def main(args):
g = generate(args.nodes, args.edges, True)
while not find_cycle(g):
g = generate(args.nodes, args.edges, True)
with open(args.output, 'w') as f:
f.write(write(g))
def main():
global dg2
while find_cycle(dg2):
dg2 = generate(8,10,directed=True)
sweep()
print dg2
print toporder
def main():
global dg2
while find_cycle(dg2):
dg2 = generate(8, 10, directed=True)
sweep()
print dg2
print toporder
def old_steels_uniwersal_basic_experiment(num_iter, agents, stimuli, interaction,
classifier = SteelsClassifier, topology = None,
inc_category_treshold = None, dump_freq = 50):
sys.path.append("../")
from cog_abm.core.agent import Agent
from cog_abm.agent.sensor import SimpleSensor
from pygraph.classes.graph import graph
from pygraph.algorithms.generators import generate
from cog_abm.core.environment import Environment
from cog_abm.stimuli.stimulus import SimpleStimulus
from cog_abm.extras.color import Color
from cog_abm.core.simulation import Simulation
num_agents = len(agents)
topology = def_value(topology,
generate(num_agents, num_agents*(num_agents-1)/2))
stimuli = def_value(stimuli, default_stimuli())
if inc_category_treshold is not None:
interaction.__class__.def_inc_category_treshold = inc_category_treshold
env = Environment(stimuli, True)
Simulation.global_environment = env
s = Simulation(topology, interaction, agents)
res = s.run(num_iter, dump_freq)
return res
def graphgen(grnodes):
"""Initialises the graph. Ensures that all nodes in the graph are connected.
Creates an image called graph.png which represents the graph.
"""
graph = generate(grnodes, int(1.2*grnodes),
directed=False, weight_range=(1, 1))
# Makes sure graphs generated by generate() have all their nodes connected.
while len(shortest_path(graph, 0)[1]) < grnodes:
graph = generate(grnodes, int(1.2*grnodes),
directed=False, weight_range=(1, 1))
# print len(shortest_path(graph, 0)[1])
# Draw as PNG
dot = write(graph)
gvv = gv.readstring(dot)
gv.layout(gvv, 'dot')
gv.render(gvv, 'png', 'graph.png')
return graph
def generate_simple_network(agents):
n = len(agents)
network = Network(generate(n, n * (n - 1) // 2, directed=False))
for i, a in enumerate(agents):
network.add_agent(a, i)
# network.add_agent(a, i, a)
# network.add_agent(a, i, str(i))
return network
def generate_simple_network(agents):
n = len(agents)
network = Network(generate(n, n * (n - 1) // 2, directed=False))
for i, a in enumerate(agents):
network.add_agent(a, i)
# network.add_agent(a, i, a)
# network.add_agent(a, i, str(i))
return network
def steels_uniwersal_basic_experiment(num_iter,
agents,
environments,
interaction,
classifier=SteelsClassifier,
topology=None,
inc_category_treshold=None,
dump_freq=50,
stimuli=None):
from cog_abm.core import Environment, Simulation
from cog_abm.core.environment import RandomStimuliChooser
from pygraph.algorithms.generators import generate
num_agents = len(agents)
topology = def_value(
topology, generate(num_agents,
num_agents * (num_agents - 1) / 2))
# if stimuli == None:
# stimuli = def_value(None, default_stimuli())
if inc_category_treshold is not None:
interaction.__class__.def_inc_category_treshold = inc_category_treshold
chooser = RandomStimuliChooser(use_distance=True, distance=50.)
env = Environment(stimuli, chooser)
for key in environments.keys():
Simulation.environments[key] =\
Environment(environments[key].stimuli, chooser)
#TODO: think about this all...
glob = Simulation.environments["global"]
Simulation.global_environment = def_value(glob, env)
s = Simulation(topology, interaction, agents)
res = s.run(num_iter, dump_freq)
# import pprint
# print pprint.pprint(error_counter)
try:
# s = sum(error_counter.values())
# for k,v in error_counter.iteritems():
# print "%s: %s" % (k, float(v)/s)
for a in agents:
print "[%s]:%s" % (len(
a.state.lexicon.known_words()), a.state.lexicon.known_words())
print "OK"
except:
pass
return res
def steels_uniwersal_basic_experiment(num_iter, agents, environments, interaction, classifier=SteelsClassifier, topology=None, inc_category_treshold=None, dump_freq=50, stimuli=None): from cog_abm.core import Environment, Simulation from cog_abm.core.environment import RandomStimuliChooser from pygraph.algorithms.generators import generate num_agents = len(agents) topology = def_value(topology, generate(num_agents, num_agents*(num_agents-1)/2)) # if stimuli == None: # stimuli = def_value(None, default_stimuli()) if inc_category_treshold is not None: interaction.__class__.def_inc_category_treshold = inc_category_treshold chooser = RandomStimuliChooser(use_distance=True, distance=50.) env = Environment(stimuli, chooser) for key in environments.keys(): Simulation.environments[key] =\ Environment(environments[key].stimuli, chooser) #TODO: think about this all... glob = Simulation.environments["global"] Simulation.global_environment = def_value(glob , env) s = Simulation(topology, interaction, agents) res = s.run(num_iter, dump_freq) # import pprint # print pprint.pprint(error_counter) try: # s = sum(error_counter.values()) # for k,v in error_counter.iteritems(): # print "%s: %s" % (k, float(v)/s) for a in agents: print "[%s]:%s" % (len(a.state.lexicon.known_words()), a.state.lexicon.known_words()) print "OK" except: pass return res
def test_bfs(bfs_func, min_size, max_size, iterations=100):
ns = []
ts = []
for n in range(min_size, max_size, 100):
ns.append(n)
# Use random generation function from python-graph package
g = generate(n, n)
search_vertex = g.nodes()[0]
# Actual BFS starts here
start = etime()
# Running the function only once was giving a time of 0
# most of the time, loop it a few times to get a meaningful
# result
for i in range(iterations):
bfs_func(search_vertex, dummy_visit, g)
end = etime()
# ANd ends here
search_time = end - start
ts.append(search_time)
return {'sizes': ns, 'times': ts}
def steels_uniwersal_basic_experiment(num_iter, agents, environments, interaction,
classifier = SteelsClassifier, topology = None,
inc_category_treshold = None, dump_freq = 50, stimuli = None):
sys.path.append("../")
from cog_abm.core.agent import Agent
from cog_abm.agent.sensor import SimpleSensor
from pygraph.classes.graph import graph
from pygraph.algorithms.generators import generate
from cog_abm.core.environment import Environment
from cog_abm.stimuli.stimulus import SimpleStimulus
from cog_abm.extras.color import Color
from cog_abm.core.simulation import Simulation
num_agents = len(agents)
topology = def_value(topology,
generate(num_agents, num_agents*(num_agents-1)/2))
if stimuli == None:
stimuli = def_value(None, default_stimuli())
if inc_category_treshold is not None:
interaction.__class__.def_inc_category_treshold = inc_category_treshold
env = Environment(stimuli, True)
for key in environments.keys():
Simulation.environments[key] = Environment(environments[key].stimuli, True)
#bez sensu...
if "global" in environments:
glob = Environment([SimpleStimulus(c) for c in environments["global"].stimuli], True)
else:
glob = None
Simulation.global_environment = def_value(glob , env)
s = Simulation(topology, interaction, agents)
res = s.run(num_iter, dump_freq)
return res
import pygraph.algorithms.generators as gen
import pygraph.algorithms.accessibility as acc
import pygraph.algorithms.minmax as minmax
graph = gen.generate(5000, 10000, weight_range=(50, 2000))
components = acc.connected_components(graph)
nodes = [g for g in graph if components[g] == 1]
print "GRAPH NODES"
for n in graph.nodes():
print n
print "GRAPH EDGES"
for e in graph.edges():
if components[e[0]] == 1:
w = graph.edge_weight(e)
print(e[0], e[1], w)
# MST = minmax.minimal_spanning_tree(graph)
# print "MST NODES"
# for n in MST.keys():
# print n
# print "MST EDGES"
# for k in MST.keys():
# if MST[k] is not None:
# print "(%d, %d)" % (k, MST[k])
# else:
# print "(%d, %d)" % (k, k)
def new_digraph(wt_range=(1, 1)):
seed(random_seed)
return generate(num_nodes[use_size],
num_edges[use_size],
directed=True,
weight_range=wt_range)
from pygraph.classes.digraph import digraph
from pygraph.algorithms.searching import depth_first_search
from pygraph.algorithms.generators import generate
from pygraph.algorithms.cycles import find_cycle
from mytranverse import tranverse
dg2 = generate(8, 10, directed=True)
node_status = dict()
node_par = dict()
toporder = []
def sweep():
global node_status
global node_par
global dg2
node_list = dg2.nodes()
for node in node_list:
node_status[node] = 'white'
for node in node_list:
if (node_status[node] == 'white'):
node_par[node] = 'none'
dfs(node)
def dfs(vnode):
global node_status
global node_par
global dg2
for node in dg2.neighbors(vnode):
if (node_status[node] == 'white'):
import pygraph.algorithms.generators as gen
import pygraph.algorithms.accessibility as acc
import pygraph.algorithms.minmax as minmax
graph = gen.generate(5000, 10000, weight_range=(50, 2000))
components = acc.connected_components(graph)
nodes = [g for g in graph if components[g] == 1]
print "GRAPH NODES"
for n in graph.nodes():
print n
print "GRAPH EDGES"
for e in graph.edges():
if components[e[0]] == 1:
w = graph.edge_weight(e)
print (e[0], e[1], w)
# MST = minmax.minimal_spanning_tree(graph)
# print "MST NODES"
# for n in MST.keys():
# print n
# print "MST EDGES"
# for k in MST.keys():
# if MST[k] is not None:
# print "(%d, %d)" % (k, MST[k])
# else:
# print "(%d, %d)" % (k, k)
def makeTestGraph(self):
G = self.test_class()
log.warn("Generating %s with random seed: %i" % ( self.test_class, self.randomseed ) )
random.seed( self.randomseed )
generate(G, self.graph_order, self.edge_count)
return G
import pygraph.algorithms.generators as Gen
import random
n = random.randint(10, 1000)
g = Gen.generate(n, n, True)
for e in g.edges():
src, dest = e
print src, dest
#!/usr/bin/env python
"""
This script requires python-graph package from
https://code.google.com/p/python-graph/
"""
import random
import sys
import pygraph.algorithms.generators as generator
if (len(sys.argv) != 3):
print "./%s num_vertex num_edge" % (sys.argv[0])
sys.exit()
num_vertex = int(sys.argv[1])
num_edge = int(sys.argv[2])
g = generator.generate(num_vertex, num_edge,
directed=False, weight_range=(1,100))
edge_list = g.edges()
with open("sample.graph", 'wb') as f:
f.write("%d\n"%num_vertex)
for x,y in edge_list:
w = random.random() * 100
f.write("%d %d %f\n" % (x, y, w))
from pygraph.classes.digraph import digraph
from pygraph.algorithms.searching import depth_first_search
from pygraph.algorithms.generators import generate
from mytranverse import tranverse
dg2 = generate(8,15,directed=True)
node_status = dict()
node_par = dict()
def sweep():
global node_status
global node_par
global dg2
node_list = dg2.nodes()
for node in node_list:
node_status[node] = 'white'
for node in node_list:
if(node_status[node] == 'white'):
node_par[node] = 'none'
dfs(node)
def dfs(vnode):
global node_status
global node_par
global dg2
for node in dg2.neighbors(vnode):
if(node_status[node] == 'white'):
node_status[node] = 'grey'
node_par[node] = vnode
dfs(node)
node_status[vnode] = 'black'
def new_dict_digraph():
seed(random_seed)
G1 = generate(num_nodes[use_size], num_edges[use_size], directed=True)
G2 = dict_digraph()
G2.add_graph(G1)
return G2
def new_digraph():
seed(random_seed)
return generate(num_nodes[use_size], num_edges[use_size], directed=True)
def new_digraph(wt_range=(1, 1)):
seed(random_seed)
return generate(num_nodes[use_size], num_edges[use_size], directed=True, weight_range=wt_range)
def generate_clique_graph(n):
return generate(n, n * (n - 1) // 2, directed=False)
def new_graph():
seed(random_seed)
return generate(num_nodes[use_size], num_edges[use_size])
