def testMaxn():
print '===== test maxn ====='
print '\n>>>normal\n'
mu = 10
sigma = 3
n = 5
x = []
y = []
for i in range(100000):
m = -sys.maxint
for j in range(n):
r = np.random.normal(mu, sigma)
x.append(r)
if r > m:
m = r
y.append(m)
ddist0 = DDist.create(x)
ddist1 = DDist.create(y)
ddist2 = maxn(n, ddist0)
print 'base', ddist0.mean, ddist0.std
print 'sim', ddist1.mean, ddist1.std
print 'ana', ddist2.mean, ddist2.std
ddist1.plot('/tmp/test_maxn_normal1.pdf')
ddist2.plot('/tmp/test_maxn_normal2.pdf')
print '\n>>>pareto\n'
a = 1.3
x0 = 0
x = []
y = []
for i in range(100000):
m = -sys.maxint
for j in range(n):
r = np.random.pareto(a) + x0
x.append(r)
if r > m:
m = r
y.append(m)
ddist0 = DDist.create(x, tail=('p', 0.1), tnh=25)
ddist1 = DDist.create(y, tail=('p', 0.1), tnh=25)
#ddist0 = DDist.create(x)
#ddist1 = DDist.create(y)
ddist2 = maxn(n, ddist0)
print 'base', ddist0.mean, ddist0.std, 'length', len(ddist0.pmfy), len(
ddist0.tpmfy)
print 'sim', ddist1.mean, ddist1.std, 'length', len(ddist1.pmfy), len(
ddist1.tpmfy)
print 'ana', ddist2.mean, ddist2.std, 'length', len(ddist2.pmfy), len(
ddist2.tpmfy)
ddist1.plot('/tmp/test_maxn_pareto1.pdf')
ddist2.plot('/tmp/test_maxn_pareto2.pdf')
print '===== end =====\n'
def testMaxn():
print '===== test maxn ====='
print '\n>>>normal\n'
mu = 10
sigma = 3
n = 5
x = []
y = []
for i in range(100000):
m = -sys.maxint
for j in range(n):
r = np.random.normal(mu, sigma)
x.append(r)
if r > m:
m = r
y.append(m)
ddist0 = DDist.create(x)
ddist1 = DDist.create(y)
ddist2 = maxn(n, ddist0)
print 'base', ddist0.mean, ddist0.std
print 'sim', ddist1.mean, ddist1.std
print 'ana', ddist2.mean, ddist2.std
ddist1.plot('/tmp/test_maxn_normal1.pdf')
ddist2.plot('/tmp/test_maxn_normal2.pdf')
print '\n>>>pareto\n'
a = 1.3
x0 = 0
x = []
y = []
for i in range(100000):
m = -sys.maxint
for j in range(n):
r = np.random.pareto(a) + x0
x.append(r)
if r > m:
m = r
y.append(m)
ddist0 = DDist.create(x, tail=('p', 0.1), tnh=25)
ddist1 = DDist.create(y, tail=('p', 0.1), tnh=25)
#ddist0 = DDist.create(x)
#ddist1 = DDist.create(y)
ddist2 = maxn(n, ddist0)
print 'base', ddist0.mean, ddist0.std, 'length', len(ddist0.pmfy), len(ddist0.tpmfy)
print 'sim', ddist1.mean, ddist1.std, 'length', len(ddist1.pmfy), len(ddist1.tpmfy)
print 'ana', ddist2.mean, ddist2.std, 'length', len(ddist2.pmfy), len(ddist2.tpmfy)
ddist1.plot('/tmp/test_maxn_pareto1.pdf')
ddist2.plot('/tmp/test_maxn_pareto2.pdf')
print '===== end =====\n'
def testCond():
print '===== test cond ====='
print '\n>>> normal\n'
p = 0.8
mu1 = 10
sigma1 = 3
mu2 = 20
sigma2 = 1
x1 = []
x2 = []
y = []
for i in range(100000):
r = np.random.random()
if r < p:
s = np.random.normal(mu1, sigma1)
x1.append(s)
y.append(s)
else:
s = np.random.normal(mu2, sigma2)
x2.append(s)
y.append(s)
ddist0 = DDist.create(x1)
ddist1 = DDist.create(x2)
ddist2 = DDist.create(y)
ddist3 = cond(p, ddist0, ddist1)
print 'base1', ddist0.mean, ddist0.std
print 'base2', ddist1.mean, ddist1.std
print 'sim', ddist2.mean, ddist2.std
print 'ana', ddist3.mean, ddist3.std
ddist2.plot('/tmp/test_cond_norm1.pdf')
ddist3.plot('/tmp/test_cond_norm2.pdf')
print '\n>>> pareto\n'
p = 0.5
a1 = 1.1
a2 = 1.2
x1 = []
x2 = []
y = []
for i in range(100000):
r = np.random.random()
if r < p:
s = np.random.pareto(a1)
x1.append(s)
y.append(s)
else:
s = np.random.pareto(a2) + 10
x2.append(s)
y.append(s)
ddist0 = DDist.create(x1, tail=('p', 0.1), tnh=10)
ddist1 = DDist.create(x2, tail=('p', 0.1), tnh=10)
ddist2 = DDist.create(y)
ddist3 = cond(p, ddist0, ddist1)
print 'base1', ddist0.mean, ddist0.std
print 'base2', ddist1.mean, ddist1.std
print 'sim', ddist2.mean, ddist2.std
print 'ana', ddist3.mean, ddist3.std
ddist2.plot('/tmp/test_cond_pareto1.pdf')
ddist3.plot('/tmp/test_cond_pareto2.pdf')
print '===== end =====\n'
def testCond():
print '===== test cond ====='
print '\n>>> normal\n'
p = 0.8
mu1 = 10
sigma1 = 3
mu2 = 20
sigma2 = 1
x1 = []
x2 = []
y = []
for i in range(100000):
r = np.random.random()
if r < p:
s = np.random.normal(mu1, sigma1)
x1.append(s)
y.append(s)
else:
s = np.random.normal(mu2, sigma2)
x2.append(s)
y.append(s)
ddist0 = DDist.create(x1)
ddist1 = DDist.create(x2)
ddist2 = DDist.create(y)
ddist3 = cond(p, ddist0, ddist1)
print 'base1', ddist0.mean, ddist0.std
print 'base2', ddist1.mean, ddist1.std
print 'sim', ddist2.mean, ddist2.std
print 'ana', ddist3.mean, ddist3.std
ddist2.plot('/tmp/test_cond_norm1.pdf')
ddist3.plot('/tmp/test_cond_norm2.pdf')
print '\n>>> pareto\n'
p = 0.5
a1 = 1.1
a2 = 1.2
x1 = []
x2 = []
y = []
for i in range(100000):
r = np.random.random()
if r < p:
s = np.random.pareto(a1)
x1.append(s)
y.append(s)
else:
s = np.random.pareto(a2) + 10
x2.append(s)
y.append(s)
ddist0 = DDist.create(x1, tail=('p', 0.1), tnh=10)
ddist1 = DDist.create(x2, tail=('p', 0.1), tnh=10)
ddist2 = DDist.create(y)
ddist3 = cond(p, ddist0, ddist1)
print 'base1', ddist0.mean, ddist0.std
print 'base2', ddist1.mean, ddist1.std
print 'sim', ddist2.mean, ddist2.std
print 'ana', ddist3.mean, ddist3.std
ddist2.plot('/tmp/test_cond_pareto1.pdf')
ddist3.plot('/tmp/test_cond_pareto2.pdf')
print '===== end =====\n'
def testoodelay():
print '===== test oodelay =====\n'
intvl = 30
def genexpointvl():
return np.random.exponential(intvl)
def genfixedintvl():
return intvl
lambd = 100
a = 1.8
m = 100
lb = 50
tb = 150
ub = 1000
def customlatency():
r = np.random.random()
if r < 0.2:
return 30
elif r < 0.4:
return 60
elif r < 0.6:
return 90
elif r < 0.8:
return 120
else:
return 180
def expolatency():
return np.random.exponential(lambd)
def paretolatency():
return np.random.pareto(a) + m
def uniformlatency():
r = np.random.random()
if r < 0.8:
rr = np.random.random()
return lb + rr * (tb - lb)
else:
rr = np.random.random()
return tb + rr * (ub - tb)
intvlkeys = {'fixed': genfixedintvl, 'poisson': genexpointvl}
intvlopts = {'fixed': 'fixed.interval', 'poisson': 'poisson.lambda'}
latencykeys = {
'custom': customlatency,
'expo': expolatency,
'pareto': paretolatency,
'uniform': uniformlatency,
}
for ik in intvlkeys:
for lk in latencykeys:
print '\n>>> %s, %s\n' % (ik, lk)
x, y = runoodelay(intvlkeys[ik], latencykeys[lk])
if lk == 'custom':
ddist0 = DDist.create(x, h=30, tail=('b', 120), tnh=2)
elif lk == 'expo':
ddist0 = DDist.create(x, h=0.5)
elif lk == 'uniform':
ddist0 = DDist.create(x, h=0.5, tail=('b', 150), tnh=10)
else:
ddist0 = DDist.create(x, h=0.5, tail=('p', 0.1), tnh=10)
if lk == 'custom':
ddist1 = DDist.create(y, h=30)
else:
ddist1 = DDist.create(y, h=0.5)
if ik == 'fixed':
ddist2 = oodelay(ddist0, {
'arrival.process': 'fixed',
'fixed.interval': intvl
})
else:
ddist2 = oodelay(ddist0, {
'arrival.process': 'poisson',
'poisson.lambda': 1.0 / intvl
})
print 'sim', ddist1.mean, ddist1.std
print 'ana', ddist2.mean, ddist2.std
if lk == 'custom':
print 'sim pmf', ddist1.getPmfxy()
print 'ana pmf', ddist2.getPmfxy()
print '===== end =====\n'
def testQuorum():
print '===== test quorum ====='
lambd = 50
a = 1.8
n = 5
f = 2
x = {'gen': [], 'exp': [], 'pareto': []}
yc = {'gen': [], 'exp': [], 'pareto': []}
yr = {'gen': [], 'exp': [], 'pareto': []}
y = {'gen': [], 'exp': [], 'pareto': []}
dists = ['gen', 'exp', 'pareto']
leader = 0
def gen():
r = np.random.random()
if r < 0.2:
return 1
elif r < 0.4:
return 2
elif r < 0.6:
return 3
elif r < 0.8:
return 4
else:
return 8
for i in range(100000):
first = {}
second = {}
latencies = {'gen': [], 'exp': [], 'pareto': []}
learner = np.random.randint(n)
for acceptor in range(n):
if leader == acceptor:
for k in dists:
first[k] = 0
else:
first['gen'] = gen()
first['exp'] = np.random.exponential(lambd)
first['pareto'] = np.random.pareto(a)
for k in dists:
x[k].append(first[k])
if acceptor == learner:
for k in dists:
second[k] = 0
else:
second['gen'] = gen()
second['exp'] = np.random.exponential(lambd)
second['pareto'] = np.random.pareto(a)
for k in dists:
x[k].append(second[k])
for k in dists:
latencies[k].append(first[k] + second[k])
for k in dists:
l = sorted(latencies[k])
y[k].append(l[n - f - 1])
if leader == learner:
assert l[0] == 0
yc[k].append(l[n - f - 1])
else:
yr[k].append(l[n - f - 1])
for k in dists:
print '\n>>> %s\n' % k
if k == 'gen':
ddist0 = DDist.create(x[k], h=1, tail=('b', 4), tnh=4)
elif k == 'exp':
ddist0 = DDist.create(x[k], h=0.5)
else:
ddist0 = DDist.create(x[k], h=0.5, tail=('p', 0.1), tnh=20)
print 'created ddist0'
if k == 'gen':
ddist1 = DDist.create(y[k], h=1, tail=('b', 8), tnh=4)
ddistc1 = DDist.create(yc[k], h=1, tail=('b', 8), tnh=4)
ddistr1 = DDist.create(yr[k], h=1, tail=('b', 8), tnh=4)
print 'base pmf', ddist1.getPmfxy()
print 'base c pmf', ddistc1.getPmfxy()
print 'base r pmf', ddistr1.getPmfxy()
else:
ddist1 = DDist.create(y[k], h=0.05)
ddistc1 = DDist.create(yc[k], h=0.05)
ddistr1 = DDist.create(yr[k], h=0.05)
ddist2 = quorum(n, f, ddist0)
print 'computed quorum'
rtrip = ddist0 + ddist0
ddistc2 = _quorumC(n - 1, n - f - 1, rtrip)
ddistr2 = _quorumR(n, n - f, ddist0, rtrip)
if k == 'gen':
print 'q pmf', ddist2.getPmfxy()
print 'q c pmf', ddistc2.getPmfxy()
print 'q r pmf', ddistr2.getPmfxy()
print 'latency', ddist0.mean, ddist0.std
print 'quorum sim', ddist1.mean, ddist1.std
print 'quorum c sim', ddistc1.mean, ddistc1.std
print 'quorum r sim', ddistr1.mean, ddistr1.std
print 'quorum ana', ddist2.mean, ddist2.std
print 'quorum c ana', ddistc2.mean, ddistc2.std
print 'quorum r ana', ddistr2.mean, ddistr2.std
ddist1.plot('/tmp/test_quorum_%s1.pdf' % k)
ddistc1.plot('/tmp/test_quorumc_%s1.pdf' % k)
ddistr1.plot('/tmp/test_quorumr_%s1.pdf' % k)
ddist2.plot('/tmp/test_quorum_%s2.pdf' % k)
ddistc2.plot('/tmp/test_quorumc_%s2.pdf' % k)
ddistr2.plot('/tmp/test_quorumr_%s2.pdf' % k)
print '===== end =====\n'
def testoodelay():
print '===== test oodelay =====\n'
intvl = 30
def genexpointvl():
return np.random.exponential(intvl)
def genfixedintvl():
return intvl
lambd = 100
a = 1.8
m = 100
lb = 50
tb = 150
ub = 1000
def customlatency():
r = np.random.random()
if r < 0.2:
return 30
elif r < 0.4:
return 60
elif r < 0.6:
return 90
elif r < 0.8:
return 120
else:
return 180
def expolatency():
return np.random.exponential(lambd)
def paretolatency():
return np.random.pareto(a) + m
def uniformlatency():
r = np.random.random()
if r < 0.8:
rr = np.random.random()
return lb + rr * (tb - lb)
else:
rr = np.random.random()
return tb + rr * (ub - tb)
intvlkeys = {'fixed' : genfixedintvl,
'poisson' : genexpointvl}
intvlopts = {'fixed' : 'fixed.interval',
'poisson' : 'poisson.lambda'}
latencykeys = {'custom' : customlatency,
'expo' : expolatency,
'pareto' : paretolatency,
'uniform' : uniformlatency,
}
for ik in intvlkeys:
for lk in latencykeys:
print '\n>>> %s, %s\n'%(ik, lk)
x, y = runoodelay(intvlkeys[ik], latencykeys[lk])
if lk == 'custom':
ddist0 = DDist.create(x, h=30, tail=('b', 120), tnh=2)
elif lk == 'expo':
ddist0 = DDist.create(x, h=0.5)
elif lk == 'uniform':
ddist0 = DDist.create(x, h=0.5, tail=('b', 150), tnh=10)
else:
ddist0 = DDist.create(x, h=0.5, tail=('p', 0.1), tnh=10)
if lk == 'custom':
ddist1 = DDist.create(y, h=30)
else:
ddist1 = DDist.create(y, h=0.5)
if ik == 'fixed':
ddist2 = oodelay(ddist0, {'arrival.process' : 'fixed',
'fixed.interval' : intvl})
else:
ddist2 = oodelay(ddist0, {'arrival.process' : 'poisson',
'poisson.lambda' : 1.0 / intvl})
print 'sim', ddist1.mean, ddist1.std
print 'ana', ddist2.mean, ddist2.std
if lk == 'custom':
print 'sim pmf', ddist1.getPmfxy()
print 'ana pmf', ddist2.getPmfxy()
print '===== end =====\n'
def testQuorum():
print '===== test quorum ====='
lambd = 50
a = 1.8
n = 5
f = 2
x = {'gen' : [], 'exp':[], 'pareto':[]}
yc = {'gen' : [], 'exp':[], 'pareto':[]}
yr = {'gen' : [], 'exp':[], 'pareto':[]}
y = {'gen' : [], 'exp':[], 'pareto':[]}
dists = ['gen', 'exp', 'pareto']
leader = 0
def gen():
r = np.random.random()
if r < 0.2:
return 1
elif r < 0.4:
return 2
elif r < 0.6:
return 3
elif r < 0.8:
return 4
else:
return 8
for i in range(100000):
first = {}
second = {}
latencies = {'gen':[], 'exp':[], 'pareto':[]}
learner = np.random.randint(n)
for acceptor in range(n):
if leader == acceptor:
for k in dists:
first[k] = 0
else:
first['gen'] = gen()
first['exp'] = np.random.exponential(lambd)
first['pareto'] = np.random.pareto(a)
for k in dists:
x[k].append(first[k])
if acceptor == learner:
for k in dists:
second[k] = 0
else:
second['gen'] = gen()
second['exp'] = np.random.exponential(lambd)
second['pareto'] = np.random.pareto(a)
for k in dists:
x[k].append(second[k])
for k in dists:
latencies[k].append(first[k] + second[k])
for k in dists:
l = sorted(latencies[k])
y[k].append(l[n - f - 1])
if leader == learner:
assert l[0] == 0
yc[k].append(l[n - f - 1])
else:
yr[k].append(l[n - f - 1])
for k in dists:
print '\n>>> %s\n'%k
if k == 'gen':
ddist0 = DDist.create(x[k], h=1, tail=('b', 4), tnh=4)
elif k == 'exp':
ddist0 = DDist.create(x[k], h=0.5)
else:
ddist0 = DDist.create(x[k], h=0.5, tail=('p', 0.1), tnh=20)
print 'created ddist0'
if k == 'gen':
ddist1 = DDist.create(y[k], h=1, tail=('b', 8), tnh=4)
ddistc1 = DDist.create(yc[k], h=1, tail=('b', 8), tnh=4)
ddistr1 = DDist.create(yr[k], h=1, tail=('b', 8), tnh=4)
print 'base pmf', ddist1.getPmfxy()
print 'base c pmf', ddistc1.getPmfxy()
print 'base r pmf', ddistr1.getPmfxy()
else:
ddist1 = DDist.create(y[k], h=0.05)
ddistc1 = DDist.create(yc[k], h=0.05)
ddistr1 = DDist.create(yr[k], h=0.05)
ddist2 = quorum(n, f, ddist0)
print 'computed quorum'
rtrip = ddist0 + ddist0
ddistc2 = _quorumC(n - 1, n - f - 1, rtrip)
ddistr2 = _quorumR(n, n - f, ddist0, rtrip)
if k == 'gen':
print 'q pmf', ddist2.getPmfxy()
print 'q c pmf', ddistc2.getPmfxy()
print 'q r pmf', ddistr2.getPmfxy()
print 'latency', ddist0.mean, ddist0.std
print 'quorum sim', ddist1.mean, ddist1.std
print 'quorum c sim', ddistc1.mean, ddistc1.std
print 'quorum r sim', ddistr1.mean, ddistr1.std
print 'quorum ana', ddist2.mean, ddist2.std
print 'quorum c ana', ddistc2.mean, ddistc2.std
print 'quorum r ana', ddistr2.mean, ddistr2.std
ddist1.plot('/tmp/test_quorum_%s1.pdf'%k)
ddistc1.plot('/tmp/test_quorumc_%s1.pdf'%k)
ddistr1.plot('/tmp/test_quorumr_%s1.pdf'%k)
ddist2.plot('/tmp/test_quorum_%s2.pdf'%k)
ddistc2.plot('/tmp/test_quorumc_%s2.pdf'%k)
ddistr2.plot('/tmp/test_quorumr_%s2.pdf'%k)
print '===== end =====\n'