# coding=utf-8
from typing import Tuple

from _framework.data_types import RATIONAL_INPUT, RATIONAL_OUTPUT, PREDICTOR_STATE
from _framework.systems.predictors.rational.abstract import RationalPredictor
from tools.regression_experiments import LinearRegressor, FullPolynomialRegressor


class RationalLinearRegression(RationalPredictor):
    def __init__(self, no_states: int, history_length: int, input_dimensions: int, output_dimensions: int, drag: int):
        super().__init__(no_states, input_dimensions, output_dimensions, drag)
        self._history_length = history_length
        self._regressions = tuple(tuple(LinearRegressor(input_dimensions * history_length, drag) for _ in range(output_dimensions)) for _ in range(no_states))

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]) -> Tuple[RATIONAL_OUTPUT, ...]:
        data_out = []
        for _regression_array, _input_history in zip(self._regressions, data_in):
            _flat_input = tuple(_value for _vector in _input_history for _value in _vector)
            _each_output = []
            for _each_regressor in _regression_array:
                _o = _each_regressor.output(_flat_input)
                _each_output.append(_o)
            data_out.append(tuple(_each_output))
        return tuple(data_out)

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...], data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _regression_array, _input_history, _each_target in zip(self._regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for _value in _vector)

            for _each_regressor, _target_value in zip(_regression_array, _each_target):
                _each_regressor.fit(_flat_input, _target_value)

    def get_state(self) -> PREDICTOR_STATE:
        return tuple()


class RationalPolynomialRegression(RationalPredictor):
    def __init__(self, no_states: int, history_length: int, input_degrees: Tuple[int, ...], output_dimensions: int, drag: int):
        super().__init__(no_states, len(input_degrees), output_dimensions, drag)
        self._regressions = tuple(FullPolynomialRegressor(input_degrees * history_length, output_dimensions) for _ in range(no_states))
        self._history_length = history_length

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]) -> Tuple[RATIONAL_OUTPUT, ...]:
        return tuple(
            each_regressor.output(
                tuple(_value for _vector in each_input_history for _value in _vector)
            )
            for each_input_history, each_regressor in zip(data_in, self._regressions)
        )

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...], data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _each_regression, _input_history, _each_target in zip(self._regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for _value in _vector)
            _each_regression.fit(_flat_input, _each_target, self._drag)

    def get_state(self) -> PREDICTOR_STATE:
        return tuple()

26397ecc14da9e89b242879cfe484650bef396de
<mask token>


class RationalLinearRegression(RationalPredictor):
    <mask token>

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]
        ) ->Tuple[RATIONAL_OUTPUT, ...]:
        data_out = []
        for _regression_array, _input_history in zip(self._regressions, data_in
            ):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            _each_output = []
            for _each_regressor in _regression_array:
                _o = _each_regressor.output(_flat_input)
                _each_output.append(_o)
            data_out.append(tuple(_each_output))
        return tuple(data_out)
    <mask token>
    <mask token>


class RationalPolynomialRegression(RationalPredictor):

    def __init__(self, no_states: int, history_length: int, input_degrees:
        Tuple[int, ...], output_dimensions: int, drag: int):
        super().__init__(no_states, len(input_degrees), output_dimensions, drag
            )
        self._regressions = tuple(FullPolynomialRegressor(input_degrees *
            history_length, output_dimensions) for _ in range(no_states))
        self._history_length = history_length

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]
        ) ->Tuple[RATIONAL_OUTPUT, ...]:
        return tuple(each_regressor.output(tuple(_value for _vector in
            each_input_history for _value in _vector)) for 
            each_input_history, each_regressor in zip(data_in, self.
            _regressions))

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...],
        data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _each_regression, _input_history, _each_target in zip(self.
            _regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            _each_regression.fit(_flat_input, _each_target, self._drag)

    def get_state(self) ->PREDICTOR_STATE:
        return tuple()

<mask token>


class RationalLinearRegression(RationalPredictor):

    def __init__(self, no_states: int, history_length: int,
        input_dimensions: int, output_dimensions: int, drag: int):
        super().__init__(no_states, input_dimensions, output_dimensions, drag)
        self._history_length = history_length
        self._regressions = tuple(tuple(LinearRegressor(input_dimensions *
            history_length, drag) for _ in range(output_dimensions)) for _ in
            range(no_states))

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]
        ) ->Tuple[RATIONAL_OUTPUT, ...]:
        data_out = []
        for _regression_array, _input_history in zip(self._regressions, data_in
            ):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            _each_output = []
            for _each_regressor in _regression_array:
                _o = _each_regressor.output(_flat_input)
                _each_output.append(_o)
            data_out.append(tuple(_each_output))
        return tuple(data_out)
    <mask token>
    <mask token>


class RationalPolynomialRegression(RationalPredictor):

    def __init__(self, no_states: int, history_length: int, input_degrees:
        Tuple[int, ...], output_dimensions: int, drag: int):
        super().__init__(no_states, len(input_degrees), output_dimensions, drag
            )
        self._regressions = tuple(FullPolynomialRegressor(input_degrees *
            history_length, output_dimensions) for _ in range(no_states))
        self._history_length = history_length

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]
        ) ->Tuple[RATIONAL_OUTPUT, ...]:
        return tuple(each_regressor.output(tuple(_value for _vector in
            each_input_history for _value in _vector)) for 
            each_input_history, each_regressor in zip(data_in, self.
            _regressions))

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...],
        data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _each_regression, _input_history, _each_target in zip(self.
            _regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            _each_regression.fit(_flat_input, _each_target, self._drag)

    def get_state(self) ->PREDICTOR_STATE:
        return tuple()

<mask token>


class RationalLinearRegression(RationalPredictor):

    def __init__(self, no_states: int, history_length: int,
        input_dimensions: int, output_dimensions: int, drag: int):
        super().__init__(no_states, input_dimensions, output_dimensions, drag)
        self._history_length = history_length
        self._regressions = tuple(tuple(LinearRegressor(input_dimensions *
            history_length, drag) for _ in range(output_dimensions)) for _ in
            range(no_states))

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]
        ) ->Tuple[RATIONAL_OUTPUT, ...]:
        data_out = []
        for _regression_array, _input_history in zip(self._regressions, data_in
            ):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            _each_output = []
            for _each_regressor in _regression_array:
                _o = _each_regressor.output(_flat_input)
                _each_output.append(_o)
            data_out.append(tuple(_each_output))
        return tuple(data_out)

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...],
        data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _regression_array, _input_history, _each_target in zip(self.
            _regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            for _each_regressor, _target_value in zip(_regression_array,
                _each_target):
                _each_regressor.fit(_flat_input, _target_value)
    <mask token>


class RationalPolynomialRegression(RationalPredictor):

    def __init__(self, no_states: int, history_length: int, input_degrees:
        Tuple[int, ...], output_dimensions: int, drag: int):
        super().__init__(no_states, len(input_degrees), output_dimensions, drag
            )
        self._regressions = tuple(FullPolynomialRegressor(input_degrees *
            history_length, output_dimensions) for _ in range(no_states))
        self._history_length = history_length

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]
        ) ->Tuple[RATIONAL_OUTPUT, ...]:
        return tuple(each_regressor.output(tuple(_value for _vector in
            each_input_history for _value in _vector)) for 
            each_input_history, each_regressor in zip(data_in, self.
            _regressions))

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...],
        data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _each_regression, _input_history, _each_target in zip(self.
            _regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            _each_regression.fit(_flat_input, _each_target, self._drag)

    def get_state(self) ->PREDICTOR_STATE:
        return tuple()

<mask token>


class RationalLinearRegression(RationalPredictor):

    def __init__(self, no_states: int, history_length: int,
        input_dimensions: int, output_dimensions: int, drag: int):
        super().__init__(no_states, input_dimensions, output_dimensions, drag)
        self._history_length = history_length
        self._regressions = tuple(tuple(LinearRegressor(input_dimensions *
            history_length, drag) for _ in range(output_dimensions)) for _ in
            range(no_states))

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]
        ) ->Tuple[RATIONAL_OUTPUT, ...]:
        data_out = []
        for _regression_array, _input_history in zip(self._regressions, data_in
            ):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            _each_output = []
            for _each_regressor in _regression_array:
                _o = _each_regressor.output(_flat_input)
                _each_output.append(_o)
            data_out.append(tuple(_each_output))
        return tuple(data_out)

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...],
        data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _regression_array, _input_history, _each_target in zip(self.
            _regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            for _each_regressor, _target_value in zip(_regression_array,
                _each_target):
                _each_regressor.fit(_flat_input, _target_value)

    def get_state(self) ->PREDICTOR_STATE:
        return tuple()


class RationalPolynomialRegression(RationalPredictor):

    def __init__(self, no_states: int, history_length: int, input_degrees:
        Tuple[int, ...], output_dimensions: int, drag: int):
        super().__init__(no_states, len(input_degrees), output_dimensions, drag
            )
        self._regressions = tuple(FullPolynomialRegressor(input_degrees *
            history_length, output_dimensions) for _ in range(no_states))
        self._history_length = history_length

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]
        ) ->Tuple[RATIONAL_OUTPUT, ...]:
        return tuple(each_regressor.output(tuple(_value for _vector in
            each_input_history for _value in _vector)) for 
            each_input_history, each_regressor in zip(data_in, self.
            _regressions))

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...],
        data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _each_regression, _input_history, _each_target in zip(self.
            _regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for
                _value in _vector)
            _each_regression.fit(_flat_input, _each_target, self._drag)

    def get_state(self) ->PREDICTOR_STATE:
        return tuple()

# coding=utf-8
from typing import Tuple

from _framework.data_types import RATIONAL_INPUT, RATIONAL_OUTPUT, PREDICTOR_STATE
from _framework.systems.predictors.rational.abstract import RationalPredictor
from tools.regression_experiments import LinearRegressor, FullPolynomialRegressor


class RationalLinearRegression(RationalPredictor):
    def __init__(self, no_states: int, history_length: int, input_dimensions: int, output_dimensions: int, drag: int):
        super().__init__(no_states, input_dimensions, output_dimensions, drag)
        self._history_length = history_length
        self._regressions = tuple(tuple(LinearRegressor(input_dimensions * history_length, drag) for _ in range(output_dimensions)) for _ in range(no_states))

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]) -> Tuple[RATIONAL_OUTPUT, ...]:
        data_out = []
        for _regression_array, _input_history in zip(self._regressions, data_in):
            _flat_input = tuple(_value for _vector in _input_history for _value in _vector)
            _each_output = []
            for _each_regressor in _regression_array:
                _o = _each_regressor.output(_flat_input)
                _each_output.append(_o)
            data_out.append(tuple(_each_output))
        return tuple(data_out)

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...], data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _regression_array, _input_history, _each_target in zip(self._regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for _value in _vector)

            for _each_regressor, _target_value in zip(_regression_array, _each_target):
                _each_regressor.fit(_flat_input, _target_value)

    def get_state(self) -> PREDICTOR_STATE:
        return tuple()


class RationalPolynomialRegression(RationalPredictor):
    def __init__(self, no_states: int, history_length: int, input_degrees: Tuple[int, ...], output_dimensions: int, drag: int):
        super().__init__(no_states, len(input_degrees), output_dimensions, drag)
        self._regressions = tuple(FullPolynomialRegressor(input_degrees * history_length, output_dimensions) for _ in range(no_states))
        self._history_length = history_length

    def _low_predict(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...]) -> Tuple[RATIONAL_OUTPUT, ...]:
        return tuple(
            each_regressor.output(
                tuple(_value for _vector in each_input_history for _value in _vector)
            )
            for each_input_history, each_regressor in zip(data_in, self._regressions)
        )

    def _low_fit(self, data_in: Tuple[Tuple[RATIONAL_INPUT, ...], ...], data_out: Tuple[RATIONAL_OUTPUT, ...]):
        for _each_regression, _input_history, _each_target in zip(self._regressions, data_in, data_out):
            _flat_input = tuple(_value for _vector in _input_history for _value in _vector)
            _each_regression.fit(_flat_input, _each_target, self._drag)

    def get_state(self) -> PREDICTOR_STATE:
        return tuple()
