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Traffic.py
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"""
Traffic.py
"""
__author__ = "[email protected]"
import numpy as np
from os import listdir
from re import split
from OU import OU
from helper import softmax
def natural_key(string_):
"""See http://www.codinghorror.com/blog/archives/001018.html"""
return [int(s) if s.isdigit() else s for s in split(r'(\d+)', string_)]
#
class Traffic():
def __init__(self, nodes_num, type, capacity):
self.nodes_num = nodes_num
self.prev_traffic = None
self.type = type
self.capacity = capacity * nodes_num / (nodes_num - 1)
self.dictionary = {}
self.dictionary['NORM'] = self.normal_traffic
self.dictionary['UNI'] = self.uniform_traffic
self.dictionary['CONTROLLED'] = self.controlled_uniform_traffic
self.dictionary['EXP'] = self.exp_traffic
self.dictionary['OU'] = self.ou_traffic
self.dictionary['STAT'] = self.stat_traffic
self.dictionary['STATEQ'] = self.stat_eq_traffic
self.dictionary['FILE'] = self.file_traffic
self.dictionary['DIR'] = self.dir_traffic
if self.type.startswith('DIR:'):
self.dir = sorted(listdir(self.type.split('DIR:')[-1]), key=lambda x: natural_key((x)))
self.static = None
self.total_ou = OU(1, self.capacity/2, 0.1, self.capacity/2)
self.nodes_ou = OU(self.nodes_num**2, 1, 0.1, 1)
def normal_traffic(self):
t = np.random.normal(capacity/2, capacity/2)
return np.asarray(t * softmax(np.random.randn(self.nodes_num, self.nodes_num))).clip(min=0.001)
def uniform_traffic(self):
t = np.random.uniform(0, self.capacity*1.25)
return np.asarray(t * softmax(np.random.uniform(0, 1, size=[self.nodes_num]*2))).clip(min=0.001)
def controlled_uniform_traffic(self):
t = np.random.uniform(0, self.capacity*1.25)
if self.prev_traffic is None:
self.prev_traffic = np.asarray(t * softmax(np.random.uniform(0, 1, size=[self.nodes_num]*2))).clip(min=0.001)
dist = [1]
dist += [0]*(self.nodes_num**2 - 1)
ch = np.random.choice(dist, [self.nodes_num]*2)
tt = np.multiply(self.prev_traffic, 1 - ch)
nt = np.asarray(t * softmax(np.random.uniform(0, 1, size=[self.nodes_num]*2))).clip(min=0.001)
nt = np.multiply(nt, ch)
self.prev_traffic = tt + nt
return self.prev_traffic
# xxxxxxx
# xxx
# 指数分布
def exp_traffic(self):
a = np.random.exponential(size=self.nodes_num)
b = np.random.exponential(size=self.nodes_num)
# https://blog.csdn.net/u011599639/article/details/77926402
# 计算向量 a,b 外积，[a,b]
T = np.outer(a, b)
# 对角线 填 -1
np.fill_diagonal(T, -1)
T[T!=-1] = np.asarray(np.random.exponential()*T[T!=-1]/np.average(T[T!=-1])).clip(min=0.001)
return T
def stat_traffic(self):
if self.static is None:
string = self.type.split('STAT:')[-1]
v = np.asarray(tuple(float(x) for x in string.split(',')[:self.nodes_num**2]))
M = np.split(v, self.nodes_num)
self.static = np.vstack(M)
return self.static
def stat_eq_traffic(self):
if self.static is None:
value = float(self.type.split('STATEQ:')[-1])
self.static = np.full([self.nodes_num]*2, value, dtype=float)
return self.static
def ou_traffic(self):
t = self.total_ou.evolve()[0]
nt = t * softmax(self.nodes_ou.evolve())
i = np.split(nt, self.nodes_num)
return np.vstack(i).clip(min=0.001)
def file_traffic(self):
if self.static is None:
fname = 'traffic/' + self.type.split('FILE:')[-1]
v = np.loadtxt(fname, delimiter=',')
self.static = np.split(v, self.nodes_num)
return self.static
def dir_traffic(self):
while len(self.dir) > 0:
tm = self.dir.pop(0)
if not tm.endswith('.txt'):
continue
fname = self.type.split('DIR:')[-1] + '/' + tm
v = np.loadtxt(fname, delimiter=',')
return np.split(v, self.nodes_num)
return False
def generate(self):
return self.dictionary[self.type.split(":")[0]]()
# 这样 dictionary [14,14] 代表什么意思呢？？？
#[[-1.00000000e+00 4.82597027e-01 1.64885219e-01 2.39937374e-01
# 4.24195039e-01 3.90513477e-01 1.73313504e-01 2.39531467e-01
# 8.81383591e-01 2.35750495e-01 9.86736084e-01 1.10174305e+00
# 2.91715890e-02 1.24369249e-01]
# [1.24568554e-01 - 1.00000000e+00 4.58794226e-02 6.67627350e-02
# 1.18032554e-01 1.08660636e-01 4.82245987e-02 6.66497910e-02
# 2.45245574e-01 6.55977330e-02 2.74559976e-01 3.06560741e-01
# 8.11701418e-03 3.46058268e-02]
# [4.41894837e-01 4.76355699e-01 - 1.00000000e+00 2.36834313e-01
# 4.18709012e-01 3.85463046e-01 1.71072076e-01 2.36433655e-01
# 8.69984838e-01 2.32701582e-01 9.73974829e-01 1.08749443e+00
# 2.87943189e-02 1.22760807e-01]
# [3.99306289e-01 4.30445913e-01 1.47067148e-01 - 1.00000000e+00
# 3.78355046e-01 3.48313231e-01 1.54584644e-01 2.13646863e-01
# 7.86138212e-01 2.10274476e-01 8.80105948e-01 9.82684859e-01
# 2.60192056e-02 1.10929475e-01]
# [8.86430104e-01 9.55557740e-01 3.26478072e-01 4.75083769e-01
# - 1.00000000e+00 7.73229329e-01 3.43166350e-01 4.74280060e-01
# 1.74516805e+00 4.66793613e-01 1.95376940e+00 2.18148691e+00
# 5.77606908e-02 2.46255140e-01]
# [6.17537874e-01 6.65696136e-01 2.27443285e-01 3.30970507e-01
# 5.85136215e-01 - 1.00000000e+00 2.39069293e-01 3.30410597e-01
# 1.21578381e+00 3.25195110e-01 1.36110743e+00 1.51974846e+00
# 4.02393985e-02 1.71555405e-01]
# [2.31567998e-01 2.49626667e-01 8.52880256e-02 1.24109275e-01
# 2.19417832e-01 2.01995810e-01 - 1.00000000e+00 1.23899316e-01
# 4.55901789e-01 1.21943582e-01 5.10396098e-01 5.69884250e-01
# 1.50892072e-02 6.43308585e-02]
# [1.68174721e+00 1.81289710e+00 6.19398623e-01 9.01335366e-01
# 1.59350744e+00 1.46698116e+00 6.51059851e-01 - 1.00000000e+00
# 3.31095647e+00 8.85607170e-01 3.70671778e+00 4.13874653e+00
# 1.09584366e-01 4.67198589e-01]
# [1.81548791e+00 1.95706747e+00 6.68656207e-01 9.73013928e-01
# 1.72023088e+00 1.58364262e+00 7.02835289e-01 9.71367859e-01
# - 1.00000000e+00 9.56034948e-01 4.00149396e+00 4.46787974e+00
# 1.18299046e-01 5.04352487e-01]
# [3.39180759e+00 3.65631535e+00 1.24922518e+00 1.81784523e+00
# 3.21384249e+00 2.95865979e+00 1.31308067e+00 1.81476994e+00
# 6.67765480e+00 - 1.00000000e+00 7.47584027e+00 8.34717123e+00
# 2.21013647e-01 9.42262733e-01]
# [3.80108803e+00 4.09751324e+00 1.39996587e+00 2.03719980e+00
# 3.60164835e+00 3.31567343e+00 1.47152663e+00 2.03375342e+00
# 7.48342971e+00 2.00165090e+00 - 1.00000000e+00 9.35440226e+00
# 2.47682778e-01 1.05596308e+00]
# [1.92350407e-01 2.07350720e-01 7.08439274e-02 1.03090538e-01
# 1.82257953e-01 1.67786468e-01 7.44651911e-02 1.02916137e-01
# 3.78691769e-01 1.01291620e-01 4.23957102e-01 - 1.00000000e+00
# 1.25337489e-02 5.34359969e-02]
# [3.30800641e-01 3.56597899e-01 1.21836065e-01 1.77293184e-01
# 3.13443828e-01 2.88556037e-01 1.28063846e-01 1.76993253e-01
# 6.51267039e-01 1.74199439e-01 7.29113514e-01 8.14093818e-01
# - 1.00000000e+00 9.18982306e-02]
# [6.67661381e-01 7.19728489e-01 2.45904104e-01 3.57834288e-01
# 6.32629787e-01 5.82398272e-01 2.58473757e-01 3.57228932e-01
# 1.31446496e+00 3.51590122e-01 1.47158401e+00 1.64310142e+00
# 4.35054974e-02 - 1.00000000e+00]]