def display(self,
covariance=None,
logalpha=None,
name=None,
extra=1,
k=3,
figures=(1, 2)):
"""Display covariances and alphas with matplotlib."""
pyplot.close('all')
super(SplineModelPCAGaussianMixture,
self).display(covariance, logalpha, name, figures[0])
figurenum = figures[1]
if PLOTTING_AVAILABLE:
print "Okay plotting"
pyplot.ioff()
pyplot.figure(figurenum)
pyplot.clf()
rows = cols = np.ceil(np.sqrt(self._ncomponent()))
if rows * cols == self._ncomponent():
rows = rows + 1
ncomp, ndim = self._means.shape
perspline = (ndim - extra) / 2
for clust in xrange(self._ncomponent()):
t = internal_knots(coef2knots(perspline))
t = np.concatenate((np.zeros(4), t, np.ones(4)))
pyplot.subplot(rows, cols, clust + 1)
means = self._pc.reconstruct(self._means[clust, :]).squeeze()
spline1, spline2 = unmix(means, k=k, extra=extra)
plot_from_spline(spline1)
plot_from_spline(spline2)
pyplot.ylim(-0.2, 1.01)
pyplot.show()
pyplot.ion()
def display(self, covariance=None, logalpha=None, name=None, extra=1,
k=3, figures=(1,2)):
"""Display covariances and alphas with matplotlib."""
pyplot.close('all')
super(SplineModelPCAGaussianMixture, self).display(covariance,
logalpha, name, figures[0])
figurenum = figures[1]
if PLOTTING_AVAILABLE:
print "Okay plotting"
pyplot.ioff()
pyplot.figure(figurenum)
pyplot.clf()
rows = cols = np.ceil(np.sqrt(self._ncomponent()))
if rows * cols == self._ncomponent():
rows = rows + 1
ncomp, ndim = self._means.shape
perspline = (ndim - extra) / 2
for clust in xrange(self._ncomponent()):
t = internal_knots(coef2knots(perspline))
t = np.concatenate((np.zeros(4),t,np.ones(4)))
pyplot.subplot(rows, cols, clust+1)
means = self._pc.reconstruct(self._means[clust, :]).squeeze()
spline1, spline2 = unmix(means,k=k,extra=extra)
plot_from_spline(spline1)
plot_from_spline(spline2)
pyplot.ylim(-0.2, 1.01)
pyplot.show()
pyplot.ion()
def display(self,
covariance=None,
logalpha=None,
name=None,
extra=1,
k=3,
figures=(1, 2)):
"""Display covariances and alphas with matplotlib."""
pyplot.close('all')
super(SplineModelGaussianMixture,
self).display(covariance, logalpha, name, figures[0])
figurenum = figures[1]
print figurenum
if PLOTTING_AVAILABLE:
pyplot.ioff()
pyplot.figure(figurenum)
pyplot.clf()
rows = np.floor(np.sqrt(self._ncomponent()))
cols = np.ceil(np.sqrt(self._ncomponent()))
while rows * cols < self._ncomponent():
rows += 1
ncomp, ndim = self._means.shape
perspline = (ndim - extra) / 2
for clust in xrange(self._ncomponent()):
t = internal_knots(coef2knots(perspline))
t = np.concatenate((np.zeros(4), t, np.ones(4)))
pyplot.subplot(rows, cols, clust + 1)
pyplot.title('Mean curves for component %d' % (clust + 1),
fontsize='medium')
pyplot.xlabel('Position along medial axis', fontsize='x-small')
pyplot.ylabel('Fraction of principal axis width',
fontsize='x-small')
spline1, spline2 = unmix(self._means[clust, :],
self._normmean,
self._normstd,
k=k,
extra=extra)
pyplot.vlines(spline1[0],
-0.2,
1.01,
color='0.8',
linestyles='dotted')
if len(self._covariances.shape) > 2:
eva, evec = np.linalg.eig(self._covariances[clust])
pc = evec[:, np.argmax(eva)]
uponestd = self._means[clust, :] + \
pc * 3*np.sqrt(np.max(eva))
downonestd = self._means[clust, :] - \
pc * 3*np.sqrt(np.max(eva))
spline1u, spline2u = unmix(uponestd,
self._normmean,
self._normstd,
k=k,
extra=extra)
spline1d, spline2d = unmix(downonestd,
self._normmean,
self._normstd,
k=k,
extra=extra)
plot_from_spline(spline1u, color='0.3')
plot_from_spline(spline1d, color='0.3')
plot_from_spline(spline2u, color='0.3')
plot_from_spline(spline2d, color='0.3')
pyplot.ylim(-0.2, 1.01)
plot_from_spline(spline1)
plot_from_spline(spline2)
pyplot.show()
pyplot.ion()
def display(self, covariance=None, logalpha=None, name=None, extra=1, k=3,
figures=(1,2)):
"""Display covariances and alphas with matplotlib."""
pyplot.close('all')
super(SplineModelGaussianMixture, self).display(covariance, logalpha,
name, figures[0])
figurenum = figures[1]
print figurenum
if PLOTTING_AVAILABLE:
pyplot.ioff()
pyplot.figure(figurenum)
pyplot.clf()
rows = np.floor(np.sqrt(self._ncomponent()))
cols = np.ceil(np.sqrt(self._ncomponent()))
while rows * cols < self._ncomponent():
rows += 1
ncomp, ndim = self._means.shape
perspline = (ndim - extra) / 2
for clust in xrange(self._ncomponent()):
t = internal_knots(coef2knots(perspline))
t = np.concatenate((np.zeros(4),t,np.ones(4)))
pyplot.subplot(rows, cols, clust + 1)
pyplot.title('Mean curves for component %d' % (clust + 1),
fontsize='medium')
pyplot.xlabel('Position along medial axis',
fontsize='x-small')
pyplot.ylabel('Fraction of principal axis width',
fontsize='x-small')
spline1, spline2 = unmix(self._means[clust, :],
self._normmean, self._normstd, k=k, extra=extra)
pyplot.vlines(spline1[0], -0.2, 1.01, color='0.8',
linestyles='dotted')
if len(self._covariances.shape) > 2:
eva, evec = np.linalg.eig(self._covariances[clust])
pc = evec[:, np.argmax(eva)]
uponestd = self._means[clust, :] + \
pc * 3*np.sqrt(np.max(eva))
downonestd = self._means[clust, :] - \
pc * 3*np.sqrt(np.max(eva))
spline1u, spline2u = unmix(uponestd, self._normmean,
self._normstd, k=k, extra=extra)
spline1d, spline2d = unmix(downonestd, self._normmean,
self._normstd, k=k, extra=extra)
plot_from_spline(spline1u, color='0.3')
plot_from_spline(spline1d, color='0.3')
plot_from_spline(spline2u, color='0.3')
plot_from_spline(spline2d, color='0.3')
pyplot.ylim(-0.2, 1.01)
plot_from_spline(spline1)
plot_from_spline(spline2)
pyplot.show()
pyplot.ion()