https://github.com/mbari-org/ecoz2rs

ECOZ2 in Rust
https://github.com/mbari-org/ecoz2rs

hidden-markov-models hmm k-means linear-predictive-coding signal-recognition supervised-learning unsupervised-learning vector-quantization

Last synced: 3 months ago
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ECOZ2 in Rust

• Host: GitHub
• URL: https://github.com/mbari-org/ecoz2rs
• Owner: mbari-org
• Created: 2019-09-03T19:57:01.000Z (almost 6 years ago)
• Default Branch: main
• Last Pushed: 2025-04-04T23:46:13.000Z (3 months ago)
• Last Synced: 2025-04-13T12:13:02.142Z (3 months ago)
• Topics: hidden-markov-models, hmm, k-means, linear-predictive-coding, signal-recognition, supervised-learning, unsupervised-learning, vector-quantization
• Language: Rust
• Homepage:
• Size: 396 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: ChangeLog.md

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README

        
# ECOZ2 in Rust

This project is mainly a "front-end" to the original

[ecoz2](https://github.com/ecoz2/ecoz2) implementation in C,

with some functionality implemented in Rust.

## Installing and running

The `ecoz2` executable is currently being built and released for

Linux and MacOS, which you can find under

[releases](https://github.com/ecoz2/ecoz2rs/releases).

Alternatively, you can also install the executable using

[`Rust`](https://www.rust-lang.org/tools/install).

For this you will also need a GNU gcc compiler on your machine.

On Linux you can run:

    $ export CC=gcc

    $ cargo install ecoz2

and on a MacOS, something like:

    $ brew install gcc

    $ export CC=gcc-10

    $ cargo install ecoz2

This may take some time to complete (example of output

[here](https://gist.github.com/carueda/0b4ede3e0152d3d670b0a0f2fc7098ce)).

Running:

    $ ecoz2

    ...

    USAGE:

        ecoz2 

    

    FLAGS:

        -h, --help       Prints help information

        -V, --version    Prints version information

    

    SUBCOMMANDS:

        csv-show    Basic csv selection info

        cversion    Show version of C code

        help        Prints this message or the help of the given subcommand(s)

        hmm         HMM operations

        lpc         Linear prediction coding

        mm          MM operations

        nb          NBayes operations

        prd         Predictor file operations

        seq         Sequence file operations

        sgn         Signal operations

        util        Utilities

        vq          VQ operations

Starting with a set of acoustic signals (WAV format) on your machine,

the typical use of the system will involve the following main subcommands

in this general order:

- `ecoz2 lpc`:         takes `*.wav` and generates predictor files `*.prd`

- `ecoz2 vq learn`     takes `*.prd` and generates codebook files `*.cbook`

- `ecoz2 vq quantize`  takes `*.cbook` and `*.prd` and generates observation sequences `*.seq`

- `ecoz2 hmm learn`    takes `*.seq` and generates an HMM model `*.hmm`

- `ecoz2 hmm classify` takes `*.hmm` and `*.seq`, performs classification

  of the sequences and reports the results

## Development

[ecoz2](https://github.com/ecoz2/ecoz2) is included as a submodule,

with selected functionality exposed via

.

    $ export CC=gcc-10

    $ cargo build [--release]

    $ cargo install --path .