def create_report_affine(state, report_id):
y_dot_mean = state.result.y_dot_stats.mean
y_dot_cov = state.result.y_dot_stats.covariance
y_dot_inf = state.result.y_dot_stats.information
y_dot_min = state.result.y_dot_stats.minimum
y_dot_max = state.result.y_dot_stats.maximum
y_mean = state.result.y_stats.mean
y_cov = state.result.y_stats.covariance
y_inf = state.result.y_stats.information
y_min = state.result.y_stats.minimum
y_max = state.result.y_stats.maximum
u_mean = state.result.u_stats.mean
u_inf = state.result.u_stats.information
u_cov = state.result.u_stats.covariance
N = state.result.N.get_value()
report_y_dot = create_report_var(
name="y_dot", mean=y_dot_mean, cov=y_dot_cov, inf=y_dot_inf, minimum=y_dot_min, maximum=y_dot_max
)
report_y = create_report_var(name="y", mean=y_mean, cov=y_cov, inf=y_inf, minimum=y_min, maximum=y_max)
report_u = create_report_var(name="u", mean=u_mean, cov=u_cov, inf=u_inf)
T = state.result.T.get_value()
report_T = create_report_figure_tensors(T, report_id="T", caption="Learned T")
Nreport = Node("n-report")
with Nreport.data("N", N).data_file("N", "image/png") as filename:
pylab.figure()
for i in range(N.shape[1]):
v = N[:, i].squeeze()
pylab.plot(v)
pylab.savefig(filename)
pylab.close()
f = Nreport.figure("nfig")
f.sub("N/N")
bT = state.result.bT.get_value()
report_bT = create_report_figure_tensors(bT, report_id="bT", caption="Learned bT")
bTn = state.result.bTn.get_value()
report_bTn = create_report_figure_tensors(bTn, report_id="bTn", caption="Learned bTn")
node = Node(id=report_id, children=[report_y, report_y_dot, report_u, report_T, Nreport, report_bT, report_bTn])
return node
def analyze_olfaction_covariance(covariance, receptors):
''' Covariance: n x n covariance matrix.
Positions: list of n positions '''
positions = [pose.get_2d_position() for pose, sens in receptors] #@UnusedVariable
positions = array(positions).transpose().squeeze()
require_shape(square_shape(), covariance)
n = covariance.shape[0]
require_shape((2, n), positions)
distances = create_distance_matrix(positions)
correlation = cov2corr(covariance)
flat_distances = distances.reshape(n * n)
flat_correlation = correlation.reshape(n * n)
# let's fit a polynomial
deg = 4
poly = polyfit(flat_distances, flat_correlation, deg=deg)
knots = linspace(min(flat_distances), max(flat_distances), 2000)
poly_int = polyval(poly, knots)
poly_fder = polyder(poly)
fder = polyval(poly_fder, distances)
Ttheta = create_olfaction_Ttheta(positions, fder)
Tx, Ty = create_olfaction_Txy(positions, fder, distances)
report = Node('olfaction-theory')
report.data('flat_distances', flat_distances)
report.data('flat_correlation', flat_correlation)
with report.data_file('dist_vs_corr', 'image/png') as filename:
pylab.figure()
pylab.plot(flat_distances, flat_correlation, '.')
pylab.plot(knots, poly_int, 'r-')
pylab.xlabel('distance')
pylab.ylabel('correlation')
pylab.title('Correlation vs distance')
pylab.legend(['data', 'interpolation deg = %s' % deg])
pylab.savefig(filename)
pylab.close()
with report.data('fder', fder).data_file('fder', 'image/png') as filename:
pylab.figure()
pylab.plot(knots, polyval(poly_fder, knots), 'r-')
pylab.title('f der')
pylab.savefig(filename)
pylab.close()
report.data('distances', distances)
report.data('correlation', correlation)
report.data('covariance', covariance)
report.data('f', polyval(poly, distances))
f = report.figure(id='cor-vs-distnace', caption='Estimated kernels',
shape=(3, 3))
f.sub('dist_vs_corr')
f.sub('fder')
f.sub('f', display='scale')
f.sub('distances', display='scale')
f.sub('correlation', display='posneg')
f.sub('covariance', display='posneg')
T = numpy.zeros(shape=(3, Tx.shape[0], Tx.shape[1]))
T[0, :, :] = Tx
T[1, :, :] = Ty
T[2, :, :] = Ttheta
T_report = create_report_figure_tensors(T, report_id='tensors',
caption="Predicted learned tensors")
report.add_child(T_report)
return report
