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create_class_train_datacreate_class_train_dataCreateClassTrainDatacreate_class_train_dataCreateClassTrainDataCreateClassTrainData (Operator)

Name

create_class_train_datacreate_class_train_dataCreateClassTrainDatacreate_class_train_dataCreateClassTrainDataCreateClassTrainData — Create a handle for training data for classifiers.

Signature

create_class_train_data( : : NumDim : ClassTrainDataHandle)

Herror create_class_train_data(const Hlong NumDim, Hlong* ClassTrainDataHandle)

Herror T_create_class_train_data(const Htuple NumDim, Htuple* ClassTrainDataHandle)

Herror create_class_train_data(const HTuple& NumDim, Hlong* ClassTrainDataHandle)

void HClassTrainData::CreateClassTrainData(const HTuple& NumDim)

void CreateClassTrainData(const HTuple& NumDim, HTuple* ClassTrainDataHandle)

void HClassTrainData::HClassTrainData(Hlong NumDim)

void HClassTrainData::CreateClassTrainData(Hlong NumDim)

void HOperatorSetX.CreateClassTrainData(
[in] VARIANT NumDim, [out] VARIANT* ClassTrainDataHandle)

void HClassTrainDataX.CreateClassTrainData([in] Hlong NumDim)

static void HOperatorSet.CreateClassTrainData(HTuple numDim, out HTuple classTrainDataHandle)

public HClassTrainData(int numDim)

void HClassTrainData.CreateClassTrainData(int numDim)

Description

create_class_train_datacreate_class_train_dataCreateClassTrainDatacreate_class_train_dataCreateClassTrainDataCreateClassTrainData creates a handle for training data for classifiers. The handle is returned in ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle. The dimension of the feature vectors is specified with NumDimNumDimNumDimNumDimNumDimnumDim. Only feature vectors of this length can be added to the handle.

Parallelization

Parameters

NumDimNumDimNumDimNumDimNumDimnumDim (input_control)  number HTupleHTupleHTupleVARIANTHtuple (integer) (int / long) (Hlong) (Hlong) (Hlong) (Hlong)

Number of dimensions of the feature vector.

Default value: 10

ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandle (output_control)  class_train_data HClassTrainData, HTupleHTupleHClassTrainData, HTupleHClassTrainDataX, VARIANTHtuple (integer) (IntPtr) (Hlong) (Hlong) (Hlong) (Hlong)

Handle of the training data.

Example (HDevelop)

* Find out which of the two features distinguishes two Classes
NameFeature1 := 'Good Feature'
NameFeature2 := 'Bad Feature'
LengthFeature1 := 3
LengthFeature2 := 2
* Create training data
create_class_train_data (LengthFeature1+LengthFeature2,\
  ClassTrainDataHandle)
* Define the features which are in the training data
set_feature_lengths_class_train_data (ClassTrainDataHandle, [LengthFeature1,\
  LengthFeature2], [NameFeature1, NameFeature2])
* Add training data
*                                                         |Feat1| |Feat2|
add_sample_class_train_data (ClassTrainDataHandle, 'row', [1,1,1,  2,1  ], 0)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,2,2,  2,1  ], 1)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [1,1,1,  3,4  ], 0)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,2,2,  3,4  ], 1)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [0,0,1,  5,6  ], 0)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,3,2,  5,6  ], 1)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [0,0,1,  5,6  ], 0)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,3,2,  5,6  ], 1)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [0,0,1,  5,6  ], 0)
add_sample_class_train_data (ClassTrainDataHandle, 'row', [2,3,2,  5,6  ], 1)
* Add more data 
* ...
* Select the better feature with the classifier of your choice
select_feature_set_knn (ClassTrainDataHandle, 'greedy', [], [], KNNHandle,\
  SelectedFeature, Score)
select_feature_set_svm (ClassTrainDataHandle, 'greedy', [], [], SVMHandle,\
  SelectedFeature, Score)
select_feature_set_mlp (ClassTrainDataHandle, 'greedy', [], [], MLPHandle,\
  SelectedFeature, Score)
select_feature_set_gmm (ClassTrainDataHandle, 'greedy', [], [], GMMHandle,\
  SelectedFeature, Score)
clear_class_train_data (ClassTrainDataHandle)
* Use the classifier
* ...
clear_class_knn (KNNHandle)
clear_class_svm (SVMHandle)
clear_class_mlp (MLPHandle)
clear_class_gmm (GMMHandle)

Result

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

Possible Successors

add_sample_class_knnadd_sample_class_knnAddSampleClassKnnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnn, train_class_knntrain_class_knnTrainClassKnntrain_class_knnTrainClassKnnTrainClassKnn

Alternatives

create_class_svmcreate_class_svmCreateClassSvmcreate_class_svmCreateClassSvmCreateClassSvm, create_class_mlpcreate_class_mlpCreateClassMlpcreate_class_mlpCreateClassMlpCreateClassMlp

See also

select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnn, read_class_knnread_class_knnReadClassKnnread_class_knnReadClassKnnReadClassKnn

Module

Foundation


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