A Rust port of LAS2 from SVDLIBC
A library that computes an svd on a sparse matrix, typically a large sparse matrix
This is a functional port (mostly a translation) of the algorithm as implemented in Doug Rohde's SVDLIBC
This library performs singular value decomposition on a sparse input Matrix using the Lanczos algorithm and returns the decomposition as ndarray components.
Input: Sparse Matrix (CSR, CSC, or COO)
Output: decomposition U,S,V where U,V are Array2 and S is Array1, packaged in a Result<SvdRec, SvdLibError>
-
svd-- simply computes an SVD -
svd_dim-- computes an SVD supplying a desired numer ofdimensions -
svd_dim_seed-- computes an SVD supplying a desired numer ofdimensionsand a fixedseedto the LAS2 algorithm (the algorithm initializes with a random vector and will generate an internal seed if one isn't supplied)
use svdlibrs::svd;
// SVD on a Compressed Sparse Row matrix
let svd = svd(&csr)?;use svdlibrs::svd_dim;
// SVD on a Compressed Sparse Column matrix specifying the desired dimensions, 3 in this example
let svd = svd_dim(&csc, 3)?;use svdlibrs::svd_dim_seed;
// SVD on a Coordinate-form matrix requesting the
// dimensions and supplying a fixed seed to the LAS2 algorithm
let svd = svd_dim_seed(&coo, dimensions, 12345)?;pub struct SvdRec {
pub d: usize, // Dimensionality (rank), the number of rows of both ut, vt and the length of s
pub ut: Array2<f64>, // Transpose of left singular vectors, the vectors are the rows of ut
pub s: Array1<f64>, // Singular values (length d)
pub vt: Array2<f64>, // Transpose of right singular vectors, the vectors are the rows of vt
pub diagnostics: Diagnostics, // Computational diagnostics
}
pub struct Diagnostics {
pub non_zero: usize, // Number of non-zeros in the input matrix
pub dimensions: usize, // Number of dimensions attempted (bounded by matrix shape)
pub iterations: usize, // Number of iterations attempted (bounded by dimensions and matrix shape)
pub transposed: bool, // True if the matrix was transposed internally
pub lanczos_steps: usize, // Number of Lanczos steps performed
pub ritz_values_stabilized: usize, // Number of ritz values
pub significant_values: usize, // Number of significant values discovered
pub singular_values: usize, // Number of singular values returned
pub end_interval: [f64; 2], // Left, Right end of interval containing unwanted eigenvalues
pub kappa: f64, // Relative accuracy of ritz values acceptable as eigenvalues
pub random_seed: u32, // Random seed provided or the seed generated
}use svdlibrs::{svd, svd_dim, svd_dim_seed, svdLAS2, SvdRec};
let svd: SvdRec = svdLAS2(
&matrix, // sparse matrix (nalgebra_sparse::{csr,csc,coo}
dimensions, // upper limit of desired number of dimensions
// supplying 0 will use the input matrix shape to determine dimensions
iterations, // number of algorithm iterations
// supplying 0 will use the input matrix shape to determine iterations
end_interval, // left, right end of interval containing unwanted eigenvalues,
// typically small values centered around zero
/// set to [-1.0e-30, 1.0e-30] for convenience methods svd(), svd_dim(), svd_dim_seed()
kappa, // relative accuracy of ritz values acceptable as eigenvalues
/// set to 1.0e-6 for convenience methods svd(), svd_dim(), svd_dim_seed()
random_seed, // a supplied seed if > 0, otherwise an internal seed will be generated
)?;SVD using R
$ Rscript -e 'options(digits=12);m<-matrix(1:9,nrow=3)^2;print(m);r<-svd(m);print(r);r$u%*%diag(r$d)%*%t(r$v)'
• The input matrix: M
[,1] [,2] [,3]
[1,] 1 16 49
[2,] 4 25 64
[3,] 9 36 81
• The diagonal matrix (singular values): S
$d
[1] 123.676578742544 6.084527896514 0.287038004183
• The left singular vectors: U
$u
[,1] [,2] [,3]
[1,] -0.415206840886 -0.753443585619 -0.509829424976
[2,] -0.556377565194 -0.233080213641 0.797569820742
[3,] -0.719755016815 0.614814099788 -0.322422608499
• The right singular vectors: V
$v
[,1] [,2] [,3]
[1,] -0.0737286909592 0.632351847728 -0.771164846712
[2,] -0.3756889918995 0.698691000150 0.608842071210
[3,] -0.9238083467338 -0.334607272761 -0.186054055373
• Recreating the original input matrix: r$u %*% diag(r$d) %*% t(r$v)
[,1] [,2] [,3]
[1,] 1 16 49
[2,] 4 25 64
[3,] 9 36 81
• Cargo.toml dependencies
[dependencies]
svdlibrs = "0.5.1"
nalgebra-sparse = "0.9.0"
ndarray = "0.15.6"
extern crate ndarray;
use ndarray::prelude::*;
use nalgebra_sparse::{coo::CooMatrix, csc::CscMatrix};
use svdlibrs::svd_dim_seed;
fn main() {
// create a CscMatrix from a CooMatrix
// use the same matrix values as the R example above
// [,1] [,2] [,3]
// [1,] 1 16 49
// [2,] 4 25 64
// [3,] 9 36 81
let mut coo = CooMatrix::<f64>::new(3, 3);
coo.push(0, 0, 1.0); coo.push(0, 1, 16.0); coo.push(0, 2, 49.0);
coo.push(1, 0, 4.0); coo.push(1, 1, 25.0); coo.push(1, 2, 64.0);
coo.push(2, 0, 9.0); coo.push(2, 1, 36.0); coo.push(2, 2, 81.0);
// our input
let csc = CscMatrix::from(&coo);
// compute the svd
// 1. supply 0 as the dimension (requesting max)
// 2. supply a fixed seed so outputs are repeatable between runs
let svd = svd_dim_seed(&csc, 0, 3141).unwrap();
// svd.d dimensions were found by the algorithm
// svd.ut is a 2-d array holding the left vectors
// svd.vt is a 2-d array holding the right vectors
// svd.s is a 1-d array holding the singular values
// assert the shape of all results in terms of svd.d
assert_eq!(svd.d, 3);
assert_eq!(svd.d, svd.ut.nrows());
assert_eq!(svd.d, svd.s.dim());
assert_eq!(svd.d, svd.vt.nrows());
// show transposed output
println!("svd.d = {}\n", svd.d);
println!("U =\n{:#?}\n", svd.ut.t());
println!("S =\n{:#?}\n", svd.s);
println!("V =\n{:#?}\n", svd.vt.t());
// Note: svd.ut & svd.vt are returned in transposed form
// M = USV*
let m_approx = svd.ut.t().dot(&Array2::from_diag(&svd.s)).dot(&svd.vt);
assert_eq!(svd.recompose(), m_approx);
// assert computed values are an acceptable approximation
let epsilon = 1.0e-12;
assert!((m_approx[[0, 0]] - 1.0).abs() < epsilon);
assert!((m_approx[[0, 1]] - 16.0).abs() < epsilon);
assert!((m_approx[[0, 2]] - 49.0).abs() < epsilon);
assert!((m_approx[[1, 0]] - 4.0).abs() < epsilon);
assert!((m_approx[[1, 1]] - 25.0).abs() < epsilon);
assert!((m_approx[[1, 2]] - 64.0).abs() < epsilon);
assert!((m_approx[[2, 0]] - 9.0).abs() < epsilon);
assert!((m_approx[[2, 1]] - 36.0).abs() < epsilon);
assert!((m_approx[[2, 2]] - 81.0).abs() < epsilon);
assert!((svd.s[0] - 123.676578742544).abs() < epsilon);
assert!((svd.s[1] - 6.084527896514).abs() < epsilon);
assert!((svd.s[2] - 0.287038004183).abs() < epsilon);
}svd.d = 3
U =
[[-0.4152068408862081, -0.7534435856189199, -0.5098294249756481],
[-0.556377565193878, -0.23308021364108839, 0.7975698207417085],
[-0.719755016814907, 0.6148140997884891, -0.3224226084985998]], shape=[3, 3], strides=[1, 3], layout=Ff (0xa), const ndim=2
S =
[123.67657874254405, 6.084527896513759, 0.2870380041828973], shape=[3], strides=[1], layout=CFcf (0xf), const ndim=1
V =
[[-0.07372869095916511, 0.6323518477280158, -0.7711648467120451],
[-0.3756889918994792, 0.6986910001499903, 0.6088420712097343],
[-0.9238083467337805, -0.33460727276072516, -0.18605405537270261]], shape=[3, 3], strides=[1, 3], layout=Ff (0xa), const ndim=2
svd = Ok(
SvdRec {
d: 3,
ut: [[-0.4152068408862081, -0.556377565193878, -0.719755016814907],
[-0.7534435856189199, -0.23308021364108839, 0.6148140997884891],
[-0.5098294249756481, 0.7975698207417085, -0.3224226084985998]], shape=[3, 3], strides=[3, 1], layout=Cc (0x5), const ndim=2,
s: [123.67657874254405, 6.084527896513759, 0.2870380041828973], shape=[3], strides=[1], layout=CFcf (0xf), const ndim=1,
vt: [[-0.07372869095916511, -0.3756889918994792, -0.9238083467337805],
[0.6323518477280158, 0.6986910001499903, -0.33460727276072516],
[-0.7711648467120451, 0.6088420712097343, -0.18605405537270261]], shape=[3, 3], strides=[3, 1], layout=Cc (0x5), const ndim=2,
diagnostics: Diagnostics {
non_zero: 9,
dimensions: 3,
iterations: 3,
transposed: false,
lanczos_steps: 3,
ritz_values_stabilized: 3,
significant_values: 3,
singular_values: 3,
end_interval: [
-1e-30,
1e-30,
],
kappa: 1e-6,
random_seed: 3141,
},
},
)