'''
Created on Nov 24, 2009
@author: johnsalvatier
'''
import model9
import pymc
import multichain_mcmc
from pylab import *
from numpy import *
from cPickle import *
sampler = multichain_mcmc.AmalaSampler(model9.gen_model)
sampler.sample(nChains = 5, ndraw = 500, maxGradient = 100)
print sampler.R
history = sampler.samples
slices = sampler.slices
print history.shape
samples = sampler.samples
print sampler.accepts_ratio
print sampler.burnIn
print sampler.time
outfile = open('samples1.pkl', 'wb')
dump(samples, outfile)
outfile = open('slices.pkl', 'wb')
def sample():
sampler = multichain_mcmc.AmalaSampler(model8.model_gen)
sampler.sample(ndraw = 500, maxGradient = 1.3, mAccept = True, mConvergence = True)
''' Created on Nov 24, 2009 @author: johnsalvatier ''' import model7 import pymc import multichain_mcmc from pylab import * import numpy variables_of_interest = ['mu'] sampler = multichain_mcmc.AmalaSampler(model7.model_gen) sampler.sample(ndraw = 500,samplesPerAdapatationParameter = .5,adaptationDecayLength = 100, variables_of_interest = variables_of_interest, ndraw_max = 10000, maxGradient = 100) slices = sampler.slices print sampler.R history = sampler.samples print history.shape samples = history.shape[0] print sampler.accepts_ratio print sampler.burnIn print "running time (s)", sampler.time meanInterceptEstimate = numpy.mean(history[:,slices['intercept']], axis = 0) meanSdEstimate = numpy.mean(history[:,slices['sd']], axis = 0) meanResponsesEstimates = numpy.mean(history[:,slices['responses']], axis = 0)
'''
Created on Nov 24, 2009
@author: johnsalvatier
'''
import model2
import pymc
import multichain_mcmc as mc
from pylab import *
import pydevd
pydevd.set_pm_excepthook()
sampler = mc.AmalaSampler(model2.model)
history, time = sampler.sample(nChains = 5, ndraw = 3000, maxGradient = 100)
print time
mc.show_samples(plot, history, ('mean', 'sd'))