The other option is not to add them in the first place?

   load 'stats/distribs'
   (|: + ~: zeroTri ) >: zeroTri rnorm 5 5
 0.346799 _1.22161    0.57274   0.556122 _0.329658
 _1.22161 0.149955   _1.77435   _1.76668  0.831557
  0.57274 _1.77435    0.77674 _0.0690683 _0.967551
 0.556122 _1.76668 _0.0690683   0.720588 _0.195658
_0.329658 0.831557  _0.967551  _0.195658  _0.12314

In other words zero the items above the diagonal then transpose and
add to the non-diagonal items.

Where zeroTri is an adverb defined as below:

NB.*zeroTri a Zeros triangular items of matrix determined by verb to left
NB. EG: < zeroTri mat   NB. zeros lower-tri items of mat
NB. EG: <: zeroTri mat  NB. zeros lower-tri, off-diag items of mat
NB. EG: > zeroTri mat   NB. zeros upper tri items of mat
NB. EG: = zeroTri mat   NB. zeros all off-diag items of mat
NB. EG: ~: zeroTri mat  NB. zeros diag items of mat
zeroTri=: 1 :'([: u/~ i.@#) * ]'

This is somewhat wasteful in that you generate a bunch of random
numbers that you then discard, but if that was an issue you could work
around it.


On Wed, Aug 22, 2012 at 5:07 PM, Owen Marschall
<[email protected]> wrote:
> You read my mind. I was thinking about this same problem tonight while at the 
> opera, and I couldn't think of any way to only divide the diagonals by 
> sqrt(2) a second time--without loops, of course. I don't quite yet understand 
> why your solution works, but I'm sure with enough staring and dictionary help 
> I'll get it.
>
> Thanks,
> Owen
>
> On Aug 21, 2012, at 8:25 PM, Henry Rich wrote:
>
>> If you have a matrix a of standard normal deviates, you can make it 
>> symmetric with
>>
>> asymm =: (+ |:) a
>>
>> but what is the variance of the items of a?
>>
>> The variance of values off the principal diagonal will be the sum of the 
>> variance of two independent standard normal deviates. i.e. 2.
>>
>> To return these values to variance 1 you need to divide by sqrt(2).
>>
>> But the variance of values ON the principal diagonal will be the sum of two 
>> perfectly correlated random variables, i. e. 4.
>>
>> So you need to treat the principal diagonal differently.  You can reduce its 
>> variance by scaling it differently after the conversion to symmetric, 
>> dividing the diagonal by sqrt(4) and the rest by sqrt(2):
>>
>> asymmgood =: asymm % %: +: >: e. i. # asymm
>>
>> (The e. i. bit is a standard idiom for making an identity matrix. Another 
>> one you see around is   = i.  but I avoid that because I think monad = was 
>> wrongly defined and should be assigned for other purposes)
>>
>> If I've made a statistical blunder I'm sure someone will tell me.
>>
>> Henry Rich
>>
>> On 8/21/2012 3:10 PM, Owen Marschall wrote:
>>> Ah, just what I needed. Thanks!
>>>
>>> On Aug 21, 2012, at 1:06 PM, Ric Sherlock wrote:
>>>
>>>> The primitive ( |: ) is transpose. E.g. :
>>>>
>>>>   |: i. 3 4
>>>> 0 4  8
>>>> 1 5  9
>>>> 2 6 10
>>>> 3 7 11
>>>> On Aug 22, 2012 6:55 AM, "Owen Marschall" <[email protected]> wrote:
>>>>
>>>>> Anyone know of an easy way to create a random symmetric matrix (more
>>>>> specifically, a matrix whose entires are each picked from a standard
>>>>> Gaussian distribution)? I can start by doing
>>>>>
>>>>> load 'stats'
>>>>> R=:normalrand N N
>>>>>
>>>>> but this is not symmetric, and I don't know of any way to symmetrize it
>>>>> without thinking in loops, which I'm training myself not to. If I could
>>>>> somehow take a transpose, that would solve the problem, but I don't know
>>>>> how to do that either.
>>>>>
>>>>> Thanks,
>>>>> Owen
>>>>> ----------------------------------------------------------------------
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>>>>>
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