This link seems to work:
http://www.cs.toronto.edu/~mackay/itprnn/book.pdf

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Skip Cave
Cave Consulting LLC

On Tue, Jan 27, 2015 at 3:33 AM, Ryan <[email protected]> wrote:

> Thanks Devon.
>
> FYI I'm learning J by implementing the exercises in
>
> Information theory, inference, and learning algorithms (Mackay 03)
> http://www.inference.phy.cam.ac.uk/mackay/itila/
>
>
>
> On 22 Jan 2015, at 0:37, Devon McCormick wrote:
>
>  Raul's example assumes your data is between 0 and 1, inclusive, so you may
>> need to scale it:
>> 13 : 'y-<./,y'   NB. Subtract the smallest from each
>> ] - [: <./ ,
>> 13 : 'y%>./,y'   NB. Divide each by the largest
>> ] % [: >./ ,
>> scale=: (] % [: >./ ,)@(] - [: <./ ,)  NB. Combine to scale from 0 to 1
>>
>> So, if
>> data=: +/~i.10
>> then, doing what Raul showed:
>> viewmat ,/0|:,/0 2|:data >:/ square
>>
>>
>> On Wed, Jan 21, 2015 at 1:55 AM, Raul Miller <[email protected]>
>> wrote:
>>
>>  It's certainly possible.
>>>
>>> You didn't give any data, and I do not feel like trying to replicate
>>> your original numbers from the image you gave, so I will just use some
>>> arbitrary numbers:
>>>
>>> data=: ?10 10$0
>>>
>>> Now, let's say that your squares are 25 pixels on a side. We can form
>>> squares like this:
>>>
>>> $square=: (>./~|i:12)%12
>>> 25 25
>>>
>>> That's a bit big for email, so here's a smaller version:
>>>
>>> 4%~>./~|i:4
>>> 1    1    1    1    1    1    1    1 1
>>> 1 0.75 0.75 0.75 0.75 0.75 0.75 0.75 1
>>> 1 0.75  0.5  0.5  0.5  0.5  0.5 0.75 1
>>> 1 0.75  0.5 0.25 0.25 0.25  0.5 0.75 1
>>> 1 0.75  0.5 0.25    0 0.25  0.5 0.75 1
>>> 1 0.75  0.5 0.25 0.25 0.25  0.5 0.75 1
>>> 1 0.75  0.5  0.5  0.5  0.5  0.5 0.75 1
>>> 1 0.75 0.75 0.75 0.75 0.75 0.75 0.75 1
>>> 1    1    1    1    1    1    1    1 1
>>>
>>> (That's basically the same expression, I just moved the division over
>>> to the left hand side, and changed the 12 to a 4, so it's 5 by 5. The
>>> point isn't the expression so much as the resulting data. It's
>>> arranged as a square with values ranging from 0 in the center to 1 at
>>> the edge.)
>>>
>>> Now, we just need to compare this to each of our original values, form
>>> them into a square, and pass that on to viewmat:
>>>
>>> require'viewmat'
>>> viewmat ,/0|:,/0 2|:data >:/ square
>>>
>>> Good enough?
>>>
>>> Or would an explanation of the steps in any of those expressions help?
>>>
>>> (You can shave off parts of an expression to see what was passed
>>> along, and you can shrink the data so you can inspect those
>>> intermediate results easier. You can also use J's trace facility
>>> and/or dissect. You can also of course read up on things in the
>>> dictionary. But sometimes, especially after you've tried some of those
>>> other approaches, it can be good to have someone else give their
>>> perspective - not so much because it's not simple but because having
>>> other people's words can help you think about things from a slightly
>>> different perspective.)
>>>
>>> Thanks,
>>>
>>> --
>>> Raul
>>>
>>> On Tue, Jan 20, 2015 at 11:48 PM, Ryan <[email protected]> wrote:
>>>
>>>> I have a matrix of probabilities that I'd like to visualise similar to
>>>>
>>> this:
>>>
>>>>
>>>>  http://emotion.inrialpes.fr/people/synnaeve/phdthesis/
>>> images/battleship_board_3_1miss.png
>>>
>>>> where the bigger the probability, the bigger the square.  The closest
>>>>
>>> I've
>>>
>>>> come is a simple
>>>> density plot
>>>>
>>>>      mymat=? 27 27$0
>>>>  load 'graphics/plot viewmat'
>>>>  'density' plot mymatrix
>>>>  NB. or
>>>>  viewmat mymatrix
>>>>
>>>> I've looked through plot's source code but don't see an option for
>>>>
>>> something
>>>
>>>> like this.
>>>>
>>>> Does someone know if it's possible to do?
>>>>
>>>> thanks for any help,
>>>> Ryan
>>>>
>>>>
>>>>
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>>
>>
>> --
>> Devon McCormick, CFA
>> ----------------------------------------------------------------------
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>>
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