surface

def graph_surface(fun, rect, offset=0.5, width=256, height=256):
    '''
    fun    ... decizijska funkcija (Nx2)->(Nx1)
    rect   ... željena domena prikaza zadana kao:
             ([x_min,y_min], [x_max,y_max])
    offset ... "nulta" vrijednost decizijske funkcije na koju
             je potrebno poravnati središte palete boja;
             tipično imamo:
             offset = 0.5 za probabilističke modele
                (npr. logistička regresija)
             offset = 0 za modele koji ne spljošćuju
                klasifikacijske mjere (npr. SVM)
    width,height ... rezolucija koordinatne mreže
    '''

    # Make linspace vector width x height resolution with rect limits
    linspace_width = np.linspace(rect[0][0], rect[1][0], width)
    linspace_height = np.linspace(rect[0][1], rect[1][1], height)

    # Make a meshgrid
    xv, yv = np.meshgrid(linspace_width, linspace_height)

    # Flatten and stack them in Nx2 shape
    x_flat = np.ndarray.flatten(xv)
    y_flat = np.ndarray.flatten(yv)
    grid_data = np.stack([x_flat, y_flat], axis=1)

    #pdb.set_trace()
    # Get point decisions width x height
    point_decision = fun(grid_data).reshape((width, height))

    # fix the range and offset
    delta = offset if offset else 0
    maxval = max(
        np.max(point_decision)-delta,
        - (np.min(point_decision)-delta))

    # draw the surface and the offset
    plt.pcolormesh(xv, yv, point_decision,
    vmin=delta-maxval, vmax=delta+maxval, cmap='jet')
    plt.colorbar()

    if offset != None:
        plt.contour(xv, yv, point_decision, colors='black', levels=[offset])