Beispiel #1
0
    def setUpClass(cls):
        cls.x_equiR_1, cls.w_equiR_1 = qnwequi(n, a, b, "R", random_state=41)
        temp, cls.w_equiR_3 = qnwequi(n_3, a_3, b_3, "R", random_state=42)

        # NOTE: I need to do a little magic here. MATLAB and numpy
        #       are generating the same random numbers, but MATLAB is
        #       column major and numpy is row major, so they are stored
        #       in different places for multi-dimensional arrays.
        #       The ravel, reshape code here moves the numpy nodes into
        #       the same relative position as the MATLAB ones. Also, in
        #       order for this to work I have to undo the shifting of
        #       the nodes, re-organize data, then re-shift. If this
        #       seems like voodoo to you, it kinda is. But, the fact
        #       that the test can pass after this kind of manipulation
        #       is a strong indicator that we are doing it correctly

        unshifted = (temp - a_3) / (b_3 - a_3)
        reshaped = np.ravel(unshifted).reshape(315, 3, order='F')
        reshifted = a_3 + reshaped * (b_3 - a_3)
        cls.x_equiR_3 = reshifted
    def setUpClass(cls):
        cls.x_equiR_1, cls.w_equiR_1 = qnwequi(n, a, b, "R", random_state=41)
        temp, cls.w_equiR_3 = qnwequi(n_3, a_3, b_3, "R", random_state=42)

        # NOTE: I need to do a little magic here. MATLAB and numpy
        #       are generating the same random numbers, but MATLAB is
        #       column major and numpy is row major, so they are stored
        #       in different places for multi-dimensional arrays.
        #       The ravel, reshape code here moves the numpy nodes into
        #       the same relative position as the MATLAB ones. Also, in
        #       order for this to work I have to undo the shifting of
        #       the nodes, re-organize data, then re-shift. If this
        #       seems like voodoo to you, it kinda is. But, the fact
        #       that the test can pass after this kind of manipulation
        #       is a strong indicator that we are doing it correctly

        unshifted = (temp - a_3) / (b_3 - a_3)
        reshaped = np.ravel(unshifted).reshape(315, 3, order='F')
        reshifted = a_3 + reshaped * (b_3 - a_3)
        cls.x_equiR_3 = reshifted
Beispiel #3
0
 def setUpClass(cls):
     cls.x_equiH_1, cls.w_equiH_1 = qnwequi(n, a, b, "H")
     cls.x_equiH_3, cls.w_equiH_3 = qnwequi(n_3, a_3, b_3, "H")
 def setUpClass(cls):
     cls.x_equiH_1, cls.w_equiH_1 = qnwequi(n, a, b, "H")
     cls.x_equiH_3, cls.w_equiH_3 = qnwequi(n_3, a_3, b_3, "H")