def test(f_discretization=10, e_discretization=30, indep=False, selection=None, subset=None, corpus=None, smoothing=None, sensitivity=5, segmentation='reasonable'):
if not selection:
selection = (db.select())
score = db.getScore1(selection)
if not corpus:
(f, e, m) = tools.chooseFeatures()
else:
(f, e, m) = tools.loadFeatures(corpus)
if m['version'] != sf.version:
print("Scorefeatures versions don't match! Corpus version: {0} Scorefeatures version: {1}".format(m['version'], sf.version))
exit(0)
if not subset:
# Select a subset by hand
subset = selectSubset(m['featureset'])
print('\n\tPerforming {0}'.format(selection))
print('\tFeatures version: {0}'.format(m['version']))
print('\tFeatureset used: [', end=' ')
print(', '.join([m['featureset'][i] for i in subset]), end=' ')
print(']')
print('\tScorefeatures discretization: {0}\n\tExpression discretization: {1}'.format(f_discretization, e_discretization))
print('\tSensitivity: {0}'.format(sensitivity))
print('\tSmoothing: {0}'.format(smoothing))
print('\tCorpus: {0}\n'.format(corpus))
if indep:
hmm = HMM_indep(2, smoothing)
else:
hmm = HMM(2, smoothing)
trainHMM(hmm, f, e, f_discretization, e_discretization, subset=subset, ignore=selection, sensitivity=sensitivity)
#trainHMM(hmm, f, e, f_discretization, e_discretization, subset=subset)
hmm.storeInfo('hmm2.txt')
print("Loading score")
melodyscore = Score(score).melody()
melody = tools.parseScore(melodyscore)
# Segmentate the the score
print("Analysing score")
if segmentation == 'reasonable':
print('Using reasonable segmentation')
onset = structure.reasonableSegmentation(melody)
elif segmentation == 'new':
print('Using new segmentation')
onset = structure.newSegmentation(melody)
elif segmentation == 'notelevel':
print('Using notelevel segmentation')
onset = structure.noteLevel(melody)
#onset = structure.groupings(structure.list_to_tree(structure.first_order_tree(structure.onset, melody, 0.1)), 1)
#namelist = []
#for group in onset:
# namelist.append([leaf.name() for leaf in group])
#print namelist
(p, expression) = render(melodyscore, onset, hmm, f_discretization, e_discretization, subset, sensitivity=sensitivity)
print("Done, resulting expression(with a probability of {1}): {0}".format(expression, p))
performance = perform.perform(score, melodyscore, onset, expression, converter=melody)
playPerformance(selection, performance, expression, onset, '{0}_{1}_on_{2}_discret_{3}-{4}_smoothing_{5}'.format(\
selection[0], selection[1], corpus, f_discretization, e_discretization, smoothing))
def loadperformance():
(selection, expression) = tools.loadPerformance()
score = db.getScore1(selection)
melodyscore = Score(score).melody()
melody = tools.parseScore(melodyscore)
segmentation = structure.reasonableSegmentation(melody)
performance = perform.perform(score, Score(score).melody(), segmentation, expression, converter=melody)
seq = Sequencer()
seq.play(performance)
def plotCorpus():
(f, e, m) = tools.chooseFeatures()
d = input('Discretization? ')
if not d == '':
s = input('Sensitivity? [5]')
if s == '':
s = 5.0
d = float(d)
s = float(s)
else: d = None
for work in f:
print('Plotting and saving {0}'.format(work))
expression = e[work]
if d:
expression = [undiscretize_expression(discretize_expression(x, d, sensitivity=s), d, sensitivity=s) for x in expression]
score = db.getScore1(work)
segmentation = structure.reasonableSegmentation(tools.parseScore(Score(score).melody()))
visualize(segmentation, expression, [0, 1, 2], '{0}-{1}-{2}'.format(work[0], work[1], work[2]), '{0}-{1} by {2}'.format(work[0], work[1], work[2]))
elif a[1] == 'align':
import train
set = [db.select()]
train.train(set)
exit(0)
elif a[1] == 'plotcorpus':
plotCorpus()
exit(0)
elif a[1] == 'plot':
(selection, expression) = tools.loadPerformance()
if selection == None:
selection = db.select()
score = db.getScore1(selection)
melodyscore = Score(score).melody()
melody = tools.parseScore(melodyscore)
segmentation = structure.reasonableSegmentation(melody)
visualize(segmentation, expression)
exit(0)
elif a[1] == 'corpora':
for x in tools.datasets():
print('Name:\t[{0}]\tInfo:\t{1}'.format(x, tools.corpusInfo(x)))
exit(0)
elif a[1] == 'corpusinfo':
choice = util.menu("Select corpus", tools.datasets())
print(tools.corpusInfo(tools.datasets()[choice]))
tools.extendedCorpusInfo(tools.datasets()[choice])
exit(0)
elif a[1] == 'features':
s = db.select()
score = Score(db.getScore1(s))
melodyscore = score.melody()