def checkSliceStudentT2(slice_method, seed):
# Simple program involving simulating from a student_t
if (backend_name() != "lite") and (slice_method == 'slice_doubling'):
raise SkipTest(
"Slice sampling with doubling only implemented in Lite.")
ripl = get_ripl(seed=seed)
ripl.assume("a", "(student_t 1.0)")
ripl.observe("(normal a 1.0)", 3.0)
ripl.predict("(normal a 1.0)", label="pid")
predictions = myCollectSamples(ripl, slice_method)
# Posterior of a is proprtional to
def postprop(a):
return stats.t(1).pdf(a) * stats.norm(loc=3).pdf(a)
import scipy.integrate as integrate
(normalize, _) = integrate.quad(postprop, -10, 10)
def posterior(a):
return postprop(a) / normalize
(meana, _) = integrate.quad(lambda x: x * posterior(x), -10, 10)
(meanasq, _) = integrate.quad(lambda x: x * x * posterior(x), -10, 10)
vara = meanasq - meana * meana
# TODO Test agreement with the whole shape of the distribution, not
# just the mean
return reportKnownMean(meana, predictions, variance=vara + 1.0)
def testStudentT2(seed):
# Simple program involving simulating from a student_t, with basic
# testing of loc and shape params.
ripl = get_ripl(seed=seed)
ripl.assume("x", "(student_t 1.0 3 2)")
ripl.assume("a", "(/ (- x 3) 2)")
ripl.observe("(normal a 1.0)", 3.0)
ripl.predict("(normal a 1.0)", label="pid")
predictions = collectSamples(ripl, "pid", infer="mixes_slowly")
# Posterior of a is proprtional to
def postprop(a):
return stats.t(1).pdf(a) * stats.norm(loc=3).pdf(a)
import scipy.integrate as integrate
(normalize, _) = integrate.quad(postprop, -10, 10)
def posterior(a):
return postprop(a) / normalize
(meana, _) = integrate.quad(lambda x: x * posterior(x), -10, 10)
(meanasq, _) = integrate.quad(lambda x: x * x * posterior(x), -10, 10)
vara = meanasq - meana * meana
# TODO Test agreement with the whole shape of the distribution, not
# just the mean
return reportKnownMean(meana, predictions, variance=vara + 1.0)
def testPoisson2(seed):
# Check that Poisson simulates correctly.
ripl = get_ripl(seed=seed)
ripl.assume("lambda", "5")
ripl.predict("(poisson lambda)", label="pid")
predictions = collectSamples(ripl, "pid")
return reportKnownMean(5, predictions)
def testApply1(seed):
# This CSP does not handle lists and symbols correctly.
ripl = get_ripl(seed=seed)
ripl.assume(
"apply",
"(lambda (op args) (eval (pair op args) (get_empty_environment)))")
ripl.predict(
"(apply mul (array (normal 10.0 1.0) (normal 10.0 1.0) (normal 10.0 1.0)))",
label="pid")
predictions = collectSamples(ripl, "pid")
return reportKnownMean(1000, predictions, variance=101**3 - 100**3)
def testGPMean2(seed):
ripl = get_ripl(seed=seed)
prep_ripl(ripl)
ripl.assume('gp', '(make_gp zero sq_exp)')
ripl.observe('(gp (array -1 1))', array([-1, 1]))
ripl.predict("(gp (array 0))", label="pid")
predictions = collectSamples(ripl, "pid")
xs = [p[0] for p in predictions]
# TODO: variance
return reportKnownMean(0, xs)
def testCMVN2D_mu1(seed):
if backend_name() != "lite": raise SkipTest("CMVN in lite only")
ripl = get_ripl(seed=seed)
ripl.assume("m0", "(array 5.0 5.0)")
ripl.assume("k0", "7.0")
ripl.assume("v0", "11.0")
ripl.assume("S0", "(matrix (array (array 13.0 0.0) (array 0.0 13.0)))")
ripl.assume("f", "(make_niw_normal m0 k0 v0 S0)")
ripl.predict("(f)", label="pid")
predictions = collectSamples(ripl, "pid")
mu1 = [p[0] for p in predictions]
return reportKnownMean(5, mu1)
def testConstrainWithAPredict2(seed):
# This test will fail at first, since we previously considered a
# program like this to be illegal and thus did not handle it
# correctly (we let the predict go stale). So we do not continually
# bewilder our users, I suggest that we handle this case WHEN WE
# CAN, which means we propagate from a constrain as long as we don't
# hit an absorbing node or a DRG node with a kernel.
ripl = get_ripl(seed=seed)
ripl.assume(
"f",
"(if (flip) (lambda () (normal 0.0 1.0)) (mem (lambda () (normal 0.0 1.0))))"
)
ripl.observe("(f)", "1.0")
ripl.predict("(* (f) 100)", label="pid")
predictions = collectSamples(ripl, "pid")
return reportKnownMean(
50, predictions) # will divide by 0 if there is no sample variance
