#genetic algorithm scaling routines
#based on galib.
#
from ga_util import *
import scipy.stats as stats
from numpy import *
# if a score is less the 2 standard deviations below, the average, its score
# is arbitrarily set to zero
class sigma_truncation_scaling:
    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 = sc - avg + self.scaling * dev
        f=choose(less_equal(f,0.),(f,0.))
        for i in range(len(pop)): pop[i].fitness(f[i])
        return pop

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

class linear_scaling:
    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 = sc * a + b
        f=choose(less_equal(f,0.),(f,0.))
        for i in range(len(pop)): pop[i].fitness(f[i])