From c53b2eb729bae1f248a2654dfcfa4a3dd3e2902b Mon Sep 17 00:00:00 2001
From: mattip <matti.picus@gmail.com>
Date: Wed, 10 Apr 2019 00:50:42 +0300
Subject: BENCH: convert bencmarks to asv format

remove files that were part of the origal repo
rework randomgen docs to integrate with numpy and fix some links
remove convenience functions, require explicit call to gen.brng
move code out of numpy.random.randomgen into numpy.random
---
 numpy/random/examples/numba/extending.py | 77 ++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 numpy/random/examples/numba/extending.py

(limited to 'numpy/random/examples/numba/extending.py')

diff --git a/numpy/random/examples/numba/extending.py b/numpy/random/examples/numba/extending.py
new file mode 100644
index 000000000..72e903b1f
--- /dev/null
+++ b/numpy/random/examples/numba/extending.py
@@ -0,0 +1,77 @@
+import datetime as dt
+
+import numpy as np
+import numba as nb
+
+from randomgen import Xoroshiro128
+
+x = Xoroshiro128()
+f = x.ctypes.next_uint32
+s = x.ctypes.state
+
+
+@nb.jit(nopython=True)
+def bounded_uint(lb, ub, state):
+    mask = delta = ub - lb
+    mask |= mask >> 1
+    mask |= mask >> 2
+    mask |= mask >> 4
+    mask |= mask >> 8
+    mask |= mask >> 16
+
+    val = f(state) & mask
+    while val > delta:
+        val = f(state) & mask
+
+    return lb + val
+
+
+print(bounded_uint(323, 2394691, s.value))
+
+
+@nb.jit(nopython=True)
+def bounded_uints(lb, ub, n, state):
+    out = np.empty(n, dtype=np.uint32)
+    for i in range(n):
+        out[i] = bounded_uint(lb, ub, state)
+
+
+bounded_uints(323, 2394691, 10000000, s.value)
+
+g = x.cffi.next_double
+cffi_state = x.cffi.state
+state_addr = x.cffi.state_address
+
+
+def normals(n, state):
+    out = np.empty(n)
+    for i in range((n + 1) // 2):
+        x1 = 2.0 * g(state) - 1.0
+        x2 = 2.0 * g(state) - 1.0
+        r2 = x1 * x1 + x2 * x2
+        while r2 >= 1.0 or r2 == 0.0:
+            x1 = 2.0 * g(state) - 1.0
+            x2 = 2.0 * g(state) - 1.0
+            r2 = x1 * x1 + x2 * x2
+        f = np.sqrt(-2.0 * np.log(r2) / r2)
+        out[2 * i] = f * x1
+        if 2 * i + 1 < n:
+            out[2 * i + 1] = f * x2
+    return out
+
+
+print(normals(10, cffi_state).var())
+# Warm up
+normalsj = nb.jit(normals, nopython=True)
+normalsj(1, state_addr)
+
+start = dt.datetime.now()
+normalsj(1000000, state_addr)
+ms = 1000 * (dt.datetime.now() - start).total_seconds()
+print('1,000,000 Polar-transform (numba/Xoroshiro128) randoms in '
+      '{ms:0.1f}ms'.format(ms=ms))
+
+start = dt.datetime.now()
+np.random.standard_normal(1000000)
+ms = 1000 * (dt.datetime.now() - start).total_seconds()
+print('1,000,000 Polar-transform (NumPy) randoms in {ms:0.1f}ms'.format(ms=ms))
-- 
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