juancamilog

juancamilog

juancamilog

juancamilog

juancamilog

import theano
import numpy as np

# inti vars
s = theano.tensor.matrix('s')
y = theano.tensor.vector('y')
r = theano.tensor.slinalg.Solve()(s, y)

# jacobian using scan
dr1 = theano.tensor.jacobian(r.flatten(), s)

juancamilog

@register_stabilize
@local_optimizer([Dot, Dot22])
def inv_as_solve(node):
    if not imported_scipy:
        return False
    if isinstance(node.op, (Dot, Dot22)):
        l, r = node.inputs
        if l.owner and l.owner.op == matrix_inverse:

            if CHECK l.owner.inputs[0] is PSD HERE:

juancamilog

import theano
import theano.tensor as T
from theano.sandbox.linalg import psd,matrix_inverse,det,cholesky
from theano.tensor.slinalg import solve_lower_triangular
import numpy as np
from time import time

def SEard(loghyp,X):
    ''' Squared exponential kernel with diagonal scaling matrix (one lengthscale per dimension)'''
    N,idims = X.shape

juancamilog

import theano
import theano.tensor as T
from theano.sandbox.linalg import psd,matrix_inverse,det,cholesky
import numpy as np
from time import time

def SEard(loghyp,X):
    ''' Squared exponential kernel with diagonal scaling matrix (one lengthscale per dimension)'''
    N,idims = X.shape

juancamilog

import theano
import theano.tensor as T
from theano.sandbox.linalg import psd,matrix_inverse,det,cholesky
import numpy as np
from time import time

def SEard(loghyp,X):
    ''' Squared exponential kernel with diagonal scaling matrix (one lengthscale per dimension)'''
    N,idims = X.shape

juancamilog

import theano
import theano.tensor as T
from theano.sandbox.linalg import psd,matrix_inverse,det,cholesky
import numpy as np
from time import time

def SEard(loghyp,X):
    ''' Squared exponential kernel with diagonal scaling matrix (one lengthscale per dimension)'''
    N,idims = X.shape