| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.minimization.line |
Provides line (scalar) minimization algorithms.
|
| gov.sandia.cognition.learning.algorithm.minimization.line.interpolator |
Provides line (scalar) interpolation/extrapolation algorithms that fit an
algebraic function to a (small) collection of data points.
|
| gov.sandia.cognition.learning.function.scalar |
Provides functions that output real numbers.
|
| gov.sandia.cognition.learning.function.vector |
Provides functions that output vectors.
|
| gov.sandia.cognition.math |
Provides classes for mathematical computation.
|
| gov.sandia.cognition.statistics.bayesian |
Provides algorithms for computing Bayesian estimates of parameters.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DirectionalVectorToDifferentiableScalarFunction
Creates a truly differentiable scalar function from a differentiable Vector
function, instead of using a forward-differences approximation to the
derivative like DirectionalVectorToScalarFunction does.
|
class |
DirectionalVectorToScalarFunction
Maps a vector function onto a scalar one by using a
directional vector and vector offset, and the parameter to the
function is a scalar value along the direction from the start-point
offset.
|
class |
LineMinimizerDerivativeBased.InternalFunction
Internal function used to map/remap/unmap the search direction.
|
| Modifier and Type | Field and Description |
|---|---|
static LineBracketInterpolator<? super DifferentiableUnivariateScalarFunction> |
LineMinimizerDerivativeBased.DEFAULT_INTERPOLATOR
Default interpolator to use to create a new candidate point to evaluate
|
| Constructor and Description |
|---|
LineMinimizerDerivativeBased(LineBracketInterpolator<? super DifferentiableUnivariateScalarFunction> interpolator,
double minFunctionValue)
Creates a new instance of LineMinimizerDerivativeBased
|
| Modifier and Type | Method and Description |
|---|---|
PolynomialFunction.ClosedForm |
LineBracketInterpolatorHermiteCubic.computePolynomial(LineBracket bracket,
DifferentiableUnivariateScalarFunction function) |
PolynomialFunction.ClosedForm |
LineBracketInterpolatorHermiteParabola.computePolynomial(LineBracket bracket,
DifferentiableUnivariateScalarFunction function) |
| Modifier and Type | Interface and Description |
|---|---|
static interface |
PolynomialFunction.ClosedForm
Describes functionality of a closed-form algebraic polynomial function
|
| Modifier and Type | Class and Description |
|---|---|
class |
AtanFunction
Returns the element-wise arctangent of the input vector, compressed between
-maxMagnitude and maxMagnitude (instead of just -PI/2 and PI/2)
|
class |
CosineFunction
A closed-form cosine function.
|
class |
HardSigmoidFunction
A hard sigmoid function, which is an approximation of a logistic sigmoid
whose output is between 0 and 1.
|
class |
HardTanHFunction
A hard sigmoid function, which is an approximation of a tanh sigmoid
whose output is between -1 and 1.
|
class |
IdentityScalarFunction
A univariate scalar identity function: f(x) = x.
|
class |
LeakyRectifiedLinearFunction
A leaky rectified linear unit.
|
class |
LinearFunction
This function acts as a simple linear function of the form f(x) = m*x + b.
|
class |
PolynomialFunction
A single polynomial term specified by a real-valued exponent.
|
static class |
PolynomialFunction.Cubic
Algebraic treatment for a polynomial of the form
y(x) = q0 + q1*x + q2*x^2 + q3*x^3
|
static class |
PolynomialFunction.Linear
Utilities for algebraic treatment of a linear polynomial of the form
y(x) = q0 + q1*x
|
static class |
PolynomialFunction.Quadratic
Utilities for algebraic treatment of a quadratic polynomial of the form
y(x) = q0 + q1*x + q2*x^2.
|
class |
RectifiedLinearFunction
A rectified linear unit, which is the maximum of its input or 0.
|
class |
SigmoidFunction
An implementation of a sigmoid squashing function.
|
class |
SoftPlusFunction
A smoothed approximation for rectified linear unit.
|
class |
TanHFunction
The hyperbolic tangent (tanh) function.
|
| Modifier and Type | Field and Description |
|---|---|
static DifferentiableUnivariateScalarFunction |
ThreeLayerFeedforwardNeuralNetwork.DEFAULT_SQUASHING_FUNCTION
Default squashing function, AtanFunction.
|
| Modifier and Type | Method and Description |
|---|---|
DifferentiableUnivariateScalarFunction |
ElementWiseDifferentiableVectorFunction.getScalarFunction() |
DifferentiableUnivariateScalarFunction |
ThreeLayerFeedforwardNeuralNetwork.getSquashingFunction()
Getter for squashingFunction
|
| Modifier and Type | Method and Description |
|---|---|
void |
ThreeLayerFeedforwardNeuralNetwork.setSquashingFunction(DifferentiableUnivariateScalarFunction squashingFunction)
Setter for squashingFunction
|
| Constructor and Description |
|---|
DifferentiableFeedforwardNeuralNetwork(int numInputs,
int numHiddens,
int numOutputs,
DifferentiableUnivariateScalarFunction scalarFunction,
java.util.Random random)
Creates a new instance of FeedforwardNeuralNetwork
|
DifferentiableGeneralizedLinearModel(int numInputs,
int numOutputs,
DifferentiableUnivariateScalarFunction scalarFunction)
Creates a new instance of GeneralizedLinearModel
|
DifferentiableGeneralizedLinearModel(MultivariateDiscriminant matrixMultiply,
DifferentiableUnivariateScalarFunction scalarSquashingFunction)
Creates a new instance of DifferentiableGeneralizedLinearModel
|
ElementWiseDifferentiableVectorFunction(DifferentiableUnivariateScalarFunction scalarFunction)
Creates a new instance of ElementWiseDifferentiableVectorFunction
|
ThreeLayerFeedforwardNeuralNetwork(int numInputs,
int numHidden,
int numOutputs,
DifferentiableUnivariateScalarFunction squashingFunction)
Creates a new instance of ThreeLayerFeedforwardNeuralNetwork
|
ThreeLayerFeedforwardNeuralNetwork(int numInputs,
int numHidden,
int numOutputs,
DifferentiableUnivariateScalarFunction squashingFunction,
int randomSeed,
double initializationRange)
Creates a new instance of ThreeLayerFeedforwardNeuralNetwork
|
| Constructor and Description |
|---|
DifferentiableFeedforwardNeuralNetwork(java.util.ArrayList<java.lang.Integer> nodesPerLayer,
java.util.ArrayList<DifferentiableUnivariateScalarFunction> layerActivationFunctions,
java.util.Random random)
Creates a new instance of DifferentiableFeedforwardNeuralNetwork
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractDifferentiableUnivariateScalarFunction
Partial implementation of DifferentiableUnivariateScalarFunction that
implements the differentiate(Double) method with a callback to the
differentiate(double) method, so that a concrete class only to implement
the differentiate(double) method
|
| Modifier and Type | Class and Description |
|---|---|
static class |
AdaptiveRejectionSampling.LineSegment
A line that has a minimum and maximum support (x-axis) value.
|