def test_estimate_X_biased_dispersion():
n = 50
random_state = np.random.RandomState(42)
X_true = random_state.rand(n, 3) * 10
dis = euclidean_distances(X_true)
alpha, beta = -3, 1
fdis = beta * dis ** alpha
fdis[np.isinf(fdis)] = 1
disp = fdis + fdis ** 2
p = fdis / (fdis + disp)
counts = random_state.negative_binomial(disp, 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]])
_, mean, variance, _ = dispersion.compute_mean_variance(counts, lengths)
mean, variance = mean[:-1], variance[:-1]
d = dispersion.ExponentialDispersion()
d.fit(mean, variance)
X = negative_binomial_structure.estimate_X(counts, alpha, beta,
dispersion=d,
random_state=random_state)
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)
def test_estimate_X():
n = 25
random_state = np.random.RandomState(42)
X_true = random_state.rand(n, 3)
dis = euclidean_distances(X_true)
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)
X = negative_binomial_structure.estimate_X(counts, alpha, beta,
factr=10,
random_state=random_state)
assert_array_almost_equal(dis,
euclidean_distances(X), 2)
X = negative_binomial_structure.estimate_X(counts.toarray(), alpha, beta,
factr=10,
use_zero_entries=True,
random_state=random_state)
def test_estimate_X():
n = 50
random_state = np.random.RandomState(42)
X_true = random_state.rand(n, 3)
dis = euclidean_distances(X_true)
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)
X = negative_binomial_structure.estimate_X(counts, alpha, beta,
random_state=random_state)
assert_array_almost_equal(dis,
euclidean_distances(X), 2)