本文整理汇总了Python中nupic.research.spatial_pooler.SpatialPooler._adaptSynapses方法的典型用法代码示例。如果您正苦于以下问题：Python SpatialPooler._adaptSynapses方法的具体用法？Python SpatialPooler._adaptSynapses怎么用？Python SpatialPooler._adaptSynapses使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nupic.research.spatial_pooler.SpatialPooler的用法示例。
在下文中一共展示了SpatialPooler._adaptSynapses方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testInhibition
# 需要导入模块: from nupic.research.spatial_pooler import SpatialPooler [as 别名]
# 或者: from nupic.research.spatial_pooler.SpatialPooler import _adaptSynapses [as 别名]
def testInhibition(self):
"""
Test if the firing number of coincidences after inhibition
equals spatial pooler numActiveColumnsPerInhArea.
"""
# Miscellaneous variables:
# n, w: n, w of encoders
# inputLen: Length of binary input
# synPermConnected: Spatial pooler synPermConnected
# synPermActiveInc: Spatial pooler synPermActiveInc
# connectPct: Initial connect percentage of permanences
# columnDimensions: Number of spatial pooler coincidences
# numActiveColumnsPerInhArea: Spatial pooler numActiveColumnsPerInhArea
# stimulusThreshold: Spatial pooler stimulusThreshold
# spSeed: Spatial pooler for initial permanences
# stimulusThresholdInh: Parameter for inhibition, default value 0.00001
# kDutyCycleFactor: kDutyCycleFactor for dutyCycleTieBreaker in
# Inhibition
# spVerbosity: Verbosity to print other sp initial parameters
# testIter: Testing iterations
n = 100
w = 15
inputLen = 300
columnDimensions = 2048
numActiveColumnsPerInhArea = 40
stimulusThreshold = 0
spSeed = 1956
stimulusThresholdInh = 0.00001
kDutyCycleFactor = 0.01
spVerbosity = 0
testIter = 100
spTest = SpatialPooler(
columnDimensions=(columnDimensions, 1),
inputDimensions=(1, inputLen),
potentialRadius=inputLen / 2,
numActiveColumnsPerInhArea=numActiveColumnsPerInhArea,
spVerbosity=spVerbosity,
stimulusThreshold=stimulusThreshold,
seed=spSeed
)
initialPermanence = spTest._initialPermanence()
spTest._masterPotentialM, spTest._masterPermanenceM = (
spTest._makeMasterCoincidences(spTest.numCloneMasters,
spTest._coincRFShape,
spTest.potentialPct,
initialPermanence,
spTest.random))
spTest._updateInhibitionObj()
boostFactors = numpy.ones(columnDimensions)
for i in range(testIter):
spTest._iterNum = i
# random binary input
input_ = numpy.zeros((1, inputLen))
nonzero = numpy.random.random(inputLen)
input_[0][numpy.where (nonzero < float(w)/float(n))] = 1
# overlap step
spTest._computeOverlapsFP(input_,
stimulusThreshold=spTest.stimulusThreshold)
spTest._overlaps *= boostFactors
onCellIndices = numpy.where(spTest._overlaps > 0)
spTest._onCells.fill(0)
spTest._onCells[onCellIndices] = 1
denseOn = spTest._onCells
# update _dutyCycleBeforeInh
spTest.dutyCyclePeriod = min(i + 1, 1000)
spTest._dutyCycleBeforeInh = (
(spTest.dutyCyclePeriod - 1) *
spTest._dutyCycleBeforeInh +denseOn) / spTest.dutyCyclePeriod
dutyCycleTieBreaker = spTest._dutyCycleAfterInh.copy()
dutyCycleTieBreaker *= kDutyCycleFactor
# inhibition step
numOn = spTest._inhibitionObj.compute(
spTest._overlaps + dutyCycleTieBreaker, spTest._onCellIndices,
stimulusThresholdInh, # stimulusThresholdInh
max(spTest._overlaps)/1000, # addToWinners
)
# update _dutyCycleAfterInh
spTest._onCells.fill(0)
onCellIndices = spTest._onCellIndices[0:numOn]
spTest._onCells[onCellIndices] = 1
denseOn = spTest._onCells
spTest._dutyCycleAfterInh = (((spTest.dutyCyclePeriod-1) *
spTest._dutyCycleAfterInh + denseOn) /
spTest.dutyCyclePeriod)
# learning step
spTest._adaptSynapses(onCellIndices, [], input_)
# update boostFactor
spTest._updateBoostFactors()
boostFactors = spTest._firingBoostFactors
# update dutyCycle and boost
if ((spTest._iterNum+1) % 50) == 0:
#.........这里部分代码省略.........
示例2: testAdaptSynapses
# 需要导入模块: from nupic.research.spatial_pooler import SpatialPooler [as 别名]
# 或者: from nupic.research.spatial_pooler.SpatialPooler import _adaptSynapses [as 别名]
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])