def apply(self, X):

        self._check_initialized()
        X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True)

        # n_classes will be equal to 1 in the binary classification or the
        # regression case.
        n_estimators, n_classes = self.estimators_.shape
        leaves = np.zeros((X.shape[0], n_estimators, n_classes))

        for i in range(n_estimators):
            for j in range(n_classes):
                estimator = self.estimators_[i, j]
                leaves[:, i, j] = estimator.apply(X, check_input=False)

        return leaves
Return the database of ground-truth regions of interest.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/gradient_boosting.py/BaseGradientBoosting.apply--crossover with--dataset/ETHPy150Open rbgirshick/fast-rcnn/lib/datasets/pascal_voc.py/pascal_voc.gt_roidb