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 | francois-a/llsmtools-master | setupFigure.m | .m | llsmtools-master/graphics/setupFigure.m | 4,671 | utf_8 | 96f99f86d4e545d13665266c8a7ef507 | %[ha, hi, hf] = setupFigure(varargin) generates a multi-panel figure
%
% Optional inputs (first, second, third arguments):
% nh: number of rows
% nw: number of columns
% na: total number of axes
%
% Options (specifier, value pairs):
% SameAxes: true|{false} omits unnecessary tick labels if the data r... |
github | francois-a/llsmtools-master | stackviewer.m | .m | llsmtools-master/graphics/stackviewer.m | 17,623 | utf_8 | 8eaac8bbdc890b42bda58e614ac55ec2 | %stackviewer(stack) displays 2D projections of a 3D stack
%
% Inputs:
% stack : 3D array
%
% Optional inputs:
% X : matrix of #points x 3 coordinates
%
% Parameters:
% 'ZAnisotropy' : anisotropy factor to adjust for z vs. x,y spacing differences in projections
% 'DynamicRange' : dynamic range for disp... |
github | francois-a/llsmtools-master | getDirFromPath.m | .m | llsmtools-master/pathfunc/getDirFromPath.m | 390 | utf_8 | c88f4ce92d983d8f0355827a94fac01f | %[dirName] = getDirFromPath(dpath) returns the last directory contained in the input path
% Francois Aguet, November 2010
function [dirName, dirPath] = getDirFromPath(dpath)
idx = regexp(dpath, filesep);
if idx(end) == length(dpath)
dirName = dpath(idx(end-1)+1:end-1);
dirPath = dpath(1:idx(end-1));
else
... |
github | francois-a/llsmtools-master | recursiveDir.m | .m | llsmtools-master/pathfunc/recursiveDir.m | 1,199 | utf_8 | 41406b169c12e5b914e96881a1a67bb6 | %[dirList] = recursiveDir(path, maxlevel) recursively lists directories found under 'path'
% Francois Aguet, 062813
function p = recursiveDir(d, varargin)
ip = inputParser;
ip.CaseSensitive = false;
ip.addRequired('d');
ip.addOptional('maxlevel', [], @(x) isempty(x) || (isnumeric(x) && abs(round(x))==x));
ip.addOpti... |
github | francois-a/llsmtools-master | getShortPath.m | .m | llsmtools-master/pathfunc/getShortPath.m | 560 | utf_8 | b64000f85bed382093af8c954de401e0 | %spath = getShortPath(data) returns the truncated path of a cell directory (3 levels)
% Francois Aguet, 05/13/2011
function spath = getShortPath(data, level)
if nargin<2
level = 3;
end
if numel(data.channels)>1
mCh = strcmp(data.channels, data.source);
sCh = setdiff(1:length(data.channels),mCh);
spa... |
github | francois-a/llsmtools-master | getMovieName.m | .m | llsmtools-master/pathfunc/getMovieName.m | 408 | utf_8 | 9b45c957bb5b35dc0b8ac2ba5aeb07da | %[str] = getMovieName(data) returns the identifier string ' date movieName' for each movie in data
% Francois Aguet 08/2013
function str = getMovieName(data)
nd = numel(data);
str = cell(1,nd);
for i = 1:nd
if isempty(data(i).date)
str{i} = [' ' getCellDir(data(i))];
else
str{i} = [' ' num2st... |
github | francois-a/llsmtools-master | sortStringsByToken.m | .m | llsmtools-master/pathfunc/sortStringsByToken.m | 786 | utf_8 | f4de716643f8388cb07776316305c528 | %[s, rm] = sortStringsByToken(s, token, mode) sorts input array by a number sequence preceding or following a specified token
%
% Inputs:
% s : cell array of strings
% token : token to match
% mode : 'pre' (sort by number sequence preceding token) or
% 'post' (by sequence following token)
%
% Outpu... |
github | francois-a/llsmtools-master | estGaussianAmplitude3D.m | .m | llsmtools-master/psdetect3d/estGaussianAmplitude3D.m | 1,763 | utf_8 | 8ab8145afc957e13740987757dc76545 | %[A_est, c_est] = estGaussianAmplitude3D(vol, sigma, varargin) calculates the
% amplitude and background coefficient for Gaussians centered on all voxels of
% the input volume.
%
% INPUTS
% vol : input volume
% sigma : standard deviation of the Gaussian PSF
% If the PSF is anisotropic, 'sigma' should b... |
github | francois-a/llsmtools-master | scaleContrast.m | .m | llsmtools-master/mathfunc/scaleContrast.m | 592 | utf_8 | 5c142a37cba1833b9b14351983e7e0f5 | %out = scaleContrast(in, rangeIn, rangeOut) adjusts the contrast of the input
%
% Inputs:
% in : input signal
% rangeIn : input range. If empty, [min(in(:)) max(in(:))]
% rangeOut : output range
% Francois Aguet (Last modified: 03/22/2011)
function out = scaleContrast(in, rangeIn, rangeOut)
if nargin<2 ... |
github | francois-a/llsmtools-master | interpln.m | .m | llsmtools-master/mathfunc/interpln.m | 579 | utf_8 | 6dc7a1d2579e693db0243e38af0b99df | %[fi] = interpln(x, f, xi) implements lower-neighbor interpolation
%
% Inputs:
% x : sample positions
% f : sample values
% xi : interpolation positions
%
% Outputs:
% fi : interpolated values
%
% Example:
% interpln([0 0.5 2.7 3.2], [0 1 0 1], [1 3])
% returns [1 0]
% Francois Aguet, 03/24/2014
function... |
github | francois-a/llsmtools-master | scaleEDFs.m | .m | llsmtools-master/mathfunc/scaleEDFs.m | 6,927 | utf_8 | e1315d02e609813f0c50246b5632be2f | %[a c medIdx] = scaleEDFs(samples, varargin) computes the x-scaling factor between the EDFs of the input sample sets
%
% Outputs:
% a : scaling factor
% c : estimated fraction of missing data
%
% Francois Aguet, 03/06/2012 (last modified 03/12/2013)
function [a, c, refIdx] = scaleEDFs(samples, varar... |
github | francois-a/llsmtools-master | getCropRegions3D.m | .m | llsmtools-master/cmeAnalysis3D/getCropRegions3D.m | 2,466 | utf_8 | 8383bfcc90ffb08d317205c5631438b6 | %[reg, hasDS] = getCropRegions3D(data, varargin) enables cropping movies prior to de-skewing in order to reduce file sizes.
% The function loops through the movies in the input structure and displays projections
% of the first and last frame to facilitate cropping.
%
% Inputs:
% data : structure returned by loadCond... |
github | francois-a/llsmtools-master | runDetection3D.m | .m | llsmtools-master/cmeAnalysis3D/runDetection3D.m | 8,402 | utf_8 | ddc2875abe01114cba58fc54bb22bed0 | %runDetection3D(data) detects CCPs using a combination of model-based (PSF) fitting and statistical tests
%
% Inputs: data : data/movie structure
% {'Sigma'} : standard deviation of the Gaussian used for fitting
% {'Overwrite'} : true | {false}
%
% Notes:
% - 3D coordinates are in pixel space, except... |
github | francois-a/llsmtools-master | loadTrackSettings.m | .m | llsmtools-master/cmeAnalysis3D/loadTrackSettings.m | 6,032 | utf_8 | 88cc5bc7c44c4bc47decda96eaaf454f | % Adapted from 'ScriptTrackGeneral' in 'trackWithGapClosing/Kalman'
% Francois Aguet, November 2010
function trackSettings = loadTrackSettings(varargin)
ip = inputParser;
ip.CaseSensitive = false;
ip.addParamValue('Radius', []);
ip.addParamValue('GapRadius', [5 10]);
ip.addParamValue('LinkRadius', [5 10]);
ip.addPar... |
github | francois-a/llsmtools-master | deskewData.m | .m | llsmtools-master/cmeAnalysis3D/deskewData.m | 13,645 | utf_8 | 006bcb279e760dcd6460f4aa3ec4be79 | %[data] = deskewData(varargin) de-skews and rotates light sheet microscope data sets
% The function launches a small GUI enabling input of acquisition parameters, after which
% it prompts for input of cropping regions to minimize file sizes. By default, the function
% generates the de-skewed data required for processin... |
github | francois-a/llsmtools-master | getVisitorIndex.m | .m | llsmtools-master/cmeAnalysis3D/getVisitorIndex.m | 795 | utf_8 | d3f56c26ebe9256602b64a1ec438802c | %[vidx] = getVisitorIndex(lftData, mCh) identifies trajectories corresponding to objects 'visiting' the TIRF field
function vidx = getVisitorIndex(lftData, mCh)
if nargin<2
mCh = 1;
end
minLength = min(arrayfun(@(i) size(i.gapMat_Ia,2), lftData));
A = arrayfun(@(i) i.A(:,1:minLength,mCh), lftData, 'UniformOutpu... |
github | francois-a/llsmtools-master | rotateDetections3D.m | .m | llsmtools-master/cmeAnalysis3D/rotateDetections3D.m | 1,711 | utf_8 | 9761acd77a058fb27d39165ac43a7450 |
function X = rotateDetections3D(data, varargin)
ip = inputParser;
ip.CaseSensitive = false;
ip.addRequired('data');
ip.addOptional('X', []);
ip.addParamValue('Crop', true, @islogical);
ip.parse(data, varargin{:});
X = ip.Results.X;
vol = double(readtiff(data.framePathsDS{1}{1}));
[ny,nx,nz] = size(vol);
theta = d... |
github | francois-a/llsmtools-master | loadTracks.m | .m | llsmtools-master/cmeAnalysis3D/loadTracks.m | 4,171 | utf_8 | 6ebec5f032e0989cf17cccc5f101f775 | %tracks = loadTracks(data, varargin) returns the tracks detected in input data
%
% Inputs:
%
% data : data structure returned by loadConditionData()
%
% Options:
%
% 'Category' : 'Ia' or 'valid' Single tracks with valid gaps
% 'Ib' Single tracks with invalid gaps
% 'Ic' Single tracks ... |
github | francois-a/llsmtools-master | getFluorPropStruct.m | .m | llsmtools-master/cmeAnalysis3D/getFluorPropStruct.m | 1,650 | utf_8 | 9b20694ca644600605d6ccd938ce88ed | % Values from http://www.olympusfluoview.com/applications/fpcolorpalette.html
% Alexa Fluors: http://www.invitrogen.com/site/us/en/home/References/Molecular-Probes-The-Handbook/Technical-Notes-and-Product-Highlights/The-Alexa-Fluor-Dye-Series.html
% Francois Aguet, October 2010
function s = getFluorPropStruct()
s(1)... |
github | francois-a/llsmtools-master | loadConditionData3D.m | .m | llsmtools-master/cmeAnalysis3D/loadConditionData3D.m | 5,812 | utf_8 | 8dc8515650479a4a66fa6c3d46adf7fd | % loadConditionData3D loads the relevant information for all the movies
% available for a specific condition; this requires a specific directory
% structure and nomenclature (see below)
%
% SYNOPSIS [data] = loadConditionData3D()
%
% INPUTS
% {condDir} : root directory where movies are located
% ... |
github | francois-a/llsmtools-master | runTracking3D.m | .m | llsmtools-master/cmeAnalysis3D/runTracking3D.m | 2,933 | utf_8 | fb50485996436d5b3ee6694baa4b7bab | %runTracking(data, varargin) tracks CCPs in the movies passed with the 'data' structure.
% This function generates a list of tracks in 'Tracking/trackedFeatures.mat' for each
% data set.
%
% Inputs
% data : list of movies, using the structure returned by loadConditionData.m
% {settings} :... |
github | francois-a/llsmtools-master | runTrackProcessing3D.m | .m | llsmtools-master/cmeAnalysis3D/runTrackProcessing3D.m | 41,225 | utf_8 | 0f04f4b7705e788d468815bf7f7119fe | %runTrackProcessing(data, varargin) processes the track structure generated by runTracking()
%
% Inputs
% data : list of movies, using the structure returned by loadConditionData.m
%
% Options
% 'Buffer' : Length of buffer readout before/after each track. Default: [5 5]
% 'Overwrite' : true|... |
github | francois-a/llsmtools-master | cropMovie3D.m | .m | llsmtools-master/cmeAnalysis3D/cropMovie3D.m | 1,195 | utf_8 | 7bffef3e3dcca395379b6a5302885c43 | % Author: Francois Aguet
function cropMovie3D(framePaths, outputDir, varargin)
nf = numel(framePaths);
ip = inputParser;
ip.CaseSensitive = false;
ip.addRequired('framePaths', @iscell);
ip.addRequired('outputDir', @ischar);
ip.addParamValue('FrameRange', [1 nf], @isnumeric);
ip.parse(framePaths, outputDir, varargin{... |
github | francois-a/llsmtools-master | cmeAnalysis3D.m | .m | llsmtools-master/cmeAnalysis3D/cmeAnalysis3D.m | 5,624 | utf_8 | f44e7aacf2f02426ccc10a262893a5c7 | %cmeAnalysis3D performs the analysis of clathrin-coated pit dynamics on data
% generated with a light sheet microscope.
% The analysis comprises detection, tracking, and selection CCP structures.
% The graphical output includes lifetime distribution and intensity cohort plots.
%
% Inputs (optional):
% data : ... |
github | francois-a/llsmtools-master | getLifetimeData.m | .m | llsmtools-master/cmeAnalysis3D/getLifetimeData.m | 8,914 | utf_8 | d53d49b540c05ef0e1f0ccd9471cc69b | %[lftData, rmIdx] = getLifetimeData(data, varargin) returns track information in compact form for lifetime analysis
% Francois Aguet, 05/2012
function [lftData, rmIdx] = getLifetimeData(data, varargin)
nd = numel(data);
nCh = numel(data(1).channels);
ip = inputParser;
ip.CaseSensitive = false;
ip.addParamValue('Ove... |
github | francois-a/llsmtools-master | getCellVolume.m | .m | llsmtools-master/cmeAnalysis3D/getCellVolume.m | 9,353 | utf_8 | 76e0a3cc5a8e0c35b8ba30ad04a3d46f | %getCellVolume(data, varargin) calculates cell volume & area based on thresholding
% of the smoothened input data
%
% Inputs:
% data : structure returned by loadConditionData3D
%
% Parameters (specifier/value pairs):
% 'SmoothingSigma' : s.d. for Gaussian smoothing of the data
% 'MinVolume' : minimum accept... |
github | francois-a/llsmtools-master | rotateTracks3D.m | .m | llsmtools-master/cmeAnalysis3D/rotateTracks3D.m | 3,680 | utf_8 | fd3b60280ca32c4c4e1c2c9fd3d9130f | %rotateTracks3D(data, varargin) rotates processed tracks to the same frame
% of reference as rotated stacks, with the coverslip horizontal
%
% See also rotateFrame3D
% Author: Francois Aguet
function rotateTracks3D(data, varargin)
ip = inputParser;
ip.CaseSensitive = false;
ip.addRequired('data');
ip.addParamValue('... |
github | gizatt/gelsight_driver-master | fast_poisson2.m | .m | gelsight_driver-master/src/matlab/3D_reconstruction/Func_3DReconstruct/fast_poisson2.m | 1,619 | utf_8 | f515a594e3093dad907d492f806370e2 | % function [img_direct] = poisson_solver_function(gx,gy,boundary_image)
% Inputs; Gx and Gy -> Gradients
% Boundary Image -> Boundary image intensities
% Gx Gy and boundary image should be of same size
% code borrowed from Ramesh Raskar,
% http://www.merl.com/people/raskar/photo/code.pdf
function [img_direct] = ... |
github | gizatt/gelsight_driver-master | matchGrad.m | .m | gelsight_driver-master/src/matlab/3D_reconstruction/Func_3DReconstruct/matchGrad.m | 1,677 | utf_8 | 024750de288bf8c0d24dc1e4cc2c2a8c | function [ImGradX, ImGradY]=matchGrad(LookupTable, dI, f0,f01, validmask)
% LookupTable is the look up table structure; dI is the difference;
% f0 is the initializaion image, f01 is the local weight. In current
% sketch, it's the sum of three channels.
% validmask is the mask for contact area, optional
size1=size(dI... |
github | AlanRace/MOOGL-master | parseRegionOfInterestList.m | .m | MOOGL-master/util/parseRegionOfInterestList.m | 980 | utf_8 | 24027c8c7e4b292c2b1717cf1a06eeb6 | function regionOfInterestList = parseRegionOfInterestList(filename)
% parseClusterGroupList Convert XML file to a MATLAB structure.
try
tree = xmlread(filename);
catch
error('Failed to read XML file %s.',filename);
end
% Recurse over child nodes. This could run into problems
% with very deeply nested trees.
tr... |
github | AlanRace/MOOGL-master | parseRegionOfInterestElement.m | .m | MOOGL-master/util/parseRegionOfInterestElement.m | 1,660 | utf_8 | f6ea11108991760769565360ccd5d7a8 | function regionOfInterest = parseRegionOfInterestElement(regionOfInterestNode)
width = str2num(regionOfInterestNode.getAttributes().getNamedItem('width').getValue());
height = str2num(regionOfInterestNode.getAttributes().getNamedItem('height').getValue());
regionOfInterest = RegionOfInterest(width, height)... |
github | MichaelXin/caffe-binary-master | classification_demo.m | .m | caffe-binary-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 | MichaelXin/caffe-binary-master | BWN.m | .m | caffe-binary-master/matlab/Binary/BWN.m | 3,913 | utf_8 | 91caec5c1db25e93041287864dba2018 | function BWN()
clear;clc;
current_dir = pwd;
caffe_dir = '../..'; cd(caffe_dir); caffe_dir = pwd;
cd(current_dir);
addpath(fullfile(caffe_dir,'matlab'));
caffe.reset_all();
caffe.set_mode_gpu();
caffe.set_device(5);
rand('seed',0);
cifar10_train_data = load(fullfile(caffe_dir,'examples','cifar10','cifar10_train_lmdb.... |
github | Markus-PP/openslide-vmic-master | reconstruct.m | .m | openslide-vmic-master/misc/reconstruct.m | 4,679 | utf_8 | d36aa0543b14ab10ff4e0467592184af | % This script is tailored for the 9th level of the CMU-1 data set.
% The files input should be a cell array of string pointing to the
% two JPEG files of which the 9th is build up. You may use split-mirax.py
% to extract all images from the CMU-1 data set.
function reconstructed_image = reconstruct(files,cvt_to_int)
... |
github | sasatatar/ConcreteTool-master | RebarTool.m | .m | ConcreteTool-master/RebarTool.m | 17,737 | utf_8 | 22f940aed36f81fd42637b3f99d92886 | function varargout = RebarTool(varargin)
% REBARTOOL MATLAB code for RebarTool.fig
% REBARTOOL, by itself, creates a new REBARTOOL or raises the existing
% singleton*.
%
% H = REBARTOOL returns the handle to a new REBARTOOL or the handle to
% the existing singleton*.
%
% REBARTOOL('CALLBACK',hO... |
github | sasatatar/ConcreteTool-master | TorsionTool.m | .m | ConcreteTool-master/TorsionTool.m | 15,971 | utf_8 | e9c38a305e4efd9485d0f3e86e94b9c8 | function varargout = TorsionTool(varargin)
% TORSIONTOOL MATLAB code for TorsionTool.fig
% TORSIONTOOL, by itself, creates a new TORSIONTOOL or raises the existing
% singleton*.
%
% H = TORSIONTOOL returns the handle to a new TORSIONTOOL or the handle to
% the existing singleton*.
%
% TORSIONTO... |
github | sasatatar/ConcreteTool-master | StirrupTool.m | .m | ConcreteTool-master/StirrupTool.m | 11,970 | utf_8 | 5dbd3ae97dc43f5b7eb433ea89572170 | function varargout = StirrupTool(varargin)
% STIRRUPTOOL MATLAB code for StirrupTool.fig
% STIRRUPTOOL, by itself, creates a new STIRRUPTOOL or raises the existing
% singleton*.
%
% H = STIRRUPTOOL returns the handle to a new STIRRUPTOOL or the handle to
% the existing singleton*.
%
% STIRRUPTO... |
github | sasatatar/ConcreteTool-master | ConcreteTool.m | .m | ConcreteTool-master/ConcreteTool.m | 42,589 | utf_8 | e1e85b3e27f24d5ef15cd8bdac80106e | function varargout = ConcreteTool(varargin)
% CONCRETETOOL MATLAB code for ConcreteTool.fig
% CONCRETETOOL, by itself, creates a new CONCRETETOOL or raises the existing
% singleton*.
%
% H = CONCRETETOOL returns the handle to a new CONCRETETOOL or the handle to
% the existing singleton*.
%
% CO... |
github | talialerner/Photometry-Analysis-Shared-master | tdt2mat.m | .m | Photometry-Analysis-Shared-master/Dropbox/MATLAB/Shared photometry code/tdt2mat.m | 3,894 | utf_8 | d0b80fe4c4b06ac2664759872911d85b | %% tdt2mat.m %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Import data from TDT system recording into Matlab structure
%
% S = tdt2mat(filepath, tank, blk, event)
%
% INPUTS
% filepath: folder where tank is stored
% tank: tank name
% blk: block name
% event: event name (ie 'Wave')
%
% OUTPUT
% data st... |
github | Ahmed-ElTahan/Deterministic-Indirect-Self-Tuning-Regulator-One-Degree-Controller-master | outputestimation.m | .m | Deterministic-Indirect-Self-Tuning-Regulator-One-Degree-Controller-master/outputestimation.m | 3,275 | utf_8 | dc6610be6960ca614e10141953a18c1c | % This function is made by Ahmed ElTahan
%{
Any system can be written as
z^(-d) B y
G = -------------------- = -----------
A u
where
--... |
github | Ahmed-ElTahan/Deterministic-Indirect-Self-Tuning-Regulator-One-Degree-Controller-master | ISTR.m | .m | Deterministic-Indirect-Self-Tuning-Regulator-One-Degree-Controller-master/ISTR.m | 9,855 | utf_8 | b4f1ea05eb2eab84ee9846216f8d1fd4 | % This function is made by Ahmed Tahan
%{
It's intended to apply the self-tuning regulator for a given system
such as
y z^(-d) Bsys
Gp = ------ = ----------------------
u ... |
github | Ahmed-ElTahan/Deterministic-Indirect-Self-Tuning-Regulator-One-Degree-Controller-master | Diophantine.m | .m | Deterministic-Indirect-Self-Tuning-Regulator-One-Degree-Controller-master/Diophantine.m | 2,916 | utf_8 | e3608c58795d57b57d400502e37b0e2a | % This function is made by Ahmed ElTahan.
%{
It's intended to solve the Diphantine equation in the form of
AR + z^(d) BS = A0Am = alpha;
where
-- A = 1 + a_1 z^-1 + a_2 z^-1 + ... + a_na z^(-na) --> [1, a_1, a_2, a_3, ..., a_na]
-- B = b_0 + b_1 z^-1 + b_2 z^-1 + ... + b_n... |
github | webrtc-uwp/zzz-obsolete.webrtc-master | rtpAnalyze.m | .m | zzz-obsolete.webrtc-master/tools_webrtc/matlab/rtpAnalyze.m | 7,892 | utf_8 | 46e63db0fa96270c14a0c205bbab42e4 | function rtpAnalyze( input_file )
%RTP_ANALYZE Analyze RTP stream(s) from a txt file
% The function takes the output from the command line tool rtp_analyze
% and analyzes the stream(s) therein. First, process your rtpdump file
% through rtp_analyze (from command line):
% $ out/Debug/rtp_analyze my_file.rtp my_f... |
github | webrtc-uwp/zzz-obsolete.webrtc-master | readDetection.m | .m | zzz-obsolete.webrtc-master/webrtc/modules/audio_processing/transient/test/readDetection.m | 927 | utf_8 | f6af5020971d028a50a4d19a31b33bcb | %
% Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | webrtc-uwp/zzz-obsolete.webrtc-master | readPCM.m | .m | zzz-obsolete.webrtc-master/webrtc/modules/audio_processing/transient/test/readPCM.m | 821 | utf_8 | 76b2955e65258ada1c1e549a4fc9bf79 | %
% Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | webrtc-uwp/zzz-obsolete.webrtc-master | plotDetection.m | .m | zzz-obsolete.webrtc-master/webrtc/modules/audio_processing/transient/test/plotDetection.m | 923 | utf_8 | e8113bdaf5dcfe4f50200a3ca29c3846 | %
% Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | webrtc-uwp/zzz-obsolete.webrtc-master | apmtest.m | .m | zzz-obsolete.webrtc-master/webrtc/modules/audio_processing/test/apmtest.m | 9,874 | utf_8 | 17ad6af59f6daa758d983dd419e46ff0 | %
% Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | webrtc-uwp/zzz-obsolete.webrtc-master | parse_delay_file.m | .m | zzz-obsolete.webrtc-master/webrtc/modules/audio_coding/neteq/test/delay_tool/parse_delay_file.m | 6,405 | utf_8 | 4cc70d6f90e1ca5901104f77a7e7c0b3 | %
% Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | webrtc-uwp/zzz-obsolete.webrtc-master | plot_neteq_delay.m | .m | zzz-obsolete.webrtc-master/webrtc/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,967 | utf_8 | cce342fed6406ef0f12d567fe3ab6eef | %
% Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | joshuaas/eldm_matlab-master | parseData.m | .m | eldm_matlab-master/Utils/parseData.m | 1,055 | utf_8 | 7d72b85a34904b49ad36dd2422aef08d | %deal with input path
function [data,m] = parseData(inpath)
parstr = strsplit(inpath,'.') ;%Split string using point
% the length of parstr is 1 or not end with 'dat' and 'mat'
if(length(parstr)==1 ||( (~strcmp(strtrim(parstr(end)),'dat') && (~strcmp(parstr(end),'mat')))))
[Y,X] = libsvmread(inpath);... |
github | joshuaas/eldm_matlab-master | calacc.m | .m | eldm_matlab-master/Utils/calacc.m | 291 | utf_8 | fa8ba8e24d24d9e7590f8a942dc684dd | %% functionname: function description
function [acc] = calacc(res,label)
mul = size(label,2)>1;
if mul
res = vec2ind(res')';
label = vec2ind(label')';
acc = sum(res == label)/size(label,1);
else
yl = 2*(res>0)-1 ;
acc = sum(yl == label)/length(label);
end
end
|
github | joshuaas/eldm_matlab-master | Run_All_ELDM_test.m | .m | eldm_matlab-master/Experiments/Run_All_ELDM_test.m | 957 | utf_8 | c973f0282016b3ebc399cc6e2db6d9b4 | %Run all the test data
function Run_All_ELDM_test(file)
files = ls([file '\*' ]);%lists the contents of the current folder.
files(1,:) =[]; %%% delete the first row
files(1,:)= []; %%% delete ..
for i = 1:size(files,1) %Traverse rows
Infile(i,:) = [file '\' files(i,:)];%the input file
names = strsplit(Infi... |
github | joshuaas/eldm_matlab-master | eval_ELDM_test.m | .m | eldm_matlab-master/Experiments/eval_ELDM_test.m | 790 | utf_8 | 2deb9818226532c0b707f88aae866990 | %execute algorithm then save
function eval_ELDM_test(inpath,outpath)
[data,ny] =parseData(inpath) ;%deal with input path and data
%data= load('E:\MATLAB\SVM\data\handwritten2.txt');
coeff= -3:3;% penalty coefficient
g=-5:5;%gamma in kernel function
c=coeff;
if(ny>1)
node = [50 100 150 200 300 1000] ;
else
node... |
github | mahmoudabdulazim/MEMCL-master | CATBot.m | .m | MEMCL-master/CATBot.m | 10,916 | utf_8 | 36ca7061cdc680d34051b647a5126612 | classdef CATBot < handle
properties
true_state;
belief_state;
map;
sensor;
particles;
target_state;
dt = 0.01;
alphas = [0.01;0.01;0.01;0.01;0.01;0.01];
z_params = [0.85; 0.03; 0.1; 0.02];
sigma_hit = 0.05;
lamda_sho... |
github | SubhankarGhosh/Leaf_recognition-master | mymain.m | .m | Leaf_recognition-master/mymain.m | 2,583 | utf_8 | 27a7d9a59b33eb372e24e4ca583ed745 | function main()
ch=input('You want to train press 1 else 2');
if ch==1
for i=1:8
diry=[pwd '\dataset\' num2str(i)];
disp(' features Extraction.....');
feature1=training(diry);
if i==1
out=feature1;
group=ones(size(feature1,1),1)*i;
else
group1=ones(size(feature1,1),1)*i;
group=[g... |
github | SubhankarGhosh/Leaf_recognition-master | main_feature.m | .m | Leaf_recognition-master/main_feature.m | 2,248 | utf_8 | 7724587111a3ef3f09d7650c729ebb57 | function main()
ch=input('You want to train press 1 else 2');
if ch==1
for i=1:8
diry=[pwd '\dataset\' num2str(i)];
disp(' features Extraction.....');
feature1=training(diry);
if i==1
out=feature1;
group=ones(size(feature1,1),1)*i;
else
group1=ones(size(feature1,1),1)*i;
group=[g... |
github | SubhankarGhosh/Leaf_recognition-master | MAIN_CODE.m | .m | Leaf_recognition-master/MAIN_CODE.m | 6,843 | utf_8 | 423c2ba706eadce95d2815653efa5c4c | function varargout = MAIN_CODE(varargin)
% MAIN_CODE MATLAB code for MAIN_CODE.fig
% MAIN_CODE, by itself, creates a new MAIN_CODE or raises the existing
% singleton*.
%
% H = MAIN_CODE returns the handle to a new MAIN_CODE or the handle to
% the existing singleton*.
%
% MAIN_CODE('CALLBACK',hO... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | BFOsc20.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/BFO_FUZZY/BFOsc20.m | 2,439 | utf_8 | 983f08e86805c8d546aa17b4beab2fa7 |
function [minvalue,xmin,fminval] = BFOsc20(num,nub,E)
% ------- initialisation ----------%
Ne=10;
Nr=10;
Nc=10;
Np=num;
Ns=num/2;
D=nub*3;
C=0.01;
Ped=0.9; % elimination dispersion probability
% x=(rand(Np,D)-0.5)*60; % x lies in [-30 30]
Lb = zeros(1,D); %%% Lower bounds
Ub = 255.*ones(1,D); %%% Upper bo... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/BFO_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/ABC_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/Differential Evolution_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | randFCR.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/JADE_FUZZY/randFCR.m | 1,186 | utf_8 | 323c76980b166af47006963da0d3d44c | function [F,CR] = randFCR(NP, CRm, CRsigma, Fm, Fsigma)
% this function generate CR according to a normal distribution with mean "CRm" and sigma "CRsigma"
% If CR > 1, set CR = 1. If CR < 0, set CR = 0.
% this function generate F according to a cauchy distribution with location parameter "Fm" and scale par... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/JADE_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/PSO_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | cuckoosc20.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/Cuckoo Search_FUZZY/cuckoosc20.m | 2,785 | utf_8 | dedbb2c81375afba43a63413c7403f31 | function [fmin,bestnest,fminval] = cuckoosc20(n,num,E)
if nargin<1
n = 25;
end
format long;
number_of_solution = num*3;
% Lb = 0.*ones(1,number_of_solution);
% [~,lowerindx] = min(E);
Lb = 0.*ones(1,number_of_solution);
Ub = 255.*ones(1,number_of_solution);
for i=1:n
nest(i,:) = Lb + (Ub - Lb).*rand(size(Lb... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/Cuckoo Search_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/Bat Algorithm_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | bat_algorithmsc20otsu.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/Bat Algorithm_FUZZY/bat_algorithmsc20otsu.m | 5,326 | utf_8 | dc4b9f61d25f8137c0c926c5ef7c59dd | % ======================================================== %
% Files of the Matlab programs included in the book: %
% Xin-She Yang, Nature-Inspired Metaheuristic Algorithms, %
% Second Edition, Luniver Press, (2010). www.luniver.com %
% ======================================================== %
% -------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | bat_algorithmsc20.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/Bat Algorithm_FUZZY/bat_algorithmsc20.m | 5,258 | utf_8 | d5942e16272104872a46878dbe76e61d | % ======================================================== %
% Files of the Matlab programs included in the book: %
% Xin-She Yang, Nature-Inspired Metaheuristic Algorithms, %
% Second Edition, Luniver Press, (2010). www.luniver.com %
% ======================================================== %
% -------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | bat_algorithmsc20kapur.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/Bat Algorithm_FUZZY/bat_algorithmsc20kapur.m | 5,317 | utf_8 | 8a4ac6b1147246d5be5ace303b4423ab | % ======================================================== %
% Files of the Matlab programs included in the book: %
% Xin-She Yang, Nature-Inspired Metaheuristic Algorithms, %
% Second Edition, Luniver Press, (2010). www.luniver.com %
% ======================================================== %
% -------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/BSA_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | bsasc20Tsallis.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/BSA_FUZZY/bsasc20Tsallis.m | 2,968 | utf_8 | cb8e8df9837f30a415013f178b2211ff |
function [globalminimum,globalminimizer,fminval] = bsasc20Tsallis(popsize,dim,DIM_RATE,low,up,epoch,E)
%INITIALIZATION
if numel(low)==1, low=low*ones(1,dim); up=up*ones(1,dim); end % this line must be adapted to your problem
pop=GeneratePopulation(popsize,dim,low,up); % see Eq.1 in [1]
for i=1:popsize
fitnesspop(... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | bsasc20kapur.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/BSA_FUZZY/bsasc20kapur.m | 2,962 | utf_8 | 6c467ab5e11a228ba4430d8db548c50b |
function [globalminimum,globalminimizer,fminval] = bsasc20kapur(popsize,dim,DIM_RATE,low,up,epoch,E)
%INITIALIZATION
if numel(low)==1, low=low*ones(1,dim); up=up*ones(1,dim); end % this line must be adapted to your problem
pop=GeneratePopulation(popsize,dim,low,up); % see Eq.1 in [1]
for i=1:popsize
fitnesspop(i)... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | bsasc20.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/BSA_FUZZY/bsasc20.m | 2,944 | utf_8 | 04d9cf37096b18f2a26b2528c12a8875 |
function [globalminimum,globalminimizer,fminval] = bsasc20(popsize,dim,DIM_RATE,low,up,epoch,E)
%INITIALIZATION
if numel(low)==1, low=low*ones(1,dim); up=up*ones(1,dim); end % this line must be adapted to your problem
pop=GeneratePopulation(popsize,dim,low,up); % see Eq.1 in [1]
for i=1:popsize
fitnesspop(i)=fitn... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | bsasc20otsu.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/BSA_FUZZY/bsasc20otsu.m | 2,971 | utf_8 | d58a9e479c476c3ed6a0b85472c1c8f6 |
function [globalminimum,globalminimizer,fminval] = bsasc20otsu(popsize,dim,DIM_RATE,low,up,epoch,E,muT)
%INITIALIZATION
if numel(low)==1, low=low*ones(1,dim); up=up*ones(1,dim); end % this line must be adapted to your problem
pop=GeneratePopulation(popsize,dim,low,up); % see Eq.1 in [1]
for i=1:popsize
fitnesspop... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | fireflysc20.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/Firefly Algorithm_FUZZY/fireflysc20.m | 5,886 | utf_8 | 4870dc8b56fb1e333f3ad20bff9833b8 | % ======================================================== %
% Files of the Matlab programs included in the book: %
% Xin-She Yang, Nature-Inspired Metaheuristic Algorithms, %
% Second Edition, Luniver Press, (2010). www.luniver.com %
% ======================================================== %
% -------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/Firefly Algorithm_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | EFOsc20otsu.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/EFO_FUZZY/EFOsc20otsu.m | 3,879 | utf_8 | 7b3e0eb288a9a40c35e2489bbcbd01aa | %**************************************************************************************************
%Reference: Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2015).
% Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm.
% Swar... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | FeatureSIM.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/EFO_FUZZY/FeatureSIM.m | 17,652 | utf_8 | e7843052febad5aeebea478be388b87c | function [FSIM, FSIMc] = FeatureSIM(imageRef, imageDis)
% ========================================================================
% FSIM Index with automatic downsampling, Version 1.0
% Copyright(c) 2010 Lin ZHANG, Lei Zhang, Xuanqin Mou and David Zhang
% All Rights Reserved.
%
% --------------------------------------... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | EFOsc20kapur.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/EFO_FUZZY/EFOsc20kapur.m | 3,870 | utf_8 | bbb380d35137d63a7e8bcb459918d8f0 | %**************************************************************************************************
%Reference: Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2015).
% Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm.
% Swar... |
github | Deepaknkumar/Fuzzy-entropy-based-optimal-thresholding-master | EFOsc20.m | .m | Fuzzy-entropy-based-optimal-thresholding-master/EFO_FUZZY/EFOsc20.m | 3,806 | utf_8 | 1d0b6007feb94bc76a3a8e56f112e91a | %**************************************************************************************************
%Reference: Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. (2015).
% Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm.
% Swar... |
github | jardamacak/NodalDKFramework-master | inductor.m | .m | NodalDKFramework-master/inductor.m | 765 | utf_8 | 16757a533e003b878885badb75947a38 | % NodalDKFramework
% Digital simulation of analog circuits
%
% Jaromir Macak
% jarda.macak@seznam.cz
%
% 14.1.2017
%
% Copyright 2017, All Rights Reserved.
%
% This software may be licensed under the terms of the
% GNU Public License v3 (LICENSE-gpl3.txt) or the custom license
% (LICENSE.txt) located at th... |
github | jardamacak/NodalDKFramework-master | pentode.m | .m | NodalDKFramework-master/pentode.m | 924 | utf_8 | c50c4e844e2a014cbfd2fe5d0a1f02f4 | % NodalDKFramework
% Digital simulation of analog circuits
%
% Jaromir Macak
% jarda.macak@seznam.cz
%
% 14.1.2017
%
% Copyright 2017, All Rights Reserved.
%
% This software may be licensed under the terms of the
% GNU Public License v3 (LICENSE-gpl3.txt) or the custom license
% (LICENSE.txt) located at th... |
github | jardamacak/NodalDKFramework-master | capacitor.m | .m | NodalDKFramework-master/capacitor.m | 766 | utf_8 | 1d6b9c5974264ce6ac44d3ba178c9a36 | % NodalDKFramework
% Digital simulation of analog circuits
%
% Jaromir Macak
% jarda.macak@seznam.cz
%
% 14.1.2017
%
% Copyright 2017, All Rights Reserved.
%
% This software may be licensed under the terms of the
% GNU Public License v3 (LICENSE-gpl3.txt) or the custom license
% (LICENSE.txt) located at th... |
github | jardamacak/NodalDKFramework-master | inputPort.m | .m | NodalDKFramework-master/inputPort.m | 766 | utf_8 | 79b2386d2731fdd2305a9407cd7a05b8 | % NodalDKFramework
% Digital simulation of analog circuits
%
% Jaromir Macak
% jarda.macak@seznam.cz
%
% 14.1.2017
%
% Copyright 2017, All Rights Reserved.
%
% This software may be licensed under the terms of the
% GNU Public License v3 (LICENSE-gpl3.txt) or the custom license
% (LICENSE.txt) located at th... |
github | jardamacak/NodalDKFramework-master | resistor.m | .m | NodalDKFramework-master/resistor.m | 903 | utf_8 | 573f67a836399b1d1bce8246ab1313e4 | % NodalDKFramework
% Digital simulation of analog circuits
%
% Jaromir Macak
% jarda.macak@seznam.cz
%
% 14.1.2017
%
% Copyright 2017, All Rights Reserved.
%
% This software may be licensed under the terms of the
% GNU Public License v3 (LICENSE-gpl3.txt) or the custom license
% (LICENSE.txt) located at th... |
github | jardamacak/NodalDKFramework-master | opa.m | .m | NodalDKFramework-master/opa.m | 835 | utf_8 | 6fda134b89ae874333497a83e87c6e31 | % NodalDKFramework
% Digital simulation of analog circuits
%
% Jaromir Macak
% jarda.macak@seznam.cz
%
% 14.1.2017
%
% Copyright 2017, All Rights Reserved.
%
% This software may be licensed under the terms of the
% GNU Public License v3 (LICENSE-gpl3.txt) or the custom license
% (LICENSE.txt) located at th... |
github | jardamacak/NodalDKFramework-master | outputPort.m | .m | NodalDKFramework-master/outputPort.m | 753 | utf_8 | 8d5488b18d4c43a9268aa8609dd6ff88 | % NodalDKFramework
% Digital simulation of analog circuits
%
% Jaromir Macak
% jarda.macak@seznam.cz
%
% 14.1.2017
%
% Copyright 2017, All Rights Reserved.
%
% This software may be licensed under the terms of the
% GNU Public License v3 (LICENSE-gpl3.txt) or the custom license
% (LICENSE.txt) located at th... |
github | mehmetgonen/bam-master | bam_pairwise_classification_variational_train.m | .m | bam-master/bam_pairwise_classification_variational_train.m | 6,488 | utf_8 | fc41fbff9fa95a9ae5fc1f97c2a1985e | function state = bam_pairwise_classification_variational_train(X, y, parameters)
rand('state', parameters.seed); %#ok<RAND>
randn('state', parameters.seed); %#ok<RAND>
D = size(X, 1);
N = size(X, 2);
log2pi = log(2 * pi);
gamma.alpha = (parameters.alpha_gamma + 0.5);
gamma.beta = paramete... |
github | mehmetgonen/bam-master | bprobit_classification_variational_train.m | .m | bam-master/bprobit_classification_variational_train.m | 4,209 | utf_8 | 29d31066e488485599473c02da671010 | function state = bprobit_classification_variational_train(X, y, parameters)
rand('state', parameters.seed); %#ok<RAND>
randn('state', parameters.seed); %#ok<RAND>
D = size(X, 1);
N = size(X, 2);
log2pi = log(2 * pi);
gamma.alpha = (parameters.alpha_gamma + 0.5);
gamma.beta = parameters.be... |
github | mehmetgonen/bam-master | bam_listwise_classification_variational_train.m | .m | bam-master/bam_listwise_classification_variational_train.m | 5,894 | utf_8 | 62afcb69f64e045b23243aa221ee302b | function state = bam_listwise_classification_variational_train(X, y, parameters)
rand('state', parameters.seed); %#ok<RAND>
randn('state', parameters.seed); %#ok<RAND>
D = size(X, 1);
N = size(X, 2);
log2pi = log(2 * pi);
gamma.alpha = (parameters.alpha_gamma + 0.5);
gamma.beta = paramete... |
github | NTCColumbia/ca_source_extraction-master | lars_regression_noise.m | .m | ca_source_extraction-master/utilities/lars_regression_noise.m | 7,310 | utf_8 | 57b6188ceb2fc027c7e65b5a96aac472 | function [Ws, lambdas, W_lam, lam, flag] = lars_regression_noise(Y, X, positive, noise)
% run LARS for regression problems with LASSO penalty, with optional positivity constraints
% Author: Eftychios Pnevmatikakis. Adapted code from Ari Pakman
% Input Parameters:
% Y: Y(:,t) is the observed data at time ... |
github | NTCColumbia/ca_source_extraction-master | kde.m | .m | ca_source_extraction-master/utilities/kde.m | 7,250 | utf_8 | 7dfcbb1965c773791727e6b713f0e062 | function [bandwidth,density,xmesh,cdf]=kde(data,n,MIN,MAX)
% Reliable and extremely fast kernel density estimator for one-dimensional data;
% Gaussian kernel is assumed and the bandwidth is chosen automatically;
% Unlike many other implementations, this one is immune to problems
% caused by mul... |
github | NTCColumbia/ca_source_extraction-master | plot_components_GUI.m | .m | ca_source_extraction-master/utilities/plot_components_GUI.m | 7,229 | utf_8 | 4f6fb1dff699bd9ebb19079d42e7fb9a | %%
function plot_components_GUI(Y,A,C,b,f,Cn,options)
defoptions = CNMFSetParms;
if nargin < 7 || isempty(options); options = []; end
if ~isfield(options,'d1') || isempty(options.d1); d1 = input('What is the total number of rows? \n'); else d1 = options.d1; end % # of rows
if ~isfield(options,'d2') || isempty(... |
github | NTCColumbia/ca_source_extraction-master | greedyROI.m | .m | ca_source_extraction-master/utilities/greedyROI.m | 12,224 | utf_8 | 7bce850e5d1188b9bd29537fbfa2e28e | function [Ain, Cin, b_in, f_in, center, res] = greedyROI(Y, K, params, ROI_list)
% component initialization using a greedy algorithm to identify neurons in 2d or 3d calcium imaging movies
%
% Usage: [Ain, Cin, bin, fin, center, res] = greedyROI2d(data, K, params)
%
% Input:
% Y d1 x d2 x (d3 x) T movie, ra... |
github | NTCColumbia/ca_source_extraction-master | greedyROI_corr.m | .m | ca_source_extraction-master/utilities/greedyROI_corr.m | 8,991 | utf_8 | 66757986ecc7cb49e3b9bae53b903f84 | function [Ain, Cin, bin, fin, center, res] = greedyROI_corr(Y, K, options, sn, debug_on, save_avi)
%% a greedy method for detecting ROIs and initializing CNMF. in each iteration,
% it searches the one with large (peak-median)/noise level and large local
% correlation
%% Input:
% Y: d X T matrx, imaging data
% K: ... |
github | NTCColumbia/ca_source_extraction-master | signalExtraction.m | .m | ca_source_extraction-master/utilities/signalExtraction.m | 3,742 | utf_8 | e8d8c6683f024e7ddc3b7e7d35b7f7b9 | function [ inferred, filtered, raw ] = signalExtraction(Y,A,C,b,f,d1,d2,extractControl)
% this code extract the signal after CNMF is ran
% inputs: Y raw data (d X T matrix, d # number of pixels, T # of timesteps)
% A matrix of spatial components (d x K matrix, K # of components)
% C matrix of tem... |
github | jte0419/Taylor_Maccoll_Supersonic_Cone-master | GUI_Taylor_Maccoll.m | .m | Taylor_Maccoll_Supersonic_Cone-master/GUI_Taylor_Maccoll.m | 29,262 | utf_8 | 050a23f83c1c7a542546995685f55393 | function varargout = GUI_Taylor_Maccoll(varargin)
% Taylor-Maccoll Simulation GUI
% Written by: JoshTheEngineer
% YouTube: www.youtube.com/joshtheengineer
% Website: www.joshtheengineer.com
% Started: 01/20/16
% Updated: 01/20/16 - Started GUI
% - Adding code from other .m files
% -... |
github | nguyenvanhoa89/tracking-master | kf_example.m | .m | tracking-master/Kalman_Filter/kf_example.m | 1,997 | utf_8 | 77848fb389701eb296740de5661e9dc4 | % Modified by Hoa V. Nguyen to demonstrate the Kalman Filter in a simple
% example: tracking a vehicle with constant velocity moving in 2D space.
% Date: September 21st 2016
% Copyright (C) 2007 Jouni Hartikainen
function kf_example
% Stepsize
dt = 1;
% Process noise variance
q = 0.1;
% Discretization of the contin... |
github | nguyenvanhoa89/tracking-master | ekf_example.m | .m | tracking-master/Kalman_Filter/EKF/ekf_example.m | 1,882 | utf_8 | 91db7750d0f28ecb0dfe565f57739aa8 | % Modified by Hoa V. Nguyen to demonstrate the Extended Kalman Filter in a simple
% example: tracking a pendulum trajectories through its alpha corner value
% Date: September 28th 2016
function ekf_example
% Stepsize
dt = 0.01;
% Process noise variance
q = 0.1;
% Discretization of the continous-time system.
Q = q^2 *... |
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