plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | kd383/GPML_SLD-master | priorSmoothBox1.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/prior/priorSmoothBox1.m | 1,771 | utf_8 | 4fc9d6491b923568cc0b19a1894509a5 | function [lp,dlp] = priorSmoothBox1(a,b,eta,x)
% Univariate smoothed box prior distribution with linear decay in the log domain
% and infinite support over the whole real axis.
% Compute log-likelihood and its derivative or draw a random sample.
% The prior distribution is parameterized as:
%
% p(x) = 1/w*sigm... |
github | kd383/GPML_SLD-master | infFITC.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/infFITC.m | 244 | utf_8 | 5fdeba09c14e9397cb2b684c841f258a | % Wrapper to infGaussLik to remain backwards compatible.
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch 2016-08-25.
function varargout = infFITC(varargin)
varargout = cell(nargout, 1); [varargout{:}] = infGaussLik(varargin{:}); |
github | kd383/GPML_SLD-master | logphi.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/logphi.m | 2,261 | utf_8 | 69fbcfc9d9913da15644d5f0a0368d5f | % Safe computation of logphi(z) = log(normcdf(z)) and its derivatives
% dlogphi(z) = normpdf(x)/normcdf(x).
% The function is based on index 5725 in Hart et al. and gsl_sf_log_erfc_e.
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2013-11-13.
function [lp,dlp,d2lp,d3lp] = logphi(z)
... |
github | kd383/GPML_SLD-master | gauher.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/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 | kd383/GPML_SLD-master | elsympol.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/elsympol.m | 992 | utf_8 | 5cc9210f574e7f44bea2f0619d1637aa | % Evaluate the order R elementary symmetric polynomials using Newton's identity,
% the Newton-Girard formulae: http://en.wikipedia.org/wiki/Newton's_identities
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch, 2010-01-10.
% speedup contributed by Truong X. Nghiem, 2016-01-20.
function ... |
github | kd383/GPML_SLD-master | minimize.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/minimize.m | 11,190 | utf_8 | 58c59070538bcf9d709052c85e4a9c2f | 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 | kd383/GPML_SLD-master | sq_dist.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/sq_dist.m | 1,967 | utf_8 | 75b906d47729b33d7567f1353ced2f83 | % 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 | kd383/GPML_SLD-master | any2vec.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/any2vec.m | 653 | utf_8 | d703596b446303a8c82e4de32786fad1 | % 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 vec2any.m.
function v = any2vec(s)
v = [];
if isnumeric(s)
v = s(:); % numeric values are r... |
github | kd383/GPML_SLD-master | glm_invlink_expexp.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/glm_invlink_expexp.m | 423 | utf_8 | 56f87c54a6964c850f79e867c5d4b291 | % Compute the log intensity for the inverse link function g(f) = exp(-exp(-f)).
% Output range: 0 <= g(f) <= 1.
%
% The function can be used in GLM likelihoods such as likBeta.
%
% Copyright (c) by Hannes Nickisch, 2016-10-04.
function [lg,dlg,d2lg,d3lg] = glm_invlink_expexp(f)
lg = -exp(-f);
if nargout>1
dlg ... |
github | kd383/GPML_SLD-master | covGrid.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/covGrid.m | 312 | utf_8 | 57193d84d2b7b76d5acaf055348cfa51 | % Wrapper to apxGrid to remain backwards compatible.
%
% Note that covGrid is not a valid covariance function on its own right.
%
% Copyright (c) by Hannes Nickisch and Andrew Wilson 2016-08-25.
function varargout = covGrid(varargin)
varargout = cell(nargout, 1); [varargout{:}] = apxGrid(varargin{:});
|
github | kd383/GPML_SLD-master | glm_invlink_logistic.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/glm_invlink_logistic.m | 709 | utf_8 | 5b8da5da4ad55f7e13652c79e9ae2954 | % Compute the log intensity for the inverse link function g(f) = log(1+exp(f))).
% Output range: 0 <= g(f).
%
% The function can be used in GLM likelihoods such as likPoisson, likGamma, and
% likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2016-10-04.
function [lg,dlg,d2lg,d3lg] = glm_invlink_logistic(f)
l1pef = ... |
github | kd383/GPML_SLD-master | vec2any.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/vec2any.m | 1,016 | utf_8 | 917a9d3bb4112736eac9cbf3730f6f40 | % 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 any2vec.m.
function [s v] = vec2any(s, v)
if isnumeric(s)
if numel(v) < numel(s)
error('The... |
github | kd383/GPML_SLD-master | infFITC_EP.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/infFITC_EP.m | 235 | utf_8 | 47597e3d3b6166acd5d61066547317f3 | % Wrapper to infEP to remain backwards compatible.
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch 2016-12-14.
function varargout = infFITC_EP(varargin)
varargout = cell(nargout, 1); [varargout{:}] = infEP(varargin{:}); |
github | kd383/GPML_SLD-master | vfe_xu_opt.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/vfe_xu_opt.m | 3,265 | utf_8 | 2c1a9383671fb35ff92e5c18815df0b0 | % Optimize inducing inputs for the VFE approximation (not FITC).
%
% One can perform a gradient-based optimisation of the inducing inputs xu by
% specifying them via hyp.xu rather than through {@apxSparse,cov,xu}.
%
% An alternative way of optimising xu (in order to overcome local minima) is
% to simply compute the exp... |
github | kd383/GPML_SLD-master | infFITC_Laplace.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/infFITC_Laplace.m | 250 | utf_8 | 4ea7b03dd38fc20a2bcd4eef8f3237b5 | % Wrapper to infLaplace to remain backwards compatible.
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch 2016-10-13.
function varargout = infFITC_Laplace(varargin)
varargout = cell(nargout, 1); [varargout{:}] = infLaplace(varargin{:}); |
github | kd383/GPML_SLD-master | infExact.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/infExact.m | 245 | utf_8 | b85c546a2f76f0535f4bc5c5cc03e804 | % Wrapper to infGaussLik to remain backwards compatible.
%
% Copyright (c) by Carl Edward Rasmussen and Hannes Nickisch 2016-08-25.
function varargout = infExact(varargin)
varargout = cell(nargout, 1); [varargout{:}] = infGaussLik(varargin{:}); |
github | kd383/GPML_SLD-master | solve_chol.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/solve_chol.m | 994 | utf_8 | f4d6cd4b9e7b0a955c2c8709a4894dd3 | % 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 | kd383/GPML_SLD-master | covFITC.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/covFITC.m | 385 | utf_8 | d4456aefc33a8cdf1f555980e65abae9 | % Wrapper to apxSparse to remain backwards compatible.
%
% Note that covFITC is not a valid covariance function on its own right.
%
% Copyright (c) by Ed Snelson, Carl Edward Rasmussen
% and Hannes Nickisch, 2016-08-25.
function varargout = covFITC(varargin)
varar... |
github | kd383/GPML_SLD-master | glm_invlink_logit.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/glm_invlink_logit.m | 835 | utf_8 | d5d14b52f5422b6f3497b6cc8a239f4e | % Compute the log intensity for the inverse link function g(f) = 1/(1+exp(-f)).
% Output range: 0 <= g(f) <= 1.
%
% The function can be used in GLM likelihoods such as likBeta.
%
% Copyright (c) by Hannes Nickisch, 2016-10-04.
function varargout = glm_invlink_logit(f)
varargout = cell(nargout, 1); % allocate the ri... |
github | kd383/GPML_SLD-master | minimize_lbfgsb_gradfun.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/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 | kd383/GPML_SLD-master | glm_invlink_logistic2.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/glm_invlink_logistic2.m | 870 | utf_8 | 1fd6d55db008cfb0517988106bc6b3e6 | % Compute the log intensity for the inverse link function (twice logistic)
% g(f) = h(f*(1+a*h(f))), where is the logistic h(f) = log(1+exp(f))).
% Output range: 0 <= g(f).
%
% The function can be used in GLM likelihoods such as likPoisson, likGamma, and
% likInvGauss.
%
% See Seeger et al., Bayesian Intermittent Deman... |
github | kd383/GPML_SLD-master | minimize_lbfgsb.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/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 | kd383/GPML_SLD-master | minimize_lbfgsb_objfun.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/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 | kd383/GPML_SLD-master | logsumexp2.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/logsumexp2.m | 454 | utf_8 | aa7e4f12a67c8f2e12bc5d9113b9abd0 | % Compute y = log( sum(exp(x),2) ), the softmax in a numerically safe way by
% subtracting the row maximum to avoid cancelation after taking the exp
% the sum is done along the rows.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [y,x] = logsumexp2(logx)
N = size(logx,2); max_logx = max(logx,[],2);
%... |
github | kd383/GPML_SLD-master | minimize_minfunc.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/minimize_minfunc.m | 5,472 | utf_8 | 1f17d182bc7f64339c8eefbd76605d25 | function [X, f, i, exitflag, output] = minimize_minfunc(X, f, options, varargin)
% Minimize a differentiable multivariate function using minFunc.
% (http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html)
% To be used with GPML toolbox.
%
% Usage: [X, f, i, exitflag, output] = ...
% minimize_minfunc(X, f, o... |
github | kd383/GPML_SLD-master | lik_epquad.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/lik_epquad.m | 1,622 | utf_8 | 9f92aef26b02e08fcee8f74617ebd05d | % Compute infEP part of a likelihood function based on the infLaplace part using
% Gaussian-Hermite quadrature.
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function varargout = lik_epquad(lik,hyp,y,mu,s2)
n = m... |
github | kd383/GPML_SLD-master | glm_invlink_exp.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/glm_invlink_exp.m | 466 | utf_8 | d88c5e637e5ed1f9db34a94bb0c3b81e | % Compute the log intensity for the inverse link function g(f) = exp(f).
% Output range: 0 <= g(f).
%
% The function can be used in GLM likelihoods such as likPoisson, likGamma, and
% likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2016-10-04.
function [lg,dlg,d2lg,d3lg] = glm_invlink_exp(f)
lg = f;
if nargout>... |
github | kd383/GPML_SLD-master | WolfeLineSearch.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/minfunc/WolfeLineSearch.m | 10,590 | utf_8 | f962bc5ae0a1e9f80202a9aaab106dab | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS_interp,LS_multi,maxLS,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function v... |
github | kd383/GPML_SLD-master | minFunc_processInputOptions.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/minfunc/minFunc_processInputOptions.m | 4,103 | utf_8 | 8822581c3541eabe5ce7c7927a57c9ab |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,optTol,progTol,method,...
corrections,c1,c2,LS_init,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCurv,precFunc... |
github | kd383/GPML_SLD-master | mexAll_octave.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/util/minfunc/mex/mexAll_octave.m | 529 | utf_8 | 71ee15c617dd2bfc3de849feaeeeadae | % minFunc
printf('Compiling minFunc files (octave version)...\n');
## working around the lack of an -outdir option in octave's mex
function mexme(fn)
cmd = sprintf("mkoctfile --mex --output ../compiled/%s.mex %s.c", fn, fn) ;
[ status output ] = system(cmd) ;
if status!=0
error("Executing command %s\... |
github | kd383/GPML_SLD-master | meanProd.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/mean/meanProd.m | 1,637 | utf_8 | 9ef1cd91495c7edf4cd263a7522833dc | function [m,dm] = meanProd(mean, hyp, x)
% meanProd - compose a mean function as the product of other mean functions.
% This function doesn't actually compute very much on its own, it merely does
% some bookkeeping, and calls other mean functions to do the actual work.
%
% m(x) = \prod_i m_i(x)
%
% Copyright (c) by Ca... |
github | kd383/GPML_SLD-master | meanWSPC.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/mean/meanWSPC.m | 1,155 | utf_8 | 63678f0a2b4719e468e81085c69dd205 | function [m,dm] = meanWSPC(d, hyp, x)
% Weighted Sum of Projected Cosines or Random Kitchen Sink features.
%
% This function represents the feature function of a zero mean GP with
% stationary covariance function. See the paper "Sparse spectrum GP regression"
% by Lazaro-Gredilla et al., JMLR, 2010 for details.
%
% m... |
github | kd383/GPML_SLD-master | meanDiscrete.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/mean/meanDiscrete.m | 938 | utf_8 | 8cd9e4ccebb561316bcac3893f21bd4b | function [m,dm] = meanDiscrete(s, hyp, x)
% Mean function for discrete inputs x. Given a function defined on the
% integers 1,2,3,..,s, the mean function is parametrized as:
%
% m(x) = mu_x,
%
% where mu is a fixed vector of length s.
%
% This implementation assumes that the inputs x are given as integers
% between 1 ... |
github | kd383/GPML_SLD-master | meanGPexact.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/mean/meanGPexact.m | 2,135 | utf_8 | ebac92262e0958da06afecabc8e8d265 | function [m,dm] = meanGPexact(mean,cov,x,y, hypz,z)
% Mean function being the predictive mean of a GP model:
%
% mu(z) = posterior mean of GP at location z as given by
% mu(z) = gp(hyp,@infExact,mean,cov,@likGauss,x,y, z) where
% hyp.mean = hyp_mean; hyp.lik = log(sn); hyp.cov = hyp.cov;
%
% The hyperparameters are:
%... |
github | kd383/GPML_SLD-master | meanPoly.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/mean/meanPoly.m | 1,144 | utf_8 | 37b5289c7c858a50ffa4834928ade29f | function [m,dm] = meanPoly(d, hyp, x)
% meanPoly - compose a mean function as a polynomial.
%
% The degree d has to be a strictly positive integer.
%
% m(x) = sum_i=1..D sum_j=1..d a_ij * x_i^j
%
% The hyperparameter is:
%
% hyp = [ a_11
% a_21
% ..
% a_D1
% a_12
% a_22
% ... |
github | kd383/GPML_SLD-master | meanSum.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/mean/meanSum.m | 1,536 | utf_8 | caf2e4aed8ec16eaca6eecb232e8a3d8 | function [m,dm] = meanSum(mean, hyp, x)
% meanSum - compose a mean function as the sum of other mean functions.
% This function doesn't actually compute very much on its own, it merely does
% some bookkeeping, and calls other mean functions to do the actual work.
%
% m(x) = \sum_i m_i(x)
%
% Copyright (c) by Carl Edwa... |
github | kd383/GPML_SLD-master | covNNone.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covNNone.m | 2,181 | utf_8 | d3739df4147646dd3127df49ceb631aa | function [K,dK] = covNNone(hyp, x, z)
% Neural network covariance function with a single parameter for the distance
% measure. The covariance function is parameterized as:
%
% k(x,z) = sf2 * asin(x'*P*z / sqrt[(1+x'*P*x)*(1+z'*P*z)])
%
% where the x and z vectors on the right hand side have an added extra bias
% entry... |
github | kd383/GPML_SLD-master | covWarp.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covWarp.m | 1,985 | utf_8 | edfc0bebca248cfb80e7f7fe6a53ba17 | function [K,dK] = covWarp(cov, p, dp, Dp, hyp, x, z)
% Apply a covariance function to p(x) rather than x i.e. warp the inputs.
%
% This function doesn't actually compute very much on its own, it merely does
% some bookkeeping, and calls another covariance function to do the actual work.
%
% The function computes:
% ... |
github | kd383/GPML_SLD-master | covZero.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covZero.m | 1,116 | utf_8 | 8e584fb7aaec194e49fd84eb2c8ade2b | function [K,dK] = covZero(hyp, x, z)
% Constant (degenerate) covariance function, with zero variance.
% The covariance function is specified as:
%
% k(x,z) = 0
%
% hyp = [ ]
%
% For more help on design of covariance functions, try "help covFunctions".
%
% Copyright (c) by Hannes Nickisch, 2016-04-17.
%
% See also COVF... |
github | kd383/GPML_SLD-master | covOne.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covOne.m | 1,112 | utf_8 | ad8b2c66ba87471c2aad1ef1da8b74e2 | function [K,dK] = covOne(hyp, x, z)
% Constant (degenerate) covariance function, with unit variance.
% The covariance function is specified as:
%
% k(x,z) = 1
%
% hyp = [ ]
%
% For more help on design of covariance functions, try "help covFunctions".
%
% Copyright (c) by Hannes Nickisch, 2016-04-17.
%
% See also COVFU... |
github | kd383/GPML_SLD-master | covRQard.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covRQard.m | 1,319 | utf_8 | 4a01d96fc2dfed96d4da74a33d401733 | function varargout = covRQard(varargin)
% Wrapper for Rational Quadratic covariance function covRQ.m.
%
% Rational Quadratic covariance function with Automatic Relevance Determination
% (ARD) distance measure. The covariance function is parameterized as:
%
% k(x,z) = sf^2 * [1 + (x-z)'*inv(P)*(x-z)/(2*alpha)]^(-alpha)... |
github | kd383/GPML_SLD-master | covW.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covW.m | 4,023 | utf_8 | 949b84c4ea6880c67f04e823beccebf8 | function [K,dK] = covW(i, hyp, x, z)
% Wiener process covariance function, i times integrated.
%
% For i= 0, this is the Wiener process covariance,
% for i= 1, this is the integrated Wiener process covariance (velocity),
% for i= 2, this is the twice-integrated Wiener process covariance (accel.),
% for i= 3, this is t... |
github | kd383/GPML_SLD-master | covPeriodicNoDC.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covPeriodicNoDC.m | 4,121 | utf_8 | ddc552a3a497084564de2a890aac7bef | function [K,dK] = covPeriodicNoDC(hyp, x, z)
% Stationary covariance function for a smooth periodic function, with period p:
%
% k(x,z) = sf^2 * [k0(pi*(x-z)/p) - f(ell)] / [1 - f(ell)]
% with k0(t) = exp( -2*sin^2(t)/ell^2 ) and f(ell) = \int 0..pi k0(t) dt.
%
% The constant (DC component) has been removed and... |
github | kd383/GPML_SLD-master | covPoly.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covPoly.m | 1,728 | utf_8 | 05dd966400369bbcb5c67bd873904ba1 | function [K,dK] = covPoly(mode,par,d,hyp,x,z)
% Polynomial covariance function. The covariance function is parameterized as:
%
% k(x,z) = sf^2 * ( c + s )^d , where s = x*inv(P)*z is the dot product
%
% The hyperparameters are:
%
% hyp = [ hyp_dot
% log(c)
% log(sf) ]
%
% Copyright (c) by Carl Edward ... |
github | kd383/GPML_SLD-master | covPP.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covPP.m | 1,920 | utf_8 | 316ff11a633a645667943d2e5b47cd44 | function varargout = covPP(mode, par, v, hyp, x, varargin)
% Piecewise Polynomial covariance function with compact support, v = 0,1,2,3.
% The covariance functions are 2v times contin. diff'ble and the corresponding
% processes are hence v times mean-square diffble. The covariance function is:
%
% k(x,z) = max(1-r,0)... |
github | kd383/GPML_SLD-master | covMaha.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covMaha.m | 8,278 | utf_8 | 0847696adc64057a0c27b85bb49a1ae8 | function [K,dK,D2] = covMaha(mode, par, k, dk, hyp, x, z)
% Mahalanobis distance-based covariance function. The covariance function is
% parameterized as:
%
% k(x,z) = k(r^2), r^2 = maha(x,P,z) = (x-z)'*inv(P)*(x-z),
%
% where the matrix P is the metric.
%
% Parameters:
% 1) mode,par:
% We offer different modes (mode)... |
github | kd383/GPML_SLD-master | apx.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/apx.m | 33,771 | utf_8 | 2265d86ef1af3ba038046c64d460356a | function K = apx(hyp,cov,x,opt)
% (Approximate) linear algebra operations involving the covariance matrix K.
%
% A) Exact covariance computations.
% There are no parameters in this mode.
% Depending on the sign of W, we switch between
% - the symmetric Cholesky mode [1], where B = I + sqrt(W)*K*sqrt(W), and
%... |
github | kd383/GPML_SLD-master | covPER.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covPER.m | 2,744 | utf_8 | 1c61459cd15e6fc06d59b8e66bf447ac | function [K,dK] = covPER(mode, cov, hyp, x, z)
% Periodic covariance function from an arbitrary covariance function k0 via
% embedding IR^D into IC^D.
% The covariance function is parameterized as:
%
% k(x,z) = k0(u(x),u(z)), u(x) = [sin(pi*x/p); cos(pi*x/p)]
%
% where the period p belongs to covPER and hyp0 belongs t... |
github | kd383/GPML_SLD-master | covGE.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covGE.m | 1,186 | utf_8 | 0408e6341483b3f84ba8d55997213e13 | function [K,dK] = covGE(mode, par, hyp, varargin)
% Gamma Exponential covariance function.
% The covariance function is parameterized as:
%
% k(x,z) = exp(-r^gamma), r = maha(x,z)
%
% where maha(x,z) is a Mahalanobis distance and gamma is the shape parameter
% for the GE covariance. The hyperparameters are:
%
% hyp = ... |
github | kd383/GPML_SLD-master | covLINone.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covLINone.m | 1,478 | utf_8 | b9498a31ed150ecc1639a5dcb13d180b | function [K,dK] = covLINone(hyp, x, z)
% Linear covariance function with a single hyperparameter. The covariance
% function is parameterized as:
%
% k(x,z) = (x'*z + 1)/t^2;
%
% where the P matrix is t2 times the unit matrix. The second term plays the
% role of the bias. The hyperparameter is:
%
% hyp = [ log(t) ]
%
%... |
github | kd383/GPML_SLD-master | covFBM.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covFBM.m | 2,452 | utf_8 | 557d97477e58c30d11b640492b88b785 | function [K,dK] = covFBM(hyp, x, z)
% Fractional Brownian motion covariance function with Hurst index h from (0,1).
%
% For h=1/2, this is the Wiener covariance, for h>1/2, the increments are
% positively correlated and for h<1/2 the increments are negatively correlated.
%
% The covariance function -- given that x,z>=... |
github | kd383/GPML_SLD-master | covADD.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covADD.m | 4,141 | utf_8 | f58af163e496f6be948dbfc1d9118972 | function [K,dK] = covADD(cov, hyp, x, z)
% Additive covariance function using 1d base covariance functions
% cov_d(x_d,z_d;hyp_d) with individual hyperparameters hyp_d, d=1..D.
%
% k (x,z) = \sum_{r \in R} sf^2_r k_r(x,z), where 1<=r<=D and
% k_r(x,z) = \sum_{|I|=r} \prod_{i \in I} cov_i(x_i,z_i;hyp_i)
%
% hyp = [ h... |
github | kd383/GPML_SLD-master | covProd.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covProd.m | 3,136 | utf_8 | 1f0519c767c2410e29139c94fa58b031 | function [K,dK] = covProd(cov, hyp, x, z)
% covProd - compose a covariance function as the product of other covariance
% functions. This function doesn't actually compute very much on its own, it
% merely does some bookkeeping, and calls other covariance functions to do the
% actual work.
%
% Note that cov = {c... |
github | kd383/GPML_SLD-master | covRQiso.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covRQiso.m | 1,165 | utf_8 | 662fd9158e87d9e809e77de537bbaca9 | function varargout = covRQiso(varargin)
% Wrapper for Rational Quadratic covariance function covRQ.m.
%
% Rational Quadratic covariance function with isotropic distance measure. The
% covariance function is parameterized as:
%
% k(x,z) = sf^2 * [1 + (x-z)'*inv(P)*(x-z)/(2*alpha)]^(-alpha)
%
% where the P matrix is ell... |
github | kd383/GPML_SLD-master | covMatern.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covMatern.m | 3,060 | utf_8 | ca45c5dd70fb047a931ac0296b64ad16 | function varargout = covMatern(mode, par, d, varargin)
% Matern covariance function with nu = d/2 and isotropic distance measure. For
% d=1 the function is also known as the exponential covariance function or the
% Ornstein-Uhlenbeck covariance in 1d. The covariance function is:
%
% k(x,z) = f( sqrt(d)*r ) * exp(-s... |
github | kd383/GPML_SLD-master | covRQ.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covRQ.m | 1,181 | utf_8 | d112eee5d6ba568af6d5f1ee22849f3f | function [K,dK] = covRQ(mode, par, hyp, varargin)
% Rational Quadratic covariance function.
% The covariance function is parameterized as:
%
% k(x,z) = [1 + maha(x,z)/(2*alpha)]^(-alpha)
%
% where maha(x,z) is a Mahalanobis distance and alpha is the shape parameter
% for the RQ covariance. The hyperparameters are:
%
%... |
github | kd383/GPML_SLD-master | covDot.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covDot.m | 4,125 | utf_8 | 389b9cdeecc1b3bf4d9b780e011f1a92 | function [K,dK,S] = covDot(mode, par, k, dk, hyp, x, z)
% Dot product-based covariance function. The covariance function is
% parameterized as:
%
% k(x,z) = k(s), s = dot(x,z) = x'*inv(P)*z
%
% where the matrix P is the metric.
%
% Parameters:
% 1) mode,par:
% We offer different modes (mode) with their respective par... |
github | kd383/GPML_SLD-master | covGabor.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covGabor.m | 2,950 | utf_8 | 95522109b2cb6e8dc9f3f68134433e3a | function [K,dK] = covGabor(mode, hyp, x, z)
% Gabor covariance function with length scale ell and period p. The
% covariance function is parameterized as:
%
% k(x,z) = h(x-z), h(t) = exp(-sum(t.^2./(2*ell.^2)))*cos(2*pi*sum(t./p)).
%
% The hyperparameters are:
%
% hyp = [ hyp_ell
% hyp_p ]
%
% We offer thr... |
github | kd383/GPML_SLD-master | covMask.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covMask.m | 2,077 | utf_8 | fbb73ed86a404c471718a7040f0d4ac6 | function [K,dK] = covMask(cov, hyp, x, z)
% Apply a covariance function to a subset of the dimensions only. The subset can
% either be specified by a 0/1 mask by a boolean mask or by an index set.
%
% This function doesn't actually compute very much on its own, it merely does
% some bookkeeping, and calls another cova... |
github | kd383/GPML_SLD-master | covSum.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covSum.m | 2,619 | utf_8 | dd217e57b3859a67a2ed06b93ce7b5da | function [K,dK] = covSum(cov, hyp, x, z)
% covSum - compose a covariance function as the sum of other covariance
% functions. This function doesn't actually compute very much on its own, it
% merely does some bookkeeping, and calls other covariance functions to do the
% actual work.
%
% Note that cov = {cov1, cov2, ..... |
github | kd383/GPML_SLD-master | covEye.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covEye.m | 1,506 | utf_8 | 74ff8cf22ddc7da30f6541ca77daebbd | function [K,dK] = covEye(hyp, x, z)
% Independent covariance function, i.e. "white noise", with unit variance.
% The covariance function is specified as:
%
% k(x^p,x^q) = \delta(p,q)
%
% \delta(p,q) is a Kronecker delta function which is 1 iff p=q and zero
% otherwise in mode 1).
% In cross covariance mode 2) two data... |
github | kd383/GPML_SLD-master | covCos.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covCos.m | 1,642 | utf_8 | fffb37510851e1faf2c666ca437a6a7b | function [K,dK] = covCos(hyp, x, z)
% Stationary covariance function for a sinusoid with period p in 1d:
%
% k(x,z) = sf^2*cos(2*pi*(x-z)/p)
%
% where the hyperparameters are:
%
% hyp = [ log(p)
% log(sf) ]
%
% Note that covPeriodicNoDC converges to covCos as ell goes to infinity.
%
% Copyright (c) by James Ro... |
github | kd383/GPML_SLD-master | covDiscrete.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covDiscrete.m | 2,444 | utf_8 | 1a59b16bbcb28d2299847efdfd9f941b | function [K,dK] = covDiscrete(s, hyp, x, z)
% Covariance function for discrete inputs. Given a function defined on the
% integers 1,2,3,..,s, the covariance function is parameterized as:
%
% k(x,z) = K_{xz},
%
% where K is a matrix of size (s x s).
%
% This implementation assumes that the inputs x and z are given as i... |
github | kd383/GPML_SLD-master | covULL.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covULL.m | 2,045 | utf_8 | cee0f7f68e01ba6bdef5fbb1755e674b | function [K,dK] = covULL(hyp, x, z)
% Stationary covariance function for an underdamped linear Langevin process
% as obtained by filtering white noise through an underdamped 2nd order system
% m * f''(x) + c * f'(x) + k * f(x) = N(0,sf^2).
%
% k(t) = a^2*exp(-mu*t) * ( sin(omega*t)/omega + cos(omega*t)/mu ),
% where ... |
github | kd383/GPML_SLD-master | covScale.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covScale.m | 3,216 | utf_8 | afdaa196951547c5f3a67e635ae0d485 | function [K,dK] = covScale(cov, lsf, hyp, x, z)
% covScale - compose a covariance function as a scaled version of another
% one to model functions of the form f(x) = sf(x) f0(x), where sf(x) is a
% scaling function determining the function's standard deviation given f0(x)
% is normalised.
%
% The covariance function i... |
github | kd383/GPML_SLD-master | covOU.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covOU.m | 3,643 | utf_8 | 02c3c9004d03310c02a3484a2c427480 | function [K,dK] = covOU(i, hyp, x, z)
% Ornstein-Uhlenbeck process covariance function, i times integrated.
%
% For i=0, this considers the stochastic differential equation
%
% ell * f'(x) + f(x) = N(0,sf^2), f(0) = N(f0,sf0^2), x>=0
%
% where 1/ell>0 is the decay rate and sf, sf0 are noise levels.
% N(m,v) is a Gau... |
github | kd383/GPML_SLD-master | covPeriodic.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covPeriodic.m | 1,834 | utf_8 | fd8160a9f4eba0c658e2e84cf0077b87 | function [K,dK] = covPeriodic(hyp, x, z)
% Stationary covariance function for a smooth periodic function, with period p
% in 1d (see covPERiso and covPERard for multivariate data):
%
% k(x,z) = sf^2 * exp( -2*sin^2( pi*(x-z)/p )/ell^2 )
%
% where the hyperparameters are:
%
% hyp = [ log(ell)
% log(p)
% ... |
github | kd383/GPML_SLD-master | covPref.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covPref.m | 2,069 | utf_8 | 7a68891ffcfb5214e9b185519e241b75 | function [K,dK] = covPref(cov, hyp, x, z)
% covPref - covariance function for preference learning. The covariance
% function corresponds to a prior on f(x1) - f(x2).
%
% k(x,z) = k_0(x1,z1) + k_0(x2,z2) - k_0(x1,z2) - k_0(x2,z1).
%
% The hyperparameters are:
%
% hyp = [ hyp_k0 ]
%
% For more help on design of covarian... |
github | kd383/GPML_SLD-master | covSM.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/covSM.m | 6,966 | utf_8 | 53a540d4830180d0289d0d1f9d049c03 | function [K,dK] = covSM(Q, hyp, x, z)
% Gaussian Spectral Mixture covariance function. The covariance function
% parametrization depends on the sign of Q.
%
% Let t(Dx1) be an offset vector in dataspace e.g. t = x-z. Then w(DxP)
% are the weights and m(Dx|Q|) = 1/p, v(Dx|Q|) = (2*pi*ell)^-2 are spectral
% means (freq... |
github | kd383/GPML_SLD-master | apxGrid.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/cov/apxGrid.m | 35,628 | utf_8 | f8130ac69ac6d56bec797472b95c15d0 | function [K,Mx,xe] = apxGrid(cov, xg, hyp, x, z, b)
% apxGrid - Covariance function approximation based on an inducing point grid.
%
% A grid covariance function k(x,z) is composed as a product
% k(x,z) = k1(x(i1),z(i1)) * .. * kp(x(ip),z(ip)) of p covariance functions
% operating on mutually disjoint components of th... |
github | kd383/GPML_SLD-master | infMCMC.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/inf/infMCMC.m | 11,425 | utf_8 | b9d0ffd6b56ddf12e1a81986654bca19 | 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 | kd383/GPML_SLD-master | infGrid.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/inf/infGrid.m | 9,908 | utf_8 | 4d801508da434be163f9b59d8cbbf94d | function [post nlZ dnlZ] = infGrid(hyp, mean, cov, lik, x, y, opt)
% Inference for a GP with grid-based approximate covariance.
%
% The (Kronecker) covariance matrix used is given by:
% K = kron( kron(...,K{2}), K{1} ) = K_p x .. x K_2 x K_1.
%
% Compute a parametrization of the posterior, the negative log marginal
... |
github | kd383/GPML_SLD-master | infEP.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/inf/infEP.m | 16,241 | utf_8 | ab38eadd10971c020f090bf044c20e7c | function [post nlZ dnlZ] = infEP(hyp, mean, cov, lik, x, y, opt)
% Expectation Propagation approximation to the posterior Gaussian Process.
% The function takes a specified covariance function (see covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMetho... |
github | kd383/GPML_SLD-master | infVB.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/inf/infVB.m | 5,862 | utf_8 | 2ce41c3e62d67935a0e8f13ae909137b | function [post, nlZ, dnlZ] = infVB(hyp, mean, cov, lik, x, y, opt)
% Variational approximation to the posterior Gaussian process.
% The function takes a specified covariance function (see covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMethods.m.
%
% ... |
github | kd383/GPML_SLD-master | infLaplace.m | .m | GPML_SLD-master/gpml-matlab-v4.1-2017-10-19/inf/infLaplace.m | 5,348 | utf_8 | 472d96f52dc0997f6629ce9ae7ae5c5b | function [post nlZ dnlZ alpha] = infLaplace(hyp, mean, cov, lik, x, y, opt)
% Laplace approximation to the posterior Gaussian process.
% The function takes a specified covariance function (see covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMethods.m.... |
github | kd383/GPML_SLD-master | demo_sound.m | .m | GPML_SLD-master/demo/sound/demo_sound.m | 3,971 | utf_8 | d2532d9afe9491656859f693775ca427 | function demo_sound(method, ninterp, hyp)
%
% Natural Sound Recovery Experiment
% Recover contiguous missing region in a waveform using different
% Training points: 59306, Testing points: 691
%
% method: Logdet Approximation Methods, {'Lanczos', 'Cheby', 'SKI', 'FITC'}
% ninterp: Number of interpolation points, defaul... |
github | kd383/GPML_SLD-master | spatiotemporal_spectral_init_poisson.m | .m | GPML_SLD-master/demo/crime/auxiliary/spatiotemporal_spectral_init_poisson.m | 4,088 | utf_8 | 9fa01d473f355c69b9c4fbd1df01811d | % Function to initialise SM kernel hyperparameters
% If varargin{1} is specified, it is the number of optimisation iterations
% to run for each random restart. Otherwise, we will just have
% initialisations and no optimisation.
% stdy should be on the log-scale
function hyp = spatiotemporal_spectral_init(inf_method... |
github | neurogeometry/BoutonAnalyzer-master | Optimize_Trace.m | .m | BoutonAnalyzer-master/Optimize_Trace.m | 5,971 | utf_8 | 7a405cf45f27f2b1d38acf3e566b401b | % This function works with AM, AMlbl for branches, or AMlbl for trees.
% Trees are optimized separately.
% Branch positions (r) are optimized, but calibers (R) remain fixed
% Branch and end points can be fixed or optimized:
% Optimize_bps = 1,0 optimize branch points.
% Optimize_tps = 1,0 optimize terminal (start,... |
github | neurogeometry/BoutonAnalyzer-master | plotAM.m | .m | BoutonAnalyzer-master/plotAM.m | 643 | utf_8 | ef459959fe9a0c21691aa0c19012211e | % This function plots the tree structure contained in AM.
% The function works with labeled or not labeled AM.
% AM can be directed or undirected.
% The labels don't have to be consecutive.
function h=plotAM(AM,r,col)
if size(r,2)==2
r=[r,zeros(size(r,1),1)];
end
AM = max(AM,AM');
AM = triu(AM);
L... |
github | neurogeometry/BoutonAnalyzer-master | FastMarchingTube.m | .m | BoutonAnalyzer-master/FastMarchingTube.m | 4,471 | utf_8 | 5d4a3e0287fa79356f6c02a4dc0be28c | % This function the Eikonal equation by using the Fast Marching algorithm
% of Sethian. T and D are the arival time and distance maps.
% Max_Known_Dist is the distance at which re-initialization is performed
% SVr contains positions of the seeds
% unisotropy is the wave speed unisotropy in a uniform intensity image
% O... |
github | neurogeometry/BoutonAnalyzer-master | profilefilters.m | .m | BoutonAnalyzer-master/profilefilters.m | 4,319 | utf_8 | dc109b321906ffba43afbcb7c0059c56 | function [II, RR]= profilefilters (r,IM,filtertype,params)
if strcmp(filtertype,'LoGxy')
%LoGxy filter options
LoGxy_R_min=params.filt.LoGxy_R_min;
LoGxy_R_step=params.filt.LoGxy_R_step;
LoGxy_R_max=params.filt.LoGxy_R_max;
LoGxy_Rz=params.filt.LoGxy_Rz;
[II,RR] = LoG_Filt_xy(IM,r,LoGxy_R_min,L... |
github | neurogeometry/BoutonAnalyzer-master | AM2swc.m | .m | BoutonAnalyzer-master/AM2swc.m | 3,317 | utf_8 | 964b9a6ab8a971b47d17295439d0695f | % This function converts AMlbl r R format to swc. Reduction done during image
% loading is inverted. AMlbl must not contain loops.
function swc_all = AM2swc(AMlbl,r,R,reduction_x,reduction_y,reduction_z)
rem_ind=(sum(AMlbl)==0);
AMlbl(rem_ind,:)=[];
AMlbl(:,rem_ind)=[];
r(rem_ind,:)=[];
R(rem_ind)=[];
... |
github | neurogeometry/BoutonAnalyzer-master | updateProfile.m | .m | BoutonAnalyzer-master/updateProfile.m | 3,418 | utf_8 | f56672094ce0bb36d08e9dde37ea8e44 | function updateProfile(hf)
%This function ensures that assigned fg.id are in a sorted order based on
%distance along the trace. In addition, removal of peaks can require
%re-labeling of existing matches, which is accomplished by the call to
%addrempeaklbl
UserData=hf.UserData;hf.UserData=[];%For speed reasons
channel=... |
github | neurogeometry/BoutonAnalyzer-master | ImportStackJ.m | .m | BoutonAnalyzer-master/ImportStackJ.m | 1,791 | utf_8 | 066057b9ee6616481e3155413ab88b5c | % This function imports images into MatLab. RGB images are converted to
% grayscale. Data format of Orig is preserved (uint8, uint16, etc).
function [Orig,sizeOrig,classOrig]=ImportStackJ(pth,file_list)
Orig=[];
sizeOrig=[];
classOrig=[];
N=length(file_list);
info = imfinfo([pth,file_list{1}]);
Npl=length... |
github | neurogeometry/BoutonAnalyzer-master | LabelBranchesAM.m | .m | BoutonAnalyzer-master/LabelBranchesAM.m | 1,097 | utf_8 | ea1bc5783fe1d5a6940da903652809ec | % This function labels individual branches in AM by using Depth First Search.
% The function works even when there are several disconnected trees in the AM
function AMlbl = LabelBranchesAM(AM)
AM=spones(AM+AM');
AM=AM-diag(diag(AM));
Remaining=find(sum(AM,1)==1 | sum(AM,1)>2);
AMlbl=AM;
AMlbl(AMlbl==1)=... |
github | neurogeometry/BoutonAnalyzer-master | BoutonAnalyzer.m | .m | BoutonAnalyzer-master/BoutonAnalyzer.m | 6,273 | utf_8 | 87972725983ca960cac7bc42612f1e9e | function BoutonAnalyzer()
temp=get(0);
fi.H=400;
fi.W=600;
fi.L=temp.ScreenSize(3)/2-fi.W/2;
fi.B=temp.ScreenSize(4)/2-fi.H/2;
hf=figure;
hf.Position=[fi.L,fi.B,fi.W,fi.H];
hf.MenuBar='none';
hf.NumberTitle='off';
hf.Name='Bouton Analyzer';
workingdir=pwd;
cbfcn=['open(''',workingdir,filesep,'User Man... |
github | neurogeometry/BoutonAnalyzer-master | gui_alignment_defineframe.m | .m | BoutonAnalyzer-master/gui_alignment_defineframe.m | 5,391 | utf_8 | 347c2a1194dd679b003548d3c7a6c8e4 | function [] = gui_alignment_defineframe(hf)
gui_alignment_layout;
%------------------------------Operation Panel-----------------------------
h_operation=uipanel('Parent',hf);h_operation.Tag='Operation';
h_operation.Units='pixels';h_operation.Position=[panel_l,operationpanel_b,panel_w,operationpanel_h];
h_oper... |
github | neurogeometry/BoutonAnalyzer-master | gui_alignment.m | .m | BoutonAnalyzer-master/gui_alignment.m | 8,260 | utf_8 | 89f47bb5669ce220107bc3f6689b9190 | function gui_alignment(src)
%Function allows
%1. view projections of all traces simultaneously and align traces
%2. annotate traces to exclude cross-overs etc
%3. editing and matching of peaks (putative boutons)
close(src);
temp=get(0);
fi.H=max([700,temp.ScreenSize(4)*0.8]);
fi.W=max([875,fi.H*5/4]);
fi.L=(fi.W.*0.1);... |
github | neurogeometry/BoutonAnalyzer-master | LabelTreesAM.m | .m | BoutonAnalyzer-master/LabelTreesAM.m | 723 | utf_8 | 3b17984d93020bca4d75064e73a9e1af | % This function finds trees in a directed or undirected AM and returns a
% labeled AMlbl.
function AMlbl = LabelTreesAM(AM)
AM = spones(AM+AM');
AMlbl=double(AM);
AV = find(sum(AM));
if ~isempty(AV)
startV=AV(1);
TreeLabel=1;
end
while ~isempty(AV)
startVnew=find(sum(AM(startV,:),1));
... |
github | neurogeometry/BoutonAnalyzer-master | gui_optimization.m | .m | BoutonAnalyzer-master/gui_optimization.m | 21,412 | utf_8 | 700e399f68aa0ec77f44b54100f76dc5 | function gui_optimization(src)
close(src);
%This section has sizes derived from screen resolution---------------------
temp=get(0);
fi.H=max([700,temp.ScreenSize(4)*0.8]);
fi.W=max([875,fi.H*5/4]);
fi.L=(fi.W.*0.1);
fi.B=(fi.H.*0.1);
%--------------------------------------------------------------------------
h... |
github | neurogeometry/BoutonAnalyzer-master | analysis_getmat.m | .m | BoutonAnalyzer-master/analysis_getmat.m | 8,683 | utf_8 | e5d2bfae9a58fae36c925993965f1573 | function [AxonMat] = analysis_getmat(An)
%This function creates matrices for analysis from registered data. Input is
%obtained from BoutonAnalyzer via saveProfile.m
channel=fieldnames(An{1}.fit);
remchannelind=false(numel(channel),1);
for i=1:numel(channel)
remchannelind(i)=isempty(fieldnames(An{1}.fit.(cha... |
github | neurogeometry/BoutonAnalyzer-master | AdjustPPM.m | .m | BoutonAnalyzer-master/AdjustPPM.m | 4,361 | utf_8 | 6916b925eb85d858d3b11a7f31944fe0 | % This function adjusts the number of points per micrometer of the trace (ppm).
% Input can be in the form of AM, AMlbl for branches, or AMlbl for trees
% The output is always in the form of AMlbl for trees
function [AMlbl,r,R] = AdjustPPM(AM,r,R,ppm)
AM=spones(AM+AM');
AMlbl = LabelBranchesAM(AM);
leng=size(... |
github | Hui-Ling/BeamformerSourceImaging-master | process_beamformer_con_speedup.m | .m | BeamformerSourceImaging-master/process_beamformer_con_speedup.m | 84,804 | utf_8 | 61b790c40d1f3c4f0a06d22c6268f587 | function varargout = process_beamformer_con_speedup( varargin )
% PROCESS_BEAMFORMER_TEST:
% @=============================================================================
% This software is part of the Brainstorm software:
% http://neuroimage.usc.edu/brainstorm
%
% Copyright (c)2000-2013 Brainstorm by the Universit... |
github | Hui-Ling/BeamformerSourceImaging-master | process_beamformer_mcb_speedup.m | .m | BeamformerSourceImaging-master/process_beamformer_mcb_speedup.m | 39,513 | utf_8 | 9ce8310d95f5258ce3de7e5363f7b2ce | function varargout = process_beamformer_mcb_speedup( varargin )
% PROCESS_BEAMFORMER_MCB (2017.01.16):
% USAGE: sInput = process_beamformer_mcb_speedup('GetDescription')
% sOutput = process_beamformer_mcb_speedup('Run', sProcess, sInput, method=[])
% INPUT:
% - Options
% |... |
github | harig00/MVHSlepian-master | slept2residBAD.m | .m | MVHSlepian-master/Fall17/slept2residBAD.m | 21,124 | utf_8 | 047d48a22b5d0885965d42125828a8f3 | function varargout=slept2resid(slept,thedates,fitwhat,givenerrors,specialterms,CC,TH,N)
% [ESTsignal,ESTresid,ftests,extravalues,total,alphavarall,totalparams,
% totalparamerrors,totalfit,functionintegrals,alphavar]
% =SLEPT2RESID(slept,thedates,fitwhat,givenerrors,specialterms,CC,TH)
%
% Takes a time series o... |
github | harig00/MVHSlepian-master | slept2resid.m | .m | MVHSlepian-master/Fall17/slept2resid.m | 21,225 | utf_8 | 0aa4fba4615ad4ae371dd2bec3a23cc1 | function varargout=slept2resid(slept,thedates,fitwhat,givenerrors,specialterms,CC,TH,N)
% [ESTsignal,ESTresid,ftests,extravalues,total,alphavarall,totalparams,
% totalparamerrors,totalfit,functionintegrals,alphavar]
% =SLEPT2RESID(slept,thedates,fitwhat,givenerrors,specialterms,CC,TH)
%
% Takes a time series o... |
github | harig00/MVHSlepian-master | intersection_reduce.m | .m | MVHSlepian-master/Spring18/utils/intersection_reduce.m | 1,009 | utf_8 | 11161e9a8e01992a62f4609a7efc9d1f | % Some code I'm working on to take as input a list of regions and return
% a new list wherein all intersecting/overlapping regions from the original
% have been unioned together.
% Not currently working, or done.
function [ reduced ] = intersectReduce(polys)
% Any intersecting polys get unioned
numPolys=numel(pol... |
github | rossimattia/light-field-super-resolution-master | nlm.m | .m | light-field-super-resolution-master/nlm.m | 5,739 | utf_8 | 2dfeb4c385a2f7cb353ba02583afbe08 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | tukeywin.m | .m | light-field-super-resolution-master/tukeywin.m | 1,096 | utf_8 | 49da297950887fc13409ec7ed2385fff |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | readhci.m | .m | light-field-super-resolution-master/readhci.m | 2,868 | utf_8 | e577d16ba369b9221d054cfa67220b05 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.