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nexedi
dream
Commits
bfa2747f
Commit
bfa2747f
authored
Oct 22, 2014
by
Georgios Dagkakis
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Plain Diff
Geometric distribution fixed
parent
f0f4e5ea
Changes
3
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3 changed files
with
8 additions
and
6 deletions
+8
-6
dream/simulation/Globals.py
dream/simulation/Globals.py
+2
-1
dream/simulation/LineGenerationJSON.py
dream/simulation/LineGenerationJSON.py
+4
-2
dream/simulation/RandomNumberGenerator.py
dream/simulation/RandomNumberGenerator.py
+2
-3
No files found.
dream/simulation/Globals.py
View file @
bfa2747f
...
...
@@ -40,7 +40,8 @@ import simpy
class
G
:
seed
=
1450
#the seed of the random number generator
Rnd
=
Random
(
seed
)
#random number generator
import
numpy
numpyRnd
=
numpy
ObjList
=
[]
#a list that holds all the CoreObjects
EntityList
=
[]
#a list that holds all the Entities
...
...
dream/simulation/LineGenerationJSON.py
View file @
bfa2747f
...
...
@@ -529,9 +529,11 @@ def main(argv=[], input_data=None):
G
.
Router
.
isInitialized
=
False
if
G
.
seed
:
G
.
Rnd
=
Random
(
'%s%s'
%
(
G
.
seed
,
i
))
G
.
Rnd
=
Random
(
'%s%s'
%
(
G
.
seed
,
i
))
G
.
numpyRnd
.
random
.
seed
(
G
.
seed
)
else
:
G
.
Rnd
=
Random
()
G
.
Rnd
=
Random
()
G
.
numpyRnd
.
random
.
seed
()
createWIP
()
initializeObjects
()
Globals
.
setWIP
(
G
.
EntityList
)
...
...
dream/simulation/RandomNumberGenerator.py
View file @
bfa2747f
...
...
@@ -25,6 +25,7 @@ Created on 14 Feb 2013
'''
holds methods for generations of numbers from different distributions
'''
class
RandomNumberGenerator
(
object
):
def
__init__
(
self
,
obj
,
distributionType
,
mean
=
0
,
stdev
=
0
,
min
=
0
,
max
=
0
,
alpha
=
0
,
beta
=
0
,
logmean
=
0
,
logsd
=
0
,
probability
=
0
,
shape
=
0
,
scale
=
0
,
location
=
0
):
...
...
@@ -43,8 +44,6 @@ class RandomNumberGenerator(object):
self
.
location
=
float
(
location
or
0
)
self
.
obj
=
obj
def
generateNumber
(
self
):
from
Globals
import
G
if
(
self
.
distributionType
==
"Fixed"
):
#if the distribution is Fixed
...
...
@@ -76,7 +75,7 @@ class RandomNumberGenerator(object):
else
:
continue
elif
(
self
.
distributionType
==
"Geometric"
):
#if the distribution is Geometric
return
1
return
G
.
numpyRnd
.
random
.
geometric
(
self
.
probability
)
elif
(
self
.
distributionType
==
"Lognormal"
):
#if the distribution is Lognormal
# XXX from the files lognormvariate(mu, sigma)
# it would be better to use same mean,stdev
...
...
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