def __init__(self, inputDimensions, columnDimensions):
    """
     Parameters:
     ----------
     _inputDimensions: The size of the input. (m,n) will give a size m x n
     _columnDimensions: The size of the 2 dimensional array of columns
     """
    self.inputDimensions = inputDimensions
    self.columnDimensions = columnDimensions
    self.inputSize = np.array(inputDimensions).prod()
    self.columnNumber = np.array(columnDimensions).prod()
    self.inputArray = np.zeros(self.inputSize, dtype=uintType)
    self.activeArray = np.zeros(self.columnNumber, dtype=uintType)

    random.seed(1)

    self.sp = SP(self.inputDimensions,
                 self.columnDimensions,
                 potentialRadius = self.inputSize,
                 numActiveColumnsPerInhArea = int(0.02*self.columnNumber),
                 globalInhibition = True,
                 seed = 1,
                 synPermActiveInc = 0.01,
                 synPermInactiveDec = 0.008)
def __init__ ( self , inputDimensions , columnDimensions ) : self . inputDimensions = inputDimensions self . columnDimensions = columnDimensions self . inputSize = np . array ( inputDimensions ) . prod ( ) self . columnNumber = np . array ( columnDimensions ) . prod ( ) self . inputArray = np . zeros ( self . inputSize , dtype = uintType ) self . activeArray = np . zeros ( self . columnNumber , dtype = uintType ) random . seed ( 1 ) self . sp = SP ( self . inputDimensions , self . columnDimensions , potentialRadius = self . inputSize , numActiveColumnsPerInhArea = int ( 0.02 * self . columnNumber ) , globalInhibition = True , seed = 1 , synPermActiveInc = 0.01 , synPermInactiveDec = 0.008 )
Parameters:
     ----------
     _inputDimensions: The size of the input. (m,n) will give a size m x n
     _columnDimensions: The size of the 2 dimensional array of columns