248 lines
9.2 KiB
Rust
248 lines
9.2 KiB
Rust
#[test]
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#[ignore]
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fn coo_no_duplicates_generates_admissible_matrices() {
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//TODO
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}
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#[cfg(feature = "slow-tests")]
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mod slow {
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use nalgebra::DMatrix;
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use nalgebra_sparse::proptest::{
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coo_no_duplicates, coo_with_duplicates, csc, csr, sparsity_pattern,
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};
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use itertools::Itertools;
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use proptest::strategy::ValueTree;
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use proptest::test_runner::TestRunner;
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use proptest::prelude::*;
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use nalgebra_sparse::csr::CsrMatrix;
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use std::collections::HashSet;
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use std::iter::repeat;
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use std::ops::RangeInclusive;
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fn generate_all_possible_matrices(
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value_range: RangeInclusive<i32>,
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rows_range: RangeInclusive<usize>,
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cols_range: RangeInclusive<usize>,
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) -> HashSet<DMatrix<i32>> {
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// Enumerate all possible combinations
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let mut all_combinations = HashSet::new();
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for nrows in rows_range {
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for ncols in cols_range.clone() {
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// For the given number of rows and columns
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let n_values = nrows * ncols;
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if n_values == 0 {
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// If we have zero rows or columns, the set of matrices with the given
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// rows and columns is a single element: an empty matrix
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all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &[]));
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} else {
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// Otherwise, we need to sample all possible matrices.
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// To do this, we generate the values as the (multi) Cartesian product
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// of the value sets. For example, for a 2x2 matrices, we consider
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// all possible 4-element arrays that the matrices can take by
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// considering all elements in the cartesian product
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// V x V x V x V
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// where V is the set of eligible values, e.g. V := -1 ..= 1
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let values_iter = repeat(value_range.clone())
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.take(n_values)
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.multi_cartesian_product();
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for matrix_values in values_iter {
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all_combinations.insert(DMatrix::from_row_slice(
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nrows,
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ncols,
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&matrix_values,
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));
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}
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}
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}
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}
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all_combinations
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}
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#[cfg(feature = "slow-tests")]
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#[test]
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fn coo_no_duplicates_samples_all_admissible_outputs() {
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// Note: This test basically mirrors a similar test for `matrix` in the `nalgebra` repo.
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// Test that the proptest generation covers all possible outputs for a small space of inputs
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// given enough samples.
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// We use a deterministic test runner to make the test "stable".
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let mut runner = TestRunner::deterministic();
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// This number needs to be high enough so that we with high probability sample
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// all possible cases
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let num_generated_matrices = 500000;
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let values = -1..=1;
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let rows = 0..=2;
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let cols = 0..=3;
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let max_nnz = rows.end() * cols.end();
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let strategy = coo_no_duplicates(values.clone(), rows.clone(), cols.clone(), max_nnz);
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// Enumerate all possible combinations
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let all_combinations = generate_all_possible_matrices(values, rows, cols);
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let visited_combinations =
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sample_matrix_output_space(strategy, &mut runner, num_generated_matrices);
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assert_eq!(visited_combinations.len(), all_combinations.len());
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assert_eq!(
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visited_combinations, all_combinations,
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"Did not sample all possible values."
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);
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}
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#[cfg(feature = "slow-tests")]
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#[test]
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fn coo_with_duplicates_samples_all_admissible_outputs() {
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// This is almost the same as the test for coo_no_duplicates, except that we need
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// a different "success" criterion, since coo_with_duplicates is able to generate
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// matrices with values outside of the value constraints. See below for details.
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// We use a deterministic test runner to make the test "stable".
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let mut runner = TestRunner::deterministic();
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// This number needs to be high enough so that we with high probability sample
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// all possible cases
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let num_generated_matrices = 500000;
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let values = -1..=1;
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let rows = 0..=2;
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let cols = 0..=3;
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let max_nnz = rows.end() * cols.end();
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let strategy = coo_with_duplicates(values.clone(), rows.clone(), cols.clone(), max_nnz, 2);
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// Enumerate all possible combinations that fit the constraints
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// (note: this is only a subset of the matrices that can be generated by
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// `coo_with_duplicates`)
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let all_combinations = generate_all_possible_matrices(values, rows, cols);
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let visited_combinations =
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sample_matrix_output_space(strategy, &mut runner, num_generated_matrices);
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// Here we cannot verify that the set of visited combinations is *equal* to
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// all possible outcomes with the given constraints, however the
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// strategy should be able to generate all matrices that fit the constraints.
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// In other words, we need to determine that set of all admissible matrices
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// is contained in the set of visited matrices
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assert!(all_combinations.is_subset(&visited_combinations));
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}
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#[cfg(feature = "slow-tests")]
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#[test]
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fn csr_samples_all_admissible_outputs() {
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// We use a deterministic test runner to make the test "stable".
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let mut runner = TestRunner::deterministic();
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// This number needs to be high enough so that we with high probability sample
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// all possible cases
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let num_generated_matrices = 500000;
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let values = -1..=1;
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let rows = 0..=2;
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let cols = 0..=3;
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let max_nnz = rows.end() * cols.end();
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let strategy = csr(values.clone(), rows.clone(), cols.clone(), max_nnz);
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let all_combinations = generate_all_possible_matrices(values, rows, cols);
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let visited_combinations =
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sample_matrix_output_space(strategy, &mut runner, num_generated_matrices);
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assert_eq!(visited_combinations.len(), all_combinations.len());
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assert_eq!(
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visited_combinations, all_combinations,
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"Did not sample all possible values."
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);
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}
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#[cfg(feature = "slow-tests")]
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#[test]
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fn csc_samples_all_admissible_outputs() {
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// We use a deterministic test runner to make the test "stable".
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let mut runner = TestRunner::deterministic();
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// This number needs to be high enough so that we with high probability sample
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// all possible cases
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let num_generated_matrices = 500000;
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let values = -1..=1;
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let rows = 0..=2;
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let cols = 0..=3;
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let max_nnz = rows.end() * cols.end();
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let strategy = csc(values.clone(), rows.clone(), cols.clone(), max_nnz);
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let all_combinations = generate_all_possible_matrices(values, rows, cols);
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let visited_combinations =
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sample_matrix_output_space(strategy, &mut runner, num_generated_matrices);
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assert_eq!(visited_combinations.len(), all_combinations.len());
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assert_eq!(
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visited_combinations, all_combinations,
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"Did not sample all possible values."
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);
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}
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#[cfg(feature = "slow-tests")]
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#[test]
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fn sparsity_pattern_samples_all_admissible_outputs() {
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let mut runner = TestRunner::deterministic();
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let num_generated_patterns = 50000;
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let major_dims = 0..=2;
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let minor_dims = 0..=3;
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let max_nnz = major_dims.end() * minor_dims.end();
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let strategy = sparsity_pattern(major_dims.clone(), minor_dims.clone(), max_nnz);
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let visited_patterns: HashSet<_> = sample_strategy(strategy, &mut runner)
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.take(num_generated_patterns)
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.map(|pattern| {
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// We represent patterns as dense matrices with 1 if an entry is occupied,
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// 0 otherwise
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let values = vec![1; pattern.nnz()];
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CsrMatrix::try_from_pattern_and_values(pattern, values).unwrap()
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})
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.map(|csr| DMatrix::from(&csr))
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.collect();
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let all_possible_patterns = generate_all_possible_matrices(0..=1, major_dims, minor_dims);
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assert_eq!(visited_patterns.len(), all_possible_patterns.len());
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assert_eq!(visited_patterns, all_possible_patterns);
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}
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fn sample_matrix_output_space<S>(
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strategy: S,
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runner: &mut TestRunner,
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num_samples: usize,
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) -> HashSet<DMatrix<i32>>
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where
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S: Strategy,
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DMatrix<i32>: for<'b> From<&'b S::Value>,
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{
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sample_strategy(strategy, runner)
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.take(num_samples)
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.map(|matrix| DMatrix::from(&matrix))
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.collect()
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}
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fn sample_strategy<'a, S: 'a + Strategy>(
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strategy: S,
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runner: &'a mut TestRunner,
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) -> impl 'a + Iterator<Item = S::Value> {
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repeat(()).map(move |_| {
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let tree = strategy
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.new_tree(runner)
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.expect("Tree generation should not fail");
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let value = tree.current();
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value
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})
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}
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}
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