get_sample_class_knnT_get_sample_class_knnGetSampleClassKnnGetSampleClassKnnget_sample_class_knn (Operator)
Name
get_sample_class_knnT_get_sample_class_knnGetSampleClassKnnGetSampleClassKnnget_sample_class_knn — Return a training sample from the training data of a k-nearest neighbors
(k-NN) classifier.
Signature
Description
get_sample_class_knnget_sample_class_knnGetSampleClassKnnGetSampleClassKnnGetSampleClassKnnget_sample_class_knn reads a training sample from the
k-nearest neighbors (k-NN) classifier given by KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleknnhandle that was
added with add_sample_class_knnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnnAddSampleClassKnnadd_sample_class_knn or
read_class_knnread_class_knnReadClassKnnReadClassKnnReadClassKnnread_class_knn. The index of the sample is
specified with IndexSampleIndexSampleIndexSampleIndexSampleindexSampleindex_sample. The index is counted from 0,
i.e., IndexSampleIndexSampleIndexSampleIndexSampleindexSampleindex_sample must be a number between 0 and
NumSamples - 1, where NumSamples can be
determined with get_sample_num_class_knnget_sample_num_class_knnGetSampleNumClassKnnGetSampleNumClassKnnGetSampleNumClassKnnget_sample_num_class_knn. The training
sample is returned in FeaturesFeaturesFeaturesFeaturesfeaturesfeatures and ClassIDClassIDClassIDClassIDclassIDclass_id.
FeaturesFeaturesFeaturesFeaturesfeaturesfeatures is a feature vector of length
NumDim (see create_class_knncreate_class_knnCreateClassKnnCreateClassKnnCreateClassKnncreate_class_knn), while ClassIDClassIDClassIDClassIDclassIDclass_id
is the class label, which is a number between 0 and the
number of classes.
Execution Information
- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Processed without parallelization.
Parameters
KNNHandleKNNHandleKNNHandleKNNHandleKNNHandleknnhandle (input_control) class_knn → HClassKnn, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)
Handle of the k-NN classifier.
IndexSampleIndexSampleIndexSampleIndexSampleindexSampleindex_sample (input_control) integer → HTupleintHTupleHtuple (integer) (int / long) (Hlong) (Hlong)
Index of the training sample.
FeaturesFeaturesFeaturesFeaturesfeaturesfeatures (output_control) real-array → HTupleSequence[float]HTupleHtuple (real) (double) (double) (double)
Feature vector of the training sample.
ClassIDClassIDClassIDClassIDclassIDclass_id (output_control) integer-array → HTupleSequence[int]HTupleHtuple (integer) (int / long) (Hlong) (Hlong)
Class of the training sample.
Result
If the parameters are valid the operator
get_sample_class_knnget_sample_class_knnGetSampleClassKnnGetSampleClassKnnGetSampleClassKnnget_sample_class_knn returns the value 2 (H_MSG_TRUE). If necessary,
an exception is raised.
Possible Predecessors
add_sample_class_train_dataadd_sample_class_train_dataAddSampleClassTrainDataAddSampleClassTrainDataAddSampleClassTrainDataadd_sample_class_train_data
See also
create_class_knncreate_class_knnCreateClassKnnCreateClassKnnCreateClassKnncreate_class_knn
Module
Foundation