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Merge pull request #4633 from spahnke/fix-matrix-adjugate

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Fix matrix adjugate
This commit is contained in:
gingerBill
2025-01-06 09:36:18 +00:00
committed by GitHub
parent c7739de891 b21fc19233
commit a3b4280cf7
6 changed files with 398 additions and 54 deletions
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tests/issues/run.bat
+1
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@@ -16,6 +16,7 @@ set COMMON=-define:ODIN_TEST_FANCY=false -file -vet -strict-style
..\..\..\odin test ..\test_issue_2637.odin %COMMON% || exit /b
..\..\..\odin test ..\test_issue_2666.odin %COMMON% || exit /b
..\..\..\odin test ..\test_issue_4210.odin %COMMON% || exit /b
..\..\..\odin test ..\test_issue_4584.odin %COMMON% || exit /b
@echo off
tests/issues/run.sh
+1
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@@ -17,6 +17,7 @@ $ODIN test ../test_issue_2615.odin $COMMON
$ODIN test ../test_issue_2637.odin $COMMON
$ODIN test ../test_issue_2666.odin $COMMON
$ODIN test ../test_issue_4210.odin $COMMON
$ODIN test ../test_issue_4584.odin $COMMON
if [[ $($ODIN build ../test_issue_2395.odin $COMMON 2>&1 >/dev/null | grep -c "Error:") -eq 2 ]] ; then
echo "SUCCESSFUL 1/1"
else
tests/issues/test_issue_4584.odin
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@@ -0,0 +1,198 @@
// Tests issue #4584 https://github.com/odin-lang/Odin/issues/4584
package test_issues
import "core:testing"
import "core:log"
import "core:math/linalg"
import glm "core:math/linalg/glsl"
import hlm "core:math/linalg/hlsl"
@test
test_adjugate_2x2 :: proc(t: ^testing.T) {
I := linalg.identity(matrix[2,2]int)
m := matrix[2,2]int {
-3, 2,
-1, 0,
}
expected := matrix[2,2]int {
0, -2,
1, -3,
}
testing.expect_value(t, linalg.adjugate(m), expected)
testing.expect_value(t, linalg.determinant(m), 2)
testing.expect_value(t, linalg.adjugate(m) * m, 2 * I)
testing.expect_value(t, m * linalg.adjugate(m), 2 * I)
testing.expect_value(t, glm.adjugate(m), expected)
testing.expect_value(t, glm.determinant(m), 2)
testing.expect_value(t, glm.adjugate(m) * m, 2 * I)
testing.expect_value(t, m * glm.adjugate(m), 2 * I)
testing.expect_value(t, hlm.adjugate(m), expected)
testing.expect_value(t, hlm.determinant(m), 2)
testing.expect_value(t, hlm.adjugate(m) * m, 2 * I)
testing.expect_value(t, m * hlm.adjugate(m), 2 * I)
}
@test
test_adjugate_3x3 :: proc(t: ^testing.T) {
I := linalg.identity(matrix[3,3]int)
m := matrix[3,3]int {
-3, 2, -5,
-1, 0, -2,
3, -4, 1,
}
expected := matrix[3,3]int {
-8, 18, -4,
-5, 12, -1,
4, -6, 2,
}
testing.expect_value(t, linalg.adjugate(m), expected)
testing.expect_value(t, linalg.determinant(m), -6)
testing.expect_value(t, linalg.adjugate(m) * m, -6 * I)
testing.expect_value(t, m * linalg.adjugate(m), -6 * I)
testing.expect_value(t, glm.adjugate(m), expected)
testing.expect_value(t, glm.determinant(m), -6)
testing.expect_value(t, glm.adjugate(m) * m, -6 * I)
testing.expect_value(t, m * glm.adjugate(m), -6 * I)
testing.expect_value(t, hlm.adjugate(m), expected)
testing.expect_value(t, hlm.determinant(m), -6)
testing.expect_value(t, hlm.adjugate(m) * m, -6 * I)
testing.expect_value(t, m * hlm.adjugate(m), -6 * I)
}
@test
test_adjugate_4x4 :: proc(t: ^testing.T) {
I := linalg.identity(matrix[4,4]int)
m := matrix[4,4]int {
-3, 2, -5, 1,
-1, 0, -2, 2,
3, -4, 1, 3,
4, 5, 6, 7,
}
expected := matrix[4,4]int {
-144, 266, -92, -16,
-57, 92, -5, -16,
105, -142, 55, 2,
33, -96, 9, -6,
}
testing.expect_value(t, linalg.adjugate(m), expected)
testing.expect_value(t, linalg.determinant(m), -174)
testing.expect_value(t, linalg.adjugate(m) * m, -174 * I)
testing.expect_value(t, m * linalg.adjugate(m), -174 * I)
testing.expect_value(t, glm.adjugate(m), expected)
testing.expect_value(t, glm.determinant(m), -174)
testing.expect_value(t, glm.adjugate(m) * m, -174 * I)
testing.expect_value(t, m * glm.adjugate(m), -174 * I)
testing.expect_value(t, hlm.adjugate(m), expected)
testing.expect_value(t, hlm.determinant(m), -174)
testing.expect_value(t, hlm.adjugate(m) * m, -174 * I)
testing.expect_value(t, m * hlm.adjugate(m), -174 * I)
}
@test
test_inverse_regression_2x2 :: proc(t: ^testing.T) {
I := linalg.identity(matrix[2,2]f32)
m := matrix[2,2]f32 {
-3, 2,
-1, 0,
}
expected := matrix[2,2]f32 {
0.0, -1.0,
1.0/2.0, -3.0/2.0,
}
expect_float_matrix_value(t, linalg.inverse(m), expected)
expect_float_matrix_value(t, linalg.inverse_transpose(m), linalg.transpose(expected))
expect_float_matrix_value(t, linalg.inverse(m) * m, I)
expect_float_matrix_value(t, m * linalg.inverse(m), I)
expect_float_matrix_value(t, glm.inverse(m), expected)
expect_float_matrix_value(t, glm.inverse_transpose(m), glm.transpose(expected))
expect_float_matrix_value(t, glm.inverse(m) * m, I)
expect_float_matrix_value(t, m * glm.inverse(m), I)
expect_float_matrix_value(t, hlm.inverse(m), expected)
expect_float_matrix_value(t, hlm.inverse_transpose(m), hlm.transpose(expected))
expect_float_matrix_value(t, hlm.inverse(m) * m, I)
expect_float_matrix_value(t, m * hlm.inverse(m), I)
}
@test
test_inverse_regression_3x3 :: proc(t: ^testing.T) {
I := linalg.identity(matrix[3,3]f32)
m := matrix[3,3]f32 {
-3, 2, -5,
-1, 0, -2,
3, -4, 1,
}
expected := matrix[3,3]f32 {
4.0/3.0, -3.0, 2.0/3.0,
5.0/6.0, -2.0, 1.0/6.0,
-2.0/3.0, 1.0, -1.0/3.0,
}
expect_float_matrix_value(t, linalg.inverse(m), expected)
expect_float_matrix_value(t, linalg.inverse_transpose(m), linalg.transpose(expected))
expect_float_matrix_value(t, linalg.inverse(m) * m, I)
expect_float_matrix_value(t, m * linalg.inverse(m), I)
expect_float_matrix_value(t, glm.inverse(m), expected)
expect_float_matrix_value(t, glm.inverse_transpose(m), glm.transpose(expected))
expect_float_matrix_value(t, glm.inverse(m) * m, I)
expect_float_matrix_value(t, m * glm.inverse(m), I)
expect_float_matrix_value(t, hlm.inverse(m), expected)
expect_float_matrix_value(t, hlm.inverse_transpose(m), hlm.transpose(expected))
expect_float_matrix_value(t, hlm.inverse(m) * m, I)
expect_float_matrix_value(t, m * hlm.inverse(m), I)
}
@test
test_inverse_regression_4x4 :: proc(t: ^testing.T) {
I := linalg.identity(matrix[4,4]f32)
m := matrix[4,4]f32 {
-3, 2, -5, 1,
-1, 0, -2, 2,
3, -4, 1, 3,
4, 5, 6, 7,
}
expected := matrix[4,4]f32 {
24.0/29.0, -133.0/87.0, 46.0/87.0, 8.0/87.0,
19.0/58.0, -46.0/87.0, 5.0/174.0, 8.0/87.0,
-35.0/58.0, 71.0/87.0, -55.0/174.0, -1.0/87.0,
-11.0/58.0, 16.0/29.0, -3.0/58.0, 1.0/29.0,
}
expect_float_matrix_value(t, linalg.inverse(m), expected)
expect_float_matrix_value(t, linalg.inverse_transpose(m), linalg.transpose(expected))
expect_float_matrix_value(t, linalg.inverse(m) * m, I)
expect_float_matrix_value(t, m * linalg.inverse(m), I)
expect_float_matrix_value(t, glm.inverse(m), expected)
expect_float_matrix_value(t, glm.inverse_transpose(m), glm.transpose(expected))
expect_float_matrix_value(t, glm.inverse(m) * m, I)
expect_float_matrix_value(t, m * glm.inverse(m), I)
expect_float_matrix_value(t, hlm.inverse(m), expected)
expect_float_matrix_value(t, hlm.inverse_transpose(m), hlm.transpose(expected))
expect_float_matrix_value(t, hlm.inverse(m) * m, I)
expect_float_matrix_value(t, m * hlm.inverse(m), I)
}
@(private="file")
expect_float_matrix_value :: proc(t: ^testing.T, value, expected: $M/matrix[$N, N]f32, loc := #caller_location, value_expr := #caller_expression(value)) -> bool {
ok := true
outer: for i in 0..<N {
for j in 0..<N {
diff := abs(value[i, j] - expected[i, j])
if diff > 1e-6 {
ok = false
break outer
}
}
}
if !ok do log.errorf("expected %v to be %v, got %v", value_expr, expected, value, location=loc)
return ok
}