def simple_plots(d):
# TO
y_cov = d.y_cov
y_dot_cov = d.y_dot_cov
y_dot_sign_cov = d.y_dot_sign_cov
vars = [ ('y', y_cov, {}),
('y_dot', y_dot_cov, {}),
('y_dot_sign', y_dot_sign_cov, {}) ]
#
# I = numpy.eye(y_cov.shape[0])
#
r = Report()
f = r.figure(cols=3)
for var in vars:
label = var[0]
cov = var[1]
corr = cov2corr(cov, zero_diagonal=False)
corr_z = cov2corr(cov, zero_diagonal=True)
n1 = r.data("cov_%s" % label, cov).display('posneg')
n2 = r.data("corr_%s" % label, corr).display('posneg')
n3 = r.data("corrz_%s" % label, corr_z).display('posneg')
f.sub(n1, 'Covariance of %s' % label)
f.sub(n2, 'Correlation of %s ' % label)
f.sub(n3, 'Correlation of %s (zeroing diagonal)' % label)
return r
def hist_plots(d):
# TO
vars = [ ('C', d.C, {}),
('y', cov2corr(d.y_cov, False), {}),
('y_dot', cov2corr(d.y_dot_cov, False), {}),
('y_dot_sign', cov2corr(d.y_dot_sign_cov, False), {}) ]
r = Report()
f = r.figure(cols=5)
for var in vars:
label = var[0]
x = var[1]
nid = "hist_%s" % label
with r.data_pylab(nid) as pylab:
pylab.hist(x.flat, bins=128)
f.sub(nid, 'histogram of correlation of %s' % label)
order = scale_score(x)
r.data('order%s' % label, order).display('posneg').add_to(f, 'ordered')
nid = "hist2_%s" % label
with r.data_pylab(nid) as pylab:
pylab.plot(x.flat, order.flat, '.', markersize=0.2)
pylab.xlabel(label)
pylab.ylabel('order')
f.sub(nid, 'histogram of correlation of %s' % label)
h = create_histogram_2d(d.C, x, resolution=128)
r.data('h2d_%s' % label, numpy.flipud(h.T)).display('scale').add_to(f)
return r
def iterations_plots(d):
r = Report('algorithmic_results')
# dummy = d.C ** 5 # -- perfect!
dummy = numpy.maximum(0, d.C ** 7)
results = cbc(dummy, num_iterations=5, ground_truth=d)
r.add_child(plot_results('dummy', results))
#
y_corr = cov2corr(d.y_cov, False)
results = cbc(y_corr, num_iterations=5, ground_truth=d)
r.add_child(plot_results('y_corr', results))
#
# y_dot_corr = cov2corr(d.y_dot_cov, False)
# results = cbc(y_dot_corr, num_iterations=5, ground_truth=d)
# r.add_child(plot_results('y_dot_corr', results))
y_dot_sign_corr = cov2corr(d.y_dot_sign_cov, False)
results = cbc(y_dot_sign_corr, num_iterations=5, ground_truth=d)
r.add_child(plot_results('y_dot_sign_corr', results))
return r
