def test_negative_binomial_obj_sparse_dispersion_biased():
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
return True
from minorswing import dispersion
mean, variance = dispersion.compute_mean_variance(
counts**2,
np.array([counts.shape[0]]))
mean, variance = mean[:-1], variance[:-1]
d = dispersion.Dispersion()
d.fit(mean, variance)
counts = np.triu(counts)
counts[np.arange(len(counts)), np.arange(len(counts))] = 0
counts = sparse.coo_matrix(counts)
obj = negative_binomial_structure.negative_binomial_obj(
X, counts, dispersion=d, alpha=alpha, beta=beta)
obj_ = negative_binomial_structure.negative_binomial_obj(
random_state.rand(*X.shape),
counts, dispersion=d, alpha=alpha, beta=beta)
assert(obj < obj_)
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_estimate_X_biased_dispersion():
n = 50
random_state = np.random.RandomState(42)
X_true = random_state.rand(n, 3)
dis = euclidean_distances(X_true)
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=np.float)
lengths = np.array([counts.shape[0]])
return True
from minorswing import dispersion
mean, variance = dispersion.compute_mean_variance(counts, lengths)
mean, variance = mean[:-1], variance[:-1]
d = dispersion.DispersionPolynomial()
d.fit(mean, variance)
X = negative_binomial_structure.estimate_X(counts, alpha, beta,
dispersion=d,
random_state=random_state)
