.. _`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'.
    If gamma is 'auto' then 1/n_features will be used instead.


  - *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*

    The seed of the pseudo random number generator to use when shuffling
    the data.  If int, random_state is the seed used by the random number
    generator; If RandomState instance, random_state is the random number
    generator; If None, the random number generator is the RandomState
    instance used by `np.random`.




: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

*Ports*:

    **Outputs**:

        :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'.
        If gamma is 'auto' then 1/n_features will be used instead.
    **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**
        The seed of the pseudo random number generator to use when shuffling
        the data.  If int, random_state is the seed used by the random number
        generator; If RandomState instance, random_state is the random number
        generator; If None, the random number generator is the RandomState
        instance used by `np.random`.

.. automodule:: node_svc

.. class:: OneClassSVM