2014 Goals For NuPIC
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New users of NuPIC should be able to build (or otherwise install) NuPIC with tests passing and sample apps running within 5 minutes. They should have sample examples of usage for them to either generate or convert data into NuPIC input and proper model parameters that produce appropriate predictions and/or anomaly detection within 6 hours.
- compelling sample use case(s) for NuPIC, with clear documentation and instructions for running
- input and output are clear
- tutorial walks users through setting up all components of the sample app, including input, model parameters, swarming, and analysis of output
- addition of a visual component to sample through an add-on feature for graphing?
- complete and automated API docs of all public interfaces
- for both python and C++ code
- from scratch tutorial (perhaps using a publicly available data stream?) on getting data into NuPIC through the OPF, getting predictions out, swarming over the data, etc.
- from scratch tutorial of the same process without using the OPF (?)
Concrete API with docs
- Firm V1.0 API surrounding "core" of NuPIC, which will not change for a long time
Easy install, easy build
- users can install NuPIC in their runtime environment
- users can build NuPIC in their development environment
Separation of concerns (core algos vs encoders vs classifiers vs OPF, etc.)
- compartmentalization of key areas of NuPIC
- users can easily see the different parts of NuPIC by browsing the file system
- language bindings for major runtimes
- ruby, jvm, python, .NET
Common Serialization Format
- allowing model states to be stored, transported, and restarted
- For at least Debian Linux, Red Hat Linux, and Mac OS X