def mcmc_advance(start, stdevs, logp, nsteps = 1e300, adapt=True, callback=None):
	"""
	Generic Metropolis MCMC. Advances the chain by nsteps.
	Called by :func:`mcmc`
	
	:param adapt: enables adaptive stepwidth alteration (converges).
	"""
	import scipy
	from numpy import log
	import progressbar
	
	prob = logp(start)
	chain = [start]
	accepts = [True]
	probs = [prob]
	assert not numpy.isnan(start).any()
	assert not numpy.isnan(stdevs).any()
	
	i = 0
	widgets=['AR', progressbar.Percentage(), progressbar.Counter('%5d'),
		progressbar.Bar(), progressbar.ETA()]
	pbar = progressbar.ProgressBar(widgets=widgets,
		maxval=nsteps).start()

	prev = start
	prev_prob = prob
	print 'MCMC: start at prob', prob
	stepchange = 0.1
	while len(chain) < nsteps:
		i = i + 1
		next = scipy.random.normal(prev, stdevs)
		next[next > 1] = 1
		next[next < 0] = 0
		next_prob = logp(next)
		assert not numpy.isnan(next).any()
		assert not numpy.isnan(next_prob).any()
		delta = next_prob - prev_prob
		dice = log(scipy.random.uniform(0, 1))
		accept = delta > dice
		if accept:
			prev = next
			prev_prob = next_prob
			if adapt: stdevs *= (1 + stepchange)
		else:
			if adapt: stdevs *= (1 + stepchange)**(-0.4) # aiming for 40% acceptance
		if callback: callback(prev_prob, prev, accept)
		chain.append(prev)
		accepts.append(accept)
		probs.append(prev_prob)
		if adapt: stepchange = min(0.1, 10. / i)
		#print 'STDEV', stdevs[:5], stepchange
		
		# compute stats
		widgets[0] = 'AR: %.03f' % numpy.mean(numpy.array(accepts[len(accepts)/3:])+0)
		pbar.update(pbar.currval + 1)
	pbar.finish()
	
	return chain, probs, accepts, stdevs
def mcmc_advance(start, stdevs, logp, nsteps = 1e300, adapt=True, callback=None):
	"""
	Generic Metropolis MCMC. Advances the chain by nsteps.
	Called by :func:`mcmc`
	
	:param adapt: enables adaptive stepwidth alteration (converges).
	"""
	import scipy
	from numpy import log
	import progressbar
	
	prob = logp(start)
	chain = [start]
	accepts = [True]
	probs = [prob]
	assert not numpy.isnan(start).any()
	assert not numpy.isnan(stdevs).any()
	
	i = 0
	widgets=['AR', progressbar.Percentage(), progressbar.Counter('%5d'),
		progressbar.Bar(), progressbar.ETA()]
	pbar = progressbar.ProgressBar(widgets=widgets,
		maxval=nsteps).start()

	prev = start
	prev_prob = prob
	print 'MCMC: start at prob', prob
	stepchange = 0.1
	while len(chain) < nsteps:
		i = i + 1
		next = scipy.random.normal(prev, stdevs)
		next[next > 1] = 1
		next[next < 0] = 0
		next_prob = logp(next)
		assert not numpy.isnan(next).any()
		assert not numpy.isnan(next_prob).any()
		delta = next_prob - prev_prob
		dice = log(scipy.random.uniform(0, 1))
		accept = delta > dice
		if accept:
			prev = next
			prev_prob = next_prob
			if adapt: stdevs *= (1 + stepchange)
		else:
			if adapt: stdevs *= (1 + stepchange)**(-0.4) # aiming for 40% acceptance
		if callback: callback(prev_prob, prev, accept)
		chain.append(prev)
		accepts.append(accept)
		probs.append(prev_prob)
		if adapt: stepchange = min(0.1, 10. / i)
		#print 'STDEV', stdevs[:5], stepchange
		
		# compute stats
		widgets[0] = 'AR: %.03f' % numpy.mean(numpy.array(accepts[len(accepts)/3:])+0)
		pbar.update(pbar.currval + 1)
	pbar.finish()
	
	return chain, probs, accepts, stdevs
def mcmc_advance ( start , stdevs , logp , nsteps = 1e300 , adapt = True , callback = None ) : import scipy from numpy import log import progressbar prob = logp ( start ) chain = [ start ] accepts = [ True ] probs = [ prob ] assert not numpy . isnan ( start ) . any ( ) assert not numpy . isnan ( stdevs ) . any ( ) i = 0 widgets = [ 'AR' , progressbar . Percentage ( ) , progressbar . Counter ( '%5d' ) , progressbar . Bar ( ) , progressbar . ETA ( ) ] pbar = progressbar . ProgressBar ( widgets = widgets , maxval = nsteps ) . start ( ) prev = start prev_prob = prob print 'MCMC: start at prob' , prob stepchange = 0.1 while len ( chain ) < nsteps : i = i + 1 next = scipy . random . normal ( prev , stdevs ) next [ next > 1 ] = 1 next [ next < 0 ] = 0 next_prob = logp ( next ) assert not numpy . isnan ( next ) . any ( ) assert not numpy . isnan ( next_prob ) . any ( ) delta = next_prob - prev_prob dice = log ( scipy . random . uniform ( 0 , 1 ) ) accept = delta > dice if accept : prev = next prev_prob = next_prob if adapt : stdevs *= ( 1 + stepchange ) else : if adapt : stdevs *= ( 1 + stepchange ) ** ( - 0.4 ) # aiming for 40% acceptance if callback : callback ( prev_prob , prev , accept ) chain . append ( prev ) accepts . append ( accept ) probs . append ( prev_prob ) if adapt : stepchange = min ( 0.1 , 10. / i ) #print 'STDEV', stdevs[:5], stepchange # compute stats widgets [ 0 ] = 'AR: %.03f' % numpy . mean ( numpy . array ( accepts [ len ( accepts ) / 3 : ] ) + 0 ) pbar . update ( pbar . currval + 1 ) pbar . finish ( ) return chain , probs , accepts , stdevs
Generic Metropolis MCMC. Advances the chain by nsteps.
	Called by :func:`mcmc`
	
	:param adapt: enables adaptive stepwidth alteration (converges).
Generic Metropolis MCMC . Advances the chain by nsteps . Called by : func : mcmc : param adapt : enables adaptive stepwidth alteration ( converges ) .