int retrialPolicy) As above with a scalar admission threshold and an explicit retrial policy.static QsysMapDcResultQsys_mapd1.qsys_mapd1(Matrix D0, Matrix D1, double s) static QsysMapDcResultQsys_mapd1.qsys_mapd1(Matrix D0, Matrix D1, double s, int maxNumComp) static QsysMapDcResultQsys_mapd1.qsys_mapd1(Matrix D0, Matrix D1, double s, int maxNumComp, int numSteps) Analyzes a MAP/D/1 queue (single server with deterministic service).static QsysMapDcResultQsys_mapdc.qsys_mapdc(Matrix D0, Matrix D1, double s, int c) static QsysMapDcResultQsys_mapdc.qsys_mapdc(Matrix D0, Matrix D1, double s, int c, int maxNumComp) static QsysMapDcResultQsys_mapdc.qsys_mapdc(Matrix D0, Matrix D1, double s, int c, int maxNumComp, int numSteps) static QsysMapDcResultQsys_mapdc.qsys_mapdc(Matrix D0, Matrix D1, double s, int c, int maxNumComp, int numSteps, int verbose) Analyzes a MAP/D/c queue (multi-server with deterministic service).static QsysMapPhResultQsys_mapg1.qsys_mapg1(Matrix D0, Matrix D1, double[] serviceMoments) static QsysMapPhResultQsys_mapg1.qsys_mapg1(Matrix D0, Matrix D1, double[] serviceMoments, int numQLMoms) static QsysMapPhResultQsys_mapg1.qsys_mapg1(Matrix D0, Matrix D1, double[] serviceMoments, int numQLMoms, int numQLProbs) static QsysMapPhResultQsys_mapg1.qsys_mapg1(Matrix D0, Matrix D1, double[] serviceMoments, int numQLMoms, int numQLProbs, int numSTMoms) Analyzes a MAP/G/1 queue.static QsysMapPhResultQsys_mapg1.qsys_mapg1(Matrix D0, Matrix D1, double meanService, double cvService) Analyzes a MAP/G/1 queue with service time specified as mean and CV.static QsysMapG1kResultQsys_mapg1k.qsys_mapg1k(Matrix D0, Matrix D1, QsysServiceLaw svc, int K) qsys_mapg1k with the reference defaults tol = 1e-12, nmax = 200000.static QsysMapG1kResultQsys_mapg1k.qsys_mapg1k(Matrix D0, Matrix D1, QsysServiceLaw svc, int K, double tol, int nmaxCap) MAP/G/1/K with tail drop.static QsysMapPhResultQsys_mapm1.qsys_mapm1(Matrix D0, Matrix D1, double mu) static QsysMapPhResultQsys_mapm1.qsys_mapm1(Matrix D0, Matrix D1, double mu, int maxNumComp) Analyzes a MAP/M/1 queue (single server with exponential service).static QsysMapPhResultQsys_mapmap1.qsys_mapmap1(Matrix C0, Matrix C1, Matrix D0, Matrix D1) static QsysMapPhResultQsys_mapmap1.qsys_mapmap1(Matrix C0, Matrix C1, Matrix D0, Matrix D1, int numQLMoms, int numQLProbs, int numSTMoms) Analyzes a MAP/MAP/1 queue.static QsysMapPhResultQsys_mapmc.qsys_mapmc(Matrix D0, Matrix D1, double mu, int c) static QsysMapPhResultQsys_mapmc.qsys_mapmc(Matrix D0, Matrix D1, double mu, int c, String mode, int maxNumComp) static QsysMapPhResultQsys_mapmc.qsys_mapmc(Matrix D0, Matrix D1, double mu, int c, String mode, int maxNumComp, int verbose) Analyzes a MAP/M/c queue (multi-server with exponential service).static QsysMapPhResultQsys_mapph1.qsys_mapph1(Matrix D0, Matrix D1, Matrix sigma, Matrix S) static QsysMapPhResultQsys_mapph1.qsys_mapph1(Matrix D0, Matrix D1, Matrix sigma, Matrix S, int numQLMoms) static QsysMapPhResultQsys_mapph1.qsys_mapph1(Matrix D0, Matrix D1, Matrix sigma, Matrix S, int numQLMoms, int numQLProbs) static QsysMapPhResultQsys_mapph1.qsys_mapph1(Matrix D0, Matrix D1, Matrix sigma, Matrix S, int numQLMoms, int numQLProbs, int numSTMoms) Analyzes a MAP/PH/1 queue.static QsysMapPhcResultQsys_mapphc.qsys_mapphc(Matrix D0, Matrix D1, Matrix alpha, Matrix S, int c) static QsysMapPhcResultQsys_mapphc.qsys_mapphc(Matrix D0, Matrix D1, Matrix alpha, Matrix S, int c, int maxNumComp, int numWMoms, Matrix wPoints) static Ret.qsys_prioQsys_mg1_fb.qsys_mg1_fb(Matrix lambda, Matrix mu, Matrix cs) Analyzes an M/G/1 queueing system with FB (Feedback/LAS) scheduling.static Ret.qsys_prioQsys_mg1_lrpt.qsys_mg1_lrpt(Matrix lambda, Matrix mu, Matrix cs) Analyzes an M/G/1 queueing system with LRPT (Longest Remaining Processing Time) scheduling.static Ret.qsys_prioQsys_mg1_prio.qsys_mg1_prio(Matrix lambda, Matrix mu, Matrix cs) Analyzes an M/G/1 queueing system with non-preemptive (Head-of-Line) priorities.static Ret.qsys_prioQsys_mg1_psjf.qsys_mg1_psjf(Matrix lambda, Matrix mu, Matrix cs) Analyzes an M/G/1 queueing system with PSJF (Preemptive Shortest Job First) scheduling.static Ret.qsys_prioQsys_mg1_setf.qsys_mg1_setf(Matrix lambda, Matrix mu, Matrix cs) Analyzes an M/G/1 queueing system with SETF (non-preemptive FB) scheduling.static Ret.qsys_prioQsys_mg1_srpt.qsys_mg1_srpt(Matrix lambda, Matrix mu, Matrix cs) static MatrixQsys_mm1_dps.qsys_mm1_dps(Matrix lambda, Matrix mu, Matrix w) Solves the M/M/1-DPS queue exactly on a truncated state space.static QsysMmapG1kResultQsys_mmapg1k.qsys_mmapg1k(Matrix D0, List<Matrix> D1c, QsysServiceLaw svc, int K) qsys_mmapg1k with the reference defaults tol = 1e-12, nmax = 200000.static QsysMmapG1kResultQsys_mmapg1k.qsys_mmapg1k(Matrix D0, List<Matrix> D1c, QsysServiceLaw svc, int K, double tol, int nmaxCap) MMAP[K]/G/1/K with tail drop.static QsysMmapGk1ResultQsys_mmapgk1.qsys_mmapgk1(MatrixCell MMAP, List<Distribution> svc, Matrix wPoints, int numWMoms, double tol, int iterMax) static QsysMapDcResultAnalyzes a PH/D/c queue.static QsysMapPhResultAnalyzes a PH/PH/1 queue with exponential service (simplified E/M/1).static QsysMapPhResultAnalyzes a PH/M/c queue.static QsysMapPhResultQsys_phph1.qsys_phph1(Matrix alpha, Matrix T, Matrix beta, Matrix S) static QsysMapPhResultQsys_phph1.qsys_phph1(Matrix alpha, Matrix T, Matrix beta, Matrix S, int numQLMoms) static QsysMapPhResultQsys_phph1.qsys_phph1(Matrix alpha, Matrix T, Matrix beta, Matrix S, int numQLMoms, int numQLProbs) static QsysMapPhResultQsys_phph1.qsys_phph1(Matrix alpha, Matrix T, Matrix beta, Matrix S, int numQLMoms, int numQLProbs, int numSTMoms) Analyzes a PH/PH/1 queue.Method parameters in jline.api.qsys with type arguments of type MatrixModifier and TypeMethodDescriptionstatic QsysMmapG1kResultQsys_mmapg1k.qsys_mmapg1k(Matrix D0, List<Matrix> D1c, QsysServiceLaw svc, int K) qsys_mmapg1k with the reference defaults tol = 1e-12, nmax = 200000.static QsysMmapG1kResultQsys_mmapg1k.qsys_mmapg1k(Matrix D0, List<Matrix> D1c, QsysServiceLaw svc, int K, double tol, int nmaxCap) MMAP[K]/G/1/K with tail drop.Constructors in jline.api.qsys with parameters of type MatrixModifierConstructorDescriptionQsysBmapM1Result(Matrix theta, double lambda, double rho, double q, Matrix A0, Matrix A1, Matrix B0, Matrix[] Bk, Matrix A, Matrix alpha, Matrix G, double drift, double decayRate, Matrix levelProb, double pi0, double meanQueueLength, double utilization, double throughput, int truncLevel, double truncError, String analyzer) QsysMapDcResult(double meanQueueLength, double meanWaitingTime, double meanSojournTime, double utilization, Matrix queueLengthDist, Matrix waitingTimeDist, String analyzer) QsysMapPhcResult(double meanQueueLength, double meanWaitingTime, double meanSojournTime, double utilization, Matrix queueLengthDist, Matrix waitingTimeMoments, Matrix waitingTimeCCDF, Matrix waitingTimePoints, double probWait, int phaseCount, String analyzer) QsysMapPhResult(double meanQueueLength, double meanWaitingTime, double meanSojournTime, double utilization, Matrix queueLengthDist, Matrix queueLengthMoments, Matrix sojournTimeMoments, String analyzer) QsysMmapGk1Result(Matrix lambdas, double arrivalRate, double utilization, Matrix idleVector, Matrix waitMoments, Matrix meanWaitingTime, Matrix meanSojournTime, double meanQueueLength, Matrix waitCDF, Matrix waitPoints, String analyzer) QsysRetrialResult(double L_orbit, double N_server, double L_system, double Utilization, double Throughput, double P_idle, double P_empty_orbit, double P_empty_system, Matrix pi, int truncLevel, double truncError, String analyzer) QsysRetrialResult(double L_orbit, double N_server, double L_system, double Utilization, double Throughput, double P_idle, double P_empty_orbit, double P_empty_system, Matrix pi, int truncLevel, String analyzer) -
Uses of Matrix in jline.api.retrieval
Fields in jline.api.retrieval declared as MatrixModifier and TypeFieldDescriptionRetrieval_mva.Result.pdh(r+1) x n, delayed-hit probabilities phi_si, s = 0..r.Retrieval_mva.Result.phith x n, hit ratios pi_ij.Retrieval_mva.Result.pmiss1 x n, miss ratios pi_i0.Methods in jline.api.retrieval with parameters of type MatrixModifier and TypeMethodDescriptionstatic Retrieval_mva.ResultRetrieval_mva.retrieval_mva(int[] m, double[] lambda, Matrix eta, Matrix gamma) -
Uses of Matrix in jline.api.sim
Methods in jline.api.sim with parameters of type MatrixModifier and TypeMethodDescriptionSimRunlength.sim_asymvar_ctmc(Matrix A, double[] f) Asymptotic variance of a reward on a CTMC: 2 sum_x pi(x)g(x)d(x) with g = f - E_pi[f] and A d = -g.SimRunlength.sim_runlength_plan(Matrix means, Matrix ciHalfWidth, double samplesUsed, double relPrecision, double confidence) How long a simulation run should have been, from the one it already did. -
Uses of Matrix in jline.api.sn
Fields in jline.api.sn declared as MatrixModifier and TypeFieldDescriptionfinal MatrixSnMapModulation.MapModulation.D0Hidden-phase generator part D0.SnBuildFjSyncMap.FjSyncMap.nodeSyncnodeSync(joinIdx, srcIdx) = groupId; groupId > 0 means srcIdx belongs to sync group groupId at joinIdx, 0 means srcIdx is an independent flow.final MatrixSnPnFiringRates.Ret.ratesFiring rate per (transition, mode) pair, as a column vector.Fields in jline.api.sn with type parameters of type MatrixModifier and TypeFieldDescriptionSnMapModulation.MapModulation.D1Per-class D1 blocks, aligned withSnMapModulation.MapModulation.classes.Methods in jline.api.sn that return MatrixModifier and TypeMethodDescriptionSnMapModulation.MapModulation.phaseGenerator()static MatrixSnGetArvRFromTput.snGetArvRFromTput(NetworkStruct sn, Matrix TN, AvgHandle TH) Calculates the average arrival rates at each station from the network throughputs.static MatrixSnGetNodeArvRFromTput.snGetNodeArvRFromTput(NetworkStruct sn, Matrix TN, AvgHandle TH, Matrix AN) static MatrixSnGetNodeTputFromTput.snGetNodeTputFromTput(NetworkStruct sn, Matrix TN, AvgHandle TH, Matrix ANn) static MatrixSnGetResidTFromRespT.snGetResidTFromRespT(NetworkStruct sn, Matrix RNclass, AvgHandle WH) Calculates the residence times at each station from the response times.static MatrixSnInterlockChain.snInterlockChain(NetworkStruct sn, Matrix ILclass) static MatrixSnJoinDroprate.snJoinDroprate(NetworkStruct sn, Matrix TN, Matrix AN) A Join is the one station where the loss identity ArvR - Tput does NOT hold, because the two rates are in different units: AN counts the SIBLINGS offered to the join (N per parent job) while TN counts the PARENT jobs released by it (one per synchronisation).Methods in jline.api.sn that return types with arguments of type MatrixModifier and TypeMethodDescriptionSnFjVisitsSpn.snFjVisitsSpn(NetworkStruct sn) Compute fork-join node visit ratios via auxiliary SPN models.static Map<StatefulNode,Matrix> SnGetStateAggr.snGetStateAggr(NetworkStruct sn) Aggregates the state of the network.SnRtnodesToRtorig.snRtnodesToRtorig(NetworkStruct sn) Converts routing matrices from nodes to original format, specifically handling class switching nodes.SnRtnodesToRtorig.snRtnodesToRtorig(NetworkStruct sn) Converts routing matrices from nodes to original format, specifically handling class switching nodes.SnRtStations.snRtStations(NetworkStruct sn) Station-to-station routing probabilities and per-station visits.SnRtStations.snRtStations(NetworkStruct sn) Station-to-station routing probabilities and per-station visits.Methods in jline.api.sn with parameters of type MatrixModifier and TypeMethodDescriptionstatic doubleSnCompatRate.snCompatRate(Matrix compat, double[] counts, double[] rates, double[] n) Rate cleared by the pools when the operands innare present.static doubleSnCompatRate.snCompatScaling(Matrix compat, double[] counts, double[] rates, double[] n) Rate scaling eta(n) a compatibility declaration imposes on its station.SnDeaggregateChainResults.snDeaggregateChainResults(NetworkStruct sn, Matrix Lchain, Matrix ST, Matrix STchain, Matrix Vchain, Matrix alpha, Matrix Qchain, Matrix Uchain, Matrix Rchain, Matrix Tchain, Matrix Cchain, Matrix Xchain) Calculate class-based performance metrics for a queueing network based on performance measures of its chains.static MatrixSnGetArvRFromTput.snGetArvRFromTput(NetworkStruct sn, Matrix TN, AvgHandle TH) Calculates the average arrival rates at each station from the network throughputs.static MatrixSnGetNodeArvRFromTput.snGetNodeArvRFromTput(NetworkStruct sn, Matrix TN, AvgHandle TH, Matrix AN) static MatrixSnGetNodeTputFromTput.snGetNodeTputFromTput(NetworkStruct sn, Matrix TN, AvgHandle TH, Matrix ANn) static MatrixSnGetResidTFromRespT.snGetResidTFromRespT(NetworkStruct sn, Matrix RNclass, AvgHandle WH) Calculates the residence times at each station from the response times.static MatrixSnInterlockChain.snInterlockChain(NetworkStruct sn, Matrix ILclass) static booleanSnIsPhaseType.snIsPhaseType(MatrixCell proc, Matrix pie) Tests a (D0, D1, ...) representation and its entry vector.static MatrixSnJoinDroprate.snJoinDroprate(NetworkStruct sn, Matrix TN, Matrix AN) A Join is the one station where the loss identity ArvR - Tput does NOT hold, because the two rates are in different units: AN counts the SIBLINGS offered to the join (N per parent job) while TN counts the PARENT jobs released by it (one per synchronisation).static doubleSnOpenProbTerms.snOpenProbTerms(NetworkStruct sn, Matrix Q, Matrix U, Matrix nir, int ist) Log-probability contributed by the open classes at one station.static voidSnPnAvgRates.snPnAvgRates(NetworkStruct sn, Matrix QN, Matrix TN, Matrix AN, Matrix RN) Rescales the Place rows of TN, AN and RN to tokens, in place.static SnPnFiringRates.RetSnPnFiringRates.snPnFiringRates(NetworkStruct sn, Matrix TN, boolean tputIsTokens) Recovers the per-mode firing rates from the Place throughputs.static NetworkStructSnRefreshVisits.snRefreshVisits(NetworkStruct sn, Matrix chains, Matrix rt, Matrix rtnodes) static boolean[]SnRegionMembers.snRegionMembers(NetworkStruct sn, int f, Matrix Rmat, Matrix memMat) Station membership mask of finite capacity region f.static NetworkStructSnSetArrival.snSetArrivalBatch(NetworkStruct sn, Matrix rates) static NetworkStructSnSetArrival.snSetArrivalBatch(NetworkStruct sn, Matrix rates, Matrix scvs) static NetworkStructSnSetArrival.snSetArrivalBatch(NetworkStruct sn, Matrix rates, Matrix scvs, ModifyMode mode) static NetworkStructSnSetArrival.snSetArrivalBatch(NetworkStruct sn, Matrix rates, Matrix scvs, ModifyMode mode, ValidationLevel validation) static NetworkStructSnSetArrival.snSetArrivalBatch(NetworkStruct sn, Matrix rates, Matrix scvs, ModifyMode mode, ValidationLevel validation, boolean autoRefresh) Sets arrival rates for multiple classes in a single operation.static NetworkStructSnSetPopulation.snSetPopulationBatch(NetworkStruct sn, Matrix njobs) static NetworkStructSnSetPopulation.snSetPopulationBatch(NetworkStruct sn, Matrix njobs, ModifyMode mode) static NetworkStructSnSetPopulation.snSetPopulationBatch(NetworkStruct sn, Matrix njobs, ModifyMode mode, ValidationLevel validation) static NetworkStructSnSetPopulation.snSetPopulationBatch(NetworkStruct sn, Matrix njobs, ModifyMode mode, ValidationLevel validation, boolean autoRefresh) Sets populations for multiple classes in a single operation.static NetworkStructSnSetPriority.snSetPriorityBatch(NetworkStruct sn, Matrix priorities) static NetworkStructSnSetPriority.snSetPriorityBatch(NetworkStruct sn, Matrix priorities, ModifyMode mode) static NetworkStructSnSetPriority.snSetPriorityBatch(NetworkStruct sn, Matrix priorities, ModifyMode mode, ValidationLevel validation) Sets priorities for multiple classes in a single operation.static NetworkStructSnSetRouting.snSetRoutingMatrix(NetworkStruct sn, Matrix rt) static NetworkStructSnSetRouting.snSetRoutingMatrix(NetworkStruct sn, Matrix rt, ModifyMode mode) static NetworkStructSnSetRouting.snSetRoutingMatrix(NetworkStruct sn, Matrix rt, ModifyMode mode, ValidationLevel validation) static NetworkStructSnSetRouting.snSetRoutingMatrix(NetworkStruct sn, Matrix rt, ModifyMode mode, ValidationLevel validation, boolean autoRefresh) Sets the entire routing matrix for stateful nodes.static NetworkStructSnSetRouting.snSetRoutingNodesMatrix(NetworkStruct sn, Matrix rtnodes) static NetworkStructSnSetRouting.snSetRoutingNodesMatrix(NetworkStruct sn, Matrix rtnodes, ModifyMode mode) static NetworkStructSnSetRouting.snSetRoutingNodesMatrix(NetworkStruct sn, Matrix rtnodes, ModifyMode mode, ValidationLevel validation) static NetworkStructSnSetRouting.snSetRoutingNodesMatrix(NetworkStruct sn, Matrix rtnodes, ModifyMode mode, ValidationLevel validation, boolean autoRefresh) Sets the entire routing matrix for all nodes.static NetworkStructSnSetServers.snSetServersBatch(NetworkStruct sn, Matrix nServers) static NetworkStructSnSetServers.snSetServersBatch(NetworkStruct sn, Matrix nServers, ModifyMode mode) static NetworkStructSnSetServers.snSetServersBatch(NetworkStruct sn, Matrix nServers, ModifyMode mode, ValidationLevel validation) Sets the number of servers for multiple stations in a single operation.static NetworkStructSnSetService.snSetServiceBatch(NetworkStruct sn, Matrix rates) static NetworkStructSnSetService.snSetServiceBatch(NetworkStruct sn, Matrix rates, Matrix scvs) static NetworkStructSnSetService.snSetServiceBatch(NetworkStruct sn, Matrix rates, Matrix scvs, ModifyMode mode) static NetworkStructSnSetService.snSetServiceBatch(NetworkStruct sn, Matrix rates, Matrix scvs, ModifyMode mode, ValidationLevel validation) static NetworkStructSnSetService.snSetServiceBatch(NetworkStruct sn, Matrix rates, Matrix scvs, ModifyMode mode, ValidationLevel validation, boolean autoRefresh) Sets service rates for multiple station-class pairs in a single operation.Constructors in jline.api.sn with parameters of type Matrix -
Uses of Matrix in jline.api.snc
Methods in jline.api.snc with parameters of type MatrixModifier and TypeMethodDescriptionstatic SncEnvelopestatic double[]Snc_env_map.snc_env_map(Matrix D0, Matrix D1, double theta) -
Uses of Matrix in jline.api.spn
Fields in jline.api.spn with type parameters of type MatrixModifier and TypeFieldDescriptionSpn_mdd.SpnMode.depMarking-dependent firing-rate multiplier; null for the unit one. -
Uses of Matrix in jline.api.sum
Fields in jline.api.sum declared as MatrixModifier and TypeFieldDescriptionfinal MatrixSum_closed.Result.QNMxR mean queue lengthsfinal MatrixSum_closing.Result.QNMxR mean queue lengths at the original stationsfinal MatrixSum_closed.Result.RNMxR residence times, RN=QN/XNfinal MatrixSum_closing.Result.RNMxR residence times at the original stationsfinal MatrixSum_closing.Result.TN1xR mean response time in the original network, TN=sum(QN)/XNfinal MatrixSum_closed.Result.UNMxR utilizations (per-server for queueing stations, X*L for IS)final MatrixSum_closing.Result.UNMxR utilizations at the original stationsfinal MatrixSum_closed.Result.XN1xR class throughputsfinal MatrixSum_closing.Result.XN1xR class throughputs (open classes approach lambda0 from below)Methods in jline.api.sum with parameters of type MatrixModifier and TypeMethodDescriptionstatic Sum_closed.ResultSummation method for closed queueing networks.static Sum_closing.ResultSum_closing.sum_closing(Matrix lambda0, Matrix scva, Matrix L, Matrix mi, Matrix scv, Matrix N, Matrix Z, double Kclosed, double tol, int maxiter) Closing method for open and mixed networks solved with SUM. -
Uses of Matrix in jline.api.trace
Fields in jline.api.trace declared as MatrixModifier and TypeFieldDescriptionfinal MatrixMtrace_summary.MtraceSummary.B1final MatrixMtrace_summary.MtraceSummary.B2final MatrixMtrace_summary.MtraceSummary.C1final MatrixMtrace_summary.MtraceSummary.C2final MatrixMtrace_summary.MtraceSummary.F1final MatrixMtrace_summary.MtraceSummary.F2final MatrixMtrace_summary.MtraceSummary.Pabfinal MatrixMtrace_summary.MtraceSummary.PcMethods in jline.api.trace that return MatrixModifier and TypeMethodDescriptionstatic Matrix[][]Mtrace_cov.mtrace_cov(double[] T, int[] A) Computes the covariance matrix for multi-type traces.static MatrixMtrace_cross_moment.mtrace_cross_moment(double[] T, int[] L, int k) 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.static MatrixMtrace_forward_moment.mtrace_forward_moment(double[] T, int[] A, int[] orders) static MatrixMtrace_forward_moment.mtrace_forward_moment(double[] T, int[] A, int[] orders, int norm) Computes the forward moments of a marked trace.static MatrixMtrace_iat2counts.mtrace_iat2counts(double[] T, int[] A, double scale) Computes the per-class counting processes of T, i.e., the counts after "scale" units of time from an arrival.static MatrixMtrace_joint.mtrace_joint(double[] T, int[] A, int[] i) 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.static MatrixMtrace_mean.mtrace_mean(double[] trace, int ntypes, int[] type) Computes the mean of a trace, divided by types.static MatrixMtrace_moment.mtrace_moment(double[] T, int[] A, int[] orders) static MatrixMtrace_moment.mtrace_moment(double[] T, int[] A, int[] orders, int after) static MatrixMtrace_moment.mtrace_moment(double[] T, int[] A, int[] orders, int after, int norm) Computes the empirical class-dependent moments of a multi-class trace.static MatrixMtrace_moment_simple.mtrace_moment_simple(double[] T, int[] L, int k) 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.static MatrixMtrace_pc.mtrace_pc(double[] T, int[] C) Computes the probabilities of arrival for each class.static MatrixMtrace_sigma.mtrace_sigma(double[] T, int[] L) Computes the empirical probability of observing a specific 2-element sequence of events, i.e.Constructors in jline.api.trace with parameters of type Matrix -
Uses of Matrix in jline.api.wf
Fields in jline.api.wf declared as MatrixMethods in jline.api.wf that return MatrixModifier and TypeMethodDescriptionWf_pattern_updater.ServiceParameters.getAlpha()Wf_analyzer.WorkflowRepresentation.getLinkMatrix()Wf_pattern_updater.UpdatedWorkflow.getLinkMatrix()Wf_pattern_updater.ServiceParameters.getT()Methods in jline.api.wf with parameters of type MatrixModifier and TypeMethodDescriptionstatic List<Wf_branch_detector.BranchPattern>Wf_branch_detector.detectBranches(Matrix linkMatrix, List<Integer> serviceNodes, List<Integer> joinNodes) Wf_loop_detector.detectLoops(Matrix linkMatrix, List<Integer> serviceNodes, List<Integer> routerNodes) Wf_loop_detector.detectLoops(Matrix linkMatrix, List<Integer> serviceNodes, List<Integer> routerNodes, List<Integer> joinNodes) Wf_parallel_detector.detectParallel(Matrix linkMatrix, List<Integer> serviceNodes, List<Integer> forkNodes, List<Integer> joinNodes) Detect parallel patterns in a workflow network.Wf_sequence_detector.detectSequences(Matrix linkMatrix, List<Integer> serviceNodes) Detect sequence patterns in a workflow network.static doubleWf_loop_detector.getLoopProbability(int serviceNode, Matrix linkMatrix, List<Integer> routerNodes) Wf_loop_detector.getLoopStats(List<Integer> loopNodes, Matrix linkMatrix, List<Integer> routerNodes) Wf_pattern_updater.getUpdateStats(Matrix originalMatrix, Wf_pattern_updater.UpdatedWorkflow updatedWorkflow) Wf_pattern_updater.updatePatterns(Matrix linkMatrix, List<Integer> serviceNodes, List<Integer> forkNodes, List<Integer> joinNodes, List<Integer> routerNodes, Map<Integer, Wf_pattern_updater.ServiceParameters> serviceParams) static booleanWf_branch_detector.validateBranchPattern(Wf_branch_detector.BranchPattern pattern, Matrix linkMatrix) static booleanWf_loop_detector.validateLoopPattern(int loopNode, Matrix linkMatrix, List<Integer> routerNodes) static booleanWf_parallel_detector.validateParallelPattern(List<Integer> pattern, Matrix linkMatrix, List<Integer> forkNodes, List<Integer> joinNodes) static booleanWf_sequence_detector.validateSequence(List<Integer> sequence, Matrix linkMatrix) Validate that a sequence chain is properly connected.Constructors in jline.api.wf with parameters of type MatrixModifierConstructorDescriptionServiceParameters(Matrix alpha, Matrix T) UpdatedWorkflow(Matrix linkMatrix, Map<Integer, Wf_pattern_updater.ServiceParameters> serviceParameters) WorkflowRepresentation(Matrix linkMatrix, List<Integer> serviceNodes, List<Integer> forkNodes, List<Integer> joinNodes, List<Integer> routerNodes, Map<Integer, Wf_pattern_updater.ServiceParameters> serviceParameters) -
Uses of Matrix in jline.bench
Methods in jline.bench that return MatrixModifier and TypeMethodDescriptionstatic MatrixBenchmarkUtils.randGallery(int rows, int cols, int seed) Generate random gallery matrix (similar to MATLAB's randgallery) This creates a random matrix with values in [0,1]Methods in jline.bench with parameters of type MatrixModifier and TypeMethodDescriptionstatic doubleCalculate mean error on sum for queue length metrics Returns mean absolute error relative to sum of each column Similar to MATLAB: mean(abs(exact-approx))/sum(exact)static doubleCalculate Mean Absolute Percentage Error (MAPE) Wrapper for Utils.mape for benchmark compatibilitystatic doubleBenchmarkUtils.maxErrorOnSum(Matrix approx, Matrix exact) 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)static doubleBenchmarkUtils.utilizationError(Matrix approx, Matrix exact, Network model) Calculate utilization error for benchmarks Excludes delay stations (row 0) from comparison -
Uses of Matrix in jline.examples
Methods in jline.examples that return MatrixModifier and TypeMethodDescriptionstatic MatrixNetworkGeneratorExample.starTopology(int n) Custom topology function - creates a star topology where node 0 is connected to all other nodes -
Uses of Matrix in jline.examples.java.basic
Methods in jline.examples.java.basic with parameters of type MatrixModifier and TypeMethodDescriptionstatic NetworkCacheRetrievalSystemModel.chain_retrieval_system_model(double[] accessProb, Matrix serviceRates, int nQueues, String itemLevelCap, SchedStrategy sched) Cache model with a chained retrieval system.static NetworkCacheRetrievalSystemModel.retrieval_system_with_probabilistic_routing(double[] accessProb, Matrix serviceRates, String itemLevelCap, SchedStrategy sched) Overload using the default 3-item routing matrices defined for the probabilistic routing model.static NetworkCacheRetrievalSystemModel.retrieval_system_with_probabilistic_routing(double[] accessProb, Matrix serviceRates, Matrix[] routingMatrices, String itemLevelCap, SchedStrategy sched) Cache model with probabilistic per-item routing through a retrieval system of 3 queues (one infinite-server, two scheduled).static NetworkCacheRetrievalSystemModel.retrieval_system_with_probabilistic_routing_and_self_loops(double[] accessProb, Matrix serviceRates, String itemLevelCap, SchedStrategy sched) Overload using the default 3-item routing matrices (with self-loops) defined for the probabilistic routing model.static NetworkCacheRetrievalSystemModel.retrieval_system_with_probabilistic_routing_and_self_loops(double[] accessProb, Matrix serviceRates, Matrix[] routingMatrices, String itemLevelCap, SchedStrategy sched) Cache model with probabilistic per-item routing including self-loops through a retrieval system of 3 queues (one infinite-server, two scheduled). -
Uses of Matrix in jline.gen
Modifier and TypeMethodDescriptionstatic MatrixNetworkGenerator.cyclicGraph(int numVertices) Generate a cyclic graph topologystatic MatrixNetworkGenerator.randGraph(int numVertices) Generate a random strongly connected graph topology This implements the algorithm from MATLAB's randGraph functionModifier and TypeMethodDescriptionvoidNetworkGenerator.setTopologyFcn(Function<Integer, Matrix> fcn) ModifierConstructorDescriptionNetworkGenerator(String schedStrat, String routingStrat, String distribution, String cclassJobLoad, boolean hasVaryingServiceRates, boolean hasMultiServerQueues, boolean hasRandomCSNodes, boolean hasMultiChainCS, boolean initializeStates, Function<Integer, Matrix> topologyFcn) Constructor with all options including state initializationNetworkGenerator(String schedStrat, String routingStrat, String distribution, String cclassJobLoad, boolean hasVaryingServiceRates, boolean hasMultiServerQueues, boolean hasRandomCSNodes, boolean hasMultiChainCS, Function<Integer, Matrix> topologyFcn) Constructor with custom settings -
Uses of Matrix in jline.inference.api
Fields in jline.inference.api declared as MatrixModifier and TypeFieldDescriptionfinal MatrixInfer_fluid_ps_rt_likelihood.FluidPsRtResult.augPhasesfinal MatrixInfer_fluid_ps_rt_likelihood.FluidPsRtResult.qIndicesMethods in jline.inference.api that return MatrixModifier and TypeMethodDescriptionstatic MatrixInfer_compute_ql_at_arrival.infer_compute_ql_at_arrival(double[] at, int[] atJobid, double[] rt, int[] rtJobid, int[] classVec, int R) Compute per-class queue lengths at arrival.static MatrixInfer_qmle.infer_qmle(Matrix Q, double[] N, double[] Z) Quick Maximum Likelihood Estimation closed-form formula.Methods in jline.inference.api that return types with arguments of type MatrixModifier and TypeMethodDescriptionInfer_get_qlen_arrival.infer_get_qlen_arrival(double[][][] data, int K) Compute queue lengths at arrival from legacy cell data format.Methods in jline.inference.api with parameters of type MatrixModifier and TypeMethodDescriptionstatic doubleInfer_fluid_ps_rt_likelihood.infer_fluid_ps_rt_solve(Infer_fluid_ps_rt_likelihood.FluidPsRtResult result, Matrix y0Levels, double Rsampled, int taggedClass) static double[]Infer_fmlps.infer_fmlps(Network model, Queue node, double[] rt, int[] classVec, Matrix ql, int W) FMLPS demand estimation using fluid-based likelihood.static double[]Infer_minps.infer_minps(Network model, Queue node, double[] rt, int[] classVec, Matrix ql) MINPS demand estimation method.static double[]Infer_mlps.infer_mlps(Network model, Queue node, double[] rt, int[] classVec, Matrix ql) static MatrixInfer_qmle.infer_qmle(Matrix Q, double[] N, double[] Z) Quick Maximum Likelihood Estimation closed-form formula.static double[]Regression for Processor Sharing (RPS) demand estimation.Constructors in jline.inference.api with parameters of type MatrixModifierConstructorDescriptionFluidPsRtResult(org.apache.commons.math3.ode.FirstOrderDifferentialEquations ode, Matrix qIndices, Matrix augPhases, int stateSize, int refIdx, int newK) -
Uses of Matrix in jline.inference.lang
Methods in jline.inference.lang that return Matrix -
Uses of Matrix in jline.inference.util
Methods in jline.inference.util that return MatrixModifier and TypeMethodDescriptionstatic MatrixSolve the NNLS problem: min ||Ax - b||^2 subject to x >= 0.Methods in jline.inference.util that return types with arguments of type MatrixModifier and TypeMethodDescriptionQuadratic program solver with non-negative constraints.Methods in jline.inference.util with parameters of type Matrix -
Uses of Matrix in jline.io
Modifier and TypeFieldDescriptionRet.lossnMCI.acceptCIRet.snGetDemands.alphaRet.snToAG.APRet.pfqnProcomom2.BRet.pfqnManjunath.blockedRet.lossnErlangFP.blockProbRet.pfqnFnc.cRet.pfqnAMVA.CRet.pfqnAMVAMS.CRet.pfqnAMVASchmidt.CWaiting time matrix (M x R)Ret.pfqnDAC.CRet.snDeaggregateChainResults.CRet.pfqnAB.CNWaiting time matrix (M x R)Ret.pfqnSchmidt.CNWaiting time matrix (M x R)Ret.pfqnQlenMoments.covRet.pfqnSensLinearizer.CovRet.pfqnSensMom.CovMatrix[][]Ret.pfqnSensMom.CovGfinal MatrixRet.pfqnLeFpi.dfinal MatrixRet.pfqnLeFpiZ.dRet.snGetDemands.DRet.snGetProductFormParams.DRet.pfqnSensLinearizer.d2mRet.pfqnSensMom.d2mRet.snGetDemands.DchainRet.pfqnSensLdmxEc.dERet.pfqnSensLdmxEc.dECRet.pfqnManjunath.delayRet.pfqnSensLdmxEc.dEprimeRet.pfqnSensLinearizer.dmRet.pfqnSensMom.dmMatrix[][]Ret.pfqnSensMom.dmGMatrix[]Ret.pfqnSens.dQMatrix[]Ret.pfqnSens.dRMatrix[]Ret.pfqnSens.dURet.pfqnSens.dXRet.pfqnLDMXEC.ERet.pfqnSensLdmxEc.ERet.pfqnLDMXEC.ECRet.pfqnSensLdmxEc.ECRet.pfqnLDMXEC.EprimeRet.pfqnSensLdmxEc.EprimeRet.npfqnNonexpApprox.etaRet.SampleResult.eventEvent sequence corresponding to the state trajectory.Ret.pfqnProcomom2.FRet.FJApprox.fjclassmapRet.FJApprox.fjforkmapRet.FJApprox.forkLambdaInitRet.cacheGamma.gammaRet.npfqnNonexpApprox.gammaRet.getHashOrAddResult.hashidRet.pfqnNcSanitize.LRet.pfqnNcSanitize.lambdaRet.snGetProductFormParams.lambdaRet.pfqnComomrm.lGbasisRet.pfqnLDMXEC.LoRet.pfqnSensLdmxEc.LoRet.lossnMCI.lossCIRet.lossnErlangFP.lossProbRet.lossnManjunath.lossProbRet.lossnMCI.lossProbRet.pfqnSensLinearizer.mRet.pfqnSensMom.mRet.pfqnSensRespt.mRet.pfqnSensLinearizer.M2Ret.pfqnSensMom.M2Ret.pfqnSensLinearizer.M3Ret.pfqnSensMom.M3Ret.pfqnQlenMoments.meanRet.pfqnFnc.muRet.pfqnOifnc.muRet.snGetProductFormParams.muRet.pfqnNcSanitize.NRet.snGetProductFormParams.NRet.snGetDemands.NchainRet.npfqnNonexpApprox.nserversRet.FJApprox.outerForkssort_forks output, cached alongside the transformation it describes.Ret.FJsortForks.outerForksRet.afterEventHashedOrAddResult.outhashfinal MatrixRet.EventResult.outpreemptPREEMPT annotation of each successor: outpreempt(i,r) counts the class-r jobs pushed back into the buffer on successor row i.Ret.afterEventHashedOrAddResult.outprobfinal MatrixRet.EventResult.outprobRet.afterEventHashedOrAddResult.outratefinal MatrixRet.EventResult.outratefinal MatrixRet.EventResult.outspacefinal MatrixRet.EventResult.outstartSTART annotation of each successor: outstart(i,r) counts the class-r jobs that begin, or resume, holding a server on successor row i.Ret.pfqnSensRespt.pRet.LinearizerResult.PMatrix[]Ret.pfqnLinearizerMSEstimate.P_1Ret.FJApprox.parentForksRet.FJsortForks.parentForksRet.LinearizerResult.PBfinal MatrixRet.pfqnEstimate.PB_1Ret.pfqnLinearizerMSEstimate.PB_1Ret.pfqnMVALDMX.PcRet.cacheMVA.piRet.pfqnDAC.piRet.pfqnMVALD.piRet.cacheMVA.pi0Ret.cacheXiFp.pi0Ret.cacheMVA.pijRet.cacheXiFp.pijRet.pfqnMVACLD.pijRet.pfqnDAC.PjointRet.pfqnProcomom2.pkRet.pfqnProcomom.PrRet.pfqnComomrmLd.probRet.pfqnComomrmMs.probRet.ProbabilityResult.probabilityThe probability value(s).Ret.snToAG.processMapRet.pfqnOifnc.PsiRet.LinearizerResult.QRet.pfqnAMVA.QRet.pfqnAMVAMS.QRet.pfqnAMVASchmidt.QQueue length matrix (M x R)Ret.pfqnBkLc.QRet.pfqnDAC.QRet.pfqnManjunath.QPer-class decomposition, null unless it was requested.Ret.pfqnMcmc.Q(M x R) mean queue lengths at the queueing stations.Ret.pfqnMVA.QRet.pfqnMVAC.QRet.pfqnMVACLD.QRet.pfqnMVALD.QRet.pfqnMVALDMX.QRet.pfqnNc.QRet.pfqnNcXQ.QRet.pfqnProcomom.QRet.pfqnSens.QRet.pfqnSensLinearizer.QRet.pfqnSensMom.QRet.pfqnSensMva.QRet.pfqnSensMvaldmx.QRet.pfqnSensRespt.QRet.snDeaggregateChainResults.QMatrix[]Ret.pfqnLinearizerEstimate.Q_1Matrix[]Ret.pfqnLinearizerMSEstimate.Q_1Matrix[][]Ret.pfqnSens.QCovMatrix[]Ret.pfqnSensMva.QCovMatrix[]Ret.pfqnSensMvaldmx.QCovMatrix[][]Ret.pfqnSensMvaldmx.QCovFullRet.pfqnMcmc.Qhi(M x R) upper end of the two-sigma interval for Q.Ret.lossnErlangFP.qLenRet.lossnManjunath.qLenRet.lossnMCI.qLenRet.pfqnMcmc.Qlo(M x R) lower end of the two-sigma interval for Q.Ret.pfqnAB.QNQueue length matrix (M x R)Ret.pfqnNcldmx.QNRet.pfqnSchmidt.QNQueue length matrix (M x R)Ret.pfqnMcmc.Qse(M x R) batch-means standard error of Q.Ret.pfqnSens.QTotVarRet.pfqnSensMva.QTotVarRet.pfqnSensMvaldmx.QTotVarRet.pfqnSens.QVarRet.pfqnSensMva.QVarRet.pfqnSensMvaldmx.QVarRet.pfqnAMVA.RRet.pfqnAMVAMS.RRet.pfqnMVA.RRet.pfqnMVAC.RRet.pfqnMVACLD.RRet.pfqnMVALD.RRet.pfqnMVALDMX.RRet.pfqnSens.RRet.pfqnSensMom.RRet.pfqnSensMva.RRet.pfqnSensMvaldmx.RRet.snDeaggregateChainResults.RMatrix[][]Ret.snToAG.RRet.snGetDemands.refstatchainRet.npfqnNonexpApprox.rhoRet.pfqnAB.RNResponse time matrix (M x R)Ret.pfqnSchmidt.RNResponse time matrix (M x R)Ret.SVD.sRet.snGetProductFormParams.SRet.npfqnNonexpApprox.scvaRet.snGetDemands.SCVchainRet.npfqnNonexpApprox.scvsRet.pfqnSensLinearizer.SkewRet.pfqnSensMom.SkewRet.SpectralDecomposition.spectrumRet.reachableSpaceGeneratorResult.SShRet.reachableSpaceGeneratorResult.SSqRet.npfqnNonexpApprox.STRet.ProbabilityResult.stateThe state specification for which the probability was computed.Ret.pfqnDAC.statesRet.snGetDemands.STchainRet.SampleResult.tTime points for the sampled trajectory.Ret.LinearizerResult.TRet.pfqnAMVA.TRet.snDeaggregateChainResults.Tfinal MatrixRet.pfqnEstimate.T_1Ret.pfqnLinearizerEstimate.T_1Ret.pfqnManjunath.thinkRet.DistributionResult.timePointsTime points for transient distributions.Ret.pfqnAB.TNThroughput matrix (M x R)Ret.pfqnSchmidt.TNThroughput matrix (M x R)Ret.cacheMVA.ufinal MatrixRet.pfqnLeFpi.ufinal MatrixRet.pfqnLeFpiZ.uRet.SVD.uRet.pfqnAMVA.URet.pfqnAMVAMS.URet.pfqnAMVASchmidt.UUtilization matrix (M x R)Ret.pfqnBkLc.URet.pfqnDAC.URet.pfqnManjunath.URet.pfqnMVA.URet.pfqnMVAC.URet.pfqnMVACLD.URet.pfqnMVALD.URet.pfqnMVALDMX.URet.pfqnSens.URet.pfqnSensLinearizer.URet.pfqnSensMom.URet.pfqnSensMva.URet.pfqnSensMvaldmx.URet.pfqnSensRespt.URet.snDeaggregateChainResults.URet.pfqnAB.UNUtilization matrix (M x R)Ret.pfqnSchmidt.UNUtilization matrix (M x R)Ret.SVD.vRet.snGetProductFormParams.VRet.Eigs.valuesRet.pfqnSensLinearizer.VarRet.pfqnSensMom.VarRet.pfqnSensRespt.VarRet.snGetDemands.VchainRet.Eigs.vectorsRet.LinearizerResult.WRet.pfqnSensLinearizer.WRet.pfqnSensRespt.Wstatic MatrixRet.qsys_prio.WMatrix[]Ret.pfqnSensRespt.WMRet.pfqnSensRespt.WresidRet.pfqnSensRespt.WSkewRet.pfqnSensRespt.WVarRet.cacheMVA.xRet.LinearizerResult.XRet.pfqnAMVA.XRet.pfqnAMVAMS.XRet.pfqnAMVASchmidt.XSystem throughput vector (1 x R)Ret.pfqnBkLc.XRet.pfqnDAC.XRet.pfqnManjunath.XRet.pfqnMcmc.X(1 x R) throughput estimates G(N-e_r)/G(N).Ret.pfqnMVA.XRet.pfqnMVAC.XRet.pfqnMVACLD.XRet.pfqnMVALD.XRet.pfqnMVALDMX.XRet.pfqnNc.XRet.pfqnNcXQ.XRet.pfqnSens.XRet.pfqnSensLinearizer.XRet.pfqnSensMom.XRet.pfqnSensMva.XRet.pfqnSensMvaldmx.XRet.pfqnSensRespt.XRet.snDeaggregateChainResults.XRet.pfqnMcmc.Xhi(1 x R) upper end of the two-sigma interval for X.Ret.cacheSpm.xiRet.cacheXiFp.xiRet.pfqnMcmc.Xlo(1 x R) lower end of the two-sigma interval for X.Ret.pfqnAB.XNSystem throughput vector (1 x R)Ret.pfqnNcldmx.XNRet.pfqnSchmidt.XNSystem throughput vector (1 x R)Ret.pfqnMcmc.Xse(1 x R) batch-means standard error of X.Ret.pfqnNcSanitize.ZRet.snGetDemands.ZRet.snGetProductFormParams.ZModifier and TypeFieldDescriptionRet.DistributionResult.cdfDataThe CDF data organized as a cell array [stations x classes].Ret.pfqnSchmidt.PNState probabilities per stationRet.pfqnProcomom2.TModifier and TypeMethodDescriptionRet.DistributionResult.getCdf(int station, int jobClass) Gets the CDF for a specific station and class.Ret.ProbabilityResult.getProbabilityMatrix()Gets the probability matrix.Ret.SampleResult.getStateMatrix()Gets the state trajectory as a matrix (for single-node sampling).Modifier and TypeMethodDescriptionRet.DistributionResult.getAllCdfData()Gets the complete CDF data structure.Ret.SampleResult.getSystemStateList()Gets the system state trajectories as a list of matrices (for system-wide sampling).Modifier and TypeMethodDescriptionvoidSets the CDF for a specific station and class.static Ret.LinearizerResultModifierConstructorDescriptionafterEventHashedOrAddResult(Matrix outhash, Matrix outrate, Matrix outprob, NetworkStruct sn) cacheGamma(Matrix gamma, int u, int n, int h) Constructor for initializing the cacheGammaLpReturn object.cacheGamma(Matrix gamma, int u, int n, int h, int[] parent) Constructor including the tree structure of the cache lists.Constructor for initializing the cacheMVAReturn object.cacheRayInt(double Z, double lZ, Matrix xi) Deprecated.Constructor for initializing the cacheSpmReturn object.Creates a new cacheXiFp result object.DistributionResult(int numStations, int numClasses, String distributionType, Matrix timePoints) Constructor for transient distributions.EventResult(Matrix outspace, Matrix outrate, Matrix outprob) FJApprox(Network nonfjmodel, Matrix fjclassmap, Matrix fjforkmap, Map<Integer, Integer> fj_auxiliary_delays, Map<Integer, Integer> fanout) FJsortForks(Matrix outerForks, Matrix parentForks) getHashOrAddResult(Matrix hashid, NetworkStruct sn) LinearizerResult(Matrix Q, Matrix W, Matrix T) LinearizerResult(Matrix Q, Matrix W, Matrix T, int iter) lossnErlangFP(Matrix q, Matrix l, Matrix b, int n) lossnManjunath(Matrix q, Matrix l, double lg, int n, long pk) npfqnNonexpApprox(Matrix ST, Matrix gamma, Matrix nservers, Matrix rho, Matrix scva, Matrix scvs, Matrix eta) pfqnAB(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, String method, int iter, double runtime) pfqnComomrm(double lG, Matrix lGbasis) pfqnComomrmLd(double GN, double lG, Matrix prob) pfqnComomrmMs(double G, double lG, Matrix prob) pfqnEstimate(MatrixCell Q_1, MatrixCell P_1, Matrix PB_1, Matrix T_1) pfqnLDMXEC(Matrix EC, Matrix E, Matrix Eprime, Matrix Lo) pfqnLeFpiZ(Matrix u, double v, Matrix d) pfqnLinearizerEstimate(Matrix[] Q_1, Matrix T_1) pfqnLinearizerMSEstimate(Matrix[] Q_1, Matrix[] P_1, Matrix PB_1) pfqnMcmc(Matrix X, Matrix Q, Matrix Xse, Matrix Xlo, Matrix Xhi, Matrix Qse, Matrix Qlo, Matrix Qhi, int batches, long samples, long burnin) pfqnMVALD(Matrix XN, Matrix QN, Matrix UN, Matrix RN, List<Double> lG, boolean isNumStable, Matrix pi) pfqnNcSanitize(Matrix lambda, Matrix L, Matrix N, Matrix Z, double lGremaind) pfqnOifnc(ToDoubleFunction<int[]> muf, Matrix Psi, Matrix mu, int[] shape) pfqnProcomom(Matrix Pr, Matrix Q) pfqnQlenMoments(double[] tail, double[] binomial, double[] factorial, double[] raw, double[] central, double[] cumulant, int[] dims, Matrix mean, Matrix cov, String route, int points, int served, int evals) pfqnSchmidt(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, List<Matrix> PN, String method, int iter, double runtime) pfqnSens(Matrix X, Matrix Q, Matrix U, Matrix R, Matrix dX, Matrix[] dQ, Matrix[] dU, Matrix[] dR, int[] paramType, int[] paramStation, int[] paramClass) pfqnSensLdmxEc(Matrix EC, Matrix E, Matrix Eprime, Matrix Lo, Matrix dEC, Matrix dE, Matrix dEprime) pfqnSensLinearizer(Matrix X, Matrix Q, Matrix U, Matrix W, Matrix m, Matrix dm, Matrix d2m, Matrix Var, Matrix Cov, Matrix M2, Matrix M3, Matrix Skew, double CovAsym, int iter) pfqnSensMom(Matrix X, Matrix Q, Matrix U, Matrix R, int G, Matrix m, Matrix dm, Matrix[][] dmG, Matrix d2m, Matrix Var, Matrix Cov, Matrix[][] CovG, Matrix M2, Matrix M3, Matrix Skew, double CovAsym) pfqnSensMva(Matrix X, Matrix Q, Matrix U, Matrix R, Matrix[] QCov, Matrix QVar, Matrix QTotVar, double QCovAsym) pfqnSensMvaldmx(Matrix X, Matrix Q, Matrix U, Matrix R, Matrix[] QCov, Matrix[][] QCovFull, Matrix QVar, Matrix QTotVar, double QCovAsym) pfqnSensRespt(Matrix X, Matrix Q, Matrix U, Matrix W, Matrix[] WM, Matrix WVar, Matrix WSkew, Matrix m, Matrix Var, Matrix p, Matrix Wresid) ProbabilityResult(Matrix probability) Constructor for matrix probability results.Constructs a qsys_prio return object.reachableSpaceGeneratorResult(Matrix SSq, Matrix SSh, NetworkStruct sn) SampleResult(String handle, Matrix t, List<Matrix> systemState, Matrix event, boolean isAggregate, int numEvents) Constructor for system-wide sampling results.SampleResult(String handle, Matrix t, Matrix state, Matrix event, boolean isAggregate, Integer nodeIndex, int numEvents) Constructor for single-node sampling results.snGetDemands(Matrix D, Matrix Z) snGetDemands(Matrix Dchain, Matrix STchain, Matrix Vchain, Matrix alpha, Matrix Nchain, Matrix SCVchain, Matrix refstatchain) snGetDemands(Matrix D, Matrix Z, Matrix Dchain, Matrix STchain, Matrix Vchain, Matrix alpha, Matrix Nchain, Matrix SCVchain, Matrix refstatchain) ModifierConstructorDescriptionpfqnSchmidt(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, List<Matrix> PN, String method, int iter, double runtime) SampleResult(String handle, Matrix t, List<Matrix> systemState, Matrix event, boolean isAggregate, int numEvents) Constructor for system-wide sampling results. -
Uses of Matrix in jline.io.tikz
Fields in jline.io.tikz declared as Matrix -
Uses of Matrix in jline.lang
Fields in jline.lang declared as MatrixModifier and TypeFieldDescriptionModelAdapter.DeaggInfo.alphaFJSync.auxallNetworkStruct.breakdownMu(nstations x 1) failure rate of an up server (0 = never fails).NetworkStruct.capNetwork.routingMatrixReturn.chainsNetworkStruct.chainsNetworkStruct.classcapNetworkStruct.classdeadlineNetworkStruct.classprioNetworkStruct.classspawnprotected MatrixChain.completesNetworkStruct.connmatrixNetworkStruct.csmaskNetworkStruct.downServiceRates(nstations x nclasses) service rate while the server is down (0 = no service).NetworkStruct.fjfinal MatrixModelAdapter.FJTagResult.fjclassmapNetworkStruct.fjclassmapNetworkStruct.gdscalingpeakNetworkStruct.hasbreakdown(nnodes x 1) 1 iff the node's server is subject to breakdowns.NetworkStruct.hassetupStation-indexed mask of queue stations carrying setup/delay-off times (function stations), as in MATLAB refreshStruct.m.NetworkStruct.immfeedNetworkStruct.isbasblocking(nnodes,1) 1 iff the node is the upstream/blocking side of a true-BAS relation (BUG-83).NetworkStruct.isbasdestination(nstations,nclasses) 1 iff refusing an arrival of that class here must BLOCK an upstream BAS station rather than lose the job.NetworkStruct.iscatastropheNetworkStruct.issignalNetworkStruct.isslcNetworkStruct.isstatedepNetworkStruct.isstatefulNetworkStruct.isstationModelAdapter.DeaggInfo.lambdaChainModelAdapter.DeaggInfo.LchainNetwork.routingMatrixReturn.linksmatNetworkStruct.lldscalingNetworkStruct.markidxModelAdapter.DeaggInfo.Nchainprotected MatrixChain.njobsNetworkStruct.njobsNetworkStruct.nodeToStatefulNetworkStruct.nodeToStationNetworkStruct.nserversNetworkStruct.nvarsNetworkStruct.phasesNetworkStruct.phaseshiftNetworkStruct.phasesszEnvironment.probEnvEnvironment.probOrigNetworkStruct.ratesNetworkStruct.refclassModelAdapter.DeaggInfo.refstatNetworkStruct.refstatModelAdapter.DeaggInfo.refstatchainNetworkStruct.regionruleNetworkStruct.regionszNetworkStruct.regionweightNetworkStruct.repairMu(nstations x 1) repair rate of a down server (0 = never repaired).NetworkStruct.replyblock(nnodes,nclasses) 1 iff the node holds a server for class r awaiting a REPLY signal (synchronous call).Network.routingMatrixReturn.rtNetworkStruct.rtNetwork.routingMatrixReturn.rtnodesNetworkStruct.rtnodesfinal MatrixRoutingMatrix.RtOrigResult.rtorigThe (nodes*classes)-square stochastic complement over the pre-class-switch nodes.NetworkStruct.schedparamNetworkStruct.scvModelAdapter.DeaggInfo.SCVchainNetworkStruct.signaltargetprotected MatrixEvent.stateModeEvent.stateNetworkStruct.statefulToNodeNetworkStruct.statefulToStationNetworkStruct.stationToNodeNetworkStruct.stationToStatefulModelAdapter.DeaggInfo.STchainNetworkStruct.syncreplyNetworkStruct.varsparamModelAdapter.DeaggInfo.Vchainprotected MatrixChain.visitsFields in jline.lang with type parameters of type MatrixModifier and TypeFieldDescriptionNetworkStruct.cdscalingNetworkStruct.cdscalingNetworkStruct.cdscalingpeakNetworkStruct.gdscalingNetworkStruct.gdscalingNetworkStruct.impatienceMuNetworkStruct.impatiencePhiNetworkStruct.impatiencePieModelAdapter.DeaggInfo.inchainNetworkStruct.inchainNetworkStruct.jdscalingNetworkStruct.jdscalingNetworkStruct.jdscalingpeakNetworkStruct.muNetworkStruct.nodevisitsNodeParam.outlinksOutbound link specifications by job class - defines outgoing connectionsNetworkStruct.phiNetworkStruct.pieEvent.probFunEvent.probFunNetworkStruct.retrialMuNetworkStruct.retrialPhiNetwork.routingMatrixReturn.rtNodesByClassNetwork.routingMatrixReturn.rtNodesByStationNetworkStruct.rtorigRoutingMatrix.RtOrigResult.rtorigcellrtorigcell[r][s] holds the node-by-node probabilities from class r to class s.NetworkStruct.spaceNetworkStruct.stateNetworkStruct.statepriorNetworkStruct.visitsNodeParam.weightedOutlinksWeighted round-robin cycle by job class: the outlink node indices with each destination repeated by its integer weight.NodeParam.weightsRouting weights by job class - controls probabilistic routing decisionsMethods in jline.lang that return MatrixModifier and TypeMethodDescriptionstatic MatrixModelAdapter.findPaths(NetworkStruct sn, Matrix P, int startNode, int endNode, int r, ArrayList<Integer> toMerge, Matrix QN, Matrix TN, double currentTime, Matrix fjclassmap, Matrix fjforkmap, Network nonfjmodel) Finds the response times along each path leading out of startNode up to (and not including) endNodestatic MatrixModelAdapter.findPaths(NetworkStruct sn, Matrix P, int startNode, int endNode, int r, ArrayList<Integer> toMerge, Matrix QN, Matrix TN, double currentTime, Matrix fjclassmap, Matrix fjforkmap, Network nonfjmodel, Set<Integer> onPath) Simple paths only; see the ONPATH note on findPathsCS.static MatrixModelAdapter.findPathsCS(NetworkStruct sn, Matrix P, int curNode, int endNode, int curClass, ArrayList<Integer> toMerge, Matrix QN, Matrix TN, double currentTime, Matrix fjclassmap, Matrix fjforkmap, Network nonfjmodel) Finds the response times along each path leading out of curNode up to (and not including) endNode Variant for models with class switchingstatic MatrixModelAdapter.findPathsCS(NetworkStruct sn, Matrix P, int curNode, int endNode, int curClass, ArrayList<Integer> toMerge, Matrix QN, Matrix TN, double currentTime, Matrix fjclassmap, Matrix fjforkmap, Network nonfjmodel, Set<Long> onPath) Enumerates the SIMPLE paths only: the call classes carry a geometric loop (server -> Aux -> server) whenever a call mean exceeds one, so the routing graph between a fork and its join is cyclic and the path set would be infinite without ONPATH.RoutingMatrix.get(int jobclass1, int jobclass2) ModelAdapter.AggregateChainResult.getAlpha()Network.getClassSwitchingMask()Network.getConnectionMatrix()Network.getCsMatrix()Network.getDemandsChain()Network.getForkJoins()Network.getGlobalDependencePeak()(nstations x nclasses) peak of the global dependence, or null if none is declared.Network.getLimitedLoadDependence()Matrix[]Region.getLinearConstraints()Gets the linear admission constraints as the pair (A, b).ClassSwitchMatrix.getMatrix()Expose the raw Matrix only if callers genuinely need itNetwork.getNumberOfJobs()Network.getReferenceClasses()Network.getReferenceStations()Event.getState()Gets the system state matrix when this event occurred.ModeEvent.getState()Network.getStatefulServers()Network.getStationServers()Methods in jline.lang that return types with arguments of type MatrixModifier and TypeMethodDescriptionRoutingMatrix.getCell()Returns the routing probabilities as a class-by-class table of node-by-node matrices, twin of MATLABgetCell.Network.getGlobalDependence()Network-level global dependence handle, or null if the model declares none.Network.getGlobalDependence()Network-level global dependence handle, or null if the model declares none.Network.getLimitedClassDependence()Gets the class-dependence functions beta_i(n) of the stations that declare one.Network.getLimitedClassDependence()Gets the class-dependence functions beta_i(n) of the stations that declare one.Network.getLimitedClassDependencePeak()Peak (max) class-dependent rate scaling per class for each class-dependent station, as a 1xR row vector (scalar declarations broadcast to R classes).Network.getLimitedJointDependence()Map from station to its joint-dependence (non-product-form) function eta_i(n); empty if none.Network.getLimitedJointDependence()Map from station to its joint-dependence (non-product-form) function eta_i(n); empty if none.Network.getLimitedJointDependencePeak()Peak joint-dependent rate scaling per class for each joint-dependent station, as a 1xR row vector.Network.getLinkedRoutingMatrix()Event.getProbFun()Gets the probability function used to dynamically compute event probability.Event.getProbFun()Gets the probability function used to dynamically compute event probability.Methods in jline.lang with parameters of type MatrixModifier and TypeMethodDescriptionstatic NetworkNetwork.cluster(Matrix lambda, Matrix D, SchedStrategy[] strategy, Matrix S, RoutingStrategy dispatching) Creates an open cluster network: Source -> Dispatcher (Router) -> Servers -> Sink.static NetworkNetwork.clusterClosed(Matrix N, Matrix Z, Matrix D, SchedStrategy[] strategy, Matrix S, RoutingStrategy dispatching) Creates a closed cluster network: Think (Delay) -> Dispatcher (Router) -> Servers -> Think.static NetworkNetwork.clusterFcfs(Matrix lambda, Matrix D, Matrix S, RoutingStrategy dispatching) Creates an open FCFS cluster with one server per queue.static NetworkNetwork.clusterMixed(Matrix lambda, Matrix N, Matrix Z, Matrix D, SchedStrategy[] strategy, Matrix S, RoutingStrategy dispatching) Creates a mixed cluster network in which open and closed classes share the dispatcher and the servers: open classes flow Source -> Dispatcher -> Servers -> Sink while closed classes cycle Think (Delay) -> Dispatcher -> Servers -> Think.static NetworkNetwork.clusterPs(Matrix lambda, Matrix D, RoutingStrategy dispatching) Creates an open PS cluster with one server per queue.static NetworkNetwork.clusterPs(Matrix lambda, Matrix D, Matrix S, RoutingStrategy dispatching) Creates an open PS cluster.static NetworkNetwork.cyclic(Matrix N, Matrix D, SchedStrategy[] strategy, Matrix S) Creates a cyclic queueing network model with specified job populations, service demands, scheduling strategies, and server counts.static NetworkNetwork.cyclicFcfs(Matrix N, Matrix D) Creates a cyclic queueing network with First Come First Served (FCFS) scheduling at all stations.static NetworkNetwork.cyclicFcfs(Matrix N, Matrix D, Matrix S) Creates a cyclic queueing network with FCFS scheduling and specified server counts.static NetworkNetwork.cyclicFcfsInf(Matrix N, Matrix D, Matrix Z) Creates a cyclic network with infinite server (delay) stations followed by FCFS queue stations.static NetworkNetwork.cyclicFcfsInf(Matrix N, Matrix D, Matrix Z, Matrix S) Creates a cyclic network with infinite server stations followed by FCFS queue stations with specified server counts.static NetworkCreates a cyclic queueing network with Processor Sharing (PS) scheduling at all stations.static NetworkCreates a cyclic queueing network with PS scheduling and specified server counts.static NetworkNetwork.cyclicPsInf(Matrix N, Matrix D, Matrix Z) Creates a cyclic network with infinite server (delay) stations followed by PS queue stations.static NetworkNetwork.cyclicPsInf(Matrix N, Matrix D, Matrix Z, Matrix S) Creates a cyclic network with infinite server stations followed by PS queue stations with specified server counts.static MatrixModelAdapter.findPaths(NetworkStruct sn, Matrix P, int startNode, int endNode, int r, ArrayList<Integer> toMerge, Matrix QN, Matrix TN, double currentTime, Matrix fjclassmap, Matrix fjforkmap, Network nonfjmodel) Finds the response times along each path leading out of startNode up to (and not including) endNodestatic MatrixModelAdapter.findPaths(NetworkStruct sn, Matrix P, int startNode, int endNode, int r, ArrayList<Integer> toMerge, Matrix QN, Matrix TN, double currentTime, Matrix fjclassmap, Matrix fjforkmap, Network nonfjmodel, Set<Integer> onPath) Simple paths only; see the ONPATH note on findPathsCS.static MatrixModelAdapter.findPathsCS(NetworkStruct sn, Matrix P, int curNode, int endNode, int curClass, ArrayList<Integer> toMerge, Matrix QN, Matrix TN, double currentTime, Matrix fjclassmap, Matrix fjforkmap, Network nonfjmodel) Finds the response times along each path leading out of curNode up to (and not including) endNode Variant for models with class switchingstatic MatrixModelAdapter.findPathsCS(NetworkStruct sn, Matrix P, int curNode, int endNode, int curClass, ArrayList<Integer> toMerge, Matrix QN, Matrix TN, double currentTime, Matrix fjclassmap, Matrix fjforkmap, Network nonfjmodel, Set<Long> onPath) Enumerates the SIMPLE paths only: the call classes carry a geometric loop (server -> Aux -> server) whenever a call mean exceeds one, so the routing graph between a fork and its join is cyclic and the path set would be infinite without ONPATH.static voidFold the auxiliary-class metric columns computed on an FJ tag-augmented struct back into the original classes: queue lengths, utilizations and throughputs of the sibling classes are exact aggregates of the class they were forked from; response times are recomputed by Little's law after folding.Network.getRoutingMatrix(Matrix arvRates, int returnVal) voidNetwork.initFromAvgQLen(Matrix AvgQLen) voidNetwork.initFromMarginal(Matrix n) voidNetwork.initFromMarginalAndRunning(Matrix n, Matrix s) voidNetwork.initFromMarginalAndStarted(Matrix n, Matrix s) static Ret.FJApproxFork-Join Transform approach to evaluate queueing networks including fork-join systems.voidNetwork.refreshRoutingMatrix(Matrix rates) Environment.ResetEnvRatesFunction.reset(Markovian originalDist, Matrix QExit, Matrix UExit, Matrix TExit) voidSets the whole node-by-node routing from one class to another, given the class indices.voidSets the whole node-by-node routing of a class onto itself, given the class index.voidvoidvoidNetwork.setConnectionMatrix(Matrix connection) voidNetwork.setCsMatrix(Matrix csMatrix) voidNetwork.setGlobalDependence(SerializableFunction<Matrix, Matrix> phi, Matrix peak) Declares a globally state-dependent service-rate scaling phi(n), where n is the FULL (nstations x nclasses) population matrix rather than the population local to one station.voidNetwork.setGlobalDependence(SerializableFunction<Matrix, Matrix> phi, Matrix peak, int wireCutoff) AsNetwork.setGlobalDependence(SerializableFunction, Matrix), with an explicit per-slot OPEN-class truncation used when phi is materialized onto the JSON wire (closed classes are tabulated up to their own population).voidRegion.setLinearConstraints(Matrix A, Matrix b) Sets general linear admission constraints An <= b.voidSets the system state matrix for this event.voidvoidstatic Ret.FJsortForksModelAdapter.sort_forks(NetworkStruct sn, NetworkStruct nonfjstruct, Matrix fjforkmap, Matrix fjclassmap, Network nonfjmodel) Determines a directed acyclic graph of relationships among fork nodes.doubleNetwork.sub_jsq(int ind, int jnd, int r, int s, Matrix linksmat, Map<Node, Matrix> state_before, Map<Node, Matrix> state_after) doubleNetwork.sub_rr_wrr(int ind, int jnd, int r, int s, Matrix linksmat, Map<Node, Matrix> state_before, Map<Node, Matrix> state_after) doubleNetwork.sub_sdr(int ind, int jnd, int r, int s, Matrix linksmat, Map<Node, Matrix> state_before, Map<Node, Matrix> state_after) Krzesinski (1987) product-form state-dependent routing, eq.doubleNetwork.sub_sq(int ind, int jnd, int r, int s, Matrix linksmat, Map<Node, Matrix> state_before, Map<Node, Matrix> state_after) Power-of-K choices marginal routing probability.static NetworkNetwork.tandem(Matrix lambda, Matrix D, SchedStrategy[] strategy, Matrix S) Creates a tandem queueing network with specified arrival rates and service demands.static NetworkNetwork.tandemFcfs(Matrix lambda, Matrix D, Matrix S) static NetworkNetwork.tandemFcfsInf(Matrix lambda, Matrix D) Creates a tandem network with FCFS infinite servers.static NetworkNetwork.tandemFcfsInf(Matrix lambda, Matrix D, Matrix Z) Creates a tandem network with FCFS infinite servers and delay centers.static NetworkNetwork.tandemFcfsInf(Matrix lambda, Matrix D, Matrix Z, Matrix S) Creates a tandem network with FCFS infinite servers, delays, and specified server counts.static Networkstatic NetworkNetwork.tandemPsInf(Matrix lambda, Matrix D) Creates a tandem network with processor sharing infinite servers.static NetworkNetwork.tandemPsInf(Matrix lambda, Matrix D, Matrix Z) Creates a tandem network with processor sharing infinite servers and delays.static NetworkNetwork.tandemPsInf(Matrix lambda, Matrix D, Matrix Z, Matrix S) StateDepRouting.toStationIndices(Matrix nodeToStation, List<String> nodeNames) Returns a copy of this structure with every center index mapped through the supplied node-to-station index vector.Method parameters in jline.lang with type arguments of type MatrixModifier and TypeMethodDescriptiondoubleComputes the probability of this event using the probability function and current system state.doubleComputes the probability of this event using the probability function and current system state.voidRelink the network from a modified rtorig map.voidNetwork.setGlobalDependence(SerializableFunction<Matrix, Matrix> phi, Matrix peak) Declares a globally state-dependent service-rate scaling phi(n), where n is the FULL (nstations x nclasses) population matrix rather than the population local to one station.voidNetwork.setGlobalDependence(SerializableFunction<Matrix, Matrix> phi, Matrix peak) Declares a globally state-dependent service-rate scaling phi(n), where n is the FULL (nstations x nclasses) population matrix rather than the population local to one station.voidNetwork.setGlobalDependence(SerializableFunction<Matrix, Matrix> phi, Matrix peak, int wireCutoff) AsNetwork.setGlobalDependence(SerializableFunction, Matrix), with an explicit per-slot OPEN-class truncation used when phi is materialized onto the JSON wire (closed classes are tabulated up to their own population).voidNetwork.setGlobalDependence(SerializableFunction<Matrix, Matrix> phi, Matrix peak, int wireCutoff) AsNetwork.setGlobalDependence(SerializableFunction, Matrix), with an explicit per-slot OPEN-class truncation used when phi is materialized onto the JSON wire (closed classes are tabulated up to their own population).voidEvent.setProbFun(SerializableFunction<Pair<Map<Node, Matrix>, Map<Node, Matrix>>, Double> probFun) Sets the probability function to dynamically compute event probability based on system state.voidEvent.setProbFun(SerializableFunction<Pair<Map<Node, Matrix>, Map<Node, Matrix>>, Double> probFun) Sets the probability function to dynamically compute event probability based on system state.doubleNetwork.sub_jsq(int ind, int jnd, int r, int s, Matrix linksmat, Map<Node, Matrix> state_before, Map<Node, Matrix> state_after) doubleNetwork.sub_rr_wrr(int ind, int jnd, int r, int s, Matrix linksmat, Map<Node, Matrix> state_before, Map<Node, Matrix> state_after) doubleNetwork.sub_sdr(int ind, int jnd, int r, int s, Matrix linksmat, Map<Node, Matrix> state_before, Map<Node, Matrix> state_after) Krzesinski (1987) product-form state-dependent routing, eq.doubleNetwork.sub_sq(int ind, int jnd, int r, int s, Matrix linksmat, Map<Node, Matrix> state_before, Map<Node, Matrix> state_after) Power-of-K choices marginal routing probability.voidUpdates sn.rtorig without replacing the entire NetworkStruct.Constructors in jline.lang with parameters of type MatrixModifierConstructorDescriptionAggregateChainResult(Network chainModel, Matrix alpha, ModelAdapter.DeaggInfo deaggInfo) ClassSwitchMatrix(Matrix matrix) Creates a new Event with all basic parameters specified.Event(EventType event, int node, int jobclass, SerializableFunction<Pair<Map<Node, Matrix>, Map<Node, Matrix>>, Double> probFun, Matrix state, double t, double job) Creates a new Event where probability is determined by a function of system state.FJTagResult(Network fjmodel, NetworkStruct fjsn, Matrix fjclassmap) ModeEvent(EventType event, int node, int mode, double weight, double prob, Matrix state, double t, double job) routingMatrixReturn(Matrix rt, Matrix rtnodes, Matrix linksmat, Matrix chains, Map<JobClass, Map<JobClass, Matrix>> rtNodesByClass, Map<Node, Map<Node, Matrix>> rtNodesByStation) RtOrigResult(List<List<Matrix>> rtorigcell, Matrix rtorig) Constructor parameters in jline.lang with type arguments of type MatrixModifierConstructorDescriptionEvent(EventType event, int node, int jobclass, SerializableFunction<Pair<Map<Node, Matrix>, Map<Node, Matrix>>, Double> probFun, Matrix state, double t, double job) Creates a new Event where probability is determined by a function of system state.Event(EventType event, int node, int jobclass, SerializableFunction<Pair<Map<Node, Matrix>, Map<Node, Matrix>>, Double> probFun, Matrix state, double t, double job) Creates a new Event where probability is determined by a function of system state.routingMatrixReturn(Matrix rt, Matrix rtnodes, Matrix linksmat, Matrix chains, Map<JobClass, Map<JobClass, Matrix>> rtNodesByClass, Map<Node, Map<Node, Matrix>> rtNodesByStation) RtOrigResult(List<List<Matrix>> rtorigcell, Matrix rtorig) -
Uses of Matrix in jline.lang.layered
Fields in jline.lang.layered declared as MatrixModifier and TypeFieldDescriptionLayeredNetworkStruct.actphaseLayeredNetworkStruct.actposttypeLayeredNetworkStruct.actpretypeLayeredNetworkStruct.actquorumprotected MatrixActivity.asyncCallMeansLayeredNetworkStruct.callpairLayeredNetworkStruct.ServerPools.compatLayeredNetworkStruct.conntasksLayeredNetworkStruct.ServerPools.countsLayeredNetworkStruct.dagLayeredNetworkStruct.fanoutprotected MatrixEntry.forwardingProbsLayeredNetworkStruct.graphLayeredNetworkStruct.hasretrievalLayeredNetworkStruct.hassetupLayeredNetworkStruct.isasynccallerLayeredNetworkStruct.iscacheLayeredNetworkStruct.iscallerLayeredNetworkStruct.isrefLayeredNetworkStruct.issynccallerLayeredNetworkElement.lcdScalingPeakPeak rate scaling per operand of lcdScaling, normalizes Util = T*S/peak.LayeredNetworkElement.linConAMatrix(C,K): admission constraint matrix on this server's layer station.LayeredNetworkElement.linConBMatrix(C,1): admission constraint capacities.LayeredNetworkElement.ljdScalingPeakPeak rate scaling per operand of ljdScaling, normalizes Util = T*S/peak.LayeredNetworkElement.lldScalingVector alpha(n): rate scaling of this server's layer station when it holds n jobs.LayeredNetworkStruct.maxmultLayeredNetworkStruct.multLayeredNetworkStruct.nitemsLayeredNetworkStruct.parentprotected MatrixActivityPrecedence.postParamsprotected MatrixActivityPrecedence.preParamsLayeredNetworkStruct.prioScheduling priority of a task, 0 on every other index.LayeredNetworkStruct.ServerPools.ratesLayeredNetworkStruct.replLayeredNetworkStruct.replacementLayeredNetworkStruct.replacestratLayeredNetworkStruct.replygraphLayeredNetworkStruct.schedidprotected MatrixActivity.schedulingprotected MatrixEntry.schedulingprotected MatrixActivity.syncCallMeansLayeredNetworkStruct.taskgraphLayeredNetworkStruct.typeFields in jline.lang.layered with type parameters of type MatrixModifier and TypeFieldDescriptionLayeredNetworkStruct.actthink_paramsLayeredNetworkStruct.arrival_paramsLayeredNetworkStruct.callproc_paramsLayeredNetworkStruct.cdscalingLayeredNetworkStruct.cdscalingLayeredNetworkStruct.cdscalingpeakLayeredNetworkStruct.delayofftime_paramsLayeredNetworkStruct.hostdem_paramsLayeredNetworkStruct.itemproc_paramsLayeredNetworkStruct.jdscalingLayeredNetworkStruct.jdscalingLayeredNetworkStruct.jdscalingpeakLayeredNetworkElement.lcdScalingHandle beta(n): product-form class-dependent scaling, n counted over this server's operands.LayeredNetworkElement.lcdScalingHandle beta(n): product-form class-dependent scaling, n counted over this server's operands.LayeredNetworkElement.ljdScalingHandle eta(n): non-product-form joint-dependent scaling, n counted over this server's operands.LayeredNetworkElement.ljdScalingHandle eta(n): non-product-form joint-dependent scaling, n counted over this server's operands.LayeredNetworkStruct.lldscalingLayeredNetworkStruct.setuptime_paramsLayeredNetworkStruct.think_paramsMethods in jline.lang.layered that return MatrixModifier and TypeMethodDescriptionActivity.getAsyncCallMeans()Entry.getForwardingProbs()Get the matrix of forwarding probabilities.Matrix[]LayeredNetworkElement.getLinearConstraints()Positional constraint pair declared on this element, before name resolution.ActivityPrecedence.getPostParams()Returns the parameters for the following activities.ActivityPrecedence.getPreParams()Returns the parameters for the preceding activities.Activity.getSyncCallMeans()Methods in jline.lang.layered that return types with arguments of type MatrixModifier and TypeMethodDescriptionLayeredNetwork.getInitMarginalBlocks()Per-layer blocks of the warm start supplied by the lastLayeredNetwork.initFromMarginal(jline.util.matrix.Matrix)call, or null if none was supplied.Methods in jline.lang.layered with parameters of type MatrixModifier and TypeMethodDescriptionstatic ActivityPrecedenceCreates an ActivityPrecedence object representing an AND-fork relationship with a specified fanout matrix.static ActivityPrecedenceCreates an ActivityPrecedence object representing an AND-fork relationship with a specified fanout matrix.static ActivityPrecedenceCreates an ActivityPrecedence object representing an AND-join relationship with a specified quorum matrix.static ActivityPrecedenceCreates an ActivityPrecedence object representing an AND-join relationship with a specified quorum matrix.static ActivityPrecedenceActivityPrecedence.fromActivities(List<Activity> preActs, List<Activity> postActs, String preType, String postType, Matrix preParams) Creates an ActivityPrecedence with Activity objects, without postParams.static ActivityPrecedenceActivityPrecedence.fromActivities(List<Activity> preActs, List<Activity> postActs, String preType, String postType, Matrix preParams, Matrix postParams) Creates an ActivityPrecedence with Activity objects.static intActivityPrecedence.getQuorumCount(Matrix preParams, int nPreActs) Returns the effective quorum count of an AND-join, i.e.voidLayeredNetwork.initFromMarginal(Matrix n) Split the aggregate (M x K) marginal queue-length matrix into per-layer blocks and warm-start each layer network.static ActivityPrecedenceCreates an ActivityPrecedence object representing a loop relationship.static ActivityPrecedencestatic ActivityPrecedenceCreates an ActivityPrecedence object representing a loop relationship.static ActivityPrecedencestatic ActivityPrecedenceCreates an ActivityPrecedence object representing a loop relationship.static ActivityPrecedenceCreates an ActivityPrecedence object representing a loop relationship.static ActivityPrecedenceCreates an ActivityPrecedence object representing an OR-fork relationship with a specified probability matrix.static ActivityPrecedenceCreates an ActivityPrecedence object representing an OR-fork relationship with a specified probability matrix.static DiscreteDistributionLayeredNetwork.reconstructDiscreteDistribution(ProcessType type, Matrix params, Double mean) Reconstruct a DiscreteDistribution object from primitive parameters.static DistributionLayeredNetwork.reconstructDistribution(ProcessType type, Matrix params, Double mean, Double scv, MatrixCell proc) Reconstruct a Distribution object from primitive parameters.Activity.setAsyncCallMeans(Matrix asyncCallMeans) voidLayeredNetworkElement.setClassDependence(SerializableFunction<Matrix, Matrix> beta, Matrix peakRatePerOperand) Sets a class-dependent service-rate scaling on this server's layer station.voidLayeredNetworkElement.setConstraint(Matrix A, Matrix b) Raw form of addConstraint, for programmatic construction.voidLayeredNetworkElement.setJointDependence(SerializableFunction<Matrix, Matrix> eta, Matrix peakRatePerOperand) Sets a joint-dependent (non-product-form) service-rate scaling on this server's layer station.voidLayeredNetworkElement.setLoadDependence(Matrix alpha) Sets the service-rate scaling of the station that represents this server in its layer: alpha[n] applies when that station holds n jobs in total, as in Queue.setLoadDependence.Activity.setSyncCallMeans(Matrix syncCallMeans) static ActivityPrecedenceCreates an ActivityPrecedence object representing an XOR relationship.static ActivityPrecedenceCreates an ActivityPrecedence object representing an XOR relationship.Method parameters in jline.lang.layered with type arguments of type MatrixModifier and TypeMethodDescriptionvoidLayeredNetworkElement.setClassDependence(SerializableFunction<Matrix, Matrix> beta, Matrix peakRatePerOperand) Sets a class-dependent service-rate scaling on this server's layer station.voidLayeredNetworkElement.setClassDependence(SerializableFunction<Matrix, Matrix> beta, Matrix peakRatePerOperand) Sets a class-dependent service-rate scaling on this server's layer station.voidLayeredNetworkElement.setJointDependence(SerializableFunction<Matrix, Matrix> eta, Matrix peakRatePerOperand) Sets a joint-dependent (non-product-form) service-rate scaling on this server's layer station.voidLayeredNetworkElement.setJointDependence(SerializableFunction<Matrix, Matrix> eta, Matrix peakRatePerOperand) Sets a joint-dependent (non-product-form) service-rate scaling on this server's layer station.Constructors in jline.lang.layered with parameters of type MatrixModifierConstructorDescriptionActivityPrecedence(List<String> preActs, List<String> postActs, String preType, String postType, Matrix preParams) Constructs an ActivityPrecedence with the specified parameters, without postParams.ActivityPrecedence(List<String> preActs, List<String> postActs, String preType, String postType, Matrix preParams, Matrix postParams) Constructs an ActivityPrecedence with the specified parameters. -
Uses of Matrix in jline.lang.nodeparam
Fields in jline.lang.nodeparam declared as MatrixModifier and TypeFieldDescriptionMatrix[][]CacheNodeParam.accostAccess cost matrix for cache items by class [items x classes x servers]CacheNodeParam.actualdelayedhitprobActual delayed-hit fractions computed during analysis (retrieval system) [1 x classes]CacheNodeParam.actualhitprobActual hit probabilities computed during analysis [items x classes]CacheNodeParam.actualhitproblistActual per-list (per-level) hit fractions computed during analysis [classes x lists]CacheNodeParam.actuallistcostMean storage cost held by each list computed during analysis [1 x lists]CacheNodeParam.actualmissprobActual miss probabilities computed during analysis [items x classes]CacheNodeParam.actualresidtActual expected latency computed during analysis [items x classes]CacheNodeParam.costcapPer-list cap on the total storage cost of the resident items [1 x lists]; null when unsetForkNodeParam.fanOutLinkVariable forking levels, all (nnodes x nclasses) and indexed by DESTINATION NODE rather than link ordinal, so a relink cannot silently permute them.ForkNodeParam.fanOutProbTransitionNodeParam.fireweightTransitionNodeParam.firingphasesTransitionNodeParam.firingprioCacheNodeParam.hitclassJob class routing matrix for cache hits [items x classes]CacheNodeParam.itemcapCapacity matrix specifying maximum number of each item type [items x 1]CacheNodeParam.itemsizePer-item storage cost (size) [1 x items]; null when unsetCacheNodeParam.missclassJob class routing matrix for cache misses [items x classes]TransitionNodeParam.nmodeserversCacheNodeParam.retrievalClassesMatrix containing the retrieval class for each item [items x classes]ServiceNodeParam.servercompatServer-class compatibility matrix: Matrix (nTypes x K), 1.0 if type t serves class r.ServiceNodeParam.serverparallelismServers seized at once by a job, per class: Matrix (1 x K), all ones unless some class declares job parallelism.ServiceNodeParam.serverspertypeNumber of servers per server type: Matrix (nTypes x 1).QueueNodeParam.swapGraphPass-and-swap (PAS) class compatibility/swap graph (nclasses x nclasses).Fields in jline.lang.nodeparam with type parameters of type MatrixModifier and TypeFieldDescriptionTransitionNodeParam.enablingTransitionNodeParam.firingTransitionNodeParam.firingdepTransitionNodeParam.firingpieForkNodeParam.fjAuxmatrixFJ tag augmentation: B x T auxiliary class indices per original classJoinNodeParam.fjAuxmatrixFJ tag augmentation: B x T auxiliary class indices per original classJoinNodeParam.fjRequiredFJ tag augmentation: required siblings per branch (B x 1) per original classTransitionNodeParam.inhibitingQueueNodeParam.svcRateFunPass-and-swap (PAS) total service rate function mu(c) of the ordered state. -
Uses of Matrix in jline.lang.nodes
Fields in jline.lang.nodes declared as MatrixModifier and TypeFieldDescriptionMatrix[][]Cache.accessProbTransition.firingPrioritiesTransition.firingWeightsprotected MatrixStation.lcdScalingPeakprotected MatrixStation.ljdScalingPeakprotected MatrixStation.lldScalingprotected MatrixNode.stateFields in jline.lang.nodes with type parameters of type MatrixModifier and TypeFieldDescriptionTransition.enablingConditionsTransition.firingOutcomesTransition.firingRateDependenceTransition.inhibitingConditionsprotected SerializableFunction<Matrix,Matrix> Station.lcdScalingprotected SerializableFunction<Matrix,Matrix> Station.lcdScalingprotected SerializableFunction<Matrix,Matrix> Station.ljdScalingprotected SerializableFunction<Matrix,Matrix> Station.ljdScalingMethods in jline.lang.nodes that return MatrixModifier and TypeMethodDescriptionCache.getAccessProb(int i, int j) Gets the access probability matrix for a specific cache level and job class.Cache.getCostCaps()Gets the per-list storage cost caps.Cache.getDelayedHitQLen()Per-item delayed-hit queue length, empty when the solver does not compute it.Cache.getDelayedHitQLenFull()Per-item delayed-hit queue length including the triggering request.Cache.getDelayedHitRatio()Gets the delayed-hit fraction per class (retrieval system); empty when the cache has no retrieval system.Matrix[]Cache.getGraph()Gets the graph structure defining the cache organization.Cache.getHitClass()For an incoming job of class r, HITCLASS[r] is the new class of that job after a hitCache.getHitRatio()Gets the actual hit probability/ratio for each job class.Cache.getHitRatioByList()Gets the per-class, per-list (per-level) hit fraction matrix [classes x lists]; empty when not computed by the solver.Cache.getItemLevelCap()Gets the capacity configuration for each cache level.Cache.getItemProb()Gets the per-item occupancy matrix [items x (lists+1)]: column 0 is the miss probability, columns 1..Cache.getItemSizes()Gets the per-item storage costs (sizes).Station.getLimitedClassDependencePeak()Gets the declared peak class-dependent rate scaling per class.Station.getLimitedJointDependencePeak()Gets the declared peak joint-dependent rate scaling per class.Station.getLimitedLoadDependence()Gets the limited load-dependent scaling matrix for this station.Cache.getListCost()Gets the mean storage cost held by each list.Cache.getMissClass()For an incoming job of class r, MISSCLASS[r] is the new class of that job after a missCache.getMissRatio()Gets the actual miss probability/ratio for each job class.Transition.getNumberOfModeServers()Cache.getResidT()Gets the actual expected latency of an item request for each job class.Cache.getRetrievalClasses()For an incoming job of class r, RETRIEVALCLASSES[i, r] is the new class of the job after a retrieval beginsNode.getState()Gets the current state of this node.StatefulNode.getState()StatefulNode.getStatePrior()StatefulNode.getStateSpace()Queue.getSwapGraph()Returns the (nclasses x nclasses) swap graph of a PAS queue, or null.Methods in jline.lang.nodes that return types with arguments of type MatrixModifier and TypeMethodDescriptionTransition.getFiringRateDependence(Mode mode) Station.getLimitedClassDependence()Gets the limited class-dependent scaling function for this station.Station.getLimitedClassDependence()Gets the limited class-dependent scaling function for this station.Station.getLimitedJointDependence()Gets the limited joint-dependent (non-product-form) scaling function.Station.getLimitedJointDependence()Gets the limited joint-dependent (non-product-form) scaling function.Queue.getServiceRateFunction()Returns the mu(c) service rate function of a PAS queue, or null.Methods in jline.lang.nodes with parameters of type MatrixModifier and TypeMethodDescriptionvoidCache.setAccessProb(Matrix[][] R) Sets the access probability matrices for all cache levels and job classes.voidQueue.setClassDependence(SerializableFunction<Matrix, Matrix> beta, Matrix peakRatePerClass) Sets a class-dependent service-rate scaling and its required peak rate per class.voidCache.setCostCaps(Matrix caps) Sets the per-list cap on the total storage cost of the resident items.voidCache.setItemSizes(Matrix sizes) Sets the storage cost (size) of each item, a positive integer vector with one entry per item.voidQueue.setJointDependence(SerializableFunction<Matrix, Matrix> eta, Matrix peakRatePerClass) Sets a joint-dependent (non-product-form) service-rate scaling and its required peak rate per class.voidStation.setLimitedClassDependence(SerializableFunction<Matrix, Matrix> gamma, Matrix peakRatePerClass) Sets the limited class-dependent scaling function and its declared peak rate scaling per class (used to normalize utilization as U = T*S/peak).voidStation.setLimitedJointDependence(SerializableFunction<Matrix, Matrix> eta, Matrix peakRatePerClass) Sets the limited joint-dependent scaling function and its declared peak rate scaling per class (used to normalize utilization as U = T*S/peak).voidStation.setLimitedLoadDependence(Matrix alpha) Sets the limited load-dependent scaling matrix for this station.voidQueue.setLoadDependence(Matrix alpha) Sets load-dependent service rate scaling factors.voidPlace.setMarking(Matrix marking) Alias forStatefulNode.setState(jline.util.matrix.Matrix)using Petri-net terminology: sets the initial per-class token marking of this place.voidCache.setResultDelayedHitProb(Matrix actualDelayedHitProb) Sets the delayed-hit fraction per class (retrieval system).voidCache.setResultDelayedHitQLen(Matrix d1, Matrix dfull) Sets the per-item delayed-hit queue length: the mean number of secondary requests waiting on the in-flight fetch of each item (d1), and the same count including the request that triggered the fetch (dfull).voidCache.setResultHitProb(Matrix actualHitProb) voidCache.setResultHitProbList(Matrix actualHitProbList) Sets the per-class, per-list (per-level) hit fraction matrix [classes x lists]; rows sum to the aggregate hit fraction.voidCache.setResultItemProb(Matrix actualItemProb) Sets the per-item occupancy matrix [items x (lists+1)]; column 0 = miss, columns 1..voidCache.setResultListCost(Matrix listCost) Sets the mean storage cost held by each list, as computed by a solver.voidCache.setResultMissProb(Matrix actualMissProb) Sets the actual miss probabilities from simulation or analysis results.voidCache.setResultResidT(Matrix actualResidT) Sets the actual expected latency from simulation or analysis results.voidSets the state of this node.voidvoidStatefulNode.setStatePrior(Matrix prior) voidStatefulNode.setStateSpace(Matrix space) voidQueue.setSwapGraph(Matrix graph) Sets the class compatibility/swap graph of a pass-and-swap (PAS) queue.Method parameters in jline.lang.nodes with type arguments of type MatrixModifier and TypeMethodDescriptionvoidQueue.setClassDependence(SerializableFunction<Matrix, Matrix> beta) Sets a class-dependent scaling function for service rates.voidQueue.setClassDependence(SerializableFunction<Matrix, Matrix> beta) Sets a class-dependent scaling function for service rates.voidQueue.setClassDependence(SerializableFunction<Matrix, Matrix> beta, Matrix peakRatePerClass) Sets a class-dependent service-rate scaling and its required peak rate per class.voidQueue.setClassDependence(SerializableFunction<Matrix, Matrix> beta, Matrix peakRatePerClass) Sets a class-dependent service-rate scaling and its required peak rate per class.voidTransition.setFiringRateDependence(Mode mode, SerializableFunction<Matrix, Double> g) Marking-dependent firing-rate multiplier for a timed mode.voidQueue.setJointDependence(SerializableFunction<Matrix, Matrix> eta, Matrix peakRatePerClass) Sets a joint-dependent (non-product-form) service-rate scaling and its required peak rate per class.voidQueue.setJointDependence(SerializableFunction<Matrix, Matrix> eta, Matrix peakRatePerClass) Sets a joint-dependent (non-product-form) service-rate scaling and its required peak rate per class.voidStation.setLimitedClassDependence(SerializableFunction<Matrix, Matrix> gamma) Refuses a class dependence declared without its peak rate: the peak is the max_n beta_{i,r}(n) that utilization is normalized by, and it cannot be recovered from the handle.voidStation.setLimitedClassDependence(SerializableFunction<Matrix, Matrix> gamma) Refuses a class dependence declared without its peak rate: the peak is the max_n beta_{i,r}(n) that utilization is normalized by, and it cannot be recovered from the handle.voidStation.setLimitedClassDependence(SerializableFunction<Matrix, Matrix> gamma, Matrix peakRatePerClass) Sets the limited class-dependent scaling function and its declared peak rate scaling per class (used to normalize utilization as U = T*S/peak).voidStation.setLimitedClassDependence(SerializableFunction<Matrix, Matrix> gamma, Matrix peakRatePerClass) Sets the limited class-dependent scaling function and its declared peak rate scaling per class (used to normalize utilization as U = T*S/peak).voidStation.setLimitedJointDependence(SerializableFunction<Matrix, Matrix> eta) Refuses a joint dependence declared without its peak rate; twin ofStation.setLimitedClassDependence(SerializableFunction).voidStation.setLimitedJointDependence(SerializableFunction<Matrix, Matrix> eta) Refuses a joint dependence declared without its peak rate; twin ofStation.setLimitedClassDependence(SerializableFunction).voidStation.setLimitedJointDependence(SerializableFunction<Matrix, Matrix> eta, Matrix peakRatePerClass) Sets the limited joint-dependent scaling function and its declared peak rate scaling per class (used to normalize utilization as U = T*S/peak).voidStation.setLimitedJointDependence(SerializableFunction<Matrix, Matrix> eta, Matrix peakRatePerClass) Sets the limited joint-dependent scaling function and its declared peak rate scaling per class (used to normalize utilization as U = T*S/peak).voidQueue.setService(SerializableFunction<Matrix, Double> muFun) Sets the total service rate function mu(c) of a pass-and-swap (PAS) queue.voidQueue.setServiceRateFunction(SerializableFunction<Matrix, Double> muFun) Sets the total service rate function mu(c) of a pass-and-swap (PAS) queue.Constructors in jline.lang.nodes with parameters of type MatrixModifierConstructorDescriptionCache(Network model, String name, int nitems, int itemLevelCap, ReplacementStrategy replPolicy, Matrix[] graph) Creates a single-level cache with the specified item capacity, replacement policy, and graph structure.Cache(Network model, String name, int nitems, Matrix itemLevelCap, ReplacementStrategy replPolicy) Creates a multi-level cache with different capacities per level and a replacement policy.Cache(Network model, String name, int nitems, Matrix itemLevelCap, ReplacementStrategy replPolicy, Matrix[] graph) Creates a multi-level cache with different capacities per level, replacement policy, and graph structure.ClassSwitch(Network model, String name, Matrix csFun) -
Uses of Matrix in jline.lang.processes
Fields in jline.lang.processes declared as MatrixModifier and TypeFieldDescriptionprotected MatrixEmpiricalCDF.dataprotected MatrixMarkovProcess.infGenprotected MatrixMarkovChain.stateSpaceprotected MatrixMarkovProcess.stateSpaceprotected MatrixMarkovChain.transMatFields in jline.lang.processes with type parameters of type MatrixMethods in jline.lang.processes that return MatrixModifier and TypeMethodDescriptionMarkovian.acf(int maxLag) Property alias for getACFDMAP.D(int i) MAP.D(int i) Marked.D(int i) Marked.D(int i, int k) Markovian.D(int i) Gets the i-th matrix of the Markovian arrival process representation.MMPP2.D(int i) Markovian.embedded()Property alias for getEmbeddedMarkovian.embeddedProb()Property alias for getEmbeddedProbMAP.evalACFT(int[] lags, double timescale) Evaluates the autocorrelation function at given lags and timescale.MarkovModulated.evalACFT(int[] lags, double timescale) MMPP2.evalACFT(int[] lags, double timescale) APH.evalCDFMatrix()Evaluates the CDF at default time points and returns as a Matrix.APH.evalCDFMatrix(double[] timePoints) Evaluates the CDF at specified time points and returns as a Matrix.Zipf.evalPMF()Evaluates the probability mass function at tME.getA()Gets the matrix parameter A.Markovian.getACF(double[] lags) Computes the autocorrelation of the inter-event times at the given lags, supplied as a plain array.Gets the autocorrelation function at the specified lags.ME.getAlpha()Gets the initial vector alpha.PHt.getAlphaAt(double t) alpha in force at t; the last segment's row past a non-cyclic horizon.BMAP.getBatchMatrix(int batchSize) Get the D_k matrix for batch size kMultivariateNormal.getCorrelation()Gets the correlation matrix from the covariance matrix.MultivariateNormal.getCovariance()Gets the covariance matrix.MAPt.getD0At(double t) D0 in force at t; the zero matrix past a non-cyclic horizon.MAPt.getD1At(double t) D1 in force at t; the zero matrix past a non-cyclic horizon.Marked.getD1k(int k) EmpiricalCDF.getData()Get the empirical dataMarkovian.getEmbedded()Gets the embedded Markov chain transition matrix.MMPP2.getEmbedded()Markovian.getEmbeddedProb()Gets the stationary probability vector of the embedded Markov chain.MMPP2.getEmbeddedProb()MarkovProcess.getGenerator()Get the infinitesimal generator matrixRAP.getH0()Gets the H0 matrix (hidden transition rates).RAP.getH1()Gets the H1 matrix (visible transition rates).APH.getInitProb()Markovian.getInitProb()Gets the initial probability vector.PH.getInitProb()MultivariateNormal.getMeanVector()Gets the mean vector.Coxian.getMu()DMAP.getMu()Row sums of (I - D0).Markovian.getMu()Gets the diagonal rate matrix containing the negative diagonal elements of D0.Coxian.getPhi()Markovian.getPhi()Gets the exit probability vector (phi).MarkovProcess.getProbState(Matrix state) Get probability of a specific state using Cramer's rulePHt.getSAt(double t) S in force at t; the zero matrix past a non-cyclic horizon.MarkovChain.getStateSpace()Get the state spaceMarkovProcess.getStateSpace()Get the state spaceAPH.getSubgenerator()Markovian.getSubgenerator()PH.getSubgenerator()DMAP.getTransitionMatrix()Embedded chain P = (I - D0)^{-1} * D1.MarkovChain.getTransMat()Get the transition matrixMMAP.getTypeMatrix(int type) Get the D_k matrix for arrival type kMarkovChain.hittingTime(int[] targetStates) Mean number of steps to reach any target state.APH.initProb()Property alias for getInitProbMarkovian.initProb()Property alias for getInitProbPH.initProb()Property alias for getInitProbCoxian.mu()Property alias for getMuMarkovian.mu()Property alias for getMuCoxian.phi()Property alias for getPhiMarkovian.phi()Property alias for getPhiMMDP.Q()Returns the generator matrix Q.MMDP2.Q()Returns the generator matrix Q (closed-form for 2 states).MMDP.r()Returns the rate vector (diagonal of R).MMDP2.r()Returns the rate vector (diagonal of R).MMDP.R()Returns the rate matrix R (diagonal).MMDP2.R()Returns the rate matrix R (diagonal, closed-form for 2 states).static Matrix[]CME.representation(int order) Builds the unit-mean (alpha, A) matrix-exponential form of a CME.MarkovChain.sample()Sample from the DTMCMarkovChain.sample(int n) Sample n steps from the DTMC starting from initial distributionSample n steps from the DTMCMarkovProcess.sample()Sample from the CTMCMarkovProcess.sample(int n) Sample n state transitions from the CTMC starting from steady-stateSample n state transitions from the CTMCMultivariateNormal.sampleMatrix(int n, Random random) Generates random samples from the distribution.Sensitivity of the stationary distribution to a scalar parameter.MarkovChain.solve()Solve the DTMC for steady-state probabilities.MarkovProcess.solve()Solve the CTMC for steady-state probabilitiesMarkovProcess.solveRelative(int refstate) Equilibrium distribution relative to a reference state, i.e.Stochastic complement of a subset of states.Stochastic complement of a subset of states.Markovian.subgenerator()Property alias for getSubgeneratorPH.subgenerator()Property alias for getSubgeneratorMarkovProcess.timeAverage(Matrix pi0, double t) Time-averaged distribution over [0,t].GMM.toMatrix()Converts this GMM to a simplified matrix representation.MarkovChain.transientProb(Matrix pi0, int steps) Distribution at each step 0,...,steps.MarkovProcess.transientProb(Matrix pi0, double t) Distribution at time t from pi0, by Jensen uniformization.MarkovProcess.transientProb(Matrix pi0, double t, String method) Distribution at time t from pi0.MarkovChain.transientUnif(Matrix pi0, double t) Distribution at time t of the DTMC seen through uniformization.Methods in jline.lang.processes that return types with arguments of type MatrixModifier and TypeMethodDescriptionAggregation-disaggregation over a macrostate partition.PHt.getAlphaSegments()PHt.RateSchedule.getAlphaSegments()MAPt.getD0Segments()MAPt.RateSchedule.getD0Segments()MAPt.getD1Segments()MAPt.RateSchedule.getD1Segments()Immediate.getPH()Det.getProcess()Gets the matrix representation of this deterministic process (for PH compatibility).PHt.getSSegments()PHt.RateSchedule.getSSegments()Det.process()Property alias for getProcessMethods in jline.lang.processes with parameters of type MatrixModifier and TypeMethodDescriptiondoubleEvaluates the probability density function.static MMAPFactory method to create a simple 2-type MMAP from two MAPsstatic BMAPBMAP.fromMAPWithBatchPMF(Matrix D0, Matrix D1, int[] batchSizes, double[] pmf) Factory method to create BMAP from a base MAP and batch size distribution Given a base MAP (D0_base, D1_base) for inter-batch arrivals and a batch size distribution, constructs the BMAP by scaling: Dk = D1_base * pmf[k-1]static GMMGMM.fromMatrix(Matrix sgmm) Creates a GMM from a simplified matrix representation.static MarkovChainMarkovChain.fromSampleSysAggr(Matrix samples) Create DTMC from sample system aggregationstatic MarkovChainMarkovChain.fromSampleSysAggr(Matrix samples, int numStates) Create DTMC from sample system aggregationstatic MarkovProcessMarkovProcess.fromSampleSysAggr(Matrix samples) Create CTMC from sample system aggregation (assuming unit sojourn times)static MarkovProcessMarkovProcess.fromSampleSysAggr(Matrix samples, Matrix sojournTimes) Create CTMC from sample system aggregationstatic MarkovProcessMarkovProcess.fromSampleSysAggr(Matrix samples, Matrix sojournTimes, int numStates) Create CTMC from sample system aggregationGets the autocorrelation function at the specified lags.doubleMMPP2.getACFDecay(Matrix lags) MarkovProcess.getProbState(Matrix state) Get probability of a specific state using Cramer's rulestatic booleanMMDP.isFeasible(Matrix Q, Matrix R) Checks if the given (Q, R) matrices define a valid MMDP.Sample n steps from the DTMCSample n state transitions from the CTMCstatic ME.NegativeDensityScanME.scanNegativeDensity(Matrix alphaRow, Matrix A) Searches the density f(t) = -alpha*expm(A*t)*A*e for a negative value.Sensitivity of the stationary distribution to a scalar parameter.voidMarkovChain.setStateSpace(Matrix stateSpace) Set the state spacevoidMarkovProcess.setStateSpace(Matrix stateSpace) Set the state spaceMarkovProcess.timeAverage(Matrix pi0, double t) Time-averaged distribution over [0,t].MarkovChain.transientProb(Matrix pi0, int steps) Distribution at each step 0,...,steps.MarkovProcess.transientProb(Matrix pi0, double t) Distribution at time t from pi0, by Jensen uniformization.MarkovProcess.transientProb(Matrix pi0, double t, String method) Distribution at time t from pi0.MarkovChain.transientUnif(Matrix pi0, double t) Distribution at time t of the DTMC seen through uniformization.Method parameters in jline.lang.processes with type arguments of type MatrixModifier and TypeMethodDescriptionstatic voidMAPt.checkCommonSupport(List<Matrix> mats, boolean ignoreDiagonal, String label) Rejects a schedule whose matrices do not share one sparsity pattern.voidDet.setProcess(Map<Integer, Matrix> proc) Sets the matrix representation of this deterministic process.Constructors in jline.lang.processes with parameters of type MatrixModifierConstructorDescriptionConstruct a BMAP from D0 and variable number of Dk matricesDiscreteSampler(Matrix p, Matrix x) Constructs a discrete distribution from a finite probability vector p at the points specified in vector xEmpiricalCDF(Matrix xdata) Creates an EmpiricalCDF with the given dataEmpiricalCDF(Matrix cdfdata, Matrix xdata) Creates an EmpiricalCDF with separate CDF and value dataCreates an n-phase hyper-exponential distribution from Matrix vectors.MarkedMarkovProcess(Matrix infGen, MatrixCell eventFilt, List<Map<String, Object>> evs) Creates a MarkedCTMC with the specified generator, event filters, and eventsMarkedMarkovProcess(Matrix infGen, MatrixCell eventFilt, List<Map<String, Object>> evs, boolean isFinite) Creates a MarkedCTMC with the specified generator, event filters, events, and finite flagMarkedMarkovProcess(Matrix infGen, MatrixCell eventFilt, List<Map<String, Object>> evs, boolean isFinite, Matrix stateSpace) Creates a MarkedCTMC with the specified generator, event filters, events, finite flag, and state spaceMarkovChain(Matrix transMat) Creates a DTMC with the specified transition matrixMarkovChain(Matrix transMat, boolean isFinite) Creates a DTMC with the specified transition matrix and finite flagMarkovProcess(Matrix infGen) Creates a CTMC with the specified infinitesimal generatorMarkovProcess(Matrix infGen, boolean isFinite) Creates a CTMC with the specified infinitesimal generator and finite flagMarkovProcess(Matrix infGen, boolean isFinite, Matrix stateSpace) Creates a CTMC with the specified infinitesimal generator, finite flag, and state spaceCreates a Matrix Exponential distribution with specified initial vector and matrix parameter.protectedCreates a Matrix Exponential distribution, optionally skipping the density scan.Construct an MMAP from D0 and a variable number of per-type matrices.Creates an MMDP with specified generator Q and rate matrix R.MultivariateNormal(double[] mu, Matrix Sigma) Creates a multivariate normal distribution.MultivariateNormal(Matrix mu, Matrix Sigma) Creates a multivariate normal distribution.Creates a Rational Arrival Process with specified H0 and H1 matrices.Constructor parameters in jline.lang.processes with type arguments of type MatrixModifierConstructorDescriptionCreates a cyclic MAP_t.Creates a MAP_t with a piecewise-constant matrix schedule.Creates a cyclic Ph_t.Creates a Ph_t with a piecewise-constant schedule. -
Uses of Matrix in jline.lang.reward
Methods in jline.lang.reward with parameters of type MatrixModifier and TypeMethodDescriptionvoidAdds a pre-computed reward value for a specific aggregated state.doubleRewardDescriptor.compute(Matrix state, NetworkStruct sn) doubleRewardFunction.compute(Matrix state, NetworkStruct sn) Compute the reward value for a given state.doubleTabulatedRewardFunction.compute(Matrix state, NetworkStruct sn) -
Uses of Matrix in jline.lang.sections
Fields in jline.lang.sections declared as MatrixModifier and TypeFieldDescriptionCacheClassSwitcher.actualDelayedHitProbCacheClassSwitcher.actualDelayedHitQLen[1 x items] mean secondary requests waiting on the in-flight fetch of each itemCacheClassSwitcher.actualDelayedHitQLenFull[1 x items] as above, including the request that triggered the fetchCacheClassSwitcher.actualHitProbCacheClassSwitcher.actualHitProbListCacheClassSwitcher.actualItemProbCacheClassSwitcher.actualListCostCacheClassSwitcher.actualMissProbCacheClassSwitcher.actualResidTCacheClassSwitcher.hitClassCacheClassSwitcher.missClassCacheClassSwitcher.retrievalClassesCSFunInput.stateOptional state matrixCSFunInput.statedepOptional state-dependent matrixMethods in jline.lang.sections with parameters of type MatrixModifier and TypeMethodDescriptiondoubleCacheClassSwitcher.simpleHitMiss(int r, int s, Matrix state) doubleCacheClassSwitcher.simpleHitMiss(int r, int s, Matrix state, Matrix statep) voidStatelessClassSwitcher.updateClassSwitch(Matrix csMatrix) Constructors in jline.lang.sections with parameters of type MatrixModifierConstructorDescriptionCacheClassSwitcher(List<JobClass> jobClasses, int items, Matrix capacity) CacheClassSwitcher(List<JobClass> jobClasses, int items, Matrix capacity, int levels) CSFunInput(int r, int s, Matrix state, Matrix statedep) Creates a new CSFunInput.StatelessClassSwitcher(List<JobClass> jobClasses, Matrix csMatrix) -
Uses of Matrix in jline.lang.state
Fields in jline.lang.state declared as MatrixModifier and TypeFieldDescriptionState.StateSpaceGeneratorResult.Adjfinal MatrixState.spaceGeneratorNodesResult.capacitycfinal MatrixEventCacheKey.inspacefinal MatrixState.EventHandleResult.isCompletionColumn vector, one entry per row of outspace: 1.0 when that outcome is a firing completion (a D1 firing of the active mode, which applies the PRE/POST place updates), 0.0 otherwise.final MatrixAfterEventContext.lldscalingLoad-dependent scaling matrix (defaulted to ones if sn.lldscaling is empty).State.StateMarginalStatistics.niState.StateMarginalStatistics.nirfinal MatrixAfterGlobalEvent.AfterGlobalEventResult.outcompColumn vector aligned with outrate: 1.0 when that outcome is a firing completion (a D1 firing of the active mode, which applies the PRE/POST place updates), 0.0 otherwise (e.g.final MatrixAfterFJEvent.AfterFJEventResult.outprobfinal MatrixAfterGlobalEvent.AfterGlobalEventResult.outprobProbability matrix for each resulting state transition.final MatrixState.EventHandleResult.outprobfinal MatrixAfterFJEvent.AfterFJEventResult.outratefinal MatrixAfterGlobalEvent.AfterGlobalEventResult.outrateTransition rates matrix for each resulting state.final MatrixState.EventHandleResult.outratefinal MatrixState.EventHandleResult.outspaceState.StateMarginalStatistics.sirState.StateSpaceGeneratorResult.SSState.StateSpaceGeneratorResult.SShMatrix[]Polling.Info.swD0D0 of the switchover entering buffer q.Matrix[]Polling.Info.swD1D1 of the switchover entering buffer q.Matrix[]Polling.Info.swPieEntry probability vector of the switchover entering buffer q.Fields in jline.lang.state with type parameters of type MatrixModifier and TypeFieldDescriptionfinal Map<Station,SerializableFunction<Matrix, Matrix>> AfterEventContext.cdscalingClass-dependent scaling functions (defaulted to the constant 1 map).final Map<Station,SerializableFunction<Matrix, Matrix>> AfterEventContext.cdscalingClass-dependent scaling functions (defaulted to the constant 1 map).final Map<StatefulNode,Matrix> State.initialStatefinal Map<StatefulNode,Matrix> State.initialStateSpaceAfterEventContext.ismkvmodclassPer-node (R x 1) indicator of MAP/MMPP2/BMAP service; stations only.State.StateMarginalStatistics.kirfinal Map<StatefulNode,Matrix> State.spaceGeneratorNodesResult.nodeStateSpaceAfterFJEvent.AfterFJEventResult.outGlobalStatesAfterGlobalEvent.AfterGlobalEventResult.outglspaceUpdated global state space after event processing.final Map<StatefulNode,Matrix> State.priorInitialStatePolling.Landing.rowsSuccessor [buffer, server, local-var] rows.State.StateSpaceGeneratorResult.QNC.spaceMethods in jline.lang.state that return MatrixModifier and TypeMethodDescriptionstatic MatrixReplyBlock.blocked(NetworkStruct sn, int ind, Matrix spaceVar) Per-class counts of servers held at nodeindby jobs that made a synchronous call and are waiting for their REPLY signal.static MatrixReplyBlock.blockedTotal(NetworkStruct sn, int ind, Matrix spaceVar) Total number of servers held for pending replies, one entry per row ofspaceVar.static MatrixFromMarginal.fromMarg(NetworkStruct sn, int ind, int ntot) Every detailed local state of node IND whose CLASS TOTAL is ntot.static MatrixGenerates the state space of a node from its TOTAL job count, all classes summed out.static MatrixFromMarginal.fromMargAndStarted(NetworkStruct sn, int ind, int ntot, int stot) Every detailed local state of node IND whose CLASS TOTAL is ntot and whose total number of started jobs is stot.static MatrixState.fromMargAndStarted(Network model, int ind, int ntot, int stot) Generates the state space of a node from its TOTAL job count and its TOTAL number of started jobs, both summed over classes.static MatrixFromMarginal.fromMarginal(NetworkStruct sn, int ind, Matrix n) static MatrixState.fromMarginal(Network model, int ind, int[] n) Generates the state space of a node from the per-class marginal job counts.static MatrixFromMarginal.fromMarginalAndRunning(Network sn, int ind, Matrix n, Matrix s) static MatrixFromMarginal.fromMarginalAndRunning(NetworkStruct sn, int ind, Matrix n, Matrix s) static MatrixFromMarginal.fromMarginalAndRunning(NetworkStruct sn, int ind, Matrix n, Matrix s, boolean optionsForce) static MatrixState.fromMarginalAndRunning(Network model, int ind, int[] n, int[] s) Generates the state space of a node from the per-class marginal job counts and the per-class number of running jobs.static MatrixFromMarginal.fromMarginalAndStarted(Network network, int ind, Matrix n, Matrix s) static MatrixFromMarginal.fromMarginalAndStarted(NetworkStruct sn, int ind, Matrix n, Matrix s) static MatrixFromMarginal.fromMarginalAndStarted(NetworkStruct sn, int ind, Matrix n, Matrix s, Boolean optionsForce) Wrapper: the discipline branches below return early from several places, so the synchronous-call (REPLY) counter columns are appended here, once, for every exit path.static MatrixState.fromMarginalAndStarted(Network model, int ind, int[] n, int[] s) Generates the state space of a node from the per-class marginal job counts and the per-class number of jobs that have just started service.static MatrixFromMarginal.fromMarginalBounds(NetworkStruct sn, int ind, Matrix ub, double cap, SolverOptions options) static MatrixPolling.project(Polling.Info pinfo, int[] trip) Project a full {pos, swk, ctr} triple onto the materialized columns.static MatrixPolling.set(Polling.Info pinfo, Matrix spaceVar, int pos, int swk, int ctr) Write the polling controller into the local-variable columns of a state row.static MatrixSignalRemoval.signalBatchPMF(NetworkStruct sn, int jobClass, int ntot) Batch-size distribution of the jobs removed by a signal class when ntot eligible jobs are present.static MatrixPolling.space(NetworkStruct sn, int ind, Matrix space, Matrix K) Append the polling controller columns to the rows of space, which must hold the [buffer, server, routing-variable] layout of a polling station.static MatrixState.spaceCachePublic(int n, Matrix m, int retrievalSystemCapacity) Make spaceCache method public Generates cache state spacestatic MatrixState.spaceClosedMulti(int M, Matrix N) static MatrixState.spaceClosedMultiCS(int M, Matrix N, Matrix chains) static MatrixState.spaceClosedMultiCSBounded(int M, Matrix N, Matrix chains, Matrix capMatrix, Set<String> visitedSums) static MatrixState.spaceClosedSinglePublic(int M, int N) Make spaceClosedSingle method public Generates state space for single-class closed networksstatic MatrixState.spaceLocalVarsPublic(NetworkStruct sn, int ind) Make spaceLocalVars method public Generates local variable state spacesstatic MatrixState.spaceLocalVarsPublic(NetworkStruct sn, int ind, int maxPending) Make spaceLocalVars method public, at a given delayed-hit truncation level.Methods in jline.lang.state with parameters of type MatrixModifier and TypeMethodDescriptionstatic Ret.EventResultState.afterEvent(NetworkStruct sn, int ind, Matrix inspace, EventType event, int jobClass, boolean isSimulation) static Ret.EventResultState.afterEvent(NetworkStruct sn, int ind, Matrix inspace, EventType event, int jobClass, boolean isSimulation, EventCache eventCache) static Ret.EventResultState.afterEvent(NetworkStruct sn, int ind, Matrix inspace, EventType event, int jobClass, boolean isSimulation, EventCache eventCache, AfterEventContext ctx) static Ret.EventResultState.afterEvent(NetworkStruct sn, int ind, Matrix inspace, EventType event, int jobClass, boolean isSimulation, EventCache eventCache, AfterEventContext ctx, boolean noPromote) noPromote: when true, a DEP at an FCFS-family station does not promote a waiting job into the vacated server.static Ret.EventResultAfterEventStation.afterEventStationPas(NetworkStruct sn, int ind, int ist, Matrix inspace, EventType event, int jobClass, int R, int V, boolean isSimulation) Event handler for pass-and-swap (PAS) / order-independent stations.static Ret.EventResultAfterEventStationReply.apply(NetworkStruct sn, int ind, int ist, int jobClass, Matrix K, Matrix Ks, Matrix S, Map<Station, Map<JobClass, Matrix>> pie, Matrix spaceBuf, Matrix spaceSrv, Matrix spaceVar) Applies a REPLY arrival at nodeind.static MatrixReplyBlock.blocked(NetworkStruct sn, int ind, Matrix spaceVar) Per-class counts of servers held at nodeindby jobs that made a synchronous call and are waiting for their REPLY signal.static MatrixReplyBlock.blockedTotal(NetworkStruct sn, int ind, Matrix spaceVar) Total number of servers held for pending replies, one entry per row ofspaceVar.static intstatic MatrixFromMarginal.fromMarginal(NetworkStruct sn, int ind, Matrix n) static MatrixFromMarginal.fromMarginalAndRunning(Network sn, int ind, Matrix n, Matrix s) static MatrixFromMarginal.fromMarginalAndRunning(NetworkStruct sn, int ind, Matrix n, Matrix s) static MatrixFromMarginal.fromMarginalAndRunning(NetworkStruct sn, int ind, Matrix n, Matrix s, boolean optionsForce) static MatrixFromMarginal.fromMarginalAndStarted(Network network, int ind, Matrix n, Matrix s) static MatrixFromMarginal.fromMarginalAndStarted(NetworkStruct sn, int ind, Matrix n, Matrix s) static MatrixFromMarginal.fromMarginalAndStarted(NetworkStruct sn, int ind, Matrix n, Matrix s, Boolean optionsForce) Wrapper: the discipline branches below return early from several places, so the synchronous-call (REPLY) counter columns are appended here, once, for every exit path.static MatrixFromMarginal.fromMarginalBounds(NetworkStruct sn, int ind, Matrix ub, double cap, SolverOptions options) static int[]Polling.get(Polling.Info pinfo, Matrix spaceVar, int srvclass) Read the polling controller out of the local-variable columns of a single state row.static Ret.getHashOrAddResultState.getHashOrAdd(NetworkStruct sn, int ind, Matrix inspace) Get hash ID for a state space, or add the state to the space if not found Migrated from MATLAB getHashOrAdd.mprotected static State.EventHandleResultState.handleEnableEvent(NetworkStruct sn, int ind, GlobalSync glevent, List<Matrix> glspace, List<Matrix> outglspace, Matrix inspace, Matrix spaceBuf, Matrix spaceSrv, Matrix spaceVar, Matrix fK, Matrix fKs, int mode, TransitionNodeParam transParam, int R) Handles ENABLE events for transitions in Stochastic Petri Net (SPN) event processing.protected static State.EventHandleResultState.handleFireEvent(NetworkStruct sn, int ind, GlobalSync glevent, List<Matrix> glspace, List<Matrix> outglspace, Matrix inspace, Matrix spaceBuf, Matrix spaceSrv, Matrix spaceVar, Matrix fK, Matrix fKs, int mode, TransitionNodeParam transParam, int R, boolean isSimulation) Handles FIRE events for transitions in Stochastic Petri Net (SPN) event processing.static Ret.EventResultSignalRemoval.handleSignalArrival(NetworkStruct sn, int ind, int ist, Matrix inspace, int jobClass, boolean isSimulation, Matrix phasessz, Matrix phaseshift, Matrix K, Matrix Ks, Matrix S, Matrix spaceBuf, Matrix spaceSrv, Matrix spaceVar) Passive arrival of a G-network signal class at station ist.static booleanstatic booleanState.isValid(NetworkStruct sn, Matrix n, Matrix s) static Polling.LandingPolling.land(Polling.Info pinfo, int q, int mode, int budget, Matrix spaceBuf, Matrix spaceSrv, Matrix spaceVar, Matrix K, Matrix Ks, Map<JobClass, Matrix> pieist, List<JobClass> jobclasses, int R) Materialize the state rows a polling server lands in afterPolling.next(jline.lang.state.Polling.Info, int, int[], int, boolean)has resolved (q, mode, budget).static AfterEventStation.PasSwapAfterEventStation.passAndSwap(int[] c, int p, Matrix G) Applies the pass-and-swap mechanism (Dorsman and Gardner 2024, Sect.static voidState.roundMarginalPreservingChains(Matrix n, NetworkStruct sn) Rounds a fractional marginal queue-length matrix (station x class) to integers with the largest remainder method, so that every closed chain keeps exactly its own population.static MatrixPolling.set(Polling.Info pinfo, Matrix spaceVar, int pos, int swk, int ctr) Write the polling controller into the local-variable columns of a state row.static MatrixPolling.space(NetworkStruct sn, int ind, Matrix space, Matrix K) Append the polling controller columns to the rows of space, which must hold the [buffer, server, routing-variable] layout of a polling station.static MatrixState.spaceCachePublic(int n, Matrix m, int retrievalSystemCapacity) Make spaceCache method public Generates cache state spacestatic MatrixState.spaceClosedMulti(int M, Matrix N) static MatrixState.spaceClosedMultiCS(int M, Matrix N, Matrix chains) static MatrixState.spaceClosedMultiCSBounded(int M, Matrix N, Matrix chains, Matrix capMatrix, Set<String> visitedSums) State.spaceGenerator(NetworkStruct sn, Matrix cutoff, SolverOptions options) Generates the state space for a queueing network using a matrix cutoff.State.spaceGeneratorNodes(NetworkStruct sn, Matrix cutoff, SolverOptions options) ToMarginal.toMarginal(NetworkStruct sn, int ind, Matrix state_i, Matrix phasesz, Matrix phaseshift, Matrix space_buf, Matrix space_srv, Matrix space_var) Computes marginal statistics for a stateful node in a Stochastic Petri Net.ToMarginal.toMarginalAggr(NetworkStruct sn, int ind, Matrix state_i, Matrix K, Matrix Ks, Matrix space_buf, Matrix space_srv, Matrix space_var) Computes aggregated marginal statistics for a stateful node using specified aggregation weights.Method parameters in jline.lang.state with type arguments of type MatrixModifier and TypeMethodDescriptionAfterFJEvent.afterFJEvent(NetworkStruct sn, FJSync fjentry, List<Matrix> glspace, boolean isSimulation, EventCache eventCache) AfterGlobalEvent.afterGlobalEvent(NetworkStruct sn, int ind, List<Matrix> glspace, GlobalSync glevent, boolean isSimulation) Processes a global event in a Stochastic Petri Net (SPN) and computes the resulting state space.static Ret.EventResultAfterEventStationReply.apply(NetworkStruct sn, int ind, int ist, int jobClass, Matrix K, Matrix Ks, Matrix S, Map<Station, Map<JobClass, Matrix>> pie, Matrix spaceBuf, Matrix spaceSrv, Matrix spaceVar) Applies a REPLY arrival at nodeind.static Map<StatefulNode,Map<String, Integer>> State.buildSpaceHashMap(Map<StatefulNode, Matrix> space) protected static State.EventHandleResultState.handleEnableEvent(NetworkStruct sn, int ind, GlobalSync glevent, List<Matrix> glspace, List<Matrix> outglspace, Matrix inspace, Matrix spaceBuf, Matrix spaceSrv, Matrix spaceVar, Matrix fK, Matrix fKs, int mode, TransitionNodeParam transParam, int R) Handles ENABLE events for transitions in Stochastic Petri Net (SPN) event processing.protected static State.EventHandleResultState.handleFireEvent(NetworkStruct sn, int ind, GlobalSync glevent, List<Matrix> glspace, List<Matrix> outglspace, Matrix inspace, Matrix spaceBuf, Matrix spaceSrv, Matrix spaceVar, Matrix fK, Matrix fKs, int mode, TransitionNodeParam transParam, int R, boolean isSimulation) Handles FIRE events for transitions in Stochastic Petri Net (SPN) event processing.static Polling.LandingPolling.land(Polling.Info pinfo, int q, int mode, int budget, Matrix spaceBuf, Matrix spaceSrv, Matrix spaceVar, Matrix K, Matrix Ks, Map<JobClass, Matrix> pieist, List<JobClass> jobclasses, int R) Materialize the state rows a polling server lands in afterPolling.next(jline.lang.state.Polling.Info, int, int[], int, boolean)has resolved (q, mode, budget).Constructors in jline.lang.state with parameters of type MatrixModifierConstructorDescriptionAfterEventContext(Matrix lldscaling, int lldlimit, Map<Station, SerializableFunction<Matrix, Matrix>> cdscaling, Map<Integer, Matrix> ismkvmodclass) Constructs a new result container for global event processing.EventCacheKey(int ind, Matrix inspace, EventType event, int jobClass, boolean isSimulation) EventCacheKey(int ind, Matrix inspace, EventType event, int jobClass, boolean isSimulation, boolean noPromote) EventHandleResult(Matrix outspace, Matrix outrate, Matrix outprob) EventHandleResult(Matrix outspace, Matrix outrate, Matrix outprob, Matrix isCompletion) spaceGeneratorNodesResult(Map<StatefulNode, Matrix> nodeStateSpace, NetworkStruct sn, Matrix capacityc) StateSpaceGeneratorResult(Matrix ss, Matrix sSh, NetworkStruct sn) Constructor parameters in jline.lang.state with type arguments of type MatrixModifierConstructorDescriptionAfterEventContext(Matrix lldscaling, int lldlimit, Map<Station, SerializableFunction<Matrix, Matrix>> cdscaling, Map<Integer, Matrix> ismkvmodclass) AfterEventContext(Matrix lldscaling, int lldlimit, Map<Station, SerializableFunction<Matrix, Matrix>> cdscaling, Map<Integer, Matrix> ismkvmodclass) Constructs a new result container for global event processing.spaceGeneratorNodesResult(Map<StatefulNode, Matrix> nodeStateSpace, NetworkStruct sn, Matrix capacityc) State(Map<StatefulNode, Matrix> initialState, Map<StatefulNode, Matrix> priorInitialState, Map<StatefulNode, Matrix> initialStateSpace) -
Uses of Matrix in jline.lang.workflow
Fields in jline.lang.workflow declared as MatrixModifier and TypeFieldDescriptionWorkflow.SPNode.alphaCached initial vector.Workflow.SPNode.TCached subgenerator.Methods in jline.lang.workflow that return types with arguments of type MatrixModifier and TypeMethodDescriptionWorkflow.composeLoopGeometric(Matrix alpha, Matrix T, double count) Geometric repetition of a PH law, the POST_LOOP semantics of an activity graph: the number of executions of the body is geometric of mean COUNT.Workflow.composeLoopGeometric(Matrix alpha, Matrix T, double count) Geometric repetition of a PH law, the POST_LOOP semantics of an activity graph: the number of executions of the body is geometric of mean COUNT.Workflow.composeMixture(List<Matrix> alphas, List<Matrix> Ts, double[] probs) Probabilistic mixture of several PH laws, which is aph_simplify pattern 3 generalised to any number of branches.Workflow.composeMixture(List<Matrix> alphas, List<Matrix> Ts, double[] probs) Probabilistic mixture of several PH laws, which is aph_simplify pattern 3 generalised to any number of branches.Workflow.composeParallel(Matrix alpha1, Matrix T1, Matrix alpha2, Matrix T2) Workflow.composeParallel(Matrix alpha1, Matrix T1, Matrix alpha2, Matrix T2) Workflow.composeRepeat(Matrix alpha, Matrix T, int count) Deterministic COUNT-fold convolution of a PH law.Workflow.composeRepeat(Matrix alpha, Matrix T, int count) Deterministic COUNT-fold convolution of a PH law.Workflow.composeSerial(Matrix alpha1, Matrix T1, Matrix alpha2, Matrix T2) Workflow.composeSerial(Matrix alpha1, Matrix T1, Matrix alpha2, Matrix T2) WorkflowActivity.getPHRepresentation()WorkflowActivity.getPHRepresentation()Methods in jline.lang.workflow with parameters of type MatrixModifier and TypeMethodDescriptionWorkflow.composeLoopGeometric(Matrix alpha, Matrix T, double count) Geometric repetition of a PH law, the POST_LOOP semantics of an activity graph: the number of executions of the body is geometric of mean COUNT.Workflow.composeParallel(Matrix alpha1, Matrix T1, Matrix alpha2, Matrix T2) Workflow.composeRepeat(Matrix alpha, Matrix T, int count) Deterministic COUNT-fold convolution of a PH law.Workflow.composeSerial(Matrix alpha1, Matrix T1, Matrix alpha2, Matrix T2) static booleanWorkflow.isAcyclicGenerator(Matrix T) True when the phase graph of T has no cycle.Method parameters in jline.lang.workflow with type arguments of type Matrix -
Uses of Matrix in jline.lib.butools
Methods in jline.lib.butools that return MatrixModifier and TypeMethodDescriptionstatic MatrixFactorialMomsFromMoms.factorialMomsFromMoms(Matrix m) Returns the factorial moments given the raw moments.static MatrixHankelMomsFromMoms.hankelMomsFromMoms(Matrix m) Returns the Hankel moments given the raw moments.static MatrixJFactorialMomsFromJMoms.jFactorialMomsFromJMoms(Matrix jm) Returns the lag-1 joint factorial moments given the lag-1 joint raw moments.static MatrixJMomsFromJFactorialMoms.jMomsFromJFactorialMoms(Matrix jfm) Returns the lag-1 joint raw moments given the lag-1 joint factorial moments.static MatrixMomsFromFactorialMoms.MomsFromFactorialMoms(Matrix fm) Returns the raw moments given the factorial moments.static MatrixMomsFromHankelMoms.momsFromHankelMoms(Matrix hm) Returns the raw moments given the Hankel moments.static MatrixMomsFromNormMoms.momsFromNormMoms(Matrix nm) Returns the raw moments given the normalized moments.static MatrixMomsFromReducedMoms.momsFromReducedMoms(Matrix rm) Returns the raw moments given the reduced moments.static Matrixstatic MatrixReducedMomsFromMoms.reducedMomsFromMoms(Matrix m) Returns the reduced moments given the raw moments.static MatrixSimilarityMatrixForVectors.SimilarityMatrixForVectors(Matrix vecA, Matrix vecB) Methods in jline.lib.butools that return types with arguments of type MatrixModifier and TypeMethodDescriptionFluidFundamentalMatrices.FluidFundamentalMatrices(Matrix Fpp, Matrix Fpm, Matrix Fmp, Matrix Fmm, Double precision_, Integer maxNumIt_, String method_) MMAPPH1FCFS.MMAPPH1FCFS(MatrixCell D, Map<Integer, Matrix> sigma, Map<Integer, Matrix> S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stDistr, boolean stDistrME, boolean stDistrPH, Double prec, Matrix classes_) MMAPPH1NPPR.MMAPPH1NPPR(MatrixCell D, MatrixCell sigma, MatrixCell S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stCdfPoints, Double prec, Integer erlMaxOrder_, Matrix classes_) MMAPPH1PRPR.MMAPPH1PRPR(MatrixCell D, MatrixCell sigma, MatrixCell S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stCdfPoints, Double prec, Integer erlMaxOrder_, Matrix classes_) QBDFundamentalMatrices.QBDFundamentalMatrices(Matrix B, Matrix L, Matrix F, Double precision_, Integer maxNumIt_, String method_, Integer Verbose_) MMAPPH1FCFS.solve(MatrixCell D, Map<Integer, Matrix> sigma, Map<Integer, Matrix> S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stDistr, boolean stDistrME, boolean stDistrPH, Double prec, Matrix classes_) Convenience alias for callers that expect asolve(...)entry point on the MMAPPH1FCFS class.Methods in jline.lib.butools with parameters of type MatrixModifier and TypeMethodDescriptionstatic booleanCheckMoments.checkMoments(Matrix m) static booleanCheckMoments.checkMoments(Matrix m, double prec) Checks if the given moment sequence is valid in the sense that it belongs to a distribution with support (0,inf).static MatrixFactorialMomsFromMoms.factorialMomsFromMoms(Matrix m) Returns the factorial moments given the raw moments.FluidFundamentalMatrices.FluidFundamentalMatrices(Matrix Fpp, Matrix Fpm, Matrix Fmp, Matrix Fmm, Double precision_, Integer maxNumIt_, String method_) static MatrixHankelMomsFromMoms.hankelMomsFromMoms(Matrix m) Returns the Hankel moments given the raw moments.static MatrixJFactorialMomsFromJMoms.jFactorialMomsFromJMoms(Matrix jm) Returns the lag-1 joint factorial moments given the lag-1 joint raw moments.static MatrixJMomsFromJFactorialMoms.jMomsFromJFactorialMoms(Matrix jfm) Returns the lag-1 joint raw moments given the lag-1 joint factorial moments.MMAPPH1FCFS.MMAPPH1FCFS(MatrixCell D, Map<Integer, Matrix> sigma, Map<Integer, Matrix> S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stDistr, boolean stDistrME, boolean stDistrPH, Double prec, Matrix classes_) MMAPPH1NPPR.MMAPPH1NPPR(MatrixCell D, MatrixCell sigma, MatrixCell S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stCdfPoints, Double prec, Integer erlMaxOrder_, Matrix classes_) MMAPPH1PRPR.MMAPPH1PRPR(MatrixCell D, MatrixCell sigma, MatrixCell S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stCdfPoints, Double prec, Integer erlMaxOrder_, Matrix classes_) static MatrixMomsFromFactorialMoms.MomsFromFactorialMoms(Matrix fm) Returns the raw moments given the factorial moments.static MatrixMomsFromHankelMoms.momsFromHankelMoms(Matrix hm) Returns the raw moments given the Hankel moments.static MatrixMomsFromNormMoms.momsFromNormMoms(Matrix nm) Returns the raw moments given the normalized moments.static MatrixMomsFromReducedMoms.momsFromReducedMoms(Matrix rm) Returns the raw moments given the reduced moments.static MatrixQBDFundamentalMatrices.QBDFundamentalMatrices(Matrix B, Matrix L, Matrix F, Double precision_, Integer maxNumIt_, String method_, Integer Verbose_) static MatrixReducedMomsFromMoms.reducedMomsFromMoms(Matrix m) Returns the reduced moments given the raw moments.static MatrixSimilarityMatrixForVectors.SimilarityMatrixForVectors(Matrix vecA, Matrix vecB) MMAPPH1FCFS.solve(MatrixCell D, Map<Integer, Matrix> sigma, Map<Integer, Matrix> S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stDistr, boolean stDistrME, boolean stDistrPH, Double prec, Matrix classes_) Convenience alias for callers that expect asolve(...)entry point on the MMAPPH1FCFS class.Method parameters in jline.lib.butools with type arguments of type MatrixModifier and TypeMethodDescriptionMMAPPH1FCFS.MMAPPH1FCFS(MatrixCell D, Map<Integer, Matrix> sigma, Map<Integer, Matrix> S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stDistr, boolean stDistrME, boolean stDistrPH, Double prec, Matrix classes_) MMAPPH1FCFS.solve(MatrixCell D, Map<Integer, Matrix> sigma, Map<Integer, Matrix> S, Integer numOfQLMoms, Integer numOfQLProbs, Integer numOfSTMoms, Matrix stDistr, boolean stDistrME, boolean stDistrPH, Double prec, Matrix classes_) Convenience alias for callers that expect asolve(...)entry point on the MMAPPH1FCFS class. -
Uses of Matrix in jline.lib.butools.dmap
Methods in jline.lib.butools.dmap that return MatrixModifier and TypeMethodDescriptionstatic MatrixLagkJointMomentsFromDMAP.lagkJointMomentsFromDMAP(Matrix D0, Matrix D1) static MatrixLagkJointMomentsFromDMAP.lagkJointMomentsFromDMAP(Matrix D0, Matrix D1, int K) static MatrixLagkJointMomentsFromDMAP.lagkJointMomentsFromDMAP(Matrix D0, Matrix D1, int K, int L) static MatrixLagkJointMomentsFromDMAP.lagkJointMomentsFromDMAP(Matrix D0, Matrix D1, int K, int L, double prec) Returns the lag-L joint moments of a discrete Markovian arrival process.static MatrixLagkJointMomentsFromDRAP.lagkJointMomentsFromDRAP(Matrix H0, Matrix H1) static MatrixLagkJointMomentsFromDRAP.lagkJointMomentsFromDRAP(Matrix H0, Matrix H1, int K) static MatrixLagkJointMomentsFromDRAP.lagkJointMomentsFromDRAP(Matrix H0, Matrix H1, int K, int L) static MatrixLagkJointMomentsFromDRAP.lagkJointMomentsFromDRAP(Matrix H0, Matrix H1, int K, int L, double prec) Returns the lag-L joint moments of a discrete rational arrival process.Methods in jline.lib.butools.dmap that return types with arguments of type MatrixModifier and TypeMethodDescriptionCanonicalFromDMAP2.canonicalFromDMAP2(Matrix D0, Matrix D1) CanonicalFromDMAP2.canonicalFromDMAP2(Matrix D0, Matrix D1) CanonicalFromDMAP2.canonicalFromDMAP2(Matrix D0, Matrix D1, double prec) Returns the canonical form of an order-2 discrete Markovian arrival process.CanonicalFromDMAP2.canonicalFromDMAP2(Matrix D0, Matrix D1, double prec) Returns the canonical form of an order-2 discrete Markovian arrival process.DMAP2FromMoments.dmap2FromMoments(double[] moms, double corr1) Returns a discrete MAP(2) which has the same 3 marginal moments and lag-1 autocorrelation as given.DMAP2FromMoments.dmap2FromMoments(double[] moms, double corr1) Returns a discrete MAP(2) which has the same 3 marginal moments and lag-1 autocorrelation as given.DMAPFromDRAP.dmapFromDRAP(Matrix H0, Matrix H1) DMAPFromDRAP.dmapFromDRAP(Matrix H0, Matrix H1) DMAPFromDRAP.dmapFromDRAP(Matrix H0, Matrix H1, double prec) Obtains a Markovian representation of a discrete rational arrival process of the same size, if possible.DMAPFromDRAP.dmapFromDRAP(Matrix H0, Matrix H1, double prec) Obtains a Markovian representation of a discrete rational arrival process of the same size, if possible.DRAPFromMoments.drapFromMoments(double[] moms, Matrix Nm) Creates a discrete rational arrival process that has the same marginal and lag-1 joint moments as given.DRAPFromMoments.drapFromMoments(double[] moms, Matrix Nm) Creates a discrete rational arrival process that has the same marginal and lag-1 joint moments as given.RandomDMAP.randomDMAP(int order) RandomDMAP.randomDMAP(int order) RandomDMAP.randomDMAP(int order, double mean) RandomDMAP.randomDMAP(int order, double mean) RandomDMAP.randomDMAP(int order, double mean, int zeroEntries) RandomDMAP.randomDMAP(int order, double mean, int zeroEntries) RandomDMAP.randomDMAP(int order, double mean, int zeroEntries, int maxTrials) RandomDMAP.randomDMAP(int order, double mean, int zeroEntries, int maxTrials) RandomDMAP.randomDMAP(int order, double mean, int zeroEntries, int maxTrials, double prec) RandomDMAP.randomDMAP(int order, double mean, int zeroEntries, int maxTrials, double prec) RandomDMAP.randomDMAP(int order, double mean, int zeroEntries, int maxTrials, double prec, Random random) Returns a random discrete Markovian arrival process.RandomDMAP.randomDMAP(int order, double mean, int zeroEntries, int maxTrials, double prec, Random random) Returns a random discrete Markovian arrival process.Methods in jline.lib.butools.dmap with parameters of type MatrixModifier and TypeMethodDescriptionCanonicalFromDMAP2.canonicalFromDMAP2(Matrix D0, Matrix D1) CanonicalFromDMAP2.canonicalFromDMAP2(Matrix D0, Matrix D1, double prec) Returns the canonical form of an order-2 discrete Markovian arrival process.static booleanCheckDMAPRepresentation.checkDMAPRepresentation(Matrix D0, Matrix D1) static booleanCheckDMAPRepresentation.checkDMAPRepresentation(Matrix D0, Matrix D1, double prec) Checks if the input matrices define a discrete time MAP.static booleanCheckDMMAPRepresentation.checkDMMAPRepresentation(Matrix[] D) static booleanCheckDMMAPRepresentation.checkDMMAPRepresentation(Matrix[] D, double prec) Overload for Matrix[].static booleanCheckDMRAPRepresentation.checkDMRAPRepresentation(Matrix[] H) static booleanCheckDMRAPRepresentation.checkDMRAPRepresentation(Matrix[] H, double prec) Overload for Matrix[].static booleanCheckDRAPRepresentation.checkDRAPRepresentation(Matrix D0, Matrix D1) static booleanCheckDRAPRepresentation.checkDRAPRepresentation(Matrix D0, Matrix D1, double prec) Checks if the input matrices define a discrete time RAP.DMAPFromDRAP.dmapFromDRAP(Matrix H0, Matrix H1) DMAPFromDRAP.dmapFromDRAP(Matrix H0, Matrix H1, double prec) Obtains a Markovian representation of a discrete rational arrival process of the same size, if possible.static MatrixCellDMMAPFromDMRAP.dmmapFromDMRAP(Matrix[] H) static MatrixCellDMMAPFromDMRAP.dmmapFromDMRAP(Matrix[] H, double prec) static MatrixCellDMRAPFromMoments.dmrapFromMoments(double[] moms, Matrix[] Nm) DRAPFromMoments.drapFromMoments(double[] moms, Matrix Nm) Creates a discrete rational arrival process that has the same marginal and lag-1 joint moments as given.static double[]LagCorrelationsFromDMAP.lagCorrelationsFromDMAP(Matrix D0, Matrix D1) static double[]LagCorrelationsFromDMAP.lagCorrelationsFromDMAP(Matrix D0, Matrix D1, int L) static double[]LagCorrelationsFromDMAP.lagCorrelationsFromDMAP(Matrix D0, Matrix D1, int L, double prec) Returns the lag autocorrelations of a discrete Markovian arrival process.static double[]LagCorrelationsFromDRAP.lagCorrelationsFromDRAP(Matrix H0, Matrix H1) static double[]LagCorrelationsFromDRAP.lagCorrelationsFromDRAP(Matrix H0, Matrix H1, int L) static double[]LagCorrelationsFromDRAP.lagCorrelationsFromDRAP(Matrix H0, Matrix H1, int L, double prec) Returns the lag autocorrelations of a discrete rational arrival process.static MatrixLagkJointMomentsFromDMAP.lagkJointMomentsFromDMAP(Matrix D0, Matrix D1) static MatrixLagkJointMomentsFromDMAP.lagkJointMomentsFromDMAP(Matrix D0, Matrix D1, int K) static MatrixLagkJointMomentsFromDMAP.lagkJointMomentsFromDMAP(Matrix D0, Matrix D1, int K, int L) static MatrixLagkJointMomentsFromDMAP.lagkJointMomentsFromDMAP(Matrix D0, Matrix D1, int K, int L, double prec) Returns the lag-L joint moments of a discrete Markovian arrival process.static MatrixCellLagkJointMomentsFromDMMAP.lagkJointMomentsFromDMMAP(Matrix[] D) static MatrixCellLagkJointMomentsFromDMMAP.lagkJointMomentsFromDMMAP(Matrix[] D, int K) static MatrixCellLagkJointMomentsFromDMMAP.lagkJointMomentsFromDMMAP(Matrix[] D, int K, int L) static MatrixCellLagkJointMomentsFromDMMAP.lagkJointMomentsFromDMMAP(Matrix[] D, int K, int L, double prec) Overload for Matrix[].static MatrixCellLagkJointMomentsFromDMRAP.lagkJointMomentsFromDMRAP(Matrix[] H) static MatrixCellLagkJointMomentsFromDMRAP.lagkJointMomentsFromDMRAP(Matrix[] H, int K) static MatrixCellLagkJointMomentsFromDMRAP.lagkJointMomentsFromDMRAP(Matrix[] H, int K, int L) static MatrixCellLagkJointMomentsFromDMRAP.lagkJointMomentsFromDMRAP(Matrix[] H, int K, int L, double prec) static MatrixLagkJointMomentsFromDRAP.lagkJointMomentsFromDRAP(Matrix H0, Matrix H1) static MatrixLagkJointMomentsFromDRAP.lagkJointMomentsFromDRAP(Matrix H0, Matrix H1, int K) static MatrixLagkJointMomentsFromDRAP.lagkJointMomentsFromDRAP(Matrix H0, Matrix H1, int K, int L) static MatrixLagkJointMomentsFromDRAP.lagkJointMomentsFromDRAP(Matrix H0, Matrix H1, int K, int L, double prec) Returns the lag-L joint moments of a discrete rational arrival process.MarginalDistributionFromDMAP.marginalDistributionFromDMAP(Matrix D0, Matrix D1) MarginalDistributionFromDMAP.marginalDistributionFromDMAP(Matrix D0, Matrix D1, double prec) Returns the discrete phase type distributed marginal distribution of a discrete Markovian arrival process.MarginalDistributionFromDMMAP.marginalDistributionFromDMMAP(Matrix[] D) MarginalDistributionFromDMMAP.marginalDistributionFromDMMAP(Matrix[] D, double prec) MarginalDistributionFromDMRAP.marginalDistributionFromDMRAP(Matrix[] H) MarginalDistributionFromDMRAP.marginalDistributionFromDMRAP(Matrix[] H, double prec) Overload for Matrix[].MarginalDistributionFromDRAP.marginalDistributionFromDRAP(Matrix H0, Matrix H1) MarginalDistributionFromDRAP.marginalDistributionFromDRAP(Matrix H0, Matrix H1, double prec) Returns the matrix geometrically distributed marginal distribution of a discrete rational arrival process.static double[]MarginalMomentsFromDMAP.marginalMomentsFromDMAP(Matrix D0, Matrix D1) static double[]MarginalMomentsFromDMAP.marginalMomentsFromDMAP(Matrix D0, Matrix D1, int K) static double[]MarginalMomentsFromDMAP.marginalMomentsFromDMAP(Matrix D0, Matrix D1, int K, double prec) Returns the moments of the marginal distribution of a discrete Markovian arrival process.static double[]MarginalMomentsFromDMMAP.marginalMomentsFromDMMAP(Matrix[] D) static double[]MarginalMomentsFromDMMAP.marginalMomentsFromDMMAP(Matrix[] D, int K) static double[]MarginalMomentsFromDMMAP.marginalMomentsFromDMMAP(Matrix[] D, int K, double prec) Overload for Matrix[].static double[]MarginalMomentsFromDMRAP.marginalMomentsFromDMRAP(Matrix[] H) static double[]MarginalMomentsFromDMRAP.marginalMomentsFromDMRAP(Matrix[] H, int K) static double[]MarginalMomentsFromDMRAP.marginalMomentsFromDMRAP(Matrix[] H, int K, double prec) Overload for Matrix[].static double[]MarginalMomentsFromDRAP.marginalMomentsFromDRAP(Matrix H0, Matrix H1) static double[]MarginalMomentsFromDRAP.marginalMomentsFromDRAP(Matrix H0, Matrix H1, int K) static double[]MarginalMomentsFromDRAP.marginalMomentsFromDRAP(Matrix H0, Matrix H1, int K, double prec) Returns the moments of the marginal distribution of a discrete rational arrival process.static int[]SamplesFromDMAP.samplesFromDMAP(Matrix D0, Matrix D1, int K) static int[]SamplesFromDMAP.samplesFromDMAP(Matrix D0, Matrix D1, int K, Integer initial) static int[]SamplesFromDMAP.samplesFromDMAP(Matrix D0, Matrix D1, int K, Integer initial, double prec) static int[]SamplesFromDMAP.samplesFromDMAP(Matrix D0, Matrix D1, int K, Integer initial, double prec, Random random) Generates random samples from a discrete Markovian arrival process.static ObjectSamplesFromDMMAP.samplesFromDMMAP(Matrix[] D, int K) static ObjectSamplesFromDMMAP.samplesFromDMMAP(Matrix[] D, int K, Integer initial, double prec, Random random) -
Uses of Matrix in jline.lib.butools.dph
Fields in jline.lib.butools.dph declared as MatrixModifier and TypeFieldDescriptionfinal MatrixMGFromMoments.MGRepresentation.Afinal MatrixMGFromMoments.MGRepresentation.alphafinal MatrixCanonicalFromDPH2.DPH2Representation.Bfinal MatrixCanonicalFromDPH3.DPH3Representation.Bfinal MatrixCanonicalFromDPH2.DPH2Representation.betafinal MatrixCanonicalFromDPH3.DPH3Representation.betaMethods in jline.lib.butools.dph that return MatrixModifier and TypeMethodDescriptionCanonicalFromDPH2.DPH2Representation.component1()MGFromMoments.MGRepresentation.component1()CanonicalFromDPH2.DPH2Representation.component2()MGFromMoments.MGRepresentation.component2()MGFromMoments.MGRepresentation.getA()MGFromMoments.MGRepresentation.getAlpha()Methods in jline.lib.butools.dph with parameters of type MatrixModifier and TypeMethodDescriptionAcyclicDPHFromMG.acyclicDPHFromMG(double[] alpha, Matrix A) AcyclicDPHFromMG.acyclicDPHFromMG(double[] alpha, Matrix A, double prec) AcyclicDPHFromMG.acyclicDPHFromMG(Matrix alpha, Matrix A) AcyclicDPHFromMG.acyclicDPHFromMG(Matrix alpha, Matrix A, double prec) Transforms a matrix-geometric representation to an acyclic DPH representation of the same size, if possible.CanonicalFromDPH2.canonicalFromDPH2(double[] alpha, Matrix A) CanonicalFromDPH2.canonicalFromDPH2(double[] alpha, Matrix A, double prec) Overload for double[] alpha.CanonicalFromDPH2.canonicalFromDPH2(Matrix alpha, Matrix A) CanonicalFromDPH2.canonicalFromDPH2(Matrix alpha, Matrix A, double prec) CanonicalFromDPH3.canonicalFromDPH3(double[] alpha, Matrix A) CanonicalFromDPH3.canonicalFromDPH3(double[] alpha, Matrix A, double prec) CanonicalFromDPH3.canonicalFromDPH3(Matrix alpha, Matrix A) CanonicalFromDPH3.canonicalFromDPH3(Matrix alpha, Matrix A, double prec) static doubleCdfFromDPH.cdfFromDPH(double[] alpha, Matrix A, int x) Overload for double[] alpha and single integer x.static double[]CdfFromDPH.cdfFromDPH(double[] alpha, Matrix A, int[] x) Overload for double[] alpha.static doubleCdfFromDPH.cdfFromDPH(Matrix alpha, Matrix A, int x) Overload for single integer x.static double[]CdfFromDPH.cdfFromDPH(Matrix alpha, Matrix A, int[] x) Returns the cumulative distribution function of a discrete phase-type distribution.static doubleOverload for double[] alpha and single integer x.static double[]Overload for double[] alpha.static doubleOverload for single integer x.static double[]Returns the cumulative distribution function of a matrix-geometric distribution.static booleanCheckDPHRepresentation.checkDPHRepresentation(double[] alpha, Matrix A) static booleanCheckDPHRepresentation.checkDPHRepresentation(double[] alpha, Matrix A, double prec) Overload for double[] alpha.static booleanCheckDPHRepresentation.checkDPHRepresentation(Matrix alpha, Matrix A) static booleanCheckDPHRepresentation.checkDPHRepresentation(Matrix alpha, Matrix A, double prec) Checks if the given vector and matrix define a valid discrete phase-type representation.static booleanCheckMGRepresentation.checkMGRepresentation(double[] alpha, Matrix A) static booleanCheckMGRepresentation.checkMGRepresentation(double[] alpha, Matrix A, double prec) static booleanCheckMGRepresentation.checkMGRepresentation(Matrix alpha, Matrix A) static booleanCheckMGRepresentation.checkMGRepresentation(Matrix alpha, Matrix A, double prec) Checks if the given vector and matrix define a valid matrix-geometric representation.Overload for double[] alpha.Obtains a Markovian representation of a matrix-geometric distribution of the same size, if possible.static double[]MomentsFromDPH.momentsFromDPH(double[] alpha, Matrix A) static double[]MomentsFromDPH.momentsFromDPH(double[] alpha, Matrix A, int K) Overload for double[] alpha.static double[]MomentsFromDPH.momentsFromDPH(Matrix alpha, Matrix A) static double[]MomentsFromDPH.momentsFromDPH(Matrix alpha, Matrix A, int K) Returns the first K moments of a discrete phase-type distribution.static double[]MomentsFromMG.momentsFromMG(double[] alpha, Matrix A) static double[]MomentsFromMG.momentsFromMG(double[] alpha, Matrix A, int K) static double[]MomentsFromMG.momentsFromMG(Matrix alpha, Matrix A) static double[]MomentsFromMG.momentsFromMG(Matrix alpha, Matrix A, int K) Returns the first K moments of a matrix-geometric distribution.static doublePmfFromDPH.pmfFromDPH(double[] alpha, Matrix A, int x) Overload for double[] alpha and single integer x.static double[]PmfFromDPH.pmfFromDPH(double[] alpha, Matrix A, int[] x) Overload for double[] alpha.static doublePmfFromDPH.pmfFromDPH(Matrix alpha, Matrix A, int x) Overload for single integer x.static double[]PmfFromDPH.pmfFromDPH(Matrix alpha, Matrix A, int[] x) Returns the probability mass function of a discrete phase-type distribution.static doublestatic double[]static doublestatic double[]Returns the probability mass function of a matrix-geometric distribution.static int[]SamplesFromDPH.samplesFromDPH(double[] alpha, Matrix A, int K) static int[]SamplesFromDPH.samplesFromDPH(double[] alpha, Matrix A, int K, Random random) Overload for double[] alpha.static int[]SamplesFromDPH.samplesFromDPH(Matrix alpha, Matrix A, int K) static int[]SamplesFromDPH.samplesFromDPH(Matrix alpha, Matrix A, int K, Random random) Generates random samples from a discrete phase-type distribution.Constructors in jline.lib.butools.dph with parameters of type MatrixModifierConstructorDescriptionDPH2Representation(Matrix beta, Matrix B) DPH3Representation(Matrix beta, Matrix B) MGRepresentation(Matrix alpha, Matrix A) -
Uses of Matrix in jline.lib.butools.fitting
Fields in jline.lib.butools.fitting declared as MatrixModifier and TypeFieldDescriptionfinal MatrixPHFromTrace.PHFitResult.Afinal MatrixPHFromTrace.PHFitResult.alphafinal MatrixMAPFitResult.D0final MatrixMAPFitResult.D1Methods in jline.lib.butools.fitting with parameters of type MatrixModifier and TypeMethodDescriptionstatic doubleLikelihoodFromTrace.likelihoodFromTraceMAP(double[] trace, Matrix D0, Matrix D1) static doubleLikelihoodFromTrace.likelihoodFromTraceMAP(double[] trace, Matrix D0, Matrix D1, double prec) Evaluates the log-likelihood of a trace with the given MAP.static doubleLikelihoodFromTrace.likelihoodFromTracePH(double[] trace, Matrix alpha, Matrix A) static doubleLikelihoodFromTrace.likelihoodFromTracePH(double[] trace, Matrix alpha, Matrix A, double prec) Evaluates the log-likelihood of a trace with the given PH distribution.static MAPFitResultMAPFromTrace.mapFromTrace(double[] trace, int[] orders, int maxIter, double stopCond, Matrix[] initialGuess) static MAPFitResultMAPFromTrace.mapFromTrace(double[] trace, int[] orders, int maxIter, double stopCond, Matrix[] initialGuess, String resultFormat) Performs MAP fitting using the EM algorithm (ErCHMM).Constructors in jline.lib.butools.fitting with parameters of type MatrixModifierConstructorDescriptionMAPFitResult(Matrix D0, Matrix D1, double logli) PHFitResult(Matrix alpha, Matrix A, double logli) -
Uses of Matrix in jline.lib.butools.mam
Fields in jline.lib.butools.mam declared as MatrixModifier and TypeFieldDescriptionfinal MatrixFluidSolve.FluidSolution.clofinal MatrixFluidSolve.FluidSolution.inifinal MatrixFluidSolve.FluidSolution.Kfinal MatrixFluidSolve.FluidSolution.mass0Fields in jline.lib.butools.mam with type parameters of type MatrixModifier and TypeFieldDescriptionLevelDependentFluidSolution.cloBLevelDependentFluidSolution.cloFLevelDependentFluidSolution.iniBLevelDependentFluidSolution.iniFLevelDependentFluidSolution.KBLevelDependentFluidSolution.KFLevelDependentFluidSolution.massesMethods in jline.lib.butools.mam that return MatrixModifier and TypeMethodDescriptionstatic MatrixBlock-diagonal [[A 0];[0 B]].static Matrix2x2 block matrix [[A B];[C D]].static MatrixFluidTools.diagFrom(double[] v) Diagonal matrix from a double array.static MatrixDiagonal matrix from a row/column vector.static Matrix[]FluidTools.expIntMoments(Matrix M, double L) {J0, J1} with J0=int_0^L expm(M u)du, J1=int_0^L u expm(M u)du via nilpotent augmentation.static MatrixFluidStationaryDistr.fluidStationaryDistr(Matrix mass0, Matrix ini, Matrix K, Matrix clo, double[] x) Returns the stationary distribution of a Markovian fluid model at specific points.GeneralFluidSolution.getClo()GeneralFluidSolution.getIni()GeneralFluidSolution.getK()GeneralFluidSolution.getMass0()static MatrixGM1FundamentalMatrix.gm1FundamentalMatrix(List<Matrix> A) static MatrixGM1FundamentalMatrix.gm1FundamentalMatrix(List<Matrix> A, double precision) static MatrixGM1FundamentalMatrix.gm1FundamentalMatrix(List<Matrix> A, double precision, int maxNumIt) static MatrixGM1FundamentalMatrix.gm1FundamentalMatrix(List<Matrix> A, double precision, int maxNumIt, GM1FundamentalMatrix.GM1Method method) Returns matrix R corresponding to the G/M/1 type Markov chain given by matrices A.static MatrixGM1StationaryDistr.gm1StationaryDistr(List<Matrix> B, Matrix R, int K) Returns the stationary distribution of the G/M/1 type Markov chain up to a given level K.static MatrixGM1StationaryDistr.gm1StationaryDistr(Matrix[] B, Matrix R, int K) Overload accepting Matrix[].static Matrix[A B] side by side (handles zero-dimension blocks).static Matrixint_0^L expm(KA u) du, robust when KA has a zero eigenvalue (deflation).static Matrix[]{int_0^L expm(KA u)du, int_0^L expm(KB u)du}, applying deflation to the near-singular one.static MatrixSolves A*X = B via partial-pivot LU (LAPACK-equivalent accuracy).static MatrixMG1FundamentalMatrix.mg1FundamentalMatrix(List<Matrix> A) static MatrixMG1FundamentalMatrix.mg1FundamentalMatrix(List<Matrix> A, double precision) static MatrixMG1FundamentalMatrix.mg1FundamentalMatrix(List<Matrix> A, double precision, int maxNumIt) static MatrixMG1FundamentalMatrix.mg1FundamentalMatrix(List<Matrix> A, double precision, int maxNumIt, MG1FundamentalMatrix.MG1Method method) Returns matrix G corresponding to the M/G/1 type Markov chain defined by matrices A.static MatrixMG1StationaryDistr.mg1StationaryDistr(List<Matrix> A) static MatrixMG1StationaryDistr.mg1StationaryDistr(List<Matrix> A, List<Matrix> B) static Matrixstatic Matrixstatic MatrixReturns the stationary distribution of the M/G/1 type Markov chain up to a given level K.static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A) static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A, Matrix[] B) static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A, Matrix[] B, Matrix G) static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A, Matrix[] B, Matrix G, int K) static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A, Matrix[] B, Matrix G, int K, double prec) Overload accepting Matrix[] for A and B.static Matrix[]Multiregime.multiregime(List<Matrix> Q, List<double[]> R, List<Matrix> Qt, List<double[]> Rt, double[] T, double[] pdfpoints, double[] cdfpoints) Solves a multi-regime feedback Markovian fluid queue.static MatrixLeft null vector of KA via the CRPSolve algorithm (column, no generator check).static Matrix[]FluidTools.orderedSchurRhp(Matrix A) Real Schur decomposition of A reordered so that eigenvalues with positive real part appear in the leading block, matching MATLAB's ordschur(U,T,'rhp').static MatrixQBDStationaryDistr.qbdStationaryDistr(double[] pi0, Matrix R, int K) Overload for double[] pi0.static MatrixQBDStationaryDistr.qbdStationaryDistr(Matrix pi0, Matrix R, int K) Returns the stationary distribution of a QBD up to a given level K.static MatrixRow vector of row-sums (each row summed across columns).static MatrixLevelDependentFluidStationary.stationaryDistr(LevelDependentFluidSolution sol, double[] T, String what, double[] points) Stationary distribution ('pdf','pdfd','cdf','cdfm') at the requested points.static MatrixSubmatrix M(ri, ci) with index arrays (0-based).static MatrixFluidTools.triSylvester(Matrix A, Matrix B, Matrix C) Solves the Sylvester equation A*X - X*B = C where A (n x n) and B (m x m) are quasi-upper-triangular (real Schur form), via column-wise back-substitution (Bartels-Stewart), handling 1x1 and 2x2 diagonal blocks of B.static MatrixColumn-major reshape of M into a single column vector.static Matrix[A; B] stacked (handles zero-dimension blocks).Methods in jline.lib.butools.mam that return types with arguments of type MatrixModifier and TypeMethodDescriptionReturns the parameters of the matrix-geometrically distributed stationary distribution of a QBD.Returns the parameters of the matrix-geometrically distributed stationary distribution of a QBD.Methods in jline.lib.butools.mam with parameters of type MatrixModifier and TypeMethodDescriptionstatic MatrixBlock-diagonal [[A 0];[0 B]].static Matrix2x2 block matrix [[A B];[C D]].static double[]Diagonal of M as a double array.static MatrixDiagonal matrix from a row/column vector.static Matrix[]FluidTools.expIntMoments(Matrix M, double L) {J0, J1} with J0=int_0^L expm(M u)du, J1=int_0^L u expm(M u)du via nilpotent augmentation.static FluidSolve.FluidSolutionFluidSolve.fluidSolve(Matrix Fpp, Matrix Fpm, Matrix Fmp, Matrix Fmm) static FluidSolve.FluidSolutionFluidSolve.fluidSolve(Matrix Fpp, Matrix Fpm, Matrix Fmp, Matrix Fmm, double prec) Returns the parameters of the matrix-exponentially distributed stationary distribution of a canonical Markovian fluid model.static MatrixFluidStationaryDistr.fluidStationaryDistr(Matrix mass0, Matrix ini, Matrix K, Matrix clo, double[] x) Returns the stationary distribution of a Markovian fluid model at specific points.static GeneralFluidSolutionGeneralFluidSolve.generalFluidSolve(Matrix Q, Matrix R) static GeneralFluidSolutionGeneralFluidSolve.generalFluidSolve(Matrix Q, Matrix R, Matrix Q0) static GeneralFluidSolutionGeneralFluidSolve.generalFluidSolve(Matrix Q, Matrix R, Matrix Q0, double prec) Returns the parameters of the matrix-exponentially distributed stationary distribution of a general Markovian fluid model.static MatrixGM1StationaryDistr.gm1StationaryDistr(List<Matrix> B, Matrix R, int K) Returns the stationary distribution of the G/M/1 type Markov chain up to a given level K.static MatrixGM1StationaryDistr.gm1StationaryDistr(Matrix[] B, Matrix R, int K) Overload accepting Matrix[].static Matrix[A B] side by side (handles zero-dimension blocks).static Matrixint_0^L expm(KA u) du, robust when KA has a zero eigenvalue (deflation).static Matrix[]{int_0^L expm(KA u)du, int_0^L expm(KB u)du}, applying deflation to the near-singular one.static MatrixSolves A*X = B via partial-pivot LU (LAPACK-equivalent accuracy).static Matrixstatic Matrixstatic MatrixReturns the stationary distribution of the M/G/1 type Markov chain up to a given level K.static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A) static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A, Matrix[] B) static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A, Matrix[] B, Matrix G) static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A, Matrix[] B, Matrix G, int K) static MatrixMG1StationaryDistr.mg1StationaryDistr(Matrix[] A, Matrix[] B, Matrix G, int K, double prec) Overload accepting Matrix[] for A and B.static doubleMinimum modulus of the eigenvalues of M.static MatrixLeft null vector of KA via the CRPSolve algorithm (column, no generator check).static Object[]FluidTools.orderedSchur(Matrix A, double tol) Real Schur decomposition of A reordered so that the eigenvalues appear grouped as [zero real-part, negative real-part, positive real-part], the ordering used by MATLAB's ordschur in the multi-regime fluid solver.static Matrix[]FluidTools.orderedSchurRhp(Matrix A) Real Schur decomposition of A reordered so that eigenvalues with positive real part appear in the leading block, matching MATLAB's ordschur(U,T,'rhp').Returns the parameters of the matrix-geometrically distributed stationary distribution of a QBD.static MatrixQBDStationaryDistr.qbdStationaryDistr(double[] pi0, Matrix R, int K) Overload for double[] pi0.static MatrixQBDStationaryDistr.qbdStationaryDistr(Matrix pi0, Matrix R, int K) Returns the stationary distribution of a QBD up to a given level K.static MatrixRow vector of row-sums (each row summed across columns).static voidAssigns src (rows x |cols|) into the given columns of dst (all rows).static voidAssigns src into dst at rows ri, cols ci.static MatrixSubmatrix M(ri, ci) with index arrays (0-based).static MatrixFluidTools.triSylvester(Matrix A, Matrix B, Matrix C) Solves the Sylvester equation A*X - X*B = C where A (n x n) and B (m x m) are quasi-upper-triangular (real Schur form), via column-wise back-substitution (Bartels-Stewart), handling 1x1 and 2x2 diagonal blocks of B.static MatrixColumn-major reshape of M into a single column vector.static Matrix[A; B] stacked (handles zero-dimension blocks).Method parameters in jline.lib.butools.mam with type arguments of type MatrixModifier and TypeMethodDescriptionstatic MatrixGM1FundamentalMatrix.gm1FundamentalMatrix(List<Matrix> A) static MatrixGM1FundamentalMatrix.gm1FundamentalMatrix(List<Matrix> A, double precision) static MatrixGM1FundamentalMatrix.gm1FundamentalMatrix(List<Matrix> A, double precision, int maxNumIt) static MatrixGM1FundamentalMatrix.gm1FundamentalMatrix(List<Matrix> A, double precision, int maxNumIt, GM1FundamentalMatrix.GM1Method method) Returns matrix R corresponding to the G/M/1 type Markov chain given by matrices A.static MatrixGM1StationaryDistr.gm1StationaryDistr(List<Matrix> B, Matrix R, int K) Returns the stationary distribution of the G/M/1 type Markov chain up to a given level K.static MatrixMG1FundamentalMatrix.mg1FundamentalMatrix(List<Matrix> A) static MatrixMG1FundamentalMatrix.mg1FundamentalMatrix(List<Matrix> A, double precision) static MatrixMG1FundamentalMatrix.mg1FundamentalMatrix(List<Matrix> A, double precision, int maxNumIt) static MatrixMG1FundamentalMatrix.mg1FundamentalMatrix(List<Matrix> A, double precision, int maxNumIt, MG1FundamentalMatrix.MG1Method method) Returns matrix G corresponding to the M/G/1 type Markov chain defined by matrices A.static MatrixMG1StationaryDistr.mg1StationaryDistr(List<Matrix> A) static MatrixMG1StationaryDistr.mg1StationaryDistr(List<Matrix> A, List<Matrix> B) static Matrixstatic Matrixstatic MatrixReturns the stationary distribution of the M/G/1 type Markov chain up to a given level K.static Matrix[]Multiregime.multiregime(List<Matrix> Q, List<double[]> R, List<Matrix> Qt, List<double[]> Rt, double[] T, double[] pdfpoints, double[] cdfpoints) Solves a multi-regime feedback Markovian fluid queue.static LevelDependentFluidSolutionSecondOrderLevelDependentFluidSolve.solve(List<Matrix> Q, List<Matrix> R, List<Matrix> S, double[] T, double[] boundaryL, double[] boundaryU, List<Matrix> Qt, double prec) Constructors in jline.lib.butools.mam with parameters of type MatrixModifierConstructorDescriptionFluidSolution(Matrix mass0, Matrix ini, Matrix K, Matrix clo) GeneralFluidSolution(Matrix mass0, Matrix ini, Matrix K, Matrix clo) Constructor parameters in jline.lib.butools.mam with type arguments of type Matrix -
Uses of Matrix in jline.lib.butools.map
Methods in jline.lib.butools.map that return MatrixModifier and TypeMethodDescriptionMAPRepresentation.component1()MAPRepresentation.component2()MAPRepresentation.getD0()MAPRepresentation.getD1()static MatrixLagkJointMomentsFromMAP.lagkJointMomentsFromMAP(Matrix D0, Matrix D1) static MatrixLagkJointMomentsFromMAP.lagkJointMomentsFromMAP(Matrix D0, Matrix D1, int K) static MatrixLagkJointMomentsFromMAP.lagkJointMomentsFromMAP(Matrix D0, Matrix D1, int K, int L) static MatrixLagkJointMomentsFromMAP.lagkJointMomentsFromMAP(Matrix D0, Matrix D1, int K, int L, double prec) Returns the lag-L joint moments of a continuous Markovian arrival process.static MatrixLagkJointMomentsFromRAP.lagkJointMomentsFromRAP(Matrix H0, Matrix H1) static MatrixLagkJointMomentsFromRAP.lagkJointMomentsFromRAP(Matrix H0, Matrix H1, int K) static MatrixLagkJointMomentsFromRAP.lagkJointMomentsFromRAP(Matrix H0, Matrix H1, int K, int L) static MatrixLagkJointMomentsFromRAP.lagkJointMomentsFromRAP(Matrix H0, Matrix H1, int K, int L, double prec) Returns the lag-L joint moments of a continuous rational arrival process.static Matrix[]MAPFromFewMomentsAndCorrelations.mapFromFewMomentsAndCorrelations(double[] moms) static Matrix[]MAPFromFewMomentsAndCorrelations.mapFromFewMomentsAndCorrelations(double[] moms, double corr1) static Matrix[]MAPFromFewMomentsAndCorrelations.mapFromFewMomentsAndCorrelations(double[] moms, double corr1, Double r) Creates a Markovian arrival process that has the given 2 or 3 marginal moments and lag-1 autocorrelation.Methods in jline.lib.butools.map that return types with arguments of type MatrixModifier and TypeMethodDescriptionCanonicalFromMAP2.canonicalFromMAP2(Matrix D0, Matrix D1) CanonicalFromMAP2.canonicalFromMAP2(Matrix D0, Matrix D1) CanonicalFromMAP2.canonicalFromMAP2(Matrix D0, Matrix D1, double prec) Returns the canonical form of an order-2 Markovian arrival process.CanonicalFromMAP2.canonicalFromMAP2(Matrix D0, Matrix D1, double prec) Returns the canonical form of an order-2 Markovian arrival process.MAP2FromMoments.map2FromMoments(double[] moms, double corr1) Returns a MAP(2) which has the same 3 marginal moments and lag-1 autocorrelation as given.MAP2FromMoments.map2FromMoments(double[] moms, double corr1) Returns a MAP(2) which has the same 3 marginal moments and lag-1 autocorrelation as given.MAPFromRAP.mapFromRAP(Matrix H0, Matrix H1) MAPFromRAP.mapFromRAP(Matrix H0, Matrix H1) MAPFromRAP.mapFromRAP(Matrix H0, Matrix H1, double prec) Obtains a Markovian representation of a continuous rational arrival process of the same size, if possible.MAPFromRAP.mapFromRAP(Matrix H0, Matrix H1, double prec) Obtains a Markovian representation of a continuous rational arrival process of the same size, if possible.MinimalRepFromRAP.minimalRepFromRAP(Matrix H0, Matrix H1) MinimalRepFromRAP.minimalRepFromRAP(Matrix H0, Matrix H1) MinimalRepFromRAP.minimalRepFromRAP(Matrix H0, Matrix H1, String how) MinimalRepFromRAP.minimalRepFromRAP(Matrix H0, Matrix H1, String how) MinimalRepFromRAP.minimalRepFromRAP(Matrix H0, Matrix H1, String how, double precision) Returns the minimal representation of a rational arrival process.MinimalRepFromRAP.minimalRepFromRAP(Matrix H0, Matrix H1, String how, double precision) Returns the minimal representation of a rational arrival process.MRAPFromMoments.rapFromMoments(double[] moms, Matrix Nm) Creates a rational arrival process that has the same marginal and lag-1 joint moments as given.MRAPFromMoments.rapFromMoments(double[] moms, Matrix Nm) Creates a rational arrival process that has the same marginal and lag-1 joint moments as given.RAPFromMomentsAndCorrelations.rapFromMomentsAndCorrelations(double[] moms, double[] corr) Returns a rational arrival process that has the same moments and lag autocorrelation coefficients as given.RAPFromMomentsAndCorrelations.rapFromMomentsAndCorrelations(double[] moms, double[] corr) Returns a rational arrival process that has the same moments and lag autocorrelation coefficients as given.Methods in jline.lib.butools.map with parameters of type MatrixModifier and TypeMethodDescriptionCanonicalFromMAP2.canonicalFromMAP2(Matrix D0, Matrix D1) CanonicalFromMAP2.canonicalFromMAP2(Matrix D0, Matrix D1, double prec) Returns the canonical form of an order-2 Markovian arrival process.static booleanCheckMAPRepresentation.checkMAPRepresentation(Matrix D0, Matrix D1) static booleanCheckMAPRepresentation.checkMAPRepresentation(Matrix D0, Matrix D1, double prec) Checks if the input matrices define a continuous time MAP.static booleanCheckMMAPRepresentation.checkMMAPRepresentation(Matrix[] D) static booleanCheckMMAPRepresentation.checkMMAPRepresentation(Matrix[] D, double prec) Overload for Matrix[].static booleanCheckMRAPRepresentation.checkMRAPRepresentation(Matrix[] H) static booleanCheckMRAPRepresentation.checkMRAPRepresentation(Matrix[] H, double prec) Overload for Matrix[].static booleanCheckMAPRepresentation.checkRAPRepresentation(Matrix H0, Matrix H1) static booleanCheckMAPRepresentation.checkRAPRepresentation(Matrix H0, Matrix H1, double prec) Checks if the input matrices define a valid RAP representation.static double[]LagCorrelationsFromMAP.lagCorrelationsFromMAP(Matrix D0, Matrix D1) static double[]LagCorrelationsFromMAP.lagCorrelationsFromMAP(Matrix D0, Matrix D1, int L) Returns the lag autocorrelations of a Markovian arrival process.static double[]LagCorrelationsFromMAP.lagCorrelationsFromRAP(Matrix H0, Matrix H1) static double[]LagCorrelationsFromMAP.lagCorrelationsFromRAP(Matrix H0, Matrix H1, int L) Returns the lag autocorrelations of a rational arrival process.static MatrixLagkJointMomentsFromMAP.lagkJointMomentsFromMAP(Matrix D0, Matrix D1) static MatrixLagkJointMomentsFromMAP.lagkJointMomentsFromMAP(Matrix D0, Matrix D1, int K) static MatrixLagkJointMomentsFromMAP.lagkJointMomentsFromMAP(Matrix D0, Matrix D1, int K, int L) static MatrixLagkJointMomentsFromMAP.lagkJointMomentsFromMAP(Matrix D0, Matrix D1, int K, int L, double prec) Returns the lag-L joint moments of a continuous Markovian arrival process.static MatrixCellLagkJointMomentsFromMMAP.lagkJointMomentsFromMMAP(Matrix[] D) static MatrixCellLagkJointMomentsFromMMAP.lagkJointMomentsFromMMAP(Matrix[] D, int K) static MatrixCellLagkJointMomentsFromMMAP.lagkJointMomentsFromMMAP(Matrix[] D, int K, int L) static MatrixCellLagkJointMomentsFromMMAP.lagkJointMomentsFromMMAP(Matrix[] D, int K, int L, double prec) Overload for Matrix[].static MatrixCellLagkJointMomentsFromMRAP.lagkJointMomentsFromMRAP(Matrix[] H, int K, int L, double prec) Overload for Matrix[].static MatrixLagkJointMomentsFromRAP.lagkJointMomentsFromRAP(Matrix H0, Matrix H1) static MatrixLagkJointMomentsFromRAP.lagkJointMomentsFromRAP(Matrix H0, Matrix H1, int K) static MatrixLagkJointMomentsFromRAP.lagkJointMomentsFromRAP(Matrix H0, Matrix H1, int K, int L) static MatrixLagkJointMomentsFromRAP.lagkJointMomentsFromRAP(Matrix H0, Matrix H1, int K, int L, double prec) Returns the lag-L joint moments of a continuous rational arrival process.MAPFromRAP.mapFromRAP(Matrix H0, Matrix H1) MAPFromRAP.mapFromRAP(Matrix H0, Matrix H1, double prec) Obtains a Markovian representation of a continuous rational arrival process of the same size, if possible.static PHRepresentationMarginalDistributionFromMAP.marginalDistributionFromMAP(Matrix D0, Matrix D1) Returns the phase type distributed marginal distribution of a Markovian arrival process.static PHRepresentationMarginalDistributionFromMMAP.marginalDistributionFromMMAP(Matrix[] D) static PHRepresentationMarginalDistributionFromMMAP.marginalDistributionFromMMAP(Matrix[] D, double prec) static PHRepresentationMarginalDistributionFromMRAP.marginalDistributionFromMRAP(Matrix[] H) static PHRepresentationMarginalDistributionFromMRAP.marginalDistributionFromMRAP(Matrix[] H, double prec) Overload for Matrix[].static PHRepresentationMarginalDistributionFromMAP.marginalDistributionFromRAP(Matrix H0, Matrix H1) Returns the matrix exponential distributed marginal distribution of a rational arrival process.static double[]MarginalMomentsFromMAP.marginalMomentsFromMAP(Matrix D0, Matrix D1) static double[]MarginalMomentsFromMAP.marginalMomentsFromMAP(Matrix D0, Matrix D1, int K) Returns the moments of the marginal distribution of a Markovian arrival process.static double[]MarginalMomentsFromMMAP.marginalMomentsFromMMAP(Matrix[] D) static double[]MarginalMomentsFromMMAP.marginalMomentsFromMMAP(Matrix[] D, int K) static double[]MarginalMomentsFromMMAP.marginalMomentsFromMMAP(Matrix[] D, int K, double prec) Overload for Matrix[].static double[]MarginalMomentsFromMRAP.marginalMomentsFromMRAP(Matrix[] H) static double[]MarginalMomentsFromMRAP.marginalMomentsFromMRAP(Matrix[] H, int K) static double[]MarginalMomentsFromMRAP.marginalMomentsFromMRAP(Matrix[] H, int K, double prec) Overload for Matrix[].static double[]MarginalMomentsFromMAP.marginalMomentsFromRAP(Matrix H0, Matrix H1) static double[]MarginalMomentsFromMAP.marginalMomentsFromRAP(Matrix H0, Matrix H1, int K) Returns the moments of the marginal distribution of a rational arrival process.static MatrixCellMinimalRepFromMRAP.minimalRepFromMRAP(Matrix[] H, String how, double precision) Overload for Matrix[].MinimalRepFromRAP.minimalRepFromRAP(Matrix H0, Matrix H1) MinimalRepFromRAP.minimalRepFromRAP(Matrix H0, Matrix H1, String how) MinimalRepFromRAP.minimalRepFromRAP(Matrix H0, Matrix H1, String how, double precision) Returns the minimal representation of a rational arrival process.static MatrixCellMMAPFromMRAP.mmapFromMRAP(Matrix[] H) static MatrixCellMMAPFromMRAP.mmapFromMRAP(Matrix[] H, double prec) Overload for Matrix[].static MatrixCellMRAPFromMoments.mrapFromMoments(double[] moms, Matrix[] Nm) Overload for Matrix[].MRAPFromMoments.rapFromMoments(double[] moms, Matrix Nm) Creates a rational arrival process that has the same marginal and lag-1 joint moments as given.static double[]SamplesFromMAP.samplesFromMAP(Matrix D0, Matrix D1, int K) static double[]SamplesFromMAP.samplesFromMAP(Matrix D0, Matrix D1, int K, Integer initial) static double[]SamplesFromMAP.samplesFromMAP(Matrix D0, Matrix D1, int K, Integer initial, Random random) Generates random samples from a Markovian arrival process.static ObjectSamplesFromMMAP.samplesFromMMAP(Matrix[] D, int K) static ObjectSamplesFromMMAP.samplesFromMMAP(Matrix[] D, int K, Integer initial, double prec, Random random) Constructors in jline.lib.butools.map with parameters of type Matrix -
Uses of Matrix in jline.lib.butools.mc
Methods in jline.lib.butools.mc that return MatrixModifier and TypeMethodDescriptionstatic Matrixstatic MatrixComputes the stationary solution of a continuous time rational process (CRP).static Matrixstatic MatrixComputes the stationary solution of a continuous time Markov chain.static MatrixComputes the stationary solution of a discrete time rational process (DRP).static Matrixstatic MatrixComputes the stationary solution of a discrete time Markov chain.Methods in jline.lib.butools.mc with parameters of type MatrixModifier and TypeMethodDescriptionstatic booleanCheckGenerator.checkGenerator(Matrix Q) static booleanCheckGenerator.checkGenerator(Matrix Q, boolean transient_) static booleanCheckGenerator.checkGenerator(Matrix Q, boolean transient_, double prec) Checks if the matrix is a valid generator matrix.static booleanCheckProbMatrix.checkProbMatrix(Matrix P) static booleanCheckProbMatrix.checkProbMatrix(Matrix P, boolean transient_) static booleanCheckProbMatrix.checkProbMatrix(Matrix P, boolean transient_, double prec) Checks if the matrix is a valid probability matrix.static booleanCheckProbVector.checkProbVector(Matrix pi) static booleanCheckProbVector.checkProbVector(Matrix pi, boolean sub) static booleanCheckProbVector.checkProbVector(Matrix pi, boolean sub, double prec) Checks if the vector is a valid probability vector: the vector has only non-negative elements, the sum of the vector elements is 1.static Matrixstatic MatrixComputes the stationary solution of a continuous time rational process (CRP).static Matrixstatic MatrixComputes the stationary solution of a continuous time Markov chain.static MatrixComputes the stationary solution of a discrete time rational process (DRP).static Matrixstatic MatrixComputes the stationary solution of a discrete time Markov chain. -
Uses of Matrix in jline.lib.butools.ph
Fields in jline.lib.butools.ph declared as MatrixModifier and TypeFieldDescriptionfinal MatrixMERepresentation.Afinal MatrixPH2From3Moments.PH2Representation.Afinal MatrixPH3Representation.Afinal MatrixPHRepresentation.Afinal MatrixMERepresentation.alphafinal MatrixPH2From3Moments.PH2Representation.alphafinal MatrixPH3Representation.alphafinal MatrixPHRepresentation.alphaMethods in jline.lib.butools.ph that return MatrixModifier and TypeMethodDescriptionMERepresentation.component1()PH2From3Moments.PH2Representation.component1()PH3Representation.component1()PHRepresentation.component1()MERepresentation.component2()PH2From3Moments.PH2Representation.component2()PH3Representation.component2()PHRepresentation.component2()MERepresentation.getA()PH3Representation.getA()PHRepresentation.getA()MERepresentation.getAlpha()PH3Representation.getAlpha()PHRepresentation.getAlpha()Methods in jline.lib.butools.ph with parameters of type MatrixModifier and TypeMethodDescriptionstatic PHRepresentationAcyclicPHFromME.acyclicPHFromME(Matrix alpha, Matrix A) static PHRepresentationAcyclicPHFromME.acyclicPHFromME(Matrix alpha, Matrix A, int maxSize) static PHRepresentationAcyclicPHFromME.acyclicPHFromME(Matrix alpha, Matrix A, int maxSize, double prec) Transforms an arbitrary matrix-exponential representation to an acyclic phase-type representation.CanonicalFromPH2.canonicalFromPH2(double[] alpha, Matrix A) CanonicalFromPH2.canonicalFromPH2(double[] alpha, Matrix A, double prec) CanonicalFromPH2.canonicalFromPH2(Matrix alpha, Matrix A) CanonicalFromPH2.canonicalFromPH2(Matrix alpha, Matrix A, double prec) Returns the canonical form of an order-2 phase-type distribution.static PH3RepresentationCanonicalFromPH3.canonicalFromPH3(double[] alpha, Matrix A) static PH3RepresentationCanonicalFromPH3.canonicalFromPH3(double[] alpha, Matrix A, double prec) static PH3RepresentationCanonicalFromPH3.canonicalFromPH3(Matrix alpha, Matrix A) static PH3RepresentationCanonicalFromPH3.canonicalFromPH3(Matrix alpha, Matrix A, double prec) Returns the canonical form of an order-3 phase-type distribution.static double[]Overload for double[] alpha.static double[]Returns the cumulative distribution function of a matrix-exponential distribution.static double[]Overload for double[] alpha.static double[]Returns the cumulative distribution function of a phase-type distribution.static booleanCheckMEPositiveDensity.checkMEPositiveDensity(Matrix alpha, Matrix A) static booleanCheckMEPositiveDensity.checkMEPositiveDensity(Matrix alpha, Matrix A, int maxSize) static booleanCheckMEPositiveDensity.checkMEPositiveDensity(Matrix alpha, Matrix A, int maxSize, double prec) Checks if the given ME distribution has positive density.static booleanCheckMERepresentation.checkMERepresentation(double[] alpha, Matrix A) static booleanCheckMERepresentation.checkMERepresentation(double[] alpha, Matrix A, double prec) Overload for double[] alpha.static booleanCheckMERepresentation.checkMERepresentation(Matrix alpha, Matrix A) static booleanCheckMERepresentation.checkMERepresentation(Matrix alpha, Matrix A, double prec) Checks if the given vector and matrix define a valid matrix- exponential representation.static booleanCheckPHRepresentation.checkPHRepresentation(double[] alpha, Matrix A) static booleanCheckPHRepresentation.checkPHRepresentation(double[] alpha, Matrix A, double prec) Overload for double[] alpha.static booleanCheckPHRepresentation.checkPHRepresentation(Matrix alpha, Matrix A) static booleanCheckPHRepresentation.checkPHRepresentation(Matrix alpha, Matrix A, double prec) Checks if the given vector and matrix define a valid phase- type representation.static booleanCheckRAPRepresentation.checkRAPRepresentation(Matrix H0, Matrix H1) static booleanCheckRAPRepresentation.checkRAPRepresentation(Matrix H0, Matrix H1, double prec) Checks if the given matrices define a valid Rational Arrival Process (RAP) representation.static Pair<double[],double[]> IntervalPdfFromPH.intervalPdfFromME(Matrix alpha, Matrix A, double[] intBounds) Returns the approximate probability density function of a matrix-exponential distribution, based on the probability of falling into intervals.static Pair<double[],double[]> IntervalPdfFromPH.intervalPdfFromPH(Matrix alpha, Matrix A, double[] intBounds) Returns the approximate probability density function of a continuous phase-type distribution, based on the probability of falling into intervals.static intstatic intstatic intReturns the order of the ME distribution (which is not necessarily equal to the size of the representation).static MERepresentationMinimalRepFromME.minimalRepFromME(Matrix alpha, Matrix A) static MERepresentationMinimalRepFromME.minimalRepFromME(Matrix alpha, Matrix A, String how) static MERepresentationMinimalRepFromME.minimalRepFromME(Matrix alpha, Matrix A, String how, double prec) Returns the minimal representation of the given ME distribution.static double[]MomentsFromME.momentsFromME(double[] alpha, Matrix A) static double[]MomentsFromME.momentsFromME(double[] alpha, Matrix A, int K) Overload for double[] alpha.static double[]MomentsFromME.momentsFromME(Matrix alpha, Matrix A) static double[]MomentsFromME.momentsFromME(Matrix alpha, Matrix A, int K) Returns the first K moments of a matrix-exponential distribution.static double[]MomentsFromME.momentsFromPH(double[] alpha, Matrix A) static double[]MomentsFromME.momentsFromPH(double[] alpha, Matrix A, int K) Overload for double[] alpha.static double[]MomentsFromME.momentsFromPH(Matrix alpha, Matrix A) static double[]MomentsFromME.momentsFromPH(Matrix alpha, Matrix A, int K) Returns the first K moments of a phase-type distribution.static PHRepresentationMonocyclicPHFromME.monocyclicPHFromME(Matrix alpha, Matrix A) static PHRepresentationMonocyclicPHFromME.monocyclicPHFromME(Matrix alpha, Matrix A, int maxSize) static PHRepresentationMonocyclicPHFromME.monocyclicPHFromME(Matrix alpha, Matrix A, int maxSize, double prec) Transforms an arbitrary matrix-exponential representation to a Markovian monocyclic representation.static double[]Overload for double[] alpha.static double[]Returns the probability density function of a matrix-exponential distribution.static double[]Overload for double[] alpha.static double[]Returns the probability density function of a phase-type distribution.static PHRepresentationstatic PHRepresentationObtains a Markovian representation of a matrix exponential distribution of the same size, if possible, using elementary similarity transformations.static double[]SamplesFromPH.samplesFromPH(double[] alpha, Matrix A, int K) static double[]SamplesFromPH.samplesFromPH(double[] alpha, Matrix A, int K, Random random) static double[]SamplesFromPH.samplesFromPH(Matrix alpha, Matrix A, int K) static double[]SamplesFromPH.samplesFromPH(Matrix alpha, Matrix A, int K, Random random) Generates random samples from a phase-type distribution.Constructors in jline.lib.butools.ph with parameters of type MatrixModifierConstructorDescriptionMERepresentation(Matrix alpha, Matrix A) PH2Representation(Matrix alpha, Matrix A) PH3Representation(Matrix alpha, Matrix A) PHRepresentation(Matrix alpha, Matrix A) -
Uses of Matrix in jline.lib.butools.queues
Methods in jline.lib.butools.queues that return MatrixModifier and TypeMethodDescriptionstatic Matrix[]FluidQueueSTD.fluFluSTD(Matrix Qin, Matrix Rin, Matrix Qout, Matrix Rout, boolean srv0stop, boolean transToPH) Sojourn-time distribution of a fluid queue in which both the arrival and the service processes are Markov-modulated fluid flows.static Matrix[]FluidQueueSTD.fluidQueueSTD(Matrix Q, Matrix Rin, Matrix Rout, Matrix Q0, boolean transToPH) Sojourn-time distribution of a fluid queue with input rate matrix Rin and output (service) rate matrix Rout, modulated by generator Q.Methods in jline.lib.butools.queues with parameters of type MatrixModifier and TypeMethodDescriptionstatic FluFluResultFluFluQueue.fluFluQueue(Matrix Qin, Matrix Rin, Matrix Qout, Matrix Rout, boolean srv0stop) static FluFluResultFluFluQueue.fluFluQueue(Matrix Qin, Matrix Rin, Matrix Qout, Matrix Rout, boolean srv0stop, int numFluidMoments, int numSojournMoments) static FluFluResultFluFluQueue.fluFluQueue(Matrix Qin, Matrix Rin, Matrix Qout, Matrix Rout, boolean srv0stop, int numFluidMoments, int numSojournMoments, double prec) Returns various performance measures of a fluid queue with independent fluid arrival and service processes.static Matrix[]FluidQueueSTD.fluFluSTD(Matrix Qin, Matrix Rin, Matrix Qout, Matrix Rout, boolean srv0stop, boolean transToPH) Sojourn-time distribution of a fluid queue in which both the arrival and the service processes are Markov-modulated fluid flows.static List<double[]>FluidPrioQueue.fluidPrioQueue(Matrix Q, Matrix R, double d, int[] classes, double prec, int erlMaxOrder, Object... measures) Returns performance measures of a continuous-time fluid priority queue.FluidQueue.fluidQueue(Matrix Q, Matrix Rin, Matrix Rout, Map<String, Object> measures, Matrix Q0, double prec) Returns various performance measures of a fluid queue.static Matrix[]FluidQueueSTD.fluidQueueSTD(Matrix Q, Matrix Rin, Matrix Rout, Matrix Q0, boolean transToPH) Sojourn-time distribution of a fluid queue with input rate matrix Rin and output (service) rate matrix Rout, modulated by generator Q.MAPMAP1.mapmap1(Matrix D0, Matrix D1, Matrix S0, Matrix S1, Map<String, Object> measures, double prec) Returns various performance measures of a continuous time MAP/MAP/1 queue.QBDQueue.qbdQueue(Matrix B, Matrix L, Matrix F, Matrix L0, Map<String, Object> measures, double prec) Returns various performance measures of a continuous time QBD queue. -
Uses of Matrix in jline.lib.butools.reptrans
Methods in jline.lib.butools.reptrans that return MatrixModifier and TypeMethodDescriptionMarkovianRepresentation.getB()MarkovianRepresentation.getBeta()static MatrixSimilarityMatrix.similarityMatrix(Matrix A1, Matrix A2) Returns the matrix that transforms A1 to A2.static MatrixTransformToAcyclic.transformToAcyclic(Matrix A) static MatrixTransformToAcyclic.transformToAcyclic(Matrix A, int maxSize) static MatrixTransformToAcyclic.transformToAcyclic(Matrix A, int maxSize, double precision) Transforms an arbitrary matrix to a Markovian bi-diagonal matrix.static MatrixTransformToMonocyclic.transformToMonocyclic(Matrix A) static MatrixTransformToMonocyclic.transformToMonocyclic(Matrix A, int maxSize) static MatrixTransformToMonocyclic.transformToMonocyclic(Matrix A, int maxSize, double precision) Transforms an arbitrary matrix to a Markovian monocyclic matrix.static MatrixTransformToOnes.transformToOnes(Matrix clovec) Returns the similarity transformation matrix B such that B*clovec = ones.Methods in jline.lib.butools.reptrans that return types with arguments of type MatrixModifier and TypeMethodDescriptionFindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun) FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun, double precision) Obtains a Markovian representation from a non-Markovian one while keeping the size the same, by applying a series of elementary transformations.FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, FindMarkovianRepresentation.TransFun transfun, FindMarkovianRepresentation.EvalFun evalfun, double precision) Overload accepting Java functional-interface flavors of the arguments.MStaircase.mStaircase(List<Matrix> Y, Matrix Z) MStaircase.mStaircase(List<Matrix> Y, Matrix Z, double precision) Computes a smaller representation using the staircase algorithm.Methods in jline.lib.butools.reptrans with parameters of type MatrixModifier and TypeMethodDescriptionstatic MarkovianRepresentationExtendToMarkovian.extendToMarkovian(Matrix alpha, Matrix A) static MarkovianRepresentationExtendToMarkovian.extendToMarkovian(Matrix alpha, Matrix A, int maxSize) static MarkovianRepresentationExtendToMarkovian.extendToMarkovian(Matrix alpha, Matrix A, int maxSize, double precision) Extends a non-Markovian initial vector to a Markovian one by appending an Erlang tail.MStaircase.mStaircase(List<Matrix> Y, Matrix Z) MStaircase.mStaircase(List<Matrix> Y, Matrix Z, double precision) Computes a smaller representation using the staircase algorithm.static MatrixSimilarityMatrix.similarityMatrix(Matrix A1, Matrix A2) Returns the matrix that transforms A1 to A2.static MatrixTransformToAcyclic.transformToAcyclic(Matrix A) static MatrixTransformToAcyclic.transformToAcyclic(Matrix A, int maxSize) static MatrixTransformToAcyclic.transformToAcyclic(Matrix A, int maxSize, double precision) Transforms an arbitrary matrix to a Markovian bi-diagonal matrix.static MatrixTransformToMonocyclic.transformToMonocyclic(Matrix A) static MatrixTransformToMonocyclic.transformToMonocyclic(Matrix A, int maxSize) static MatrixTransformToMonocyclic.transformToMonocyclic(Matrix A, int maxSize, double precision) Transforms an arbitrary matrix to a Markovian monocyclic matrix.static MatrixTransformToOnes.transformToOnes(Matrix clovec) Returns the similarity transformation matrix B such that B*clovec = ones.Method parameters in jline.lib.butools.reptrans with type arguments of type MatrixModifier and TypeMethodDescriptiondoubleFindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun) FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun) FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun) FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun) FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun, double precision) Obtains a Markovian representation from a non-Markovian one while keeping the size the same, by applying a series of elementary transformations.FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun, double precision) Obtains a Markovian representation from a non-Markovian one while keeping the size the same, by applying a series of elementary transformations.FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun, double precision) Obtains a Markovian representation from a non-Markovian one while keeping the size the same, by applying a series of elementary transformations.FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, BiFunction<List<Matrix>, Matrix, List<Matrix>> transfun, BiFunction<List<Matrix>, Integer, Double> evalfun, double precision) Obtains a Markovian representation from a non-Markovian one while keeping the size the same, by applying a series of elementary transformations.FindMarkovianRepresentation.findMarkovianRepresentation(List<Matrix> rep, FindMarkovianRepresentation.TransFun transfun, FindMarkovianRepresentation.EvalFun evalfun, double precision) Overload accepting Java functional-interface flavors of the arguments.MStaircase.mStaircase(List<Matrix> Y, Matrix Z) MStaircase.mStaircase(List<Matrix> Y, Matrix Z, double precision) Computes a smaller representation using the staircase algorithm.Constructors in jline.lib.butools.reptrans with parameters of type Matrix -
Uses of Matrix in jline.lib.butools.trace
Methods in jline.lib.butools.trace that return MatrixModifier and TypeMethodDescriptionstatic MatrixLagCorrelationsFromTrace.lagkJointMomentsFromTrace(double[] trace, int K) static MatrixLagCorrelationsFromTrace.lagkJointMomentsFromTrace(double[] trace, int K, int L) Returns the lag-L joint moments of a trace. -
Uses of Matrix in jline.lib.fjcodes
Fields in jline.lib.fjcodes declared as MatrixModifier and TypeFieldDescriptionfinal MatrixComputeT.ComputeTResult.A_jumpfinal MatrixReturnWaitResult.alfafinal MatrixSRKResult.Kcfinal MatrixSRKResult.Kefinal MatrixFJArrival.lambda0final MatrixFJArrival.lambda1final MatrixPiResult.pi0final MatrixSRKResult.R0final MatrixComputeT.ComputeTResult.Sfinal MatrixComputeT.ComputeTResult.S_Arrfinal MatrixSRKResult.S_lastfinal MatrixSRKResult.S_longfinal MatrixSRKResult.Sefinal MatrixSRKResult.Sestarfinal MatrixFJService.STfinal MatrixComputeT.ComputeTResult.sum_Ajumpfinal MatrixComputeT.ComputeTResult.Tfinal MatrixFJService.tau_stfinal MatrixReturnWaitResult.wait_alphafinal MatrixReturnWaitResult.wait_SmatMethods in jline.lib.fjcodes that return MatrixModifier and TypeMethodDescriptionstatic MatrixFjCodesUtils.build_index(int m, int cr) Build combinatorial index patterns: enumerate all ways to distributecritems intombins.static MatrixFJUtils.build_index(int m, int cr) Build combinatorial index patterns.ComputeT.ComputeTResult.component1()GenerateServiceResult.component1()SAResult.component1()ComputeT.ComputeTResult.component2()SAResult.component2()ComputeT.ComputeTResult.component3()ComputeT.ComputeTResult.component4()ComputeT.ComputeTResult.component5()static MatrixComputeT_NARE.computeT_NARE(Matrix D0, Matrix D1, Matrix S, Matrix A_jump) static MatrixComputeT_Sylvester.computeT_Sylvester(Matrix D0, Matrix D1, Matrix S, Matrix A_jump) Compute T-matrix via Sylvester equation methodstatic MatrixFJStateSpace.constructNotAllBusy(int C, FJService services, FJServiceH service_h) Construct state space matrix for not-all-busy states.SAResult.getA_jump()ReturnWaitResult.getAlfa()FJServiceH.getBeta()SRKResult.getKc()SRKResult.getKe()FJArrival.getLambda0()FJArrival.getLambda1()PiResult.getPi0()SRKResult.getR0()FJServiceH.getS()SAResult.getS()SRKResult.getS_last()SRKResult.getS_long()SRKResult.getSe()FJServiceH.getService_phases()SRKResult.getSestar()FJService.getSt()FJService.getST()GenerateServiceResult.getT()FJService.getTau_st()ReturnWaitResult.getWait_alpha()ReturnWaitResult.getWait_Smat()static MatrixCompute Kronecker sum of two matrices: A xor B = A x I + I x B.static MatrixCompute Kronecker sum of two matrices.static MatrixExtract percentiles from a Phase-Type (PH) distributionstatic MatrixCompute response time percentiles for K=1 Fork-Join queue.static Matrixstatic MatrixCompute response time percentiles for K=2 Fork-Join queue.Methods in jline.lib.fjcodes with parameters of type MatrixModifier and TypeMethodDescriptionstatic PiResultComputePi.computePi(Matrix T, FJArrival arrival, FJService services, FJServiceH service_h, int C, Matrix S, Matrix A_jump) Compute steady-state distribution for the case with 2 replicas.static MatrixComputeT_NARE.computeT_NARE(Matrix D0, Matrix D1, Matrix S, Matrix A_jump) static MatrixComputeT_Sylvester.computeT_Sylvester(Matrix D0, Matrix D1, Matrix S, Matrix A_jump) Compute T-matrix via Sylvester equation methodstatic SRKResultFJStateSpace.constructSRK(int C, FJService services, FJServiceH service_h, Matrix S) Construct extended state space matrices (Se, Sestar, R0, Ke, Kc).static GenerateServiceResultGenerateService.generateService(FJService services, FJServiceH service_h, int C, Matrix S) Generate Phase-Type representation for service time.static double[]FjCodesUtils.getRowAsArray(Matrix matrix, int row) Extract rowrowofmatrixas adouble[].static double[]FJUtils.getRowAsArray(Matrix matrix, int row) Extract a row from a Matrix as a double[].static MatrixCompute Kronecker sum of two matrices: A xor B = A x I + I x B.static MatrixCompute Kronecker sum of two matrices.static MatrixExtract percentiles from a Phase-Type (PH) distributionstatic ReturnWaitResultReturnWait.returnWait(double En1, Matrix pi0, Matrix T, Matrix phi, Matrix sum_Ajump) Compute Phase-Type representation of stationary waiting time.static voidFjCodesUtils.setSubMatrix(Matrix target, int startRow, int startCol, Matrix source) Copysourceintotargetstarting at the given (0-based) row/column position.static voidFJUtils.setSubMatrix(Matrix target, int startRow, int startCol, Matrix source) Copy a submatrix into a target matrix at specified position.static intFind the 1-based index of the row inmatrixthat matchesrowelementwise (within 1e-10), or -1 if no row matches.static intFind the row index in a matrix that matches a given row vector.Constructors in jline.lib.fjcodes with parameters of type MatrixModifierConstructorDescriptionFJServiceH(Matrix service_phases, Matrix beta, Matrix S) GenerateServiceResult(Matrix T, int newdim, int dim_notbusy) ReturnWaitResult(Matrix wait_alpha, Matrix wait_Smat, double prob_wait, Matrix alfa) -
Uses of Matrix in jline.lib.kpctoolbox
Methods in jline.lib.kpctoolbox that return MatrixModifier and TypeMethodDescriptionstatic MatrixMarkovChain.ctmcSteadyState(Matrix Q) static MatrixMarkovChain.ctmcTransient(Matrix Q, double t) static MatrixMarkovChain.dtmcSteadyState(Matrix P) Methods in jline.lib.kpctoolbox that return types with arguments of type MatrixModifier and TypeMethodDescriptionAPH.fromMoments(Matrix moments) APH.fromMoments(Matrix moments) Methods in jline.lib.kpctoolbox with parameters of type MatrixModifier and TypeMethodDescriptionstatic MatrixMarkovChain.ctmcSteadyState(Matrix Q) static MatrixMarkovChain.ctmcTransient(Matrix Q, double t) static MatrixMarkovChain.dtmcSteadyState(Matrix P) static MatrixCellstatic MatrixCellstatic MatrixCellstatic MatrixCellstatic MatrixCellAPH.fromMoments(Matrix moments) -
Uses of Matrix in jline.lib.kpctoolbox.aph
Methods in jline.lib.kpctoolbox.aph that return types with arguments of type MatrixModifier and TypeMethodDescriptionAPH.aph_convpara(List<Pair<double[], Matrix>> distributions) APH.aph_convseq(List<Pair<double[], Matrix>> distributions) APH.aph_simplify(double[] a1, Matrix T1, double[] a2, Matrix T2, double p1, double p2, APH.ConvolutionPattern pattern) APH.aph_simplify(double[] a1, Matrix T1, double[] a2, Matrix T2, APH.ConvolutionPattern pattern) Methods in jline.lib.kpctoolbox.aph with parameters of type MatrixModifier and TypeMethodDescriptionAPH.aph_simplify(double[] a1, Matrix T1, double[] a2, Matrix T2, double p1, double p2, APH.ConvolutionPattern pattern) APH.aph_simplify(double[] a1, Matrix T1, double[] a2, Matrix T2, APH.ConvolutionPattern pattern) Method parameters in jline.lib.kpctoolbox.aph with type arguments of type Matrix -
Uses of Matrix in jline.lib.kpctoolbox.basic
Methods in jline.lib.kpctoolbox.basic that return MatrixModifier and TypeMethodDescriptionSpectralDecomposition.component3()SpectralDecomposition.component4()static MatrixBasicUtils.e(int n) Creates a vector e(n) = [1, 1, ..., 1]^T used in matrix operations.static MatrixBasicUtils.eye(int n) Creates an identity matrix.SpectralDecomposition.getEigenvalueMatrix()SpectralDecomposition.getEigenvectors()static MatrixBasicUtils.ones(int n) Creates a column vector of ones.static MatrixBasicUtils.zeros(int m, int n) Creates a zero matrix.Methods in jline.lib.kpctoolbox.basic that return types with arguments of type MatrixModifier and TypeMethodDescriptionSpectralDecomposition.component2()SpectralDecomposition.getProjectors()Methods in jline.lib.kpctoolbox.basic with parameters of type MatrixModifier and TypeMethodDescriptionstatic SpectralDecompositionComputes the spectral decomposition of a matrix.Constructors in jline.lib.kpctoolbox.basic with parameters of type MatrixModifierConstructorDescriptionSpectralDecomposition(double[] spectrum, List<Matrix> projectors, Matrix eigenvectors, Matrix eigenvalueMatrix) Constructor parameters in jline.lib.kpctoolbox.basic with type arguments of type MatrixModifierConstructorDescriptionSpectralDecomposition(double[] spectrum, List<Matrix> projectors, Matrix eigenvectors, Matrix eigenvalueMatrix) -
Uses of Matrix in jline.lib.kpctoolbox.mc
Fields in jline.lib.kpctoolbox.mc declared as MatrixMethods in jline.lib.kpctoolbox.mc that return MatrixModifier and TypeMethodDescriptionstatic MatrixCTMC.ctmc_makeinfgen(Matrix Q) Normalizes a matrix to be a valid infinitesimal generator.static MatrixCTMC.ctmc_rand(int n) Generates a random infinitesimal generator matrix.static MatrixCTMC.ctmc_timereverse(Matrix Q) Computes the time-reversed generator of a CTMC.static MatrixDTMC.dtmc_makestochastic(Matrix P) static MatrixDTMC.dtmc_rand(int n) static MatrixDTMC.dtmc_stochcomp(Matrix P) static MatrixDTMC.dtmc_stochcomp(Matrix P, int[] I) static MatrixDTMC.dtmc_timereverse(Matrix P) Methods in jline.lib.kpctoolbox.mc that return types with arguments of type MatrixModifier and TypeMethodDescriptionCTMC.ctmc_randomization(Matrix Q) CTMC.ctmc_randomization(Matrix Q, Double q) Applies uniformization (randomization) to transform a CTMC into a DTMC.Methods in jline.lib.kpctoolbox.mc with parameters of type MatrixModifier and TypeMethodDescriptionstatic MatrixCTMC.ctmc_makeinfgen(Matrix Q) Normalizes a matrix to be a valid infinitesimal generator.CTMC.ctmc_randomization(Matrix Q) CTMC.ctmc_randomization(Matrix Q, Double q) Applies uniformization (randomization) to transform a CTMC into a DTMC.static double[]CTMC.ctmc_relsolve(Matrix Q) Computes the equilibrium distribution relative to a reference state.static double[]CTMC.ctmc_relsolve(Matrix Q, int refstate) static double[]CTMC.ctmc_solve(Matrix Q) Computes the equilibrium distribution of a continuous-time Markov chain.static CTMC.CTMCSolveResultCTMC.ctmc_solveFull(Matrix Q) Computes the equilibrium distribution with full details.static MatrixCTMC.ctmc_timereverse(Matrix Q) Computes the time-reversed generator of a CTMC.CTMC.ctmc_uniformization(double[] pi0, Matrix Q, double t) Computes transient probabilities using uniformization method.CTMC.ctmc_uniformization(double[] pi0, Matrix Q, double t, double tol, int maxiter) static intDTMC.dtmc_isfeasible(Matrix P) static MatrixDTMC.dtmc_makestochastic(Matrix P) static int[]DTMC.dtmc_simulate(Matrix P, double[] pi0, int nSteps) static double[]DTMC.dtmc_solve(Matrix P) static MatrixDTMC.dtmc_stochcomp(Matrix P) static MatrixDTMC.dtmc_stochcomp(Matrix P, int[] I) static MatrixDTMC.dtmc_timereverse(Matrix P) DTMC.dtmc_uniformization(double[] pi0, Matrix P) DTMC.dtmc_uniformization(double[] pi0, Matrix P, double t) DTMC.dtmc_uniformization(double[] pi0, Matrix P, double t, double tol) DTMC.dtmc_uniformization(double[] pi0, Matrix P, double t, double tol, int maxiter) static CTMC.ConnectedComponentsCTMC.weaklyconncomp(Matrix G) Finds weakly connected components in a directed graph.Constructors in jline.lib.kpctoolbox.mc with parameters of type MatrixModifierConstructorDescriptionCTMCSolveResult(double[] equilibriumDistribution, Matrix generator, int numComponents, int[] componentAssignment) -
Uses of Matrix in jline.lib.kpctoolbox.mvph
Methods in jline.lib.kpctoolbox.mvph with parameters of type MatrixModifier and TypeMethodDescriptionstatic doublestatic doublestatic doubleMVPH.mvph_joint(double[] alpha, Matrix S, Matrix T, Matrix D, int n1, int n2) Computes the joint moment E[X^n1 * Y^n2] of a bivariate phase-type distribution.static doubleMVPH.mvph_mean_x(double[] alpha, Matrix S, Matrix T, Matrix D) static doubleMVPH.mvph_mean_y(double[] alpha, Matrix S, Matrix T, Matrix D) -
Uses of Matrix in jline.lib.kpctoolbox.smp
Methods in jline.lib.kpctoolbox.smp that return Matrix -
Uses of Matrix in jline.lib.m3a
Methods in jline.lib.m3a with parameters of type MatrixModifier and TypeMethodDescriptionstatic MTraceM3aFit.m3afit_init(Matrix S, Matrix C) Prepares multiclass trace for M3A fitting from Matrix inputs. -
Uses of Matrix in jline.lib.perm
Fields in jline.lib.perm declared as MatrixMethods in jline.lib.perm that return MatrixMethods in jline.lib.perm that return types with arguments of type MatrixModifier and TypeMethodDescriptionQueueingNetwork.preprocessingDS(Matrix M) Make a matrix doubly stochastic via the Sinkhorn algorithm.Methods in jline.lib.perm with parameters of type MatrixModifier and TypeMethodDescriptionstatic int[]PermSupport.maxWeightAssignment(Matrix weight) Maximum-weight perfect assignment of a square weight matrix.QueueingNetwork.preprocessingDS(Matrix M) Make a matrix doubly stochastic via the Sinkhorn algorithm.static voidPermSupport.requireFullSupport(Matrix matrix, String caller) Refuse a matrix the permanent approximations cannot take.Constructors in jline.lib.perm with parameters of type MatrixModifierConstructorDescriptionAdaPartSampler(Matrix matrix) AdaPartSampler(Matrix matrix, int maximumAcceptedSamples, long maximumTime, int maximumSamples, String mode, boolean solve) BethePermanent(Matrix matrix) BethePermanent(Matrix matrix, double epsilon, int maxIteration) BethePermanent(Matrix matrix, double epsilon, int maxIteration, boolean solve) HeuristicPermanent(Matrix matrix) Constructor with just matrix parameter.HeuristicPermanent(Matrix matrix, boolean solve) Constructor with just matrix and solve parameters.HeuristicPermanent(Matrix matrix, double tolerance, int maxIterations, boolean solve) Constructor with all parameters.HuberLawSampler(Matrix matrix) HuberLawSampler(Matrix matrix, double delta, double alpha2, double epsilon, String mode, int numberOfSamples, long maximumTime, boolean solve) NaivePermanent(Matrix matrix) NaivePermanent(Matrix matrix, boolean solve) PermSolver(Matrix matrix) RyzerPermanent(Matrix matrix) RyzerPermanent(Matrix matrix, String mode) RyzerPermanent(Matrix matrix, String mode, boolean solve) SaddlePointPermanent(Matrix matrix) Unit multiplicities, not solved on construction.SaddlePointPermanent(Matrix matrix, boolean solve) Unit multiplicities, which requires a square matrix.SaddlePointPermanent(Matrix matrix, int[] m, boolean solve) Multiplicities and defaults for tolerance (1e-11) and maxIterations (10000).SaddlePointPermanent(Matrix matrix, int[] m, double tolerance, int maxIterations, boolean solve) Constructor with all parameters. -
Uses of Matrix in jline.lib.qmam
Fields in jline.lib.qmam declared as MatrixModifier and TypeFieldDescriptionfinal MatrixMMAPKPHK1Result.qlTotalfinal MatrixDTQueueResult.queueLengthfinal MatrixMAPDcResult.queueLengthfinal MatrixMAPMAP1Result.queueLengthfinal MatrixMAPMcResult.queueLengthfinal MatrixPHPH1Result.queueLengthfinal MatrixQ_RAP_RAP_1.RAPRAP1Result.queueLengthfinal MatrixMAPMAP1Result.Smatfinal MatrixMAPMcResult.Smatfinal MatrixMMAPKPHK1Result.Smatfinal MatrixMAPMAP1Result.sojAlphafinal MatrixMAPMAP1Result.waitAlphafinal MatrixMAPMcResult.waitAlphafinal MatrixPHPH1Result.waitAlphafinal MatrixMAPDcResult.waitingTimefinal MatrixPHPH1Result.waitTFields in jline.lib.qmam with type parameters of type MatrixModifier and TypeFieldDescriptionMMAPKPHK1Result.qlPerTypeMMAPKPHK1Result.sojAlphaMMAPKPHK1Result.waitAlphaMethods in jline.lib.qmam that return MatrixModifier and TypeMethodDescriptionDTQueueResult.component1()MAPMAP1Result.component1()PHPH1Result.component1()MAPMAP1Result.component2()PHPH1Result.component2()MAPMAP1Result.component3()PHPH1Result.component3()MAPMAP1Result.component4()MMAPKPHK1Result.getQlTotal()DTQueueResult.getQueueLength()MAPDcResult.getQueueLength()MAPMAP1Result.getQueueLength()MAPMcResult.getQueueLength()PHPH1Result.getQueueLength()MAPMAP1Result.getSmat()MAPMcResult.getSmat()MMAPKPHK1Result.getSmat()MAPMAP1Result.getSojAlpha()MAPMAP1Result.getWaitAlpha()MAPMcResult.getWaitAlpha()PHPH1Result.getWaitAlpha()MAPDcResult.getWaitingTime()PHPH1Result.getWaitT()static MatrixQ_Sylvest.qSylvest(ComplexMatrix U, ComplexMatrix T, Matrix B) Solves the equation X*kron(A,I)+B*X=-I, where kron(A,I)=U*T*U' is given by its complex Schur decomposition (U unitary, T upper triangular).Methods in jline.lib.qmam that return types with arguments of type MatrixModifier and TypeMethodDescriptionMMAPKPHK1Result.getQlPerType()MMAPKPHK1Result.getSojAlpha()MMAPKPHK1Result.getWaitAlpha()Methods in jline.lib.qmam with parameters of type MatrixModifier and TypeMethodDescriptionstatic MAPDcResultstatic MAPDcResultQ_CT_MAP_D_C.qCtMapDC(Matrix D0, Matrix D1, double s, int c, MAPDcOptions options) Computes queue length and waiting time distribution for a MAP/D/c/FCFS queue.static MAPMAP1ResultQ_CT_MAP_MAP_1.qCtMapMap1(Matrix C0, Matrix C1, Matrix D0, Matrix D1) static MAPMAP1ResultQ_CT_MAP_MAP_1.qCtMapMap1(Matrix C0, Matrix C1, Matrix D0, Matrix D1, MAPMAP1Options options) Computes queue length and time distributions for a MAP/MAP/1/FCFS queue.static MAPMcResultstatic MAPMcResultQ_CT_MAP_M_C.qCtMapMC(Matrix D0, Matrix D1, double mu, int c, MAPMcOptions options) static MMAPKPHK1Resultstatic MMAPKPHK1ResultQ_CT_MMAPK_PHK_1.qCtMmapkPhk1(Matrix D0, List<Matrix> D, List<Matrix> alpha, List<Matrix> S, MMAPKPHK1Options options) static PHPH1Resultstatic PHPH1ResultComputes queue length and waiting time distribution for a PH/PH/1/FCFS queue.static DTQueueResultQ_DT_MAP_MAP_1.qDtMapMap1(Matrix C0, Matrix C1, Matrix D0, Matrix D1, MAPMAP1Options options) Queue length distribution of a discrete-time DMAP/DMAP/1/FCFS queue.static DTQueueResultQueue length distribution of a discrete-time DPH/DPH/1/FCFS queue.static Q_RAP_RAP_1.RAPRAP1Resultstatic Q_RAP_RAP_1.RAPRAP1ResultQ_RAP_RAP_1.qRapRap1(Matrix C0, Matrix C1, Matrix D0, Matrix D1, Q_RAP_RAP_1.RAPRAP1Options options) Computes queue length distribution for a RAP/RAP/1/FCFS queue.static MatrixQ_Sylvest.qSylvest(ComplexMatrix U, ComplexMatrix T, Matrix B) Solves the equation X*kron(A,I)+B*X=-I, where kron(A,I)=U*T*U' is given by its complex Schur decomposition (U unitary, T upper triangular).static Map<String,ComplexMatrix> Q_Sylvest.schurDecomposition(Matrix A) Computes the complex Schur decomposition of A, equivalent to MATLAB's[U,T] = schur(A,'complex')with A = U*T*U'.Method parameters in jline.lib.qmam with type arguments of type MatrixModifier and TypeMethodDescriptionstatic MMAPKPHK1Resultstatic MMAPKPHK1ResultQ_CT_MMAPK_PHK_1.qCtMmapkPhk1(Matrix D0, List<Matrix> D, List<Matrix> alpha, List<Matrix> S, MMAPKPHK1Options options) Constructors in jline.lib.qmam with parameters of type MatrixModifierConstructorDescriptionDTQueueResult(Matrix queueLength) MAPDcResult(Matrix queueLength, Matrix waitingTime) MAPMAP1Result(Matrix queueLength, Matrix sojAlpha, Matrix waitAlpha, Matrix Smat) MAPMcResult(Matrix queueLength, Matrix waitAlpha, Matrix Smat) MMAPKPHK1Result(List<Matrix> qlPerType, Matrix qlTotal, List<Matrix> sojAlpha, List<Matrix> waitAlpha, Matrix Smat) PHPH1Result(Matrix queueLength, Matrix waitAlpha, Matrix waitT) RAPRAP1Result(Matrix queueLength) Constructor parameters in jline.lib.qmam with type arguments of type Matrix -
Uses of Matrix in jline.lib.smc
Fields in jline.lib.smc declared as MatrixModifier and TypeFieldDescriptionfinal MatrixGIM1PiOptions.boundaryfinal MatrixMG1_ETAQA.ETAQAResult.Gfinal MatrixMG1_ETAQA.ETAQAResult.piMethods in jline.lib.smc that return MatrixModifier and TypeMethodDescriptionGIM1PiOptions.component1()GIM1CaudalResult.component2()MG1DecayResult.component2()GIM1PiOptions.getBoundary()MG1PiOptions.getBoundary()MG1_ETAQA.ETAQAResult.getG()MG1_ETAQA.ETAQAResult.getPi()MG1FIOptions.getStartValue()MG1DecayResult.getUT()GIM1CaudalResult.getV()static Matrixstatic MatrixGIM1_pi.gim1_pi(Matrix B, Matrix R, GIM1PiOptions options) Computes the stationary distribution of a GI/M/1-type Markov chain.static MatrixGIM1_ETAQA.gim1_pi_etaqa(Matrix Bin, Matrix Ain, Matrix R, Matrix B0in) Aggregated stationary vector [pi0, pi1, pi2+pi3+...] of a GI/M/1-type chain.static Matrixstatic MatrixGIM1_R.gim1_R(Matrix A, GIM1_R.GIM1ROptions options) Computes the R matrix for a GI/M/1-type Markov chain.static MatrixGIM1_R.gim1_R_dual(Matrix A, String dual, String algor) R of a GI/M/1-type chain through the G of its DUAL.static MatrixGIM1_ETAQA.gim1_r_etaqa(Matrix A) R of a GI/M/1-type chain, uniformized first.static MatrixComputes the G matrix using Cyclic Reduction for M/G/1-type Markov Chains.static MatrixMG1_pi.mg1_cr(Matrix A, MG1CROptions options) static Matrixstatic MatrixDetermines G directly if rank(A0)=1 for M/G/1-type Markov chains.static Matrixstatic MatrixMG1_FI.mg1_fi(Matrix A, MG1FIOptions options) Functional Iterations for M/G/1-Type Markov Chains.static MatrixMG1_ETAQA.mg1_g_etaqa(Matrix A) Computes the G matrix for M/G/1-type Markov chains.static Matrixstatic MatrixMG1_pi.mg1_pi(Matrix B, Matrix A, MG1PiOptions options) Stationary vector of an M/G/1-type chain by the stable Ramaswami formula.static MatrixMG1_ETAQA.mg1_pi_etaqa(Matrix B, Matrix A) static MatrixMG1_ETAQA.mg1_pi_etaqa(Matrix B, Matrix A, Matrix G) static MatrixMG1_ETAQA.mg1_pi_etaqa(Matrix B, Matrix A, Matrix G, Matrix C0) Computes the aggregated stationary probability vector for an M/G/1-type Markov chain using the ETAQA method.static Matrixstatic MatrixNSF_GHT.nsfGht(Matrix A, int N, NSFGHTOptions options) Gail-Hantler-Taylor algorithm for Non-Skip-Free Markov chains.static Matrixstatic MatrixNSF_GHT.nsfPi(Matrix B, Matrix A, Matrix G, NSFPiOptions options) NSF_pi - Stationary vector of a Non-Skip-Free Markov chain.static MatrixQBD_NI_Sylvest.QBD_NI_Sylvest(Matrix A, Matrix B, Matrix C, Matrix D) Solves the Sylvester equation AXB + CX = D using complex Schur and Hessenberg-triangular decomposition.static MatrixQBD_pi: Stationary vector of a Quasi-Birth-Death Markov Chains [Neuts]static MatrixQBD_pi.QBD_pi(Matrix B0, Matrix B1, Matrix R, int MaxNumComp, int Verbose, Matrix Boundary, int RAPComp) static MatrixMethods in jline.lib.smc that return types with arguments of type MatrixModifier and TypeMethodDescriptionMG1_Shifts.mg1_shifts(Matrix A) MG1_Shifts.mg1_shifts(Matrix A) MG1_Shifts.mg1_shifts(Matrix A, String shiftType) Applies the shift technique to an M/G/1-type block matrix.MG1_Shifts.mg1_shifts(Matrix A, String shiftType) Applies the shift technique to an M/G/1-type block matrix.QBD_CR.QBD_CR(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) QBD_FI.QBD_FI(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Matrix StartValue_, Integer RAPComp_) QBD_IS.QBD_IS(Matrix A0, Matrix A1, Matrix A2, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) QBD_LR.QBD_LR(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) QBD_NI.QBD_NI(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) QBD_CR.solve(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) Convenience alias for callers that expect asolve(...)entry point on the QBD_CR class.QBD_FI.solve(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Matrix StartValue_, Integer RAPComp_) Convenience alias for callers that expect asolve(...)entry point on the QBD_FI class.Methods in jline.lib.smc with parameters of type MatrixModifier and TypeMethodDescriptionstatic GIM1CaudalResultGIM1_Caudal.gim1_caudal(Matrix A) static GIM1CaudalResultGIM1_Caudal.gim1_caudal(Matrix A, boolean dual) static GIM1CaudalResultGIM1_Caudal.gim1_caudal(Matrix A, boolean dual, boolean computeEigenvector) Computes the dominant eigenvalue of the R matrix for GI/M/1-type chains.static Matrixstatic MatrixGIM1_pi.gim1_pi(Matrix B, Matrix R, GIM1PiOptions options) Computes the stationary distribution of a GI/M/1-type Markov chain.static MatrixGIM1_ETAQA.gim1_pi_etaqa(Matrix Bin, Matrix Ain, Matrix R, Matrix B0in) Aggregated stationary vector [pi0, pi1, pi2+pi3+...] of a GI/M/1-type chain.static doublen-th moment of the level of a GI/M/1-type chain from the ETAQA aggregates.static Matrixstatic MatrixGIM1_R.gim1_R(Matrix A, GIM1_R.GIM1ROptions options) Computes the R matrix for a GI/M/1-type Markov chain.static MatrixGIM1_R.gim1_R_dual(Matrix A, String dual, String algor) R of a GI/M/1-type chain through the G of its DUAL.static MatrixGIM1_ETAQA.gim1_r_etaqa(Matrix A) R of a GI/M/1-type chain, uniformized first.static MatrixComputes the G matrix using Cyclic Reduction for M/G/1-type Markov Chains.static MatrixMG1_pi.mg1_cr(Matrix A, MG1CROptions options) static MG1DecayResultstatic MG1DecayResultComputes the decay rate of a recurrent M/G/1 type Markov chain.static Matrixstatic MatrixDetermines G directly if rank(A0)=1 for M/G/1-type Markov chains.static Matrixstatic MatrixMG1_FI.mg1_fi(Matrix A, MG1FIOptions options) Functional Iterations for M/G/1-Type Markov Chains.static MatrixMG1_ETAQA.mg1_g_etaqa(Matrix A) Computes the G matrix for M/G/1-type Markov chains.static Matrixstatic MatrixMG1_pi.mg1_pi(Matrix B, Matrix A, MG1PiOptions options) Stationary vector of an M/G/1-type chain by the stable Ramaswami formula.static MatrixMG1_ETAQA.mg1_pi_etaqa(Matrix B, Matrix A) static MatrixMG1_ETAQA.mg1_pi_etaqa(Matrix B, Matrix A, Matrix G) static MatrixMG1_ETAQA.mg1_pi_etaqa(Matrix B, Matrix A, Matrix G, Matrix C0) Computes the aggregated stationary probability vector for an M/G/1-type Markov chain using the ETAQA method.static doubleMG1_ETAQA.mg1_qlen_etaqa(Matrix B, Matrix A, Matrix pi, int n) Computes the n-th moment of the level (the queue length) of an M/G/1-type chain from the ETAQA aggregates.MG1_Shifts.mg1_shifts(Matrix A) MG1_Shifts.mg1_shifts(Matrix A, String shiftType) Applies the shift technique to an M/G/1-type block matrix.static Matrixstatic MatrixNSF_GHT.nsfGht(Matrix A, int N, NSFGHTOptions options) Gail-Hantler-Taylor algorithm for Non-Skip-Free Markov chains.static Matrixstatic MatrixNSF_GHT.nsfPi(Matrix B, Matrix A, Matrix G, NSFPiOptions options) NSF_pi - Stationary vector of a Non-Skip-Free Markov chain.static doubleQBD_CAUDAL.QBD_CAUDAL(Matrix A0, Matrix A1, Matrix A2) static doubleQBD_CAUDAL.QBD_CAUDAL(Matrix A0, Matrix A1, Matrix A2, boolean Dual) QBD_CR.QBD_CR(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) QBD_FI.QBD_FI(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Matrix StartValue_, Integer RAPComp_) QBD_IS.QBD_IS(Matrix A0, Matrix A1, Matrix A2, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) QBD_LR.QBD_LR(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) QBD_NI.QBD_NI(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) static MatrixQBD_NI_Sylvest.QBD_NI_Sylvest(Matrix A, Matrix B, Matrix C, Matrix D) Solves the Sylvester equation AXB + CX = D using complex Schur and Hessenberg-triangular decomposition.static voidQBD_ParsePara.QBD_ParsePara(Matrix A0, Matrix A1, Matrix A2) static MatrixQBD_pi: Stationary vector of a Quasi-Birth-Death Markov Chains [Neuts]static MatrixQBD_pi.QBD_pi(Matrix B0, Matrix B1, Matrix R, int MaxNumComp, int Verbose, Matrix Boundary, int RAPComp) static voidQBD_RAP_ParsePara.QBD_RAP_ParsePara(Matrix A0, Matrix A1, Matrix A2) QBD_RAP_ParsePara checks the validity of the input matrices A0, A1 and A2 for a QBD with RAP components.QBD_CR.solve(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Integer RAPComp_) Convenience alias for callers that expect asolve(...)entry point on the QBD_CR class.QBD_FI.solve(Matrix A0arg, Matrix A1arg, Matrix A2arg, Integer MaxNumIt_, Integer Verbose_, String Mode_, Matrix StartValue_, Integer RAPComp_) Convenience alias for callers that expect asolve(...)entry point on the QBD_FI class.static MatrixConstructors in jline.lib.smc with parameters of type MatrixModifierConstructorDescriptionETAQAResult(Matrix pi, Matrix G) GIM1CaudalResult(double eta, Matrix v) GIM1PiOptions(Matrix boundary, int maxNumComp, int verbose) MG1DecayResult(double eta, Matrix uT) MG1FIOptions(String mode, int maxNumIt, int verbose, String shiftType, Matrix startValue, int[] nonZeroBlocks) MG1PiOptions(Matrix boundary, int maxNumComp, int precision, String solver, boolean verbose, String mode) -
Uses of Matrix in jline.opt.variables
Methods in jline.opt.variables that return MatrixModifier and TypeMethodDescriptionprotected static MatrixDecisionVariable.connectionMatrix(Network model) Node-by-node binary connection (adjacency) matrix. -
Uses of Matrix in jline.solvers
Fields in jline.solvers declared as MatrixModifier and TypeFieldDescriptionSolverResult.ANMean arrival rates [stations x classes]SolverResult.ANfcrFCR mean arrival rates (attempted entries) [regions x classes]QrfParams.BB(MR x M) blocking state: BB(m,i) is 1 iff queue i is blocked in configuration m.SolverResult.CNMean system response times [1 x chains]Matrix[][]SolverResult.CNtTransient system response times [time_points][1 x chains]NetworkMomentStationResult.CovM x M, Cov(i,j) = Cov[Q_i,Q_j]; the diagonal isVar.Matrix[][]NetworkMomentStationResult.CovGThe general (M x G x M x G) covariance,CovG[i][g]being an M x G matrix whose (j,g2) entry is Cov[Q_(i,g),Q_(j,g2)].SolverOptions.cutoffCutoff threshold for numerical computations.SolverResult.DropRateJoinFork-Join sibling-drop rate [stations x classes]: at a quorum/PARTIAL Join, the rate of forked siblings discarded because they arrive after the quorum already fired.SolverResult.DropRateNfcrFCR mean drop rate: the rate of jobs refused entry to the region and dropped (DROP rule), per [regions x classes].SolverOptions.Config.init_covFluid 'kp': initial covariance Sigma(0), dim-by-dim in the same layout asSolverOptions.Config.kp_init_sol.SolverOptions.init_solInitial solution for iterative solversSolverOptions.Config.interlockInterlock matrix of Franks (1999), Eq.SolverOptions.Config.interlock_chainThe same matrix aggregated to the chain basis, set by the AMVA handler for the iteration it is about to run.NetworkMomentStationResult.mM x 1, m(i) = E[Q_i], the mean total queue length at station i.NetworkMomentStationResult.M2M x 1, E[Q_i^2].NetworkMomentStationResult.M3M x 1, E[Q_i^3].SolverResult.MemOccNfcrFCR mean memory occupation (time-average of sum of per-class classSize times in-region job count), the JMT "FCR Memory Occupation" measure [regions x classes].QrfParams.MM(MR x .) blocking order, 1-based; only column 0 is read, the head that takes f's freed slot.QrfParams.MM1(MR x M) successor map: MM1(m,j) is the configuration reached when queue j becomes blocked.Matrix[]SolverOptions.Config.moment_covFluid moment closure: per-station coordinate covariance blocks closing the capacity-share RATIO of the PS/FCFS/DPS/GPS branches, which is a separate closure from the min().SolverResult.pi_tTransient probability distributions over timeLayeredSolverResult.PNMean processor utilization [processors x 1]NetworkMomentResult.QMq x R mean queue length, one row per queueing station in node order.Matrix[]NetworkMomentResult.QCovPer-station covariance blocks;QCov[i]is R x R with entry (r,s) = Cov[n(i,r),n(i,s)].SolverResult.QNMean queue lengths [stations x classes]SolverResult.QNfcrFCR mean queue lengths (jobs waiting at ingress) [regions x classes]Matrix[][]SolverResult.QNtNetworkMomentResult.QVarMq x R queue-length variance, Var[n(i,r)].SolverOptions.Config.rate_traj_mmatFluid closing ODE: event rate multiplier matrix of the caller-supplied trajectory, of size numEvents x numel(rate_traj_tgrid).LayeredSolverResult.rawEdgesWaitingWaiting time by call edge [calls x 1]LayeredSolverResult.rawPhase1ServiceTimePhase 1 service time by node [nodes x 1]LayeredSolverResult.rawPhase1UtilizationPhase 1 utilization by node [nodes x 1]LayeredSolverResult.rawPhase2ServiceTimePhase 2 service time by node [nodes x 1]LayeredSolverResult.rawPhase2UtilizationPhase 2 utilization by node [nodes x 1]LayeredSolverResult.rawProcUtilizationProcessor utilization by node [nodes x 1]LayeredSolverResult.rawProcWaitingProcessor waiting time by node [nodes x 1]LayeredSolverResult.rawThroughputThroughput by node [nodes x 1]LayeredSolverResult.rawUtilizationRaw utilization by node [nodes x 1]SolverResult.RNMean response times [stations x classes]SolverResult.RNfcrFCR mean response times (wait time at ingress) [regions x classes]Matrix[][]SolverResult.RNtTransient response times [time_points][stations x classes]NetworkMomentStationResult.SkewM x 1, skewness of Q_i; NaN where the variance vanishes.LayeredSolverResult.SNMean service time [tasks x 1]SolverResult.SSSteady-state probability distributionSolverResult.SysTardNMean system tardiness [1 x classes]SolverResult.tTime points for transient analysisSolverResult.TardNMean tardiness [stations x classes]SolverResult.TNMean throughputs [stations x classes]SolverResult.TNfcrFCR mean throughputs (entry rate) [regions x classes]Matrix[][]SolverResult.TNtTransient throughputs [time_points][stations x classes]SolverResult.UNMean server utilizations [stations x classes]SolverResult.UNfcrFCR mean utilizations (capacity utilization) [regions x classes]Matrix[][]SolverResult.UNtTransient utilizations [time_points][stations x classes]NetworkMomentStationResult.VarM x 1, Var[Q_i].SolverResult.WeightNfcrFCR mean total weight (time-average of sum of per-class classWeight times in-region job count), the JMT "FCR Total Weight" measure [regions x classes].SolverResult.WNMean residence times [stations x classes]SolverResult.WNfcrFCR mean residence times [regions x classes]NetworkMomentResult.X1 x R throughput of the queue-length path, or null on the purely open branch.SolverResult.XNMean system throughputs [1 x chains]Matrix[][]SolverResult.XNtTransient system throughputs [time_points][1 x chains]Methods in jline.solvers that return MatrixModifier and TypeMethodDescriptionNetworkSolver.avgArvR()NetworkSolver.avgArvRChain()NetworkSolver.avgNodeArvRChain()NetworkSolver.avgNodeQLenChain()NetworkSolver.avgNodeResidTChain()NetworkSolver.avgNodeRespTChain()NetworkSolver.avgNodeTputChain()NetworkSolver.avgNodeUtilChain()NetworkSolver.avgQLen()NetworkSolver.avgQLenChain()NetworkSolver.avgResidT()NetworkSolver.avgResidTChain()NetworkSolver.avgRespT()NetworkSolver.avgRespTChain()NetworkSolver.avgSysRespT()NetworkSolver.avgSysTput()NetworkSolver.avgTput()NetworkSolver.avgTputChain()NetworkSolver.avgUtil()NetworkSolver.avgUtilChain()NetworkSolver.avgWaitT()NetworkSolver.getAvgArvR()Computes and returns average arrival rates at steady-state.NetworkSolver.getAvgArvRChain()Returns average arrival rates aggregated by job chains.NetworkSolver.getAvgNodeArvRChain()Returns average node arrival rates aggregated by job chains.NetworkSolver.getAvgNodeQLenChain()Returns average node queue lengths aggregated by job chains.NetworkSolver.getAvgNodeResidTChain()Returns average node residence times aggregated by job chains.NetworkSolver.getAvgNodeRespTChain()Returns average node response times aggregated by job chains.NetworkSolver.getAvgNodeTputChain()Returns average node throughputs aggregated by job chains.NetworkSolver.getAvgNodeUtilChain()Returns average node utilizations aggregated by job chains.NetworkSolver.getAvgOrbit()Mean number of jobs waiting in the ORBIT of each retrial station, as an (nstations x nclasses) matrix.NetworkSolver.getAvgQLen()Computes and returns average queue lengths at steady-state.NetworkSolver.getAvgQLenChain()Returns average queue lengths aggregated by job chains.NetworkSolver.getAvgResidT()Computes and returns average residence times in queue (including service).NetworkSolver.getAvgResidTChain()Returns average residence times aggregated by job chains.NetworkSolver.getAvgRespT()Computes and returns average response times at steady-state.NetworkSolver.getAvgRespTChain()Returns average response times aggregated by job chains.NetworkSolver.getAvgSysRespT()Returns average system response times at steady state.NetworkSolver.getAvgSysTput()Returns average system throughputs at steady state.NetworkSolver.getAvgTput()NetworkSolver.getAvgTputChain()Returns average throughputs aggregated by job chains.NetworkSolver.getAvgUtil()Computes and returns average server utilizations at steady-state.NetworkSolver.getAvgUtilChain()Returns average server utilizations aggregated by job chains.NetworkSolver.getAvgWaitT()Computes and returns average waiting times in queue excluding service time.SolverOptions.getCutoffMatrix(int nstations, int nclasses) Ensures the cutoff is properly dimensioned for the given network structure.AvgTable.getData()Returns the underlying matrix data.NetworkSolver.getPerctRespT(double[] percentiles, String method) AvgTable.toMatrix()Returns the table data as a Matrix after sanitizing numerical values.static MatrixWarmStart.warmStartPlacement(NetworkSolver initSolver, NetworkStruct sn) Integer job placement (nstations x nclasses) decided by the steady-state solution of the auxiliary solver.Methods in jline.solvers with parameters of type MatrixModifier and TypeMethodDescriptionSets the numerical cutoff threshold as a matrix (builder pattern).Returns marginal state probabilities for a specific node and state.NetworkSolver.getProbAggr(int node, Matrix state_a) Probability of a SPECIFIC per-class job distribution at a station.NetworkSolver.getProbMarg(int node, int jobclass, Matrix state_m) Probability distribution for queue length of a SINGLE class at a station.NetworkSolver.getProbSysMarg(Matrix nvec) Returns the joint probability of the per-station TOTAL queue lengths, all classes summed out.NetworkSolver.getProbSysMarg(Matrix nvec, String engine) Returns the joint probability of the per-station total queue lengths, evaluated with a chosen permanent engine.voidNetworkSolver.setAvgResults(Matrix Q, Matrix U, Matrix R, Matrix T, Matrix A, Matrix W, Matrix C, Matrix X, double runtime, String method, int iter) protected final voidNetworkSolver.setDistribResults(Matrix RD, double runtime) Stores distribution metrics at steady-state.voidNetworkSolver.setTranAvgResults(Matrix[][] Qt, Matrix[][] Ut, Matrix[][] Rt, Matrix[][] Tt, Matrix[][] Ct, Matrix[][] Xt, double runtimet) Stores computed transient average metrics.protected final voidNetworkSolver.setTranProb(Matrix t, Matrix pi_t, Matrix SS, double runtimet) Stores transient probability distributions.Constructors in jline.solvers with parameters of type Matrix -
Uses of Matrix in jline.solvers.ag
Fields in jline.solvers.ag declared as MatrixModifier and TypeFieldDescriptionAGResult.actionRatesConverged reversed rates, one per synchronizing action.Fields in jline.solvers.ag with type parameters of type MatrixModifier and TypeFieldDescriptionAGResult.equilibriumStationary vector of every agent at the fixed point.AGResult.generatorsGenerator of every agent at the fixed point.Methods in jline.solvers.ag that return MatrixModifier and TypeMethodDescriptionstatic MatrixAgWire.fromTriplets(com.google.gson.JsonArray a, int n) Rebuild an n-by-n matrix from the triplets written byAgWire.triplets(jline.util.matrix.Matrix).static MatrixAgAgent.generator(int k, Matrix x, Matrix[] Aa, Matrix[] Pb, Matrix[] L, int[] ACT, int[] PSV, int numActions, int[] N) Agent k's generator at the reversed rates x.static MatrixAgAgent.stationary(Matrix Qk, int mph, int nlev, int[] level) Agent k's stationary vector from the QBD shape carried explicitly, for a worker that holds the agent's layout without holding the whole model.static MatrixAgAgent.stationary(Matrix Qk, RCATModel rcat, int k) Agent k's stationary vector, given its generator.static MatrixAgWire.toRates(com.google.gson.JsonArray a) The inverse ofAgWire.rates(jline.util.matrix.Matrix), as the column vector the solver uses.static MatrixAgWire.toVector(com.google.gson.JsonArray a) The inverse ofAgWire.vector(jline.util.matrix.Matrix).Methods in jline.solvers.ag that return types with arguments of type MatrixModifier and TypeMethodDescriptionOne sweep: send the reversed rates, receive the owned agents' stationary vectors in the order they were assigned.Methods in jline.solvers.ag with parameters of type MatrixModifier and TypeMethodDescriptionstatic com.google.gson.JsonObjectAgWire.agent(int k, Matrix[] Aa, Matrix[] Pb, Matrix[] L, int[] ACT, int[] PSV, int numActions, int[] N, RCATModel rcat) The static description of agent k, as the worker needs it.voidAgWorkerClient.assign(List<Integer> agents, Matrix[] Aa, Matrix[] Pb, Matrix[] L, int[] ACT, int[] PSV, int numActions, int[] N, RCATModel rcat) Ship the static half of the owned agents.static MatrixAgAgent.generator(int k, Matrix x, Matrix[] Aa, Matrix[] Pb, Matrix[] L, int[] ACT, int[] PSV, int numActions, int[] N) Agent k's generator at the reversed rates x.static com.google.gson.JsonArrayThe reversed-rate vector, the whole per-sweep payload.static MatrixAgAgent.stationary(Matrix Qk, int mph, int nlev, int[] level) Agent k's stationary vector from the QBD shape carried explicitly, for a worker that holds the agent's layout without holding the whole model.static MatrixAgAgent.stationary(Matrix Qk, RCATModel rcat, int k) Agent k's stationary vector, given its generator.voidAgExec.sweep(Matrix x, Matrix[] Aa, Matrix[] Pb, Matrix[] L, int[] ACT, int[] PSV, int numProcesses, int numActions, int[] N, RCATModel rcat, Matrix[] Qs, Matrix[] pis) Evaluate every agent at the reversed rates x, writing agent k's generator intoQs[k]and its stationary vector intopis[k].One sweep: send the reversed rates, receive the owned agents' stationary vectors in the order they were assigned.static com.google.gson.JsonArrayNon-zero entries of M as a JSON array of [row, col, value] triplets.static com.google.gson.JsonArrayA stationary vector as a JSON array. -
Uses of Matrix in jline.solvers.ag.handlers
Fields in jline.solvers.ag.handlers declared as MatrixModifier and TypeFieldDescriptionfinal MatrixRCATModel.APfinal MatrixQbdTail.busyRcatComponent.Da0RcatComponent.Da1RcatComponent.Ds0RcatComponent.Ds1RcatComponent.Dsvcfinal MatrixQbdTail.pi0final MatrixQbdTail.pi1final MatrixRCATModel.processMapfinal MatrixQbdTail.Rfinal Matrix[][]RCATModel.Rfinal MatrixINAPResult.xFields in jline.solvers.ag.handlers with type parameters of type MatrixMethods in jline.solvers.ag.handlers that return MatrixModifier and TypeMethodDescriptionstatic MatrixSolver_ag_inap.agentGenerator(int k, Matrix x, Matrix[] Aa, Matrix[] Pb, Matrix[] L, int[] ACT, int[] PSV, int numActions, int[] N) Agent k's generator at the current reversed rates.static MatrixSolver_ag_inap.agentStationary(Matrix Qk, RCATModel rcat, int k) Agent k's stationary vector, given its generator.static MatrixSolver_ag_inap.agentStationaryOf(Matrix Qk, int mph, int nlev, int[] level) The same solve from the agent's layout given explicitly, for a worker that holds one agent rather than the whole RCATModel.INAPResult.component1()INAPResult.getX()Methods in jline.solvers.ag.handlers that return types with arguments of type MatrixModifier and TypeMethodDescriptionINAPResult.component2()INAPResult.component3()INAPResult.getPi()INAPResult.getQ()Methods in jline.solvers.ag.handlers with parameters of type MatrixModifier and TypeMethodDescriptionstatic MatrixSolver_ag_inap.agentGenerator(int k, Matrix x, Matrix[] Aa, Matrix[] Pb, Matrix[] L, int[] ACT, int[] PSV, int numActions, int[] N) Agent k's generator at the current reversed rates.static MatrixSolver_ag_inap.agentStationary(Matrix Qk, RCATModel rcat, int k) Agent k's stationary vector, given its generator.static MatrixSolver_ag_inap.agentStationaryOf(Matrix Qk, int mph, int nlev, int[] level) The same solve from the agent's layout given explicitly, for a worker that holds one agent rather than the whole RCATModel.Constructors in jline.solvers.ag.handlers with parameters of type MatrixModifierConstructorDescriptionINAPResult(Matrix x, List<Matrix> pi, List<Matrix> Q, int iter, double[] rhoProc, boolean[] isGeomProc, QbdTail[] geomData, double rcatRes) RCATModel(Matrix[][] R, Matrix AP, Matrix processMap, List<ActionInfo> actionMap, int[] N, int[] nlev, int[] mph, int[][] level, double[][] svcrate, double[][] svcdown) Constructor parameters in jline.solvers.ag.handlers with type arguments of type Matrix -
Uses of Matrix in jline.solvers.auto
Methods in jline.solvers.auto that return MatrixModifier and TypeMethodDescriptionSolverAUTO.avgArvR()Alias for getAvgArvR()SolverAUTO.avgArvRChain()Alias for getAvgArvRChain()SolverAUTO.avgQLen()Alias for getAvgQLen()SolverAUTO.avgQLenChain()Alias for getAvgQLenChain()SolverAUTO.avgResidT()Alias for getAvgResidT()SolverAUTO.avgResidTChain()Alias for getAvgResidTChain()SolverAUTO.avgRespT()Alias for getAvgRespT()SolverAUTO.avgRespTChain()Alias for getAvgRespTChain()SolverAUTO.avgSysRespT()Alias for getAvgSysRespT()SolverAUTO.avgSysTput()Alias for getAvgSysTput()SolverAUTO.avgTput()Alias for getAvgTput()SolverAUTO.avgTputChain()Alias for getAvgTputChain()SolverAUTO.avgUtil()Alias for getAvgUtil()SolverAUTO.avgUtilChain()Alias for getAvgUtilChain()SolverAUTO.avgWaitT()Alias for getAvgWaitT()SolverAUTO.getAvgArvR()Get average arrival rates at steady-stateSolverAUTO.getAvgArvRChain()SolverAUTO.getAvgNodeArvRChain()Get average node arrival rate by chainSolverAUTO.getAvgNodeQLenChain()Get average node queue length by chainSolverAUTO.getAvgNodeResidTChain()Get average node residence time by chainSolverAUTO.getAvgNodeRespTChain()Get average node response time by chainSolverAUTO.getAvgNodeTputChain()Get average node throughput by chainSolverAUTO.getAvgNodeUtilChain()Get average node utilization by chainSolverAUTO.getAvgQLen()Get average queue lengths at steady-stateSolverAUTO.getAvgQLenChain()SolverAUTO.getAvgResidT()Get average residence times at steady-stateSolverAUTO.getAvgResidTChain()Get average residence time by chainSolverAUTO.getAvgRespT()Get average response times at steady-stateSolverAUTO.getAvgRespTChain()SolverAUTO.getAvgSysRespT()SolverAUTO.getAvgSysTput()SolverAUTO.getAvgTput()Get average throughputs at steady-stateSolverAUTO.getAvgTputChain()SolverAUTO.getAvgUtil()Get average utilizations at steady-stateSolverAUTO.getAvgUtilChain()SolverAUTO.getAvgWaitT()Get average waiting times (queue time excluding service)Matrix[]SolverAUTO.getCdfAoI()Age-of-Information CDFs, a property of the ODE representation, so fluid-only.Matrix[]Age-of-Information CDFs on caller-supplied time points, fluid-only.SolverAUTO.getPerctRespT(double[] percentiles) Get response time percentilesMatrix[]SolverAUTO.getTranProb(Node node) Matrix[]SolverAUTO.getTranProbAggr(Node node) Matrix[]SolverAUTO.getTranProbSys()Matrix[]SolverAUTO.getTranProbSysAggr()SolverAUTO.perctRespT(double[] percentiles) Alias for getPerctRespT()Methods in jline.solvers.auto that return types with arguments of type MatrixModifier and TypeMethodDescriptionSolverAUTO.getCdfSysRespT()System response time CDF per chain, always from SolverCTMC -- like the state-space accessors, it is a property of the CTMC representation, so it resolves on the CTMC candidate rather than the ranked selection, as the reference@SolverAUTO/getCdfSysRespTdoes.Methods in jline.solvers.auto with parameters of type MatrixModifier and TypeMethodDescriptionMatrix[]Age-of-Information CDFs on caller-supplied time points, fluid-only.doubledoubleSolverAUTO.getProbAggr(Node node, Matrix state_a) doubleSolverAUTO.getProbMarg(Node node, int jobclass, Matrix state_m) Get marginalized state probabilitydoubleAlias for getProb()doubleAlias for getProbAggr()doubleAlias for getProbMarg() -
Uses of Matrix in jline.solvers.ba
Fields in jline.solvers.ba declared as MatrixModifier and TypeFieldDescriptionSolverBA.Bounds.QlowerSolverBA.Bounds.QupperSolverBA.Bounds.TlowerSolverBA.Bounds.TupperMethods in jline.solvers.ba that return MatrixModifier and TypeMethodDescriptionSolverBA.getBacklogPerc(double eps) Returns the per-station queue-length QUANTILE, in jobs, at violation probability eps.SolverBA.getDelayPerc(double eps) Returns the per-station response-time QUANTILE at violation probability eps: the smallest d for whichP{D_ir > d} <= epsis certified by the stochastic network calculus bound. -
Uses of Matrix in jline.solvers.ctmc
Fields in jline.solvers.ctmc declared as MatrixModifier and TypeFieldDescriptionCTMCResult.cftpHorizonCTMCResult.cftpSamplesPerfect-sampling output of thecftpmethod: the drawn states, one per row (samples x stations), and the per-sample coalescence horizon (cftp) or number of mixing steps (cftp.approx).SolverCTMC.AnalyzerResult.CNSolverCTMC.TransientResult.CNtSolverCTMC.StochCompResult.entryFirst-entry matrix (-Q22)^-1 Q21: row i is where vanishing state i lands.CTMCResult.infGenSolverCTMC.generatorResult.infGenSolverCTMC.AnalyzerResult.InfGenSolverCTMC.TransientResult.InfGenCTMCResult.infGenWorkSolverCTMC.AnalyzerResult.InfGenWorkStationary distribution the analyzer solved for, and the generator and state spaces it is indexed by.CTMCResult.Prob.jointSolverCTMC.FirstPassageMomentsResult.m(1 x nmax) moments for a passage started uniformly in A.SolverCTMC.FirstPassageMomentsResult.mall(nstates x nmax), one row per starting state; 0 on B, Inf where B is unreachable.CTMCResult.Prob.marginalCTMCResult.piStationary distribution over the state space the analyzer solved on, and the generator and state spaces it is indexed by.SolverCTMC.AnalyzerResult.piStationary distribution the analyzer solved for, and the generator and state spaces it is indexed by.CTMCResult.TranProbAggrResult.pitCTMCResult.TranProbResult.pitCTMCResult.TranProbSysAggrResult.pitCTMCResult.TranProbSysResult.pitSolverCTMC.TransientResult.pitMatrix[][]CTMCResult.preemptFiltMatrix[][]SolverCTMC.AnalyzerResult.preemptFiltThe filtrations those rates reduce, indexed [station][class].SolverCTMC.AnalyzerResult.PreemptNDerived START/PREEMPT rates, (stations x classes): how often per unit time a class-r service starts at station i, and how often a class-r job in service is pushed back into the buffer there.CTMCResult.preemptRateSolverCTMC.StochCompResult.Q11SolverCTMC.StochCompResult.Q12SolverCTMC.StochCompResult.Q21SolverCTMC.StochCompResult.Q22SolverCTMC.AnalyzerResult.QNSolverCTMC.TransientResult.QNtSolverCTMC.FirstPassageResult.RD(n x 2) matrix, first column F(t), second column t.SolverCTMC.AnalyzerResult.RNSolverCTMC.TransientResult.RNtSolverCTMC.StochCompResult.SCTMCResult.solverSpecificCTMCResult.spaceCTMCResult.spaceAggrCTMCResult.spaceAggrWorkCTMCResult.spaceWorkMatrix[][]CTMCResult.startFiltDerived START/PREEMPT filtrations, indexed [station][class].Matrix[][]SolverCTMC.AnalyzerResult.startFiltThe filtrations those rates reduce, indexed [station][class].SolverCTMC.AnalyzerResult.StartNDerived START/PREEMPT rates, (stations x classes): how often per unit time a class-r service starts at station i, and how often a class-r job in service is pushed back into the buffer there.CTMCResult.startRate(stations x classes) rates the two filtrations above reduce to.SolverCTMC.SampleResult.stateSolverCTMC.SampleSysResult.stateCTMCResult.TranProbResult.stateSpaceCTMCResult.TranProbSysResult.stateSpaceprotected MatrixResultCTMC.stateSpaceSolverCTMC.StateSpace.stateSpaceSolverCTMC.symbolicGeneratorResult.stateSpaceSolverCTMC.AnalyzerResult.StateSpaceSolverCTMC.TransientResult.StateSpaceCTMCResult.TranProbSysAggrResult.stateSpaceAggrSolverCTMC.AnalyzerResult.StateSpaceAggrSolverCTMC.TransientResult.StateSpaceAggrSolverCTMC.AnalyzerResult.StateSpaceAggrWorkStationary distribution the analyzer solved for, and the generator and state spaces it is indexed by.SolverCTMC.AnalyzerResult.StateSpaceWorkStationary distribution the analyzer solved for, and the generator and state spaces it is indexed by.CTMCResult.TranProbAggrResult.tCTMCResult.TranProbResult.tCTMCResult.TranProbSysAggrResult.tCTMCResult.TranProbSysResult.tSolverCTMC.SampleResult.tSolverCTMC.SampleSysResult.tSolverCTMC.TransientResult.tSolverCTMC.StochCompResult.TSolverCTMC.AnalyzerResult.TNSolverCTMC.TransientResult.TNtSolverCTMC.AnalyzerResult.UNSolverCTMC.TransientResult.UNtSolverCTMC.AnalyzerResult.XNSolverCTMC.TransientResult.XNtFields in jline.solvers.ctmc with type parameters of type MatrixModifier and TypeFieldDescriptionCTMCResult.nodeSpaceCTMCResult.AVG.QCTMCResult.AVG.TCTMCResult.AVG.UMethods in jline.solvers.ctmc that return MatrixModifier and TypeMethodDescriptionSolverCTMC.symbolicGeneratorResult.evalInfGen(double[] x) Evaluate the symbolic generator at the given symbol values.SolverCTMC.symbolicGeneratorResult.evalInfGen(Map<String, Double> assignment) Evaluate the symbolic generator at the given symbol assignment.SolverCTMC.getCdfRespT(Matrix R) Get the cumulative distribution function of response times using tagged job methodologyMatrix[][]SolverCTMC.getEventFiltration(EventType eventType) Filtration of a DERIVED event type, indexed [station][class]: the (s,ns) entry is the rate at which the transition s -> ns carries one such event at that station for that class.ResultCTMCMargAggr.getPi()Get the steady-state distributionResultCTMC.getPi0()ResultCTMCMargAggr.getPnir()Get the marginal probabilities at stationsSolverCTMC.SolverCtmcJointResult.getPnir()Matrix[][]ResultCTMC.getPreemptFilt()SolverCTMC.getPreemptRate()(stations x classes) rate at which a class-r job HOLDING A SERVER at station i is pushed back into the buffer.ResultCTMC.getQ()SolverCTMC.getRewardValueFunction(String rewardName) Get the value function for a specific reward.Matrix[][]ResultCTMC.getStartFilt()SolverCTMC.getStartRate()(stations x classes) rate at which a class-r job BEGINS or RESUMES holding a server at station i, i.e.ResultCTMC.getStateSpace()SolverCTMC.CtmcSsgResult.getStateSpace()ResultCTMC.getStateSpaceAggr()SolverCTMC.CtmcSsgResult.getStateSpaceAggr()SolverCTMC.getStateSpaceAggr()SolverCTMC.CtmcSsgResult.getStateSpaceHashed()SolverCTMC.getTransMat()static MatrixCtmcStationary.initialDistribution(Matrix Q, Matrix stateSpace, NetworkStruct sn, SolverOptions options) Point mass at the initial state ofsn, or null when that state is absent fromstateSpace(stochastic complementation may have removed it, e.g.static MatrixCtmcStationary.solve(Matrix Q, Matrix stateSpace, NetworkStruct sn, SolverOptions options) Stationary distribution ofQ, seeded from the initial state ofsnwhen that state can be located instateSpace.Methods in jline.solvers.ctmc that return types with arguments of type MatrixModifier and TypeMethodDescriptionSolverCTMC.getCdfSysRespT()The SYSTEM response-time distribution: one law per CHAIN, not a number.Methods in jline.solvers.ctmc with parameters of type MatrixModifier and TypeMethodDescriptionSolverCTMC.getCdfFirstPassT(Matrix A, Matrix B) Distribution of the FIRST PASSAGE TIME from state set A into state set B, on the CTMC underlying this model.SolverCTMC.getCdfRespT(Matrix R) Get the cumulative distribution function of response times using tagged job methodologySolverCTMC.getFirstPassTMoments(Matrix A, Matrix B) Moments of order 1..3, the reference's default.SolverCTMC.getFirstPassTMoments(Matrix A, Matrix B, int nmax) Moments of order 1..nmax of the first passage time from state set A into state set B.SolverCTMC.getProb(StatefulNode node, Matrix state) doubleStationary probability of a single state of the user-supplied chain, identified by its row in the chain state space or, when the chain carries none, by its 1-based state index.SolverCTMC.getProbAggr(int node, Matrix state_a) SolverCTMC.getProbAggr(Node node, Matrix state_a) SolverCTMC.getProbAggr(StatefulNode node, Matrix state_a) SolverCTMC.getProbSys(Matrix sysState) SolverCTMC.getProbSysAggr(Matrix)for the joint (non-aggregated) getter.SolverCTMC.getProbSysAggr(Matrix sysState) getProbSysAggr for a caller-named system state, given as one row of per-class job counts per STATION, station-major.static MatrixCtmcStationary.initialDistribution(Matrix Q, Matrix stateSpace, NetworkStruct sn, SolverOptions options) Point mass at the initial state ofsn, or null when that state is absent fromstateSpace(stochastic complementation may have removed it, e.g.static voidSolverCTMC.printEventFilt(MatrixCell eventFilt, Matrix SS) static voidSolverCTMC.printInfGen(Matrix Q, Matrix SS) voidResultCTMC.setAuxFilt(Matrix[][] startFilt, Matrix[][] preemptFilt) voidstatic MatrixCtmcStationary.solve(Matrix Q, Matrix stateSpace, NetworkStruct sn, SolverOptions options) Stationary distribution ofQ, seeded from the initial state ofsnwhen that state can be located instateSpace.Constructors in jline.solvers.ctmc with parameters of type MatrixModifierConstructorDescriptionAnalyzerResult(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, Matrix InfGen, Matrix StateSpace, Matrix StateSpaceAggr, MatrixCell EventFiltration, double runtime, String fname, NetworkStruct sncopy) AnalyzerResult(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, Matrix InfGen, Matrix StateSpace, Matrix StateSpaceAggr, MatrixCell EventFiltration, double runtime, String fname, NetworkStruct sncopy, Matrix pi, Matrix StateSpaceWork, Matrix StateSpaceAggrWork, Matrix InfGenWork) CtmcSsgResult(Matrix stateSpace, Matrix stateSpaceAggr, Matrix stateSpaceHashed, Map<StatefulNode, Matrix> nodeStateSpace, NetworkStruct sn) generatorResult(Matrix infGen, MatrixCell eventFilt, Map<Integer, Sync> ev) ResultCTMC(Matrix q, Matrix stateSpace, Matrix stateSpaceAggr, MatrixCell dfilt, double[][][] arvRates, double[][][] depRates, NetworkStruct sn) ResultCTMCMargAggr(Matrix Pnir, Matrix pi, double runtime, String fname) SolverCtmcJointResult(Matrix pnir, double runtime, String fname) StateSpace(Matrix stateSpace, MatrixCell localStateSpace) symbolicGeneratorResult(MatrixCell eventFilt, MatrixCell infGenTerms, List<String> symbols, boolean invertSymbol, Matrix stateSpace, MatrixCell nodeStateSpace, Map<Integer, Sync> syncInfo) TransientResult(Matrix t, Matrix pit, Matrix QNt, Matrix UNt, Matrix RNt, Matrix TNt, Matrix CNt, Matrix XNt, Matrix InfGen, Matrix StateSpace, Matrix StateSpaceAggr, MatrixCell EventFiltration, double runtime, String fname) Constructor parameters in jline.solvers.ctmc with type arguments of type MatrixModifierConstructorDescriptionCtmcSsgResult(Matrix stateSpace, Matrix stateSpaceAggr, Matrix stateSpaceHashed, Map<StatefulNode, Matrix> nodeStateSpace, NetworkStruct sn) -
Uses of Matrix in jline.solvers.ctmc.analyzers
Fields in jline.solvers.ctmc.analyzers declared as MatrixModifier and TypeFieldDescriptionSolver_ctmc_cftp_analyzer.CftpResult.CNSolver_ctmc_mdd_analyzer.MddResult.CNSolver_ctmc_cftp_analyzer.CftpResult.horizonPer-sample coalescence horizon or mixing steps.Solver_ctmc_cftp_analyzer.CftpResult.piEmpirical probability of each row of spaceAggr.Solver_ctmc_cftp_analyzer.CftpResult.QNSolver_ctmc_mdd_analyzer.MddResult.QNSolver_ctmc_cftp_analyzer.CftpResult.RNSolver_ctmc_mdd_analyzer.MddResult.RNSolver_ctmc_cftp_analyzer.CftpResult.samplesSampled states, one per row (samples x stations).Solver_ctmc_cftp_analyzer.CftpResult.spaceAggrDistinct sampled states.Solver_ctmc_cftp_analyzer.CftpResult.TNSolver_ctmc_mdd_analyzer.MddResult.TNSolver_ctmc_cftp_analyzer.CftpResult.UNSolver_ctmc_mdd_analyzer.MddResult.UNSolver_ctmc_cftp_analyzer.CftpResult.XNSolver_ctmc_mdd_analyzer.MddResult.XNMethods in jline.solvers.ctmc.analyzers that return MatrixMethods in jline.solvers.ctmc.analyzers that return types with arguments of type MatrixConstructors in jline.solvers.ctmc.analyzers with parameters of type MatrixModifierConstructorDescriptionRewardResult(Map<String, Matrix> valueFunction, double[] time, List<String> rewardNames, Matrix stateSpace, Map<String, Double> steadyState, double runtime) Constructor parameters in jline.solvers.ctmc.analyzers with type arguments of type Matrix -
Uses of Matrix in jline.solvers.ctmc.handlers
Fields in jline.solvers.ctmc.handlers declared as MatrixModifier and TypeFieldDescriptionfinal MatrixSolver_ctmc_fcr_waitq.Result.basBlockQCtmc_avg_from_pi.Result.CNCtmc_avg_from_pi.Result.QNCtmc_avg_from_pi.Result.RNfinal MatrixSolver_ctmc_fcr_waitq.Result.stateSpacefinal MatrixSolver_ctmc_fcr_waitq.Result.stateSpaceAggrfinal MatrixSolver_ctmc_fcr_waitq.Result.stateSpaceHashedCtmc_avg_from_pi.Result.TNCtmc_avg_from_pi.Result.UNCtmc_avg_from_pi.Result.XNMethods in jline.solvers.ctmc.handlers that return MatrixModifier and TypeMethodDescriptionstatic MatrixSolver_ctmc_marg.solver_ctmc_marg(NetworkStruct sn, SolverOptions options) Methods in jline.solvers.ctmc.handlers with parameters of type MatrixModifier and TypeMethodDescriptionstatic double[]CtmcSignalBusy.busyFraction(NetworkStruct sn, int ind, int ist, SchedStrategy sched, double nservers, Matrix stateSpace, Matrix istSpaceShift, Matrix wset, Matrix probSysState, int K) static Ctmc_avg_from_pi.ResultCtmc_avg_from_pi.ctmc_avg_from_pi(NetworkStruct sn, Matrix pivec, Matrix StateSpace, Matrix StateSpaceAggr, double[][][] arvRates, double[][][] depRates) static Ctmc_avg_from_pi.ResultCtmc_avg_from_pi.ctmc_avg_from_pi(NetworkStruct sn, Matrix pivec, Matrix StateSpace, Matrix StateSpaceAggr, double[][][] arvRates, double[][][] depRates, SolverOptions options) -
Uses of Matrix in jline.solvers.env
Fields in jline.solvers.env declared as MatrixModifier and TypeFieldDescriptionSolverENV.SamplePathResult.SamplePathSegment.finalQSolverENV.SamplePathResult.SamplePathSegment.finalTSolverENV.SamplePathResult.SamplePathSegment.finalUSolverENV.SamplePathResult.SamplePathSegment.initialQSolverENV.SamplePathResult.SamplePathSegment.initialTSolverENV.SamplePathResult.SamplePathSegment.initialUMatrix[][]SolverENV.SamplePathResult.SamplePathSegment.QNtMatrix[][]SolverENV.EnvGeneratorResult.renvEventFiltSolverENV.EnvGeneratorResult.renvInfGenMatrix[]SolverENV.EnvGeneratorResult.stageInfGenSolverENV.SamplePathResult.SamplePathSegment.tMatrix[][]SolverENV.SamplePathResult.SamplePathSegment.TNtMatrix[][]SolverENV.SamplePathResult.SamplePathSegment.UNtMethods in jline.solvers.env with parameters of type MatrixModifier and TypeMethodDescriptionstatic jline.solvers.env.SolverENV.Compression_resultSolverENV.ctmc_courtois(Matrix Q, MatrixCell MS) static jline.solvers.env.SolverENV.Compression_resultSolverENV.ctmc_courtois(Matrix Q, MatrixCell MS, double q) static jline.solvers.env.SolverENV.Compression_resultSolverENV.ctmc_decompose(Matrix Q, MatrixCell MS, SolverOptions options) Perform CTMC decomposition using the configured method.Constructors in jline.solvers.env with parameters of type MatrixModifierConstructorDescriptionEnvGeneratorResult(Matrix[] stageInfGen, Matrix renvInfGen, MatrixCell[] stageEventFilt, Matrix[][] renvEventFilt, Map<Integer, Sync>[] stageEvents, List<RenvEvent> renvEvents) RenvEvent(int nodeIdx, int jobclassIdx, double prob, Matrix state, double t, double job, Pair<Integer, Integer> envTransition) -
Uses of Matrix in jline.solvers.fj
Fields in jline.solvers.fj declared as MatrixModifier and TypeFieldDescriptionFJFixedPoint.FJState.fjForkLambdaAuxiliary-class arrival rates, the warm start of the next call.FJFixedPoint.FJOutcome.QNConstructors in jline.solvers.fj with parameters of type Matrix -
Uses of Matrix in jline.solvers.fluid
Fields in jline.solvers.fluid declared as MatrixModifier and TypeFieldDescriptionFluidResult.daeCapacityBRight-hand side of each capacity constraint.FluidResult.daeCapacityValueValue each constrained quantity actually took at the fixed point.FluidResult.daeDrainRate each waiting queue drains at, one per binding cap.FluidResult.daeStagingMass held OUTSIDE a capped region, per staging coordinate.Matrix[][]FluidResult.distribCDistribution matrices for response time and passage time CDFs [stations][classes]FluidResult.hitProbCache hit probabilities [nodes x classes] from cacheqn analysisFluidResult.missProbCache miss probabilities [nodes x classes] from cacheqn analysisFluidResult.momentCacheMissProbVarPer-cache, per-class variance of the miss probability that class sees.Matrix[]FluidResult.momentCachePi0Per-cache per-item miss probability, aligned withFluidResult.momentCacheSigma.Matrix[]FluidResult.momentCacheSigmaPer-cache stationary covariance of the item occupancy under the linear noise approximation, one entry per cache that has a drift-based fluid model.FluidResult.momentQVarPer-(station,class) queue-length variance of the moment-closure methods.FluidResult.momentSigmaState-level stationary covariance of the moment-closure methods, i.e.FluidResult.momentSigma2Per-station population variance of the moment-closure methods.FluidResult.momentSigma2DriftThe same variance as it entered the DRIFT, i.e.FluidResult.odeStateVecODE state vector solution from fluid approximationMatrix[][]FluidResult.QVartTransient queue-length VARIANCE per station and class, [stations][classes], each a column over the time grid int.Matrix[]FluidResult.SigmatFull state covariance Sigma(t) of the "kp" method, one dim-by-dim matrix per time point, so cross-station and cross-class covariances survive rather than only the per-block totals inFluidResult.QVart.FluidODEsExporter.SymODEs.WMethods in jline.solvers.fluid that return MatrixModifier and TypeMethodDescriptionMatrix[]SolverFluid.getCdfAoI()Get CDF of Age of Information with automatic time range.Matrix[]Get CDF of Age of Information.Matrix[][]SolverFluid.getTranAvgVar()Transient queue-length VARIANCE per station and class, [stations][classes].Methods in jline.solvers.fluid with parameters of type MatrixModifier and TypeMethodDescriptionMatrix[]Get CDF of Age of Information.SolverFluid.getProbAggr(int ist, Matrix state_a) Probability of a given per-class job distribution at a station.voidSolverFluid.setFluidDistribResults(Matrix RD, double runtime) Set distribution results with enhanced metadata for fluid solvervoidSolverFluid.setFluidTranAvgResults(Matrix[][] Qt, Matrix[][] Ut, Matrix[][] Rt, Matrix[][] Tt, Matrix[][] Ct, Matrix[][] Xt, double runtimet) Set transient average results with proper validationMethod parameters in jline.solvers.fluid with type arguments of type MatrixModifier and TypeMethodDescriptionstatic Map<Station,Map<JobClass, MatrixCell>> FluidNhpp.substituteNhppProc(NetworkStruct sn, Map<Station, Map<JobClass, Matrix>> mu, Map<Station, Map<JobClass, Matrix>> phi) Returns the process map to hand to the closing-rate builder, with every NHPP station-class replaced by the equivalent one-phase exponential MAP {D0, D1} = {-lam, lam} at its nominal ratemu(i,k)(0), so thatmap_piesees a valid Markovian representation. -
Uses of Matrix in jline.solvers.fluid.analyzers
Fields in jline.solvers.fluid.analyzers declared as MatrixModifier and TypeFieldDescriptionDaeAnalyzer.capacityBRight-hand side of each capacity constraint.DaeAnalyzer.capacityValueValue each constrained quantity actually took.DaeAnalyzer.drainRate each waiting queue drains at, one per binding cap.DaeAnalyzer.qVarPer-(station,class) queue-length variance.MinNormalAnalyzer.qVarPer-(station,class) queue-length variance.Matrix[]DaeAnalyzer.qVartTransient per-(station,class) variance, flattened asi*K+k.DaeAnalyzer.sigmaMatrixState-level stationary covariance at the converged fixed point.MinNormalAnalyzer.sigmaMatrixState-level stationary covariance at the converged fixed point.Matrix[]DaeAnalyzer.sigmatTransient state covariance, one matrix per reported time.DaeAnalyzer.stagingMass held OUTSIDE a capped region, per staging coordinate.DaeAnalyzer.tvarThe time grid the transient covariance is reported on.ClosingAndStateDepMethodsAnalyzer.xvec_itMatrixMethodAnalyzer.xvec_itClosingAndStateDepMethodsAnalyzer.xvec_tMatrixMethodAnalyzer.xvec_tMethods in jline.solvers.fluid.analyzers that return MatrixModifier and TypeMethodDescriptionClosingAndStateDepMethodsAnalyzer.getXVecIt()DiffusionAnalyzer.getXVecIt()FluidAnalyzer.getXVecIt()KoPenderAnalyzer.getXVecIt()MatrixMethodAnalyzer.getXVecIt()MFQAnalyzer.getXVecIt()QsysLimitAnalyzer.getXVecIt()RMFAnalyzer.getXVecIt()Methods in jline.solvers.fluid.analyzers with parameters of type MatrixModifier and TypeMethodDescriptionprotected voidClosingAndStateDepMethodsAnalyzer.solver_fluid_iteration(NetworkStruct sn, Map<Station, Map<JobClass, Matrix>> mu, Map<Station, Map<JobClass, Matrix>> phi, Matrix S, double[] yDefault, Matrix slowrate, SolverOptions options, SolverResult result) protected voidTbiAnalyzer.solver_fluid_iteration(NetworkStruct sn, Map<Station, Map<JobClass, Matrix>> mu, Map<Station, Map<JobClass, Matrix>> phi, Matrix S, double[] yDefault, Matrix slowrate, SolverOptions options, SolverResult result) Method parameters in jline.solvers.fluid.analyzers with type arguments of type MatrixModifier and TypeMethodDescriptionprotected voidClosingAndStateDepMethodsAnalyzer.solver_fluid_iteration(NetworkStruct sn, Map<Station, Map<JobClass, Matrix>> mu, Map<Station, Map<JobClass, Matrix>> phi, Matrix S, double[] yDefault, Matrix slowrate, SolverOptions options, SolverResult result) protected voidTbiAnalyzer.solver_fluid_iteration(NetworkStruct sn, Map<Station, Map<JobClass, Matrix>> mu, Map<Station, Map<JobClass, Matrix>> phi, Matrix S, double[] yDefault, Matrix slowrate, SolverOptions options, SolverResult result) -
Uses of Matrix in jline.solvers.fluid.handlers
Fields in jline.solvers.fluid.handlers declared as MatrixModifier and TypeFieldDescriptionImmediateElimination.EliminationResult.absorb[nStates x nStates] projector for the initial condition: identity on the timed rows, the absorption distribution on the immediate ones.ImmediateElimination.EliminationResult.allJumpsReducedImmediateElimination.EliminationResult.Emap[nEventsReduced x nEventsOriginal] expected firings of each original event.ImmediateElimination.EliminationResult.eventIdxReducedImmediateElimination.EliminationResult.rateBaseReducedMethodStepHandler.tVecTransientDataHandler.tVecTime vector storing integration time pointsMethodStepHandler.xVecTransientDataHandler.xVecState vector matrix storing system state at each time point [time x dimensions]Methods in jline.solvers.fluid.handlers that return MatrixModifier and TypeMethodDescriptionPassageTimeODE.calculateFactors(double[] x, double[] sigma2, Matrix[] covblk) Per-coordinate service shares evaluated at an explicit closure variance.PassageTimeODE.calculateFactorsJacobian(double[] x, double[] sigma2, Matrix[] covblk) Analytic Jacobian of the per-coordinate service shares.PassageTimeODE.calculateRatesClosing(double[] x) Raw closing-method event-rate vector, mirroring MATLAB ode_rates_closing: the per-index scheduling correction gathered over the event index set and scaled by the rate base.PassageTimeODE.calculateRatesClosing(double t, double[] x) PassageTimeODE.getAllJumps()Precomputed jump matrix of the closing method (dimension x numEvents).PassageTimeODE.getEventIdx()Precomputed per-event source state coordinate of the closing method.PassageTimeODE.getImmediateAbsorb()Projector onto the coordinates that survive the immediate elimination, null when nothing was eliminated.PassageTimeODE.getImmediateEmap()Expected firings of each ORIGINAL event per firing of each reduced one, null when nothing was eliminated (which the caller reads as the identity).PassageTimeODE.getKic()Precomputed per-(station,class) phase count of the closing method.PassageTimeODE.getOriginalEventIdx()Source coordinate of each event BEFORE the immediate elimination.PassageTimeODE.getQIndices()Precomputed per-(station,class) starting state index of the closing method.PassageTimeODE.getRateBase()Precomputed per-event constant rate factor of the closing method.Methods in jline.solvers.fluid.handlers with parameters of type MatrixModifier and TypeMethodDescriptionstatic FluidRateMultiplierFluidRateMultiplier.build(int numEvents, boolean[][] enabled, Matrix qIndices, Matrix kic, Map<Station, Map<JobClass, Matrix>> mu, List<Station> stations, List<JobClass> jobclasses, Matrix eventIdx, SolverOptions options) Builds the multiplier for the closing machinery described by the given event mapping, or returns null when no time-varying channel is set.PassageTimeODE.calculateFactors(double[] x, double[] sigma2, Matrix[] covblk) Per-coordinate service shares evaluated at an explicit closure variance.PassageTimeODE.calculateFactorsJacobian(double[] x, double[] sigma2, Matrix[] covblk) Analytic Jacobian of the per-coordinate service shares.ImmediateElimination.eliminateImmediate(Matrix allJumps, Matrix rateBase, Matrix eventIdx, NetworkStruct sn, SolverOptions options) Eliminate the instantaneous coordinates from the fluid event set.Method parameters in jline.solvers.fluid.handlers with type arguments of type MatrixModifier and TypeMethodDescriptionstatic FluidRateMultiplierFluidRateMultiplier.build(int numEvents, boolean[][] enabled, Matrix qIndices, Matrix kic, Map<Station, Map<JobClass, Matrix>> mu, List<Station> stations, List<JobClass> jobclasses, Matrix eventIdx, SolverOptions options) Builds the multiplier for the closing machinery described by the given event mapping, or returns null when no time-varying channel is set.Constructors in jline.solvers.fluid.handlers with parameters of type MatrixModifierConstructorDescriptionEliminationResult(Matrix allJumpsReduced, Matrix rateBaseReduced, Matrix eventIdxReduced, int[] stateMap, Matrix Emap, Matrix absorb) MatrixMethodODE(Matrix W, Matrix SQ, Matrix S, Matrix Qa, Matrix ALambda, int numDimensions, boolean[] isSourceState) MatrixMethodODE(Matrix W, Matrix SQ, Matrix S, Matrix Qa, Matrix ALambda, int numDimensions, boolean[] isSourceState, NetworkStruct sn, List<Double> pStarValues) MatrixMethodODE(Matrix W, Matrix SQ, Matrix S, Matrix Qa, Matrix ALambda, int numDimensions, boolean[] isSourceState, NetworkStruct sn, List<Double> pStarValues, FluidStateRateMultiplier stateMult) MatrixMethodODE(Matrix W, Matrix SQ, Matrix S, Matrix Qa, Matrix ALambda, int numDimensions, boolean[] isSourceState, FluidStateRateMultiplier stateMult) PassageTimeODE(NetworkStruct sn, Map<Station, Map<JobClass, Matrix>> mu, Map<Station, Map<JobClass, Matrix>> phi, Map<Station, Map<JobClass, MatrixCell>> proc, Matrix rt, Matrix S, SolverOptions options) PassageTimeODE(NetworkStruct sn, Map<Station, Map<JobClass, Matrix>> mu, Map<Station, Map<JobClass, Matrix>> phi, Map<Station, Map<JobClass, MatrixCell>> proc, Matrix rt, Matrix S, SolverOptions options, int numDimensions) Constructor parameters in jline.solvers.fluid.handlers with type arguments of type MatrixModifierConstructorDescriptionPassageTimeODE(NetworkStruct sn, Map<Station, Map<JobClass, Matrix>> mu, Map<Station, Map<JobClass, Matrix>> phi, Map<Station, Map<JobClass, MatrixCell>> proc, Matrix rt, Matrix S, SolverOptions options) PassageTimeODE(NetworkStruct sn, Map<Station, Map<JobClass, Matrix>> mu, Map<Station, Map<JobClass, Matrix>> phi, Map<Station, Map<JobClass, MatrixCell>> proc, Matrix rt, Matrix S, SolverOptions options, int numDimensions) -
Uses of Matrix in jline.solvers.fluid.moments
Fields in jline.solvers.fluid.moments declared as MatrixModifier and TypeFieldDescriptionfinal MatrixFluidClosures.ShareResult.cnfinal MatrixFluidMomentTerms.DJump matrix (nstate x nevents).final MatrixFluidClosures.ShareResult.dcnfinal MatrixFluidClosures.ShareResult.dsfinal MatrixFluidMomentTerms.Emap[nEventsReduced x nEventsOriginal] expected firings of each original event per firing of each reduced one; the identity when no immediate coordinate was eliminated.final MatrixFluidMomentTerms.immediateAbsorbProjector taking an initial condition onto the surviving coordinates: the identity on a coordinate the immediate reduction kept and the absorption distribution on one it folded away, so a zero diagonal marks an eliminated coordinate.final MatrixFluidMomentTerms.Kicfinal MatrixFluidMomentTerms.lldscalingfinal MatrixFluidMomentTerms.qIndicesfinal MatrixFluidMomentTerms.rateBaseConstant rate factor of each event (nevents x 1).final MatrixFluidClosures.ShareResult.sfinal MatrixFluidMomentTerms.SEffective server counts, with an infinite server replaced by the closed population.final MatrixFluidLyapunov.Result.sigmaMethods in jline.solvers.fluid.moments that return MatrixModifier and TypeMethodDescriptionFluid drift F(x) = D*r(x).Per-coordinate service shares at the given closure variance.Per-coordinate service share, before event indexing and before the constant rate factors are applied.Analytic Jacobian dF/dx = D*(rateBase .* G(eventIdx,:)).Analytic Jacobian dg/dx of the rate factors, mirroringFluidRateFactors.factors(double[], double[], jline.util.matrix.Matrix[])branch by branch.static MatrixOrthonormal basis of the column space, mirroring MATLABorth: the left singular vectors whose singular value exceedsmax(size(A))*eps*max(s).Event rates, i.e.Methods in jline.solvers.fluid.moments with parameters of type MatrixModifier and TypeMethodDescriptionFluid drift F(x) = D*r(x).Per-coordinate service shares at the given closure variance.Per-coordinate service share, before event indexing and before the constant rate factors are applied.static FluidClosures.ShareResultExpected capacity share of a GPS station under a normal marginal, mirroring MATLABfluid_gps_share.Analytic Jacobian dF/dx = D*(rateBase .* G(eventIdx,:)).Analytic Jacobian dg/dx of the rate factors, mirroringFluidRateFactors.factors(double[], double[], jline.util.matrix.Matrix[])branch by branch.static MatrixOrthonormal basis of the column space, mirroring MATLABorth: the left singular vectors whose singular value exceedsmax(size(A))*eps*max(s).Event rates, i.e.static FluidRefineMeanfield.ResultFluidRefineMeanfield.refine(double[] x, double[] sigma2, Matrix sigma, FluidMomentTerms terms, Matrix[] covblk) The O(1/N) correction of a fluid fixed point.static FluidClosures.ShareResultFluidClosures.shareClosure(Matrix x, Matrix wv, Matrix cov, boolean wantJac) The share closure without the joint-closure covariance.static FluidClosures.ShareResultFluidClosures.shareClosure(Matrix x, Matrix wv, Matrix cov, boolean wantJac, boolean wantCov) Second-order closure of the capacity share of a sharing discipline, mirroring MATLABfluid_share_closure.static FluidLyapunov.ResultSolves the Lyapunov equation on the reachable subspace.Constructors in jline.solvers.fluid.moments with parameters of type MatrixModifierConstructorDescriptionFluidConservationGuard(NetworkStruct sn, Matrix phases) FluidRateFactors(int M, int K, boolean[][] enabled, Matrix qIndices, Matrix Kic, Matrix nservers, Matrix w, SchedStrategy[] sched, Matrix lldscaling) ShareResult(Matrix s, Matrix ds) ShareResult(Matrix s, Matrix ds, Matrix cn, Matrix dcn) -
Uses of Matrix in jline.solvers.fluid.petri
Fields in jline.solvers.fluid.petri declared as MatrixModifier and TypeFieldDescriptionPetriConstraints.A(nrows x nstate) capacity rows.PetriConservation.C(nrows x nstate) conservation rows.PetriTerms.DPetriMode.D0PetriMode.D1PetriSolver.PetriReport.markingPetriSolver.PetriReport.markingVarPetriSolver.Result.QNPetriSolver.Result.QStdPetriSolver.Result.QVarPetriSolver.Result.RNPetriSolver.PetriReport.SigmaPetriSolver.Result.SigmaPetriSolver.Result.TNPetriSolver.Result.UNFields in jline.solvers.fluid.petri with type parameters of type MatrixModifier and TypeFieldDescriptionPetriMode.depMarking-dependent firing multiplier, null when the mode declares none.Methods in jline.solvers.fluid.petri that return MatrixModifier and TypeMethodDescriptionstatic MatrixPetriSystem.jacobian(PetriTerms t, PetriSystem.Theta th) Drift Jacobian A = D * dR/dX.Methods in jline.solvers.fluid.petri with parameters of type MatrixModifier and TypeMethodDescriptionstatic PetriClosures.MinMultiPetriClosures.minMultiClosure(double[] mu, Matrix S, double c) Min-normal closure of E[min(X_1,...,X_A,c)] by Clark's recursion. -
Uses of Matrix in jline.solvers.ldes
Fields in jline.solvers.ldes declared as MatrixModifier and TypeFieldDescriptionLNLDESResult.ALNArrival rate per LQN element [1 x nidx]LDESResult.ANCIConfidence interval half-widths for arrival rates [stations x classes]LDESResult.avgOrbitSizeAverage orbit size (time-weighted mean jobs in orbit) [stations x classes]LDESResult.avgRenegingWaitTimeAverage wait time before reneging [stations x classes]LDESResult.balkedCustomersNumber of customers who balked (refused to join upon arrival) [stations x classes]LDESResult.balkingProbabilityBalking probability (balked / arrivals) [stations x classes]LDESResult.busyPeriodCountNumber of completed busy periods per target (rows) and order (columns).LDESResult.busyPeriodMeanMean busy period duration per target (rows) and order (columns), populated whenLDESOptions.busyPeriodOrdersis positive.LNLDESResult.QLNQueue length per LQN element [1 x nidx]LNLDESResult.QLNCIQueue length confidence interval half-widths [1 x nidx]LDESResult.QNCIConfidence interval half-widths for queue lengths [stations x classes]LDESResult.QNRelPrecRelative precision achieved for queue lengths (CI half-width / mean) [stations x classes]LDESResult.QNSamplesNumber of samples (observations) used for queue length estimation [stations x classes]LDESResult.renegedCustomersNumber of customers who reneged (abandoned queue due to expired patience) [stations x classes]LDESResult.renegingRateReneging rate (reneged / (completed + reneged + dropped)) [stations x classes]LDESResult.retrialDroppedNumber of customers dropped after exceeding max retrial attempts [stations x classes]LDESResult.retriedCustomersNumber of successful retrial attempts (customers who re-entered queue from orbit) [stations x classes]LDESResult.rewardTimeTime points associated withLDESResult.tranReward.LNLDESResult.RLNResponse time per LQN element [1 x nidx]LNLDESResult.RLNCIResponse time confidence interval half-widths [1 x nidx]LDESResult.RNCIConfidence interval half-widths for response times [stations x classes]LDESResult.RNRelPrecRelative precision achieved for response times (CI half-width / mean) [stations x classes]LDESResult.RNSamplesNumber of samples (observations) used for response time estimation [stations x classes]LDESResult.stateHistogramSpaceExact joint-state residence-time histogram (populated whenLDESOptions.exportStateHistogramis set).LDESResult.stateHistogramTimeResidence time per state inLDESResult.stateHistogramSpace(column vector).LDESResult.stateTrajectorySpaceInteger joint-state trajectory (one aggregated state per row, same layout asLDESResult.stateHistogramSpace) sampled along the simulated path, downsampled to a bounded number of points.LDESResult.stateTrajectoryTimeTime points associated withLDESResult.stateTrajectorySpace(column vector).LNLDESResult.TCallLNRate at which each call is dispatched [1 x ncalls].LNLDESResult.TLNThroughput per LQN element [1 x nidx]LNLDESResult.TLNCIThroughput confidence interval half-widths [1 x nidx]LDESResult.TNCIConfidence interval half-widths for throughputs [stations x classes]LDESResult.TNRelPrecRelative precision achieved for throughputs (CI half-width / mean) [stations x classes]LDESResult.TNSamplesNumber of samples (observations) used for throughput estimation [stations x classes]LDESResult.tranProbPitTransient state probabilities over time [numTimePoints x numStates].LDESResult.tranProbStateSpaceTransient state space matrix [numStates x stateVectorLength].LDESResult.tranProbTTime points for transient probability analysis.LDESResult.tranSyncTransient synchronization matrix for multi-class coordinationLNLDESResult.UCallLNPer-call occupancy of the callee's thread pool, in job units [1 x ncalls].LNLDESResult.UEntryClassLNOccupancy contributed by an entry's OWN request stream, in job units [1 x nentries], indexed by local entry index.LNLDESResult.ULNUtilization per LQN element [1 x nidx]LNLDESResult.ULNCIUtilization confidence interval half-widths [1 x nidx]LDESResult.UNCIConfidence interval half-widths for utilizations [stations x classes]LDESResult.UNRelPrecRelative precision achieved for utilizations (CI half-width / mean) [stations x classes]LDESResult.UNSamplesNumber of samples (observations) used for utilization estimation [stations x classes]LNLDESResult.WLNResidence/waiting time per LQN element [1 x nidx]LDESResult.WNCIConfidence interval half-widths for residence times [stations x classes]LNLDESResult.ZLNMean think time of a task, at task indices and zero elsewhere [1 x nidx].Fields in jline.solvers.ldes with type parameters of type MatrixModifier and TypeFieldDescriptionLDESResult.tranProbAggrPitTransient aggregated state probabilities per node [nodeIdx] -> [numTimePoints x numAggrStates].LDESResult.tranProbAggrStateSpaceTransient aggregated state space per node [nodeIdx] -> [numAggrStates x numClasses].LDESResult.tranSysStateTransient system state matrices indexed by time pointsMethods in jline.solvers.ldes that return MatrixModifier and TypeMethodDescriptionSolverLDES.getLNAvgCallTput()Rate at which each call is dispatched, counted at the caller.SolverLDES.getLNAvgCallUtil()Per-call occupancy of the called task's thread pool, in job units.SolverLDES.getLNAvgEntryClassUtil()Occupancy contributed by each entry's own request stream, in job units.SolverLDES.getLNAvgThinkTime()Mean think time of every LQN element, at task indices and zero elsewhere.SolverLDES.getRewardTimeVector()Time vector associated withSolverLDES.getTranReward().Matrix[]SolverLDES.getStateHistogram()Runs the simulation with state-histogram export enabled and returns the exact joint-state residence-time histogram.Matrix[]SolverLDES.getStateTrajectory()Runs the simulation with state export enabled and returns the integer joint-state trajectory along the sampled path:{space, time}where rowiof space is the aggregated state (CTMCstateSpaceAggrlayout) attime[i].Methods in jline.solvers.ldes that return types with arguments of type MatrixMethods in jline.solvers.ldes with parameters of type MatrixModifier and TypeMethodDescriptionEstimates state probability using node index.SolverLDES.getProb(StatefulNode node, Matrix state) Estimates the steady-state probability of a specific state at a node via LDES simulation.SolverLDES.getProbAggr(int nodeIndex, Matrix stateAggr) Estimates aggregated state probability using node index.SolverLDES.getProbAggr(StatefulNode node, Matrix stateAggr) Estimates the steady-state probability of a specific aggregated (per-class) state at a node.Constructors in jline.solvers.ldes with parameters of type MatrixModifierConstructorDescriptionLDESResult(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, Map<Integer, Matrix> tranSysState, Matrix tranSync, NetworkStruct sn) Constructs a LDESResult with the specified performance metrics and state information.Constructor parameters in jline.solvers.ldes with type arguments of type Matrix -
Uses of Matrix in jline.solvers.ln
Fields in jline.solvers.ln declared as MatrixModifier and TypeFieldDescriptionSolverLN.arvproc_classes_updmapSolverLN.call_classes_updmapSolverLN.callresidtSolverLN.LNState.callresidtSolverLN.callresidt_prevSolverLN.LNState.callresidtPrevSolverLN.callservtSolverLN.LNState.callservtSolverLN.callservt_prevSolverLN.LNState.callservtPrevSolverLN.ignoreSolverLN.ilscalingSolverLN.LNState.ilscalingSolverLN.jointMatrix[]SolverLN.layerChainsCached sn.chains of a constrained layer, per ensemble index.SolverLN.LNState.njobsSolverLN.njobsSolverLN.njobsorigSolverLN.prOvertakeSolverLN.LNState.ptaskcallersSolverLN.ptaskcallersMatrix[][]LNTranAvgResult.QNtTransient queue lengths, block-diagonal [rows][cols]; each cell Tmax x 1 or null.SolverLN.LNState.residtSolverLN.residtSolverLN.residt_prevSolverLN.LNState.residtPrevSolverLN.route_prob_updmapSolverLN.LNState.servtSolverLN.servtSolverLN.servt_classes_updmapSolverLN.servt_ph1SolverLN.servt_ph2SolverLN.servt_prevSolverLN.servtmatrixSolverLN.LNState.servtPrevSolverLN.stochResidtAvgSolverLN.stochServtAvgMatrix[][]LNTranAvgResult.tTime instants per cell, block-diagonal [rows][cols]; each cell Tmax x 1 or null.SolverLN.LNState.thinktSolverLN.thinktSolverLN.thinkt_classes_updmapSolverLN.thinkt_prevSolverLN.LNState.thinktPrevMatrix[][]LNTranAvgResult.TNtTransient throughputs, block-diagonal [rows][cols]; each cell Tmax x 1 or null.SolverLN.LNState.tputSolverLN.tputSolverLN.tput_prevSolverLN.LNState.tputPrevSolverLN.unique_route_prob_updmapMatrix[][]LNTranAvgResult.UNtTransient utilizations, block-diagonal [rows][cols]; each cell Tmax x 1 or null.SolverLN.LNState.utilSolverLN.utilSolverLN.util_ph1SolverLN.util_ph2Fields in jline.solvers.ln with type parameters of type MatrixModifier and TypeFieldDescriptionSolverLN.callservtcdfSolverLN.ptaskcallers_stepSolverLN.servtcdfMethods in jline.solvers.ln that return MatrixModifier and TypeMethodDescriptionSolverLN.getArvproc_classes_updmap()SolverLN.getCall_classes_updmap()Matrix[]SolverLN.getEnsembleAvgPH()LQN-level results.SolverLN.getEntryServiceMatrix()SolverLN.getEntryServiceMatrixRecursion(LayeredNetworkStruct lqn, int aidx, int eidx, Matrix U) SolverLN.getRoute_prob_updmap()SolverLN.getServt_classes_updmap()SolverLN.getThinkt_classes_updmap()SolverLN.integerMapToMatrix(Map<Integer, List<Integer[]>> cell) Methods in jline.solvers.ln that return types with arguments of type MatrixModifier and TypeMethodDescriptionSolverLN.getCdfRespT()Response time distribution of every entry of the layered network.Methods in jline.solvers.ln with parameters of type MatrixModifier and TypeMethodDescriptionSolverLN.getEntryServiceMatrixRecursion(LayeredNetworkStruct lqn, int aidx, int eidx, Matrix U) Constructors in jline.solvers.ln with parameters of type MatrixModifierConstructorDescriptionLNTranAvgResult(Matrix[][] QNt, Matrix[][] UNt, Matrix[][] TNt, Matrix[][] t) -
Uses of Matrix in jline.solvers.mam
Fields in jline.solvers.mam declared as MatrixFields in jline.solvers.mam with type parameters of type MatrixMethods in jline.solvers.mam that return MatrixModifier and TypeMethodDescriptionMAMFJResult.getQN()MAMFJResult.getRN()MAMFJResult.getTN()MAMFJResult.getUN()MAMFJResult.getXN()Methods in jline.solvers.mam with parameters of type MatrixModifier and TypeMethodDescriptionJoint (level, phase) state probability, port of @SolverMAM/getProb.m.SolverMAM.getProbMarg(int node, int jobclass, Matrix state_m) Constructors in jline.solvers.mam with parameters of type MatrixModifierConstructorDescriptionMAMFJResult(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix XN, List<MainFJ.FJPercentileResult> percentileResults) -
Uses of Matrix in jline.solvers.mam.handlers
Fields in jline.solvers.mam.handlers declared as MatrixModifier and TypeFieldDescriptionMam_bgchain_env.Env.ALumped generator, (me x me).final MatrixMam_svc_mixture.Result.alpha1 x n, the arrival-weighted initial probability row.final MatrixMetricsResult.CNfinal MatrixMam_svc_mixture.Result.Dsumsum_k D_arr(k+1), the aggregate arrival matrix.final MatrixBMAPMAP1Result.GG matrixfinal MatrixMam_truncate_renorm.Result.p_normrenormalized truncated marginal, 1x(capK+1).final MatrixBMAPMAP1Result.piAggregated stationary probabilities [pi0, pi1, piStar]Mam_bgchain_ctmc.Result.piStationary distribution, one row.final MatrixMAPBMAP1Result.piMam_bgchain_ctmc.Result.QGenerator.Solver_mam_ldqbd_statevec.Flat.Qfinal MatrixMetricsResult.QNSolver_mam_ldqbd_statevec.Avg.QNfinal MatrixMAPBMAP1Result.Rfinal MatrixMetricsResult.RNSolver_mam_ldqbd_statevec.Avg.RNfinal MatrixMam_svc_mixture.Result.Tn x n, the block-diagonal subgenerator.final MatrixMetricsResult.TNSolver_mam_ldqbd_statevec.Avg.TNfinal MatrixMetricsResult.UNSolver_mam_ldqbd_statevec.Avg.UNfinal MatrixMetricsResult.XNFields in jline.solvers.mam.handlers with type parameters of type MatrixModifier and TypeFieldDescriptionSolver_mam_ldqbd_statevec.Ld.Q0Solver_mam_ldqbd_statevec.Ld.Q1Solver_mam_ldqbd_statevec.Ld.Q2Methods in jline.solvers.mam.handlers that return MatrixModifier and TypeMethodDescriptionBMAPMAP1Result.component5()BMAPMAP1Result.component6()MetricsResult.getCN()BMAPMAP1Result.getG()BMAPMAP1Result.getPi()MetricsResult.getQN()Matrix[][]TransientResult.getQt()MetricsResult.getRN()MetricsResult.getTN()Matrix[][]TransientResult.getTt()MetricsResult.getUN()Matrix[][]TransientResult.getUt()MetricsResult.getXN()Methods in jline.solvers.mam.handlers with parameters of type MatrixModifier and TypeMethodDescriptionMam_bgchain_station.mam_bgchain_station(Matrix Da0, Matrix Da1, Matrix alphaS, Matrix T, Matrix A, int[] esup, double nservers, double[] gref, int Kmax, SolverOptions options) static doubleQna_superpos.qna_superpos(Matrix lambda, Matrix a2) static BMAPMAP1ResultSolver_mam_bmap_map_1.solver_mam_bmap_map_1(List<Matrix> D, Matrix S0, Matrix S1) static BMAPMAP1ResultSolver_mam_bmap_map_1.solver_mam_bmap_map_1(List<Matrix> D, Matrix S0, Matrix S1, int nMoments) Solves a BMAP/MAP/1 queue using M/G/1 type matrix-analytic methods.Solver_mam_ldqbd_statevec.solver_mam_ldqbd_avg(Solver_mam_ldqbd_statevec.Ld ld, Matrix piflat, int[] levelOf) Map a flat LD-QBD distribution to per-(station,class) mean metrics.static MAPBMAP1ResultSolver_mam_map_bmap_1.solver_mam_map_bmap_1(Matrix C0, Matrix C1, List<Matrix> D) Method parameters in jline.solvers.mam.handlers with type arguments of type MatrixModifier and TypeMethodDescriptionstatic Mam_bgchain_ctmc.ResultMam_bgchain_ctmc.mam_bgchain_ctmc(int[] Nb, double[][] STb, List<Matrix> Pb, SchedStrategy[] sched, double[] nservers, double[][] cshare, boolean[][] supp, SolverOptions options) Builds and solves the background chain.static Mam_svc_mixture.ResultMam_svc_mixture.mam_svc_mixture(MatrixCell D_arr, Map<Integer, Matrix> pie_cell, Map<Integer, Matrix> D0_cell) static Mam_truncate_renorm.ResultMam_truncate_renorm.mam_truncate_renorm(MatrixCell D_arr, Map<Integer, Matrix> pie_cell, Map<Integer, Matrix> D0_cell, int capK) static BMAPMAP1ResultSolver_mam_bmap_map_1.solver_mam_bmap_map_1(List<Matrix> D, Matrix S0, Matrix S1) static BMAPMAP1ResultSolver_mam_bmap_map_1.solver_mam_bmap_map_1(List<Matrix> D, Matrix S0, Matrix S1, int nMoments) Solves a BMAP/MAP/1 queue using M/G/1 type matrix-analytic methods.static MAPBMAP1ResultSolver_mam_map_bmap_1.solver_mam_map_bmap_1(Matrix C0, Matrix C1, List<Matrix> D) Constructors in jline.solvers.mam.handlers with parameters of type MatrixModifierConstructorDescriptionBMAPMAP1Result(double meanQueueLength, double utilization, double meanResponseTime, double throughput, Matrix pi, Matrix G, double meanBatchSize) MAPBMAP1Result(double meanQueueLength, double utilization, double meanResponseTime, double throughput, Matrix pi, Matrix R, double meanBatchSize) TransientResult(Matrix[][] Qt, Matrix[][] Ut, Matrix[][] Tt) -
Uses of Matrix in jline.solvers.mva
Fields in jline.solvers.mva declared as MatrixModifier and TypeFieldDescriptionMVAResult.hitProbCache hit probabilities [items x classes] (used by cache analyzers)MVAResult.missProbCache miss probabilities [items x classes] (used by cache analyzers)Fields in jline.solvers.mva with type parameters of type MatrixModifier and TypeFieldDescriptionMVAResult.cacheItemProbPer-cache per-item occupancy [nitems x (lists+1)], keyed by node index (used by the cache+queueing analyzer to populate getAvgItemTable)Methods in jline.solvers.mva that return MatrixModifier and TypeMethodDescriptionSolverMVA.getForkWarmStart()Returns the retained MMT fixed-point iterate, or null if none is held.ProbabilityResult.getMarginal(int station, int jobclass) Get the marginal probability distribution for a specific station and class.ProbabilityResult.getProbabilityVector()Get the probability distribution vector.Methods in jline.solvers.mva with parameters of type MatrixModifier and TypeMethodDescriptionSolverMVA.getProbMarg(int ist, int jobclass, Matrix state_m) Get marginalized state probabilities for a specific station and job class with state filterstatic StringSolverMVA.schmidtExtReason(Matrix N, List<Boolean> fcfs, String method) Refusal reason for the extended Schmidt method, empty when it may run.voidSolverMVA.setForkWarmStart(Matrix fjForkLambda) Seeds the MMT fixed point with a previously retained iterate.voidProbabilityResult.setMarginal(int station, int jobclass, Matrix probVector) Set the marginal probability distribution for a specific station and class.Constructors in jline.solvers.mva with parameters of type MatrixModifierConstructorDescriptionProbabilityResult(Matrix probabilityVector) Constructor for probability distribution result. -
Uses of Matrix in jline.solvers.mva.handlers
Methods in jline.solvers.mva.handlers that return MatrixModifier and TypeMethodDescriptionMVARunner.getFjForkLambda()The MMT fixed-point iterate this run ended on, for the owning solver to keep.Methods in jline.solvers.mva.handlers that return types with arguments of type MatrixModifier and TypeMethodDescriptionSolver_amvald.solver_amvald_forward(List<Matrix> gamma, Matrix tau, Matrix Xchain_ref_in, Matrix Qchain_in, Matrix Xchain_in, Matrix Uchain_in, Matrix STchain_in, Matrix Vchain_in, Matrix Nchain_in, Matrix SCVchain_in, double Nt, double delta, Matrix deltaclass, List<Integer> ocl, List<Integer> ccl, List<Integer> nnzclasses, Map<Integer, List<Integer>> nnzclasses_eprio, Map<Integer, List<Integer>> nnzclasses_hprio, Map<Integer, List<Integer>> nnzclasses_ehprio, Map<Integer, List<Integer>> nnzclasses_lprio, int M, int K, Matrix nservers, Matrix schedparam, Matrix lldscaling_in, Map<Station, SerializableFunction<Matrix, Matrix>> cdscaling, Map<Station, SerializableFunction<Matrix, Matrix>> jdscaling, List<SchedStrategy> sched, List<Station> stations, SolverOptions options) Solver_amvald.solver_amvald_forward(List<Matrix> gamma, Matrix tau, Matrix Xchain_ref_in, Matrix Qchain_in, Matrix Xchain_in, Matrix Uchain_in, Matrix STchain_in, Matrix Vchain_in, Matrix Nchain_in, Matrix SCVchain_in, double Nt, double delta, Matrix deltaclass, List<Integer> ocl, List<Integer> ccl, List<Integer> nnzclasses, Map<Integer, List<Integer>> nnzclasses_eprio, Map<Integer, List<Integer>> nnzclasses_hprio, Map<Integer, List<Integer>> nnzclasses_ehprio, Map<Integer, List<Integer>> nnzclasses_lprio, int M, int K, Matrix nservers, Matrix schedparam, Matrix lldscaling_in, Map<Station, SerializableFunction<Matrix, Matrix>> cdscaling, Map<Station, SerializableFunction<Matrix, Matrix>> jdscaling, List<SchedStrategy> sched, List<Station> stations, SolverOptions options) Methods in jline.solvers.mva.handlers with parameters of type MatrixModifier and TypeMethodDescriptionstatic doubleSolver_qna.qna_superpos(Matrix lambda, Matrix a2) Solver_amvald.solver_amvald_forward(List<Matrix> gamma, Matrix tau, Matrix Xchain_ref_in, Matrix Qchain_in, Matrix Xchain_in, Matrix Uchain_in, Matrix STchain_in, Matrix Vchain_in, Matrix Nchain_in, Matrix SCVchain_in, double Nt, double delta, Matrix deltaclass, List<Integer> ocl, List<Integer> ccl, List<Integer> nnzclasses, Map<Integer, List<Integer>> nnzclasses_eprio, Map<Integer, List<Integer>> nnzclasses_hprio, Map<Integer, List<Integer>> nnzclasses_ehprio, Map<Integer, List<Integer>> nnzclasses_lprio, int M, int K, Matrix nservers, Matrix schedparam, Matrix lldscaling_in, Map<Station, SerializableFunction<Matrix, Matrix>> cdscaling, Map<Station, SerializableFunction<Matrix, Matrix>> jdscaling, List<SchedStrategy> sched, List<Station> stations, SolverOptions options) Method parameters in jline.solvers.mva.handlers with type arguments of type MatrixModifier and TypeMethodDescriptionSolver_amvald.solver_amvald_forward(List<Matrix> gamma, Matrix tau, Matrix Xchain_ref_in, Matrix Qchain_in, Matrix Xchain_in, Matrix Uchain_in, Matrix STchain_in, Matrix Vchain_in, Matrix Nchain_in, Matrix SCVchain_in, double Nt, double delta, Matrix deltaclass, List<Integer> ocl, List<Integer> ccl, List<Integer> nnzclasses, Map<Integer, List<Integer>> nnzclasses_eprio, Map<Integer, List<Integer>> nnzclasses_hprio, Map<Integer, List<Integer>> nnzclasses_ehprio, Map<Integer, List<Integer>> nnzclasses_lprio, int M, int K, Matrix nservers, Matrix schedparam, Matrix lldscaling_in, Map<Station, SerializableFunction<Matrix, Matrix>> cdscaling, Map<Station, SerializableFunction<Matrix, Matrix>> jdscaling, List<SchedStrategy> sched, List<Station> stations, SolverOptions options) Constructors in jline.solvers.mva.handlers with parameters of type MatrixModifierConstructorDescriptionMVARunner(Network model, SolverOptions options, boolean enableChecks, Ret.FJApprox mmtCache, Matrix fjForkLambda) -
Uses of Matrix in jline.solvers.nc
Fields in jline.solvers.nc declared as MatrixModifier and TypeFieldDescriptionSolverNC.SolverNCLDReturn.CNCResult.hitProbSolverNC.SolverNCMargReturn.lPrNCResult.missProbNCResult.pijSolverNC.SolverNCLDReturn.QSolverNC.SolverNCReturn.QSolverNC.SolverNCLDReturn.RSolverNC.SolverNCReturn.RNCResult.STeffSolverNC.SolverNCReturn.STeffSolverNC.SolverNCLDReturn.TSolverNC.SolverNCReturn.TSolverNC.SolverNCLDReturn.USolverNC.SolverNCReturn.USolverNC.SolverNCLDReturn.XSolverNC.SolverNCReturn.XFields in jline.solvers.nc with type parameters of type MatrixModifier and TypeFieldDescriptionNCResult.cacheItemProbPer-item occupancy of each cache node, (nitems x lists+1) keyed by node index; column 0 = miss.Methods in jline.solvers.nc that return MatrixModifier and TypeMethodDescriptionSolverNC.getCdfRespTFirstProbe()First-probe-time summary of the response time CDF, one scalar per station and class.Methods in jline.solvers.nc with parameters of type MatrixModifier and TypeMethodDescriptionSolverNC.getProbAggr(Node node, Matrix state_a) Get aggregated probability for a specific node and stateSolverNC.getProbMarg(int ist, int jobclass, Matrix state_m) Marginal queue-length distribution at a station, routing the CLI's (station, class) call to the exact procomom/enumeration path inSolverNC.getProbMarg(Node).SolverNC.getProbSysMarg(Matrix nvec) Joint probability that station i holds nvec(i) jobs IN TOTAL, all classes summed out.SolverNC.getProbSysMarg(Matrix nvec, String engine) Joint probability of the per-station total queue lengths, evaluated with a chosen permanent engine.Constructors in jline.solvers.nc with parameters of type MatrixModifierConstructorDescriptionSolverNCLDReturn(Matrix Q, Matrix U, Matrix R, Matrix T, Matrix C, Matrix X, double lG, double runtime, int it, String method) SolverNCMargReturn(Matrix lPr, double G, double lG, double runtime) SolverNCReturn(Matrix Q, Matrix U, Matrix R, Matrix T, int C, Matrix X, Double lG, Matrix STeff, int it, double runtime, String method) -
Uses of Matrix in jline.solvers.nc.analyzers
Methods in jline.solvers.nc.analyzers with parameters of type MatrixModifier and TypeMethodDescriptionstatic voidSolver_nc_retrieval_analyzer.setStationMetrics(NetworkStruct sn, Cache_retrieval_inputs.Inputs in, double[][] pdh, double[] pmiss, Matrix QN, Matrix UN, Matrix RN, Matrix TN) Shared station-metric population (QN=UN=phi_s, TN=fetch throughput, RN=Little). -
Uses of Matrix in jline.solvers.nc.handlers
Methods in jline.solvers.nc.handlers with parameters of type MatrixModifier and TypeMethodDescriptionSolver_nc_jointmarg.solver_nc_jointmarg(NetworkStruct sn, SolverOptions options, Matrix nvec, String engine, Double lG) -
Uses of Matrix in jline.solvers.ssa
Fields in jline.solvers.ssa declared as MatrixModifier and TypeFieldDescriptionSSAResult.ANCIConfidence interval half-widths for arrival rates [stations x classes]final MatrixSSAValues.piSSAResult.preemptRateDerived PREEMPT rate [stations x classes]; zero at a non-preemptive station.SSAResult.QNCIConfidence interval half-widths for queue lengths [stations x classes]SSAResult.RNCIConfidence interval half-widths for response times [stations x classes]final MatrixSSAValues.SSqSSAResult.startRateDerived START rate [stations x classes]: how often per unit time a class-r job begins, or resumes, holding a server at station i.SampleNodeState.stateState information at each time pointSampleNodeState.tTime points of the samplingSampleSysState.tTime points of the samplingSSAResult.TNCIConfidence interval half-widths for throughputs [stations x classes]SSAResult.tranSyncTransient synchronization matrix for multi-class coordinationfinal MatrixSSAValues.tranSyncSSAResult.UNCIConfidence interval half-widths for utilizations [stations x classes]SSAResult.WNCIConfidence interval half-widths for residence times [stations x classes]Fields in jline.solvers.ssa with type parameters of type MatrixModifier and TypeFieldDescriptionSSAValues.arvRatesSSAValues.depRatesSSAValues.dlyRatesRate of the cache merge transitions, i.e.SSAValues.preemptRatesDerived PREEMPT rates, laid out like startRates.SSAResult.spaceState space matrices for each station in the networkSSAValues.startRatesDerived START rates per unique state, laid out like arvRates/depRates: how fast the transitions enabled in that state start a class-r service at a stateful node.SampleSysState.stateState information for each stateful node at each time pointSSAResult.tranSysStateTransient system state matrices indexed by time pointsSSAValues.tranSysStateMethods in jline.solvers.ssa that return MatrixModifier and TypeMethodDescriptionSolverSSA.getPreemptRate()(stations x classes) rate at which a class-r job HOLDING A SERVER at station i is pushed back into the buffer.SolverSSA.getStartRate()(stations x classes) rate at which a class-r job BEGINS or RESUMES holding a server at station i, estimated over the simulated path exactly as the throughput is.Methods in jline.solvers.ssa with parameters of type MatrixModifier and TypeMethodDescriptionGet marginal probability for a specific node state (by node index).doubleGet probability for a specific node stateSolverSSA.getProbAggr(int node, Matrix state) Aggregated probability for a node state, addressed by NODE INDEX.doubleSolverSSA.getProbAggr(Node node, Matrix state) Get aggregated probability for a specific node stateSolverSSA.planRunLength(SolverOptions options, Matrix means, Matrix ciHalfWidth, double samplesUsed) The run length the caller would need for the precision they asked for.Method parameters in jline.solvers.ssa with type arguments of type MatrixModifier and TypeMethodDescriptionvoidAttach the derived START/PREEMPT rates measured over the sampled path.Constructors in jline.solvers.ssa with parameters of type MatrixModifierConstructorDescriptionSampleSysState(List<Node> handle, Matrix t, List<Matrix> state, List<Event> event, boolean isaggregate) SSAResult(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, Map<Integer, Matrix> tranSysState, Matrix tranSync, NetworkStruct sn) Constructs an SSAResult with the specified performance metrics and state information.SSAValues(Matrix pi, Matrix SSq, Map<Integer, Matrix> arvRates, Map<Integer, Matrix> depRates, Map<Integer, Matrix> dlyRates, Map<Integer, Matrix> tranSysState, Matrix tranSync, NetworkStruct sn) SSAValues(Matrix pi, Matrix SSq, Map<Integer, Matrix> arvRates, Map<Integer, Matrix> depRates, Map<Integer, Matrix> tranSysState, Matrix tranSync, NetworkStruct sn) Constructor parameters in jline.solvers.ssa with type arguments of type MatrixModifierConstructorDescriptionSSAResult(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, Map<Integer, Matrix> tranSysState, Matrix tranSync, NetworkStruct sn) Constructs an SSAResult with the specified performance metrics and state information.SSAValues(Matrix pi, Matrix SSq, Map<Integer, Matrix> arvRates, Map<Integer, Matrix> depRates, Map<Integer, Matrix> dlyRates, Map<Integer, Matrix> tranSysState, Matrix tranSync, NetworkStruct sn) SSAValues(Matrix pi, Matrix SSq, Map<Integer, Matrix> arvRates, Map<Integer, Matrix> depRates, Map<Integer, Matrix> tranSysState, Matrix tranSync, NetworkStruct sn) -
Uses of Matrix in jline.solvers.ssa.analyzers
Method parameters in jline.solvers.ssa.analyzers with type arguments of type MatrixModifier and TypeMethodDescriptionstatic SSAResultSolver_ssa_analyzer_nrm.solver_ssa_analyzer_nrm(NetworkStruct sn, Map<StatefulNode, Matrix> init_state, SolverOptions options) static SSAResultSolver_ssa_analyzer_parallel.solver_ssa_analyzer_parallel(NetworkStruct sn, Map<StatefulNode, Matrix> init_state, SolverOptions options, SolverSSA solverSSA) static SSAResultSolver_ssa_analyzer_serial.solver_ssa_analyzer_serial(NetworkStruct sn, boolean hash, Map<StatefulNode, Matrix> init_state, SolverOptions options, SolverSSA solverSSA) -
Uses of Matrix in jline.solvers.ssa.handlers
Fields in jline.solvers.ssa.handlers declared as MatrixModifier and TypeFieldDescriptionfinal MatrixSolverSSAResultNRM.CNfinal MatrixSolver_ssa_nrm_space.SolverSSAResultNRMSpace.depRatesfinal MatrixSolver_ssa_nrm_space.SolverSSAResultNRMSpace.outspacefinal MatrixSolver_ssa_nrm_space.SolverSSAResultNRMSpace.piSolverSSAResultNRM.PreemptNDerived PREEMPT rate [stations x classes].final MatrixSolverSSAResultNRM.QNfinal MatrixSolverSSAResultNRM.RNSolverSSAResultNRM.StartNDerived START rate [stations x classes]: counts of service starts over the simulated time.final MatrixSolver_ssa_nrm_space.NrmSpaceResult.statesfinal MatrixSolverSSAResultNRM.TNfinal MatrixSolverSSAResultNRM.UNfinal MatrixSolverSSAResultNRM.XNMethods in jline.solvers.ssa.handlers that return MatrixModifier and TypeMethodDescriptionSolverSSAResultNRM.getCN()SolverSSAResultNRM.getQN()SolverSSAResultNRM.getRN()SolverSSAResultNRM.getTN()SolverSSAResultNRM.getUN()SolverSSAResultNRM.getXN()Methods in jline.solvers.ssa.handlers with parameters of type MatrixModifier and TypeMethodDescriptionstatic voidSolver_ssa_nrm.modified_nrm_direct(Matrix S, List<Integer>[] D, BiFunction<Matrix, ArrayDeque<Integer>[], Double>[] a, Matrix nvec0, ArrayDeque<Integer>[] buffers0, int samples, SolverOptions options, Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, Matrix StartN, Matrix PreemptN, NetworkStruct sn, List<Integer> fromIdx, List<int[]> fromIR, double[] mi, jline.solvers.ssa.handlers.Solver_ssa_nrm.Fcr fcr, jline.solvers.ssa.handlers.Solver_ssa_nrm.Balk balk, jline.solvers.ssa.handlers.Solver_ssa_nrm.Sig sig, jline.solvers.ssa.handlers.Solver_ssa_nrm.Rr rrp, boolean[] isRenegeRx, boolean[] isRetryRx, jline.solvers.ssa.handlers.Solver_ssa_nrm.Smap sm, int nDepRx, boolean[] isPhaseRx, double[][][] svcph, boolean[] bufPHNode, boolean[] isBufSvcRx, List<Integer> depPhase, int[] phaseToArr) static voidSolver_ssa_nrm.nrm_direct(Matrix S, List<Integer>[] D, BiFunction<Matrix, ArrayDeque<Integer>[], Double>[] a, Matrix nvec0, ArrayDeque<Integer>[] buffers0, int samples, SolverOptions options, Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, Matrix StartN, Matrix PreemptN, NetworkStruct sn, List<Integer> fromIdx, List<int[]> fromIR, double[] mi, jline.solvers.ssa.handlers.Solver_ssa_nrm.Fcr fcr, jline.solvers.ssa.handlers.Solver_ssa_nrm.Balk balk, jline.solvers.ssa.handlers.Solver_ssa_nrm.Sig sig, jline.solvers.ssa.handlers.Solver_ssa_nrm.Rr rrp, boolean[] isRenegeRx, boolean[] isRetryRx, jline.solvers.ssa.handlers.Solver_ssa_nrm.Smap sm, int nDepRx, boolean[] isPhaseRx, boolean anyPoll, Polling.Info[] pinfo, boolean[] isPollNode, int[][] pollCtrl, boolean[] isPollSwRx, int[] pollSwNode, double[][][] svcph, boolean[] bufPHNode, boolean[] isBufSvcRx, List<Integer> depPhase, int[] phaseToArr, boolean[] isCacheRx, int[] cacheHitSlotArr, int[] cacheMissSlotArr, boolean[] isCacheNode, int[][] cacheContents0, long[][] cacheProd, long[][] cacheDly, int[][] cacheRetrDest) Solver_ssa_nrm_space.nrm_space(Matrix S, List<List<Integer>> D, BiFunction<Matrix, ArrayDeque<Integer>[], Double>[] a, Matrix nvec0, ArrayDeque<Integer>[] buffers0, int samples, SolverOptions options, Map<String, double[]> reactcache, List<int[]> fromIR, double[] mi, int R, NetworkStruct sn) voidSolverSSAResultNRM.setTagRates(Matrix StartN, Matrix PreemptN) static voidSolver_ssa_findenabled.solver_ssa_findenabled(NetworkStruct sn, EventCache eventCache, int A, Map<Integer, Integer> node_a, Map<Integer, Map<Integer, Matrix>> next_state, Map<Integer, Matrix> stateCell, Map<Integer, EventType> event_a, Map<Integer, Integer> class_a, boolean isSimulation, Map<Integer, Double> outprob_a, Map<Integer, Integer> node_p, int local, Map<Integer, EventType> event_p, Map<Integer, Integer> class_p, Map<Integer, Double> outprob_p, Map<Integer, Double> prob_sync_p, Map<Integer, Sync> sync, Map<Integer, Integer> node_a_sf, Map<Integer, Integer> node_p_sf, Map<Integer, Matrix> startRatesSamples, Map<Integer, Matrix> preemptRatesSamples, Map<Integer, int[][]> enabled_tags, Map<Integer, Matrix> depRatesSamples, int samples_collected, Map<Integer, Matrix> arvRatesSamples, Map<Integer, Matrix> dlyRatesSamples, int[] cacheVarW, Matrix csmask, Map<Integer, Double> enabled_rates, Map<Integer, Integer> enabled_sync, Map<Integer, int[]> enabled_fcr, SolverSSA solverSSA, AfterEventContext aectx) static org.ejml.data.DMatrixRMajSolver_ssa.update_paddings_dense(NetworkStruct sn, Map<Integer, Matrix> stateCell, Matrix statelen, org.ejml.data.DMatrixRMaj tranStateD) Method parameters in jline.solvers.ssa.handlers with type arguments of type MatrixModifier and TypeMethodDescriptionstatic voidSolver_ssa.save_log_dense(double dt, Map<Integer, Matrix> cur_state, org.ejml.data.DMatrixRMaj tranStateD, int samples_collected, double[] tranSyncData, Map<Integer, Integer> enabled_sync, int firing_ctr, NetworkStruct sn, Map<Integer, Matrix> nir, Map<Integer, Matrix> stateCell, org.ejml.data.DMatrixRMaj SSqD) static voidSolver_ssa.save_log_dense(double dt, Map<Integer, Matrix> cur_state, org.ejml.data.DMatrixRMaj tranStateD, int samples_collected, double[] tranSyncData, Map<Integer, Integer> enabled_sync, int firing_ctr, NetworkStruct sn, Map<Integer, Matrix> nir, Map<Integer, Matrix> stateCell, org.ejml.data.DMatrixRMaj SSqD, Collector streamingCollector, double curTime) static SSAValuesSolver_ssa.solver_ssa(NetworkStruct sn_in, EventCache eventCache, Map<StatefulNode, Matrix> init_state, SolverOptions optionsIn, SolverSSA solverSSA) static org.ejml.data.DMatrixRMajSolver_ssa.update_paddings_dense(NetworkStruct sn, Map<Integer, Matrix> stateCell, Matrix statelen, org.ejml.data.DMatrixRMaj tranStateD) Constructors in jline.solvers.ssa.handlers with parameters of type MatrixModifierConstructorDescriptionNrmSpaceResult(List<Double> times, Matrix states, List<ArrayDeque<Integer>[]> bufferStates) SolverSSAResultNRM(Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, NetworkStruct sn) SolverSSAResultNRMSpace(Matrix pi, Matrix outspace, Matrix depRates, NetworkStruct sn) -
Uses of Matrix in jline.solvers.tr
Fields in jline.solvers.tr declared as MatrixModifier and TypeFieldDescriptionfinal MatrixFJTagTransform.Lifted.ANfinal MatrixTransformSolve.Lifted.Cfinal MatrixTransformSolve.Result.Cfinal MatrixFJTagTransform.Lifted.CNfinal MatrixFJTagTransform.Context.fjclassmapAuxiliary-to-original class map produced by the augmentation.final MatrixTransformSolve.Inner.Qfinal MatrixTransformSolve.Lifted.Qfinal MatrixTransformSolve.Result.Qfinal MatrixFJTagTransform.Lifted.QNfinal MatrixTransformSolve.Inner.Rfinal MatrixTransformSolve.Lifted.Rfinal MatrixTransformSolve.Result.Rfinal MatrixFJTagTransform.Lifted.RNfinal MatrixTransformSolve.Inner.Tfinal MatrixTransformSolve.Lifted.Tfinal MatrixTransformSolve.Result.Tfinal MatrixFJTagTransform.Lifted.TNfinal MatrixTransformSolve.Inner.Ufinal MatrixTransformSolve.Lifted.Ufinal MatrixTransformSolve.Result.Ufinal MatrixFJTagTransform.Lifted.UNfinal MatrixTransformSolve.Inner.XThe per-chain SYSTEM throughput, from getAvgSysTput.final MatrixTransformSolve.Lifted.Xfinal MatrixTransformSolve.Result.Xfinal MatrixFJTagTransform.Lifted.XNMethods in jline.solvers.tr with parameters of type MatrixModifier and TypeMethodDescriptionstatic FJTagTransform.LiftedFJTagTransform.lift(FJTagTransform.Context ctx, Matrix QN, Matrix UN, Matrix RN, Matrix TN, Matrix CN, Matrix XN, AvgHandle T) Folds the auxiliary sibling classes back onto the original ones.Constructors in jline.solvers.tr with parameters of type Matrix -
Uses of Matrix in jline.solvers.uq
Fields in jline.solvers.uq declared as MatrixModifier and TypeFieldDescriptionfinal MatrixSolverUQ.Interval.QloQueue-length endpoints.final MatrixSolverUQ.Interval.QupQueue-length endpoints.final MatrixSolverUQ.Interval.RloResponse-time endpoints.SolverUQ.Interval.RtotTotal response-time interval (1 x 2); null off the exact path.final MatrixSolverUQ.Interval.RupResponse-time endpoints.final MatrixSolverUQ.Interval.TloThroughput endpoints.final MatrixSolverUQ.Interval.TupThroughput endpoints.final MatrixSolverUQ.Interval.UloUtilization endpoints.final MatrixSolverUQ.Interval.UupUtilization endpoints.final MatrixSolverUQ.Interval.WloResidence-time endpoints.final MatrixSolverUQ.Interval.WupResidence-time endpoints.SolverUQ.Interval.XSystem throughput interval (1 x 2); null off the exact path. -
Uses of Matrix in jline.solvers.wrappers.jmt
Fields in jline.solvers.wrappers.jmt declared as MatrixModifier and TypeFieldDescriptionJMTResult.cacheANCache node arrival rates [nnodes x nclasses].JMTResult.cacheTNCache node throughputs [nnodes x nclasses].JMTResult.TransientProbabilityResult.Pi_tJMTResult.TransientProbabilityResult.SSnode_aMethods in jline.solvers.wrappers.jmt that return MatrixModifier and TypeMethodDescriptionMatrix[][]SolverJMT.getTranQLen()Returns transient queue length results from the last getTranAvg() call.Matrix[][]SolverJMT.getTranTput()Returns transient throughput results from the last getTranAvg() call.Matrix[][]SolverJMT.getTranUtil()Returns transient utilization results from the last getTranAvg() call.Methods in jline.solvers.wrappers.jmt with parameters of type MatrixConstructors in jline.solvers.wrappers.jmt with parameters of type Matrix -
Uses of Matrix in jline.solvers.wrappers.jmt.handlers
Fields in jline.solvers.wrappers.jmt.handlers declared as MatrixModifier and TypeFieldDescriptionfinal MatrixSaveHandlers.ServerPools.compatPool-class compatibility, (nTypes x K), 1.0 where the pool serves the class.final MatrixSaveHandlers.ServerPools.countsServers held by each pool.final MatrixSaveHandlers.ServerPools.parallelismServers seized at once by a job, (1 x K). -
Uses of Matrix in jline.solvers.wrappers.jmt.io
Methods in jline.solvers.wrappers.jmt.io that return MatrixModifier and TypeMethodDescriptionMatrix[][][]JMTIO.parseLogs(boolean[] isNodeLogged, MetricType metric) Parses JMT log files for the specified metric type.Matrix[][]JMTIO.parseTranRespT(List<String[]> arvData, List<String[]> depData) Parses transient response time data from arrival and departure logs.Methods in jline.solvers.wrappers.jmt.io with parameters of type Matrix -
Uses of Matrix in jline.solvers.wrappers.qns
Methods in jline.solvers.wrappers.qns with parameters of type MatrixConstructors in jline.solvers.wrappers.qns with parameters of type Matrix -
Uses of Matrix in jline.streaming
Methods in jline.streaming with parameters of type MatrixModifier and TypeMethodDescriptionvoidCollector.recordState(double simulationTime, double dt, Matrix nirState, Matrix depRates, Matrix arvRates) Record state observation from simulation loop. -
Uses of Matrix in jline.util
Fields in jline.util declared as MatrixModifier and TypeFieldDescriptionMaths.laplaceApproxReturn.HPopulationLattice.sprodResult.nPopulationLattice.sprodResult.sPopulationLattice.sprodResult.Sfinal MatrixUniqueRowResult.sortedMatrixfinal MatrixUniqueRowResult.viMethods in jline.util that return MatrixModifier and TypeMethodDescriptionApplies the class dependence function to the given state.static MatrixImplementation of MATLAB "hist": puts elements of v into k binsstatic MatrixMaths.circul(int c) Returns a circulant matrix of order c.static MatrixReturns a circulant matrix from the given first column.static MatrixAdapted from jblas and IHMC Original documentation:static MatrixMaths.multichoose(double n, double k) static MatrixMaths.multichoose(int n, int k) Generates all combinations of n objects taken k at a time with repetition (multichoose).static MatrixMaths.multichoosecon(Matrix n, int S) Pick vectors of S units from the units available in vector n.static MatrixMaths.multiChooseCon(Matrix n, double S) static MatrixMaths.multisetPerms(Matrix vec) All distinct permutations of the multiset held in a row vector, one per row.static MatrixComputes the combinations of the elements in v taken k at a timestatic MatrixMaths.num_grad_h(Matrix x0, double h, SerializableFunction<Matrix, Matrix> hfun) static MatrixMaths.num_hess_h(Matrix x0, double h, SerializableFunction<Matrix, Matrix> hfun) static MatrixMaths.permutations(Matrix vec) static MatrixInitializes a population product iterator.static MatrixAdvances to the next population state in lexicographic order.static Matrixstatic MatrixSet difference operation for Matrix objects that are vectors.static MatrixGMMUtils.sgmmConvolve(Matrix sgmm1, Matrix sgmm2) Convolves two simplified GMMs (sum of independent random variables).static MatrixGMMUtils.sgmmMixture(Matrix sgmm1, Matrix sgmm2, double p1, double p2) Creates a mixture of two simplified GMMs.static MatrixMaths.uniqueAndSort(Matrix space) Methods in jline.util that return types with arguments of type MatrixModifier and TypeMethodDescriptionMaths.multiChooseCombinations(int k, int n) Generate all combinations of distributing n items into k binsMethods in jline.util with parameters of type MatrixModifier and TypeMethodDescriptionvoidAdds a pre-computed value for a specific state.voidAdds a pre-computed per-class value for a specific state, for a handle that returns one entry per class (as the flow-equivalent-server aggregation does, beta_r(n) = X_r(n)|n|/n_r).Applies the class dependence function to the given state.Applies the rate function to the given ordered class sequence.static MatrixImplementation of MATLAB "hist": puts elements of v into k binsstatic MatrixReturns a circulant matrix from the given first column.static intComputes a hash index for a population vector within a lattice.static intComputes a hash index for a population vector using precomputed products.static Maths.laplaceApproxReturnMaths.laplaceapprox_h(Matrix x0, SerializableFunction<Matrix, Matrix> h) Maths.laplaceapprox_h_complex(Matrix x0, SerializableFunction<Matrix, ComplexMatrix> h) static doubleMaths.logmeanexp(Matrix x) protected doubleLooks up the tabulated value for a state.protected double[]PrecomputedTableFunction.lookupVector(Matrix state) Looks up the tabulated per-class value for a state.static doubleReturn mean absolute percentage error of approx with respect to exactstatic Utils.MapeResultUtils.mapeWithNanMean(Matrix approx, Matrix exact) Polymorphic version that returns both MAPE and nanMean.static MatrixAdapted from jblas and IHMC Original documentation:static intReturns the position of the maximum value in a vector.static int[]Returns the positions of the n largest values in a vector.static intReturns the position of the minimum value in a vector.static int[]Returns the positions of the n smallest values in a vector.static MatrixMaths.multichoosecon(Matrix n, int S) Pick vectors of S units from the units available in vector n.static MatrixMaths.multiChooseCon(Matrix n, double S) static doubleMaths.multinomialln(Matrix n) static MatrixMaths.multisetPerms(Matrix vec) All distinct permutations of the multiset held in a row vector, one per row.static MatrixComputes the combinations of the elements in v taken k at a timestatic MatrixMaths.num_grad_h(Matrix x0, double h, SerializableFunction<Matrix, Matrix> hfun) static ComplexMatrixMaths.num_grad_h_complex(Matrix x0, double h, SerializableFunction<Matrix, ComplexMatrix> hfun) static MatrixMaths.num_hess_h(Matrix x0, double h, SerializableFunction<Matrix, Matrix> hfun) static ComplexMatrixMaths.num_hess_h_complex(Matrix x0, double h, SerializableFunction<Matrix, ComplexMatrix> hfun) static MatrixMaths.permutations(Matrix vec) static MatrixInitializes a population product iterator.static MatrixAdvances to the next population state in lexicographic order.static Matrixstatic intMaths.probchoose(Matrix p) Choose an element index according to probability vector.static org.apache.commons.math3.complex.Complex[]static voidMatFileUtils.saveCTMCWorkspace(Matrix stateSpace, Matrix infGen, Matrix pi, String filename) Saves CTMC solver workspace to a .mat filestatic voidMatFileUtils.saveMatrix(Matrix matrix, String variableName, String filename) Saves a single matrix to a .mat filestatic MatrixSet difference operation for Matrix objects that are vectors.static MatrixGMMUtils.sgmmConvolve(Matrix sgmm1, Matrix sgmm2) Convolves two simplified GMMs (sum of independent random variables).static doubleCalculates the first moment (E[X]) of a simplified GMM.static doubleCalculates the second moment (E[X²]) of a simplified GMM.static doubleCalculates the mean of a simplified GMM directly from matrix representation.static MatrixGMMUtils.sgmmMixture(Matrix sgmm1, Matrix sgmm2, double p1, double p2) Creates a mixture of two simplified GMMs.static double[]Generates random samples from a simplified GMM representation.static doubleCalculates the standard deviation of a simplified GMM.static doubleCalculates the variance of a simplified GMM.static doubleMaths.simplex_fun(double[] x, Matrix L, Matrix N) Computes a specialized function used in simplex-based optimization algorithms.static doubleMaths.simplex_logfun(double[] x, Matrix L, Matrix N) Log-domain form ofMaths.simplex_fun(double[], jline.util.matrix.Matrix, jline.util.matrix.Matrix): returns the exponent, i.e.PopulationLattice.sprod(Matrix s, Matrix S, MatrixCell D) static MatrixMaths.uniqueAndSort(Matrix space) Method parameters in jline.util with type arguments of type MatrixModifier and TypeMethodDescriptionstatic Maths.laplaceApproxReturnMaths.laplaceapprox_h(Matrix x0, SerializableFunction<Matrix, Matrix> h) static Maths.laplaceApproxReturnMaths.laplaceapprox_h(Matrix x0, SerializableFunction<Matrix, Matrix> h) Maths.laplaceapprox_h_complex(Matrix x0, SerializableFunction<Matrix, ComplexMatrix> h) static MatrixMaths.num_grad_h(Matrix x0, double h, SerializableFunction<Matrix, Matrix> hfun) static MatrixMaths.num_grad_h(Matrix x0, double h, SerializableFunction<Matrix, Matrix> hfun) static ComplexMatrixMaths.num_grad_h_complex(Matrix x0, double h, SerializableFunction<Matrix, ComplexMatrix> hfun) static MatrixMaths.num_hess_h(Matrix x0, double h, SerializableFunction<Matrix, Matrix> hfun) static MatrixMaths.num_hess_h(Matrix x0, double h, SerializableFunction<Matrix, Matrix> hfun) static ComplexMatrixMaths.num_hess_h_complex(Matrix x0, double h, SerializableFunction<Matrix, ComplexMatrix> hfun) static voidMatFileUtils.saveWorkspace(Map<String, Matrix> matrices, String filename) Saves multiple matrices to a .mat file as a workspaceConstructors in jline.util with parameters of type MatrixModifierConstructorDescriptionlaplaceApproxReturn(Matrix H, double I, double logI) sprodResult(Matrix s, Matrix n, Matrix S, MatrixCell D) -
Uses of Matrix in jline.util.graph
Methods in jline.util.graph that return MatrixModifier and TypeMethodDescriptionstatic MatrixPerforms topological sorting using Kahn's algorithm.DirectedGraph.toMatrix()UndirectedGraph.toMatrix()Methods in jline.util.graph with parameters of type MatrixModifier and TypeMethodDescriptionstatic booleanTests if the given adjacency matrix represents a Directed Acyclic Graph (DAG).static MatrixPerforms topological sorting using Kahn's algorithm.Constructors in jline.util.graph with parameters of type MatrixModifierConstructorDescriptionDirectedGraph(Matrix param) Constructs a directed graph with the given adjacency matrix (no column filtering).DirectedGraph(Matrix param, Set<Integer> colsToIgnore) Constructs a directed graph with the given adjacency matrix and column filter.UndirectedGraph(Matrix param) UndirectedGraph(Matrix param, Set<Integer> colsToIgnore) Constructs an undirected graph with the given adjacency matrix and column filter.UndirectedGraph(Matrix param, Set<Integer> colsToIgnore, boolean normalize) Constructs an undirected graph with optional weight normalization. -
Uses of Matrix in jline.util.matrix
Fields in jline.util.matrix declared as MatrixModifier and TypeFieldDescriptionComplexMatrix.imThe imaginary component of the complex matrixComplexMatrix.realThe real component of the complex matrixMethods in jline.util.matrix that return MatrixModifier and TypeMethodDescriptionMatrix.add(double alpha) Adds a scalar multiple of the all-ones matrix to this matrix:this + alpha * 1.Addsalpha * matrixto this matrix.Adds another matrix to this matrix:this + matrix.static MatrixReturns all elements in a matrix except the first onesMatrix.autoFormat()Re-evaluates the storage format of this matrix by fill ratio whensetAutoFormatSwitch(boolean)is enabled; no-op otherwise.static MatrixMatrix.broadcastColPlusRow(Matrix colVector, Matrix rowVector) Computes the element-wise sum of a column vector and a row vector, producing a matrix where each entry (i, j) is the sum of colVector[i] and rowVector[j].static MatrixCartesian product of two matrices.Matrix.ceil()Returns a new matrix where each element is the ceiling of the corresponding element in this matrix.Matrix.ceilEq()Applies the ceiling operation in-place to each element of this matrix.static MatrixConcatenates a collection of matrices stored in a map into a single row vector.static MatrixComputes the element-wise sum of all matrices stored in a map.MatrixCell.cellsum()Computes the element-wise sum of all matrices in this cell.Matrix.colon()Returns the matrix in column-major order.Matrix.columnMajorOrder()Equivalent to the colon operator in MATLAB (:).Matrix.compatibleSizesAdd(Matrix b) Performs element-wise addition with shape broadcasting where applicable.Matrix.concatCols(Matrix other) Concatenates this matrix with another matrix horizontally.static MatrixMatrix.concatColumns(Matrix left, Matrix right, Matrix out) Concatenates two matrices horizontally (column-wise).static MatrixMatrix.concatRows(Matrix top, Matrix bottom, Matrix out) Concatenates two matrices vertically (row-wise).Matrix.copy()Returns a deep copy of this matrix.Matrix.countEachRow(double val) Counts the number of occurrences of a value in each row of the matrix.Matrix.createBlockDiagonal(Matrix matrix2) Creates a block diagonal matrix by placing the current matrix in the top-left and another matrix (if provided) in the bottom-right.static MatrixMatrix.createLike(Matrix B) Matrix.cumsumViaCol()Computes the cumulative sum of the matrix down each column.Matrix.cumsumViaRow()Computes the cumulative sum of the matrix across each row.static Matrixstatic MatrixMatrix.dense(int numRows, int numCols) Creates a zeroed matrix backed by DENSE storage.static MatrixMatrix.diag(double... values) Creates a square diagonal matrix from the given values.static MatrixMatrix.diagMatrix(double[] values) Creates a square diagonal matrix from a given array of values.static MatrixMatrix.diagMatrix(Matrix values) Creates a square diagonal matrix from the elements of a column or row vector matrix.static MatrixMatrix.diagMatrix(Matrix A, double[] values, int offset, int length) Creates or fills a square diagonal matrix with the specified values.Performs element-wise division between this matrix and the provided matrix.static MatrixSolves the discrete Sylvester equation A * X * B - X + C = 0.Matrix.elementDiv(Matrix B) Performs element-wise division of this matrix by another matrix.Matrix.elementDivide(Matrix b) Performs element-wise division with another matrix.Matrix.elementIncrease(double val) Increases each element of the matrix by a constant value.Matrix.elementMult(Matrix B) Performs element-wise multiplication with another matrix, as MATLAB's.*.Matrix.elementMult(Matrix B, Matrix output) Performs element-wise multiplication with another matrix, storing the result in the given output matrix.Matrix.elementMultEq(Matrix B) Performs in-place element-wise multiplication with another matrix, overwriting this matrix with the product.Matrix.elementMultWithVector(Matrix B) Performs element-wise multiplication between this matrix and a row vector.Matrix.elementPow(double a) Raises each non-zero element of the matrix to the specified power.Matrix.elementPower(double t) Raises each element of the matrix to the given power.Matrix.exp()Applies the exponential function to each element of the matrix.Matrix.expm()Computes the matrix exponential.Matrix.expm_higham()Computes the matrix exponential using Higham's scaling and squaring method.static MatrixExtracts a rectangular submatrix from the given source matrix.Matrix.extractCols(int col0, int col1) Extracts a range of columns from this matrix (instance method).static MatrixMatrix.extractColumn(Matrix A, int column, Matrix out) Extracts a single column from the given matrix.static MatrixMatrix.extractColumns(Matrix A, int col0, int col1) Extracts a range of columns from the matrix [col0:col1).static MatrixMatrix.extractColumns(Matrix A, int col0, int col1, Matrix out) Extracts a range of columns from the matrix [col0:col1) into a destination matrix.Matrix.extractRows(int row0, int row1) Extracts rows from this matrix (instance method).static MatrixMatrix.extractRows(Matrix A, int row0, int row1) Extracts a range of rows from the matrix [row0:row1).static MatrixMatrix.extractRows(Matrix A, int row0, int row1, Matrix out) Extracts a range of rows from the matrix [row0:row1) into a destination matrix.static MatrixMatrix.eye(int length) Creates an identity matrix of given size.Matrix.fact()Computes the factorial of each element in the matrix.Matrix.factln()Computes the natural logarithm of the factorial for each element.static MatrixComputes the natural logarithm of the factorial (log(x!)) for each element in the input matrix.Matrix.fill(double val) Fills all entries in the matrix with the given value.Matrix.find()Returns the linear indices of all non-zero elements in the matrix.Matrix.findNonNegative()Returns the linear indices of all elements that are non-negative (≥ 0).Matrix.findNumber(double number) Finds all linear indices where the matrix has a specific value.Matrix.findZero()Finds all linear indices where the matrix value is zero.Matrix.fromArray2D(double[][] matrix) Populates the matrix from a 2D double array.Matrix.fromArray2D(int[][] matrix) Populates the matrix from a 2D integer array.static MatrixConstructs a Matrix from a list of rows, where each inner list is a row.MatrixCell.get(int i) Retrieves the matrix stored at the specified index.Matrix.getColumn(int j) Returns a column of the matrix as a new single-column matrix.Matrix.getRow(int i) Returns the specified row as a single-row matrix.Matrix.getRowsFrom(int row) Returns a new matrix consisting of rows from the given start index to the end.Matrix.getSlice(boolean[] rowFlags, boolean[] colFlags) Extracts a submatrix from rows/columns marked as true in input flags.Matrix.getSlice(int r0, int r1, int c0, int c1) Extracts a submatrix based on row/column bounds.static MatrixMatrix.getSubMatrix(Matrix sourceMatrix, int x0, int x1, int y0, int y1) Extracts a submatrix from the source matrix using zero-based index ranges.Matrix.getSubMatrix(Matrix rows, Matrix cols) Returns a matrix consisting of rows and columns selected by two index matrices.Performs the Hadamard (element-wise) product of two matrices.Matrix.inv()Computes the inverse of the matrix.static MatrixComputes the inverse of the given matrix.Computes the Kronecker product of this matrix and another matrix.Computes the Kronecker sum of two matrices: A ⊕ B = A \otimes I + I \otimes B, where \otimes is the Kronecker product and I is the identity matrix of matching size.Matrix.leftMatrixDivide(Matrix b) Solves the equation AX = B for X, where A is this matrix and B is the right-hand side.Matrix.log()Applies the natural logarithm element-wise.MatrixEquation.lookupSimple(String token) Retrieves a matrix result from the equation context by variable name.static MatrixSolves the Sylvester equation A·X + X·B + C = 0, matching the three-argument MATLAB lyap(A,B,C) convention; the Lyapunov equation A·X + X·Aᵀ + Q = 0 corresponds to B = Aᵀ, C = Q.static MatrixMatrix.matrixAddVector(Matrix matrix, Matrix vector) Adds a vector to each row or column of the matrix, depending on the vector's orientation.Matrix.meanCol()Computes the mean of each column.Matrix.meanRow()Computes the mean of each row.Performs matrix multiplication: this * BPerforms matrix multiplication: this * BMatrix.neg()Returns the negation of this matrix (all elements multiplied by -1).static MatrixNegates all elements in the matrix and returns the result.static MatrixMatrix.oneMinusMatrix(Matrix matrix) Computes the matrix 1 - A, where diagonal entries become 1 - A(i, i) and off-diagonal entries become -A(i, j).static MatrixDecreases a single element of an integer vector by one.static MatrixDecreases multiple elements of an integer vector by one.static MatrixMatrix.ones(int rows, int cols) Creates a matrix of the given shape filled with ones.Matrix.pinv()Computes the Moore-Penrose pseudo-inverse of the matrix using SVD.static MatrixComputes the matrix power A^b for a non-negative integer exponent b.static MatrixMatrix.readFromFile(String fileName) Reads a CSV-formatted matrix from a file.Matrix.reciprocal()Computes the element-wise reciprocal (1/x) of the matrix.Matrix.repmat(int rows, int cols) Repeats the matrix to match the specified row and column replication.Matrix.reverse()Reverses the elements of a vector (row or column) matrix.Matrix.reverseRows()Reverses the order of rows in the matrix.Matrix.rightMatrixDivide(Matrix b) Performs right matrix division A / B = A * inv(B)static MatrixMatrix.robustLeftDivide(Matrix A, Matrix B) Robust linear solve for A·X = B that handles singular matrices.Matrix.scale(double scalar) Scales the matrix by the given scalar value.static MatrixMultiplies all elements of a matrix by a scalar.Stores a matrix at the specified index.Sets the specified column to the values in the given vector.Matrix.setColumns(int j0, int j1, Matrix cols) Sets multiple columns starting from column index j0 to j1 (exclusive) using values from another matrix.Sets the specified row to the values in the given vector.Sets multiple rows starting from row index i0 to i1 (exclusive) using values from another matrix.Returns a new matrix representing the updated slice after assigning values from newSlice.static MatrixMatrix.singleton(double value) Creates a 1×1 matrix containing a single scalar value.Matrix.sort()Returns a new matrix with the non-zero values sorted in ascending order.Matrix.sortEq()Sorts the matrix values in place following MATLAB semantics: vectors are sorted as a whole (zeros included), matrices are sorted column by column.Matrix.sqrt()Computes the element-wise square root of the matrix.Matrix.square()Computes the matrix multiplied by itself.Matrix.sub(double x) Subtract a scalar from all elements.Subtracts alpha-scaled version of the provided matrix.Subtracts another matrix.Matrix.sumCols()Sums the values in each column and returns the results as a row vector.Matrix.sumCols(int startRow, int endRow) Computes the sum of a subset of rows for each column.Matrix.sumRows()Computes the sum of each row and returns the result as a column vector.Matrix.sumRows(int startCol, int endCol) Computes the sum over a subrange of columns for each row.static MatrixSolves the Sylvester equation A·X + X·B = -C via Bartels-Stewart on the complex Schur form, mirroring MATLAB lyap(A,B,C).Matrix.toDense()Switches this matrix in place to dense (row-major) storage.Matrix.toSparse()Switches this matrix in place to sparse (CSC) storage, dropping explicit zeros.Matrix.transpose()Computes the transpose of the current matrix.static MatrixReturns the lower triangular part of a matrix (zeroing elements above the main diagonal).static MatrixReturns the elements on and below the kth diagonal of matrix A.static MatrixComputes the union of two matrices A and B.Matrix.uniqueInCol(int colIdx) Finds unique integer values in the specified column of the matrix.Matrix.uniqueInRow(int rowIdx) Finds unique integer values in the specified row of the matrix.Matrix.uniqueNonNegativeInCol(int colIdx) Finds unique positive values (strictly greater than 0) in the specified column.Matrix.uniqueNonNegativeInRow(int rowIdx) Finds unique positive values (strictly greater than 0) in the specified row.Matrix.uniqueNonZerosInCol(int colIdx) Finds unique non-zero values in the specified column.Matrix.uniqueNonZerosInRow(int rowIdx) Finds unique non-zero values in the specified row.static MatrixMatrix.zeros(int rows, int cols) Creates a matrix of the specified shape filled with zeros.Methods in jline.util.matrix that return types with arguments of type MatrixModifier and TypeMethodDescriptionMatrix.hess()Computes the Hessenberg decomposition of the matrix, equivalent to MATLAB's[V,H] = hess(A): V is orthogonal, H is upper Hessenberg, andA = V*H*V'(equivalentlyV'*A*V = H).Computes the generalized Hessenberg-triangular decomposition of the pencil (this, B), equivalent to MATLAB's[LBAR,NBAR,W,V] = hess(A,B): W and V are orthogonal,W*A*V = LBARis upper Hessenberg andW*B*V = NBARis upper triangular.Matrix.qr()Performs QR decomposition on the matrix.Matrix.schur()Computes the Schur decomposition with the default method and iteration count.Computes the Schur decomposition of the matrix.MatrixCell.toMap()Creates a copy of the internal mapping from indices to matrices.Methods in jline.util.matrix with parameters of type MatrixModifier and TypeMethodDescriptionAddsalpha * matrixto this matrix.Adds another matrix to this matrix:this + matrix.voidAddsalpha * matrixto this matrix in-place:this += alpha * matrix.voidAdds another matrix to this matrix in-place:this += matrix.static MatrixReturns all elements in a matrix except the first onesstatic MatrixMatrix.broadcastColPlusRow(Matrix colVector, Matrix rowVector) Computes the element-wise sum of a column vector and a row vector, producing a matrix where each entry (i, j) is the sum of colVector[i] and rowVector[j].static MatrixCartesian product of two matrices.static double[]Matrix.columnMatrixToDoubleArray(Matrix columnMatrix) Converts a column matrix (of size m x 1) to a dense double array of length m.static booleanCompares two matrices element-wise using a specified comparison operator.booleanMatrix.compareMatrix(Matrix matrix) Compares this matrix to another matrix for approximate equality.Matrix.compatibleSizesAdd(Matrix b) Performs element-wise addition with shape broadcasting where applicable.Matrix.concatCols(Matrix other) Concatenates this matrix with another matrix horizontally.static MatrixMatrix.concatColumns(Matrix left, Matrix right, Matrix out) Concatenates two matrices horizontally (column-wise).static MatrixMatrix.concatRows(Matrix top, Matrix bottom, Matrix out) Concatenates two matrices vertically (row-wise).Matrix.createBlockDiagonal(Matrix matrix2) Creates a block diagonal matrix by placing the current matrix in the top-left and another matrix (if provided) in the bottom-right.static MatrixMatrix.createLike(Matrix B) static Matrixstatic MatrixMatrix.diagMatrix(Matrix values) Creates a square diagonal matrix from the elements of a column or row vector matrix.static MatrixMatrix.diagMatrix(Matrix A, double[] values, int offset, int length) Creates or fills a square diagonal matrix with the specified values.Performs element-wise division between this matrix and the provided matrix.voidPerforms in-place element-wise division between this matrix and the provided matrix.voidDivides this matrix by a scalar and stores the result in the output matrix.static MatrixSolves the discrete Sylvester equation A * X * B - X + C = 0.doubleRowView.dotProduct(Matrix columnVector) Computes the dot product of this row with a column vector.Matrix.elementDiv(Matrix B) Performs element-wise division of this matrix by another matrix.Matrix.elementDivide(Matrix b) Performs element-wise division with another matrix.static doubleMatrix.elementMinNonZero(Matrix matrix) Returns the smallest non-zero positive element in the given matrix.Matrix.elementMult(Matrix B) Performs element-wise multiplication with another matrix, as MATLAB's.*.Matrix.elementMult(Matrix B, Matrix output) Performs element-wise multiplication with another matrix, storing the result in the given output matrix.Matrix.elementMultEq(Matrix B) Performs in-place element-wise multiplication with another matrix, overwriting this matrix with the product.Matrix.elementMultWithVector(Matrix B) Performs element-wise multiplication between this matrix and a row vector.static MatrixExtracts a rectangular submatrix from the given source matrix.static voidMatrix.extract(Matrix src, int srcX0, int srcX1, int srcY0, int srcY1, Matrix dst, int dstY0, int dstX0) Extracts a rectangular submatrix from a source matrix and stores it in a destination matrix.static MatrixMatrix.extractColumn(Matrix A, int column, Matrix out) Extracts a single column from the given matrix.static MatrixMatrix.extractColumns(Matrix A, int col0, int col1) Extracts a range of columns from the matrix [col0:col1).static MatrixMatrix.extractColumns(Matrix A, int col0, int col1, Matrix out) Extracts a range of columns from the matrix [col0:col1) into a destination matrix.static voidMatrix.extractDiag(Matrix A, Matrix outputB) Extracts the diagonal elements of a matrix and stores them in a destination matrix.static MatrixMatrix.extractRows(Matrix A, int row0, int row1) Extracts a range of rows from the matrix [row0:row1).static MatrixMatrix.extractRows(Matrix A, int row0, int row1, Matrix out) Extracts a range of rows from the matrix [row0:row1) into a destination matrix.static MatrixComputes the natural logarithm of the factorial (log(x!)) for each element in the input matrix.Matrix.findIndexWithZeroSum(Matrix matrix, boolean isRow) Finds the indices of all rows or columns in a matrix that have a sum of zero.Finds the indices of all rows in a matrix that exactly match a given row vector.static doubleComputes the 1-norm (maximum absolute column sum) of the matrix.static MatrixMatrix.getSubMatrix(Matrix sourceMatrix, int x0, int x1, int y0, int y1) Extracts a submatrix from the source matrix using zero-based index ranges.Matrix.getSubMatrix(Matrix rows, Matrix cols) Returns a matrix consisting of rows and columns selected by two index matrices.Performs the Hadamard (element-wise) product of two matrices.Computes the generalized Hessenberg-triangular decomposition of the pencil (this, B), equivalent to MATLAB's[LBAR,NBAR,W,V] = hess(A,B): W and V are orthogonal,W*A*V = LBARis upper Hessenberg andW*B*V = NBARis upper triangular.static doubleComputes the infinity norm (maximum absolute row sum) of the matrix.voidMatrix.insertSubMatrix(int start_row, int start_col, int end_row, int end_col, Matrix matrix_to_be_inserted) Inserts a sub-matrix into the current matrix at the specified location.Computes the intersection of scalar values present in two matrices.static MatrixComputes the inverse of the given matrix.booleanChecks if two matrices are exactly equal.booleanMatrix.isEqualToTol(Matrix m, double tol) Checks if two matrices are equal within a specified tolerance.Computes the Kronecker product of this matrix and another matrix.Computes the Kronecker sum of two matrices: A ⊕ B = A \otimes I + I \otimes B, where \otimes is the Kronecker product and I is the identity matrix of matching size.Matrix.leftMatrixDivide(Matrix b) Solves the equation AX = B for X, where A is this matrix and B is the right-hand side.static doubleComputes the sum of the natural logarithms of all elements in the matrix.static doubleComputes log(sum_i exp(x_i)) in a numerically stable way (log-sum-exp trick).static MatrixSolves the Sylvester equation A·X + X·B + C = 0, matching the three-argument MATLAB lyap(A,B,C) convention; the Lyapunov equation A·X + X·Aᵀ + Q = 0 corresponds to B = Aᵀ, C = Q.static intReturns the index of the row in the matrix that exactly matches the given row vector.static MatrixMatrix.matrixAddVector(Matrix matrix, Matrix vector) Adds a vector to each row or column of the matrix, depending on the vector's orientation.static doubleMatrix.maxAbsDiff(Matrix a, Matrix b) Computes the maximum relative absolute difference between corresponding elements of two matrices: max(abs((a - b) / b)).Performs matrix multiplication: this * BPerforms matrix multiplication: this * BvoidReplaces this matrix with the result of this * B.static MatrixNegates all elements in the matrix and returns the result.static MatrixMatrix.oneMinusMatrix(Matrix matrix) Computes the matrix 1 - A, where diagonal entries become 1 - A(i, i) and off-diagonal entries become -A(i, j).static MatrixDecreases a single element of an integer vector by one.static MatrixDecreases multiple elements of an integer vector by one.static MatrixComputes the matrix power A^b for a non-negative integer exponent b.static double[][][]Matrix.removeRows(double[][][] array, Matrix rowsToRemove) Matrix.rightMatrixDivide(Matrix b) Performs right matrix division A / B = A * inv(B)static MatrixMatrix.robustLeftDivide(Matrix A, Matrix B) Robust linear solve for A·X = B that handles singular matrices.voidScales this matrix by the given scalar value and stores the result in the provided output matrix.static MatrixMultiplies all elements of a matrix by a scalar.Stores a matrix at the specified index.Sets the specified column to the values in the given vector.Matrix.setColumns(int j0, int j1, Matrix cols) Sets multiple columns starting from column index j0 to j1 (exclusive) using values from another matrix.Sets the specified row to the values in the given vector.Sets multiple rows starting from row index i0 to i1 (exclusive) using values from another matrix.Returns a new matrix representing the updated slice after assigning values from newSlice.voidMatrix.setSliceEq(int rowStart, int rowEnd, int colStart, int colEnd, Matrix newSlice) Sets the values of a submatrix (in-place) using the specified newSlice matrix.voidCopies the data from another matrix into this matrix.static booleanSolves the sparse linear system Ax = b.static booleanMatrix.solveDirect(Matrix a, Matrix b, Matrix x) Solves the linear system A*x = b directly using LU decomposition without singularity checks.static booleanSolves the linear system Ax = b for x, handling singular matrices gracefully.static Ret.SpectralDecompositionComputes the spectral decomposition of a matrix A using its eigendecomposition.Subtracts alpha-scaled version of the provided matrix.Subtracts another matrix.voidSubtracts a scaled matrix from the current matrix in place.voidSubtracts a matrix from the current matrix in place.static doubleMatrix.sumCumprod(Matrix matrix) Computes the sum of the cumulative product along a row vector.static MatrixSolves the Sylvester equation A·X + X·B = -C via Bartels-Stewart on the complex Schur form, mirroring MATLAB lyap(A,B,C).org.ejml.data.DMatrixSparseCSCMatrix.toDMatrixSparseCSC(Matrix matrix) Converts a specified matrix to a copy of its underlyingDMatrixSparseCSCstructure.static MatrixReturns the lower triangular part of a matrix (zeroing elements above the main diagonal).static MatrixReturns the elements on and below the kth diagonal of matrix A.static MatrixComputes the union of two matrices A and B.static UniqueRowResultMatrix.uniqueRowIndexes(Matrix m) Finds the indices of unique rows in a matrix without returning a matrix of the unique rows themselves.static UniqueRowResultMatrix.uniqueRowIndexesFromColumn(Matrix m, int startCol) Identifies unique rows in a matrix starting from a specified column index.static UniqueRowResultMatrix.uniqueRows(Matrix m) Finds all unique rows in a matrix and returns the unique sorted rows, along with mapping indices to/from the original matrix.Matrix.weaklyConnect(Matrix param, Set<Integer> colsToIgnore) Weakly-connected components of a sub-matrix.Method parameters in jline.util.matrix with type arguments of type MatrixModifier and TypeMethodDescriptionstatic MatrixConcatenates a collection of matrices stored in a map into a single row vector.static MatrixComputes the element-wise sum of all matrices stored in a map.static intMatrix.getColIndexSum(Map<Integer, Matrix> cellArray) Computes the total number of elements across all matrices in a map, summingnumRows * numColsfor each matrix.Constructors in jline.util.matrix with parameters of type MatrixModifierConstructorDescriptionComplexMatrix(Matrix real) Creates a complex matrix from a real matrix with zero imaginary component.ComplexMatrix(Matrix real, Matrix im) Creates a complex matrix from separate real and imaginary component matrices.Creates a copy of the specified matrix.MatrixCell(Matrix[] x) Creates a MatrixCell from an array of matrices.MatrixCell(Matrix D0, Matrix D1) Creates a MatrixCell containing exactly two matrices.