132 lines
4.9 KiB
Python
132 lines
4.9 KiB
Python
# Author: Mathieu Blondel
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# License: BSD 3 clause
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from .stochastic_gradient import BaseSGDClassifier
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class Perceptron(BaseSGDClassifier):
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"""Perceptron
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Read more in the :ref:`User Guide <perceptron>`.
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Parameters
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----------
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penalty : None, 'l2' or 'l1' or 'elasticnet'
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The penalty (aka regularization term) to be used. Defaults to None.
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alpha : float
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Constant that multiplies the regularization term if regularization is
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used. Defaults to 0.0001
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fit_intercept : bool
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Whether the intercept should be estimated or not. If False, the
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data is assumed to be already centered. Defaults to True.
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max_iter : int, optional
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The maximum number of passes over the training data (aka epochs).
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It only impacts the behavior in the ``fit`` method, and not the
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`partial_fit`.
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Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None.
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.. versionadded:: 0.19
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tol : float or None, optional
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The stopping criterion. If it is not None, the iterations will stop
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when (loss > previous_loss - tol). Defaults to None.
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Defaults to 1e-3 from 0.21.
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.. versionadded:: 0.19
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shuffle : bool, optional, default True
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Whether or not the training data should be shuffled after each epoch.
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verbose : integer, optional
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The verbosity level
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eta0 : double
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Constant by which the updates are multiplied. Defaults to 1.
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n_jobs : integer, optional
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The number of CPUs to use to do the OVA (One Versus All, for
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multi-class problems) computation. -1 means 'all CPUs'. Defaults
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to 1.
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random_state : int, RandomState instance or None, optional, default None
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The seed of the pseudo random number generator to use when shuffling
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the data. If int, random_state is the seed used by the random number
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generator; If RandomState instance, random_state is the random number
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generator; If None, the random number generator is the RandomState
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instance used by `np.random`.
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class_weight : dict, {class_label: weight} or "balanced" or None, optional
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Preset for the class_weight fit parameter.
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Weights associated with classes. If not given, all classes
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are supposed to have weight one.
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The "balanced" mode uses the values of y to automatically adjust
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weights inversely proportional to class frequencies in the input data
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as ``n_samples / (n_classes * np.bincount(y))``
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warm_start : bool, optional
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When set to True, reuse the solution of the previous call to fit as
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initialization, otherwise, just erase the previous solution.
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n_iter : int, optional
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The number of passes over the training data (aka epochs).
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Defaults to None. Deprecated, will be removed in 0.21.
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.. versionchanged:: 0.19
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Deprecated
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Attributes
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----------
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coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes,\
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n_features]
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Weights assigned to the features.
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intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
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Constants in decision function.
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n_iter_ : int
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The actual number of iterations to reach the stopping criterion.
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For multiclass fits, it is the maximum over every binary fit.
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Notes
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-----
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`Perceptron` and `SGDClassifier` share the same underlying implementation.
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In fact, `Perceptron()` is equivalent to `SGDClassifier(loss="perceptron",
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eta0=1, learning_rate="constant", penalty=None)`.
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See also
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--------
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SGDClassifier
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References
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----------
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https://en.wikipedia.org/wiki/Perceptron and references therein.
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"""
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def __init__(self, penalty=None, alpha=0.0001, fit_intercept=True,
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max_iter=None, tol=None, shuffle=True, verbose=0, eta0=1.0,
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n_jobs=1, random_state=0, class_weight=None,
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warm_start=False, n_iter=None):
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super(Perceptron, self).__init__(loss="perceptron",
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penalty=penalty,
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alpha=alpha, l1_ratio=0,
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fit_intercept=fit_intercept,
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max_iter=max_iter,
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tol=tol,
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shuffle=shuffle,
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verbose=verbose,
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random_state=random_state,
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learning_rate="constant",
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eta0=eta0,
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power_t=0.5,
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warm_start=warm_start,
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class_weight=class_weight,
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n_jobs=n_jobs,
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n_iter=n_iter)
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