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annotate pylearn/sampling/tests/test_hmc.py @ 1503:1ee532a6f33b

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Fix import.
author Frederic Bastien <nouiz@nouiz.org>
date Mon, 12 Sep 2011 10:24:24 -0400
parents fbe470217937
children 5804e44d7a1b
rev   line source
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1 import numpy
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2 import theano
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3 from theano import tensor
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4
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5 from pylearn.sampling.hmc import HMC_sampler
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6
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7 def _sampler_on_2d_gaussian(sampler_cls, burnin, n_samples):
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8 batchsize=3
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9
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10 rng = numpy.random.RandomState(234)
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11
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12 #
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13 # Define a covariance and mu for a gaussian
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14 #
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15 tmp = rng.randn(2,2).astype(theano.config.floatX)
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16 tmp[0] += tmp[1] #induce some covariance
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17 cov = numpy.dot(tmp, tmp.T)
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18 cov_inv = numpy.linalg.inv(cov).astype(theano.config.floatX)
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19 mu = numpy.asarray([5, 9.5], dtype=theano.config.floatX)
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20
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21 def gaussian_energy(xlist):
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22 x = xlist
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23 return 0.5 * (tensor.dot((x-mu),cov_inv)*(x-mu)).sum(axis=1)
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25
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26 position = theano.shared(rng.randn(batchsize, 2).astype(theano.config.floatX))
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27 sampler = sampler_cls(position, gaussian_energy)
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28
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29 print 'initial position', position.get_value(borrow=True)
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30 print 'initial stepsize', sampler.stepsize.get_value(borrow=True)
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31
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32 # DRAW SAMPLES
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33
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34 samples = [sampler.draw() for r in xrange(burnin)] #burn-in
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35 samples = numpy.asarray([sampler.draw() for r in xrange(n_samples)])
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37 assert sampler.avg_acceptance_rate.get_value() > 0
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38 assert sampler.avg_acceptance_rate.get_value() < 1
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39
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40 # TEST THAT THEY ARE FROM THE RIGHT DISTRIBUTION
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41
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42 # samples.shape == (1000, 3, 2)
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43
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44 print 'target mean:', mu
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45 print 'empirical mean: ', samples.mean(axis=0)
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46 #assert numpy.all(abs(mu - samples.mean(axis=0)) < 1)
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48
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49 print 'final stepsize', sampler.stepsize.get_value()
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50 print 'final acceptance_rate', sampler.avg_acceptance_rate.get_value()
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51
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52 print 'target cov', cov
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53 s = samples[:,0,:]
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54 empirical_cov = numpy.cov(samples[:,0,:].T)
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55 print ''
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56 print 'cov/empirical_cov', cov/empirical_cov
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57 empirical_cov = numpy.cov(samples[:,1,:].T)
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58 print 'cov/empirical_cov', cov/empirical_cov
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59 empirical_cov = numpy.cov(samples[:,2,:].T)
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60 print 'cov/empirical_cov', cov/empirical_cov
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61 return sampler
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62
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63 def test_hmc():
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64 print ('HMC')
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65 sampler = _sampler_on_2d_gaussian(HMC_sampler.new_from_shared_positions, burnin=3000/20, n_samples=90000/20)
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66 assert abs(sampler.avg_acceptance_rate.get_value() - sampler.target_acceptance_rate) < .1
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67 assert sampler.stepsize.get_value() >= sampler.stepsize_min
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68 assert sampler.stepsize.get_value() <= sampler.stepsize_max
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69