diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml
index 77101b0..91d201a 100644
--- a/.github/workflows/pytest.yml
+++ b/.github/workflows/pytest.yml
@@ -12,7 +12,8 @@ jobs:
           - '3.8'
           - '3.9'
           - '3.10'
-    runs-on: ubuntu-latest
+          - '3.11'
+    runs-on: ubuntu-20.04
     steps:
       - uses: actions/checkout@v2
       - uses: actions/setup-python@v2
diff --git a/LICENSE b/LICENSE
index c150506..60c4a91 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2019-2022 Jungtaek Kim
+Copyright (c) 2019-2023 Jungtaek Kim
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/README.md b/README.md
index d81f761..6bb47f7 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,16 @@
-# BayesO Benchmarks
+<p align="center">
+<img src="https://github.com/jungtaekkim/bayeso/blob/main/docs/_static/assets/logo_bayeso_capitalized.svg" width="400" />
+</p>
+
+# BayesO Benchmarks: Benchmark Functions for Bayesian Optimization
 [![Build Status](https://github.com/jungtaekkim/bayeso-benchmarks/actions/workflows/pytest.yml/badge.svg)](https://github.com/jungtaekkim/bayeso-benchmarks/actions/workflows/pytest.yml)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
-Benchmarks for Bayesian optimization.
+This repository provides the implementation of benchmark functions for Bayesian optimization.
 The details of benchmark functions can be found in [these notes](https://jungtaek.github.io/notes/benchmarks_bo.pdf).
 
+* [https://bayeso.org](https://bayeso.org)
+
 ## Installation
 We recommend installing it with `virtualenv`.
 You can choose one of three installation options.
@@ -64,8 +70,5 @@ Y = obj_fun.output(X)
 Y_noise = obj_fun.output_gaussian_noise(X)
 ```
 
-## Author
-* [Jungtaek Kim](https://jungtaek.github.io) (POSTECH)
-
 ## License
 [MIT License](LICENSE)
diff --git a/bayeso_benchmarks/__init__.py b/bayeso_benchmarks/__init__.py
index e112df7..d9eb7e4 100644
--- a/bayeso_benchmarks/__init__.py
+++ b/bayeso_benchmarks/__init__.py
@@ -1,16 +1,20 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: October 23, 2021
+# last updated: January 6, 2023
 #
 
 
-__version__ = '0.1.6'
+__version__ = '0.1.7'
 
 
 from bayeso_benchmarks.inf_dim_ackley import Ackley
 from bayeso_benchmarks.inf_dim_cosines import Cosines
+from bayeso_benchmarks.inf_dim_griewank import Griewank
+from bayeso_benchmarks.inf_dim_levy import Levy
+from bayeso_benchmarks.inf_dim_rastrigin import Rastrigin
 from bayeso_benchmarks.inf_dim_rosenbrock import Rosenbrock
 from bayeso_benchmarks.inf_dim_sphere import Sphere
+from bayeso_benchmarks.inf_dim_zakharov import Zakharov
 
 from bayeso_benchmarks.one_dim_constant import Constant
 from bayeso_benchmarks.one_dim_gramacyandlee2012 import GramacyAndLee2012
@@ -20,8 +24,10 @@
 from bayeso_benchmarks.two_dim_beale import Beale
 from bayeso_benchmarks.two_dim_bohachevsky import Bohachevsky
 from bayeso_benchmarks.two_dim_branin import Branin
+from bayeso_benchmarks.two_dim_bukin6 import Bukin6
 from bayeso_benchmarks.two_dim_dejong5 import DeJong5
 from bayeso_benchmarks.two_dim_dropwave import DropWave
+from bayeso_benchmarks.two_dim_easom import Easom
 from bayeso_benchmarks.two_dim_eggholder import Eggholder
 from bayeso_benchmarks.two_dim_goldsteinprice import GoldsteinPrice
 from bayeso_benchmarks.two_dim_holdertable import HolderTable
@@ -29,8 +35,47 @@
 from bayeso_benchmarks.two_dim_kim2 import Kim2
 from bayeso_benchmarks.two_dim_kim3 import Kim3
 from bayeso_benchmarks.two_dim_michalewicz import Michalewicz
+from bayeso_benchmarks.two_dim_shubert import Shubert
 from bayeso_benchmarks.two_dim_sixhumpcamel import SixHumpCamel
 from bayeso_benchmarks.two_dim_threehumpcamel import ThreeHumpCamel
 
+from bayeso_benchmarks.four_dim_colville import Colville
 from bayeso_benchmarks.three_dim_hartmann3d import Hartmann3D
 from bayeso_benchmarks.six_dim_hartmann6d import Hartmann6D
+
+
+all_benchmarks = [
+    Ackley,
+    Cosines,
+    Griewank,
+    Levy,
+    Rastrigin,
+    Rosenbrock,
+    Sphere,
+    Zakharov,
+    Constant,
+    GramacyAndLee2012,
+    Linear,
+    Step,
+    Beale,
+    Bohachevsky,
+    Branin,
+    Bukin6,
+    DeJong5,
+    DropWave,
+    Easom,
+    Eggholder,
+    GoldsteinPrice,
+    HolderTable,
+    Kim1,
+    Kim2,
+    Kim3,
+    Michalewicz,
+    Shubert,
+    SixHumpCamel,
+    ThreeHumpCamel,
+    Colville,
+    Hartmann3D,
+    Hartmann6D,
+]
+num_benchmarks = len(all_benchmarks)
diff --git a/bayeso_benchmarks/benchmark_base.py b/bayeso_benchmarks/benchmark_base.py
index acda74a..c3d5472 100644
--- a/bayeso_benchmarks/benchmark_base.py
+++ b/bayeso_benchmarks/benchmark_base.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: May 1, 2021
+# last updated: December 13, 2022
 #
 
 import numpy as np
@@ -8,14 +8,15 @@
 EPSILON = 1e-4
 
 
-class Function(object):
+class Function:
     def __init__(self, dimensionality, bounds, global_minimizers, global_minimum, function, dim_problem=None, seed=None):
         assert isinstance(dimensionality, int) or dimensionality is np.inf
         assert isinstance(bounds, np.ndarray)
         assert isinstance(global_minimizers, np.ndarray)
         assert isinstance(global_minimum, float)
         assert callable(function)
-        assert isinstance(dim_problem, int) or dim_problem is None
+        assert isinstance(dim_problem, (type(None), int))
+        assert isinstance(seed, (type(None), int))
         assert len(bounds.shape) == 2
         assert bounds.shape[1] == 2
         assert (bounds[:, 0] <= bounds[:, 1]).all()
@@ -30,6 +31,7 @@ def __init__(self, dimensionality, bounds, global_minimizers, global_minimum, fu
         self.random_state = np.random.RandomState(seed)
 
         self.validate_properties()
+        self.set_name()
 
     @property
     def dimensionality(self):
@@ -47,6 +49,14 @@ def global_minimizers(self):
     def global_minimum(self):
         return self._global_minimum
 
+    def set_name(self):
+        name = self.__class__.__name__.lower()
+
+        if self.dimensionality is np.inf:
+            self.name = f'{name}_{self.dim_problem}'
+        else:
+            self.name = name
+
     def get_bounds(self):
         if self.dimensionality is np.inf:
             return np.array(list(self.bounds) * self.dim_problem)
@@ -134,7 +144,7 @@ def output_sparse_gaussian_noise(self, X, scale_noise=0.1, sparsity=0.01):
 
         noise = self.random_state.randn(num_X)
         mask = self.random_state.uniform(low=0.0, high=1.0, size=num_X) < sparsity
-        noise *= mask.astype(np.float)
+        noise *= mask.astype(float)
         by += scale_noise * noise
 
         Y = np.expand_dims(by, axis=1)
@@ -172,7 +182,7 @@ def output_sparse_student_t_noise(self, X, scale_noise=0.1, dof=4.0, sparsity=0.
 
         noise = self.random_state.standard_t(dof, size=num_X)
         mask = self.random_state.uniform(low=0.0, high=1.0, size=num_X) < sparsity
-        noise *= mask.astype(np.float)
+        noise *= mask.astype(float)
         by += scale_noise * noise
 
         Y = np.expand_dims(by, axis=1)
@@ -228,3 +238,6 @@ def sample_uniform(self, num_points, seed=None):
         points = bounds[:, 0] + (bounds[:, 1] - bounds[:, 0]) * points
 
         return points
+
+    def __call__(self, X):
+        return self.output(X)
diff --git a/bayeso_benchmarks/four_dim_colville.py b/bayeso_benchmarks/four_dim_colville.py
new file mode 100644
index 0000000..1416c3d
--- /dev/null
+++ b/bayeso_benchmarks/four_dim_colville.py
@@ -0,0 +1,48 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: December 30, 2022
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    y = 100.0 * (bx[0]**2 - bx[1])**2
+    y += (bx[0] - 1.0)**2
+    y += (bx[2] - 1.0)**2
+    y += 90.0 * (bx[2]**2 - bx[3])**2
+    y += 10.1 * ((bx[1] - 1.0)**2 + (bx[3] - 1.0)**2)
+    y += 19.8 * (bx[1] - 1.0) * (bx[3] - 1.0)
+
+    return y
+
+
+class Colville(Function):
+    def __init__(self, seed=None):
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = 4
+        bounds = np.array([
+            [-10.0, 10.0],
+            [-10.0, 10.0],
+            [-10.0, 10.0],
+            [-10.0, 10.0],
+        ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
+        global_minimizers = np.array([
+            [1.0, 1.0, 1.0, 1.0],
+        ])
+        global_minimum = 0.0
+        function = lambda bx: fun_target(bx, dim_bx)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Colville, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
diff --git a/bayeso_benchmarks/inf_dim_griewank.py b/bayeso_benchmarks/inf_dim_griewank.py
new file mode 100644
index 0000000..c20eb46
--- /dev/null
+++ b/bayeso_benchmarks/inf_dim_griewank.py
@@ -0,0 +1,45 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 6, 2023
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    first_term = np.sum(bx**2 / 4000.0)
+
+    second_term = 1.0
+    for ind in range(1, dim_bx + 1):
+        second_term *= np.cos(bx[ind - 1] / np.sqrt(ind))
+
+    y = first_term - second_term + 1.0
+    return y
+
+
+class Griewank(Function):
+    def __init__(self, dim_problem, seed=None):
+        assert isinstance(dim_problem, int)
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = np.inf
+        bounds = np.array([
+            [-600.0, 600.0],
+        ])
+        global_minimizers = np.array([
+            [0.0],
+        ])
+        global_minimum = 0.0
+        dim_problem = dim_problem
+
+        function = lambda bx: fun_target(bx, dim_problem)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        except:
+            super(Griewank, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
diff --git a/bayeso_benchmarks/inf_dim_levy.py b/bayeso_benchmarks/inf_dim_levy.py
new file mode 100644
index 0000000..833e81e
--- /dev/null
+++ b/bayeso_benchmarks/inf_dim_levy.py
@@ -0,0 +1,51 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: December 29, 2022
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx,
+):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    bw = []
+    for x in bx:
+        w = 1.0 + (x - 1.0) / 4.0
+        bw.append(w)
+    bw = np.array(bw)
+
+    y = np.sin(np.pi * bw[0])**2
+
+    for w in bw[:-1]:
+        y += (w - 1.0)**2 * (1.0 + 10.0 * np.sin(np.pi * w + 1.0)**2)
+
+    y += (bw[-1] - 1.0)**2 * (1.0 + np.sin(2.0 * np.pi * bw[-1])**2)
+    return y
+
+
+class Levy(Function):
+    def __init__(self, dim_problem, seed=None):
+        assert isinstance(dim_problem, int)
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = np.inf
+        bounds = np.array([
+            [-10.0, 10.0],
+        ])
+        global_minimizers = np.array([
+            [1.0],
+        ])
+        global_minimum = 0.0
+        dim_problem = dim_problem
+
+        function = lambda bx: fun_target(bx, dim_problem)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        except:
+            super(Levy, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
diff --git a/bayeso_benchmarks/inf_dim_rastrigin.py b/bayeso_benchmarks/inf_dim_rastrigin.py
new file mode 100644
index 0000000..43faa7e
--- /dev/null
+++ b/bayeso_benchmarks/inf_dim_rastrigin.py
@@ -0,0 +1,43 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: December 20, 2022
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    y = 10.0 * dim_bx
+
+    for ind in range(0, dim_bx):
+        y += bx[ind]**2 - 10.0 * np.cos(2.0 * np.pi * bx[ind])
+
+    return y
+
+
+class Rastrigin(Function):
+    def __init__(self, dim_problem, seed=None):
+        assert isinstance(dim_problem, int)
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = np.inf
+        bounds = np.array([
+            [-5.12, 5.12],
+        ])
+        global_minimizers = np.array([
+            [0.0],
+        ])
+        global_minimum = 0.0
+        dim_problem = dim_problem
+
+        function = lambda bx: fun_target(bx, dim_problem)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        except:
+            super(Rastrigin, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
diff --git a/bayeso_benchmarks/inf_dim_zakharov.py b/bayeso_benchmarks/inf_dim_zakharov.py
new file mode 100644
index 0000000..bdb9918
--- /dev/null
+++ b/bayeso_benchmarks/inf_dim_zakharov.py
@@ -0,0 +1,48 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 4, 2023
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    first_term = np.sum(bx**2)
+
+    inner_term = 0.0
+    for ind in range(1, dim_bx + 1):
+        inner_term += 0.5 * ind * bx[ind - 1]
+
+    second_term = inner_term**2
+    third_term = inner_term**4
+
+    y = first_term + second_term + third_term
+    return y
+
+
+class Zakharov(Function):
+    def __init__(self, dim_problem, seed=None):
+        assert isinstance(dim_problem, int)
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = np.inf
+        bounds = np.array([
+            [-5.0, 10.0],
+        ])
+        global_minimizers = np.array([
+            [0.0],
+        ])
+        global_minimum = 0.0
+        dim_problem = dim_problem
+
+        function = lambda bx: fun_target(bx, dim_problem)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
+        except:
+            super(Zakharov, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, dim_problem=dim_problem, seed=seed)
diff --git a/bayeso_benchmarks/one_dim_constant.py b/bayeso_benchmarks/one_dim_constant.py
index cd59f76..102a7d6 100644
--- a/bayeso_benchmarks/one_dim_constant.py
+++ b/bayeso_benchmarks/one_dim_constant.py
@@ -19,7 +19,7 @@ def fun_target(bx, dim_bx, constant):
 
 class Constant(Function):
     def __init__(self,
-        bounds = np.array([
+        bounds=np.array([
             [-10.0, 10.0],
         ]),
         constant=0.0,
diff --git a/bayeso_benchmarks/one_dim_gramacyandlee2012.py b/bayeso_benchmarks/one_dim_gramacyandlee2012.py
index 5559f1d..aa2ab1c 100644
--- a/bayeso_benchmarks/one_dim_gramacyandlee2012.py
+++ b/bayeso_benchmarks/one_dim_gramacyandlee2012.py
@@ -24,6 +24,9 @@ def __init__(self, seed=None):
         bounds = np.array([
             [0.5, 2.5],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [0.54856405],
         ])
diff --git a/bayeso_benchmarks/one_dim_step.py b/bayeso_benchmarks/one_dim_step.py
index 9610fe9..2fb01e0 100644
--- a/bayeso_benchmarks/one_dim_step.py
+++ b/bayeso_benchmarks/one_dim_step.py
@@ -49,6 +49,9 @@ def __init__(self,
         bounds = np.array([
             [np.min(steps), np.max(steps)],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [steps[np.argmin(step_values)]],
         ])
diff --git a/bayeso_benchmarks/six_dim_hartmann6d.py b/bayeso_benchmarks/six_dim_hartmann6d.py
index 690e396..3bc9b29 100644
--- a/bayeso_benchmarks/six_dim_hartmann6d.py
+++ b/bayeso_benchmarks/six_dim_hartmann6d.py
@@ -38,24 +38,20 @@ def fun_target(bx, dim_bx):
 
 
 class Hartmann6D(Function):
-    def __init__(self,
-        bounds=np.array([
+    def __init__(self, seed=None):
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = 6
+        bounds = np.array([
             [0.0, 1.0],
             [0.0, 1.0],
             [0.0, 1.0],
             [0.0, 1.0],
             [0.0, 1.0],
             [0.0, 1.0],
-        ]),
-        seed=None
-    ):
-        assert isinstance(bounds, np.ndarray)
-        assert isinstance(seed, (type(None), int))
-        assert len(bounds.shape) == 2
-        assert bounds.shape[1] == 2
-
-        dim_bx = 6
+        ])
         assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
 
         global_minimizers = np.array([
             [0.20169, 0.150011, 0.476874, 0.275332, 0.311652, 0.6573],
diff --git a/bayeso_benchmarks/three_dim_hartmann3d.py b/bayeso_benchmarks/three_dim_hartmann3d.py
index 0e528a3..8379272 100644
--- a/bayeso_benchmarks/three_dim_hartmann3d.py
+++ b/bayeso_benchmarks/three_dim_hartmann3d.py
@@ -38,21 +38,17 @@ def fun_target(bx, dim_bx):
 
 
 class Hartmann3D(Function):
-    def __init__(self,
-        bounds=np.array([
-            [0.0, 1.0],
-            [0.0, 1.0],
-            [0.0, 1.0],
-        ]),
-        seed=None
-    ):
-        assert isinstance(bounds, np.ndarray)
+    def __init__(self, seed=None):
         assert isinstance(seed, (type(None), int))
-        assert len(bounds.shape) == 2
-        assert bounds.shape[1] == 2
 
         dim_bx = 3
+        bounds = np.array([
+            [0.0, 1.0],
+            [0.0, 1.0],
+            [0.0, 1.0],
+        ])
         assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
 
         global_minimizers = np.array([
             [0.114614, 0.555649, 0.852547],
diff --git a/bayeso_benchmarks/two_dim_beale.py b/bayeso_benchmarks/two_dim_beale.py
index 3abc875..e98e8da 100644
--- a/bayeso_benchmarks/two_dim_beale.py
+++ b/bayeso_benchmarks/two_dim_beale.py
@@ -25,6 +25,9 @@ def __init__(self, seed=None):
             [-4.5, 4.5],
             [-4.5, 4.5],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [3.0, 0.5],
         ])
diff --git a/bayeso_benchmarks/two_dim_bohachevsky.py b/bayeso_benchmarks/two_dim_bohachevsky.py
index c82364c..b421f76 100644
--- a/bayeso_benchmarks/two_dim_bohachevsky.py
+++ b/bayeso_benchmarks/two_dim_bohachevsky.py
@@ -25,6 +25,9 @@ def __init__(self, seed=None):
             [-100.0, 100.0],
             [-100.0, 100.0],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [0.0, 0.0],
         ])
diff --git a/bayeso_benchmarks/two_dim_branin.py b/bayeso_benchmarks/two_dim_branin.py
index 28979fc..3f0df1e 100644
--- a/bayeso_benchmarks/two_dim_branin.py
+++ b/bayeso_benchmarks/two_dim_branin.py
@@ -45,6 +45,9 @@ def __init__(self,
             [-5, 10],
             [0, 15],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [-np.pi, 12.275],
             [np.pi, 2.275],
diff --git a/bayeso_benchmarks/two_dim_bukin6.py b/bayeso_benchmarks/two_dim_bukin6.py
new file mode 100644
index 0000000..07c9e51
--- /dev/null
+++ b/bayeso_benchmarks/two_dim_bukin6.py
@@ -0,0 +1,40 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 6, 2023
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    y = 100.0 * np.sqrt(np.abs(bx[1] - 0.01 * bx[0]**2)) + 0.01 * np.abs(bx[0] + 10.0)
+    return y
+
+
+class Bukin6(Function):
+    def __init__(self, seed=None):
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = 2
+        bounds = np.array([
+            [-15, -5],
+            [-3, 3],
+        ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
+        global_minimizers = np.array([
+            [-10.0, 1.0],
+        ])
+        global_minimum = 0.0
+        function = lambda bx: fun_target(bx, dim_bx)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Bukin6, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
diff --git a/bayeso_benchmarks/two_dim_dejong5.py b/bayeso_benchmarks/two_dim_dejong5.py
index 0199891..bdb0d63 100644
--- a/bayeso_benchmarks/two_dim_dejong5.py
+++ b/bayeso_benchmarks/two_dim_dejong5.py
@@ -35,6 +35,9 @@ def __init__(self, seed=None):
             [-65.536, 65.536],
             [-65.536, 65.536],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [-31.97707837, -31.97795471],
             [-31.99140499, -31.99140499],
diff --git a/bayeso_benchmarks/two_dim_dropwave.py b/bayeso_benchmarks/two_dim_dropwave.py
index 405eb67..447c1ec 100644
--- a/bayeso_benchmarks/two_dim_dropwave.py
+++ b/bayeso_benchmarks/two_dim_dropwave.py
@@ -25,6 +25,9 @@ def __init__(self, seed=None):
             [-5.12, 5.12],
             [-5.12, 5.12],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [0.0, 0.0],
         ])
diff --git a/bayeso_benchmarks/two_dim_easom.py b/bayeso_benchmarks/two_dim_easom.py
new file mode 100644
index 0000000..2ccc071
--- /dev/null
+++ b/bayeso_benchmarks/two_dim_easom.py
@@ -0,0 +1,40 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 3, 2023
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    y = -1.0 * np.cos(bx[0]) * np.cos(bx[1]) * np.exp(-1.0 * (bx[0] - np.pi)**2 - (bx[1] - np.pi)**2)
+    return y
+
+
+class Easom(Function):
+    def __init__(self, seed=None):
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = 2
+        bounds = np.array([
+            [-100, 100],
+            [-100, 100],
+        ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
+        global_minimizers = np.array([
+            [np.pi, np.pi],
+        ])
+        global_minimum = -1.0
+        function = lambda bx: fun_target(bx, dim_bx)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Easom, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
diff --git a/bayeso_benchmarks/two_dim_eggholder.py b/bayeso_benchmarks/two_dim_eggholder.py
index 5395e62..8dc145c 100644
--- a/bayeso_benchmarks/two_dim_eggholder.py
+++ b/bayeso_benchmarks/two_dim_eggholder.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: January 13, 2023
 #
 
 import numpy as np
@@ -17,20 +17,16 @@ def fun_target(bx, dim_bx):
 
 
 class Eggholder(Function):
-    def __init__(self,
-        bounds=np.array([
-            [-512.0, 512.0],
-            [-512.0, 512.0],
-        ]),
-        seed=None
-    ):
-        assert isinstance(bounds, np.ndarray)
+    def __init__(self, seed=None):
         assert isinstance(seed, (type(None), int))
-        assert len(bounds.shape) == 2
-        assert bounds.shape[1] == 2
 
         dim_bx = 2
+        bounds = np.array([
+            [-512.0, 512.0],
+            [-512.0, 512.0],
+        ])
         assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
 
         global_minimizers = np.array([
             [512.0, 404.2319],
diff --git a/bayeso_benchmarks/two_dim_goldsteinprice.py b/bayeso_benchmarks/two_dim_goldsteinprice.py
index 940499f..22ce195 100644
--- a/bayeso_benchmarks/two_dim_goldsteinprice.py
+++ b/bayeso_benchmarks/two_dim_goldsteinprice.py
@@ -33,6 +33,9 @@ def __init__(self, seed=None):
             [-2.0, 2.0],
             [-2.0, 2.0],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [0.0, -1.0],
         ])
diff --git a/bayeso_benchmarks/two_dim_holdertable.py b/bayeso_benchmarks/two_dim_holdertable.py
index 6011272..62f904a 100644
--- a/bayeso_benchmarks/two_dim_holdertable.py
+++ b/bayeso_benchmarks/two_dim_holdertable.py
@@ -25,6 +25,9 @@ def __init__(self, seed=None):
             [-10.0, 10.0],
             [-10.0, 10.0],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [8.05502347, 9.66459002],
             [8.05502347, -9.66459002],
diff --git a/bayeso_benchmarks/two_dim_kim1.py b/bayeso_benchmarks/two_dim_kim1.py
index 11b6595..ea82e06 100644
--- a/bayeso_benchmarks/two_dim_kim1.py
+++ b/bayeso_benchmarks/two_dim_kim1.py
@@ -25,6 +25,9 @@ def __init__(self, seed=None):
             [-16.0, 16.0],
             [-16.0, 16.0],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [4.72130726, 3.17086303],
         ])
diff --git a/bayeso_benchmarks/two_dim_kim2.py b/bayeso_benchmarks/two_dim_kim2.py
index 27ec15f..e6aa747 100644
--- a/bayeso_benchmarks/two_dim_kim2.py
+++ b/bayeso_benchmarks/two_dim_kim2.py
@@ -30,6 +30,9 @@ def __init__(self, seed=None):
             [-128.0, 128.0],
             [-128.0, 128.0],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [-2.1013466, 34.14526252],
         ])
diff --git a/bayeso_benchmarks/two_dim_kim3.py b/bayeso_benchmarks/two_dim_kim3.py
index bb0ffd7..a899fc9 100644
--- a/bayeso_benchmarks/two_dim_kim3.py
+++ b/bayeso_benchmarks/two_dim_kim3.py
@@ -40,6 +40,9 @@ def __init__(self, seed=None):
             [-256.0, 256.0],
             [-256.0, 256.0],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [48.12477173, 34.19859065],
         ])
diff --git a/bayeso_benchmarks/two_dim_michalewicz.py b/bayeso_benchmarks/two_dim_michalewicz.py
index 886a5f9..a3f464c 100644
--- a/bayeso_benchmarks/two_dim_michalewicz.py
+++ b/bayeso_benchmarks/two_dim_michalewicz.py
@@ -30,6 +30,9 @@ def __init__(self, seed=None):
             [0.0, np.pi],
             [0.0, np.pi],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [2.20290552, 1.57079632],
         ])
diff --git a/bayeso_benchmarks/two_dim_shubert.py b/bayeso_benchmarks/two_dim_shubert.py
new file mode 100644
index 0000000..cb600d0
--- /dev/null
+++ b/bayeso_benchmarks/two_dim_shubert.py
@@ -0,0 +1,48 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 3, 2023
+#
+
+import numpy as np
+
+from bayeso_benchmarks.benchmark_base import Function
+
+
+def fun_target(bx, dim_bx):
+    assert len(bx.shape) == 1
+    assert bx.shape[0] == dim_bx
+
+    first_term = 0.0
+    for ind in range(1, 6):
+        first_term += ind * np.cos((ind + 1.0) * bx[0] + ind)
+
+    second_term = 0.0
+    for ind in range(1, 6):
+        second_term += ind * np.cos((ind + 1.0) * bx[1] + ind)
+
+    y = first_term * second_term
+    return y
+
+
+class Shubert(Function):
+    def __init__(self, seed=None):
+        assert isinstance(seed, (type(None), int))
+
+        dim_bx = 2
+        bounds = np.array([
+            [-10, 10],
+            [-10, 10],
+        ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
+        global_minimizers = np.array([
+            [-7.08350641, -7.70831374],
+        ])
+        global_minimum = -186.73090883
+        function = lambda bx: fun_target(bx, dim_bx)
+
+        try:
+            super().__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
+        except:
+            super(Shubert, self).__init__(dim_bx, bounds, global_minimizers, global_minimum, function, seed=seed)
diff --git a/bayeso_benchmarks/two_dim_sixhumpcamel.py b/bayeso_benchmarks/two_dim_sixhumpcamel.py
index 80c6283..767c064 100644
--- a/bayeso_benchmarks/two_dim_sixhumpcamel.py
+++ b/bayeso_benchmarks/two_dim_sixhumpcamel.py
@@ -25,6 +25,9 @@ def __init__(self, seed=None):
             [-3.0, 3.0],
             [-2.0, 2.0],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [0.0898, -0.7126],
             [-0.0898, 0.7126],
diff --git a/bayeso_benchmarks/two_dim_threehumpcamel.py b/bayeso_benchmarks/two_dim_threehumpcamel.py
index 9bf97e8..5c3e92c 100644
--- a/bayeso_benchmarks/two_dim_threehumpcamel.py
+++ b/bayeso_benchmarks/two_dim_threehumpcamel.py
@@ -25,6 +25,9 @@ def __init__(self, seed=None):
             [-5.0, 5.0],
             [-5.0, 5.0],
         ])
+        assert bounds.shape[0] == dim_bx
+        assert bounds.shape[1] == 2
+
         global_minimizers = np.array([
             [0.0, 0.0],
         ])
diff --git a/bayeso_benchmarks/utils.py b/bayeso_benchmarks/utils.py
index 7374b7d..4fc6189 100644
--- a/bayeso_benchmarks/utils.py
+++ b/bayeso_benchmarks/utils.py
@@ -1,10 +1,65 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: October 27, 2021
+# last updated: January 4, 2023
 #
 
 import numpy as np
 
+import bayeso_benchmarks as bb
+
+
+def get_benchmark(str_fun, seed=None, **kwargs):
+    count = 0
+
+    for class_benchmark in bb.all_benchmarks:
+        if str_fun == class_benchmark.__name__.lower():
+            target_class = class_benchmark
+            count += 1
+
+    if count == 0:
+        raise ValueError('missing str_fun.')
+    elif count > 1:
+        raise ValueError('duplicate class name.')
+
+    if str_fun in [
+        'ackley',
+        'cosines',
+        'griewank',
+        'levy',
+        'rastrigin',
+        'rosenbrock',
+        'sphere',
+        'zakharov',
+    ]:
+        assert 'dim' in kwargs
+        dim = kwargs['dim']
+
+        benchmark = target_class(dim, seed=seed)
+    elif str_fun == 'constant':
+        assert 'bounds' in kwargs
+        assert 'constant' in kwargs
+        bounds = kwargs['bounds']
+        constant = kwargs['constant']
+
+        benchmark = target_class(bounds=bounds, constant=constant, seed=seed)
+    elif str_fun == 'linear':
+        assert 'bounds' in kwargs
+        assert 'slope' in kwargs
+        bounds = kwargs['bounds']
+        slope = kwargs['slope']
+
+        benchmark = target_class(bounds=bounds, slope=slope, seed=seed)
+    elif str_fun == 'step':
+        assert 'steps' in kwargs
+        assert 'step_values' in kwargs
+        steps = kwargs['steps']
+        step_values = kwargs['step_values']
+
+        benchmark = target_class(steps=steps, step_values=step_values, seed=seed)
+    else:
+        benchmark = target_class(seed=seed)
+
+    return benchmark
 
 def pdf_two_dim_normal(bx, mu, Cov):
     assert bx.shape[0] == mu.shape[0] == Cov.shape[0] == Cov.shape[1] == 2
diff --git a/examples/plot_benchmarks.py b/examples/plot_benchmarks.py
index 3813462..d025f02 100644
--- a/examples/plot_benchmarks.py
+++ b/examples/plot_benchmarks.py
@@ -8,9 +8,13 @@ def plot_1d(obj_fun,
     str_x_axis=r'$x$',
     str_y_axis=r'$f(x)$',
     str_figures='../figures',
+    show_figure=False,
+    bounds=None, # for zooming in on part of a figure
 ):
     print(str_fun)
-    bounds = obj_fun.get_bounds()
+
+    if bounds is None:
+        bounds = obj_fun.get_bounds()
     print(bounds)
     assert bounds.shape[0] == 1
 
@@ -43,7 +47,10 @@ def plot_1d(obj_fun,
         transparent=True,
         bbox_inches='tight')
 
-    plt.show()
+    if show_figure:
+        plt.show()
+
+    plt.close('all')
 
 def plot_2d(obj_fun,
     str_fun,
@@ -51,14 +58,36 @@ def plot_2d(obj_fun,
     str_x2_axis=r'$x_2$',
     str_y_axis=r'$f(\mathbf{x})$',
     str_figures='../figures',
+    show_figure=False,
+    bounds=None, # for zooming in on part of a figure
 ):
     print(str_fun)
-    bounds = obj_fun.get_bounds()
+
+    if bounds is None:
+        bounds = obj_fun.get_bounds()
     print(bounds)
     assert bounds.shape[0] == 2
 
-    X1 = np.linspace(bounds[0, 0], bounds[0, 1], 200)
-    X2 = np.linspace(bounds[1, 0], bounds[1, 1], 200)
+    num_grids = 300
+
+    X1 = np.linspace(bounds[0, 0], bounds[0, 1], num_grids)
+    X2 = np.linspace(bounds[1, 0], bounds[1, 1], num_grids)
+
+    if obj_fun.name == 'easom':
+        num_grids_additional = 300
+
+        X1 = np.concatenate([
+                X1,
+                np.linspace(np.pi - 3.0, np.pi + 3.0, num_grids_additional)
+            ], axis=0)
+        X2 = np.concatenate([
+                X2,
+                np.linspace(np.pi - 3.0, np.pi + 3.0, num_grids_additional)
+            ], axis=0)
+
+        X1 = np.sort(X1)
+        X2 = np.sort(X2)
+
     X1, X2 = np.meshgrid(X1, X2)
     X = np.concatenate((X1[..., np.newaxis], X2[..., np.newaxis]), axis=2)
     X = np.reshape(X, (X.shape[0] * X.shape[1], X.shape[2]))
@@ -106,14 +135,17 @@ def plot_2d(obj_fun,
         transparent=True,
         bbox_inches='tight')
 
-    plt.show()
+    if show_figure:
+        plt.show()
+
+    plt.close('all')
 
 
 if __name__ == '__main__':
     # one dim.
     from bayeso_benchmarks.one_dim_gramacyandlee2012 import GramacyAndLee2012 as target_class
     obj_fun = target_class()
-    plot_1d(obj_fun, 'gramacyandlee2012')
+    plot_1d(obj_fun, 'gramacyandlee2012_1d')
 
     from bayeso_benchmarks.inf_dim_ackley import Ackley as target_class
     obj_fun = target_class(1)
@@ -123,10 +155,27 @@ def plot_2d(obj_fun,
     obj_fun = target_class(1)
     plot_1d(obj_fun, 'cosines_1d')
 
+    from bayeso_benchmarks.inf_dim_griewank import Griewank as target_class
+    obj_fun = target_class(1)
+    plot_1d(obj_fun, 'griewank_1d')
+    plot_1d(obj_fun, 'griewank_zoom_in_1d', bounds=np.array([[-50, 50]]))
+
+    from bayeso_benchmarks.inf_dim_levy import Levy as target_class
+    obj_fun = target_class(1)
+    plot_1d(obj_fun, 'levy_1d')
+
+    from bayeso_benchmarks.inf_dim_rastrigin import Rastrigin as target_class
+    obj_fun = target_class(1)
+    plot_1d(obj_fun, 'rastrigin_1d')
+
     from bayeso_benchmarks.inf_dim_sphere import Sphere as target_class
     obj_fun = target_class(1)
     plot_1d(obj_fun, 'sphere_1d')
 
+    from bayeso_benchmarks.inf_dim_zakharov import Zakharov as target_class
+    obj_fun = target_class(1)
+    plot_1d(obj_fun, 'zakharov_1d')
+
     # two dim.
     from bayeso_benchmarks.two_dim_beale import Beale as target_class
     obj_fun = target_class()
@@ -140,6 +189,10 @@ def plot_2d(obj_fun,
     obj_fun = target_class()
     plot_2d(obj_fun, 'branin_2d')
 
+    from bayeso_benchmarks.two_dim_bukin6 import Bukin6 as target_class
+    obj_fun = target_class()
+    plot_2d(obj_fun, 'bukin6_2d')
+
     from bayeso_benchmarks.two_dim_dejong5 import DeJong5 as target_class
     obj_fun = target_class()
     plot_2d(obj_fun, 'dejong5_2d')
@@ -148,6 +201,10 @@ def plot_2d(obj_fun,
     obj_fun = target_class()
     plot_2d(obj_fun, 'dropwave_2d')
 
+    from bayeso_benchmarks.two_dim_easom import Easom as target_class
+    obj_fun = target_class()
+    plot_2d(obj_fun, 'easom_2d')
+
     from bayeso_benchmarks.two_dim_eggholder import Eggholder as target_class
     obj_fun = target_class()
     plot_2d(obj_fun, 'eggholder_2d')
@@ -176,6 +233,10 @@ def plot_2d(obj_fun,
     obj_fun = target_class()
     plot_2d(obj_fun, 'michalewicz_2d')
 
+    from bayeso_benchmarks.two_dim_shubert import Shubert as target_class
+    obj_fun = target_class()
+    plot_2d(obj_fun, 'shubert_2d')
+
     from bayeso_benchmarks.two_dim_sixhumpcamel import SixHumpCamel as target_class
     obj_fun = target_class()
     plot_2d(obj_fun, 'sixhumpcamel_2d')
@@ -192,6 +253,19 @@ def plot_2d(obj_fun,
     obj_fun = target_class(2)
     plot_2d(obj_fun, 'cosines_2d')
 
+    from bayeso_benchmarks.inf_dim_griewank import Griewank as target_class
+    obj_fun = target_class(2)
+    plot_2d(obj_fun, 'griewank_2d')
+    plot_2d(obj_fun, 'griewank_zoom_in_2d', bounds=np.array([[-50, 50], [-50, 50]]))
+
+    from bayeso_benchmarks.inf_dim_levy import Levy as target_class
+    obj_fun = target_class(2)
+    plot_2d(obj_fun, 'levy_2d')
+
+    from bayeso_benchmarks.inf_dim_rastrigin import Rastrigin as target_class
+    obj_fun = target_class(2)
+    plot_2d(obj_fun, 'rastrigin_2d')
+
     from bayeso_benchmarks.inf_dim_rosenbrock import Rosenbrock as target_class
     obj_fun = target_class(2)
     plot_2d(obj_fun, 'rosenbrock_2d')
@@ -199,3 +273,7 @@ def plot_2d(obj_fun,
     from bayeso_benchmarks.inf_dim_sphere import Sphere as target_class
     obj_fun = target_class(2)
     plot_2d(obj_fun, 'sphere_2d')
+
+    from bayeso_benchmarks.inf_dim_zakharov import Zakharov as target_class
+    obj_fun = target_class(2)
+    plot_2d(obj_fun, 'zakharov_2d')
diff --git a/figures/ackley_1d.pdf b/figures/ackley_1d.pdf
index 610fef5..40e40ce 100644
Binary files a/figures/ackley_1d.pdf and b/figures/ackley_1d.pdf differ
diff --git a/figures/ackley_2d.pdf b/figures/ackley_2d.pdf
index 92e2f9a..82675f5 100644
Binary files a/figures/ackley_2d.pdf and b/figures/ackley_2d.pdf differ
diff --git a/figures/beale_2d.pdf b/figures/beale_2d.pdf
index d06fee7..ed2b8c3 100644
Binary files a/figures/beale_2d.pdf and b/figures/beale_2d.pdf differ
diff --git a/figures/bohachevsky_2d.pdf b/figures/bohachevsky_2d.pdf
index 92bfd5e..1a8c69e 100644
Binary files a/figures/bohachevsky_2d.pdf and b/figures/bohachevsky_2d.pdf differ
diff --git a/figures/branin_2d.pdf b/figures/branin_2d.pdf
index 7328230..63e7f35 100644
Binary files a/figures/branin_2d.pdf and b/figures/branin_2d.pdf differ
diff --git a/figures/bukin6_2d.pdf b/figures/bukin6_2d.pdf
new file mode 100644
index 0000000..0e25cdc
Binary files /dev/null and b/figures/bukin6_2d.pdf differ
diff --git a/figures/cosines_1d.pdf b/figures/cosines_1d.pdf
index 05020b8..0e2c752 100644
Binary files a/figures/cosines_1d.pdf and b/figures/cosines_1d.pdf differ
diff --git a/figures/cosines_2d.pdf b/figures/cosines_2d.pdf
index 931316b..df6e0e1 100644
Binary files a/figures/cosines_2d.pdf and b/figures/cosines_2d.pdf differ
diff --git a/figures/dejong5_2d.pdf b/figures/dejong5_2d.pdf
index 8e2fd33..26762ad 100644
Binary files a/figures/dejong5_2d.pdf and b/figures/dejong5_2d.pdf differ
diff --git a/figures/dropwave_2d.pdf b/figures/dropwave_2d.pdf
index 82d8ebf..ed7b147 100644
Binary files a/figures/dropwave_2d.pdf and b/figures/dropwave_2d.pdf differ
diff --git a/figures/easom_2d.pdf b/figures/easom_2d.pdf
new file mode 100644
index 0000000..bd36c1d
Binary files /dev/null and b/figures/easom_2d.pdf differ
diff --git a/figures/eggholder_2d.pdf b/figures/eggholder_2d.pdf
index 15dcc12..33af4fd 100644
Binary files a/figures/eggholder_2d.pdf and b/figures/eggholder_2d.pdf differ
diff --git a/figures/goldsteinprice_2d.pdf b/figures/goldsteinprice_2d.pdf
index af8faee..a092f85 100644
Binary files a/figures/goldsteinprice_2d.pdf and b/figures/goldsteinprice_2d.pdf differ
diff --git a/figures/gramacyandlee2012.pdf b/figures/gramacyandlee2012_1d.pdf
similarity index 98%
rename from figures/gramacyandlee2012.pdf
rename to figures/gramacyandlee2012_1d.pdf
index 4affb26..4e7becf 100644
Binary files a/figures/gramacyandlee2012.pdf and b/figures/gramacyandlee2012_1d.pdf differ
diff --git a/figures/griewank_1d.pdf b/figures/griewank_1d.pdf
new file mode 100644
index 0000000..d52cc1a
Binary files /dev/null and b/figures/griewank_1d.pdf differ
diff --git a/figures/griewank_2d.pdf b/figures/griewank_2d.pdf
new file mode 100644
index 0000000..fd5e8f4
Binary files /dev/null and b/figures/griewank_2d.pdf differ
diff --git a/figures/griewank_zoom_in_1d.pdf b/figures/griewank_zoom_in_1d.pdf
new file mode 100644
index 0000000..84e8a2d
Binary files /dev/null and b/figures/griewank_zoom_in_1d.pdf differ
diff --git a/figures/griewank_zoom_in_2d.pdf b/figures/griewank_zoom_in_2d.pdf
new file mode 100644
index 0000000..431235a
Binary files /dev/null and b/figures/griewank_zoom_in_2d.pdf differ
diff --git a/figures/holdertable_2d.pdf b/figures/holdertable_2d.pdf
index ea5948b..1687296 100644
Binary files a/figures/holdertable_2d.pdf and b/figures/holdertable_2d.pdf differ
diff --git a/figures/kim1_2d.pdf b/figures/kim1_2d.pdf
index 5f8b90e..e12c60f 100644
Binary files a/figures/kim1_2d.pdf and b/figures/kim1_2d.pdf differ
diff --git a/figures/kim2_2d.pdf b/figures/kim2_2d.pdf
index 4172c3e..591fc98 100644
Binary files a/figures/kim2_2d.pdf and b/figures/kim2_2d.pdf differ
diff --git a/figures/kim3_2d.pdf b/figures/kim3_2d.pdf
index 8a1b595..c1caa12 100644
Binary files a/figures/kim3_2d.pdf and b/figures/kim3_2d.pdf differ
diff --git a/figures/levy_1d.pdf b/figures/levy_1d.pdf
new file mode 100644
index 0000000..c298ccc
Binary files /dev/null and b/figures/levy_1d.pdf differ
diff --git a/figures/levy_2d.pdf b/figures/levy_2d.pdf
new file mode 100644
index 0000000..80fe9e4
Binary files /dev/null and b/figures/levy_2d.pdf differ
diff --git a/figures/michalewicz_2d.pdf b/figures/michalewicz_2d.pdf
index 8f88935..ef98398 100644
Binary files a/figures/michalewicz_2d.pdf and b/figures/michalewicz_2d.pdf differ
diff --git a/figures/rastrigin_1d.pdf b/figures/rastrigin_1d.pdf
new file mode 100644
index 0000000..c1265bb
Binary files /dev/null and b/figures/rastrigin_1d.pdf differ
diff --git a/figures/rastrigin_2d.pdf b/figures/rastrigin_2d.pdf
new file mode 100644
index 0000000..816cfe0
Binary files /dev/null and b/figures/rastrigin_2d.pdf differ
diff --git a/figures/rosenbrock_2d.pdf b/figures/rosenbrock_2d.pdf
index ed00706..90c2f80 100644
Binary files a/figures/rosenbrock_2d.pdf and b/figures/rosenbrock_2d.pdf differ
diff --git a/figures/shubert_2d.pdf b/figures/shubert_2d.pdf
new file mode 100644
index 0000000..9e8e3fc
Binary files /dev/null and b/figures/shubert_2d.pdf differ
diff --git a/figures/sixhumpcamel_2d.pdf b/figures/sixhumpcamel_2d.pdf
index 524e667..60f401e 100644
Binary files a/figures/sixhumpcamel_2d.pdf and b/figures/sixhumpcamel_2d.pdf differ
diff --git a/figures/sphere_1d.pdf b/figures/sphere_1d.pdf
index 45f36d6..dde04de 100644
Binary files a/figures/sphere_1d.pdf and b/figures/sphere_1d.pdf differ
diff --git a/figures/sphere_2d.pdf b/figures/sphere_2d.pdf
index 3671f3d..59808fc 100644
Binary files a/figures/sphere_2d.pdf and b/figures/sphere_2d.pdf differ
diff --git a/figures/threehumpcamel_2d.pdf b/figures/threehumpcamel_2d.pdf
index bf5378f..4d992a3 100644
Binary files a/figures/threehumpcamel_2d.pdf and b/figures/threehumpcamel_2d.pdf differ
diff --git a/figures/zakharov_1d.pdf b/figures/zakharov_1d.pdf
new file mode 100644
index 0000000..03a01d7
Binary files /dev/null and b/figures/zakharov_1d.pdf differ
diff --git a/figures/zakharov_2d.pdf b/figures/zakharov_2d.pdf
new file mode 100644
index 0000000..a41f76f
Binary files /dev/null and b/figures/zakharov_2d.pdf differ
diff --git a/setup.py b/setup.py
index f5f770d..85c2dd4 100644
--- a/setup.py
+++ b/setup.py
@@ -8,10 +8,10 @@
 
 setup(
     name='bayeso-benchmarks',
-    version='0.1.6',
+    version='0.1.7',
     author='Jungtaek Kim',
     author_email='jtkim@postech.ac.kr',
-    url='https://github.com/jungtaekkim/bayeso-benchmarks',
+    url='https://bayeso.org',
     license='MIT',
     description='Benchmarks for Bayesian optimization',
     packages=list_packages,
diff --git a/tests/test_four_dim_colville.py b/tests/test_four_dim_colville.py
new file mode 100644
index 0000000..80c16f7
--- /dev/null
+++ b/tests/test_four_dim_colville.py
@@ -0,0 +1,226 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: December 30, 2022
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.four_dim_colville import *
+
+class_fun = Colville
+str_name = 'colville'
+
+TEST_EPSILON = 1e-5
+
+
+def test_init():
+    obj_fun = class_fun()
+
+    with pytest.raises(AssertionError) as error:
+        class_fun(seed='abc')
+    with pytest.raises(AssertionError) as error:
+        class_fun(seed=2.1)
+
+def test_validate_properties():
+    obj_fun = class_fun()
+    obj_fun.validate_properties()
+
+def test_output():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [2304082.],
+        [2111692.],
+        [1939322.],
+        [1223962.],
+        [1211572.],
+        [1219202.],
+        [2304042.],
+        [2111652.],
+        [1939282.],
+        [1103962.],
+        [911572.],
+        [739202.],
+        [23842.],
+        [11452.],
+        [19082.],
+        [1103922.],
+        [911532.],
+        [739162.],
+        [2304042.],
+        [2111652.],
+        [1939282.],
+        [1223922.],
+        [1211532.],
+        [1219162.],
+        [2304002.],
+        [2111612.],
+        [1939242.],
+        [2090692.],
+        [1900282.],
+        [1729892.],
+        [1010572.],
+        [1000162.],
+        [1009772.],
+        [2090652.],
+        [1900242.],
+        [1729852.],
+        [1090572.],
+        [900162.],
+        [729772.],
+        [10452.],
+        [42.],
+        [9652.],
+        [1090532.],
+        [900122.],
+        [729732.],
+        [2090652.],
+        [1900242.],
+        [1729852.],
+        [1010532.],
+        [1000122.],
+        [1009732.],
+        [2090612.],
+        [1900202.],
+        [1729812.],
+        [1899322.],
+        [1710892.],
+        [1542482.],
+        [819202.],
+        [810772.],
+        [822362.],
+        [1899282.],
+        [1710852.],
+        [1542442.],
+        [1099202.],
+        [910772.],
+        [742362.],
+        [19082.],
+        [10652.],
+        [22242.],
+        [1099162.],
+        [910732.],
+        [742322.],
+        [1899282.],
+        [1710852.],
+        [1542442.],
+        [819162.],
+        [810732.],
+        [822322.],
+        [1899242.],
+        [1710812.],
+        [1542402.],
+    ])
+    outputs = obj_fun(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+    assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [2304082.],
+        [2111692.],
+        [1939322.],
+        [1223962.],
+        [1211572.],
+        [1219202.],
+        [2304042.],
+        [2111652.],
+        [1939282.],
+        [1103962.],
+        [911572.],
+        [739202.],
+        [23842.],
+        [11452.],
+        [19082.],
+        [1103922.],
+        [911532.],
+        [739162.],
+        [2304042.],
+        [2111652.],
+        [1939282.],
+        [1223922.],
+        [1211532.],
+        [1219162.],
+        [2304002.],
+        [2111612.],
+        [1939242.],
+        [2090692.],
+        [1900282.],
+        [1729892.],
+        [1010572.],
+        [1000162.],
+        [1009772.],
+        [2090652.],
+        [1900242.],
+        [1729852.],
+        [1090572.],
+        [900162.],
+        [729772.],
+        [10452.],
+        [42.],
+        [9652.],
+        [1090532.],
+        [900122.],
+        [729732.],
+        [2090652.],
+        [1900242.],
+        [1729852.],
+        [1010532.],
+        [1000122.],
+        [1009732.],
+        [2090612.],
+        [1900202.],
+        [1729812.],
+        [1899322.],
+        [1710892.],
+        [1542482.],
+        [819202.],
+        [810772.],
+        [822362.],
+        [1899282.],
+        [1710852.],
+        [1542442.],
+        [1099202.],
+        [910772.],
+        [742362.],
+        [19082.],
+        [10652.],
+        [22242.],
+        [1099162.],
+        [910732.],
+        [742322.],
+        [1899282.],
+        [1710852.],
+        [1542442.],
+        [819162.],
+        [810732.],
+        [822322.],
+        [1899242.],
+        [1710812.],
+        [1542402.],
+    ])
+    outputs = obj_fun(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+    assert np.all(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_global_minimum.py b/tests/test_global_minimum.py
index 8269c03..734bd03 100644
--- a/tests/test_global_minimum.py
+++ b/tests/test_global_minimum.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: July 12, 2021
+# last updated: January 3, 2023
 #
 
 import numpy as np
@@ -14,7 +14,9 @@
 from bayeso_benchmarks import Kim1
 from bayeso_benchmarks import Kim2
 from bayeso_benchmarks import Kim3
+from bayeso_benchmarks import Rastrigin
 from bayeso_benchmarks import Michalewicz
+from bayeso_benchmarks import Shubert
 
 
 TEST_EPSILON = 1e-7
@@ -36,7 +38,7 @@ def _test_global_minimum(obj_fun):
 
     ind_minimum = np.argmin(np.squeeze(list_by))
     bx_best = list_bx[ind_minimum]
-    y_best = list_by[ind_minimum][0]
+    y_best = list_by[ind_minimum]
 
     print(bx_best)
     print(obj_fun.global_minimum)
@@ -93,8 +95,8 @@ def test_global_minimum_kim3():
 
     _test_global_minimum(obj_fun)
 
-def test_global_minimum_michalewicz():
-    class_fun = Michalewicz
+def test_global_minimum_shubert():
+    class_fun = Shubert
     obj_fun = class_fun()
 
     _test_global_minimum(obj_fun)
diff --git a/tests/test_import.py b/tests/test_import.py
index 100bee6..d5fa8ef 100644
--- a/tests/test_import.py
+++ b/tests/test_import.py
@@ -1,12 +1,70 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: January 4, 2023
 #
 
 
 def test_import_benchmarks():
     import bayeso_benchmarks
 
+def test_all_benchmarks():
+    import bayeso_benchmarks
+
+    list_str_names = [
+        'ackley',
+        'cosines',
+        'levy',
+        'rastrigin',
+        'rosenbrock',
+        'sphere',
+        'zakharov',
+        'constant',
+        'gramacyandlee2012',
+        'linear',
+        'step',
+        'colville',
+        'hartmann3d',
+        'hartmann6d',
+        'beale',
+        'bohachevsky',
+        'branin',
+        'bukin6',
+        'dejong5',
+        'dropwave',
+        'easom',
+        'eggholder',
+        'goldsteinprice',
+        'holdertable',
+        'kim1',
+        'kim2',
+        'kim3',
+        'michalewicz',
+        'shubert',
+        'sixhumpcamel',
+        'threehumpcamel',
+    ]
+
+    names = []
+    for class_benchmark in bayeso_benchmarks.all_benchmarks:
+        print(class_benchmark.__name__.lower())
+        names.append(class_benchmark.__name__.lower())
+
+    for str_name in list_str_names:
+        assert str_name in names
+
+    qualnames = []
+    for class_benchmark in bayeso_benchmarks.all_benchmarks:
+        print(class_benchmark.__qualname__.lower())
+        qualnames.append(class_benchmark.__qualname__.lower())
+
+    for str_name in list_str_names:
+        assert str_name in qualnames
+
+def test_num_benchmarks():
+    import bayeso_benchmarks
+
+    assert bayeso_benchmarks.num_benchmarks == 32
+
 def test_import_benchmark_base():
     import bayeso_benchmarks.benchmark_base
     from bayeso_benchmarks.benchmark_base import Function
@@ -46,11 +104,21 @@ def test_import_two_dim_branin():
     from bayeso_benchmarks.two_dim_branin import Branin
     from bayeso_benchmarks import Branin
 
+def test_import_two_dim_bukin6():
+    import bayeso_benchmarks.two_dim_bukin6
+    from bayeso_benchmarks.two_dim_bukin6 import Bukin6
+    from bayeso_benchmarks import Bukin6
+
 def test_import_two_dim_dropwave():
     import bayeso_benchmarks.two_dim_dropwave
     from bayeso_benchmarks.two_dim_dropwave import DropWave
     from bayeso_benchmarks import DropWave
 
+def test_import_two_dim_easom():
+    import bayeso_benchmarks.two_dim_easom
+    from bayeso_benchmarks.two_dim_easom import Easom
+    from bayeso_benchmarks import Easom
+
 def test_import_two_dim_eggholder():
     import bayeso_benchmarks.two_dim_eggholder
     from bayeso_benchmarks.two_dim_eggholder import Eggholder
@@ -86,6 +154,11 @@ def test_import_two_dim_michalewicz():
     from bayeso_benchmarks.two_dim_michalewicz import Michalewicz
     from bayeso_benchmarks import Michalewicz
 
+def test_import_two_dim_shubert():
+    import bayeso_benchmarks.two_dim_shubert
+    from bayeso_benchmarks.two_dim_shubert import Shubert
+    from bayeso_benchmarks import Shubert
+
 def test_import_two_dim_sixhumpcamel():
     import bayeso_benchmarks.two_dim_sixhumpcamel
     from bayeso_benchmarks.two_dim_sixhumpcamel import SixHumpCamel
@@ -96,6 +169,11 @@ def test_import_two_dim_threehumpcamel():
     from bayeso_benchmarks.two_dim_threehumpcamel import ThreeHumpCamel
     from bayeso_benchmarks import ThreeHumpCamel
 
+def test_import_four_dim_colville():
+    import bayeso_benchmarks.four_dim_colville
+    from bayeso_benchmarks.four_dim_colville import Colville
+    from bayeso_benchmarks import Colville
+
 def test_import_three_dim_hartmann3d():
     import bayeso_benchmarks.three_dim_hartmann3d
     from bayeso_benchmarks.three_dim_hartmann3d import Hartmann3D
@@ -111,6 +189,26 @@ def test_import_inf_dim_ackley():
     from bayeso_benchmarks.inf_dim_ackley import Ackley
     from bayeso_benchmarks import Ackley
 
+def test_import_inf_dim_cosines():
+    import bayeso_benchmarks.inf_dim_cosines
+    from bayeso_benchmarks.inf_dim_cosines import Cosines
+    from bayeso_benchmarks import Cosines
+
+def test_import_inf_dim_griewank():
+    import bayeso_benchmarks.inf_dim_griewank
+    from bayeso_benchmarks.inf_dim_griewank import Griewank
+    from bayeso_benchmarks import Griewank
+
+def test_import_inf_dim_levy():
+    import bayeso_benchmarks.inf_dim_levy
+    from bayeso_benchmarks.inf_dim_levy import Levy
+    from bayeso_benchmarks import Levy
+
+def test_import_inf_dim_rastrigin():
+    import bayeso_benchmarks.inf_dim_rastrigin
+    from bayeso_benchmarks.inf_dim_rastrigin import Rastrigin
+    from bayeso_benchmarks import Rastrigin
+
 def test_import_inf_dim_rosenbrock():
     import bayeso_benchmarks.inf_dim_rosenbrock
     from bayeso_benchmarks.inf_dim_rosenbrock import Rosenbrock
@@ -120,3 +218,8 @@ def test_import_inf_dim_sphere():
     import bayeso_benchmarks.inf_dim_sphere
     from bayeso_benchmarks.inf_dim_sphere import Sphere
     from bayeso_benchmarks import Sphere
+
+def test_import_inf_dim_zakharov():
+    import bayeso_benchmarks.inf_dim_zakharov
+    from bayeso_benchmarks.inf_dim_zakharov import Zakharov
+    from bayeso_benchmarks import Zakharov
diff --git a/tests/test_inf_dim_ackley.py b/tests/test_inf_dim_ackley.py
index f78150f..285a087 100644
--- a/tests/test_inf_dim_ackley.py
+++ b/tests/test_inf_dim_ackley.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.inf_dim_ackley import *
 
 class_fun = Ackley
+str_name = 'ackley'
 
 TEST_EPSILON = 1e-5
 
@@ -70,3 +71,56 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun(3)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [2.15703112e+01],
+        [2.11187470e+01],
+        [2.15703112e+01],
+        [2.11187470e+01],
+        [2.02411230e+01],
+        [2.11187470e+01],
+        [2.15703112e+01],
+        [2.11187470e+01],
+        [2.15703112e+01],
+        [2.11187470e+01],
+        [2.02411230e+01],
+        [2.11187470e+01],
+        [2.02411230e+01],
+        [4.44089210e-16],
+        [2.02411230e+01],
+        [2.11187470e+01],
+        [2.02411230e+01],
+        [2.11187470e+01],
+        [2.15703112e+01],
+        [2.11187470e+01],
+        [2.15703112e+01],
+        [2.11187470e+01],
+        [2.02411230e+01],
+        [2.11187470e+01],
+        [2.15703112e+01],
+        [2.11187470e+01],
+        [2.15703112e+01],
+    ])
+    
+    print(grids)
+    print(obj_fun(grids))
+    print(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun(2)
+    assert obj_fun.name == str_name + '_2'
+
+    obj_fun = class_fun(4)
+    assert obj_fun.name == str_name + '_4'
+
+    obj_fun = class_fun(16)
+    assert obj_fun.name == str_name + '_16'
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_inf_dim_cosines.py b/tests/test_inf_dim_cosines.py
index 3c12c46..c274d56 100644
--- a/tests/test_inf_dim_cosines.py
+++ b/tests/test_inf_dim_cosines.py
@@ -1,15 +1,15 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
 import pytest
 
 from bayeso_benchmarks.inf_dim_cosines import *
-# last updated: February 8, 2021
 
 class_fun = Cosines
+str_name = 'cosines'
 
 TEST_EPSILON = 1e-5
 
@@ -71,3 +71,16 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun(2)
+    assert obj_fun.name == str_name + '_2'
+
+    obj_fun = class_fun(4)
+    assert obj_fun.name == str_name + '_4'
+
+    obj_fun = class_fun(16)
+    assert obj_fun.name == str_name + '_16'
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_inf_dim_griewank.py b/tests/test_inf_dim_griewank.py
new file mode 100644
index 0000000..1a2322f
--- /dev/null
+++ b/tests/test_inf_dim_griewank.py
@@ -0,0 +1,417 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 6, 2023
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.inf_dim_griewank import *
+
+class_fun = Griewank
+str_name = 'griewank'
+
+TEST_EPSILON = 1e-5
+SCALE_NOISE = 2.0
+SEED = 42
+
+
+def test_init():
+    obj_fun = class_fun(16)
+
+    with pytest.raises(AssertionError) as error:
+        class_fun(1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun('abc')
+    with pytest.raises(AssertionError) as error:
+        class_fun(4, seed=1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun(4, seed='abc')
+
+def test_validate_properties():
+    obj_fun = class_fun(8)
+    obj_fun.validate_properties()
+
+def test_output():
+    obj_fun = class_fun(4)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+        [271.02183025],
+        [180.01205465],
+        [271.02183025],
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+        [270.98533321],
+        [181.66375729],
+        [270.98533321],
+        [180.97814841],
+        [91.98891104],
+        [180.97814841],
+        [270.98533321],
+        [181.66375729],
+        [270.98533321],
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+        [271.02183025],
+        [180.01205465],
+        [271.02183025],
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+        [270.98518323],
+        [181.67054476],
+        [270.98518323],
+        [180.97792496],
+        [91.99902348],
+        [180.97792496],
+        [270.98518323],
+        [181.67054476],
+        [270.98518323],
+        [181.01483126],
+        [90.3287998],
+        [181.01483126],
+        [91.02209662],
+        [0.],
+        [91.02209662],
+        [181.01483126],
+        [90.3287998],
+        [181.01483126],
+        [270.98518323],
+        [181.67054476],
+        [270.98518323],
+        [180.97792496],
+        [91.99902348],
+        [180.97792496],
+        [270.98518323],
+        [181.67054476],
+        [270.98518323],
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+        [271.02183025],
+        [180.01205465],
+        [271.02183025],
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+        [270.98533321],
+        [181.66375729],
+        [270.98533321],
+        [180.97814841],
+        [91.98891104],
+        [180.97814841],
+        [270.98533321],
+        [181.66375729],
+        [270.98533321],
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+        [271.02183025],
+        [180.01205465],
+        [271.02183025],
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+    ])
+    outputs = obj_fun.output(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun(2)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [180.01205465],
+        [91.98891104],
+        [180.01205465],
+        [91.99902348],
+        [0.],
+        [91.99902348],
+        [180.01205465],
+        [91.98891104],
+        [180.01205465],
+    ])
+    outputs = obj_fun(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_constant_noise():
+    obj_fun = class_fun(2)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [182.01205465],
+        [93.98891104],
+        [182.01205465],
+        [93.99902348],
+        [2.],
+        [93.99902348],
+        [182.01205465],
+        [93.98891104],
+        [182.01205465],
+    ])
+    outputs = obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+
+def test_output_gaussian_noise():
+    obj_fun = class_fun(4, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [362.00808078],
+        [270.06036228],
+        [362.31002955],
+        [274.06788997],
+        [179.5437479],
+        [270.55355634],
+        [364.1730781],
+        [271.87176034],
+        [360.0757037],
+        [272.07045329],
+        [180.73692191],
+        [270.0538737],
+        [181.46207295],
+        [88.16235055],
+        [177.52831274],
+        [269.86075815],
+        [179.63809505],
+        [271.61382787],
+        [359.19860432],
+        [267.51228348],
+        [363.94595001],
+        [270.57027765],
+        [180.14711106],
+        [268.17233388],
+        [359.92588702],
+        [270.55873606],
+        [358.71266531],
+        [271.73657926],
+        [180.46926738],
+        [270.40179573],
+        [179.77451173],
+        [95.70357985],
+        [180.95093051],
+        [268.86976137],
+        [183.31563459],
+        [268.54349593],
+        [181.43255845],
+        [86.40945955],
+        [178.35845916],
+        [91.41581909],
+        [1.47693316],
+        [91.36483318],
+        [180.78353469],
+        [89.72659241],
+        [178.05778727],
+        [269.54549481],
+        [180.74926722],
+        [273.09942768],
+        [181.66516154],
+        [88.47294317],
+        [181.6260929],
+        [270.21501867],
+        [180.31670076],
+        [272.20853581],
+        [363.07665151],
+        [272.19945112],
+        [359.33621742],
+        [270.4034055],
+        [180.67458151],
+        [272.97292051],
+        [360.05630399],
+        [269.96557293],
+        [358.80198252],
+        [268.59291996],
+        [183.28880894],
+        [273.69781326],
+        [180.83412817],
+        [93.99597684],
+        [181.70142046],
+        [269.6950937],
+        [182.3865485],
+        [274.06140634],
+        [360.94300039],
+        [273.46617819],
+        [355.77516226],
+        [272.66563526],
+        [180.18614879],
+        [270.42381555],
+        [361.19817402],
+        [266.36175305],
+        [360.57530869],
+    ])
+    outputs = obj_fun.output_gaussian_noise(grids, scale_noise=SCALE_NOISE)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_gaussian_noise():
+    obj_fun = class_fun(4, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [361.01465247],
+        [270.33689088],
+        [362.31002955],
+        [274.06788997],
+        [179.5437479],
+        [270.55355634],
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+        [270.98533321],
+        [181.66375729],
+        [270.0538737],
+        [180.97814841],
+        [91.98891104],
+        [180.97814841],
+        [270.98533321],
+        [181.66375729],
+        [271.61382787],
+        [359.19860432],
+        [270.33689088],
+        [361.01465247],
+        [271.02183025],
+        [180.14711106],
+        [271.02183025],
+        [361.01465247],
+        [270.55873606],
+        [358.71266531],
+        [270.98518323],
+        [181.67054476],
+        [270.98518323],
+        [180.97792496],
+        [91.99902348],
+        [180.97792496],
+        [270.98518323],
+        [181.67054476],
+        [268.54349593],
+        [181.01483126],
+        [90.3287998],
+        [178.35845916],
+        [91.41581909],
+        [0.],
+        [91.02209662],
+        [180.78353469],
+        [89.72659241],
+        [181.01483126],
+        [269.54549481],
+        [180.74926722],
+        [270.98518323],
+        [180.97792496],
+        [88.47294317],
+        [180.97792496],
+        [270.98518323],
+        [180.31670076],
+        [270.98518323],
+        [361.01465247],
+        [270.33689088],
+        [361.01465247],
+        [271.02183025],
+        [180.67458151],
+        [271.02183025],
+        [361.01465247],
+        [269.96557293],
+        [358.80198252],
+        [270.98533321],
+        [181.66375729],
+        [273.69781326],
+        [180.97814841],
+        [93.99597684],
+        [180.97814841],
+        [269.6950937],
+        [181.66375729],
+        [270.98533321],
+        [361.01465247],
+        [273.46617819],
+        [361.01465247],
+        [272.66563526],
+        [180.01205465],
+        [271.02183025],
+        [361.19817402],
+        [270.33689088],
+        [361.01465247],
+    ])
+    outputs = obj_fun.output_sparse_gaussian_noise(grids, scale_noise=SCALE_NOISE, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_student_t_noise():
+    obj_fun = class_fun(1, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [93.14825808],
+        [-2.16338047],
+        [94.63714051],
+    ])
+    outputs = obj_fun.output_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_student_t_noise():
+    obj_fun = class_fun(2, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [181.16128925],
+        [89.82553057],
+        [180.01205465],
+        [91.99902348],
+        [0.],
+        [91.99902348],
+        [176.08376221],
+        [91.98891104],
+        [180.01205465],
+    ])
+    outputs = obj_fun.output_sparse_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun(2)
+    assert obj_fun.name == str_name + '_2'
+
+    obj_fun = class_fun(4)
+    assert obj_fun.name == str_name + '_4'
+
+    obj_fun = class_fun(16)
+    assert obj_fun.name == str_name + '_16'
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_inf_dim_levy.py b/tests/test_inf_dim_levy.py
new file mode 100644
index 0000000..0391023
--- /dev/null
+++ b/tests/test_inf_dim_levy.py
@@ -0,0 +1,1784 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: December 29, 2022
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.inf_dim_levy import *
+
+class_fun = Levy
+str_name = 'levy'
+
+TEST_EPSILON = 1e-5
+SCALE_NOISE = 2.0
+SEED = 42
+
+
+def test_init():
+    obj_fun = class_fun(16)
+
+    with pytest.raises(AssertionError) as error:
+        class_fun(1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun('abc')
+    with pytest.raises(AssertionError) as error:
+        class_fun(4, seed=1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun(4, seed='abc')
+
+def test_validate_properties():
+    obj_fun = class_fun(8)
+    obj_fun.validate_properties()
+
+def test_output():
+    obj_fun = class_fun(4)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [254.89842686],
+        [239.89842686],
+        [249.89842686],
+        [175.23146246],
+        [160.23146246],
+        [170.23146246],
+        [228.53220902],
+        [213.53220902],
+        [223.53220902],
+        [175.23146246],
+        [160.23146246],
+        [170.23146246],
+        [95.56449806],
+        [80.56449806],
+        [90.56449806],
+        [148.86524462],
+        [133.86524462],
+        [143.86524462],
+        [228.53220902],
+        [213.53220902],
+        [223.53220902],
+        [148.86524462],
+        [133.86524462],
+        [143.86524462],
+        [202.16599118],
+        [187.16599118],
+        [197.16599118],
+        [175.23146246],
+        [160.23146246],
+        [170.23146246],
+        [95.56449806],
+        [80.56449806],
+        [90.56449806],
+        [148.86524462],
+        [133.86524462],
+        [143.86524462],
+        [95.56449806],
+        [80.56449806],
+        [90.56449806],
+        [15.89753366],
+        [0.89753366],
+        [10.89753366],
+        [69.19828022],
+        [54.19828022],
+        [64.19828022],
+        [148.86524462],
+        [133.86524462],
+        [143.86524462],
+        [69.19828022],
+        [54.19828022],
+        [64.19828022],
+        [122.49902679],
+        [107.49902679],
+        [117.49902679],
+        [228.53220902],
+        [213.53220902],
+        [223.53220902],
+        [148.86524462],
+        [133.86524462],
+        [143.86524462],
+        [202.16599118],
+        [187.16599118],
+        [197.16599118],
+        [148.86524462],
+        [133.86524462],
+        [143.86524462],
+        [69.19828022],
+        [54.19828022],
+        [64.19828022],
+        [122.49902679],
+        [107.49902679],
+        [117.49902679],
+        [202.16599118],
+        [187.16599118],
+        [197.16599118],
+        [122.49902679],
+        [107.49902679],
+        [117.49902679],
+        [175.79977335],
+        [160.79977335],
+        [170.79977335],
+    ])
+    outputs = obj_fun.output(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun(6)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [414.41404476],
+        [399.41404476],
+        [409.41404476],
+        [334.74708036],
+        [319.74708036],
+        [329.74708036],
+        [388.04782692],
+        [373.04782692],
+        [383.04782692],
+        [334.74708036],
+        [319.74708036],
+        [329.74708036],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [388.04782692],
+        [373.04782692],
+        [383.04782692],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [334.74708036],
+        [319.74708036],
+        [329.74708036],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [388.04782692],
+        [373.04782692],
+        [383.04782692],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [334.74708036],
+        [319.74708036],
+        [329.74708036],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [95.74618717],
+        [80.74618717],
+        [90.74618717],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [388.04782692],
+        [373.04782692],
+        [383.04782692],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [308.94917342],
+        [293.94917342],
+        [303.94917342],
+        [334.74708036],
+        [319.74708036],
+        [329.74708036],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [95.74618717],
+        [80.74618717],
+        [90.74618717],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [95.74618717],
+        [80.74618717],
+        [90.74618717],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [95.74618717],
+        [80.74618717],
+        [90.74618717],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [95.74618717],
+        [80.74618717],
+        [90.74618717],
+        [16.07922277],
+        [1.07922277],
+        [11.07922277],
+        [69.37996933],
+        [54.37996933],
+        [64.37996933],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [69.37996933],
+        [54.37996933],
+        [64.37996933],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [69.37996933],
+        [54.37996933],
+        [64.37996933],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [69.37996933],
+        [54.37996933],
+        [64.37996933],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [229.28220902],
+        [214.28220902],
+        [224.28220902],
+        [388.04782692],
+        [373.04782692],
+        [383.04782692],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [308.94917342],
+        [293.94917342],
+        [303.94917342],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [69.37996933],
+        [54.37996933],
+        [64.37996933],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [229.28220902],
+        [214.28220902],
+        [224.28220902],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [308.94917342],
+        [293.94917342],
+        [303.94917342],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [229.28220902],
+        [214.28220902],
+        [224.28220902],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [308.94917342],
+        [293.94917342],
+        [303.94917342],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [229.28220902],
+        [214.28220902],
+        [224.28220902],
+        [308.94917342],
+        [293.94917342],
+        [303.94917342],
+        [229.28220902],
+        [214.28220902],
+        [224.28220902],
+        [282.58295558],
+        [267.58295558],
+        [277.58295558],
+    ])
+    outputs = obj_fun(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_constant_noise():
+    obj_fun = class_fun(2)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [97.38280895],
+        [17.71584455],
+        [71.01659112],
+        [82.38280895],
+        [2.71584455],
+        [56.01659112],
+        [92.38280895],
+        [12.71584455],
+        [66.01659112],
+    ])
+
+    print(grids)
+    print(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE))
+    print(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+    assert np.all(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+
+def test_output_gaussian_noise():
+    obj_fun = class_fun(4, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [255.89185516],
+        [239.62189825],
+        [251.19380393],
+        [178.27752217],
+        [159.76315571],
+        [169.76318854],
+        [231.69063465],
+        [215.06707848],
+        [222.59326025],
+        [176.31658254],
+        [159.30462707],
+        [169.30000295],
+        [96.0484226],
+        [76.73793757],
+        [87.1146624],
+        [147.74066956],
+        [131.83958238],
+        [144.49373929],
+        [226.71616087],
+        [210.70760162],
+        [226.46350656],
+        [148.41369202],
+        [134.00030103],
+        [141.01574825],
+        [201.07722574],
+        [187.38783636],
+        [194.86400403],
+        [175.98285849],
+        [159.03018508],
+        [169.64807496],
+        [94.36108484],
+        [84.26905443],
+        [90.53750361],
+        [146.74982276],
+        [135.51033445],
+        [141.42355732],
+        [95.98222525],
+        [76.64515781],
+        [87.90812596],
+        [16.29125613],
+        [2.37446682],
+        [11.24027022],
+        [68.96698366],
+        [53.59607283],
+        [61.24123624],
+        [147.42555621],
+        [132.94396708],
+        [145.97948907],
+        [69.8855168],
+        [50.67219991],
+        [64.84644816],
+        [121.72886223],
+        [106.14518279],
+        [118.72237936],
+        [230.59420807],
+        [215.39476926],
+        [221.85377397],
+        [148.24681987],
+        [134.52777149],
+        [145.81633488],
+        [201.20764271],
+        [186.79467323],
+        [194.95332124],
+        [146.47283137],
+        [135.49029627],
+        [146.57772468],
+        [69.05425998],
+        [56.20534602],
+        [64.92155227],
+        [121.20878728],
+        [108.221818],
+        [120.57509992],
+        [202.09433911],
+        [190.2952785],
+        [191.92650098],
+        [124.1428318],
+        [107.67312092],
+        [116.90101209],
+        [175.9832949],
+        [156.82463552],
+        [170.36042957],
+    ])
+    outputs = obj_fun.output_gaussian_noise(grids, scale_noise=SCALE_NOISE)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_gaussian_noise():
+    obj_fun = class_fun(6, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [415.40747307],
+        [399.41404476],
+        [410.70942184],
+        [334.74708036],
+        [319.27877361],
+        [329.74708036],
+        [388.04782692],
+        [373.04782692],
+        [382.10887815],
+        [335.83220045],
+        [318.82024498],
+        [329.74708036],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [308.38086253],
+        [291.35520029],
+        [303.38086253],
+        [388.04782692],
+        [373.04782692],
+        [383.04782692],
+        [307.92930993],
+        [293.51591894],
+        [303.38086253],
+        [360.59284364],
+        [346.68160909],
+        [354.37962193],
+        [334.74708036],
+        [318.54580298],
+        [329.74708036],
+        [255.08011596],
+        [243.78467233],
+        [250.08011596],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [255.08011596],
+        [236.16077572],
+        [247.42374387],
+        [175.80687404],
+        [160.41315157],
+        [170.75588813],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [308.38086253],
+        [292.45958498],
+        [305.49510698],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [265.66080069],
+        [277.01464469],
+        [388.04782692],
+        [373.04782692],
+        [381.36939188],
+        [308.38086253],
+        [293.38086253],
+        [305.33195278],
+        [361.68160909],
+        [346.31029114],
+        [356.68160909],
+        [308.38086253],
+        [295.00591417],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [361.60995701],
+        [346.68160909],
+        [351.44211888],
+        [282.01464469],
+        [267.01464469],
+        [276.41662999],
+        [335.31539125],
+        [320.31539125],
+        [329.87604748],
+        [334.74708036],
+        [319.74708036],
+        [328.71053992],
+        [255.08011596],
+        [239.07660188],
+        [250.08011596],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [255.08011596],
+        [240.08011596],
+        [250.08011596],
+        [175.41315157],
+        [159.62893526],
+        [170.41315157],
+        [229.30613868],
+        [214.23600867],
+        [223.72412504],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.0284691],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [255.08011596],
+        [240.08011596],
+        [246.24257353],
+        [175.36012382],
+        [160.41315157],
+        [175.33963579],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [95.74618717],
+        [80.74618717],
+        [90.74618717],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [130.94560687],
+        [144.18405968],
+        [202.34768029],
+        [188.29486515],
+        [195.50883182],
+        [311.48073134],
+        [291.81435594],
+        [302.73673949],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [263.79967822],
+        [277.01464469],
+        [228.71389813],
+        [215.27754387],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [188.04057671],
+        [197.34768029],
+        [282.47915209],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [194.9650733],
+        [256.96153407],
+        [238.69906352],
+        [252.22259606],
+        [388.04782692],
+        [373.04782692],
+        [384.97457918],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [361.68160909],
+        [345.17413676],
+        [356.68160909],
+        [308.38086253],
+        [293.38086253],
+        [304.06316648],
+        [228.71389813],
+        [215.36826463],
+        [223.73990191],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [362.93294378],
+        [346.68160909],
+        [354.53982409],
+        [282.97958952],
+        [267.01464469],
+        [277.01464469],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [308.38086253],
+        [292.48783262],
+        [303.38086253],
+        [228.71389813],
+        [211.22242057],
+        [223.71389813],
+        [282.01464469],
+        [265.24692982],
+        [277.3220949],
+        [228.71389813],
+        [211.42795753],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [282.01464469],
+        [268.04474006],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [282.01464469],
+        [271.64396182],
+        [277.01464469],
+        [335.31539125],
+        [317.08995951],
+        [330.31539125],
+        [284.19254588],
+        [267.01464469],
+        [277.01464469],
+        [200.91707287],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.73957054],
+        [249.34522616],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [256.02133549],
+        [240.64842686],
+        [250.64842686],
+        [308.94917342],
+        [293.71970054],
+        [303.94917342],
+        [334.74708036],
+        [319.74708036],
+        [329.74708036],
+        [255.08011596],
+        [240.08011596],
+        [253.61102444],
+        [308.38086253],
+        [293.38086253],
+        [305.21658642],
+        [255.08011596],
+        [240.08011596],
+        [247.04137603],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [229.60153698],
+        [213.71389813],
+        [223.71389813],
+        [308.38086253],
+        [293.38086253],
+        [301.33208724],
+        [228.71389813],
+        [211.21833176],
+        [226.97872074],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [255.08011596],
+        [240.08011596],
+        [252.40644347],
+        [175.41315157],
+        [160.41315157],
+        [170.41315157],
+        [229.11201752],
+        [213.71389813],
+        [223.71389813],
+        [174.64252437],
+        [160.64018626],
+        [170.41315157],
+        [95.74618717],
+        [78.27055617],
+        [90.74618717],
+        [145.14275813],
+        [134.04693373],
+        [144.04693373],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [148.06093186],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [185.9618611],
+        [199.14688004],
+        [308.99546157],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [283.23538522],
+        [267.01464469],
+        [277.01464469],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [133.61157132],
+        [146.24448743],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [282.01464469],
+        [268.37855063],
+        [276.39411118],
+        [202.34768029],
+        [187.34768029],
+        [197.54167222],
+        [255.64842686],
+        [239.01198549],
+        [250.64842686],
+        [253.0680812],
+        [240.08011596],
+        [250.08011596],
+        [175.41315157],
+        [161.6613912],
+        [171.66984258],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [174.05882814],
+        [160.41315157],
+        [170.41315157],
+        [95.74618717],
+        [80.10341549],
+        [90.74618717],
+        [147.91948463],
+        [132.40249294],
+        [144.04693373],
+        [229.20383127],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [141.23200618],
+        [202.34768029],
+        [187.34768029],
+        [197.96949542],
+        [175.41315157],
+        [162.12847081],
+        [170.09327451],
+        [95.74618717],
+        [78.74112844],
+        [90.74618717],
+        [149.04693373],
+        [134.04693373],
+        [142.39247184],
+        [96.7848802],
+        [83.81166499],
+        [90.74618717],
+        [16.07922277],
+        [2.45951075],
+        [11.07922277],
+        [69.8281543],
+        [54.37996933],
+        [64.37996933],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [69.37996933],
+        [54.37996933],
+        [68.68633425],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [129.79914228],
+        [142.99542369],
+        [200.82941497],
+        [187.64846787],
+        [197.34768029],
+        [152.79927541],
+        [134.04693373],
+        [144.04693373],
+        [67.58313999],
+        [54.37996933],
+        [64.37996933],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [198.92141123],
+        [187.34768029],
+        [197.34768029],
+        [122.6807159],
+        [104.49186058],
+        [117.6807159],
+        [175.99194986],
+        [161.07542365],
+        [170.08133151],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [279.56999833],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [202.34768029],
+        [187.34768029],
+        [196.849752],
+        [282.01464469],
+        [267.01464469],
+        [279.75190781],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [252.13094788],
+        [240.64842686],
+        [250.64842686],
+        [228.17508446],
+        [215.14898264],
+        [223.71389813],
+        [149.19512329],
+        [134.04693373],
+        [144.04693373],
+        [198.94091541],
+        [187.34768029],
+        [197.34768029],
+        [149.04693373],
+        [134.04693373],
+        [144.04693373],
+        [66.77103033],
+        [54.37996933],
+        [64.37996933],
+        [120.80095632],
+        [107.6807159],
+        [117.6807159],
+        [202.34768029],
+        [189.25796493],
+        [197.34768029],
+        [122.6807159],
+        [106.62020066],
+        [116.09497023],
+        [175.76740174],
+        [160.98146246],
+        [170.98146246],
+        [282.01464469],
+        [267.01464469],
+        [277.08517179],
+        [202.34768029],
+        [187.34768029],
+        [197.12302419],
+        [255.20648766],
+        [241.87676026],
+        [250.64842686],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [179.27139788],
+        [160.48339038],
+        [172.13457638],
+        [256.27092716],
+        [240.64842686],
+        [250.64842686],
+        [175.72562727],
+        [159.07038158],
+        [170.98146246],
+        [229.28220902],
+        [214.28220902],
+        [224.28220902],
+        [386.75468116],
+        [370.88473092],
+        [383.04782692],
+        [308.38086253],
+        [293.36491724],
+        [303.38086253],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [226.96266162],
+        [210.94829867],
+        [223.71389813],
+        [282.01464469],
+        [264.21750954],
+        [277.01464469],
+        [361.68160909],
+        [345.70735832],
+        [356.68160909],
+        [282.01464469],
+        [267.01464469],
+        [275.35274446],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.91256274],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [227.38865061],
+        [213.71389813],
+        [223.71389813],
+        [149.04693373],
+        [130.79184885],
+        [144.04693373],
+        [202.34768029],
+        [185.53904704],
+        [197.34768029],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [361.68160909],
+        [346.68160909],
+        [356.68160909],
+        [282.01464469],
+        [267.01464469],
+        [275.42085418],
+        [335.31539125],
+        [319.90930048],
+        [330.31539125],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [198.02288562],
+        [255.64842686],
+        [239.67321441],
+        [249.78331048],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [308.94917342],
+        [293.94917342],
+        [299.87292435],
+        [308.38086253],
+        [293.38086253],
+        [303.38086253],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [228.71389813],
+        [215.25562852],
+        [220.75672564],
+        [151.33444182],
+        [134.04693373],
+        [144.04693373],
+        [203.61324403],
+        [187.34768029],
+        [197.7114128],
+        [282.01464469],
+        [267.01464469],
+        [275.31495595],
+        [202.34768029],
+        [185.63551264],
+        [197.49081277],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [228.71389813],
+        [213.71389813],
+        [223.71389813],
+        [147.0894063],
+        [134.04693373],
+        [144.80153472],
+        [203.86165753],
+        [187.34768029],
+        [199.08689213],
+        [149.04693373],
+        [134.04693373],
+        [147.80052536],
+        [69.37996933],
+        [51.89065993],
+        [64.37996933],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [202.90761755],
+        [187.34768029],
+        [197.34768029],
+        [122.6807159],
+        [107.6807159],
+        [117.6807159],
+        [176.94348092],
+        [161.42923051],
+        [170.98146246],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [186.32524894],
+        [197.34768029],
+        [255.39685302],
+        [240.64842686],
+        [250.64842686],
+        [202.34768029],
+        [190.40678093],
+        [197.34768029],
+        [122.6807159],
+        [107.6807159],
+        [114.37100255],
+        [175.98146246],
+        [160.98146246],
+        [168.40154066],
+        [253.05826931],
+        [239.97685746],
+        [250.64842686],
+        [175.98146246],
+        [160.98146246],
+        [170.48997633],
+        [229.28220902],
+        [214.28220902],
+        [224.28220902],
+        [361.68160909],
+        [348.07402182],
+        [356.68160909],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [335.31539125],
+        [320.31539125],
+        [330.34324984],
+        [281.96639452],
+        [267.01464469],
+        [277.01464469],
+        [201.20035628],
+        [186.25396241],
+        [197.28217375],
+        [255.64842686],
+        [239.22273529],
+        [250.64842686],
+        [334.80543682],
+        [320.31539125],
+        [325.01345164],
+        [255.64842686],
+        [243.14059724],
+        [250.64842686],
+        [308.26379823],
+        [293.94917342],
+        [303.94917342],
+        [282.01464469],
+        [267.01464469],
+        [277.01464469],
+        [202.34768029],
+        [187.34768029],
+        [197.34768029],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [202.34768029],
+        [188.77367715],
+        [197.34768029],
+        [122.6807159],
+        [109.10263583],
+        [117.6807159],
+        [175.25953013],
+        [160.98146246],
+        [168.8193358],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [175.98146246],
+        [160.98146246],
+        [170.98146246],
+        [228.91240475],
+        [214.28220902],
+        [226.38022747],
+        [335.31539125],
+        [320.31539125],
+        [330.31539125],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [304.7853146],
+        [293.94917342],
+        [304.37120835],
+        [255.64842686],
+        [240.64842686],
+        [250.64842686],
+        [175.90619305],
+        [160.98146246],
+        [171.20991776],
+        [229.28220902],
+        [213.5549846],
+        [224.28220902],
+        [308.94917342],
+        [293.94917342],
+        [303.94917342],
+        [229.28220902],
+        [214.28220902],
+        [224.28220902],
+        [282.58295558],
+        [267.58295558],
+        [277.58295558],
+    ])
+    outputs = obj_fun.output_sparse_gaussian_noise(grids, scale_noise=SCALE_NOISE, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_student_t_noise():
+    obj_fun = class_fun(1, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [16.7742346],
+        [-1.53838047],
+        [13.26311703],
+    ])
+    outputs = obj_fun.output_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_student_t_noise():
+    obj_fun = class_fun(2, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [96.53204355],
+        [13.55246408],
+        [69.01659112],
+        [80.38280895],
+        [0.71584455],
+        [54.01659112],
+        [86.45451651],
+        [10.71584455],
+        [64.01659112],
+    ])
+    outputs = obj_fun.output_sparse_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun(2)
+    assert obj_fun.name == str_name + '_2'
+
+    obj_fun = class_fun(4)
+    assert obj_fun.name == str_name + '_4'
+
+    obj_fun = class_fun(16)
+    assert obj_fun.name == str_name + '_16'
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_inf_dim_rastrigin.py b/tests/test_inf_dim_rastrigin.py
new file mode 100644
index 0000000..9219b55
--- /dev/null
+++ b/tests/test_inf_dim_rastrigin.py
@@ -0,0 +1,1784 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: December 20, 2022
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.inf_dim_rastrigin import *
+
+class_fun = Rastrigin
+str_name = 'rastrigin'
+
+TEST_EPSILON = 1e-5
+SCALE_NOISE = 2.0
+SEED = 42
+
+
+def test_init():
+    obj_fun = class_fun(16)
+
+    with pytest.raises(AssertionError) as error:
+        class_fun(1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun('abc')
+    with pytest.raises(AssertionError) as error:
+        class_fun(4, seed=1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun(4, seed='abc')
+
+def test_validate_properties():
+    obj_fun = class_fun(8)
+    obj_fun.validate_properties()
+
+def test_output():
+    obj_fun = class_fun(4)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [28.92471373],
+        [0.00000000],
+        [28.92471373],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+    ])
+    outputs = obj_fun.output(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun(6)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [28.92471373],
+        [0.00000000],
+        [28.92471373],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+    ])
+    outputs = obj_fun(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_constant_noise():
+    obj_fun = class_fun(2)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [59.84942745],
+        [30.92471373],
+        [59.84942745],
+        [30.92471373],
+        [2.0],
+        [30.92471373],
+        [59.84942745],
+        [30.92471373],
+        [59.84942745],
+    ])
+
+    print(grids)
+    print(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE))
+    print(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+    assert np.all(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+
+def test_output_gaussian_noise():
+    obj_fun = class_fun(4, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [116.69228321],
+        [86.49761258],
+        [116.99423198],
+        [89.82020089],
+        [57.38112070],
+        [86.30586726],
+        [118.85728053],
+        [88.30901064],
+        [114.75990613],
+        [87.85926126],
+        [56.92259207],
+        [85.84268167],
+        [58.33335199],
+        [25.09815324],
+        [54.39959179],
+        [85.64956612],
+        [55.82376521],
+        [87.40263584],
+        [113.88280675],
+        [83.94953377],
+        [118.63015244],
+        [86.32258858],
+        [57.98448386],
+        [83.92464480],
+        [114.61008945],
+        [86.99598636],
+        [113.39686775],
+        [87.52553721],
+        [56.64815007],
+        [86.19075368],
+        [56.64601423],
+        [32.62927009],
+        [57.82243300],
+        [84.65871932],
+        [59.49451728],
+        [84.33245388],
+        [58.26715464],
+        [25.00537348],
+        [55.19305535],
+        [29.31843620],
+        [1.47693316],
+        [29.26745029],
+        [57.61813089],
+        [28.32250633],
+        [54.89238347],
+        [85.33445276],
+        [56.92814991],
+        [88.88838563],
+        [58.53666403],
+        [25.39863342],
+        [58.49759539],
+        [86.00397662],
+        [56.49558345],
+        [87.99749376],
+        [117.76085395],
+        [88.63670142],
+        [114.02041986],
+        [86.15571643],
+        [58.51195431],
+        [88.72523143],
+        [114.74050643],
+        [86.40282322],
+        [113.48618496],
+        [84.38172793],
+        [59.47447910],
+        [89.48662123],
+        [57.70540721],
+        [30.93177952],
+        [58.57269950],
+        [85.48390167],
+        [58.57221866],
+        [89.85021431],
+        [115.62720282],
+        [89.90342849],
+        [110.45936469],
+        [88.41794619],
+        [58.02352159],
+        [86.17612648],
+        [115.88237646],
+        [82.79900335],
+        [115.25951113],
+    ])
+    outputs = obj_fun.output_gaussian_noise(grids, scale_noise=SCALE_NOISE)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_gaussian_noise():
+    obj_fun = class_fun(6, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [174.54171066],
+        [144.62356863],
+        [174.84365943],
+        [144.62356863],
+        [115.23054815],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [172.60933358],
+        [145.70868872],
+        [114.77201952],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [113.67319266],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.17201603],
+        [115.83391131],
+        [144.62356863],
+        [172.45951691],
+        [144.62356863],
+        [171.24629520],
+        [144.62356863],
+        [114.49757752],
+        [144.62356863],
+        [115.69885490],
+        [90.47869755],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [82.85480093],
+        [113.04248281],
+        [87.16786365],
+        [57.84942745],
+        [87.11687774],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [114.77757736],
+        [146.73781308],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [114.34501090],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [171.86984731],
+        [144.62356863],
+        [115.69885490],
+        [146.57465888],
+        [173.54828235],
+        [144.25225068],
+        [173.54828235],
+        [144.62356863],
+        [117.32390655],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.47663028],
+        [144.62356863],
+        [168.30879215],
+        [144.62356863],
+        [115.69885490],
+        [144.02555393],
+        [173.54828235],
+        [144.62356863],
+        [173.10893858],
+        [144.62356863],
+        [115.69885490],
+        [143.58702819],
+        [115.69885490],
+        [85.77062709],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.06521115],
+        [86.77414118],
+        [116.29109546],
+        [87.29625172],
+        [115.70908182],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.01342587],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [111.86131247],
+        [86.72111343],
+        [57.84942745],
+        [91.70062540],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [54.74810059],
+        [86.91126713],
+        [115.69885490],
+        [87.72132604],
+        [113.86000643],
+        [147.72343744],
+        [114.13234832],
+        [143.97944560],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [112.48388843],
+        [144.62356863],
+        [115.69885490],
+        [88.33778692],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [87.46703760],
+        [115.69885490],
+        [145.08807602],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [113.31624791],
+        [145.93667585],
+        [113.74949156],
+        [146.19773784],
+        [173.54828235],
+        [144.62356863],
+        [175.47503461],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [143.11609630],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [145.30587258],
+        [115.69885490],
+        [88.42850768],
+        [115.72485869],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [174.79961705],
+        [144.62356863],
+        [171.40649736],
+        [145.58851346],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [114.80582500],
+        [144.62356863],
+        [115.69885490],
+        [84.28266362],
+        [115.69885490],
+        [144.62356863],
+        [113.93114003],
+        [144.93101884],
+        [115.69885490],
+        [84.48820058],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [116.72895028],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [120.32817204],
+        [144.62356863],
+        [173.54828235],
+        [141.39813689],
+        [173.54828235],
+        [146.80146982],
+        [115.69885490],
+        [144.62356863],
+        [114.26824748],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.78999858],
+        [143.32036793],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.99647726],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.39409575],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [119.22976338],
+        [144.62356863],
+        [115.69885490],
+        [146.45929252],
+        [115.69885490],
+        [86.77414118],
+        [112.66011497],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [116.58649376],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [142.57479335],
+        [115.69885490],
+        [84.27857481],
+        [118.96367751],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [118.02518241],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [116.09697429],
+        [86.77414118],
+        [115.69885490],
+        [86.00351398],
+        [58.07646214],
+        [86.77414118],
+        [57.84942745],
+        [26.44908273],
+        [57.84942745],
+        [82.86996558],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [85.78813931],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [85.38832199],
+        [117.49805465],
+        [145.23816767],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [145.84430916],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.41406505],
+        [88.97169488],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [117.06276085],
+        [144.00303512],
+        [115.69885490],
+        [86.77414118],
+        [115.89284683],
+        [144.62356863],
+        [114.06241354],
+        [144.62356863],
+        [113.68682014],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [59.09766709],
+        [88.03083220],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [85.41981775],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.28194204],
+        [57.84942745],
+        [85.64669207],
+        [56.20498666],
+        [86.77414118],
+        [116.18878805],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [83.95921363],
+        [115.69885490],
+        [86.77414118],
+        [116.32067003],
+        [86.77414118],
+        [59.56474670],
+        [86.45426412],
+        [57.84942745],
+        [26.91965500],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [85.11967929],
+        [58.88812048],
+        [31.99019155],
+        [57.84942745],
+        [28.92471373],
+        [1.38028798],
+        [28.92471373],
+        [58.29761242],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [28.92471373],
+        [62.15579237],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [53.60163600],
+        [85.72263113],
+        [114.18058958],
+        [87.07492875],
+        [115.69885490],
+        [90.52648286],
+        [57.84942745],
+        [86.77414118],
+        [56.05259811],
+        [28.92471373],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [112.27258584],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [54.66057213],
+        [86.77414118],
+        [115.70934230],
+        [86.86810236],
+        [114.79872396],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [147.17892227],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [115.20092661],
+        [144.62356863],
+        [115.69885490],
+        [147.36083174],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [141.10608966],
+        [115.69885490],
+        [144.62356863],
+        [115.16004123],
+        [88.20922569],
+        [115.69885490],
+        [86.92233074],
+        [57.84942745],
+        [86.77414118],
+        [112.29209002],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [55.24048845],
+        [28.92471373],
+        [57.84942745],
+        [84.89438160],
+        [57.84942745],
+        [86.77414118],
+        [115.69885490],
+        [88.68442582],
+        [115.69885490],
+        [86.77414118],
+        [56.78891221],
+        [85.18839551],
+        [115.48479418],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.69409573],
+        [115.69885490],
+        [86.77414118],
+        [115.47419880],
+        [144.18162943],
+        [116.92718830],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [118.98879033],
+        [86.27606910],
+        [116.85196883],
+        [145.24606894],
+        [115.69885490],
+        [144.62356863],
+        [115.44301972],
+        [84.86306030],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [172.25513659],
+        [142.46047262],
+        [173.54828235],
+        [144.62356863],
+        [115.68290962],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [113.94761840],
+        [84.00854172],
+        [115.69885490],
+        [144.62356863],
+        [112.90171976],
+        [144.62356863],
+        [173.54828235],
+        [143.64931786],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [142.96166840],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.89751951],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [114.37360739],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [54.59434258],
+        [86.77414118],
+        [115.69885490],
+        [84.96550793],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [143.02977812],
+        [173.54828235],
+        [144.21747786],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [116.37406023],
+        [144.62356863],
+        [114.72364245],
+        [143.75845225],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [169.47203328],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [88.31587157],
+        [112.74168241],
+        [89.06164926],
+        [57.84942745],
+        [86.77414118],
+        [116.96441864],
+        [86.77414118],
+        [116.06258741],
+        [144.62356863],
+        [115.69885490],
+        [142.92387989],
+        [115.69885490],
+        [85.06197353],
+        [115.84198738],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [84.81661375],
+        [57.84942745],
+        [87.52874216],
+        [117.21283214],
+        [86.77414118],
+        [117.43806674],
+        [86.77414118],
+        [57.84942745],
+        [90.52773280],
+        [57.84942745],
+        [26.43540432],
+        [57.84942745],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [116.25879216],
+        [86.77414118],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [86.77414118],
+        [116.66087337],
+        [87.22190923],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [85.75170982],
+        [115.69885490],
+        [144.37199479],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [89.83324182],
+        [115.69885490],
+        [86.77414118],
+        [57.84942745],
+        [83.46442783],
+        [115.69885490],
+        [86.77414118],
+        [113.11893310],
+        [142.03341108],
+        [115.02728550],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.20736877],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [146.01598136],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.57614094],
+        [144.57531845],
+        [115.69885490],
+        [144.62356863],
+        [114.55153089],
+        [85.68042329],
+        [115.63334836],
+        [144.62356863],
+        [114.27316334],
+        [144.62356863],
+        [173.03832792],
+        [144.62356863],
+        [168.24634274],
+        [144.62356863],
+        [118.19102529],
+        [144.62356863],
+        [172.86290717],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [88.20013804],
+        [115.69885490],
+        [86.77414118],
+        [59.27134739],
+        [86.77414118],
+        [114.97692257],
+        [86.77414118],
+        [113.53672825],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.69885490],
+        [86.77414118],
+        [115.69885490],
+        [144.25376436],
+        [115.69885490],
+        [146.72158708],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [169.38442354],
+        [144.62356863],
+        [173.97031729],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [115.62358550],
+        [86.77414118],
+        [115.92731020],
+        [144.62356863],
+        [114.97163048],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [115.69885490],
+        [144.62356863],
+        [173.54828235],
+        [144.62356863],
+        [173.54828235],
+    ])
+    outputs = obj_fun.output_sparse_gaussian_noise(grids, scale_noise=SCALE_NOISE, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_student_t_noise():
+    obj_fun = class_fun(1, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [30.07394833],
+        [-2.16338047],
+        [31.56283076],
+    ])
+    outputs = obj_fun.output_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_student_t_noise():
+    obj_fun = class_fun(2, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [58.99866205],
+        [26.76133325],
+        [57.84942745],
+        [28.92471373],
+        [0.0],
+        [28.92471373],
+        [53.92113501],
+        [28.92471373],
+        [57.84942745],
+    ])
+    outputs = obj_fun.output_sparse_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun(2)
+    assert obj_fun.name == str_name + '_2'
+
+    obj_fun = class_fun(4)
+    assert obj_fun.name == str_name + '_4'
+
+    obj_fun = class_fun(16)
+    assert obj_fun.name == str_name + '_16'
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_inf_dim_rosenbrock.py b/tests/test_inf_dim_rosenbrock.py
index 647f1a2..6fe5447 100644
--- a/tests/test_inf_dim_rosenbrock.py
+++ b/tests/test_inf_dim_rosenbrock.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.inf_dim_rosenbrock import *
 
 class_fun = Rosenbrock
+str_name = 'rosenbrock'
 
 TEST_EPSILON = 1e-5
 
@@ -70,3 +71,16 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun(2)
+    assert obj_fun.name == str_name + '_2'
+
+    obj_fun = class_fun(4)
+    assert obj_fun.name == str_name + '_4'
+
+    obj_fun = class_fun(16)
+    assert obj_fun.name == str_name + '_16'
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_inf_dim_sphere.py b/tests/test_inf_dim_sphere.py
index bca9569..a84d203 100644
--- a/tests/test_inf_dim_sphere.py
+++ b/tests/test_inf_dim_sphere.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.inf_dim_sphere import *
 
 class_fun = Sphere
+str_name = 'sphere'
 
 TEST_EPSILON = 1e-5
 
@@ -70,3 +71,16 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun(2)
+    assert obj_fun.name == str_name + '_2'
+
+    obj_fun = class_fun(4)
+    assert obj_fun.name == str_name + '_4'
+
+    obj_fun = class_fun(16)
+    assert obj_fun.name == str_name + '_16'
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_inf_dim_zakharov.py b/tests/test_inf_dim_zakharov.py
new file mode 100644
index 0000000..0a1aeb4
--- /dev/null
+++ b/tests/test_inf_dim_zakharov.py
@@ -0,0 +1,416 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 4, 2023
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.inf_dim_zakharov import *
+
+class_fun = Zakharov
+str_name = 'zakharov'
+
+TEST_EPSILON = 1e-5
+SCALE_NOISE = 2.0
+SEED = 42
+
+
+def test_init():
+    obj_fun = class_fun(16)
+
+    with pytest.raises(AssertionError) as error:
+        class_fun(1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun('abc')
+    with pytest.raises(AssertionError) as error:
+        class_fun(4, seed=1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun(4, seed='abc')
+
+def test_validate_properties():
+    obj_fun = class_fun(8)
+    obj_fun.validate_properties()
+
+def test_output():
+    obj_fun = class_fun(4)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [391350.],
+        [10181.25],
+        [825.],
+        [36014.94140625],
+        [66.50390625],
+        [70149.31640625],
+        [220.3125],
+        [24726.5625],
+        [572920.3125],
+        [204441.50390625],
+        [1627.44140625],
+        [6094.62890625],
+        [10162.5],
+        [693.75],
+        [160537.5],
+        [160.25390625],
+        [70130.56640625],
+        [954882.12890625],
+        [94270.3125],
+        [201.5625],
+        [24820.3125],
+        [1721.19140625],
+        [6075.87890625],
+        [318961.81640625],
+        [900.],
+        [160631.25],
+        [1502175.],
+        [94176.5625],
+        [107.8125],
+        [24726.5625],
+        [1627.44140625],
+        [5982.12890625],
+        [318868.06640625],
+        [806.25],
+        [160537.5],
+        [1502081.25],
+        [35996.19140625],
+        [47.75390625],
+        [70130.56640625],
+        [89.0625],
+        [24595.3125],
+        [572789.0625],
+        [6075.87890625],
+        [318849.31640625],
+        [2256404.00390625],
+        [10256.25],
+        [787.5],
+        [160631.25],
+        [141.50390625],
+        [70111.81640625],
+        [954863.37890625],
+        [24801.5625],
+        [572882.8125],
+        [3264651.5625],
+        [10275.],
+        [806.25],
+        [160650.],
+        [160.25390625],
+        [70130.56640625],
+        [954882.12890625],
+        [24820.3125],
+        [572901.5625],
+        [3264670.3125],
+        [1721.19140625],
+        [6075.87890625],
+        [318961.81640625],
+        [787.5],
+        [160518.75],
+        [1502062.5],
+        [70224.31640625],
+        [954863.37890625],
+        [4578033.69140625],
+        [295.3125],
+        [24801.5625],
+        [572995.3125],
+        [6169.62890625],
+        [318943.06640625],
+        [2256497.75390625],
+        [160725.],
+        [1502156.25],
+        [6252900.],
+    ])
+    outputs = obj_fun.output(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun(2)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [3270.3125],
+        [243.06640625],
+        [125.],
+        [31.25],
+        [224.31640625],
+        [3326.5625],
+        [3345.3125],
+        [16250.87890625],
+        [51050.],
+    ])
+    outputs = obj_fun(grids)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_constant_noise():
+    obj_fun = class_fun(2)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [3.27231250e+03],
+        [2.45066406e+02],
+        [1.27000000e+02],
+        [3.32500000e+01],
+        [2.26316406e+02],
+        [3.32856250e+03],
+        [3.34731250e+03],
+        [1.62528789e+04],
+        [5.10520000e+04],
+    ])
+
+    print(grids)
+    print(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE))
+    print(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+    assert np.all(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+
+def test_output_gaussian_noise():
+    obj_fun = class_fun(4, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [391350.99342831],
+        [10180.9734714],
+        [826.29537708],
+        [36017.98746596],
+        [66.0355995],
+        [70148.84813234],
+        [223.47092563],
+        [24728.09736946],
+        [572919.37355123],
+        [204442.58902634],
+        [1626.51457086],
+        [6093.69744674],
+        [10162.98392454],
+        [689.92343951],
+        [160534.05016433],
+        [159.12933119],
+        [70128.54074401],
+        [954882.75740092],
+        [94268.49645185],
+        [198.7378926],
+        [24823.24379754],
+        [1720.73985365],
+        [6076.01396266],
+        [318958.96690988],
+        [898.91123455],
+        [160631.47184518],
+        [1502172.69801285],
+        [94177.31389604],
+        [106.61122262],
+        [24725.9791125],
+        [1626.23799303],
+        [5985.83346262],
+        [318868.0394118],
+        [804.13457814],
+        [160539.14508982],
+        [1502078.8083127],
+        [35996.60913344],
+        [43.834566],
+        [70127.91003415],
+        [89.45622247],
+        [24596.78943316],
+        [572789.40523656],
+        [6075.64760969],
+        [318848.71419886],
+        [2256401.04686227],
+        [10254.81031158],
+        [786.57872246],
+        [160633.36424445],
+        [142.19114283],
+        [70108.29032594],
+        [954864.02707419],
+        [24800.79233544],
+        [572881.458656],
+        [3264652.78585258],
+        [10277.06199904],
+        [808.11256024],
+        [160648.32156495],
+        [159.6354815],
+        [70131.22893311],
+        [954884.0799965],
+        [24819.35415152],
+        [572901.19118205],
+        [3264668.09983005],
+        [1718.798993],
+        [6077.50395789],
+        [318964.52888631],
+        [787.35597976],
+        [160520.7570658],
+        [1502063.22327205],
+        [70223.02616674],
+        [954864.10169746],
+        [4578036.76747938],
+        [295.24084792],
+        [24804.69178731],
+        [572990.07300979],
+        [6171.27271126],
+        [318943.24050039],
+        [2256497.15589155],
+        [160725.18352155],
+        [1502152.27486217],
+        [6252899.56065622],
+    ])
+    outputs = obj_fun.output_gaussian_noise(grids, scale_noise=SCALE_NOISE)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_gaussian_noise():
+    obj_fun = class_fun(4, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [391350.],
+        [10181.25],
+        [826.29537708],
+        [36017.98746596],
+        [66.0355995],
+        [70148.84813234],
+        [220.3125],
+        [24726.5625],
+        [572920.3125],
+        [204441.50390625],
+        [1627.44140625],
+        [6093.69744674],
+        [10162.5],
+        [693.75],
+        [160537.5],
+        [160.25390625],
+        [70130.56640625],
+        [954882.75740092],
+        [94268.49645185],
+        [201.5625],
+        [24820.3125],
+        [1721.19140625],
+        [6076.01396266],
+        [318961.81640625],
+        [900.],
+        [160631.47184518],
+        [1502172.69801285],
+        [94176.5625],
+        [107.8125],
+        [24726.5625],
+        [1627.44140625],
+        [5982.12890625],
+        [318868.06640625],
+        [806.25],
+        [160537.5],
+        [1502078.8083127],
+        [35996.19140625],
+        [47.75390625],
+        [70127.91003415],
+        [89.45622247],
+        [24595.3125],
+        [572789.0625],
+        [6075.64760969],
+        [318848.71419886],
+        [2256404.00390625],
+        [10254.81031158],
+        [786.57872246],
+        [160631.25],
+        [141.50390625],
+        [70108.29032594],
+        [954863.37890625],
+        [24801.5625],
+        [572881.458656],
+        [3264651.5625],
+        [10275.],
+        [806.25],
+        [160650.],
+        [160.25390625],
+        [70131.22893311],
+        [954882.12890625],
+        [24820.3125],
+        [572901.19118205],
+        [3264668.09983005],
+        [1721.19140625],
+        [6075.87890625],
+        [318964.52888631],
+        [787.5],
+        [160520.7570658],
+        [1502062.5],
+        [70223.02616674],
+        [954863.37890625],
+        [4578033.69140625],
+        [295.3125],
+        [24804.69178731],
+        [572995.3125],
+        [6171.27271126],
+        [318943.06640625],
+        [2256497.75390625],
+        [160725.18352155],
+        [1502156.25],
+        [6252900.],
+    ])
+    outputs = obj_fun.output_sparse_gaussian_noise(grids, scale_noise=SCALE_NOISE, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_student_t_noise():
+    obj_fun = class_fun(1, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [71.4617346],
+        [8.09052578],
+        [752.63811703],
+    ])
+    outputs = obj_fun.output_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_student_t_noise():
+    obj_fun = class_fun(2, seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [3271.4617346],
+        [240.90302578],
+        [125.],
+        [31.25],
+        [224.31640625],
+        [3326.5625],
+        [3341.38420756],
+        [16250.87890625],
+        [51050.],
+    ])
+    outputs = obj_fun.output_sparse_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun(2)
+    assert obj_fun.name == str_name + '_2'
+
+    obj_fun = class_fun(4)
+    assert obj_fun.name == str_name + '_4'
+
+    obj_fun = class_fun(16)
+    assert obj_fun.name == str_name + '_16'
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_one_dim_constant.py b/tests/test_one_dim_constant.py
index c53d313..67e76f6 100644
--- a/tests/test_one_dim_constant.py
+++ b/tests/test_one_dim_constant.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.one_dim_constant import *
 
 class_fun = Constant
+str_name = 'constant'
 
 TEST_EPSILON = 1e-5
 
@@ -54,3 +55,26 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [0.0],
+        [0.0],
+        [0.0],
+    ])
+    
+    print(grids)
+    print(obj_fun(grids))
+    print(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_one_dim_gramacyandlee2012.py b/tests/test_one_dim_gramacyandlee2012.py
index 13237ab..e24cf34 100644
--- a/tests/test_one_dim_gramacyandlee2012.py
+++ b/tests/test_one_dim_gramacyandlee2012.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.one_dim_gramacyandlee2012 import *
 
 class_fun = GramacyAndLee2012
+str_name = 'gramacyandlee2012'
 
 TEST_EPSILON = 1e-5
 
@@ -40,3 +41,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_one_dim_linear.py b/tests/test_one_dim_linear.py
index 8457528..3b7eee5 100644
--- a/tests/test_one_dim_linear.py
+++ b/tests/test_one_dim_linear.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.one_dim_linear import *
 
 class_fun = Linear
+str_name = 'linear'
 
 TEST_EPSILON = 1e-5
 
@@ -66,3 +67,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_one_dim_step.py b/tests/test_one_dim_step.py
index 0fe08cb..7dc9ba2 100644
--- a/tests/test_one_dim_step.py
+++ b/tests/test_one_dim_step.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.one_dim_step import *
 
 class_fun = Step
+str_name = 'step'
 
 TEST_EPSILON = 1e-5
 
@@ -55,3 +56,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_six_dim_hartmann6d.py b/tests/test_six_dim_hartmann6d.py
index ef86d2d..361ad2f 100644
--- a/tests/test_six_dim_hartmann6d.py
+++ b/tests/test_six_dim_hartmann6d.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.six_dim_hartmann6d import *
 
 class_fun = Hartmann6D
+str_name = 'hartmann6d'
 
 TEST_EPSILON = 1e-5
 
@@ -766,3 +767,752 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [-5.08911288e-03],
+        [-1.39417909e-01],
+        [-7.03642418e-02],
+        [-1.28869208e-02],
+        [-3.53479936e-01],
+        [-1.78361572e-01],
+        [-2.54689221e-04],
+        [-3.71921487e-03],
+        [-1.06424677e-03],
+        [-7.71014520e-03],
+        [-2.03302912e-01],
+        [-1.02872411e-01],
+        [-1.90092977e-02],
+        [-5.15263796e-01],
+        [-2.60720627e-01],
+        [-4.31653377e-04],
+        [-1.82166544e-03],
+        [-5.57722931e-04],
+        [-1.34601938e-04],
+        [-2.04523802e-03],
+        [-1.02727639e-03],
+        [-2.49175535e-04],
+        [-5.18358536e-03],
+        [-2.60138785e-03],
+        [-6.29850385e-05],
+        [-1.21979697e-04],
+        [-3.33663146e-05],
+        [-2.01973752e-02],
+        [-3.79140190e-01],
+        [-1.72776742e-01],
+        [-5.31354735e-02],
+        [-9.88341220e-01],
+        [-3.77506742e-01],
+        [-4.34890158e-02],
+        [-6.52731890e-01],
+        [-1.79563032e-01],
+        [-1.52764672e-02],
+        [-3.35264377e-01],
+        [-1.78871699e-01],
+        [-3.90210622e-02],
+        [-8.60382551e-01],
+        [-3.87506085e-01],
+        [-1.92650287e-02],
+        [-2.84407512e-01],
+        [-7.83150773e-02],
+        [-6.16609104e-04],
+        [-1.03409268e-02],
+        [-3.30619590e-02],
+        [-1.53231605e-03],
+        [-2.51342950e-02],
+        [-1.72736777e-02],
+        [-1.48864416e-03],
+        [-2.14985611e-02],
+        [-5.96035595e-03],
+        [-3.57243075e-03],
+        [-9.53672207e-02],
+        [-1.65567290e-01],
+        [-9.10553914e-03],
+        [-2.34536318e-01],
+        [-1.49844278e-01],
+        [-1.64812933e-03],
+        [-2.46029476e-02],
+        [-6.99010494e-03],
+        [-5.02661669e-03],
+        [-1.29938794e-01],
+        [-1.85603313e-01],
+        [-1.22155389e-02],
+        [-3.21392180e-01],
+        [-1.97089704e-01],
+        [-1.04757111e-03],
+        [-1.08440287e-02],
+        [-3.21702233e-03],
+        [-1.23405921e-04],
+        [-5.08713433e-03],
+        [-1.18652035e-01],
+        [-2.19152932e-04],
+        [-4.87166863e-03],
+        [-3.73950140e-02],
+        [-1.11007245e-04],
+        [-8.17050467e-04],
+        [-4.19803489e-04],
+        [-4.98627198e-03],
+        [-1.32458341e-01],
+        [-6.70204937e-02],
+        [-1.23500430e-02],
+        [-3.35717210e-01],
+        [-1.69856989e-01],
+        [-2.51130043e-04],
+        [-1.25998552e-03],
+        [-3.83966561e-04],
+        [-1.18067119e-02],
+        [-1.94142004e-01],
+        [-9.81966234e-02],
+        [-2.25031920e-02],
+        [-4.91644737e-01],
+        [-2.48857619e-01],
+        [-4.47221414e-03],
+        [-9.50824094e-04],
+        [-2.57363332e-04],
+        [-8.96938712e-04],
+        [-1.96888061e-03],
+        [-9.74727532e-04],
+        [-9.92865242e-04],
+        [-4.92524484e-03],
+        [-2.46720543e-03],
+        [-7.67213164e-04],
+        [-7.73899702e-05],
+        [-1.10377943e-05],
+        [-1.02145839e-02],
+        [-2.23252421e-01],
+        [-1.28189573e-01],
+        [-2.61884889e-02],
+        [-5.72693200e-01],
+        [-2.58912791e-01],
+        [-1.34364265e-02],
+        [-1.99122393e-01],
+        [-5.48427318e-02],
+        [-1.51665230e-02],
+        [-2.60067875e-01],
+        [-1.55614367e-01],
+        [-3.10431745e-02],
+        [-6.60493738e-01],
+        [-3.26162430e-01],
+        [-1.03515667e-02],
+        [-8.71324338e-02],
+        [-2.40128029e-02],
+        [-1.07842858e-03],
+        [-5.38157507e-03],
+        [-3.16372526e-02],
+        [-1.43191616e-03],
+        [-1.18269962e-02],
+        [-1.35338549e-02],
+        [-1.22548209e-03],
+        [-6.59622416e-03],
+        [-1.85266330e-03],
+        [-3.24646485e-03],
+        [-8.60695727e-02],
+        [-1.62042071e-01],
+        [-7.94897734e-03],
+        [-2.10194240e-01],
+        [-1.40928970e-01],
+        [-6.80649908e-04],
+        [-7.58417793e-03],
+        [-2.30729429e-03],
+        [-9.28302411e-03],
+        [-1.22277348e-01],
+        [-1.82060775e-01],
+        [-1.58335639e-02],
+        [-3.01279219e-01],
+        [-1.88253232e-01],
+        [-4.82231893e-03],
+        [-3.64585053e-03],
+        [-1.17571514e-03],
+        [-9.05731357e-04],
+        [-4.88738991e-03],
+        [-1.18473356e-01],
+        [-9.66875140e-04],
+        [-4.28343285e-03],
+        [-3.71602329e-02],
+        [-8.10268545e-04],
+        [-2.94248069e-04],
+        [-2.65456530e-04],
+        [-1.01866285e-03],
+        [-2.81614256e-02],
+        [-1.42683928e-02],
+        [-2.58074116e-03],
+        [-7.13682077e-02],
+        [-3.61563426e-02],
+        [-1.96640000e-06],
+        [-3.90396482e-05],
+        [-1.92547684e-05],
+        [-1.50675227e-03],
+        [-4.13036311e-02],
+        [-2.09262322e-02],
+        [-3.79755641e-03],
+        [-1.04672823e-01],
+        [-5.30289519e-02],
+        [-1.45246242e-05],
+        [-5.55811549e-05],
+        [-2.76113094e-05],
+        [-1.70921654e-05],
+        [-4.08358502e-04],
+        [-2.08294922e-04],
+        [-3.97023325e-05],
+        [-1.03459954e-03],
+        [-5.24518085e-04],
+        [-2.18273812e-06],
+        [-7.17532844e-07],
+        [-2.92797021e-07],
+        [-1.22074825e-03],
+        [-3.45514485e-02],
+        [-4.68866044e-02],
+        [-3.09402230e-03],
+        [-8.55414071e-02],
+        [-5.21413638e-02],
+        [-2.93023413e-05],
+        [-4.53875241e-04],
+        [-1.84464412e-04],
+        [-1.79439560e-03],
+        [-5.01235291e-02],
+        [-5.51586428e-02],
+        [-4.52633232e-03],
+        [-1.24968147e-01],
+        [-7.22725892e-02],
+        [-2.70749465e-05],
+        [-2.44283645e-04],
+        [-1.32013401e-04],
+        [-2.02828415e-05],
+        [-1.38096103e-03],
+        [-2.93775538e-02],
+        [-4.76589378e-05],
+        [-1.51174593e-03],
+        [-9.42872578e-03],
+        [-3.14626116e-06],
+        [-1.56943790e-05],
+        [-5.27334259e-05],
+        [-6.16929640e-04],
+        [-2.06445976e-02],
+        [-1.26429383e-01],
+        [-1.56243078e-03],
+        [-4.42787658e-02],
+        [-5.74711985e-02],
+        [-2.33338971e-06],
+        [-4.36448190e-05],
+        [-2.12476855e-04],
+        [-9.17683116e-04],
+        [-2.86381883e-02],
+        [-1.31921681e-01],
+        [-2.30369846e-03],
+        [-6.44351026e-02],
+        [-6.81199561e-02],
+        [-1.46101013e-05],
+        [-4.60953511e-05],
+        [-2.17810367e-04],
+        [-1.14595526e-05],
+        [-3.75956349e-03],
+        [-1.14981197e-01],
+        [-2.50731250e-05],
+        [-1.68766734e-03],
+        [-3.50220260e-02],
+        [-2.28614353e-06],
+        [-6.81639226e-06],
+        [-1.92367509e-04],
+        [-5.65385855e-03],
+        [-9.67927426e-02],
+        [-4.87783652e-02],
+        [-1.10377689e-02],
+        [-2.45253274e-01],
+        [-1.23633381e-01],
+        [-2.16022665e-03],
+        [-3.00626559e-03],
+        [-8.27667950e-04],
+        [-5.96745804e-02],
+        [-1.43673045e-01],
+        [-7.12804483e-02],
+        [-6.67466444e-02],
+        [-3.59809313e-01],
+        [-1.80639418e-01],
+        [-5.05533708e-02],
+        [-3.99159225e-03],
+        [-4.25845136e-04],
+        [-9.46885958e-03],
+        [-1.90313229e-03],
+        [-7.19448618e-04],
+        [-9.41744789e-03],
+        [-4.07612031e-03],
+        [-1.80669595e-03],
+        [-8.71468740e-03],
+        [-5.41662418e-04],
+        [-2.61241964e-05],
+        [-1.75032197e-02],
+        [-2.86778842e-01],
+        [-2.61287764e-01],
+        [-4.25031506e-02],
+        [-7.39107298e-01],
+        [-3.17239160e-01],
+        [-3.61907088e-02],
+        [-5.12932271e-01],
+        [-1.41292654e-01],
+        [-6.72086761e-02],
+        [-2.47990525e-01],
+        [-2.64118285e-01],
+        [-8.38363686e-02],
+        [-6.23231468e-01],
+        [-3.16432662e-01],
+        [-6.69396289e-02],
+        [-2.26098945e-01],
+        [-6.17418747e-02],
+        [-1.01429584e-02],
+        [-1.23748708e-02],
+        [-1.55824804e-01],
+        [-1.07147972e-02],
+        [-2.08773304e-02],
+        [-5.25559751e-02],
+        [-1.01090629e-02],
+        [-1.73576281e-02],
+        [-4.90015774e-03],
+        [-4.72686643e-03],
+        [-8.33353958e-02],
+        [-6.51676513e-01],
+        [-8.61164570e-03],
+        [-1.69529355e-01],
+        [-2.66539798e-01],
+        [-3.32982852e-03],
+        [-1.94516375e-02],
+        [-6.36568260e-03],
+        [-5.99005776e-02],
+        [-1.09856252e-01],
+        [-6.72115749e-01],
+        [-6.41271814e-02],
+        [-2.31382042e-01],
+        [-3.00974859e-01],
+        [-5.29470770e-02],
+        [-1.12155571e-02],
+        [-3.40987785e-03],
+        [-9.81945025e-03],
+        [-2.00546011e-02],
+        [-6.05580038e-01],
+        [-9.75203927e-03],
+        [-8.77035978e-03],
+        [-1.84066791e-01],
+        [-9.08191769e-03],
+        [-1.13102974e-03],
+        [-1.18665197e-03],
+        [-3.28854104e-02],
+        [-9.33026658e-02],
+        [-4.64416001e-02],
+        [-3.75782465e-02],
+        [-2.34134839e-01],
+        [-1.17687234e-01],
+        [-2.73967061e-02],
+        [-2.37410310e-03],
+        [-2.93622690e-04],
+        [-7.61874317e-01],
+        [-1.73296523e-01],
+        [-6.80342229e-02],
+        [-7.58740328e-01],
+        [-3.78859295e-01],
+        [-1.72399548e-01],
+        [-7.00116229e-01],
+        [-3.65832813e-02],
+        [-1.91975636e-04],
+        [-1.30680296e-01],
+        [-8.06032335e-03],
+        [-6.82655295e-04],
+        [-1.28927185e-01],
+        [-1.00162035e-02],
+        [-1.71222014e-03],
+        [-1.20809885e-01],
+        [-6.24859784e-03],
+        [-8.85605209e-06],
+        [-3.78399741e-02],
+        [-1.66427904e-01],
+        [-2.26487863e-01],
+        [-4.91552890e-02],
+        [-4.15694906e-01],
+        [-2.24902689e-01],
+        [-3.86169732e-02],
+        [-1.57879606e-01],
+        [-4.33021740e-02],
+        [-7.88231366e-01],
+        [-2.27025613e-01],
+        [-2.46238350e-01],
+        [-7.88644125e-01],
+        [-5.05314992e-01],
+        [-2.69486569e-01],
+        [-7.26783266e-01],
+        [-1.05432244e-01],
+        [-1.90795014e-02],
+        [-1.34935782e-01],
+        [-1.48902631e-02],
+        [-1.54599672e-01],
+        [-1.33325522e-01],
+        [-1.67617823e-02],
+        [-4.95970773e-02],
+        [-1.24974560e-01],
+        [-1.15727948e-02],
+        [-1.67290629e-03],
+        [-3.28126972e-02],
+        [-7.78216699e-02],
+        [-6.48633370e-01],
+        [-3.56677578e-02],
+        [-1.52716537e-01],
+        [-2.59844642e-01],
+        [-2.87686859e-02],
+        [-7.42935626e-03],
+        [-2.68601275e-03],
+        [-7.88791593e-01],
+        [-1.41680122e-01],
+        [-6.69137063e-01],
+        [-7.82393820e-01],
+        [-2.53690230e-01],
+        [-2.94549338e-01],
+        [-7.26880074e-01],
+        [-4.01007774e-02],
+        [-1.80725208e-03],
+        [-1.35632767e-01],
+        [-2.63342699e-02],
+        [-6.05004574e-01],
+        [-1.33786771e-01],
+        [-1.46716790e-02],
+        [-1.83756753e-01],
+        [-1.25414609e-01],
+        [-6.68055294e-03],
+        [-1.06493476e-03],
+        [-7.88704703e-04],
+        [-1.95121337e-02],
+        [-9.89065653e-03],
+        [-1.86961061e-03],
+        [-4.94416263e-02],
+        [-2.50478224e-02],
+        [-7.81956838e-05],
+        [-3.12038273e-05],
+        [-1.34061382e-05],
+        [-3.17111006e-03],
+        [-2.87210161e-02],
+        [-1.45026596e-02],
+        [-4.72820200e-03],
+        [-7.26155878e-02],
+        [-3.67355781e-02],
+        [-1.97745668e-03],
+        [-1.39868750e-04],
+        [-1.91674993e-05],
+        [-3.78943109e-04],
+        [-3.01976202e-04],
+        [-1.50958168e-04],
+        [-3.89469331e-04],
+        [-7.35373946e-04],
+        [-3.65354470e-04],
+        [-3.41008654e-04],
+        [-1.79807622e-05],
+        [-2.16660605e-07],
+        [-9.32256819e-04],
+        [-2.80043448e-02],
+        [-1.65034993e-01],
+        [-2.22999296e-03],
+        [-6.05174297e-02],
+        [-7.61799080e-02],
+        [-1.02079775e-04],
+        [-3.63245556e-04],
+        [-3.59881912e-04],
+        [-3.43816804e-03],
+        [-3.89431695e-02],
+        [-1.72411676e-01],
+        [-5.30041339e-03],
+        [-8.79320643e-02],
+        [-9.06178826e-02],
+        [-2.04951519e-03],
+        [-2.97099260e-04],
+        [-3.20413163e-04],
+        [-3.92909681e-04],
+        [-4.92882310e-03],
+        [-1.49594128e-01],
+        [-4.06543231e-04],
+        [-2.26174402e-03],
+        [-4.55839513e-02],
+        [-3.52504410e-04],
+        [-3.67615719e-05],
+        [-2.52998682e-04],
+        [-5.14052700e-04],
+        [-3.02878422e-02],
+        [-6.10212903e-01],
+        [-1.16754138e-03],
+        [-3.55069860e-02],
+        [-1.97729966e-01],
+        [-8.14529696e-05],
+        [-6.24894125e-05],
+        [-1.02153856e-03],
+        [-2.84441490e-03],
+        [-3.61327903e-02],
+        [-6.20517230e-01],
+        [-3.77332715e-03],
+        [-4.96364609e-02],
+        [-2.07070224e-01],
+        [-2.05236226e-03],
+        [-1.64737930e-04],
+        [-1.03588577e-03],
+        [-3.89435933e-04],
+        [-1.82072400e-02],
+        [-5.89235961e-01],
+        [-3.93233080e-04],
+        [-5.89709436e-03],
+        [-1.78236811e-01],
+        [-3.53993075e-04],
+        [-4.89687028e-05],
+        [-9.85413505e-04],
+        [-6.59238442e-03],
+        [-1.20054976e-02],
+        [-5.88472482e-03],
+        [-7.16337480e-03],
+        [-2.99557347e-02],
+        [-1.49188254e-02],
+        [-5.73435279e-03],
+        [-7.95863576e-04],
+        [-1.41626265e-04],
+        [-1.58503337e-01],
+        [-2.51058655e-02],
+        [-8.58597372e-03],
+        [-1.57239929e-01],
+        [-5.10505477e-02],
+        [-2.17604739e-02],
+        [-1.46028887e-01],
+        [-7.75075671e-03],
+        [-6.98757292e-05],
+        [-2.72517233e-02],
+        [-1.57489907e-03],
+        [-8.64394364e-05],
+        [-2.68803827e-02],
+        [-1.82043389e-03],
+        [-2.18546903e-04],
+        [-2.51975042e-02],
+        [-1.31333070e-03],
+        [-4.58467785e-06],
+        [-8.81688553e-03],
+        [-4.36456441e-02],
+        [-2.04818492e-02],
+        [-1.27688328e-02],
+        [-1.13807862e-01],
+        [-4.08846274e-02],
+        [-1.18697031e-02],
+        [-9.01972308e-02],
+        [-2.47288254e-02],
+        [-1.64465619e-01],
+        [-4.14853618e-02],
+        [-1.92229612e-02],
+        [-1.64730523e-01],
+        [-9.35514262e-02],
+        [-3.69097743e-02],
+        [-1.53242895e-01],
+        [-4.69088018e-02],
+        [-1.07807200e-02],
+        [-2.81794379e-02],
+        [-2.77742264e-03],
+        [-5.67148756e-03],
+        [-2.79052355e-02],
+        [-4.57072125e-03],
+        [-2.57043884e-03],
+        [-2.61911117e-02],
+        [-4.29877788e-03],
+        [-8.22924646e-04],
+        [-6.72515139e-03],
+        [-9.06477998e-03],
+        [-2.52860989e-02],
+        [-7.14557312e-03],
+        [-2.11513705e-02],
+        [-1.62476551e-02],
+        [-6.14315216e-03],
+        [-3.68763318e-03],
+        [-9.67810048e-04],
+        [-1.64292995e-01],
+        [-1.98115625e-02],
+        [-2.70307765e-02],
+        [-1.62620961e-01],
+        [-3.57296690e-02],
+        [-2.00493568e-02],
+        [-1.51664456e-01],
+        [-9.28514856e-03],
+        [-4.47158849e-04],
+        [-2.82874358e-02],
+        [-2.23288187e-03],
+        [-2.09746765e-02],
+        [-2.79013952e-02],
+        [-1.98218096e-03],
+        [-6.47577875e-03],
+        [-2.61619296e-02],
+        [-1.45866944e-03],
+        [-6.56436061e-05],
+        [-8.59241826e-02],
+        [-1.54658359e-02],
+        [-5.59406400e-03],
+        [-8.53439946e-02],
+        [-3.23848795e-02],
+        [-1.41776954e-02],
+        [-7.91016625e-02],
+        [-4.23440094e-03],
+        [-4.83247082e-05],
+        [-2.19086006e+00],
+        [-1.28927151e-01],
+        [-8.19575789e-03],
+        [-2.16111393e+00],
+        [-1.52170833e-01],
+        [-2.07621247e-02],
+        [-2.02556653e+00],
+        [-1.04357711e-01],
+        [-3.33704982e-05],
+        [-3.77963549e-01],
+        [-1.96177796e-02],
+        [-8.25084491e-05],
+        [-3.72684501e-01],
+        [-1.95931770e-02],
+        [-2.07134029e-04],
+        [-3.49534811e-01],
+        [-1.79984504e-02],
+        [-2.29591959e-06],
+        [-8.93010949e-02],
+        [-2.67856599e-02],
+        [-1.45415617e-02],
+        [-8.97934230e-02],
+        [-6.17215001e-02],
+        [-2.50612786e-02],
+        [-8.34202455e-02],
+        [-3.16147332e-02],
+        [-7.55051221e-03],
+        [-2.26055311e+00],
+        [-1.39614600e-01],
+        [-1.63196907e-02],
+        [-2.23044003e+00],
+        [-1.73892940e-01],
+        [-2.92155744e-02],
+        [-2.09046707e+00],
+        [-1.19524938e-01],
+        [-3.30628821e-03],
+        [-3.89946799e-01],
+        [-2.07198647e-02],
+        [-5.47804038e-03],
+        [-3.84534811e-01],
+        [-2.11117028e-02],
+        [-2.06329506e-03],
+        [-3.60657558e-01],
+        [-1.94657058e-02],
+        [-2.57948625e-04],
+        [-8.90381136e-02],
+        [-1.22259248e-02],
+        [-2.48993264e-02],
+        [-8.82031892e-02],
+        [-2.24920412e-02],
+        [-1.52788414e-02],
+        [-8.21675069e-02],
+        [-5.26420602e-03],
+        [-3.23119186e-04],
+        [-2.27392323e+00],
+        [-1.27622303e-01],
+        [-2.66943813e-02],
+        [-2.24267185e+00],
+        [-1.40762494e-01],
+        [-1.92176473e-02],
+        [-2.10261399e+00],
+        [-1.08698362e-01],
+        [-1.70895763e-04],
+        [-3.92333810e-01],
+        [-2.09430258e-02],
+        [-2.09484462e-02],
+        [-3.86851230e-01],
+        [-2.03812838e-02],
+        [-6.44687435e-03],
+        [-3.62827834e-01],
+        [-1.87126054e-02],
+        [-4.51927134e-05],
+        [-3.26294369e-04],
+        [-2.36067775e-03],
+        [-1.18991047e-03],
+        [-4.53174149e-04],
+        [-5.96331690e-03],
+        [-3.01493025e-03],
+        [-2.23373504e-04],
+        [-1.48656613e-05],
+        [-1.63942185e-06],
+        [-6.30676167e-03],
+        [-3.76227163e-03],
+        [-1.74507714e-03],
+        [-6.41129847e-03],
+        [-9.04181589e-03],
+        [-4.42183260e-03],
+        [-5.71750323e-03],
+        [-2.98950322e-04],
+        [-2.33080844e-06],
+        [-1.06805061e-03],
+        [-8.89639198e-05],
+        [-1.75125348e-05],
+        [-1.05501271e-03],
+        [-1.40413277e-04],
+        [-4.37825127e-05],
+        [-9.86595170e-04],
+        [-5.08424046e-05],
+        [-2.81245054e-08],
+        [-3.51268205e-04],
+        [-2.98691434e-03],
+        [-6.71697126e-03],
+        [-5.04794192e-04],
+        [-7.19077504e-03],
+        [-5.20076875e-03],
+        [-2.34188602e-04],
+        [-7.20489018e-05],
+        [-2.61784599e-05],
+        [-6.52659463e-03],
+        [-4.59778100e-03],
+        [-7.44050157e-03],
+        [-6.66552681e-03],
+        [-1.07787090e-02],
+        [-6.88692805e-03],
+        [-5.90011395e-03],
+        [-3.33758882e-04],
+        [-1.84295783e-05],
+        [-1.10209415e-03],
+        [-2.55682914e-04],
+        [-5.18503546e-03],
+        [-1.08900624e-03],
+        [-2.07823581e-04],
+        [-1.61289536e-03],
+        [-1.01794449e-03],
+        [-5.45656746e-05],
+        [-9.17478486e-06],
+        [-3.01998702e-04],
+        [-2.07285020e-03],
+        [-2.16034133e-02],
+        [-3.77356905e-04],
+        [-3.80778858e-03],
+        [-8.13461009e-03],
+        [-2.31938881e-04],
+        [-1.69651123e-05],
+        [-3.64435778e-05],
+        [-6.49269710e-03],
+        [-3.06083421e-03],
+        [-2.21989194e-02],
+        [-6.51850527e-03],
+        [-5.80231567e-03],
+        [-9.06401290e-03],
+        [-5.93489370e-03],
+        [-3.10231795e-04],
+        [-3.70027561e-05],
+        [-1.10814922e-03],
+        [-7.00301267e-04],
+        [-2.03726271e-02],
+        [-1.09378813e-03],
+        [-2.96601645e-04],
+        [-6.17908587e-03],
+        [-1.02411386e-03],
+        [-5.38568035e-05],
+        [-3.40853927e-05],
+    ])
+
+    print(grids)
+    print(obj_fun(grids))
+    print(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_three_dim_hartmann3d.py b/tests/test_three_dim_hartmann3d.py
index c364257..62417f9 100644
--- a/tests/test_three_dim_hartmann3d.py
+++ b/tests/test_three_dim_hartmann3d.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.three_dim_hartmann3d import *
 
 class_fun = Hartmann3D
+str_name = 'hartmann3d'
 
 TEST_EPSILON = 1e-5
 
@@ -64,3 +65,50 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [-6.79741166e-02],
+        [-1.36461045e-01],
+        [-9.13324430e-02],
+        [-9.71082067e-02],
+        [-1.85406663e-01],
+        [-9.02038776e-02],
+        [-3.09547170e-02],
+        [-7.29043824e-02],
+        [-8.47693855e-02],
+        [-1.80480228e-02],
+        [-8.39060933e-01],
+        [-1.99426284e+00],
+        [-2.57290548e-02],
+        [-6.28022015e-01],
+        [-1.95703928e+00],
+        [-8.19356834e-03],
+        [-2.25915245e-01],
+        [-1.82665019e+00],
+        [-2.73536768e-04],
+        [-2.26230774e+00],
+        [-3.34829168e-01],
+        [-2.04016877e-04],
+        [-1.48565839e+00],
+        [-3.25957854e-01],
+        [-3.77271851e-05],
+        [-2.24631259e-01],
+        [-3.00476074e-01],
+    ])
+
+    print(grids)
+    print(obj_fun(grids))
+    print(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_beale.py b/tests/test_two_dim_beale.py
index a3f8f2e..f4aba0d 100644
--- a/tests/test_two_dim_beale.py
+++ b/tests/test_two_dim_beale.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_beale import *
 
 class_fun = Beale
+str_name = 'beale'
 
 TEST_EPSILON = 1e-3
 
@@ -46,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_bohachevsky.py b/tests/test_two_dim_bohachevsky.py
index a0b9918..007e942 100644
--- a/tests/test_two_dim_bohachevsky.py
+++ b/tests/test_two_dim_bohachevsky.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_bohachevsky import *
 
 class_fun = Bohachevsky
+str_name = 'bohachevsky'
 
 TEST_EPSILON = 1e-5
 
@@ -46,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_branin.py b/tests/test_two_dim_branin.py
index 5017ccd..3c7509c 100644
--- a/tests/test_two_dim_branin.py
+++ b/tests/test_two_dim_branin.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_branin import *
 
 class_fun = Branin
+str_name = 'branin'
 
 TEST_EPSILON = 1e-5
 SCALE_NOISE = 2.0
@@ -61,6 +62,28 @@ def test_output():
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
 
+def test_call():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [308.12909601],
+        [10.30790849],
+        [10.96088904],
+        [106.56869776],
+        [24.12996441],
+        [22.16653996],
+        [17.50829952],
+        [150.45202034],
+        [145.87219088],
+    ])
+
+    print(grids)
+    print(obj_fun(grids))
+    print(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+
 def test_output_constant_noise():
     obj_fun = class_fun()
     bounds = obj_fun.get_bounds()
@@ -174,3 +197,10 @@ def test_output_sparse_student_t_noise():
     print(outputs)
     print(np.abs(outputs - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_bukin6.py b/tests/test_two_dim_bukin6.py
new file mode 100644
index 0000000..6f3175c
--- /dev/null
+++ b/tests/test_two_dim_bukin6.py
@@ -0,0 +1,194 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 6, 2023
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.two_dim_bukin6 import *
+
+class_fun = Bukin6
+str_name = 'bukin6'
+
+TEST_EPSILON = 1e-5
+SCALE_NOISE = 2.0
+SEED = 42
+
+
+def test_init():
+    obj_fun = class_fun()
+
+    with pytest.raises(AssertionError) as error:
+        class_fun(seed=1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun(seed='abc')
+
+def test_validate_properties():
+    obj_fun = class_fun()
+    obj_fun.validate_properties()
+
+def test_output():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [229.17878475],
+        [200.0],
+        [180.32756377],
+        [150.05],
+        [100.0],
+        [50.05],
+        [86.65254038],
+        [141.42135624],
+        [165.88123952],
+    ])
+
+    print(grids)
+    print(obj_fun.output(grids))
+    print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [229.17878475],
+        [200.0],
+        [180.32756377],
+        [150.05],
+        [100.0],
+        [50.05],
+        [86.65254038],
+        [141.42135624],
+        [165.88123952],
+    ])
+
+    print(grids)
+    print(obj_fun(grids))
+    print(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+
+def test_output_constant_noise():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [231.17878475],
+        [202.0],
+        [182.32756377],
+        [152.05],
+        [102.0],
+        [52.05],
+        [88.65254038],
+        [143.42135624],
+        [167.88123952],
+    ])
+
+    print(grids)
+    print(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE))
+    print(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+    assert np.all(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+
+def test_output_gaussian_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [230.17221305],
+        [199.7234714],
+        [181.62294085],
+        [153.09605971],
+        [99.53169325],
+        [49.58172609],
+        [89.81096601],
+        [142.9562257],
+        [164.94229075],
+    ])
+    outputs = obj_fun.output_gaussian_noise(grids, scale_noise=SCALE_NOISE)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_gaussian_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [230.17221305],
+        [199.7234714],
+        [180.32756377],
+        [150.05],
+        [100.0],
+        [49.58172609],
+        [86.65254038],
+        [142.9562257 ],
+        [164.94229075],
+    ])
+    outputs = obj_fun.output_sparse_gaussian_noise(grids, scale_noise=SCALE_NOISE, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_student_t_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [230.32801935],
+        [197.83661953],
+        [182.96568081],
+        [148.46644847],
+        [101.4728255],
+        [53.83670737],
+        [82.72424794],
+        [140.30935909],
+        [164.7380196],
+    ])
+    outputs = obj_fun.output_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_student_t_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [230.32801935],
+        [197.83661953],
+        [180.32756377],
+        [150.05],
+        [100.0],
+        [50.05],
+        [82.72424794],
+        [141.42135624],
+        [165.88123952],
+    ])
+    outputs = obj_fun.output_sparse_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_dejong5.py b/tests/test_two_dim_dejong5.py
index 17483ac..6c3e137 100644
--- a/tests/test_two_dim_dejong5.py
+++ b/tests/test_two_dim_dejong5.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_dejong5 import *
 
 class_fun = DeJong5
+str_name = 'dejong5'
 
 TEST_EPSILON = 1e-3
 
@@ -46,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_dropwave.py b/tests/test_two_dim_dropwave.py
index 529378f..2fbc3ed 100644
--- a/tests/test_two_dim_dropwave.py
+++ b/tests/test_two_dim_dropwave.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_dropwave import *
 
 class_fun = DropWave
+str_name = 'dropwave'
 
 TEST_EPSILON = 1e-3
 
@@ -46,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_easom.py b/tests/test_two_dim_easom.py
new file mode 100644
index 0000000..f7d6cc7
--- /dev/null
+++ b/tests/test_two_dim_easom.py
@@ -0,0 +1,194 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 3, 2023
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.two_dim_easom import *
+
+class_fun = Easom
+str_name = 'easom'
+
+TEST_EPSILON = 1e-5
+SCALE_NOISE = 2.0
+SEED = 42
+
+
+def test_init():
+    obj_fun = class_fun()
+
+    with pytest.raises(AssertionError) as error:
+        class_fun(seed=1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun(seed='abc')
+
+def test_validate_properties():
+    obj_fun = class_fun()
+    obj_fun.validate_properties()
+
+def test_output():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [0.0],
+        [0.0],
+        [0.0],
+        [0.0],
+        [-2.67528799e-09],
+        [0.0],
+        [0.0],
+        [0.0],
+        [0.0],
+    ])
+
+    print(grids)
+    print(obj_fun.output(grids))
+    print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [0.0],
+        [0.0],
+        [0.0],
+        [0.0],
+        [-2.67528799e-09],
+        [0.0],
+        [0.0],
+        [0.0],
+        [0.0],
+    ])
+
+    print(grids)
+    print(obj_fun(grids))
+    print(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+
+def test_output_constant_noise():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [2.0],
+        [2.0],
+        [2.0],
+        [2.0],
+        [2.0],
+        [2.0],
+        [2.0],
+        [2.0],
+        [2.0],
+    ])
+
+    print(grids)
+    print(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE))
+    print(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+    assert np.all(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+
+def test_output_gaussian_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [0.99342831],
+        [-0.2765286],
+        [1.29537708],
+        [3.04605971],
+        [-0.46830675],
+        [-0.46827391],
+        [3.15842563],
+        [1.53486946],
+        [-0.93894877],
+    ])
+    outputs = obj_fun.output_gaussian_noise(grids, scale_noise=SCALE_NOISE)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_gaussian_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [9.93428306e-01],
+        [-2.76528602e-01],
+        [0.0],
+        [0.0],
+        [-2.67528799e-09],
+        [-4.68273914e-01],
+        [0.0],
+        [1.53486946],
+        [-9.38948772e-01],
+    ])
+    outputs = obj_fun.output_sparse_gaussian_noise(grids, scale_noise=SCALE_NOISE, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_student_t_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [1.1492346],
+        [-2.16338047],
+        [2.63811703],
+        [-1.58355153],
+        [1.4728255],
+        [3.78670737],
+        [-3.92829244],
+        [-1.11199715],
+        [-1.14321992],
+    ])
+    outputs = obj_fun.output_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_student_t_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [1.14923460],
+        [-2.16338047],
+        [0.0],
+        [0.0],
+        [-2.67528799e-09],
+        [0.0],
+        [-3.92829244],
+        [0.0],
+        [0.0],
+    ])
+    outputs = obj_fun.output_sparse_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_eggholder.py b/tests/test_two_dim_eggholder.py
index eb71991..4cfbebb 100644
--- a/tests/test_two_dim_eggholder.py
+++ b/tests/test_two_dim_eggholder.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: January 13, 2023
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_eggholder import *
 
 class_fun = Eggholder
+str_name = 'eggholder'
 
 TEST_EPSILON = 1e-5
 
@@ -16,10 +17,6 @@
 def test_init():
     obj_fun = class_fun()
 
-    with pytest.raises(AssertionError) as error:
-        class_fun(bounds='abc')
-    with pytest.raises(AssertionError) as error:
-        class_fun(bounds=2.1)
     with pytest.raises(AssertionError) as error:
         class_fun(seed='abc')
     with pytest.raises(AssertionError) as error:
@@ -50,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_goldsteinprice.py b/tests/test_two_dim_goldsteinprice.py
index c0b4793..913ae0b 100644
--- a/tests/test_two_dim_goldsteinprice.py
+++ b/tests/test_two_dim_goldsteinprice.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_goldsteinprice import *
 
 class_fun = GoldsteinPrice
+str_name = 'goldsteinprice'
 
 TEST_EPSILON = 1e-5
 
@@ -46,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_holdertable.py b/tests/test_two_dim_holdertable.py
index 3f6ccb9..bd11d85 100644
--- a/tests/test_two_dim_holdertable.py
+++ b/tests/test_two_dim_holdertable.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_holdertable import *
 
 class_fun = HolderTable
+str_name = 'holdertable'
 
 TEST_EPSILON = 1e-5
 
@@ -46,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_kim1.py b/tests/test_two_dim_kim1.py
index 45ff5d9..fbc74d1 100644
--- a/tests/test_two_dim_kim1.py
+++ b/tests/test_two_dim_kim1.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: October 27, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_kim1 import *
 
 class_fun = Kim1
+str_name = 'kim1'
 
 TEST_EPSILON = 1e-5
 SCALE_NOISE = 2.0
@@ -102,3 +103,10 @@ def test_output_gaussian_noise():
     print(outputs)
     print(np.abs(outputs - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_kim2.py b/tests/test_two_dim_kim2.py
index b4a821d..7c0a54b 100644
--- a/tests/test_two_dim_kim2.py
+++ b/tests/test_two_dim_kim2.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: October 27, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_kim2 import *
 
 class_fun = Kim2
+str_name = 'kim2'
 
 TEST_EPSILON = 1e-5
 SCALE_NOISE = 2.0
@@ -102,3 +103,10 @@ def test_output_gaussian_noise():
     print(outputs)
     print(np.abs(outputs - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_kim3.py b/tests/test_two_dim_kim3.py
index ef6423f..44bdec8 100644
--- a/tests/test_two_dim_kim3.py
+++ b/tests/test_two_dim_kim3.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: October 27, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_kim3 import *
 
 class_fun = Kim3
+str_name = 'kim3'
 
 TEST_EPSILON = 1e-5
 SCALE_NOISE = 2.0
@@ -102,3 +103,10 @@ def test_output_gaussian_noise():
     print(outputs)
     print(np.abs(outputs - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_michalewicz.py b/tests/test_two_dim_michalewicz.py
index c06e496..e61ada4 100644
--- a/tests/test_two_dim_michalewicz.py
+++ b/tests/test_two_dim_michalewicz.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_michalewicz import *
 
 class_fun = Michalewicz
+str_name = 'michalewicz'
 
 TEST_EPSILON = 1e-3
 
@@ -46,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_shubert.py b/tests/test_two_dim_shubert.py
new file mode 100644
index 0000000..f9bbfdb
--- /dev/null
+++ b/tests/test_two_dim_shubert.py
@@ -0,0 +1,194 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 3, 2023
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.two_dim_shubert import *
+
+class_fun = Shubert
+str_name = 'shubert'
+
+TEST_EPSILON = 1e-5
+SCALE_NOISE = 2.0
+SEED = 42
+
+
+def test_init():
+    obj_fun = class_fun()
+
+    with pytest.raises(AssertionError) as error:
+        class_fun(seed=1.0)
+    with pytest.raises(AssertionError) as error:
+        class_fun(seed='abc')
+
+def test_validate_properties():
+    obj_fun = class_fun()
+    obj_fun.validate_properties()
+
+def test_output():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [0.06674108],
+        [1.15175294],
+        [0.86375707],
+        [1.15175294],
+        [19.87583625],
+        [14.90588261],
+        [0.86375707],
+        [14.90588261],
+        [11.17866608],
+    ])
+
+    print(grids)
+    print(obj_fun.output(grids))
+    print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_call():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [0.06674108],
+        [1.15175294],
+        [0.86375707],
+        [1.15175294],
+        [19.87583625],
+        [14.90588261],
+        [0.86375707],
+        [14.90588261],
+        [11.17866608],
+    ])
+
+    print(grids)
+    print(obj_fun(grids))
+    print(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(obj_fun(grids) - truths_grids) < TEST_EPSILON)
+
+def test_output_constant_noise():
+    obj_fun = class_fun()
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [2.06674108],
+        [3.15175294],
+        [2.86375707],
+        [3.15175294],
+        [21.87583625],
+        [16.90588261],
+        [2.86375707],
+        [16.90588261],
+        [13.17866608],
+    ])
+
+    print(grids)
+    print(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE))
+    print(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+    assert np.all(np.abs(obj_fun.output_constant_noise(grids, scale_noise=SCALE_NOISE) - truths_grids) < TEST_EPSILON + SCALE_NOISE)
+
+def test_output_gaussian_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [1.06016939],
+        [0.87522434],
+        [2.15913415],
+        [4.19781266],
+        [19.4075295],
+        [14.4376087],
+        [4.02218271],
+        [16.44075207],
+        [10.2397173],
+    ])
+    outputs = obj_fun.output_gaussian_noise(grids, scale_noise=SCALE_NOISE)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_gaussian_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [1.06016939],
+        [0.87522434],
+        [0.86375707],
+        [1.15175294],
+        [19.87583625],
+        [14.4376087],
+        [0.86375707],
+        [16.44075207],
+        [10.2397173],
+    ])
+    outputs = obj_fun.output_sparse_gaussian_noise(grids, scale_noise=SCALE_NOISE, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_student_t_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [1.21597568],
+        [-1.01162753],
+        [3.50187411],
+        [-0.43179858],
+        [21.34866175],
+        [18.69258998],
+        [-3.06453537],
+        [13.79388546],
+        [10.03544615],
+    ])
+    outputs = obj_fun.output_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_output_sparse_student_t_noise():
+    obj_fun = class_fun(seed=SEED)
+    bounds = obj_fun.get_bounds()
+
+    grids = obj_fun.sample_grids(3)
+    truths_grids = np.array([
+        [1.21597568],
+        [-1.01162753],
+        [0.86375707],
+        [1.15175294],
+        [19.87583625],
+        [14.90588261],
+        [-3.06453537],
+        [14.90588261],
+        [11.17866608],
+    ])
+    outputs = obj_fun.output_sparse_student_t_noise(grids, scale_noise=SCALE_NOISE, dof=4.0, sparsity=0.3)
+
+    print(grids)
+    print(outputs)
+    print(np.abs(outputs - truths_grids) < TEST_EPSILON)
+    assert np.all(np.abs(outputs - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_sixhumpcamel.py b/tests/test_two_dim_sixhumpcamel.py
index 1303007..d3e5044 100644
--- a/tests/test_two_dim_sixhumpcamel.py
+++ b/tests/test_two_dim_sixhumpcamel.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_sixhumpcamel import *
 
 class_fun = SixHumpCamel
+str_name = 'sixhumpcamel'
 
 TEST_EPSILON = 1e-5
 
@@ -46,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_two_dim_threehumpcamel.py b/tests/test_two_dim_threehumpcamel.py
index 6dc72c6..29f4a08 100644
--- a/tests/test_two_dim_threehumpcamel.py
+++ b/tests/test_two_dim_threehumpcamel.py
@@ -1,6 +1,6 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: February 8, 2021
+# last updated: December 4, 2022
 #
 
 import numpy as np
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_threehumpcamel import *
 
 class_fun = ThreeHumpCamel
+str_name = 'threehumpcamel'
 
 TEST_EPSILON = 1e-5
 
@@ -46,3 +47,10 @@ def test_output():
     print(obj_fun.output(grids))
     print(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
     assert np.all(np.abs(obj_fun.output(grids) - truths_grids) < TEST_EPSILON)
+
+def test_name():
+    obj_fun = class_fun()
+    assert obj_fun.name == str_name
+
+    assert obj_fun.__class__.__name__.lower() == str_name
+    assert obj_fun.__class__.__qualname__.lower() == str_name
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000..09f9aec
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,131 @@
+#
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: January 6, 2023
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks import utils
+
+TEST_EPSILON = 1e-5
+
+
+def test_get_benchmark():
+    with pytest.raises(TypeError) as error:
+        benchmark = utils.get_benchmark()
+    with pytest.raises(ValueError) as error:
+        benchmark = utils.get_benchmark('abc', seed=None)
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('ackley')
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('ackley', seed='abc')
+
+    benchmark = utils.get_benchmark('ackley', dim=4, seed=42)
+    print(benchmark.output(np.array([0.0, 0.0, 0.0, 0.0])))
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('cosines')
+
+    benchmark = utils.get_benchmark('cosines', dim=4, seed=None)
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('griewank')
+
+    benchmark = utils.get_benchmark('griewank', dim=4, seed=None)
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('levy')
+
+    benchmark = utils.get_benchmark('levy', dim=2, seed=None)
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('rastrigin')
+
+    benchmark = utils.get_benchmark('rastrigin', dim=8, seed=None)
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('rosenbrock')
+
+    benchmark = utils.get_benchmark('rosenbrock', dim=8, seed=None)
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('sphere')
+
+    benchmark = utils.get_benchmark('sphere', dim=16, seed=None)
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('zakharov')
+
+    benchmark = utils.get_benchmark('zakharov', dim=16, seed=None)
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('constant')
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('constant', constant=None)
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('constant', bounds=None)
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('constant', bounds=np.array([0.0, 10.0]), constant=10.0, seed=None)
+
+    benchmark = utils.get_benchmark('constant', bounds=np.array([[0.0, 10.0]]), constant=10.0, seed=None)
+
+    benchmark = utils.get_benchmark('gramacyandlee2012')
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('linear')
+
+    benchmark = utils.get_benchmark('linear', bounds=np.array([[0.0, 10.0]]), slope=-1.2, seed=None)
+
+    with pytest.raises(AssertionError) as error:
+        benchmark = utils.get_benchmark('step')
+
+    benchmark = utils.get_benchmark('step', steps=[0.0, 3.0, 7.0, 10.0], step_values=[-2.1, 4.0, 10.0], seed=None)
+
+    benchmark = utils.get_benchmark('beale')
+    benchmark = utils.get_benchmark('bohachevsky')
+
+    benchmark = utils.get_benchmark('branin')
+    print(benchmark.output(np.array([1.0, 1.0])))
+
+    benchmark = utils.get_benchmark('bukin6')
+    benchmark = utils.get_benchmark('dejong5')
+    benchmark = utils.get_benchmark('dropwave')
+    benchmark = utils.get_benchmark('easom')
+    benchmark = utils.get_benchmark('eggholder')
+    benchmark = utils.get_benchmark('goldsteinprice')
+    benchmark = utils.get_benchmark('holdertable')
+    benchmark = utils.get_benchmark('kim1')
+    benchmark = utils.get_benchmark('kim2')
+    benchmark = utils.get_benchmark('kim3')
+    benchmark = utils.get_benchmark('michalewicz')
+    benchmark = utils.get_benchmark('shubert')
+    benchmark = utils.get_benchmark('sixhumpcamel')
+    benchmark = utils.get_benchmark('threehumpcamel')
+
+    benchmark = utils.get_benchmark('colville')
+    benchmark = utils.get_benchmark('hartmann3d')
+    benchmark = utils.get_benchmark('hartmann6d')
+
+def test_pdf_two_dim_normal():
+    bx = np.array([0.0, 1.0])
+    mu = np.array([1.0, 1.0])
+    Cov = np.array([
+        [2.0, 1.0],
+        [1.0, 2.0],
+    ])
+
+    with pytest.raises(AssertionError) as error:
+        value = utils.pdf_two_dim_normal(np.array([1.0, 1.0, 1.0]), mu, Cov)
+    with pytest.raises(AssertionError) as error:
+        value = utils.pdf_two_dim_normal(np.array([2.0]), mu, Cov)
+    with pytest.raises(AssertionError) as error:
+        value = utils.pdf_two_dim_normal(bx, np.array([1.0, 1.0, 1.0]), Cov)
+    with pytest.raises(AssertionError) as error:
+        value = utils.pdf_two_dim_normal(bx, np.array([3.0]), Cov)
+
+    value = utils.pdf_two_dim_normal(bx, mu, Cov)
+    print(value)
+
+    assert np.abs(0.06584073599896273 - value) < TEST_EPSILON
diff --git a/tests/test_version.py b/tests/test_version.py
index f0bb691..d967e4c 100644
--- a/tests/test_version.py
+++ b/tests/test_version.py
@@ -1,10 +1,10 @@
 #
 # author: Jungtaek Kim (jtkim@postech.ac.kr)
-# last updated: October 23, 2021
+# last updated: October 19, 2022
 #
 
 
-STR_VERSION = '0.1.6'
+STR_VERSION = '0.1.7'
 
 
 def test_version_bayeso():