How to use the neuraxle.hyperparams.distributions.HyperparameterDistribution.was_narrowed_from function in neuraxle

def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.0) -> HyperparameterDistribution:
        """
        Will narrow the space. If the cumulative kept_space_ratio gets to be under or equal to 1/len(choice_list),
        then the list is crunched to a single item as a FixedHyperparameter to reflect this narrowing.
        So once a small enough kept_space_ratio is reached, the list becomes a fixed unique item from the best guess.
        Otherwise, a deepcopy of self is returned.

        :param best_guess: the best item of the list to keep if truly narrowing.
        :param kept_space_ratio: the ratio of the space to keep.
        :return: a deepcopy of self, or else a FixedHyperparameter of the best_guess.
        """
        new_narrowing = self.get_current_narrowing_value() * kept_space_ratio

        if len(self.choice_list) == 0 or len(self.choice_list) == 1 or new_narrowing <= 1.0 / len(self.choice_list):
            return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self)

        return copy.deepcopy(self).was_narrowed_from(kept_space_ratio, self)

def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> HyperparameterDistribution:
        """
        Will narrow the underlying distribution towards the best guess.

        :param best_guess: the value towards which we want to narrow down the space. Should be between 0.0 and 1.0.
        :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5).
        :return: a new HyperparameterDistribution that has been narrowed down.
        """
        lost_space_ratio = 1.0 - kept_space_ratio
        new_min_included = round(self.min_included * kept_space_ratio + best_guess * lost_space_ratio)
        new_max_included = round(self.max_included * kept_space_ratio + best_guess * lost_space_ratio)
        if new_max_included <= new_min_included or kept_space_ratio == 0.0:
            return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self)
        return RandInt(new_min_included, new_max_included).was_narrowed_from(kept_space_ratio, self)

Will narrow the distribution towards the new best_guess.
        The mean will move towards the new best guess, and the standard deviation
        will be multiplied by the kept_space_ratio.
        The hard clip limit is unchanged.

        :param best_guess: the value towards which we want to narrow down the space's mean. Should be between 0.0 and 1.0.
        :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5).
        :return: a new HyperparameterDistribution that has been narrowed down.
        """
        lost_space_ratio = 1.0 - kept_space_ratio
        if isinstance(self.mean, tuple):
            self.mean = self.mean[0]
        new_mean = self.mean * kept_space_ratio + best_guess * lost_space_ratio
        new_std = self.std * kept_space_ratio
        if new_std <= 0.0:
            return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self)
        return Normal(
            new_mean, new_std, self.hard_clip_min, self.hard_clip_max
        ).was_narrowed_from(kept_space_ratio, self)

def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> HyperparameterDistribution:
        """
        Will narrow the underlying distribution towards the best guess.

        :param best_guess: the value towards which we want to narrow down the space. Should be between 0.0 and 1.0.
        :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5).
        :return: a new HyperparameterDistribution that has been narrowed down.
        """
        lost_space_ratio = 1.0 - kept_space_ratio
        new_min_included = self.min_included * kept_space_ratio + best_guess * lost_space_ratio
        new_max_included = self.max_included * kept_space_ratio + best_guess * lost_space_ratio
        if new_max_included <= new_min_included or kept_space_ratio == 0.0:
            return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self)
        return Uniform(new_min_included, new_max_included).was_narrowed_from(kept_space_ratio, self)

def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> HyperparameterDistribution:
        """
        Will narrow, in log space, the distribution towards the new best_guess.

        :param best_guess: the value towards which we want to narrow down the space. Should be between 0.0 and 1.0.
        :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5).
        :return: a new HyperparameterDistribution that has been narrowed down.
        """
        log2_best_guess = math.log2(best_guess)
        lost_space_ratio = 1.0 - kept_space_ratio
        new_min_included = self.log2_min_included * kept_space_ratio + log2_best_guess * lost_space_ratio
        new_max_included = self.log2_max_included * kept_space_ratio + log2_best_guess * lost_space_ratio
        if new_max_included <= new_min_included or kept_space_ratio == 0.0:
            return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self)
        return LogUniform(2 ** new_min_included, 2 ** new_max_included).was_narrowed_from(kept_space_ratio, self)

def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> HyperparameterDistribution:
        """
        Will narrow, in log space, the distribution towards the new best_guess.

        :param best_guess: the value towards which we want to narrow down the space. Should be between 0.0 and 1.0.
        :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5).
        :return: a new HyperparameterDistribution that has been narrowed down.
        """
        log2_best_guess = math.log2(best_guess)
        lost_space_ratio = 1.0 - kept_space_ratio
        new_min_included = self.log2_min_included * kept_space_ratio + log2_best_guess * lost_space_ratio
        new_max_included = self.log2_max_included * kept_space_ratio + log2_best_guess * lost_space_ratio
        if new_max_included <= new_min_included or kept_space_ratio == 0.0:
            return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self)
        return LogUniform(2 ** new_min_included, 2 ** new_max_included).was_narrowed_from(kept_space_ratio, self)

if (
                len(self.choice_list) == 0
                or len(self.choice_list) == 1
                or new_size <= 1
                or kept_space_ratio <= 1.0 / len(self.choice_list)
        ):
            return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self)

        # Bring best_guess to front
        idx = self.choice_list.index(best_guess)
        del self.choice_list[idx]
        self.choice_list = [best_guess] + self.choice_list

        # Narrowing of the list.
        maybe_reduced_list = self.choice_list[:new_size]
        return PriorityChoice(maybe_reduced_list).was_narrowed_from(kept_space_ratio, self)

The mean will move towards the new best guess, and the standard deviation
        will be multiplied by the kept_space_ratio.
        The hard clip limit is unchanged.

        :param best_guess: the value towards which we want to narrow down the space's mean. Should be between 0.0 and 1.0.
        :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5).
        :return: a new HyperparameterDistribution that has been narrowed down.
        """
        lost_space_ratio = 1.0 - kept_space_ratio
        if isinstance(self.mean, tuple):
            self.mean = self.mean[0]
        new_mean = self.mean * kept_space_ratio + best_guess * lost_space_ratio
        new_std = self.std * kept_space_ratio
        if new_std <= 0.0:
            return FixedHyperparameter(best_guess).was_narrowed_from(kept_space_ratio, self)
        return Normal(
            new_mean, new_std, self.hard_clip_min, self.hard_clip_max
        ).was_narrowed_from(kept_space_ratio, self)

def narrow_space_from_best_guess(self, best_guess, kept_space_ratio: float = 0.5) -> 'Quantized':
        """
        Will narrow the underlying distribution and re-wrap it under a Quantized.

        :param best_guess: the value towards which we want to narrow down the space.
        :param kept_space_ratio: what proportion of the space is kept. Default is to keep half the space (0.5).
        :return:
        """
        return Quantized(
            self.hd.narrow_space_from_best_guess(best_guess, kept_space_ratio)
        ).was_narrowed_from(kept_space_ratio, self)