104 lines
3.6 KiB
Python
104 lines
3.6 KiB
Python
# Author: Mathieu Blondel
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# License: BSD Style.
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from .stochastic_gradient import BaseSGDClassifier
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from ..feature_selection.selector_mixin import SelectorMixin
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class Perceptron(BaseSGDClassifier, SelectorMixin):
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"""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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n_iter: int, optional
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The number of passes over the training data (aka epochs).
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Defaults to 5.
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shuffle: bool, optional
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Whether or not the training data should be shuffled after each epoch.
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Defaults to False.
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random_state: int seed, RandomState instance, or None (default)
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The seed of the pseudo random number generator to use when
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shuffling the data.
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verbose: integer, optional
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The verbosity level
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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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eta0 : double
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Constant by which the updates are multiplied. Defaults to 1.
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class_weight : dict, {class_label : weight} or "auto" 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 "auto" mode uses the values of y to automatically adjust
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weights inversely proportional to class frequencies.
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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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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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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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http://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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n_iter=5, shuffle=False, verbose=0, eta0=1.0, n_jobs=1,
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random_state=0, class_weight=None, warm_start=False):
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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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n_iter=n_iter,
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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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