dendritic
| Crates.io | dendritic |
| lib.rs | dendritic |
| version | 1.5.0 |
| source | src |
| created_at | 2024-10-28 12:39:11.920739 |
| updated_at | 2024-11-01 19:42:25.974113 |
| description | Machine learning library for Rust |
| homepage | |
| repository | |
| max_upload_size | |
| id | 1425578 |
| size | 21,055 |
Shay (shaysingh818)
documentation
README
Dendritic
Dendrite is a general purpose supervised/un-supervised machine learning library written for the rust ecosystem. It contains the required data structures & algorithms needed for general machine learning. It acts as core library with packages for predictive data modeling.
Disclaimer
The dendritic project is a toy machine learning library built for learning and research purposes. It is not advised by the maintainer to use this library as a production ready machine learning library. This is a project that is still very much a work in progress.
Published Crates
| Rust Crate | Description |
|---|---|
| dendritic_ndarray | N Dimensional array library for numerical computing |
| dendritic_datasets | Variety of datasets for regression and classification tasks |
| dendritic_autodiff | Autodifferentiation crate for backward and forward operations |
| dendritic_metrics | Metrics package for measuring loss and activiation functions for non linear boundaries |
| dendritic_preprocessing | Preprocessing library for normalization and encoding of data |
| dendritic_bayes | Bayesian statistics package |
| dendritic_clustering | Clustering package utilizing various distance metrics |
| dendritic_knn | K Nearest Neighbors for regression and classification |
| dendritic_models | Pre-trained models for testing dendritic functionality |
| dendritic_regression | Regression package for linear modeling & multi class classification |
| dendritic_trees | Tree based models using decision trees and random forests |
Building The Dendritic Packages
Dendritic is made up of multiple indepedent packages that can be built separatley.
To install a package, add the following to your Cargo.toml file.
[dependencies]
dendritic = { version = "<LATEST_VERSION>", features = ["bundled"] }
Example IRIS Flowers Prediction
Down below is an example of using a multi class logstic regression model on the well known iris flowers dataset.
For more examples, refer to the dendritic-models/src/main.rs file.
use dendritic_datasets::iris::*;
use dendritic_regression::logistic::*;
use dendritic_metrics::loss::*;
use dendritic_metrics::activations::*;
use dendritic_preprocessing::encoding::*;
fn main() {
// load data
let data_path = "../../datasets/data/iris.parquet";
let (x_train, y_train) = load_iris(data_path).unwrap();
// encode the target variables
let mut encoder = OneHotEncoding::new(y_train.clone()).unwrap();
let y_train_encoded = encoder.transform();
// create logistic regression model
let mut log_model = MultiClassLogistic::new(
&x_train,
&y_train_encoded,
softmax,
0.1
).unwrap();
log_model.sgd(500, true, 5);
let sample_index = 100;
let x_test = x_train.batch(5).unwrap();
let y_test = y_train.batch(5).unwrap();
let y_pred = log_model.predict(x_test[sample_index].clone());
println!("Actual: {:?}", y_test[sample_index]);
println!("Prediction: {:?}", y_pred.values());
let loss = mse(&y_test[sample_index], &y_pred).unwrap();
println!("LOSS: {:?}", loss);
}