# HG changeset patch
# User Pascal Lamblin <lamblinp@iro.umontreal.ca>
# Date 1284748180 14400
# Node ID 77b6ed85d3f787cb700dfed1e789f879839b79bb
# Parent  7a8dcf87d78018c23f24159f39b990feaebc3931
Update doc of learner's API

diff -r 7a8dcf87d780 -r 77b6ed85d3f7 doc/v2_planning/API_learner.txt
--- a/doc/v2_planning/API_learner.txt	Fri Sep 17 13:57:46 2010 -0400
+++ b/doc/v2_planning/API_learner.txt	Fri Sep 17 14:29:40 2010 -0400
@@ -1,11 +1,5 @@
-
+# A list of "task types"
 
-def bagging(learner_factory):
-    for i in range(N):
-        learner_i = learner_factory.new()
-        # todo: get dataset_i ??
-        learner_i.use_dataset(dataset_i)
-        learner_i.train()
 '''
  List of tasks types:
   Attributes
@@ -44,8 +38,22 @@
    - some are applied to an example, others on a batch
    - most statistics are on the dataset
 '''
+
+
 class Learner(Object):
-    
+    '''
+    Takes data as inputs, and learns a prediction function (or several).
+
+    A learner is parametrized by hyper-parameters, which can be set from the
+    outside (a "client" from Learner, that can be a HyperLearner, a
+    Tester,...).
+
+    The data can be given all at a time as a data set, or incrementally.
+    Some learner need to be fully trained in one step, whereas other can be
+    trained incrementally.
+
+    The question of statistics collection during training remains open.
+    '''
     #def use_dataset(dataset)
 
     # return a dictionary of hyperparameters names(keys)
@@ -70,7 +78,18 @@
 
     # 
 
+
+# Some example cases
+
 class HyperLearner(Learner):
 
     ### def get_hyper_parameter_distribution(name)
     def set_hyper_parameters_distribution(dictionary)
+
+
+def bagging(learner_factory):
+    for i in range(N):
+        learner_i = learner_factory.new()
+        # todo: get dataset_i ??
+        learner_i.use_dataset(dataset_i)
+        learner_i.train()