DOC: specify that sample_weight is optional in in _multinomial_loss_*
@@ -260,9 +260,8 @@ def _multinomial_loss(w, X, Y, alpha, sample_weight):
alpha : float
Regularization parameter. alpha is equal to 1 / C.
- sample_weight : array-like, shape (n_samples,) optional
+ sample_weight : array-like, shape (n_samples,)
Array of weights that are assigned to individual samples.
- If not provided, then each sample is given unit weight.
Returns
-------
@@ -317,7 +316,7 @@ def _multinomial_loss_grad(w, X, Y, alpha, sample_weight):
alpha : float
Regularization parameter. alpha is equal to 1 / C.
- sample_weight : array-like, shape (n_samples,) optional
+ sample_weight : array-like, shape (n_samples,)
Array of weights that are assigned to individual samples.
Returns
@@ -371,7 +370,7 @@ def _multinomial_grad_hess(w, X, Y, alpha, sample_weight):
alpha : float
Regularization parameter. alpha is equal to 1 / C.
- sample_weight : array-like, shape (n_samples,) optional
+ sample_weight : array-like, shape (n_samples,)
Array of weights that are assigned to individual samples.
Returns
