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Add an Example for HiddenMarkovModel(TDistribution, TObservation) Class #697
Description
Please add an example for HiddenMarkovModel(TDistribution, TObservation) Class.
Please l will like to see an example of a multivariatedistribution being modeled within the HMM. The univariate examples works perfect, however my dataset has more than 2 variables per data point hence l need a multivariate
"Time" "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8" "V9" "V10" "V11" "V12" "V13" "V14" "V15" "V16" "V17" "V18" "V19" "V20" "V21" "V22" "V23" "V24" "V25" "V26" "V27" "V28" "Amount" "Class"
0 -1.3598071336738 -0.0727811733098497 2.53634673796914 1.37815522427443 -0.338320769942518 0.462387777762292 0.239598554061257 0.0986979012610507 0.363786969611213 0.0907941719789316 -0.551599533260813 -0.617800855762348 -0.991389847235408 -0.311169353699879 1.46817697209427 -0.470400525259478 0.207971241929242 0.0257905801985591 0.403992960255733 0.251412098239705 -0.018306777944153 0.277837575558899 -0.110473910188767 0.0669280749146731 0.128539358273528 -0.189114843888824 0.133558376740387 -0.0210530534538215 149.62 "0"
0 1.19185711131486 0.26615071205963 0.16648011335321 0.448154078460911 0.0600176492822243 -0.0823608088155687 -0.0788029833323113 0.0851016549148104 -0.255425128109186 -0.166974414004614 1.61272666105479 1.06523531137287 0.48909501589608 -0.143772296441519 0.635558093258208 0.463917041022171 -0.114804663102346 -0.183361270123994 -0.145783041325259 -0.0690831352230203 -0.225775248033138 -0.638671952771851 0.101288021253234 -0.339846475529127 0.167170404418143 0.125894532368176 -0.00898309914322813 0.0147241691924927 2.69 "0"
1 -1.35835406159823 -1.34016307473609 1.77320934263119 0.379779593034328 -0.503198133318193 1.80049938079263 0.791460956450422 0.247675786588991 -1.51465432260583 0.207642865216696 0.624501459424895 0.066083685268831 0.717292731410831 -0.165945922763554 2.34586494901581 -2.89008319444231 1.10996937869599 -0.121359313195888 -2.26185709530414 0.524979725224404 0.247998153469754 0.771679401917229 0.909412262347719 -0.689280956490685 -0.327641833735251 -0.139096571514147 -0.0553527940384261 -0.0597518405929204 378.66 "0"
1 -0.966271711572087 -0.185226008082898 1.79299333957872 -0.863291275036453 -0.0103088796030823 1.24720316752486 0.23760893977178 0.377435874652262 -1.38702406270197 -0.0549519224713749 -0.226487263835401 0.178228225877303 0.507756869957169 -0.28792374549456 -0.631418117709045 -1.0596472454325 -0.684092786345479 1.96577500349538 -1.2326219700892 -0.208037781160366 -0.108300452035545 0.00527359678253453 -0.190320518742841 -1.17557533186321 0.647376034602038 -0.221928844458407 0.0627228487293033 0.0614576285006353 123.5 "0"
2 -1.15823309349523 0.877736754848451 1.548717846511 0.403033933955121 -0.407193377311653 0.0959214624684256 0.592940745385545 -0.270532677192282 0.817739308235294 0.753074431976354 -0.822842877946363 0.53819555014995 1.3458515932154 -1.11966983471731 0.175121130008994 -0.451449182813529 -0.237033239362776 -0.0381947870352842 0.803486924960175 0.408542360392758 -0.00943069713232919 0.79827849458971 -0.137458079619063 0.141266983824769 -0.206009587619756 0.502292224181569 0.219422229513348 0.215153147499206 69.99 "0"