use bhtsne;

 const N: usize = 150;         // Number of vectors to embed.
 const D: usize = 4;           // The dimensionality of the
                               // original space.
 const THETA: f32 = 0.5;       // Parameter used by the Barnes-Hut algorithm.
                               // Small values improve accuracy but increase complexity.

 const PERPLEXITY: f32 = 10.0; // Perplexity of the conditional distribution.
 const EPOCHS: usize = 2000;   // Number of fitting iterations.
 const NO_DIMS: u8 = 2;        // Dimensionality of the embedded space.
 
 // Loads the data from a csv file skipping the first row,
 // treating it as headers and skipping the 5th column,
 // treating it as a class label.
 // Do note that you can also switch to f64s for higher precision.
 let data: Vec<f32> = bhtsne::load_csv("iris.csv", true, Some(&[4]), |float| {
         float.parse().unwrap()
 })?;
 let samples: Vec<&[f32]> = data.chunks(D).collect();
 // Executes the Barnes-Hut approximation of the algorithm and writes the embedding to the
 // specified csv file.
 bhtsne::tSNE::new(&samples)
     .embedding_dim(NO_DIMS)
     .perplexity(PERPLEXITY)
     .epochs(EPOCHS)
     .barnes_hut(THETA, |sample_a, sample_b| {
             sample_a
             .iter()
             .zip(sample_b.iter())
             .map(|(a, b)| (a - b).powi(2))
             .sum::<f32>()
             .sqrt()
     })
     .write_csv("iris_embedding.csv")?;