\c@s*ddlmZdefdYZdS(i(tBaseSGDClassifiert PerceptroncBsGeZdZddeddeddddedddeddZRS(sPerceptron Read more in the :ref:`User Guide `. 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 : int or None, optional (default=None) The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. 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`. early_stopping : bool, default=False Whether to use early stopping to terminate training when validation. score is not improving. If set to True, it will automatically set aside a fraction of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs. .. versionadded:: 0.20 validation_fraction : float, default=0.1 The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1. Only used if early_stopping is True. .. versionadded:: 0.20 n_iter_no_change : int, default=5 Number of iterations with no improvement to wait before early stopping. .. versionadded:: 0.20 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. See :term:`the Glossary `. 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`` is a classification algorithm which shares the same underlying implementation with ``SGDClassifier``. In fact, ``Perceptron()`` is equivalent to `SGDClassifier(loss="perceptron", eta0=1, learning_rate="constant", penalty=None)`. Examples -------- >>> from sklearn.datasets import load_digits >>> from sklearn.linear_model import Perceptron >>> X, y = load_digits(return_X_y=True) >>> clf = Perceptron(tol=1e-3, random_state=0) >>> clf.fit(X, y) Perceptron(alpha=0.0001, class_weight=None, early_stopping=False, eta0=1.0, fit_intercept=True, max_iter=None, n_iter=None, n_iter_no_change=5, n_jobs=None, penalty=None, random_state=0, shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0, warm_start=False) >>> clf.score(X, y) # doctest: +ELLIPSIS 0.946... See also -------- SGDClassifier References ---------- https://en.wikipedia.org/wiki/Perceptron and references therein. g-C6?ig?g?ic)Cstt|jddd|d|ddd|d|d |d |d |d | d dd|d| d| d| ddd|d|d| d|dS(Ntlosst perceptrontpenaltytalphatl1_ratioit fit_intercepttmax_iterttoltshuffletverboset random_statet learning_ratetconstantteta0tearly_stoppingtvalidation_fractiontn_iter_no_changetpower_tg?t warm_startt class_weighttn_jobstn_iter(tsuperRt__init__(tselfRRRRR R R RRR RRRRRR((s>lib/python2.7/site-packages/sklearn/linear_model/perceptron.pyRs N(t__name__t __module__t__doc__tNonetTruetFalseR(((s>lib/python2.7/site-packages/sklearn/linear_model/perceptron.pyRs   N(tstochastic_gradientRR(((s>lib/python2.7/site-packages/sklearn/linear_model/perceptron.pyts