def test_continuous_mh_exp():
"""
Test on a distribution with support on the positive reals
"""
def dens(x):
#mixture of gaussian
lamb = 2.47
if x < 0:
return -np.inf
else:
return -x * lamb
# return util.log_norm_dens(x, 0, 1.0)
rng = irm.RNG()
ITERS = 1000000
x = 0
results = np.zeros(ITERS)
for i in range(ITERS):
x = irm.continuous_mh_sample(x, dens, rng, 10, -4, 4)
results[i] = x
MIN = -1
MAX = 4
BINS = 101
x = np.linspace(MIN, MAX, BINS)
bin_width = x[1] - x[0]
y = [dens(a + bin_width/2) for a in x[:-1]]
p = np.exp(y)
p = p/np.sum(p)/(x[1]-x[0])
hist, bin_edges = np.histogram(results, x, normed=True)
kl= util.kl(hist, p)
assert kl < 0.1
def test_cont_mh_normal():
def dens(x):
#mixture of gaussian
mus = [-1.5, 2]
vars = [1, 1]
pis = [0.25, 0.75]
return np.logaddexp.accumulate([(np.log(pi) + util.log_norm_dens(x, mu, var)) for (pi, mu, var) in zip(pis, mus, vars)])[-1]
# return util.log_norm_dens(x, 0, 1.0)
rng = irm.RNG()
ITERS = 100000
x = 0
results = np.zeros(ITERS)
for i in range(ITERS):
x = irm.continuous_mh_sample(x, dens, rng, 10, -4, 4)
results[i] = x
MIN = -5
MAX = 5
BINS = 100
x = np.linspace(MIN, MAX, BINS)
bin_width = x[1] - x[0]
y = [dens(a + bin_width/2) for a in x[:-1]]
p = np.exp(y)
p = p/np.sum(p)/(x[1]-x[0])
hist, bin_edges = np.histogram(results, x, normed=True)
print hist
print p
pylab.plot(p)
pylab.plot(hist)
pylab.show()
kl= util.kl(hist, p)
assert kl < 0.1
