def testVerbosity(self):
"""
Test that nothing is printed out when verbosity=0
"""
_stdout = sys.stdout
sys.stdout = _stringio = StringIO()
encoder = RandomDistributedScalarEncoder(name="mv", resolution=1.0, verbosity=0)
output = numpy.zeros(encoder.getWidth(), dtype=defaultDtype)
encoder.encodeIntoArray(23.0, output)
encoder.getBucketIndices(23.0)
sys.stdout = _stdout
self.assertEqual(len(_stringio.getvalue()), 0, "zero verbosity doesn't lead to zero output")
def testVerbosity(self):
"""
Test that nothing is printed out when verbosity=0
"""
_stdout = sys.stdout
sys.stdout = _stringio = StringIO()
encoder = RandomDistributedScalarEncoder(name="mv", resolution=1.0,
verbosity=0)
output = numpy.zeros(encoder.getWidth(), dtype=defaultDtype)
encoder.encodeIntoArray(23.0, output)
encoder.getBucketIndices(23.0)
sys.stdout = _stdout
self.assertEqual(len(_stringio.getvalue()), 0,
"zero verbosity doesn't lead to zero output")
def runSimpleSequence(self, resets, repetitions=1):
scalarEncoder = RandomDistributedScalarEncoder(0.88, n=2048, w=41)
instances = self._createInstances(cellsPerColumn=32)
times = [0.0] * len(self.contestants)
duration = 10000 * repetitions
increment = 4
sequenceLength = 25
sequence = (i % (sequenceLength * 4)
for i in xrange(0, duration * increment, increment))
t = 0
encodedValue = numpy.zeros(2048, dtype=numpy.int32)
for value in sequence:
scalarEncoder.encodeIntoArray(value, output=encodedValue)
activeBits = encodedValue.nonzero()[0]
for i in xrange(len(self.contestants)):
tmInstance = instances[i]
computeFn = self.contestants[i][2]
if resets:
if value == 0:
tmInstance.reset()
start = time.clock()
computeFn(tmInstance, encodedValue, activeBits)
times[i] += time.clock() - start
printProgressBar(t, duration, 50)
t += 1
clearProgressBar(50)
results = []
for i in xrange(len(self.contestants)):
name = self.contestants[i][3]
results.append((
name,
times[i],
))
return results
def runHotgym(self, cellsPerColumn, repetitions=1):
scalarEncoder = RandomDistributedScalarEncoder(0.88, n=2048, w=41)
instances = self._createInstances(cellsPerColumn=cellsPerColumn)
times = [0.0] * len(self.contestants)
t = 0
duration = HOTGYM_LENGTH * repetitions
for _ in xrange(repetitions):
with open(HOTGYM_PATH) as fin:
reader = csv.reader(fin)
reader.next()
reader.next()
reader.next()
encodedValue = numpy.zeros(2048, dtype=numpy.int32)
for timeStr, valueStr in reader:
value = float(valueStr)
scalarEncoder.encodeIntoArray(value, output=encodedValue)
activeBits = encodedValue.nonzero()[0]
for i in xrange(len(self.contestants)):
tmInstance = instances[i]
computeFn = self.contestants[i][2]
start = time.clock()
computeFn(tmInstance, encodedValue, activeBits)
times[i] += time.clock() - start
printProgressBar(t, duration, 50)
t += 1
clearProgressBar(50)
results = []
for i in xrange(len(self.contestants)):
name = self.contestants[i][3]
results.append((
name,
times[i],
))
return results
def runSimpleSequence(self, resets, repetitions=1):
scalarEncoder = RandomDistributedScalarEncoder(0.88, n=2048, w=41)
instances = self._createInstances(cellsPerColumn=32)
times = [0.0] * len(self.contestants)
duration = 10000 * repetitions
increment = 4
sequenceLength = 25
sequence = (i % (sequenceLength * 4)
for i in xrange(0, duration * increment, increment))
t = 0
encodedValue = numpy.zeros(2048, dtype=numpy.int32)
for value in sequence:
scalarEncoder.encodeIntoArray(value, output=encodedValue)
activeBits = encodedValue.nonzero()[0]
for i in xrange(len(self.contestants)):
tmInstance = instances[i]
computeFn = self.contestants[i][2]
if resets:
if value == 0:
tmInstance.reset()
start = time.clock()
computeFn(tmInstance, encodedValue, activeBits)
times[i] += time.clock() - start
printProgressBar(t, duration, 50)
t += 1
clearProgressBar(50)
results = []
for i in xrange(len(self.contestants)):
name = self.contestants[i][3]
results.append((name,
times[i],))
return results
def runHotgym(self, cellsPerColumn, repetitions=1):
scalarEncoder = RandomDistributedScalarEncoder(0.88, n=2048, w=41)
instances = self._createInstances(cellsPerColumn=cellsPerColumn)
times = [0.0] * len(self.contestants)
t = 0
duration = HOTGYM_LENGTH * repetitions
for _ in xrange(repetitions):
with open(HOTGYM_PATH) as fin:
reader = csv.reader(fin)
reader.next()
reader.next()
reader.next()
encodedValue = numpy.zeros(2048, dtype=numpy.uint32)
for timeStr, valueStr in reader:
value = float(valueStr)
scalarEncoder.encodeIntoArray(value, output=encodedValue)
activeBits = encodedValue.nonzero()[0]
for i in xrange(len(self.contestants)):
tmInstance = instances[i]
computeFn = self.contestants[i][2]
start = time.clock()
computeFn(tmInstance, encodedValue, activeBits)
times[i] += time.clock() - start
printProgressBar(t, duration, 50)
t += 1
clearProgressBar(50)
results = []
for i in xrange(len(self.contestants)):
name = self.contestants[i][3]
results.append((name,
times[i],))
return results
class DendriteDetector(AnomalyDetector):
def initialize(self):
# Keep track of value range for spatial anomaly detection.
self.minVal = None
self.maxVal = None
# Time of day encoder
self.timeOfDayEncoder = DateEncoder(timeOfDay=(21, 9.49),
name='time_enc')
# RDSE encoder for the time series value.
minResolution = 0.001
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = self.inputMax + rangePadding
numBuckets = 130
resolution = max(minResolution, (maxVal - minVal) / numBuckets)
self.value_enc = RandomDistributedScalarEncoder(resolution=resolution,
name='value_rdse')
# Spatial Pooler.
encodingWidth = self.timeOfDayEncoder.getWidth(
) + self.value_enc.getWidth()
self.sp = SpatialPooler(
inputDimensions=(encodingWidth, ),
columnDimensions=(2048, ),
potentialPct=0.8,
potentialRadius=encodingWidth,
globalInhibition=1,
numActiveColumnsPerInhArea=40,
synPermInactiveDec=0.0005,
synPermActiveInc=0.003,
synPermConnected=0.2,
boostStrength=0.0,
seed=1956,
wrapAround=True,
)
self.tm = TemporalMemory(
columnDimensions=(2048, ),
cellsPerColumn=32,
activationThreshold=20,
initialPermanence=.5, # Increased to connectedPermanence.
connectedPermanence=.5,
minThreshold=13,
maxNewSynapseCount=31,
permanenceIncrement=0.04,
permanenceDecrement=0.008,
predictedSegmentDecrement=0.001,
maxSegmentsPerCell=128,
maxSynapsesPerSegment=
128, # Changed meaning. Also see connections.topology[2]
seed=1993,
)
# Initialize the anomaly likelihood object
numentaLearningPeriod = int(math.floor(self.probationaryPeriod / 2.0))
self.anomalyLikelihood = anomaly_likelihood.AnomalyLikelihood(
learningPeriod=numentaLearningPeriod,
estimationSamples=self.probationaryPeriod - numentaLearningPeriod,
reestimationPeriod=100,
)
self.age = 0
def getAdditionalHeaders(self):
"""Returns a list of strings."""
return ["raw_score"]
def handleRecord(self, inputData):
"""
Argument inputData is {"value": instantaneous_value, "timestamp": pandas.Timestamp}
Returns a tuple (anomalyScore, rawScore).
Internally to NuPIC "anomalyScore" corresponds to "likelihood_score"
and "rawScore" corresponds to "anomaly_score". Sorry about that.
"""
# Check for spatial anomalies and update min/max values.
value = inputData["value"]
spatialAnomaly = False
if self.minVal != self.maxVal:
tolerance = (self.maxVal - self.minVal) * SPATIAL_TOLERANCE
maxExpected = self.maxVal + tolerance
minExpected = self.minVal - tolerance
if value > maxExpected or value < minExpected:
spatialAnomaly = True
if self.maxVal is None or value > self.maxVal:
self.maxVal = value
if self.minVal is None or value < self.minVal:
self.minVal = value
# Run the HTM stack. First Encoders.
timestamp = inputData["timestamp"]
timeOfDayBits = np.zeros(self.timeOfDayEncoder.getWidth())
self.timeOfDayEncoder.encodeIntoArray(timestamp, timeOfDayBits)
valueBits = np.zeros(self.value_enc.getWidth())
self.value_enc.encodeIntoArray(value, valueBits)
encoding = np.concatenate([timeOfDayBits, valueBits])
# Spatial Pooler.
activeColumns = np.zeros(self.sp.getNumColumns())
self.sp.compute(encoding, True, activeColumns)
activeColumnIndices = np.nonzero(activeColumns)[0]
# Temporal Memory and Anomaly.
predictions = self.tm.getPredictiveCells()
predictedColumns = list(self.tm.mapCellsToColumns(predictions).keys())
self.tm.compute(activeColumnIndices, learn=True)
activeCells = self.tm.getActiveCells()
rawScore = anomaly.computeRawAnomalyScore(activeColumnIndices,
predictedColumns)
# Compute log(anomaly likelihood)
anomalyScore = self.anomalyLikelihood.anomalyProbability(
inputData["value"], rawScore, inputData["timestamp"])
finalScore = logScore = self.anomalyLikelihood.computeLogLikelihood(
anomalyScore)
if spatialAnomaly:
finalScore = 1.0
if False:
# Plot correlation of excitement versus compartmentalization.
if self.age == 0:
print("Correlation Plots ENABLED.")
if False:
start_age = 1000
end_age = 1800
else:
start_age = 4000
end_age = 7260
if self.age == start_age:
import correlation
import random
self.cor_samplers = []
sampled_cells = []
while len(self.cor_samplers) < 20:
n = random.choice(xrange(self.tm.numberOfCells()))
if n in sampled_cells:
continue
else:
sampled_cells.append(n)
neuron = self.tm.connections.dataForCell(n)
if neuron._roots:
c = correlation.CorrelationSampler(neuron._roots[0])
c.random_sample_points(100)
self.cor_samplers.append(c)
print("Created %d Correlation Samplers" %
len(self.cor_samplers))
if self.age >= start_age:
for smplr in self.cor_samplers:
smplr.sample()
if self.age == end_age:
import matplotlib.pyplot as plt
for idx, smplr in enumerate(self.cor_samplers):
if smplr.num_samples == 0:
print("No samples, plot not shown.")
continue
plt.figure("Sample %d" % idx)
smplr.plot(period=64) # Different value!
plt.show()
if False:
# Plot excitement of a typical detection on a dendrite.
if self.age == 7265:
#if self.age == 1800:
import matplotlib.pyplot as plt
import random
from connections import SYN_CONNECTED_ACTIVE
sampled_cells = set()
for figure_num in xrange(40):
plt.figure("(%d)" % figure_num)
# Find an active cell to view.
cell = None
for attempt in range(100):
event = random.choice(self.tm.activeEvents)
cell = event.cell # This is an integer.
if cell is not None and cell not in sampled_cells:
break
else:
break
sampled_cells.add(cell)
cell = self.tm.connections.dataForCell(cell)
# Organize the data.
EPSPs = []
excitement = []
distance_to_root = 0
segment_offsets = {}
branch = cell._roots[0]
while True:
segment_offsets[branch] = distance_to_root
distance_to_root += len(branch._synapses)
excitement.extend(branch.excitement)
for syn in branch._synapses:
if syn is None:
EPSPs.append(0)
else:
EPSPs.append(syn.state == SYN_CONNECTED_ACTIVE)
if branch.children:
branch = random.choice(branch.children)
else:
break
plt.plot(
np.arange(distance_to_root),
EPSPs,
'r',
np.arange(distance_to_root),
excitement,
'b',
)
plt.title(
"Dendrite Activation\n Horizontal line is activation threshold, Vertical lines are segment bifurcations"
)
plt.xlabel("Distance along Dendrite", )
plt.ylabel("EPSPs are Red, Excitement is Blue")
# Show lines where the excitement crosses thresholds.
plt.axhline(20, color='k') # Hard coded parameter value.
for offset in segment_offsets.values():
if offset != 0:
plt.axvline(offset, color='k')
print("\nShowing %d excitement plots." % len(sampled_cells))
plt.show()
self.age += 1
return (finalScore, rawScore)
def runHotgym():
timeOfDayEncoder = DateEncoder(timeOfDay=(21,1))
weekendEncoder = DateEncoder(weekend=21)
scalarEncoder = RandomDistributedScalarEncoder(0.88)
encodingWidth = timeOfDayEncoder.getWidth() \
+ weekendEncoder.getWidth() \
+ scalarEncoder.getWidth()
sp = SpatialPooler(
# How large the input encoding will be.
inputDimensions=(encodingWidth),
# How many mini-columns will be in the Spatial Pooler.
columnDimensions=(2048),
# What percent of the columns's receptive field is available for potential
# synapses?
potentialPct=0.85,
# This means that the input space has no topology.
globalInhibition=True,
localAreaDensity=-1.0,
# Roughly 2%, giving that there is only one inhibition area because we have
# turned on globalInhibition (40 / 2048 = 0.0195)
numActiveColumnsPerInhArea=40.0,
# How quickly synapses grow and degrade.
synPermInactiveDec=0.005,
synPermActiveInc=0.04,
synPermConnected=0.1,
# boostStrength controls the strength of boosting. Boosting encourages
# efficient usage of SP columns.
boostStrength=3.0,
# Random number generator seed.
seed=1956,
# Determines if inputs at the beginning and end of an input dimension should
# be considered neighbors when mapping columns to inputs.
wrapAround=False
)
tm = TemporalMemory(
# Must be the same dimensions as the SP
columnDimensions=(2048, ),
# How many cells in each mini-column.
cellsPerColumn=32,
# A segment is active if it has >= activationThreshold connected synapses
# that are active due to infActiveState
activationThreshold=16,
initialPermanence=0.21,
connectedPermanence=0.5,
# Minimum number of active synapses for a segment to be considered during
# search for the best-matching segments.
minThreshold=12,
# The max number of synapses added to a segment during learning
maxNewSynapseCount=20,
permanenceIncrement=0.1,
permanenceDecrement=0.1,
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=128,
maxSynapsesPerSegment=32,
seed=1960
)
classifier = SDRClassifierFactory.create()
with open (_INPUT_FILE_PATH) as fin:
reader = csv.reader(fin)
headers = reader.next()
reader.next()
reader.next()
for count, record in enumerate(reader):
# Convert data string into Python date object.
dateString = datetime.datetime.strptime(record[0], "%m/%d/%y %H:%M")
# Convert data value string into float.
consumption = float(record[1])
# To encode, we need to provide zero-filled numpy arrays for the encoders
# to populate.
timeOfDayBits = numpy.zeros(timeOfDayEncoder.getWidth())
weekendBits = numpy.zeros(weekendEncoder.getWidth())
consumptionBits = numpy.zeros(scalarEncoder.getWidth())
# Now we call the encoders create bit representations for each value.
timeOfDayEncoder.encodeIntoArray(dateString, timeOfDayBits)
weekendEncoder.encodeIntoArray(dateString, weekendBits)
scalarEncoder.encodeIntoArray(consumption, consumptionBits)
# Concatenate all these encodings into one large encoding for Spatial
# Pooling.
encoding = numpy.concatenate(
[timeOfDayBits, weekendBits, consumptionBits]
)
# Create an array to represent active columns, all initially zero. This
# will be populated by the compute method below. It must have the same
# dimensions as the Spatial Pooler.
activeColumns = numpy.zeros(2048)
# Execute Spatial Pooling algorithm over input space.
sp.compute(encoding, True, activeColumns)
activeColumnIndices = numpy.nonzero(activeColumns)[0]
# Execute Temporal Memory algorithm over active mini-columns.
tm.compute(activeColumnIndices, learn=True)
activeCells = tm.getActiveCells()
# Get the bucket info for this input value for classification.
bucketIdx = scalarEncoder.getBucketIndices(consumption)[0]
# Run classifier to translate active cells back to scalar value.
classifierResult = classifier.compute(
recordNum=count,
patternNZ=activeCells,
classification={
"bucketIdx": bucketIdx,
"actValue": consumption
},
learn=True,
infer=True
)
# Print the best prediction for 1 step out.
probability, value = sorted(
zip(classifierResult[1], classifierResult["actualValues"]),
reverse=True
)[0]
print("1-step: {:16} ({:4.4}%)".format(value, probability * 100))
def runHotgym(numRecords):
with open(_PARAMS_PATH, "r") as f:
modelParams = yaml.safe_load(f)["modelParams"]
enParams = modelParams["sensorParams"]["encoders"]
spParams = modelParams["spParams"]
tmParams = modelParams["tmParams"]
timeOfDayEncoder = DateEncoder(
timeOfDay=enParams["timestamp_timeOfDay"]["timeOfDay"])
weekendEncoder = DateEncoder(
weekend=enParams["timestamp_weekend"]["weekend"])
scalarEncoder = RandomDistributedScalarEncoder(
enParams["consumption"]["resolution"])
encodingWidth = (timeOfDayEncoder.getWidth()
+ weekendEncoder.getWidth()
+ scalarEncoder.getWidth())
sp = SpatialPooler(
# How large the input encoding will be.
inputDimensions=(encodingWidth),
# How many mini-columns will be in the Spatial Pooler.
columnDimensions=(spParams["columnCount"]),
# What percent of the columns"s receptive field is available for potential
# synapses?
potentialPct=spParams["potentialPct"],
# This means that the input space has no topology.
globalInhibition=spParams["globalInhibition"],
localAreaDensity=spParams["localAreaDensity"],
# Roughly 2%, giving that there is only one inhibition area because we have
# turned on globalInhibition (40 / 2048 = 0.0195)
numActiveColumnsPerInhArea=spParams["numActiveColumnsPerInhArea"],
# How quickly synapses grow and degrade.
synPermInactiveDec=spParams["synPermInactiveDec"],
synPermActiveInc=spParams["synPermActiveInc"],
synPermConnected=spParams["synPermConnected"],
# boostStrength controls the strength of boosting. Boosting encourages
# efficient usage of SP columns.
boostStrength=spParams["boostStrength"],
# Random number generator seed.
seed=spParams["seed"],
# TODO: is this useful?
# Determines if inputs at the beginning and end of an input dimension should
# be considered neighbors when mapping columns to inputs.
wrapAround=False
)
tm = TemporalMemory(
# Must be the same dimensions as the SP
columnDimensions=(tmParams["columnCount"],),
# How many cells in each mini-column.
cellsPerColumn=tmParams["cellsPerColumn"],
# A segment is active if it has >= activationThreshold connected synapses
# that are active due to infActiveState
activationThreshold=tmParams["activationThreshold"],
initialPermanence=tmParams["initialPerm"],
# TODO: This comes from the SP params, is this normal
connectedPermanence=spParams["synPermConnected"],
# Minimum number of active synapses for a segment to be considered during
# search for the best-matching segments.
minThreshold=tmParams["minThreshold"],
# The max number of synapses added to a segment during learning
maxNewSynapseCount=tmParams["newSynapseCount"],
permanenceIncrement=tmParams["permanenceInc"],
permanenceDecrement=tmParams["permanenceDec"],
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=tmParams["maxSegmentsPerCell"],
maxSynapsesPerSegment=tmParams["maxSynapsesPerSegment"],
seed=tmParams["seed"]
)
classifier = SDRClassifierFactory.create()
results = []
with open(_INPUT_FILE_PATH, "r") as fin:
reader = csv.reader(fin)
headers = reader.next()
reader.next()
reader.next()
for count, record in enumerate(reader):
if count >= numRecords: break
# Convert data string into Python date object.
dateString = datetime.datetime.strptime(record[0], "%m/%d/%y %H:%M")
# Convert data value string into float.
consumption = float(record[1])
# To encode, we need to provide zero-filled numpy arrays for the encoders
# to populate.
timeOfDayBits = numpy.zeros(timeOfDayEncoder.getWidth())
weekendBits = numpy.zeros(weekendEncoder.getWidth())
consumptionBits = numpy.zeros(scalarEncoder.getWidth())
# Now we call the encoders create bit representations for each value.
timeOfDayEncoder.encodeIntoArray(dateString, timeOfDayBits)
weekendEncoder.encodeIntoArray(dateString, weekendBits)
scalarEncoder.encodeIntoArray(consumption, consumptionBits)
# Concatenate all these encodings into one large encoding for Spatial
# Pooling.
encoding = numpy.concatenate(
[timeOfDayBits, weekendBits, consumptionBits]
)
# Create an array to represent active columns, all initially zero. This
# will be populated by the compute method below. It must have the same
# dimensions as the Spatial Pooler.
activeColumns = numpy.zeros(spParams["columnCount"])
# Execute Spatial Pooling algorithm over input space.
sp.compute(encoding, True, activeColumns)
activeColumnIndices = numpy.nonzero(activeColumns)[0]
# Execute Temporal Memory algorithm over active mini-columns.
tm.compute(activeColumnIndices, learn=True)
activeCells = tm.getActiveCells()
# Get the bucket info for this input value for classification.
bucketIdx = scalarEncoder.getBucketIndices(consumption)[0]
# Run classifier to translate active cells back to scalar value.
classifierResult = classifier.compute(
recordNum=count,
patternNZ=activeCells,
classification={
"bucketIdx": bucketIdx,
"actValue": consumption
},
learn=True,
infer=True
)
# Print the best prediction for 1 step out.
oneStepConfidence, oneStep = sorted(
zip(classifierResult[1], classifierResult["actualValues"]),
reverse=True
)[0]
print("1-step: {:16} ({:4.4}%)".format(oneStep, oneStepConfidence * 100))
results.append([oneStep, oneStepConfidence * 100, None, None])
return results
class NumentaTMLowLevelDetector(AnomalyDetector):
"""The 'numentaTM' detector, but not using the CLAModel or network API """
def __init__(self, *args, **kwargs):
super(NumentaTMLowLevelDetector, self).__init__(*args, **kwargs)
self.valueEncoder = None
self.encodedValue = None
self.timestampEncoder = None
self.encodedTimestamp = None
self.sp = None
self.spOutput = None
self.tm = None
self.anomalyLikelihood = None
# Set this to False if you want to get results based on raw scores
# without using AnomalyLikelihood. This will give worse results, but
# useful for checking the efficacy of AnomalyLikelihood. You will need
# to re-optimize the thresholds when running with this setting.
self.useLikelihood = True
def getAdditionalHeaders(self):
"""Returns a list of strings."""
return ["raw_score"]
def initialize(self):
# Initialize the RDSE with a resolution; calculated from the data min and
# max, the resolution is specific to the data stream.
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = (self.inputMax + rangePadding
if self.inputMin != self.inputMax
else self.inputMin + 1)
numBuckets = 130.0
resolution = max(0.001, (maxVal - minVal) / numBuckets)
self.valueEncoder = RandomDistributedScalarEncoder(resolution, seed=42)
self.encodedValue = np.zeros(self.valueEncoder.getWidth(),
dtype=np.uint32)
# Initialize the timestamp encoder
self.timestampEncoder = DateEncoder(timeOfDay=(21, 9.49, ))
self.encodedTimestamp = np.zeros(self.timestampEncoder.getWidth(),
dtype=np.uint32)
inputWidth = (self.timestampEncoder.getWidth() +
self.valueEncoder.getWidth())
self.sp = SpatialPooler(**{
"globalInhibition": True,
"columnDimensions": [2048],
"inputDimensions": [inputWidth],
"potentialRadius": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 0.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
})
self.spOutput = np.zeros(2048, dtype=np.float32)
self.tm = TemporalMemory(**{
"activationThreshold": 20,
"cellsPerColumn": 32,
"columnDimensions": (2048,),
"initialPermanence": 0.24,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 128,
"minThreshold": 13,
"maxNewSynapseCount": 31,
"permanenceDecrement": 0.008,
"permanenceIncrement": 0.04,
"seed": 1960,
})
if self.useLikelihood:
learningPeriod = math.floor(self.probationaryPeriod / 2.0)
self.anomalyLikelihood = anomaly_likelihood.AnomalyLikelihood(
claLearningPeriod=learningPeriod,
estimationSamples=self.probationaryPeriod - learningPeriod,
reestimationPeriod=100
)
def handleRecord(self, inputData):
"""Returns a tuple (anomalyScore, rawScore)."""
# Encode the input data record
self.valueEncoder.encodeIntoArray(
inputData["value"], self.encodedValue)
self.timestampEncoder.encodeIntoArray(
inputData["timestamp"], self.encodedTimestamp)
# Run the encoded data through the spatial pooler
self.sp.compute(np.concatenate((self.encodedTimestamp,
self.encodedValue,)),
True, self.spOutput)
# At the current state, the set of the region's active columns and the set
# of columns that have previously-predicted cells are used to calculate the
# raw anomaly score.
activeColumns = set(self.spOutput.nonzero()[0].tolist())
prevPredictedColumns = set(self.tm.columnForCell(cell)
for cell in self.tm.getPredictiveCells())
rawScore = (len(activeColumns - prevPredictedColumns) /
float(len(activeColumns)))
self.tm.compute(activeColumns)
if self.useLikelihood:
# Compute the log-likelihood score
anomalyScore = self.anomalyLikelihood.anomalyProbability(
inputData["value"], rawScore, inputData["timestamp"])
logScore = self.anomalyLikelihood.computeLogLikelihood(anomalyScore)
return (logScore, rawScore)
return (rawScore, rawScore)
class DistalTimestamps1CellPerColumnDetector(AnomalyDetector):
"""The 'numenta' detector, with the following changes:
- Use pure Temporal Memory, not the classic TP that uses backtracking.
- Don't spatial pool the timestamp. Pass it in as distal input.
- 1 cell per column.
- Use w=41 in the scalar encoding, rather than w=21, to make up for the
lost timestamp input to the spatial pooler.
"""
def __init__(self, *args, **kwargs):
super(DistalTimestamps1CellPerColumnDetector,
self).__init__(*args, **kwargs)
self.valueEncoder = None
self.encodedValue = None
self.timestampEncoder = None
self.encodedTimestamp = None
self.activeExternalCells = []
self.prevActiveExternalCells = []
self.sp = None
self.spOutput = None
self.etm = None
self.anomalyLikelihood = None
def getAdditionalHeaders(self):
"""Returns a list of strings."""
return ["raw_score"]
def initialize(self):
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = (self.inputMax + rangePadding
if self.inputMin != self.inputMax else self.inputMin + 1)
numBuckets = 130.0
resolution = max(0.001, (maxVal - minVal) / numBuckets)
self.valueEncoder = RandomDistributedScalarEncoder(resolution,
w=41,
seed=42)
self.encodedValue = np.zeros(self.valueEncoder.getWidth(),
dtype=np.uint32)
self.timestampEncoder = DateEncoder(timeOfDay=(
21,
9.49,
))
self.encodedTimestamp = np.zeros(self.timestampEncoder.getWidth(),
dtype=np.uint32)
inputWidth = self.valueEncoder.getWidth()
self.sp = SpatialPooler(
**{
"globalInhibition": True,
"columnDimensions": [2048],
"inputDimensions": [inputWidth],
"potentialRadius": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 0.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
})
self.spOutput = np.zeros(2048, dtype=np.float32)
self.etm = ExtendedTemporalMemory(
**{
"activationThreshold": 13,
"cellsPerColumn": 1,
"columnDimensions": (2048, ),
"basalInputDimensions": (self.timestampEncoder.getWidth(), ),
"initialPermanence": 0.21,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 32,
"minThreshold": 10,
"maxNewSynapseCount": 20,
"permanenceDecrement": 0.1,
"permanenceIncrement": 0.1,
"seed": 1960,
"checkInputs": False,
})
learningPeriod = math.floor(self.probationaryPeriod / 2.0)
self.anomalyLikelihood = anomaly_likelihood.AnomalyLikelihood(
claLearningPeriod=learningPeriod,
estimationSamples=self.probationaryPeriod - learningPeriod,
reestimationPeriod=100)
def handleRecord(self, inputData):
"""Returns a tuple (anomalyScore, rawScore)."""
self.valueEncoder.encodeIntoArray(inputData["value"],
self.encodedValue)
self.timestampEncoder.encodeIntoArray(inputData["timestamp"],
self.encodedTimestamp)
self.prevActiveExternalCells = self.activeExternalCells
self.activeExternalCells = self.encodedTimestamp.nonzero()[0]
self.sp.compute(self.encodedValue, True, self.spOutput)
activeColumns = self.spOutput.nonzero()[0]
activeColumnsSet = set(activeColumns.tolist())
prevPredictedColumns = set(
self.etm.columnForCell(cell)
for cell in self.etm.getPredictiveCells())
rawScore = (len(activeColumnsSet - prevPredictedColumns) /
float(len(activeColumns)))
anomalyScore = self.anomalyLikelihood.anomalyProbability(
inputData["value"], rawScore, inputData["timestamp"])
logScore = self.anomalyLikelihood.computeLogLikelihood(anomalyScore)
self.etm.compute(
activeColumns,
activeCellsExternalBasal=self.activeExternalCells,
reinforceCandidatesExternalBasal=self.prevActiveExternalCells,
growthCandidatesExternalBasal=self.prevActiveExternalCells)
return (logScore, rawScore)
class DistalTimestamps1CellPerColumnDetector(AnomalyDetector):
"""The 'numenta' detector, with the following changes:
- Use pure Temporal Memory, not the classic TP that uses backtracking.
- Don't spatial pool the timestamp. Pass it in as distal input.
- 1 cell per column.
- Use w=41 in the scalar encoding, rather than w=21, to make up for the
lost timestamp input to the spatial pooler.
"""
def __init__(self, *args, **kwargs):
super(DistalTimestamps1CellPerColumnDetector, self).__init__(*args,
**kwargs)
self.valueEncoder = None
self.encodedValue = None
self.timestampEncoder = None
self.encodedTimestamp = None
self.activeExternalCells = []
self.prevActiveExternalCells = []
self.sp = None
self.spOutput = None
self.etm = None
self.anomalyLikelihood = None
def getAdditionalHeaders(self):
"""Returns a list of strings."""
return ["raw_score"]
def initialize(self):
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = (self.inputMax + rangePadding
if self.inputMin != self.inputMax
else self.inputMin + 1)
numBuckets = 130.0
resolution = max(0.001, (maxVal - minVal) / numBuckets)
self.valueEncoder = RandomDistributedScalarEncoder(resolution,
w=41,
seed=42)
self.encodedValue = np.zeros(self.valueEncoder.getWidth(),
dtype=np.uint32)
self.timestampEncoder = DateEncoder(timeOfDay=(21,9.49,))
self.encodedTimestamp = np.zeros(self.timestampEncoder.getWidth(),
dtype=np.uint32)
inputWidth = self.valueEncoder.getWidth()
self.sp = SpatialPooler(**{
"globalInhibition": True,
"columnDimensions": [2048],
"inputDimensions": [inputWidth],
"potentialRadius": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 0.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
})
self.spOutput = np.zeros(2048, dtype=np.float32)
self.etm = ExtendedTemporalMemory(**{
"activationThreshold": 13,
"cellsPerColumn": 1,
"columnDimensions": (2048,),
"basalInputDimensions": (self.timestampEncoder.getWidth(),),
"initialPermanence": 0.21,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 32,
"minThreshold": 10,
"maxNewSynapseCount": 20,
"permanenceDecrement": 0.1,
"permanenceIncrement": 0.1,
"seed": 1960,
"checkInputs": False,
})
learningPeriod = math.floor(self.probationaryPeriod / 2.0)
self.anomalyLikelihood = anomaly_likelihood.AnomalyLikelihood(
claLearningPeriod=learningPeriod,
estimationSamples=self.probationaryPeriod - learningPeriod,
reestimationPeriod=100
)
def handleRecord(self, inputData):
"""Returns a tuple (anomalyScore, rawScore)."""
self.valueEncoder.encodeIntoArray(inputData["value"],
self.encodedValue)
self.timestampEncoder.encodeIntoArray(inputData["timestamp"],
self.encodedTimestamp)
self.prevActiveExternalCells = self.activeExternalCells
self.activeExternalCells = self.encodedTimestamp.nonzero()[0]
self.sp.compute(self.encodedValue, True, self.spOutput)
activeColumns = self.spOutput.nonzero()[0]
activeColumnsSet = set(activeColumns.tolist())
prevPredictedColumns = set(self.etm.columnForCell(cell)
for cell in self.etm.getPredictiveCells())
rawScore = (len(activeColumnsSet - prevPredictedColumns) /
float(len(activeColumns)))
anomalyScore = self.anomalyLikelihood.anomalyProbability(
inputData["value"], rawScore, inputData["timestamp"])
logScore = self.anomalyLikelihood.computeLogLikelihood(anomalyScore)
self.etm.compute(activeColumns,
activeCellsExternalBasal=self.activeExternalCells,
reinforceCandidatesExternalBasal=self.prevActiveExternalCells,
growthCandidatesExternalBasal=self.prevActiveExternalCells)
return (logScore, rawScore)
class BaseNetwork(object):
def __init__(self, inputMin=None, inputMax=None, runSanity=False):
self.inputMin = inputMin
self.inputMax = inputMax
self.runSanity = runSanity
self.encoder = None
self.encoderOutput = None
self.sp = None
self.spOutput = None
self.spOutputNZ = None
self.tm = None
self.anomalyScore = None
if runSanity:
self.sanity = None
self.defaultEncoderResolution = 0.0001
self.numColumns = 2048
self.cellsPerColumn = 32
self.predictedActiveCells = None
self.previouslyPredictiveCells = None
def initialize(self):
# Scalar Encoder
resolution = self.getEncoderResolution()
self.encoder = RandomDistributedScalarEncoder(resolution, seed=42)
self.encoderOutput = np.zeros(self.encoder.getWidth(), dtype=np.uint32)
# Spatial Pooler
spInputWidth = self.encoder.getWidth()
self.spParams = {
"globalInhibition": True,
"columnDimensions": [self.numColumns],
"inputDimensions": [spInputWidth],
"potentialRadius": spInputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 0.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
}
self.sp = SpatialPooler(**self.spParams)
self.spOutput = np.zeros(self.numColumns, dtype=np.uint32)
# Temporal Memory
self.tmParams = {
"activationThreshold": 20,
"cellsPerColumn": self.cellsPerColumn,
"columnDimensions": (self.numColumns,),
"initialPermanence": 0.24,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 128,
"minThreshold": 13,
"maxNewSynapseCount": 31,
"permanenceDecrement": 0.008,
"permanenceIncrement": 0.04,
"seed": 1960,
}
self.tm = TemporalMemory(**self.tmParams)
# Sanity
if self.runSanity:
self.sanity = sanity.SPTMInstance(self.sp, self.tm)
def handleRecord(self, scalarValue, label=None, skipEncoding=False,
learningMode=True):
"""Process one record."""
if self.runSanity:
self.sanity.waitForUserContinue()
# Encode the input data record if it hasn't already been encoded.
if not skipEncoding:
self.encodeValue(scalarValue)
# Run the encoded data through the spatial pooler
self.sp.compute(self.encoderOutput, learningMode, self.spOutput)
self.spOutputNZ = self.spOutput.nonzero()[0]
# WARNING: this needs to happen here, before the TM runs.
self.previouslyPredictiveCells = self.tm.getPredictiveCells()
# Run SP output through temporal memory
self.tm.compute(self.spOutputNZ)
self.predictedActiveCells = _computePredictedActiveCells(
self.tm.getActiveCells(), self.previouslyPredictiveCells)
# Anomaly score
self.anomalyScore = _computeAnomalyScore(self.spOutputNZ,
self.previouslyPredictiveCells,
self.cellsPerColumn)
# Run Sanity
if self.runSanity:
self.sanity.appendTimestep(self.getEncoderOutputNZ(),
self.getSpOutputNZ(),
self.previouslyPredictiveCells,
{
'value': scalarValue,
'label':label
})
def encodeValue(self, scalarValue):
self.encoder.encodeIntoArray(scalarValue, self.encoderOutput)
def getEncoderResolution(self):
"""
Compute the Random Distributed Scalar Encoder (RDSE) resolution. It's
calculated from the data min and max, specific to the data stream.
"""
if self.inputMin is None or self.inputMax is None:
return self.defaultEncoderResolution
else:
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = (self.inputMax + rangePadding
if self.inputMin != self.inputMax
else self.inputMin + 1)
numBuckets = 130.0
return max(self.defaultEncoderResolution, (maxVal - minVal) / numBuckets)
def getEncoderOutputNZ(self):
return self.encoderOutput.nonzero()[0]
def getSpOutputNZ(self):
return self.spOutputNZ
def getTmPredictiveCellsNZ(self):
return self.tm.getPredictiveCells()
def getTmActiveCellsNZ(self):
return self.tm.getActiveCells()
def getTmPredictedActiveCellsNZ(self):
return self.predictedActiveCells
def getRawAnomalyScore(self):
return self.anomalyScore
anom_score = np.zeros((N_DATA + 1, ))
anom_logscore = np.zeros((N_DATA + 1, ))
anomaly_score = Anomaly(slidingWindowSize=25)
anomaly_likelihood = AnomalyLikelihood(learningPeriod=500,
historicWindowSize=213)
dd = 0
for i, linha in enumerate(teste):
#####################################################
scalar_encoder.encodeIntoArray(linha[1], bits_scalar)
time_encoder.encodeIntoArray(linha[0], bits_time)
encoder_output = np.concatenate((bits_time, bits_scalar))
####################################################
sdr_output = np.zeros(N_COLUMNS)
sp.compute(encoder_output, True, sdr_output)
active_columns = np.nonzero(sdr_output)[0]
####################################################
tm.compute(active_columns, learn=True)
####################################################
def runHotgym(numRecords):
with open(_PARAMS_PATH, "r") as f:
modelParams = yaml.safe_load(f)["modelParams"]
enParams = modelParams["sensorParams"]["encoders"]
spParams = modelParams["spParams"]
tmParams = modelParams["tmParams"]
timeOfDayEncoder = DateEncoder(
timeOfDay=enParams["timestamp_timeOfDay"]["timeOfDay"])
weekendEncoder = DateEncoder(
weekend=enParams["timestamp_weekend"]["weekend"])
CtEncoder = RandomDistributedScalarEncoder(enParams["Ct"]["resolution"])
ZIP_10467Encoder = RandomDistributedScalarEncoder(
enParams["ZIP_10467"]["resolution"])
# ZIP_10462Encoder = RandomDistributedScalarEncoder(enParams["ZIP_10462"]["resolution"])
# ZIP_10475Encoder = RandomDistributedScalarEncoder(enParams["ZIP_10475"]["resolution"])
# ZIP_10466Encoder = RandomDistributedScalarEncoder(enParams["ZIP_10466"]["resolution"])
# ZIP_10469Encoder = RandomDistributedScalarEncoder(enParams["ZIP_10469"]["resolution"])
# DEPT_11Encoder = RandomDistributedScalarEncoder(enParams["DEPT_11"]["resolution"])
# DEPT_24Encoder = RandomDistributedScalarEncoder(enParams["DEPT_24"]["resolution"])
# DEPT_41Encoder = RandomDistributedScalarEncoder(enParams["DEPT_41"]["resolution"])
# DEPT_34Encoder = RandomDistributedScalarEncoder(enParams["DEPT_34"]["resolution"])
# DEPT_31Encoder = RandomDistributedScalarEncoder(enParams["DEPT_31"]["resolution"])
# DEPT_60Encoder = RandomDistributedScalarEncoder(enParams["DEPT_60"]["resolution"])
# AGE_0_9Encoder = RandomDistributedScalarEncoder(enParams["AGE_0_9"]["resolution"])
# AGE_10_19Encoder = RandomDistributedScalarEncoder(enParams["AGE_10_19"]["resolution"])
# AGE_20_29Encoder = RandomDistributedScalarEncoder(enParams["AGE_20_29"]["resolution"])
# AGE_30_39Encoder = RandomDistributedScalarEncoder(enParams["AGE_30_39"]["resolution"])
# AGE_40_49Encoder = RandomDistributedScalarEncoder(enParams["AGE_40_49"]["resolution"])
# AGE_50_59Encoder = RandomDistributedScalarEncoder(enParams["AGE_50_59"]["resolution"])
# AGE_60_69Encoder = RandomDistributedScalarEncoder(enParams["AGE_60_69"]["resolution"])
# AGE_70_79Encoder = RandomDistributedScalarEncoder(enParams["AGE_70_79"]["resolution"])
# AGE_80_89Encoder = RandomDistributedScalarEncoder(enParams["AGE_80_89"]["resolution"])
# AGE_90_99Encoder = RandomDistributedScalarEncoder(enParams["AGE_90_99"]["resolution"])
# DIST_1_7Encoder = RandomDistributedScalarEncoder(enParams["DIST_1_7"]["resolution"])
# DIST_8_14Encoder = RandomDistributedScalarEncoder(enParams["DIST_8_14"]["resolution"])
# DIST_15_21Encoder = RandomDistributedScalarEncoder(enParams["DIST_15_21"]["resolution"])
# DIST_22_28Encoder = RandomDistributedScalarEncoder(enParams["DIST_22_28"]["resolution"])
# DIST_29_35Encoder = RandomDistributedScalarEncoder(enParams["DIST_29_35"]["resolution"])
# DIST_36_42Encoder = RandomDistributedScalarEncoder(enParams["DIST_36_42"]["resolution"])
# DIST_43_49Encoder = RandomDistributedScalarEncoder(enParams["DIST_43_49"]["resolution"])
# DIST_50_56Encoder = RandomDistributedScalarEncoder(enParams["DIST_50_56"]["resolution"])
# DIST_57_63Encoder = RandomDistributedScalarEncoder(enParams["DIST_57_63"]["resolution"])
# DIST_64_70Encoder = RandomDistributedScalarEncoder(enParams["DIST_64_70"]["resolution"])
encodingWidth = (timeOfDayEncoder.getWidth() + weekendEncoder.getWidth() +
CtEncoder.getWidth() * 2)
sp = SpatialPooler(
inputDimensions=(encodingWidth, ),
columnDimensions=(spParams["columnCount"], ),
potentialPct=spParams["potentialPct"],
potentialRadius=encodingWidth,
globalInhibition=spParams["globalInhibition"],
localAreaDensity=spParams["localAreaDensity"],
numActiveColumnsPerInhArea=spParams["numActiveColumnsPerInhArea"],
synPermInactiveDec=spParams["synPermInactiveDec"],
synPermActiveInc=spParams["synPermActiveInc"],
synPermConnected=spParams["synPermConnected"],
boostStrength=spParams["boostStrength"],
seed=spParams["seed"],
wrapAround=True)
tm = TemporalMemory(
columnDimensions=(tmParams["columnCount"], ),
cellsPerColumn=tmParams["cellsPerColumn"],
activationThreshold=tmParams["activationThreshold"],
initialPermanence=tmParams["initialPerm"],
connectedPermanence=spParams["synPermConnected"],
minThreshold=tmParams["minThreshold"],
maxNewSynapseCount=tmParams["newSynapseCount"],
permanenceIncrement=tmParams["permanenceInc"],
permanenceDecrement=tmParams["permanenceDec"],
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=tmParams["maxSegmentsPerCell"],
maxSynapsesPerSegment=tmParams["maxSynapsesPerSegment"],
seed=tmParams["seed"])
classifier = SDRClassifierFactory.create()
results = []
with open(_INPUT_FILE_PATH, "r") as fin:
reader = csv.reader(fin)
headers = reader.next()
reader.next()
reader.next()
output = output_anomaly_generic_v1.NuPICFileOutput(_FILE_NAME)
for count, record in enumerate(reader):
if count >= numRecords: break
# Convert data string into Python date object.
dateString = datetime.datetime.strptime(record[0],
"%Y-%m-%d %H:%M:%S")
# Convert data value string into float.
Ct = float(record[1])
ZIP_10467 = float(record[2])
# ZIP_10462 = float(record[3])
# ZIP_10475 = float(record[4])
# ZIP_10466 = float(record[5])
# ZIP_10469 = float(record[6])
# DEPT_11 = float(record[7])
# DEPT_24 = float(record[8])
# DEPT_41 = float(record[9])
# DEPT_34 = float(record[10])
# DEPT_31 = float(record[11])
# DEPT_60 = float(record[12])
# AGE_0_9 = float(record[13])
# AGE_10_19 = float(record[14])
# AGE_20_29 = float(record[15])
# AGE_30_39 = float(record[16])
# AGE_40_49 = float(record[17])
# AGE_50_59 = float(record[18])
# AGE_60_69 = float(record[19])
# AGE_70_79 = float(record[20])
# AGE_80_89 = float(record[21])
# AGE_90_99 = float(record[22])
# DIST_1_7 = float(record[23])
# DIST_8_14 = float(record[24])
# DIST_15_21 = float(record[25])
# DIST_22_28 = float(record[26])
# DIST_29_35 = float(record[27])
# DIST_36_42 = float(record[28])
# DIST_43_49 = float(record[29])
# DIST_50_56 = float(record[30])
# DIST_57_63 = float(record[31])
# DIST_64_70 = float(record[31])
# To encode, we need to provide zero-filled numpy arrays for the encoders
# to populate.
timeOfDayBits = numpy.zeros(timeOfDayEncoder.getWidth())
weekendBits = numpy.zeros(weekendEncoder.getWidth())
CtBits = numpy.zeros(CtEncoder.getWidth())
ZIP_10467Bits = numpy.zeros(ZIP_10467Encoder.getWidth())
# ZIP_10462Bits = numpy.zeros(ZIP_10462Encoder.getWidth())
# ZIP_10475Bits = numpy.zeros(ZIP_10475Encoder.getWidth())
# ZIP_10466Bits = numpy.zeros(ZIP_10466Encoder.getWidth())
# ZIP_10469Bits = numpy.zeros(ZIP_10469Encoder.getWidth())
# DEPT_11Bits = numpy.zeros(DEPT_11Encoder.getWidth())
# DEPT_24Bits = numpy.zeros(DEPT_24Encoder.getWidth())
# DEPT_41Bits = numpy.zeros(DEPT_41Encoder.getWidth())
# DEPT_34Bits = numpy.zeros(DEPT_34Encoder.getWidth())
# DEPT_31Bits = numpy.zeros(DEPT_31Encoder.getWidth())
# DEPT_60Bits = numpy.zeros(DEPT_60Encoder.getWidth())
# AGE_0_9Bits = numpy.zeros(AGE_0_9Encoder.getWidth())
# AGE_10_19Bits = numpy.zeros(AGE_10_19Encoder.getWidth())
# AGE_20_29Bits = numpy.zeros(AGE_20_29Encoder.getWidth())
# AGE_30_39Bits = numpy.zeros(AGE_30_39Encoder.getWidth())
# AGE_40_49Bits = numpy.zeros(AGE_40_49Encoder.getWidth())
# AGE_50_59Bits = numpy.zeros(AGE_50_59Encoder.getWidth())
# AGE_60_69Bits = numpy.zeros(AGE_60_69Encoder.getWidth())
# AGE_70_79Bits = numpy.zeros(AGE_70_79Encoder.getWidth())
# AGE_80_89Bits = numpy.zeros(AGE_80_89Encoder.getWidth())
# AGE_90_99Bits = numpy.zeros(AGE_90_99Encoder.getWidth())
# DIST_1_7Bits = numpy.zeros(DIST_1_7Encoder.getWidth())
# DIST_8_14Bits = numpy.zeros(DIST_8_14Encoder.getWidth())
# DIST_15_21Bits = numpy.zeros(DIST_15_21Encoder.getWidth())
# DIST_22_28Bits = numpy.zeros(DIST_22_28Encoder.getWidth())
# DIST_29_35Bits = numpy.zeros(DIST_29_35Encoder.getWidth())
# DIST_36_42Bits = numpy.zeros(DIST_36_42Encoder.getWidth())
# DIST_43_49Bits = numpy.zeros(DIST_43_49Encoder.getWidth())
# DIST_50_56Bits = numpy.zeros(DIST_50_56Encoder.getWidth())
# DIST_57_63Bits = numpy.zeros(DIST_57_63Encoder.getWidth())
# DIST_64_70Bits = numpy.zeros(DIST_64_70Encoder.getWidth())
# Now we call the encoders to create bit representations for each value.
timeOfDayEncoder.encodeIntoArray(dateString, timeOfDayBits)
weekendEncoder.encodeIntoArray(dateString, weekendBits)
CtEncoder.encodeIntoArray(Ct, CtBits)
ZIP_10467Encoder.encodeIntoArray(ZIP_10467, ZIP_10467Bits)
# ZIP_10462Encoder.encodeIntoArray(ZIP_10462, ZIP_10462Bits)
# ZIP_10475Encoder.encodeIntoArray(ZIP_10475, ZIP_10475Bits)
# ZIP_10466Encoder.encodeIntoArray(ZIP_10466, ZIP_10466Bits)
# ZIP_10469Encoder.encodeIntoArray(ZIP_10469, ZIP_10469Bits)
# DEPT_11Encoder.encodeIntoArray(DEPT_11, DEPT_11Bits)
# DEPT_24Encoder.encodeIntoArray(DEPT_24, DEPT_24Bits)
# DEPT_41Encoder.encodeIntoArray(DEPT_41, DEPT_41Bits)
# DEPT_34Encoder.encodeIntoArray(DEPT_34, DEPT_34Bits)
# DEPT_31Encoder.encodeIntoArray(DEPT_31, DEPT_31Bits)
# DEPT_60Encoder.encodeIntoArray(DEPT_60, DEPT_60Bits)
# AGE_0_9Encoder.encodeIntoArray(AGE_0_9, AGE_0_9Bits)
# AGE_10_19Encoder.encodeIntoArray(AGE_10_19, AGE_10_19Bits)
# AGE_20_29Encoder.encodeIntoArray(AGE_20_29, AGE_20_29Bits)
# AGE_30_39Encoder.encodeIntoArray(AGE_30_39, AGE_30_39Bits)
# AGE_40_49Encoder.encodeIntoArray(AGE_40_49, AGE_40_49Bits)
# AGE_50_59Encoder.encodeIntoArray(AGE_50_59, AGE_50_59Bits)
# AGE_60_69Encoder.encodeIntoArray(AGE_60_69, AGE_60_69Bits)
# AGE_70_79Encoder.encodeIntoArray(AGE_70_79, AGE_70_79Bits)
# AGE_80_89Encoder.encodeIntoArray(AGE_80_89, AGE_80_89Bits)
# AGE_90_99Encoder.encodeIntoArray(AGE_90_99, AGE_90_99Bits)
# DIST_1_7Encoder.encodeIntoArray(DIST_1_7, DIST_1_7Bits)
# DIST_8_14Encoder.encodeIntoArray(DIST_8_14, DIST_8_14Bits)
# DIST_15_21Encoder.encodeIntoArray(DIST_15_21, DIST_15_21Bits)
# DIST_22_28Encoder.encodeIntoArray(DIST_22_28, DIST_22_28Bits)
# DIST_29_35Encoder.encodeIntoArray(DIST_29_35, DIST_29_35Bits)
# DIST_36_42Encoder.encodeIntoArray(DIST_36_42, DIST_36_42Bits)
# DIST_43_49Encoder.encodeIntoArray(DIST_43_49, DIST_43_49Bits)
# DIST_50_56Encoder.encodeIntoArray(DIST_50_56, DIST_50_56Bits)
# DIST_57_63Encoder.encodeIntoArray(DIST_57_63, DIST_57_63Bits)
# DIST_64_70Encoder.encodeIntoArray(DIST_64_70, DIST_64_70Bits)
# Concatenate all these encodings into one large encoding for Spatial
# Pooling.
encoding = numpy.concatenate(
[timeOfDayBits, weekendBits, CtBits, ZIP_10467Bits])
# encoding = numpy.concatenate(
# [timeOfDayBits, weekendBits, CtBits,
# ZIP_10467Bits, ZIP_10462Bits, ZIP_10475Bits, ZIP_10466Bits, ZIP_10469Bits,
# DEPT_11Bits, DEPT_24Bits, DEPT_41Bits, DEPT_34Bits, DEPT_31Bits,
# DEPT_60Bits, AGE_0_9Bits, AGE_10_19Bits, AGE_20_29Bits, AGE_30_39Bits,
# AGE_40_49Bits, AGE_50_59Bits, AGE_60_69Bits, AGE_70_79Bits, AGE_80_89Bits,
# AGE_90_99Bits, DIST_1_7Bits, DIST_8_14Bits, DIST_15_21Bits, DIST_22_28Bits,
# DIST_29_35Bits, DIST_36_42Bits, DIST_43_49Bits, DIST_50_56Bits, DIST_57_63Bits,
# DIST_64_70Bits])
# Create an array to represent active columns, all initially zero. This
# will be populated by the compute method below. It must have the same
# dimensions as the Spatial Pooler.
activeColumns = numpy.zeros(spParams["columnCount"])
# Execute Spatial Pooling algorithm over input space.
sp.compute(encoding, True, activeColumns)
activeColumnIndices = numpy.nonzero(activeColumns)[0]
# Execute Temporal Memory algorithm over active mini-columns.
tm.compute(activeColumnIndices, learn=True)
activeCells = tm.getActiveCells()
# Get the bucket info for this input value for classification.
bucketIdx = CtEncoder.getBucketIndices(Ct)[0]
# Run classifier to translate active cells back to scalar value.
classifierResult = classifier.compute(recordNum=count,
patternNZ=activeCells,
classification={
"bucketIdx": bucketIdx,
"actValue": Ct
},
learn=True,
infer=True)
# Print the best prediction for 1 step out.
oneStepConfidence, oneStep = sorted(zip(
classifierResult[1], classifierResult["actualValues"]),
reverse=True)[0]
# print("1-step: {:16} ({:4.4}%)".format(oneStep, oneStepConfidence * 100))
# results.append([oneStep, oneStepConfidence * 100, None, None])
results.append([record[0], Ct, oneStep, oneStepConfidence * 100])
output.write(record[0], Ct, oneStep, oneStepConfidence * 100)
output.close()
return results
class BaseNetwork(object):
def __init__(self, inputMin=None, inputMax=None, runSanity=False):
self.inputMin = inputMin
self.inputMax = inputMax
self.runSanity = runSanity
self.encoder = None
self.encoderOutput = None
self.sp = None
self.spOutput = None
self.spOutputNZ = None
self.tm = None
self.anomalyScore = None
if runSanity:
self.sanity = None
self.defaultEncoderResolution = 0.0001
self.numColumns = 2048
self.cellsPerColumn = 32
self.predictedActiveCells = None
self.previouslyPredictiveCells = None
def initialize(self):
# Scalar Encoder
resolution = self.getEncoderResolution()
self.encoder = RandomDistributedScalarEncoder(resolution, seed=42)
self.encoderOutput = np.zeros(self.encoder.getWidth(), dtype=np.uint32)
# Spatial Pooler
spInputWidth = self.encoder.getWidth()
self.spParams = {
"globalInhibition": True,
"columnDimensions": [self.numColumns],
"inputDimensions": [spInputWidth],
"potentialRadius": spInputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 5.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
}
self.sp = SpatialPooler(**self.spParams)
self.spOutput = np.zeros(self.numColumns, dtype=np.uint32)
# Temporal Memory
self.tmParams = {
"activationThreshold": 20,
"cellsPerColumn": self.cellsPerColumn,
"columnDimensions": (self.numColumns,),
"initialPermanence": 0.24,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 128,
"minThreshold": 13,
"maxNewSynapseCount": 31,
"permanenceDecrement": 0.008,
"permanenceIncrement": 0.04,
"seed": 1960,
}
self.tm = TemporalMemory(**self.tmParams)
# Sanity
if self.runSanity:
self.sanity = sanity.SPTMInstance(self.sp, self.tm)
def handleRecord(self, scalarValue, label=None, skipEncoding=False,
learningMode=True):
"""Process one record."""
if self.runSanity:
self.sanity.waitForUserContinue()
# Encode the input data record if it hasn't already been encoded.
if not skipEncoding:
self.encodeValue(scalarValue)
# Run the encoded data through the spatial pooler
self.sp.compute(self.encoderOutput, learningMode, self.spOutput)
self.spOutputNZ = self.spOutput.nonzero()[0]
# WARNING: this needs to happen here, before the TM runs.
self.previouslyPredictiveCells = self.tm.getPredictiveCells()
# Run SP output through temporal memory
self.tm.compute(self.spOutputNZ)
self.predictedActiveCells = _computePredictedActiveCells(
self.tm.getActiveCells(), self.previouslyPredictiveCells)
# Anomaly score
self.anomalyScore = _computeAnomalyScore(self.spOutputNZ,
self.previouslyPredictiveCells,
self.cellsPerColumn)
# Run Sanity
if self.runSanity:
self.sanity.appendTimestep(self.getEncoderOutputNZ(),
self.getSpOutputNZ(),
self.previouslyPredictiveCells,
{
'value': scalarValue,
'label':label
})
def encodeValue(self, scalarValue):
self.encoder.encodeIntoArray(scalarValue, self.encoderOutput)
def getEncoderResolution(self):
"""
Compute the Random Distributed Scalar Encoder (RDSE) resolution. It's
calculated from the data min and max, specific to the data stream.
"""
if self.inputMin is None or self.inputMax is None:
return self.defaultEncoderResolution
else:
rangePadding = abs(self.inputMax - self.inputMin) * 0.2
minVal = self.inputMin - rangePadding
maxVal = (self.inputMax + rangePadding
if self.inputMin != self.inputMax
else self.inputMin + 1)
numBuckets = 130.0
return max(self.defaultEncoderResolution, (maxVal - minVal) / numBuckets)
def getEncoderOutputNZ(self):
return self.encoderOutput.nonzero()[0]
def getSpOutputNZ(self):
return self.spOutputNZ
def getTmPredictiveCellsNZ(self):
return self.tm.getPredictiveCells()
def getTmActiveCellsNZ(self):
return self.tm.getActiveCells()
def getTmPredictedActiveCellsNZ(self):
return self.predictedActiveCells
def getRawAnomalyScore(self):
return self.anomalyScore
def go():
valueEncoder = RandomDistributedScalarEncoder(resolution=0.88, seed=42)
timestampEncoder = DateEncoder(timeOfDay=(
21,
9.49,
))
inputWidth = timestampEncoder.getWidth() + valueEncoder.getWidth()
sp = SpatialPooler(
**{
"globalInhibition": True,
"columnDimensions": [2048],
"inputDimensions": [inputWidth],
"potentialRadius": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"boostStrength": 0.0,
"synPermActiveInc": 0.003,
"synPermConnected": 0.2,
"synPermInactiveDec": 0.0005,
})
tm = TemporalMemory(
**{
"activationThreshold": 20,
"cellsPerColumn": 32,
"columnDimensions": (2048, ),
"initialPermanence": 0.24,
"maxSegmentsPerCell": 128,
"maxSynapsesPerSegment": 128,
"minThreshold": 13,
"maxNewSynapseCount": 31,
"permanenceDecrement": 0.008,
"permanenceIncrement": 0.04,
"seed": 1961,
})
inputPath = os.path.join(os.path.dirname(__file__),
"data/rec-center-hourly.csv")
inputFile = open(inputPath, "rb")
csvReader = csv.reader(inputFile)
csvReader.next()
csvReader.next()
csvReader.next()
encodedValue = np.zeros(valueEncoder.getWidth(), dtype=np.uint32)
encodedTimestamp = np.zeros(timestampEncoder.getWidth(), dtype=np.uint32)
spOutput = np.zeros(2048, dtype=np.float32)
sanityInstance = sanity.SPTMInstance(sp, tm)
for timestampStr, consumptionStr in csvReader:
sanityInstance.waitForUserContinue()
timestamp = datetime.datetime.strptime(timestampStr, "%m/%d/%y %H:%M")
consumption = float(consumptionStr)
timestampEncoder.encodeIntoArray(timestamp, encodedTimestamp)
valueEncoder.encodeIntoArray(consumption, encodedValue)
sensoryInput = np.concatenate((
encodedTimestamp,
encodedValue,
))
sp.compute(sensoryInput, True, spOutput)
activeColumns = np.flatnonzero(spOutput)
predictedCells = tm.getPredictiveCells()
tm.compute(activeColumns)
activeInputBits = np.flatnonzero(sensoryInput)
displayText = {
"timestamp": timestampStr,
"consumption": consumptionStr
}
sanityInstance.appendTimestep(activeInputBits, activeColumns,
predictedCells, displayText)
def runHotgym(numRecords):
with open(_PARAMS_PATH, "r") as f:
modelParams = yaml.safe_load(f)["modelParams"]
enParams = modelParams["sensorParams"]["encoders"]
spParams = modelParams["spParams"]
tmParams = modelParams["tmParams"]
timeOfDayEncoder = DateEncoder(
timeOfDay=enParams["timestamp_timeOfDay"]["timeOfDay"])
weekendEncoder = DateEncoder(
weekend=enParams["timestamp_weekend"]["weekend"])
scalarEncoder = RandomDistributedScalarEncoder(
enParams["consumption"]["resolution"])
encodingWidth = (timeOfDayEncoder.getWidth()
+ weekendEncoder.getWidth()
+ scalarEncoder.getWidth())
sp = SpatialPooler(
inputDimensions=(encodingWidth,),
columnDimensions=(spParams["columnCount"],),
potentialPct=spParams["potentialPct"],
potentialRadius=encodingWidth,
globalInhibition=spParams["globalInhibition"],
localAreaDensity=spParams["localAreaDensity"],
numActiveColumnsPerInhArea=spParams["numActiveColumnsPerInhArea"],
synPermInactiveDec=spParams["synPermInactiveDec"],
synPermActiveInc=spParams["synPermActiveInc"],
synPermConnected=spParams["synPermConnected"],
boostStrength=spParams["boostStrength"],
seed=spParams["seed"],
wrapAround=True
)
tm = TemporalMemory(
columnDimensions=(tmParams["columnCount"],),
cellsPerColumn=tmParams["cellsPerColumn"],
activationThreshold=tmParams["activationThreshold"],
initialPermanence=tmParams["initialPerm"],
connectedPermanence=spParams["synPermConnected"],
minThreshold=tmParams["minThreshold"],
maxNewSynapseCount=tmParams["newSynapseCount"],
permanenceIncrement=tmParams["permanenceInc"],
permanenceDecrement=tmParams["permanenceDec"],
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=tmParams["maxSegmentsPerCell"],
maxSynapsesPerSegment=tmParams["maxSynapsesPerSegment"],
seed=tmParams["seed"]
)
classifier = SDRClassifierFactory.create()
results = []
with open(_INPUT_FILE_PATH, "r") as fin:
reader = csv.reader(fin)
headers = reader.next()
reader.next()
reader.next()
for count, record in enumerate(reader):
if count >= numRecords: break
# Convert data string into Python date object.
dateString = datetime.datetime.strptime(record[0], "%m/%d/%y %H:%M")
# Convert data value string into float.
consumption = float(record[1])
# To encode, we need to provide zero-filled numpy arrays for the encoders
# to populate.
timeOfDayBits = numpy.zeros(timeOfDayEncoder.getWidth())
weekendBits = numpy.zeros(weekendEncoder.getWidth())
consumptionBits = numpy.zeros(scalarEncoder.getWidth())
# Now we call the encoders to create bit representations for each value.
timeOfDayEncoder.encodeIntoArray(dateString, timeOfDayBits)
weekendEncoder.encodeIntoArray(dateString, weekendBits)
scalarEncoder.encodeIntoArray(consumption, consumptionBits)
# Concatenate all these encodings into one large encoding for Spatial
# Pooling.
encoding = numpy.concatenate(
[timeOfDayBits, weekendBits, consumptionBits]
)
# Create an array to represent active columns, all initially zero. This
# will be populated by the compute method below. It must have the same
# dimensions as the Spatial Pooler.
activeColumns = numpy.zeros(spParams["columnCount"])
# Execute Spatial Pooling algorithm over input space.
sp.compute(encoding, True, activeColumns)
activeColumnIndices = numpy.nonzero(activeColumns)[0]
# Execute Temporal Memory algorithm over active mini-columns.
tm.compute(activeColumnIndices, learn=True)
activeCells = tm.getActiveCells()
# Get the bucket info for this input value for classification.
bucketIdx = scalarEncoder.getBucketIndices(consumption)[0]
# Run classifier to translate active cells back to scalar value.
classifierResult = classifier.compute(
recordNum=count,
patternNZ=activeCells,
classification={
"bucketIdx": bucketIdx,
"actValue": consumption
},
learn=True,
infer=True
)
# Print the best prediction for 1 step out.
oneStepConfidence, oneStep = sorted(
zip(classifierResult[1], classifierResult["actualValues"]),
reverse=True
)[0]
print("1-step: {:16} ({:4.4}%)".format(oneStep, oneStepConfidence * 100))
results.append([oneStep, oneStepConfidence * 100, None, None])
return results
def runHotgym(numRecords):
with open(_PARAMS_PATH, "r") as f:
modelParams = yaml.safe_load(f)["modelParams"]
enParams = modelParams["sensorParams"]["encoders"]
spParams = modelParams["spParams"]
tmParams = modelParams["tmParams"]
scalarEncoder = RandomDistributedScalarEncoder(
enParams["consumption"]["resolution"])
scalarEncoder2 = RandomDistributedScalarEncoder(
enParams["consumption2"]["resolution"])
encodingWidth = (scalarEncoder.getWidth() + scalarEncoder2.getWidth())
sp = SpatialPooler(
inputDimensions=(encodingWidth, ),
columnDimensions=(spParams["columnCount"], ),
potentialPct=spParams["potentialPct"],
potentialRadius=encodingWidth,
globalInhibition=spParams["globalInhibition"],
localAreaDensity=spParams["localAreaDensity"],
numActiveColumnsPerInhArea=spParams["numActiveColumnsPerInhArea"],
synPermInactiveDec=spParams["synPermInactiveDec"],
synPermActiveInc=spParams["synPermActiveInc"],
synPermConnected=spParams["synPermConnected"],
boostStrength=spParams["boostStrength"],
seed=spParams["seed"],
wrapAround=True)
tm = TemporalMemory(
columnDimensions=(tmParams["columnCount"], ),
cellsPerColumn=tmParams["cellsPerColumn"],
activationThreshold=tmParams["activationThreshold"],
initialPermanence=tmParams["initialPerm"],
connectedPermanence=spParams["synPermConnected"],
minThreshold=tmParams["minThreshold"],
maxNewSynapseCount=tmParams["newSynapseCount"],
permanenceIncrement=tmParams["permanenceInc"],
permanenceDecrement=tmParams["permanenceDec"],
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=tmParams["maxSegmentsPerCell"],
maxSynapsesPerSegment=tmParams["maxSynapsesPerSegment"],
seed=tmParams["seed"])
classifier = SDRClassifierFactory.create()
results = []
with open(_INPUT_FILE_PATH, "r") as fin:
reader = csv.reader(fin)
headers = reader.next()
reader.next()
reader.next()
output = output_anomaly_generic_v1.NuPICFileOutput(_FILE_NAME)
for count, record in enumerate(reader):
if count >= numRecords: break
# Convert data string into Python date object.
# dateString = datetime.datetime.strptime(record[0], "%m/%d/%y %H:%M")
# Convert data value string into float.
prediction = float(record[1])
prediction2 = float(record[2])
# To encode, we need to provide zero-filled numpy arrays for the encoders
# to populate.
consumptionBits = numpy.zeros(scalarEncoder.getWidth())
consumptionBits2 = numpy.zeros(scalarEncoder2.getWidth())
# Now we call the encoders to create bit representations for each value.
scalarEncoder.encodeIntoArray(prediction, consumptionBits)
scalarEncoder2.encodeIntoArray(prediction2, consumptionBits2)
# Concatenate all these encodings into one large encoding for Spatial
# Pooling.
encoding = numpy.concatenate([consumptionBits, consumptionBits2])
# Create an array to represent active columns, all initially zero. This
# will be populated by the compute method below. It must have the same
# dimensions as the Spatial Pooler.
activeColumns = numpy.zeros(spParams["columnCount"])
# Execute Spatial Pooling algorithm over input space.
sp.compute(encoding, True, activeColumns)
activeColumnIndices = numpy.nonzero(activeColumns)[0]
# Execute Temporal Memory algorithm over active mini-columns.
tm.compute(activeColumnIndices, learn=True)
activeCells = tm.getActiveCells()
# Get the bucket info for this input value for classification.
bucketIdx = scalarEncoder.getBucketIndices(prediction)[0]
# Run classifier to translate active cells back to scalar value.
classifierResult = classifier.compute(recordNum=count,
patternNZ=activeCells,
classification={
"bucketIdx": bucketIdx,
"actValue": prediction
},
learn=True,
infer=True)
# Print the best prediction for 1 step out.
oneStepConfidence, oneStep = sorted(zip(
classifierResult[1], classifierResult["actualValues"]),
reverse=True)[0]
# print("1-step: {:16} ({:4.4}%)".format(oneStep, oneStepConfidence * 100))
# results.append([oneStep, oneStepConfidence * 100, None, None])
results.append(
[record[0], prediction, oneStep, oneStepConfidence * 100])
output.write(record[0], prediction, oneStep,
oneStepConfidence * 100)
output.close()
return results
def runHotgym(numRecords):
with open(_PARAMS_PATH, "r") as f:
modelParams = yaml.safe_load(f)["modelParams"]
enParams = modelParams["sensorParams"]["encoders"]
spParams = modelParams["spParams"]
tmParams = modelParams["tmParams"]
timeOfDayEncoder = DateEncoder(
timeOfDay=enParams["timestamp_timeOfDay"]["timeOfDay"])
weekendEncoder = DateEncoder(
weekend=enParams["timestamp_weekend"]["weekend"])
scalarEncoder = RandomDistributedScalarEncoder(
enParams["consumption"]["resolution"])
encodingWidth = (timeOfDayEncoder.getWidth()
+ weekendEncoder.getWidth()
+ scalarEncoder.getWidth())
sp = SpatialPooler(
# How large the input encoding will be.
inputDimensions=(encodingWidth),
# How many mini-columns will be in the Spatial Pooler.
columnDimensions=(spParams["columnCount"]),
# What percent of the columns"s receptive field is available for potential
# synapses?
potentialPct=spParams["potentialPct"],
# This means that the input space has no topology.
globalInhibition=spParams["globalInhibition"],
localAreaDensity=spParams["localAreaDensity"],
# Roughly 2%, giving that there is only one inhibition area because we have
# turned on globalInhibition (40 / 2048 = 0.0195)
numActiveColumnsPerInhArea=spParams["numActiveColumnsPerInhArea"],
# How quickly synapses grow and degrade.
synPermInactiveDec=spParams["synPermInactiveDec"],
synPermActiveInc=spParams["synPermActiveInc"],
synPermConnected=spParams["synPermConnected"],
# boostStrength controls the strength of boosting. Boosting encourages
# efficient usage of SP columns.
boostStrength=spParams["boostStrength"],
# Random number generator seed.
seed=spParams["seed"],
# TODO: is this useful?
# Determines if inputs at the beginning and end of an input dimension should
# be considered neighbors when mapping columns to inputs.
wrapAround=False
)
tm = TemporalMemory(
# Must be the same dimensions as the SP
columnDimensions=(tmParams["columnCount"],),
# How many cells in each mini-column.
cellsPerColumn=tmParams["cellsPerColumn"],
# A segment is active if it has >= activationThreshold connected synapses
# that are active due to infActiveState
activationThreshold=tmParams["activationThreshold"],
initialPermanence=tmParams["initialPerm"],
# TODO: This comes from the SP params, is this normal
connectedPermanence=spParams["synPermConnected"],
# Minimum number of active synapses for a segment to be considered during
# search for the best-matching segments.
minThreshold=tmParams["minThreshold"],
# The max number of synapses added to a segment during learning
maxNewSynapseCount=tmParams["newSynapseCount"],
permanenceIncrement=tmParams["permanenceInc"],
permanenceDecrement=tmParams["permanenceDec"],
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=tmParams["maxSegmentsPerCell"],
maxSynapsesPerSegment=tmParams["maxSynapsesPerSegment"],
seed=tmParams["seed"]
)
classifier = SDRClassifierFactory.create()
results = []
with open(_INPUT_FILE_PATH, "r") as fin:
reader = csv.reader(fin)
headers = reader.next()
reader.next()
reader.next()
for count, record in enumerate(reader):
if count >= numRecords: break
# Convert data string into Python date object.
dateString = datetime.datetime.strptime(record[0], "%m/%d/%y %H:%M")
# Convert data value string into float.
consumption = float(record[1])
# To encode, we need to provide zero-filled numpy arrays for the encoders
# to populate.
timeOfDayBits = numpy.zeros(timeOfDayEncoder.getWidth())
weekendBits = numpy.zeros(weekendEncoder.getWidth())
consumptionBits = numpy.zeros(scalarEncoder.getWidth())
# Now we call the encoders create bit representations for each value.
timeOfDayEncoder.encodeIntoArray(dateString, timeOfDayBits)
weekendEncoder.encodeIntoArray(dateString, weekendBits)
scalarEncoder.encodeIntoArray(consumption, consumptionBits)
# Concatenate all these encodings into one large encoding for Spatial
# Pooling.
encoding = numpy.concatenate(
[timeOfDayBits, weekendBits, consumptionBits]
)
# Create an array to represent active columns, all initially zero. This
# will be populated by the compute method below. It must have the same
# dimensions as the Spatial Pooler.
activeColumns = numpy.zeros(spParams["columnCount"])
# Execute Spatial Pooling algorithm over input space.
sp.compute(encoding, True, activeColumns)
activeColumnIndices = numpy.nonzero(activeColumns)[0]
# Execute Temporal Memory algorithm over active mini-columns.
tm.compute(activeColumnIndices, learn=True)
activeCells = tm.getActiveCells()
# Get the bucket info for this input value for classification.
bucketIdx = scalarEncoder.getBucketIndices(consumption)[0]
# Run classifier to translate active cells back to scalar value.
classifierResult = classifier.compute(
recordNum=count,
patternNZ=activeCells,
classification={
"bucketIdx": bucketIdx,
"actValue": consumption
},
learn=True,
infer=True
)
# Print the best prediction for 1 step out.
oneStepConfidence, oneStep = sorted(
zip(classifierResult[1], classifierResult["actualValues"]),
reverse=True
)[0]
print("1-step: {:16} ({:4.4}%)".format(oneStep, oneStepConfidence * 100))
results.append([oneStep, oneStepConfidence * 100, None, None])
return results
