srenblad/nalgebra
1
0
Fork 0
forked from M-Labs/nalgebra
Code Pull Requests Activity

Move nalgebra proptest slow tests into slow submodule

Browse Source 
This way it's easier to keep track of what imports are only
necessary for the slow tests.
This commit is contained in:
Andreas Longva 2020-11-23 13:41:10 +01:00
parent f909638bf4
commit 402de4d045
1 changed files with 74 additions and 63 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

137
tests/proptest/mod.rs
View File

@ -6,13 +6,6 @@ use proptest::prelude::*;
use proptest::strategy::ValueTree;
use proptest::test_runner::TestRunner;
#[cfg(feature = "slow-tests")]
use {
itertools::Itertools,
std::iter::repeat,
std::collections::HashSet,
};
/// Generate a proptest that tests that all matrices generated with the
/// provided rows and columns conform to the constraints defined by the
/// input.
@ -96,62 +89,6 @@ proptest! {
fn ensure_arbitrary_test_compiles_dvector(_: DVector<i32>) {}
}
#[cfg(feature = "slow-tests")]
#[test]
fn matrix_samples_all_possible_outputs() {
// Test that the proptest generation covers all possible outputs for a small space of inputs
// given enough samples.
// We use a deterministic test runner to make the test "stable".
let mut runner = TestRunner::deterministic();
// This number needs to be high enough so that we with high probability sample
// all possible cases
let num_generated_matrices = 200000;
let values = -1..=1;
let rows = 0..=2;
let cols = 0..=3;
let strategy = matrix(values.clone(), rows.clone(), cols.clone());
// Enumerate all possible combinations
let mut all_combinations = HashSet::new();
for nrows in rows {
for ncols in cols.clone() {
// For the given number of rows and columns
let n_values = nrows * ncols;
if n_values == 0 {
// If we have zero rows or columns, the set of matrices with the given
// rows and columns is a single element: an empty matrix
all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &[]));
} else {
// Otherwise, we need to sample all possible matrices.
// To do this, we generate the values as the (multi) Cartesian product
// of the value sets. For example, for a 2x2 matrices, we consider
// all possible 4-element arrays that the matrices can take by
// considering all elements in the cartesian product
// V x V x V x V
// where V is the set of eligible values, e.g. V := -1 ..= 1
for matrix_values in repeat(values.clone()).take(n_values).multi_cartesian_product() {
all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &matrix_values));
}
}
}
}
let mut visited_combinations = HashSet::new();
for _ in 0..num_generated_matrices {
let tree = strategy
.new_tree(&mut runner)
.expect("Tree generation should not fail");
let matrix = tree.current();
visited_combinations.insert(matrix.clone());
}
assert_eq!(visited_combinations, all_combinations, "Did not sample all possible values.");
}
#[test]
fn matrix_shrinking_satisfies_constraints() {
// We use a deterministic test runner to make the test "stable".
@ -206,3 +143,77 @@ fn matrix_shrinking_satisfies_constraints() {
maybeprintln!("========================== (end of generation process)");
}
#[cfg(feature = "slow-tests")]
mod slow {
use super::*;
use itertools::Itertools;
use std::collections::HashSet;
use std::iter::repeat;
#[cfg(feature = "slow-tests")]
#[test]
fn matrix_samples_all_possible_outputs() {
// Test that the proptest generation covers all possible outputs for a small space of inputs
// given enough samples.
// We use a deterministic test runner to make the test "stable".
let mut runner = TestRunner::deterministic();
// This number needs to be high enough so that we with high probability sample
// all possible cases
let num_generated_matrices = 200000;
let values = -1..=1;
let rows = 0..=2;
let cols = 0..=3;
let strategy = matrix(values.clone(), rows.clone(), cols.clone());
// Enumerate all possible combinations
let mut all_combinations = HashSet::new();
for nrows in rows {
for ncols in cols.clone() {
// For the given number of rows and columns
let n_values = nrows * ncols;
if n_values == 0 {
// If we have zero rows or columns, the set of matrices with the given
// rows and columns is a single element: an empty matrix
all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &[]));
} else {
// Otherwise, we need to sample all possible matrices.
// To do this, we generate the values as the (multi) Cartesian product
// of the value sets. For example, for a 2x2 matrices, we consider
// all possible 4-element arrays that the matrices can take by
// considering all elements in the cartesian product
// V x V x V x V
// where V is the set of eligible values, e.g. V := -1 ..= 1
for matrix_values in repeat(values.clone())
.take(n_values)
.multi_cartesian_product()
{
all_combinations.insert(DMatrix::from_row_slice(
nrows,
ncols,
&matrix_values,
));
}
}
}
}
let mut visited_combinations = HashSet::new();
for _ in 0..num_generated_matrices {
let tree = strategy
.new_tree(&mut runner)
.expect("Tree generation should not fail");
let matrix = tree.current();
visited_combinations.insert(matrix.clone());
}
assert_eq!(
visited_combinations, all_combinations,
"Did not sample all possible values."
);
}
}