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
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
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
