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 | jianxiongxiao/ProfXkit-master | dirSmart.m | .m | ProfXkit-master/dirSmart/dirSmart.m | 1,476 | utf_8 | 937e09fdced78e2b7c9bb27706116c62 | % example usage: imageFiles = dirSmart(fullfile(...),'jpg');
function files = dirSmart(page, tag)
[files, status] = urldir(page, tag);
if status == 0
files = dir(fullfile(page, ['*.' tag]));
end
end
function [files, status] = urldir(page, tag)
if nargin == 1
tag = '/';
else
tag ... |
github | jianxiongxiao/ProfXkit-master | duplicateRemoval.m | .m | ProfXkit-master/duplicateRemoval/duplicateRemoval.m | 13,686 | utf_8 | 9a2d5d0eca38443523c43d23346de6a4 | function [images, image2keep, image2delete]=duplicateRemoval(rootPath,category)
% find all duplicates under the path fullfile(rootPath,category)
% and DELETE all the duplicated images!!! (file deletion will happen!!!)
% Written by Jianxiong Xiao @ 20130812
if ~exist('rootPath','var')
rootPath = '/data/vision/torr... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | meanTesterGeneral.m | .m | AgnosticMeanAndCovarianceCode-master/meanTesterGeneral.m | 2,260 | utf_8 | 6cf708f98619aa376136460d3c3e2e55 | % Output: Several vectors of values corresponding to the performance of
% the algorithm, the coordinate-wise median, and the mean of the true
% samples
% vecs contains the vectors for the algorithm's estimate and the mean of
% the true samples for dimension equal to the largest value of drange
% This code also plots th... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | covTesterGeneral.m | .m | AgnosticMeanAndCovarianceCode-master/covTesterGeneral.m | 3,978 | utf_8 | 526f5f44d82b36c11eada85bfbe4473e | % Output: Several vectors of values corresponding to the performance of
% the algorithm, the coordinate-wise median, and the mean of the true
% samples
% vecs contains the vectors for the algorithm's estimate and the mean of
% the true samples for dimension equal to the largest value of drange
% This code also plots th... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | noisyG.m | .m | AgnosticMeanAndCovarianceCode-master/noisyG.m | 646 | utf_8 | 2ec7a1383e477d64b10e6b615992815c | % Method for generating points from a spherical Gaussian with noise placed
% at a single point
%
% Input: mean, covariance matrix, noise fraction eta, number of samples m,
% and a noise point z. Mean and z are column vectors in n dimensions
%
% Output: a matrix X of samples, where in expectation, first 1-eta
% fractio... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | agnosticMeanGeneral.m | .m | AgnosticMeanAndCovarianceCode-master/agnosticMeanGeneral.m | 857 | utf_8 | d77761516d49c3db9c7ed9e63c4ef926 | % Agnostic algorithm for computing mean of a general distribution with
% bounded fouth moments
%
% Input: X = noisy data from a general distribution with bounded fourth
% moments, noise fraction eta
% Output: est = estimate for the mean
function est = agnosticMeanGeneral(X, eta)
n = size(X,2);
if n <= 1
est = e... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | generateGMMsamples.m | .m | AgnosticMeanAndCovarianceCode-master/generateGMMsamples.m | 303 | utf_8 | 679bd5af6f168a8fa24843e805386e31 | % Generates m samples from a general GMM with the given parameters
function x = generateGMMsamples(m, w1, mu1, Sigma1, mu2, Sigma2)
d = size(mu1, 2);
x = zeros(m, d);
numOnes = binornd(m, w1);
x(1:numOnes,:) = mvnrnd(mu1, Sigma1, numOnes);
x(numOnes+1:end,:) = mvnrnd(mu2, Sigma2, m - numOnes);
end
|
github | kevinalai/AgnosticMeanAndCovarianceCode-master | recursivePCA.m | .m | AgnosticMeanAndCovarianceCode-master/recursivePCA.m | 701 | utf_8 | 69d5d69cc551640850c7441656b65498 | % Agnostic algorithm for computing mean of a Gaussian
%
% Input: data X from a Gaussian, outlierRemoval procedure
% Output: estimate for the mean
function est = recursivePCA(X,sig,outlierRemoval)
m = length(X);
n = size(X,2);
if n<=2
est = median(X);
return;
end
% iter = ceil(m/2);
% R = zeros(iter,1);
% f... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | estG1D.m | .m | AgnosticMeanAndCovarianceCode-master/estG1D.m | 590 | utf_8 | 2a0a2d58bf29151c01e9792ec94f14c6 | % Algorithm for estimating 1D mean and variance of a Gaussian in a
% direction v
%
% Input: Noisy samples from a general Gaussian
% Output: estimate of the mean and variance along the direction v
function [mu, sigma2] = estG1D(X, v)
v = v/norm(v); %normalize
m = size(X,1);
Z = X*v;
mu = median(Z);... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | agnosticCovarianceGeneral.m | .m | AgnosticMeanAndCovarianceCode-master/agnosticCovarianceGeneral.m | 821 | utf_8 | 3933997e1758b32366fb3ee0b79cf9ee | % Algorithm for estimating general covariance
% Assume mean of X's is 0
function [muHat, SigmaEst, centeredX] = agnosticCovarianceGeneral(X, eta)
m = size(X, 1);
n = size(X, 2);
%muHat = agnosticMeanGeneral(X, eta);
muHat = zeros(1, n);
tic;
Z = X - repmat(muHat, m, 1);
C = num2ce... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | estGeneral1D.m | .m | AgnosticMeanAndCovarianceCode-master/estGeneral1D.m | 718 | utf_8 | c3c13e6cc219840e6d497e99c10eb60c | % Algorithm for estimating the 1D mean of a general distribution with
% bounded fourth moments in a direction v
%
% Input: Noisy samples from a general distribution with bounded fourth
% moments, column vector v, noise fraction eta
% Output: estimate of the mean and variance along the direction v
function mu = estGene... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | outRemBall.m | .m | AgnosticMeanAndCovarianceCode-master/outRemBall.m | 673 | utf_8 | 3f50404576c02f2e35495df7a4d97486 | % Removes points outside of a ball containing (1-eta)^2 fraction of the
% points. The ball is centered at the coordinate-wise median.
% The weight vector returned has 0 weight for points from X that are
% outside this ball.
%
% Input: X = sample from a distribution with bounded fourth moments,
% noise fraction eta
%
%... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | agnosticMeanG.m | .m | AgnosticMeanAndCovarianceCode-master/agnosticMeanG.m | 1,177 | utf_8 | 1544f8ae91b45b7dfd7a2dec75b3129c | % Agnostic algorithm for computing mean of a general Gaussian
%
% Input: X = noisy data from a general Gaussian
% Output: est = estimate for the mean
function est = agnosticMeanG(X, eta)
m = size(X,1);
n = size(X,2);
if n<=2
est = median(X);
return;
end
w = outlierRemoval(X, eta);
muHat = w'*X/m;
norm(muHa... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | tester.m | .m | AgnosticMeanAndCovarianceCode-master/tester.m | 946 | utf_8 | d7f0f0d73a362c7cb7e40db450157d48 | % Testing code for agnosticMeanG
% Compares the quality of agnosticMeanG's output to the sample mean and
% sample median for noise all at the ones vector times 100
%
% Input: eta = noise fraction
% m = number of samples to test
% Output: norms of agnosticMeanG estimate, sample mean, and sample median
% for vari... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | cauchyfit.m | .m | AgnosticMeanAndCovarianceCode-master/Cauchy code/cauchyfit.m | 5,565 | utf_8 | 026e4e274726fdf96ecf7abf0cddf87b | function [mlepars, output]= cauchyfit(varargin)
% USAGE:
% [mlepars, res]= cauchyfit(x) Fit parameters to data x.
% [mlepars, res]= cauchyfit(x, xpars) Fit parameters to data x, with one known parameter.
% [mlepars, res]= cauchyfit(n, npars) Debugging: generate a n-size sample and fit it...
% [mlepar... |
github | kevinalai/AgnosticMeanAndCovarianceCode-master | paxmle.m | .m | AgnosticMeanAndCovarianceCode-master/Cauchy code/paxmle.m | 6,174 | utf_8 | e3a492151051b3c6fb196ba07e8436ef | function [mlepars, output]= paxmle(pars, negloglike, varargin)
% USAGE:
% [mlepars, output]= paxmle(pars, negloglike)
% [mlepars, output]= paxmle(pars, negloglike, lBounds)
% [mlepars, output]= paxmle(pars, negloglike, lBounds, uBounds)
% [mlepars, output]= paxmle(..., options)
%
% Calculate the best parameter fit g... |
github | joe-of-all-trades/czifinfo-master | czifinfo.m | .m | czifinfo-master/czifinfo.m | 5,263 | utf_8 | 77e512aa3e745b1d6c0d642e3ee955ab | function fileInfo = czifinfo( filename, varargin )
%CZIFINFO returns informaion of Zeiss CZI file
%
% czifinfo returns information of czi file includingl pixel type,
% compression method, fileGUID, file version number, a structure
% recording various information of raw image data including data start
% positi... |
github | onalbach/caffe-deep-shading-master | classification_demo.m | .m | caffe-deep-shading-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | antipa/proxMin-master | tv3d_iso_Haar.m | .m | proxMin-master/tv3d_iso_Haar.m | 2,349 | utf_8 | 9dbca1f3c36fa28e8cf93711c0874c42 | function y = tv3d_iso_Haar(x, tau, alpha)
% Private functions here
% circshift does circular shifting
% indexing: x(5:10), 1 indexed. Use x(5:4:end-6) to index in strides of 4
% to the 6th-to-last element
D = 3;
gamma = 1; %step size
thresh = sqrt(2) * 2 * D * tau * gamma;
y = zeros(size(x), 'like', ... |
github | antipa/proxMin-master | proxMin.m | .m | proxMin-master/proxMin.m | 8,566 | utf_8 | 31302ab2b5b63c6185b13bd08ca43337 | function [out,varargout] = proxMin(GradErrHandle,ProxFunc,xk,b,options)
% Out = proxMin(GradErrHanle,ProxHandle,AxyTxy0,measurement,options)
%
% GradErrHandle: handle for function that computes error and gradient at
% each step
%
% ProxFunc: handle for function that does projection step
%
% AxyTxy0: initialization N... |
github | antipa/proxMin-master | tv2d_aniso_haar.m | .m | proxMin-master/tv2d_aniso_haar.m | 2,249 | utf_8 | 3d7154b33f0fde5be8941b98e3824b62 | function y = tv2dApproxHaar(x, tau)
% Private functions here
% circshift does circular shifting
% indexing: x(5:10), 1 indexed. Use x(5:4:end-6) to index in strides of 4
% to the 6th-to-last element
D = 2;
gamma = 1; %step size
thresh = sqrt(2) * 2 * D * tau * gamma;
y = zeros(size(x), 'like', x);
... |
github | antipa/proxMin-master | conv2c.m | .m | proxMin-master/conv2c.m | 1,666 | utf_8 | fbd814f6dbc22cf39b0304b1630681a6 | function y = conv2c(x,h)
% Circular 2D convolution
x=wraparound(x,h);
y=conv2(x,h,'valid');
function y = wraparound(x, m)
% Extend x so as to wrap around on both axes, sufficient to allow a
% "valid" convolution with m to return the cyclical convolution.
% We assume mask origin near centre of mask for compat... |
github | oussamamoslah/Democratic-RPSO-master | simeditcb.m | .m | Democratic-RPSO-master/simeditcb.m | 14,986 | utf_8 | e773b67088c3cf2660ecb16b0639b3c6 | function simeditcb(action)
nameSim = 'MRSim - Multi-Robot Simulator v1.0';
switch action
case 'import'
% ***************** Import bitmap ********************
h = findobj('Tag','ListStore'); % We need to check the list
list = get(h,'UserData'); % Get it
if ~isempty(li... |
github | oussamamoslah/Democratic-RPSO-master | gui_senscb.m | .m | Democratic-RPSO-master/gui_senscb.m | 9,298 | utf_8 | 22009f4c455b3ff73eacfc8a5718e513 | function gui_senscb(action);
if nargin == 0
action = 'initialize';
end
% Data - in SensStore (static text)
% SensAdd (add button) - 1 if new sensor
% Shape in SensCancel
% SensOK = OK Button - number of callbacking robot (when called from robot's uicm)
switch(action)
case 'initialize'
... |
github | joe-of-all-trades/xml2struct-master | xml2struct.m | .m | xml2struct-master/xml2struct.m | 6,351 | utf_8 | 0feee43c103f51c376a7dab2ac8457d0 | function outStruct = xml2struct(input)
%XML2STRUCT converts xml file into a MATLAB structure
%
% outStruct = xml2struct2(input)
%
% xml2struct2 takes either a java xml object, an xml file, or a string in
% xml format as input and returns a parsed xml tree in structure.
%
% Please note that the following characters... |
github | inria-larsen/toolbox-probabilistic_movement_primitives-master | visualisation.m | .m | toolbox-probabilistic_movement_primitives-master/toolbox_promps/visualisation.m | 510 | utf_8 | 0100f613e186d950ded2e0809db7067d | % Function that plot the matrix with the color col1. x is the line of the
% matrix, y the number of colonnes
function y = visualisation(matrix, x,y, z, col1, nameFig)
tall = size(nameFig,2);
for i=1:x
for j=1:y
val(i,j) = matrix(y*(i-1)+j);
end
end
if(isa(col1, 'char'))
%for i=1:x
i=z;
... |
github | inria-larsen/toolbox-probabilistic_movement_primitives-master | computeBasisFunction.m | .m | toolbox-probabilistic_movement_primitives-master/toolbox_promps/computeBasisFunction.m | 1,618 | utf_8 | 854c5ead441fdfc45654ce74ccef107d | %In this function, we create basis function matrix corresponding to the
%number of input information we have and the number of basis function we
%have defined with their bandwith h.
function PSI = computeBasisFunction(z,nbFunctions, nbDof, alpha, totalTime, center_gaussian, h, nbData)
%creating the center of basis... |
github | inria-larsen/toolbox-probabilistic_movement_primitives-master | visualisation2.m | .m | toolbox-probabilistic_movement_primitives-master/toolbox_promps/visualisation2.m | 674 | utf_8 | dd838d01f4acf9892cc1fd8ad967d9da | % Function that plot the matrix with the color col1. x is the line of the
% matrix, y the number of colonnes
%take into account the alpha
function y = visualisation2(matrix, x,y, z, col1, alpha, nameFig)
tall = size(nameFig,2);
for i=1:x
for j=1:y
val(i,j) = matrix(y*(i-1)+j);
end
end
if(isa(col1, 'ch... |
github | inria-larsen/toolbox-probabilistic_movement_primitives-master | visualisation3D.m | .m | toolbox-probabilistic_movement_primitives-master/toolbox_promps/visualisation3D.m | 668 | utf_8 | 4acca5e107cb4216bd2b761c1339c88a | % Function that plot the matrix with the color col1. x is the number of line of the
% matrix, y the number of colonnes, type is the reference of the kind of data you want to plot, nameFig is the fig
function y = visualisation3D(matrix, x, y, type, nbDof, col1, nameFig)
tall= size(nameFig,2);
for i=1:x
for j=1:y
... |
github | inria-larsen/toolbox-probabilistic_movement_primitives-master | visualisation3D2.m | .m | toolbox-probabilistic_movement_primitives-master/toolbox_promps/visualisation3D2.m | 816 | utf_8 | 60fec35072c7d3c8166bb3632e59270e | % Function that plot the matrix with the color col1. x is the number of line of the
% matrix, y the number of colonnes, bool=1 if we want forces, 0 if we want
% cartesian position, col1 is the color of the fig, nameFig is the fig
function y = visualisation3D2(matrix, , y, type, col1, alpha, nameFig)
tall= size(nameFi... |
github | inria-larsen/toolbox-probabilistic_movement_primitives-master | logLikelihood.m | .m | toolbox-probabilistic_movement_primitives-master/toolbox_promps/logLikelihood.m | 276 | utf_8 | b0809bc3dbdd70c471b3ca61227a78cd | %function that compute the log likelihood
% If A positif symetric
% R = chol(A) where R'*R = A
function log_p = logLikelihood(x,mu,S)
Sigma = chol(2*pi*S);
logdetSigma = sum(log(diag(Sigma))); % logdetSigma
log_p = -2*logdetSigma -(1/2)*(x-mu)*(S\(x-mu)');
end
|
github | sergiocastellanos/switch_mexico_data-master | UIExample.m | .m | switch_mexico_data-master/SAM/sam-sdk-2016-3-14-r3/languages/matlab/UIExample.m | 586,602 | utf_8 | 0aa9c0ca12306d99f27e822c55971cd2 | function varargout = UIExample(varargin)
% UIEXAMPLE MATLAB code for UIExample.fig
% UIEXAMPLE, by itself, creates a new UIEXAMPLE or raises the existing
% singleton*.
%
% H = UIEXAMPLE returns the handle to a new UIEXAMPLE or the handle to
% the existing singleton*.
%
% UIEXAMPLE('CALLBACK',hO... |
github | sergiocastellanos/switch_mexico_data-master | ssccall.m | .m | switch_mexico_data-master/SAM/sam-sdk-2016-3-14-r3/languages/matlab/+SSC/ssccall.m | 8,963 | utf_8 | 61a4af2373d48538fcfc4a2dd42eda7c | function [result] = ssccall(action, arg0, arg1, arg2 )
% SAM Simulation Core (SSC) MATLAB API
% Copyright (c) 2012 National Renewable Energy Laboratory
% author: Aron P. Dobos and Steven H. Janzou
% automatically detect architecture to load proper dll.
[pathstr, fn, fext] = fileparts(mfilename('fullpath'... |
github | sergiocastellanos/switch_mexico_data-master | UIExample.m | .m | switch_mexico_data-master/SAM/SDK/languages/matlab/UIExample.m | 586,602 | utf_8 | 0aa9c0ca12306d99f27e822c55971cd2 | function varargout = UIExample(varargin)
% UIEXAMPLE MATLAB code for UIExample.fig
% UIEXAMPLE, by itself, creates a new UIEXAMPLE or raises the existing
% singleton*.
%
% H = UIEXAMPLE returns the handle to a new UIEXAMPLE or the handle to
% the existing singleton*.
%
% UIEXAMPLE('CALLBACK',hO... |
github | sergiocastellanos/switch_mexico_data-master | ssccall.m | .m | switch_mexico_data-master/SAM/SDK/languages/matlab/+SSC/ssccall.m | 8,963 | utf_8 | 61a4af2373d48538fcfc4a2dd42eda7c | function [result] = ssccall(action, arg0, arg1, arg2 )
% SAM Simulation Core (SSC) MATLAB API
% Copyright (c) 2012 National Renewable Energy Laboratory
% author: Aron P. Dobos and Steven H. Janzou
% automatically detect architecture to load proper dll.
[pathstr, fn, fext] = fileparts(mfilename('fullpath'... |
github | fizyr-forks/caffe-master | classification_demo.m | .m | caffe-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | nmovshov/CMS-planet-master | CMSPlanet.m | .m | CMS-planet-master/CMSPlanet.m | 45,067 | utf_8 | 5572d78661126b36c626c37bf3e4baf2 | classdef CMSPlanet < handle
%CMSPLANET Interior model of rotating fluid planet.
% This class implements a model of a rotating fluid planet using
% Concentric Maclaurin Spheroids to calculate the hydrostatic equilibrium
% shape and resulting gravity field. A CMSPlanet object is defined by a
% ... |
github | nmovshov/CMS-planet-master | cmsset.m | .m | CMS-planet-master/cmsset.m | 2,632 | utf_8 | 7a832d5c0ef8fe7bcac0c07172e51730 | function options = cmsset(varargin)
%CMSSET Create options structure used by CMSPlanet class methods.
% OPTIONS = CMSSET('NAME1',VALUE1,'NAME2',VALUE2,...) creates an options
% structure OPTIONS in which the named properties have the specified values.
% Any unspecified properties have default values. Case is igno... |
github | nmovshov/CMS-planet-master | cms.m | .m | CMS-planet-master/cms.m | 17,189 | utf_8 | f23b04b52e20488a26fc3db9df9258ec | function [Js, out] = cms(zvec, dvec, qrot, varargin)
%CMS Concentric Maclaurin Spheroids equilibrium shape and gravity.
% Js = CMS(zvec, dvec, qrot) returns 1-by-16 vector Js of gravity
% coefficients J0 through J30 of a rotating fluid planet in hydrostatic
% equilibrium. Coefficients are stored in ascending orde... |
github | nmovshov/CMS-planet-master | Polytrope.m | .m | CMS-planet-master/+barotropes/Polytrope.m | 1,505 | utf_8 | 5def90a212dafec35c75aed8b89395ce | classdef Polytrope < barotropes.Barotrope
%POLYTROPE A barotrope of the form P = K*rho^(1 + 1/n).
%% Properties
properties (SetAccess = private)
K % polytropic constant, dimensions depend on index
n % polytropic index, dimensionless
alpha % 1 + 1/n
end
%% The constr... |
github | nmovshov/CMS-planet-master | Tabular.m | .m | CMS-planet-master/+barotropes/Tabular.m | 2,338 | utf_8 | 3d41e90db26d52cbd5eacc4521c5a1af | classdef Tabular < barotropes.Barotrope
%TABULAR A base class for a generic table barotrope.
%% Properties
properties (SetAccess = protected)
P_vals % a vector of pressure values
rho_vals % a vector of density values
end
properties (Access = public)
interpolation_metho... |
github | mcyeh/aaltd16_fusion-master | trainTask1Classifier.m | .m | aaltd16_fusion-master/trainTask1Classifier.m | 3,952 | utf_8 | 4a9e5c59630def2d8883663ac78df516 | % Train the classifier using the training data
% Chin-Chia Michael Yeh 05/28/2016
%
% trainTask1Classifier(dataPath, classPath, dicNum, spletLen, spletNum)
% Input:
% dataPath: path to the training data (string)
% classPath: path to the output directory (string)
% dicNum: number of dictionary element for sp... |
github | mcyeh/aaltd16_fusion-master | applyTask1Classifier.m | .m | aaltd16_fusion-master/applyTask1Classifier.m | 2,991 | utf_8 | 67f695bc5e1a7e7b708106cc2b1acfdc | % Apply the classifier to task 1's test data
% Chin-Chia Michael Yeh 05/28/2016
%
% applyTask1Classifier(dataPath, classPath, dicNum, spletLen, spletNum)
% Input:
% dataPath: path to task 1's test data (string)
% classPath: path to the output directory (string)
% dicNum: number of dictionary element for spa... |
github | mcyeh/aaltd16_fusion-master | distanceProfile.m | .m | aaltd16_fusion-master/distanceProfile.m | 1,179 | utf_8 | 9014f01902d4f8d1c7e8a7d385eddce7 | % Compute the distance profile on a given time series with the query
% Modify by Chin-Chia Michael Yeh 05/28/2016
% Original from http://www.cs.unm.edu/~mueen/FastestSimilaritySearch.html
%
% dist = distanceProfile(data, query)
% Output:
% dist: distance profile (vector)
% Input:
% data: the time series (vector... |
github | mcyeh/aaltd16_fusion-master | randSpletSele.m | .m | aaltd16_fusion-master/randSpletSele.m | 1,001 | utf_8 | 643d94e985a0e1f00e0247ef204aee2c | % Randomly select shapelet from the dataset
% Chin-Chia Michael Yeh 06/16/2016
%
% splet = randSpletSele(data, spletLen, spletNum)
% Output:
% splet: shaplet set, each row is a shapelet (matrix)
% Input:
% data: training set, each row is a training data (cell)
% spletLen: shaplet length (scalar)
% splet... |
github | mcyeh/aaltd16_fusion-master | normalizeData.m | .m | aaltd16_fusion-master/normalizeData.m | 674 | utf_8 | 9f72db7df8188f8f8df2ef7acbd233dc | % Perform power normalization (cube root), consecutive frame concatenation, and
% unit-norm normalize to the input data
% Chin-Chia Michael Yeh 05/28/2016
%
% data = normalizeData(data)
% Output:
% data: the normalized multi dimensional time series (matrix)
% Input:
% data: the multi dimensional time series (ma... |
github | mcyeh/aaltd16_fusion-master | learnDic.m | .m | aaltd16_fusion-master/learnDic.m | 622 | utf_8 | c4bea5eda0b8d68bd85e4f5ec0781ce0 | % Learn dictionary from the dataset
% Chin-Chia Michael Yeh 05/30/2016
%
% dic = learnDic(data, dicNum)
% Output:
% dic: dictionary, each column is a dictionary element (matrix)
% Input:
% data: training set, each row is a training data (cell)
% dicNum: number of dictionary element (scalar)
%
function dic ... |
github | mcyeh/aaltd16_fusion-master | applyTask2Classifier.m | .m | aaltd16_fusion-master/applyTask2Classifier.m | 5,954 | utf_8 | c02a19b80fb4b73c15134ef9a2df4125 | % Apply the classifier to task 2's test data
% Chin-Chia Michael Yeh 05/28/2016
%
% applyTask1Classifier(dataPath, classPath, dicNum, spletLen, spletNum)
% Input:
% dataPath: path to task 2's test data (string)
% classPath: path to the output directory (string)
% dicNum: number of dictionary element for spa... |
github | mcyeh/aaltd16_fusion-master | sparseCodeTran.m | .m | aaltd16_fusion-master/sparseCodeTran.m | 663 | utf_8 | 451fab4e711dc77f697c0f828b7bcb37 | % Compute the sparse coding of the input data, pool the sparse coding result
% with mean, and power normalized (cube root) the pooled result
% Chin-Chia Michael Yeh 05/28/2016
%
% feat = sparseCodeTran(data, dic)
% Output:
% feat: pooled and normalized sparse coding output (vector)
% Input:
% data: the multi di... |
github | mcyeh/aaltd16_fusion-master | spletTran.m | .m | aaltd16_fusion-master/spletTran.m | 707 | utf_8 | 60f253335139479b25221db4188f7d66 | % Shapelet transform and power normalized (square root) the output
% Chin-Chia Michael Yeh 05/28/2016
%
% dataTran = spletTran(data, splet)
% Output:
% dataTran: shapelet trasformation output (vector)
% Input:
% data: the multi dimensional time series (matrix)
% splet: shaplet set, each row is a shapelet (m... |
github | swag-kaust/ASOFI3D-master | write_asofi3D_json.m | .m | ASOFI3D-master/mfiles/write_asofi3D_json.m | 1,074 | utf_8 | 0cc977a208c79a5d7a0c858fd57fa85e | function write_asofi3D_json(filename, config)
% writes to json file
%% make all numbers strings
field_list = fieldnames(config);
for field_n = 1:length(field_list)
config.(field_list{field_n}) = ...
num2str(config.(field_list{field_n}));
end
%% encode to json
bb = jsonencode(config);
% replace manually ... |
github | swag-kaust/ASOFI3D-master | plot_2Dslices.m | .m | ASOFI3D-master/mfiles/plot_2Dslices.m | 25,867 | utf_8 | 315f4f842972061d8c2b83ae1947f73f | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---script for the visualization of snapshots gained from the ASOFI simulation
%---most parameters are as specified in ASOFI parameter-file, e.g. sofi3D.json
%---Please note : y denotes the vertical axis!!
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%close... |
github | swag-kaust/ASOFI3D-master | read_asofi3D_json.m | .m | ASOFI3D-master/mfiles/read_asofi3D_json.m | 922 | utf_8 | 572c5ceb8ee2a49a9b110240dbf26c43 | function config = read_asofi3D_json(filename)
%READ_ASOFI3D_JSON Read configuration file into `struct`.
% json_config = read_asofi3D_json('in_and_out/sofi3D.json') reads file
% 'in_and_out/sofi3D.json' relative to the current directory.
json_text = fileread(filename);
i = find(json_text=='{');
j = find(json_text=... |
github | swag-kaust/ASOFI3D-master | snap3D_ASOFI.m | .m | ASOFI3D-master/mfiles/snap3D_ASOFI.m | 16,756 | utf_8 | ae3f6d8fa159ce4bda3d5aef9588f3d6 | function [opts, plot_opts, D] = snap3D_ASOFI(folder_out, diffFlag, config_file)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%---script for the visualization of snapshots gained from the ASOFI simulation
%---most parameters are as specified in ASOFI parameter-file, e.g. sofi3D.json
%---Please note : y denotes ... |
github | swag-kaust/ASOFI3D-master | run_and_snap.m | .m | ASOFI3D-master/mfiles/run_and_snap.m | 3,456 | utf_8 | 3260c20b15fe9a0556b9050e371101a5 | function run_and_snap
%% run modeling
% load config
config = read_asofi3D_json('../par/in_and_out/asofi3D_hom.json');
jsonPath = '../par/in_and_out/asofi3D.json';
config.jsonPath = jsonPath;
config_hom = config;
%% generate homogeneous data
config.DH1 = 1e6;
write_asofi3D_json(jsonPath, config);
% run modeling
ru... |
github | swag-kaust/ASOFI3D-master | savefig.m | .m | ASOFI3D-master/mfiles/utils/savefig.m | 13,343 | utf_8 | 5e55383fee448146f66f14d4c342b027 | function savefig(fname, varargin)
% Usage: savefig(filename, fighdl, options)
%
% Saves a pdf, eps, png, jpeg, and/or tiff of the contents of the fighandle's (or current) figure.
% It saves an eps of the figure and the uses Ghostscript to convert to the other formats.
% The result is a cropped, clean picture. There a... |
github | swag-kaust/ASOFI3D-master | rdbuMap.m | .m | ASOFI3D-master/mfiles/utils/rdbuMap.m | 220 | utf_8 | a4b9788f7d2378e6472061570a33ad37 | % creates colormap blue-white-red
% (c) Vladimir Kazei, 2019
function a = rdbuMap()
a = zeros(2001,3);
a(:,:) = NaN;
a(1,:) = [0 0 1];
a(1001,:) = [0.85 0.95 0.85];
a(2001,:) = [1 0 0];
a = fillmissing(a,'linear',1);
end |
github | swag-kaust/ASOFI3D-master | qgsls.m | .m | ASOFI3D-master/mfiles/attenuation_tools/qgsls.m | 419 | utf_8 | ee9ed1e0880ff718c9e25bbb36e746d2 |
function q=qgsls(te,ts,L,w)
% Q fuer den L-fachen standard linear solid:
sumzQ=0;sumnQ=0;
for l=1:L,
d=1.0+w.*w*ts(l)*ts(l);
sumzQ=((1.0+w.*w*te(l)*ts(l))./d)+sumzQ;
sumnQ=(w*(te(l)-ts(l))./d)+sumnQ;
end
% Qu... |
github | swag-kaust/ASOFI3D-master | qflt.m | .m | ASOFI3D-master/mfiles/attenuation_tools/qflt.m | 698 | utf_8 | 0fced6eb730fe5fed0ef8e9d9957c78d | % This function computes the difference between a
% frequency indepent quality factor and Q as function of
% relaxation frequencies and tau (written for optimization with leastsq).
function delta=qstd(x)
global L w Qf1 Qf2
fl=x(1:L);
t=x(L+1);
% computing telaxation time... |
github | swag-kaust/ASOFI3D-master | source.m | .m | ASOFI3D-master/mfiles/old_scripts/source.m | 1,269 | utf_8 | e4b570daa2f489f0dac5304bd6462abc | % The function computes amplitude spectrum of a Ricker, Fumue
% or extern source wavelet (central frequency: fs, spectral sampling: df)
function [amp,f]=source(s,fp1,fp2,df,fs)
if ~strcmp(s,'from_file'),
tsour=1/fs;
dt=tsour/256;
k=5;
tbeg=0;
tend=k*tsour;
t=[tbeg:dt:tend];
n=length(t);
lsou... |
github | benoitberanger/FunctionalLocalizer-master | FunctionalLocalizer_GUI.m | .m | FunctionalLocalizer-master/FunctionalLocalizer_GUI.m | 28,972 | utf_8 | 95aa8b0810a0f3270a2d22ed06e28883 | function varargout = FunctionalLocalizer_GUI
% global handles
%% Open a singleton figure
% Is the GUI already open ?
figPtr = findall(0,'Tag',mfilename);
if isempty(figPtr) % Create the figure
clc
% Create a figure
figHandle = figure( ...
'HandleVisibility', 'off',... % close all does ... |
github | thomaspingel/mackskill-matlab-master | mackskill.m | .m | mackskill-matlab-master/mackskill.m | 11,461 | utf_8 | db972920708c5fc02fcf0833526bf16f | % The Mack-Skillings Statistical Test
% A nonparametric two-way ANOVA used for unbalanced incomplete block
% designs, when the number of observations in each treatment/block pair is
% one or greater. The test is equivalent to the Friedman test when
% balanced and there are no missing observations.
%
% Syntax:
% [p s... |
github | GYZHikari/Semantic-Cosegmentation-master | imagesAlign.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/imagesAlign.m | 8,167 | utf_8 | d125eb5beb502d940be5bd145521f34b | function [H,Ip] = imagesAlign( I, Iref, varargin )
% Fast and robust estimation of homography relating two images.
%
% The algorithm for image alignment is a simple but effective variant of
% the inverse compositional algorithm. For a thorough overview, see:
% "Lucas-kanade 20 years on A unifying framework,"
% S. B... |
github | GYZHikari/Semantic-Cosegmentation-master | opticalFlow.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/opticalFlow.m | 7,385 | utf_8 | 0fdca13d3caa4421fc488d0031e7838c | function [Vx,Vy,reliab] = opticalFlow( I1, I2, varargin )
% Coarse-to-fine optical flow using Lucas&Kanade or Horn&Schunck.
%
% Implemented 'type' of optical flow estimation:
% LK: http://en.wikipedia.org/wiki/Lucas-Kanade_method
% HS: http://en.wikipedia.org/wiki/Horn-Schunck_method
% SD: Simple block-based sum of ... |
github | GYZHikari/Semantic-Cosegmentation-master | seqWriterPlugin.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/seqWriterPlugin.m | 8,280 | utf_8 | 597792f79fff08b8bb709313267c3860 | function varargout = seqWriterPlugin( cmd, h, varargin )
% Plugin for seqIo and videoIO to allow writing of seq files.
%
% Do not call directly, use as plugin for seqIo or videoIO instead.
% The following is a list of commands available (swp=seqWriterPlugin):
% h=swp('open',h,fName,info) % Open a seq file for writing ... |
github | GYZHikari/Semantic-Cosegmentation-master | kernelTracker.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/kernelTracker.m | 9,315 | utf_8 | 4a7d0235f1e518ab5f1c9f1b5450b3f0 | function [allRct, allSim, allIc] = kernelTracker( I, prm )
% Kernel Tracker from Comaniciu, Ramesh and Meer PAMI 2003.
%
% Implements the algorithm described in "Kernel-Based Object Tracking" by
% Dorin Comaniciu, Visvanathan Ramesh and Peter Meer, PAMI 25, 564-577,
% 2003. This is a fast tracking algorithm that utili... |
github | GYZHikari/Semantic-Cosegmentation-master | seqIo.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/seqIo.m | 17,019 | utf_8 | 9c631b324bb527372ec3eed3416c5dcc | function out = seqIo( fName, action, varargin )
% Utilities for reading and writing seq files.
%
% A seq file is a series of concatentated image frames with a fixed size
% header. It is essentially the same as merging a directory of images into
% a single file. seq files are convenient for storing videos because: (1)
%... |
github | GYZHikari/Semantic-Cosegmentation-master | seqReaderPlugin.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/videos/seqReaderPlugin.m | 9,617 | utf_8 | ad8f912634cafe13df6fc7d67aeff05a | function varargout = seqReaderPlugin( cmd, h, varargin )
% Plugin for seqIo and videoIO to allow reading of seq files.
%
% Do not call directly, use as plugin for seqIo or videoIO instead.
% The following is a list of commands available (srp=seqReaderPlugin):
% h = srp('open',h,fName) % Open a seq file for reading ... |
github | GYZHikari/Semantic-Cosegmentation-master | pcaApply.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/pcaApply.m | 3,320 | utf_8 | a06fc0e54d85930cbc0536c874ac63b7 | function varargout = pcaApply( X, U, mu, k )
% Companion function to pca.
%
% Use pca.m to retrieve the principal components U and the mean mu from a
% set of vectors x, then use pcaApply to get the first k coefficients of
% x in the space spanned by the columns of U. See pca for general usage.
%
% If x is large, pcaAp... |
github | GYZHikari/Semantic-Cosegmentation-master | forestTrain.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/forestTrain.m | 6,138 | utf_8 | de534e2a010f452a7b13167dbf9df239 | function forest = forestTrain( data, hs, varargin )
% Train random forest classifier.
%
% Dimensions:
% M - number trees
% F - number features
% N - number input vectors
% H - number classes
%
% USAGE
% forest = forestTrain( data, hs, [varargin] )
%
% INPUTS
% data - [NxF] N length F feature vectors
% hs ... |
github | GYZHikari/Semantic-Cosegmentation-master | fernsRegTrain.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/fernsRegTrain.m | 5,914 | utf_8 | b9ed2d87a22cb9cbb1e2632495ddaf1d | function [ferns,ysPr] = fernsRegTrain( data, ys, varargin )
% Train boosted fern regressor.
%
% Boosted regression using random ferns as the weak regressor. See "Greedy
% function approximation: A gradient boosting machine", Friedman, Annals of
% Statistics 2001, for more details on boosted regression.
%
% A few notes ... |
github | GYZHikari/Semantic-Cosegmentation-master | rbfDemo.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/rbfDemo.m | 2,929 | utf_8 | 14cc64fb77bcac3edec51cf6b84ab681 | function rbfDemo( dataType, noiseSig, scale, k, cluster, show )
% Demonstration of rbf networks for regression.
%
% See rbfComputeBasis for discussion of rbfs.
%
% USAGE
% rbfDemo( dataType, noiseSig, scale, k, cluster, show )
%
% INPUTS
% dataType - 0: 1D sinusoid
% 1: 2D sinusoid
% 2: ... |
github | GYZHikari/Semantic-Cosegmentation-master | pdist2.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/pdist2.m | 5,162 | utf_8 | 768ff9e8818251f756c8325368ee7d90 | function D = pdist2( X, Y, metric )
% Calculates the distance between sets of vectors.
%
% Let X be an m-by-p matrix representing m points in p-dimensional space
% and Y be an n-by-p matrix representing another set of points in the same
% space. This function computes the m-by-n distance matrix D where D(i,j)
% is the ... |
github | GYZHikari/Semantic-Cosegmentation-master | pca.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/pca.m | 3,244 | utf_8 | 848f2eb05c18a6e448e9d22af27b9422 | function [U,mu,vars] = pca( X )
% Principal components analysis (alternative to princomp).
%
% A simple linear dimensionality reduction technique. Use to create an
% orthonormal basis for the points in R^d such that the coordinates of a
% vector x in this basis are of decreasing importance. Instead of using all
% d bas... |
github | GYZHikari/Semantic-Cosegmentation-master | kmeans2.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/classify/kmeans2.m | 5,251 | utf_8 | f941053f03c3e9eda40389a4cc64ee00 | function [ IDX, C, d ] = kmeans2( X, k, varargin )
% Fast version of kmeans clustering.
%
% Cluster the N x p matrix X into k clusters using the kmeans algorithm. It
% returns the cluster memberships for each data point in the N x 1 vector
% IDX and the K x p matrix of cluster means in C.
%
% This function is in some w... |
github | GYZHikari/Semantic-Cosegmentation-master | acfModify.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/acfModify.m | 4,202 | utf_8 | 7a49406d51e7a9431b8fd472be0476e8 | function detector = acfModify( detector, varargin )
% Modify aggregate channel features object detector.
%
% Takes an object detector trained by acfTrain() and modifies it. Only
% certain modifications are allowed to the detector and the detector should
% never be modified directly (this may cause the detector to be in... |
github | GYZHikari/Semantic-Cosegmentation-master | acfDetect.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/acfDetect.m | 3,659 | utf_8 | cf1384311b16371be6fa4715140e5c81 | function bbs = acfDetect( I, detector, fileName )
% Run aggregate channel features object detector on given image(s).
%
% The input 'I' can either be a single image (or filename) or a cell array
% of images (or filenames). In the first case, the return is a set of bbs
% where each row has the format [x y w h score] and... |
github | GYZHikari/Semantic-Cosegmentation-master | acfSweeps.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/acfSweeps.m | 10,730 | utf_8 | 78d640ed4b5b62600dd5164118a15408 | function acfSweeps
% Parameter sweeps for ACF pedestrian detector.
%
% Running the parameter sweeps requires altering internal flags.
% The sweeps are not well documented, use at your own discretion.
%
% Piotr's Computer Vision Matlab Toolbox Version NEW
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Lic... |
github | GYZHikari/Semantic-Cosegmentation-master | bbGt.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/bbGt.m | 34,046 | utf_8 | 69e66c9a0cc143fb9a794fbc9233246e | function varargout = bbGt( action, varargin )
% Bounding box (bb) annotations struct, evaluation and sampling routines.
%
% bbGt gives access to two types of routines:
% (1) Data structure for storing bb image annotations.
% (2) Routines for evaluating the Pascal criteria for object detection.
%
% The bb annotation sto... |
github | GYZHikari/Semantic-Cosegmentation-master | bbApply.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/detector/bbApply.m | 21,195 | utf_8 | 8c02a6999a84bfb5fcbf2274b8b91a97 | function varargout = bbApply( action, varargin )
% Functions for manipulating bounding boxes (bb).
%
% A bounding box (bb) is also known as a position vector or a rectangle
% object. It is a four element vector with the fields: [x y w h]. A set of
% n bbs can be stores as an [nx4] array, most funcitons below can handle... |
github | GYZHikari/Semantic-Cosegmentation-master | imwrite2.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/imwrite2.m | 5,086 | utf_8 | c98d66c2cddd9ec90beb9b1bbde31fe0 | function I = imwrite2( I, mulFlag, imagei, path, ...
name, ext, nDigits, nSplits, spliti, varargin )
% Similar to imwrite, except follows a strict naming convention.
%
% Wrapper for imwrite that writes file to the filename:
% fName = [path name int2str2(i,nDigits) '.' ext];
% Using imwrite:
% imwrite( I, fName, wri... |
github | GYZHikari/Semantic-Cosegmentation-master | convnFast.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/convnFast.m | 9,102 | utf_8 | 03d05e74bb7ae2ecb0afd0ac115fda39 | function C = convnFast( A, B, shape )
% Fast convolution, replacement for both conv2 and convn.
%
% See conv2 or convn for more information on convolution in general.
%
% This works as a replacement for both conv2 and convn. Basically,
% performs convolution in either the frequency or spatial domain, depending
% on wh... |
github | GYZHikari/Semantic-Cosegmentation-master | imMlGauss.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/imMlGauss.m | 5,674 | utf_8 | 56ead1b25fbe356f7912993d46468d02 | function varargout = imMlGauss( G, symmFlag, show )
% Calculates max likelihood params of Gaussian that gave rise to image G.
%
% Suppose G contains an image of a gaussian distribution. One way to
% recover the parameters of the gaussian is to threshold the image, and
% then estimate the mean/covariance based on the c... |
github | GYZHikari/Semantic-Cosegmentation-master | montage2.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/montage2.m | 7,484 | utf_8 | 828f57d7b1f67d36eeb6056f06568ebf | function varargout = montage2( IS, prm )
% Used to display collections of images and videos.
%
% Improved version of montage, with more control over display.
% NOTE: Can convert between MxNxT and MxNx3xT image stack via:
% I = repmat( I, [1,1,1,3] ); I = permute(I, [1,2,4,3] );
%
% USAGE
% varargout = montage2( IS, ... |
github | GYZHikari/Semantic-Cosegmentation-master | jitterImage.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/jitterImage.m | 5,252 | utf_8 | 3310f8412af00fd504c6f94b8c48992c | function IJ = jitterImage( I, varargin )
% Creates multiple, slightly jittered versions of an image.
%
% Takes an image I, and generates a number of images that are copies of the
% original image with slight translation, rotation and scaling applied. If
% the input image is actually an MxNxK stack of images then applie... |
github | GYZHikari/Semantic-Cosegmentation-master | movieToImages.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/images/movieToImages.m | 889 | utf_8 | 28c71798642af276951ee27e2d332540 | function I = movieToImages( M )
% Creates a stack of images from a matlab movie M.
%
% Repeatedly calls frame2im. Useful for playback with playMovie.
%
% USAGE
% I = movieToImages( M )
%
% INPUTS
% M - a matlab movie
%
% OUTPUTS
% I - MxNxT array (of images)
%
% EXAMPLE
% load( 'images.mat' ); [X,map]=gray2ind... |
github | GYZHikari/Semantic-Cosegmentation-master | toolboxUpdateHeader.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/toolboxUpdateHeader.m | 2,255 | utf_8 | 7a5b75e586be48da97c84d20b59887ff | function toolboxUpdateHeader
% Update the headers of all the files.
%
% USAGE
% toolboxUpdateHeader
%
% INPUTS
%
% OUTPUTS
%
% EXAMPLE
%
% See also
%
% Piotr's Computer Vision Matlab Toolbox Version 3.40
% Copyright 2014 Piotr Dollar. [pdollar-at-gmail.com]
% Licensed under the Simplified BSD License [see extern... |
github | GYZHikari/Semantic-Cosegmentation-master | toolboxGenDoc.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/toolboxGenDoc.m | 3,639 | utf_8 | 4c21fb34fa9b6002a1a98a28ab40c270 | function toolboxGenDoc
% Generate documentation, must run from dir toolbox.
%
% 1) Make sure to update and run toolboxUpdateHeader.m
% 2) Update history.txt appropriately, including w current version
% 3) Update overview.html file with the version/date/link to zip:
% edit external/m2html/templates/frame-piotr/overv... |
github | GYZHikari/Semantic-Cosegmentation-master | toolboxHeader.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/toolboxHeader.m | 2,391 | utf_8 | 30c24a94fb54ca82622719adcab17903 | function [y1,y2] = toolboxHeader( x1, x2, x3, prm )
% One line description of function (will appear in file summary).
%
% General commments explaining purpose of function [width is 75
% characters]. There may be multiple paragraphs. In special cases some or
% all of these guidelines may need to be broken.
%
% Next come... |
github | GYZHikari/Semantic-Cosegmentation-master | mdot.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/mdot.m | 2,516 | utf_8 | 34a14428c433e118d1810e23f5a6caf5 | function mdot(mmat, dotfile,f)
%MDOT - Export a dependency graph into DOT language
% MDOT(MMAT, DOTFILE) loads a .mat file generated by M2HTML using option
% ('save','on') and writes an ascii file using the DOT language that can
% be drawn using <dot> or <neato> .
% MDOT(MMAT, DOTFILE,F) builds the graph containing... |
github | GYZHikari/Semantic-Cosegmentation-master | m2html.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/m2html.m | 49,063 | utf_8 | 472047b4c36a4f8b162012840e31b59b | function m2html(varargin)
%M2HTML - Documentation Generator for Matlab M-files and Toolboxes in HTML
% M2HTML by itself generates an HTML documentation of the Matlab M-files found
% in the direct subdirectories of the current directory. HTML files are
% written in a 'doc' directory (created if necessary). All the o... |
github | GYZHikari/Semantic-Cosegmentation-master | doxysearch.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/private/doxysearch.m | 7,724 | utf_8 | 8331cde8495f34b86aef8c18656b37f2 | function result = doxysearch(query,filename)
%DOXYSEARCH Search a query in a 'search.idx' file
% RESULT = DOXYSEARCH(QUERY,FILENAME) looks for request QUERY
% in FILENAME (Doxygen search.idx format) and returns a list of
% files responding to the request in RESULT.
%
% See also DOXYREAD, DOXYWRITE
% Copyright (C)... |
github | GYZHikari/Semantic-Cosegmentation-master | doxywrite.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/private/doxywrite.m | 3,584 | utf_8 | 3255d8f824957ebc173dde374d0f78af | function doxywrite(filename, kw, statinfo, docinfo)
%DOXYWRITE Write a 'search.idx' file compatible with DOXYGEN
% DOXYWRITE(FILENAME, KW, STATINFO, DOCINFO) writes file FILENAME
% (Doxygen search.idx. format) using the cell array KW containing the
% word list, the sparse matrix (nbword x nbfile) with non-null value... |
github | GYZHikari/Semantic-Cosegmentation-master | doxyread.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/m2html/private/doxyread.m | 3,093 | utf_8 | 3152e7d26bf7ac64118be56f72832a20 | function [statlist, docinfo] = doxyread(filename)
%DOXYREAD Read a 'search.idx' file generated by DOXYGEN
% STATLIST = DOXYREAD(FILENAME) reads FILENAME (Doxygen search.idx
% format) and returns the list of keywords STATLIST as a cell array.
% [STATLIST, DOCINFO] = DOXYREAD(FILENAME) also returns a cell array
% con... |
github | GYZHikari/Semantic-Cosegmentation-master | imwrite2split.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/imwrite2split.m | 1,617 | utf_8 | 4222fd45df123e6dec9ef40ae793004f | % Writes/reads a large set of images into/from multiple directories.
%
% This is useful since certain OS handle very large directories (of say
% >20K images) rather poorly (I'm talking to you Bill). Thus, can take
% 100K images, and write into 5 separate directories, then read them back
% in.
%
% USAGE
% I = imwrite2... |
github | GYZHikari/Semantic-Cosegmentation-master | playmovies.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/playmovies.m | 1,935 | utf_8 | ef2eaad8a130936a1a281f1277ca0ea1 | % [4D] shows R videos simultaneously as a movie.
%
% Plays a movie.
%
% USAGE
% playmovies( I, [fps], [loop] )
%
% INPUTS
% I - MxNxTxR or MxNx1xTxR or MxNx3xTxR array (if MxNxT calls
% playmovie)
% fps - [100] maximum number of frames to display per second use
% fps==0 to introduce n... |
github | GYZHikari/Semantic-Cosegmentation-master | pca_apply_large.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/pca_apply_large.m | 2,062 | utf_8 | af84a2179b9d8042519bc6b378736a88 | % Wrapper for pca_apply that allows for application to large X.
%
% Wrapper for pca_apply that splits and processes X in parts, this may be
% useful if processing cannot be done fully in parallel because of memory
% constraints. See pca_apply for usage.
%
% USAGE
% same as pca_apply
%
% INPUTS
% same as pca_apply
%
%... |
github | GYZHikari/Semantic-Cosegmentation-master | montages2.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/montages2.m | 2,269 | utf_8 | 505e2be915d65fff8bfef8473875cc98 | % MONTAGES2 [4D] Used to display R sets of T images each.
%
% Displays one montage (see montage2) per row. Each of the R image sets is
% flattened to a single long image by concatenating the T images in the
% set. Alternative to montages.
%
% USAGE
% varargout = montages2( IS, [montage2prms], [padSiz] )
%
% INPUTS
% ... |
github | GYZHikari/Semantic-Cosegmentation-master | filter_gauss_1D.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/filter_gauss_1D.m | 1,137 | utf_8 | 94a453b82dcdeba67bd886e042d552d9 | % 1D Gaussian filter.
%
% Equivalent to (but faster then):
% f = fspecial('Gaussian',[2*r+1,1],sigma);
% f = filter_gauss_nD( 2*r+1, r+1, sigma^2 );
%
% USAGE
% f = filter_gauss_1D( r, sigma, [show] )
%
% INPUTS
% r - filter size=2r+1, if r=[] -> r=ceil(2.25*sigma)
% sigma - standard deviation of filter
% ... |
github | GYZHikari/Semantic-Cosegmentation-master | clfEcoc.m | .m | Semantic-Cosegmentation-master/code/Util/pdollar_toolbox/external/deprecated/clfEcoc.m | 1,493 | utf_8 | e77e1b4fd5469ed39f47dd6ed15f130f | function clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets)
% Wrapper for ecoc that makes ecoc compatible with nfoldxval.
%
% Requires the SVM toolbox by Anton Schwaighofer.
%
% USAGE
% clf = clfEcoc(p,clfInit,clfparams,nclasses,use01targets)
%
% INPUTS
% p - data dimension
% clfInit - bi... |
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