def test_multiple_entry_calls():
# Test that calls with multiple x vectors as matrix work
np.random.seed(2846)
n = np.random.randint(1, 32)
alpha = np.random.uniform(10e-10, 100, n)
d = dirichlet(alpha)
num_tests = 10
num_multiple = 5
xm = None
for i in range(num_tests):
for m in range(num_multiple):
x = np.random.uniform(10e-10, 100, n)
x /= np.sum(x)
if xm is not None:
xm = np.vstack((xm, x))
else:
xm = x
rm = d.pdf(xm.T)
rs = None
for xs in xm:
r = d.pdf(xs)
if rs is not None:
rs = np.append(rs, r)
else:
rs = r
assert_array_almost_equal(rm, rs)
def test_multiple_entry_calls():
# Test that calls with multiple x vectors as matrix work
np.random.seed(2846)
n = np.random.randint(1, 32)
alpha = np.random.uniform(10e-10, 100, n)
d = dirichlet(alpha)
num_tests = 10
num_multiple = 5
xm = None
for i in range(num_tests):
for m in range(num_multiple):
x = np.random.uniform(10e-10, 100, n)
x /= np.sum(x)
if xm is not None:
xm = np.vstack((xm, x))
else:
xm = x
rm = d.pdf(xm.T)
rs = None
for xs in xm:
r = d.pdf(xs)
if rs is not None:
rs = np.append(rs, r)
else:
rs = r
assert_array_almost_equal(rm, rs)
def test_simple_values():
alpha = np.array([1, 1])
d = dirichlet(alpha)
assert_almost_equal(d.mean(), 0.5)
assert_almost_equal(d.var(), 1. / 12.)
b = beta(1, 1)
assert_almost_equal(d.mean(), b.mean())
assert_almost_equal(d.var(), b.var())
def test_simple_values():
alpha = np.array([1, 1])
d = dirichlet(alpha)
assert_almost_equal(d.mean(), 0.5)
assert_almost_equal(d.var(), 1. / 12.)
b = beta(1, 1)
assert_almost_equal(d.mean(), b.mean())
assert_almost_equal(d.var(), b.var())
def test_K_and_K_minus_1_calls_equal():
# Test that calls with K and K-1 entries yield the same results.
np.random.seed(2846)
n = np.random.randint(1, 32)
alpha = np.random.uniform(10e-10, 100, n)
d = dirichlet(alpha)
num_tests = 10
for i in range(num_tests):
x = np.random.uniform(10e-10, 100, n)
x /= np.sum(x)
assert_almost_equal(d.pdf(x[:-1]), d.pdf(x))
def test_K_and_K_minus_1_calls_equal():
# Test that calls with K and K-1 entries yield the same results.
np.random.seed(2846)
n = np.random.randint(1, 32)
alpha = np.random.uniform(10e-10, 100, n)
d = dirichlet(alpha)
num_tests = 10
for i in range(num_tests):
x = np.random.uniform(10e-10, 100, n)
x /= np.sum(x)
assert_almost_equal(d.pdf(x[:-1]), d.pdf(x))
def test_2D_dirichlet_is_beta():
np.random.seed(2846)
alpha = np.random.uniform(10e-10, 100, 2)
d = dirichlet(alpha)
b = beta(alpha[0], alpha[1])
num_tests = 10
for i in range(num_tests):
x = np.random.uniform(10e-10, 100, 2)
x /= np.sum(x)
assert_almost_equal(b.pdf(x), d.pdf([x]))
assert_almost_equal(b.mean(), d.mean()[0])
assert_almost_equal(b.var(), d.var()[0])
def test_2D_dirichlet_is_beta():
np.random.seed(2846)
alpha = np.random.uniform(10e-10, 100, 2)
d = dirichlet(alpha)
b = beta(alpha[0], alpha[1])
num_tests = 10
for i in range(num_tests):
x = np.random.uniform(10e-10, 100, 2)
x /= np.sum(x)
assert_almost_equal(b.pdf(x), d.pdf([x]))
assert_almost_equal(b.mean(), d.mean()[0])
assert_almost_equal(b.var(), d.var()[0])
def test_frozen_dirichlet():
np.random.seed(2846)
n = np.random.randint(1, 32)
alpha = np.random.uniform(10e-10, 100, n)
d = dirichlet(alpha)
assert_equal(d.var(), dirichlet.var(alpha))
assert_equal(d.mean(), dirichlet.mean(alpha))
assert_equal(d.entropy(), dirichlet.entropy(alpha))
num_tests = 10
for i in range(num_tests):
x = np.random.uniform(10e-10, 100, n)
x /= np.sum(x)
assert_equal(d.pdf(x[:-1]), dirichlet.pdf(x[:-1], alpha))
assert_equal(d.logpdf(x[:-1]), dirichlet.logpdf(x[:-1], alpha))
def test_frozen_dirichlet():
np.random.seed(2846)
n = np.random.randint(1, 32)
alpha = np.random.uniform(10e-10, 100, n)
d = dirichlet(alpha)
assert_equal(d.var(), dirichlet.var(alpha))
assert_equal(d.mean(), dirichlet.mean(alpha))
assert_equal(d.entropy(), dirichlet.entropy(alpha))
num_tests = 10
for i in range(num_tests):
x = np.random.uniform(10e-10, 100, n)
x /= np.sum(x)
assert_equal(d.pdf(x[:-1]), dirichlet.pdf(x[:-1], alpha))
assert_equal(d.logpdf(x[:-1]), dirichlet.logpdf(x[:-1], alpha))
