Uses of Interface
gov.sandia.cognition.math.matrix.VectorizableVectorFunction

Packages that use VectorizableVectorFunction

Package	Description
gov.sandia.cognition.learning.algorithm.gradient	Provides gradient based learning algorithms.
gov.sandia.cognition.learning.algorithm.regression	Provides regression algorithms, such as Linear Regression.
gov.sandia.cognition.learning.function.vector	Provides functions that output vectors.
gov.sandia.cognition.math.matrix	Provides interfaces and classes for linear algebra.
gov.sandia.cognition.math.signals	Provides mathematical signal processing methods.

Uses of VectorizableVectorFunction in gov.sandia.cognition.learning.algorithm.gradient

Subinterfaces of VectorizableVectorFunction in gov.sandia.cognition.learning.algorithm.gradient

Modifier and Type	Interface and Description
interface	GradientDescendable Defines the functionality of an object that is required in order to apply the gradient descent algorithm to it.

Classes in gov.sandia.cognition.learning.algorithm.gradient that implement VectorizableVectorFunction

Modifier and Type	Class and Description
class	GradientDescendableApproximator Creates a radientDescendable from a VectorizableVectorFunction by estimating the parameter gradient using a forward-difference approximation of the parameter Jacobian.

Methods in gov.sandia.cognition.learning.algorithm.gradient that return VectorizableVectorFunction

Modifier and Type	Method and Description
VectorizableVectorFunction	GradientDescendableApproximator.getFunction() Getter for function

Methods in gov.sandia.cognition.learning.algorithm.gradient with parameters of type VectorizableVectorFunction

Modifier and Type	Method and Description
static Matrix	GradientDescendableApproximator.computeParameterGradient(VectorizableVectorFunction function, Vector input, double deltaSize) Computes a forward-differences approximation to the parameter Jacobian
void	GradientDescendableApproximator.setFunction(VectorizableVectorFunction function) Setter for function

Constructors in gov.sandia.cognition.learning.algorithm.gradient with parameters of type VectorizableVectorFunction

Constructor and Description
GradientDescendableApproximator(VectorizableVectorFunction function) Creates a new instance of GradientDescendableApproximator
GradientDescendableApproximator(VectorizableVectorFunction function, double deltaSize) Creates a new instance of GradientDescendableApproximator

Uses of VectorizableVectorFunction in gov.sandia.cognition.learning.algorithm.regression

Classes in gov.sandia.cognition.learning.algorithm.regression with type parameters of type VectorizableVectorFunction

Modifier and Type	Class and Description
class	AbstractMinimizerBasedParameterCostMinimizer<ResultType extends VectorizableVectorFunction,EvaluatorType extends Evaluator<? super Vector,? extends java.lang.Double>> Partial implementation of ParameterCostMinimizer, based on the algorithms from the minimization package.
class	AbstractParameterCostMinimizer<ResultType extends VectorizableVectorFunction,CostFunctionType extends SupervisedCostFunction<Vector,Vector>> Partial implementation of ParameterCostMinimizer.
interface	ParameterCostMinimizer<ResultType extends VectorizableVectorFunction> A anytime algorithm that is used to estimate the locally minimum-cost parameters of an object.

Constructors in gov.sandia.cognition.learning.algorithm.regression with parameters of type VectorizableVectorFunction

Constructor and Description
ParameterCostEvaluatorDerivativeFree(VectorizableVectorFunction internalFunction, SupervisedCostFunction<Vector,Vector> costFunction) Creates a new instance of ParameterCostEvaluatorDerivativeFree

Uses of VectorizableVectorFunction in gov.sandia.cognition.learning.function.vector

Classes in gov.sandia.cognition.learning.function.vector that implement VectorizableVectorFunction

Modifier and Type	Class and Description
class	DifferentiableFeedforwardNeuralNetwork A feedforward neural network that can have an arbitrary number of layers, and an arbitrary differentiable squashing (activation) function assigned to each layer.
class	DifferentiableGeneralizedLinearModel A GradientDescenable version of a GeneralizedLinearModel, in other words, a GeneralizedLinearModel where the squashing function is differentiable
class	FeedforwardNeuralNetwork A feedforward neural network that can have an arbitrary number of layers, and an arbitrary squashing (activation) function assigned to each layer.
class	GeneralizedLinearModel A VectorizableVectorFunction that is a matrix multiply followed by a VectorFunction...
class	MultivariateDiscriminant Allows learning algorithms (vectorizing, differentiating) on a matrix*vector multiply.
class	MultivariateDiscriminantWithBias A multivariate discriminant (matrix multiply) plus a constant vector that gets added to the output of the discriminant.
class	ThreeLayerFeedforwardNeuralNetwork This is a "standard" feedforward neural network with a single hidden layer.

Uses of VectorizableVectorFunction in gov.sandia.cognition.math.matrix

Subinterfaces of VectorizableVectorFunction in gov.sandia.cognition.math.matrix

Modifier and Type	Interface and Description
interface	VectorizableDifferentiableVectorFunction A VectorizableVectorFunction that also define a derivative (this is needed for GradientDescendable).

Methods in gov.sandia.cognition.math.matrix that return VectorizableVectorFunction

Modifier and Type	Method and Description
VectorizableVectorFunction	VectorizableVectorFunction.clone() Creates a new clone (shallow copy) of this object.

Uses of VectorizableVectorFunction in gov.sandia.cognition.math.signals

Classes in gov.sandia.cognition.math.signals that implement VectorizableVectorFunction

Modifier and Type	Class and Description
class	LinearDynamicalSystem A generic Linear Dynamical System of the form x_n = A*x_(n-1) + B*u_n y_n = C*x_n, where x_(n-1) is the previous state, x_n is the current state, u_n is the current input, y_n is the current output, A is the system matrix, B is the input-gain matrix, and C is the output-selector matrix