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A fast and easy to use dense matrix linear algebra library written in Java.
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/*
* Copyright (c) 2009-2014, Peter Abeles. All Rights Reserved.
*
* This file is part of Efficient Java Matrix Library (EJML).
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.ejml.ops;
import org.ejml.UtilEjml;
import org.ejml.alg.dense.decomposition.chol.CholeskyDecompositionInner_D64;
import org.ejml.alg.dense.mult.VectorVectorMult;
import org.ejml.data.Complex64F;
import org.ejml.data.D1Matrix64F;
import org.ejml.data.DenseMatrix64F;
import org.ejml.data.ReshapeMatrix64F;
import org.ejml.factory.DecompositionFactory;
import org.ejml.interfaces.decomposition.EigenDecomposition;
import org.ejml.interfaces.decomposition.LUDecomposition;
import org.ejml.interfaces.decomposition.SingularValueDecomposition;
/**
*
* Used to compute features that describe the structure of a matrix.
*
*
*
* Unless explicitly stated otherwise it is assumed that the elements of input matrices
* contain only real numbers. If an element is NaN or infinite then the behavior is undefined.
* See IEEE 754 for more information on this issue.
*
*
* @author Peter Abeles
*/
public class MatrixFeatures {
/**
* Checks to see if any element in the matrix is NaN.
*
* @param m A matrix. Not modified.
* @return True if any element in the matrix is NaN.
*/
public static boolean hasNaN( D1Matrix64F m )
{
int length = m.getNumElements();
for( int i = 0; i < length; i++ ) {
if( Double.isNaN(m.get(i)))
return true;
}
return false;
}
/**
* Checks to see if any element in the matrix is NaN of Infinite.
*
* @param m A matrix. Not modified.
* @return True if any element in the matrix is NaN of Infinite.
*/
public static boolean hasUncountable( D1Matrix64F m )
{
int length = m.getNumElements();
for( int i = 0; i < length; i++ ) {
double a = m.get(i);
if( Double.isNaN(a) || Double.isInfinite(a))
return true;
}
return false;
}
/**
* Checks to see all the elements in the matrix are zeros
*
* @param m A matrix. Not modified.
* @return True if all elements are zeros or false if not
*/
public static boolean isZeros( D1Matrix64F m , double tol )
{
int length = m.getNumElements();
for( int i = 0; i < length; i++ ) {
if( Math.abs(m.get(i)) > tol )
return false;
}
return true;
}
/**
* Checks to see if the matrix is a vector or not.
*
* @param mat A matrix. Not modified.
*
* @return True if it is a vector and false if it is not.
*/
public static boolean isVector( D1Matrix64F mat ) {
return (mat.numCols == 1 || mat.numRows == 1);
}
/**
*
* Checks to see if the matrix is positive definite.
*
*
* xT A x > 0
* for all x where x is a non-zero vector and A is a symmetric matrix.
*
*
* @param A square symmetric matrix. Not modified.
*
* @return True if it is positive definite and false if it is not.
*/
public static boolean isPositiveDefinite( DenseMatrix64F A ) {
if( !isSquare(A))
return false;
CholeskyDecompositionInner_D64 chol = new CholeskyDecompositionInner_D64(true);
if( chol.inputModified() )
A = A.copy();
return chol.decompose(A);
}
/**
*
* Checks to see if the matrix is positive semidefinite:
*
*
* xT A x >= 0
* for all x where x is a non-zero vector and A is a symmetric matrix.
*
*
* @param A square symmetric matrix. Not modified.
*
* @return True if it is positive semidefinite and false if it is not.
*/
public static boolean isPositiveSemidefinite( DenseMatrix64F A ) {
if( !isSquare(A))
return false;
EigenDecomposition eig = DecompositionFactory.eig(A.numCols,false);
if( eig.inputModified() )
A = A.copy();
eig.decompose(A);
for( int i = 0; i < A.numRows; i++ ) {
Complex64F v = eig.getEigenvalue(i);
if( v.getReal() < 0 )
return false;
}
return true;
}
/**
* Checks to see if it is a square matrix. A square matrix has
* the same number of rows and columns.
*
* @param mat A matrix. Not modified.
* @return True if it is a square matrix and false if it is not.
*/
public static boolean isSquare( D1Matrix64F mat ) {
return mat.numCols == mat.numRows;
}
/**
*
* Returns true if the matrix is symmetric within the tolerance. Only square matrices can be
* symmetric.
*
*
* A matrix is symmetric if:
* |aij - aji| ≤ tol
*
*
* @param m A matrix. Not modified.
* @param tol Tolerance for how similar two elements need to be.
* @return true if it is symmetric and false if it is not.
*/
public static boolean isSymmetric( DenseMatrix64F m , double tol ) {
if( m.numCols != m.numRows )
return false;
double max = CommonOps.elementMaxAbs(m);
for( int i = 0; i < m.numRows; i++ ) {
for( int j = 0; j < i; j++ ) {
double a = m.get(i,j)/max;
double b = m.get(j,i)/max;
double diff = Math.abs(a-b);
if( !(diff <= tol) ) {
return false;
}
}
}
return true;
}
/**
*
* Returns true if the matrix is perfectly symmetric. Only square matrices can be symmetric.
*
*
* A matrix is symmetric if:
* aij == aji
*
*
* @param m A matrix. Not modified.
* @return true if it is symmetric and false if it is not.
*/
public static boolean isSymmetric( DenseMatrix64F m ) {
return isSymmetric(m,0.0);
}
/**
*
* Checks to see if a matrix is skew symmetric with in tolerance:
*
* -A = AT
* or
* |aij + aji| ≤ tol
*
*
* @param A The matrix being tested.
* @param tol Tolerance for being skew symmetric.
* @return True if it is skew symmetric and false if it is not.
*/
public static boolean isSkewSymmetric( DenseMatrix64F A , double tol ){
if( A.numCols != A.numRows )
return false;
for( int i = 0; i < A.numRows; i++ ) {
for( int j = 0; j < i; j++ ) {
double a = A.get(i,j);
double b = A.get(j,i);
double diff = Math.abs(a+b);
if( !(diff <= tol) ) {
return false;
}
}
}
return true;
}
/**
* Checks to see if the two matrices are inverses of each other.
*
* @param a A matrix. Not modified.
* @param b A matrix. Not modified.
*/
public static boolean isInverse( DenseMatrix64F a , DenseMatrix64F b , double tol ) {
if( a.numRows != b.numRows || a.numCols != b.numCols ) {
return false;
}
int numRows = a.numRows;
int numCols = a.numCols;
for( int i = 0; i < numRows; i++ ) {
for( int j = 0; j < numCols; j++ ) {
double total = 0;
for( int k = 0; k < numCols; k++ ) {
total += a.get(i,k)*b.get(k,j);
}
if( i == j ) {
if( !(Math.abs(total-1) <= tol) )
return false;
} else if( !(Math.abs(total) <= tol) )
return false;
}
}
return true;
}
/**
*
* Checks to see if each element in the two matrices are within tolerance of
* each other: tol ≥ |aij - bij|.
*
*
*
* NOTE: If any of the elements are not countable then false is returned.
* NOTE: If a tolerance of zero is passed in this is equivalent to calling
* {@link #isEquals(org.ejml.data.D1Matrix64F, org.ejml.data.D1Matrix64F)}
*
*
* @param a A matrix. Not modified.
* @param b A matrix. Not modified.
* @param tol How close to being identical each element needs to be.
* @return true if equals and false otherwise.
*/
public static boolean isEquals( D1Matrix64F a , D1Matrix64F b , double tol )
{
if( a.numRows != b.numRows || a.numCols != b.numCols ) {
return false;
}
if( tol == 0.0 )
return isEquals(a,b);
final int length = a.getNumElements();
for( int i = 0; i < length; i++ ) {
if( !(tol >= Math.abs(a.get(i) - b.get(i))) ) {
return false;
}
}
return true;
}
/**
*
* Checks to see if each element in the upper or lower triangular portion of the two matrices are within tolerance of
* each other: tol ≥ |aij - bij|.
*
*
*
* NOTE: If any of the elements are not countable then false is returned.
* NOTE: If a tolerance of zero is passed in this is equivalent to calling
* {@link #isEquals(org.ejml.data.D1Matrix64F, org.ejml.data.D1Matrix64F)}
*
*
* @param a A matrix. Not modified.
* @param b A matrix. Not modified.
* @param upper true of upper triangular and false for lower.
* @param tol How close to being identical each element needs to be.
* @return true if equals and false otherwise.
*/
public static boolean isEqualsTriangle(ReshapeMatrix64F a, ReshapeMatrix64F b, boolean upper, double tol)
{
if( a.numRows != b.numRows || a.numCols != b.numCols ) {
return false;
}
if( upper ) {
for( int i = 0; i < a.numRows; i++ ) {
for( int j = i; j < a.numCols; j++ ) {
if( Math.abs(a.get(i,j)-b.get(i,j)) > tol )
return false;
}
}
} else {
for( int i = 0; i < a.numRows; i++ ) {
int end = Math.min(i,a.numCols-1);
for( int j = 0; j <= end; j++ ) {
if( Math.abs(a.get(i,j)-b.get(i,j)) > tol )
return false;
}
}
}
return true;
}
/**
*
* Checks to see if each element in the two matrices are equal:
* aij == bij
*
*
*
* NOTE: If any of the elements are NaN then false is returned. If two corresponding
* elements are both positive or negative infinity then they are equal.
*
*
* @param a A matrix. Not modified.
* @param b A matrix. Not modified.
* @return true if identical and false otherwise.
*/
public static boolean isEquals( D1Matrix64F a, D1Matrix64F b ) {
if( a.numRows != b.numRows || a.numCols != b.numCols ) {
return false;
}
final int length = a.getNumElements();
for( int i = 0; i < length; i++ ) {
if( !(a.get(i) == b.get(i)) ) {
return false;
}
}
return true;
}
/**
*
* Checks to see if each corresponding element in the two matrices are
* within tolerance of each other or have the some symbolic meaning. This
* can handle NaN and Infinite numbers.
*
*
*
* If both elements are countable then the following equality test is used:
* |aij - bij| ≤ tol.
* Otherwise both numbers must both be Double.NaN, Double.POSITIVE_INFINITY, or
* Double.NEGATIVE_INFINITY to be identical.
*
*
* @param a A matrix. Not modified.
* @param b A matrix. Not modified.
* @param tol Tolerance for equality.
* @return true if identical and false otherwise.
*/
public static boolean isIdentical( D1Matrix64F a, D1Matrix64F b , double tol ) {
if( a.numRows != b.numRows || a.numCols != b.numCols ) {
return false;
}
if( tol < 0 )
throw new IllegalArgumentException("Tolerance must be greater than or equal to zero.");
final int length = a.getNumElements();
for( int i = 0; i < length; i++ ) {
double valA = a.get(i);
double valB = b.get(i);
// if either is negative or positive infinity the result will be positive infinity
// if either is NaN the result will be NaN
double diff = Math.abs(valA-valB);
// diff = NaN == false
// diff = infinity == false
if( tol >= diff )
continue;
if( Double.isNaN(valA) ) {
return Double.isNaN(valB);
} else if( Double.isInfinite(valA) ) {
return valA == valB;
} else {
return false;
}
}
return true;
}
/**
*
* Checks to see if a matrix is orthogonal or isometric.
*
*
* @param Q The matrix being tested. Not modified.
* @param tol Tolerance.
* @return True if it passes the test.
*/
public static boolean isOrthogonal( DenseMatrix64F Q , double tol )
{
if( Q.numRows < Q.numCols ) {
throw new IllegalArgumentException("The number of rows must be more than or equal to the number of columns");
}
DenseMatrix64F u[] = CommonOps.columnsToVector(Q, null);
for( int i = 0; i < u.length; i++ ) {
DenseMatrix64F a = u[i];
for( int j = i+1; j < u.length; j++ ) {
double val = VectorVectorMult.innerProd(a,u[j]);
if( !(Math.abs(val) <= tol))
return false;
}
}
return true;
}
/**
* Checks to see if the rows of the provided matrix are linearly independent.
*
* @param A Matrix whose rows are being tested for linear independence.
* @return true if linearly independent and false otherwise.
*/
public static boolean isRowsLinearIndependent( DenseMatrix64F A )
{
// LU decomposition
LUDecomposition lu = DecompositionFactory.lu(A.numRows,A.numCols);
if( lu.inputModified() )
A = A.copy();
if( !lu.decompose(A))
throw new RuntimeException("Decompositon failed?");
// if they are linearly independent it should not be singular
return !lu.isSingular();
}
/**
* Checks to see if the provided matrix is within tolerance to an identity matrix.
*
* @param mat Matrix being examined. Not modified.
* @param tol Tolerance.
* @return True if it is within tolerance to an identify matrix.
*/
public static boolean isIdentity( DenseMatrix64F mat , double tol )
{
// see if the result is an identity matrix
int index = 0;
for( int i = 0; i < mat.numRows; i++ ) {
for( int j = 0; j < mat.numCols; j++ ) {
if( i == j ) {
if( !(Math.abs(mat.get(index++)-1) <= tol) )
return false;
} else {
if( !(Math.abs(mat.get(index++)) <= tol) )
return false;
}
}
}
return true;
}
/**
* Checks to see if every value in the matrix is the specified value.
*
* @param mat The matrix being tested. Not modified.
* @param val Checks to see if every element in the matrix has this value.
* @param tol True if all the elements are within this tolerance.
* @return true if the test passes.
*/
public static boolean isConstantVal( DenseMatrix64F mat , double val , double tol )
{
// see if the result is an identity matrix
int index = 0;
for( int i = 0; i < mat.numRows; i++ ) {
for( int j = 0; j < mat.numCols; j++ ) {
if( !(Math.abs(mat.get(index++)-val) <= tol) )
return false;
}
}
return true;
}
/**
* Checks to see if all the diagonal elements in the matrix are positive.
*
* @param a A matrix. Not modified.
* @return true if all the diagonal elements are positive, false otherwise.
*/
public static boolean isDiagonalPositive( DenseMatrix64F a ) {
for( int i = 0; i < a.numRows; i++ ) {
if( !(a.get(i,i) >= 0) )
return false;
}
return true;
}
// TODO write this
public static boolean isFullRank( DenseMatrix64F a ) {
throw new RuntimeException("Implement");
}
/**
*
* Checks to see if the two matrices are the negative of each other:
*
* aij = -bij
*
*
* @param a First matrix. Not modified.
* @param b Second matrix. Not modified.
* @param tol Numerical tolerance.
* @return True if they are the negative of each other within tolerance.
*/
public static boolean isNegative(D1Matrix64F a, D1Matrix64F b, double tol) {
if( a.numRows != b.numRows || a.numCols != b.numCols )
throw new IllegalArgumentException("Matrix dimensions must match");
int length = a.getNumElements();
for( int i = 0; i < length; i++ ) {
if( !(Math.abs(a.get(i)+b.get(i)) <= tol) )
return false;
}
return true;
}
/**
*
* Checks to see if a matrix is upper triangular or Hessenberg. A Hessenberg matrix of degree N
* has the following property:
*
* aij ≤ 0 for all i < j+N
*
* A triangular matrix is a Hessenberg matrix of degree 0.
*
* @param A Matrix being tested. Not modified.
* @param hessenberg The degree of being hessenberg.
* @param tol How close to zero the lower left elements need to be.
* @return If it is an upper triangular/hessenberg matrix or not.
*/
public static boolean isUpperTriangle(DenseMatrix64F A , int hessenberg , double tol ) {
if( A.numRows != A.numCols )
return false;
for( int i = hessenberg+1; i < A.numRows; i++ ) {
for( int j = 0; j < i-hessenberg; j++ ) {
if( !(Math.abs(A.get(i,j)) <= tol) ) {
return false;
}
}
}
return true;
}
/**
* Computes the rank of a matrix using a default tolerance.
*
* @param A Matrix whose rank is to be calculated. Not modified.
* @return The matrix's rank.
*/
public static int rank( DenseMatrix64F A ) {
return rank(A, UtilEjml.EPS*100);
}
/**
* Computes the rank of a matrix using the specified tolerance.
*
* @param A Matrix whose rank is to be calculated. Not modified.
* @param threshold The numerical threshold used to determine a singular value.
* @return The matrix's rank.
*/
public static int rank( DenseMatrix64F A , double threshold ) {
SingularValueDecomposition svd = DecompositionFactory.svd(A.numRows,A.numCols,false,false,true);
if( svd.inputModified() )
A = A.copy();
if( !svd.decompose(A) )
throw new RuntimeException("Decomposition failed");
return SingularOps.rank(svd, threshold);
}
/**
* Computes the nullity of a matrix using the default tolerance.
*
* @param A Matrix whose rank is to be calculated. Not modified.
* @return The matrix's nullity.
*/
public static int nullity( DenseMatrix64F A ) {
return nullity(A, UtilEjml.EPS*100);
}
/**
* Computes the nullity of a matrix using the specified tolerance.
*
* @param A Matrix whose rank is to be calculated. Not modified.
* @param threshold The numerical threshold used to determine a singular value.
* @return The matrix's nullity.
*/
public static int nullity( DenseMatrix64F A , double threshold ) {
SingularValueDecomposition svd = DecompositionFactory.svd(A.numRows,A.numCols,false,false,true);
if( svd.inputModified() )
A = A.copy();
if( !svd.decompose(A) )
throw new RuntimeException("Decomposition failed");
return SingularOps.nullity(svd,threshold);
}
}