def depolarizeCells(self, basalInput, apicalInput, learn):
    """
    Calculate predictions.

    @param basalInput (numpy array)
    List of active input bits for the basal dendrite segments

    @param apicalInput (numpy array)
    List of active input bits for the apical dendrite segments

    @param learn (bool)
    Whether learning is enabled. Some TM implementations may depolarize cells
    differently or do segment activity bookkeeping when learning is enabled.
    """
    (activeApicalSegments,
     matchingApicalSegments,
     apicalPotentialOverlaps) = self._calculateApicalSegmentActivity(
       self.apicalConnections, apicalInput, self.connectedPermanence,
       self.activationThreshold, self.minThreshold)

    if learn or self.useApicalModulationBasalThreshold==False:
      reducedBasalThresholdCells = ()
    else:
      reducedBasalThresholdCells = self.apicalConnections.mapSegmentsToCells(
        activeApicalSegments)

    (activeBasalSegments,
     matchingBasalSegments,
     basalPotentialOverlaps) = self._calculateBasalSegmentActivity(
       self.basalConnections, basalInput, reducedBasalThresholdCells,
       self.connectedPermanence,
       self.activationThreshold, self.minThreshold, self.reducedBasalThreshold)

    predictedCells = self._calculatePredictedCells(activeBasalSegments,
                                                   activeApicalSegments)

    self.predictedCells = predictedCells
    self.activeBasalSegments = activeBasalSegments
    self.activeApicalSegments = activeApicalSegments
    self.matchingBasalSegments = matchingBasalSegments
    self.matchingApicalSegments = matchingApicalSegments
    self.basalPotentialOverlaps = basalPotentialOverlaps
    self.apicalPotentialOverlaps = apicalPotentialOverlaps
def depolarizeCells ( self , basalInput , apicalInput , learn ) : ( activeApicalSegments , matchingApicalSegments , apicalPotentialOverlaps ) = self . _calculateApicalSegmentActivity ( self . apicalConnections , apicalInput , self . connectedPermanence , self . activationThreshold , self . minThreshold ) if learn or self . useApicalModulationBasalThreshold == False : reducedBasalThresholdCells = ( ) else : reducedBasalThresholdCells = self . apicalConnections . mapSegmentsToCells ( activeApicalSegments ) ( activeBasalSegments , matchingBasalSegments , basalPotentialOverlaps ) = self . _calculateBasalSegmentActivity ( self . basalConnections , basalInput , reducedBasalThresholdCells , self . connectedPermanence , self . activationThreshold , self . minThreshold , self . reducedBasalThreshold ) predictedCells = self . _calculatePredictedCells ( activeBasalSegments , activeApicalSegments ) self . predictedCells = predictedCells self . activeBasalSegments = activeBasalSegments self . activeApicalSegments = activeApicalSegments self . matchingBasalSegments = matchingBasalSegments self . matchingApicalSegments = matchingApicalSegments self . basalPotentialOverlaps = basalPotentialOverlaps self . apicalPotentialOverlaps = apicalPotentialOverlaps
Calculate predictions.

    @param basalInput (numpy array)
    List of active input bits for the basal dendrite segments

    @param apicalInput (numpy array)
    List of active input bits for the apical dendrite segments

    @param learn (bool)
    Whether learning is enabled. Some TM implementations may depolarize cells
    differently or do segment activity bookkeeping when learning is enabled.