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github | Hadisalman/stoec-master | logphi.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | gauher.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | elsympol.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | minimize.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/util/minimize.m | 11,191 | utf_8 | 69603a3c319cf5374483af20b033f10e | 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 | Hadisalman/stoec-master | minimize_v2.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/util/minimize_v2.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 | Hadisalman/stoec-master | sq_dist.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | cov_deriv_sq_dist.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/util/cov_deriv_sq_dist.m | 1,906 | utf_8 | 625e697b220630f920d967bce06884e7 | % Compute derivative k'(x^p,x^q) of a stationary covariance k(d2) (ard or iso)
% w.r.t. to squared distance d2 = (x^p - x^q)'*inv(P)*(x^p - x^q) measure. Here
% P is either diagonal with ARD parameters ell_1^2,...,ell_D^2 where D is the
% dimension of the input space or ell^2 times the unit matrix for isotropic
% covar... |
github | Hadisalman/stoec-master | unwrap.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | glm_invlink_expexp.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/util/glm_invlink_expexp.m | 427 | utf_8 | 99a5cdb9880a947109671401c7398199 | % Compute the log intensity for the inverse link function g(f) = exp(-exp(-f)).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [lg,dlg,d2lg,d3lg] = glm_invlink_expexp(f)
lg = -exp(-f);
if nargout>1
... |
github | Hadisalman/stoec-master | glm_invlink_logistic.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/util/glm_invlink_logistic.m | 686 | utf_8 | b21f086f037b6560c290e0044e0beef5 | % Compute the log intensity for the inverse link function g(f) = log(1+exp(f))).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [lg,dlg,d2lg,d3lg] = glm_invlink_logistic(f)
l1pef = max(0,f) + log(1+exp(-a... |
github | Hadisalman/stoec-master | minimize_v1.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/util/minimize_v1.m | 11,202 | utf_8 | cd58ba0b83b1121423ed9a53b33562a1 | function [X, fX, i] = minimize_old(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 f... |
github | Hadisalman/stoec-master | rewrap.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | solve_chol.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | glm_invlink_logit.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/util/glm_invlink_logit.m | 786 | utf_8 | b2fc9a03b835c7f6643f37b29eac8c0b | % Compute the log intensity for the inverse link function g(f) = 1/(1+exp(-f)).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function varargout = glm_invlink_logit(f)
varargout = cell(nargout, 1); % allocate th... |
github | Hadisalman/stoec-master | minimize_lbfgsb_gradfun.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | minimize_lbfgsb.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | minimize_lbfgsb_objfun.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | logsumexp2.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | lik_epquad.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | glm_invlink_exp.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/util/glm_invlink_exp.m | 443 | utf_8 | af4bb74d42054f7b470ed8aecfcf4607 | % Compute the log intensity for the inverse link function g(f) = exp(f).
%
% The function is used in GLM likelihoods such as likPoisson, likGamma, likBeta
% and likInvGauss.
%
% Copyright (c) by Hannes Nickisch, 2013-10-16.
function [lg,dlg,d2lg,d3lg] = glm_invlink_exp(f)
lg = f;
if nargout>1
dlg = ones(size(f... |
github | Hadisalman/stoec-master | covPeriodicNoDC.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/cov/covPeriodicNoDC.m | 3,630 | utf_8 | 32d02bd08932f22fe8302ce97b797d39 | function K = covPeriodicNoDC(hyp, x, z, i)
% Stationary covariance function for a smooth periodic function, with period p:
%
% k(x,x') = sf^2 * [k0(pi*(x-x')/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 | Hadisalman/stoec-master | covGrid.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/cov/covGrid.m | 7,051 | utf_8 | 45064e9c69d8e20f1122e20679e25085 | function [K,Mx,xe] = covGrid(cov, xg, hyp, x, z, i)
% covGrid - Kronecker covariance function based on a grid.
%
% The grid g is represented by its p axes xg = {x1,x2,..xp}. An axis xi is of
% size (ni,di) and the grid g has size (n1,n2,..,np,D), where D=d1+d2+..+dp.
% Hence, the grid contains N=n1*n2*..*np data point... |
github | Hadisalman/stoec-master | covPERiso.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/cov/covPERiso.m | 3,145 | utf_8 | 7308c3f2001744df0d77cd5dc190c637 | function K = covPERiso(cov, hyp, x, z, i)
% Stationary periodic covariance function for an isotropic stationary covariance
% function k0 such as covMaterniso, covPPiso, covRQiso and covSEiso.
% Isotropic stationary means that the covariance function k0(x,z) depends on the
% data points x,z only through the squared dis... |
github | Hadisalman/stoec-master | covADD.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/cov/covADD.m | 3,632 | utf_8 | 45875a6c0e52c3f98448f40f6b6fc599 | 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 | Hadisalman/stoec-master | covPERard.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/cov/covPERard.m | 3,588 | utf_8 | 47dfb8b9857ef5e9bc3693bb6e5c0aa9 | function K = covPERard(cov, hyp, x, z, i)
% Stationary periodic covariance function for a stationary covariance function
% k0 such as covMaternard, covPPard, covRQard and covSEard.
% Stationary means that the covariance function k0(x,z) depends on the
% data points x,z only through the squared distance
% dxz = (x-z)'*... |
github | Hadisalman/stoec-master | infMCMC.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/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 | Hadisalman/stoec-master | infKL.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/inf/infKL.m | 10,925 | utf_8 | 25a0bb16b3bced105beb3520acf7f57e | function [post nlZ dnlZ] = infKL(hyp, mean, cov, lik, x, y)
% Approximation to the posterior Gaussian Process by minimization of the
% KL-divergence. The function is structurally very similar to infEP; the
% only difference being the local divergence measure minimised.
% In infEP, one minimises KL(p,q) whereas in inf... |
github | Hadisalman/stoec-master | infFITC_EP.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/inf/infFITC_EP.m | 12,117 | utf_8 | a2c1fccebe29502421d32ed7ab5fcd14 | 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.... |
github | Hadisalman/stoec-master | infFITC_Laplace.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/inf/infFITC_Laplace.m | 11,364 | utf_8 | 1de345e37242cee549b4d7841e348f84 | 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 | Hadisalman/stoec-master | infGrid.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/inf/infGrid.m | 7,627 | utf_8 | cca2d1208eb8763b1f518d5106ec6fd5 | function [post nlZ dnlZ] = infGrid(hyp, mean, cov, lik, x, y, opt)
% Inference for a GP with Gaussian likelihood and covGrid 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 ma... |
github | Hadisalman/stoec-master | infEP.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/inf/infEP.m | 5,886 | utf_8 | bc70721e1eea7c28c46653d36d1cd851 | 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 covFunctions.m) and
% likelihood function (see likFunctions.m), and is designed to be used with
% gp.m. See also infMethods.m.... |
github | Hadisalman/stoec-master | infVB.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/inf/infVB.m | 6,024 | utf_8 | ff46ca9c2cce23402f0058955fe60b93 | 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 | Hadisalman/stoec-master | infLaplace.m | .m | stoec-master/code/Include/gpml-matlab-v3.5-2014-12-08/inf/infLaplace.m | 7,949 | utf_8 | 297780be514bf1d87f826763094f104f | function [post nlZ dnlZ] = 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.
%
% C... |
github | Hadisalman/stoec-master | ipdm.m | .m | stoec-master/code/Include/InterPointDistanceMatrix/ipdm.m | 39,104 | utf_8 | d686ea7e03c771fcb892fb17417b3f23 | function d = ipdm(data1,varargin)
% ipdm: Inter-Point Distance Matrix
% usage: d = ipdm(data1)
% usage: d = ipdm(data1,data2)
% usage: d = ipdm(data1,prop,value)
% usage: d = ipdm(data1,data2,prop,value)
%
% Arguments: (input)
% data1 - array of data points, each point is one row. p dimensional
% data will be... |
github | Hadisalman/stoec-master | domain2meshgrid.m | .m | stoec-master/code/Include/vectorized_meshgrid/domain2meshgrid.m | 2,502 | utf_8 | 8adb8734c239cfe0c94f31fb942ea979 | function [X, Y, Z] = domain2meshgrid(domain, resolution)
%DOMAIN2MESHGRID(domain, resolution) generate meshgrid on parallelepiped
% [X, Y] = DOMAIN2MESHGRID(domain, resolution) creates the matrices
% X, Y definining a meshgrid covering the 2D rectangular domain
% domain = [xmin, xmax, ymin, ymax] with resol... |
github | Hadisalman/stoec-master | example_vectorized_surf_plot.m | .m | stoec-master/code/Include/vectorized_meshgrid/example_vectorized_surf_plot.m | 696 | utf_8 | 1d885f126747d5f8ca76f6df48533d2a | function [] = example_vectorized_surf_plot
% File: example_vectorized_surf_plot.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.01.14 - 2012.02.11
% Language: MATLAB R2011b
% Purpose: test meshgrid interface to functions taking as argument a
% matrix of column vectors... |
github | Hadisalman/stoec-master | meshgrid2vec.m | .m | stoec-master/code/Include/vectorized_meshgrid/meshgrid2vec.m | 1,541 | utf_8 | 9b720737c64121c57177aa22a8b26e88 | function [q] = meshgrid2vec(xgv, ygv, zgv)
%MESHGRID2VEC meshgrid matrices to matrix of column vectors
%
% [q] = MESHGRIDVEC(xgv, ygv) takes the matrix of abscissas XGV and
% ordinates YGV of meshgrid points as returned by MESHGRID and arranges
% them as vectors comprising the columns of matrix Q.
%
% ... |
github | Hadisalman/stoec-master | stlread.m | .m | stoec-master/code/Include/CMU_include/stlread.m | 3,981 | utf_8 | f1c11b51cd13528daae6802cfcd3b539 | function varargout = stlread(file)
% STLREAD imports geometry from an STL file into MATLAB.
% FV = STLREAD(FILENAME) imports triangular faces from the ASCII or binary
% STL file idicated by FILENAME, and returns the patch struct FV, with fields
% 'faces' and 'vertices'.
%
% [F,V] = STLREAD(FILENAME) r... |
github | Hadisalman/stoec-master | icp.m | .m | stoec-master/code/Include/CMU_include/icp.m | 18,647 | utf_8 | eb909b597a19b75b55810daee50f1676 | function [TR, TT, ER, t] = icp(q,p,varargin)
% Perform the Iterative Closest Point algorithm on three dimensional point
% clouds.
%
% [TR, TT] = icp(q,p) returns the rotation matrix TR and translation
% vector TT that minimizes the distances from (TR * p + TT) to q.
% p is a 3xm matrix and q is a 3xn matrix.
%... |
github | Hadisalman/stoec-master | normcdf.m | .m | stoec-master/code/Include/GP optimization/normcdf.m | 3,991 | utf_8 | 8a98cdb0d640bfd5a9a0a8fc1b3be30e | function [varargout] = normcdf(x,varargin)
%NORMCDF Normal cumulative distribution function (cdf).
% P = NORMCDF(X,MU,SIGMA) returns the cdf of the normal distribution with
% mean MU and standard deviation SIGMA, evaluated at the values in X.
% The size of P is the common size of X, MU and SIGMA. A scalar input
... |
github | Hadisalman/stoec-master | EI.m | .m | stoec-master/code/Include/GP optimization/EI.m | 300 | utf_8 | 5b9d33eba552e84709c55f4269598d1a | % Elif Ayvali 06/16/2015 eayvali@gmail.com
% yEI: the value of y to 'improve over'.
% ymu: the mean of GP posterior
% ys: the standard deviation of GP posterior
function res = EI(yEI,ymu,ys2)
eps=0.01;
ys=sqrt(ys2);
res = (ymu-yEI-eps).*normcdf((ymu-yEI-eps)./ys)+ys.*normpdf((ymu-yEI-eps)./ys);
end
|
github | Hadisalman/stoec-master | dEI.m | .m | stoec-master/code/Include/GP optimization/dEI.m | 300 | utf_8 | 5b9d33eba552e84709c55f4269598d1a | % Elif Ayvali 06/16/2015 eayvali@gmail.com
% yEI: the value of y to 'improve over'.
% ymu: the mean of GP posterior
% ys: the standard deviation of GP posterior
function res = EI(yEI,ymu,ys2)
eps=0.01;
ys=sqrt(ys2);
res = (ymu-yEI-eps).*normcdf((ymu-yEI-eps)./ys)+ys.*normpdf((ymu-yEI-eps)./ys);
end
|
github | Hadisalman/stoec-master | UCB.m | .m | stoec-master/code/Include/GP optimization/UCB.m | 291 | utf_8 | 7f1ab1acc4b7626fd0040a1744b963ab | % Elif Ayvali 11/03/2015 eayvali@gmail.com
% Upper Confidence Bound
% ymu: the mean of GP posterior
% ys: the standard deviation of GP posterior
function res = UCB(ymu,ys2,k)
switch nargin
case 2
beta=1.96;
case 3
beta = k;
end
ys=sqrt(ys2);
res =ymu+beta.*ys;
end
|
github | Hadisalman/stoec-master | wEI.m | .m | stoec-master/code/Include/GP optimization/wEI.m | 388 | utf_8 | 755514e7605e817c6b08a6b344557ade | % Elif Ayvali 06/16/2015 eayvali@gmail.com
% yEI: the value of y to 'improve over'.
% ymu: the mean of GP posterior
% ys: the standard deviation of GP posterior
% w = 0 global exploration
% w = 1 local exploitation
%w=0.5 wEI becomes EI
function res = wEI(yEI,ymu,ys2,w)
eps=0.01;
ys=sqrt(ys2);
res = w*(ymu-yEI-eps).*n... |
github | cdebacco/SpringRank-master | crossValidation.m | .m | SpringRank-master/matlab/crossValidation.m | 4,379 | utf_8 | 14bd7c4736dcb2871ea12cb112972663 | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% [sig_a,sig... |
github | cdebacco/SpringRank-master | betaLocal.m | .m | SpringRank-master/matlab/betaLocal.m | 1,031 | utf_8 | f1ad8be3f40d4591b7210456f33b5576 | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% b = betaLo... |
github | cdebacco/SpringRank-master | colleyMatrix.m | .m | SpringRank-master/matlab/colleyMatrix.m | 1,090 | utf_8 | 62e2ddddd76c55ce9c4418f954bee9e3 | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% r = colley... |
github | cdebacco/SpringRank-master | springRank.m | .m | SpringRank-master/matlab/springRank.m | 1,565 | utf_8 | 05483c2c0427fb4ac7d99a357effccca | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% s = spring... |
github | cdebacco/SpringRank-master | ranks2svg.m | .m | SpringRank-master/matlab/ranks2svg.m | 2,966 | utf_8 | 85d15849f335c0cfa3085e3c096f0db9 | % Code by Daniel Larremore
% Santa Fe Institute
% larremore@santafe.edu
% http://danlarremore.com
% v3
function [energy] = ranks2svg(A,s,filename)
[r,c,v] = find(A);
energy = (s(r)-s(c)-1).^2;
energy = lin(energy/max(energy),0.05,0.3);
% wid = 800; % Must be at least 400
hei = 800; % Must be at least 400
aspectRatio =... |
github | cdebacco/SpringRank-master | pageRank.m | .m | SpringRank-master/matlab/pageRank.m | 843 | utf_8 | ed72c5ff10741e4c8c07d5eddf3ea0d2 | % Parameter M adjacency matrix where M_i,j represents the link from 'j' to 'i', such that for all 'j'
% sum(i, M_i,j) = 1
% Parameter d damping factor
% Parameter v_quadratic_error quadratic error for v
% Return v, a vector of ranks such that v_i is the i-th rank from [0, 1]
function v = pageRank(A, d, v_quadratic... |
github | cdebacco/SpringRank-master | globalAccuracy.m | .m | SpringRank-master/matlab/globalAccuracy.m | 1,078 | utf_8 | a5b96f21a21c715028ec162644c233c2 | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% y = global... |
github | cdebacco/SpringRank-master | rankCentrality.m | .m | SpringRank-master/matlab/rankCentrality.m | 1,424 | utf_8 | a8fd8534646737e069618cad2e5473da | % Rank Centrality
% Implemented by Dan Larremore, University of Colorado Boulder
% April 8, 2018
%
% Based on the manuscript
% Rank Centrality: Ranking from Pairwise Comparisons
% Sahand Negahban, Sewoong Oh, Devavrat Shah
% 2017
%
function [rc] = rankCentrality(A)
% In their text, a_ij = # of times j is preferred over... |
github | cdebacco/SpringRank-master | springRankHamiltonian.m | .m | SpringRank-master/matlab/springRankHamiltonian.m | 1,292 | utf_8 | c5d299fe7f9d0c8d492690a9ecaa1037 | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% H = spring... |
github | cdebacco/SpringRank-master | globalAccuracy_BTL.m | .m | SpringRank-master/matlab/globalAccuracy_BTL.m | 440 | utf_8 | b2b67332100fdf1041556c3ca6219483 | % Code by Daniel Larremore
% Santa Fe Institute
% larremore@santafe.edu
% http://danlarremore.com
% evaluate the local accuracy of edge direction prediction
function y = globalAccuracy_BTL(A,g)
n = length(g);
y = 0;
for i=1:n
for j=1:n
p = g(i)/(g(i)+g(j)); % BTL probability
if p==0 || p==1 || isn... |
github | cdebacco/SpringRank-master | mvr.m | .m | SpringRank-master/matlab/mvr.m | 3,709 | utf_8 | 0fba16216b6fa253d5fe1c15b5843c39 | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% [order,vio... |
github | cdebacco/SpringRank-master | btl.m | .m | SpringRank-master/matlab/btl.m | 1,384 | utf_8 | 7a35f00eac95fc913ce8d85daf13199e | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% g = btl(A,... |
github | cdebacco/SpringRank-master | localAccuracy_BTL.m | .m | SpringRank-master/matlab/localAccuracy_BTL.m | 460 | utf_8 | 982e9879799160f78b833eb9ff58d5e9 | % Code by Daniel Larremore
% Santa Fe Institute
% larremore@santafe.edu
% http://danlarremore.com
% evaluate the local accuracy of edge direction prediction
function a = localAccuracy_BTL(A,g)
m = sum(sum(A));
n = length(g);
y = 0;
for i=1:n
for j=1:n
p = g(i)/(g(i)+g(j)); % BTL probability
if isn... |
github | cdebacco/SpringRank-master | betaGlobal.m | .m | SpringRank-master/matlab/betaGlobal.m | 1,016 | utf_8 | 5ce0dabb8de3c08dc206f76792c59ffa | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% b = betaGl... |
github | cdebacco/SpringRank-master | localAccuracy.m | .m | SpringRank-master/matlab/localAccuracy.m | 1,047 | utf_8 | c2e66140ef330c17dadfea25fb924b1e | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% a = localA... |
github | cdebacco/SpringRank-master | networkComponents.m | .m | SpringRank-master/matlab/networkComponents.m | 2,678 | utf_8 | 71e8c66191349a50896bef05ed228869 | % [nComponents,sizes,members] = networkComponents(A)
%
% Daniel Larremore
% April 24, 2014
% larremor@hsph.harvard.edu
% http://danlarremore.com
% Comments and suggestions always welcome.
%
% INPUTS:
% A Matrix. This function takes as an input a
% network adjacency matrix A, for a network that is ... |
github | cdebacco/SpringRank-master | davidScore.m | .m | SpringRank-master/matlab/davidScore.m | 836 | utf_8 | d9ab89c7af3f1d00a6344d70f6eb069b | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% s = davidS... |
github | cdebacco/SpringRank-master | pvalueNullModel.m | .m | SpringRank-master/matlab/pvalueNullModel.m | 2,323 | utf_8 | d5d34bc9134164f5885f99d0ac17b9e6 | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% [p,H0,H] =... |
github | cdebacco/SpringRank-master | generativeModel.m | .m | SpringRank-master/matlab/generativeModel.m | 976 | utf_8 | 6e6489e870ab1ebdb5db9b2a7b8d5569 | % SpringRank
% CODE -> https://github.com/cdebacco/SpringRank
% PAPER -> http://danlarremore.com/pdf/SpringRank_2017_PrePrint.pdf
% Code by Daniel Larremore
% University of Colorado at Boulder
% BioFrontiers Institute & Dept of Computer Science
% daniel.larremore@colorado.edu
% http://danlarremore.com
%
% [A,P] = ge... |
github | aamiranis/sampling_theory-master | test_eig_lopcg_proj.m | .m | sampling_theory-master/test_eig_lopcg_proj.m | 2,708 | utf_8 | 790f3462b25ee42dba9720dc49b4a527 | function test_eig_lopcg_proj(Ln, S)
k = 8;
N = length(Ln);
% Ln = Ln + 0.1 * speye(length(Ln));
Ln_k = Ln^k;
% S = rand(length(Ln),1) > 0.05;
% S = true(length(Ln),1); S(10) = false;
[y1,s1] = eigs(Ln_k(S,S), 1, 'sm');
s1
% function x = operatorA(x)
% for i = 1:k
% x = Ln * x;
% end
% end
%
% C =... |
github | aamiranis/sampling_theory-master | compute_S_L_k_lobpcg.m | .m | sampling_theory-master/sampling_methods/max_lambda_min_L_k/compute_S_L_k_lobpcg.m | 3,369 | utf_8 | 371f985ac6d087cbc8ef29edaa7ebd8d | function [ S_opt, count ] = compute_S_L_k_lobpcg( L, prec_fun, k, num_nodes_to_add, current_S_opt )
% AUTHOR: Aamir Anis, USC
% This function computes the optimal sampling set of a given size
% "S_opt_size" that maximizes the cutoff frequency.
% % %
% PARAMETER DESCRIPTION
%
% INPUT
% L_k: kth power of Laplaci... |
github | aamiranis/sampling_theory-master | compute_S_L_k_lobpcg_proj.m | .m | sampling_theory-master/sampling_methods/max_lambda_min_L_k/compute_S_L_k_lobpcg_proj.m | 2,083 | utf_8 | eb8313912525ab67ab720b05bdd3c2f0 | function [ S_opt, count ] = compute_S_L_k_lobpcg_proj( L, prec_fun, k, num_nodes_to_add, current_S_opt )
% AUTHOR: Aamir Anis, USC
% This function computes the optimal sampling set of a given size
% "S_opt_size" that maximizes the cutoff frequency.
% % %
% PARAMETER DESCRIPTION
%
% INPUT
% L_k: kth power of La... |
github | aamiranis/sampling_theory-master | pdftops.m | .m | sampling_theory-master/results/exportfig/pdftops.m | 3,053 | utf_8 | 6eb261c6107aedd03ceace4ccbce285c | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for futur... |
github | aamiranis/sampling_theory-master | isolate_axes.m | .m | sampling_theory-master/results/exportfig/isolate_axes.m | 3,307 | utf_8 | 43cbadba85146816219993a4e1de54cb | %ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes specified, and
% also their associated legends and colorbars. The axes specified must all
% be in the same figur... |
github | aamiranis/sampling_theory-master | pdf2eps.m | .m | sampling_theory-master/results/exportfig/pdf2eps.m | 1,524 | utf_8 | 037f9109e96ab4385d13019a29db4639 | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.c... |
github | aamiranis/sampling_theory-master | print2array.m | .m | sampling_theory-master/results/exportfig/print2array.m | 6,161 | utf_8 | 155b53ad27b25177fbcb3cd67ec6615e | %PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% [A bcol] = print2array(...)
%
% This function outputs a bitmap image of the given fig... |
github | aamiranis/sampling_theory-master | eps2pdf.m | .m | sampling_theory-master/results/exportfig/eps2pdf.m | 5,151 | utf_8 | b356d73460fdebe8ef6fa428d5b2c125 | %EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop, append, gray, quality)
%
% This function converts an eps file... |
github | aamiranis/sampling_theory-master | copyfig.m | .m | sampling_theory-master/results/exportfig/copyfig.m | 846 | utf_8 | 289162022c603c9e11a52b6d56329188 | %COPYFIG Create a copy of a figure, without changing the figure
%
% Examples:
% fh_new = copyfig(fh_old)
%
% This function will create a copy of a figure, but not change the figure,
% as copyobj sometimes does, e.g. by changing legends.
%
% IN:
% fh_old - The handle of the figure to be copied. Default: gc... |
github | aamiranis/sampling_theory-master | user_string.m | .m | sampling_theory-master/results/exportfig/user_string.m | 2,339 | utf_8 | f9b2326571e9d13eccc99ce441efd788 | %USER_STRING Get/set a user specific string
%
% Examples:
% string = user_string(string_name)
% saved = user_string(string_name, new_string)
%
% Function to get and set a string in a system or user specific file. This
% enables, for example, system specific paths to binaries to be saved.
%
% IN:
% string_name - ... |
github | aamiranis/sampling_theory-master | export_fig.m | .m | sampling_theory-master/results/exportfig/export_fig.m | 29,468 | utf_8 | e1fc4fe8c0dcd6f758389b63c10a52bf | %EXPORT_FIG Exports figures suitable for publication
%
% Examples:
% im = export_fig
% [im alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -transparent
% export_fig ... -native
% export_fig ... -m<val>
% export_fig... |
github | aamiranis/sampling_theory-master | ghostscript.m | .m | sampling_theory-master/results/exportfig/ghostscript.m | 4,215 | utf_8 | 621b90eb2972a74b0f4094afa317e96d | function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path... |
github | aamiranis/sampling_theory-master | print2eps.m | .m | sampling_theory-master/results/exportfig/print2eps.m | 6,295 | utf_8 | afc45df95a67e7d634c24d5c2b265207 | %PRINT2EPS Prints figures to eps with improved line styles
%
% Examples:
% print2eps filename
% print2eps(filename, fig_handle)
% print2eps(filename, fig_handle, options)
%
% This function saves a figure as an eps file, with two improvements over
% MATLAB's print command. First, it improves the line styl... |
github | aamiranis/sampling_theory-master | sgwt_cheby_square.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_cheby_square.m | 1,940 | utf_8 | 5dd1536abce104317a8094bb4c7fcc51 | % sgwt_cheby_square : Chebyshev coefficients for square of polynomial
%
% function d=sgwt_cheby_square(c)
%
% Inputs :
% c - Chebyshev coefficients for p(x) = sum c(1+k) T_k(x) ; 0<=K<=M
%
% Outputs :
% d - Chebyshev coefficients for p(x)^2 = sum d(1+k) T_k(x) ;
% 0<=k<=2*M
% This file is part of the SGWT toolbox ... |
github | aamiranis/sampling_theory-master | sgwt_kernel_abspline3.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_kernel_abspline3.m | 1,879 | utf_8 | 2cb063d47b5ee454c07302c6428e7dc5 | % sgwt_kernel_abspline3 : Monic polynomial / cubic spline / power law decay kernel
%
% function r = sgwt_kernel_abspline3(x,alpha,beta,t1,t2)
%
% defines function g(x) with g(x) = c1*x^alpha for 0<x<x1
% g(x) = c3/x^beta for x>t2
% cubic spline for t1<x<t2,
% Satisfying g(t1)=g(t2)=1
%
% Inputs :
% x : array of indepen... |
github | aamiranis/sampling_theory-master | sgwt_kernel_abspline5.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_kernel_abspline5.m | 2,174 | utf_8 | f6125a68524de3c1b9d68a60c20b04b9 | % sgwt_kernel_abspline5 : Monic polynomial / quintic spline / power law decay kernel
%
% function r = sgwt_kernel_abspline5(x,alpha,beta,t1,t2)
%
% Defines function g(x) with g(x) = c1*x^alpha for 0<x<x1
% g(x) = c3/x^beta for x>t2
% quintic spline for t1<x<t2,
% Satisfying g(t1)=g(t2)=1
% g'(t1)=g'(t2)
% g''(t1)=g''(t... |
github | aamiranis/sampling_theory-master | sgwt_adjoint.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_adjoint.m | 1,328 | utf_8 | a1b915af5342360927ac1e544a9e812c | % sgwt_adjoint : Compute adjoint of sgw transform
%
% function adj=sgwt_inverse(y,L,c,arange)
%
% Inputs:
% y - sgwt coefficients
% L - laplacian
% c - cell array of Chebyshev coefficients defining transform
% arange - spectral approximation range
%
% Outputs:
% adj - computed sgwt adjoint applied to y
% This file is ... |
github | aamiranis/sampling_theory-master | sgwt_cheby_coeff.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_cheby_coeff.m | 1,552 | utf_8 | fcab96b5fc2ee0008daebc18b441206e | % sgwt_cheby_coeff : Compute Chebyshev coefficients of given function
%
% function c=sgwt_cheby_coeff(g,m,N,arange)
%
% Inputs:
% g - function handle, should define function on arange
% m - maximum order Chebyshev coefficient to compute
% N - grid order used to compute quadrature (default is m+1)
% arange - interval of... |
github | aamiranis/sampling_theory-master | sgwt_meshmat.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_meshmat.m | 2,230 | utf_8 | 8be6b355e7542c5588dd4ccf2006a51c | % sgwt_meshmat : Adjacency matrix for regular 2d mesh
%
% function A=meshmat_p(dim,varargin)
%
% Inputs:
% dim - size of 2d mesh
% Selectable control parameters:
% boundary - 'rectangle' or 'torus'
%
% Outputs:
% A - adjacency matrix
% This file is part of the SGWT toolbox (Spectral Graph Wavelet Transform toolbox)
... |
github | aamiranis/sampling_theory-master | sgwt_irregular_meshmat.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_irregular_meshmat.m | 2,155 | utf_8 | d04f2a817116506446dea051cf5100f7 | % sgwt_irregular_meshmat : Adjacency matrix from irregular domain mask
%
% function A = sgwt_irregular_meshmat(mask)
%
% Computes the adjaceny matrix of graph for given 2-d irregular
% domain. Vertices of graph correspond to nonzero elements of
% mask. Edges in graph connect to (up to) 4 nearest neighbors.
%
% Inputs... |
github | aamiranis/sampling_theory-master | sgwt_view_design.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_view_design.m | 2,044 | utf_8 | 247bd54a5a76a94390e1b9c63c10f32b | % sgwt_view_design : display filter design in spectral domain
%
% function sgwt_view_design(g,t,arange)
%
% This function graphs the input scaling function and wavelet
% kernels, indicates the wavelet scales by legend, and also shows
% the sum of squares G and corresponding frame bounds for the transform.
%
% Inputs :
... |
github | aamiranis/sampling_theory-master | sgwt_randmat.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_randmat.m | 1,167 | utf_8 | 11ba319fb1710fe43c0b282b8f4fbd31 | % sgwt_randmat : Compute random (Erdos-Renyi model) graph
%
% function A=sgwt_randmat(N,thresh)
%
% Inputs :
% N - number of vertices
% thresh - probability of connection of each edge
%
% Outputs :
% A - adjacency matrix
% This file is part of the SGWT toolbox (Spectral Graph Wavelet Transform toolbox)
% Copyright (... |
github | aamiranis/sampling_theory-master | sgwt_rough_lmax.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_rough_lmax.m | 1,675 | utf_8 | e86284557ee6b70d9b5bc8538c8677d6 | % sgwt_rough_lmax : Rough upper bound on maximum eigenvalue of L
%
% function lmax=sgwt_rough_lmax(L)
%
% Runs Arnoldi algorithm with a large tolerance, then increases
% calculated maximum eigenvalue by 1 percent. For much of the SGWT
% machinery, we need to approximate the wavelet kernels on an
% interval that conta... |
github | aamiranis/sampling_theory-master | sgwt_kernel_meyer.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_kernel_meyer.m | 1,224 | utf_8 | 5537c2610259b92be34a5fea5be86ac8 | % sgwt_kernel_meyer : evaluates meyer wavelet kernel and scaling function
% function r=sgwt_kernel_meyer(x,kerneltype)
%
% Inputs
% x : array of independent variable values
% kerneltype : string, either 'sf' or 'wavelet'
%
% Ouputs
% r : array of function values, same size as x.
%
% meyer wavelet kernel : supported on... |
github | aamiranis/sampling_theory-master | sgwt_cheby_eval.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_cheby_eval.m | 1,740 | utf_8 | 351350401b5c3068e214d45848ee0f76 | % sgwt_cheby_eval : Evaluate shifted Chebyshev polynomial on given domain
%
% function r=sgwt_cheby_eval(x,c,arange)
%
% Compute Chebyshev polynomial of laplacian applied to input.
% This is primarily for visualization
%
% Inputs:
% x - input values to evaluate polynomial on
% c - Chebyshev coefficients (c(1+j) is jth ... |
github | aamiranis/sampling_theory-master | sgwt_cheby_op.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_cheby_op.m | 2,505 | utf_8 | 5183cc2390cb62eeed472b015abc0fd2 | % sgwt_cheby_op : Chebyshev polynomial of Laplacian applied to vector
%
% function r=sgwt_cheby_op(f,L,c,arange)
%
% Compute (possibly multiple) polynomials of laplacian (in Chebyshev
% basis) applied to input.
%
% Coefficients for multiple polynomials may be passed as a cell array. This is
% equivalent to setting
% r{... |
github | aamiranis/sampling_theory-master | sgwt_inverse.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_inverse.m | 1,944 | utf_8 | f162cbfdf8957b52d971032bbf6d8e5c | % sgwt_inverse : Compute inverse sgw transform, via conjugate gradients
%
% function r=sgwt_inverse(y,L,c,arange)
%
% Inputs:
% y - sgwt coefficients
% L - laplacian
% c - cell array of Chebyshev coefficients defining transform
% arange - spectral approximation range
%
% Selectable Control Parameters
% tol - tolerance ... |
github | aamiranis/sampling_theory-master | sgwt_kernel_simple_tf.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_kernel_simple_tf.m | 941 | utf_8 | b1e5ddf012d9da6e0f1dc36cb52e473c | % sgwt_kernel_simple_tf : evaluates "simple" tight-frame kernel
%
% this is similar to meyer kernel, but simpler
%
% function is essentially sin^2(x) in ascending part,
% essentially cos^2 in descending part.
%
% function r= sgwt_kernel_simple_tf(x,kerneltype)
%
% Inputs
% x : array of independent variable values
% ker... |
github | aamiranis/sampling_theory-master | sgwt_check_connected.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_check_connected.m | 1,099 | utf_8 | f1f8b67da83442e06b1d3e495595fb2c | % sgwt_check_connected : Check connectedness of graph
%
% function r=sgwt_check_connected(A)
%
% returns 1 if graph is connected, 0 otherwise
% Uses boost graph library breadth first search
%
% Inputs :
% A - adjacency matrix
%
% Outputs :
% r - result
%
% This file is part of the SGWT toolbox (Spectral Graph Wavele... |
github | aamiranis/sampling_theory-master | sgwt_framebounds.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_framebounds.m | 1,434 | utf_8 | 9d7475831d87b18cd390d84dd8e5317e | % sgwt_framebounds : Compute approximate frame bounds for given sgw transform
%
% function [A,B,sg2,x]=sgwt_framebounds(g,lmin,lmax)
%
% Inputs :
% g - function handles computing sgwt scaling function and wavelet
% kernels
% lmin,lmax - minimum nonzero, maximum eigenvalue
%
% Outputs :
% A , B - frame bounds
% sg2 - a... |
github | aamiranis/sampling_theory-master | sgwt_delta.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_delta.m | 1,086 | utf_8 | 35b91034385c7a5ad54d4c636df108d8 | % sgwt_delta : Return vector with one nonzero entry equal to 1.
%
% function r=sgwt_delta(N,j)
%
% Returns length N vector with r(j)=1, all others zero
%
% Inputs :
% N - length of vector
% j - position of "delta" impulse
%
% Outputs:
% r - returned vector
% This file is part of the SGWT toolbox (Spectral Graph Wavele... |
github | aamiranis/sampling_theory-master | sgwt_laplacian.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_laplacian.m | 2,419 | utf_8 | c69f646e26bdc6127f0acea3a9ea5778 | % sgwt_laplacian : Compute graph laplacian from connectivity matrix
%
% function L = sgwt_laplacian(A,varargin)
%
% Connectivity matrix A must be symmetric. A may have arbitrary
% non-negative values, in which case the graph is a weighted
% graph. The weighted graph laplacian follows the definition in
% "Spectral Gra... |
github | aamiranis/sampling_theory-master | sgwt_filter_design.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_filter_design.m | 3,113 | utf_8 | 1fe642bdc562bf76e4ef0d65828d3be3 | % sgwt_filter_design : Return list of scaled wavelet kernels and derivatives
%
% g{1} is scaling function kernel,
% g{2} ... g{Nscales+1} are wavelet kernels
%
% function [g,t]=sgwt_filter_design(lmax,Nscales,varargin)
%
% Inputs :
% lmax - upper bound on spectrum
% Nscales - number of wavelet scales
%
% selectable par... |
github | aamiranis/sampling_theory-master | sgwt_setscales.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_setscales.m | 1,879 | utf_8 | f3a9d5e3ffe5388b2b3f13f7e9ed799f | % sgwt_setscales : Compute a set of wavelet scales adapted to spectrum bounds
%
% function s=sgwt_setscales(lmin,lmax,Nscales)
%
% returns a (possibly good) set of wavelet scales given minimum nonzero and
% maximum eigenvalues of laplacian
%
% returns scales logarithmicaly spaced between minimum and maximum
% "effec... |
github | aamiranis/sampling_theory-master | sgwt_ftsd.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/sgwt_ftsd.m | 1,488 | utf_8 | c93b3db59098b389b8e86258845f0900 | % sgwt_ftsd : Compute forward transform in spectral domain
%
% function r=sgwt_ftsd(f,g,t,L)
%
% Compute forward transform by explicitly computing eigenvectors and
% eigenvalues of graph laplacian
%
% Uses persistent variables to store eigenvectors, so decomposition
% will be computed only on first call
%
% Inputs:
%... |
github | aamiranis/sampling_theory-master | sgwt_demo3.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/demo/sgwt_demo3.m | 4,022 | utf_8 | 172a5b137553c10796f84fb7239e2804 | % sgwt_demo3 : Image decomposition with SGWT wavelets based on local adjacency.
%
% This demo builds the SGWT transform on a graph representing
% adjacency on a pixel mesh with 4-nearest neighbor connectivity.
% This demonstrates inverse on problem with large dimension.
%
% The demo loads an image file and decomposes ... |
github | aamiranis/sampling_theory-master | sgwt_demo2.m | .m | sampling_theory-master/reconstruction_methods/pocs_bandlimited/sgwt_toolbox/demo/sgwt_demo2.m | 6,384 | utf_8 | 7bab07e6514e903305e5afaf1ba24716 | % sgwt_demo2 : Allows exploring wavelet scale and approximation accuracy
%
% This demo builds the SGWT for the minnesota traffic graph, a graph
% representing the connectivity of the minnesota highway system. One center
% vertex is chosen, and then the exact (naive forward transform) and the
% approximate (via chebyshe... |
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