def reset(self, params, repetition):
random.seed(params['seed'])
if params['encoding'] == 'basic':
self.encoder = BasicEncoder(params['encoding_num'])
elif params['encoding'] == 'distributed':
self.encoder = DistributedEncoder(params['encoding_num'],
maxValue=params['encoding_max'],
minValue=params['encoding_min'],
classifyWithRandom=params[
'classify_with_random'])
else:
raise Exception("Encoder not found")
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'])
else:
raise Exception("Dataset not found")
self.computeCounter = 0
self.history = []
self.resets = []
self.randoms = []
self.currentSequence = self.dataset.generateSequence()
self.net = None
self.sequenceCounter = 0
def __init__(self, numPredictions, resultsDir):
random.seed(43)
self.numPredictions = numPredictions
if not os.path.exists(resultsDir):
os.makedirs(resultsDir)
self.resultsFile = open(os.path.join(resultsDir, "0.log"), 'w')
self.model = ModelFactory.create(MODEL_PARAMS)
self.model.enableInference({"predictedField": "element"})
self.shifter = InferenceShifter()
self.mapping = getEncoderMapping(self.model)
self.correct = []
self.numPredictedActiveCells = []
self.numPredictedInactiveCells = []
self.numUnpredictedActiveColumns = []
self.iteration = 0
self.perturbed = False
self.randoms = []
self.verbosity = 1
self.dataset = HighOrderDataset(numPredictions=self.numPredictions)
self.sequences = []
self.currentSequence = []
self.replenish_sequence()
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['encoding'] == 'basic':
self.encoder = BasicEncoder(params['encoding_num'])
elif params['encoding'] == 'distributed':
self.encoder = DistributedEncoder(
params['encoding_num'],
maxValue=params['encoding_max'],
minValue=params['encoding_min'],
classifyWithRandom=params['classify_with_random'])
else:
raise Exception("Encoder not found")
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'])
else:
raise Exception("Dataset not found")
self.computeCounter = 0
self.history = []
self.resets = []
self.randoms = []
self.currentSequence = []
self.targetPrediction = []
self.replenishSequence(params, iteration=0)
self.net = buildNetwork(params['encoding_num'],
params['num_cells'],
params['encoding_num'],
hiddenclass=LSTMLayer,
bias=True,
outputbias=params['output_bias'],
recurrent=True)
self.trainer = BackpropTrainer(self.net,
dataset=SequentialDataSet(
params['encoding_num'],
params['encoding_num']),
learningrate=0.01,
momentum=0,
verbose=params['verbosity'] > 0)
self.sequenceCounter = 0
def reset(self, params, repetition):
random.seed(params['seed'])
if params['encoding'] == 'basic':
self.encoder = BasicEncoder(params['encoding_num'])
elif params['encoding'] == 'distributed':
self.encoder = DistributedEncoder(params['encoding_num'],
params['encoding_num_non_random'],
maxValue=params['encoding_max'],
minValue=params['encoding_min'])
elif params['encoding'] == 'sparse-distributed':
self.encoder = SparseDistributedEncoder(params['encoding_num'],
params['encoding_num_non_random'],
params['encoding_active_bits'])
else:
raise Exception("Encoder not found")
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'])
else:
raise Exception("Dataset not found")
self.numLags = params['num_lags']
self.computeCounter = 0
self.history = []
self.resets = []
self.finishInitializeX = False
self.randoms = []
self.currentSequence = []
self.targetPrediction = []
self.replenishSequence(params, iteration=0)
self.net = buildNetwork(params['encoding_num'] * params['num_lags'],
params['num_cells'],
params['encoding_num'],
bias=True,
outputbias=True)
# self.trainer = BackpropTrainer(self.net, dataset=trndata, momentum=0.1, verbose=True, weightdecay=0.01)
self.sequenceCounter = 0
def reset(self, params, repetition):
random.seed(params['seed'])
if params['encoding'] == 'basic':
self.encoder = BasicEncoder(params['encoding_num'])
elif params['encoding'] == 'distributed':
self.encoder = DistributedEncoder(
params['encoding_num'],
params['encoding_num_non_random'],
maxValue=params['encoding_max'],
minValue=params['encoding_min'])
elif params['encoding'] == 'sparse-distributed':
self.encoder = SparseDistributedEncoder(
params['encoding_num'], params['encoding_num_non_random'],
params['encoding_active_bits'])
else:
raise Exception("Encoder not found")
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'])
else:
raise Exception("Dataset not found")
self.numLags = params['num_lags']
self.history = []
self.resets = []
self.finishInitializeX = False
self.randoms = []
self.currentSequence = []
self.targetPrediction = []
self.replenishSequence(params, iteration=0)
self.net = initializeELMnet(params['encoding_num'] *
params['num_lags'],
params['encoding_num'],
numNeurons=params['num_cells'])
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
