Web: https://deepcausality.com
DeepCausality is a hyper-geometric computational causality library that enables fast and deterministic context-aware causal reasoning over complex multi-stage causality models. Deep Causality adds only minimal overhead and thus is suitable for real-time applications without additional acceleration hardware. Take a look at how is deep causality different from deep learning?
1) DeepCausality is written in Rust with production-grade safety, reliability, and performance in mind. 2) DeepCausality provides recursive causal data structures that concisely express arbitrary complex causal structures. 3) DeepCausality enables context awareness across data-like, time-like, space-like, spacetime-like entities stored within (multiple) context-hyper-graphs. 4) DeepCausality simplifies modeling of complex tempo-spatial patterns. 5) DeepCausality comes with Causal State Machine (CSM)
Just run:
bash
cargo add deep_causality
Alternatively, add the following to your Cargo.toml
toml
deep_causality = "0.2.7"
See:
A causal state machine models a context-free system where each cause maps to a known effect. The example below models a sensor network that screens an industry site for smoke, fire, and explosions. Because the sensors are reliable, an alert will be raised whenever the sensor exceeds a certain threshold. You could implement this kind of system in many different ways, but as the example shows, the causal state machine makes the system relatively easy to maintain. New sensors, for example, from a drone inspection, can be added and evaluated dynamically.
Cargo works as expected, but in addition to cargo, a makefile exists that abstracts over several additional tools you may have to install before all make commands work:
toml
make build Builds the code base incrementally (fast).
make bench Runs all benchmarks across all crates.
make check Checks the code base for security vulnerabilities.
make coverage Checks test coverage and generates a html report.
make example Runs the default example: Smoking.
make fix Auto-fixes linting issues as reported by cargo and clippy.
make test Runs all tests across all crates.
Contributions are welcomed especially related to documentation, example code, and fixes. If unsure where to start, open an issue and ask. For more significant code contributions, please run make test and make check locally before opening a PR.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in deep_causality by you, shall be licensed under the MIT license without additional terms or conditions.
For details:
The project took inspiration from several researchers and their projects in the field:
Parts of the implementation are inspired by:
Finally, inspiration, especially related to the hypergraph structure, was derived from reading the Quanta Magazine.
This project is licensed under the MIT license.
For details about security, please read the security policy.