Merge branch 'docs'

12 files changed, 103 insertions, 478 deletions

diff --git a/docs/auto_functions.rst b/docs/auto_functions.rst
index 2a6596f..de8438d 100644
--- a/docs/auto_functions.rst
+++ b/docs/auto_functions.rst
@@ -1,3 +1,5 @@
+Parameter Grids
+===============
 
 .. py:function:: load_grid_tiny()
    :noindex:
diff --git a/docs/changelog.rst b/docs/changelog.rst
new file mode 100644
index 0000000..446252e
--- /dev/null
+++ b/docs/changelog.rst
@@ -0,0 +1 @@
+.. include:: ./CHANGELOG.rst
diff --git a/docs/cls_gensvm.rst b/docs/cls_gensvm.rst
index b4bc9a7..80a2b25 100644
--- a/docs/cls_gensvm.rst
+++ b/docs/cls_gensvm.rst
@@ -1,3 +1,5 @@
+GenSVM
+======
 
 .. 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:
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, )
-      
diff --git a/docs/cls_gridsearch.rst b/docs/cls_gridsearch.rst
index 6a2c05e..13cce92 100644
--- a/docs/cls_gridsearch.rst
+++ b/docs/cls_gridsearch.rst
@@ -1,3 +1,5 @@
+GenSVMGridSearchCV
+==================
 
 .. py:class:: GenSVMGridSearchCV(param_grid='tiny', scoring=None, iid=True, cv=None, refit=True, verbose=0, return_train_score=True)
    :noindex:
diff --git a/docs/cls_gridsearch.txt b/docs/cls_gridsearch.txt
deleted file mode 100644
index 6a2c05e..0000000
--- a/docs/cls_gridsearch.txt
+++ /dev/null
@@ -1,285 +0,0 @@
-
-.. py:class:: GenSVMGridSearchCV(param_grid='tiny', scoring=None, iid=True, cv=None, refit=True, verbose=0, return_train_score=True)
-   :noindex:
-   :module: gensvm.gridsearch
-
-   GenSVM cross validated grid search
-   
-   This class implements efficient GenSVM grid search with cross validation.
-   One of the strong features of GenSVM is that seeding the classifier
-   properly can greatly reduce total training time. This class ensures that
-   the grid search is done in the most efficient way possible.
-   
-   The implementation of this class is based on the `GridSearchCV`_ class in
-   scikit-learn. The documentation of the various parameters is therefore
-   mostly the same. This is done to provide the user with a familiar and
-   easy-to-use interface to doing a grid search with GenSVM. A separate class
-   was needed to benefit from the fast low-level C implementation of grid
-   search in the GenSVM library.
-   
-   :param param_grid: If a string, it must be either 'tiny', 'small', or 'full' to load the
-                      predefined parameter grids (see the functions :func:`load_grid_tiny`,
-                      :func:`load_grid_small`, and :func:`load_grid_full`).
-   
-                      Otherwise, a dictionary of parameter names (strings) as keys and lists
-                      of parameter settings to evaluate as values, or a list of such dicts.
-                      The GenSVM model will be evaluated at all combinations of the
-                      parameters.
-   :type param_grid: string, dict, or list of dicts
-   :param scoring: A single string (see :ref:`scoring_parameter`) or a callable (see
-                   :ref:`scoring`) to evaluate the predictions on the test set.
-   
-                   For evaluating multiple metrics, either give a list of (unique) strings
-                   or a dict with names as keys and callables as values.
-   
-                   NOTE that when using custom scorers, each scorer should return a single
-                   value. Metric functions returning a list/array of values can be wrapped
-                   into multiple scorers that return one value each.
-   
-                   If None, the `accuracy_score`_ is used.
-   :type scoring: string, callable, list/tuple, dict or None
-   :param iid: If True, the data is assumed to be identically distributed across the
-               folds, and the loss minimized is the total loss per sample and not the
-               mean loss across the folds.
-   :type iid: boolean, default=True
-   :param cv: Determines the cross-validation splitting strategy. Possible inputs for
-              cv are:
-   
-                - None, to use the default 5-fold cross validation,
-                - integer, to specify the number of folds in a `(Stratified)KFold`,
-                - An object to be used as a cross-validation generator.
-                - An iterable yielding train, test splits.
-   
-              For integer/None inputs, :class:`StratifiedKFold
-              <sklearn.model_selection.StratifiedKFold>` is used.  In all other
-              cases, :class:`KFold <sklearn.model_selection.KFold>` is used.
-   
-              Refer to the `scikit-learn User Guide on cross validation`_ for the
-              various strategies that can be used here.
-   
-              NOTE: At the moment, the ShuffleSplit and StratifiedShuffleSplit are
-              not supported in this class. If you need these, you can use the GenSVM
-              classifier directly with the GridSearchCV object from scikit-learn.
-              (these methods require significant changes in the low-level library
-              before they can be supported).
-   :type cv: int, cross-validation generator or an iterable, optional
-   :param refit: Refit the GenSVM estimator with the best found parameters on the whole
-                 dataset.
-   
-                 For multiple metric evaluation, this needs to be a string denoting the
-                 scorer to be used to find the best parameters for refitting the
-                 estimator at the end.
-   
-                 The refitted estimator is made available at the `:attr:best_estimator_
-                 <.GenSVMGridSearchCV.best_estimator_>` attribute and allows the user to
-                 use the :func:`~GenSVMGridSearchCV.predict` method directly on this
-                 :class:`.GenSVMGridSearchCV` instance.
-   
-                 Also for multiple metric evaluation, the attributes :attr:`best_index_
-                 <.GenSVMGridSearchCV.best_index_>`, :attr:`best_score_
-                 <.GenSVMGridSearchCV.best_score_>` and :attr:`best_params_
-                 <.GenSVMGridSearchCV:best_params_>` will only be available if ``refit``
-                 is set and all of them will be determined w.r.t this specific scorer.
-   
-                 See ``scoring`` parameter to know more about multiple metric
-                 evaluation.
-   :type refit: boolean, or string, default=True
-   :param verbose: Controls the verbosity: the higher, the more messages.
-   :type verbose: integer
-   :param return_train_score: If ``False``, the :attr:`cv_results_ <.GenSVMGridSearchCV.cv_results_>`
-                              attribute will not include training scores.
-   :type return_train_score: boolean, default=True
-   
-   .. rubric:: Examples
-   
-   >>> from gensvm import GenSVMGridSearchCV
-   >>> from sklearn.datasets import load_iris
-   >>> iris = load_iris()
-   >>> param_grid = {'p': [1.0, 2.0], 'kappa': [-0.9, 0.0, 1.0]}
-   >>> clf = GenSVMGridSearchCV(param_grid)
-   >>> clf.fit(iris.data, iris.target)
-   GenSVMGridSearchCV(cv=None, iid=True,
-         param_grid={'p': [1.0, 2.0], 'kappa': [-0.9, 0.0, 1.0]},
-         refit=True, return_train_score=True, scoring=None, verbose=0)
-   
-   .. attribute:: cv_results_
-   
-      *dict of numpy (masked) ndarrays* -- A dict with keys as column headers and values as columns, that can be
-      imported into a pandas `DataFrame`_.
-   
-      For instance the below given table
-   
-      +------------+-----------+------------+-----------------+---+---------+
-      |param_kernel|param_gamma|param_degree|split0_test_score|...|rank_t...|
-      +============+===========+============+=================+===+=========+
-      |  'poly'    |     --    |      2     |        0.8      |...|    2    |
-      +------------+-----------+------------+-----------------+---+---------+
-      |  'poly'    |     --    |      3     |        0.7      |...|    4    |
-      +------------+-----------+------------+-----------------+---+---------+
-      |  'rbf'     |     0.1   |     --     |        0.8      |...|    3    |
-      +------------+-----------+------------+-----------------+---+---------+
-      |  'rbf'     |     0.2   |     --     |        0.9      |...|    1    |
-      +------------+-----------+------------+-----------------+---+---------+
-   
-      will be represented by a ``cv_results_`` dict of::
-   
-          {
-          'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
-                                       mask = [False False False False]...)
-          'param_gamma': masked_array(data = [-- -- 0.1 0.2],
-                                      mask = [ True  True False False]...),
-          'param_degree': masked_array(data = [2.0 3.0 -- --],
-                                       mask = [False False  True  True]...),
-          'split0_test_score'  : [0.8, 0.7, 0.8, 0.9],
-          'split1_test_score'  : [0.82, 0.5, 0.7, 0.78],
-          'mean_test_score'    : [0.81, 0.60, 0.75, 0.82],
-          'std_test_score'     : [0.02, 0.01, 0.03, 0.03],
-          'rank_test_score'    : [2, 4, 3, 1],
-          'split0_train_score' : [0.8, 0.9, 0.7],
-          'split1_train_score' : [0.82, 0.5, 0.7],
-          'mean_train_score'   : [0.81, 0.7, 0.7],
-          'std_train_score'    : [0.03, 0.03, 0.04],
-          'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
-          'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
-          'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
-          'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
-          'params'             : [{'kernel': 'poly', 'degree': 2}, ...],
-          }
-   
-      NOTE:
-   
-      The key ``'params'`` is used to store a list of parameter settings
-      dicts for all the parameter candidates.
-   
-      The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
-      ``std_score_time`` are all in seconds.
-   
-      For multi-metric evaluation, the scores for all the scorers are
-      available in the :attr:`cv_results_ <.GenSVMGridSearchCV.cv_results_>`
-      dict at the keys ending with that scorer's name (``'_<scorer_name>'``)
-      instead of ``'_score'`` shown above. ('split0_test_precision',
-      'mean_train_precision' etc.)
-   
-   .. attribute:: best_estimator_
-   
-      *estimator or dict* -- Estimator that was chosen by the search, i.e. estimator which gave
-      highest score (or smallest loss if specified) on the left out data. Not
-      available if ``refit=False``.
-   
-      See ``refit`` parameter for more information on allowed values.
-   
-   .. attribute:: best_score_
-   
-      *float* -- Mean cross-validated score of the best_estimator
-   
-      For multi-metric evaluation, this is present only if ``refit`` is
-      specified.
-   
-   .. attribute:: best_params_
-   
-      *dict* -- Parameter setting that gave the best results on the hold out data.
-   
-      For multi-metric evaluation, this is present only if ``refit`` is
-      specified.
-   
-   .. attribute:: best_index_
-   
-      *int* -- The index (of the ``cv_results_`` arrays) which corresponds to the best
-      candidate parameter setting.
-   
-      The dict at ``search.cv_results_['params'][search.best_index_]`` gives
-      the parameter setting for the best model, that gives the highest mean
-      score (``search.best_score_``).
-   
-      For multi-metric evaluation, this is present only if ``refit`` is
-      specified.
-   
-   .. attribute:: scorer_
-   
-      *function or a dict* -- Scorer function used on the held out data to choose the best parameters
-      for the model.
-   
-      For multi-metric evaluation, this attribute holds the validated
-      ``scoring`` dict which maps the scorer key to the scorer callable.
-   
-   .. attribute:: n_splits_
-   
-      *int* -- The number of cross-validation splits (folds/iterations).
-   
-   .. rubric:: Notes
-   
-   The parameters selected are those that maximize the score of the left out
-   data, unless an explicit score is passed in which case it is used instead.
-   
-   .. seealso::
-   
-      `ParameterGrid`_:
-          Generates all the combinations of a hyperparameter grid.
-   
-      :class:`.GenSVM`:
-          The GenSVM classifier
-   
-      .. _GridSearchCV:
-          http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html
-      .. _accuracy_score:
-          http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html
-      .. _scikit-learn User Guide on cross validation:
-          http://scikit-learn.org/stable/modules/cross_validation.html
-   
-      .. _ParameterGrid:
-          http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.ParameterGrid.html
-      .. _DataFrame:
-          https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html
-   
-   
-   .. py:method:: GenSVMGridSearchCV.fit(X, y, groups=None)
-      :noindex:
-      :module: gensvm.gridsearch
-   
-      Run GenSVM grid search with all sets of parameters
-      
-      :param X: Training data, where n_samples is the number of observations and
-                n_features is the number of features.
-      :type X: array-like, shape = (n_samples, n_features)
-      :param y: Target vector for the training data.
-      :type y: array-like, shape = (n_samples, )
-      :param groups: Group labels for the samples used while splitting the dataset into
-                     train/test sets.
-      :type groups: array-like, with shape (n_samples, ), optional
-      
-      :returns: **self** -- Return self.
-      :rtype: object
-      
-   
-   .. py:method:: GenSVMGridSearchCV.predict(X, trainX=None)
-      :noindex:
-      :module: gensvm.gridsearch
-   
-      Predict the class labels on the test data
-      
-      :param X: Test data, where n_samples is the number of observations and
-                n_features is the number of features.
-      :type X: array-like, shape = (n_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-like, shape = (n_samples, )
-      
-   
-   .. py:method:: GenSVMGridSearchCV.score(X, y)
-      :noindex:
-      :module: gensvm.gridsearch
-   
-      Compute the score on the test data given the true labels
-      
-      :param X: Test data, where n_samples is the number of observations and
-                n_features is the number of features.
-      :type X: array-like, shape = (n_samples, n_features)
-      :param y: True labels for the test data.
-      :type y: array-like, shape = (n_samples, )
-      
-      :returns: **score**
-      :rtype: float
-      
diff --git a/docs/generate_autodocs.py b/docs/generate_autodocs.py
index a0544ef..a2f7e49 100644
--- a/docs/generate_autodocs.py
+++ b/docs/generate_autodocs.py
@@ -42,11 +42,11 @@ FULL_NAMES = {
 }
 
 OUTPUT_FILES = {
-    "GenSVMGridSearchCV": os.path.join(DOCDIR, "cls_gridsearch.txt"),
-    "GenSVM": os.path.join(DOCDIR, "cls_gensvm.txt"),
-    "load_grid_tiny": os.path.join(DOCDIR, "auto_functions.txt"),
-    "load_grid_small": os.path.join(DOCDIR, "auto_functions.txt"),
-    "load_grid_full": os.path.join(DOCDIR, "auto_functions.txt"),
+    "GenSVMGridSearchCV": os.path.join(DOCDIR, "cls_gridsearch.rst"),
+    "GenSVM": os.path.join(DOCDIR, "cls_gensvm.rst"),
+    "load_grid_tiny": os.path.join(DOCDIR, "auto_functions.rst"),
+    "load_grid_small": os.path.join(DOCDIR, "auto_functions.rst"),
+    "load_grid_full": os.path.join(DOCDIR, "auto_functions.rst"),
 }
 
 
@@ -83,11 +83,13 @@ def generate_class_autodoc(app, cls):
     documenter.generate(all_members=True)
 
     with open(OUTPUT_FILES[cls], "w") as fid:
+        fid.write(cls + '\n')
+        fid.write(''.join(['=']*len(cls)) + '\n')
         for line in ad.result:
             fid.write(line + "\n")
 
 
-def generate_func_autodoc(app, func):
+def generate_func_autodoc(app, func, add_title=True):
     ad = AutoDirective(
         name="autofunc",
         arguments=[FULL_NAMES[func]],
@@ -108,7 +110,11 @@ def generate_func_autodoc(app, func):
     documenter = FunctionDocumenter(ad, ad.arguments[0])
     documenter.generate(all_members=True)
 
+    title = 'Parameter Grids'
     with open(OUTPUT_FILES[func], "a") as fid:
+        if add_title:
+            fid.write(title + '\n')
+            fid.write(''.join(['=']*len(title)) + '\n')
         for line in ad.result:
             fid.write(line + "\n")
 
@@ -121,8 +127,10 @@ def main():
     app = load_app()
     for cls in CLASSES:
         generate_class_autodoc(app, cls)
+    add_title = True
     for func in FUNCTIONS:
-        generate_func_autodoc(app, func)
+        generate_func_autodoc(app, func, add_title=add_title)
+        add_title = False
 
 
 if __name__ == "__main__":
diff --git a/docs/index.rst b/docs/index.rst
index 6845d73..55b6a10 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -3,29 +3,24 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-.. toctree::
-
-.. include:: ./README.rst
-
-Classes
--------
-
-GenSVM
-^^^^^^
-
-.. include:: ./cls_gensvm.txt
+.. include:: ./readme.rst
 
-GenSVMGridSearchCV
-^^^^^^^^^^^^^^^^^^
-
-.. include:: ./cls_gridsearch.txt
+.. toctree::
+   :maxdepth: 2
 
-Functions
----------
+   readme
 
-.. include:: ./auto_functions.txt
+.. toctree::
+   :caption: API Documentation
+   :maxdepth: 2
 
-.. include:: ./kernels.txt
+   cls_gensvm
+   cls_gridsearch
+   auto_functions
+   kernels
 
-.. include:: ./CHANGELOG.rst
+.. toctree::
+   :caption: Further Documentation
+   :maxdepth: 2
 
+   changelog
diff --git a/docs/kernels.txt b/docs/kernels.rst
index 479b6c0..479b6c0 100644
--- a/docs/kernels.txt
+++ b/docs/kernels.rst
diff --git a/docs/readme.rst b/docs/readme.rst
new file mode 100644
index 0000000..d62c29e
--- /dev/null
+++ b/docs/readme.rst
@@ -0,0 +1 @@
+.. include:: ./README.rst
diff --git a/gensvm/core.py b/gensvm/core.py
index 45d59ad..169a30c 100644
--- a/gensvm/core.py
+++ b/gensvm/core.py
@@ -104,7 +104,7 @@ class GenSVM(BaseEstimator, ClassifierMixin):
     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 
+    The :meth:`~GenSVM.fit` and :meth:`~GenSVM.predict` methods of this class 
     use the GenSVM C library for the actual computations.
 
     Parameters
@@ -123,7 +123,7 @@ class GenSVM(BaseEstimator, ClassifierMixin):
         '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 
+        through the :meth:`~GenSVM.fit` method. If so, it will override the 
         setting provided here.
 
     kernel : string, optional (default='linear')
@@ -183,7 +183,7 @@ class GenSVM(BaseEstimator, ClassifierMixin):
 
     See Also
     --------
-    :class:`.GenSVMGridSearchCV`:
+    :class:`~.gridsearch.GenSVMGridSearchCV`:
         Helper class to run an efficient grid search for GenSVM.
 
 
@@ -257,8 +257,8 @@ class GenSVM(BaseEstimator, ClassifierMixin):
     def fit(self, X, y, sample_weight=None, seed_V=None):
         """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.
+        The model can be fit with or without a seed matrix (`seed_V`). This can 
+        be used to provide warm starts for the algorithm.
 
         Parameters
         ----------
@@ -280,14 +280,13 @@ class GenSVM(BaseEstimator, ClassifierMixin):
             <.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.
-
+            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.
 
         Returns
         -------
@@ -354,7 +353,13 @@ class GenSVM(BaseEstimator, ClassifierMixin):
                     )
                 )
 
-        self.coef_, self.intercept_, self.n_iter_, self.n_support_, self.SVs_ = _fit_gensvm(
+        (
+            self.coef_,
+            self.intercept_,
+            self.n_iter_,
+            self.n_support_,
+            self.SVs_,
+        ) = _fit_gensvm(
             X,
             y,
             n_class,
diff --git a/gensvm/gridsearch.py b/gensvm/gridsearch.py
index 22125a4..550ae64 100644
--- a/gensvm/gridsearch.py
+++ b/gensvm/gridsearch.py
@@ -65,7 +65,7 @@ def _validate_param_grid(param_grid):
     """Check if the parameter values are valid
 
     This basically does the same checks as in the constructor of the 
-    :class:`core.GenSVM` class, but for the entire parameter grid.
+    :class:`~.core.GenSVM` class, but for the entire parameter grid.
 
     """
     # the conditions that the parameters must satisfy
@@ -169,16 +169,24 @@ def _format_results(
             score_time = 0
 
             if return_train_score:
-                train_pred = predictions[cv_idx != test_idx,]
-                y_train = true_y[cv_idx != test_idx,]
+                train_pred = predictions[
+                    cv_idx != test_idx,
+                ]
+                y_train = true_y[
+                    cv_idx != test_idx,
+                ]
                 train_score, score_t = _wrap_score(
                     train_pred, y_train, scorers, is_multimetric
                 )
                 score_time += score_t
                 ret.append(train_score)
 
-            test_pred = predictions[cv_idx == test_idx,]
-            y_test = true_y[cv_idx == test_idx,]
+            test_pred = predictions[
+                cv_idx == test_idx,
+            ]
+            y_test = true_y[
+                cv_idx == test_idx,
+            ]
             test_score, score_t = _wrap_score(
                 test_pred, y_test, scorers, is_multimetric
             )
@@ -232,7 +240,7 @@ def _fit_grid_gensvm(
     Returns
     -------
     cv_results_ : dict
-        The cross validation results. See :func:`~GenSVMGridSearchCV.fit`.
+        The cross validation results. See :meth:`~GenSVMGridSearchCV.fit`.
 
     """
 
@@ -349,7 +357,7 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin):
 
         The refitted estimator is made available at the `:attr:best_estimator_ 
         <.GenSVMGridSearchCV.best_estimator_>` attribute and allows the user to 
-        use the :func:`~GenSVMGridSearchCV.predict` method directly on this 
+        use the :meth:`~GenSVMGridSearchCV.predict` method directly on this 
         :class:`.GenSVMGridSearchCV` instance.
 
         Also for multiple metric evaluation, the attributes :attr:`best_index_ 
@@ -623,7 +631,7 @@ class GenSVMGridSearchCV(BaseEstimator, MetaEstimatorMixin):
                 and not self.best_params_["kernel"] == "linear"
                 and not "gamma" in self.best_params_
             ):
-                self.best_params_["gamma"] = 1. / X.shape[1]
+                self.best_params_["gamma"] = 1.0 / X.shape[1]
             self.best_estimator_ = GenSVM(**self.best_params_)
             # y_orig because GenSVM fit must know the conversion for predict to
             # work correctly
@@ -711,9 +719,24 @@ def load_grid_tiny():
     """
 
     pg = [
-        {"p": [2.0], "kappa": [5.0], "lmd": [pow(2, -16)], "weights": ["unit"]},
-        {"p": [2.0], "kappa": [5.0], "lmd": [pow(2, -18)], "weights": ["unit"]},
-        {"p": [2.0], "kappa": [0.5], "lmd": [pow(2, -18)], "weights": ["unit"]},
+        {
+            "p": [2.0],
+            "kappa": [5.0],
+            "lmd": [pow(2, -16)],
+            "weights": ["unit"],
+        },
+        {
+            "p": [2.0],
+            "kappa": [5.0],
+            "lmd": [pow(2, -18)],
+            "weights": ["unit"],
+        },
+        {
+            "p": [2.0],
+            "kappa": [0.5],
+            "lmd": [pow(2, -18)],
+            "weights": ["unit"],
+        },
         {
             "p": [2.0],
             "kappa": [5.0],
@@ -726,7 +749,12 @@ def load_grid_tiny():
             "lmd": [pow(2, -18)],
             "weights": ["unit"],
         },
-        {"p": [2.0], "kappa": [5.0], "lmd": [pow(2, -14)], "weights": ["unit"]},
+        {
+            "p": [2.0],
+            "kappa": [5.0],
+            "lmd": [pow(2, -14)],
+            "weights": ["unit"],
+        },
         {
             "p": [2.0],
             "kappa": [0.5],
@@ -739,7 +767,12 @@ def load_grid_tiny():
             "lmd": [pow(2, -18)],
             "weights": ["unit"],
         },
-        {"p": [2.0], "kappa": [0.5], "lmd": [pow(2, -16)], "weights": ["unit"]},
+        {
+            "p": [2.0],
+            "kappa": [0.5],
+            "lmd": [pow(2, -16)],
+            "weights": ["unit"],
+        },
         {
             "p": [2.0],
             "kappa": [0.5],