def main():

    domain = np.linspace(-4, 4, 1000) 
    print E
    print f
    print p


    pylab.hold(True)
    pylab.plot(domain, p(domain))

    pylab.draw()
    output_file = "/u/alaingui/Dropbox/umontreal/denoising_autoencoder/iclr2013_paper/presentation/1.png"
    pylab.savefig(output_file, dpi=150)
    print "Wrote %s" % (output_file,)
    pylab.close()

    if False:
        x = np.linspace(-1, 1, 100)
        p = np.exp(-x**2/2)
        noise_stddev = 0.1

        r_cae = infinite_capacity.fit_cae_1D(x,p,noise_stddev)
        print r_cae

        r_dae = infinite_capacity.fit_dae_1D(x,p,noise_stddev)
        print r_dae

        X = np.vstack( (x, np.zeros(x.shape)) ).T
        r_dae = infinite_capacity.fit_dae(X,p,noise_stddev)
        print r_dae
Exemple #2
0
def main():

    domain = np.linspace(-4, 4, 1000)
    print E
    print f
    print p

    pylab.hold(True)
    pylab.plot(domain, p(domain))

    pylab.draw()
    output_file = "/u/alaingui/Dropbox/umontreal/denoising_autoencoder/iclr2013_paper/presentation/1.png"
    pylab.savefig(output_file, dpi=150)
    print "Wrote %s" % (output_file, )
    pylab.close()

    if False:
        x = np.linspace(-1, 1, 100)
        p = np.exp(-x**2 / 2)
        noise_stddev = 0.1

        r_cae = infinite_capacity.fit_cae_1D(x, p, noise_stddev)
        print r_cae

        r_dae = infinite_capacity.fit_dae_1D(x, p, noise_stddev)
        print r_dae

        X = np.vstack((x, np.zeros(x.shape))).T
        r_dae = infinite_capacity.fit_dae(X, p, noise_stddev)
        print r_dae
Exemple #3
0
def main():

    N = 1000
    Na = N * 0.3 / 1.5 / 2
    Nb = N - Na
    #domain = np.linspace(-1.2, 1.2, 1000)
    domain = np.linspace(-1.5, 1.5, 1000)
    print "(N, Na, Nb) = (%d, %d, %d)" % (N, Na, Nb)
    print "[domain[Na], domain[Nb]] = [%f, %f]" % (domain[Na], domain[Nb])
    output_dir = "/u/alaingui/Dropbox/umontreal/denoising_autoencoder/iclr2013_paper/presentation/grad_movie_4"

    # ground truth for all three plots
    legend_names = {
        "pdf": r"$p(x)$",
        "E": r"$-E(x)$",
        "grad_E": r"$-\nabla E(x)$"
    }
    colors = {"pdf": "#f62c9e", "E": "#0e7410", "grad_E": "#0e7410"}
    for (name, func) in [("pdf", f), ("E", lambda x: -E(x)),
                         ("grad_E", lambda x: -grad_E(x))]:
        pylab.hold(True)
        if name in ["pdf"]:
            pa = pylab.plot(domain,
                            func(domain),
                            linestyle="-",
                            color=colors[name],
                            linewidth="4")
        elif name in ["E", "grad_E"]:
            #pa = pylab.plot(domain[Na:Nb], func(domain[Na:Nb]), linestyle="-", color=colors[name], linewidth="4")
            pa = pylab.plot(domain,
                            func(domain),
                            linestyle="-",
                            color=colors[name],
                            linewidth="4")
            pylab.xlim([domain[0], domain[-1]])

        if name in ["E"]:
            pylab.ylim([-4, 0])

        if name in ["grad_E"]:
            pylab.plot(domain,
                       np.zeros(domain.shape),
                       linestyle="--",
                       color="#000000",
                       linewidth="2")
            pylab.ylim([-3, 4])

        pylab.legend([pa], [legend_names[name]], loc=1)

        pylab.draw()
        output_file = os.path.join(output_dir, "%s.png" % (name, ))
        pylab.savefig(output_file, dpi=200)
        print "Wrote %s" % (output_file, )
        pylab.close()

    #quit()

    # combined graphs RCAE DAE for grad_E
    #noise_stddevs = [1.0, 0.7, 0.1, 0.07, 0.01]
    n_frames = 100
    noise_stddevs = np.exp(np.linspace(0, -4.6, n_frames))
    for (noise_stddev, i) in zip(noise_stddevs, range(n_frames)):
        r_cae = infinite_capacity.fit_cae_1D(domain, f(domain), noise_stddev)
        r_dae = infinite_capacity.fit_dae_1D(domain, f(domain), noise_stddev)

        pylab.hold(True)

        #pa = pylab.plot(domain[Na:Nb], grad_E(domain[Na:Nb]), linestyle="-", color="#0e7410", linewidth="4", label=r"$\nabla E(x)$")
        #pb = pylab.plot(domain[Na:Nb], -(r_cae[Na:Nb] - domain[Na:Nb])/noise_stddev**2, linestyle="-", color="#f9a21d", linewidth="4", label=r"RCAE $\left(r(x)-x\right)/\sigma^2$")
        #pc = pylab.plot(domain[Na:Nb], -(r_dae[Na:Nb] - domain[Na:Nb])/noise_stddev**2, linestyle="-", color="#411ced", linewidth="4", label=r"DAE $\left(r(x)-x\right)/\sigma^2$")
        pa = pylab.plot(domain,
                        -grad_E(domain),
                        linestyle="-",
                        color="#0e7410",
                        linewidth="4",
                        label=r"$-\nabla E(x)$")
        pb = pylab.plot(domain,
                        +(r_cae - domain) / noise_stddev**2,
                        linestyle="-",
                        color="#f9a21d",
                        linewidth="4",
                        label=r"RCAE $ \left(r(x)-x\right)/\sigma^2$")
        pc = pylab.plot(domain,
                        +(r_dae - domain) / noise_stddev**2,
                        linestyle="-",
                        color="#411ced",
                        linewidth="4",
                        label=r"DAE $ \left(r(x)-x\right)/\sigma^2$")

        pylab.legend([pa, pb, pc], [
            r"$-\nabla E(x)$", r"RCAE $\left(r(x)-x\right)/\sigma^2$",
            r"DAE $\left(r(x)-x\right)/\sigma^2$"
        ],
                     loc=3)
        pylab.plot(domain,
                   np.zeros(domain.shape),
                   linestyle="--",
                   color="#000000",
                   linewidth="2")
        pylab.text(0.5, 3.0, r"$\sigma = %0.2f$" % noise_stddev, fontsize=30)
        pylab.xlim([domain[0], domain[-1]])
        pylab.ylim([-3, 4])
        pylab.draw()
        output_file = os.path.join(output_dir,
                                   "combined_grad_E_frame_%0.3d.png" % i)
        #output_file = os.path.join(output_dir, "combined_grad_E_noise_stddev_%0.3f.png" % noise_stddev)
        pylab.savefig(output_file, dpi=200)
        print "Wrote %s" % (output_file, )
        pylab.close()

    # Now we'll be printing the reconstructed p(x),
    # but it's not really worthwhile.
    # Is it ?

    if False:
        x = np.linspace(-1, 1, 100)
        p = np.exp(-x**2 / 2)
        noise_stddev = 0.1

        r_cae = infinite_capacity.fit_cae_1D(x, p, noise_stddev)
        print r_cae

        r_dae = infinite_capacity.fit_dae_1D(x, p, noise_stddev)
        print r_dae

        X = np.vstack((x, np.zeros(x.shape))).T
        r_dae = infinite_capacity.fit_dae(X, p, noise_stddev)
        print r_dae
def main():

    N = 1000
    Na = N*0.3/1.5/2
    Nb = N - Na
    #domain = np.linspace(-1.2, 1.2, 1000)
    domain = np.linspace(-1.5, 1.5, 1000)
    print "(N, Na, Nb) = (%d, %d, %d)" % (N, Na, Nb)
    print "[domain[Na], domain[Nb]] = [%f, %f]" % (domain[Na], domain[Nb])
    output_dir = "/u/alaingui/Dropbox/umontreal/denoising_autoencoder/iclr2013_paper/presentation/grad_movie_4"

    # ground truth for all three plots
    legend_names = {"pdf":r"$p(x)$", "E":r"$-E(x)$", "grad_E":r"$-\nabla E(x)$"}
    colors = {"pdf":"#f62c9e", "E":"#0e7410", "grad_E":"#0e7410"}
    for (name, func) in [("pdf", f), ("E", lambda x : -E(x)), ("grad_E", lambda x: -grad_E(x))]:
        pylab.hold(True)
        if name in ["pdf"]:
            pa = pylab.plot(domain, func(domain), linestyle="-", color=colors[name], linewidth="4")
        elif name in ["E", "grad_E"]:
            #pa = pylab.plot(domain[Na:Nb], func(domain[Na:Nb]), linestyle="-", color=colors[name], linewidth="4")
            pa = pylab.plot(domain, func(domain), linestyle="-", color=colors[name], linewidth="4")
            pylab.xlim([domain[0], domain[-1]])

        if name in ["E"]:
            pylab.ylim([-4,0])

        if name in ["grad_E"]:
            pylab.plot(domain, np.zeros(domain.shape), linestyle="--", color="#000000", linewidth="2")
            pylab.ylim([-3,4])

        pylab.legend([pa], [legend_names[name]],loc=1)

        pylab.draw()
        output_file = os.path.join(output_dir, "%s.png" % (name,) )
        pylab.savefig(output_file, dpi=200)
        print "Wrote %s" % (output_file,)
        pylab.close()

    #quit()

    # combined graphs RCAE DAE for grad_E
    #noise_stddevs = [1.0, 0.7, 0.1, 0.07, 0.01]
    n_frames = 100
    noise_stddevs = np.exp(np.linspace(0, -4.6, n_frames))
    for (noise_stddev, i) in zip(noise_stddevs, range(n_frames)):
        r_cae = infinite_capacity.fit_cae_1D(domain, f(domain), noise_stddev)
        r_dae = infinite_capacity.fit_dae_1D(domain, f(domain), noise_stddev)

        pylab.hold(True)

        #pa = pylab.plot(domain[Na:Nb], grad_E(domain[Na:Nb]), linestyle="-", color="#0e7410", linewidth="4", label=r"$\nabla E(x)$")
        #pb = pylab.plot(domain[Na:Nb], -(r_cae[Na:Nb] - domain[Na:Nb])/noise_stddev**2, linestyle="-", color="#f9a21d", linewidth="4", label=r"RCAE $\left(r(x)-x\right)/\sigma^2$")
        #pc = pylab.plot(domain[Na:Nb], -(r_dae[Na:Nb] - domain[Na:Nb])/noise_stddev**2, linestyle="-", color="#411ced", linewidth="4", label=r"DAE $\left(r(x)-x\right)/\sigma^2$")
        pa = pylab.plot(domain, -grad_E(domain), linestyle="-", color="#0e7410", linewidth="4", label=r"$-\nabla E(x)$")
        pb = pylab.plot(domain, +(r_cae - domain)/noise_stddev**2, linestyle="-", color="#f9a21d", linewidth="4", label=r"RCAE $ \left(r(x)-x\right)/\sigma^2$")
        pc = pylab.plot(domain, +(r_dae - domain)/noise_stddev**2, linestyle="-", color="#411ced", linewidth="4", label=r"DAE $ \left(r(x)-x\right)/\sigma^2$")

        pylab.legend([pa,pb,pc], [r"$-\nabla E(x)$", r"RCAE $\left(r(x)-x\right)/\sigma^2$", r"DAE $\left(r(x)-x\right)/\sigma^2$"], loc=3)
        pylab.plot(domain, np.zeros(domain.shape), linestyle="--", color="#000000", linewidth="2")
        pylab.text(0.5, 3.0, r"$\sigma = %0.2f$" % noise_stddev, fontsize=30)
        pylab.xlim([domain[0], domain[-1]])
        pylab.ylim([-3,4])
        pylab.draw()
        output_file = os.path.join(output_dir, "combined_grad_E_frame_%0.3d.png" % i)
        #output_file = os.path.join(output_dir, "combined_grad_E_noise_stddev_%0.3f.png" % noise_stddev)
        pylab.savefig(output_file, dpi=200)
        print "Wrote %s" % (output_file,)
        pylab.close()

    # Now we'll be printing the reconstructed p(x),
    # but it's not really worthwhile.
    # Is it ?


    if False:
        x = np.linspace(-1, 1, 100)
        p = np.exp(-x**2/2)
        noise_stddev = 0.1

        r_cae = infinite_capacity.fit_cae_1D(x,p,noise_stddev)
        print r_cae

        r_dae = infinite_capacity.fit_dae_1D(x,p,noise_stddev)
        print r_dae

        X = np.vstack( (x, np.zeros(x.shape)) ).T
        r_dae = infinite_capacity.fit_dae(X,p,noise_stddev)
        print r_dae