| Package | Description |
|---|---|
| gov.sandia.cognition.evaluator |
Provides interfaces and classes to do with the
Evaluator interface. |
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| gov.sandia.cognition.learning.algorithm.tree |
Provides decision tree learning algorithms.
|
| gov.sandia.cognition.learning.data |
Provides data set utilities for learning.
|
| gov.sandia.cognition.learning.experiment |
Provides experiments for validating the performance of learning algorithms.
|
| gov.sandia.cognition.learning.function.cost |
Provides cost functions.
|
| gov.sandia.cognition.math.signals |
Provides mathematical signal processing methods.
|
| gov.sandia.cognition.util |
Provides general utility classes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CompositeEvaluatorPair<InputType,IntermediateType,OutputType>
Implements a composition of two evaluators.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
LogisticRegression.Function
Class that is a linear discriminant, followed by a sigmoid function.
|
| Modifier and Type | Method and Description |
|---|---|
DefaultPair<java.lang.Double,java.lang.Double> |
AbstractVectorThresholdMaximumGainLearner.computeBestGainAndThreshold(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data,
int dimension,
DefaultDataDistribution<OutputType> baseCounts)
Computes the best gain and threshold for a given dimension using the
computeSplitGain method for each potential split point of values for the
given dimension.
|
protected DefaultPair<java.lang.Double,java.lang.Double> |
AbstractVectorThresholdMaximumGainLearner.computeBestGainAndThreshold(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,OutputType>> data,
int dimension,
DefaultDataDistribution<OutputType> baseCounts,
java.util.ArrayList<DefaultWeightedValue<OutputType>> values)
Computes the best gain and threshold for a given dimension using the
computeSplitGain method for each potential split point of values for the
given dimension.
|
DefaultPair<java.lang.Double,java.lang.Double> |
VectorThresholdVarianceLearner.computeBestGainThreshold(java.util.Collection<? extends InputOutputPair<? extends Vectorizable,java.lang.Double>> data,
int dimension,
double baseVariance)
Computes the best information gain-threshold pair for the given
dimension on the given data.
|
| Modifier and Type | Method and Description |
|---|---|
static <DataType> DefaultPair<java.util.LinkedList<DataType>,java.util.LinkedList<DataType>> |
DatasetUtil.splitDatasets(java.util.Collection<? extends InputOutputPair<? extends DataType,java.lang.Boolean>> data)
Splits a dataset of input-output pair into two datasets, one for the
inputs that have a "true" output and another for the inputs that have
a "false" output
|
| Modifier and Type | Field and Description |
|---|---|
protected DefaultPair<java.util.ArrayList<StatisticType>,java.util.ArrayList<StatisticType>> |
LearnerComparisonExperiment.statistics
The performance evaluations made during the experiment.
|
protected DefaultPair<SummaryType,SummaryType> |
LearnerComparisonExperiment.summaries
The summaries of performance.
|
| Modifier and Type | Method and Description |
|---|---|
DefaultPair<java.util.ArrayList<StatisticType>,java.util.ArrayList<StatisticType>> |
LearnerComparisonExperiment.getStatistics()
Gets the performance evaluations for the trials of the experiment.
|
DefaultPair<SummaryType,SummaryType> |
LearnerComparisonExperiment.getSummaries()
Gets the summaries of the experiment.
|
DefaultPair<SummaryType,SummaryType> |
LearnerComparisonExperiment.Result.getSummaries()
Gets the summary of performance for the learners.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
LearnerComparisonExperiment.setStatistics(DefaultPair<java.util.ArrayList<StatisticType>,java.util.ArrayList<StatisticType>> statistics)
Sets the performance evaluations for the trials of the experiment.
|
protected void |
LearnerComparisonExperiment.setSummaries(DefaultPair<SummaryType,SummaryType> summaries)
Sets the summaries of the experiment.
|
void |
LearnerComparisonExperiment.Result.setSummaries(DefaultPair<SummaryType,SummaryType> summaries)
Sets the summary of performance for the learners.
|
| Constructor and Description |
|---|
Result(ConfidenceStatistic confidence,
DefaultPair<SummaryType,SummaryType> summaries)
Creates a new instance of Result.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
SumSquaredErrorCostFunction.GradientPartialSSE
Partial result from the SSE gradient computation
|
| Modifier and Type | Method and Description |
|---|---|
DefaultPair<FiniteCapacityBuffer<java.lang.Double>,FiniteCapacityBuffer<java.lang.Double>> |
AutoRegressiveMovingAverageFilter.createDefaultState() |
| Modifier and Type | Class and Description |
|---|---|
class |
DefaultWeightedPair<FirstType,SecondType>
The
DefaultWeightedPair class extends the DefaultPair class
to add a weight to the pair. |
| Modifier and Type | Method and Description |
|---|---|
DefaultPair<FirstType,SecondType> |
DefaultPair.clone() |
static <FirstType,SecondType> |
DefaultPair.create()
Creates a new, empty
DefaultPair with both values being null. |
static <FirstType,SecondType> |
DefaultPair.create(FirstType first,
SecondType second)
Creates a new
DefaultPair from the given values. |
| Modifier and Type | Method and Description |
|---|---|
static java.util.ArrayList<DefaultPair<java.lang.reflect.Field,java.lang.Object>> |
ObjectUtil.getAllFieldValues(java.lang.Object o)
Returns an ArrayList of field/value pairs
|
static <FirstType,SecondType> |
DefaultPair.mergeCollections(java.util.Collection<FirstType> firsts,
java.util.Collection<SecondType> seconds)
Takes two collections of data of the same size and creates a new single
ArrayList<DefaultPair> out of their elements. |