class _LstsqL2Solver(Solver):
"""Base class for L2-regularized least-squares solvers."""
reg = NumberParam('reg', low=0)
solver = LeastSquaresSolverParam('solver')
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
class _LstsqNoiseSolver(Solver):
"""Base class for least-squares solvers with noise."""
noise = NumberParam('noise', low=0)
solver = LeastSquaresSolverParam('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.
"""
super(_LstsqNoiseSolver, self).__init__(weights=weights)
self.noise = noise
self.solver = solver
class LstsqL2(Solver):
"""Least-squares solver with L2 regularization."""
reg = NumberParam("reg", low=0)
solver = LeastSquaresSolverParam("solver")
def __init__(self, weights=False, reg=0.1, solver=lstsq.Cholesky()):
super().__init__(weights=weights)
self.reg = reg
self.solver = solver
def __call__(self, A, Y, rng=np.random):
tstart = time.time()
sigma = self.reg * A.max()
X, info = self.solver(A, Y, sigma, rng=rng)
info["time"] = time.time() - tstart
return X, info
class LstsqNoise(Solver):
"""Least-squares solver with additive Gaussian white noise."""
noise = NumberParam("noise", low=0)
solver = LeastSquaresSolverParam("solver")
def __init__(self, weights=False, noise=0.1, solver=lstsq.Cholesky()):
super().__init__(weights=weights)
self.noise = noise
self.solver = solver
def __call__(self, A, Y, rng=np.random):
tstart = time.time()
sigma = self.noise * np.amax(np.abs(A))
A = A + rng.normal(scale=sigma, size=A.shape)
X, info = self.solver(A, Y, 0, rng=rng)
info["time"] = time.time() - tstart
return X, info
