forked from M-Labs/nalgebra
41 lines
1.9 KiB
Rust
41 lines
1.9 KiB
Rust
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//! TODO
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//!
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//! TODO: Clarify that this module needs proptest-support feature
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use crate::coo::CooMatrix;
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use proptest::prelude::*;
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use proptest::collection::{SizeRange, vec};
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use nalgebra::Scalar;
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/// TODO
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pub fn coo<T>(
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value_strategy: T,
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rows: impl Strategy<Value=usize> + 'static,
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cols: impl Strategy<Value=usize> + 'static,
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max_nonzeros: usize) -> BoxedStrategy<CooMatrix<T::Value>>
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where
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T: Strategy + Clone + 'static,
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T::Value: Scalar,
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{
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(rows, cols, (0 ..= max_nonzeros))
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.prop_flat_map(move |(nrows, ncols, nnz)| {
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// If the numbers of rows and columns are small in comparison with the
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// max nnz, it will lead to small matrices essentially always turning out to be dense.
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// To address this, we correct the nnz by computing the modulo with the
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// maximum number of non-zeros (ignoring duplicates) we can have for
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// the given dimensions.
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// This way we can still generate very sparse matrices for small matrices.
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let max_nnz = nrows * ncols;
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let nnz = if max_nnz == 0 { 0 } else { nnz % max_nnz };
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let row_index_strategy = if nrows > 0 { 0 .. nrows } else { 0 .. 1 };
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let col_index_strategy = if ncols > 0 { 0 .. ncols } else { 0 .. 1 };
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let row_indices = vec![row_index_strategy.clone(); nnz];
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let col_indices = vec![col_index_strategy.clone(); nnz];
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let values_strategy = vec![value_strategy.clone(); nnz];
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(Just(nrows), Just(ncols), row_indices, col_indices, values_strategy)
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}).prop_map(|(nrows, ncols, row_indices, col_indices, values)| {
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CooMatrix::try_from_triplets(nrows, ncols, row_indices, col_indices, values)
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.expect("We should always generate valid COO data.")
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}).boxed()
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}
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