X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fanalysis%2FPCA.java;h=60dd901ea27a889da51c232ddf754159a9cc8226;hb=a1984b1c8c273ed33c7ce9283039f4027dcae2de;hp=3ec7995f9f67aa84c2553fcd154df0b4c934a223;hpb=aba253e57b22ce7d1f4fe376935e42aeb4f6d591;p=jalview.git

diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java
index 3ec7995..60dd901 100755
--- a/src/jalview/analysis/PCA.java
+++ b/src/jalview/analysis/PCA.java
@@ -22,7 +22,9 @@ package jalview.analysis;
 
 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;
@@ -32,28 +34,37 @@ import java.io.PrintStream;
  */
 public class PCA implements Runnable
 {
-  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 pairwiseScores;
 
-  final private AlignmentView seqs;
+  private MatrixI tridiagonal;
 
-  private ScoreModelI scoreModel;
-  
-  private SimilarityParamsI similarityParams;
+  private MatrixI eigenMatrix;
 
-  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;
   }
 
   /**
@@ -66,7 +77,7 @@ public class PCA implements Runnable
    */
   public double getEigenvalue(int i)
   {
-    return eigenvector.getD()[i];
+    return eigenMatrix.getD()[i];
   }
 
   /**
@@ -83,15 +94,16 @@ public class PCA implements Runnable
    * 
    * @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;
@@ -132,83 +144,111 @@ public class PCA implements Runnable
   {
     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];
   }
 
+  /**
+   * Answers a formatted text report of the PCA calculation results (matrices
+   * and eigenvalues) suitable for display
+   * 
+   * @return
+   */
   public String getDetails()
   {
-    return details.toString();
+    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();
   }
 
   /**
-   * DOCUMENT ME!
+   * Performs the PCA calculation
    */
   @Override
   public void run()
   {
+    try
+    {
+      /*
+       * 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.error("Error computing PCA:  " + q.getMessage());
+      q.printStackTrace();
+    }
+  }
+
+  /**
+   * 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)
       {
-        details.append(x);
+        sb.append(x);
       }
 
       @Override
       public void println()
       {
-        details.append("\n");
+        sb.append("\n");
       }
     };
-
-    // long now = System.currentTimeMillis();
-    try
-    {
-      eigenvector = scoreModel.findSimilarities(seqs, similarityParams);
-
-      details.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");
-      eigenvector.printD(ps, "%15.4e");
-      ps.println();
-      details.append("--- E vector ---\n");
-      eigenvector.printE(ps, "%15.4e");
-      ps.println();
-
-      // Now produce the diagonalization matrix
-      eigenvector.tqli();
-    } catch (Exception q)
-    {
-      q.printStackTrace();
-      details.append("\n*** Unexpected exception when performing PCA ***\n"
-              + q.getLocalizedMessage());
-      details.append("*** Matrices below may not be fully diagonalised. ***\n");
-    }
-
-    details.append(" --- New diagonalization matrix ---\n");
-    eigenvector.print(ps, "%8.2f");
-    details.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"));
+    return ps;
   }
 
   /**
@@ -220,6 +260,42 @@ public class PCA implements Runnable
   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;
   }
 }