Benchmark functions were locked into one of the tests

skopt/benchmarks.py

@@ -0,0 +1,35 @@
+"""A collection of benchmark problems"""
+
+import numpy as np
+
+
+def branin(x, a=1, b=5.1 / (4 * np.pi**2), c=5. / np.pi,
+           r=6, s=10, t=1. / (8 * np.pi)):
+    """Branin-Hoo function is defined on the square x1 ∈ [-5, 10], x2 ∈ [0, 15].
+
+    It has three minima with f(x*) = 0.397887 at x* = (-pi, 12.275),
+    (+pi, 2.275), and (9.42478, 2.475).
+
+    More details: <http://www.sfu.ca/~ssurjano/branin.html>
+    """
+    return (a * (x[1] - b * x[0]**2 + c * x[0] - r)**2 +
+            s * (1 - t) * np.cos(x[0]) + s)
+
+def hartmann_6(x,
+               alpha=np.asarray([1.0, 1.2, 3.0, 3.2]),
+               P=10**-4 * np.asarray([[1312, 1696, 5569, 124, 8283, 5886],
+                                      [2329, 4135, 8307, 3736, 1004, 9991],
+                                      [2348, 1451, 3522, 2883, 3047, 6650],
+                                      [4047, 8828, 8732, 5743, 1091, 381]]),
+               A=np.asarray([[10, 3, 17, 3.50, 1.7, 8],
+                             [0.05, 10, 17, 0.1, 8, 14],
+                             [3, 3.5, 1.7, 10, 17, 8],
+                             [17, 8, 0.05, 10, 0.1, 14]])):
+    """The six dimensional Hartmann function is defined on the unit hypercube.
+
+    It has six local minima and one global minimum f(x*) = -3.32237 at
+    x* = (0.20169, 0.15001, 0.476874, 0.275332, 0.311652, 0.6573).
+
+    More details: <http://www.sfu.ca/~ssurjano/hart6.html>
+    """
+    return -np.sum(alpha * np.exp(-np.sum(A * (x - P)**2, axis=1)))

skopt/tests/test_benchmarks.py

@@ -0,0 +1,19 @@
+import numpy as np
+from sklearn.utils.testing import assert_array_almost_equal
+from sklearn.utils.testing import assert_almost_equal
+
+from skopt.benchmarks import branin, hartmann_6
+
+
+def test_branin():
+    xstars = np.asarray([(-np.pi, 12.275), (+np.pi, 2.275), (9.42478, 2.475)])
+    f_at_xstars = np.asarray([branin(xstar) for xstar in xstars])
+    branin_min = np.array([0.397887] * xstars.shape[0])
+    assert_array_almost_equal(f_at_xstars, branin_min)
+
+
+def test_hartmann6():
+    assert_almost_equal(hartmann_6((0.20169, 0.15001, 0.476874,
+                                    0.275332, 0.311652, 0.6573)),
+                        -3.32237,
+                        decimal=5)

skopt/tests/test_gp_opt.py

@@ -3,25 +3,7 @@
 from sklearn.utils.testing import assert_less
 
 from skopt.gp_opt import gp_minimize
-
-
-def branin(x, a=1, b=5.1 / (4 * pi ** 2), c=5. / pi,
-           r=6, s=10, t=1. / (8 * pi)):
-    return (a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 +
-            s * (1 - t) * cos(x[0]) + s)
-
-
-def hartmann_6(x,
-               alpha=np.asarray([1.0, 1.2, 3.0, 3.2]),
-               P=10**-4 * np.asarray([[1312, 1696, 5569, 124, 8283, 5886],
-                                      [2329, 4135, 8307, 3736, 1004, 9991],
-                                      [2348, 1451, 3522, 2883, 3047, 6650],
-                                      [4047, 8828, 8732, 5743, 1091, 381]]),
-               A=np.asarray([[10, 3, 17, 3.50, 1.7, 8],
-                             [0.05, 10, 17, 0.1, 8, 14],
-                             [3, 3.5, 1.7, 10, 17, 8],
-                             [17, 8, 0.05, 10, 0.1, 14]])):
-    return -np.sum(alpha * np.exp(-np.sum(A * (x - P)**2, axis=1)))
+from skopt.benchmarks import branin, hartmann_6
 
 
 def check_branin(search):