Scala 矩阵求逆
呃，是的，我真的需要一个没有创作者眼睛的人的快速输入。根据我的 scalacheck 测试，这里有些问题......但我对此了解不够，不知道问题出在哪里。
case class Matrix(_1: (Float, Float, Float, Float), _2: (Float, Float, Float, Float),
_3: (Float, Float, Float, Float), _4: (Float, Float, Float, Float)) extends Immutable {
def invert = {
val _11 = _2._2 * _3._3 * _4._4 - _2._2 * _3._4 * _4._3 - _3._2 * _2._3 * _4._4
+_3._2 * _2._4 * _4._3 + _4._2 * _2._3 * _3._4 - _4._2 * _2._4 * _3._3
val _21 = -_2._1 * _3._3 * _4._4 + _2._1 * _3._4 * _4._3 + _3._1 * _2._3 * _4._4
-_3._1 * _2._4 * _4._3 - _4._1 * _2._3 * _3._4 + _4._1 * _2._4 * _3._3
val _31 = _2._1 * _3._2 * _4._4 - _2._1 * _3._4 * _4._2 - _3._1 * _2._2 * _4._4
+_3._1 * _2._4 * _4._2 + _4._1 * _2._2 * _3._4 - _4._1 * _2._4 * _3._2
val _41 = -_2._1 * _3._2 * _4._3 + _2._1 * _3._3 * _4._2 + _3._1 * _2._2 * _4._3
-_3._1 * _2._3 * _4._2 - _4._1 * _2._2 * _3._3 + _4._1 * _2._3 * _3._2
val _12 = -_1._2 * _3._3 * _4._4 + _1._2 * _3._4 * _4._3 + _3._2 * _1._3 * _4._4
-_3._2 * _1._4 * _4._3 - _4._2 * _1._3 * _3._4 + _4._2 * _1._4 * _3._3
val _22 = _1._1 * _3._3 * _4._4 - _1._1 * _3._4 * _4._3 - _3._1 * _1._3 * _4._4
+_3._1 * _1._4 * _4._3 + _4._1 * _1._3 * _3._4 - _4._1 * _1._4 * _3._3
val _32 = -_1._1 * _3._2 * _4._4 + _1._1 * _3._4 * _4._2 + _3._1 * _1._2 * _4._4
-_3._1 * _1._4 * _4._2 - _4._1 * _1._2 * _3._4 + _4._1 * _1._4 * _3._2
val _42 = _1._1 * _3._2 * _4._3 - _1._1 * _3._3 * _4._2 - _3._1 * _1._2 * _4._3
+_3._1 * _1._3 * _4._2 + _4._1 * _1._2 * _3._3 - _4._1 * _1._3 * _3._2
val _13 = _1._2 * _2._3 * _4._4 - _1._2 * _2._4 * _4._3 - _2._2 * _1._3 * _4._4
+_2._2 * _1._4 * _4._3 + _4._2 * _1._3 * _2._4 - _4._2 * _1._4 * _2._3
val _23 = -_1._1 * _2._3 * _4._4 + _1._1 * _2._4 * _4._3 + _2._1 * _1._3 * _4._4
-_2._1 * _1._4 * _4._3 - _4._1 * _1._3 * _2._4 + _4._1 * _1._4 * _2._3
val _33 = _1._1 * _2._2 * _4._4 - _1._1 * _2._4 * _4._2 - _2._1 * _1._2 * _4._4
+_2._1 * _1._4 * _4._2 + _4._1 * _1._2 * _2._4 - _4._1 * _1._4 * _2._2
val _43 = -_1._1 * _2._2 * _4._3 + _1._1 * _2._3 * _4._2 + _2._1 * _1._2 * _4._3
-_2._1 * _1._3 * _4._2 - _4._1 * _1._2 * _2._3 + _4._1 * _1._3 * _2._2
val _14 = -_1._2 * _2._3 * _3._4 + _1._2 * _2._4 * _3._3 + _2._2 * _1._3 * _3._4
-_2._2 * _1._4 * _3._3 - _3._2 * _1._3 * _2._4 + _3._2 * _1._4 * _2._3
val _24 = _1._1 * _2._3 * _3._4 - _1._1 * _2._4 * _3._3 - _2._1 * _1._3 * _3._4
+_2._1 * _1._4 * _3._3 + _3._1 * _1._3 * _2._4 - _3._1 * _1._4 * _2._3
val _34 = -_1._1 * _2._2 * _3._4 + _1._1 * _2._4 * _3._2 + _2._1 * _1._2 * _3._4
-_2._1 * _1._4 * _3._2 - _3._1 * _1._2 * _2._4 + _3._1 * _1._4 * _2._2
val _44 = _1._1 * _2._2 * _3._3 - _1._1 * _2._3 * _3._2 - _2._1 * _1._2 * _3._3
+_2._1 * _1._3 * _3._2 + _3._1 * _1._2 * _2._3 - _3._1 * _1._3 * _2._2
val det = _1._1 * _11 + _1._2 * _21 + _1._3 * _31 + _1._4 * _41
if (det == 0) this
else Matrix(
(_11, _12, _13, _14),
(_21, _22, _23, _24),
(_31, _32, _33, _34),
(_41, _42, _43, _44)
) * (1 / det)
}
def *(f: Float) = Matrix(
(_1._1 * f, _1._2 * f, _1._3 * f, _1._4 * f),
(_2._1 * f, _2._2 * f, _2._3 * f, _2._4 * f),
(_3._1 * f, _3._2 * f, _3._3 * f, _3._4 * f),
(_4._1 * f, _4._2 * f, _4._3 * f, _4._4 * f)
)
}
另外，我可以将此矩阵加载到 OpenGL 中还是必须先转置它。我真的总是对这个数学感到困惑。
Uh, yeah, I'd really need a quick input from someone without creator's eyes. Something's wrong in here, according to my scalacheck tests... but I don't really know enough about it to know where it's wrong.
case class Matrix(_1: (Float, Float, Float, Float), _2: (Float, Float, Float, Float),
_3: (Float, Float, Float, Float), _4: (Float, Float, Float, Float)) extends Immutable {
def invert = {
val _11 = _2._2 * _3._3 * _4._4 - _2._2 * _3._4 * _4._3 - _3._2 * _2._3 * _4._4
+_3._2 * _2._4 * _4._3 + _4._2 * _2._3 * _3._4 - _4._2 * _2._4 * _3._3
val _21 = -_2._1 * _3._3 * _4._4 + _2._1 * _3._4 * _4._3 + _3._1 * _2._3 * _4._4
-_3._1 * _2._4 * _4._3 - _4._1 * _2._3 * _3._4 + _4._1 * _2._4 * _3._3
val _31 = _2._1 * _3._2 * _4._4 - _2._1 * _3._4 * _4._2 - _3._1 * _2._2 * _4._4
+_3._1 * _2._4 * _4._2 + _4._1 * _2._2 * _3._4 - _4._1 * _2._4 * _3._2
val _41 = -_2._1 * _3._2 * _4._3 + _2._1 * _3._3 * _4._2 + _3._1 * _2._2 * _4._3
-_3._1 * _2._3 * _4._2 - _4._1 * _2._2 * _3._3 + _4._1 * _2._3 * _3._2
val _12 = -_1._2 * _3._3 * _4._4 + _1._2 * _3._4 * _4._3 + _3._2 * _1._3 * _4._4
-_3._2 * _1._4 * _4._3 - _4._2 * _1._3 * _3._4 + _4._2 * _1._4 * _3._3
val _22 = _1._1 * _3._3 * _4._4 - _1._1 * _3._4 * _4._3 - _3._1 * _1._3 * _4._4
+_3._1 * _1._4 * _4._3 + _4._1 * _1._3 * _3._4 - _4._1 * _1._4 * _3._3
val _32 = -_1._1 * _3._2 * _4._4 + _1._1 * _3._4 * _4._2 + _3._1 * _1._2 * _4._4
-_3._1 * _1._4 * _4._2 - _4._1 * _1._2 * _3._4 + _4._1 * _1._4 * _3._2
val _42 = _1._1 * _3._2 * _4._3 - _1._1 * _3._3 * _4._2 - _3._1 * _1._2 * _4._3
+_3._1 * _1._3 * _4._2 + _4._1 * _1._2 * _3._3 - _4._1 * _1._3 * _3._2
val _13 = _1._2 * _2._3 * _4._4 - _1._2 * _2._4 * _4._3 - _2._2 * _1._3 * _4._4
+_2._2 * _1._4 * _4._3 + _4._2 * _1._3 * _2._4 - _4._2 * _1._4 * _2._3
val _23 = -_1._1 * _2._3 * _4._4 + _1._1 * _2._4 * _4._3 + _2._1 * _1._3 * _4._4
-_2._1 * _1._4 * _4._3 - _4._1 * _1._3 * _2._4 + _4._1 * _1._4 * _2._3
val _33 = _1._1 * _2._2 * _4._4 - _1._1 * _2._4 * _4._2 - _2._1 * _1._2 * _4._4
+_2._1 * _1._4 * _4._2 + _4._1 * _1._2 * _2._4 - _4._1 * _1._4 * _2._2
val _43 = -_1._1 * _2._2 * _4._3 + _1._1 * _2._3 * _4._2 + _2._1 * _1._2 * _4._3
-_2._1 * _1._3 * _4._2 - _4._1 * _1._2 * _2._3 + _4._1 * _1._3 * _2._2
val _14 = -_1._2 * _2._3 * _3._4 + _1._2 * _2._4 * _3._3 + _2._2 * _1._3 * _3._4
-_2._2 * _1._4 * _3._3 - _3._2 * _1._3 * _2._4 + _3._2 * _1._4 * _2._3
val _24 = _1._1 * _2._3 * _3._4 - _1._1 * _2._4 * _3._3 - _2._1 * _1._3 * _3._4
+_2._1 * _1._4 * _3._3 + _3._1 * _1._3 * _2._4 - _3._1 * _1._4 * _2._3
val _34 = -_1._1 * _2._2 * _3._4 + _1._1 * _2._4 * _3._2 + _2._1 * _1._2 * _3._4
-_2._1 * _1._4 * _3._2 - _3._1 * _1._2 * _2._4 + _3._1 * _1._4 * _2._2
val _44 = _1._1 * _2._2 * _3._3 - _1._1 * _2._3 * _3._2 - _2._1 * _1._2 * _3._3
+_2._1 * _1._3 * _3._2 + _3._1 * _1._2 * _2._3 - _3._1 * _1._3 * _2._2
val det = _1._1 * _11 + _1._2 * _21 + _1._3 * _31 + _1._4 * _41
if (det == 0) this
else Matrix(
(_11, _12, _13, _14),
(_21, _22, _23, _24),
(_31, _32, _33, _34),
(_41, _42, _43, _44)
) * (1 / det)
}
def *(f: Float) = Matrix(
(_1._1 * f, _1._2 * f, _1._3 * f, _1._4 * f),
(_2._1 * f, _2._2 * f, _2._3 * f, _2._4 * f),
(_3._1 * f, _3._2 * f, _3._3 * f, _3._4 * f),
(_4._1 * f, _4._2 * f, _4._3 * f, _4._4 * f)
)
}
Also, can I load this Matrix into OpenGL or do I have to transpose it first. I really always get confused about this maths.
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评论（4）
对矩阵求逆通常是一个坏主意，因为计算可能是病态的。
如果您想求解方程组，最好使用 LU 分解和前后向替换等方法来求解，特别是如果您可以重复使用分解来求解多个右侧向量。
此链接显示了使用旋转进行高斯消除的 Java 示例。
这里还有另一个想法：也许你可以只使用 Java 库，比如 Apache Commons Math（JAMA 的后继者），你的申请？
如果您有特定的情况，我建议将其输入到 Wolfram Alpha 中在开始编码之前您可以看到答案应该是什么。
Inverting a matrix is usually a bad idea, because the calculations can be ill-conditioned.
If you want to solve a system of equations it's a better idea to do it using something like LU decomposition and forward-backward substitution, especially if you can reuse the decomposition to solve for several right hand side vectors.
This link shows a Java example for Gaussian elimination with pivoting.
Here's another thought: maybe you can just use Java libraries like Apache Commons Math, the successor to JAMA, in your application?
If you have a particular case in mind, I'd recommend entering it into Wolfram Alpha so you can see what the answer should be before you start coding.
我非常确定 Simplex3D 实现了此计算（并且很可能在那里正确完成）。
I'm pretty sure Simplex3D implements this calculation (and very likely it's done correctly there).
如果您想玩数字游戏 - 无论如何，请自己动手 - Jesper 和 duffymo 提供了一些很好的建议（求逆矩阵在实践中没有用 - 请研究 LU 分解）。
但是，如果您只是想完成任务TM，请查看Scalala 和 Scalalab。
无论哪种方式，您都需要线性代数背景知识 - 这对于许多领域来说都是非常有用的数学。
If you want to play with numerics - by all means go ahead and do it yourself - you have some good suggestions from Jesper and duffymo (inverting matrices is not useful in practice - look into LU decomposition).
If however if you just want to Get Stuff DoneTM look into Scalala and Scalalab.
Either way you will need Linear Algebra background knowledge - which is incredibly useful math for lots of fields.
请参阅 Wikipedia 上的可逆矩阵：解析解。顶部的全部计算计算矩阵的辅助，其中行列式是计算，逆矩阵就是
1 / det乘以辅助矩阵。整个计算是针对代码中的 4 x 4 矩阵显式写出的，因此如果存在错误需要花费一些精力来检查整个事情。维基百科文章解释了它应该如何工作。
Look at Invertible matrix: Analytic solution on Wikipedia. The whole bunch of calculations at the top compute the adjugate of the matrix, from which the determinant is calculated, and the inverse is then
1 / dettimes the adjugate matrix.The whole calculation is written out explicitly for a 4 x 4 matrix in your code, so if there's a bug in it will take some effort to check the whole thing. The Wikipedia articles explain how it's supposed to work.