def logp(X):
IVI = det(V)
return bound(
((n - p - 1) * log(IVI) - trace(solve(V, X)) -
n * p * log(
2) - n * log(IVI) - 2 * multigammaln(p, n / 2)) / 2,
n > p - 1)
import theano
import theano.sandbox.linalg as linalg
mu = theano.tensor.matrix('mu')
Sigma = theano.tensor.matrix('Sigma')
H = theano.tensor.matrix('H')
R = theano.tensor.matrix('R')
data = theano.tensor.matrix('data')
dot = theano.tensor.dot
A = dot(Sigma, H.T); A.name = 'A'
B = R + dot(H, dot(Sigma, H.T)); B.name = 'B'
new_mu = mu + dot(A, linalg.solve(B, dot(H, mu) - data))
new_mu.name = "updated_mu"
new_Sigma = Sigma - dot(dot(A, linalg.solve(B, H)), Sigma)
new_Sigma.name = "updated_Sigma"
inputs = [mu, Sigma, H, R, data]
outputs = [new_mu, new_Sigma]
theano.gof.graph.utils.give_variables_names(
theano.gof.graph.variables(inputs, outputs))
n = 500
input_shapes = {mu: (n, 1),
Sigma: (n, n),
H: (n, n),
R: (n, n),
data: (n, 1)}
import theano
import theano.sandbox.linalg as linalg
mu = theano.tensor.matrix('mu')
Sigma = theano.tensor.matrix('Sigma')
H = theano.tensor.matrix('H')
R = theano.tensor.matrix('R')
data = theano.tensor.matrix('data')
dot = theano.tensor.dot
A = dot(Sigma, H.T)
A.name = 'A'
B = R + dot(H, dot(Sigma, H.T))
B.name = 'B'
new_mu = mu + dot(A, linalg.solve(B, dot(H, mu) - data))
new_mu.name = "updated_mu"
new_Sigma = Sigma - dot(dot(A, linalg.solve(B, H)), Sigma)
new_Sigma.name = "updated_Sigma"
inputs = [mu, Sigma, H, R, data]
outputs = [new_mu, new_Sigma]
theano.gof.graph.utils.give_variables_names(
theano.gof.graph.variables(inputs, outputs))
n = 500
input_shapes = {mu: (n, 1), Sigma: (n, n), H: (n, n), R: (n, n), data: (n, 1)}
def logp(X):
IVI = det(V)
return bound(
((n - p - 1) * log(IVI) - trace(solve(V, X)) - n * p * log(2) -
n * log(IVI) - 2 * multigammaln(p, n / 2)) / 2, n > p - 1)