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annotate pylearn/sampling/tests/test_hmc.py @ 1528:dee276045768

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make test don't run in debug mode as it is too slow.
author Frederic Bastien <nouiz@nouiz.org>
date Fri, 09 Nov 2012 17:06:30 -0500
parents 211b217f9691
children
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
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8 def _sampler_on_2d_gaussian(sampler_cls, burnin, n_samples):
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9 batchsize = 3
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10
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11 rng = numpy.random.RandomState(234)
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12
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13 #
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14 # Define a covariance and mu for a gaussian
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15 #
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16 tmp = rng.randn(2, 2).astype(theano.config.floatX)
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17 tmp[0] += tmp[1] # induce some covariance
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18 cov = numpy.dot(tmp, tmp.T)
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19 cov_inv = numpy.linalg.inv(cov).astype(theano.config.floatX)
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20 mu = numpy.asarray([5, 9.5], dtype=theano.config.floatX)
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21
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22 def gaussian_energy(xlist):
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23 x = xlist
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24 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,
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27 2).astype(theano.config.floatX))
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28 sampler = sampler_cls(position, gaussian_energy)
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29
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30 print 'initial position', position.get_value(borrow=True)
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31 print 'initial stepsize', sampler.stepsize.get_value(borrow=True)
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32
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33 # DRAW SAMPLES
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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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36
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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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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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47
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48 print 'final stepsize', sampler.stepsize.get_value()
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49 print 'final acceptance_rate', sampler.avg_acceptance_rate.get_value()
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50
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51 print 'target cov', cov
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52 s = samples[:, 0, :]
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53 empirical_cov = numpy.cov(samples[:, 0, :].T)
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54 print ''
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55 print 'cov/empirical_cov', cov / empirical_cov
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56 empirical_cov = numpy.cov(samples[:, 1, :].T)
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57 print 'cov/empirical_cov', cov / empirical_cov
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58 empirical_cov = numpy.cov(samples[:, 2, :].T)
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59 print 'cov/empirical_cov', cov / empirical_cov
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60 return sampler
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61
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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 try:
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66 orig_mode = theano.config.mode
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67 if theano.config.mode in ["DebugMode", "DEBUG_MODE"]:
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68 theano.config.mode = "FAST_RUN"
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69 sampler = _sampler_on_2d_gaussian(
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70 HMC_sampler.new_from_shared_positions,
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71 burnin=3000 / 20, n_samples=90000 / 20)
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72 finally:
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73 theano.config.mode = orig_mode
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74
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75
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76 assert abs(sampler.avg_acceptance_rate.get_value() -
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77 sampler.target_acceptance_rate) < .1
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78 assert sampler.stepsize.get_value() >= sampler.stepsize_min
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79 assert sampler.stepsize.get_value() <= sampler.stepsize_max