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"""! |
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@brief CCORE Wrapper for ant colony based algorithm for travelling salesman problem. |
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@authors Andrei Novikov, Alexey Kukushkin ([email protected]) |
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@date 2014-2017 |
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@copyright GNU Public License |
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@cond GNU_PUBLIC_LICENSE |
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PyClustering is free software: you can redistribute it and/or modify |
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it under the terms of the GNU General Public License as published by |
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the Free Software Foundation, either version 3 of the License, or |
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(at your option) any later version. |
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PyClustering is distributed in the hope that it will be useful, |
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but WITHOUT ANY WARRANTY; without even the implied warranty of |
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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GNU General Public License for more details. |
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You should have received a copy of the GNU General Public License |
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along with this program. If not, see <http://www.gnu.org/licenses/>. |
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@endcond |
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""" |
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from pyclustering.core.wrapper import *; |
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import types; |
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from pyclustering.core.pyclustering_package import pyclustering_package,\ |
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package_builder, package_extractor |
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CITIES_DISTANCE_SET_BY_MATRIX = True |
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CITIES_DISTANCE_SET_BY_LIST_OF_COORDINATES = False |
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class c_antcolony_tsp_parameters(Structure): |
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""" |
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double q; |
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double ro; |
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double alpha; |
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double beta; |
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double gamma; |
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double initial_pheramone; |
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unsigned int iterations; |
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unsigned int ants_per_iteration; |
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""" |
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_fields_ = [("q" , c_double), |
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("ro" , c_double), |
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("alpha" , c_double), |
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("beta" , c_double), |
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("gamma" , c_double), |
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("qinit_pheramone" , c_double), |
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("iterations" , c_uint), |
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("ants_per_iteration" , c_uint) ]; |
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class c_antcolony_tsp_objects(Structure): |
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""" |
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unsigned int size; |
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unsigned int dimension; |
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double *data; |
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""" |
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_fields_ = [("size" , c_uint), |
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("dimension" , c_uint), |
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("data" , POINTER(c_double)) ]; |
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class c_antcolony_tsp_matrix(Structure): |
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""" |
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unsigned int size; |
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double **data; |
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""" |
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_fields_ = [("size" , c_uint), |
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("data" , POINTER(POINTER(c_double))) ]; |
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class c_antcolony_tsp_result(Structure): |
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""" |
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unsigned int size; |
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double path_length; |
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unsigned int *cities_num; |
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""" |
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_fields_ = [("size" , c_uint), |
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("path_length" , c_double), |
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("object_sequence" , POINTER(c_uint)) ]; |
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def get_algo_params(params): |
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algorithm_params = c_antcolony_tsp_parameters(); |
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algorithm_params.q = c_double(params.q); |
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algorithm_params.ro = c_double(params.ro); |
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algorithm_params.alpha = c_double(params.alpha); |
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algorithm_params.beta = c_double(params.beta); |
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algorithm_params.gamma = c_double(params.gamma); |
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algorithm_params.qinit_pheramone = c_double(params.qinit_pheramone); |
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algorithm_params.iterations = c_uint(params.iterations); |
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algorithm_params.ants_per_iteration = c_uint(params.ants_per_iteration); |
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algorithm_params = pointer(algorithm_params); |
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return algorithm_params |
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def antcolony_tsp_prepare_matrix(matrix): |
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dist_matrix = c_antcolony_tsp_matrix() |
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dist_matrix.size = len(matrix) |
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p_dist = (POINTER(c_double) * dist_matrix.size)(); |
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for i in range(dist_matrix.size): |
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tmp_p_dist = (c_double * dist_matrix.size)(); |
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for j in range(dist_matrix.size): |
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tmp_p_dist[j] = matrix[i][j]; |
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p_dist[i] = cast(tmp_p_dist, POINTER(c_double)); |
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dist_matrix.data = cast(p_dist, POINTER(POINTER(c_double))); |
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dist_matrix = pointer(dist_matrix) |
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return dist_matrix |
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def antcolony_tsp_prepare_cities_list(cities): |
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dimension = len(cities[0]); |
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cities_coord = c_antcolony_tsp_objects(); |
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cities_coord.size = c_uint(len(cities) * dimension); |
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cities_coord.dimension = c_uint(dimension); |
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cities_coord.data = (c_double * cities_coord.size)(); |
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for i in range(0, cities_coord.size): |
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cities_coord.data[i] = cities[i // dimension][i % dimension]; |
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cities_coord = pointer(cities_coord); |
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return cities_coord |
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def antcolony_tsp_process(cities, params, citiesDistRepresent = CITIES_DISTANCE_SET_BY_LIST_OF_COORDINATES): |
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algorithm_params = get_algo_params(params) |
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ccore = load_core(); |
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if citiesDistRepresent == CITIES_DISTANCE_SET_BY_MATRIX: |
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cities_coord = package_builder(cities, c_double).create(); # antcolony_tsp_prepare_matrix(cities); |
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ccore.antcolony_tsp_process_by_matrix.restype = POINTER(pyclustering_package); |
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result_package = ccore.antcolony_tsp_process_by_matrix(cities_coord, algorithm_params); |
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else: |
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cities_coord = package_builder(cities, c_double).create(); # antcolony_tsp_prepare_cities_list(cities); |
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ccore.antcolony_tsp_process.restype = POINTER(pyclustering_package); |
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result_package = ccore.antcolony_tsp_process(cities_coord, algorithm_params); |
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result = package_extractor(result_package).extract(); |
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ccore.free_pyclustering_package(result_package); |
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return result; |
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annoviko /
pyclustering
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