def __init__(self, prox_list: list):
# Range is stored in each Prox individually
Prox.__init__(self, range=None)
if len(prox_list) == 0:
raise ValueError('prox_list must have at least one Prox')
self.prox_list = prox_list
self.n_proxs = len(self.prox_list)
def __init__(self,
strength: float,
n_rows: int = None,
range: tuple = None,
positive: bool = False):
Prox.__init__(self, range)
self.positive = positive
self.strength = strength
self.n_rows = n_rows
self.rank_max = None
def __init__(self,
strength: float,
fdr: float = 0.6,
range: tuple = None,
positive: bool = False):
Prox.__init__(self, range)
self.strength = strength
self.fdr = fdr
self.positive = positive
self.weights = None
self._prox = self._build_cpp_prox("float64")
def __init__(self,
strength: float,
fdr: float = 0.6,
range: tuple = None,
positive: bool = False):
Prox.__init__(self, range)
self.strength = strength
self.fdr = fdr
self.positive = positive
self.weights = None
if range is None:
self._prox = _ProxSlope(self.strength, self.fdr, self.positive)
else:
self._prox = _ProxSlope(self.strength, self.fdr, self.range[0],
self.range[1], self.positive)
def set_prox(self, prox: Prox):
"""Set proximal operator in the solver
Parameters
----------
prox : `Prox`
The proximal operator of the penalization function
Returns
-------
output : `Solver`
The solver with given prox
Notes
-----
In some solvers, ``set_model`` must be called before
``set_prox``, otherwise and error might be raised
"""
if not isinstance(prox, Prox):
raise ValueError('Passed object of class %s is not a '
'Prox class' % prox.name)
if self.dtype is None or self.model is None:
raise ValueError("Solver must call set_model before set_prox")
if prox.dtype != self.dtype:
prox = prox.astype(self.dtype)
self._set("prox", prox)
return self
def __init__(self,
strength: float,
fdr: float = 0.6,
weights_type: str = "bh",
range: tuple = None,
positive: bool = False):
Prox.__init__(self, range)
self.strength = strength
self.fdr = fdr
self.weights_type = weights_type
self.positive = positive
self.weights = None
if range is None:
self._prox = _ProxSortedL1(self.strength, self.fdr,
self._weights_type, self.positive)
else:
self._prox = _ProxSortedL1(self.strength, self.fdr,
self._weights_type, self.range[0],
self.range[1], self.positive)
def set_prox(self, prox: Prox):
if prox._prox is None:
raise ValueError("Prox %s is not compatible with stochastic "
"solver %s" % (prox.__class__.__name__,
self.__class__.__name__))
# Give the C++ wrapped prox to the solver
if self.dtype is None or self.model is None:
raise ValueError("Solver must call set_model before set_prox")
if prox.dtype != self.dtype:
prox = prox.astype(self.dtype)
self._solver.set_prox(prox._prox)
return self
def _as_dict(self):
dd = Prox._as_dict(self)
del dd["weights"]
return dd
