- eigenvector = scoreModel.findSimilarities(seqs, similarityParams);
-
- sb.append(" --- OrigT * Orig ---- \n");
- eigenvector.print(ps, "%8.2f");
-
- pairwiseScores = eigenvector.copy();
-
- eigenvector.tred();
-
- afterTred = eigenvector.copy();
-
- sb.append(" ---Tridiag transform matrix ---\n");
- sb.append(" --- D vector ---\n");
- afterTred.printD(ps, "%15.4e");
- ps.println();
- sb.append("--- E vector ---\n");
- afterTred.printE(ps, "%15.4e");
- ps.println();
-
- // Now produce the diagonalization matrix
- eigenvector.tqli();
+ /*
+ * sequence pairwise similarity scores
+ */
+ pairwiseScores = scoreModel.findSimilarities(seqs, similarityParams);
+
+ /*
+ * tridiagonal matrix
+ */
+ tridiagonal = pairwiseScores.copy();
+ tridiagonal.tred();
+
+ /*
+ * the diagonalization matrix
+ */
+ eigenMatrix = tridiagonal.copy();
+ eigenMatrix.tqli();