Approximate furthest neighbor search
An implementation of two strategies for furthest neighbor search. This can be used to compute the furthest neighbor of query point(s) from a set of points.
Available Predicates
- initDrusillaModelNoTrain/2
- initDrusillaModelWithTrain/4
- searchDrusilla/7
- trainDrusilla/4
- initQDAFNModelNoTrain/2
- initQDAFNModelWithTrain/4
- searchQDAFN/7
- trainQDAFN/4
initDrusillaModelNoTrain/2
Initiates the DrusillaSearch Model but doesn’t train it.
trainDrusilla has to be used before searchDrusilla can be used.
%% part of the definition
initDrusillaModelNoTrain( +integer, +integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| l | +integer | Number of projections | 5 |
| m | +integer | Number of elements to store for each projection | 5 |
initDrusillaModelWithTrain/4
Initiates the DrusillaSearch Model and trains it with the given reference Set.
Afterwards searchDrusilla can be used.
%% part of the definition
initDrusillaModelWithTrain( +pointer(float_array), +integer, +integer,
+integer, +integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| referenceSet | +matrix | Matrix containing the reference dataset | - |
| l | +integer | Number of projections | 5 |
| m | +integer | Number of elements to store for each projection | 5 |
searchDrusilla/7
Run Search on the given Queryset with the Drusilla Search Policy.
%% part of the predicate definition
searchDrusilla( +pointer(float_array), +integer, +integer,
+integer,
-pointer(float_array), -integer, -integer,
-pointer(float_array), -integer, -integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| querySet | +matrix | Matrix containing query points | - |
| k | +integer | Number of furthest neighbors to search for | 1 |
| neighbors | -matrix | Matrix to save furthest neighbor distances to | - |
| distance | -matrix | Matrix to save neighbor indices to | - |
trainDrusilla/4
Trains the DrusillaSearch Model with the given reference Set.
%% part of the predicate definition
trainDrusilla( +pointer(float_array), +integer, +integer,
+integer, +integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| referenceSet | +matrix | Matrix containing the reference dataset | - |
| l | +integer | Number of projections | 5 |
| m | +integer | Number of elements to store for each projection | 5 |
initQDAFNModelNoTrain/2
Initiates the QDAFNSearch Model but doesn’t train it.
trainQDAFN has to be used before searchQDAFN can be used.
%% part of the definition
initQDAFNModelNoTrain( +integer, +integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| l | +integer | Number of projections | 5 |
| m | +integer | Number of elements to store for each projection | 5 |
initQDAFNModelWithTrain/4
Initiates the QDAFNSearch Model and trains it with the given reference Set.
Afterwards searchQDAFN can be used.
%% part of the definition
initQDAFNModelWithTrain( +pointer(float_array), +integer, +integer,
+integer, +integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| referenceSet | +matrix | Matrix containing the reference dataset | - |
| l | +integer | Number of projections | 5 |
| m | +integer | Number of elements to store for each projection | 5 |
searchQDAFN/7
Run Search on the given Queryset with the QDAFN Search Policy.
%% part of the predicate definition
searchQDAFN( +pointer(float_array), +integer, +integer,
+integer,
-pointer(float_array), -integer, -integer,
-pointer(float_array), -integer, -integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| querySet | +matrix | Matrix containing query points | - |
| k | +integer | Number of furthest neighbors to search for | 1 |
| neighbors | -matrix | Matrix to save furthest neighbor distances to | - |
| distance | -matrix | Matrix to save neighbor indices to | - |
trainQDAFN/4
Trains the QDAFNSearch Model with the given reference Set.
%% part of the predicate definition
trainQDAFN( +pointer(float_array), +integer, +integer,
+integer, +integer).Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| referenceSet | +matrix | Matrix containing the reference dataset | - |
| l | +integer | Number of projections | 5 |
| m | +integer | Number of elements to store for each projection | 5 |
Connected Links/Resources
If you want a more detailed explanation, then go to the python documentation. There is most of the time a good explanation on how the methods work and what the parameters do.
- MLpack::DrusillaSearch_C++_documentation
- MLpack::QDAFN_C++_documentation
- MLpack::Approx_KFN_Python_documentation
added some of the links from the python documentation