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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | gijzelaerr/sonic-gesture-master | parzendc.m | .m | sonic-gesture-master/evaluate/part1/prtools/parzendc.m | 3,112 | utf_8 | 5499152fa3da34991b55b84c764f1a56 | %PARZENDC Parzen density based classifier
%
% [W,H] = PARZENDC(A)
% W = PARZENDC(A,H)
%
% INPUT
% A Dataset
% H Smoothing parameters (optional; default: estimated from A for each class)
%
% OUTPUT
% W Trained Parzen classifier
% H Smoothing parameters, estimated from the data
%
% DESCRIPTION
% For e... |
github | gijzelaerr/sonic-gesture-master | ksmoothr.m | .m | sonic-gesture-master/evaluate/part1/prtools/ksmoothr.m | 1,034 | utf_8 | 1bb121254911d38d3aed55d5c30bd04d | %KSMOOTHR Kernel smoother
%
% W = KSMOOTHR(X,H)
%
% INPUT
% X Regression dataset
% H Width parameter (default H=1)
%
% OUTPUT
% W Kernel smoother mapping
%
% DESCRIPTION
% Train a kernel smoothing W on data X, with width parameter H.
%
% SEE ALSO
% KNNR, TESTR, PLOTR
% Copyright: D.M.J. Tax, D.M.J... |
github | gijzelaerr/sonic-gesture-master | isparallel.m | .m | sonic-gesture-master/evaluate/part1/prtools/isparallel.m | 727 | utf_8 | c236c6aaf876afb62259dc6dea58e2a5 | %ISPARALLEL Test on parallel mapping
%
% N = ISPARALLEL(W)
% ISPARALLEL(W)
%
% INPUT
% W input mapping
%
% OUTPUT
% N logical value
%
% DESCRIPTION
% Returns true for parallel mappings. If no output is required,
% false outputs are turned into errors. This may be used for
% assertion.
%
% SEE ALSO
% ISMAP... |
github | gijzelaerr/sonic-gesture-master | gencirc.m | .m | sonic-gesture-master/evaluate/part1/prtools/gencirc.m | 1,003 | utf_8 | b95f991f81ebe9c78ff8cf68f51694dd | %GENCIRC Generation of a one-class circular dataset
%
% A = GENCIRC(N,S)
%
% INPUT
% N Size of dataset (optional; default: 50)
% S Standard deviation (optional; default: 0.1)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% Generation of a uniformly distributed one-class 2D circular
% dataset with radius 1 ... |
github | gijzelaerr/sonic-gesture-master | averagec.m | .m | sonic-gesture-master/evaluate/part1/prtools/averagec.m | 1,494 | utf_8 | 1e23e2eef193ec91ec199c2ac34645d5 | %AVERAGEC Combining of linear classifiers by averaging coefficients
%
% W = AVERAGEC(V)
% W = V*AVERAGEC
%
% INPUT
% V A set of affine base classifiers.
%
% OUTPUT
% W Combined classifier.
%
% DESCRIPTION
% Let V = [V1,V2,V3, ... ] is a set of affine classifiers trained on the same
% classes, then W is the aver... |
github | gijzelaerr/sonic-gesture-master | perlc.m | .m | sonic-gesture-master/evaluate/part1/prtools/perlc.m | 3,940 | utf_8 | ab6cd9ecba1cb940276cbd81aea4b8b0 | % PERLC - Train a linear perceptron classifier
%
% W = PERLC(A)
% W = PERLC(A,MAXITER,ETA,W_INI,TYPE)
%
% INPUT
% A Training dataset
% MAXITER Maximum number of iterations (default 100)
% ETA Learning rate (default 0.1)
% W_INI Initial weights, as affine mapping, e.g W_INI = NMC(A)
% ... |
github | gijzelaerr/sonic-gesture-master | rejectm.m | .m | sonic-gesture-master/evaluate/part1/prtools/rejectm.m | 1,951 | utf_8 | fa3364ba0a378a5cb9db889c924a2b4c | %REJECTM Rejection mapping
%
% W = REJECTM(A,FRAC)
%
% DESCRIPTION
% Train the threshold of a rejection mapping W such that a fraction FRAC
% of the training data A is rejected. Dataset A is usually the output of
% a classifier. The mapping REJECTM will add one extra reject class.
%
% W = REJECTM(A,FRAC,REJNAME)
... |
github | gijzelaerr/sonic-gesture-master | testp.m | .m | sonic-gesture-master/evaluate/part1/prtools/testp.m | 2,691 | utf_8 | b23721ebac675abf1169567eb38380a6 | %TESTP Error estimation of Parzen classifier
%
% E = TESTP(A,H,T)
% E = TESTP(A,H)
%
% INPUT
% A input dataset
% H matrix smoothing parameters (optional, def: determined via
% parzenc)
% T test dataset (optional)
%
% OUTPUT
% E estimated error rate
%
% DESCRIPTION
% Tests a d... |
github | gijzelaerr/sonic-gesture-master | prtver.m | .m | sonic-gesture-master/evaluate/part1/prtools/prtver.m | 917 | utf_8 | 49fcbe9eb3983b8c6c1533be8d0cdcbb | %PRTVER Get PRTools version
%
%This routine is intended for internal use in PRTools only
function prtversion = prtver
persistent PRTVERSION
if ~isempty (PRTVERSION)
prtversion = PRTVERSION;
return
end
verstring = version;
if strcmp(computer,'MAC2') | verstring(1) == '5';
% name = fileparts(which('fis... |
github | gijzelaerr/sonic-gesture-master | pcaklm.m | .m | sonic-gesture-master/evaluate/part1/prtools/pcaklm.m | 5,662 | utf_8 | f4ef48bffcd04f38149f303063cfaee0 | %PCAKLM Principal Component Analysis/Karhunen-Loeve Mapping
% (PCA or MCA of overall/mean covariance matrix)
%
% [W,FRAC] = PCAKLM(TYPE,A,N)
% [W,N] = PCAKLM(TYPE,A,FRAC)
%
% INPUT
% A Dataset
% TYPE Type of mapping: 'pca' or 'klm'. Default: 'pca'.
% N or FRAC Number of dimensions (>= ... |
github | gijzelaerr/sonic-gesture-master | loglc.m | .m | sonic-gesture-master/evaluate/part1/prtools/loglc.m | 3,458 | utf_8 | 4c1da4f17c22125827b02b458af22294 | %LOGLC Logistic Linear Classifier
%
% W = LOGLC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Logistic linear classifier
%
% DESCRIPTION
% Computation of the linear classifier for the dataset A by maximizing the
% likelihood criterion using the logistic (sigmoid) function.
% This routine becomes very slow for ... |
github | gijzelaerr/sonic-gesture-master | modeseek.m | .m | sonic-gesture-master/evaluate/part1/prtools/modeseek.m | 1,991 | utf_8 | 8fe1d02f08dc5537527b58387dd22cae | %MODESEEK Clustering by mode-seeking
%
% [LAB,J] = MODESEEK(D,K)
%
% INPUT
% D Distance matrix or distance dataset (square)
% K Number of neighbours to search for local mode (default: 10)
%
% OUTPUT
% LAB Cluster assignments, 1..K
% J Indices of modal samples
%
% DESCRIPTION
% A K-NN mo... |
github | gijzelaerr/sonic-gesture-master | plsm.m | .m | sonic-gesture-master/evaluate/part1/prtools/plsm.m | 2,555 | utf_8 | 44a925147418e1579d1827ebc1614079 | % PLSM Partial Least Squares Feature Extraction
%
% W = PLSM
% W = PLSM([],MAXLV,METHOD)
%
% [W, INFORM] = PLSM(A,MAXLV,METHOD)
%
% INPUT
% A training dataset
% MAXLV maximal number of latent variables (will be corrected
% if > rank(A));
% MAXLV=inf means MAX... |
github | gijzelaerr/sonic-gesture-master | pls_apply.m | .m | sonic-gesture-master/evaluate/part1/prtools/pls_apply.m | 1,626 | utf_8 | 961a8eadfab5964c53573af62c6b64f2 | %pls_apply Partial Least Squares (applying)
%
% Y = pls_apply(X,B)
% Y = pls_apply(X,B,Options)
%
% INPUT
% X [N -by- d_X] the input data matrix, N samples, d_X variables
% B [d_X -by- d_Y] regression matrix: Y_new = X_new*B
% (X_new here after preprocessing, Y_new before
%... |
github | gijzelaerr/sonic-gesture-master | parallel.m | .m | sonic-gesture-master/evaluate/part1/prtools/parallel.m | 6,282 | utf_8 | dd4ed7f1a92c7fafca46ca8ca5ee2e14 | %PARALLEL Combining classifiers in different feature spaces
%
% WC = PARALLEL(W1,W2,W3, ....) or WC = [W1;W2;W3; ...]
% WC = PARALLEL({W1;W2;W3; ...}) or WC = [{W1;W2;W3; ...}]
% WC = PARALLEL(WC,W1,W2, ....) or WC = [WC;W2;W3; ...]
% WC = PARALELL(C);
% WC = PARALLEL(WC,N);
%
% INPUT
% W1,W2,... Ba... |
github | gijzelaerr/sonic-gesture-master | im_fill_norm.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_fill_norm.m | 1,152 | utf_8 | fcd880b1620e4cf27165ee515172ac83 | %IM_FILL_NORM Fill and normalize image for display puproses
%
% B = IM_FILL_NORM(A,N,BACKGROUND)
%
%Low level routine for the DATAFILE/SHOW command to display non-square
%images of the datafile A, inside square of NxN pixels. Empty areas are
%filled with gray.
%Empty parts of images are given the value BACKGRO... |
github | gijzelaerr/sonic-gesture-master | isfeatim.m | .m | sonic-gesture-master/evaluate/part1/prtools/isfeatim.m | 621 | utf_8 | 9e19b7be2892fcb9bd5d884c251c94c5 | %ISFEATIM
%
% N = ISFEATIM(A);
%
% INPUT
% A Input dataset
%
% OUTPUT
% N 1/0 if dataset A does/doesn't contain images
%
% DESCRIPTION
% True if dataset contains features that are images.
%
% SEE ALSO
% ISDATASET, ISMAPPING, ISDATAIM
% $Id: isfeatim.m,v 1.2 2006/03/08 22:06:58 duin Exp $
function n = isfeati... |
github | gijzelaerr/sonic-gesture-master | pls_prepro.m | .m | sonic-gesture-master/evaluate/part1/prtools/pls_prepro.m | 1,715 | utf_8 | d87b6dd6fe49928ae30c51efbc000dbc | % [X,centering,scaling] = pls_prepro(X,centering,scaling, flag)
function [X,centering,scaling] = pls_prepro(X,centering,scaling, flag)
% Copyright: S.Verzakov, serguei@ph.tn.tudelft.nl
% Faculty of Applied Sciences, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands
if nargin<4
flag = 1;... |
github | gijzelaerr/sonic-gesture-master | clevalf.m | .m | sonic-gesture-master/evaluate/part1/prtools/clevalf.m | 4,409 | utf_8 | cf5e535696e36f4d01d8c9746d141c1a | %CLEVALF Classifier evaluation (feature size curve)
%
% E = CLEVALF(A,CLASSF,FEATSIZES,LEARNSIZE,NREPS,T,TESTFUN)
%
% INPUT
% A Training dataset.
% CLASSF The untrained classifier to be tested.
% FEATSIZES Vector of feature sizes (default: all sizes)
% LEARNSIZE Number of objects/fraction of ... |
github | gijzelaerr/sonic-gesture-master | distm.m | .m | sonic-gesture-master/evaluate/part1/prtools/distm.m | 2,392 | utf_8 | 86750607d43a524f999ca5c5de2345f8 | %DISTM Compute square Euclidean distance matrix
%
% D = DISTM(A,B)
% D = DISTM(A);
% D = A*DISTM
%
% INPUT
% A,B Datasets or matrices; B is optional, default B = A
%
% OUTPUT
% D Square Euclidean distance dataset or matrix
%
% DESCRIPTION
% Computation of the square Euclidean distance matrix D betw... |
github | gijzelaerr/sonic-gesture-master | svo.m | .m | sonic-gesture-master/evaluate/part1/prtools/svo.m | 5,695 | utf_8 | a9b5f6ada2a4fc27d55d2577eb06d58f | %SVO Support Vector Optimizer
%
% [V,J,C,NU] = SVO(K,NLAB,C,OPTIONS)
%
% INPUT
% K Similarity matrix
% NLAB Label list consisting of -1/+1
% C Scalar for weighting the errors (optional; default: 1)
% OPTIONS
% .PD_CHECK force positive definiteness of the kernel by adding a small constant ... |
github | gijzelaerr/sonic-gesture-master | prcursor.m | .m | sonic-gesture-master/evaluate/part1/prtools/prcursor.m | 1,012 | utf_8 | f0ffc26e1d4b1085e240482c53ee31cb | %PRCURSOR Show object ident.
%
% PRCURSOR(H)
%
% Enable the datacursor in a scatterplot. This can be used to
% investigate the object identifier by clicking on the object.
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
fu... |
github | gijzelaerr/sonic-gesture-master | clevalb.m | .m | sonic-gesture-master/evaluate/part1/prtools/clevalb.m | 5,779 | utf_8 | 49bc4933f35f2f156671760c1c794679 | %CLEVALB Classifier evaluation (learning curve), bootstrap version
%
% E = CLEVALB(A,CLASSF,TRAINSIZES,N)
%
% INPUT
% A Training dataset
% CLASSF Classifier to evaluate
% TRAINSIZES Vector of class sizes, used to generate subsets of A
% (default [2,3,5,7,10,15,20,30,50,70,100])
% ... |
github | gijzelaerr/sonic-gesture-master | klms.m | .m | sonic-gesture-master/evaluate/part1/prtools/klms.m | 1,499 | utf_8 | c5280fd52bab9dc81ed54a1061e8b099 | %KLMS Karhunen Loeve Mapping, followed by scaling
%
% [W,FRAC] = KLMS(A,N)
% [W,N] = KLMS(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions (>= 1) or fraction of variance (< 1)
% to retain; if > 0, perform PCA; otherwise MCA. Default: N = inf.
%
% OUTPUT
% W ... |
github | gijzelaerr/sonic-gesture-master | knn_map.m | .m | sonic-gesture-master/evaluate/part1/prtools/knn_map.m | 3,521 | utf_8 | 28cd04eb09f56ceb0eb0c9a40f405a7d | %KNN_MAP Map a dataset on a K-NN classifier
%
% F = KNN_MAP(A,W)
%
% INPUT
% A Dataset
% W K-NN classifier trained by KNNC
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps the dataset A by the K-NN classifier W on the [0,1] interval for
% each of the classes that W is trained on. The posteri... |
github | gijzelaerr/sonic-gesture-master | im_measure.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_measure.m | 4,741 | utf_8 | 765294902b9550e1a36b7693028b2d73 | %IM_MEASURE Computation by DIP_Image of feature measurements
%
% F = IM_MEASURE(A,GRAY,FEATURES)
%
% INPUT
% A Dataset with binary object images dataset (possibly multi-band)
% GRAY Gray-valued images (matched with A, optional)
% FEATURES Features to be computed
%
% OUTPUT
% F Dataset with co... |
github | gijzelaerr/sonic-gesture-master | mds_stress.m | .m | sonic-gesture-master/evaluate/part1/prtools/mds_stress.m | 1,539 | utf_8 | 9d18dc6dbb2205f7949662a11fb0146e | %MDS_STRESS - Sammon stress between dissimilarity matrices
%
% E = MDS_STRESS(q,Ds,D)
%
% INPUT
% q Indicator of the Sammon stress; q = -2,-1,0,1,2
% Ds Original distance matrix
% D Approximated distance matrix
%
% OUTPUT
% E Sammon stress
%
% DESCRIPTION
% Computes the Sammon stress between the ori... |
github | gijzelaerr/sonic-gesture-master | closemess.m | .m | sonic-gesture-master/evaluate/part1/prtools/closemess.m | 414 | utf_8 | 74b3dc0ce1e07c2c0a6f04f464762113 | %CLOSEMESS Close progress message
%
% CLOSEMESS(FID,N)
%
% Closes a progress message of length N on file-id FID
%
% This routine is obsolete now and just preserved to get
% old code running.
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 260... |
github | gijzelaerr/sonic-gesture-master | gendatsin.m | .m | sonic-gesture-master/evaluate/part1/prtools/gendatsin.m | 1,008 | utf_8 | 9b2a9a557eb3beb99a42a96d70edff8d | %GENREGSIN Generate sinusoidal regression data
%
% X = GENDATSIN(N,SIGMA)
%
% INPUT
% N Number of objects to generate
% SIGMA Standard deviation of the noise
%
% OUTPUT
% X Regression dataset
%
% DESCRIPTION
% Generate an artificial regression dataset [X,Y] with:
%
% y = sin(4x) + noise.
%
%... |
github | gijzelaerr/sonic-gesture-master | im_gauss.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_gauss.m | 1,627 | utf_8 | 0d1be89edc304ad76adb7ebd7ddf0bf3 | %IM_GAUSS Gaussian filter of images stored in a dataset/datafile (Matlab)
%
% B = IM_GAUSS(A,SX,SY)
% B = A*IM_GAUSS([],SX,SY)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% SX Desired horizontal standard deviation for filter, default SX = 1
% SY Desired vertical standard devia... |
github | gijzelaerr/sonic-gesture-master | emclust.m | .m | sonic-gesture-master/evaluate/part1/prtools/emclust.m | 7,248 | utf_8 | 239d82760474b6ec630b238a2a51be88 | %EMCLUST Expectation-Maximization clustering
%
% [LABELS,W_EM] = EMCLUST (A,W_CLUST,K,LABTYPE,FID)
%
% INPUT
% A Dataset, possibly labeled
% W_CLUST Cluster model mapping, untrained (default: nmc)
% K Number of clusters (default: 2)
% LABTYPE Label type: 'crisp' or 'soft' (default: label ty... |
github | gijzelaerr/sonic-gesture-master | normal_map.m | .m | sonic-gesture-master/evaluate/part1/prtools/normal_map.m | 8,134 | utf_8 | 77e33f0dee2b9f0e6a636ddff92e37d9 | %NORMAL_MAP Map a dataset on normal-density classifiers or mappings
%
% F = NORMAL_MAP(A,W)
%
% INPUT
% A Dataset
% W Mapping
%
% OUTPUT
% F Density estimation for classes in A
%
% DESCRIPTION
% Maps the dataset A by the normal density based classifier or mapping W.
% For each object in A, F returns the ... |
github | gijzelaerr/sonic-gesture-master | circles3d.m | .m | sonic-gesture-master/evaluate/part1/prtools/circles3d.m | 930 | utf_8 | bf2367f2ff9b48e17f9421654f4ea159 | % CIRCLES3D Create a data set containing 2 circles in 3 dimensions.
%
% DATA = CIRCLES3D(N)
%
% Creates a data set containing N points in 3 dimensions.
%
% If N is a vector of sizes, exactly N(I) objects are generated
% for class I, I = 1,2.Default: N = [50 50].
%
% See also DATASETS, PRDATASETS
% Copyright: E. Pe... |
github | gijzelaerr/sonic-gesture-master | nodatafile.m | .m | sonic-gesture-master/evaluate/part1/prtools/nodatafile.m | 421 | utf_8 | d83c026d864d8bc78ddc3ad2b862a7ae | %NODATAFILE Error return in case of datafile
%
% NODATAFILE
%
% Error message
%
% B = NODATAFILE(A)
% B = A*NODATAFILE
%
% Error message in case A is a datafile, otherwise B = A
function a = nodatafile(a)
if (nargin == 0 & nargout == 0) | (nargin == 1 & isdatafile(a) & nargout == 0)
error('Command not implemen... |
github | gijzelaerr/sonic-gesture-master | gendatr.m | .m | sonic-gesture-master/evaluate/part1/prtools/gendatr.m | 784 | utf_8 | 5282de6dceaaa18c3d45f24df4b4b109 | %GENDATR Generation of regression data
%
% A = GENDATR(X,Y)
%
% INPUT
% X data matrix
% Y target values
%
% OUTPUT
% A regression dataset
%
% DESCRIPTION
% Generate a regression data from the data X and the target values Y.
%
% SEE ALSO
% SCATTERR, GENDATSINC
% Copyright: D.M.J. Tax, D.M.J.Tax@prt... |
github | gijzelaerr/sonic-gesture-master | tree_map.m | .m | sonic-gesture-master/evaluate/part1/prtools/tree_map.m | 2,570 | utf_8 | cfb50d5529a9f524e2f717ffeeeb2533 | %TREE_MAP Map a dataset by binary decision tree
%
% F = TREE_MAP(A,W)
%
% INPUT
% A Dataset
% W Decision tree mapping
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps the dataset A by the binary decision tree classifier W on the
% [0,1] interval for each of the classes W is trained on. The
% pos... |
github | gijzelaerr/sonic-gesture-master | nu_svro.m | .m | sonic-gesture-master/evaluate/part1/prtools/nu_svro.m | 8,512 | utf_8 | f5e1da68470cd4080b3b5e0b71ceb0f4 | %NU_SVRO Support Vector Optimizer
%
% [V,J] = NU_SVRO(K,Y,C)
%
% INPUT
% K Similarity matrix
% NLAB Label list consisting of -1/+1
% C Scalar for weighting the errors (optional; default: 10)
%
% OUTPUT
% V Vector of weights for the support vectors
% J Index vector pointing to the support ve... |
github | gijzelaerr/sonic-gesture-master | lines5d.m | .m | sonic-gesture-master/evaluate/part1/prtools/lines5d.m | 1,045 | utf_8 | 97363967a36f35b3569e57b8804c04df | %LINES5D Generates three 5-dimensional lines
%
% A = LINES5D(N);
%
% Generates a data set of N points, on 3 non-crossing, non-parallel lines
% in 5 dimensions.
%
% If N is a vector of sizes, exactly N(I) objects are generated
% for class I, I = 1,2.Default: N = [50 50 50].
%
% See also DATASETS, PRDATASETS
% Copyrig... |
github | gijzelaerr/sonic-gesture-master | pinvr.m | .m | sonic-gesture-master/evaluate/part1/prtools/pinvr.m | 2,833 | utf_8 | db6ec97e9daf736f3faa8926c39eaa2d | %PINVR PSEUDO-INVERSE REGRESSION (PCR)
%
% [W,J,C] = PINVR(A,TYPE,PAR,C,SVR_TYPE,EPS_TOL,MC,PD)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (optional; default: 'p')
% PAR Kernel parameter (optional; default: 1)
%
% MC Do or do not data mean-centering (optional; default: 1 (to do))
% PD Do o... |
github | gijzelaerr/sonic-gesture-master | parzenc.m | .m | sonic-gesture-master/evaluate/part1/prtools/parzenc.m | 4,367 | utf_8 | cf34ee89811b6ad98cb4765651de431c | %PARZENC Optimisation of the Parzen classifier
%
% [W,H] = PARZENC(A)
% W = PARZENC(A,H,FID)
%
% INPUT
% A dataset
% H smoothing parameter (may be scalar, vector of per-class
% parameters, or matrix with parameters for each class (rows) and
% dimension (columns))
% FID File ID to write progres... |
github | gijzelaerr/sonic-gesture-master | prversion.m | .m | sonic-gesture-master/evaluate/part1/prtools/prversion.m | 726 | utf_8 | 31fae3be7d1e6e9b4cc4eca5bf907ad6 | %PRVERSION PRtools version number
%
% [VERSION,STR,DATE] = PRVERSION
%
% OUTPUT
% VERSION Version number (double)
% STR Version number (string)
% DATE Version date (string)
%
% DESCRIPTION
% Returns the numerical version number of PRTools VER (e.g. VER = 3.2050)
% and as a string, e.g. STR = '3.2.5'. In DAT... |
github | gijzelaerr/sonic-gesture-master | im_center.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_center.m | 1,438 | utf_8 | 00ed7fe66c0c585fdbfffedd6331d429 | %IM_CENTER Shift all binary images in dataset: center to center of gravity
%
% B = IM_CENTER(A)
% B = A*IM_CENTER
%
% The objects in the binary images are shifted such that their centers of
% gravities are in the image center.
%
% B = IM_CENTER(A,N)
%
% In all directions N rows and columns are added after shifti... |
github | gijzelaerr/sonic-gesture-master | gendatlin.m | .m | sonic-gesture-master/evaluate/part1/prtools/gendatlin.m | 940 | utf_8 | f1930c8927d3b84b82eba5af2f1893ac | %GENDATLIN Generation of linear regression data
%
% A = GENDATLIN(N,B0,B1,SIGMA)
%
% INPUT
% N Number of objects to generate
% B0 Offset
% B1 Slope
% SIGMA Standard deviation of the noise
%
% OUTPUT
% A Regression dataset
%
% DESCRIPTION
% Generate regression data A, containing N ... |
github | gijzelaerr/sonic-gesture-master | image_dbr.m | .m | sonic-gesture-master/evaluate/part1/prtools/image_dbr.m | 18,808 | utf_8 | 9b920c6c4de759707dd093ddb4db2928 | function varargout = image_dbr(varargin)
%IMAGE_DBR M-file for image_dbr.fig
% IMAGE_DBR, by itself, creates a new IMAGE_DBR or raises the existing
% singleton*.
%
% H = IMAGE_DBR returns the handle to a new IMAGE_DBR or the handle to
% the existing singleton*.
%
% IMAGE_DBR('Property','Value',... |
github | gijzelaerr/sonic-gesture-master | wvotec.m | .m | sonic-gesture-master/evaluate/part1/prtools/wvotec.m | 3,605 | utf_8 | e97b52d375f85b99e5bfbf4719945011 | %WVOTEC Weighted combiner (Adaboost weights)
%
% W = WVOTEC(A,V) compute weigths and store
% W = WVOTEC(V,U) Construct weighted combiner using weights U
%
% INPUT
% A Labeled dataset
% V Parallel or stacked set of trained classifiers
% U Set of classifier weights
%
% OUTPUT
% ... |
github | gijzelaerr/sonic-gesture-master | im_mean.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_mean.m | 1,216 | utf_8 | 2bd973e381a00889d4869daeb84c6e27 | %IM_MEAN Computation of the centers of gravity of images
%
% B = IM_MEAN(A)
% B = A*IM_MEAN
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
%
% OUTPUT
% B Dataset with centers-of-gravity replacing images
% (possibly multi-band). The first component is always meas... |
github | gijzelaerr/sonic-gesture-master | preig.m | .m | sonic-gesture-master/evaluate/part1/prtools/preig.m | 422 | utf_8 | 30d2f7223bb8aef5fff931f20106ba95 | %PREIG Call to EIG() including PRWAITBAR
%
% [E,D] = PREIG(A)
%
% This calls [E,D] = EIG(A) and includes a message to PRWAITBAR
% in case of a large A
function [E,D] = preig(A)
[m,n] = size(A);
if min([m,n]) > 500
%prwaitbaronce('Computing %i x %i eigenvectors ...',[m,n]);
if nargout == 1
E = eig(A);
else
[E,... |
github | gijzelaerr/sonic-gesture-master | mlrc.m | .m | sonic-gesture-master/evaluate/part1/prtools/mlrc.m | 3,609 | utf_8 | abbaafe79e6764055df8ab5351d3f24d | % MLRC Muli-response Linear Regression Combiner
%
% W = A*(WU*MLRC)
% W = WT*MLRC(B*WT)
% D = C*W
%
% INPUT
% A Dataset used for training base classifiers as well as combiner
% B Dataset used for training combiner of trained base classifiers
% C Dataset used for testing (executing) the combiner
... |
github | gijzelaerr/sonic-gesture-master | obj2feat.m | .m | sonic-gesture-master/evaluate/part1/prtools/obj2feat.m | 419 | utf_8 | b73488a7727dd82b3e8f672296fe8d39 | %OBJ2FEAT Transform object images to feature images in dataset
%
% B = OBJ2FEAT(A)
%
% INPUT
% A Dataset with object images, possible with multiple bands
%
% OUTPUT
% B Dataset with features images
%
% SEE ALSO
% DATASETS, IM2OBJ, IM2FEAT, DATA2IM, FEAT2OBJ
function b = obj2feat(a)
prtra... |
github | gijzelaerr/sonic-gesture-master | minc.m | .m | sonic-gesture-master/evaluate/part1/prtools/minc.m | 1,724 | utf_8 | b6414ed51e0df782bc8e731755a0e0d3 | %MINC Minimum combining classifier
%
% W = MINC(V)
% W = V*MINC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Minimum combining classifier on V
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the
% same classes and W is the maximum combiner: it selects the class
% with th... |
github | gijzelaerr/sonic-gesture-master | knnr.m | .m | sonic-gesture-master/evaluate/part1/prtools/knnr.m | 986 | utf_8 | 6f361a2a60209246c49a0820cef1a076 | %KNNR Nearest neighbor regression
%
% Y = KNNR(X,K)
%
% INPUT
% X Regression dataset
% K number of neighbors (default K=3)
%
% OUTPUT
% Y k-nearest neighbor regression
%
% DESCRIPTION
% Define a k-Nearest neighbor regression on dataset X.
%
% SEE ALSO
% LINEARR, TESTR, PLOTR
% Copyright: D.M.J. Tax,... |
github | gijzelaerr/sonic-gesture-master | kmeans.m | .m | sonic-gesture-master/evaluate/part1/prtools/kmeans.m | 3,537 | utf_8 | c12405092e32824030d39fa86dfaa233 | %KMEANS k-means clustering
%
% [LABELS,A] = KMEANS(A,K,MAXIT,INIT,FID)
%
% INPUT
% A Matrix or dataset
% K Number of clusters to be found (optional; default: 2)
% MAXIT maximum number of iterations (optional; default: 50)
% INIT Labels for initialisation, or
% 'rand' : take at random... |
github | gijzelaerr/sonic-gesture-master | im_norm.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_norm.m | 974 | utf_8 | 108b2fee72ae6fd49c5ae1efc8afac78 | %IM_NORM Mapping for normalizing images: mean, variance
%
% B = IM_NORM(A)
% B = A*IM_NORM
%
% INPUT
% A Dataset or datafile
%
% OUTPUT
% B Dataset or datafile
%
% DESCRIPTION
% The objects stored as images in the dataset or datafile A are normalised
% w.r.t. their mean (0) and variance (1)... |
github | gijzelaerr/sonic-gesture-master | logdens.m | .m | sonic-gesture-master/evaluate/part1/prtools/logdens.m | 1,743 | utf_8 | f19d2f4edeb5a881769faebd6e7e4b2c | %LOGDENS Force density based classifiers to use log-densities
%
% V = LOGDENS(W)
% V = W*LOGDENS
%
% INPUT
% W Density based trained classifier
%
% OUTPUT
% V Log-density based trained classifier
%
% DESCRIPTION
% Density based classifiers suffer from a low numeric accuracy in the tails
% of th... |
github | gijzelaerr/sonic-gesture-master | plsr.m | .m | sonic-gesture-master/evaluate/part1/prtools/plsr.m | 2,956 | utf_8 | 5ca665d50587aa96474a51fd0a120597 | % PLSR Partial Least Squares Regression
%
% W = PLSR
% W = PLSR([],MAXLV,METHOD)
%
% [W, INFORM] = PLSR(A,MAXLV,METHOD)
%
% INPUT
% A training dataset
% MAXLV maximal number of latent variables (will be corrected
% if > rank(A));
% MAXLV=inf means MAXLV=min(s... |
github | gijzelaerr/sonic-gesture-master | im_select_blob.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_select_blob.m | 935 | utf_8 | e1e9f866b26bbf9e94a36e83d99d1fe3 | %IM_SELECT_BLOB Select largest blob in binary images in dataset (DIP_Image)
%
% B = IM_SELECT_BLOB(IM)
%
% Just the largest object in the image is returned.
%
% SEE ALSO
% DATASETS, DATAFILES, DIP_IMAGE
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031... |
github | gijzelaerr/sonic-gesture-master | featrank.m | .m | sonic-gesture-master/evaluate/part1/prtools/featrank.m | 1,548 | utf_8 | 1b6fbcb41238f457e3235100517ed770 | %FEATRANK Feature ranking on individual performance for classification
%
% [I,F] = FEATRANK(A,CRIT,T)
%
% INPUT
% A input dataset
% CRIT string name of a method or untrained mapping
% T validation dataset (optional)
%
% OUTPUT
% I vector with sorted feature indices
% F ... |
github | gijzelaerr/sonic-gesture-master | udc.m | .m | sonic-gesture-master/evaluate/part1/prtools/udc.m | 1,305 | utf_8 | 5f7a31ca4be7e6246f97f82f5ef2c63d | %UDC Uncorrelated normal based quadratic Bayes classifier (BayesNormal_U)
%
% W = UDC(A)
% W = A*UDC
%
% INPUT
% A input dataset
%
% OUTPUT
% W output mapping
%
% DESCRIPTION
% Computation a quadratic classifier between the classes in the
% dataset A assuming normal densities with uncorrelated features.
%
% T... |
github | gijzelaerr/sonic-gesture-master | naivebc.m | .m | sonic-gesture-master/evaluate/part1/prtools/naivebc.m | 5,057 | utf_8 | 682c8b1b678f2b2f98010d395b6ca79f | %NAIVEBC Naive Bayes classifier
%
% W = NAIVEBC(A,N)
% W = A*NAIVEBC([],N)
%
% W = NAIVEBC(A,DENSMAP)
% W = A*NAIVEBC([],DENSMAP)
%
% INPUT
% A Training dataset
% N Scalar number of bins (default: 10)
% DENSMAP Untrained mapping for density estimation
%
% OUTPUT
% W Naive Bayes classifi... |
github | gijzelaerr/sonic-gesture-master | im_profile.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_profile.m | 1,839 | utf_8 | 4ac4ef7c3a6157021c8a985dd9cd1c34 | %IM_PROFILE Computation of horizontal and vertical image profile
%
% P = IM_PROFILE(A,NX,NY)
% P = A*IM_PROFILE([],NX,NY)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% NX Number of bins for horizontal profile
% NY Number of bins for vertical profile
%
% OUTPUT
% P ... |
github | gijzelaerr/sonic-gesture-master | plotf.m | .m | sonic-gesture-master/evaluate/part1/prtools/plotf.m | 2,216 | utf_8 | e952c5f78e0c02fd34f82ba6895f5e72 | %PLOTF Plot feature distribution, special version
%
% h = PLOTF(A,N)
%
% Produces 1-D density plots for all the features in dataset A. The
% densities are estimated using PARZENML. N is the number of
% feature density plots on a row.
%
% See also DATASETS, PARZENML
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.... |
github | gijzelaerr/sonic-gesture-master | mds_init.m | .m | sonic-gesture-master/evaluate/part1/prtools/mds_init.m | 2,981 | utf_8 | d3873ccbbaa3a28678a465dd0a47b32b | %MDS_INIT Initialization for MDS (variants of Sammon) mapping
%
% Y = MDS_INIT (D,N,INIT)
%
% INPUT
% D Square dissimilarity matrix of the size M x M
% N Desired output dimensionality (optional; default: 2)
% INIT Initialization method (optional; default: 'randnp')
%
% OUTPUT
% Y Initial configuration for ... |
github | gijzelaerr/sonic-gesture-master | plotm.m | .m | sonic-gesture-master/evaluate/part1/prtools/plotm.m | 4,530 | utf_8 | af07489432cd67f10f4d42a6a96e2fc6 | %PLOTM Plot mapping values, contours or surface
%
% H = PLOTM(W,S,N)
%
% INPUT
% W Trained mapping
% S Plot strings, or scalar selecting type of plot
% 1: density plot;
% 2: contour plot (default);
% 3: 3D surface plot;
% 4: 3D surface plot above 2D contour plot;
% ... |
github | gijzelaerr/sonic-gesture-master | datunif.m | .m | sonic-gesture-master/evaluate/part1/prtools/datunif.m | 1,690 | utf_8 | c63527e249a4fe53eaca6011b7f02698 | %DATUNIF Apply uniform filter on images in a dataset
%
% B = DATUNIF(A,NX,NY)
%
% INPUT
% A Dataset containing images
% NX,NY Filtersize in X- and Y-direction (default: NY = NX)
%
% OUTPUT
% B Dataset with filtered images
%
% DESCRIPTION
% All images stored as objects (rows) or as features (colum... |
github | gijzelaerr/sonic-gesture-master | regoptc.m | .m | sonic-gesture-master/evaluate/part1/prtools/regoptc.m | 5,370 | utf_8 | 02e08726d2d767122b8a1c7c730e6aed | %REGOPTC Optimise regularisation and complexity parameters by crossvalidation
%
% [W,PARS] = REGOPTC(A,CLASSF,PARS,DEFS,NPAR,PAR_MIN_MAX,TESTFUN,REALINT)
%
% INPUT
% A Dataset, training set
% CLASSF Untrained classifiers (mapping)
% PARS Cell array with parameters for CLASSF
% DEFS Default... |
github | gijzelaerr/sonic-gesture-master | gendatc.m | .m | sonic-gesture-master/evaluate/part1/prtools/gendatc.m | 2,506 | utf_8 | 6b89a3ef6b64f2046f10c2d6190fd8cc | %GENDATC Generation of two spherical classes with different variances
%
% A = GENDATC(N,K,U,LABTYPE)
%
% INPUT
% N Vector with class sizes (default: [50,50])
% K Dimensionality of the dataset (default: 2)
% U Mean of class 1 (default: 0)
% LABTYPE 'crisp' or 'soft' labels (default: 'cri... |
github | gijzelaerr/sonic-gesture-master | gridsize.m | .m | sonic-gesture-master/evaluate/part1/prtools/gridsize.m | 1,279 | utf_8 | d7cf4b33766da4b039ee2ecd0d4dbf0a | %GRIDSIZE Set gridsize used in the plot commands
%
% O = GRIDSIZE(N)
%
% INPUT
% N New grid size (optional, default: display current gridsize)
%
% OUTPUT
% O New grid size (optional)
%
% DESCRIPTION
% The initial gridsize is 30, enabling fast plotting of PLOTC and PLOTM.
% This is, however, insufficien... |
github | gijzelaerr/sonic-gesture-master | gendatsinc.m | .m | sonic-gesture-master/evaluate/part1/prtools/gendatsinc.m | 912 | utf_8 | 28f4043efaddf36174a9db40003229ae | %GENDATSINC Generate Sinc data
%
% A = GENDATSINC(N,SIGMA)
%
% INPUT
% N Number of objects to generate
% SIGMA Standard deviation of the noise (default SIGMA=0.1)
%
% OUTPUT
% A Regression dataset
%
% DESCRIPTION
%
% Generate the standard 1D Sinc data containing N objects, with Gaussian
% noise... |
github | gijzelaerr/sonic-gesture-master | parzenml.m | .m | sonic-gesture-master/evaluate/part1/prtools/parzenml.m | 5,834 | utf_8 | c96aea24e5e46fcc58f6c854e494f330 | %PARZENML Optimum smoothing parameter in Parzen density estimation.
%
% H = PARZENML(A)
%
% INPUT
% A Input dataset
%
% OUTPUT
% H Scalar smoothing parameter (in case of crisp labels)
% Vector with smoothing parameters (in case of soft labels)
%
% DESCRIPTION
% Maximum likelihood estimation for th... |
github | gijzelaerr/sonic-gesture-master | lassor.m | .m | sonic-gesture-master/evaluate/part1/prtools/lassor.m | 973 | utf_8 | ba861862740a37071cd2dbdd02b46801 | %LASSOR LASSO regression
%
% W = LASSOR(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter
%
% OUTPUT
% W LASSO regression mapping
%
% DESCRIPTION
% The 'Least Absolute Shrinkage and Selection Operator' regression,
% using the regularization parameter LAMBDA.
%
% SEE AL... |
github | gijzelaerr/sonic-gesture-master | bamc.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/bamc.m | 3,913 | utf_8 | 76b7d7de16910fbaf3c2222bee795370 | function [w,zeta] = bamc(x, C, rtype, par, unitnorm)
% w = bamc(x, C, rtype, par, unitnorm)
%
% Optimize AUC on dataset X and reg. param. C. The AUC constraints can
% be sampled in different ways:
% rtype par
% 'full', - use all constraints
% 'subs', N subsample just N constraints
% 'knn' k use... |
github | gijzelaerr/sonic-gesture-master | lessmcSF.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmcSF.m | 2,260 | utf_8 | 91746fc50a2c9d13692b240fc0988f22 | %LESS-MC: MultiClass LESS-Classifier
%
% W = LESSMC(A,C,VARS,ESTIMATE,COMBINER,COMBINERSTYLE)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data map... |
github | gijzelaerr/sonic-gesture-master | lessmcMR.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmcMR.m | 2,260 | utf_8 | 507097757fe439c9a2bfe1a5020c2776 | %LESS-MC: MultiClass LESS-Classifier
%
% W = LESSMC(A,C,VARS,ESTIMATE,COMBINER,COMBINERSTYLE)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data map... |
github | gijzelaerr/sonic-gesture-master | avgprec.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/avgprec.m | 1,428 | utf_8 | c037e49ec4e79f3629b9fc76c39c69cb | % AVGPREC Compute Average Precision of classified dataset (a*w)
% + Rows are objects and columns are posteriors per class
% + For two-class problems also only one column may be used,
% where negative and positive values differentiate the
% classes.
function ap=avgprec(b)
ap=0;
... |
github | gijzelaerr/sonic-gesture-master | lessmc.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmc.m | 11,707 | utf_8 | cd646edff6c628c593afaaae6ef8e4ec | %LESS-MC: MultiClass LESS-Classifier
%
% W = LESSMC(A,C,VARS,ESTIMATE,COMBINER,COMBINERSTYLE)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data map... |
github | gijzelaerr/sonic-gesture-master | lessmcSR.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmcSR.m | 2,261 | utf_8 | e7566aa6f002c2b0c475d8c2d8f93bdf | %LESS-MC: MultiClass LESS-Classifier
%
% W = LESSMC(A,C,VARS,ESTIMATE,COMBINER,COMBINERSTYLE)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data map... |
github | gijzelaerr/sonic-gesture-master | simpleless.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/simpleless.m | 3,689 | utf_8 | a8717a1104adfadf1d88201f9246aa49 | %LESS LESS-Classifier
%
% W = LESSC(A,C,VARS)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data mapping
% default: VARS=0
%
% OUTPUT
% W ... |
github | gijzelaerr/sonic-gesture-master | lessmcXX.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmcXX.m | 12,411 | utf_8 | ae5016b5bf901e26cf44a0932d2bf943 | %LESS-MC: MultiClass LESS-Classifier
%
% W = LESSMC(A,C,VARS,ESTIMATE,COMBINER,COMBINERSTYLE)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data map... |
github | gijzelaerr/sonic-gesture-master | lessc.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessc.m | 7,775 | utf_8 | 85cbca4bffa909c95089d246e191f862 | %LESS LESS-Classifier
%
% W = LESSC(A,C,VARS)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data mapping
% default: VARS=0
%
% OUTPUT
% W ... |
github | gijzelaerr/sonic-gesture-master | lessmedian.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmedian.m | 7,610 | utf_8 | 0f6a5ce714b79df119e62bb9a3fc9cc0 | %LESS LESS-Classifier
%
% W = LESSC(A,C,VARS)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data mapping
% default: VARS=0
%
% OUTPUT
% W ... |
github | gijzelaerr/sonic-gesture-master | lessmcMF.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmcMF.m | 2,259 | utf_8 | 8e5af60fb10c0cfe2f7a163275698e39 | %LESS-MC: MultiClass LESS-Classifier
%
% W = LESSMC(A,C,VARS,ESTIMATE,COMBINER,COMBINERSTYLE)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data map... |
github | gijzelaerr/sonic-gesture-master | lessmc_23022006.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmc_23022006.m | 11,861 | utf_8 | 6d968096d6c8c1716246c3e1375bc2ae | %LESS-MC: MultiClass LESS-Classifier
%
% W = LESSMC(A,C,VARS,ESTIMATE,COMBINER,COMBINERSTYLE)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data map... |
github | gijzelaerr/sonic-gesture-master | lassoc.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lassoc.m | 1,793 | utf_8 | b7914789bfe7cfd044a3f9becc458f09 | %LASSO LASSO-Classifier
%
% W = LASSOC(A,C,mustScale)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% MUSTSCALE
% Boolean variable indicating whether the data must be scaled or
% not
% default: mustScale=1
%
% OUTPUT
% W LAS... |
github | gijzelaerr/sonic-gesture-master | lessmeanvar.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmeanvar.m | 7,616 | utf_8 | 3480fe8bf2afb3ff86a17e042c0317a6 | %LESS LESS-Classifier
%
% W = LESSC(A,C,VARS)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data mapping
% default: VARS=0
%
% OUTPUT
% W ... |
github | gijzelaerr/sonic-gesture-master | liknonc.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/liknonc.m | 2,367 | utf_8 | 43f5c2a47eaafe65209fd2632847cb50 | %LIKNON LIKNON-Classifier
%
% W = LIKNONC(A,C)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
%
% OUTPUT
% W LIKNON classifier
%
% SEE ALSO
% MAPPINGS, DATASETS, NMC, NMSC, SVC
% Copyright: Cor J. Veenman, C.J.Veenman@uva.nl
% Computer... |
github | gijzelaerr/sonic-gesture-master | lessmedianvar.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessmedianvar.m | 7,624 | utf_8 | fd8c20bbc0ed8aee499c4e60d8bcf22f | %LESS LESS-Classifier
%
% W = LESSC(A,C,VARS)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data mapping
% default: VARS=0
%
% OUTPUT
% W ... |
github | gijzelaerr/sonic-gesture-master | lessqdc.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/lessqdc.m | 3,253 | utf_8 | 0e2096d68d4467a5bd3098b879b119f8 | %LESS LESS-Classifier
%
% LESS with distance to class means optimised per class. This in contrast
% to LESS with variance scaling, where the optimal scaling is estimated
% through the variance per class.
%
% W = LESSQDC(A,C)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% defau... |
github | gijzelaerr/sonic-gesture-master | lessc.m | .m | sonic-gesture-master/evaluate/part1/mp-tools/public/lessc.m | 3,629 | utf_8 | a1028be77f7703315974a17e5a41e8ce | %LESS LESS-Classifier
%
% W = LESSC(A,C,VARS)
%
% INPUT
% A Dataset
% C Regularization parameter, C>=0
% default: C=1
% VARS Boolean variable indicating whether or not the class variance
% should be included in data mapping
% default: VARS=0
%
% OUTPUT
% W ... |
github | lawrennd/ensmlp-master | mixhypergradchek.m | .m | ensmlp-master/matlab/mixhypergradchek.m | 1,368 | utf_8 | 075f19d55eb2a73f8eb6712dba4988da | function mixhypergradchek(net, x, t)
% MIXHYPERGRADCHEK Check gradient of hyper parameters.
% ENSMLP
epsilon = 1.0e-6;
%net = ensupdatehyperpar(net, x, t);
%net = enshypermoments(net);
w = mixenspakpar(net);
nparams = length(w);
deltaf = zeros(1, nparams);
step = zeros(1, nparams);
for i = 1:length(w)
% Move a... |
github | lawrennd/ensmlp-master | enshess.m | .m | ensmlp-master/matlab/enshess.m | 2,252 | utf_8 | ae866abcbe426e65fbc8b6a0a95140cf | function [h, dh] = enshess(net, x, t, dh)
% ENSHESS Evaluate the Hessian matrix for a multi-layer perceptron network.
% FORMAT
% DESC takes an MLP network data structure NET, a
% matrix X of input values, and a matrix T of target values and returns
% the full Hessian matrix H corresponding to the second derivatives of... |
github | lawrennd/ensmlp-master | mixparsgrad.m | .m | ensmlp-master/matlab/mixparsgrad.m | 4,448 | utf_8 | 447776688665ccac296c3db6e0b84c71 | function [g, gprior, gdata, gentropy] = mixparsgrad(net, x, t)
% MIXPARSGRAD Gradient of error function with respect to mixture parameters.
% FORMAT
% DESC takes the network structure from a mixture of ensembles and returns
% the gradient with respect to the mixture distribution parameters.
% ARG net : network for whi... |
github | lawrennd/ensmlp-master | mixensgrad.m | .m | ensmlp-master/matlab/mixensgrad.m | 5,072 | utf_8 | 90731fbec540c53c5c038a52f8fc7af5 | function [g, gprior, gdata, gentropy] = mixensgrad(net, x, t)
% MIXENSGRAD Evaluate gradient of error function for 2-layer mixture ensemble network.
% FORMAT
% DESC takes a network data structure NET together with a matrix X of input
% vectors and a matrix T of target vectors, and evaluates the gradient G of
% the err... |
github | lawrennd/ensmlp-master | ensderiv.m | .m | ensmlp-master/matlab/ensderiv.m | 4,401 | utf_8 | 668b7862b4068a5152f62122c53fba1f | function g = ensderiv(net, x)
% ENSDERIV Evaluate derivatives of network outputs with respect to weights.
% FORMAT
% DESC takes a network data structure NET and a matrix
% of input vectors X and returns a three-index matrix G whose I, J, K
% element contains the derivative of network output K with respect to
% weight o... |
github | enmaskarado/vendor_st-ericsson_u8500-master | TEQ_filter_design.m | .m | vendor_st-ericsson_u8500-master/multimedia/audio/libeffects/libtransducer_equalizer/src/matlab/TEQ_filter_design.m | 33,224 | utf_8 | 93625bb70acd6ce55db6ec625d0e5dbe | function TEQ_filter_design(filter_type, name, n, order_l, gain_table_l, freq_table_l, order_r, gain_table_r, freq_table_r, sampling_freq, stereo, same_filter_l_r)
if (stereo ~= 0) && (same_filter_l_r == 0)
fprintf(1, 'left ');
compute_right = 1;
else
compute_right = 0;
end
% le... |
github | enmaskarado/vendor_st-ericsson_u8500-master | mdrc_gains.m | .m | vendor_st-ericsson_u8500-master/multimedia/audio/libeffects/libmdrc5b/src/tuning/matlab/mdrc_gains.m | 14,356 | utf_8 | 545d063f7e1a178bb8125a46beed8180 | function [gains global_response band_responses index error] = mdrc_gains(bands)
sampling_freq = 48000;
FreqCutoff = [0 1 2 3 4 5 6 7 8 9 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 110 120 130 140 150 160 170] * 100;
% b2, -b1/2, b0, -a1/2, a2
LPCoefTab... |
github | enmaskarado/vendor_st-ericsson_u8500-master | compare_mdrc_filters.m | .m | vendor_st-ericsson_u8500-master/multimedia/audio/libeffects/libmdrc5b/src/matlab/compare_mdrc_filters.m | 33,157 | utf_8 | 7921f72cda40d3538f08cc365dbd01b7 | function compare_mdrc_filters(CutFreq)
FIR_order = 41; % odd value (linear phase FIR filter)
biquad_order = 2;
fs = 48000;
biquad_order_legacy = 2; % 2 is mandatory for legacy biquads, any value for Butterworth biquads
fs_legacy ... |
github | enmaskarado/vendor_st-ericsson_u8500-master | compare_mdrc_filters.m | .m | vendor_st-ericsson_u8500-master/multimedia/audio/libeffects/libmdrc5b/libfilterdesign/matlab/compare_mdrc_filters.m | 41,123 | utf_8 | 740bf5878e64c8f9f151829e50761a11 | function compare_mdrc_filters(CutFreq, gains_dB, sampling_freq, biquad_size, FIR_size)
FIR_order = 41; % odd value (linear phase FIR filter)
biquad_order = 2;
fs = 48000;
biquad_order_legacy = 2; % 2 is mandatory for legacy biquads, an... |
github | enmaskarado/vendor_st-ericsson_u8500-master | mdrc_biquad_filters.m | .m | vendor_st-ericsson_u8500-master/multimedia/audio/libeffects/libmdrc5b/libfilterdesign/matlab/mdrc_biquad_filters.m | 9,435 | utf_8 | c80b37630b0d280891acd4a47a4ac23a | function mdrc_biquad_filters(CutFreq, gains_dB, sampling_freq, biquad_size)
biquad_order = 2;
fs = 48000;
if nargin < 1
fprintf(1, 'need at leat 1 parameter : cut frequencies table !\n');
return;
end;
assert(size(CutFreq, 1) == 1);
if nargin > 1
assert(size(gai... |
github | enmaskarado/vendor_st-ericsson_u8500-master | fhil.m | .m | vendor_st-ericsson_u8500-master/multimedia/audio/libeffects/libresampling/matlab/fhil.m | 435 | utf_8 | 6b7e3a8faa12cea48b6ee4dd81878ea9 | % [B,A] = fhil(N) where B is the set of N odd coefficents
% which can be used as a fir on a real input signal to calculate
% its imaginary output and A is the set of N odd coefficients
% which can be used as a fir on a real input signal to calculate
% its real output
function [B,A] = fhil(N)
C=(N-1)/2;
K=1:2:(C-1);
H(... |
github | enmaskarado/vendor_st-ericsson_u8500-master | dhtm.m | .m | vendor_st-ericsson_u8500-master/multimedia/audio/libeffects/libresampling/matlab/dhtm.m | 501 | utf_8 | 7d3a6da33ae6347e5272ad4429998490 | %function to return the modified 1-d discrete hilbert transform
%The code uses N pt. Fast Fourier Transforms
%Niranjan Damera-Venkata,
%Brian L. Evans and Shawn R. McCaslin
%% Takes arguements (Magnitude Spectrum,Truncation length)
function y=dhtm(mag,N,s)
sig(1:(N/2))=sign(linspace(1,(N/2),(N/2)));
sig((N/2)+1)... |
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