rng gpu

Author: rflamary

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)