Source code for nachos.constraints.mean_tuple

from nachos.constraints.mean import Mean
from nachos.constraints import register
from typing import Any, Generator, Union


[docs]@register('mean_tuple') class MeanTuple(Mean): ''' Summary: Defines the constraint on the mean value of a factor. The constraint is that the mean for two datasets should be close to a specified value. '''
[docs] @classmethod def build(cls, conf: dict): return cls(*conf['mean_tuple'])
[docs] def __init__(self, s1_mean: Any, s2_mean: Any): super().__init__() self.s1_mean = s1_mean self.s2_mean = s2_mean
[docs] def __call__(self, c1: Union[list, Generator], c2: Union[list, Generator], ) -> float: r''' Summary: Given a tuple .. math:: \mu = \left(\mu_1, \mu_2\right) compute .. math:: \lvert \frac{1}{|c1|} \sum c1 - \mu_1 \rvert + \lvert \frac{1}{|c2|} \sum c2 - \mu_2 \rvert Inputs ----------------------- :param c1: the list of values to constrain associated with dataset 1 :type c1: Union[list, Generator] :param c2: the list of values to constrain associated with dataset 2 :type c2: Union[list, Generator] Returns ----------------------- :return: the constraint score (how close the constraints are met) :rtype: float ''' c1, c2 = list(c1), list(c2) return abs(self.stat(c1) - self.s1_mean) + abs(self.stat(c2) - self.s2_mean)