Mercurial > pylearn
changeset 1076:20a1af112a75
dataset: Looked into datasets from some other ML libraries
| author | Olivier Delalleau <delallea@iro> |
|---|---|
| date | Fri, 10 Sep 2010 12:11:10 -0400 |
| parents | d422f726c156 |
| children | 5c14d2ffcbb3 |
| files | doc/v2_planning/dataset.txt |
| diffstat | 1 files changed, 10 insertions(+), 0 deletions(-) [+] |
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--- a/doc/v2_planning/dataset.txt Fri Sep 10 11:42:48 2010 -0400 +++ b/doc/v2_planning/dataset.txt Fri Sep 10 12:11:10 2010 -0400 @@ -24,6 +24,16 @@ - PyML: notion of dataset containers: VectorDataSet, SparseDataSet, KernelData, PairDataSet, Aggregate. Ultimately, the learner decides - mlpy: very primitive notions of data +- PyBrain: Datasets are geared towards specific tasks: ClassificationDataSet, + SequentialDataSet, ReinforcementDataSet, ... Each class is quite + constrained and may have a different interface. +- MDP: Seems to have restrictions on the type of data being passed around, as + well as its dimensionality ("Input array data is typically assumed to be + two-dimensional and ordered such that observations of the same variable are + stored on rows and different variables are stored on columns.") +- Orange: Data matrices, with names and types associated to each column. + Basically there seems to be only one base dataset class that contains the + data. Data points are lists (of values corresponding to each column). - (still going through the other ones) A few things that our dataset containers should support at a minimum: