def main():
"""if len(sys.argv) < 2:
print('Usage: application2.py <graph.txt>')
return"""
network_graph = alg_application2_provided.load_graph(sys.argv[1])
print('network_grap has %d edges', num_of_edges(network_graph))
N = len(network_graph)
p = 0.00171
# trials = 10
# print('avg # of edges %d' % avg([num_of_edges(algorithm_er(N, p)) for _ in range(trials)]))
er_graph = algorithm_er(N, p)
print('# of edges in er_graph %d' % num_of_edges(er_graph))
m = 2
upa_graph = algorithm_upa(N, m)
print('# of edges in upa_graph %d' % num_of_edges(upa_graph))
question1(network_graph, er_graph, upa_graph, random_order,
'random order attack', 'resilience-random.png')
plt.cla()
question3('performance.png')
plt.cla()
question1(network_graph, er_graph, upa_graph, fast_targeted_order,
'targeted order attack', 'resilience-targeted.png')
plt.show()
def load_network_graph():
'''
Helper function to load the provided undirected network graph
'''
network_graph = provided.load_graph(provided.NETWORK_URL)
return network_graph
def main():
if len(sys.argv) < 2:
print('Usage: application2.py <graph.txt>')
return
network_graph = alg_application2_provided.load_graph(sys.argv[1])
print('network_grap has %d edges', num_of_edges(network_graph))
N = len(network_graph)
p = 0.00171
# trials = 10
# print('avg # of edges %d' % avg([num_of_edges(algorithm_er(N, p)) for _ in range(trials)]))
er_graph = algorithm_er(N, p)
print('# of edges in er_graph %d' % num_of_edges(er_graph))
m = 2
upa_graph = algorithm_upa(N, m)
print('# of edges in upa_graph %d' % num_of_edges(upa_graph))
question1(network_graph, er_graph, upa_graph, random_order,
'random order attack', 'pic/1-resilience-random.png')
plt.cla()
question3('pic/3-performance.png')
plt.cla()
question1(network_graph, er_graph, upa_graph, fast_targeted_order,
'targeted order attack', 'pic/4-resilience-targeted.png')
plt.show()
def ques4_plot():
"""
Plot an example with two curves with legends
"""
ERGraph = uERAlgo(1239,0.004)
UPAGraph = UPAalgo(1239,3)
computer_nw = alg_application2_provided.load_graph(alg_application2_provided.NETWORK_URL)
xvals = range(0,1240)
yvals1 = module2.compute_resilience(ERGraph,alg_application2_provided.targeted_order(ERGraph))
yvals2 = module2.compute_resilience(UPAGraph,alg_application2_provided.targeted_order(UPAGraph))
yvals3 = module2.compute_resilience(computer_nw,alg_application2_provided.targeted_order(computer_nw))
#yvals1 = module2.compute_resilience(ERGraph,fast_targeted_order(ERGraph))
#yvals2 = module2.compute_resilience(UPAGraph,fast_targeted_order(UPAGraph))
#yvals3 = module2.compute_resilience(computer_nw,fast_targeted_order(computer_nw))
plt.xlabel("Number of Nodes Removed")
plt.ylabel("Size of Largest Connected Component")
plt.title("Size of Largest Connected Component vs Nodes Removed (using Targeted Order)")
plt.plot(xvals, yvals1, '-g', label='ERGraph n = 1239,p = 0.004')
plt.plot(xvals, yvals2, '-r', label='UPAGraph n = 1239, m = 3')
plt.plot(xvals, yvals3, '-b', label='computer network')
plt.legend(loc='upper right')
plt.show()
#ques4_plot()
def question4():
NETWORK_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_rf7.txt"
network_graph = app2.load_graph(NETWORK_URL)
network_idd = prj1.in_degree_distribution(network_graph)
network_nodes = sum(network_idd.values())
network_edges = sum([x * y for (x, y) in network_idd.iteritems()]) / 2
network_prob = float(network_edges) / network_nodes / (network_nodes - 1)
print "Network_nodes", network_nodes
print "Network_edges", network_edges
print "Network_prob", network_prob
print "Network_m", network_edges / float(network_nodes)
er_graph = er_makegraph(1347, 0.00172)
er_idd = prj1.in_degree_distribution(er_graph)
er_nodes = sum(er_idd.values())
er_edges = sum([x * y for (x, y) in er_idd.iteritems()]) / 2
print "ER_nodes", er_nodes
print "ER_edges", er_edges
upa_graph = upa_makegraph(1347, 2)
upa_idd = prj1.in_degree_distribution(upa_graph)
upa_nodes = sum(upa_idd.values())
upa_edges = sum([x * y for (x, y) in upa_idd.iteritems()]) / 2
print "UPA_nodes", upa_nodes
print "UPA_edges", upa_edges
network_resilience = prj2.compute_resilience(
network_graph, fast_targeted_order(network_graph))
er_resilience = prj2.compute_resilience(er_graph,
fast_targeted_order(er_graph))
upa_resilience = prj2.compute_resilience(upa_graph,
fast_targeted_order(upa_graph))
plt.plot(range(len(network_resilience)),
network_resilience,
'-r',
label='Network graph')
plt.plot(range(len(er_resilience)),
er_resilience,
'-b',
label='ER-generated (p = 0.00172)')
plt.plot(range(len(upa_resilience)),
upa_resilience,
'-g',
label='UPA-generated (m = 2)')
plt.title('Comparison of Networks Resilience')
plt.ylabel('Size of the largest connected component')
plt.xlabel('Number of nodes removed')
plt.legend()
plt.show()
#question4()
def question4_plot():
"""
Loads the three graphs in the problem, calculates a sequence of targeted
attacks using fast_targeted_attack and calculates the resilience of each
graph based on this attack. Plots the results.
"""
# Loading the provided computer network example
example_graph = graph_provided.load_graph(NETWORK_URL)
# Creating a random ER graph with 1239 nodes and p = .004
er_graph = make_random_ugraph(1239, .004)
# Creating an UPA graph, m=12
upa_graph = mod_upa.create_UPA_graph(1239, 2)
# Calculating attack sequences with the provided targeted_order
#example_attack = graph_provided.targeted_order(example_graph)
#er_attack = graph_provided.targeted_order(er_graph)
#upa_attack = graph_provided.targeted_order(upa_graph)
# Calculating attack sequences with fast_targeted_order
example_attack = fast_targeted_order(example_graph)
er_attack = fast_targeted_order(er_graph)
upa_attack = fast_targeted_order(upa_graph)
# Calculating resilience
example_resilience = mod_resilience.compute_resilience(example_graph,
example_attack)
er_resilience = mod_resilience.compute_resilience(er_graph,
er_attack)
upa_resilience = mod_resilience.compute_resilience(upa_graph,
upa_attack)
# Plotting the outcome
xvals = range(1240)
plt.plot(xvals, example_resilience, '-c',
label='computer network example (provided)')
plt.plot(xvals, er_resilience, '-y',
label='generated ER graph (p = .004)')
plt.plot(xvals, upa_resilience, '-m',
label='generated UPA graph (m = 2)')
plt.legend(loc='upper right')
plt.title('Graphs resilience to targeted attack')
plt.xlabel('Nodes removed')
plt.ylabel('Biggest connected element size')
plt.show()
def legend_example():
"""
Plot an example with two curves with legends
"""
ERGraph = uERAlgo(1239,0.004)
UPAGraph = UPAalgo(1239,3)
computer_nw = alg_application2_provided.load_graph(alg_application2_provided.NETWORK_URL)
print countedges(ERGraph)
print countedges(UPAGraph)
print countedges(computer_nw)
#deg_dict = compute_degrees(computer_nw)
#total_deg = (sum(x for x in deg_dict.values()))/1239
#print total_deg
xvals = range(0,1240)
yvals1 = module2.compute_resilience(ERGraph,random_order(ERGraph))
yvals2 = module2.compute_resilience(UPAGraph,random_order(UPAGraph))
yvals3 = module2.compute_resilience(computer_nw,random_order(computer_nw))
#yvals1 = module2.compute_resilience(ERGraph,alg_application2_provided.targeted_order(ERGraph))
#yvals2 = module2.compute_resilience(UPAGraph,alg_application2_provided.targeted_order(UPAGraph))
#yvals3 = module2.compute_resilience(computer_nw,alg_application2_provided.targeted_order(computer_nw))
#yvals1 = module2.compute_resilience(ERGraph,fast_targeted_order(ERGraph))
#yvals2 = module2.compute_resilience(UPAGraph,fast_targeted_order(UPAGraph))
#yvals3 = module2.compute_resilience(computer_nw,fast_targeted_order(computer_nw))
print len(yvals1)
print len(yvals2)
print len(yvals3)
plt.xlabel("Number of Nodes Removed")
plt.ylabel("Size of Largest Connected Component")
plt.title("Size of Largest Connected Component vs Nodes Removed")
plt.plot(xvals, yvals1, '-g', label='ERGraph n = 1239,p = 0.004')
plt.plot(xvals, yvals2, '-r', label='UPAGraph n = 1239, m = 3')
plt.plot(xvals, yvals3, '-b', label='computer network')
plt.legend(loc='upper right')
plt.show()
def question4():
NETWORK_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_rf7.txt"
network_graph = app2.load_graph(NETWORK_URL)
network_idd = prj1.in_degree_distribution(network_graph)
network_nodes = sum(network_idd.values())
network_edges = sum([x * y for (x, y) in network_idd.iteritems()]) / 2
network_prob = float(network_edges) / network_nodes / (network_nodes - 1)
print "Network_nodes", network_nodes
print "Network_edges", network_edges
print "Network_prob", network_prob
print "Network_m", network_edges /float(network_nodes)
er_graph = er_makegraph(1347, 0.00172)
er_idd = prj1.in_degree_distribution(er_graph)
er_nodes = sum(er_idd.values())
er_edges = sum([x * y for (x, y) in er_idd.iteritems()]) / 2
print "ER_nodes", er_nodes
print "ER_edges", er_edges
upa_graph = upa_makegraph(1347, 2)
upa_idd = prj1.in_degree_distribution(upa_graph)
upa_nodes = sum(upa_idd.values())
upa_edges = sum([x * y for (x, y) in upa_idd.iteritems()]) / 2
print "UPA_nodes", upa_nodes
print "UPA_edges", upa_edges
network_resilience = prj2.compute_resilience(network_graph, fast_targeted_order(network_graph))
er_resilience = prj2.compute_resilience(er_graph, fast_targeted_order(er_graph))
upa_resilience = prj2.compute_resilience(upa_graph, fast_targeted_order(upa_graph))
plt.plot(range(len(network_resilience)), network_resilience, '-r', label='Network graph')
plt.plot(range(len(er_resilience)), er_resilience, '-b', label='ER-generated (p = 0.00172)')
plt.plot(range(len(upa_resilience)), upa_resilience, '-g', label='UPA-generated (m = 2)')
plt.title('Comparison of Networks Resilience')
plt.ylabel('Size of the largest connected component')
plt.xlabel('Number of nodes removed')
plt.legend()
plt.show()
#question4()
def Question4():
n = 1239
xvals = range(n+1)
UPA_graph = UPA_generator(2,n)
ER_graph = make_ER_graph_2(n,.0034)
CN_graph = alg_application2_provided.load_graph(NETWORK_URL)
UPA_fast_order = alg_application2_provided.fast_targeted_order(UPA_graph)
ER_fast_order = alg_application2_provided.fast_targeted_order(ER_graph)
CN_fast_order = alg_application2_provided.fast_targeted_order(CN_graph)
UPA_resilience = module2_project.compute_resilience(UPA_graph, UPA_fast_order)
ER_resilience = module2_project.compute_resilience(ER_graph, ER_fast_order)
CN_resilience = module2_project.compute_resilience(CN_graph, CN_fast_order)
yvals1 = UPA_resilience
yvals2 = ER_resilience
yvals3 = CN_resilience
plt.plot(xvals, yvals1, '-b', label='UPA_resilience m = 2')
plt.plot(xvals, yvals2, '-r', label='ER_resilience, p = .0034')
plt.plot(xvals, yvals3, '-g', label='ComputerNetworkGraph_resilience')
plt.legend(loc='upper right')
plt.title('plot of resilience of three graphs ')
plt.xlabel('number of removed nodes')
plt.ylabel('size of the largest connected component')
plt.show()
def Question4():
n = 1239
xvals = range(n + 1)
UPA_graph = UPA_generator(2, n)
ER_graph = make_ER_graph_2(n, .0034)
CN_graph = alg_application2_provided.load_graph(NETWORK_URL)
UPA_fast_order = alg_application2_provided.fast_targeted_order(UPA_graph)
ER_fast_order = alg_application2_provided.fast_targeted_order(ER_graph)
CN_fast_order = alg_application2_provided.fast_targeted_order(CN_graph)
UPA_resilience = module2_project.compute_resilience(
UPA_graph, UPA_fast_order)
ER_resilience = module2_project.compute_resilience(ER_graph, ER_fast_order)
CN_resilience = module2_project.compute_resilience(CN_graph, CN_fast_order)
yvals1 = UPA_resilience
yvals2 = ER_resilience
yvals3 = CN_resilience
plt.plot(xvals, yvals1, '-b', label='UPA_resilience m = 2')
plt.plot(xvals, yvals2, '-r', label='ER_resilience, p = .0034')
plt.plot(xvals, yvals3, '-g', label='ComputerNetworkGraph_resilience')
plt.legend(loc='upper right')
plt.title('plot of resilience of three graphs ')
plt.xlabel('number of removed nodes')
plt.ylabel('size of the largest connected component')
plt.show()
return output_graph
def random_order(ugraph):
"""
:param ugraph: input an undisrected graph
:return: a list of nodes in the graph in some random order
"""
random_nodes_list = []
for node in ugraph.keys():
random_nodes_list.append(node)
random.shuffle(random_nodes_list)
return random_nodes_list
#graph of computer network
cn_graph = aa2p.load_graph(aa2p.NETWORK_URL)
print edges_in_undirected_graph(cn_graph)
#graph of ER
er_graph = er_algorithm(len(cn_graph.keys()), 0.002)
#print er_graph
print edges_in_undirected_graph(er_graph)
#graph of UPA
upa_graph = get_upa_graph(len(cn_graph.keys()), 2)
print edges_in_undirected_graph(upa_graph)
#random attack
cn_attack_order = random_order(cn_graph)
er_attack_order = random_order(er_graph)
upa_attack_order = random_order(upa_graph)
def load_network_graph():
graph_url = '/home/novneet/PycharmProjects/AlgoThinking1/week2/application/res/alg_rf7.txt'
network_graph = alg2.load_graph(graph_url)
return network_graph
def load_network_graph():
graph_url = '/home/novneet/PycharmProjects/AlgoThinking1/week2/application/res/alg_rf7.txt'
network_graph = alg2.load_graph(graph_url)
return network_graph
'''
Algorithmic Thinking (part1), programming application 2, question 5
Connected components and graph resilience,
https://class.coursera.org/algorithmicthink1-003/human_grading/view/courses/975649/assessments/32/submissions
Author: Sakari Hakala
'''
import alg_application2_provided as provided
import matplotlib.pyplot as plt
import algo_pa2 as pa2
import algo_app2_1 as app2
cn = provided.load_graph(provided.NETWORK_URL)
upa = app2.create_UPA(1239, 2)
uer = app2.undirected_ER(1239, 0.004)
order_cn = app2.fastTargetedOrder(cn)
order_upa = app2.fastTargetedOrder(upa)
order_uer = app2.fastTargetedOrder(uer)
resillience_cn = pa2.compute_resilience(cn, order_cn)
resillience_upa = pa2.compute_resilience(upa, order_upa)
resillience_uer = pa2.compute_resilience(uer, order_uer)
plt.plot(resillience_cn, '-b', label='Provided computer network')
plt.plot(resillience_upa, '-r', label='UPA graph, m = 2')
plt.plot(resillience_uer, '-g', label='uER graph, p = 0.004')
plt.title('Graph resillience under targeted attack')
plt.xlabel('Number of nodes attacked')
#n=10
#m=2
#print [graph_size(UPA_generator(m,n), n) for _ in np.arange(20)]
NETWORK_URL = "http://storage.googleapis.com/codeskulptor-alg/alg_rf7.txt"
import matplotlib.pyplot as plt
t0 = time.clock()
n = 1239
xvals = range(n+1)
UPA_graph = UPA_generator(2,n)
ER_graph = make_ER_graph_2(n,.0034)
CN_graph = alg_application2_provided.load_graph(NETWORK_URL)
UPA_resilience = module2_project.compute_resilience(UPA_graph, random_order(UPA_graph))
ER_resilience = module2_project.compute_resilience(ER_graph, random_order(ER_graph))
CN_resilience = module2_project.compute_resilience(CN_graph, random_order(CN_graph))
yvals1 = UPA_resilience
yvals2 = ER_resilience
yvals3 = CN_resilience
plt.plot(xvals, yvals1, '-b', label='UPA_resilience m = 2')
plt.plot(xvals, yvals2, '-r', label='ER_resilience, p = .0034')
plt.plot(xvals, yvals3, '-g', label='ComputerNetworkGraph_resilience')
plt.legend(loc='upper right')
plt.title('plot of resilience of three graphs ')
plt.xlabel('number of removed nodes')
plt.ylabel('size of the largest connected component')
plt.show()
plt.savefig('q2_1.png')
attack_list.append(u_node)
degree_sets[k_degree].remove(u_node)
for v_neighbor in ugraph[u_node]:
v_degree = len(ugraph[v_neighbor])
degree_sets[v_degree].remove(v_neighbor)
degree_sets[v_degree - 1].add(v_neighbor)
graph_provided.delete_node(graph, u_node)
index += 1
return attack_list
# Tests for fast_targeted_order
#my_graph = mod_upa.create_UPA_graph(10, 3)
#print 'my graph = ' + str(my_graph)
#print 'provided attack order = ' + str(graph_provided.targeted_order(my_graph))
my_graph = graph_provided.load_graph(NETWORK_URL)
attack = fast_targeted_order(my_graph)
resilience = mod_resilience.compute_resilience(my_graph, attack)
def question3_plot():
"""
Calculates a series of targeted attacks using both functions (provided and
the fast targeted implemented one), computes the running time of these
processes and plots the outcome
"""
# Initializing the data series
xvals = range(10 , 1000, 10)
targeted_times = []
fast_targeted_times = []
for size in range(10, 1000, 10):
