use np.random.default_rng with a seed consistently

inducer · Aug 6, 2022 · c7718d2 · c7718d2
1 parent 8bce4f6
commit c7718d2
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diff --git a/doc/algorithm.rst b/doc/algorithm.rst
@@ -241,7 +241,8 @@ Here's a usage example::
     knl = InclusiveScanKernel(context, np.int32, "a+b")
 
     n = 2**20-2**18+5
-    host_data = np.random.randint(0, 10, n).astype(np.int32)
+    rng = np.random.default_rng(seed=42)
+    host_data = rng.integers(0, 10, size=n, dtype=np.int32)
     dev_data = cl_array.to_device(queue, host_data)
 
     knl(dev_data)