create_class_train_dataT_create_class_train_dataCreateClassTrainDataCreateClassTrainDatacreate_class_train_data (算子名称)

名称

create_class_train_dataT_create_class_train_dataCreateClassTrainDataCreateClassTrainDatacreate_class_train_data — Create a handle for training data for classifiers.

参数签名

描述

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

运行信息

• Multithreading type: reentrant (runs in parallel with non-exclusive operators).
• Multithreading scope: global (may be called from any thread).
• Processed without parallelization.

This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.

参数表

例程 (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)
* Use the classifier
* ...

结果

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

可能的后置算子

add_sample_class_knnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnnAddSampleClassKnnadd_sample_class_knn, train_class_knntrain_class_knnTrainClassKnnTrainClassKnnTrainClassKnntrain_class_knn

可替代算子

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm, create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp

参考其它

select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnnSelectFeatureSetKnnselect_feature_set_knn, read_class_knnread_class_knnReadClassKnnReadClassKnnReadClassKnnread_class_knn

模块

Foundation