def update_choice(self):
'''
Updates every choice performed.
'''
global glb_num_population
global glb_alg_choice
# N of people
self.__number_population = glb_num_population
# Chosed algorithm
if (glb_alg_choice == glb_key_random ):
# Random
print("Chosed Random algorithm")
params = Common_alg_params(self.__number_population)
self.__fitness_alg = Random_alg(params)
elif (glb_alg_choice == glb_key_pseudoME) :
# Pseudo ME
print("Chosed pseudoMacroevolutionary algorithm")
params = Common_alg_params(self.__number_population)
self.__fitness_alg = MA_alg(params)
elif (glb_alg_choice == glb_key_ga):
# Genetic Algorithm
print("Chosed Genetic algorithm")
params = Params_GA(self.__number_population)
params.set_size_population(self.__number_population)
self.__fitness_alg = GA_alg(params)
elif (glb_alg_choice == glb_key_mixed):
# Own algorithm
print("Chosed own (mixed) algorithm")
params = Common_alg_params(self.__number_population)
self.__fitness_alg = Mixed_alg(params)
# Pause worker and re-start
self.init_population()
print("The choice is : {0}".format(glb_alg_choice))
class GraphicPlot(wx.Panel):
def __init__(self,parent):
'''
Graphic plot conforms the zone to view and control the main flow of the simulation.
'''
wx.Panel.__init__(self, parent,-1)
# Draws the map
self.draw_map()
#Base parameters (from GUI).
self.update_choice()
# Distributes the population among the map
self.init_population()
def draw_map(self):
'''
It draws the landscape according to some function (to try to minimize)
'''
global glb_landscape_functions
'''
Base map contains the region to plot.
By default an XY plane between (-10,-10) and (10,10)
'''
self.upper_limit=10
self.lower_limit=-10
self.npts = 500
self.function = glb_landscape_functions.get_function()
self.points = []
# Add Countor graph
self.new_countour()
def update_choice(self):
'''
Updates every choice performed.
'''
global glb_num_population
global glb_alg_choice
# N of people
self.__number_population = glb_num_population
# Chosed algorithm
if (glb_alg_choice == glb_key_random ):
# Random
print("Chosed Random algorithm")
params = Common_alg_params(self.__number_population)
self.__fitness_alg = Random_alg(params)
elif (glb_alg_choice == glb_key_pseudoME) :
# Pseudo ME
print("Chosed pseudoMacroevolutionary algorithm")
params = Common_alg_params(self.__number_population)
self.__fitness_alg = MA_alg(params)
elif (glb_alg_choice == glb_key_ga):
# Genetic Algorithm
print("Chosed Genetic algorithm")
params = Params_GA(self.__number_population)
params.set_size_population(self.__number_population)
self.__fitness_alg = GA_alg(params)
elif (glb_alg_choice == glb_key_mixed):
# Own algorithm
print("Chosed own (mixed) algorithm")
params = Common_alg_params(self.__number_population)
self.__fitness_alg = Mixed_alg(params)
# Pause worker and re-start
self.init_population()
print("The choice is : {0}".format(glb_alg_choice))
def init_population(self):
'''
Initializes a certain population (with his kind)
'''
self.__fitness_alg.new_population()
def new_countour(self):
'''
It draws a new map and also inits new population
'''
# Once every time..
self.xi = linspace(self.lower_limit-0.1, self.upper_limit+0.1, self.npts)
self.yi = linspace(self.lower_limit-0.1, self.upper_limit+0.1, self.npts)
self.x = []
self.y = []
self.z = []
#seed(0)
global glb_landscape_functions
functionXY = glb_landscape_functions.get_function()
self.x = uniform(self.lower_limit, self.upper_limit, self.npts)
self.y = uniform(self.lower_limit, self.upper_limit, self.npts)
for index in range(0,len(self.x)):
self.z.append(functionXY(self.x[index],self.y[index]))
self.zgrid = griddata(self.x,self.y,self.z,self.xi,self.yi)
# Distributes the population among the map
#self.init_population()
self.draw_base_plot()
def draw_base_plot(self):
# Configure graph
self.figure = matplotlib.figure.Figure()
self.axes = self.figure.add_subplot(111)
self.axes.cla()
self.axes.contour(self.xi, self.yi, self.zgrid,linewidths=0.5,colors='k')
self.axes.contourf(self.xi, self.yi, self.zgrid)
self.canvas = FigureCanvas(self, -1, self.figure)
def add(self, point):
self.points.append(point)
def clear(self):
self.points = []
def do_step(self):
'''One step more, apply the algorithm'''
self.draw_base_plot()
self.__fitness_alg.update_population()
#print("{0} individuals to draw !".format(len(self.__fitness_alg.get_individuals())))
# Plot each individual
for indy in self.__fitness_alg.get_individuals():
self.axes.plot(indy.x,indy.y,'wo',ms=5)
#print "pos ind {0},{1}".format(indy.x, indy.y)
self.figure.canvas.draw()
