文件名称:EppsteinAlgorithm
介绍说明－－下载内容均来自于网络，请自行研究使用
The algorithm developed by Eppstein not looking for simple paths. In
Indeed, the paths returned by the algorithm may contain rings or
loops. It applies only on graphs where the weights are positive.
This algorithm is based on extensive use of heap. These piles will allow
construct a graph of P such that the maximum level is 4 and a path
in P corresponds to a path in the starting graph.
A Python scr ipt (ksp.py) is provided in order to test both algorithms
directly the command line. The parameters are the file name
containing the graph, the origin of the roads, the roads end and the number
paths. It should also add the -a option followed by the choice of algorithm
(Eppstein or yen). Two additional optional parameters have been added:
-s which saves the graph in an image whose name is passed
-p parameter and that saves the graph P generated Eppstein algorithm.
Both options require Graphviz to generate images and the
library PyGraphviz-The algorithm developed by Eppstein not looking for simple paths. In
Indeed, the paths returned by the algorithm may contain rings or
loops. It applies only on graphs where the weights are positive.
This algorithm is based on extensive use of heap. These piles will allow
construct a graph of P such that the maximum level is 4 and a path
in P corresponds to a path in the starting graph.
A Python scr ipt (ksp.py) is provided in order to test both algorithms
directly the command line. The parameters are the file name
containing the graph, the origin of the roads, the roads end and the number
paths. It should also add the -a option followed by the choice of algorithm
(Eppstein or yen). Two additional optional parameters have been added:
-s which saves the graph in an image whose name is passed
-p parameter and that saves the graph P generated Eppstein algorithm.
Both options require Graphviz to generate images and the
library PyGraphviz
Indeed, the paths returned by the algorithm may contain rings or
loops. It applies only on graphs where the weights are positive.
This algorithm is based on extensive use of heap. These piles will allow
construct a graph of P such that the maximum level is 4 and a path
in P corresponds to a path in the starting graph.
A Python scr ipt (ksp.py) is provided in order to test both algorithms
directly the command line. The parameters are the file name
containing the graph, the origin of the roads, the roads end and the number
paths. It should also add the -a option followed by the choice of algorithm
(Eppstein or yen). Two additional optional parameters have been added:
-s which saves the graph in an image whose name is passed
-p parameter and that saves the graph P generated Eppstein algorithm.
Both options require Graphviz to generate images and the
library PyGraphviz-The algorithm developed by Eppstein not looking for simple paths. In
Indeed, the paths returned by the algorithm may contain rings or
loops. It applies only on graphs where the weights are positive.
This algorithm is based on extensive use of heap. These piles will allow
construct a graph of P such that the maximum level is 4 and a path
in P corresponds to a path in the starting graph.
A Python scr ipt (ksp.py) is provided in order to test both algorithms
directly the command line. The parameters are the file name
containing the graph, the origin of the roads, the roads end and the number
paths. It should also add the -a option followed by the choice of algorithm
(Eppstein or yen). Two additional optional parameters have been added:
-s which saves the graph in an image whose name is passed
-p parameter and that saves the graph P generated Eppstein algorithm.
Both options require Graphviz to generate images and the
library PyGraphviz
(系统自动生成,下载前可以参看下载内容)
下载文件列表
heap.py
ksp.py
measures.py
README.txt
graphs\complete.txt
......\exampleEppstein.svg
......\exampleEppstein.txt
......\exampleEppsteinP.svg
......\exampleYen.svg
......\exampleYen.txt
......\exampleYenP.svg
......\format.txt
......\twopaths.svg
......\twopaths.txt
networkx\algorithms\approximation\clique.py
........\..........\.............\clustering_coefficient.py
........\..........\.............\dominating_set.py
........\..........\.............\independent_set.py
........\..........\.............\matching.py
........\..........\.............\ramsey.py
........\..........\.............\tests\test_approx_clust_coeff.py
........\..........\.............\.....\test_clique.py
........\..........\.............\.....\test_dominating_set.py
........\..........\.............\.....\test_independent_set.py
........\..........\.............\.....\test_matching.py
........\..........\.............\.....\test_ramsey.py
........\..........\.............\.....\test_vertex_cover.py
........\..........\.............\vertex_cover.py
........\..........\.............\__init__.py
........\..........\.ssortativity\connectivity.py
........\..........\.............\connectivity.pyc
........\..........\.............\correlation.py
........\..........\.............\correlation.pyc
........\..........\.............\mixing.py
........\..........\.............\mixing.pyc
........\..........\.............\neighbor_degree.py
........\..........\.............\neighbor_degree.pyc
........\..........\.............\pairs.py
........\..........\.............\pairs.pyc
........\..........\.............\tests\base_test.py
........\..........\.............\.....\test_connectivity.py
........\..........\.............\.....\test_correlation.py
........\..........\.............\.....\test_mixing.py
........\..........\.............\.....\test_neighbor_degree.py
........\..........\.............\.....\test_pairs.py
........\..........\.............\__init__.py
........\..........\.............\__init__.pyc
........\..........\bipartite\basic.py
........\..........\.........\basic.pyc
........\..........\.........\centrality.py
........\..........\.........\centrality.pyc
........\..........\.........\cluster.py
........\..........\.........\cluster.pyc
........\..........\.........\projection.py
........\..........\.........\projection.pyc
........\..........\.........\redundancy.py
........\..........\.........\redundancy.pyc
........\..........\.........\spectral.py
........\..........\.........\spectral.pyc
........\..........\.........\tests\test_basic.py
........\..........\.........\.....\test_centrality.py
........\..........\.........\.....\test_cluster.py
........\..........\.........\.....\test_project.py
........\..........\.........\.....\test_spectral_bipartivity.py
........\..........\.........\__init__.py
........\..........\.........\__init__.pyc
........\..........\block.py
........\..........\block.pyc
........\..........\boundary.py
........\..........\boundary.pyc
........\..........\centrality\betweenness.py
........\..........\..........\betweenness.pyc
........\..........\..........\betweenness_subset.py
........\..........\..........\betweenness_subset.pyc
........\..........\..........\closeness.py
........\..........\..........\closeness.pyc
........\..........\..........\communicability_alg.py
........\..........\..........\communicability_alg.pyc
........\..........\..........\current_flow_betweenness.py
........\..........\..........\current_flow_betweenness.pyc
........\..........\..........\current_flow_betweenness_subset.py
........\..........\..........\current_flow_betweenness_subset.pyc
........\..........\..........\current_flow_closeness.py
........\..........\..........\current_flow_closeness.pyc
........\..........\..........\degree_alg.py
........\..........\..........\degree_alg.pyc
........\..........\..........\dispersion.py
........\..........\..........\dispersion.pyc
........\..........\..........\eigenvector.py
........\..........\..........\eigenvector.pyc
........\..........\..........\flow_matrix.py
....