Merge pull request #79 from drufat/numpy

Extending monte_carlo_pi example to support numpy

Author: pavanky

examples/benchmarks/monte_carlo_pi.py

@@ -11,26 +11,35 @@
 
 from random import random
 from time import time
-from arrayfire import (array, randu)
 import arrayfire as af
 import sys
 
+try:
+    import numpy as np
+except:
+    np = None
+
 #alias range / xrange because xrange is faster than range in python2
 try:
     frange = xrange  #Python2
 except NameError:
     frange = range   #Python3
 
-
-def calc_pi_device(samples):
-    x = randu(samples)
-    y = randu(samples)
-    return 4 * af.sum((x * x  + y * y) < 1) / samples
-
 # Having the function outside is faster than the lambda inside
 def in_circle(x, y):
     return (x*x + y*y) < 1
 
+def calc_pi_device(samples):
+    x = af.randu(samples)
+    y = af.randu(samples)
+    return 4 * af.sum(in_circle(x, y)) / samples
+
+def calc_pi_numpy(samples):
+    np.random.seed(1)
+    x = np.random.rand(samples).astype(np.float32)
+    y = np.random.rand(samples).astype(np.float32)
+    return 4 * np.sum(in_circle(x, y)) / samples
+
 def calc_pi_host(samples):
     count = sum(1 for k in frange(samples) if in_circle(random(), random()))
     return 4 * float(count) / samples
@@ -53,4 +62,6 @@ def bench(calc_pi, samples=1000000, iters=25):
     af.info()
 
     bench(calc_pi_device)
+    if np:
+        bench(calc_pi_numpy)
     bench(calc_pi_host)