plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | SASVDDwt/sa_svdd-master | doublem.m | .m | sa_svdd-master/matlab/prtools/doublem.m | 405 | utf_8 | 47eea486dc8e01143b697647d6c9d72b | %DOUBLEM Datafile mapping for conversion to double
%
% B = DOUBLEM(A)
% B = A*DOUBLEM
%
% For datasets B = A, in all other cases A is converted to double.
function a = doublem(a)
prtrace(mfilename);
if nargin < 1 | isempty(a)
a = mapping(mfilename,'fixed');
a = setname(a,'double');
elseif isdataset(a)
;
e... |
github | SASVDDwt/sa_svdd-master | im_harris.m | .m | sa_svdd-master/matlab/prtools/im_harris.m | 13,080 | utf_8 | 90cc1dec7c6840c1cfc8a404986e90ec | %IM_HARRIS Harris corner detector
%
% X = IM_HARRIS(A,N,SIGMA)
%
% INPUT
% A Datafile or dataset with images
% N Number of desired Harris points per image (default 100)
% SIGMA Smoothing size (default 3)
%
% OUTPUT
% X Dataset with a [N,3] array with for every image
% x, y and strengt... |
github | SASVDDwt/sa_svdd-master | filtm.m | .m | sa_svdd-master/matlab/prtools/filtm.m | 3,233 | utf_8 | b508e965c37eaffb7a146133779ae9ef | %FILTM Mapping to filter objects in datasets and datafiles
%
% B = FILTM(A,FILTER_COMMAND,{PAR1,PAR2,....},SIZE)
% B = A*FILTM([],FILTER_COMMAND,{PAR1,PAR2,....},SIZE)
%
% INPUT
% A Dataset or datafile
% FILTER_COMMAND String with function name
% {PAR1, ... } Cell array with optional pa... |
github | SASVDDwt/sa_svdd-master | mds_cs.m | .m | sa_svdd-master/matlab/prtools/mds_cs.m | 3,083 | utf_8 | 3146fafdcca70fc84952a465cab553ce | % MDS_CS Classical scaling
%
% W = MDS_CS(D,N)
%
% INPUT
% D Square dissimilarity matrix of the size M x M
% N Desired output dimensionality (optional; default: 2)
%
% OUTPUT
% W Classical scaling mapping
%
% DESCRIPTION
% A linear mapping of objects given by a symmetric distance matrix D with
% a zero diago... |
github | SASVDDwt/sa_svdd-master | rsquared.m | .m | sa_svdd-master/matlab/prtools/rsquared.m | 727 | utf_8 | c4f5d33e644e6c2cdbbdc3edefb6c7e4 | %RSQUARED R^2 statistic
%
% E = RSQUARED(X,W)
% E = RSQUARED(X*W)
% E = X*W*RSQUARED
%
% INPUT
% X Regression dataset
% W Regression mapping
%
% OUTPUT
% E The R^2-statistic
%
% DESCRIPTION
% Compute the R^2 statistic of regression W on dataset X.
%
% SEE ALSO
% TESTR
% Copyright: D.M... |
github | SASVDDwt/sa_svdd-master | im_skel.m | .m | sa_svdd-master/matlab/prtools/im_skel.m | 816 | utf_8 | 8b6fef0e8819522166a1b00bf7a945ca | %IM_SKEL Skeleton of binary images stored in a dataset (DIP_Image)
%
% B = IM_SKEL(A)
% B = A*IM_SKEL
%
% INPUT
% A Dataset with binary object images dataset
%
% OUTPUT
% B Dataset with skeleton images
%
% SEE ALSO
% DATASETS, DATAFILES, DIP_IMAGE, BSKELETON
% Copyright: R.P.W. Duin, r.p.w.duin@prto... |
github | SASVDDwt/sa_svdd-master | klm.m | .m | sa_svdd-master/matlab/prtools/klm.m | 1,967 | utf_8 | d875ae891450009ebf5dfea7d03bb079 | %KLM Karhunen-Loeve Mapping (PCA or MCA of mean covariance matrix)
%
% [W,FRAC] = KLM(A,N)
% [W,N] = KLM(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.... |
github | SASVDDwt/sa_svdd-master | im_gray.m | .m | sa_svdd-master/matlab/prtools/im_gray.m | 1,528 | utf_8 | 5522afb900b3e97d26b7150df6f7a005 | %IM_GRAY Conversion of multi-band images into gray images
%
% B = IM_GRAY(A,V)
% B = A*IM_GRAY([],V)
%
% INPUT
% A Multiband image or dataset with multi-band images as objects
% V Weight vector, one weight per band. Default: equal weights.
%
% OUTPUT
% B Output image or dataset.
%
% DESCRIPTION
% The m... |
github | SASVDDwt/sa_svdd-master | quadrc.m | .m | sa_svdd-master/matlab/prtools/quadrc.m | 3,694 | utf_8 | 6f114a67232dc8685fba7e6a75adee29 | %QUADRC Quadratic Discriminant Classifier
%
% W = QUADRC(A,R,S)
%
% INPUT
% A Dataset
% R,S 0 <= R,S <= 1, regularization parameters (default: R = 0, S = 0)
%
% OUTPUT
% W Quadratic Discriminant Classifier mapping
%
% DESCRIPTION
% Computation of the quadratic classifier between the classes of the dat... |
github | SASVDDwt/sa_svdd-master | linearr.m | .m | sa_svdd-master/matlab/prtools/linearr.m | 1,242 | utf_8 | 126ce6ae982b3f26b7508b5eb9b65698 | %LINEARR Linear regression
%
% Y = LINEARR(X,LAMBDA,N)
%
% INPUT
% X Dataset
% LAMBDA Regularization parameter (default: no regularization)
% N Order of polynomial (optional)
%
% OUTPUT
% Y Linear (or higher order) regression
%
% DESCRIPTION
% Perform a linear regression on dataset X, wit... |
github | SASVDDwt/sa_svdd-master | svc_nu.m | .m | sa_svdd-master/matlab/prtools/svc_nu.m | 4,270 | utf_8 | 9096d329b41233a41eb3ca323a3e85da | %SVC_NU Support Vector Classifier: NU algorithm
%
% This routine is outdated, use NUSVC instead
%
% [W,J,C] = SVC(A,TYPE,PAR,NU,MC,PD)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (optional; default: 'p')
% PAR Kernel parameter (optional; default: 1)
% NU Regularization parameter (0 < NU < 1): e... |
github | SASVDDwt/sa_svdd-master | mclassc.m | .m | sa_svdd-master/matlab/prtools/mclassc.m | 3,837 | utf_8 | 918d82e98d79b7cb32b13d8613d90076 | %MCLASSC Computation of multi-class classifier from 2-class discriminants
%
% W = MCLASSC(A,CLASSF,MODE)
%
% INPUT
% A Dataset
% CLASSF Untrained classifier
% MODE Type of handling multi-class problems (optional; default: 'single')
%
% OUTPUT
% W Combined classifier
%
% DESCRIPTION
% For defaul... |
github | SASVDDwt/sa_svdd-master | disperror.m | .m | sa_svdd-master/matlab/prtools/disperror.m | 2,483 | utf_8 | aa9422a1321ed41f48c803488f043e98 | %DISPERROR Display error matrix with information on classifiers and datasets
%
% DISPERROR(DATA,CLASSF,ERROR,STD,FID)
%
% INPUT
% DATA Cell array of M datasets or dataset names (strings)
% CLASSF Cell array of N mappings or mapping names (strings)
% ERROR M*N matrix of (average) error estimates
% STD ... |
github | SASVDDwt/sa_svdd-master | parzendc.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | ksmoothr.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | isparallel.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | gencirc.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | averagec.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | perlc.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | rejectm.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | testp.m | .m | sa_svdd-master/matlab/prtools/testp.m | 2,687 | utf_8 | 9b011462f5be5f4415b70e46d5782840 | %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 | SASVDDwt/sa_svdd-master | prtver.m | .m | sa_svdd-master/matlab/prtools/prtver.m | 886 | utf_8 | ebc66a2954b1fc2ee5a576225b2c0a0d | %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('fisherc'))
% [pp,... |
github | SASVDDwt/sa_svdd-master | pcaklm.m | .m | sa_svdd-master/matlab/prtools/pcaklm.m | 5,534 | utf_8 | f24483da13af9da3188bf1cafc9c9cd4 | %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 (>= 1) o... |
github | SASVDDwt/sa_svdd-master | loglc.m | .m | sa_svdd-master/matlab/prtools/loglc.m | 3,454 | utf_8 | c41fa2a0b0dd8ac25c10605517a41546 | %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 | SASVDDwt/sa_svdd-master | modeseek.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | plsm.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | pls_apply.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | parallel.m | .m | sa_svdd-master/matlab/prtools/parallel.m | 5,868 | utf_8 | 19a90c50a7ece3849862edf727edd894 | %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 | SASVDDwt/sa_svdd-master | im_fill_norm.m | .m | sa_svdd-master/matlab/prtools/im_fill_norm.m | 780 | utf_8 | 2cc1ba8eb354b7cbaa664a2a13814628 | %IM_FILL_NORM Fill and normalize image for display puproses
%
% B = IM_FILL_NORM(A,N)
%
%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.
function b = im_fill_norm(a,n)
if isa(a,'dataset')
isobjim(a);
o... |
github | SASVDDwt/sa_svdd-master | isfeatim.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | pls_prepro.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | clevalf.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | distm.m | .m | sa_svdd-master/matlab/prtools/distm.m | 2,290 | utf_8 | a470c27a4418bb16c07bd2f5103713fb | %DISTM Compute square Euclidean distance matrix
%
% D = DISTM(A,B)
%
% 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 between two
% sets A and B. If A has M... |
github | SASVDDwt/sa_svdd-master | svo.m | .m | sa_svdd-master/matlab/prtools/svo.m | 5,614 | utf_8 | ba1a1e2608e89a78064092758fe79c2d | %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 | SASVDDwt/sa_svdd-master | prcursor.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | clevalb.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | klms.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | knn_map.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | im_measure.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | mds_stress.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | closemess.m | .m | sa_svdd-master/matlab/prtools/closemess.m | 396 | utf_8 | 07c939dc4af1b5a1ddca3256c7ffaa7d | %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, 2600 GA Delft,... |
github | SASVDDwt/sa_svdd-master | gendatsin.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | im_gauss.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | emclust.m | .m | sa_svdd-master/matlab/prtools/emclust.m | 7,246 | utf_8 | 5f8d92ecb1f8f7e7d929cb6135c4286b | %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 | SASVDDwt/sa_svdd-master | normal_map.m | .m | sa_svdd-master/matlab/prtools/normal_map.m | 8,007 | utf_8 | a24dc4d17fef45a99a4a572a0de320a5 | %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 | SASVDDwt/sa_svdd-master | circles3d.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | nodatafile.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | gendatr.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | tree_map.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | nu_svro.m | .m | sa_svdd-master/matlab/prtools/nu_svro.m | 8,512 | utf_8 | 52019a5052842bde176deaf7c81f0c60 | %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 | SASVDDwt/sa_svdd-master | lines5d.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | pinvr.m | .m | sa_svdd-master/matlab/prtools/pinvr.m | 2,808 | utf_8 | 1e2263f5e975eb13c9b444c77cc78052 | %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 | SASVDDwt/sa_svdd-master | parzenc.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | prversion.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | im_center.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | gendatlin.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | image_dbr.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | wvotec.m | .m | sa_svdd-master/matlab/prtools/wvotec.m | 3,490 | utf_8 | 2c7790456f6bcc4d7a1cd9de2d6daace | %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
% W C... |
github | SASVDDwt/sa_svdd-master | im_mean.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | obj2feat.m | .m | sa_svdd-master/matlab/prtools/obj2feat.m | 396 | utf_8 | 4998c6d2d063ace035d1d6ad752c1446 | %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)
prtrace(mfilename);
... |
github | SASVDDwt/sa_svdd-master | minc.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | knnr.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | kmeans.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | im_norm.m | .m | sa_svdd-master/matlab/prtools/im_norm.m | 930 | utf_8 | 2b77fc02e6fe3c2312d322bedb10b972 | %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).
%% SEE ALS... |
github | SASVDDwt/sa_svdd-master | logdens.m | .m | sa_svdd-master/matlab/prtools/logdens.m | 1,573 | utf_8 | 378ada1aa16d40f5111608c461309e30 | %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 the distributio... |
github | SASVDDwt/sa_svdd-master | plsr.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | im_select_blob.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | featrank.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | udc.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | naivebc.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | im_profile.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | plotf.m | .m | sa_svdd-master/matlab/prtools/plotf.m | 2,137 | utf_8 | bfe3f7799dc4dc61ba4e47d96a9c466f | %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 | SASVDDwt/sa_svdd-master | mds_init.m | .m | sa_svdd-master/matlab/prtools/mds_init.m | 2,979 | utf_8 | f468ebec29d7b88e59a648ddbda5914e | %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 | SASVDDwt/sa_svdd-master | plotm.m | .m | sa_svdd-master/matlab/prtools/plotm.m | 4,472 | utf_8 | e086c67ea6e5531ef9e3e71c7032f0cb | %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 | SASVDDwt/sa_svdd-master | datunif.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | regoptc.m | .m | sa_svdd-master/matlab/prtools/regoptc.m | 4,949 | utf_8 | 69fc496fbc5174c7075991033123e5d5 | %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 Defaults for PA... |
github | SASVDDwt/sa_svdd-master | gendatc.m | .m | sa_svdd-master/matlab/prtools/gendatc.m | 2,500 | utf_8 | 38746a5b15dcef16864e16fc91a675ac | %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 | SASVDDwt/sa_svdd-master | gridsize.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | gendatsinc.m | .m | sa_svdd-master/matlab/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 | SASVDDwt/sa_svdd-master | parzenml.m | .m | sa_svdd-master/matlab/prtools/parzenml.m | 5,796 | utf_8 | 3adead0df04803d3fdcc11cc0e6f2c89 | %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 | SASVDDwt/sa_svdd-master | lassor.m | .m | sa_svdd-master/matlab/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 | diazlab/scell-master | choose_kmeans_params.m | .m | scell-master/mfiles/choose_kmeans_params.m | 10,966 | utf_8 | 661b5ce3c5f3659ccede1567910df596 | function varargout = choose_kmeans_params(varargin)
% CHOOSE_kmeans_params MATLAB code for choose_kmeans_params.fig
% CHOOSE_kmeans_params by itself, creates a new CHOOSE_kmeans_params or raises the
% existing singleton*.
%
% H = CHOOSE_kmeans_params returns the handle to a new CHOOSE_kmeans_params or th... |
github | diazlab/scell-master | dist_euclidean.m | .m | scell-master/mfiles/dist_euclidean.m | 1,323 | utf_8 | cb527d8b80ae466b58a1bf8ca63ca936 | % Calculates the Euclidean distance between vectors [FAST].
%
% Assume X is an m-by-p matrix representing m points in p-dimensional space and Y is an
% n-by-p matrix representing another set of points in the same space. This function
% compute the m-by-n distance matrix D where D(i,j) is the SQUARED Euclidean distance
... |
github | diazlab/scell-master | brewermap.m | .m | scell-master/mfiles/brewermap.m | 17,337 | utf_8 | 0f64db81d32dbe481628fe0fb2dd44ed | function [map,num,typ] = brewermap(N,scheme)
% The complete selection of ColorBrewer colorschemes (RGB colormaps).
%
% (c) 2014 Stephen Cobeldick
%
% ### Function ###
%
% Returns an RGB colormap of one of the ColorBrewer colorschemes, especially
% intended for mapping and plots with attractive, distinguishable ... |
github | diazlab/scell-master | mcdcov.m | .m | scell-master/mfiles/mcdcov.m | 61,895 | utf_8 | 0ef7555add8bce66103328ecd2e7a3cb | function [rew,raw]=mcdcov(x,varargin)
%MCDCOV computes the MCD estimator of a multivariate data set. This
% estimator is given by the subset of h observations with smallest covariance
% determinant. The MCD location estimate is then the mean of those h points,
% and the MCD scatter estimate is their covariance ... |
github | diazlab/scell-master | colorspace.m | .m | scell-master/mfiles/colorspace.m | 16,178 | utf_8 | 2ca0aee9ae4d0f5c12a7028c45ef2b8d | function varargout = colorspace(Conversion,varargin)
%COLORSPACE Transform a color image between color representations.
% B = COLORSPACE(S,A) transforms the color representation of image A
% where S is a string specifying the conversion. The input array A
% should be a real full double array of size Mx3 or MxN... |
github | diazlab/scell-master | CiK_Means.m | .m | scell-master/mfiles/CiK_Means.m | 4,969 | utf_8 | 8e5df65c39ac8f82126adfca6bcadde9 | function FinalCentroids = CiK_Means(Data, SmallClusterThreshold, IsDataStandarized, MustLinkIndex, CannotLinkIndex, CannotClusterIndex)
%Constrained iK-means
%For more info see: R. C. de Amorim (2008) Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge, The 2nd International Conference ... |
github | diazlab/scell-master | alert.m | .m | scell-master/mfiles/alert.m | 7,007 | utf_8 | ea999289acfae342cf6ac3b626627143 | function varargout = alert(varargin)
% ALERT MATLAB code for alert.fig
% ALERT by itself, creates a new ALERT or raises the
% existing singleton*.
%
% H = ALERT returns the handle to a new ALERT or the handle to
% the existing singleton*.
%
% ALERT('CALLBACK',hObject,eventData,handles,...) call... |
github | diazlab/scell-master | pca_tool2.m | .m | scell-master/mfiles/pca_tool2.m | 23,703 | utf_8 | d0f4a7bbe9360f10b3f0d98727d037df | function varargout = pca_tool2(varargin)
%PCA_TOOL2 M-file for pca_tool2.fig
% PCA_TOOL2, by itself, creates a new PCA_TOOL2 or raises the existing
% singleton*.
%
% H = PCA_TOOL2 returns the handle to a new PCA_TOOL2 or the handle to
% the existing singleton*.
%
% PCA_TOOL2('Property','Value',... |
github | diazlab/scell-master | set_sample_id.m | .m | scell-master/mfiles/set_sample_id.m | 6,709 | utf_8 | 24abacbc8fee32e9c314a3bceb687e9a | function varargout = set_sample_id(varargin)
% SET_SAMPLE_ID MATLAB code for set_sample_id.fig
% SET_SAMPLE_ID by itself, creates a new SET_SAMPLE_ID or raises the
% existing singleton*.
%
% H = SET_SAMPLE_ID returns the handle to a new SET_SAMPLE_ID or the handle to
% the existing singleton*.
%
% ... |
github | diazlab/scell-master | choose_corr_type.m | .m | scell-master/mfiles/choose_corr_type.m | 8,946 | utf_8 | a9268af991f5a9ec6182400295fdeaff | function varargout = choose_corr_type(varargin)
% CHOOSE_FILE_TYPE MATLAB code for choose_file_type.fig
% CHOOSE_FILE_TYPE by itself, creates a new CHOOSE_FILE_TYPE or raises the
% existing singleton*.
%
% H = CHOOSE_FILE_TYPE returns the handle to a new CHOOSE_FILE_TYPE or the handle to
% the exist... |
github | diazlab/scell-master | choose_mink_params.m | .m | scell-master/mfiles/choose_mink_params.m | 9,485 | utf_8 | 26cfbdfca4dc4795e776f72e92e93cab | function varargout = choose_gauss_params(varargin)
% CHOOSE_gauss_params MATLAB code for choose_gauss_params.fig
% CHOOSE_gauss_params by itself, creates a new CHOOSE_gauss_params or raises the
% existing singleton*.
%
% H = CHOOSE_gauss_params returns the handle to a new CHOOSE_gauss_params or the handl... |
github | diazlab/scell-master | gene_select_tool.m | .m | scell-master/mfiles/gene_select_tool.m | 14,259 | utf_8 | ff9143c05516b4ce08db0f9baeb4c823 | function varargout = gene_select_tool(varargin)
% GENE_SELECT_TOOL MATLAB code for gene_select_tool.fig
% GENE_SELECT_TOOL, by itself, creates a new GENE_SELECT_TOOL or raises the existing
% singleton*.
%
% H = GENE_SELECT_TOOL returns the handle to a new GENE_SELECT_TOOL or the handle to
% the exis... |
github | diazlab/scell-master | SubWkMeans.m | .m | scell-master/mfiles/SubWkMeans.m | 8,279 | utf_8 | 5dcf01a4b1f676b53616bd641eebbcfc | function [U, W, Z, UDistToZ, LoopCount] = SubWkMeans (Data, k, Beta, InitialCentroids, InitialW, p, lnk)
%function [U, W, Z, UDistToZ, LoopCount] = SubWkMeans (Data, k, Beta, InitialCentroids, InitialW, p, lnk)
%
%Parameters:
%Data
% Dataset, format: Entities x Features
%k
% Total number of clusters in... |
github | diazlab/scell-master | dbscan.m | .m | scell-master/mfiles/dbscan.m | 4,235 | utf_8 | 474630ba5efa42e442d2fd01d0a22170 | % -------------------------------------------------------------------------
% Function: [class,type]=dbscan(x,k,Eps)
% -------------------------------------------------------------------------
% Aim:
% Clustering the data with Density-Based Scan Algorithm with Noise (DBSCAN)
% -----------------------------------------... |
github | diazlab/scell-master | main.m | .m | scell-master/mfiles/main.m | 29,949 | utf_8 | 39e25207935bc5a69f30a88f2e91cd0c | function varargout = main(varargin)
% MAIN MATLAB code for main.fig
% MAIN, by itself, creates a new MAIN or raises the existing
% singleton*.
%
% H = MAIN returns the handle to a new MAIN or the handle to
% the existing singleton*.
%
% MAIN('CALLBACK',hObject,eventData,handles,...) calls the l... |
github | diazlab/scell-master | notBoxPlot.m | .m | scell-master/mfiles/notBoxPlot.m | 6,631 | utf_8 | 5168ba3f770ec8ce0786ef91585613fb | function varargout=notBoxPlot(y,x,jitter,style)
% notBoxPlot - Doesn't plot box plots!
%
% function notBoxPlot(y,x,jitter,style)
%
%
% Purpose
% An alternative to a box plot, where the focus is on showing raw
% data. Plots columns of y as different groups located at points
% along the x axis defined by the optional vec... |
github | diazlab/scell-master | splash.m | .m | scell-master/mfiles/splash.m | 7,915 | utf_8 | 54a45518b7ecceab6b5af39015d64f05 | function varargout = splash(varargin)
%SPLASH Creates a splash screen.
% SPLASH(FILENAME,FMT,TIME) creates a splash screen using the image from the
% file specified by the string FILENAME, where the string FMT specifies
% the format of the file and TIME is the duration time of the splash
% screen in milli... |
github | diazlab/scell-master | pca_tool.m | .m | scell-master/mfiles/pca_tool.m | 44,200 | utf_8 | 0b2999cd381aa8c9be6657a542c9e6aa | function varargout = pareto_plt_gui(varargin)
% PARETO_PLT_GUI MATLAB code for pareto_plt_gui.fig
% PARETO_PLT_GUI, by itself, creates a new PARETO_PLT_GUI or raises the existing
% singleton*.
%
% H = PARETO_PLT_GUI returns the handle to a new PARETO_PLT_GUI or the handle to
% the existing singleton... |
github | diazlab/scell-master | norm_tool.m | .m | scell-master/mfiles/norm_tool.m | 22,367 | utf_8 | e87be418ea45299bb4ed705e2ea5ea24 | function varargout = norm_tool(varargin)
% NORM_TOOL MATLAB code for norm_tool.fig
% NORM_TOOL, by itself, creates a new NORM_TOOL or raises the existing
% singleton*.
%
% H = NORM_TOOL returns the handle to a new NORM_TOOL or the handle to
% the existing singleton*.
%
% NORM_TOOL('CALLBACK',hO... |
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