def fit(self, X, y=None, weights=None):
"""Compute the empirical Exponential Barycenter mean.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
y : array-like, shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in
regression).
Ignored
weights : array-like, shape=[n_samples,], optional
Returns
-------
self : object
Returns self.
"""
if isinstance(self.group, Euclidean):
mean = linear_mean(points=X, weights=weights)
# TODO(nguigs): use closed form expression for special euclidean
# group as before PR #537
else:
mean = _default_gradient_descent(points=X,
weights=weights,
group=self.group,
max_iter=self.max_iter,
epsilon=self.epsilon,
step=self.step,
verbose=self.verbose)
self.estimate_ = mean
return self
def fit(self, X, y=None, weights=None):
"""Compute the empirical Exponential Barycenter mean.
Parameters
----------
X : {array-like, sparse matrix}, shape=[..., n_features]
Training input samples.
y : array-like, shape=[...,] or [..., n_outputs]
Target values (class labels in classification, real numbers in
regression).
Ignored.
weights : array-like, shape=[...,]
Weights associated to the points.
Optional, default: None.
Returns
-------
self : object
Returns self.
"""
if isinstance(self.group, Euclidean):
mean = linear_mean(points=X, weights=weights)
# TODO (nguigs): use closed form expression for special euclidean
# group as before PR #537
else:
mean = _default_gradient_descent(
group=self.group,
points=X,
weights=weights,
max_iter=self.max_iter,
init_step_size=self.init_step_size,
epsilon=self.epsilon,
verbose=self.verbose,
)
self.estimate_ = mean
return self