datasets = [
noisy_2d_fx(100, lambda x: x, [lambda x: x], center, noise_std=0.5),
noisy_2d_fx(50,
lambda x: x, [lambda x: x, lambda x: -x],
center,
noise_std=0.5),
noisy_2d_fx(50,
lambda x: x, [lambda x: x, lambda x: 0],
center,
noise_std=0.5),
]
ndatasets = len(datasets)
nmappers = len(mappers.keys())
pl.figure(figsize=(8, 8))
fig = 1
for ds in datasets:
for mname, mapper in mappers.iteritems():
mapper.train(ds)
dproj = mapper.forward(ds.samples)
mproj = mapper.proj
pl.subplot(ndatasets, nmappers, fig)
if fig <= 3:
pl.title(mname)
pl.axis('equal')
pl.scatter(ds.samples[:, 0] - center[0],
ds.samples[:, 1] - center[1],
'SVD': SVDMapper(),
'ICA': ICAMapper(alg='CuBICA'),
}
datasets = [
noisy_2d_fx(100, lambda x: x, [lambda x: x],
center, noise_std=0.5),
noisy_2d_fx(50, lambda x: x, [lambda x: x, lambda x: -x],
center, noise_std=0.5),
noisy_2d_fx(50, lambda x: x, [lambda x: x, lambda x: 0],
center, noise_std=0.5),
]
ndatasets = len(datasets)
nmappers = len(mappers.keys())
pl.figure(figsize=(8,8))
fig = 1
for ds in datasets:
for mname, mapper in mappers.iteritems():
mapper.train(ds)
dproj = mapper.forward(ds.samples)
mproj = mapper.proj
pl.subplot(ndatasets, nmappers, fig)
if fig <= 3:
pl.title(mname)
pl.axis('equal')
pl.scatter(ds.samples[:, 0] - center[0],
ds.samples[:, 1] - center[1],
(the sum of two Gaussians) to the total of 10 histograms.
"""
histfit = fit2histogram(raw_data,
dual_gaussian, (1000, 0.5, 0.1, 1000, 0.8, 0.05),
nbins=20)
H, bin_left, bin_width, fit = histfit
"""
All that is left to do is composing a figure -- showing the accuracy
histogram and its variation across folds, as well as the two estimated
Gaussians.
"""
# new figure
pl.figure()
# Gaussian parameters
params = fit[0]
# plot the histogram
plot_bars(H.T, xloc=bin_left, width=bin_width, yerr='std')
# show the Gaussians
x = np.linspace(0, 1, 100)
# first gaussian
pl.plot(x, params[0] * norm.pdf(x, params[1], params[2]), "r-", zorder=2)
pl.axvline(params[1], color='r', linestyle='--', alpha=0.6)
# second gaussian
pl.plot(x, params[3] * norm.pdf(x, params[4], params[5]), "b-", zorder=3)
pl.axvline(params[4], color='b', linestyle='--', alpha=0.6)