def plotPosteriorMax(mix, axis):
"""
@param axis: matlab-like axis coordinates: [x_start, x_end, y_start, y_end]
"""
# -5, 10.0, -5.0, 10.0
x = pylab.arange(axis[0],axis[1],0.1)
y = pylab.arange(axis[2],axis[3],0.1)
#print len(x)
#print len(y)
# XXX colors hard coded here XXX
color = ['b','r','g','m','c','y']
assert mix.G <= len(color)
z = np.zeros( (len(y),len(x)),dtype='Float64' )
for i in range(len(y)):
dat = np.zeros((len(x),2),dtype='Float64' )
dat[:,1] = y[i]
dat[:,0] = x
#print np.exp(mix.pdf(dat))
#print "---------------------------\n",dat
l = mixture.get_posterior(mix,dat)
#print l
# XXX pdf is log valued, we want the true value XXX
#print np.exp(mix.pdf(dat)).tolist()
for j in range(len(x)):
z[i,j] = np.argmax(l[:,j])
#print dat[j,:] ,":",l[:,j],np.argmax(l[:,j])
#print dat[j,:] ,":",l[:,j], "=",z[i,j]
#print "---------------------------\n"
#print "z", len(z),'x', len(z[0]) ,'=', len(z) * len(z[0])
print "max",z.max()
#max_val = z.max()
max_val = np.log(mix.G) # maximum entropy for a vector of length mix.G
print "theor. max", max_val
step = max_val / 40.0
print "step",step
#pylab.figure(1)
#pylab.contour(x,y,z)
#pylab.figure(2)
#pylab.contour(x,y,z,pylab.arange(0,max_val,step))
#pylab.legend()
pylab.figure()
# pylab.colorbar()
pylab.contourf(x,y,z,cmap=pylab.cm.hsv) # pylab.arange(0,max_val,step)
pylab.title('Posterior Maximum Plot')
def plotMixtureEntropy(mix, axis):
"""
@param axis: matlab-like axis coordinates: [x_start, x_end, y_start, y_end]
"""
# -5, 10.0, -5.0, 10.0
x = pylab.arange(axis[0],axis[1]+0.1,0.1)
y = pylab.arange(axis[2],axis[3]+0.1,0.1)
#print x
#print y
#print len(x)
#print len(y)
#X,Y = pylab.meshgrid(x,y)
#z = pylab.exp(-(X*X + Y*Y)) + 0.6*pylab.exp(-((X+1.8)**2 + Y**2))
#pylab.contour(x,y,z)
z = np.zeros( (len(y),len(x)),dtype='Float64' )
for i in range(len(y)):
dat = np.zeros((len(x),2),dtype='Float64' )
dat[:,1] = y[i]
dat[:,0] = x
#print np.exp(mix.pdf(dat))
#print "---------------------------\n",dat
data = mixture.DataSet()
data.fromList(dat)
data.internalInit(mix)
l = mixture.get_posterior(mix,data,logreturn=False)
#print l
#print np.exp(mix.pdf(dat)).tolist()
for j in range(len(x)):
z[i,j] = mixture.entropy(l[:,j])
#print dat[j,:] ,":",l[:,j], "=",z[i,j]
#print "---------------------------\n"
#print "z", len(z),'x', len(z[0]) ,'=', len(z) * len(z[0])
print "max",z.max()
#max_val = z.max()
max_val = np.log(mix.G) # maximum entropy for a vector of length mix.G
print "theor. max", max_val
step = max_val / 10.0
print "step",step
#pylab.figure(1)
#pylab.contour(x,y,z)
#pylab.figure(2)
#pylab.contour(x,y,z,pylab.arange(0,max_val,step))
#pylab.legend()
# pylab.colorbar()
pylab.contourf(x,y,z,) # pylab.arange(0,max_val,step)
pylab.title('Posterior Entropy Plot')
