def reset(self, params, repetition):
random.seed(params['seed'])
if params['dataset'] == 'simple':
self.dataset = SimpleDataset()
elif params['dataset'] == 'reber':
self.dataset = ReberDataset(maxLength=params['max_length'])
elif params['dataset'] == 'high-order':
self.dataset = HighOrderDataset(numPredictions=params['num_predictions'],
seed=params['seed'])
print "Sequence dataset: "
print " Symbol Number {}".format(self.dataset.numSymbols)
for seq in self.dataset.sequences:
print seq
elif params['dataset'] == 'high-order-long':
self.dataset = LongHighOrderDataset(params['sequence_length'],
seed=params['seed'])
print "Sequence dataset: "
print " Symbol Number {}".format(self.dataset.numSymbols)
for seq in self.dataset.sequences:
print seq
else:
raise Exception("Dataset not found")
self.randomStart = self.dataset.numSymbols + 1
self.randomEnd = self.randomStart + 5000
MODEL_PARAMS['modelParams']['sensorParams']['encoders']['element']\
['categoryList'] = range(self.randomEnd)
# if not os.path.exists(resultsDir):
# os.makedirs(resultsDir)
# self.resultsFile = open(os.path.join(resultsDir, "0.log"), 'w')
if params['verbosity'] > 0:
print " initializing HTM model..."
self.model = ModelFactory.create(MODEL_PARAMS)
self.model.enableInference({"predictedField": "element"})
# self.classifier = SDRClassifier(steps=[1], alpha=0.001)
self.mapping = getEncoderMapping(self.model, self.dataset.numSymbols)
self.numPredictedActiveCells = []
self.numPredictedInactiveCells = []
self.numUnpredictedActiveColumns = []
self.currentSequence = []
self.targetPrediction = []
self.replenish_sequence(params, iteration=0)
self.resets = []
self.randoms = []
self.verbosity = 1
self.sequenceCounter = 0
def reset(self, params, repetition):
random.seed(params['seed'])
if params['dataset'] == 'simple':
self.dataset = SimpleDataset()
elif params['dataset'] == 'reber':
self.dataset = ReberDataset(maxLength=params['max_length'])
elif params['dataset'] == 'high-order':
self.dataset = HighOrderDataset(
numPredictions=params['num_predictions'],
seed=params['seed'],
smallAlphabet=params['use_small_alphabet'])
print "Sequence dataset: "
print " Symbol Number {}".format(self.dataset.numSymbols)
for seq in self.dataset.sequences:
print seq
elif params['dataset'] == 'high-order-long':
self.dataset = LongHighOrderDataset(params['sequence_length'],
seed=params['seed'])
print "Sequence dataset: "
print " Symbol Number {}".format(self.dataset.numSymbols)
for seq in self.dataset.sequences:
print seq
else:
raise Exception("Dataset not found")
self.randomStart = self.dataset.numSymbols + 1
self.randomEnd = self.randomStart + 5000
MODEL_PARAMS['modelParams']['sensorParams']['encoders']['element']\
['categoryList'] = range(self.randomEnd)
# if not os.path.exists(resultsDir):
# os.makedirs(resultsDir)
# self.resultsFile = open(os.path.join(resultsDir, "0.log"), 'w')
if params['verbosity'] > 0:
print " initializing HTM model..."
# print MODEL_PARAMS
self.model = ModelFactory.create(MODEL_PARAMS)
self.model.enableInference({"predictedField": "element"})
# self.classifier = SDRClassifier(steps=[1], alpha=0.001)
print "finish initializing HTM model "
if params['kill_cell_percent'] > 0:
# a hack to use faulty temporal memory instead
self.model._getTPRegion().getSelf()._tfdr = MonitoredFaultyTPShim(
numberOfCols=2048,
cellsPerColumn=32,
newSynapseCount=32,
maxSynapsesPerSegment=128,
maxSegmentsPerCell=128,
initialPerm=0.21,
connectedPerm=0.50,
permanenceInc=0.10,
permanenceDec=0.10,
predictedSegmentDecrement=0.01,
minThreshold=15,
activationThreshold=15,
seed=1960,
)
self.mapping = getEncoderMapping(self.model, self.dataset.numSymbols)
self.numPredictedActiveCells = []
self.numPredictedInactiveCells = []
self.numUnpredictedActiveColumns = []
self.currentSequence = []
self.targetPrediction = []
self.replenish_sequence(params, iteration=0)
self.resets = []
self.randoms = []
self.verbosity = 1
self.sequenceCounter = 0
