X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fanalysis%2FPCA.java;h=06a139bb6df27db1c145b93309e3dff54063298d;hb=4d7f98a6dd54d9863ba449ec79dcd95d25ed863d;hp=979968f092b5cd57d3c61feedf9191ae53ba9e00;hpb=27b77d2219147d3741d4af7377e13918a8ae972a;p=jalview.git
diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java
index 979968f..06a139b 100755
--- a/src/jalview/analysis/PCA.java
+++ b/src/jalview/analysis/PCA.java
@@ -1,31 +1,33 @@
/*
- * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8.0b1)
- * Copyright (C) 2014 The Jalview Authors
+ * 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 .
+ * You should have received a copy of the GNU General Public License
+ * along with Jalview. If not, see .
* 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.schemes.ResidueProperties;
import jalview.schemes.ScoreMatrix;
+import java.io.PrintStream;
+
/**
* Performs Principal Component Analysis on given sequences
*
@@ -72,6 +74,7 @@ public class PCA implements Runnable
{
this(s, nucleotides, null);
}
+
public PCA(String[] s, boolean nucleotides, String s_m)
{
@@ -88,15 +91,17 @@ public class PCA implements Runnable
BinarySequence[] bs2 = new BinarySequence[s.length];
ii = 0;
ScoreMatrix smtrx = null;
- String sm=s_m;
- if (sm!=null)
+ String sm = s_m;
+ if (sm != null)
{
smtrx = ResidueProperties.getScoreMatrix(sm);
}
- if (smtrx==null)
+ 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"));
+ // 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");
@@ -262,41 +267,45 @@ public class PCA implements Runnable
}
};
- try {
- details.append("PCA Calculation Mode is "
- + (jvCalcMode ? "Jalview variant" : "Original SeqSpace") + "\n");
- Matrix mt = m.transpose();
-
- details.append(" --- OrigT * Orig ---- \n");
- if (!jvCalcMode)
+ try
{
- eigenvector = mt.preMultiply(m); // standard seqspace comparison matrix
- }
- else
- {
- eigenvector = mt.preMultiply(m2); // jalview variation on seqsmace method
- }
+ details.append("PCA Calculation Mode is "
+ + (jvCalcMode ? "Jalview variant" : "Original SeqSpace")
+ + "\n");
+ Matrix mt = m.transpose();
- eigenvector.print(ps);
+ 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
+ }
- symm = eigenvector.copy();
+ eigenvector.print(ps);
- eigenvector.tred();
+ symm = eigenvector.copy();
- 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.tred();
+
+ 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();
- // Now produce the diagonalization matrix
- eigenvector.tqli();
+ // 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("\n*** Unexpected exception when performing PCA ***\n"
+ + q.getLocalizedMessage());
details.append("*** Matrices below may not be fully diagonalised. ***\n");
}