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# coding: UTF-8
# Copyright 2009, 2010 Thomas Jourdan
#
# 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, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
import ka_utils
import model_locus
import model_random
import model_constraintpool
import ep_sampler_randomwalk
from gettext import gettext as _
SECTIONS_CONSTRAINT = 'sectionsconstraint'
class CenteredWalkSampler(ep_sampler_randomwalk.RandomWalkSampler):
"""CenteredWalkSampler:
inv: len(self.direction_steps) > 0
"""
cdef = [{'bind' : SECTIONS_CONSTRAINT,
'name' : 'Number of sections.',
'domain': model_constraintpool.INT_RANGE,
'min' : 1, 'max': 100},
]
def __init__(self, trunk):
"""Constructor for a random walk."""
super(CenteredWalkSampler, self).__init__(trunk)
def __eq__(self, other):
"""Equality."""
equal = isinstance(other, CenteredWalkSampler) \
and super(CenteredWalkSampler, self).__eq__(other)
return equal
def crossingover(self, other):
"""
pre: isinstance(other, CenteredWalkSampler)
pre: isinstance(self, CenteredWalkSampler)
# check for distinct references, needs to copy content, not references
post: __return__ is not self
post: __return__ is not other
post: model_locus.unique_check(__return__, self, other) == ''
"""
new_one = CenteredWalkSampler(self.get_trunk())
new_one.direction_steps = model_random.crossingover_list(self.direction_steps,
other.direction_steps)
return new_one
@staticmethod
def _numeric_compare(point1, point2):
"""Comparison function based on Euklidian distance from center 0.0."""
diff1 = point1[0]*point1[0] + point1[1]*point1[1]
diff2 = point2[0]*point2[0] + point2[1]*point2[1]
if diff1 > diff2:
return 1
elif diff1 < diff2:
return -1
return 0
def get_sample_points(self):
""" Produces a list of sampling points.
The points describes an random walk starting near (0.5, 0.5).
"""
sample_points = super(CenteredWalkSampler, self).get_sample_points()
sample_points.sort(cmp=CenteredWalkSampler._numeric_compare,
reverse=False)
return sample_points
def explain(self):
"""
post: len(__return__) == 3
"""
head = _('Centered random walk sampler: %d points') \
% (len(self.direction_steps))
return ka_utils.explain_points(head, self.get_sample_points())
def copy(self):
"""A copy constructor.
post: isinstance(__return__, CenteredWalkSampler)
# check for distinct references, needs to copy content, not references
post: __return__ is not self
"""
new_one = CenteredWalkSampler(self.get_trunk())
new_one.direction_steps = model_random.copy_list(self.direction_steps)
return new_one
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