def _fifi2fid(x):
x = kept_within(0.0, x)
cdf = cNexpo2(means, x)
pdf = dNexpo2(means, x)
fi = cdf - prob
if pdf <= 0.0:
if fi == 0.0: fi2fid = 1.0
else: fi2fid = MAXFLOAT
else:
fi2fid = fi/pdf
return fi, fi2fid
def rNexpo2(self, means, xmax=float('inf'), pmax=1.0):
"""
Generator of random variates from a distribution of the sum of
exponentially distributed random variables. means[k] = 1.0/lambda[k].
NB No two numbers in the means vector can be exactly equal! If this
is desired, use rNexpo !
"""
assert xmax >= 0.0, "xmax must be a non-negative float in rNexpo2!"
self._checkpmax(pmax, 'rNexpo2')
pmx = pmax
if xmax < float('inf'): pmx = min(pmax, cNexpo2(means, xmax))
p = pmx * self.runif01()
x = iNexpo2(p, means)
return x
