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github | alexalex222/My-MATLAB-Tooboxes-master | likLaplace.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/lik/likLaplace.m | 11,152 | iso_8859_13 | 64102178a902207cd938dedbe5a255d1 | function [varargout] = likLaplace(hyp, y, mu, s2, inf, i)
% likLaplace - Laplacian likelihood function for regression.
% The expression for the likelihood is
% likLaplace(t) = exp(-|t-y|/b)/(2*b) with b = sn/sqrt(2),
% where y is the mean and sn^2 is the variance.
%
% The hyperparameters are:
%
% hyp = [ log(sn) ... |
github | alexalex222/My-MATLAB-Tooboxes-master | likPoisson.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/lik/likPoisson.m | 6,315 | utf_8 | 464a93907c3fe117d6e260de85e37ca3 | function [varargout] = likPoisson(kind, hyp, y, mu, s2, inf, i)
% likPoisson - Poisson likelihood function for count data y. The expression for
% the likelihood is
% likPoisson(f) = mu^y * exp(-mu) / y! with mean=variance=mu
% where mu = g(f) is the Poisson intensity, f is a
% Gaussian process, y is the non-negativ... |
github | alexalex222/My-MATLAB-Tooboxes-master | likLogistic.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/lik/likLogistic.m | 8,181 | utf_8 | 3c63d63bb39e2f462465fdd0e5677a43 | function [varargout] = likLogistic(hyp, y, mu, s2, inf, i)
% likLogistic - logistic function for binary classification or logit regression.
% The expression for the likelihood is
% likLogistic(t) = 1./(1+exp(-t)).
%
% Several modes are provided, for computing likelihoods, derivatives and moments
% respectively, see... |
github | alexalex222/My-MATLAB-Tooboxes-master | likSech2.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/lik/likSech2.m | 11,140 | utf_8 | cfade3d75712b667f7a97d9c1da54cf5 | function [varargout] = likSech2(hyp, y, mu, s2, inf, i)
% likSech2 - sech-square likelihood function for regression. Often, the sech-
% square distribution is also referred to as the logistic distribution not to be
% confused with the logistic function for classification. The expression for the
% likelihood is
% li... |
github | alexalex222/My-MATLAB-Tooboxes-master | likMix.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/lik/likMix.m | 8,590 | utf_8 | f9f6a474ae6c850aff8793ba315a17ff | function [varargout] = likMix(lik, hyp, varargin)
% likMix - Mixture of likelihoods for regression/classification.
% The expression for the likelihood is
% log( likMix(t) ) = sum_i=1..m w_i * log( lik_i(t) ),
% where lik_i are the m individual likelihood functions combined by a weighted
% sum in the log domain wi... |
github | alexalex222/My-MATLAB-Tooboxes-master | lbfgsb.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/lbfgsb.m | 4,993 | utf_8 | e8376ef952af2fc29fe1b08e69366bf1 | % LBFGSB Call the nonlinear bound-constrained solver that uses
% limited-memory BFGS quasi-Newton updates.
%
% The basic function call is
%
% LBFGSB(x0,lb,ub,objfunc,gradfunc)
%
% The first input argument x0 is either a matrix or a cell array of
% matrices. It declares the starting point for the s... |
github | alexalex222/My-MATLAB-Tooboxes-master | gauher.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/gauher.m | 2,245 | utf_8 | 441ef6c145fe66f1b7ca9da6207f6003 | % compute abscissas and weight factors for Gaussian-Hermite quadrature
%
% CALL: [x,w] = gauher(N)
%
% x = base points (abscissas)
% w = weight factors
% N = number of base points (abscissas) (integrates an up to (2N-1)th order
% polynomial exactly)
%
% p(x)=exp(-x^2/2)/sqrt(2*pi), a =-Inf, b = Inf
%
% Th... |
github | alexalex222/My-MATLAB-Tooboxes-master | elsympol.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/elsympol.m | 699 | utf_8 | 33e751b982c07eb890d26629bf71f595 | % Evaluate the order R elementary symmetric polynomial Newton's identity aka
% the Newton–Girard formulae: http://en.wikipedia.org/wiki/Newton's_identities
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2010-01-10.
function E = elsympol(Z,R)
% evaluate 'power sums' of the individual terms in Z
sz = si... |
github | alexalex222/My-MATLAB-Tooboxes-master | minimize.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/minimize.m | 11,191 | utf_8 | ac4d9dfffeb0cae49b3172d877a03ee6 | function [X, fX, i] = minimize(X, f, length, varargin)
% Minimize a differentiable multivariate function using conjugate gradients.
%
% Usage: [X, fX, i] = minimize(X, f, length, P1, P2, P3, ... )
%
% X initial guess; may be of any type, including struct and cell array
% f the name or pointer to the funct... |
github | alexalex222/My-MATLAB-Tooboxes-master | minimize_new.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/minimize_new.m | 11,952 | utf_8 | d8aad9cf50639371a892fbcc202eed7c | % minimize.m - minimize a smooth differentiable multivariate function using
% LBFGS (Limited memory LBFGS) or CG (Conjugate Gradients)
% Usage: [X, fX, i] = minimize(X, F, p, other, ... )
% where
% X is an initial guess (any type: vector, matrix, cell array, struct)
% F is the objective function (function poi... |
github | alexalex222/My-MATLAB-Tooboxes-master | sq_dist.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/sq_dist.m | 1,967 | utf_8 | 4b47740ab9df8ebf0acd5ae2d557acef | % sq_dist - a function to compute a matrix of all pairwise squared distances
% between two sets of vectors, stored in the columns of the two matrices, a
% (of size D by n) and b (of size D by m). If only a single argument is given
% or the second matrix is empty, the missing matrix is taken to be identical
% to the fir... |
github | alexalex222/My-MATLAB-Tooboxes-master | unwrap.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/unwrap.m | 651 | utf_8 | 47d4deafec9cfdde0a4c291b3825c401 | % Extract the numerical values from "s" into the column vector "v". The
% variable "s" can be of any type, including struct and cell array.
% Non-numerical elements are ignored. See also the reverse rewrap.m.
function v = unwrap(s)
v = [];
if isnumeric(s)
v = s(:); % numeric values are re... |
github | alexalex222/My-MATLAB-Tooboxes-master | rewrap.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/rewrap.m | 1,014 | utf_8 | 64b6d7c0f51a8c77ddd012370a288b20 | % Map the numerical elements in the vector "v" onto the variables "s" which can
% be of any type. The number of numerical elements must match; on exit "v"
% should be empty. Non-numerical entries are just copied. See also unwrap.m.
function [s v] = rewrap(s, v)
if isnumeric(s)
if numel(v) < numel(s)
error('The ... |
github | alexalex222/My-MATLAB-Tooboxes-master | solve_chol.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/solve_chol.m | 993 | utf_8 | 50d81a361032ceb40d9102492db78fe9 | % solve_chol - solve linear equations from the Cholesky factorization.
% Solve A*X = B for X, where A is square, symmetric, positive definite. The
% input to the function is R the Cholesky decomposition of A and the matrix B.
% Example: X = solve_chol(chol(A),B);
%
% NOTE: The program code is written in the C language ... |
github | alexalex222/My-MATLAB-Tooboxes-master | minimize_lbfgsb_gradfun.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/minimize_lbfgsb_gradfun.m | 2,390 | utf_8 | 0eca58fc12d068780d735fd5a83ebdfa | function G = minimize_lbfgsb_gradfun(X,varargin)
% extract input arguments
varargin = varargin{1}; strctX = varargin{2}; f = varargin{1};
% global variables serve as communication interface between calls
global minimize_lbfgsb_iteration_number
global minimize_lbfgsb_objective
global minimize_lbfgsb_gradie... |
github | alexalex222/My-MATLAB-Tooboxes-master | minimize_lbfgsb.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/minimize_lbfgsb.m | 4,476 | utf_8 | 10c2d1fef0bdc071cd35d3904c88f0ed | function [X, fX, i] = minimize_lbfgsb(X, f, length, varargin)
% Minimize a differentiable multivariate function using quasi Newton.
%
% Usage: [X, fX, i] = minimize_lbfgsb(X, f, length, P1, P2, P3, ... )
%
% X initial guess; may be of any type, including struct and cell array
% f the name or pointer to th... |
github | alexalex222/My-MATLAB-Tooboxes-master | minimize_lbfgsb_objfun.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/util/minimize_lbfgsb_objfun.m | 2,695 | utf_8 | d9bbd3614b193a06603c12f33f877104 | function y = minimize_lbfgsb_objfun(X,varargin)
% extract input arguments
varargin = varargin{1}; strctX = varargin{2}; f = varargin{1};
% global variables serve as communication interface between calls
global minimize_lbfgsb_iteration_number
global minimize_lbfgsb_objective
global minimize_lbfgsb_gradien... |
github | alexalex222/My-MATLAB-Tooboxes-master | covADD.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/cov/covADD.m | 3,664 | utf_8 | 1b8b5711d67b327547d34b486852b399 | function K = covADD(cov, hyp, x, z, i)
% Additive covariance function using a 1d base covariance function
% cov(x^p,x^q;hyp) with individual hyperparameters hyp.
%
% k(x^p,x^q) = \sum_{r \in R} sf_r \sum_{|I|=r}
% \prod_{i \in I} cov(x^p_i,x^q_i;hyp_i)
%
% hyp = [ hyp_1
% hyp_2
% ...
... |
github | alexalex222/My-MATLAB-Tooboxes-master | infMCMC.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/inf/infMCMC.m | 10,673 | utf_8 | 346201720f95a22a681c50bd2535b84c | function [post nlZ dnlZ] = infMCMC(hyp, mean, cov, lik, x, y, par)
% Markov Chain Monte Carlo (MCMC) sampling from posterior and
% Annealed Importance Sampling (AIS) for marginal likelihood estimation.
%
% The algorithms are not to be used as a black box, since the acceptance rate
% of the samplers need to be careful... |
github | alexalex222/My-MATLAB-Tooboxes-master | infFITC_EP.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/inf/infFITC_EP.m | 10,312 | utf_8 | 1abd5f8600d7dd7db01a9ca3fde0e2d5 | function [post nlZ dnlZ] = infFITC_EP(hyp, mean, cov, lik, x, y)
% FITC-EP approximation to the posterior Gaussian process. The function is
% equivalent to infEP with the covariance function:
% Kt = Q + G; G = diag(g); g = diag(K-Q); Q = Ku'*inv(Kuu + snu2*eye(nu))*Ku;
% where Ku and Kuu are covariances w.r.t. to i... |
github | alexalex222/My-MATLAB-Tooboxes-master | infFITC_Laplace.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/inf/infFITC_Laplace.m | 9,692 | utf_8 | d55d9988952c1a49cded6e5879266ffe | function [post nlZ dnlZ] = infFITC_Laplace(hyp, mean, cov, lik, x, y)
% FITC-Laplace approximation to the posterior Gaussian process. The function is
% equivalent to infLaplace with the covariance function:
% Kt = Q + G; G = diag(g); g = diag(K-Q); Q = Ku'*inv(Kuu + snu2*eye(nu))*Ku;
% where Ku and Kuu are covarian... |
github | alexalex222/My-MATLAB-Tooboxes-master | infEP.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/inf/infEP.m | 5,964 | utf_8 | c0ad6163dfaa5740138133b31d0b2fa1 | function [post nlZ dnlZ] = infEP(hyp, mean, cov, lik, x, y)
% Expectation Propagation approximation to the posterior Gaussian Process.
% The function takes a specified covariance function (see covFunction.m) and
% likelihood function (see likFunction.m), and is designed to be used with
% gp.m. See also infFunctions.m.... |
github | alexalex222/My-MATLAB-Tooboxes-master | infVB.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/inf/infVB.m | 6,054 | utf_8 | bd5ba48650a5268bf66dd60db54f4bc5 | function [post, nlZ, dnlZ] = infVB(hyp, mean, cov, lik, x, y)
% Variational approximation to the posterior Gaussian process with MKL
% covariance function hyperparameter optimisation.
% The function takes a likelihood function (see likFunction.m), and is designed
% to be used with gp.m. See also infFunctions.m.
%
% M... |
github | alexalex222/My-MATLAB-Tooboxes-master | infLaplace.m | .m | My-MATLAB-Tooboxes-master/gpml-matlab-v3.2-2013-01-15/inf/infLaplace.m | 7,122 | utf_8 | 0afd81c506ee903bca07bb187d8dd1ae | function [post nlZ dnlZ] = infLaplace(hyp, mean, cov, lik, x, y)
% Laplace approximation to the posterior Gaussian process.
% The function takes a specified covariance function (see covFunction.m) and
% likelihood function (see likFunction.m), and is designed to be used with
% gp.m. See also infFunctions.m.
%
% Copyri... |
github | alexalex222/My-MATLAB-Tooboxes-master | plscv.m | .m | My-MATLAB-Tooboxes-master/PLS/plscv.m | 4,105 | utf_8 | 4ffb9cdadcd09c9162d4d54ed7440738 | function pls_cv = plscv(x,y,vl,da)
%
% Leave-one-out cross-validation for PLS regression or discriminant analysis
%
% pls_cv = plscv(x,y,vl,'da')
%
% input:
% x (samples x descriptors) for cross-validation
% y (samples x variables) for regression or
% (samples x classes) for discriminant analysis. Classes numbers... |
github | alexalex222/My-MATLAB-Tooboxes-master | pls.m | .m | My-MATLAB-Tooboxes-master/PLS/pls.m | 6,474 | utf_8 | fe412d442c9026e4884250aea0ba16e7 | function pls_model = pls(x,y,vl,da)
%
% Model for Partial Least Squares regression or discriminant analysis
%
% pls_model = pls(x,y,vl,'da')
%
% input:
% x (samples x descriptors) for calibration
% y (samples x variables) for regression or
% (samples x classes) for discriminant analysis. Classes numbers must be... |
github | alexalex222/My-MATLAB-Tooboxes-master | f1.m | .m | My-MATLAB-Tooboxes-master/UKF/f1.m | 402 | utf_8 | f03e846f88a1acd0049465c5fb83d6ae | % 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 = f1(x)
x_n(1,1)=0.63*x(1,... |
github | alexalex222/My-MATLAB-Tooboxes-master | h1.m | .m | My-MATLAB-Tooboxes-master/UKF/h1.m | 397 | utf_8 | 5dfb3b114c63aac28e99ddd62a936dd9 | % 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 = h1(x)
f = x(1,:);
a = ... |
github | alexalex222/My-MATLAB-Tooboxes-master | pcamat.m | .m | My-MATLAB-Tooboxes-master/FastICA_2.5/FastICA_25/pcamat.m | 12,075 | utf_8 | bcb1117d4132558d0d54d8b7b616a902 | function [E, D] = pcamat(vectors, firstEig, lastEig, s_interactive, ...
s_verbose);
%PCAMAT - Calculates the pca for data
%
% [E, D] = pcamat(vectors, firstEig, lastEig, ...
% interactive, verbose);
%
% Calculates the PCA matrices for given data (row) vectors. Returns
% the eigenvector (E) and diag... |
github | alexalex222/My-MATLAB-Tooboxes-master | icaplot.m | .m | My-MATLAB-Tooboxes-master/FastICA_2.5/FastICA_25/icaplot.m | 13,259 | utf_8 | dde3e6d852f657a3c1eaacbd03f5dcc7 | function icaplot(mode, varargin);
%ICAPLOT - plot signals in various ways
%
% ICAPLOT is mainly for plottinf and comparing the mixed signals and
% separated ica-signals.
%
% ICAPLOT has many different modes. The first parameter of the function
% defines the mode. Other parameters and their order depends on the
% mode. ... |
github | alexalex222/My-MATLAB-Tooboxes-master | mwpls.m | .m | My-MATLAB-Tooboxes-master/libPLS_1.6/libPLS_1.6/mwpls.m | 1,162 | utf_8 | 64702fb8b9af6b7707935089f5995bc5 | function [WP,RMSEF]=mwpls(X,y,A,width)
%+++ moving window PLS
%+++ A: number of maximal latent variables;
%+++ width: window width, must be a odd number, e.g. 5,7,15 etc
if nargin<4;width=15;end
if nargin<3;A=5;end
[Mx,Nx]=size(X);
X=pretreat(X,'center');
y=pretreat(y,'center');
%+++ main
w=(width-1)/2;
WP=w+1:Nx-w... |
github | alexalex222/My-MATLAB-Tooboxes-master | carspls.m | .m | My-MATLAB-Tooboxes-master/libPLS_1.6/libPLS_1.6/carspls.m | 3,321 | utf_8 | 20b0c9d350f7fa1121c833c7dd9deb9d | function CARS=carspls(X,y,A,fold,method,num)
%+++ CARS: Competitive Adaptive Reweighted Sampling method for variable selection.
%+++ X: The data matrix of size m x p
%+++ y: The reponse vector of size m x 1
%+++ A: the maximal principle to extract.
%+++ fold: the group number for cross validation.
%+++ num: the numbe... |
github | alexalex222/My-MATLAB-Tooboxes-master | ks.m | .m | My-MATLAB-Tooboxes-master/libPLS_1.6/libPLS_1.6/ks.m | 1,069 | utf_8 | b647e3a4bb1547c4854bfa188898259e | function Rank=ks(X)
%+++ Employ the K-S algorithm for selecting the representative samples;
%+++ X: a m x n matrix with m samples and n variables.
%+++ Rank: sample index ordered by the representitiveness. if you want to select for example the most
%+++ representitive 10 samples, select the samples corresponding ... |
github | alexalex222/My-MATLAB-Tooboxes-master | carspls_simplify.m | .m | My-MATLAB-Tooboxes-master/libPLS_1.6/libPLS_1.6/carspls_simplify.m | 2,898 | utf_8 | e1ae85314e48ff55183f9557e8523a72 | function CARS=carspls_simplify(X,y,A,fold,method,num)
%+++ CARS: Simplified version of Competitive Adaptive Reweighted Sampling method for variable selection.
%+++ X: The data matrix of size m x p
%+++ y: The reponse vector of size m x 1
%+++ A: the maximal principle to extract.
%+++ fold: the group number for cross v... |
github | alexalex222/My-MATLAB-Tooboxes-master | randomfrog_plslda.m | .m | My-MATLAB-Tooboxes-master/libPLS_1.6/libPLS_1.6/randomfrog_plslda.m | 2,755 | utf_8 | 471bd62a382821e9118b4836f77a4820 | function F=randomfrog_plslda(X,Y,A,method,N,Q,weight)
%+++ Random Frog for variable selection for high dimensional data.
%+++ Input: X: m x n (Sample matrix)
% Y: m x 1 (measured property)
% A: The maximal number of latent variables for
% cross-validation
% method: data pret... |
github | alexalex222/My-MATLAB-Tooboxes-master | randomfrog_pls.m | .m | My-MATLAB-Tooboxes-master/libPLS_1.6/libPLS_1.6/randomfrog_pls.m | 2,907 | utf_8 | b3402d6fcff61f0758d8503e55232afc | function F=randomfrog_pls(X,Y,A,method,N,Q,criterion)
%+++ Random Frog for variable selection for high dimensional data.
%+++ Input: X: m x n (Sample matrix)
% Y: m x 1 (measured property)
% A: The maximal number of latent variables for
% cross-validation
% method: data pret... |
github | alexalex222/My-MATLAB-Tooboxes-master | kcc.m | .m | My-MATLAB-Tooboxes-master/StrAP_Matlab_full/kcc.m | 2,868 | utf_8 | dade24895f0bb44762c9f965767ba238 | % [idx,dpsim]=kcc(S,k,nruns,maxits)
%
% Performs k-centers clustering (aka k-medoids clustering) to find k
% data centers (data points), by starting with a randomly selected
% set of k centers and iteratively refining this set using up to a
% maximum of maxits iterations. For N data points, the input is an NxN
% simila... |
github | alexalex222/My-MATLAB-Tooboxes-master | apclustermex.m | .m | My-MATLAB-Tooboxes-master/StrAP_Matlab_full/apclustermex.m | 8,244 | utf_8 | 3f9e45bb58535e74c5a5447f5d214125 | %APCLUSTERMEX Affinity Propagation Clustering (Frey/Dueck, Science 2007)
% [idx,netsim,dpsim,expref]=APCLUSTERMEX(s,p) clusters data, using a set
% of real-valued pairwise data point similarities as input. Clusters
% are each represented by a cluster center data point (the "exemplar").
% The method is iterative and ... |
github | alexalex222/My-MATLAB-Tooboxes-master | gauher.m | .m | My-MATLAB-Tooboxes-master/StrAP_Matlab_full/gpml-matlab/gpml/gauher.m | 2,235 | utf_8 | 470f2e21a6e4f909868c550b5fe4637f | % compute abscissas and weight factors for Gaussian-Hermite quadrature
%
% CALL: [x,w]=gauher(N)
%
% x = base points (abscissas)
% w = weight factors
% N = number of base points (abscissas) (integrates a (2N-1)th order
% polynomial exactly)
%
% p(x)=exp(-x^2/2)/sqrt(2*pi), a =-Inf, b = Inf
%
% The Gaussia... |
github | alexalex222/My-MATLAB-Tooboxes-master | approxEP.m | .m | My-MATLAB-Tooboxes-master/StrAP_Matlab_full/gpml-matlab/gpml/approxEP.m | 5,097 | utf_8 | b787297e5f04f6d89e6fc5e602ee76a1 | function [alpha, sW, L, nlZ, dnlZ] = approxEP(hyper, covfunc, lik, x, y)
% Expectation Propagation approximation to the posterior Gaussian Process.
% The function takes a specified covariance function (see covFunction.m) and
% likelihood function (see likelihoods.m), and is designed to be used with
% binaryGP.m. See a... |
github | alexalex222/My-MATLAB-Tooboxes-master | sq_dist.m | .m | My-MATLAB-Tooboxes-master/StrAP_Matlab_full/gpml-matlab/gpml/sq_dist.m | 2,186 | utf_8 | 3b01028d6b07c19a397a274229dd24ee | % sq_dist - a function to compute a matrix of all pairwise squared distances
% between two sets of vectors, stored in the columns of the two matrices, a
% (of size D by n) and b (of size D by m). If only a single argument is given
% or the second matrix is empty, the missing matrix is taken to be identical
% to the fir... |
github | alexalex222/My-MATLAB-Tooboxes-master | logistic.m | .m | My-MATLAB-Tooboxes-master/StrAP_Matlab_full/gpml-matlab/gpml/logistic.m | 4,453 | utf_8 | 727c105366930b647b840ed749d3ae7a | function [out1, out2, out3, out4] = logistic(y, f, var)
% logistic - logistic likelihood function. The expression for the likelihood is
% logistic(t) = 1./(1+exp(-t)).
%
% Three modes are provided, for computing likelihoods, derivatives and moments
% respectively, see likelihoods.m for the details. In general, care is... |
github | alexalex222/My-MATLAB-Tooboxes-master | solve_chol.m | .m | My-MATLAB-Tooboxes-master/StrAP_Matlab_full/gpml-matlab/gpml/solve_chol.m | 991 | utf_8 | 906bce3c6bfbaf11908ec2e326cffd7e | % solve_chol - solve linear equations from the Cholesky factorization.
% Solve A*X = B for X, where A is square, symmetric, positive definite. The
% input to the function is R the Cholesky decomposition of A and the matrix B.
% Example: X = solve_chol(chol(A),B);
%
% NOTE: The program code is written in the C language ... |
github | alaa-saade/macbeth_matlab-master | macbeth_demo.m | .m | macbeth_matlab-master/macbeth_demo.m | 3,066 | utf_8 | c1d5d5f1e92eedee659bbfe8b49cc33b | function RMSE = macbeth_demo(varargin)
% MaCBetH : Matrix Completion with the Bethe Hessian
%
% Companion paper: Matrix Completion from Fewer Entries: Spectral Detectability
% and Rank Estimation.
%
% This is a demo code. run `macbeth_demo;` to run an example with default parameters.
% macbeth_demo a... |
github | alaa-saade/macbeth_matlab-master | local_optimization.m | .m | macbeth_matlab-master/subroutines/local_optimization.m | 2,122 | utf_8 | ab51d80c4b04d821ccb4063f2a63c207 | function [inferred_X,inferred_Y] = local_optimization(starting_vec,r,A,n,m,stop_val,maxiter,verbose)
[I,J,VAL] = find(A);
Nnz = length(I);
global count;
count = 0;
if (exist('minFunc')==2)
fprintf('Cleaning using minFunc from: \nhttp://www.cs.ubc.ca/~schmidtm/Software/minFunc.html\n');
options.progTol=Nn... |
github | alaa-saade/macbeth_matlab-master | build_BH.m | .m | macbeth_matlab-master/subroutines/build_BH.m | 1,216 | utf_8 | dee5c8445c3faca06c6b41ce8d9a6332 | function BH = build_BH(A,n,m,tol_bet,c_1,c_2,verbose)
% lin,col,val = findnz(J)
% N = length(J(1,:));
bet = solve_beta(A,tol_bet,c_1,c_2);
J = [sparse(n,n) A ; A' sparse(m,m)];
N = length(J(1,:));
if max(max(abs(tanh(bet*J)))) < 0.99
if verbose
disp('Using canonical formulation of the Bethe Hessian');
en... |
github | psiorx/OnlinePlanning-master | PlaneVisualizer.m | .m | OnlinePlanning-master/PlaneVisualizer.m | 6,736 | utf_8 | 4b7747fe6d30d93341ffb85be00c6af4 | classdef PlaneVisualizer < Visualizer
% Implements the draw function for the MURI Plane
% Takes in a Forest of Trees if trees are to be drawn with the
% plane/trajectory
properties
angleRep = 'euler';
forest = [];
end
methods
function obj = PlaneVisualizer(coordinateFrame, forest,angleRep)
... |
github | psiorx/OnlinePlanning-master | sampledFiniteTimeReach_B0_robust.m | .m | OnlinePlanning-master/FunnelLibrary/sampledFiniteTimeReach_B0_robust.m | 61,440 | utf_8 | 67fbbd86a8be47efcf3aeee54ad02720 | function [V,rho,Phi] = sampledFiniteTimeReach_B0_robust(sys,polyOrig,Vtraj0,G,R0,tv,ts,xtraj0,utraj,options,Phi,rho)
% % % Implements time-varying reachability computation.
% % The approach searches for a Lyapunov function
% % and attempts to minimize the size of the funnel.
% %
% % @param sys Taylor expanded closed lo... |
github | psiorx/OnlinePlanning-master | sampledFiniteTimeReach.m | .m | OnlinePlanning-master/FunnelLibrary/sampledFiniteTimeReach.m | 22,762 | utf_8 | 4d203a8a93f729cbba58b1b23ed54a5e | function [V,rho,Phi] = sampledFiniteTimeReach(sys,polyOrig,Vtraj0,G,tv,ts,xtraj0,utraj,options,Phi,rho)
% % Implements time-varying reachability computation.
% The approach searches for a Lyapunov function
% and attempts to minimize the size of the funnel.
%
% @param sys Taylor expanded closed loop system (with tvlqr)
... |
github | psiorx/OnlinePlanning-master | sampledFiniteTimeReach_B0.m | .m | OnlinePlanning-master/FunnelLibrary/sampledFiniteTimeReach_B0.m | 64,707 | utf_8 | 8270f9b9ff649f7d05fa6c651c3ccdbb | function [V,rho,Phi] = sampledFiniteTimeReach_B0(sys,polyOrig,Vtraj0,G,R0,tv,ts,xtraj0,utraj,options,Phi,rho)
% % Implements time-varying reachability computation.
% The approach searches for a Lyapunov function
% and attempts to minimize the size of the funnel.
%
% @param sys Taylor expanded closed loop system (with t... |
github | psiorx/OnlinePlanning-master | cvxsolve.m | .m | OnlinePlanning-master/OnlinePlanner/OldStuff/cvxsolve.m | 882 | utf_8 | d1dd8ad09f1ecd19c4af3f55d453cc26 | % Produced by CVXGEN, 2013-07-30 13:25:11 -0400.
% CVXGEN is Copyright (C) 2006-2012 Jacob Mattingley, jem@cvxgen.com.
% The code in this file is Copyright (C) 2006-2012 Jacob Mattingley.
% CVXGEN, or solvers produced by CVXGEN, cannot be used for commercial
% applications without prior written permission from Jacob Ma... |
github | psiorx/OnlinePlanning-master | cvxgen.m | .m | OnlinePlanning-master/OnlinePlanner/OldStuff/cvxgen.m | 1,380 | utf_8 | 1310a6e83300b69dfa4ca40812d8c997 | % Creates a local directory called cvxgen_CODENUM, and places a solver called
% csolve in your local directory.
% TODO: make platform independent.
function cvxgen(code)
code = num2str(code);
url = ['http://b.cvxgen.com/matlab_test/' code];
if ~strcmp(urlread(url), 'success')
disp(['Failed to retrieve problem ' co... |
github | psiorx/OnlinePlanning-master | cvxsolve.m | .m | OnlinePlanning-master/OnlinePlanner/OldStuff/cvxgen/cvxsolve.m | 882 | utf_8 | d1dd8ad09f1ecd19c4af3f55d453cc26 | % Produced by CVXGEN, 2013-07-30 13:25:11 -0400.
% CVXGEN is Copyright (C) 2006-2012 Jacob Mattingley, jem@cvxgen.com.
% The code in this file is Copyright (C) 2006-2012 Jacob Mattingley.
% CVXGEN, or solvers produced by CVXGEN, cannot be used for commercial
% applications without prior written permission from Jacob Ma... |
github | psiorx/OnlinePlanning-master | generate_terrain.m | .m | OnlinePlanning-master/Visualization/generate_terrain.m | 4,122 | utf_8 | 591c905ef9c512f8129dac3ee12c211a | function [x, y, h, hm, xm, ym] = generate_terrain(n, mesh_size, h0, r0, rr, xlims, ylims)
% [x, y, h, hm, xm, ym] = generate_terrain(n, mesh_size, h0, r0, rr)
%
% This function generates a series of points that approximate terrain
% according to a simple algorithm and very few parameters.
%
% Inputs:
% n - Nu... |
github | psiorx/OnlinePlanning-master | changeView.m | .m | OnlinePlanning-master/Visualization/funnelMovie/changeView.m | 1,567 | utf_8 | d76dce6b4d4dbdef11d7033cc0057e05 | function im_funnel = changeView(figNum,camPosition,camOrientation,camUp,cameraParameters)
figure(figNum);
% Make it run in the background (without showing the image on the screen)
set(figNum,'Visible','Off');
% Image size
im_sz = [1920, 1080];
% Camera properties
camera_pos = camPosition;
camera_target = camOrientat... |
github | psiorx/OnlinePlanning-master | makeFunnelImage.m | .m | OnlinePlanning-master/Visualization/funnelMovie/makeFunnelImage.m | 2,888 | utf_8 | 6d0e5d7c060c284ee2c8b4b0334a060d | function [im_funnel,figNum] = makeFunnelImage(funnel,funnelPos,camPosition,camOrientation,camUp,cameraParameters)
% Plots funnel image and makes the Matlab viewing camera have the same
% focal length as the goPro so we can take the image of the funnel and
% superimpose it on top of a goPro image.
%
% @param funnel Ele... |
github | psiorx/OnlinePlanning-master | superImposeFunnel.m | .m | OnlinePlanning-master/Visualization/funnelMovie/superImposeFunnel.m | 927 | utf_8 | 7e44bd5702b90d271c3da8eeee4402d9 | function F = superImposeFunnel(im_pic,im_funnel,alpha_param)
% close all;
% Display image
figNum = 100;
figure(figNum);
% Make it run in the background (without showing the image on the screen)
set(figNum,'Visible','Off');
clf;
imshow(im_pic);
% Plot funnel image
hold on;
h = imshow(im_funnel);
% Get cdata of funn... |
github | paulstav/LeggedRobot-master | state_2legs_EEKF_all.m | .m | LeggedRobot-master/ROS/IMUprocessTest/state_2legs_EEKF_all.m | 6,789 | utf_8 | ebb430647f3bc9ac39f9c1bb9a703e4d | function [r ,u ,q, p1, p2, b_w] = state_2legs_EEKF(z_leg1, z_leg2, .......
omega, f, dt,g,touched, foot1pos, foot2pos, touch_first)
%all the required vectors are column vectors
global wf ww wbw wp %sigma values of noise
global P Kal_gain first C R dx H S
global r_es u_es q_es p1_es bias_w p2_es call r_es_p wp1... |
github | vigente/gerardus-master | TutorialMatlab.m | .m | gerardus-master/cpp/src/third-party/Ipopt-3.11.6/Ipopt/tutorial/CodingExercise/Matlab/3-solution/TutorialMatlab.m | 3,609 | utf_8 | 2bc5eccfdcf5dc22144b8503230f6b44 | % Copyright (C) 2009 International Business Machines
% All Rights Reserved.
% This code is published under the Eclipse Public License.
%
% $Id: hs071_c.c 699 2006-04-05 21:05:18Z andreasw $
%
% Author: Andreas Waechter IBM 2009-04-02
%
% This file is part of the Ipopt tutorial. It is a correct versi... |
github | vigente/gerardus-master | TutorialMatlab.m | .m | gerardus-master/cpp/src/third-party/Ipopt-3.11.6/Ipopt/tutorial/CodingExercise/Matlab/2-mistake/TutorialMatlab.m | 3,643 | utf_8 | 578035e52af32a4c1043bdda03276f3c | % Copyright (C) 2009 International Business Machines
% All Rights Reserved.
% This code is published under the Eclipse Public License.
%
% $Id: hs071_c.c 699 2006-04-05 21:05:18Z andreasw $
%
% Author: Andreas Waechter IBM 2009-04-02
%
% This file is part of the Ipopt tutorial. It is a version with
... |
github | vigente/gerardus-master | TutorialMatlab.m | .m | gerardus-master/cpp/src/third-party/Ipopt-3.11.6/Ipopt/tutorial/CodingExercise/Matlab/1-skeleton/TutorialMatlab.m | 2,732 | utf_8 | f5e68321a1a25030feef4f51479a5f11 | % Copyright (C) 2009 International Business Machines
% All Rights Reserved.
% This code is published under the Eclipse Public License.
%
% $Id: hs071_c.c 699 2006-04-05 21:05:18Z andreasw $
%
% Author: Andreas Waechter IBM 2009-04-02
%
% This file is part of the Ipopt tutorial. It is the skeleton fo... |
github | vigente/gerardus-master | examplehs038.m | .m | gerardus-master/cpp/src/third-party/Ipopt-3.11.6/Ipopt/contrib/MatlabInterface/examples/examplehs038.m | 2,544 | utf_8 | f3bb807d1f00d1e9805debf9f63341e3 | % Test the "ipopt" Matlab interface on the Hock & Schittkowski test problem
% #38. See: Willi Hock and Klaus Schittkowski. (1981) Test Examples for
% Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical
% Systems Vol. 187, Springer-Verlag.
%
% Copyright (C) 2008 Peter Carbonetto. All Rights Reserved... |
github | vigente/gerardus-master | examplehs051.m | .m | gerardus-master/cpp/src/third-party/Ipopt-3.11.6/Ipopt/contrib/MatlabInterface/examples/examplehs051.m | 2,029 | utf_8 | 8d5dd0450d9974af1ab8d426b6e561fc | % Test the "ipopt" Matlab interface on the Hock & Schittkowski test problem
% #51. See: Willi Hock and Klaus Schittkowski. (1981) Test Examples for
% Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical
% Systems Vol. 187, Springer-Verlag.
%
% Copyright (C) 2008 Peter Carbonetto. All Rights Reserved... |
github | vigente/gerardus-master | examplehs071.m | .m | gerardus-master/cpp/src/third-party/Ipopt-3.11.6/Ipopt/contrib/MatlabInterface/examples/examplehs071.m | 2,355 | utf_8 | 43b7820bab7b701f36f625fa5a0401e2 | % Test the "ipopt" Matlab interface on the Hock & Schittkowski test problem
% #71. See: Willi Hock and Klaus Schittkowski. (1981) Test Examples for
% Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical
% Systems Vol. 187, Springer-Verlag.
%
% Copyright (C) 2008 Peter Carbonetto. All Rights Reserved... |
github | vigente/gerardus-master | lasso.m | .m | gerardus-master/cpp/src/third-party/Ipopt-3.11.6/Ipopt/contrib/MatlabInterface/examples/lasso.m | 3,110 | utf_8 | c7d23cb1f2f1c621ca899be600f724a0 | % This function executes IPOPT to find the maximum likelihood solution to
% least squares regression with L1 regularization or the "Lasso". The inputs
% are the data matrix A (in which each row is an example vector), the vector
% of regression outputs y, and the penalty parameter lambda, a number
% greater than zero. T... |
github | vigente/gerardus-master | get_gerardus_dependencies.m | .m | gerardus-master/matlab/get_gerardus_dependencies.m | 10,515 | utf_8 | e2371844d0dde04eb54ce95b156b0165 | function get_gerardus_dependencies(files)
% GET_GERARDUS_DEPENDENCIES Grab Gerardus files that a script depends on.
%
% This function processes one or more Matlab scripts to get a list of
% functions it depends on to run. It then creates a "gerardus" directory
% and makes a copy of those dependencies and other files t... |
github | vigente/gerardus-master | im2col3.m | .m | gerardus-master/matlab/FiltersToolbox/im2col3.m | 6,000 | utf_8 | b815e1ef8c0feb0c6ec1127420ffe40e | function [reshaped3D2D] = im2col3(varargin)
%IM2COL3 rearranges 3D image blocks into columns.
% 16 September 2015
%
% im2col3 does the same as im2col, only for 3D arrays.
% The inputs are a 3D dataset and a vector for the blocksize, [x y z],
% and the kind of block ('sliding' or 'distinct')
% x,y&z have to be diviso... |
github | vigente/gerardus-master | bw_sb_interp.m | .m | gerardus-master/matlab/FiltersToolbox/bw_sb_interp.m | 20,837 | utf_8 | 0f5ed381b8c2e8cb1f96dfc9bff87cca | function thres_img=bw_sb_interp(data_SBintrp, inter_slice_no)
% BW_SB_INTERP Function that performs the shape-base interpolation
% following Herman et al. (1992) paper
%
% THRES_IMG = bw_sb_interp(DATA_SBINTRP, INTER_SLICE_NO)
%
% DATA_SBINTRP is the 3D binary image (or segmentation) that we want to
% interpolate. ... |
github | vigente/gerardus-master | scimat_skeleton_prune.m | .m | gerardus-master/matlab/FiltersToolbox/scimat_skeleton_prune.m | 13,259 | utf_8 | a5ecfd2154e8560553b647c70344d999 | function scimatsk = scimat_skeleton_prune(scimatsk, scimat, minlen, lratio, alphamax, p)
% SCIMAT_SKELETON_PRUNE Prune branches in a segmentation's skeletonization
%
% This function prunes the leaves of a segmentation skeleton in three
% steps:
%
% 1. Erode clumps of voxels to look like branches
%
% 2. Prune very ... |
github | vigente/gerardus-master | blockproc3.m | .m | gerardus-master/matlab/FiltersToolbox/blockproc3.m | 9,885 | utf_8 | b6bd5c83168062c058a6949fa1c8bb5d | function im2 = blockproc3(im, blksz, fun, border, numworkers)
% BLOCKPROC3 Block processing for a 3D image
%
% Sometimes images are too large to be processed in one block. Matlab
% provides function blockproc(), that works on 2D images only. This
% blockproc3() function, on the other hand, works also with 3D images.
%... |
github | vigente/gerardus-master | ccwrap.m | .m | gerardus-master/matlab/FiltersToolbox/ccwrap.m | 3,859 | utf_8 | 7ca51ef369bcff2e123b7ca0f194d193 | function varargout = ccwrap(f, varargin)
% CCWRAP Wrapper that enables CTRL+C interruption of MEX files
%
% Matlab code (.m) can be interrupted by the user pressing the keys CTRL+C.
% This is not the case with MEX functions (e.g. C++ code compiled to run as
% a MEX function).
%
% To overcome this limitation, this func... |
github | vigente/gerardus-master | scimat_blockproc3.m | .m | gerardus-master/matlab/FiltersToolbox/scimat_blockproc3.m | 10,852 | utf_8 | 7b3b8bfe4b69b734e0727849098aa598 | function scimat2 = scimat_blockproc3(scimat, blksz, fun, border, numworkers)
% SCIMAT_BLOCKPROC3 Block processing for a 3D SCI MAT volume
%
% Sometimes images are too large to be processed in one block. Matlab
% provides function blockproc(), that works on 2D images only. This
% scimat_blockproc3() function, on the ot... |
github | vigente/gerardus-master | skeleton_label.m | .m | gerardus-master/matlab/FiltersToolbox/skeleton_label.m | 38,484 | utf_8 | ef056691abd7b79f5ad3ac3dd998dc34 | function [sk, cc, bifcc, mcon, madj, cc2, mmerge] = skeleton_label(sk, im, res, alphamax, p, SINGLEMERGE, CORRECT)
% SKELETON_LABEL Give each branch of a skeleton a different label, and
% sort the voxels within each branch.
%
% [LAB, CC, BIFCC, MCON, MADJ] = skeleton_label(SK, [], RES)
%
% SK is a 3D segmentation ma... |
github | vigente/gerardus-master | alphavol.m | .m | gerardus-master/matlab/ThirdPartyToolbox/alphavol.m | 6,284 | utf_8 | 36c0b405cf5c3ededc3f9e2f9be540aa | function [V,S] = alphavol(X,R,fig)
% ALPHAVOL Alpha shape of 2D or 3D point set.
%
% V = ALPHAVOL(X,R) gives the area or volume V of the basic alpha shape
% for a 2D or 3D point set. X is a coordinate matrix of size Nx2 or Nx3.
%
% R is the probe radius with default value R = Inf. In the default case
% the basi... |
github | vigente/gerardus-master | gridfit.m | .m | gerardus-master/matlab/ThirdPartyToolbox/gridfit.m | 34,993 | utf_8 | a877691ac9f4c8e6c543a930b0666d75 | function [zgrid,xgrid,ygrid] = gridfit(x,y,z,xnodes,ynodes,varargin)
% gridfit: estimates a surface on a 2d grid, based on scattered data
% Replicates are allowed. All methods extrapolate to the grid
% boundaries. Gridfit uses a modified ridge estimator to
% generate the surface, where the bi... |
github | vigente/gerardus-master | tiff_read_header.m | .m | gerardus-master/matlab/ThirdPartyToolbox/tiff_read_header.m | 11,647 | utf_8 | a8fc28beeb654ee3ecd5db364be82ef1 | function [TiffInfo,Img]=tiff_read_header(filename)
% This function tiff_read_header can be used to read the header(s) of
% complex tiff files. It implements a part of the Tiff version 5 and 6
% specifications.
%
%
% Info = tiff_read_header( Filename )
% or
% [Info,I] = tiff_read_header( Filename )
%
% inputs,
% Fi... |
github | vigente/gerardus-master | gplot3d.m | .m | gerardus-master/matlab/ThirdPartyToolbox/gplot3d.m | 3,049 | utf_8 | 080c1f20b69d71a649231969e8bb16fc | function [Xout,Yout,Zout]=gplot3d(A,xyz,lc)
%GPLOT3D Plot graph, as in "graph theory". This is a modification of
%MATLAB's GPLOT function for vertices in 3D
%
% GPLOT3D(A,xyz) plots the graph specified by A and xyz. A graph, G, is
% a set of nodes numbered from 1 to n, and a set of connections, or
% edges, betwee... |
github | vigente/gerardus-master | perform_farthest_point_sampling.m | .m | gerardus-master/matlab/ThirdPartyToolbox/FastMarchingToolbox/perform_farthest_point_sampling.m | 3,253 | utf_8 | 271c5c1557a202369b41332981b02b64 | function [points,D] = perform_farthest_point_sampling( W, points, npoints, options )
% perform_farthest_point_sampling - samples points using farthest seeding strategy
%
% points = perform_farthest_point_sampling( W, points, npoints );
%
% points can be [] or can be a (2,npts) matrix of already computed
% sam... |
github | vigente/gerardus-master | perform_farthest_point_sampling_mesh.m | .m | gerardus-master/matlab/ThirdPartyToolbox/FastMarchingToolbox/perform_farthest_point_sampling_mesh.m | 1,797 | utf_8 | 3e863c0d0d0a4b012240b09688d4c8ef | function [points,D] = perform_farthest_point_sampling_mesh( vertex,faces, points, nbr_iter, options )
% perform_farthest_point_sampling - samples points using farthest seeding strategy
%
% [points,D] = perform_farthest_point_sampling_mesh( vertex,faces, points, nbr_iter, options );
%
% points can be [] or can be a (... |
github | vigente/gerardus-master | divgrad.m | .m | gerardus-master/matlab/ThirdPartyToolbox/FastMarchingToolbox/divgrad.m | 1,749 | utf_8 | 531f368d44593d78a30eea65ad3a8134 | function G = divgrad(M,options)
% divgrad - compute either gradient or divergence.
%
% G = divgrad(M);
%
% if M is a 2D array, compute gradient,
% if M is a 3D array, compute divergence.
% Use centered finite differences.
%
% Copyright (c) 2007 Gabriel Peyre
options.null = 0;
if size(M,3)==2
G = mydiv(... |
github | vigente/gerardus-master | compute_voronoi_triangulation.m | .m | gerardus-master/matlab/ThirdPartyToolbox/FastMarchingToolbox/compute_voronoi_triangulation.m | 2,806 | utf_8 | 6ed546707daebe1eb6f945f8834ceb61 | function faces = compute_voronoi_triangulation(Q, vertex)
% compute_voronoi_triangulation - compute a triangulation
%
% face = compute_voronoi_triangulation(Q);
%
% Q is a Voronoi partition function, computed using
% perform_fast_marching.
% face(:,i) is the ith face.
%
% Works in 2D and in 3D.
%
% Cop... |
github | vigente/gerardus-master | vol3d.m | .m | gerardus-master/matlab/ThirdPartyToolbox/FastMarchingToolbox/vol3d.m | 5,700 | utf_8 | d4c8aeec81bea6ff37ed75bb28e20663 | function [model] = vol3d(varargin)
%H = VOL3D Volume render 3-D data.
% VOL3D uses the orthogonal plane 2-D texture mapping technique for
% volume rending 3-D data in OpenGL. Use the 'texture' option to fine
% tune the texture mapping technique. This function is best used with
% fast OpenGL hardware.
%
% H = vol3d('... |
github | vigente/gerardus-master | compute_geodesic_mesh.m | .m | gerardus-master/matlab/ThirdPartyToolbox/FastMarchingToolbox/compute_geodesic_mesh.m | 4,234 | utf_8 | f3e2ddf4b7d47f9f8d9226288e74c5d7 | function [path,vlist,plist] = compute_geodesic_mesh(D, vertex, face, x, options)
% compute_geodesic_mesh - extract a discrete geodesic on a mesh
%
% [path,vlist,plist] = compute_geodesic_mesh(D, vertex, face, x, options);
%
% D is the set of geodesic distances.
%
% path is a 3D curve that is the shortest path st... |
github | vigente/gerardus-master | load_image.m | .m | gerardus-master/matlab/ThirdPartyToolbox/FastMarchingToolbox/toolbox/load_image.m | 18,777 | utf_8 | 46b5ed29e3aa2b0736eb6fb78a3ed113 | function M = load_image(type, n, options)
% load_image - load benchmark images.
%
% M = load_image(name, n, options);
%
% name can be:
% Synthetic images:
% 'chessboard1', 'chessboard', 'square', 'squareregular', 'disk', 'diskregular', 'quaterdisk', '3contours', 'line',
% 'line_vertical', 'line_horiz... |
github | vigente/gerardus-master | check_face_vertex.m | .m | gerardus-master/matlab/ThirdPartyToolbox/FastMarchingToolbox/toolbox/check_face_vertex.m | 630 | utf_8 | 5112ad0482fa3700123a6c770f8eb622 | function [vertex,face] = check_face_vertex(vertex,face, options)
% check_face_vertex - check that vertices and faces have the correct size
%
% [vertex,face] = check_face_vertex(vertex,face);
%
% Copyright (c) 2007 Gabriel Peyre
vertex = check_size(vertex);
face = check_size(face);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
f... |
github | vigente/gerardus-master | updateOptions.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/updateOptions.m | 2,197 | utf_8 | 1df0316e2a2fbea1dfd99319e345a2df | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | match_param_hie.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/match_param_hie.m | 2,291 | utf_8 | 98a4b93a96e45734e875e04a22af6d8f | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | SpharmMatUtilImportObjs.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/SpharmMatUtilImportObjs.m | 4,170 | utf_8 | f723b8ae427644d01ad7a88a1434a719 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | SpharmMatUtilAverageObjs.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/SpharmMatUtilAverageObjs.m | 3,610 | utf_8 | 4c6119577e212e51939d22cd6a946a43 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | performPCA.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/performPCA.m | 2,650 | utf_8 | b9a6415ee7186175ee7cc629fb587869 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | patch_lighta.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/patch_lighta.m | 2,393 | utf_8 | 232ed37a48aa94937e04ec21e33d2ef8 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | patch_mesh.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/patch_mesh.m | 1,704 | utf_8 | 987900d47edb6972e7ccfe1a7b21422d | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | performTstat.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/performTstat.m | 6,413 | utf_8 | 1e4bb76f893ec34c804d91b080ff32df | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | do_pca.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/do_pca.m | 2,131 | utf_8 | 026446df827f5dc2168f238118c878e1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | utl_sgm.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/utl_sgm.m | 1,574 | utf_8 | e7ccefea5c23a801073a7e74936cceb0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | readM.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/readM.m | 1,506 | utf_8 | 459a86ecacc995c29dd0659da6c55d90 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | stlreadASCII.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/stlreadASCII.m | 4,193 | utf_8 | 5abccceeef3ee307db33452a499ce3fa | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | fix_bad_topology.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/fix_bad_topology.m | 21,317 | utf_8 | 05bb0fbab65dad2f71deffd02edbeee1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | initParamCALD.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/initParamCALD.m | 17,050 | utf_8 | e9831668885ccfa505dfab6a6a7809fd | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
github | vigente/gerardus-master | write_gipl.m | .m | gerardus-master/matlab/ThirdPartyToolbox/SpharmToolbox/code/write_gipl.m | 2,538 | utf_8 | 67f1bbd544874c26fd8e51e02aec34f0 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Spherical Harmonic Modeling and Analysis Toolkit (SPHARM-MAT) is a 3D
% shape modeling and analysis toolkit.
% It is a software package developed at Shenlab in Center for Neuroimaging,
% Indiana University (SpharmMat@gmail.com, http://www.... |
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