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)