def test_leapfrog_reversible():
n = 3
start, model, _ = models.non_normal(n)
with model:
h = pm.find_hessian(start, model=model)
step = pm.HamiltonianMC(model.vars, h, model=model)
bij = pm.DictToArrayBijection(step.ordering, start)
logp, dlogp = map(bij.mapf, step.fs)
H = Hamiltonian(logp, dlogp, step.potential)
q0 = bij.map(start)
p0 = np.ones(n) * .05
for e in [.01, .1, 1.2]:
for L in [1, 2, 3, 4, 20]:
q, p = q0, p0
q, p = leapfrog(H, q, p, L, e)
q, p = leapfrog(H, q, -p, L, e)
close_to(q, q0, 1e-8, str((L, e)))
close_to(-p, p0, 1e-8, str((L, e)))
def test_leapfrog_reversible():
n=3
start, model, _ = models.non_normal(n)
with model:
h = pm.find_hessian(start, model = model)
step = pm.HamiltonianMC(model.vars, h, model = model)
bij = pm.DictToArrayBijection(step.ordering, start)
logp, dlogp = map(bij.mapf, step.fs)
H = Hamiltonian(logp, dlogp, step.potential)
q0 = bij.map(start)
p0 = np.ones(n)*.05
for e in [.01, .1, 1.2]:
for L in [1,2,3,4, 20]:
q,p = q0,p0
q,p = leapfrog(H, q,p,L, e)
q,p = leapfrog(H, q,-p,L,e)
close_to(q, q0, 1e-8, str((L, e)))
close_to(-p, p0, 1e-8, str((L, e)))
def test_plots_multidimensional():
# Test single trace
from .models import multidimensional_model
start, model, _ = multidimensional_model()
with model as model:
h = np.diag(find_hessian(start))
step = Metropolis(model.vars, h)
trace = sample(3000, step, start)
traceplot(trace)
def test_plots():
# Test single trace
from pymc.examples import arbitrary_stochastic as asmod
with asmod.model as model:
start = model.test_point
h = find_hessian(start)
step = Metropolis(model.vars, h)
trace = sample(3000, step, start)
forestplot(trace)
autocorrplot(trace)
