easy-ml 1.10.0

Machine learning library providing matrices, named tensors, linear algebra and automatic differentiation aimed at being easy to use
Documentation 
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#![allow(non_snake_case)]

extern crate easy_ml;

/// A simple linear regression acceptance test / example.

#[cfg(test)]
mod linear_regression {
    use easy_ml::linear_algebra;
    use easy_ml::matrices::Matrix;

    #[test]
    fn linear_regression() {
        // First create some data to perform regression on
        let x = Matrix::column(vec![
            0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,
        ]);
        let y = Matrix::column(vec![
            1.0, 3.0, 3.5, 8.0, 9.0, 11.0, 13.0, 15.5, 17.5, 19.0, 21.0, 23.0, 25.0,
        ]);
        // Try to fit a line y = mx + c

        // We will compute w as a column vector with values corresponding to c then m

        // Define the design matrix consisting of 1 and each value for x in each row
        // by inserting a column of 1s
        let mut X = x.clone();
        X.insert_column(0, 1.0);
        println!("X = {}", &X);

        // w is given by inverse(XT*X) * (XT * y)
        #[rustfmt::skip]
        let w = linear_algebra::inverse::<f32>(&(X.transpose() * &X))
            .unwrap()
            * (X.transpose() * &y);
        let error = error_function(&w, &X, &y);
        println!("error {:?}", error);
        println!("w = {}", w);
        println!("y = {}\nprediction = {}", y, (&X * &w));
        assert!(error < 3.7);
        assert!(error > 3.5);
    }

    /*
     * A sum of squares error function. Xw computes each ith prediction as a column vector
     */
    fn error_function(w: &Matrix<f32>, X: &Matrix<f32>, y: &Matrix<f32>) -> f32 {
        let error = y - (X * w);
        (error.transpose() * error).scalar()
    }
}