.. _`One Class SVM`:

.. _`org.sysess.sympathy.machinelearning.one_class_svm`:

One Class SVM
~~~~~~~~~~~~~

.. image:: outliers.svg
   :width: 48


Unsupervised outlier detection based on support vector machines

:Configuration:


  - *kernel*

    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*

    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*

    Degree of the polynomial kernel function ('poly').
    Ignored by all other kernels.


  - *gamma*

    Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.

    Current default is 'auto' which uses 1 / n_features,
    if ``gamma='scale'`` is passed then it uses 1 / (n_features * X.var())
    as value of gamma. The current default of gamma, 'auto', will change
    to 'scale' in version 0.22. 'auto_deprecated', a deprecated version of
    'auto' is used as a default indicating that no explicit value of gamma
    was passed.


  - *coef0*

    Independent term in kernel function.
    It is only significant in 'poly' and 'sigmoid'.


  - *shrinking*

    Whether to use the shrinking heuristic.


  - *tol*

    Tolerance for stopping criterion.


  - *max_iter*

    Hard limit on iterations within solver, or -1 for no limit.


  - *random_state*

    Ignored.

    .. deprecated:: 0.20
       ``random_state`` has been deprecated in 0.20 and will be removed in
       0.22.




:Attributes:


  - *support_*

    Indices of support vectors.


  - *support_vectors_*

    Support vectors.


  - *dual_coef_*

    Coefficients of the support vectors in the decision function.


  - *coef_*

    Coefficients of the support vectors in the decision function.


  - *intercept_*

    Constant in the decision function.




:Inputs:


:Outputs:
    **model** : model
        Model

*Output ports*:
    :model: model

        Model

*Configuration*:
    **kernel**
        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**
        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**
        Degree of the polynomial kernel function ('poly').
        Ignored by all other kernels.
    **gamma**
        Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.

        Current default is 'auto' which uses 1 / n_features,
        if ``gamma='scale'`` is passed then it uses 1 / (n_features * X.var())
        as value of gamma. The current default of gamma, 'auto', will change
        to 'scale' in version 0.22. 'auto_deprecated', a deprecated version of
        'auto' is used as a default indicating that no explicit value of gamma
        was passed.
    **coef0**
        Independent term in kernel function.
        It is only significant in 'poly' and 'sigmoid'.
    **shrinking**
        Whether to use the shrinking heuristic.
    **tol**
        Tolerance for stopping criterion.
    **max_iter**
        Hard limit on iterations within solver, or -1 for no limit.
    **random_state**
        Ignored.

        .. deprecated:: 0.20
           ``random_state`` has been deprecated in 0.20 and will be removed in
           0.22.

.. automodule:: node_svc

.. class:: OneClassSVM