def __init__(
self,
threshold=0.1,
nu=1.0,
tol=1e-5,
thresholder="l0",
max_iter=30,
normalize=False,
fit_intercept=False,
copy_X=True,
):
super(SR3, self).__init__(
max_iter=max_iter,
normalize=normalize,
fit_intercept=fit_intercept,
copy_X=copy_X,
)
if threshold < 0:
raise ValueError("threshold cannot be negative")
if nu <= 0:
raise ValueError("nu must be positive")
if tol <= 0:
raise ValueError("tol must be positive")
self.threshold = threshold
self.nu = nu
self.tol = tol
self.thresholder = thresholder
self.prox = get_prox(thresholder)
def __init__(
self,
threshold=0.1,
nu=1.0,
tol=1e-5,
thresholder="l0",
max_iter=30,
trimming_fraction=0.0,
trimming_initialization=None,
trimming_step_size=1.0,
constraint_lhs=None,
constraint_rhs=None,
normalize=False,
fit_intercept=False,
copy_X=True,
):
super(SR3Enhanced, self).__init__(
max_iter=max_iter,
normalize=normalize,
fit_intercept=fit_intercept,
copy_X=copy_X,
)
if threshold < 0:
raise ValueError("threshold cannot be negative")
if nu <= 0:
raise ValueError("nu must be positive")
if tol <= 0:
raise ValueError("tol must be positive")
self.threshold = threshold
self.nu = nu
self.tol = tol
self.thresholder = thresholder
self.prox = get_prox(thresholder)
self.reg = get_reg(thresholder)
if trimming_fraction == 0.0:
self.use_trimming = False
else:
self.use_trimming = True
self.trimming_fraction = trimming_fraction
self.trimming_initialization = trimming_initialization
self.trimming_step_size = trimming_step_size
self.use_constraints = (constraint_lhs is not None) and (constraint_rhs
is not None)
if self.use_constraints:
self.n_constraints = constraint_lhs.shape[0]
self.constraint_lhs = constraint_lhs
self.constraint_rhs = constraint_rhs
def __init__(
self,
threshold=0.1,
nu=1.0,
tol=1e-5,
thresholder="l0",
trimming_fraction=0.0,
trimming_step_size=1.0,
max_iter=30,
normalize=False,
fit_intercept=False,
copy_X=True,
):
super(SR3, self).__init__(
max_iter=max_iter,
normalize=normalize,
fit_intercept=fit_intercept,
copy_X=copy_X,
)
if threshold < 0:
raise ValueError("threshold cannot be negative")
if nu <= 0:
raise ValueError("nu must be positive")
if tol <= 0:
raise ValueError("tol must be positive")
if (trimming_fraction < 0) or (trimming_fraction > 1):
raise ValueError("trimming fraction must be between 0 and 1")
self.threshold = threshold
self.nu = nu
self.tol = tol
self.thresholder = thresholder
self.prox = get_prox(thresholder)
if trimming_fraction == 0.0:
self.use_trimming = False
else:
self.use_trimming = True
self.trimming_fraction = trimming_fraction
self.trimming_step_size = trimming_step_size
def __init__(
self,
n_forcing_params,
forcing_functions,
threshold=0.1,
nu=1.0,
tol=1e-5,
thresholder="l0",
max_iter=30,
normalize=False,
fit_intercept=False,
copy_X=True,
):
super(SR3Forcing, self).__init__(
max_iter=max_iter,
normalize=normalize,
fit_intercept=fit_intercept,
copy_X=copy_X,
)
if threshold < 0:
raise ValueError("threshold cannot be negative")
if nu <= 0:
raise ValueError("nu must be positive")
if tol <= 0:
raise ValueError("tol must be positive")
self.threshold = threshold
self.nu = nu
self.tol = tol
self.thresholder = thresholder
self.prox = get_prox(thresholder)
self.reg = get_reg(thresholder)
self.n_forcing_params = n_forcing_params
self.forcing_params_ = None
self.forcing_functions = forcing_functions