Proper working implementation of a cycle detection algorithm, that
returns the cycles (rather than printing them) by constructing the
path between the two nodes that were found to be cyclic.
Patch by: Sverre Rabbelier
# Copyright (c) 2007-2008 Pedro Matiello <pmatiello@gmail.com>
# Zsolt Haraszti <zsolt@drawwell.net>
#
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation
# files (the "Software"), to deal in the Software without
# restriction, including without limitation the rights to use,
# copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following
# conditions:
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
# OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
# WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
# OTHER DEALINGS IN THE SOFTWARE.
"""
Random graph generators for python-graph.
@sort: generate
"""
# Imports
import graph as classes
from random import randint
# Generator
def generate(graph, num_nodes, num_edges, weight_range=(1, 1)):
"""
Add nodes and random edges to the graph.
@type graph: graph
@param graph: Graph.
@type num_nodes: number
@param num_nodes: Number of nodes.
@type num_edges: number
@param num_edges: Number of edges.
@type weight_range: tuple
@param weight_range: tuple of two integers as lower and upper limits on randomly generated
weights (uniform distribution).
"""
# Discover if graph is directed or not
directed = (type(graph) == classes.digraph)
# Nodes first
nodes = xrange(num_nodes)
graph.add_nodes(nodes)
# Build a list of all possible edges
edges = []
edges_append = edges.append
for x in nodes:
for y in nodes:
if ((directed and x != y) or (x > y)):
edges_append((x, y))
# Randomize the list
for i in xrange(len(edges)):
r = randint(0, len(edges)-1)
edges[i], edges[r] = edges[r], edges[i]
# Add edges to the graph
min_wt = min(weight_range)
max_wt = max(weight_range)
for i in xrange(num_edges):
each = edges[i]
graph.add_edge(each[0], each[1], wt = randint(min_wt, max_wt))