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github | canlab/MediationToolbox-master | mediation_brain_print_tables.m | .m | MediationToolbox-master/mediation_toolbox/mediation_brain_print_tables.m | 11,225 | utf_8 | 8f4b4908e324004d3e897c3d8ab5e8d7 | function [clpos_data, clneg_data] = mediation_brain_print_tables(clpos_data, clneg_data, varargin)
%
% [clpos_data, clneg_data] = mediation_brain_print_tables(clpos_data, clneg_data, [do ranks flag],[other optional inputs])
%
% optional: doranks, ranks, rank : rank data before calculating partial
% ... |
github | canlab/MediationToolbox-master | mediation_threepaths.m | .m | MediationToolbox-master/mediation_toolbox/mediation_threepaths.m | 49,844 | utf_8 | 5a954733e4ec3c6985f9ea3d8798e7c8 | function [paths, varargout] = mediation_threepaths(X, Y, M1, M2, varargin)
% function [paths, varargout] = mediation_threepaths(X, Y, M1, M2, varargin)
%
% Usage: This function tests the three-path mediation effect (X -> M1 -> M2 -> Y).
%
% This is based on Tor Wager's original mediation function (mediation.m) and als... |
github | canlab/MediationToolbox-master | mediation_extract_data.m | .m | MediationToolbox-master/mediation_toolbox/mediation_extract_data.m | 4,989 | utf_8 | 65435325ff45b7c721170882380d0002 | function cl = mediation_extract_data(cl, meth, domergeclusters)
%cl = mediation_extract_data(cl)
%
% Works with robust dir too, if you enter:
% cl = mediation_extract_data(cl, 'rob');
%
%help not done -- see medation_+brain_results.m
%
% Start in mediation or robfit directory with a val... |
github | canlab/MediationToolbox-master | mediation_permutation_svc_fwe.m | .m | MediationToolbox-master/mediation_toolbox/mediation_permutation_svc_fwe.m | 7,245 | utf_8 | 1d207e2ce67eb0d0aa1b64bb299d6024 | % MC_FWE = mediation_permutation_svc_fwe(X, Y, dat, nperms, varargin)
% % Takes all standard options that mediation.m does
%
% Single-level mediation only!
%
% Permutation test to assess familywise error rate corrected p-values for
% mediation test in a region of interest.
%
% Permutes rows of data matrix dat and cond... |
github | canlab/MediationToolbox-master | glmfit_multilevel_brain_wrapper.m | .m | MediationToolbox-master/mediation_toolbox/glmfit_multilevel_brain_wrapper.m | 2,663 | utf_8 | 28e5c266417c4c9bb8325cc7561818bc | % [a, b, c1, c, ab, aste, bste, c1ste, cste, abste, ap, bp, c1p, cp, abp, ...
% aind, bind, c1ind, cind, abind, aiste, biste, c1iste, ciste, abiste] = ...
% glmfit_brain_multilev_wrapper(dmpfc, hr, pag, 'boot');
%
% Wrapper function for mediation_brain_multilev
% Returns separate outputs for each variable that deserves... |
github | canlab/MediationToolbox-master | mediation_brain_results.m | .m | MediationToolbox-master/mediation_toolbox/mediation_brain_results.m | 56,112 | utf_8 | 62e88e1fe015d8ec2e6537abde3bb760 | function [clpos, clneg, clpos_data, clneg_data, clpos_data2, clneg_data2] = mediation_brain_results(meth, varargin)
%
% [clpos, clneg, clpos_data, clneg_data, clpos_data2, clneg_data2] = mediation_brain_results(meth, varargin)
%
% This is a results-printing utility that will get thresholded results
% from three types o... |
github | canlab/MediationToolbox-master | M3.m | .m | MediationToolbox-master/mediation_toolbox/M3.m | 239 | utf_8 | e85547544b89ff72f1bf233586a5d041 | % Opening function for the M3 toolbox. For info on the GUI layout, see spm_config_mediation.m
function M3(varargin)
addpath(fullfile(spm('dir'),'toolbox','M3'));
spm_jobman('interactive','','jobs.tools.mediation');
return
end |
github | canlab/MediationToolbox-master | mediation_sim_single_level2.m | .m | MediationToolbox-master/mediation_toolbox/mediation_sim_single_level2.m | 5,120 | utf_8 | 58313dedb7efcc6bbac6d905bde9c3d8 |
% function mediation_sim2_igls(iter,varargin)
% Simulation for power and false positive rates for mediation analysis
%
% tor wager, Feb. 2007, Updated March 2007
% -------------------------------------------------------------------------
% Default: Bootstrap 1000 samples, AR(2), hierarchical weighting, no
% shift/late... |
github | canlab/MediationToolbox-master | mediation_plots.m | .m | MediationToolbox-master/mediation_toolbox/mediation_plots.m | 9,614 | utf_8 | 95a6f3ab4964f89fd6efb14ff114e2e7 | % Plotting function for mediation output.
% Several kinds of plots can be created.
%
% This is used in mediation.m
%
% See also mediation_scatterplots.
% Tor Wager, Jan 2009
%
% Usage:
% -------------------------------------------------------------------------
% Plot individual slopes of regressions, using conf. interv... |
github | canlab/MediationToolbox-master | mediation_threepaths_singlelevel.m | .m | MediationToolbox-master/mediation_toolbox/mediation_threepaths_singlelevel.m | 54,482 | utf_8 | 7e92775ab1cc385ea05660020f25afc5 | function [paths, varargout] = mediation_threepaths_singlelevel(X, Y, M1, M2, varargin)
% function [paths, varargout] = mediation_threepaths(X, Y, M1, M2, varargin)
%
% Usage: This function tests the three-path mediation effect (X -> M1 -> M2 -> Y).
%
% This is based on Tor Wager's original mediation function (mediatio... |
github | canlab/MediationToolbox-master | mediation_sim3.m | .m | MediationToolbox-master/mediation_toolbox/mediation_sim3.m | 9,106 | utf_8 | 1d91e6199e576c21ac34b114888176e4 | function [pvals, results_table, stats, randseed] = mediation_sim3(n, t, s_g, s_a, s_b, apop, bpop, abcov, l2m_acov, l2m_bcov, color, varargin)
% Simulated results and significance rates (power or false positives) for multilevel mediation.
% generates a dataset (in an encapusulated subfunction) and runs multiple iterati... |
github | canlab/MediationToolbox-master | mediationIVObserver.m | .m | MediationToolbox-master/mediation_toolbox/mediationIVObserver.m | 2,545 | utf_8 | 6af2c15159c7e9f757de999792b6a3a5 | % obs = mediationIVObserver(varargin)
% Computes mediation parameters for a given point, displays independently of the InteractiveViewer,
% and then saves the mediation results to the workspace
%
% E.g.
% To search the brain for mediators between existing X and Y variables:
%
% load('mediation_SETUP')
% X = SETUP.... |
github | canlab/MediationToolbox-master | mediation_path_diagram.m | .m | MediationToolbox-master/mediation_toolbox/mediation_path_diagram.m | 5,119 | utf_8 | 036c070f7bbbd3d5cfb508ec9970ed30 | function mediation_path_diagram(stats)
wh = which('intersectLinePolygon');
if isempty(wh)
disp('Warning: To create a mediation path diagram, you must have the external');
disp('geom2d toolbox on your path. I can''t find it, so the path diagram will be skipped.');
return
end
... |
github | canlab/MediationToolbox-master | mediation_brain_multilevel.m | .m | MediationToolbox-master/mediation_toolbox/mediation_brain_multilevel.m | 11,370 | utf_8 | 8cb0bdb94009f8fa450a344705f6815f | function mediation_brain_multilevel(X, Y, M, SETUP, varargin)
% mediation_brain_multilevel(X, Y, M, SETUP, [mediation optional inputs])
%
% Multilevel mediation on a set of brain images
%
% Inputs
% ------------------------------------------------
% X data matrix of t tim... |
github | canlab/MediationToolbox-master | xcorr_multisubject_old.m | .m | MediationToolbox-master/mediation_toolbox/xcorr_multisubject_old.m | 8,507 | utf_8 | d76fc1bb0b470ce7016d5c24c762ef49 | function OUT = xcorr_multisubject(data, varargin)
% Cross-correlation and partial correlation matrices for 3-D data, i.e., a cell array of subject data matrices
%
% Usage:
% -------------------------------------------------------------------------
% OUT = xcorr_multisubject(data, [optional inputs])
%
% Author and copyr... |
github | canlab/MediationToolbox-master | mediation_brain.m | .m | MediationToolbox-master/mediation_toolbox/mediation_brain.m | 17,454 | utf_8 | 9d71f0c143d417768b8cf9bce52c1216 | % med_results = mediation_brain(X, Y, M, ['thresholds', thresholds], ['mask', maskname], ['names', names], ...
% ['robust'|'norobust'], ['signperm'], ['arorder', arorder], ['boot'|'noboot'], ['multilevel'|'summarystats'], ['covs', covariates], ...
% ['covnames', covnames])
%
% This function does a robust/nonrobust ... |
github | canlab/MediationToolbox-master | timeseries_interactive_plot.m | .m | MediationToolbox-master/mediation_toolbox/timeseries_interactive_plot.m | 2,801 | utf_8 | dc50921ac207306c7aab47a1eb79f97e | function timeseries_interactive_plot(xnames)
% timeseries_interactive_plot(xnames)
%
% create interactive timeseries-plot that pops up in spm_orthviews window
%
% Tor Wager, Matthew Davidson
% find the spm window, or make one from a p-image
% -----------------------------------------------
spm_handle = findobj('Tag',... |
github | canlab/MediationToolbox-master | mediation_latent_sse.m | .m | MediationToolbox-master/mediation_toolbox/mediation_latent_sse.m | 3,701 | utf_8 | 945e1afd34181a327e731ad4e9952f1d | function [totalsse paths hrf_xmy] = mediation_latent_sse(hrfparams,x, y, m, intcpt,bf)
% [totalsse paths hrf_xmy] = mediation_latent_sse(hrfparams,x, y, m,intcpt,bf)
%
% hrfparams = [x1 x2; m1 m2; y1 y2]';
%
%%% **** action item: make subfunction; persistent px pmx
% if search y, save px, pmx, a... |
github | canlab/MediationToolbox-master | mediation_multilev_reformat_cl.m | .m | MediationToolbox-master/mediation_toolbox/mediation_multilev_reformat_cl.m | 1,790 | utf_8 | fe474b9ed691e04ba3602f0a2548281e | % output_cl = mediation_multilev_reformat_cl(input_cl)
%
% This function takes as input a multi-subject cl structure with one cell
% per subject (this is the format returned in clpos_data and clneg_data of
% mediation_brain_results) and re-arranges the data fields so that the
% output clusters structure is a "typical" ... |
github | canlab/MediationToolbox-master | mediation_path_coefficients.m | .m | MediationToolbox-master/mediation_toolbox/mediation_path_coefficients.m | 18,202 | utf_8 | d0d0daf117d9ae6480aeb31b37175a34 | function [paths, abetas, bbetas, cpbetas, cbetas, sterrs, intcpt, n, residm, residy, residy2, Vxm, Vmy, Vxy] = mediation_path_coefficients(x,y,m,domultilev,dorobust,boot1,logistic_Y,varargin)
% [paths, abetas, bbetas, cpbetas, cbetas, sterrs, intcpt, n, residm, residy, residy2, Vxm, Vmy, Vxy] = mediation_path_coeff... |
github | canlab/MediationToolbox-master | spm_config_mediation.m | .m | MediationToolbox-master/mediation_toolbox/spm_config_mediation.m | 9,747 | utf_8 | 8b49eeb56497a11e2ebbf3e93f600819 | function med = spm_config_mediation(varargin)
% Configuration file for mediation analysis
%_______________________________________________________________________
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | canlab/MediationToolbox-master | enclosingCircle.m | .m | MediationToolbox-master/geom2d/enclosingCircle.m | 1,841 | utf_8 | 59f94c65fc98908a140554be725d46b4 | function circle = enclosingCircle(pts)
%ENCLOSINGCIRCLE find the minimum circle enclosing a set of points.
%
% usage :
% CIRCLE = enclosingCircle(POINTS);
% compute cirlce CIRCLE=[xc yc r] which enclose all points POINTS given
% as an [Nx2] array.
%
%
% Rewritten from a file from
% Yazan A... |
github | canlab/MediationToolbox-master | PDMN.m | .m | MediationToolbox-master/PDM_toolbox/PDMN.m | 4,885 | utf_8 | 5c30fdad3e88d07048cacea0543d8adb | function [w_N,theta,flag,WMi]= PDMN(x,y,m, W, varargin)
% Compute the Nth Principal Direction of Mediation
%
% This code can be used iteratively to compute each principal direction of mediation
%
% INPUT:
%
% x - treatment (N X 1 vector)
% y - outcome (N X 1 vector)
% m - mediator (N X p matrix)
% W ... |
github | smilesheng/SRRS-master | SRRS.m | .m | SRRS-master/SRRS.m | 2,647 | utf_8 | 6c81abe8ec4d727489dd53697dc8d0c6 | function [Z,E,P] = SRRS(X, label, para)
% The main function of Supervised Regularization based Robust Subspace (SRRS) Method.
% Input:
% X: d*n data matrix
% label: label vector
% para: parameter
%
% Output:
% Z: coefficient matrix
% E: error matrix
% P: projection matrix
%
% Autho... |
github | SASVDDwt/sa_svdd-master | apcluster.m | .m | sa_svdd-master/matlab/apcluster.m | 10,923 | utf_8 | 5208778db5c9d38bc7c53e80b67c728a | %APCLUSTER Affinity Propagation Clustering (Frey/Dueck, Science 2007)
% [idx,netsim,dpsim,expref]=APCLUSTER(s,p) clusters data, using a set
% of real-valued pairwise data point similarities as input. Clusters
% are each represented by a cluster center data point (the "exemplar").
% The method is iterative and search... |
github | SASVDDwt/sa_svdd-master | mapminmax.m | .m | sa_svdd-master/matlab/mapminmax.m | 9,491 | utf_8 | 84141a307144540348e53607beb13882 | function [out1,out2] = mapminmax(in1,in2,in3,in4)
%MAPMINMAX Map matrix row minimum and maximum values to [-1 1].
%
% Syntax
%
% [y,ps] = mapminmax(x,ymin,ymax)
% [y,ps] = mapminmax(x,fp)
% y = mapminmax('apply',x,ps)
% x = mapminmax('reverse',y,ps)
% dx_dy = mapminmax('dx',x,y,ps)
% dx_dy = mapminmax('dx',x,[],ps)
%... |
github | SASVDDwt/sa_svdd-master | kmeans.m | .m | sa_svdd-master/matlab/kmeans.m | 36,424 | utf_8 | 41671e384ef747fc0b6cd4316a9740ef | function [idxbest, Cbest, sumDbest, Dbest] = kmeans(X, k, varargin)
%KMEANS K-means clustering.
% IDX = KMEANS(X, K) partitions the points in the N-by-P data matrix X
% into K clusters. This partition minimizes the sum, over all clusters, of
% the within-cluster sums of point-to-cluster-centroid distances. Rows... |
github | SASVDDwt/sa_svdd-master | mog_threshold.m | .m | sa_svdd-master/matlab/dd_tools/mog_threshold.m | 1,467 | utf_8 | 2b7c0faa66de5074d54983a3780fa2b9 | %MOG_THRESHOLD Set threshold of a MoG
%
% W = MOG_THRESHOLD(W,X,FRACREJ)
%
% Set the threshold of the Mixture of Gaussians mapping W. The threshold
% is set such that a pre-specified fraction FRACREJ of the target data X
% is rejected.
%
% I still have problems to be sure when the obtained decision boundary
% is c... |
github | SASVDDwt/sa_svdd-master | dknndd.m | .m | sa_svdd-master/matlab/dd_tools/dknndd.m | 2,862 | utf_8 | 4d315e92131cb902bf4e67c07755b098 | %DKNNDD Distance K-Nearest neighbour data description method.
%
% W = DKNNDD(D,FRACREJ,K,METHOD)
%
% Calculates the K-Nearest neighbour data description on distance
% dataset D. Two methods are defined to compute a distance to the
% dataset using the k-nearest neighbours:
%
% METHOD does:
% 'kappa' us... |
github | SASVDDwt/sa_svdd-master | knndd.m | .m | sa_svdd-master/matlab/dd_tools/knndd.m | 3,296 | utf_8 | c3a2be3e58fc316a1613ba08928794d5 | %KNNDD K-Nearest neighbour data description method.
%
% W = KNNDD(A,FRACREJ,K,METHOD)
%
% Calculates the K-Nearest neighbour data description on dataset A.
% Three methods are defined to compute a distance to the dataset using
% the k-nearest neighbours:
%
% METHOD uses the
% 'kappa' distance to the k-... |
github | SASVDDwt/sa_svdd-master | rankboostc.m | .m | sa_svdd-master/matlab/dd_tools/rankboostc.m | 4,187 | utf_8 | ff1bd4248d33a856763a2aea04ee5b9f | %RANKBOOSTB Binary rankboost
%
% W = RANKBOOSTC(A,FRACREJ,T)
%
% Train a simple binary version of rankboost containing T weak
% classifiers. The base (weak) classifiers only threshold a single
% feature.
%
% See also dd_auc, auclpm
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of T... |
github | SASVDDwt/sa_svdd-master | incsvdd.m | .m | sa_svdd-master/matlab/dd_tools/incsvdd.m | 3,506 | utf_8 | 680ee4797fc3d4984987c03dbcc70061 | %INCSVDD Incremental Support Vector Classifier
%
% W = INCSVDD(A,FRACERR,KTYPE,PAR)
%
% Use the incremental version of the SVDD. The kernel is defined by
% KTYPE, with the free parameter PAR. See inckernel.m for more
% information on the available kernels and the parameters to choose.
% FRACERR defines the error on... |
github | SASVDDwt/sa_svdd-master | nndist_range.m | .m | sa_svdd-master/matlab/dd_tools/nndist_range.m | 741 | utf_8 | bec5713622f69cc016863f60e886cf4b | %NNDIST_RANGE Give a vector of scales
%
% D = NNDIST_RANGE(X)
% D = NNDIST_RANGE(X,NR)
%
% Give the average nearest neighbor distance in dataset X. When NR is
% specified, the first NR nearest distances are returned.
%
% Default: NR = 1
%
% See also: svdd
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Facult... |
github | SASVDDwt/sa_svdd-master | lpball_dist.m | .m | sa_svdd-master/matlab/dd_tools/lpball_dist.m | 839 | utf_8 | e6cf07857d5c4126cf051a50e750ff52 | %LPBALL_DIST Compute Lp distance to a mean
%
% [F,G,H] = LPBALL_DIST(M,X,P,FRAC)
%
% Compute the maximum distance of objects X to the mean M, using Lp
% distances with P. To make the distance a bit more robust, just a
% fraction FRAC of the data is taken into account. The distance is
% returned in F, the derivative... |
github | SASVDDwt/sa_svdd-master | mog_dd.m | .m | sa_svdd-master/matlab/dd_tools/mog_dd.m | 4,180 | utf_8 | a33de7bb2c0b6139ebf846e374d206ad | %MOG_DD Mixture of Gaussians data description
%
% W = MOG_DD(A,FRACREJ,[N1 N2],CTYPE,REG,NUMITERS)
%
% Train a Mixture of Gaussians model on data A, using N1 clusters to
% model the target class, and N2 clusters for the outlier data. The
% position, size and priors of each of the clusters is optimized using
% the EM ... |
github | SASVDDwt/sa_svdd-master | autoenc_dd.m | .m | sa_svdd-master/matlab/dd_tools/autoenc_dd.m | 2,125 | utf_8 | e1ef7f4176e2cddf28cdc2c233e42e86 | %AUTOENC_DD Auto-Encoder data description.
%
% W = AUTOENC_DD(A,FRACREJ,N)
%
% Train an Auto-Encoder network with N hidden units. The network should
% recover the original data A at its output. The difference between the
% network output and the original pattern (in MSE sense) is used as a
% charaterization of ... |
github | SASVDDwt/sa_svdd-master | rob_gauss_dd.m | .m | sa_svdd-master/matlab/dd_tools/rob_gauss_dd.m | 2,714 | utf_8 | 657fc445300b78f74d5035a27d63845d | %ROB_GAUSS_DD Robust Gaussian data description.
%
% W = ROB_GAUSS_DD(A,FRACREJ)
%
% Fit a robust Gaussian density on dataset A. The algorithm is taken
% from
% Huber, P.J. "Robust Statistics", John Wiley&Sons, 1981, pg 238
%
% To be perfectly honest, there are some personal choices for some weighting
% factor... |
github | SASVDDwt/sa_svdd-master | nparzen_dd.m | .m | sa_svdd-master/matlab/dd_tools/nparzen_dd.m | 2,650 | utf_8 | 2731a96f7024f6d65ea0c818e46813c1 | %NPARZEN_DD Naive Parzen data description.
%
% W = nparzen_dd(A,fracrej)
%
% Fit a Parzen density on each individual feature in dataset A and
% multiply the results for the final density estimate. This is similar
% to the Naive Bayes approach used for classification.
% The threshold is put such that fracrej of ... |
github | SASVDDwt/sa_svdd-master | kcenter_dd.m | .m | sa_svdd-master/matlab/dd_tools/kcenter_dd.m | 1,417 | utf_8 | 462d4e2ac1c70c6335c93efd50a7325d | %KCENTER_DD k-center data description.
%
% W = kcenter_dd(A,fracrej,K)
%
% Train a k-center method with K prototypes on dataset A.
%
% See also kmeans_dd, som_dd, dd_roc
% 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... |
github | SASVDDwt/sa_svdd-master | gauss_dd.m | .m | sa_svdd-master/matlab/dd_tools/gauss_dd.m | 2,070 | utf_8 | 368eb2b61179757fa789a2c235b4c83f | %GAUSS_DD Gaussian data description.
%
% W = gauss_dd(A,fracrej,r)
%
% Fit a Gaussian density on dataset A. If requested, the r can be
% given to add some regularization to the estimated covariance matrix:
% sig_new = (1-r)*sig + r*eye(dim). Default r = 0.01!!! (might be
% dangerous!)
%
% This version actually ... |
github | SASVDDwt/sa_svdd-master | find_target.m | .m | sa_svdd-master/matlab/dd_tools/find_target.m | 800 | utf_8 | 0829f7c0eaa5136dc52621bf44ea5380 | %FIND_TARGET extract the indices of the target and outlier objects
%
% [It,Io] = FIND_TARGET(A)
%
% Return the indices of the objects from dataset A which are labeled
% 'target' and 'outlier' in the index vectors It and Io
% respectively. A warning is given when no target objects can be
% found.
%
% [It,Io] = FIND_... |
github | SASVDDwt/sa_svdd-master | istarget.m | .m | sa_svdd-master/matlab/dd_tools/istarget.m | 960 | utf_8 | bc084eab32103dc619a1744384a32703 | %ISTARGET true if the label is target
%
% I = ISTARGET(A)
%
% Returns true for the objects from dataset A which are labeled
% 'target'.
%
% I = ISTARGET(LABA)
%
% It also works when no dataset but a label matrix given
%
% See also: isocset, gendatoc, oc_set
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty ... |
github | SASVDDwt/sa_svdd-master | ball_dd.m | .m | sa_svdd-master/matlab/dd_tools/ball_dd.m | 2,996 | utf_8 | 71e3a8310ef57e63874bc535e40d208a | %BALL_DD L_p ball description
%
% W = BALL_DD(X,FRACREJ,P)
%
% Fit a L_p ball around the data X by optimizing the weights:
% min w_0
% s.t. \sum_j w_j|x_ij-a_j|^p <= w_0
% \sum_j w_j = 1, w_j>=0
% The vector a is taken as the mean of dataset X.
%
% When the (feature-) weigths w are optimized, t... |
github | SASVDDwt/sa_svdd-master | kmeans_dd.m | .m | sa_svdd-master/matlab/dd_tools/kmeans_dd.m | 1,594 | utf_8 | fc4f3d8e7bdbb99c71b0e98f3722a012 | %KMEANS_DD k-means data description.
%
% W = KMEANS_DD(A,FRACREJ,K)
%
% Train a k-means method with K prototypes on dataset A. Parameter
% fracrej gives the fraction of the target set which will be rejected.
%
% Optionally, one may give the error tolerance as last argument as
% stopping criterion.
%
% See als... |
github | SASVDDwt/sa_svdd-master | kwhiten.m | .m | sa_svdd-master/matlab/dd_tools/kwhiten.m | 3,146 | utf_8 | 5d485aa5b3a3703abb0f7a29f84db715 | %KWHITEN Whiten the data in kernel space.
%
% W = kwhiten(A,DIM,KTYPE,PAR1)
%
% Apply a kernel PCA to dataset A and retain DIM dimensions, or a
% fraction DIM of the total variance. The data A is then rescaled to
% unit variance in the feature space. The kernel space is defined by
% the kernel function KTYPE, w... |
github | SASVDDwt/sa_svdd-master | stump_dd.m | .m | sa_svdd-master/matlab/dd_tools/stump_dd.m | 2,331 | utf_8 | a2aa383b82062e1891361a6fd70d63f8 | %STUMP_DD Threshold one dim. one-class classifier
%
% W = STUMP_DD(A,FRACREJ,DIM)
%
% Put a threshold on one of the feature dimensions DIM of dataset A. The
% threshold is put such that a fraction FRACREJ of the targets is
% rejected.
%
% See also: dd_threshold, dd_roc, dd_error
% Copyright: D.M.J. Tax, D.M.J.... |
github | SASVDDwt/sa_svdd-master | pca_dd.m | .m | sa_svdd-master/matlab/dd_tools/pca_dd.m | 1,975 | utf_8 | cf20ae016f18ac340b15e0f3640b6f72 | %PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% 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 Neth... |
github | SASVDDwt/sa_svdd-master | som_dd.m | .m | sa_svdd-master/matlab/dd_tools/som_dd.m | 1,915 | utf_8 | 1ec771c8cc85508058bd6d0e6d119c39 | %SOM_DD Self-Organizing Map data description
%
% W = SOM_DD(X,FRACREJ,K)
%
% Train a 2D SOM on dataset X. In K the size of the map is defined. The
% map can maximally be 2D. When K contains just a single value, it is
% assumed that a 1D map should be trained.
%
% For further features of SOM_DD, see som.m (th... |
github | SASVDDwt/sa_svdd-master | svdd_optrbf.m | .m | sa_svdd-master/matlab/dd_tools/svdd_optrbf.m | 2,210 | utf_8 | 895d9b3315a781aa65af51f35b7b7752 | %SVDD_OPTRBF Quadratic optimizer for the SVDD
%
% [ALF,R2,DX,I] = SVDD_OPTRBF(SIGMA,X,LABX,C)
%
% Quadratic optimizer for the SVDD. Preferably called by svdd.m.
%
% Given the dataset X with labels LABX, and the parameters SIGMA and C the
% quadratic optimization is performed, and the resulting weights ALF and R2
% ... |
github | SASVDDwt/sa_svdd-master | isocc.m | .m | sa_svdd-master/matlab/dd_tools/isocc.m | 1,153 | utf_8 | 5946910c24f49a3e52b4c0c1941bed53 | %ISOCC True for one-class classifiers
%
% isocc(w) returns true if the classifier w is a one-class classifier,
% outputting only classes 'target' and/or 'outlier' and having a
% structure with threshold stored.
%
% Only problem is when you have an empty oc-classifier, this will
% return false. I cannot help it:-(
% Co... |
github | SASVDDwt/sa_svdd-master | multic.m | .m | sa_svdd-master/matlab/dd_tools/multic.m | 5,313 | utf_8 | e109fa84ed2d43ec7240770fd30fa54e | %MULTIC Make a multi-class classifier
%
% W = MULTIC(A,V)
%
% Train the (untrained!) one-class classifier V on each of the classes
% in A, and combine it to a multi-class classifier W. If an object is
% rejected by all one-class classifiers, it will be classified
% 'outlier'. If it is accepted by more than one one-c... |
github | SASVDDwt/sa_svdd-master | lpball_dd.m | .m | sa_svdd-master/matlab/dd_tools/lpball_dd.m | 3,552 | utf_8 | 5f38706a8088ae5d3ef9cf875a194ecf | %LPBALL_DD L_p ball description
%
% W = LPBALL_DD(X,FRACREJ,BTYPE,P)
%
% Optimize a L_p ball around dataset X, rejecting FRACREJ fraction of
% the data. The type of ball can be:
% BTYPE :
% w optimize the weights per feature
% center optimize the center
% p optimize the center and p
%
%... |
github | SASVDDwt/sa_svdd-master | myproxm.m | .m | sa_svdd-master/matlab/dd_tools/myproxm.m | 4,891 | utf_8 | cd16365ef60a7df02a278bbbc881dfdd | %MYPROXM MyProximity mapping
%
% W = MYPROXM(A,TYPE,P,G)
%
% Computation of the k*m proximity mapping (or kernel) defined by
% the m*k dataset A.
% The proximities are defined by the following possible TYPEs:
%
% 'linear' | 'l': a*b'
% 'polynomial' | 'p': sign(a*b'+1).*(a*b'+1).^p
% 'exponential' | ... |
github | SASVDDwt/sa_svdd-master | mogEMupdate.m | .m | sa_svdd-master/matlab/dd_tools/mogEMupdate.m | 3,709 | utf_8 | bcacaf9b52d90089f98499b4e15c9853 | function [means,invcovs,priors] = mogEMupdate(x,covtype,means,invcovs,priors,nriters,fixedcl,reg)
%MOGEMUPDATE Apply EM to a MoG
%
% [MEANS,INVCOVS,PRIORS] = MOGEMUPDATE(X,COVTYPE,MEANS,INVCOVS,PRIORS,...
% NRITERS)
%
% Apply Expectation-Maximization to update the MEANS, INVCOVS and PRIOR... |
github | SASVDDwt/sa_svdd-master | mykmeans.m | .m | sa_svdd-master/matlab/dd_tools/mykmeans.m | 924 | utf_8 | eacdffcf1dc687877171905e76ffa7b5 | %MYKMEANS K-means clustering
%
% [LABS,MEANS] = MYKMEANS(X,K)
%
% Place K centers in the data X using the k-means procedure.
% 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
function [labs,means,err] = mykmeans(x,k,errtol... |
github | SASVDDwt/sa_svdd-master | dd_normc.m | .m | sa_svdd-master/matlab/dd_tools/dd_normc.m | 1,447 | utf_8 | 47fc20d3d54a3572d5d003491d8653ed | %DD_NORMC Normalize the output of a oc-classifier
%
% B = DD_NORMC(A)
% B = A*W*DD_NORMC
% W = DD_NORMC
%
% Normalize the mapped dataset A to standard 'posterior probability'
% estimates (or something which looks similar to that). It basically
% means that all rows sum to 1. For the output of distan... |
github | SASVDDwt/sa_svdd-master | ksvdd.m | .m | sa_svdd-master/matlab/dd_tools/ksvdd.m | 7,248 | utf_8 | 57ee16b53fbba81c8a6153f9b0b97c9f | %KSVDD Support Vector Data Description on general kernel matrix
%
% W = KSVDD(X,FRACERR,WK)
%
% Train an SVDD on the data X, which is first mapped by mapping WK
% (see for possibilities myproxm). The mapping WK should be an
% untrained mapping! A fraction FRACERR of the data is outside the
% description.
% For e... |
github | SASVDDwt/sa_svdd-master | scale_range.m | .m | sa_svdd-master/matlab/dd_tools/scale_range.m | 1,372 | utf_8 | 69fa989fe46216d8c2e0a424657b01c1 | %SCALE_RANGE Give a vector of scales
%
% SIG = SCALE_RANGE(X,NR)
%
% Give a reasonable range of scales SIG for the dataset X. The largest
% scale is given first. If NR is given, the number of scales is NR.
% This function is useful in consistent_occ.
%
% SIG = SCALE_RANGE(X,NR,NMAX)
%
% The (reasonable) range o... |
github | SASVDDwt/sa_svdd-master | mcd_gauss_dd.m | .m | sa_svdd-master/matlab/dd_tools/mcd_gauss_dd.m | 1,809 | utf_8 | dbb2b57fb4849687affdcd9e5e60ab04 | %MCD_GAUSS_DD Minimum Covariance Determinant Robust Gaussian data description.
%
% W = MCD_GAUSS_DD(A,FRACREJ)
%
% Fit a Minimum-Covariance-Determinant Gaussian density on dataset A. The
% algorithm is taken from :
%
% Rousseeuw, P.J. and Van Driessen, Katrien, "A fast algorithm for
% the minimum covariance d... |
github | SASVDDwt/sa_svdd-master | dnndd.m | .m | sa_svdd-master/matlab/dd_tools/dnndd.m | 1,732 | utf_8 | f3c46546b796f0187e4f971a2744e8e3 | %DNNDD Distance nearest neighbour data description method.
%
% W = dnndd(D,fracrej)
%
% Calculates the Nearest neighbour data description on distance data.
% Training only consists of the computation of the resemblance of all
% training objects to the training data using Leave-one-out.
%
% See also datasets, m... |
github | SASVDDwt/sa_svdd-master | mst_dd.m | .m | sa_svdd-master/matlab/dd_tools/mst_dd.m | 4,466 | utf_8 | 6756c5104f21e21cec89491b79817bf5 | %MST_DD Minimum Spanning Tree Data Description.
%
% [W,TREE,A] = MST_DD(A,FRACREJ,N)
%
% INPUT
% A one-class dataset
% FRACREJ fraction rejection [0,1]; (default 0.1)
% N complexity parameter equals a number of
% paths of max length; (default 0, entire mst)
%
% OUTPUT
% W ... |
github | SASVDDwt/sa_svdd-master | is_ocset.m | .m | sa_svdd-master/matlab/dd_tools/is_ocset.m | 624 | utf_8 | 737ffee31727e633c2ec98947113d9aa | %IS_OCSET True for one-class datasets
%
% is_ocset(a) returns true if the dataset a is a one-class dataset,
% containing only classes 'target' and/or 'outlier'.
% 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
function out = i... |
github | SASVDDwt/sa_svdd-master | is_occ.m | .m | sa_svdd-master/matlab/dd_tools/is_occ.m | 1,171 | utf_8 | dd1baf04d2582ee1a6354b49d02797b3 | %IS_OCC True for one-class classifiers
%
% IS_OCC(W) returns true if the classifier W is a one-class classifier,
% outputting only classes 'target' and/or 'outlier' and having a
% structure with threshold stored.
%
% Only problem is when you have an empty oc-classifier, this will
% return false. I cannot help it:-(
%
%... |
github | SASVDDwt/sa_svdd-master | random_dd.m | .m | sa_svdd-master/matlab/dd_tools/random_dd.m | 2,243 | utf_8 | 411564c84d2698c9592e7cf735c5f7b6 | %RANDOM_DD Random one-class classifier
%
% W = RANDOM_DD(A,FRACREJ)
%
% This is the trivial one-class classifier, randomly assigning labels
% and rejecting FRACREJ of the data objects. This procedure is just to
% show the basic setup of a Prtools classifier, and what is required
% to define a one-class classifi... |
github | SASVDDwt/sa_svdd-master | dkcenter_dd.m | .m | sa_svdd-master/matlab/dd_tools/dkcenter_dd.m | 1,520 | utf_8 | fa3f112b606ad8ed9002f200b5b23625 | %DKCENTER_DD Distance k-center data description.
%
% W = DKCENTER_DD(D,FRACREJ,K)
%
% Train a k-center method with K prototypes on distance dataset D.
%
% See also datasets, mappings, dd_roc, kcenter_dd
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 503... |
github | SASVDDwt/sa_svdd-master | plotg3.m | .m | sa_svdd-master/matlab/dd_tools/plotg3.m | 1,253 | utf_8 | 975af46efc2684e75f2cee0bd2749737 | %PLOTG Plot the function values z on a 2D grid
%
% H = PLOTG(GRID,Z)
%
% Plot the function values given in Z on the 2D grid. The GRID is a
% 2xN dataset, where N is nxn. Vector Z has therefore also length N.
%
% H = PLOTG(GRID,Z,HGT)
%
% By setting HGT one contour at height HGT can be plotted.
%
% When you alread... |
github | SASVDDwt/sa_svdd-master | mpm_dd.m | .m | sa_svdd-master/matlab/dd_tools/mpm_dd.m | 2,674 | utf_8 | 0893dec09b6ca85beed86783048c98c6 | %MPM_DD Minimax prob. machine.
%
% W = MPM_DD(X,FRACREJ,SIGMA,LAMBDA)
%
% Computes the minimax probability machine of Lanckriet, using the RBF
% kernel with kernel-width SIGMA and quantile FRACREJ. It tries to find
% the linear classifier that separates the data from the origin,
% rejecting maximally FRACREJ of th... |
github | SASVDDwt/sa_svdd-master | parzen_dd.m | .m | sa_svdd-master/matlab/dd_tools/parzen_dd.m | 1,746 | utf_8 | 2f3bbad1b96ede662cbae6452c0cfe27 | %PARZEN_DD Parzen data description.
%
% W = parzen_dd(A,fracrej)
%
% Fit a Parzen density on dataset A. The threshold is put such that
% fracrej of the target objects is rejected.
%
% W = parzen_dd(A,fracrej,h)
%
% If the width parameter is known, it can be given as third parameter,
% otherwise it is op... |
github | SASVDDwt/sa_svdd-master | svddpath.m | .m | sa_svdd-master/matlab/dd_tools/svddpath.m | 2,781 | utf_8 | f819b5ba4dbcee91196513aaae331cb9 | %SVDDPATH SVDD for different lambda/C
%
% W = SVDDPATH(A,FRACREJ,KTYPE,KPAR)
%
% Optimize the SVDD over the complete regularization path by changing C
% (or lambda). The SVDD is defined by the kernel KTYPE with parameter
% KPAR. For the definition of the kernel, see dd_kernel.m.
%
% To get the path, please have a l... |
github | SASVDDwt/sa_svdd-master | nndd.m | .m | sa_svdd-master/matlab/dd_tools/nndd.m | 1,754 | utf_8 | 737586c934604589ee628ebfe16bb369 | %NNDD Nearest neighbour data description method.
%
% W = NNDD(A,FRACREJ)
%
% Calculates the Nearest neighbour data description. Training only
% consists of the computation of the resemblance of all training
% objects to the training data using Leave-one-out.
%
% WARNING: this method is basically a wrapper aroun... |
github | SASVDDwt/sa_svdd-master | lpdd.m | .m | sa_svdd-master/matlab/dd_tools/lpdd.m | 2,445 | utf_8 | 7715e73898257f025727f2d1c4374d6b | %LPDD Linear programming distance data description
%
% W = LPDD(X,NU,S,DTYPE,P)
%
% One-class classifier put into a linear programming framework. From
% the data X the distance matrix is computed (using distance DTYPE,
% see myproxm for the possibilities). The distances are then
% transformed using a sigmoidal... |
github | SASVDDwt/sa_svdd-master | dd_eer.m | .m | sa_svdd-master/matlab/dd_tools/dd_eer.m | 1,094 | utf_8 | 6744f2fc44f6fb5efa755ea694df570f | %EER Equal error rate
%
% E = DD_EER(R)
% E = A*W*DD_EER
%
% Compute the Equal error rate for ROC-curve R, or from the roc-curve
% derived from dataset A applied to (one-class) classifier W. Output E
% returns two values, the FPr and the FNr. In the case the ROC curve is
% sampled very well, these two values shou... |
github | SASVDDwt/sa_svdd-master | fastmcd.m | .m | sa_svdd-master/matlab/dd_tools/fastmcd.m | 64,709 | utf_8 | 7edeada5150063ac40e13783dbfbd4e0 | function [res,raw]=fastmcd(data,options);
% version 22/12/2000, revised 19/01/2001, new reweighted correction factors and old cutoff 9/07/2001
%
% FASTMCD 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... |
github | SASVDDwt/sa_svdd-master | mst.m | .m | sa_svdd-master/matlab/dd_tools/private/mst.m | 3,024 | utf_8 | 8528544bd11a598bdab20a02af56bb8f | function [tree,A] = mst(d)
% [tree,A] = mst(d)
% minimum spanning tree
%
% INPUT
% d [m x m] distance matrix
% OUTPUT
% tree [m-1 x 2] list of edges
% A [m x m] adjecency matrix
%
% See also mst_dd,datasets, mappings
% Copyright: Piotr Juszczak, p.juszczak@tudelft.nl
% I... |
github | SASVDDwt/sa_svdd-master | p_map.m | .m | sa_svdd-master/matlab/dd_tools/private/p_map.m | 2,208 | utf_8 | 8d1235dcc4962429fa41287c64c2303c | %PARZEN_MAP Map a dataset on a Parzen densities based classifier
%
% F = p_map(A,W)
%
% Maps the dataset A by the Parzen density based classfier W. It
% outputs just the raw class probabilities (i.e. non-normalized).
% W should be trained by a
% classifier like parzenc. This routine is called automatically to
% so... |
github | SASVDDwt/sa_svdd-master | mykcentres.m | .m | sa_svdd-master/matlab/dd_tools/private/mykcentres.m | 1,535 | utf_8 | 0e602bfad853f9407b9d46128d8afb05 | % KCENTRES Find k centres objects from distance matrix
%
% [labels,J,dmin] = kcentres(D,k,n)
%
% If D is a square distance matrix between m objects then J is the set of centre
% points, i.e. the subset of k objects that minimizes dmin, the maximum of the
% distances over all objects to the nearest centre point. For k >... |
github | SASVDDwt/sa_svdd-master | dd_mem.m | .m | sa_svdd-master/matlab/dd_tools/private/dd_mem.m | 747 | utf_8 | e477bfc44c0aecb09c1848c325f2b00d | %DD_MEM Size of memory and loops for intermediate results
%
% [loops,rows,last] = dd_mem(m,k)
%
% The numbers of loops and rows are determined that are needed if in
% total an intermediate array of m*k is needed such that rows*k <
% PRMEMORY. The final number of rows for the last loop is returned
% in last.
% C... |
github | SASVDDwt/sa_svdd-master | knnc.m | .m | sa_svdd-master/matlab/prtools/knnc.m | 3,535 | utf_8 | 20362e51c361d7899c025ded631e1d9b | %KNNC K-Nearest Neighbor Classifier
%
% [W,K,E] = KNNC(A,K)
% [W,K,E] = KNNC(A)
%
% INPUT
% A Dataset
% K Number of the nearest neighbors (optional; default: K is
% optimized with respect to the leave-one-out error on A)
%
% OUTPUT
% W k-NN classifier
% K Number of the nearest neighbors used
% ... |
github | SASVDDwt/sa_svdd-master | im_skel_meas.m | .m | sa_svdd-master/matlab/prtools/im_skel_meas.m | 1,669 | utf_8 | dcdcd014bc93aaef5301141e3c64512a | %IM_SKEL_MEASURE Computation by DIP_Image of skeleton-based features
%
% F = IM_SKEL_MEASURE(A,FEATURES)
%
% INPUT
% A Dataset with binary object images dataset
% FEATURES Features to be computed
%
% OUTPUT
% F Dataset with computed features
%
% DESCRIPTION
% The following features may be compute... |
github | SASVDDwt/sa_svdd-master | im_fft.m | .m | sa_svdd-master/matlab/prtools/im_fft.m | 859 | utf_8 | 9c39c2a03e24449fb0baa8cf48e786b2 | %IM_FFT 2D FFT of all images in dataset
%
% F = IM_FFT(A)
%
% INPUT
% A Dataset with object images (possibly multi-band)
%
% OUTPUT
% F Dataset with FFT images
%
% SEE ALSO
% DATASETS, DATAFILES, FFT2
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O... |
github | SASVDDwt/sa_svdd-master | parzenm.m | .m | sa_svdd-master/matlab/prtools/parzenm.m | 2,629 | utf_8 | fc2c033dbde1f0e376cebdb6f43eb220 | %PARZENM Estimate Parzen densities
%
% W = PARZENM(A,H)
% W = A*PARZENM([],H)
%
% D = B*W
%
% INPUT
% A Input dataset
% H Smoothing parameters (scalar, vector)
%
% OUTPUT
% W output mapping
%
% DESCRIPTION
% A Parzen distribution is estimated for the labeled objects in A. Unlabeled
% objects are neglecte... |
github | SASVDDwt/sa_svdd-master | col2gray.m | .m | sa_svdd-master/matlab/prtools/col2gray.m | 1,596 | utf_8 | 8f52ea4434366e7be840bf8ffebaf7dd | %COL2GRAY Mapping for converting multi-band images into single band images
%
% B = COL2GRAY(A,V)
% B = A*COL2GRAY([],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.
%
% ... |
github | SASVDDwt/sa_svdd-master | cleval.m | .m | sa_svdd-master/matlab/prtools/cleval.m | 6,207 | utf_8 | 25adf4cdc01eb3c30a03ff917d67a36f | %CLEVAL Classifier evaluation (learning curve)
%
% E = CLEVAL(A,CLASSF,TRAINSIZES,NREPS,T,TESTFUN)
%
% INPUT
% A Training dataset
% CLASSF Classifier to evaluate
% TRAINSIZE Vector of class sizes, used to generate subsets of A
% (default [2,3,5,7,10,15,20,30,50,70,100])
% NREPS ... |
github | SASVDDwt/sa_svdd-master | classc.m | .m | sa_svdd-master/matlab/prtools/classc.m | 2,225 | utf_8 | 5ffc0222026cc4981615df3dc0b45370 | %CLASSC Convert mapping to classifier
%
% W = CLASSC(W)
% W = W*CLASSC
%
% INPUT
% W Any mapping or dataset
%
% OUTPUT
% W Classifier mapping or normalized dataset: outputs/features sum to 1
%
% DESCRIPTION
% The mapping W is converted into a classifier by normalizing the outputs:
% the sum of the outputs for on... |
github | SASVDDwt/sa_svdd-master | featselb.m | .m | sa_svdd-master/matlab/prtools/featselb.m | 2,850 | utf_8 | 242e3f5afed7c113de4956acdbf3b569 | %FEATSELB Backward feature selection for classification
%
% [W,R] = FEATSELB(A,CRIT,K,T,FID)
% [W,R] = FEATSELB(A,CRIT,K,N,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping
% (optional; default: 'NN', i.e. 1-Nearest Neighbor error)
% K Number of features to ... |
github | SASVDDwt/sa_svdd-master | issym.m | .m | sa_svdd-master/matlab/prtools/issym.m | 768 | utf_8 | d029ef5dca799d320df7ee0deb0d19fa | %ISSYM Checks whether a matrix is symmetric
%
% OK = ISSYM(A,DELTA)
%
% INPUT
% A Dataset
% DELTA Parameter for the precision check (optional; default: 1e-12)
%
% OUTPUT
% OK 1 if the matrix A is symmetric and 0, otherwise.
%
% DESCRIPTION
% A is considered as a symmetric matrix, when it is square and
... |
github | SASVDDwt/sa_svdd-master | isdataset.m | .m | sa_svdd-master/matlab/prtools/isdataset.m | 501 | utf_8 | 0b61fa069741029a5c4bf06e4ba660c4 | %ISDATASET Test whether the argument is a dataset
%
% N = ISDATASET(A);
%
% INPUT
% A Input argument
%
% OUTPUT
% N 1/0 if A is/isn't a dataset
%
% DESCRIPTION
% The function ISDATASET test if A is a dataset object.
%
% SEE ALSO
% ISMAPPING, ISDATAIM, ISFEATIM
% $Id: isdataset.m,v 1.3 2007/03/22 08:54:59 duin Ex... |
github | SASVDDwt/sa_svdd-master | stumpc.m | .m | sa_svdd-master/matlab/prtools/stumpc.m | 14,250 | utf_8 | 2a509d754a43b68333308bf4d064d903 | %STUMPC Decision stump classifier
%
% W = STUMPC(A,CRIT,N)
%
% Computation of a decision tree classifier out of a dataset A using
% a binary splitting criterion CRIT:
% INFCRIT - information gain
% MAXCRIT - purity (default)
% FISHCRIT - Fisher criterion
% Just N (default N=1) nodes are computed.
%
% s... |
github | SASVDDwt/sa_svdd-master | plote.m | .m | sa_svdd-master/matlab/prtools/plote.m | 6,571 | utf_8 | fef2e79155d7f5fbab03ea26fef49a88 | %PLOTE Plot error curves
%
% H = PLOTE(E,LINEWIDTH,S,FONTSIZE,OPTIONS)
%
% INPUT
% E Structure containing error curves (see e.g. CLEVAL)
% LINEWIDTH Line width, < 5 (default 1.5)
% S Plot strings
% FONTSIZE Font size, >= 5 (default 5)
% OPTIONS Character strings:
% 'nole... |
github | SASVDDwt/sa_svdd-master | data2im.m | .m | sa_svdd-master/matlab/prtools/data2im.m | 2,596 | utf_8 | c14acb77e7e389b9700d82cfc55549e3 | %DATA2IM Convert PRTools dataset or datafile to image
%
% IM = DATA2IM(A,J)
% IM = DATA2IM(A(J,:))
%
% INPUT
% A Dataset or datafile containing images
% J Desired images
%
% OUTPUT
% IM If A is dataset, IM is a X*Y*N*K matrix with K images.
% K is the number of images (length(J))
% N is the... |
github | SASVDDwt/sa_svdd-master | feateval.m | .m | sa_svdd-master/matlab/prtools/feateval.m | 4,370 | utf_8 | c05ca89a6f9e115fd0c6d064e694400f | %FEATEVAL Evaluation of feature set for classification
%
% J = FEATEVAL(A,CRIT,T)
% J = FEATEVAL(A,CRIT,N)
%
% INPUT
% A input dataset
% CRIT string name of a method or untrained mapping
% T validation dataset (optional)
% N number of cross-validations (optional)
%
% OUTPUT
... |
github | SASVDDwt/sa_svdd-master | gendatm.m | .m | sa_svdd-master/matlab/prtools/gendatm.m | 1,444 | utf_8 | 052286de99e508d15b878b8079f7d6c7 | %GENDATM Generation of multi-class 2-D data
%
% A = GENDATM(N)
%
% INPUT
% N Vector of class sizes (default: 20)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% Generation of N samples in 8 classes of 2 dimensionally distributed data
% vectors. Classes have equal prior probabilities. If N is a vector of
% sizes, ex... |
github | SASVDDwt/sa_svdd-master | crossval.m | .m | sa_svdd-master/matlab/prtools/crossval.m | 6,676 | utf_8 | 8fc38c0d24bebea305b194f5b9aaed5f | %CROSSVAL Error/performance estimation by cross validation (rotation)
%
% [ERR,CERR,NLAB_OUT] = CROSSVAL(A,CLASSF,N,1,TESTFUN)
% [ERR,STDS] = CROSSVAL(A,CLASSF,N,NREP,TESTFUN)
% R = CROSSVAL(A,[],N,0)
%
% INPUT
% A Input dataset
% CLASSF The untrained classifier to be ... |
github | SASVDDwt/sa_svdd-master | baggingc.m | .m | sa_svdd-master/matlab/prtools/baggingc.m | 2,725 | utf_8 | 88b0a50a556993052bb0c8407885d693 | %BAGGINGC Bootstrapping and aggregation of classifiers
%
% W = BAGGINGC (A,CLASSF,N,ACLASSF,T)
%
% INPUT
% A Training dataset.
% CLASSF The base classifier (default: nmc)
% N Number of base classifiers to train (default: 100)
% ACLASSF Aggregating classifier (default: nmc), [] for no a... |
github | SASVDDwt/sa_svdd-master | svo_nu.m | .m | sa_svdd-master/matlab/prtools/svo_nu.m | 3,960 | utf_8 | 9c30e006ea59434cea25006140960d8d | %SVO_NU Support Vector Optimizer: NU algorithm
%
% [V,J,C] = SVO(K,NLAB,NU,PD)
%
% INPUT
% K Similarity matrix
% NLAB Label list consisting of -1/+1
% NU Regularization parameter (0 < NU < 1): expected fraction of SV (optional; default: 0.25)
%
% PD Do or do not the check of the positive definitene... |
github | SASVDDwt/sa_svdd-master | im_dbr.m | .m | sa_svdd-master/matlab/prtools/im_dbr.m | 3,931 | utf_8 | ca05d5f7297ffe05ed40a9511f866585 | %IM_DBR Image Database Retrieval GUI
%
% [RANK,TARG,OUTL] = IM_DBR(DBASE,FSETS,CLASSF,COMB)
%
% INPUT
% DBASE - Dataset or datafile with N object images
% FSETS - Cell array with maximum 4 feature sets
% CLASSF - Cell array with untrained classifiers (Default: KNNC([],1))
% COMB - Combining classifie... |
github | SASVDDwt/sa_svdd-master | testr.m | .m | sa_svdd-master/matlab/prtools/testr.m | 806 | utf_8 | 5e59ef193ee8cd878561c221ebf52cb2 | %TESTR MSE for regression
%
% E = TESTR(X,W)
% E = TESTR(X*W)
% E = X*W*TESTR
%
% INPUT
% X Regression dataset
% W Regression mapping
%
% OUTPUT
% E Mean squared error
%
% DESCRIPTION
% Compute the the mean squared error of regression W on dataset X.
%
% SEE ALSO
% RSQUARED, TESTC
% Copy... |
github | SASVDDwt/sa_svdd-master | stacked.m | .m | sa_svdd-master/matlab/prtools/stacked.m | 4,700 | utf_8 | 7936e0ebb5dd8fbfeaa38c6e71b421c9 | %STACKED Combining classifiers in the same feature space
%
% WC = STACKED(W1,W2,W3, ....) or WC = [W1,W2,W3, ...]
% WC = STACKED({W1,W2,W3, ...}) or WC = [{W1,W2,W3, ...}]
% WC = STACKED(WC,W1,W2, ....) or WC = [WC,W2,W3, ...]
%
% INPUT
% W1,W2,W3 Set of classifiers
%
% OUTPUT
% WC Combined classifi... |
github | SASVDDwt/sa_svdd-master | bandsel.m | .m | sa_svdd-master/matlab/prtools/bandsel.m | 2,566 | utf_8 | 526ee5ca5c5f30ea620541aa76184bf0 | %BANDSEL Selection of bands from object images
%
% B = BANDSEL(A,J)
% W = BANDSEL([],J)
% B = A*BANDSEL([],J)
%
% INPUT
% A Dataset or datafile with multi-band object images
% J Bands to be selected
%
% OUTPUT
% W Mapping performing the band selection
% B Dataset with selected bands (oredered ... |
github | SASVDDwt/sa_svdd-master | datfilt.m | .m | sa_svdd-master/matlab/prtools/datfilt.m | 1,208 | utf_8 | 31f36e31d9f2ba195de6f48b03120f3c | %DATFILT Filtering of dataset images
%
% B = DATFILT(A,F)
%
% INPUT
% A Dataset with image data
% F Matrix with the convolution mask
%
% OUTPUT
% B Dataset containing all the images after filtering
%
% DESCRIPTION
% All images stored in the dataset A are horizontally and vertically
% convoluted by the 1-dime... |
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