# HG changeset patch
# User Frederic Bastien <nouiz@nouiz.org>
# Date 1315842387 14400
# Node ID b709f6b53b1776a6e0c2c686400d49bc3a21f64f
# Parent  b28e8730c94896c506f768f0db4987c8a21fdf04
auto fix white space.

diff -r b28e8730c948 -r b709f6b53b17 pylearn/algorithms/tests/test_mcRBM.py
--- a/pylearn/algorithms/tests/test_mcRBM.py	Mon Sep 12 11:45:56 2011 -0400
+++ b/pylearn/algorithms/tests/test_mcRBM.py	Mon Sep 12 11:46:27 2011 -0400
@@ -100,7 +100,7 @@
     trainer = trainer_alloc(
             rbm_alloc(n_I=n_vis),
             train_batch,
-            batchsize, 
+            batchsize,
             initial_lr_per_example=lr_per_example,
             l1_penalty=l1_penalty,
             l1_penalty_start=l1_penalty_start,
@@ -109,7 +109,7 @@
 
     if persistent_chains:
         grads = trainer.contrastive_grads()
-        learn_fn = theano.function([batch_idx], 
+        learn_fn = theano.function([batch_idx],
                 outputs=[grads[0].norm(2), grads[0].norm(2), grads[1].norm(2)],
                 updates=trainer.cd_updates())
     else:
@@ -170,7 +170,7 @@
 
             print 'l2(U)', l2(rbm.U.value),
             print 'l2(W)', l2(rbm.W.value),
-            print 'l1_penalty', 
+            print 'l1_penalty',
             try:
                 print trainer.effective_l1_penalty.value
             except:
@@ -183,7 +183,7 @@
             print 'c min max', rbm.c.value.min(), rbm.c.value.max()
 
             if persistent_chains:
-                print 'parts min', smplr.positions.value.min(), 
+                print 'parts min', smplr.positions.value.min(),
                 print 'max',smplr.positions.value.max(),
             print 'HMC step', smplr.stepsize.value,
             print 'arate', smplr.avg_acceptance_rate.value
@@ -231,7 +231,7 @@
     # Set up mcRBM Trainer
     # Initialize P using topological 3x3 overlapping patches thing
     # start learning P matrix after 2 passes through dataset
-    # 
+    #
     rbm_filename = 'mcRBM.rbm.%06i.pkl'%46000
     try:
         open(rbm_filename).close()
@@ -252,7 +252,7 @@
         trainer = mcRBMTrainer.alloc_for_P(
                 rbm=mcRBM_withP.alloc_topo_P(n_I=n_vis, n_J=81),
                 visible_batch=train_batch,
-                batchsize=batchsize, 
+                batchsize=batchsize,
                 initial_lr_per_example=0.05,
                 l1_penalty=1e-3,
                 l1_penalty_start=sys.maxint,
@@ -277,7 +277,7 @@
 
                     print 'l2(U)', l2(rbm.U.value),
                     print 'l2(W)', l2(rbm.W.value),
-                    print 'l1_penalty', 
+                    print 'l1_penalty',
                     try:
                         print trainer.effective_l1_penalty.value
                     except:
@@ -317,9 +317,9 @@
         feat_idx = tensor.lscalar()
         feat_idx_range = feat_idx * batchsize + tensor.arange(batchsize)
         train_batch_x, train_batch_y = pylearn.dataset_ops.cifar10.cifar10(
-                feat_idx_range, 
-                split='all', 
-                dtype='uint8', 
+                feat_idx_range,
+                split='all',
+                dtype='uint8',
                 rasterized=False,
                 color='rgb')
 
@@ -397,7 +397,7 @@
         #l1_regularization = float(sys.argv[1]) #1.e-3
         #l2_regularization = float(sys.argv[2]) #1.e-3*0
 
-        feature_logreg = LogisticRegression.new(x_i, 
+        feature_logreg = LogisticRegression.new(x_i,
                 n_in = 11025, n_out=10,
                 dtype=x_i.dtype)
 
@@ -407,7 +407,7 @@
         traincost = feature_logreg.nll(y_i).sum()
         traincost = traincost + abs(feature_logreg.w).sum() * l1_regularization
         #traincost = traincost + (feature_logreg.w**2).sum() * l2_regularization
-        train_logreg_fn = theano.function([x_i, y_i, lr], 
+        train_logreg_fn = theano.function([x_i, y_i, lr],
                 [feature_logreg.nll(y_i).mean(),
                     feature_logreg.errors(y_i).mean()],
                 updates=pylearn.gd.sgd.sgd_updates(
@@ -459,7 +459,7 @@
                     y_i = valid_labels[i*batchsize:(i+1)*batchsize]
 
                     #lr=0.0 -> no learning, safe for validation set
-                    nll, l01 = train_logreg_fn(preproc(x_i), y_i, 0.0) 
+                    nll, l01 = train_logreg_fn(preproc(x_i), y_i, 0.0)
                     nlls.append(nll)
                     l01s.append(l01)
                 print 'validate log_reg', numpy.mean(nlls), numpy.mean(l01s)
@@ -473,7 +473,7 @@
                 y_i = test_labels[i*batchsize:(i+1)*batchsize]
 
                 #lr=0.0 -> no learning, safe for validation set
-                nll, l01 = train_logreg_fn(preproc(x_i), y_i, 0.0) 
+                nll, l01 = train_logreg_fn(preproc(x_i), y_i, 0.0)
                 nlls.append(nll)
                 l01s.append(l01)
             print 'test log_reg', numpy.mean(nlls), numpy.mean(l01s)
@@ -495,7 +495,7 @@
 import pickle as cPickle
 #import cPickle
 if __name__ == '__main__':
-    if 0: 
+    if 0:
         #learning 16 x 16 pinwheel filters from official cifar patches (MAR)
         rbm,smplr = test_reproduce_ranzato_hinton_2010(
                 as_unittest=False,
@@ -524,4 +524,3 @@
         def checkpoint():
             return checkpoint
         run_classif_experiment(checkpoint=checkpoint)
-