Class QuadraticDiscriminantAnalysisScikitsLearnNode

Quadratic Discriminant Analysis

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

A classifier with a quadratic decision boundary, generated
by fitting class conditional densities to the data
and using Bayes' rule.

The model fits a Gaussian density to each class.

.. versionadded:: 0.17
   *QuadraticDiscriminantAnalysis*

.. versionchanged:: 0.17
   Deprecated :class:`qda.QDA` have been moved to *QuadraticDiscriminantAnalysis*.

**Parameters**

priors : array, optional, shape = [n_classes]
    Priors on classes

reg_param : float, optional
    Regularizes the covariance estimate as
    ``(1-reg_param)*Sigma + reg_param*np.eye(n_features)``

**Attributes**

``covariances_`` : list of array-like, shape = [n_features, n_features]
    Covariance matrices of each class.

``means_`` : array-like, shape = [n_classes, n_features]
    Class means.

``priors_`` : array-like, shape = [n_classes]
    Class priors (sum to 1).

``rotations_`` : list of arrays
    For each class k an array of shape [n_features, n_k], with
    ``n_k = min(n_features, number of elements in class k)``
    It is the rotation of the Gaussian distribution, i.e. its
    principal axis.

``scalings_`` : list of arrays
    For each class k an array of shape [n_k]. It contains the scaling
    of the Gaussian distributions along its principal axes, i.e. the
    variance in the rotated coordinate system.

store_covariances : boolean
    If True the covariance matrices are computed and stored in the
    `self.covariances_` attribute.

    .. versionadded:: 0.17

tol : float, optional, default 1.0e-4
    Threshold used for rank estimation.

    .. versionadded:: 0.17

**Examples**

>>> from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> y = np.array([1, 1, 1, 2, 2, 2])
>>> clf = QuadraticDiscriminantAnalysis()
>>> clf.fit(X, y)
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0,
                              store_covariances=False, tol=0.0001)
>>> print(clf.predict([[-0.8, -1]]))
[1]

See also

sklearn.discriminant_analysis.LinearDiscriminantAnalysis: Linear
    Discriminant Analysis

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Perform classification on an array of test vectors X.

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

The predicted class C for each sample in X is returned.

Parameters

X : array-like, shape = [n_samples, n_features]

Returns

C : array, shape = [n_samples]

Overrides: ClassifierNode.label

Fit the model according to the given training data and parameters.

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

    .. versionchanged:: 0.17
       Deprecated *store_covariance* have been moved to main constructor.

    .. versionchanged:: 0.17
       Deprecated *tol* have been moved to main constructor.

**Parameters**

X : array-like, shape = [n_samples, n_features]
    Training vector, where n_samples in the number of samples and
    n_features is the number of features.

y : array, shape = [n_samples]
    Target values (integers)

Overrides: Node.stop_training