133 lines
4.9 KiB
Python
133 lines
4.9 KiB
Python
|
# Author: Mathieu Blondel
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
from .stochastic_gradient import BaseSGDClassifier
|
||
|
|
|
||
|
|
|
||
|
|
class Perceptron(BaseSGDClassifier):
|
||
|
|
"""Perceptron
|
||
|
|
|
||
|
|
Read more in the :ref:`User Guide <perceptron>`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
|
||
|
|
penalty : None, 'l2' or 'l1' or 'elasticnet'
|
||
|
|
The penalty (aka regularization term) to be used. Defaults to None.
|
||
|
|
|
||
|
|
alpha : float
|
||
|
|
Constant that multiplies the regularization term if regularization is
|
||
|
|
used. Defaults to 0.0001
|
||
|
|
|
||
|
|
fit_intercept : bool
|
||
|
|
Whether the intercept should be estimated or not. If False, the
|
||
|
|
data is assumed to be already centered. Defaults to True.
|
||
|
|
|
||
|
|
max_iter : int, optional
|
||
|
|
The maximum number of passes over the training data (aka epochs).
|
||
|
|
It only impacts the behavior in the ``fit`` method, and not the
|
||
|
|
`partial_fit`.
|
||
|
|
Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None.
|
||
|
|
|
||
|
|
.. versionadded:: 0.19
|
||
|
|
|
||
|
|
tol : float or None, optional
|
||
|
|
The stopping criterion. If it is not None, the iterations will stop
|
||
|
|
when (loss > previous_loss - tol). Defaults to None.
|
||
|
|
Defaults to 1e-3 from 0.21.
|
||
|
|
|
||
|
|
.. versionadded:: 0.19
|
||
|
|
|
||
|
|
shuffle : bool, optional, default True
|
||
|
|
Whether or not the training data should be shuffled after each epoch.
|
||
|
|
|
||
|
|
verbose : integer, optional
|
||
|
|
The verbosity level
|
||
|
|
|
||
|
|
eta0 : double
|
||
|
|
Constant by which the updates are multiplied. Defaults to 1.
|
||
|
|
|
||
|
|
n_jobs : integer, optional
|
||
|
|
The number of CPUs to use to do the OVA (One Versus All, for
|
||
|
|
multi-class problems) computation. -1 means 'all CPUs'. Defaults
|
||
|
|
to 1.
|
||
|
|
|
||
|
|
random_state : int, RandomState instance or None, optional, 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
|
||
|
|
generator; If None, the random number generator is the RandomState
|
||
|
|
instance used by `np.random`.
|
||
|
|
|
||
|
|
class_weight : dict, {class_label: weight} or "balanced" or None, optional
|
||
|
|
Preset for the class_weight fit parameter.
|
||
|
|
|
||
|
|
Weights associated with classes. If not given, all classes
|
||
|
|
are supposed to have weight one.
|
||
|
|
|
||
|
|
The "balanced" mode uses the values of y to automatically adjust
|
||
|
|
weights inversely proportional to class frequencies in the input data
|
||
|
|
as ``n_samples / (n_classes * np.bincount(y))``
|
||
|
|
|
||
|
|
warm_start : bool, optional
|
||
|
|
When set to True, reuse the solution of the previous call to fit as
|
||
|
|
initialization, otherwise, just erase the previous solution.
|
||
|
|
|
||
|
|
n_iter : int, optional
|
||
|
|
The number of passes over the training data (aka epochs).
|
||
|
|
Defaults to None. Deprecated, will be removed in 0.21.
|
||
|
|
|
||
|
|
.. versionchanged:: 0.19
|
||
|
|
Deprecated
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
coef_ : array, 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]
|
||
|
|
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.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
|
||
|
|
`Perceptron` and `SGDClassifier` share the same underlying implementation.
|
||
|
|
In fact, `Perceptron()` is equivalent to `SGDClassifier(loss="perceptron",
|
||
|
|
eta0=1, learning_rate="constant", penalty=None)`.
|
||
|
|
|
||
|
|
See also
|
||
|
|
--------
|
||
|
|
|
||
|
|
SGDClassifier
|
||
|
|
|
||
|
|
References
|
||
|
|
----------
|
||
|
|
|
||
|
|
https://en.wikipedia.org/wiki/Perceptron and references therein.
|
||
|
|
"""
|
||
|
|
def __init__(self, penalty=None, alpha=0.0001, fit_intercept=True,
|
||
|
|
max_iter=None, tol=None, shuffle=True, verbose=0, eta0=1.0,
|
||
|
|
n_jobs=1, random_state=0, class_weight=None,
|
||
|
|
warm_start=False, n_iter=None):
|
||
|
|
super(Perceptron, self).__init__(loss="perceptron",
|
||
|
|
penalty=penalty,
|
||
|
|
alpha=alpha, l1_ratio=0,
|
||
|
|
fit_intercept=fit_intercept,
|
||
|
|
max_iter=max_iter,
|
||
|
|
tol=tol,
|
||
|
|
shuffle=shuffle,
|
||
|
|
verbose=verbose,
|
||
|
|
random_state=random_state,
|
||
|
|
learning_rate="constant",
|
||
|
|
eta0=eta0,
|
||
|
|
power_t=0.5,
|
||
|
|
warm_start=warm_start,
|
||
|
|
class_weight=class_weight,
|
||
|
|
n_jobs=n_jobs,
|
||
|
|
n_iter=n_iter)
|