create_class_train_dataT_create_class_train_dataCreateClassTrainDataCreateClassTrainDatacreate_class_train_data (算子)

名称

create_class_train_dataT_create_class_train_dataCreateClassTrainDataCreateClassTrainDatacreate_class_train_data — 为分类器的训练数据创建句柄。

签名

create_class_train_data( : : NumDim : ClassTrainDataHandle)

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.

执行信息

多线程类型：可重入（与非独占算子并行运行）。
多线程作用域：全局（可从任何线程调用）。
未采用并行化处理。

此算子返回一个句柄。请注意，即使该句柄被用作特定算子的输入参数，这些算子仍可能改变此句柄类型的实例状态。

参数

NumDimNumDimNumDimNumDimnumDimnum_dim (输入控制) number → (integer)

Number of dimensions of the feature vector.

默认值： 10

ClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleClassTrainDataHandleclassTrainDataHandleclass_train_data_handle (输出控制) class_train_data → (handle)

训练数据的句柄。

示例（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

如果参数有效，算子 create_class_train_datacreate_class_train_dataCreateClassTrainDataCreateClassTrainDataCreateClassTrainDatacreate_class_train_data 返回值 2 ( H_MSG_TRUE )。如有必要，则抛出异常。

可能的后继

add_sample_class_knnadd_sample_class_knnAddSampleClassKnnAddSampleClassKnnAddSampleClassKnnadd_sample_class_knn, train_class_knntrain_class_knnTrainClassKnnTrainClassKnnTrainClassKnntrain_class_knn

替代

create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvmcreate_class_svm, create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlpcreate_class_mlp

另见

select_feature_set_knnselect_feature_set_knnSelectFeatureSetKnnSelectFeatureSetKnnSelectFeatureSetKnnselect_feature_set_knn, read_class_knnread_class_knnReadClassKnnReadClassKnnReadClassKnnread_class_knn

模块

基础

算子