# 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

 ## 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.

 ## Example Usage
 This is an example of using the one hot encoder for data with multiple class labels
 ```rust
 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); 

 }
 ```