X-Git-Url: http://source.jalview.org/gitweb/?a=blobdiff_plain;f=src%2Fjalview%2Fanalysis%2FPCA.java;h=4a3cfec1e48fcb11838fede41fd91c254c42e7d5;hb=cb8e52fbbc5f725e3f7f48c672cdddb0690bd978;hp=500573223c98c967eede401eac7382c907c9b963;hpb=174230b4233d9ce80f94527768d2cd2f76da11ab;p=jalview.git diff --git a/src/jalview/analysis/PCA.java b/src/jalview/analysis/PCA.java index 5005732..4a3cfec 100755 --- a/src/jalview/analysis/PCA.java +++ b/src/jalview/analysis/PCA.java @@ -1,241 +1,301 @@ -/* -* Jalview - A Sequence Alignment Editor and Viewer -* Copyright (C) 2006 AM Waterhouse, J Procter, G Barton, M Clamp, S Searle -* -* This program 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 2 -* of the License, or (at your option) any later version. -* -* This program 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 this program; if not, write to the Free Software -* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA -*/ -package jalview.analysis; - -import jalview.datamodel.*; - -import jalview.math.*; - -import java.io.*; - -/** - * Performs Principal Component Analysis on given sequences - * - * @author $author$ - * @version $Revision$ - */ -public class PCA implements Runnable -{ - Matrix m; - Matrix symm; - Matrix m2; - double[] eigenvalue; - Matrix eigenvector; - StringBuffer details = new StringBuffer(); - - - /** - * Creates a new PCA object. - * - * @param s Set of sequences to perform PCA on - */ - public PCA(String[] s) - { - - BinarySequence[] bs = new BinarySequence[s.length]; - int ii = 0; - - while ((ii < s.length) && (s[ii] != null)) - { - bs[ii] = new BinarySequence(s[ii]); - bs[ii].encode(); - ii++; - } - - BinarySequence[] bs2 = new BinarySequence[s.length]; - ii = 0; - - while ((ii < s.length) && (s[ii] != null)) - { - bs2[ii] = new BinarySequence(s[ii]); - bs2[ii].blosumEncode(); - 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; - - while (i < count) - { - seqmat[i] = bs[i].getDBinary(); - seqmat2[i] = bs2[i].getDBinary(); - 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); - - } - - /** - * Returns the matrix used in PCA calculation - * - * @return java.math.Matrix object - */ - - public Matrix getM() - { - return m; - } - - /** - * Returns Eigenvalue - * - * @param i Index of diagonal within matrix - * - * @return Returns value of diagonal from matrix - */ - public double getEigenvalue(int i) - { - return eigenvector.d[i]; - } - - /** - * DOCUMENT ME! - * - * @param l DOCUMENT ME! - * @param n DOCUMENT ME! - * @param mm DOCUMENT ME! - * @param factor DOCUMENT ME! - * - * @return DOCUMENT ME! - */ - public float[][] getComponents(int l, int n, int mm, float factor) - { - float[][] out = new float[m.rows][3]; - - for (int i = 0; i < m.rows; 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; - } - - return out; - } - - /** - * DOCUMENT ME! - * - * @param n DOCUMENT ME! - * - * @return DOCUMENT ME! - */ - public double[] component(int n) - { - // n = index of eigenvector - double[] out = new double[m.rows]; - - for (int i = 0; i < m.rows; i++) - { - out[i] = component(i, n); - } - - return out; - } - - /** - * DOCUMENT ME! - * - * @param row DOCUMENT ME! - * @param n DOCUMENT ME! - * - * @return DOCUMENT ME! - */ - double component(int row, int n) - { - double out = 0.0; - - for (int i = 0; i < symm.cols; i++) - { - out += (symm.value[row][i] * eigenvector.value[i][n]); - } - - return out / eigenvector.d[n]; - } - - public String getDetails() - { - return details.toString(); - } - - - /** - * DOCUMENT ME! - */ - public void run() - { - Matrix mt = m.transpose(); - - details.append(" --- OrigT * Orig ---- \n"); - eigenvector = mt.preMultiply(m2); - - PrintStream ps = new PrintStream(System.out) - { - public void print(String x) { - details.append(x); - } - public void println() - { - details.append("\n"); - } - }; - - - eigenvector.print( ps ); - - symm = eigenvector.copy(); - - 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(); - - - details.append(" --- New diagonalization matrix ---\n"); - details.append(" --- Eigenvalues ---\n"); - eigenvector.printD(ps); - ps.println(); - // taps.println(); - // taps.println("Transformed sequences = "); - // Matrix trans = m.preMultiply(eigenvector); - // trans.print(System.out); - } -} +/* + * 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. + * + * 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 . + * The Jalview Authors are detailed in the 'AUTHORS' file. + */ +package jalview.analysis; + +import jalview.api.analysis.ScoreModelI; +import jalview.api.analysis.SimilarityParamsI; +import jalview.bin.Console; +import jalview.datamodel.AlignmentView; +import jalview.datamodel.Point; +import jalview.math.MatrixI; + +import java.io.PrintStream; + +/** + * Performs Principal Component Analysis on given sequences + */ +public class PCA implements Runnable +{ + /* + * inputs + */ + final private AlignmentView seqs; + + final private ScoreModelI scoreModel; + + final private SimilarityParamsI similarityParams; + + /* + * outputs + */ + private MatrixI pairwiseScores; + + private MatrixI tridiagonal; + + private MatrixI eigenMatrix; + + /** + * 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 = sequences; + this.scoreModel = sm; + this.similarityParams = options; + } + + /** + * Returns Eigenvalue + * + * @param i + * Index of diagonal within matrix + * + * @return Returns value of diagonal from matrix + */ + public double getEigenvalue(int i) + { + return eigenMatrix.getD()[i]; + } + + /** + * DOCUMENT ME! + * + * @param l + * DOCUMENT ME! + * @param n + * DOCUMENT ME! + * @param mm + * DOCUMENT ME! + * @param factor + * DOCUMENT ME! + * + * @return DOCUMENT ME! + */ + public Point[] getComponents(int l, int n, int mm, float factor) + { + Point[] out = new Point[getHeight()]; + + for (int i = 0; i < getHeight(); i++) + { + 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; + } + + /** + * DOCUMENT ME! + * + * @param n + * DOCUMENT ME! + * + * @return DOCUMENT ME! + */ + public double[] component(int n) + { + // n = index of eigenvector + double[] out = new double[getHeight()]; + + for (int i = 0; i < out.length; i++) + { + out[i] = component(i, n); + } + + return out; + } + + /** + * DOCUMENT ME! + * + * @param row + * DOCUMENT ME! + * @param n + * DOCUMENT ME! + * + * @return DOCUMENT ME! + */ + double component(int row, int n) + { + double out = 0.0; + + for (int i = 0; i < pairwiseScores.width(); i++) + { + out += (pairwiseScores.getValue(row, i) * eigenMatrix.getValue(i, 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() + { + 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(); + } + + /** + * 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) + { + Console.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) + { + sb.append(x); + } + + @Override + public void println() + { + sb.append("\n"); + } + }; + return ps; + } + + /** + * Answers the N dimensions of the NxN PCA matrix. This is the number of + * sequences involved in the pairwise score calculation. + * + * @return + */ + public int getHeight() + { + // TODO can any of seqs[] be null? + 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; + } +}