import jalview.api.analysis.ScoreModelI;
import jalview.api.analysis.SimilarityParamsI;
import jalview.datamodel.AlignmentView;
+import jalview.datamodel.Point;
import jalview.math.MatrixI;
import java.io.PrintStream;
*/
public class PCA implements Runnable
{
- MatrixI symm;
+ /*
+ * inputs
+ */
+ final private AlignmentView seqs;
+
+ final private ScoreModelI scoreModel;
- double[] eigenvalue;
+ final private SimilarityParamsI similarityParams;
- MatrixI eigenvector;
+ /*
+ * outputs
+ */
+ private MatrixI pairwiseScores;
- StringBuilder details = new StringBuilder(1024);
+ private MatrixI afterTred;
- final private AlignmentView seqs;
+ private MatrixI eigenvector;
- private ScoreModelI scoreModel;
-
- private SimilarityParamsI similarityParams;
+ private String details;
- public PCA(AlignmentView s, ScoreModelI sm, SimilarityParamsI options)
+ /**
+ * Constructor given the sequences to compute for, the similarity model to
+ * use, and a set of parameters for sequence comparison
+ *
+ * @param sequences
+ * @param sm
+ * @param options
+ */
+ public PCA(AlignmentView sequences, ScoreModelI sm, SimilarityParamsI options)
{
- this.seqs = s;
- this.similarityParams = options;
+ this.seqs = sequences;
this.scoreModel = sm;
-
- details.append("PCA calculation using " + sm.getName()
- + " sequence similarity matrix\n========\n\n");
+ this.similarityParams = options;
}
/**
*
* @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) * eigenvector.getValue(i, n));
}
return out / eigenvector.getD()[n];
}
+ /**
+ * Answers a formatted text report of the PCA calculation results (matrices
+ * and eigenvalues) suitable for display
+ *
+ * @return
+ */
public String getDetails()
{
- return details.toString();
+ 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);
+
+ sb.append(" --- OrigT * Orig ---- \n");
+ pairwiseScores.print(ps, "%8.2f");
+
+ 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();
+
+ sb.append(" --- New diagonalization matrix ---\n");
+ eigenvector.print(ps, "%8.2f");
+ sb.append(" --- Eigenvalues ---\n");
+ eigenvector.printD(ps, "%15.4e");
+ ps.println();
+
+ return sb.toString();
+ */
}
/**
- * DOCUMENT ME!
+ * Performs the PCA calculation
*/
@Override
public void run()
{
- PrintStream ps = new PrintStream(System.out)
- {
- @Override
- public void print(String x)
- {
- details.append(x);
- }
-
- @Override
- public void println()
- {
- details.append("\n");
- }
- };
+ /*
+ * 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);
- // long now = System.currentTimeMillis();
try
{
eigenvector = scoreModel.findSimilarities(seqs, similarityParams);
- details.append(" --- OrigT * Orig ---- \n");
+ sb.append(" --- OrigT * Orig ---- \n");
eigenvector.print(ps, "%8.2f");
- symm = eigenvector.copy();
+ pairwiseScores = eigenvector.copy();
eigenvector.tred();
- details.append(" ---Tridiag transform matrix ---\n");
- details.append(" --- D vector ---\n");
- eigenvector.printD(ps, "%15.4e");
+ afterTred = eigenvector.copy();
+
+ sb.append(" ---Tridiag transform matrix ---\n");
+ sb.append(" --- D vector ---\n");
+ afterTred.printD(ps, "%15.4e");
ps.println();
- details.append("--- E vector ---\n");
- eigenvector.printE(ps, "%15.4e");
+ sb.append("--- E vector ---\n");
+ afterTred.printE(ps, "%15.4e");
ps.println();
// Now produce the diagonalization matrix
} catch (Exception q)
{
q.printStackTrace();
- details.append("\n*** Unexpected exception when performing PCA ***\n"
+ sb.append("\n*** Unexpected exception when performing PCA ***\n"
+ q.getLocalizedMessage());
- details.append("*** Matrices below may not be fully diagonalised. ***\n");
+ sb.append(
+ "*** Matrices below may not be fully diagonalised. ***\n");
}
- details.append(" --- New diagonalization matrix ---\n");
+ sb.append(" --- New diagonalization matrix ---\n");
eigenvector.print(ps, "%8.2f");
- details.append(" --- Eigenvalues ---\n");
+ sb.append(" --- Eigenvalues ---\n");
eigenvector.printD(ps, "%15.4e");
ps.println();
- /*
- * for (int seq=0;seq<symm.rows;seq++) { ps.print("\"Seq"+seq+"\""); for
- * (int ev=0;ev<symm.rows; ev++) {
- *
- * ps.print(","+component(seq, ev)); } ps.println(); }
- */
- // System.out.println(("PCA.run() took "
- // + (System.currentTimeMillis() - now) + "ms"));
+
+ details = sb.toString();
+ }
+
+ /**
+ * Returns a PrintStream that wraps (appends its output to) the given
+ * StringBuilder
+ *
+ * @param sb
+ * @return
+ */
+ protected PrintStream wrapOutputBuffer(StringBuilder sb)
+ {
+ PrintStream ps = new PrintStream(System.out)
+ {
+ @Override
+ public void print(String x)
+ {
+ sb.append(x);
+ }
+
+ @Override
+ public void println()
+ {
+ sb.append("\n");
+ }
+ };
+ return ps;
}
/**
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 eigenvector;
+ }
+
+ public void setEigenmatrix(MatrixI m)
+ {
+ eigenvector = m;
+ }
+
+ public void setDetails(String d)
+ {
+ details = d;
}
}
git://source.jalview.org / jalview.git / blobdiff