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Open Source Chemistry Library
package com.actelion.research.calc;
import com.actelion.research.calc.regression.linear.pls.SimPLS;
import com.actelion.research.util.IO;
import com.actelion.research.util.datamodel.ModelXY;
import java.util.Date;
import java.util.Random;
/**
* MatrixData
* Copyright: Actelion Ltd., Inc. All Rights Reserved
* This software is the proprietary information of Actelion Pharmaceuticals, Ltd.
* Use is subject to license terms.
* @author Modest von Korff
* @version 1.0
* Sep 13, 2013 MvK Start implementation
*/
public class MatrixTests {
/**
* Creates a multivariate test dataset
* The regression factor factor is the col number, starting with 1.
* @param rows
* @param cols
* @return
*/
public static ModelXY getMultivariate(int rows, int cols){
ModelXY modelXY = new ModelXY();
Matrix X = new Matrix(rows, cols);
Matrix Y = new Matrix(rows, 1);
double max = 10;
Random rnd = new Random();
for (int i = 0; i < cols; i++) {
for (int j = 0; j < rows; j++) {
double v = rnd.nextDouble() * max;
X.set(j,i,v);
}
}
for (int i = 0; i < rows; i++) {
double y = 0;
for (int j = 0; j < cols; j++) {
double v = X.get(i, j);
y += v*(j+1);
}
Y.set(i, 0, y);
}
modelXY.X = X;
modelXY.Y = Y;
return modelXY;
}
public static Matrix test00() {
double [][] A = {{1.001},
{1.002},
{1.003},
{1.004}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix test01() {
double [][] A = {{1.004},
{1.003},
{1.002},
{1.001}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix test02() {
double [][] A = {{1,3,4},
{2,3,4},
{3,3,2},
{4,3,1}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix test03() {
double [][] A = {{1,0,0,0},
{1,0,0,0},
{0,1,0,0},
{0,1,0,0},
{0,0,1,0},
{0,0,1,0},
{0,0,0,1},
{0,0,0,1}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix test04() {
double [][] A = {{1,1,1,1},
{1,1,1,1},
{2,20,2,2},
{2,20,2,2},
{3,30,3,3},
{3,30,3,3},
{4,40,40,4},
{4,40,40,4}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix test05() {
double [][] A = {{1,1,1,1},
{1,2,1,1},
{1,3,1,1},
{1,4,1,1},
{0,5,1,1},
{0,6,1,1},
{0,7,1,1},
{0,8,1,1}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix test06() {
double [][] A = {{1,0},
{1,0},
{1,0},
{1,0},
{0,1},
{0,1},
{0,1},
{0,1}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix test07() {
double [][] A = {{1,1,0,0},
{1,1,0,0},
{1,1,0,0},
{1,1,0,0},
{0,0,1,1},
{0,0,1,1},
{0,0,1,1},
{0,0,1,1}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix test08() {
double [][] A = {{1,1,1,0},
{1,0,0,0},
{1,1,0,0},
{1,1,0,0},
{0,1,1,1},
{0,0,1,0},
{0,0,1,1},
{0,0,1,1}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix testMatrix02() {
double [][] A = {{16, 2, 3, 13},
{ 5, 11, 10, 8},
{ 9, 7, 6, 12},
{ 4, 14, 15, 1}};
Matrix ma = new Matrix(A);
return ma;
}
/**
* Validation data for PLS from Abdi, PLS, Encyclopedia of Social Sciences
* Reasearch Methods (2003).
* @return Matrix
*/
public static Matrix testMatrix_YWine() {
double [][] A = {{ 14, 7, 8},
{ 10, 7, 6},
{ 8, 5, 5},
{ 2, 4, 7},
{ 6, 2, 4}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix testMatrix_XWine() {
double [][] A = {{ 7, 7, 13, 7},
{ 4, 3, 14, 7},
{ 10, 5, 12, 5},
{ 16, 7, 11, 3},
{ 13, 3, 10, 3}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix testMatrixHenrion01() {
double [][] A = {{ 4, 0},
{ 0, 8},
{ 4, 4},
{ 2, 0},
{ 0, 8}};
Matrix ma = new Matrix(A);
return ma;
}
/**
* http://www.itl.nist.gov/div898/strd/lls/data/Longley.shtml
* Y Matrix
* @return Y matrix
*/
public static Matrix testLonglyY() {
double [][] A = {{ 60323 },
{ 61122 },
{ 60171 },
{ 61187 },
{ 63221 },
{ 63639 },
{ 64989 },
{ 63761 },
{ 66019 },
{ 67857 },
{ 68169 },
{ 66513 },
{ 68655 },
{ 69564 },
{ 69331 },
{ 70551 }};
Matrix ma = new Matrix(A);
return ma;
}
/**
* http://itl.nist.gov/div898/strd/lls/data/LINKS/DATA/Longley.dat
* @return
*/
public static Matrix testLonglyX() {
double[][] A = {
{83, 234289, 2356, 1590, 107608, 1947},
{88.5, 259426, 2325, 1456, 108632, 1948},
{88.2, 258054, 3682, 1616, 109773, 1949},
{89.5, 284599, 3351, 1650, 110929, 1950},
{96.2, 328975, 2099, 3099, 112075, 1951},
{98.1, 346999, 1932, 3594, 113270, 1952},
{99, 365385, 1870, 3547, 115094, 1953},
{100, 363112, 3578, 3350, 116219, 1954},
{101.2, 397469, 2904, 3048, 117388, 1955},
{104.6, 419180, 2822, 2857, 118734, 1956},
{108.4, 442769, 2936, 2798, 120445, 1957},
{110.8, 444546, 4681, 2637, 121950, 1958},
{112.6, 482704, 3813, 2552, 123366, 1959},
{114.2, 502601, 3931, 2514, 125368, 1960},
{115.7, 518173, 4806, 2572, 127852, 1961},
{116.9, 554894, 4007, 2827, 130081, 1962},
};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix testDescriptor01X() {
double[][] A = {
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{0, 0, 0, 1, 1, 1},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
{1, 1, 1, 0, 0, 0},
};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix testDescriptor01Y() {
double[][] A = {
{1,0},
{1,0},
{1,0},
{1,0},
{1,0},
{1,0},
{1,0},
{1,0},
{1,0},
{1,0},
{0,1},
{0,1},
{0,1},
{0,1},
{0,1},
{0,1},
{0,1},
{0,1},
{0,1},
{0,1},
{0,1},
};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix testSimple1Y() {
double [][] A = {{55},{56},{57},{58},{59},{60},{61},{62}};
Matrix ma = new Matrix(A);
return ma;
}
public static Matrix testSimple1X(int cols) {
double [][] a = {{55},
{56},
{57},
{58},
{59},
{60},
{61},
{62}};
Matrix Xrnd = Matrix.getRND(a.length, cols);
for (int i = 0; i < a.length; i++) {
Xrnd.set(i,0, a[i][0]);
}
return Xrnd;
}
public static Matrix testSimple2X(int cols) {
double [][] a = {{55,0},
{56,0},
{57,0},
{58,0},
{0,59},
{0,60},
{0,61},
{0,62}};
Matrix Xrnd = Matrix.getRND(a.length, cols);
for (int i = 0; i < a.length; i++) {
Xrnd.set(i,0, a[i][0]);
Xrnd.set(i,1, a[i][1]);
}
return Xrnd;
}
/**
* Checks for the correctness of the Eigenvector and Eigenvalues calculation.
* The test relies on the X V = V E equation (Henrion^2 (1995),p219).
* X is the symmetric original matrix. V is the diagonal matrix of the
* eigenvalues and E is the matrix of the corresponding eigenvectors.
* @return true if the check is ok.
*/
public static boolean checkForEigenvaluesAndEigenvectors() {
boolean bCheckOK = true;
Matrix A = testMatrix02();
Matrix AtA = A.multiply(true,false,A);
Matrix d = new Matrix(1,1);
Matrix e = new Matrix(1,1);
Matrix EV = new Matrix(AtA.getArray());
Matrix.getEigenvector(EV, EV.getColDim(), d, e);
Matrix D = d.diagonalize();
Matrix C = EV.multiply(false,false,D);
Matrix F = AtA.multiply(false,false,EV);
bCheckOK = C.equal(F, Matrix.TINY);
// System.out.println(C);
// System.out.println(F);
return bCheckOK;
}
static protected Matrix pls(Matrix X, Matrix Y,
String sPatternHeaderX,
int iNumPrincipalComponents,
boolean bLogarithm,
String sFileDataSummaryOut) {
Matrix R = null;
if(bLogarithm)
X = X.log();
// System.out.println(X.toString(4));
// Perform PLS
SimPLS pls = new SimPLS();
Matrix Xc = X.getCenteredMatrix();
Matrix Yc = Y.getCenteredMatrix();
String sSummary = "Xc(standardized):\r\n" + Xc + "\r\n\r\n";
sSummary += "Yc:\r\n" + Yc + "\r\n\r\n";
IO.write(sFileDataSummaryOut, sSummary, true);
pls.simPlsSave(Xc,Yc,iNumPrincipalComponents);
Matrix P = pls.getP();
R = pls.getR();
Matrix U = pls.getU();
Matrix V = pls.getV();
Matrix Q = pls.getQ();
Matrix T = pls.getT();
// Result matrices to summary file
sSummary = "Matrices from the PLS decomposition of the Training data.\r\n\r\n";
sSummary += "P:\r\n" + P + "\r\n\r\n";
sSummary += "R:\r\n" + R + "\r\n\r\n";
sSummary += "U:\r\n" + U + "\r\n\r\n";
sSummary += "V:\r\n" + V + "\r\n\r\n";
sSummary += "Q:\r\n" + Q + "\r\n\r\n";
sSummary += "T:\r\n" + T + "\r\n\r\n";
IO.write(sFileDataSummaryOut, sSummary, true);
return R;
}
@SuppressWarnings("unused")
public static void testMain01() {
int repeat = 10;
int rowsA = 1000;
int colsA = 1000;
int colsB = 100;
int n = 10;
Matrix X = MatrixFunctions.getRandomMatrix(rowsA,colsA);
Matrix Y = MatrixFunctions.getRandomMatrix(colsA,colsB);
Matrix Eleft = new Matrix(X);
Matrix Eright = new Matrix();
Matrix D = new Matrix();
Date dateStart = new Date();
Matrix.getEigenvector(Eleft, n, D, Eright);
Date dateEnd = new Date();
long delta = dateEnd.getTime() - dateStart.getTime();
System.out.println("Time: " + delta);
// System.out.println("D: " + D.toString());
// Date dateStart = new Date();
// Matrix C = null;
// for (int ii = 0; ii < repeat; ii++) {
// C = X.multiply(false, false, Y);
// }
// Date dateEnd = new Date();
// long delta = dateEnd.getTime() - dateStart.getTime();
// System.out.println("Time: " + delta);
// System.out.println("size: " + (C.getColDim() * C.getRowDim()));
//
// dateStart = new Date();
// for (int ii = 0; ii < repeat; ii++) {
// C = X.multiplyBig(false, false, Y);
// }
//
// dateEnd = new Date();
// delta = dateEnd.getTime() - dateStart.getTime();
// System.out.println("Time: " + delta);
// System.out.println("size: " + (C.getColDim() * C.getRowDim()));
}
public static void testMainHenrion() {
Matrix X = testMatrixHenrion01();
Matrix Xc = X.getCenteredMatrix();
System.out.println(Xc);
Matrix Xstand = X.getStandardDeviationCols();
System.out.println(Xstand);
Matrix Xs = X.getStandardized();
System.out.println(Xs);
// checkForEigenvaluesAndEigenvectors();
}
}
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