def _copyAllocatedStates(self):
    """If state is allocated in CPP, copy over the data into our numpy arrays."""

    # Get learn states if we need to print them out
    if self.verbosity > 1 or self.retrieveLearningStates:
      (activeT, activeT1, predT, predT1) = self.cells4.getLearnStates()
      self.lrnActiveState['t-1'] = activeT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.lrnActiveState['t'] = activeT.reshape((self.numberOfCols, self.cellsPerColumn))
      self.lrnPredictedState['t-1'] = predT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.lrnPredictedState['t'] = predT.reshape((self.numberOfCols, self.cellsPerColumn))

    if self.allocateStatesInCPP:
      assert False
      (activeT, activeT1, predT, predT1, colConfidenceT, colConfidenceT1, confidenceT,
       confidenceT1) = self.cells4.getStates()
      self.cellConfidence['t'] = confidenceT.reshape((self.numberOfCols, self.cellsPerColumn))
      self.cellConfidence['t-1'] = confidenceT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.colConfidence['t'] = colConfidenceT.reshape(self.numberOfCols)
      self.colConfidence['t-1'] = colConfidenceT1.reshape(self.numberOfCols)
      self.infActiveState['t-1'] = activeT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.infActiveState['t'] = activeT.reshape((self.numberOfCols, self.cellsPerColumn))
      self.infPredictedState['t-1'] = predT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.infPredictedState['t'] = predT.reshape((self.numberOfCols, self.cellsPerColumn))
def _copyAllocatedStates(self):
    """If state is allocated in CPP, copy over the data into our numpy arrays."""

    # Get learn states if we need to print them out
    if self.verbosity > 1 or self.retrieveLearningStates:
      (activeT, activeT1, predT, predT1) = self.cells4.getLearnStates()
      self.lrnActiveState['t-1'] = activeT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.lrnActiveState['t'] = activeT.reshape((self.numberOfCols, self.cellsPerColumn))
      self.lrnPredictedState['t-1'] = predT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.lrnPredictedState['t'] = predT.reshape((self.numberOfCols, self.cellsPerColumn))

    if self.allocateStatesInCPP:
      assert False
      (activeT, activeT1, predT, predT1, colConfidenceT, colConfidenceT1, confidenceT,
       confidenceT1) = self.cells4.getStates()
      self.cellConfidence['t'] = confidenceT.reshape((self.numberOfCols, self.cellsPerColumn))
      self.cellConfidence['t-1'] = confidenceT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.colConfidence['t'] = colConfidenceT.reshape(self.numberOfCols)
      self.colConfidence['t-1'] = colConfidenceT1.reshape(self.numberOfCols)
      self.infActiveState['t-1'] = activeT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.infActiveState['t'] = activeT.reshape((self.numberOfCols, self.cellsPerColumn))
      self.infPredictedState['t-1'] = predT1.reshape((self.numberOfCols, self.cellsPerColumn))
      self.infPredictedState['t'] = predT.reshape((self.numberOfCols, self.cellsPerColumn))
def _copyAllocatedStates ( self ) : # Get learn states if we need to print them out if self . verbosity > 1 or self . retrieveLearningStates : ( activeT , activeT1 , predT , predT1 ) = self . cells4 . getLearnStates ( ) self . lrnActiveState [ 't-1' ] = activeT1 . reshape ( ( self . numberOfCols , self . cellsPerColumn ) ) self . lrnActiveState [ 't' ] = activeT . reshape ( ( self . numberOfCols , self . cellsPerColumn ) ) self . lrnPredictedState [ 't-1' ] = predT1 . reshape ( ( self . numberOfCols , self . cellsPerColumn ) ) self . lrnPredictedState [ 't' ] = predT . reshape ( ( self . numberOfCols , self . cellsPerColumn ) ) if self . allocateStatesInCPP : assert False ( activeT , activeT1 , predT , predT1 , colConfidenceT , colConfidenceT1 , confidenceT , confidenceT1 ) = self . cells4 . getStates ( ) self . cellConfidence [ 't' ] = confidenceT . reshape ( ( self . numberOfCols , self . cellsPerColumn ) ) self . cellConfidence [ 't-1' ] = confidenceT1 . reshape ( ( self . numberOfCols , self . cellsPerColumn ) ) self . colConfidence [ 't' ] = colConfidenceT . reshape ( self . numberOfCols ) self . colConfidence [ 't-1' ] = colConfidenceT1 . reshape ( self . numberOfCols ) self . infActiveState [ 't-1' ] = activeT1 . reshape ( ( self . numberOfCols , self . cellsPerColumn ) ) self . infActiveState [ 't' ] = activeT . reshape ( ( self . numberOfCols , self . cellsPerColumn ) ) self . infPredictedState [ 't-1' ] = predT1 . reshape ( ( self . numberOfCols , self . cellsPerColumn ) ) self . infPredictedState [ 't' ] = predT . reshape ( ( self . numberOfCols , self . cellsPerColumn ) )
If state is allocated in CPP, copy over the data into our numpy arrays.
If state is allocated in CPP copy over the data into our numpy arrays .