MyOneVersusNoneLocalGainAlgo Class Reference

A subclass of MyOneVersusNoneGainAlgo that implements a guilt-by-association algorithm that does not use negative examples. See. More...

#include <gain-algorithm.h>

Inheritance diagram for MyOneVersusNoneLocalGainAlgo:

List of all members.

Public Member Functions
	MyOneVersusNoneLocalGainAlgo (MyGainGraph &g, MyAnnotations &a, MyGainParams &p, Reporter &r, GeneOntology *go=NULL)
virtual MyGainTriState	computeState (MyNode &node)
virtual string	getName ()
virtual void	run (const MyNodeIdList &nodesToAnnotate)
	Run the algorithm by looping over all the nodes in nodesToAnnotate once.

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Detailed Description

A subclass of MyOneVersusNoneGainAlgo that implements a guilt-by-association algorithm that does not use negative examples. See.

See also:

MyOneVersusAllLocalGainAlgo for a version of guilt-by-association that uses both positive and negative examples.

This algorithm computes node states that are either HYPOTHETICAL_STATE or ANNOTATED_STATE. The confidence that a particular node should be annotated with a function is simply the total weight of the edges connecting that nodes to its neighbours that are annotated with that function.

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Member Function Documentation

MyGainTriState MyOneVersusNoneLocalGainAlgo::computeState

(

MyNode &

node

)

[virtual]

This method should be a member of this class since the method needs access to the states of the nodes (stored in subclasses of the class).

Reimplemented from MyOneVersusAllGainAlgo.

virtual string MyOneVersusNoneLocalGainAlgo::getName

(

)

[inline, virtual]

Return the name of the algorithm.

Reimplemented from MyOneVersusNoneGainAlgo.

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The documentation for this class was generated from the following files:

• /home/poirel/src/c++/biorithm/gain/gain-algorithm.h
• /home/poirel/src/c++/biorithm/gain/gain-algorithm.C