diff options
Diffstat (limited to 'gensvm/core.py')
| -rw-r--r-- | gensvm/core.py | 33 |
1 files changed, 19 insertions, 14 deletions
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, |