Package | Description |
---|---|
de.citec.tcs.alignment.comparators |
This module defines the interfaces for comparators in the TCS
Alignment Toolbox.
|
de.citec.tcs.alignment.wrappers |
This module contains some wrappers to make usage of the
TCSAlignmentToolbox easier.
|
Modifier and Type | Method and Description |
---|---|
double |
DTWKernelComparator.calculateLocalDerivative(int paramIdent,
VectorialValue a,
VectorialValue b,
OperationType type)
TODO: think about opterationType
This is called by an AlignmentDerivativeAlgorithm and should calculate
the local derivative for only one comparison of the values a and b.
|
double |
DTWKernelComparator.compare(VectorialValue a,
VectorialValue b)
This has to return a distance between a and b with the following
restrictions:
The return value has to lie between 0 and 1.
1 means maximum dissimilarity between a and b.
0 means maximum similarity between a and b (especially it should be
true that a = b implies a zero distance).
distance(a,b) should be equal or at least close to
distance(b,a)
Please note that this has to be well-defined, even if an input is null.
|
double |
L1NormComparator.compare(VectorialValue a,
VectorialValue b)
This has to return a distance between a and b with the following
restrictions:
The return value has to lie between 0 and 1.
1 means maximum dissimilarity between a and b.
0 means maximum similarity between a and b (especially it should be
true that a = b implies a zero distance).
distance(a,b) should be equal or at least close to
distance(b,a)
Please note that this has to be well-defined, even if an input is null.
|
double |
ZeroNormComparator.compare(VectorialValue a,
VectorialValue b)
This has to return a distance between a and b with the following
restrictions:
The return value has to lie between 0 and 1.
1 means maximum dissimilarity between a and b.
0 means maximum similarity between a and b (especially it should be
true that a = b implies a zero distance).
distance(a,b) should be equal or at least close to
distance(b,a)
Please note that this has to be well-defined, even if an input is null.
|
double |
EuclideanComparator.compare(VectorialValue a,
VectorialValue b)
This has to return a distance between a and b with the following
restrictions:
The return value has to lie between 0 and 1.
1 means maximum dissimilarity between a and b.
0 means maximum similarity between a and b (especially it should be
true that a = b implies a zero distance).
distance(a,b) should be equal or at least close to
distance(b,a)
Please note that this has to be well-defined, even if an input is null.
|
double |
L1NormComparator.comparisonDerivative(int paramIdx,
VectorialValue a,
VectorialValue b)
This should implement the local derivative calculation in case of
comparisons.
|
double |
ZeroNormComparator.comparisonDerivative(int i,
VectorialValue v,
VectorialValue v1)
This should implement the local derivative calculation in case of
comparisons.
|
double |
EuclideanComparator.comparisonDerivative(int paramIdx,
VectorialValue a,
VectorialValue b)
This should implement the local derivative calculation in case of
comparisons.
|
SparseLocalDerivative |
L1NormComparator.sparseComparisonDerivative(VectorialValue a,
VectorialValue b)
This should implement the local derivative calculation in case of
comparisons.
|
SparseLocalDerivative |
ZeroNormComparator.sparseComparisonDerivative(VectorialValue v,
VectorialValue v1)
This should implement the local derivative calculation in case of
comparisons.
|
SparseLocalDerivative |
EuclideanComparator.sparseComparisonDerivative(VectorialValue a,
VectorialValue b)
This should implement the local derivative calculation in case of
comparisons.
|
Constructor and Description |
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EuclideanComparator(Normalizer normalizer,
VectorialValue deleteComparison,
VectorialValue insertComparison,
VectorialValue skipDeleteComparison,
VectorialValue skipInsertComparison) |
EuclideanComparator(VectorialValue deleteComparison,
VectorialValue insertComparison,
VectorialValue skipDeleteComparison,
VectorialValue skipInsertComparison) |
L1NormComparator(Normalizer normalizer,
VectorialValue deleteComparison,
VectorialValue insertComparison,
VectorialValue skipDeleteComparison,
VectorialValue skipInsertComparison) |
L1NormComparator(VectorialValue deleteComparison,
VectorialValue insertComparison,
VectorialValue skipDeleteComparison,
VectorialValue skipInsertComparison) |
Modifier and Type | Method and Description |
---|---|
static AlignmentSpecification |
VectorialSequences.setUpSpecification(Sequence[] dataSpace,
GapComparator<VectorialValue> comparator)
Sets up an AlignmentSpecification for an array of vectorial sequences.
|
static AlignmentSpecification |
VectorialSequences.setUpSpecification(Sequence[] dataSpace,
GapComparator<VectorialValue> comparator,
double[] weights)
Sets up an AlignmentSpecification for an array of vectorial sequences.
|
Copyright (C) 2013-2015 Benjamin Paaßen, Georg Zentgraf, AG Theoretical Computer Science, Centre of Excellence Cognitive Interaction Technology (CITEC), University of Bielefeld, licensed under the AGPL v. 3: http://openresearch.cit-ec.de/projects/tcs