def test_user_pdf(self):
pdf = UserPDF()
bounds = PDF(pdf)
assert_equal(bounds.valid(1.0), 1)
bounds.rvs(1)
assert_equal(bounds.logp(1.0), 0)
def test_pdf(self):
pdf = PDF(norm)
# even if it's really far out it's still a valid value
assert_equal(pdf.valid(1003), 1003)
# logp
assert_equal(pdf.logp(0), norm.logpdf(0))
# examine dist with finite support
pdf = PDF(truncnorm(-1, 1))
assert_equal(pdf.logp(-2), -np.inf)
assert_equal(pdf.logp(-0.5), truncnorm.logpdf(-0.5, -1, 1))
# obtain a random value of a bounds instance
vals = pdf.rvs(size=1000)
assert_(np.min(vals) >= -1)
assert_(np.min(vals) <= 1)
# test a uniform distribution
pdf = PDF(uniform(1, 9))
assert_equal(pdf.logp(2), np.log(1.0 / 9.0))
assert_equal(pdf.logp(10.0), np.log(1.0 / 9.0))
# test the invcdf
rando = np.random.uniform(size=10)
pdf = PDF(truncnorm(-1, 1))
assert_equal(pdf.invcdf(rando), truncnorm.ppf(rando, -1, 1))
def test_pdf(self):
pdf = PDF(norm)
# even if it's really far out it's still a valid value
assert_equal(pdf.valid(1003), 1003)
# logp
assert_equal(pdf.lnprob(0), norm.logpdf(0))
# examine dist with finite support
pdf = PDF(truncnorm(-1, 1), seed=1)
assert_equal(pdf.lnprob(-2), -np.inf)
assert_equal(pdf.lnprob(-0.5), truncnorm.logpdf(-0.5, -1, 1))
# obtain a random value of a bounds instance
vals = pdf.rvs(size=1000)
assert_(np.min(vals) >= -1)
assert_(np.min(vals) <= 1)