def binomial_log_pdf_ddd(k, p, n):
from qy.math import ln_choose
@qy.if_(k > n)
def _():
qy.return_(-numpy.inf)
@qy.if_(p == 0.0)
def _():
qy.return_(qy.select(k == 0.0, 0.0, -numpy.inf))
@qy.if_(p == 1.0)
def _():
qy.return_(qy.select(k == n, 0.0, -numpy.inf))
qy.return_(ln_choose(n, k) + k * qy.log(p) + (n - k) * qy.log1p(-p))
def binomial_log_pdf_ddd(k, p, n):
from qy.math import ln_choose
@qy.if_(k > n)
def _():
qy.return_(-numpy.inf)
@qy.if_(p == 0.0)
def _():
qy.return_(qy.select(k == 0.0, 0.0, -numpy.inf))
@qy.if_(p == 1.0)
def _():
qy.return_(qy.select(k == n, 0.0, -numpy.inf))
qy.return_(
ln_choose(n, k) + k * qy.log(p) + (n - k) * qy.log1p(-p))
def _(then):
if then:
qy.return_(-numpy.inf)
else:
qy.return_(a +
qy.log1p(qy.exp(qy.select(s, y_in, x_in) - a)))
def _():
v0 = qy.log1p(math.e - 1.0)
@qy.python(v0)
def _(v0_py):
assert_equal(v0_py, 1.0)
def _(then):
if then:
qy.return_(-numpy.inf)
else:
qy.return_(a + qy.log1p(qy.exp(qy.select(s, y_in, x_in) - a)))
