def fit(self, X, y=None, groups=None):
return self._fit(X, y, groups, ParameterGrid(self.param_grid))<EOL>
Run fit with all sets of parameters.

        Parameters
        ----------

        X : array-like, shape = [n_samples, n_features]
            Training vector, where n_samples is the number of samples and
            n_features is the number of features.

        y : array-like, shape = [n_samples] or [n_samples, n_output], optional
            Target relative to X for classification or regression;
            None for unsupervised learning.

        groups : array-like, with shape (n_samples,), optional
            Group labels for the samples used while splitting the dataset into
            train/test set.