def run(self, plot_file=None):
"""
GA repeatedly creates a village, runs it with values, then judges its
performance and modifies values.
Runs in groups of 10 villages.
"""
gen = 0
total_results = []
P = self.init_P
printing = False
villages_per_gen = 30
num_families = 10
max_gens = 100
max_fitness = 500
use_ga = True
elitism = True
catastrophe = False
presentation = True
if use_ga:
suptitle = 'Village evolution with GA'
else:
suptitle = 'Village evolution without GA'
if catastrophe:
suptitle += ' and with catastrophes'
if presentation:
fig = plt.figure(figsize=(8, 6), dpi=150,)
else:
fig = plt.figure(figsize=(12, 10), dpi=200,)
ax = fig.add_subplot(111, aspect='auto')
ax.set_xlabel('Generation')
ax.set_ylabel('village trial')
# FOR LIGHT PLOT
#fig.suptitle(suptitle, fontsize=14, fontweight='bold')
#cmap = 'GnBu'
#facecolor = 'white'
#fontcolor = 'black'
# FOR DARK PLOT
fig.set_facecolor('black')
ax.set_axis_bgcolor('white')
fig.suptitle(suptitle, fontsize=14, fontweight='bold', color='white')
ax.spines['bottom'].set_color('gray')
ax.spines['top'].set_color('gray')
ax.spines['left'].set_color('gray')
ax.spines['right'].set_color('gray')
xa = ax.xaxis
xa.set_tick_params(labelcolor='white')
xa.label.set_color('white')
ya = ax.yaxis
ya.set_tick_params(labelcolor='white')
ya.label.set_color('white')
cmap='gist_earth'
facecolor = 'black'
fontcolor = 'white'
if presentation:
cmap = 'gist_ncar'
fitness_array = np.zeros([villages_per_gen, max_gens])
fit_plot = plt.imshow(fitness_array, interpolation='nearest',
cmap=cmap, origin='lower', vmin=0, vmax=max_fitness)
ax.set_aspect('auto')
cbar = plt.colorbar(fit_plot)
cbytick_obj = plt.getp(cbar.ax.axes, 'yticklabels')
plt.setp(cbytick_obj, color=fontcolor)
# BEGIN EVOLUTION
while gen < max_gens:
results = []
vill = None
fitness = None
print gen
# RUN ONE VILLAGE FOR EACH GENOME
P_index = 0
for prof_designations in P:
vill = Village(num_families, prof_designations)
fitness = vill.run_village(max_fitness, printing,
catastrophe, presentation)
#fitness, count_villagers, count_fams = 1, 2, 3
results.append((fitness, prof_designations))
vill = None
# animation
#curr_village = P.index(prof_designations)
fitness_array[P_index][gen] = fitness
#print 'f %s: %s' % (P_index, fitness)
if presentation and P_index % 10 == 0:
fit_plot = plt.imshow(fitness_array, interpolation='nearest',
cmap=cmap, origin='lower', vmin=0, vmax=400)
ax.set_aspect('auto')
plt.draw()
plt.show(block=False)
P_index += 1
# append set of 10 results to main set of results
total_results.append(results)
fit_plot = plt.imshow(fitness_array, interpolation='nearest',
cmap=cmap, origin='lower', vmin=0, vmax=400)
ax.set_aspect('auto')
if elitism:
fitnesses = [run[0] for run in results]
indexes = range(len(P))
max_fit_index = fitnesses.index(max(fitnesses))
best_genome = P[max_fit_index][:]
else:
best_genome = None
if use_ga:
P = self.tournament_select(P, results, elitism, best_genome)
#P = self.mutate(P, elitism, best_genome)
#P = self.mutate_blocks(P, elitism, best_genome)
P = self.mutate_elems(P, elitism, best_genome)
P = self.alternate_crossover(P, elitism, best_genome)
#P = self.three_point_crossover(P)
else:
# BASE: replace P with random vals
P = []
for x in range(villages_per_gen):
P.append(self.generate_genome())
gen += 1
plt.savefig(plot_file, bbox_inches=0, facecolor=facecolor)
return total_results
