| Package | Description |
|---|---|
| gov.sandia.cognition.data.convert |
Provides utilities for doing data type conversion.
|
| gov.sandia.cognition.data.convert.number |
Provides utilities for doing data type conversion with numbers.
|
| gov.sandia.cognition.data.convert.vector |
Provides utilities for doing data type conversion with vectors.
|
| gov.sandia.cognition.evaluator |
Provides interfaces and classes to do with the
Evaluator interface. |
| gov.sandia.cognition.framework.learning |
Provides a mechanism for putting learned objects into the Cognitive
Framework.
|
| gov.sandia.cognition.hash |
Provides hash function implementations.
|
| gov.sandia.cognition.learning.algorithm |
Provides general interfaces for learning algorithms.
|
| gov.sandia.cognition.learning.algorithm.bayes |
Provides algorithms for computing Bayesian categorizers.
|
| gov.sandia.cognition.learning.algorithm.delta |
Provides an abstract class for helping to implement variants of the Burrows'
Delta algorithm.
|
| gov.sandia.cognition.learning.algorithm.ensemble |
Provides ensemble methods.
|
| gov.sandia.cognition.learning.algorithm.factor.machine |
Provides factorization machine algorithms.
|
| gov.sandia.cognition.learning.algorithm.gradient |
Provides gradient based learning algorithms.
|
| gov.sandia.cognition.learning.algorithm.minimization |
Provides minimization algorithms.
|
| 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.algorithm.minimization.matrix |
Provides matrix solving algorithms.
|
| gov.sandia.cognition.learning.algorithm.nearest |
Provides algorithms for Nearest-Neighbor memory-based functions.
|
| gov.sandia.cognition.learning.algorithm.pca |
Provides implementations of Principle Components Analysis (PCA).
|
| gov.sandia.cognition.learning.algorithm.perceptron |
Provides the Perceptron algorithm and some of its variations.
|
| gov.sandia.cognition.learning.algorithm.perceptron.kernel | |
| gov.sandia.cognition.learning.algorithm.regression |
Provides regression algorithms, such as Linear Regression.
|
| gov.sandia.cognition.learning.algorithm.root |
Provides algorithms for finding the roots, or zero crossings, of scalar functions.
|
| gov.sandia.cognition.learning.algorithm.semisupervised.valence | |
| gov.sandia.cognition.learning.algorithm.tree |
Provides decision tree learning algorithms.
|
| gov.sandia.cognition.learning.data.feature |
Provides data feature extractors.
|
| gov.sandia.cognition.learning.experiment |
Provides experiments for validating the performance of learning algorithms.
|
| gov.sandia.cognition.learning.function |
Provides function objects for learning algorithms.
|
| gov.sandia.cognition.learning.function.categorization |
Provides functions that output a discrete set of categories.
|
| gov.sandia.cognition.learning.function.cost |
Provides cost functions.
|
| gov.sandia.cognition.learning.function.distance |
Provides distance functions.
|
| gov.sandia.cognition.learning.function.kernel |
Provides kernel functions.
|
| gov.sandia.cognition.learning.function.regression |
Provides functions that output real numbers from some input data structure.
|
| 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.learning.performance |
Provides performance measures.
|
| gov.sandia.cognition.math |
Provides classes for mathematical computation.
|
| gov.sandia.cognition.math.matrix |
Provides interfaces and classes for linear algebra.
|
| gov.sandia.cognition.math.signals |
Provides mathematical signal processing methods.
|
| gov.sandia.cognition.statistics |
Provides the inheritance hierarchy for general statistical methods and distributions.
|
| gov.sandia.cognition.statistics.bayesian |
Provides algorithms for computing Bayesian estimates of parameters.
|
| gov.sandia.cognition.statistics.distribution |
Provides statistical distributions.
|
| gov.sandia.cognition.statistics.method |
Provides algorithms for evaluating statistical data and conducting statistical inference, particularly frequentist methods.
|
| gov.sandia.cognition.statistics.montecarlo |
Provides Monte Carlo procedures for numerical integration and sampling.
|
| gov.sandia.cognition.text.convert |
Provides classes for converting objects to a textual representation.
|
| gov.sandia.cognition.text.spelling |
Provides classes for spelling.
|
| gov.sandia.cognition.text.term.filter |
Provides classes for filtering and transforming terms.
|
| gov.sandia.cognition.text.term.vector |
Provides methods for handling documents represented as term vectors.
|
| gov.sandia.cognition.text.term.vector.weighter |
Provides term weighting algorithms.
|
| gov.sandia.cognition.text.topic |
Provides topic modeling algorithms.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
DataConverter<InputType,OutputType>
Defines an object used to convert data from one type to another.
|
interface |
ReversibleDataConverter<InputType,OutputType>
Represents a
DataConverter whose conversion can be reversed. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractDataConverter<InputType,OutputType>
Abstract implementation of
DataConverter interface. |
class |
AbstractReverseCachedDataConverter<InputType,OutputType,ReverseConverterType extends DataConverter<? super OutputType,? extends InputType>>
Abstract implementation of
ReversibleDataConverter that caches the
reverse converter. |
class |
AbstractReversibleDataConverter<InputType,OutputType>
Abstract implementation of sthe
ReversibleDataConverter interface. |
class |
IdentityDataConverter<DataType>
A pass-through converter that just returns the given value.
|
class |
ObjectToStringConverter
Converts an
Object to a String using the toString
method. |
| Modifier and Type | Class and Description |
|---|---|
class |
DefaultBooleanToNumberConverter
Converts a
Boolean to a Number by using predefined values
for true, false, and (optionally) null. |
class |
DefaultBooleanToNumberConverter.Reverse
The reverse converter for the
DefaultBooleanToNumberConverter. |
class |
StringToDoubleConverter
Converts a
String to a Double using the
Double.valueOf method. |
class |
StringToIntegerConverter
Converts a
String to a Integer using the
Integer.valueOf method. |
| Modifier and Type | Interface and Description |
|---|---|
interface |
DataToVectorEncoder<InputType>
Defines a converter that can be used to encode data into a
Vector. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractToVectorEncoder<InputType>
An abstract implementation of the
DataToVectorEncoder interface. |
class |
NumberConverterToVectorAdapter<InputType>
Adapts a
DataConverter that outputs a number to be a
VectorEncoder. |
class |
NumberToVectorEncoder
An encoder that encodes a number as an element of a
Vector. |
class |
UniqueBooleanVectorEncoder<InputType>
An encoder for arbitrary objects that encodes an equality comparison between
a given input and a set of unique values.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ForwardReverseEvaluatorPair<InputType,OutputType,ForwardType extends Evaluator<? super InputType,? extends OutputType>,ReverseType extends Evaluator<? super OutputType,? extends InputType>>
Represents a both a (normal) forward evaluator and its reverse as a pair.
|
class |
ForwardReverseEvaluatorPair<InputType,OutputType,ForwardType extends Evaluator<? super InputType,? extends OutputType>,ReverseType extends Evaluator<? super OutputType,? extends InputType>>
Represents a both a (normal) forward evaluator and its reverse as a pair.
|
interface |
ReversibleEvaluator<InputType,OutputType,ReverseType extends Evaluator<? super OutputType,? extends InputType>>
Represents a
Evaluator whose evaluation can be reversed. |
| Modifier and Type | Interface and Description |
|---|---|
interface |
ReversibleEvaluator<InputType,OutputType,ReverseType extends Evaluator<? super OutputType,? extends InputType>>
Represents a
Evaluator whose evaluation can be reversed. |
interface |
StatefulEvaluator<InputType,OutputType,StateType extends CloneableSerializable>
The
StatefulEvaluator interface defines the functionality of an
Evaluator that maintains an internal state. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractStatefulEvaluator<InputType,OutputType,StateType extends CloneableSerializable>
The
AbstractStatefulEvalutor class is an abstract implementation of
the StatefulEvalutor interface. |
class |
CompositeEvaluatorList<InputType,OutputType>
Implements the composition of a list of evaluators.
|
class |
CompositeEvaluatorPair<InputType,IntermediateType,OutputType>
Implements a composition of two evaluators.
|
class |
CompositeEvaluatorTriple<InputType,FirstIntermediateType,SecondIntermediateType,OutputType>
Implements a composition of three evaluators.
|
class |
ForwardReverseEvaluatorPair<InputType,OutputType,ForwardType extends Evaluator<? super InputType,? extends OutputType>,ReverseType extends Evaluator<? super OutputType,? extends InputType>>
Represents a both a (normal) forward evaluator and its reverse as a pair.
|
class |
IdentityEvaluator<DataType>
An identity function that returns its input as its output.
|
class |
ValueClamper<DataType extends java.lang.Comparable<DataType>>
An evaluator that clamps a number between minimum and maximum values.
|
class |
ValueMapper<InputType,OutputType>
An evaluator that uses a map to map input values to their appropriate output
values.
|
| Modifier and Type | Field and Description |
|---|---|
protected ForwardType |
ForwardReverseEvaluatorPair.forward
The forward evaluator from input type to output type.
|
protected ReverseType |
ForwardReverseEvaluatorPair.reverse
The reverse evaluator from output type to input type.
|
| Modifier and Type | Method and Description |
|---|---|
static <InputType,OutputType,ForwardType extends Evaluator<? super InputType,? extends OutputType>,ReverseType extends Evaluator<? super OutputType,? extends InputType>> |
ForwardReverseEvaluatorPair.create(ForwardType forward,
ReverseType reverse)
Convenience method for creating a new forward-reverse evaluator pair.
|
static <InputType,OutputType,ForwardType extends Evaluator<? super InputType,? extends OutputType>,ReverseType extends Evaluator<? super OutputType,? extends InputType>> |
ForwardReverseEvaluatorPair.create(ForwardType forward,
ReverseType reverse)
Convenience method for creating a new forward-reverse evaluator pair.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<Evaluator<?,?>> |
CompositeEvaluatorList.getEvaluators()
Gets the list of evaluators that is being composed together.
|
| Modifier and Type | Method and Description |
|---|---|
static <InputType,FirstIntermediateType,SecondIntermediateType,OutputType> |
CompositeEvaluatorTriple.create(Evaluator<? super InputType,? extends FirstIntermediateType> first,
Evaluator<? super FirstIntermediateType,? extends SecondIntermediateType> second,
Evaluator<? super SecondIntermediateType,? extends OutputType> third)
A convenience method for creating composite evaluators.
|
static <InputType,FirstIntermediateType,SecondIntermediateType,OutputType> |
CompositeEvaluatorTriple.create(Evaluator<? super InputType,? extends FirstIntermediateType> first,
Evaluator<? super FirstIntermediateType,? extends SecondIntermediateType> second,
Evaluator<? super SecondIntermediateType,? extends OutputType> third)
A convenience method for creating composite evaluators.
|
static <InputType,FirstIntermediateType,SecondIntermediateType,OutputType> |
CompositeEvaluatorTriple.create(Evaluator<? super InputType,? extends FirstIntermediateType> first,
Evaluator<? super FirstIntermediateType,? extends SecondIntermediateType> second,
Evaluator<? super SecondIntermediateType,? extends OutputType> third)
A convenience method for creating composite evaluators.
|
static <InputType,IntermediateType,OutputType> |
CompositeEvaluatorPair.create(Evaluator<? super InputType,? extends IntermediateType> first,
Evaluator<? super IntermediateType,? extends OutputType> second)
A convenience method for creating composite evaluators.
|
static <InputType,IntermediateType,OutputType> |
CompositeEvaluatorPair.create(Evaluator<? super InputType,? extends IntermediateType> first,
Evaluator<? super IntermediateType,? extends OutputType> second)
A convenience method for creating composite evaluators.
|
void |
CompositeEvaluatorList.setEvaluators(Evaluator<?,?>... evaluatorsArray)
Sets the array of evaluators to compose together.
|
| Modifier and Type | Method and Description |
|---|---|
void |
CompositeEvaluatorList.setEvaluators(java.util.Collection<? extends Evaluator<?,?>> evaluators)
Sets the list of evaluators to compose together.
|
| Constructor and Description |
|---|
CompositeEvaluatorList(Evaluator<?,?>... evaluatorsArray)
Creates a new
CompositeEvaluatorList from the given array of
evaluators. |
CompositeEvaluatorPair(Evaluator<? super InputType,? extends IntermediateType> first,
Evaluator<? super IntermediateType,? extends OutputType> second)
Creates a new
CompositeEvaluatorPair from the two given evaluators. |
CompositeEvaluatorPair(Evaluator<? super InputType,? extends IntermediateType> first,
Evaluator<? super IntermediateType,? extends OutputType> second)
Creates a new
CompositeEvaluatorPair from the two given evaluators. |
CompositeEvaluatorTriple(Evaluator<? super InputType,? extends FirstIntermediateType> first,
Evaluator<? super FirstIntermediateType,? extends SecondIntermediateType> second,
Evaluator<? super SecondIntermediateType,? extends OutputType> third)
Creates a new
CompositeEvaluatorTriple from the three given
evaluators. |
CompositeEvaluatorTriple(Evaluator<? super InputType,? extends FirstIntermediateType> first,
Evaluator<? super FirstIntermediateType,? extends SecondIntermediateType> second,
Evaluator<? super SecondIntermediateType,? extends OutputType> third)
Creates a new
CompositeEvaluatorTriple from the three given
evaluators. |
CompositeEvaluatorTriple(Evaluator<? super InputType,? extends FirstIntermediateType> first,
Evaluator<? super FirstIntermediateType,? extends SecondIntermediateType> second,
Evaluator<? super SecondIntermediateType,? extends OutputType> third)
Creates a new
CompositeEvaluatorTriple from the three given
evaluators. |
| Constructor and Description |
|---|
CompositeEvaluatorList(java.util.Collection<? extends Evaluator<?,?>> evaluators)
Creates a new
CompositeEvaluatorList from the given collection
of evaluators. |
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,? extends OutputType> |
EvaluatorBasedCognitiveModule.getEvaluator()
Gets the underlying evaluator that is being wrapped in this module.
|
Evaluator<? super InputType,? extends OutputType> |
EvaluatorBasedCognitiveModuleSettings.getEvaluator()
Gets the evaluator to be used by the module.
|
| Modifier and Type | Method and Description |
|---|---|
BatchLearner<? super java.util.Collection<LearningDataType>,? extends Evaluator<? super InputType,? extends OutputType>> |
EvaluatorBasedCognitiveModuleFactoryLearner.getLearner()
Gets the learner used to create the Evaluator for the module.
|
| Modifier and Type | Method and Description |
|---|---|
void |
EvaluatorBasedCognitiveModuleSettings.setEvaluator(Evaluator<? super InputType,? extends OutputType> evaluator)
Sets the evaluator to be used by the module.
|
| Modifier and Type | Method and Description |
|---|---|
void |
EvaluatorBasedCognitiveModuleFactoryLearner.setLearner(BatchLearner<? super java.util.Collection<LearningDataType>,? extends Evaluator<? super InputType,? extends OutputType>> learner)
Sets the learner used to create the Evaluator for the module.
|
| Constructor and Description |
|---|
EvaluatorBasedCognitiveModuleSettings(Evaluator<? super InputType,? extends OutputType> evaluator,
CogxelConverter<InputType> inputConverter,
CogxelConverter<OutputType> outputConverter)
Creates a new instance of EvaluatorBasedCognitiveModuleSettings.
|
| Constructor and Description |
|---|
EvaluatorBasedCognitiveModuleFactoryLearner(BatchLearner<? super java.util.Collection<LearningDataType>,? extends Evaluator<? super InputType,? extends OutputType>> learner,
java.lang.String name,
CogxelConverter<InputType> inputConverter,
CogxelConverter<OutputType> outputConverter,
CogxelConverter<LearningDataType> learningDataConverter)
Creates a new instance of CognitiveModuleFactoryEvaluatorLearner.
|
EvaluatorBasedCognitiveModuleFactoryLearner(BatchLearner<? super java.util.Collection<LearningDataType>,? extends Evaluator<InputType,OutputType>> learner,
java.lang.String name)
Creates a new instance of CognitiveModuleFactoryEvaluatorLearner.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
HashFunction
A hash function maps an arbitrarily large input and maps it onto a
finite-length output.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractHashFunction
Partial implementation of HashFunction
|
class |
Eva32Hash
This implements the 32-bit "evahash" due to Robert Jenkins.
|
class |
Eva64Hash
This implements the 64-bit "evahash" due to Robert Jenkins.
|
class |
FNV1a32Hash
Implementation of the 32-bit (4-byte) Fowler-Noll-Vo (FNV-1a) hash function.
|
class |
FNV1a64Hash
Implementation of the 32-bit (4-byte) Fowler-Noll-Vo (FNV-1a) hash function.
|
class |
MD5Hash
Implementation of the MD5 128-bit (16-byte) cryptographic hash function.
|
class |
Murmur32Hash
Implementation of the MurmurHash2 32-bit (4-byte) non-cryptographic hash
function.
|
class |
Prime32Hash
Implementation of the prime-hash function using 32-bit (4-byte) precision.
|
class |
Prime64Hash
Implementation of the prime-hash function using 64-bit (8-byte) precision.
|
class |
SHA1Hash
A Java implementation of the Secure Hash Algorithm, SHA-1, as defined
in FIPS PUB 180-1.
|
class |
SHA256Hash
The SHA-2, 256-bit (32 byte) hash function.
|
class |
SHA512Hash
The SHA-2 512-bit (64-byte) hash function.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractAnytimeSupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
The
AbstractAnytimeSupervisedBatchLearner abstract class extends
the AbstractAnytimeBatchLearner to implement the
SupervisedBatchLearner interface. |
class |
AbstractSupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
An abstract implementation of the batch and incremental learning for
an incremental supervised learner.
|
interface |
SupervisedBatchAndIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
Interface for a class that is a supervised learning algorithm that can be
used both batch and incremental contexts.
|
interface |
SupervisedBatchLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
The
BatchSupervisedLearner interface is an extension of the
BatchLearner interface that contains the typical generic definition
conventions for a batch, supervised learning algorithm. |
interface |
SupervisedIncrementalLearner<InputType,OutputType,ResultType extends Evaluator<? super InputType,? extends OutputType>>
Interface for supervised incremental learning algorithms.
|
class |
TimeSeriesPredictionLearner<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>
A learner used to predict the future of a sequence of data by wrapping
another learner and created a future-aligned data set.
|
| Modifier and Type | Field and Description |
|---|---|
protected BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> |
CompositeBatchLearnerPair.firstLearner
The first learner that is trained on the input data.
|
protected BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> |
InputOutputTransformedBatchLearner.inputLearner
The unsupervised learning algorithm for creating the input
transformation.
|
protected BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> |
CompositeBatchLearnerPair.secondLearner
The second learner that is trained on the output of the evaluator
created by the first learner.
|
| Modifier and Type | Method and Description |
|---|---|
BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> |
CompositeBatchLearnerPair.getFirst() |
BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> |
CompositeBatchLearnerPair.getFirstLearner()
Gets the first learner that is applied to the input data.
|
BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> |
InputOutputTransformedBatchLearner.getInputLearner()
Gets the unsupervised learning algorithm for creating the input
transformation.
|
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> |
CompositeBatchLearnerPair.getSecond() |
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> |
CompositeBatchLearnerPair.getSecondLearner()
Gets the second learner that is applied to the output of the first
learner.
|
| Modifier and Type | Method and Description |
|---|---|
static <InputType,TransformedInputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.create(Evaluator<? super InputType,? extends TransformedInputType> inputTransform,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner)
Creates a new
InputOutputTransformedBatchLearner from the
input transform, supervised learner, and unsupervised output transform
learner. |
static <InputType,TransformedInputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.create(Evaluator<? super InputType,? extends TransformedInputType> inputTransform,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform)
Creates a new
InputOutputTransformedBatchLearner from the
predefined input and output transforms and the supervised learner. |
static <InputType,IntermediateType,OutputType> |
CompositeBatchLearnerPair.createInputTransformed(Evaluator<? super InputType,? extends IntermediateType> inputTransform,
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> learner)
Creates a new
CompositeBatchLearnerPair from the given input
transform and learner. |
static <InputType,TransformedInputType,OutputType> |
InputOutputTransformedBatchLearner.createInputTransformed(Evaluator<? super InputType,? extends TransformedInputType> inputTransform,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,OutputType>>,? extends Evaluator<? super TransformedInputType,? extends OutputType>> learner)
Creates a new
InputOutputTransformedBatchLearner from the
predefined input transform and the supervised learner. |
static <InputType,IntermediateType,OutputType> |
CompositeBatchLearnerPair.createOutputTransformed(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> learner,
Evaluator<? super IntermediateType,? extends OutputType> outputTransform)
Creates a new
CompositeBatchLearnerPair from the given learner
and output transform.. |
| Modifier and Type | Method and Description |
|---|---|
static <InputType,IntermediateType,OutputType> |
CompositeBatchLearnerPair.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner,
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner)
Creates a new
CompositeBatchLearnerPair from the given learners. |
static <InputType,IntermediateType,OutputType> |
CompositeBatchLearnerPair.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner,
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner)
Creates a new
CompositeBatchLearnerPair from the given learners. |
static <InputType,TransformedInputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner)
Creates a new
InputOutputTransformedBatchLearner from the
three learners. |
static <InputType,TransformedInputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner)
Creates a new
InputOutputTransformedBatchLearner from the
three learners. |
static <InputType,TransformedInputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform)
Creates a new
InputOutputTransformedBatchLearner from the
unsupervised input transform learner, supervised learners, and output
transform. |
static <InputType,TransformedInputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.create(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform)
Creates a new
InputOutputTransformedBatchLearner from the
unsupervised input transform learner, supervised learners, and output
transform. |
static <InputType,TransformedInputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.create(Evaluator<? super InputType,? extends TransformedInputType> inputTransform,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner)
Creates a new
InputOutputTransformedBatchLearner from the
input transform, supervised learner, and unsupervised output transform
learner. |
static <InputType,TransformedInputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.create(Evaluator<? super InputType,? extends TransformedInputType> inputTransform,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform)
Creates a new
InputOutputTransformedBatchLearner from the
predefined input and output transforms and the supervised learner. |
static <InputType,TransformedInputType,OutputType> |
InputOutputTransformedBatchLearner.createInputTransformed(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,OutputType>>,? extends Evaluator<? super TransformedInputType,? extends OutputType>> learner)
Creates a new
InputOutputTransformedBatchLearner from the
input and supervised learners, performing no transformation on the
output type. |
static <InputType,TransformedInputType,OutputType> |
InputOutputTransformedBatchLearner.createInputTransformed(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,OutputType>>,? extends Evaluator<? super TransformedInputType,? extends OutputType>> learner)
Creates a new
InputOutputTransformedBatchLearner from the
input and supervised learners, performing no transformation on the
output type. |
static <InputType,IntermediateType,OutputType> |
CompositeBatchLearnerPair.createInputTransformed(Evaluator<? super InputType,? extends IntermediateType> inputTransform,
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> learner)
Creates a new
CompositeBatchLearnerPair from the given input
transform and learner. |
static <InputType,TransformedInputType,OutputType> |
InputOutputTransformedBatchLearner.createInputTransformed(Evaluator<? super InputType,? extends TransformedInputType> inputTransform,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,OutputType>>,? extends Evaluator<? super TransformedInputType,? extends OutputType>> learner)
Creates a new
InputOutputTransformedBatchLearner from the
predefined input transform and the supervised learner. |
static <InputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.createOutputTransformed(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,TransformedOutputType>>,? extends Evaluator<? super InputType,? extends TransformedOutputType>> learner,
BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner)
Creates a new
InputOutputTransformedBatchLearner from the
supervised and output learners, performing no transformation on the
input type. |
static <InputType,TransformedOutputType,OutputType> |
InputOutputTransformedBatchLearner.createOutputTransformed(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,TransformedOutputType>>,? extends Evaluator<? super InputType,? extends TransformedOutputType>> learner,
ReversibleEvaluator<OutputType,TransformedOutputType,?> outputTransform)
Creates a new
InputOutputTransformedBatchLearner from the
predefined output transforms and the supervised learner. |
static <InputType,IntermediateType,OutputType> |
CompositeBatchLearnerPair.createOutputTransformed(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> learner,
Evaluator<? super IntermediateType,? extends OutputType> outputTransform)
Creates a new
CompositeBatchLearnerPair from the given learner
and output transform.. |
void |
CompositeBatchLearnerPair.setFirstLearner(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner)
Sets the first learner that is applied to the input data.
|
void |
InputOutputTransformedBatchLearner.setInputLearner(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner)
Sets the unsupervised learning algorithm for creating the input
transformation.
|
void |
CompositeBatchLearnerPair.setSecondLearner(BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner)
Sets the second learner that is applied to the output of the first
learner.
|
| Constructor and Description |
|---|
CompositeBatchLearnerPair(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner,
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner)
Creates a new
CompositeBatchLearnerPair with the given two
learner. |
CompositeBatchLearnerPair(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends IntermediateType>> firstLearner,
BatchLearner<? super java.util.Collection<? extends IntermediateType>,? extends Evaluator<? super IntermediateType,? extends OutputType>> secondLearner)
Creates a new
CompositeBatchLearnerPair with the given two
learner. |
InputOutputTransformedBatchLearner(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner)
Creates a new
InputOutputTransformedBatchLearner with the given
learners. |
InputOutputTransformedBatchLearner(BatchLearner<? super java.util.Collection<? extends InputType>,? extends Evaluator<? super InputType,? extends TransformedInputType>> inputLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends TransformedInputType,TransformedOutputType>>,? extends Evaluator<? super TransformedInputType,? extends TransformedOutputType>> learner,
BatchLearner<? super java.util.Collection<? extends OutputType>,? extends ReversibleEvaluator<OutputType,TransformedOutputType,?>> outputLearner)
Creates a new
InputOutputTransformedBatchLearner with the given
learners. |
| Modifier and Type | Class and Description |
|---|---|
class |
DiscreteNaiveBayesCategorizer<InputType,CategoryType>
Implementation of a Naive Bayes Classifier for Discrete Data.
|
class |
VectorNaiveBayesCategorizer<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>
A naive Bayesian categorizer that takes an input vector and applies an
independent scalar probability density function to each one.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractDeltaCategorizer<CategoryType>
The Burrows Delta algorithm is primarily used for authorship attribution, but
can be used for other applications.
|
class |
BurrowsDeltaCategorizer<CategoryType>
The regular Burrows' Delta algorithm implementation.
|
class |
CosineDeltaCategorizer<CategoryType>
The Cosine Delta algorithm implementation.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractBaggingLearner<InputType,OutputType,MemberType,EnsembleType extends Evaluator<? super InputType,? extends OutputType>>
Learns an ensemble by randomly sampling with replacement
(duplicates allowed) some percentage of the size of the data (defaults to
100%) on each iteration to train a new ensemble member.
|
class |
AdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>>
An ensemble of regression functions that determine the result by adding
their outputs together.
|
class |
AveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>>
An ensemble for regression functions that averages together the output value
of each ensemble member to get the final output.
|
class |
OnlineBaggingCategorizerLearner<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
An implementation of an online version of the Bagging algorithm for learning
an ensemble of categorizers.
|
class |
VotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
An ensemble of categorizers that determine the result based on an
equal-weight vote.
|
class |
WeightedAdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>>
An implementation of an ensemble that takes a weighted sum of the values
returned by its members.
|
class |
WeightedAveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>>
An implementation of an ensemble that takes the weighted average of its
members.
|
class |
WeightedBinaryEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Boolean>>
The
WeightedBinaryEnsemble class implements an Ensemble of
BinaryCategorizer objects where each categorizer is assigned a
weight and the category is selected by choosing the one with the largest
sum of weights. |
class |
WeightedVotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
An ensemble of categorizers where each ensemble member is evaluated with the
given input to find the category to which its weighted votes are assigned.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>>
An ensemble of regression functions that determine the result by adding
their outputs together.
|
class |
AveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>>
An ensemble for regression functions that averages together the output value
of each ensemble member to get the final output.
|
class |
VotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
An ensemble of categorizers that determine the result based on an
equal-weight vote.
|
class |
WeightedAdditiveEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>>
An implementation of an ensemble that takes a weighted sum of the values
returned by its members.
|
class |
WeightedAveragingEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Number>>
An implementation of an ensemble that takes the weighted average of its
members.
|
class |
WeightedBinaryEnsemble<InputType,MemberType extends Evaluator<? super InputType,? extends java.lang.Boolean>>
The
WeightedBinaryEnsemble class implements an Ensemble of
BinaryCategorizer objects where each categorizer is assigned a
weight and the category is selected by choosing the one with the largest
sum of weights. |
class |
WeightedVotingCategorizerEnsemble<InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>>
An ensemble of categorizers where each ensemble member is evaluated with the
given input to find the category to which its weighted votes are assigned.
|
| Modifier and Type | Field and Description |
|---|---|
protected Evaluator<? super InputType,? extends CategoryType> |
IVotingCategorizerLearner.currentMember
The currently learned member of the ensemble.
|
protected EnsembleType |
AbstractBaggingLearner.ensemble
The ensemble being created by the learner.
|
| Modifier and Type | Field and Description |
|---|---|
protected WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>> |
AdaBoost.ensemble
The ensemble learned by the algorithm.
|
protected WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>> |
IVotingCategorizerLearner.ensemble
The current ensemble.
|
protected WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>> |
MultiCategoryAdaBoost.ensemble
The ensemble learned by the algorithm.
|
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> |
IVotingCategorizerLearner.learner
The learner used to produce each ensemble member.
|
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> |
AdaBoost.weakLearner
The "weak learner" that must learn from the weighted input-output pairs
on each iteration.
|
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> |
MultiCategoryAdaBoost.weakLearner
The "weak learner" that must learn from the weighted input-output pairs
on each iteration.
|
| Modifier and Type | Method and Description |
|---|---|
static <InputType,CategoryType,MemberType extends Evaluator<? super InputType,? extends CategoryType>> |
OnlineBaggingCategorizerLearner.create(IncrementalLearner<? super InputOutputPair<? extends InputType,CategoryType>,MemberType> learner,
int ensembleSize,
double percentToSample,
java.util.Random random)
Convenience method for creating an
OnlineBaggingCategorizerLearner. |
| Modifier and Type | Method and Description |
|---|---|
protected WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>> |
BaggingCategorizerLearner.createInitialEnsemble() |
protected AveragingEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Number>> |
BaggingRegressionLearner.createInitialEnsemble() |
protected WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>> |
BinaryBaggingLearner.createInitialEnsemble() |
WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>> |
AdaBoost.getEnsemble()
Gets the ensemble created by this learner.
|
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> |
AdaBoost.getLearner() |
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> |
IVotingCategorizerLearner.getLearner()
Gets the learner used to learn each ensemble member.
|
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> |
MultiCategoryAdaBoost.getLearner() |
WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>> |
AdaBoost.getResult() |
WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>> |
IVotingCategorizerLearner.getResult() |
WeightedVotingCategorizerEnsemble<InputType,CategoryType,Evaluator<? super InputType,? extends CategoryType>> |
MultiCategoryAdaBoost.getResult() |
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> |
AdaBoost.getWeakLearner()
Gets the weak learner that is passed the weighted training data on each
step of the algorithm.
|
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> |
MultiCategoryAdaBoost.getWeakLearner()
Gets the weak learner that is passed the weighted training data on each
step of the algorithm.
|
| Modifier and Type | Method and Description |
|---|---|
protected void |
BinaryBaggingLearner.addEnsembleMember(Evaluator<? super InputType,? extends java.lang.Boolean> member) |
protected void |
BaggingCategorizerLearner.addEnsembleMember(Evaluator<? super InputType,? extends CategoryType> member) |
protected void |
BaggingRegressionLearner.addEnsembleMember(Evaluator<? super InputType,? extends java.lang.Number> member) |
| Modifier and Type | Method and Description |
|---|---|
protected void |
AdaBoost.setEnsemble(WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>> ensemble)
Sets the ensemble created by this learner.
|
void |
IVotingCategorizerLearner.setLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner)
Sets the learner used to learn each ensemble member.
|
void |
AdaBoost.setWeakLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> weakLearner)
Sets the weak learner that is passed the weighted training data on each
step of the algorithm.
|
void |
MultiCategoryAdaBoost.setWeakLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> weakLearner)
Sets the weak learner that is passed the weighted training data on each
step of the algorithm.
|
| Constructor and Description |
|---|
AdaBoost(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> weakLearner)
Creates a new instance of AdaBoost.
|
AdaBoost(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> weakLearner,
int maxIterations)
Creates a new instance of AdaBoost.
|
BaggingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner)
Creates a new instance of BaggingCategorizerLearner.
|
BaggingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
int maxIterations,
double percentToSample,
java.util.Random random)
Creates a new instance of BaggingCategorizerLearner.
|
BaggingRegressionLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,? extends java.lang.Number>> learner)
Creates a new instance of BaggingRegressionLearner.
|
BaggingRegressionLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,? extends java.lang.Number>> learner,
int maxIterations,
double percentToSample,
java.util.Random random)
Creates a new instance of BaggingRegressionLearner.
|
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner)
Creates a new instance of BinaryBaggingLearner.
|
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner,
int maxIterations)
Creates a new instance of BinaryBaggingLearner.
|
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner,
int maxIterations,
double percentToSample,
java.util.Random random)
Creates a new instance of BinaryBaggingLearner.
|
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner,
int maxIterations,
java.util.Random random)
Creates a new instance of BinaryBaggingLearner.
|
CategoryBalancedBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner)
Creates a new instance of CategoryBalancedBaggingLearner.
|
CategoryBalancedBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
int maxIterations,
double percentToSample,
java.util.Random random)
Creates a new instance of CategoryBalancedBaggingLearner.
|
CategoryBalancedIVotingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
int maxIterations,
double percentToSample,
double proportionIncorrectInSample,
boolean voteOutOfBagOnly,
Factory<? extends DataDistribution<CategoryType>> counterFactory,
java.util.Random random)
Creates a new
CategoryBalancedIVotingLearner. |
CategoryBalancedIVotingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
int maxIterations,
double percentToSample,
java.util.Random random)
Creates a new
CategoryBalancedIVotingLearner. |
IVotingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
int maxIterations,
double percentToSample,
double proportionIncorrectInSample,
boolean voteOutOfBagOnly,
Factory<? extends DataDistribution<CategoryType>> counterFactory,
java.util.Random random)
Creates a new
IVotingCategorizerLearner. |
IVotingCategorizerLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner,
int maxIterations,
double percentToSample,
java.util.Random random)
Creates a new
IVotingCategorizerLearner. |
MultiCategoryAdaBoost(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> weakLearner,
int maxIterations)
Creates a new
MultiCategoryAdaBoost with the given parameters. |
| Modifier and Type | Class and Description |
|---|---|
class |
FactorizationMachine
Implements a Factorization Machine.
|
| 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.
|
interface |
ParameterGradientEvaluator<InputType,OutputType,GradientType>
Interface for computing the derivative of the output with respect to the
parameters for a given input.
|
| 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. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractAnytimeFunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>
A partial implementation of a minimization algorithm that is iterative,
stoppable, and approximate.
|
interface |
FunctionMinimizer<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>
Interface for unconstrained minimization of nonlinear functions.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractAnytimeLineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Partial AnytimeAlgorithm implementation of a LineMinimizer.
|
interface |
LineMinimizer<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Defines the functionality of a line-minimization algorithm, often called a
"line search" algorithm.
|
| 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 Evaluator<java.lang.Double,java.lang.Double>> |
LineMinimizerDerivativeFree.DEFAULT_INTERPOLATOR
Default interpolation algorithm, LineBracketInterpolatorBrent.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super Vector,? extends java.lang.Double> |
DirectionalVectorToScalarFunction.getVectorScalarFunction()
Getter for vectorScalarFunction
|
| Modifier and Type | Method and Description |
|---|---|
void |
DirectionalVectorToScalarFunction.setVectorScalarFunction(Evaluator<? super Vector,? extends java.lang.Double> vectorScalarFunction)
Setter for vectorScalarFunction
|
| Constructor and Description |
|---|
DirectionalVectorToScalarFunction(Evaluator<? super Vector,? extends java.lang.Double> vectorScalarFunction,
Vector vectorOffset,
Vector direction)
Creates a new function that restricts the vectorFunction to a
particular vector direction
|
| Constructor and Description |
|---|
LineMinimizerDerivativeFree(LineBracketInterpolator<? super Evaluator<java.lang.Double,java.lang.Double>> interpolator)
Creates a new instance of LineMinimizerDerivativeFree
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractLineBracketInterpolator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Partial implementation of LinearBracketInterpolator
|
class |
AbstractLineBracketInterpolatorPolynomial<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Partial implementation of a LineBracketInterpolator based on a closed-form
polynomial function.
|
interface |
LineBracketInterpolator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Definition of an interpolator/extrapolator for a LineBracket.
|
| Modifier and Type | Method and Description |
|---|---|
PolynomialFunction.Linear |
LineBracketInterpolatorLinear.computePolynomial(LineBracket bracket,
Evaluator<java.lang.Double,java.lang.Double> function) |
PolynomialFunction.ClosedForm |
LineBracketInterpolatorParabola.computePolynomial(LineBracket bracket,
Evaluator<java.lang.Double,java.lang.Double> function) |
double |
LineBracketInterpolatorBrent.findMinimum(LineBracket bracket,
double minx,
double maxx,
Evaluator<java.lang.Double,java.lang.Double> function) |
double |
LineBracketInterpolatorGoldenSection.findMinimum(LineBracket bracket,
double minx,
double maxx,
Evaluator<java.lang.Double,java.lang.Double> function) |
| Modifier and Type | Class and Description |
|---|---|
class |
MatrixVectorMultiplier
The necessary multiplication wrapper class to match the FunctionMinimizer
interface.
|
class |
MatrixVectorMultiplierDiagonalPreconditioner
Implements a diagonal preconditioner for a matrix-vector multiplier.
|
class |
MatrixVectorMultiplierWithPreconditioner
As various preconditioner operations could be created, this class defines the
interface that must be followed by them.
|
class |
OverconstrainedMatrixVectorMultiplier
Implements an overconstrainted matrix-vector multiplication.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
KNearestNeighbor<InputType,OutputType>
A generic k-nearest-neighbor classifier.
|
interface |
NearestNeighbor<InputType,OutputType>
The
NearestNeighborExhaustive class implements a simple evaluator
that looks up a given input object in a collection of input-output pair
examples and returns the output associated with the most similar input. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractKNearestNeighbor<InputType,OutputType>
Partial implementation of KNearestNeighbor.
|
class |
AbstractNearestNeighbor<InputType,OutputType>
Partial implementation of KNearestNeighbor.
|
class |
KNearestNeighborExhaustive<InputType,OutputType>
A generic k-nearest-neighbor classifier.
|
static class |
KNearestNeighborExhaustive.Learner<InputType,OutputType>
This is a BatchLearner interface for creating a new KNearestNeighborExhaustive
from a given dataset, simply a pass-through to the constructor of
KNearestNeighborExhaustive
|
class |
KNearestNeighborKDTree<InputType extends Vectorizable,OutputType>
A KDTree-based implementation of the k-nearest neighbor algorithm.
|
static class |
KNearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
This is a BatchLearner interface for creating a new KNearestNeighbor
from a given dataset, simply a pass-through to the constructor of
KNearestNeighbor
|
class |
NearestNeighborExhaustive<InputType,OutputType>
The
NearestNeighborExhaustive class implements a simple evaluator
that looks up a given input object in a collection of input-output pair
examples and returns the output associated with the most similar input. |
class |
NearestNeighborKDTree<InputType extends Vectorizable,OutputType>
A KDTree-based implementation of the nearest neighbor algorithm.
|
static class |
NearestNeighborKDTree.Learner<InputType extends Vectorizable,OutputType>
This is a BatchLearner interface for creating a new NearestNeighbor
from a given dataset, simply a pass-through to the constructor of
NearestNeighbor
|
| Modifier and Type | Class and Description |
|---|---|
static class |
KernelPrincipalComponentsAnalysis.Function<DataType>
The resulting transformation function learned by Kernel Principal
Components Analysis.
|
class |
PrincipalComponentsAnalysisFunction
This VectorFunction maps a high-dimension input space onto a (hopefully)
simple low-dimensional output space by subtracting the mean of the input
data, and passing the zero-mean input through a dimension-reducing matrix
multiplication function.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
OnlineShiftingPerceptron.LinearResult
This is the result learned by the shifting perceptron.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
Forgetron.Result<InputType>
The result object learned by the
Forgetron, which extends
the DefaultKernelBinaryCategorizer with some additional state
information needed in the update step. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>>
Abstract partial implementation for logistic regression classes.
|
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.
|
interface |
MultivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends Vectorizable>>
A regression algorithm that maps one or more independent (input) variables
onto multiple output variables.
|
interface |
Regression<InputType,OutputType,EvaluatorType extends Evaluator<? super InputType,? extends OutputType>>
A supervised learning algorithm that attempts to interpolate/extrapolate
inputs given a training set of input/output pairs.
|
interface |
UnivariateRegression<InputType,EvaluatorType extends Evaluator<? super InputType,? extends java.lang.Double>>
A type of Regression algorithm that has a single dependent (output) variable
that we are trying to predict.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LocallyWeightedFunction<InputType,OutputType>
LocallyWeightedFunction is a generalization of the k-nearest neighbor
concept, also known as "Instance-Based Learning", "Memory-Based Learning",
"Nonparametric Regression", "Case-Based Regression", or
"Kernel-Based Regression".
|
static class |
LogisticRegression.Function
Class that is a linear discriminant, followed by a sigmoid function.
|
static class |
ParameterDerivativeFreeCostMinimizer.ParameterCostEvaluatorDerivativeFree
Function that maps the parameters of an object to its inputs, so that
minimization algorithms can tune the parameters of an object against
a cost function.
|
static class |
ParameterDifferentiableCostMinimizer.ParameterCostEvaluatorDerivativeBased
Function that maps the parameters of an object to its inputs, so that
minimization algorithms can tune the parameters of an object against
a cost function.
|
| Modifier and Type | Field and Description |
|---|---|
protected FunctionType |
AbstractLogisticRegression.objectToOptimize
The object to optimize, used as a factory on successive runs of the
algorithm.
|
protected FunctionType |
AbstractLogisticRegression.result
Return value from the algorithm
|
| Modifier and Type | Field and Description |
|---|---|
static FunctionMinimizer<Vector,java.lang.Double,Evaluator<? super Vector,java.lang.Double>> |
ParameterDerivativeFreeCostMinimizer.DEFAULT_FUNCTION_MINIMIZER
Default function minimizer, FunctionMinimizerDirectionSetPowell
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,Vector> |
LinearBasisRegression.getInputToVectorMap()
Getter for inputToVectorMap
|
Evaluator<? super InputType,? extends OutputType> |
LocallyWeightedFunction.getLocalApproximator()
Getter for localApproximator
|
Evaluator<? super InputType,? extends OutputType> |
KernelWeightedRobustRegression.getResult() |
| Modifier and Type | Method and Description |
|---|---|
void |
LinearBasisRegression.setInputToVectorMap(Evaluator<? super InputType,Vector> inputToVectorMap)
Setter for inputToVectorMap
|
void |
KernelWeightedRobustRegression.setLearned(Evaluator<InputType,OutputType> result)
Getter for result
|
void |
LocallyWeightedFunction.setLocalApproximator(Evaluator<? super InputType,? extends OutputType> localApproximator)
Setter for localApproximator
|
| Constructor and Description |
|---|
LinearBasisRegression(Evaluator<? super InputType,Vector> inputToVectorMap)
Creates a new instance of LinearRegression
|
| Constructor and Description |
|---|
LinearBasisRegression(java.util.Collection<? extends Evaluator<? super InputType,java.lang.Double>> basisFunctions)
Creates a new instance of LinearRegression
|
| Modifier and Type | Class and Description |
|---|---|
class |
SolverFunction
Evaluator that allows RootFinders to solve for nonzero values by setting
a "target" parameter.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<java.lang.Double,java.lang.Double> |
SolverFunction.getInternalFunction()
Getter for internalFunction
|
| Modifier and Type | Method and Description |
|---|---|
InputOutputPair<java.lang.Double,java.lang.Double> |
MinimizerBasedRootFinder.learn(Evaluator<java.lang.Double,java.lang.Double> data) |
void |
SolverFunction.setInternalFunction(Evaluator<java.lang.Double,java.lang.Double> internalFunction)
Setter for internalFunction.
|
| Constructor and Description |
|---|
SolverFunction(double target,
Evaluator<java.lang.Double,java.lang.Double> internalFunction)
Creates a new instance of SolverFunction
|
| Constructor and Description |
|---|
MinimizerBasedRootFinder(LineMinimizer<Evaluator<java.lang.Double,java.lang.Double>> algorithm)
Creates a new instance of MinimizerBasedRootFinder
|
| Modifier and Type | Class and Description |
|---|---|
class |
MultipartiteValenceMatrix
This class implements a semi-supervised learning algorithm for spreading
"valence" across a multi-partite graph.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CategorizationTree<InputType,OutputType>
The
CategorizationTree class extends the DecisionTree class
to implement a decision tree that does categorization. |
class |
DecisionTree<InputType,OutputType>
The
DecisionTree class implements a standard decision tree that is
made up of DecisionTreeNode objects. |
class |
RegressionTree<InputType>
The
RegressionTree class extends the DecisionTree class
to implement a decision tree that does regression. |
| Modifier and Type | Field and Description |
|---|---|
protected Evaluator<? super InputType,java.lang.Double> |
RegressionTreeNode.scalarFunction
The function to apply for leaf nodes.
|
| Modifier and Type | Field and Description |
|---|---|
protected BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> |
RegressionTreeLearner.regressionLearner
The learning algorithm for the regression function.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,java.lang.Double> |
RegressionTreeNode.getScalarFunction()
Gets the scalar function applied to the input when the node is a leaf
node.
|
| Modifier and Type | Method and Description |
|---|---|
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> |
RegressionTreeLearner.getRegressionLearner()
Gets the regression learner that is to be used to fit a function
approximator to the values in the tree.
|
| Modifier and Type | Method and Description |
|---|---|
void |
RegressionTreeNode.setScalarFunction(Evaluator<? super InputType,java.lang.Double> scalarFunction)
Sets the scalar function applied to the input when the node is a leaf
node.
|
| Modifier and Type | Method and Description |
|---|---|
void |
RegressionTreeLearner.setRegressionLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> regressionLearner)
Sets the regression learner that is to be used to fit a function
approximator to the values in the tree.
|
| Constructor and Description |
|---|
RegressionTreeNode(DecisionTreeNode<InputType,java.lang.Double> parent,
Categorizer<? super InputType,? extends InteriorType> decider,
Evaluator<? super InputType,java.lang.Double> scalarFunction,
double value,
java.lang.Object incomingValue)
Creates a new instance of RegressionTreeNode.
|
RegressionTreeNode(DecisionTreeNode<InputType,java.lang.Double> parent,
Evaluator<? super InputType,java.lang.Double> scalarFunction,
double value)
Creates a new instance of RegressionTreeNode.
|
RegressionTreeNode(DecisionTreeNode<InputType,java.lang.Double> parent,
Evaluator<? super InputType,java.lang.Double> scalarFunction,
double value,
java.lang.Object incomingValue)
Creates a new instance of RegressionTreeNode.
|
| Constructor and Description |
|---|
RegressionTreeLearner(DeciderLearner<? super InputType,java.lang.Double,?,?> deciderLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> regressionLearner)
Creates a new instance of CategorizationTreeLearner.
|
RegressionTreeLearner(DeciderLearner<? super InputType,java.lang.Double,?,?> deciderLearner,
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,? extends Evaluator<? super InputType,java.lang.Double>> regressionLearner,
int leafCountThreshold,
int maxDepth)
Creates a new instance of CategorizationTreeLearner.
|
| Modifier and Type | Class and Description |
|---|---|
class |
DelayFunction<DataType>
Delays the input and returns the input from the parameterized number of
samples previous.
|
class |
FeatureHashing
Implements a function that applies vector feature hashing.
|
class |
LinearRegressionCoefficientExtractor
Takes a sampled sequence of equal-dimension vectors as input and computes
the linear regression coefficients for each dimension in the vectors.
|
class |
MultivariateDecorrelator
Decorrelates a data using a mean and full or diagonal covariance matrix.
|
class |
StandardDistributionNormalizer
The
StandardDistributionNormalizer class implements a normalization
method where a real value is converted onto a standard distribution. |
| Modifier and Type | Class and Description |
|---|---|
class |
ConstantEvaluator<OutputType>
The
ConstantEvaluator class implements an Evaluator that
always returns the same output value. |
class |
LinearCombinationFunction<InputType,OutputType>
A function whose output is a weighted linear combination of (potentially)
nonlinear basis function.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.ArrayList<? extends Evaluator<? super InputType,? extends OutputType>> |
LinearCombinationFunction.getBasisFunctions()
Getter for basisFunctions
|
| Modifier and Type | Method and Description |
|---|---|
void |
LinearCombinationFunction.setBasisFunctions(java.util.ArrayList<? extends Evaluator<? super InputType,? extends OutputType>> basisFunctions)
Setter for basisFunctions
|
| Constructor and Description |
|---|
LinearCombinationFunction(java.util.ArrayList<? extends Evaluator<? super InputType,? extends OutputType>> basisFunctions,
Vector coefficients)
Creates a new instance of LinearCombinationFunction
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
BinaryCategorizer<InputType>
The
BinaryCategorizer extends the Categorizer
interface by enforcing that the output categories are boolean values, which
means that the categorizer is performing binary categorization. |
interface |
Categorizer<InputType,CategoryType>
The
Categorizer interface defines the functionality of an
object that can take an input and evaluate what category out of a fixed
set of categories it belongs to. |
interface |
ConfidenceWeightedBinaryCategorizer
Interface for a confidence-weighted binary categorizer, which defines a
distribution over linear binary categorizers.
|
interface |
DiscriminantBinaryCategorizer<InputType>
Interface for a linear discriminant categorizer in the binary categorization
domain.
|
interface |
DiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends java.lang.Comparable<? super DiscriminantType>>
Interface for a
Categorizer that can produce a value to discriminate
between how well different instances fit a given category. |
interface |
ThresholdBinaryCategorizer<InputType>
Interface for a binary categorizer that uses a threshold to determine the
categorization.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractBinaryCategorizer<InputType>
The
AbstractBinaryCategorizer implements the commonality of
the BinaryCategorizer, holding the collection of possible
values. |
class |
AbstractCategorizer<InputType,CategoryType>
An abstract implementation of the
Categorizer interface. |
class |
AbstractConfidenceWeightedBinaryCategorizer
Unit tests for class AbstractConfidenceWeightedBinaryCategorizer.
|
class |
AbstractDiscriminantBinaryCategorizer<InputType>
An abstract implementation of the
DiscriminantBinaryCategorizer
interface. |
class |
AbstractDiscriminantCategorizer<InputType,CategoryType,DiscriminantType extends java.lang.Comparable<? super DiscriminantType>>
An abstract implementation of the
DiscriminantCategorizer interface. |
class |
AbstractThresholdBinaryCategorizer<InputType>
Categorizer that first maps the input space onto a real value, then
uses a threshold to map the result onto lowValue (for strictly less than the
threshold) or highValue (for greater than or equal to the threshold).
|
class |
BinaryVersusCategorizer<InputType,CategoryType>
An adapter that allows binary categorizers to be adapted for multi-category
problems by applying a binary categorizer to each pair of categories.
|
class |
CompositeCategorizer<InputType,IntermediateType,CategoryType>
Composes a preprocessor function with a categorizer.
|
class |
DefaultConfidenceWeightedBinaryCategorizer
A default implementation of the
ConfidenceWeightedBinaryCategorizer
that stores a full mean and covariance matrix. |
class |
DefaultKernelBinaryCategorizer<InputType>
A default implementation of the
KernelBinaryCategorizer that uses
the standard way of representing the examples (supports) using a
DefaultWeightedValue. |
class |
DiagonalConfidenceWeightedBinaryCategorizer
A confidence-weighted linear predictor with a diagonal covariance,
which is stored as a vector.
|
class |
EvaluatorToCategorizerAdapter<InputType,CategoryType>
The
EvaluatorToCategorizerAdapter class implements an adapter from a
general Evaluator to be a Categorizer. |
class |
FisherLinearDiscriminantBinaryCategorizer
A Fisher Linear Discriminant classifier, which creates an optimal linear
separating plane between two Gaussian classes of different covariances.
|
class |
KernelBinaryCategorizer<InputType,EntryType extends WeightedValue<? extends InputType>>
The
KernelBinaryCategorizer class implements a binary
categorizer that uses a kernel to do its categorization. |
class |
LinearBinaryCategorizer
The
LinearBinaryCategorizer class implements a binary
categorizer that is implemented by a linear function. |
class |
LinearMultiCategorizer<CategoryType>
A multi-category version of the LinearBinaryCategorizer that keeps a separate
LinearBinaryCategorizer for each category.
|
class |
MaximumAPosterioriCategorizer<ObservationType,CategoryType>
Categorizer that returns the category with the highest posterior likelihood
for a given observation.
|
class |
ScalarFunctionToBinaryCategorizerAdapter<InputType>
Adapts a scalar function to be a categorizer using a threshold.
|
class |
ScalarThresholdBinaryCategorizer
The
ScalarThresholdBinaryCategorizer class implements a binary
categorizer that uses a threshold to categorize a given double. |
class |
VectorElementThresholdCategorizer
The
VectorElementThresholdCategorizer class implements a
BinaryCategorizer that categorizes an input vector by applying a
threshold to an element in a the vector. |
class |
WinnerTakeAllCategorizer<InputType,CategoryType>
Adapts an evaluator that outputs a vector to be used as a categorizer.
|
| Modifier and Type | Field and Description |
|---|---|
protected Evaluator<? super InputType,java.lang.Double> |
ScalarFunctionToBinaryCategorizerAdapter.evaluator
The scalar evaluator.
|
protected Evaluator<? super InputType,? extends Vectorizable> |
WinnerTakeAllCategorizer.evaluator
The evaluator that outputs a vector to return.
|
protected Evaluator<? super InputType,? extends IntermediateType> |
CompositeCategorizer.preprocessor
The preprocessor for the input data.
|
| Modifier and Type | Field and Description |
|---|---|
protected java.util.Map<Pair<CategoryType,CategoryType>,Evaluator<? super InputType,java.lang.Boolean>> |
BinaryVersusCategorizer.categoryPairsToEvaluatorMap
Maps false-true category pairs .
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,? extends CategoryType> |
EvaluatorToCategorizerAdapter.getEvaluator()
Gets the evaluator that is to be adapted to be a categorizer.
|
Evaluator<? super InputType,java.lang.Double> |
ScalarFunctionToBinaryCategorizerAdapter.getEvaluator()
Gets the scalar function that the adapter wraps.
|
Evaluator<? super InputType,? extends Vectorizable> |
WinnerTakeAllCategorizer.getEvaluator()
Gets the wrapped evaluator.
|
Evaluator<? super InputType,? extends IntermediateType> |
CompositeCategorizer.getPreprocessor()
Gets the preprocessor, which takes the input and produces an intermediate
value that is then passed to the categorizer.
|
| Modifier and Type | Method and Description |
|---|---|
java.util.Map<Pair<CategoryType,CategoryType>,Evaluator<? super InputType,java.lang.Boolean>> |
BinaryVersusCategorizer.getCategoryPairsToEvaluatorMap()
Gets the mapping of false-true category pairs to the binary categorizer
that distinguishes them.
|
BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> |
EvaluatorToCategorizerAdapter.Learner.getLearner()
Gets the learner whose output is to be adapted to be a categorizer.
|
| Modifier and Type | Method and Description |
|---|---|
void |
EvaluatorToCategorizerAdapter.setEvaluator(Evaluator<? super InputType,? extends CategoryType> evaluator)
Sets the evaluator that is to be adapted to be a categorizer.
|
void |
WinnerTakeAllCategorizer.setEvaluator(Evaluator<? super InputType,? extends Vectorizable> evaluator)
Sets the wrapped evaluator.
|
void |
ScalarFunctionToBinaryCategorizerAdapter.setEvaluator(Evaluator<? super InputType,java.lang.Double> evaluator)
Sets the scalar function that the adapter wraps.
|
void |
CompositeCategorizer.setPreprocessor(Evaluator<? super InputType,? extends IntermediateType> preprocessor)
Sets the preprocessor, which takes the input and produces an intermediate
value that is then passed to the categorizer.
|
| Modifier and Type | Method and Description |
|---|---|
void |
BinaryVersusCategorizer.setCategoryPairsToEvaluatorMap(java.util.Map<Pair<CategoryType,CategoryType>,Evaluator<? super InputType,java.lang.Boolean>> categoryPairsToEvaluatorMap)
Sets the mapping of false-true category pairs to the binary categorizer
that distinguishes them.
|
void |
EvaluatorToCategorizerAdapter.Learner.setLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner)
Sets the learner whose output is to be adapted to be a categorizer.
|
| Constructor and Description |
|---|
CompositeCategorizer(Evaluator<? super InputType,? extends IntermediateType> preprocessor,
Categorizer<? super IntermediateType,? extends CategoryType> categorizer)
Creates a new
CompositeCategorizer. |
EvaluatorToCategorizerAdapter(Evaluator<? super InputType,? extends CategoryType> evaluator,
java.util.Set<CategoryType> categories)
Creates a new
EvaluatorToCategorizerAdapter. |
ScalarFunctionToBinaryCategorizerAdapter(Evaluator<? super InputType,java.lang.Double> evaluator)
Creates a new
ScalarFunctionToBinaryCategorizerAdapter with the
given evaluator and a default threshold of 0.0. |
ScalarFunctionToBinaryCategorizerAdapter(Evaluator<? super InputType,java.lang.Double> evaluator,
double threshold)
Creates a new
ScalarFunctionToBinaryCategorizerAdapter with the
given evaluator and threshold. |
WinnerTakeAllCategorizer(Evaluator<? super InputType,? extends Vectorizable> evaluator,
java.util.Set<CategoryType> categories)
Creates a new
WinnerTakesAllCategorizer. |
| Constructor and Description |
|---|
BinaryVersusCategorizer(java.util.Set<CategoryType> categories,
java.util.Map<Pair<CategoryType,CategoryType>,Evaluator<? super InputType,java.lang.Boolean>> categoryPairsToEvaluatorMap)
Creates a new
BinaryVersusCategorizer. |
Learner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,java.lang.Boolean>> learner)
Creates a new
BinaryVersusCategorizer.Learner with an
binary categorizer learner to learn category versus category. |
Learner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,CategoryType>>,? extends Evaluator<? super InputType,? extends CategoryType>> learner)
Creates a new
EvaluatorToCategorizerAdapter. |
Learner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,Evaluator<? super InputType,? extends Vectorizable>> learner)
Creates a new learner adapter with the given internal learner.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
CostFunction<EvaluatedType,CostParametersType>
The CostFunction interface defines the interface to evaluate some object to
determine its cost.
|
interface |
DifferentiableCostFunction
The
DifferentiableCostFunction is a cost function that can
be differentiated. |
interface |
ParallelizableCostFunction
Interface describing a cost function that can (largely) be computed in
parallel.
|
interface |
SupervisedCostFunction<InputType,TargetType>
A type of CostFunction normally used in supervised-learning applications.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractCostFunction<EvaluatedType,CostParametersType>
Partial implementation of CostFunction.
|
class |
AbstractParallelizableCostFunction
Partial implementation of the ParallelizableCostFunction
|
class |
AbstractSupervisedCostFunction<InputType,TargetType>
Partial implementation of SupervisedCostFunction
|
class |
ClusterDistortionMeasure<DataType,ClusterType extends Cluster<DataType>>
Computes the objective measure for a clustering algorithm, based on the
internal "distortion" of each cluster.
|
class |
EuclideanDistanceCostFunction
The EuclideanDistanceCostFunction class implements a CostFunction that
calculates the Euclidean distance the given Vectorizable and the goal
vector.
|
class |
KolmogorovSmirnovDivergence<DataType extends java.lang.Number>
CostFunction that induces a CDF that most-closely resembles the
target distribution according to the Kolmogorov-Smirnov (K-S) test.
|
class |
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.
|
class |
MeanSquaredErrorCostFunction
The MeanSquaredErrorCostFunction implements a cost function for functions
that take as input a vector and return a vector.
|
class |
NegativeLogLikelihood<DataType>
CostFunction for computing the maximum likelihood
(because we are minimizing the negative of the log likelihood)
|
class |
ParallelClusterDistortionMeasure<DataType,ClusterType extends Cluster<DataType>>
A parallel implementation of ClusterDistortionMeasure.
|
class |
ParallelizedCostFunctionContainer
A cost function that automatically splits a ParallelizableCostFunction
across multiple cores/processors to speed up computation.
|
class |
ParallelNegativeLogLikelihood<DataType>
Parallel implementation of the NegativeLogLikleihood cost function
|
class |
SumSquaredErrorCostFunction
This is the sum-squared error cost function
|
| Modifier and Type | Method and Description |
|---|---|
java.lang.Double |
AbstractSupervisedCostFunction.evaluate(Evaluator<? super InputType,? extends TargetType> evaluator) |
java.lang.Double |
AbstractParallelizableCostFunction.evaluate(Evaluator<? super Vector,? extends Vector> evaluator) |
java.lang.Double |
ParallelizedCostFunctionContainer.evaluate(Evaluator<? super Vector,? extends Vector> evaluator) |
java.lang.Object |
ParallelizableCostFunction.evaluatePartial(Evaluator<? super Vector,? extends Vector> evaluator)
Computes the partial (linear) component of the cost function.
|
java.lang.Object |
SumSquaredErrorCostFunction.evaluatePartial(Evaluator<? super Vector,? extends Vector> evaluator) |
| Constructor and Description |
|---|
SubCostEvaluate(ParallelizableCostFunction costFunction,
Evaluator<? super Vector,? extends Vector> evaluator)
Creates a new instance of SubCostEvaluate
|
| Modifier and Type | Class and Description |
|---|---|
class |
DivergencesEvaluator<InputType,ValueType>
Evaluates the divergence (distance) between an input and a list of values,
storing the resulting divergence values in a vector.
|
| Modifier and Type | Field and Description |
|---|---|
protected Evaluator<? super InputType,java.lang.Double> |
ScalarFunctionKernel.function
The scalar function for the kernel to use.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,java.lang.Double> |
ScalarFunctionKernel.getFunction()
Gets the scalar function the kernel is using.
|
| Modifier and Type | Method and Description |
|---|---|
void |
ScalarFunctionKernel.setFunction(Evaluator<? super InputType,java.lang.Double> function)
Sets the scalar function for the kernel to use.
|
| Constructor and Description |
|---|
ScalarFunctionKernel(Evaluator<? super InputType,java.lang.Double> function)
Creates a new instance of RealFunctionKernel.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
Regressor<InputType>
Defines the functionality of a regression function, which is the model
created by regression algorithms.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractRegressor<InputType>
An abstract implementation of the
Regressor interface. |
| 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 |
KernelScalarFunction<InputType>
The
KernelScalarFunction class implements a scalar function that
uses a kernel to compute its output value. |
class |
KolmogorovSmirnovEvaluator
You can specify a particular CDF.
|
class |
LeakyRectifiedLinearFunction
A leaky rectified linear unit.
|
class |
LinearCombinationScalarFunction<InputType>
A weighted linear combination of scalar functions.
|
class |
LinearDiscriminant
LinearDiscriminant takes the dot product between the weight Vector and
the input Vector.
|
class |
LinearDiscriminantWithBias
A LinearDiscriminant with an additional bias term that gets added to the
output of the dot product.
|
class |
LinearFunction
This function acts as a simple linear function of the form f(x) = m*x + b.
|
class |
LinearVectorScalarFunction
The
LinearVectorScalarFunction class implements a scalar
function that is implemented by a linear function. |
class |
LocallyWeightedKernelScalarFunction<InputType>
The
LocallyWeightedKernelScalarFunction class implements a scalar
function that uses kernels and does local weighting on them to get the
result value. |
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.
|
class |
ThresholdFunction
Maps the input space onto the set {LOW_VALUE,HIGH_VALUE}.
|
class |
VectorEntryFunction
An evaluator that returns the value of an input vector at a specified index.
|
class |
VectorFunctionLinearDiscriminant<InputType>
This class takes a function that maps a generic InputType to a Vector.
|
class |
VectorFunctionToScalarFunction<InputType>
The
VectorFunctionToScalarFunction class implements an adapter for
using a vector function that outputs a single-dimensional vector as a
scalar function. |
| Modifier and Type | Field and Description |
|---|---|
protected Evaluator<? super InputType,? extends Vectorizable> |
VectorFunctionToScalarFunction.vectorFunction
The function that takes a given input and outputs a 1-dimensional
vector.
|
| Modifier and Type | Field and Description |
|---|---|
protected BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>> |
VectorFunctionToScalarFunction.Learner.vectorLearner
The supervised learner that learns on vectors as outputs.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,Vector> |
VectorFunctionLinearDiscriminant.getVectorFunction()
Getter for vectorFunction
|
Evaluator<? super InputType,? extends Vectorizable> |
VectorFunctionToScalarFunction.getVectorFunction()
Gets the vector function with a one-dimensional output that is being
converted to a scalar function.
|
| Modifier and Type | Method and Description |
|---|---|
void |
VectorFunctionToScalarFunction.setVectorFunction(Evaluator<? super InputType,? extends Vectorizable> vectorFunction)
Sets the vector function with a one-dimensional output that is being
converted to a scalar function.
|
void |
VectorFunctionLinearDiscriminant.setVectorFunction(Evaluator<? super InputType,Vector> vectorFunction)
Setter for vectorFunction
|
| Constructor and Description |
|---|
VectorFunctionLinearDiscriminant(Evaluator<? super InputType,Vector> vectorFunction,
LinearDiscriminant discriminant)
Creates a new instance of VectorFunctionLinearDiscriminant
|
VectorFunctionToScalarFunction(Evaluator<? super InputType,? extends Vectorizable> vectorFunction)
Creates a new instance of
VectorFunctionToScalarFunction. |
| Constructor and Description |
|---|
Learner(BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>> vectorLearner)
Creates a new
VectorFunctionToScalarFunction.Learner. |
LinearCombinationScalarFunction(java.util.ArrayList<? extends Evaluator<InputType,java.lang.Double>> basisFunctions,
Vector coefficients)
Creates a new instance of LinearCombinationFunction
|
LinearCombinationScalarFunction(java.util.Collection<? extends Evaluator<InputType,java.lang.Double>> basisFunctions)
Creates a new instance of LinearCombinationFunction
|
| 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 |
ElementWiseDifferentiableVectorFunction
An ElementWiseVectorFunction that is also a DifferentiableVectorFunction
|
class |
ElementWiseVectorFunction
A VectorFunction that operates on each element of the Vector indepenently
of all others.
|
class |
EntropyEvaluator
Takes a vector of inputs and computes the log base 2 entropy of the input.
|
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 |
GaussianContextRecognizer
Uses a MixtureOfGaussians to compute the probability of the different
constituent MultivariateGaussians (that is, the contexts)
|
class |
GeneralizedLinearModel
A VectorizableVectorFunction that is a matrix multiply followed by a
VectorFunction...
|
class |
LinearCombinationVectorFunction
A weighted linear combination of scalar functions.
|
class |
LinearVectorFunction
The
LinearFunction class is a simple
VectorFunction that just scales the given input vector by a
scalar value. |
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 |
ScalarBasisSet<InputType>
Collection of scalar basis functions, where the ith function operates
on the ith element of the output Vector
|
class |
SubVectorEvaluator
Extracts the given set of indices from an input vector to create a new
vector containing the input vector's elements at those indices.
|
class |
ThreeLayerFeedforwardNeuralNetwork
This is a "standard" feedforward neural network with a single hidden
layer.
|
class |
VectorizableVectorConverter
The
VectorizableVectorConverter class implements a conversion
between a Vectorizable and an Vector by calling the proper
conversion method on the Vectorizable. |
class |
VectorizableVectorConverterWithBias
The
VectorizableVectorConverterWithBias class extends the
VectorizableVectorConverter class to append a constant bias value of
1.0 to the vector returned by the converter. |
| Modifier and Type | Method and Description |
|---|---|
java.util.Collection<? extends Evaluator<? super InputType,java.lang.Double>> |
ScalarBasisSet.getBasisFunctions()
Getter for basisFunctions
|
| Modifier and Type | Method and Description |
|---|---|
void |
ScalarBasisSet.setBasisFunctions(java.util.Collection<? extends Evaluator<? super InputType,java.lang.Double>> basisFunctions)
Setter for basisFunctions
|
| Constructor and Description |
|---|
LinearCombinationVectorFunction(java.util.ArrayList<? extends Evaluator<? super Vector,? extends Vector>> basisFunctions,
Vector coefficients)
Creates a new instance of LinearCombinationFunction
|
LinearCombinationVectorFunction(java.util.Collection<? extends Evaluator<? super Vector,? extends Vector>> basisFunctions)
Creates a new instance of LinearCombinationFunction
|
ScalarBasisSet(java.util.Collection<? extends Evaluator<? super InputType,java.lang.Double>> basisFunctions)
Creates a new instance of ScalarBasisSet
|
| Modifier and Type | Class and Description |
|---|---|
class |
MeanZeroOneErrorEvaluator<InputType,DataType>
The
MeanZeroOneErrorEvaluator class implements a method for
computing the performance of a supervised learner by the mean number of
incorrect values between the target and estimated outputs. |
| Modifier and Type | Method and Description |
|---|---|
ResultType |
AbstractSupervisedPerformanceEvaluator.evaluatePerformance(Evaluator<? super InputType,? extends EstimateType> evaluator,
java.util.Collection<? extends InputOutputPair<? extends InputType,TargetType>> data)
Evaluates the performance accuracy of the given estimates against the
given targets.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ClosedFormDifferentiableEvaluator<InputType,OutputType,DerivativeType>
A differentiable function that has a closed-form derivative.
|
interface |
DifferentiableEvaluator<InputType,OutputType,DerivativeType>
Interface that indicates that the Evaluator can be differentiated about the
given input.
|
interface |
DifferentiableUnivariateScalarFunction
A differentiable univariate scalar function
|
interface |
ScalarFunction<InputType>
Interface for a function that maps some input onto a double.
|
interface |
UnivariateScalarFunction
Simple interface that describes a function that maps the reals to the reals,
has a Double to Double and double to double.
|
| 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
|
class |
AbstractScalarFunction<InputType>
An abstract implementation of the
ScalarFunction interface. |
class |
AbstractUnivariateScalarFunction
Abstract implementation of ScalarFunction where the evaluate(Double) method
calls back into the evaluate(double) method.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<InputType,DerivativeType> |
ClosedFormDifferentiableEvaluator.getDerivative()
Gets the closed-form derivative of the function.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
DifferentiableVectorFunction
A VectorFunction that can is also differentiable
|
interface |
VectorFunction
A vector function is a type of Evaluator that takes a Vector for its input
and output.
|
interface |
VectorInputEvaluator<InputType extends Vectorizable,OutputType>
An interface for an evaluator that takes a vector of a fixed dimensionality.
|
interface |
VectorizableDifferentiableVectorFunction
A VectorizableVectorFunction that also define a derivative
(this is needed for GradientDescendable).
|
interface |
VectorizableVectorFunction
The VectorizableVectorFunction interface defines a useful interface for
doing machine learning, which is a function that takes and returns vectors
and also is parameterizable as a vector.
|
interface |
VectorOutputEvaluator<InputType,OutputType extends Vectorizable>
An interface for an evaluator that produces a vector of a fixed
dimensionality.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NumericalDifferentiator<InputType,OutputType,DerivativeType>
Automatically differentiates a function by the method of forward differences.
|
static class |
NumericalDifferentiator.DoubleJacobian
Numerical differentiator based on a Vector Jacobian.
|
static class |
NumericalDifferentiator.MatrixJacobian
Numerical differentiator based on a Matrix Jacobian.
|
static class |
NumericalDifferentiator.VectorJacobian
Numerical differentiator based on a Vector Jacobian.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super InputType,OutputType> |
NumericalDifferentiator.getInternalFunction()
Getter for internalFunction
|
| Modifier and Type | Method and Description |
|---|---|
static java.lang.Double |
NumericalDifferentiator.DoubleJacobian.differentiate(double input,
Evaluator<? super java.lang.Double,java.lang.Double> f)
Static access to the numerical differentiation procedure.
|
static java.lang.Double |
NumericalDifferentiator.DoubleJacobian.differentiate(double input,
Evaluator<? super java.lang.Double,java.lang.Double> f,
double h)
Static access to the numerical differentiation procedure.
|
static Matrix |
NumericalDifferentiator.MatrixJacobian.differentiate(Vector input,
Evaluator<? super Vector,Vector> f)
Static access to the numerical differentiation procedure.
|
static Vector |
NumericalDifferentiator.VectorJacobian.differentiate(Vectorizable input,
Evaluator<? super Vector,java.lang.Double> f)
Static access to the numerical differentiation procedure.
|
static Vector |
NumericalDifferentiator.VectorJacobian.differentiate(Vectorizable input,
Evaluator<? super Vector,java.lang.Double> f,
double h)
Static access to the numerical differentiation procedure.
|
static Matrix |
NumericalDifferentiator.MatrixJacobian.differentiate(Vectorizable input,
Evaluator<? super Vector,Vector> f,
double h)
Static access to the numerical differentiation procedure.
|
void |
NumericalDifferentiator.setInternalFunction(Evaluator<? super InputType,OutputType> internalFunction)
Setter for internalFunction
|
| Constructor and Description |
|---|
DoubleJacobian(Evaluator<? super java.lang.Double,java.lang.Double> internalFunction)
Creates a new instance of VectorJacobian
|
DoubleJacobian(Evaluator<? super java.lang.Double,java.lang.Double> internalFunction,
double delta)
Create a new instance of VectorJacobian
|
MatrixJacobian(Evaluator<? super Vector,Vector> internalFunction)
Creates a new instance of VectorJacobian
|
MatrixJacobian(Evaluator<? super Vector,Vector> internalFunction,
double delta)
Create a new instance of VectorJacobian
|
NumericalDifferentiator(Evaluator<? super InputType,OutputType> internalFunction,
double delta)
Creates a new instance of NumericalDifferentiator
|
VectorJacobian(Evaluator<? super Vector,java.lang.Double> internalFunction)
Creates a new instance of VectorJacobian
|
VectorJacobian(Evaluator<? super Vector,java.lang.Double> internalFunction,
double delta)
Create a new instance of VectorJacobian
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
DiscreteTimeFilter<StateType extends CloneableSerializable>
A discrete-time filter.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AutoRegressiveMovingAverageFilter
A type of filter using a moving-average calculation.
|
class |
FourierTransform
Computes the Fast Fourier Transform, or brute-force discrete Fourier
transform, of a discrete input sequence.
|
static class |
FourierTransform.Inverse
Evaluator that inverts a Fourier transform.
|
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 |
class |
MovingAverageFilter
A type of filter using a moving-average calculation.
|
class |
PIDController
This class defines a Proportional-plus-Integral-plus-Derivative set-point
controller.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
ClosedFormCumulativeDistributionFunction<DomainType extends java.lang.Number>
Functionality of a cumulative distribution function that's defined with
closed-form parameters.
|
interface |
CumulativeDistributionFunction<NumberType extends java.lang.Number>
Functionality of a cumulative distribution function.
|
static interface |
DataDistribution.PMF<KeyType>
Interface for the probability mass function (PMF) of a data distribution.
|
interface |
InvertibleCumulativeDistributionFunction<NumberType extends java.lang.Number>
A cumulative distribution function that is empirically invertible.
|
interface |
ProbabilityDensityFunction<DataType>
Defines a probability density function.
|
interface |
ProbabilityFunction<DataType>
A Distribution that has an evaluate method that indicates p(x), such as
a probability density function or a probability mass function (but NOT
a cumulative distribution function).
|
interface |
ProbabilityMassFunction<DataType>
The
ProbabilityMassFunction interface defines the functionality of
a probability mass function. |
interface |
SmoothCumulativeDistributionFunction
This defines a CDF that has an associated derivative, which is its PDF.
|
interface |
UnivariateProbabilityDensityFunction
A PDF that takes doubles as input.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
AdaptiveRejectionSampling.LogEvaluator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Wraps an Evaluator and takes the natural logarithm of the evaluate method
|
| Modifier and Type | Class and Description |
|---|---|
class |
AdaptiveRejectionSampling.AbstractEnvelope
Describes an enveloping function comprised of a sorted sequence of lines
|
static class |
AdaptiveRejectionSampling.LineSegment
A line that has a minimum and maximum support (x-axis) value.
|
static class |
AdaptiveRejectionSampling.LogEvaluator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
Wraps an Evaluator and takes the natural logarithm of the evaluate method
|
class |
AdaptiveRejectionSampling.LowerEnvelope
Define the lower envelope for Adaptive Rejection Sampling
|
static class |
AdaptiveRejectionSampling.PDFLogEvaluator
Wraps a PDF so that it returns the logEvaluate method.
|
class |
AdaptiveRejectionSampling.UpperEnvelope
Constructs the upper envelope for sampling.
|
class |
BayesianLinearRegression.PredictiveDistribution
Creates the predictive distribution for the likelihood of a given point.
|
class |
BayesianRobustLinearRegression.PredictiveDistribution
Predictive distribution of future data given the posterior of
the weights given the data.
|
protected class |
ExtendedKalmanFilter.ModelJacobianEvaluator
Holds the input constant while perturbing the state to estimate
the Jacobian (A) matrix
|
class |
GaussianProcessRegression.PredictiveDistribution
Predictive distribution for Gaussian Process Regression.
|
class |
RejectionSampling.ScalarEstimator.MinimizerFunction
Minimization function that measures the difference between the
logarithm of the sampler function minus the logarithm of the
conjunctive distribution.
|
| Modifier and Type | Field and Description |
|---|---|
protected EvaluatorType |
AdaptiveRejectionSampling.LogEvaluator.function
Evaluator to wrap and compute the natural logarithm of.
|
protected Evaluator<Vector,Vector> |
ExtendedKalmanFilter.observationModel
Model that determines how the state is observed.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<? super Vectorizable,? extends ClosedFormDistribution<OutputType>> |
BayesianRegression.createPredictiveDistribution(PosteriorType posterior)
Creates the predictive distribution of outputs given the weight posterior
|
Evaluator<Vector,Vector> |
ExtendedKalmanFilter.getObservationModel()
Getter for observationModel
|
| Modifier and Type | Method and Description |
|---|---|
void |
ExtendedKalmanFilter.setObservationModel(Evaluator<Vector,Vector> observationModel)
Setter for observationModel
|
| Constructor and Description |
|---|
ExtendedKalmanFilter(StatefulEvaluator<Vector,Vector,Vector> motionModel,
Evaluator<Vector,Vector> observationModel,
Vector currentInput,
Matrix modelCovariance,
Matrix measurementCovariance)
Creates a new instance of ExtendedKalmanFilter
|
| Modifier and Type | Class and Description |
|---|---|
static class |
BernoulliDistribution.CDF
CDF of a Bernoulli distribution.
|
static class |
BernoulliDistribution.PMF
PMF of the Bernoulli distribution.
|
static class |
BetaBinomialDistribution.CDF
CDF of BetaBinomialDistribution
|
static class |
BetaBinomialDistribution.PMF
PMF of the BetaBinomialDistribution
|
static class |
BetaDistribution.CDF
CDF of the Beta-family distribution
|
static class |
BetaDistribution.PDF
Beta distribution probability density function
|
static class |
BinomialDistribution.CDF
CDF of the Binomial distribution, which is the probability of getting
up to "x" successes in "N" trials with a Bernoulli probability of "p"
|
static class |
BinomialDistribution.PMF
The Probability Mass Function of a binomial distribution.
|
static class |
CategoricalDistribution.PMF
PMF of the Categorical Distribution
|
static class |
CauchyDistribution.CDF
CDF of the CauchyDistribution.
|
static class |
CauchyDistribution.PDF
PDF of the CauchyDistribution.
|
static class |
ChineseRestaurantProcess.PMF
PMF of the Chinese Restaurant Process
|
static class |
ChiSquareDistribution.CDF
Cumulative Distribution Function (CDF) of a Chi-Square Distribution
|
static class |
ChiSquareDistribution.PDF
PDF of the Chi-Square distribution
|
static class |
DefaultDataDistribution.PMF<KeyType>
PMF of the DefaultDataDistribution
|
static class |
DeterministicDistribution.CDF
CDF of the deterministic distribution.
|
static class |
DeterministicDistribution.PMF
PMF of the deterministic distribution.
|
static class |
DirichletDistribution.PDF
PDF of the Dirichlet distribution.
|
static class |
ExponentialDistribution.CDF
CDF of the ExponentialDistribution.
|
static class |
ExponentialDistribution.PDF
PDF of the ExponentialDistribution.
|
static class |
GammaDistribution.CDF
CDF of the Gamma distribution
|
static class |
GammaDistribution.PDF
Closed-form PDF of the Gamma distribution
|
static class |
GeometricDistribution.CDF
CDF of the Geometric distribution
|
static class |
GeometricDistribution.PMF
PMF of the Geometric distribution
|
static class |
InverseGammaDistribution.CDF
CDF of the inverseRootFinder-gamma distribution.
|
static class |
InverseGammaDistribution.PDF
PDF of the inverseRootFinder-Gamma distribution.
|
static class |
InverseWishartDistribution.PDF
PDF of the Inverse-Wishart distribution, though I have absolutely no
idea why anybody would evaluate the PDF of an Inverse-Wishart...
|
static class |
KolmogorovDistribution.CDF
Contains the Cumulative Distribution Function description for the "D"
statistic used within the Kolmogorov-Smirnov test.
|
static class |
LaplaceDistribution.CDF
CDF of the Laplace distribution.
|
static class |
LaplaceDistribution.PDF
The PDF of a Laplace Distribution.
|
static class |
LogisticDistribution.CDF
CDF of the LogisticDistribution
|
static class |
LogisticDistribution.PDF
PDF of the LogisticDistribution
|
static class |
LogNormalDistribution.CDF
CDF of the Log-Normal Distribution
|
static class |
LogNormalDistribution.PDF
PDF of a Log-normal distribution
|
static class |
MixtureOfGaussians.PDF
PDF of the MixtureOfGaussians
|
static class |
MultinomialDistribution.PMF
Probability Mass Function of the Multinomial Distribution.
|
static class |
MultivariateGaussian.PDF
PDF of a multivariate Gaussian
|
static class |
MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>>
PDF of the MultivariateMixtureDensityModel
|
static class |
MultivariatePolyaDistribution.PMF
PMF of the MultivariatePolyaDistribution
|
static class |
MultivariateStudentTDistribution.PDF
PDF of the MultivariateStudentTDistribution
|
static class |
NegativeBinomialDistribution.CDF
CDF of the NegativeBinomialDistribution
|
static class |
NegativeBinomialDistribution.PMF
PMF of the NegativeBinomialDistribution.
|
static class |
NormalInverseGammaDistribution.PDF
PDF of the NormalInverseGammaDistribution
|
static class |
NormalInverseWishartDistribution.PDF
PDF of the normal inverse-Wishart distribution.
|
static class |
ParetoDistribution.CDF
CDF of the Pareto Distribution.
|
static class |
ParetoDistribution.PDF
PDF of the ParetoDistribution
|
static class |
PoissonDistribution.CDF
CDF of the PoissonDistribution
|
static class |
PoissonDistribution.PMF
PMF of the PoissonDistribution.
|
static class |
ScalarDataDistribution.CDF
CDF of the ScalarDataDistribution, maintains the keys/domain in
sorted order (TreeMap), so it's slower than it's peers.
|
static class |
ScalarDataDistribution.PMF
PMF of the ScalarDataDistribution
|
static class |
ScalarMixtureDensityModel.CDF
CDFof the SMDM
|
static class |
ScalarMixtureDensityModel.PDF
PDF of the SMDM
|
static class |
SnedecorFDistribution.CDF
CDF of the F-distribution.
|
static class |
StudentizedRangeDistribution.CDF
CDF of the StudentizedRangeDistribution
|
static class |
StudentTDistribution.CDF
Evaluator that computes the Cumulative Distribution Function (CDF) of
a Student-t distribution with a fixed number of degrees of freedom
|
static class |
StudentTDistribution.PDF
Evaluator that computes the Probability Density Function (CDF) of
a Student-t distribution with a fixed number of degrees of freedom
|
static class |
UniformDistribution.CDF
Cumulative Distribution Function of a uniform
|
static class |
UniformDistribution.PDF
Probability density function of a Uniform Distribution
|
static class |
UniformIntegerDistribution.CDF
Implements the cumulative distribution function for the discrete
uniform distribution.
|
static class |
UniformIntegerDistribution.PMF
Probability mass function of a discrete uniform distribution.
|
static class |
UnivariateGaussian.CDF
CDF of the underlying Gaussian.
|
static class |
UnivariateGaussian.CDF.Inverse
Inverts the CumulativeDistribution function.
|
static class |
UnivariateGaussian.ErrorFunction
Gaussian Error Function, useful for computing the cumulative distribution
function for a Gaussian.
|
static class |
UnivariateGaussian.ErrorFunction.Inverse
Inverse of the ErrorFunction
|
static class |
UnivariateGaussian.PDF
PDF of the underlying Gaussian.
|
static class |
WeibullDistribution.CDF
CDF of the Weibull distribution
|
static class |
WeibullDistribution.PDF
PDF of the Weibull distribution
|
static class |
YuleSimonDistribution.CDF
CDF of the Yule-Simon Distribution
|
static class |
YuleSimonDistribution.PMF
PMF of the Yule-Simon Distribution
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| Modifier and Type | Class and Description |
|---|---|
class |
ConvexReceiverOperatingCharacteristic
Computes the convex hull of the Receiver Operating Characteristic (ROC),
which a mathematician might call a "concave down" function.
|
protected class |
DistributionParameterEstimator.DistributionWrapper
Maps the parameters of a Distribution and a CostFunction into a
Vector/Double Evaluator.
|
class |
ReceiverOperatingCharacteristic
Class that describes a Receiver Operating Characteristic (usually called an
"ROC Curve").
|
| Modifier and Type | Method and Description |
|---|---|
static <ValueType> |
ImportanceSampling.sample(ProbabilityDensityFunction<ValueType> importanceDistribution,
Evaluator<ValueType,java.lang.Double> targetDistribution,
java.util.Random random,
int numSamples)
Importance sampling is a technique for estimating properties of
a target distribution, while only having samples generated from an
"importance" distribution rather than the target distribution.
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
MonteCarloSampler<DataType,SampleType,FunctionType extends Evaluator<? super DataType,java.lang.Double>>
A sampling technique based on the Monte Carlo method.
|
| Modifier and Type | Method and Description |
|---|---|
<SampleType> |
UnivariateMonteCarloIntegrator.integrate(java.util.Collection<? extends SampleType> samples,
Evaluator<? super SampleType,? extends java.lang.Double> expectationFunction) |
<SampleType> |
MonteCarloIntegrator.integrate(java.util.Collection<? extends SampleType> samples,
Evaluator<? super SampleType,? extends OutputType> expectationFunction)
Integrates the given function given samples from another function.
|
<SampleType> |
MultivariateMonteCarloIntegrator.integrate(java.util.Collection<? extends SampleType> samples,
Evaluator<? super SampleType,? extends Vector> expectationFunction) |
<SampleType> |
UnivariateMonteCarloIntegrator.integrate(java.util.List<? extends WeightedValue<? extends SampleType>> samples,
Evaluator<? super SampleType,? extends java.lang.Double> expectationFunction) |
<SampleType> |
MonteCarloIntegrator.integrate(java.util.List<? extends WeightedValue<? extends SampleType>> samples,
Evaluator<? super SampleType,? extends OutputType> expectationFunction)
Integrates the given function given weighted samples from another
function.
|
<SampleType> |
MultivariateMonteCarloIntegrator.integrate(java.util.List<? extends WeightedValue<? extends SampleType>> samples,
Evaluator<? super SampleType,? extends Vector> expectationFunction) |
java.util.ArrayList<DefaultWeightedValue<DataType>> |
ImportanceSampler.sample(Evaluator<? super DataType,java.lang.Double> targetFunction,
java.util.Random random,
int numSamples) |
| Modifier and Type | Interface and Description |
|---|---|
interface |
MultiTextualConverter<InputType,OutputType extends Textual>
Interface for an
TextConverter that converts an input into possibly
multiple output textual objects. |
interface |
SingleTextualConverter<InputType,OutputType extends Textual>
Interface for an
TextConverter that converts an input into a single
output. |
| Modifier and Type | Class and Description |
|---|---|
class |
AbstractMultiTextualConverter<InputType,OutputType extends Textual>
An abstract implementation of the
MultiTextualConverter interface. |
class |
AbstractSingleTextualConverter<InputType,OutputType extends Textual>
An abstract implementation of the
SingleTextualConverter interface. |
class |
DocumentFieldConcatenator
A document-text converter that concatenates multiple text fields from a
document together for further processing.
|
class |
DocumentSingleFieldConverter
Extracts a single field from a document.
|
class |
ObjectToStringTextualConverter
A text converter that can take in any type of object and then returns a
new
DefaultTextual that wraps that object's toString(). |
class |
SingleToMultiTextualConverterAdapter<InputType,OutputType extends Textual>
Adapts a
SingleTextualConverter to work within the interface of an
MultiTextualConverter. |
| Modifier and Type | Class and Description |
|---|---|
class |
SimpleStatisticalSpellingCorrector
A simple statistical spelling corrector based on word counts that looks at
possible one and two-character edits.
|
| Modifier and Type | Field and Description |
|---|---|
protected Evaluator<java.lang.String,java.lang.String> |
StringEvaluatorSingleTermFilter.evaluator
The evaluator to adapt.
|
| Modifier and Type | Method and Description |
|---|---|
Evaluator<java.lang.String,java.lang.String> |
StringEvaluatorSingleTermFilter.getEvaluator()
Gets the evaluator being used as a filter.
|
| Modifier and Type | Method and Description |
|---|---|
void |
StringEvaluatorSingleTermFilter.setEvaluator(Evaluator<java.lang.String,java.lang.String> evaluator)
Sets the evaluator being used as a filter.
|
| Constructor and Description |
|---|
StringEvaluatorSingleTermFilter(Evaluator<java.lang.String,java.lang.String> evaluator)
Creates a new
StringEvaluatorSingleTermFilter with a given
evaluator. |
| Modifier and Type | Class and Description |
|---|---|
class |
BagOfWordsTransform
Transforms a list of term occurrences into a vector of counts.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CompositeLocalGlobalTermWeighter
Composes together local and global term weighters along with a normalizer.
|
| Modifier and Type | Class and Description |
|---|---|
static class |
LatentSemanticAnalysis.Transform
The result from doing latent semantic analysis (LSA).
|
static class |
ProbabilisticLatentSemanticAnalysis.Result
The dimensionality transform created by probabilistic latent semantic
analysis.
|