This is a tutorial of how to use *args and **kwargs
For defining the default value of arguments that is not assigned in key words when calling the function:
def func(**keywargs):
if 'my_word' not in keywargs:
word = 'default_msg'
print(word)
else:
word = keywargs['my_word']
print(word)
call this by:
func()
func(my_word='love')
you’ll get:
default_msg
love
read more about *args and **kwargs in python: https://www.digitalocean.com/community/tutorials/how-to-use-args-and-kwargs-in-python-3
Add more about default value if arg key is not assigned:
def make_hastie_10_2(n_samples=12000, random_state=None):
"""Generates data for binary classification used in
Hastie et al. 2009, Example 10.2.
The ten features are standard independent Gaussian and
the target ``y`` is defined by::
y[i] = 1 if np.sum(X[i] ** 2) > 9.34 else -1
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int, optional (default=12000)
The number of samples.
random_state : int, RandomState instance or None (default)
Determines random number generation for dataset creation. Pass an int
for reproducible output across multiple function calls.
See :term:`Glossary <random_state>`.
Returns
-------
X : array of shape [n_samples, 10]
The input samples.
y : array of shape [n_samples]
The output values.
References
----------
.. [1] T. Hastie, R. Tibshirani and J. Friedman, "Elements of Statistical
Learning Ed. 2", Springer, 2009.
See also
--------
make_gaussian_quantiles: a generalization of this dataset approach
"""
rs = check_random_state(random_state)
shape = (n_samples, 10)
X = rs.normal(size=shape).reshape(shape)
y = ((X ** 2.0).sum(axis=1) > 9.34).astype(np.float64, copy=False)
y[y == 0.0] = -1.0
return X, y