Esempio n. 1
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def test_plot_samples():
    # create a gp model
    model = pygp.BasicGP(sn=1, sf=1, ell=1)

    # create a prior datastructure
    priors1 = {'sn': pygp.priors.Uniform(0.01, 1.0),
               'sf': pygp.priors.Uniform(0.01, 5.0),
               'ell': pygp.priors.Uniform(0.01, 1.0),
               'mu': pygp.priors.Uniform(-2, 2)}

    # create a prior datastructure which holds fixed all but one parameter
    priors2 = {'sn': pygp.priors.Uniform(0.01, 1.0),
               'sf': None,
               'ell': None,
               'mu': None}

    # sample and plot the models
    pg.plot_samples(pygp.meta.SMC(model, priors1, 200))
    pg.plot_samples(pygp.meta.SMC(model, priors2, 200))
Esempio n. 2
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    mcmc = pygp.meta.MCMC(model, priors, n=200, burn=100)
    smc = pygp.meta.SMC(model, priors, n=200)

    pl.figure(1)
    pl.clf()

    pl.subplot(131)
    pp.plot_posterior(model)
    pl.title('Type-II ML')
    pl.legend(loc='best')

    axis = pl.axis()

    pl.subplot(132)
    pp.plot_posterior(mcmc)
    pl.axis(axis)
    pl.title('MCMC')

    pl.subplot(133)
    pp.plot_posterior(mcmc)
    pl.axis(axis)
    pl.title('SMC')
    pl.draw()

    pl.figure(2)
    pp.plot_samples(mcmc)
    pl.suptitle('Samples generated by MCMC')
    pl.draw()

    pl.show()
Esempio n. 3
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def callback(model, bounds, info, x, index, ftrue):
    """
    Plot the current posterior and the index.
    """
    xx0, xx1 = np.meshgrid(                             # define grid
        np.linspace(bounds[0, 0], bounds[0, 1], 200),
        np.linspace(bounds[1, 0], bounds[1, 1], 200))
    xx = np.c_[xx0.flat, xx1.flat]

    ff = ftrue(xx).reshape(xx0.shape)                   # compute true function
    acq = index(xx).reshape(xx0.shape)                  # compute acquisition

    mu, _ = model.posterior(xx)                         # compute the posterior
    mu = mu.reshape(xx0.shape)

    X = info['x']                                       # get observations and
    xbest = info['xbest']                               # recommendations

    fig = pl.figure(1)
    pl.clf()

    pl.subplot(221)                                     # top left subplot
    pl.contour(xx0, xx1, ff, 20)                        # plot true function
    pl.scatter(x[0], x[1], color='k', zorder=2)         # plot current selection
    pl.scatter(X[:, 0], X[:, 1],                        # plot previous data
               facecolors='none', color='k', zorder=2)
    pl.scatter(xbest[-1, 0], xbest[-1, 1], marker='+',  # plot recommendation
               s=60, lw=2, color='g', zorder=2)
    pl.axis(bounds.flatten())
    pl.title('true function')

    pl.subplot(222)                                     # top right subplot
    pl.contour(xx0, xx1, mu, 20)                        # plot posterior
    pl.scatter(xbest[-1, 0], xbest[-1, 1], marker='+',  # plot recommendation
               s=60, lw=2, color='g', zorder=2)
    pl.axis(bounds.flatten())
    pl.title('posterior mean')

    pl.subplot(223)                                     # bottom left subplot
    pl.contour(xx0, xx1, acq, 20)                       # plot acquisition
    pl.scatter(x[0], x[1], color='k', zorder=2)         # plot current selection
    pl.axis(bounds.flatten())
    pl.title('acquisition')

    if not np.all(np.isnan(xbest)):
        pl.subplot(224)                                 # bottom right subplot
        pl.plot(ftrue(xbest), 'g', lw=2, alpha=0.5)     # plot value of recom.
        pl.axis('tight')
        pl.title('value of recommendation')

    for ax in fig.axes:                                 # remove tick labels
        ax.set_xticklabels([])
        ax.set_yticklabels([])

    pl.draw()
    pl.show(block=False)

    pl.figure(2)                                        # plot hyperparameter
    pp.plot_samples(model)                              # samples

    pl.draw()
    pl.show(block=False)
Esempio n. 4
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    mcmc = pygp.meta.MCMC(model, priors, n=200, burn=100)
    smc = pygp.meta.SMC(model, priors, n=200)

    pl.figure(1)
    pl.clf()

    pl.subplot(131)
    pp.plot_posterior(model)
    pl.title('Type-II ML')
    pl.legend(loc='best')

    axis = pl.axis()

    pl.subplot(132)
    pp.plot_posterior(mcmc)
    pl.axis(axis)
    pl.title('MCMC')

    pl.subplot(133)
    pp.plot_posterior(mcmc)
    pl.axis(axis)
    pl.title('SMC')
    pl.draw()

    pl.figure(2)
    pp.plot_samples(mcmc)
    pl.suptitle('Samples generated by MCMC')
    pl.draw()

    pl.show()