def sample_hmc_mlabwrap(m,
nsamples,
burnin=1000,
steps=10,
step_sz=.2,
diagnostics=False,
persistence=0):
from scikits.mlabwrap import mlab
mlab.addpath('matlab')
mlab.addpath('matlab/f_energy')
opts = mlab.hmc2_opt()
opts.nsamples = nsamples
opts.nomit = burnin
opts.steps = steps
opts.stepadj = step_sz
opts.persistence = persistence
samps, E, diagn = mlab.hmc2('f_phasedist',
np.zeros(m.shape[0] / 2, float),
opts,
'g_phasedist',
m,
nout=3)
print opts
print diagn
if diagnostics:
return smod(samps.T), E, diagn
else:
return smod(samps.T)
def sample_hmc(m,
nsamples,
burnin=1000,
steps=10,
step_sz=.2,
diagnostics=False,
persistence=0):
from hmc2 import opt, hmc2
from f_energy import f_phasedist, g_phasedist
opts = opt(nsamples=nsamples,
nomit=burnin,
steps=steps,
stepadj=step_sz,
persistence=persistence,
display=True)
samps, E, diagn = hmc2(f=f_phasedist,
x=np.zeros(m.shape[0] / 2, float),
options=opts,
gradf=g_phasedist,
args=(m, ),
return_energies=True,
return_diagnostics=True)
print opts
print diagn
if diagnostics:
return smod(samps.T), E, diagn
else:
return smod(samps.T)
def sample_hmc(m,nsamples,burnin=1000,steps=10,step_sz=.2,diagnostics=False,persistence=0):
from hmc2 import opt, hmc2
from f_energy import f_phasedist, g_phasedist
opts = opt(nsamples=nsamples,nomit=burnin,steps=steps,stepadj=step_sz,
persistence=persistence,display=True)
samps, E, diagn = hmc2(f=f_phasedist, x=np.zeros(m.shape[0]/2,float),
options=opts, gradf=g_phasedist, args=(m,),
return_energies=True, return_diagnostics=True)
print opts
print diagn
if diagnostics:
return smod(samps.T),E,diagn
else:
return smod(samps.T)
def sample_hmc_mlabwrap(m,nsamples,burnin=1000,steps=10,step_sz=.2,diagnostics=False,persistence=0):
from scikits.mlabwrap import mlab
mlab.addpath('matlab')
mlab.addpath('matlab/f_energy')
opts = mlab.hmc2_opt()
opts.nsamples = nsamples
opts.nomit = burnin
opts.steps = steps
opts.stepadj = step_sz
opts.persistence = persistence
samps, E, diagn = mlab.hmc2('f_phasedist',np.zeros(m.shape[0]/2,float),opts,'g_phasedist',m,nout=3)
print opts
print diagn
if diagnostics:
return smod(samps.T),E,diagn
else:
return smod(samps.T)
def plot_phasedist_1d(phases,nbins=37,plot_fit=True,fig=None,ax=None,linewidth=2,**kargs):
if fig is None and ax is None: fig=plt.figure()
if ax is None: ax = fig.add_subplot(111)
# default plot arguments
plotargs = dict(color='b',alpha=.75)
plotargs.update(kargs)
phases = utils.smod(phases,2*np.pi)
(N, bins, patches) = ax.hist(phases,np.linspace(-np.pi,np.pi,nbins+1),normed=1,**kargs)
plt.setp(patches, 'facecolor', plotargs['color'], 'alpha', plotargs['alpha'])
(kappa,mu,p,n) = model.phasedist(phases)
if plot_fit:
plt.plot (np.linspace(-np.pi,np.pi,100),model.mises(np.linspace(-np.pi,np.pi,100),
kappa,mu),'k',linewidth=linewidth)
ax.set_xlim([-np.pi,np.pi])
ax.set_xlabel('phase (rad)')
ax.set_title ('[kappa=%4.2f, mu=%3.1f]' % (kappa,180.*mu/np.pi))
print 'kappa = %4.2f, mu = %1.2f, N = %d' % (kappa,mu,n)
return kappa,mu
