pylearn: doc/v2_planning/API_learner.txt comparison

Rename learn_meeting.py to API_learner.txt

author	Pascal Lamblin <lamblinp@iro.umontreal.ca>
date	Fri, 17 Sep 2010 13:57:46 -0400
parents	doc/v2_planning/learn_meeting.py@8c448829db30
children	77b6ed85d3f7

comparison

equal deleted inserted replaced

-:ec1e93663656
+:7a8dcf87d780
+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)

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