[Neurodebian-devel] MDP patches in AFNI

[Tiziano Zito]

Hi,

I had a quick look at the AFNI localized copy of MDP. It seems they
imported a fairly recent git snapshot. If the ica_nodes.py
patch would get integrated in MDP, I think they could use the
released MDP 3.3 version without any incompatibility.

Is this 2 stage convergence switching also present in the original
MATLAB reference version of FastICA? I am far away from the ICA
world since a long time now, so I may have missed some important
developments lately. If this approach is indeed novel and introduced
by PK, wouldn't be better to allow the user to choose which
approach to use -- standard or 2-stage -- at instantiation time?
This would make MDP users happy, because their results are not going
to look different after upgrading to the next MDP release, and AFNI
could still use exactly the same algorithm their are using now...

What do you think?

Ciao,
Tiziano

PS: Ccing the mdp-dev mailing list.

On Thu 15 Nov, 15:08, Yaroslav Halchenko wrote:
> Hi Tiziano,
> 
> NB Ccing PK -- original author of the patch
> 
> As you might (or not) know, AFNI now carries a copy of MDP (of state
> somewhere before actual 3.3 release as far as I see) with slight
> patching of ica_nodes.py to have 2 stages of convergence with switching
> to gFine later in the loop (PK, please correct me if I am wrong)
> 
> I am attaching patch for your consideration to get absorbed into MDP
> since we would like to avoid maintaining two copies of it ;)
> 
> if needed (may be there is more patches, PK?) you can find
> complete AFNI source with this mdp copy at
> 
> http://git.debian.org/?p=pkg-exppsy/afni.git
> 
> branch upstream.
> 
> -- 
> Yaroslav O. Halchenko
> Postdoctoral Fellow,   Department of Psychological and Brain Sciences
> Dartmouth College, 419 Moore Hall, Hinman Box 6207, Hanover, NH 03755
> Phone: +1 (603) 646-9834                       Fax: +1 (603) 646-1419
> WWW:   http://www.linkedin.com/in/yarik        

> --- pkundu/meica.libs/mdp/nodes/ica_nodes_old.py	2012-11-15 14:33:53.000000000 -0500
> +++ pkundu/meica.libs/mdp/nodes/ica_nodes.py	2012-11-15 14:33:53.000000000 -0500
> @@ -1,3 +1,4 @@
> +
>  __docformat__ = "restructuredtext en"
>  
>  import math
> @@ -325,8 +326,8 @@
>      def __init__(self, approach = 'defl', g = 'pow3', guess = None,
>                   fine_g = 'pow3', mu = 1,
>                   sample_size = 1, fine_tanh = 1, fine_gaus = 1,
> -                 max_it = 1000, max_it_fine = 100,
> -                 failures = 5, limit = 0.001, verbose = False,
> +                 max_it = 5000, max_it_fine = 100,
> +                 failures = 5, primary_limit=0.01, limit = 0.001,  verbose = False,
>                   whitened = False, white_comp = None, white_parm = None,
>                   input_dim = None, dtype=None):
>          """
> @@ -346,7 +347,9 @@
>                        It is passed directly to the WhiteningNode constructor.
>                        Ex: white_parm = { 'svd' : True }
>  
> -        limit -- convergence threshold.
> +        limit -- final convergence threshold.
> +
> +	primary_limit -- initial convergence threshold, to switch to fine function, (i.e. linear to non-linear). PK 26-6-12.
>  
>          Specific for FastICA:
>  
> @@ -417,6 +420,7 @@
>          self.fine_gaus = fine_gaus
>          self.max_it = max_it
>          self.max_it_fine = max_it_fine
> +        self.primary_limit = primary_limit
>          self.failures = failures
>          self.guess = guess
>  
> @@ -458,6 +462,7 @@
>                  guess = mult(guess, self.white.get_recmatrix(transposed=1))
>  
>          limit = self.limit
> +        primary_limit = self.primary_limit
>          max_it = self.max_it
>          max_it_fine = self.max_it_fine
>          failures = self.failures
> @@ -501,6 +506,7 @@
>          used_g = gOrig
>          stroke = 0
>          fine_tuned = False
> +        in_secondary = False
>          lng = False
>  
>          # SYMMETRIC APPROACH
> @@ -529,10 +535,16 @@
>                  v2 = 1.-abs((mult(Q.T, QOldF)).diagonal()).min(axis=0)
>                  convergence_fine.append(v2)
>  
> +                if self.g!=self.fine_g and convergence[round] < primary_limit and not in_secondary:
> +                    if verbose:
> +                        print 'Primary convergence, switching to fine cost...'
> +                    used_g = gFine
> +                    in_secondary = True
> +
>                  if convergence[round] < limit:
>                      if fine_tuning and (not fine_tuned):
>                          if verbose:
> -                            print 'Initial convergence, fine-tuning...'
> +                            print 'Secondary convergence, fine-tuning...'
>                          fine_tuned = True
>                          used_g = gFine
>                          mu = muK * self.mu
> @@ -569,7 +581,7 @@
>                  # Show the progress...
>                  if verbose:
>                      msg = ('Step no. %d,'
> -                           ' convergence: %.3f' % (round+1,convergence[round]))
> +                           ' convergence: %.7f' % (round+1,convergence[round]))
>                      print msg
>  
>