def cross_validate(self, X, y):

        print "fitting {} to the training set".format(self.name)
        if self.param_grid is not None:
            param_sets = list(ParameterGrid(self.param_grid))
            n_param_sets = len(param_sets)
            param_scores = []
            for j, param_set in enumerate(param_sets):

                print "--------------"
                print "training the classifier..."
                print "parameter set:"
                for k, v in param_set.iteritems():
                    print "{}:{}".format(k, v)

                param_score = self.evaluate(X, y, param_set=param_set)
                param_scores.append(param_score)
                p = np.argmax(np.array(param_scores))
                self.best_param_set = param_sets[p]
                print "best parameter set", self.best_param_set
                print "best score:", param_scores[p]
        else:
            score = self.evaluate(X, y) 

def fit(self, X, y=None):
        """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.
        """
        return self._fit(X, y, ParameterGrid(self.param_grid)) 

def fit(self, frame):
        """Fit the grid search.

        Parameters
        ----------

        frame : H2OFrame, shape=(n_samples, n_features)
            The training frame on which to fit.
        """
        return self._fit(frame, ParameterGrid(self.param_grid)) 

def create_misc_confs():
    from sklearn.grid_search import ParameterGrid
    params = {'break_width': [1.5, 2.0, 3.6, 5.0], 
              'recognizer': ['probout', 'hmm'], 'combine_hangoff': [.4, .6, .8], 
              'postprocess': [True, False], 'segmenter': ['experimental', 'stochastic'],
              'line_cluster_pos': ['top', 'center'],
              }
    grid = ParameterGrid(params)
    for pr in grid:
        Config(save_conf=True, **pr) 

def __get_param_iterable(self, param_grid):
        if self.ramdonized_search_enable:
            parameter_iterable = ParameterSampler(param_grid,
                                          self.randomized_search_n_iter,
                                          random_state=self.ramdonized_search_random_state)
        else:
            parameter_iterable = ParameterGrid(param_grid)
                 
        return parameter_iterable 

def fit(self, Z):
        return self._fit(Z, ParameterGrid(self.param_grid))