def plotGauss1D(mu, sigma2, color='b', ax_handle=None, **kwargs):
if ax_handle is not None:
pylab.sca(ax_handle)
mu = np.squeeze(mu)
sigma = np.sqrt(np.squeeze(sigma2))
assert mu.size == 1 and mu.ndim == 0
assert sigma.size == 1 and sigma.ndim == 0
xs = mu + sigma * np.arange(-4, 4, 0.01)
ps = 1. / np.sqrt(2 * np.pi) * 1. / sigma * \
np.exp(-0.5 * (xs - mu)**2 / sigma**2)
pylab.plot(xs, ps, '.', markerfacecolor=color, markeredgecolor=color)
def plotGauss2DContour(
mu,
Sigma,
color='b',
radiusLengths=[1.0, 3.0],
markersize=3.0,
ax_handle=None,
):
''' Plot elliptical contours for provided mean mu, covariance Sigma.
Uses only the first 2 dimensions.
Post Condition
--------------
Plot created on current axes
'''
if ax_handle is not None:
pylab.sca(ax_handle)
mu = np.asarray(mu)
Sigma = np.asarray(Sigma)
mu = mu[:2]
Sigma = Sigma[:2, :2]
D, V = np.linalg.eig(Sigma)
sqrtSigma = np.dot(V, np.sqrt(np.diag(D)))
# Prep for plotting elliptical contours
# by creating grid of (x,y) points along perfect circle
ts = np.arange(-np.pi, np.pi, 0.03)
x = np.sin(ts)
y = np.cos(ts)
Zcirc = np.vstack([x, y])
# Warp circle into ellipse defined by Sigma's eigenvectors
Zellipse = np.dot(sqrtSigma, Zcirc)
# plot contour lines across several radius lengths
# TODO: instead, choose radius by percentage of prob mass contained within
for r in radiusLengths:
Z = r * Zellipse + mu[:, np.newaxis]
pylab.plot(Z[0],
Z[1],
'.',
markersize=markersize,
markerfacecolor=color,
markeredgecolor=color)
def plotGauss2DFromHModel(hmodel,
compListToPlot=None,
compsToHighlight=None,
activeCompIDs=None,
MaxKToDisplay=50,
proba_thr=0.0001,
ax_handle=None,
dataset=None,
Colors=Colors,
samp_colors=None,
**kwargs):
''' Plot 2D contours for components in hmodel in current pylab figure
Args
----
hmodel : bnpy HModel object
compListToPlot : array-like of integer IDs of components within hmodel
compsToHighlight : int or array-like
integer IDs to highlight
if None, all components get unique colors
if not None, only highlighted components get colors.
proba_thr : float
Minimum weight assigned to component in order to be plotted.
All components with weight below proba_thr are ignored.
'''
if ax_handle is not None:
pylab.sca(ax_handle)
if compsToHighlight is not None:
compsToHighlight = np.asarray(compsToHighlight)
if compsToHighlight.ndim == 0:
compsToHighlight = np.asarray([compsToHighlight])
else:
compsToHighlight = list()
if compListToPlot is None:
compListToPlot = np.arange(0, hmodel.obsModel.K)
if activeCompIDs is None:
activeCompIDs = np.arange(0, hmodel.obsModel.K)
# Load appearance probabilities as single vector
if hmodel.allocModel.K == hmodel.obsModel.K:
w = hmodel.allocModel.get_active_comp_probs()
else:
w = np.ones(hmodel.obsModel.K)
if dataset is not None and hasattr(dataset, 'X'):
if samp_colors:
for (start, stop), value in samp_colors.items():
pylab.plot(dataset.X[start:stop, 0],
dataset.X[start:stop, 1],
'.',
color=value,
alpha=0.5)
else:
pylab.plot(dataset.X[:, 0],
dataset.X[:, 1],
'.',
color=(0.3, 0.3, 0.3),
alpha=0.5)
nSkip = 0
nGood = 0
for ii, compID in enumerate(compListToPlot):
if compID not in activeCompIDs:
continue
kk = np.flatnonzero(activeCompIDs == compID)
assert kk.size == 1
kk = kk[0]
if w[kk] < proba_thr and compID not in compsToHighlight:
nSkip += 1
continue
mu = hmodel.obsModel.get_mean_for_comp(kk)
Sigma = hmodel.obsModel.get_covar_mat_for_comp(kk)
if len(compsToHighlight) == 0 or compID in compsToHighlight:
color = Colors[ii % len(Colors)]
plotGauss2DContour(mu, Sigma, color=color)
elif kk not in compsToHighlight:
plotGauss2DContour(mu, Sigma, color='k')
nGood += 1
if nGood >= MaxKToDisplay:
print 'DISPLAY LIMIT EXCEEDED. Showing %d/%d components' \
% (nGood, len(activeCompIDs))
break
if nSkip > 0:
print 'SKIPPED %d comps with size below %.2f' % (nSkip, proba_thr)
def plotGauss1DFromHModel(hmodel,
compListToPlot=None,
compsToHighlight=None,
activeCompIDs=None,
MaxKToDisplay=50,
proba_thr=0.0001,
ax_handle=None,
Colors=Colors,
dataset=None,
**kwargs):
''' Make line plot of pdf for each component (1D observations).
'''
if ax_handle is not None:
pylab.sca(ax_handle)
if compsToHighlight is not None:
compsToHighlight = as1D(np.asarray(compsToHighlight))
else:
compsToHighlight = list()
if compListToPlot is None:
compListToPlot = np.arange(0, hmodel.obsModel.K)
if activeCompIDs is None:
activeCompIDs = np.arange(0, hmodel.obsModel.K)
# Load appearance probabilities as single vector
if hmodel.allocModel.K == hmodel.obsModel.K:
w = hmodel.allocModel.get_active_comp_probs()
else:
w = np.ones(hmodel.obsModel.K)
if dataset is not None:
if hasattr(dataset, 'X'):
pylab.hist(dataset.X[:, 0], 50, normed=1)
#Xtile = np.tile(Data.X[:, 0], (2, 1))
#ys = 0.1 * np.arange(2)
#pylab.plot(Xtile, ys, 'k-')
nSkip = 0
nGood = 0
for ii, compID in enumerate(compListToPlot):
if compID not in activeCompIDs:
continue
kk = np.flatnonzero(activeCompIDs == compID)
assert kk.size == 1
kk = kk[0]
if w[kk] < proba_thr and compID not in compsToHighlight:
nSkip += 1
continue
mu = hmodel.obsModel.get_mean_for_comp(kk)
sigma2 = hmodel.obsModel.get_covar_mat_for_comp(kk)
if len(compsToHighlight) == 0 or compID in compsToHighlight:
color = Colors[ii % len(Colors)]
plotGauss1D(mu, sigma2, color=color)
elif kk not in compsToHighlight:
plotGauss1D(mu, sigma2, color='k')
nGood += 1
if nGood >= MaxKToDisplay:
print 'DISPLAY LIMIT EXCEEDED. Showing %d/%d components' \
% (nGood, len(activeCompIDs))
break
if nSkip > 0:
print 'SKIPPED %d comps with size below %.2f' % (nSkip, proba_thr)