def testQueue(self):
"""
Test that RawValues executes correctly.
"""
net = Network()
# Create simple region to pass motor commands as displacement vectors (dx, dy)
motor = net.addRegion("motor", "py.RawValues",
json.dumps({"outputWidth": 2}))
motor.executeCommand('addDataToQueue', [0, 0], 1)
motor.executeCommand('addDataToQueue', [1, 1])
net.run(1)
output = list(np.array(motor.getOutputArray("dataOut")))
self.assertEqual([0, 0], output)
output = list(np.array(motor.getOutputArray("resetOut")))
self.assertEqual([True], output)
net.run(1)
output = list(np.array(motor.getOutputArray("dataOut")))
self.assertEqual([1, 1], output)
output = list(np.array(motor.getOutputArray("resetOut")))
self.assertEqual([False], output)
def testGetParametersCustomRegions(self):
Network.cleanup() # removes all previous registrations
registerAllAdvancedRegions()
json_list = Network.getRegistrations()
#print(json_list)
y = json.loads(json_list)
self.assertTrue("py.ColumnPoolerRegion" in y)
net = Network()
#print(net.getSpecJSON("py.ColumnPoolerRegion"))
cp = net.addRegion(
"py.ColumnPoolerRegion", "py.ColumnPoolerRegion", """{
"activationThresholdDistal": 20,
"cellCount": 4096,
"connectedPermanenceDistal": 0.5,
"connectedPermanenceProximal": 0.5,
"initialDistalPermanence": 0.51,
"initialProximalPermanence": 0.6,
"minThresholdProximal": 5,
"sampleSizeDistal": 30,
"sampleSizeProximal": 10,
"sdrSize": 40,
"synPermDistalDec": 0.001,
"synPermDistalInc": 0.1,
"synPermProximalDec": 0.001,
"synPermProximalInc": 0.1
}""")
# expected results from getParameters() (in Spec order)
expected = """{
"learningMode": true,
"onlineLearning": false,
"cellCount": 4096,
"inputWidth": 16384,
"numOtherCorticalColumns": 0,
"sdrSize": 40,
"maxSdrSize": 0,
"minSdrSize": 0,
"synPermProximalInc": 0.100000,
"synPermProximalDec": 0.001000,
"initialProximalPermanence": 0.600000,
"sampleSizeProximal": 10,
"minThresholdProximal": 5,
"connectedPermanenceProximal": 0.500000,
"predictedInhibitionThreshold": 20.000000,
"synPermDistalInc": 0.100000,
"synPermDistalDec": 0.001000,
"initialDistalPermanence": 0.510000,
"sampleSizeDistal": 30,
"activationThresholdDistal": 20,
"connectedPermanenceDistal": 0.500000,
"inertiaFactor": 1.000000,
"seed": 42,
"defaultOutputType": "active"
}"""
json_list = cp.getParameters()
#print(json_list)
self.assertEqual(json_list, expected)
def testGetParameters(self):
"""
A test of the getParameters( ) and getParameterJSON() functions.
"""
expected = """{
"activeBits": 200,
"category": false,
"noise": 0.010000,
"radius": 0.030000,
"resolution": 0.000150,
"seed": 2019,
"sensedValue": 0.000000,
"size": 1000,
"sparsity": 0.200000
}"""
net = Network()
encoder = net.addRegion(
"encoder", "RDSEEncoderRegion",
"{size: 1000, sparsity: 0.2, radius: 0.03, seed: 2019, noise: 0.01}"
)
json_str = encoder.getParameters()
self.assertEqual(json_str, expected)
json.loads(json_str) # test if json package can load it
json_str = encoder.getParameterJSON("activeBits", False)
self.assertEqual(json_str, "200")
json_str = encoder.getParameterJSON("activeBits", True)
self.assertEqual(json_str, "{\"value\": 200, \"type\": \"UInt32\"}")
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.getParameterUInt32("outputWidth"), l2c.getParameterUInt32("inputWidth"), "Incorrect outputWidth parameter")
rawSensor.executeCommand('addDataToQueue', [2, 4, 6], 0, 42)
rawSensor.executeCommand('addDataToQueue', [2, 42, 1023], 1, 43)
rawSensor.executeCommand('addDataToQueue', [18, 19, 20], 0, 44)
# Run the network and check outputs are as expected
net.run(3)
def testSPRegionParametersAreWritable(self):
"""
Test that the SPRegion parameters can be set.
"""
net = Network()
# Create simple region to pass sensor commands as displacement vectors (dx, dy)
sp = net.addRegion("sp", "SPRegion", json.dumps(spParams))
def testSPRegionIsCreatable(self):
"""
Test that the SPRegion can be created in Python.
"""
net = Network()
# Create simple region to pass sensor commands as displacement vectors (dx, dy)
sp = net.addRegion("sp", "SPRegion", json.dumps({}))
def testGetSpec(self):
"""
Test of region.getSpec() function. Testing if pybind pointers are correctly handled
"""
net = Network()
dateRegion = net.addRegion(
'dateEncoder', 'DateEncoderRegion',
str(dict(timeOfDay_width=30, timeOfDay_radius=1,
weekend_width=21)))
dateRegion.getSpec() # twice times to check if no double free arises
dateRegion.getSpec()
def testGetParametersGridCell(self):
# a test of arrays in parameters
Network.cleanup() # removes all previous registrations
registerAllAdvancedRegions()
json_list = Network.getRegistrations()
#print(json_list)
y = json.loads(json_list)
self.assertTrue("py.GridCellLocationRegion" in y)
# only provide one orentation to shorten the test. Full test in location_region_test.py
net = Network()
#print(net.getSpecJSON("py.GridCellLocationRegion"))
cp = net.addRegion(
"grid", "py.GridCellLocationRegion", """{
"moduleCount": 1,
"cellsPerAxis": 10,
"scale": [1],
"orientation": [0.5],
"anchorInputSize": 1,
"activeFiringRate": 10
}""")
# expected results from getParameters() (in Spec order)
expected = """{
"moduleCount": 1,
"cellsPerAxis": 10,
"scale": [1],
"orientation": [0.5],
"anchorInputSize": 1,
"activeFiringRate": 10.000000,
"bumpSigma": 0.181720,
"activationThreshold": 10,
"initialPermanence": 0.210000,
"connectedPermanence": 0.500000,
"learningThreshold": 10,
"sampleSize": 20,
"permanenceIncrement": 0.100000,
"permanenceDecrement": 0.000000,
"maxSynapsesPerSegment": -1,
"bumpOverlapMethod": "probabilistic",
"learningMode": false,
"dualPhase": true,
"dimensions": 2,
"seed": 42
}"""
json_list = cp.getParameters()
#print(json_list)
self.assertEqual(json_list, expected)
def testExecuteCommandListWithNoReset(self):
"""
Test that execute command executes the correct command with a list with reset 0.
"""
net = Network()
# Create simple region to pass motor commands as displacement vectors (dx, dy)
motor = net.addRegion("motor", "py.RawValues",
json.dumps({"outputWidth": 2}))
motor.executeCommand('addDataToQueue', [0, 0], 0)
net.run(1)
output = list(np.array(motor.getOutputArray("dataOut")))
self.assertEqual([0, 0], output)
output = list(np.array(motor.getOutputArray("resetOut")))
self.assertEqual([False], output)
def testEmptyQueue(self):
"""
Test that RawValues detects empty queue.
"""
net = Network()
# Create simple region to pass motor commands as displacement vectors (dx, dy)
motor = net.addRegion("motor", "py.RawValues",
json.dumps({"outputWidth": 2}))
try:
net.run(1)
output = list(np.array(motor.getOutputArray("dataOut")))
self.assertEqual([0, 0], output)
self.fail("Empty queue should throw exception")
except:
pass
def testExecuteCommandListWithReset(self):
"""
Test that execute command executes the correct command with a list and reset set.
"""
net = Network()
# Create simple region to pass sensor commands as displacement vectors (dx, dy)
sensor = net.addRegion("sensor", "py.RawSensor",
json.dumps({"outputWidth": 8}))
sensor.executeCommand('addDataToQueue', [0, 1], 1, 0)
net.run(1)
output = list(np.array(sensor.getOutputArray("dataOut")))
self.assertEqual([1, 1, 0, 0, 0, 0, 0, 0], output)
output = list(np.array(sensor.getOutputArray("resetOut")))
self.assertEqual([True], output)
def testSequence(self):
"""
Test that RawSensor executes correctly.
"""
net = Network()
# Create simple region to pass sensor commands as displacement vectors (dx, dy)
sensor = net.addRegion("sensor", "py.RawSensor",
json.dumps({"outputWidth": 8}))
sensor.executeCommand('addDataToQueue', [0, 1], 1, 0)
sensor.executeCommand('addDataToQueue', [1, 2], 0, 1)
sensor.executeCommand('addDataToQueue', [2, 3], 0, 2)
net.run(1)
output = list(np.array(sensor.getOutputArray("dataOut")))
self.assertEqual([1, 1, 0, 0, 0, 0, 0, 0], output)
output = list(np.array(sensor.getOutputArray("resetOut")))
self.assertEqual([True], output)
output = list(np.array(sensor.getOutputArray("sequenceIdOut")))
self.assertEqual([0], output)
net.run(1)
output = list(np.array(sensor.getOutputArray("dataOut")))
self.assertEqual([0, 1, 1, 0, 0, 0, 0, 0], output)
output = list(np.array(sensor.getOutputArray("resetOut")))
self.assertEqual([False], output)
output = list(np.array(sensor.getOutputArray("sequenceIdOut")))
self.assertEqual([1], output)
net.run(1)
output = list(np.array(sensor.getOutputArray("dataOut")))
self.assertEqual([0, 0, 1, 1, 0, 0, 0, 0], output)
output = list(np.array(sensor.getOutputArray("resetOut")))
self.assertEqual([False], output)
output = list(np.array(sensor.getOutputArray("sequenceIdOut")))
self.assertEqual([2], output)
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 range(5):
for _ in range(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 main(parameters=default_parameters, argv=None, verbose=True):
if verbose:
import pprint
print("Parameters:")
pprint.pprint(parameters, indent=4)
print("")
# Read the input file.
records = []
with open(_INPUT_FILE_PATH, "r") as fin:
reader = csv.reader(fin)
headers = next(reader)
next(reader)
next(reader)
for record in reader:
records.append(record)
net = Network()
# Make the Encoders. These will convert input data into binary representations.
dateEncoderRegion = net.addRegion(
'dateEncoder', 'DateEncoderRegion',
str(
dict(timeOfDay_width=parameters["enc"]["time"]["timeOfDay"][0],
timeOfDay_radius=parameters["enc"]["time"]["timeOfDay"][1],
weekend_width=parameters["enc"]["time"]["weekend"])))
valueEncoderRegion = net.addRegion(
'valueEncoder', 'RDSEEncoderRegion',
str(
dict(size=parameters["enc"]["value"]["size"],
sparsity=parameters["enc"]["value"]["sparsity"],
resolution=parameters["enc"]["value"]["resolution"])))
# Make the HTM. SpatialPooler & TemporalMemory & associated tools.
spParams = parameters["sp"]
spRegion = net.addRegion(
'sp',
'SPRegion',
str(
dict(
columnCount=spParams['columnCount'],
potentialPct=spParams["potentialPct"],
potentialRadius=0, # 0 is auto assign as inputWith
globalInhibition=True,
localAreaDensity=spParams["localAreaDensity"],
synPermInactiveDec=spParams["synPermInactiveDec"],
synPermActiveInc=spParams["synPermActiveInc"],
synPermConnected=spParams["synPermConnected"],
boostStrength=spParams["boostStrength"],
wrapAround=True)))
tmParams = parameters["tm"]
tmRegion = net.addRegion(
'tm', 'TMRegion',
str(
dict(columnCount=spParams['columnCount'],
cellsPerColumn=tmParams["cellsPerColumn"],
activationThreshold=tmParams["activationThreshold"],
initialPermanence=tmParams["initialPerm"],
connectedPermanence=spParams["synPermConnected"],
minThreshold=tmParams["minThreshold"],
maxNewSynapseCount=tmParams["newSynapseCount"],
permanenceIncrement=tmParams["permanenceInc"],
permanenceDecrement=tmParams["permanenceDec"],
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=tmParams["maxSegmentsPerCell"],
maxSynapsesPerSegment=tmParams["maxSynapsesPerSegment"])))
net.link('dateEncoder', 'sp', '', '', 'encoded', 'bottomUpIn')
net.link('valueEncoder', 'sp', '', '', 'encoded', 'bottomUpIn')
net.link('sp', 'tm', '', '', 'bottomUpOut', 'bottomUpIn')
net.initialize()
# Iterate through every datum in the dataset, record the inputs & outputs.
inputs = []
anomaly = []
for count, record in enumerate(records):
# Convert date string into Python date object.
dateString = datetime.datetime.strptime(record[0], "%m/%d/%y %H:%M")
# Convert data value string into float.
consumption = float(record[1])
inputs.append(consumption)
# Call the encoders to create bit representations for each value. These are SDR objects.
dateEncoderRegion.setParameterInt64('sensedTime',
int(dateString.timestamp()))
valueEncoderRegion.setParameterReal64('sensedValue', consumption)
net.run(1)
anomaly.append(np.array(tmRegion.getOutputArray("anomaly"))[0])
try:
import matplotlib.pyplot as plt
except:
print("WARNING: failed to import matplotlib, plots cannot be shown.")
return
plt.title("Anomaly Score")
plt.xlabel("Time")
plt.ylabel("Power Consumption")
inputs = np.array(inputs) / max(inputs)
plt.plot(
np.arange(len(inputs)),
inputs,
'red',
np.arange(len(inputs)),
anomaly,
'blue',
)
plt.legend(labels=('Input', 'Anomaly Score'))
plt.show()
return