def plot_true_clusters():
for k in range(K):
c = k % len(GaussViz.Colors)
GaussViz.plotGauss2DContour(
mus[k],
sigmas[k],
color=GaussViz.Colors[c])
def _viz_Gauss_before_after(
curModel=None, propModel=None,
curSS=None, propSS=None,
Plan=None,
propLscore=None, curLscore=None,
Data_b=None, Data_t=None,
**kwargs):
pylab.subplots(
nrows=1, ncols=2, figsize=(8, 4), num=1)
h1 = pylab.subplot(1, 2, 1)
h1.clear()
GaussViz.plotGauss2DFromHModel(
curModel, compsToHighlight=Plan['btargetCompID'], figH=h1)
if curLscore is not None:
pylab.title('%.4f' % (curLscore))
h2 = pylab.subplot(1, 2, 2, sharex=h1, sharey=h1)
h2.clear()
newCompIDs = np.arange(curModel.obsModel.K, propModel.obsModel.K)
GaussViz.plotGauss2DFromHModel(
propModel, compsToHighlight=newCompIDs, figH=h2, Data=Data_t)
if propLscore is not None:
pylab.title('%.4f' % (propLscore))
Lgain = propLscore - curLscore
if Lgain > 0:
pylab.xlabel('ACCEPT +%.2f' % (Lgain))
else:
pylab.xlabel('REJECT %.2f' % (Lgain))
pylab.draw()
pylab.subplots_adjust(hspace=0.1, top=0.9, bottom=0.15,
left=0.15, right=0.95)
def plot_true_clusters():
from bnpy.viz import GaussViz
for k in range(K):
c = k % len(GaussViz.Colors)
GaussViz.plotGauss2DContour(Mu[k], Sigma[k], color=GaussViz.Colors[c])
def plot_true_clusters():
from bnpy.viz import GaussViz
for k in range(K):
c = k % len(GaussViz.Colors)
GaussViz.plotGauss2DContour( Mu[k], Sigma[k], color=GaussViz.Colors[c] )