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Scikit-learn Documentation Template
"""
This file has an example function, with a documentation string which should
serve as a template for scikit-learn docstrings.
"""
def sklearn_template(X, y, a=1, flag=True, f=None, **kwargs):
"""This is where a short one-line description goes
This is where a longer, multi-line description goes. It's not
required, but might be helpful if more information is needed.
It can also refer to sections below, such as Notes, See Also,
etc.
Parameters
----------
X : array_like or sparse matrix
Array of shape (n_samples, n_features). Other information about the
array here. Keep it to ~2 lines: refer to Notes section for more.
y : array_like
Array of shape (n_samples,). Other information about the
array here. Keep it to ~2 lines: refer to Notes section for more.
a : int (optional, default=1)
Description of what a does
flag : bool (optional, default=True)
If true, then do one thing.
If false, then do another thing.
f : callable (optional, default=None)
Call-back function. If not specified, then some other function
will be used
**kwargs :
Additional keyword arguments will be passed to name_of_function
Returns
-------
z : ndarray
result of shape (n_samples,). Note that here we use "ndarray" rather
than "array_like", because we assure we'll return a numpy array.
xmin, xmax : integers
if multiple parameters have similar description, then they can
be combined.
optional_info : dict
returned only if flag is True. More info about this return value.
Examples
--------
>>> X = np.ones((4, 3))
>>> y = np.ones(4)
>>> sklearn_template(X, y)
(z, xmin, xmax) # this should match the actual output
Notes
-----
More information. This can be in paragraph form, and uses markdown to
- show lists
- like this
- with as many items as you want
Or to show code blocks, with two colons::
import pylab as pl
x = np.arange(10)
y = np.sin(x)
pl.plot(x, y)
We use a code block for a pylab example, because plotting does not
play well with doctests (doctests runs all the example code, and checks
that the output matches).
See Also
--------
- numpy.some_related_function : short description (optional)
- sklearn.some_other_function : short description
"""
# put the code here
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