Repository: incubator-systemml Updated Branches: refs/heads/gh-pages 51da13ee3 -> 16950600d
[SYSTEMML-1241] Fix diag description in DML Language Reference Fix incorrect description of diag() in DML Language Reference. Make diag error message more descriptive. Closes #387. Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/16950600 Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/16950600 Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/16950600 Branch: refs/heads/gh-pages Commit: 16950600dcf067ca729ab3378a0de7db1d29a472 Parents: 51da13e Author: Deron Eriksson <de...@us.ibm.com> Authored: Fri Feb 10 10:57:41 2017 -0800 Committer: Deron Eriksson <de...@us.ibm.com> Committed: Fri Feb 10 10:57:41 2017 -0800 ---------------------------------------------------------------------- dml-language-reference.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/16950600/dml-language-reference.md ---------------------------------------------------------------------- diff --git a/dml-language-reference.md b/dml-language-reference.md index f3fba3b..05625fd 100644 --- a/dml-language-reference.md +++ b/dml-language-reference.md @@ -835,7 +835,7 @@ sign() | Returns a matrix representing the signs of the input matrix elements, w Function | Description | Parameters | Example -------- | ----------- | ---------- | ------- cholesky() | Computes the Cholesky decomposition of symmetric input matrix A | Input: (A <matrix>) <br/> Output: <matrix> | <span style="white-space: nowrap;">A = matrix("4 12 -16 12 37 -43</span> -16 -43 98", rows=3, cols=3) <br/> B = cholesky(A)<br/> Matrix B: [[2, 0, 0], [6, 1, 0], [-8, 5, 3]] -diag() | Create diagonal matrix from (n x 1) or (1 x n) matrix, or take diagonal from square matrix | Input: (n x 1) or (1 x n) matrix, or (n x n) matrix <br/> Output: (n x n) matrix, or (n x 1) matrix | diag(X) +diag() | Create diagonal matrix from (n x 1) matrix, or take diagonal from square matrix | Input: (n x 1) matrix, or (n x n) matrix <br/> Output: (n x n) matrix, or (n x 1) matrix | D = diag(matrix(1.0, rows=3, cols=1))<br/> E = diag(matrix(1.0, rows=3, cols=3)) eigen() | Computes Eigen decomposition of input matrix A. The Eigen decomposition consists of two matrices V and w such that A = V %\*% diag(w) %\*% t(V). The columns of V are the eigenvectors of the original matrix A. And, the eigen values are given by w. <br/> It is important to note that this function can operate only on small-to-medium sized input matrix that can fit in the main memory. For larger matrices, an out-of-memory exception is raised. | Input : (A <matrix>) <br/> Output : [w <(m x 1) matrix>, V <matrix>] <br/> A is a square symmetric matrix with dimensions (m x m). This function returns two matrices w and V, where w is (m x 1) and V is of size (m x m). | [w, V] = eigen(A) lu() | Computes Pivoted LU decomposition of input matrix A. The LU decomposition consists of three matrices P, L, and U such that P %\*% A = L %\*% U, where P is a permutation matrix that is used to rearrange the rows in A before the decomposition can be computed. L is a lower-triangular matrix whereas U is an upper-triangular matrix. <br/> It is important to note that this function can operate only on small-to-medium sized input matrix that can fit in the main memory. For larger matrices, an out-of-memory exception is raised. | Input : (A <matrix>) <br/> Output : [<matrix>, <matrix>, <matrix>] <br/> A is a square matrix with dimensions m x m. This function returns three matrices P, L, and U, all of which are of size m x m. | [P, L, U] = lu(A) qr() | Computes QR decomposition of input matrix A using Householder reflectors. The QR decomposition of A consists of two matrices Q and R such that A = Q%\*%R where Q is an orthogonal matrix (i.e., Q%\*%t(Q) = t(Q)%\*%Q = I, identity matrix) and R is an upper triangular matrix. For efficiency purposes, this function returns the matrix of Householder reflector vectors H instead of Q (which is a large m x m potentially dense matrix). The Q matrix can be explicitly computed from H, if needed. In most applications of QR, one is interested in calculating Q %\*% B or t(Q) %\*% B â and, both can be computed directly using H instead of explicitly constructing the large Q matrix. <br/> It is important to note that this function can operate only on small-to-medium sized input matrix that can fit in the main memory. For larger matrices, an out-of-memory exception is raised. | Input : (A <matrix>) <br/> Output : [<matrix>, <matrix>] <br/> A is a (m x n) matrix, which can e ither be a square matrix (m=n) or a rectangular matrix (m != n). This function returns two matrices H and R of size (m x n) i.e., same size as of the input matrix A. | [H, R] = qr(A)
