forked from M-Labs/nalgebra
Implement remaining conversions (missing docs)
This commit is contained in:
Andreas Longva
parent
6083d24dd6
commit
95ee65fa8e
@ -1,8 +1,10 @@
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use crate::coo::CooMatrix;
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use crate::convert::serial::{convert_dense_coo, convert_coo_dense};
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use crate::convert::serial::*;
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use nalgebra::{Matrix, Scalar, Dim, ClosedAdd, DMatrix};
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use nalgebra::storage::{Storage};
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use num_traits::Zero;
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use crate::csr::CsrMatrix;
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use crate::csc::CscMatrix;
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impl<'a, T, R, C, S> From<&'a Matrix<T, R, C, S>> for CooMatrix<T>
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where
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@ -23,4 +25,100 @@ where
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fn from(coo: &'a CooMatrix<T>) -> Self {
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convert_coo_dense(coo)
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}
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}
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impl<'a, T> From<&'a CooMatrix<T>> for CsrMatrix<T>
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where
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T: Scalar + Zero + ClosedAdd
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{
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fn from(matrix: &'a CooMatrix<T>) -> Self {
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convert_coo_csr(matrix)
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}
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}
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impl<'a, T> From<&'a CsrMatrix<T>> for CooMatrix<T>
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where
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T: Scalar + Zero + ClosedAdd
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{
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fn from(matrix: &'a CsrMatrix<T>) -> Self {
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convert_csr_coo(matrix)
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}
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}
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impl<'a, T, R, C, S> From<&'a Matrix<T, R, C, S>> for CsrMatrix<T>
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where
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T: Scalar + Zero,
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R: Dim,
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C: Dim,
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S: Storage<T, R, C>
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{
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fn from(matrix: &'a Matrix<T, R, C, S>) -> Self {
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convert_dense_csr(matrix)
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}
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}
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impl<'a, T> From<&'a CsrMatrix<T>> for DMatrix<T>
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where
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T: Scalar + Zero + ClosedAdd
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{
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fn from(matrix: &'a CsrMatrix<T>) -> Self {
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convert_csr_dense(matrix)
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}
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}
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impl<'a, T> From<&'a CooMatrix<T>> for CscMatrix<T>
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where
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T: Scalar + Zero + ClosedAdd
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{
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fn from(matrix: &'a CooMatrix<T>) -> Self {
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convert_coo_csc(matrix)
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}
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}
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impl<'a, T> From<&'a CscMatrix<T>> for CooMatrix<T>
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where
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T: Scalar + Zero
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{
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fn from(matrix: &'a CscMatrix<T>) -> Self {
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convert_csc_coo(matrix)
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}
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}
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impl<'a, T, R, C, S> From<&'a Matrix<T, R, C, S>> for CscMatrix<T>
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where
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T: Scalar + Zero,
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R: Dim,
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C: Dim,
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S: Storage<T, R, C>
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{
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fn from(matrix: &'a Matrix<T, R, C, S>) -> Self {
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convert_dense_csc(matrix)
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}
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}
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impl<'a, T> From<&'a CscMatrix<T>> for DMatrix<T>
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where
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T: Scalar + Zero + ClosedAdd
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{
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fn from(matrix: &'a CscMatrix<T>) -> Self {
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convert_csc_dense(matrix)
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}
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}
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impl<'a, T> From<&'a CscMatrix<T>> for CsrMatrix<T>
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where
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T: Scalar + Zero
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{
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fn from(matrix: &'a CscMatrix<T>) -> Self {
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convert_csc_csr(matrix)
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}
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}
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impl<'a, T> From<&'a CsrMatrix<T>> for CscMatrix<T>
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where
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T: Scalar + Zero
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{
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fn from(matrix: &'a CsrMatrix<T>) -> Self {
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convert_csr_csc(matrix)
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}
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}
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@ -1,10 +1,12 @@
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//! TODO
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use nalgebra::{DMatrix, Scalar, Matrix, Dim};
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use crate::coo::CooMatrix;
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use crate::csr::CsrMatrix;
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use num_traits::Zero;
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use std::ops::{Add, AddAssign};
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use nalgebra::{DMatrix, Scalar, Matrix, Dim, ClosedAdd};
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use nalgebra::storage::Storage;
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use num_traits::Zero;
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use std::ops::{Add};
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use crate::csc::CscMatrix;
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/// TODO
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pub fn convert_dense_coo<T, R, C, S>(dense: &Matrix<T, R, C, S>) -> CooMatrix<T>
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@ -33,7 +35,7 @@ where
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/// TODO: What should the actual trait bounds be?
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pub fn convert_coo_dense<T>(coo: &CooMatrix<T>) -> DMatrix<T>
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where
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T: Scalar + Zero + Add + AddAssign,
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T: Scalar + Zero + ClosedAdd,
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{
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let mut output = DMatrix::repeat(coo.nrows(), coo.ncols(), T::zero());
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for (i, j, v) in coo.triplet_iter() {
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@ -43,6 +45,430 @@ where
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}
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/// TODO
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pub fn convert_coo_csr<T>(_coo: &CooMatrix<T>) -> CsrMatrix<T> {
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todo!()
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pub fn convert_coo_csr<T>(coo: &CooMatrix<T>) -> CsrMatrix<T>
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where
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T: Scalar + Zero
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{
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let (offsets, indices, values) = convert_coo_cs(coo.nrows(),
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coo.row_indices(),
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coo.col_indices(),
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coo.values());
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// TODO: Avoid "try_from" since it validates the data? (requires unsafe, should benchmark
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// to see if it can be justified for performance reasons)
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CsrMatrix::try_from_csr_data(coo.nrows(), coo.ncols(), offsets, indices, values)
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.expect("Internal error: Invalid CSR data during COO->CSR conversion")
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}
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/// TODO
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pub fn convert_csr_coo<T: Scalar>(csr: &CsrMatrix<T>) -> CooMatrix<T>
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{
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let mut result = CooMatrix::new(csr.nrows(), csr.ncols());
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for (i, j, v) in csr.triplet_iter() {
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result.push(i, j, v.inlined_clone());
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}
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result
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}
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/// TODO
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pub fn convert_csr_dense<T>(csr:& CsrMatrix<T>) -> DMatrix<T>
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where
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T: Scalar + ClosedAdd + Zero
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{
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let mut output = DMatrix::zeros(csr.nrows(), csr.ncols());
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for (i, j, v) in csr.triplet_iter() {
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output[(i, j)] += v.inlined_clone();
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}
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output
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}
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/// TODO
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pub fn convert_dense_csr<T, R, C, S>(dense: &Matrix<T, R, C, S>) -> CsrMatrix<T>
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where
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T: Scalar + Zero,
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R: Dim,
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C: Dim,
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S: Storage<T, R, C>
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{
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let mut row_offsets = Vec::with_capacity(dense.nrows() + 1);
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let mut col_idx = Vec::new();
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let mut values = Vec::new();
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// We have to iterate row-by-row to build the CSR matrix, which is at odds with
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// nalgebra's column-major storage. The alternative would be to perform an initial sweep
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// to count number of non-zeros per row.
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row_offsets.push(0);
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for i in 0 .. dense.nrows() {
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for j in 0 .. dense.ncols() {
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let v = dense.index((i, j));
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if v != &T::zero() {
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col_idx.push(j);
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values.push(v.inlined_clone());
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}
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}
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row_offsets.push(col_idx.len());
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}
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// TODO: Consider circumventing the data validity check here
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// (would require unsafe, should benchmark)
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CsrMatrix::try_from_csr_data(dense.nrows(), dense.ncols(), row_offsets, col_idx, values)
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.expect("Internal error: Invalid CsrMatrix format during dense-> CSR conversion")
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}
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/// TODO
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pub fn convert_coo_csc<T>(coo: &CooMatrix<T>) -> CscMatrix<T>
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where
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T: Scalar + Zero
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{
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let (offsets, indices, values) = convert_coo_cs(coo.ncols(),
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coo.col_indices(),
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coo.row_indices(),
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coo.values());
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// TODO: Avoid "try_from" since it validates the data? (requires unsafe, should benchmark
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// to see if it can be justified for performance reasons)
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CscMatrix::try_from_csc_data(coo.nrows(), coo.ncols(), offsets, indices, values)
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.expect("Internal error: Invalid CSC data during COO->CSC conversion")
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}
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/// TODO
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pub fn convert_csc_coo<T>(csc: &CscMatrix<T>) -> CooMatrix<T>
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where
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T: Scalar
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{
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let mut coo = CooMatrix::new(csc.nrows(), csc.ncols());
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for (i, j, v) in csc.triplet_iter() {
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coo.push(i, j, v.inlined_clone());
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}
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coo
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}
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/// TODO
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pub fn convert_csc_dense<T>(csc: &CscMatrix<T>) -> DMatrix<T>
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where
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T: Scalar + ClosedAdd + Zero
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{
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let mut output = DMatrix::zeros(csc.nrows(), csc.ncols());
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for (i, j, v) in csc.triplet_iter() {
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output[(i, j)] += v.inlined_clone();
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}
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output
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}
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/// TODO
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pub fn convert_dense_csc<T, R, C, S>(dense: &Matrix<T, R, C, S>) -> CscMatrix<T>
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where
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T: Scalar + Zero,
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R: Dim,
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C: Dim,
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S: Storage<T, R, C>
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{
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let mut col_offsets = Vec::with_capacity(dense.ncols() + 1);
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let mut row_idx = Vec::new();
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let mut values = Vec::new();
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col_offsets.push(0);
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for j in 0 .. dense.ncols() {
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for i in 0 .. dense.nrows() {
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let v = dense.index((i, j));
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if v != &T::zero() {
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row_idx.push(i);
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values.push(v.inlined_clone());
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}
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}
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col_offsets.push(row_idx.len());
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}
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// TODO: Consider circumventing the data validity check here
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// (would require unsafe, should benchmark)
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CscMatrix::try_from_csc_data(dense.nrows(), dense.ncols(), col_offsets, row_idx, values)
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.expect("Internal error: Invalid CscMatrix format during dense-> CSC conversion")
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}
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/// TODO
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pub fn convert_csr_csc<T>(csr: &CsrMatrix<T>) -> CscMatrix<T>
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where
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T: Scalar + Zero
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{
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let (offsets, indices, values) = transpose_cs(csr.nrows(),
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csr.ncols(),
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csr.row_offsets(),
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csr.col_indices(),
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csr.values());
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// TODO: Avoid data validity check?
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CscMatrix::try_from_csc_data(csr.nrows(), csr.ncols(), offsets, indices, values)
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.expect("Internal error: Invalid CSC data during CSR->CSC conversion")
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}
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/// TODO
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pub fn convert_csc_csr<T>(csc: &CscMatrix<T>) -> CsrMatrix<T>
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where
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T: Scalar + Zero
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{
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let (offsets, indices, values) = transpose_cs(csc.ncols(),
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csc.nrows(),
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csc.col_offsets(),
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csc.row_indices(),
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csc.values());
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// TODO: Avoid data validity check?
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CsrMatrix::try_from_csr_data(csc.nrows(), csc.ncols(), offsets, indices, values)
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.expect("Internal error: Invalid CSR data during CSC->CSR conversion")
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}
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fn convert_coo_cs<T>(major_dim: usize,
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major_indices: &[usize],
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minor_indices: &[usize],
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values: &[T])
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-> (Vec<usize>, Vec<usize>, Vec<T>)
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where
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T: Scalar + Zero
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{
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assert_eq!(major_indices.len(), minor_indices.len());
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assert_eq!(minor_indices.len(), values.len());
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let nnz = major_indices.len();
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let (unsorted_major_offsets, unsorted_minor_idx, unsorted_vals) = {
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let mut offsets = vec![0usize; major_dim + 1];
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let mut minor_idx = vec![0usize; nnz];
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let mut vals = vec![T::zero(); nnz];
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coo_to_unsorted_cs(
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&mut offsets,
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&mut minor_idx,
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&mut vals,
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major_dim,
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major_indices,
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minor_indices,
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values,
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);
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(offsets, minor_idx, vals)
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};
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// TODO: If input is sorted and/or without duplicates, we can avoid additional allocations
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// and work. Might want to take advantage of this.
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// At this point, assembly is essentially complete. However, we must ensure
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// that minor indices are sorted within each lane and without duplicates.
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let mut sorted_major_offsets = Vec::new();
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let mut sorted_minor_idx = Vec::new();
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let mut sorted_vals = Vec::new();
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sorted_major_offsets.push(0);
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// We need some temporary storage when working with each lane. Since lanes often have a
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// very small number of non-zero entries, we try to amortize allocations across
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// lanes by reusing workspace vectors
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let mut idx_workspace = Vec::new();
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let mut perm_workspace = Vec::new();
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let mut values_workspace = Vec::new();
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for lane in 0..major_dim {
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let begin = unsorted_major_offsets[lane];
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let end = unsorted_major_offsets[lane + 1];
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let count = end - begin;
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let range = begin..end;
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// Ensure that workspaces can hold enough data
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perm_workspace.resize(count, 0);
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idx_workspace.resize(count, 0);
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values_workspace.resize(count, T::zero());
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sort_lane(
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&mut idx_workspace[..count],
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&mut values_workspace[..count],
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&unsorted_minor_idx[range.clone()],
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&unsorted_vals[range.clone()],
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&mut perm_workspace[..count],
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);
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let sorted_ja_current_len = sorted_minor_idx.len();
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combine_duplicates(
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|idx| sorted_minor_idx.push(idx),
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|val| sorted_vals.push(val),
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&idx_workspace[..count],
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&values_workspace[..count],
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&Add::add,
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);
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let new_col_count = sorted_minor_idx.len() - sorted_ja_current_len;
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sorted_major_offsets.push(sorted_major_offsets.last().unwrap() + new_col_count);
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}
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(sorted_major_offsets, sorted_minor_idx, sorted_vals)
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}
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/// Converts matrix data given in triplet format to unsorted CSR/CSC, retaining any duplicated
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/// indices.
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///
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/// Here `major/minor` is `row/col` for CSR and `col/row` for CSC.
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fn coo_to_unsorted_cs<T: Clone>(
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major_offsets: &mut [usize],
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cs_minor_idx: &mut [usize],
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cs_values: &mut [T],
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major_dim: usize,
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major_indices: &[usize],
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minor_indices: &[usize],
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coo_values: &[T],
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) {
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assert_eq!(major_offsets.len(), major_dim + 1);
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assert_eq!(cs_minor_idx.len(), cs_values.len());
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assert_eq!(cs_values.len(), major_indices.len());
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assert_eq!(major_indices.len(), minor_indices.len());
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assert_eq!(minor_indices.len(), coo_values.len());
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// Count the number of occurrences of each row
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for major_idx in major_indices {
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major_offsets[*major_idx] += 1;
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}
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convert_counts_to_offsets(major_offsets);
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{
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// TODO: Instead of allocating a whole new vector storing the current counts,
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// I think it's possible to be a bit more clever by storing each count
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// in the last of the column indices for each row
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let mut current_counts = vec![0usize; major_dim + 1];
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let triplet_iter = major_indices.iter().zip(minor_indices).zip(coo_values);
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for ((i, j), value) in triplet_iter {
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let current_offset = major_offsets[*i] + current_counts[*i];
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cs_minor_idx[current_offset] = *j;
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cs_values[current_offset] = value.clone();
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current_counts[*i] += 1;
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}
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}
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}
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/// Sort the indices of the given lane.
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///
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/// The indices and values in `minor_idx` and `values` are sorted according to the
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/// minor indices and stored in `minor_idx_result` and `values_result` respectively.
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///
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/// All input slices are expected to be of the same length. The contents of mutable slices
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/// can be arbitrary, as they are anyway overwritten.
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fn sort_lane<T: Clone>(
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minor_idx_result: &mut [usize],
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values_result: &mut [T],
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minor_idx: &[usize],
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values: &[T],
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workspace: &mut [usize],
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) {
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assert_eq!(minor_idx_result.len(), values_result.len());
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assert_eq!(values_result.len(), minor_idx.len());
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||||
assert_eq!(minor_idx.len(), values.len());
|
||||
assert_eq!(values.len(), workspace.len());
|
||||
|
||||
let permutation = workspace;
|
||||
// Set permutation to identity
|
||||
for (i, p) in permutation.iter_mut().enumerate() {
|
||||
*p = i;
|
||||
}
|
||||
|
||||
// Compute permutation needed to bring minor indices into sorted order
|
||||
// Note: Using sort_unstable here avoids internal allocations, which is crucial since
|
||||
// each lane might have a small number of elements
|
||||
permutation.sort_unstable_by_key(|idx| minor_idx[*idx]);
|
||||
|
||||
apply_permutation(minor_idx_result, minor_idx, permutation);
|
||||
apply_permutation(values_result, values, permutation);
|
||||
}
|
||||
|
||||
/// Transposes the compressed format.
|
||||
///
|
||||
/// This means that major and minor roles are switched. This is used for converting between CSR
|
||||
/// and CSC formats.
|
||||
fn transpose_cs<T>(major_dim: usize,
|
||||
minor_dim: usize,
|
||||
source_major_offsets: &[usize],
|
||||
source_minor_indices: &[usize],
|
||||
values: &[T])
|
||||
-> (Vec<usize>, Vec<usize>, Vec<T>)
|
||||
where
|
||||
T: Scalar + Zero
|
||||
{
|
||||
assert_eq!(source_major_offsets.len(), major_dim + 1);
|
||||
assert_eq!(source_minor_indices.len(), values.len());
|
||||
let nnz = values.len();
|
||||
|
||||
// Count the number of occurences of each minor index
|
||||
let mut minor_counts = vec![0; minor_dim];
|
||||
for minor_idx in source_minor_indices {
|
||||
minor_counts[*minor_idx] += 1;
|
||||
}
|
||||
convert_counts_to_offsets(&mut minor_counts);
|
||||
let mut target_offsets = minor_counts;
|
||||
target_offsets.push(nnz);
|
||||
let mut target_indices = vec![usize::MAX; nnz];
|
||||
let mut target_values = vec![T::zero(); nnz];
|
||||
|
||||
// Keep track of how many entries we have placed in each target major lane
|
||||
let mut current_target_major_counts = vec![0; minor_dim];
|
||||
|
||||
for source_major_idx in 0 .. major_dim {
|
||||
let source_lane_begin = source_major_offsets[source_major_idx];
|
||||
let source_lane_end = source_major_offsets[source_major_idx + 1];
|
||||
let source_lane_indices = &source_minor_indices[source_lane_begin .. source_lane_end];
|
||||
let source_lane_values = &values[source_lane_begin .. source_lane_end];
|
||||
|
||||
for (&source_minor_idx, val) in source_lane_indices.iter().zip(source_lane_values) {
|
||||
// Compute the offset in the target data for this particular source entry
|
||||
let target_lane_count = &mut current_target_major_counts[source_minor_idx];
|
||||
let entry_offset = target_offsets[source_minor_idx] + *target_lane_count;
|
||||
target_indices[entry_offset] = source_major_idx;
|
||||
target_values[entry_offset] = val.inlined_clone();
|
||||
*target_lane_count += 1;
|
||||
}
|
||||
}
|
||||
|
||||
(target_offsets, target_indices, target_values)
|
||||
}
|
||||
|
||||
fn convert_counts_to_offsets(counts: &mut [usize]) {
|
||||
// Convert the counts to an offset
|
||||
let mut offset = 0;
|
||||
for i_offset in counts.iter_mut() {
|
||||
let count = *i_offset;
|
||||
*i_offset = offset;
|
||||
offset += count;
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: Move this into `utils` or something?
|
||||
fn apply_permutation<T: Clone>(out_slice: &mut [T], in_slice: &[T], permutation: &[usize]) {
|
||||
assert_eq!(out_slice.len(), in_slice.len());
|
||||
assert_eq!(out_slice.len(), permutation.len());
|
||||
for (out_element, old_pos) in out_slice.iter_mut().zip(permutation) {
|
||||
*out_element = in_slice[*old_pos].clone();
|
||||
}
|
||||
}
|
||||
|
||||
/// Given *sorted* indices and corresponding scalar values, combines duplicates with the given
|
||||
/// associative combiner and calls the provided produce methods with combined indices and values.
|
||||
fn combine_duplicates<T: Clone>(
|
||||
mut produce_idx: impl FnMut(usize),
|
||||
mut produce_value: impl FnMut(T),
|
||||
idx_array: &[usize],
|
||||
values: &[T],
|
||||
combiner: impl Fn(T, T) -> T,
|
||||
) {
|
||||
assert_eq!(idx_array.len(), values.len());
|
||||
|
||||
let mut i = 0;
|
||||
while i < idx_array.len() {
|
||||
let idx = idx_array[i];
|
||||
let mut combined_value = values[i].clone();
|
||||
let mut j = i + 1;
|
||||
while j < idx_array.len() && idx_array[j] == idx {
|
||||
let j_val = values[j].clone();
|
||||
combined_value = combiner(combined_value, j_val);
|
||||
j += 1;
|
||||
}
|
||||
produce_idx(idx);
|
||||
produce_value(combined_value);
|
||||
i = j;
|
||||
}
|
||||
}
|
||||
@ -45,14 +45,17 @@
|
||||
//! - [x] "Disassemble" the COO matrix into its underlying triplet arrays.
|
||||
//! - Format conversion:
|
||||
//! - [x] COO -> Dense
|
||||
//! - [ ] CSR -> Dense
|
||||
//! - [ ] CSC -> Dense
|
||||
//! - [ ] COO -> CSR
|
||||
//! - [ ] COO -> CSC
|
||||
//! - [ ] CSR -> CSC
|
||||
//! - [ ] CSC -> CSR
|
||||
//! - [ ] CSR -> COO
|
||||
//! - [ ] CSC -> COO
|
||||
//! - [x] CSR -> Dense
|
||||
//! - [x] CSC -> Dense
|
||||
//! - [x] COO -> CSR
|
||||
//! - [x] COO -> CSC
|
||||
//! - [x] CSR -> CSC
|
||||
//! - [x] CSC -> CSR
|
||||
//! - [x] CSR -> COO
|
||||
//! - [x] CSC -> COO
|
||||
//! - [x] Dense -> COO
|
||||
//! - [x] Dense -> CSR
|
||||
//! - [x] Dense -> CSC
|
||||
//! - Arithmetic. In general arithmetic is only implemented between instances of the same format,
|
||||
//! or between dense and instances of a given format. For example, we do not implement
|
||||
//! CSR * CSC, only CSR * CSR and CSC * CSC.
|
||||
|
||||
@ -1,9 +1,16 @@
|
||||
use nalgebra_sparse::coo::CooMatrix;
|
||||
use nalgebra_sparse::convert::serial::{convert_coo_dense, convert_dense_coo};
|
||||
use nalgebra_sparse::proptest::coo_with_duplicates;
|
||||
use nalgebra_sparse::convert::serial::{convert_coo_dense, convert_coo_csr,
|
||||
convert_dense_coo, convert_csr_dense,
|
||||
convert_csr_coo, convert_dense_csr,
|
||||
convert_csc_coo, convert_coo_csc,
|
||||
convert_csc_dense, convert_dense_csc,
|
||||
convert_csr_csc, convert_csc_csr};
|
||||
use nalgebra_sparse::proptest::{coo_with_duplicates, coo_no_duplicates, csr, csc};
|
||||
use nalgebra::proptest::matrix;
|
||||
use proptest::prelude::*;
|
||||
use nalgebra::DMatrix;
|
||||
use nalgebra_sparse::csr::CsrMatrix;
|
||||
use nalgebra_sparse::csc::CscMatrix;
|
||||
|
||||
#[test]
|
||||
fn test_convert_dense_coo() {
|
||||
@ -49,10 +56,244 @@ fn test_convert_dense_coo() {
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_convert_coo_csr() {
|
||||
// No duplicates
|
||||
{
|
||||
let coo = {
|
||||
let mut coo = CooMatrix::new(3, 4);
|
||||
coo.push(1, 3, 4);
|
||||
coo.push(0, 1, 2);
|
||||
coo.push(2, 0, 1);
|
||||
coo.push(2, 3, 2);
|
||||
coo.push(2, 2, 1);
|
||||
coo
|
||||
};
|
||||
|
||||
let expected_csr = CsrMatrix::try_from_csr_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 1, 2, 5],
|
||||
vec![1, 3, 0, 2, 3],
|
||||
vec![2, 4, 1, 1, 2]
|
||||
).unwrap();
|
||||
|
||||
assert_eq!(convert_coo_csr(&coo), expected_csr);
|
||||
}
|
||||
|
||||
// Duplicates
|
||||
{
|
||||
let coo = {
|
||||
let mut coo = CooMatrix::new(3, 4);
|
||||
coo.push(1, 3, 4);
|
||||
coo.push(2, 3, 2);
|
||||
coo.push(0, 1, 2);
|
||||
coo.push(2, 0, 1);
|
||||
coo.push(2, 3, 2);
|
||||
coo.push(0, 1, 3);
|
||||
coo.push(2, 2, 1);
|
||||
coo
|
||||
};
|
||||
|
||||
let expected_csr = CsrMatrix::try_from_csr_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 1, 2, 5],
|
||||
vec![1, 3, 0, 2, 3],
|
||||
vec![5, 4, 1, 1, 4]
|
||||
).unwrap();
|
||||
|
||||
assert_eq!(convert_coo_csr(&coo), expected_csr);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_convert_csr_coo() {
|
||||
let csr = CsrMatrix::try_from_csr_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 1, 2, 5],
|
||||
vec![1, 3, 0, 2, 3],
|
||||
vec![5, 4, 1, 1, 4]
|
||||
).unwrap();
|
||||
|
||||
let expected_coo = CooMatrix::try_from_triplets(
|
||||
3,
|
||||
4,
|
||||
vec![0, 1, 2, 2, 2],
|
||||
vec![1, 3, 0, 2, 3],
|
||||
vec![5, 4, 1, 1, 4]
|
||||
).unwrap();
|
||||
|
||||
assert_eq!(convert_csr_coo(&csr), expected_coo);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_convert_coo_csc() {
|
||||
// No duplicates
|
||||
{
|
||||
let coo = {
|
||||
let mut coo = CooMatrix::new(3, 4);
|
||||
coo.push(1, 3, 4);
|
||||
coo.push(0, 1, 2);
|
||||
coo.push(2, 0, 1);
|
||||
coo.push(2, 3, 2);
|
||||
coo.push(2, 2, 1);
|
||||
coo
|
||||
};
|
||||
|
||||
let expected_csc = CscMatrix::try_from_csc_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 1, 2, 3, 5],
|
||||
vec![2, 0, 2, 1, 2],
|
||||
vec![1, 2, 1, 4, 2]
|
||||
).unwrap();
|
||||
|
||||
assert_eq!(convert_coo_csc(&coo), expected_csc);
|
||||
}
|
||||
|
||||
// Duplicates
|
||||
{
|
||||
let coo = {
|
||||
let mut coo = CooMatrix::new(3, 4);
|
||||
coo.push(1, 3, 4);
|
||||
coo.push(2, 3, 2);
|
||||
coo.push(0, 1, 2);
|
||||
coo.push(2, 0, 1);
|
||||
coo.push(2, 3, 2);
|
||||
coo.push(0, 1, 3);
|
||||
coo.push(2, 2, 1);
|
||||
coo
|
||||
};
|
||||
|
||||
let expected_csc = CscMatrix::try_from_csc_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 1, 2, 3, 5],
|
||||
vec![2, 0, 2, 1, 2],
|
||||
vec![1, 5, 1, 4, 4]
|
||||
).unwrap();
|
||||
|
||||
assert_eq!(convert_coo_csc(&coo), expected_csc);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_convert_csc_coo() {
|
||||
let csc = CscMatrix::try_from_csc_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 1, 2, 3, 5],
|
||||
vec![2, 0, 2, 1, 2],
|
||||
vec![1, 2, 1, 4, 2]
|
||||
).unwrap();
|
||||
|
||||
let expected_coo = CooMatrix::try_from_triplets(
|
||||
3,
|
||||
4,
|
||||
vec![2, 0, 2, 1, 2],
|
||||
vec![0, 1, 2, 3, 3],
|
||||
vec![1, 2, 1, 4, 2]
|
||||
).unwrap();
|
||||
|
||||
assert_eq!(convert_csc_coo(&csc), expected_coo);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_convert_csr_csc_bidirectional() {
|
||||
let csr = CsrMatrix::try_from_csr_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 3, 4, 6],
|
||||
vec![1, 2, 3, 0, 1, 3],
|
||||
vec![5, 3, 2, 2, 1, 4],
|
||||
).unwrap();
|
||||
|
||||
let csc = CscMatrix::try_from_csc_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 1, 3, 4, 6],
|
||||
vec![1, 0, 2, 0, 0, 2],
|
||||
vec![2, 5, 1, 3, 2, 4],
|
||||
).unwrap();
|
||||
|
||||
assert_eq!(convert_csr_csc(&csr), csc);
|
||||
assert_eq!(convert_csc_csr(&csc), csr);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_convert_csr_dense_bidirectional() {
|
||||
let csr = CsrMatrix::try_from_csr_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 3, 4, 6],
|
||||
vec![1, 2, 3, 0, 1, 3],
|
||||
vec![5, 3, 2, 2, 1, 4],
|
||||
).unwrap();
|
||||
|
||||
#[rustfmt::skip]
|
||||
let dense = DMatrix::from_row_slice(3, 4, &[
|
||||
0, 5, 3, 2,
|
||||
2, 0, 0, 0,
|
||||
0, 1, 0, 4
|
||||
]);
|
||||
|
||||
assert_eq!(convert_csr_dense(&csr), dense);
|
||||
assert_eq!(convert_dense_csr(&dense), csr);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_convert_csc_dense_bidirectional() {
|
||||
let csc = CscMatrix::try_from_csc_data(
|
||||
3,
|
||||
4,
|
||||
vec![0, 1, 3, 4, 6],
|
||||
vec![1, 0, 2, 0, 0, 2],
|
||||
vec![2, 5, 1, 3, 2, 4],
|
||||
).unwrap();
|
||||
|
||||
#[rustfmt::skip]
|
||||
let dense = DMatrix::from_row_slice(3, 4, &[
|
||||
0, 5, 3, 2,
|
||||
2, 0, 0, 0,
|
||||
0, 1, 0, 4
|
||||
]);
|
||||
|
||||
assert_eq!(convert_csc_dense(&csc), dense);
|
||||
assert_eq!(convert_dense_csc(&dense), csc);
|
||||
}
|
||||
|
||||
fn coo_strategy() -> impl Strategy<Value=CooMatrix<i32>> {
|
||||
coo_with_duplicates(-5 ..= 5, 0..=6usize, 0..=6usize, 40, 2)
|
||||
}
|
||||
|
||||
fn coo_no_duplicates_strategy() -> impl Strategy<Value=CooMatrix<i32>> {
|
||||
coo_no_duplicates(-5 ..= 5, 0..=6usize, 0..=6usize, 40)
|
||||
}
|
||||
|
||||
fn csr_strategy() -> impl Strategy<Value=CsrMatrix<i32>> {
|
||||
csr(-5 ..= 5, 0..=6usize, 0..=6usize, 40)
|
||||
}
|
||||
|
||||
fn csc_strategy() -> impl Strategy<Value=CscMatrix<i32>> {
|
||||
csc(-5 ..= 5, 0..=6usize, 0..=6usize, 40)
|
||||
}
|
||||
|
||||
/// Avoid generating explicit zero values so that it is possible to reason about sparsity patterns
|
||||
fn non_zero_csr_strategy() -> impl Strategy<Value=CsrMatrix<i32>> {
|
||||
csr(1 ..= 5, 0..=6usize, 0..=6usize, 40)
|
||||
}
|
||||
|
||||
/// Avoid generating explicit zero values so that it is possible to reason about sparsity patterns
|
||||
fn non_zero_csc_strategy() -> impl Strategy<Value=CscMatrix<i32>> {
|
||||
csc(1 ..= 5, 0..=6usize, 0..=6usize, 40)
|
||||
}
|
||||
|
||||
fn dense_strategy() -> impl Strategy<Value=DMatrix<i32>> {
|
||||
matrix(-5..=5, 0..=6, 0..=6)
|
||||
}
|
||||
|
||||
proptest! {
|
||||
|
||||
#[test]
|
||||
@ -78,7 +319,125 @@ proptest! {
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn from_dense_coo_roundtrip(dense in matrix(-5..=5, 0..=6, 0..=6)) {
|
||||
fn coo_from_dense_roundtrip(dense in dense_strategy()) {
|
||||
prop_assert_eq!(&dense, &DMatrix::from(&CooMatrix::from(&dense)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_coo_csr_agrees_with_csr_dense(coo in coo_strategy()) {
|
||||
let coo_dense = convert_coo_dense(&coo);
|
||||
let csr = convert_coo_csr(&coo);
|
||||
let csr_dense = convert_csr_dense(&csr);
|
||||
prop_assert_eq!(csr_dense, coo_dense);
|
||||
|
||||
// It might be that COO matrices have a higher nnz due to duplicates,
|
||||
// so we can only check that the CSR matrix has no more than the original COO matrix
|
||||
prop_assert!(csr.nnz() <= coo.nnz());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_coo_csr_nnz(coo in coo_no_duplicates_strategy()) {
|
||||
// Check that the NNZ are equal when converting from a CooMatrix without
|
||||
// duplicates to a CSR matrix
|
||||
let csr = convert_coo_csr(&coo);
|
||||
prop_assert_eq!(csr.nnz(), coo.nnz());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_csr_coo_roundtrip(csr in csr_strategy()) {
|
||||
let coo = convert_csr_coo(&csr);
|
||||
let csr2 = convert_coo_csr(&coo);
|
||||
prop_assert_eq!(csr2, csr);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coo_from_csr_roundtrip(csr in csr_strategy()) {
|
||||
prop_assert_eq!(&csr, &CsrMatrix::from(&CooMatrix::from(&csr)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn csr_from_dense_roundtrip(dense in dense_strategy()) {
|
||||
prop_assert_eq!(&dense, &DMatrix::from(&CsrMatrix::from(&dense)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_csr_dense_roundtrip(csr in non_zero_csr_strategy()) {
|
||||
// Since we only generate CSR matrices with non-zero values, we know that the
|
||||
// number of explicitly stored entries when converting CSR->Dense->CSR should be
|
||||
// unchanged, so that we can verify that the result is the same as the input
|
||||
let dense = convert_csr_dense(&csr);
|
||||
let csr2 = convert_dense_csr(&dense);
|
||||
prop_assert_eq!(csr2, csr);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_csc_coo_roundtrip(csc in csc_strategy()) {
|
||||
let coo = convert_csc_coo(&csc);
|
||||
let csc2 = convert_coo_csc(&coo);
|
||||
prop_assert_eq!(csc2, csc);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn coo_from_csc_roundtrip(csc in csc_strategy()) {
|
||||
prop_assert_eq!(&csc, &CscMatrix::from(&CooMatrix::from(&csc)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_csc_dense_roundtrip(csc in non_zero_csc_strategy()) {
|
||||
// Since we only generate CSC matrices with non-zero values, we know that the
|
||||
// number of explicitly stored entries when converting CSC->Dense->CSC should be
|
||||
// unchanged, so that we can verify that the result is the same as the input
|
||||
let dense = convert_csc_dense(&csc);
|
||||
let csc2 = convert_dense_csc(&dense);
|
||||
prop_assert_eq!(csc2, csc);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn csc_from_dense_roundtrip(dense in dense_strategy()) {
|
||||
prop_assert_eq!(&dense, &DMatrix::from(&CscMatrix::from(&dense)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_coo_csc_agrees_with_csc_dense(coo in coo_strategy()) {
|
||||
let coo_dense = convert_coo_dense(&coo);
|
||||
let csc = convert_coo_csc(&coo);
|
||||
let csc_dense = convert_csc_dense(&csc);
|
||||
prop_assert_eq!(csc_dense, coo_dense);
|
||||
|
||||
// It might be that COO matrices have a higher nnz due to duplicates,
|
||||
// so we can only check that the CSR matrix has no more than the original COO matrix
|
||||
prop_assert!(csc.nnz() <= coo.nnz());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_coo_csc_nnz(coo in coo_no_duplicates_strategy()) {
|
||||
// Check that the NNZ are equal when converting from a CooMatrix without
|
||||
// duplicates to a CSR matrix
|
||||
let csc = convert_coo_csc(&coo);
|
||||
prop_assert_eq!(csc.nnz(), coo.nnz());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_csc_csr_roundtrip(csc in csc_strategy()) {
|
||||
let csr = convert_csc_csr(&csc);
|
||||
let csc2 = convert_csr_csc(&csr);
|
||||
prop_assert_eq!(csc2, csc);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn convert_csr_csc_roundtrip(csr in csr_strategy()) {
|
||||
let csc = convert_csr_csc(&csr);
|
||||
let csr2 = convert_csc_csr(&csc);
|
||||
prop_assert_eq!(csr2, csr);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn csc_from_csr_roundtrip(csr in csr_strategy()) {
|
||||
prop_assert_eq!(&csr, &CsrMatrix::from(&CscMatrix::from(&csr)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn csr_from_csc_roundtrip(csc in csc_strategy()) {
|
||||
prop_assert_eq!(&csc, &CscMatrix::from(&CsrMatrix::from(&csc)));
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user