def synth_model_plot_component_matching():
with open('results/synth_model_component_robustness_190smp_4comps.bin', 'r') as f:
d = cPickle.load(f)
Uopt = d['Uopt']
Ur = d['Ur']
mean_comp = d['mean']
var_comp = d['var']
perm, sf = match_components_munkres(Uopt, Ur)
perm = perm[:3]
sf = sf[:, :3]
rst = np.setdiff1d(np.arange(Ur.shape[1]), np.sort(perm[:3]), False)
print rst
Urm = np.hstack([Ur[:, perm] * sf, Ur[:, rst]])
print Urm.shape
perm, sf = match_components_munkres(Uopt, mean_comp)
perm = perm[:3]
sf = sf[:, :3]
rst = np.setdiff1d(np.arange(mean_comp.shape[1]), np.sort(perm[:3]), False)
print rst
Umvm = np.hstack([mean_comp[:, perm] * sf, mean_comp[:, rst]])
Umvv = np.hstack([var_comp[:, perm], var_comp[:, rst]])
print Umvm.shape
print Umvv.shape
p2 = list(perm)
for i in range(mean_comp.shape[1]):
if not i in p2:
p2.append(i)
print estimate_snr(Uopt, Urm[:, :3])
print estimate_snr(Uopt, Umvm[:, :3])
print mse_error(Uopt, Urm[:, :3])
print mse_error(Uopt, Umvm[:, :3])
print marpe_error(Uopt, Urm[:, :3])
print marpe_error(Uopt, Umvm[:, :3])
print np.sum(Umvv, axis = 0)
print np.sum(Umvm ** 2, axis = 0)
print np.sum(Umvm ** 2, axis = 0) / np.sum(Umvv[:, p2], axis = 0)
plt.figure(figsize = (12, 16))
plt.subplots_adjust(left = 0.05, bottom = 0.05, right = 0.95, top = 0.95)
for i in range(4):
plt.subplot(4, 3, i*3+1)
if(i < 3):
plt.plot(Uopt[:,i], 'r-')
plt.plot(Umvm[:,i], 'b-')
plt.plot(np.ones((Uopt.shape[0],1)) * (1.0 / Uopt.shape[0]**0.5), 'g-', linewidth = 2)
plt.title('Component %d [bootstrap mean]' % (i+1))
plt.subplot(4, 3, i*3+2)
if(i < 3):
plt.plot(Uopt[:,i], 'r-')
plt.plot(Urm[:,i], 'b-')
plt.plot(np.ones((Uopt.shape[0],1)) * (1.0 / Uopt.shape[0]**0.5), 'g-', linewidth = 2)
plt.title('Component %d [data]' % (i+1))
plt.subplot(4, 3, i*3+3)
plt.plot(Umvv[:,i], 'b-')
plt.title('Component %d [variance]' % (i+1))
plt.show()
var_comp += delta * (Urb - mean_comp)
var_comp /= (num_comp - 1)
del slam_list
# change variance to a small positive value to prevent NaN warning
var_comp[var_comp == 0] = 1e-6
# how do we now estimate the SNRs?
# a. from data
# b. from bootstrap means and stdevs -> T-values
Urm = matched_components(Uopt, Ur)
# Umvm = matched_components(Uopt, mean_comp / (var_comp ** 0.5))
Umvm = matched_components(Uopt, mean_comp)
print estimate_snr(Uopt, Urm)
print estimate_snr(Uopt, Umvm)
print mse_error(Uopt, Urm)
print mse_error(Uopt, Umvm)
print marpe_error(Uopt, Urm)
print marpe_error(Uopt, Umvm)
plt.figure(figsize = (12, 6))
for i in range(3):
plt.subplot(320+i*2+1)
plt.plot(Uopt[:,i], 'r-')
plt.plot(Umvm[:,i], 'b-')
plt.plot(np.ones((Uopt.shape[0],1)) * (1.0 / Uopt.shape[0]**0.5), 'g-')
var_comp += delta * (Urb - mean_comp)
var_comp /= (num_comp - 1)
del slam_list
# change variance to a small positive value to prevent NaN warning
var_comp[var_comp == 0] = 1e-6
# how do we now estimate the SNRs?
# a. from data
# b. from bootstrap means and stdevs -> T-values
Urm = matched_components(Uopt, Ur)
# Umvm = matched_components(Uopt, mean_comp / (var_comp ** 0.5))
Umvm = matched_components(Uopt, mean_comp)
print estimate_snr(Uopt, Urm)
print estimate_snr(Uopt, Umvm)
print mse_error(Uopt, Urm)
print mse_error(Uopt, Umvm)
print marpe_error(Uopt, Urm)
print marpe_error(Uopt, Umvm)
plt.figure(figsize=(12, 6))
for i in range(3):
plt.subplot(320 + i * 2 + 1)
plt.plot(Uopt[:, i], 'r-')
plt.plot(Umvm[:, i], 'b-')
plt.plot(