Update dejong5 and dropwave

jungtaekkim · Feb 10, 2021 · 59edd11 · 59edd11
1 parent 90deaf0
commit 59edd11
Show file tree

Hide file tree

Showing 26 changed files with 204 additions and 0 deletions.
diff --git a/bayeso_benchmarks/plot_benchmarks.py b/bayeso_benchmarks/plot_benchmarks.py
@@ -110,6 +110,7 @@ def plot_2d(obj_fun,
 
 
 if __name__ == '__main__':
+    # one dim.
     from one_dim_gramacyandlee2012 import GramacyAndLee2012 as target_class
     obj_fun = target_class()
     plot_1d(obj_fun, 'gramacyandlee2012')
@@ -126,6 +127,7 @@ def plot_2d(obj_fun,
     obj_fun = target_class(1)
     plot_1d(obj_fun, 'sphere_1d')
 
+    # two dim.
     from two_dim_beale import Beale as target_class
     obj_fun = target_class()
     plot_2d(obj_fun, 'beale_2d')
@@ -138,6 +140,14 @@ def plot_2d(obj_fun,
     obj_fun = target_class()
     plot_2d(obj_fun, 'branin_2d')
 
+    from two_dim_dejong5 import DeJong5 as target_class
+    obj_fun = target_class()
+    plot_2d(obj_fun, 'dejong5_2d')
+
+    from two_dim_dropwave import DropWave as target_class
+    obj_fun = target_class()
+    plot_2d(obj_fun, 'dropwave_2d')
+
     from two_dim_eggholder import Eggholder as target_class
     obj_fun = target_class()
     plot_2d(obj_fun, 'eggholder_2d')

diff --git a/bayeso_benchmarks/two_dim_dejong5.py b/bayeso_benchmarks/two_dim_dejong5.py
@@ -0,0 +1,57 @@
+#
+# author: Jungtaek Kim ([email protected])
+# last updated: February 8, 2021
+#
+
+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
+
+    A = np.array([
+        [-32.0, -16.0, 0.0, 16.0, 32.0, -32.0, -16.0, 0.0, 16.0, 32.0, -32.0, -16.0, 0.0, 16.0, 32.0, -32.0, -16.0, 0.0, 16.0, 32.0, -32.0, -16.0, 0.0, 16.0, 32.0],
+        [-32.0, -32.0, -32.0, -32.0, -32.0, -16.0, -16.0, -16.0, -16.0, -16.0, 0.0, 0.0, 0.0, 0.0, 0.0, 16.0, 16.0, 16.0, 16.0, 16.0, 32.0, 32.0, 32.0, 32.0, 32.0],
+    ])
+    y = 0.002
+
+    for ind in range(0, 25):
+        cur_y = 1.0 / (ind + 1.0 + (bx[0] - A[0, ind])**6 + (bx[1] - A[1, ind])**6)
+        y += cur_y
+    y = y**(-1)
+
+    return y
+
+
+class DeJong5(Function):
+    def __init__(self):
+        dim_bx = 2
+        bounds = np.array([
+            [-65.536, 65.536],
+            [-65.536, 65.536],
+        ])
+        global_minimizers = np.array([
+            [-32.10428207, -32.13705826],
+            [-32.07150588, -32.13705826],
+            [-32.03872968, -32.13705826],
+            [-32.00595349, -32.13705826],
+            [-31.97317729, -32.13705826],
+            [-31.9404011, -32.13705826],
+            [-31.90762491, -32.13705826],
+            [-32.13705826, -32.10428207],
+            [-32.10428207, -32.10428207],
+            [-32.07150588, -32.10428207],
+            [-32.03872968, -32.10428207],
+            [-32.00595349, -32.10428207],
+            [-31.97317729, -32.10428207],
+            [-31.9404011, -32.10428207],
+            [-31.90762491, -32.10428207],
+            [-31.87484871, -32.10428207],
+        ])
+        global_minimum = 0.9980038753198021
+        function = lambda bx: fun_target(bx, dim_bx)
+
+        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function)
diff --git a/bayeso_benchmarks/two_dim_dropwave.py b/bayeso_benchmarks/two_dim_dropwave.py
@@ -0,0 +1,32 @@
+#
+# author: Jungtaek Kim ([email protected])
+# last updated: February 9, 2021
+#
+
+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 * (1 + np.cos(12.0 * np.sqrt(bx[0]**2 + bx[1]**2))) / (0.5 * (bx[0]**2 + bx[1]**2) + 2.0)
+    return y
+
+
+class DropWave(Function):
+    def __init__(self):
+        dim_bx = 2
+        bounds = np.array([
+            [-5.12, 5.12],
+            [-5.12, 5.12],
+        ])
+        global_minimizers = np.array([
+            [0.0, 0.0],
+        ])
+        global_minimum = -1.0
+        function = lambda bx: fun_target(bx, dim_bx)
+
+        Function.__init__(self, dim_bx, bounds, global_minimizers, global_minimum, function)
diff --git a/figures/ackley_1d.pdf b/figures/ackley_1d.pdf
diff --git a/figures/ackley_2d.pdf b/figures/ackley_2d.pdf
diff --git a/figures/beale_2d.pdf b/figures/beale_2d.pdf
diff --git a/figures/bohachevsky_2d.pdf b/figures/bohachevsky_2d.pdf
diff --git a/figures/branin_2d.pdf b/figures/branin_2d.pdf
diff --git a/figures/cosines_1d.pdf b/figures/cosines_1d.pdf
diff --git a/figures/cosines_2d.pdf b/figures/cosines_2d.pdf
diff --git a/figures/dejong5_2d.pdf b/figures/dejong5_2d.pdf
diff --git a/figures/dropwave_2d.pdf b/figures/dropwave_2d.pdf
diff --git a/figures/eggholder_2d.pdf b/figures/eggholder_2d.pdf
diff --git a/figures/goldsteinprice_2d.pdf b/figures/goldsteinprice_2d.pdf
diff --git a/figures/gramacyandlee2012.pdf b/figures/gramacyandlee2012.pdf
diff --git a/figures/holdertable_2d.pdf b/figures/holdertable_2d.pdf
diff --git a/figures/michalewicz_2d.pdf b/figures/michalewicz_2d.pdf
diff --git a/figures/rosenbrock_2d.pdf b/figures/rosenbrock_2d.pdf
diff --git a/figures/sixhumpcamel_2d.pdf b/figures/sixhumpcamel_2d.pdf
diff --git a/figures/sphere_1d.pdf b/figures/sphere_1d.pdf
diff --git a/figures/sphere_2d.pdf b/figures/sphere_2d.pdf
diff --git a/figures/threehumpcamel_2d.pdf b/figures/threehumpcamel_2d.pdf
diff --git a/tests/test_global_minimum.py b/tests/test_global_minimum.py
@@ -9,6 +9,7 @@
 from bayeso_benchmarks.two_dim_branin import *
 from bayeso_benchmarks.two_dim_eggholder import *
 from bayeso_benchmarks.two_dim_michalewicz import *
+from bayeso_benchmarks.two_dim_dejong5 import *
 
 TEST_EPSILON = 1e-5
 
@@ -54,3 +55,17 @@ def test_global_minimum_michalewicz():
     print(grids[ind_minimum])
     print(obj_fun.global_minimum - global_minimum_brute_force)
     assert (obj_fun.global_minimum - global_minimum_brute_force) < TEST_EPSILON
+
+def test_global_minimum_dejong5():
+    class_fun = DeJong5
+
+    obj_fun = class_fun()
+    grids = obj_fun.get_grids(100)
+    vals_grids = obj_fun.output(grids)
+    ind_minimum = np.argmin(vals_grids)
+    global_minimum_brute_force = np.min(vals_grids)
+
+    print(global_minimum_brute_force)
+    print(grids[((vals_grids - global_minimum_brute_force) < TEST_EPSILON)[:, 0]])
+    print(obj_fun.global_minimum - global_minimum_brute_force)
+    assert (obj_fun.global_minimum - global_minimum_brute_force) < TEST_EPSILON
diff --git a/tests/test_import.py b/tests/test_import.py
@@ -39,6 +39,10 @@ def test_import_two_dim_branin():
     import bayeso_benchmarks.two_dim_branin
     from bayeso_benchmarks.two_dim_branin import Branin
 
+def test_import_two_dim_dropwave():
+    import bayeso_benchmarks.two_dim_dropwave
+    from bayeso_benchmarks.two_dim_dropwave import DropWave
+
 def test_import_two_dim_eggholder():
     import bayeso_benchmarks.two_dim_eggholder
     from bayeso_benchmarks.two_dim_eggholder import Eggholder

diff --git a/tests/test_two_dim_dejong5.py b/tests/test_two_dim_dejong5.py
@@ -0,0 +1,43 @@
+#
+# author: Jungtaek Kim ([email protected])
+# last updated: February 8, 2021
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.two_dim_dejong5 import *
+
+class_fun = DeJong5
+
+TEST_EPSILON = 1e-3
+
+
+def test_init():
+    obj_fun = class_fun()
+
+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.get_grids(3)
+    truths_grids = np.array([
+        [499.99985236],
+        [499.99917292],
+        [499.99985236],
+        [499.99917292],
+        [12.67050581],
+        [499.99917292],
+        [499.99985236],
+        [499.99917292],
+        [499.99985236],
+    ])
+
+    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)
diff --git a/tests/test_two_dim_dropwave.py b/tests/test_two_dim_dropwave.py
@@ -0,0 +1,43 @@
+#
+# author: Jungtaek Kim ([email protected])
+# last updated: February 8, 2021
+#
+
+import numpy as np
+import pytest
+
+from bayeso_benchmarks.two_dim_dropwave import *
+
+class_fun = DropWave
+
+TEST_EPSILON = 1e-3
+
+
+def test_init():
+    obj_fun = class_fun()
+
+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.get_grids(3)
+    truths_grids = np.array([
+        [-0.05229446],
+        [-0.07797539],
+        [-0.05229446],
+        [-0.07797539],
+        [-1.0],
+        [-0.07797539],
+        [-0.05229446],
+        [-0.07797539],
+        [-0.05229446],
+    ])
+
+    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)