| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm |
Provides general interfaces for learning algorithms.
|
| gov.sandia.cognition.learning.algorithm.annealing |
Provides the Simulated Annealing algorithm.
|
| gov.sandia.cognition.learning.algorithm.genetic |
Provides a genetic algorithm implementation.
|
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| gov.sandia.cognition.learning.function.cost |
Provides cost functions.
|
| gov.sandia.cognition.learning.performance |
Provides performance measures.
|
| gov.sandia.cognition.statistics.method |
Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.
|
| Class and Description |
|---|
| CostFunction
The CostFunction interface defines the interface to evaluate some object to
determine its cost.
|
| Class and Description |
|---|
| CostFunction
The CostFunction interface defines the interface to evaluate some object to
determine its cost.
|
| Class and Description |
|---|
| CostFunction
The CostFunction interface defines the interface to evaluate some object to
determine its cost.
|
| Class and Description |
|---|
| DifferentiableCostFunction
The
DifferentiableCostFunction is a cost function that can
be differentiated. |
| SupervisedCostFunction
A type of CostFunction normally used in supervised-learning applications.
|
| Class and Description |
|---|
| AbstractCostFunction
Partial implementation of CostFunction.
|
| AbstractParallelizableCostFunction
Partial implementation of the ParallelizableCostFunction
|
| AbstractSupervisedCostFunction
Partial implementation of SupervisedCostFunction
|
| ClusterDistortionMeasure
Computes the objective measure for a clustering algorithm, based on the
internal "distortion" of each cluster.
|
| CostFunction
The CostFunction interface defines the interface to evaluate some object to
determine its cost.
|
| DifferentiableCostFunction
The
DifferentiableCostFunction is a cost function that can
be differentiated. |
| EuclideanDistanceCostFunction
The EuclideanDistanceCostFunction class implements a CostFunction that
calculates the Euclidean distance the given Vectorizable and the goal
vector.
|
| MeanL1CostFunction
Cost function that evaluates the mean 1-norm error (absolute value of
difference) weighted by a sample "weight" that is embedded in each sample.
|
| MeanSquaredErrorCostFunction
The MeanSquaredErrorCostFunction implements a cost function for functions
that take as input a vector and return a vector.
|
| NegativeLogLikelihood
CostFunction for computing the maximum likelihood
(because we are minimizing the negative of the log likelihood)
|
| ParallelizableCostFunction
Interface describing a cost function that can (largely) be computed in
parallel.
|
| ParallelizedCostFunctionContainer
A cost function that automatically splits a ParallelizableCostFunction
across multiple cores/processors to speed up computation.
|
| ParallelNegativeLogLikelihood.NegativeLogLikelihoodTask
Task for computing partial log likelihoods
|
| SumSquaredErrorCostFunction
This is the sum-squared error cost function
|
| SumSquaredErrorCostFunction.Cache
Caches often-used values for the Cost Function
|
| SupervisedCostFunction
A type of CostFunction normally used in supervised-learning applications.
|
| Class and Description |
|---|
| AbstractSupervisedCostFunction
Partial implementation of SupervisedCostFunction
|
| CostFunction
The CostFunction interface defines the interface to evaluate some object to
determine its cost.
|
| SupervisedCostFunction
A type of CostFunction normally used in supervised-learning applications.
|
| Class and Description |
|---|
| CostFunction
The CostFunction interface defines the interface to evaluate some object to
determine its cost.
|