File with functions to calcule NNMF using betadivergence methods with Intel Xeon Phi (MIC) More...

Functions
int	dbdivg_mic (const int m, const int n, const int k, const double A, double W, double *H, const double expo, const int uType, const int nIter)
	dbdivg_mic performs the NNMF using beta-divergence when beta is != 1 and !=2, using double precision More...

int	sbdivg_mic (const int m, const int n, const int k, const float A, float W, float *H, const float expo, const int uType, const int nIter)
	sbdivg_mic performs NNMF using betadivergence for general case (beta <> 1 and 2) using simple precision More...

int	dbdivone_mic (const int m, const int n, const int k, const double A, double W, double *H, const int uType, const int nIter)
	dbdivone_mic performs NNMF using betadivergence when beta=1 using double precision More...

int	sbdivone_mic (const int m, const int n, const int k, const float A, float W, float *H, const int uType, const int nIter)
	sbdivone_mic performs NNMF using betadivergence when beta=1 using simple precision More...

int	dbdiv_mic (const int m, const int n, const int k, const double A, double W, double *H, const double beta, const int uType, const int nIter)
	dbdiv_mic is a wrapper that calls the adequate function to performs NNMF using betadivergence using double precision with MIC More...

int	sbdiv_mic (const int m, const int n, const int k, const float A, float W, float *H, const float beta, const int uType, const int nIter)

Detailed Description

File with functions to calcule NNMF using betadivergence methods with Intel Xeon Phi (MIC)

Author: Information Retrieval and Parallel Computing Group (IRPCG); University of Oviedo, Spain; Interdisciplinary Computation and Communication Group (INCO2); Universitat Politecnica de Valencia, Spain.; Contact: nnmfp.nosp@m.ack@.nosp@m.gmail.nosp@m..com

Date: 04/11/14

Definition in file bdiv_mic.c.

Function Documentation

◆ dbdiv_mic()

int dbdiv_mic	(	const int	m,
		const int	n,
		const int	k,
		const double *	A,
		double *	W,
		double *	H,
		const double	beta,
		const int	uType,
		const int	nIter
	)

dbdiv_mic is a wrapper that calls the adequate function to performs NNMF using betadivergence using double precision with MIC

Parameters

m	(input) Number of rows of matrix A and matrix W
n	(input) Number of columns of matrix A and matrix H
k	(input) Number of columns of matrix W and rows of H
A	(input) Double precision input matrix of (m * n) number of elements stored using 1D column-major
W	(inout) Double precision input/output matrix of (m * k) number of elements stored using 1D column-major
H	(inout) Double precision input/output matrix of (k * n) number of elements stored using 1D column-major
beta	(input) Double precision value. The parameter beta of betadivergence method
uType	(input) It can be UpdateAll (W and H are updated), UpdateW (only H is updated) or UpdateH (only W is updated)
nIter	(input) Number of iterations

Returns: : 0 if all is OK, -1 otherwise

Definition at line 274 of file bdiv_mic.c.

◆ dbdivg_mic()

int dbdivg_mic	(	const int	m,
		const int	n,
		const int	k,
		const double *	A,
		double *	W,
		double *	H,
		const double	expo,
		const int	uType,
		const int	nIter
	)

dbdivg_mic performs the NNMF using beta-divergence when beta is != 1 and !=2, using double precision

The algorithm is
  repit nIter times
    STEP 1
      L=W*H
      L1=L(.^)(beta-2)
      L2=L1(.*)A
      L1=L1(.*)L
      B=W'L2
      C=W'*L1
      H=H(.)B(./)C

    STEP 2
      L=W*H
      L1=L(.^)(beta-2)
      L2=L1(.*)A
      L1=L1(.*)L
      D=L2*H'
      E=L1*H'
      W=W(.*)D(./)E
  end repit
End algorithm

   In real live L1 and L2 are (m*n) matrices used in STEP 1 and 2. For performance
   reasons only one 1D colum-mayor buffer, named R in next code, of size 2*m*n is used
   In STEP 1, first part of R (m*n positions) is L2 and the second part is L1.
   In STEP 2, fisrt column of R (2*m positions) is composed by the first column of L2
   following first column of L1. Second column of R (2*m positions) is composed by the
   sencond column of L2 following second column of L1. 3rd column of R ... and so on

   In real live B and C are (k*n) matrices used in STEP 1, and D and E are (m*k)
   matrices used in STEP 2. B/C and D/E are independent. However we do not have L1
   and L2, we have R, and we can do B=W'*L2 and C=W'*L1 (or D=L2*H' and E=L1*H') at
   the same time. For this reason only one matrix is declared to save space. This is
   matrix M with size 2*max(m,n)*k

Parameters

m	(input) Number of rows of matrix A and matrix W
n	(input) Number of columns of matrix A and matrix H
k	(input) Number of columns of matrix W and rows of H
A	(input) Double precision input matrix of (m * n) number of elements stored using 1D column-major
W	(inout) Double precision input/output matrix of (m * k) number of elements stored using 1D column-major
H	(inout) Double precision input/output matrix of (k * n) number of elements stored using 1D column-major
expo	(input) Double precision value. The exponent beta of betadivergence method
uType	(input) It can be UpdateAll (W and H are updated), UpdateW (only H is updated) or UpdateH (only W is updated)
nIter	(input) Number of iterations

It returns 0 if all is OK.

Definition at line 88 of file bdiv_mic.c.

◆ dbdivone_mic()

int dbdivone_mic	(	const int	m,
		const int	n,
		const int	k,
		const double *	A,
		double *	W,
		double *	H,
		const int	uType,
		const int	nIter
	)

dbdivone_mic performs NNMF using betadivergence when beta=1 using double precision

The algorithm is
  repit nIter times
    STEP 1
      y(i)=sum(W(j,i) for all j in range) for all i in range
      L=W*H
      L=A(./)L
      B=W'L
      B(i,j)=B(i,j) / y(i) for all B elements
      H=H(.)B

    STEP 2
      y(i)=sum(H(i,j) for all j in range) for all i in range
      L=W*H
      L=A(./)L
      D=L*H'
      B(i,j)=B(i,j) / y(j) for all B elements
      W=W(.*)D
    end repit
End algorithm

In real live B is a (k*n) matrix used in STEP 1, and D is a (m*k) matrix used in STEP 2. B and D are independent. For this reason only 1 matrix of size max(m,n)*k is declared/used

Parameters

m	(input) Number of rows of matrix A and matrix W
n	(input) Number of columns of matrix A and matrix H
k	(input) Number of columns of matrix W and rows of H
A	(input) Double precision input matrix of (m * n) number of elements stored using 1D column-major
W	(inout) Double precision input/output matrix of (m * k) number of elements stored using 1D column-major
H	(inout) Double precision input/output matrix of (k * n) number of elements stored using 1D column-major
uType	(input) It can be UpdateAll (W and H are updated), UpdateW (only H is updated) or UpdateH (only W is updated)
nIter	(input) Number of iterations

Returns: : 0 if all is OK, -1 otherwise

Definition at line 195 of file bdiv_mic.c.

◆ sbdiv_mic()

int sbdiv_mic	(	const int	m,
		const int	n,
		const int	k,
		const float *	A,
		float *	W,
		float *	H,
		const float	beta,
		const int	uType,
		const int	nIter
	)

Definition at line 304 of file bdiv_mic.c.

◆ sbdivg_mic()

int sbdivg_mic	(	const int	m,
		const int	n,
		const int	k,
		const float *	A,
		float *	W,
		float *	H,
		const float	expo,
		const int	uType,
		const int	nIter
	)

sbdivg_mic performs NNMF using betadivergence for general case (beta <> 1 and 2) using simple precision

Parameters

m	(input) Number of rows of matrix A and matrix W
n	(input) Number of columns of matrix A and matrix H
k	(input) Number of columns of matrix W and rows of H
A	(input) Simple precision input matrix of (m * n) number of elements stored using 1D column-major
W	(inout) Simple precision input/output matrix of (m * k) number of elements stored using 1D column-major
H	(inout) Simple precision input/output matrix of (k * n) number of elements stored using 1D column-major
expo	(input) Double precision value. The exponent beta of betadivergence method
uType	(input) It can be UpdateAll (W and H are updated), UpdateW (only H is updated) or UpdateH (only W is updated)
nIter	(input) Number of iterations

Returns: : 0 if all is OK, -1 otherwise

Definition at line 128 of file bdiv_mic.c.

◆ sbdivone_mic()

int sbdivone_mic	(	const int	m,
		const int	n,
		const int	k,
		const float *	A,
		float *	W,
		float *	H,
		const int	uType,
		const int	nIter
	)

sbdivone_mic performs NNMF using betadivergence when beta=1 using simple precision

Parameters

m	(input) Number of rows of matrix A and matrix W
n	(input) Number of columns of matrix A and matrix H
k	(input) Number of columns of matrix W and rows of H
A	(input) Simple precision input matrix of (m * n) number of elements stored using 1D column-major
W	(inout) Simple precision input/output matrix of (m * k) number of elements stored using 1D column-major
H	(inout) Simple precision input/output matrix of (k * n) number of elements stored using 1D column-major
uType	(input) It can be UpdateAll (W and H are updated), UpdateW (only H is updated) or UpdateH (only W is updated)
nIter	(input) Number of iterations

Returns: : 0 if all is OK, -1 otherwise

Definition at line 234 of file bdiv_mic.c.

Functions

Detailed Description

Function Documentation

◆ dbdiv_mic()

◆ dbdivg_mic()

◆ dbdivone_mic()

◆ sbdiv_mic()

◆ sbdivg_mic()

◆ sbdivone_mic()