371 lines
13 KiB
Rust
371 lines
13 KiB
Rust
//! Tests for proptest-related functionality.
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#![allow(dead_code)]
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use nalgebra::allocator::Allocator;
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use nalgebra::base::dimension::*;
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use nalgebra::proptest::{DimRange, MatrixStrategy};
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use nalgebra::{
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DMatrix, DVector, DefaultAllocator, Dim, DualQuaternion, Isometry2, Isometry3, Matrix3,
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OMatrix, Point2, Point3, Quaternion, Rotation2, Rotation3, Scalar, Similarity3, Translation2,
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Translation3, UnitComplex, UnitDualQuaternion, UnitQuaternion, Vector3, U3, U4,
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};
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use num_complex::Complex;
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use proptest::prelude::*;
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use proptest::strategy::{Strategy, ValueTree};
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use proptest::test_runner::TestRunner;
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use std::ops::RangeInclusive;
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pub const PROPTEST_MATRIX_DIM: RangeInclusive<usize> = 1..=20;
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pub const PROPTEST_F64: RangeInclusive<f64> = -100.0..=100.0;
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pub use nalgebra::proptest::{matrix, vector};
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pub fn point2() -> impl Strategy<Value = Point2<f64>> {
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vector2().prop_map(|v| Point2::from(v))
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}
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pub fn point3() -> impl Strategy<Value = Point3<f64>> {
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vector3().prop_map(|v| Point3::from(v))
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}
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pub fn translation2() -> impl Strategy<Value = Translation2<f64>> {
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vector2().prop_map(|v| Translation2::from(v))
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}
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pub fn translation3() -> impl Strategy<Value = Translation3<f64>> {
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vector3().prop_map(|v| Translation3::from(v))
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}
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pub fn rotation2() -> impl Strategy<Value = Rotation2<f64>> {
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PROPTEST_F64.prop_map(|v| Rotation2::new(v))
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}
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pub fn rotation3() -> impl Strategy<Value = Rotation3<f64>> {
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vector3().prop_map(|v| Rotation3::new(v))
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}
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pub fn unit_complex() -> impl Strategy<Value = UnitComplex<f64>> {
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PROPTEST_F64.prop_map(|v| UnitComplex::new(v))
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}
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pub fn isometry2() -> impl Strategy<Value = Isometry2<f64>> {
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vector3().prop_map(|v| Isometry2::new(v.xy(), v.z))
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}
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pub fn isometry3() -> impl Strategy<Value = Isometry3<f64>> {
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vector6().prop_map(|v| Isometry3::new(v.xyz(), Vector3::new(v.w, v.a, v.b)))
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}
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// pub fn similarity2() -> impl Strategy<Value = Similarity2<f64>> {
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// vector4().prop_map(|v| Similarity2::new(v.xy(), v.z, v.w))
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// }
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pub fn similarity3() -> impl Strategy<Value = Similarity3<f64>> {
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vector(PROPTEST_F64, Const::<7>)
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.prop_map(|v| Similarity3::new(v.xyz(), Vector3::new(v[3], v[4], v[5]), v[6]))
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}
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pub fn unit_dual_quaternion() -> impl Strategy<Value = UnitDualQuaternion<f64>> {
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isometry3().prop_map(|iso| UnitDualQuaternion::from_isometry(&iso))
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}
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pub fn dual_quaternion() -> impl Strategy<Value = DualQuaternion<f64>> {
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vector(PROPTEST_F64, Const::<8>).prop_map(|v| {
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DualQuaternion::from_real_and_dual(
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Quaternion::new(v[0], v[1], v[2], v[3]),
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Quaternion::new(v[4], v[5], v[6], v[7]),
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)
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})
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}
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pub fn quaternion() -> impl Strategy<Value = Quaternion<f64>> {
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vector4().prop_map(|v| Quaternion::from(v))
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}
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pub fn unit_quaternion() -> impl Strategy<Value = UnitQuaternion<f64>> {
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vector3().prop_map(|v| UnitQuaternion::new(v))
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}
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pub fn complex_f64() -> impl Strategy<Value = Complex<f64>> + Clone {
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vector(PROPTEST_F64, Const::<2>).prop_map(|v| Complex::new(v.x, v.y))
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}
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pub fn dmatrix() -> impl Strategy<Value = DMatrix<f64>> {
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matrix(PROPTEST_F64, PROPTEST_MATRIX_DIM, PROPTEST_MATRIX_DIM)
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}
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pub fn dvector() -> impl Strategy<Value = DVector<f64>> {
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vector(PROPTEST_F64, PROPTEST_MATRIX_DIM)
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}
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pub fn dmatrix_<ScalarStrategy>(
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scalar_strategy: ScalarStrategy,
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) -> impl Strategy<Value = OMatrix<ScalarStrategy::Value, Dynamic, Dynamic>>
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where
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ScalarStrategy: Strategy + Clone + 'static,
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ScalarStrategy::Value: Scalar,
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DefaultAllocator: Allocator<ScalarStrategy::Value, Dynamic, Dynamic>,
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{
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matrix(scalar_strategy, PROPTEST_MATRIX_DIM, PROPTEST_MATRIX_DIM)
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}
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// pub fn dvector_<T>(range: RangeInclusive<T>) -> impl Strategy<Value = DVector<T>>
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// where
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// RangeInclusive<T>: Strategy<Value = T>,
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// T: Scalar + PartialEq + Copy,
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// DefaultAllocator: Allocator<T, Dynamic>,
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// {
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// vector(range, PROPTEST_MATRIX_DIM)
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// }
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macro_rules! define_strategies(
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($($strategy_: ident $strategy: ident<$nrows: literal, $ncols: literal>),*) => {$(
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pub fn $strategy() -> impl Strategy<Value = OMatrix<f64, Const<$nrows>, Const<$ncols>>> {
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matrix(PROPTEST_F64, Const::<$nrows>, Const::<$ncols>)
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}
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pub fn $strategy_<ScalarStrategy>(scalar_strategy: ScalarStrategy) -> impl Strategy<Value = OMatrix<ScalarStrategy::Value, Const<$nrows>, Const<$ncols>>>
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where
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ScalarStrategy: Strategy + Clone + 'static,
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ScalarStrategy::Value: Scalar, {
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matrix(scalar_strategy, Const::<$nrows>, Const::<$ncols>)
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}
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)*}
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);
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define_strategies!(
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matrix1_ matrix1<1, 1>,
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matrix2_ matrix2<2, 2>,
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matrix3_ matrix3<3, 3>,
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matrix4_ matrix4<4, 4>,
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matrix5_ matrix5<5, 5>,
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matrix6_ matrix6<6, 6>,
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matrix5x2_ matrix5x2<5, 2>,
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matrix2x5_ matrix2x5<2, 5>,
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matrix5x3_ matrix5x3<5, 3>,
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matrix3x5_ matrix3x5<3, 5>,
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matrix5x4_ matrix5x4<5, 4>,
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matrix4x5_ matrix4x5<4, 5>,
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vector1_ vector1<1, 1>,
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vector2_ vector2<2, 1>,
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vector3_ vector3<3, 1>,
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vector4_ vector4<4, 1>,
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vector5_ vector5<5, 1>,
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vector6_ vector6<6, 1>
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);
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/// Generate a proptest that tests that all matrices generated with the
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/// provided rows and columns conform to the constraints defined by the
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/// input.
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macro_rules! generate_matrix_sanity_test {
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($test_name:ident, $rows:expr, $cols:expr) => {
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proptest! {
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#[test]
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fn $test_name(a in matrix(-5 ..= 5i32, $rows, $cols)) {
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// let a: OMatrix<_, $rows, $cols> = a;
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let rows_range = DimRange::from($rows);
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let cols_range = DimRange::from($cols);
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prop_assert!(a.nrows() >= rows_range.lower_bound().value()
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&& a.nrows() <= rows_range.upper_bound().value());
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prop_assert!(a.ncols() >= cols_range.lower_bound().value()
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&& a.ncols() <= cols_range.upper_bound().value());
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prop_assert!(a.iter().all(|x_ij| *x_ij >= -5 && *x_ij <= 5));
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}
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}
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};
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}
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// Test all fixed-size matrices with row/col dimensions up to 3
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generate_matrix_sanity_test!(test_matrix_u0_u0, Const::<0>, Const::<0>);
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generate_matrix_sanity_test!(test_matrix_u1_u0, Const::<1>, Const::<0>);
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generate_matrix_sanity_test!(test_matrix_u0_u1, Const::<0>, Const::<1>);
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generate_matrix_sanity_test!(test_matrix_u1_u1, Const::<1>, Const::<1>);
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generate_matrix_sanity_test!(test_matrix_u2_u1, Const::<2>, Const::<1>);
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generate_matrix_sanity_test!(test_matrix_u1_u2, Const::<1>, Const::<2>);
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generate_matrix_sanity_test!(test_matrix_u2_u2, Const::<2>, Const::<2>);
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generate_matrix_sanity_test!(test_matrix_u3_u2, Const::<3>, Const::<2>);
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generate_matrix_sanity_test!(test_matrix_u2_u3, Const::<2>, Const::<3>);
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generate_matrix_sanity_test!(test_matrix_u3_u3, Const::<3>, Const::<3>);
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// Similarly test all heap-allocated but fixed dim ranges
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generate_matrix_sanity_test!(test_matrix_0_0, 0, 0);
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generate_matrix_sanity_test!(test_matrix_0_1, 0, 1);
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generate_matrix_sanity_test!(test_matrix_1_0, 1, 0);
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generate_matrix_sanity_test!(test_matrix_1_1, 1, 1);
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generate_matrix_sanity_test!(test_matrix_2_1, 2, 1);
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generate_matrix_sanity_test!(test_matrix_1_2, 1, 2);
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generate_matrix_sanity_test!(test_matrix_2_2, 2, 2);
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generate_matrix_sanity_test!(test_matrix_3_2, 3, 2);
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generate_matrix_sanity_test!(test_matrix_2_3, 2, 3);
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generate_matrix_sanity_test!(test_matrix_3_3, 3, 3);
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// Test arbitrary inputs
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generate_matrix_sanity_test!(test_matrix_input_1, Const::<5>, 1..=5);
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generate_matrix_sanity_test!(test_matrix_input_2, 3..=4, 1..=5);
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generate_matrix_sanity_test!(test_matrix_input_3, 1..=2, Const::<3>);
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generate_matrix_sanity_test!(test_matrix_input_4, 3, Const::<4>);
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#[test]
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fn test_matrix_output_types() {
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// Test that the dimension types are correct for the given inputs
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let _: MatrixStrategy<_, U3, U4> = matrix(-5..5, Const::<3>, Const::<4>);
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let _: MatrixStrategy<_, U3, U3> = matrix(-5..5, Const::<3>, Const::<3>);
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let _: MatrixStrategy<_, U3, Dynamic> = matrix(-5..5, Const::<3>, 1..=5);
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let _: MatrixStrategy<_, Dynamic, U3> = matrix(-5..5, 1..=5, Const::<3>);
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let _: MatrixStrategy<_, Dynamic, Dynamic> = matrix(-5..5, 1..=5, 1..=5);
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}
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// Below we have some tests to ensure that specific instances of OMatrix are usable
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// in a typical proptest scenario where we (implicitly) use the `Arbitrary` trait
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proptest! {
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#[test]
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fn ensure_arbitrary_test_compiles_matrix3(_: Matrix3<i32>) {}
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#[test]
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fn ensure_arbitrary_test_compiles_matrixmn_u3_dynamic(_: OMatrix<i32, U3, Dynamic>) {}
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#[test]
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fn ensure_arbitrary_test_compiles_matrixmn_dynamic_u3(_: OMatrix<i32, Dynamic, U3>) {}
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#[test]
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fn ensure_arbitrary_test_compiles_dmatrix(_: DMatrix<i32>) {}
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#[test]
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fn ensure_arbitrary_test_compiles_vector3(_: Vector3<i32>) {}
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#[test]
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fn ensure_arbitrary_test_compiles_dvector(_: DVector<i32>) {}
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}
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#[test]
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fn matrix_shrinking_satisfies_constraints() {
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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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let strategy = matrix(-1..=2, 1..=3, 2..=4);
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let num_matrices = 25;
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macro_rules! maybeprintln {
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($($arg:tt)*) => {
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// Uncomment the below line to enable printing of matrix sequences. This is handy
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// for manually inspecting the sequences of simplified matrices.
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// println!($($arg)*)
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};
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}
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maybeprintln!("========================== (begin generation process)");
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for _ in 0..num_matrices {
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let mut tree = strategy
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.new_tree(&mut runner)
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.expect("Tree generation should not fail.");
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let mut current = Some(tree.current());
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maybeprintln!("------------------");
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while let Some(matrix) = current {
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maybeprintln!("{}", matrix);
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assert!(
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matrix.iter().all(|&v| v >= -1 && v <= 2),
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"All matrix elements must satisfy constraints"
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);
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assert!(
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matrix.nrows() >= 1 && matrix.nrows() <= 3,
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"Number of rows in matrix must satisfy constraints."
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);
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assert!(
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matrix.ncols() >= 2 && matrix.ncols() <= 4,
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"Number of columns in matrix must satisfy constraints."
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);
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current = if tree.simplify() {
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Some(tree.current())
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} else {
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None
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}
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}
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}
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maybeprintln!("========================== (end of generation process)");
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}
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#[cfg(feature = "slow-tests")]
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mod slow {
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use super::*;
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use itertools::Itertools;
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use std::collections::HashSet;
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use std::iter::repeat;
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#[cfg(feature = "slow-tests")]
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#[test]
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fn matrix_samples_all_possible_outputs() {
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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 = 200000;
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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 strategy = matrix(values.clone(), rows.clone(), cols.clone());
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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 {
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for ncols in cols.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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for matrix_values in repeat(values.clone())
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.take(n_values)
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.multi_cartesian_product()
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{
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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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let mut visited_combinations = HashSet::new();
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for _ in 0..num_generated_matrices {
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let tree = strategy
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.new_tree(&mut runner)
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.expect("Tree generation should not fail");
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let matrix = tree.current();
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visited_combinations.insert(matrix.clone());
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}
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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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}
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