def generate_graph_laplacian_list(self, node_size, orders):
if self.graph_topology is None:
print("Network topology is not initialized yet")
return 0
self.graph_laplacian_list = list()
self.graph_laplacian_list.append(np.identity(node_size))
lap = rd.make_laplacian_matrix(self.graph_topology)
base = rd.make_laplacian_matrix(self.graph_topology)
self.graph_laplacian_list.append(
rd.make_laplacian_matrix(self.graph_topology))
if orders > 2:
for i in range(2, orders):
lap = np.matmul(lap, base)
self.graph_laplacian_list.append(lap)
return self.graph_laplacian_list
LOG_FILE = "/home/yanzx1993/VNR_A3C_LOG/LOG"
ENTROPY_WEIGHT = 0.5
EPS = 1e-6
INPUT_FEATURES = 5
EXTRACTED_FEATURES = INPUT_FEATURES * 64
SNODE_SIZE = 100
VNODE_FEATURES_SIZE = 3
#NUM_AGENT = mp.cpu_count()#4 or 8 i guess
NUM_AGENT = 24
ORDERS = 3
GAMMA = 0.99
ALIVE_TIME = 50000
#saver=tf.train.Saver()
LAPLACIAN = rd.make_laplacian_matrix(G)
LAPLACIAN_LIST = rd.make_laplacian_list(G, SNODE_SIZE, ORDERS)
LAPLACIAN_TENSOR = np.stack(LAPLACIAN_LIST)
RANDOM_SEED = 93
def random_gen(rate):
rv = poisson(rate)
plist = []
for i in range(int(rate * 100 + 1)):
plist.append(rv.cdf(i))
rnd = np.random.rand()
for i in range(len(plist)):
if (rnd <= plist[i]):
#print(i)
import numpy as np
import tensorflow as tf
import random_graph_gen as rd
import time
start = time.clock()
node = 100
input_features = 4
output_features = 4 * 32
orders = 3
learning_rate = 0.000001
max_steps = 90
n = rd.make_random_graph(node, 0.5)
adj = rd.make_adj_matrix(n)
lap = rd.make_laplacian_matrix(n)
base = rd.make_laplacian_matrix(n)
sess = tf.InteractiveSession()
with tf.name_scope("graph_laplacian"):
lap_ls = []
lap_ls.append(np.identity(node))
lap_ls.append(lap)
for i in range(2, orders):
lap = np.matmul(lap, base)
lap_ls.append(lap)
with tf.name_scope("placeholders"):
x = tf.placeholder(tf.float32, [input_features, node], name="state")
y_ = tf.placeholder(tf.float32, [None, node], name="actions")
