def init_row_clustering(data_matrix, isotropic, num_iter=200):
m, n = data_matrix.m, data_matrix.n
state = crp.fit_model(data_matrix,
isotropic_w=isotropic,
isotropic_b=isotropic,
num_iter=num_iter)
U = np.zeros((m, state.assignments.max() + 1), dtype=int)
U[np.arange(m), state.assignments] = 1
left = recursive.MultinomialNode(U)
if isotropic:
right = recursive.GaussianNode(state.centers, 'scalar',
state.sigma_sq_b)
else:
right = recursive.GaussianNode(state.centers, 'col', state.sigma_sq_b)
pred = state.centers[state.assignments, :]
X = data_matrix.sample_latent_values(pred,
state.sigma_sq_w * np.ones((m, n)))
if isotropic:
noise = recursive.GaussianNode(X - pred, 'scalar', state.sigma_sq_w)
else:
noise = recursive.GaussianNode(X - pred, 'col', state.sigma_sq_w)
return recursive.SumNode([recursive.ProductNode([left, right]), noise])
def init_row_binary(data_matrix, num_iter=200):
state = ibp.fit_model(data_matrix, num_iter=num_iter)
left = recursive.BernoulliNode(state.Z)
right = recursive.GaussianNode(state.A, 'scalar', state.sigma_sq_f)
pred = np.dot(state.Z, state.A)
X = data_matrix.sample_latent_values(pred, state.sigma_sq_n)
noise = recursive.GaussianNode(X - pred, 'scalar', state.sigma_sq_n)
return recursive.SumNode([recursive.ProductNode([left, right]), noise])
def init_row_chain(data_matrix, num_iter=200):
states, sigma_sq_D, sigma_sq_N = chains.fit_model(data_matrix, num_iter=num_iter)
integ = chains.integration_matrix(data_matrix.m_orig)[data_matrix.row_ids, :]
left = recursive.IntegrationNode(integ)
temp = np.vstack([states[0, :][nax, :],
states[1:, :] - states[:-1, :]])
right = recursive.GaussianNode(temp, 'scalar', sigma_sq_D)
pred = states[data_matrix.row_ids, :]
X = data_matrix.sample_latent_values(pred, sigma_sq_N)
noise = recursive.GaussianNode(X - pred, 'scalar', sigma_sq_N)
return recursive.SumNode([recursive.ProductNode([left, right]), noise])
def init_low_rank(data_matrix, num_iter=200):
m, n = data_matrix.m, data_matrix.n
state, X = low_rank_poisson.fit_model(data_matrix, 2, num_iter=num_iter)
U, V, ssq_U, ssq_N = state.U, state.V, state.ssq_U, state.ssq_N
U /= ssq_U[nax, :] ** 0.25
V *= ssq_U[:, nax] ** 0.25
left = recursive.GaussianNode(U, 'col', np.sqrt(ssq_U))
right = recursive.GaussianNode(V, 'row', np.sqrt(ssq_U))
pred = np.dot(U, V)
X = data_matrix.sample_latent_values(pred, ssq_N)
noise = recursive.GaussianNode(X - pred, 'scalar', ssq_N)
return recursive.SumNode([recursive.ProductNode([left, right]), noise])
def dummy(self):
return recursive.SumNode([c.dummy() for c in self.children])