import jalview.api.analysis.ScoreModelI;
import jalview.api.analysis.SimilarityParamsI;
+import jalview.bin.Cache;
import jalview.datamodel.AlignmentView;
+import jalview.datamodel.Point;
import jalview.math.MatrixI;
import java.io.PrintStream;
/*
* outputs
*/
- private MatrixI symm;
+ private MatrixI pairwiseScores;
- private MatrixI eigenvector;
+ private MatrixI tridiagonal;
- private String details;
+ private MatrixI eigenMatrix;
/**
* Constructor given the sequences to compute for, the similarity model to
*/
public double getEigenvalue(int i)
{
- return eigenvector.getD()[i];
+ return eigenMatrix.getD()[i];
}
/**
*
* @return DOCUMENT ME!
*/
- public float[][] getComponents(int l, int n, int mm, float factor)
+ public Point[] getComponents(int l, int n, int mm, float factor)
{
- float[][] out = new float[getHeight()][3];
+ Point[] out = new Point[getHeight()];
for (int i = 0; i < getHeight(); i++)
{
- out[i][0] = (float) component(i, l) * factor;
- out[i][1] = (float) component(i, n) * factor;
- out[i][2] = (float) component(i, mm) * factor;
+ float x = (float) component(i, l) * factor;
+ float y = (float) component(i, n) * factor;
+ float z = (float) component(i, mm) * factor;
+ out[i] = new Point(x, y, z);
}
return out;
{
double out = 0.0;
- for (int i = 0; i < symm.width(); i++)
+ for (int i = 0; i < pairwiseScores.width(); i++)
{
- out += (symm.getValue(row, i) * eigenvector.getValue(i, n));
+ out += (pairwiseScores.getValue(row, i) * eigenMatrix.getValue(i, n));
}
- return out / eigenvector.getD()[n];
+ return out / eigenMatrix.getD()[n];
}
/**
*/
public String getDetails()
{
- return details;
+ StringBuilder sb = new StringBuilder(1024);
+ 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(" ---Tridiag transform matrix ---\n");
+ sb.append(" --- D vector ---\n");
+ tridiagonal.printD(ps, "%15.4e");
+ ps.println();
+ sb.append("--- E vector ---\n");
+ tridiagonal.printE(ps, "%15.4e");
+ ps.println();
+
+ /*
+ * eigenvalues matrix, with D vector
+ */
+ sb.append(" --- New diagonalization matrix ---\n");
+ eigenMatrix.print(ps, "%8.2f");
+ sb.append(" --- Eigenvalues ---\n");
+ eigenMatrix.printD(ps, "%15.4e");
+ ps.println();
+
+ return sb.toString();
}
/**
@Override
public void run()
{
- /*
- * print details to a string buffer as they are computed
- */
- StringBuilder sb = new StringBuilder(1024);
- sb.append("PCA calculation using ").append(scoreModel.getName())
- .append(" sequence similarity matrix\n========\n\n");
- PrintStream ps = wrapOutputBuffer(sb);
-
try
{
- eigenvector = scoreModel.findSimilarities(seqs, similarityParams);
-
- sb.append(" --- OrigT * Orig ---- \n");
- eigenvector.print(ps, "%8.2f");
-
- symm = eigenvector.copy();
-
- eigenvector.tred();
-
- sb.append(" ---Tridiag transform matrix ---\n");
- sb.append(" --- D vector ---\n");
- eigenvector.printD(ps, "%15.4e");
- ps.println();
- sb.append("--- E vector ---\n");
- eigenvector.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();
} catch (Exception q)
{
+ Cache.log.error("Error computing PCA: " + q.getMessage());
q.printStackTrace();
- sb.append("\n*** Unexpected exception when performing PCA ***\n"
- + q.getLocalizedMessage());
- sb.append(
- "*** Matrices below may not be fully diagonalised. ***\n");
}
-
- sb.append(" --- New diagonalization matrix ---\n");
- eigenvector.print(ps, "%8.2f");
- sb.append(" --- Eigenvalues ---\n");
- eigenvector.printD(ps, "%15.4e");
- ps.println();
-
- details = sb.toString();
}
/**
- * Returns a PrintStream that wraps (sends its output to) the given
+ * Returns a PrintStream that wraps (appends its output to) the given
* StringBuilder
*
* @param sb
public int getHeight()
{
// TODO can any of seqs[] be null?
- return seqs.getSequences().length;
+ return pairwiseScores.height();// seqs.getSequences().length;
+ }
+
+ /**
+ * Answers the sequence pairwise similarity scores which were the first step
+ * of the PCA calculation
+ *
+ * @return
+ */
+ public MatrixI getPairwiseScores()
+ {
+ return pairwiseScores;
+ }
+
+ public void setPairwiseScores(MatrixI m)
+ {
+ pairwiseScores = m;
+ }
+
+ public MatrixI getEigenmatrix()
+ {
+ return eigenMatrix;
+ }
+
+ public void setEigenmatrix(MatrixI m)
+ {
+ eigenMatrix = m;
+ }
+
+ public MatrixI getTridiagonal()
+ {
+ return tridiagonal;
+ }
+
+ public void setTridiagonal(MatrixI tridiagonal)
+ {
+ this.tridiagonal = tridiagonal;
}
}