def testReal(self):
m1 = _getMapping()
m2 = resample(m1, pxPerDeg=15, method='mean')
m2.checkPlateCarree()
m3 = resample(m2, arcsecPerPx=100, method='nearest')
m3.checkPlateCarree()
assert_array_approx_equal(_bbToArray(m2.boundingBox), _bbToArray(m1.boundingBox), 1)
assert_array_approx_equal(_bbToArray(m3.boundingBox), _bbToArray(m1.boundingBox), 1)
def test_joint_estmator_point(self):
X = array([[0, 1], [1, 0], [1, 1]])
y = array([[0, 1], [1, 0], [1, 0]])
assert_array_approx_equal(
joint_estimator_point(X, y), [[.5, 0], [.25, .25]])
assert_array_approx_equal(
joint_estimator_point(csr_matrix(X), csr_matrix(y)),
[[.5, 0], [.25, .25]])
def test_joint_estmator_point(self):
X = array([[0, 1], [1, 0], [1, 1]])
y = array([[0, 1], [1, 0], [1, 0]])
assert_array_approx_equal(joint_estimator_point(X, y),
[[.5, 0], [.25, .25]])
assert_array_approx_equal(
joint_estimator_point(csr_matrix(X), csr_matrix(y)),
[[.5, 0], [.25, .25]])
def test_pointwise_mutual_information(self):
X = array([[0, 1], [1, 0], [1, 1]])
y = array([[0, 1], [1, 0], [1, 0]])
assert_array_approx_equal(pointwise_mutual_information(X, y),
[0.1178, 0.1178],
decimal=3)
assert_array_approx_equal(pointwise_mutual_information(
csr_matrix(X), csr_matrix(y)), [0.1178, 0.1178],
decimal=3)
def test_mutual_information(self):
X = array([[0, 1],
[1, 0],
[1, 1]])
y = array([[0, 1],
[1, 0],
[1, 0]])
assert_array_approx_equal(mutual_information(X, y), [-0.37489, -0.605939], decimal=3)
assert_array_approx_equal(mutual_information(csr_matrix(X), csr_matrix(y)), [-0.37489, -0.605939], decimal=3)
def _testReal(self):
m1 = _getMapping()
m2 = resample(m1, pxPerDeg=15, method='mean')
m2.checkPlateCarree()
m3 = resample(m2, arcsecPerPx=100, method='nearest')
m3.checkPlateCarree()
assert_array_approx_equal(_bbToArray(m2.boundingBox),
_bbToArray(m1.boundingBox), 1)
assert_array_approx_equal(_bbToArray(m3.boundingBox),
_bbToArray(m1.boundingBox), 1)
def test_mutual_information(self):
X = array([[0, 1], [1, 0], [1, 1]])
y = array([[0, 1], [1, 0], [1, 0]])
assert_array_approx_equal(mutual_information(X, y),
[-0.37489, -0.605939],
decimal=3)
assert_array_approx_equal(mutual_information(csr_matrix(X),
csr_matrix(y)),
[-0.37489, -0.605939],
decimal=3)
def test_pointwise_mutual_information(self):
X = array([[0, 1],
[1, 0],
[1, 1]])
y = array([[0, 1],
[1, 0],
[1, 0]])
assert_array_approx_equal(pointwise_mutual_information(X, y), [0.1178, 0.1178], decimal=3)
assert_array_approx_equal(pointwise_mutual_information(csr_matrix(X), csr_matrix(y)),
[0.1178, 0.1178], decimal=3)
def test_joint_estimator_full(self):
X = array([[0, 1], [1, 0], [1, 1]])
y = array([[0, 1], [1, 0], [1, 0]])
assert_array_approx_equal(
joint_estimator_full(X, y),
[[[.1667, .0], [.0833, .0833]], [[.0, .1667], [.0833, .0833]],
[[.0, .0833], [.0833, .0]], [[.0833, .0], [.0, .0833]]],
decimal=3)
assert_array_approx_equal(
joint_estimator_full(csr_matrix(X), csr_matrix(y)),
[[[.1667, .0], [.0833, .0833]], [[.0, .1667], [.0833, .0833]],
[[.0, .0833], [.0833, .0]], [[.0833, .0], [.0, .0833]]],
decimal=3)
def test_npmi(self):
X = array([[0, 1],
[1, 0],
[1, 1]])
y = array([[0, 1],
[1, 0],
[1, 0]])
transformer = TermWeightTransformer(method='npmi')
transformer.fit(X, y)
assert_array_approx_equal(transformer._weights, [0.1699, 0.0850], decimal=3)
assert_array_approx_equal(transformer.transform(X), array([[0., 0.0850],
[0.1700, 0.],
[0.1700, 0.0850]]),
decimal=3)
transformer = TermWeightTransformer(method='npmi')
X = csr_matrix(X)
y = csr_matrix(y)
transformer.fit(X, y)
newX = transformer.transform(X)
assert_array_approx_equal(transformer._weights, [0.1700, 0.0850], decimal=3)
assert_true(issparse(newX))
assert_array_approx_equal(newX.todense(), array([[0., 0.0850],
[0.1700, 0.],
[0.1700, 0.0850]]),
decimal=3)
def test_joint_estimator_full(self):
X = array([[0, 1], [1, 0], [1, 1]])
y = array([[0, 1], [1, 0], [1, 0]])
assert_array_approx_equal(
joint_estimator_full(X, y),
[[[.1667, .0], [.0833, .0833]], [[.0, .1667], [.0833, .0833]],
[[.0, .0833], [.0833, .0]], [[.0833, .0], [.0, .0833]]],
decimal=3)
assert_array_approx_equal(
joint_estimator_full(csr_matrix(X), csr_matrix(y)),
[[[.1667, .0], [.0833, .0833]], [[.0, .1667], [.0833, .0833]],
[[.0, .0833], [.0833, .0]], [[.0833, .0], [.0, .0833]]],
decimal=3)
def test_auto_guide(auto_class, init_loc_fn, num_particles):
latent_dim = 3
def model(obs):
a = numpyro.sample("a", Normal(0, 1))
return numpyro.sample("obs", Bernoulli(logits=a), obs=obs)
obs = Bernoulli(0.5).sample(random.PRNGKey(0), (10, latent_dim))
rng_key = random.PRNGKey(0)
guide_key, stein_key = random.split(rng_key)
inner_guide = auto_class(model, init_loc_fn=init_loc_fn())
with handlers.seed(rng_seed=guide_key), handlers.trace() as inner_guide_tr:
inner_guide(obs)
steinvi = SteinVI(
model,
auto_class(model, init_loc_fn=init_loc_fn()),
Adam(1.0),
Trace_ELBO(),
RBFKernel(),
num_particles=num_particles,
)
state = steinvi.init(stein_key, obs)
init_params = steinvi.get_params(state)
for name, site in inner_guide_tr.items():
if site.get("type") == "param":
assert name in init_params
inner_param = site
init_value = init_params[name]
expected_shape = (num_particles, *np.shape(inner_param["value"]))
assert init_value.shape == expected_shape
if "auto_loc" in name or name == "b":
assert np.alltrue(init_value != np.zeros(expected_shape))
assert np.unique(init_value).shape == init_value.reshape(
-1).shape
elif "scale" in name:
assert_array_approx_equal(init_value,
np.full(expected_shape, 0.1))
else:
assert_array_approx_equal(init_value,
np.full(expected_shape, 0.0))
def _testPoleBug(self):
m1 = _getMapping()
m2 = resample(m1, arcsecPerPx=100, method='mean')
assert_array_approx_equal(_bbToArray(m2.boundingBox),
_bbToArray(m1.boundingBox), 1)
def testPoleBug(self):
m1 = _getMapping()
m2 = resample(m1, arcsecPerPx=100, method='mean')
assert_array_approx_equal(_bbToArray(m2.boundingBox), _bbToArray(m1.boundingBox), 1)
