class Patcher(object):
def __init__(self, dataDir="/tmp/cerebro2/model"):
self.paths = Paths(dataDir, deleteExisting=True)
def patchCLAModel(self, model):
# Get internal Spatial Pooler Python object
sp = model._getSPRegion().getSelf()._sfdr
self.patchSP(sp)
# Get internal Temporal Pooler Python object
tp = model._getTPRegion().getSelf()._tfdr
self.patchTP(tp, sp=sp)
def patchSP(self, sp):
SPPatch(self).patch(sp)
def patchTP(self, tp, sp=None):
TPPatch(self).patch(tp, sp=sp)
def patchEncoder(self, encoder, name):
EncoderPatch(self).patch(encoder, name)
def patchCoordinateEncoder(self, encoder, name):
CoordinateEncoderPatch(self).patch(encoder, name)
def saveDimensions(self, dimensions, layer):
writeJSON(dimensions, self.paths.dimensions(layer))
def saveActiveColumns(self, activeColumns, layer, iteration):
writeJSON(activeColumns, self.paths.activeColumns(layer, iteration))
def saveActiveCells(self, activeCells, layer, iteration):
writeJSON(activeCells, self.paths.activeCells(layer, iteration))
def savePredictedCells(self, predictedCells, layer, iteration):
writeJSON(predictedCells, self.paths.predictedCells(layer, iteration))
def saveProximalSynapses(self, proximalSynapses, layer, iteration):
writeJSON(proximalSynapses, self.paths.proximalSynapses(layer, iteration))
class Patcher(object):
def __init__(self, dataDir="/tmp/cerebro2/model"):
self.paths = Paths(dataDir, deleteExisting=True)
def patchCLAModel(self, model):
# Get internal Spatial Pooler Python object
sp = model._getSPRegion().getSelf()._sfdr
self.patchSP(sp)
# Get internal Temporal Pooler Python object
tp = model._getTPRegion().getSelf()._tfdr
self.patchTP(tp, sp=sp)
def patchSP(self, sp):
SPPatch(self).patch(sp)
def patchTP(self, tp, sp=None):
TPPatch(self).patch(tp, sp=sp)
def patchEncoder(self, encoder, name):
EncoderPatch(self).patch(encoder, name)
def patchCoordinateEncoder(self, encoder, name):
CoordinateEncoderPatch(self).patch(encoder, name)
def saveDimensions(self, dimensions, layer):
writeJSON(dimensions, self.paths.dimensions(layer))
def saveActiveColumns(self, activeColumns, layer, iteration):
writeJSON(activeColumns, self.paths.activeColumns(layer, iteration))
def saveActiveCells(self, activeCells, layer, iteration):
writeJSON(activeCells, self.paths.activeCells(layer, iteration))
def savePredictedCells(self, predictedCells, layer, iteration):
writeJSON(predictedCells, self.paths.predictedCells(layer, iteration))
def saveProximalSynapses(self, proximalSynapses, layer, iteration):
writeJSON(proximalSynapses, self.paths.proximalSynapses(layer, iteration))
def __init__(self, dataDir="/tmp/cerebro2/model"): self.paths = Paths(dataDir, deleteExisting=True)
def __init__(self, dataDir="/tmp/cerebro2/model"): self.paths = Paths(dataDir, deleteExisting=True)
def GET(self):
paths = Paths(DATA_DIR)
statesDir = paths.states()
states = os.listdir(statesDir)
states = [state for state in states if not state.startswith(".")]
return jsonResponse(len(states))
def __init__(self, dataDir='/tmp/cerebro2/model', modelID=None):
self._paths = Paths(dataDir, deleteExisting=False)
class History():
def __init__(self, dataDir='/tmp/cerebro2/model', modelID=None):
self._paths = Paths(dataDir, deleteExisting=False)
def getNumberOfIterations(self):
fileNames = os.listdir(self._paths.states())
listOfIterations = [state for state in fileNames if not state.startswith(".")]
return len(listOfIterations)
def inputDimensions(self):
return readJSON(self._paths.dimensions("input"))
def dimensions(self):
return readJSON(self._paths.dimensions("output"))
def inputVector(self):
inputVector = []
for iteration in xrange(1,self.getNumberOfIterations()+1):
inputVector.append(readJSON(self._paths.activeCells("input", iteration)))
return inputVector
def activeColumns(self):
activeColumns = []
for iteration in xrange(1,self.getNumberOfIterations()+1):
activeColumns.append(readJSON(self._paths.activeColumns("output", iteration)))
return activeColumns
def activeCells(self):
activeCells = []
for iteration in xrange(1,self.getNumberOfIterations()+1):
activeCells.append(readJSON(self._paths.activeCells("output", iteration)))
return activeCells
def predictiveCells(self):
predictiveCells = []
for iteration in xrange(1,self.getNumberOfIterations()+1):
predictiveCells.append(readJSON(self._paths.predictedCells("output", iteration)))
return predictiveCells
def proximalSynapses(self):
proximalSynapses = []
for iteration in xrange(1,self.getNumberOfIterations()+1):
proximalSynapses.append(readJSON(self._paths.proximalSynapses("output", iteration)))
return proximalSynapses
#encoder stuff
def encoderInputs(self):
encoderInputs = {}
for encoderDetails in readJSON(self._paths.encoders()):
name = encoderDetails['name']
encoderInputs[name] = []
for iteration in xrange(1,self.getNumberOfIterations()+1):
encoderInputs[name].append(readJSON(self._paths.encoderInput(name, iteration)))
return encoderInputs
def encoderOutputs(self):
encoderOutputs = {}
for encoderDetails in readJSON(self._paths.encoders()):
name = encoderDetails['name']
encoderOutputs[name] = []
for iteration in xrange(1,self.getNumberOfIterations()+1):
encoderOutputs[name].append(readJSON(self._paths.encoderOutput(name, iteration)))
return encoderOutputs
def encoderNeighbors(self):
encoderNeighbors = {}
for encoderDetails in readJSON(self._paths.encoders()):
name = encoderDetails['name']
encoderNeighbors[name] = []
for iteration in xrange(1,self.getNumberOfIterations()+1):
encoderNeighbors[name].append(readJSON(self._paths.coordinateEncoderNeighbors(name, iteration)))
return encoderNeighbors
def encoderTopWCoordinates(self):
encoderTopWCoordinates = {}
for encoderDetails in readJSON(self._paths.encoders()):
name = encoderDetails['name']
encoderTopWCoordinates[name] = []
for iteration in xrange(1,self.getNumberOfIterations()+1):
encoderTopWCoordinates[name].append(readJSON(self._paths.coordinateEncoderTopWCoordinates(name, iteration)))
return encoderTopWCoordinates
