parameterspace.transformations.zero_one

ZeroOneFloat (BaseTransformation)

Maps a bounded interval to [0, 1] via a linear transformation.

Source code in parameterspace/transformations/zero_one.py

class ZeroOneFloat(BaseTransformation):
    """Maps a bounded interval to [0, 1] via a linear transformation."""

    @store_init_arguments
    def __init__(self, bounds: Optional[Tuple]):
        super().__init__(bounds, (0, 1))
        self.interval_size = bounds[1] - bounds[0]

    def inverse(self, numerical_value: float) -> float:
        return float(
            np.clip(
                self.input_bounds[0] + numerical_value * (self.interval_size),
                self.input_bounds[0],
                self.input_bounds[1],
            )
        )

    def __call__(self, value: Any) -> float:
        return float((value - self.input_bounds[0]) / self.interval_size)

    def __eq__(self, other):
        return np.allclose(self.input_bounds, other.input_bounds)

    def jacobian_factor(self, numerical_value: float) -> float:
        return 1.0 / self.interval_size

inverse(self, numerical_value)

Convert the numerical representation back to the true value with the proper type.

Parameters:

Name	Type	Description	Default
numerical_value	float	Transformed/Numerical representation of a value.	required

Returns:

Type	Description
float	The value corresponding to the given value. Type depends no the kind of transformation.

Source code in parameterspace/transformations/zero_one.py

def inverse(self, numerical_value: float) -> float:
    return float(
        np.clip(
            self.input_bounds[0] + numerical_value * (self.interval_size),
            self.input_bounds[0],
            self.input_bounds[1],
        )
    )

jacobian_factor(self, numerical_value)

Factor to correct the likelihood based on the non-linear transformation.

Parameters:

Name	Type	Description	Default
numerical_value	float	Transformed/Numerical representation of a value.	required

Returns:

Type	Description
float	Jacobian factor to properly transform the likelihood.

Source code in parameterspace/transformations/zero_one.py

def jacobian_factor(self, numerical_value: float) -> float:
    return 1.0 / self.interval_size

ZeroOneInteger (BaseTransformation)

Maps a bounded interval of integers to [0, 1] via a linear transformation.

Source code in parameterspace/transformations/zero_one.py

class ZeroOneInteger(BaseTransformation):
    """Maps a bounded interval of integers to [0, 1] via a linear transformation."""

    @store_init_arguments
    def __init__(self, bounds: Optional[Tuple]):
        super().__init__(bounds, (0, 1))
        self.interval_size = bounds[1] - bounds[0] + 1

    def inverse(self, numerical_value: float) -> int:
        return int(
            np.clip(
                np.around(
                    self.input_bounds[0] - 0.5 + numerical_value * (self.interval_size)
                ),
                self.input_bounds[0],
                self.input_bounds[1],
            )
        )

    def __call__(self, value: Any) -> float:
        return float((value - self.input_bounds[0] + 0.5) / self.interval_size)

    def __eq__(self, other):
        return np.allclose(self.input_bounds, other.input_bounds)

    def jacobian_factor(self, numerical_value: float) -> float:
        return 1.0 / self.interval_size

inverse(self, numerical_value)

Convert the numerical representation back to the true value with the proper type.

Parameters:

Name	Type	Description	Default
numerical_value	float	Transformed/Numerical representation of a value.	required

Returns:

Type	Description
int	The value corresponding to the given value. Type depends no the kind of transformation.

Source code in parameterspace/transformations/zero_one.py

def inverse(self, numerical_value: float) -> int:
    return int(
        np.clip(
            np.around(
                self.input_bounds[0] - 0.5 + numerical_value * (self.interval_size)
            ),
            self.input_bounds[0],
            self.input_bounds[1],
        )
    )

jacobian_factor(self, numerical_value)

Factor to correct the likelihood based on the non-linear transformation.

Parameters:

Name	Type	Description	Default
numerical_value	float	Transformed/Numerical representation of a value.	required

Returns:

Type	Description
float	Jacobian factor to properly transform the likelihood.

Source code in parameterspace/transformations/zero_one.py

def jacobian_factor(self, numerical_value: float) -> float:
    return 1.0 / self.interval_size