| 1 | |
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| 2 | #include <stat/libEF.h> |
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| 3 | #include <estim/merger.h> |
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| 4 | |
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| 5 | using namespace bdm; |
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| 6 | |
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| 7 | //These lines are needed for use of cout and endl |
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| 8 | using std::cout; |
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| 9 | using std::endl; |
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| 10 | |
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| 11 | int main() { |
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| 12 | |
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| 13 | RNG_randomize(); |
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| 14 | |
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| 15 | RV y ( "{y }","1" ); |
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| 16 | RV u1 ( "{u1 }","1" ); |
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| 17 | RV u2 ( "{u2 }","1" ); |
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| 18 | |
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| 19 | RV all = y; |
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| 20 | all.add ( u1 ); |
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| 21 | all.add ( u2 ); |
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| 22 | |
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| 23 | mlnorm<fsqmat> f1; f1.set_rv( y ); f1.set_rvc( u1 ); |
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| 24 | mlnorm<fsqmat> f2; f2.set_rv( y ); f2.set_rvc( u2 ); |
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| 25 | |
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| 26 | //Differneces in constant term are essential |
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| 27 | f1.set_parameters ( "0.4","1",mat ( "0.04" ) ); |
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| 28 | f2.set_parameters ( "0.2","-1",mat ( "0.08" ) ); |
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| 29 | |
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| 30 | Array<mpdf* > A ( 2 ); |
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| 31 | A ( 0 ) =&f1; |
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| 32 | A ( 1 ) =&f2; |
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| 33 | |
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| 34 | merger M ( A ); |
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| 35 | enorm<fsqmat> g0; g0.set_rv ( all ); |
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| 36 | g0.set_parameters ( vec ( "1 -1 1" ),3*eye ( 3 ));// +1*ones ( 3,3 ) ); |
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| 37 | |
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| 38 | M.set_parameters ( 1.2,1000,1 ); |
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| 39 | |
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| 40 | int Ntrials=100; |
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| 41 | vec A1s ( Ntrials ); |
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| 42 | vec A2s ( Ntrials ); |
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| 43 | vec R1s ( Ntrials ); |
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| 44 | vec R2s ( Ntrials ); |
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| 45 | vec C1s ( Ntrials ); |
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| 46 | vec C2s ( Ntrials ); |
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| 47 | |
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| 48 | for ( int it=0;it<Ntrials;it++ ) { |
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| 49 | M.merge ( &g0 ); |
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| 50 | |
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| 51 | MixEF &MM = M._Mix(); |
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| 52 | epdf* MP = MM._Coms ( 0 )->epredictor ( ); |
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| 53 | |
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| 54 | RV yu1 = y; yu1.add ( u1 ); |
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| 55 | RV yu2 = y; yu2.add ( u2 ); |
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| 56 | enorm<ldmat>* P1m= ( enorm<ldmat>* ) MP->marginal ( yu1 ); |
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| 57 | enorm<ldmat>* P2m= ( enorm<ldmat>* ) MP->marginal ( yu2 ); |
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| 58 | mlnorm<ldmat>* P1c= ( mlnorm<ldmat>* ) ( P1m->condition ( y ) ); |
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| 59 | mlnorm<ldmat>* P2c= ( mlnorm<ldmat>* ) ( P2m->condition ( y ) ); |
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| 60 | |
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| 61 | A1s(it) = P1c->_A()(0,0); |
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| 62 | A2s(it) = P2c->_A()(0,0); |
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| 63 | R1s(it) = P1c->_R()(0,0); |
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| 64 | R2s(it) = P2c->_R()(0,0); |
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| 65 | C1s(it) = P1c->_mu_const()(0); |
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| 66 | C2s(it) = P2c->_mu_const()(0); |
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| 67 | |
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| 68 | } |
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| 69 | double A1mean = sum(A1s)/Ntrials; |
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| 70 | double A2mean = sum(A2s)/Ntrials; |
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| 71 | double C1mean = sum(C1s)/Ntrials; |
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| 72 | double C2mean = sum(C2s)/Ntrials; |
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| 73 | double R1mean = sum(R1s)/Ntrials; |
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| 74 | double R2mean = sum(R2s)/Ntrials; |
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| 75 | cout << "A1: " << A1mean << " +- " << 2*(sum_sqr(A1s)/Ntrials-A1mean*A1mean) <<endl; |
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| 76 | cout << "A2: " << A2mean << " +- " << 2*(sum_sqr(A2s)/Ntrials-A2mean*A2mean) <<endl; |
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| 77 | cout << "C1: " << C1mean << " +- " << 2*(sum_sqr(C1s)/Ntrials-C1mean*C1mean) <<endl; |
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| 78 | cout << "C2: " << C2mean << " +- " << 2*(sum_sqr(C2s)/Ntrials-C2mean*C2mean) <<endl; |
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| 79 | cout << "R1: " << R1mean << " +- " << 2*(sum_sqr(R1s)/Ntrials-R1mean*R1mean) <<endl; |
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| 80 | cout << "R2: " << R2mean << " +- " << 2*(sum_sqr(R2s)/Ntrials-R2mean*R2mean) <<endl; |
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| 81 | } |
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