update _alpha_grid class in _coordinate_descent.py
@@ -43,26 +43,26 @@ def _alpha_grid(X, y, Xy=None, l1_ratio=1.0, fit_intercept=True,
y : ndarray, shape (n_samples,)
Target values
- Xy : array-like, optional
+ Xy : array-like, default=None
Xy = np.dot(X.T, y) that can be precomputed.
- l1_ratio : float
+ l1_ratio : float, default=1.0
The elastic net mixing parameter, with ``0 < l1_ratio <= 1``.
For ``l1_ratio = 0`` the penalty is an L2 penalty. (currently not
supported) ``For l1_ratio = 1`` it is an L1 penalty. For
``0 < l1_ratio <1``, the penalty is a combination of L1 and L2.
- eps : float, optional
+ eps : float, default=1e-3
Length of the path. ``eps=1e-3`` means that
``alpha_min / alpha_max = 1e-3``
- n_alphas : int, optional
+ n_alphas : int, default=100
Number of alphas along the regularization path
- fit_intercept : boolean, default True
+ fit_intercept : boolean, default=True
Whether to fit an intercept or not
- normalize : boolean, optional, default False
+ normalize : boolean, 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.
@@ -70,7 +70,7 @@ def _alpha_grid(X, y, Xy=None, l1_ratio=1.0, fit_intercept=True,
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit``
on an estimator with ``normalize=False``.
- copy_X : boolean, optional, default True
+ copy_X : boolean, optional, default=True
If ``True``, X will be copied; else, it may be overwritten.
"""
if l1_ratio == 0:
