<|file_name|>garch.py<|end_file_name|><｜fim▁begin｜># -------------------------------------------------------------
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# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
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#   http://www.apache.org/licenses/LICENSE-2.0
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# -------------------------------------------------------------

# Autogenerated By   : src/main/python/generator/generator.py
# Autogenerated From : scripts/builtin/garch.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 <|fim_middle|>(X: Matrix,
          kmax: int,
          momentum: float,
          start_stepsize: float,
          end_stepsize: float,
          start_vicinity: float,
          end_vicinity: float,
          sim_seed: int,
          verbose: bool):
    """
    :param X: The input Matrix to apply Arima on.
    :param kmax: Number of iterations
    :param momentum: Momentum for momentum-gradient descent (set to 0 to deactivate)
    :param start_stepsize: Initial gradient-descent stepsize
    :param end_stepsize: gradient-descent stepsize at end (linear descent)
    :param start_vicinity: proportion of randomness of restart-location for gradient descent at beginning
    :param end_vicinity: same at end (linear decay)
    :param sim_seed: seed for simulation of process on fitted coefficients
    :param verbose: verbosity, comments during fitting
    :return: 'OperationNode' containing simulated garch(1,1) process on fitted coefficients & variances of simulated fitted process & constant term of fitted process & 1-st arch-coefficient of fitted process & 1-st garch-coefficient of fitted process & drawbacks: slow convergence of optimization (sort of simulated annealing/gradient descent) 
    """
    params_dict = {'X': X, 'kmax': kmax, 'momentum': momentum, 'start_stepsize': start_stepsize, 'end_stepsize': end_stepsize, 'start_vicinity': start_vicinity, 'end_vicinity': end_vicinity, 'sim_seed': sim_seed, 'verbose': verbose}
    
    vX_0 = Matrix(X.sds_context, '')
    vX_1 = Matrix(X.sds_context, '')
    vX_2 = Scalar(X.sds_context, '')
    vX_3 = Scalar(X.sds_context, '')
    vX_4 = Scalar(X.sds_context, '')
    output_nodes = [vX_0, vX_1, vX_2, vX_3, vX_4, ]

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

    vX_0._unnamed_input_nodes = [op]
    vX_1._unnamed_input_nodes = [op]
    vX_2._unnamed_input_nodes = [op]
    vX_3._unnamed_input_nodes = [op]
    vX_4._unnamed_input_nodes = [op]

    return op
<｜fim▁end｜>
garch