--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/doc/v2_planning/API_learner.txt	Fri Sep 17 16:13:58 2010 -0400
@@ -0,0 +1,95 @@
+# A list of "task types"
+
+'''
+ List of tasks types:
+  Attributes
+
+   sequential
+   spatial
+   structured
+   semi-supervised
+   missing-values
+
+
+  Supervised (x,y)
+
+   classification
+   regression
+   probabilistic classification
+   ranking
+   conditional density estimation
+   collaborative filtering
+   ordinal regression ?= ranking 
+
+  Unsupervised (x)
+
+   de-noising
+   feature learning ( transformation ) PCA, DAA
+   density estimation
+   inference
+
+  Other
+
+   generation (sampling)
+   structure learning ???
+
+
+Notes on metrics & statistics:
+   - 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)
+    # and value(values) 
+    def get_hyper_parameters()
+    def set_hyper_parameters(dictionary)
+
+
+    
+    
+    # Ver B
+    def eval(dataset)
+    def predict(dataset)
+
+    # Trainable
+    def train(dataset)   # train until complition
+
+    # Incremental
+    def use_dataset(dataset)
+    def adapt(n_steps =1)
+    def has_converged()
+
+    # 
+
+
+# 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()