GINNet 2.5.2 JavaDoc
| Package | Description |
|---|---|
| fr.loria.cortex.ginnet.data |
Data management, save, import and export.
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| fr.loria.cortex.ginnet.data.signal |
EEG signals treatments.
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| fr.loria.cortex.ginnet.dynnet.corpus |
Corpus are datas ready to be treated.
|
| fr.loria.cortex.ginnet.dynnet.corpus.filter |
Corpus filters.
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| fr.loria.cortex.ginnet.dynnet.corpus.patterns |
Patterns contain classes that describe an Attribute and a set of interfaces determining how to use patterns.
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| fr.loria.cortex.ginnet.dynnet.corpus.patterns.impl |
Concrete implementations of patterns interfaces.
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| fr.loria.cortex.ginnet.dynnet.metalearners |
Meta-learners are groups of supervised models able to learn how to learn
and have better results than simple learners.
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| fr.loria.cortex.ginnet.dynnet.metalearners.combiners |
Package combiners regroups re-usable combiners.
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| fr.loria.cortex.ginnet.dynnet.metalearners.models.arcing |
Arcing algorithm implementations.
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| fr.loria.cortex.ginnet.dynnet.metalearners.models.bagging |
Bagging algorithm implementations.
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| fr.loria.cortex.ginnet.dynnet.metalearners.models.boosting |
Boosting algorithm implementation.
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| fr.loria.cortex.ginnet.dynnet.metalearners.samplers |
Package samplers regroups re-usable samplers.
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| fr.loria.cortex.ginnet.dynnet.methods |
Methods describes the hierarchy of data-mining methods of GINNet.
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| fr.loria.cortex.ginnet.dynnet.methods.entries |
Entries are patterns more easily manipulable than corpuses.
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| fr.loria.cortex.ginnet.dynnet.methods.management |
Manages methods feature selection.
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| fr.loria.cortex.ginnet.dynnet.methods.management.wrapper |
Wrappers.
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| fr.loria.cortex.ginnet.dynnet.methods.stoppingfunctions |
Stopping functions are used to determine when a stoppable data-mining method should stop learning.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks |
All neural networks basic classes.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.competitivenetworks |
Unsupervised networks doesn't work by teaching (correspondance between desired output and computed output) but by data organization.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.competitivenetworks.aggregatingfunctions | |
| fr.loria.cortex.ginnet.dynnet.neuralnetworks.competitivenetworks.distancefunctions | |
| fr.loria.cortex.ginnet.dynnet.neuralnetworks.learningratefunctions |
Learning rate functions determines how to update the learning rate of the network while learning.
|
| fr.loria.cortex.ginnet.dynnet.neuralnetworks.models.gng |
Growing Neural Gas networks.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.models.kmeans |
KMeans algorithm implementation.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.models.kohonen |
Kohonen self-organizing maps.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.models.kohonen.kernelfunctions |
Kernel functions determine the nodes to move with the winner in Kohonen networks.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.models.kohonen.topologyfunctions |
Topology functions etermine the distances between nodes and winner nodes in Kohonen networks.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.models.neuralgas |
Neural gas networks.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.models.perceptron |
Simple and multi-layer Perceptron network.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.models.tom |
Temporal Organization Map spatio-temporal grid.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.supervisednetworks |
Supervised network contain all information relative to non supervised networks.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.supervisednetworks.embedded |
Embedded methods for supervised networks.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.supervisednetworks.embedded.evaluationcriteria |
Evaluation criteria available for embedded pruning methods.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.supervisednetworks.errorfunctions |
Error functions compute network mean error for a cycle.
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| fr.loria.cortex.ginnet.dynnet.neuralnetworks.supervisednetworks.transfertfunctions |
Transfert functions determine the output of units according to their inputs.
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| fr.loria.cortex.ginnet.dynnet.trees |
Decision trees.
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| fr.loria.cortex.ginnet.dynnet.trees.informationfunctions |
Information functions relatives to any decision tree.
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| fr.loria.cortex.ginnet.dynnet.trees.models |
models package regroups all decision tree concrete models.
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| fr.loria.cortex.ginnet.dynnet.utils |
utils package regroups all utilitees usable by DynNet or GINNet.
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| fr.loria.cortex.ginnet.dynnet.utils.charts | |
| fr.loria.cortex.ginnet.dynnet.utils.criteriastrategy |
criteriastrategy is the root package for evaluation and stopping criterion.
This criteria are used for feature selection. |
| fr.loria.cortex.ginnet.dynnet.utils.criteriastrategy.evaluationcriteria |
evaluationcriteria package contains all evaluation's criteria usable for feature selection.
|
| fr.loria.cortex.ginnet.dynnet.utils.criteriastrategy.stoppingcriteria |
stoppingcriteria package contains all stopping's criteria usable for feature selection.
|
| fr.loria.cortex.ginnet.dynnet.utils.math |
math package regroups all mathematical utilitees usable by DynNet or GINNet.
This includes vector and matrix operation, and statistical fonction. |
| fr.loria.cortex.ginnet.dynnet.utils.modelfromdynnetcreation |
This packages regroups all classes and files needed to generate Jar file from a DataMining model.
|
| fr.loria.cortex.ginnet.dynnet.utils.preferences |
preferences package concerns all DynNet (and GINNet) saved preferences.
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| fr.loria.cortex.ginnet.dynnet.utils.results |
results package regroups classes intended to format results in HTML.
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| fr.loria.cortex.ginnet.dynnet.utils.strategy |
Strategy design pattern allow to propose several concrete strategies to implement a computation.
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| fr.loria.cortex.ginnet.dynnet.utils.task |
Long task is used to display task advancing (message and progress).
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| fr.loria.cortex.ginnet.gui |
The Graphical User Interface for the DynNet library.
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| fr.loria.cortex.ginnet.gui.commandpanels |
Command panels are panels displayed in the bottom of the frame.
|
| fr.loria.cortex.ginnet.gui.commandpanels.networkcommandpanels |
Corpus exporters are dialog interfaces that create networks.
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| fr.loria.cortex.ginnet.gui.corpusexporters |
All the command panels used to export data to corpus.
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| fr.loria.cortex.ginnet.gui.data |
All classes relative to data tab.
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| fr.loria.cortex.ginnet.gui.dialogs |
All kind of dialogs.
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| fr.loria.cortex.ginnet.gui.dialogs.featureselection | |
| fr.loria.cortex.ginnet.gui.dialogs.networkdialogs |
Dialog for network creations.
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| fr.loria.cortex.ginnet.gui.dialogs.networkdialogs.subpanels |
Panels used by network dialogs.
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| fr.loria.cortex.ginnet.gui.dialogs.patterns | |
| fr.loria.cortex.ginnet.gui.dialogs.preferences |
GUI elements used to watch and edit GINNet preferences.
|
| fr.loria.cortex.ginnet.gui.graphs |
All graphs.
|
| fr.loria.cortex.ginnet.gui.help |
Elements used for graphical help.
|
| fr.loria.cortex.ginnet.gui.jarexporting | |
| fr.loria.cortex.ginnet.gui.strategypanels |
Strategy panels allow dynamical interface that automatically create interfaces for several implementation of strategies.
|
| fr.loria.cortex.ginnet.gui.tablemodels |
All table models.
|
| fr.loria.cortex.ginnet.gui.tabs |
All GINNet tabs : Data, Stats, Corpus, Network and Tree.
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| fr.loria.cortex.ginnet.gui.tabs.network | |
| fr.loria.cortex.ginnet.gui.tabs.stats | |
| fr.loria.cortex.ginnet.gui.visitor |
Neural network visitors used in GUI.
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| fr.loria.cortex.ginnet.gui.visitor.metalearning |
Meta-learner GUI is displayed by MetaLearnerGUIVisitor and its relative classes.
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| fr.loria.cortex.ginnet.gui.visitor.metalearning.combining |
Combiner sub-components of meta-learner GUI.
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| fr.loria.cortex.ginnet.gui.visitor.metalearning.resulting |
Result sub-component of meta-learner GUI.
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| fr.loria.cortex.ginnet.gui.visitor.metalearning.sampling |
Sampler sub-components of meta-learner GUI.
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