Dendritic Preprocessing Crate

This crate contains functionality for performing normalization of data during the preprocessing stage for a model. Contains preprocessing for encoding and standard scaling.

Features

• Standard Scalar: Standard scalar and min max normlization of data.
• Encoding: One hot encoding for multi class data

Getting Started

To get started, add this to your Cargo.toml:

[dependencies]
dendritic-preprocessing = "0.1"

Example Usage

This is an example of using the one hot encoder for data with multiple class labels

use dendritic_ndarray::ndarray::NDArray;
use dendritic_preprocessing::encoding::{OneHotEncoding};

fn main() {

// Data to one hot encode for multi class classification
let x = NDArray::array(vec![10, 1], vec![
1.0,2.0,0.0,2.0,0.0,
0.0,1.0,0.0,2.0,2.0
]).unwrap();

let mut encoder = OneHotEncoding::new(x).unwrap();
println!("Max Value: {:?}", encoder.max_value()); // 3.0
println!("Num Samples: {:?}", encoder.num_samples()); // 10.0 

let encoded_vals = encoder.transform();
println!("Encoded Values: {:?}", encoded_vals); 

}

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.