pep8

nouiz · nouiz · commit 8b4e1f945a82 · 2012-03-20T15:01:38.000-04:00
diff --git a/code/mcrbm/test_hmc.py b/code/mcrbm/test_hmc.py
@@ -4,21 +4,23 @@
 
 from hmc import HMC_sampler
 
+
 def sampler_on_nd_gaussian(sampler_cls, burnin, n_samples, dim=10):
-    batchsize=3
+    batchsize = 3
 
     rng = numpy.random.RandomState(123)
 
     # Define a covariance and mu for a gaussian
-    mu  = numpy.array(rng.rand(dim) * 10, dtype=theano.config.floatX)
+    mu = numpy.array(rng.rand(dim) * 10, dtype=theano.config.floatX)
     cov = numpy.array(rng.rand(dim, dim), dtype=theano.config.floatX)
     cov = (cov + cov.T) / 2.
     cov[numpy.arange(dim), numpy.arange(dim)] = 1.0
     cov_inv = linalg.inv(cov)
 
     # Define energy function for a multi-variate Gaussian
     def gaussian_energy(x):
-        return 0.5 * (theano.tensor.dot((x-mu),cov_inv)*(x-mu)).sum(axis=1)
+        return 0.5 * (theano.tensor.dot((x - mu), cov_inv) *
+                      (x - mu)).sum(axis=1)
 
     # Declared shared random variable for positions
     position = rng.randn(batchsize, dim).astype(theano.config.floatX)
@@ -29,11 +31,12 @@ def gaussian_energy(x):
             initial_stepsize=1e-3, stepsize_max=0.5)
 
     # Start with a burn-in process
-    garbage = [sampler.draw() for r in xrange(burnin)] #burn-in
-    # Draw `n_samples`: result is a 3D tensor of dim [n_samples, batchsize, dim]
+    garbage = [sampler.draw() for r in xrange(burnin)]  # burn-in Draw
+    # `n_samples`: result is a 3D tensor of dim [n_samples, batchsize,
+    # dim]
     _samples = numpy.asarray([sampler.draw() for r in xrange(n_samples)])
     # Flatten to [n_samples * batchsize, dim]
-    samples = _samples.T.reshape(dim,-1).T
+    samples = _samples.T.reshape(dim, -1).T
 
     print '****** TARGET VALUES ******'
     print 'target mean:', mu
@@ -49,10 +52,11 @@ def gaussian_energy(x):
 
     return sampler
 
+
 def test_hmc():
     sampler = sampler_on_nd_gaussian(HMC_sampler.new_from_shared_positions,
             burnin=1000, n_samples=1000, dim=5)
-    assert abs(sampler.avg_acceptance_rate - sampler.target_acceptance_rate) < .1
+    assert abs(sampler.avg_acceptance_rate -
+               sampler.target_acceptance_rate) < .1
     assert sampler.stepsize.get_value() >= sampler.stepsize_min
     assert sampler.stepsize.get_value() <= sampler.stepsize_max
-
