Difference between revisions of "184: Matrix Transform"
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{{comic | {{comic | ||
| number = 184 | | number = 184 | ||
| − | | date = | + | | date = November 15, 2006 |
| title = Matrix Transformation | | title = Matrix Transformation | ||
| image = matrix_transform.png | | image = matrix_transform.png | ||
| − | | titletext = In fact, draw all your rotational matrices sideways. | + | | titletext = In fact, draw all your rotational matrices sideways. Your professors will love it! And then they'll go home and shrink. |
}} | }} | ||
==Explanation== | ==Explanation== | ||
| − | A rotational matrix transformation (i.e. the big brackets with a few "cos" and "sin" in them) is used in computer graphics to rotate an image. | + | A rotational matrix transformation (i.e. the big brackets with a few "cos" and "sin" in them) is used in computer graphics to rotate an image. The product of the transform matrix and the argument vector (a1, a2) is a rotated version of the argument vector. Here, the joke is that the author turned the image of the vector rather than writing the correct answer. Rotational matrix transformations are a special case of the general linear matrix transform, which can do other things to images, including shrinking them. However, turning the rotational matrix sideways does not make it a shrinking matrix. |
| − | Rotational matrix transformations are a special case of the general linear matrix transform, which can do other things to images, including shrinking them. | ||
==Transcript== | ==Transcript== | ||
| − | + | : | |
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{{comic discussion}} | {{comic discussion}} | ||
| − | + | [[Category:Math]] | |
Revision as of 00:54, 13 February 2013
| Matrix Transformation |
 Title text: In fact, draw all your rotational matrices sideways. Your professors will love it! And then they'll go home and shrink. |
Explanation
A rotational matrix transformation (i.e. the big brackets with a few "cos" and "sin" in them) is used in computer graphics to rotate an image. The product of the transform matrix and the argument vector (a1, a2) is a rotated version of the argument vector. Here, the joke is that the author turned the image of the vector rather than writing the correct answer. Rotational matrix transformations are a special case of the general linear matrix transform, which can do other things to images, including shrinking them. However, turning the rotational matrix sideways does not make it a shrinking matrix.
Transcript
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This baby needs a bit more rigor. --Quicksilver (talk) 05:23, 24 August 2013 (UTC) 173.245.62.84 07:54, 7 April 2014 (UTC) I think this is also a reference to the movie "The Matrix", specifically the now famous scene where Neo does 90degree back bending to dodge bullets. 173.245.62.84 07:54, 7 April 2014 (UTC)
Maybe another reference: They'll go home (translation matrix) and shrink (scale matrix). Translation, scale and rotate are probably the most popular linear transformations. 108.162.218.89 23:39, 14 May 2014 (UTC)
I've been teach that the rotation was anticlockwise. Do computer turn the other way round that math teachers? by the way (left ( matrix{0 # 1 ## -1 # 0} right )*left ( binom 1 0 right)=left (binom 0 1 right) if you know what i mean. get fun. Yomismo (talk) 09:28, 18 March 2015 (UTC)
IIIII'm pretty sure the "shrink" is a typo, and he meant "drink". My evidence is the xkcd book, where he just flat-out says it.
On clockwise/counterclockwise: I think Randall is using screen coordinates (where the top left is (0,0) and the point (1,4) is one below (1,3)), instead of standard Cartesian coordinates where the bottom left is (0,0) and going from (1,3) to (1,4) means going upwards. 162.158.155.170 13:07, 17 May 2019 (UTC)
