read_sampset — Read a training data set from a file.
read_sampset is obsolete and is only provided for
reasons of backward compatibility. New applications should use the
MLP, SVM, KNN or GMM operators instead.
The training examples are accessible with the key SampKey by
calling the operators clear_sampset and learn_sampset_box.
You may edit the file using an editor. Every row contains an array of
attributes with corresponding class.
An example for a format might be:
(1.0, 25.3, * , 17 | 3) This row specifies an array of attributes which belong to class 3. In this array the third attribute is unknown. Attributes upwards 5 are supposed to be unknown, too. You may insert comments like /* .. */ in any place.
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.
FileName (input_control) filename.read → (string)
Filename of the data set to train.
Default value: 'sampset1'
SampKey (output_control) feature_set → (handle)
Identification of the data set to train.
read_sampset returns 2 (H_MSG_TRUE).
An exception is raised if it is not possible to open the file or
it contains syntax errors or there is not enough memory.
test_sampset_box,
enquire_class_box,
write_class_box,
close_class_box,
clear_sampset
test_sampset_box,
clear_sampset,
learn_sampset_box
Foundation