# HG changeset patch
# User Yoshua Bengio <bengioy@iro.umontreal.ca>
# Date 1216759482 14400
# Node ID fa4a5fee53ce7de880acfd9760524f71aac6dfa5
# Parent  d7611a3811f2be8297d442dbc0825312f9d92470
Showing the path to an online version of linear regressor.

diff -r d7611a3811f2 -r fa4a5fee53ce linear_regression.py
--- a/linear_regression.py	Tue Jul 22 15:20:25 2008 -0400
+++ b/linear_regression.py	Tue Jul 22 16:44:42 2008 -0400
@@ -35,14 +35,6 @@
 
     The predictor parameters are obtained analytically from the training set.
 
-    *** NOT IMPLEMENTED YET ***
-    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,::
     
        output_t = b + W * input_t
@@ -189,3 +181,17 @@
             return ds
         
 
+#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
+
+
+
+