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.. py:class:: GenSVM(p=1.0, lmd=1e-05, kappa=0.0, epsilon=1e-06, weights='unit', kernel='linear', gamma='auto', coef=0.0, degree=2.0, kernel_eigen_cutoff=1e-08, verbose=0, random_state=None, max_iter=100000000.0)
:noindex:
:module: gensvm.core
Generalized Multiclass Support Vector Machine Classification.
This class implements the basic GenSVM classifier. GenSVM is a generalized
multiclass SVM which is flexible in the weighting of misclassification
errors. It is this flexibility that makes it perform well on diverse
datasets.
The :func:`~GenSVM.fit` and :func:`~GenSVM.predict` methods of this class
use the GenSVM C library for the actual computations.
:param p: Parameter for the L_p norm of the loss function (1.0 <= p <= 2.0)
:type p: float, optional (default=1.0)
:param lmd: Parameter for the regularization term of the loss function (lmd > 0)
:type lmd: float, optional (default=1e-5)
:param kappa: Parameter for the hinge function in the loss function (kappa > -1.0)
:type kappa: float, optional (default=0.0)
:param weights: Type of sample weights to use. Options are 'unit' for unit weights and
'group' for group size correction weights (equation 4 in the paper).
:type weights: string, optional (default='unit')
:param kernel: Specify the kernel type to use in the classifier. It must be one of
'linear', 'poly', 'rbf', or 'sigmoid'.
:type kernel: string, optional (default='linear')
:param gamma: Kernel parameter for the rbf, poly, and sigmoid kernel. If gamma is
'auto' then 1/n_features will be used. See `Kernels in GenSVM
<gensvm_kernels_>`_ for the exact implementation of the kernels.
:type gamma: float, optional (default='auto')
:param coef: Kernel parameter for the poly and sigmoid kernel. See `Kernels in
GenSVM <gensvm_kernels_>`_ for the exact implementation of the kernels.
:type coef: float, optional (default=0.0)
:param degree: Kernel parameter for the poly kernel. See `Kernels in GenSVM
<gensvm_kernels_>`_ for the exact implementation of the kernels.
:type degree: float, optional (default=2.0)
:param kernel_eigen_cutoff: Cutoff point for the reduced eigendecomposition used with nonlinear
GenSVM. Eigenvectors for which the ratio between their corresponding
eigenvalue and the largest eigenvalue is smaller than the cutoff will
be dropped.
:type kernel_eigen_cutoff: float, optional (default=1e-8)
:param verbose: Enable verbose output
:type verbose: int, (default=0)
:param max_iter: The maximum number of iterations to be run.
:type max_iter: int, (default=1e8)
.. attribute:: coef_
*array, shape = [n_features, n_classes-1]* -- Weights assigned to the features (coefficients in the primal problem)
.. attribute:: intercept_
*array, shape = [n_classes-1]* -- Constants in the decision function
.. attribute:: combined_coef_
*array, shape = [n_features+1, n_classes-1]* -- Combined weights matrix for the seed_V parameter to the fit method
.. attribute:: n_iter_
*int* -- The number of iterations that were run during training.
.. attribute:: n_support_
*int* -- The number of support vectors that were found
.. seealso::
:class:`.GenSVMGridSearchCV`
Helper class to run an efficient grid search for GenSVM.
.. _gensvm_kernels:
https://gensvm.readthedocs.io/en/latest/#kernels-in-gensvm
.. py:method:: GenSVM.fit(X, y, seed_V=None)
:noindex:
:module: gensvm.core
Fit the GenSVM model on the given data
The model can be fit with or without a seed matrix (``seed_V``). This
can be used to provide warm starts for the algorithm.
:param X: The input data. It is expected that only numeric data is given.
:type X: array, shape = (n_observations, n_features)
:param y: The label vector, labels can be numbers or strings.
:type y: array, shape = (n_observations, )
:param seed_V: Seed coefficient array to use as a warm start for the optimization.
It can for instance be the :attr:`combined_coef_
<.GenSVM.combined_coef_>` attribute of a different GenSVM model.
This is only supported for the linear kernel.
NOTE: the size of the seed_V matrix is ``n_features+1`` by
``n_classes - 1``. The number of columns of ``seed_V`` is leading
for the number of classes in the model. For example, if ``y``
contains 3 different classes and ``seed_V`` has 3 columns, we
assume that there are actually 4 classes in the problem but one
class is just represented in this training data. This can be useful
for problems were a certain class has only a few samples.
:type seed_V: array, shape = (n_features+1, n_classes-1), optional
:returns: **self** -- Returns self.
:rtype: object
.. py:method:: GenSVM.predict(X)
:noindex:
:module: gensvm.core
Predict the class labels on the given data
:param X:
:type X: array, shape = [n_samples, n_features]
:returns: **y_pred**
:rtype: array, shape = (n_samples, )
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