--- a/linear_regression.py	Mon Aug 04 16:21:59 2008 -0400
+++ b/linear_regression.py	Mon Aug 04 16:29:30 2008 -0400
@@ -4,9 +4,9 @@
 the use of theano.
 """
 
-from pylearn.learner import OfflineLearningAlgorithm
+from pylearn.learner import OfflineLearningAlgorithm,OnlineLearningAlgorithm
 from theano import tensor as T
-from theano.others_ops import prepend_1_to_each_row
+from nnet_ops import prepend_1_to_each_row
 from theano.scalar import as_scalar
 from common.autoname import AutoName
 import theano
@@ -34,11 +34,6 @@
     we want to compute the squared errors.
 
     The predictor parameters are obtained analytically from the training set.
-    Training can proceed sequentially (with multiple calls to update with
-    different disjoint subsets of the training sets). After each call to
-    update the predictor is ready to be used (and optimized for the union
-    of all the training sets passed to update since construction or since
-    the last call to forget).
 
     For each (input[t],output[t]) pair in a minibatch,::
     
@@ -74,7 +69,7 @@
     def __init__(self, L2_regularizer=0,minibatch_size=10000):
         self.L2_regularizer=L2_regularizer
         self.equations = LinearRegressionEquations()
-        self.minibatch_size=1000
+        self.minibatch_size=minibatch_size
 
     def __call__(self,trainset):
         first_example = trainset[0]
@@ -186,3 +181,21 @@
             return ds
         
 
+def linear_predictor(inputs,params,*otherargs):
+  p = LinearPredictor(params)
+  return p.compute_outputs(inputs)
+
+#TODO : an online version
+class OnlineLinearRegression(OnlineLearningAlgorithm):
+    """
+    Training can proceed sequentially (with multiple calls to update with
+    different disjoint subsets of the training sets). After each call to
+    update the predictor is ready to be used (and optimized for the union
+    of all the training sets passed to update since construction or since
+    the last call to forget).
+    """
+    pass
+
+
+
+