Merge pull request #145 from jaberg/linalg_misc

theano/sandbox/linalg/ops.py

+import logging
+
+logger = logging.getLogger(__name__)
 import numpy
 
 from theano.gof import Op, Apply
@@ -57,6 +60,7 @@ def hints(variable):
 @local_optimizer([])
 def remove_hint_nodes(node):
     if is_hint_node(node):
+        # transfer hints from graph to Feature
         try:
             for k,v in node.op.hints:
                 node.env.hints_feature.add_hint(node.inputs[0], k, v)
@@ -95,7 +99,7 @@ class HintsFeature(object):
 
     """
     def add_hint(self, r, k, v):
-        print 'adding hint', r, k, v
+        logger.debug('adding hint; %s, %s, %s' % (r, k, v))
         self.hints[r][k] = v
 
     def ensure_init_r(self, r):
@@ -171,9 +175,8 @@ def is_positive(v):
         return True
     #TODO: how to handle this - a registry?
     #      infer_hints on Ops?
-    print 'is_positive', v
+    logger.debug('is_positive: %s' % str(v))
     if v.owner and v.owner.op == tensor.pow:
-        print 'try for pow', v, v.owner.inputs
         try:
             exponent = tensor.get_constant_value(v.owner.inputs[1])
         except TypeError:
@@ -250,7 +253,6 @@ def local_log_prod_sqr(node):
             # we cannot always make this substitution because
             # the prod might include negative terms
             p = x.owner.inputs[0]
-            print "AAA", p
 
             # p is the matrix we're reducing with prod
             if is_positive(p):
@@ -316,7 +318,7 @@ class Cholesky(Op):
             destr = 'destructive'
         else:
             destr = 'non-destructive'
-        return 'Cholesky{%s,%s}'% (lu,destr)
+        return 'Cholesky{%s,%s}' % (lu, destr)
     def make_node(self, x):
         x = as_tensor_variable(x)
         return Apply(self, [x], [x.type()])
@@ -378,7 +380,10 @@ class Solve(Op):
     def make_node(self, A, b):
         A = as_tensor_variable(A)
         b = as_tensor_variable(b)
-        return Apply(self, [A,b], [b.type()])
+        otype = tensor.tensor(
+                broadcastable=b.broadcastable,
+                dtype = (A*b).dtype)
+        return Apply(self, [A,b], [otype])
     def perform(self, node, inputs, output_storage):
         A, b = inputs
         #TODO: use the A_structure to go faster
@@ -461,6 +466,7 @@ def diag(x):
 
 class Det(Op):
     """matrix determinant
+
     TODO: move this op to another file that request scipy.
     """
     def make_node(self, x):