- OBJECT - Static variable in class org.ai_heuristic.util.AiTypeConst
-
Defines an Object class parameter type
- op - Variable in class org.ai_heuristic.eval.Condition
-
The math operator.
- OPERATOR - Static variable in class org.ai_heuristic.util.AiHeuristicConst
-
Operator key type
- OR - Static variable in class org.ai_heuristic.util.TextConst
-
Defines or combination
- orderBagOfWords() - Method in class org.ai_heuristic.model.BagOfWords
-
Create a ordered list of most to least counts for the bag of words.
- org.ai_heuristic.algo - package org.ai_heuristic.algo
-
Some base classes for the algorithms, where they can be divided into ones that directly
compare their own dataset with other datasets, or ones that 'learn' a function first
and then use that to classify or cluster, with emphasis on information or text-based content.
- org.ai_heuristic.algo.classify - package org.ai_heuristic.algo.classify
-
Classification algorithms that categorise more than cluster.
- org.ai_heuristic.algo.cluster - package org.ai_heuristic.algo.cluster
-
Clustering algorithms that try to group datasets together.
- org.ai_heuristic.algo.cluster.som.kohonen - package org.ai_heuristic.algo.cluster.som.kohonen
-
Clustering using a SOM neural network, the main cluster learning algorithms, code from http://jknnl.sourceforge.net/, but modified.
- org.ai_heuristic.algo.cluster.som.neighbourhood - package org.ai_heuristic.algo.cluster.som.neighbourhood
-
Clustering using a SOM neural network, for defining architecture neighbourhoods, code from http://jknnl.sourceforge.net/, but modified.
- org.ai_heuristic.algo.cluster.som.network - package org.ai_heuristic.algo.cluster.som.network
-
Clustering using a SOM neural network, the neural network and node types, code from http://jknnl.sourceforge.net/, but modified.
- org.ai_heuristic.algo.cluster.som.topology - package org.ai_heuristic.algo.cluster.som.topology
-
Clustering using a SOM neural network, the network topologies, code from http://jknnl.sourceforge.net/, but modified.
- org.ai_heuristic.algo.prob - package org.ai_heuristic.algo.prob
-
Statistical probability algorithms, including state-based.
- org.ai_heuristic.algo.sort - package org.ai_heuristic.algo.sort
-
Some well known sorting algorithms.
- org.ai_heuristic.data - package org.ai_heuristic.data
-
Can be used for reading data files.
- org.ai_heuristic.def - package org.ai_heuristic.def
-
Some common definition interfaces that the system uses to identify types of class.
- org.ai_heuristic.eval - package org.ai_heuristic.eval
-
For comparing and evaluating the different data types, with emphasis on generic classes that can be adapted to evaluate any data object.
- org.ai_heuristic.eval.metric - package org.ai_heuristic.eval.metric
-
Class model specific to this package for evaluating single data objects, or making comparisons between them.
- org.ai_heuristic.eval.text - package org.ai_heuristic.eval.text
-
Implementations of known metrics or evaluation functions for text-specific data.
- org.ai_heuristic.functs - package org.ai_heuristic.functs
-
Base classes for writing the evaluation functions or metrics.
- org.ai_heuristic.functs.activate - package org.ai_heuristic.functs.activate
-
Implementations of known activation functions that measure a firing limit threshold,
but may work with other number types, through the org.ai_heuristic.eval.metric
data structures.
- org.ai_heuristic.functs.learn - package org.ai_heuristic.functs.learn
-
Implementations of known functions that map a double value to another double value,
but may work with other number types, through the org.ai_heuristic.eval.metric
data structures.
- org.ai_heuristic.functs.metric - package org.ai_heuristic.functs.metric
-
Implementations of known metrics or evaluation functions, mostly distance or similarity,
but adapted to allow for the evaluation of any type of data object and must use the
org.ai_heuristic.eval.metric data structures.
- org.ai_heuristic.functs.similar - package org.ai_heuristic.functs.similar
-
Similarity comparisons, mostly between two text-based values, but this package might
include implementations that allow for any data type, with the appropriate evaluator,
to be defined and not force the use of the org.ai_heuristic.eval.metric data structures.
- org.ai_heuristic.functs.test - package org.ai_heuristic.functs.test
-
Test package.
- org.ai_heuristic.model - package org.ai_heuristic.model
-
For storing more complex data types, mainly text-based.
- org.ai_heuristic.parser - package org.ai_heuristic.parser
-
For parsing the data structures to or from XML.
- org.ai_heuristic.tree - package org.ai_heuristic.tree
-
Can be used to build tree-like structures.
- org.ai_heuristic.util - package org.ai_heuristic.util
-
Mostly pre-defined constant values, but also some utility methods.
- otherIsSame(MetaBagOfWords) - Method in class org.ai_heuristic.model.MetaBagOfWords
-
Compare the other metadata with this bag-of-words metadata and return true
if they are the same.