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 | jacksky64/imageProcessing-master | savedatafile.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/savedatafile.m | 8,730 | utf_8 | 95058a04ca5983f539f85772424cc56a | %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
% Default: as it is.
% NAME Desired name of directory
% N... |
github | jacksky64/imageProcessing-master | pls_transform.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | prdatafiles.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prdatafiles.m | 3,108 | utf_8 | 2a830c3be91c9058ada3978a7534d5bd | %PRDATAFILES Checks availability of a PRTools datafile
%
% PRDATAFILES
%
% Checks the availability of the 'prdatafiles' directory, downloads the
% Contents file and m-files if necessary and adds it to the search path.
% Lists Contents file.
%
% PRDATAFILES(DFILE)
%
% Checks the availability of the particu... |
github | jacksky64/imageProcessing-master | vandermondem.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/vandermondem.m | 1,043 | utf_8 | 908771c54d917ff448e9c41eb1bface9 | %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 | jacksky64/imageProcessing-master | modselc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/modselc.m | 5,399 | utf_8 | 3a438f73efa5f4bc8e705b530915c7f8 | % MODSELC Model selection
%
% V = MODSELC(A,W,N,NREP)
% V = A*(W*MODSELC([],N,NREP))
%
% INPUT
% A Dataset used for training base classifiers and/or selection
% B Dataset used for testing (executing) the selector
% W Set of trained or untrained base classifiers
% N Number of cross... |
github | jacksky64/imageProcessing-master | classd.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | prcov.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prcov.m | 353 | utf_8 | d356173c3d4ecd94c33278a0d2f06dbc | %PRCOV Call to COV() including PRWAITBAR
%
% B = PRCOV(A,tol)
%
% This calls C = COV(A, ...) and includes a message to PRWAITBAR
% in case of a large A
function B = prcov(varargin)
[m,n] = size(varargin{1});
if m*n*n > 1e9
prwaitbaronce('covariance of %i x %i matrix ...',[m,n]);
B = cov(varargin{:});
prwaitbar(0)... |
github | jacksky64/imageProcessing-master | im_stretch.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | prodc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | scatterdui.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/scatterdui.m | 8,832 | utf_8 | 2a9a07a5ef7fa77da861992b9fb0dddd | % 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 | jacksky64/imageProcessing-master | primport.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | testn.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/testn.m | 2,970 | utf_8 | 6ca41edfc202366ae9aec6995f980f3b | %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 | jacksky64/imageProcessing-master | plotc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/plotc.m | 5,575 | utf_8 | e41895c418931e871fbdff32d1fcf052 | %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 | jacksky64/imageProcessing-master | prsvd.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prsvd.m | 426 | utf_8 | b8c97273dada58abf140fc88b5c1f239 | %PRSVD Call to SVD() including PRWAITBAR
%
% VARARGOUT = PRSVD(VARARGIN)
%
% This calls B = RANK(A,tol) and includes a message to PRWAITBAR
% in case of a large A
function varargout = prsvd(varargin)
[m,n] = size(varargin{1});
varargout = cell(1,nargout);
if min([m,n]) >= 500
prwaitbaronce('SVD of %i x %i matrix ..... |
github | jacksky64/imageProcessing-master | im2obj.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | getwindows.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/getwindows.m | 3,090 | utf_8 | 843fd3b7f8928e6809061d465551396d | %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 vec... |
github | jacksky64/imageProcessing-master | getopt_pars.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/getopt_pars.m | 795 | utf_8 | 320c88660dd14251da5693f9e2f3d6d7 | %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 = []... |
github | jacksky64/imageProcessing-master | map.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/map.m | 9,939 | utf_8 | 83957b0af503a00779627d5493540bc5 | %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. ... |
github | jacksky64/imageProcessing-master | filtim.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/filtim.m | 5,011 | utf_8 | bd97d5621d52f453ea77064e9fa1a408 | %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 multi-band image objects
% FILTER_COMMAND String with function name... |
github | jacksky64/imageProcessing-master | resizem.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/resizem.m | 1,270 | utf_8 | ce577df4423128217dbf0c54502e6915 | %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
... |
github | jacksky64/imageProcessing-master | testdatasize.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/testdatasize.m | 2,682 | utf_8 | 28a7eda7986d21fdcc452f3bf4accf70 | %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 | jacksky64/imageProcessing-master | nu_svr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/nu_svr.m | 4,319 | utf_8 | 8eaeaa6ed5f4f5b39397258f05ca1b8e | %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 | jacksky64/imageProcessing-master | reducm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/reducm.m | 1,511 | utf_8 | 17bcfd079a7d710c4b823060e4dc27e4 | %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 | jacksky64/imageProcessing-master | classim.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/classim.m | 1,941 | utf_8 | f32ac66484a886f0f8055dc61dd99584 | %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 | jacksky64/imageProcessing-master | gendatb.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatb.m | 1,475 | utf_8 | 785af17a0d25f1cc4ff2a4c43265dabc | %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 | jacksky64/imageProcessing-master | im_invert.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | disnorm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/disnorm.m | 1,646 | utf_8 | 17034f60f84d0b97b101998541c77e85 | %DISNORM Normalization of a dissimilarity matrix
%
% V = DISNORM(D,OPT)
% F = E*V
%
% INPUT
% D NxN dissimilarity matrix or dataset, which sets the norm
% E Matrix to be normalized, e.g. D itself
% OPT 'max' : maximum dissimilarity is set to 1 by global rescaling
% 'mean': average dissimilarity is se... |
github | jacksky64/imageProcessing-master | prinv.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prinv.m | 309 | utf_8 | 89acc1f05a67a2900135b943a8cd4614 | %PRINV Call to INV() including PRWAITBAR
%
% B = PRINV(A)
%
% This calls B = INV(A) and includes a message to PRWAITBAR
% in case of a large A
function B = prinv(A)
[m,n] = size(A);
if min([m,n]) >= 500
prwaitbaronce('Inverting %i x %i matrix ...',[m,n]);
B = inv(A);
prwaitbar(0);
else
B = inv(A);
end |
github | jacksky64/imageProcessing-master | matchlablist.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/matchlablist.m | 1,366 | utf_8 | ac9318c7c49e2c458090ca4d64934eea | %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 | jacksky64/imageProcessing-master | gendatl.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | invsigm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | isdataim.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | mogc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/mogc.m | 2,240 | utf_8 | cc799611be99de1d4a78f49de3d7a6d6 | %MOGC Mixture of Gaussian classifier
%
% W = MOGC(A,N)
% W = A*MOGC([],N);
% W = A*MOGC([],N,R,S);
%
% 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 ... |
github | jacksky64/imageProcessing-master | sigm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | klldc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | weakc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/weakc.m | 1,898 | utf_8 | f1146600c4e707fdea7974fe5cb918a0 | %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: u... |
github | jacksky64/imageProcessing-master | bhatm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/bhatm.m | 3,010 | utf_8 | 14b1869e8b30889e9d06b9665b57b7a4 | %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 ... |
github | jacksky64/imageProcessing-master | prwaitbarnext.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prwaitbarnext.m | 684 | utf_8 | c8d48c4427ac50247a6b8ab6481e7c2c | %PRWAITBARNEXT Low level routine to simplify PRWAITBAR next calls
%
% COUNT = PRWAITBARNEXT(N,STRING,COUNT)
%
% Update call for PRWAITBAR after initialisation by PRWAITBARINIT.
%
% In case COUNT = 0 it initializes as well and a separate call to
% PRWAITBARINIT is not needed.
%
% SEE PRWAITBARINIT
function c... |
github | jacksky64/imageProcessing-master | libsvc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/libsvc.m | 4,941 | utf_8 | fc38617bb5605c8cb8f39d1e7cb6a360 | %LIBSVC Support Vector Classifier by libsvm
%
% [W,J] = LIBSVC(A,KERNEL,C)
%
% INPUT
% A Dataset
% KERNEL Mapping to compute kernel by A*MAP(A,KERNEL)
% or string to compute kernel by FEVAL(KERNEL,A,A)
% or cell array with strings and parameters to compute kernel by
% F... |
github | jacksky64/imageProcessing-master | randreset.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/randreset.m | 673 | utf_8 | c649bdd2f643bc2dc74a2f7aab5fabbc | %RANDRESET Reset state of random generators
%
% RANDRESET - reset states to 1
% RANDRESET(STATE) - reset states to STATE
% STATE = RANDRESET - retrieve present state (no reset)
% OLDSTATE = RANDRESET(STATE) - retrieve present state and reset
function output = randreset(s... |
github | jacksky64/imageProcessing-master | rsscc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/rsscc.m | 1,918 | utf_8 | b6e779e3c66d0dcceb1f7815cd10ef16 | %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
% Th... |
github | jacksky64/imageProcessing-master | fisherc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/fisherc.m | 3,055 | utf_8 | 4284de41090f4d633d00ff52b68a8594 | %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 | jacksky64/imageProcessing-master | dyadicm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/dyadicm.m | 3,915 | utf_8 | fd78b4e2ab69cf6302874f80423e54e0 | %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... |
github | jacksky64/imageProcessing-master | mds.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/mds.m | 34,203 | utf_8 | ad1048b580af1294a41a46963ebcd5e1 | %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 | jacksky64/imageProcessing-master | fixedcc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/fixedcc.m | 6,296 | utf_8 | 7f063504ddd492c6c189c15418da0f1a | %FIXEDCC Construction of fixed combiners
%
% V = FIXEDCC(A,W,TYPE,NAME,PAR)
%
% INPUT
% A Dataset
% W A set of classifier mappings
% TYPE The type of combination rule
% NAME The name of this combination rule
% PAR Possible parameter for combiner defined by TYPE
%
% OUTPUT
% V Mapping... |
github | jacksky64/imageProcessing-master | gendath.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendath.m | 1,639 | utf_8 | a279b0efc2f6f7597ef201039460de3f | %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 | jacksky64/imageProcessing-master | plotgtm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/plotgtm.m | 4,021 | utf_8 | b23f60d48591b5a6f674a0de4bca4108 | %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 Ridd... |
github | jacksky64/imageProcessing-master | drbmc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/drbmc.m | 20,444 | utf_8 | fc2e95de4fb99b6c90dbe11a9b994b18 | function [B, ll] = drbmc(A, W, L, I)
%DRBMC Discriminative Restricted Boltzmann Machine classifier
%
% W = DRBMC(A)
% W = DRBMC(A, N)
% W = DRBMC(A, N, L)
%
% INPUT
% A Dataset
% N Number of hidden units
% L Regularization parameter (L2)
%
% OUTPUT
% W Discriminative Restricted Boltzmann Machine... |
github | jacksky64/imageProcessing-master | prtrace.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | featselm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/featselm.m | 3,336 | utf_8 | ca470807d2d85e5ff55358b1b70152ae | %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 by featself (default)
%... |
github | jacksky64/imageProcessing-master | seldat.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/seldat.m | 5,055 | utf_8 | 44125bd3175a5107456aafda72e2e865 | %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 | jacksky64/imageProcessing-master | gtm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gtm.m | 7,622 | utf_8 | 01519a52974eadcfbb3cd939ee1f98b7 | %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 | jacksky64/imageProcessing-master | naivebcc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/naivebcc.m | 4,467 | utf_8 | 98b2b2891939f7514f2b34f6ca7d6cac | % NAIVEBCC Naive Bayes Combining Classifier
%
% W = A*(WU*NAIVEBCC)
% W = WT*NAIVEBCC(B*WT)
% D = C*W
%
% INPUT
% A Dataset used for training base classifiers as well as combiner
% B Dataset used for training combiner of trained base classifiers
% C Dataset used for testing (executing) the combi... |
github | jacksky64/imageProcessing-master | gendatgauss.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendatgauss.m | 4,913 | utf_8 | 979e2ea312308dfc22d0d67f3d74fb28 | %GENDATGAUSS (Formerly GAUSS) Generation of a multivariate Gaussian dataset
%
% A = GENDATGAUSS(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 matr... |
github | jacksky64/imageProcessing-master | marksize.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/marksize.m | 421 | utf_8 | 5f56b132318a2fe2d3554b9581dfe7b6 | %MARKSIZE Change markersize in lineplot or scatterplot
%
% marksize(size,marker)
%
% The marker fontsize (default set by matlab to 6) is reset
% to size for the given marker, default: all markers.
%
function marksize(siz,marker)
if nargin == 1, marker = ' '; end
h = get(gca,'Children')';
for i = h
if strcmp(get(i,'Ty... |
github | jacksky64/imageProcessing-master | svmr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/svmr.m | 2,133 | utf_8 | a4b79163f7829d338e4df40e406fc30c | %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 | jacksky64/imageProcessing-master | scatterr.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | istrained.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/istrained.m | 719 | utf_8 | 16db91f39638d441eeea6d5517bdd52a | %ISTRAINED Test on trained mapping
%
% I = ISTRAINED(W)
% ISTRAINED(W)
%
% True if the mapping type of W is 'trained' (see HELP MAPPINGS). If
% called without an output argument ISTRAINED generates an error if the
% mapping type of W is not 'trained'.
% $Id: istrained.m,v 1.1 2009/03/18 16:12:41 duin Exp $
fun... |
github | jacksky64/imageProcessing-master | testk.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/testk.m | 1,874 | utf_8 | e4041302fe206e0842baa466bfa2e810 | %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 | jacksky64/imageProcessing-master | userkernel.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/userkernel.m | 1,998 | utf_8 | 67ccb0c570bbc7742a528966a9f20de7 | %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
% ... |
github | jacksky64/imageProcessing-master | ldc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/ldc.m | 5,010 | utf_8 | 852b8584fa6dc210f4cb125a86fe288c | %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 | jacksky64/imageProcessing-master | gendati.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendati.m | 1,667 | utf_8 | d55740da279d0ca29c66d7e9a9581d96 | %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 objec... |
github | jacksky64/imageProcessing-master | im_bdilation.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | ridger.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/ridger.m | 1,036 | utf_8 | f3d6e200f6fabf110cd0dc0db2f057b6 | %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 | jacksky64/imageProcessing-master | bpxnc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | edicon.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/edicon.m | 5,932 | utf_8 | feac26635d4fbb0b972e4435a7e6bf14 | % 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... |
github | jacksky64/imageProcessing-master | isstacked.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/isstacked.m | 796 | utf_8 | eb8484829ee9efe2498d3fae02ee6066 | %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 | jacksky64/imageProcessing-master | gendat.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/gendat.m | 4,567 | utf_8 | 741d390eee21a0c39a0f761c86e04757 | %GENDAT Random sampling of datasets for training and testing
%
% [A,B,IA,IB] = GENDAT(X,N)
% A = X*GENDAT([],N)
% [A,B,IA,IB] = GENDAT(X)
% [A,B,IA,IB] = GENDAT(X,ALF)
% A = X*GENDAT([],ALF)
%
% INPUT
% X Dataset
% N,ALF Number/fraction of objects to be selected
% (optiona... |
github | jacksky64/imageProcessing-master | pcldc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | im_maxf.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | prdownload.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/prdownload.m | 2,080 | utf_8 | b8843c8b48d744580c216fc478b612d3 | %PRDOWNLOAD Download well defined data and toolboxes
%
% STATUS = PRDOWNLOAD(URL,DIRNAME)
%
% INPUT
% URL String containing URL of file to be downloaded
% DIRNAME Final directory for download, created if necessary
% README Name of possible readme-file to be preserved
%
% DESCRIPTION... |
github | jacksky64/imageProcessing-master | votec.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | dataim.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | hclust.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | feat2obj.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/feat2obj.m | 420 | utf_8 | 40b24bd841ad312efce9f97e06480b2e | %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)
prtra... |
github | jacksky64/imageProcessing-master | plotd.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | prwarning.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/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 | jacksky64/imageProcessing-master | is_scalar.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/is_scalar.m | 408 | utf_8 | 6714336395873cf354df615113a965da | %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 (n... |
github | jacksky64/imageProcessing-master | compute_kernel.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/compute_kernel.m | 502 | utf_8 | e17f2af48790f6d9308283c48906dab5 |
function K = compute_kernel(a,s,kernel)
% compute a kernel matrix for the objects a w.r.t. the support objects s
% given a kernel description
if isstr(kernel) % routine supplied to compute kernel
K = feval(kernel,a,s);
elseif iscell(kernel)
K = feval(kernel{1},a,s,kernel{2:end});
elseif ismapping(k... |
github | jacksky64/imageProcessing-master | im_moments.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_moments.m | 10,678 | utf_8 | 43751390d5cb45c351dcb6db219ffaa6 | %IM_MOMENTS PRTools routine for computing central moments of object images
%
% M = IM_MOMENTS(A,TYPE,MOMENTS)
% M = A*IM_MOMENTS([],TYPE,MOMENTS)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% TYPE Desired type of moments
% MOMENTS Desired moments
%
% OUTPUT
% ... |
github | jacksky64/imageProcessing-master | svc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/svc.m | 5,562 | utf_8 | 86dd8221e27b55319d8d9001503e5f92 | %SVC Support Vector Classifier
%
% [W,J] = SVC(A,KERNEL,C)
% [W,J] = SVC(A,TYPE,PAR,C)
% W = A*SVC([],KERNEL,C)
% W = A*SVC([],TYPE,PAR,C)
%
% INPUT
% A Dataset
% KERNEL - Untrained mapping to compute kernel by A*(A*KERNEL) during
% training, or B*(A*KERNEL) during testing with dat... |
github | jacksky64/imageProcessing-master | rblibsvc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/rblibsvc.m | 1,915 | utf_8 | d62453cf819c693d66f8887bf17adde8 | %RBSVC Automatic radial basis Support Vector Classifier using LIBSVM
%
% [W,KERNEL,NU] = RBLIBSVC(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 N... |
github | jacksky64/imageProcessing-master | im_bpropagation.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_bpropagation.m | 2,387 | utf_8 | c75c17355488eceb9ea5baddea69c6ff | %IM_BPROPAGATION Binary propagation of images stored in a dataset (DIP_Image)
%
% B = IM_BPROPAGATION(A1,A2,N,CONNECTIVITY,EDGE_CONDITION)
%
% INPUT
% A1 Dataset with binary object images dataset (possibly multi-band)
% to be treated as seed for the propagation
% A2 Dataset with binary obje... |
github | jacksky64/imageProcessing-master | im_unif.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_unif.m | 1,204 | utf_8 | 7f05e468c2eabcdf937dabf7598b1738 | %IM_UNIF Uniform filter of images stored in a dataset/datafile
%
% B = IM_UNIF(A,SX,SY)
% B = A*IM_UNIF([],SX,SY)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% SX Desired horizontal width for filter, default SX = 3
% SY Desired vertical width for filter, default SY = SX
%
% OU... |
github | jacksky64/imageProcessing-master | newfig.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/newfig.m | 942 | utf_8 | 88e9e3d9057179f2294f97a98ab73c79 | %NEWFIG Create new figure on given position
%
% NEWFIG(FIGURE_NUMBER,FIGURE_PER_ROW)
%
% INPUT
% FIGURE_NUMBER Number of the figure
% FIGURE_PER_ROW Figures per row (default: 4)
%
% OUTPUT
%
% DESCRIPTION
% Creates figure number FIGURE_NUMBER and places it on the screen,
% such that (when sufficient figures are ... |
github | jacksky64/imageProcessing-master | scalem.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/scalem.m | 3,471 | utf_8 | ea9e240e095bc805e8a74b190c3b73f7 | %SCALEM Compute scaling map
%
% W = SCALEM(A,T)
%
% INPUT
% A Dataset
% T Type of scaling (optional; default: the class priors weighted mean of A
% is shifted to the origin)
%
% OUTPUT
% W Scaling mapping
%
% DESCRIPTION
% Computes a scaling map W, whose type depends on the parameter T:
%
% [], 'c-mea... |
github | jacksky64/imageProcessing-master | mclassm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/mclassm.m | 2,458 | utf_8 | dfa95c18e9a18bd2606c311f0d9acf3e | %MCLASSM Computation of a combined, multi-class based mapping
%
% W = MCLASSM(A,MAPPING,MODE,PAR)
%
% INPUT
% A Dataset
% MAPPING Untrained mapping
% MODE Combining mode (optional; default: 'weight')
% PAR Parameter needed for the combining
%
% OUTPUT
% W Combined mapping
%
% DESCRIPTION
... |
github | jacksky64/imageProcessing-master | cdats.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/cdats.m | 1,958 | utf_8 | 2686ee521a7698c29fa5e642699dbd21 | %CDATS Support routine for checking datasets
%
% [B,C,LABLIST,P] = CDATS(A,REDUCE)
%
% INPUT
% A Dataset or double
% REDUCE 0/1, Reduce A to labeled samples (1) or not (0, default), optional.
%
% OUTPUT
% B Dataset
% C Number of classes
% LABLIST Label list of A
% P ... |
github | jacksky64/imageProcessing-master | proxm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/proxm.m | 5,944 | utf_8 | 87c619ec24866c3bd3663663f102710f | %PROXM Proximity mapping
%
% W = PROXM(A,TYPE,P,WEIGHTS)
% W = A*PROXM([],TYPE,P,WEIGHTS)
%
% INPUT
% A Dataset
% TYPE Type of the proximity (optional; default: 'distance')
% P Parameter of the proximity (optional; default: 1)
% WEIGHTS Weights (optional; default: all 1)
%
% OUTPUT
% W Pro... |
github | jacksky64/imageProcessing-master | createdatafile.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/createdatafile.m | 8,747 | utf_8 | 8012e1f74bccfd7b83ae3557a6a3c0c5 | %CREATEDATAFILE Create datafile on disk
%
% B = CREATEDATAFILE(A,DIR,ROOT,TYPE,CMD,FMT)
%
% INPUT
% A Datafile
% DIR Name of datafile, default: name of A
% ROOT Root directory in which datafile should be created
% default: present directory
% TYPE Datafile type... |
github | jacksky64/imageProcessing-master | chernoffm.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/chernoffm.m | 2,908 | utf_8 | f68913a263bb7296e579bd8fe2007935 | %CHERNOFFM Suboptimal discrimination linear mapping (Chernoff mapping)
%
% W = CHERNOFFM(A,N,R)
%
% INPUT
% A Dataset
% N Number of dimensions to map to, N < C, where C is the number of classes
% (default: min(C,K)-1, where K is the number of features in A)
% R Regularization variable, 0 <= r <= 1, default i... |
github | jacksky64/imageProcessing-master | band2obj.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/band2obj.m | 2,883 | utf_8 | 19a57f14d1c5f11326b820a3fb24691f | %BAND2OBJ Mapping image bands to objects
%
% B = BAND2OBJ(A,N)
% W = BAND2OBJ([],N)
% B = A*W
%
% INPUT
% A Dataset or datafile with multiband image objects.
% N Number of successive bands to be combined in an object.
% The number of image bands in A should be multiple of N.
% Defaul... |
github | jacksky64/imageProcessing-master | libsvmcheck.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/libsvmcheck.m | 474 | utf_8 | 403ba4e83ee91c67c682c9edc2565eac | %LIBSVMCHECK Check whether the LIBSVM toolbox is in the path
function libsvmcheck
if exist('svmpredict','file') ~= 3 & exist('svmpredict.dll','file') ~= 2
error([newline 'The LIBSVM package is not found. Download and install it from' ...
newline 'http://www.csie.ntu.edu.tw/~cjlin/libsvm/'])
else
p ... |
github | jacksky64/imageProcessing-master | labcmp.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/labcmp.m | 1,172 | utf_8 | bfc40e4bb42265de6ee75522b63e650f | %LABCMP Compare label sets
%
% [JNE,JEQ] = LABCMP(LABELS1,LABELS2)
%
% INPUT
% LABELS1 - list of labels (strings or numeric)
% LABELS2 - list of labels (strings or numeric)
%
% OUTPUT
% JNE - Indices of non-matching labels
% JEQ - indices of matching labels
%
% DESCRIPTION
% The comparison ... |
github | jacksky64/imageProcessing-master | testd.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/testd.m | 1,602 | utf_8 | bf21d1853b98c052c9eafb4cbdd51fef | %TESTD Find classification error of dataset applied to given classifier
%
% [E,C] = TESTD(A,W)
% [E,C] = TESTD(A*W)
% E = A*W*TESTD
%
% INPUT
% A Dataset
% W Classifier
%
% OUTPUT
% E Fraction of labeled objects in A that is erroneously classified
% C Vector with numbers of erroneously cla... |
github | jacksky64/imageProcessing-master | im_stat.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/im_stat.m | 2,355 | utf_8 | c9d64e86fc06111f67c8b0dae44e983c | %IM_STAT Computation of some image statistics
%
% B = IM_STAT(A,STAT)
% B = A*IM_STAT([],STAT)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% STAT String cell array or series of statistics
%
% OUTPUT
% B Dataset with statistics replacing images (possibly multi-band)
%
%... |
github | jacksky64/imageProcessing-master | cnormc.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/cnormc.m | 2,947 | utf_8 | 556f4711a78ff297eba70268c848f9dd | %CNORMC Classifier normalisation for ML posterior probabilities
%
% W = CNORMC(W,A)
%
% INPUT
% W Classifier mapping
% A Labeled dataset
%
% OUTPUT
% W Scaled classifier mapping
%
% DESCRIPTION
% The mapping W is scaled such that the likelihood of the posterior
% probabilities of the samples in A, estimated b... |
github | jacksky64/imageProcessing-master | immoments.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/immoments.m | 10,334 | utf_8 | 1b7a586afad05f118e8011c8bf59958a | %IMMOMENTS PRTools routine for computing central moments of object images
%
% M = IMMOMENTS(A,TYPE,MOMENTS)
%
% INPUT
% A Dataset with object images dataset
% TYPE Desired type of moments
% MOMENTS Desired moments
%
% OUTPUT
% M Dataset with moments replacing images
%
% DESCRIPT... |
github | jacksky64/imageProcessing-master | treec.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/treec.m | 16,984 | utf_8 | 29eeede09436dd3d3938e71eb5adf663 | %TREEC Build a decision tree classifier
%
% W = TREEC(A,CRIT,PRUNE,T)
%
% 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
%
% Pruning is defined by prune:
% ... |
github | jacksky64/imageProcessing-master | getfeat.m | .m | imageProcessing-master/Matlab PRTools/prtools_com/prtools/getfeat.m | 855 | utf_8 | d0e5811fcde6732e0e08c00c43e50989 | %GETFEAT Get feature labels of a dataset or a mapping
%
% LABELS = GETFEAT(A)
% LABELS = GETFEAT(W)
%
% INPUT
% A,W Dataset or mapping
%
% OUTPUT
% LABELS Label vector with feature labels
%
% DESCRIPTION
% Returns the labels of the features in the dataset A or the labels
% assigned by the mapping W.
%
% Note... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.