def testNetwork(testPath="test_images/testing", savedNetworkFile="imageNet_net.nta"):
net = Network(savedNetworkFile)
sensor = net.regions["sensor"]
sp = net.regions["SP"]
classifier = net.regions["classifier"]
print "Reading test images"
sensor.executeCommand(["loadMultipleImages", testPath])
numTestImages = sensor.getParameter("numImages")
print "Number of test images", numTestImages
start = time.time()
# Various region parameters
sensor.setParameter("explorer", "ExhaustiveSweep")
classifier.setParameter("inferenceMode", 1)
classifier.setParameter("learningMode", 0)
sp.setParameter("inferenceMode", 1)
sp.setParameter("learningMode", 0)
numCorrect = 0
for i in range(numTestImages):
net.run(1)
inferredCategory = classifier.getOutputData("categoriesOut").argmax()
if sensor.getOutputData("categoryOut") == inferredCategory:
numCorrect += 1
if i % (numTestImages / 100) == 0:
print "Iteration", i, "numCorrect=", numCorrect
# Some interesting statistics
print "Testing time:", time.time() - start
print "Number of test images", numTestImages
print "num correct=", numCorrect
print "pct correct=", (100.0 * numCorrect) / numTestImages
def testGetSelf(self):
# Create network
net = Network()
# Register ImageSensor region
Network.registerRegion(ImageSensor)
# Add sensor
sensor = net.addRegion("sensor", "py.ImageSensor",
"{width: 100, height: 50}")
pysensor = sensor.getSelf()
# Verify set parameters
self.assertTrue(isinstance(pysensor, ImageSensor))
self.assertEqual(pysensor.height, 50)
self.assertEqual(pysensor.width, 100)
self.assertEqual(pysensor.width, sensor.getParameter('width'))
self.assertEqual(pysensor.height, sensor.getParameter('height'))
sensor.setParameter('width', 444)
sensor.setParameter('height', 444)
self.assertEqual(pysensor.width, 444)
self.assertEqual(pysensor.height, 444)
# Verify py object is not a copy
sensor.getSelf().height = 100
sensor.getSelf().width = 200
self.assertEqual(pysensor.height, 100)
self.assertEqual(pysensor.width, 200)
pysensor.height = 50
pysensor.width = 100
self.assertEqual(sensor.getSelf().height, 50)
self.assertEqual(sensor.getSelf().width, 100)
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 testScalars(self):
scalars = [("int32Param", 32, int, 35), ("uint32Param", 33, int, 36),
("int64Param", 64, int, 74), ("uint64Param", 65, int, 75),
("real32Param", 32.1, float, 33.1),
("real64Param", 64.1, float, 65.1),
("stringParam", "nodespec value", str, "new value")]
n = Network()
l1 = n.addRegion("l1", "TestNode", "")
x = l1.getParameter("uint32Param")
for paramName, initval, paramtype, newval in scalars:
# Check the initial value for each parameter.
x = l1.getParameter(paramName)
self.assertEqual(type(x), paramtype)
if initval is None:
continue
if type(x) == float:
self.assertTrue(abs(x - initval) < 0.00001)
else:
self.assertEqual(x, initval)
# Now set the value, and check to make sure the value is updated
l1.setParameter(paramName, newval)
x = l1.getParameter(paramName)
self.assertEqual(type(x), paramtype)
if type(x) == float:
self.assertTrue(abs(x - newval) < 0.00001)
else:
self.assertEqual(x, newval)
def testRunPCANode(self):
from nupic.engine import *
rgen = numpy.random.RandomState(37)
inputSize = 8
net = Network()
sensor = 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 testArrays(self):
arrays = [
("real32ArrayParam",
[0 * 32, 1 * 32, 2 * 32, 3 * 32, 4 * 32, 5 * 32, 6 * 32,
7 * 32], "Real32"),
("int64ArrayParam", [0 * 64, 1 * 64, 2 * 64, 3 * 64], "Int64")
]
n = Network()
l1 = n.addRegion("l1", "TestNode", "")
for paramName, initval, paramtype in arrays:
x = l1.getParameter(paramName)
self.assertTrue(isinstance(x,
nupic.bindings.engine_internal.Array))
self.assertEqual(x.getType(), paramtype)
self.assertEqual(len(x), len(initval))
for i in range(len(x)):
self.assertEqual(x[i], initval[i])
for i in range(len(x)):
x[i] = x[i] * 2
l1.setParameter(paramName, x)
x = l1.getParameter(paramName)
self.assertTrue(isinstance(x,
nupic.bindings.engine_internal.Array))
self.assertEqual(x.getType(), paramtype)
self.assertEqual(len(x), len(initval))
for i in range(len(x)):
self.assertEqual(x[i], 2 * initval[i])
def testScalars(self):
scalars = [
("int32Param", 32, int, 35),
("uint32Param", 33, int, 36),
("int64Param", 64, long, 74),
("uint64Param", 65, long, 75),
("real32Param", 32.1, float, 33.1),
("real64Param", 64.1, float, 65.1),
("stringParam", "nodespec value", str, "new value")]
n = Network()
l1= n.addRegion("l1", "TestNode", "")
x = l1.getParameter("uint32Param")
for paramName, initval, paramtype, newval in scalars:
# Check the initial value for each parameter.
x = l1.getParameter(paramName)
self.assertEqual(type(x), paramtype)
if initval is None:
continue
if type(x) == float:
self.assertTrue(abs(x - initval) < 0.00001)
else:
self.assertEqual(x, initval)
# Now set the value, and check to make sure the value is updated
l1.setParameter(paramName, newval)
x = l1.getParameter(paramName)
self.assertEqual(type(x), paramtype)
if type(x) == float:
self.assertTrue(abs(x - newval) < 0.00001)
else:
self.assertEqual(x, newval)
def testNetwork(testPath="mnist/testing", savedNetworkFile="mnist_net.nta"):
net = Network(savedNetworkFile)
sensor = net.regions['sensor']
sp = net.regions["SP"]
classifier = net.regions['classifier']
print "Reading test images"
sensor.executeCommand(["loadMultipleImages", testPath])
numTestImages = sensor.getParameter('numImages')
print "Number of test images", numTestImages
start = time.time()
# Various region parameters
sensor.setParameter('explorer', 'Flash')
classifier.setParameter('inferenceMode', 1)
classifier.setParameter('learningMode', 0)
sp.setParameter('inferenceMode', 1)
sp.setParameter('learningMode', 0)
numCorrect = 0
for i in range(numTestImages):
net.run(1)
inferredCategory = classifier.getOutputData('categoriesOut').argmax()
if sensor.getOutputData('categoryOut') == inferredCategory:
numCorrect += 1
if i % (numTestImages / 100) == 0:
print "Iteration", i, "numCorrect=", numCorrect
# Some interesting statistics
print "Testing time:", time.time() - start
print "Number of test images", numTestImages
print "num correct=", numCorrect
print "pct correct=", (100.0 * numCorrect) / numTestImages
def testGetSelf(self):
# Create network
net = Network()
# Register ImageSensor region
Network.registerRegion(ImageSensor)
# Add sensor
sensor = net.addRegion("sensor", "py.ImageSensor", "{width: 100, height: 50}")
pysensor = sensor.getSelf()
# Verify set parameters
self.assertTrue(isinstance(pysensor, ImageSensor))
self.assertEqual(pysensor.height, 50)
self.assertEqual(pysensor.width, 100)
self.assertEqual(pysensor.width, sensor.getParameter("width"))
self.assertEqual(pysensor.height, sensor.getParameter("height"))
sensor.setParameter("width", 444)
sensor.setParameter("height", 444)
self.assertEqual(pysensor.width, 444)
self.assertEqual(pysensor.height, 444)
# Verify py object is not a copy
sensor.getSelf().height = 100
sensor.getSelf().width = 200
self.assertEqual(pysensor.height, 100)
self.assertEqual(pysensor.width, 200)
pysensor.height = 50
pysensor.width = 100
self.assertEqual(sensor.getSelf().height, 50)
self.assertEqual(sensor.getSelf().width, 100)
def testNetwork(testPath="mnist/testing", savedNetworkFile="mnist_net.nta"):
net = Network(savedNetworkFile)
sensor = net.regions['sensor']
sp = net.regions["SP"]
classifier = net.regions['classifier']
print "Reading test images"
sensor.executeCommand(["loadMultipleImages",testPath])
numTestImages = sensor.getParameter('numImages')
print "Number of test images",numTestImages
start = time.time()
# Various region parameters
sensor.setParameter('explorer','Flash')
classifier.setParameter('inferenceMode', 1)
classifier.setParameter('learningMode', 0)
sp.setParameter('inferenceMode', 1)
sp.setParameter('learningMode', 0)
numCorrect = 0
for i in range(numTestImages):
net.run(1)
inferredCategory = classifier.getOutputData('categoriesOut').argmax()
if sensor.getOutputData('categoryOut') == inferredCategory:
numCorrect += 1
if i%(numTestImages/10)== 0:
print "Iteration",i,"numCorrect=",numCorrect
# Some interesting statistics
print "Testing time:",time.time()-start
print "Number of test images",numTestImages
print "num correct=",numCorrect
print "pct correct=",(100.0*numCorrect) / numTestImages
def __init__(self, numColumns, L2Params, L4Params, L6aParams, repeat,
logCalls=False):
"""
Create a network consisting of multiple columns. Each column contains one L2,
one L4 and one L6a layers. In addition all the L2 columns are fully
connected to each other through their lateral inputs.
:param numColumns: Number of columns to create
:type numColumns: int
:param L2Params: constructor parameters for :class:`ColumnPoolerRegion`
:type L2Params: dict
:param L4Params: constructor parameters for :class:`ApicalTMPairRegion`
:type L4Params: dict
:param L6aParams: constructor parameters for :class:`GridCellLocationRegion`
:type L6aParams: dict
:param repeat: Number of times each pair should be seen to be learned
:type repeat: int
:param logCalls: If true, calls to main functions will be logged internally.
The log can then be saved with saveLogs(). This allows us
to recreate the complete network behavior using
rerunExperimentFromLogfile which is very useful for
debugging.
:type logCalls: bool
"""
# Handle logging - this has to be done first
self.logCalls = logCalls
self.numColumns = numColumns
self.repeat = repeat
network = Network()
self.network = createMultipleL246aLocationColumn(network=network,
numberOfColumns=self.numColumns,
L2Params=L2Params,
L4Params=L4Params,
L6aParams=L6aParams)
network.initialize()
self.sensorInput = []
self.motorInput = []
self.L2Regions = []
self.L4Regions = []
self.L6aRegions = []
for i in xrange(self.numColumns):
col = str(i)
self.sensorInput.append(network.regions["sensorInput_" + col].getSelf())
self.motorInput.append(network.regions["motorInput_" + col].getSelf())
self.L2Regions.append(network.regions["L2_" + col])
self.L4Regions.append(network.regions["L4_" + col])
self.L6aRegions.append(network.regions["L6a_" + col])
if L6aParams is not None and "dimensions" in L6aParams:
self.dimensions = L6aParams["dimensions"]
else:
self.dimensions = 2
self.sdrSize = L2Params["sdrSize"]
# will be populated during training
self.learnedObjects = {}
def testArrays(self):
arrays = [
("real32ArrayParam",
[0*32, 1*32, 2*32, 3*32, 4*32, 5*32, 6*32, 7*32],
"Real32"),
("int64ArrayParam",
[0*64, 1*64, 2*64, 3*64],
"Int64")
]
n = Network()
l1= n.addRegion("l1", "TestNode", "")
for paramName, initval, paramtype in arrays:
x = l1.getParameter(paramName)
self.assertTrue(isinstance(x, nupic.bindings.engine_internal.Array))
self.assertEqual(x.getType(), paramtype)
self.assertEqual(len(x), len(initval))
for i in xrange(len(x)):
self.assertEqual(x[i], initval[i])
for i in xrange(len(x)):
x[i] = x[i] * 2
l1.setParameter(paramName, x)
x = l1.getParameter(paramName)
self.assertTrue(isinstance(x, nupic.bindings.engine_internal.Array))
self.assertEqual(x.getType(), paramtype)
self.assertEqual(len(x), len(initval))
for i in xrange(len(x)):
self.assertEqual(x[i], 2 * initval[i])
def _createNetwork():
"""Create network with one RecordSensor region."""
network = Network()
network.addRegion('sensor', 'py.RecordSensor', '{}')
sensorRegion = network.regions['sensor'].getSelf()
# Add an encoder.
encoderParams = {'consumption': {'fieldname': 'consumption',
'resolution': 0.88,
'seed': 1,
'name': 'consumption',
'type': 'RandomDistributedScalarEncoder'}}
encoder = MultiEncoder()
encoder.addMultipleEncoders(encoderParams)
sensorRegion.encoder = encoder
# Add a data source.
testDir = os.path.dirname(os.path.abspath(__file__))
inputFile = os.path.join(testDir, 'fixtures', 'gymdata-test.csv')
dataSource = FileRecordStream(streamID=inputFile)
sensorRegion.dataSource = dataSource
# Get and set what field index we want to predict.
network.regions['sensor'].setParameter('predictedField', 'consumption')
return network
def testGetSelf(self):
# Create network
net = Network()
# Add sensor
sensor = net.addRegion("sensor", "py.ImageSensor",
"{width: 100, height: 50}")
pysensor = sensor.getSelf()
# Verify set parameters
self.assertEqual(type(pysensor), ImageSensor)
self.assertEqual(pysensor.height, 50)
self.assertEqual(pysensor.width, 100)
self.assertEqual(pysensor.width, sensor.getParameter('width'))
self.assertEqual(pysensor.height, sensor.getParameter('height'))
sensor.setParameter('width', 444)
sensor.setParameter('height', 444)
self.assertEqual(pysensor.width, 444)
self.assertEqual(pysensor.height, 444)
# Verify py object is not a copy
sensor.getSelf().height = 100
sensor.getSelf().width = 200
self.assertEqual(pysensor.height, 100)
self.assertEqual(pysensor.width, 200)
pysensor.height = 50
pysensor.width = 100
self.assertEqual(sensor.getSelf().height, 50)
self.assertEqual(sensor.getSelf().width, 100)
def testRunPCANode(self):
from nupic.engine import *
numpy.random.RandomState(37)
inputSize = 8
net = Network()
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 testNetwork(testPath, savedNetworkFile="mnist_net.nta"):
net = Network(savedNetworkFile)
sensor = net.regions["sensor"]
sp = net.regions["SP"]
classifier = net.regions["classifier"]
print "Reading test images"
sensor.executeCommand(["loadMultipleImages",testPath])
numTestImages = sensor.getParameter("numImages")
print "Number of test images",numTestImages
start = time.time()
# Various region parameters
sensor.setParameter("explorer", yaml.dump(["RandomFlash",
{"replacement": False}]))
classifier.setParameter("inferenceMode", 1)
classifier.setParameter("learningMode", 0)
sp.setParameter("inferenceMode", 1)
sp.setParameter("learningMode", 0)
numCorrect = 0
for i in range(numTestImages):
net.run(1)
inferredCategory = classifier.getOutputData("categoriesOut").argmax()
if sensor.getOutputData("categoryOut") == inferredCategory:
numCorrect += 1
if i%(numTestImages/100)== 0:
print "Iteration",i,"numCorrect=",numCorrect
# Some interesting statistics
print "Testing time:",time.time()-start
print "Number of test images",numTestImages
print "num correct=",numCorrect
print "pct correct=",(100.0*numCorrect) / numTestImages
def createSensorRegion(network, sensorType, encoders, dataSource, numCats):
"""
Initializes the sensor region with an encoder and data source.
@param network (Network)
@param sensorType (str) Specific type of region, e.g.
"py.RecordSensor"; possible options can be found in /nupic/regions/.
@param encoders (dict, encoder) If adding multiple encoders, pass a dict
as specified in createEncoder() docstring. Otherwise an encoder object is
expected.
@param dataSource (RecordStream) Sensor region reads data from here.
@param numCats (int) Maximum number of categories of the input data.
@return (Region) Sensor region of the network.
"""
# Sensor region may be non-standard, so add custom region class to the network
sensorName = sensorType.split(".")[1]
sensorModule = sensorName # conveniently have the same name
if sensorName not in PY_REGIONS:
# Add new region class to the network
try:
module = __import__(sensorModule, {}, {}, sensorName)
sensorClass = getattr(module, sensorName)
Network.registerRegion(sensorClass)
# Add region to list of registered PyRegions
PY_REGIONS.append(sensorName)
except ImportError:
raise RuntimeError("Could not find sensor \'{}\' to import.".
format(sensorName))
try:
# Add region to network
regionParams = json.dumps({"verbosity": _VERBOSITY,
"numCategories": numCats})
network.addRegion("sensor", sensorType, regionParams)
except RuntimeError:
print ("Custom region not added correctly. Possible issues are the spec is "
"wrong or the region class is not in the Python path.")
return
# getSelf() returns the actual region, instead of a region wrapper
sensorRegion = network.regions["sensor"].getSelf()
# Specify how the sensor encodes input values
if isinstance(encoders, dict):
# Add encoder(s) from params dict:
sensorRegion.encoder = createEncoder(encoders)
else:
sensorRegion.encoder = encoders
# Specify the dataSource as a file RecordStream instance
sensorRegion.dataSource = dataSource
return sensorRegion
def createNetwork(dataSource):
network = Network()
network.addRegion("sensor", "py.RecordSensor",
json.dumps({"verbosity": _VERBOSITY}))
sensor = network.regions["sensor"].getSelf()
sensor.encoder = createEncoder()
sensor.dataSource = dataSource
return network
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 __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 testVaryingNumberOfCategories(self):
# Setup network with sensor; max number of categories = 2
net = Network()
sensorRegion = net.addRegion("sensor", "py.RecordSensor",
"{'numCategories': 2}")
sensor = sensorRegion.getSelf()
# Test for # of output categories = max
data = {
"_timestamp": None,
"_category": [0, 1],
"label": "0 1",
"_sequenceId": 0,
"y": 2.624902024,
"x": 0.0,
"_timestampRecordIdx": None,
"_reset": 0
}
sensorOutput = numpy.array([0, 0], dtype="int32")
sensor.populateCategoriesOut(data["_category"], sensorOutput)
self.assertSequenceEqual([0, 1], sensorOutput.tolist(
), "Sensor failed to populate the array with record of two categories."
)
# Test for # of output categories > max
data["_category"] = [1, 2, 3]
sensorOutput = numpy.array([0, 0], dtype="int32")
sensor.populateCategoriesOut(data["_category"], sensorOutput)
self.assertSequenceEqual([1, 2], sensorOutput.tolist(
), "Sensor failed to populate the array w/ record of three categories."
)
# Test for # of output categories < max
data["_category"] = [3]
sensorOutput = numpy.array([0, 0], dtype="int32")
sensor.populateCategoriesOut(data["_category"], sensorOutput)
self.assertSequenceEqual(
[3, -1], sensorOutput.tolist(),
"Sensor failed to populate the array w/ record of one category.")
# Test for no output categories
data["_category"] = [None]
sensorOutput = numpy.array([0, 0], dtype="int32")
sensor.populateCategoriesOut(data["_category"], sensorOutput)
self.assertSequenceEqual([-1, -1], sensorOutput.tolist(
), "Sensor failed to populate the array w/ record of zero categories.")
def testParameters(self):
# Test setting and getting parameters
net = Network()
# Add sensor to the network
sensor = net.addRegion("sensor", "py.ImageSensor",
"{width: 100, height: 50}")
# Verify get parameters
self.assertEqual(sensor.getParameter('height'), 50)
self.assertEqual(sensor.getParameter('width'), 100)
# Verify set parameters
sensor.setParameter('width', 42)
self.assertEqual(sensor.getParameter('width'), 42)
def testParameters(self):
# Test setting and getting parameters
net = Network()
# Add sensor to the network
sensor = net.addRegion("sensor", "py.ImageSensor",
"{width: 100, height: 50}")
# Verify get parameters
self.assertEqual(sensor.getParameter('height'), 50)
self.assertEqual(sensor.getParameter('width'), 100)
# Verify set parameters
sensor.setParameter('width', 42)
self.assertEqual(sensor.getParameter('width'), 42)
def _testRepeatCount(self):
"""Test setting and getting repeat count using parameters."""
# Check default repeat count
n = Network(self.filename)
sensor = n.regions[self.nodeName]
res = sensor.executeCommand(['dump'])
expected = self.sensorName + \
' isLabeled = 0 repeatCount = 1 vectorCount = 0 iterations = 0\n'
self.assertEqual(
res, expected,
"repeat count test:\n expected '%s'\n got '%s'\n" %
(expected, res))
# Set to 42, check it and return it back to 1
sensor.setParameter('repeatCount', 42)
res = sensor.getParameter('repeatCount')
self.assertEqual(
res, 42, "set repeatCount to 42:\n got back '%d'\n" % res)
res = sensor.executeCommand(['dump'])
expected = (self.sensorName +
' isLabeled = 0 repeatCount = 42 vectorCount = 0 '
'iterations = 0\n')
self.assertEqual(
res, expected,
"set to 42 test:\n expected '%s'\n got '%s'\n" %
(expected, res))
sensor.setParameter('repeatCount', 1)
def testSaveAndReload(self):
"""
This function tests saving and loading. It will train a network for 500
iterations, then save it and reload it as a second network instance. It will
then run both networks for 100 iterations and ensure they return identical
results.
"""
print "Creating network..."
netOPF = _createOPFNetwork()
level1OPF = netOPF.regions['level1SP']
# ==========================================================================
print "Training network for 500 iterations"
level1OPF.setParameter('learningMode', 1)
level1OPF.setParameter('inferenceMode', 0)
netOPF.run(500)
level1OPF.setParameter('learningMode', 0)
level1OPF.setParameter('inferenceMode', 1)
# ==========================================================================
# Save network and reload as a second instance. We need to reset the data
# source for the unsaved network so that both instances start at the same
# place
print "Saving and reload network"
_, tmpNetworkFilename = _setupTempDirectory("trained.nta")
netOPF.save(tmpNetworkFilename)
netOPF2 = Network(tmpNetworkFilename)
level1OPF2 = netOPF2.regions['level1SP']
sensor = netOPF.regions['sensor'].getSelf()
trainFile = resource_filename("nupic.datafiles", "extra/gym/gym.csv")
sensor.dataSource = FileRecordStream(streamID=trainFile)
sensor.dataSource.setAutoRewind(True)
# ==========================================================================
print "Running inference on the two networks for 100 iterations"
for _ in xrange(100):
netOPF2.run(1)
netOPF.run(1)
l1outputOPF2 = level1OPF2.getOutputData("bottomUpOut")
l1outputOPF = level1OPF.getOutputData("bottomUpOut")
opfHash2 = l1outputOPF2.nonzero()[0].sum()
opfHash = l1outputOPF.nonzero()[0].sum()
self.assertEqual(opfHash2, opfHash)
def testSaveAndReload(self):
"""
This function tests saving and loading. It will train a network for 500
iterations, then save it and reload it as a second network instance. It will
then run both networks for 100 iterations and ensure they return identical
results.
"""
print "Creating network..."
netOPF = _createOPFNetwork()
level1OPF = netOPF.regions['level1SP']
# ==========================================================================
print "Training network for 500 iterations"
level1OPF.setParameter('learningMode', 1)
level1OPF.setParameter('inferenceMode', 0)
netOPF.run(500)
level1OPF.setParameter('learningMode', 0)
level1OPF.setParameter('inferenceMode', 1)
# ==========================================================================
# Save network and reload as a second instance. We need to reset the data
# source for the unsaved network so that both instances start at the same
# place
print "Saving and reload network"
_, tmpNetworkFilename = _setupTempDirectory("trained.nta")
netOPF.save(tmpNetworkFilename)
netOPF2 = Network(tmpNetworkFilename)
level1OPF2 = netOPF2.regions['level1SP']
sensor = netOPF.regions['sensor'].getSelf()
trainFile = resource_filename("nupic.datafiles", "extra/gym/gym.csv")
sensor.dataSource = FileRecordStream(streamID=trainFile)
sensor.dataSource.setAutoRewind(True)
# ==========================================================================
print "Running inference on the two networks for 100 iterations"
for _ in xrange(100):
netOPF2.run(1)
netOPF.run(1)
l1outputOPF2 = level1OPF2.getOutputData("bottomUpOut")
l1outputOPF = level1OPF.getOutputData("bottomUpOut")
opfHash2 = l1outputOPF2.nonzero()[0].sum()
opfHash = l1outputOPF.nonzero()[0].sum()
self.assertEqual(opfHash2, opfHash)
def createNetwork(dataSource):
'''
Create and initialize a network.
'''
with open(_PARAMS_PATH, "r") as f:
modelParams = yaml.safe_load(f)["modelParams"]
# Create a network that will hold the regions.
network = Network()
# Add a sensor region.
network.addRegion("sensor", "py.RecordSensor", "{}")
# Set the encoder and data source of the sensor region.
sensorRegion = network.regions["sensor"].getSelf()
sensorRegion.encoder = createEncoder(
modelParams["sensorParams"]["encoders"])
sensorRegion.dataSource = dataSource
# Make sure the SP input width matches the sensor region output width.
modelParams["spParams"]["inputWidth"] = sensorRegion.encoder.getWidth()
# Add SP and TP regions.
network.addRegion("SP", "py.SPRegion", json.dumps(modelParams["spParams"]))
network.addRegion("TM", "py.TMRegion", json.dumps(modelParams["tmParams"]))
# Add a classifier region.
clName = "py.%s" % modelParams["clParams"].pop("regionName")
network.addRegion("classifier", clName,
json.dumps(modelParams["clParams"]))
classifierRegion = network.regions["classifier"].getSelf()
# Add all links
createSensorToClassifierLinks(network, "sensor", "classifier")
createDataOutLink(network, "sensor", "SP")
createFeedForwardLink(network, "SP", "TM")
createFeedForwardLink(network, "TM", "classifier")
# Reset links are optional, since the sensor region does not send resets.
createResetLink(network, "sensor", "SP")
createResetLink(network, "sensor", "TM")
# Make sure all objects are initialized.
network.initialize()
return network
def initModels():
global model1
model1.dataSource = NetworkModel.getOrCreateDataSource()
model1.network = Network("network1.nta")
#reset the dataSource
model1.network.regions[_RECORD_SENSOR].dataSource = model1.dataSource
"""
def testParameters(self):
# Test setting and getting parameters
net = Network()
# Register ImageSensor region
Network.registerRegion(ImageSensor)
# Add sensor to the network
sensor = net.addRegion("sensor", "py.ImageSensor", "{width: 100, height: 50}")
# Verify get parameters
self.assertEqual(sensor.getParameter("height"), 50)
self.assertEqual(sensor.getParameter("width"), 100)
# Verify set parameters
sensor.setParameter("width", 42)
self.assertEqual(sensor.getParameter("width"), 42)
def createSensorRegion(network, sensorType, encoders, dataSource):
"""
Initializes the sensor region with an encoder and data source.
@param network (Network)
@param sensorType (str) Specific type of region, e.g.
"py.RecordSensor"; possible options can be found in /nupic/regions/.
@param encoders (dict, encoder) If adding multiple encoders, pass a dict
as specified in createEncoder() docstring. Otherwise an encoder object is
expected.
@param dataSource (RecordStream) Sensor region reads data from here.
@return (Region) Sensor region of the network.
"""
# Sensor region may be non-standard, so add custom region class to the network
if sensorType.split(".")[1] not in PY_REGIONS:
# Add new region class to the network
Network.registerRegion(EEGSensor)
try:
# Add region to network
regionParams = json.dumps({"verbosity": _VERBOSITY})
network.addRegion("sensor", sensorType, regionParams)
except RuntimeError:
print ("Custom region not added correctly. Possible issues are the spec is "
"wrong or the region class is not in the Python path.")
return
# getSelf() returns the actual region, instead of a region wrapper
sensorRegion = network.regions["sensor"].getSelf()
# Specify how RecordSensor encodes input values
if isinstance(encoders, dict):
# Multiple encoders to add
sensorRegion.encoder = createEncoder(encoders)
else:
sensorRegion.encoder = encoders
# Specify the dataSource as a file RecordStream instance
sensorRegion.dataSource = dataSource
return sensorRegion
def _registerRegion(regionTypeName, moduleName=None):
"""
A region may be non-standard, so add custom region class to the network.
@param regionTypeName: (str) type name of the region. E.g SensorRegion.
@param moduleName: (str) location of the region class, only needed if
registering a region that is outside the expected "regions/" dir.
"""
if moduleName is None:
# the region is located in the regions/ directory
moduleName = "htmresearch.regions." + regionTypeName
if regionTypeName not in _PY_REGIONS:
# Add new region class to the network.
module = __import__(moduleName, {}, {}, regionTypeName)
unregisteredClass = getattr(module, regionTypeName)
Network.registerRegion(unregisteredClass)
# Add region to list of registered PyRegions
_PY_REGIONS.append(regionTypeName)
def _registerRegion(regionTypeName, moduleName=None):
"""
A region may be non-standard, so add custom region class to the network.
@param regionTypeName: (str) type name of the region. E.g SensorRegion.
@param moduleName: (str) location of the region class, only needed if
registering a region that is outside the expected "regions/" dir.
"""
if moduleName is None:
# the region is located in the regions/ directory
moduleName = "htmresearch.regions." + regionTypeName
if regionTypeName not in _PY_REGIONS:
# Add new region class to the network.
module = __import__(moduleName, {}, {}, regionTypeName)
unregisteredClass = getattr(module, regionTypeName)
Network.registerRegion(unregisteredClass)
# Add region to list of registered PyRegions
_PY_REGIONS.append(regionTypeName)
def _deSerializeExtraData(self, extraDataDir):
"""
Protected method that is called during deserialization (after __setstate__)
with an external directory path. We override it here to load the Network API
instance.
@param extraDataDir (string) Model's extra data directory path
"""
self.network = Network(os.path.join(extraDataDir, "network.nta"))
self._initializeRegionHelpers()
def _registerRegion(regionTypeName, moduleName=None):
"""
A region may be non-standard, so add custom region class to the network.
@param regionTypeName: (str) type name of the region. E.g SensorRegion.
"""
if moduleName is None:
moduleName = regionTypeName
if regionTypeName not in _PY_REGIONS:
# Add new region class to the network.
try:
module = __import__(moduleName, {}, {}, regionTypeName)
unregisteredClass = getattr(module, regionTypeName)
Network.registerRegion(unregisteredClass)
# Add region to list of registered PyRegions
_PY_REGIONS.append(regionTypeName)
except ImportError:
raise RuntimeError(
"Could not import sensor \'{}\'.".format(regionTypeName))
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
}))
return network
def registerResearchRegion(regionTypeName, moduleName=None):
"""
Register this region so that NuPIC can later find it.
@param regionTypeName: (str) type name of the region. E.g LanguageSensor.
@param moduleName: (str) location of the region class, only needed if
registering a region that is outside the expected "regions/" dir.
"""
global _PY_REGIONS
if moduleName is None:
# the region is located in the regions/ directory
moduleName = "htmresearch.regions." + regionTypeName
if regionTypeName not in _PY_REGIONS:
# Add new region class to the network.
module = __import__(moduleName, {}, {}, regionTypeName)
unregisteredClass = getattr(module, regionTypeName)
Network.registerRegion(unregisteredClass)
# Add region to list of registered PyRegions
_PY_REGIONS.append(regionTypeName)
def loadFromFile(self, filename):
""" Load a serialized network
:param filename: Where the network should be loaded from
"""
print "Loading network from {file}...".format(file=filename)
Network.unregisterRegion(ImageSensor.__name__)
Network.registerRegion(ImageSensor)
self.net = Network(filename)
self.networkSensor = self.net.regions["sensor"]
self.networkSensor.setParameter("numSaccades", SACCADES_PER_IMAGE_TESTING)
self.networkSP = self.net.regions["SP"]
self.networkClassifier = self.net.regions["classifier"]
self.setLearningMode(learningSP=False,
learningClassifier=False)
self.numCorrect = 0
def registerResearchRegion(regionTypeName, moduleName=None):
"""
Register this region so that NuPIC can later find it.
@param regionTypeName: (str) type name of the region. E.g LanguageSensor.
@param moduleName: (str) location of the region class, only needed if
registering a region that is outside the expected "regions/" dir.
"""
global _PY_REGIONS
if moduleName is None:
# the region is located in the regions/ directory
moduleName = "htmresearch.regions." + regionTypeName
if regionTypeName not in _PY_REGIONS:
# Add new region class to the network.
module = __import__(moduleName, {}, {}, regionTypeName)
unregisteredClass = getattr(module, regionTypeName)
Network.registerRegion(unregisteredClass)
# Add region to list of registered PyRegions
_PY_REGIONS.append(regionTypeName)
def testVaryingNumberOfCategories(self):
# Setup network with sensor; max number of categories = 2
net = Network()
sensorRegion = net.addRegion(
"sensor", "py.RecordSensor", "{'numCategories': 2}")
sensor = sensorRegion.getSelf()
# Test for # of output categories = max
data = {"_timestamp": None, "_category": [0, 1], "label": "0 1",
"_sequenceId": 0, "y": 2.624902024, "x": 0.0,
"_timestampRecordIdx": None, "_reset": 0}
sensorOutput = numpy.array([0, 0], dtype="int32")
sensor.populateCategoriesOut(data["_category"], sensorOutput)
self.assertSequenceEqual([0, 1], sensorOutput.tolist(),
"Sensor failed to populate the array with record of two categories.")
# Test for # of output categories > max
data["_category"] = [1, 2, 3]
sensorOutput = numpy.array([0, 0], dtype="int32")
sensor.populateCategoriesOut(data["_category"], sensorOutput)
self.assertSequenceEqual([1, 2], sensorOutput.tolist(),
"Sensor failed to populate the array w/ record of three categories.")
# Test for # of output categories < max
data["_category"] = [3]
sensorOutput = numpy.array([0, 0], dtype="int32")
sensor.populateCategoriesOut(data["_category"], sensorOutput)
self.assertSequenceEqual([3, -1], sensorOutput.tolist(),
"Sensor failed to populate the array w/ record of one category.")
# Test for no output categories
data["_category"] = [None]
sensorOutput = numpy.array([0, 0], dtype="int32")
sensor.populateCategoriesOut(data["_category"], sensorOutput)
self.assertSequenceEqual([-1, -1], sensorOutput.tolist(),
"Sensor failed to populate the array w/ record of zero categories.")
def _createNetwork():
"""Create a network with a RecordSensor region and a SDRClassifier region"""
network = Network()
network.addRegion('sensor', 'py.RecordSensor', '{}')
network.addRegion('classifier', 'py.SDRClassifierRegion', '{}')
_createSensorToClassifierLinks(network, 'sensor', 'classifier')
# Add encoder to sensor region.
sensorRegion = network.regions['sensor'].getSelf()
encoderParams = {'consumption': {'fieldname': 'consumption',
'resolution': 0.88,
'seed': 1,
'name': 'consumption',
'type': 'RandomDistributedScalarEncoder'}}
encoder = MultiEncoder()
encoder.addMultipleEncoders(encoderParams)
sensorRegion.encoder = encoder
# Add data source.
testDir = os.path.dirname(os.path.abspath(__file__))
inputFile = os.path.join(testDir, 'fixtures', 'gymdata-test.csv')
dataSource = FileRecordStream(streamID=inputFile)
sensorRegion.dataSource = dataSource
# Get and set what field index we want to predict.
predictedIdx = dataSource.getFieldNames().index('consumption')
network.regions['sensor'].setParameter('predictedFieldIdx', predictedIdx)
return network
def testLoadImages(self):
# Create a simple network with an ImageSensor. You can't actually run
# the network because the region isn't connected to anything
net = Network()
Network.registerRegion(ImageSensor)
net.addRegion("sensor", "py.ImageSensor", "{width: 32, height: 32}")
sensor = net.regions['sensor']
# 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 and check we loaded the correct number
sensor.executeCommand(["loadMultipleImages", tmpDir])
numImages = sensor.getParameter('numImages')
self.assertEqual(numImages, 2)
# Load a single image (this will replace the previous images)
sensor.executeCommand(["loadSingleImage",
os.path.join(tmpDir,'1','im1.png')])
numImages = sensor.getParameter('numImages')
self.assertEqual(numImages, 1)
# 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 testLoadImages(self):
# Create a simple network with an ImageSensor. You can't actually run
# the network because the region isn't connected to anything
net = Network()
net.addRegion("sensor", "py.ImageSensor", "{width: 32, height: 32}")
sensor = net.regions['sensor']
# 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 and check we loaded the correct number
sensor.executeCommand(["loadMultipleImages", tmpDir])
numImages = sensor.getParameter('numImages')
self.assertEqual(numImages, 2)
# Load a single image (this will replace the previous images)
sensor.executeCommand(
["loadSingleImage",
os.path.join(tmpDir, '1', 'im1.png')])
numImages = sensor.getParameter('numImages')
self.assertEqual(numImages, 1)
# 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 plotPermanences(network=None,
savedNetworkFile="mnist_net.nta",
columnList=None,
iteration=0):
"""
Plots the permanences of the top columns into a single master image
If columnList is specified, uses those columns otherwise extracts the
most active columns from the spatial pooler using duty cycle.
"""
# Get the spatial pooler from the network, otherwise read it from checkpoint.
if network is None:
network = Network(savedNetworkFile)
spRegion = network.regions["SP"]
spSelf = spRegion.getSelf()
sp = spSelf._sfdr
# If we are not given a column list, retrieve columns with highest duty cycles
dutyCycles = numpy.zeros(sp.getNumColumns(), dtype=GetNTAReal())
sp.getActiveDutyCycles(dutyCycles)
if columnList is None:
mostActiveColumns = list(dutyCycles.argsort())
mostActiveColumns.reverse()
columnList = mostActiveColumns[0:400]
#print columnList
# Create empty master image with the top 25 columns. We will paste
# individual column images into this image
numImagesPerRowInMaster = 20
masterImage = Image.new("L", ((32 + 2) * numImagesPerRowInMaster,
(32 + 2) * numImagesPerRowInMaster), 255)
for rank, col in enumerate(columnList):
#print "Col=",col,"rank=",rank,"dutyCycle=",dutyCycles[col]
pyPerm = numpy.zeros(sp.getNumInputs(), dtype=GetNTAReal())
sp.getPermanence(col, pyPerm)
# Create small image for each column
pyPerm = pyPerm / pyPerm.max()
pyPerm = (pyPerm * 255.0)
pyPerm = pyPerm.reshape((32, 32))
pyPerm = (pyPerm).astype('uint8')
img = Image.fromarray(pyPerm)
# Paste it into master image
if rank < numImagesPerRowInMaster * numImagesPerRowInMaster:
x = rank % numImagesPerRowInMaster * (32 + 2)
y = (rank / numImagesPerRowInMaster) * (32 + 2)
masterImage.paste(img, (x, y))
# Save master image
masterImage.save("master_%05d.png" % (iteration))
def inspect(element, showRun=True, icon=None):
"""
Launch an Inspector for the provided element.
element -- A network, region or a path to a network directory.
showRun -- Whether to show the RuntimeInspector in the dropdown, which lets
the user run the network.
"""
if isinstance(element, basestring):
element = Network(element)
else:
assert isinstance(element, Network)
if len(element.regions) == 0:
raise Exception("Unable to inspect an empty network")
# Network must be initialized before it can be inspected
element.initialize()
from wx import GetApp, PySimpleApp
if GetApp():
useApp = True
else:
useApp = False
from nupic.analysis.inspectors.MultiInspector import MultiInspector
if not useApp:
app = PySimpleApp()
inspector = MultiInspector(element=element, showRun=showRun, icon=icon)
if not useApp:
app.MainLoop()
app.Destroy()
else:
return inspector
def inspect(element, showRun=True, icon=None):
"""
Launch an Inspector for the provided element.
element -- A network, region or a path to a network directory.
showRun -- Whether to show the RuntimeInspector in the dropdown, which lets
the user run the network.
"""
if isinstance(element, basestring):
element = Network(element)
else:
assert isinstance(element, Network)
if len(element.regions) == 0:
raise Exception('Unable to inspect an empty network')
# Network must be initialized before it can be inspected
element.initialize()
from wx import GetApp, PySimpleApp
if GetApp():
useApp = True
else:
useApp = False
from nupic.analysis.inspectors.MultiInspector import MultiInspector
if not useApp:
app = PySimpleApp()
inspector = MultiInspector(element=element, showRun=showRun, icon=icon)
if not useApp:
app.MainLoop()
app.Destroy()
else:
return inspector
def _testNetLoad(self):
"""Test loading a network with this sensor in it."""
n = Network()
r = n.addRegion(self.nodeName, self.sensorName, '{ activeOutputCount: 11}')
r.dimensions = Dimensions([1])
n.save(self.filename)
n = Network(self.filename)
n.initialize()
self.testsPassed += 1
# Check that vectorCount parameter is zero
r = n.regions[self.nodeName]
res = r.getParameter('vectorCount')
self.assertEqual(
res, 0, "getting vectorCount:\n Expected '0', got back '%d'\n" % res)
self.sensor = r
def createNetwork(dataSource):
"""Create and initialize a network."""
with open(_PARAMS_PATH, "r") as f:
modelParams = yaml.safe_load(f)["modelParams"]
# Create a network that will hold the regions.
network = Network()
# Add a sensor region.
network.addRegion("sensor", "py.RecordSensor", '{}')
# Set the encoder and data source of the sensor region.
sensorRegion = network.regions["sensor"].getSelf()
sensorRegion.encoder = createEncoder(modelParams["sensorParams"]["encoders"])
sensorRegion.dataSource = dataSource
# Make sure the SP input width matches the sensor region output width.
modelParams["spParams"]["inputWidth"] = sensorRegion.encoder.getWidth()
# Add SP and TM regions.
network.addRegion("SP", "py.SPRegion", json.dumps(modelParams["spParams"]))
network.addRegion("TM", "py.TMRegion", json.dumps(modelParams["tmParams"]))
# Add a classifier region.
clName = "py.%s" % modelParams["clParams"].pop("regionName")
network.addRegion("classifier", clName, json.dumps(modelParams["clParams"]))
# Add all links
createSensorToClassifierLinks(network, "sensor", "classifier")
createDataOutLink(network, "sensor", "SP")
createFeedForwardLink(network, "SP", "TM")
createFeedForwardLink(network, "TM", "classifier")
# Reset links are optional, since the sensor region does not send resets.
createResetLink(network, "sensor", "SP")
createResetLink(network, "sensor", "TM")
# Make sure all objects are initialized.
network.initialize()
return network
def createNetwork(networkConfig):
"""
Create and initialize the specified network instance.
@param networkConfig: (dict) the configuration of this network.
@return network: (Network) The actual network
"""
registerAllResearchRegions()
network = Network()
if networkConfig["networkType"] == "L4L2Column":
return createL4L2Column(network, networkConfig, "_0")
elif networkConfig["networkType"] == "MultipleL4L2Columns":
return createMultipleL4L2Columns(network, networkConfig)
def loadFromFile(self, filename):
""" Load a serialized network
:param filename: Where the network should be loaded from
"""
print "Loading network from {file}...".format(file=filename)
Network.unregisterRegion(SaccadeSensor.__name__)
Network.registerRegion(SaccadeSensor)
Network.registerRegion(ExtendedTMRegion)
self.net = Network(filename)
self.networkSensor = self.net.regions["sensor"]
self.networkSensor.setParameter("numSaccades", SACCADES_PER_IMAGE_TESTING)
self.networkSP = self.net.regions["SP"]
self.networkClassifier = self.net.regions["classifier"]
self.numCorrect = 0
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 _testNetLoad(self):
"""Test loading a network with this sensor in it."""
n = Network()
r = n.addRegion(self.nodeName, self.sensorName, '{ activeOutputCount: 11}')
r.dimensions = Dimensions([1])
n.save(self.filename)
n = Network(self.filename)
n.initialize()
self.testsPassed += 1
# Check that vectorCount parameter is zero
r = n.regions[self.nodeName]
res = r.getParameter('vectorCount')
self.assertEqual(
res, 0, "getting vectorCount:\n Expected '0', got back '%d'\n" % res)
self.sensor = r
def saveAndLoadNetwork(network):
# Save network
proto1 = NetworkProto_capnp.NetworkProto.new_message()
network.write(proto1)
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
# Load network
proto2 = NetworkProto_capnp.NetworkProto.read(f)
loadedNetwork = Network.read(proto2)
# Set loaded network's datasource
sensor = network.regions["sensor"].getSelf()
loadedSensor = loadedNetwork.regions["sensor"].getSelf()
loadedSensor.dataSource = sensor.dataSource
# Initialize loaded network
loadedNetwork.initialize()
return loadedNetwork
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 saveAndLoadNetwork(network):
# Save network
proto1 = NetworkProto_capnp.NetworkProto.new_message()
network.write(proto1)
with tempfile.TemporaryFile() as f:
proto1.write(f)
f.seek(0)
# Load network
proto2 = NetworkProto_capnp.NetworkProto.read(f)
loadedNetwork = Network.read(proto2)
# Set loaded network's datasource
sensor = network.regions["sensor"].getSelf()
loadedSensor = loadedNetwork.regions["sensor"].getSelf()
loadedSensor.dataSource = sensor.dataSource
# Initialize loaded network
loadedNetwork.initialize()
return loadedNetwork
def getOutputElementCount(self, outputName):
"""Returns the width of dataOut."""
# Check if classifier has a 'maxCategoryCount' attribute
if not hasattr(self, "maxCategoryCount"):
# Large default value for backward compatibility
self.maxCategoryCount = 1000
if outputName == "categoriesOut":
return len(self.stepsList)
elif outputName == "probabilities":
return len(self.stepsList) * self.maxCategoryCount
elif outputName == "actualValues":
return self.maxCategoryCount
else:
raise ValueError("Unknown output {}.".format(outputName))
if __name__ == "__main__":
from nupic.engine import Network
n = Network()
classifier = n.addRegion(
'classifier',
'py.CLAClassifierRegion',
'{ steps: "1,2", maxAge: 1000}'
)
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
class ClaClassifier():
def __init__(self, net_structure, sensor_params, dest_region_params, class_encoder_params):
self.run_number = 0
# for classifier
self.classifier_encoder_list = {}
self.classifier_input_list = {}
self.prevPredictedColumns = {}
# TODO: 消したいパラメータ
self.predict_value = class_encoder_params.keys()[0]
self.predict_step = 0
# default param
self.default_params = {
'SP_PARAMS': {
"spVerbosity": 0,
"spatialImp": "cpp",
"globalInhibition": 1,
"columnCount": 2024,
"inputWidth": 0, # set later
"numActiveColumnsPerInhArea": 20,
"seed": 1956,
"potentialPct": 0.8,
"synPermConnected": 0.1,
"synPermActiveInc": 0.05,
"synPermInactiveDec": 0.0005,
"maxBoost": 2.0,
},
'TP_PARAMS': {
"verbosity": 0,
"columnCount": 2024,
"cellsPerColumn": 32,
"inputWidth": 2024,
"seed": 1960,
"temporalImp": "cpp",
"newSynapseCount": 20,
"maxSynapsesPerSegment": 32,
"maxSegmentsPerCell": 128,
"initialPerm": 0.21,
"permanenceInc": 0.2,
"permanenceDec": 0.1,
"globalDecay": 0.0,
"maxAge": 0,
"minThreshold": 12,
"activationThreshold": 16,
"outputType": "normal",
"pamLength": 1,
},
'CLASSIFIER_PARAMS': {
"clVerbosity": 0,
"alpha": 0.005,
"steps": "0"
}
}
# tp
self.tp_enable = True
# net structure
self.net_structure = OrderedDict()
self.net_structure['sensor3'] = ['region1']
self.net_structure['region1'] = ['region2']
self.net_structure = net_structure
# region change params
self.dest_region_params = dest_region_params
# sensor change params
self.sensor_params = sensor_params
self.class_encoder_params = class_encoder_params
self._createNetwork()
def _makeRegion(self, name, params):
sp_name = "sp_" + name
if self.tp_enable:
tp_name = "tp_" + name
class_name = "class_" + name
# addRegion
self.network.addRegion(sp_name, "py.SPRegion", json.dumps(params['SP_PARAMS']))
if self.tp_enable:
self.network.addRegion(tp_name, "py.TPRegion", json.dumps(params['TP_PARAMS']))
self.network.addRegion( class_name, "py.CLAClassifierRegion", json.dumps(params['CLASSIFIER_PARAMS']))
encoder = MultiEncoder()
encoder.addMultipleEncoders(self.class_encoder_params)
self.classifier_encoder_list[class_name] = encoder
if self.tp_enable:
self.classifier_input_list[class_name] = tp_name
else:
self.classifier_input_list[class_name] = sp_name
def _linkRegion(self, src_name, dest_name):
sensor = src_name
sp_name = "sp_" + dest_name
tp_name = "tp_" + dest_name
class_name = "class_" + dest_name
if self.tp_enable:
self.network.link(sensor, sp_name, "UniformLink", "")
self.network.link(sp_name, tp_name, "UniformLink", "")
self.network.link(tp_name, class_name, "UniformLink", "")
else:
self.network.link(sensor, sp_name, "UniformLink", "")
self.network.link(sp_name, class_name, "UniformLink", "")
def _initRegion(self, name):
sp_name = "sp_"+ name
tp_name = "tp_"+ name
class_name = "class_"+ name
# setting sp
SP = self.network.regions[sp_name]
SP.setParameter("learningMode", True)
SP.setParameter("anomalyMode", True)
# # setting tp
if self.tp_enable:
TP = self.network.regions[tp_name]
TP.setParameter("topDownMode", False)
TP.setParameter("learningMode", True)
TP.setParameter("inferenceMode", True)
TP.setParameter("anomalyMode", False)
# classifier regionを定義.
classifier = self.network.regions[class_name]
classifier.setParameter('inferenceMode', True)
classifier.setParameter('learningMode', True)
def _createNetwork(self):
def deepupdate(original, update):
"""
Recursively update a dict.
Subdict's won't be overwritten but also updated.
"""
if update is None:
return None
for key, value in original.iteritems():
if not key in update:
update[key] = value
elif isinstance(value, dict):
deepupdate(value, update[key])
return update
self.network = Network()
# check
# if self.selectivity not in self.dest_region_params.keys():
# raise Exception, "There is no selected region : " + self.selectivity
if not len(self.net_structure.keys()) == len(set(self.net_structure.keys())):
raise Exception, "There is deplicated net_structure keys : " + self.net_structure.keys()
# sensor
for sensor_name, params in self.sensor_params.items():
self.network.addRegion(sensor_name, "py.RecordSensor", json.dumps({"verbosity": 0}))
sensor = self.network.regions[sensor_name].getSelf()
# set encoder
#params = deepupdate(cn.SENSOR_PARAMS, params)
encoder = MultiEncoder()
encoder.addMultipleEncoders( params )
sensor.encoder = encoder
sensor.dataSource = DataBuffer()
# network
print 'create element ...'
for name in self.dest_region_params.keys():
change_params = self.dest_region_params[name]
params = deepupdate(self.default_params, change_params)
# input width
input_width = 0
for source in [s for s,d in self.net_structure.items() if name in d]:
if source in self.sensor_params.keys():
sensor = self.network.regions[source].getSelf()
input_width += sensor.encoder.getWidth()
else:
input_width += params['TP_PARAMS']['cellsPerColumn'] * params['TP_PARAMS']['columnCount']
params['SP_PARAMS']['inputWidth'] = input_width
self._makeRegion(name, params)
# link
print 'link network ...'
for source, dest_list in self.net_structure.items():
for dest in dest_list:
if source in self.sensor_params.keys():
self._linkRegion(source, dest)
else:
if self.tp_enable:
self._linkRegion("tp_" + source, dest)
else:
self._linkRegion("sp_" + source, dest)
# initialize
print 'initializing network ...'
self.network.initialize()
for name in self.dest_region_params.keys():
self._initRegion(name)
return
#@profile
def run(self, input_data, learn=True, class_learn=True,learn_layer=None):
"""
networkの実行.
学習したいときは, learn=True, ftypeを指定する.
予測したいときは, learn=False, ftypeはNoneを指定する.
学習しているときも, 予測はしているがな.
input_data = {'xy_value': [1.0, 2.0], 'ftype': 'sin'}
"""
self.enable_learning_mode(learn, learn_layer)
self.enable_class_learning_mode(class_learn)
self.run_number += 1
# calc encoder, SP, TP
for sensor_name in self.sensor_params.keys():
self.network.regions[sensor_name].getSelf().dataSource.push(input_data)
self.network.run(1)
#self.layer_output(input_data)
#self.debug(input_data)
# learn classifier
inferences = {}
for name in self.dest_region_params.keys():
class_name = "class_" + name
inferences['classifier_'+name] = self._learn_classifier_multi(class_name, actValue=input_data[self.predict_value], pstep=self.predict_step)
# anomaly
#inferences["anomaly"] = self._calc_anomaly()
return inferences
def _learn_classifier_multi(self, region_name, actValue=None, pstep=0):
"""
classifierの計算を行う.
直接customComputeを呼び出さずに, network.runの中でやりたいところだけど,
計算した内容の取り出し方法がわからない.
"""
# TODO: networkとclassifierを完全に切り分けたいな.
# networkでは, sensor,sp,tpまで計算を行う.
# その計算結果の評価/利用は外に出す.
classifier = self.network.regions[region_name]
encoder = self.classifier_encoder_list[region_name].getEncoderList()[0]
class_input = self.classifier_input_list[region_name]
tp_bottomUpOut = self.network.regions[class_input].getOutputData("bottomUpOut").nonzero()[0]
#tp_bottomUpOut = self.network.regions["TP"].getSelf()._tfdr.infActiveState['t'].reshape(-1).nonzero()[0]
if actValue is not None:
bucketIdx = encoder.getBucketIndices(actValue)[0]
classificationIn = {
'bucketIdx': bucketIdx,
'actValue': actValue
}
else:
classificationIn = {'bucketIdx': 0,'actValue': 'no'}
clResults = classifier.getSelf().customCompute(
recordNum=self.run_number,
patternNZ=tp_bottomUpOut,
classification=classificationIn
)
inferences= self._get_inferences(clResults, pstep, summary_tyep='sum')
return inferences
def _get_inferences(self, clResults, steps, summary_tyep='sum'):
"""
classifierの計算結果を使いやすいように変更するだけ.
"""
likelihoodsVec = clResults[steps]
bucketValues = clResults['actualValues']
likelihoodsDict = defaultdict(int)
bestActValue = None
bestProb = None
if summary_tyep == 'sum':
for (actValue, prob) in zip(bucketValues, likelihoodsVec):
likelihoodsDict[actValue] += prob
if bestProb is None or likelihoodsDict[actValue] > bestProb:
bestProb = likelihoodsDict[actValue]
bestActValue = actValue
elif summary_tyep == 'best':
for (actValue, prob) in zip(bucketValues, likelihoodsVec):
if bestProb is None or prob > bestProb:
likelihoodsDict[actValue] = prob
bestProb = prob
bestActValue = actValue
return {'likelihoodsDict': likelihoodsDict, 'best': {'value': bestActValue, 'prob':bestProb}}
def _calc_anomaly(self):
"""
各層のanomalyを計算
"""
score = 0
anomalyScore = {}
for name in self.dest_region_params.keys():
#sp_bottomUpOut = self.network.regions["sp_"+name].getOutputData("bottomUpOut").nonzero()[0]
sp_bottomUpOut = self.network.regions["tp_"+name].getInputData("bottomUpIn").nonzero()[0]
if self.prevPredictedColumns.has_key(name):
score = computeAnomalyScore(sp_bottomUpOut, self.prevPredictedColumns[name])
#topdown_predict = self.network.regions["TP"].getSelf()._tfdr.topDownCompute().copy().nonzero()[0]
topdown_predict = self.network.regions["tp_"+name].getSelf()._tfdr.topDownCompute().nonzero()[0]
self.prevPredictedColumns[name] = copy.deepcopy(topdown_predict)
anomalyScore[name] = score
return anomalyScore
def reset(self):
"""
reset sequence
"""
# for name in self.dest_region_params.keys():
# self.network.regions["tp_"+name].getSelf().resetSequenceStates()
return
# for sensor_name in self.sensor_params.keys():
# sensor = self.network.regions[sensor_name].getSelf()
# sensor.dataSource = DataBuffer()
def enable_class_learning_mode(self, enable):
for name in self.dest_region_params.keys():
self.network.regions["class_"+name].setParameter("learningMode", enable)
def enable_learning_mode(self, enable, layer_name = None):
"""
各層のSP, TP, ClassifierのlearningModeを変更
"""
if layer_name is None:
for name in self.dest_region_params.keys():
self.network.regions["sp_"+name].setParameter("learningMode", enable)
if self.tp_enable:
self.network.regions["tp_"+name].setParameter("learningMode", enable)
self.network.regions["class_"+name].setParameter("learningMode", enable)
else:
for name in self.dest_region_params.keys():
self.network.regions["sp_"+name].setParameter("learningMode", not enable)
if self.tp_enable:
self.network.regions["tp_"+name].setParameter("learningMode", not enable)
self.network.regions["class_"+name].setParameter("learningMode", not enable)
for name in layer_name:
self.network.regions["sp_"+name].setParameter("learningMode", enable)
if self.tp_enable:
self.network.regions["tp_"+name].setParameter("learningMode", enable)
self.network.regions["class_"+name].setParameter("learningMode", enable)
def print_inferences(self, input_data, inferences):
"""
計算結果を出力する
"""
# print "%10s, %10s, %1s" % (
# int(input_data['xy_value'][0]),
# int(input_data['xy_value'][1]),
# input_data['label'][:1]),
print "%5s" % (
input_data['label']),
try:
for name in sorted(self.dest_region_params.keys()):
print "%5s" % (inferences['classifier_'+name]['best']['value']),
for name in sorted(self.dest_region_params.keys()):
print "%6.4f," % (inferences['classifier_'+name]['likelihoodsDict'][input_data[self.predict_value]]),
except:
pass
# for name in sorted(self.dest_region_params.keys()):
# print "%3.2f," % (inferences["anomaly"][name]),
# for name in sorted(self.dest_region_params.keys()):
# print "%5s," % name,
print
def layer_output(self, input_data, region_name=None):
if region_name is not None:
Region = self.network.regions[region_name]
print Region.getOutputData("bottomUpOut").nonzero()[0]
return
for name in self.dest_region_params.keys():
SPRegion = self.network.regions["sp_"+name]
if self.tp_enable:
TPRegion = self.network.regions["tp_"+name]
print "#################################### ", name
print
print "==== SP layer ===="
print "input: ", SPRegion.getInputData("bottomUpIn").nonzero()[0][:20]
print "output: ", SPRegion.getOutputData("bottomUpOut").nonzero()[0][:20]
print
if self.tp_enable:
print "==== TP layer ===="
print "input: ", TPRegion.getInputData("bottomUpIn").nonzero()[0][:20]
print "output: ", TPRegion.getOutputData("bottomUpOut").nonzero()[0][:20]
print
print "==== Predict ===="
print TPRegion.getSelf()._tfdr.topDownCompute().copy().nonzero()[0][:20]
print
def save(self, path):
import pickle
with open(path, 'wb') as modelPickleFile:
pickle.dump(self, modelPickleFile)
def _createNetwork(self):
def deepupdate(original, update):
"""
Recursively update a dict.
Subdict's won't be overwritten but also updated.
"""
if update is None:
return None
for key, value in original.iteritems():
if not key in update:
update[key] = value
elif isinstance(value, dict):
deepupdate(value, update[key])
return update
self.network = Network()
# check
# if self.selectivity not in self.dest_region_params.keys():
# raise Exception, "There is no selected region : " + self.selectivity
if not len(self.net_structure.keys()) == len(set(self.net_structure.keys())):
raise Exception, "There is deplicated net_structure keys : " + self.net_structure.keys()
# sensor
for sensor_name, params in self.sensor_params.items():
self.network.addRegion(sensor_name, "py.RecordSensor", json.dumps({"verbosity": 0}))
sensor = self.network.regions[sensor_name].getSelf()
# set encoder
#params = deepupdate(cn.SENSOR_PARAMS, params)
encoder = MultiEncoder()
encoder.addMultipleEncoders( params )
sensor.encoder = encoder
sensor.dataSource = DataBuffer()
# network
print 'create element ...'
for name in self.dest_region_params.keys():
change_params = self.dest_region_params[name]
params = deepupdate(self.default_params, change_params)
# input width
input_width = 0
for source in [s for s,d in self.net_structure.items() if name in d]:
if source in self.sensor_params.keys():
sensor = self.network.regions[source].getSelf()
input_width += sensor.encoder.getWidth()
else:
input_width += params['TP_PARAMS']['cellsPerColumn'] * params['TP_PARAMS']['columnCount']
params['SP_PARAMS']['inputWidth'] = input_width
self._makeRegion(name, params)
# link
print 'link network ...'
for source, dest_list in self.net_structure.items():
for dest in dest_list:
if source in self.sensor_params.keys():
self._linkRegion(source, dest)
else:
if self.tp_enable:
self._linkRegion("tp_" + source, dest)
else:
self._linkRegion("sp_" + source, dest)
# initialize
print 'initializing network ...'
self.network.initialize()
for name in self.dest_region_params.keys():
self._initRegion(name)
return