def test_slice_sample_uniform():
tutil.npseed()
def logpdf(x):
if x > 1 or x < 0:
return -np.inf
return 0
w = np.array([0.5])
x0 = np.array([0.0])
samples = util.slice_sample(logpdf, 10000, w, xval=x0, nburn=10, freq=1)
hist, bins = np.histogram(samples, bins=5, normed=True, range=[0, 1])
assert (np.abs(hist - 1) < 0.05).all()
def test_slice_sample_uniform():
tutil.npseed()
def logpdf(x):
if x > 1 or x < 0:
return -np.inf
return 0
w = np.array([0.5])
x0 = np.array([0.0])
samples = util.slice_sample(logpdf, 10000, w, xval=x0, nburn=10, freq=1)
hist, bins = np.histogram(samples, bins=5, normed=True, range=[0, 1])
assert (np.abs(hist - 1) < 0.05).all()
def test_slice_sample_normal():
tutil.npseed()
def logpdf(x):
return (-(x ** 2) / 2.) - 0.5 * np.log(2 * np.pi)
w = np.array([1.0])
x0 = np.array([0.0])
samples = util.slice_sample(logpdf, 10000, w, xval=x0, nburn=10, freq=1)
hist, bins = np.histogram(samples, bins=10, normed=True)
centers = (bins[:-1] + bins[1:]) / 2.
bin_pdf = np.exp(logpdf(centers))
assert (np.abs(bin_pdf - hist) < 0.02).all()
def test_slice_sample_normal():
tutil.npseed()
def logpdf(x):
return (-(x**2) / 2.) - 0.5 * np.log(2 * np.pi)
w = np.array([1.0])
x0 = np.array([0.0])
samples = util.slice_sample(logpdf, 10000, w, xval=x0, nburn=10, freq=1)
hist, bins = np.histogram(samples, bins=10, normed=True)
centers = (bins[:-1] + bins[1:]) / 2.
bin_pdf = np.exp(logpdf(centers))
assert (np.abs(bin_pdf - hist) < 0.02).all()