--- a/pylearn/sampling/tests/test_hmc.py	Fri Sep 09 10:54:17 2011 -0400
+++ b/pylearn/sampling/tests/test_hmc.py	Mon Sep 12 10:24:24 2011 -0400
@@ -1,25 +1,29 @@
-from pylearn.sampling.hmc import *
+import numpy
+import theano
+from theano import tensor
+
+from pylearn.sampling.hmc import HMC_sampler
 
 def _sampler_on_2d_gaussian(sampler_cls, burnin, n_samples):
     batchsize=3
 
-    rng = np.random.RandomState(234)
+    rng = numpy.random.RandomState(234)
 
     #
     # Define a covariance and mu for a gaussian
     #
     tmp = rng.randn(2,2).astype(theano.config.floatX)
     tmp[0] += tmp[1] #induce some covariance
-    cov = np.dot(tmp, tmp.T)
-    cov_inv = np.linalg.inv(cov).astype(theano.config.floatX)
-    mu = np.asarray([5, 9.5], dtype=theano.config.floatX)
+    cov = numpy.dot(tmp, tmp.T)
+    cov_inv = numpy.linalg.inv(cov).astype(theano.config.floatX)
+    mu = numpy.asarray([5, 9.5], dtype=theano.config.floatX)
 
     def gaussian_energy(xlist):
         x = xlist
-        return 0.5 * (TT.dot((x-mu),cov_inv)*(x-mu)).sum(axis=1)
+        return 0.5 * (tensor.dot((x-mu),cov_inv)*(x-mu)).sum(axis=1)
 
 
-    position = shared(rng.randn(batchsize, 2).astype(theano.config.floatX))
+    position = theano.shared(rng.randn(batchsize, 2).astype(theano.config.floatX))
     sampler = sampler_cls(position, gaussian_energy)
 
     print 'initial position', position.get_value(borrow=True)
@@ -28,7 +32,7 @@
     # DRAW SAMPLES
 
     samples = [sampler.draw() for r in xrange(burnin)] #burn-in
-    samples = np.asarray([sampler.draw() for r in xrange(n_samples)])
+    samples = numpy.asarray([sampler.draw() for r in xrange(n_samples)])
 
     assert sampler.avg_acceptance_rate.get_value() > 0
     assert sampler.avg_acceptance_rate.get_value() < 1
@@ -39,7 +43,7 @@
 
     print 'target mean:', mu
     print 'empirical mean: ', samples.mean(axis=0)
-    #assert np.all(abs(mu - samples.mean(axis=0)) < 1)
+    #assert numpy.all(abs(mu - samples.mean(axis=0)) < 1)
 
 
     print 'final stepsize', sampler.stepsize.get_value()
@@ -47,12 +51,12 @@
 
     print 'target cov', cov
     s = samples[:,0,:]
-    empirical_cov = np.cov(samples[:,0,:].T)
+    empirical_cov = numpy.cov(samples[:,0,:].T)
     print ''
     print 'cov/empirical_cov', cov/empirical_cov
-    empirical_cov = np.cov(samples[:,1,:].T)
+    empirical_cov = numpy.cov(samples[:,1,:].T)
     print 'cov/empirical_cov', cov/empirical_cov
-    empirical_cov = np.cov(samples[:,2,:].T)
+    empirical_cov = numpy.cov(samples[:,2,:].T)
     print 'cov/empirical_cov', cov/empirical_cov
     return sampler