# HG changeset patch
# User Razvan Pascanu <r.pascanu@gmail.com>
# Date 1284251614 14400
# Node ID 8c448829db3040ee02a91932460270a1f23291e9
# Parent  65ac0f493830568d48182df51b5d01da5176bde3
learning committee first draft of an api

diff -r 65ac0f493830 -r 8c448829db30 doc/v2_planning/learn_meeting.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/doc/v2_planning/learn_meeting.py	Sat Sep 11 20:33:34 2010 -0400
@@ -0,0 +1,76 @@
+
+
+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
+
+   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):
+    
+    #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()
+
+    # 
+
+class HyperLearner(Learner):
+
+    ### def get_hyper_parameter_distribution(name)
+    def set_hyper_parameters_distribution(dictionary)