def _train(self, x, labels): """Add the sampel points to the classes. labels -- Can be a list, tuple or array of labels (one for each data point) or a single label, in which case all input data is assigned to the same class (computationally this is more efficient). """ if isinstance(labels, (list, tuple, numx.ndarray)): labels = numx.asarray(labels) for label in set(labels): x_label = numx.compress(labels == label, x, axis=0) self._add_samples(x_label, label) else: self._add_samples(x, labels)
def _train(self, x, labels): """Update the mean information for the different classes. :param x: The data. :type x: numpy.ndarray :param labels: Can be a list, tuple or array of labels (one for each data point) or a single label, in which case all input data is assigned to the same class (computationally this is more efficient). """ if isinstance(labels, (list, tuple, numx.ndarray)): labels = numx.asarray(labels) for label in set(labels): x_label = numx.compress(labels == label, x, axis=0) self._update_mean(x_label, label) else: self._update_mean(x, labels)
def _train(self, x, labels): """ :param x: Data :type x: numpy.ndarray :param labels: Can be a list, tuple or array of labels (one for each data point) or a single label, in which case all input data is assigned to the same class. """ # if labels is a number, all x's belong to the same class if isinstance(labels, (list, tuple, numx.ndarray)): labels_ = numx.asarray(labels) # get all classes from cl for lbl in set(labels_): x_lbl = numx.compress(labels_ == lbl, x, axis=0) self._update_covs(x_lbl, lbl) else: self._update_covs(x, labels)
def _train(self, x, labels): """ :Arguments: x data labels Can be a list, tuple or array of labels (one for each data point) or a single label, in which case all input data is assigned to the same class. """ # if labels is a number, all x's belong to the same class if isinstance(labels, (list, tuple, numx.ndarray)): labels_ = numx.asarray(labels) # get all classes from cl for lbl in set(labels_): x_lbl = numx.compress(labels_ == lbl, x, axis=0) self._update_covs(x_lbl, lbl) else: self._update_covs(x, labels)