2.1 KiB
Generic Distribution Library
This library provides one interface to generic distributions. These distributions can either be symbolic, point set (xy-coordinates of the shape), or sample set (arrays of random samples).
Different internal formats (symbolic, point set, sample set) allow for benefits and features. It's common for distributions to be converted into either point sets or sample sets to enable certain functions.
In addition to this interface, there's a second, generic function, for calling functions on this generic distribution type. This genericOperation standardizes the inputs and outputs for these various function calls. See it's run() function.
Performance is very important. Some operations can take a long time to run, and even then, be inaccurate. Because of this, we plan to have a lot of logging and stack tracing functionality eventually built in.
Diagram of Distribution Types
graph TD
A[Generic Distribution] -->B{Point Set}
A --> C{Sample Set}
A --> D{Symbolic}
B ---> continuous(Continuous)
B ---> discrete(Discrete)
B --> mixed(Mixed)
continuous -.-> XYshape(XYshape)
discrete -.-> XYshape(XYshape)
mixed -.-> continuous
mixed -.-> discrete
D --> Normal(Normal)
D --> Lognormal(Lognormal)
D --> Triangular(Triangular)
D --> Beta(Beta)
D --> Uniform(Uniform)
D --> Float(Float)
D --> Exponential(Exponential)
D --> Cauchy(Cauchy)
Diagram of Generic Distribution Types
Todo
- Lots of cleanup
- Simple test story
- Provide decent stack traces for key calls in GenericOperation. This could be very useful for debugging.
- Cleanup Sample Set library
- Add memoization for calculations
- Performance bechmarking reports
- Remove most of DistPlus, much of which is not needed anymore
- More functions for Sample Set, which is very minimal now
- Allow these functions to be run on web workers
- Refactor interpreter to use GenericDist. This might not be necessary, as the new reducer-inspired interpreter is integrated.