def getDescription(datasets):
# ========================================================================
# Encoder for the sensor
encoder = MultiEncoder()
if config["encodingFieldStyleA"] == "contiguous":
encoder.addEncoder(
"fieldA",
ScalarEncoder(
w=config["encodingOnBitsA"],
n=config["encodingFieldWidthA"],
minval=0,
maxval=config["numAValues"],
periodic=True,
name="fieldA",
),
)
elif config["encodingFieldStyleA"] == "sdr":
encoder.addEncoder(
"fieldA",
SDRCategoryEncoder(
w=config["encodingOnBitsA"],
n=config["encodingFieldWidthA"],
categoryList=range(config["numAValues"]),
name="fieldA",
),
)
else:
assert False
if config["encodingFieldStyleB"] == "contiguous":
encoder.addEncoder(
"fieldB",
ScalarEncoder(
w=config["encodingOnBitsB"],
n=config["encodingFieldWidthB"],
minval=0,
maxval=config["numBValues"],
periodic=True,
name="fieldB",
),
)
elif config["encodingFieldStyleB"] == "sdr":
encoder.addEncoder(
"fieldB",
SDRCategoryEncoder(
w=config["encodingOnBitsB"],
n=config["encodingFieldWidthB"],
categoryList=range(config["numBValues"]),
name="fieldB",
),
)
else:
assert False
# ========================================================================
# Network definition
# ------------------------------------------------------------------
# Node params
# The inputs are long, horizontal vectors
inputShape = (1, encoder.getWidth())
# Layout the coincidences vertically stacked on top of each other, each
# looking at the entire input field.
coincidencesShape = (config["spCoincCount"], 1)
inputBorder = inputShape[1] / 2
if inputBorder * 2 >= inputShape[1]:
inputBorder -= 1
sensorParams = dict(
# encoder/datasource are not parameters so don't include here
verbosity=config["sensorVerbosity"]
)
CLAParams = dict(
inputShape=inputShape,
inputBorder=inputBorder,
coincidencesShape=coincidencesShape,
coincInputRadius=inputShape[1] / 2,
coincInputPoolPct=1.0,
gaussianDist=0,
commonDistributions=0, # should be False if possibly not training
localAreaDensity=-1, # 0.05,
numActivePerInhArea=config["spNumActivePerInhArea"],
dutyCyclePeriod=1000,
stimulusThreshold=1,
synPermInactiveDec=config["spSynPermInactiveDec"],
synPermActiveInc=0.02,
synPermActiveSharedDec=0.0,
synPermOrphanDec=0.0,
minPctDutyCycleBeforeInh=0.001,
minPctDutyCycleAfterInh=config["spMinPctDutyCycleAfterInh"],
minDistance=0.05,
computeTopDown=1,
spVerbosity=config["spVerbosity"],
spSeed=1,
printPeriodicStats=int(config["spPeriodicStats"]),
# TP params
disableTemporal=1,
# General params
trainingStep="spatial",
)
trainingDataSource = FileRecordStream(datasets["trainingFilename"])
description = dict(
options=dict(logOutputsDuringInference=False),
network=dict(
sensorDataSource=trainingDataSource,
sensorEncoder=encoder,
sensorParams=sensorParams,
CLAType="py.CLARegion",
CLAParams=CLAParams,
classifierType=None,
classifierParams=None,
),
)
if config["trainSP"]:
description["spTrain"] = (
dict(
iterationCount=config["iterationCount"],
# iter=displaySPCoincidences(50),
finish=printSPCoincidences(),
),
)
else:
description["spTrain"] = dict(
# need to train with one iteration just to initialize data structures
iterationCount=1
)
# ============================================================================
# Inference tests
inferSteps = []
# ----------------------------------------
# Training dataset
if True:
datasetName = "bothTraining"
inferSteps.append(
dict(
name="%s_baseline" % datasetName,
iterationCount=config["iterationCount"],
setup=[sensorOpen(datasets["trainingFilename"])],
ppOptions=dict(printLearnedCoincidences=True),
)
)
inferSteps.append(
dict(
name="%s_acc" % datasetName,
iterationCount=config["iterationCount"],
setup=[sensorOpen(datasets["trainingFilename"])],
ppOptions=dict(
onlyClassificationAcc=True,
tpActivationThresholds=config["tpActivationThresholds"],
computeDistances=True,
verbosity=1,
),
)
)
# ----------------------------------------
# Testing dataset
if "testingFilename" in datasets:
datasetName = "bothTesting"
inferSteps.append(
dict(
name="%s_baseline" % datasetName,
iterationCount=config["iterationCount"],
setup=[sensorOpen(datasets["testingFilename"])],
ppOptions=dict(printLearnedCoincidences=False),
)
)
inferSteps.append(
dict(
name="%s_acc" % datasetName,
iterationCount=config["iterationCount"],
setup=[sensorOpen(datasets["testingFilename"])],
ppOptions=dict(onlyClassificationAcc=True, tpActivationThresholds=config["tpActivationThresholds"]),
)
)
description["infer"] = inferSteps
return description
def getDescription(datasets):
# ========================================================================
# Encoder for the sensor
encoder = MultiEncoder()
if config['encodingFieldStyleA'] == 'contiguous':
encoder.addEncoder('fieldA', ScalarEncoder(w=config['encodingOnBitsA'],
n=config['encodingFieldWidthA'], minval=0,
maxval=config['numAValues'], periodic=True, name='fieldA'))
elif config['encodingFieldStyleA'] == 'sdr':
encoder.addEncoder('fieldA', SDRCategoryEncoder(w=config['encodingOnBitsA'],
n=config['encodingFieldWidthA'],
categoryList=range(config['numAValues']), name='fieldA'))
else:
assert False
if config['encodingFieldStyleB'] == 'contiguous':
encoder.addEncoder('fieldB', ScalarEncoder(w=config['encodingOnBitsB'],
n=config['encodingFieldWidthB'], minval=0,
maxval=config['numBValues'], periodic=True, name='fieldB'))
elif config['encodingFieldStyleB'] == 'sdr':
encoder.addEncoder('fieldB', SDRCategoryEncoder(w=config['encodingOnBitsB'],
n=config['encodingFieldWidthB'],
categoryList=range(config['numBValues']), name='fieldB'))
else:
assert False
# ========================================================================
# Network definition
# ------------------------------------------------------------------
# Node params
# The inputs are long, horizontal vectors
inputDimensions = (1, encoder.getWidth())
# Layout the coincidences vertically stacked on top of each other, each
# looking at the entire input field.
columnDimensions = (config['spCoincCount'], 1)
sensorParams = dict(
# encoder/datasource are not parameters so don't include here
verbosity=config['sensorVerbosity']
)
CLAParams = dict(
inputDimensions = inputDimensions,
columnDimensions = columnDimensions,
potentialRadius = inputDimensions[1]/2,
potentialPct = 1.0,
gaussianDist = 0,
commonDistributions = 0, # should be False if possibly not training
localAreaDensity = -1, #0.05,
numActiveColumnsPerInhArea = config['spNumActivePerInhArea'],
dutyCyclePeriod = 1000,
stimulusThreshold = 1,
synPermInactiveDec = config['spSynPermInactiveDec'],
synPermActiveInc = 0.02,
synPermActiveSharedDec=0.0,
synPermOrphanDec = 0.0,
minPctDutyCycleBeforeInh = 0.001,
minPctDutyCycleAfterInh = config['spMinPctDutyCycleAfterInh'],
minDistance = 0.05,
computeTopDown = 1,
spVerbosity = config['spVerbosity'],
spSeed = 1,
printPeriodicStats = int(config['spPeriodicStats']),
# TP params
disableTemporal = 1,
# General params
trainingStep = 'spatial',
)
trainingDataSource = FileRecordStream(datasets['trainingFilename'])
description = dict(
options = dict(
logOutputsDuringInference = False,
),
network = dict(
sensorDataSource = trainingDataSource,
sensorEncoder = encoder,
sensorParams = sensorParams,
CLAType = 'py.CLARegion',
CLAParams = CLAParams,
classifierType = None,
classifierParams = None),
)
if config['trainSP']:
description['spTrain'] = dict(
iterationCount=config['iterationCount'],
#iter=displaySPCoincidences(50),
finish=printSPCoincidences()
),
else:
description['spTrain'] = dict(
# need to train with one iteration just to initialize data structures
iterationCount=1)
# ============================================================================
# Inference tests
inferSteps = []
# ----------------------------------------
# Training dataset
if True:
datasetName = 'bothTraining'
inferSteps.append(
dict(name = '{0!s}_baseline'.format(datasetName),
iterationCount = config['iterationCount'],
setup = [sensorOpen(datasets['trainingFilename'])],
ppOptions = dict(printLearnedCoincidences=True),
)
)
inferSteps.append(
dict(name = '{0!s}_acc'.format(datasetName),
iterationCount = config['iterationCount'],
setup = [sensorOpen(datasets['trainingFilename'])],
ppOptions = dict(onlyClassificationAcc=True,
tpActivationThresholds=config['tpActivationThresholds'],
computeDistances=True,
verbosity = 1),
)
)
# ----------------------------------------
# Testing dataset
if 'testingFilename' in datasets:
datasetName = 'bothTesting'
inferSteps.append(
dict(name = '{0!s}_baseline'.format(datasetName),
iterationCount = config['iterationCount'],
setup = [sensorOpen(datasets['testingFilename'])],
ppOptions = dict(printLearnedCoincidences=False),
)
)
inferSteps.append(
dict(name = '{0!s}_acc'.format(datasetName),
iterationCount = config['iterationCount'],
setup = [sensorOpen(datasets['testingFilename'])],
ppOptions = dict(onlyClassificationAcc=True,
tpActivationThresholds=config['tpActivationThresholds']),
)
)
description['infer'] = inferSteps
return description
def getDescription(datasets):
encoder = MultiEncoder()
encoder.addEncoder("date", DateEncoder(timeOfDay=3))
encoder.addEncoder("amount", LogEncoder(name="amount", maxval=1000))
for i in xrange(0, nRandomFields):
s = ScalarEncoder(name="scalar", minval=0, maxval=randomFieldWidth, resolution=1, w=3)
encoder.addEncoder("random%d" % i, s)
dataSource = FunctionSource(generateFunction, dict(nRandomFields=nRandomFields,
randomFieldWidth=randomFieldWidth))
inputShape = (1, encoder.getWidth())
# Layout the coincidences vertically stacked on top of each other, each
# looking at the entire input field.
coincidencesShape = (nCoincidences, 1)
# TODO: why do we need input border?
inputBorder = inputShape[1]/2
if inputBorder*2 >= inputShape[1]:
inputBorder -= 1
nodeParams = dict()
spParams = dict(
commonDistributions=0,
inputShape = inputShape,
inputBorder = inputBorder,
coincidencesShape = coincidencesShape,
coincInputRadius = inputShape[1]/2,
coincInputPoolPct = 0.75,
gaussianDist = 0,
localAreaDensity = 0.10,
# localAreaDensity = 0.04,
numActivePerInhArea = -1,
dutyCyclePeriod = 1000,
stimulusThreshold = 5,
synPermInactiveDec=0.08,
# synPermInactiveDec=0.02,
synPermActiveInc=0.02,
synPermActiveSharedDec=0.0,
synPermOrphanDec = 0.0,
minPctDutyCycleBeforeInh = 0.05,
# minPctDutyCycleAfterInh = 0.1,
# minPctDutyCycleBeforeInh = 0.05,
minPctDutyCycleAfterInh = 0.05,
# minPctDutyCycleAfterInh = 0.4,
seed = 1,
)
otherParams = dict(
disableTemporal=1,
trainingStep='spatial',
)
nodeParams.update(spParams)
nodeParams.update(otherParams)
def mySetupCallback(experiment):
print "Setup function called"
description = dict(
options = dict(
logOutputsDuringInference = False,
),
network = dict(
sensorDataSource = dataSource,
sensorEncoder = encoder,
CLAType = "py.CLARegion",
CLAParams = nodeParams,
classifierType = None,
classifierParams = None),
# step
spTrain = dict(
name="phase1",
setup=mySetupCallback,
iterationCount=5000,
#iter=displaySPCoincidences(100),
finish=printSPCoincidences()),
tpTrain = None, # same format as sptrain if non-empty
infer = None, # same format as sptrain if non-empty
)
return description
def getDescription(datasets):
# ========================================================================
# Encoder for the sensor
encoder = MultiEncoder()
if config['encodingFieldStyleA'] == 'contiguous':
encoder.addEncoder('fieldA', ScalarEncoder(w=config['encodingOnBitsA'],
n=config['encodingFieldWidthA'], minval=0,
maxval=config['numAValues'], periodic=True, name='fieldA'))
elif config['encodingFieldStyleA'] == 'sdr':
encoder.addEncoder('fieldA', SDRCategoryEncoder(w=config['encodingOnBitsA'],
n=config['encodingFieldWidthA'],
categoryList=range(config['numAValues']), name='fieldA'))
else:
assert False
if config['encodingFieldStyleB'] == 'contiguous':
encoder.addEncoder('fieldB', ScalarEncoder(w=config['encodingOnBitsB'],
n=config['encodingFieldWidthB'], minval=0,
maxval=config['numBValues'], periodic=True, name='fieldB'))
elif config['encodingFieldStyleB'] == 'zero':
encoder.addEncoder('fieldB', SDRRandomEncoder(w=0, n=config['encodingFieldWidthB'],
name='fieldB'))
elif config['encodingFieldStyleB'] == 'sdr':
encoder.addEncoder('fieldB', SDRCategoryEncoder(w=config['encodingOnBitsB'],
n=config['encodingFieldWidthB'],
categoryList=range(config['numBValues']), name='fieldB'))
else:
assert False
# ========================================================================
# Network definition
# ------------------------------------------------------------------
# Node params
# The inputs are long, horizontal vectors
inputShape = (1, encoder.getWidth())
# Layout the coincidences vertically stacked on top of each other, each
# looking at the entire input field.
coincidencesShape = (config['spCoincCount'], 1)
inputBorder = inputShape[1]/2
if inputBorder*2 >= inputShape[1]:
inputBorder -= 1
sensorParams = dict(
# encoder/datasource are not parameters so don't include here
verbosity=config['sensorVerbosity']
)
CLAParams = dict(
inputShape = inputShape,
inputBorder = inputBorder,
coincidencesShape = coincidencesShape,
coincInputRadius = inputShape[1]/2,
coincInputPoolPct = 1.0,
gaussianDist = 0,
commonDistributions = 0, # should be False if possibly not training
localAreaDensity = -1, #0.05,
numActivePerInhArea = config['spNumActivePerInhArea'],
dutyCyclePeriod = 1000,
stimulusThreshold = 1,
synPermInactiveDec = config['spSynPermInactiveDec'],
synPermActiveInc = 0.02,
synPermActiveSharedDec=0.0,
synPermOrphanDec = 0.0,
minPctDutyCycleBeforeInh = 0.001,
minPctDutyCycleAfterInh = config['spMinPctDutyCycleAfterInh'],
minDistance = 0.05,
computeTopDown = 1,
spVerbosity = config['spVerbosity'],
spSeed = 1,
printPeriodicStats = int(config['spPeriodicStats']),
# TP params
disableTemporal = 1,
# General params
trainingStep = 'spatial',
)
trainingDataSource = FileRecordStream(datasets['trainingFilename'])
description = dict(
options = dict(
logOutputsDuringInference = False,
),
network = dict(
sensorDataSource = trainingDataSource,
sensorEncoder = encoder,
sensorParams = sensorParams,
CLAType = 'py.CLARegion',
CLAParams = CLAParams,
classifierType = None,
classifierParams = None),
)
if config['trainSP']:
description['spTrain'] = dict(
iterationCount=config['iterationCount'],
#iter=displaySPCoincidences(50),
finish=printSPCoincidences()
),
else:
description['spTrain'] = dict(
# need to train with one iteration just to initialize data structures
iterationCount=1)
# ============================================================================
# Inference tests
inferSteps = []
# ----------------------------------------
# Training dataset
if True:
datasetName = 'bothTraining'
inferSteps.append(
dict(name = '%s_baseline' % datasetName,
iterationCount = config['iterationCount'],
setup = [sensorOpen(datasets['trainingFilename'])],
ppOptions = dict(printLearnedCoincidences=True),
)
)
inferSteps.append(
dict(name = '%s_acc' % datasetName,
iterationCount = config['iterationCount'],
setup = [sensorOpen(datasets['trainingFilename'])],
ppOptions = dict(onlyClassificationAcc=True,
tpActivationThresholds=config['tpActivationThresholds'],
computeDistances=True,
verbosity = 1),
)
)
# ----------------------------------------
# Testing dataset
if 'testingFilename' in datasets:
datasetName = 'bothTesting'
inferSteps.append(
dict(name = '%s_baseline' % datasetName,
iterationCount = config['iterationCount'],
setup = [sensorOpen(datasets['testingFilename'])],
ppOptions = dict(printLearnedCoincidences=False),
)
)
inferSteps.append(
dict(name = '%s_acc' % datasetName,
iterationCount = config['iterationCount'],
setup = [sensorOpen(datasets['testingFilename'])],
ppOptions = dict(onlyClassificationAcc=True,
tpActivationThresholds=config['tpActivationThresholds']),
)
)
description['infer'] = inferSteps
return description
def getDescription(datasets):
encoder = MultiEncoder()
encoder.addEncoder("date", DateEncoder(timeOfDay=3))
encoder.addEncoder("amount", LogEncoder(name="amount", maxval=1000))
for i in xrange(0, nRandomFields):
s = ScalarEncoder(name="scalar",
minval=0,
maxval=randomFieldWidth,
resolution=1,
w=3)
encoder.addEncoder("random%d" % i, s)
dataSource = FunctionSource(
generateFunction,
dict(nRandomFields=nRandomFields, randomFieldWidth=randomFieldWidth))
inputShape = (1, encoder.getWidth())
# Layout the coincidences vertically stacked on top of each other, each
# looking at the entire input field.
coincidencesShape = (nCoincidences, 1)
# TODO: why do we need input border?
inputBorder = inputShape[1] / 2
if inputBorder * 2 >= inputShape[1]:
inputBorder -= 1
nodeParams = dict()
spParams = dict(
commonDistributions=0,
inputShape=inputShape,
inputBorder=inputBorder,
coincidencesShape=coincidencesShape,
coincInputRadius=inputShape[1] / 2,
coincInputPoolPct=0.75,
gaussianDist=0,
localAreaDensity=0.10,
# localAreaDensity = 0.04,
numActivePerInhArea=-1,
dutyCyclePeriod=1000,
stimulusThreshold=5,
synPermInactiveDec=0.08,
# synPermInactiveDec=0.02,
synPermActiveInc=0.02,
synPermActiveSharedDec=0.0,
synPermOrphanDec=0.0,
minPctDutyCycleBeforeInh=0.05,
# minPctDutyCycleAfterInh = 0.1,
# minPctDutyCycleBeforeInh = 0.05,
minPctDutyCycleAfterInh=0.05,
# minPctDutyCycleAfterInh = 0.4,
seed=1,
)
otherParams = dict(
disableTemporal=1,
trainingStep='spatial',
)
nodeParams.update(spParams)
nodeParams.update(otherParams)
def mySetupCallback(experiment):
print "Setup function called"
description = dict(
options=dict(logOutputsDuringInference=False, ),
network=dict(sensorDataSource=dataSource,
sensorEncoder=encoder,
CLAType="py.CLARegion",
CLAParams=nodeParams,
classifierType=None,
classifierParams=None),
# step
spTrain=dict(
name="phase1",
setup=mySetupCallback,
iterationCount=5000,
#iter=displaySPCoincidences(100),
finish=printSPCoincidences()),
tpTrain=None, # same format as sptrain if non-empty
infer=None, # same format as sptrain if non-empty
)
return description
