#  This software and supporting documentation are distributed by
#      Institut Federatif de Recherche 49
#      CEA/NeuroSpin, Batiment 145,
#      91191 Gif-sur-Yvette cedex
#      France
#
# This software is governed by the CeCILL license version 2 under
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from brainvisa.processes import *
import shfjGlobals
try:
  from soma import aims
except:
  def validation():
    raise RuntimeError( _t_( 'module <em>aims</em> not available' ) )

name = 'Train SVM'
userLevel = 2


signature = Signature(
  'classifier', ReadDiskItem( 'Classifier', [ 'SVM classifier' ] ), 
  'output_classifier', WriteDiskItem( 'Classifier', [ 'SVM classifier' ] ), 
  'input_data', ReadDiskItem( '2D image', shfjGlobals.aimsVolumeFormats ),
  'svm_mode', Choice( 'classifier', 'probability', 'regression', 'quality',
                      'decision', 'one_class' ),
  'sigma', Float(),
  'C', Float(), 
  )


def initialization( self ):
  self.linkParameters( 'output_classifier', 'classifier' )
  self.sigma = 1.
  self.C = 1.


def execution( self, context ):
    r = aims.Reader( {} )
    im = r.read( self.input_data.fullPath() )
    input = context.temporary( 'Text file' )
    f = open( input.fullPath(), 'w' )
    for y in xrange( im.getSizeY() ):
        for x in xrange( im.getSizeX() ):
            val = im.value( x, y )
            if val:
              f.write( '%d\t1:%f\t2:%f\n' % \
                       ( val-1, float(x)/im.getSizeX(),
                         float(y)/im.getSizeY() ) )
    f.close()
    if self.svm_mode == 'regression':
      context.system( 'svm-train', '-p', 0, '-s', 3, '-b', '1', '-c', self.C,
                      '-g', self.sigma,
                      input, self.output_classifier )
    elif self.svm_mode == 'one_class':
      context.system( 'svm-train', '-s', 2, '-c', self.C,
                      '-g', self.sigma,
                      input, self.output_classifier )
      # TODO
      self.svm_mode = 'classifier'
    else:
      context.system( 'svm-train', '-b', '1', '-c', self.C, '-g', self.sigma,
                      input, self.output_classifier )
    self.output_classifier.updateMinf( { 'svm_mode' : self.svm_mode } )