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github | gijzelaerr/sonic-gesture-master | find_target.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/find_target.m | 833 | utf_8 | 037f0dc52f22da91639151d3e35a9256 | %FIND_TARGET extract the indices of the target and outlier objects
%
% [It,Io] = FIND_TARGET(A)
%
% Return the indices of the objects from dataset A which are labeled
% 'target' and 'outlier' in the index vectors It and Io
% respectively. A warning is given when no target objects can be
% found.
%
% [It,Io... |
github | gijzelaerr/sonic-gesture-master | istarget.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/istarget.m | 1,004 | utf_8 | e8243e454f02e6f55d16a3552b55cf77 | %ISTARGET true if the label is target
%
% I = ISTARGET(A)
%
% Returns true for the objects from dataset A which are labeled
% 'target'.
%
% I = ISTARGET(LABA)
%
% It also works when no dataset but a label matrix given
%
% See also: isocset, gendatoc, oc_set
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.o... |
github | gijzelaerr/sonic-gesture-master | auclpm.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/auclpm.m | 6,221 | utf_8 | 7a7117a5e5d08b93af9d7c0eacb5ef70 | %AUCLPM Find linear mapping with optimized AUC
%
% W = AUCLPM(X, C, RTYPE, PAR)
%
% Optimize the AUC on dataset X and reg. param. C. This is done by
% finding the weights W for which the ordering of the objects mapped
% onto the line defined by W, is optimal. That means that objects from
% class +1 is always mapped ... |
github | gijzelaerr/sonic-gesture-master | ball_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/ball_dd.m | 2,996 | utf_8 | 71e3a8310ef57e63874bc535e40d208a | %BALL_DD L_p ball description
%
% W = BALL_DD(X,FRACREJ,P)
%
% Fit a L_p ball around the data X by optimizing the weights:
% min w_0
% s.t. \sum_j w_j|x_ij-a_j|^p <= w_0
% \sum_j w_j = 1, w_j>=0
% The vector a is taken as the mean of dataset X.
%
% When the (feature-) weigths w are optimized, t... |
github | gijzelaerr/sonic-gesture-master | kmeans_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/kmeans_dd.m | 1,594 | utf_8 | fc4f3d8e7bdbb99c71b0e98f3722a012 | %KMEANS_DD k-means data description.
%
% W = KMEANS_DD(A,FRACREJ,K)
%
% Train a k-means method with K prototypes on dataset A. Parameter
% fracrej gives the fraction of the target set which will be rejected.
%
% Optionally, one may give the error tolerance as last argument as
% stopping criterion.
%
% See als... |
github | gijzelaerr/sonic-gesture-master | kwhiten.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/kwhiten.m | 3,146 | utf_8 | 5d485aa5b3a3703abb0f7a29f84db715 | %KWHITEN Whiten the data in kernel space.
%
% W = kwhiten(A,DIM,KTYPE,PAR1)
%
% Apply a kernel PCA to dataset A and retain DIM dimensions, or a
% fraction DIM of the total variance. The data A is then rescaled to
% unit variance in the feature space. The kernel space is defined by
% the kernel function KTYPE, w... |
github | gijzelaerr/sonic-gesture-master | stump_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/stump_dd.m | 2,333 | utf_8 | 99aba74246f9fc566bb02b98b7be9144 | %STUMP_DD Threshold one dim. one-class classifier
%
% W = STUMP_DD(A,FRACREJ,DIM)
%
% Put a threshold on one of the feature dimensions DIM of dataset A. The
% threshold is put such that a fraction FRACREJ of the targets is
% rejected.
%
% See also: dd_threshold, dd_roc, dd_error
% Copyright: D.M.J. Tax, D.M.J.... |
github | gijzelaerr/sonic-gesture-master | pca_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/pca_dd.m | 2,295 | utf_8 | e83d951bb316c344dc1fb63460094771 | %PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Neth... |
github | gijzelaerr/sonic-gesture-master | som_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/som_dd.m | 1,988 | utf_8 | ce218e8842839fdfe91b694c4284511b | %SOM_DD Self-Organizing Map data description
%
% W = SOM_DD(X,FRACREJ,K)
%
% Train a 2D SOM on dataset X. In K the size of the map is defined. The
% map can maximally be 2D. When K contains just a single value, it is
% assumed that a 1D map should be trained.
%
% For further features of SOM_DD, see s... |
github | gijzelaerr/sonic-gesture-master | svdd_optrbf.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/svdd_optrbf.m | 2,210 | utf_8 | 895d9b3315a781aa65af51f35b7b7752 | %SVDD_OPTRBF Quadratic optimizer for the SVDD
%
% [ALF,R2,DX,I] = SVDD_OPTRBF(SIGMA,X,LABX,C)
%
% Quadratic optimizer for the SVDD. Preferably called by svdd.m.
%
% Given the dataset X with labels LABX, and the parameters SIGMA and C the
% quadratic optimization is performed, and the resulting weights ALF and R2
% ... |
github | gijzelaerr/sonic-gesture-master | isocc.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/isocc.m | 1,202 | utf_8 | 1b10c4c3a92d1cdf1155c8c50742c418 | %ISOCC True for one-class classifiers
%
% isocc(w) returns true if the classifier w is a one-class classifier,
% outputting only classes 'target' and/or 'outlier' and having a
% structure with threshold stored.
%
% Only problem is when you have an empty oc-classifier, this will
% return false. I cannot help it:-... |
github | gijzelaerr/sonic-gesture-master | multic.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/multic.m | 5,276 | utf_8 | f13e3142ceab36e01be1a6a605bcdaa8 | %MULTIC Make a multi-class classifier
%
% W = MULTIC(A,V)
%
% Train the (untrained!) one-class classifier V on each of the classes
% in A, and combine it to a multi-class classifier W. If an object is
% rejected by all one-class classifiers, it will be classified
% 'outlier'. If it is accepted by more than one one-c... |
github | gijzelaerr/sonic-gesture-master | lpball_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/lpball_dd.m | 3,552 | utf_8 | 5f38706a8088ae5d3ef9cf875a194ecf | %LPBALL_DD L_p ball description
%
% W = LPBALL_DD(X,FRACREJ,BTYPE,P)
%
% Optimize a L_p ball around dataset X, rejecting FRACREJ fraction of
% the data. The type of ball can be:
% BTYPE :
% w optimize the weights per feature
% center optimize the center
% p optimize the center and p
%
%... |
github | gijzelaerr/sonic-gesture-master | myproxm.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/myproxm.m | 4,895 | utf_8 | 5304721ae6227b667922bf4848f18273 | %MYPROXM MyProximity mapping
%
% W = MYPROXM(A,TYPE,P,G)
%
% Computation of the k*m proximity mapping (or kernel) defined by
% the m*k dataset A.
% The proximities are defined by the following possible TYPEs:
%
% 'linear' | 'l': a*b'
% 'polynomial' | 'p': sign(a*b'+1).*(a*b'+1).^p
% 'exponential' | ... |
github | gijzelaerr/sonic-gesture-master | mogEMupdate.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/mogEMupdate.m | 3,697 | utf_8 | df99e325094e9c478ec990d148f30df5 | function [means,invcovs,priors] = mogEMupdate(x,covtype,means,invcovs,priors,nriters,fixedcl,reg)
%MOGEMUPDATE Apply EM to a MoG
%
% [MEANS,INVCOVS,PRIORS] = MOGEMUPDATE(X,COVTYPE,MEANS,INVCOVS,PRIORS,...
% NRITERS)
%
% Apply Expectation-Maximization to update the MEANS, INVCOVS and PRIOR... |
github | gijzelaerr/sonic-gesture-master | mykmeans.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/mykmeans.m | 924 | utf_8 | eacdffcf1dc687877171905e76ffa7b5 | %MYKMEANS K-means clustering
%
% [LABS,MEANS] = MYKMEANS(X,K)
%
% Place K centers in the data X using the k-means procedure.
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
function [labs,means,err] = mykmeans(x,k,errtol... |
github | gijzelaerr/sonic-gesture-master | dd_normc.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/dd_normc.m | 1,447 | utf_8 | 47fc20d3d54a3572d5d003491d8653ed | %DD_NORMC Normalize the output of a oc-classifier
%
% B = DD_NORMC(A)
% B = A*W*DD_NORMC
% W = DD_NORMC
%
% Normalize the mapped dataset A to standard 'posterior probability'
% estimates (or something which looks similar to that). It basically
% means that all rows sum to 1. For the output of distan... |
github | gijzelaerr/sonic-gesture-master | ksvdd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/ksvdd.m | 7,248 | utf_8 | 57ee16b53fbba81c8a6153f9b0b97c9f | %KSVDD Support Vector Data Description on general kernel matrix
%
% W = KSVDD(X,FRACERR,WK)
%
% Train an SVDD on the data X, which is first mapped by mapping WK
% (see for possibilities myproxm). The mapping WK should be an
% untrained mapping! A fraction FRACERR of the data is outside the
% description.
% For e... |
github | gijzelaerr/sonic-gesture-master | scale_range.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/scale_range.m | 1,372 | utf_8 | 69fa989fe46216d8c2e0a424657b01c1 | %SCALE_RANGE Give a vector of scales
%
% SIG = SCALE_RANGE(X,NR)
%
% Give a reasonable range of scales SIG for the dataset X. The largest
% scale is given first. If NR is given, the number of scales is NR.
% This function is useful in consistent_occ.
%
% SIG = SCALE_RANGE(X,NR,NMAX)
%
% The (reasonable) range o... |
github | gijzelaerr/sonic-gesture-master | mcd_gauss_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/mcd_gauss_dd.m | 1,809 | utf_8 | dbb2b57fb4849687affdcd9e5e60ab04 | %MCD_GAUSS_DD Minimum Covariance Determinant Robust Gaussian data description.
%
% W = MCD_GAUSS_DD(A,FRACREJ)
%
% Fit a Minimum-Covariance-Determinant Gaussian density on dataset A. The
% algorithm is taken from :
%
% Rousseeuw, P.J. and Van Driessen, Katrien, "A fast algorithm for
% the minimum covariance d... |
github | gijzelaerr/sonic-gesture-master | dnndd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/dnndd.m | 1,732 | utf_8 | f3c46546b796f0187e4f971a2744e8e3 | %DNNDD Distance nearest neighbour data description method.
%
% W = dnndd(D,fracrej)
%
% Calculates the Nearest neighbour data description on distance data.
% Training only consists of the computation of the resemblance of all
% training objects to the training data using Leave-one-out.
%
% See also datasets, m... |
github | gijzelaerr/sonic-gesture-master | mst_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/mst_dd.m | 4,466 | utf_8 | 6756c5104f21e21cec89491b79817bf5 | %MST_DD Minimum Spanning Tree Data Description.
%
% [W,TREE,A] = MST_DD(A,FRACREJ,N)
%
% INPUT
% A one-class dataset
% FRACREJ fraction rejection [0,1]; (default 0.1)
% N complexity parameter equals a number of
% paths of max length; (default 0, entire mst)
%
% OUTPUT
% W ... |
github | gijzelaerr/sonic-gesture-master | is_ocset.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/is_ocset.m | 650 | utf_8 | 698846457e1edec40849632e3c344930 | %IS_OCSET True for one-class datasets
%
% is_ocset(a) returns true if the dataset a is a one-class dataset,
% containing only classes 'target' and/or 'outlier'.
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
functio... |
github | gijzelaerr/sonic-gesture-master | is_occ.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/is_occ.m | 1,216 | utf_8 | 08dc1b7a5073b415abba967431c92ac6 | %IS_OCC True for one-class classifiers
%
% IS_OCC(W) returns true if the classifier W is a one-class classifier,
% outputting only classes 'target' and/or 'outlier' and having a
% structure with threshold stored.
%
% Only problem is when you have an empty oc-classifier, this will
% return false. I cannot help it... |
github | gijzelaerr/sonic-gesture-master | random_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/random_dd.m | 2,243 | utf_8 | 411564c84d2698c9592e7cf735c5f7b6 | %RANDOM_DD Random one-class classifier
%
% W = RANDOM_DD(A,FRACREJ)
%
% This is the trivial one-class classifier, randomly assigning labels
% and rejecting FRACREJ of the data objects. This procedure is just to
% show the basic setup of a Prtools classifier, and what is required
% to define a one-class classifi... |
github | gijzelaerr/sonic-gesture-master | dkcenter_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/dkcenter_dd.m | 1,520 | utf_8 | fa3f112b606ad8ed9002f200b5b23625 | %DKCENTER_DD Distance k-center data description.
%
% W = DKCENTER_DD(D,FRACREJ,K)
%
% Train a k-center method with K prototypes on distance dataset D.
%
% See also datasets, mappings, dd_roc, kcenter_dd
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 503... |
github | gijzelaerr/sonic-gesture-master | mpm_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/mpm_dd.m | 2,674 | utf_8 | 0893dec09b6ca85beed86783048c98c6 | %MPM_DD Minimax prob. machine.
%
% W = MPM_DD(X,FRACREJ,SIGMA,LAMBDA)
%
% Computes the minimax probability machine of Lanckriet, using the RBF
% kernel with kernel-width SIGMA and quantile FRACREJ. It tries to find
% the linear classifier that separates the data from the origin,
% rejecting maximally FRACREJ of th... |
github | gijzelaerr/sonic-gesture-master | parzen_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/parzen_dd.m | 1,746 | utf_8 | 2f3bbad1b96ede662cbae6452c0cfe27 | %PARZEN_DD Parzen data description.
%
% W = parzen_dd(A,fracrej)
%
% Fit a Parzen density on dataset A. The threshold is put such that
% fracrej of the target objects is rejected.
%
% W = parzen_dd(A,fracrej,h)
%
% If the width parameter is known, it can be given as third parameter,
% otherwise it is op... |
github | gijzelaerr/sonic-gesture-master | svddpath.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/svddpath.m | 2,891 | utf_8 | 394960d3c6b25bcc0dcc107a0dfa7509 | %SVDDPATH SVDD for different lambda/C
%
% W = SVDDPATH(A,FRACREJ,KTYPE,KPAR)
%
% Optimize the SVDD over the complete regularization path by changing C
% (or lambda). The SVDD is defined by the kernel KTYPE with parameter
% KPAR. For the definition of the kernel, see dd_kernel.m.
%
% To get the path, please ... |
github | gijzelaerr/sonic-gesture-master | nndd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/nndd.m | 1,754 | utf_8 | 737586c934604589ee628ebfe16bb369 | %NNDD Nearest neighbour data description method.
%
% W = NNDD(A,FRACREJ)
%
% Calculates the Nearest neighbour data description. Training only
% consists of the computation of the resemblance of all training
% objects to the training data using Leave-one-out.
%
% WARNING: this method is basically a wrapper aroun... |
github | gijzelaerr/sonic-gesture-master | lpdd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/lpdd.m | 2,445 | utf_8 | 7715e73898257f025727f2d1c4374d6b | %LPDD Linear programming distance data description
%
% W = LPDD(X,NU,S,DTYPE,P)
%
% One-class classifier put into a linear programming framework. From
% the data X the distance matrix is computed (using distance DTYPE,
% see myproxm for the possibilities). The distances are then
% transformed using a sigmoidal... |
github | gijzelaerr/sonic-gesture-master | dd_eer.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/dd_eer.m | 1,094 | utf_8 | 6744f2fc44f6fb5efa755ea694df570f | %EER Equal error rate
%
% E = DD_EER(R)
% E = A*W*DD_EER
%
% Compute the Equal error rate for ROC-curve R, or from the roc-curve
% derived from dataset A applied to (one-class) classifier W. Output E
% returns two values, the FPr and the FNr. In the case the ROC curve is
% sampled very well, these two values shou... |
github | gijzelaerr/sonic-gesture-master | fastmcd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/fastmcd.m | 64,715 | utf_8 | f65b4955cb0d9cfbcb02438a8482f39f | function [res,raw]=fastmcd(data,options);
% version 22/12/2000, revised 19/01/2001, new reweighted correction factors and old cutoff 9/07/2001
%
% FASTMCD computes the MCD estimator of a multivariate data set. This
% estimator is given by the subset of h observations with smallest covariance
% determinant. The MCD... |
github | gijzelaerr/sonic-gesture-master | mst.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/private/mst.m | 3,174 | utf_8 | 4f15713a7575b4c09230de569f1ed312 | function [tree,A] = mst(d)
% [tree,A] = mst(d)
% minimum spanning tree
%
% INPUT
% d [m x m] distance matrix
% OUTPUT
% tree [m-1 x 2] list of edges
% A [m x m] adjecency matrix
%
% See also mst_dd,datasets, mappings
% Copyright: Piotr Juszczak, p.juszczak@tud... |
github | gijzelaerr/sonic-gesture-master | p_map.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/private/p_map.m | 2,208 | utf_8 | 8d1235dcc4962429fa41287c64c2303c | %PARZEN_MAP Map a dataset on a Parzen densities based classifier
%
% F = p_map(A,W)
%
% Maps the dataset A by the Parzen density based classfier W. It
% outputs just the raw class probabilities (i.e. non-normalized).
% W should be trained by a
% classifier like parzenc. This routine is called automatically to
% so... |
github | gijzelaerr/sonic-gesture-master | mykcentres.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/private/mykcentres.m | 1,535 | utf_8 | 0e602bfad853f9407b9d46128d8afb05 | % KCENTRES Find k centres objects from distance matrix
%
% [labels,J,dmin] = kcentres(D,k,n)
%
% If D is a square distance matrix between m objects then J is the set of centre
% points, i.e. the subset of k objects that minimizes dmin, the maximum of the
% distances over all objects to the nearest centre point. For k >... |
github | gijzelaerr/sonic-gesture-master | dd_mem.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/private/dd_mem.m | 747 | utf_8 | e477bfc44c0aecb09c1848c325f2b00d | %DD_MEM Size of memory and loops for intermediate results
%
% [loops,rows,last] = dd_mem(m,k)
%
% The numbers of loops and rows are determined that are needed if in
% total an intermediate array of m*k is needed such that rows*k <
% PRMEMORY. The final number of rows for the last loop is returned
% in last.
% C... |
github | gijzelaerr/sonic-gesture-master | knnc.m | .m | sonic-gesture-master/evaluate/part1/prtools/knnc.m | 3,535 | utf_8 | 20362e51c361d7899c025ded631e1d9b | %KNNC K-Nearest Neighbor Classifier
%
% [W,K,E] = KNNC(A,K)
% [W,K,E] = KNNC(A)
%
% INPUT
% A Dataset
% K Number of the nearest neighbors (optional; default: K is
% optimized with respect to the leave-one-out error on A)
%
% OUTPUT
% W k-NN classifier
% K Number of the nearest neighbors used
% ... |
github | gijzelaerr/sonic-gesture-master | im_skel_meas.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_skel_meas.m | 1,669 | utf_8 | dcdcd014bc93aaef5301141e3c64512a | %IM_SKEL_MEASURE Computation by DIP_Image of skeleton-based features
%
% F = IM_SKEL_MEASURE(A,FEATURES)
%
% INPUT
% A Dataset with binary object images dataset
% FEATURES Features to be computed
%
% OUTPUT
% F Dataset with computed features
%
% DESCRIPTION
% The following features may be compute... |
github | gijzelaerr/sonic-gesture-master | im_fft.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_fft.m | 859 | utf_8 | 9c39c2a03e24449fb0baa8cf48e786b2 | %IM_FFT 2D FFT of all images in dataset
%
% F = IM_FFT(A)
%
% INPUT
% A Dataset with object images (possibly multi-band)
%
% OUTPUT
% F Dataset with FFT images
%
% SEE ALSO
% DATASETS, DATAFILES, FFT2
% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org
% Faculty EWI, Delft University of Technology
% P.O... |
github | gijzelaerr/sonic-gesture-master | parzenm.m | .m | sonic-gesture-master/evaluate/part1/prtools/parzenm.m | 2,629 | utf_8 | fc2c033dbde1f0e376cebdb6f43eb220 | %PARZENM Estimate Parzen densities
%
% W = PARZENM(A,H)
% W = A*PARZENM([],H)
%
% D = B*W
%
% INPUT
% A Input dataset
% H Smoothing parameters (scalar, vector)
%
% OUTPUT
% W output mapping
%
% DESCRIPTION
% A Parzen distribution is estimated for the labeled objects in A. Unlabeled
% objects are neglecte... |
github | gijzelaerr/sonic-gesture-master | col2gray.m | .m | sonic-gesture-master/evaluate/part1/prtools/col2gray.m | 1,596 | utf_8 | 8f52ea4434366e7be840bf8ffebaf7dd | %COL2GRAY Mapping for converting multi-band images into single band images
%
% B = COL2GRAY(A,V)
% B = A*COL2GRAY([],V)
%
% INPUT
% A Multiband image or dataset with multi-band images as objects
% V Weight vector, one weight per band. Default: equal weights.
%
% OUTPUT
% B Output image or dataset.
%
% ... |
github | gijzelaerr/sonic-gesture-master | nulibsvc.m | .m | sonic-gesture-master/evaluate/part1/prtools/nulibsvc.m | 5,081 | utf_8 | f22dc30b5705477f3cc1829d91d1bd1f | %NULIBSVC Support Vector Classifier by libsvm, nu version
%
% [W,J] = NULIBSVC(A,KERNEL,NU)
%
% 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 kerne... |
github | gijzelaerr/sonic-gesture-master | cleval.m | .m | sonic-gesture-master/evaluate/part1/prtools/cleval.m | 6,779 | utf_8 | 51f621337d021488d5d648febc52522b | %CLEVAL Classifier evaluation (learning curve)
%
% E = CLEVAL(A,CLASSF,TRAINSIZES,NREPS,T,TESTFUN)
%
% INPUT
% A Training dataset
% CLASSF Classifier to evaluate
% TRAINSIZE Vector of class sizes, used to generate subsets of A
% (default [2,3,5,7,10,15,20,30,50,70,100])
% NREPS ... |
github | gijzelaerr/sonic-gesture-master | classc.m | .m | sonic-gesture-master/evaluate/part1/prtools/classc.m | 3,622 | utf_8 | 095c4dd1edad8a43a38da8b9d9263191 | %CLASSC Convert classifier to normalized classifier (yielding confidences)
%
% V = CLASSC(W)
% V = W*CLASSC
% D = CLASSC(A*W)
% D = A*W*CLASSC
% D = CLASSC(A,W)
%
% INPUT
% W Trained or untrained classifier
% A Dataset
%
% OUTPUT
% V Normalized classifier producing confidences instead of
% densities or... |
github | gijzelaerr/sonic-gesture-master | featselb.m | .m | sonic-gesture-master/evaluate/part1/prtools/featselb.m | 2,850 | utf_8 | 242e3f5afed7c113de4956acdbf3b569 | %FEATSELB Backward feature selection for classification
%
% [W,R] = FEATSELB(A,CRIT,K,T,FID)
% [W,R] = FEATSELB(A,CRIT,K,N,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping
% (optional; default: 'NN', i.e. 1-Nearest Neighbor error)
% K Number of features to ... |
github | gijzelaerr/sonic-gesture-master | issym.m | .m | sonic-gesture-master/evaluate/part1/prtools/issym.m | 768 | utf_8 | d029ef5dca799d320df7ee0deb0d19fa | %ISSYM Checks whether a matrix is symmetric
%
% OK = ISSYM(A,DELTA)
%
% INPUT
% A Dataset
% DELTA Parameter for the precision check (optional; default: 1e-12)
%
% OUTPUT
% OK 1 if the matrix A is symmetric and 0, otherwise.
%
% DESCRIPTION
% A is considered as a symmetric matrix, when it is square and
... |
github | gijzelaerr/sonic-gesture-master | isdataset.m | .m | sonic-gesture-master/evaluate/part1/prtools/isdataset.m | 501 | utf_8 | 0b61fa069741029a5c4bf06e4ba660c4 | %ISDATASET Test whether the argument is a dataset
%
% N = ISDATASET(A);
%
% INPUT
% A Input argument
%
% OUTPUT
% N 1/0 if A is/isn't a dataset
%
% DESCRIPTION
% The function ISDATASET test if A is a dataset object.
%
% SEE ALSO
% ISMAPPING, ISDATAIM, ISFEATIM
% $Id: isdataset.m,v 1.3 2007/03/22 08:54:59 duin Ex... |
github | gijzelaerr/sonic-gesture-master | stumpc.m | .m | sonic-gesture-master/evaluate/part1/prtools/stumpc.m | 14,270 | utf_8 | eafef64246953de2e60096c0899fd40e | %STUMPC Decision stump classifier
%
% W = STUMPC(A,CRIT,N)
%
% Computation of a decision tree classifier out of a dataset A using
% a binary splitting criterion CRIT:
% INFCRIT - information gain
% MAXCRIT - purity (default)
% FISHCRIT - Fisher criterion
% Just N (default N=1) nodes are computed.
%
% s... |
github | gijzelaerr/sonic-gesture-master | plote.m | .m | sonic-gesture-master/evaluate/part1/prtools/plote.m | 7,933 | utf_8 | de6db24ff3cb019ce78ed26736d5e0bc | %PLOTE Plot error curves
%
% H = PLOTE(E,LINEWIDTH,S,FONTSIZE,OPTIONS)
%
% INPUT
% E Structure containing error curves (see e.g. CLEVAL)
% LINEWIDTH Line width, < 5 (default 2)
% S Plot strings
% FONTSIZE Font size, >= 5 (default 16)
% OPTIONS Character strings:
% 'noleg... |
github | gijzelaerr/sonic-gesture-master | data2im.m | .m | sonic-gesture-master/evaluate/part1/prtools/data2im.m | 2,951 | utf_8 | 8f8c9a64fb18ac5f48f9b0bb34ef588d | %DATA2IM Convert PRTools dataset or datafile to image
%
% IM = DATA2IM(A,J)
% IM = DATA2IM(A(J,:))
%
% INPUT
% A Dataset or datafile containing images
% J Desired images
%
% OUTPUT
% IM If A is dataset, IM is a X*Y*N*K matrix with K images.
% K is the number of images (length(J))
% ... |
github | gijzelaerr/sonic-gesture-master | feateval.m | .m | sonic-gesture-master/evaluate/part1/prtools/feateval.m | 4,914 | utf_8 | ad2e0e02919a65973caa8cd07be7ea65 | %FEATEVAL Evaluation of feature set for classification
%
% J = FEATEVAL(A,CRIT,T)
% J = FEATEVAL(A,CRIT,N)
%
% INPUT
% A input dataset
% CRIT string name of a method or untrained mapping
% T validation dataset (optional)
% N number of cross-validations (optional)
%
% OUTPUT
... |
github | gijzelaerr/sonic-gesture-master | dcsc.m | .m | sonic-gesture-master/evaluate/part1/prtools/dcsc.m | 7,568 | utf_8 | e21c176dfd20be277f7484f749f2319b | % DCSC Dynamic Classifier Selection Combiner
%
% V = DCSC(A,W,K,TYPE)
% V = A*(W*DCSC([],K,TYPE))
% D = B*V
%
% INPUT
% A Dataset used for training base classifiers as well as combiner
% B Dataset used for testing (executing) the combiner
% W Set of trained or untrained base classifier... |
github | gijzelaerr/sonic-gesture-master | gendatm.m | .m | sonic-gesture-master/evaluate/part1/prtools/gendatm.m | 1,444 | utf_8 | 052286de99e508d15b878b8079f7d6c7 | %GENDATM Generation of multi-class 2-D data
%
% A = GENDATM(N)
%
% INPUT
% N Vector of class sizes (default: 20)
%
% OUTPUT
% A Dataset
%
% DESCRIPTION
% Generation of N samples in 8 classes of 2 dimensionally distributed data
% vectors. Classes have equal prior probabilities. If N is a vector of
% sizes, ex... |
github | gijzelaerr/sonic-gesture-master | crossval.m | .m | sonic-gesture-master/evaluate/part1/prtools/crossval.m | 8,429 | utf_8 | 1478ac227f824721769614e25d762596 | %CROSSVAL Error/performance estimation by cross validation (rotation)
%
% [ERR,CERR,NLAB_OUT] = CROSSVAL(A,CLASSF,NFOLDS,1,TESTFUN)
% [ERR,STDS] = CROSSVAL(A,CLASSF,NFOLDS,NREP,TESTFUN)
% [ERR,CERR,NLAB_OUT] = CROSSVAL(A,CLASSF,NFOLDS,'DPS',TESTFUN)
% R = CROSSVAL(A,[],NFOLDS,0)
%
% ... |
github | gijzelaerr/sonic-gesture-master | baggingc.m | .m | sonic-gesture-master/evaluate/part1/prtools/baggingc.m | 2,478 | utf_8 | e1527f453891053e0d7cc405f8973486 | %BAGGINGC Bootstrapping and aggregation of classifiers
%
% W = BAGGINGC (A,CLASSF,N,ACLASSF,T)
%
% INPUT
% A Training dataset.
% CLASSF The base classifier (default: nmc)
% N Number of base classifiers to train (default: 100)
% ACLASSF Aggregating classifier (default: meanc), [] for no... |
github | gijzelaerr/sonic-gesture-master | svo_nu.m | .m | sonic-gesture-master/evaluate/part1/prtools/svo_nu.m | 3,962 | utf_8 | 396fa04268e3a40213f79e02eb83ee67 | %SVO_NU Support Vector Optimizer: NU algorithm
%
% [V,J,C] = SVO(K,NLAB,NU,PD)
%
% INPUT
% K Similarity matrix
% NLAB Label list consisting of -1/+1
% NU Regularization parameter (0 < NU < 1): expected fraction of SV (optional; default: 0.25)
%
% PD Do or do not the check of the positive definitene... |
github | gijzelaerr/sonic-gesture-master | im_dbr.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_dbr.m | 4,020 | utf_8 | af0771a52e215a85ee7b958c4661a1ce | %IM_DBR Image Database Retrieval GUI
%
% [RANK,TARG,OUTL] = IM_DBR(DBASE,FSETS,CLASSF,COMB)
%
% INPUT
% DBASE - Dataset or datafile with N object images
% FSETS - Cell array with maximum 4 feature sets
% CLASSF - Cell array with untrained classifiers (Default: KNNC([],1))
% COMB - Combining c... |
github | gijzelaerr/sonic-gesture-master | testr.m | .m | sonic-gesture-master/evaluate/part1/prtools/testr.m | 806 | utf_8 | 5e59ef193ee8cd878561c221ebf52cb2 | %TESTR MSE for regression
%
% E = TESTR(X,W)
% E = TESTR(X*W)
% E = X*W*TESTR
%
% INPUT
% X Regression dataset
% W Regression mapping
%
% OUTPUT
% E Mean squared error
%
% DESCRIPTION
% Compute the the mean squared error of regression W on dataset X.
%
% SEE ALSO
% RSQUARED, TESTC
% Copy... |
github | gijzelaerr/sonic-gesture-master | stacked.m | .m | sonic-gesture-master/evaluate/part1/prtools/stacked.m | 4,900 | utf_8 | 43a1eca5520eaef38afbe170b53df4d8 | %STACKED Combining classifiers in the same feature space
%
% WC = STACKED(W1,W2,W3, ....) or WC = [W1,W2,W3, ...]
% WC = STACKED({W1,W2,W3, ...}) or WC = [{W1,W2,W3, ...}]
% WC = STACKED(WC,W1,W2, ....) or WC = [WC,W2,W3, ...]
%
% INPUT
% W1,W2,W3 Set of classifiers
%
% OUTPUT
% WC Combined classifi... |
github | gijzelaerr/sonic-gesture-master | bandsel.m | .m | sonic-gesture-master/evaluate/part1/prtools/bandsel.m | 4,320 | utf_8 | b397f93a16fc65c469e370035595021d | %BANDSEL Selection of bands from object images
%
% B = BANDSEL(A,J)
% W = BANDSEL([],J)
% B = A*BANDSEL([],J)
%
% INPUT
% A Dataset or datafile with multi-band object images
% J Indices of bands to be selected
%
% OUTPUT
% W Mapping performing the band selection
% B Dataset with se... |
github | gijzelaerr/sonic-gesture-master | datfilt.m | .m | sonic-gesture-master/evaluate/part1/prtools/datfilt.m | 1,208 | utf_8 | 31f36e31d9f2ba195de6f48b03120f3c | %DATFILT Filtering of dataset images
%
% B = DATFILT(A,F)
%
% INPUT
% A Dataset with image data
% F Matrix with the convolution mask
%
% OUTPUT
% B Dataset containing all the images after filtering
%
% DESCRIPTION
% All images stored in the dataset A are horizontally and vertically
% convoluted by the 1-dime... |
github | gijzelaerr/sonic-gesture-master | linewidth.m | .m | sonic-gesture-master/evaluate/part1/prtools/linewidth.m | 605 | utf_8 | 38780117b4f229c290a054070793bd28 | %LINEWIDTH Set linewidth in plot
%
% linewidth(width)
%Set linewidth for current figure
function linewidth(width)
if strcmp(get(gca,'type'),'line')
set(gca,'linewidth',width);
end
children = get(gca,'children');
set_linewidth_children(children,width)
return
function set_linewidth_children(children,width)
if isempty(... |
github | gijzelaerr/sonic-gesture-master | medianc.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | im_rotate.m | .m | sonic-gesture-master/evaluate/part1/prtools/im_rotate.m | 1,230 | utf_8 | adfb824fe371037f44f120c02c27249f | %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 | gijzelaerr/sonic-gesture-master | ploto.m | .m | sonic-gesture-master/evaluate/part1/prtools/ploto.m | 1,981 | utf_8 | a555d41642039708da0330e072b05329 | %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 | gijzelaerr/sonic-gesture-master | iscomdset.m | .m | sonic-gesture-master/evaluate/part1/prtools/iscomdset.m | 1,531 | utf_8 | bb64e77ba3eacdf8784ce2bf189903c3 | %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... |
github | gijzelaerr/sonic-gesture-master | prdata.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | affine.m | .m | sonic-gesture-master/evaluate/part1/prtools/affine.m | 6,573 | utf_8 | 7e992ef7777af6a337523962c7c0f57f | %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 | gijzelaerr/sonic-gesture-master | show.m | .m | sonic-gesture-master/evaluate/part1/prtools/show.m | 1,818 | utf_8 | 9bc796d7fe51619e16e991c5d4e4ab3f | %SHOW PRTools general show
%
% H = SHOW(A,N,B)
%
% INPUT
% A Image
% N Number of images on a row
% B Intensity value of background (default 0.5);
%
% OUTPUT
% H Graphics handle
%
% DESCRIPTION
% PRTools offers a SHOW command for variables of the data classes DATASET
% and DA... |
github | gijzelaerr/sonic-gesture-master | gauss.m | .m | sonic-gesture-master/evaluate/part1/prtools/gauss.m | 4,857 | utf_8 | 35f15aa2d6ecffdef3fe9eb323f047e4 | %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 | gijzelaerr/sonic-gesture-master | nlabcmp.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | featsellr.m | .m | sonic-gesture-master/evaluate/part1/prtools/featsellr.m | 9,276 | utf_8 | 899156db13a08ae7791a4a549dd9d1c7 | %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 | gijzelaerr/sonic-gesture-master | gentrunk.m | .m | sonic-gesture-master/evaluate/part1/prtools/gentrunk.m | 1,849 | utf_8 | 3a239e0c7a1bf3700f24ce43dc0e7187 | %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 | gijzelaerr/sonic-gesture-master | setdat.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | testc.m | .m | sonic-gesture-master/evaluate/part1/prtools/testc.m | 15,126 | utf_8 | 0e592c7a3aa4f3ae954c260954d4f5be | %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 | gijzelaerr/sonic-gesture-master | labeld.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | nmsc.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | testauc.m | .m | sonic-gesture-master/evaluate/part1/prtools/testauc.m | 2,064 | utf_8 | c78feb6f45972f5303640aa6668b7fee | %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 ROC is computed for the datset A w.r.t. the
% classifer... |
github | gijzelaerr/sonic-gesture-master | genclass.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | prtools_news.m | .m | sonic-gesture-master/evaluate/part1/prtools/prtools_news.m | 2,101 | utf_8 | 07a2963d609a86fca42d81185ff9a30d | %PRTOOLS_NEWS List PRTools news and download new versions
%
% PRTOOLS_NEWS List PRTools news
% PRTOOLS_NEWS(DIRNAME,UNZIP) Reload PRTools
%
% DIRNAME is the directory to download PRTools. If UNZIP == 1
% (default 0) it is unzipped.
function out = prtools_news(dirname,unzip_li... |
github | gijzelaerr/sonic-gesture-master | gendatw.m | .m | sonic-gesture-master/evaluate/part1/prtools/gendatw.m | 808 | utf_8 | f3395f59b4d881cec04c978b0936835e | %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 prio... |
github | gijzelaerr/sonic-gesture-master | kernelm.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | rbsvc.m | .m | sonic-gesture-master/evaluate/part1/prtools/rbsvc.m | 2,053 | utf_8 | 41cca5dbfaed944ed3654474ee369f0b | %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
%
% DESCR... |
github | gijzelaerr/sonic-gesture-master | gendatp.m | .m | sonic-gesture-master/evaluate/part1/prtools/gendatp.m | 2,981 | utf_8 | 1d7832af2fdce2ffd55c5daae2048971 | %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 | gijzelaerr/sonic-gesture-master | spirals.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | plotdg.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | newline.m | .m | sonic-gesture-master/evaluate/part1/prtools/newline.m | 174 | utf_8 | 2a39d991937508030bfbf1e69ec3c1a6 | %NEWLINE The platform dependent newline character
%
% c = newline
% $Id: newline.m,v 1.3 2010/03/18 12:25:21 duin Exp $
function c = newline
c = sprintf('\n');
return
|
github | gijzelaerr/sonic-gesture-master | genlab.m | .m | sonic-gesture-master/evaluate/part1/prtools/genlab.m | 3,076 | utf_8 | f6ea44f4198613eeb28e3339d6174aa7 | %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 | gijzelaerr/sonic-gesture-master | im_berosion.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | im_minf.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | setname.m | .m | sonic-gesture-master/evaluate/part1/prtools/setname.m | 287 | utf_8 | 038915ac208df9ac19248da017fefef0 | %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 | gijzelaerr/sonic-gesture-master | subsc.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | reject.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | rejectc.m | .m | sonic-gesture-master/evaluate/part1/prtools/rejectc.m | 2,045 | utf_8 | 6f43b167ee7845e9dc2399ead1ec2432 | %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.... |
github | gijzelaerr/sonic-gesture-master | gendatk.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | nusvc.m | .m | sonic-gesture-master/evaluate/part1/prtools/nusvc.m | 4,682 | utf_8 | 0b034917e1481824ef730ac3d7734280 | %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 | gijzelaerr/sonic-gesture-master | prarff.m | .m | sonic-gesture-master/evaluate/part1/prtools/prarff.m | 3,226 | utf_8 | b9a5520deaa586036751cc082ae2f646 | %PRARFF COnvert ARFF file into PRTools dataset
%
% A = PRARFF(FILE)
%
% INPUT
% FILE ARFF file
%
% OUTPUT
% A Dataset in PRTools format
%
% DESCRIPTION
% ARFF files as used in WEKA are converted into PRTools format. In case
% they don't fit (non-numeric features, varying feature length) an err... |
github | gijzelaerr/sonic-gesture-master | prmemory.m | .m | sonic-gesture-master/evaluate/part1/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 | gijzelaerr/sonic-gesture-master | im_scale.m | .m | sonic-gesture-master/evaluate/part1/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.... |
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