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# Author: Mathieu Blondel
# License: BSD Style.
from .stochastic_gradient import SGDClassifier
class Perceptron(SGDClassifier):
"""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.
n_iter: int, optional
The number of passes over the training data (aka epochs).
Defaults to 5.
shuffle: bool, optional
Whether or not the training data should be shuffled after each epoch.
Defaults to False.
seed: int, optional
The seed of the pseudo random number generator to use when
shuffling the data.
verbose: integer, optional
The verbosity level
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.
eta0 : double
Constant by which the updates are multiplied. Defaults to 1.
class_weight : dict, {class_label : weight} or "auto" 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 "auto" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies.
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.
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.
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
----------
http://en.wikipedia.org/wiki/Perceptron and references therein.
"""
def __init__(self, penalty=None, alpha=0.0001, fit_intercept=True,
n_iter=5, shuffle=False, verbose=0, eta0=1.0,
n_jobs=1, seed=0, class_weight=None, warm_start=False):
super(Perceptron, self).__init__(loss="perceptron",
penalty=penalty,
alpha=alpha, rho=0,
fit_intercept=fit_intercept,
n_iter=n_iter,
shuffle=shuffle,
verbose=verbose,
seed=seed,
learning_rate="constant",
eta0=eta0,
power_t=0.5,
warm_start=warm_start,
class_weight=class_weight,
n_jobs=n_jobs)
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