set_params_class_knnT_set_params_class_knnSetParamsClassKnnSetParamsClassKnnset_params_class_knn (Operator)
Name
set_params_class_knnT_set_params_class_knnSetParamsClassKnnSetParamsClassKnnset_params_class_knn — Set parameters for k-NN classification.
Signature
Description
set_params_class_knnset_params_class_knnSetParamsClassKnnSetParamsClassKnnSetParamsClassKnnset_params_class_knn sets parameters for the classification
of the k-nearest neighbors (k-NN) classifier KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleknnhandle.
It controls the behavior of classify_class_knnclassify_class_knnClassifyClassKnnClassifyClassKnnClassifyClassKnnclassify_class_knn.
The value of 'k'"k""k""k""k""k" can be set via GenParamNameGenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and
GenParamValueGenParamValueGenParamValueGenParamValuegenParamValuegen_param_value. Increasing 'k'"k""k""k""k""k" also increases the accuracy
of the resulting neighbors and increases the run time.
The results can either be the determined class of the feature vector or
the indices of the nearest neighbors. The result behavior
can be selected with set_params_class_knnset_params_class_knnSetParamsClassKnnSetParamsClassKnnSetParamsClassKnnset_params_class_knn via the generic parameters
'method'"method""method""method""method""method" and 'max_num_classes'"max_num_classes""max_num_classes""max_num_classes""max_num_classes""max_num_classes":
- 'classes_distance'"classes_distance""classes_distance""classes_distance""classes_distance""classes_distance":
returns the nearest samples
for each of maximally 'max_num_classes'"max_num_classes""max_num_classes""max_num_classes""max_num_classes""max_num_classes" different classes,
if they have a representative in the nearest 'k'"k""k""k""k""k" neighbors.
The results are classes sorted by their minimal
distance. There is no efficient way to determine in a
k-NN-tree the nearest neighbor for exactly 'max_num_classes'"max_num_classes""max_num_classes""max_num_classes""max_num_classes""max_num_classes"
classes.
- 'classes_frequency'"classes_frequency""classes_frequency""classes_frequency""classes_frequency""classes_frequency":
counts the occurrences of certain
classes among the nearest 'k'"k""k""k""k""k" neighbors and returns the occurrent
classes sorted by their relative frequency that is
returned, too. Again, maximally 'max_num_classes'"max_num_classes""max_num_classes""max_num_classes""max_num_classes""max_num_classes"
values are returned.
- 'classes_weighted_frequencies'"classes_weighted_frequencies""classes_weighted_frequencies""classes_weighted_frequencies""classes_weighted_frequencies""classes_weighted_frequencies":
counts the occurrences of certain classes among the
nearest 'k'"k""k""k""k""k" neighbors and returns the occurrent classes sorted
by their relative frequency weighted with the
average distance that is returned, too.
Again, maximally 'max_num_classes'"max_num_classes""max_num_classes""max_num_classes""max_num_classes""max_num_classes" values are returned.
- 'neighbors_distance'"neighbors_distance""neighbors_distance""neighbors_distance""neighbors_distance""neighbors_distance":
returns the indices of the nearest
'k'"k""k""k""k""k" neighbors and the distances.
The default behavior is 'classes_distance'"classes_distance""classes_distance""classes_distance""classes_distance""classes_distance".
The option 'num_checks'"num_checks""num_checks""num_checks""num_checks""num_checks" allows to set the number of maximal runs
through the trees. The parameter has to be positive and the default value is
32. The higher this value is, the more accurate the results will be.
As a trade-off, the running time will also be higher. Setting this parameter
to 0 triggers an exact search.
The option 'epsilon'"epsilon""epsilon""epsilon""epsilon""epsilon" allows to set a stop criteria if the value
is increased from the default value 0.0.
The higher the value is set, the less accurate results of the
estimated neighbors can be expected, while it might speed up the search.
Execution Information
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
This operator modifies the state of the following input parameter:
During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.
Parameters
KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleknnhandle (input_control, state is modified) class_knn → HClassKnn, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
Handle of the k-NN classifier.
GenParamNameGenParamNameGenParamNameGenParamNamegenParamNamegen_param_name (input_control) string-array → HTupleSequence[str]HTupleHtuple (string) (string) (HString) (char*)
Names of the generic parameters that can be adjusted
for the k-NN classifier.
Default value:
['method','k','max_num_classes']
["method","k","max_num_classes"]
["method","k","max_num_classes"]
["method","k","max_num_classes"]
["method","k","max_num_classes"]
["method","k","max_num_classes"]
List of values: 'epsilon'"epsilon""epsilon""epsilon""epsilon""epsilon", 'k'"k""k""k""k""k", 'max_num_classes'"max_num_classes""max_num_classes""max_num_classes""max_num_classes""max_num_classes", 'method'"method""method""method""method""method", 'num_checks'"num_checks""num_checks""num_checks""num_checks""num_checks"
GenParamValueGenParamValueGenParamValueGenParamValuegenParamValuegen_param_value (input_control) number-array → HTupleSequence[Union[int, float, str]]HTupleHtuple (integer / real / string) (int / long / double / string) (Hlong / double / HString) (Hlong / double / char*)
Values of the generic parameters that can be adjusted
for the k-NN classifier.
Default value:
['classes_distance',5,1]
["classes_distance",5,1]
["classes_distance",5,1]
["classes_distance",5,1]
["classes_distance",5,1]
["classes_distance",5,1]
Suggested values: 'classes_distance'"classes_distance""classes_distance""classes_distance""classes_distance""classes_distance", 'classes_frequency'"classes_frequency""classes_frequency""classes_frequency""classes_frequency""classes_frequency", 'classes_weighted_frequencies'"classes_weighted_frequencies""classes_weighted_frequencies""classes_weighted_frequencies""classes_weighted_frequencies""classes_weighted_frequencies", 'neighbors_distance'"neighbors_distance""neighbors_distance""neighbors_distance""neighbors_distance""neighbors_distance", 32, 0.0, 0.02, 0, 1, 2, 3, 4, 5, 6
Result
If the parameters are valid, the operator set_params_class_knnset_params_class_knnSetParamsClassKnnSetParamsClassKnnSetParamsClassKnnset_params_class_knn
returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
Possible Predecessors
train_class_knntrain_class_knnTrainClassKnnTrainClassKnnTrainClassKnntrain_class_knn,
read_class_knnread_class_knnReadClassKnnReadClassKnnReadClassKnnread_class_knn
Possible Successors
classify_class_knnclassify_class_knnClassifyClassKnnClassifyClassKnnClassifyClassKnnclassify_class_knn
See also
create_class_knncreate_class_knnCreateClassKnnCreateClassKnnCreateClassKnncreate_class_knn,
read_class_knnread_class_knnReadClassKnnReadClassKnnReadClassKnnread_class_knn,
get_params_class_knnget_params_class_knnGetParamsClassKnnGetParamsClassKnnGetParamsClassKnnget_params_class_knn
References
Marius Muja, David G. Lowe: “Fast Approximate Nearest Neighbors with
Automatic Algorithm Configuration”;
International Conference on Computer Vision Theory
and Applications (VISAPP 09); 2009.
Module
Foundation