https://github.com/open-net-libraries/open.evaluation

This set of interfaces and classes can be used to create serializable functions for use with other systems that require a reproducable structure.
https://github.com/open-net-libraries/open.evaluation

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This set of interfaces and classes can be used to create serializable functions for use with other systems that require a reproducable structure.

• Host: GitHub
• URL: https://github.com/open-net-libraries/open.evaluation
• Owner: Open-NET-Libraries
• License: mit
• Created: 2017-01-23T03:25:38.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2024-11-22T05:52:14.000Z (2 months ago)
• Last Synced: 2024-12-22T20:48:00.150Z (29 days ago)
• Language: C#
• Size: 566 KB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# Open.Evaluation

Open.Evaluation is a framework for constructing and evaluating any hierarchal formula or complex structure.  Typically used for evaluating math or algorithms.

This set of interfaces and classes can be used to create serializable (human readable) functions for use with other systems that require a reproducable structure.

For example: Given a specific mathmatical formula (or function), it is possible to not only resolve/evaluate the result given a set of parameters, it is also possible to inspect the formula's structure and see how the values are being used.

## Evaluation Classes

(See Core/EvaluationBase.cs)

### Immutability 

The main idea here is to envorce immutable classes and related sub classes.

No changes allowed after construction.  A clone can only be created by 'recreating' or 'reconstructing'.

It is very important to maintain this since these classes should not change during operation.

### Unidirectional Referencing

To avoid potential memory problems, all heirarchies should be unidirectional: "The parent is aware of the children, but the children are unaware of the parent."

This allows for child sharing and has the potential to signficantly reduce the memory footprint.