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nexedi
dream
Commits
7cd15c8c
Commit
7cd15c8c
authored
Jun 13, 2014
by
panos
Committed by
Jérome Perrin
Jun 17, 2014
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New example added to demonstrate the use of the ConfidenceIntercals objext
parent
3612ba1b
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dream/KnowledgeExtraction/KEtool_examples/ConfidenceIntervals/ConfidenceIntervals_example.py
...amples/ConfidenceIntervals/ConfidenceIntervals_example.py
+40
-0
dream/KnowledgeExtraction/KEtool_examples/ConfidenceIntervals/DataSet.csv
...xtraction/KEtool_examples/ConfidenceIntervals/DataSet.csv
+201
-0
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dream/KnowledgeExtraction/KEtool_examples/ConfidenceIntervals/ConfidenceIntervals_example.py
0 → 100644
View file @
7cd15c8c
'''
Created on 13 Jun 2014
@author: Panos
'''
#===========================================================================
# Copyright 2013 University of Limerick
#
# This file is part of DREAM.
#
# DREAM is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# DREAM is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with DREAM. If not, see <http://www.gnu.org/licenses/>.
# ===========================================================================
from
ImportCSVdata
import
Import_CSV
from
ConfidenceIntervals
import
Intervals
filename
=
(
"DataSet.csv"
)
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
=
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
#print the confidence intervals of the data sets applying either 90% or 95% probability
print
CI
.
ConfidIntervals
(
ProcTime
,
0.95
)
print
CI
.
ConfidIntervals
(
MTTF
,
0.90
)
print
CI
.
ConfidIntervals
(
MTTR
,
0.95
)
\ No newline at end of file
dream/KnowledgeExtraction/KEtool_examples/ConfidenceIntervals/DataSet.csv
0 → 100644
View file @
7cd15c8c
ProcessingTimes,MTTF,MTTR
0.244348299,0.821417417,0.673141665
0.356772955,0.557264448,0.944193512
0.845419754,0.393255702,0.270017308
0.273231138,0.631987756,0.120011257
0.200046377,0.51631315,0.857021184
0.640128181,0.643033348,0.881389765
0.578735622,0.716773075,0.314106089
0.996385373,0.457847744,0.770978692
0.92722687,0.893791782,0.861785565
0.672702473,0.63019863,0.607014913
0.323240807,0.851207249,0.722162596
0.694269656,0.322774484,0.240750485
0.873223515,0.940929275,0.843516877
0.196289047,0.297817863,0.590291493
0.969731768,0.434041093,0.792679063
0.788809391,0.253841521,0.093194379
0.92058734,0.549898254,0.855697522
0.065780998,0.176827725,0.162613094
0.624755732,0.834824833,0.016795064
0.492693905,0.264748324,0.090957798
0.861827429,0.250311982,0.6496143
0.846934493,0.888114338,0.68499297
0.73524109,0.30377131,0.327762839
0.244015705,0.884171671,0.571923527
0.29260347,0.141533832,0.292892309
0.944574806,0.14442337,0.747256417
0.293183651,0.836559342,0.751553338
0.024415113,0.063740086,0.423910368
0.688527632,0.310661676,0.924224229
0.924272804,0.350761349,0.783824064
0.51155886,0.388905519,0.604406331
0.895576824,0.477540233,0.95734478
0.231353006,0.470609629,0.530935904
0.971559887,0.725885333,0.302987433
0.061372303,0.898496525,0.780488618
0.22738673,0.578615066,0.97574445
0.403014029,0.879109722,0.112487013
0.248396939,0.704838901,0.761908866
0.071824714,0.198061447,0.025785103
0.389326797,0.999732845,0.225423476
0.643803956,0.328977973,0.302487722
0.85225217,0.524051594,0.246369698
0.075357436,0.981919454,0.658538275
0.240823459,0.068576738,0.000701793
0.988418367,0.266281903,0.403786022
0.371428517,0.965371301,0.865176415
0.569949118,0.418132208,0.798035233
0.800870444,0.593208345,0.159754963
0.018308733,0.62561658,0.963641938
0.284832919,0.70279377,0.393928877
0.348884795,0.989127196,0.376676983
0.339045155,0.158021077,0.921501518
0.935784889,0.908901752,0.075931735
0.529725974,0.116111498,0.701198489
0.018638641,0.764226529,0.192218789
0.053014846,0.98378422,0.222391481
0.860853572,0.935160391,0.812615337
0.429075865,0.203585105,0.799141236
0.179437117,0.75895991,0.77216644
0.369073457,0.19765367,0.832066455
0.797089597,0.700829612,0.808611683
0.93862975,0.928737195,0.483896626
0.859144465,0.63764556,0.375831455
0.391575118,0.664991281,0.932467818
0.690302219,0.543577948,0.219571044
0.364004149,0.951116883,0.605002311
0.879621159,0.180563528,0.506357331
0.786377464,0.549207737,0.681883705
0.406260939,0.49102904,0.844085805
0.161720183,0.868973053,0.768035649
0.954476376,0.194981883,0.033715634
0.636651874,0.945756938,0.946266775
0.187969315,0.234813896,0.861189759
0.273153275,0.919853836,0.829086849
0.426627811,0.061183363,0.406850358
0.423205261,0.792740625,0.775762635
0.774013305,0.269985243,0.228500842
0.153571759,0.490667589,0.475202433
0.698877901,0.493995964,0.77022049
0.360469393,0.575125682,0.98747614
0.240174531,0.071083525,0.116741911
0.922059632,0.471389823,0.070771427
0.255662318,0.909090032,0.282770784
0.950852233,0.476929259,0.476337626
0.795378313,0.104664087,0.128293655
0.926824965,0.604674164,0.499953525
0.071941875,0.187541528,0.106471519
0.459289568,0.500669778,0.875508558
0.227208062,0.764540737,0.858606031
0.518629118,0.56787545,0.113948966
0.527557139,0.121515253,0.113737628
0.364719892,0.9221987,0.536809359
0.889815361,0.208815311,0.233965719
0.404150321,0.597380531,0.193431314
0.24144174,0.025685419,0.991015873
0.568590903,0.207059302,0.377139721
0.802065023,0.272644933,0.212301908
0.893104083,0.624014562,0.405912991
0.295972361,0.865639787,0.418402166
0.424512268,0.231007371,0.948515831
0.690958747,0.566966247,0.716447645
0.721777494,0.927995277,0.392195707
0.8079432,0.937950489,0.405759321
0.439583856,0.455564626,0.84354921
0.666079804,0.294614555,0.437734072
0.037288067,0.821366061,0.941714723
0.592943636,0.513266529,0.178979915
0.193327164,0.708463151,0.067355554
0.674445392,0.56872425,0.573794279
0.488603447,0.895231179,0.310904869
0.454109336,0.373303621,0.567405481
0.197664194,0.619254014,0.515036835
0.104548589,0.675310307,0.347678911
0.218128674,0.555857231,0.31435142
0.793893431,0.201764647,0.093716837
0.537664757,0.433296036,0.088702349
0.374679933,0.249686753,0.133766937
0.732658895,0.024589389,0.235549755
0.071772901,0.935249087,0.548796846
0.64164859,0.149733938,0.045514473
0.024284404,0.493520142,0.549042691
0.510253618,0.926161183,0.049054121
0.042028514,0.505287464,0.661617575
0.95505841,0.567147645,0.78227471
0.848598119,0.585444228,0.290214317
0.699583895,0.90300218,0.795151474
0.568499385,0.386982622,0.256399096
0.490769172,0.087588681,0.81658994
0.482577999,0.576309464,0.952940586
0.686645315,0.428967708,0.011081965
0.165524499,0.040911514,0.754658597
0.927679286,0.152260406,0.8295449
0.687792303,0.521165616,0.417882179
0.791950763,0.818953165,0.553298893
0.550182683,0.300349901,0.944000121
0.256062862,0.305519697,0.47603591
0.887511744,0.366859143,0.192894404
0.099122024,0.301233167,0.582898193
0.280396384,0.094206117,0.132463693
0.67030415,0.546268735,0.088700823
0.166866499,0.842950184,0.509831744
0.149331952,0.218193719,0.322613067
0.910635325,0.79565795,0.89830561
0.268955819,0.945646112,0.151525168
0.373592127,0.860167356,0.750079258
0.653720168,0.293492107,0.599568966
0.691996992,0.841033603,0.975754904
0.287847093,0.461756963,0.262967161
0.655448355,0.606556434,0.838654418
0.649464334,0.020972618,0.135015578
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0.566005686,0.640768029,0.886535734
0.738116685,0.183818367,0.587210651
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0.110301485,0.23547056,0.126868053
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0.317220876,0.136251238,0.809475538
0.188350365,0.575495566,0.605847912
0.217836256,0.805115712,0.94039292
0.593039565,0.941220747,0.708428372
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0.238797144,0.758223579,0.346314681
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0.089135344,0.509400985,0.757525604
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0.630022301,0.361779691,0.158873646
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