Description of slmulvec

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slmulvec

PURPOSE

SLMULVEC multiplies a vector to columns or rows of a matrix

SYNOPSIS

function Y = slmulvec(X, v, d)

DESCRIPTION

SLMULVEC multiplies a vector to columns or rows of a matrix

 $ Syntax $
   - Y = slmulvec(X, v, d)
   - Y = slmulvec(X, v)

 $ Arguments $
   - X:        The original matrix
   - v:        The addend vector
   - d:        The dimension along which the vector is to add
   - Y:        The resultant matrix

 $ Description $
   - Y = slmulvec(X, v, d) selects the most efficienct way to multiple a 
     vector v to every column/row of X. If d == 1, then v should be 
     a column vector, and is multiplied to each column of X, if d == 2,
     then v should be a row vector, and is multiplied to each row of X.

   - Y = slmulvec(X, v) will automatically determine d according to
     the shape of v.

 $ Remarks $
   - The implementation simply wraps the mex function vecop_core.

 $ History $
   - Created by Dahua Lin, on Sep 10, 2006

CROSS-REFERENCE INFORMATION

This function calls:

This function is called by:

• sllabeledsum SLLABELEDSUM Sums the numbers according to labels
• slmetric_cp SLMETRIC_CP Computes the metrics between corresponding pairs of samples
• slmetric_pw SLMETRIC_PW Compute the metric between column vectors pairwisely
• slnormalize SLNORMALIZE Normalize the sub-arrays
• slsymgeig SLSYMGEIG Solve the generalized eigen decomposition for symmetric matrices
• slkpca SLPCA Learns a Kernel PCA model from training samples
• slgembed SLGEMBED Solves the general graph-based embedding
• sllemap SLLEMAP Solves Laplacian Eigenmap Embedding
• sllocaltanspace SLLOCALTANSPACE Solves the local tangent spaces
• sllinreg SLLINREG Performs Multivariate Linear Regression and Ridge Regression
• sllogistreg SLLOGISTREG Performs Multivariate Logistic Regression
• slcov SLCOV Compute the covariance matrix
• slfmm SLFMM Learns a Finite Mixture Model (FMM)
• slgaussest SLGAUSSEST Estimates the Gaussian models from samples
• slgaussrnd SLGAUSSRND Generates random samples from Gaussian models
• slmean SLMEAN Compute the mean vector of samples
• slposteriori SLPOSTERIORI Computes the posterioris
• slposterioritrue SLPOSTERIORITRUE Computes the posteriori that samples belong to true class
• slwhiten_from_cov SLWHITEN_FROM_COV Compute the whitening transform from covariance matrix
• slwhiten_from_samples SLWHITEN_FROM_SAMPLES Compute the whitening matrix from sample matrix
• slcopca SLCOPCA Performs Coupled PCA Learning
• sldlda SLDLDA Performs Direct Linear Discriminant Analysis
• slnlda SLNLDA Performs Nullspace-based Linear Discriminant Analysis
• slpca SLPCA Learns a PCA model from training samples

SOURCE CODE

0001 function Y = slmulvec(X, v, d)
0002 %SLMULVEC multiplies a vector to columns or rows of a matrix
0003 %
0004 % $ Syntax $
0005 %   - Y = slmulvec(X, v, d)
0006 %   - Y = slmulvec(X, v)
0007 %
0008 % $ Arguments $
0009 %   - X:        The original matrix
0010 %   - v:        The addend vector
0011 %   - d:        The dimension along which the vector is to add
0012 %   - Y:        The resultant matrix
0013 %
0014 % $ Description $
0015 %   - Y = slmulvec(X, v, d) selects the most efficienct way to multiple a
0016 %     vector v to every column/row of X. If d == 1, then v should be
0017 %     a column vector, and is multiplied to each column of X, if d == 2,
0018 %     then v should be a row vector, and is multiplied to each row of X.
0019 %
0020 %   - Y = slmulvec(X, v) will automatically determine d according to
0021 %     the shape of v.
0022 %
0023 % $ Remarks $
0024 %   - The implementation simply wraps the mex function vecop_core.
0025 %
0026 % $ History $
0027 %   - Created by Dahua Lin, on Sep 10, 2006
0028 %
0029 
0030 if nargin < 3
0031     if size(v, 2) == 1
0032         d = 1;
0033     else
0034         d = 2;
0035     end
0036 end
0037 
0038 Y = vecop_core(X, v, d, 2);  % 2 is the opcode of multiplication in vecop_core

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