def testBumpUpWeakColumns(self):
sp = SpatialPooler(inputDimensions=[8],
columnDimensions=[5])
sp._synPermBelowStimulusInc = 0.01
sp._synPermTrimThreshold = 0.05
sp._overlapDutyCycles = numpy.array([0, 0.009, 0.1, 0.001, 0.002])
sp._minOverlapDutyCycles = numpy.array(5*[0.01])
sp._potentialPools = SparseBinaryMatrix(
[[1, 1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 1, 0, 1],
[0, 0, 1, 0, 1, 1, 1, 0],
[1, 1, 1, 0, 0, 0, 1, 0],
[1, 1, 1, 1, 1, 1, 1, 1]])
sp._permanences = SparseMatrix(
[[0.200, 0.120, 0.090, 0.040, 0.000, 0.000, 0.000, 0.000],
[0.150, 0.000, 0.000, 0.000, 0.180, 0.120, 0.000, 0.450],
[0.000, 0.000, 0.014, 0.000, 0.032, 0.044, 0.110, 0.000],
[0.041, 0.000, 0.000, 0.000, 0.000, 0.000, 0.178, 0.000],
[0.100, 0.738, 0.045, 0.002, 0.050, 0.008, 0.208, 0.034]])
truePermanences = [
[0.210, 0.130, 0.100, 0.000, 0.000, 0.000, 0.000, 0.000],
# Inc Inc Inc Trim - - - -
[0.160, 0.000, 0.000, 0.000, 0.190, 0.130, 0.000, 0.460],
# Inc - - - Inc Inc - Inc
[0.000, 0.000, 0.014, 0.000, 0.032, 0.044, 0.110, 0.000], #unchanged
# - - - - - - - -
[0.051, 0.000, 0.000, 0.000, 0.000, 0.000, 0.188, 0.000],
# Inc Trim Trim - - - Inc -
[0.110, 0.748, 0.055, 0.000, 0.060, 0.000, 0.218, 0.000]]
sp._bumpUpWeakColumns()
for i in xrange(sp._numColumns):
perm = list(sp._permanences.getRow(i))
for j in xrange(sp._numInputs):
self.assertAlmostEqual(truePermanences[i][j], perm[j])
def testAdaptSynapses(self):
sp = SpatialPooler(inputDimensions=[8],
columnDimensions=[4],
synPermInactiveDec=0.01,
synPermActiveInc=0.1,
synPermActiveSharedDec=0.02,
synPermOrphanDec=0.03)
sp._synPermTrimThreshold = 0.05
sp._potentialPools = SparseBinaryMatrix(
[[1, 1, 1, 1, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 1, 0, 1],
[0, 0, 1, 0, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 1, 0]])
inputVector = numpy.array([1, 0, 0, 1, 1, 0, 1, 0])
sharedInputs = numpy.where(numpy.array(
[1, 0, 0, 0, 0, 0, 1, 0]) > 0)[0]
activeColumns = numpy.array([0,1,2])
sp._permanences = SparseMatrix(
[[0.200, 0.120, 0.090, 0.040, 0.000, 0.000, 0.000, 0.000],
[0.150, 0.000, 0.000, 0.000, 0.180, 0.120, 0.000, 0.450],
[0.000, 0.000, 0.014, 0.000, 0.000, 0.000, 0.110, 0.000],
[0.040, 0.000, 0.000, 0.000, 0.000, 0.000, 0.178, 0.000]])
truePermanences = [
[0.280, 0.110, 0.080, 0.140, 0.000, 0.000, 0.000, 0.000],
# Inc/Sh Dec Dec Inc - - - -
[0.230, 0.000, 0.000, 0.000, 0.280, 0.110, 0.000, 0.440],
# Inc/Sh - - - Inc Dec - Dec
[0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.190, 0.000],
# - - Trim - - - Inc/Sh -
[0.040, 0.000, 0.000, 0.000, 0.000, 0.000, 0.178, 0.000]]
# - - - - - - - - -
sp._adaptSynapses(inputVector,sharedInputs, activeColumns)
for i in xrange(sp._numColumns):
perm = list(sp._permanences.getRow(i))
for j in xrange(sp._numInputs):
self.assertAlmostEqual(truePermanences[i][j], perm[j])
# test orphan columns
sp._potentialPools = SparseBinaryMatrix(
[[1, 1, 1, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 1, 0]])
inputVector = numpy.array([1, 0, 0, 1, 1, 0, 1, 0])
sharedInputs = numpy.where(numpy.array([1, 0, 0, 1, 0, 0, 0, 0]) > 0)[0]
activeColumns = numpy.array([0,1,2])
sp._permanences = SparseMatrix(
[[0.200, 0.120, 0.090, 0.000, 0.000, 0.000, 0.000, 0.000],
[0.000, 0.017, 0.232, 0.400, 0.000, 0.000, 0.000, 0.000],
[0.000, 0.000, 0.014, 0.051, 0.730, 0.000, 0.000, 0.000],
[0.170, 0.000, 0.000, 0.000, 0.000, 0.000, 0.380, 0.000]])
truePermanences = [
[0.280, 0.110, 0.080, 0.000, 0.000, 0.000, 0.000, 0.000],
# Inc/Sh Dec Dec - - - - -
[0.000, 0.000, 0.222, 0.480, 0.000, 0.000, 0.000, 0.000],
# - Trim Dec Inc/Sh - - - -
[0.000, 0.000, 0.000, 0.131, 0.830, 0.000, 0.000, 0.000],
# - - Trim Inc/Sh Inc - - -
[0.170, 0.000, 0.000, 0.000, 0.000, 0.000, 0.380, 0.000]]
# - - - - - - - -
sp._adaptSynapses(inputVector,sharedInputs, activeColumns)
for i in xrange(sp._numColumns):
perm = list(sp._permanences.getRow(i))
for j in xrange(sp._numInputs):
self.assertAlmostEqual(truePermanences[i][j], perm[j])
