You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: fast_ica.rs
    path of file: ./repos/linfa/algorithms/linfa-ica/examples
    
    the code of the file until where you have to start completion: use linfa::{
    dataset::DatasetBase,
    traits::{Fit, Predict},
};
use linfa_ica::fast_ica::{FastIca, GFunc};
use ndarray::{array, concatenate};
use ndarray::{Array, Array2, Axis};
use ndarray_npy::write_npy;
use ndarray_rand::{rand::SeedableRng, rand_distr::Uniform, RandomExt};
use rand_xoshiro::Xoshiro256Plus;
use std::error::Error;

fn main() -> Result<(), Box<dyn Error>> {
    // Create sample dataset for the model
    // `sources_original` has the unmixed sources (we merely have it to save to disk)
    // `sources_mixed` is the mixed source that will be unmixed using ICA
    // Shape of the data will be (2000 x 2)
    let (sources_original, sources_mixed) = create_data();

    // Fitting the model
    // We set the G function used in the approximation of neg-entropy as logcosh
    // with its alpha value as 1
    // `ncomponents` 
    
fn main() -> Result<(), Box<dyn Error>> {
    // Create sample dataset for the model
    // `sources_original` has the unmixed sources (we merely have it to save to disk)
    // `sources_mixed` is the mixed source that will be unmixed using ICA
    // Shape of the data will be (2000 x 2)
    let (sources_original, sources_mixed) = create_data();

    // Fitting the model
    // We set the G function used in the approximation of neg-entropy as logcosh
    // with its alpha value as 1
    // `ncomponents` is not set, it will be automatically be assigned 2 from
    // the input
    let ica = FastIca::params().gfunc(GFunc::Logcosh(1.0));
    let ica = ica.fit(&DatasetBase::from(sources_mixed.view()))?;

    // Here we unmix the data to recover back the original signals
    let sources_ica = ica.predict(&sources_mixed);

    // Saving to disk
    write_npy("sources_original.npy", &sources_original).expect("Failed to write .npy file");
    write_npy("sources_mixed.npy", &sources_mixed).expect("Failed to write .npy file");
    write_npy("sources_ica.npy", &sources_ica).expect("Failed to write .npy file");

    Ok(())
}