def evolution_step(step, pop, fitnesses,
save_best=False, display_chords=True, console_prints=True):
if display_chords:
f.canvas.set_window_title('Step {}/{}'.format(step+1, NUM_STEPS))
if console_prints:
print('BEGINNING STEP NUMBER', step)
print('Size of pop:', len(pop))
kill_ratio = interpolations['Kill Ratio'].interpolate(step)
penalty = interpolations['Incorrect Num Notes Penalty'].interpolate(step)
# **Display each chord**
if display_chords:
for i in range(MAX_SHOW_SIZE):
plt.subplot(n_rows, n_cols, i+1)
pop[i].plot()
chord_pos[pop[i]] = i+1
pop[i].subplot = i+1
plt.title('Chord {}: f=unknown'.format(i+1))
plt.pause(FITNESS_PAUSE)
# **Calculate and assign fitness to each organism**
row = []
for c, chord in enumerate(pop):
frequencies = Guitar.frequency_list(chord.read())
try:
fitness = fitness_eval(frequencies, penalty)
chord.fitness = fitness
if display_chords:
if c<MAX_SHOW_SIZE:
plt.subplot(n_rows,n_cols,c+1)
plt.title('Chord {}: f={}'.format(c+1,round(fitness,2)))
row.append(fitness)
except Exception as e:
print(e,frequencies)
if display_chords:
plt.pause(FITNESS_PAUSE)
avg_f = np.mean(row)
if save_best:
if len(fitnesses) > 0:
if avg_f < min(np.mean(fitnesses, axis=1)):
if console_prints:
print('Saving this population to file...')
with open('best.pickle', 'wb') as file:
pickle.dump(pop, file)
fitnesses.append(row)
# **Kill the weakest chords**
# The bottom 50% or so of the population in terms of fitness
# will be eliminated and will not have a chance to breed.
# - sort by fitness
# - remove the bottom half
pop.sort(key=lambda x: (x.fitness), reverse=False)
kill_count = np.ceil(len(pop)*(kill_ratio))
if console_prints:
print('Killing {} organisms with the lowest fitness...'.format(kill_count))
for c, chord in enumerate(pop[-1:int(len(pop)*(1-kill_ratio)):-1]):
if display_chords:
if chord in chord_pos:
plt.subplot(n_rows, n_cols,chord_pos[chord])
plt.title('Killed')
draw_x()
chord.alive = False
#print('Killed Chord {} with f={}'.format(chord_pos[chord],chord.fitness))
pop.remove(chord)
if display_chords:
plt.pause(KILL_PAUSE)
# **Breed the organisms to refill the population**
# - Lowest fitness should breed the most
# - Use chord_breeder.py to generate offspring
candidates = np.copy(pop)
[np.append(candidates,pop[0]) for _ in range(first_place_bonus)]
[np.append(candidates,pop[1]) for _ in range(second_place_bonus)]
[np.append(candidates,pop[2]) for _ in range(third_place_bonus)]
candidates = np.repeat(candidates,4)
candidates = np.random.permutation(candidates)
if console_prints:
print('Beginning breeding of remaining population...')
diff = MAX_POP_SIZE - len(pop)
i = 0
while len(pop) < MAX_POP_SIZE:
chord_breeder.set_mutation_rate(interpolations['Mutation Rate'].interpolate(step))
offspring_a = chord_breeder.breed(candidates[i*2], candidates[i*2+1], debug=False)
offspring_b = chord_breeder.breed(candidates[i*2], candidates[i*2+1], debug=False)
pop.append(offspring_a)
empty_spot = find_empty_spot()
if empty_spot is not None:
chord_pos[offspring_a] = empty_spot
if(len(pop)< MAX_POP_SIZE):
pop.append(offspring_b)
empty_spot = find_empty_spot()
if empty_spot is not None:
chord_pos[offspring_b] = empty_spot
i+=1
if console_prints:
print('Bred {} new chords.'.format(diff))
if display_chords:
plt.pause(KILL_PAUSE)
step = 0
while patient(fitnesses, monitor='mean'):
plt.clf()
evolution_step(step, pop, fitnesses,
save_best=True, display_chords=display_chords,
console_prints=False)
step+=1
if __name__ == "__main__":
# Defining master chord
f1 = Finger(string=2, technique='Single_Note', start_fret=1)
f2 = Finger(string=4, technique='Single_Note', increment=1)
f3 = Finger(string=5, technique='Single_Note_Mute_Above', increment=1)
#f4 = Finger(string=1, technique='Single_Note', increment=3)
master_chord = Chord(fingers=[f1,f2,f3])
master_frequencies = Guitar.frequency_list(master_chord.read())
master_chord.plot()
plt.title('Master Chord')
plt.show()
#master_frequencies = q_transform.analyze('./tmp/target.wav', plot_q_transform=True, debug=True)
print('Master frequencies:',master_frequencies)
print('Showing {0}% of organisms in the population'.format(MAX_SHOW_SIZE/MAX_POP_SIZE*100))
# Initializing the population randomly
f, axs = plt.subplots(n_rows, n_cols, sharex=True)
#f.tight_layout(pad=1.0)
chord_pos = {}
#mng = plt.get_current_fig_manager()
#mng.window.state('zoomed')
kill_interp = LinearInterpolation(start=.3,end=.6,end_step=25)
mutation_interp = LinearInterpolation(start=.1,end=.6,end_step=60)
