Source code for

.. module:: PrototypesSelectorRandom
   :synopsis: Selector of prototypes using spanning strategy.

.. moduleauthor:: Marco Melis <>

from import CPrototypesSelector
from secml.array import CArray

[docs]class CPSRandom(CPrototypesSelector): """Selection of Prototypes using random strategy. Attributes ---------- class_type : 'random' """ __class_type = 'random'
[docs] def select(self, dataset, n_prototypes, random_state=None): """Selects the prototypes from input dataset. Parameters ---------- dataset : CDataset Dataset from which prototypes should be selected n_prototypes : int Number of prototypes to be selected. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, is the RandomState instance used by np.random. Returns ------- reduced_ds : CDataset Dataset with selected prototypes. """ sel_idx = CArray.randsample(CArray(list(range(dataset.num_samples))), shape=n_prototypes, random_state=random_state) self.logger.debug("Selecting samples: {:}".format(sel_idx.tolist())) self._sel_idx = sel_idx # Returning the reduced training set return dataset[self._sel_idx, :]