"""Gumbel (minimum) probability distribution.
.. math:: 1/\\rho exp((x-\\alpha)/\\rho - exp((x-\\alpha)/\\rho))
+
+ Integral
+
+ .. math:: -exp(-exp((x-\\alpha)/\\rho))
+
+ So integrated over the range x = [0,\\infty]
+
+ .. math:: -exp(-\\infty) - (-exp(-exp(-\\alpha/\\rho)))
+ = exp(-exp(-\\alpha/\\rho)))
"""
p = params # convenient alias
p[1] = abs(p[1]) # cannot normalize negative rho.
xs = (x - p[0]) / p[1]
- return (1.0/p[1]) * exp(xs - exp(xs))
+ return (exp(exp(-p[0]/p[1]))/p[1]) * exp(xs - exp(xs))
class GumbelModelFitter (HistogramModelFitter):
sim = sawsim_histogram(sawsim_runner, param_string, N=N,
bin_edges=theory.bin_edges)
-
+
e = GumbelModelFitter(sim)
params = e.fit()
sim_alpha = params[0]
sim_rho = abs(params[1])
for s,t,n in [(sim_alpha, alpha, 'alpha'), (sim_rho, rho, 'rho')]:
- assert (s - t)/t < 0.1, 'simulation %s = %g != %g = %s' % (n,s,t,n)
+ assert (s - t)/t < 0.1, 'simulation %s = %g != %g = %s' % (n,s,t,n))
return sim.residual(theory)