diff options
Diffstat (limited to 'Lib/random.py')
| -rw-r--r-- | Lib/random.py | 25 |
1 files changed, 24 insertions, 1 deletions
diff --git a/Lib/random.py b/Lib/random.py index 72b422f480..e0c015d8e8 100644 --- a/Lib/random.py +++ b/Lib/random.py @@ -13,6 +13,7 @@ distributions on the real line: ------------------------------ uniform + triangular normal (Gaussian) lognormal negative exponential @@ -35,6 +36,7 @@ General notes on the underlying Mersenne Twister core generator: """ +from __future__ import division from warnings import warn as _warn from types import MethodType as _MethodType, BuiltinMethodType as _BuiltinMethodType from math import log as _log, exp as _exp, pi as _pi, e as _e, ceil as _ceil @@ -44,7 +46,7 @@ from binascii import hexlify as _hexlify __all__ = ["Random","seed","random","uniform","randint","choice","sample", "randrange","shuffle","normalvariate","lognormvariate", - "expovariate","vonmisesvariate","gammavariate", + "expovariate","vonmisesvariate","gammavariate","triangular", "gauss","betavariate","paretovariate","weibullvariate", "getstate","setstate", "getrandbits", "SystemRandom"] @@ -333,6 +335,25 @@ class Random(_random.Random): """Get a random number in the range [a, b).""" return a + (b-a) * self.random() +## -------------------- triangular -------------------- + + def triangular(self, low=0.0, high=1.0, mode=None): + """Triangular distribution. + + Continuous distribution bounded by given lower and upper limits, + and having a given mode value in-between. + + http://en.wikipedia.org/wiki/Triangular_distribution + + """ + u = self.random() + c = 0.5 if mode is None else (mode - low) / (high - low) + if u > c: + u = 1.0 - u + c = 1.0 - c + low, high = high, low + return low + (high - low) * (u * c) ** 0.5 + ## -------------------- normal distribution -------------------- def normalvariate(self, mu, sigma): @@ -671,6 +692,7 @@ def _test(N=2000): _test_generator(N, gammavariate, (200.0, 1.0)) _test_generator(N, gauss, (0.0, 1.0)) _test_generator(N, betavariate, (3.0, 3.0)) + _test_generator(N, triangular, (0.0, 1.0, 1.0/3.0)) # Create one instance, seeded from current time, and export its methods # as module-level functions. The functions share state across all uses @@ -682,6 +704,7 @@ _inst = Random() seed = _inst.seed random = _inst.random uniform = _inst.uniform +triangular = _inst.triangular randint = _inst.randint choice = _inst.choice randrange = _inst.randrange |