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nexedi
dream
Commits
f52b201a
Commit
f52b201a
authored
9 years ago
by
panos
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Example ammended based on the objects name changes
parent
c9e66a58
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dream/KnowledgeExtraction/KEtool_examples/ConfidenceIntervals/ConfidenceIntervals_example.py
...amples/ConfidenceIntervals/ConfidenceIntervals_example.py
+6
-6
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dream/KnowledgeExtraction/KEtool_examples/ConfidenceIntervals/ConfidenceIntervals_example.py
View file @
f52b201a
...
...
@@ -22,9 +22,9 @@ Created on 13 Jun 2014
# along with DREAM. If not, see <http://www.gnu.org/licenses/>.
# ===========================================================================
from
dream.KnowledgeExtraction.ImportCSVdata
import
Import
_CSV
from
dream.KnowledgeExtraction.ConfidenceIntervals
import
Intervals
from
dream.KnowledgeExtraction.DataManipulation
import
DataMan
agement
from
dream.KnowledgeExtraction.ImportCSVdata
import
Import
CSVdata
from
dream.KnowledgeExtraction.ConfidenceIntervals
import
Confidence
Intervals
from
dream.KnowledgeExtraction.DataManipulation
import
DataMan
ipulation
def
main
(
test
=
0
,
CSVFileName
=
'DataSet.csv'
,
csvFile
=
None
):
...
...
@@ -32,15 +32,15 @@ def main(test=0, CSVFileName='DataSet.csv',
CSVFileName
=
csvFile
.
name
filename
=
CSVFileName
data
=
Import
_CSV
()
#call the Import_CSV module and using its method Input_data import the data set from the CSV file to the tool
data
=
Import
CSVdata
()
#call the Import_CSV module and using its method Input_data import the data set from the CSV file to the tool
Data
=
data
.
Input_data
(
filename
)
ProcTime
=
Data
.
get
(
'ProcessingTimes'
,[])
#get from the returned Python dictionary the three data sets
MTTF
=
Data
.
get
(
'MTTF'
,[])
MTTR
=
Data
.
get
(
'MTTR'
,[])
CI
=
Intervals
()
#create a Intervals object
DM
=
DataMan
agement
()
CI
=
Confidence
Intervals
()
#create a Intervals object
DM
=
DataMan
ipulation
()
if
test
:
return
DM
.
round
(
CI
.
ConfidIntervals
(
ProcTime
,
0.95
)),
CI
.
ConfidIntervals
(
MTTR
,
0.95
),
DM
.
ceiling
(
CI
.
ConfidIntervals
(
MTTF
,
0.90
))
#print the confidence intervals of the data sets applying either 90% or 95% probability
...
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