Class NuSVCScikitsLearnNode

Nu-Support Vector Classification.

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

Similar to SVC but uses a parameter to control the number of support
vectors.

The implementation is based on libsvm.

Read more in the :ref:`User Guide <svm_classification>`.

**Parameters**

nu : float, optional (default=0.5)
    An upper bound on the fraction of training errors and a lower
    bound of the fraction of support vectors. Should be in the
    interval (0, 1].

kernel : string, optional (default='rbf')
     Specifies the kernel type to be used in the algorithm.
     It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
     a callable.
     If none is given, 'rbf' will be used. If a callable is given it is
     used to precompute the kernel matrix.

degree : int, optional (default=3)
    Degree of the polynomial kernel function ('poly').
    Ignored by all other kernels.

gamma : float, optional (default='auto')
    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.
    If gamma is 'auto' then 1/n_features will be used instead.

coef0 : float, optional (default=0.0)
    Independent term in kernel function.
    It is only significant in 'poly' and 'sigmoid'.

probability : boolean, optional (default=False)
    Whether to enable probability estimates. This must be enabled prior
    to calling `fit`, and will slow down that method.

shrinking : boolean, optional (default=True)
    Whether to use the shrinking heuristic.

tol : float, optional (default=1e-3)
    Tolerance for stopping criterion.

cache_size : float, optional
    Specify the size of the kernel cache (in MB).

class_weight : {dict, 'auto'}, optional
    Set the parameter C of class i to class_weight[i]*C for
    SVC. If not given, all classes are supposed to have
    weight one. The 'auto' mode uses the values of y to
    automatically adjust weights inversely proportional to
    class frequencies.

verbose : bool, default: False
    Enable verbose output. Note that this setting takes advantage of a
    per-process runtime setting in libsvm that, if enabled, may not work
    properly in a multithreaded context.

max_iter : int, optional (default=-1)
    Hard limit on iterations within solver, or -1 for no limit.

decision_function_shape : 'ovo', 'ovr' or None, default=None
    Whether to return a one-vs-rest ('ovr') ecision function of shape
    (n_samples, n_classes) as all other classifiers, or the original
    one-vs-one ('ovo') decision function of libsvm which has shape
    (n_samples, n_classes * (n_classes - 1) / 2).
    The default of None will currently behave as 'ovo' for backward
    compatibility and raise a deprecation warning, but will change 'ovr'
    in 0.18.

    .. versionadded:: 0.17
       *decision_function_shape='ovr'* is recommended.

    .. versionchanged:: 0.17
       Deprecated *decision_function_shape='ovo' and None*.

random_state : int seed, RandomState instance, or None (default)
    The seed of the pseudo random number generator to use when
    shuffling the data for probability estimation.

**Attributes**

``support_`` : array-like, shape = [n_SV]
    Indices of support vectors.

``support_vectors_`` : array-like, shape = [n_SV, n_features]
    Support vectors.

``n_support_`` : array-like, dtype=int32, shape = [n_class]
    Number of support vectors for each class.

``dual_coef_`` : array, shape = [n_class-1, n_SV]
    Coefficients of the support vector in the decision function.
    For multiclass, coefficient for all 1-vs-1 classifiers.
    The layout of the coefficients in the multiclass case is somewhat
    non-trivial. See the section about multi-class classification in
    the SVM section of the User Guide for details.

``coef_`` : array, shape = [n_class-1, n_features]
    Weights assigned to the features (coefficients in the primal
    problem). This is only available in the case of a linear kernel.

    `coef_` is readonly property derived from `dual_coef_` and
    `support_vectors_`.

``intercept_`` : array, shape = [n_class * (n_class-1) / 2]
    Constants in decision function.

**Examples**

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm import NuSVC
>>> clf = NuSVC()
>>> clf.fit(X, y) #doctest: +NORMALIZE_WHITESPACE
NuSVC(cache_size=200, class_weight=None, coef0=0.0,
      decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
      max_iter=-1, nu=0.5, probability=False, random_state=None,
      shrinking=True, tol=0.001, verbose=False)
>>> print(clf.predict([[-0.8, -1]]))
[1]

See also

SVC
    Support Vector Machine for classification using libsvm.

LinearSVC
    Scalable linear Support Vector Machine for classification using
    liblinear.

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Perform classification on samples in X.

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

For an one-class model, +1 or -1 is returned.

Parameters

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

For kernel="precomputed", the expected shape of X is [n_samples_test, n_samples_train]

Returns

y_pred : array, shape (n_samples,)

Class labels for samples in X.

Overrides: ClassifierNode.label

Fit the SVM model according to the given training data.

This node has been automatically generated by wrapping the sklearn.svm.classes.NuSVC 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 vectors, where n_samples is the number of samples and n_features is the number of features. For kernel="precomputed", the expected shape of X is (n_samples, n_samples).

y : array-like, shape (n_samples,)

Target values (class labels in classification, real numbers in regression)

sample_weight : array-like, shape (n_samples,)

Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns

self : object

Returns self.

Notes

If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.

If X is a dense array, then the other methods will not support sparse matrices as input.

Overrides: Node.stop_training