X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fanalysis%2FPCA.java;h=7d6b8224523986eb33ae86c2c55a2aa139cd48d2;hb=0ce9577d119e1cc646f4b0d2e961f698d994fcc5;hp=6498b6d1dbecd52e08a6efef77c54f775acc0709;hpb=6d94bfcf539e558b09a08102b228e65e670f77d8;p=jalview.git

diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java
index 6498b6d..7d6b822 100755
--- a/src/jalview/analysis/PCA.java
+++ b/src/jalview/analysis/PCA.java
@@ -1,53 +1,58 @@
 /*
- * Jalview - A Sequence Alignment Editor and Viewer (Version 2.7)
- * Copyright (C) 2011 J Procter, AM Waterhouse, G Barton, M Clamp, S Searle
+ * Jalview - A Sequence Alignment Editor and Viewer ($$Version-Rel$$)
+ * Copyright (C) $$Year-Rel$$ The Jalview Authors
  * 
  * This file is part of Jalview.
  * 
  * Jalview is free software: you can redistribute it and/or
  * modify it under the terms of the GNU General Public License 
- * as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
- * 
+ * as published by the Free Software Foundation, either version 3
+ * of the License, or (at your option) any later version.
+ *  
  * Jalview is distributed in the hope that it will be useful, but 
  * WITHOUT ANY WARRANTY; without even the implied warranty 
  * of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
  * PURPOSE.  See the GNU General Public License for more details.
  * 
- * You should have received a copy of the GNU General Public License along with Jalview.  If not, see <http://www.gnu.org/licenses/>.
+ * You should have received a copy of the GNU General Public License
+ * along with Jalview.  If not, see <http://www.gnu.org/licenses/>.
+ * The Jalview Authors are detailed in the 'AUTHORS' file.
  */
 package jalview.analysis;
 
-import java.io.*;
-
-import jalview.datamodel.*;
+import jalview.datamodel.BinarySequence;
 import jalview.datamodel.BinarySequence.InvalidSequenceTypeException;
-import jalview.math.*;
+import jalview.math.Matrix;
+import jalview.math.MatrixI;
+import jalview.math.SparseMatrix;
 import jalview.schemes.ResidueProperties;
 import jalview.schemes.ScoreMatrix;
 
+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 sequence similarity matrices
+   * Creates a new PCA object. By default, uses blosum62 matrix to generate
+   * sequence similarity matrices
+   * 
    * @param s
    *          Set of amino acid sequences to perform PCA on
    */
@@ -55,34 +60,52 @@ public class PCA implements Runnable
   {
     this(s, false);
   }
-  
+
   /**
-   * Creates a new PCA object.
-   * By default, uses blosum62 matrix to generate sequence similarity matrices
+   * Creates a new PCA object. By default, uses blosum62 matrix to generate
+   * sequence similarity matrices
+   * 
    * @param s
    *          Set of sequences to perform PCA on
-   *          @param nucleotides if true, uses standard DNA/RNA matrix for sequence similarity calculation.
+   * @param nucleotides
+   *          if true, uses standard DNA/RNA matrix for sequence similarity
+   *          calculation.
    */
   public PCA(String[] s, boolean nucleotides)
   {
+    this(s, nucleotides, null);
+  }
+
+  public PCA(String[] s, boolean nucleotides, String s_m)
+  {
 
     BinarySequence[] bs = new BinarySequence[s.length];
     int ii = 0;
 
     while ((ii < s.length) && (s[ii] != null))
     {
-      bs[ii] = new BinarySequence(s[ii],nucleotides);
+      bs[ii] = new BinarySequence(s[ii], nucleotides);
       bs[ii].encode();
       ii++;
     }
 
     BinarySequence[] bs2 = new BinarySequence[s.length];
+    ScoreMatrix smtrx = null;
+    String sm = s_m;
+    if (sm != null)
+    {
+      smtrx = ResidueProperties.getScoreMatrix(sm);
+    }
+    if (smtrx == null)
+    {
+      // either we were given a non-existent score matrix or a scoremodel that
+      // isn't based on a pairwise symbol score matrix
+      smtrx = ResidueProperties.getScoreMatrix(sm = (nucleotides ? "DNA"
+              : "BLOSUM62"));
+    }
+    details.append("PCA calculation using " + sm
+            + " sequence similarity matrix\n========\n\n");
     ii = 0;
-
-    String sm=nucleotides ? "DNA" : "BLOSUM62";
-    ScoreMatrix smtrx=ResidueProperties.getScoreMatrix(sm);
-    details.append("PCA calculation using "+sm+" sequence similarity matrix\n========\n\n");
-    
     while ((ii < s.length) && (s[ii] != null))
     {
       bs2[ii] = new BinarySequence(s[ii], nucleotides);
@@ -99,19 +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();
@@ -119,21 +139,22 @@ 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);
 
   }
 
   /**
    * Returns the matrix used in PCA calculation
    * 
-   * @return java.math.Matrix object
+   * @return
    */
 
-  public Matrix getM()
+  public MatrixI getM()
   {
     return m;
   }
@@ -148,7 +169,7 @@ public class PCA implements Runnable
    */
   public double getEigenvalue(int i)
   {
-    return eigenvector.d[i];
+    return eigenvector.getD()[i];
   }
 
   /**
@@ -167,9 +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;
@@ -190,9 +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);
     }
@@ -214,12 +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()
@@ -230,48 +251,64 @@ public class PCA implements Runnable
   /**
    * DOCUMENT ME!
    */
+  @Override
   public void run()
   {
-    Matrix mt = m.transpose();
-
-    details.append(" --- OrigT * Orig ---- \n");
-    // eigenvector = mt.preMultiply(m); // standard seqspace comparison matrix
-    eigenvector = mt.preMultiply(m2); // jalview variation on seqsmace method
-
     PrintStream ps = new PrintStream(System.out)
     {
+      @Override
       public void print(String x)
       {
         details.append(x);
       }
 
+      @Override
       public void println()
       {
         details.append("\n");
       }
     };
 
-    eigenvector.print(ps);
+    // long now = System.currentTimeMillis();
+    try
+    {
+      details.append("PCA Calculation Mode is "
+              + (jvCalcMode ? "Jalview variant" : "Original SeqSpace")
+              + "\n");
+      MatrixI mt = m.transpose();
 
-    symm = eigenvector.copy();
+      details.append(" --- OrigT * Orig ---- \n");
 
-    eigenvector.tred();
+      eigenvector = mt.preMultiply(jvCalcMode ? m2 : m);
 
-    details.append(" ---Tridiag transform matrix ---\n");
-    details.append(" --- D vector ---\n");
-    eigenvector.printD(ps);
-    ps.println();
-    details.append("--- E vector ---\n");
-    eigenvector.printE(ps);
-    ps.println();
+      eigenvector.print(ps, "%8.2f");
 
-    // Now produce the diagonalization matrix
-    eigenvector.tqli();
+      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);
+    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
@@ -279,5 +316,12 @@ public class PCA implements Runnable
      * 
      * ps.print(","+component(seq, ev)); } ps.println(); }
      */
+    // System.out.println(("PCA.run() took "
+    // + (System.currentTimeMillis() - now) + "ms"));
+  }
+
+  public void setJvCalcMode(boolean calcMode)
+  {
+    this.jvCalcMode = calcMode;
   }
 }