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@ -233,12 +233,13 @@ public: |
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oobError /= n_oob; |
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if( rparams.calcVarImportance && n_oob > 1 ) |
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{ |
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Mat sample_clone; |
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oobperm.resize(n_oob); |
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for( i = 0; i < n_oob; i++ ) |
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oobperm[i] = oobidx[i]; |
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for (i = n_oob - 1; i > 0; --i) //Randomly shuffle indices so we can permute features
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{ |
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int r_i = rng.uniform(0, i + 1); |
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int r_i = rng.uniform(0, n_oob); |
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std::swap(oobperm[i], oobperm[r_i]); |
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} |
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@ -252,7 +253,7 @@ public: |
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j = oobidx[i]; |
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int vj = oobperm[i]; |
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sample0 = Mat( nallvars, 1, CV_32F, psamples + sstep0*w->sidx[j], sstep1*sizeof(psamples[0]) ); |
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Mat sample_clone = sample0.clone(); //create a copy so we don't mess up the original data
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sample0.copyTo(sample_clone); //create a copy so we don't mess up the original data
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sample_clone.at<float>(vi) = psamples[sstep0*w->sidx[vj] + sstep1*vi]; |
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double val = predictTrees(Range(treeidx, treeidx+1), sample_clone, predictFlags); |
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