diff options
Diffstat (limited to 'gi/posterior-regularisation/train_pr_parallel.py')
| -rw-r--r-- | gi/posterior-regularisation/train_pr_parallel.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/gi/posterior-regularisation/train_pr_parallel.py b/gi/posterior-regularisation/train_pr_parallel.py index d5df87b5..4de7f504 100644 --- a/gi/posterior-regularisation/train_pr_parallel.py +++ b/gi/posterior-regularisation/train_pr_parallel.py @@ -37,8 +37,6 @@ for line in sys.stdin: num_edges += 1 -print 'Read in', num_edges, 'edges and', len(types), 'word types' - # # Step 2: initialise the model parameters # @@ -53,6 +51,8 @@ local = sys.argv[2] == 'local' if len(sys.argv) >= 2: seed(int(sys.argv[3])) +print 'Read in', num_edges, 'edges', num_phrases, 'phrases', num_contexts, 'contexts and', len(types), 'word types' + def normalise(a): return a / float(sum(a)) @@ -131,7 +131,7 @@ class GlobalDualObjective: for j, (phrase, edges) in enumerate(edges_phrase_to_context): for i, (context, count) in enumerate(edges): for t in range(num_tags): - cons[j,t] -= ls[index,t] + cons[j,t] -= ls[index,t] * count index += 1 return cons.ravel() @@ -142,7 +142,7 @@ class GlobalDualObjective: for j, (phrase, edges) in enumerate(edges_phrase_to_context): for i, (context, count) in enumerate(edges): for t in range(num_tags): - gradient[j,t,index,t] -= 1 + gradient[j,t,index,t] -= count index += 1 return gradient.reshape((num_phrases*num_tags, num_edges*num_tags)) @@ -231,7 +231,7 @@ class LocalDualObjective: cons = ones(num_tags) * self.scale for t in range(num_tags): for i, (context, count) in enumerate(edges): - cons[t] -= ls[i,t] + cons[t] -= ls[i,t] * count return cons def constraints_gradient(self, ls): @@ -240,7 +240,7 @@ class LocalDualObjective: gradient = zeros((num_tags, len(edges), num_tags)) for t in range(num_tags): for i, (context, count) in enumerate(edges): - gradient[t,i,t] -= 1 + gradient[t,i,t] -= count return gradient.reshape((num_tags, len(edges)*num_tags)) def optimize(self): |