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Viewing changes to stats/EST_ols.cc

  • Committer: Bazaar Package Importer
  • Author(s): Kumar Appaiah, Kartik Mistry, Kumar Appaiah
  • Date: 2010-07-17 11:32:04 UTC
  • mfrom: (3.1.6 sid)
  • Revision ID: james.westby@ubuntu.com-20100717113204-mnse3jo236j107q8
Tags: 1:2.0.95~beta-1
[ Kartik Mistry ]
* debian/control:
  + [Lintian] Added missing ${misc:Depends}
  + Updated Standards-Version to 3.8.4 (no changes needed)
* debian/patches/const_char.diff:
  + Added missing patch header
* Removed unused patch invalid_const_char_conversion_fixes.diff

[ Kumar Appaiah ]
* New upstream release.
* Standards Version is now 3.9.0 (No changes needed)
* Update debian/rules to specify version numbers.

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Lines of Context:
stats/EST_ols.cc
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                                  float &bscore,
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                                  int &best_feat,
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                                  const EST_FMatrix &Xtest,
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                                  const EST_FMatrix &Ytest);
 
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                                  const EST_FMatrix &Ytest,
 
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                                  const EST_StrList &feat_names
 
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                                  );
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int ols(const EST_FMatrix &X,const EST_FMatrix &Y, EST_FMatrix &coeffs)
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{
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            for (s=i=0; i<singularity; i++)
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            {
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                s++;
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                while (!included(s)) s++;
 
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                while ((included(s) == FALSE) ||
 
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                       (included(s) == OLS_IGNORE))
 
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                       s++;
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            }
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            if (!included(s))
 
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            if (included(s) == FALSE)
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            {   // oops
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                cerr << "OLS: found singularity twice, shouldn't happen" 
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                    << endl;
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    int i,j,k,width;
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    for (width=i=0; i<included.length(); i++)
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        if (included(i))
 
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        if (included(i) == TRUE)
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            width++;
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    Xl.resize(X.num_rows(),width);
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    for (i=0; i<X.num_rows(); i++)
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        for (k=j=0; j < X.num_columns(); j++)
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            if (included(j))
 
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            if (included(j) == TRUE)
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            {
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                Xl.a_no_check(i,k) = X.a_no_check(i,j);
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                k++;
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                 float limit,
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                 EST_FMatrix &coeffs,
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                 const EST_FMatrix &Xtest,
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                 const EST_FMatrix &Ytest)
 
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                 const EST_FMatrix &Ytest,
 
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                 EST_IVector &included)
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{
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    // Find the features that contribute to the correlation using a
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    // a greedy algorithm
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    EST_FMatrix coeffsl;
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    EST_IVector included;
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    float best_score=0.0,bscore;
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    int i,best_feat;
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    int nf=1;  // for nice printing of progress
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    included.resize(X.num_columns());
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    included[0] = TRUE;  // always guarantee interceptor
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    for (i=1; i<included.length(); i++)
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         included.a_no_check(i) = FALSE;
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    for (i=1; i < X.num_columns(); i++)
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    {
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        if (!ols_stepwise_find_best(X,Y,included,coeffsl,
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                                    bscore,best_feat,Xtest,Ytest))
 
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                                    bscore,best_feat,Xtest,Ytest,
 
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                                    feat_names))
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        {
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            cerr << "OLS: stepwise failed" << endl;
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            return FALSE;
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                                  float &bscore,
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                                  int &best_feat,
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                                  const EST_FMatrix &Xtest,
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                                  const EST_FMatrix &Ytest)
 
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                                  const EST_FMatrix &Ytest,
 
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                                  const EST_StrList &feat_names
 
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                                  )
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{
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    EST_FMatrix coeffsl;
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    bscore = 0;
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            if (!robust_ols(X,Y,included,coeffsl))
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                return FALSE;  // failed for some reason
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            ols_apply(Xtest,coeffsl,pred);
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            ols_test(Ytest,pred,cor,rmse);
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            if (cor > bscore)
 
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            ols_test(Ytest,pred,cor,rmse);
 
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            printf("tested %d %s %f best %f\n",
 
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                   i,(const char *)feat_names.nth(i),cor,bscore);
 
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            if (fabs(cor) > bscore)
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            {
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                bscore = cor;
 
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                bscore = fabs(cor);
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                coeffs = coeffsl;
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                best_feat = i;
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            }
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    if (v3 <= 0)
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    {   // happens when there's very little variation in x
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        correlation = -3;
 
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        correlation = 0;
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        rmse = se.mean();
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        return FALSE;
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    }