def Special_Case4_Occurance():
max_repeats = 1000
max_nodes = 10
n = 2
repeat = True
case4 = []
while repeat == True:
for repeats in range(max_repeats):
G = random_graph_generator(n, n * 3)
sc4 = special_4_check(G)
case4.append(sc4)
case4_occur = case4.count(True)
case4 = []
print(str(n) + "\t" + str(case4_occur / max_repeats))
if n == max_nodes:
repeat = False
n += 1
def Special_Case3_Occurance():
max_repeats = 1000
max_nodes = 10
n = 2
repeat = True
case3 = []
while repeat == True:
for repeats in range(max_repeats):
G = random_graph_generator(n, (n * (n - 1)) / 2)
sc3 = special_3_check(G, n)
case3.append(sc3)
case3_occur = case3.count(True)
case3 = []
print(str(n) + "\t" + str(case3_occur / max_repeats))
if n == max_nodes:
repeat = False
n += 1
def Greedy_Approximation():
print("n\tApproximation")
max_repeats = 1000
max_nodes = 1000
n = 100
repeat = True
approx = []
while repeat == True:
for repeats in range(max_repeats):
G = random_graph_generator(n, n * 2)
a = Perc_Hamiltonian(Greedy_Search(G))
approx.append(a)
approx_final = sum(approx)
approx = []
print(str(n) + "\t" + str((approx_final / max_repeats)))
points_n.append(n)
points_a.append(approx_final / max_repeats)
if n == max_nodes:
repeat = False
n += 100
def __init__(self, nNodes, nEdges):
self.graph = random_graph_generator(nNodes, nEdges)
self.solution = None
self.graspCurrentSolution = None
self.neighbourArray = []