val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?priors:Ndarray.t ->
?reg_param:float ->
?store_covariance:bool ->
?tol:float ->
unit ->
tQuadratic Discriminant Analysis
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*
Read more in the :ref:`User Guide <lda_qda>`.
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)``
store_covariance : boolean If True the covariance matrices are computed and stored in the `self.covariance_` attribute.
.. versionadded:: 0.17
tol : float, optional, default 1.0e-4 Threshold used for rank estimation.
.. versionadded:: 0.17
Attributes ---------- covariance_ : list of array-like of shape (n_features, n_features) Covariance matrices of each class.
means_ : array-like of shape (n_classes, n_features) Class means.
priors_ : array-like of 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.
classes_ : array-like, shape (n_classes,) Unique class labels.
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) QuadraticDiscriminantAnalysis() >>> print(clf.predict([-0.8, -1])) 1
See also -------- sklearn.discriminant_analysis.LinearDiscriminantAnalysis: Linear Discriminant Analysis
Apply decision function to an array of samples.
Parameters ---------- X : array-like of shape (n_samples, n_features) Array of samples (test vectors).
Returns ------- C : ndarray of shape (n_samples,) or (n_samples, n_classes) Decision function values related to each class, per sample. In the two-class case, the shape is n_samples,, giving the log likelihood ratio of the positive class.
Fit the model according to the given training data and parameters.
.. versionchanged:: 0.19 ``store_covariances`` has been moved to main constructor as ``store_covariance``
.. versionchanged:: 0.19 ``tol`` has been moved to main constructor.
Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features.
y : array, shape = n_samples Target values (integers)
val get_params : ?deep:bool -> t -> Py.Object.tGet parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Perform classification on an array of test vectors X.
The predicted class C for each sample in X is returned.
Parameters ---------- X : array-like of shape (n_samples, n_features)
Returns ------- C : ndarray of shape (n_samples,)
Return posterior probabilities of classification.
Parameters ---------- X : array-like of shape (n_samples, n_features) Array of samples/test vectors.
Returns ------- C : ndarray of shape (n_samples, n_classes) Posterior log-probabilities of classification per class.
Return posterior probabilities of classification.
Parameters ---------- X : array-like of shape (n_samples, n_features) Array of samples/test vectors.
Returns ------- C : ndarray of shape (n_samples, n_classes) Posterior probabilities of classification per class.
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val covariance_ : t -> Py.Object.tAttribute covariance_: see constructor for documentation
val rotations_ : t -> Py.Object.tAttribute rotations_: see constructor for documentation
val scalings_ : t -> Py.Object.tAttribute scalings_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.