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github | lmthang/nmt.hybrid-master | contextLayerBackprop.m | .m | nmt.hybrid-master/code/layers/contextLayerBackprop.m | 2,173 | utf_8 | a469bbc703c40e56990d79337753ff91 | %%%
%
% For attention-based models, given:
% grad_contextVecs: lstmSize * batchSize
% alignWeights: numPositions * batchSize.
% srcHidVecs: lstmSize * batchSize * numPositions.
% compute the following grads:
% grad_srcHidVecs: lstmSize * batchSize * numPositions
% grad_alignWeights: numPositions * batchSize
%... |
github | lmthang/nmt.hybrid-master | srcCompareLayerBackprop.m | .m | nmt.hybrid-master/code/layers/srcCompareLayerBackprop.m | 679 | utf_8 | 1049437b565182414df86c8c8105f2dc | %%%
% For attention-based models, given:
% grad_scores: numPositions * batchSize
% srcHidVecs: lstmSize * batchSize * numPositions
% h_t: lstmSize * batchSize
% compute the following grads:
% grad_ht: lstmSize * batchSize
% grad_srcHidVecs: lstmSize * batchSize * numPositions
%
% Thang Luong @ 2015, <lmthang@... |
github | lmthang/nmt.hybrid-master | srcCompareLayerForward.m | .m | nmt.hybrid-master/code/layers/srcCompareLayerForward.m | 893 | utf_8 | a0eb1c381aa63b792fe01de9615d86e0 | %%%
%
% For attention-based models, given:
% srcHidVecs: lstmSize * curBatchSize * numPositions
% h_t: lstmSize * curBatchSize
% compute:
% alignScores: numPositions * curBatchSize
%
% Thang Luong @ 2015, <lmthang@stanford.edu>
%
%%%
function [alignScores] = srcCompareLayerForward(srcHidVecs, h_t, params)
alig... |
github | lmthang/nmt.hybrid-master | prepareVocabs.m | .m | nmt.hybrid-master/code/preprocess/prepareVocabs.m | 4,556 | utf_8 | 82b4589110a9967d150499268c995c6b | function [params] = prepareVocabs(params)
% prepareVocabs - prepare vocabs for the model
%
% Input:
% params: parameter settings
%
% Output:
% params: updated parameter settings with vocab fields
%
% Authors:
% Thang Luong @ 2015, <lmthang@stanford.edu>
%
%% grad check
if params.isGradCheck
% word
i... |
github | lmthang/nmt.hybrid-master | loadWordSimData.m | .m | nmt.hybrid-master/code/wordsim/code/loadWordSimData.m | 1,193 | utf_8 | ec613e7520261381130e71b339066ff5 | %% Load word similarity file and save in Matlab format
%% Assumed format: word1\tword2\tscore[Optional]
function [wordPairs, humanScores] = loadWordSimData(inFile, isHeader, delimiter, numLastLineExcluded, word1Index, word2Index, scoreIndex)
% outFile,
fid = fopen(inFile, 'r');
fileLines = textscan(fid, '%s',... |
github | lmthang/nmt.hybrid-master | evaluateWordSim.m | .m | nmt.hybrid-master/code/wordsim/code/evaluateWordSim.m | 3,471 | utf_8 | 40809c0bbcc9de4303786b2548c7edaa | function [corrScores, data] = evaluateWordSim(modelFile, modelFormat, lang, We, words)
%%
% Run word similarity evaluation
%
% modelFile contains either 'We', 'words' or allW, 'params', 'words'
% modelFormat: 0 -- mat file,
% 1 -- text file with a header line <numWords> <embDim>.
% Subsequen... |
github | lmthang/nmt.hybrid-master | slgetroc.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/perfeval/slgetroc.m | 3,483 | utf_8 | 87ac4e0c9d5db6c7f26c4088b00ed745 | function [thr, fa, fr] = slgetroc(thrs, fars, frrs, item, itempara)
%SLGETROC Computes some point from ROC Curve
%
% $ Syntax $
% - [thr, fa, fr] = slgetroc(thrs, fars, frrs, item, itempara)
%
% $ Arguments $
% - thrs: the sampled threshold values
% - fars: the false accept rates at the sa... |
github | lmthang/nmt.hybrid-master | slverifyroc_blks.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/perfeval/slverifyroc_blks.m | 3,328 | utf_8 | cbe1c23a36f8ce2fcc164ecc324bb22b | function [thrs, fars, frrs] = slverifyroc_blks(scores, blocks, labels1, labels2, op, npts)
%SLVERIFYROC_BLKS Computes the verification ROC for blockwise score matrix
%
% $ Syntax $
% - [thrs, fars, frrs] = slverifyroc_blks(scores, blocks, labels1, labels2, op)
% - [thrs, fars, frrs] = slverifyroc_blks(scores, ... |
github | lmthang/nmt.hybrid-master | slhistmetric_cp.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/discrete/slhistmetric_cp.m | 4,924 | utf_8 | fbde416d976c392bcd5249bb9b38db5e | function D = slhistmetric_cp(H1, H2, mtype, varargin)
%SLHISTMETRIC_CP Computes the metrics between corresponding pairs of histograms
%
% $ Syntax $
% - D = slhistmetric_cp(H1, H2, mtype, ...)
%
% $ Arguments $
% - H1, H2: The histograms for metric computing (d x n)
% - mtype: The metric type
% -... |
github | lmthang/nmt.hybrid-master | slcountvote.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/discrete/slcountvote.m | 4,765 | utf_8 | f6cfec416e31c632bec5489061985b86 | function H = slcountvote(m, n, V, w, countrule)
%SLCOUNTRULE Counts the votings to make histogram
%
% $ Syntax $
% - H = slcountvote(m, n, V, w, countrule)
%
% $ Arguments $
% - m: The number of models to be voted for
% - n: The number of samples
% - V: The voting results
% - ... |
github | lmthang/nmt.hybrid-master | slvechist.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/discrete/slvechist.m | 9,635 | utf_8 | 043f4db9c8c59642e4b1ea5930edd06e | function H = slvechist(X0, X, varargin)
%SLVECHIST Makes the histogram on prototype vectors by voting
%
% $ Syntax $
% - H = slvechist(X0, X, ...)
%
% $ Arguments $
% - X0: The sample matrix of the prototypes (be voted)
% - X: The samples to vote (voters)
% - H: The resultant histogram
%
% ... |
github | lmthang/nmt.hybrid-master | slhistmetric_pw.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/discrete/slhistmetric_pw.m | 4,724 | utf_8 | c04307d8427e5c262a3bff232934b501 | function D = slhistmetric_pw(H1, H2, mtype, varargin)
%SLHISTMETRIC_PW Computes distance metrics between histograms pairwisely
%
% $ Syntax $
% - D = slhistmetric_pw(H1, H2, mtype, ...)
%
% $ Arguments $
% - H1, H2: The matrices of histogram sets (d1 x n1, d2 x n2)
% - mtype: The metric type
% - D:... |
github | lmthang/nmt.hybrid-master | sl2dpcaex.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace_ex/sl2dpcaex.m | 9,040 | utf_8 | 0510f596f09c46c601f7758674239d28 | function [Mm, PL, PR, info] = sl2dpcaex(data, matsiz, n, method, varargin)
%SL2DPCAEX Learns Extended 2D PCA on a set of matrix samples
%
% $ Syntax $
% - [Mm, PL, PR] = sl2dpcaex(data, matsiz, n, method, ...)
% - [Mm, PL, PR, info] = sl2dpcaex(data, matsiz, n, method, ...)
%
% $ Arguments $
% - data: ... |
github | lmthang/nmt.hybrid-master | slpartitionpca_apply.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace_ex/slpartitionpca_apply.m | 4,194 | utf_8 | dfc05c0443f9436739de4d047c6759c3 | function feas = slpartitionpca_apply(S, modeldir, data, n, k)
%SLPARTITIONPCA_APPLY applies partition-based PCA to a set of arrays
%
% $ Syntax $
% - feas = slpartitionpca_apply(S, modeldir, data, n)
% - feas = slpartitionpca_apply(S, modeldir, data, n, k)
%
% $ Arguments $
% - S: the partition... |
github | lmthang/nmt.hybrid-master | slpartitionpca.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace_ex/slpartitionpca.m | 19,683 | utf_8 | 99d7c4bd46511f1de0a4a7c83c7e76ac | function slpartitionpca(data, arrsiz, n, ps, filepath, varargin)
%SLPARTITIONPCA Performs Partition-based PCA and saves the models
%
% $ Syntax $
% - slpartitionpca(data, arrsiz, n, ps, filepath, ...)
%
% $ Arguments $
% - data: the super-array of the unit arrays, or the set of
% ... |
github | lmthang/nmt.hybrid-master | sl2dpca_apply.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace_ex/sl2dpca_apply.m | 2,447 | utf_8 | f4c24b026a99390f8ce02f663c0eea4e | function Y = sl2dpca_apply(Mm, PL, PR, data, matsiz, n)
%SL2DPCA_APPLY Applies 2D PCA onto a set of matrices to extract features
%
% $ Syntax $
% - Y = sl2dpca_apply(Mm, PL, PR, data, matsiz, n)
%
% $ Description $
% - Mm: the mean matrix
% - PL: the left projection matrix
% - PR: ... |
github | lmthang/nmt.hybrid-master | sl2dmatcov.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace_ex/sl2dmatcov.m | 7,698 | utf_8 | 3c4715547c3b15653f906a9d1fbbae72 | function varargout = sl2dmatcov(type, data, matsiz, n, meanmat, PL, PR, w)
%SL2DMATCOV Computes the 2D matrix-covariances
%
% $ Syntax $
% - CL = sl2dmatcov('CL', data, matsiz, n, meanmat, PL, PR, w)
% - CR = sl2dmatcov('CR', data, matsiz, n, meanmat, PL, PR, w)
% - [CL, CR] = sl2dmatcov('Both', data, matsi... |
github | lmthang/nmt.hybrid-master | slarrmean.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace_ex/slarrmean.m | 3,642 | utf_8 | e076b45c3a0012209ea0525e04d3763c | function arrMean = slarrmean(data, arrsiz, n, varargin)
%SLARRMEAN Computes the mean of a set of arrays
%
% $ Syntax $
% - slarrmean(arrs, arrsiz, n, ...)
% - slarrmean(fns, arrsiz, n, ...)
%
% $ Arguments $
% - arrs: the super-array consisting of all arrays
% - fns: the file paths of ... |
github | lmthang/nmt.hybrid-master | sllogistreg.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/regression/sllogistreg.m | 6,755 | utf_8 | 3b2b8e24a5f51d3cc7f21bafce332581 | function [A, b, props, info] = sllogistreg(X, nums, varargin)
%SLLOGISTREG Performs Multivariate Logistic Regression
%
% $ Syntax $
% - [A, b] = sllogistreg(X, nums, ...)
% - [A, b, props] = sllogistreg(X, nums, ...)
% - [A, b, props, info] = sllogistreg(X, nums, ...)
%
% $ Arguments $
% - X: The inp... |
github | lmthang/nmt.hybrid-master | slsharedisp_attach.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/utils/slsharedisp_attach.m | 1,647 | utf_8 | 68d0885d5b91e92b24ff32d507351f78 | function slsharedisp_attach(name, varargin)
%SLSHAREDISP_ATTACH Attachs to global display options
%
% $ Syntax $
% - slsharedisp_attach(name, ...)
%
% $ Arguments $
% - name: the name of the invoker
%
% $ Description $
% - slsharedisp_attach(name, ...) attachs the current function to
% the glob... |
github | lmthang/nmt.hybrid-master | slclassify.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/utils/slclassify.m | 4,536 | utf_8 | 8631feedc1a6721e6ecb22ebaf7b829e | function [decisions, decscores] = slclassify(scores, clabels, op, varargin)
%SLCLASSIFY Classifies a set of samples according to final scores
%
% $ Syntax $
% - decisions = slclassify(scores, clabels, op, ...)
% - [decisions, decscores] = slclassify(scores, clabels, op, ...)
%
% $ Arguments $
% - scores: ... |
github | lmthang/nmt.hybrid-master | slpartition.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/utils/slpartition.m | 8,926 | utf_8 | 80473608fa9c4dcf7a60b747573ff4e0 | function PS = slpartition(whole_size, spec_item, varargin)
%SLPARTITION Partition a range into blocks in a specified manner
%
% $ Syntax $
% - PS = slpartition(whole_size, 'numblks', nblks_dim1, nblks_dim2, ...);
% - PS = slpartition(whole_size, 'numblks', [nblks_dim1, nblks_dim2, ...]);
% - PS = slpartitio... |
github | lmthang/nmt.hybrid-master | slparseprops.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/utils/slparseprops.m | 3,071 | utf_8 | abef74973ecd918fe2ee24a7d6b01a19 | function P = slparseprops(P0, varargin)
%SLPARSEPROPS Parses input parameters
%
% $ Syntax $
% - P = slparseprops(P0, property_struct)
% - P = slparseprops(P0, property_name1, property_value1, ...)
%
% $ Syntax $
% - P = slparseprops(P0, property_struct) parses the properties from a
% property struc... |
github | lmthang/nmt.hybrid-master | sladdpath.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/utils/sladdpath.m | 1,601 | utf_8 | c55acad8c4a826d0f257cc3da8194950 | function paths = sladdpath(filenames, dirpath)
%SLADDPATH Adds dirpath to precede the filenames
%
% $ Syntax $
% - paths = sladdpath(filenames, dirpath)
%
% $ Arguments $
% - filenames: the filenames without root path
% - dirpath: the preceding dirpath to be added
% - paths: ... |
github | lmthang/nmt.hybrid-master | slclassify_blks.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/utils/slclassify_blks.m | 4,613 | utf_8 | 24dc1a708800052b2862067d0bddbc3d | function [decisions, decscores] = slclassify_blks(scores, n, blocks, clabels, op, varargin)
%SLCLASSIFY_BLKS Classifies samples according to blockwise scores
%
% $ Syntax $
% - [decisions, decscores] = slclassify_blks(scores, n, blocks, clabels, op, ...)
%
% $ Arguments $
% - scores: the score matrix
... |
github | lmthang/nmt.hybrid-master | slproglearn.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/learn/slproglearn.m | 4,561 | utf_8 | 64c88a0336b93aa88739702c54289048 | function [models, info] = slproglearn(source, getter, learnfunctor, varargin)
%SLPROGLEARN Performs Progressive Learning from sample source
%
% $ Syntax $
% - models = slproglearn(source, getter, learnfunctor, ...)
% - [models, info] = slproglearn(source, getter, learnfunctor, ...)
%
% $ Arguments $
% ... |
github | lmthang/nmt.hybrid-master | slreevallearn.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/learn/slreevallearn.m | 3,868 | utf_8 | bb1d90dcc2a864dcda2130c87505b8fc | function [models, Q, info] = slreevallearn(models, Q, data, estfunctor, evalfunctor, cmpfunctor, varargin)
%SLREEVALLEARN Performs an iterative learning based on re-evaluation
%
% $ Syntax $
% - [models, Q] = slreevallearn(models, data, estfunctor, evalfunctor, cmpfunctor, ...)
% - [models, Q, info] = slreeval... |
github | lmthang/nmt.hybrid-master | annsearch.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/ann/annsearch.m | 5,502 | utf_8 | 9a864bc2e01db7a0318289b51ee3a1b1 | function [nnidx, dists] = annsearch(X0, X, k, varargin)
%ANNSEARCH Approximate Nearest Neighbor Search
%
% $ Syntax $
% - nnidx = annsearch(X0, [], k)
% - nnidx = annsearch(X0, X, k)
% - nnidx = annsearch(X0, [], k, ...)
% - nnidx = annsearch(X0, X, k, ...)
% - [nnidx, dists] = annsearch(...)
%
% $ ... |
github | lmthang/nmt.hybrid-master | slnbreconweights.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/manifold/slnbreconweights.m | 8,453 | utf_8 | 4e1fca926949e25699d9deb52ec51472 | function WG = slnbreconweights(X0, X, G, varargin)
%SLNBRECONWEIGHTS Solve the optimal reconstruction weights on given neighbors
%
% $ Syntax $
% - WG = slnbreconweights(X0, X, G, ...)
%
% $ Arguments $
% - X0: The reference samples to reconstruct the query samples
% - X: The query samples
% - G: ... |
github | lmthang/nmt.hybrid-master | sllemap.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/manifold/sllemap.m | 4,987 | utf_8 | eaff0018af85c5e9a691e26801b04ee4 | function [Y, spectrum] = sllemap(G, d, sch)
%SLLEMAP Solves Laplacian Eigenmap Embedding
%
% $ Syntax $
% - Y = sllemap(G, d)
% - Y = sllemap(G, d, sch)
% - [Y, spectrum] = sllemap(...)
%
% $ Arguments $
% - G: The affinity graph (in any acceptable form): n x n
% - d: The embedding dimension
% ... |
github | lmthang/nmt.hybrid-master | sllocaltanspace.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/manifold/sllocaltanspace.m | 3,388 | utf_8 | 54948d248323ac685e3adb80072749f2 | function [LM, LP, LS] = sllocaltanspace(X0, G, dl)
%SLLOCALTANSPACE Solves the local tangent spaces
%
% $ Syntax $
% - [LM, LP] = sllocaltanspace(X0, G, dl)
% - [LM, LP, LS] = sllocaltanspace(...)
%
% $ Arguments $
% - X0: The referenced sample matrix (d0 x n0)
% - G: The neighborhood graph (n0 x ... |
github | lmthang/nmt.hybrid-master | sllocalcoordalign.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/manifold/sllocalcoordalign.m | 5,256 | utf_8 | 3dc085dbc15784fb23c4c77a5944ef6d | function [GC, spectrum, LT] = sllocalcoordalign(GM, LC, dg)
%SLLOCALCOORDALIGN Performs optimal local coordinate alignment
%
% $ Syntax $
% - [GC, spectrum, LT] = sllocalcoordalign(GM, LC)
% - [GC, spectrum, LT] = sllocalcoordalign(GM, LC, dg)
% - [GC, spectrum] = sllocalcoordalign(...)
%
% $ Arguments $
% - GM... |
github | lmthang/nmt.hybrid-master | slgembed.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/manifold/slgembed.m | 6,016 | utf_8 | f8d0d7f7bfa1b2603961adb3f68f74bd | function [Y, spectrum] = slgembed(G, Gc, d, fm, varargin)
%SLGEMBED Solves the general graph-based embedding
%
% $ Syntax $
% - Y = slgembed(G, Gc, d, fm, ...)
% - [Y, spectrum] = slgembed(G, Gc, d, fm, ...)
%
% $ Arguments $
% - G: The graph to be optimized
% - Gc: The constraint graph
% - d: ... |
github | lmthang/nmt.hybrid-master | slsymgraph.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/graph/slsymgraph.m | 5,767 | utf_8 | 4712a128cac886176fc2c9b59a9ded41 | function As = slsymgraph(A, symmethod)
%SLSYMGRAPH Forces symmetry of the adjacency matrix of a graph
%
% $ Syntax $
% - As = slsymgraph(A)
% - As = slsymgraph(A, symmethod)
%
% $ Arguments $
% - A: The adjacency matrix of the original graph
% - symmethod: The method to symmetrize the graph
% - ... |
github | lmthang/nmt.hybrid-master | slaffinitymat.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/graph/slaffinitymat.m | 4,520 | utf_8 | 337f48f1ff8a6b50aee378976e7d403f | function A = slaffinitymat(X, X2, nnparams, varargin)
%SLAFFINITYMAT Constructs an affinity matrix
%
% $ Syntax $
% - A = slaffinitymat(X, [], nnparams, ...)
% - A = slaffinitymat(X, X2, nnparams, ...)
%
% $ Arguments $
% - X: The sample matrix of the (source) nodes
% - X2: The sample matrix of the... |
github | lmthang/nmt.hybrid-master | slmakeadjmat.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/graph/slmakeadjmat.m | 3,548 | utf_8 | dadcb4a88eea54307e6733eac611302d | function A = slmakeadjmat(n, nt, edges, vals, islogic, isspar)
%SLMAKEADJMAT Makes an adjacency matrix using edges and corresponing values
%
% $ Syntax $
% - A = slmakeadjmat(n, nt, edges, vals, islogic, issparse)
%
% $ Arguments $
% - n: The number of (source) nodes
% - nt: The number of (target) no... |
github | lmthang/nmt.hybrid-master | slfindnn.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/graph/slfindnn.m | 8,953 | utf_8 | eca5384c007eb54744d25c2ad98239ca | function [nnidx, dists] = slfindnn(X0, X, method, varargin)
%SLFINDNN Finds the nearest neighbors using specified strategy
%
% $ Syntax $
% - [nnidx, dists] = slfindnn(X0, X, method, ...)
%
% $ Arguments $
% - X0: The referenced samples in which the neighbors are found
% - X: The query sampl... |
github | lmthang/nmt.hybrid-master | slpruneedgeset.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/graph/slpruneedgeset.m | 7,648 | utf_8 | a7bac199ec30737c990b520d561bc362 | function edges = slpruneedgeset(n, nt, edges, method)
%SLPRUNEEDGESET Prunes the edge set
%
% $ Syntax $
% - edges = slpruneedgeset(n, nt, edges)
% - edges = slpruneedgeset(n, nt, edges, method)
%
% $ Arguments $
% - n: The number of (source) nodes
% - nt: The number of (target) nodes
% -... |
github | lmthang/nmt.hybrid-master | slpwmetricgraph.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/graph/slpwmetricgraph.m | 4,899 | utf_8 | 0177081cec25b9699d7880f2ae8eaf56 | function G = slpwmetricgraph(X, varargin)
%SLPWMETRICGRAPH Constructs a graph based on pairwise metrics
%
% $ Syntax $
% - G = slpwmetricgraph(X, ...)
% - G = slpwmetricgraph(X, Xt, ...)
%
% $ Arguments $
% - X: The sample matrix with each column as a (source) node
% - Xt: The sample matrix with ea... |
github | lmthang/nmt.hybrid-master | slgraphinfo.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/graph/slgraphinfo.m | 6,437 | utf_8 | 014232df968b07c584e6fc9712a483fd | function gi = slgraphinfo(G, conds)
%SLGRAPHINFO Extracts basic information of a given graph representation
%
% $ Syntax $
% - gi = slgraphinfo(G)
% - gi = slgraphinfo(G, conds)
%
% $ Arguments $
% - G: The input graph
% - conds: The cell array of conditions to be checked
% - gi: the informati... |
github | lmthang/nmt.hybrid-master | sladjmat.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/graph/sladjmat.m | 3,007 | utf_8 | a84de6c39188f5ed6915d1a89c01fae7 | function A = sladjmat(G, varargin)
%SLADJMAT Constructs the adjacency matrix representation of a graph
%
% $ Syntax $
% - A = sladjmat(G, ...)
%
% $ Arguments $
% - G: The input graph
% - A: The adjacency matrix representation of the graph
%
% $ Description $
% - A = sladjmat(G, ...) constructs th... |
github | lmthang/nmt.hybrid-master | edl_go.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/ExpDL/edl_go.m | 7,701 | utf_8 | 721cff372a8334506753694dcf2faeed | function edl_go(expdef, type, name, filter, runopt)
%EDL_GO The Top interface for doing experiments in EDL
%
% $ Syntax $
% - edl_go(expdef, type, name)
% - edl_go(expdef, type, name, filter, runopt)
%
% $ Arguments $
% - expdef: the experiment definition
% - type: the type of the action
... |
github | lmthang/nmt.hybrid-master | edl_batchexp.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/ExpDL/edl_batchexp.m | 4,712 | utf_8 | c3d923496ea5954e0613adb53fe1e945 | function edl_batchexp(expfun, scrpath, env, logger, filter, runopt)
%EDL_BATCHEXP Performs Batch experiments according to scheme
%
% $ Syntax $
% - edl_batchexp(expfun, scrpath, env, logger)
% - edl_batchexp(expfun, scrpath, env, logger, filter, runopt)
%
% $ Arguments $
% - expfun: the experiment funct... |
github | lmthang/nmt.hybrid-master | edl_readexpdefs.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/ExpDL/edl_readexpdefs.m | 11,932 | utf_8 | 2d4c500b10cee3a98b67347f875de0f6 | function ED = edl_readexpdefs(filename)
%EDL_READEXPDEFS Reads in an experiment definition from XML file
%
% $ Syntax $
% - ED = edl_readexpdefs(filename)
%
% $ Arguments $
% - filename: the filename of the experiment definition XML
% - ED: the read experiment definition struct
%
% $ Descr... |
github | lmthang/nmt.hybrid-master | edl_readenvvars.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/ExpDL/edl_readenvvars.m | 999 | utf_8 | 5262741e014cd5c671932e50ffed6369 | function S = edl_readenvvars(envfile)
%EDL_READENVVARS Reads in a file with environment variables
%
% $ Syntax $
% - S = edl_readenvvars(envfile)
%
% $ Arguments $
% - envfile: the environment filename
% - S: the struct of all environment variables
%
% $ Description $
% - S = edl_read... |
github | lmthang/nmt.hybrid-master | get.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/ExpDL/@dataset/get.m | 5,600 | utf_8 | 8ca614b3a0497fbb4709e7d929f62757 | function R = get(DS, props)
%GET gets the properties of the dataset
%
% $ Syntax $
% - R = get(DS, propname)
% - R = get(DS, propname cell array)
%
% $ Description $
% - R = get(DS, propname) gets a property value with its name specified
% by propname
%
% - R = get(DS, propname cell array) get... |
github | lmthang/nmt.hybrid-master | readfile.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/ExpDL/@dataset/readfile.m | 2,725 | utf_8 | c1ebed369944670de7fbf222e86eecf0 | function DS = readfile(DS, filename)
%READFILE Reads the dataset from DSDML file
%
% $ Syntax $
% - DS = readfile(DS, filename)
%
% $ Arguments $
% - DS: the dataset to be loaded from file
% - filename: the DSDML file describing the dataset.
%
% $ Description $
% - DS = readfile(DS, fi... |
github | lmthang/nmt.hybrid-master | writefile.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/ExpDL/@dataset/writefile.m | 2,853 | utf_8 | 4a05b177d56e38d2595b4d1dbae758b8 | function writefile(DS, filename)
%WRITEFILE Writes a dataset to a DSDML file
%
% $ Syntax $
% - writefile(DS, filename)
%
% $ Arguments $
% - DS: the dataset object
% - filename: the name of the file to be written to
%
% $ Description $
% - writefile(DS, filename) writes a dataset objec... |
github | lmthang/nmt.hybrid-master | slcountlines.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/fileio/slcountlines.m | 6,976 | utf_8 | 57314c53b1349897531626597d50335c | function R = slcountlines(folderpath, fnreport)
%SLCOUNTLINES Count the lines of m-files in a folder and make a report
%
% $ Syntax $
% - R = slcountlines(folderpath)
% - slcountlines(folderpath, fnreport)
% - R = slcountlines(folderpath, fnreport)
%
% $ Arguments $
% - folderpath: the path of th... |
github | lmthang/nmt.hybrid-master | slchangefilepart.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/fileio/slchangefilepart.m | 1,711 | utf_8 | e4f4a0a638a04192466019311977e1b0 | function newfp = slchangefilepart(fp, varargin)
%SLCHANGEFILEPART Changes some parts of the file path
%
% $ Syntax $
% - newfp = slchangefilepart(fp, partname1, part1, ...)
%
% $ Description $
% - newfp = slchangefilepart(fp, partname1, part1, ...) changes the
% specified part of a path to a new value ... |
github | lmthang/nmt.hybrid-master | sltensor_multiply.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/tensor/sltensor_multiply.m | 2,845 | utf_8 | 66a2f7abd12474a0212355e89dfd1563 | function T2 = sltensor_multiply(T, varargin)
%SLTENSOR_MULTIPLY Multiplies a tensor and a matrix
%
% $ Syntax $
% - T2 = sltensor_multiply(T, M, k)
% - T2 = sltensor_multiply(T, Ms)
% - T2 = sltensor_multiply(T, Ms, ks)
% - T2 = sltensor_multiply(T, M1, k1, M2, k2, ...)
%
% $ Description $
% - T2 = ... |
github | lmthang/nmt.hybrid-master | slpixlinnorm.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/imgproc/slpixlinnorm.m | 1,871 | utf_8 | 5d97db7fd526303fa6d200c384d99ba2 | function dstimgs = slpixlinnorm(imgs, mu, sigma)
%SLPIXLINNORM Performs linear normalization on pixel values
%
% $ Syntax $
% - dstimgs = slpixlinnorm(imgs);
% - dstimgs = slpixlinnorm(imgs, mu, sigma)
%
% $ Arguments $
% - imgs: the array of images
% - mu: the mean pixel value to be normaliz... |
github | lmthang/nmt.hybrid-master | slapplyfilterband.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/imgproc/slapplyfilterband.m | 3,496 | utf_8 | 38526bb5175f469b6b896cfd73e747e4 | function fimgs = slapplyfilterband(imgs, filterband, filtersize, varargin)
%SLAPPLYFILTERBAND Applies filter band to filter images in batch
%
% $ Syntax $
% - fimgs = slapplyfilterband(imgs, filterband, filtersize, ...)
%
% $ Arguments $
% - imgs: The images to be filtered
% - filterband: The se... |
github | lmthang/nmt.hybrid-master | slgaborbands.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/imgproc/slgaborbands.m | 1,711 | utf_8 | 79ac036c6a6a0766033133b0b9ef85f2 | function FB = slgaborbands(w, scales, orientations)
%SLGABORBANDS Generates a set of Gabor kernels
%
% $ Syntax $
% - FB = slgaborbands(w, scales, radians)
%
% $ Arguments $
% - w: the kernel window size is wxw
% - scales: the scales (number of scales is m)
% - orientatio... |
github | lmthang/nmt.hybrid-master | slimginterp.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/imgproc/slimginterp.m | 4,554 | utf_8 | a292916a533922476ab7a012e9572527 | function V = slimginterp(A, I, J, interpker)
%SLIMGINTERP Performs image based interpolation
%
% $ Syntax $
% - V = slimginterp(A, I, J)
% - V = slimginterp(A, I, J, interpker)
%
% $ Arguments $
% - A: The reference image array
% - I, J: The coordinates at which the values are inter... |
github | lmthang/nmt.hybrid-master | slpadimg.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/imgproc/slpadimg.m | 4,619 | utf_8 | 5f2397259afdf87ce4d7cbd696efe279 | function imgpadded = slpadimg(img, padsize, varargin)
%SLPADIMG Pads an image with boundary
%
% $ Syntax $
% - imgpadded = slpadimg(img, padsize, padval)
% - imgpadded = slpadimg(img, padsize, padtype)
%
% $ Arguments $
% - img: The original input image
% - padsize: The boundary widths i... |
github | lmthang/nmt.hybrid-master | slbenchmark_batchfilter.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/imgproc/slbenchmark_batchfilter.m | 1,277 | utf_8 | 8079e8e6ecec42586fc0122db78ef4d8 | function recs = slbenchmark_batchfilter(imgsiz, nimgs, filtersiz, nfilters)
%SLBENCHMARK_BATCHFILTER Compares the efficiency of batch filter
%
% Input:
% imgsiz: the size of each image
% nimgs: the number of images
% filtersiz: The size of each filter
% nfilters: the list of numbers of filter... |
github | lmthang/nmt.hybrid-master | slkernel.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/kernel/slkernel.m | 4,397 | utf_8 | a287bbd44e6c14f5c7014d7a85ba78c6 | function K = slkernel(varargin)
%SLKERNEL Computes the kernel for samples
%
% $ Syntax $
% - K = slkernel(X0, kernel_type, ...)
% - K = slkernel(X0, X, kernel_type, ...)
%
% $ Description $
% - K = slkernel(X0, kernel_type, ...) Computes the Gram matrix for
% the samples in matrix X0 using the kernel... |
github | lmthang/nmt.hybrid-master | sldim_by_eigval.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/sldim_by_eigval.m | 3,482 | utf_8 | 7cdcd3819c2b228fed364c398b4e8fdb | function d = sldim_by_eigval(eigvals, sch, varargin)
%SLDIM_BY_EIGVAL Determines the dimension of principal subspace by eigenvalues
%
% $ Syntax $
% - d = sldim_by_eigval(eigvals)
% - d = sldim_by_eigval(eigvals, sch, ...)
%
% $ Arguments $
% - eigvals: the eigenvalues (energies) of each dimension gi... |
github | lmthang/nmt.hybrid-master | sldlda.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/sldlda.m | 5,260 | utf_8 | ac2a098eef787f3e6c609db624a28d3c | function T = sldlda(X, nums, varargin)
%SLDLDA Performs Direct Linear Discriminant Analysis
%
% $ Syntax $
% - T = sldlda(X, nums)
% - T = sldlda(X, nums, ...)
%
% $ Arguments $
% - X: the training sample matrix
% - nums: the numbers of samples in all classes
% - T: the solved tran... |
github | lmthang/nmt.hybrid-master | sllda.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/sllda.m | 4,961 | utf_8 | 624d8db4e5ea5e2ce834cb508c533454 | function T = sllda(X, nums, method, varargin)
%SLLDA Trains a Linear Discriminant Model using specified method
%
% $ Syntax $
% - T = sllda(X, nums, method, ...)
%
% $ Arguments $
% - X: the sample matrix, with each column representing a sample
% - nums: the numbers of samples in all classes
%... |
github | lmthang/nmt.hybrid-master | slfld.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/slfld.m | 6,395 | utf_8 | 0974cb161bc415ff11f20714b190c0ce | function T = slfld(X, nums, varargin)
%SLFLD Performs Fisher Linear Discriminant Analysis
%
% $ Syntax $
% - T = slfld(X, nums)
% - T = slfld(X, nums, ...)
%
% $ Arguments $
% - X: the training sample matrix
% - nums: the numbers of samples in all classes
% - T: the solved transfor... |
github | lmthang/nmt.hybrid-master | slgbfe.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/slgbfe.m | 4,381 | utf_8 | 8322bc02c4cb5eec4d3c5c213c765cd4 | function T = slgbfe(X, G, Gc, dy, fm, varargin)
%SLGBFE Performs Graph-based Feature Extraction Learning
%
% $ Syntax $
% - T = slgbfe(X, G, Gc, dy, fm, ...)
%
% $ Arguments $
% - X: The sample matrix
% - G: The graph to be optimized
% - Gc: The constraint graph
% - dy: The dimensio... |
github | lmthang/nmt.hybrid-master | slscatter.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/slscatter.m | 9,525 | utf_8 | 724caf5336e1186ddd498155dfc8cb3a | function S = slscatter(X, type, varargin)
%SLSCATTER Compute the scatter matrix
%
% $ Syntax $
% - S = slscatter(X, type, ...)
%
% $ Arguments $
% - X: the sample matrix with each column representing a sample
% - type: the type of scatter matrix to compute
% - S: the resulting scatte... |
github | lmthang/nmt.hybrid-master | slpca.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/slpca.m | 4,690 | utf_8 | 9f70f2345974b764785ba92ee3de2e30 | function S = slpca(X, varargin)
%SLPCA Learns a PCA model from training samples
%
% $ Syntax $
% - S = slpca(X)
% - S = slpca(X, ...)
%
% $ Arguments $
% - X: the training sample matrix
% - S: the struct representing the learned PCA
%
% $ Description $
% - S = slpca(X) learns a PCA m... |
github | lmthang/nmt.hybrid-master | slpcareduce.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/slpcareduce.m | 4,284 | utf_8 | e17290546dd258b1a4545a6048d3b38c | function S = slpcareduce(S, cri, varargin)
%SLPCAREDUCE Reduces a PCA model to lower dimension
%
% $ Syntax $
% - S = slpcareduce(S, cri, ...)
%
% $ Arguments $
% - S: the target PCA model
% - cri: the criterion for PCA model reduction
%
% $ Description $
% - S = slpcareduce(S, cri, ...) ... |
github | lmthang/nmt.hybrid-master | slnlda.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/slnlda.m | 6,220 | utf_8 | 33ce48a824707c3a9c61f9d91786cd6e | function T = slnlda(X, nums, varargin)
%SLNLDA Performs Nullspace-based Linear Discriminant Analysis
%
% $ Syntax $
% - T = slnlda(X, nums)
% - T = slnlda(X, nums, ...)
%
% $ Arguments $
% - X: the training sample matrix
% - nums: the numbers of samples in all classes
% - T: the so... |
github | lmthang/nmt.hybrid-master | slcopca.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/subspace/slcopca.m | 4,626 | utf_8 | 186290ce05bc6bc5e4eb4b21f74985cc | function [P1, P2, spectrum] = slcopca(X1, X2, d, varargin)
%SLCOPCA Performs Coupled PCA Learning
%
% $ Syntax $
% - [P1, P2] = slcopca(X1, X2, sch, ...)
% - [P1, P2, spectrum] = slcopca(...)
%
% $ Arguments $
% - X1: The samples in the first modality
% - X2: The samples in the second modality
% -... |
github | lmthang/nmt.hybrid-master | slgausstype.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slgausstype.m | 2,561 | utf_8 | a963eb0bcede4dd399e752c6942b0fa8 | function tyinfo = slgausstype(GS)
%SLGAUSSTYPE Judges the type of a Gaussian model struct
%
% $ Syntax $
% - tyinfo = slgausstype(GS)
%
% $ Arguments $
% - GS: the Gaussian model struct
% - tyinfo: the type information structure with following fields
% - varform: the form of variance: 'univ... |
github | lmthang/nmt.hybrid-master | slgausscomb.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slgausscomb.m | 4,846 | utf_8 | 774397e5e31e2c0f1d8bdbf90b6524f5 | function GS = slgausscomb(varargin)
%SLGAUSSCOMB Collects the means and variances/covariances to form GS
%
% $ Syntax $
% - GS = slgausscomb('means', means, 'vars', vars, ...)
% - GS = slgausscomb('means', means, 'covs', covs, ...)
%
% $ Arguments $
% - means: the mean vectors
% - vars: the varia... |
github | lmthang/nmt.hybrid-master | slcovs.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slcovs.m | 5,644 | utf_8 | cff9c98b7de950309b052144323e4e0e | function varargout = slcovs(X, w, nums, M)
%SLCOVS Computes the sample covariance matrix
%
% $ Syntax $
% - C = slcovs(X)
% - C = slcovs(X, w)
% - Cs = slcovs(X, [], nums)
% - Cs = slcovs(X, w, nums)
% - C = slcovs(X, w, [], M)
% - Cs = slcovs(X, w, nums, M)
% - [Cs, Cpool] = slcovs(...)
%
% $... |
github | lmthang/nmt.hybrid-master | slinvcov.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slinvcov.m | 3,142 | utf_8 | b5c9cb40b73f1e25257d946e7adaac4d | function R = slinvcov(C, method, r)
%SLINVCOV Compute the inverse of an covariance matrix
%
% $ Syntax $
% - R = slinvcov(C)
% - R = slinvcov(C, method, r)
%
% $ Arguments $
% - C: the covariance matrix (matrices)
% - method: the method of inverse calculation
% - r: the additional pa... |
github | lmthang/nmt.hybrid-master | slgaussrnd.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slgaussrnd.m | 3,005 | utf_8 | fea0b2d230aeeb24d4f43b98efd3f936 | function X = slgaussrnd(GS, nums)
%SLGAUSSRND Generates random samples from Gaussian models
%
% $ Syntax $
% - X = slgaussrnd(GS, nums)
%
% $ Arguments $
% - GS: The Gaussian model struct
% - nums: the number of samples from the models
%
% $ Description $
% - X = slgaussrnd(GS, nums) randomly draws sa... |
github | lmthang/nmt.hybrid-master | slgmm.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slgmm.m | 6,962 | utf_8 | 728750f7be5cc5c80f5c7a9b7648e271 | function [GS, pp, info] = slgmm(X, varargin)
%SLGMM Learns Gaussian Mixture model from samples
%
% $ Syntax $
% - GS = slgmm(X, ...)
% - [GS, pp] = slgmm(X, ...)
% - [GS, pp, info] = slgmm(X, ...)
%
% $ Arguments $
% - GS: The Gaussian model struct with mixture weights
% - pp: the poster... |
github | lmthang/nmt.hybrid-master | slcovlarge.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slcovlarge.m | 5,646 | utf_8 | a8bb44052bef42a6d6f7823e8447d2d3 | function C = slcovlarge(X, w, vmean, cachesize)
%SLCOVLARGE Computes large covariance matrix using memory-efficient way
%
% $ Syntax $
% - C = slcovlarge(X, w, vmean, cachesize)
%
% $ Arguments $
% - X: the sample matrix
% - w: the weights of samples (default = [])
% ... |
github | lmthang/nmt.hybrid-master | slgaussmdist.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slgaussmdist.m | 2,776 | utf_8 | 1f707e0d03313ecd45f3050da69b39b3 | function dists = slgaussmdist(GS, X)
%SLGAUSSMDIST Computes the Malanobis distance between samples and centers
%
% $ Syntax $
% - dists = slgaussmdist(GS, X)
%
% $ Arguments $
% - GS: the Gaussian models
% - X: the sample matrix
% - dists: the distances of samples to the model centers
... |
github | lmthang/nmt.hybrid-master | slwhiten_from_samples.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slwhiten_from_samples.m | 4,401 | utf_8 | 2fa376461be16be2931b7a0c51381f54 | function W = slwhiten_from_samples(X, varargin)
%SLWHITEN_FROM_SAMPLES Compute the whitening matrix from sample matrix
%
% $ Syntax $
% - W = slwhiten_from_samples(X)
% - W = slwhiten_from_samples(X, ...)
%
% $ Arguments $
% - X: the sample matrix
% - W: the computed whitening tra... |
github | lmthang/nmt.hybrid-master | slgausspdf.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slgausspdf.m | 2,567 | utf_8 | 76eb617008f32266cab77b2086fb4c83 | function P = slgausspdf(GS, X, varargin)
%SLGAUSSPDF Computes the probability density of Gaussian models
%
% $ Syntax $
% - P = slgausspdf(GS, X, ...)
%
% $ Arguments $
% - GS: The Gaussian model struct
% - X: the sample matrix
% - P: the computed results
%
% $ Description $
% ... |
github | lmthang/nmt.hybrid-master | slfmm.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slfmm.m | 14,235 | utf_8 | a39d6a10cec6bf26bbd876186208d853 | function [S, cw, pp, info] = slfmm(X, n, estfunctor, evalfunctor, varargin)
%SLFMM Learns a Finite Mixture Model (FMM)
%
% $ Syntax $
% - [S, cw] = slfmm(X, n, estfunctor, evalfunctor, ...)
% - [S, cw, pp] = slfmm(X, n, estfunctor, evalfunctor, ...)
% - [S, cw, pp, info] = slfmm(X, n, estfunctor, evalfuncto... |
github | lmthang/nmt.hybrid-master | slgaussest.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/stat/slgaussest.m | 8,145 | utf_8 | 99af5a98704a8da214453b0bf135a819 | function GS = slgaussest(X, varargin)
%SLGAUSSEST Estimates the Gaussian models from samples
%
% $ Syntax $
% - GS = slgaussest(X, ...)
%
% $ Arguments $
% - X: the sample matrix
% - GS: the gaussian model struct
%
% $ Description $
% - GS = slgaussest(X, ...) estimates the parameters of Gaussian m... |
github | lmthang/nmt.hybrid-master | sldistmean.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/core/sldistmean.m | 4,617 | utf_8 | f6f990dde12fe79e88524b20e10ad201 | function m = sldistmean(X1, X2, varargin)
%SLDISTMEAN Uses fast method to compute means of pairwise distances
%
% $ Syntax $
% - m = sldistmean(X1, X2, mtype, ...)
% - m = sldistmean(X1, X2, w1, w2, mtype, ...)
%
% $ Arguments $
% - X1, X2: The samples to compute the mean of pairwise distances
% - mtype:... |
github | lmthang/nmt.hybrid-master | slsymgeig.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/core/slsymgeig.m | 5,007 | utf_8 | 543d82ff44b04d94c7e07cb43a75f18f | function [evals, evecs] = slsymgeig(A, B, method, r)
%SLSYMGEIG Solve the generalized eigen decomposition for symmetric matrices
%
% $ Syntax $
% - [evals, evecs] = slsymgeig(A, B)
% - [evals, evecs] = slsymgeig(A, B, method, r)
%
% $ Description $
% - [evals, evecs] = slsymgeig(A, B) solves the generaliz... |
github | lmthang/nmt.hybrid-master | slmetric_cp.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/core/slmetric_cp.m | 4,936 | utf_8 | 97cbabfc1f8c96b26a212add9c7ab6b7 | function M = slmetric_cp(X1, X2, mtype, varargin)
%SLMETRIC_CP Computes the metrics between corresponding pairs of samples
%
% $ Syntax $
% - M = slmetric_cp(X1, X2, mtype, ...);
%
% $ Arguments $
% - X1, X2: The sample matrices with each column being a sample
% - mtype: The metric type
% - M: ... |
github | lmthang/nmt.hybrid-master | slmetric_pw.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/core/slmetric_pw.m | 11,906 | utf_8 | e3864f6e2643ac4e2c007a18ef7febc6 | function M = slmetric_pw(X1, X2, mtype, varargin)
%SLMETRIC_PW Compute the metric between column vectors pairwisely
%
% [ Syntax ]
% - M = slmetric_pw(X1, X2, mtype);
% - M = slmetric_pw(X1, X2, mtype, ...);
%
% [ Arguments ]
% - X1, X2: the sample matrices
% - mtype: the string indicating... |
github | lmthang/nmt.hybrid-master | slkmeansex.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/cluster/slkmeansex.m | 6,378 | utf_8 | 93ec5976a390681c5668ef8db318d599 | function [centers, labels, info] = slkmeansex(X, n, estfunctor, clsfunctor, varargin)
%SLKMEANSEX Performs Generalized K-means
%
% $ Syntax $
% - [centers, labels] = slkmeansex(X, n, estfunctor, clsfunctor, ...)
% - [centers, labels, info] = slkmeansex(X, n, estfunctor, clsfunctor, ...)
%
% $ Arguments $
% ... |
github | lmthang/nmt.hybrid-master | slkmeans.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/cluster/slkmeans.m | 5,327 | utf_8 | aa93907c74fc6b2c766d9aa57c298314 | function [means, labels] = slkmeans(X, varargin)
%SLKMEANS Performs K-Means Clustering on samples
%
% $ Syntax $
% - [means, labels] = slkmeans(X, ...)
%
% $ Arguments $
% - X: the sample matrix
% - means: the center(mean) vectors of the clusters
% - labels: the labels of the clusters which... |
github | lmthang/nmt.hybrid-master | slgetinterpkernel.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/interp/slgetinterpkernel.m | 2,887 | utf_8 | 22c27adb54540a2739b3ce5d368efbf4 | function [f, r] = slgetinterpkernel(kername)
%SLGETINTERPKERNEL Gets the interpolation kernel function
%
% $ Syntax $
% - [f, r] = slgetinterpkernel(kername)
%
% $ Arguments $
% - kername: The name of the interpolation kernel
% - f: The function handle to the kernel
% - r: ... |
github | lmthang/nmt.hybrid-master | sldrawpts.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/visualize/sldrawpts.m | 3,512 | utf_8 | 0c23d486ae44664f901dcc65da9d904c | function h = sldrawpts(X, varargin)
% SLDRAWPTS Draws a set of sample points on axes
%
% $ Syntax $
% - h = sldrawpts(X, ...)
% - h = sldrawpts(X, plotsyms, ...)
% - h = sldrawpts(X, nums, ...)
% - h = sldrawpts(X, nums, plotsyms, ...)
%
% $ Arguments $
% - X: the sample matrix with each column as a sa... |
github | lmthang/nmt.hybrid-master | sldrawmultiellipse.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/visualize/sldrawmultiellipse.m | 4,686 | utf_8 | 3d26341edb45956a594fabac84867a55 | function h = sldrawmultiellipse(centers, vars, npts, plotsyms, varargin)
%SLDRAWMULTIELLIPSE Draws multiple ellipses on axies
%
% $ Syntax $
% - sldrawmultiellipse(centers, vars, npts)
% - sldrawmultiellipse(centers, vars, npts, plotsyms, ...)
% - h = sldrawmultiellipse(...)
%
% $ Arguments $
% - cente... |
github | lmthang/nmt.hybrid-master | sldrawellipse.m | .m | nmt.hybrid-master/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/visualize/sldrawellipse.m | 3,176 | utf_8 | ef95098997746819863bd0f51b4ed238 | function h = sldrawellipse(center, shape, n, varargin)
%SLDRAWELLIPSE Draws an ellipse on current axis
%
% $ Syntax $
% - sldrawellipse(center, shape)
% - sldrawellipse(center, shape, n)
% - sldrawellipse(center, shape, n, ...)
% - h = sldrawellipse(...)
%
% $ Description $
% - sldrawellipse(center,... |
github | HildoBijl/GPRT-master | pdftops.m | .m | GPRT-master/ExportFig/pdftops.m | 3,574 | utf_8 | 92ff676904575e16046dfff010b4e145 | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for future refere... |
github | HildoBijl/GPRT-master | crop_borders.m | .m | GPRT-master/ExportFig/crop_borders.m | 4,618 | utf_8 | ef7a590c4064b21d59940fc3f95b7f46 | function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding, crop_amounts)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how much... |
github | HildoBijl/GPRT-master | isolate_axes.m | .m | GPRT-master/ExportFig/isolate_axes.m | 4,721 | utf_8 | 253cd7b7d8fc7cb00d0cc55926f32de5 | function fh = isolate_axes(ah, vis)
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
%... |
github | HildoBijl/GPRT-master | im2gif.m | .m | GPRT-master/ExportFig/im2gif.m | 6,048 | utf_8 | 5a7437140f8d013158a195de1e372737 | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function converts a mu... |
github | HildoBijl/GPRT-master | read_write_entire_textfile.m | .m | GPRT-master/ExportFig/read_write_entire_textfile.m | 924 | utf_8 | 779e56972f5d9778c40dee98ddbd677e | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fname - Pathn... |
github | HildoBijl/GPRT-master | pdf2eps.m | .m | GPRT-master/ExportFig/pdf2eps.m | 1,471 | utf_8 | a1f41f0c7713c73886a2323e53ed982b | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.com/xpdf ... |
github | HildoBijl/GPRT-master | print2array.m | .m | GPRT-master/ExportFig/print2array.m | 9,369 | utf_8 | ca18a1e6c5a944b591a0557bd69f1c2c | function [A, bcol] = print2array(fig, res, renderer, gs_options)
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% A = print2array(figure_handle... |
github | HildoBijl/GPRT-master | append_pdfs.m | .m | GPRT-master/ExportFig/append_pdfs.m | 2,678 | utf_8 | 949c7c4ec3f5af6ff23099f17b1dfd79 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the output fi... |
github | HildoBijl/GPRT-master | using_hg2.m | .m | GPRT-master/ExportFig/using_hg2.m | 1,002 | utf_8 | b1620dd31f4d0b8acea2723e354a3518 | %USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for improved per... |
github | HildoBijl/GPRT-master | eps2pdf.m | .m | GPRT-master/ExportFig/eps2pdf.m | 8,435 | utf_8 | 95432e4216ee24df69e7e5720c6c4039 | function eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop... |
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