def __add__(self, other):
"""
Return the result of an addition.
"""
other = qy.value_from_any(other).cast_to(self.type_)
value = RealValue(qy.get().builder.fadd(self._value, other._value))
if qy.get().test_for_nan:
qy.assert_(~value.is_nan, "result of %s + %s is not a number", other, self)
return value
def _ll(self, parameter, sample, out):
"""
Compute log probability under this distribution.
"""
p = parameter.data.load()
k = sample.data.gep(0, 0).load()
n = sample.data.gep(0, 1).load()
if get_qy().test_for_nan:
qy.assert_(p >= 0.0, "invalid p = %s", p)
qy.assert_(p <= 1.0, "invalid p = %s", p)
qy.assert_(k >= 0, "invalid k = %s", k)
qy.assert_(n >= 0, "invalid n = %s", n)
qy.assert_(k <= n, "invalid k = %s (> n = %s)", k, n)
binomial_log_pdf(k, p, n).store(out)
def _ll(self, parameter, sample, out):
"""
Compute log probability under this distribution.
"""
p = parameter.data.load()
k = sample.data.gep(0, 0).load()
n = sample.data.gep(0, 1).load()
if get_qy().test_for_nan:
qy.assert_(p >= 0.0, "invalid p = %s" , p )
qy.assert_(p <= 1.0, "invalid p = %s" , p )
qy.assert_(k >= 0 , "invalid k = %s" , k )
qy.assert_(n >= 0 , "invalid n = %s" , n )
qy.assert_(k <= n , "invalid k = %s (> n = %s)", k, n)
binomial_log_pdf(k, p, n).store(out)
def _():
qy.assert_(False)
def _():
qy.assert_(True)