rng gpu

test/test_gpu.py

@@ -14,16 +14,16 @@
 @pytest.mark.skipif(nogpu, reason="No GPU available")
 def test_gpu_sinkhorn():
 
- np.random.seed(0)
+ rng = np.random.RandomState(0)
 
  def describe_res(r):
  print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format(
  np.min(r), np.max(r), np.mean(r), np.std(r)))
 
  for n_samples in [50, 100, 500, 1000]:
  print(n_samples)
- a = np.random.rand(n_samples // 4, 100)
- b = np.random.rand(n_samples, 100)
+ a = rng.rand(n_samples // 4, 100)
+ b = rng.rand(n_samples, 100)
  time1 = time.time()
  transport = ot.da.OTDA_sinkhorn()
  transport.fit(a, b)
@@ -43,17 +43,18 @@ def describe_res(r):
 
 @pytest.mark.skipif(nogpu, reason="No GPU available")
 def test_gpu_sinkhorn_lpl1():
- np.random.seed(0)
+
+ rng = np.random.RandomState(0)
 
  def describe_res(r):
  print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}"
  .format(np.min(r), np.max(r), np.mean(r), np.std(r)))
 
  for n_samples in [50, 100, 500]:
  print(n_samples)
- a = np.random.rand(n_samples // 4, 100)
+ a = rng.rand(n_samples // 4, 100)
  labels_a = np.random.randint(10, size=(n_samples // 4))
- b = np.random.rand(n_samples, 100)
+ b = rng.rand(n_samples, 100)
  time1 = time.time()
  transport = ot.da.OTDA_lpl1()
  transport.fit(a, labels_a, b)