distributions on the real line:
------------------------------
uniform
+ triangular
normal (Gaussian)
lognormal
negative exponential
__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","jumpahead", "WichmannHill", "getrandbits",
"SystemRandom"]
"""Get a random number in the range [a, b)."""
return a + (b-a) * self.random()
+## -------------------- triangular --------------------
+
+ def triangular(self, low, high, mode):
+ """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 = (mode - low) / (high - low)
+ if u > c:
+ u = 1 - u
+ c = 1 - c
+ low, high = high, low
+ return low + (high - low) * (u * c) ** 0.5
+
## -------------------- normal distribution --------------------
def normalvariate(self, mu, sigma):
_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
seed = _inst.seed
random = _inst.random
uniform = _inst.uniform
+triangular = _inst.triangular
randint = _inst.randint
choice = _inst.choice
randrange = _inst.randrange
g.random = x[:].pop; g.gammavariate(1.0, 1.0)
g.random = x[:].pop; g.gammavariate(200.0, 1.0)
g.random = x[:].pop; g.betavariate(3.0, 3.0)
+ g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
def test_avg_std(self):
# Use integration to test distribution average and standard deviation.
x = [i/float(N) for i in xrange(1,N)]
for variate, args, mu, sigmasqrd in [
(g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12),
+ (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0),
(g.expovariate, (1.5,), 1/1.5, 1/1.5**2),
(g.paretovariate, (5.0,), 5.0/(5.0-1),
5.0/((5.0-1)**2*(5.0-2))),