Remove header (copy-paste error) and wrong impl of additive and multi…

…plicative indicators

deap/tools/indicator.py

@@ -1,19 +1,3 @@
-#    Copyright (C) 2010 Simon Wessing
-#    TU Dortmund University
-#
-#    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, either version 3 of the License, or
-#    (at your option) any later version.
-#
-#    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/>.
-
 import numpy
 
 try:
@@ -46,49 +30,5 @@ def contribution(i):
     # Select the maximum hypervolume value (correspond to the minimum difference)
     return numpy.argmax(contrib_values)
 
-def additive_epsilon(front, **kargs):
-    """Returns the index of the individual with the least the additive epsilon
-    contribution. The provided *front* should be a set of non-dominated
-    individuals having each a :attr:`fitness` attribute.
-
-    .. warning::
-
-       This function has not been tested.
-    """
-    wobj = numpy.array([ind.fitness.wvalues for ind in front]) * -1
-
-    def contribution(i):
-        mwobj = numpy.ma.array(wobj)
-        mwobj[i] = numpy.ma.masked
-        return numpy.min(numpy.max(wobj[i] - mwobj, axis=1))
-
-    contrib_values = map(contribution, range(len(front)))
-
-    # Select the minimum contribution value
-    return numpy.argmin(contrib_values)
-
-
-def multiplicative_epsilon(front, **kargs):
-    """Returns the index of the individual with the least the multiplicative epsilon
-    contribution. The provided *front* should be a set of non-dominated
-    individuals having each a :attr:`fitness` attribute.
-
-    .. warning::
-
-       This function has not been tested.
-    """
-    wobj = numpy.array([ind.fitness.wvalues for ind in front]) * -1
-
-    def contribution(i):
-        mwobj = numpy.ma.array(wobj)
-        mwobj[i] = numpy.ma.masked
-        return numpy.min(numpy.max(wobj[i] / mwobj, axis=1))
-
-    contrib_values = map(contribution, range(len(front)))
-
-    # Select the minimum contribution value
-    return numpy.argmin(contrib_values)
-
-
 
-__all__ = ["hypervolume", "additive_epsilon", "multiplicative_epsilon"]
+__all__ = ["hypervolume"]