diagnostics.py 文件源码

def show_chains(rbm, state, dataset, num_particles=20, num_samples=20, show_every=10, display=True,
                figname='Gibbs chains', figtitle='Gibbs chains'):
    samples = gnp.zeros((num_particles, num_samples, state.v.shape[1]))
    state = state[:num_particles, :, :]

    for i in range(num_samples):
        samples[:, i, :] = rbm.vis_expectations(state.h)

        for j in range(show_every):
            state = rbm.step(state)

    npix = dataset.num_rows * dataset.num_cols
    rows = [vm.hjoin([samples[i, j, :npix].reshape((dataset.num_rows, dataset.num_cols)).as_numpy_array()
                      for j in range(num_samples)],
                     normalize=False)
            for i in range(num_particles)]
    grid = vm.vjoin(rows, normalize=False)

    if display:
        pylab.figure(figname)
        pylab.matshow(grid, cmap='gray', fignum=False)
        pylab.title(figtitle)
        pylab.gcf().canvas.draw()

    return grid