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github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_pr.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_pr.m | 3,763 | utf_8 | 4d1da5ccda1a7df2bec35b8f12fdd620 | function results = vl_test_pr(varargin)
% VL_TEST_PR
vl_test_init ;
function s = setup()
s.scores0 = [5 4 3 2 1] ;
s.scores1 = [5 3 4 2 1] ;
s.labels = [1 1 -1 -1 -1] ;
function test_perfect_tptn(s)
[rc,pr] = vl_pr(s.labels,s.scores0) ;
vl_assert_almost_equal(pr, [1 1/1 2/2 2/3 2/4 2/5]) ;
vl_assert_almost_equal(rc, ... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_hog.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_hog.m | 1,555 | utf_8 | eed7b2a116d142040587dc9c4eb7cd2e | function results = vl_test_hog(varargin)
% VL_TEST_HOG
vl_test_init ;
function s = setup()
s.im = im2single(vl_impattern('roofs1')) ;
[x,y]= meshgrid(linspace(-1,1,128)) ;
s.round = single(x.^2+y.^2);
s.imSmall = s.im(1:128,1:128,:) ;
s.imSmall = s.im ;
s.imSmallFlipped = s.imSmall(:,end:-1:1,:) ;
function test_basic... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_argparse.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_argparse.m | 795 | utf_8 | e72185b27206d0ee1dfdc19fe77a5be6 | function results = vl_test_argparse(varargin)
% VL_TEST_ARGPARSE
vl_test_init ;
function test_basic()
opts.field1 = 1 ;
opts.field2 = 2 ;
opts.field3 = 3 ;
opts_ = opts ;
opts_.field1 = 3 ;
opts_.field2 = 10 ;
opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ;
assert(isequal(opts, opts_)) ;
opts_.field1 = 9 ;
... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_liop.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_liop.m | 1,023 | utf_8 | a162be369073bed18e61210f44088cf3 | function results = vl_test_liop(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
randn('state',0) ;
s.patch = randn(65,'single') ;
xr = -32:32 ;
[x,y] = meshgrid(xr) ;
s.blob = - single(x.^2+y.^2) ;
function test_basic(s)
d = vl_liop(s.patch) ;
function test_blob(s)
% with a blob, all local intensity ord... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_test_binsearch.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/xtest/vl_test_binsearch.m | 1,339 | utf_8 | 85dc020adce3f228fe7dfb24cf3acc63 | function results = vl_test_binsearch(varargin)
% VL_TEST_BINSEARCH
vl_test_init ;
function test_inf_bins()
x = [-inf -1 0 1 +inf] ;
vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ;
vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ;
vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ;
vl_a... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_roc.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/plotop/vl_roc.m | 8,747 | utf_8 | 6b8b4786c9242d5112ca90a616db507a | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
%VL_ROC ROC curve.
% [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
% Characteristic (ROC) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative one. SCORES are... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_click.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/plotop/vl_click.m | 2,661 | utf_8 | 6982e869cf80da57fdf68f5ebcd05a86 | function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any k... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_pr.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/plotop/vl_pr.m | 9,135 | utf_8 | c5d1b9d67f843d10c0b2c6b48fab3c53 | function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR Precision-recall curve.
% [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
% precision-recall (PR) curve. LABELS are the ground truth labels,
% greather than zero for a positive sample and smaller than zero for
% a negative on... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_ubcread.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/sift/vl_ubcread.m | 3,015 | utf_8 | e8ddd3ecd87e76b6c738ba153fef050f | function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', '... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_frame2oell.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/sift/vl_frame2oell.m | 2,806 | utf_8 | c93792632f630743485fa4c2cf12d647 | function eframes = vl_frame2oell(frames)
% VL_FRAMES2OELL Convert a geometric frame to an oriented ellipse
% EFRAME = VL_FRAME2OELL(FRAME) converts the generic FRAME to an
% oriented ellipses EFRAME. FRAME and EFRAME can be matrices, with
% one frame per column.
%
% A frame is either a point, a disc, an orien... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | vl_plotsiftdescriptor.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/toolbox/sift/vl_plotsiftdescriptor.m | 5,114 | utf_8 | a4e125a8916653f00143b61cceda2f23 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptor D. If D is a
% matrix, it plots one descriptor per column. D has the same format
% used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warpe... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | phow_caltech101.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/apps/phow_caltech101.m | 11,594 | utf_8 | 7f4890a2e6844ca56debbfe23cca64f3 | function phow_caltech101()
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedu... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | sift_mosaic.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/apps/sift_mosaic.m | 4,621 | utf_8 | 8fa3ad91b401b8f2400fb65944c79712 | function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAI... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | encodeImage.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/apps/recognition/encodeImage.m | 5,278 | utf_8 | 5d9dc6161995b8e10366b5649bf4fda4 | function descrs = encodeImage(encoder, im, varargin)
% ENCODEIMAGE Apply an encoder to an image
% DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER
% to image IM, returning a corresponding code vector PSI.
%
% IM can be an image, the path to an image, or a cell array of
% the same, to operate on multiple ... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | experiments.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/apps/recognition/experiments.m | 6,905 | utf_8 | 1e4a4911eed4a451b9488b9e6cc9b39c | function experiments()
% EXPERIMENTS Run image classification experiments
% The experimens download a number of benchmark datasets in the
% 'data/' subfolder. Make sure that there are several GBs of
% space available.
%
% By default, experiments run with a lite option turned on. This
% quickly runs all... |
github | shenjianbing/Generalized-pooling-for-robust-object-tracking-master | getDenseSIFT.m | .m | Generalized-pooling-for-robust-object-tracking-master/dependency/vlfeat-0.9.18-bin/vlfeat-0.9.18/apps/recognition/getDenseSIFT.m | 1,679 | utf_8 | 2059c0a2a4e762226d89121408c6e51c | function features = getDenseSIFT(im, varargin)
% GETDENSESIFT Extract dense SIFT features
% FEATURES = GETDENSESIFT(IM) extract dense SIFT features from
% image IM.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made availab... |
github | joe-of-all-trades/vtkwrite-master | vtkwrite.m | .m | vtkwrite-master/vtkwrite.m | 11,698 | utf_8 | b2d2311772bb3c962cf4c421b43f3ea2 | function vtkwrite( filename,dataType,varargin )
% VTKWRITE Writes 3D Matlab array into VTK file format.
% vtkwrite(filename,'structured_grid',x,y,z,'vectors',title,u,v,w) writes
% a structured 3D vector data into VTK file, with name specified by the string
% filename. (u,v,w) are the vector components at the points ... |
github | krrish94/caffe-keypoint-master | classification_demo.m | .m | caffe-keypoint-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 | ijameslive/coursera-machine-learning-1-master | submit.m | .m | coursera-machine-learning-1-master/mlclass-ex8/submit.m | 17,515 | utf_8 | 2949fbde41e47f99c42171e2e0a39efc | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | ijameslive/coursera-machine-learning-1-master | submitWeb.m | .m | coursera-machine-learning-1-master/mlclass-ex8/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | ijameslive/coursera-machine-learning-1-master | submit.m | .m | coursera-machine-learning-1-master/mlclass-ex6/submit.m | 16,836 | utf_8 | d4c87e5dbf32a81bdaf04fd017fe4cb3 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | ijameslive/coursera-machine-learning-1-master | porterStemmer.m | .m | coursera-machine-learning-1-master/mlclass-ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | ijameslive/coursera-machine-learning-1-master | submitWeb.m | .m | coursera-machine-learning-1-master/mlclass-ex6/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | ijameslive/coursera-machine-learning-1-master | submit.m | .m | coursera-machine-learning-1-master/mlclass-ex4/submit.m | 17,129 | utf_8 | 917c487f37cf14037c77e3c57ad78ce1 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | ijameslive/coursera-machine-learning-1-master | submitWeb.m | .m | coursera-machine-learning-1-master/mlclass-ex4/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | ijameslive/coursera-machine-learning-1-master | submit.m | .m | coursera-machine-learning-1-master/mlclass-ex1/submit.m | 17,317 | utf_8 | 14dfeccc6eb749406cb5d77fabb6bf47 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | ijameslive/coursera-machine-learning-1-master | submitWeb.m | .m | coursera-machine-learning-1-master/mlclass-ex1/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | ijameslive/coursera-machine-learning-1-master | submit.m | .m | coursera-machine-learning-1-master/mlclass-ex2/submit.m | 17,086 | utf_8 | 7b02ce6b9daa919a9a66ef0adb401b07 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | ijameslive/coursera-machine-learning-1-master | submitWeb.m | .m | coursera-machine-learning-1-master/mlclass-ex2/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | ijameslive/coursera-machine-learning-1-master | submit.m | .m | coursera-machine-learning-1-master/mlclass-ex3/submit.m | 17,041 | utf_8 | 07a62d95df0814b4ffbc6c2f4b433e22 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | ijameslive/coursera-machine-learning-1-master | submitWeb.m | .m | coursera-machine-learning-1-master/mlclass-ex3/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | ijameslive/coursera-machine-learning-1-master | submit.m | .m | coursera-machine-learning-1-master/mlclass-ex5/submit.m | 17,211 | utf_8 | 057662350ffa8db95583373185a26a6b | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | ijameslive/coursera-machine-learning-1-master | submitWeb.m | .m | coursera-machine-learning-1-master/mlclass-ex5/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | ijameslive/coursera-machine-learning-1-master | submit.m | .m | coursera-machine-learning-1-master/mlclass-ex7/submit.m | 16,958 | utf_8 | cd11307f72915c0d3b58176b66081197 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | ijameslive/coursera-machine-learning-1-master | submitWeb.m | .m | coursera-machine-learning-1-master/mlclass-ex7/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | cgtuebingen/Product-Quantization-Tree-master | ivecs_read.m | .m | Product-Quantization-Tree-master/cpu_version/matlab/ivecs_read.m | 1,361 | utf_8 | a6dcf18c53cf54bf185c73f31ed016ea | % Read a set of vectors stored in the ivec format (int + n * int)
% The function returns a set of output vector (one vector per column)
%
% Syntax:
% v = ivecs_read (filename) -> read all vectors
% v = ivecs_read (filename, n) -> read n vectors
% v = ivecs_read (filename, [a b]) -> read the vectors from a ... |
github | cgtuebingen/Product-Quantization-Tree-master | fvecs_read.m | .m | Product-Quantization-Tree-master/cpu_version/matlab/fvecs_read.m | 1,363 | utf_8 | 267b271a3740ad6bf22d8f14965b7c4a | % Read a set of vectors stored in the fvec format (int + n * float)
% The function returns a set of output vector (one vector per column)
%
% Syntax:
% v = fvecs_read (filename) -> read all vectors
% v = fvecs_read (filename, n) -> read n vectors
% v = fvecs_read (filename, [a b]) -> read the vectors from ... |
github | claassengroup/matLeap-master | isFbcEnabled.m | .m | matLeap-master/libSBML-5.12.0-matlab/isFbcEnabled.m | 2,380 | utf_8 | 52e85daac8468f2c9bf66de5b51e04fe | function fbcEnabled = isFbcEnabled()
% Checks whether the version of libSBML has been built with
% the FBC package extension enabled
% Filename : isFbcEnabled.m
% Description : check fbc status
% Author(s) : SBML Team <sbml-team@caltech.edu>
% Organization: EMBL-EBI, Caltech
% Created : 2011-02-08
%
% This f... |
github | claassengroup/matLeap-master | buildSBML.m | .m | matLeap-master/libSBML-5.12.0-matlab/buildSBML.m | 30,786 | utf_8 | 90b3ea6ab3341cde3685da49306e5977 | function buildSBML(varargin)
% Builds the MATLAB language interface for libSBML.
%
% This script is meant to be invoked from libSBML's MATLAB bindings
% source directory. LibSBML must already have been compiled and
% installed on your system. This script makes the following
% assumptions:
%
% * Linux and Mac systems:... |
github | claassengroup/matLeap-master | CheckAndConvert.m | .m | matLeap-master/libSBML-5.12.0-matlab/CheckAndConvert.m | 13,028 | utf_8 | 00513eb63c558601962fb1952bb6ae86 | function Formula = CheckAndConvert(Input)
% converts from MathML in-fix to MATLAB functions
% Filename : CheckAndConvert.m
% Description : converts from MathML in-fix to MATLAB functions
% Author(s) : SBML Team <sbml-team@caltech.edu>
% Organization: University of Hertfordshire STRC
% Created : 2004-12-13
%
%... |
github | claassengroup/matLeap-master | ConvertFormulaToMathML.m | .m | matLeap-master/libSBML-5.12.0-matlab/ConvertFormulaToMathML.m | 9,356 | utf_8 | 340134515b50305b849db7c47490aef3 | function Formula = ConvertFormulaToMathML(Input)
% converts from MATLAB to MathML in-fix functions
% Filename : ConvertFormulaToMathML.m
%
% This file is part of libSBML. Please visit http://sbml.org for more
% information about SBML, and the latest version of libSBML.
%
% Copyright (C) 2013-2014 jointly by the f... |
github | claassengroup/matLeap-master | installSBML.m | .m | matLeap-master/libSBML-5.12.0-matlab/installSBML.m | 12,505 | utf_8 | d29eeee59c9bb8682a5b13deeeabf9b5 | function installSBML(varargin)
% Installs the MATLAB language interface for libSBML.
%
% This script assumes that the libsbml matlab binding executables files already
% exist; either because the user has built them using buildSBML (only
% in the src release) or the binding is being installed from an installer.
%
% Curr... |
github | claassengroup/matLeap-master | isSBML_Model.m | .m | matLeap-master/libSBML-5.12.0-matlab/isSBML_Model.m | 107,187 | utf_8 | 498389acf901590bf5411ed06382e606 | function [valid, message] = isSBML_Model(varargin)
% [valid, message] = isSBML_Model(SBMLModel)
%
% Takes
%
% 1. SBMLModel, an SBML Model structure
% 2. extensions_allowed (optional) =
% - 0, structures should contain ONLY required fields
% - 1, structures may contain additional fields (default)
%
% Returns
%
% 1. ... |
github | compneuro-da/rsHRF-master | rsHRF_mvgc.m | .m | rsHRF-master/rsHRF_mvgc.m | 14,061 | utf_8 | 288e227bc8fac13e21ff0084b5cd9265 | function [F,pvalue] = rsHRF_mvgc(data,order,regmode,flag_1to2,flag_pvalue)
% Calculate time-domain multivariate Granger causalities
% data: nvars x nobs x ntrials
% F(i,j): from i to j.
% pvalue: F-test.
%
%% References
% [1] L. Barnett and A. K. Seth, The MVGC The MVGC multivariate Granger causality toolbox:
% A... |
github | compneuro-da/rsHRF-master | rsHRF_inpaint_nans3.m | .m | rsHRF-master/rsHRF_inpaint_nans3.m | 8,268 | utf_8 | 312e077493b6496d54e1e9bc634bab03 | function B=rsHRF_inpaint_nans3(A,method)
% INPAINT_NANS3: in-paints over nans in a 3-D array
% usage: B=INPAINT_NANS3(A) % default method (0)
% usage: B=INPAINT_NANS3(A,method) % specify method used
%
% Solves approximation to a boundary value problem to
% interpolate and extrapolate holes in a 3-D array.
% ... |
github | compneuro-da/rsHRF-master | rsHRF_band_filter.m | .m | rsHRF-master/rsHRF_band_filter.m | 1,907 | utf_8 | 0a42ce81cbe9a24d88c672994eebab72 | function x = rsHRF_band_filter(x,TR,Bands,m)
% data: x nobs*nvar
if nargin<4
m = 5000; %block size
end
nvar = size(x,2);
nbin = ceil(nvar/m);
for i=1:nbin
if i~=nbin
ind_X = (i-1)*m+1:i*m ;
else
ind_X = (i-1)*m+1:nvar ;
end
x1 = x(:,ind_X);
x1 = conn_filter(TR,Bands,x1,'full... |
github | compneuro-da/rsHRF-master | rsHRF_deleteoutliers.m | .m | rsHRF-master/rsHRF_deleteoutliers.m | 3,398 | utf_8 | afbe5d17fc43047bd25f0386a8ff14b3 | function [b,idx,outliers] = rsHRF_deleteoutliers(a,alpha,rep);
% [B, IDX, OUTLIERS] = DELETEOUTLIERS(A, ALPHA, REP)
%
% For input vector A, returns a vector B with outliers (at the significance
% level alpha) removed. Also, optional output argument idx returns the
% indices in A of outlier values. Optional output argu... |
github | compneuro-da/rsHRF-master | rsHRF_ROI_sig_job.m | .m | rsHRF-master/rsHRF_ROI_sig_job.m | 2,097 | utf_8 | 4fa12ffe69a0327b9e3b1ead60f2170d | function rsHRF_ROI_sig_job(job)
ROI = job.Datasig; %cell file
[data_txt,mat_name]= rsHRF_check_ROIsig(ROI);
tmp = data_txt(:,1);
if job.para_global.combine_ROI
data = cell2mat(tmp'); % combine all ROI together
tmp={}; tmp{1} = data;
[~,outname,~] = fileparts(data_txt{1,2});
fprintf('Combine all input R... |
github | compneuro-da/rsHRF-master | rsHRF_estimation_temporal_basis.m | .m | rsHRF-master/rsHRF_estimation_temporal_basis.m | 11,630 | utf_8 | 1f2b9bf962a3d78fb4dc5359a2f908e3 | function [beta_hrf, bf, event_bold] = rsHRF_estimation_temporal_basis(data,xBF,temporal_mask,flag_parfor)
% xBF.TR = 2;
% xBF.T = 8;
% xBF.T0 = fix(xBF.T/2); (reference time bin, see slice timing)
% xBF.dt = xBF.TR/xBF.T;
% xBF.AR_lag = 1;
% xBF.thr = 1;
% xBF.len = 25;
% xBF.localK = 2;
% temporal_mask: scrubbing mask... |
github | compneuro-da/rsHRF-master | rsHRF_viewer.m | .m | rsHRF-master/rsHRF_viewer.m | 13,643 | utf_8 | 31ffdd725e57f1fd4c1d762605d380a4 | function st = rsHRF_viewer(job);
% gronwu@gmail.com ; Guo-Rong Wu
% 2017, 18th May.
% 2019, 25th Oct, updated.
% 2020, 9th Oct, colorbar Yticklabel removed.
underlay_img = job.underlay_nii{1};
img = job.stat_nii{1};
HRF_mat = job.HRF_mat;
clear st;
spm_orthviews('Reset');
global st
st.fig = figu... |
github | compneuro-da/rsHRF-master | rsHRF_conn_run.m | .m | rsHRF-master/rsHRF_conn_run.m | 15,141 | utf_8 | d03feabaea5d3171c10d2d0cb822b2bc | function rsHRF_conn_run(data, connroinfo,v0,name,outdir,flag_pval_pwgc,flag_nii_gii);
%data: nobs x nvar (3D index)
fprintf('Connectivity analysis...\n ')
meastr = {'pwGC','CGC','PCGC','Pearson','PartialPearson','Spearman','PartialSpearman',};
para_global = rsHRF_global_para;
regmode = para_global.regmode; % for GC
fo... |
github | compneuro-da/rsHRF-master | rsHRF_estimation_impulseest.m | .m | rsHRF-master/rsHRF_estimation_impulseest.m | 3,318 | utf_8 | 70435fdafab02c8b3db7641bff6ee530 | function [hrfa,event_bold] = rsHRF_estimation_impulseest(data,para);
% Nonparametric impulse response estimation.
% System Identification Toolbox is required.
%
% By: Guo-Rong Wu (gronwu@gmail.com).
% Faculty of Psychology, Southwest University.
% History:
% - 2015-04-17 - Initial version.
% para.thr=[1];
% pa... |
github | compneuro-da/rsHRF-master | rsHRF_knee_pt.m | .m | rsHRF-master/rsHRF_knee_pt.m | 5,902 | utf_8 | f22baf6900ab2ca29cb21378d8bdef01 | function [res_x, idx_of_result] = rsHRF_knee_pt(y)
res_x=[];
[~,id] = knee_pt(y);
[~,idm] = min(y);
ratio = abs(y(id)-y(idm))/range(y);
if ratio>0.5
idx_of_result = idm;
else
idx_of_result = id;
end
end
function [res_x, idx_of_result] = knee_pt(y,x,just_return)
%Returns the x-location of a (single) knee of cu... |
github | compneuro-da/rsHRF-master | tbx_cfg_rsHRF.m | .m | rsHRF-master/tbx_cfg_rsHRF.m | 38,917 | utf_8 | f8345a7a0e695df20bd7cf743caa5b5e | function HRFrs = tbx_cfg_rsHRF
% Configuration file for toolbox 'rsHRF'
% https://github.com/guorongwu/rsHRF
% $Id: rsHRF.m
if ~isdeployed
addpath(fullfile(spm('Dir'),'toolbox','rsHRF'));
end
% ---------------------------------------------------------------------
% NIfTI Data
% ---------------------------... |
github | compneuro-da/rsHRF-master | rsHRF_estimation_FIR.m | .m | rsHRF-master/rsHRF_estimation_FIR.m | 5,644 | utf_8 | f525c14ab84e3a3979b752846777a816 | function [beta_rshrf,event_bold] = rsHRF_estimation_FIR(data,para,temporal_mask,flag_parfor)
% temporal_mask: generated from scrubbing.
% By: Guo-Rong Wu (gronwu@gmail.com).
% Faculty of Psychology, Southwest University.
% History:
% - 2015-04-17 - Initial version.
if nargin<4
flag_parfor = 1;
end
para.t... |
github | compneuro-da/rsHRF-master | rsHRF_denoise_job.m | .m | rsHRF-master/rsHRF_denoise_job.m | 5,773 | utf_8 | 636514ba272caf404eab51266b00844e | function [data,data_nuisancerm] = rsHRF_denoise_job(job,data)
Nscans = size(data,1);
flag_delete = job.para_global.delete_files; % delete temporary files (generated wm/csf/brainmask)
covariates = job.Denoising.generic;
if ~isempty(covariates)
fprintf('Reading Covariates ...\n')
[txt,mat,nii]= wgr_check_covaria... |
github | compneuro-da/rsHRF-master | test_rsHRF_VolumeROI_cfg_job.m | .m | rsHRF-master/unittests/test_rsHRF_VolumeROI_cfg_job.m | 3,599 | utf_8 | eecfea0bae7198621a2519e43ff4ce03 | function tests = test_rsHRF_VolumeROI_cfg_job
% Unit Tests for (Volume) ROI rsHRF estimation/deconvolution and FC analysis
tests = functiontests(localfunctions);
function test_VolumeROI(testCase)
out_dir = tempdir;
conprefix = 'Conntest_';
deconvprefix = 'Deconvtest_';
filename = 'rsHRF_demo';
expected_output... |
github | compneuro-da/rsHRF-master | test_rsHRF_estimation_impulseest.m | .m | rsHRF-master/unittests/test_rsHRF_estimation_impulseest.m | 907 | utf_8 | abe8a389bdc31bef81d29d08ff88c27c | function tests = test_rsHRF_estimation_impulseest
% Unit Tests for rsHRF_estimation_impulseest
tests = functiontests(localfunctions);
function test_rsHRF_impulseest(testCase)
import matlab.unittest.constraints.*
fpath = fileparts(which('rsHRF.m'));
cd(fullfile(fpath,'demo_codes'))
load('HCP_100307_rfMRI_REST1_... |
github | compneuro-da/rsHRF-master | test_rsHRF_estimation_deconvolution.m | .m | rsHRF-master/unittests/test_rsHRF_estimation_deconvolution.m | 2,473 | utf_8 | b547768210512d21a0a5be08f2a9de4b | function tests = test_rsHRF_estimation_deconvolution
% Unit Tests for band filter, HRF (parameter) estimation and (iterative Wiener) deconvolution
tests = functiontests(localfunctions);
function test_rsHRF_core(testCase)
import matlab.unittest.constraints.*
fpath = fileparts(which('rsHRF.m'));
cd(fullfile(fpath... |
github | compneuro-da/rsHRF-master | test_rsHRF_Signals_cfg_job.m | .m | rsHRF-master/unittests/test_rsHRF_Signals_cfg_job.m | 3,627 | utf_8 | 4b435d5e34417d5d19a2e39f11845c98 | function tests = test_rsHRF_Signals_cfg_job
% Unit Tests for (Signal)ROI rsHRF estimation/deconvolution and FC analysis
tests = functiontests(localfunctions);
function test_Signals(testCase)
out_dir = tempdir;
conprefix = 'Conntest_';
deconvprefix = 'Deconvtest_';
filename = 'rsHRF_demo';
expected_output = {f... |
github | compneuro-da/rsHRF-master | test_rsHRF_Voxelwise_cfg_job.m | .m | rsHRF-master/unittests/test_rsHRF_Voxelwise_cfg_job.m | 4,580 | utf_8 | f87d49d4621efdea5bb8f9a023c5e8f9 | function tests = test_rsHRF_Voxelwise_cfg_job
% Unit Tests for voxelwise rsHRF estimation/deconvolution and FC/GC analysis
tests = functiontests(localfunctions);
function test_voxelwise(testCase)
out_dir = tempdir;
conprefix = 'Conntest_';
deconvprefix = 'Deconvtest_';
filename = 'rsHRF_demo';
expected_output... |
github | compneuro-da/rsHRF-master | test_rsHRF_SurfROI_cfg_job.m | .m | rsHRF-master/unittests/test_rsHRF_SurfROI_cfg_job.m | 4,263 | utf_8 | 2013a3fc83ac25d8c8a1acc35293762c | function tests = test_rsHRF_SurfROI_cfg_job
% Unit Tests for (surface) ROI rsHRF estimation/deconvolution and FC/GC analysis
tests = functiontests(localfunctions);
function test_SurfROI(testCase)
out_dir = tempdir;
conprefix = 'Conntest_';
deconvprefix = 'Deconvtest_';
filename = 'rsHRF_demo';
expected_output... |
github | compneuro-da/rsHRF-master | test_rsHRF_find_event_vector.m | .m | rsHRF-master/unittests/test_rsHRF_find_event_vector.m | 451 | utf_8 | 4edc3607ff94c7875c9bd03d8d332592 | function tests = test_rsHRF_find_event_vector
% Unit Tests for rsHRF_find_event_vector
tests = functiontests(localfunctions);
function test_rsHRF_find_event_vector_1(testCase)
import matlab.unittest.constraints.*
matrix = randn(200,1);
ts = [20,60,100 150];
matrix(ts)=10; % plot(matrix)
event = rsHRF_find_ev... |
github | compneuro-da/rsHRF-master | test_rsHRF_Viewer_cfg_job.m | .m | rsHRF-master/unittests/test_rsHRF_Viewer_cfg_job.m | 2,559 | utf_8 | 63817f367f41761dadd3b7993ce9ac50 | function tests = test_rsHRF_Viewer_cfg_job
% Unit Tests for voxelwise rsHRF display
tests = functiontests(localfunctions);
function test_Viewer(testCase)
out_dir = tempdir;
deconvprefix = 'Deconvtest_';
filename = 'rsHRF_demo';
expected_output = {fullfile(out_dir,[deconvprefix,filename,'_job.mat'])
fullfile(o... |
github | compneuro-da/rsHRF-master | test_rsHRF_Vertexwise_cfg_job.m | .m | rsHRF-master/unittests/test_rsHRF_Vertexwise_cfg_job.m | 4,546 | utf_8 | e9b9a5b0e1b3b62113143415f517edd9 | function tests = test_rsHRF_Vertexwise_cfg_job
% Unit Tests for vertexwise rsHRF estimation/deconvolution and FC/GC analysis
tests = functiontests(localfunctions);
function test_vertexwise(testCase)
out_dir = tempdir;
conprefix = 'Conntest_';
deconvprefix = 'Deconvtest_';
filename = 'rsHRF_demo';
expected_out... |
github | compneuro-da/rsHRF-master | rsHRF_estimation_impulseest_IO.m | .m | rsHRF-master/demo_codes/rsHRF_estimation_impulseest_IO.m | 11,440 | utf_8 | 99bc4a399cc92d67ce2bbff4a3eb25e2 | function [hrf] = rsHRF_estimation_impulseest_IO(input, output, para)
%% detrend
if ~isempty(para.flag_detrend)
input = spm_detrend(input,para.flag_detrend);
output = spm_detrend(output,para.flag_detrend);
end
%% filtering
if ~isempty(para.Band)
output = bandpass_filt(output, 1/para.TR, para.Band, 10... |
github | compneuro-da/rsHRF-master | rsHRF_demo_UCLA_sub_gamma3_wm.m | .m | rsHRF-master/demo_codes/rsHRF_demo_UCLA/rsHRF_demo_UCLA_sub_gamma3_wm.m | 2,409 | utf_8 | 1647fe41b830244fd71da5d19fd54003 | function rsHRF_demo_UCLA_sub_gamma3_wm(fl,fnii,niimask,mainoutdir,csf_mat)
[fpath,name,~] = fileparts(fl);
id = strfind(name,'_task');
subid = name(1:id(1)-1);
gunzip([fnii,'.gz'])
a= spm_load(fl);
b = load(csf_mat);
acomp = [b.csf_acompcor];
Q1 = [a.X, a.Y, a.Z, a.RotX, a.RotY, a.RotZ];
HM = [Q1, [zeros(1,... |
github | compneuro-da/rsHRF-master | rsHRF_demo_UCLA_sub_Fourier_surf_ROI.m | .m | rsHRF-master/demo_codes/rsHRF_demo_UCLA/rsHRF_demo_UCLA_sub_Fourier_surf_ROI.m | 2,687 | utf_8 | bd72bb48f9957960369ac13f17f771ba | %-----------------------------------------------------------------------
% Job saved on 01-Oct-2020 17:30:46 by cfg_util (rev $Rev: 7345 $)
% spm SPM - SPM12 (7771)
% cfg_basicio BasicIO - Unknown
%-----------------------------------------------------------------------
function rsHRF_demo_UCLA_sub_Fourier_surf_ROI... |
github | compneuro-da/rsHRF-master | W_Calculate_RVR.m | .m | rsHRF-master/demo_codes/rsHRF_demo_UCLA/RVR/W_Calculate_RVR.m | 2,383 | utf_8 | 1c9ded04ddc186db35091bbb267ac855 |
function [w_Brain, model_All] = W_Calculate_RVR(Subjects_Data, Subjects_Scores, Covariates, Pre_Method, ResultantFolder)
%
% Subject_Data:
% m*n matrix
% m is the number of subjects
% n is the number of features
%
% Subject_Scores:
% the continuous variable to be predicted,[1*m]... |
github | compneuro-da/rsHRF-master | W_Calculate_RVR_SGE.m | .m | rsHRF-master/demo_codes/rsHRF_demo_UCLA/RVR/W_Calculate_RVR_SGE.m | 389 | utf_8 | ed548906c32e0c3951f1c7fc894f7392 |
function W_Calculate_RVR_SGE(Subjects_Data_Path, Rand_Scores, ID, Covariates, Pre_Method, ResultantFolder)
tmp = load(Subjects_Data_Path);
FieldName = fieldnames(tmp);
for i = 1:length(ID)
disp(ID(i));
[w_Brain, ~] = W_Calculate_RVR(tmp.(FieldName{1}), Rand_Scores{i}, Covariates, Pre_Method);
save([Resul... |
github | compneuro-da/rsHRF-master | SurfStatWriteVol1.m | .m | rsHRF-master/demo_codes/rsHRF_demo_UCLA/RVR/SurfStatWriteVol1.m | 19,785 | utf_8 | bad41c0fa1d7c525f5619d09cbf3db77 | function SurfStatWriteVol( d, Z, T );
%Writes volumetric image data in MINC, ANALYZE, NIFTI or AFNI format.
%
% Usage: fmris_write_image( d [, Z, T] ).
%
% d.file_name = file name with extension .mnc, .img, .nii or .brik as above
% Z = vector of slices.
% T = vector of times.
% If Z and T are o... |
github | compneuro-da/rsHRF-master | SurfStatReadVol1.m | .m | rsHRF-master/demo_codes/rsHRF_demo_UCLA/RVR/SurfStatReadVol1.m | 17,058 | utf_8 | a06b1de60d4f9453d2516675335e9cad | function d = SurfStatReadVol1( file, Z, T );
%Reads a single volumetric file in MINC, ANALYZE, NIFTI or AFNI format.
%
% Usage: d = SurfStatReadVol1( file [, Z, T] ).
%
% file = file name with extension .mnc, .img, .nii or .brik as above.
% Z = vector of slices.
% T = vector of times.
% If Z and T are both 0... |
github | compneuro-da/rsHRF-master | stat_threshold.m | .m | rsHRF-master/demo_codes/rsHRF_demo_UCLA/RVR/stat_threshold.m | 27,286 | utf_8 | 75dcc644078e278b556f0acc871d984d | function [ peak_threshold, extent_threshold, ...
peak_threshold_1, extent_threshold_1, t, rho ] = ...
stat_threshold( search_volume, num_voxels, fwhm, df, p_val_peak, ...
cluster_threshold, p_val_extent, nconj, nvar, EC_file, expr, nprint );
%Thresholds and P-values of peaks and clusters of random field... |
github | compneuro-da/rsHRF-master | SurfStatResels.m | .m | rsHRF-master/demo_codes/rsHRF_demo_UCLA/RVR/SurfStatResels.m | 17,594 | utf_8 | 7a46dc3835272228e7108dd7cfc676d2 | function [resels, reselspvert, edg] = SurfStatResels( slm, mask );
%Resels of surface or volume data inside a mask.
%
% Usage: [resels, reselspvert, edg] = SurfStatResels( slm [, mask] )
%
% slm.resl = e x k matrix of sum over observations of squares of
% differences of normalized residuals along eac... |
github | compneuro-da/rsHRF-master | RVR_W_Permutation.m | .m | rsHRF-master/demo_codes/rsHRF_demo_UCLA/RVR/RVR_W_Permutation.m | 1,696 | utf_8 | 527f7c4ad4cfdbe7ec572b365bb43319 |
function RVR_W_Permutation(Data_Path, Scores, Perm_times_Range, RandIndex_Folder, Covariates, Pre_Method, ResultantFolder, Queue)
TaskQuantity = 100;
JobsPerTask = fix(length(Perm_times_Range) / TaskQuantity);
JobsRemain = mod(length(Perm_times_Range), TaskQuantity * JobsPerTask);
mkdir([ResultantFolder filesep 'ran... |
github | lhmRyan/deep-supervised-hashing-DSH-master | classification_demo.m | .m | deep-supervised-hashing-DSH-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 | kpzhang93/MTCNN_face_detection_alignment-master | test.m | .m | MTCNN_face_detection_alignment-master/code/codes/camera_demo/test.m | 8,824 | utf_8 | 484a3e8719f2102fd5aa6c841209b5ed | function varargout = test(varargin)
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @test_OpeningFcn, ...
'gui_OutputFcn', @test_OutputFcn, ...
'gui_LayoutFcn', [] ... |
github | dailymoyuan/Compressed-Air-Energy-Storage-for-wind-energy-storage-master | air_tank.m | .m | Compressed-Air-Energy-Storage-for-wind-energy-storage-master/air_tank.m | 713 | utf_8 | e066bd0364ff84d95ed16714340c9d7e | %---------------air storage tank-----------------%
function [Cp_nrm,Turbine_trq] = fcn(v_wind,wp)
Cpmax=0.4825;
rated_trb=36;
Pw_rated=600;
w=wp*pi/30;
air_rho=1.225;
r_blade=30;
swept_area=pi*r_blade^2;
lambda=(r_blade*w)/v_wind;
beta=0;
c1=0.51760*0.15;
c2=116*1.512;
c3=0.4;
c4=5;
c5=21*1.36;
... |
github | dailymoyuan/Compressed-Air-Energy-Storage-for-wind-energy-storage-master | expension_stage.m | .m | Compressed-Air-Energy-Storage-for-wind-energy-storage-master/expension_stage.m | 1,022 | utf_8 | 989a4e95cfda9e8f92cdc3e960ee60fa | %-----------expansion stage--------------%
function [Pout_caes,Tout_LP, Tin_LP,Tout_HP,pout_HP,pout_LP, P_HP, P_LP] = fcn(P_need_left, dm, pin_HP, Tin_HP)
B1 = 3; % compression ratio for high turbines
B2 = 15; % compression ratio for low turbines
n_t = 0.85; % efficiency of both turbines
nHX =... |
github | dailymoyuan/Compressed-Air-Energy-Storage-for-wind-energy-storage-master | charge_mode_supervisor_control_logic.m | .m | Compressed-Air-Energy-Storage-for-wind-energy-storage-master/charge_mode_supervisor_control_logic.m | 1,933 | utf_8 | 6f936eba5c79806e40c79bfeff7e47e8 | %--------battery charging/discharging mode transtion login definition with time step count-----------\\
function [P_excess,P_need,P_real,P_without_supplu] = fcn(P_auto,real_Tload,auto_Tload,wm,P_aero)
P_real = real_Tload * (wm *pi/30);
if P_auto > P_real
P_without_supplu = 0;
else
... |
github | dailymoyuan/Compressed-Air-Energy-Storage-for-wind-energy-storage-master | compression_stage.m | .m | Compressed-Air-Energy-Storage-for-wind-energy-storage-master/compression_stage.m | 560 | utf_8 | 22222521bd8765bab9448af0f63d4045 | %------setting Pelec as the power input from P_exceed of wind farm-----%
%------used to compress air and store it in the air tank---------------%
function dm_c = fcn(Tout, P_ex, p)
dm_c = 0;
n_c = 0.877; %efficiency of each compressor
Tin = 293; %environment temperature [k]
R = 0.287; ... |
github | dailymoyuan/Compressed-Air-Energy-Storage-for-wind-energy-storage-master | storage_size_def.m | .m | Compressed-Air-Energy-Storage-for-wind-energy-storage-master/storage_size_def.m | 400 | utf_8 | 5d699f45672b6e9e824e1d3b655d4972 | %turbine output is messured in Kwh - energy
% n is the efficiency of the turbine
% a is the number of turbines used
% pm is the maximum pressure of the tank in [Pa]
function [sc,m,V] = fcn(turbine_out,Tin_ct,Tout_ct,Ts)
n = 0.85;
a = 2;
pm = 4e+6;
cp = 1.005;
R = 287.06;
sc = t... |
github | kunzhan/Pulse-coupled_neural_networks_PCNN-master | GrayStretch.m | .m | Pulse-coupled_neural_networks_PCNN-master/functions/GrayStretch.m | 1,449 | utf_8 | 59810661bc80ef21e7702aeaf24fe2b3 | function GS = GrayStretch(I,Per)
% The code was written by Jicai Teng, Jinhui Shi, Kun Zhan
% $Revision: 1.0.0.0 $ $Date: 2014/12/06 $ 17:58:47 $
% Reference:
% [1] K Zhan, J Shi, Q Li, J Teng, M Wang,
% "Image segmentation using fast linking SCM,"
% in Proc. of IJCNN, IEEE, vol. 25, pp. 2093-210... |
github | herenvarno/caffe-master | classification_demo.m | .m | caffe-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 | zongwave/IPASS-master | BM3DDEB.m | .m | IPASS-master/BM3D/BM3D/BM3DDEB.m | 17,170 | utf_8 | 1ca4e4613bf8a4a204335f69e5b9a1ca | function [ISNR, y_hat_RWI] = BM3DDEB(experiment_number, test_image_name)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Copyright (c) 2008-2014 Tampere University of Technology. All rights reserved.
% This work should only be used for nonprofit purposes.
%
% AUTHORS:
% Kostadin Dabov... |
github | zongwave/IPASS-master | CBM3D.m | .m | IPASS-master/BM3D/BM3D/CBM3D.m | 28,530 | utf_8 | 9a3e40b8b0f177169d223c8676892b13 | function [PSNR, yRGB_est] = CBM3D(yRGB, zRGB, sigma, profile, print_to_screen, colorspace)
%
% CBM3D is algorithm for attenuation of additive white Gaussian noise from
% color RGB images. This algorithm reproduces the results from the article:
%
% [1] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, "Color image
... |
github | zongwave/IPASS-master | BM3D.m | .m | IPASS-master/BM3D/BM3D/BM3D.m | 22,747 | utf_8 | 7e2312aaf69cead1edcfd768d113392d | function [PSNR, y_est] = BM3D(y, z, sigma, profile, print_to_screen)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% BM3D is an algorithm for attenuation of additive white Gaussian noise from
% grayscale images. This algorithm reproduces the results from the article:
%
% [1] K. Dab... |
github | zongwave/IPASS-master | Demo_IDDBM3D.m | .m | IPASS-master/BM3D/BM3D/IDDBM3D/Demo_IDDBM3D.m | 9,349 | utf_8 | 092ac706ce756d878d1d006a5698eb72 | function [isnr, y_hat] = Demo_IDDBM3D(experiment_number, test_image_name)
% ------------------------------------------------------------------------------------------
%
% Demo software for BM3D-frame based image deblurring
% Public release ver. 0.8 (beta) (June 03, 2011)
%
% -------------------... |
github | zongwave/IPASS-master | function_CreateLPAKernels.m | .m | IPASS-master/BM3D/BM3D/BM3D-SAPCA/function_CreateLPAKernels.m | 5,192 | utf_8 | b5ac9173b2024d53c79babfd89c0aaf9 | % Creates LPA kernels cell array (function_CreateLPAKernels)
%
% Alessandro Foi - Tampere University of Technology - 2003-2005
% ---------------------------------------------------------------
%
% Builds kernels cell arrays kernels{direction,size}
% and kernels_higher_order{direction,s... |
github | zongwave/IPASS-master | function_Window2D.m | .m | IPASS-master/BM3D/BM3D/BM3D-SAPCA/function_Window2D.m | 2,356 | utf_8 | d7eb1c5259345f71197ec8f94d93107c | % Returns a scalar/matrix weights (window function) for the LPA estimates
% function w=function_Window2D(X,Y,window,sig_wind, beta);
% X,Y scalar/matrix variables
% window - type of the window weight
% sig_wind - std scaling for the Gaussian ro-weight
% beta -parameter of the degree in the weights
%--------------... |
github | zongwave/IPASS-master | function_LPAKernelMatrixTheta.m | .m | IPASS-master/BM3D/BM3D/BM3D-SAPCA/function_LPAKernelMatrixTheta.m | 6,420 | utf_8 | e4d4b5ab9a0cc7fe4e417116778164e7 | % Return the discrete kernels for LPA estimation and their degrees matrix
%
% function [G, G1, index_polynomials]=function_LPAKernelMatrixTheta(h2,h1,window_type,sig_wind,TYPE,theta, m)
%
%
% Outputs:
%
% G kernel for function estimation
% G1 kernels for function and derivative estimation
% G1(:,:,j), j... |
github | zongwave/IPASS-master | bfilter.m | .m | IPASS-master/Bilateral/bfilter.m | 4,290 | utf_8 | 14e396b5cb0f534b1709d7c6fc512e35 | % Douglas R. Lanman, Brown University, September 2006.
% dlanman@brown.edu, http://mesh.brown.edu/dlanman
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Pre-process input and select appropriate filter.
function B = bfilter(A,w,sigma)
% Verify that the input image exists and is valid.
if ~exist(... |
github | zongwave/IPASS-master | main_3DNR.m | .m | IPASS-master/3DNR/main_3DNR.m | 6,682 | utf_8 | f177462edf06fa0a49921cb8a9670c37 | function main_3DNR()
clear;
clc;
close all;
[filename, pathname, filterindex] = uigetfile( ...
{ '*.bmp','Bitmap files (*.bmp)'; ...
'*.png','PNG files (*.png)'; ...
'*.jpg','JPEG files (*.jpg)'; ...
'*.*', 'All Files (*.*)'}, ...
'Pick a file', ...
'MultiSelect', 'on');
if (iscell(fi... |
github | zongwave/IPASS-master | import_video.m | .m | IPASS-master/VideoStab/import_video.m | 1,568 | utf_8 | 4f3b5d25af12d202e844be7fb4c54d3f | % import_video.m - import original video
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% Unless required by applicable law ... |
github | zongwave/IPASS-master | import_image_keypoints.m | .m | IPASS-master/VideoStab/import_image_keypoints.m | 1,618 | utf_8 | 4c6de30ee56355c8aaaf8c830b95763b | % import_image_keypoints.m - import image keypoints to do calibration
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% Unles... |
github | zongwave/IPASS-master | import_camera_pose.m | .m | IPASS-master/VideoStab/import_camera_pose.m | 6,954 | utf_8 | f0fd1745dcac8421aaaae5600dc0f7e0 | % import_camera_pose.m - import camera pose data acquired from Gyroscope
% & Accelerometer
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licen... |
github | zongwave/IPASS-master | analyze_projective2d.m | .m | IPASS-master/VideoStab/analyze_projective2d.m | 3,766 | utf_8 | 34bcc79f60bb6e618bdf8dbb62dfb619 | % analyze_projective2d.m - Plot projection matrix to analyze
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% Unless require... |
github | zongwave/IPASS-master | import_camera_intrinsics.m | .m | IPASS-master/VideoStab/import_camera_intrinsics.m | 2,899 | utf_8 | 6c47f85b592721490e586123d7eec883 | % import_camera_intrinsics.m - import camera calibration data to get
% intrinsic parameters
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% Unless required by applicable law o... |
github | zongwave/IPASS-master | video_stabilization.m | .m | IPASS-master/VideoStab/video_stabilization.m | 3,444 | utf_8 | 63a76012bd6a73fb8e6c53ea0bde733f | % video_stabilizaton.m - main function to launch video stabilization
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apache.org/licenses/LICENSE-2.0
%
% Unless... |
github | zongwave/IPASS-master | sensor_calibraion.m | .m | IPASS-master/VideoStab/sensor_calibraion.m | 2,075 | utf_8 | 87988e8b55e6952551ee3fa327ddeba4 | % sensor_calibration.m - Optimal camera & gyrocope calibration parameters
% by build in function 'fminunc'
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http://www.apac... |
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