Data commit

Task/Monte-Carlo-methods/Python/monte-carlo-methods-1.py

@@ -0,0 +1,16 @@
+>>> import random, math
+>>> throws = 1000
+>>> 4.0 * sum(math.hypot(*[random.random()*2-1
+	                 for q in [0,1]]) < 1
+              for p in xrange(throws)) / float(throws)
+3.1520000000000001
+>>> throws = 1000000
+>>> 4.0 * sum(math.hypot(*[random.random()*2-1
+	                 for q in [0,1]]) < 1
+              for p in xrange(throws)) / float(throws)
+3.1396359999999999
+>>> throws = 100000000
+>>> 4.0 * sum(math.hypot(*[random.random()*2-1
+	                 for q in [0,1]]) < 1
+              for p in xrange(throws)) / float(throws)
+3.1415666400000002

Task/Monte-Carlo-methods/Python/monte-carlo-methods-2.py

@@ -0,0 +1,17 @@
+from random import random
+from math import hypot
+try:
+    import psyco
+    psyco.full()
+except:
+    pass
+
+def pi(nthrows):
+    inside = 0
+    for i in xrange(nthrows):
+        if hypot(random(), random()) < 1:
+            inside += 1
+    return 4.0 * inside / nthrows
+
+for n in [10**4, 10**6, 10**7, 10**8]:
+    print "%9d: %07f" % (n, pi(n))

Task/Monte-Carlo-methods/Python/monte-carlo-methods-3.py

@@ -0,0 +1,4 @@
+import numpy as np
+
+n = input('Number of samples: ')
+print np.sum(np.random.rand(n)**2+np.random.rand(n)**2<1)/float(n)*4