def test_negative_binomial_grad_sparse():
n = 10
random_state = np.random.RandomState(seed=42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
counts = beta * dis**alpha
grad_dense = negative_binomial.negative_binomial_gradient(X, counts)
grad_sparse = negative_binomial.negative_binomial_gradient(
X, sparse.coo_matrix(np.triu(counts, 1)))
assert_array_almost_equal(grad_dense, grad_sparse)
assert_array_almost_equal(np.zeros(grad_dense.shape), grad_dense)
def test_negative_binomial_grad_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.T * bias
counts_dense = counts
counts_sparse = sparse.coo_matrix(np.triu(counts, 1))
grad_dense = negative_binomial.negative_binomial_gradient(X,
counts_dense,
bias=bias)
grad_sparse = negative_binomial.negative_binomial_gradient(X,
counts_sparse,
bias=bias)
assert_array_almost_equal(grad_dense, grad_sparse)
assert_array_almost_equal(np.array([0, 0.]), grad_dense)
def test_negative_binomial_grad_dense():
n = 10
random_state = np.random.RandomState(seed=42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
counts = beta * dis**alpha
grad_dense = negative_binomial.negative_binomial_gradient(
X, counts, use_zero_counts=True)
# Checking that the gradient at the solution is 0
assert np.all(grad_dense == 0)
def test_negative_binomial_grad_sparse():
n = 10
random_state = np.random.RandomState(seed=42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
counts = beta * dis ** alpha
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
counts = sparse.coo_matrix(counts)
# grad_dense = negative_binomial.negative_binomial_gradient(X, counts)
grad_sparse = negative_binomial.negative_binomial_gradient(
X, sparse.coo_matrix(counts))
# assert_array_almost_equal(grad_dense, grad_sparse)
assert_array_almost_equal(np.zeros(grad_sparse.shape),
grad_sparse)
def test_negative_binomial_grad_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 = counts
counts_sparse = sparse.coo_matrix(np.triu(counts))
# grad_dense = negative_binomial.negative_binomial_gradient(
# X, counts_dense, -3,
# bias=bias,
# beta=1)
grad_sparse = negative_binomial.negative_binomial_gradient(
X, counts_sparse, -3,
bias=bias,
beta=1)
assert_array_almost_equal(np.array([0, 0.]), grad_sparse)
