Operator Reference

HALCON Operator Reference / Classification / K-Nearest Neighbors

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train_class_knnT_train_class_knnTrainClassKnnTrainClassKnntrain_class_knn (Operator)

train_class_knnT_train_class_knnTrainClassKnnTrainClassKnntrain_class_knn — Creates the search trees for a k-NN classifier.

Signature

train_class_knn( : : KNNHandle, GenParamName, GenParamValue : )

Herror T_train_class_knn(const Htuple KNNHandle, const Htuple GenParamName, const Htuple GenParamValue)

void TrainClassKnn(const HTuple& KNNHandle, const HTuple& GenParamName, const HTuple& GenParamValue)

void HClassKnn::TrainClassKnn(const HTuple& GenParamName, const HTuple& GenParamValue) const

static void HOperatorSet.TrainClassKnn(HTuple KNNHandle, HTuple genParamName, HTuple genParamValue)

void HClassKnn.TrainClassKnn(HTuple genParamName, HTuple genParamValue)

def train_class_knn(knnhandle: HHandle, gen_param_name: Sequence[str], gen_param_value: Sequence[Union[int, str, float]]) -> None

Description

train_class_knntrain_class_knnTrainClassKnnTrainClassKnntrain_class_knn creates the search trees for a k-NN classifier.

It is possible to set the number of trees via the parameters GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value by 'num_trees'"num_trees""num_trees""num_trees""num_trees". The default value for the number of search trees is 4. A higher number of trees improves the accuracy of the search, but increases the run time.

It is possible to add more samples after training using the operator add_sample_class_knnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnnadd_sample_class_knn. The added data affects the classification only, if train_class_knntrain_class_knnTrainClassKnnTrainClassKnntrain_class_knn is called again.

Automatic feature normalization can be activated by setting 'normalization'"normalization""normalization""normalization""normalization" in GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and 'true'"true""true""true""true" in GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value. The feature vectors are normalized by normalizing each dimension separately. For each dimension, the mean and standard deviation is calculated over the training samples. Every feature vector is normalized by subtracting the mean and dividing by the standard deviation of the individual dimension. This results in a normalization, where each dimension has zero mean and unit variance. If the standard deviation happens to be zero, only the mean is subtracted. Please note however, that a feature dimension with no standard deviation does not change the classification result and should be removed. Automatic feature normalization will change the stored training data, but the original data can be restored at any time by calling train_class_knntrain_class_knnTrainClassKnnTrainClassKnntrain_class_knn with 'normalization'"normalization""normalization""normalization""normalization" set to 'false'"false""false""false""false". If normalization is used, the operator classify_class_knnclassify_class_knnClassifyClassKnnClassifyClassKnnclassify_class_knn interprets the input data as unnormalized and performs normalization internally as it has been defined in the last call to train_class_knntrain_class_knnTrainClassKnnTrainClassKnntrain_class_knn.

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:

• KNNHandleKNNHandleKNNHandleKNNHandleknnhandle

During execution of this operator, access to the value of this parameter must be synchronized if it is used across multiple threads.

Parameters

KNNHandleKNNHandleKNNHandleKNNHandleknnhandle (input_control, state is modified)  class_knn → HClassKnn, HTupleHHandleHTupleHtuple (handle) (IntPtr) (HHandle) (handle)

Handle of the k-NN classifier.

GenParamNameGenParamNameGenParamNamegenParamNamegen_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 creation.

Default: []

List of values: 'normalization'"normalization""normalization""normalization""normalization", 'num_trees'"num_trees""num_trees""num_trees""num_trees"

GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value (input_control)  number-array → HTupleSequence[Union[int, str, float]]HTupleHtuple (integer / string / real) (int / long / string / double) (Hlong / HString / double) (Hlong / char* / double)

Values of the generic parameters that can be adjusted for the k-NN classifier creation.

Default: []

Suggested values: 4, 'false'"false""false""false""false", 'true'"true""true""true""true"

Result

If the parameters are valid, the operator train_class_knntrain_class_knnTrainClassKnnTrainClassKnntrain_class_knn returns the value 2 ( H_MSG_TRUE) . If necessary, an exception is raised.

Possible Predecessors

add_sample_class_knnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnnadd_sample_class_knn, read_class_knnread_class_knnReadClassKnnReadClassKnnread_class_knn

Alternatives

select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnnselect_feature_set_knn

See also

create_class_knncreate_class_knnCreateClassKnnCreateClassKnncreate_class_knn, read_class_knnread_class_knnReadClassKnnReadClassKnnread_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

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