def bayes_kill_max_frank(currentModel, newModel, kn, u, v, s, q, Q, zita, chain):
current = np.sort(currentModel)
new = np.sort(newModel)
t1 = current[current != 0]
min_old = np.min(t1)
max_old = np.max(t1)
L = len(u)
l = len(new)
ss = -1
R = 3/4
if np.any(new):
t2 = new[new != 0]
min_new = np.min(t2)
max_new = np.max(t2)
if min_old < min_new:
if(s[1] == 2 and s[2] == 2):
result1 = allfrank(u[:min_old], v[:min_old])
result2 = allfrank(u[min_old:min_new], v[min_old:min_new])
R = R * 1/3
else:
if(s[1] == 2 and s[2] == 1):
result1 = allfrank(u[:min_old], v[:min_old])
result2 = allclayton(u[min_old:min_new], v[min_old:min_new])
R = R * 2/3
else:
if(s[1] == 1 and s[2] == 2):
result1 = allclayton(u[:min_old], v[:min_old])
result2 = allfrank(u[min_old:min_new], v[min_old:min_new])
R = R * 2/3
else:
if(s[1] == 2 and s[2] == 3):
result1 = allfrank(u[:min_old], v[:min_old])
result2 = allgumbel(u[min_old:min_new], v[min_old:min_new])
R = R * 2/3
else:
if(s[1] == 3 and s[2] == 2):
result2 = allgumbel(u[:min_old], v[:min_old])
result1 = allfrank(u[min_old:min_new], v[min_old:min_new])
R = R * 2/3
resultOld = allfrank(u[:min_new], v[:min_new])
BFu = resultOld["BFu"] - result1["BFu"] - result2["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, ((zita ** (chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
QQ = Q
w = 13
else:
new_model = current
rejected = new
QQ = q
w = 14
else:
if max_new < max_old and max_new != 0:
if(s[1] == 2 and s[2] == 2):
result1 = allfrank(u[max_new - 1:max_old], v[max_new - 1:max_old])
result2 = allfrank(u[max_old:L], v[max_old:L])
R = R * 1/3
else:
if(s[1] == 1 and s[2] == 2):
result2 = allclayton(u[max_new - 1:max_old], v[max_new - 1:max_old])
result1 = allfrank(u[max_old:L], v[max_old:L])
R = R * 2/3
else:
if(s[1] == 2 and s[2] == 1):
result2 = allfrank(u[max_new - 1:max_old], v[max_new - 1:max_old])
result1 = allclayton(u[max_old:L], v[max_old:L])
R = R * 2/3
else:
if(s[1] == 2 and s[2] == 3):
result2 = allfrank(u[max_new - 1:max_old], v[max_new - 1:max_old])
result1 = allgumbel(u[max_old:L], v[max_old:L])
R = R * 2/3
else:
if(s[1] == 3 and s[2] == 2):
result1 = allgumbel(u[max_new - 1:max_old], v[max_new - 1:max_old])
result2 = allfrank(u[max_old:L], v[max_old:L])
R = R * 2/3
resultOld = allfrank(u[max_new - 1:L], v[max_new - 1:L])
BFu = resultOld["BFu"] - result1["BFu"] - result2["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, ((zita ** (chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
QQ = Q
w = 15
else:
new_model = current
rejected = new
QQ = q
w = 16
else:
place = kn
if(s[1] == 2 and s[2] == 2):
result1 = allfrank(u[current[place - 2] - 1:current[place - 1]], v[current[place - 2] - 1:current[place - 1]])
result2 = allfrank(u[current[place - 1]:current[place]], v[current[place - 1]:current[place]])
R = R * 1/3
else:
if(s[1] == 2 and s[2] == 1):
result1 = allfrank(u[current[place - 2] - 1:current[place - 1]], v[current[place - 2] - 1:current[place - 1]])
result2 = allclayton(u[current[place - 1]:current[place]], v[current[place - 1]:current[place]])
R = R * 2/3
else:
if(s[1] == 1 and s[2] == 2):
result1 = allclayton(u[current[place - 2] - 1:current[place - 1]], v[current[place - 2] - 1:current[place - 1]])
result2 = allfrank(u[current[place - 1]:current[place]], v[current[place - 1]:current[place]])
R = R * 2/3
else:
if(s[1] == 2 and s[2] == 3):
result1 = allfrank(u[current[place - 2] - 1:current[place - 1]], v[current[place - 2] - 1:current[place - 1]])
result2 = allgumbel(u[current[place - 1]:current[place]], v[current[place - 1]:current[place]])
R = R * 2/3
else:
if(s[1] == 3 and s[2] == 2):
result1 = allgumbel(u[current[place - 2] - 1:current[place - 1]], v[current[place - 2] - 1:current[place - 1]])
result2 = allfrank(u[current[place - 1]:current[place]], v[current[place - 1]:current[place]])
R = R * 2/3
resultOld = allfrank(u[current[place - 2] - 1:current[place]], v[current[place - 2] - 1:current[place]])
BFu = resultOld["BFu"] - result1["BFu"] - result2["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, ((zita ** (chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
QQ = Q
w = 17
else:
new_model = current
rejected = new
QQ = q
w = 18
else:
if not np.any(new):
R = 2
if(s[1] == 2 and s[2] == 2):
result1 = allfrank(u[:max_old], v[:max_old])
result2 = allfrank(u[max_old:L], v[max_old:L])
R = R * 1/3
else:
if(s[1] == 2 and s[2] == 1):
result1 = allfrank(u[:max_old], v[:max_old])
result2 = allclayton(u[max_old:L], v[max_old:L])
R = R * 2/3
else:
if(s[1] == 1 and s[2] == 2):
result1 = allclayton(u[:max_old], v[:max_old])
result2 = allfrank(u[max_old:L], v[max_old:L])
R = R * 2/3
else:
if(s[1] == 3 and s[2] == 2):
result1 = allgumbel(u[:max_old], v[:max_old])
result2 = allfrank(u[max_old:L], v[max_old:L])
R = R * 2/3
else:
if(s[1] == 2 and s[2] == 3):
result2 = allfrank(u[:max_old], v[:max_old])
result1 = allgumbel(u[max_old:L], v[max_old:L])
R = R * 2/3
resultOld = allfrank(u, v)
BFu = resultOld["BFu"] - result1["BFu"] - result2["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, ((zita ** (chain - 1)) * BFu) + np.log(R))) and BFu.imag == 0:
new_model = new
rejected = current
QQ = Q
w = 19
else:
new_model = current
rejected = new
QQ = q
w = 20
result = {"new_model": new_model, "rejected": rejected, "w": w, "QQ": QQ, "ss": ss}
return result
def bayes_change(currentModel, u, v, q, numbrk, zita, chain):
current = np.sort(currentModel)
l = len(currentModel)
L = len(u)
R = 1 / 3
ss = -1
q_new = 0
if np.count_nonzero(current) != 0:
temp = current[current != 0]
z = temp.shape[0]
p = np.random.randint(low=1, high=z + 2)
if p > 1 and p <= z:
lower = temp[p - 2]
upper = temp[p - 1]
if q[p - 1] == 1:
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allclayton(u[lower - 1:upper],
v[lower - 1:upper])
result2 = allfrank(u[lower - 1:upper], v[lower - 1:upper])
q_new = 2
else:
result1 = allclayton(u[lower - 1:upper],
v[lower - 1:upper])
result2 = allgumbel(u[lower - 1:upper], v[lower - 1:upper])
q_new = 3
else:
if q[p - 1] == 2:
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allfrank(u[lower - 1:upper],
v[lower - 1:upper])
result2 = allclayton(u[lower - 1:upper],
v[lower - 1:upper])
q_new = 1
else:
result1 = allfrank(u[lower - 1:upper],
v[lower - 1:upper])
result2 = allgumbel(u[lower - 1:upper],
v[lower - 1:upper])
q_new = 3
else:
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allgumbel(u[lower - 1:upper],
v[lower - 1:upper])
result2 = allfrank(u[lower - 1:upper],
v[lower - 1:upper])
q_new = 2
else:
result1 = allgumbel(u[lower - 1:upper],
v[lower - 1:upper])
result2 = allclayton(u[lower - 1:upper],
v[lower - 1:upper])
q_new = 1
BFu = result2["BFu"] - result1["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = current
q[p - 1] = q_new
QQ = q
rejected = current
w = 37
else:
new_model = current
QQ = q
rejected = current
w = 38
else:
if p > z and p != 1:
if q[p - 1] == 1:
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allclayton(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
result2 = allfrank(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
q_new = 2
j = 0
else:
result1 = allclayton(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
result2 = allgumbel(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
q_new = 3
j = 1
else:
if q[p - 1] == 2:
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allfrank(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
result2 = allclayton(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
q_new = 1
j = 3
else:
result1 = allfrank(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
result2 = allgumbel(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
q_new = 3
j = 4
else:
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allgumbel(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
result2 = allclayton(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
q_new = 1
j = 5
else:
result1 = allgumbel(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
result2 = allfrank(u[np.max(temp) - 1:L],
v[np.max(temp) - 1:L])
q_new = 2
j = 6
BFu = result2["BFu"] - result1["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = current
q[p - 1] = q_new
QQ = q
j = 7
QQ[p - 1:numbrk +
1] = q_new * np.ones(numbrk + 1 - (p - 1), dtype=int)
rejected = current
w = 41
else:
new_model = current
QQ = q
j = 8
rejected = current
w = 42
else:
if p == 1:
if q[p - 1] == 1:
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allclayton(u[:np.min(temp)],
v[:np.min(temp)])
result2 = allfrank(u[:np.min(temp)],
v[:np.min(temp)])
q_new = 2
else:
result1 = allclayton(u[:np.min(temp)],
v[:np.min(temp)])
result2 = allgumbel(u[:np.min(temp)],
v[:np.min(temp)])
q_new = 3
else:
if q[p - 1] == 2:
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allfrank(u[:np.min(temp)],
v[:np.min(temp)])
result2 = allclayton(u[:np.min(temp)],
v[:np.min(temp)])
q_new = 1
else:
result1 = allfrank(u[:np.min(temp)],
v[:np.min(temp)])
result2 = allgumbel(u[:np.min(temp)],
v[:np.min(temp)])
q_new = 3
else:
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allgumbel(u[:np.min(temp)],
v[:np.min(temp)])
result2 = allclayton(u[:np.min(temp)],
v[:np.min(temp)])
q_new = 1
else:
result1 = allgumbel(u[:np.min(temp)],
v[:np.min(temp)])
result2 = allfrank(u[:np.min(temp)],
v[:np.min(temp)])
q_new = 2
BFu = result2["BFu"] - result1["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, ((zita**(chain - 1)) * BFu) +
np.log(R))) and not BFu.imag:
new_model = current
q[p - 1] = q_new
QQ = q
#QQ[p - 1:numbrk + 1] = q_new * np.ones(numbrk + 1 - (p - 1), dtype=int)
rejected = current
w = 61
else:
new_model = current
QQ = q
rejected = current
w = 62
else:
if np.count_nonzero(current == 0) != 0:
if (q == 2 * np.ones(numbrk + 1)).all():
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allfrank(u[:L], v[:L])
result2 = allclayton(u[:L], v[:L])
q_new = 1
else:
result1 = allfrank(u[:L], v[:L])
result2 = allgumbel(u[:L], v[:L])
q_new = 3
else:
if (q == np.ones(numbrk + 1)).all():
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allclayton(u[:L], v[:L])
result2 = allfrank(u[:L], v[:L])
q_new = 2
else:
result1 = allclayton(u[:L], v[:L])
result2 = allgumbel(u[:L], v[:L])
q_new = 3
else:
if (q == 3 * np.ones(numbrk + 1)).all():
a = np.random.uniform(low=np.nextafter(0.0, 1.0))
if a <= 1 / 2:
result1 = allgumbel(u[:L], v[:L])
result2 = allfrank(u[:L], v[:L])
q_new = 2
else:
result1 = allgumbel(u[:L], v[:L])
result2 = allclayton(u[:L], v[:L])
q_new = 1
BFu = result2["BFu"] - result1["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = current
QQ = q_new * np.ones(numbrk + 1, dtype=int)
rejected = current
w = 49
else:
new_model = current
QQ = q
rejected = current
w = 50
result = {
"new_model": new_model,
"rejected": rejected,
"w": w,
"QQ": QQ,
"ss": ss
}
return result
def bayes_birth_only_clay(currentModel, newModel, kn, u, v, s, q, Q, zita,
chain):
current = np.sort(currentModel)
new = np.sort(newModel)
t2 = new[new != 0]
min_new = np.min(t2)
max_new = np.max(t2)
L = len(u)
l = len(current)
ss = -1
R = 1 / 2
if not np.any(current):
if (s[1] == 1 and s[2] == 1):
result1 = allclayton(u[:max_new], v[:max_new])
result2 = allclayton(u[max_new:L], v[max_new:L])
R = R * 3
else:
if (s[1] == 1 and s[2] == 2):
result1 = allclayton(u[:max_new], v[:max_new])
result2 = allfrank(u[max_new:L], v[max_new:L])
R = R * 3 / 2
else:
if (s[1] == 2 and s[2] == 1):
result1 = allfrank(u[:max_new], v[:max_new])
result2 = allclayton(u[max_new:L], v[max_new:L])
R = R * 3 / 2
else:
if (s[1] == 1 and s[2] == 3):
result1 = allclayton(u[:max_new], v[:max_new])
result2 = allgumbel(u[max_new:L], v[max_new:L])
R = R * 3 / 2
else:
if (s[1] == 3 and s[2] == 1):
result1 = allgumbel(u[:max_new], v[:max_new])
result2 = allclayton(u[max_new:L], v[max_new:L])
R = R * 3 / 2
resultOld = allclayton(u, v)
BFu = result1["BFu"] + result2["BFu"] - resultOld["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
QQ = Q
w = 35
else:
new_model = current
rejected = new
QQ = q
w = 36
result = {
"new_model": new_model,
"rejected": rejected,
"w": w,
"QQ": QQ,
"ss": ss
}
else:
result = bayes_birth_clay(currentModel, newModel, kn, u, v, s, q, Q,
zita, chain)
return result
def bayes_birth_clay(currentModel, newModel, kn, u, v, s, q, Q, zita, chain):
current = np.sort(currentModel)
new = np.sort(newModel)
j2 = np.count_nonzero(new == 0, axis=0)
t2 = new[new != 0]
min_new = np.min(t2)
max_new = np.max(t2)
L = len(u)
l = len(current)
ss = -1
if j2 == 0:
R = 4 / 3
else:
R = 1
if np.any(current):
t1 = current[current != 0]
min_old = np.min(t1)
max_old = np.max(t1)
if min_new < min_old:
if (s[1] == 1 and s[2] == 1):
result1 = allclayton(u[:min_new], v[:min_new])
result2 = allclayton(u[min_new:min_old], v[min_new:min_old])
R = R * 3
else:
if (s[1] == 1 and s[2] == 2):
result1 = allclayton(u[:min_new], v[:min_new])
result2 = allfrank(u[min_new:min_old], v[min_new:min_old])
R = R * 3 / 2
else:
if (s[1] == 2 and s[2] == 1):
result1 = allfrank(u[:min_new], v[:min_new])
result2 = allclayton(u[min_new:min_old],
v[min_new:min_old])
R = R * 3 / 2
else:
if (s[1] == 1 and s[2] == 3):
result1 = allclayton(u[:min_new], v[:min_new])
result2 = allgumbel(u[min_new:min_old],
v[min_new:min_old])
R = R * 3 / 2
else:
if (s[1] == 3 and s[2] == 1):
result1 = allgumbel(u[:min_new], v[:min_new])
result2 = allclayton(u[min_new:min_old],
v[min_new:min_old])
R = R * 3 / 2
resultOld = allclayton(u[:min_old], v[:min_old])
BFu = result1["BFu"] + result2["BFu"] - resultOld["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
QQ = Q
w = 29
else:
new_model = current
rejected = new
QQ = q
w = 30
else:
if max_new > max_old and max_old != 0:
if (s[1] == 1 and s[2] == 1):
result1 = allclayton(u[max_old - 1:max_new],
v[max_old - 1:max_new])
result2 = allclayton(u[max_new:L], v[max_new:L])
R = R * 3
else:
if (s[1] == 1 and s[2] == 2):
result1 = allclayton(u[max_old - 1:max_new],
v[max_old - 1:max_new])
result2 = allfrank(u[max_new:L], v[max_new:L])
R = R * 3 / 2
else:
if (s[1] == 2 and s[2] == 1):
result1 = allfrank(u[max_old - 1:max_new],
v[max_old - 1:max_new])
result2 = allclayton(u[max_new:L], v[max_new:L])
R = R * 3 / 2
else:
if (s[1] == 1 and s[2] == 3):
result1 = allclayton(u[max_old - 1:max_new],
v[max_old - 1:max_new])
result2 = allgumbel(u[max_new:L], v[max_new:L])
R = R * 3 / 2
else:
if (s[1] == 3 and s[2] == 1):
result1 = allgumbel(
u[max_old:max_new], v[max_old:max_new])
result2 = allclayton(
u[max_new:L], v[max_new:L])
R = R * 3 / 2
resultOld = allclayton(u[max_old - 1:L], v[max_old - 1:L])
BFu = result1["BFu"] + result2["BFu"] - resultOld["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
QQ = Q
w = 31
else:
new_model = current
rejected = new
QQ = q
w = 32
else:
place = np.where(new == kn)[0][0] + 1
if (s[1] == 1 and s[2] == 1):
result1 = allclayton(u[new[place - 2] - 1:new[place - 1]],
v[new[place - 2] - 1:new[place - 1]])
result2 = allclayton(u[new[place - 1]:new[place]],
v[new[place - 1]:new[place]])
R = R * 3
else:
if (s[1] == 1 and s[2] == 2):
result1 = allclayton(
u[new[place - 2] - 1:new[place - 1]],
v[new[place - 2] - 1:new[place - 1]])
result2 = allfrank(u[new[place - 1]:new[place]],
v[new[place - 1]:new[place]])
R = R * 3 / 2
else:
if (s[1] == 2 and s[2] == 1):
result1 = allfrank(
u[new[place - 2] - 1:new[place - 1]],
v[new[place - 2] - 1:new[place - 1]])
result2 = allclayton(u[new[place - 1]:new[place]],
v[new[place - 1]:new[place]])
R = R * 3 / 2
else:
if (s[1] == 1 and s[2] == 3):
result1 = allclayton(
u[new[place - 2] - 1:new[place - 1]],
v[new[place - 2] - 1:new[place - 1]])
result2 = allgumbel(
u[new[place - 1]:new[place]],
v[new[place - 1]:new[place]])
R = R * 3 / 2
else:
if (s[1] == 3 and s[2] == 1):
result1 = allgumbel(
u[new[place - 2] - 1:new[place - 1]],
v[new[place - 2] - 1:new[place - 1]])
result2 = allclayton(
u[new[place - 1]:new[place]],
v[new[place - 1]:new[place]])
R = R * 3 / 2
resultOld = allclayton(u[new[place - 2] - 1:new[place]],
v[new[place - 2] - 1:new[place]])
BFu = result1["BFu"] + result2["BFu"] - resultOld["BFu"]
if BFu.imag:
ss = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
QQ = Q
w = 33
else:
new_model = current
rejected = new
QQ = q
w = 34
result = {
"new_model": new_model,
"rejected": rejected,
"w": w,
"QQ": QQ,
"ss": ss
}
else:
result = bayes_birth_only_clay(currentModel, newModel, kn, u, v, s, q,
Q, zita, chain)
return result
def bayes_move_clay(currentModel, newModel, kn, u, v, q, zita, chain):
current = np.sort(currentModel)
new = np.sort(newModel)
t1 = current[current != 0]
t2 = new[new != 0]
min_old = np.min(t1)
max_old = np.max(t1)
min_new = np.min(t2)
max_new = np.max(t2)
L = len(u)
l = len(current)
s = -1
R = 1
if min_new < min_old and max_old != min_old:
result1 = allclayton(u[:min_new], v[:min_new])
result2 = allclayton(u[min_new:t1[1]], v[min_new:t1[1]])
resultOld1 = allclayton(u[:min_old], v[:min_old])
resultOld2 = allclayton(u[min_old:t1[1]], v[min_old:t1[1]])
BFu = result1["BFu"] + result2["BFu"] - resultOld1["BFu"] - resultOld2[
"BFu"]
if BFu.imag:
s = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
w = 1
else:
new_model = current
rejected = new
w = 2
else:
if min_new > min_old and max_old != min_old:
result1 = allclayton(u[:min_new], v[:min_new])
result2 = allclayton(u[min_new:t1[1]], v[min_new:t1[1]])
resultOld1 = allclayton(u[:min_old], v[:min_old])
resultOld2 = allclayton(u[min_old:t1[1]], v[min_old:t1[1]])
BFu = result1["BFu"] + result2["BFu"] - resultOld1[
"BFu"] - resultOld2["BFu"]
if BFu.imag:
s = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
w = 3
else:
new_model = current
rejected = new
w = 4
elif (min_new < min_old or max_new > max_old) and max_old == min_old:
result1 = allclayton(u[:t2[0]], v[:t2[0]])
result2 = allclayton(u[t2[0]:L], v[t2[0]:L])
resultOld1 = allclayton(u[:t1[0]], v[:t1[0]])
resultOld2 = allclayton(u[t1[0]:L], v[t1[0]:L])
BFu = result1["BFu"] + result2["BFu"] - resultOld1[
"BFu"] - resultOld2["BFu"]
if BFu.imag:
s = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
w = 5
else:
new_model = current
rejected = new
w = 6
elif max_new > max_old and max_old != min_old:
result1 = allclayton(u[t1[kn - 2] - 1:max_new],
v[t1[kn - 2] - 1:max_new])
result2 = allclayton(u[max_new:L], v[max_new:L])
resultOld1 = allclayton(u[t1[kn - 2] - 1:t1[kn - 1]],
v[t1[kn - 2] - 1:t1[kn - 1]])
resultOld2 = allclayton(u[t1[kn - 1]:L], v[t1[kn - 1]:L])
BFu = result1["BFu"] + result2["BFu"] - resultOld1[
"BFu"] - resultOld2["BFu"]
if BFu.imag:
s = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
w = 7
else:
new_model = current
rejected = new
w = 8
elif max_new < max_old and max_old != min_old:
result1 = allclayton(u[t1[kn - 2] - 1:max_new],
v[t1[kn - 2] - 1:max_new])
result2 = allclayton(u[max_new:L], v[max_new:L])
resultOld1 = allclayton(u[t1[kn - 2] - 1:t1[kn - 1]],
v[t1[kn - 2] - 1:t1[kn - 1]])
resultOld2 = allclayton(u[t1[kn - 1]:L], v[t1[kn - 1]:L])
BFu = result1["BFu"] + result2["BFu"] - resultOld1[
"BFu"] - resultOld2["BFu"]
if BFu.imag:
s = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
w = 9
else:
new_model = current
rejected = new
w = 10
elif min_new == min_old and max_old == max_old:
result1 = allclay(u[t2[kn - 2] - 1:t2[kn - 1]],
v[t2[kn - 2] - 1:t2[kn - 1]])
result2 = allclay(u[t2[kn - 1] - 1:t2[kn]],
v[t2[kn - 1] - 1:t2[kn]])
resultOld1 = allclay(u[t1[kn - 2] - 1:t1[kn - 1]],
v[t1[kn - 2] - 1:t1[kn - 1]])
resultOld2 = allclay(u[t1[kn - 1] - 1:t1[kn]],
v[t1[kn - 1] - 1:t1[kn]])
BFu = result1["BFu"] + result2["BFu"] - resultOld1[
"BFu"] - resultOld2["BFu"]
if BFu.imag:
s = -2
U2 = np.random.uniform(low=np.nextafter(0.0, 1.0))
if (np.log(U2) < min(0, (
(zita**(chain - 1)) * BFu) + np.log(R))) and not BFu.imag:
new_model = new
rejected = current
w = 11
else:
new_model = current
rejected = new
w = 12
QQ = q
result = {
"new_model": new_model,
"rejected": rejected,
"w": w,
"QQ": QQ,
"s": s
}
return result
new_model = new
rejected = current
QQ = Q
w = 17
else:
new_model = current
rejected = new
QQ = q
w = 18
else:
if not np.any(new):
R = 2
if(s[1] == 1 and s[2] == 1):
result1 = allclayton(u[:max_old], v[:max_old])
result2 = allclayton(u[max_old:L], v[max_old:L])
R = R * 1/3
else:
if(s[1] == 1 and s[2] == 2):
result1 = allclayton(u[:max_old], v[:max_old])
result2 = allfrank(u[max_old:L], v[max_old:L])
R = R * 2/3
else:
if(s[1] == 2 and s[2] == 1):
result1 = allfrank(u[:max_old], v[:max_old])
result2 = allclayton(u[max_old:L], v[max_old:L])
R = R * 2/3
else:
if(s[1] == 1 and s[2] == 3):
result1 = allclayton(u[:max_old], v[:max_old])