Difference between revisions of "Matlab to EJML"

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To help Matlab users quickly learn how to use EJML a list of equivalent functions is provided below
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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 codeIn Matlab for loops are very expensive and often extracting sub-matrices is the preferred methodJava 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.
 
 
Many functions in Matlab have equivalent or similar functions in EJMLTo help port Matlab code into EJML two list are provided for SimpleMatrix and the procedural APIIf 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 =
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= SimpleMatrix =
 
= 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.
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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.
  
 
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Revision as of 20:48, 21 March 2015

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)