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switched the magnitude function to use the alpha max + beta min algorithm
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@ -378,13 +378,6 @@ impl<I: FixedWidthSignedInteger, const N: usize> Num<I, N> {
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}
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}
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}
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}
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impl<const N: usize> Num<i32, N> {
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pub fn sqrt(self) -> Self {
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assert_eq!(N % 2, 0, "N must be even to be able to square root");
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Self(syscall::sqrt(self.0) << (N / 2))
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}
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}
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#[test_case]
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#[test_case]
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fn test_numbers(_gba: &mut super::Gba) {
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fn test_numbers(_gba: &mut super::Gba) {
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// test addition
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// test addition
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@ -696,9 +689,17 @@ impl<const N: usize> Vector2D<Num<i32, N>> {
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self.x.abs() + self.y.abs()
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self.x.abs() + self.y.abs()
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}
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}
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// calculates the magnitude of a vector using the alpha max plus beta min
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// algorithm https://en.wikipedia.org/wiki/Alpha_max_plus_beta_min_algorithm
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// this has a maximum error of less than 4% of the true magnitude, probably
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// depending on the size of your fixed point approximation
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pub fn magnitude(self) -> Num<i32, N> {
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pub fn magnitude(self) -> Num<i32, N> {
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self.magnitude_squared().sqrt()
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let max = core::cmp::max(self.x, self.y);
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let min = core::cmp::min(self.x, self.y);
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max * num!(0.960433870103) + min * num!(0.397824734759)
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}
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}
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pub fn normalise(self) -> Self {
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pub fn normalise(self) -> Self {
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self / self.magnitude()
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self / self.magnitude()
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}
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}
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