diff --git a/bob/bio/base/script/fusion_llr.py b/bob/bio/base/script/fusion_llr.py
new file mode 100755
index 0000000000000000000000000000000000000000..66c5d2a0c2c93f137fcea1686f37b92666e73e05
--- /dev/null
+++ b/bob/bio/base/script/fusion_llr.py
@@ -0,0 +1,116 @@
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# Laurent El Shafey <laurent.el-shafey@idiap.ch>
+# Elie El Khoury <elie.khoury@idiap.ch>
+#Mon 13 Jul 11:55:34 CEST 2015
+#
+# Copyright (C) 2011-2013 Idiap Research Institute, Martigny, Switzerland
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program. If not, see <http://www.gnu.org/licenses/>.
+
+"""This script fuses scores from various systems,
+from a score file in four or five column format.
+
+Note: The score file has to contain the exact probe file names as the 3rd (4column) or 4th (5column) column.
+"""
+
+
+
+import bob, os, sys
+import bob.learn.linear
+
+def parse_command_line(command_line_options):
+ """Parse the program options"""
+
+ usage = 'usage: %s [arguments]' % os.path.basename(sys.argv[0])
+
+ import argparse
+ parser = argparse.ArgumentParser(usage=usage, description=__doc__, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+ # This option is not normally shown to the user...
+ parser.add_argument('--self-test', action = 'store_true', help = argparse.SUPPRESS)
+ parser.add_argument('-s', '--score-development', nargs='*', required = True, help = 'The score file in 4 or 5 column format to train the fusion parameters.')
+ parser.add_argument('-t', '--score-evaluation', nargs='*', required = True, help = 'The score file in 4 or 5 column format to calibrate.')
+ parser.add_argument('-f', '--score-fused-development-file', required = True, help = 'The calibrated development score file in 4 or 5 column format to calibrate.')
+ parser.add_argument('-g', '--score-fused-evaluation-file', required = True, help = 'The calibrated evaluation score file in 4 or 5 column format to calibrate.')
+ parser.add_argument('-p', '--parser', default = '4column', choices = ('4column', '5column'), help = 'The type of the score file.')
+
+ args = parser.parse_args(command_line_options)
+
+ return args
+
+def main(command_line_options = None):
+ """Score Fusion using logistic regresssion"""
+ args = parse_command_line(command_line_options)
+
+ # read data
+ n_systems = len(args.score_development)
+ for i in range(n_systems):
+ if not os.path.isfile(args.score_development[i]): raise IOError("The given score file does not exist")
+ # pythonic way: create inline dictionary "{...}", index with desired value "[...]", execute function "(...)"
+ data = []
+ for i in range(n_systems):
+ data.append({'4column' : bob.measure.load.split_four_column, '5column' : bob.measure.load.split_five_column}[args.parser](args.score_development[i]))
+ import numpy
+
+ data_neg = numpy.vstack([data[k][0] for k in range(n_systems)]).T.copy()
+ data_pos = numpy.vstack([data[k][1] for k in range(n_systems)]).T.copy()
+ trainer = bob.learn.linear.CGLogRegTrainer(0.5, 1e-10, 10000)
+ machine = trainer.train(data_neg, data_pos)
+
+ # fuse development scores
+ data_dev = []
+ for i in range(n_systems):
+ data_dev.append({'4column' : bob.measure.load.four_column, '5column' : bob.measure.load.five_column}[args.parser](args.score_development[i]))
+
+ data_dev2 = []
+ for i in range(n_systems):
+ data_sys = []
+ for (client_id, probe_id, file_id, score) in data_dev[i]:
+ data_sys.append([client_id, probe_id, file_id, score])
+ data_dev2.append(data_sys)
+ data_dev = data_dev2
+
+ ndata = len(data_dev[0])
+ outf = open(args.score_fused_development_file, 'w')
+ for k in range(ndata):
+ scores = numpy.array([[v[k][-1] for v in data_dev]], dtype=numpy.float64)
+ s_fused = machine.forward(scores)[0,0]
+ line = " ".join(data_dev[0][k][0:-1]) + " " + str(s_fused) + "\n"
+ outf.write(line)
+
+ # fuse evaluation scores
+ data_eval = []
+ for i in range(n_systems):
+ data_eval.append({'4column' : bob.measure.load.four_column, '5column' : bob.measure.load.five_column}[args.parser](args.score_evaluation[i]))
+
+ data_eval2 = []
+ for i in range(n_systems):
+ data_sys = []
+ for (client_id, probe_id, file_id, score) in data_eval[i]:
+ data_sys.append([client_id, probe_id, file_id, score])
+ data_eval2.append(data_sys)
+ data_eval = data_eval2
+
+ ndata = len(data_eval[0])
+ outf = open(args.score_fused_evaluation_file, 'w')
+ for k in range(ndata):
+ scores = numpy.array([[v[k][-1] for v in data_eval]], dtype=numpy.float64)
+ s_fused = machine.forward(scores)[0,0]
+ line = " ".join(data_eval[0][k][0:-1]) + " " + str(s_fused) + "\n"
+ outf.write(line)
+
+ return 0
+
+if __name__ == '__main__':
+ main(sys.argv[1:])
diff --git a/setup.py b/setup.py
index 406e6ef5ec9a55a400257f5bd008929ed0cc8807..174cd090f99bda7a191271ebfce133b769413ff1 100644
--- a/setup.py
+++ b/setup.py
@@ -113,6 +113,7 @@ setup(
'extract.py = bob.bio.base.script.extract:main',
'enroll.py = bob.bio.base.script.enroll:main',
'score.py = bob.bio.base.script.score:main',
+ 'fusion_llr.py = bob.bio.base.script.fusion_llr:main',
],
'bob.bio.database': [