From 0165668b7a2ea3206c25abfae3f469c9cf6dd8e5 Mon Sep 17 00:00:00 2001 From: axreyes Date: Wed, 8 Oct 2025 04:07:43 +0000 Subject: [PATCH 1/2] Fix: attribute error 'np.Infinity' was removed in the NumPy 2.0 release --- utils/sampling_utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/utils/sampling_utils.py b/utils/sampling_utils.py index 8ece402..31fff23 100644 --- a/utils/sampling_utils.py +++ b/utils/sampling_utils.py @@ -160,7 +160,7 @@ def update(self, samples, scores, alpha_max, **kwargs): return indices - def run_sampling(self, evaluator, num_samples, n_iter, minimize=False, alpha_max=1.0, early_stopping=np.Infinity, + def run_sampling(self, evaluator, num_samples, n_iter, minimize=False, alpha_max=1.0, early_stopping=np.inf, save_path='./sampling', n_parallel=1, plot_contour=False, executor=mp.Pool, verbose=True, init_samples=None): ''' Function to maximize given black-box function and save results to ./sampling/ @@ -677,7 +677,7 @@ def get_pairs(self, num_pairs): distance_mat[i][j] = np.sum((samples[i]-samples[j])**2) distance_mat[j][i] = np.sum((samples[i]-samples[j])**2) for i in range(len(scores)): - distance_mat[i,i] = np.Infinity + distance_mat[i,i] = np.inf pair_each_point = np.zeros(len(scores)).astype(np.int32) id0 = 0 while(len(pairs) Date: Wed, 8 Oct 2025 04:24:32 +0000 Subject: [PATCH 2/2] Fix: incorrect clip target in Gaussian local sampling loop --- utils/sampling_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/utils/sampling_utils.py b/utils/sampling_utils.py index 31fff23..b8bb63c 100644 --- a/utils/sampling_utils.py +++ b/utils/sampling_utils.py @@ -563,7 +563,7 @@ def sample(self, num_samples, verbose=True, **kwargs): while len(local_samples) < local_sampling: count = local_sampling - len(local_samples) new_samples = gaussian_mix.sample(n_samples=count)[0] - new_samples = np.clip(local_samples, self.boundaries[:,0], self.boundaries[:,1]) + new_samples = np.clip(new_samples, self.boundaries[:,0], self.boundaries[:,1]) constraint_check = np.asarray(list(map(self.constraint_fn, new_samples))) new_samples = new_samples[np.where(constraint_check)[0]]