Name | Description | Type | Package | Framework |
AbelianGroup | An Abelian group is a group with a binary additive operation (+), satisfying the group axioms: closureassociativityexistence of additive identityexistence of additive oppositecommutativity of addition | Interface | com.numericalmethod.suanshu.algebra.structure | SuanShu |
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ABMPredictorCorrector | The Adams-Bashforth predictor and the Adams-Moulton corrector pair. | Interface | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton | SuanShu |
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ABMPredictorCorrector1 | The Adams-Bashforth predictor and the Adams-Moulton corrector of order 1. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton | SuanShu |
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ABMPredictorCorrector2 | The Adams-Bashforth predictor and the Adams-Moulton corrector of order 2. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton | SuanShu |
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ABMPredictorCorrector3 | The Adams-Bashforth predictor and the Adams-Moulton corrector of order 3. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton | SuanShu |
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ABMPredictorCorrector4 | The Adams-Bashforth predictor and the Adams-Moulton corrector of order 4. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton | SuanShu |
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ABMPredictorCorrector5 | The Adams-Bashforth predictor and the Adams-Moulton corrector of order 5. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton | SuanShu |
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AbsoluteErrorPenalty | This penalty function sums up the absolute error penalties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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AbsoluteTolerance | The stopping criteria is that the norm of the residual r is equal to or smaller than the specified tolerance, that is, | Class | com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance | SuanShu |
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AbstractBivariateEVD | | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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AbstractBivariateProbabilityDistribution | | Class | com.numericalmethod.suanshu.stats.distribution.multivariate | SuanShu |
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AbstractBivariateRealFunction | A bivariate real function takes two real arguments and outputs one real value. | Class | com.numericalmethod.suanshu.analysis.function.rn2r1 | SuanShu |
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AbstractGridExecutor | Provides basic default implementations of GridExecutor functions on top of the map operation. | Class | com.numericalmethod.suanshu.grid.executor | SuanShu |
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AbstractHybridMCMC | Hybrid Monte Carlo, or Hamiltonian Monte Carlo, is a method that combines the traditional Metropolis algorithm, with molecular dynamics simulation. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
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AbstractMetropolis | The Metropolis algorithm is a Markov Chain Monte Carlo algorithm, which requires only a function f proportional to the PDF from which we wish to sample. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
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AbstractR1RnFunction | This is a function that takes one real argument and outputs one vector value. | Class | com.numericalmethod.suanshu.analysis.function.rn2rm | SuanShu |
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AbstractRealScalarFunction | This abstract implementation implements Function. | Class | com.numericalmethod.suanshu.analysis.function.rn2r1 | SuanShu |
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AbstractRealVectorFunction | This abstract implementation implements Function. | Class | com.numericalmethod.suanshu.analysis.function.rn2rm | SuanShu |
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AbstractTrivariateRealFunction | A trivariate real function takes three real arguments and outputs one real value. | Class | com.numericalmethod.suanshu.analysis.function.rn2r1 | SuanShu |
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AbstractUnivariateRealFunction | A univariate real function takes one real argument and outputs one real value. | Class | com.numericalmethod.suanshu.analysis.function.rn2r1.univariate | SuanShu |
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ACERAnalysis | Average Conditional Exceedance Rate (ACER) method is for estimating the cdf of the maxima (M) distribution from observations. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer | SuanShu |
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ACERByCounting | Estimate epsilons by counting conditional exceedances from the observations. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical | SuanShu |
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ACERConfidenceInterval | Using the given (estimated) ACER function as the mean, find the ACER parameters at the lower and upper bounds of the estimated confidence interval of ACER values. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer | SuanShu |
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ACERFunction | The ACER (Average Conditional Exceedance Rate) function (epsilon_k(eta)) approximates the epsilon_k(eta) = Pr(X_k > eta | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer | SuanShu |
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ACERInverseFunction | The inverse of the ACER function. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer | SuanShu |
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ACERLogFunction | The ACER function in log scale (base e), i. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer | SuanShu |
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ACERReturnLevel | Given an ACER function, compute the return level (eta) for a given return period (R). | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer | SuanShu |
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ACERUtils | Utility functions used in ACER empirical analysis. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical | SuanShu |
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ActiveList | This interface defines the node popping strategy used in a branch-and-bound algorithm, e. | Interface | com.numericalmethod.suanshu.misc.algorithm.bb | SuanShu |
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ActiveSet | This class keeps track of the active and inactive indices. | Class | com.numericalmethod.suanshu.misc.algorithm | SuanShu |
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ActorProps | Static factory class that contains all of the common Props, to make the code that uses them more readable. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka | SuanShu |
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AdamsBashforthMoulton | This class uses an Adams-Bashford predictor and an Adams-Moulton corrector of the specified order. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.multistep.adamsbashforthmoulton | SuanShu |
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AdditiveModel | The additive model of a time series is an additive composite of the trend, seasonality and irregular random components. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess | SuanShu |
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ADFAsymptoticDistribution | This class computes the asymptotic distribution of the Augmented Dickey-Fuller (ADF) test There are three main versions of the test and thus three possible asymptotic distributions: | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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ADFAsymptoticDistribution1 | This is the asymptotic distribution of the Augmented Dickey-Fuller test statistic, for the TrendType. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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ADFDistribution | This represents an Augmented Dickey Fuller distribution. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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ADFDistributionTable | A table contains the simulated observations/values of an empirical ADF distribution for a given set of parameters. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf.table | SuanShu |
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ADFDistributionTable_CONSTANT_lag0 | This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf.table | SuanShu |
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ADFDistributionTable_CONSTANT_TIME_lag0 | This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf.table | SuanShu |
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ADFDistributionTable_NO_CONSTANT_lag0 | This table contains the quantile values of both finite (for various sample sizes) and infinite (asymptotic) distributions of the Augmented Dicky Fuller test statistics for the | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf.table | SuanShu |
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ADFFiniteSampleDistribution | This class computes the finite sample distribution of the Augmented Dickey-Fuller (ADF) test There are three main versions of the test and thus three possible asymptotic distributions: | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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AfterIterations | Stops after a given number of iterations. | Class | com.numericalmethod.suanshu.misc.algorithm.stopcondition | SuanShu |
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AfterNoImprovement | | Class | com.numericalmethod.suanshu.misc.algorithm.stopcondition | SuanShu |
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AhatEstimation | Estimates the coefficient of a VAR(1) model by penalized maximum likelihood. | Class | com.numericalmethod.suanshu.model.daspremont2008 | SuanShu |
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AkkaGridExecutor | Uses Akka to distribute the computational load between multiple machines. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka | SuanShu |
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AkkaGridExecutorFactory | Creates instances of GridExecutorFactory that use Akka's remoting to distribute computation, from a configuration object. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka | SuanShu |
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AkkaUtils | Utility methods for Akka. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka | SuanShu |
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AlternatingDirectionImplicitMethod | Alternating direction implicit (ADI) method is an implicit method for obtaining numerical approximations to the solution of a HeatEquation2D. | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2 | SuanShu |
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AndersonDarling | This algorithm calculates the Anderson-Darling k-sample test statistics and p-values. | Class | com.numericalmethod.suanshu.stats.test.distribution | SuanShu |
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AndersonDarlingPValue | This algorithm calculates the p-value when the Anderson-Darling statistic and the number of samples are given. | Class | com.numericalmethod.suanshu.stats.test.distribution | SuanShu |
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AndStopConditions | Combines an arbitrary number of stop conditions, terminating when all conditions are met. | Class | com.numericalmethod.suanshu.misc.algorithm.stopcondition | SuanShu |
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AnnealingFunction | An annealing function or a tempered proposal function gives the next proposal/state from the current state and temperature. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
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AntitheticVariates | The antithetic variates technique consists, for every sample path obtained, in taking its antithetic path - that is given a path (varepsilon_1,dots,varepsilon_M) to also take, for | Class | com.numericalmethod.suanshu.stats.random.variancereduction | SuanShu |
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AntoniouLu2007 | This implementation is based on Algorithm 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | SuanShu |
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AR1GARCH11Model | An AR1-GARCH11 model takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch | SuanShu |
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Arc | An arc is an ordered pair of vertices. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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ArgumentAssertion | Utility class for checking numerical arguments. | Class | com.numericalmethod.suanshu.misc | SuanShu |
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ARIMAForecast | Forecasts an ARIMA time series using the innovative algorithm. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima | SuanShu |
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ARIMAForecastMultiStep | Makes forecasts for a time series assuming an ARIMA model using the innovative algorithm. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima | SuanShu |
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ARIMAModel | An ARIMA(p, d, q) process, Xt, is such that (1 - B)^d X_t = Y_t | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima | SuanShu |
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ARIMASim | This class simulates an ARIMA (AutoRegressive Integrated Moving Average) process. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima | SuanShu |
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ARIMAXModel | The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima | SuanShu |
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ARMAFit | | Interface | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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ARMAForecast | Forecasts an ARMA time series using the innovative algorithm. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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ARMAForecastMultiStep | Computes the h-step ahead prediction of a causal ARMA model, by the innovative algorithm. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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ARMAForecastOneStep | Computes the one-step ahead prediction of a causal ARMA model, by the innovative algorithm. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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ARMAGARCHFit | This implementation fits, for a data set, an ARMA-GARCH model by Quasi-Maximum Likelihood "QMLE" stands for Quasi-Maximum Likelihood Estimation, which assumes Normal distribution and | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch | SuanShu |
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ARMAGARCHModel | An ARMA-GARCH model takes this form: X_t = mu + sum_{i=1}^p phi_i X_{t-i} + sum_{i=1}^q heta_j epsilon_{t-j} + epsilon_t, | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.armagarch | SuanShu |
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ARMAModel | A univariate ARMA model, Xt, takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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ARMAXModel | The ARMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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ARModel | This class represents an AR model. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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ArrayUtils | Get a left shifted array. | Class | com.numericalmethod.suanshu.misc | SuanShu |
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ARResamplerFactory | | Class | com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler | SuanShu |
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AS159 | Algorithm AS 159 accepts a table shape (the number of rows and columns), and two vectors, the lists of row and column sums. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
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AtThreshold | Stops when the value reaches a given value with a given precision. | Class | com.numericalmethod.suanshu.misc.algorithm.stopcondition | SuanShu |
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AugmentedDickeyFuller | The Augmented Dickey Fuller test tests whether a one-time differencing (d = 1) will make the time That is, whether the series has a unit root. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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AutoARIMAFit | Selects the order and estimates the coefficients of an ARIMA model automatically by AIC or AICC. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.arima | SuanShu |
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AutoCorrelation | Compute the Auto-Correlation Function (ACF) for an AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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AutoCorrelationFunction | This is the auto-correlation function of a univariate time series {xt}. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate | SuanShu |
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AutoCovariance | Computes the Auto-CoVariance Function (ACVF) for an AutoRegressive Moving Average (ARMA) model by The R equivalent functions are ARMAacf and TacvfAR in package FitAR. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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AutoCovarianceFunction | This is the auto-covariance function of a univariate time series {xt}. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate | SuanShu |
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AutoParallelMatrixMathOperation | This class uses ParallelMatrixMathOperation when the first input matrix argument's size is greater than the defined threshold; otherwise, it uses SimpleMatrixMathOperation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation | SuanShu |
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BackwardElimination | Constructs a GLM model for a set of observations using the backward elimination method. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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BackwardSubstitution | Backward substitution solves a matrix equation in the form Ux = b by an iterative process for an upper triangular matrix U. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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Bartlett | Bartlett's test is used to test if k samples are from populations with equal variances, hence homoscedasticity. | Class | com.numericalmethod.suanshu.stats.test.variance | SuanShu |
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Basis | A basis is a set of linearly independent vectors spanning a vector space. | Class | com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation | SuanShu |
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BaumWelch | | Class | com.numericalmethod.suanshu.stats.hmm.discrete | SuanShu |
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BBNode | A branch-and-bound algorithm maintains a tree of nodes to keep track of the search paths and the pruned paths. | Interface | com.numericalmethod.suanshu.misc.algorithm.bb | SuanShu |
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BernoulliTrial | A Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success, p, is the same every time | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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Best1Bin | The Best-1-Bin rule is the same as the Rand-1-Bin rule, except that it always pick the best candidate in the population to be the base. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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Best2Bin | The Best-1-Bin rule always picks the best chromosome as the base. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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Beta | The beta function defined as: B(x,y) = frac{Gamma(x)Gamma(y)}{Gamma(x+y)}= int_0^1t^{x-1}(1-t)^{y-1},dt, x > 0, y > 0 | Class | com.numericalmethod.suanshu.analysis.function.special.beta | SuanShu |
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BetaDistribution | | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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BetaMixtureDistribution | The HMM states use the Beta distribution to model the observations. | Class | com.numericalmethod.suanshu.stats.hmm.mixture.distribution | SuanShu |
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BetaRegularized | The Regularized Incomplete Beta function is defined as: I_x(p,q) = frac{B(x;,p,q)}{B(p,q)} = frac{1}{B(p,q)} int_0^x t^{p-1},(1-t)^{q-1},dt, p > 0, q > 0 | Class | com.numericalmethod.suanshu.analysis.function.special.beta | SuanShu |
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BetaRegularizedInverse | The inverse of the Regularized Incomplete Beta function is defined at: x = I^{-1}_{(p,q)}(u), 0 le u le 1 | Class | com.numericalmethod.suanshu.analysis.function.special.beta | SuanShu |
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BFGSMinimizer | The Broyden-Fletcher-Goldfarb-Shanno method is a quasi-Newton method to solve unconstrained nonlinear optimization problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
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BFS | This class implements the breadth-first-search using iteration. | Class | com.numericalmethod.suanshu.graph.algorithm.traversal | SuanShu |
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BiconjugateGradientSolver | The Biconjugate Gradient method (BiCG) is useful for solving non-symmetric n-by-n linear systems. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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BiconjugateGradientStabilizedSolver | The Biconjugate Gradient Stabilized (BiCGSTAB) method is useful for solving non-symmetric n-by-n linear systems. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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BicubicInterpolation | Bicubic interpolation is the two-dimensional equivalent of cubic Hermite spline interpolation. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate | SuanShu |
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BicubicSpline | Bicubic splines are the two-dimensional equivalent of cubic splines. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate | SuanShu |
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BiDiagonalization | | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization | SuanShu |
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BiDiagonalizationByGolubKahanLanczos | This implementation uses Golub-Kahan-Lanczos algorithm with reorthogonalization. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization | SuanShu |
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BiDiagonalizationByHouseholder | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization | SuanShu |
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BidiagonalMatrix | A bi-diagonal matrix is either upper or lower diagonal. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal | SuanShu |
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BidiagonalSVDbyMR3 | Given a bidiagonal matrix A, computes the singular value decomposition (SVD) of A, using "Algorithm of Multiple Relatively Robust Representations" (MRRR). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3 | SuanShu |
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BigDecimalUtils | These are the utility functions to manipulate BigDecimal. | Class | com.numericalmethod.suanshu.number.big | SuanShu |
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BigIntegerUtils | These are the utility functions to manipulate BigInteger. | Class | com.numericalmethod.suanshu.number.big | SuanShu |
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BilinearInterpolation | Bilinear interpolation is the 2-dimensional equivalent of linear interpolation. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate | SuanShu |
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BinomialDistribution | The binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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BinomialMixtureDistribution | The HMM states use the Binomial distribution to model the observations. | Class | com.numericalmethod.suanshu.stats.hmm.mixture.distribution | SuanShu |
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BinomialRNG | This random number generator samples from the binomial distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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Bins | This class divides the items based on their keys into a number of bins. | Class | com.numericalmethod.suanshu.misc.algorithm | SuanShu |
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BisectionRoot | The bisection method repeatedly bisects an interval and then selects a subinterval in which a root must lie for further processing. | Class | com.numericalmethod.suanshu.analysis.root.univariate | SuanShu |
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BivariateArrayGrid | | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate | SuanShu |
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BivariateEVD | Bivariate Extreme Value (BEV) distribution is the joint distribution of component-wise maxima of two-dimensional iid random vectors. | Interface | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateEVDAsymmetricLogistic | The bivariate asymmetric logistic model. | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateEVDAsymmetricMixed | The asymmetric mixed model. | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateEVDAsymmetricNegativeLogistic | The bivariate asymmetric negative logistic model. | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateEVDBilogistic | The bilogistic model. | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateEVDColesTawn | The Coles-Tawn model. | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateEVDHuslerReiss | The Husler-Reiss model. | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateEVDLogistic | The bivariate logistic model. | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateEVDNegativeBilogistic | The negative bilogistic model. | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateEVDNegativeLogistic | The bivariate negative logistic model. | Class | com.numericalmethod.suanshu.stats.evt.evd.bivariate | SuanShu |
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BivariateGrid | A rectilinear (meaning that grid lines are not necessarily equally-spaced) bivariate grid of double values. | Interface | com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate | SuanShu |
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BivariateGridInterpolation | A bivariate interpolation, which requires the input to form a rectilinear grid. | Interface | com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate | SuanShu |
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BivariateProbabilityDistribution | A bivariate or joint probability distribution for X_1, X_2 is a probability distribution that gives the probability that each of X_1, X_2, . | Interface | com.numericalmethod.suanshu.stats.distribution.multivariate | SuanShu |
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BivariateRealFunction | A bivariate real function takes two real arguments and outputs one real value. | Interface | com.numericalmethod.suanshu.analysis.function.rn2r1 | SuanShu |
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BivariateRegularGrid | A regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes (e. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate | SuanShu |
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BlockSplitPointSearch | Computes the splitting points with the given threshold. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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BlockWinogradAlgorithm | This implementation accelerates matrix multiplication via a combination of the Strassen algorithm and block matrix multiplication. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu |
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BMSDE | A Brownian motion is a stochastic process with the following properties. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete | SuanShu |
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BoltzAnnealingFunction | Matlab: @annealingboltz - The step has length square root of temperature, with direction uniformly at random. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
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BoltzTemperatureFunction | (T_k = T_0 / ln(k)). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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BootstrapEstimator | This class estimates the statistic of a sample using a bootstrap method. | Class | com.numericalmethod.suanshu.stats.random.sampler.resampler | SuanShu |
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BorderedHessian | A bordered Hessian matrix consists of the Hessian of a multivariate function f, and the gradient of a multivariate function g. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
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BottomUp | This implementation traverses a directed acyclic graph starting from the leaves at the bottom, and reaches the roots. | Class | com.numericalmethod.suanshu.graph.algorithm.traversal | SuanShu |
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BoxConstraints | This represents the lower and upper bounds for a variable. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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BoxGeneralizedSimulatedAnnealingMinimizer | This is an extension to GeneralizedSimulatedAnnealingMinimizer, which allows adding box constraints to bound solutions. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.box | SuanShu |
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BoxGSAAcceptanceProbabilityFunction | This probability function boxes an unconstrained probability function so that when a proposed state is outside the box, it has a probability of 0. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
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BoxGSAAnnealingFunction | | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
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BoxMinimizer | A box minimizer solves a BoxOptimProblem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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BoxMuller | The Box-Muller transform (by George Edward Pelham Box and Mervin Edgar Muller 1958) is a pseudo-random number sampling method for generating pairs of independent standard | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
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BoxOptimProblem | A box constrained optimization problem, for which a solution must be within fixed bounds. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
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BoxPierce | The Box-Pierce test (named for George E. | Class | com.numericalmethod.suanshu.stats.test.timeseries.portmanteau | SuanShu |
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BracketSearchMinimizer | This class provides implementation support for those univariate optimization algorithms that are based on bracketing. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
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BranchAndBound | Branch-and-Bound (BB or B&B) is a general algorithm for finding optimal solutions of various optimization problems, especially in discrete and combinatorial optimization. | Class | com.numericalmethod.suanshu.misc.algorithm.bb | SuanShu |
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BrentCetaMaximizer | | Class | com.numericalmethod.suanshu.model.lai2010.ceta.maximizer | SuanShu |
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BrentMinimizer | Brent's algorithm is the preferred method for finding the minimum of a univariate function. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
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BrentRoot | Brent's root-finding algorithm combines super-linear convergence with reliability of bisection. | Class | com.numericalmethod.suanshu.analysis.root.univariate | SuanShu |
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BreuschPagan | The Breusch-Pagan test tests for conditional heteroskedasticity. | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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BroadcastMessage | A message that is sent to each slave by the master. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.message | SuanShu |
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BrownForsythe | The Brown-Forsythe test is a statistical test for the equality of group variances based on performing an ANOVA on a transformation of the response variable. | Class | com.numericalmethod.suanshu.stats.test.variance | SuanShu |
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BruteForceIPMinimizer | This implementation solves an integral constrained minimization problem by brute force search for all possible integer combinations. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce | SuanShu |
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BruteForceIPProblem | This implementation is an integral constrained minimization problem that has enumerable integral domains. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.bruteforce | SuanShu |
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Bt | This is a FiltrationFunction that returns (B(t_i)), the Brownian motion value at the i-th time point. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration | SuanShu |
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BurlischStoerExtrapolation | Burlisch-Stoer extrapolation (or Gragg-Bulirsch-Stoer (GBS)) algorithm combines three powerful ideas: Richardson extrapolation, the use of rational function extrapolation in Richardson-type | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation | SuanShu |
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BurnInRNG | A burn-in random number generator discards the first M samples. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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BurnInRVG | A burn-in random number generator discards the first M samples. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
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C1 | See Also:Wikipedia: Smooth functionGet the gradient function, g, of a real valued function f. | Interface | com.numericalmethod.suanshu.analysis.differentiation.differentiability | SuanShu |
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C2 | | Interface | com.numericalmethod.suanshu.analysis.differentiation.differentiability | SuanShu |
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C2OptimProblem | This is an optimization problem of a real valued function that is twice differentiable. | Interface | com.numericalmethod.suanshu.optimization.problem | SuanShu |
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C2OptimProblemImpl | This is an optimization problem of a real valued function: (max_x f(x)). | Class | com.numericalmethod.suanshu.optimization.problem | SuanShu |
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CartesianProduct | The Cartesian product can be generalized to the n-ary Cartesian product over n sets X1, . | Class | com.numericalmethod.suanshu.misc.algorithm | SuanShu |
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CaseResamplingReplacement | This is the classical bootstrap method described in the reference. | Class | com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap | SuanShu |
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CauchyPolynomial | The Cauchy's polynomial of a polynomial takes this form: C(x) = | Class | com.numericalmethod.suanshu.analysis.function.polynomial | SuanShu |
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CentralPath | A central path is a solution to both the primal and dual problems of a semi-definite programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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Ceta | | Class | com.numericalmethod.suanshu.model.lai2010.ceta | SuanShu |
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CetaMaximizer | | Interface | com.numericalmethod.suanshu.model.lai2010.ceta.maximizer | SuanShu |
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ChangeOfVariable | Change of variable can easy the computation of some integrals, such as improper integrals. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann | SuanShu |
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CharacteristicPolynomial | The characteristic polynomial of a square matrix is the function The zeros of this polynomial are the eigenvalues of A. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
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ChebyshevRule | | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule | SuanShu |
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Cheng1978 | Cheng, 1978, is a new rejection method for generating beta variates. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.beta | SuanShu |
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ChiSquareDistribution | The Chi-square distribution is the distribution of the sum of the squares of a set of statistically independent standard Gaussian random variables. | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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ChiSquareIndependenceTest | Pearson's chi-square test of independence assesses whether paired observations on two variables, expressed in a contingency table, are independent of each other. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
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Chol | Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
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Cholesky | Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
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CholeskyBanachiewicz | Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
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CholeskyBanachiewiczParallelized | This is a parallelized version of CholeskyBanachiewicz. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
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CholeskySparse | Cholesky decomposition decomposes a real, symmetric (hence square), and positive definite matrix A into A = L * Lt, where L is a lower triangular matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
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CholeskyWang2006 | Cholesky decomposition works only for a positive definite matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle.cholesky | SuanShu |
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Chromosome | A chromosome is a representation of a solution to an optimization problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm | SuanShu |
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ClusterAnalyzer | This class counts clusters of exceedances based on observations above a given threshold, and the discontinuity of exceedances can be tolerated by an interval length r. | Class | com.numericalmethod.suanshu.stats.evt.cluster | SuanShu |
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Clusters | Store cluster information obtained by cluster analysis. | Class | com.numericalmethod.suanshu.stats.evt.cluster | SuanShu |
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CointegrationMLE | Two or more time series are cointegrated if they each share a common type of stochastic drift, that is, to a limited degree they share a certain type of behavior in terms of their long-term fluctuations, | Class | com.numericalmethod.suanshu.stats.cointegration | SuanShu |
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CollectWorkerCounts | Request to collect the number of workers managed by the slaves. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.message | SuanShu |
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ColumnBindMatrix | A fast "cbind" matrix from vectors. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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CombinedCetaMaximizer | | Class | com.numericalmethod.suanshu.model.lai2010.ceta.maximizer | SuanShu |
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CombinedVectorByRef | For efficiency, this wrapper concatenates two or more vectors by references (without data copying). | Class | com.numericalmethod.suanshu.algebra.linear.vector.doubles | SuanShu |
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CommonRandomNumbers | The common random numbers is a variance reduction technique to apply when we are comparing two random systems, e. | Class | com.numericalmethod.suanshu.stats.random.variancereduction | SuanShu |
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Complex | A complex number is a number consisting of a real number part and an imaginary number part. | Class | com.numericalmethod.suanshu.number.complex | SuanShu |
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ComplexMatrix | This is a Complex matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype | SuanShu |
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CompositeDoubleArrayOperation | It is desirable to have multiple implementations and switch between them for, e. | Class | com.numericalmethod.suanshu.number.doublearray | SuanShu |
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CompositeLinearCongruentialGenerator | A composite generator combines a number of simple LinearCongruentialGenerator, such as Lehmer, to form one longer period generator by first summing values and then taking modulus. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
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ConcurrentCachedGenerator | A generic wrapper that makes an underlying item generator thread-safe by caching generated items in a concurrently-accessible list. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.cache | SuanShu |
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ConcurrentCachedRLG | This is a fast thread-safe wrapper for random long generators. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.cache | SuanShu |
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ConcurrentCachedRNG | This is a fast thread-safe wrapper for random number generators. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.cache | SuanShu |
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ConcurrentCachedRVG | This is a fast thread-safe wrapper for random vector generators. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.cache | SuanShu |
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ConcurrentStandardNormalRNG | | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
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ConditionalSumOfSquares | The method Conditional Sum of Squares (CSS) fits an ARIMA model by minimizing the conditional sum of squares. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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ConfidenceInterval | This class stores information for a list of confidence intervals, with the same confidence level. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting | SuanShu |
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CongruentMatrix | Given a matrix A and an invertible matrix P, we create the congruent matrixSee Also:Wikipedia: Matrix congruence | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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ConjugateGradientMinimizer | A conjugate direction optimization method is performed by using sequential line search along directions that bear a strict mathematical relationship to one another. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
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ConjugateGradientNormalErrorSolver | For an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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ConjugateGradientNormalResidualSolver | For an under-determined system of linear equations, Ax = b, or when the coefficient matrix A is non-symmetric and nonsingular, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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ConjugateGradientSolver | The Conjugate Gradient method (CG) is useful for solving a symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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ConjugateGradientSquaredSolver | The Conjugate Gradient Squared method (CGS) is useful for solving a non-symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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ConstantDriftVector | The class represents a constant drift function. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients | SuanShu |
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Constants | This class lists the global parameters and constants in this SuanShu library. | Class | com.numericalmethod.suanshu.misc | SuanShu |
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ConstantSeeder | A wrapper that seeds each given seedable random number generator with the given seed(s). | Class | com.numericalmethod.suanshu.stats.random.rng | SuanShu |
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ConstantSigma1 | The class represents a constant diffusion coefficient function. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients | SuanShu |
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ConstantSigma2 | The class represents a constant diffusion coefficient function. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients | SuanShu |
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ConstrainedCellFactory | This defines a Differential Evolution operator that takes in account constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained | SuanShu |
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ConstrainedLASSObyLARS | This class solves the constrained form of LASSO by modified least angle regression (LARS) and linear interpolation: | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
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ConstrainedLASSOProblem | A LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
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ConstrainedMinimizer | A constrained minimizer solves a constrained optimization problem, namely, ConstrainedOptimProblem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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ConstrainedOptimProblem | A constrained optimization problem takes this form. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
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ConstrainedOptimProblemImpl1 | This implements a constrained optimization problem for a function f subject to equality and less-than-or-equal-to constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
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ConstrainedOptimSubProblem | A constrained optimization sub-problem takes this form. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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Constraints | A set of constraints for a (real-valued) optimization problem is a set of functions. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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ConstraintsUtils | These are the utility functions for manipulating Constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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ContextRNG | This uniform number generator generates independent sequences of random numbers per context. | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.context | SuanShu |
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ContinuedFraction | A continued fraction representation of a number has this form: z = b_0 + cfrac{a_1}{b_1 + cfrac{a_2}{b_2 + cfrac{a_3}{b_3 + cfrac{a_4}{b_4 + ddots,}}}} | Class | com.numericalmethod.suanshu.analysis.function.rn2r1.univariate | SuanShu |
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ControlVariates | Control variates method is a variance reduction technique that exploits information about the errors in estimates of known quantities to reduce the error of an estimate of an unknown | Class | com.numericalmethod.suanshu.stats.random.variancereduction | SuanShu |
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ConvectionDiffusionEquation1D | | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation | SuanShu |
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ConvergenceFailure | This exception is thrown by IterativeLinearSystemSolver. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative | SuanShu |
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CorrelationMatrix | The correlation matrix of n random variables X1, . | Class | com.numericalmethod.suanshu.stats.descriptive.correlation | SuanShu |
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Corvalan2005 | | Class | com.numericalmethod.suanshu.model.corvalan2005 | SuanShu |
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Counter | A counter keeps track of the number of occurrences of numbers. | Class | com.numericalmethod.suanshu.combinatorics | SuanShu |
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CountMonitor | This IterationMonitor counts the number of iterates generated, hence the number of iterations. | Class | com.numericalmethod.suanshu.misc.algorithm.iterative.monitor | SuanShu |
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CourantPenalty | This penalty function sums up the squared error penalties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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Covariance | Covariance is a measure of how much two variables change together. | Class | com.numericalmethod.suanshu.stats.descriptive.covariance | SuanShu |
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CovarianceEstimation | Estimates the covariance matrix by maximum likelihood. | Class | com.numericalmethod.suanshu.model.daspremont2008 | SuanShu |
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CovarianceSelectionGLASSOFAST | GLASSOFAST is the Graphical LASSO algorithm to solve the covariance selection problem. | Class | com.numericalmethod.suanshu.model.covarianceselection.lasso | SuanShu |
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CovarianceSelectionLASSO | The LASSO approach of covariance selection. | Class | com.numericalmethod.suanshu.model.covarianceselection.lasso | SuanShu |
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CovarianceSelectionProblem | This class defines the covariance selection problem outlined in d'Aspremont (2008). | Class | com.numericalmethod.suanshu.model.covarianceselection | SuanShu |
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CovarianceSelectionSolver | Get the estimated Covariance matrix of the selection problem. | Interface | com.numericalmethod.suanshu.model.covarianceselection | SuanShu |
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CramerVonMises2Samples | This algorithm calculates the two sample Cramer-Von Mises test statistic and p-value. | Class | com.numericalmethod.suanshu.stats.test.distribution | SuanShu |
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CrankNicolsonConvectionDiffusionEquation1D | This class uses the Crank-Nicolson scheme to obtain a numerical solution of a one-dimensional convection-diffusion PDE. | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.convectiondiffusionequation | SuanShu |
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CrankNicolsonHeatEquation1D | The Crank-Nicolson method is an algorithm for obtaining a numerical solution to parabolic PDE problems. | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation | SuanShu |
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CSDPMinimizer | See Also:"Borchers, Brian, "CSDP, a C Library for Semidefinite Programming", Optimization Methods and Software 11(1): 613-623, 1999. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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CSRSparseMatrix | The Compressed Sparse Row (CSR) format for sparse matrix has this representation: (value, col_ind, row_ptr). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
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CubicHermite | Cubic Hermite spline interpolation is a piecewise spline interpolation, in which each polynomial is in Hermite form which consists of two control points and two control tangents. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate | SuanShu |
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CubicRoot | This is a cubic equation solver. | Class | com.numericalmethod.suanshu.analysis.function.polynomial.root | SuanShu |
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CubicSpline | The (natural) cubic spline interpolation fits a cubic polynomial between each pair of adjacent points such that adjacent cubics are continuous in their first and second derivative. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate | SuanShu |
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CumulativeNormalHastings | Hastings algorithm is faster but less accurate way to compute the cumulative standard Normal. | Class | com.numericalmethod.suanshu.analysis.function.special.gaussian | SuanShu |
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CumulativeNormalInverse | The inverse of the cumulative standard Normal distribution function is defined as: This implementation uses the Beasley-Springer-Moro algorithm. | Class | com.numericalmethod.suanshu.analysis.function.special.gaussian | SuanShu |
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CumulativeNormalMarsaglia | Marsaglia is about 3 times slower but is more accurate to compute the cumulative standard Normal. | Class | com.numericalmethod.suanshu.analysis.function.special.gaussian | SuanShu |
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CurveFitting | Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints. | Interface | com.numericalmethod.suanshu.analysis.curvefit | SuanShu |
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DAgostino | D'Agostino's K2 test is a goodness-of-fit measure of departure from normality. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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DAGraph | A directed acyclic graph (DAG), is a directed graph with no directed cycles. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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Dai2011HMM | Creates a two-state Geometric Brownian Motion with a constant volatility. | Class | com.numericalmethod.suanshu.model.dai2011 | SuanShu |
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Dai2011Solver | Solves the stochastic control problem in the referenced paper to get the two Min Dai, Qing Zhang and Qiji Jim Zhu, "Optimal Trend Following Trading | Class | com.numericalmethod.suanshu.model.dai2011 | SuanShu |
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DateTimeGenericTimeSeries | | Class | com.numericalmethod.suanshu.stats.timeseries.datastructure | SuanShu |
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DateTimeTimeSeries | This is a time series has its double values indexed by DateTime. | Class | com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate | SuanShu |
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DBeta | This is the first order derivative function of the Beta function w. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
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DBetaRegularized | This is the first order derivative function of the Regularized Incomplete Beta function, BetaRegularized, w. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
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DeepCopyable | This interface provides a way to do polymorphic copying. | Interface | com.numericalmethod.suanshu.misc | SuanShu |
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DefaultDynamicCreatorConfiguration | Default settings for DynamicCreatorConfiguration. | Class | com.numericalmethod.suanshu.grid.config.dc | SuanShu |
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DefaultGridExecutorFactory | The default factory that creates instances of GridExecutor. | Class | com.numericalmethod.suanshu.grid.executor | SuanShu |
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DefaultMatrixStorage | There are multiple ways to implement the storage data structure depending on the matrix type for optimization purpose. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype | SuanShu |
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DefaultSimplex | A simplex optimization algorithm, e. | Class | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
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DefaultTestRemoteConfiguration | Simple remote configuration that replaces everything but the hosts with the default remote configuration - similar to leaving out the relevant elements in an XML configuration files. | Class | com.numericalmethod.suanshu.grid.test.config | SuanShu |
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Deflation | A deflation found in a Hessenberg (or tridiagonal in symmetric case) matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
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DeflationCriterion | Determines whether a sub-diagonal entry is sufficiently small to be neglected. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
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DenseData | This implementation of the storage of a dense matrix stores the data of a 2D matrix as an 1D In general, computing index for a double[] is faster than that for a double[][]. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense | SuanShu |
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DenseMatrix | This class implements the standard, dense, double based matrix representation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense | SuanShu |
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DenseMatrixMultiplication | Matrix operation that multiplies two matrices. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu |
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DenseMatrixMultiplicationByBlock | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu |
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DenseMatrixMultiplicationByIjk | parallel execution with multiple threads. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu |
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DenseVector | This class implements the standard, dense, double based vector representation. | Class | com.numericalmethod.suanshu.algebra.linear.vector.doubles.dense | SuanShu |
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Densifiable | This interface specifies whether a matrix implementation can be efficiently converted to the standard dense matrix representation. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense | SuanShu |
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DEOptim | Differential Evolution (DE) is a global optimization method. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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DEOptimCellFactory | A DEOptimCellFactory produces DEOptimCellFactory. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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DErf | This is the first order derivative function of the Error function, Erf. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
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DerivativeFunction | Defines the derivative function F(x, y) for ODE problems. | Interface | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem | SuanShu |
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Dfdx | The first derivative is a measure of how a function changes as its input changes. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
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DFPMinimizer | The Davidon-Fletcher-Powell method is a quasi-Newton method to solve unconstrained nonlinear optimization problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
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DFS | This class implements the depth-first-search using iteration. | Class | com.numericalmethod.suanshu.graph.algorithm.traversal | SuanShu |
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DGamma | This is the first order derivative function of the Gamma function, ({d mathrm{Gamma}(x) over dx}). | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
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DGaussian | This is the first order derivative function of a Gaussian function, ({d mathrm{phi}(x) over dx}). | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
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DiagonalMatrix | A diagonal matrix has non-zero entries only on the main diagonal. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal | SuanShu |
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DiagonalSum | Add diagonal elements to a matrix, an efficient implementation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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DifferencedIntTimeTimeSeries | Differencing of a time series xt in discrete time t is the transformation of the series to a new time series (1-B)xt where the new values | Class | com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime | SuanShu |
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Diffusion | | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients | SuanShu |
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DiffusionMatrix | | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients | SuanShu |
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DiffusionSigma | This class implements the diffusion term in the form of a diffusion matrix. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients | SuanShu |
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Digamma | The digamma function is defined as the logarithmic derivative of the gamma function. | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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DiGraph | A directed graph or digraph is a graph, or set of nodes connected by edges, where the edges have a direction associated with them. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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DimensionCheck | These are the utility functions for checking table dimension. | Class | com.numericalmethod.suanshu.misc.datastructure | SuanShu |
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DirichletDistribution | The Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted Dir(a), is a family of continuous multivariate probability distributions parametrized by a vector | Class | com.numericalmethod.suanshu.stats.distribution.multivariate | SuanShu |
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DiscreteHMM | This is the discrete hidden Markov model as defined by Rabiner. | Class | com.numericalmethod.suanshu.stats.hmm.discrete | SuanShu |
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DiscreteSDE | This interface represents the discrete approximation of a univariate SDE. | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete | SuanShu |
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DiversificationMeasure | Defines the diversification of a portfolio. | Interface | com.numericalmethod.suanshu.model.corvalan2005.diversification | SuanShu |
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DividedDifferences | Divided differences is recursive division process for calculating the coefficients in the interpolation polynomial in the Newton form. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate | SuanShu |
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DLM | This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification. | Class | com.numericalmethod.suanshu.stats.dlm.univariate | SuanShu |
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DLMSeries | This is a simulator for a multivariate controlled dynamic linear model process. | Class | com.numericalmethod.suanshu.stats.dlm.univariate | SuanShu |
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DLMSim | This is a simulator for a univariate controlled dynamic linear model process. | Class | com.numericalmethod.suanshu.stats.dlm.univariate | SuanShu |
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DOKSparseMatrix | The Dictionary Of Key (DOK) format for sparse matrix uses the coordinates of non-zero entries in the matrix as keys. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
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Doolittle | Doolittle algorithm is a LU decomposition algorithm which decomposes a square matrix P is an n x n permutation matrix;L is an n x n (unit) lower triangular matrix;U is an n x n upper triangular matrix, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle | SuanShu |
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DoubleArrayMath | These are the math functions that operate on double[]. | Class | com.numericalmethod.suanshu.number.doublearray | SuanShu |
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DoubleArrayOperation | It is possible to provide different implementations for different platforms, hardware, etc. | Interface | com.numericalmethod.suanshu.number.doublearray | SuanShu |
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DoubleExponential | This transformation speeds up the convergence of the Trapezoidal Rule exponentially. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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DoubleExponential4HalfRealLine | This transformation is good for the region ((0, +infty)). | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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DoubleExponential4RealLine | This transformation is good for the region ((-infty, +infty)). | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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DoubleUtils | These are the utility functions to manipulate double and int. | Class | com.numericalmethod.suanshu.number | SuanShu |
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DPolynomial | This is the first order derivative function of a Polynomial, which, again, is a polynomial. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
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DQDS | Computes all the eigenvalues of the symmetric positive definite tridiagonal matrix associated with the qd-array Z to high relative accuracy. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds | SuanShu |
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Drift | | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.coefficients | SuanShu |
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DriftVector | | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients | SuanShu |
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DuplicatedAbscissae | This exception is thrown when a function has two same x-abscissae, hence ill-defined. | Class | com.numericalmethod.suanshu.analysis.function.tuple | SuanShu |
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DynamicCreator | Performs the Dynamic Creation algorithm (DC) to generate parameters for MersenneTwister. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation | SuanShu |
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DynamicCreatorConfig | Java class for dynamicCreatorConfig complex type. | Class | com.numericalmethod.suanshu.grid.config.xml.schema | SuanShu |
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DynamicCreatorConfiguration | Configuration for the Mersenne Twister Dynamic Creator (MT-DC), that is used to generate independent random number generators. | Interface | com.numericalmethod.suanshu.grid.config.dc | SuanShu |
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DynamicCreatorException | Indicates that a problem has occurred in the dynamic creation process, usually because suitable parameters were not found. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation | SuanShu |
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Edge | An edge connects a pair of vertices. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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EdgeBetweeness | The edge betweenness centrality is defined as the number of the shortest paths that go through an edge in a graph or network. | Class | com.numericalmethod.suanshu.graph.community | SuanShu |
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Eigen | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
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EigenBoundUtils | Utility methods for computing bounds of eigenvalues. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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EigenCount | Counts the number of eigenvalues in a symmetric tridiagonal matrix T that are less than aSee Also:"W. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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EigenCountInRange | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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EigenDecomposition | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
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EigenProperty | EigenProperty is a read-only structure that contains the information about a particular eigenvalue, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
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EigenvalueByDQDS | Computes all the eigenvalues of a symmetric tridiagonal matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds | SuanShu |
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ElementaryFunction | This class contains some elementary functions for complex number, Complex. | Class | com.numericalmethod.suanshu.number.complex | SuanShu |
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ElementaryOperation | There are three elementary row operations which are equivalent to left multiplying an elementary They are row switching, row multiplication, and row addition. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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EliminationByAIC | In each step, a factor is dropped if the resulting model has the least AIC, until no factor removal can result in a model with AIC lower than the current AIC. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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EliminationByZValue | In each step, the factor with the least z-value is dropped, until all z-values are greater than the critical value (given by the significance level). | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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Elliott2005DLM | This class implements the Kalman filter model as in Elliott's paper. | Class | com.numericalmethod.suanshu.model.elliott2005 | SuanShu |
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ElliottOnlineFilter | It is important to note that this algorithm does not guarantee that Therefore, we need to check the outputs. | Class | com.numericalmethod.suanshu.model.elliott2005 | SuanShu |
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EmpiricalACER | This class contains empirical ACER (hat{epsilon_k}(eta_i)) values and other related statistics estimated from observations. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical | SuanShu |
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EmpiricalACEREstimation | This class estimates empirical ACER values from the given observations. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical | SuanShu |
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EmpiricalACERStatistics | This class contains the computed statistics of the estimated ACERs. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical | SuanShu |
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EmpiricalDistribution | An empirical cumulative probability distribution function is a cumulative probability distribution function that | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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EpsilonStatisticsCalculator | Compute statistics: mean, confidence interval of estimated ACER values (epsilon_k(eta_i)). | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer.empirical | SuanShu |
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EqualityConstraints | The domain of an optimization problem may be restricted by equality constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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Erf | The Error function is defined as: operatorname{erf}(x) = frac{2}{sqrt{pi}}int_{0}^x e^{-t^2} dt | Class | com.numericalmethod.suanshu.analysis.function.special.gaussian | SuanShu |
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Erfc | This complementary Error function is defined as: operatorname{erfc}(x) | Class | com.numericalmethod.suanshu.analysis.function.special.gaussian | SuanShu |
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ErfInverse | The inverse of the Error function is defined as: operatorname{erf}^{-1}(x) | Class | com.numericalmethod.suanshu.analysis.function.special.gaussian | SuanShu |
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ErgodicHybridMCMC | A simple decorator which will randomly vary dt between each sample. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
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Estimator | Gets the expectation of the estimator. | Interface | com.numericalmethod.suanshu.stats.random | SuanShu |
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EulerMethod | The Euler method is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver | SuanShu |
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EulerSDE | The Euler scheme is the first order approximation of an SDE. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete | SuanShu |
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EvenlySpacedGrid | This is an evenly spaced time grid. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.timegrid | SuanShu |
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ExceptionUtils | Exception-related utility functions. | Class | com.numericalmethod.suanshu.misc | SuanShu |
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ExpectationAtEndTime | This class computes the expectation (mean) and the variance of a stochastic process, by Monte Carlo simulation, at the end of an interval: (E(X_T)). | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random | SuanShu |
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ExplicitCentralDifference1D | This explicit central difference method is a numerical technique for solving the one-dimensional wave equation by the following explicit | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1 | SuanShu |
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ExplicitCentralDifference2D | This explicit central difference method is a numerical technique for solving the two-dimensional wave equation by the following explicit | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2 | SuanShu |
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Exponential | This transformation is good for when the lower limit is finite, the upper limit is infinite, and the integrand falls off exponentially. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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ExponentialDistribution | The exponential distribution describes the times between events in a Poisson process, a process in which events occur continuously and independently at a constant average rate. | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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ExponentialMixtureDistribution | The HMM states use the Exponential distribution to model the observations. | Class | com.numericalmethod.suanshu.stats.hmm.mixture.distribution | SuanShu |
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ExpTemperatureFunction | Logarithmic decay, where (T_k = T_0 * 0. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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ExtremalGeneralizedEigenvalueByGreedySearch | [ min_x frac{x'Ax}{x'Bx} \ extup{s. | Class | com.numericalmethod.suanshu.model.daspremont2008 | SuanShu |
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ExtremalGeneralizedEigenvalueBySDP | Solves the problem described in Section 3. | Class | com.numericalmethod.suanshu.model.daspremont2008 | SuanShu |
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ExtremalGeneralizedEigenvalueSolver | Computes the solution to the problem described in Section 3. | Interface | com.numericalmethod.suanshu.model.daspremont2008 | SuanShu |
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ExtremalIndexByClusterSizeReciprocal | This class estimates the extremal index by the reciprocal of the average cluster size. | Class | com.numericalmethod.suanshu.stats.evt.exi | SuanShu |
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ExtremalIndexByFerroSeegers | This class estimates the extremal index from observations by the algorithm proposed by Ferro and The R equivalent function is evd::exi. | Class | com.numericalmethod.suanshu.stats.evt.exi | SuanShu |
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ExtremalIndexEstimation | The extremal index ( heta in [0,1]) characterizes the degree of local dependence in the extremes of a stationary time series. | Interface | com.numericalmethod.suanshu.stats.evt.exi | SuanShu |
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ExtremeValueMC | Simulation of first order extreme value Markov chains such that each pair of consecutive values has the dependence structure of given bivariate extreme value distributions. | Class | com.numericalmethod.suanshu.stats.evt.markovchain | SuanShu |
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F | The F-test tests whether two normal populations have the same variance. | Class | com.numericalmethod.suanshu.stats.test.variance | SuanShu |
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F_Sum_BtDt | This represents a function of this integral I = int_{0}^{1} B(t)dt | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration | SuanShu |
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F_Sum_tBtDt | This represents a function of this integral int_{0}^{1} (t - 0. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration | SuanShu |
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FactorAnalysis | Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved | Class | com.numericalmethod.suanshu.stats.factoranalysis | SuanShu |
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FAEstimator | These are the estimators (estimated psi, loading matrix, scores, degrees of freedom, test statistics, p-value, etc. | Class | com.numericalmethod.suanshu.stats.factoranalysis | SuanShu |
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FailureDetectingTestRemoteConfiguration | Similar to DefaultTestRemoteConfiguration but adds failure detection. | Class | com.numericalmethod.suanshu.grid.test.config | SuanShu |
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FailureDetectionConfig | Java class for failureDetectionConfig complex type. | Class | com.numericalmethod.suanshu.grid.config.xml.schema | SuanShu |
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FailureDetector | Acts on behalf of the master and keeps track of all the work that was delegated, as well as the responses that were received. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.actor | SuanShu |
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FastAnnealingFunction | Matlab default: @annealingfast - The step has length temperature, with direction uniformly at random. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
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FastKroneckerProduct | This is a fast and memory-saving implementation of computing the Kronecker product. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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FastTemperatureFunction | Linear decay, where (T_k = T_0 / k). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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FDistribution | The F distribution is the distribution of the ratio of two independent chi-squared variates. | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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FerrisMangasarianWrightPhase1 | The phase 1 procedure finds a feasible table from an infeasible one by pivoting the simplex table of a related problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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FerrisMangasarianWrightPhase2 | This implementation solves a canonical linear programming problem that does not need preprocessing its simplex table. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver | SuanShu |
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FerrisMangasarianWrightScheme2 | The scheme 2 procedure removes equalities and free variables. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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Fibonacci | A Fibonacci sequence starts with 0 and 1 as the first two numbers. | Class | com.numericalmethod.suanshu.analysis.sequence | SuanShu |
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FibonaccMinimizer | The Fibonacci search is a dichotomous search where a bracketing interval is sub-divided by the Fibonacci ratio. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
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Field | As an algebraic structure, every field is a ring, but not every ring is a field. | Class | com.numericalmethod.suanshu.algebra.structure | SuanShu |
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Filter | A filter, for signal processing, takes (real) input signal and transforms it to (real) output signal. | Interface | com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles | SuanShu |
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Filtration | This class represents the filtration information known at the end of time. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration | SuanShu |
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FiltrationFunction | A filtration function, parameterized by a fixed filtration, is a function of time, (f(mathfrak{F_{t_i}})). | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.filtration | SuanShu |
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FiniteDifference | A finite difference (divided by a small increment) is an approximation of the derivative of a The accuracy depends on the function to take the derivative of. | Class | com.numericalmethod.suanshu.analysis.differentiation.univariate | SuanShu |
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FirstGeneration | This interface allows customization of how the first pool of chromosomes is generated. | Interface | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration | SuanShu |
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FirstOrderMinimizer | This implements the steepest descent line search using the first order expansion of the Taylor's series. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
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FisherExactDistribution | Fisher's exact test distribution is, as its name states, exact, and can therefore be used regardless of the sample characteristics. | Class | com.numericalmethod.suanshu.stats.test.distribution.pearson | SuanShu |
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FixedEffectsModel | Fits the panel data to this linear model: y_{it} = alpha_{i}+X_{it}mathbf{eta}+u_{it} | Class | com.numericalmethod.suanshu.stats.regression.linear.panel | SuanShu |
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FletcherLineSearch | This is Fletcher's inexact line search method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
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FletcherPenalty | This penalty function sums up the squared costs penalties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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FletcherReevesMinimizer | The Fletcher-Reeves method is a variant of the Conjugate-Gradient method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
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FlexibleTable | This is a 2D table that can shrink or grow by row or by column. | Class | com.numericalmethod.suanshu.misc.datastructure | SuanShu |
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Forest | A forest is a disjoint union of trees. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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ForwardBackwardProcedure | The forward-backward procedure is an inference algorithm for hidden Markov models which computes the posterior marginals of all hidden state variables | Class | com.numericalmethod.suanshu.stats.hmm | SuanShu |
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ForwardSelection | Constructs a GLM model for a set of observations using the forward selection method. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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ForwardSubstitution | Forward substitution solves a matrix equation in the form Lx = b by an iterative process for a lower triangular matrix L. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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FrechetDistribution | | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate | SuanShu |
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Ft | This represents the concept 'Filtration', the information available at time t. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde | SuanShu |
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FtAdaptedFunction | | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde | SuanShu |
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FtAdaptedRealFunction | | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde | SuanShu |
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FtAdaptedVectorFunction | | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde | SuanShu |
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FtWt | | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde | SuanShu |
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Function | The mathematical concept of a function expresses the idea that one quantity (the argument of the function, also known as the input) completely determines another quantity (the value, or output). | Interface | com.numericalmethod.suanshu.analysis.function | SuanShu |
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Function | A function in the classical sense, that it maps from an input to an output. | Interface | com.numericalmethod.suanshu.grid.function | SuanShu |
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FunctionOps | These are some commonly used mathematical functions. | Class | com.numericalmethod.suanshu.analysis.function | SuanShu |
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Gamma | The Gamma function is an extension of the factorial function to real and complex numbers, with its argument shifted down by 1. | Interface | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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GammaDistribution | This gamma distribution, when k is an integer, is the distribution of the sum of k independent exponentially distributed random variables, | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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GammaGergoNemes | The Gergo Nemes' algorithm is very simple and quick to compute the Gamma function, if accuracy is not critical. | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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GammaLanczos | Lanczos approximation provides a way to compute the Gamma function such that the accuracy can be made arbitrarily precise. | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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GammaLanczosQuick | Lanczos approximation, computations are done in double. | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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GammaLowerIncomplete | The Lower Incomplete Gamma function is defined as: gamma(s,x) = int_0^x t^{s-1},e^{-t},{
m d}t = P(s,x)Gamma(s) | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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GammaMixtureDistribution | The HMM states use the Gamma distribution to model the observations. | Class | com.numericalmethod.suanshu.stats.hmm.mixture.distribution | SuanShu |
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GammaRegularizedP | The Regularized Incomplete Gamma P function is defined as: P(s,x) = frac{gamma(s,x)}{Gamma(s)} = 1 - Q(s,x), s geq 0, x geq 0 | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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GammaRegularizedPInverse | The inverse of the Regularized Incomplete Gamma P function is defined as: x = P^{-1}(s,u), 0 geq u geq 1 | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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GammaRegularizedQ | The Regularized Incomplete Gamma Q function is defined as: Q(s,x)=frac{Gamma(s,x)}{Gamma(s)}=1-P(s,x), s geq 0, x geq 0 | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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GammaUpperIncomplete | The Upper Incomplete Gamma function is defined as: Gamma(s,x) = int_x^{infty} t^{s-1},e^{-t},{
m d}t = Q(s,x) imes Gamma(s) | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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GARCH11Model | An GARCH11 model takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch | SuanShu |
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GARCHFit | This implementation fits, for a data set, a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch | SuanShu |
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GARCHModel | The GARCH(p, q) model takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch | SuanShu |
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GARCHResamplerFactory | | Class | com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler | SuanShu |
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GARCHSim | This class simulates the GARCH models of this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.garch | SuanShu |
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GaussChebyshevQuadrature | Gauss-Chebyshev Quadrature uses the following weighting function: w(x) = frac{1}{sqrt{1 - x^2}} | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian | SuanShu |
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GaussHermiteQuadrature | Gauss-Hermite quadrature exploits the fact that quadrature approximations are open integration formulas (that is, the values of the endpoints are not required) to evaluate of integrals in the | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian | SuanShu |
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Gaussian | The Gaussian function is defined as: f(x) = a e^{- { frac{(x-b)^2 }{ 2 c^2} } } | Class | com.numericalmethod.suanshu.analysis.function.special.gaussian | SuanShu |
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GaussianElimination | The Gaussian elimination performs elementary row operations to reduce a matrix to the row echelon form. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination | SuanShu |
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GaussianElimination4SquareMatrix | This is a wrapper for GaussianElimination but applies only to square matrices. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination | SuanShu |
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GaussianProposalFunction | A proposal generator where each perturbation is a random vector, where each element is drawn from a standard Normal distribution, multiplied by a scale matrix. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction | SuanShu |
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GaussianQuadrature | A quadrature rule is a method of numerical integration in which we approximate the integral of a function by a weighted sum of sample points. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian | SuanShu |
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GaussianQuadratureRule | This interface defines a Gaussian quadrature rule used in Gaussian quadrature. | Interface | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule | SuanShu |
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GaussJordanElimination | Gauss-Jordan elimination performs elementary row operations to reduce a matrix to the reduced row echelon form. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.gaussianelimination | SuanShu |
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GaussLaguerreQuadrature | Gauss-Laguerre quadrature exploits the fact that quadrature approximations are open integration formulas (i. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian | SuanShu |
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GaussLegendreQuadrature | Gauss-Legendre quadrature considers the simplest case of uniform weighting: (w(x) = 1). | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian | SuanShu |
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GaussNewtonMinimizer | The Gauss-Newton method is a steepest descent method to minimize a real vector function in the form: f(x) = [f_1(x), f_2(x), . | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
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GaussSeidelSolver | Similar to the Jacobi method, the Gauss-Seidel method (GS) solves each equation in sequential order. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
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GBMProcess | A Geometric Brownian motion (GBM) (occasionally, exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process | SuanShu |
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GeneralConstraints | The real-valued constraints define the domain (feasible regions) for a real-valued objective function in a constrained optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general | SuanShu |
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GeneralEqualityConstraints | This is the collection of equality constraints for an optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general | SuanShu |
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GeneralGreaterThanConstraints | This is the collection of greater-than-or-equal-to constraints for an optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general | SuanShu |
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GeneralizedConjugateResidualSolver | The Generalized Conjugate Residual method (GCR) is useful for solving a non-symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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GeneralizedEVD | | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate | SuanShu |
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GeneralizedLinearModel | The Generalized Linear Model (GLM) is a flexible generalization of the Ordinary Least Squares regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
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GeneralizedLinearModelQuasiFamily | GLM for the quasi-families. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
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GeneralizedMinimalResidualSolver | The Generalized Minimal Residual method (GMRES) is useful for solving a non-symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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GeneralizedParetoDistribution | Generalized Pareto distribution (GPD) is used for modeling exceedances over (or shortfalls below) a threshold. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate | SuanShu |
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GeneralizedSimulatedAnnealingMinimizer | Tsallis and Stariolo (1996) proposed this variant of SimulatedAnnealingMinimizer (SA). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing | SuanShu |
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GeneralLessThanConstraints | This is the collection of less-than or equal-to constraints for an optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.general | SuanShu |
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GenericFieldMatrix | This is a generic matrix over a Field. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype | SuanShu |
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GenericMatrix | This class defines a matrix over a field. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.generic | SuanShu |
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GenericMatrixAccess | This interface defines the methods for accessing entries in a matrix over a field. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.generic | SuanShu |
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GenericTimeTimeSeries | This is a univariate time series indexed by some notion of time. | Class | com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate | SuanShu |
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GeneticAlgorithm | A genetic algorithm (GA) is a search heuristic that mimics the process of natural evolution. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm | SuanShu |
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GetResults | Basic message that is used to ask the for the final result. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.message | SuanShu |
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Getvec | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3.getvec | SuanShu |
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GEVFittingByMaximumLikelihood | Estimate the GeneralizedEVD parameter from the observations by maximum likelihood approach. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting | SuanShu |
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GirvanNewman | | Class | com.numericalmethod.suanshu.graph.community | SuanShu |
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GirvanNewmanUnDiGraph | | Class | com.numericalmethod.suanshu.graph.community | SuanShu |
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GivensMatrix | Givens rotation is a rotation in the plane spanned by two coordinates axes. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype | SuanShu |
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Glejser | The Glejser test tests for conditional heteroskedasticity. | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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GLMBeta | | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
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GLMBinomial | This is the Binomial distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
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GLMExponentialDistribution | This interface represents a probability distribution from the exponential family. | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
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GLMFamily | Family provides a convenient way to specify the error distribution and link function used in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
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GLMFitting | | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
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GLMGamma | This is the Gamma distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
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GLMGaussian | This is the Gaussian distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
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GLMInverseGaussian | This is the Inverse Gaussian distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
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GLMModelSelection | Given a set of observations {y, X}, we would like to construct a GLM to explain the data. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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GLMPoisson | This is the Poisson distribution of the error distribution in GLM model. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution | SuanShu |
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GLMProblem | This is a Generalized Linear regression problem. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
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GLMResiduals | Residual analysis of the results of a Generalized Linear Model regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
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GlobalSearchByLocalMinimizer | This minimizer is a global optimization method. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local | SuanShu |
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GoldenMinimizer | This is the golden section univariate minimization algorithm. | Class | com.numericalmethod.suanshu.optimization.univariate.bracketsearch | SuanShu |
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GoldfeldQuandtTrotter | Goldfeld, Quandt and Trotter propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite | SuanShu |
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GolubKahanSVD | Golub-Kahan algorithm does the SVD decomposition of a tall matrix in two stages. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd | SuanShu |
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GomoryMixedCutMinimizer | This cutting-plane implementation uses Gomory's mixed cut method. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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GomoryPureCutMinimizer | This cutting-plane implementation uses Gomory's pure cut method for pure integer programming, in which all variables are integral. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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Gradient | The gradient of a scalar field is a vector field which points in the direction of the greatest rate of increase of the scalar field, and of which the magnitude is the greatest rate of change. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
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GradientFunction | The gradient function, g(x), evaluates the gradient of a real scalar function f at a point x. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
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GramSchmidt | The Gram-Schmidt process is a method for orthogonalizing a set of vectors in an inner product space. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr | SuanShu |
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Graph | A graph is a representation of a set of objects where some pairs of the objects are connected by links. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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GraphTraversal | A spanning tree T of a connected, undirected graph G is a tree composed of all the vertices and some (or perhaps all) of the edges of G. | Interface | com.numericalmethod.suanshu.graph.algorithm.traversal | SuanShu |
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GraphUtils | These are the utility functions to manipulate Graph. | Class | com.numericalmethod.suanshu.graph | SuanShu |
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GreaterThanConstraints | The domain of an optimization problem may be restricted by greater-than or equal-to constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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GridConfig | Java class for gridConfig complex type. | Class | com.numericalmethod.suanshu.grid.config.xml.schema | SuanShu |
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GridExecutor | Instances of this class can run code that can be distributed across multiple machines (or CPU Note that if a function invocation fails, for example by throwing an exception, null will | Interface | com.numericalmethod.suanshu.grid.executor | SuanShu |
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GridExecutorFactory | Factory that creates GridExecutor. | Interface | com.numericalmethod.suanshu.grid.executor | SuanShu |
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GridExecutorFactoryFromConfig | Generates GridExecutor according to the settings in the configuration file. | Class | com.numericalmethod.suanshu.grid.config.xml | SuanShu |
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GridRouterConfig | Assigns work to slaves (that is, routing) in an efficient manner. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.actor | SuanShu |
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GridSearchCetaMaximizer | | Class | com.numericalmethod.suanshu.model.lai2010.ceta.maximizer | SuanShu |
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GridSearchMinimizer | This performs a grid search to find the minimum of a univariate function. | Class | com.numericalmethod.suanshu.optimization.univariate | SuanShu |
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GroupResampler | | Class | com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate | SuanShu |
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GroupResamplerFactory | Creates re-samplers that do re-sampling for the whole group of stocks together. | Class | com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler | SuanShu |
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GSAAcceptanceProbabilityFunction | The GSA acceptance probability function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
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GSAAnnealingFunction | The GSA proposal/annealing function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
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GSATemperatureFunction | The GSA temperature function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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GumbelDistribution | The Gumbel distribution is a special case (Type I) of the generalized extreme value distribution, The cumulative distribution function is | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate | SuanShu |
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HalleyRoot | Halley's method is an iterative root finding method for a univariate function with a continuous second derivative, i. | Class | com.numericalmethod.suanshu.analysis.root.univariate | SuanShu |
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HarveyGodfrey | The Harvey-Godfrey test tests for conditional heteroskedasticity. | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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HConstruction | A construction of extreme and trade points based on H discretization, ignoring changes smaller than H. | Class | com.numericalmethod.suanshu.model.hvolatility | SuanShu |
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HeatEquation1D | A one-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the frac{partial u}{partial t} = eta frac{partial^2 u}{partial x^2}, | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim1.heatequation | SuanShu |
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HeatEquation2D | A two-dimensional heat equation (or diffusion equation) is a parabolic PDE that takes the frac{partial u}{partial t} | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.parabolic.dim2 | SuanShu |
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HermitePolynomials | A Hermite polynomial is defined by the recurrence relation below. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule | SuanShu |
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HermiteRule | | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule | SuanShu |
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Hessenberg | An upper Hessenberg matrix is a square matrix which has zero entries below the first 0 & 9 & 10 & 11 & \ | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
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HessenbergDecomposition | Given a square matrix A, we find Q such that Q' * A * Q = H where H is a Hessenberg matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
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HessenbergDeflationSearch | Given a Hessenberg matrix, this class searches the largest unreduced Hessenberg sub-matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
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Hessian | The Hessian matrix is the square matrix of the second-order partial derivatives of a multivariate function. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
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HessianFunction | The Hessian function, H(x), evaluates the Hessian of a real scalar function f at a point x. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
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Heteroskedasticity | A heteroskedasticity test tests, for a linear regression model, whether the estimated variance of the residuals from a regression is dependent on the values of the independent variables (regressors). | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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HiddenMarkovModel | | Class | com.numericalmethod.suanshu.stats.hmm | SuanShu |
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HilbertMatrix | A Hilbert matrix, H, is a symmetric matrix with entries being the unit fractions H[i][j] = 1 / (i + j -1) | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype | SuanShu |
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HilbertSpace | A Hilbert space is an inner product space, an abstract vector space in which distances and angles can be measured. | Interface | com.numericalmethod.suanshu.algebra.structure | SuanShu |
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HmmInnovation | An HMM innovation consists of a state and an observation in the state. | Class | com.numericalmethod.suanshu.stats.hmm | SuanShu |
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HMMRNG | In a (discrete) hidden Markov model, the state is not directly visible, but output, dependent on the state, is visible. | Class | com.numericalmethod.suanshu.stats.hmm | SuanShu |
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HomogeneousPathFollowingMinimizer | This implementation solves a Semi-Definite Programming problem using the Homogeneous Self-Dual Path-Following algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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HornerScheme | Horner scheme is an algorithm for the efficient evaluation of polynomials in monomial form. | Class | com.numericalmethod.suanshu.analysis.function.polynomial | SuanShu |
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Host | Defines a host on a remote (or local) port. | Class | com.numericalmethod.suanshu.grid.config.remote | SuanShu |
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Householder4SubVector | Faster implementation of Householder reflection for sub-vectors at a given index. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
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Householder4ZeroGenerator | Faster implementation of Householder reflection for zero generator vector. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
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HouseholderContext | This is the context information about a Householder transformation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
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HouseholderInPlace | Maintains the matrix to be transformed by a sequence of Householder reflections. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
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HouseholderQR | Successive Householder reflections gradually transform a matrix A to the upper triangular For example, the first step is to multiply A with a Householder matrix | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr | SuanShu |
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HouseholderReflection | A Householder transformation in the 3-dimensional space is the reflection of a vector in the The plane, containing the origin, is uniquely defined by a unit vector, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.householder | SuanShu |
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Hp | This is the symmetrization operator as defined in equation (6) in the reference. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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HuangMinimizer | Huang's updating formula is a family of formulas which encompasses the rank-one, DFP, BFGS as well as some other formulas. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
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HybridMCMC | This class implements a hybrid MCMC algorithm. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
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HybridMCMCProposalFunction | | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction | SuanShu |
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HyperEdge | A hyper-edge connects a set of vertices of any size. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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HypersphereRVG | Generates uniformly distributed points on a unit hypersphere. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
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HypothesisTest | A statistical hypothesis test is a method of making decisions using experimental data. | Class | com.numericalmethod.suanshu.stats.test | SuanShu |
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IdentityHashSet | This class implements the Set interface with a hash table, using reference-equality in place of object-equality when comparing keys and values. | Class | com.numericalmethod.suanshu.misc.datastructure | SuanShu |
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IdentityPreconditioner | This identity preconditioner is used when no preconditioning is applied. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
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IID | An i. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
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ILPBranchAndBoundMinimizer | This is a Branch-and-Bound algorithm that solves Integer Linear Programming problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb | SuanShu |
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ILPNode | This is the branch-and-bound node used in conjunction with ILPBranchAndBoundMinimizer to solve an Integer Linear Programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.bb | SuanShu |
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ILPProblem | A linear program in real variables is said to be integral if it has at least one optimal solution which is integral. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem | SuanShu |
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ILPProblemImpl1 | This implementation is an ILP problem, in which the variables can be real or integral. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem | SuanShu |
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ImmutableMatrix | This is a read-only view of a Matrix instance. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
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ImmutableVector | This is a read-only view of a Vector instance. | Class | com.numericalmethod.suanshu.algebra.linear.vector.doubles | SuanShu |
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ImportanceSampling | Importance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated from a different distribution rather than the | Class | com.numericalmethod.suanshu.stats.random.variancereduction | SuanShu |
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IndependentCoVAR | This algorithm finds the independent variables based on the covariance matrix. | Class | com.numericalmethod.suanshu.model.daspremont2008 | SuanShu |
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Infantino2010PCA | The objective is to predict the next H-period accumulated returns from the past H-period dimensionally reduced returns. | Class | com.numericalmethod.suanshu.model.infantino2010 | SuanShu |
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Infantino2010Regime | Detects the current regime (mean reversion or momentum) by cross-sectional volatility. | Class | com.numericalmethod.suanshu.model.infantino2010 | SuanShu |
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InitDynamicCreator | A message sent to Slaves to indicate that it should run the DynamicCreator algorithm for all workers according or to Workers to indicate that they should run it to | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.message | SuanShu |
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InitialsFactory | Some optimization algorithms, e. | Interface | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
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InnerProduct | The Frobenius inner product is the component-wise inner product of two matrices as though they are vectors. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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InnovationsAlgorithm | The innovations algorithm is an efficient way to obtain a one step least square linear predictor for a univariate linear time series with known auto-covariance and these properties (not limited | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess | SuanShu |
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Integral | The class represents an integral of a function, in the Lebesgue sense. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration | SuanShu |
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IntegralConstrainedCellFactory | This implementation defines the constrained Differential Evolution operators that solve an Integer Programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim.constrained | SuanShu |
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IntegralDB | This class evaluates the following class of integrals. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration | SuanShu |
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IntegralDt | This class evaluates the following class of integrals. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration | SuanShu |
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IntegralExpectation | This class computes the expectation of the following class of integrals. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.integration | SuanShu |
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Integrator | This defines the interface for the numerical integration of definite integrals of univariate functions. | Interface | com.numericalmethod.suanshu.analysis.integration.univariate.riemann | SuanShu |
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Interpolation | Interpolation is a method of constructing new data points within the range of a discrete set of known data points. | Interface | com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate | SuanShu |
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Interval | | Class | com.numericalmethod.suanshu.interval | SuanShu |
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IntervalRelation | enum IntervalRelationAllen's Interval Algebra is a calculus for temporal reasoning that was introduced by James F. | Class | com.numericalmethod.suanshu.interval | SuanShu |
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Intervals | This is a disjoint set of intervals. | Class | com.numericalmethod.suanshu.interval | SuanShu |
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IntTimeTimeSeries | This is a univariate time series indexed by integers. | Interface | com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime | SuanShu |
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InvalidLicense | This is the LicenseError thrown when calling a class or method that is not yet licensed. | Class | com.numericalmethod.suanshu.misc.license | SuanShu |
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Inverse | For a square matrix A, the inverse, A-1, if exists, satisfies A. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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InverseIteration | Inverse iteration is an iterative eigenvalue algorithm. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
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InverseTransformSampling | Inverse transform sampling (also known as inversion sampling, the inverse probability integral transform, the inverse transformation method, Smirnov transform, golden rule, etc. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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InverseTransformSamplingEVDRNG | Generate random numbers according to a given univariate extreme value distribution, by inverse transform sampling. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.rng | SuanShu |
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InverseTransformSamplingExpRNG | This is a pseudo random number generator that samples from the exponential distribution using the inverse transform sampling method. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.exp | SuanShu |
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InverseTransformSamplingGammaRNG | This is a pseudo random number generator that samples from the gamma distribution using the inverse transform sampling method. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
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InverseTransformSamplingTruncatedNormalRNG | A random variate x defined as x = Phi^{-1}( Phi(alpha) + Ucdot(Phi(eta)-Phi(alpha)))sigma + mu | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal.truncated | SuanShu |
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InvertingVariable | This is the inverting-variable transformation. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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IPMinimizer | An Integer Programming minimizer minimizes an objective function subject to equality/inequality constraints as well as integral constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer | SuanShu |
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IPProblem | An Integer Programming problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer | SuanShu |
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IPProblemImpl1 | This is an implementation of a general Integer Programming problem in which some variables take only integers. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer | SuanShu |
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IteratesMonitor | This IterationMonitor stores all states generated during iterations. | Class | com.numericalmethod.suanshu.misc.algorithm.iterative.monitor | SuanShu |
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IterationBody | This interface defines the code snippet to be run in parallel. | Interface | com.numericalmethod.suanshu.misc.parallel | SuanShu |
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IterationMonitor | To debug an iterative algorithm, such as in IterativeMethod, it is useful to keep track of the all states generated in the iterations. | Interface | com.numericalmethod.suanshu.misc.algorithm.iterative.monitor | SuanShu |
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IterativeC2Maximizer | A maximization problem is simply minimizing the negative of the objective function. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
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IterativeC2Minimizer | This is a minimizer that minimizes a twice continuously differentiable, multivariate function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
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IterativeCentralDifference | An iterative central difference algorithm to obtain a numerical approximation to Poisson's equations with Dirichlet boundary conditions. | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2 | SuanShu |
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IterativeIntegrator | An iterative integrator computes an integral by a series of sums, which approximates the value of the integral. | Interface | com.numericalmethod.suanshu.analysis.integration.univariate.riemann | SuanShu |
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IterativeLinearSystemSolver | An iterative method for solving an N-by-N (or non-square) linear system Ax = b involves a sequence of matrix-vector multiplications. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative | SuanShu |
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IterativeMethod | An iterative method is a mathematical procedure that generates a sequence of improving approximate solutions for a class of problems. | Interface | com.numericalmethod.suanshu.misc.algorithm.iterative | SuanShu |
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IterativeMinimizer | This is an iterative multivariate minimizer. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained | SuanShu |
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IterativeSolution | Many minimization algorithms work by starting from some given initials and iteratively moving toward an approximate solution. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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IWLS | | Class | com.numericalmethod.suanshu.stats.regression.linear.glm | SuanShu |
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Jacobian | The Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
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JacobianFunction | The Jacobian function, J(x), evaluates the Jacobian of a real vector-valued function f at a point x. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
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JacobiPreconditioner | The Jacobi (or diagonal) preconditioner is one of the simplest forms of preconditioning, such that the preconditioner is the diagonal of | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
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JacobiSolver | The Jacobi method solves sequentially n equations in a linear system Ax = b in isolation in each iteration. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
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JarqueBera | The Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the sample kurtosis and skewness. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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JarqueBeraDistribution | Jarque-Bera distribution is the distribution of the Jarque-Bera statistics, which measures the departure from normality. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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JenkinsTraubReal | The Jenkins-Traub algorithm is a fast globally convergent iterative method for solving for polynomial roots. | Class | com.numericalmethod.suanshu.analysis.function.polynomial.root.jenkinstraub | SuanShu |
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JodaTimeUtils | These are the utility functions to manipulate JodaTime. | Class | com.numericalmethod.suanshu.misc.datastructure.time | SuanShu |
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JohansenAsymptoticDistribution | Johansen provides the asymptotic distributions of the two hypothesis testings (Eigen and Trace tests), | Class | com.numericalmethod.suanshu.stats.cointegration | SuanShu |
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JohansenTest | The maximum number of cointegrating relations among a multivariate time series is the rank of the To determine the (most likely) number of cointegrating relations r, | Class | com.numericalmethod.suanshu.stats.cointegration | SuanShu |
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JordanExchange | Jordan Exchange swaps the r-th entering variable (row) with the s-th leaving variable (column) in a matrix A. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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Kagi | KAGI construction of a random process. | Class | com.numericalmethod.suanshu.model.hvolatility | SuanShu |
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Kagi2 | KAGI construction of a random process which is assumed to be equi-distance in time. | Class | com.numericalmethod.suanshu.model.hvolatility | SuanShu |
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KagiModel | Maintains the states of a KAGI model. | Class | com.numericalmethod.suanshu.model.hvolatility | SuanShu |
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KendallRankCorrelation | | Class | com.numericalmethod.suanshu.stats.descriptive.correlation | SuanShu |
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Kernel | The kernel or null space (also nullspace) of a matrix A is the set of all vectors x for which Ax = 0. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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Kernel | A kernel that can be used for standalone operation of a Slave, i. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka | SuanShu |
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KnightSatchellTran1995 | See Also:Emmanual Acar, Stephen Satchell. | Class | com.numericalmethod.suanshu.model.kst1995 | SuanShu |
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KnightSatchellTran1995MLE | Fits a KST model from returns. | Class | com.numericalmethod.suanshu.model.kst1995 | SuanShu |
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Knuth1969 | This is a random number generator that generates random deviates according to the Poisson Generating Poisson-distributed random variables | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.poisson | SuanShu |
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KolmogorovDistribution | The Kolmogorov distribution is the distribution of the Kolmogorov-Smirnov statistic. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovOneSidedDistribution | | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovSmirnov | The Kolmogorov-Smirnov test (KS test) compares a sample with a reference probability distribution (one-sample KS test), or to compare two samples (two-sample KS test). | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovSmirnov1Sample | The one-sample Kolmogorov-Smirnov test (one-sample KS test) compares a sample with a reference probability distribution. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovSmirnov2Samples | The two-sample Kolmogorov-Smirnov test (two-sample KS test) tests for the equality of the distributions of two samples. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KolmogorovTwoSamplesDistribution | Compute the p-values for the generalized (conditionally distribution-free) Smirnov homogeneity test. | Class | com.numericalmethod.suanshu.stats.test.distribution.kolmogorov | SuanShu |
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KroneckerProduct | Given an m-by-n matrix A and a p-by-q matrix B, their Kronecker product C, also called their matrix direct product, is | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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KruskalWallis | The Kruskal-Wallis test is a non-parametric method for testing the equality of population medians among groups. | Class | com.numericalmethod.suanshu.stats.test.rank | SuanShu |
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KryoSerializer | | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.serialization | SuanShu |
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KunduGupta2007 | Kundu-Gupta propose a very convenient way to generate gamma random variables using generalized exponential distribution, | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
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Kurtosis | Kurtosis measures the "peakedness" of the probability distribution of a real-valued random Higher kurtosis means that there are more infrequent extreme deviations than frequent modestly | Class | com.numericalmethod.suanshu.stats.descriptive.moment | SuanShu |
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LaguerrePolynomials | Laguerre polynomials are defined by the recurrence relation below. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule | SuanShu |
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LaguerreRule | | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule | SuanShu |
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Lai2010NPEBModel | The Non-Parametric Empirical Bayes (NPEB) model described in the reference computes the optimal weights for asset allocation. | Class | com.numericalmethod.suanshu.model.lai2010 | SuanShu |
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Lanczos | The Lanczos approximation is a method for computing the Gamma function numerically, published by Cornelius Lanczos in 1964. | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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LARSFitting | This class computes the entire LARS sequence of coefficients and fits, starting from zero to theSee Also:B. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso.lars | SuanShu |
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LARSProblem | Least Angle Regression (LARS) is a regression algorithm for high-dimensional data. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso.lars | SuanShu |
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LDDecomposition | Represents a L D LT decomposition of a shifted symmetric tridiagonal matrix where T is a symmetric tridiagonal matrix, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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LDFactorizationFromRoot | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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LDLt | The LDL decomposition decomposes a real and symmetric (hence square) matrix A into A = L * D * Lt. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle | SuanShu |
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LeapFrogging | The leap-frogging algorithm is a method for simulating Molecular Dynamics, which isSee Also:"Jun S. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
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LeastPth | The least p-th minmax algorithm minimizes the maximal error/loss (function): min_x max_{omega in S} e(x, omega) | Class | com.numericalmethod.suanshu.optimization.multivariate.minmax | SuanShu |
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LeastSquares | This method obtains a least squares estimate of a polynomial to fit the input data, by a weighted sum of orthogonal polynomials up to a specified order. | Class | com.numericalmethod.suanshu.analysis.curvefit | SuanShu |
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Lebesgue | Lebesgue integration is the general theory of integration of a function with respect to a general measure. | Class | com.numericalmethod.suanshu.analysis.integration.univariate | SuanShu |
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LEcuyer | This is the uniform random number generator recommended by L'Ecuyer in 1996. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
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LedoitWolf2004 | To estimate the covariance matrix, Ledoit and Wolf (2004) suggests using the matrix obtained from the sample covariance matrix through a transformation called shrinkage. | Class | com.numericalmethod.suanshu.stats.descriptive.covariance | SuanShu |
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LegendrePolynomials | A Legendre polynomial is defined by the recurrence relation below. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule | SuanShu |
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LegendreRule | | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule | SuanShu |
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Lehmer | Lehmer proposed a general linear congruential generator that generates pseudo-random numbers in xi+1 = (a * xi + c) mod m | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
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LessThanConstraints | The domain of an optimization problem may be restricted by less-than or equal-to constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint | SuanShu |
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Levene | The Levene test tests for the equality of variance of groups. | Class | com.numericalmethod.suanshu.stats.test.variance | SuanShu |
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License | This is the license management system for the library. | Class | com.numericalmethod.suanshu.misc.license | SuanShu |
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LicenseError | General error regarding the license, e. | Class | com.numericalmethod.suanshu.misc.license | SuanShu |
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Lilliefors | Lilliefors test tests the null hypothesis that data come from a normally distributed population with an estimated sample mean and variance. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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LILSparseMatrix | The list of lists (LIL) format for sparse matrix stores one list per row, where each entry stores a column index and value. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
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LinearCongruentialGenerator | A linear congruential generator (LCG) produces a sequence of pseudo-random numbers based on a linear recurrence relation. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
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LinearConstraints | This is a collection of linear constraints for a real-valued optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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LinearEqualityConstraints | This is a collection of linear equality constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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LinearFit | Find the parameters for the ACER function from the given empirical epsilon, using OLS regression on the logarithm of the values. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer | SuanShu |
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LinearGreaterThanConstraints | This is a collection of linear greater-than-or-equal-to constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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LinearInterpolation | (Piecewise-)Linear interpolation fits a curve by interpolating linearly between two adjacent data-points. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate | SuanShu |
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LinearInterpolator | Define a univariate function by linearly interpolating between adjacent points. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation | SuanShu |
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LinearKalmanFilter | The Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time, | Class | com.numericalmethod.suanshu.stats.dlm.univariate | SuanShu |
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LinearLessThanConstraints | This is a collection of linear less-than-or-equal-to constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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LinearModel | A linear model provides fitting and the residual analysis (goodness of fit). | Interface | com.numericalmethod.suanshu.stats.regression.linear | SuanShu |
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LinearRepresentation | The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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LinearRoot | This is a solver for finding the roots of a linear equation. | Class | com.numericalmethod.suanshu.analysis.function.polynomial.root | SuanShu |
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LinearSystemSolver | Solve a system of linear equations in the form: We assume that, after row reduction, A has no more rows than columns. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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LineSearch | A line search is often used in another minimization algorithm to improve the current solution in one iteration step. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.linesearch | SuanShu |
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LineSegment | Represent a line segment. | Class | com.numericalmethod.suanshu.geometry | SuanShu |
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LinkCloglog | This class represents the complementary log-log link function: g(x) = log(-log(1 - x)) | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
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LinkFunction | This interface represents a link function g(x) in Generalized Linear Model (GLM). | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
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LinkIdentity | This class represents the identity link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
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LinkInverse | This class represents the inverse link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
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LinkInverseSquared | This class represents the inverse-squared link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
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LinkLog | This class represents the log link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
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LinkLogit | This class represents the logit link function: g(x) = log(frac{mu}{1-mu}) | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
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LinkProbit | This class represents the Probit link function, which is the inverse of cumulative distribution function of the standard Normal distribution N(0, 1). | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
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LinkSqrt | This class represents the square-root link function:See Also:GeneralizedLinearModel | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.distribution.link | SuanShu |
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LjungBox | The Ljung-Box test (named for Greta M. | Class | com.numericalmethod.suanshu.stats.test.timeseries.portmanteau | SuanShu |
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LMBeta | Beta coefficients are the outcomes of fitting a linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear | SuanShu |
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LMDiagnostics | This class collects some diagnostics measures for the goodness of fit based on the residulas for a linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear.residualanalysis | SuanShu |
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LMInformationCriteria | The information criteria measure the goodness of fit of an estimated statistical model. | Class | com.numericalmethod.suanshu.stats.regression.linear.residualanalysis | SuanShu |
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LMProblem | This is a linear regression or a linear model (LM) problem. | Class | com.numericalmethod.suanshu.stats.regression.linear | SuanShu |
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LMResiduals | This is the residual analysis of the results of a linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear.residualanalysis | SuanShu |
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LocalConfig | Java class for localConfig complex type. | Class | com.numericalmethod.suanshu.grid.config.xml.schema | SuanShu |
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LocalConfiguration | Defines the configuration for a local execution. | Interface | com.numericalmethod.suanshu.grid.config.local | SuanShu |
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LocalGridExecutor | Interface for classes that execute all their tasks locally (in the same JVM). | Interface | com.numericalmethod.suanshu.grid.executor.local | SuanShu |
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LocalGridExecutorFactory | Creates local instances of GridExecutors from a configuration object. | Class | com.numericalmethod.suanshu.grid.executor.local | SuanShu |
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LocalParallelGridExecutor | Grid executor that executes everything locally. | Class | com.numericalmethod.suanshu.grid.executor.local | SuanShu |
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LocalSearchCellFactory | A LocalSearchCellFactory produces LocalSearchCellFactory. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.local | SuanShu |
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LogBeta | This class represents the log of Beta function log(B(x, y)). | Class | com.numericalmethod.suanshu.analysis.function.special.beta | SuanShu |
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LogGamma | The log-Gamma function, (log (Gamma(z))), for positive real numbers, is the log of the Gamma function. | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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LogisticBeta | | Class | com.numericalmethod.suanshu.stats.regression.linear.logistic | SuanShu |
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LogisticProblem | A logistic regression problem is a variation of the OLS regression problem. | Class | com.numericalmethod.suanshu.stats.regression.linear.logistic | SuanShu |
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LogisticRegression | A logistic regression (sometimes called the logistic model or logit model) is used for prediction of the probability of occurrence of an event by fitting data to a logit function logistic curve. | Class | com.numericalmethod.suanshu.stats.regression.linear.logistic | SuanShu |
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LogisticResiduals | Residual analysis of the results of a logistic regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.logistic | SuanShu |
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LogNormalDistribution | A log-normal distribution is a probability distribution of a random variable whose logarithm is normally distributed. | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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LogNormalMixtureDistribution | The HMM states use the Log-Normal distribution to model the observations. | Class | com.numericalmethod.suanshu.stats.hmm.mixture.distribution | SuanShu |
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LogNormalRNG | This random number generator samples from the log-normal distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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LoopBody | The implementation of this interface contains the code inside a for-loopThis method contains the code inside the for-loop, as in a native | Interface | com.numericalmethod.suanshu.misc.parallel | SuanShu |
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LowerBoundConstraints | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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LowerTriangularMatrix | A lower triangular matrix has 0 entries where column index > row index. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle | SuanShu |
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LPBoundedMinimizer | This is the solution to a bounded linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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LPCanonicalProblem1 | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPCanonicalProblem2 | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPCanonicalSolver | This is an LP solver that solves a canonical LP problem in the following form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver | SuanShu |
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LPDimensionNotMatched | This is the exception thrown when the dimensions of the objective function and constraints of a linear programming problem are inconsistent. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPEmptyCostVector | This is the exception thrown when there is no objective function in a linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPException | This is the exception thrown when there is any problem when solving a linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPInfeasible | This is the exception thrown when the LP problem is infeasible, i. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPMinimizer | An LP minimizer minimizes the objective of an LP problem, satisfying all the constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp | SuanShu |
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LPNoConstraint | This is the exception thrown when there is no linear constraint found for the LP problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPProblem | A linear programming (LP) problem minimizes a linear objective function subject to a collection of linear constraints. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPProblemImpl1 | This is an implementation of a linear programming problem, LPProblem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPRuntimeException | This is the exception thrown when there is any problem when constructing a linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPSimplexMinimizer | A simplex LP minimizer can be read off from the solution simplex table. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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LPSimplexSolution | The solution to a linear programming problem using a simplex method contains an LPSimplexMinimizer. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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LPSimplexSolver | A simplex solver works toward an LP solution by sequentially applying Jordan exchange to a simplex table. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver | SuanShu |
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LPSolution | A solution to an LP problem contains all information about solving an LP problem such as whether the problem has a solution (bounded), how many minimizers it has, and the minimum. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp | SuanShu |
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LPSolver | An LP solver solves a Linear Programming (LP) problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp | SuanShu |
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LPStandardProblem | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.problem | SuanShu |
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LPTwoPhaseSolver | This implementation solves a linear programming problem, LPProblem, using a two-step approach. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solver | SuanShu |
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LPUnbounded | This is the exception thrown when the LP problem is unbounded. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.exception | SuanShu |
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LPUnboundedMinimizer | This is the solution to an unbounded linear programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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LPUnboundedMinimizerScheme2 | This is the solution to an unbounded linear programming problem found in scheme 2. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.solution | SuanShu |
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LSProblem | This is the problem of solving a system of linear equations. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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LU | LU decomposition decomposes an n x n matrix A so that P * A = L * U. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle | SuanShu |
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LUDecomposition | LU decomposition decomposes an n x n matrix A so that P * A = L * U. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.triangle | SuanShu |
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LUSolver | Use LU decomposition to solve Ax = b where A is square and The dimensions of A and b must match. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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MADecomposition | This class decomposes a time series into the trend, seasonal and stationary random components using the Moving Average Estimation method with symmetric window. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess | SuanShu |
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MAModel | This class represents a univariate MA model. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess.arma | SuanShu |
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MARMAModel | Simulation of max autoregressive moving average processes, i. | Class | com.numericalmethod.suanshu.stats.evt.timeseries | SuanShu |
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MARMASim | Generate random numbers based on a given MARMA model. | Class | com.numericalmethod.suanshu.stats.evt.timeseries | SuanShu |
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MARModel | This is equivalent to MARMA(p, 0). | Class | com.numericalmethod.suanshu.stats.evt.timeseries | SuanShu |
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MarsagliaBray1964 | The polar method (attributed to George Marsaglia, 1964) is a pseudo-random number sampling method for generating a pair of independent standard normal random variables. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
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MarsagliaTsang2000 | Marsaglia-Tsang is a procedure for generating a gamma variate as the cube of a suitably scaled normal variate. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
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Master | Delegates Work to one or more slaves and forwards Results to a predefined Also forwards work and results to the failure detector, which will keep track of their relative | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.actor | SuanShu |
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MAT | MAT is the inverse operator of SVEC. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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MathTable | A mathematical table consists of numbers showing the results of calculation with varying arguments. | Class | com.numericalmethod.suanshu.misc.datastructure | SuanShu |
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Matrix | This interface defines a Matrix as a Ring, a Table, and a few more methods not already defined in its mathematical definition. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
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MatrixAccess | This interface defines the methods for accessing entries in a matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
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MatrixAccessException | This is the runtime exception thrown when trying to access an invalid entry in a matrix, e. | Class | com.numericalmethod.suanshu.algebra.linear.matrix | SuanShu |
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MatrixCoordinate | The location of a matrix entry is specified by a 2D coordinates (i, j), where i and j are the row-index and column-index of the entry respectively. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
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MatrixFactory | These are the utility functions to create a new matrix/vector from existing ones. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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MatrixMathOperation | This interface defines some standard operations for generic matrices. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation | SuanShu |
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MatrixMeasure | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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MatrixMismatchException | This is the runtime exception thrown when an operation acts on matrices that have incompatible dimensions. | Class | com.numericalmethod.suanshu.algebra.linear.matrix | SuanShu |
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MatrixPropertyUtils | These are the boolean operators that take matrices or vectors and check if they satisfy aChecks if all matrices are SparseMatrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
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MatrixRing | | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
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MatrixRootByDiagonalization | The square root of a matrix extends the notion of square root from numbers to matrices. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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MatrixSingularityException | This is the runtime exception thrown when an operation acts on a singular matrix, e. | Class | com.numericalmethod.suanshu.algebra.linear.matrix | SuanShu |
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MatrixTable | A matrix is represented by a rectangular table structure with accessors. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles | SuanShu |
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MatrixUtils | These are the utility functions to apply to matrices. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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MatthewsDavies | Matthews and Davies propose the following way to coerce a non-positive definite Hessian matrix to become symmetric, positive definite. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite | SuanShu |
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Max | The maximum of a sample is the biggest value in the sample. | Class | com.numericalmethod.suanshu.stats.descriptive.rank | SuanShu |
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MaximaDistribution | The distribution of (M), where (M=max(x_1,x_2,. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate | SuanShu |
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MaximizationSolution | This is the solution to a maximization problem. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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MaximumLikelihoodFitting | This interface defines model fitting by maximum likelihood algorithm. | Interface | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting | SuanShu |
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Maxmizer | This interface represents an optimization algorithm that maximizers a real valued objective function, one or multi dimension. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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McCormickMinimizer | This is the McCormick method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
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MCUtils | These are the utility functions to examine a Markov chain. | Class | com.numericalmethod.suanshu.stats.markovchain | SuanShu |
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Mean | The mean of a sample is the sum of all numbers in the sample, divided by the sample size. | Class | com.numericalmethod.suanshu.stats.descriptive.moment | SuanShu |
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MeanEstimator | Defines how to estimate the mean price. | Interface | com.numericalmethod.suanshu.model.volarb | SuanShu |
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MeanEstimator | | Interface | com.numericalmethod.suanshu.stats.random | SuanShu |
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MeanEstimatorMaxLevelShift | | Class | com.numericalmethod.suanshu.model.volarb | SuanShu |
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MersenneExponent | enum MersenneExponentThe value of a Mersenne Exponent p is a parameter for creating a Mersenne-Twister random | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation | SuanShu |
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MersenneTwister | Mersenne Twister is one of the best pseudo random number generators available. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister | SuanShu |
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MersenneTwisterParam | Immutable parameters for creating a MersenneTwister RNG. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister | SuanShu |
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MersenneTwisterParamSearcher | Searches for Mersenne-Twister parameters. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.mersennetwister.dynamiccreation | SuanShu |
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Metropolis | This basic Metropolis implementation assumes using symmetric proposal function. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
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MetropolisAcceptanceProbabilityFunction | Uses the classic Metropolis rule, f_{t+1}/f_t. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
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MetropolisHastings | A generalization of the Metropolis algorithm, which allows asymmetric proposal Metropolis-HastingsLiu, Jun S. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
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MetropolisUtils | Utility functions for Metropolis algorithms. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
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Midpoint | The midpoint rule computes an approximation to a definite integral, made by finding the area of a collection of rectangles whose heights are determined by the values of the function. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes | SuanShu |
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MilsteinSDE | Milstein scheme is a first-order approximation to a continuous-time SDE. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.discrete | SuanShu |
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Min | The minimum of a sample is the smallest value in the sample. | Class | com.numericalmethod.suanshu.stats.descriptive.rank | SuanShu |
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MinimaDistribution | The distribution of (M), where (M=min(x_1,x_2,. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate | SuanShu |
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MinimalResidualSolver | The Minimal Residual method (MINRES) is useful for solving a symmetric n-by-n linear system (possibly indefinite or singular). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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MinimizationSolution | This is the solution to a minimization problem. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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Minimizer | This interface represents an optimization algorithm that minimizes a real valued objective function, one or multi dimension. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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MinimumWeights | This constraint puts lower bounds on weights. | Class | com.numericalmethod.suanshu.model.corvalan2005.constraint | SuanShu |
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MinMaxMinimizer | A minmax minimizer minimizes a minmax problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.minmax | SuanShu |
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MixedRule | The mixed rule is good for functions that fall off rapidly at infinity, e. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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MixtureDistribution | This is the conditional distribution of the observations in each state (possibly differently parameterized) of a mixture hidden Markov model. | Interface | com.numericalmethod.suanshu.stats.hmm.mixture.distribution | SuanShu |
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MixtureHMM | This is the mixture hidden Markov model (HMM). | Class | com.numericalmethod.suanshu.stats.hmm.mixture | SuanShu |
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MixtureHMMEM | The EM algorithm is used to find the unknown parameters of a hidden Markov model (HMM) by making use of the forward-backward algorithm. | Class | com.numericalmethod.suanshu.stats.hmm.mixture | SuanShu |
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MMAModel | This is equivalent to MARMA(0, q). | Class | com.numericalmethod.suanshu.stats.evt.timeseries | SuanShu |
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ModelResamplerFactory | | Class | com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler | SuanShu |
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Moments | Compute the central moment of a data set incrementally. | Class | com.numericalmethod.suanshu.stats.descriptive.moment | SuanShu |
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MomentsEstimatorLedoitWolf | | Class | com.numericalmethod.suanshu.model.returns.moments | SuanShu |
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Monoid | | Interface | com.numericalmethod.suanshu.algebra.structure | SuanShu |
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MovingAverage | This applies a linear filter to a univariate time series using the moving average estimation. | Class | com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles | SuanShu |
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MovingAverageByExtension | This implements a moving average filter with these properties: 1) both past and future observations are used in smoothing; | Class | com.numericalmethod.suanshu.dsp.univariate.operation.system.doubles | SuanShu |
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MR3 | Computes eigenvalues and eigenvectors of a given symmetric tridiagonal matrix T using "Algorithm of Multiple Relatively Robust Representations" (MRRR). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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MRG | A Multiple Recursive Generator (MRG) is a linear congruential generator which takes this form: xi = (a1 * xi-1 + a2 * xi-2 + . | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform.linear | SuanShu |
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MRModel | A Mean Reversion Model computes the target position given the current price. | Interface | com.numericalmethod.suanshu.model.volarb | SuanShu |
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MRModelRanged | | Class | com.numericalmethod.suanshu.model.volarb | SuanShu |
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MultiCubicSpline | algorithm works by recursively calling lower order cubic spline interpolation. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate | SuanShu |
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MultiDimensionalArray | A generic multi-dimensional array, with an arbitrary number of dimensions. | Class | com.numericalmethod.suanshu.misc.datastructure | SuanShu |
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MultiDimensionalCollection | A generic collection with an arbitrary number of dimensions. | Interface | com.numericalmethod.suanshu.misc.datastructure | SuanShu |
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MultiDimensionalGrid | An arbitrary dimensional grid. | Class | com.numericalmethod.suanshu.misc.datastructure | SuanShu |
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MultiLinearInterpolation | by recursively calling lower order linear interpolation. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate | SuanShu |
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MultinomialBetaFunction | A multinomial Beta function is defined as: frac{prod_{i=1}^K Gamma(alpha_i)}{Gammaleft(sum_{i=1}^K | Class | com.numericalmethod.suanshu.analysis.function.special.beta | SuanShu |
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MultinomialDistribution | | Class | com.numericalmethod.suanshu.stats.distribution.multivariate | SuanShu |
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MultinomialRVG | A multinomial distribution puts N objects into K bins according to the bins' probabilities. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
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MultipleExecutionException | This exception is thrown when any of the parallel tasks throws an exception during execution. | Class | com.numericalmethod.suanshu.misc.parallel | SuanShu |
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MultiplicativeModel | The multiplicative model of a time series is a multiplicative composite of the trend, seasonality and irregular random components. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.stationaryprocess | SuanShu |
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MultiplierPenalty | A multiplier penalty function allows different weights to be assigned to the constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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MultipointHybridMCMC | A multi-point Hybrid Monte Carlo is an extension of HybridMCMC, where during the proposal generation instead of considering only the last configuration after the dynamics | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.hybrid | SuanShu |
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MultivariateArrayGrid | MultiDimensionalCollection instance. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate | SuanShu |
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MultivariateAutoCorrelationFunction | This is the auto-correlation function of a multi-dimensional time series {Xt}. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate | SuanShu |
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MultivariateAutoCovarianceFunction | This is the auto-covariance function of a multi-dimensional time series {Xt}, K(i, j) = E((X_i - mu_i) imes (X_j - mu_j)') | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate | SuanShu |
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MultivariateBrownianRRG | This is the Random Walk construction of a multivariate Brownian motion. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random | SuanShu |
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MultivariateBrownianSDE | A multivariate Brownian motion is a stochastic process with the following properties. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete | SuanShu |
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MultivariateDiscreteSDE | This interface represents the discrete approximation of a multivariate SDE. | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete | SuanShu |
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MultivariateDLM | This is the multivariate controlled DLM (controlled Dynamic Linear Model) specification. | Class | com.numericalmethod.suanshu.stats.dlm.multivariate | SuanShu |
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MultivariateDLMSeries | This is a simulator for a multivariate controlled dynamic linear model process. | Class | com.numericalmethod.suanshu.stats.dlm.multivariate | SuanShu |
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MultivariateDLMSim | This is a simulator for a multivariate controlled dynamic linear model process. | Class | com.numericalmethod.suanshu.stats.dlm.multivariate | SuanShu |
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MultivariateEulerSDE | The Euler scheme is the first order approximation of an SDE. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.discrete | SuanShu |
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MultivariateFiniteDifference | A partial derivative of a multivariate function is the derivative with respect to one of the variables with the others held constant. | Class | com.numericalmethod.suanshu.analysis.differentiation.multivariate | SuanShu |
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MultivariateForecastOneStep | The innovation algorithm is an efficient way to obtain a one step least square linear predictor for a multivariate linear time series | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess | SuanShu |
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MultivariateFt | This represents the concept 'Filtration', the information available at time t. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde | SuanShu |
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MultivariateFtWt | This is a filtration implementation that includes the path-dependent information,See Also:MultivariateFt | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde | SuanShu |
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MultivariateGenericTimeTimeSeries | This is a multivariate time series indexed by some notion of time. | Class | com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate | SuanShu |
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MultivariateGrid | A multivariate rectilinear (not necessarily uniform) grid of double values. | Interface | com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate | SuanShu |
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MultivariateGridInterpolation | Interpolation on a rectilinear multi-dimensional grid. | Interface | com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate | SuanShu |
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MultivariateInnovationAlgorithm | | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess | SuanShu |
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MultivariateIntTimeTimeSeries | This is a multivariate time series indexed by integers. | Interface | com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime | SuanShu |
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MultivariateLinearKalmanFilter | The Kalman filter, also known as linear quadratic estimation (LQE), is an algorithm which uses a series of measurements observed over time, | Class | com.numericalmethod.suanshu.stats.dlm.multivariate | SuanShu |
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MultivariateMinimizer | This is a minimizer that minimizes a multivariate function or a Vector function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained | SuanShu |
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MultivariateNormalDistribution | The multivariate Normal distribution or multivariate Gaussian distribution, is a generalization of the one-dimensional (univariate) Normal distribution to higher dimensions. | Class | com.numericalmethod.suanshu.stats.distribution.multivariate | SuanShu |
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MultivariateObservationEquation | This is the observation equation in a controlled dynamic linear model. | Class | com.numericalmethod.suanshu.stats.dlm.multivariate | SuanShu |
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MultivariateProbabilityDistribution | A multivariate or joint probability distribution for X, Y, . | Interface | com.numericalmethod.suanshu.stats.distribution.multivariate | SuanShu |
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MultivariateRandomProcess | This interface represents a multivariate random process a. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random | SuanShu |
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MultivariateRandomRealizationGenerator | This interface defines a generator to construct random realizations from a multivariate stochastic process. | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random | SuanShu |
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MultivariateRandomRealizationOfRandomProcess | This class generates random realizations from a multivariate random/stochastic process. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random | SuanShu |
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MultivariateRandomWalk | This is the Random Walk construction of a multivariate stochastic process per SDE specification. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.random | SuanShu |
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MultivariateRealization | A multivariate realization is a multivariate time series indexed by real numbers, e. | Interface | com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime | SuanShu |
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MultivariateRegularGrid | A regular grid is a tessellation of n-dimensional Euclidean space by congruent parallelotopes (e. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate | SuanShu |
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MultivariateResampler | This is the interface of a multivariate re-sampler method. | Interface | com.numericalmethod.suanshu.stats.random.sampler.resampler.multivariate | SuanShu |
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MultivariateSDE | This class represents a multi-dimensional, continuous-time Stochastic Differential Equation (SDE) of this form: dX_t = mu(t,X_t,Z_t,. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde | SuanShu |
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MultivariateSimpleTimeSeries | This simple multivariate time series has its vectored values indexed by integers. | Class | com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate.realtime.inttime | SuanShu |
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MultivariateStateEquation | This is the state equation in a controlled dynamic linear model. | Class | com.numericalmethod.suanshu.stats.dlm.multivariate | SuanShu |
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MultivariateTDistribution | The multivariate T distribution or multivariate Student distribution, is a generalization of the one-dimensional (univariate) Student's t-distribution to higher dimensions. | Class | com.numericalmethod.suanshu.stats.distribution.multivariate | SuanShu |
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MultivariateTimeSeries | A multivariate time series is a sequence of vectors indexed by some notion of time. | Interface | com.numericalmethod.suanshu.stats.timeseries.datastructure.multivariate | SuanShu |
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Mutex | Provides mutual exclusive execution of a Runnable. | Class | com.numericalmethod.suanshu.misc.parallel | SuanShu |
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MVOptimizer | Solves for the optimal weight using Mean-Variance optimization. | Interface | com.numericalmethod.suanshu.model.lai2010.optimizer | SuanShu |
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MVOptimizerMinWeights | Solves for weights by (active set) quadratic programming. | Class | com.numericalmethod.suanshu.model.lai2010.optimizer | SuanShu |
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MVOptimizerNoConstraint | | Class | com.numericalmethod.suanshu.model.lai2010.optimizer | SuanShu |
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MWC8222 | Marsaglia's MWC256 (also known as MWC8222) is a multiply-with-carry generator. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform | SuanShu |
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NaiveRule | This pivoting rule chooses the column with the most negative reduced cost. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting | SuanShu |
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NelderMeadMinimizer | The Nelder-Mead method is a nonlinear optimization technique, which is well-defined for twice differentiable and unimodal problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2 | SuanShu |
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NevilleTable | Neville's algorithm is a polynomial interpolation algorithm. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation | SuanShu |
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NewtonCotes | The Newton-Cotes formulae, also called the Newton-Cotes quadrature rules or simply Newton-Cotes rules, are a group of formulae for numerical integration (also called quadrature) based on evaluating the integrand at equally-spaced points. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes | SuanShu |
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NewtonPolynomial | Newton polynomial is the interpolation polynomial for a given set of data points in the Newton form. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.univariate | SuanShu |
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NewtonRaphsonMinimizer | The Newton-Raphson method is a second order steepest descent method that is based on the quadratic approximation of the Taylor series. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
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NewtonRoot | The Newton-Raphson method is as follows: one starts with an initial guess which is reasonably close to the true root, then the function is approximated by its tangent line (which can be | Class | com.numericalmethod.suanshu.analysis.root.univariate | SuanShu |
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NewtonSystemRoot | This class solves the root for a non-linear system of equations. | Class | com.numericalmethod.suanshu.analysis.root.multivariate | SuanShu |
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NoChangeOfVariable | This is a dummy substitution rule that does not change any variable. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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NoConstraints | Weights are unconstrained and constraints() returns null. | Class | com.numericalmethod.suanshu.model.corvalan2005.constraint | SuanShu |
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NonlinearFit | Fit log-ACER function by sequential quadratic programming (SQP) minimization (of weighted RSS), using LinearFit's solution as the initial guess. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.acer | SuanShu |
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NonNegativityConstraintOptimProblem | This is a constrained optimization problem for a function which has all non-negative variables. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.problem | SuanShu |
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NonNegativityConstraints | These constraints ensures that for all variables are non-negative. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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NoOpActor | | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.actor | SuanShu |
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NormalDistribution | The Normal distribution has its density a Gaussian function. | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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NormalMixtureDistribution | The HMM states use the Normal distribution to model the observations. | Class | com.numericalmethod.suanshu.stats.hmm.mixture.distribution | SuanShu |
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NormalOfExpFamily1 | Normal distribution, univariate, unknown mean, known variance. | Class | com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily | SuanShu |
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NormalOfExpFamily2 | Normal distribution, univariate, unknown mean, unknown variance. | Class | com.numericalmethod.suanshu.stats.distribution.univariate.exponentialfamily | SuanShu |
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NormalRNG | This is a random number generator that generates random deviates according to the NormalSee Also:Wikipedia: Normal | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
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NormalRVG | A multivariate Normal random vector is said to be p-variate normally distributed if every linear combination of its p components has a univariate normal distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
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NoRootFoundException | This is the Exception thrown when it fails to find a root. | Class | com.numericalmethod.suanshu.analysis.root.univariate | SuanShu |
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NoShortSelling | Weights cannot be negative. | Class | com.numericalmethod.suanshu.model.corvalan2005.constraint | SuanShu |
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NPEBMomentsEstimator | | Class | com.numericalmethod.suanshu.model.lai2010.ceta.npeb | SuanShu |
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NullMonitor | This IterationMonitor does nothing when a new iterate is added. | Class | com.numericalmethod.suanshu.misc.algorithm.iterative.monitor | SuanShu |
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NumberUtils | These are the utility functions to manipulate Numbers. | Class | com.numericalmethod.suanshu.number | SuanShu |
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ObjectFactory | This object contains factory methods for each Java content interface and Java element interface | Class | com.numericalmethod.suanshu.grid.config.xml.schema | SuanShu |
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ObservationEquation | This is the observation equation in a controlled dynamic linear model. | Class | com.numericalmethod.suanshu.stats.dlm.univariate | SuanShu |
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ODE | An ordinary differential equation (ODE) is an equation in which there is only one independent variable and one or more derivatives of a dependent variable with respect to the independent | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem | SuanShu |
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ODE1stOrder | A first order ordinary differential equation (ODE) initial value problem (IVP) takes the where y0 is known, and the solution of the problem is y(x) for the | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem | SuanShu |
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ODE1stOrderWith2ndDerivative | Some ODE solvers require the second derivative for more accurate Taylor series approximation. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.problem | SuanShu |
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ODEIntegrator | This defines the interface for the numerical integration of a first order ODE, for a sequence of pre-defined steps. | Interface | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver | SuanShu |
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ODESolution | Solution to an ODE problem. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver | SuanShu |
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ODESolver | Solver for first order ODE problems. | Interface | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver | SuanShu |
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OLSBeta | | Class | com.numericalmethod.suanshu.stats.regression.linear.ols | SuanShu |
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OLSRegression | (Weighted) Ordinary Least Squares (OLS) is a method for fitting a linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear.ols | SuanShu |
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OLSResiduals | This is the residual analysis of the results of an ordinary linear regression model. | Class | com.numericalmethod.suanshu.stats.regression.linear.ols | SuanShu |
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OLSSolver | This class solves an over-determined system of linear equations in the ordinary least square sense. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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OLSSolverByQR | This class solves an over-determined system of linear equations in the ordinary least square sense. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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OLSSolverBySVD | This class solves an over-determined system of linear equations in the ordinary least square sense. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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OneDimensionTimeSeries | This class constructs a univariate realization from a multivariate realization by taking one of its dimension (coordinate). | Class | com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime | SuanShu |
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OneWayANOVA | The One-Way ANOVA test tests for the equality of the means of several groups. | Class | com.numericalmethod.suanshu.stats.test.mean | SuanShu |
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OnlineInterpolator | An online interpolator allows dynamically adding more points for interpolation. | Interface | com.numericalmethod.suanshu.analysis.curvefit.interpolation | SuanShu |
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Optimizer | Optimization, or mathematical programming, refers to choosing the best element from some set of available alternatives. | Interface | com.numericalmethod.suanshu.optimization | SuanShu |
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OptimProblem | This is an optimization problem that minimizes a real valued objective function, one or multi dimension. | Interface | com.numericalmethod.suanshu.optimization.problem | SuanShu |
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OrderedAccumulator | Collects all results in a list, ensuring that the order according to the indices is preserved in Results for indices may arrive multiple times and may be null due to a failure. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.actor | SuanShu |
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OrderedPairs | Cartesian products and binary relations (and hence the ubiquitous functions) are defined in termsSee Also:Wikipedia: Ordered pair | Interface | com.numericalmethod.suanshu.analysis.function.tuple | SuanShu |
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OrderStatisticsDistribution | The asymptotic nondegenerate distributions of the r-th smallest (largest) order statistic. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate | SuanShu |
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OrnsteinUhlenbeckProcess | This class represents a univariate Ornstein-Uhlenbeck (OU) process. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou | SuanShu |
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OrStopConditions | Combines an arbitrary number of stop conditions, terminating when the first condition is met. | Class | com.numericalmethod.suanshu.misc.algorithm.stopcondition | SuanShu |
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OrthogonalPolynomialFamily | This factory class produces a family of orthogonal polynomials. | Interface | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.gaussian.rule | SuanShu |
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OUFitting | This interface defines an estimation procedure to fit a univariate Ornstein-Uhlenbeck process. | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou | SuanShu |
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OUFittingMLE | This class fits a univariate Ornstein-Uhlenbeck process by using MLE. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou | SuanShu |
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OUFittingOLS | This class fits a univariate Ornstein-Uhlenbeck process by using least squares regression. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou | SuanShu |
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OUProcess | Get the overall mean. | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou | SuanShu |
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OUSim | This class simulates a discrete path of a univariate Ornstein-Uhlenbeck (OU) process. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde.process.ou | SuanShu |
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OuterProduct | The outer product of two vectors a and b, is a row vector multiplied on the left by a column vector. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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Package | | Class | com.numericalmethod.suanshu.misc.license | SuanShu |
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Pair | An ordered pair (x,y) is a pair of mathematical objects. | Class | com.numericalmethod.suanshu.analysis.function.tuple | SuanShu |
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PairComparatorByAbscissaFirst | | Class | com.numericalmethod.suanshu.analysis.function.tuple | SuanShu |
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PairComparatorByAbscissaOnly | | Class | com.numericalmethod.suanshu.analysis.function.tuple | SuanShu |
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PanelData | A panel data refers to multi-dimensional data frequently involving measurements over time. | Class | com.numericalmethod.suanshu.stats.regression.linear.panel | SuanShu |
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PanelRegression | Panel (data) analysis is a statistical method, widely used in social science, epidemiology, and econometrics, which deals with two-dimensional (cross sectional/times series) panel data. | Interface | com.numericalmethod.suanshu.stats.regression.linear.panel | SuanShu |
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ParallelDoubleArrayOperation | This is a multi-threaded implementation of the array math operations. | Class | com.numericalmethod.suanshu.number.doublearray | SuanShu |
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ParallelExecutor | This class provides a framework for executing an algorithm in parallel. | Class | com.numericalmethod.suanshu.misc.parallel | SuanShu |
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PartialDerivativesByCenteredDifferencing | This implementation computes the partial derivatives by centered differencing. | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.bivariate | SuanShu |
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PartialFunction | | Class | com.numericalmethod.suanshu.analysis.function.tuple | SuanShu |
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PattonPolitisWhite2009 | This class implements the stationary and circular block bootstrapping method with optimized blockSee Also:Politis, N. | Class | com.numericalmethod.suanshu.stats.random.sampler.resampler.bootstrap.block | SuanShu |
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PCA | Principal Component Analysis (PCA) is a mathematical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of | Interface | com.numericalmethod.suanshu.stats.pca | SuanShu |
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PCAbyEigen | This class performs Principal Component Analysis (PCA) on a data matrix, using eigen decomposition on the correlation or covariance matrix. | Class | com.numericalmethod.suanshu.stats.pca | SuanShu |
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PCAbySVD | This class performs Principal Component Analysis (PCA) on a data matrix, using the preferred Singular Value Decomposition (SVD) method. | Class | com.numericalmethod.suanshu.stats.pca | SuanShu |
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PDE | A partial differential equation (PDE) is a differential equation that contains unknown multivariable functions and their partial derivatives. | Interface | com.numericalmethod.suanshu.analysis.differentialequation.pde | SuanShu |
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PDESolutionGrid2D | A solution to a bivariate PDE, which is applicable to methods which produce the solution as a two-dimensional grid. | Interface | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference | SuanShu |
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PDESolutionTimeSpaceGrid1D | A solution to an one-dimensional PDE, which is applicable to methods which produce the solution | Interface | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference | SuanShu |
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PDESolutionTimeSpaceGrid2D | A solution to a two-dimensional PDE, which is applicable to methods which produce the solution as a three-dimensional grid of time and space. | Interface | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference | SuanShu |
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PDESolver | | Interface | com.numericalmethod.suanshu.analysis.differentialequation.pde | SuanShu |
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PDETimeSpaceGrid1D | This grid numerically solves a 1D PDE, e. | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference | SuanShu |
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PeaksOverThreshold | Peaks Over Threshold (POT) method estimates the parameters for generalized Pareto distribution (GPD) using maximum likelihood on the observations that are over a given threshold. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot | SuanShu |
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PeaksOverThresholdOnClusters | Similar to POT, but only use the peak observations in clusters for the parametric estimation. | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate.fitting.pot | SuanShu |
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PearsonMinimizer | This is the Pearson method. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
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PenaltyFunction | A function P: Rn -> R is a penalty function for a constrained optimization problem if it has these properties. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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PenaltyMethodMinimizer | The penalty method is an algorithm for solving a constrained minimization problem with general It replaces a constrained optimization problem by a series of unconstrained problems | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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PermutationMatrix | A permutation matrix is a square matrix that has exactly one entry '1' in each row and each column and 0's elsewhere. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype | SuanShu |
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PerturbationAroundPoint | The initial population is generated by adding a variance around a given initial. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration | SuanShu |
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PhysicalConstants | A collection of fundamental physical constants. | Class | com.numericalmethod.suanshu.misc | SuanShu |
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Point | Represent a n-dimensional point. | Class | com.numericalmethod.suanshu.geometry | SuanShu |
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PoissonDistribution | The Poisson distribution (or Poisson law of small numbers) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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PoissonEquation2D | Poisson's equation is an elliptic PDE that takes the following general form. | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.elliptic.dim2 | SuanShu |
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PoissonMixtureDistribution | The HMM states use the Poisson distribution to model the observations. | Class | com.numericalmethod.suanshu.stats.hmm.mixture.distribution | SuanShu |
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PolygonalChain | A polygonal chain, polygonal curve, polygonal path, or piecewise linear curve, is a connected series of line segments. | Interface | com.numericalmethod.suanshu.geometry.polyline | SuanShu |
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PolygonalChainByArray | An immutable PolygonalChain that is backed by an ArrayList. | Class | com.numericalmethod.suanshu.geometry.polyline | SuanShu |
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Polynomial | A polynomial is a UnivariateRealFunction that represents a finite length expression constructed from variables and constants, using the operations of addition, subtraction, multiplication, and constant non-negative whole number exponents. | Class | com.numericalmethod.suanshu.analysis.function.polynomial | SuanShu |
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PolyRoot | This is a solver for finding the roots of a polynomial equation. | Class | com.numericalmethod.suanshu.analysis.function.polynomial.root | SuanShu |
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PolyRootSolver | A root (or a zero) of a polynomial p is a member x in the domain of p such that p(x) vanishes. | Interface | com.numericalmethod.suanshu.analysis.function.polynomial.root | SuanShu |
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PositiveDefiniteMatrixByPositiveDiagonal | This class "converts" a matrix into a symmetric, positive definite matrix, if it is not already so, by forcing the diagonal entries in the eigen decomposition to a small non-negative number, | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite | SuanShu |
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PositiveSemiDefiniteMatrixNonNegativeDiagonal | This class "converts" a matrix into a symmetric, positive semi-definite matrix, if it is not already so, by forcing the negative diagonal entries in the eigen decomposition to 0. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation.positivedefinite | SuanShu |
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Pow | This is a square matrix A to the power of an integer n, An. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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PowellMinimizer | Powell's algorithm, starting from an initial point, performs a series of line searches in one iteration. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
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PowerLawSingularity | This transformation is good for an integral which diverges at one of the end points. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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Preconditioner | Preconditioning reduces the condition number of the coefficient matrix of a linear system to accelerate the convergence | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
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PreconditionerFactory | This constructs a new instance of Preconditioner for a coefficient matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
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PrimalDualInteriorPointMinimizer | Solves a Dual Second Order Conic Programming problem using the Primal Dual Interior Point 2014/1/9: This solver is tested up to 6000 variables and 26000 constraints. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | SuanShu |
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PrimalDualPathFollowingMinimizer | The Primal-Dual Path-Following algorithm is an interior point method that solves Semi-Definite Programming problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.pathfollowing | SuanShu |
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PrimalDualSolution | The vector set {x, s, y} is a solution to both the primal and dual SOCP problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | SuanShu |
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ProbabilityDistribution | A univariate probability distribution completely characterizes a random variable by stipulating the probability of each value of a random variable (when the variable is discrete), or the | Interface | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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ProbabilityMassFunction | A probability mass function (pmf) is a function that gives the probability that a discrete random variable is exactly equal to some value. | Interface | com.numericalmethod.suanshu.stats.distribution.discrete | SuanShu |
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ProbabilityMassQuantile | As probability mass function is discrete, there are gaps between values in the domain of its cdf, The quantile function is: | Class | com.numericalmethod.suanshu.stats.distribution.discrete | SuanShu |
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ProbabilityMassSampler | A random sampler that is constructed ad-hoc from a list of values and their probabilities. | Class | com.numericalmethod.suanshu.stats.distribution.discrete | SuanShu |
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ProductOfWeights | | Class | com.numericalmethod.suanshu.model.corvalan2005.diversification | SuanShu |
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Projection | Project a vector v on another vector w or a set of vectors (basis) {wi}. | Class | com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation | SuanShu |
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ProposalFunction | A proposal function goes from the current state to the next state, where a state is a vector. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.proposalfunction | SuanShu |
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PseudoInverse | The Moore-Penrose pseudo-inverse of an m x n matrix A is A+. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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PureILPProblem | This is a pure integer linear programming problem, in which all variables are integral. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.problem | SuanShu |
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QPDualActiveSetMinimizer | This implementation solves a Quadratic Programming problem using the dual active set algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activeset | SuanShu |
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QPException | This is the exception thrown when there is an error solving a quadratic programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp | SuanShu |
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QPInfeasible | This is the exception thrown by a quadratic programming solver when the quadratic programming problem is infeasible, i. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp | SuanShu |
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QPPrimalActiveSetMinimizer | This implementation solves a Quadratic Programming problem using the Primal Active Set algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.activeset | SuanShu |
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QPProblem | Quadratic Programming is the problem of optimizing (minimizing) a quadratic function of several variables subject to linear constraints on these variables. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem | SuanShu |
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QPProblemOnlyEqualityConstraints | A quadratic programming problem with only equality constraints can be converted into a equivalent quadratic programming problem without constraints, hence a mere quadratic function. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.problem | SuanShu |
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QPSimpleMinimizer | These are the utility functions to solve simple quadratic programming problems that admit analytical solutions. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp | SuanShu |
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QPSolution | This is a solution to a quadratic programming problem. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp | SuanShu |
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QR | QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr | SuanShu |
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QRAlgorithm | The QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a That is, Q'AQ = T where Q is orthogonal, and T is quasi-triangular. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
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QRDecomposition | QR decomposition of a matrix decomposes an m x n matrix A so that A = Q * R. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.qr | SuanShu |
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QuadraticFunction | A quadratic function takes this form: (f(x) = frac{1}{2} imes x'Hx + x'p + c). | Class | com.numericalmethod.suanshu.analysis.function.rn2r1 | SuanShu |
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QuadraticMonomial | A quadratic monomial has this form: x2 + ux + v. | Class | com.numericalmethod.suanshu.analysis.function.polynomial | SuanShu |
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QuadraticRoot | This is a solver for finding the roots of a quadratic equation, (ax^2 + bx + c = 0). | Class | com.numericalmethod.suanshu.analysis.function.polynomial.root | SuanShu |
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QuadraticSyntheticDivision | Divide a polynomial P(x) by a quadratic monomial (x2 + ux + v) to give the quotient Q(x) and the remainder (b * (x + u) + a). | Class | com.numericalmethod.suanshu.analysis.function.polynomial | SuanShu |
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Quantile | Quantiles are points taken at regular intervals from the cumulative distribution function (CDF) of a random variable. | Class | com.numericalmethod.suanshu.stats.descriptive.rank | SuanShu |
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QuarticRoot | This is a quartic equation solver that solves (ax^4 + bx^3 + cx^2 + dx + e = 0). | Class | com.numericalmethod.suanshu.analysis.function.polynomial.root | SuanShu |
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QuarticRootFerrari | This is a quartic equation solver that solves (ax^4 + bx^3 + cx^2 + dx + e = 0) using the Ferrari method. | Class | com.numericalmethod.suanshu.analysis.function.polynomial.root | SuanShu |
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QuarticRootFormula | This is a quartic equation solver that solves (ax^4 + bx^3 + cx^2 + dx + e = 0) using a root-finding formula. | Class | com.numericalmethod.suanshu.analysis.function.polynomial.root | SuanShu |
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QuasiBinomial | This is the quasi Binomial distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
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QuasiDistribution | This interface represents the quasi-distribution used in GLM. | Interface | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
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QuasiFamily | This interface represents the quasi-family used in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
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QuasiGamma | This is the quasi Gamma distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
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QuasiGaussian | This is the quasi Gaussian distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
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QuasiGLMBeta | | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
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QuasiGLMNewtonRaphson | | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
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QuasiGLMProblem | This class represents a quasi generalized linear regression problem. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
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QuasiGLMResiduals | Residual analysis of the results of a quasi Generalized Linear Model regression. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi | SuanShu |
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QuasiInverseGaussian | This is the quasi Inverse-Gaussian distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
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QuasiMinimalResidualSolver | The Quasi-Minimal Residual method (QMR) is useful for solving a non-symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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QuasiNewtonMinimizer | The Quasi-Newton methods in optimization are for finding local maxima and minima of functions. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
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QuasiPoisson | This is the quasi Poisson distribution in GLM. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.quasi.family | SuanShu |
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R1Projection | Projection creates a real-valued function RealScalarFunction from a vector-valued function RealVectorFunction by taking only one of its coordinate components in the vector output. | Class | com.numericalmethod.suanshu.analysis.function.rn2r1 | SuanShu |
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R1toConstantMatrix | A constant matrix function maps a real number to a constant matrix: (R^n
ightarrow A). | Class | com.numericalmethod.suanshu.analysis.function.matrix | SuanShu |
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R1toMatrix | This is a function that maps from R1 to a Matrix space. | Class | com.numericalmethod.suanshu.analysis.function.matrix | SuanShu |
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R2toMatrix | This is a function that maps from R2 to a Matrix space. | Class | com.numericalmethod.suanshu.analysis.function.matrix | SuanShu |
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RamerDouglasPeucker | The Ramer-Douglas-Peucker algorithm simplifies a PolygonalChain by removing vertices which do not affect the shape of the curve to a given tolerance. | Class | com.numericalmethod.suanshu.geometry.polyline | SuanShu |
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Rand1Bin | The Rand-1-Bin rule is defined by: mutation by adding a scaled, randomly sampled vector difference to a third vector | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.deoptim | SuanShu |
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RandomBetaGenerator | This is a random number generator that generates random deviates according to the Beta distribution. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.beta | SuanShu |
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RandomExpGenerator | This is a random number generator that generates random deviates according to the exponential distribution. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.exp | SuanShu |
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RandomGammaGenerator | This is a random number generator that generates random deviates according to the Gamma distribution. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
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RandomizedFunction | A variant of Function, which permits subclasses to offer randomized functions. | Class | com.numericalmethod.suanshu.grid.function.random | SuanShu |
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RandomLongGenerator | A (pseudo) random number generator that generates a sequence of longs that lack any pattern and are uniformly distributed. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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RandomNumberGenerator | A (pseudo) random number generator is an algorithm designed to generate a sequence of numbers that lack any pattern. | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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RandomProcess | This interface represents a univariate random process a. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random | SuanShu |
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RandomRealizationGenerator | This interface defines a generator to construct random realizations from a univariate stochastic process. | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random | SuanShu |
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RandomRealizationOfRandomProcess | This class generates random realizations from a random/stochastic process. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random | SuanShu |
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RandomStandardNormalGenerator | | Interface | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
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RandomVectorGenerator | A (pseudo) multivariate random number generator samples a random vector from a multivariate distribution. | Interface | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
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RandomWalk | This is the Random Walk construction of a stochastic process per SDE specification. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.random | SuanShu |
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Rank | Rank is a relationship between a set of items such that, for any two items, the first is either "ranked higher than", "ranked lower than" or "ranked equal to" the second. | Class | com.numericalmethod.suanshu.stats.descriptive.rank | SuanShu |
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RankOneMinimizer | The Rank One method is a quasi-Newton method to solve unconstrained nonlinear optimization problems. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.quasinewton | SuanShu |
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Rastrigin | The Rastrigin function is a non-convex function used as a performance test problem for optimization algorithms. | Class | com.numericalmethod.suanshu.analysis.function.special | SuanShu |
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RayleighDistribution | The L2 norm of (x1, x2), where xi's are normal, uncorrelated, equal variance and have the Rayleigh distributions. | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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RayleighRNG | This random number generator samples from the Rayleigh distribution using the inverse transform sampling method. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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Real | A real number is an arbitrary precision number. | Class | com.numericalmethod.suanshu.number | SuanShu |
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RealInterval | This is an interval on the real line. | Class | com.numericalmethod.suanshu.interval | SuanShu |
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Realization | This is a univariate time series indexed real numbers. | Interface | com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime | SuanShu |
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RealMatrix | This is a Real matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.generic.matrixtype | SuanShu |
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RealScalarFunction | A real valued function a (R^n
ightarrow R) function, (y = f(x_1, . | Interface | com.numericalmethod.suanshu.analysis.function.rn2r1 | SuanShu |
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RealScalarFunctionChromosome | This chromosome encodes a real valued function. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
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RealScalarSubFunction | This constructs a RealScalarFunction from another RealScalarFunction by restricting/fixing the values of a subset of | Class | com.numericalmethod.suanshu.analysis.function.rn2r1 | SuanShu |
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RealVectorFunction | A vector-valued function a (R^n
ightarrow R^m) function, ([y_1,. | Interface | com.numericalmethod.suanshu.analysis.function.rn2rm | SuanShu |
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RealVectorSpace | A vector space is a set of vectors that are closed under some operations. | Class | com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation | SuanShu |
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RealVectorSubFunction | This constructs a RealVectorFunction from another RealVectorFunction by restricting/fixing the values of a subset of variables. | Class | com.numericalmethod.suanshu.analysis.function.rn2rm | SuanShu |
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RecursiveGridInterpolation | This algorithm works by recursively calling lower order interpolation (hence the cost is exponential), until the given univariate algorithm can be used when the remaining dimension | Class | com.numericalmethod.suanshu.analysis.curvefit.interpolation.multivariate | SuanShu |
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Reference | | Class | com.numericalmethod.suanshu.misc.parallel | SuanShu |
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RelativeTolerance | The stopping criteria is that the norm of the residual r relative to the input base is equal to or smaller than the specified | Class | com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance | SuanShu |
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RemoteConfig | Java class for remoteConfig complex type. | Class | com.numericalmethod.suanshu.grid.config.xml.schema | SuanShu |
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RemoteConfiguration | | Interface | com.numericalmethod.suanshu.grid.config.remote | SuanShu |
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RemoteGridExecutor | Interface for classes that execute their tasks remotely. | Interface | com.numericalmethod.suanshu.grid.executor.remote | SuanShu |
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RemoteGridExecutorTestHelper | When using this class directly, you should remember to call shutdown() after you're done This class is designed to help build unit tests for distributed computations using the grid | Class | com.numericalmethod.suanshu.grid.test | SuanShu |
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Resampler | This is the interface of a re-sampler method. | Interface | com.numericalmethod.suanshu.stats.random.sampler.resampler | SuanShu |
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ResamplerModel | | Interface | com.numericalmethod.suanshu.model.lai2010.fit | SuanShu |
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Result | Simple immutable message class to communicate results. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.message | SuanShu |
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ReturnLevel | Given a GEV distribution of a random variable (X), the return level (eta) is the value that is expected to be exceeded on average once every interval of time (T), with a probability of | Class | com.numericalmethod.suanshu.stats.evt.function | SuanShu |
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ReturnPeriod | The return period (R) of a level (eta) for a random variable (X) is the mean number of trials that must be done for (X) to exceed (eta). | Class | com.numericalmethod.suanshu.stats.evt.function | SuanShu |
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ReturnsMoments | Contains the estimated moments of asset returns. | Class | com.numericalmethod.suanshu.model.returns.moments | SuanShu |
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ReturnsMomentsEstimator | | Interface | com.numericalmethod.suanshu.model.lai2010.fit | SuanShu |
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ReturnsResamplerFactory | This is a factory interface to construct new instances of multivariate resamplers. | Interface | com.numericalmethod.suanshu.model.lai2010.ceta.npeb.resampler | SuanShu |
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ReversedWeibullDistribution | The Reversed Weibull distribution is a special case (Type III) of the generalized extreme value distribution, with (xi<0). | Class | com.numericalmethod.suanshu.stats.evt.evd.univariate | SuanShu |
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Ridders | Ridders' method computes the numerical derivative of a function. | Class | com.numericalmethod.suanshu.analysis.differentiation | SuanShu |
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Riemann | This is a wrapper class that integrates a function by using an appropriate integrator together with Romberg's method. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann | SuanShu |
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Ring | | Interface | com.numericalmethod.suanshu.algebra.structure | SuanShu |
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RLGFunction | Randomized function that requires a single RLG for evaluation. | Class | com.numericalmethod.suanshu.grid.function.random | SuanShu |
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RngConfig | Java class for rngConfig complex type. | Class | com.numericalmethod.suanshu.grid.config.xml.schema | SuanShu |
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RNGs | | Interface | com.numericalmethod.suanshu.grid.function.random | SuanShu |
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RNGUtils | Provides static methods that wraps random number generators to produce synchronized generators. | Class | com.numericalmethod.suanshu.stats.random.rng | SuanShu |
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RntoMatrix | This interface is a function that maps from Rn to a Matrix space. | Interface | com.numericalmethod.suanshu.analysis.function.matrix | SuanShu |
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RobustAdaptiveMetropolis | A variation of Metropolis, that uses the estimated covariance of the target distribution in the proposal distribution, based on a paper by Vihola (2011). | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate.mcmc.metropolis | SuanShu |
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Romberg | Romberg's method computes an integral by generating a sequence of estimations of the integral value and then doing an extrapolation. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes | SuanShu |
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RootedTree | A rooted tree is a directed graph, and has a root to measure distance from theSee Also:Wikipedia: Simple graph | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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RungeKutta | The Runge-Kutta methods are an important family of implicit and explicit iterative methods for the approximation of solutions of ordinary differential equations. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKutta1 | This is the first-order Runge-Kutta formula, which is the same as the Euler method. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKutta10 | This is the tenth-order Runge-Kutta formula. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKutta2 | This is the second-order Runge-Kutta formula, which can be implemented efficiently with a three-step algorithm. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKutta3 | This is the third-order Runge-Kutta formula. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKutta4 | This is the fourth-order Runge-Kutta formula. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKutta5 | This is the fifth-order Runge-Kutta formula. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKutta6 | This is the sixth-order Runge-Kutta formula. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKutta7 | This is the seventh-order Runge-Kutta formula. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
|
RungeKutta8 | This is the eighth-order Runge-Kutta formula. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKuttaFehlberg | The Runge-Kutta-Fehlberg method is a version of the classic Runge-Kutta method, which additionally uses step-size control and hence allows specification of a local truncation error | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKuttaIntegrator | This integrator works with a single-step stepper which estimates the solution for the next step given the solution of the current step. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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RungeKuttaStepper | | Interface | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.rungekutta | SuanShu |
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SampleAutoCorrelation | This is the sample Auto-Correlation Function (ACF) for a univariate data set. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample | SuanShu |
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SampleAutoCovariance | This is the sample Auto-Covariance Function (ACVF) for a univariate data set. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample | SuanShu |
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SampleCovariance | This class computes the Covariance matrix of a matrix, where the (i, j) entry is the covariance of the i-th column and j-th column of the matrix. | Class | com.numericalmethod.suanshu.stats.descriptive.covariance | SuanShu |
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SamplePartialAutoCorrelation | This is the sample partial Auto-Correlation Function (PACF) for a univariate data set. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.univariate.sample | SuanShu |
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ScaledPolynomial | This constructs a scaled polynomial that has neither too big or too small coefficients, hence avoiding overflow or underflow. | Class | com.numericalmethod.suanshu.analysis.function.polynomial | SuanShu |
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ScientificNotation | | Class | com.numericalmethod.suanshu.number | SuanShu |
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SDE | This class represents a univariate, continuous-time Stochastic Differential Equation (SDE) of the following form. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde | SuanShu |
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SDPDualProblem | A dual SDP problem, as in equation 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem | SuanShu |
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SDPPrimalProblem | A Primal SDP problem, as in equation 14. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.problem | SuanShu |
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SDPT3v4 | This implements Algorithm_IPC, the SOCP interior point algorithm in SDPT3 version 4. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.interiorpoint | SuanShu |
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Seedable | A seed-able experiment allow the same experiment to be repeated in exactly the same way. | Interface | com.numericalmethod.suanshu.stats.random | SuanShu |
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SelectionByAIC | In each step, a factor is added if the resulting model has the highest AIC, until no factor addition can result in a model with AIC higher than the current AIC. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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SelectionByZValue | In each step, the most significant factor is added, until all remaining factors are insignificant. | Class | com.numericalmethod.suanshu.stats.regression.linear.glm.modelselection | SuanShu |
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SemiImplicitExtrapolation | Semi-Implicit Extrapolation is a method of solving ordinary differential equations, that is similar to Burlisch-Stoer extrapolation. | Class | com.numericalmethod.suanshu.analysis.differentialequation.ode.ivp.solver.extrapolation | SuanShu |
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Sequence | A sequence is an ordered list of (real) numbers. | Interface | com.numericalmethod.suanshu.analysis.sequence | SuanShu |
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ShapiroWilk | The Shapiro-Wilk test tests the null hypothesis that a sample comes from a normally distributed population. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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ShapiroWilkDistribution | Shapiro-Wilk distribution is the distribution of the Shapiro-Wilk statistics, which tests the null hypothesis that a sample comes from a normally distributed population. | Class | com.numericalmethod.suanshu.stats.test.distribution.normality | SuanShu |
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ShortestPath | In graph theory, a shortest path algorithm finds a path between two vertices in a graph such that the sum of the weights of its constituent edges is minimized. | Interface | com.numericalmethod.suanshu.graph.algorithm.shortestpath | SuanShu |
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SHR0 | SHR0 is a simple uniform random number generator. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform | SuanShu |
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SHR3 | SHR3 is a 3-shift-register generator with period 2^32-1. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform | SuanShu |
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SiegelTukey | The Siegel-Tukey test tests for differences in scale (variability) between two groups. | Class | com.numericalmethod.suanshu.stats.test.rank | SuanShu |
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SimilarMatrix | Given a matrix A and an invertible matrix P, we construct the similar matrixSee Also:Wikipedia: Similar matrix | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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SimpleAnnealingFunction | This annealing function takes a random step in a uniform direction, where the step size depends only on the temperature. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.annealingfunction | SuanShu |
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SimpleAR1Fit | This class does a quick AR(1) fitting to the time series, essentially treating the returns as independent. | Class | com.numericalmethod.suanshu.model.lai2010.fit | SuanShu |
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SimpleAR1Moments | | Class | com.numericalmethod.suanshu.model.lai2010.fit | SuanShu |
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SimpleArc | A simple arc has two vertices: head and tail. | Class | com.numericalmethod.suanshu.graph.type | SuanShu |
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SimpleCellFactory | A SimpleCellFactory produces SimpleCellFactory. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
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SimpleDoubleArrayOperation | This is a simple, single-threaded implementation of the array math operations. | Class | com.numericalmethod.suanshu.number.doublearray | SuanShu |
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SimpleEdge | A simple edge has two vertices. | Class | com.numericalmethod.suanshu.graph.type | SuanShu |
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SimpleGARCHFit | This class does a quick GARCH(1,1) fitting to the time series, essentially treating the returns as independent. | Class | com.numericalmethod.suanshu.model.lai2010.fit | SuanShu |
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SimpleGARCHMoments | Estimates the moments by GARCH model. | Class | com.numericalmethod.suanshu.model.lai2010.fit | SuanShu |
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SimpleGridMinimizer | This minimizer is a simple global optimization method. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid | SuanShu |
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SimpleMatrixMathOperation | This is a generic, single-threaded implementation of matrix math operations. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation | SuanShu |
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SimpleMC | This is a time-homogeneous Markov chain with a finite state space. | Class | com.numericalmethod.suanshu.stats.markovchain | SuanShu |
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SimpleTemperatureFunction | Abstract class for the common case where (T^V_t = T^A_t). | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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SimpleTestRemoteConfiguration | Doesn't perform failure detection. | Class | com.numericalmethod.suanshu.grid.test.config | SuanShu |
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SimpleTimeSeries | This simple univariate time series simply wraps a double[] to form a time series. | Class | com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate.realtime.inttime | SuanShu |
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SimplexCuttingPlaneMinimizer | The use of cutting planes to solve Mixed Integer Linear Programming (MILP) problems was introduced by Ralph E Gomory. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.integer.linear.cuttingplane | SuanShu |
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SimplexPivoting | A simplex pivoting finds a row and column to exchange to reduce the cost function. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting | SuanShu |
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SimplexTable | This is a simplex table used to solve a linear programming problem using a simplex method. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex | SuanShu |
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Simpson | Simpson's rule can be thought of as a special case of Romberg's method. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes | SuanShu |
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SimulatedAnnealingMinimizer | Simulated Annealing is a global optimization meta-heuristic that is inspired by annealing in metallurgy. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing | SuanShu |
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SingleRLG | RNGs that contains a single RLG instance. | Class | com.numericalmethod.suanshu.grid.function.random | SuanShu |
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SingularValueByDQDS | Computes all the singular values of a bidiagonal matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.dqds | SuanShu |
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Skewness | Skewness is a measure of the asymmetry of the probability distribution. | Class | com.numericalmethod.suanshu.stats.descriptive.moment | SuanShu |
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Slave | Slave that is meant to run on a remote machine and that creates the Worker instances. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.actor | SuanShu |
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SlaveConfig | Java class for slaveConfig complex type. | Class | com.numericalmethod.suanshu.grid.config.xml.schema | SuanShu |
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SlavesConfig | Java class for slavesConfig complex type. | Class | com.numericalmethod.suanshu.grid.config.xml.schema | SuanShu |
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SmallestSubscriptRule | Bland's smallest-subscript rule is for anti-cycling in choosing a pivot. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.qp.lp.simplex.pivoting | SuanShu |
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SOCPDualProblem | This is the Dual Second Order Conic Programming problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
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SOCPGeneralConstraint | This represents the SOCP general constraint of this form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
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SOCPGeneralConstraints | This represents a set of SOCP general constraints of this form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
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SOCPGeneralProblem | Many convex programming problems can be represented in the following form. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem | SuanShu |
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SOCPPortfolioConstraint | An SOCP constraint for portfolio optimization, e. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SOCPPortfolioObjectiveFunction | Constructs the objective function for portfolio optimization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SOCPPortfolioProblem | Constructs an SOCP problem for portfolio optimization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SOCPRiskConstraint | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.convex.sdp.socp.problem.portfoliooptimization | SuanShu |
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SORSweep | This is a building block for to perform the forward or backward sweep. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
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SortableArray | These arrays can be sorted according to the dictionary order. | Class | com.numericalmethod.suanshu.misc.datastructure | SuanShu |
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SortedOrderedPairs | The ordered pairs are first sorted by abscissa, then by ordinate. | Class | com.numericalmethod.suanshu.analysis.function.tuple | SuanShu |
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SparseDAGraph | This class implements the sparse directed acyclic graph representation. | Class | com.numericalmethod.suanshu.graph.type | SuanShu |
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SparseDiGraph | This class implements the sparse directed graph representation. | Class | com.numericalmethod.suanshu.graph.type | SuanShu |
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SparseGraph | This class implements the sparse graph representation. | Class | com.numericalmethod.suanshu.graph.type | SuanShu |
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SparseMatrix | A sparse matrix stores only non-zero values. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
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SparseMatrixUtils | These are the utility functions for SparseMatrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
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SparseStructure | This interface defines common operations on sparse structures such as sparse vector or sparse matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
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SparseTree | This class implements the sparse tree representation. | Class | com.numericalmethod.suanshu.graph.type | SuanShu |
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SparseUnDiGraph | This class implements the sparse undirected graph representation. | Class | com.numericalmethod.suanshu.graph.type | SuanShu |
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SparseVector | A sparse vector stores only non-zero values. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse | SuanShu |
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SpearmanRankCorrelation | Spearman's rank correlation coefficient or Spearman's rho is a non-parametric measure of statistical dependence between two variables. | Class | com.numericalmethod.suanshu.stats.descriptive.correlation | SuanShu |
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Spectrum | A spectrum is the set of eigenvalues of a matrix. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen | SuanShu |
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SQPActiveSetMinimizer | Sequential quadratic programming (SQP) is an iterative method for nonlinear optimization. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
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SQPActiveSetOnlyEqualityConstraint1Minimizer | This implementation is a modified version of Algorithm 15. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPActiveSetOnlyEqualityConstraint2Minimizer | This particular implementation of SQPActiveSetOnlyEqualityConstraint1Minimizer uses SQPASEVariation2. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPActiveSetOnlyInequalityConstraintMinimizer | This implementation is a modified version of Algorithm 15. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
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SQPASEVariation | This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem with only equality constraints using Sequential Quadratic Programming. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPASEVariation1 | This implementation is a modified version of the algorithm in the reference to solve a general constrained minimization problem using Sequential Quadratic Programming. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPASEVariation2 | This implementation tries to find an exact positive definite Hessian whenever possible. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset.equalityconstraint | SuanShu |
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SQPASVariation | This interface allows customization of certain operations in the Active Set algorithm to solve a general constrained minimization problem using Sequential Quadratic Programming. | Interface | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
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SQPASVariation1 | This implementation is a modified version of Algorithm 15. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.sqp.activeset | SuanShu |
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SSORPreconditioner | SSOR preconditioner is derived from a symmetric coefficient matrix A which is decomposed as | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.preconditioner | SuanShu |
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StandardCumulativeNormal | The cumulative Normal distribution function describes the probability of a Normal random variable falling in the interval ((-infty, x]). | Interface | com.numericalmethod.suanshu.analysis.function.special.gaussian | SuanShu |
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StandardInterval | This transformation is for mapping integral region from [a, b] to [-1, 1]. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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StandardNormalRNG | An alias for Zignor2005 to provide a default implementation for sampling from the standard Normal distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
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StateEquation | This is the state equation in a controlled dynamic linear model. | Class | com.numericalmethod.suanshu.stats.dlm.univariate | SuanShu |
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Statistic | A statistic (singular) is a single measure of some attribute of a sample (e. | Interface | com.numericalmethod.suanshu.stats.descriptive | SuanShu |
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StatisticFactory | A factory to construct a new Statistic. | Interface | com.numericalmethod.suanshu.stats.descriptive | SuanShu |
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SteepestDescentMinimizer | A steepest descent algorithm finds the minimum by moving along the negative of the steepest gradient direction. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.steepestdescent | SuanShu |
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SteepestDescentSolver | The Steepest Descent method (SDM) solves a symmetric n-by-n linear system. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.nonstationary | SuanShu |
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StepFunction | A step function (or staircase function) is a finite linear combination of indicator functions of Informally speaking, a step function is a piecewise constant function having only finitely many | Class | com.numericalmethod.suanshu.analysis.function.rn2r1.univariate | SuanShu |
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StopCondition | Defines when an algorithm stops (the iterations). | Interface | com.numericalmethod.suanshu.misc.algorithm.stopcondition | SuanShu |
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StringUtils | Utility methods for string manipulation. | Class | com.numericalmethod.suanshu.misc | SuanShu |
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SturmCount | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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SubFunction | A sub-function, g, is defined over a subset of the domain of another (original) function, | Class | com.numericalmethod.suanshu.analysis.function | SuanShu |
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SubMatrixBlock | Sub-matrix block representation for block algorithm. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.mathoperation.multiplication | SuanShu |
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SubMatrixRef | This is a 'reference' to a sub-matrix of a larger matrix without copying it. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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SubProblemMinimizer | This minimizer solves a constrained optimization sub-problem where the values for some variables are held fixed for the original optimization problem. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained | SuanShu |
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SubstitutionRule | A substitution rule specifies (x(t)) and (frac{mathrm{d} x}{mathrm{d} t}). | Interface | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.substitution | SuanShu |
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SubVectorRef | Represents a sub-vector backed by the referenced vector, without data copying. | Class | com.numericalmethod.suanshu.algebra.linear.vector.doubles | SuanShu |
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SuccessiveOverrelaxationSolver | The Successive Overrelaxation method (SOR), is devised by applying extrapolation to the Gauss-Seidel method. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
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Summation | Summation is the operation of adding a sequence of numbers; the result is their sum or total. | Class | com.numericalmethod.suanshu.analysis.sequence | SuanShu |
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SumOfPenalties | This penalty function sums up the costs from a set of constituent penalty functions. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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SumOfPoweredWeights | Defines portfolio diversification as D(w) = sum_i w_i^P | Class | com.numericalmethod.suanshu.model.corvalan2005.diversification | SuanShu |
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SumOfSquaredWeights | Defines portfolio diversification as D(w) = sum_i w_i^2 | Class | com.numericalmethod.suanshu.model.corvalan2005.diversification | SuanShu |
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SumOfWLogW | Defines portfolio diversification as D(w) = sum_i w_i ln(w_i) | Class | com.numericalmethod.suanshu.model.corvalan2005.diversification | SuanShu |
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SVD | SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, U' * A * V = D, or U * D * V' = A. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd | SuanShu |
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SVDbyMR3 | Given a matrix A, computes its singular value decomposition (SVD), using "Algorithm of Multiple Relatively Robust Representations" (MRRR). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd.mr3 | SuanShu |
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SVDDecomposition | SVD decomposition decomposes a matrix A of dimension m x n, where m >= n, such that U' * A * V = D, or U * D * V' = A. | Interface | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd | SuanShu |
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SVEC | SVEC converts a symmetric matrix K = {Kij} into a vector of dimension n(n+1)/2. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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SymmetricEigenByMR3 | Computes eigen decomposition for a symmetric matrix using "Algorithm of Multiple Relatively Robust Representations" (MRRR). | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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SymmetricEigenFor2x2Matrix | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.mr3 | SuanShu |
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SymmetricKronecker | Compute the symmetric Kronecker product of two matrices. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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SymmetricMatrix | A symmetric matrix is a square matrix such that its transpose equals to itself, i. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle | SuanShu |
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SymmetricQRAlgorithm | The symmetric QR algorithm is an eigenvalue algorithm by computing the real Schur canonical form of a square, symmetric matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
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SymmetricSuccessiveOverrelaxationSolver | The Symmetric Successive Overrelaxation method (SSOR) is like SOR, but it performs in each | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.sparse.solver.iterative.stationary | SuanShu |
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SymmetricSVD | This algorithm calculates the Singular Value Decomposition (SVD) of a square, symmetric matrix A using QR algorithm. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.svd | SuanShu |
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SynchronizedStatistic | This is a thread-safe wrapper of Statistic by synchronizing all public methods so that only one thread at a time can access the instance. | Class | com.numericalmethod.suanshu.stats.descriptive | SuanShu |
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T | Student's t-test tests for the equality of means, for the one-sample case, against a hypothetical mean, | Class | com.numericalmethod.suanshu.stats.test.mean | SuanShu |
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Table | A table is a means of arranging data in rows and columns. | Interface | com.numericalmethod.suanshu.misc.datastructure | SuanShu |
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TDistribution | The Student t distribution is the probability distribution of t, where t = frac{ar{x} - mu}{s / sqrt N} | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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TemperatureFunction | A temperature function defines a temperature schedule used in simulated annealing. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.temperaturefunction | SuanShu |
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TemperedAcceptanceProbabilityFunction | A tempered acceptance probability function computes the probability that the next state transition will be accepted. | Interface | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.annealing.acceptanceprobabilityfunction | SuanShu |
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TestRemoteConfigurationFactory | Used to allow the test to customize its configuration, after the slaves have been configured by a RemoteGridExecutorTestHelper. | Interface | com.numericalmethod.suanshu.grid.test | SuanShu |
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ThinRNG | Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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ThinRVG | Thinning is a scheme that returns every m-th item, discarding the last m-1 items for each draw. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
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ThomasAlgorithm | Thomas algorithm is an efficient algorithm to solve a linear tridiagonal matrix equation. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.linearsystem | SuanShu |
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ThreadIDRLG | | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.context | SuanShu |
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ThreadIDRNG | | Class | com.numericalmethod.suanshu.stats.random.rng.concurrent.context | SuanShu |
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ThreadLocalRngGridExecutor | A simple adaptor, which allows for execution of RandomizedFunctions, using a random number generator for each thread (with the same thread name). | Class | com.numericalmethod.suanshu.grid.executor.local | SuanShu |
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Ties | Count the number of occurrences of each distinctive value. | Class | com.numericalmethod.suanshu.combinatorics | SuanShu |
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TimeGrid | Specify the time points in a grid or axis. | Interface | com.numericalmethod.suanshu.stats.stochasticprocess.timegrid | SuanShu |
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TimeInterval | This is a time interval. | Class | com.numericalmethod.suanshu.misc.datastructure.time | SuanShu |
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TimeIntervals | This is a collection of time intervals TimeInterval. | Class | com.numericalmethod.suanshu.misc.datastructure.time | SuanShu |
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TimeSeries | A time series is a serially indexed collection of items. | Interface | com.numericalmethod.suanshu.stats.timeseries.datastructure | SuanShu |
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Tolerance | The tolerance criteria for an iterative algorithm to stop. | Interface | com.numericalmethod.suanshu.misc.algorithm.iterative.tolerance | SuanShu |
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Trapezoidal | The Trapezoidal rule is a closed type Newton-Cotes formula, where the integral interval is evenly divided into N sub-intervals. | Class | com.numericalmethod.suanshu.analysis.integration.univariate.riemann.newtoncotes | SuanShu |
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TraversalFromRoots | A graph traversal is the problem of visiting all the nodes in a graph in a particular manner. | Class | com.numericalmethod.suanshu.graph.algorithm.traversal | SuanShu |
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Tree | A tree is an undirected graph in which any two vertices are connected by exactly one simple path. | Class | com.numericalmethod.suanshu.graph | SuanShu |
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TrendType | These are the three versions of the Augmented Dickey-Fuller (ADF) test. | Class | com.numericalmethod.suanshu.stats.test.timeseries.adf | SuanShu |
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TriangularDistribution | | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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TridiagonalDeflationSearch | This class locates deflation in a tridiagonal matrix. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.eigen.qr | SuanShu |
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TriDiagonalization | A tri-diagonal matrix A is a matrix such that it has non-zero elements only in the main diagonal, the first diagonal below, and the first | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.factorization.diagonalization | SuanShu |
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TridiagonalMatrix | A tri-diagonal matrix has non-zero entries only on the super, main and sub diagonals. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.diagonal | SuanShu |
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Trigamma | The trigamma function is defined as the logarithmic derivative of the digamma function. | Class | com.numericalmethod.suanshu.analysis.function.special.gamma | SuanShu |
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TrigMath | A collection of trigonometric functions complementary to those in Java's Math class. | Class | com.numericalmethod.suanshu.geometry | SuanShu |
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Triple | A triple is a tuple of length three. | Class | com.numericalmethod.suanshu.analysis.function.tuple | SuanShu |
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TrivariateRealFunction | A trivariate real function takes three real arguments and outputs one real value. | Interface | com.numericalmethod.suanshu.analysis.function.rn2r1 | SuanShu |
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TruncatedNormalDistribution | The truncated Normal distribution is the probability distribution of a normally distributed random variable whose value is either bounded below or above (or both). | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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Twiddle | Generates all combinations of M elements drawn without replacement from a set of N elements. | Class | com.numericalmethod.suanshu.combinatorics | SuanShu |
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UnconstrainedLASSObyCoordinateDescent | This class solves the unconstrained form of LASSO, that is, min_w left { left | Xw - y
ight |_2^2 + lambda * left | w | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
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UnconstrainedLASSObyQP | This class solves the unconstrained form of LASSO (i. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
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UnconstrainedLASSOProblem | A LASSO (least absolute shrinkage and selection operator) problem focuses on solving an RSS (residual sum of squared errors) problem with L1 regularization. | Class | com.numericalmethod.suanshu.stats.regression.linear.lasso | SuanShu |
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UnDiGraph | An undirected graph is a graph, or set of nodes connected by edges, where an edge does not differentiate between (a, b) or (b, a). | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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UndirectedEdge | A tagging interface for implementations of an undirected graph that accept only undirected edges. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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UniformDistributionOverBox | This random vector generator uniformly samples points over a box region. | Class | com.numericalmethod.suanshu.stats.random.rng.multivariate | SuanShu |
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UniformDistributionOverBox1 | This algorithm, by sampling uniformly in each dimension, generates a set of initials uniformly distributed over a box region, | Class | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
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UniformDistributionOverBox2 | This algorithm, by perturbing each grid point by a small random scale, generates a set of initials uniformly distributed over a box region, | Class | com.numericalmethod.suanshu.optimization.multivariate.initialization | SuanShu |
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UniformMeshOverRegion | The initial population is generated by putting a uniform mesh/grid/net over the entire region. | Class | com.numericalmethod.suanshu.optimization.multivariate.geneticalgorithm.minimizer.simplegrid.firstgeneration | SuanShu |
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UniformRNG | A pseudo uniform random number generator samples numbers from the unit interval, [0, 1], in such a way that there are equal probabilities of them falling in any same length sub-interval. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.uniform | SuanShu |
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Uniroot | A root-finding algorithm is a numerical algorithm for finding a value x such that f(x) = 0, for a given function f. | Interface | com.numericalmethod.suanshu.analysis.root.univariate | SuanShu |
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UnitGrid | This is the sequence of time points [0, 1, . | Class | com.numericalmethod.suanshu.stats.stochasticprocess.timegrid | SuanShu |
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UnivariateEVD | Distribution of extreme values (e. | Interface | com.numericalmethod.suanshu.stats.evt.evd.univariate | SuanShu |
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UnivariateMinimizer | A univariate minimizer minimizes a univariate function. | Interface | com.numericalmethod.suanshu.optimization.univariate | SuanShu |
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UnivariateRealFunction | A univariate real function takes one real argument and outputs one real value. | Interface | com.numericalmethod.suanshu.analysis.function.rn2r1.univariate | SuanShu |
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UnivariateTimeSeries | This is a univariate time series indexed by some notion of time. | Interface | com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate | SuanShu |
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UnivariateTimeSeriesUtils | These are the utility functions to manipulate a univariate time series. | Class | com.numericalmethod.suanshu.stats.timeseries.datastructure.univariate | SuanShu |
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UnsatisfiableErrorCriterionException | An exception that is thrown when the error criterion cannot be met. | Class | com.numericalmethod.suanshu.analysis.differentialequation | SuanShu |
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UpperBoundConstraints | | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.constraint.linear | SuanShu |
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UpperTriangularMatrix | An upper triangular matrix has 0 entries where row index is greater than column index. | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.matrixtype.dense.triangle | SuanShu |
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VanDerWaerden | The Van der Waerden test tests for the equality of all population distribution functions. | Class | com.numericalmethod.suanshu.stats.test.rank | SuanShu |
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VanDerWaerden1969 | | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.beta | SuanShu |
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VARFit | This class construct a VAR model by estimating the coefficients using OLS regression. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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Variance | The variance of a sample is the average squared deviations from the sample mean. | Class | com.numericalmethod.suanshu.stats.descriptive.moment | SuanShu |
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VariancebtX | | Class | com.numericalmethod.suanshu.algebra.linear.matrix.doubles.operation | SuanShu |
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VARIMAModel | An ARIMA(p, d, q) process, Yt, is such that X_t = (1 - L)^d Y_t | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima | SuanShu |
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VARIMASim | This class simulates a multivariate ARIMA process. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima | SuanShu |
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VARIMAXModel | The ARIMAX model (ARIMA model with eXogenous inputs) is a generalization of the ARIMA model by incorporating exogenous variables. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.arima | SuanShu |
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VARLinearRepresentation | The linear representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of AR terms. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VARMAAutoCorrelation | Compute the Auto-Correlation Function (ACF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VARMAAutoCovariance | Compute the Auto-CoVariance Function (ACVF) for a vector AutoRegressive Moving Average (ARMA) model, assuming that This implementation solves the Yule-Walker equation. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VARMAForecastOneStep | This is an implementation, adapted for an ARMA process, of the innovation algorithm, which is an efficient way of obtaining a one step least square linear predictor. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VARMAModel | A multivariate ARMA model, Xt, takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VARMAXModel | The ARMAX model (ARMA model with eXogenous inputs) is a generalization of the ARMA model by incorporating exogenous variables. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VARModel | This class represents a VAR model. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VARXModel | A VARX (Vector AutoRegressive model with eXogeneous inputs) model, Xt, takes Y_t = mu + Sigma phi_i * Y_{t-i} + Psi * D_t + epsilon_t | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VECM | A Vector Error Correction Model (VECM(p)) has one of the following specifications: Delta Y_t = mu + Pi Y_{t-1} + sum left ( Gamma_i Y_{t-1}
ight ) + Psi D_t + epsilon_t, i = 1, 2, . | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VECMLongrun | The long-run Vector Error Correction Model (VECM(p)) takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VECMTransitory | A transitory Vector Error Correction Model (VECM(p)) takes this form. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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Vector | An Euclidean vector is a geometric object that has both a magnitude/length and a direction. | Interface | com.numericalmethod.suanshu.algebra.linear.vector.doubles | SuanShu |
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VectorAccessException | This is the exception thrown when any invalid access to a Vector instance is detected, e. | Class | com.numericalmethod.suanshu.algebra.linear.vector | SuanShu |
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VectorFactory | These are the utility functions that create new instances of vectors from existing ones. | Class | com.numericalmethod.suanshu.algebra.linear.vector.doubles.operation | SuanShu |
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VectorMonitor | This IterationMonitor stores all vectors generated during iterations. | Class | com.numericalmethod.suanshu.misc.algorithm.iterative.monitor | SuanShu |
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VectorSizeMismatch | This is the exception thrown when an operation is performed on two vectors with differentSee Also:Serialized Form | Class | com.numericalmethod.suanshu.algebra.linear.vector | SuanShu |
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VectorSpace | A vector space is a set V together with two binary operations that combine two entities to yield a third, called vector addition and scalar multiplication. | Interface | com.numericalmethod.suanshu.algebra.structure | SuanShu |
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VertexTree | A VertexTree is both a tree and a vertex/node. | Class | com.numericalmethod.suanshu.graph.type | SuanShu |
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Viterbi | The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states - called the Viterbi path - that results in | Class | com.numericalmethod.suanshu.stats.hmm | SuanShu |
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VMAInvertibility | The inverse representation of an Autoregressive Moving Average (ARMA) model is a (truncated) infinite sum of the Moving Averages. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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VMAModel | This class represents a multivariate MA model. | Class | com.numericalmethod.suanshu.stats.timeseries.linear.multivariate.stationaryprocess.arma | SuanShu |
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WaveEquation1D | A one-dimensional wave equation is a hyperbolic PDE that takes the following form. | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim1 | SuanShu |
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WaveEquation2D | A two-dimensional wave equation is a hyperbolic PDE that takes the following form. | Class | com.numericalmethod.suanshu.analysis.differentialequation.pde.finitedifference.hyperbolic.dim2 | SuanShu |
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WeibullDistribution | The Weibull distribution interpolates between the exponential distribution k = 1 and the Rayleigh distribution (k = 2), | Class | com.numericalmethod.suanshu.stats.distribution.univariate | SuanShu |
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WeibullRNG | This random number generator samples from the Weibull distribution using the inverse transform sampling method. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate | SuanShu |
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WeightedArc | A weighted arc is an arc that has a weight or a cost associated with it. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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WeightedEdge | A weighted edge has a weight or a cost associated with it. | Interface | com.numericalmethod.suanshu.graph | SuanShu |
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WeightedMean | The weighted mean is defined as ar{x} = frac{ sum_{i=1}^N w_i x_i}{sum_{i=1}^N w_i} | Class | com.numericalmethod.suanshu.stats.descriptive.moment.weighted | SuanShu |
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WeightedRSS | Weighted sum of squared residuals (RSS) for a given function (f(. | Class | com.numericalmethod.suanshu.stats.regression | SuanShu |
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WeightedVariance | The weighted sample variance is defined as follows. | Class | com.numericalmethod.suanshu.stats.descriptive.moment.weighted | SuanShu |
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White | The White test tests for conditional heteroskedasticity. | Class | com.numericalmethod.suanshu.stats.test.regression.linear.heteroskedasticity | SuanShu |
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WilcoxonRankSum | The Wilcoxon rank sum test tests for the equality of means of two populations, or whether the means differ by an offset. | Class | com.numericalmethod.suanshu.stats.test.rank.wilcoxon | SuanShu |
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WilcoxonRankSumDistribution | Compute the exact distribution of the Wilcoxon rank sum test statistic. | Class | com.numericalmethod.suanshu.stats.test.rank.wilcoxon | SuanShu |
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WilcoxonSignedRank | The Wilcoxon signed rank test tests, for the one-sample case, the median of the distribution against a hypothetical median, and | Class | com.numericalmethod.suanshu.stats.test.rank.wilcoxon | SuanShu |
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WilcoxonSignedRankDistribution | Compute the exact distribution of the Wilcoxon signed rank test statistic. | Class | com.numericalmethod.suanshu.stats.test.rank.wilcoxon | SuanShu |
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Work | Represents a unit of work as done by an actor (worker). | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.message | SuanShu |
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WorkAssignment | Utility class that contains the method that performs assignment of a task with a given index to a slave/worker with a given index (given the numbers of slaves and workers). | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.actor | SuanShu |
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Worker | The actor who does the real work. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.actor | SuanShu |
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WorkerCountCollector | Collects the number of workers managed by each of the given slaves. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.actor | SuanShu |
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WorkerCountReply | Reply for WorkerCountRequest. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.message | SuanShu |
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WorkerCountRequest | Request for the number of workers managed by a slave. | Class | com.numericalmethod.suanshu.grid.executor.remote.akka.message | SuanShu |
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XiTanLiu2010a | Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
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XiTanLiu2010b | Xi, Tan and Liu proposed two simple algorithms to generate gamma random numbers based on the ratio-of-uniforms method and logarithmic transformations of gamma random variable. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.gamma | SuanShu |
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XtAdaptedFunction | This represents an Ft-adapted function that depends only on X(t). | Class | com.numericalmethod.suanshu.stats.stochasticprocess.univariate.sde | SuanShu |
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ZangwillMinimizer | Zangwill's algorithm is an improved version of Powell's algorithm. | Class | com.numericalmethod.suanshu.optimization.multivariate.unconstrained.c2.conjugatedirection | SuanShu |
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ZeroDriftVector | This class represents a 0 drift function. | Class | com.numericalmethod.suanshu.stats.stochasticprocess.multivariate.sde.coefficients | SuanShu |
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ZeroPenalty | This is a dummy zero cost (no cost) penalty function. | Class | com.numericalmethod.suanshu.optimization.multivariate.constrained.general.penaltymethod | SuanShu |
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Ziggurat2000 | The Ziggurat algorithm is an algorithm for pseudo-random number sampling from the Normal distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |
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Ziggurat2000Exp | This implements the ziggurat algorithm to sample from the exponential distribution. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.exp | SuanShu |
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Zignor2005 | This is an improved version of the Ziggurat algorithm as proposed in the reference. | Class | com.numericalmethod.suanshu.stats.random.rng.univariate.normal | SuanShu |