def test_pickle(self):
pass
startvalue = 0.2
proposal = sampler.Multivariate_Normal_Sampler(scale=1)
mcmc = Metropolis_Like_MCMC(ll_normal_noise, startvalue, proposal)
import numpy as np
from scipy import stats
from matplotlib import pyplot
from fit import sampler, mcmc
def posterior(x):
return np.square(x)
# remember, np scales with exp( (x/scale)^2 /2)
def pdf(x):
return (stats.norm.pdf(x, scale = 1.0/np.sqrt(2) ))
startvalue = 0.2
proposal = sampler.Multivariate_Normal_Sampler(scale=2)
mcmcsampler = mcmc.Metropolis_Like_MCMC(posterior,startvalue, proposal, nll=True)
mcmcsampler.save_intervals = 1000
mcmcsampler.run(10000)
mcmcsampler.chain.convert_list_to_nparray()
#plotting
pyplot.figure(num=3)
pyplot.plot(np.arange(-1,1,0.01), posterior(np.arange(-1,1,0.01)))
pyplot.title("target distribution")
pyplot.hist(mcmcsampler.chain.statearray[:,0], bins=100, normed=True)
pyplot.plot(np.arange(-5,5,0.01), pdf(np.arange(-5,5,0.01)))
pyplot.show()
'''
Created on 01.02.2010
@author: floha
'''
import numpy as np
from scipy import stats
from fit import sampler, mcmc
def model(x):
scale = (1, 20, 0.1, 2, 20000)
y = stats.norm.pdf(x, scale=scale)
return np.multiply.reduce(y)
startvalue = np.array([0, 0, 0, 0, 0])
proposal = sampler.Multivariate_Normal_Sampler(scale=1, dimension_proposal=1)
mcmcsampler = mcmc.Metropolis_Like_MCMC(model, startvalue, proposal)
#mcmcsampler.run(10000)
mcmcsampler.run_adaptive_metropolis(times=20)
print mcmcsampler.chain.get_acceptance_per_dimension(-2000)
print mcmcsampler.chain.get_acceptance_rate(-2000)
Created on 01.02.2010
@author: floha
'''
import numpy as np
from scipy import stats
from matplotlib import pyplot
from fit import sampler, mcmc
print "================================="
print "explains the recording of additional information, for the rest see 1dnormal"
model = stats.norm.pdf
def testfunction():
return 3
startvalue = 0
proposal = sampler.Multivariate_Normal_Sampler(covariance=100)
mcmcsampler = mcmc.Metropolis_Like_MCMC(model, startvalue, proposal)
mcmcsampler.set_recording_function(testfunction, 10)
mcmcsampler.run(100)
mcmcsampler.chain.save_data()
print "done, look at the record file in the chain output folder"
print "here's a printout"
print mcmcsampler.chain.records
'''
Created on 20.03.2010
@author: floha
'''
import numpy as np
from scipy import stats
from matplotlib import pyplot
from fit import sampler, mcmc
current3 = np.array([0, 0, 0])
generator = sampler.Multivariate_Normal_Sampler((1, 1, 1))
print generator.get_proposal(current3) # one proposal
print generator.get_proposal(current3, 3) # several proposals, as list
print generator.get_proposal(current3,
1) # careful, this produces a list with one entry
generator = sampler.Multivariate_Uniform_Sampler()
generator.set_scale(lower=100, upper=200)
x = generator.get_proposal(0.0, 5)
print x