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# Autogenerated By   : src/main/python/generator/generator.py
# Autogenerated From : scripts/builtin/alsCG.dml

from typing import Dict, Iterable

from systemds.operator import OperationNode, Matrix, Frame, List, MultiReturn, Scalar
from systemds.script_building.dag import OutputType
from systemds.utils.consts import VALID_INPUT_TYPES


def alsCG(X: Matrix,
          **kwargs: Dict[str, VALID_INPUT_TYPES]):
    """
     This script computes an approximate factorization of a low-rank matrix X into two matrices U and V
     using the Alternating-Least-Squares (ALS) algorithm with conjugate gradient.
     Matrices U and V are computed by minimizing a loss function (with regularization).
    
    
    
    :param X: Location to read the input matrix X to be factorized
    :param rank: Rank of the factorization
    :param regType: Regularization:
        "L2" = L2 regularization;
        f (U, V) = 0.5 * sum (W * (U %*% V - X) ^ 2)
        + 0.5 * reg * (sum (U ^ 2) + sum (V ^ 2))
        "wL2" = weighted L2 regularization
        f (U, V) = 0.5 * sum (W * (U %*% V - X) ^ 2)
        + 0.5 * reg * (sum (U ^ 2 * row_nonzeros)
        + sum (V ^ 2 * col_nonzeros))
    :param reg: Regularization parameter, no regularization if 0.0
    :param maxi: Maximum number of iterations
    :param check: Check for convergence after every iteration, i.e., updating U and V once
    :param thr: Assuming check is set to TRUE, the algorithm stops and convergence is declared
        if the decrease in loss in any two consecutive iterations falls below this threshold;
        if check is FALSE thr is ignored
    :param seed: The seed to random parts of the algorithm
    :param verbose: If the algorithm should run verbosely
    :return: An m x r matrix where r is the factorization rank
    :return: An m x r matrix where r is the factorization rank
    """

    params_dict = {'X': X}
    params_dict.update(kwargs)
    
    vX_0 = Matrix(X.sds_context, '')
    vX_1 = Matrix(X.sds_context, '')
    output_nodes = [vX_0, vX_1, ]

    op = MultiReturn(X.sds_context, 'alsCG', output_nodes, named_input_nodes=params_dict)

    vX_0._unnamed_input_nodes = [op]
    vX_1._unnamed_input_nodes = [op]

    return op
