I believe paeth forward filter will give the following
output for i. 5 10
bbp=1
0 1 1 1 1 1 1 1 1 1
10 1 1 1 1 1 1 1 1 1
10 1 1 1 1 1 1 1 1 1
10 1 1 1 1 1 1 1 1 1
10 1 1 1 1 1 1 1 1 1
bbp=3
0 1 2 3 3 3 3 3 3 3
10 10 10 3 3 3 3 3 3 3
10 10 10 3 3 3 3 3 3 3
10 10 10 3 3 3 3 3 3 3
10 10 10 3 3 3
Right. So this is the way to get the original from the forward filter.
If it works.
Henry Rich
On 9/1/2014 9:48 PM, bill lam wrote:
Sorry, I might use the wrong terminology. I meant the filter
that would give the original data from the output of the
forward filter, ie
data <--> m&backwad m&
Sorry, I might use the wrong terminology. I meant the filter
that would give the original data from the output of the
forward filter, ie
data <--> m&backwad m&forward data
forward: (encode):
> Paeth(x) = Raw(x) - PaethPredictor(Raw(x-bpp), Prior(x), Prior(x-bpp))
backward (decode, back-feed,
backward. 'paeth' is the paeth filter, which could be forward or
backward, depending on use. Backward was what you wanted, no?
Henry Rich
On 9/1/2014 9:09 PM, bill lam wrote:
Just want to confirm, is you code for forward or backward
filter?
Пн, 01 сен 2014, Henry Rich написал(а):
Well, I s
Just want to confirm, is you code for forward or backward
filter?
Пн, 01 сен 2014, Henry Rich написал(а):
> Well, I said it was untested. It's just the framework. Here's a version
> that at least produces a result:
>
>
> y =. i. 5 10
> 'height width'=: $y
> pixels=: ,y
> NB. convert to array,
Erm, apologies, I meant:
inv=: [: gesv_jlapack_ ] ; [: =@i. $
On 09/01/2014 05:10 PM, Scott Locklin wrote:
So, I've been screwing around with neural nets, and built a learner
with simple linear regression output nodes. The nature of this beast
is you have to invert a big matrix which is clo
So, I've been screwing around with neural nets, and built a learner with
simple linear regression output nodes. The nature of this beast is you
have to invert a big matrix which is close to singular. Because I am a
big numerics nerd, I remembered to add a small number to the diagonal in
order t
Well, I said it was untested. It's just the framework. Here's a
version that at least produces a result:
y =. i. 5 10
'height width'=: $y
pixels=: ,y
NB. convert to array, reverse, add bottom/right 0
array =: ({.~ >:@$);.0 (height, width) $ pixels
NB. x is unused,above), y is left,upper-left
I assume your code is for backward filter (from paeth to raw).
I tried with test data: y =. i. 5 10
'height width'=. $y
pixels=. ,y
but it ran into error
|index error: scanlinepair
| xformimage=.1 1&}.;.0 scanlinepair/\.array
Also the bpp meant comparison with bpp byte apart. this bpp can
Untested. Here is a framework. It's not pretty, but this
computation is just not parallelizable.
NB. convert to array, reverse, add bottom/right 0
array =. ({.~ >:@$);.0 (height, width) $ pixels
NB. y is 2x2 of (unused,above),:(left,upper-left)
NB. Result is Paeth predictor
paeth =. ({~ (i.
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