auto: Rebuild docs.

Author: github-actions

_downloads/06743557a28bcbf4dca92af04c48319b/pybads_example_1_basic_usage.py

@@ -18,14 +18,14 @@ def rosenbrocks_fcn(x):
 
 target = rosenbrocks_fcn
 
-lb = np.array([-20, -20])  # Lower bounds
-ub = np.array([20, 20])  # Upper bounds
-plb = np.array([-5, -5])  # Plausible lower bounds
-pub = np.array([5, 5])  # Plausible upper bounds
+lower_bounds = np.array([-20, -20])
+upper_bounds = np.array([20, 20])
+plausible_lower_bounds = np.array([-5, -5])
+plausible_upper_bounds = np.array([5, 5])
 x0 = np.array([0, 0])
 # Starting point
 
-bads = BADS(target, x0, lb, ub, plb, pub)
+bads = BADS(target, x0, lower_bounds, upper_bounds, plausible_lower_bounds, plausible_upper_bounds)
 optimize_result = bads.optimize()
 
 x_min = optimize_result["x"]

_downloads/66a4a0a202d9e4aaa38aa89b13027fda/pybads_example_5_extended_usage.py

@@ -19,10 +19,10 @@ def camelback6(x):
     return f
 
 
-lb = np.array([-3, -2])  # Lower bounds
-ub = np.array([3, 2])  # Upper bounds
-plb = np.array([-2.9, -1.9])  # Plausible lower bounds
-pub = np.array([2.9, 1.9])  # Plausible upper bounds
+lower_bounds = np.array([-3, -2])
+upper_bounds = np.array([3, 2])
+plausible_lower_bounds = np.array([-2.9, -1.9])
+plausible_upper_bounds = np.array([2.9, 1.9])
 
 options = {
     "display": "off",  # We switch off the printing
@@ -31,13 +31,15 @@ def camelback6(x):
 
 num_opts = 10
 optimize_results = []
-x_vec = np.zeros((num_opts, lb.shape[0]))
+x_vec = np.zeros((num_opts,lower_bounds.shape[0]))
 fval_vec = np.zeros(num_opts)
 
 for opt_count in range(num_opts):
-    print("Running optimization " + str(opt_count) + "...")
-    x0 = np.random.uniform(low=plb, high=pub)
-    bads = BADS(camelback6, x0, lb, ub, plb, pub, options=options)
+    print('Running optimization ' + str(opt_count) + '...')
+    options['random_seed'] = opt_count
+    bads = BADS(
+        camelback6, None, lower_bounds, upper_bounds, plausible_lower_bounds, plausible_upper_bounds, options=options
+    )
     optimize_results.append(bads.optimize())
     x_vec[opt_count] = optimize_results[opt_count].x
     fval_vec[opt_count] = optimize_results[opt_count].fval

_downloads/74f3ba26c810ad06a6a8c73b48d40b97/pybads_example_4_user_provided_noise.py

@@ -15,12 +15,11 @@ def noisy_sphere_estimated_noise(x, scale=1.0):
     return y, sigma
 
 
-x0 = np.array([-3, -3])
-# Starting point
-lb = np.array([-5, -5])  # Lower bounds
-ub = np.array([5, 5])  # Upper bounds
-plb = np.array([-2, -2])  # Plausible lower bounds
-pub = np.array([2, 2])  # Plausible upper bounds
+x0 = np.array([-3, -3])  # Starting point
+lower_bounds = np.array([-5, -5])
+upper_bounds = np.array([5, 5])
+plausible_lower_bounds = np.array([-2, -2])
+plausible_upper_bounds = np.array([2, 2])
 
 options = {
     "uncertainty_handling": True,
@@ -29,7 +28,8 @@ def noisy_sphere_estimated_noise(x, scale=1.0):
 }
 
 bads = BADS(
-    noisy_sphere_estimated_noise, x0, lb, ub, plb, pub, options=options
+    noisy_sphere_estimated_noise, x0, lower_bounds, upper_bounds, plausible_lower_bounds, plausible_upper_bounds, 
+    options=options
 )
 optimize_result = bads.optimize()
 

_downloads/e438e0f5fc735fbcda36ef39de78ec93/pybads_example_3_noisy_objective.py

@@ -14,20 +14,19 @@ def noisy_sphere(x, sigma=1.0):
     return f + noise
 
 
-x0 = np.array([-3, -3])
-# Starting point
-lb = np.array([-5, -5])  # Lower bounds
-ub = np.array([5, 5])  # Upper bounds
-plb = np.array([-2, -2])  # Plausible lower bounds
-pub = np.array([2, 2])  # Plausible upper bounds
+x0 = np.array([-3, -3])  # Starting point
+lower_bounds = np.array([-5, -5])
+upper_bounds = np.array([5, 5])
+plausible_lower_bounds = np.array([-2, -2])
+plausible_upper_bounds = np.array([2, 2])
 
 options = {
     "uncertainty_handling": True,
     "max_fun_evals": 300,
     "noise_final_samples": 100,
 }
 
-bads = BADS(noisy_sphere, x0, lb, ub, plb, pub, options=options)
+bads = BADS(noisy_sphere, x0, lower_bounds, upper_bounds, plausible_lower_bounds, plausible_upper_bounds, options=options)
 optimize_result = bads.optimize()
 
 x_min = optimize_result["x"]

_downloads/f3c67215dbe688f40d2a84390d9ecb79/pybads_example_2_nonbox_constraints.py

@@ -16,10 +16,9 @@ def rosenbrocks_fcn(x):
     )
 
 
-x0 = np.array([0, 0])
-# Starting point
-lb = np.array([-1, -1])  # Lower bounds
-ub = np.array([1, 1])  # Upper bounds
+x0 = np.array([0, 0])  # Starting point
+lower_bounds = np.array([-1, -1])
+upper_bounds = np.array([1, 1])
 
 
 def circle_constr(x):
@@ -29,7 +28,9 @@ def circle_constr(x):
     return np.sum(x_2d**2, axis=1) > 1
 
 
-bads = BADS(rosenbrocks_fcn, x0, lb, ub, non_box_cons=circle_constr)
+options = {}
+options["rng_seed"] = 3
+bads = BADS(rosenbrocks_fcn, x0, lower_bounds, upper_bounds, non_box_cons=circle_constr)
 optimize_result = bads.optimize()
 
 x_min = optimize_result["x"]