# Dendritic Metrics Crate

 This crate contains metrics for measuring loss, accuracy of general ML models available for dendritic.
 Metrics contain loss and activiation functions.

 ## Features
 - **Activations**: Activation functions for non linear data.
 - **Loss**: Loss functions for measuring accuracy of classifiers/regressors

 ## 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 some of the loss and activation functions dendritic has to offer
 ```rust
 use dendritic_ndarray::ndarray::NDArray;
 use dendritic_ndarray::ops::*;
 use dendritic_metrics::activations::*; 
 use dendritic_metrics::loss::*; 
 
 fn main() {

     // Mocked Prediction values
     let y_pred: NDArray<f64> = NDArray::array(
         vec![10, 1],
         vec![
             0.0, 0.0, 1.0, 0.0, 1.0,
             1.0, 1.0, 1.0, 1.0, 1.0
         ]
      ).unwrap();

      // Mocked true values
      let y_true: NDArray<f64> = NDArray::array(
         vec![10, 1],
         vec![
             0.19, 0.33, 0.47, 0.7, 0.74,
             0.81, 0.86, 0.94, 0.97, 0.99
         ]
      ).unwrap();

      // Calculate binary cross entropy for predicted and true values
      let result = binary_cross_entropy(&y_true, &y_pred).unwrap();
      println!("{:?}", result); 

      // Input dataset to perform softmax activation
      let input: NDArray<f64> = NDArray::array(
         vec![3, 1],
         vec![1.0, 1.0, 1.0]
      ).unwrap();

      let sm_result = softmax_prime(input);
      println!("{:?}", sm_result.values()); 
 }
 ```