def initModules(self, categories, inputIdx):
modulesNames = {
'wordSP', 'wordTM', 'actionSP', 'actionTM', 'generalTM'
}
if (self.modulesParams is not None) and\
(set(self.modulesParams) == modulesNames):
self.modulesParams['wordSP'].update(self.defaultWordSPParams)
self.modulesParams['wordTM'].update(self.defaultWordTMParams)
self.modulesParams['actionSP'].update(self.defaultActionSPParams)
self.modulesParams['actionTM'].update(self.defaultActionTMParams)
self.wordSP = SpatialPooler(**self.modulesParams['wordSP'])
self.wordTM = TemporalMemory(**self.modulesParams['wordTM'])
self.actionSP = SpatialPooler(**self.modulesParams['actionSP'])
self.actionTM = TemporalMemory(**self.modulesParams['actionTM'])
defaultGeneralTMParams = {
'columnDimensions': (2,
max(self.wordTM.numberOfCells(),
self.actionTM.numberOfCells())),
'seed':
self.tmSeed
}
self.modulesParams['generalTM'].update(defaultGeneralTMParams)
self.generalTM = TemporalMemory(**self.modulesParams['generalTM'])
print("Using external Parameters!")
else:
self.wordSP = SpatialPooler(**self.defaultWordSPParams)
self.wordTM = TemporalMemory(**self.defaultWordTMParams)
self.actionSP = SpatialPooler(**self.defaultActionSPParams)
self.actionTM = TemporalMemory(**self.defaultActionTMParams)
print("External parameters invalid or not found, using"\
" the default ones")
defaultGeneralTMParams = {
'columnDimensions': (2,
max(self.wordTM.numberOfCells(),
self.actionTM.numberOfCells())),
'seed':
self.tmSeed
}
self.generalTM = TemporalMemory(**defaultGeneralTMParams)
self.classifier = CLAClassifierCond(steps=[1, 2, 3],
alpha=0.1,
actValueAlpha=0.3,
verbosity=0)
self.startPointOverlap = CommonOverlap('==',
1,
self.actionTM.columnDimensions,
threshold=0.5)
def initModules(self, categories, inputIdx):
modulesNames = {'generalSP', 'generalTM'}
nWords = len(categories[inputIdx['wordInput']])
nActions = len(categories[inputIdx['actionInput']])
inputDimensions = max(
self.wordEncoder.getWidth(),
self.actionEncoder.getWidth()
)
columnDimensions = (4 * max((nWords + nActions),
len(self.trainingData)), )
defaultGeneralSPParams = {
'inputDimensions': inputDimensions,
'columnDimensions': columnDimensions,
'seed': self.spSeed
}
defaultGeneralTMParams = {
'columnDimensions': columnDimensions,
'seed': self.tmSeed
}
if (self.modulesParams is not None) and\
(set(self.modulesParams) == modulesNames):
self.modulesParams['generalSP'].update(defaultGeneralSPParams)
self.modulesParams['generalTM'].update(defaultGeneralTMParams)
self.generalSP = SpatialPooler(**self.modulesParams['generalSP'])
self.generalTM = TemporalMemory(**self.modulesParams['generalTM'])
else:
self.generalSP = SpatialPooler(**defaultGeneralSPParams)
self.generalTM = TemporalMemory(**defaultGeneralTMParams)
self.classifier = CLAClassifierCond(
steps=[1, 2, 3],
alpha=0.1,
actValueAlpha=0.3,
verbosity=0
)
def initModules(self, categories, inputIdx):
modulesNames = {'wordSP', 'wordTM', 'actionSP', 'actionTM',
'generalSP', 'generalTM'}
if (self.modulesParams is not None) and\
(set(self.modulesParams) == modulesNames):
self.modulesParams['wordSP'].update(self.defaultWordSPParams)
self.modulesParams['wordTM'].update(self.defaultWordTMParams)
self.modulesParams['actionSP'].update(self.defaultActionSPParams)
self.modulesParams['actionTM'].update(self.defaultActionTMParams)
self.wordSP = SpatialPooler(**self.modulesParams['wordSP'])
self.wordTM = TemporalMemory(**self.modulesParams['wordTM'])
self.actionSP = SpatialPooler(**self.modulesParams['actionSP'])
self.actionTM = TemporalMemory(**self.modulesParams['actionTM'])
generalInputDimensions = max(
self.wordTM.numberOfCells() + 1,
self.actionTM.numberOfCells() + 1
)
generalColumnDimensions = (len(self.trainingData) * 3,)
defaultGeneralSPParams = {
'inputDimensions': generalInputDimensions,
'columnDimensions': generalColumnDimensions,
'seed': self.spSeed
}
defaultGeneralTMParams = {
'columnDimensions': generalColumnDimensions,
'seed': self.tmSeed
}
self.modulesParams['generalSP'].update(defaultGeneralSPParams)
self.modulesParams['generalTM'].update(defaultGeneralTMParams)
self.generalSP = SpatialPooler(**self.modulesParams['generalSP'])
self.generalTM = TemporalMemory(**self.modulesParams['generalTM'])
print("Using external Parameters!")
else:
self.wordSP = SpatialPooler(**self.defaultWordSPParams)
self.wordTM = TemporalMemory(**self.defaultWordTMParams)
self.actionSP = SpatialPooler(**self.defaultActionSPParams)
self.actionTM = TemporalMemory(**self.defaultActionTMParams)
print("External parameters invalid or not found, using"\
" the default ones")
generalInputDimensions = max(
self.wordTM.numberOfCells() + 1,
self.actionTM.numberOfCells() + 1
)
generalColumnDimensions = (len(self.trainingData) * 3,)
defaultGeneralSPParams = {
'inputDimensions': generalInputDimensions,
'columnDimensions': generalColumnDimensions,
'seed': self.spSeed
}
defaultGeneralTMParams = {
'columnDimensions': generalColumnDimensions,
'seed': self.tmSeed
}
self.generalSP = SpatialPooler(**defaultGeneralSPParams)
self.generalTM = TemporalMemory(**defaultGeneralTMParams)
self.classifier = CLAClassifierCond(
steps=[1, 2, 3],
alpha=0.1,
actValueAlpha=0.3,
verbosity=0
)
maxBoost=3,
seed=42,
spVerbosity=0)
tm = TemporalMemory(
columnDimensions=sp.getColumnDimensions(),
initialPermanence=0.4,
connectedPermanence=0.5,
minThreshold=4,
maxNewSynapseCount=4,
permanenceDecrement=0.05,
permanenceIncrement=0.05,
activationThreshold=4,
)
cla = CLAClassifierCond((1, 2))
spIn = numpy.zeros(sp.getNumInputs(), dtype=numpy.uint8)
spOut = numpy.zeros(sp.getNumColumns(), dtype=numpy.uint8)
trainingData = [['a', 'b', 'c'], ['a', 'c', 'b']]
trainingData2 = [(['b', 'c', 'a'], ['z', 'x', 'y'])]
recordNum = 0
for i in xrange(20):
# recordNum = 0
# for dataList in trainingData:
# print("----------dataList = {}----------".format(dataList))
#
