def __init__(self,
degree=1,
interaction_only=False,
fit_intercept=True,
regularization='none',
kwds=None):
self.degree = degree
self.interaction_only = interaction_only
LinearRegression.__init__(self, fit_intercept, regularization, kwds)
def __init__(self,
basis_func='gaussian',
fit_intercept=True,
regularization='none',
kwds=None,
**kwargs):
self.basis_func = basis_func
self.kwargs = kwargs
LinearRegression.__init__(self, fit_intercept, regularization, kwds)
def __init__(self,
fit_intercept=True,
normalize=False,
copy_X=True,
n_jobs=1,
delta=0.0001,
max_iter=10,
quantile=0.5,
verbose=False):
"""
Parameters
----------
fit_intercept: boolean, optional, default True
whether to calculate the intercept for this model. If set
to False, no intercept will be used in calculations
(e.g. data is expected to be already centered).
normalize: boolean, optional, default False
This parameter is ignored when ``fit_intercept`` is set to False.
If True, the regressors X will be normalized before regression by
subtracting the mean and dividing by the l2-norm.
If you wish to standardize, please use
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on
an estimator with ``normalize=False``.
copy_X: boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
n_jobs: int, optional, default 1
The number of jobs to use for the computation.
If -1 all CPUs are used. This will only provide speedup for
n_targets > 1 and sufficient large problems.
max_iter: int, optional, default 1
The number of iteration to do at training time.
This parameter is specific to the quantile regression.
delta: float, optional, default 0.0001
Used to ensure matrices has an inverse
(*M + delta*I*).
quantile: float, by default 0.5,
determines which quantile to use
to estimate the regression.
verbose: bool, optional, default False
Prints error at each iteration of the optimisation.
"""
LinearRegression.__init__(self,
fit_intercept=fit_intercept,
normalize=normalize,
copy_X=copy_X,
n_jobs=n_jobs)
self.max_iter = max_iter
self.verbose = verbose
self.delta = delta
self.quantile = quantile
def __init__(self,
args,
fit_intercept=True,
normalize=False,
copy_X=True,
n_jobs=None):
LinearRegression.__init__(self,
fit_intercept=fit_intercept,
normalize=normalize,
copy_X=copy_X,
n_jobs=n_jobs)
# set all params not passed to LinReg constructor; created in modeling process (e.g. coef_, intercept_, etc.)
for arg, value in args.items():
setattr(self, arg, value)
def __init__(self,
degree=2,
interaction_only=False,
include_bias=True,
order='C',
normalize=False,
copy_X=True,
n_jobs=None):
PolynomialFeatures.__init__(self,
degree=degree,
interaction_only=interaction_only,
include_bias=include_bias,
order=order)
LinearRegression.__init__(self,
fit_intercept=False,
normalize=normalize,
copy_X=copy_X,
n_jobs=n_jobs)
def __init__(self):
LinearRegression.__init__(self)
def __init__(self, subset_size=1):
LinearRegression.__init__(self)
self.subset_size = subset_size
def __init__(self, basis_func='gaussian', fit_intercept=True,
regularization='none', kwds=None, **kwargs):
self.basis_func = basis_func
self.kwargs = kwargs
LinearRegression.__init__(self, fit_intercept, regularization, kwds)
def __init__(self, degree=1, interaction_only=False,
fit_intercept=True,
regularization='none', kwds=None):
self.degree = degree
self.interaction_only = interaction_only
LinearRegression.__init__(self, fit_intercept, regularization, kwds)
def __init__(self, degree=1, **kwargs):
self.degree = degree
LinearRegression.__init__(self, **kwargs)
