--- a/linear_regression.py	Fri Nov 14 16:38:30 2008 -0500
+++ b/linear_regression.py	Fri Nov 14 16:39:59 2008 -0500
@@ -6,7 +6,7 @@
 
 from pylearn.learner import OfflineLearningAlgorithm,OnlineLearningAlgorithm
 from theano import tensor as T
-from nnet_ops import prepend_1_to_each_row
+from theano.tensor.nnet import prepend_1_to_each_row
 from theano.scalar import as_scalar
 from common.autoname import AutoName
 import theano
@@ -96,11 +96,11 @@
 
     __compiled = False
     @classmethod
-    def compile(cls,linker='c|py'):
+    def compile(cls, mode = "FAST_RUN"):
         if cls.__compiled:
             return
         def fn(input_vars,output_vars):
-            return staticmethod(theano.function(input_vars,output_vars, linker=linker))
+            return staticmethod(theano.function(input_vars, output_vars, mode=mode))
 
         cls.compute_outputs = fn([cls.inputs,cls.theta],[cls.outputs])
         cls.compute_errors = fn([cls.outputs,cls.targets],[cls.squared_errors])
@@ -115,17 +115,17 @@
     XtX = T.matrix() # (n_inputs+1) x (n_inputs+1)
     XtY = T.matrix() # (n_inputs+1) x n_outputs
     extended_input = prepend_1_to_each_row(P.inputs)
-    new_XtX = T.add_inplace(XtX,T.dot(extended_input.T,extended_input))
-    new_XtY = T.add_inplace(XtY,T.dot(extended_input.T,P.targets))
+    new_XtX = T.add(XtX,T.dot(extended_input.T,extended_input))
+    new_XtY = T.add(XtY,T.dot(extended_input.T,P.targets))
 
     __compiled = False
     
     @classmethod
-    def compile(cls,linker='c|py'):
+    def compile(cls, mode="FAST_RUN"):
         if cls.__compiled:
             return
         def fn(input_vars,output_vars):
-            return staticmethod(theano.function(input_vars,output_vars, linker=linker))
+            return staticmethod(theano.function(input_vars, output_vars, mode=mode))
 
         cls.update = fn([cls.XtX,cls.XtY,cls.P.inputs,cls.P.targets],[cls.new_XtX,cls.new_XtY])