def __init__(self, weights=False, reg=0.1, solver=lstsq.Cholesky()):
"""
Parameters
----------
weights : bool, optional (Default: False)
If False, solve for decoders. If True, solve for weights.
reg : float, optional (Default: 0.1)
Amount of regularization, as a fraction of the neuron activity.
solver : `.LeastSquaresSolver`, optional (Default: ``Cholesky()``)
Subsolver to use for solving the least squares problem.
Attributes
----------
reg : float
Amount of regularization, as a fraction of the neuron activity.
solver : `.LeastSquaresSolver`
Subsolver to use for solving the least squares problem.
weights : bool
If False, solve for decoders. If True, solve for weights.
"""
super(_LstsqL2Solver, self).__init__(weights=weights)
self.reg = reg
self.solver = solver
def __init__(self, weights=False, noise=0.1, solver=lstsq.Cholesky()):
"""
Parameters
----------
weights : bool, optional (Default: False)
If False, solve for decoders. If True, solve for weights.
noise : float, optional (Default: 0.1)
Amount of noise, as a fraction of the neuron activity.
solver : `.LeastSquaresSolver`, optional (Default: ``Cholesky()``)
Subsolver to use for solving the least squares problem.
Attributes
----------
noise : float
Amount of noise, as a fraction of the neuron activity.
solver : `.LeastSquaresSolver`
Subsolver to use for solving the least squares problem.
weights : bool
If False, solve for decoders. If True, solve for weights.
"""
self.weights = weights
self.noise = noise
self.solver = solver
def __init__(self, weights=False, reg=0.1, solver=lstsq.Cholesky()):
super().__init__(weights=weights)
self.reg = reg
self.solver = solver
def __init__(self, weights=False, noise=0.1, solver=lstsq.Cholesky()):
super().__init__(weights=weights)
self.noise = noise
self.solver = solver
