netReg: Network-Regularized Regression Models

# netReg: graph-regularized linear regression models.
#
# Copyright (C) 2015 - 2020 Simon Dirmeier
#
# This file is part of netReg.
#
# netReg is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# netReg is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with netReg. If not, see <http://www.gnu.org/licenses/>.


#' @noRd
#' @import tensorflow
cast_float <- function(x) {
  tensorflow::tf$cast(x, tensorflow::tf$float32)
}


#' @noRd
#' @import tensorflow
constant_float <- function(x) {
  tensorflow::tf$constant(x, tensorflow::tf$float32)
}


#' @noRd
#' @import tensorflow
init_matrix <- function(m, n, trainable = TRUE) {
  initializer <- tf$keras.initializers$glorot_normal(23L)
  tensorflow::tf$Variable(initializer(shape(m, n), tensorflow::tf$float32),
    trainable = trainable
  )
}


#' @noRd
#' @import tensorflow
init_vector <- function(m, trainable = TRUE) {
  initializer <- tf$keras.initializers$glorot_normal(23L)
  tensorflow::tf$Variable(initializer(shape(m), tensorflow::tf$float32),
    trainable = trainable
  )
}

#' @noRd
#' @import tensorflow
init_zero_scalar <- function(trainable = TRUE) {
  tensorflow::tf$Variable(tf$zeros(shape(), tensorflow::tf$float32),
    trainable = trainable
  )
}