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diff --git a/docs/cls_gensvm.txt b/docs/cls_gensvm.txt deleted file mode 100644 index b4bc9a7..0000000 --- a/docs/cls_gensvm.txt +++ /dev/null @@ -1,139 +0,0 @@ - -.. py:class:: GenSVM(p=1.0, lmd=1e-05, kappa=0.0, epsilon=1e-06, weights='unit', kernel='linear', gamma='auto', coef=1.0, 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). - - It is also possible to provide an explicit vector of sample weights - through the :func:`~GenSVM.fit` method. If so, it will override the - setting provided here. - :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=1.0) - :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 random_state: The seed for the random number generation used for initialization where - necessary. See the documentation of - ``sklearn.utils.check_random_state`` for more info. - :type random_state: None, int, instance of RandomState - :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 - - .. attribute:: SVs_ - - *array, shape = [n_observations, ]* -- Index vector that marks the support vectors (1 = SV, 0 = no SV) - - .. 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, sample_weight=None, 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 sample_weight: Array of weights that are assigned to individual samples. If not - provided, then the weight specification in the constructor is used - ('unit' or 'group'). - :type sample_weight: 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, trainX=None) - :noindex: - :module: gensvm.core - - Predict the class labels on the given data - - :param X: Data for which to predict the labels - :type X: array, shape = [n_test_samples, n_features] - :param trainX: Only for nonlinear prediction with kernels: the training data used - to train the model. - :type trainX: array, shape = [n_train_samples, n_features] - - :returns: **y_pred** -- Predicted class labels of the data in X. - :rtype: array, shape = (n_samples, ) - |