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