def testGradientOfSimulateOfLookup2():
from venture.lite.sp_registry import builtInSPs
sp = builtInSPs()["lookup"]
args = [vv.VentureArrayUnboxed([0, 0], t.Number), vv.VentureNumber(1)]
grad = sp.outputPSP.gradientOfSimulate(MockArgs(args, sp.constructSPAux()),
vv.VentureNumber(0),
vv.VentureNumber(1))
assert grad[0].lookup(vv.VentureNumber(0)) == vv.VentureNumber(0)
assert grad[0].lookup(vv.VentureNumber(1)) == vv.VentureNumber(1)
assert grad[1] == 0
def testForeignDeterminismSmoke():
get_ripl() # Build the SP registry (TODO !?)
lite_normal_sp = builtInSPs()["normal"]
def doit(seed):
r = get_ripl(seed=seed)
r.bind_foreign_sp("my_normal", lite_normal_sp)
r.assume("x", "(my_normal 0 1)")
r.infer("(default_markov_chain 3)")
return r.sample("x")
ans = doit(1)
assert isinstance(ans, numbers.Number)
eq_(ans, doit(1))
assert ans != doit(2)
def testInvWishartAssess():
psi = 3 # Parameterization from https://en.wikipedia.org/wiki/Inverse-Wishart_distribution
dof = 5
n = 3000
low = 0.00001
high = 500
get_ripl() # Make sure the SP registry is built (!)
inv_wishart_sp = builtInSPs()["inv_wishart"]
scale_matrix = [[psi]]
def inv_wishart(x):
return logDensity(inv_wishart_sp, no_wrapper=True)([[x]], [scale_matrix, dof])
inv_wisharts = np.vectorize(inv_wishart)(np.linspace(low, high, n))
inv_gamma = scipy.stats.invgamma(dof*0.5, scale=psi*0.5).logpdf
inv_gammas = np.vectorize(inv_gamma)(np.linspace(low, high, n))
cum_w = math.exp(logsumexp(inv_wisharts)) * (high - low) / n
cum_g = math.exp(logsumexp(inv_gammas)) * (high - low) / n
np.testing.assert_allclose([1, 1], [cum_w, cum_g], rtol=1e-2)
np.testing.assert_allclose(inv_wisharts, inv_gammas)
def testVectorArrayExample():
from venture.lite.sp_registry import builtInSPs
from venture.lite.value import VentureArray
propRiplAgreesWithDeterministicSimulate([[VentureArray([])]], "is_vector",
builtInSPs()["is_vector"])