Revision 278, 1.1 kB
(checked in by smidl, 16 years ago)
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1 | #include <estim/arx.h> |
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2 | using namespace bdm; |
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3 | |
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4 | int main() { |
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5 | // Setup model : ARX for 1D Gaussian |
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6 | //Test constructor |
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7 | mat V0 = 0.00001*eye(2); V0(0,0)= 0.1; // |
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8 | ARX Ar; Ar.set_statistics(1, V0, -1.0); |
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9 | |
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10 | mat mu(1,1); |
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11 | mat R(1,1); |
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12 | Ar._e()->mean_mat(mu,R); |
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13 | cout << "Prior moments: mu="<< mu << ", R=" << R <<endl; |
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14 | |
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15 | int ndat = 200; |
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16 | vec smp=randn(ndat); |
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17 | // |
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18 | mat Smp=ones(2,ndat); |
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19 | Smp.set_row(0,smp); |
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20 | // |
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21 | Ar.bayesB(Smp); |
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22 | // Ar is now filled with estimates of N(0,1); |
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23 | cout << "Empirical moments: mu=" << sum(smp)/ndat << ", R=" << sum_sqr(smp)/ndat - pow(sum(smp)/ndat,2) << endl; |
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24 | Ar._e()->mean_mat(mu,R); |
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25 | cout << "Posterior moments: mu="<< mu << ", R=" << R <<endl; |
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26 | |
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27 | //////// TEST prediction |
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28 | vec x=linspace(-3.0,3.0,100); |
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29 | double xstep = 6.0/100.0; |
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30 | mat X(1,100); |
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31 | mat X2(2,100); |
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32 | X.set_row(0,x); |
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33 | X2.set_row(0,x); |
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34 | |
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35 | mlstudent* Ap = Ar.predictor_student(); |
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36 | vec Ap_x=Ap->evallogcond_m(X,vec_1(1.0)); |
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37 | vec ll_x = Ar.logpred_m(X2); |
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38 | |
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39 | cout << "normalize : " << xstep*sum(exp(Ap_x)) << endl; |
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40 | cout << "normalize : " << xstep*sum(exp(ll_x)) << endl; |
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41 | |
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42 | it_file it("arx_elem_test.it"); |
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43 | it << Name("Ap_x") << Ap_x; |
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44 | it << Name("ll_x") << ll_x; |
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45 | } |
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