Talk:Deconvolution/2D+: Difference between revisions
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I got interested in higher dimensional deconvolution as a way of looking for trading indicators in financial time series. I set this task as an example of something that I'm speculating can be done well with functional and array processing languages, but only with difficulty otherwise, which I hope someone will weigh in to confirm or refute. In case anyone wants to know, consistent test data were generated partly by this higher dimensional convolution function, |
I got interested in higher dimensional deconvolution when contacted by someone about using it as a way of looking for trading indicators in financial time series. I set this task as an example of something that I'm speculating can be done well with functional and array processing languages, but only with difficulty otherwise, which I hope someone will weigh in to confirm or refute. In case anyone wants to know, consistent test data were generated partly by this higher dimensional convolution function, |
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<lang Ursala>conv = +^|(~&x+,*DrlDSNiCK9xxSNiCK9K7iFS+ *)=>times+ **+ *K7|\x+ iota; * ! plus:-0</lang> |
<lang Ursala>conv = +^|(~&x+,*DrlDSNiCK9xxSNiCK9K7iFS+ *)=>times+ **+ *K7|\x+ iota; * ! plus:-0</lang> |
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invoked as <code>(conv d)(h,f)</code> with dimension <code>d > 0</code> and conforming <code>h</code> and <code>f</code>. I suggest a development methodology based on warming up with [[Deconvolution/1D]], then hand coding the solutions for the next few dimensions, and then looking for the pattern. |
invoked as <code>(conv d)(h,f)</code> with dimension <code>d > 0</code> and conforming <code>h</code> and <code>f</code>. I suggest a development methodology based on warming up with [[Deconvolution/1D]], then hand coding the solutions for the next few dimensions, and then looking for the pattern. |
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Revision as of 03:34, 23 February 2010
I got interested in higher dimensional deconvolution when contacted by someone about using it as a way of looking for trading indicators in financial time series. I set this task as an example of something that I'm speculating can be done well with functional and array processing languages, but only with difficulty otherwise, which I hope someone will weigh in to confirm or refute. In case anyone wants to know, consistent test data were generated partly by this higher dimensional convolution function,
<lang Ursala>conv = +^|(~&x+,*DrlDSNiCK9xxSNiCK9K7iFS+ *)=>times+ **+ *K7|\x+ iota; * ! plus:-0</lang>
invoked as (conv d)(h,f) with dimension d > 0 and conforming h and f. I suggest a development methodology based on warming up with Deconvolution/1D, then hand coding the solutions for the next few dimensions, and then looking for the pattern.
--Sluggo 03:24, 23 February 2010 (UTC)