def test_poisson_exp_sparse():
random_state = np.random.RandomState(seed=42)
n = 50
X = random_state.rand(n, 3)
counts = euclidean_distances(X)**(-3)
counts[np.isinf(counts) | np.isnan(counts)] = 0
counts_dense = counts
counts_sparse = sparse.coo_matrix(np.triu(counts))
exp_dense = poisson_model.poisson_exp(X, counts_dense, -3,
use_empty_entries=False)
exp_sparse = poisson_model.poisson_exp(X, counts_sparse, -3,
use_empty_entries=False)
assert_almost_equal(exp_dense, exp_sparse)
def test_poisson_exp():
random_state = np.random.RandomState(seed=42)
n = 50
X = random_state.rand(n, 3)
counts = euclidean_distances(X)**(-3)
counts[np.isinf(counts) | np.isnan(counts)] = 0
eps = poisson_model.poisson_exp(X, counts, -2)
assert eps < 1e-6
def test_poisson_exp():
random_state = np.random.RandomState(seed=42)
n = 50
X = random_state.rand(n, 3)
counts = euclidean_distances(X)**(-3)
counts[np.isinf(counts) | np.isnan(counts)] = 0
eps = poisson_model.poisson_exp(X, counts, -2)
assert eps < 1e-6
def test_poisson_exp_sparse():
random_state = np.random.RandomState(seed=42)
n = 50
X = random_state.rand(n, 3)
counts = euclidean_distances(X)**(-3)
counts[np.isinf(counts) | np.isnan(counts)] = 0
counts_dense = counts
counts_sparse = sparse.coo_matrix(np.triu(counts))
exp_dense = poisson_model.poisson_exp(X,
counts_dense,
-3,
use_empty_entries=False)
exp_sparse = poisson_model.poisson_exp(X,
counts_sparse,
-3,
use_empty_entries=False)
assert_array_almost_equal(exp_dense, exp_sparse)
def test_poisson_exp_biased():
random_state = np.random.RandomState(seed=42)
n = 50
bias = 0.1 + random_state.rand(n)
bias = bias.reshape(n, 1)
X = random_state.rand(n, 3)
counts = euclidean_distances(X)**(-3)
counts *= bias * bias.T
counts[np.isinf(counts) | np.isnan(counts)] = 0
eps = poisson_model.poisson_exp(X, counts, -2, bias=bias)
assert eps < 1e-6
def test_poisson_exp_biased():
random_state = np.random.RandomState(seed=42)
n = 50
bias = 0.1 + random_state.rand(n)
bias = bias.reshape(n, 1)
X = random_state.rand(n, 3)
counts = euclidean_distances(X)**(-3)
counts *= bias * bias.T
counts[np.isinf(counts) | np.isnan(counts)] = 0
eps = poisson_model.poisson_exp(X, counts, -2, bias=bias)
assert eps < 1e-6
