def test_negative_binomial_gradient_sparse_dispersed():
n = 10
random_state = np.random.RandomState(42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
fdis = beta * dis**alpha
fdis[np.isinf(fdis)] = 0
dispersion_estimated = fdis + fdis ** 2
p = fdis / (fdis + dispersion_estimated)
# counts = random_state.negative_binomial(dispersion, 1 - p)
# counts = np.triu(counts)
counts = np.triu(fdis)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
counts = sparse.coo_matrix(counts, dtype=float)
_, mean, variance, _ = dispersion.compute_mean_variance(
counts,
np.array([counts.shape[0]]))
mean, variance = mean[:-1], variance[:-1]
d = dispersion.ExponentialDispersion()
d.fit(mean, variance)
gradient_sparse = negative_binomial_structure.negative_binomial_gradient(
X, counts, dispersion=d)
gradient_dense = negative_binomial_structure.negative_binomial_gradient(
X, counts.toarray(), dispersion=d)
assert_array_almost_equal(gradient_dense, gradient_sparse)
assert_array_almost_equal(
np.zeros(gradient_sparse.shape), gradient_sparse, -5)
def test_negative_binomial_gradient_sparse_dispersed():
n = 10
random_state = np.random.RandomState(42)
X = random_state.rand(n, 3)
dis = euclidean_distances(X)
alpha, beta = -3, 1
fdis = beta * dis**alpha
fdis[np.isinf(fdis)] = 1
dispersion = fdis + fdis ** 2
p = fdis / (fdis + dispersion)
counts = random_state.negative_binomial(dispersion, 1 - p)
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
counts = sparse.coo_matrix(counts, dtype=float)
return True
# from minorswing import dispersion
mean, variance = dispersion.compute_mean_variance(
counts,
np.array([counts.shape[0]]))
mean, variance = mean[:-1], variance[:-1]
d = dispersion.DispersionPolynomial()
d.fit(mean, variance)
gradient_sparse = negative_binomial_structure.negative_binomial_gradient(
X, counts, dispersion=d)
def test_negative_binomial_gradient_sparse():
n = 10
random_state = np.random.RandomState(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)
gradient_sparse = negative_binomial_structure.negative_binomial_gradient(
X, counts)
rand_X = random_state.randn(*X.shape)
assert_array_almost_equal(np.zeros(gradient_sparse.shape), gradient_sparse)
rand_grad_sparse = negative_binomial_structure.negative_binomial_gradient(
rand_X, counts)
rand_grad_dense = negative_binomial_structure.negative_binomial_gradient(
rand_X, counts.toarray())
assert_array_almost_equal(rand_grad_sparse, rand_grad_dense)
def test_negative_binomial_gradient_sparse():
n = 10
random_state = np.random.RandomState(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)
gradient_sparse = negative_binomial_structure.negative_binomial_gradient(
X, counts)
assert_array_almost_equal(np.zeros(gradient_sparse.shape), gradient_sparse)
