dstein64

def matrix_factorization_sgd(
    D, P, Q, steps=5000, alpha=0.0002, beta=0.02):
  P = theano.shared(P.astype(floatX))
  Q = theano.shared(Q.astype(floatX))
  P_i = T.vector()
  Q_j = T.vector()
  i = T.iscalar()
  j = T.iscalar()
  x = T.scalar()
  error = T.sqr(P_i.dot(Q_j) - x)

dstein64

import numpy as np
import numpy.ma as ma
import theano
from theano import tensor as T

floatX = theano.config.floatX


def getmask(D):
  return ma.getmaskarray(D) if ma.isMA(D) else np.zeros(D.shape, dtype=bool)