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Open learning is a string pattern matcher than can learn to find regularities into strings. To use this you will need a dataset of "normal strings" like:


Apple Tree
Banana Split
Pen Pineapple Apple Pen
Cury Rice
French fries

And a dataset that contains the thing you want to search:


Apple pie
Banana cake
Rice cake
Pineapple pie
Strawberries tart

And you will get a train dataset:

$ open_learning learn


["apple pie", " cake"]

And now we can test with an example:

$ open_learning test "cheese cake"
   Reading Done in 0ms
   Correspond true

As you can see it correspond with the model.

$ open_learning test "Apple juice"
   Reading Done in 0ms
   Correspond false

It work's fine.

Command usage

$ open_learning learn [-d DATASET-FILE] [-n NORMAL-FILE] [-o MODEL-OUTPUT-FILE]

$ open_learning test [-i INPUT-FILE] <INPUT-STRING>


$ open_learning learn -d "dataset.txt" -n "normal.txt" -o "model.txt"

$ open_learning test -i "model.txt" "Apple pen"

Integration into programs

OpenLearningAPI - A rust library to use the trained model

Why rust ?

I have chosen rust for four reasons: performance, safety, ecosystem, concurrency

Download executables

Linux executable: 0.1.0beta

Compile from source

Download the OpenLearning folder and run into it:

$ cargo build --release

Rust is needed: Download rust

Futures features (in progress)

Console colorized Learner [DONE] Linear String matching [DONE] Positional String matching [TO DO] Patern string matching [TO DO] Semantic matching [TO DO]

Error Percentage Calculator [TO DO] Valid Percentage Calculator [TO DO] More validity and more error option [TO DO]

More examples [TO DO]

Rust Font-End [DONE] JS Font-End [TO DO] Python Font-End [TO DO] Java Font-End [TO DO]

Specify specification [TO DO]

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