Matlab to EJML
To help Matlab users quickly learn how to use EJML a list of equivalent functions is provided below
Many functions in Matlab have equivalent or similar functions in EJML. To help port Matlab code into EJML two list are provided for SimpleMatrix and the procedural API. If a function is not provided by SimpleMatrix it is probably provided by the more advanced procedural API.
Looking for a Matlab interface to use in Java? Check out the new EJML module Equations.
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) |