def test_negative_binomial_obj_sparse():
n = 10
random_state = np.random.RandomState(42)
X = random_state.rand(n, 3)
counts = random_state.randn(n, n)
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
obj_dense = negative_binomial.negative_binomial_obj(X, counts)
obj_sparse = negative_binomial.negative_binomial_obj(
X, sparse.coo_matrix(counts))
assert_almost_equal(obj_dense, obj_sparse)
def test_negative_binomial_obj_sparse():
n = 10
random_state = np.random.RandomState(42)
X = random_state.rand(n, 3)
counts = random_state.randn(n, n)
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
# obj_dense = negative_binomial.negative_binomial_obj(X, counts)
obj_sparse = negative_binomial.negative_binomial_obj(
X, sparse.coo_matrix(counts))
def test_negative_binomial_obj_sparse_biased():
random_state = np.random.RandomState(seed=42)
n = 50
X = random_state.rand(n, 3)
bias = 0.1 + random_state.rand(n)
bias = bias.reshape(n, 1)
counts = euclidean_distances(X)**(-3)
counts[np.isinf(counts) | np.isnan(counts)] = 0
counts *= bias * bias.T
counts_dense = np.triu(counts, 1)
counts_sparse = sparse.coo_matrix(np.triu(counts, 1))
exp_dense = negative_binomial.negative_binomial_obj(X,
counts_dense,
-3,
bias=bias)
exp_sparse = negative_binomial.negative_binomial_obj(X,
counts_sparse,
-3,
bias=bias)
assert_almost_equal(exp_dense, exp_sparse)
def test_negative_binomial_obj_sparse_biased():
random_state = np.random.RandomState(seed=42)
n = 50
X = random_state.rand(n, 3)
bias = 0.1 + random_state.rand(n)
bias = bias.reshape(n, 1)
counts = euclidean_distances(X)**(-3)
counts[np.isinf(counts) | np.isnan(counts)] = 0
counts *= bias * bias.T
counts_dense = np.triu(counts)
counts_sparse = sparse.coo_matrix(np.triu(counts))
#exp_dense = negative_binomial.negative_binomial_obj(
# X, counts_dense, -3, bias=bias)
exp_sparse = negative_binomial.negative_binomial_obj(
X, counts_sparse, -3, bias=bias)
