ADD: sixhumpcamel

bayeso/benchmarks.py

@@ -41,6 +41,19 @@
     'global_minimum_y': -959.6407,
 }
 
+INFO_SIXHUMPCAMEL = {
+    'dim_fun': 2,
+    'bounds': np.array([
+        [-3.0, 3.0],
+        [-2.0, 2.0],
+    ]),
+    'global_minimum_X': np.array([
+        [0.0898, -0.7126],
+        [-0.0898, 0.7126],
+    ]),
+    'global_minimum_y': -1.0316,
+}
+
 def branin(X,
     a=1.0,
     b=5.1 / (4.0 * np.pi**2),
@@ -95,3 +108,15 @@ def eggholder(X):
     Y = -1.0 * (X[:, 1] + 47.0) * np.sin(np.sqrt(np.abs(X[:, 1] + X[:, 0] / 2.0 + 47.0))) - X[:, 0] * np.sin(np.sqrt(np.abs(X[:, 0] - (X[:, 1] + 47.0))))
     return Y
 
+def sixhumpcamel(X):
+    assert isinstance(X, np.ndarray)
+    assert len(X.shape) == 1 or len(X.shape) == 2
+    if len(X.shape) == 1:
+        assert X.shape[0] == 2
+        X = np.expand_dims(X, axis=0)
+    elif len(X.shape) == 2:
+        assert X.shape[1] == 2
+
+    Y = (4.0 - 2.1 * X[:, 0]**2 + X[:, 0]**4 / 3.0) * X[:, 0]**2 + X[:, 0] * X[:, 1] + (-4.0 + 4.0 * X[:, 1]**2) * X[:, 1]**2
+    return Y
+

examples/benchmarks/example_benchmarks_sixhumpcamel_bo_ei.py

@@ -0,0 +1,53 @@
+# example_benchmarks_sixhumpcamel_ei
+# author: Jungtaek Kim (jtkim@postech.ac.kr)
+# last updated: October 04, 2018
+
+import numpy as np
+import os
+
+from bayeso import bo
+from bayeso import benchmarks
+from bayeso.utils import utils_bo
+from bayeso.utils import utils_plotting
+from bayeso.utils import utils_benchmarks
+
+INFO_TARGET = benchmarks.INFO_SIXHUMPCAMEL
+STR_FUN_TARGET = 'sixhumpcamel'
+PATH_SAVE = '../figures/benchmarks/'
+
+
+def fun_target(X):
+    return benchmarks.sixhumpcamel(X)
+
+def main():
+    int_bo = 5
+    int_iter = 50
+    int_init = 3
+
+    int_dim = 2
+
+    bounds = utils_benchmarks.get_bounds(INFO_TARGET, int_dim)
+    model_bo = bo.BO(bounds, debug=True)
+    list_Y = []
+    list_time = []
+    for ind_bo in range(0, int_bo):
+        print('BO Iteration', ind_bo)
+        X_final, Y_final, time_final = utils_bo.optimize_many_with_random_init(model_bo, fun_target, int_init, int_iter, str_initial_method_bo='uniform', str_initial_method_ao='uniform', int_samples_ao=100)
+        print(X_final)
+        print(Y_final)
+        print(time_final)
+        list_Y.append(Y_final)
+        list_time.append(time_final)
+
+    arr_Y = np.array(list_Y)
+    arr_Y = np.expand_dims(np.squeeze(arr_Y), axis=0)
+    arr_time = np.array(list_time)
+    arr_time = np.expand_dims(arr_time, axis=0)
+    utils_plotting.plot_minimum(arr_Y, [STR_FUN_TARGET], int_init, True, path_save=PATH_SAVE, str_postfix=STR_FUN_TARGET)
+    utils_plotting.plot_minimum_time(arr_time, arr_Y, [STR_FUN_TARGET], int_init, True, path_save=PATH_SAVE, str_postfix=STR_FUN_TARGET)
+
+if __name__ == '__main__':
+    if not os.path.isdir(PATH_SAVE):
+        os.makedirs(PATH_SAVE)
+    main()
+