.. _`Support Vector Classifier`:

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

Support Vector Classifier
~~~~~~~~~~~~~~~~~~~~~~~~~

.. image:: svm.svg
   :width: 48


Support vector machine (SVM) based classifier

:Configuration:


  - *C*

    Penalty parameter C of the error term.


  - *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 pre-compute the kernel matrix from data matrices; that matrix
    should be an array of shape ``(n_samples, n_samples)``.


  - *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'.


  - *probability*

    Whether to enable probability estimates. This must be enabled prior
    to calling `fit`, and will slow down that method.


  - *shrinking*

    Whether to use the shrinking heuristic.


  - *tol*

    Tolerance for stopping criterion.


  - *class_weight*

    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 "balanced" mode uses the values of y to automatically adjust
    weights inversely proportional to class frequencies in the input data
    as ``n_samples / (n_classes * np.bincount(y))``


  - *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.


  - *n_support_*

    Number of support vectors for each class.


  - *dual_coef_*

    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_*

    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.


  - *intercept_*

    Constants in decision function.




:Inputs:


:Outputs:
    **model** : model
        Model

*Ports*:

    **Outputs**:

        :model: model

            Model

*Configuration*:

    **C**
        Penalty parameter C of the error term.
    **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 pre-compute the kernel matrix from data matrices; that matrix
        should be an array of shape ``(n_samples, n_samples)``.
    **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'.
    **probability**
        Whether to enable probability estimates. This must be enabled prior
        to calling `fit`, and will slow down that method.
    **shrinking**
        Whether to use the shrinking heuristic.
    **tol**
        Tolerance for stopping criterion.
    **class_weight**
        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 "balanced" mode uses the values of y to automatically adjust
        weights inversely proportional to class frequencies in the input data
        as ``n_samples / (n_classes * np.bincount(y))``
    **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:: SupportVectorClassifier