def testDefaultArgs(self):
fs = FunctionSource(dataFunction,
state=None,
resetFieldName=None,
sequenceIdFieldName=None)
self.assertIsNotNone(fs)
r = fs.getNextRecordDict()
self.assertIsNotNone(r)
def testResetAndSequenceFields(self):
fs = FunctionSource(dataFunction,
state=None,
resetFieldName="reset",
sequenceIdFieldName="sequence")
self.assertIsNotNone(fs)
r = fs.getNextRecordDict()
self.assertIsNotNone(r)
def testState(self):
state = dict(val=100)
fs = FunctionSource(dataFunction, state=state, resetFieldName="reset", sequenceIdFieldName="sequence")
self.assertIsNotNone(fs)
r = fs.getNextRecordDict()
self.assertIsNotNone(r)
r = fs.getNextRecordDict()
r = fs.getNextRecordDict()
self.assertEqual(103, state["val"])
def testState(self):
state = dict(val=100)
fs = FunctionSource(dataFunction,
state=state,
resetFieldName="reset",
sequenceIdFieldName="sequence")
self.assertIsNotNone(fs)
r = fs.getNextRecordDict()
self.assertIsNotNone(r)
r = fs.getNextRecordDict()
r = fs.getNextRecordDict()
self.assertEqual(103, state["val"])
def testPickle(self):
state = dict(val=100)
fs = FunctionSource(dataFunction, state=state, resetFieldName="reset", sequenceIdFieldName="sequence")
self.assertIsNotNone(fs)
r = fs.getNextRecordDict()
self.assertIsNotNone(r)
pkl = pickle.dumps(fs)
self.assertIsNotNone(pkl)
fs2 = pickle.loads(pkl)
self.assertIsNotNone(fs2)
r = fs2.getNextRecordDict()
r = fs2.getNextRecordDict()
self.assertEqual(103, fs2.state["val"])
def testPickle(self):
state = dict(val=100)
fs = FunctionSource(dataFunction,
state=state,
resetFieldName="reset",
sequenceIdFieldName="sequence")
self.assertIsNotNone(fs)
r = fs.getNextRecordDict()
self.assertIsNotNone(r)
pkl = pickle.dumps(fs)
self.assertIsNotNone(pkl)
fs2 = pickle.loads(pkl)
self.assertIsNotNone(fs2)
r = fs2.getNextRecordDict()
r = fs2.getNextRecordDict()
self.assertEqual(103, fs2.state["val"])
def testResetAndSequenceFields(self):
fs = FunctionSource(dataFunction, state=None, resetFieldName="reset", sequenceIdFieldName="sequence")
self.assertIsNotNone(fs)
r = fs.getNextRecordDict()
self.assertIsNotNone(r)
def testDefaultArgs(self):
fs = FunctionSource(dataFunction, state=None, resetFieldName=None, sequenceIdFieldName=None)
self.assertIsNotNone(fs)
r = fs.getNextRecordDict()
self.assertIsNotNone(r)
def getDescription(datasets):
encoder = MultiEncoder()
encoder.addEncoder("date", DateEncoder(timeOfDay=3))
encoder.addEncoder("amount", LogEncoder(name="amount", maxval=1000))
for i in xrange(0, nRandomFields):
s = ScalarEncoder(name="scalar",
minval=0,
maxval=randomFieldWidth,
resolution=1,
w=3)
encoder.addEncoder("random%d" % i, s)
dataSource = FunctionSource(
generateFunction,
dict(nRandomFields=nRandomFields, randomFieldWidth=randomFieldWidth))
inputShape = (1, encoder.getWidth())
# Layout the coincidences vertically stacked on top of each other, each
# looking at the entire input field.
coincidencesShape = (nCoincidences, 1)
# TODO: why do we need input border?
inputBorder = inputShape[1] / 2
if inputBorder * 2 >= inputShape[1]:
inputBorder -= 1
nodeParams = dict()
spParams = dict(
commonDistributions=0,
inputShape=inputShape,
inputBorder=inputBorder,
coincidencesShape=coincidencesShape,
coincInputRadius=inputShape[1] / 2,
coincInputPoolPct=0.75,
gaussianDist=0,
localAreaDensity=0.10,
# localAreaDensity = 0.04,
numActivePerInhArea=-1,
dutyCyclePeriod=1000,
stimulusThreshold=5,
synPermInactiveDec=0.08,
# synPermInactiveDec=0.02,
synPermActiveInc=0.02,
synPermActiveSharedDec=0.0,
synPermOrphanDec=0.0,
minPctDutyCycleBeforeInh=0.05,
# minPctDutyCycleAfterInh = 0.1,
# minPctDutyCycleBeforeInh = 0.05,
minPctDutyCycleAfterInh=0.05,
# minPctDutyCycleAfterInh = 0.4,
seed=1,
)
otherParams = dict(
disableTemporal=1,
trainingStep='spatial',
)
nodeParams.update(spParams)
nodeParams.update(otherParams)
def mySetupCallback(experiment):
print "Setup function called"
description = dict(
options=dict(logOutputsDuringInference=False, ),
network=dict(sensorDataSource=dataSource,
sensorEncoder=encoder,
CLAType="py.CLARegion",
CLAParams=nodeParams,
classifierType=None,
classifierParams=None),
# step
spTrain=dict(
name="phase1",
setup=mySetupCallback,
iterationCount=5000,
#iter=displaySPCoincidences(100),
finish=printSPCoincidences()),
tpTrain=None, # same format as sptrain if non-empty
infer=None, # same format as sptrain if non-empty
)
return description
