How to use the vector.cross function in vector

g = list(numpy.array(numpy.matrix(x[3:]).transpose()))
        fac = 1 # weight deviation
        def dip(y, z):
            n = min(max(vector.dot(y, z)/vector.norm(y), -1), 1)
            return n
        r1 = lmap(lambda y, z : fac*beta[2]*(beta[3]-dip(y, z)), m, g)
        return r0 + r1
    new_sphere2d_fit = FitLeastSq([0, 0, initial[3], 0], f_new_sphere2, zpoints, 2)
    if not new_sphere2d_fit or new_sphere2d_fit[2] < 0 or abs(new_sphere2d_fit[3]) >= 1:
        debug('FitLeastSq sphere2 failed!!!! ', len(points), new_sphere2d_fit)
        return False
    new_sphere2d_fit = lmap(lambda x, a, b: x + new_sphere2d_fit[0]*a + new_sphere2d_fit[1]*b, initial[:3], u, v) + [new_sphere2d_fit[2], math.degrees(math.asin(new_sphere2d_fit[3]))]
    new_sphere2d_fit = [new_sphere2d_fit, ComputeDeviation(points, new_sphere2d_fit), 2]

    if plane_max_dev < 1.2:
        ang = math.degrees(math.asin(vector.norm(vector.cross(plane_fit[1], norm))))
        
        debug('plane fit found, 2D fit only', ang, plane_fit, plane_dev, plane_max_dev)
        if ang > 30:
            debug('angle of plane not aligned to normal: no 2d fit')
            new_sphere2d_fit = False

        return [new_sphere1d_fit, new_sphere2d_fit, False]

    # ok to use best guess for 3d fit
    initial = guess
    '''
    def f_sphere3(beta, x):
        bias = beta[:3]
        b = numpy.matrix(lmap(lambda a, b : a - b, x[:3], bias))
        m = list(numpy.array(b.transpose()))
        r0 = lmap(lambda y : beta[3] - vector.norm(y), m)

return r0 + r1
    new_sphere1d_fit = FitLeastSq([0, initial[3], 0], f_new_sphere1, zpoints, 2)
    if not new_sphere1d_fit or new_sphere1d_fit[1] < 0 or abs(new_sphere1d_fit[2]) > 1:
        debug('FitLeastSq new_sphere1 failed!!!! ', len(points), new_sphere1d_fit)
        new_sphere1d_fit = current
    else:
        new_sphere1d_fit = lmap(lambda x, a: x + new_sphere1d_fit[0]*a, initial[:3], norm) + [new_sphere1d_fit[1], math.degrees(math.asin(new_sphere1d_fit[2]))]
    new_sphere1d_fit = [new_sphere1d_fit, ComputeDeviation(points, new_sphere1d_fit), 1]
        #print('new sphere1 fit', new_sphere1d_fit)

    if line_max_dev < 2:
        debug('line fit found, insufficient data', line_dev, line_max_dev)
        return False
    
    # 2d sphere fit across normal vector
    u = vector.cross(norm, [norm[1]-norm[2], norm[2]-norm[0], norm[0]-norm[1]])
    v = vector.cross(norm, u)
    u = vector.normalize(u)
    v = vector.normalize(v)

    # initial is the closest to guess on the uv plane containing current
    initial = vector.add(guess[:3], vector.project(vector.sub(current[:3], guess[:3]), norm))
    initial.append(current[3])
    #debug('initial 2d fit', initial)
    
    '''
    def f_sphere2(beta, x):
        bias = lmap(lambda x, a, b: x + beta[0]*a + beta[1]*b, initial[:3], u, v)
        b = numpy.matrix(lmap(lambda a, b : a - b, x[:3], bias))
        m = list(numpy.array(b.transpose()))
        r0 = lmap(lambda y : beta[2] - vector.norm(y), m)
        return r0

new_sphere1d_fit = FitLeastSq([0, initial[3], 0], f_new_sphere1, zpoints, 2)
    if not new_sphere1d_fit or new_sphere1d_fit[1] < 0 or abs(new_sphere1d_fit[2]) > 1:
        debug('FitLeastSq new_sphere1 failed!!!! ', len(points), new_sphere1d_fit)
        new_sphere1d_fit = current
    else:
        new_sphere1d_fit = lmap(lambda x, a: x + new_sphere1d_fit[0]*a, initial[:3], norm) + [new_sphere1d_fit[1], math.degrees(math.asin(new_sphere1d_fit[2]))]
    new_sphere1d_fit = [new_sphere1d_fit, ComputeDeviation(points, new_sphere1d_fit), 1]
        #print('new sphere1 fit', new_sphere1d_fit)

    if line_max_dev < 2:
        debug('line fit found, insufficient data', line_dev, line_max_dev)
        return False
    
    # 2d sphere fit across normal vector
    u = vector.cross(norm, [norm[1]-norm[2], norm[2]-norm[0], norm[0]-norm[1]])
    v = vector.cross(norm, u)
    u = vector.normalize(u)
    v = vector.normalize(v)

    # initial is the closest to guess on the uv plane containing current
    initial = vector.add(guess[:3], vector.project(vector.sub(current[:3], guess[:3]), norm))
    initial.append(current[3])
    #debug('initial 2d fit', initial)
    
    '''
    def f_sphere2(beta, x):
        bias = lmap(lambda x, a, b: x + beta[0]*a + beta[1]*b, initial[:3], u, v)
        b = numpy.matrix(lmap(lambda a, b : a - b, x[:3], bias))
        m = list(numpy.array(b.transpose()))
        r0 = lmap(lambda y : beta[2] - vector.norm(y), m)
        return r0
    sphere2d_fit = FitLeastSq([0, 0, initial[3]], f_sphere2, zpoints)