def fit(self, X, y, recycle=True, **grow_params):

        """Build a linear mixed forest of trees from the training set (X, y).

        Parameters
        ----------
        X : array-like of shape = [n_samples, n_features]
            The training input samples.

        y : array-like, shape = [n_samples] or [n_samples, 1]
            The real valued targets

        Returns
        -------
        self : object
            Returns self.
        """
        if isinstance(self.kernel, str) and self.kernel == 'data':
            self.kernel = SC.estimateKernel(X, maf=1.0/X.shape[0])
        elif isinstance(self.kernel, str) and self.kernel == 'iid':
            self.kernel = SP.identity(X.shape[0])
        # Use dedicated part of data as background model
        elif self.kernel.size == X.shape[1]:
            tmp_ind = self.kernel
            self.kernel = utils.estimateKernel(X[:, self.kernel],
                                               maf=1.0/X.shape[0])
            X = X[:, ~tmp_ind]
        # Extract and reshape data
        self.y = y.reshape(-1, 1)
        self.X = X
        self.n, self.m = self.X.shape
        if self.delta is None:
            self.BLUP = BLUP.BLUP()
            if self.verbose > 1:
                print('fitting BLUP')
            self.BLUP.fit(XTrain=self.X, yTrain=self.y, KTrain=self.kernel,
                          delta=self.delta)
            if self.verbose > 1:
                print('done fitting BLUP')
            # Update delta if it used to be 'None'
            self.delta = self.BLUP.delta
        self.max_features = SP.maximum(SP.int_(self.ratio_features*self.m), 1)
        self.var_used = SP.zeros(self.m)
        self.log_importance = SP.zeros(self.m)
        self.depth = 0

        if self.verbose > 0:
            print(('log(delta) fitted to ', SP.log(self.delta)))

        # Initialize individual trees
        if recycle and self.trees != []:
            for tree in self.trees:
                tree.cut_to_stump()
        else:
            n_trees = 0
            self.trees = []
            while n_trees < self.n_estimators:
                if self.verbose > 1:
                    print(('init. tree number ', n_trees))
                subsample = self.tree_sample()
                tree = MixedForestTree(self, subsample)
                self.trees.append(tree)
                n_trees += 1

        # Fitting with optimal depth constraint
        if self.fit_optimal_depth or self.update_delta:
            self.opt_depth = 0
            self.min_oob_err = self.get_oob_error(self.depth)
            if self.verbose > 0:
                print(('initial oob error is:', self.min_oob_err))
            grow_further = True
            curr_depth = self.depth
            while grow_further:
                # Updating ensemble increasing its depth by one
                self.further(depth=self.depth+1)
                if self.update_delta:
                    self.delta = self.delta_update()
                    if self.verbose > 0:
                        print(('delta was fitted to', self.delta))
                if self.verbose > 0:
                    print(('depth is:', self.depth))
                oob_err = self.get_oob_error(self.depth)
                if self.verbose > 0:
                    print(('oob error is:', oob_err))
                if oob_err < self.min_oob_err:
                    self.min_oob_err = oob_err
                    self.opt_depth = self.depth
                # Decide whether tree needs to be furthered
                grow_further = (curr_depth < self.depth) and\
                    (self.depth < self.max_depth)
                if self.build_to_opt_depth and (self.depth >= self.min_depth):
                    grow_further = grow_further and\
                        (oob_err == self.min_oob_err)
                    pass
                curr_depth = self.depth
        #####################################################
        # Growing full tree one by one
        else:
            self.further(depth=self.max_depth)
        return self
Build a linear mixed forest of trees from the training set (X, y).

        Parameters
        ----------
        X : array-like of shape = [n_samples, n_features]
            The training input samples.

        y : array-like, shape = [n_samples] or [n_samples, 1]
            The real valued targets

        Returns
        -------
        self : object
            Returns self.