# HG changeset patch
# User Yoshua Bengio <bengioy@iro.umontreal.ca>
# Date 1210118083 14400
# Node ID c4916445e0258bd897c7d7d736b73f7e24f2b0bc
# Parent  05cfe011ca201921ba780e43df1fb2a62bfd34e9
Comments from Pascal V.

diff -r 05cfe011ca20 -r c4916445e025 learner.py
--- a/learner.py	Tue May 06 10:53:28 2008 -0400
+++ b/learner.py	Tue May 06 19:54:43 2008 -0400
@@ -61,6 +61,8 @@
         """
         A Learner may have attributes that it wishes to export to other objects. To automate
         such export, sub-classes should define here the names (list of strings) of these attributes.
+
+        @todo By default, attributeNames looks for all dictionary entries whose name does not start with _.
         """
         return []
 
@@ -68,7 +70,7 @@
     """
     TLearner is a virtual class of Learners that attempts to factor out of the definition
     of a learner the steps that are common to many implementations of learning algorithms,
-    so as to leave only "the equations" to define in particular sub-classes, using Theano.
+    so as to leave only 'the equations' to define in particular sub-classes, using Theano.
 
     In the default implementations of use and update, it is assumed that the 'use' and 'update' methods
     visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done,
@@ -85,14 +87,6 @@
                           or by a stats collector.
       - defaultOutputFields(input_fields): return a list of default dataset output fields when
                           None are provided by the caller of use.
-      - update_start(), update_end(), update_minibatch(minibatch): functions
-                          executed at the beginning, the end, and in the middle
-                          (for each minibatch) of the update method. This model only
-                          works for 'online' or one-short learning that requires
-                          going only once through the training data. For more complicated
-                          models, more specialized subclasses of TLearner should be used
-                          or a learning-algorithm specific update method should be defined.
-
     The following naming convention is assumed and important.
     Attributes whose names are listed in attributeNames() can be of any type,
     but those that can be referenced as input/output dataset fields or as
@@ -102,6 +96,9 @@
     the TLearner, created in the sub-class constructor) should be _<name>.
     Typically <name> will be numpy ndarray and _<name> will be the corresponding
     Theano Tensor (for symbolic manipulation).
+
+    @todo pousser dans Learner toute la poutine qui peut l'etre sans etre
+    dependant de Theano
     """
 
     def __init__(self):
@@ -148,10 +145,14 @@
             else:
                 return [self.__getattr__(name).data for name in names]
 
-    def use(self,input_dataset,output_fieldnames=None,output_attributes=None,
+    def use(self,input_dataset,output_fieldnames=None,output_attributes=[],
             test_stats_collector=None,copy_inputs=True):
         """
-        The learner tries to compute in the output dataset the output fields specified 
+        The learner tries to compute in the output dataset the output fields specified
+
+        @todo check if some of the learner attributes are actually SPECIFIED
+        as attributes of the input_dataset, and if so use their values instead
+        of the ones in the learner.
         """
         minibatchwise_use_function = _minibatchwise_use_functions(input_dataset.fieldNames(),
                                                                   output_fieldnames,
@@ -165,6 +166,8 @@
         if copy_inputs:
             output_dataset = input_dataset | output_dataset
         # copy the wanted attributes in the dataset
+        if output_attributes is None:
+            output_attributes = self.attributeNames()
         if output_attributes:
             assert set(output_attributes) <= set(self.attributeNames())
             output_dataset.setAttributes(output_attributes,
@@ -175,21 +178,46 @@
                                          test_stats_collector.attributes())
         return output_dataset
 
+
+class OneShotTLearner(TLearner):
+    """
+    This adds to TLearner a 
+      - update_start(), update_end(), update_minibatch(minibatch), end_epoch():
+                          functions executed at the beginning, the end, in the middle
+                          (for each minibatch) of the update method, and at the end
+                          of each epoch. This model only
+                          works for 'online' or one-shot learning that requires
+                          going only once through the training data. For more complicated
+                          models, more specialized subclasses of TLearner should be used
+                          or a learning-algorithm specific update method should be defined.
+    """
+
+    def __init__(self):
+        TLearner.__init__(self)
+        
     def update_start(self): pass
     def update_end(self): pass
     def update_minibatch(self,minibatch):
         raise AbstractFunction()
     
     def update(self,training_set,train_stats_collector=None):
-        
+        """
+        @todo check if some of the learner attributes are actually SPECIFIED
+        in as attributes of the training_set.
+        """
         self.update_start()
-        for minibatch in training_set.minibatches(self.training_set_input_fields,
-                                                  minibatch_size=self.minibatch_size):
-            self.update_minibatch(minibatch)
+        stop=False
+        while not stop:
             if train_stats_collector:
-                minibatch_set = minibatch.examples()
-                minibatch_set.setAttributes(self.attributeNames(),self.attributes())
-                train_stats_collector.update(minibatch_set)
+                train_stats_collector.forget() # restart stats collectin at the beginning of each epoch
+            for minibatch in training_set.minibatches(self.training_set_input_fields,
+                                                      minibatch_size=self.minibatch_size):
+                self.update_minibatch(minibatch)
+                if train_stats_collector:
+                    minibatch_set = minibatch.examples()
+                    minibatch_set.setAttributes(self.attributeNames(),self.attributes())
+                    train_stats_collector.update(minibatch_set)
+            stop = self.end_epoch()
         self.update_end()
         return self.use
 
diff -r 05cfe011ca20 -r c4916445e025 linear_regression.py
--- a/linear_regression.py	Tue May 06 10:53:28 2008 -0400
+++ b/linear_regression.py	Tue May 06 19:54:43 2008 -0400
@@ -6,7 +6,7 @@
 
 # this is one of the simplest example of learner, and illustrates
 # the use of theano 
-class LinearRegression(Learner):
+class LinearRegression(OneShotTLearner):
     """
     Implement linear regression, with or without L2 regularization
     (the former is called Ridge Regression and the latter Ordinary Least Squares).
@@ -48,8 +48,6 @@
        - 'output' (optionally produced by use as an output dataset field)
        - 'squared_error' (optionally produced by use as an output dataset field, needs 'target') = example-wise squared error
 
-     - optional input attributes (optionally expected as input_dataset attributes)
-
      - optional attributes (optionally expected as input_dataset attributes)
        (warning, this may be dangerous, the 'use' method will use those provided in the 
        input_dataset rather than those learned during 'update'; currently no support
@@ -59,28 +57,29 @@
        - 'b' 
        - 'W' 
        - 'regularization_term' 
-       - 'XtX'
-       - 'XtY'
+
     """
 
     def attributeNames(self):
-        return ["lambda","b","W","regularization_term","XtX","XtY"]
+        return ["lambda","b","W","regularization_term"]
     
-# definitions specifiques a la regression lineaire:
 
-
-    def global_inputs(self):
+    def __init__(self):
+        self.input = t.matrix('input') # n_examples x n_inputs
+        self.target = t.matrix('target') # n_examples x n_outputs
         self.lambda = as_scalar(0.,'lambda')
         self.theta = t.matrix('theta')
         self.W = self.theta[:,1:] 
         self.b = self.theta[:,0]
         self.XtX = t.matrix('XtX')
         self.XtY = t.matrix('XtY')
-
-    def global_outputs(self):
         self.regularizer = self.lambda * t.dot(self.W,self.W)
+        self.squared_error = 
         self.loss = self.regularizer + t.sum(self.squared_error) # this only makes sense if the whole training set fits in memory in a minibatch
         self.loss_function = Function([self.W,self.lambda,self.squared_error],[self.loss])
+        self.new_XtX = self.XtX + t.dot(self.extended_input.T,self.extended_input)
+        self.new_XtY = self.XtY + t.dot(self.extended_input.T,self.target)
+        self.new_theta = t.solve(self.XtX,self.XtY)
 
     def initialize(self):
         self.XtX.resize((1+self.n_inputs,1+self.n_inputs))
@@ -90,14 +89,8 @@
         numpy.diag(self.XtX.data)[1:]=self.lambda.data
         
     def updated_variables(self):
-        self.new_XtX = self.XtX + t.dot(self.extended_input.T,self.extended_input)
-        self.new_XtY = self.XtY + t.dot(self.extended_input.T,self.target)
-        self.new_theta = t.solve(self.XtX,self.XtY)
     
     def minibatch_wise_inputs(self):
-        self.input = t.matrix('input') # n_examples x n_inputs
-        self.target = t.matrix('target') # n_examples x n_outputs
-        
     def minibatch_wise_outputs(self):
         # self.input is a (n_examples, n_inputs) minibatch matrix
         self.extended_input = t.prepend_one_to_each_row(self.input)