*/
package jalview.analysis;
-import jalview.analysis.scoremodels.PIDModel;
-import jalview.api.analysis.DistanceScoreModelI;
import jalview.api.analysis.ScoreModelI;
import jalview.api.analysis.SimilarityParamsI;
-import jalview.api.analysis.SimilarityScoreModelI;
import jalview.datamodel.AlignmentView;
import jalview.math.MatrixI;
*/
public class PCA implements Runnable
{
- boolean jvCalcMode = true;
-
- MatrixI symm;
+ /*
+ * inputs
+ */
+ final private AlignmentView seqs;
- double[] eigenvalue;
+ final private ScoreModelI scoreModel;
- MatrixI eigenvector;
+ final private SimilarityParamsI similarityParams;
- StringBuilder details = new StringBuilder(1024);
+ /*
+ * outputs
+ */
+ private MatrixI symm;
- 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 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;
}
/**
- * 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
{
- details.append("PCA Calculation Mode is "
- + (jvCalcMode ? "Jalview variant" : "Original SeqSpace")
- + "\n");
+ eigenvector = scoreModel.findSimilarities(seqs, similarityParams);
- eigenvector = computeSimilarity(seqs);
-
- details.append(" --- OrigT * Orig ---- \n");
+ sb.append(" --- OrigT * Orig ---- \n");
eigenvector.print(ps, "%8.2f");
symm = eigenvector.copy();
eigenvector.tred();
- details.append(" ---Tridiag transform matrix ---\n");
- details.append(" --- D vector ---\n");
+ sb.append(" ---Tridiag transform matrix ---\n");
+ sb.append(" --- D vector ---\n");
eigenvector.printD(ps, "%15.4e");
ps.println();
- details.append("--- E vector ---\n");
+ sb.append("--- E vector ---\n");
eigenvector.printE(ps, "%15.4e");
ps.println();
} 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();
}
/**
- * Computes a pairwise similarity matrix for the given sequence regions using
- * the configured score model. If the score model is a similarity model, then
- * it computes the result directly. If it is a distance model, then use it to
- * compute pairwise distances, and convert these to similarity scores.
+ * Returns a PrintStream that wraps (sends its output to) the given
+ * StringBuilder
*
- * @param av
+ * @param sb
* @return
*/
- MatrixI computeSimilarity(AlignmentView av)
+ protected PrintStream wrapOutputBuffer(StringBuilder sb)
{
- MatrixI result = null;
- if (scoreModel instanceof SimilarityScoreModelI)
+ PrintStream ps = new PrintStream(System.out)
{
- result = ((SimilarityScoreModelI) scoreModel).findSimilarities(av,
- similarityParams);
- if (scoreModel instanceof PIDModel)
+ @Override
+ public void print(String x)
{
- /*
- * scale % identities to width of alignment for backwards
- * compatibility with Jalview 2.10.1 SeqSpace PCA calculation
- */
- result.multiply(av.getWidth() / 100d);
+ sb.append(x);
}
- }
- else if (scoreModel instanceof DistanceScoreModelI)
- {
- /*
- * find distances and convert to similarity scores
- * reverseRange(false) preserves but reverses the min-max range
- */
- result = ((DistanceScoreModelI) scoreModel).findDistances(av,
- similarityParams);
- result.reverseRange(false);
- }
- else
- {
- System.err
- .println("Unexpected type of score model, cannot calculate similarity");
- }
- return result;
- }
-
- public void setJvCalcMode(boolean calcMode)
- {
- this.jvCalcMode = calcMode;
+ @Override
+ public void println()
+ {
+ sb.append("\n");
+ }
+ };
+ return ps;
}
/**
git://source.jalview.org / jalview.git / blobdiff