DOC Add None as valid option to random_state in neural_network module
@@ -56,7 +56,7 @@ class BernoulliRBM(TransformerMixin, BaseEstimator):
verbose : int, default=0
The verbosity level. The default, zero, means silent mode.
- random_state : integer or RandomState, default=None
+ random_state : int, RandomState instance or None, default=None
Determines random number generation for:
- Gibbs sampling from visible and hidden layers.
@@ -70,17 +70,17 @@ class BernoulliRBM(TransformerMixin, BaseEstimator):
Attributes
----------
- intercept_hidden_ : array-like, shape (n_components,)
+ intercept_hidden_ : array-like of shape (n_components,)
Biases of the hidden units.
- intercept_visible_ : array-like, shape (n_features,)
+ intercept_visible_ : array-like of shape (n_features,)
Biases of the visible units.
- components_ : array-like, shape (n_components, n_features)
+ components_ : array-like of shape (n_components, n_features)
Weight matrix, where n_features in the number of
visible units and n_components is the number of hidden units.
- h_samples_ : array-like, shape (batch_size, n_components)
+ h_samples_ : array-like of shape (batch_size, n_components)
Hidden Activation sampled from the model distribution,
where batch_size in the number of examples per minibatch and
n_components is the number of hidden units.
@@ -159,7 +159,7 @@ class BernoulliRBM(TransformerMixin, BaseEstimator):
v : ndarray of shape (n_samples, n_features)
Values of the visible layer to sample from.
- rng : RandomState
+ rng : RandomState instance
Random number generator to use.
Returns
@@ -178,7 +178,7 @@ class BernoulliRBM(TransformerMixin, BaseEstimator):
h : ndarray of shape (n_samples, n_components)
Values of the hidden layer to sample from.
- rng : RandomState
+ rng : RandomState instance
Random number generator to use.
Returns
@@ -274,7 +274,7 @@ class BernoulliRBM(TransformerMixin, BaseEstimator):
v_pos : ndarray of shape (n_samples, n_features)
The data to use for training.
- rng : RandomState
+ rng : RandomState instance
Random number generator to use for sampling.
"""
h_pos = self._mean_hiddens(v_pos)
