Class PerceptronScikitsLearnNode

Perceptron

This node has been automatically generated by wrapping the ``sklearn.linear_model.perceptron.Perceptron`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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.

n_iter : int, optional
    The number of passes over the training data (aka epochs).
    Defaults to 5.

shuffle : bool, optional, default True
    Whether or not the training data should be shuffled after each epoch.

random_state : int seed, RandomState instance, or None (default)
    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 "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.

**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.

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict class labels for samples in X.

This node has been automatically generated by wrapping the sklearn.linear_model.perceptron.Perceptron class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Samples.

Returns

C : array, shape = [n_samples]

Predicted class label per sample.

Overrides: ClassifierNode.label

Fit linear model with Stochastic Gradient Descent.

This node has been automatically generated by wrapping the sklearn.linear_model.perceptron.Perceptron class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Training data

y : numpy array, shape (n_samples,)

Target values

coef_init : array, shape (n_classes, n_features)

The initial coefficients to warm-start the optimization.

intercept_init : array, shape (n_classes,)

The initial intercept to warm-start the optimization.

sample_weight : array-like, shape (n_samples,), optional

Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the contructor) if class_weight is specified

Returns

self : returns an instance of self.

Overrides: Node.stop_training