diff --git a/utils/sampling_utils.py b/utils/sampling_utils.py
index 8ece402..b8bb63c 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/
@@ -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]]
@@ -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)<num_pairs):
@@ -936,7 +936,7 @@ def divide(self, max_id, max_dim):
 			self.split_region(max_id, ids)
 
 
-	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/