Update documentation

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, )