from performance import Performance
from goody import irange
from graph_goody import random_graph,spanning_tree
# Put script below to generate data for Problem #1
# In case you fail, the data appears in sample8.pdf in the helper folder
for i in range(8):
size=(2**i)*1000
g=random_graph(size,lambda x:x*10)
p=Performance(lambda :spanning_tree(g),lambda :None,5,'\n\nSpanning Tree of size ' + str(size))
p.evaluate()
p.analyze()
from performance import Performance
from goody import irange
from graph_goody import random, spanning_tree
# Put script here to generate data for Problem #1
g = None
def create_random(n):
global g
g = random(n, lambda n : 10*n)
for i in irange(0,7) :
n = 1000 * 2**i
p = Performance(lambda : spanning_tree(g), lambda : create_random(n),5,'Spanning Tree of size {}'.format(n))
p.evaluate()
p.analyze()
print()
def span_time(n):
global rand_graph
spanning_tree(rand_graph)
from performance import Performance
from goody import irange
from graph_goody import random_graph,spanning_tree
from graph import Graph
# Put script below to generate data for Problem #1
# In case you fail, the data appears in sample8.pdf in the helper folder
global nodes
global graph
if __name__ == '__main__':
nodes = 1000
while nodes <= 128000:
graph = random_graph(nodes,lambda n : 10*n)
perf = Performance(lambda: spanning_tree(graph),setup=lambda:None,times_to_measure=5,title='Spanning Tree Timings for '+str(nodes)+' nodes')
perf.evaluate()
perf.analyze()
nodes *= 2
def spamming_tree():
return spanning_tree(create_random())
from performance import Performance
from goody import irange
from graph_goody import random, spanning_tree
# Put script here to generate data for Problem #1
def create_random():
global dog
dog = random(x*1000, lambda n : 10*n)
if __name__ == '__main__':
x=1
while x<129:
y = Performance(lambda:spanning_tree(dog), lambda:create_random(), title = "Spanning Tree of Size " + str(x*1000))
y.evaluate()
y.analyze()
print()
x = 2*x
from performance import Performance
from goody import irange
from graph_goody import random_graph, spanning_tree
# Put script here to generate data for Problem #1
# In case you fail, the data appears in sample8.pdf in the helper folder
def creat_random(n):
global correct_size
correct_size = random_graph(n, lambda n: n * 10)
for i in irange(0, 7):
n = 1000
n = n * (2**i)
p = Performance(lambda: spanning_tree(correct_size),
lambda: creat_random(n),
5,
title='Spanning Tree of size {}'.format(n))
p.evaluate()
p.analyze()
print()
from performance import Performance
from graph_goody import random_graph, spanning_tree
# Put script below to generate data for Problem #1
# In case you fail, the data appears in sample8.pdf in the helper folder
def create_random(size: int) -> 'Graph':
return random_graph(size, lambda n: 10 * n)
graph = None
size = 1
while size <= 128:
graph = create_random(size * 1000)
p = Performance(lambda: spanning_tree(graph),
title=f'Spanning Tree of size {size*1000}')
p.evaluate()
p.analyze()
print()
size *= 2
from performance import Performance
from goody import irange
from graph_goody import random_graph,spanning_tree
# Put script below to generate data for Problem #1
# In case you fail, the data appears in sample8.pdf in the helper folder
def create_random(i):
global graph
graph = random_graph(i, lambda n : 10*n)
for i in irange(0,7):
p = Performance(lambda : spanning_tree(graph), lambda: create_random(2**i * 1000), 5, f'\nSpanning Tree of size {2**i *1000}')
p.evaluate()
p.analyze()