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+// Tests issue #4584 https://github.com/odin-lang/Odin/issues/4584
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+package test_issues
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+
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+import "core:testing"
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+import "core:log"
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+import "core:math/linalg"
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+import glm "core:math/linalg/glsl"
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+import hlm "core:math/linalg/hlsl"
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+
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+@test
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+test_adjugate_2x2 :: proc(t: ^testing.T) {
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+ I := linalg.identity(matrix[2,2]int)
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+ m := matrix[2,2]int {
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+ -3, 2,
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+ -1, 0,
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+ }
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+ expected := matrix[2,2]int {
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+ 0, -2,
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+ 1, -3,
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+ }
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+ testing.expect_value(t, linalg.adjugate(m), expected)
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+ testing.expect_value(t, linalg.determinant(m), 2)
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+ testing.expect_value(t, linalg.adjugate(m) * m, 2 * I)
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+ testing.expect_value(t, m * linalg.adjugate(m), 2 * I)
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+
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+ testing.expect_value(t, glm.adjugate(m), expected)
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+ testing.expect_value(t, glm.determinant(m), 2)
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+ testing.expect_value(t, glm.adjugate(m) * m, 2 * I)
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+ testing.expect_value(t, m * glm.adjugate(m), 2 * I)
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+
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+ testing.expect_value(t, hlm.adjugate(m), expected)
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+ testing.expect_value(t, hlm.determinant(m), 2)
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+ testing.expect_value(t, hlm.adjugate(m) * m, 2 * I)
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+ testing.expect_value(t, m * hlm.adjugate(m), 2 * I)
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+}
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+
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+@test
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+test_adjugate_3x3 :: proc(t: ^testing.T) {
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+ I := linalg.identity(matrix[3,3]int)
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+ m := matrix[3,3]int {
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+ -3, 2, -5,
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+ -1, 0, -2,
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+ 3, -4, 1,
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+ }
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+ expected := matrix[3,3]int {
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+ -8, 18, -4,
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+ -5, 12, -1,
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+ 4, -6, 2,
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+ }
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+ testing.expect_value(t, linalg.adjugate(m), expected)
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+ testing.expect_value(t, linalg.determinant(m), -6)
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+ testing.expect_value(t, linalg.adjugate(m) * m, -6 * I)
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+ testing.expect_value(t, m * linalg.adjugate(m), -6 * I)
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+
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+ testing.expect_value(t, glm.adjugate(m), expected)
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+ testing.expect_value(t, glm.determinant(m), -6)
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+ testing.expect_value(t, glm.adjugate(m) * m, -6 * I)
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+ testing.expect_value(t, m * glm.adjugate(m), -6 * I)
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+
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+ testing.expect_value(t, hlm.adjugate(m), expected)
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+ testing.expect_value(t, hlm.determinant(m), -6)
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+ testing.expect_value(t, hlm.adjugate(m) * m, -6 * I)
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+ testing.expect_value(t, m * hlm.adjugate(m), -6 * I)
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+}
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+
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+@test
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+test_adjugate_4x4 :: proc(t: ^testing.T) {
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+ I := linalg.identity(matrix[4,4]int)
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+ m := matrix[4,4]int {
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+ -3, 2, -5, 1,
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+ -1, 0, -2, 2,
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+ 3, -4, 1, 3,
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+ 4, 5, 6, 7,
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+ }
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+ expected := matrix[4,4]int {
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+ -144, 266, -92, -16,
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+ -57, 92, -5, -16,
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+ 105, -142, 55, 2,
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+ 33, -96, 9, -6,
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+ }
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+ testing.expect_value(t, linalg.adjugate(m), expected)
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+ testing.expect_value(t, linalg.determinant(m), -174)
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+ testing.expect_value(t, linalg.adjugate(m) * m, -174 * I)
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+ testing.expect_value(t, m * linalg.adjugate(m), -174 * I)
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+
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+ testing.expect_value(t, glm.adjugate(m), expected)
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+ testing.expect_value(t, glm.determinant(m), -174)
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+ testing.expect_value(t, glm.adjugate(m) * m, -174 * I)
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+ testing.expect_value(t, m * glm.adjugate(m), -174 * I)
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+
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+ testing.expect_value(t, hlm.adjugate(m), expected)
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+ testing.expect_value(t, hlm.determinant(m), -174)
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+ testing.expect_value(t, hlm.adjugate(m) * m, -174 * I)
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+ testing.expect_value(t, m * hlm.adjugate(m), -174 * I)
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+}
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+
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+@test
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+test_inverse_regression_2x2 :: proc(t: ^testing.T) {
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+ I := linalg.identity(matrix[2,2]f32)
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+ m := matrix[2,2]f32 {
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+ -3, 2,
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+ -1, 0,
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+ }
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+ expected := matrix[2,2]f32 {
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+ 0.0, -1.0,
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+ 1.0/2.0, -3.0/2.0,
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+ }
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+ expect_float_matrix_value(t, linalg.inverse(m), expected)
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+ expect_float_matrix_value(t, linalg.inverse_transpose(m), linalg.transpose(expected))
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+ expect_float_matrix_value(t, linalg.inverse(m) * m, I)
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+ expect_float_matrix_value(t, m * linalg.inverse(m), I)
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+
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+ expect_float_matrix_value(t, glm.inverse(m), expected)
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+ expect_float_matrix_value(t, glm.inverse_transpose(m), glm.transpose(expected))
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+ expect_float_matrix_value(t, glm.inverse(m) * m, I)
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+ expect_float_matrix_value(t, m * glm.inverse(m), I)
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+
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+ expect_float_matrix_value(t, hlm.inverse(m), expected)
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+ expect_float_matrix_value(t, hlm.inverse_transpose(m), hlm.transpose(expected))
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+ expect_float_matrix_value(t, hlm.inverse(m) * m, I)
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+ expect_float_matrix_value(t, m * hlm.inverse(m), I)
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+}
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+
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+@test
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+test_inverse_regression_3x3 :: proc(t: ^testing.T) {
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+ I := linalg.identity(matrix[3,3]f32)
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+ m := matrix[3,3]f32 {
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+ -3, 2, -5,
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+ -1, 0, -2,
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+ 3, -4, 1,
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+ }
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+ expected := matrix[3,3]f32 {
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+ 4.0/3.0, -3.0, 2.0/3.0,
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+ 5.0/6.0, -2.0, 1.0/6.0,
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+ -2.0/3.0, 1.0, -1.0/3.0,
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+ }
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+ expect_float_matrix_value(t, linalg.inverse(m), expected)
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+ expect_float_matrix_value(t, linalg.inverse_transpose(m), linalg.transpose(expected))
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+ expect_float_matrix_value(t, linalg.inverse(m) * m, I)
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+ expect_float_matrix_value(t, m * linalg.inverse(m), I)
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+
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+ expect_float_matrix_value(t, glm.inverse(m), expected)
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+ expect_float_matrix_value(t, glm.inverse_transpose(m), glm.transpose(expected))
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+ expect_float_matrix_value(t, glm.inverse(m) * m, I)
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+ expect_float_matrix_value(t, m * glm.inverse(m), I)
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+
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+ expect_float_matrix_value(t, hlm.inverse(m), expected)
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+ expect_float_matrix_value(t, hlm.inverse_transpose(m), hlm.transpose(expected))
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+ expect_float_matrix_value(t, hlm.inverse(m) * m, I)
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+ expect_float_matrix_value(t, m * hlm.inverse(m), I)
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+}
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+
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+@test
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+test_inverse_regression_4x4 :: proc(t: ^testing.T) {
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+ I := linalg.identity(matrix[4,4]f32)
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+ m := matrix[4,4]f32 {
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+ -3, 2, -5, 1,
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+ -1, 0, -2, 2,
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+ 3, -4, 1, 3,
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+ 4, 5, 6, 7,
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+ }
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+ expected := matrix[4,4]f32 {
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+ 24.0/29.0, -133.0/87.0, 46.0/87.0, 8.0/87.0,
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+ 19.0/58.0, -46.0/87.0, 5.0/174.0, 8.0/87.0,
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+ -35.0/58.0, 71.0/87.0, -55.0/174.0, -1.0/87.0,
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+ -11.0/58.0, 16.0/29.0, -3.0/58.0, 1.0/29.0,
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+ }
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+ expect_float_matrix_value(t, linalg.inverse(m), expected)
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+ expect_float_matrix_value(t, linalg.inverse_transpose(m), linalg.transpose(expected))
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+ expect_float_matrix_value(t, linalg.inverse(m) * m, I)
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+ expect_float_matrix_value(t, m * linalg.inverse(m), I)
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+
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+ expect_float_matrix_value(t, glm.inverse(m), expected)
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+ expect_float_matrix_value(t, glm.inverse_transpose(m), glm.transpose(expected))
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+ expect_float_matrix_value(t, glm.inverse(m) * m, I)
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+ expect_float_matrix_value(t, m * glm.inverse(m), I)
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+
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+ expect_float_matrix_value(t, hlm.inverse(m), expected)
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+ expect_float_matrix_value(t, hlm.inverse_transpose(m), hlm.transpose(expected))
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+ expect_float_matrix_value(t, hlm.inverse(m) * m, I)
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+ expect_float_matrix_value(t, m * hlm.inverse(m), I)
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+}
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+
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+@(private="file")
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+expect_float_matrix_value :: proc(t: ^testing.T, value, expected: $M/matrix[$N, N]f32, loc := #caller_location, value_expr := #caller_expression(value)) -> bool {
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+ ok := true
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+ outer: for i in 0..<N {
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+ for j in 0..<N {
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+ diff := abs(value[i, j] - expected[i, j])
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+ if diff > 1e-6 {
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+ ok = false
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+ break outer
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+ }
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+ }
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+ }
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+ if !ok do log.errorf("expected %v to be %v, got %v", value_expr, expected, value, location=loc)
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+ return ok
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+}
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