--- a/pylearn/sampling/tests/test_hmc.py	Tue Mar 08 12:50:37 2011 -0500
+++ b/pylearn/sampling/tests/test_hmc.py	Wed Mar 16 20:20:02 2011 -0400
@@ -22,16 +22,16 @@
     position = shared(rng.randn(batchsize, 2).astype(theano.config.floatX))
     sampler = sampler_cls(position, gaussian_energy)
 
-    print 'initial position', position.value
-    print 'initial stepsize', sampler.stepsize.value
+    print 'initial position', position.get_value(borrow=True)
+    print 'initial stepsize', sampler.stepsize.get_value(borrow=True)
 
     # DRAW SAMPLES
 
     samples = [sampler.draw() for r in xrange(burnin)] #burn-in
     samples = np.asarray([sampler.draw() for r in xrange(n_samples)])
 
-    assert sampler.avg_acceptance_rate.value > 0
-    assert sampler.avg_acceptance_rate.value < 1
+    assert sampler.avg_acceptance_rate.get_value() > 0
+    assert sampler.avg_acceptance_rate.get_value() < 1
 
     # TEST THAT THEY ARE FROM THE RIGHT DISTRIBUTION
 
@@ -42,8 +42,8 @@
     #assert np.all(abs(mu - samples.mean(axis=0)) < 1)
 
 
-    print 'final stepsize', sampler.stepsize.value
-    print 'final acceptance_rate', sampler.avg_acceptance_rate.value
+    print 'final stepsize', sampler.stepsize.get_value()
+    print 'final acceptance_rate', sampler.avg_acceptance_rate.get_value()
 
     print 'target cov', cov
     s = samples[:,0,:]
@@ -59,7 +59,7 @@
 def test_hmc():
     print ('HMC')
     sampler = _sampler_on_2d_gaussian(HMC_sampler.new_from_shared_positions, burnin=3000/20, n_samples=90000/20)
-    assert abs(sampler.avg_acceptance_rate.value - sampler.target_acceptance_rate) < .1
-    assert sampler.stepsize.value >= sampler.stepsize_min
-    assert sampler.stepsize.value <= sampler.stepsize_max
+    assert abs(sampler.avg_acceptance_rate.get_value() - sampler.target_acceptance_rate) < .1
+    assert sampler.stepsize.get_value() >= sampler.stepsize_min
+    assert sampler.stepsize.get_value() <= sampler.stepsize_max