nalgebra/tests/proptest/mod.rs

371 lines
13 KiB
Rust

//! Tests for proptest-related functionality.
#![allow(dead_code)]
use nalgebra::allocator::Allocator;
use nalgebra::base::dimension::*;
use nalgebra::proptest::{DimRange, MatrixStrategy};
use nalgebra::{
DMatrix, DVector, DefaultAllocator, Dim, DualQuaternion, Isometry2, Isometry3, Matrix3,
OMatrix, Point2, Point3, Quaternion, Rotation2, Rotation3, Scalar, Similarity3, Translation2,
Translation3, UnitComplex, UnitDualQuaternion, UnitQuaternion, Vector3, U3, U4,
};
use num_complex::Complex;
use proptest::prelude::*;
use proptest::strategy::{Strategy, ValueTree};
use proptest::test_runner::TestRunner;
use std::ops::RangeInclusive;
pub const PROPTEST_MATRIX_DIM: RangeInclusive<usize> = 1..=20;
pub const PROPTEST_F64: RangeInclusive<f64> = -100.0..=100.0;
pub use nalgebra::proptest::{matrix, vector};
pub fn point2() -> impl Strategy<Value = Point2<f64>> {
vector2().prop_map(|v| Point2::from(v))
}
pub fn point3() -> impl Strategy<Value = Point3<f64>> {
vector3().prop_map(|v| Point3::from(v))
}
pub fn translation2() -> impl Strategy<Value = Translation2<f64>> {
vector2().prop_map(|v| Translation2::from(v))
}
pub fn translation3() -> impl Strategy<Value = Translation3<f64>> {
vector3().prop_map(|v| Translation3::from(v))
}
pub fn rotation2() -> impl Strategy<Value = Rotation2<f64>> {
PROPTEST_F64.prop_map(|v| Rotation2::new(v))
}
pub fn rotation3() -> impl Strategy<Value = Rotation3<f64>> {
vector3().prop_map(|v| Rotation3::new(v))
}
pub fn unit_complex() -> impl Strategy<Value = UnitComplex<f64>> {
PROPTEST_F64.prop_map(|v| UnitComplex::new(v))
}
pub fn isometry2() -> impl Strategy<Value = Isometry2<f64>> {
vector3().prop_map(|v| Isometry2::new(v.xy(), v.z))
}
pub fn isometry3() -> impl Strategy<Value = Isometry3<f64>> {
vector6().prop_map(|v| Isometry3::new(v.xyz(), Vector3::new(v.w, v.a, v.b)))
}
// pub fn similarity2() -> impl Strategy<Value = Similarity2<f64>> {
// vector4().prop_map(|v| Similarity2::new(v.xy(), v.z, v.w))
// }
pub fn similarity3() -> impl Strategy<Value = Similarity3<f64>> {
vector(PROPTEST_F64, Const::<7>)
.prop_map(|v| Similarity3::new(v.xyz(), Vector3::new(v[3], v[4], v[5]), v[6]))
}
pub fn unit_dual_quaternion() -> impl Strategy<Value = UnitDualQuaternion<f64>> {
isometry3().prop_map(|iso| UnitDualQuaternion::from_isometry(&iso))
}
pub fn dual_quaternion() -> impl Strategy<Value = DualQuaternion<f64>> {
vector(PROPTEST_F64, Const::<8>).prop_map(|v| {
DualQuaternion::from_real_and_dual(
Quaternion::new(v[0], v[1], v[2], v[3]),
Quaternion::new(v[4], v[5], v[6], v[7]),
)
})
}
pub fn quaternion() -> impl Strategy<Value = Quaternion<f64>> {
vector4().prop_map(|v| Quaternion::from(v))
}
pub fn unit_quaternion() -> impl Strategy<Value = UnitQuaternion<f64>> {
vector3().prop_map(|v| UnitQuaternion::new(v))
}
pub fn complex_f64() -> impl Strategy<Value = Complex<f64>> + Clone {
vector(PROPTEST_F64, Const::<2>).prop_map(|v| Complex::new(v.x, v.y))
}
pub fn dmatrix() -> impl Strategy<Value = DMatrix<f64>> {
matrix(PROPTEST_F64, PROPTEST_MATRIX_DIM, PROPTEST_MATRIX_DIM)
}
pub fn dvector() -> impl Strategy<Value = DVector<f64>> {
vector(PROPTEST_F64, PROPTEST_MATRIX_DIM)
}
pub fn dmatrix_<ScalarStrategy>(
scalar_strategy: ScalarStrategy,
) -> impl Strategy<Value = DMatrix<ScalarStrategy::Value>>
where
ScalarStrategy: Strategy + Clone + 'static,
ScalarStrategy::Value: Scalar,
DefaultAllocator: Allocator<ScalarStrategy::Value, Dynamic, Dynamic>,
{
matrix(scalar_strategy, PROPTEST_MATRIX_DIM, PROPTEST_MATRIX_DIM)
}
// pub fn dvector_<T>(range: RangeInclusive<T>) -> impl Strategy<Value = DVector<T>>
// where
// RangeInclusive<T>: Strategy<Value = T>,
// T: Scalar + PartialEq + Copy,
// DefaultAllocator: Allocator<T, Dynamic>,
// {
// vector(range, PROPTEST_MATRIX_DIM)
// }
macro_rules! define_strategies(
($($strategy_: ident $strategy: ident<$nrows: literal, $ncols: literal>),*) => {$(
pub fn $strategy() -> impl Strategy<Value = OMatrix<f64, Const<$nrows>, Const<$ncols>>> {
matrix(PROPTEST_F64, Const::<$nrows>, Const::<$ncols>)
}
pub fn $strategy_<ScalarStrategy>(scalar_strategy: ScalarStrategy) -> impl Strategy<Value = OMatrix<ScalarStrategy::Value, Const<$nrows>, Const<$ncols>>>
where
ScalarStrategy: Strategy + Clone + 'static,
ScalarStrategy::Value: Scalar, {
matrix(scalar_strategy, Const::<$nrows>, Const::<$ncols>)
}
)*}
);
define_strategies!(
matrix1_ matrix1<1, 1>,
matrix2_ matrix2<2, 2>,
matrix3_ matrix3<3, 3>,
matrix4_ matrix4<4, 4>,
matrix5_ matrix5<5, 5>,
matrix6_ matrix6<6, 6>,
matrix5x2_ matrix5x2<5, 2>,
matrix2x5_ matrix2x5<2, 5>,
matrix5x3_ matrix5x3<5, 3>,
matrix3x5_ matrix3x5<3, 5>,
matrix5x4_ matrix5x4<5, 4>,
matrix4x5_ matrix4x5<4, 5>,
vector1_ vector1<1, 1>,
vector2_ vector2<2, 1>,
vector3_ vector3<3, 1>,
vector4_ vector4<4, 1>,
vector5_ vector5<5, 1>,
vector6_ vector6<6, 1>
);
/// Generate a proptest that tests that all matrices generated with the
/// provided rows and columns conform to the constraints defined by the
/// input.
macro_rules! generate_matrix_sanity_test {
($test_name:ident, $rows:expr, $cols:expr) => {
proptest! {
#[test]
fn $test_name(a in matrix(-5 ..= 5i32, $rows, $cols)) {
// let a: OMatrix<_, $rows, $cols> = a;
let rows_range = DimRange::from($rows);
let cols_range = DimRange::from($cols);
prop_assert!(a.nrows() >= rows_range.lower_bound().value()
&& a.nrows() <= rows_range.upper_bound().value());
prop_assert!(a.ncols() >= cols_range.lower_bound().value()
&& a.ncols() <= cols_range.upper_bound().value());
prop_assert!(a.iter().all(|x_ij| *x_ij >= -5 && *x_ij <= 5));
}
}
};
}
// Test all fixed-size matrices with row/col dimensions up to 3
generate_matrix_sanity_test!(test_matrix_u0_u0, Const::<0>, Const::<0>);
generate_matrix_sanity_test!(test_matrix_u1_u0, Const::<1>, Const::<0>);
generate_matrix_sanity_test!(test_matrix_u0_u1, Const::<0>, Const::<1>);
generate_matrix_sanity_test!(test_matrix_u1_u1, Const::<1>, Const::<1>);
generate_matrix_sanity_test!(test_matrix_u2_u1, Const::<2>, Const::<1>);
generate_matrix_sanity_test!(test_matrix_u1_u2, Const::<1>, Const::<2>);
generate_matrix_sanity_test!(test_matrix_u2_u2, Const::<2>, Const::<2>);
generate_matrix_sanity_test!(test_matrix_u3_u2, Const::<3>, Const::<2>);
generate_matrix_sanity_test!(test_matrix_u2_u3, Const::<2>, Const::<3>);
generate_matrix_sanity_test!(test_matrix_u3_u3, Const::<3>, Const::<3>);
// Similarly test all heap-allocated but fixed dim ranges
generate_matrix_sanity_test!(test_matrix_0_0, 0, 0);
generate_matrix_sanity_test!(test_matrix_0_1, 0, 1);
generate_matrix_sanity_test!(test_matrix_1_0, 1, 0);
generate_matrix_sanity_test!(test_matrix_1_1, 1, 1);
generate_matrix_sanity_test!(test_matrix_2_1, 2, 1);
generate_matrix_sanity_test!(test_matrix_1_2, 1, 2);
generate_matrix_sanity_test!(test_matrix_2_2, 2, 2);
generate_matrix_sanity_test!(test_matrix_3_2, 3, 2);
generate_matrix_sanity_test!(test_matrix_2_3, 2, 3);
generate_matrix_sanity_test!(test_matrix_3_3, 3, 3);
// Test arbitrary inputs
generate_matrix_sanity_test!(test_matrix_input_1, Const::<5>, 1..=5);
generate_matrix_sanity_test!(test_matrix_input_2, 3..=4, 1..=5);
generate_matrix_sanity_test!(test_matrix_input_3, 1..=2, Const::<3>);
generate_matrix_sanity_test!(test_matrix_input_4, 3, Const::<4>);
#[test]
fn test_matrix_output_types() {
// Test that the dimension types are correct for the given inputs
let _: MatrixStrategy<_, U3, U4> = matrix(-5..5, Const::<3>, Const::<4>);
let _: MatrixStrategy<_, U3, U3> = matrix(-5..5, Const::<3>, Const::<3>);
let _: MatrixStrategy<_, U3, Dynamic> = matrix(-5..5, Const::<3>, 1..=5);
let _: MatrixStrategy<_, Dynamic, U3> = matrix(-5..5, 1..=5, Const::<3>);
let _: MatrixStrategy<_, Dynamic, Dynamic> = matrix(-5..5, 1..=5, 1..=5);
}
// Below we have some tests to ensure that specific instances of OMatrix are usable
// in a typical proptest scenario where we (implicitly) use the `Arbitrary` trait
proptest! {
#[test]
fn ensure_arbitrary_test_compiles_matrix3(_: Matrix3<i32>) {}
#[test]
fn ensure_arbitrary_test_compiles_matrixmn_u3_dynamic(_: OMatrix<i32, U3, Dynamic>) {}
#[test]
fn ensure_arbitrary_test_compiles_matrixmn_dynamic_u3(_: OMatrix<i32, Dynamic, U3>) {}
#[test]
fn ensure_arbitrary_test_compiles_dmatrix(_: DMatrix<i32>) {}
#[test]
fn ensure_arbitrary_test_compiles_vector3(_: Vector3<i32>) {}
#[test]
fn ensure_arbitrary_test_compiles_dvector(_: DVector<i32>) {}
}
#[test]
fn matrix_shrinking_satisfies_constraints() {
// We use a deterministic test runner to make the test "stable".
let mut runner = TestRunner::deterministic();
let strategy = matrix(-1..=2, 1..=3, 2..=4);
let num_matrices = 25;
macro_rules! maybeprintln {
($($arg:tt)*) => {
// Uncomment the below line to enable printing of matrix sequences. This is handy
// for manually inspecting the sequences of simplified matrices.
// println!($($arg)*)
};
}
maybeprintln!("========================== (begin generation process)");
for _ in 0..num_matrices {
let mut tree = strategy
.new_tree(&mut runner)
.expect("Tree generation should not fail.");
let mut current = Some(tree.current());
maybeprintln!("------------------");
while let Some(matrix) = current {
maybeprintln!("{}", matrix);
assert!(
matrix.iter().all(|&v| v >= -1 && v <= 2),
"All matrix elements must satisfy constraints"
);
assert!(
matrix.nrows() >= 1 && matrix.nrows() <= 3,
"Number of rows in matrix must satisfy constraints."
);
assert!(
matrix.ncols() >= 2 && matrix.ncols() <= 4,
"Number of columns in matrix must satisfy constraints."
);
current = if tree.simplify() {
Some(tree.current())
} else {
None
}
}
}
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."
);
}
}