def fitness(self, ind, user_feedback):
'''
Compute fitness.
user_feedback is a list of the user's input,
the list contents are ordered the same as the
feedback variable in the config yaml file.
[best]
'''
# needs to be rewritten
best = user_feedback[0]
subj = 0
for attr_name, chrom in ind.genome.iteritems():
if chrom:
subj += ham(best.genome[attr_name], chrom)
ind.scalefit = ind.fitness = subj
def fitness(self, ind, user_feedback):
'''
Compute fitness.
user_feedback is [best,worst]
'''
best = user_feedback[0]
subj = 0
widgetNum = self.widgetNum
for attr_name, chrom in ind.genome.iteritems():
if chrom:
subj += ham(best.genome[attr_name], chrom)
#-------------------------------------------------------------#
# BEGIN OBJECTIVE EVALUATION
#-------------------------------------------------------------#
grid, myValues = self.decode(ind)
red = green = blue = 0
# and the background color
# A high value is good
# window color is stored at pos 0
red = myValues[1][0] - myValues[0][0]
green = myValues[1][1] - myValues[0][1]
blue = myValues[1][2] - myValues[0][2]
distance = (red*red + green*green + blue*blue)**0.5
obj = distance / self.maxDist * 100.
#-------------------------------------------------------------#
# END OBJECTIVE EVALUATION
#-------------------------------------------------------------#
subj = float(subj)
# Scale objective and subjective fitness components
objFitness = (obj-self.objMin)/(self.objMax-self.objMin) * self.objScaleMax + self.objScaleMin
subjFitness = (subj-self.subjMin)/(self.subjMax-self.subjMin) * self.subjScaleMax + self.subjScaleMin
ind.objFitness = objFitness
ind.subjFitness = subjFitness
ind.fitness = [self.objScaleMax-objFitness, self.subjScaleMax-subjFitness]