Modifier and Type | Method and Description |
---|---|
ImmutableVector |
LSProblem.b()
Gets the non-homogeneous part, the right-hand side vector, of the linear system.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
ImmutableVector.deepCopy() |
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
Projection.getOrthogonalVector()
Get the orthogonal vector which is equal to v minus the projection of v on {wi}.
|
ImmutableVector |
Projection.getProjectionVector(int i)
Get the i-th projected vector of v on {wi}.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
QuadraticFunction.p() |
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
Point.getCoordinates()
Get the coordinates of the point.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
BranchAndBound.minimizer() |
ImmutableVector |
BBNode.solution()
the solution to the sub-problem associated with this node
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
LinearConstraints.b()
Get the constraint values.
|
Modifier and Type | Field and Description |
---|---|
ImmutableVector |
CentralPath.y
This is the maximizer for the dual problem.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
SDPDualProblem.b()
Gets b.
|
Modifier and Type | Field and Description |
---|---|
ImmutableVector |
PrimalDualSolution.s
This is the auxiliary helper to solve the dual problem.
|
ImmutableVector |
PrimalDualSolution.x
This is the minimizer for the primal problem.
|
ImmutableVector |
PrimalDualSolution.y
This is the maximizer for the dual problem.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
QPSolution.minimizer()
Get a minimizing vector.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
LPProblemImpl1.b() |
ImmutableVector |
LPProblem.b()
Get the values, b, of the greater-than-or-equal-to constraints A * x ≥ b.
|
ImmutableVector |
LPProblemImpl1.beq() |
ImmutableVector |
LPProblem.beq()
Get the values, beq, of the equality constraints Aeq * x ≥ beq.
|
ImmutableVector |
LPProblemImpl1.c() |
ImmutableVector |
LPProblem.c()
Get the objective function.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
SimplexTable.minimizer() |
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
LPBoundedMinimizer.minimizer() |
ImmutableVector |
LPUnboundedMinimizer.minimizer()
This is the same as the u vector, such that the direction of arbitrarily negative can
be computed by adjusting λ.
|
ImmutableVector |
LPUnboundedMinimizerScheme2.minimizer() |
ImmutableVector[] |
LPBoundedMinimizer.minimizers()
Get all optimal minimizers.
|
ImmutableVector |
LPUnboundedMinimizer.v()
When the problem is unbounded, the direction of arbitrarily negative can be computed by
adjusting λ.
|
ImmutableVector |
LPUnboundedMinimizerScheme2.v() |
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
LPRevisedSimplexSolver.Problem.b() |
ImmutableVector |
LPRevisedSimplexSolver.Problem.beq() |
ImmutableVector |
LPRevisedSimplexSolver.Problem.c() |
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
QPProblem.b()
Get the values of the inequality constraints: b as in \(Ax \geq b\).
|
ImmutableVector |
QPProblem.beq()
Get the values of the equality constraints: beq as in \(A_{eq}x = b_{eq}\).
|
ImmutableVector |
QPProblemOnlyEqualityConstraints.getSolutionToOriginalProblem(Vector phi)
Backs out the solution for the original (constrained) problem, if the modified
(unconstrained) problem can be solved.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
ILPNode.solution() |
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
ILPProblemImpl1.b() |
ImmutableVector |
ILPProblemImpl1.beq() |
ImmutableVector |
ILPProblemImpl1.c() |
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
SimpleGridMinimizer.Solution.minimizer() |
ImmutableVector |
RealScalarFunctionChromosome.x()
Get the candidate solution.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
NelderMeadMinimizer.Solution.minimizer() |
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
SteepestDescentMinimizer.SteepestDescentImpl.minimizer() |
Modifier and Type | Field and Description |
---|---|
ImmutableVector |
MultivariateDLMSim.Innovation.observation
the simulated observation
|
ImmutableVector |
MultivariateDLMSim.Innovation.state
the simulated state
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
MultivariateLinearKalmanFilter.getFittedState(int t)
Get the posterior expected state.
|
ImmutableVector |
MultivariateLinearKalmanFilter.getPredictedObservation(int t)
Get the prior observation prediction.
|
ImmutableVector |
MultivariateLinearKalmanFilter.getPredictedState(int t)
Get the prior expected state.
|
ImmutableVector |
MultivariateDLM.m0()
Get the the mean of x0.
|
ImmutableVector |
MultivariateStateEquation.xt(int t,
Vector xt_1)
Evaluates the state equation without the control variable.
|
ImmutableVector |
MultivariateStateEquation.xt(int t,
Vector xt_1,
Vector ut)
Evaluates the state equation.
|
ImmutableVector |
MultivariateObservationEquation.yt_mean(int t,
Vector xt)
Predicts the next observation.
|
ImmutableVector |
MultivariateObservationEquation.yt(int t,
Vector xt)
Evaluates the observation equation.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
EstimateByLogLikelihood.getFittedParameters()
Get the fitted parameters.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
FAEstimator.psi()
Gets the estimated (optimal) psi, E(ee'), p.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
SimpleMC.PI()
Gets the initial state probabilities.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
LMBeta.betaHat()
Gets the coefficient estimates, β^.
|
ImmutableVector |
LMBeta.stderr()
Gets the standard errors of the coefficients β^.
|
ImmutableVector |
LMBeta.t()
Gets the t- or z- value of the regression coefficients β^.
|
ImmutableVector |
LMProblem.weights()
Gets the weights assigned to each observation.
|
ImmutableVector |
LMProblem.wy()
Gets the weighted response vector.
|
ImmutableVector |
LMProblem.y()
Gets the response vector, the regressands, the dependent variables.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
IWLS.betaHat() |
ImmutableVector |
GLMFitting.betaHat()
Gets the estimates of β, β^, as in
E(Y) = μ = g-1(Xβ)
|
ImmutableVector |
GLMResiduals.devianceResiduals()
Gets the deviances residuals.
|
ImmutableVector |
IWLS.mu() |
ImmutableVector |
GLMFitting.mu()
Gets μ as in
E(Y) = μ = g-1(Xβ)
|
ImmutableVector |
IWLS.weights() |
ImmutableVector |
GLMFitting.weights()
Gets the weights assigned to the observations.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
QuasiGLMNewtonRaphson.betaHat() |
ImmutableVector |
QuasiGLMNewtonRaphson.mu() |
ImmutableVector |
QuasiGLMNewtonRaphson.weights() |
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
LogisticResiduals.devianceResiduals()
Gets the residuals, ε.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
LMDiagnostics.cookDistances()
Cook distances.
|
ImmutableVector |
LMDiagnostics.DFFITS()
DFFITS, Welsch and Kuh Measure.
|
ImmutableVector |
LMResiduals.fitted()
Gets the fitted values, y^.
|
ImmutableVector |
LMDiagnostics.Hadi()
Hadi's influence measure.
|
ImmutableVector |
LMResiduals.leverage()
Gets the leverage.
|
ImmutableVector |
LMResiduals.residuals()
Gets the residuals, ε, the differences between sample and fitted values.
|
ImmutableVector |
LMResiduals.standardized()
standard residual = residual / v1 / sqrt(RSS / (n-m))
|
ImmutableVector |
LMResiduals.studentized()
studentized residual = standardized * sqrt((n-m-1) / (n-m-standardized^2))
|
ImmutableVector |
LMResiduals.weightedFittedValues()
Gets the weighted, fitted values.
|
ImmutableVector |
LMResiduals.weightedResiduals()
Gets the weighted residuals.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
VARIMAXModel.mu()
Get the intercept (constant) vector.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
MultivariateForecastOneStep.xHat(int n)
Get the one-step prediction \(\hat{X}_{n+1} = P_{\mathfrak{S_n}}X_{n+1}\), made at time n.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
VECM.mu()
Get the intercept vector.
|
ImmutableVector |
VARMAForecastOneStep.xHat(int n)
Get the one-step prediction \(\hat{X}_{n+1} = P_{\mathfrak{S_n}}X_{n+1}\), made at time n.
|
Modifier and Type | Method and Description |
---|---|
ImmutableVector |
ConditionalSumOfSquares.stderr()
Get the asymptotic standard errors of the estimated parameters,
φ and θ.
|
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