b18e95863def3c4b60b248503e78c95e97675b38,skopt/tests/test_forest_opt.py,,test_tree_based_minimize,#,51

Before Change

        // when x < 0 and otherwise.
        // Hence sample uniformly from [-6, 0] and
        // [0, 6] and provide a warm-start.
        X1 = (-6 + 6 * rng.rand(5, 1)).tolist()
        X2 = (6 * rng.rand(5, 1)).tolist()
        X0 = X1 + X2
        yield (check_minimize, minimizer, bench2, -5,
               [(-6.0, 6.0)], 0.05, 100, 0, X0)

        yield (check_minimize, minimizer, bench3, -0.9,

After Change

        // if all the randomly sampled points are one side of the
        // the minimum, since for a decision tree any point greater than
        // max(sampled_points) would give a constant value.
        X0 = [[-5.6], [-5.8], [5.8], [5.6]]
        yield (check_minimize, minimizer, bench2, -5,
               [(-6.0, 6.0)], 0.1, 100, 10, X0)

        yield (check_minimize, minimizer, bench3, -0.9,

Instances