class Model():
def __init__(self,
numberOfCols=16384, cellsPerColumn=8,
initialPerm=0.5, connectedPerm=0.5,
minThreshold=164, newSynapseCount=164,
permanenceInc=0.1, permanenceDec=0.0,
activationThreshold=164,
pamLength=10,
checkpointDir=None):
self.tp = TP(numberOfCols=numberOfCols, cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm, connectedPerm=connectedPerm,
minThreshold=minThreshold, newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc, permanenceDec=permanenceDec,
# 1/2 of the on bits = (16384 * .02) / 2
activationThreshold=activationThreshold,
globalDecay=0, burnIn=1,
checkSynapseConsistency=False,
pamLength=pamLength)
self.checkpointDir = checkpointDir
self.checkpointPath = None
self._initCheckpoint()
def _initCheckpoint(self):
if self.checkpointDir:
if not os.path.exists(self.checkpointDir):
os.makedirs(self.checkpointDir)
self.checkpointPath = self.checkpointDir + "/model.data"
def canCheckpoint(self):
return self.checkpointDir != None
def hasCheckpoint(self):
return os.path.exists(self.checkpointPath)
def load(self):
if not self.checkpointDir:
raise(Exception("No checkpoint directory specified"))
if not self.hasCheckpoint():
raise(Exception("Could not find checkpoint file"))
self.tp.loadFromFile(self.checkpointPath)
def save(self):
if not self.checkpointDir:
raise(Exception("No checkpoint directory specified"))
self.tp.saveToFile(self.checkpointPath)
def feedTerm(self, term):
""" Feed a Term to model, returning next predicted Term """
tp = self.tp
array = numpy.array(term.toArray(), dtype="uint32")
tp.compute(array, enableLearn = True, computeInfOutput = True)
predictedCells = tp.getPredictedState()
predictedColumns = predictedCells.max(axis=1)
predictedBitmap = predictedColumns.nonzero()[0].tolist()
return Term().createFromBitmap(predictedBitmap)
def resetSequence(self):
self.tp.reset()
class Model():
def __init__(self,
numberOfCols=16384,
cellsPerColumn=8,
initialPerm=0.5,
connectedPerm=0.5,
minThreshold=164,
newSynapseCount=164,
permanenceInc=0.1,
permanenceDec=0.0,
activationThreshold=164,
pamLength=10,
checkpointDir=None):
self.tp = TP(
numberOfCols=numberOfCols,
cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm,
connectedPerm=connectedPerm,
minThreshold=minThreshold,
newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc,
permanenceDec=permanenceDec,
# 1/2 of the on bits = (16384 * .02) / 2
activationThreshold=activationThreshold,
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=pamLength)
self.checkpointDir = checkpointDir
self.checkpointPath = None
self._initCheckpoint()
def _initCheckpoint(self):
if self.checkpointDir:
if not os.path.exists(self.checkpointDir):
os.mkdir(self.checkpointDir)
self.checkpointPath = self.checkpointDir + "/model.data"
def canCheckpoint(self):
return self.checkpointDir != None
def hasCheckpoint(self):
return os.path.exists(self.checkpointPath)
def load(self):
if not self.checkpointDir:
raise (Exception("No checkpoint directory specified"))
if not self.hasCheckpoint():
raise (Exception("Could not find checkpoint file"))
self.tp.loadFromFile(self.checkpointPath)
def save(self):
if not self.checkpointDir:
raise (Exception("No checkpoint directory specified"))
self.tp.saveToFile(self.checkpointPath)
def feedTerm(self, term):
""" Feed a Term to model, returning next predicted Term """
tp = self.tp
array = numpy.array(term.toArray(), dtype="uint32")
tp.compute(array, enableLearn=True, computeInfOutput=True)
predictedCells = tp.getPredictedState()
predictedColumns = predictedCells.max(axis=1)
predictedBitmap = predictedColumns.nonzero()[0].tolist()
return Term().createFromBitmap(predictedBitmap)
def resetSequence(self):
self.tp.reset()
class Model():
def __init__(self,
numberOfCols=20480, cellsPerColumn=8,
initialPerm=0.5, connectedPerm=0.5,
minThreshold=164, newSynapseCount=164,
permanenceInc=0.1, permanenceDec=0.0,
activationThreshold=164,
pamLength=10,
checkpointDir=None):
self.tp = TP(numberOfCols=numberOfCols, cellsPerColumn=cellsPerColumn,
initialPerm=initialPerm, connectedPerm=connectedPerm,
minThreshold=minThreshold, newSynapseCount=newSynapseCount,
permanenceInc=permanenceInc, permanenceDec=permanenceDec,
# 1/2 of the on bits = (16384 * .02) / 2
activationThreshold=activationThreshold,
globalDecay=0, burnIn=1,
checkSynapseConsistency=False,
pamLength=pamLength)
self.phonemes=[
"AA",
"AE",
"AH",
"AO",
"AW",
"AY",
"B",
"CH",
"D",
"DH",
"EH",
"ER",
"EY",
"F",
"G",
"HH",
"IH",
"IY",
"JH",
"K",
"L",
"M",
"N",
"NG",
"OW",
"OY",
"P",
"R",
"S",
"SH",
"T",
"TH",
"UH",
"UW",
"V",
"W",
"Y",
"Z",
"ZH",
"SIL"
]
self.checkpointDir = checkpointDir
self.checkpointPklPath = None
self.checkpointDataPath = None
self._initCheckpoint()
def _initCheckpoint(self):
if self.checkpointDir:
if not os.path.exists(self.checkpointDir):
os.makedirs(self.checkpointDir)
self.checkpointPklPath = self.checkpointDir + "/model.pkl"
self.checkpointDataPath = self.checkpointDir + "/model.data"
def canCheckpoint(self):
return self.checkpointDir != None
def hasCheckpoint(self):
return (os.path.exists(self.checkpointPklPath) and
os.path.exists(self.checkpointDataPath))
def load(self):
if not self.checkpointDir:
raise(Exception("No checkpoint directory specified"))
if not self.hasCheckpoint():
raise(Exception("Could not find checkpoint file"))
with open(self.checkpointPklPath, 'rb') as f:
self.tp = pickle.load(f)
self.tp.loadFromFile(self.checkpointDataPath)
def save(self):
if not self.checkpointDir:
raise(Exception("No checkpoint directory specified"))
self.tp.saveToFile(self.checkpointDataPath)
with open(self.checkpointPklPath, 'wb') as f:
pickle.dump(self.tp, f)
def feedTermAndPhonemes(self, term, phonemes_arr, learn=True):
""" Feed a Term to model, returning next predicted Term """
tp = self.tp
array = term.toArray()
array += self.phonemeToBytes(phonemes_arr)
array = numpy.array(array, dtype="uint32")
tp.compute(array, enableLearn = learn, computeInfOutput = True)
predictedCells = tp.getPredictedState()
predictedColumns = predictedCells.max(axis=1)
# get only the first 16384 bits back
predictedBitmap = predictedColumns[:16384].nonzero()[0].tolist()
return Term().createFromBitmap(predictedBitmap)
def resetSequence(self):
self.tp.reset()
def phonemeToBytes(self, phonemes_arr):
"""
param: python array of phonemes
ex: ["AA", "L", "OW"]
"""
phonemes_bytes = []
for i in range(0, 4):
if i < len(phonemes_arr):
for j in range(0, len(self.phonemes)):
if phonemes_arr[i] == self.phonemes[j]:
phonemes_bytes += [1] * int(1024/len(self.phonemes))
else:
phonemes_bytes += [0] * int(1024/len(self.phonemes))
else:
phonemes_bytes += [0] * 1024
return phonemes_bytes
tp = TP(numberOfCols=date_enc.width, cellsPerColumn=1795,
initialPerm=0.5, connectedPerm=0.5,
minThreshold=10, newSynapseCount=10,
permanenceInc=0.1, permanenceDec=0.01,
activationThreshold=5,
globalDecay=0, burnIn=1,
checkSynapseConsistency=False,
pamLength=7)
days = [datetime.date(y, m, d) for y in range(1998, 2013) for m in range(1, 13) for d in range(1, calendar.monthrange(y, m)[1] + 1)]
print days[0], days[1], days[-2], days[-1]
input_array = numpy.zeros(date_enc.width, dtype="int32")
for pres in xrange(10):
print 'Pass', pres
for i, d in enumerate(days):
if (i + 1) % 100 == 0:
print i + 1
if (i + 1) % 28 == 0:
tp.reset()
input_array[:] = numpy.concatenate(date_enc.encodeEachField(DateRecord(d)))
tp.compute(input_array, enableLearn=True, computeInfOutput=False)
#input_array[:] = numpy.concatenate(date_enc.encodeEachField(DateRecord(days[i + 1])))
#tp.compute(input_array, enableLearn=True, computeInfOutput=False)
print "saving TP to tp.p and tp.tp"
tp.saveToFile("tp.tp")
with open("tp.p", "w") as f:
pickle.dump(tp, f)
globalDecay=0,
burnIn=1,
checkSynapseConsistency=False,
pamLength=7)
days = [
datetime.date(y, m, d) for y in range(1998, 2013)
for m in range(1, 13) for d in range(1,
calendar.monthrange(y, m)[1] + 1)
]
print days[0], days[1], days[-2], days[-1]
input_array = numpy.zeros(date_enc.width, dtype="int32")
for pres in xrange(10):
print 'Pass', pres
for i, d in enumerate(days):
if (i + 1) % 100 == 0:
print i + 1
if (i + 1) % 28 == 0:
tp.reset()
input_array[:] = numpy.concatenate(
date_enc.encodeEachField(DateRecord(d)))
tp.compute(input_array, enableLearn=True, computeInfOutput=False)
#input_array[:] = numpy.concatenate(date_enc.encodeEachField(DateRecord(days[i + 1])))
#tp.compute(input_array, enableLearn=True, computeInfOutput=False)
print "saving TP to tp.p and tp.tp"
tp.saveToFile("tp.tp")
with open("tp.p", "w") as f:
pickle.dump(tp, f)
