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]
'''
ind.fitness = lcs(ind.genome, best.genome)
def fitness(self, ind, user_feedback):
'''
Compute fitness.
user_feedback is [best]
'''
best = user_feedback[0]
widgetNum = self.widgetNum
subj = 0
subj += lcs(ind.genome['grid'], best.genome['grid'])
subj += lcs(ind.genome['style'], best.genome['style'])
#-------------------------------------------------------------#
# 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['window_red'] - myValues['widget_red']
green = myValues['window_green'] - myValues['widget_green']
blue = myValues['window_blue'] - myValues['widget_blue']
distance = (red*red + green*green + blue*blue)**0.5
obj = distance / self.maxDist * 100.
grid_max = self.gridLen - 1.
ui = self.uiWidgets
layout_score = 0.
num_binds = 0
for id in grid:
if id:
if ui[id]['bind']:
other = grid.index(ui[id]['bind'])
me = grid.index(id)
diff = abs(other - me)
if diff < 3:
layout_score += 1.
else:
layout_score += (grid_max-diff)/grid_max
num_binds += 1.
if num_binds:
val = layout_score/num_binds * 100.
obj += val
else:
obj += 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]
