def cluster(self, u, ndim, keepRadius=False): """ """ if self.verbose: print 'building region ...' if len(u) > 10: if keepRadius and self.region is not None and 'maxdistance' in self.region: maxdistance = self.region['maxdistance'] else: if self.radial: if self.jackknife: #maxdistance = initial_rdistance_guess(u, k=1, metric=self.metric) maxdistance = nearest_rdistance_guess(u, metric=self.metric) else: maxdistance = find_rdistance(u, nbootstraps=50, metric=self.metric, verbose=self.verbose) else: maxdistance = find_maxdistance(u) if self.force_shrink and self.region is not None and 'maxdistance' in self.region: maxdistance = min(maxdistance, self.region['maxdistance']) ulow = numpy.max([u.min(axis=0) - maxdistance, numpy.zeros(ndim)], axis=0) uhigh = numpy.min([u.max(axis=0) + maxdistance, numpy.ones(ndim)], axis=0) else: maxdistance = None ulow = numpy.zeros(ndim) uhigh = numpy.ones(ndim) if self.verbose: print 'setting sampling region:', (ulow, uhigh), maxdistance self.region = dict(members=u, maxdistance=maxdistance, ulow=ulow, uhigh=uhigh) self.generator = None
def cluster(self, u, ndim, keepRadius=False):
"""
"""
if self.verbose: print('building region ...')
if len(u) > 10:
if keepRadius and self.region is not None and 'maxdistance' in self.region:
maxdistance = self.region['maxdistance']
else:
if self.radial:
if self.jackknife:
#maxdistance = initial_rdistance_guess(u, k=1, metric=self.metric)
maxdistance = nearest_rdistance_guess(
u, metric=self.metric)
else:
maxdistance = find_rdistance(u,
nbootstraps=20,
metric=self.metric,
verbose=self.verbose)
else:
maxdistance = find_maxdistance(u)
if self.force_shrink and self.region is not None and 'maxdistance' in self.region:
maxdistance = min(maxdistance, self.region['maxdistance'])
if self.keep_phantom_points and len(self.phantom_points) > 0:
# add phantoms to u now
print('including phantom points in cluster members',
self.phantom_points)
u = numpy.vstack((u, self.phantom_points))
ulow = numpy.max([u.min(axis=0) - maxdistance,
numpy.zeros(ndim)],
axis=0)
uhigh = numpy.min([u.max(axis=0) + maxdistance,
numpy.ones(ndim)],
axis=0)
else:
maxdistance = None
ulow = numpy.zeros(ndim)
uhigh = numpy.ones(ndim)
if self.verbose:
print('setting sampling region:', (ulow, uhigh), maxdistance)
self.region = dict(members=u,
maxdistance=maxdistance,
ulow=ulow,
uhigh=uhigh)
self.generator = None
def draw_constrained(self, Lmin, priortransform, loglikelihood, ndim,
previous, startu, startx, startL, starti, **kwargs):
# compute RadFriends spheres
u = numpy.array([u for u, _, L in previous if L >= Lmin])
rebuilt = False
if self.region is None or len(previous) % 50 == 0:
# jackknife, is fast
maxdistance = nearest_rdistance_guess(u, metric='euclidean')
#maxdistance = find_rdistance(u, nbootstraps=50, metric='euclidean', verbose=False)
# make sure we only shrink
if self.region is not None and 'maxdistance' in self.region:
maxdistance = min(maxdistance, self.region['maxdistance'])
#print 'new distance:', maxdistance
# compute enclosing rectangle for quick checks
ulow = numpy.max([u.min(axis=0) - maxdistance, numpy.zeros(ndim)], axis=0)
uhigh = numpy.min([u.max(axis=0) + maxdistance, numpy.ones(ndim)], axis=0)
self.region = dict(members=u, maxdistance=maxdistance, ulow=ulow, uhigh=uhigh)
self.direct_generator = self.generate_direct(ndim=ndim)
self.friends_generator = self.generate_from_friends(ndim=ndim)
rebuilt = True
ntoaccept = 0
if self.phase == 0:
# draw from rectangle until sphere rejection drops below 1%
for u, ntotal in self.direct_generator:
x = priortransform(u)
L = loglikelihood(x)
self.nfriends += 1
ntoaccept += 1
if L >= Lmin:
if ntotal >= 200:
print('Drawing from prior is becoming inefficient: %d draws before accept' % ntotal)
print()
if ntoaccept >= 200:
print('RadFriends is becoming inefficient: %d draws until accept' % ntoaccept)
print()
self.nfriends_accepts += 1
return u, x, L, ntoaccept
if ntotal >= 1000:
# drawing directly from prior
# becomes inefficient as we go to
# small region
# switch to drawing from Friends
print('switching to Friends sampling phase')
print()
self.phase = 1
break
if ntoaccept >= 2000:
print('switching to local steps sampling phase')
print()
self.phase = 2
# drawing using RadFriends can become
# inefficient in high dimensionality
# switch to local step sampling
break
if self.phase == 1:
# draw from spheres until acceptance rate drops below 0.05%
for u, ntotal in self.friends_generator:
x = priortransform(u)
L = loglikelihood(x)
self.nfriends += 1
ntoaccept += 1
if L >= Lmin:
if ntoaccept >= 200:
print('RadFriends is becoming inefficient: %d draws until accept' % ntoaccept)
print()
self.nfriends_accepts += 1
return u, x, L, ntoaccept
if ntoaccept >= 2000:
print( 'switching to local steps sampling phase')
print()
self.phase = 2
# drawing using RadFriends can become
# inefficient in high dimensionality
# switch to local step sampling
break
#print 'falling through...'
# then do local step sampling
i = starti # particle ID
u1, x1, L1, k = self.local_step_sample(i, startu, startx, startL,
Lmin, priortransform, loglikelihood, u)
#self.stats()
self.adapt()
return u1, x1, L1, k + ntoaccept
