# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1283540731 14400
# Node ID d38cb039c6629d5881c3366232fdc7fe286b3319
# Parent  c24c98da5615b2c150f4628117ffa2ae66e62534
debugging mcRBM

diff -r c24c98da5615 -r d38cb039c662 pylearn/algorithms/mcRBM.py
--- a/pylearn/algorithms/mcRBM.py	Fri Sep 03 12:36:53 2010 -0400
+++ b/pylearn/algorithms/mcRBM.py	Fri Sep 03 15:05:31 2010 -0400
@@ -328,16 +328,22 @@
             rng = np.random.RandomState(rng)
         if n_visible is None:
             n_visible = self.n_visible_units()
-        rval = HMC_sampler(
-            positions = [shared(
+        rval = HMC_sampler.new_from_shared_positions(
+            shared_positions = shared(
                 rng.randn(
                     n_particles,
                     n_visible).astype(floatX),
-                name='particles')],
+                name='particles'),
             energy_fn=self.free_energy_given_v,
             seed=int(rng.randint(2**30)))
         return rval
 
+    def as_feedforward_layer(self, v):
+        return dict(
+                outputs = self.expected_h_g_given_v(v),
+                params = [self.U, self.W, self.b, self.c],
+                )
+
     @classmethod
     def alloc(cls, n_I, n_K, n_J, rng = 8923402190):
         """
@@ -383,13 +389,13 @@
         #TODO: write the docstring
         U_norms = TT.sqrt((new_U**2).sum(axis=0))
         new_normVF = .95 * self.normVF + .05 * TT.mean(U_norms)
-        return new_U * this_normVF / U_norms), new_normVF
+        return (new_U * this_normVF / U_norms), new_normVF
 
     def contrastive_grads(self, visible_batch, params=None):
         if params is not None:
             params = self.rbm.params
         return contrastive_grad(
-                free_energy_fn=rbm.free_energy_given_v,
+                free_energy_fn=self.rbm.free_energy_given_v,
                 pos_v=visible_batch, 
                 neg_v=self.sampler.positions,
                 params=params,
diff -r c24c98da5615 -r d38cb039c662 pylearn/sampling/hmc.py
--- a/pylearn/sampling/hmc.py	Fri Sep 03 12:36:53 2010 -0400
+++ b/pylearn/sampling/hmc.py	Fri Sep 03 15:05:31 2010 -0400
@@ -30,6 +30,7 @@
 """
 import sys
 import logging
+import numpy
 import numpy as np
 from theano import function, shared
 from theano import tensor as TT
@@ -183,7 +184,7 @@
         positions_shape = shared_positions.value.shape
         batchsize = shared_positions.value.shape[0]
 
-        stepsize = shared(initial_stepsize, 'hmc_stepsize')
+        stepsize = shared(numpy.asarray(initial_stepsize).astype(theano.config.floatX), 'hmc_stepsize')
         avg_acceptance_rate = shared(target_acceptance_rate, 'avg_acceptance_rate')
         s_rng = TT.shared_randomstreams.RandomStreams(seed)