11.3.1.3. astroML.linear_model.BasisFunctionRegression

class astroML.linear_model.BasisFunctionRegression(basis_func='gaussian', fit_intercept=True, regularization='none', kwds=None, **kwargs)

Basis Function with errors in y

Parameters

basis_funcstr or function

specify the basis function to use. This should take an input matrix of size (n_samples, n_features), along with optional parameters, and return a matrix of size (n_samples, n_bases).

fit_interceptbool (optional)

if True (default) then fit the intercept of the data

regularizationstring (optional)

[‘l1’|’l2’|’none’] Use L1 (Lasso) or L2 (Ridge) regression

kwds: dict

additional keyword arguments passed to sklearn estimators: LinearRegression, Lasso (L1), or Ridge (L2)

Attributes

coef_

Methods

fit
predict

__init__(basis_func='gaussian', fit_intercept=True, regularization='none', kwds=None, **kwargs)

Initialize self. See help(type(self)) for accurate signature.