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