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_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