def random_stub():
# pylint: disable = unused-variable
import numpy.random as numpy_module
from nums.core.array.random import NumsRandomState
from nums.core.application_manager import instance
app = instance()
sys = app.system
rs_inst = NumsRandomState(system=sys, seed=1337)
numpy_items = sorted(
systems_utils.get_module_functions(numpy_module).items())
nums_items = sorted(systems_utils.get_instance_functions(rs_inst).items())
raise NotImplementedError()
def __init__(
self,
penalty="none",
alpha=1.0,
l1_ratio=0.5,
tol=0.0001,
max_iter=100,
solver="newton",
lr=0.01,
random_state=None,
fit_intercept=True,
normalize=False,
):
if fit_intercept is False:
raise NotImplementedError(
"fit_incercept=False currently not supported.")
if normalize is True:
raise NotImplementedError(
"normalize=True currently not supported.")
self._app = _instance()
if random_state is None:
self.rs: NumsRandomState = self._app.random
elif array_utils.is_int(random_state):
self.rs: NumsRandomState = NumsRandomState(cm=self._app.cm,
seed=random_state)
elif isinstance(random_state, NumsRandomState):
self.rs: NumsRandomState = random_state
else:
raise Exception("Unexpected type for random_state %s" %
str(type(random_state)))
self._penalty = None if penalty == "none" else penalty
if self._penalty not in (None, "l1", "l2", "elasticnet"):
raise NotImplementedError("%s penalty not supported" %
self._penalty)
# All sources use lambda as regularization term, and alpha l1/l2 ratio.
self._lambda = alpha
self._l1penalty = None
self._l1penalty_vec = None
self._l2penalty = None
self._l2penalty_vec = None
self._l2penalty_diag = None
self.alpha = l1_ratio
self._tol = tol
self._max_iter = max_iter
self._opt = solver
self._lr = lr
self._beta = None
self._beta0 = None
def __init__(
self,
penalty="none",
C=1.0,
tol=0.0001,
max_iter=100,
solver="newton-cg",
lr=0.01,
random_state=None,
fit_intercept=True,
normalize=False,
):
if fit_intercept is False:
raise NotImplementedError("fit_incercept=False currently not supported.")
if normalize is True:
raise NotImplementedError("normalize=True currently not supported.")
self._app = _instance()
if random_state is None:
self.rs: NumsRandomState = self._app.random
elif array_utils.is_int(random_state):
self.rs: NumsRandomState = NumsRandomState(
cm=self._app.cm, seed=random_state
)
elif isinstance(random_state, NumsRandomState):
self.rs: NumsRandomState = random_state
else:
raise Exception(
"Unexpected type for random_state %s" % str(type(random_state))
)
self._penalty = None if penalty == "none" else penalty
if not (self._penalty is None or self._penalty == "l2"):
raise NotImplementedError("%s penalty not supported" % self._penalty)
self._lambda = 1.0 / C
self._lambda_vec = None
self._tol = tol
self._max_iter = max_iter
self._opt = solver
self._lr = lr
self._beta = None
self._beta0 = None
def random_state(self, seed=None):
return NumsRandomState(self.cm, seed)