comparison doc/v2_planning/learn_meeting.py @ 1087:8c448829db30

learning committee first draft of an api
author Razvan Pascanu <r.pascanu@gmail.com>
date Sat, 11 Sep 2010 20:33:34 -0400
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1086:65ac0f493830 1087:8c448829db30
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3 def bagging(learner_factory):
4 for i in range(N):
5 learner_i = learner_factory.new()
6 # todo: get dataset_i ??
7 learner_i.use_dataset(dataset_i)
8 learner_i.train()
9 '''
10 List of tasks types:
11 Attributes
12
13 sequential
14 spatial
15 structured
16 semi-supervised
17 missing-values
18
19
20 Supervised (x,y)
21
22 classification
23 regression
24 probabilistic classification
25 ranking
26 conditional density estimation
27 collaborative filtering
28 ordinal regression ?= ranking
29
30 Unsupervised (x)
31
32 de-noising
33 feature learning ( transformation ) PCA, DAA
34 density estimation
35 inference
36
37 Other
38
39 generation (sampling)
40 structure learning ???
41
42
43 Notes on metrics & statistics:
44 - some are applied to an example, others on a batch
45 - most statistics are on the dataset
46 '''
47 class Learner(Object):
48
49 #def use_dataset(dataset)
50
51 # return a dictionary of hyperparameters names(keys)
52 # and value(values)
53 def get_hyper_parameters()
54 def set_hyper_parameters(dictionary)
55
56
57
58
59 # Ver B
60 def eval(dataset)
61 def predict(dataset)
62
63 # Trainable
64 def train(dataset) # train until complition
65
66 # Incremental
67 def use_dataset(dataset)
68 def adapt(n_steps =1)
69 def has_converged()
70
71 #
72
73 class HyperLearner(Learner):
74
75 ### def get_hyper_parameter_distribution(name)
76 def set_hyper_parameters_distribution(dictionary)