DOC fixes style in develop.rst to follow documentation guideline
@@ -148,19 +148,19 @@ The ``fit()`` method takes the training data as arguments, which can be one
array in the case of unsupervised learning, or two arrays in the case
of supervised learning.
-Note that the model is fitted using X and y, but the object holds no
-reference to X and y. There are, however, some exceptions to this, as in
+Note that the model is fitted using ``X`` and ``y``, but the object holds no
+reference to ``X`` and ``y``. There are, however, some exceptions to this, as in
the case of precomputed kernels where this data must be stored for use by
the predict method.
============= ======================================================
Parameters
============= ======================================================
-X array-like, shape (n_samples, n_features)
+X array-like of shape (n_samples, n_features)
-y array, shape (n_samples,)
+y array-like of shape (n_samples,)
-kwargs optional data-dependent parameters.
+kwargs optional data-dependent parameters
============= ======================================================
``X.shape[0]`` should be the same as ``y.shape[0]``. If this requisite
@@ -653,21 +653,22 @@ Here's a simple example of code using some of the above guidelines::
from sklearn.utils import check_array, check_random_state
def choose_random_sample(X, random_state=0):
- """
- Choose a random point from X
+ """Choose a random point from X.
Parameters
----------
- X : array-like, shape (n_samples, n_features)
- array representing the data
- random_state : RandomState or an int seed (0 by default)
- A random number generator instance to define the state of the
- random permutations generator.
+ X : array-like of shape (n_samples, n_features)
+ An array representing the data.
+ random_state : int or RandomState instance, default=0
+ The seed of the pseudo random number generator that selects a
+ random sample. Pass an int for reproducible output across multiple
+ function calls.
+ See :term:`Glossary <random_state>`.
Returns
-------
- x : numpy array, shape (n_features,)
- A random point selected from X
+ x : ndarray of shape (n_features,)
+ A random point selected from X.
"""
X = check_array(X)
random_state = check_random_state(random_state)
