revanalyzer.vectorizers.cf_vectorizer.CFVectorizer

class revanalyzer.vectorizers.cf_vectorizer.CFVectorizer(norm=2, mode='max')

Bases: BasicVectorizer

Class describing CF vectorizer.

Input:

mode (str): can be ‘all’ or ‘max’. If mode = ‘all’, CF calculated for ‘x’, ‘y’ and ‘z’ directions are concatenated into one vector during vectorization. If mode = ‘max’, CF calculared for different directions are vectorizes independently. Then at the analisys step, maximal differences and deviations over 3 directions are taking for REV sizes calculation. Default: ‘max’;

norm (int): Norm of vectors used in REV analysis. The same, as parameter ‘ord’ in numpy.linalg.norm function, default: 2.

Methods

vectorize

Vectorize the vector metric values for a given pair of subsamples.

vectorize(v1, v2)

Vectorize the vector metric values for a given pair of subsamples.

Input:

v1 (list(dtype = float)): data for the first subsample;

v2 (list(dtype = float)): data for the second subsample.

Output:

Depends on the chosen mode.

If mode = ‘all’:

(list(dtype = float), list(dtype = float), float) - a tuple, in which the first two elements are vectorized metric values for a given pair of subsamples, and the last one is the normalized distance between these vectors.

If mode = ‘max:

(list(list(dtype = float)), list(list(dtype = float)), list(float)) - a tuple, in which the first two elements are vectorized metric values in ‘x’, ‘y’ and ‘z’ directions for a given pair of subsamples, and the last one is a list of normalized distances between these vectors.