Skip to content

Change optional and deafult value in doc of neural network class #15968

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 9 commits into from
Dec 26, 2019
Merged

Change optional and deafult value in doc of neural network class #15968

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
9 commits
Select commit Hold shift + click to select a range
f93c262
Fix deafult value in doc of perceptron
ankishb Dec 24, 2019
0d7b46f
Correct dict and array keyword to ndarray
ankishb Dec 24, 2019
91c04a7
Fix spacing error in perceptron file
ankishb Dec 24, 2019
b7fb10e
Fix default value, array-like keywork etc in ridge
ankishb Dec 24, 2019
c5c1c9e
Fix default value and array-like keyword of nn-perceptron
ankishb Dec 24, 2019
2d49f41
Fix default value and array-like keyword of nn-rbm
ankishb Dec 24, 2019
f748146
Fix default value and array-like keyword of nn-optimizer
ankishb Dec 24, 2019
e482629
change ndarray to array-like when it is in option with sparse-matrix
ankishb Dec 26, 2019
afa8f49
change ndarray to array-like when it is in option with sparse-matrix …
ankishb Dec 26, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
36 changes: 18 additions & 18 deletions sklearn/linear_model/_perceptron.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,25 +12,25 @@ class Perceptron(BaseSGDClassifier):
Parameters
----------

penalty : None, 'l2' or 'l1' or 'elasticnet'
The penalty (aka regularization term) to be used. Defaults to None.
penalty : {'l2','l1','elasticnet'}, default=None
The penalty (aka regularization term) to be used.

alpha : float
alpha : float, default=0.0001
Constant that multiplies the regularization term if regularization is
used. Defaults to 0.0001
used.

fit_intercept : bool
fit_intercept : bool, default=True
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
data is assumed to be already centered.

max_iter : int, optional (default=1000)
max_iter : int, default=1000
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
:meth:`partial_fit` method.

.. versionadded:: 0.19

tol : float or None, optional (default=1e-3)
tol : float, default=1e-3
The stopping criterion. If it is not None, the iterations will stop
when (loss > previous_loss - tol).

Expand All @@ -39,20 +39,20 @@ class Perceptron(BaseSGDClassifier):
shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.

verbose : integer, default=0
verbose : int, default=0
The verbosity level

eta0 : double
Constant by which the updates are multiplied. Defaults to 1.
eta0 : double, default=1
Constant by which the updates are multiplied.

n_jobs : int or None, optional (default=None)
n_jobs : int, default=None
The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.

random_state : int, RandomState instance or None, optional, default None
random_state : int, RandomState instance, default=None
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
Expand Down Expand Up @@ -80,7 +80,7 @@ class Perceptron(BaseSGDClassifier):

.. versionadded:: 0.20

class_weight : dict, {class_label: weight} or "balanced" or None, optional
class_weight : dict, {class_label: weight} or "balanced", default=None
Preset for the class_weight fit parameter.

Weights associated with classes. If not given, all classes
Expand All @@ -97,18 +97,18 @@ class Perceptron(BaseSGDClassifier):

Attributes
----------
coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes,\
n_features]
coef_ : ndarray of shape = [1, n_features] if n_classes == 2 else \
[n_classes, n_features]
Weights assigned to the features.

intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
intercept_ : ndarray of shape = [1] if n_classes == 2 else [n_classes]
Constants in decision function.

n_iter_ : int
The actual number of iterations to reach the stopping criterion.
For multiclass fits, it is the maximum over every binary fit.

classes_ : array of shape (n_classes,)
classes_ : ndarray of shape (n_classes,)
The unique classes labels.

t_ : int
Expand Down
Loading