--- a/linear_regression.py	Tue Jul 15 12:57:21 2008 -0400
+++ b/linear_regression.py	Sat Jul 19 10:11:22 2008 -0400
@@ -34,12 +34,15 @@
     we want to compute the squared errors.
 
     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
@@ -74,7 +77,7 @@
     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]