def __init__(self, mytable):
self.probs = numpy.array(mytable, float)
s = self.probs.sum()
if s > 0:
self.probs /= s
else:
raise ValueError, "sum of table frequencies must be > 0"
self.sampler = _intsampler(self.probs)
def rdiscrete(pvals, numsamples=1):
"""Sample from discrete distribution with probabilities given by pvals.
If size is given, a matrix of the given size is filled with samples,
otherwise a single value (between 0 and len(pvals) is returned.
"""
if HAVE_INTSAMPLER and numsamples > 100:
sampler = _intsampler(pvals)
return sampler.sample(numsamples)
else:
cdf = cumsum(pvals)
unif = R.random_sample(size=numsamples)
return cdf.searchsorted(unif)
