Merge pull request #4 from yarikoptic/master

FastICA -- introduce coarse_limit to trigger switching to fine_g before reaching target limit. 

Thanks to Prantik Kundu and Yaroslav Halchenko for the patch!

mdp/nodes/ica_nodes.py

@@ -320,13 +320,14 @@ class FastICANode(ICANode):
     - 25.5.2005 now independent from scipy. Requires Numeric or numarray
     - 26.6.2006 converted to numpy
     - 14.9.2007 updated to Matlab version 2.5
+    - 26.6.2012 added ability to run two stages of optimization [PK]
     """
 
     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, coarse_limit=None, limit = 0.001,  verbose = False,
                  whitened = False, white_comp = None, white_parm = None,
                  input_dim = None, dtype=None):
         """
@@ -374,6 +375,11 @@ def __init__(self, approach = 'defl', g = 'pow3', guess = None,
       sample_size -- Percentage of samples used in one iteration. If
                      sample_size < 1, samples are chosen in random order.
 
+     coarse_limit -- initial convergence threshold, to switch to
+                     fine_g function (i.e. linear to non-linear) even
+                     before reaching the limit and final tuning. Set
+                     it to a value higher than limit to be in effect.
+
         fine_tanh -- parameter for 'tanh' nonlinearity
         fine_gaus -- parameter for 'gaus' nonlinearity
 
@@ -417,6 +423,7 @@ def __init__(self, approach = 'defl', g = 'pow3', guess = None,
         self.fine_gaus = fine_gaus
         self.max_it = max_it
         self.max_it_fine = max_it_fine
+        self.coarse_limit = coarse_limit
         self.failures = failures
         self.guess = guess
 
@@ -458,6 +465,7 @@ def core(self, data):
                 guess = mult(guess, self.white.get_recmatrix(transposed=1))
 
         limit = self.limit
+        coarse_limit = self.coarse_limit
         max_it = self.max_it
         max_it_fine = self.max_it_fine
         failures = self.failures
@@ -501,6 +509,7 @@ def core(self, data):
         used_g = gOrig
         stroke = 0
         fine_tuned = False
+        coarse_limit_reached = False
         lng = False
 
         # SYMMETRIC APPROACH
@@ -529,6 +538,15 @@ def core(self, data):
                 v2 = 1.-abs((mult(Q.T, QOldF)).diagonal()).min(axis=0)
                 convergence_fine.append(v2)
 
+                if self.g != self.fine_g \
+                   and coarse_limit is not None \
+                   and convergence[round] < coarse_limit \
+                   and not coarse_limit_reached:
+                    if verbose:
+                        print 'Coarse convergence, switching to fine cost...'
+                    used_g = gFine
+                    coarse_limit_reached = True
+
                 if convergence[round] < limit:
                     if fine_tuning and (not fine_tuned):
                         if verbose:
@@ -569,7 +587,7 @@ def core(self, data):
                 # Show the progress...
                 if verbose:
                     msg = ('Step no. %d,'
-                           ' convergence: %.3f' % (round+1,convergence[round]))
+                           ' convergence: %.7f' % (round+1,convergence[round]))
                     print msg
 
 

mdp/test/test_fastica.py

@@ -12,6 +12,7 @@ def _fastica_test_factory(metafunc):
                  'fine_g': [None, 'pow3', 'tanh', 'gaus', 'skew'],
                  'sample_size': [1, 0.99999],
                  'mu': [1, 0.999999],
+                 'coarse_limit': [None, 0.01],
                  }
 
     for parms in mdp.utils.orthogonal_permutations(fica_parm):
@@ -20,7 +21,11 @@ def _fastica_test_factory(metafunc):
             continue
         if parms['g'] == 'skew' and parms['fine_g'] != 'skew':
             continue
-
+        # skip testing coarse_limit if g's are the same --
+        # it should not be in effect
+        if parms['coarse_limit'] is not None \
+          and parms['g'] == parms['fine_g']:
+          continue
         funcargs = dict(parms=parms)
         theid = fastICA_id(parms)
         metafunc.addcall(funcargs, id=theid)
@@ -29,6 +34,7 @@ def fastICA_id(parms):
     app =     'AP:'+parms['approach']
     nl =      'NL:'+parms['g']
     fine_nl = 'FT:'+str(parms['fine_g'])
+    coarse_l = 'CL:'+str(parms['coarse_limit'])
     if parms['sample_size'] == 1:
         compact = 'SA:01 '
     else:
@@ -37,7 +43,7 @@ def fastICA_id(parms):
         compact += 'S:01'
     else:
         compact += 'S:<1'
-    desc = ' '.join([app, nl, fine_nl, compact])
+    desc = ' '.join([app, nl, fine_nl, compact, coarse_l])
     return desc