org.biojava3.alignment.template
Interface Scorer

All Known Subinterfaces:

Aligner<S,C>, MatrixAligner<S,C>, PairInProfileScorer<S,C>, PairwiseSequenceAligner<S,C>, PairwiseSequenceScorer<S,C>, PartitionRefiner<S,C>, ProfileProfileAligner<S,C>, ProfileProfileScorer<S,C>, RescoreRefiner<S,C>

All Known Implementing Classes:

AbstractMatrixAligner, AbstractPairwiseSequenceAligner, AbstractProfileProfileAligner, AbstractScorer, AnchoredPairwiseSequenceAligner, FractionalIdentityInProfileScorer, FractionalIdentityScorer, FractionalSimilarityInProfileScorer, FractionalSimilarityScorer, GuanUberbacher, NeedlemanWunsch, SimpleProfileProfileAligner, SmithWaterman, StandardRescoreRefiner

public interface Scorer

Defines an algorithm which computes a score.

Author:

Mark Chapman

Method Summary

double	getDistance() Returns score as a distance between 0.0 and 1.0.
double	getDistance(double scale) Returns score as a distance between 0.0 and scale.
int	getMaxScore() Returns maximum possible score.
int	getMinScore() Returns minimum possible score.
int	getScore() Returns score resulting from algorithm.
double	getSimilarity() Returns score as a similarity between 0.0 and 1.0.
double	getSimilarity(double scale) Returns score as a similarity between 0.0 and scale.

Method Detail

getDistance

double getDistance()

Returns score as a distance between 0.0 and 1.0. This equals (getMaxScore() - getScore()) / (getMaxScore() - getMinScore()).

Returns:

score as a distance between 0.0 and 1.0

getDistance

double getDistance(double scale)

Returns score as a distance between 0.0 and scale. This equals scale * (getMaxScore() - getScore()) / (getMaxScore() - getMinScore()).

Parameters:

scale - maximum distance

Returns:

score as a distance between 0.0 and scale

getMaxScore

int getMaxScore()

Returns maximum possible score.

Returns:

maximum possible score

getMinScore

int getMinScore()

Returns minimum possible score.

Returns:

minimum possible score

getScore

int getScore()

Returns score resulting from algorithm. This should normalize between 0 and 1 by calculating (getScore() - getMinScore()) / (getMaxScore() - getMinScore()).

Returns:

score resulting from algorithm

getSimilarity

double getSimilarity()

Returns score as a similarity between 0.0 and 1.0. This equals (getScore() - getMinScore()) / (getMaxScore() - getMinScore()).

Returns:

score as a similarity between 0.0 and 1.0

getSimilarity

double getSimilarity(double scale)

Returns score as a similarity between 0.0 and scale. This equals scale * (getScore() - getMinScore()) / (getMaxScore() - getMinScore()).

Parameters:

scale - maximum similarity

Returns:

score as a similarity between 0.0 and scale