get_sample_class_gmm — Return a training sample from the training data of a Gaussian Mixture Models (GMM).
get_sample_class_gmm reads out a training sample from the Gaussian Mixture Model (GMM) given by GMMHandle that was stored with add_sample_class_gmm or add_samples_image_class_gmm. The index of the sample is specified with NumSample. The index is counted from 0, i.e., NumSample must be a number between 0 and NumSamples - 1, where NumSamples can be determined with get_sample_num_class_gmm. The training sample is returned in Features and ClassID. Features is a feature vector of length NumDim, while ClassID is its class (see add_sample_class_gmm and create_class_gmm).
get_sample_class_gmm can, for example, be used to reclassify the training data with classify_class_gmm in order to determine which training samples, if any, are classified incorrectly.
Index of the stored training sample.
Feature vector of the training sample.
Class of the training sample.
create_class_gmm (2, 2, [1,10], 'spherical', 'none', 2, 42, GMMHandle) read_samples_class_gmm (GMMHandle, 'samples.gsf') train_class_gmm (GMMHandle, 100, 1e-4, 'training', 1e-4, Centers, Iter) * Reclassify the training samples get_sample_num_class_gmm (GMMHandle, NumSamples) for I := 0 to NumSamples-1 by 1 get_sample_class_gmm (GMMHandle, I, Features, Class) classify_class_gmm (GMMHandle, Features, 2, ClassID, ClassProb,\ Density, KSigmaProb) if (not (Class == ClassProb)) * classified incorrectly endif endfor clear_class_gmm (GMMHandle)
If the parameters are valid, the operator get_sample_class_gmm returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.
add_sample_class_gmm, add_samples_image_class_gmm, read_samples_class_gmm, get_sample_num_class_gmm