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 | RenderToolbox/RenderToolbox4-master | rtbReadDAT.m | .m | RenderToolbox4-master/Utilities/rtbReadDAT.m | 3,401 | utf_8 | bbe4781931fa28cede10a032a640bfb2 | function [imageData, imageSize, lens] = rtbReadDAT(filename, varargin)
%% Get multispectral image data out of a .dat file from Stanford.
%
% imageData = rtbReadDAT(filename)
% Reads multi-spectral .dat image data from the fiven filename. The .dat
% format is described by Andy Lin on the Stanford Vision and Imaging
% S... |
github | RenderToolbox/RenderToolbox4-master | rtbFindFiles.m | .m | RenderToolbox4-master/Utilities/rtbFindFiles.m | 3,711 | utf_8 | 7579e729148749eec7a77f8b3fcd853c | function fileList = rtbFindFiles(varargin)
% Locate files by recursively searching a folder and subfolders.
%
% fileList = rtbFindFiles() searches the current folder (pwd()) for files and
% returns a cell array of files found. Excludes files that start with '.',
% or end with '~' or '.asv'.
%
% fileList = rtbFindFiles... |
github | RenderToolbox/RenderToolbox4-master | struct2xml.m | .m | RenderToolbox4-master/Utilities/External/struct2xml.m | 7,303 | utf_8 | 9fc8ec5aadbfdd4dca83370bde807a18 | function varargout = struct2xml( s, varargin )
%Convert a MATLAB structure into a xml file
% [ ] = struct2xml( s, file )
% xml = struct2xml( s )
%
% A structure containing:
% s.XMLname.Attributes.attrib1 = "Some value";
% s.XMLname.Element.Text = "Some text";
% s.XMLname.DifferentElement{1}.Attributes.attrib2 = "2";
%... |
github | RenderToolbox/RenderToolbox4-master | rtbMakeTestScene.m | .m | RenderToolbox4-master/ExampleScenes/SceneFromScratch/rtbMakeTestScene.m | 9,890 | utf_8 | eddd35974c2cdca1d1e2e134de0db26c | %% Make Mexximp scene from scratch.
%
% This is intended as a well-known input or "fixture" to be used by the
% accompanying tests. It's not inteded to be a general-purpose utility.
%
% It's also an explicit and long-winded deomonstration of how to construct
% a valid scene. Being explicit and long-winded seems good ... |
github | RenderToolbox/RenderToolbox4-master | displayNicelyFormattedStruct.m | .m | RenderToolbox4-master/ExampleScenes/WildScene/displayNicelyFormattedStruct.m | 2,853 | utf_8 | 358223f4a2ba37aee9dc46b149195d75 | % Method to display a nicely formatted view of all the fields in a nested structure
%
% This function borrowed from Nicolas Cottaris and UnitTestToolbox. Thanks!
% https://github.com/isetbio/UnitTestToolbox
%
function s = displayNicelyFormattedStruct(datum, datumName, s, maxFieldWidth)
s = displayStruct(datum, da... |
github | RenderToolbox/RenderToolbox4-master | rtbPublishReferenceData.m | .m | RenderToolbox4-master/Admin/rtbPublishReferenceData.m | 3,501 | utf_8 | 6449bfac69499d00c34b5230ddb96842 | function artifacts = rtbPublishReferenceData(varargin)
% Use RemoteDataToolbox to publish reference data to brainard-archiva.
%
% Archiva server "brainard-archiva" on AWS at http://brainard-archiva.psych.upenn.edu/
% and repository called RenderToolbox.
% see rdt-config-render-toolbox.json
%
% Reference data on Amazon ... |
github | RenderToolbox/RenderToolbox4-master | rtbPrintRecipeLog.m | .m | RenderToolbox4-master/RecipeAPI/rtbPrintRecipeLog.m | 2,741 | utf_8 | 7dfbbfcaf9c257464d53a0612442fe8d | function summary = rtbPrintRecipeLog(recipe, varargin)
%% Print a recipe's log as formatted text.
%
% summary = rtbPrintRecipeLog(recipe) prints a compact summary of the log
% data for the given as nicely formatted text.
%
% rtbPrintRecipeLog( ... 'verbose', verbose) specify whether to print
% verbose log data (true) o... |
github | RenderToolbox/RenderToolbox4-master | rtbPackUpRecipe.m | .m | RenderToolbox4-master/RecipeAPI/rtbPackUpRecipe.m | 3,382 | utf_8 | 859d55272de54f784b46ed54476339ef | function archiveName = rtbPackUpRecipe(recipe, archiveName, varargin)
%% Save a recipe and its file dependencies to a zip file.
%
% archiveName = rtbPackUpRecipe(recipe, archiveName) Creates a new zip
% archive named archiveName which contains the given recipe (in a mat-file)
% along with its file dependencies from the... |
github | RenderToolbox/RenderToolbox4-master | rtbMakeRecipeSceneFiles.m | .m | RenderToolbox4-master/RecipeAPI/rtbMakeRecipeSceneFiles.m | 1,885 | utf_8 | df1f29384254867019ca812139818649 | function recipe = rtbMakeRecipeSceneFiles(recipe)
%% Generate native scene files for the given recipe.
%
% recipe = rtbMakeRecipeSceneFiles(recipe) Uses the given recipe's parent
% scene file, conditions file, and mappings file to generate
% renderer-native scene files for the renderer
% specified in recipe.input.hints... |
github | RenderToolbox/RenderToolbox4-master | rtbChangeToRecipeFolder.m | .m | RenderToolbox4-master/RecipeAPI/rtbChangeToRecipeFolder.m | 1,382 | utf_8 | f04f3e5ecd30b393d540a4c966128310 | %%% RenderToolbox4 Copyright (c) 2012-2016 The RenderToolbox Team.
%%% About Us://github.com/RenderToolbox/RenderToolbox4/wiki/About-Us
%%% RenderToolbox4 is released under the MIT License. See LICENSE file.
%
% cd() to the working folder for a recipe.
% @param recipe a recipe struct
%
% @details
% Attempts to chang... |
github | RenderToolbox/RenderToolbox4-master | rtbConfigureForRecipe.m | .m | RenderToolbox4-master/RecipeAPI/rtbConfigureForRecipe.m | 1,559 | utf_8 | 7b3cc15fe6d86dea502a3d93f462b236 | %%% RenderToolbox4 Copyright (c) 2012-2016 The RenderToolbox Team.
%%% About Us://github.com/RenderToolbox/RenderToolbox4/wiki/About-Us
%%% RenderToolbox4 is released under the MIT License. See LICENSE file.
%
% Configure RenderToolbox4 to run the given recipe.
% @param recipe a recipe struct
%
% @details
% Attempts... |
github | RenderToolbox/RenderToolbox4-master | rtbRunEpicExamples.m | .m | RenderToolbox4-master/Test/Interactive/rtbRunEpicExamples.m | 4,218 | utf_8 | cb30a198b8eadbaaba5d718414defbf0 | function results = rtbRunEpicExamples(varargin)
%% Run all "rtbMake..." scripts in the ExampleScenes/ folder.
%
% results = rtbRunEpicExamples() renders example scenes by invoking
% all of the "rtbMake..." executive sripts found within the ExampleScenes/
% folder
%
% Returns a struct with information about each executi... |
github | RenderToolbox/RenderToolbox4-master | rtbRunEpicComparison.m | .m | RenderToolbox4-master/Test/Interactive/Comparison/rtbRunEpicComparison.m | 4,997 | utf_8 | 7fab981871dde1b39cfa4a265e776b4d | function [comparisons, matchInfo, figs] = rtbRunEpicComparison(folderA, folderB, varargin)
%% Compare sets of renderings for similarity.
%
% comparisons = rtbRunEpicComparison(folderA, folderB) locates renderings
% in folderA and folderB, compares pairs of renderings found between the
% two folders, and plots a summary... |
github | RenderToolbox/RenderToolbox4-master | rtbPlotManyRecipeComparisons.m | .m | RenderToolbox4-master/Test/Interactive/Comparison/rtbPlotManyRecipeComparisons.m | 4,261 | utf_8 | cf42e867627876429fa2a23f6da2e2e9 | function fig = rtbPlotManyRecipeComparisons(comparisons, varargin)
%% Plot a many recipe comparisons from rtbCompareManyRecipes().
%
% fig = fig = rtbPlotManyRecipeComparisons(comparisons) makes a plot to
% visualize the given struct array of comparison results, as produced by
% rtbCompareManyRecipes().
%
%%% RenderToo... |
github | RenderToolbox/RenderToolbox4-master | rtbCompareManyRecipes.m | .m | RenderToolbox4-master/Test/Interactive/Comparison/rtbCompareManyRecipes.m | 4,572 | utf_8 | 5f7c7e922396475dfa21bc0b446c62ea | function [comparisons, matchInfo] = rtbCompareManyRecipes(folderA, folderB, varargin)
%% Compare paris of renderings across two folders.
%
% comparisons = rtbCompareManyRecipes(folderA, folderB) finds rendering
% data files in the given folderA and folderB and attempts to match up
% pairs of renderings that came from t... |
github | RenderToolbox/RenderToolbox4-master | rtbCompareRenderings.m | .m | RenderToolbox4-master/Test/Interactive/Comparison/rtbCompareRenderings.m | 6,433 | utf_8 | d75612efae955b074422dcd2af392f60 | function comparison = rtbCompareRenderings(renderingA, renderingB, varargin)
%% Compare two renderings for difference images and statistics.
%
% comparison = rtbCompareRenderings(renderingA, renderingB) compares the
% given renderingA against the given renderingB. Each must be a rendering
% record as returned from rtb... |
github | RenderToolbox/RenderToolbox4-master | rtbIlluminantMetamerExample.m | .m | RenderToolbox4-master/RenderData/Macbeth-D65Metamers/rtbIlluminantMetamerExample.m | 1,914 | utf_8 | 9750181a8ae4a269cb90851c05293cca | %%% RenderToolbox4 Copyright (c) 2012-2016 The RenderToolbox Team.
%%% About Us://github.com/RenderToolbox/RenderToolbox4/wiki/About-Us
%%% RenderToolbox4 is released under the MIT License. See LICENSE file.
%
% Make a D65 metamer for a given Macbeth ColorChcekr tile.
% @param whichSur the number of a ColorChecker t... |
github | canlab/wagerlabtools_supplement-master | cluster_table_test.m | .m | wagerlabtools_supplement-master/matlab_functions/cluster_table_test.m | 17,360 | utf_8 | b7548f8da0dd88ebc9ee21096ac80d58 | function clusters = cluster_table(clusters, varargin)
% function cluster_table(clusters, [opt] subclusters)
% Print output of clusters in table
% Tor Wager
%
% Option to print text labels from Carmack atlas
% Database loading is done from talairach_info.mat which should be in the
% path.
% To speed up performa... |
github | canlab/wagerlabtools_supplement-master | histo_wani.m | .m | wagerlabtools_supplement-master/matlab_functions/histo_wani.m | 1,063 | utf_8 | 1da7c56cf0113c920dba44a8e9d54108 | function histo_wani(dat)
clf;
N = size(dat.dat,2);
for i=1:size(dat.dat,2)
dattmp = dat.dat(:,i);
for j=1:100
if N > (j+1)*j
j=j+1;
else
k=j;
break
end
end
subplot(k,k+1,i);
[h, x] = hist(dattmp, 100);
han = bar(x, h);
set(han, 'Fa... |
github | canlab/wagerlabtools_supplement-master | mediation_dream_wani.m | .m | wagerlabtools_supplement-master/matlab_functions/mediation_dream_wani.m | 7,642 | utf_8 | 239082776decabde63ed0aec522043a9 | function mediation_dream_wani(med_vars, models, jobn, mask, code_filename, study_scriptdir)
% mediation_dream_wani(med_vars, models, jobn, mask, code_filename, study_scriptdir)
%
% med_vars: x, y, m or m1, m2, imgs, covs.. these will be used in models.fns{i}
% models: models.fns{i}, models.savepaths{i} = [1,2,5]; mode... |
github | canlab/wagerlabtools_supplement-master | cluster_table_wani.m | .m | wagerlabtools_supplement-master/matlab_functions/cluster_table_wani.m | 21,542 | utf_8 | fec4d1e466f9db2039062241e701ee72 | function clusters = cluster_table_wani(clusters, varargin)
% function cluster_table_wani(clusters, [opt] subclusters)
% Print output of clusters in table
% Tor Wager
%
% WANI made his custumized cluster_table function - cluster_table_wani
%
% To use AAL atlas data, you need to change the following line
% ... |
github | canlab/wagerlabtools_supplement-master | cluster_table_aal.m | .m | wagerlabtools_supplement-master/matlab_functions/cluster_table_aal.m | 18,291 | utf_8 | 2a94b3b0620ae1456164e9d906469614 | function clusters = cluster_table_aal(clusters, varargin)
% function cluster_table(clusters, [opt] subclusters)
% Print output of clusters in table
% Tor Wager
%
% Option to print text labels from Carmack atlas
% Database loading is done from talairach_info.mat which should be in the
% path.
% To speed up perf... |
github | canlab/wagerlabtools_supplement-master | community_modularity.m | .m | wagerlabtools_supplement-master/matlab_functions/Wani_network_functions/community_modularity.m | 4,379 | utf_8 | 464cfe8de128081d85d29b62f2605535 | function [max_z, max_q, outinfo] = community_modularity(A, varargin)
% function [z, q, outinfo] = community_modularity(A, optional_inputs)
%
% feature: This function conduct the greedy agglomerative algorithm to find
% community structure that maximizes the network's modularity (Q).
%
% input: A adjac... |
github | canlab/wagerlabtools_supplement-master | KL_heuristic_k2.m | .m | wagerlabtools_supplement-master/matlab_functions/Wani_network_functions/KL_heuristic_k2.m | 5,170 | utf_8 | 429ec5697d0afa8529ec15a52b9b5f7d | function [bestL, bestP, info] = KL_heuristic_k2(A, varargin)
% usage: [bestL, bestP, info] = KL_heuristic_k2(A, varargin)
%
% feature: use the Kernighan-Lin (KL) heuristic to optimize any partition
% score function, e.g., modularity Q or stochastic block model's
% likelihood function. This works onl... |
github | f-leno/DOO-Q_BRACIS2016-master | generateGraphFromBurlapFile.m | .m | DOO-Q_BRACIS2016-master/generateGraphFromBurlapFile.m | 6,027 | utf_8 | 1a7ff0e21dac1c8268f0da8ad1513be6 | % Author: Felipe Leno da Silva
% This code reads .csv files generated by Burlap and print graphs. This code is only intended to generate graphs
% for the BRACIS 2016 conference, and is highly recommended that you implement your own function to generate graphs if you want to use it to any other purpose.
function gener... |
github | superyyzg/deep-filter-panorama-master | classification_demo.m | .m | deep-filter-panorama-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 | Digiducer/matlab-master | Digiducer_Data_Analyzer.m | .m | matlab-master/Digiducer_Data_Analyzer.m | 33,799 | utf_8 | e4d432024ec15ec42b2cd5a0ab66ff4c | function varargout = Digiducer_Data_Analyzer(varargin)
% DIGIDUCER_DATA_ANALYZER MATLAB code for Digiducer_Data_Analyzer.fig
% DIGIDUCER_DATA_ANALYZER, by itself, creates a new DIGIDUCER_DATA_ANALYZER or raises the existing
% singleton*.
%
% H = DIGIDUCER_DATA_ANALYZER returns the handle to a new DIGIDUC... |
github | Digiducer/matlab-master | spectralcalc.m | .m | matlab-master/spectralcalc.m | 4,120 | utf_8 | e3b800c848a4750faabc7b5846f65c0f | % Written by Jim Elliott for The Modal Shop, Inc.
% Modifications and documentation by Alex Lambert
function SpectrumObject = spectralcalc(timedata,offset,size,windowType)
% SpectrumObject = spectralcalc(timedata,offset,size)
% Inputs:
% timedata: The time history of amplitudes.
% offset: An offset, in sample... |
github | Akhilkumar1307/Vision-based-pick-and-place-robotic-arm-master | imTransD.m | .m | Vision-based-pick-and-place-robotic-arm-master/imTransD.m | 3,965 | utf_8 | 253939b60899cb7418ae8eebee0f8b87 | % IMTRANSD - Homogeneous transformation of an image.
%
% This is a stripped down version of imTrans which does not apply any origin
% shifting to the transformed image
%
% Applies a geometric transform to an image
%
% newim = imTransD(im, T, sze, lhrh);
%
% Arguments:
% im - The image to be transformed.
%... |
github | Akhilkumar1307/Vision-based-pick-and-place-robotic-arm-master | homography2d.m | .m | Vision-based-pick-and-place-robotic-arm-master/homography2d.m | 2,493 | utf_8 | 60985e0ab95fe690d769c83adff61080 | % HOMOGRAPHY2D - computes 2D homography
%
% Usage: H = homography2d(x1, x2)
% H = homography2d(x)
%
% Arguments:
% x1 - 3xN set of homogeneous points
% x2 - 3xN set of homogeneous points such that x1<->x2
%
% x - If a single argument is supplied it is assumed ... |
github | hacklabcbba/HacklabDrone-master | UdpTest1.m | .m | HacklabDrone-master/tests/matlab/UdpTest1.m | 4,415 | utf_8 | 8a09d696dcf37cfb7b67c63c15887ef6 | function data = UdpTest(~)
host = '127.0.0.1';
% host = '169.254.1.1';
% host = '10.0.0.200';
% host = '192.168.1.109';
port = 5000;
timeout = 5;
packetLength = 500;
plotLength = 500;
import java.io.*
import java.net.DatagramSocket
import java.net.DatagramPacket
import java.net.InetAddress
%% Setup socket
% Create s... |
github | hacklabcbba/HacklabDrone-master | UdpPlot.m | .m | HacklabDrone-master/tests/matlab/UdpPlot.m | 3,844 | utf_8 | ef7cb8cf9993edf76cb9c56a2597653c | function data = UdpPlot(ip, port, timeout, numSample)
if ~exist('ip'), ip = '127.0.0.1'; end
if ~exist('port'), port = 5000; end
if ~exist('timeout'), timeout = 10; end
if ~exist('numSample'), numSample = 500; end
import java.io.*
import java.net.DatagramSocket
import java.net.DatagramPacket
import java.net.InetAddres... |
github | vSpaces/vAcademia-master | echo_diagnostic.m | .m | vAcademia-master/Voip/mumble/mumble-1.2.3/speex/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | CMU-Perceptual-Computing-Lab/caffe_rtpose-master | classification_demo.m | .m | caffe_rtpose-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 | M-MohammadPour/PSOAdaBoost-master | predStump.m | .m | PSOAdaBoost-master/predStump.m | 240 | utf_8 | 33aef76407d65dfa83f957307334842c | % Make prediction based on a decision stump
function label = predStump(X, stump)
N = size(X, 1);
x = X(:, stump.dim);
idx = logical(x >= stump.threshold); % N x 1
label = zeros(N, 1);
label(idx) = stump.more;
label(~idx) = stump.less;
end
|
github | DFSM3101H16/syllabus-master | rhs_1D_drag.m | .m | syllabus-master/rhs_1D_drag.m | 585 | utf_8 | ec1bb0d454fcaea36262435c2ef2f03c | % Dette skriptet inneholder høyresida i ligningene
function v = rhs_1D_drag(t, x)
n = length(x); % antall variable og antall ligninger, strengt tatt
% ikke nødvendig
a = -9.81; % tyngdens akselerasjon, [a] = m/s^2
D = 1e-1; % Drag koeffisienten
A = 1e-1; % Arealet til det som faller
rho = 1.225; % Mas... |
github | LLNL/GridDyn-master | sheetwrite.m | .m | GridDyn-master/matlab/sheetwrite.m | 5,150 | utf_8 | a1685e610e76b37b70db8db4abe222fa | %% -*- Mode:matlab; c-file-style:"gnu"; indent-tabs-mode:nil; eval: (c-set-offset 'innamespace 0); -*- */
%
% LLNS Copyright Start
% Copyright (c) 2016, Lawrence Livermore National Security
% This work was performed under the auspices of the U.S. Department
% of Energy by Lawrence Livermore National Laboratory in part... |
github | LLNL/GridDyn-master | sheetread.m | .m | GridDyn-master/matlab/sheetread.m | 11,726 | utf_8 | 59d259bba8138bd93465fcef53fee48e | %% -*- Mode:matlab; c-file-style:"gnu"; indent-tabs-mode:nil; eval: (c-set-offset 'innamespace 0); -*- */
%
% LLNS Copyright Start
% Copyright (c) 2016, Lawrence Livermore National Security
% This work was performed under the auspices of the U.S. Department
% of Energy by Lawrence Livermore National Laboratory in part... |
github | LLNL/GridDyn-master | readStateFile.m | .m | GridDyn-master/matlab/readStateFile.m | 2,580 | utf_8 | 58f0899fdfcfa6960a7aa8466a9549f7 | %% -*- Mode:matlab; c-file-style:"gnu"; indent-tabs-mode:nil; eval: (c-set-offset 'innamespace 0); -*- */
%
% LLNS Copyright Start
% Copyright (c) 2016, Lawrence Livermore National Security
% This work was performed under the auspices of the U.S. Department
% of Energy by Lawrence Livermore National Laboratory in part... |
github | LLNL/GridDyn-master | case_info.m | .m | GridDyn-master/test/test_files/validation_tests/case_info.m | 24,435 | utf_8 | 1b96ce45c0e9b4632739ae2c55a09c11 | function [groupss, isolated] = case_info(mpc, fd)
%CASE_INFO Prints information about islands in a network.
% CASE_INFO(MPC)
% CASE_INFO(MPC, FD)
% [GROUPS, ISOLATED] = CASE_INFO(...)
%
% Prints out detailed information about a MATPOWER case. Optionally prints
% to an open file, whose file identifier, as retu... |
github | matteomaspero/pseudo-CT_generation-master | view3dgui.m | .m | pseudo-CT_generation-master/utilsRT/view3dgui.m | 86,882 | utf_8 | 51b8f51a15ef9411af679b43423ce2ee | function varargout = view3dgui(varargin)
%
% view3dgui(img3d,[dx dy dz])
% view3dgui(img3d,dicom_info_structure)
% view3dgui(img3d,...,mask)
% view3dgui(img3d,...,'mask',mask)
% view3dgui(img3d,...,'mvx',mvx,'mvy',mvx,'mvz',mvz)
% view3dgui(img3d,...,'dvf_grid_size',[dx dy dz])
%
% Programmed by Deshan Yang, W... |
github | mlapierre/dual-task-mot-vwm-data-master | ZhangM.m | .m | dual-task-mot-vwm-data-master/experiment_1ab/Core/ZhangM.m | 171 | utf_8 | 2b327b1324727a533d8cce9b130311b4 | % Calculate mean number of objects tracked (as per Zhang et al. 2010)
function m = ZhangM(n, hr, cr)
m = n*((hr + cr - 1)/cr);
if m < 0
m = 0;
end
end
|
github | mlapierre/dual-task-mot-vwm-data-master | ZhangK.m | .m | dual-task-mot-vwm-data-master/experiment_1ab/Core/ZhangK.m | 175 | utf_8 | 10ad431b5986631ad557d08fac7b589f | % Calculate mean number of conjunctions remembered (as per Zhang et al. 2010)
function k = ZhangK(n, hr, cr)
k = (n*hr+n-1-sqrt((n*hr+n-1)^2 - 4*n*(n-1)*(hr+cr-1)))/2;
end |
github | mlapierre/dual-task-mot-vwm-data-master | CowanK.m | .m | dual-task-mot-vwm-data-master/experiment_1ab/Core/CowanK.m | 155 | utf_8 | d6ae1db351b813289a719eba2cb5096f | % Calculate mean number of objects tracked (as per Cowan 2001, cited in Fougnie & Marois 2006)
function k = CowanK(n, hr, cr)
k = (hr + cr - 1)*n;
end
|
github | mlapierre/dual-task-mot-vwm-data-master | MOTWindow.m | .m | dual-task-mot-vwm-data-master/experiment_1ab/Core/MOTWindow.m | 12,449 | utf_8 | a16f04fea45299524b0b9c6eda391eef | classdef MOTWindow
properties
WinHandle
InterFrameInterval
ScreenRes
WinCentre
BackgroundColour = [125 125 125];
end
properties (SetAccess = private, GetAccess = private)
OldVisualDebugLevel
OldSupressAllWarnings
end
methods
f... |
github | mlapierre/dual-task-mot-vwm-data-master | simSession.m | .m | dual-task-mot-vwm-data-master/experiment_2b/data/simSession.m | 1,824 | utf_8 | 60526c4978658eeae31695ffb8d559ab | function results = simSession(subject_name)
data_fn = ['data' filesep subject_name '.mat'];
if exist(data_fn, 'file')
vars = whos('-file', data_fn);
if ismember('results', {vars.name})
load(data_fn, 'results');
fprintf('Data loaded from %s\n', data_fn);
end
en... |
github | mlapierre/dual-task-mot-vwm-data-master | analyse.m | .m | dual-task-mot-vwm-data-master/experiment_2b/data/analyse.m | 9,323 | utf_8 | 5c24215eadefc361c4e4ad6034758d9b | function [raw_data, stats, anovatab] = analyse(subject_name, sessions)
if nargin < 1
subject_name = [];
end
if nargin < 2
sessions = [];
end
if isempty(subject_name)
subject_names = getSubjectNames();
elseif ~iscellstr(subject_name) && isempty(regexp(subject_name, '[\W]+'... |
github | mlapierre/dual-task-mot-vwm-data-master | analyseVWMMCS.m | .m | dual-task-mot-vwm-data-master/experiment_2b/Core/analyseVWMMCS.m | 1,166 | utf_8 | 3f73d8373c9c9807da444ec883b2c9cf | function [disc_count q] = analyseVWMMCS(subject_name, attempt_num)
data_fn = ['data' filesep subject_name '.mat'];
if exist(data_fn, 'file') && ~exist('vwm_mcs_data', 'var')
load(data_fn);
fprintf('Data and loaded from %s\n', data_fn);
end
s = [];
c = [];
for i = attempt_num... |
github | mlapierre/dual-task-mot-vwm-data-master | ZhangM.m | .m | dual-task-mot-vwm-data-master/experiment_2b/Core/ZhangM.m | 171 | utf_8 | 2b327b1324727a533d8cce9b130311b4 | % Calculate mean number of objects tracked (as per Zhang et al. 2010)
function m = ZhangM(n, hr, cr)
m = n*((hr + cr - 1)/cr);
if m < 0
m = 0;
end
end
|
github | mlapierre/dual-task-mot-vwm-data-master | MOT_MCS.m | .m | dual-task-mot-vwm-data-master/experiment_2b/Core/MOT_MCS.m | 2,322 | utf_8 | 9db5d06bcbffa5835b8155601d2c7a9b | function est_speed = MOT_MCS(subject_name, num_trials, base_speed, speed_inc)
% MOT calibration
% subject_name: The name of the participant.
% num_trials: The number of trials on which the participant will be tested.
% base_speed: The base speed at which the dots will move, i.e., the speed
% ... |
github | mlapierre/dual-task-mot-vwm-data-master | VWM_MCS.m | .m | dual-task-mot-vwm-data-master/experiment_2b/Core/VWM_MCS.m | 2,286 | utf_8 | d17ed56479162b78498eefbd81023d92 | function est_discs = VWM_MCS(subject_name, num_trials, disc_range, speed)
% VWM calibration
% subject_name: The name of the participant.
% num_trials: The number of trials on which the participant will be tested.
% disc_range: The range of number of discs that will be displayed.
st = dbstack(1);
if... |
github | mlapierre/dual-task-mot-vwm-data-master | ZhangK.m | .m | dual-task-mot-vwm-data-master/experiment_2b/Core/ZhangK.m | 175 | utf_8 | 10ad431b5986631ad557d08fac7b589f | % Calculate mean number of conjunctions remembered (as per Zhang et al. 2010)
function k = ZhangK(n, hr, cr)
k = (n*hr+n-1-sqrt((n*hr+n-1)^2 - 4*n*(n-1)*(hr+cr-1)))/2;
end |
github | mlapierre/dual-task-mot-vwm-data-master | CowanK.m | .m | dual-task-mot-vwm-data-master/experiment_2b/Core/CowanK.m | 155 | utf_8 | d6ae1db351b813289a719eba2cb5096f | % Calculate mean number of objects tracked (as per Cowan 2001, cited in Fougnie & Marois 2006)
function k = CowanK(n, hr, cr)
k = (hr + cr - 1)*n;
end
|
github | mlapierre/dual-task-mot-vwm-data-master | MOTWindow.m | .m | dual-task-mot-vwm-data-master/experiment_2b/Core/MOTWindow.m | 12,738 | utf_8 | e55d91c8f5e509d6bfd52c3e31b6d8e6 | classdef MOTWindow
properties
WinHandle
InterFrameInterval
ScreenRes
WinCentre
BackgroundColour = [125 125 125];
end
properties (SetAccess = private, GetAccess = private)
OldVisualDebugLevel
OldSupressAllWarnings
end
methods
f... |
github | mlapierre/dual-task-mot-vwm-data-master | analyseMOTMCS.m | .m | dual-task-mot-vwm-data-master/experiment_2b/Core/analyseMOTMCS.m | 1,075 | utf_8 | 097ba16991030a3653be1a68d7a5fef0 | function [speed q] = analyseMOTMCS(subject_name, attempt_num)
data_fn = ['data' filesep subject_name '.mat'];
if exist(data_fn, 'file') && ~exist('mot_mcs_data', 'var')
load(data_fn);
end
s = [];
c = [];
for i = attempt_num
s = [s mot_mcs_data{i}.speed];
c = [c mot_m... |
github | mlapierre/dual-task-mot-vwm-data-master | StartSession.m | .m | dual-task-mot-vwm-data-master/experiment_2/StartSession.m | 3,141 | utf_8 | b811029917f515f871aae56553082d94 | function StartSession(subjectName, session_config, num_blocks, data_set)
win = MOTWindow();
%win = MockWin();
try
data_log_fn = sprintf('Data/%s.log', subjectName);
for i = 1:num_blocks
% Determine the appropriate session number
if exist(data_log_fn, 'file')
... |
github | mlapierre/dual-task-mot-vwm-data-master | ZhangM.m | .m | dual-task-mot-vwm-data-master/experiment_2/Core/ZhangM.m | 171 | utf_8 | 2b327b1324727a533d8cce9b130311b4 | % Calculate mean number of objects tracked (as per Zhang et al. 2010)
function m = ZhangM(n, hr, cr)
m = n*((hr + cr - 1)/cr);
if m < 0
m = 0;
end
end
|
github | mlapierre/dual-task-mot-vwm-data-master | ZhangK.m | .m | dual-task-mot-vwm-data-master/experiment_2/Core/ZhangK.m | 175 | utf_8 | 10ad431b5986631ad557d08fac7b589f | % Calculate mean number of conjunctions remembered (as per Zhang et al. 2010)
function k = ZhangK(n, hr, cr)
k = (n*hr+n-1-sqrt((n*hr+n-1)^2 - 4*n*(n-1)*(hr+cr-1)))/2;
end |
github | mlapierre/dual-task-mot-vwm-data-master | CowanK.m | .m | dual-task-mot-vwm-data-master/experiment_2/Core/CowanK.m | 155 | utf_8 | d6ae1db351b813289a719eba2cb5096f | % Calculate mean number of objects tracked (as per Cowan 2001, cited in Fougnie & Marois 2006)
function k = CowanK(n, hr, cr)
k = (hr + cr - 1)*n;
end
|
github | mlapierre/dual-task-mot-vwm-data-master | MOTWindow.m | .m | dual-task-mot-vwm-data-master/experiment_2/Core/MOTWindow.m | 12,663 | utf_8 | ff65523341c7520beba5659ab237436e | classdef MOTWindow
properties
WinHandle
InterFrameInterval
ScreenRes
WinCentre
BackgroundColour = [125 125 125];
end
properties (SetAccess = private, GetAccess = private)
OldVisualDebugLevel
OldSupressAllWarnings
end
methods
f... |
github | IPGP/mapping-lib-master | comprose.m | .m | mapping-lib-master/comprose/comprose.m | 4,486 | ibm852 | f01bc19c4f7ac99bc4d28eb8e9949b60 | function ho=comprose(x,y,n,w,az,varargin)
%COMPROSE Compass rose plot
%
% COMPROSE(X,Y,N,W,AZ) adds a compass rose on current axis located at
% position X,Y with N points (N is 1, 4, 8 or 16), width W (radius)
% and North pointing to azimuth AZ (in degree, AZ = 0 means an arrow
% pointing ... |
github | IPGP/mapping-lib-master | dem.m | .m | mapping-lib-master/dem/dem.m | 39,571 | UNKNOWN | 75aa3bccabe79acaf37928cc76a9385b | function varargout=dem(x,y,z,varargin)
%DEM Shaded relief image plot
%
% DEM(X,Y,Z) plots the Digital Elevation Model defined by X and Y
% coordinate vectors and elevation matrix Z, as a lighted image using
% specific "landcolor" and "seacolor" colormaps. DEM uses IMAGESC
% function which is much faster than SURF... |
github | IPGP/mapping-lib-master | greatcircle.m | .m | mapping-lib-master/greatcircle/greatcircle.m | 5,502 | utf_8 | 316116b0ff972cce13b4b9a4e0d52716 | function varargout=greatcircle(varargin)
%GREATCIRCLE "As the crow flies" path, distance and bearing.
%
% GREATCIRCLE(LAT1,LON1,LAT2,LON2) returns the shortest distance (in km)
% along the great circle between two points defined by spherical
% coordinates latitude and longitude (in decimal degrees). If input
% a... |
github | IPGP/mapping-lib-master | loxodrome.m | .m | mapping-lib-master/greatcircle/loxodrome.m | 4,061 | ibm852 | 2bfc2917f00d931ec80e67370c35b1be | function [lat,lon,dist,bear]=loxodrome(varargin)
%LOXODROME Rhumb line path and distance.
%
% [LAT,LON]=LOXODROME(LAT1,LON1,LAT2,LON2) computes the line between two
% points defined by their spherical coordinates latitude and longitude
% (in decimal degrees), crossing all meridians of longitude at the same
% a... |
github | gmiaslab/mathiomica-master | MathIOmica.m | .m | mathiomica-master/MathIOmica/MathIOmica.m | 556,993 | utf_8 | 245427ca4eeb2c2071329a851bf0f1ad | (* ::Package:: *)
(* Wolfram Language Package *)
(* Created by the Wolfram Workbench Nov 25, 2015 *)
(*The MIT License (MIT)
Copyright (c) 2016-21 George I. Mias, G. Mias Lab, Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing 48824.
Permission is hereby granted, free of charge,... |
github | erichall87/HawkesCode-master | HawkesMV.m | .m | HawkesCode-master/HawkesMV.m | 1,726 | utf_8 | fac5eb27661db41305e7c54aabb03742 | % This code implements the method of Dassios and Zhao to synthetically
% generate a Multivariate Hawkes process with exponentially decaying
% intensity rates
%
% Paper: Exact simulation of Hawkes process with exponentially decaying
% intensity - Angelos Dassios and Hongbiao Zhao
%
% The code has been written to conform... |
github | zhaoweicai/mscnn-master | classification_demo.m | .m | mscnn-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 | wangs11678/CVPR10-LLC-master | LLC_coding_appr.m | .m | CVPR10-LLC-master/LLC_coding_appr.m | 1,507 | utf_8 | 4c1a387904780ff3d92f87f99d4c6e5b | % ========================================================================
% USAGE: [Coeff]=LLC_coding_appr(B,X,knn,lambda)
% Approximated Locality-constraint Linear Coding
%
% Inputs
% B -M x d codebook, M entries in a d-dim space
% X -N x d matrix, N data points in a d-dim space
% knn ... |
github | wangs11678/CVPR10-LLC-master | LLC_pooling.m | .m | CVPR10-LLC-master/LLC_pooling.m | 1,709 | utf_8 | c55f0d46517516d181f5a9b1a6e025ba | % ========================================================================
% Pooling the llc codes to form the image feature
% USAGE: [beta] = LLC_pooling(feaSet, B, pyramid, knn)
% Inputs
% feaSet -the coordinated local descriptors
% B -the codebook for llc coding
% pyramid -the sp... |
github | wangs11678/CVPR10-LLC-master | cont.m | .m | CVPR10-LLC-master/lbp/cont.m | 4,206 | utf_8 | 5b26a585b2a7f3cadea762ef8273c9cf | %C computes the VAR descriptor.
% J = CONT(I,R,N,LIMS,MODE) returns either a rotation invariant local
% variance (VAR) image or a VAR histogram of the image I. The VAR values
% are determined for all pixels having neighborhood defined by the input
% arguments. The VAR operator calculates variance on a circumference ... |
github | wangs11678/CVPR10-LLC-master | getmapping.m | .m | CVPR10-LLC-master/lbp/getmapping.m | 5,222 | utf_8 | 54332fd445f20849f7554078eddeccef | %GETMAPPING returns a structure containing a mapping table for LBP codes.
% MAPPING = GETMAPPING(SAMPLES,MAPPINGTYPE) returns a
% structure containing a mapping table for
% LBP codes in a neighbourhood of SAMPLES sampling
% points. Possible values for MAPPINGTYPE are
% 'u2' for uniform LBP
% 'ri' fo... |
github | wangs11678/CVPR10-LLC-master | make.m | .m | CVPR10-LLC-master/Liblinear/matlab/make.m | 1,198 | utf_8 | 72532ef957c850421c786167742d0912 | % This make.m is for MATLAB and OCTAVE under Windows, Mac, and Unix
function make()
try
% This part is for OCTAVE
if(exist('OCTAVE_VERSION', 'builtin'))
mex libsvmread.c
mex libsvmwrite.c
mex -I.. train.c linear_model_matlab.c ../linear.cpp ../tron.cpp ../blas/daxpy.c ../blas/ddot.c ../blas/dnrm2.c ../blas/dsca... |
github | WanliXue/matlab_code-master | Dict_Train.m | .m | matlab_code-master/Car_collection/Dict_Train.m | 434 | utf_8 | 7cc8e29b5731711e75b31a332473a485 | % Train with SPAMS
function Psi=Dict_Train(X, limit)
if exist('limit','var') == 0, limit = 8; end;
param.K = limit ; % change limit*2 to limit so the size square
param.iter = 500; % 10000
param.modeParam = 0;
param.mode = 0; % done the testing with 1,2,3,4. do not produce good results.
param.posAlpha = 0;
param.posD ... |
github | alliedel/videofeatures-master | plotROC.m | .m | videofeatures-master/code/scripts/plotting/plotROC.m | 962 | utf_8 | 808d1405ef434eef5fef06c835f3e857 |
function plotROC(pars, plotPars)
upsize_to_gt = 0;
gt_file = load(pars.paths.files.pathToGroundTruth,'volLabel'); %;
y_file = load(fullfile(pars.paths.folders.pathToResults,'an'));
fnLoc_file = load(pars.paths.files.finalFeatMATfile);
volFile = GenerateVolname(pars.paths.files.pathToVideo);
... |
github | alliedel/videofeatures-master | Movie_GT.m | .m | videofeatures-master/code/scripts/plotting/Movie_GT.m | 285 | utf_8 | ebe94c39e7afc28795037e5409d85e79 |
function Movie_GT(pars, plotPars)
gt_file = load(pars.paths.files.pathToGroundTruth,'volLabel'); %;
for i = 1:length(gt_file.volLabel)
imshow(gt_file.volLabel{i});
title(sprintf('%03d/%03d',i,length(gt_file.volLabel)));
drawnow;
end
end
|
github | alliedel/videofeatures-master | ClipSignals.m | .m | videofeatures-master/code/scripts/plotting/ClipSignals.m | 235 | utf_8 | 9e8a1e12ae09a75ef6f2fb4bdcffa194 |
function [siga,sigb] = ClipSignals(siga,sigb,tol)
a = length(siga);
b = length(sigb);
if tol < abs((a-b)/a)
error('vectors aren''t close in length. Something is wrong');
end
l = min(a,b);
siga = siga(1:l);
sigb = sigb(1:l);
end
|
github | alliedel/videofeatures-master | An1dTo3d.m | .m | videofeatures-master/code/scripts/formatdata/An1dTo3d.m | 244 | utf_8 | 154f86e0ee2ca4815e630db9dcaff703 |
function an3 = An1dTo3d(an, LocV3, BKH, BKW, T)
sig = an./(1-an);
Err = sig(:)';
AbEvent = zeros(BKH, BKW, T);
for ii = 1 : length(Err)
AbEvent(LocV3(1,ii),LocV3(2,ii),LocV3(3,ii)) = Err(ii);
end
an3 = smooth3( AbEvent, 'box', 5);
end
|
github | alliedel/videofeatures-master | wrap_DetectAnomalies.m | .m | videofeatures-master/code/scripts/wrappers/wrap_DetectAnomalies.m | 2,010 | utf_8 | 6e4e0132561b8dd12c7a771da6271e75 | function wrap_DetectAnomalies(pars)
% Features should already be computed. Will error if false.
% Will call the correct method (ours or competitor's)
%% Anomaly Detection
if ~exist(fullfile(pars.paths.folders.pathToResults,'an'),'file')
if strcmpi(pars.methodType,'mine')
anomalyDetect(pars);
elseif st... |
github | alliedel/videofeatures-master | fullRun.m | .m | videofeatures-master/code/src/fullRun.m | 1,734 | utf_8 | a6767472bc29e8b4fd770ad9ae2040c8 | function pars = fullRun( args, scriptArgs)
%FULLRUN Detect anomalies on video
% Calculates dense trajectory features and uses a variant of density
% ratio estimation to rate the anomalousness of each frame.
%% Parse inputs and add paths
parsScript = ParseScriptArgs(scriptArgs{:});
AddToolPaths(parsScript);
pars = ... |
github | alliedel/videofeatures-master | GetPaths_anomalyDetection.m | .m | videofeatures-master/code/src/parse/GetPaths_anomalyDetection.m | 1,542 | utf_8 | c7e850427153f0bf6d4d6f8ef0977b0d | function [paths] = GetPaths_anomalyDetection(pars)
% In case we're running this for debugging
% if eval('pars.argsString')
% pars.argsString = '';
% warning('argsString not set; hopefully you''re not running fullRun.m and you''re just debugging.');
% end
% Some preliminary stuff (non-technical)
[pth,name,~] = f... |
github | alliedel/videofeatures-master | GetTags_anomalyDetection.m | .m | videofeatures-master/code/src/parse/GetTags_anomalyDetection.m | 4,683 | utf_8 | fbe54dbfbbedaa55f8e3bac2c3d9c4b0 | function tags = GetTags_anomalyDetection(pars)
% - system:
tags.datestring = datestr(now,'yyyy_mm_dd');
tags.timestring = datestr(now,'HH_MM_SS');
% - inputs: video, groundtruth
% - features
[~,name,~] = fileparts(pars.pathToVideo); %output = [pathstr, name, ext]
if ~isinf(pars.endFrame)
error('Filename doesn''... |
github | alliedel/videofeatures-master | StitchNames.m | .m | videofeatures-master/code/src/parse/StitchNames.m | 6,739 | utf_8 | c196dd04264fb60a380b654b23e55b58 | function paths = StitchNames(tags, pars)
% - features
paths = StitchFeatureNames(tags, pars);
% - inputs: video, groundtruth
[pth,name,~] = fileparts(pars.pathToVideo); [~,collection,~] = fileparts(pth);
paths.name = name;
paths.folders.pathToGndTruth = fullfile(pars.anomDetectRoot,sprintf('data/input/groundTruth/%s/... |
github | alliedel/videofeatures-master | Wrapper_CreateFinalFeatMATFile_liu.m | .m | videofeatures-master/code/src/competitors/Wrapper_CreateFinalFeatMATFile_liu.m | 2,329 | utf_8 | 5bf2ef48cb6eb74cccfae7c722675a9f | function Wrapper_CreateFinalFeatMATFile_liu(pars)
flds = fieldnames(pars);
% export the liu-relevant features to a new structure it can handle
for i = 1:length(flds)
parsfld = flds{i};
a = strfind(parsfld,'liu_');
if isempty(a)
continue;
else
paramfld = parsfld(a+length('liu_') : end);
... |
github | alliedel/videofeatures-master | GridToFrames.m | .m | videofeatures-master/code/src/features/raw/GridToFrames.m | 1,181 | utf_8 | 0d3c7e445197fe6aac75feeab8df7f5f | function [rct, sz] = GridToFrames(gridIdxs, blockSize, blockStride, videoSize, center)
% Center: center the point in the grid. Otherwise, returns the start locs of the grid.
if ~exist('center','var')
center = 0;
end
[rst,cst,tst, sz] = ComputeGridStartLocsAndSz(blockSize, blockStride, videoSize);
rct = zeros(len... |
github | alliedel/videofeatures-master | FramesToGrid.m | .m | videofeatures-master/code/src/features/raw/FramesToGrid.m | 1,227 | utf_8 | cac88c257f6f93c01e2e20adfc34a994 | function gridIdxs = FramesToGrid(rct, blockSize, blockStride, videoSize) % r,c,t from DT:
% ** at the moment, only works for one-to-one mappings!
% rct = [row col frame]
[rst,cst,tst, sz] = ComputeGridStartLocsAndSz(blockSize, blockStride, videoSize);
r=rct(:,1);
c=rct(:,2);
t=rct(:,3);
if any(r > videoSize(1)) || a... |
github | alliedel/videofeatures-master | ComputeGridStartLocsAndSz.m | .m | videofeatures-master/code/src/features/raw/ComputeGridStartLocsAndSz.m | 1,150 | utf_8 | 2f3589e7ca88ed1eea016663bfa8b052 | function [rs,cs,ts, sz] = ComputeGridStartLocsAndSz(blockSize, blockStride, videoSize)
% sz = [dr dc dt]
if any(blockSize(1:2) > 1) % fraction of frame
error('I haven''t implemented non-fraction blockSizes')
end
if any(blockStride(1:2) > 1) % fraction of frame
error('I haven''t implemented non-fraction blockSiz... |
github | alliedel/videofeatures-master | MakeRawFeatures.m | .m | videofeatures-master/code/src/features/raw/MakeRawFeatures.m | 2,499 | utf_8 | 8c7ac742abdf6a2b8aaf09006554c891 | function MakeRawFeatures(pars)
paths = pars.paths;
if exist(paths.files.rawFeatMATfile,'file')
fprintf('Raw feat file already exists in %s\n',paths.files.rawFeatMATfile);
return;
end
% Extract features if needed
pathToFeatTxt = paths.files.dt_Txtfile;
pathToFeatMAT = paths.fi... |
github | siddharth-maddali/HierarchicalSmooth-master | DifferentiateFaces.m | .m | HierarchicalSmooth-master/Src/Matlab/DifferentiateFaces.m | 2,686 | utf_8 | 32129ddd9bf2885a3e1f7e6b089ce1d2 | % Copyright (c) 2016-2018, Siddharth Maddali
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% * Redistributions of source code must retain the above copyright notice,
% ... |
github | siddharth-maddali/HierarchicalSmooth-master | ExtractFace.m | .m | HierarchicalSmooth-master/Src/Matlab/ExtractFace.m | 2,063 | utf_8 | daa38aadc153a08e1b3a49b40266fa54 | % Copyright (c) 2016-2018, Siddharth Maddali
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% * Redistributions of source code must retain the above copyright notice,
% ... |
github | siddharth-maddali/HierarchicalSmooth-master | GraphLaplacian.m | .m | HierarchicalSmooth-master/Src/Matlab/GraphLaplacian.m | 2,238 | utf_8 | b811fe3095cf60e21d06ce9a4837b406 | % Copyright (c) 2016-2018, Siddharth Maddali
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% * Redistributions of source code must retain the above copyright notice,
% ... |
github | siddharth-maddali/HierarchicalSmooth-master | Smooth.m | .m | HierarchicalSmooth-master/Src/Matlab/Smooth.m | 4,600 | utf_8 | f1dbcbe7a01d21619a09f62b3d2b20bb | % Copyright (c) 2016-2018, Siddharth Maddali
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% * Redistributions of source code must retain the above copyright notice,
% ... |
github | siddharth-maddali/HierarchicalSmooth-master | FastChainLinkSort.m | .m | HierarchicalSmooth-master/Src/Matlab/FastChainLinkSort.m | 1,977 | utf_8 | 3ed56141437155abd50fb7d0603f95fd | % Copyright (c) 2016-2018, Siddharth Maddali
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% * Redistributions of source code must retain the above copyright notice,
% ... |
github | siddharth-maddali/HierarchicalSmooth-master | Laplacian2D.m | .m | HierarchicalSmooth-master/Src/Matlab/Laplacian2D.m | 1,786 | utf_8 | 05d4acd9d47e340419cb6098ddbb3836 | % Copyright (c) 2016-2018, Siddharth Maddali
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% * Redistributions of source code must retain the above copyright notice,
% ... |
github | siddharth-maddali/HierarchicalSmooth-master | HierarchicalSmooth.m | .m | HierarchicalSmooth-master/Src/Matlab/HierarchicalSmooth.m | 8,813 | utf_8 | 26f802a9607a8ace308a7ddf808b6c98 | % Copyright (c) 2016-2018, Siddharth Maddali
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% * Redistributions of source code must retain the above copyright notice,
% ... |
github | Mirkes/PQSQ-regularized-regression-master | PQSQRegularRegr.m | .m | PQSQ-regularized-regression-master/PQSQRegularRegr.m | 48,325 | utf_8 | 254d4db2d225af03ab55b120881b5ae6 | function [B, FitInfo] = PQSQRegularRegr(X, Y, varargin)
%PQSQRegularRegr calculates PQSQ regularization of linear regression.
%Syntax:
% B = PQSQRegularRegr(X, Y)
% B = PQSQRegularRegr(X, Y, Name, Value)
% [B, FitInfo] = PQSQRegularRegr(X, Y)
% [B, FitInfo] = PQSQRegularRegr(X, Y, Name, Value)
%
%Examples:
% ... |
github | Mirkes/PQSQ-regularized-regression-master | fastRegularisedRegression.m | .m | PQSQ-regularized-regression-master/fastRegularisedRegression.m | 35,715 | utf_8 | 2e69cbe2ae6e43bfe04efb39b288798d | function [ res ] = fastRegularisedRegression(X, Y, varargin)
%fastRegularisedRegression perform feature selection for regression which
%is regularized by Tikhonov regularization (ridge regression) with
%automated selection of the optimal value of regularization parameter
%Alpha.
%
%Inputs:
% X is numeric matrix with ... |
github | Mirkes/PQSQ-regularized-regression-master | PQSQRegularRegrPlot.m | .m | PQSQ-regularized-regression-master/PQSQRegularRegrPlot.m | 28,973 | utf_8 | 606415bffdfeffd619a9644d845a1367 | function [axh,figh] = PQSQRegularRegrPlot( B, plotData, varargin )
%PQSQRegularRegrPlot plots coefficient values or goodness of fit of PQSQ
% regularised regression fits.
%
% [AXH, FIGH] = PQSQRegularRegrPlot(B, PLOTDATA) creates a Trace Plot
% showing the sequence of coefficient values B produced by a
% ... |
github | Mirkes/PQSQ-regularized-regression-master | PQSQRegularRegr.m | .m | PQSQ-regularized-regression-master/For paper/PQSQRegularRegr.m | 25,702 | utf_8 | fd194c4f2eed4b58cfab855a85a036f7 | function [B, FitInfo] = PQSQRegularRegr(X, Y, varargin)
%PQSQRegularRegr calculates PQSQ regularization of linear regression.
%Syntax:
% B = PQSQRegularRegr(X, Y)
% B = PQSQRegularRegr(X, Y, Name, Value)
% [B, FitInfo] = PQSQRegularRegr(X, Y)
% [B, FitInfo] = PQSQRegularRegr(X, Y, Name, Value)
%Inputs
% X is ... |
github | ghazi94/IRIS-Segmentation-master | MainApplication.m | .m | IRIS-Segmentation-master/Equivalent_MATLAB_Code/MainApplication.m | 989 | utf_8 | d11d977d68248e9290d2eb69ef016c3f | %function to detect the pupil boundary
%it searches a certain subset of the image
%with a given radius range(rmin,rmax)
%around a 10*10 neighbourhood of the point x,y given as input
%INPUTS:
%im:image to be processed
%rmin:minimum radius
%rmax:maximum radius
%x:x-coordinate of centre point
%y:y-coordinate of ... |
github | ghazi94/IRIS-Segmentation-master | drawcircle.m | .m | IRIS-Segmentation-master/Equivalent_MATLAB_Code/drawcircle.m | 1,212 | utf_8 | a4b898d11fcee80316df5a4da992a05e | %function to generate the pixels on the boundary of a regular polygon of n sides
%the polygon approximates a circle of radius r and is used to draw the circle
%INPUTS:
%1.I:Image to be processed
%2.C(x,y):Centre coordinates of the circumcircle
%Coordinate system :
%origin of coordinates is at the top left corner
... |
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