X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fviewmodel%2FPCAModel.java;h=31fce1581c7b491787b381c1f18b9b1d59798d54;hb=e3f1e488b159029d15bbb86284667f4b482c8f69;hp=64f2497cb4b4b1e48c363d8bc054c158daaa4f9b;hpb=3e9eba89979511361b51f1c78882c9877874f2ba;p=jalview.git
diff --git a/src/jalview/viewmodel/PCAModel.java b/src/jalview/viewmodel/PCAModel.java
index 64f2497..31fce15 100644
--- a/src/jalview/viewmodel/PCAModel.java
+++ b/src/jalview/viewmodel/PCAModel.java
@@ -1,3 +1,20 @@
+/*
+ * Jalview - A Sequence Alignment Editor and Viewer (Version 2.8)
+ * Copyright (C) 2012 J Procter, AM Waterhouse, LM Lui, J Engelhardt, G Barton, M Clamp, S Searle
+ *
+ * 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.
+ *
+ * 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 .
+ */
package jalview.viewmodel;
import java.util.Vector;
@@ -14,27 +31,27 @@ public class PCAModel
public PCAModel(AlignmentView seqstrings2, SequenceI[] seqs2,
boolean nucleotide2)
{
- seqstrings=seqstrings2;
- seqs=seqs2;
- nucleotide=nucleotide2;
+ seqstrings = seqstrings2;
+ seqs = seqs2;
+ nucleotide = nucleotide2;
}
private volatile PCA pca;
-
+
int top;
-
+
AlignmentView seqstrings;
SequenceI[] seqs;
/**
- * use the identity matrix for calculating similarity between sequences.
+ * use the identity matrix for calculating similarity between sequences.
*/
- private boolean nucleotide=false;
+ private boolean nucleotide = false;
private Vector points;
- private boolean jvCalcMode=true;
+ private boolean jvCalcMode = true;
public boolean isJvCalcMode()
{
@@ -43,7 +60,7 @@ public class PCAModel
public void run()
{
-
+
pca = new PCA(seqstrings.getSequenceStrings(' '), nucleotide);
pca.setJvCalcMode(jvCalcMode);
pca.run();
@@ -76,7 +93,7 @@ public class PCAModel
SequencePoint sp = new SequencePoint(seqs[i], scores[i]);
points.addElement(sp);
}
-
+
}
public void updateRc(RotatableCanvasI rc)
@@ -88,9 +105,10 @@ public class PCAModel
{
return nucleotide;
}
+
public void setNucleotide(boolean nucleotide)
{
- this.nucleotide=nucleotide;
+ this.nucleotide = nucleotide;
}
/**
@@ -106,15 +124,17 @@ public class PCAModel
/**
* update the 2d coordinates for the list of points to the given dimensions
* Principal dimension is getTop(). Next greatest eigenvector is getTop()-1.
- * Note - pca.getComponents starts counting the spectrum from rank-2 to zero, rather than rank-1, so getComponents(dimN ...) == updateRcView(dimN+1 ..)
- * @param dim1
+ * Note - pca.getComponents starts counting the spectrum from rank-2 to zero,
+ * rather than rank-1, so getComponents(dimN ...) == updateRcView(dimN+1 ..)
+ *
+ * @param dim1
* @param dim2
* @param dim3
*/
public void updateRcView(int dim1, int dim2, int dim3)
{
// note: actual indices for components are dim1-1, etc (patch for JAL-1123)
- float[][] scores = pca.getComponents(dim1-1, dim2-1, dim3-1, 100);
+ float[][] scores = pca.getComponents(dim1 - 1, dim2 - 1, dim3 - 1, 100);
for (int i = 0; i < pca.getM().rows; i++)
{
@@ -131,7 +151,9 @@ public class PCAModel
{
return seqstrings;
}
- public String getPointsasCsv(boolean transformed, int xdim, int ydim, int zdim)
+
+ public String getPointsasCsv(boolean transformed, int xdim, int ydim,
+ int zdim)
{
StringBuffer csv = new StringBuffer();
csv.append("\"Sequence\"");
@@ -198,7 +220,7 @@ public class PCAModel
public void setJvCalcMode(boolean state)
{
- jvCalcMode=state;
+ jvCalcMode = state;
}
}