Source code for nachos.constraints.sum_tuple

from nachos.constraints.sum import Sum
from nachos.constraints import register
from typing import Union, Generator


[docs]@register('sum_tuple') class SumTuple(Sum): ''' 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['sum_tuple'])
[docs] def __init__(self, s1_sum: float, s2_sum: float): super().__init__() self.s1_sum = s1_sum self.s2_sum = s2_sum
[docs] def __call__(self, c1: Union[list, Generator], c2: Union[list, Generator], ) -> float: r''' Summary: Computes .. math:: \lvert \sum c1 - \mu_1\rvert + \lvert \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 ''' return abs(self.stat(c1) - self.s1_sum) + abs(self.stat(c2) - self.s2_sum)