(self)
Set up a random NNF
        Then calculate the distances to fill up the NND
        :return:
Set up a random NNF
        Then calculate the distances to fill up the NND
        :return:
Set up a random NNF Then calculate the distances to fill up the NND : return :
def initialise_nnf(self):
        """
        Set up a random NNF
        Then calculate the distances to fill up the NND
        :return:
        """
        self.nnf = self.nnf.transpose((2, 0, 1))
        self.nnf[0] = np.random.randint(self.B.shape[1], size=(self.A.shape[0], self.A.shape[1]))
        self.nnf[1] = np.random.randint(self.B.shape[0], size=(self.A.shape[0], self.A.shape[1]))
        self.nnf = self.nnf.transpose((1, 2, 0))
        self.nnf = self.nnf.copy("C")
def initialise_nnf ( self ) : self . nnf = self . nnf . transpose ( ( 2 , 0 , 1 ) ) self . nnf [ 0 ] = np . random . randint ( self . B . shape [ 1 ] , size = ( self . A . shape [ 0 ] , self . A . shape [ 1 ] ) ) self . nnf [ 1 ] = np . random . randint ( self . B . shape [ 0 ] , size = ( self . A . shape [ 0 ] , self . A . shape [ 1 ] ) ) self . nnf = self . nnf . transpose ( ( 1 , 2 , 0 ) ) self . nnf = self . nnf . copy ( "C" )