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Diffstat (limited to 'docs/cls_gensvm.rst')
| -rw-r--r-- | docs/cls_gensvm.rst | 33 |
1 files changed, 26 insertions, 7 deletions
diff --git a/docs/cls_gensvm.rst b/docs/cls_gensvm.rst index fc19bf4..b4bc9a7 100644 --- a/docs/cls_gensvm.rst +++ b/docs/cls_gensvm.rst @@ -1,5 +1,5 @@ -.. 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) +.. 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 @@ -21,6 +21,10 @@ :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'. @@ -31,7 +35,7 @@ :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) + :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) @@ -42,6 +46,10 @@ :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) @@ -65,6 +73,10 @@ *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` @@ -75,7 +87,7 @@ - .. py:method:: GenSVM.fit(X, y, seed_V=None) + .. py:method:: GenSVM.fit(X, y, sample_weight=None, seed_V=None) :noindex: :module: gensvm.core @@ -88,6 +100,10 @@ :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. @@ -106,15 +122,18 @@ :rtype: object - .. py:method:: GenSVM.predict(X) + .. py:method:: GenSVM.predict(X, trainX=None) :noindex: :module: gensvm.core Predict the class labels on the given data - :param X: - :type X: array, shape = [n_samples, n_features] + :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** + :returns: **y_pred** -- Predicted class labels of the data in X. :rtype: array, shape = (n_samples, ) |