def turnNoteOn(self, noteNum):
if noteNum >= len(self.currNoteState) or noteNum < 0:
return
assert(self.currNoteState[noteNum] == 0)
self.soundMapping[noteNum].play(loops=-1)
self.currNoteState[noteNum] = 1
print noteNum, " : ", self.currNoteState[noteNum]
print "current note: ", utils.midiNoteMapping[noteNum]
if True in self.predictionState:
predicted = self.predictionState.index(True)
self.confMatrix[noteNum % 12, predicted % 12] += 1
self.mmConfMatrix[noteNum % 12, self.mmPreds[-1][0]] += 1
self.mm3ConfMatrix[noteNum % 12, self.mm3Preds[-1][0]] += 1
self.hmmConfMatrix[noteNum % 12, self.hmmPreds[-1][0]] += 1
self.qConfMatrix[noteNum % 12, self.qPreds[-1][0]] += 1
if self.predictor == "Q":
print "begin reward sequence"
reward = 0
if (noteNum % 12) == (predicted % 12):
print "good reward!"
reward = 1
prevNote = self.allNotes[-1][0]
print "before learning: ", self.qModel.q
self.qModel.learn(prevNote, predicted, reward, noteNum)
print "after learning: ", self.qModel.q
#learn q learning here
note = [noteNum, pygame.time.get_ticks(), -1]
print note
self.noteRects.append(utils.makeNoteRect(noteNum, 1))
self.allNotes.append(note)
if (len(self.allNotes) > 10):
self.predictNotes()
def __init__(self, showPredictions=False, predictor="MM", dataFile="data/noteData_ex_random.pickle"):
self.model = Model(dataFile)
import utils
self.showPredictions = showPredictions
self.currNoteState = [0] * utils.numNotes
self.predictionState = [False] * utils.numNotes
self.keyRects = []
self.actionQueue = []
if predictor not in ["MM", "MM3", "HMM", "Q"]:
predictor = "MM"
self.predictor = predictor
self.octave = 1 #assumes that note 48 is in the data...
for note, val in enumerate(self.currNoteState):
self.keyRects.append(utils.makeNoteRect(note, utils.numNotes))
self.allNotes = []
self.modelRects = [] #(note, (left, top, width, height))
self.noteRects = [] #(note, (left, top, width, height))
self.soundMapping = utils.initSoundMappings()
self.confMatList = []
self.confMatrix = np.zeros((12, 12), dtype=np.int)
self.avgF1s = []
self.memoryList = []
self.cpuList = []
self.mmPreds = []
self.mmConfMatList = []
self.mmConfMatrix = np.zeros((12, 12), dtype=np.int)
self.mmAvgF1s = []
self.mm3Preds = []
self.mm3ConfMatList = []
self.mm3ConfMatrix = np.zeros((12, 12), dtype=np.int)
self.mm3AvgF1s = []
self.hmmPreds = []
self.hmmConfMatList = []
self.hmmConfMatrix = np.zeros((12, 12), dtype=np.int)
self.hmmAvgF1s = []
self.qPreds = []
self.qConfMatList = []
self.qConfMatrix = np.zeros((12, 12), dtype=np.int)
self.qAvgF1s = []
#MODELS#
mmModel, mmModel3, hmmModel, qModel = self.model.train()
self.mmModel = mmModel
self.mmModel3 = mmModel3
self.hmmModel = hmmModel
self.qModel = qModel
