class Embed(ExpSwitcher):
def __init__(self, statistic='y_corr', scale_score=False, **kwargs): # @UnusedVariable
ExpSwitcher.__init__(self, **kwargs)
self.statistic = statistic
self.scale_score = False
def init(self, boot_spec):
ExpSwitcher.init(self, boot_spec)
if len(boot_spec.get_observations().shape()) != 1:
raise UnsupportedSpec('I assume 1D signals.')
self.y_stats = MeanCovariance()
self.y_dot_stats = MeanCovariance()
self.y_dot_sgn_stats = MeanCovariance()
self.y_dot_abs_stats = MeanCovariance()
self.count = 0
self.y_deriv = DerivativeBox()
def get_similarity(self, which):
if which == 'y_corr':
return self.y_stats.get_correlation()
if which == 'y_dot_corr':
return self.y_dot_stats.get_correlation()
if which == 'y_dot_sgn_corr':
return self.y_dot_sgn_stats.get_correlation()
if which == 'y_dot_abs_corr':
return self.y_dot_abs_stats.get_correlation()
raise ValueError()
# check_contained(statistic, self.statistics, 'statistic')
def process_observations(self, obs):
y = obs['observations']
dt = obs['dt'].item()
self.y_deriv.update(y, dt)
if self.y_deriv.ready():
y, y_dot = self.y_deriv.get_value()
self.y_stats.update(y, dt)
self.y_dot_stats.update(y_dot, dt)
self.y_dot_sgn_stats.update(np.sign(y_dot), dt)
self.y_dot_abs_stats.update(np.abs(y_dot), dt)
self.count += 1
def get_S(self, dimensions=2, pub=None):
similarity = self.get_similarity(self.statistic)
if pub is not None:
pub.array_as_image('similarity', similarity,
caption='Similarity statistic')
plot_spectrum(pub, 'similarity', similarity)
if self.scale_score:
R = scale_score(similarity).astype('float32')
R = R / R.max()
if pub is not None:
pub.array_as_image('scale_score', R)
else:
R = similarity
D = 1 - R
D = D * np.pi / D.max()
np.fill_diagonal(D, 0)
if pub is not None:
# pub.array_as_image('D', D)
P = D * D
B = double_center(P)
# plot_spectrum(pub, 'D', D)
# plot_spectrum(pub, 'P', P)
plot_spectrum(pub, 'B', B)
S = mds(D, ndim=dimensions)
# S = inner_product_embedding(similarity, 3)
# S = S[1:3, :]
return S
def get_S_discrete(self, dimensions=2, pub=None):
R = self.y_dot_abs_stats.get_correlation()
Dis = discretize(-R, 2)
np.fill_diagonal(Dis, 0)
R = R * R
C = np.maximum(R, 0)
if pub is not None:
pub.array_as_image('Dis', Dis)
pub.array_as_image('R', R)
pub.array_as_image('C', C)
S = inner_product_embedding(Dis, dimensions)
# for i in range(R.shape[0]):
# R[i, i] = np.NaN
# C[i, i] = np.NaN
return S
def publish(self, pub):
if self.count < 10:
pub.text('warning', 'Too early to publish anything.')
return
pub.text('info', 'Using statistics: %s' % self.statistic)
if False: # TODO: make option
S = self.get_S_discrete(2, pub=pub.section('computation'))
else:
S = self.get_S(2, pub=pub.section('computation'))
with pub.plot('S') as pylab:
style_ieee_halfcol_xy(pylab)
pylab.plot(S[0, :], S[1, :], 's')
with pub.plot('S_joined') as pylab:
style_ieee_halfcol_xy(pylab)
pylab.plot(S[0, :], S[1, :], '-')
self.y_stats.publish(pub.section('y_stats'))
self.y_dot_stats.publish(pub.section('y_dot_stats'))
self.y_dot_sgn_stats.publish(pub.section('y_dot_sgn_stats'))
self.y_dot_abs_stats.publish(pub.section('y_dot_abs_stats'))
class EstStats(ExpSwitcher):
'''
A simple agent that estimates various statistics
of the observations.
'''
def init(self, boot_spec):
ExpSwitcher.init(self, boot_spec)
if len(boot_spec.get_observations().shape()) != 1:
raise UnsupportedSpec('I assume 1D signals.')
self.y_stats = MeanCovariance()
def merge(self, other):
self.y_stats.merge(other.y_stats)
def process_observations(self, obs):
y = obs['observations']
dt = obs['dt'].item()
self.y_stats.update(y, dt)
def get_state(self):
return dict(y_stats=self.y_stats)
def set_state(self, state):
self.y_stats = state['y_stats']
def publish(self, pub):
if self.y_stats.get_num_samples() == 0:
pub.text('warning', 'Too early to publish anything.')
return
Py = self.y_stats.get_covariance()
Ry = self.y_stats.get_correlation()
Py_inv = self.y_stats.get_information()
Ey = self.y_stats.get_mean()
y_max = self.y_stats.get_maximum()
y_min = self.y_stats.get_minimum()
Ry0 = Ry.copy()
np.fill_diagonal(Ry0, np.NaN)
Py0 = Py.copy()
np.fill_diagonal(Py0, np.NaN)
pub.text('stats', 'Num samples: %s' % self.y_stats.get_num_samples())
with pub.plot('y_bounds') as pylab:
style_ieee_fullcol_xy(pylab)
pylab.plot(Ey, label='E(y)')
pylab.plot(y_max, label='y_max')
pylab.plot(y_min, label='y_min')
pylab.legend()
all_positive = (np.min(Ey) > 0
and np.min(y_max) > 0
and np.min(y_min) > 0)
if all_positive:
with pub.plot('y_stats_log') as pylab:
style_ieee_fullcol_xy(pylab)
pylab.semilogy(Ey, label='E(y)')
pylab.semilogy(y_max, label='y_max')
pylab.semilogy(y_min, label='y_min')
pylab.legend()
pub.array_as_image('Py', Py, caption='cov(y)')
pub.array_as_image('Py0', Py0, caption='cov(y) - no diagonal')
pub.array_as_image('Ry', Ry, caption='corr(y)')
pub.array_as_image('Ry0', Ry0, caption='corr(y) - no diagonal')
pub.array_as_image('Py_inv', Py_inv)
pub.array_as_image('Py_inv_n', cov2corr(Py_inv))
with pub.plot('Py_svd') as pylab: # XXX: use spectrum
style_ieee_fullcol_xy(pylab)
_, s, _ = np.linalg.svd(Py)
s /= s[0]
pylab.semilogy(s, 'bx-')
with pub.subsection('y_stats') as sub:
self.y_stats.publish(sub)
