def testRunPCANode(self):
from nupic.engine import *
numpy.random.RandomState(37)
inputSize = 8
net = Network()
Network.registerRegion(ImageSensor)
net.addRegion('sensor', 'py.ImageSensor' ,
'{ width: %d, height: %d }' % (inputSize, inputSize))
params = """{bottomUpCount: %d,
SVDSampleCount: 5,
SVDDimCount: 2}""" % inputSize
pca = net.addRegion('pca', 'py.PCANode', params)
#nodeAbove = CreateNode("py.ImageSensor", phase=0, categoryOut=1, dataOut=3,
# width=3, height=1)
#net.addElement('nodeAbove', nodeAbove)
linkParams = '{ mapping: in, rfSize: [%d, %d] }' % (inputSize, inputSize)
net.link('sensor', 'pca', 'UniformLink', linkParams, 'dataOut', 'bottomUpIn')
net.initialize()
for i in range(10):
pca.getSelf()._testInputs = numpy.random.random([inputSize])
net.run(1)
def main():
# Create Network instance
network = Network()
# Add three TestNode regions to network
network.addRegion("region1", "TestNode", "")
network.addRegion("region2", "TestNode", "")
network.addRegion("region3", "TestNode", "")
# Set dimensions on first region
region1 = network.getRegions().getByName("region1")
region1.setDimensions(Dimensions([1, 1]))
# Link regions
network.link("region1", "region2", "UniformLink", "")
network.link("region2", "region1", "UniformLink", "")
network.link("region1", "region3", "UniformLink", "")
network.link("region2", "region3", "UniformLink", "")
# Initialize network
network.initialize()
# Initialize Network Visualizer
viz = NetworkVisualizer(network)
# Render w/ graphviz
viz.render(renderer=GraphVizRenderer)
# Render w/ networkx
viz.render(renderer=NetworkXRenderer)
def _createLPFNetwork(addSP = True, addTP = False):
"""Create an 'old-style' network ala LPF and return it."""
# ==========================================================================
# Create the encoder and data source stuff we need to configure the sensor
sensorParams = dict(verbosity = _VERBOSITY)
encoder = _createEncoder()
trainFile = findDataset("extra/gym/gym.csv")
dataSource = FileRecordStream(streamID=trainFile)
dataSource.setAutoRewind(True)
# Create all the stuff we need to configure the CLARegion
g_claConfig['spEnable'] = addSP
g_claConfig['tpEnable'] = addTP
claParams = _getCLAParams(encoder = encoder, config= g_claConfig)
claParams['spSeed'] = g_claConfig['spSeed']
claParams['tpSeed'] = g_claConfig['tpSeed']
# ==========================================================================
# Now create the network itself
n = Network()
n.addRegion("sensor", "py.RecordSensor", json.dumps(sensorParams))
sensor = n.regions['sensor'].getSelf()
sensor.encoder = encoder
sensor.dataSource = dataSource
n.addRegion("level1", "py.CLARegion", json.dumps(claParams))
n.link("sensor", "level1", "UniformLink", "")
n.link("sensor", "level1", "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
return n
def _createLPFNetwork(addSP=True, addTP=False):
"""Create an 'old-style' network ala LPF and return it."""
# ==========================================================================
# Create the encoder and data source stuff we need to configure the sensor
sensorParams = dict(verbosity=_VERBOSITY)
encoder = _createEncoder()
trainFile = findDataset("extra/gym/gym.csv")
dataSource = FileRecordStream(streamID=trainFile)
dataSource.setAutoRewind(True)
# Create all the stuff we need to configure the CLARegion
g_claConfig["spEnable"] = addSP
g_claConfig["tpEnable"] = addTP
claParams = _getCLAParams(encoder=encoder, config=g_claConfig)
claParams["spSeed"] = g_claConfig["spSeed"]
claParams["tpSeed"] = g_claConfig["tpSeed"]
# ==========================================================================
# Now create the network itself
n = Network()
n.addRegion("sensor", "py.RecordSensor", json.dumps(sensorParams))
sensor = n.regions["sensor"].getSelf()
sensor.encoder = encoder
sensor.dataSource = dataSource
n.addRegion("level1", "py.CLARegion", json.dumps(claParams))
n.link("sensor", "level1", "UniformLink", "")
n.link("sensor", "level1", "UniformLink", "", srcOutput="resetOut", destInput="resetIn")
return n
def runExperiment():
Network.unregisterRegion("ImageSensor")
Network.registerRegion(ImageSensor)
Network.registerRegion(PCANode)
inputSize = 8
net = Network()
sensor = net.addRegion(
"sensor", "py.ImageSensor",
"{ width: %d, height: %d }" % (inputSize, inputSize))
params = ("{bottomUpCount: %s, "
" SVDSampleCount: 5, "
" SVDDimCount: 2}" % inputSize)
pca = net.addRegion("pca", "py.PCANode", params)
linkParams = "{ mapping: in, rfSize: [%d, %d] }" % (inputSize, inputSize)
net.link("sensor", "pca", "UniformLink", linkParams, "dataOut",
"bottomUpIn")
net.initialize()
for i in range(10):
pca.getSelf()._testInputs = numpy.random.random([inputSize])
net.run(1)
print s.sendRequest("nodeOPrint pca_node")
def runExperiment():
Network.unregisterRegion("ImageSensor")
Network.registerRegion(ImageSensor)
Network.registerRegion(PCANode)
inputSize = 8
net = Network()
sensor = net.addRegion(
"sensor", "py.ImageSensor" ,
"{ width: %d, height: %d }" % (inputSize, inputSize))
params = ("{bottomUpCount: %s, "
" SVDSampleCount: 5, "
" SVDDimCount: 2}" % inputSize)
pca = net.addRegion("pca", "py.PCANode", params)
linkParams = "{ mapping: in, rfSize: [%d, %d] }" % (inputSize, inputSize)
net.link("sensor", "pca", "UniformLink", linkParams, "dataOut", "bottomUpIn")
net.initialize()
for i in range(10):
pca.getSelf()._testInputs = numpy.random.random([inputSize])
net.run(1)
print s.sendRequest("nodeOPrint pca_node")
def testRunPCANode(self):
from nupic.engine import *
numpy.random.RandomState(37)
inputSize = 8
net = Network()
Network.registerRegion(ImageSensor)
net.addRegion('sensor', 'py.ImageSensor',
'{ width: %d, height: %d }' % (inputSize, inputSize))
params = """{bottomUpCount: %d,
SVDSampleCount: 5,
SVDDimCount: 2}""" % inputSize
pca = net.addRegion('pca', 'py.PCANode', params)
#nodeAbove = CreateNode("py.ImageSensor", phase=0, categoryOut=1, dataOut=3,
# width=3, height=1)
#net.addElement('nodeAbove', nodeAbove)
linkParams = '{ mapping: in, rfSize: [%d, %d] }' % (inputSize,
inputSize)
net.link('sensor', 'pca', 'UniformLink', linkParams, 'dataOut',
'bottomUpIn')
net.initialize()
for i in range(10):
pca.getSelf()._testInputs = numpy.random.random([inputSize])
net.run(1)
def testSensor(self):
# Create a simple network to test the sensor
rawParams = {"outputWidth": 1029}
net = Network()
rawSensor = net.addRegion("raw","py.RawSensor", json.dumps(rawParams))
vfe = net.addRegion("output","VectorFileEffector","")
net.link("raw", "output", "UniformLink", "")
self.assertEqual(rawSensor.getParameter("outputWidth"),1029,
"Incorrect outputWidth parameter")
# Add vectors to the queue using two different add methods. Later we
# will check to ensure these are actually output properly.
rawSensor.executeCommand(["addDataToQueue", "[2, 4, 6]", "0", "42"])
rawSensorPy = rawSensor.getSelf()
rawSensorPy.addDataToQueue([2, 42, 1023], 1, 43)
rawSensorPy.addDataToQueue([18, 19, 20], 0, 44)
# Set an output file before we run anything
vfe.setParameter("outputFile",os.path.join(self.tmpDir,"temp.csv"))
# Run the network and check outputs are as expected
net.run(1)
self.assertEqual(rawSensor.getOutputData("dataOut").nonzero()[0].sum(),
sum([2, 4, 6]), "Value of dataOut incorrect")
self.assertEqual(rawSensor.getOutputData("resetOut").sum(),0,
"Value of resetOut incorrect")
self.assertEqual( rawSensor.getOutputData("sequenceIdOut").sum(),42,
"Value of sequenceIdOut incorrect")
net.run(1)
self.assertEqual(rawSensor.getOutputData("dataOut").nonzero()[0].sum(),
sum([2, 42, 1023]), "Value of dataOut incorrect")
self.assertEqual(rawSensor.getOutputData("resetOut").sum(),1,
"Value of resetOut incorrect")
self.assertEqual( rawSensor.getOutputData("sequenceIdOut").sum(),43,
"Value of sequenceIdOut incorrect")
# Make sure we can save and load the network after running
net.save(os.path.join(self.tmpDir,"rawNetwork.nta"))
net2 = Network(os.path.join(self.tmpDir,"rawNetwork.nta"))
rawSensor2 = net2.regions.get("raw")
vfe2 = net2.regions.get("output")
# Ensure parameters are preserved
self.assertEqual(rawSensor2.getParameter("outputWidth"),1029,
"Incorrect outputWidth parameter")
# Ensure the queue is preserved through save/load
vfe2.setParameter("outputFile",os.path.join(self.tmpDir,"temp.csv"))
net2.run(1)
self.assertEqual(rawSensor2.getOutputData("dataOut").nonzero()[0].sum(),
sum([18, 19, 20]), "Value of dataOut incorrect")
self.assertEqual(rawSensor2.getOutputData("resetOut").sum(),0,
"Value of resetOut incorrect")
self.assertEqual( rawSensor2.getOutputData("sequenceIdOut").sum(),44,
"Value of sequenceIdOut incorrect")
def testSensor(self):
# Create a simple network to test the sensor
params = {
"activeBits": self.encoder.w,
"outputWidth": self.encoder.n,
"radius": 2,
"verbosity": self.encoder.verbosity,
}
net = Network()
region = net.addRegion("coordinate", "py.CoordinateSensorRegion", json.dumps(params))
vfe = net.addRegion("output", "VectorFileEffector", "")
net.link("coordinate", "output", "UniformLink", "")
self.assertEqual(region.getParameter("outputWidth"), self.encoder.n, "Incorrect outputWidth parameter")
# Add vectors to the queue using two different add methods. Later we
# will check to ensure these are actually output properly.
region.executeCommand(["addDataToQueue", "[2, 4, 6]", "0", "42"])
regionPy = region.getSelf()
regionPy.addDataToQueue([2, 42, 1023], 1, 43)
regionPy.addDataToQueue([18, 19, 20], 0, 44)
# Set an output file before we run anything
vfe.setParameter("outputFile", os.path.join(self.tmpDir, "temp.csv"))
# Run the network and check outputs are as expected
net.run(1)
expected = self.encoder.encode((numpy.array([2, 4, 6]), params["radius"]))
actual = region.getOutputData("dataOut")
self.assertEqual(actual.sum(), expected.sum(), "Value of dataOut incorrect")
self.assertEqual(region.getOutputData("resetOut"), 0, "Value of resetOut incorrect")
self.assertEqual(region.getOutputData("sequenceIdOut"), 42, "Value of sequenceIdOut incorrect")
net.run(1)
expected = self.encoder.encode((numpy.array([2, 42, 1023]), params["radius"]))
actual = region.getOutputData("dataOut")
self.assertEqual(actual.sum(), expected.sum(), "Value of dataOut incorrect")
self.assertEqual(region.getOutputData("resetOut"), 1, "Value of resetOut incorrect")
self.assertEqual(region.getOutputData("sequenceIdOut"), 43, "Value of sequenceIdOut incorrect")
# Make sure we can save and load the network after running
net.save(os.path.join(self.tmpDir, "coordinateNetwork.nta"))
net2 = Network(os.path.join(self.tmpDir, "coordinateNetwork.nta"))
region2 = net2.regions.get("coordinate")
vfe2 = net2.regions.get("output")
# Ensure parameters are preserved
self.assertEqual(region2.getParameter("outputWidth"), self.encoder.n, "Incorrect outputWidth parameter")
# Ensure the queue is preserved through save/load
vfe2.setParameter("outputFile", os.path.join(self.tmpDir, "temp.csv"))
net2.run(1)
expected = self.encoder.encode((numpy.array([18, 19, 20]), params["radius"]))
actual = region2.getOutputData("dataOut")
self.assertEqual(actual.sum(), expected.sum(), "Value of dataOut incorrect")
self.assertEqual(region2.getOutputData("resetOut"), 0, "Value of resetOut incorrect")
self.assertEqual(region2.getOutputData("sequenceIdOut"), 44, "Value of sequenceIdOut incorrect")
def createNetwork():
"""Create the Network instance.
The network has a sensor region reading data from `rataSource` and passing
the encoded representation to an SPRegion. The SPRegion output is passed to
a TPRegion.
:param dataSource: a RecordStream instance to get data from
:returns: a Network instance ready to run
"""
network = Network()
# Create Sensor
network.addRegion("sensor", "py.RecordSensor", json.dumps({"verbosity":
0}))
sensor = network.regions["sensor"].getSelf()
sensor.encoder = createSensorEncoder()
sensor.dataSource = DataBuffer()
# Add the spatial pooler region
PARAMS['SP']["inputWidth"] = sensor.encoder.getWidth()
network.addRegion("spatialPoolerRegion", "py.SPRegion",
json.dumps(PARAMS['SP']))
network.link("sensor", "spatialPoolerRegion", "UniformLink", "")
# Add the TPRegion on top of the SPRegion
network.addRegion("temporalPoolerRegion", "py.TPRegion",
json.dumps(PARAMS['TP']))
network.link("spatialPoolerRegion", "temporalPoolerRegion", "UniformLink",
"")
# Add classifier
network.addRegion("classifierRegion", "py.CLAClassifierRegion",
json.dumps(PARAMS['CL']))
network.initialize()
# Make sure learning is enabled
spatialPoolerRegion = network.regions["spatialPoolerRegion"]
spatialPoolerRegion.setParameter("learningMode", True)
spatialPoolerRegion.setParameter("anomalyMode", True)
temporalPoolerRegion = network.regions["temporalPoolerRegion"]
temporalPoolerRegion.setParameter("topDownMode", False)
temporalPoolerRegion.setParameter("learningMode", True)
temporalPoolerRegion.setParameter("inferenceMode", True)
temporalPoolerRegion.setParameter("anomalyMode", False)
classifierRegion = network.regions["classifierRegion"]
classifierRegion.setParameter('inferenceMode', True)
classifierRegion.setParameter('learningMode', True)
return network
def createNetwork(dataSource):
"""Create the Network instance.
The network has a sensor region reading data from `dataSource` and passing
the encoded representation to an Identity Region.
:param dataSource: a RecordStream instance to get data from
:returns: a Network instance ready to run
"""
network = Network()
# Our input is sensor data from the gym file. The RecordSensor region
# allows us to specify a file record stream as the input source via the
# dataSource attribute.
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": _VERBOSITY}))
sensor = network.regions["sensor"].getSelf()
# The RecordSensor needs to know how to encode the input values
sensor.encoder = createEncoder()
# Specify the dataSource as a file record stream instance
sensor.dataSource = dataSource
# CUSTOM REGION
# Add path to custom region to PYTHONPATH
# NOTE: Before using a custom region, please modify your PYTHONPATH
# export PYTHONPATH="<path to custom region module>:$PYTHONPATH"
# In this demo, we have modified it using sys.path.append since we need it to
# have an effect on this program.
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from custom_region.identity_region import IdentityRegion
# Add custom region class to the network
Network.registerRegion(IdentityRegion)
# Create a custom region
network.addRegion("identityRegion", "py.IdentityRegion",
json.dumps(I_PARAMS))
# Link the Identity region to the sensor input
network.link("sensor",
"identityRegion",
"UniformLink",
"",
srcOutput="sourceOut",
destInput="in")
network.initialize()
return network
def createNetwork():
"""Create the Network instance.
The network has a sensor region reading data from `rataSource` and passing
the encoded representation to an SPRegion. The SPRegion output is passed to
a TPRegion.
:param dataSource: a RecordStream instance to get data from
:returns: a Network instance ready to run
"""
network = Network()
# Create Sensor
network.addRegion("sensor", "py.RecordSensor", json.dumps({"verbosity": 0}))
sensor = network.regions["sensor"].getSelf()
sensor.encoder = createSensorEncoder()
sensor.dataSource = DataBuffer()
# Add the spatial pooler region
PARAMS['SP']["inputWidth"] = sensor.encoder.getWidth()
network.addRegion("spatialPoolerRegion", "py.SPRegion", json.dumps(PARAMS['SP']))
network.link("sensor", "spatialPoolerRegion", "UniformLink", "")
# Add the TPRegion on top of the SPRegion
network.addRegion("temporalPoolerRegion", "py.TPRegion", json.dumps(PARAMS['TP']))
network.link("spatialPoolerRegion", "temporalPoolerRegion", "UniformLink", "")
# Add classifier
network.addRegion( "classifierRegion", "py.CLAClassifierRegion", json.dumps(PARAMS['CL']))
network.initialize()
# Make sure learning is enabled
spatialPoolerRegion = network.regions["spatialPoolerRegion"]
spatialPoolerRegion.setParameter("learningMode", True)
spatialPoolerRegion.setParameter("anomalyMode", True)
temporalPoolerRegion = network.regions["temporalPoolerRegion"]
temporalPoolerRegion.setParameter("topDownMode", False)
temporalPoolerRegion.setParameter("learningMode", True)
temporalPoolerRegion.setParameter("inferenceMode", True)
temporalPoolerRegion.setParameter("anomalyMode", False)
classifierRegion = network.regions["classifierRegion"]
classifierRegion.setParameter('inferenceMode', True)
classifierRegion.setParameter('learningMode', True)
return network
def createNet(self):
""" Set up the structure of the network """
net = Network()
Network.unregisterRegion(SaccadeSensor.__name__)
Network.registerRegion(SaccadeSensor)
Network.registerRegion(TMRegion)
imageSensorParams = copy.deepcopy(DEFAULT_IMAGESENSOR_PARAMS)
if self.loggingDir is not None:
imageSensorParams["logDir"] = "sensorImages/" + self.loggingDir
imageSensorParams["logOutputImages"] = 1
imageSensorParams["logOriginalImages"] = 1
imageSensorParams["logFilteredImages"] = 1
imageSensorParams["logLocationImages"] = 1
imageSensorParams["logLocationOnOriginalImage"] = 1
net.addRegion("sensor", "py.SaccadeSensor",
yaml.dump(imageSensorParams))
sensor = net.regions["sensor"].getSelf()
DEFAULT_SP_PARAMS["columnCount"] = sensor.getOutputElementCount("dataOut")
net.addRegion("SP", "py.SPRegion", yaml.dump(DEFAULT_SP_PARAMS))
sp = net.regions["SP"].getSelf()
DEFAULT_TM_PARAMS["columnDimensions"] = (sp.getOutputElementCount("bottomUpOut"),)
net.addRegion("TM", "py.TMRegion", yaml.dump(DEFAULT_TM_PARAMS))
net.addRegion("classifier","py.KNNClassifierRegion",
yaml.dump(DEFAULT_CLASSIFIER_PARAMS))
net.link("sensor", "SP", "UniformLink", "",
srcOutput="dataOut", destInput="bottomUpIn")
net.link("SP", "TM", "UniformLink", "",
srcOutput="bottomUpOut", destInput="activeColumns")
net.link("sensor", "TM", "UniformLink", "",
srcOutput="saccadeOut", destInput="activeExternalCells")
net.link("TM", "classifier", "UniformLink", "",
srcOutput="predictedActiveCells", destInput="bottomUpIn")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput="categoryOut", destInput="categoryIn")
self.net = net
self.networkSensor = self.net.regions["sensor"]
self.networkSP = self.net.regions["SP"]
self.networkTM = self.net.regions["TM"]
self.networkClassifier = self.net.regions["classifier"]
def createNetwork(dataSource):
"""Create the Network instance.
The network has a sensor region reading data from `dataSource` and passing
the encoded representation to an Identity Region.
:param dataSource: a RecordStream instance to get data from
:returns: a Network instance ready to run
"""
network = Network()
# Our input is sensor data from the gym file. The RecordSensor region
# allows us to specify a file record stream as the input source via the
# dataSource attribute.
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": _VERBOSITY}))
sensor = network.regions["sensor"].getSelf()
# The RecordSensor needs to know how to encode the input values
sensor.encoder = createEncoder()
# Specify the dataSource as a file record stream instance
sensor.dataSource = dataSource
# CUSTOM REGION
# Add path to custom region to PYTHONPATH
# NOTE: Before using a custom region, please modify your PYTHONPATH
# export PYTHONPATH="<path to custom region module>:$PYTHONPATH"
# In this demo, we have modified it using sys.path.append since we need it to
# have an effect on this program.
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from custom_region.identity_region import IdentityRegion
# Add custom region class to the network
Network.registerRegion(IdentityRegion)
# Create a custom region
network.addRegion("identityRegion", "py.IdentityRegion",
json.dumps({
"dataWidth": sensor.encoder.getWidth(),
}))
# Link the Identity region to the sensor output
network.link("sensor", "identityRegion", "UniformLink", "")
network.initialize()
return network
def testOverlap(self):
"""Create a simple network to test the region."""
rawParams = {"outputWidth": 8 * 2048}
net = Network()
rawSensor = net.addRegion("raw", "py.RawSensor", json.dumps(rawParams))
l2c = net.addRegion("L2", "py.ColumnPoolerRegion", "")
net.link("raw", "L2", "UniformLink", "")
self.assertEqual(rawSensor.getParameter("outputWidth"),
l2c.getParameter("inputWidth"),
"Incorrect outputWidth parameter")
rawSensorPy = rawSensor.getSelf()
rawSensorPy.addDataToQueue([2, 4, 6], 0, 42)
rawSensorPy.addDataToQueue([2, 42, 1023], 1, 43)
rawSensorPy.addDataToQueue([18, 19, 20], 0, 44)
# Run the network and check outputs are as expected
net.run(3)
def testNetworkCreate(self):
"""Create a simple network to test the region."""
rawParams = {"outputWidth": 16*2048}
net = Network()
rawSensor = net.addRegion("raw","py.RawSensor", json.dumps(rawParams))
l2c = net.addRegion("L2", "py.L2Column", "")
net.link("raw", "L2", "UniformLink", "")
self.assertEqual(rawSensor.getParameter("outputWidth"),
l2c.getParameter("inputWidth"),
"Incorrect outputWidth parameter")
rawSensorPy = rawSensor.getSelf()
rawSensorPy.addDataToQueue([2, 4, 6], 0, 42)
rawSensorPy.addDataToQueue([2, 42, 1023], 1, 43)
rawSensorPy.addDataToQueue([18, 19, 20], 0, 44)
# Run the network and check outputs are as expected
net.run(3)
def createNetwork():
"""
Set up the following simple network and return it:
ImageSensorRegion -> SP -> KNNClassifier Region
"""
net = Network()
# Register the ImageSensor region with the network
Network.registerRegion(ImageSensor)
# Add the three regions
net.addRegion("sensor", "py.ImageSensor",
yaml.dump(DEFAULT_IMAGESENSOR_PARAMS))
net.addRegion("SP", "py.SPRegion", yaml.dump(DEFAULT_SP_PARAMS))
net.addRegion("classifier","py.KNNClassifierRegion",
yaml.dump(DEFAULT_CLASSIFIER_PARAMS))
# Link up the regions. Note that we need to create a link from the sensor
# to the classifier to send in the category labels.
net.link("sensor", "SP", "UniformLink", "",
srcOutput = "dataOut", destInput = "bottomUpIn")
net.link("SP", "classifier", "UniformLink", "",
srcOutput = "bottomUpOut", destInput = "bottomUpIn")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput = "categoryOut", destInput = "categoryIn")
return net
def createNet(self):
""" Set up the structure of the network """
net = Network()
Network.unregisterRegion(ImageSensor.__name__)
Network.registerRegion(ImageSensor)
imageSensorParams = copy.deepcopy(DEFAULT_IMAGESENSOR_PARAMS)
if self.loggingDir is not None:
imageSensorParams["logDir"] = "sensorImages/" + self.loggingDir
imageSensorParams["logOutputImages"] = 1
imageSensorParams["logOriginalImages"] = 1
imageSensorParams["logFilteredImages"] = 1
imageSensorParams["logLocationImages"] = 1
imageSensorParams["logLocationOnOriginalImage"] = 1
net.addRegion("sensor", "py.ImageSensor",
yaml.dump(imageSensorParams))
net.addRegion("SP", "py.SPRegion", yaml.dump(DEFAULT_SP_PARAMS))
net.addRegion("classifier","py.KNNClassifierRegion",
yaml.dump(DEFAULT_CLASSIFIER_PARAMS))
net.link("sensor", "SP", "UniformLink", "",
srcOutput = "dataOut", destInput = "bottomUpIn")
net.link("SP", "classifier", "UniformLink", "",
srcOutput = "bottomUpOut", destInput = "bottomUpIn")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput = "categoryOut", destInput = "categoryIn")
self.net = net
self.networkSensor = self.net.regions["sensor"]
self.networkSP = self.net.regions["SP"]
self.networkClassifier = self.net.regions["classifier"]
def createNetwork():
"""
Set up the following simple network and return it:
ImageSensorRegion -> SP -> KNNClassifier Region
"""
net = Network()
# Add the three regions
net.addRegion("sensor", "py.ImageSensor",
json.dumps(DEFAULT_IMAGESENSOR_PARAMS))
net.addRegion("SP", "py.SPRegion", json.dumps(DEFAULT_SP_PARAMS))
net.addRegion("classifier","py.KNNClassifierRegion",
json.dumps(DEFAULT_CLASSIFIER_PARAMS))
# Link up the regions
net.link("sensor", "SP", "UniformLink", "",
srcOutput = "dataOut", destInput = "bottomUpIn")
net.link("SP", "classifier", "UniformLink", "",
srcOutput = "bottomUpOut", destInput = "bottomUpIn")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput = "categoryOut", destInput = "categoryIn")
return net
def createNetwork():
"""
Set up the following simple network and return it:
ImageSensorRegion -> SP -> KNNClassifier Region
"""
net = Network()
# Add the three regions
net.addRegion("sensor", "py.ImageSensor",
json.dumps(DEFAULT_IMAGESENSOR_PARAMS))
net.addRegion("SP", "py.SPRegion", json.dumps(DEFAULT_SP_PARAMS))
net.addRegion("classifier","py.KNNClassifierRegion",
json.dumps(DEFAULT_CLASSIFIER_PARAMS))
# Link up the regions. Note that we need to create a link from the sensor
# to the classifier to send in the category labels.
net.link("sensor", "SP", "UniformLink", "",
srcOutput = "dataOut", destInput = "bottomUpIn")
net.link("SP", "classifier", "UniformLink", "",
srcOutput = "bottomUpOut", destInput = "bottomUpIn")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput = "categoryOut", destInput = "categoryIn")
# Make sure all objects are initialized
net.initialize()
return net
def main():
# Create Network instance
network = Network()
# Add three TestNode regions to network
network.addRegion("region1", "TestNode", "")
network.addRegion("region2", "TestNode", "")
network.addRegion("region3", "TestNode", "")
# Set dimensions on first region
region1 = network.getRegions().getByName("region1")
region1.setDimensions(Dimensions([1, 1]))
# Link regions
network.link("region1", "region2", "UniformLink", "")
network.link("region2", "region1", "UniformLink", "")
network.link("region1", "region3", "UniformLink", "")
network.link("region2", "region3", "UniformLink", "")
# Initialize network
network.initialize()
# Initialize Network Visualizer
viz = NetworkVisualizer(network)
# Render w/ graphviz
viz.render(renderer=GraphVizRenderer)
# Render w/ networkx
viz.render(renderer=NetworkXRenderer)
# Render to dot (stdout)
viz.render(renderer=DotRenderer)
# Render to dot (file)
viz.render(renderer=lambda: DotRenderer(open("example.dot", "w")))
def create_network():
network = Network()
m_sensor = network.addRegion("Measurement", 'ScalarSensor',
json.dumps(_SCALAR_ENCODER))
dt_sensor = network.addRegion("DT", 'py.PluggableEncoderSensor', "")
dt_sensor.getSelf().encoder = DateEncoder(**_DATE_ENCODER)
# Add a SPRegion, a region containing a spatial pooler
scalar_n = m_sensor.getParameter('n')
dt_n = dt_sensor.getSelf().encoder.getWidth()
_SP_PARAMS["inputWidth"] = scalar_n + dt_n
network.addRegion("sp", "py.SPRegion", json.dumps(_SP_PARAMS))
# Input to the Spatial Pooler
network.link("Measurement", "sp", "UniformLink", "")
network.link("DT", "sp", "UniformLink", "")
# Add a TPRegion, a region containing a Temporal Memory
network.addRegion("tm", "py.TMRegion", json.dumps(_TM_PARAMS))
# Set up links
network.link("sp", "tm", "UniformLink", "")
network.link("tm",
"sp",
"UniformLink",
"",
srcOutput="topDownOut",
destInput="topDownIn")
network.regions['sp'].setParameter("learningMode", True)
network.regions['sp'].setParameter("anomalyMode", False)
# network.regions['tm'].setParameter("topDownMode", True) # check this
# Make sure learning is enabled (this is the default)
network.regions['tm'].setParameter("learningMode", True)
# Enable anomalyMode so the tm calculates anomaly scores
network.regions['tm'].setParameter("anomalyMode", True)
# Enable inference mode to be able to get predictions
network.regions['tm'].setParameter("inferenceMode", True)
# TODO: enable all inferences
return network
def testLinkingDownwardDimensions(self):
#
# Linking can induce dimensions downward
#
net = Network()
level1 = net.addRegion("level1", "TestNode", "")
level2 = net.addRegion("level2", "TestNode", "")
dims = Dimensions([3, 2])
level2.setDimensions(dims)
net.link("level1", "level2", "TestFanIn2", "")
net.initialize()
# Level1 should now have dimensions [6, 4]
self.assertEquals(level1.getDimensions()[0], 6)
self.assertEquals(level1.getDimensions()[1], 4)
#
# We get nice error messages when network can't be initialized
#
LOGGER.info("=====")
LOGGER.info("Creating a 3 level network in which levels 1 and 2 have")
LOGGER.info("dimensions but network initialization will fail because")
LOGGER.info("level3 does not have dimensions")
LOGGER.info("Error message follows:")
net = Network()
level1 = net.addRegion("level1", "TestNode", "")
level2 = net.addRegion("level2", "TestNode", "")
_level3 = net.addRegion("level3", "TestNode", "")
dims = Dimensions([6, 4])
level1.setDimensions(dims)
net.link("level1", "level2", "TestFanIn2", "")
self.assertRaises(RuntimeError, net.initialize)
LOGGER.info("=====")
LOGGER.info("======")
LOGGER.info("Creating a link with incompatible dimensions. \
Error message follows")
net.link("level2", "level3", "TestFanIn2", "")
self.assertRaises(RuntimeError, net.initialize)
def testLinkingDownwardDimensions(self):
#
# Linking can induce dimensions downward
#
net = Network()
level1 = net.addRegion("level1", "TestNode", "")
level2 = net.addRegion("level2", "TestNode", "")
dims = Dimensions([3, 2])
level2.setDimensions(dims)
net.link("level1", "level2", "TestFanIn2", "")
net.initialize()
# Level1 should now have dimensions [6, 4]
self.assertEqual(level1.getDimensions()[0], 6)
self.assertEqual(level1.getDimensions()[1], 4)
#
# We get nice error messages when network can't be initialized
#
LOGGER.info("=====")
LOGGER.info("Creating a 3 level network in which levels 1 and 2 have")
LOGGER.info("dimensions but network initialization will fail because")
LOGGER.info("level3 does not have dimensions")
LOGGER.info("Error message follows:")
net = Network()
level1 = net.addRegion("level1", "TestNode", "")
level2 = net.addRegion("level2", "TestNode", "")
_level3 = net.addRegion("level3", "TestNode", "")
dims = Dimensions([6, 4])
level1.setDimensions(dims)
net.link("level1", "level2", "TestFanIn2", "")
self.assertRaises(RuntimeError, net.initialize)
LOGGER.info("=====")
LOGGER.info("======")
LOGGER.info("Creating a link with incompatible dimensions. \
Error message follows")
net.link("level2", "level3", "TestFanIn2", "")
self.assertRaises(RuntimeError, net.initialize)
def createAnomalyNetwork(dataSource):
network = Network()
#sensor region
network.addRegion("sensor", "py.RecordSensor", json.dumps({"verbosity": _VERBOSITY}))
#encoder setup
sensorRegion = network.regions["sensor"].getSelf()
sensorRegion.encoder = createEncoder()
sensorRegion.dataSource = dataSource
#SP width must have sensor output width
SP_PARAMS["inputWidth"] = sensorRegion.encoder.getWidth()
#Add SP and TM regions
network.addRegion("SP", "py.SPRegion", json.dumps(SP_PARAMS))
network.link("sensor", "SP", "UniformLink", "")
network.link("sensor", "SP", "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
network.link("SP", "sensor", "UniformLink", "",
srcOutput="spatialTopDownOut", destInput="spatialTopDownIn")
network.link("SP", "sensor","UniformLink", "",
srcOutput ="temporalTopDownOut", destInput="temporalTopDownIn")
network.addRegion("TM", "py.TMRegion", json.dumps(TM_PARAMS))
network.link("SP", "TM", "UniformLink", "")
network.link("TM", "SP", "UniformLink", "", srcOutput="topDownOut", destInput="topDownIn")
#Add anomalyLikeliHood
network.addRegion("ALH", "py.AnomalyLikelihoodRegion", json.dumps({}))
network.link("TM", "ALH", "UniformLink", "", srcOutput="anomalyScore", destInput="rawAnomalyScore")
network.link("sensor", "ALH", "UniformLink", "", srcOutput="sourceOut", destInput="metricValue")
#set layer parameters
spRegion = network.regions["SP"]
spRegion.setParameter("learningMode", True)
spRegion.setParameter("anomalyMode", False)
tmRegion = network.regions["TM"]
tmRegion.setParameter("topDownMode", True)
tmRegion.setParameter("learningMode", True)
tmRegion.setParameter("inferenceMode", True)
tmRegion.setParameter("anomalyMode", True)
return network
def testSimpleMulticlassNetwork(self):
# Setup data record stream of fake data (with three categories)
filename = _getTempFileName()
fields = [("timestamp", "datetime", "T"), ("value", "float", ""),
("reset", "int", "R"), ("sid", "int", "S"),
("categories", "list", "C")]
records = ([datetime(day=1, month=3, year=2010), 0.0, 1, 0, ""], [
datetime(day=2, month=3, year=2010), 1.0, 0, 0, "1 2"
], [datetime(day=3, month=3, year=2010), 1.0, 0, 0,
"1 2"], [datetime(day=4, month=3, year=2010), 2.0, 0, 0, "0"], [
datetime(day=5, month=3, year=2010), 3.0, 0, 0, "1 2"
], [datetime(day=6, month=3, year=2010), 5.0, 0, 0,
"1 2"], [datetime(day=7, month=3, year=2010), 8.0, 0, 0, "0"],
[datetime(day=8, month=3, year=2010), 13.0, 0, 0, "1 2"])
dataSource = FileRecordStream(streamID=filename,
write=True,
fields=fields)
for r in records:
dataSource.appendRecord(list(r))
# Create the network and get region instances.
net = Network()
net.addRegion("sensor", "py.RecordSensor", "{'numCategories': 3}")
net.addRegion("classifier", "py.KNNClassifierRegion",
"{'k': 2,'distThreshold': 0,'maxCategoryCount': 3}")
net.link("sensor",
"classifier",
"UniformLink",
"",
srcOutput="dataOut",
destInput="bottomUpIn")
net.link("sensor",
"classifier",
"UniformLink",
"",
srcOutput="categoryOut",
destInput="categoryIn")
sensor = net.regions["sensor"]
classifier = net.regions["classifier"]
# Setup sensor region encoder and data stream.
dataSource.close()
dataSource = FileRecordStream(filename)
sensorRegion = sensor.getSelf()
sensorRegion.encoder = MultiEncoder()
sensorRegion.encoder.addEncoder(
"value", ScalarEncoder(21, 0.0, 13.0, n=256, name="value"))
sensorRegion.dataSource = dataSource
# Get ready to run.
net.initialize()
# Train the network (by default learning is ON in the classifier, but assert
# anyway) and then turn off learning and turn on inference mode.
self.assertEqual(classifier.getParameter("learningMode"), 1)
net.run(8)
classifier.setParameter("inferenceMode", 1)
classifier.setParameter("learningMode", 0)
# Assert learning is OFF and that the classifier learned the dataset.
self.assertEqual(classifier.getParameter("learningMode"), 0,
"Learning mode is not turned off.")
self.assertEqual(classifier.getParameter("inferenceMode"), 1,
"Inference mode is not turned on.")
self.assertEqual(
classifier.getParameter("categoryCount"), 3,
"The classifier should count three total categories.")
# classififer learns 12 patterns b/c there are 12 categories amongst the
# records:
self.assertEqual(
classifier.getParameter("patternCount"), 12,
"The classifier should've learned 12 samples in total.")
# Test the network on the same data as it trained on; should classify with
# 100% accuracy.
expectedCats = ([0.0, 0.5, 0.5], [0.0, 0.5, 0.5], [0.0, 0.5, 0.5],
[0.5, 0.5, 0.0], [0.0, 0.5,
0.5], [0.0, 0.5,
0.5], [0.5, 0.5,
0.0], [0.0, 0.5, 0.5])
dataSource.rewind()
for i in xrange(8):
net.run(1)
inferredCats = classifier.getOutputData("categoriesOut")
self.assertSequenceEqual(
expectedCats[i], inferredCats.tolist(),
"Classififer did not infer expected category probabilites for record "
"number {}.".format(i))
# Close data stream, delete file.
dataSource.close()
os.remove(filename)
def createMultiLevelNetwork(dataSource):
network = Network()
# Create and add a record sensor and a SP region
sensor = NetworkUtils.createRecordSensor(network, name=_RECORD_SENSOR,
dataSource=dataSource, multilevelAnomaly=True)
NetworkUtils.createSpatialPooler(network, name=_L1_SPATIAL_POOLER,
inputWidth=sensor.encoder.getWidth())
# Link the SP region to the sensor input
linkType = "UniformLink"
linkParams = ""
network.link(_RECORD_SENSOR, _L1_SPATIAL_POOLER, linkType, linkParams)
# Create and add a TM region
l1temporalMemory = NetworkUtils.createTemporalMemory(network, _L1_TEMPORAL_MEMORY)
# Link SP region to TM region in the feedforward direction
network.link(_L1_SPATIAL_POOLER, _L1_TEMPORAL_MEMORY, linkType, linkParams)
# Add a classifier
classifierParams = { # Learning rate. Higher values make it adapt faster.
'alpha': 0.005,
# A comma separated list of the number of steps the
# classifier predicts in the future. The classifier will
# learn predictions of each order specified.
'steps': '1,2,3,4,5,6,7',
# The specific implementation of the classifier to use
# See SDRClassifierFactory#create for options
'implementation': 'py',
# Diagnostic output verbosity control;
# 0: silent; [1..6]: increasing levels of verbosity
'verbosity': 0}
l1Classifier = network.addRegion(_L1_CLASSIFIER, "py.SDRClassifierRegion",
json.dumps(classifierParams))
l1Classifier.setParameter('inferenceMode', True)
l1Classifier.setParameter('learningMode', True)
network.link(_L1_TEMPORAL_MEMORY, _L1_CLASSIFIER, linkType, linkParams,
srcOutput="bottomUpOut", destInput="bottomUpIn")
network.link(_RECORD_SENSOR, _L1_CLASSIFIER, linkType, linkParams,
srcOutput="categoryOut", destInput="categoryIn")
network.link(_RECORD_SENSOR, _L1_CLASSIFIER, linkType, linkParams,
srcOutput="bucketIdxOut", destInput="bucketIdxIn")
network.link(_RECORD_SENSOR, _L1_CLASSIFIER, linkType, linkParams,
srcOutput="actValueOut", destInput="actValueIn")
# Second Level
l2inputWidth = l1temporalMemory.getSelf().getOutputElementCount("bottomUpOut")
NetworkUtils.createSpatialPooler(network, name=_L2_SPATIAL_POOLER, inputWidth=l2inputWidth)
network.link(_L1_TEMPORAL_MEMORY, _L2_SPATIAL_POOLER, linkType, linkParams)
NetworkUtils.createTemporalMemory(network, _L2_TEMPORAL_MEMORY)
network.link(_L2_SPATIAL_POOLER, _L2_TEMPORAL_MEMORY, linkType, linkParams)
l2Classifier = network.addRegion(_L2_CLASSIFIER, "py.SDRClassifierRegion",
json.dumps(classifierParams))
l2Classifier.setParameter('inferenceMode', True)
l2Classifier.setParameter('learningMode', True)
network.link(_L2_TEMPORAL_MEMORY, _L2_CLASSIFIER, linkType, linkParams,
srcOutput="bottomUpOut", destInput="bottomUpIn")
network.link(_RECORD_SENSOR, _L2_CLASSIFIER, linkType, linkParams,
srcOutput="categoryOut", destInput="categoryIn")
network.link(_RECORD_SENSOR, _L2_CLASSIFIER, linkType, linkParams,
srcOutput="bucketIdxOut", destInput="bucketIdxIn")
network.link(_RECORD_SENSOR, _L2_CLASSIFIER, linkType, linkParams,
srcOutput="actValueOut", destInput="actValueIn")
steps = l2Classifier.getSelf().stepsList
# initialize the results matrix, after the classifer has been defined
w, h = len(steps), len(steps)+1
global results
results = [[-1 for x in range(w)] for y in range(h)]
global l1ErrorSum
l2ErrorSum = [-1 for x in range(h)]
#print("Length: "+str(len(steps)))
return network
def _createOPFNetwork(addSP=True, addTP=False):
"""Create a 'new-style' network ala OPF and return it.
If addSP is true, an SPRegion will be added named 'level1SP'.
If addTP is true, a TPRegion will be added named 'level1TP'
"""
# ==========================================================================
# Create the encoder and data source stuff we need to configure the sensor
sensorParams = dict(verbosity=_VERBOSITY)
encoder = _createEncoder()
trainFile = resource_filename("nupic.datafiles", "extra/gym/gym.csv")
dataSource = FileRecordStream(streamID=trainFile)
dataSource.setAutoRewind(True)
# ==========================================================================
# Now create the network itself
n = Network()
n.addRegion("sensor", "py.RecordSensor", json.dumps(sensorParams))
sensor = n.regions['sensor'].getSelf()
sensor.encoder = encoder
sensor.dataSource = dataSource
# ==========================================================================
# Add the SP if requested
if addSP:
print "Adding SPRegion"
g_spRegionConfig['inputWidth'] = encoder.getWidth()
n.addRegion("level1SP", "py.SPRegion", json.dumps(g_spRegionConfig))
n.link("sensor", "level1SP", "UniformLink", "")
n.link("sensor",
"level1SP",
"UniformLink",
"",
srcOutput="resetOut",
destInput="resetIn")
n.link("level1SP",
"sensor",
"UniformLink",
"",
srcOutput="spatialTopDownOut",
destInput="spatialTopDownIn")
n.link("level1SP",
"sensor",
"UniformLink",
"",
srcOutput="temporalTopDownOut",
destInput="temporalTopDownIn")
# ==========================================================================
if addTP and addSP:
# Add the TP on top of SP if requested
# The input width of the TP is set to the column count of the SP
print "Adding TPRegion on top of SP"
g_tpRegionConfig['inputWidth'] = g_spRegionConfig['columnCount']
n.addRegion("level1TP", "py.TPRegion", json.dumps(g_tpRegionConfig))
n.link("level1SP", "level1TP", "UniformLink", "")
n.link("level1TP",
"level1SP",
"UniformLink",
"",
srcOutput="topDownOut",
destInput="topDownIn")
n.link("sensor",
"level1TP",
"UniformLink",
"",
srcOutput="resetOut",
destInput="resetIn")
elif addTP:
# Add a lone TPRegion if requested
# The input width of the TP is set to the encoder width
print "Adding TPRegion"
g_tpRegionConfig['inputWidth'] = encoder.getWidth()
n.addRegion("level1TP", "py.TPRegion", json.dumps(g_tpRegionConfig))
n.link("sensor", "level1TP", "UniformLink", "")
n.link("sensor",
"level1TP",
"UniformLink",
"",
srcOutput="resetOut",
destInput="resetIn")
return n
def testSimpleMulticlassNetwork(self):
# Setup data record stream of fake data (with three categories)
filename = _getTempFileName()
fields = [("timestamp", "datetime", "T"),
("value", "float", ""),
("reset", "int", "R"),
("sid", "int", "S"),
("categories", "list", "C")]
records = (
[datetime(day=1, month=3, year=2010), 0.0, 1, 0, ""],
[datetime(day=2, month=3, year=2010), 1.0, 0, 0, "1 2"],
[datetime(day=3, month=3, year=2010), 1.0, 0, 0, "1 2"],
[datetime(day=4, month=3, year=2010), 2.0, 0, 0, "0"],
[datetime(day=5, month=3, year=2010), 3.0, 0, 0, "1 2"],
[datetime(day=6, month=3, year=2010), 5.0, 0, 0, "1 2"],
[datetime(day=7, month=3, year=2010), 8.0, 0, 0, "0"],
[datetime(day=8, month=3, year=2010), 13.0, 0, 0, "1 2"])
dataSource = FileRecordStream(streamID=filename, write=True, fields=fields)
for r in records:
dataSource.appendRecord(list(r))
# Create the network and get region instances.
net = Network()
net.addRegion("sensor", "py.RecordSensor", "{'numCategories': 3}")
net.addRegion("classifier","py.KNNClassifierRegion",
"{'k': 2,'distThreshold': 0,'maxCategoryCount': 3}")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput = "dataOut", destInput = "bottomUpIn")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput = "categoryOut", destInput = "categoryIn")
sensor = net.regions["sensor"]
classifier = net.regions["classifier"]
# Setup sensor region encoder and data stream.
dataSource.close()
dataSource = FileRecordStream(filename)
sensorRegion = sensor.getSelf()
sensorRegion.encoder = MultiEncoder()
sensorRegion.encoder.addEncoder(
"value", ScalarEncoder(21, 0.0, 13.0, n=256, name="value"))
sensorRegion.dataSource = dataSource
# Get ready to run.
net.initialize()
# Train the network (by default learning is ON in the classifier, but assert
# anyway) and then turn off learning and turn on inference mode.
self.assertEqual(classifier.getParameter("learningMode"), 1)
net.run(8)
classifier.setParameter("inferenceMode", 1)
classifier.setParameter("learningMode", 0)
# Assert learning is OFF and that the classifier learned the dataset.
self.assertEqual(classifier.getParameter("learningMode"), 0,
"Learning mode is not turned off.")
self.assertEqual(classifier.getParameter("inferenceMode"), 1,
"Inference mode is not turned on.")
self.assertEqual(classifier.getParameter("categoryCount"), 3,
"The classifier should count three total categories.")
# classififer learns 12 patterns b/c there are 12 categories amongst the
# records:
self.assertEqual(classifier.getParameter("patternCount"), 12,
"The classifier should've learned 12 samples in total.")
# Test the network on the same data as it trained on; should classify with
# 100% accuracy.
expectedCats = ([0.0, 0.5, 0.5],
[0.0, 0.5, 0.5],
[0.0, 0.5, 0.5],
[0.5, 0.5, 0.0],
[0.0, 0.5, 0.5],
[0.0, 0.5, 0.5],
[0.5, 0.5, 0.0],
[0.0, 0.5, 0.5])
dataSource.rewind()
for i in xrange(8):
net.run(1)
inferredCats = classifier.getOutputData("categoriesOut")
self.assertSequenceEqual(expectedCats[i], inferredCats.tolist(),
"Classififer did not infer expected category probabilites for record "
"number {}.".format(i))
# Close data stream, delete file.
dataSource.close()
os.remove(filename)
def createNetwork():
network = Network()
#
# Sensors
#
# C++
consumptionSensor = network.addRegion(
'consumptionSensor', 'ScalarSensor',
json.dumps({
'n': 120,
'w': 21,
'minValue': 0.0,
'maxValue': 100.0,
'clipInput': True
}))
# Python
timestampSensor = network.addRegion("timestampSensor",
'py.PluggableEncoderSensor', "")
timestampSensor.getSelf().encoder = DateEncoder(timeOfDay=(21, 9.5),
name="timestamp_timeOfDay")
#
# Add a SPRegion, a region containing a spatial pooler
#
consumptionEncoderN = consumptionSensor.getParameter('n')
timestampEncoderN = timestampSensor.getSelf().encoder.getWidth()
inputWidth = consumptionEncoderN + timestampEncoderN
network.addRegion(
"sp", "py.SPRegion",
json.dumps({
"spatialImp": "cpp",
"globalInhibition": 1,
"columnCount": 2048,
"inputWidth": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"synPermConnected": 0.1,
"synPermActiveInc": 0.0001,
"synPermInactiveDec": 0.0005,
"boostStrength": 0.0,
}))
#
# Input to the Spatial Pooler
#
network.link("consumptionSensor", "sp", "UniformLink", "")
network.link("timestampSensor", "sp", "UniformLink", "")
#
# Add a TPRegion, a region containing a Temporal Memory
#
network.addRegion(
"tm", "py.TMRegion",
json.dumps({
"columnCount": 2048,
"cellsPerColumn": 32,
"inputWidth": 2048,
"seed": 1960,
"temporalImp": "cpp",
"newSynapseCount": 20,
"maxSynapsesPerSegment": 32,
"maxSegmentsPerCell": 128,
"initialPerm": 0.21,
"permanenceInc": 0.1,
"permanenceDec": 0.1,
"globalDecay": 0.0,
"maxAge": 0,
"minThreshold": 9,
"activationThreshold": 12,
"outputType": "normal",
"pamLength": 3,
}))
network.link("sp", "tm", "UniformLink", "")
network.link("tm",
"sp",
"UniformLink",
"",
srcOutput="topDownOut",
destInput="topDownIn")
# Enable anomalyMode so the tm calculates anomaly scores
network.regions['tm'].setParameter("anomalyMode", True)
# Enable inference mode to be able to get predictions
network.regions['tm'].setParameter("inferenceMode", True)
return network
def _createNetwork(inverseReadoutResolution, anchorInputSize, dualPhase=False):
"""
Create a simple network connecting sensor and motor inputs to the location
region. Use :meth:`RawSensor.addDataToQueue` to add sensor input and growth
candidates. Use :meth:`RawValues.addDataToQueue` to add motor input.
::
+----------+
[ sensor* ] --> | | --> [ activeCells ]
[ candidates* ] --> | location | --> [ learnableCells ]
[ motor ] --> | | --> [ sensoryAssociatedCells ]
+----------+
:param inverseReadoutResolution:
Specifies the diameter of the circle of phases in the rhombus encoded by a
bump.
:type inverseReadoutResolution: int
:type anchorInputSize: int
:param anchorInputSize:
The number of input bits in the anchor input.
.. note::
(*) This function will only add the 'sensor' and 'candidates' regions when
'anchorInputSize' is greater than zero. This is useful if you would like to
compute locations ignoring sensor input
.. seealso::
- :py:func:`htmresearch.frameworks.location.path_integration_union_narrowing.createRatModuleFromReadoutResolution`
"""
net = Network()
# Create simple region to pass motor commands as displacement vectors (dx, dy)
net.addRegion("motor", "py.RawValues", json.dumps({
"outputWidth": 2
}))
if anchorInputSize > 0:
# Create simple region to pass growth candidates
net.addRegion("candidates", "py.RawSensor", json.dumps({
"outputWidth": anchorInputSize
}))
# Create simple region to pass sensor input
net.addRegion("sensor", "py.RawSensor", json.dumps({
"outputWidth": anchorInputSize
}))
# Initialize region with 5 modules varying scale by sqrt(2) and 4 different
# random orientations for each scale
scale = []
orientation = []
for i in xrange(5):
for _ in xrange(4):
angle = np.radians(random.gauss(7.5, 7.5))
orientation.append(random.choice([angle, -angle]))
scale.append(10.0 * (math.sqrt(2) ** i))
# Create location region
params = computeRatModuleParametersFromReadoutResolution(inverseReadoutResolution)
params.update({
"moduleCount": len(scale),
"scale": scale,
"orientation": orientation,
"anchorInputSize": anchorInputSize,
"activationThreshold": 8,
"initialPermanence": 1.0,
"connectedPermanence": 0.5,
"learningThreshold": 8,
"sampleSize": 10,
"permanenceIncrement": 0.1,
"permanenceDecrement": 0.0,
"dualPhase": dualPhase,
"bumpOverlapMethod": "probabilistic"
})
net.addRegion("location", "py.GridCellLocationRegion", json.dumps(params))
if anchorInputSize > 0:
# Link sensor
net.link("sensor", "location", "UniformLink", "",
srcOutput="dataOut", destInput="anchorInput")
net.link("sensor", "location", "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
net.link("candidates", "location", "UniformLink", "",
srcOutput="dataOut", destInput="anchorGrowthCandidates")
# Link motor input
net.link("motor", "location", "UniformLink", "",
srcOutput="dataOut", destInput="displacement")
# Initialize network objects
net.initialize()
return net
def createNet(self):
""" Set up the structure of the network """
net = Network()
Network.unregisterRegion(SaccadeSensor.__name__)
Network.registerRegion(SaccadeSensor)
Network.unregisterRegion(ExtendedTMRegion.__name__)
Network.registerRegion(ExtendedTMRegion)
Network.unregisterRegion(ColumnPoolerRegion.__name__)
Network.registerRegion(ColumnPoolerRegion)
imageSensorParams = copy.deepcopy(DEFAULT_IMAGESENSOR_PARAMS)
if self.loggingDir is not None:
imageSensorParams["logDir"] = "sensorImages/" + self.loggingDir
imageSensorParams["logOutputImages"] = 1
imageSensorParams["logOriginalImages"] = 1
imageSensorParams["logFilteredImages"] = 1
imageSensorParams["logLocationImages"] = 1
imageSensorParams["logLocationOnOriginalImage"] = 1
net.addRegion("sensor", "py.SaccadeSensor",
yaml.dump(imageSensorParams))
sensor = net.regions["sensor"].getSelf()
DEFAULT_SP_PARAMS["columnCount"] = sensor.getOutputElementCount(
"dataOut")
net.addRegion("SP", "py.SPRegion", yaml.dump(DEFAULT_SP_PARAMS))
sp = net.regions["SP"].getSelf()
DEFAULT_TM_PARAMS["columnDimensions"] = (
sp.getOutputElementCount("bottomUpOut"), )
DEFAULT_TM_PARAMS["basalInputWidth"] = sensor.getOutputElementCount(
"saccadeOut")
net.addRegion("TM", "py.ExtendedTMRegion",
yaml.dump(DEFAULT_TM_PARAMS))
net.addRegion("TP", "py.ColumnPoolerRegion",
yaml.dump(DEFAULT_TP_PARAMS))
net.addRegion("classifier", "py.KNNClassifierRegion",
yaml.dump(DEFAULT_CLASSIFIER_PARAMS))
net.link("sensor",
"SP",
"UniformLink",
"",
srcOutput="dataOut",
destInput="bottomUpIn")
net.link("SP",
"TM",
"UniformLink",
"",
srcOutput="bottomUpOut",
destInput="activeColumns")
net.link("sensor",
"TM",
"UniformLink",
"",
srcOutput="saccadeOut",
destInput="basalInput")
net.link("TM",
"TP",
"UniformLink",
"",
srcOutput="predictedActiveCells",
destInput="feedforwardInput")
net.link("TP",
"TM",
"UniformLink",
"",
srcOutput="feedForwardOutput",
destInput="apicalInput")
net.link("TP",
"classifier",
"UniformLink",
"",
srcOutput="feedForwardOutput",
destInput="bottomUpIn")
#net.link("TM", "classifier", "UniformLink", "",
# srcOutput="predictedActiveCells", destInput="bottomUpIn")
net.link("sensor",
"classifier",
"UniformLink",
"",
srcOutput="categoryOut",
destInput="categoryIn")
self.net = net
self.networkSensor = self.net.regions["sensor"]
self.networkSP = self.net.regions["SP"]
self.networkTM = self.net.regions["TM"]
self.networkTP = self.net.regions["TP"]
self.networkClassifier = self.net.regions["classifier"]
def createNetwork(dataSource):
"""Create the Network instance.
The network has a sensor region reading data from `dataSource` and passing
the encoded representation to an SPRegion. The SPRegion output is passed to
a TPRegion.
:param dataSource: a RecordStream instance to get data from
:returns: a Network instance ready to run
"""
network = Network()
# Our input is sensor data from the gym file. The RecordSensor region
# allows us to specify a file record stream as the input source via the
# dataSource attribute.
network.addRegion("sensor", "py.RecordSensor", json.dumps({"verbosity": _VERBOSITY}))
sensor = network.regions["sensor"].getSelf()
# The RecordSensor needs to know how to encode the input values
sensor.encoder = createEncoder()
# Specify the dataSource as a file record stream instance
sensor.dataSource = dataSource
# Create the spatial pooler region
SP_PARAMS["inputWidth"] = sensor.encoder.getWidth()
network.addRegion("spatialPoolerRegion", "py.SPRegion", json.dumps(SP_PARAMS))
# Link the SP region to the sensor input
network.link("sensor", "spatialPoolerRegion", "UniformLink", "")
network.link("sensor", "spatialPoolerRegion", "UniformLink", "", srcOutput="resetOut", destInput="resetIn")
network.link(
"spatialPoolerRegion", "sensor", "UniformLink", "", srcOutput="spatialTopDownOut", destInput="spatialTopDownIn"
)
network.link(
"spatialPoolerRegion",
"sensor",
"UniformLink",
"",
srcOutput="temporalTopDownOut",
destInput="temporalTopDownIn",
)
# Add the TPRegion on top of the SPRegion
network.addRegion("temporalPoolerRegion", "py.TPRegion", json.dumps(TP_PARAMS))
network.link("spatialPoolerRegion", "temporalPoolerRegion", "UniformLink", "")
network.link(
"temporalPoolerRegion", "spatialPoolerRegion", "UniformLink", "", srcOutput="topDownOut", destInput="topDownIn"
)
# Add the AnomalyRegion on top of the TPRegion
network.addRegion("anomalyRegion", "py.AnomalyRegion", json.dumps({}))
network.link(
"spatialPoolerRegion", "anomalyRegion", "UniformLink", "", srcOutput="bottomUpOut", destInput="activeColumns"
)
network.link(
"temporalPoolerRegion", "anomalyRegion", "UniformLink", "", srcOutput="topDownOut", destInput="predictedColumns"
)
spatialPoolerRegion = network.regions["spatialPoolerRegion"]
# Make sure learning is enabled
spatialPoolerRegion.setParameter("learningMode", True)
# We want temporal anomalies so disable anomalyMode in the SP. This mode is
# used for computing anomalies in a non-temporal model.
spatialPoolerRegion.setParameter("anomalyMode", False)
temporalPoolerRegion = network.regions["temporalPoolerRegion"]
# Enable topDownMode to get the predicted columns output
temporalPoolerRegion.setParameter("topDownMode", True)
# Make sure learning is enabled (this is the default)
temporalPoolerRegion.setParameter("learningMode", True)
# Enable inference mode so we get predictions
temporalPoolerRegion.setParameter("inferenceMode", True)
# Enable anomalyMode to compute the anomaly score. This actually doesn't work
# now so doesn't matter. We instead compute the anomaly score based on
# topDownOut (predicted columns) and SP bottomUpOut (active columns).
temporalPoolerRegion.setParameter("anomalyMode", True)
return network
def testTwoNode(self):
# =====================================================
# Build and run the network
# =====================================================
net = Network()
level1 = net.addRegion("level1", "TestNode", "{int32Param: 15}")
dims = Dimensions([6, 4])
level1.setDimensions(dims)
level2 = net.addRegion("level2", "TestNode", "{real64Param: 128.23}")
net.link("level1", "level2", "TestFanIn2", "")
# Could call initialize here, but not necessary as net.run()
# initializes implicitly.
# net.initialize()
net.run(1)
LOGGER.info("Successfully created network and ran for one iteration")
# =====================================================
# Check everything
# =====================================================
dims = level1.getDimensions()
self.assertEquals(len(dims), 2)
self.assertEquals(dims[0], 6)
self.assertEquals(dims[1], 4)
dims = level2.getDimensions()
self.assertEquals(len(dims), 2)
self.assertEquals(dims[0], 3)
self.assertEquals(dims[1], 2)
# Check L1 output. "False" means don't copy, i.e.
# get a pointer to the actual output
# Actual output values are determined by the TestNode
# compute() behavior.
l1output = level1.getOutputData("bottomUpOut")
self.assertEquals(len(l1output), 48) # 24 nodes; 2 values per node
for i in xrange(24):
self.assertEquals(l1output[2*i], 0) # size of input to each node is 0
self.assertEquals(l1output[2*i+1], i) # node number
# check L2 output.
l2output = level2.getOutputData("bottomUpOut", )
self.assertEquals(len(l2output), 12) # 6 nodes; 2 values per node
# Output val = node number + sum(inputs)
# Can compute from knowing L1 layout
#
# 00 01 | 02 03 | 04 05
# 06 07 | 08 09 | 10 11
# ---------------------
# 12 13 | 14 15 | 16 17
# 18 19 | 20 21 | 22 23
outputVals = []
outputVals.append(0 + (0 + 1 + 6 + 7))
outputVals.append(1 + (2 + 3 + 8 + 9))
outputVals.append(2 + (4 + 5 + 10 + 11))
outputVals.append(3 + (12 + 13 + 18 + 19))
outputVals.append(4 + (14 + 15 + 20 + 21))
outputVals.append(5 + (16 + 17 + 22 + 23))
for i in xrange(6):
self.assertEquals(l2output[2*i], 8) # size of input for each node is 8
self.assertEquals(l2output[2*i+1], outputVals[i])
# =====================================================
# Run for one more iteration
# =====================================================
LOGGER.info("Running for a second iteration")
net.run(1)
# =====================================================
# Check everything again
# =====================================================
# Outputs are all the same except that the first output is
# incremented by the iteration number
for i in xrange(24):
self.assertEquals(l1output[2*i], 1)
self.assertEquals(l1output[2*i+1], i)
for i in xrange(6):
self.assertEquals(l2output[2*i], 9)
self.assertEquals(l2output[2*i+1], outputVals[i] + 4)
def createNetwork():
# c network: create Network instance
network = Network()
# --------------------------------------------------
# Add sensors to network
# c param_f_consumptionSensor: parameter for consumptionSensor
param_f_consumptionSensor={
'n': 120,
'w': 21,
'minValue': 0.0,
'maxValue': 100.0,
'clipInput': True}
# c jparam_f_cs: json param for consumptionSensor
jparam_f_cs=json.dumps(param_f_consumptionSensor)
# C++
# c consumptionSensor: add consumptionSensor region into network
consumptionSensor = network.addRegion(
'consumptionSensor', 'ScalarSensor', jparam_f_cs)
# --------------------------------------------------
# Python
# c timestampSensor: add timestampSensor region into network
timestampSensor = network.addRegion(
"timestampSensor",'py.PluggableEncoderSensor', "")
# c date_encoder: create date encoder
date_encoder=DateEncoder(timeOfDay=(21, 9.5), name="timestamp_timeOfDay")
# c date_encoder: assing date encoder into timestampSensor
timestampSensor.getSelf().encoder = date_encoder
# --------------------------------------------------
# c consumptionEncoderN: get number of bits "n" from consumptionSensor
consumptionEncoderN = consumptionSensor.getParameter('n')
# print("consumptionEncoderN",consumptionEncoderN)
# ('consumptionEncoderN', 120)
# print("timestampSensor.getSelf()",timestampSensor.getSelf())
# <nupic.regions.pluggable_encoder_sensor.PluggableEncoderSensor object at 0x7fa428bf31d0>
# c encoder_of_tss: encoder of timestampSensor
encoder_of_tss=timestampSensor.getSelf().encoder
# c timestampEncoderN: width of encoder of timestampSensor
timestampEncoderN = encoder_of_tss.getWidth()
# print("timestampEncoderN",timestampEncoderN)
# ('timestampEncoderN', 54)
# c inputWidth: width of input
inputWidth = consumptionEncoderN + timestampEncoderN
# print("inputWidth",inputWidth)
# ('inputWidth', 174)
# --------------------------------------------------
# c param_f_SP: parameter for spatial pooler
param_f_SP={
# c spatialImp: spatial pooler implementation in C++
"spatialImp": "cpp",
# c globalInhibition: 1 -> on
"globalInhibition": 1,
"columnCount": 2048,
"inputWidth": inputWidth,
# c numActiveColumnsPerInhArea: number of active columns per inhibition area
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
# c potentialPct: potential pool percent
"potentialPct": 0.8,
# c "synPermConnected: synaptic permanence connected
"synPermConnected": 0.1,
# c synPermActiveInc: synaptic permanence active increment
"synPermActiveInc": 0.0001,
# c synPermInactiveDec: synaptic permanence inactive decrement
"synPermInactiveDec": 0.0005,
"boostStrength": 0.0,}
# c param_f_SP_j: parameter for spatial pooler in JSON
param_f_SP_j=json.dumps(param_f_SP)
# c Add "SPRegion" into network
# SPRegion can contain spatial pooler
network.addRegion("sp", "py.SPRegion", param_f_SP_j)
# --------------------------------------------------
# Link each configured one in network
network.link("consumptionSensor", "sp", "UniformLink", "")
network.link("timestampSensor", "sp", "UniformLink", "")
# --------------------------------------------------
# c param_f_TM: parameter for temporal memory learning algorithm
param_f_TM={
"columnCount": 2048,
"cellsPerColumn": 32,
"inputWidth": 2048,
"seed": 1960,
"temporalImp": "cpp",
"newSynapseCount": 20,
# c maxSynapsesPerSegment: maximum number of synapses per segment
"maxSynapsesPerSegment": 32,
# c maxSegmentsPerCell: maximum number of segments per cell
"maxSegmentsPerCell": 128,
# c initialPerm: initial permanence value for newly created synapses
"initialPerm": 0.21,
# c permanenceInc: active synapses get their permanence counts incremented by this value
"permanenceInc": 0.1,
# c permanenceDec: all other synapses get their permanence counts decremented by this value
"permanenceDec": 0.1,
"globalDecay": 0.0,
"maxAge": 0,
"minThreshold": 9,
# c activationThreshold: if "number of active connected synapses" on segment is
# c activationThreshold: at least this threshold, segment is said to be active
"activationThreshold": 12,
"outputType": "normal",
"pamLength": 3,}
# c param_f_TM_j: parameter for temporal memory learning algorithm in JSON
param_f_TM_j=json.dumps(param_f_TM)
# Add TMRegion into network
# TMRegion is region containing "Temporal Memory Learning algorithm"
network.addRegion("tm", "py.TMRegion", param_f_TM_j)
# --------------------------------------------------
network.link("sp", "tm", "UniformLink", "")
network.link("tm", "sp", "UniformLink", "", srcOutput="topDownOut", destInput="topDownIn")
# --------------------------------------------------
# Enable anomalyMode so TM calculates anomaly scores
network.regions['tm'].setParameter("anomalyMode", True)
# Enable inference mode to be able to get predictions
network.regions['tm'].setParameter("inferenceMode", True)
return network
def createTemporalAnomaly_chemical(recordParams, spatialParams, temporalParams,
verbosity):
inputFilePath = recordParams["inputFilePath"]
scalarEncoder1Args = recordParams["scalarEncoder1Args"]
scalarEncoder2Args = recordParams["scalarEncoder2Args"]
scalarEncoder3Args = recordParams["scalarEncoder3Args"]
scalarEncoder4Args = recordParams["scalarEncoder4Args"]
scalarEncoder5Args = recordParams["scalarEncoder5Args"]
scalarEncoder6Args = recordParams["scalarEncoder6Args"]
scalarEncoder7Args = recordParams["scalarEncoder7Args"]
dateEncoderArgs = recordParams["dateEncoderArgs"]
scalarEncoder1 = ScalarEncoder(**scalarEncoder1Args)
scalarEncoder2 = ScalarEncoder(**scalarEncoder2Args)
scalarEncoder3 = ScalarEncoder(**scalarEncoder3Args)
scalarEncoder4 = ScalarEncoder(**scalarEncoder4Args)
scalarEncoder5 = ScalarEncoder(**scalarEncoder5Args)
scalarEncoder6 = ScalarEncoder(**scalarEncoder6Args)
scalarEncoder7 = ScalarEncoder(**scalarEncoder7Args)
dateEncoder = DateEncoder(**dateEncoderArgs)
encoder = MultiEncoder()
encoder.addEncoder(scalarEncoder1Args["name"], scalarEncoder1)
encoder.addEncoder(scalarEncoder2Args["name"], scalarEncoder2)
encoder.addEncoder(scalarEncoder3Args["name"], scalarEncoder3)
encoder.addEncoder(scalarEncoder4Args["name"], scalarEncoder4)
encoder.addEncoder(scalarEncoder5Args["name"], scalarEncoder5)
encoder.addEncoder(scalarEncoder6Args["name"], scalarEncoder6)
encoder.addEncoder(scalarEncoder7Args["name"], scalarEncoder7)
encoder.addEncoder(dateEncoderArgs["name"], dateEncoder)
network = Network()
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": verbosity}))
sensor = network.regions["sensor"].getSelf()
sensor.encoder = encoder
sensor.dataSource = FileRecordStream(streamID=inputFilePath)
# Create the spatial pooler region
spatialParams["inputWidth"] = sensor.encoder.getWidth()
network.addRegion("spatialPoolerRegion", "py.SPRegion",
json.dumps(spatialParams))
# Link the SP region to the sensor input
network.link("sensor", "spatialPoolerRegion", "UniformLink", "")
network.link("sensor",
"spatialPoolerRegion",
"UniformLink",
"",
srcOutput="resetOut",
destInput="resetIn")
network.link("spatialPoolerRegion",
"sensor",
"UniformLink",
"",
srcOutput="spatialTopDownOut",
destInput="spatialTopDownIn")
network.link("spatialPoolerRegion",
"sensor",
"UniformLink",
"",
srcOutput="temporalTopDownOut",
destInput="temporalTopDownIn")
# Add the TPRegion on top of the SPRegion
network.addRegion("temporalPoolerRegion", "py.TMRegion",
json.dumps(temporalParams))
network.link("spatialPoolerRegion", "temporalPoolerRegion", "UniformLink",
"")
network.link("temporalPoolerRegion",
"spatialPoolerRegion",
"UniformLink",
"",
srcOutput="topDownOut",
destInput="topDownIn")
# Add the AnomalyLikelihoodRegion on top of the TMRegion
network.addRegion("anomalyLikelihoodRegion", "py.AnomalyLikelihoodRegion",
json.dumps({}))
network.link("temporalPoolerRegion",
"anomalyLikelihoodRegion",
"UniformLink",
"",
srcOutput="anomalyScore",
destInput="rawAnomalyScore")
network.link("sensor",
"anomalyLikelihoodRegion",
"UniformLink",
"",
srcOutput="sourceOut",
destInput="metricValue")
spatialPoolerRegion = network.regions["spatialPoolerRegion"]
# Make sure learning is enabled
spatialPoolerRegion.setParameter("learningMode", True)
# We want temporal anomalies so disable anomalyMode in the SP. This mode is
# used for computing anomalies in a non-temporal model.
spatialPoolerRegion.setParameter("anomalyMode", False)
temporalPoolerRegion = network.regions["temporalPoolerRegion"]
# Enable topDownMode to get the predicted columns output
temporalPoolerRegion.setParameter("topDownMode", True)
# Make sure learning is enabled (this is the default)
temporalPoolerRegion.setParameter("learningMode", True)
# Enable inference mode so we get predictions
temporalPoolerRegion.setParameter("inferenceMode", True)
# Enable anomalyMode to compute the anomaly score.
temporalPoolerRegion.setParameter("anomalyMode", True)
return network
def createTemporalAnomaly(recordParams, spatialParams=_SP_PARAMS,
temporalParams=_TP_PARAMS,
verbosity=_VERBOSITY):
"""Generates a Network with connected RecordSensor, SP, TP.
This function takes care of generating regions and the canonical links.
The network has a sensor region reading data from a specified input and
passing the encoded representation to an SPRegion.
The SPRegion output is passed to a TPRegion.
Note: this function returns a network that needs to be initialized. This
allows the user to extend the network by adding further regions and
connections.
:param recordParams: a dict with parameters for creating RecordSensor region.
:param spatialParams: a dict with parameters for creating SPRegion.
:param temporalParams: a dict with parameters for creating TPRegion.
:param verbosity: an integer representing how chatty the network will be.
"""
inputFilePath= recordParams["inputFilePath"]
scalarEncoderArgs = recordParams["scalarEncoderArgs"]
dateEncoderArgs = recordParams["dateEncoderArgs"]
scalarEncoder = ScalarEncoder(**scalarEncoderArgs)
dateEncoder = DateEncoder(**dateEncoderArgs)
encoder = MultiEncoder()
encoder.addEncoder(scalarEncoderArgs["name"], scalarEncoder)
encoder.addEncoder(dateEncoderArgs["name"], dateEncoder)
network = Network()
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": verbosity}))
sensor = network.regions["sensor"].getSelf()
sensor.encoder = encoder
sensor.dataSource = FileRecordStream(streamID=inputFilePath)
# Create the spatial pooler region
spatialParams["inputWidth"] = sensor.encoder.getWidth()
network.addRegion("spatialPoolerRegion", "py.SPRegion",
json.dumps(spatialParams))
# Link the SP region to the sensor input
network.link("sensor", "spatialPoolerRegion", "UniformLink", "")
network.link("sensor", "spatialPoolerRegion", "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
network.link("spatialPoolerRegion", "sensor", "UniformLink", "",
srcOutput="spatialTopDownOut", destInput="spatialTopDownIn")
network.link("spatialPoolerRegion", "sensor", "UniformLink", "",
srcOutput="temporalTopDownOut", destInput="temporalTopDownIn")
# Add the TPRegion on top of the SPRegion
network.addRegion("temporalPoolerRegion", "py.TPRegion",
json.dumps(temporalParams))
network.link("spatialPoolerRegion", "temporalPoolerRegion", "UniformLink", "")
network.link("temporalPoolerRegion", "spatialPoolerRegion", "UniformLink", "",
srcOutput="topDownOut", destInput="topDownIn")
spatialPoolerRegion = network.regions["spatialPoolerRegion"]
# Make sure learning is enabled
spatialPoolerRegion.setParameter("learningMode", True)
# We want temporal anomalies so disable anomalyMode in the SP. This mode is
# used for computing anomalies in a non-temporal model.
spatialPoolerRegion.setParameter("anomalyMode", False)
temporalPoolerRegion = network.regions["temporalPoolerRegion"]
# Enable topDownMode to get the predicted columns output
temporalPoolerRegion.setParameter("topDownMode", True)
# Make sure learning is enabled (this is the default)
temporalPoolerRegion.setParameter("learningMode", True)
# Enable inference mode so we get predictions
temporalPoolerRegion.setParameter("inferenceMode", True)
# Enable anomalyMode to compute the anomaly score.
temporalPoolerRegion.setParameter("anomalyMode", True)
return network
def myCreateNetwork(self, networkConfig):
suffix = '_0'
network = Network()
sensorInputName = "sensorInput" + suffix
L4ColumnName = "L4Column" + suffix
L2ColumnName = "L2Column" + suffix
L4Params = copy.deepcopy(networkConfig["L4Params"])
L4Params["apicalInputWidth"] = networkConfig["L2Params"]["cellCount"]
network.addRegion(
sensorInputName, "py.RawSensor",
json.dumps({"outputWidth": networkConfig["sensorInputSize"]}))
network.addRegion(L4ColumnName, networkConfig["L4RegionType"],
json.dumps(L4Params))
network.addRegion(L2ColumnName, "py.ColumnPoolerRegion",
json.dumps(networkConfig["L2Params"]))
network.setPhases(sensorInputName, [0])
# L4 and L2 regions always have phases 2 and 3, respectively
network.setPhases(L4ColumnName, [2])
network.setPhases(L2ColumnName, [3])
network.link(sensorInputName,
L4ColumnName,
"UniformLink",
"",
srcOutput="dataOut",
destInput="activeColumns")
# Link L4 to L2
network.link(L4ColumnName,
L2ColumnName,
"UniformLink",
"",
srcOutput="activeCells",
destInput="feedforwardInput")
network.link(L4ColumnName,
L2ColumnName,
"UniformLink",
"",
srcOutput="winnerCells",
destInput="feedforwardGrowthCandidates")
# Link L2 feedback to L4
network.link(L2ColumnName,
L4ColumnName,
"UniformLink",
"",
srcOutput="feedForwardOutput",
destInput="apicalInput",
propagationDelay=1)
# Link reset output to L2 and L4.
network.link(sensorInputName,
L2ColumnName,
"UniformLink",
"",
srcOutput="resetOut",
destInput="resetIn")
network.link(sensorInputName,
L4ColumnName,
"UniformLink",
"",
srcOutput="resetOut",
destInput="resetIn")
#enableProfiling(network)
for region in network.regions.values():
region.enableProfiling()
return network
def createTemporalAnomaly(recordParams,
spatialParams=_SP_PARAMS,
temporalParams=_TP_PARAMS,
verbosity=_VERBOSITY):
"""Generates a Network with connected RecordSensor, SP, TP.
This function takes care of generating regions and the canonical links.
The network has a sensor region reading data from a specified input and
passing the encoded representation to an SPRegion.
The SPRegion output is passed to a TPRegion.
Note: this function returns a network that needs to be initialized. This
allows the user to extend the network by adding further regions and
connections.
:param recordParams: a dict with parameters for creating RecordSensor region.
:param spatialParams: a dict with parameters for creating SPRegion.
:param temporalParams: a dict with parameters for creating TPRegion.
:param verbosity: an integer representing how chatty the network will be.
"""
inputFilePath = recordParams["inputFilePath"]
scalarEncoderArgs = recordParams["scalarEncoderArgs"]
dateEncoderArgs = recordParams["dateEncoderArgs"]
scalarEncoder = ScalarEncoder(**scalarEncoderArgs)
dateEncoder = DateEncoder(**dateEncoderArgs)
encoder = MultiEncoder()
encoder.addEncoder(scalarEncoderArgs["name"], scalarEncoder)
encoder.addEncoder(dateEncoderArgs["name"], dateEncoder)
network = Network()
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": verbosity}))
sensor = network.regions["sensor"].getSelf()
sensor.encoder = encoder
sensor.dataSource = FileRecordStream(streamID=inputFilePath)
# Create the spatial pooler region
spatialParams["inputWidth"] = sensor.encoder.getWidth()
network.addRegion("spatialPoolerRegion", "py.SPRegion",
json.dumps(spatialParams))
# Link the SP region to the sensor input
network.link("sensor", "spatialPoolerRegion", "UniformLink", "")
network.link("sensor",
"spatialPoolerRegion",
"UniformLink",
"",
srcOutput="resetOut",
destInput="resetIn")
network.link("spatialPoolerRegion",
"sensor",
"UniformLink",
"",
srcOutput="spatialTopDownOut",
destInput="spatialTopDownIn")
network.link("spatialPoolerRegion",
"sensor",
"UniformLink",
"",
srcOutput="temporalTopDownOut",
destInput="temporalTopDownIn")
# Add the TPRegion on top of the SPRegion
network.addRegion("temporalPoolerRegion", "py.TPRegion",
json.dumps(temporalParams))
network.link("spatialPoolerRegion", "temporalPoolerRegion", "UniformLink",
"")
network.link("temporalPoolerRegion",
"spatialPoolerRegion",
"UniformLink",
"",
srcOutput="topDownOut",
destInput="topDownIn")
spatialPoolerRegion = network.regions["spatialPoolerRegion"]
# Make sure learning is enabled
spatialPoolerRegion.setParameter("learningMode", True)
# We want temporal anomalies so disable anomalyMode in the SP. This mode is
# used for computing anomalies in a non-temporal model.
spatialPoolerRegion.setParameter("anomalyMode", False)
temporalPoolerRegion = network.regions["temporalPoolerRegion"]
# Enable topDownMode to get the predicted columns output
temporalPoolerRegion.setParameter("topDownMode", True)
# Make sure learning is enabled (this is the default)
temporalPoolerRegion.setParameter("learningMode", True)
# Enable inference mode so we get predictions
temporalPoolerRegion.setParameter("inferenceMode", True)
# Enable anomalyMode to compute the anomaly score.
temporalPoolerRegion.setParameter("anomalyMode", True)
return network
def createNetwork(dataSource):
"""Create the Network instance.
:param dataSource: a RecordStream instance to get data from
:returns: a Network instance ready to run
"""
network = Network()
#----- SENSOR REGION -----#
# Input data comes from a CSV file (scalar values, labels). The RecordSensor region
# allows us to specify a file record stream as the input source via the
# dataSource attribute.
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": _VERBOSITY}))
sensor = network.regions["sensor"].getSelf()
# The RecordSensor needs to know how to encode the input values
sensor.encoder = createScalarEncoder()
# Specify the dataSource as a file record stream instance
sensor.dataSource = dataSource
# Region width
prevRegionWidth = sensor.encoder.getWidth()
#----- SPATIAL POOLER -----#
# Create the spatial pooler region
SP_PARAMS["inputWidth"] = prevRegionWidth
network.addRegion("SP", "py.SPRegion", json.dumps(SP_PARAMS))
# Link the SP region to the sensor input
network.link("sensor", "SP", "UniformLink", "")
# Forward the sensor region sequence reset to the SP
network.link("sensor", "SP", "UniformLink", "", srcOutput="resetOut", destInput="resetIn")
# Make sure learning is ON
spatialPoolerRegion = network.regions["SP"]
spatialPoolerRegion.setParameter("learningMode", True)
# Inference mode outputs the current inference (e.g. active columns).
# It's ok to always leave inference mode on - it's only there for some corner cases.
spatialPoolerRegion.setParameter('inferenceMode', True)
# Region width
prevRegionWidth = SP_PARAMS['columnCount']
#----- TEMPORAL MEMORY -----#
# Make sure region widths fit
assert TM_PARAMS['columnCount'] == prevRegionWidth
TM_PARAMS['inputWidth'] = TM_PARAMS['columnCount']
# Create the TM region
network.addRegion("TM", "py.TPRegion", json.dumps(TM_PARAMS))
# Feed forward link from SP to TM
network.link("SP", "TM", "UniformLink", "", srcOutput="bottomUpOut", destInput="bottomUpIn")
# Feedback links (unnecessary ?)
network.link("TM", "SP", "UniformLink", "", srcOutput="topDownOut", destInput="topDownIn")
network.link("TM", "sensor", "UniformLink", "", srcOutput="topDownOut", destInput="temporalTopDownIn")
# Forward the sensor region sequence reset to the TM
network.link("sensor", "TM", "UniformLink", "", srcOutput="resetOut", destInput="resetIn")
# Make sure learning is enabled (this is the default)
temporalMemoryRegion = network.regions["TM"]
temporalMemoryRegion.setParameter("learningMode", False)
# Inference mode outputs the current inference (e.g. active cells).
# It's ok to always leave inference mode on - it's only there for some corner cases.
temporalMemoryRegion.setParameter('inferenceMode', True)
# Region width
prevRegionWidth = TM_PARAMS['inputWidth']
#----- CLASSIFIER REGION -----#
# create classifier region
network.addRegion('classifier', 'py.CLAClassifierRegion', json.dumps(CLA_CLASSIFIER_PARAMS))
# feed the TM states to the classifier
network.link("TM", "classifier", "UniformLink", "", srcOutput = "bottomUpOut", destInput = "bottomUpIn")
# create a link from the sensor to the classifier to send in category labels.
# TODO: this link is actually useless right now because the CLAclassifier region compute() function doesn't work
# and that we are feeding TM states & categories manually to the classifier via the customCompute() function.
network.link("sensor", "classifier", "UniformLink", "", srcOutput = "categoryOut", destInput = "categoryIn")
# disable learning for now (will be enables in a later training phase)
classifier = network.regions["classifier"]
classifier.setParameter('learningMode', False)
# Inference mode outputs the current inference. We can always leave it on.
classifier.setParameter('inferenceMode', True)
#------ INITIALIZE -----#
# The network until you try to run it. Make sure it's initialized right away.
network.initialize()
return network
def _createOPFNetwork(addSP = True, addTP = False):
"""Create a 'new-style' network ala OPF and return it.
If addSP is true, an SPRegion will be added named 'level1SP'.
If addTP is true, a TPRegion will be added named 'level1TP'
"""
# ==========================================================================
# Create the encoder and data source stuff we need to configure the sensor
sensorParams = dict(verbosity = _VERBOSITY)
encoder = _createEncoder()
trainFile = resource_filename("nupic.datafiles", "extra/gym/gym.csv")
dataSource = FileRecordStream(streamID=trainFile)
dataSource.setAutoRewind(True)
# ==========================================================================
# Now create the network itself
n = Network()
n.addRegion("sensor", "py.RecordSensor", json.dumps(sensorParams))
sensor = n.regions['sensor'].getSelf()
sensor.encoder = encoder
sensor.dataSource = dataSource
# ==========================================================================
# Add the SP if requested
if addSP:
print "Adding SPRegion"
g_spRegionConfig['inputWidth'] = encoder.getWidth()
n.addRegion("level1SP", "py.SPRegion", json.dumps(g_spRegionConfig))
n.link("sensor", "level1SP", "UniformLink", "")
n.link("sensor", "level1SP", "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
n.link("level1SP", "sensor", "UniformLink", "",
srcOutput="spatialTopDownOut", destInput="spatialTopDownIn")
n.link("level1SP", "sensor", "UniformLink", "",
srcOutput="temporalTopDownOut", destInput="temporalTopDownIn")
# ==========================================================================
if addTP and addSP:
# Add the TP on top of SP if requested
# The input width of the TP is set to the column count of the SP
print "Adding TPRegion on top of SP"
g_tpRegionConfig['inputWidth'] = g_spRegionConfig['columnCount']
n.addRegion("level1TP", "py.TPRegion", json.dumps(g_tpRegionConfig))
n.link("level1SP", "level1TP", "UniformLink", "")
n.link("level1TP", "level1SP", "UniformLink", "",
srcOutput="topDownOut", destInput="topDownIn")
n.link("sensor", "level1TP", "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
elif addTP:
# Add a lone TPRegion if requested
# The input width of the TP is set to the encoder width
print "Adding TPRegion"
g_tpRegionConfig['inputWidth'] = encoder.getWidth()
n.addRegion("level1TP", "py.TPRegion", json.dumps(g_tpRegionConfig))
n.link("sensor", "level1TP", "UniformLink", "")
n.link("sensor", "level1TP", "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
return n
class HTMusicModel(object):
def __init__(self, model_params):
# Init an HTM network
self.network = Network()
# Getting parameters for network regions
self.sensor_params = model_params['Sensor']
self.spatial_pooler_params = model_params['SpatialPooler']
self.temporal_memory_params = model_params['TemporalMemory']
self.classifiers_params = model_params['Classifiers']
self.encoders_params = model_params['Encoders']
# Adding regions to HTM network
self.network.addRegion('DurationEncoder', 'ScalarSensor',
json.dumps(self.encoders_params['duration']))
self.network.addRegion('VelocityEncoder', 'ScalarSensor',
json.dumps(self.encoders_params['pitch']))
self.network.addRegion('PitchEncoder', 'ScalarSensor',
json.dumps(self.encoders_params['velocity']))
self.network.addRegion('SpatialPooler', 'py.SPRegion',
json.dumps(self.spatial_pooler_params))
self.network.addRegion('TemporalMemory', 'py.TMRegion',
json.dumps(self.temporal_memory_params))
# Creating outer classifiers for multifield prediction
dclp = self.classifiers_params['duration']
vclp = self.classifiers_params['pitch']
pclp = self.classifiers_params['velocity']
self.duration_classifier = SDRClassifier(
steps=(1, ),
verbosity=dclp['verbosity'],
alpha=dclp['alpha'],
actValueAlpha=dclp['actValueAlpha'])
self.velocity_classifier = SDRClassifier(
steps=(1, ),
verbosity=vclp['verbosity'],
alpha=vclp['alpha'],
actValueAlpha=vclp['actValueAlpha'])
self.pitch_classifier = SDRClassifier(
steps=(1, ),
verbosity=pclp['verbosity'],
alpha=pclp['alpha'],
actValueAlpha=pclp['actValueAlpha'])
self._link_all_regions()
self._enable_learning()
self._enable_inference()
self.network.initialize()
def _link_all_regions(self):
# Linking regions
self.network.link('DurationEncoder', 'SpatialPooler', 'UniformLink',
'')
self.network.link('VelocityEncoder', 'SpatialPooler', 'UniformLink',
'')
self.network.link('PitchEncoder', 'SpatialPooler', 'UniformLink', '')
self.network.link('SpatialPooler',
'TemporalMemory',
'UniformLink',
'',
srcOutput='bottomUpOut',
destInput='bottomUpIn')
def _enable_learning(self):
# Enable learning for all regions.
self.network.regions["SpatialPooler"].setParameter("learningMode", 1)
self.network.regions["TemporalMemory"].setParameter("learningMode", 1)
def _enable_inference(self):
# Enable inference for all regions.
self.network.regions["SpatialPooler"].setParameter("inferenceMode", 1)
self.network.regions["TemporalMemory"].setParameter("inferenceMode", 1)
def train(self, duration, pitch, velocity):
records_total = self.network.regions['SpatialPooler'].getSelf(
).getAlgorithmInstance().getIterationNum()
self.network.regions['DurationEncoder'].setParameter(
'sensedValue', duration)
self.network.regions['PitchEncoder'].setParameter('sensedValue', pitch)
self.network.regions['VelocityEncoder'].setParameter(
'sensedValue', velocity)
self.network.run(1)
# Getting active cells of TM and bucket indicies of encoders to feed classifiers
active_cells = numpy.array(
self.network.regions['TemporalMemory'].getOutputData(
'bottomUpOut')).nonzero()[0]
duration_bucket = numpy.array(
self.network.regions['DurationEncoder'].getOutputData('bucket'))
pitch_bucket = numpy.array(
self.network.regions['PitchEncoder'].getOutputData('bucket'))
velocity_bucket = numpy.array(
self.network.regions['VelocityEncoder'].getOutputData('bucket'))
duration_classifier_result = self.duration_classifier.compute(
recordNum=records_total,
patternNZ=active_cells,
classification={
'bucketIdx': duration_bucket[0],
'actValue': duration
},
learn=True,
infer=False)
pitch_classifier_result = self.pitch_classifier.compute(
recordNum=records_total,
patternNZ=active_cells,
classification={
'bucketIdx': pitch_bucket[0],
'actValue': pitch
},
learn=True,
infer=False)
velocity_classifier_result = self.velocity_classifier.compute(
recordNum=records_total,
patternNZ=active_cells,
classification={
'bucketIdx': velocity_bucket[0],
'actValue': velocity
},
learn=True,
infer=False)
def generate(self, seed, output_dir, event_amount):
records_total = self.network.regions['SpatialPooler'].getSelf(
).getAlgorithmInstance().getIterationNum()
seed = seed
midi = pretty_midi.PrettyMIDI()
midi_program = pretty_midi.instrument_name_to_program(
'Acoustic Grand Piano')
piano = pretty_midi.Instrument(program=midi_program)
clock = 0
for iters in tqdm(range(records_total, records_total + event_amount)):
duration = seed[0]
pitch = seed[1]
velocity = seed[2]
self.network.regions['DurationEncoder'].setParameter(
'sensedValue', duration)
self.network.regions['PitchEncoder'].setParameter(
'sensedValue', pitch)
self.network.regions['VelocityEncoder'].setParameter(
'sensedValue', velocity)
self.network.run(1)
# Getting active cells of TM and bucket indicies of encoders to feed classifiers
active_cells = numpy.array(
self.network.regions['TemporalMemory'].getOutputData(
'bottomUpOut')).nonzero()[0]
duration_bucket = numpy.array(
self.network.regions['DurationEncoder'].getOutputData(
'bucket'))
pitch_bucket = numpy.array(
self.network.regions['PitchEncoder'].getOutputData('bucket'))
velocity_bucket = numpy.array(
self.network.regions['VelocityEncoder'].getOutputData(
'bucket'))
# Getting up classifiers result
duration_classifier_result = self.duration_classifier.compute(
recordNum=records_total,
patternNZ=active_cells,
classification={
'bucketIdx': duration_bucket[0],
'actValue': duration
},
learn=False,
infer=True)
pitch_classifier_result = self.pitch_classifier.compute(
recordNum=records_total,
patternNZ=active_cells,
classification={
'bucketIdx': pitch_bucket[0],
'actValue': pitch
},
learn=False,
infer=True)
velocity_classifier_result = self.velocity_classifier.compute(
recordNum=records_total,
patternNZ=active_cells,
classification={
'bucketIdx': velocity_bucket[0],
'actValue': velocity
},
learn=False,
infer=True)
du = duration_classifier_result[1].argmax()
pi = pitch_classifier_result[1].argmax()
ve = velocity_classifier_result[1].argmax()
duration_top_probs = duration_classifier_result[1][
0:2] / duration_classifier_result[1][0:2].sum()
predicted_duration = duration_classifier_result['actualValues'][du]
# predicted_duration = duration_classifier_result['actualValues'][du]
predicted_pitch = pitch_classifier_result['actualValues'][pi]
predicted_velocity = velocity_classifier_result['actualValues'][ve]
# print duration_classifier_result
note = pretty_midi.Note(velocity=int(predicted_velocity),
pitch=int(predicted_pitch),
start=float(clock),
end=float(clock + predicted_duration))
piano.notes.append(note)
clock = clock + 0.25
seed[0] = predicted_duration
seed[1] = predicted_pitch
seed[2] = predicted_velocity
midi.instruments.append(piano)
midi.remove_invalid_notes()
time = datetime.datetime.now().strftime('%Y-%m-%d %H:%m:%S')
midi.write(output_dir + time + '.mid')
def load_model(self, load_path):
# Loading SpatialPooler
print 'Loading SpatialPooler'
with open(load_path + 'sp.bin', 'rb') as sp:
sp_builder = SpatialPoolerProto.read(
sp, traversal_limit_in_words=2**61)
self.network.regions['SpatialPooler'].getSelf(
)._sfdr = self.network.regions['SpatialPooler'].getSelf()._sfdr.read(
sp_builder)
# Loading TemporalMemory
print 'Loading TemporalMemory'
self.network.regions['TemporalMemory'].getSelf().getAlgorithmInstance(
).loadFromFile(load_path + 'tm.bin')
# Loading end classifier
print 'Loading duration classifier'
with open(load_path + 'dcl.bin', 'rb') as dcl:
dcl_builder = SdrClassifierProto.read(
dcl, traversal_limit_in_words=2**61)
self.duration_classifier = self.duration_classifier.read(dcl_builder)
# Loading pitch classifier
print 'Loading pitch classifier'
with open(load_path + 'pcl.bin', 'rb') as pcl:
pcl_builder = SdrClassifierProto.read(
pcl, traversal_limit_in_words=2**61)
self.pitch_classifier = self.pitch_classifier.read(pcl_builder)
# Loading velocity classifier
print 'Loading velocity classifier'
with open(load_path + 'vcl.bin', 'rb') as vcl:
vcl_builder = SdrClassifierProto.read(
vcl, traversal_limit_in_words=2**61)
self.velocity_classifier = self.velocity_classifier.read(vcl_builder)
def save_model(self, save_path):
# Saving SpatialPooler
print 'Saving SpatialPooler'
sp_builder = SpatialPoolerProto.new_message()
self.network.regions['SpatialPooler'].getSelf().getAlgorithmInstance(
).write(sp_builder)
with open(save_path + 'sp.bin', 'w+b') as sp:
sp_builder.write(sp)
# Saving TemporalMemory
print 'Saving TemporalMemory'
self.network.regions['TemporalMemory'].getSelf().getAlgorithmInstance(
).saveToFile(save_path + 'tm.bin')
# Saving end classifier
print 'Saving duration classifier'
dcl_builder = SdrClassifierProto.new_message()
self.duration_classifier.write(dcl_builder)
with open(save_path + 'dcl.bin', 'w+b') as dcl:
dcl_builder.write(dcl)
# Saving pitch classifier
print 'Saving pitch classifier'
pcl_builder = SdrClassifierProto.new_message()
self.pitch_classifier.write(pcl_builder)
with open(save_path + 'pcl.bin', 'w+b') as pcl:
pcl_builder.write(pcl)
# Saving velocity classifier
print 'Saving velocity classifier'
vcl_builder = SdrClassifierProto.new_message()
self.velocity_classifier.write(vcl_builder)
with open(save_path + 'vcl.bin', 'w+b') as vcl:
vcl_builder.write(vcl)
def testSerialization(self):
n = Network()
imageDims = (42, 38)
params = dict(width=imageDims[0],
height=imageDims[1],
mode="bw",
background=1,
invertOutput=1)
sensor = n.addRegion("sensor", "py.ImageSensor", json.dumps(params))
sensor.setDimensions(Dimensions(imageDims[0], imageDims[1]))
params = dict(inputShape=imageDims,
coincidencesShape=imageDims,
disableTemporal=1,
tpSeed=43,
spSeed=42,
nCellsPerCol=1)
l1 = n.addRegion("l1", "py.CLARegion", json.dumps(params))
params = dict(maxCategoryCount=48,
SVDSampleCount=400,
SVDDimCount=5,
distanceNorm=0.6)
_classifier = n.addRegion("classifier", "py.KNNClassifierRegion",
json.dumps(params))
# TODO: link params should not be required. Dest region dimensions are
# already specified as [1]
params = dict(mapping="in", rfSize=imageDims)
n.link("sensor", "l1", "UniformLink", json.dumps(params))
n.link("l1", "classifier", "UniformLink", "", "bottomUpOut",
"bottomUpIn")
n.link("sensor", "classifier", "UniformLink", "", "categoryOut",
"categoryIn")
n.initialize()
n.save("fdr.nta")
# Make sure the network bundle has all the expected files
self.assertTrue(os.path.exists("fdr.nta/network.yaml"))
self.assertTrue(os.path.exists("fdr.nta/R0-pkl"))
self.assertTrue(os.path.exists("fdr.nta/R1-pkl"))
self.assertTrue(os.path.exists("fdr.nta/R2-pkl"))
n2 = Network("fdr.nta")
n2.initialize() # should not fail
# Make sure the network is actually the same
sensor = n2.regions['sensor']
self.assertEqual(sensor.type, "py.ImageSensor")
# would like to directly compare, but can't -- NPC-6
self.assertEqual(str(sensor.dimensions), str(Dimensions(42, 38)))
self.assertEqual(sensor.getParameter("width"), 42)
self.assertEqual(sensor.getParameter("height"), 38)
self.assertEqual(sensor.getParameter("mode"), "bw")
self.assertEqual(sensor.getParameter("background"), 1)
self.assertEqual(sensor.getParameter("invertOutput"), 1)
l1 = n2.regions['l1']
self.assertEqual(l1.type, "py.CLARegion")
self.assertEqual(str(l1.dimensions), str(Dimensions(1)))
a = l1.getParameter("inputShape")
self.assertEqual(len(a), 2)
self.assertEqual(a[0], 42)
self.assertEqual(a[1], 38)
a = l1.getParameter("coincidencesShape")
self.assertEqual(len(a), 2)
self.assertEqual(a[0], 42)
self.assertEqual(a[1], 38)
self.assertEqual(l1.getParameter("disableTemporal"), 1)
self.assertEqual(l1.getParameter("spSeed"), 42)
self.assertEqual(l1.getParameter("tpSeed"), 43)
cl = n2.regions['classifier']
self.assertEqual(cl.type, "py.KNNClassifierRegion")
self.assertEqual(cl.getParameter("maxCategoryCount"), 48)
self.assertEqual(cl.getParameter("SVDSampleCount"), 400)
self.assertEqual(cl.getParameter("SVDDimCount"), 5)
self.assertLess((cl.getParameter("distanceNorm") - 0.6), 0.0001)
self.assertEqual(str(cl.dimensions), str(Dimensions(1)))
n2.save("fdr2.nta")
# now compare the two network bundles -- should be the same
c = filecmp.dircmp("fdr.nta", "fdr2.nta")
self.assertEqual(len(c.left_only), 0,
"fdr.nta has extra files: %s" % c.left_only)
self.assertEqual(len(c.right_only), 0,
"fdr2.nta has extra files: %s" % c.right_only)
if len(c.diff_files) > 0:
_LOGGER.warn(
"Some bundle files differ: %s\n"
"This is expected, as pickle.load() followed by "
"pickle.dump() doesn't produce the same file", c.diff_files)
def testSimpleImageNetwork(self):
# Create the network and get region instances
net = Network()
net.addRegion("sensor", "py.ImageSensor", "{width: 32, height: 32}")
net.addRegion("classifier","py.KNNClassifierRegion",
"{distThreshold: 0.01, maxCategoryCount: 2}")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput = "dataOut", destInput = "bottomUpIn")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput = "categoryOut", destInput = "categoryIn")
net.initialize()
sensor = net.regions['sensor']
classifier = net.regions['classifier']
# Create a dataset with two categories, one image in each category
# Each image consists of a unique rectangle
tmpDir = tempfile.mkdtemp()
os.makedirs(os.path.join(tmpDir,'0'))
os.makedirs(os.path.join(tmpDir,'1'))
im0 = Image.new("L",(32,32))
draw = ImageDraw.Draw(im0)
draw.rectangle((10,10,20,20), outline=255)
im0.save(os.path.join(tmpDir,'0','im0.png'))
im1 = Image.new("L",(32,32))
draw = ImageDraw.Draw(im1)
draw.rectangle((15,15,25,25), outline=255)
im1.save(os.path.join(tmpDir,'1','im1.png'))
# Load the dataset
sensor.executeCommand(["loadMultipleImages", tmpDir])
numImages = sensor.getParameter('numImages')
self.assertEqual(numImages, 2)
# Ensure learning is turned ON
self.assertEqual(classifier.getParameter('learningMode'), 1)
# Train the network (by default learning is ON in the classifier)
# and then turn off learning and turn on inference mode
net.run(2)
classifier.setParameter('inferenceMode', 1)
classifier.setParameter('learningMode', 0)
# Check to make sure learning is turned OFF and that the classifier learned
# something
self.assertEqual(classifier.getParameter('learningMode'), 0)
self.assertEqual(classifier.getParameter('inferenceMode'), 1)
self.assertEqual(classifier.getParameter('categoryCount'),2)
self.assertEqual(classifier.getParameter('patternCount'),2)
# Now test the network to make sure it categories the images correctly
numCorrect = 0
for i in range(2):
net.run(1)
inferredCategory = classifier.getOutputData('categoriesOut').argmax()
if sensor.getOutputData('categoryOut') == inferredCategory:
numCorrect += 1
self.assertEqual(numCorrect,2)
# Cleanup the temp files
os.unlink(os.path.join(tmpDir,'0','im0.png'))
os.unlink(os.path.join(tmpDir,'1','im1.png'))
os.removedirs(os.path.join(tmpDir,'0'))
os.removedirs(os.path.join(tmpDir,'1'))
def createNetwork(dataSource):
"""Create the Network instance.
The network has a sensor region reading data from `dataSource` and passing
the encoded representation to an SPRegion. The SPRegion output is passed to
a TPRegion.
:param dataSource: a RecordStream instance to get data from
:returns: a Network instance ready to run
"""
network = Network()
# Our input is sensor data from the gym file. The RecordSensor region
# allows us to specify a file record stream as the input source via the
# dataSource attribute.
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": _VERBOSITY}))
sensor = network.regions["sensor"].getSelf()
# The RecordSensor needs to know how to encode the input values
sensor.encoder = createEncoder()
# Specify the dataSource as a file record stream instance
sensor.dataSource = dataSource
# Create the spatial pooler region
SP_PARAMS["inputWidth"] = sensor.encoder.getWidth()
network.addRegion("spatialPoolerRegion", "py.SPRegion",
json.dumps(SP_PARAMS))
# Link the SP region to the sensor input
network.link("sensor", "spatialPoolerRegion", "UniformLink", "")
network.link("sensor",
"spatialPoolerRegion",
"UniformLink",
"",
srcOutput="resetOut",
destInput="resetIn")
network.link("spatialPoolerRegion",
"sensor",
"UniformLink",
"",
srcOutput="spatialTopDownOut",
destInput="spatialTopDownIn")
network.link("spatialPoolerRegion",
"sensor",
"UniformLink",
"",
srcOutput="temporalTopDownOut",
destInput="temporalTopDownIn")
# Add the TPRegion on top of the SPRegion
network.addRegion("temporalPoolerRegion", "py.TPRegion",
json.dumps(TP_PARAMS))
network.link("spatialPoolerRegion", "temporalPoolerRegion", "UniformLink",
"")
network.link("temporalPoolerRegion",
"spatialPoolerRegion",
"UniformLink",
"",
srcOutput="topDownOut",
destInput="topDownIn")
network.initialize()
spatialPoolerRegion = network.regions["spatialPoolerRegion"]
# Make sure learning is enabled
spatialPoolerRegion.setParameter("learningMode", True)
# We want temporal anomalies so disable anomalyMode in the SP. This mode is
# used for computing anomalies in a non-temporal model.
spatialPoolerRegion.setParameter("anomalyMode", False)
temporalPoolerRegion = network.regions["temporalPoolerRegion"]
# Enable topDownMode to get the predicted columns output
temporalPoolerRegion.setParameter("topDownMode", True)
# Make sure learning is enabled (this is the default)
temporalPoolerRegion.setParameter("learningMode", True)
# Enable inference mode so we get predictions
temporalPoolerRegion.setParameter("inferenceMode", True)
# Enable anomalyMode to compute the anomaly score. This actually doesn't work
# now so doesn't matter. We instead compute the anomaly score based on
# topDownOut (predicted columns) and SP bottomUpOut (active columns).
temporalPoolerRegion.setParameter("anomalyMode", True)
return network
def createNetwork(dataSource):
"""Creates and returns a new Network with a sensor region reading data from
'dataSource'. There are two hierarchical levels, each with one SP and one TP.
@param dataSource - A RecordStream containing the input data
@returns a Network ready to run
"""
network = Network()
# Create and add a record sensor and a SP region
sensor = createRecordSensor(network, name=_RECORD_SENSOR,
dataSource=dataSource)
createSpatialPooler(network, name=_L1_SPATIAL_POOLER,
inputWidth=sensor.encoder.getWidth())
# Link the SP region to the sensor input
linkType = "UniformLink"
linkParams = ""
network.link(_RECORD_SENSOR, _L1_SPATIAL_POOLER, linkType, linkParams)
# Create and add a TP region
l1temporalMemory = createTemporalMemory(network, _L1_TEMPORAL_MEMORY)
# Link SP region to TP region in the feedforward direction
network.link(_L1_SPATIAL_POOLER, _L1_TEMPORAL_MEMORY, linkType, linkParams)
# Add a classifier
classifierParams = { # Learning rate. Higher values make it adapt faster.
'alpha': 0.005,
# A comma separated list of the number of steps the
# classifier predicts in the future. The classifier will
# learn predictions of each order specified.
'steps': '1',
# The specific implementation of the classifier to use
# See SDRClassifierFactory#create for options
'implementation': 'py',
# Diagnostic output verbosity control;
# 0: silent; [1..6]: increasing levels of verbosity
'verbosity': 0}
l1Classifier = network.addRegion(_L1_CLASSIFIER, "py.SDRClassifierRegion",
json.dumps(classifierParams))
l1Classifier.setParameter('inferenceMode', True)
l1Classifier.setParameter('learningMode', True)
network.link(_L1_TEMPORAL_MEMORY, _L1_CLASSIFIER, linkType, linkParams,
srcOutput="bottomUpOut", destInput="bottomUpIn")
# Second Level
l2inputWidth = l1temporalMemory.getSelf().getOutputElementCount("bottomUpOut")
createSpatialPooler(network, name=_L2_SPATIAL_POOLER, inputWidth=l2inputWidth)
network.link(_L1_TEMPORAL_MEMORY, _L2_SPATIAL_POOLER, linkType, linkParams)
createTemporalMemory(network, _L2_TEMPORAL_MEMORY)
network.link(_L2_SPATIAL_POOLER, _L2_TEMPORAL_MEMORY, linkType, linkParams)
l2Classifier = network.addRegion(_L2_CLASSIFIER, "py.SDRClassifierRegion",
json.dumps(classifierParams))
l2Classifier.setParameter('inferenceMode', True)
l2Classifier.setParameter('learningMode', True)
network.link(_L2_TEMPORAL_MEMORY, _L2_CLASSIFIER, linkType, linkParams,
srcOutput="bottomUpOut", destInput="bottomUpIn")
return network
def createNetwork(dataSource):
"""Creates and returns a new Network with a sensor region reading data from
'dataSource'. There are two hierarchical levels, each with one SP and one TP.
@param dataSource - A RecordStream containing the input data
@returns a Network ready to run
"""
network = Network()
# Create and add a record sensor and a SP region
sensor = createRecordSensor(network,
name=_RECORD_SENSOR,
dataSource=dataSource)
createSpatialPooler(network,
name=_L1_SPATIAL_POOLER,
inputWidth=sensor.encoder.getWidth())
# Link the SP region to the sensor input
linkType = "UniformLink"
linkParams = ""
network.link(_RECORD_SENSOR, _L1_SPATIAL_POOLER, linkType, linkParams)
# Create and add a TP region
l1temporalMemory = createTemporalMemory(network, _L1_TEMPORAL_MEMORY)
# Link SP region to TP region in the feedforward direction
network.link(_L1_SPATIAL_POOLER, _L1_TEMPORAL_MEMORY, linkType, linkParams)
# Add a classifier
classifierParams = { # Learning rate. Higher values make it adapt faster.
'alpha': 0.005,
# A comma separated list of the number of steps the
# classifier predicts in the future. The classifier will
# learn predictions of each order specified.
'steps': '1',
# The specific implementation of the classifier to use
# See SDRClassifierFactory#create for options
'implementation': 'py',
# Diagnostic output verbosity control;
# 0: silent; [1..6]: increasing levels of verbosity
'verbosity': 0
}
l1Classifier = network.addRegion(_L1_CLASSIFIER, "py.SDRClassifierRegion",
json.dumps(classifierParams))
l1Classifier.setParameter('inferenceMode', True)
l1Classifier.setParameter('learningMode', True)
network.link(_L1_TEMPORAL_MEMORY,
_L1_CLASSIFIER,
linkType,
linkParams,
srcOutput="bottomUpOut",
destInput="bottomUpIn")
# Second Level
l2inputWidth = l1temporalMemory.getSelf().getOutputElementCount(
"bottomUpOut")
createSpatialPooler(network,
name=_L2_SPATIAL_POOLER,
inputWidth=l2inputWidth)
network.link(_L1_TEMPORAL_MEMORY, _L2_SPATIAL_POOLER, linkType, linkParams)
createTemporalMemory(network, _L2_TEMPORAL_MEMORY)
network.link(_L2_SPATIAL_POOLER, _L2_TEMPORAL_MEMORY, linkType, linkParams)
l2Classifier = network.addRegion(_L2_CLASSIFIER, "py.SDRClassifierRegion",
json.dumps(classifierParams))
l2Classifier.setParameter('inferenceMode', True)
l2Classifier.setParameter('learningMode', True)
network.link(_L2_TEMPORAL_MEMORY,
_L2_CLASSIFIER,
linkType,
linkParams,
srcOutput="bottomUpOut",
destInput="bottomUpIn")
return network
def createNetwork(dataSource, networkConfig, encoder=None):
"""
Create and initialize the network instance with regions for the sensor, SP,
TM, and classifier. Before running, be sure to init w/ network.initialize().
@param dataSource: (RecordStream) Sensor region reads data from here.
@param networkConfig: (dict) the configuration of this network.
@param encoder: (Encoder) encoding object to use instead of specifying in
networkConfig.
@return network: (Network) HTM network.
E.g. Sensor -> SP -> TM -> SDRClassifier
"""
network = Network()
# Create sensor region (always enabled)
sensorRegionConfig = networkConfig["sensorRegionConfig"]
sensorRegionName = sensorRegionConfig["regionName"]
sensorRegion = _createSensorRegion(network,
sensorRegionConfig,
dataSource,
encoder)
# Keep track of the previous region name and width to validate and link the
# input/output width of two consecutive regions.
previousRegion = sensorRegionName
previousRegionWidth = sensorRegion.encoder.getWidth()
networkRegions = [r for r in networkConfig.keys()
if networkConfig[r]["regionEnabled"]]
if "spRegionConfig" in networkRegions:
# create SP region, if enabled
regionConfig = networkConfig["spRegionConfig"]
regionName = regionConfig["regionName"]
regionParams = regionConfig["regionParams"]
regionParams["inputWidth"] = sensorRegion.encoder.getWidth()
spRegion = _createRegion(network, regionConfig)
_validateRegionWidths(previousRegionWidth, spRegion.getSelf().inputWidth)
_linkRegions(network,
sensorRegionName,
previousRegion,
regionName)
previousRegion = regionName
previousRegionWidth = spRegion.getSelf().columnCount
if "tmRegionConfig" in networkRegions:
# create TM region, if enabled
regionConfig = networkConfig["tmRegionConfig"]
regionName = regionConfig["regionName"]
regionParams = regionConfig["regionParams"]
regionParams["inputWidth"] = regionParams["columnCount"]
tmRegion = _createRegion(network, regionConfig)
tmRegion.setParameter("computePredictedActiveCellIndices", True)
tmRegion.setParameter("anomalyMode", True)
_validateRegionWidths(previousRegionWidth, tmRegion.getSelf().columnCount)
_linkRegions(network,
sensorRegionName,
previousRegion,
regionName)
previousRegion = regionName
previousRegionWidth = (tmRegion.getSelf().columnCount *
tmRegion.getSelf().cellsPerColumn)
if "tpRegionConfig" in networkRegions:
# create TP region, if enabled
regionConfig = networkConfig["tpRegionConfig"]
regionName = regionConfig["regionName"]
regionParams = regionConfig["regionParams"]
regionParams["inputWidth"] = previousRegionWidth
tpRegion = _createRegion(network, regionConfig,
moduleName="htmresearch.regions.TemporalPoolerRegion")
_validateRegionWidths(previousRegionWidth,
tpRegion.getSelf()._inputWidth)
_linkRegions(network,
sensorRegionName,
previousRegion,
regionName)
previousRegion = regionName
# Create classifier region (always enabled)
regionConfig = networkConfig["classifierRegionConfig"]
regionName = regionConfig["regionName"]
_createRegion(network, regionConfig)
# Link the classifier to previous region and sensor region - to send in
# category labels.
network.link(previousRegion, regionName, "UniformLink", "")
network.link(sensorRegionName,
regionName,
"UniformLink",
"",
srcOutput="categoryOut",
destInput="categoryIn")
# Link in sequenceId/partitionId if the appropriate input exists
classifierSpec = network.regions[regionName].getSpec()
if classifierSpec.inputs.contains('partitionIn'):
network.link(sensorRegionName, regionName, "UniformLink", "",
srcOutput="sequenceIdOut", destInput="partitionIn")
return network
def runNodesTest(self, nodeType1, nodeType2):
# =====================================================
# Build and run the network
# =====================================================
LOGGER.info('test(level1: %s, level2: %s)', nodeType1, nodeType2)
net = Network()
level1 = net.addRegion("level1", nodeType1, "{int32Param: 15}")
dims = Dimensions([6, 4])
level1.setDimensions(dims)
level2 = net.addRegion("level2", nodeType2, "{real64Param: 128.23}")
net.link("level1", "level2", "TestFanIn2", "")
# Could call initialize here, but not necessary as net.run()
# initializes implicitly.
# net.initialize()
net.run(1)
LOGGER.info("Successfully created network and ran for one iteration")
# =====================================================
# Check everything
# =====================================================
dims = level1.getDimensions()
self.assertEqual(len(dims), 2)
self.assertEqual(dims[0], 6)
self.assertEqual(dims[1], 4)
dims = level2.getDimensions()
self.assertEqual(len(dims), 2)
self.assertEqual(dims[0], 3)
self.assertEqual(dims[1], 2)
# Check L1 output. "False" means don't copy, i.e.
# get a pointer to the actual output
# Actual output values are determined by the TestNode
# compute() behavior.
l1output = level1.getOutputData("bottomUpOut")
self.assertEqual(len(l1output), 48) # 24 nodes; 2 values per node
for i in xrange(24):
self.assertEqual(l1output[2 * i],
0) # size of input to each node is 0
self.assertEqual(l1output[2 * i + 1], i) # node number
# check L2 output.
l2output = level2.getOutputData("bottomUpOut")
self.assertEqual(len(l2output), 12) # 6 nodes; 2 values per node
# Output val = node number + sum(inputs)
# Can compute from knowing L1 layout
#
# 00 01 | 02 03 | 04 05
# 06 07 | 08 09 | 10 11
# ---------------------
# 12 13 | 14 15 | 16 17
# 18 19 | 20 21 | 22 23
outputVals = []
outputVals.append(0 + (0 + 1 + 6 + 7))
outputVals.append(1 + (2 + 3 + 8 + 9))
outputVals.append(2 + (4 + 5 + 10 + 11))
outputVals.append(3 + (12 + 13 + 18 + 19))
outputVals.append(4 + (14 + 15 + 20 + 21))
outputVals.append(5 + (16 + 17 + 22 + 23))
for i in xrange(6):
if l2output[2 * i] != 8:
LOGGER.info(l2output[2 * i])
# from dbgp.client import brk; brk(port=9019)
self.assertEqual(l2output[2 * i],
8) # size of input for each node is 8
self.assertEqual(l2output[2 * i + 1], outputVals[i])
# =====================================================
# Run for one more iteration
# =====================================================
LOGGER.info("Running for a second iteration")
net.run(1)
# =====================================================
# Check everything again
# =====================================================
# Outputs are all the same except that the first output is
# incremented by the iteration number
for i in xrange(24):
self.assertEqual(l1output[2 * i], 1)
self.assertEqual(l1output[2 * i + 1], i)
for i in xrange(6):
self.assertEqual(l2output[2 * i], 9)
self.assertEqual(l2output[2 * i + 1], outputVals[i] + 4)
# =====================================================
# Demonstrate a few other features
# =====================================================
#
# Linking can induce dimensions downward
#
net = Network()
level1 = net.addRegion("level1", nodeType1, "")
level2 = net.addRegion("level2", nodeType2, "")
dims = Dimensions([3, 2])
level2.setDimensions(dims)
net.link("level1", "level2", "TestFanIn2", "")
net.initialize()
# Level1 should now have dimensions [6, 4]
self.assertEqual(level1.getDimensions()[0], 6)
self.assertEqual(level1.getDimensions()[1], 4)
def testSerialization(self):
n = Network()
imageDims = (42, 38)
params = dict(
width=imageDims[0],
height=imageDims[1],
mode="bw",
background=1,
invertOutput=1)
sensor = n.addRegion("sensor", "py.ImageSensor", json.dumps(params))
sensor.setDimensions(Dimensions(imageDims[0], imageDims[1]))
params = dict(
inputShape=imageDims,
coincidencesShape=imageDims,
disableTemporal=1,
tpSeed=43,
spSeed=42,
nCellsPerCol=1)
l1 = n.addRegion("l1", "py.CLARegion", json.dumps(params))
params = dict(
maxCategoryCount=48,
SVDSampleCount=400,
SVDDimCount=5,
distanceNorm=0.6)
_classifier = n.addRegion("classifier", "py.KNNClassifierRegion",
json.dumps(params))
# TODO: link params should not be required. Dest region dimensions are
# already specified as [1]
params = dict(
mapping="in",
rfSize=imageDims)
n.link("sensor", "l1", "UniformLink", json.dumps(params))
n.link("l1", "classifier", "UniformLink", "", "bottomUpOut", "bottomUpIn")
n.link("sensor", "classifier", "UniformLink", "", "categoryOut",
"categoryIn")
n.initialize()
n.save("fdr.nta")
# Make sure the network bundle has all the expected files
self.assertTrue(os.path.exists("fdr.nta/network.yaml"))
self.assertTrue(os.path.exists("fdr.nta/R0-pkl"))
self.assertTrue(os.path.exists("fdr.nta/R1-pkl"))
self.assertTrue(os.path.exists("fdr.nta/R2-pkl"))
n2 = Network("fdr.nta")
n2.initialize() # should not fail
# Make sure the network is actually the same
sensor = n2.regions['sensor']
self.assertEqual(sensor.type, "py.ImageSensor")
# would like to directly compare, but can't -- NPC-6
self.assertEqual(str(sensor.dimensions), str(Dimensions(42, 38)))
self.assertEqual(sensor.getParameter("width"), 42)
self.assertEqual(sensor.getParameter("height"), 38)
self.assertEqual(sensor.getParameter("mode"), "bw")
self.assertEqual(sensor.getParameter("background"), 1)
self.assertEqual(sensor.getParameter("invertOutput"), 1)
l1 = n2.regions['l1']
self.assertEqual(l1.type, "py.CLARegion")
self.assertEqual(str(l1.dimensions), str(Dimensions(1)))
a = l1.getParameter("inputShape")
self.assertEqual(len(a), 2)
self.assertEqual(a[0], 42)
self.assertEqual(a[1], 38)
a = l1.getParameter("coincidencesShape")
self.assertEqual(len(a), 2)
self.assertEqual(a[0], 42)
self.assertEqual(a[1], 38)
self.assertEqual(l1.getParameter("disableTemporal"), 1)
self.assertEqual(l1.getParameter("spSeed"), 42)
self.assertEqual(l1.getParameter("tpSeed"), 43)
cl = n2.regions['classifier']
self.assertEqual(cl.type, "py.KNNClassifierRegion")
self.assertEqual(cl.getParameter("maxCategoryCount"), 48)
self.assertEqual(cl.getParameter("SVDSampleCount"), 400)
self.assertEqual(cl.getParameter("SVDDimCount"), 5)
self.assertLess((cl.getParameter("distanceNorm") - 0.6), 0.0001)
self.assertEqual(str(cl.dimensions), str(Dimensions(1)))
n2.save("fdr2.nta")
# now compare the two network bundles -- should be the same
c = filecmp.dircmp("fdr.nta", "fdr2.nta")
self.assertEqual(len(c.left_only), 0,
"fdr.nta has extra files: %s" % c.left_only)
self.assertEqual(len(c.right_only), 0,
"fdr2.nta has extra files: %s" % c.right_only)
if len(c.diff_files) > 0:
_LOGGER.warn("Some bundle files differ: %s\n"
"This is expected, as pickle.load() followed by "
"pickle.dump() doesn't produce the same file", c.diff_files)
def _createNetwork(inverseReadoutResolution, anchorInputSize, dualPhase=False):
"""
Create a simple network connecting sensor and motor inputs to the location
region. Use :meth:`RawSensor.addDataToQueue` to add sensor input and growth
candidates. Use :meth:`RawValues.addDataToQueue` to add motor input.
::
+----------+
[ sensor* ] --> | | --> [ activeCells ]
[ candidates* ] --> | location | --> [ learnableCells ]
[ motor ] --> | | --> [ sensoryAssociatedCells ]
+----------+
:param inverseReadoutResolution:
Specifies the diameter of the circle of phases in the rhombus encoded by a
bump.
:type inverseReadoutResolution: int
:type anchorInputSize: int
:param anchorInputSize:
The number of input bits in the anchor input.
.. note::
(*) This function will only add the 'sensor' and 'candidates' regions when
'anchorInputSize' is greater than zero. This is useful if you would like to
compute locations ignoring sensor input
.. seealso::
- :py:func:`htmresearch.frameworks.location.path_integration_union_narrowing.createRatModuleFromReadoutResolution`
"""
net = Network()
# Create simple region to pass motor commands as displacement vectors (dx, dy)
net.addRegion("motor", "py.RawValues", json.dumps({"outputWidth": 2}))
if anchorInputSize > 0:
# Create simple region to pass growth candidates
net.addRegion("candidates", "py.RawSensor",
json.dumps({"outputWidth": anchorInputSize}))
# Create simple region to pass sensor input
net.addRegion("sensor", "py.RawSensor",
json.dumps({"outputWidth": anchorInputSize}))
# Initialize region with 5 modules varying scale by sqrt(2) and 4 different
# random orientations for each scale
scale = []
orientation = []
for i in xrange(5):
for _ in xrange(4):
angle = np.radians(random.gauss(7.5, 7.5))
orientation.append(random.choice([angle, -angle]))
scale.append(10.0 * (math.sqrt(2)**i))
# Create location region
params = computeRatModuleParametersFromReadoutResolution(
inverseReadoutResolution)
params.update({
"moduleCount": len(scale),
"scale": scale,
"orientation": orientation,
"anchorInputSize": anchorInputSize,
"activationThreshold": 8,
"initialPermanence": 1.0,
"connectedPermanence": 0.5,
"learningThreshold": 8,
"sampleSize": 10,
"permanenceIncrement": 0.1,
"permanenceDecrement": 0.0,
"dualPhase": dualPhase,
"bumpOverlapMethod": "probabilistic"
})
net.addRegion("location", "py.GridCellLocationRegion", json.dumps(params))
if anchorInputSize > 0:
# Link sensor
net.link("sensor",
"location",
"UniformLink",
"",
srcOutput="dataOut",
destInput="anchorInput")
net.link("sensor",
"location",
"UniformLink",
"",
srcOutput="resetOut",
destInput="resetIn")
net.link("candidates",
"location",
"UniformLink",
"",
srcOutput="dataOut",
destInput="anchorGrowthCandidates")
# Link motor input
net.link("motor",
"location",
"UniformLink",
"",
srcOutput="dataOut",
destInput="displacement")
# Initialize network objects
net.initialize()
return net
def testSimpleImageNetwork(self):
# Create the network and get region instances
net = Network()
net.addRegion("sensor", "py.ImageSensor", "{width: 32, height: 32}")
net.addRegion("classifier", "py.KNNClassifierRegion",
"{distThreshold: 0.01, maxCategoryCount: 2}")
net.link("sensor",
"classifier",
"UniformLink",
"",
srcOutput="dataOut",
destInput="bottomUpIn")
net.link("sensor",
"classifier",
"UniformLink",
"",
srcOutput="categoryOut",
destInput="categoryIn")
net.initialize()
sensor = net.regions['sensor']
classifier = net.regions['classifier']
# Create a dataset with two categories, one image in each category
# Each image consists of a unique rectangle
tmpDir = tempfile.mkdtemp()
os.makedirs(os.path.join(tmpDir, '0'))
os.makedirs(os.path.join(tmpDir, '1'))
im0 = Image.new("L", (32, 32))
draw = ImageDraw.Draw(im0)
draw.rectangle((10, 10, 20, 20), outline=255)
im0.save(os.path.join(tmpDir, '0', 'im0.png'))
im1 = Image.new("L", (32, 32))
draw = ImageDraw.Draw(im1)
draw.rectangle((15, 15, 25, 25), outline=255)
im1.save(os.path.join(tmpDir, '1', 'im1.png'))
# Load the dataset
sensor.executeCommand(["loadMultipleImages", tmpDir])
numImages = sensor.getParameter('numImages')
self.assertEqual(numImages, 2)
# Ensure learning is turned ON
self.assertEqual(classifier.getParameter('learningMode'), 1)
# Train the network (by default learning is ON in the classifier)
# and then turn off learning and turn on inference mode
net.run(2)
classifier.setParameter('inferenceMode', 1)
classifier.setParameter('learningMode', 0)
# Check to make sure learning is turned OFF and that the classifier learned
# something
self.assertEqual(classifier.getParameter('learningMode'), 0)
self.assertEqual(classifier.getParameter('inferenceMode'), 1)
self.assertEqual(classifier.getParameter('categoryCount'), 2)
self.assertEqual(classifier.getParameter('patternCount'), 2)
# Now test the network to make sure it categories the images correctly
numCorrect = 0
for i in range(2):
net.run(1)
inferredCategory = classifier.getOutputData(
'categoriesOut').argmax()
if sensor.getOutputData('categoryOut') == inferredCategory:
numCorrect += 1
self.assertEqual(numCorrect, 2)
# Cleanup the temp files
os.unlink(os.path.join(tmpDir, '0', 'im0.png'))
os.unlink(os.path.join(tmpDir, '1', 'im1.png'))
os.removedirs(os.path.join(tmpDir, '0'))
os.removedirs(os.path.join(tmpDir, '1'))
def testSimpleMulticlassNetworkPY(self):
# Setup data record stream of fake data (with three categories)
filename = _getTempFileName()
fields = [("timestamp", "datetime", "T"), ("value", "float", ""),
("reset", "int", "R"), ("sid", "int", "S"),
("categories", "list", "C")]
records = ([datetime(day=1, month=3, year=2010), 0.0, 1, 0, "0"], [
datetime(day=2, month=3, year=2010), 1.0, 0, 0, "1"
], [datetime(day=3, month=3, year=2010), 0.0, 0, 0,
"0"], [datetime(day=4, month=3, year=2010), 1.0, 0, 0,
"1"], [datetime(day=5, month=3, year=2010), 0.0, 0, 0, "0"],
[datetime(day=6, month=3, year=2010), 1.0, 0, 0, "1"
], [datetime(day=7, month=3, year=2010), 0.0, 0, 0, "0"],
[datetime(day=8, month=3, year=2010), 1.0, 0, 0, "1"])
dataSource = FileRecordStream(streamID=filename,
write=True,
fields=fields)
for r in records:
dataSource.appendRecord(list(r))
# Create the network and get region instances.
net = Network()
net.addRegion("sensor", "py.RecordSensor", "{'numCategories': 3}")
net.addRegion("classifier", "py.SDRClassifierRegion",
"{steps: '0', alpha: 0.001, implementation: 'py'}")
net.link("sensor",
"classifier",
"UniformLink",
"",
srcOutput="dataOut",
destInput="bottomUpIn")
net.link("sensor",
"classifier",
"UniformLink",
"",
srcOutput="categoryOut",
destInput="categoryIn")
sensor = net.regions["sensor"]
classifier = net.regions["classifier"]
# Setup sensor region encoder and data stream.
dataSource.close()
dataSource = FileRecordStream(filename)
sensorRegion = sensor.getSelf()
sensorRegion.encoder = MultiEncoder()
sensorRegion.encoder.addEncoder(
"value", ScalarEncoder(21, 0.0, 13.0, n=256, name="value"))
sensorRegion.dataSource = dataSource
# Get ready to run.
net.initialize()
# Train the network (by default learning is ON in the classifier, but assert
# anyway) and then turn off learning and turn on inference mode.
self.assertEqual(classifier.getParameter("learningMode"), 1)
net.run(8)
# Test the network on the same data as it trained on; should classify with
# 100% accuracy.
classifier.setParameter("inferenceMode", 1)
classifier.setParameter("learningMode", 0)
# Assert learning is OFF and that the classifier learned the dataset.
self.assertEqual(classifier.getParameter("learningMode"), 0,
"Learning mode is not turned off.")
self.assertEqual(classifier.getParameter("inferenceMode"), 1,
"Inference mode is not turned on.")
# make sure we can access all the parameters with getParameter
self.assertEqual(classifier.getParameter("maxCategoryCount"), 2000)
self.assertAlmostEqual(float(classifier.getParameter("alpha")), 0.001)
self.assertEqual(int(classifier.getParameter("steps")), 0)
self.assertTrue(classifier.getParameter("implementation") == "py")
self.assertEqual(classifier.getParameter("verbosity"), 0)
expectedCats = (
[0.0],
[1.0],
[0.0],
[1.0],
[0.0],
[1.0],
[0.0],
[1.0],
)
dataSource.rewind()
for i in xrange(8):
net.run(1)
inferredCats = classifier.getOutputData("categoriesOut")
self.assertSequenceEqual(
expectedCats[i], inferredCats.tolist(),
"Classififer did not infer expected category "
"for record number {}.".format(i))
# Close data stream, delete file.
dataSource.close()
os.remove(filename)
def myCreateNetwork(self, networkConfig):
suffix = '_0'
network = Network()
sensorInputName = "sensorInput" + suffix
L4ColumnName = "L4Column" + suffix
L2ColumnName = "L2Column" + suffix
L4Params = copy.deepcopy(networkConfig["L4Params"])
L4Params["apicalInputWidth"] = networkConfig["L2Params"]["cellCount"]
network.addRegion(
sensorInputName, "py.RawSensor",
json.dumps({"outputWidth": networkConfig["sensorInputSize"]}))
network.addRegion(
L4ColumnName, networkConfig["L4RegionType"],
json.dumps(L4Params))
network.addRegion(
L2ColumnName, "py.ColumnPoolerRegion",
json.dumps(networkConfig["L2Params"]))
network.setPhases(sensorInputName,[0])
# L4 and L2 regions always have phases 2 and 3, respectively
network.setPhases(L4ColumnName,[2])
network.setPhases(L2ColumnName,[3])
network.link(sensorInputName, L4ColumnName, "UniformLink", "",
srcOutput="dataOut", destInput="activeColumns")
# Link L4 to L2
network.link(L4ColumnName, L2ColumnName, "UniformLink", "",
srcOutput="activeCells", destInput="feedforwardInput")
network.link(L4ColumnName, L2ColumnName, "UniformLink", "",
srcOutput="winnerCells",
destInput="feedforwardGrowthCandidates")
# Link L2 feedback to L4
network.link(L2ColumnName, L4ColumnName, "UniformLink", "",
srcOutput="feedForwardOutput", destInput="apicalInput",
propagationDelay=1)
# Link reset output to L2 and L4.
network.link(sensorInputName, L2ColumnName, "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
network.link(sensorInputName, L4ColumnName, "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
#enableProfiling(network)
for region in network.regions.values():
region.enableProfiling()
return network
def testSimpleMulticlassNetworkPY(self):
# Setup data record stream of fake data (with three categories)
filename = _getTempFileName()
fields = [("timestamp", "datetime", "T"),
("value", "float", ""),
("reset", "int", "R"),
("sid", "int", "S"),
("categories", "list", "C")]
records = (
[datetime(day=1, month=3, year=2010), 0.0, 1, 0, "0"],
[datetime(day=2, month=3, year=2010), 1.0, 0, 0, "1"],
[datetime(day=3, month=3, year=2010), 0.0, 0, 0, "0"],
[datetime(day=4, month=3, year=2010), 1.0, 0, 0, "1"],
[datetime(day=5, month=3, year=2010), 0.0, 0, 0, "0"],
[datetime(day=6, month=3, year=2010), 1.0, 0, 0, "1"],
[datetime(day=7, month=3, year=2010), 0.0, 0, 0, "0"],
[datetime(day=8, month=3, year=2010), 1.0, 0, 0, "1"])
dataSource = FileRecordStream(streamID=filename, write=True, fields=fields)
for r in records:
dataSource.appendRecord(list(r))
# Create the network and get region instances.
net = Network()
net.addRegion("sensor", "py.RecordSensor", "{'numCategories': 3}")
net.addRegion("classifier", "py.SDRClassifierRegion",
"{steps: '0', alpha: 0.001, implementation: 'py'}")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput="dataOut", destInput="bottomUpIn")
net.link("sensor", "classifier", "UniformLink", "",
srcOutput="categoryOut", destInput="categoryIn")
sensor = net.regions["sensor"]
classifier = net.regions["classifier"]
# Setup sensor region encoder and data stream.
dataSource.close()
dataSource = FileRecordStream(filename)
sensorRegion = sensor.getSelf()
sensorRegion.encoder = MultiEncoder()
sensorRegion.encoder.addEncoder(
"value", ScalarEncoder(21, 0.0, 13.0, n=256, name="value"))
sensorRegion.dataSource = dataSource
# Get ready to run.
net.initialize()
# Train the network (by default learning is ON in the classifier, but assert
# anyway) and then turn off learning and turn on inference mode.
self.assertEqual(classifier.getParameter("learningMode"), 1)
net.run(8)
# Test the network on the same data as it trained on; should classify with
# 100% accuracy.
classifier.setParameter("inferenceMode", 1)
classifier.setParameter("learningMode", 0)
# Assert learning is OFF and that the classifier learned the dataset.
self.assertEqual(classifier.getParameter("learningMode"), 0,
"Learning mode is not turned off.")
self.assertEqual(classifier.getParameter("inferenceMode"), 1,
"Inference mode is not turned on.")
# make sure we can access all the parameters with getParameter
self.assertEqual(classifier.getParameter("maxCategoryCount"), 2000)
self.assertAlmostEqual(float(classifier.getParameter("alpha")), 0.001)
self.assertEqual(int(classifier.getParameter("steps")), 0)
self.assertTrue(classifier.getParameter("implementation") == "py")
self.assertEqual(classifier.getParameter("verbosity"), 0)
expectedCats = ([0.0], [1.0], [0.0], [1.0], [0.0], [1.0], [0.0], [1.0],)
dataSource.rewind()
for i in xrange(8):
net.run(1)
inferredCats = classifier.getOutputData("categoriesOut")
self.assertSequenceEqual(expectedCats[i], inferredCats.tolist(),
"Classififer did not infer expected category "
"for record number {}.".format(i))
# Close data stream, delete file.
dataSource.close()
os.remove(filename)
def createNetwork(dataSource, networkConfig, encoder=None):
"""
Create and initialize the network instance with regions for the sensor, SP,
TM, and classifier. Before running, be sure to init w/ network.initialize().
@param dataSource: (RecordStream) Sensor region reads data from here.
@param networkConfig: (dict) the configuration of this network.
@param encoder: (Encoder) encoding object to use instead of specifying in
networkConfig.
@return network: (Network) Sample network. E.g. Sensor -> SP -> TM -> Classif.
"""
network = Network()
# Create sensor region (always enabled)
sensorRegionConfig = networkConfig["sensorRegionConfig"]
sensorRegionName = sensorRegionConfig["regionName"]
sensorRegion = _createSensorRegion(network,
sensorRegionConfig,
dataSource,
encoder)
# Keep track of the previous region name and width to validate and link the
# input/output width of two consecutive regions.
previousRegion = sensorRegionName
previousRegionWidth = sensorRegion.encoder.getWidth()
networkRegions = [r for r in networkConfig.keys() if networkConfig[r]["regionEnabled"]]
if "spRegionConfig" in networkRegions:
# create SP region, if enabled
regionConfig = networkConfig["spRegionConfig"]
regionName = regionConfig["regionName"]
regionParams = regionConfig["regionParams"]
regionParams["inputWidth"] = sensorRegion.encoder.getWidth()
spRegion = _createRegion(network, regionConfig)
_validateRegionWidths(previousRegionWidth, spRegion.getSelf().inputWidth)
_linkRegions(network,
sensorRegionName,
previousRegion,
regionName)
previousRegion = regionName
previousRegionWidth = spRegion.getSelf().columnCount
if "tmRegionConfig" in networkRegions:
# create TM region, if enabled
regionConfig = networkConfig["tmRegionConfig"]
regionName = regionConfig["regionName"]
regionParams = regionConfig["regionParams"]
regionParams["inputWidth"] = regionParams["columnCount"]
tmRegion = _createRegion(network, regionConfig)
_validateRegionWidths(previousRegionWidth, tmRegion.getSelf().columnCount)
_linkRegions(network,
sensorRegionName,
previousRegion,
regionName)
previousRegion = regionName
previousRegionWidth = tmRegion.getSelf().cellsPerColumn
if "upRegionConfig" in networkRegions:
# create UP region, if enabled
# this req's the union_pooling dir to be on your system path
# add w/ >>> import sys; sys.path.append(path/to/union_pooling)
regionConfig = networkConfig["upRegionConfig"]
regionName = regionConfig["regionName"]
regionParams = regionConfig["regionParams"]
regionParams["inputWidth"] = previousRegionWidth
upRegion = _createRegion(network, regionConfig,
moduleName="union_pooling.PoolingRegion")
_validateRegionWidths(previousRegionWidth,
upRegion.getSelf().cellsPerColumn)
_linkRegions(network,
sensorRegionName,
previousRegion,
regionName)
previousRegion = regionName
# Create classifier region (always enabled)
regionConfig = networkConfig["classifierRegionConfig"]
regionName = regionConfig["regionName"]
_createRegion(network, regionConfig)
# Link the classifier to previous region and sensor region - to send in
# category labels.
network.link(previousRegion, regionName, "UniformLink", "")
network.link(sensorRegionName,
regionName,
"UniformLink",
"",
srcOutput="categoryOut",
destInput="categoryIn")
return network
def createNetwork():
network = Network()
#
# Sensors
#
# C++
consumptionSensor = network.addRegion('consumptionSensor', 'ScalarSensor',
json.dumps({'n': 120,
'w': 21,
'minValue': 0.0,
'maxValue': 100.0,
'clipInput': True}))
# Python
timestampSensor = network.addRegion("timestampSensor",
'py.PluggableEncoderSensor', "")
timestampSensor.getSelf().encoder = DateEncoder(timeOfDay=(21, 9.5),
name="timestamp_timeOfDay")
#
# Add a SPRegion, a region containing a spatial pooler
#
consumptionEncoderN = consumptionSensor.getParameter('n')
timestampEncoderN = timestampSensor.getSelf().encoder.getWidth()
inputWidth = consumptionEncoderN + timestampEncoderN
network.addRegion("sp", "py.SPRegion",
json.dumps({
"spatialImp": "cpp",
"globalInhibition": 1,
"columnCount": 2048,
"inputWidth": inputWidth,
"numActiveColumnsPerInhArea": 40,
"seed": 1956,
"potentialPct": 0.8,
"synPermConnected": 0.1,
"synPermActiveInc": 0.0001,
"synPermInactiveDec": 0.0005,
"maxBoost": 1.0,
}))
#
# Input to the Spatial Pooler
#
network.link("consumptionSensor", "sp", "UniformLink", "")
network.link("timestampSensor", "sp", "UniformLink", "")
#
# Add a TPRegion, a region containing a Temporal Memory
#
network.addRegion("tm", "py.TPRegion",
json.dumps({
"columnCount": 2048,
"cellsPerColumn": 32,
"inputWidth": 2048,
"seed": 1960,
"temporalImp": "cpp",
"newSynapseCount": 20,
"maxSynapsesPerSegment": 32,
"maxSegmentsPerCell": 128,
"initialPerm": 0.21,
"permanenceInc": 0.1,
"permanenceDec": 0.1,
"globalDecay": 0.0,
"maxAge": 0,
"minThreshold": 9,
"activationThreshold": 12,
"outputType": "normal",
"pamLength": 3,
}))
network.link("sp", "tm", "UniformLink", "")
network.link("tm", "sp", "UniformLink", "", srcOutput="topDownOut",
destInput="topDownIn")
# Enable anomalyMode so the tm calculates anomaly scores
network.regions['tm'].setParameter("anomalyMode", True)
# Enable inference mode to be able to get predictions
network.regions['tm'].setParameter("inferenceMode", True)
return network
def createNetwork(dataSource):
"""Create the Network instance.
The network has a sensor region reading data from `dataSource` and passing
the encoded representation to an SPRegion. The SPRegion output is passed to
a TPRegion.
:param dataSource: a RecordStream instance to get data from
:returns: a Network instance ready to run
"""
network = Network()
# Our input is sensor data from the gym file. The RecordSensor region
# allows us to specify a file record stream as the input source via the
# dataSource attribute.
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": _VERBOSITY}))
sensor = network.regions["sensor"].getSelf()
# The RecordSensor needs to know how to encode the input values
sensor.encoder = createEncoder()
# Specify the dataSource as a file record stream instance
sensor.dataSource = dataSource
# Create the spatial pooler region
SP_PARAMS["inputWidth"] = sensor.encoder.getWidth()
network.addRegion("spatialPoolerRegion", "py.SPRegion", json.dumps(SP_PARAMS))
# Link the SP region to the sensor input
network.link("sensor", "spatialPoolerRegion", "UniformLink", "")
network.link("sensor", "spatialPoolerRegion", "UniformLink", "",
srcOutput="resetOut", destInput="resetIn")
network.link("spatialPoolerRegion", "sensor", "UniformLink", "",
srcOutput="spatialTopDownOut", destInput="spatialTopDownIn")
network.link("spatialPoolerRegion", "sensor", "UniformLink", "",
srcOutput="temporalTopDownOut", destInput="temporalTopDownIn")
# Add the TPRegion on top of the SPRegion
# TODO: Needs TMRegion
network.addRegion("temporalMemoryRegion", "py.TPRegion",
json.dumps(TP_PARAMS))
network.link("spatialPoolerRegion", "temporalMemoryRegion", "UniformLink", "")
network.link("temporalMemoryRegion", "spatialPoolerRegion", "UniformLink", "",
srcOutput="topDownOut", destInput="topDownIn")
# Register TPRegion since we aren't in nupic
curDirectory = os.path.dirname(os.path.abspath(__file__))
# directory containing the union pooler directory is 2 directories above this file
unionTemporalPoolerDirectory = os.path.split((os.path.split(curDirectory))[0])[0]
sys.path.append(unionTemporalPoolerDirectory)
Network.registerRegionPackage("union_temporal_pooling")
# Add the TPRegion on top of the TPRegion
temporal = network.regions["temporalMemoryRegion"].getSelf()
UP_PARAMS["inputWidth"] = temporal.getOutputElementCount("bottomUpOut")
network.addRegion("unionTemporalPoolerRegion", "py.TemporalPoolerRegion", json.dumps(UP_PARAMS))
network.link("temporalMemoryRegion", "unionTemporalPoolerRegion", "UniformLink", "",
srcOutput="activeCells", destInput="activeCells")
network.link("temporalMemoryRegion", "unionTemporalPoolerRegion", "UniformLink", "",
srcOutput="predictedActiveCells", destInput="predictedActiveCells")
network.initialize()
spatial = network.regions["spatialPoolerRegion"].getSelf()
# Make sure learning is enabled (this is the default)
spatial.setParameter("learningMode", 1, True)
# We want temporal anomalies so disable anomalyMode in the SP. This mode is
# used for computing anomalies in a non-temporal model.
spatial.setParameter("anomalyMode", 1, False)
# Enable topDownMode to get the predicted columns output
temporal.setParameter("topDownMode", 1, True)
# Make sure learning is enabled (this is the default)
temporal.setParameter("learningMode", 1, True)
# Enable inference mode so we get predictions
temporal.setParameter("inferenceMode", 1, True)
temporal.setParameter("computePredictedActiveCellIndices", 1, True)
union = network.regions["unionTemporalPoolerRegion"].getSelf()
# Make sure learning is enabled (this is the default)
union.setParameter("learningMode", 1, True)
return network