def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config_json',
help='location of json configuration',
default='config.json')
options = vars(parser.parse_args())
config_json = read_config(options['config_json'])
#url = 'https://api.openweathermap.org/data/2.5/weather?zip={}&appid={}'.format(options['zip'], options['owmkey'])
#r = requests.get(url)
ticks_per_quarter = config_json['midi']['ticks_per_quarter']
midi_output = midi.Midi(ticks_per_quarter)
trk_chk = midi.Midi.TrackChunk(ticks_per_quarter)
# result sequence is a list containing a single list containing the resulting measures from the algorithm
result_sequence = genetic.run(config_json)
for measure in result_sequence[0]:
print("Result measure: {}".format(measure))
for note in measure.notes:
note_length_ticks = int(ticks_per_quarter * note.note_len * 4)
trk_chk.add_event(1, 0, note.midi_num, 96, 0)
trk_chk.add_event(0, 0, note.midi_num, 0, note_length_ticks)
midi_output.chunks.append(trk_chk)
midi_output.write_to_file(config_json['midi']['output'])
def main():
"""Run imported algorithms and show statistics."""
if OMIT_OUTPUT:
sys.stdout = open(os.devnull, 'w')
genetic_time, lcv_time, mrv_time, ph_time = [], [], [], []
for _ in range(NO_OF_TESTS):
now = time.time()
genetic.run()
genetic_time.append(time.time() - now)
now = time.time()
least_constraining_value.run()
lcv_time.append(time.time() - now)
now = time.time()
minimum_remaining_values.run()
mrv_time.append(time.time() - now)
now = time.time()
power_heuristic.run()
ph_time.append(time.time() - now)
if OMIT_OUTPUT:
sys.stdout = sys.__stdout__
algorithms = [
'Genetic', 'Least constraining value', 'Minimum remaining values',
'Power heuristic'
]
stats = [genetic_time, lcv_time, mrv_time, ph_time]
for name, t in zip(algorithms, stats):
avg_time = round(sum(t) / NO_OF_TESTS * 1000, ACCURACY)
print(f"Statistics for {name} algorithm: {avg_time} ms.")
def test(stat,time):
mystats = team.calcTeamStats(mystarters, stat, players, time)
head = team.statidHeader(stat)
t1,f1=genetic.run(mystarters,TeamsToInclude=[0,MYTEAMID],StatsToMaximize=stat,
TimeFrame=time, Population=1000, Generations=60)
print('\n\n\n\n\n\n\n')
[print(head[i],mystats[i]) for i in range(len(head))]
print()
gastats = team.calcTeamStats(t1, stat, players, time)
[print(head[i],gastats[i]) for i in range(len(head))]
m = set(mystarters)
g = set(t1)
k = m&g
d = m-g
u = g-m
print('\nKeep:')
for x in k:
p = players[x]
print(p['stats_percentown'],p['stats_percentowndelta'],p['player_name'],p['position_eligible'])
print('\nDrop:')
for x in d:
p = players[x]
print(p['stats_percentown'],p['stats_percentowndelta'],p['player_name'],p['position_eligible'])
print('\nPickup:')
for x in u:
p = players[x]
print(p['stats_percentown'],p['stats_percentowndelta'],p['player_name'],p['position_eligible'])
mystats = team.calcTeamStats(mystarters, st, players, time)
gastats = team.calcTeamStats(t1, st, players, time)
print (gastats)
print (mystats)
print (len(mystats))
print ( sum( [a/b for a,b in zip(gastats,mystats)] ) )
def initialize():
preamble()
generate_detectors()
initialize_state()
genetic.run()
def initialize():
preamble()
generate_detectors()
initialize_state()
genetic.run()
