See: Description
| Interface | Description |
|---|---|
| DivergenceFunctionContainer<FirstType,SecondType> |
Interface for a class that holds a divergence function.
|
| Class | Description |
|---|---|
| ChebyshevDistanceMetric |
An implementation of the Chebyshev distance, which is the absolute value of
the largest difference between two vectors in a single dimension.
|
| CosineDistanceMetric |
The
CosineDistanceMetric class implements a semimetric between
two vectors based on the cosine between the vectors. |
| DefaultDivergenceFunctionContainer<FirstType,SecondType> |
The
DefaultDivergenceFunctionContainer class implements an object
that holds a divergence function. |
| DivergencesEvaluator<InputType,ValueType> |
Evaluates the divergence (distance) between an input and a list of values,
storing the resulting divergence values in a vector.
|
| DivergencesEvaluator.Learner<DataType,InputType,ValueType> |
A learner adapter for the
DivergencesEvaluator. |
| EuclideanDistanceMetric |
The
EuclideanDistanceMetric implements a distance metric that
computes the Euclidean distance between two points. |
| EuclideanDistanceSquaredMetric |
The
EuclideanDistanceSquaredMetric implements a distance metric
that computes the squared Euclidean distance between two points. |
| IdentityDistanceMetric |
A distance metric that is 0 if two objects are equal and 1 if they are not.
|
| ManhattanDistanceMetric |
The
ManhattanDistanceMetric class implements a distance metric
between two vectors that is implemented as the sum of the absolute value of
the difference between the elements in the vectors. |
| MinkowskiDistanceMetric |
An implementation of the Minkowski distance metric.
|
| WeightedEuclideanDistanceMetric |
A distance metric that weights each dimension of a vector differently before
computing Euclidean distance.
|