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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | alexalex222/My-MATLAB-Tooboxes-master | split.m | .m | My-MATLAB-Tooboxes-master/MB_VDP/split.m | 2,676 | utf_8 | ea3ad069d09b2c28e7cd429e3b4c004a | function new_q_z = split(data,c,q_z,posterior,options)
if c > size(posterior.B,2) - 1
cluster_mean = posterior.m(:,1);
if options.DIAG
cluster_cov = posterior.B(:,1)/posterior.eta(1);
else
cluster_cov = posterior.B(:,:,1)/posterior.eta(1);
end
else
... |
github | alexalex222/My-MATLAB-Tooboxes-master | split_merge.m | .m | My-MATLAB-Tooboxes-master/MB_VDP/split_merge.m | 14,746 | utf_8 | 76f7071fedce1323fae03a33b3af6041 | % The split merge inference routine
function [free_energy, posterior] = split_merge(data,posterior,prior,options)
c_max = 5;
% first update the current posterior with the new data
[free_energy, posterior, q_z] = iterate_posterior(data,posterior,prior,options);
fe_improved = true;
K = size(q_z.single... |
github | alexalex222/My-MATLAB-Tooboxes-master | iterate_posterior.m | .m | My-MATLAB-Tooboxes-master/MB_VDP/iterate_posterior.m | 2,228 | utf_8 | 60f8cc639a62482c1f820bc28d9100dd | function [free_energy, posterior, q_z] = iterate_posterior(data,posterior,prior,options,iter,do_sort,suppress_output)
if nargin < 6;
do_sort = true;
iter = inf;
suppress_output = false;
end
if ~suppress_output
disp('Updating until convergence...');
end
... |
github | alexalex222/My-MATLAB-Tooboxes-master | generate_prior.m | .m | My-MATLAB-Tooboxes-master/MB_VDP/generate_prior.m | 613 | utf_8 | 5f7b1a5a350cf6e46e34eab27cf4e315 | %Borrowed from Kenichi Kurihara's NIP 2006 implementation.
function hp_prior = generate_prior(data,alpha)
hp_prior.xi0 = 0.01;
eta_p = 1;
if nargin > 1
hp_prior.alpha = alpha;
else
hp_prior.alpha = 1;
end
[D,N] = size(data);
covariance = cov(data');
hp... |
github | alexalex222/My-MATLAB-Tooboxes-master | MB_VDP.m | .m | My-MATLAB-Tooboxes-master/MB_VDP/MB_VDP.m | 6,880 | utf_8 | 017c50349d966b725781900dcdbd6d84 | %% Memory Bounded Variational Dirichlet Process
% X: Each column is a D dimensional data point. Passing it in as one big matrix
% will work when the data fits in memory. For larger problems, modify
% the main loop to retrieve E data points from disk during each round.
% M: The memory bound, measured as the max... |
github | alexalex222/My-MATLAB-Tooboxes-master | eval_delta_fe.m | .m | My-MATLAB-Tooboxes-master/MB_VDP/eval_delta_fe.m | 4,246 | utf_8 | 2ccb399eae737089fa70e3703d406b38 | % fast evaluation of the change in free energy when points are hard
% assigned to partitions. This is used to evaluate split-merge proposals
% and to determine which partitions to split when determining clumps
function delta_fe = eval_delta_fe(subdata,post_posterior,pre_posterior, ...
... |
github | alexalex222/My-MATLAB-Tooboxes-master | exportfig.m | .m | My-MATLAB-Tooboxes-master/exportfig/exportfig.m | 32,769 | utf_8 | c64155519b7fc569dfcf501d94376e63 | function varargout = exportfig(varargin)
%EXPORTFIG Export a figure.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(H, FILENAME, OPTIONS) writes the figure H to FILENAME
% with options init... |
github | alexalex222/My-MATLAB-Tooboxes-master | maximize.m | .m | My-MATLAB-Tooboxes-master/exportfig/maximize.m | 492 | utf_8 | 0579256b578239a27a43e1fd52626d85 | %MAXIMIZE Maximize a figure window to fill the entire screen
%
% Examples:
% maximize
% maximize(hFig)
%
% Maximizes the current or input figure so that it fills the whole of the
% screen that the figure is currently on. This function is platform
% independent.
%
%IN:
% hFig - Handle of figure to maximize. Defau... |
github | alexalex222/My-MATLAB-Tooboxes-master | format_ticks.m | .m | My-MATLAB-Tooboxes-master/figure/format_ticks.m | 17,358 | utf_8 | 86786fceb9c2ff6c1422dc6d2a2434f9 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% BEGIN HEADER
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
... |
github | alexalex222/My-MATLAB-Tooboxes-master | cf_pt.m | .m | My-MATLAB-Tooboxes-master/pcamv/cf_pt.m | 1,089 | utf_8 | fa90e99d355379781d35386af4f51d14 | % CF_PT - Cost for PCA model with maximum a posteriori estimation
%
% Compute the cost function for the PCA model with maximum a
% posteriori estimation of the parameters. The function also returns
% the matrix of reconstruction errors errMx and the rms error.
%
% See also PCA_PT
% This software is provided "as ... |
github | alexalex222/My-MATLAB-Tooboxes-master | addebars.m | .m | My-MATLAB-Tooboxes-master/pcamv/addebars.m | 1,162 | utf_8 | 7d6b86cc58f524a8492d557974ecefab | % ADDEBARS - Add error bars to current figure
%
% ADDEBARS(Yb) adds error bars in Yb to the current figure which
% should be created by TSPLOT. Yb should contain error bars for
% corresponding elements in Y displayed with TSPLOT.
%
% See also TSPLOT
% This software is provided "as is", without warranty of any ki... |
github | alexalex222/My-MATLAB-Tooboxes-master | compute_rms.m | .m | My-MATLAB-Tooboxes-master/pcamv/compute_rms.m | 779 | utf_8 | 2917b83737ac3fe2ca16ebe3e445b20a | % COMPUTE_RMS - RMS error and reconstruction error matrix
%
% [rms,errMx] = COMPUTE_RMS( X, A, S, M, ndata ) computes a matrix
% errMx of reconstruction errors (X - A*S) and the rms reconstruction
% error.
%
% [rms,errMx] = COMPUTE_RMS( ..., numCPU ) also specifies the number
% numCPU of CPUs used for parallel co... |
github | alexalex222/My-MATLAB-Tooboxes-master | tsplot.m | .m | My-MATLAB-Tooboxes-master/pcamv/tsplot.m | 1,076 | utf_8 | 62c037f10d4f7cc2e06a9e1eea4783bc | % TSPLOT - Plot time series
%
% TSPLOT(Y) plots time series in the rows of Y on a separate
% subplot. Y can be followed by parameter/value pairs to specify
% additional properties of the lines.
%
% See also ADDTSPLOT
% This software is provided "as is", without warranty of any kind.
% Alexander Ilin, Tapani Rai... |
github | alexalex222/My-MATLAB-Tooboxes-master | addtsplot.m | .m | My-MATLAB-Tooboxes-master/pcamv/addtsplot.m | 947 | utf_8 | 4f2d834681cd52d4ccb673fae6906367 | % ADDTSPLOT - Add time series to current figure
%
% ADDTSPLOT(Y) adds time series in the rows of Y to the current
% figure which should be created by TSPLOT. Y can be followed by
% parameter/value pairs to specify additional properties of the
% lines.
%
% See also TSPLOT
% This software is provided "as is", wit... |
github | alexalex222/My-MATLAB-Tooboxes-master | rmempty.m | .m | My-MATLAB-Tooboxes-master/pcamv/rmempty.m | 2,660 | utf_8 | 661d8a38abee789e51ef90808704bbf5 | % RMEMPTY - Remove empty columns or rows from data matrix
%
% [ X, Xprobe, Ir, Ic, init ] = RMEMPTY( X, Xprobe, init, verbose )
% removes empty columns or rows from data matrix X and matrix Xprobe
% of probing data. Those columns and rows do not affect the found
% solution.
%
% See also ADDMCOLS, ADDMROWS
% Thi... |
github | alexalex222/My-MATLAB-Tooboxes-master | pcaimput.m | .m | My-MATLAB-Tooboxes-master/pcamv/pcaimput.m | 5,763 | utf_8 | 8a3f023873ce4462448b80890e37a796 | % PCAIMPUT - Imputation algorithm for PCA with missing values
%
% [ A, S, Mu, LC ] = PCAIMPUT( X, N ) finds the PCA decomposition X =
% AS + [Mu .. Mu] for the given data matrix X and number of principal
% components N using repetitive imputation. Matrix X can be either
% sparse with only observed values included ... |
github | alexalex222/My-MATLAB-Tooboxes-master | addmcols.m | .m | My-MATLAB-Tooboxes-master/pcamv/addmcols.m | 1,085 | utf_8 | ec87073acb186e8c281b09b9e0915e99 | % ADDMCOLS - Add unobserved columns to PCA solution
%
% [ S_new, Sv_new, Isv_new ] = ADDMCOLS( S, Sv, Ic, n2x, Isv ) adds
% the columns removed by RMEMPTY to the found parameter S.
%
% See also RMEMPTY, ADDMROWS
% This software is provided "as is", without warranty of any kind.
% Alexander Ilin, Tapani Raiko
fu... |
github | alexalex222/My-MATLAB-Tooboxes-master | pca_full.m | .m | My-MATLAB-Tooboxes-master/pcamv/pca_full.m | 17,597 | utf_8 | 3542d603bd78e5d165879a64fcae111e | % PCA_FULL - PCA with unrestricted Gaussian posterior
%
% PCA with unrestricted Gaussian pdfs (full covariance matrices) for
% approximating the posterior distributions of A(i,:) and S(:,j) in
% the model X(:,j) = Mu + A*S(:,j) + Noise. The noise is isotropic
% and Gaussian with the variance V.
%
% [ A, S, Mu, V,... |
github | alexalex222/My-MATLAB-Tooboxes-master | cov_d2f.m | .m | My-MATLAB-Tooboxes-master/pcamv/cov_d2f.m | 182 | utf_8 | 673bb72a3f9c05750b1de5120cf9183d | % COV_D2F -
function Sv = cov_d2f( Sv )
if isnumeric(Sv)
Sv_in = Sv;
Sv = cell(1,size(Sv,2));
for i = 1:size(Sv,2)
Sv{i} = diag(Sv_in(:,i));
end
end
|
github | alexalex222/My-MATLAB-Tooboxes-master | cf_diag.m | .m | My-MATLAB-Tooboxes-master/pcamv/cf_diag.m | 2,949 | utf_8 | 4c9400517b2c152c8201e87cf576c6bd | % CF_DIAG - Cost for PCA model with fully factorial posterior
%
% cost = CF_DIAG( X, A, S, Mu, V, cv, hp ) computes the cost function
% for the PCA model with fully factorial Gaussian pdf (diagonal
% covariance matrix) for approximating the posterior distributions of
% A and S. The input parameters are returned by... |
github | alexalex222/My-MATLAB-Tooboxes-master | subspace2d.m | .m | My-MATLAB-Tooboxes-master/pcamv/subspace2d.m | 3,901 | utf_8 | ab44697fbe1f9dd8645aec47eadae137 | % SUBSPACE2D - Visualize principal components in 2D
%
% SUBSPACE2D( S, Sv, Sr ) visualizes principal components in S and
% their posterior variances Sv. Each column of S should be
% two-dimensional, Sv should contain the posterior variances of
% S. Function COVDG is useful for obtaining Sv from the results
% retu... |
github | alexalex222/My-MATLAB-Tooboxes-master | converg_check.m | .m | My-MATLAB-Tooboxes-master/pcamv/converg_check.m | 2,035 | utf_8 | 2ba7f3046752ce80431f9c72019c3a91 | % CONVERG_CHECK - Check convergence criteria
%
% convmsg = CONVERG_CHECK( opts, lc, angleA, sd_iter ) checks
% convergence criteria and generates a message that should be
% displayed.
% This software is provided "as is", without warranty of any kind.
% Alexander Ilin, Tapani Raiko
function convmsg = converg_che... |
github | alexalex222/My-MATLAB-Tooboxes-master | pca_diag.m | .m | My-MATLAB-Tooboxes-master/pcamv/pca_diag.m | 13,996 | utf_8 | 970a0d17d477b23b7999516c90ba908a | % PCA_DIAG - PCA with fully factorial Gaussian posterior
%
% PCA with fully factorial Gaussian pdf (diagonal covariance matrix)
% for approximating the posterior distributions of A and S in the
% model X(:,j) = Mu + A*S(:,j) + Noise. The noise is isotropic and
% Gaussian with the variance V.
%
% [ A, S, Mu, V, CV... |
github | alexalex222/My-MATLAB-Tooboxes-master | lspca.m | .m | My-MATLAB-Tooboxes-master/pcamv/lspca.m | 11,445 | utf_8 | 683f60ac12c002ce1b6a813b68fc54be | % LSPCA - Least-squares approach to PCA with missing values
%
% [ A, S, Mu, LC ] = LSPCA( X, N ) finds the PCA decomposition X = AS
% + [Mu .. Mu] for the given data matrix X and number of principal
% components N using the unregularized least-squares approach. Matrix
% X can be either sparse with only observed va... |
github | alexalex222/My-MATLAB-Tooboxes-master | cov_f2d.m | .m | My-MATLAB-Tooboxes-master/pcamv/cov_f2d.m | 747 | utf_8 | 331f2865882e61f7fb3ed65075f8b7ea | % COV_F2D - Extract posterior variance of principal components
%
% Sv = COV_F2D( Sv, Isv ) extracts the posterior variances of
% principal components (elements of matrix S) from the structures
% returned by PCA_FULL.
%
% See also SUBSPACE2D, ADDEBARS
% This software is provided "as is", without warranty of any k... |
github | alexalex222/My-MATLAB-Tooboxes-master | pca_pt.m | .m | My-MATLAB-Tooboxes-master/pcamv/pca_pt.m | 15,306 | utf_8 | 1236994486e8ddbbebdf2109462ceb6d | % PCA_PT - PCA with maximum a posteriori estimation
%
% PCA with maximum a posteriori estimates for the parameters of the
% model X(:,j) = Mu + A*S(:,j) + Noise. The noise is isotropic and
% Gaussian with the variance V. Parameters Mu, A and S are assigned
% Gaussian priors.
%
% [ A, S, Mu, V, HP, LC ] = PCA_PT( ... |
github | alexalex222/My-MATLAB-Tooboxes-master | argschk.m | .m | My-MATLAB-Tooboxes-master/pcamv/argschk.m | 1,069 | utf_8 | 0a7407c1b139fab070a54de98b205b5a | % ARGSCHK - Check of parameter/value pairs
%
% [ opts, errmsg, wrnmsg ] = ARGSCHK( defopts, varargin ) checks
% parameter/value pairs.
% This software is provided "as is", without warranty of any kind.
% Alexander Ilin, Tapani Raiko
function [ opts, errmsg, wrnmsg ] = argschk( defopts, varargin )
errmsg = '';
w... |
github | alexalex222/My-MATLAB-Tooboxes-master | miscomb.m | .m | My-MATLAB-Tooboxes-master/pcamv/miscomb.m | 938 | utf_8 | f22f80498a4ce5a508f4f234292ba13f | % MISCOMB - Find combinations of missing values in columns
%
% [ nobscomb, obscombj, Isv ] = MISCOMB(M,verbose) computes the
% combinations of missing values in each column of a data matrix.
% This is needed for faster implementation.
%
% See also PCA_FULL, PCA_PT, LSPCA
% This software is provided "as is", with... |
github | alexalex222/My-MATLAB-Tooboxes-master | cf_full.m | .m | My-MATLAB-Tooboxes-master/pcamv/cf_full.m | 4,405 | utf_8 | fcad1eae0669fd04d4b2b23bdea4a227 | % CF_FULL - Cost for PCA model with unrestricted Gaussian posterior
%
% cost = CF_FULL( X, A, S, Mu, V, cv, hp ) computes the cost function
% for the PCA model with unrestricted Gaussian pdfs (full covariance
% matrices) for A(i,:) and S(:,j). The input parameters are returned
% by PCA_FULL.
%
% See also PCA_FULL... |
github | alexalex222/My-MATLAB-Tooboxes-master | addmrows.m | .m | My-MATLAB-Tooboxes-master/pcamv/addmrows.m | 1,317 | utf_8 | b2064d3880d4b9c596c1806fd6773cd8 | % ADDMROWS - Add unobserved rows to PCA solution
%
% [ A_new, Av_new ] = ADDMROWS( A, Av, Ir, n1x, Va ) adds the columns
% removed by RMEMPTY to the found parameter A.
%
% Va is the variance of the prior for A. Va=inf corresponds to
% no prior for A.
%
% See also RMEMPTY, ADDMCOLS
% This software is provided "a... |
github | alexalex222/My-MATLAB-Tooboxes-master | ekf_predict2.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ekf_predict2.m | 3,343 | UNKNOWN | e361d1331b5970202696843b127cb7e9 | %EKF_PREDICT2 2nd order Extended Kalman Filter prediction step
%
% Syntax:
% [M,P] = EKF_PREDICT2(M,P,[A,F,Q,a,W,param])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% A - Derivative of a() with respect to state as
% matrix, inline function, function ... |
github | alexalex222/My-MATLAB-Tooboxes-master | ut_transform.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ut_transform.m | 3,564 | UNKNOWN | f75fd5abda99dff018cbeec9e7061f9f | %UT_TRANSFORM Perform unscented transform
%
% Syntax:
% [mu,S,C,X,Y,w] = UT_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,param) as
% matrix, inline function, fu... |
github | alexalex222/My-MATLAB-Tooboxes-master | quad_transform.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/quad_transform.m | 2,486 | UNKNOWN | c1cc63e44335d77b4d42a0e5b2732cb5 | %UT_TRANSFORM Perform quadratic approximation based transform of a Gaussian rv
%
%
% Syntax:
% [mu,S,C,X,Y,w] = QUAD_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,para... |
github | alexalex222/My-MATLAB-Tooboxes-master | uimm_predict.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/uimm_predict.m | 3,833 | utf_8 | c3e69773e05f6724c702dc85eebd5e72 | %IMM_PREDICT UKF based Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = UIMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% ... |
github | alexalex222/My-MATLAB-Tooboxes-master | imm_filter.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/imm_filter.m | 4,152 | utf_8 | 71128ac12e721c163f0c582ff2af4e6d | %IMM_FILTER Interacting Multiple Model (IMM) Filter prediction and update steps
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_FILTER(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,Q,Y,H,R)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_... |
github | alexalex222/My-MATLAB-Tooboxes-master | ukf_update1.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ukf_update1.m | 3,047 | UNKNOWN | c6fa396ccaa417dcc9a2e8bfebc304a5 | %UKF_UPDATE1 - Additive form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,S,LH] = UKF_UPDATE1(M,P,Y,h,R,param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement model functio... |
github | alexalex222/My-MATLAB-Tooboxes-master | imm_smooth.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/imm_smooth.m | 8,587 | utf_8 | 299bb594c62b9fe2b393c54e2dfd9625 | %IMM_SMOOTH Fixed-interval IMM smoother using two IMM-filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = IMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,Q,R,H,Y)
%
% In:
% MM - NxK matrix containing the means of forward-time
% IMM-filter on each time step
% PP - NxNxK matrix containing the c... |
github | alexalex222/My-MATLAB-Tooboxes-master | ukf_predict3.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ukf_predict3.m | 2,671 | UNKNOWN | f53661137ceedfac68cf499295cec7a8 | %UKF_PREDICT3 Augmented (state, process and measurement noise) UKF prediction step
%
% Syntax:
% [M,P,X,w] = UKF_PREDICT3(M,P,f,Q,R,f_param,alpha,beta,kappa)
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as inline function,
% ... |
github | alexalex222/My-MATLAB-Tooboxes-master | kf_loop.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/kf_loop.m | 1,888 | utf_8 | 0db0b34d194f33879dab4a27c7fbf0da | %KF_LOOP Performs the prediction and update steps of the Kalman filter
% for a set of measurements.
%
% Syntax:
% [MM,PP] = KF_LOOP(X,P,H,R,Y,A,Q)
%
% In:
% X - Nx1 initial estimate for the state mean
% P - NxN initial estimate for the state covariance
% H - DxN measurement matrix
% R - DxD meas... |
github | alexalex222/My-MATLAB-Tooboxes-master | imm_update.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/imm_update.m | 2,533 | utf_8 | 860499b62caf6e4b660ec41b713d4e0e | %IMM_UPDATE Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,R)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j state covari... |
github | alexalex222/My-MATLAB-Tooboxes-master | ukf_update3.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ukf_update3.m | 3,076 | UNKNOWN | d6a6f273f4495654caee03c01ff019e8 | %UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,IS,LH] = UKF_UPDATE3(M,P,Y,h,R,X,w,h_param,alpha,beta,kappa,mat,sigmas)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement... |
github | alexalex222/My-MATLAB-Tooboxes-master | ut_weights.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ut_weights.m | 1,585 | UNKNOWN | 5ae13a5f605593674a60c12f8f55bbe8 | %UT_WEIGHTS - Generate unscented transformation weights
%
% Syntax:
% [WM,WC,c] = ut_weights(n,alpha,beta,kappa)
%
% In:
% n - Dimensionality of random variable
% alpha - Transformation parameter (optional, default 0.5)
% beta - Transformation parameter (optional, default 2)
% kappa - Transformation pa... |
github | alexalex222/My-MATLAB-Tooboxes-master | ukf_predict2.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ukf_predict2.m | 2,307 | UNKNOWN | da4d237360c2ea17a88a15b18de0a65d | %UKF_PREDICT2 Augmented (state and process noise) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT2(M,P,a,Q,[param,alpha,beta,kappa])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as inline function,
% function handle ... |
github | alexalex222/My-MATLAB-Tooboxes-master | ukf_predict1.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ukf_predict1.m | 2,297 | UNKNOWN | e470b6641ff341b56241835f6b908dd3 | %UKF_PREDICT1 Nonaugmented (Additive) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT1(M,P,f,Q,f_param,alpha,beta,kappa,mat)
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as a matrix A defining
% linear function a(x) ... |
github | alexalex222/My-MATLAB-Tooboxes-master | imm_predict.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/imm_predict.m | 3,459 | utf_8 | a557c545bef965afe036ee2e59f5274b | %IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = IMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_ip - Cell arra... |
github | alexalex222/My-MATLAB-Tooboxes-master | eimm_predict.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/eimm_predict.m | 4,185 | utf_8 | 0e65c7ab1a1e1717c2e76e785f151d22 | %IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = EIMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_ip - ... |
github | alexalex222/My-MATLAB-Tooboxes-master | uimm_smooth.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/uimm_smooth.m | 9,008 | utf_8 | f165a3da19e62f9a637fc4caac8ad45f | %UIMM_SMOOTH UKF based Fixed-interval IMM smoother using two IMM-UKF filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = UIMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,a,a_param,Q,R,H,h,h_param,Y)
%
% In:
% MM - Means of forward-time IMM-filter on each time step
% PP - Covariances of forward-time IMM... |
github | alexalex222/My-MATLAB-Tooboxes-master | eimm_update.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/eimm_update.m | 3,594 | utf_8 | 2be8defc9605899d693c67d6496abc39 | %IMM_UPDATE Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,h,R,param)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j stat... |
github | alexalex222/My-MATLAB-Tooboxes-master | urts_smooth1.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/urts_smooth1.m | 3,687 | UNKNOWN | 3e5bdb3c14f06c08d0f41cf62ad81e82 | %URTS_SMOOTH1 Additive form Unscented Rauch-Tung-Striebel smoother
%
% Syntax:
% [M,P,D] = URTS_SMOOTH1(M,P,f,Q,[f_param,alpha,beta,kappa,mat,same_p])
%
% In:
% M - NxK matrix of K mean estimates from Unscented Kalman filter
% P - NxNxK matrix of K state covariances from Unscented Kalman Filter
% f - Dynamic m... |
github | alexalex222/My-MATLAB-Tooboxes-master | lin_transform.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/lin_transform.m | 1,798 | UNKNOWN | a145a61ccd833f3d7100c7d651dfb779 | %UT_TRANSFORM Perform linearization based transform of a Gaussian rv
%
%
% Syntax:
% [mu,S,C,X,Y,w] = LIN_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,param) as
% ... |
github | alexalex222/My-MATLAB-Tooboxes-master | ckf_update.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ckf_update.m | 2,193 | utf_8 | 2b4cf6b96fb1094a10fea58afc4df3c3 |
function [M,P,K,MU,S,LH] = ckf_update(M,P,Y,h,R,h_param)
% CKF_UPDATE - Cubature Kalman filter update step
%
% Syntax:
% [M,P,K,MU,S,LH] = CKF_UPDATE(M,P,Y,h,R,param)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measu... |
github | alexalex222/My-MATLAB-Tooboxes-master | uimm_update.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/uimm_update.m | 2,973 | utf_8 | e386b64264ff328d4c5e5fa35c945487 | %IMM_UPDATE UKF based Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,R)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j st... |
github | alexalex222/My-MATLAB-Tooboxes-master | ukf_update2.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/ukf_update2.m | 3,187 | UNKNOWN | 0a303ec8bcb980daf834c0d75ff65fb8 | %UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,IS,LH] = UKF_UPDATE2(M,P,Y,h,R,h_param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement model func... |
github | alexalex222/My-MATLAB-Tooboxes-master | urts_smooth2.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/urts_smooth2.m | 3,098 | UNKNOWN | eafbc7975b06ca22a4d6002269610046 | %URTS_SMOOTH2 Augmented form Unscented Rauch-Tung-Striebel smoother
%
% Syntax:
% [M,P,S] = URTS_SMOOTH2(M,P,f,Q,[f_param,alpha,beta,kappa,mat,same_p])
%
% In:
% M - NxK matrix of K mean estimates from Unscented Kalman filter
% P - NxNxK matrix of K state covariances from Unscented Kalman Filter
% f - Dynamic ... |
github | alexalex222/My-MATLAB-Tooboxes-master | eimm_smooth.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/eimm_smooth.m | 10,081 | utf_8 | 14cc37296d6aa91b1c0ded61dd76c73f | %EIMM_SMOOTH EKF based fixed-interval IMM smoother using two IMM-EKF filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = EIMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,a,a_param,Q,R,H,h,h_param,Y)
%
% In:
% MM - Means of forward-time IMM-filter on each time step
% PP - Covariances of forward-time IMM-f... |
github | alexalex222/My-MATLAB-Tooboxes-master | f_turn.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/eimm_demo/f_turn.m | 924 | utf_8 | 7ef714a1030afbd53e53eab3ff996db5 | %
% A coordinated turn model for extended IMM filter demonstration
%
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function x_k = f_turn(x,param)
... |
github | alexalex222/My-MATLAB-Tooboxes-master | bot_d2h_dx2.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/eimm_demo/bot_d2h_dx2.m | 991 | utf_8 | 121065207e815dd017245b07a9f807b6 | % Hessian of the measurement function in BOT-demo.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dY = bot_d2h_dx2(x,s)
% Space for Hessia... |
github | alexalex222/My-MATLAB-Tooboxes-master | f_turn_inv.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/eimm_demo/f_turn_inv.m | 919 | utf_8 | 81845969367ed59c79f6f535a775dddc | %
% Inverse prediction for the coordinated turn model
% used in extended IMM filter demonstration
%
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
f... |
github | alexalex222/My-MATLAB-Tooboxes-master | ekf_sine_d2h_dx2.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ekf_sine_demo/ekf_sine_d2h_dx2.m | 473 | utf_8 | 6a5d46b28c8d2b088180bbe20bc81c1b | % Hessian of the measurement model function in the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function df = ekf_sine_d2h_... |
github | alexalex222/My-MATLAB-Tooboxes-master | ekf_sine_h.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ekf_sine_demo/ekf_sine_h.m | 411 | utf_8 | 4636d4ebcbeacdd8a0dd352e576511d2 | % Measurement model function for the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function Y = ekf_sine_h(x,param)
f = x... |
github | alexalex222/My-MATLAB-Tooboxes-master | ekf_sine_dh_dx.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ekf_sine_demo/ekf_sine_dh_dx.m | 420 | utf_8 | d75fee0ef60b44bc6f56b32735b4b1ea | % Jacobian of the measurement model function in the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dY = ekf_sine_dh_... |
github | alexalex222/My-MATLAB-Tooboxes-master | ekf_sine_f.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ekf_sine_demo/ekf_sine_f.m | 479 | utf_8 | 3e305873b26ce735e10f3cc3bf0cbee1 | % Dynamical model function for the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function x_n = ekf_sine_f(x,param)
dt =... |
github | alexalex222/My-MATLAB-Tooboxes-master | ungm_d2h_dx2.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ungm_demo/ungm_d2h_dx2.m | 354 | utf_8 | 7dfa2e46358bf758dafc4800e05ce95f | % Hessian of the measurement model function in UNGM-model.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function d = ungm_d2h_dx2(x,param)
d = ... |
github | alexalex222/My-MATLAB-Tooboxes-master | ungm_d2f_dx2.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ungm_demo/ungm_d2f_dx2.m | 356 | utf_8 | 9ffb3dfcf6d01d351901d55c4e263b84 | % Hessian of the state transition function in UNGM-model.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function d = ungm_d2f_dx2(x,param)
d = 25... |
github | alexalex222/My-MATLAB-Tooboxes-master | ungm_h.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ungm_demo/ungm_h.m | 382 | utf_8 | 0f3485c123b9aa66a21f68bce20aa18a | % Measurement model function for the UNGM-model.
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function y_n = ungm_h(x_n,param)
y_n = x_n(1,:).*x_n... |
github | alexalex222/My-MATLAB-Tooboxes-master | ungm_dh_dx.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ungm_demo/ungm_dh_dx.m | 328 | utf_8 | f502cd540115362bbb125287a8f4c55c | % Jacobian of the measurement model function for the UNGM-model.
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dh = ungm_dh_dx(x,param)
dh... |
github | alexalex222/My-MATLAB-Tooboxes-master | ungm_f.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ungm_demo/ungm_f.m | 440 | utf_8 | 6923e25b28640fe8a310079cafe82c97 | % State transition function for the UNGM-model.
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function x_n = ungm_f(x,param)
n = param(1);
x_n = 0.... |
github | alexalex222/My-MATLAB-Tooboxes-master | ungm_df_dx.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/ungm_demo/ungm_df_dx.m | 358 | utf_8 | 2b593c3666a1c0e89fbf5e9ce173953f | % Jacobian of the state transition function for the UNGM-model.
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function df = ungm_df_dx(x,param)
df ... |
github | alexalex222/My-MATLAB-Tooboxes-master | kf_cwpa_demo.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/kf_cwpa_demo/kf_cwpa_demo.m | 7,431 | utf_8 | 83f5b06ca7f301fb9a3c358fa88bba60 | % Demonstration for Kalman filter and smoother using a 2D CWPA model
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function kf_cwpa_demo
% Transit... |
github | alexalex222/My-MATLAB-Tooboxes-master | bot_d2h_dx2.m | .m | My-MATLAB-Tooboxes-master/ekfukf_1_3/ekfukf/demos/bot_demo/bot_d2h_dx2.m | 991 | utf_8 | 121065207e815dd017245b07a9f807b6 | % Hessian of the measurement function in BOT-demo.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dY = bot_d2h_dx2(x,s)
% Space for Hessia... |
github | alexalex222/My-MATLAB-Tooboxes-master | plotroc2009b.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/plotroc2009b.m | 5,374 | utf_8 | 8514d02f6d7933274b7e7f0164c72f38 | function result = plotroc2009b(varargin)
%PLOTROC Plot receiver operating characteristic.
%
% Syntax
%
% plotroc(targets,outputs)
% plotroc(targets1,outputs1,'name1',targets,outputs2,'name2', ...)
%
% Description
%
% PLOTROC(TARGETS,OUTPUTS) plots the receiver operating characteristic
% for each output cl... |
github | alexalex222/My-MATLAB-Tooboxes-master | recdis.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/recdis.m | 1,774 | utf_8 | caa65c1f80c486b258f0b787c741bf9a | % RECDIS.M (RECombination DIScrete)
%
% This function performs discret recombination between pairs of individuals
% and returns the new individuals after mating.
%
% Syntax: NewChrom = recdis(OldChrom, XOVR)
%
% Input parameters:
% OldChrom - Matrix containing the chromosomes of the old
% popu... |
github | alexalex222/My-MATLAB-Tooboxes-master | select.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/select.m | 2,333 | utf_8 | b47cd4eb63dac6daa67b0fde300452e0 | % SELECT.M (universal SELECTion)
%
% This function performs universal selection. The function handles
% multiple populations and calls the low level selection function
% for the actual selection process.
%
% Syntax: SelCh = select(SEL_F, Chrom, FitnV, GGAP, SUBPOP)
%
% Input parameters:
% SEL_F - Name... |
github | alexalex222/My-MATLAB-Tooboxes-master | xovshrs.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovshrs.m | 1,049 | utf_8 | f872954b60e93a8c59f4a3c1c7a6d74f | % XOVSHRS.M (CROSSOVer SHuffle with Reduced Surrogate)
%
% This function performs shuffle 'reduced surrogate' crossover between
% pairs of individuals and returns the current generation after mating.
%
% Syntax: NewChrom = xovshrs(OldChrom, XOVR)
%
% Input parameters:
% OldChrom - Matrix containing the chrom... |
github | alexalex222/My-MATLAB-Tooboxes-master | migrate.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/migrate.m | 7,057 | utf_8 | f5347623804b8e202b5799118f889682 | % MIGRATE.M (MIGRATion of individuals between subpopulations)
%
% This function performs migration of individuals.
%
% Syntax: [Chrom, ObjV] = migrate(Chrom, SUBPOP, MigOpt, ObjV)
%
% Input parameters:
% Chrom - Matrix containing the individuals of the current
% population. Each row correspo... |
github | alexalex222/My-MATLAB-Tooboxes-master | sus.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/sus.m | 1,279 | utf_8 | f2a8222f57502e1de92890f97377cae7 | % SUS.M (Stochastic Universal Sampling)
%
% This function performs selection with STOCHASTIC UNIVERSAL SAMPLING.
%
% Syntax: NewChrIx = sus(FitnV, Nsel)
%
% Input parameters:
% FitnV - Column vector containing the fitness values of the
% individuals in the population.
% Nsel - nu... |
github | alexalex222/My-MATLAB-Tooboxes-master | ranking.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/ranking.m | 4,585 | utf_8 | 764e33df698db260012d0c3d2f12cdeb | % RANKING.M (RANK-based fitness assignment)
%
% This function performs ranking of individuals.
%
% Syntax: FitnV = ranking(ObjV, RFun, SUBPOP)
%
% This function ranks individuals represented by their associated
% cost, to be *minimized*, and returns a column vector FitnV
% containing the corresponding individual ... |
github | alexalex222/My-MATLAB-Tooboxes-master | recombin.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/recombin.m | 2,370 | utf_8 | eb464a3e9c3ab4ee39fd8ba7314e6cfe | % RECOMBIN.M (RECOMBINation high-level function)
%
% This function performs recombination between pairs of individuals
% and returns the new individuals after mating. The function handles
% multiple populations and calls the low-level recombination function
% for the actual recombination process.
%
% Syntax: New... |
github | alexalex222/My-MATLAB-Tooboxes-master | bs2rv.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/bs2rv.m | 3,103 | utf_8 | dc467c5bc085074cdce377944607e359 | % BS2RV.m - Binary string to real vector
%
% This function decodes binary chromosomes into vectors of reals. The
% chromosomes are seen as the concatenation of binary strings of given
% length, and decoded into real numbers in a specified interval using
% either standard binary or Gray decoding.
%
% Syntax: Phen ... |
github | alexalex222/My-MATLAB-Tooboxes-master | reins.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/reins.m | 5,452 | utf_8 | 270d878d55dc8b5ca7eb8b327560b146 | % REINS.M (RE-INSertion of offspring in population replacing parents)
%
% This function reinserts offspring in the population.
%
% Syntax: [Chrom, ObjVCh] = reins(Chrom, SelCh, SUBPOP, InsOpt, ObjVCh, ObjVSel)
%
% Input parameters:
% Chrom - Matrix containing the individuals (parents) of the current
% ... |
github | alexalex222/My-MATLAB-Tooboxes-master | mut.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/mut.m | 1,556 | utf_8 | 97652aabfb892dcbbd240102963cfa0a | % MUT.m
%
% This function takes the representation of the current population,
% mutates each element with given probability and returns the resulting
% population.
%
% Syntax: NewChrom = mut(OldChrom,Pm,BaseV)
%
% Input parameters:
%
% OldChrom - A matrix containing the chromosomes of the
% current population. Ea... |
github | alexalex222/My-MATLAB-Tooboxes-master | recmut.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/recmut.m | 4,735 | utf_8 | e7037b945697583b7b681ae93ef56665 | % RECLIN.M (line RECombination with MUTation features)
%
% This function performs line recombination with mutation features between
% pairs of individuals and returns the new individuals after mating.
%
% Syntax: NewChrom = recmut(OldChrom, FieldDR, MutOpt)
%
% Input parameters:
% OldChrom - Matrix containin... |
github | alexalex222/My-MATLAB-Tooboxes-master | xovsprs.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovsprs.m | 1,059 | utf_8 | bd0059a9b0685fb0d25ad669c7de984e | % XOVSPRS.M (CROSSOVer Single-Point with Reduced Surrogate)
%
% This function performs single-point 'reduced surrogate' crossover between
% pairs of individuals and returns the current generation after mating.
%
% Syntax: NewChrom = xovsprs(OldChrom, XOVR)
%
% Input parameters:
% OldChrom - Matrix containing... |
github | alexalex222/My-MATLAB-Tooboxes-master | scaling.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/scaling.m | 1,219 | utf_8 | d8872170a387a6e841955b0efd3456e1 | % SCALING.m - linear fitness scaling
%
% This function implements a linear fitness scaling algorithm as described
% by Goldberg in "Genetic Algorithms in Search, Optimization and Machine
% Learning", Addison Wesley, 1989. It use is not recommended when fitness
% functions produce negative results as the scaling will b... |
github | alexalex222/My-MATLAB-Tooboxes-master | crtrp.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/crtrp.m | 2,039 | utf_8 | 1c83222a36bb7c88e41346cac82d549d | % CRTRP.M (CReaTe an initial (Real-value) Population)
%
% This function creates a population of given size of random real-values.
%
% Syntax: Chrom = crtrp(Nind,FieldDR);
%
% Input parameters:
% Nind - A scalar containing the number of individuals in the new
% population.
%
% Fi... |
github | alexalex222/My-MATLAB-Tooboxes-master | crtbp.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/crtbp.m | 2,116 | utf_8 | 0262d6199e6215c6de17ffcdd480c09e | % CRTP.m - Create an initial population
%
% This function creates a binary population of given size and structure.
%
% Syntax: [Chrom Lind BaseV] = crtbp(Nind, Lind, Base)
%
% Input Parameters:
%
% Nind - Either a scalar containing the number of individuals
% in the new population or a row vector of length two
% ... |
github | alexalex222/My-MATLAB-Tooboxes-master | rep.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/rep.m | 1,163 | utf_8 | 522fc66ca0b4d8c634ff478c54fc3174 | % REP.m Replicate a matrix
%
% This function replicates a matrix in both dimensions.
%
% Syntax: MatOut = rep(MatIn,REPN);
%
% Input parameters:
% MatIn - Input Matrix (before replicating)
%
% REPN - Vector of 2 numbers, how many replications in each dimension
% REPN(1): replicate... |
github | alexalex222/My-MATLAB-Tooboxes-master | rws.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/rws.m | 1,060 | utf_8 | 3e57371be37c2afe31e534bacac38ef1 | % RWS.m - Roulette Wheel Selection
%
% Syntax:
% NewChrIx = rws(FitnV, Nsel)
%
% This function selects a given number of individuals Nsel from a
% population. FitnV is a column vector containing the fitness
% values of the individuals in the population.
%
% The function retur... |
github | alexalex222/My-MATLAB-Tooboxes-master | recint.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/recint.m | 1,841 | utf_8 | 2e2ce4e7253b27b11965de41bbb06e97 | % RECINT.M (RECombination extended INTermediate)
%
% This function performs extended intermediate recombination between
% pairs of individuals and returns the new individuals after mating.
%
% Syntax: NewChrom = recint(OldChrom, XOVR)
%
% Input parameters:
% OldChrom - Matrix containing the chromosomes of th... |
github | alexalex222/My-MATLAB-Tooboxes-master | xovdp.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovdp.m | 1,011 | utf_8 | 307a96a23a58ae75230f93ac3305abdd | % XOVDP.M (CROSSOVer Double Point)
%
% This function performs double point crossover between pairs of
% individuals and returns the current generation after mating.
%
% Syntax: NewChrom = xovdp(OldChrom, XOVR)
%
% Input parameters:
% OldChrom - Matrix containing the chromosomes of the old
% p... |
github | alexalex222/My-MATLAB-Tooboxes-master | resplot.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/resplot.m | 2,025 | utf_8 | 0f9e36690c2696ad8733a25ca62f6e66 | % RESPLOT.M (RESult PLOTing)
%
% This function plots some results during computation.
%
% Syntax: resplot(Chrom,IndAll,ObjV,Best,gen)
%
% Input parameters:
% Chrom - Matrix containing the chromosomes of the current
% population. Each line corresponds to one individual.
% IndAll - Matr... |
github | alexalex222/My-MATLAB-Tooboxes-master | mutbga.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/mutbga.m | 4,831 | utf_8 | 798dcff17df5007819ffccc1692be180 | % MUTBGA.M (real-value MUTation like Breeder Genetic Algorithm)
%
% This function takes a matrix OldChrom containing the real
% representation of the individuals in the current population,
% mutates the individuals with probability MutR and returns
% the resulting population.
%
% This function implements the muta... |
github | alexalex222/My-MATLAB-Tooboxes-master | xovdprs.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovdprs.m | 1,059 | utf_8 | 1a80762a7f575ac6f9b3814857714a63 | % XOVDPRS.M (CROSSOVer Double-Point with Reduced Surrogate)
%
% This function performs double-point 'reduced surrogate' crossover between
% pairs of individuals and returns the current generation after mating.
%
% Syntax: NewChrom = xovdprs(OldChrom, XOVR)
%
% Input parameters:
% OldChrom - Matrix containing... |
github | alexalex222/My-MATLAB-Tooboxes-master | mutate.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/mutate.m | 3,356 | utf_8 | 76b01fbb1235eaa8ad0d2b8e6c202e52 | % MUTATE.M (MUTATion high-level function)
%
% This function takes a matrix OldChrom containing the
% representation of the individuals in the current population,
% mutates the individuals and returns the resulting population.
%
% The function handles multiple populations and calls the low-level
% mutation functi... |
github | alexalex222/My-MATLAB-Tooboxes-master | crtbase.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/crtbase.m | 1,122 | utf_8 | 4992ebac5f7aa555598ac6fb56477894 | % CRTBASE.m - Create base vector
%
% This function creates a vector containing the base of the loci
% in a chromosome.
%
% Syntax: BaseVec = crtbase(Lind, Base)
%
% Input Parameters:
%
% Lind - A scalar or vector containing the lengths
% of the alleles. Sum(Lind) is the length of
% the corresponding chromoso... |
github | alexalex222/My-MATLAB-Tooboxes-master | xovsh.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovsh.m | 1,001 | utf_8 | 8351ff203d7566d23a533028f1fc42d2 | % XOVSH.M (CROSSOVer SHuffle)
%
% This function performs shuffle crossover between pairs of
% individuals and returns the current generation after mating.
%
% Syntax: NewChrom = xovsh(OldChrom, XOVR)
%
% Input parameters:
% OldChrom - Matrix containing the chromosomes of the old
% population.... |
github | alexalex222/My-MATLAB-Tooboxes-master | reclin.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/reclin.m | 1,897 | utf_8 | 1b854d6c172c3c64433646f4815e800b | % RECLIN.M (RECombination extended LINe)
%
% This function performs extended line recombination between
% pairs of individuals and returns the new individuals after mating.
%
% Syntax: NewChrom = reclin(OldChrom, XOVR)
%
% Input parameters:
% OldChrom - Matrix containing the chromosomes of the old
% ... |
github | alexalex222/My-MATLAB-Tooboxes-master | xovmp.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovmp.m | 2,712 | utf_8 | 2fcf985e764ebd2f8abf1c4efc9d9b44 | % XOVMP.m Multi-point crossover
%
% Syntax: NewChrom = xovmp(OldChrom, Px, Npt, Rs)
%
% This function takes a matrix OldChrom containing the binary
% representation of the individuals in the current population,
% applies crossover to consecutive pairs of individuals with
% ... |
github | alexalex222/My-MATLAB-Tooboxes-master | xovsp.m | .m | My-MATLAB-Tooboxes-master/libsvm-3.1-[FarutoUltimate3.1Mcode]/matlab-implement[by faruto]/myprivate/gatbx[Sheffield]/xovsp.m | 1,012 | utf_8 | 930745320d1d2c50db84573794038686 | % XOVSP.M (CROSSOVer Single-Point)
%
% This function performs single-point crossover between pairs of
% individuals and returns the current generation after mating.
%
% Syntax: NewChrom = xovsp(OldChrom, XOVR)
%
% Input parameters:
% OldChrom - Matrix containing the chromosomes of the old
% ... |
github | alexalex222/My-MATLAB-Tooboxes-master | likT.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/lik/likT.m | 6,161 | utf_8 | 7ed3bbaf1c71cd2e56e1818df722f18d | function [varargout] = likT(hyp, y, mu, s2, inf, i)
% likT - Student's t likelihood function for regression.
% The expression for the likelihood is
% likT(t) = Z * ( 1 + (t-y)^2/(nu*sn^2) ).^(-(nu+1)/2),
% where Z = gamma((nu+1)/2) / (gamma(nu/2)*sqrt(nu*pi)*sn)
% and y is the mean (for nu>1) and nu*sn^2/(nu-2) is ... |
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