#genetic algorithm scaling routines
#based on galib.

from numpy import clip, inf
from ga_util import GAError, my_mean, my_std


# if a score is less the 2 standard deviations below, the average, its score
# is arbitrarily set to zero
class sigma_truncation_scaling(object):
    def __init__(self, scaling = 2):
        self.scaling = scaling

    def scale(self, pop):
        sc = pop.scores()
        avg = my_mean(sc)
        if len(sc) > 1:
            dev = my_std(sc)
        else:
            dev = 0
        f = clip(sc - avg + self.scaling * dev, 0, inf)
        for i in range(len(pop)):
            pop[i].fitness(f[i])
        return pop

class no_scaling(object):
    def scale(self,pop):
        for ind in pop:
            ind.fitness(ind.score())
        return pop

class linear_scaling(object):
    def __init__(self,mult = 1.2):
        self.mult = mult

    def scale(self,pop):
        sc = pop.scores()
        pmin = min(sc)
        if pmin < 0:
            raise GAError('linear scaling does not work with objective scores < 0')
        pmax = max(sc)
        pavg = my_mean(sc)
        if pavg == pmax:
            a = 1.
            b = 0.
        elif pmin > (self.mult * pavg - pmax)/(self.mult - 1.):
            delta = pmax - pavg
            a = (self.mult - 1.) * pavg / delta
            b = pavg * (pmax - self.mult * pavg) / delta
        else:
            delta = pavg - pmin
            a = pavg / delta
            b = -pmin * pavg / delta
        f = clip(sc * a + b, 0, inf)
        for i in range(len(pop)):
            pop[i].fitness(f[i])