Add matrixmarket parser.
This commit is contained in:
sebcrozet
parent
383a18f083
commit
ed07b78b97
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@ -26,6 +26,7 @@ abomonation-serialize = [ "abomonation" ]
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sparse = [ ]
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debug = [ ]
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alloc = [ ]
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io = [ "pest", "pest_derive" ]
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[dependencies]
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typenum = "1.10"
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@ -41,6 +42,8 @@ serde_derive = { version = "1.0", optional = true }
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abomonation = { version = "0.5", optional = true }
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mint = { version = "0.5", optional = true }
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quickcheck = { version = "0.6", optional = true }
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pest = { version = "2.0", optional = true }
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pest_derive = { version = "2.0", optional = true }
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[dev-dependencies]
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serde_json = "1.0"
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@ -0,0 +1,16 @@
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WHITESPACE = _{ " " }
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Comments = _{ "%" ~ (!NEWLINE ~ ANY)* }
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Header = { "%%" ~ (!NEWLINE ~ ANY)* }
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Shape = { Dimension ~ Dimension ~ Dimension }
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Document = {
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SOI ~
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NEWLINE ~
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Header ~
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(NEWLINE ~ Comments)* ~
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(NEWLINE ~ Shape) ~
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(NEWLINE ~ Entry?)*
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}
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Dimension = @{ ASCII_DIGIT+ }
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Value = @{ ("+" | "-")? ~ NUMBER+ ~ ("." ~ NUMBER+)? ~ ("e" ~ ("+" | "-")? ~ NUMBER+)? }
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Entry = { Dimension ~ Dimension ~ Value }
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@ -0,0 +1,51 @@
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use std::fs;
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use std::path::Path;
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use pest::Parser;
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use sparse::CsMatrix;
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use Real;
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#[derive(Parser)]
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#[grammar = "io/matrix_market.pest"]
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struct MatrixMarketParser;
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// FIXME: return an Error instead of an Option.
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pub fn cs_matrix_from_matrix_market<N: Real, P: AsRef<Path>>(path: P) -> Option<CsMatrix<N>> {
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let file = fs::read_to_string(path).ok()?;
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cs_matrix_from_matrix_market_str(&file)
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}
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// FIXME: return an Error instead of an Option.
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pub fn cs_matrix_from_matrix_market_str<N: Real>(data: &str) -> Option<CsMatrix<N>> {
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let file = MatrixMarketParser::parse(Rule::Document, data)
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.unwrap()
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.next()?;
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let mut shape = (0, 0, 0);
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let mut rows: Vec<usize> = Vec::new();
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let mut cols: Vec<usize> = Vec::new();
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let mut data: Vec<N> = Vec::new();
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for line in file.into_inner() {
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match line.as_rule() {
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Rule::Header => {}
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Rule::Shape => {
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let mut inner = line.into_inner();
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shape.0 = inner.next()?.as_str().parse::<usize>().ok()?;
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shape.1 = inner.next()?.as_str().parse::<usize>().ok()?;
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shape.2 = inner.next()?.as_str().parse::<usize>().ok()?;
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}
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Rule::Entry => {
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let mut inner = line.into_inner();
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// NOTE: indices are 1-based.
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rows.push(inner.next()?.as_str().parse::<usize>().ok()? - 1);
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cols.push(inner.next()?.as_str().parse::<usize>().ok()? - 1);
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data.push(::convert(inner.next()?.as_str().parse::<f64>().ok()?));
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}
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_ => return None, // FIXME: return an Err instead.
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}
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}
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Some(CsMatrix::from_triplet(
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shape.0, shape.1, &rows, &cols, &data,
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))
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}
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@ -0,0 +1,3 @@
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pub use self::matrix_market::*;
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mod matrix_market;
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@ -7,8 +7,43 @@ use std::slice;
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use allocator::Allocator;
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use constraint::{AreMultipliable, DimEq, SameNumberOfRows, ShapeConstraint};
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use sparse::cs_utils;
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use storage::{Storage, StorageMut};
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use {DefaultAllocator, Dim, Matrix, MatrixMN, Real, Scalar, Vector, VectorN, U1};
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use {
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DVector, DefaultAllocator, Dim, Dynamic, Matrix, MatrixMN, MatrixVec, Real, Scalar, Vector,
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VectorN, U1,
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};
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pub struct ColumnEntries<'a, N> {
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curr: usize,
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i: &'a [usize],
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v: &'a [N],
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}
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impl<'a, N> ColumnEntries<'a, N> {
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#[inline]
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pub fn new(i: &'a [usize], v: &'a [N]) -> Self {
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assert_eq!(i.len(), v.len());
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ColumnEntries { curr: 0, i, v }
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}
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}
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impl<'a, N: Copy> Iterator for ColumnEntries<'a, N> {
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type Item = (usize, N);
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#[inline]
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fn next(&mut self) -> Option<(usize, N)> {
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if self.curr >= self.i.len() {
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None
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} else {
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let res = Some((unsafe { *self.i.get_unchecked(self.curr) }, unsafe {
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*self.v.get_unchecked(self.curr)
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}));
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self.curr += 1;
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res
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}
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}
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}
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// FIXME: this structure exists for now only because impl trait
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// cannot be used for trait method return types.
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@ -17,12 +52,15 @@ pub trait CsStorageIter<'a, N, R, C = U1> {
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type ColumnRowIndices: Iterator<Item = usize>;
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fn column_row_indices(&'a self, j: usize) -> Self::ColumnRowIndices;
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#[inline(always)]
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fn column_entries(&'a self, j: usize) -> Self::ColumnEntries;
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}
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pub trait CsStorageIterMut<'a, N: 'a, R, C = U1> {
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type ValuesMut: Iterator<Item = &'a mut N>;
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type ColumnEntriesMut: Iterator<Item = (usize, &'a mut N)>;
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fn values_mut(&'a mut self) -> Self::ValuesMut;
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fn column_entries_mut(&'a mut self, j: usize) -> Self::ColumnEntriesMut;
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}
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@ -41,7 +79,7 @@ pub trait CsStorageMut<N, R, C = U1>:
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{
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}
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#[derive(Clone, Debug)]
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#[derive(Clone, Debug, PartialEq)]
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pub struct CsVecStorage<N: Scalar, R: Dim, C: Dim>
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where
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DefaultAllocator: Allocator<usize, C>,
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@ -59,6 +97,12 @@ where
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pub fn values(&self) -> &[N] {
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&self.vals
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}
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pub fn p(&self) -> &[usize] {
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self.p.as_slice()
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}
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pub fn i(&self) -> &[usize] {
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&self.i
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}
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}
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impl<N: Scalar, R: Dim, C: Dim> CsVecStorage<N, R, C> where DefaultAllocator: Allocator<usize, C> {}
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@ -67,17 +111,13 @@ impl<'a, N: Scalar, R: Dim, C: Dim> CsStorageIter<'a, N, R, C> for CsVecStorage<
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where
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DefaultAllocator: Allocator<usize, C>,
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{
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type ColumnEntries =
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iter::Zip<iter::Cloned<slice::Iter<'a, usize>>, iter::Cloned<slice::Iter<'a, N>>>;
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type ColumnEntries = ColumnEntries<'a, N>;
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type ColumnRowIndices = iter::Cloned<slice::Iter<'a, usize>>;
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#[inline]
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fn column_entries(&'a self, j: usize) -> Self::ColumnEntries {
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let rng = self.column_range(j);
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self.i[rng.clone()]
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.iter()
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.cloned()
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.zip(self.vals[rng].iter().cloned())
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ColumnEntries::new(&self.i[rng.clone()], &self.vals[rng])
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}
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#[inline]
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@ -137,8 +177,14 @@ impl<'a, N: Scalar, R: Dim, C: Dim> CsStorageIterMut<'a, N, R, C> for CsVecStora
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where
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DefaultAllocator: Allocator<usize, C>,
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{
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type ValuesMut = slice::IterMut<'a, N>;
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type ColumnEntriesMut = iter::Zip<iter::Cloned<slice::Iter<'a, usize>>, slice::IterMut<'a, N>>;
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#[inline]
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fn values_mut(&'a mut self) -> Self::ValuesMut {
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self.vals.iter_mut()
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}
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#[inline]
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fn column_entries_mut(&'a mut self, j: usize) -> Self::ColumnEntriesMut {
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let rng = self.column_range(j);
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@ -163,13 +209,18 @@ pub struct CsSliceStorage<'a, N: Scalar, R: Dim, C: DimAdd<U1>> {
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}*/
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/// A compressed sparse column matrix.
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#[derive(Clone, Debug)]
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pub struct CsMatrix<N: Scalar, R: Dim, C: Dim, S: CsStorage<N, R, C> = CsVecStorage<N, R, C>> {
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#[derive(Clone, Debug, PartialEq)]
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pub struct CsMatrix<
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N: Scalar,
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R: Dim = Dynamic,
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C: Dim = Dynamic,
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S: CsStorage<N, R, C> = CsVecStorage<N, R, C>,
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> {
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pub data: S,
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_phantoms: PhantomData<(N, R, C)>,
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}
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pub type CsVector<N, R, S = CsVecStorage<N, R, U1>> = CsMatrix<N, R, U1, S>;
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pub type CsVector<N, R = Dynamic, S = CsVecStorage<N, R, U1>> = CsMatrix<N, R, U1, S>;
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impl<N: Scalar, R: Dim, C: Dim> CsMatrix<N, R, C>
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where
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@ -198,22 +249,66 @@ where
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_phantoms: PhantomData,
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}
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}
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}
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fn cumsum<D: Dim>(a: &mut VectorN<usize, D>, b: &mut VectorN<usize, D>) -> usize
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where
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DefaultAllocator: Allocator<usize, D>,
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{
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assert!(a.len() == b.len());
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let mut sum = 0;
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pub fn from_parts_generic(
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nrows: R,
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ncols: C,
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p: VectorN<usize, C>,
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i: Vec<usize>,
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vals: Vec<N>,
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) -> Self
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where
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N: Zero + ClosedAdd,
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DefaultAllocator: Allocator<N, R>,
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{
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assert_eq!(ncols.value(), p.len(), "Invalid inptr size.");
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assert_eq!(i.len(), vals.len(), "Invalid value size.");
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for i in 0..a.len() {
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b[i] = sum;
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sum += a[i];
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a[i] = b[i];
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// Check p.
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for ptr in &p {
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assert!(*ptr < i.len(), "Invalid inptr value.");
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}
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sum
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for ptr in p.as_slice().windows(2) {
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assert!(ptr[0] <= ptr[1], "Invalid inptr ordering.");
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}
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// Check i.
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for i in &i {
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assert!(*i < nrows.value(), "Invalid row ptr value.")
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}
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let mut res = CsMatrix {
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data: CsVecStorage {
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shape: (nrows, ncols),
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p,
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i,
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vals,
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},
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_phantoms: PhantomData,
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};
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// Sort and remove duplicates.
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res.sort();
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res.dedup();
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res
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}
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}
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impl<N: Scalar + Zero + ClosedAdd> CsMatrix<N> {
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pub fn from_parts(
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nrows: usize,
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ncols: usize,
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p: Vec<usize>,
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i: Vec<usize>,
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vals: Vec<N>,
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) -> Self {
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let nrows = Dynamic::new(nrows);
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let ncols = Dynamic::new(ncols);
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let p = DVector::from_data(MatrixVec::new(ncols, U1, p));
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Self::from_parts_generic(nrows, ncols, p, i, vals)
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}
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}
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impl<N: Scalar, R: Dim, C: Dim, S: CsStorage<N, R, C>> CsMatrix<N, R, C, S> {
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@ -288,7 +383,7 @@ impl<N: Scalar, R: Dim, C: Dim, S: CsStorage<N, R, C>> CsMatrix<N, R, C, S> {
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workspace[row_id] += 1;
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}
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let _ = cumsum(&mut workspace, &mut res.data.p);
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let _ = cs_utils::cumsum(&mut workspace, &mut res.data.p);
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// Fill the result.
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for j in 0..ncols.value() {
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@ -305,6 +400,13 @@ impl<N: Scalar, R: Dim, C: Dim, S: CsStorage<N, R, C>> CsMatrix<N, R, C, S> {
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}
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}
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impl<N: Scalar, R: Dim, C: Dim, S: CsStorageMut<N, R, C>> CsMatrix<N, R, C, S> {
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#[inline]
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pub fn values_mut(&mut self) -> impl Iterator<Item = &mut N> {
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self.data.values_mut()
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}
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}
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impl<N: Scalar, R: Dim, C: Dim> CsMatrix<N, R, C>
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where
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DefaultAllocator: Allocator<usize, C>,
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@ -341,4 +443,46 @@ where
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}
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}
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}
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// Remove dupliate entries on a sorted CsMatrix.
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pub(crate) fn dedup(&mut self)
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where
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N: Zero + ClosedAdd,
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{
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let mut curr_i = 0;
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for j in 0..self.ncols() {
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let range = self.data.column_range(j);
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self.data.p[j] = curr_i;
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if range.start != range.end {
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let mut value = N::zero();
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let mut irow = self.data.i[range.start];
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for idx in range {
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let curr_irow = self.data.i[idx];
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if curr_irow == irow {
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value += self.data.vals[idx];
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} else {
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self.data.i[curr_i] = irow;
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self.data.vals[curr_i] = value;
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value = self.data.vals[idx];
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irow = curr_irow;
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curr_i += 1;
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}
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}
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// Handle the last entry.
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self.data.i[curr_i] = irow;
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self.data.vals[curr_i] = value;
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curr_i += 1;
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}
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}
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self.data.i.truncate(curr_i);
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self.data.i.shrink_to_fit();
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self.data.vals.truncate(curr_i);
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self.data.vals.shrink_to_fit();
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}
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}
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@ -7,9 +7,67 @@ use std::slice;
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use allocator::Allocator;
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use constraint::{AreMultipliable, DimEq, SameNumberOfRows, ShapeConstraint};
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use sparse::cs_utils;
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use sparse::{CsMatrix, CsStorage, CsVector};
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use storage::{Storage, StorageMut};
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use {DefaultAllocator, Dim, Matrix, MatrixMN, Real, Scalar, Vector, VectorN, U1};
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use {DefaultAllocator, Dim, Dynamic, Matrix, MatrixMN, Real, Scalar, Vector, VectorN, U1};
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impl<'a, N: Scalar + Zero + ClosedAdd> CsMatrix<N> {
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// FIXME: implement for dimensions other than Dynamic too.
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pub fn from_triplet(
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nrows: usize,
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ncols: usize,
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irows: &[usize],
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icols: &[usize],
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vals: &[N],
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) -> Self {
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Self::from_triplet_generic(Dynamic::new(nrows), Dynamic::new(ncols), irows, icols, vals)
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}
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}
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impl<'a, N: Scalar + Zero + ClosedAdd, R: Dim, C: Dim> CsMatrix<N, R, C>
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where
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DefaultAllocator: Allocator<usize, C> + Allocator<N, R>,
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{
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pub fn from_triplet_generic(
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nrows: R,
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ncols: C,
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irows: &[usize],
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icols: &[usize],
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vals: &[N],
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) -> Self {
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assert!(vals.len() == irows.len());
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assert!(vals.len() == icols.len());
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let mut res = CsMatrix::new_uninitialized_generic(nrows, ncols, vals.len());
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let mut workspace = res.data.p.clone();
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// Column count.
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for j in icols.iter().cloned() {
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workspace[j] += 1;
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}
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let _ = cs_utils::cumsum(&mut workspace, &mut res.data.p);
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// Fill i and vals.
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for ((i, j), val) in irows
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.iter()
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.cloned()
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.zip(icols.iter().cloned())
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.zip(vals.iter().cloned())
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{
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let offset = workspace[j];
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res.data.i[offset] = i;
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res.data.vals[offset] = val;
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workspace[j] = offset + 1;
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}
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// Sort the result.
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res.sort();
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res.dedup();
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res
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}
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}
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impl<'a, N: Scalar + Zero, R: Dim, C: Dim, S> From<CsMatrix<N, R, C, S>> for MatrixMN<N, R, C>
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where
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|
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|
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@ -0,0 +1,18 @@
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use allocator::Allocator;
|
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use {DefaultAllocator, Dim, VectorN};
|
||||
|
||||
pub fn cumsum<D: Dim>(a: &mut VectorN<usize, D>, b: &mut VectorN<usize, D>) -> usize
|
||||
where
|
||||
DefaultAllocator: Allocator<usize, D>,
|
||||
{
|
||||
assert!(a.len() == b.len());
|
||||
let mut sum = 0;
|
||||
|
||||
for i in 0..a.len() {
|
||||
b[i] = sum;
|
||||
sum += a[i];
|
||||
a[i] = b[i];
|
||||
}
|
||||
|
||||
sum
|
||||
}
|
||||
|
|
@ -8,3 +8,4 @@ mod cs_matrix_cholesky;
|
|||
mod cs_matrix_conversion;
|
||||
mod cs_matrix_ops;
|
||||
mod cs_matrix_solve;
|
||||
pub mod cs_utils;
|
||||
|
|
|
|||
|
|
@ -1,9 +1,8 @@
|
|||
#![cfg_attr(rustfmt, rustfmt_skip)]
|
||||
|
||||
use na::{Matrix4x5, CsMatrix};
|
||||
use na::{CsMatrix, DMatrix, Matrix4x5};
|
||||
|
||||
#[test]
|
||||
fn cs_from_to_matrix() {
|
||||
#[cfg_attr(rustfmt, rustfmt_skip)]
|
||||
let m = Matrix4x5::new(
|
||||
5.0, 6.0, 0.0, 8.0, 15.0,
|
||||
9.0, 10.0, 11.0, 12.0, 0.0,
|
||||
|
|
@ -17,3 +16,74 @@ fn cs_from_to_matrix() {
|
|||
let m2: Matrix4x5<_> = cs.into();
|
||||
assert_eq!(m2, m);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn cs_matrix_from_triplet() {
|
||||
let mut irows = vec![0, 0, 0, 0, 1, 1, 1, 1, 2, 3, 3, 3];
|
||||
let mut icols = vec![0, 1, 3, 4, 0, 1, 2, 3, 2, 1, 2, 4];
|
||||
let mut vals = vec![
|
||||
5.0, 6.0, 8.0, 15.0, 9.0, 10.0, 11.0, 12.0, 13.0, 1.0, 4.0, 14.0,
|
||||
];
|
||||
|
||||
#[cfg_attr(rustfmt, rustfmt_skip)]
|
||||
let expected = DMatrix::from_row_slice(4, 5, &[
|
||||
5.0, 6.0, 0.0, 8.0, 15.0,
|
||||
9.0, 10.0, 11.0, 12.0, 0.0,
|
||||
0.0, 0.0, 13.0, 0.0, 0.0,
|
||||
0.0, 1.0, 4.0, 0.0, 14.0,
|
||||
]);
|
||||
let cs_expected = CsMatrix::from_parts(
|
||||
4,
|
||||
5,
|
||||
vec![0, 2, 5, 8, 10],
|
||||
vec![0, 1, 0, 1, 3, 1, 2, 3, 0, 1, 0, 3],
|
||||
vec![
|
||||
5.0, 9.0, 6.0, 10.0, 1.0, 11.0, 13.0, 4.0, 8.0, 12.0, 15.0, 14.0,
|
||||
],
|
||||
);
|
||||
|
||||
let cs_mat = CsMatrix::from_triplet(4, 5, &irows, &icols, &vals);
|
||||
println!("Mat from triplet: {:?}", cs_mat);
|
||||
assert!(cs_mat.is_sorted());
|
||||
assert_eq!(cs_mat, cs_expected);
|
||||
|
||||
let m: DMatrix<_> = cs_mat.into();
|
||||
assert_eq!(m, expected);
|
||||
|
||||
/*
|
||||
* Try again with some permutations.
|
||||
*/
|
||||
let permutations = [(2, 5), (0, 4), (8, 10), (1, 11)];
|
||||
|
||||
for (i, j) in &permutations {
|
||||
irows.swap(*i, *j);
|
||||
icols.swap(*i, *j);
|
||||
vals.swap(*i, *j);
|
||||
}
|
||||
|
||||
let cs_mat = CsMatrix::from_triplet(4, 5, &irows, &icols, &vals);
|
||||
println!("Mat from triplet: {:?}", cs_mat);
|
||||
assert!(cs_mat.is_sorted());
|
||||
assert_eq!(cs_mat, cs_expected);
|
||||
|
||||
let m: DMatrix<_> = cs_mat.into();
|
||||
assert_eq!(m, expected);
|
||||
|
||||
/*
|
||||
* Try again, duplicating all entries.
|
||||
*/
|
||||
let mut ir = irows.clone();
|
||||
let mut ic = icols.clone();
|
||||
let mut va = vals.clone();
|
||||
irows.append(&mut ir);
|
||||
icols.append(&mut ic);
|
||||
vals.append(&mut va);
|
||||
|
||||
let cs_mat = CsMatrix::from_triplet(4, 5, &irows, &icols, &vals);
|
||||
println!("Mat from triplet: {:?}", cs_mat);
|
||||
assert!(cs_mat.is_sorted());
|
||||
assert_eq!(cs_mat, cs_expected * 2.0);
|
||||
|
||||
let m: DMatrix<_> = cs_mat.into();
|
||||
assert_eq!(m, expected * 2.0);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,55 @@
|
|||
#![cfg_attr(rustfmt, rustfmt_skip)]
|
||||
|
||||
|
||||
use na::io;
|
||||
use na::DMatrix;
|
||||
|
||||
#[test]
|
||||
fn cs_matrix_market() {
|
||||
let file_str = r#"
|
||||
%%MatrixMarket matrix coordinate real general
|
||||
%=================================================================================
|
||||
%
|
||||
% This ASCII file represents a sparse MxN matrix with L
|
||||
% nonzeros in the following Matrix Market format:
|
||||
%
|
||||
% +----------------------------------------------+
|
||||
% |%%MatrixMarket matrix coordinate real general | <--- header line
|
||||
% |% | <--+
|
||||
% |% comments | |-- 0 or more comment lines
|
||||
% |% | <--+
|
||||
% | M N L | <--- rows, columns, entries
|
||||
% | I1 J1 A(I1, J1) | <--+
|
||||
% | I2 J2 A(I2, J2) | |
|
||||
% | I3 J3 A(I3, J3) | |-- L lines
|
||||
% | . . . | |
|
||||
% | IL JL A(IL, JL) | <--+
|
||||
% +----------------------------------------------+
|
||||
%
|
||||
% Indices are 1-based, i.e. A(1,1) is the first element.
|
||||
%
|
||||
%=================================================================================
|
||||
5 5 8
|
||||
1 1 1.000e+00
|
||||
2 2 1.050e+01
|
||||
3 3 1.500e-02
|
||||
1 4 6.000e+00
|
||||
4 2 2.505e+02
|
||||
4 4 -2.800e+02
|
||||
4 5 3.332e+01
|
||||
5 5 1.200e+01
|
||||
"#;
|
||||
|
||||
let cs_mat = io::cs_matrix_from_matrix_market_str(file_str).unwrap();
|
||||
println!("CS mat: {:?}", cs_mat);
|
||||
let mat: DMatrix<_> = cs_mat.into();
|
||||
let expected = DMatrix::from_row_slice(5, 5, &[
|
||||
1.0, 0.0, 0.0, 6.0, 0.0,
|
||||
0.0, 10.5, 0.0, 0.0, 0.0,
|
||||
0.0, 0.0, 0.015, 0.0, 0.0,
|
||||
0.0, 250.5, 0.0, -280.0, 33.32,
|
||||
0.0, 0.0, 0.0, 0.0, 12.0,
|
||||
]);
|
||||
|
||||
assert_eq!(mat, expected);
|
||||
}
|
||||
|
|
@ -2,5 +2,7 @@ mod cs_cholesky;
|
|||
mod cs_construction;
|
||||
mod cs_conversion;
|
||||
mod cs_matrix;
|
||||
#[cfg(feature = "io")]
|
||||
mod cs_matrix_market;
|
||||
mod cs_ops;
|
||||
mod cs_solve;
|
||||
|
|
|
|||
Loading…
Reference in New Issue