def collectFeats(trainFeats, featType):
#trainFeats structure:
# [
# {
# name:'score1',
# scoreFeats:{ feat1name:[1,2,3...], feat2name:[2,3,4...] ...},
# perfFeats:{ feat1name:[1,2,3...], feat2name:[2,3,4...] ...}
# }
# {
# name:'score2',
# scoreFeats:{ feat1name:[1,2,3...], feat2name:[2,3,4...] ...},
# perfFeats:{ feat1name:[1,2,3...], feat2name:[2,3,4...] ...}
# }
# ]
#
# collectedFeats structure (scoreFeats for example):
# {
# feat1name: [1,2,3....2 3 4,...3 9 4,...],
# feat2name: [1,2,3....2 3 4,...3 9 4,...],
# }
#
#logging.printDebug("trainFeats: ")
#logging.printDebug(trainFeats)
collectedFeats = {}
for featName in getattr(config, featType+'List'):
collectedFeats[featName] = []
#logging.printDebug(trainFeats)
logging.printDebug(featName)
for score in trainFeats:
logging.printDebug(score)
#logging.printDebug("featName: " + featName)
#logging.printDebug("featType: " + featType)
collectedFeats[featName].extend(score[featType][featName])
return collectedFeats
def collectFeats(trainFeats, featType):
#trainFeats structure:
# [
# {
# name:'score1',
# scoreFeats:{ feat1name:[1,2,3...], feat2name:[2,3,4...] ...},
# perfFeats:{ feat1name:[1,2,3...], feat2name:[2,3,4...] ...}
# }
# {
# name:'score2',
# scoreFeats:{ feat1name:[1,2,3...], feat2name:[2,3,4...] ...},
# perfFeats:{ feat1name:[1,2,3...], feat2name:[2,3,4...] ...}
# }
# ]
#
# collectedFeats structure (scoreFeats for example):
# {
# feat1name: [1,2,3....2 3 4,...3 9 4,...],
# feat2name: [1,2,3....2 3 4,...3 9 4,...],
# }
#
#logging.printDebug("trainFeats: ")
#logging.printDebug(trainFeats)
collectedFeats = {}
for featName in getattr(config, featType + 'List'):
collectedFeats[featName] = []
#logging.printDebug(trainFeats)
logging.printDebug(featName)
for score in trainFeats:
logging.printDebug(score)
#logging.printDebug("featName: " + featName)
#logging.printDebug("featType: " + featType)
collectedFeats[featName].extend(score[featType][featName])
return collectedFeats
def extractOnsetDiffQNote(sample):
scoreOffsets = [s['offset'] for s in sample['score'].flat.notes.offsetMap]
perfOffsets = [p['offset'] for p in sample['perf'].flat.notes.offsetMap]
perfStartOffset = perfOffsets[0]
ratio = extractTempoRatio(sample)
normalizedPerfOffsets = [(p - perfStartOffset) / ratio
for p in perfOffsets]
weakStartOffset = getWeakStartOffset(sample)
shiftedPerfOffsets = map(lambda x: x + weakStartOffset,
normalizedPerfOffsets)
logging.printDebug("scoreOffsets:")
logging.printDebug(scoreOffsets)
logging.printDebug("perfOffsets:")
logging.printDebug(perfOffsets)
logging.printDebug("shiftedPerfOffsets:")
logging.printDebug(shiftedPerfOffsets)
logging.printDebug("normalizedPerfOffsets:")
logging.printDebug(normalizedPerfOffsets)
offsetDiff = [s - p for s, p in zip(scoreOffsets, shiftedPerfOffsets)]
return offsetDiff
def extractOnsetDiffQNote1(sample):
scoreOffsets = [s['offset'] for s in sample['score'].flat.notes.offsetMap]
perfOffsets = [p['offset'] for p in sample['perf'].flat.notes.offsetMap]
perfStartOffset = perfOffsets[0]
ratio = extractTempoRatio1(sample)
normalizedPerfOffsets= [(p - perfStartOffset) / ratio for p in perfOffsets]
weakStartOffset = getWeakStartOffset(sample)
shiftedPerfOffsets = map(lambda x: x + weakStartOffset, normalizedPerfOffsets)
logging.printDebug("scoreOffsets:")
logging.printDebug(scoreOffsets)
logging.printDebug("perfOffsets:")
logging.printDebug(perfOffsets)
logging.printDebug("shiftedPerfOffsets:")
logging.printDebug(shiftedPerfOffsets)
logging.printDebug("normalizedPerfOffsets:")
logging.printDebug(normalizedPerfOffsets)
offsetDiff = [ s - p for s, p in zip(scoreOffsets, shiftedPerfOffsets)]
return offsetDiff