def basicTest(self):
"""Basic test (creation, pickling, basic run of learning and inference)"""
# Create TM object
tm = BacktrackingTMCPP(numberOfCols=10,
cellsPerColumn=3,
initialPerm=.2,
connectedPerm=0.8,
minThreshold=2,
newSynapseCount=5,
permanenceInc=.1,
permanenceDec=.05,
permanenceMax=1,
globalDecay=.05,
activationThreshold=4,
doPooling=False,
segUpdateValidDuration=5,
seed=SEED,
verbosity=VERBOSITY)
tm.retrieveLearningStates = True
# Save and reload
tm.makeCells4Ephemeral = False
pickle.dump(tm, open("test_tm_cpp.pkl", "wb"))
tm2 = pickle.load(open("test_tm_cpp.pkl"))
self.assertTrue(fdrutils.tmDiff2(tm, tm2, VERBOSITY,
checkStates=False))
# Learn
for i in xrange(5):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
tm.learn(x)
# Save and reload after learning
tm.reset()
tm.makeCells4Ephemeral = False
pickle.dump(tm, open("test_tm_cpp.pkl", "wb"))
tm2 = pickle.load(open("test_tm_cpp.pkl"))
self.assertTrue(fdrutils.tmDiff2(tm, tm2, VERBOSITY))
## Infer
patterns = numpy.zeros((4, tm.numberOfCols), dtype='uint32')
for i in xrange(4):
_RGEN.initializeUInt32Array(patterns[i], 2)
for i in xrange(10):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
tm.infer(x)
if i > 0:
tm._checkPrediction(patterns)
def test_cpp_py_tms(self):
# Create cpp tm
tm = BacktrackingTMCPP(numberOfCols=10,
cellsPerColumn=1,
verbosity=VERBOSITY)
tm.cells4.setCellSegmentOrder(True)
# Create Python tm from same characteristics
tmPy = BacktrackingTM(numberOfCols=tm.numberOfCols,
cellsPerColumn=tm.cellsPerColumn,
initialPerm=tm.initialPerm,
connectedPerm=tm.connectedPerm,
minThreshold=tm.minThreshold,
newSynapseCount=tm.newSynapseCount,
permanenceInc=tm.permanenceInc,
permanenceDec=tm.permanenceDec,
permanenceMax=tm.permanenceMax,
globalDecay=tm.globalDecay,
activationThreshold=tm.activationThreshold,
doPooling=tm.doPooling,
segUpdateValidDuration=tm.segUpdateValidDuration,
pamLength=tm.pamLength,
maxAge=tm.maxAge,
maxSeqLength=tm.maxSeqLength,
maxSegmentsPerCell=tm.maxSegmentsPerCell,
maxSynapsesPerSegment=tm.maxSynapsesPerSegment,
seed=tm.seed,
verbosity=tm.verbosity)
# Check if both tm's are equal
self.assertTrue(fdrutils.tmDiff2(tm, tmPy, VERBOSITY, False))
# Build up sequences
numPatterns = 1
numRepetitions = 1
activity = 1
sequence = fdrutils.generateCoincMatrix(nCoinc=numPatterns,
length=tm.numberOfCols,
activity=activity)
# print(sequence)
sequence_row = sequence.getRow(0)
y1 = tm.learn(sequence_row)
y2 = tmPy.learn(sequence_row)
num_segments_cpp = tm.getNumSegments()
num_segments_py = tmPy.getNumSegments()
# fails since num_segments_cpp = 0 and num_segments_py = 1
self.assertTrue(num_segments_cpp=num_segments_py)
# Check if both tm's are equal
# self.assertTrue(fdrutils.tmDiff2(tm, tmPy, VERBOSITY, False))
return
def basicTest(self):
"""Basic test (creation, pickling, basic run of learning and inference)"""
# Create TM object
tm = BacktrackingTMCPP(numberOfCols=10, cellsPerColumn=3,
initialPerm=.2, connectedPerm= 0.8,
minThreshold=2, newSynapseCount=5,
permanenceInc=.1, permanenceDec= .05,
permanenceMax=1, globalDecay=.05,
activationThreshold=4, doPooling=False,
segUpdateValidDuration=5, seed=SEED,
verbosity=VERBOSITY)
tm.retrieveLearningStates = True
# Save and reload
tm.makeCells4Ephemeral = False
pickle.dump(tm, open("test_tm_cpp.pkl", "wb"))
tm2 = pickle.load(open("test_tm_cpp.pkl"))
self.assertTrue(fdrutils.tmDiff2(tm, tm2, VERBOSITY, checkStates=False))
# Learn
for i in xrange(5):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
tm.learn(x)
# Save and reload after learning
tm.reset()
tm.makeCells4Ephemeral = False
pickle.dump(tm, open("test_tm_cpp.pkl", "wb"))
tm2 = pickle.load(open("test_tm_cpp.pkl"))
self.assertTrue(fdrutils.tmDiff2(tm, tm2, VERBOSITY))
## Infer
patterns = numpy.zeros((4, tm.numberOfCols), dtype='uint32')
for i in xrange(4):
_RGEN.initializeUInt32Array(patterns[i], 2)
for i in xrange(10):
x = numpy.zeros(tm.numberOfCols, dtype='uint32')
_RGEN.initializeUInt32Array(x, 2)
tm.infer(x)
if i > 0:
tm.checkPrediction2(patterns)
