JAL-2379 use SparseMatrix for encoded sequences, tidy unneeded code

diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java
index 06a139b..9fc6027 100755 (executable)
--- a/src/jalview/analysis/PCA.java
+++ b/src/jalview/analysis/PCA.java
@@ -23,6 +23,8 @@ package jalview.analysis;
 import jalview.datamodel.BinarySequence;
 import jalview.datamodel.BinarySequence.InvalidSequenceTypeException;
 import jalview.math.Matrix;
+import jalview.math.MatrixI;
+import jalview.math.SparseMatrix;
 import jalview.schemes.ResidueProperties;
 import jalview.schemes.ScoreMatrix;
 
@@ -30,23 +32,22 @@ import java.io.PrintStream;
 
 /**
  * Performs Principal Component Analysis on given sequences
- * 
- * @author $author$
- * @version $Revision$
  */
 public class PCA implements Runnable
 {
-  Matrix m;
+  boolean jvCalcMode = true;
+
+  MatrixI m;
 
-  Matrix symm;
+  MatrixI symm;
 
-  Matrix m2;
+  MatrixI m2;
 
   double[] eigenvalue;
 
-  Matrix eigenvector;
+  MatrixI eigenvector;
 
-  StringBuffer details = new StringBuffer();
+  StringBuilder details = new StringBuilder(1024);
 
   /**
    * Creates a new PCA object. By default, uses blosum62 matrix to generate
@@ -89,7 +90,6 @@ public class PCA implements Runnable
     }
 
     BinarySequence[] bs2 = new BinarySequence[s.length];
-    ii = 0;
     ScoreMatrix smtrx = null;
     String sm = s_m;
     if (sm != null)
@@ -105,6 +105,7 @@ public class PCA implements Runnable
     }
     details.append("PCA calculation using " + sm
             + " sequence similarity matrix\n========\n\n");
+    ii = 0;
     while ((ii < s.length) && (s[ii] != null))
     {
       bs2[ii] = new BinarySequence(s[ii], nucleotides);
@@ -121,19 +122,16 @@ public class PCA implements Runnable
       ii++;
     }
 
-    // System.out.println("Created binary encoding");
-    // printMemory(rt);
     int count = 0;
-
     while ((count < bs.length) && (bs[count] != null))
     {
       count++;
     }
 
-    double[][] seqmat = new double[count][bs[0].getDBinary().length];
-    double[][] seqmat2 = new double[count][bs2[0].getDBinary().length];
-    int i = 0;
+    double[][] seqmat = new double[count][];
+    double[][] seqmat2 = new double[count][];
 
+    int i = 0;
     while (i < count)
     {
       seqmat[i] = bs[i].getDBinary();
@@ -141,11 +139,12 @@ public class PCA implements Runnable
       i++;
     }
 
-    // System.out.println("Created array");
-    // printMemory(rt);
-    // System.out.println(" --- Original matrix ---- ");
-    m = new Matrix(seqmat, count, bs[0].getDBinary().length);
-    m2 = new Matrix(seqmat2, count, bs2[0].getDBinary().length);
+    /*
+     * using a SparseMatrix to hold the encoded sequences matrix
+     * greatly speeds up matrix multiplication as these are mostly zero
+     */
+    m = new SparseMatrix(seqmat);
+    m2 = new Matrix(seqmat2);
 
   }
 
@@ -155,7 +154,7 @@ public class PCA implements Runnable
    * @return java.math.Matrix object
    */
 
-  public Matrix getM()
+  public MatrixI getM()
   {
     return m;
   }
@@ -170,7 +169,7 @@ public class PCA implements Runnable
    */
   public double getEigenvalue(int i)
   {
-    return eigenvector.d[i];
+    return eigenvector.getD()[i];
   }
 
   /**
@@ -189,9 +188,9 @@ public class PCA implements Runnable
    */
   public float[][] getComponents(int l, int n, int mm, float factor)
   {
-    float[][] out = new float[m.rows][3];
+    float[][] out = new float[m.height()][3];
 
-    for (int i = 0; i < m.rows; i++)
+    for (int i = 0; i < m.height(); i++)
     {
       out[i][0] = (float) component(i, l) * factor;
       out[i][1] = (float) component(i, n) * factor;
@@ -212,9 +211,9 @@ public class PCA implements Runnable
   public double[] component(int n)
   {
     // n = index of eigenvector
-    double[] out = new double[m.rows];
+    double[] out = new double[m.height()];
 
-    for (int i = 0; i < m.rows; i++)
+    for (int i = 0; i < m.height(); i++)
     {
       out[i] = component(i, n);
     }
@@ -236,12 +235,12 @@ public class PCA implements Runnable
   {
     double out = 0.0;
 
-    for (int i = 0; i < symm.cols; i++)
+    for (int i = 0; i < symm.width(); i++)
     {
-      out += (symm.value[row][i] * eigenvector.value[i][n]);
+      out += (symm.getValue(row, i) * eigenvector.getValue(i, n));
     }
 
-    return out / eigenvector.d[n];
+    return out / eigenvector.getD()[n];
   }
 
   public String getDetails()
@@ -252,40 +251,37 @@ public class PCA implements Runnable
   /**
    * DOCUMENT ME!
    */
+  @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");
       }
     };
 
+    // long now = System.currentTimeMillis();
     try
     {
       details.append("PCA Calculation Mode is "
               + (jvCalcMode ? "Jalview variant" : "Original SeqSpace")
               + "\n");
-      Matrix mt = m.transpose();
+      MatrixI mt = m.transpose();
 
       details.append(" --- OrigT * Orig ---- \n");
-      if (!jvCalcMode)
-      {
-        eigenvector = mt.preMultiply(m); // standard seqspace comparison matrix
-      }
-      else
-      {
-        eigenvector = mt.preMultiply(m2); // jalview variation on seqsmace
-                                          // method
-      }
 
-      eigenvector.print(ps);
+      eigenvector = mt.preMultiply(jvCalcMode ? m2 : m);
+
+      eigenvector.print(ps, "%8.2f");
 
       symm = eigenvector.copy();
 
@@ -293,10 +289,10 @@ public class PCA implements Runnable
 
       details.append(" ---Tridiag transform matrix ---\n");
       details.append(" --- D vector ---\n");
-      eigenvector.printD(ps);
+      eigenvector.printD(ps, "%15.4e");
       ps.println();
       details.append("--- E vector ---\n");
-      eigenvector.printE(ps);
+      eigenvector.printE(ps, "%15.4e");
       ps.println();
 
       // Now produce the diagonalization matrix
@@ -310,9 +306,9 @@ public class PCA implements Runnable
     }
 
     details.append(" --- New diagonalization matrix ---\n");
-    eigenvector.print(ps);
+    eigenvector.print(ps, "%8.2f");
     details.append(" --- Eigenvalues ---\n");
-    eigenvector.printD(ps);
+    eigenvector.printD(ps, "%15.4e");
     ps.println();
     /*
      * for (int seq=0;seq<symm.rows;seq++) { ps.print("\"Seq"+seq+"\""); for
@@ -320,10 +316,10 @@ public class PCA implements Runnable
      * 
      * ps.print(","+component(seq, ev)); } ps.println(); }
      */
+    // System.out.println(("PCA.run() took "
+    // + (System.currentTimeMillis() - now) + "ms"));
   }
 
-  boolean jvCalcMode = true;
-
   public void setJvCalcMode(boolean calcMode)
   {
     this.jvCalcMode = calcMode;

diff --git a/src/jalview/datamodel/BinarySequence.java b/src/jalview/datamodel/BinarySequence.java
index ed57dce..62ee974 100755 (executable)
--- a/src/jalview/datamodel/BinarySequence.java
+++ b/src/jalview/datamodel/BinarySequence.java
@@ -69,13 +69,9 @@ public class BinarySequence extends Sequence
   {
     int nores = (isNa) ? ResidueProperties.maxNucleotideIndex
             : ResidueProperties.maxProteinIndex;
-    // Set all matrix to 0
+
     dbinary = new double[getSequence().length * nores];
 
-    for (int i = 0; i < dbinary.length; i++)
-    {
-      dbinary[i] = 0.0;
-    }
     return nores;
   }
 
@@ -134,14 +130,8 @@ public class BinarySequence extends Sequence
 
   private void matrixEncode(final int[] aaIndex, final int[][] matrix)
   {
-    // Set all matrix to 0
-    // dbinary = new double[getSequence().length * 21];
-
     int nores = initMatrixGetNoRes();
 
-    // for (int i = 0; i < dbinary.length; i++) {
-    // dbinary[i] = 0.0;
-    // }
     for (int i = 0, iSize = getSequence().length; i < iSize; i++)
     {
       int aanum = nores - 1;

diff --git a/src/jalview/viewmodel/PCAModel.java b/src/jalview/viewmodel/PCAModel.java
index b0af302..cd8eb15 100644 (file)
--- a/src/jalview/viewmodel/PCAModel.java
+++ b/src/jalview/viewmodel/PCAModel.java
@@ -69,7 +69,6 @@ public class PCAModel
 
   public void run()
   {
-
     pca = new PCA(seqstrings.getSequenceStrings(' '), nucleotide,
             score_matrix);
     pca.setJvCalcMode(jvCalcMode);
@@ -83,32 +82,32 @@ public class PCAModel
       ii++;
     }
 
-    double[][] comps = new double[ii][ii];
+    // comps is not used - commenting out
+    // double[][] comps = new double[ii][];
+    //
+    // for (int i = 0; i < ii; i++)
+    // {
+    // if (pca.getEigenvalue(i) > 1e-4)
+    // {
+    // comps[i] = pca.component(i);
+    // }
+    // }
 
-    for (int i = 0; i < ii; i++)
-    {
-      if (pca.getEigenvalue(i) > 1e-4)
-      {
-        comps[i] = pca.component(i);
-      }
-    }
-
-    top = pca.getM().rows - 1;
+    top = pca.getM().height() - 1;
 
     points = new Vector<SequencePoint>();
     float[][] scores = pca.getComponents(top - 1, top - 2, top - 3, 100);
 
-    for (int i = 0; i < pca.getM().rows; i++)
+    for (int i = 0; i < pca.getM().height(); i++)
     {
       SequencePoint sp = new SequencePoint(seqs[i], scores[i]);
       points.addElement(sp);
     }
-
   }
 
   public void updateRc(RotatableCanvasI rc)
   {
-    rc.setPoints(points, pca.getM().rows);
+    rc.setPoints(points, pca.getM().height());
   }
 
   public boolean isNucleotide()
@@ -146,9 +145,9 @@ public class PCAModel
     // note: actual indices for components are dim1-1, etc (patch for JAL-1123)
     float[][] scores = pca.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 100);
 
-    for (int i = 0; i < pca.getM().rows; i++)
+    for (int i = 0; i < pca.getM().height(); i++)
     {
-      ((SequencePoint) points.elementAt(i)).coord = scores[i];
+      points.elementAt(i).coord = scores[i];
     }
   }