active_inference_basic.py 文件源码

def plot_colormeshmatrix_reduced(
        X, Y, ymin = None, ymax = None,
        title = "plot_colormeshmatrix_reduced"):

    print "plot_colormeshmatrix_reduced X.shape", X.shape, "Y.shape", Y.shape
    # input_cols  = [i for i in df.columns if i.startswith("X")]
    # output_cols = [i for i in df.columns if i.startswith("Y")]
    # Xs = df[input_cols]
    # Ys = df[output_cols]

    # numsamples = df.shape[0]
    # print "plot_scattermatrix_reduced: numsamples = %d" % numsamples

    # # numplots = Xs.shape[1] * Ys.shape[1]
    # # print "numplots = %d" % numplots

    cbar_orientation = "vertical" # "horizontal"
    gs = gridspec.GridSpec(Y.shape[2], X.shape[2]/2)
    pl.ioff()
    fig = pl.figure()
    fig.suptitle(title)
    # # alpha = 1.0 / np.power(numsamples, 1.0/(Xs.shape[1] - 0))
    # alpha = 0.2
    # print "alpha", alpha
    # cols = ["k", "b", "r", "g", "c", "m", "y"]
    for i in range(X.shape[2]/2):
        for j in range(Y.shape[2]):
            # print "i, j", i, j, Xs, Ys
            ax = fig.add_subplot(gs[j, i])
            pcm = ax.pcolormesh(X[:,:,i], X[:,:,X.shape[2]/2+i], Y[:,:,j], vmin = ymin, vmax = ymax)
            # ax.plot(Xs.as_matrix()[:,i], Ys.as_matrix()[:,j], "ko", alpha = alpha)
            ax.set_xlabel("goal")
            ax.set_ylabel("error")
            cbar = fig.colorbar(mappable = pcm, ax=ax, orientation=cbar_orientation)
            ax.set_aspect(1)
    if SAVEPLOTS:
        fig.savefig("fig_%03d_colormeshmatrix_reduced.pdf" % (fig.number), dpi=300)
    fig.show()