add_sample_class_mlp — Add a training sample to the training data of a multilayer perceptron.
add_sample_class_mlp adds a training sample to the multilayer perceptron (MLP) given by MLPHandle. The training sample is given by Features and Target. Features is the feature vector of the sample, and consequently must be a real vector of length NumInput, as specified in create_class_mlp. Target is the target vector of the sample, which must have the length NumOutput (see create_class_mlp) for all three types of activation functions of the MLP (exception: see below). If the MLP is used for regression (function approximation), i.e., if OutputFunction = 'linear', Target is the value of the function at the coordinate Features. In this case, Target can contain arbitrary real numbers. For OutputFunction = 'logistic', Target can only contain the values 0.0 and 1.0. A value of 1.0 specifies that the attribute in question is present, while a value of 0.0 specifies that the attribute is absent. Because in this case the attributes are independent, arbitrary combinations of 0.0 and 1.0 can be passed. For OutputFunction = 'softmax', Target also can only contain the values 0.0 and 1.0. In contrast to OutputFunction = 'logistic', the value 1.0 must be present for exactly one element of the tuple Target. The location in the tuple designates the class of the sample. For ease of use, a single integer value may be passed if OutputFunction = 'softmax'. This value directly designates the class of the sample, which is counted from 0, i.e., the class must be an integer between 0 and NumOutput - 1. The class is converted to a target vector of length NumOutput internally.
Before the MLP can be trained with train_class_mlp, all training samples must be added to the MLP with add_sample_class_mlp.
The number of currently stored training samples can be queried with get_sample_num_class_mlp. Stored training samples can be read out again with get_sample_class_mlp.
Normally, it is useful to save the training samples in a file with write_samples_class_mlp to facilitate reusing the samples, and to facilitate that, if necessary, new training samples can be added to the data set, and hence to facilitate that a newly created MLP can be trained anew with the extended data set.
This operator modifies the state of the following input parameter:
Feature vector of the training sample to be stored.
Class or target vector of the training sample to be stored.
If the parameters are valid, the operator add_sample_class_mlp returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
clear_samples_class_mlp, get_sample_num_class_mlp, get_sample_class_mlp