Matlab to EJML
To help Matlab users quickly learn how to use EJML a list of equivalent functions is provided in the sections below. Keep in mind that directly porting Matlab code will often result in inefficient code. In Matlab for loops are very expensive and often extracting sub-matrices is the preferred method. Java like C++ can handle for loops much better and extracting and inserting a matrix can be much less efficient than direct manipulation of the matrix itself.
Equations
Equations is very similar to Matlab but there are a few differences. For a description of the syntax and list of available functions checkout the Equations tutorial.
SimpleMatrix
A subset of EJML's functionality is provided in SimpleMatrix. If SimpleMatrix does not provide the functionality you desire then look at the list of #Procedural functions below.
| Matlab | SimpleMatrix |
|---|---|
| eye(3) | SimpleMatrix.identity(3) |
| diag({{{[1 2 3]}}}) | SimpleMatrix.diag(1,2,3) |
| C(1,2) = 5 | A.set(0,1,5) |
| C(:) = A | C.set(A) |
| C(:) = 5 | C.set(5) |
| C(2,:) = [1,2,3] | C.setRow(1,0,1,2,3) |
| C(:,2) = [1,2,3] | C.setColumn(1,0,1,2,3) |
| C = A(2:4,3:8) | C = A.extractMatrix(1,4,2,8) |
| A(:,2:end) = B | A.insertIntoThis(0,1,B); |
| C = diag(A) | C = A.extractDiag() |
| C = [A,B] | C = A.combine(0,A.numCols(),B) |
| C = A' | C = A.transpose() |
| C = -A | C = A.negative() |
| C = A{{{*}}}B | C = A.mult(B) |
| C = A + B | C = A.plus(B) |
| C = A - B | C = A.minus(B) |
| C = 2{{{*}}}A | C = A.scale(2) |
| C = A / 2 | C = A.divide(2) |
| C = inv(A) | C = A.invert() |
| C = pinv(A) | C = A.pinv() |
| C = A \ B | C = A.solve(B) |
| C = trace(A) | C = A.trace() |
| det(A) | A.det() |
| C=kron(A,B) | C=A.kron(B) |
| norm(A,"fro") | A.normf() |
| max(abs(A(:))) | A.elementMaxAbs() |
| sum(A(:)) | A.elementSum() |
| rank(A) | A.svd(true).rank() |
| [U,S,V] = svd(A) | A.svd(false) |
| [U,S,V] = svd(A,0) | A.svd(true) |
| [V,L] = eig(A) | A.eig() |
Procedural
Functions and classes in the procedural interface use DenseMatrix64F as input. Since SimpleMatrix is a wrapper around DenseMatrix64F its internal matrix can be extracted and passed into any of these functions.
| Matlab | Procedural |
|---|---|
| eye(3) | CommonOps.identity(3) |
| C(1,2) = 5 | A.set(0,1,5) |
| C(:) = A | C.setTo(A) |
| C(2,:) = [1,2,3] | CommonOps.insert(new DenseMatrix64F(1,3,true,1,2,3),C,1,0) |
| C(:,2) = [1,2,3] | CommonOps.insert(new DenseMatrix64F(3,1,true,1,2,3),C,0,1) |
| C = A(2:4,3:8) | CommonOps.extract(A,1,4,2,8) |
| diag({{{[1 2 3]}}}) | CommonOps.diag(1,2,3) |
| C = A' | CommonOps.transpose(A,C) |
| A = A' | CommonOps.transpose(A) |
| A = -A | CommonOps.changeSign(A) |
| C = A {{{*}}} B | CommonOps.mult(A,B,C) |
| C = A .{{{*}}} B | CommonOps.elementMult(A,B,C) |
| A = A .{{{*}}} B | CommonOps.elementMult(A,B) |
| C = A ./ B | CommonOps.elementDiv(A,B,C) |
| A = A ./ B | CommonOps.elementDiv(A,B) |
| C = A + B | CommonOps.add(A,B,C) |
| C = A - B | CommonOps.sub(A,B,C) |
| C = 2 {{{*}}} A | CommonOps.scale(2,A,C) |
| A = 2 {{{*}}} A | CommonOps.scale(2,A) |
| C = A / 2 | CommonOps.divide(2,A,C) |
| A = A / 2 | CommonOps.divide(2,A) |
| C = inv(A) | CommonOps.invert(A,C) |
| A = inv(A) | CommonOps.invert(A) |
| C = pinv(A) | CommonOps.pinv(A) |
| C = trace(A) | C = CommonOps.trace(A) |
| C = det(A) | C = CommonOps.det(A) |
| C=kron(A,B) | CommonOps.kron(A,B,C) |
| B=rref(A) | B = CommonOps.rref(A,-1,null) |
| norm(A,"fro") | NormOps.normf(A) |
| norm(A,1) | NormOps.normP1(A) |
| norm(A,2) | NormOps.normP2(A) |
| norm(A,Inf) | NormOps.normPInf(A) |
| max(abs(A(:))) | CommonOps.elementMaxAbs(A) |
| sum(A(:)) | CommonOps.elementSum(A) |
| rank(A,tol) | svd.decompose(A); SingularOps.rank(svd,tol) |
| [U,S,V] = svd(A) | DecompositionFactory.svd(A.numRows,A.numCols,true,true,false) |
| SingularOps.descendingOrder(U,false,S,V,false) | |
| [U,S,V] = svd(A,0) | DecompositionFactory.svd(A.numRows,A.numCols,true,true,true) |
| SingularOps.descendingOrder(U,false,S,V,false) | |
| S = svd(A) | DecompositionFactory.svd(A.numRows,A.numCols,false,false,true) |
| [V,D] = eig(A) | eig = DecompositionFactory.eig(A.numCols); eig.decompose(A) |
| V = EigenOps.createMatrixV(eig); D = EigenOps.createMatrixD(eig) | |
| [Q,R] = qr(A) | decomp = DecompositionFactory.qr(A.numRows,A.numCols) |
| Q = decomp.getQ(null,false); R = decomp.getR(null,false) | |
| [Q,R] = qr(A,0) | decomp = DecompositionFactory.qr(A.numRows,A.numCols) |
| Q = decomp.getQ(null,true); R = decomp.getR(null,true) | |
| [Q,R,P] = qr(A) | decomp = DecompositionFactory.qrp(A.numRows,A.numCols) |
| Q = decomp.getQ(null,false); R = decomp.getR(null,false) | |
| P = decomp.getPivotMatrix(null) | |
| [Q,R,P] = qr(A,0) | decomp = DecompositionFactory.qrp(A.numRows,A.numCols) |
| Q = decomp.getQ(null,true); R = decomp.getR(null,true) | |
| P = decomp.getPivotMatrix(null) | |
| R = chol(A) | DecompositionFactory.chol(A.numCols,false) |
| [L,U,P] = lu(A) | DecompositionFactory.lu(A.numCols) |