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 | medianc.m | .m | sa_svdd-master/matlab/prtools/medianc.m | 1,428 | utf_8 | b359e761a6b0660209c316be6d90edfb | %MEDIANC Median combining classifier
%
% W = MEDIANC(V)
% W = V*MEDIANC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Median combining classifier on V
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the same
% classes, then W is the median combiner: it selects the clas... |
github | SASVDDwt/sa_svdd-master | im_rotate.m | .m | sa_svdd-master/matlab/prtools/im_rotate.m | 1,209 | utf_8 | d41ac83e1acd7920c98caa4301fc2755 | %IM_ROTATE Rotate all images in dataset
%
% B = IM_ROTATE(A,ALF)
%
% INPUT
% A Dataset with object images (possibly multi-band)
% ALF Rotation angle (in radians),
% default: rotation to main axis
%
% OUTPUT
% B Dataset with rotated object images
%
% SEE ALSO
% DATASETS, DATAFILES,... |
github | SASVDDwt/sa_svdd-master | ploto.m | .m | sa_svdd-master/matlab/prtools/ploto.m | 1,735 | utf_8 | abb34348c3a7669eb987b2ac19de812a | %PLOTO Plot objects as 1-D functions of the feature number
%
% [HH HO HC] = PLOTO(A,N)
%
% INPUT
% A Dataset
% N Integer
%
% OUTPUT
% HH Lines handles
% HO Object identifier handles
% HC Class number handles
%
% DESCRIPTION
% Produces 1-D function plots for all the objects in dataset A. The plots
%... |
github | SASVDDwt/sa_svdd-master | iscomdset.m | .m | sa_svdd-master/matlab/prtools/iscomdset.m | 1,463 | utf_8 | e7b638fcb7d060eae2f0d310d9003976 | %ISCOMDSET Test whether datasets are compatible
%
% N = ISCOMDSET(A,B,CLAS);
%
% INPUT
% A Input argument, to be tested on dataset
% B Input argument, to be tested on compatibility with A
% CLAS 1/0, test on equal classes (1) or don't test (0)
% (optional; default 1)
%
% OUTPUT
% N 1/0 if A and... |
github | SASVDDwt/sa_svdd-master | prdata.m | .m | sa_svdd-master/matlab/prtools/prdata.m | 1,605 | utf_8 | 5945233e9aca3aff5de534a83caf9910 | %PRDATA Read data files
%
% A = PRDATA(FILENAME,FLAG)
%
% INPUT
% FILENAME Name of delimited ASCII file containing rows of data
% FLAG If not 0, first column is assumed to contain labels (default 1)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% Reads data into the dataset A. The first word of each ... |
github | SASVDDwt/sa_svdd-master | affine.m | .m | sa_svdd-master/matlab/prtools/affine.m | 6,471 | utf_8 | ab3fc191ca4f45804eab5c533c20d72a | %AFFINE Construct affine (linear) mapping from parameters
%
% W = AFFINE(R,OFFSET,LABLIST_IN,LABLIST_OUT,SIZE_IN,SIZE_OUT)
% W = AFFINE(R,OFFSET,A)
% W = AFFINE(W1,W2)
%
% INPUT
% R Matrix of a linear mapping from a K- to an L-dimensional space
% OFFSET Shift applied after R; a row vector of... |
github | SASVDDwt/sa_svdd-master | show.m | .m | sa_svdd-master/matlab/prtools/show.m | 1,605 | utf_8 | c2b4d13676eb67fa5df7d955ac2203e1 | %SHOW PRTools general show
%
% H = SHOW(A,N)
%
% INPUT
% A Image
% N Number of images on a row
%
% OUTPUT
% H Graphics handle
%
% DESCRIPTION
% PRTools offers a SHOW command for variables of the data classes DATASET
% and DATAFILE. In order to have a simliar command for images not converted
% to... |
github | SASVDDwt/sa_svdd-master | gauss.m | .m | sa_svdd-master/matlab/prtools/gauss.m | 4,836 | utf_8 | 0b2a291f6bd1b62648f0b98dd4a5d471 | %GAUSS Generation of a multivariate Gaussian dataset
%
% A = GAUSS(N,U,G,LABTYPE)
%
% INPUT (in case of generation a 1-class dataset in K dimensions)
% N Number of objects to be generated (default 50).
% U Desired mean (vector of length K).
% G K x K covariance matrix. Default eye(K).
% LABTY... |
github | SASVDDwt/sa_svdd-master | nlabcmp.m | .m | sa_svdd-master/matlab/prtools/nlabcmp.m | 975 | utf_8 | 91014c1ad5b78b2874f44cf02e296fcb | %NLABCMP Compare two label lists and count the differences
%
% [N,C] = NLABCMP(LAB1,LAB2)
%
% INPUT
% LAB1,
% LAB2 Label lists
%
% OUTPUT
% C A 0/1 vector pointing to different/equal labels
% N Number of differences in LAB1 and LAB2
%
% DESCRIPTION
% Compares two label lists and counts the disa... |
github | SASVDDwt/sa_svdd-master | featsellr.m | .m | sa_svdd-master/matlab/prtools/featsellr.m | 9,217 | utf_8 | 1d4044912afa68b33e567b470d6c838d | %FEATSELLR Plus-L-takeaway-R feature selection for classification
%
% [W,RES] = FEATSELLR(A,CRIT,K,L,R,T,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 select
% (o... |
github | SASVDDwt/sa_svdd-master | gentrunk.m | .m | sa_svdd-master/matlab/prtools/gentrunk.m | 1,843 | utf_8 | eab5ed7e45535b180aa94930ba5fb226 | %GENTRUNK Generation of Trunk's classification problem of 2 Gaussian classes
%
% A = GENTRUNK(N,K)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K Dimensionality of the dataset to be generated (default: 2).
%
% OUTPUT
% A Dataset.
%
% DESCRIPTION
% Gener... |
github | SASVDDwt/sa_svdd-master | setdat.m | .m | sa_svdd-master/matlab/prtools/setdat.m | 1,279 | utf_8 | 1b8788b1fe07cafce24eb7baef438956 | %SETDAT Reset data and feature labels of dataset
%
% A = SETDAT(A,DATA,W)
%
% INPUT
% A Dataset
% DATA Dataset or double
% W Mapping (optional)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% The data in the dataset A is replaced by DATA (dataset or double). The
% number of objects in A and ... |
github | SASVDDwt/sa_svdd-master | testc.m | .m | sa_svdd-master/matlab/prtools/testc.m | 14,741 | utf_8 | 341765623a705854e9cabdcf5720ded0 | %TESTC Test classifier, error / performance estimation
%
% [E,C] = TESTC(A*W,TYPE)
% [E,C] = TESTC(A,W,TYPE)
% E = A*W*TESTC([],TYPE)
%
% [E,F] = TESTC(A*W,TYPE,LABEL)
% [E,F] = TESTC(A,W,TYPE,LABEL)
% E = A*W*TESTC([],TYPE,LABEL)
%
% INPUT
% A Dataset
% W Trained classifier mapping
% ... |
github | SASVDDwt/sa_svdd-master | labeld.m | .m | sa_svdd-master/matlab/prtools/labeld.m | 3,045 | utf_8 | 29685d816665b295ccaac0573773b9b0 | %LABELD Find labels of classification dataset (perform crisp classification)
%
% LABELS = LABELD(Z)
% LABELS = Z*LABELD
% LABELS = LABELD(A,W)
% LABELS = A*W*LABELD
% LABELS = LABELD(Z,THRESH)
% LABELS = Z*LABELD([],THRESH)
% LABELS = LABELD(A,W,THRESH)
% LABELS = A*W*LABELD([],THRESH)
%
% INPUT
% Z ... |
github | SASVDDwt/sa_svdd-master | nmsc.m | .m | sa_svdd-master/matlab/prtools/nmsc.m | 2,013 | utf_8 | d6f267790fb4836284cfe76cb9641f13 | %NMSC Nearest Mean Scaled Classifier
%
% W = NMSC(A)
% W = A*NMSC
%
% INPUT
% A Trainign dataset
%
% OUTPUT
% W Nearest Mean Scaled Classifier mapping
%
% DESCRIPTION
% Computation of the linear discriminant for the classes in the dataset A
% assuming normal distributions with zero covariances and equal cl... |
github | SASVDDwt/sa_svdd-master | testauc.m | .m | sa_svdd-master/matlab/prtools/testauc.m | 1,999 | utf_8 | 7ca602b737123e08a8d0a1279dff5123 | %TESTAUC Multiclass error area under the ROC
%
% E = TESTAUC(A*W)
% E = TESTAUC(A,W)
% E = A*W*TESTAUC
%
% INPUT
% A Dataset to be classified
% W Classifier
%
% OUTPUT
% E Error, Area under the ROC
%
% DESCRIPTION
% The area under the error ROC is computed for the datset A w.r.t. the
% classifer W. The e... |
github | SASVDDwt/sa_svdd-master | genclass.m | .m | sa_svdd-master/matlab/prtools/genclass.m | 1,579 | utf_8 | b363022121705e34497b3a5c067eabd1 | %GENCLASS Generate class frequency distribution
%
% M = GENCLASS(N,P)
%
% INPUT
% N Number (scalar)
% P Prior probabilities
%
% OUTPUT
% M Class frequency distribution
%
% DESCRIPTION
% Generates a class frequency distribution M of N (scalar) samples
% over a set of classes with prior probabilities given b... |
github | SASVDDwt/sa_svdd-master | gendatw.m | .m | sa_svdd-master/matlab/prtools/gendatw.m | 764 | utf_8 | 886a119ac1453c1e191ea0c98dee8af0 | %GENDATW Sample dataset by given weigths
%
% B = GENDATW(A,V,N)
%
% INPUT
% A Dataset
% V Vector with weigths for each object in A
% N Number of objects to be generated (default size A);
%
% OUTPUT
% B Dataset
%
% DESCRIPTION
% The dataset A is sampled using the weigths in V as a prior distributio... |
github | SASVDDwt/sa_svdd-master | kernelm.m | .m | sa_svdd-master/matlab/prtools/kernelm.m | 5,202 | utf_8 | c623a8b05eb023f2cfd2fb8ab965d6ae | %KERNELM Kernel mapping, dissimilarity representation
%
% [W,J] = KERNELM(A,KERNEL,SELECT,P1,P2 , ...)
% W = A*KERNELM([],KERNEL,SELECT,P1,P2 , ...)
% K = B*W
%
% INPUT
% A,B Datasets
% KERNEL Untrained kernel / dissimilarity representation,
% a mapping computing proximitie... |
github | SASVDDwt/sa_svdd-master | rbsvc.m | .m | sa_svdd-master/matlab/prtools/rbsvc.m | 1,769 | utf_8 | a95209f34a5b4fbc2d80e580efb33c88 | %RBSVC Automatic radial basis Support Vector Classifier
%
% [W,KERNEL,NU] = RBSVC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Mapping: Radial Basis Support Vector Classifier
% KERNEL Untrained mapping, representing the optimised kernel
% NU Resulting value for NU from NUSVC
%
% DESCRIPTION
% Thi... |
github | SASVDDwt/sa_svdd-master | gendatp.m | .m | sa_svdd-master/matlab/prtools/gendatp.m | 2,944 | utf_8 | 739b1f5b524716f60d8096624f35ada5 | %GENDATP Parzen density data generation
%
% B = GENDATP(A,N,S,G)
%
% INPUT
% A Dataset
% N Number(s) of points to be generated (optional; default: 50 per class)
% S Smoothing parameter(s)
% (optional; default: a maximum likelihood estimate based on A)
% G Covariance matrix used for generation of t... |
github | SASVDDwt/sa_svdd-master | spirals.m | .m | sa_svdd-master/matlab/prtools/spirals.m | 553 | utf_8 | 9c160bf41b06bee2e4e497139043f7f5 | %SPIRALS 194 objects with 2 features in 2 classes
%
% A = SPIRALS
% A = SPIRALS(M,N)
%
% Load the dataset in A, select the objects and features according to the
% index vectors M and N. This is one of the Spiral dataset implementations.
%
% See also DATASETS, PRDATASETS
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org... |
github | SASVDDwt/sa_svdd-master | plotdg.m | .m | sa_svdd-master/matlab/prtools/plotdg.m | 1,889 | utf_8 | a5fda91cafdb6a5f1d4df34153470a49 | %PLOTDG Plot dendrogram
%
% PLOTDG(DENDROGRAM,K)
%
% INPUT
% DENDROGRAM Dendrogram
% K Number of clusters
%
% OUTPUT
%
% DESCRIPTION
% Plots a dendrogram as generated by HCLUST. If the optional K is given the
% dendrogram is compressed first to K clusters. Along the horizontal axis
% the numbers stored... |
github | SASVDDwt/sa_svdd-master | newline.m | .m | sa_svdd-master/matlab/prtools/newline.m | 280 | utf_8 | f689d04b4cfc4871db4b8517eb101090 | %NEWLINE The platform dependent newline character
%
% c = newline
% $Id: newline.m,v 1.2 2006/03/08 22:06:58 duin Exp $
function c = newline
if strcmp(computer,'MAC2')
c = setstr(13);
elseif strcmp(computer,'PCWIN')
c = setstr(10);
else
c = setstr(10);
end
return
|
github | SASVDDwt/sa_svdd-master | genlab.m | .m | sa_svdd-master/matlab/prtools/genlab.m | 3,026 | utf_8 | fe5a25e4de06ffb1104b3fc9ae448c3c | %GENLAB Generate labels for classes
%
% LABELS = GENLAB(N,LABLIST)
%
% INPUT
% N Number of labels to be generated
% LABLIST Label names (optional; default: numeric labels 1,2,3,...)
%
% OUTPUT
% LABELS Labels in a column vector or strinag array
%
% DESCRIPTION
% Generate a set of labels as defined... |
github | SASVDDwt/sa_svdd-master | im_berosion.m | .m | sa_svdd-master/matlab/prtools/im_berosion.m | 1,256 | utf_8 | ea9ba28359fad8fd157dc7a9cb947476 | %IM_BEROSION Binary erosion of images stored in a dataset (DIP_Image)
%
% B = IM_BEROSION(A,N,CONNECTIVITY,EDGE_CONDITION)
% B = A*IM_BEROSION([],N,CONNECTIVITY,EDGE_CONDITION)
%
% INPUT
% A Dataset with binary object images dataset (possibly multi-band)
% N Number of iterations (default 1)
% CONNEC... |
github | SASVDDwt/sa_svdd-master | im_minf.m | .m | sa_svdd-master/matlab/prtools/im_minf.m | 1,134 | utf_8 | fe024d15ba47c051482648153256c205 | %IM_MINF Minimum filter of images stored in a dataset (DIP_Image)
%
% B = IM_MINF(A,SIZE,SHAPE)
% B = A*IM_MINF([],SIZE,SHAPE)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% SIZE Filter width in pixels, default SIZE = 7
% SHAPE String with shape:'rectangular', 'elliptic', '... |
github | SASVDDwt/sa_svdd-master | setname.m | .m | sa_svdd-master/matlab/prtools/setname.m | 274 | utf_8 | 0cbc90d50f92b12d89062cdb1bd00aab | %SETNAME Mapping for easy name setting
%
% A = A*SETNAME([],NAME)
% W = W*SETNAME([],NAME)
%
%Set name of dataset A or mapping W
function a = setname(a,varargin)
if nargin < 1 | isempty(a)
a = mapping(mfilename,'combiner',varargin);
else
a = setname(a,varargin);
end |
github | SASVDDwt/sa_svdd-master | subsc.m | .m | sa_svdd-master/matlab/prtools/subsc.m | 4,467 | utf_8 | 6622a9f94945b44abfdbc13f5cf2cdb6 | %SUBSC Subspace Classifier
%
% W = SUBSC(A,N)
% W = SUBSC(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Desired model dimensionality or fraction of retained
% variance per class
%
% OUTPUT
% W Subspace classifier
%
% DESCRIPTION
% Each class in the trainingset A is described by ... |
github | SASVDDwt/sa_svdd-master | reject.m | .m | sa_svdd-master/matlab/prtools/reject.m | 3,470 | utf_8 | e28c512648bc1dc90ebb01253058ff9d | %REJECT Compute the error-reject trade-off curve
%
% E = REJECT(D);
% E = REJECT(A,W);
%
% INPUT
% D Classification result, D = A*W
% A Dataset
% W Cell array of trained classifiers
%
% OUTPUT
% E Structure storing the error curve and information needed for plotting
%
% DESCRIPTION
% E = REJECT(D)... |
github | SASVDDwt/sa_svdd-master | rejectc.m | .m | sa_svdd-master/matlab/prtools/rejectc.m | 1,970 | utf_8 | 75a06f69a6036f1fbba69b48403a6693 | %REJECTC Construction of a rejecting classifier
%
% WR = REJECTC(A,W,FRAC,TYPE)
%
% INPUT
% A Dataset
% W Trained or untrained classifier
% FRAC Fraction to be rejected. Default: 0.05
% TYPE String with reject type: 'ambiguity' or 'outlier'.
% 'a' and 'o' are supported as well. Default ... |
github | SASVDDwt/sa_svdd-master | gendatk.m | .m | sa_svdd-master/matlab/prtools/gendatk.m | 3,710 | utf_8 | 750061e1cf645e01287511112e378f39 | %GENDATK K-Nearest neighbor data generation
%
% B = GENDATK(A,N,K,S)
%
% INPUT
% A Dataset
% N Number of points (optional; default: 50)
% K Number of nearest neighbors (optional; default: 1)
% S Standard deviation (optional; default: 1)
%
% OUTPUT
% B Generated dataset
%
% DESCRIPTION
% Generation of... |
github | SASVDDwt/sa_svdd-master | nusvc.m | .m | sa_svdd-master/matlab/prtools/nusvc.m | 4,225 | utf_8 | a98a0bb2aeb6a1c05d873e659f105f70 | %NUSVC Support Vector Classifier: NU algorithm
%
% [W,J] = NUSVC(A,KERNEL,NU)
% [W,J] = NUSVC(A,TYPE,PAR,NU)
% W = A*SVC([],KERNEL,NU)
% W = A*SVC([],TYPE,PAR,NU)
%
% INPUT
% A Dataset
% KERNEL - Untrained mapping to compute kernel by A*(A*KERNEL) during
% training, or B*(A*KERNEL)... |
github | SASVDDwt/sa_svdd-master | prmemory.m | .m | sa_svdd-master/matlab/prtools/prmemory.m | 1,955 | utf_8 | 6581e143a25ca54cf6d6abf520223783 | %PRMEMORY Set/get size of memory usage
%
% N = PRMEMORY(N)
%
% N : The desired / retrieved maximum size data of matrices (in
% matrix elements)
%
% DESCRIPTION
% This retoutine sets or retrieves a global variable GLOBALPRMEMORY that
% controls the maximum size of data matrices in PRTools. Routines like
% K... |
github | SASVDDwt/sa_svdd-master | im_scale.m | .m | sa_svdd-master/matlab/prtools/im_scale.m | 1,217 | utf_8 | 523e0781344f11a37de848be741dc80c | %IM_SCALE Scale all binary images in a datafile to a giving fraction of pixels 'on'
%
% B = IM_SCALE(A,P)
% B = A*IM_SCALE([],P)
%
% B is a zoomed in / out version of A such that about a fraction
% P of the image pixels is 'on' (1).
%
% SEE ALSO
% DATASETS, DATAFILES, IM_BOX, IM_CENTER
% Copyright: R.P.W. Duin, r.... |
github | SASVDDwt/sa_svdd-master | kcentres.m | .m | sa_svdd-master/matlab/prtools/kcentres.m | 3,830 | utf_8 | a91828c86455572413cc04b4370497a6 | %KCENTRES Finds K center objects from a distance matrix
%
% [LAB,J,DM] = KCENTRES(D,K,N,FID)
%
% INPUT
% D Distance matrix between, e.g. M objects (may be a dataset)
% K Number of center objects to be found (optional; default: 1)
% N Number of trials starting from a random initialization
% (opt... |
github | SASVDDwt/sa_svdd-master | savedatafile.m | .m | sa_svdd-master/matlab/prtools/savedatafile.m | 8,184 | utf_8 | f3236c25deedeaa0fe57849701edf9c9 | %SAVEDATAFILE Save datafile
%
% B = SAVEDATAFILE(A,FEATSIZE,NAME,NBITS,FILESIZE)
%
% INPUT
% A Datafile, or cell array with datafiles and/or datasets
% FEATSIZE Feature size, i.e. image size of a single object in B
% NAME Desired name of directory
% NBITS # of bits in case of rescaling (8... |
github | SASVDDwt/sa_svdd-master | pls_transform.m | .m | sa_svdd-master/matlab/prtools/pls_transform.m | 1,490 | utf_8 | 5ebad849b7f357810fd3db9d4b823ba5 | %pls_transform Partial Least Squares transformation
%
% T = pls_transform(X,R)
% T = pls_transform(X,R,Options)
%
% INPUT
% X [N -by- d_X] the input data matrix, N samples, d_X variables
% R [d_X -by- nLV] the transformation matrix: T_new = X_new*R
% (X_new here after preprocessing, prepr... |
github | SASVDDwt/sa_svdd-master | vandermondem.m | .m | sa_svdd-master/matlab/prtools/vandermondem.m | 1,041 | utf_8 | c3f5516ce784c9807569a9a61b40c44d | %VANDERMONDEM Extend data matrix
%
% Z = VANDERMONDEM(X,N)
%
% INPUT
% X Data matrix
% N Order of the polynomail
%
% OUTPUT
% Z New data matrix containing X upto order N
%
% DESCRIPTION
% Construct the Vandermonde matrix Z from the original data matrix X by
% including all orders upto N. Note that ... |
github | SASVDDwt/sa_svdd-master | classd.m | .m | sa_svdd-master/matlab/prtools/classd.m | 550 | utf_8 | a9ffc4f859a1a00eb46ce2c2c2d1e9ab | %CLASSD Return labels of classified dataset, outdated, use LABELD instead
% $Id: classd.m,v 1.2 2006/03/08 22:06:58 duin Exp $
function labels = classd(a,w)
prtrace(mfilename);
global CLASSD_REPLACED_BY_LABELD
if isempty(CLASSD_REPLACED_BY_LABELD)
disp([newline 'CLASSD has been replaced by LABELD, please use ... |
github | SASVDDwt/sa_svdd-master | im_stretch.m | .m | sa_svdd-master/matlab/prtools/im_stretch.m | 1,275 | utf_8 | 25193a277ae8513dbcd8c6a259d0b553 | %IM_STRETCH Contrast stretching of images stored in a dataset (DIP_Image)
%
% B = IM_STRETCH(A,LOW,HIGH,MIN,MAX)
% B = A*IM_STRETCH([],LOW,HIGH,MIN,MAX)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% LOW Lower percentile (default 0)
% HIGH Highest percentile (default 100)... |
github | SASVDDwt/sa_svdd-master | prodc.m | .m | sa_svdd-master/matlab/prtools/prodc.m | 1,633 | utf_8 | d89f9184765750c0c705dfc45192da63 | %PRODC Product combining classifier
%
% W = PRODC(V)
% W = V*PRODC
%
% INPUT
% V Set of classifiers trained on the same classes
%
% OUTPUT
% W Product combiner
%
% DESCRIPTION
% It defines the product combiner on a set of classifiers, e.g.
% V=[V1,V2,V3] trained on the same classes, by selecting the cl... |
github | SASVDDwt/sa_svdd-master | scatterdui.m | .m | sa_svdd-master/matlab/prtools/scatterdui.m | 8,439 | utf_8 | d2c8f58a10ab26a90560077e64cb4681 | % SCATTERDUI Scatter plot with user interactivity
%
% SCATTERDUI (A)
% SCATTERDUI (A,DIM,S,CMAP,FONTSIZE,'label','both','legend','gridded')
%
% INPUT
% DATA Dataset
% ... See SCATTERD
%
% OUTPUT
%
% DESCRIPTION
% SCATTERDUI is a wrapper around SCATTERD (see SCATTERD for the options). If
% the user clicks o... |
github | SASVDDwt/sa_svdd-master | primport.m | .m | sa_svdd-master/matlab/prtools/primport.m | 2,968 | utf_8 | 67b864e93a49244199462b27e7dbbc95 | % PRIMPORT import the old-format prtools datasets
%
% OUT = PRIMPORT(A)
%
% INPUT
% A The Structure to be converted.
%
% OUTPUT
% OUT The imported dataset
%
% DESCRIPTION
% This routine converts old prtools datasets into the new prtools 4.x
% format. Structure A is tested for existence of all the fields ... |
github | SASVDDwt/sa_svdd-master | testn.m | .m | sa_svdd-master/matlab/prtools/testn.m | 2,964 | utf_8 | 10c47fdb62ac7407a2ee4be8856c9d4e | %TESTN Error estimate of discriminant for normal distribution.
%
% E = TESTN(W,U,G,N)
%
% INPUT
% W Trained classifier mapping
% U C x K dataset with C class means, labels and priors (default: [0 .. 0])
% G K x K x C matrix with C class covariance matrices (default: identity)
% N Number of test examples ... |
github | SASVDDwt/sa_svdd-master | plotc.m | .m | sa_svdd-master/matlab/prtools/plotc.m | 5,410 | utf_8 | 2d333f0a9334bb3df2cb48b4dda93599 | %PLOTC Plot classifiers
%
% PLOTC(W,S,LINE_WIDTH)
% PLOTC(W,LINE_WIDTH,S)
%
% Plots the discriminant as given by the mapping W on predefined axis,
% typically set by scatterd. Discriminants are defined by the points
% where class differences for mapping values are zero.
%
% S is the plot string, e.g. S = 'b--'. ... |
github | SASVDDwt/sa_svdd-master | im2obj.m | .m | sa_svdd-master/matlab/prtools/im2obj.m | 3,036 | utf_8 | f910d69b3caaaaddcaab0f7e7dc9a09d | %IM2OBJ Convert Matlab images or datafile to dataset object
%
% B = IM2OBJ(IM,A)
% B = IM2OBJ(IM,FEATSIZE)
%
% INPUT
% IM X*Y image, X*Y*C image, X*Y*K array of K images,
% X*Y*C*K array of color images, or cell-array of images
% The images may be given as a datafile.
% A Input... |
github | SASVDDwt/sa_svdd-master | getwindows.m | .m | sa_svdd-master/matlab/prtools/getwindows.m | 2,996 | utf_8 | aae1cba2e33f470e7c12aa66f297ca37 | %GETWINDOWS Get pixel feature vectors around given pixels in image dataset
%
% L = GETWINDOWS(A,INDEX,WSIZE,INCLUDE)
% L = GETWINDOWS(A,[ROW,COL],WSIZE,INCLUDE)
%
% INPUT
% A Dataset containing feature images
% INDEX Index vector of target pixels in the images (Objects in A)
% ROW Column vector of r... |
github | SASVDDwt/sa_svdd-master | getopt_pars.m | .m | sa_svdd-master/matlab/prtools/getopt_pars.m | 761 | utf_8 | 304ce5721d89375f6489d64c821ee442 | %GETOPT_PARS Get optimal parameters from REGOPTC
%
% PARS = GETOPT_PARS
% GETOPT_PARS
%
% DESCRIPTION
% This routine retrieves the parameters as used in the final call
% in computing a classifier they are optimised by REGOPTC.
function pars = getopt_pars
global REGOPT_PARS
if nargout == 0
s = [];
for j = 1:l... |
github | SASVDDwt/sa_svdd-master | map.m | .m | sa_svdd-master/matlab/prtools/map.m | 6,377 | utf_8 | 3823bcba3009a13137b3b6960d1118b9 | %MAP Map a dataset, train a mapping or classifier, or combine mappings
%
% B = MAP(A,W) or B = A*W
%
% Maps a dataset A by a fixed or trained mapping (or classifier) W, generating
% a new dataset B. This is done object by object. So B has as many objects
% (rows) as A. The number of features of B is determined by W. Al... |
github | SASVDDwt/sa_svdd-master | filtim.m | .m | sa_svdd-master/matlab/prtools/filtim.m | 4,897 | utf_8 | 1ab9a0566b518ba8a5dde0e00718ee89 | %FILTIM Mapping to filter multiband image objects in datasets and datafiles
%
% B = FILTIM(A,FILTER_COMMAND,{PAR1,PAR2,....},SIZE)
% B = A*FILTIM([],FILTER_COMMAND,{PAR1,PAR2,....},SIZE)
%
% INPUT
% A Dataset or datafile with image objects
% FILTER_COMMAND String with function name
% {PAR1... |
github | SASVDDwt/sa_svdd-master | resizem.m | .m | sa_svdd-master/matlab/prtools/resizem.m | 1,221 | utf_8 | 7f0962ffd2d8b7f4e2a1fe5387354a23 | %RESIZEM Mapping for resizing object images in datasets and datafiles
%(outdated, rplaced by im_resize)
%
% B = RESIZEM(A,SIZE,METHOD)
% B = A*RESIZEM([],SIZE,METHOD)
%
% INPUT
% A Dataset or datafile
% SIZE Desired size
% METHOD Method, see IMRESIZE
%
% OUTPUT
% B Dataset or datafile
%
% DESCRIPT... |
github | SASVDDwt/sa_svdd-master | testdatasize.m | .m | sa_svdd-master/matlab/prtools/testdatasize.m | 2,508 | utf_8 | c04effab6dbe1a9ec827c289c72adab9 | %TESTDATASIZE of datafiles and convert to dataset
%
% B = TESTDATASIZE(A,STRING,FLAG)
%
% INPUT
% A DATAFILE or DATASET
% STRING 'data' (default) or 'features' or 'objects'
% FLAG TRUE / FALSE, (1/0) (Default TRUE)
%
% OUTPUT
% B DATASET (if FLAG == 1 and conversion possible)
% ... |
github | SASVDDwt/sa_svdd-master | nu_svr.m | .m | sa_svdd-master/matlab/prtools/nu_svr.m | 4,298 | utf_8 | ef139c1ae3e2a13467771ac706bab213 | %NU_SVR Support Vector Classifier: NU algorithm
%
% [W,J,C] = NU_SVR(A,TYPE,PAR,C,SVR_TYPE,NU_EPS,MC,PD)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (optional; default: 'p')
% PAR Kernel parameter (optional; default: 1)
% C Regularization parameter (0 < C < 1): expected fraction of SV
% ... |
github | SASVDDwt/sa_svdd-master | reducm.m | .m | sa_svdd-master/matlab/prtools/reducm.m | 1,436 | utf_8 | 2576197c9bee1d077b3255a5bdeaa1e4 | %REDUCM Reduce to minimal space
%
% W = REDUCM(A)
%
% Ortho-normal mapping to a space in which the dataset A exactly fits.
% This is useful for datasets with more features than objects. For the
% objects in B = A*W holds that their dimensionality is minimum, their mean
% is zero, the covariance matrix is diagonal wit... |
github | SASVDDwt/sa_svdd-master | classim.m | .m | sa_svdd-master/matlab/prtools/classim.m | 1,976 | utf_8 | d4c0e46961530bfa3b63e701cc6ef506 | %CLASSIM Classify image and return resulting label image
%
% LABELS = CLASSIM(Z)
% LABELS = CLASSIM(A,W)
% LABELS = A*W*CLASSIM
%
% INPUT
% Z Classified dataset, or
% A,W Dataset and classifier mapping
%
% OUTPUT
% LABELS Label image
% When no output is requested, the label image is display... |
github | SASVDDwt/sa_svdd-master | gendatb.m | .m | sa_svdd-master/matlab/prtools/gendatb.m | 1,478 | utf_8 | d618860fc51cdfe15cc986bcd7a519ea | %GENDATB Generation of banana shaped classes
%
% A = GENDATB(N,S)
%
% INPUT
% N number of generated samples of vector with
% number of samples per class
% S variance of the normal distribution (opt, def: s=1)
%
% OUTPUT
% A generated dataset
%
% DESCRIPTION
% Generation of a ... |
github | SASVDDwt/sa_svdd-master | im_invert.m | .m | sa_svdd-master/matlab/prtools/im_invert.m | 743 | utf_8 | 934a5a84ab57a0240f1787e6af4d17c7 | %IM_INVERT Inversion of images stored in a dataset
%
% A = IM_INVERT(A)
% A = A*IM_INVERT
%
% Inverts image A by subtracting it from its maximum
%
% SEE ALSO
% DATASETS, DATAFILES
% Copyright: D. de Ridder, 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 | matchlablist.m | .m | sa_svdd-master/matlab/prtools/matchlablist.m | 1,266 | utf_8 | 9b644b98bab1c00b7be97ddf3e06549e | %MATCHLABLIST Match entries of lablist1 with lablist2
%
% I = MATCHLABLIST(LABLIST1,LABLIST2)
%
% INPUT
% LABLIST1 list of class names
% LABLIST2 list of class names
%
% OUTPUT
% I indices for LABLIST1 appearing in LABLIST2
%
% DESCRIPTION
% Find the indices of places where the entries of LABLIST1... |
github | SASVDDwt/sa_svdd-master | gendatl.m | .m | sa_svdd-master/matlab/prtools/gendatl.m | 1,532 | utf_8 | aba983637cf024f09083ee34afe47bb2 | %GENDATL Generation of Lithuanian classes
%
% A = GENDATL(N,S)
%
% INPUT
% N Number of objects per class (optional; default: [50 50])
% S Standard deviation for the data generation (optional; default: 1)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% Generation of Lithuanian classes, a 2-dimensional, 2-class datase... |
github | SASVDDwt/sa_svdd-master | invsigm.m | .m | sa_svdd-master/matlab/prtools/invsigm.m | 1,435 | utf_8 | 15b41aa9cbad0fb4cad94337d3a9eb78 | %INVSIGM Inverse sigmoid map
%
% W = W*INVSIGM
% B = INVSIGM(ARG)
%
% INPUT
% ARG Mapping/Dataset
%
% OUTPUT
% W Mapping transforming posterior probabilities into distances.
%
% DESCRIPTION
% The inverse sigmoidal transformation to transform a classifier to a
% mapping, transforming posterior probabilities int... |
github | SASVDDwt/sa_svdd-master | isdataim.m | .m | sa_svdd-master/matlab/prtools/isdataim.m | 956 | utf_8 | 541eb52b33cb75affee631ee0b4db63f | %ISDATAIM Returns true if a dataset contains image objects or image features
%
% N = ISDATAIM(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% N Scalar: 1 if A contains images as objects or features, otherwise 0
%
% DESCRIPTION
% If no output argument is given, the function will produce an error if A does
% not contain ima... |
github | SASVDDwt/sa_svdd-master | mogc.m | .m | sa_svdd-master/matlab/prtools/mogc.m | 2,212 | utf_8 | 3a22b2eed98f9fcab1ce1891d5578d6e | %MOGC Mixture of Gaussian classifier
%
% W = MOGC(A,N)
% W = A*MOGC([],N);
%
% INPUT
% A Dataset
% N Number of mixtures (optional; default 2)
% R,S Regularization parameters, 0 <= R,S <= 1, see QDC
% OUTPUT
%
% DESCRIPTION
% For each class j in A a density estimate is made by GAUSSM, using N(j)
% mixtu... |
github | SASVDDwt/sa_svdd-master | sigm.m | .m | sa_svdd-master/matlab/prtools/sigm.m | 1,231 | utf_8 | 6c752530c0b60971e1d8bf944baa4512 | %SIGM Sigmoid map
%
% W = W*SIGM
% B = A*SIGM
% W = W*SIGM([],SCALE)
% B = SIGM(A,SCALE)
%
% INPUT
% A Dataset (optional)
% SCALE Scaling parameter (optional, default: 1)
%
% OUTPUT
% W Sigmoid mapping, or
% B Dataset A mapped by sigmoid mapping
%
% DESCRIPTION
% Sigmoidal tra... |
github | SASVDDwt/sa_svdd-master | klldc.m | .m | sa_svdd-master/matlab/prtools/klldc.m | 1,713 | utf_8 | ee649fb58c45cab210ff1f23d1817d73 | %KLLDC Linear classifier built on the KL expansion of the common covariance matrix
%
% W = KLLDC(A,N)
% W = KLLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of significant eigenvectors
% ALF 0 < ALF <= 1, percentage of the total variance explained (default: 0.9)
%
% OUTPUT
% W Linear classifier
%
% DES... |
github | SASVDDwt/sa_svdd-master | weakc.m | .m | sa_svdd-master/matlab/prtools/weakc.m | 1,816 | utf_8 | 25d90023dbdad32a6256f815a75b5037 | %WEAKC Weak Classifier
%
% [W,V] = WEAKC(A,ALF,ITER,R)
% VC = WEAKC(A,ALF,ITER,R,1)
%
% INPUT
% A Dataset
% ALF Fraction of objects to be used for training (def: 0.5)
% ITER Number of trials
% R R = 0: use NMC (default)
% R = 1: use FISHERC
% R = 2: use UDC
% R = 3: use QDC
% ... |
github | SASVDDwt/sa_svdd-master | bhatm.m | .m | sa_svdd-master/matlab/prtools/bhatm.m | 2,908 | utf_8 | 9d832aa35bcfae93d03a81d6c3426c02 | %BHATM Bhattacharryya linear feature extraction mapping
%
% W = BHATM(A,N)
%
% INPUT
% A Dataset
% N Number of dimensions to map to (N >= 1), or fraction of cumulative
% contribution to retain (0 < N < 1)
%
% OUTPUT
% W Bhattacharryya mapping
%
% DESCRIPTION
% Finds a mapping of the labeled d... |
github | SASVDDwt/sa_svdd-master | randreset.m | .m | sa_svdd-master/matlab/prtools/randreset.m | 331 | utf_8 | 9c6339f8ef9559c8f0e4baa6287dcf4c |
function output = randreset(state)
if nargin < 1
state = 1;
end
randstate = cell(1,2);
randstate{1} = rand('state');
randstate{2} = randn('state');
if iscell(state)
rand('state',state{1});
randn('state',state{2});
else
rand('state',state);
randn('state',state);
end
if nargout > 0
output = randstat... |
github | SASVDDwt/sa_svdd-master | rsscc.m | .m | sa_svdd-master/matlab/prtools/rsscc.m | 1,580 | utf_8 | fbf5092c81f9ba8863b3567ed3adb06c | %RSSCC Random subspace combining classifier
%
% W = RSSCC(A,CLASSF,NFEAT,NCLASSF)
%
% INPUT
% A Dataset
% CLASSF Untrained base classifier
% NFEAT Number of features for training CLASSF
% NCLASSF Number of base classifiers
%
% OUTPUT
% W Combined classifer
%
% DESCRIPTION
% This procedure c... |
github | SASVDDwt/sa_svdd-master | fisherc.m | .m | sa_svdd-master/matlab/prtools/fisherc.m | 2,939 | utf_8 | 87f32eb3e6edffc81e2719ebea018f54 | %FISHERC Fisher's Least Square Linear Classifier
%
% W = FISHERC(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% W Fisher's linear classifier
%
% DESCRIPTION
% Finds the linear discriminant function between the classes in the
% dataset A by minimizing the errors in the least square sense. This
% is a multi-class i... |
github | SASVDDwt/sa_svdd-master | dyadicm.m | .m | sa_svdd-master/matlab/prtools/dyadicm.m | 2,697 | utf_8 | 073c564513e7d1106a2d46ed26b2b787 | %DYADICM Dyadic dataset mapping
%
% B = DYADICM(A,P,Q,SIZE)
%
% INPUT
% A Input dataset
% P Scalar multiplication factor (default 1)
% or string (name of a routine)
% Q Scalar multiplication factor (default 1)
% or feature size needed for splitting A
% SIZE Desired images si... |
github | SASVDDwt/sa_svdd-master | mds.m | .m | sa_svdd-master/matlab/prtools/mds.m | 34,177 | utf_8 | 21711e28d38d7b518c28318feaf04d33 | %MDS - Multidimensional Scaling - a variant of Sammon mapping
%
% [W,J,stress] = MDS(D,Y,OPTIONS)
% [W,J,stress] = MDS(D,N,OPTIONS)
%
% INPUT
% D Square (M x M) dissimilarity matrix
% Y M x N matrix containing starting configuration, or
% N Desired output dimensionality
% OPTIONS Various... |
github | SASVDDwt/sa_svdd-master | fixedcc.m | .m | sa_svdd-master/matlab/prtools/fixedcc.m | 5,461 | utf_8 | ada023da4f0089cadcc1779002558cc4 | %FIXEDCC Construction of fixed combiners
%
% V = FIXEDCC(A,W,TYPE,NAME)
%
% INPUT
% A Dataset
% W A set of classifier mappings
% TYPE The type of combination rule
% NAME The name of this combination rule
%
% OUTPUT
% V Mapping
%
% DESCRIPTION
% Define a mapping V which applies the combina... |
github | SASVDDwt/sa_svdd-master | gendath.m | .m | sa_svdd-master/matlab/prtools/gendath.m | 1,633 | utf_8 | 1df4eaadfe7399ed2ca2dc9bc35ac880 | %GENDATH Generation of Highleyman classes
%
% A = GENDATH(N,LABTYPE)
%
% INPUT
% N Number of objects (optional; default: [50,50])
% LABTYPE Label type (optional; default: 'crisp')
%
% OUTPUT
% A Generated dataset
%
% DESCRIPTION
% Generation of a 2-dimensional 2-class dataset A of N objects
% accor... |
github | SASVDDwt/sa_svdd-master | plotgtm.m | .m | sa_svdd-master/matlab/prtools/plotgtm.m | 3,880 | utf_8 | 04b442307ecda373a0475a1d31dee4ba | %PLOTGTM Plot a trained GTM mapping in 1D, 2D or 3D
%
% H = PLOTGTM (W)
%
% INPUT
% W Trained GTM mapping
%
% OUTPUT
% H Graphics handles
%
% DESCRIPTION
% Creates a plot of the GTM manifold in the original data space, but at
% most in 3D.
%
% SEE ALSO
% GTM, SOM, PLOTSOM
% (c) Dick de Ridder, 2003
% Inform... |
github | SASVDDwt/sa_svdd-master | prtrace.m | .m | sa_svdd-master/matlab/prtools/prtrace.m | 2,266 | utf_8 | 2492aac5f515f3f1e84df641d32beb43 | %PRTRACE Trace PRTools routines
%
% Routine is outdated and will directly return
%
% PRTRACE ON Tracing of the PRTools routines is switched on
% PRTRACE OFF Tracing of the PRTools routines is switched off
% PRTRACE(MESSAGE,LEVEL)
%
% INPUT
% MESSAGE String
% LEVEL Trace level (option... |
github | SASVDDwt/sa_svdd-master | featselm.m | .m | sa_svdd-master/matlab/prtools/featselm.m | 3,152 | utf_8 | 0938e48fa3e42bf7d83377e4c9f3d829 | %FEATSELM Feature selection map
%
% [W,R] = FEATSELM(A,CRIT,METHOD,K,T,PAR1,...)
%
% INPUT
% A Training dataset
% CRIT Name of criterion: 'in-in', 'maha-s', 'NN' or others (see FEATEVAL)
% or an untrained classifier V (default: 'NN')
% METHOD - 'forward' : selection b... |
github | SASVDDwt/sa_svdd-master | seldat.m | .m | sa_svdd-master/matlab/prtools/seldat.m | 5,032 | utf_8 | 2395fb27eb8c26730d2e483863b10354 | %SELDAT Select subset of dataset
%
% [B,J] = SELDAT(A,C,F,N)
% B = A*SELDAT([],C,F,N)
% [B,J] = SELDAT(A,D)
%
% INPUT
% A Dataset
% C Indexes of classes (optional; default: all)
% F Indexes of features (optional; default: all)
% N Indices of objects extracted from classes in C
% Should be cell... |
github | SASVDDwt/sa_svdd-master | gtm.m | .m | sa_svdd-master/matlab/prtools/gtm.m | 7,614 | utf_8 | dc1d3e58c2c43d0b05800b3f4cf4324f | %GTM Fit a Generative Topographic Mapping using the
% expectation-maximisation algorithm.
%
% [W,L] = GTM (A,K,M,MAPTYPE,REG,EPS,MAXITER)
%
% INPUT
% A Dataset or double matrix
% K Vector containing number of nodes per dimension (default: [5 5], 2D map)
% M Vector containing number of ba... |
github | SASVDDwt/sa_svdd-master | svmr.m | .m | sa_svdd-master/matlab/prtools/svmr.m | 2,126 | utf_8 | 82721267a2ee4e4e9574fbe07d2b0b60 | %SVMR SVM regression
%
% W = SVMR(X,NU,KTYPE,KPAR,EP)
%
% INPUT
% X Regression dataset
% NU Fraction of objects outside the 'data tube'
% KTYPE Kernel type (default KTYPE='p', for polynomial)
% KPAR Extra parameter for the kernel
% EP Epsilon, with of the 'data tube'
%
% OUTPUT
% W ... |
github | SASVDDwt/sa_svdd-master | scatterr.m | .m | sa_svdd-master/matlab/prtools/scatterr.m | 729 | utf_8 | 7d9a51df0874b46f92b6d1850ff9eaaf | %SCATTERR Scatter regression data
%
% H = SCATTERR(X,CLRS)
%
% INPUT
% X Regression dataset
% CLRS Plot string (default CLRS = 'k.')
%
% OUTPUT
% H Vector of handles
%
% DESCRIPTION
% Scatter the regression dataset X with marker colors CLRS.
%
% SEE ALSO
% PLOTR
% Copyright: D.M.J. Tax, D.M.... |
github | SASVDDwt/sa_svdd-master | testk.m | .m | sa_svdd-master/matlab/prtools/testk.m | 1,786 | utf_8 | f0b1db1dfadf169c748bd4f7d0ab782e | %TESTK Error estimation of the K-NN rule
%
% E = TESTK(A,K,T)
%
% INPUT
% A Training dataset
% K Number of nearest neighbors (default 1)
% T Test dataset (default [], i.e. find leave-one-out estimate on A)
%
% OUTPUT
% E Estimated error of the K-NN rule
%
% DESCRIPTION
% Tests a dataset T on the training da... |
github | SASVDDwt/sa_svdd-master | userkernel.m | .m | sa_svdd-master/matlab/prtools/userkernel.m | 1,930 | utf_8 | d214708aecab61fa512fa212c7b22fb4 | %USERKERNEL Construct user defined kernel mapping
%
% K = USERKERNEL(B,R,FUNC,P1,P2, ...)
% K = B*USERKERNEL([],R,FUNC,P1,P2, ...)
% W = USERKERNEL([],R,FUNC,P1,P2, ...)
% W = R*USERKERNEL([],[],FUNC,P1,P2, ...)
% K = B*W
%
% INPUT
% R Dataset, representation set, default B
% B Dataset
% FUNC Stri... |
github | SASVDDwt/sa_svdd-master | ldc.m | .m | sa_svdd-master/matlab/prtools/ldc.m | 4,833 | utf_8 | d38e6a7bb54590abc80d83b2dd8e99d1 | %LDC Linear Bayes Normal Classifier (BayesNormal_1)
%
% [W.R,S,M] = LDC(A,R,S,M)
% W = A*LDC([],R,S,M);
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0 <= R,S <= 1
% (optional; default: no regularization, i.e. R,S = 0)
% M Dimension of subspace structure in covariance matrix (default: ... |
github | SASVDDwt/sa_svdd-master | gendati.m | .m | sa_svdd-master/matlab/prtools/gendati.m | 1,596 | utf_8 | 4d8fb3f8dc6e5ddaceb1246198e5d869 | %GENDATI Create dataset from randomly selected windows in a given image
%
% A = GENDATI(IMAGE,WSIZE,N,LABEL)
%
% INPUT
% IMAGE - Image of any dimensionality
% WSIZE - Vector with size of the window
% N - Number windows to be generated
% LABEL - Optional string or number with label for all objects
%
% O... |
github | SASVDDwt/sa_svdd-master | im_bdilation.m | .m | sa_svdd-master/matlab/prtools/im_bdilation.m | 1,266 | utf_8 | 58572ed6d27092bdd0a812440507dfcb | %IM_BDILATION Binary dilation of images stored in a dataset (DIP_Image)
%
% B = IM_BDILATION(A,N,CONNECTIVITY,EDGE_CONDITION)
% B = A*IM_BDILATION([],N,CONNECTIVITY,EDGE_CONDITION)
%
% INPUT
% A Dataset with binary object images dataset (possibly multi-band)
% N Number of iterations (default 1)
% CO... |
github | SASVDDwt/sa_svdd-master | ridger.m | .m | sa_svdd-master/matlab/prtools/ridger.m | 1,033 | utf_8 | b9ded92eddd3d3a31caa8d6522fbfff4 | %RIDGER Ridge Regression
%
% W = RIDGER(X,LAMBDA)
%
% INPUT
% X Regression dataset
% LAMBDA Regularization parameter (default LAMBDA=1)
%
% OUTPUT
% W Ridge regression mapping
%
% DESCRIPTION
% Perform a ridge regression on dataset X, with the regularization
% parameter LAMBDA.
%
% SEE ALSO
% ... |
github | SASVDDwt/sa_svdd-master | bpxnc.m | .m | sa_svdd-master/matlab/prtools/bpxnc.m | 1,765 | utf_8 | 7e46cf4aa7c956f4e576e53a00508a32 | %BPXNC Back-propagation trained feed-forward neural net classifier
%
% [W,HIST] = BPXNC (A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each hidden layer (default: [5])
% ITER Number of iterations to train (default: inf)
% W_INI Weight initialisation netw... |
github | SASVDDwt/sa_svdd-master | edicon.m | .m | sa_svdd-master/matlab/prtools/edicon.m | 5,758 | utf_8 | e3a1e1ef083918d9c8d00eddf3aac37e | % EDICON Multi-edit and condense a training set
%
% J = EDICON(D,NSETS,NITERS,NTRIES)
%
% INPUT
% D Distance matrix dataset
% NSETS Number of subsets for editing, or [] for no editing (default: 3)
% NITERS Number of iterations for editing (default: 5)
% NTRIES Number of tries for condensing, or [] ... |
github | SASVDDwt/sa_svdd-master | isstacked.m | .m | sa_svdd-master/matlab/prtools/isstacked.m | 754 | utf_8 | 6c8a1327bdcfbba008476230c0649370 | %ISSTACKED Test on stacked mapping
%
% N = ISSTACKED(W)
% ISSTACKED(W)
%
% INPUT
% W Mapping
%
% OUTPUT
% N Scalar, 1 if W is a stacked mapping, 0 otherwise
%
% DESCRIPTION
% Returns 1 for stacked mappings. If no output is requested, false outputs
% are turned into errors. This may be used for assertion.
%
% ... |
github | SASVDDwt/sa_svdd-master | gendat.m | .m | sa_svdd-master/matlab/prtools/gendat.m | 4,212 | utf_8 | 4fb8d260bc9116759ef606e7cae3059c | %GENDAT Random generation of datasets for training and testing
%
% [A,B,IA,IB] = GENDAT(X,N)
% [A,B,IA,IB] = GENDAT(X)
% [A,B,IA,IB] = GENDAT(X,ALF)
%
% INPUT
% X Dataset
% N,ALF Number/fraction of objects to be selected
% (optional; default: bootstrapping)
%
% OUTPUT
% A,B Datasets
% IA... |
github | SASVDDwt/sa_svdd-master | pcldc.m | .m | sa_svdd-master/matlab/prtools/pcldc.m | 1,556 | utf_8 | 6795cecd9c8f254cdc84b6cf74d6ece4 | %PCLDC Linear classifier using PC expansion on the joint data.
%
% W = PCLDC(A,N)
% W = PCLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of eigenvectors
% ALF Total explained variance (default: ALF = 0.9)
%
% OUTPUT
% W Mapping
%
% DESCRIPTION
% Finds the linear discriminant function W for the dataset A ... |
github | SASVDDwt/sa_svdd-master | im_maxf.m | .m | sa_svdd-master/matlab/prtools/im_maxf.m | 1,134 | utf_8 | 2f834256ee1f68b17045bb73babd63ff | %IM_MAXF Maximum filter of images stored in a dataset (DIP_Image)
%
% B = IM_MAXF(A,SIZE,SHAPE)
% B = A*IM_MAXF([],SIZE,SHAPE)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% SIZE Filter width in pixels, default SIZE = 7
% SHAPE String with shape:'rectangular', 'elliptic', '... |
github | SASVDDwt/sa_svdd-master | votec.m | .m | sa_svdd-master/matlab/prtools/votec.m | 1,705 | utf_8 | ecfba23be0f9a5b45b42b9d0caabaae5 | %VOTEC Voting combining classifier
%
% W = VOTEC(V)
% W = V*VOTEC
%
% INPUT
% V Set of classifiers
%
% OUTPUT
% W Voting combiner
%
% DESCRIPTION
% If V = [V1,V2,V3,...] is a stacked set of classifiers trained for the
% same classes, W is the voting combiner: it selects the class with the
% highest vote of th... |
github | SASVDDwt/sa_svdd-master | dataim.m | .m | sa_svdd-master/matlab/prtools/dataim.m | 2,017 | utf_8 | 114c84e258c8243a70363e9e342145a0 | %DATAIM Image operation on dataset images
%
% B = DATAIM(A,'IMAGE_COMMAND',PAR1,PAR2,....)
%
% INPUT
% A Dataset containing images
% IMAGE_COMMAND Function name
% PAR1, ... Optional parameters to IMAGE_COMMAND
%
% OUTPUT
% B Dataset containing images processed by IMAGE... |
github | SASVDDwt/sa_svdd-master | hclust.m | .m | sa_svdd-master/matlab/prtools/hclust.m | 3,650 | utf_8 | 2c71eb6b426dcac1014b6a0cfa5230c7 | %HCLUST hierarchical clustering
%
% [LABELS, DENDROGRAM] = HCLUST(D,TYPE,K)
% DENDROGRAM = HCLUST(D,TYPE)
%
% INPUT
% D dissimilarity matrix
% TYPE string name of clustering criterion (optional)
% 's' or 'single' : single linkage (default_
% 'c' or 'complete' : complete lin... |
github | SASVDDwt/sa_svdd-master | feat2obj.m | .m | sa_svdd-master/matlab/prtools/feat2obj.m | 397 | utf_8 | e7b4d418265b0530ec0a8062755ff715 | %FEAT2OBJ Transform feature images to object images in dataset
%
% B = FEAT2OBJ(A)
%
% INPUT
% A Dataset with object images, possible with multiple bands
%
% OUTPUT
% B Dataset with features images
%
% SEE ALSO
% DATASETS, IM2OBJ, IM2FEAT, DATA2IM, OBJ2FEAT
function b = feat2obj(a)
prtrace(mfilename);
... |
github | SASVDDwt/sa_svdd-master | plotd.m | .m | sa_svdd-master/matlab/prtools/plotd.m | 427 | utf_8 | 8dc4baf0dd8f08102e21faafd7850f50 | %PLOTD Plot classifiers, outdated, use PLOTC instead
% $Id: plotd.m,v 1.2 2006/03/08 22:06:58 duin Exp $
function handle = plotd(varargin)
prtrace(mfilename);
global PLOTD_REPLACED_BY_PLOTC
if isempty(PLOTD_REPLACED_BY_PLOTC)
disp([newline 'PLOTD has been replaced by PLOTC, please use it'])
PLOTD_REPLACED_B... |
github | SASVDDwt/sa_svdd-master | prwarning.m | .m | sa_svdd-master/matlab/prtools/prwarning.m | 1,697 | utf_8 | c0d8edc3c3037c62d44ea9d6c3f46f96 | %PRWARNING Show PRTools warning
%
% PRWARNING(LEVEL,FORMAT,...)
%
% Shows the message (given as FORMAT and a variable number of arguments),
% if the current PRWARNING level is >= LEVEL. Output is written to standard
% error ouput (FID = 2).
%
% PRWARNING(LEVEL) - Set the current PRWARNING level
%
% Set the PRWARNING... |
github | SASVDDwt/sa_svdd-master | is_scalar.m | .m | sa_svdd-master/matlab/prtools/is_scalar.m | 387 | utf_8 | 3d8f89155b7a6d731aae836d6e206525 | %IS_SCALAR Test on scalar (size = [1,1])
%
% N = IS_SCALAR(P);
%
% INPUT
% P Input argument
%
% OUTPUT
% N 1/0 if A is/isn't scalar
%
% DESCRIPTION
% The function IS_SCALAR tests if P is scalar.
function n = is_scalar(p)
prtrace(mfilename);
n = all(size(p) == ones(1,length(size(p))));
if (nargout == 0) & (n... |
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