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This is possibly talked about in #202 but I'm pretty sure the chapter on convolution came after that issue...
There is always a 0 element in the linear convolution.
The code says
# full convolution, output will be the size of x + y full_linear_output = convolve_linear(x, y, length(x) + length(y))
for example,
julia> convolve_linear([1,2], [7, 8], 4) 4-element Array{Float64,1}: 7.0 22.0 16.0 0.0
The last element is always 0 because it corresponds to a situation when the signal and filter functions don't overlap at all.
1 2 1 2 1 2 1 2 | | | | 8 7 8 7 8 7 8 7 7 22 16 0
# full convolution, output will be the size of x + y - 1 full_linear_output = convolve_linear(x, y, length(x) + length(y) - 1)
julia> convolve_linear([1,2], [7, 8], 3) 4-element Array{Float64,1}: 7.0 22.0 16.0
The text was updated successfully, but these errors were encountered:
[algorithm-archivists#852] [algorithm-archivists#853] Fix for Julia 1…
7eab3b7
…D convolution
[#852] [#852] Fix for Julia 1D convolution (#854)
c249854
* [#852] [#853] Fix for Julia 1D convolution * fixing line numbers for jie's PR Co-authored-by: James Schloss <[email protected]>
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This is possibly talked about in #202 but I'm pretty sure the chapter on convolution came after that issue...
Bug Report
Description
There is always a 0 element in the linear convolution.
Steps to Reproduce
The code says
for example,
The last element is always 0 because it corresponds to a situation when the signal and filter functions don't overlap at all.
Expected behavior
For Algorithm Archive Developers
The text was updated successfully, but these errors were encountered: