Drawing values from normal distribution to be used in Monte Carlo Simulation
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I have defined a function, where the output is produced by a Monte Carlo Simulation.
When I make the call y(p,m,n,d), the output always stays the same "n" --> y = n
What am I doing wrong?
k = [0, 100]
for i in k:
p = np.random.normal(40,3,i)
m = np.random.normal(35,1,i)
n = np.random.normal(50,4,i)
d = np.random.normal(27,2.5,i)
def fct(p,m,n,d):
global u1
global u2
if np.any( n > 0):
return n
u1, u2 = np.asarray[np.log(0.6*n)], np.asarray[(math.e**d)**0.5]
if np.any(u1 != 0):
return u1
if np.any(u2 != 0):
return u2
if np.any( p > 0):
return p
G = np.log(p**2) + np.asarray(6*[math.e**(-m)]/u1) + 3/u2
return G
y = fct(p,m,n,d)
python function numpy gaussian montecarlo
edited Nov 20 at 11:56
Nils Gudat
2,09511433
asked Nov 20 at 10:34
Plop
104
add a comment |
up vote
0
down vote
favorite
I have defined a function, where the output is produced by a Monte Carlo Simulation.
When I make the call y(p,m,n,d), the output always stays the same "n" --> y = n
What am I doing wrong?
k = [0, 100]
for i in k:
p = np.random.normal(40,3,i)
m = np.random.normal(35,1,i)
n = np.random.normal(50,4,i)
d = np.random.normal(27,2.5,i)
def fct(p,m,n,d):
global u1
global u2
if np.any( n > 0):
return n
u1, u2 = np.asarray[np.log(0.6*n)], np.asarray[(math.e**d)**0.5]
if np.any(u1 != 0):
return u1
if np.any(u2 != 0):
return u2
if np.any( p > 0):
return p
G = np.log(p**2) + np.asarray(6*[math.e**(-m)]/u1) + 3/u2
return G
y = fct(p,m,n,d)
python function numpy gaussian montecarlo
edited Nov 20 at 11:56
Nils Gudat
2,09511433
asked Nov 20 at 10:34
Plop
104
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I have defined a function, where the output is produced by a Monte Carlo Simulation.
When I make the call y(p,m,n,d), the output always stays the same "n" --> y = n
What am I doing wrong?
k = [0, 100]
for i in k:
p = np.random.normal(40,3,i)
m = np.random.normal(35,1,i)
n = np.random.normal(50,4,i)
d = np.random.normal(27,2.5,i)
def fct(p,m,n,d):
global u1
global u2
if np.any( n > 0):
return n
u1, u2 = np.asarray[np.log(0.6*n)], np.asarray[(math.e**d)**0.5]
if np.any(u1 != 0):
return u1
if np.any(u2 != 0):
return u2
if np.any( p > 0):
return p
G = np.log(p**2) + np.asarray(6*[math.e**(-m)]/u1) + 3/u2
return G
y = fct(p,m,n,d)
python function numpy gaussian montecarlo
edited Nov 20 at 11:56
Nils Gudat
2,09511433
asked Nov 20 at 10:34
Plop
104
I have defined a function, where the output is produced by a Monte Carlo Simulation.
When I make the call y(p,m,n,d), the output always stays the same "n" --> y = n
What am I doing wrong?
k = [0, 100]
for i in k:
p = np.random.normal(40,3,i)
m = np.random.normal(35,1,i)
n = np.random.normal(50,4,i)
d = np.random.normal(27,2.5,i)
def fct(p,m,n,d):
global u1
global u2
if np.any( n > 0):
return n
u1, u2 = np.asarray[np.log(0.6*n)], np.asarray[(math.e**d)**0.5]
if np.any(u1 != 0):
return u1
if np.any(u2 != 0):
return u2
if np.any( p > 0):
return p
G = np.log(p**2) + np.asarray(6*[math.e**(-m)]/u1) + 3/u2
return G
y = fct(p,m,n,d)
python function numpy gaussian montecarlo
python function numpy gaussian montecarlo
edited Nov 20 at 11:56
Nils Gudat
2,09511433
asked Nov 20 at 10:34
Plop
104
edited Nov 20 at 11:56
Nils Gudat
2,09511433
asked Nov 20 at 10:34
Plop
104
edited Nov 20 at 11:56
Nils Gudat
2,09511433
edited Nov 20 at 11:56
Nils Gudat
2,09511433
edited Nov 20 at 11:56
Nils Gudat
2,09511433
2,09511433
asked Nov 20 at 10:34
Plop
104
asked Nov 20 at 10:34
Plop
104
asked Nov 20 at 10:34
Plop
104
104
add a comment |
add a comment |
1 Answer
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oldest
votes
up vote
0
down vote
You are always hitting this case:
if np.any( n > 0):
return n
This is happening because of the way you define n:
n = np.random.normal(50,4,i)
The mean is at 50 and the standard deviation is 4. So, you have a range between 46 and 54 which will contain only positive values.
answered Nov 20 at 10:41
Konstantin Grigorov
866
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
You are always hitting this case:
if np.any( n > 0):
return n
This is happening because of the way you define n:
n = np.random.normal(50,4,i)
The mean is at 50 and the standard deviation is 4. So, you have a range between 46 and 54 which will contain only positive values.
answered Nov 20 at 10:41
Konstantin Grigorov
866
add a comment |
up vote
0
down vote
You are always hitting this case:
if np.any( n > 0):
return n
This is happening because of the way you define n:
n = np.random.normal(50,4,i)
The mean is at 50 and the standard deviation is 4. So, you have a range between 46 and 54 which will contain only positive values.
answered Nov 20 at 10:41
Konstantin Grigorov
866
add a comment |
up vote
0
down vote
up vote
0
down vote
You are always hitting this case:
if np.any( n > 0):
return n
This is happening because of the way you define n:
n = np.random.normal(50,4,i)
The mean is at 50 and the standard deviation is 4. So, you have a range between 46 and 54 which will contain only positive values.
answered Nov 20 at 10:41
Konstantin Grigorov
866
You are always hitting this case:
if np.any( n > 0):
return n
This is happening because of the way you define n:
n = np.random.normal(50,4,i)
The mean is at 50 and the standard deviation is 4. So, you have a range between 46 and 54 which will contain only positive values.
answered Nov 20 at 10:41
Konstantin Grigorov
866
edited Nov 20 at 10:57
answered Nov 20 at 10:41
Konstantin Grigorov
866
answered Nov 20 at 10:41
Konstantin Grigorov
866
answered Nov 20 at 10:41
Konstantin Grigorov
866
866
add a comment |
add a comment |
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