diff options
| -rw-r--r-- | docs/cls_gensvm.rst | 24 | ||||
| -rw-r--r-- | docs/cls_gridsearch.rst | 2 | ||||
| -rw-r--r-- | gensvm/core.py | 14 |
3 files changed, 20 insertions, 20 deletions
diff --git a/docs/cls_gensvm.rst b/docs/cls_gensvm.rst index 80a2b25..643af4e 100644 --- a/docs/cls_gensvm.rst +++ b/docs/cls_gensvm.rst @@ -12,7 +12,7 @@ GenSVM 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. :param p: Parameter for the L_p norm of the loss function (1.0 <= p <= 2.0) @@ -25,7 +25,7 @@ GenSVM '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. :type weights: string, optional (default='unit') :param kernel: Specify the kernel type to use in the classifier. It must be one of @@ -81,7 +81,7 @@ GenSVM .. seealso:: - :class:`.GenSVMGridSearchCV` + :class:`~.gridsearch.GenSVMGridSearchCV`: Helper class to run an efficient grid search for GenSVM. .. _gensvm_kernels: @@ -95,8 +95,8 @@ GenSVM 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. :param X: The input data. It is expected that only numeric data is given. :type X: array, shape = (n_observations, n_features) @@ -111,13 +111,13 @@ GenSVM <.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. :type seed_V: array, shape = (n_features+1, n_classes-1), optional :returns: **self** -- Returns self. diff --git a/docs/cls_gridsearch.rst b/docs/cls_gridsearch.rst index 13cce92..7c41672 100644 --- a/docs/cls_gridsearch.rst +++ b/docs/cls_gridsearch.rst @@ -74,7 +74,7 @@ GenSVMGridSearchCV 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_ diff --git a/gensvm/core.py b/gensvm/core.py index 169a30c..837df67 100644 --- a/gensvm/core.py +++ b/gensvm/core.py @@ -280,13 +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 ------- |