11.3.1.3. astroML.linear_model.BasisFunctionRegression¶
-
class
astroML.linear_model.BasisFunctionRegression(basis_func='gaussian', fit_intercept=True, regularization='none', kwds=None, **kwargs)[source]¶ 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
get_params([deep])Get parameters for this estimator.
set_params(**params)Set the parameters of this estimator.
fit
predict