import jalview.api.analysis.ScoreModelI;
import jalview.api.analysis.SimilarityParamsI;
-import jalview.bin.Cache;
+import jalview.bin.Console;
import jalview.datamodel.AlignmentView;
import jalview.datamodel.Point;
import jalview.math.MatrixI;
* @param sm
* @param options
*/
- public PCA(AlignmentView sequences, ScoreModelI sm, SimilarityParamsI options)
+ public PCA(AlignmentView sequences, ScoreModelI sm,
+ SimilarityParamsI options)
{
this.seqs = sequences;
this.scoreModel = sm;
sb.append("PCA calculation using ").append(scoreModel.getName())
.append(" sequence similarity matrix\n========\n\n");
PrintStream ps = wrapOutputBuffer(sb);
-
+
/*
* pairwise similarity scores
*/
sb.append(" --- OrigT * Orig ---- \n");
pairwiseScores.print(ps, "%8.2f");
-
+
/*
* tridiagonal matrix, with D and E vectors
*/
sb.append("--- E vector ---\n");
tridiagonal.printE(ps, "%15.4e");
ps.println();
-
+
/*
* eigenvalues matrix, with D vector
*/
sb.append(" --- Eigenvalues ---\n");
eigenMatrix.printD(ps, "%15.4e");
ps.println();
-
+
return sb.toString();
}
eigenMatrix.tqli();
} catch (Exception q)
{
- Cache.log.error("Error computing PCA: " + q.getMessage());
+ Console.error("Error computing PCA: " + q.getMessage());
q.printStackTrace();
}
}