|
Class Summary |
| Clustering |
Wrapper class for a clustering of many motifs. |
| EuclideanDistance |
The Minkowski distance for the special case p=2. |
| ForwardClustering |
This agglomerative clustering strategy builds up clusters beforehand,
and decides by means of the threshold whether a cluster can be accepted
or not. |
| GreedyClustering |
This agglomerative clustering strategy builds up clusters by means of
the optimal relation between any two consensus motifs. |
| LikelihoodMeasure |
Calculates the likelyhood between two vectors. |
| ManhattanDistance |
The Minkowski distance for the special case p=1. |
| MatrixAlignment |
Class for aligning two matrices. |
| MatrixDistance |
Class for calculating the similarity between two matrices. |
| MatrixLikelihood |
Calculates the likelihood between two matrices. |
| MatrixLoglikelihood |
Calculates the loglikelihood between two matrices. |
| MatrixRelationMeasure |
Superclass for all relations defined on two or more matrices filled with
numerical values. |
| MatrixSimilarity |
Class for calculating the similarity between two matrices. |
| MinkowskiDistance |
Calculates the distance between two vectors. |
| MotifAlignment |
Class for generell alignment between two or more motifs. |
| MultiMotifAlignment |
Defines the alignment between two MultiMotifs. |
| RelationalCalculation |
This class is designed to simply calculate all possible distance values
between every two motifs and then print these values in a huge table. |
| RelationMeasure |
This class is designed to act as a wrapper for any relation between two
or more objects. |
| SingleMotifAlignment |
Defines the Alignement between two single motifs. |
| VectorRelationMeasure |
Superclass for all relations defined on two or more vectors. |