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):
"""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)
