def generate_data(sample_size):
data = []
for i in range(sample_size):
x = start.generate_random_graph(10, 0.8, 10)
y = start.edmonds_karp(x)
data.append((x, y))
return data
def generate_data(sample_size):
data = []
for i in range(int(sample_size / 4)):
x = start.generate_random_graph(5, 0.8, 10)
y = start.edmonds_karp(x)
x.shape = ([1, 25])
data.append((x, y))
for i in range(int(sample_size / 4)):
x = start.generate_random_graph(5, 0.6, 10)
y = start.edmonds_karp(x)
x.shape = ([1, 25])
data.append((x, y))
for i in range(int(sample_size / 4)):
x = start.generate_random_graph(5, 0.4, 10)
y = start.edmonds_karp(x)
x.shape = ([1, 25])
data.append((x, y))
for i in range(int(sample_size / 4)):
x = start.generate_random_graph(5, 0.2, 10)
y = start.edmonds_karp(x)
x.shape = ([1, 25])
data.append((x, y))
return data
v+=1
for j in range(10):
if self.reward_graph[i][j] > -1:
if self.q[i][j] > maxq:
maxq = self.q[i][j]
self.q[self.current][i] = self.reward_graph[self.current][i]+self.gamma*maxq
if i == self.sink:
end = True
else:
self.current = i
if v > 10:
end = True
episodes = 500
ex_graph_1 = start.generate_random_graph(10, 0.4, 15)
print(start.edmonds_karp(ex_graph_1))
done = False
maxf = 0
while not done:
graph = np.zeros([10, 10])
for i in range(len(graph[0])):
for j in range(len(graph[0])):
if ex_graph_1[i][j] > 0:
if j == 9:
graph[i][j] = 1
else:
graph[i][j] = 0
else:
graph[i][j] = -1
bond = agent(1, 0.9, graph)
for i in range(episodes):