def best_job(self, jobs):
best_job = jobs[0]
best_data = job_data(best_job)
best_avg = average(best_data['fitness'])
for run in jobs:
run_data = job_data(run)
run_avg = average(run_data['fitness'])
if run_avg < best_avg:
best_job = run
best_data = run_data
best_avg = run_avg
return best_job, best_data
def compare(self, jobs):
'Runs and compares the results of two jobs.'
results_jobs = self.launch(jobs)
def adjust(stats_list):
'Transforms the list found in stats to a plottable list.'
return [fit[0] for fit in stats_list[0]]
plt.figure()
for job in results_jobs:
data = job_data(job)
size = len(data['stats'][0]['min'][0])
# # Plotting individual runs:
# for d in data['stats']:
# d = adjust(d['min'])
# plt.plot(d, color_run + '--')
# Plotting average:
plt.plot(
[
average([adjust(d['min'])[i] for d in data['stats']])
for i in xrange(size)
],
# color_avg + '-',
# linewidth=2.0,
label=job['operators']['name'],
)
plt.xlabel('Generations')
plt.ylabel('Fitness')
plt.legend()
plt.show()
def compare(self, jobs):
'Runs and compares the results of two jobs.'
results_jobs = self.launch(jobs)
def adjust(stats_list):
'Transforms the list found in stats to a plottable list.'
return [fit[0] for fit in stats_list[0]]
plt.figure()
for job in results_jobs:
data = job_data(job)
size = len(data['stats'][0]['min'][0])
# # Plotting individual runs:
# for d in data['stats']:
# d = adjust(d['min'])
# plt.plot(d, color_run + '--')
# Plotting average:
plt.plot(
[average([adjust(d['min'])[i]
for d in data['stats']])
for i in xrange(size)],
# color_avg + '-',
# linewidth=2.0,
label=job['operators']['name'],
)
plt.xlabel('Generations')
plt.ylabel('Fitness')
plt.legend()
plt.show()
