ndarray_linalg/lobpcg/
svd.rs

///! Truncated singular value decomposition
///!
///! This module computes the k largest/smallest singular values/vectors for a dense matrix.
use super::lobpcg::{lobpcg, LobpcgResult, Order};
use crate::error::Result;
use crate::generate;
use cauchy::Scalar;
use lax::Lapack;
use ndarray::prelude::*;
use ndarray::ScalarOperand;
use num_traits::{Float, NumCast};
use std::ops::DivAssign;

/// The result of a eigenvalue decomposition, not yet transformed into singular values/vectors
///
/// Provides methods for either calculating just the singular values with reduced cost or the
/// vectors with additional cost of matrix multiplication.
#[derive(Debug)]
pub struct TruncatedSvdResult<A> {
    eigvals: Array1<A>,
    eigvecs: Array2<A>,
    problem: Array2<A>,
    ngm: bool,
}

impl<A: Float + PartialOrd + DivAssign<A> + 'static + MagnitudeCorrection> TruncatedSvdResult<A> {
    /// Returns singular values ordered by magnitude with indices.
    fn singular_values_with_indices(&self) -> (Array1<A>, Vec<usize>) {
        // numerate eigenvalues
        let mut a = self.eigvals.iter().enumerate().collect::<Vec<_>>();

        // sort by magnitude
        a.sort_by(|(_, x), (_, y)| x.partial_cmp(y).unwrap().reverse());

        // calculate cut-off magnitude (borrowed from scipy)
        let cutoff = A::epsilon() * // float precision
                     A::correction() * // correction term (see trait below)
                     *a[0].1; // max eigenvalue

        // filter low singular values away
        let (values, indices): (Vec<A>, Vec<usize>) = a
            .into_iter()
            .filter(|(_, x)| *x > &cutoff)
            .map(|(a, b)| (b.sqrt(), a))
            .unzip();

        (Array1::from(values), indices)
    }

    /// Returns singular values ordered by magnitude
    pub fn values(&self) -> Array1<A> {
        let (values, _) = self.singular_values_with_indices();

        values
    }

    /// Returns singular values, left-singular vectors and right-singular vectors
    pub fn values_vectors(&self) -> (Array2<A>, Array1<A>, Array2<A>) {
        let (values, indices) = self.singular_values_with_indices();

        // branch n > m (for A is [n x m])
        let (u, v) = if self.ngm {
            let vlarge = self.eigvecs.select(Axis(1), &indices);
            let mut ularge = self.problem.dot(&vlarge);

            ularge
                .columns_mut()
                .into_iter()
                .zip(values.iter())
                .for_each(|(mut a, b)| a.mapv_inplace(|x| x / *b));

            (ularge, vlarge)
        } else {
            let ularge = self.eigvecs.select(Axis(1), &indices);

            let mut vlarge = self.problem.t().dot(&ularge);
            vlarge
                .columns_mut()
                .into_iter()
                .zip(values.iter())
                .for_each(|(mut a, b)| a.mapv_inplace(|x| x / *b));

            (ularge, vlarge)
        };

        (u, values, v.reversed_axes())
    }
}

/// Truncated singular value decomposition
///
/// Wraps the LOBPCG algorithm and provides convenient builder-pattern access to
/// parameter like maximal iteration, precision and constraint matrix.
pub struct TruncatedSvd<A: Scalar> {
    order: Order,
    problem: Array2<A>,
    precision: f32,
    maxiter: usize,
}

impl<A: Float + Scalar + ScalarOperand + Lapack + PartialOrd + Default> TruncatedSvd<A> {
    pub fn new(problem: Array2<A>, order: Order) -> TruncatedSvd<A> {
        TruncatedSvd {
            precision: 1e-5,
            maxiter: problem.len_of(Axis(0)) * 2,
            order,
            problem,
        }
    }

    pub fn precision(mut self, precision: f32) -> Self {
        self.precision = precision;

        self
    }

    pub fn maxiter(mut self, maxiter: usize) -> Self {
        self.maxiter = maxiter;

        self
    }

    // calculate the eigenvalue decomposition
    pub fn decompose(self, num: usize) -> Result<TruncatedSvdResult<A>> {
        if num < 1 {
            panic!("The number of singular values to compute should be larger than zero!");
        }

        let (n, m) = (self.problem.nrows(), self.problem.ncols());

        // generate initial matrix
        let x: Array2<f32> = generate::random((usize::min(n, m), num));
        let x = x.mapv(|x| NumCast::from(x).unwrap());

        // square precision because the SVD squares the eigenvalue as well
        let precision = self.precision * self.precision;

        // use problem definition with less operations required
        let res = if n > m {
            lobpcg(
                |y| self.problem.t().dot(&self.problem.dot(&y)),
                x,
                |_| {},
                None,
                precision,
                self.maxiter,
                self.order.clone(),
            )
        } else {
            lobpcg(
                |y| self.problem.dot(&self.problem.t().dot(&y)),
                x,
                |_| {},
                None,
                precision,
                self.maxiter,
                self.order.clone(),
            )
        };

        // convert into TruncatedSvdResult
        match res {
            LobpcgResult::Ok(vals, vecs, _) | LobpcgResult::Err(vals, vecs, _, _) => {
                Ok(TruncatedSvdResult {
                    problem: self.problem,
                    eigvals: vals,
                    eigvecs: vecs,
                    ngm: n > m,
                })
            }
            LobpcgResult::NoResult(err) => Err(err),
        }
    }
}

pub trait MagnitudeCorrection {
    fn correction() -> Self;
}

impl MagnitudeCorrection for f32 {
    fn correction() -> Self {
        1.0e3
    }
}

impl MagnitudeCorrection for f64 {
    fn correction() -> Self {
        1.0e6
    }
}

#[cfg(test)]
mod tests {
    use super::Order;
    use super::TruncatedSvd;
    use crate::{close_l2, generate};

    use ndarray::{arr1, arr2, Array2};

    #[test]
    fn test_truncated_svd() {
        let a = arr2(&[[3., 2., 2.], [2., 3., -2.]]);

        let res = TruncatedSvd::new(a, Order::Largest)
            .precision(1e-5)
            .maxiter(10)
            .decompose(2)
            .unwrap();

        let (_, sigma, _) = res.values_vectors();

        close_l2(&sigma, &arr1(&[5.0, 3.0]), 1e-5);
    }

    #[test]
    fn test_truncated_svd_random() {
        let mut rng = rand_pcg::Mcg128Xsl64::new(0xcafef00dd15ea5e5);
        let a: Array2<f64> = generate::random_using((50, 10), &mut rng);

        let res = TruncatedSvd::new(a.clone(), Order::Largest)
            .precision(1e-5)
            .maxiter(10)
            .decompose(10)
            .unwrap();

        let (u, sigma, v_t) = res.values_vectors();
        let reconstructed = u.dot(&Array2::from_diag(&sigma).dot(&v_t));

        close_l2(&a, &reconstructed, 1e-5);
    }
}
ndarray_linalg/lobpcg/svd.rs

ndarray_linalg/lobpcg/
svd.rs
