Class OneClassSVMScikitsLearnNode

Unsupervised Outlier Detection.

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

Estimate the support of a high-dimensional distribution.

The implementation is based on libsvm.

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

**Parameters**

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.

nu : float, optional
    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]. By default 0.5
    will be taken.

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'.

tol : float, optional
    Tolerance for stopping criterion.

shrinking : boolean, optional
    Whether to use the shrinking heuristic.

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

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.

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 = [nSV, n_features]
    Support vectors.

``dual_coef_`` : array, shape = [n_classes-1, n_SV]
    Coefficients of the support vectors in the decision function.

``coef_`` : array, shape = [n_classes-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_classes-1]
    Constants in decision function.

Overrides: object.__init__

Perform regression on samples in X.

This node has been automatically generated by wrapping the sklearn.svm.classes.OneClassSVM 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,)

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Detects the soft boundary of the set of samples X.

This node has been automatically generated by wrapping the sklearn.svm.classes.OneClassSVM 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)

Set of samples, where n_samples is the number of samples and n_features is the number of features.

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 is not a C-ordered contiguous array it is copied.

Overrides: Node.stop_training