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/* This file is part of the jgpml Project.
* http://github.com/renzodenardi/jgpml
*
* Copyright (c) 2011 Renzo De Nardi and Hugo Gravato-Marques
*
* Permission is hereby granted, free of charge, to any person
* obtaining a copy of this software and associated documentation
* files (the "Software"), to deal in the Software without
* restriction, including without limitation the rights to use,
* copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following
* conditions:
*
* The above copyright notice and this permission notice shall be
* included in all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
* WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
* OTHER DEALINGS IN THE SOFTWARE.
*/
package jgpml.covariancefunctions;
import Jama.Matrix;
import org.apache.commons.math3.util.FastMath;
/**
* Some useful operations defined over Matrices
*/
public class MatrixOperations {
/**
* Computes the exponential of the input Matrix
*
* @param A input Matrix
* @return exp(A) result
*/
public static Matrix exp(Matrix A) {
Matrix out = new Matrix(A.getRowDimension(), A.getColumnDimension());
for (int i = 0; i < A.getRowDimension(); i++)
for (int j = 0; j < A.getColumnDimension(); j++)
out.set(i, j, FastMath.exp(A.get(i, j)));
return out;
}
/**
* Sums across the rows of the Matrix and return the result as a single column MAtrix
*
* @param A input Matrix
* @return result
*/
public static Matrix sumRows(Matrix A) {
Matrix sum = new Matrix(A.getRowDimension(), 1);
for (int i = 0; i < A.getColumnDimension(); i++)
sum.plusEquals(A.getMatrix(0, A.getRowDimension() - 1, i, i));
return sum;
}
/**
* Adds a value to each elemnts of the Matrix
*
* @param A Matrix
* @param val value to be added
* @return result
*/
public static Matrix addValue(Matrix A, double val) {
for (int i = 0; i < A.getRowDimension(); i++)
for (int j = 0; j < A.getColumnDimension(); j++)
A.set(i, j, A.get(i, j) + val);
return A;
}
/**
* Computes the arcsin of the input Matrix (element by element)
*
* @param A input Matrix
* @return asin(A) result
*/
public static Matrix asin(Matrix A) {
Matrix out = new Matrix(A.getRowDimension(), A.getColumnDimension());
for (int i = 0; i < A.getRowDimension(); i++)
for (int j = 0; j < A.getColumnDimension(); j++)
out.set(i, j, FastMath.asin(A.get(i, j)));
return out;
}
/**
* Computes the square root of the input Matrix (element by element)
*
* @param A input Matrix
* @return sqrt(A) result
*/
public static Matrix sqrt(Matrix A) {
Matrix out = new Matrix(A.getRowDimension(), A.getColumnDimension());
for (int i = 0; i < A.getRowDimension(); i++)
for (int j = 0; j < A.getColumnDimension(); j++)
out.set(i, j, FastMath.sqrt(A.get(i, j)));
return out;
}
/**
* If the argument is a row or column Matrix it returns a new diagonal Matrix with the
* input as diagonal elements. If the argument is a Matrix it returns the diagonal elements as a single
* column Matrix Is a clone of the Matlab's function diag(A)
*
* @param A input Matrix
* @return diag(A) result
*/
public static Matrix diag(Matrix A) {
Matrix diag = null;
if (A.getColumnDimension() == 1 || A.getRowDimension() == 1) {
if (A.getColumnDimension() == 1) {
diag = new Matrix(A.getRowDimension(), A.getRowDimension());
for (int i = 0; i < diag.getColumnDimension(); i++)
diag.set(i, i, A.get(i, 0));
} else {
diag = new Matrix(A.getColumnDimension(), A.getColumnDimension());
for (int i = 0; i < diag.getRowDimension(); i++)
diag.set(i, i, A.get(0, i));
}
} else {
diag = new Matrix(A.getRowDimension(), 1);
for (int i = 0; i < diag.getRowDimension(); i++)
diag.set(i, 0, A.get(i, i));
}
return diag;
}
public static Matrix mean(Matrix A) {
if (A.getRowDimension() == 1) {
double m = 0;
for (int i = 0; i < A.getColumnDimension(); i++) m += A.get(0, i);
Matrix M = new Matrix(1, 1);
M.set(0, 0, m / A.getColumnDimension());
return M;
} else {
Matrix M = new Matrix(1, A.getColumnDimension());
for (int i = 0; i < A.getColumnDimension(); i++) {
double m = 0;
for (int j = 0; j < A.getRowDimension(); j++) {
m += A.get(j, i);
}
M.set(0, i, m / A.getRowDimension());
}
return M;
}
}
public static Matrix std(Matrix A) {
if (A.getRowDimension() == 1) {
double m = 0;
double var = 0;
for (int i = 0; i < A.getColumnDimension(); i++) {
m = (m * (i - 1) + A.get(0, i)) / i;
var = var * (i - 1) / i + ((A.get(0, i) - m) * (A.get(0, i) - m)) / (i - 1);
}
Matrix M = new Matrix(1, 1);
M.set(0, 0, FastMath.sqrt(var));
return M;
} else {
Matrix M = new Matrix(1, A.getColumnDimension());
for (int i = 0; i < A.getColumnDimension(); i++) {
double m = 0;
double var = 0;
for (int j = 0; j < A.getRowDimension(); j++) {
m = (m * (j - 1) + A.get(j, i)) / j;
var = var * (j - 1) / j + ((A.get(j, i) - m) * (A.get(j, i) - m)) / (j - 1);
}
M.set(0, i, FastMath.sqrt(var));
}
return M;
}
}
}
