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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 |
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github | atcollab/at-master | mpi_sweep_octave_example.m | .m | at-master/utils/mpi_sweep/mpi_sweep_octave_example.m | 1,866 | utf_8 | e5088c7fd19ff8ffbebb60ba590827e1 | #!/usr/bin/env octave
function mpi_sweep_octave_example
## Make sure that AT source files are in path.
## For this go to atoctave folder and run
## > octave --eval 'bootstrap;savepath'
## this code will run on all MPI nodes
D1.FamName = 'DR01';
D1.Length = 3;
D1.PassMethod = 'DriftPass';
QF.FamName... |
github | atcollab/at-master | updateContents.m | .m | at-master/atmat/updateContents.m | 5,941 | utf_8 | 8148907889c137a3c59883ffcfc38592 | function updateContents(folder)
%UPDATECONTENTS Create a Contents.m file including subdirectories
%
% UPDATECONTENTS scans through the current directory, and
% its subdirectories, and builds a Contents file similar to Matlab's
% report-generated Contents.m files. Any existing Contents.m file will be
% overwrit... |
github | atcollab/at-master | getContents.m | .m | at-master/atmat/getContents.m | 6,308 | utf_8 | 3b7c2a87d47634bac780fdaf77e0a825 | function [cont,dirflag] = getContents(directory,varargin)
%GETCONTENTS Get the contents of a specified directory
%
% This function returns the contents of a specified directory.
%
% CONT = IOSR.GENERAL.GETCONTENTS(DIRECTORY) returns the files and
% folders in a directory and returns them to the cell array cont. I... |
github | atcollab/at-master | atsurvey2spos.m | .m | at-master/atmat/pubtools/atsurvey2spos.m | 1,453 | utf_8 | e8ffa61b0458986160f7eafd9cd58d4e | function [s,distance]=atsurvey2spos(r,xycoord,varargin)
% returns closest lattics s coordinates to xycoord points
%
% input:
% r: AT lattice
% xycoord: 2xN vector of [x,y] cartesian coordinates
% 'slices', value: number of slices to split r
% (more slices = more precision, longer computation ... |
github | atcollab/at-master | freqsearch.m | .m | at-master/atmat/pubtools/freqsearch.m | 12,397 | utf_8 | 1143d29bbd7a626f9c9191e6855bc291 | function varargout = freqsearch3(data, varargin)
% =========================================================================
% Find the frequency terms in the data set with the use of filters and FFT
% or a "search" algorithm (slower but more accurate). The function returns
% the number of oscillations per unit tim... |
github | atcollab/at-master | calc_TouschekPM.m | .m | at-master/atmat/pubtools/calc_TouschekPM.m | 4,625 | utf_8 | d66ee65b0c9d7c29acb6ab9a729fc914 | function tauT = calc_TouschekPM(TD,dppPM,Trf,Ib,U0,coupling, sigE, emit_x)
%tauT = calc_TouschekPM(TD,dppPM,Trf,Ib,U0,coupling, sigE, emit_x)
%tauT = calc_TouschekPM(TD,dppPM,alpha,Ib,U0,coupling, sigE, emit_x)
% Ib, mA, single bunch current
% U0, MeV, one-turn energy loss
% emit_x, nm-rad
% couplin... |
github | atcollab/at-master | atundulator.m | .m | at-master/atmat/pubtools/atundulator.m | 3,079 | utf_8 | d56ceb8dae400fe56478e891f72882d5 | function undulator=atundulator(LUnd,nperiod,varargin)
% define undulator model
%
% input:
% Lund= undulator length
% nperiod = number of periods
% 'BendAngle', value : half pole bending angle in rad
% 'B0andEnergy', value (2x1): [half pole B0 field in T, Energy in eV]
% converts ... |
github | atcollab/at-master | atdynap.m | .m | at-master/atmat/pubtools/atdynap.m | 1,887 | utf_8 | 48612afda2e9f15e132e97e7e074d0ff | function [xx,zz]=atdynap(ring,nt,dpp,rfrac)
%ATDYNAP Compute the dynamic aperture
%
%
%[XX,ZZ]=ATDYNAP(RING,NTURNS,DPP,RFRAC)
%
%XX,ZZ : limit of the dynamic aperture (betatron amplitudes in m)
%RING : Structure for tracking
%NTURNS: Number of turns
%DPP : Off-momentum value (default: 0)
%RFRAC : Resolution of the g... |
github | atcollab/at-master | calc_Touschek.m | .m | at-master/atmat/pubtools/calc_Touschek.m | 4,772 | utf_8 | 723aa4acb63d395f0cb6103c70bbc674 | function tauT = calc_Touschek(THERING,Ib,varargin)
%tauT = calc_Touschek(THERING, Ib)
%tauT = calc_Touschek(THERING, Ib,hori_acceptance)
%tauT = calc_Touschek(THERING, Ib,hori_acceptance,U0)
%tauT = calc_Touschek(THERING, Ib,hori_acceptance,U0,coupling)
%tauT = calc_Touschek(THERING, Ib,hori_acceptance,U0,coupling... |
github | atcollab/at-master | fitgaussian.m | .m | at-master/atmat/pubtools/haissinski/fitgaussian.m | 7,136 | utf_8 | 209df9e5c867353dfd6bfa850f67c4fa | function varargout = fitgaussian(varargin)
% GAUSSIAN_PARAM FITERR GAUSSFIT SIGERROR]= FITGAUSSIAN(DATA,[property_value_pair]);
%
% DATA is a 1D vector to which we want to fit a gaussian profile. The
% function will return a structure with various fitted parameters.
% SCALEFACTOR is optional and is applied in t... |
github | atcollab/at-master | atset_s_shift.m | .m | at-master/atmat/pubtools/LatticeTuningFunctions/errors/atset_s_shift.m | 3,577 | utf_8 | a6ba1e6975dcd22aa1ac788c38c0fff6 | function rerr=atset_s_shift(r,pos,DS)
%ATSET_S_SHIFT Implements DS longitudinal position drift
% by changing drifts at the sides of the
% elements defined by pos in r
%
% for dipoles the T2(1) field is also changed and the the out DS is
% modified:
% T2(1)=DS*sin(bendignangle)
% DSout=DS*cos(bendignangle)
%
% pos and D... |
github | atcollab/at-master | atsettiltdipole.m | .m | at-master/atmat/pubtools/LatticeTuningFunctions/errors/atsettiltdipole.m | 3,004 | utf_8 | 5bd60291c9e71410d38e824b88914a14 | function ring=atsettiltdipole(varargin)
%ATSETTILTDIPOLE sets the entrance and exit rotation matrices
% of an element or a group of elements in THERING
%
% RING=ATSETTILTDIPOLE(RING,ELEMINDEX, PSI)
% ELEMINDEX contains indexes of elements to be rotated
% PSI - angle(s) of rotation in RADIANS
% POSITIVE PSI correspond... |
github | atcollab/at-master | getMagGroupsFromGirderIndex.m | .m | at-master/atmat/pubtools/LatticeTuningFunctions/errors/errorsmanipulation/getMagGroupsFromGirderIndex.m | 559 | utf_8 | 28b01fd7aaceb36986e1ab6c9514de1a | function maggroups=getMagGroupsFromGirderIndex(r)
%GETMAGGROUPSFROMGIRDERINDEX Gets magnets on a girder
% output maggroups in r with indexes between GS and GE markers.
%
% maggroups is a cell array of magnet indexes describing a single magnet in
% reality, but sliced in the lattice
% a single magnet has the same MagNum... |
github | atcollab/at-master | ThetaPhiGirder.m | .m | at-master/atmat/pubtools/LatticeTuningFunctions/errors/errorsmanipulation/ThetaPhiGirder.m | 1,249 | utf_8 | bdf76010e76c46b04836bfbd30da6bfe | function rtp=ThetaPhiGirder(r,mag_gr)
%rtp=ThetaPhiGirder(r,mag_gr)
%
% sets misalignment to model theta, phi errors for magnets on girder
%
% mag_gr is the output of getMagGroupsFromGirderIndex(ring)
%
%see also: GetExistingErrors setANYshift setTiltAbout seterrorrand
% get girder centers
gm=cellfun(@(mg)gmisal(r... |
github | atcollab/at-master | EquivalentGradientsFromAlignments6D.m | .m | at-master/atmat/pubtools/LatticeTuningFunctions/correction/RDT/EquivalentGradientsFromAlignments6D.m | 2,854 | utf_8 | 71d7239491abcbbe844a8e780d5a1d84 | function [kn,ks,ind]=EquivalentGradientsFromAlignments6D(r,inCOD)
%EQUIVALENTGRADIENTSFROMALIGNMENTS6D Estimated normal quad gradients from sext offsets
%[kn, 1) estimated normal quad gradients from sext offsets, quad
% errors in quadrupoles and sextupoles.
% ks, 2) estimated skew quad gradients from se... |
github | atcollab/at-master | semrdtresp_mod.m | .m | at-master/atmat/pubtools/LatticeTuningFunctions/correction/RDT/semrdtresp_mod.m | 1,993 | utf_8 | ee46c3c10b71f273b696acabb09f2800 | function [f1,f2,skew]=semrdtresp_mod(mach,bpmidx,skewidx)
%SEMRDT compute resonance driving terms at BPM locations
%
%[f1,f2,skew]=semrdtresp_mod(mach,bpmidx,skewidx)
%
% mach : AT lattice
% bpmindx : BPM indexes
% skewidx : skew quadrupole indexes
%
% f1 : f1001 RDT
% f2 : f1010 RDT
% skew : skew.beta skew... |
github | atcollab/at-master | DisplayCorrectionEffect.m | .m | at-master/atmat/pubtools/LatticeTuningFunctions/correction/correction_chain/DisplayCorrectionEffect.m | 7,457 | utf_8 | a232fd9497506ab41bf0b0245ff89459 | function [d0,de,dc]=DisplayCorrectionEffect(...
r0,...
rerr,...
rcor,...
inCODe,...
inCODc,...
refpts,...
indHCor,...
indVCor,...
indQCor,...
indSCor)
% [d0,de,dc]=DisplayCorrectionEffect(...
% r0,... 1) reference lattice
% rerr,... 2) lattice with errors
% ... |
github | atcollab/at-master | atmatchtunedelta.m | .m | at-master/atmat/pubtools/LatticeTuningFunctions/correction/tune/atmatchtunedelta.m | 1,408 | utf_8 | 594b0a1ba71cd2b3b662ce2905629a04 | function arctune0=atmatchtunedelta(arc,tune,quadfams)
% function arcchrom0=atmatchtunedelta(arc,c,quadfams)
%
% arc : at lattice
% tune : tune to get (with integer part) size(tune)=[2,1]
% quadfams: {[findcells(arc,'FamName','QF1','QF2')],...
% [findcells(arc,'FamName','QD1','QD2')] }
%
% delta on q... |
github | atcollab/at-master | BumpAtBPM.m | .m | at-master/atmat/pubtools/LatticeTuningFunctions/correction/orbitbumps/matching/BumpAtBPM.m | 2,920 | utf_8 | 115949ead276c24cea46066103bd887f | function [rbump,hs,vs]=BumpAtBPM(ring0,inCOD,bumph,bumpv,indBPMbump,indHCor,indVCor)
% function roff=BumpAtBPM(...
% ring0,... AT lattice structure
% inCOD,... initial 6x1 coordinate guess
% bumph,... hor. bump value at indBPMbump
% bumpv,... ver. bump value at indBPMbump
% indBPMbump, bump positio... |
github | atcollab/at-master | distance2curve.m | .m | at-master/atmat/pubtools/distance2curve/distance2curve.m | 55,867 | utf_8 | ad166e4d19008dd21756da4a9d60319e | function [xy,distance,t_a] = distance2curve(curvexy,mapxy,interpmethod)
%DISTANCE2CURVE Gets the minimum distance from a point to a general curvilinear n-dimensional arc
% usage: [xy,distance,t] = distance2curve(curvexy,mapxy) % uses linear curve segments
% usage: [xy,distance,t] = distance2curve(curvexy,mapxy,interpme... |
github | atcollab/at-master | atreadbeta.m | .m | at-master/atmat/pubtools/lattice_tools/atreadbeta.m | 7,602 | utf_8 | b82299ba84340d21116de4990fde8cef | function [superp,periods]=atreadbeta(filename,cavipass,bendpass,quadpass)
%ATREADBETA reads a BETA file
%
%ring=ATREADBETA(fname,cavipass,bendpass,quadpass,multipass)
%
%FILENAME: BETA file
%CAVIPASS: pass method for cavities (default IdentityPass)
%BENDPASS: pass method for dipoles (default BndMPoleSymplectic4Pass)... |
github | atcollab/at-master | intlat.m | .m | at-master/atmat/atgui/intlat.m | 12,947 | utf_8 | 4ae7078bdd2fb416bd61740364f2fee9 | function intlat(varargin)
%INTLAT Interactive AT lattice editor
% INTLAT(DIRECTION)
% Direction is the initial angle[rad] of the orbit with respect
% to the plot axis
global THERING
if nargin < 1 | isnumeric(varargin{1})
if nargin == 1
STARTANGLE = varargin{1};
else
STARTANGLE = 0;
end
... |
github | atcollab/at-master | ataddmpoleerrors.m | .m | at-master/atmat/lattice/ataddmpoleerrors.m | 2,135 | utf_8 | fa44b965ae74e661bb1a4b2edcfca94e | function newring = ataddmpoleerrors(ring,type,newindex,strength,radius,randflag)
%ataddrandmpole adds a random multipole component to all elements of type
%'type' where type can be 'dipole', 'quadrupole', or 'sextupole'
%
%[newring] = ATRANDMPOLE(ring,type,newindex,strength,radius)
%
%ring = input ring
%type = 'dipole'... |
github | atcollab/at-master | readmad.m | .m | at-master/atmat/lattice/Converters/readmad.m | 7,722 | utf_8 | 8357572071d29729e9cebd24c41b84da | function ATLATTICE = readmad(FILENAME)
%READMAD reads the file output of MAD commands
% TWISS, STRUCTURE, SURVEY.
%
% ATLATTICE = readmad(FILENAME)
%
% READMAD reads the MAD file header to determine the number of elements
% in the lattice, symmetry flag, the number of supperperiods etc.
%
% Then it interprets... |
github | atcollab/at-master | atfrommadx.m | .m | at-master/atmat/lattice/Converters/MADX2AT/atfrommadx.m | 27,333 | utf_8 | 0e8d4629973f9993e9113666184071bc | function atfrommadx(seqfilemadX,E0,outfilename)
%function atfrommadx(seqfilemadX,E0,outfilename)
% tansform madX sequence file (savesequence) file into AT lattice structure.
%
% This procedure reads a saved lattice (sequence in madx) in madX
% and converts it to an AT lattice
%
% (madx comands to save the sequences :
%... |
github | atcollab/at-master | plotERAperture.m | .m | at-master/atmat/atplot/plotfunctions/plotERAperture.m | 1,961 | utf_8 | 4a9c3a29f1b184a849d8fbaea79cee22 | function varargout=plotERAperture(varargin)
%PLOTERAPERTURE Plot RApertures EApertures
%
%Helper function for atplot:
% plot the Elliptic and Rectangular physical apertures
%
% USAGE:
% >> atbaseplot(ring,@plotERAperture);
% >> atplot(ring,@plotERAperture); (obsolete)
%
%See also atplot atbaseplot
if nargout == ... |
github | atcollab/at-master | BunchLength.m | .m | at-master/atmat/atphysics/LongitudinalDynamics/BunchLength.m | 1,651 | utf_8 | b9ba831b0e8ab4233e84dbdc40897841 | function BL = BunchLength (Ib,Zn,Vrf,U0,E0,h,alpha,sigdelta,circ)
% bunch length due to the potential well effect
% the output is the zerocurrent bunch length x bunch lengthening
%
% BL = BunchLength (Ib,Zn,Vrf,U0,E0,h,alpha,sigdelta,circ)
%
% Ib is the bunch current [A] (it may be a vector for multiple values)
% Zn... |
github | atcollab/at-master | findtune.m | .m | at-master/atmat/atphysics/TuneAndChromaticity/findtune.m | 2,153 | utf_8 | 430281c9b2bf8858079c67247c3f5100 | function [tune,spectrum]=findtune(pos,method)
%FINDTUNE get the tune value from turn by turn positions
%
%TUNE=FINDTUNE(POS,METHOD)
%
%POS: Tune-by-turn particle position
%METHOD: Method for tune determination:
% 1: Highest peak in fft
% 2: Interpolation on fft results
% ... |
github | atcollab/at-master | thinmpoleraddiffm.m | .m | at-master/atmat/atphysics/Radiation/thinmpoleraddiffm.m | 3,006 | utf_8 | 9c934f83e1f4500d880e450a91af7eaf | function [B66, M, rout] = findthinmpoleraddiffm(rin, PolynomA, PolynomB, L, irho, E0, max_order)
%FINDTHINMPOLERADDIFFM
% Physical constants used in calculations
persistent TWOPI CGAMMA M0C2 LAMBDABAR CER CU
if isempty(TWOPI) %Initialize constansts on the first call
TWOPI = 2*pi;
CGAMMA = 8.8... |
github | atcollab/at-master | findthinmpoleraddiffm.m | .m | at-master/atmat/atphysics/Radiation/findthinmpoleraddiffm.m | 3,006 | utf_8 | 9c934f83e1f4500d880e450a91af7eaf | function [B66, M, rout] = findthinmpoleraddiffm(rin, PolynomA, PolynomB, L, irho, E0, max_order)
%FINDTHINMPOLERADDIFFM
% Physical constants used in calculations
persistent TWOPI CGAMMA M0C2 LAMBDABAR CER CU
if isempty(TWOPI) %Initialize constansts on the first call
TWOPI = 2*pi;
CGAMMA = 8.8... |
github | atcollab/at-master | thickmpoleraddiffm.m | .m | at-master/atmat/atphysics/Radiation/thickmpoleraddiffm.m | 1,708 | utf_8 | cc43d4f668b8b722cf435a9b6b4b175c | function [Bcum, Mcum, r] = findthickmpoleraddifm(rin, PolynomA, PolynomB,L, irho, E0, max_order,num_steps)
%FINDTHICKMPOLERADDIFFM
% Fourth order-symplectic integrator constants
persistent DRIFT1 DRIFT2 KICK1 KICK2
if isempty(DRIFT1)
DRIFT1 = 0.6756035959798286638;
DRIFT2 = -0.1756035959798286639;
... |
github | atcollab/at-master | findthickmpoleraddiffm.m | .m | at-master/atmat/atphysics/Radiation/findthickmpoleraddiffm.m | 1,709 | utf_8 | ebc35623ce562ec6ce4133a19cb63d42 | function [Bcum, Mcum, r] = findthickmpoleraddifm(rin, PolynomA, PolynomB,L, irho, E0, max_order,num_steps)
%FINDTHICKMPOLERADDIFFM
% Fourth order-symplectic integrator constants
persistent DRIFT1 DRIFT2 KICK1 KICK2
if isempty(DRIFT1)
DRIFT1 = 0.6756035959798286638;
DRIFT2 = -0.1756035959798286639;
... |
github | atcollab/at-master | naff_cc.m | .m | at-master/atmat/atphysics/nafflib/naff_cc.m | 2,023 | utf_8 | f4beeffc5c9f251ee33da68512ec0199 | function naff_cc
%NAFF_CC Compile nafflibrary for Matlab
%
% Modified by Laurent S. Nadolski
% April 6th, 2007
cd_old = pwd;
cd(fileparts(which('naff_cc')))
disp(['Compiling NAFF routines on ', computer,'.'])
switch computer
case 'SOL2'
PLATFORMOPTION = ['-D',computer,' '];
case 'GLNXA64'
PL... |
github | atcollab/at-master | twissring.m | .m | at-master/atmat/atphysics/ParameterSummaryFunctions/twissring.m | 4,892 | utf_8 | 7e51fadcac888999286ae29c5d767c9c | function [TD, varargout] = twissring(RING,DP,varargin)
%TWISSRING calculates linear optics functions for an UNCOUPLED ring
%
% [TwissData, tune] = TWISSRING(LATTICE,DP) calculates twiss parameters
% and closed orbit coordinates at the RING entrance assuming
% constant energy deviation DP.
%
% [TwissData,... |
github | atcollab/at-master | twissline.m | .m | at-master/atmat/atphysics/ParameterSummaryFunctions/twissline.m | 6,230 | utf_8 | 83e74ff872e535dae8dba2a0ae9e1efc | function [TD, varargout] = twissline(LINE,DP,TWISSDATAIN,varargin)
%TWISSLINE calculates linear optics functions for an UNCOUPLED transport line
%
% TwissData = TWISSLINE(LATTICE,DP,TWISSDATAIN) propagates twiss
% parameters and closed orbit coordinates from the LINE entrance
% given by TWISSDATAIN assumin... |
github | smallcorgi/3D-Deepbox-master | writeLabels.m | .m | 3D-Deepbox-master/visualization/writeLabels.m | 2,759 | utf_8 | 9f25c784ee622dc760d160581deac377 | function writeLabels(objects,label_dir,img_idx)
% parse input file
fid = fopen(sprintf('%s/%06d.txt',label_dir,img_idx),'w');
% for all objects do
for o = 1:numel(objects)
% set label, truncation, occlusion
if isfield(objects(o),'type'), fprintf(fid,'%s ',objects(o).type);
else ... |
github | mmclkv/caffe-mask-rcnn-master | classification_demo.m | .m | caffe-mask-rcnn-master/matlab/demo/classification_demo.m | 5,466 | utf_8 | 45745fb7cfe37ef723c307dfa06f1b97 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | rpng/clatt-master | isspd.m | .m | clatt-master/isspd.m | 717 | utf_8 | 264b483d9cf3dbe9867b79e3e554a1c4 |
function [t,R] = isspd(Sigma)
%ISPDS Test if a matrix is positive definite symmetric
% T = ISPDS(SIGMA) returns a logical indicating whether the matrix SIGMA is
% square, symmetric, and positive definite, i.e., it is a valid full rank
% covariance matrix.
%
% [T,R] = ISPDS(SIGMA) returns the cholesky factor of ... |
github | rpng/clatt-master | rws.m | .m | clatt-master/rws.m | 10,484 | utf_8 | 0102e197e65e4fb724d6fb3ec9d3ea8b | %% Real-world simulation
function [v_m,omega_m,v,omega,xR_true,zr,Rr, zl,Rl, xT_true,PHI,Qd,zt,Rt ] = rws(nR,nSteps, nL,xL_true, dt, ...
v_true,omega_true,sigma_v,sigma_w, sigma_r,sigma_th,sigma_p, ...
nT, vt, sigma_a, at, sigma_j,dim_target, ...
max_range,min_range, r_max,omega_max,DORANDOM,SIGPERCEN... |
github | rezazad68/Dynamic-3D-Action-Recognition-on-RGB-D-Videos-master | elm_kernel.m | .m | Dynamic-3D-Action-Recognition-on-RGB-D-Videos-master/elm_kernel.m | 8,062 | utf_8 | 5f5cf9aaa5bfe2cd1d8e863c871cbbf9 | function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy, TY, ConfusMatrix] = elm_kernel(train_data, test_data, Elm_Type, Regularization_coefficient, Kernel_type, Kernel_para)
% Usage: elm(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction)
% OR: [TrainingTime,... |
github | luciana-marques/orps-master | DataGeneration.m | .m | orps-master/DataGeneration.m | 22,194 | utf_8 | f81037670752ee4a6c4925a030e28ca6 | %=========================================================================
% Load Scheduling Problem - Data Generation
% Based on: Vasirani and Ossowski (2012) and Mohsenian-Rad et al. (2010)
% Institution: Federal University of Minas Gerais (UFMG)
% Department: Graduate Program in Electrical Engineering
% Author:... |
github | luciana-marques/orps-master | LocalSearch.m | .m | orps-master/LocalSearch.m | 3,859 | utf_8 | a4994e40245585afd181436240ec5a32 | %===============================================================
% Load Scheduling Problem - Local Search
% Institution: Federal University of Minas Gerais (UFMG)
% Department: Graduate Program in Electrical Engineering
% Course: Network Optimization
% Author: Luciana Sant'Ana Marques
% Date: Feb 23th, 2018 at 16... |
github | luciana-marques/orps-master | SimulatedAnnealing.m | .m | orps-master/SimulatedAnnealing.m | 5,346 | utf_8 | 144c6ee658cf75cc304607c331e11b8e | %===============================================================
% Simulated Annealing for Scheduling Problem
% Institution: Federal University of Minas Gerais (UFMG)
% Department: Graduate Program in Electrical Engineering
% Author: Luciana Sant'Ana Marques
% Date: Feb 23th, 2018 at 16:28
%============================... |
github | luciana-marques/orps-master | InstanceGeneration.m | .m | orps-master/InstanceGeneration.m | 1,439 | utf_8 | 0d71f8b8798c208034da7601cb1bb7b7 | %===============================================================
% Load Scheduling Problem - Instance Generation
% Institution: Federal University of Minas Gerais (UFMG)
% Department: Graduate Program in Electrical Engineering
% Author: Luciana Sant'Ana Marques
% Date: Feb 20th, 2018 at 16:33
%===================... |
github | luciana-marques/orps-master | UpdateCost.m | .m | orps-master/UpdateCost.m | 2,422 | utf_8 | fbea553a182a9a10e872c40f7a1b2aec | %===============================================================
% Calculate Total Cost of a Solution Considering only the
% Modified load
% Institution: Federal University of Minas Gerais (UFMG)
% Department: Graduate Program in Electrical Engineering
% Author: Luciana Sant'Ana Marques
% Date: Feb 23th, 2018 at ... |
github | luciana-marques/orps-master | TotalCostF.m | .m | orps-master/TotalCostF.m | 2,162 | utf_8 | e552444b4471acef52f53422169cc3eb | %===============================================================
% Calculate Total Cost of a Solution
% Institution: Federal University of Minas Gerais (UFMG)
% Department: Graduate Program in Electrical Engineering
% Author: Luciana Sant'Ana Marques
% Date: Feb 1923th, 2018 at 16:29
%============================... |
github | luciana-marques/orps-master | Neighborhood.m | .m | orps-master/Neighborhood.m | 3,657 | utf_8 | 9f1cb2f303c98193530522ba645ddfdc | %===============================================================
% Demand Response Problem
% Title: Neighborhood Strucutre Generation
% Institution: Federal University of Minas Gerais (UFMG)
% Department: Graduate Program in Electrical Engineering
% Author: Luciana Sant'Ana Marques Arnoux and Isabella
% Date: Jun... |
github | luciana-marques/orps-master | RankingHeuristic.m | .m | orps-master/RankingHeuristic.m | 3,836 | utf_8 | a4a0503f13019128111b426b16b2801b | %===============================================================
% Ranking Heuristic to Load Scheduling Problem
% Construction of an initial solution
% Based on: Wu et al. (2012) - with many modifications
% Institution: Federal University of Minas Gerais (UFMG)
% Department: Graduate Program in Electrical Engineer... |
github | remega/LEDOV-eye-tracking-database-master | pm_norm.m | .m | LEDOV-eye-tracking-database-master/metrics/pm_norm.m | 114 | utf_8 | c065a431c1f58d5599e0824c785841dd |
function map_new=pm_norm(map)
tempmin=min(map(:));
temp2=map-tempmin;
map_new=temp2./max(temp2(:));
end |
github | remega/LEDOV-eye-tracking-database-master | NSS.m | .m | LEDOV-eye-tracking-database-master/metrics/NSS.m | 530 | utf_8 | 76acfa14c05ad2755d4c797e413301d9 | % created: Zoya Bylinskii, Aug 2014
% This finds the normalized scanpath saliency between two different
% saliency maps as the mean value of the normalized saliency map at
% fixation locations.
function score = NSS(saliencyMap, fixationMap)
% saliencyMap is the saliency map
% fixationMap is the human fixation map... |
github | remega/LEDOV-eye-tracking-database-master | TestLEDOV.m | .m | LEDOV-eye-tracking-database-master/metrics/TestLEDOV.m | 3,232 | utf_8 | 670739a69a2d2a617081f2f199427c46 |
function res = TestLEDOV(saldir)
load('./LEDOV/VideoNameList.mat','VideoNameList')
load('./LEDOV/namelist.mat','testlist','trainlist','vaildlist')
videoDir='./LEDOV';
countvid=0;
for m=testlist'
tic
InputVideoName_short=VideoNameList{m};
InputVideoName=[InputVideoName_short '.mp4'];
load([videoDir '... |
github | jun-zhang/WebRTC-VideoEngine-Demo-master | apmtest.m | .m | WebRTC-VideoEngine-Demo-master/webrtc_videoengine_demo/webrtc/modules/audio_processing/test/apmtest.m | 9,470 | utf_8 | ad72111888b4bb4b7c4605d0bf79d572 | function apmtest(task, testname, filepath, casenumber, legacy)
%APMTEST is a tool to process APM file sets and easily display the output.
% APMTEST(TASK, TESTNAME, CASENUMBER) performs one of several TASKs:
% 'test' Processes the files to produce test output.
% 'list' Prints a list of cases in the test set,... |
github | jun-zhang/WebRTC-VideoEngine-Demo-master | exportfig.m | .m | WebRTC-VideoEngine-Demo-master/webrtc_videoengine_demo/webrtc/modules/video_coding/codecs/test_framework/exportfig.m | 14,995 | utf_8 | d7427be6e56c37d4aec2f2c91c9a6341 | function exportfig(varargin)
%EXPORTFIG Export a figure to Encapsulated Postscript.
% EXPORTFIG(H, FILENAME) writes the figure H to FILENAME. H is
% a figure handle and FILENAME is a string that specifies the
% name of the output file.
%
% EXPORTFIG(...,PARAM1,VAL1,PARAM2,VAL2,...) specifies
% parameters th... |
github | jun-zhang/WebRTC-VideoEngine-Demo-master | plotBenchmark.m | .m | WebRTC-VideoEngine-Demo-master/webrtc_videoengine_demo/webrtc/modules/video_coding/codecs/test_framework/plotBenchmark.m | 11,672 | utf_8 | a80ed712ca3895c1e7b6383d4cc07d38 | function plotBenchmark(fileNames, export)
%PLOTBENCHMARK Plots and exports video codec benchmarking results.
% PLOTBENCHMARK(FILENAMES, EXPORT) parses the video codec benchmarking result
% files given by the cell array of strings FILENAME. It plots the results and
% optionally exports each plot to an appropriatel... |
github | sudrag/Perception-and-Computer-Vision-in-MATLAB-master | dpsimplify.m | .m | Perception-and-Computer-Vision-in-MATLAB-master/AR Tag Detection/Scripts/dpsimplify.m | 6,599 | utf_8 | ec1b680dd31937dca16da7df9996aeba | function [ps,ix] = dpsimplify(p,tol)
% Recursive Douglas-Peucker Polyline Simplification, Simplify
%
% [ps,ix] = dpsimplify(p,tol)
%
% dpsimplify uses the recursive Douglas-Peucker line simplification
% algorithm to reduce the number of vertices in a piecewise linear curve
% according to a specified toleranc... |
github | sudrag/Perception-and-Computer-Vision-in-MATLAB-master | homography2d.m | .m | Perception-and-Computer-Vision-in-MATLAB-master/AR Tag Detection/Scripts/homography2d.m | 2,957 | utf_8 | 5d39781c0ed194cfeea3f44954b4ea80 | % 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 | sudrag/Perception-and-Computer-Vision-in-MATLAB-master | normalise2dpts.m | .m | Perception-and-Computer-Vision-in-MATLAB-master/AR Tag Detection/Scripts/normalise2dpts.m | 2,430 | utf_8 | f0428a0b70a640f4503e444e7e286535 | % NORMALISE2DPTS - normalises 2D homogeneous points
%
% Function translates and normalises a set of 2D homogeneous points
% so that their centroid is at the origin and their mean distance from
% the origin is sqrt(2). This process typically improves the
% conditioning of any equations used to solve homographies... |
github | sudrag/Perception-and-Computer-Vision-in-MATLAB-master | ransacfitfundmatrix.m | .m | Perception-and-Computer-Vision-in-MATLAB-master/Visual Odometry/code/ransacfitfundmatrix.m | 5,973 | utf_8 | ade5620bd3b104b73b18d18a647de42c | % RANSACFITFUNDMATRIX - fits fundamental matrix using RANSAC
%
% Usage: [F, inliers] = ransacfitfundmatrix(x1, x2, t)
%
% Arguments:
% x1 - 2xN or 3xN set of homogeneous points. If the data is
% 2xN it is assumed the homogeneous scale factor is 1.
% x2 - 2xN or 3xN set of ho... |
github | sudrag/Perception-and-Computer-Vision-in-MATLAB-master | ransac.m | .m | Perception-and-Computer-Vision-in-MATLAB-master/Visual Odometry/code/ransac.m | 10,480 | utf_8 | c5e0917ad9d7194d2ab04f6741400fac | % RANSAC - Robustly fits a model to data with the RANSAC algorithm
%
% Usage:
%
% [M, inliers] = ransac(x, fittingfn, distfn, degenfn s, t, feedback, ...
% maxDataTrials, maxTrials)
%
% Arguments:
% x - Data sets to which we are seeking to fit a model M
% It is... |
github | sudrag/Perception-and-Computer-Vision-in-MATLAB-master | normalise2dpts.m | .m | Perception-and-Computer-Vision-in-MATLAB-master/Visual Odometry/code/normalise2dpts.m | 2,501 | utf_8 | 00a5d4cb7272f147a49e349c2772fe72 | % NORMALISE2DPTS - normalises 2D homogeneous points
%
% Function translates and normalises a set of 2D homogeneous points
% so that their centroid is at the origin and their mean distance from
% the origin is sqrt(2). This process typically improves the
% conditioning of any equations used to solve homographies... |
github | johnfgibson/whyjulia-master | ksbenchmark.m | .m | whyjulia-master/codes/ksbenchmark.m | 2,820 | utf_8 | 823086545f955482f5a264767f751251 | function ksbenchmark(Nx, printnorms)
% ksbenchmark: run a Kuramoto-Sivashinky simulation, benchmark, and plot
% Nx = number of gridpoints
% printnorms = 1 => print norm(u0) and norm(uT), 0 => don't
Lx = Nx/16*pi; % spatial domain [0, L] periodic
dt = 1/16; % discrete time step
T = 200; ... |
github | sajeed786/Earthquake-Prediction-master | MFO.m | .m | Earthquake-Prediction-master/MFO.m | 5,867 | utf_8 | ded7613a78e724ccac5ece6882985164 | %______________________________________________________________________________________________
% Moth-Flame Optimization Algorithm (MFO)
% Source codes demo version 1.0
% ... |
github | sajeed786/Earthquake-Prediction-master | obj.m | .m | Earthquake-Prediction-master/obj.m | 406 | utf_8 | 81454c92b7086ffa726f5fd97b409dca | function [lb,ub,dim,fobj] = obj()
lb=-1;
ub=1;
dim=40;
fobj = @get_cost_mse;
end
function mse = get_cost_mse(y,x,w)
%fprintf('inside cost_mse function ');
pred_random = x * w';
O = ones(437,1);
act_op = O ./ (O + exp(-pred_random));
maxm = max(y);
minm = min(y);
denorm_op = ((act_op - 0.1) / 0.8) * (maxm... |
github | sajeed786/Earthquake-Prediction-master | initialization.m | .m | Earthquake-Prediction-master/initialization.m | 2,272 | utf_8 | 092dd93bd670c6c2b240828b47f0ef0a | %______________________________________________________________________________________________
% Moth-Flame Optimization Algorithm (MFO)
% Source codes demo version 1.0
% ... |
github | uncledickHe/basic_beamforming-master | espritBeamforming.m | .m | basic_beamforming-master/esprit/espritBeamforming.m | 25,178 | utf_8 | f069d37c8ba26ff11e24f8ce3fc1cb1f | function espritBeamforming()
% ------------------------------------------------
% ESPRIT BEAMFORMING DEMO
% Simulation of several sources around the array
%
% Jose Ignacio Dominguez Simon
%
% Array Signal Processing
% Aalborg University - 2015
... |
github | yluthu/fpga-nn-experiment-master | hexdump.m | .m | fpga-nn-experiment-master/matlab/hexdump.m | 1,013 | utf_8 | 0a4bda98e31a5a278a5b4b452bc39400 | % dump data as single precision numbers into Intel HEX format
function [] = hexdump(data, filename)
data = single(data);
line = [];
twos = [];
result = [];
for i = 1:length(data)
% size address type data
line = [':04', dec2hex(i - 1, 4), '00', ... |
github | eslamtharwat/2-IRIS-Detection-and-Recognition-master | face.m | .m | 2-IRIS-Detection-and-Recognition-master/face.m | 955 | utf_8 | 95628a1095dc996f9ace708adc32baff | % function [face,skin_region]=face(I);
%
% skin_region=skin(I);
%
% se = strel('disk',3);
% dil = imdilate(skin_region,se); % morphologic dilation
% d2 = imfill(dil, 'holes'); % morphologic fill
% face = bwdist(~d2); % computing minimal euclidean distance to non-white pixel
% figure;imsho... |
github | danielemarinazzo/multiscaleGrangerCausality-master | egc_SetLag.m | .m | multiscaleGrangerCausality-master/egc_SetLag.m | 795 | utf_8 | 05c6dc2b94538906b60bf5c932dddab6 | %% Sets the vector of indexes for series and lags to be used in Conditional Entropy estimation
% inputs:
% p: vector of embedding dimensions (one for each series; if 0, the series is excluded)
% tau: vector of embedding delays (one for each series)
% u: vector of propagation times (one for each series)
% zerolag: ... |
github | danielemarinazzo/multiscaleGrangerCausality-master | iss_varma2iss.m | .m | multiscaleGrangerCausality-master/iss_varma2iss.m | 1,210 | utf_8 | a0ce27bd483b148861a8982d8af50c31 | %% VARMA with B0 term to (Innovations form) State Space parameters
% computes innovations form parameters for a state space model from VARMA
% parameters using Aoki's method - this version allows for zero-lag MA coefficients
function [A,C,K,R,lambda0] = iss_varma2iss(Am,Bm,V,B0)
% INPUT: VARMA parameters Am, ... |
github | danielemarinazzo/multiscaleGrangerCausality-master | egc_LinReg_Ftest.m | .m | multiscaleGrangerCausality-master/egc_LinReg_Ftest.m | 1,710 | utf_8 | c67017c67f4810a95b07f8d1dc4c6bbf | %% Statistics of difference in conditional entropies estimated through linear regression
% Upu: reiduals of unrestricted regression
% Upr: reiduals of restricted regression
% Nu: number of coefficients for unrestricted regression
% Nr: number of coefficients for restricted regression
function [p_value] = egc_L... |
github | danielemarinazzo/multiscaleGrangerCausality-master | eMVAR_MVARfilter.m | .m | multiscaleGrangerCausality-master/eMVAR_MVARfilter.m | 620 | utf_8 | 83b62a141b725ae83f035fc783ccfd21 | %% FILTER A VECTOR NOISE WITH A SPECIFIED STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)
%%% INPUT
% A=[A(1)...A(p)]: M*pM matrix of the MVAR model coefficients (strictly causal model)
% U: M*N matrix of innovations
%%% OUTPUT
% Y: M*N matrix of simulated time series
function [Y]=eMVAR_MVAR... |
github | danielemarinazzo/multiscaleGrangerCausality-master | surrshuf.m | .m | multiscaleGrangerCausality-master/surrshuf.m | 194 | utf_8 | 3f27b575a884ee11981562fdb7eb740d | %genera x surrogato con sample shuffling - distruggeanche gli autospettri
function xs=surrshuf(x)
sx=size(x);
p=randperm(sx(1));
xs=zeros(sx(1),1);
for k = 1:sx(1)
xs(k)=x(p(k));
end
|
github | danielemarinazzo/multiscaleGrangerCausality-master | egc_buildvectors.m | .m | multiscaleGrangerCausality-master/egc_buildvectors.m | 1,046 | utf_8 | c0fabb97db424e857b26211aa6ff0c39 | %% form embedding matrix (for entropy computation)
% Y: (quantized) input multiple time series, dimension M*N
% V: list of candidates, dimension Nc*2, Nc is number of candidates; 1st column: index of the signal; 2nd column: index of the lag
% A: output matrix of the vectors specified from the signals Y according to ... |
github | danielemarinazzo/multiscaleGrangerCausality-master | eMVAR_idMVAR.m | .m | multiscaleGrangerCausality-master/eMVAR_idMVAR.m | 1,452 | utf_8 | d1cedfd57662c15c9969b53a64ab836b | %% IDENTIFICATION OF STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)
% makes use of autocovariance method (vector least squares)
%%% input:
% Y, M*N matrix of time series (each time series is in a row)
% p, model order
% Mode, determines estimation algorithm (0:builtin least squares, else other m... |
github | danielemarinazzo/multiscaleGrangerCausality-master | eMVAR_InstModelfilter.m | .m | multiscaleGrangerCausality-master/eMVAR_InstModelfilter.m | 2,355 | utf_8 | a77cbc2e982dff26e398b5f61504ed3e | %% realization of the instantaneous model : U = L*W
%%% OUTPUT
% U: N*M matrix of filtered noises
% INPUT
% N data length
% C: input covariance matrix (may be interpreted as Su or Sw, see above)
% B0: M*M matrix of instantaneous effects (when relevant)
% when flag='StrictlyCausal':
% given Su, applies Chole... |
github | danielemarinazzo/multiscaleGrangerCausality-master | egc_LinReg.m | .m | multiscaleGrangerCausality-master/egc_LinReg.m | 738 | utf_8 | 4044f07d3346650cddc464cec66273c7 | %% LINEAR REGRESSION
%%% INPUTS:
% data: N*M matrix of the M signals each having length N
% j: index (column) of the series considered as output, the one we want to describe
% V: two column vector of series (col 1) and lag (col 2) indexes
function [S,Up,Am]=egc_LinReg(data,j,V)
if isempty(V) %if no conditio... |
github | danielemarinazzo/multiscaleGrangerCausality-master | msgc.m | .m | multiscaleGrangerCausality-master/msgc.m | 2,184 | utf_8 | 08c1d9bb93a08811c532e7ee5ba4e736 | %% MULTISCALE GC computation
%%% inputs
% Am, Su: VAR parameters (Am: M x pM coeff matrix; Su: M x M innovation covariance matrix)
% tau: scale factor
% ncoeff: number of coefficients of FIR filter (if no averaging)
% whichfilter: 'F' for FIR (default), 'A' for averaging
%%% outputs
% GCdws: GC at scale tau ... |
github | danielemarinazzo/multiscaleGrangerCausality-master | egc_gcMVAR.m | .m | multiscaleGrangerCausality-master/egc_gcMVAR.m | 1,845 | utf_8 | cbbb1c17fc5a26db10138e66abb9af3f | %% GRANGER CAUSALITY FROM STRICTLY CAUSAL MVAR MODEL: Y(n)=A(1)Y(n-1)+...+A(p)Y(n-p)+U(n)
% estimates Granger Causality in multiple time series from MVAR model fitted on data
% performs also row-by-row MVAR identification, equivalent to idMVAR (with vector least squares)
%%% input:
% Y, M*N matrix of time series ... |
github | danielemarinazzo/multiscaleGrangerCausality-master | surriaafft.m | .m | multiscaleGrangerCausality-master/surriaafft.m | 1,502 | utf_8 | f60f7c2acff33418e0cc6efa5895b5ab | % genera iterative amplitude adjusted fourier tranform surrogates
% algoritmo di Schreiber e Schmitz - Physical Review Letters 1996
% y: serie da surrogare
% nit: numero di iterazioni volute (default 7)
% stop: se metto 'spe' esce con lo spettro conservato, se metto 'dis' esce con la distribuzione conservata
... |
github | avinashk94/CVIP-master | harris.m | .m | CVIP-master/Assignment/hw2/50248877_hw2/code/harris.m | 3,097 | utf_8 | c3a11b6fc77fa908635b8f9b14ad52a2 | % HARRIS - Harris corner detector
%
% Usage: [cim, r, c] = harris(im, sigma, thresh, radius, disp)
%
% Arguments:
% im - image to be processed.
% sigma - standard deviation of smoothing Gaussian. Typical
% values to use might be 1-3.
% thresh - thres... |
github | avinashk94/CVIP-master | harris.m | .m | CVIP-master/Assignment/hw2/hw2/code/harris.m | 3,097 | utf_8 | c3a11b6fc77fa908635b8f9b14ad52a2 | % HARRIS - Harris corner detector
%
% Usage: [cim, r, c] = harris(im, sigma, thresh, radius, disp)
%
% Arguments:
% im - image to be processed.
% sigma - standard deviation of smoothing Gaussian. Typical
% values to use might be 1-3.
% thresh - thres... |
github | avinashk94/CVIP-master | find_sift.m | .m | CVIP-master/Assignment/hw3/hw3/code/find_sift.m | 5,054 | utf_8 | bd661341ed3535975182b3f451a3c152 | function sift_arr = find_sift(I, circles, enlarge_factor)
%%
%% Compute non-rotation-invariant SIFT descriptors of a set of circles
%% I is the image
%% circles is an Nx3 array where N is the number of circles, where the
%% first column is the x-coordinate, the second column is the y-coordinate,
%% and the... |
github | avinashk94/CVIP-master | harris.m | .m | CVIP-master/Assignment/hw3/hw3/code/harris.m | 3,097 | utf_8 | c3a11b6fc77fa908635b8f9b14ad52a2 | % HARRIS - Harris corner detector
%
% Usage: [cim, r, c] = harris(im, sigma, thresh, radius, disp)
%
% Arguments:
% im - image to be processed.
% sigma - standard deviation of smoothing Gaussian. Typical
% values to use might be 1-3.
% thresh - thres... |
github | avinashk94/CVIP-master | distanceToSet.m | .m | CVIP-master/Assignment/hw1/release/custom/distanceToSet.m | 1,570 | utf_8 | e468b68972d12528f3824237c308520c | function histInter = distanceToSet(wordHist, histograms)
% Sum of minimums (Baseline implementation)
%intersections = bsxfun(@min, histograms, repmat(wordHist, 1, size(histograms, 2)));
%histInter = sum(intersections);
% Bhattacharyya coefficient
%intersections = bsxfun(@times, sqrt(histograms), repmat(sqrt(wordHist)... |
github | avinashk94/CVIP-master | computeDictionary.m | .m | CVIP-master/Assignment/hw1/release/matlab/computeDictionary.m | 356 | utf_8 | 7baa8b2034d3469217474b5c13857ac2 | % Computes filter bank and dictionary, and saves it in dictionary.mat
function computeDictionary()
load('../data/traintest.mat');
interval= 1;
train_imagenames = train_imagenames(1:interval:end);
[filterBank,dictionary] = getFilterBankAndDictionary(strcat(['../data/'],train_imagenames));
save('dictionary.mat... |
github | numpde/as-master | sample_network3.m | .m | as-master/p/network/20171002-RatPathways-EPFL/B-subgraphs/sample_network3.m | 4,012 | utf_8 | e569b5292d7cf526f9ed50bf6bd28064 | % Subsampling from a large network.
% Pick a seed according to the degree distribution.
% Carry out random walks in the network.
% Stop when the number of subsampled nodes reaches the pre-specified number.
% Difference from sample_network2: attempt to control both node and edge sizes.
% Select the seed according to the... |
github | numpde/as-master | construct_clique_complex.m | .m | as-master/p/network/20171002-RatPathways-EPFL/B-subgraphs/construct_clique_complex.m | 2,403 | utf_8 | fd8bd622b6598d8ae7862faa4dd7c5f5 | % Construct the clique complex from an undirected graph.
% Apply only to small graphs.
function [nsimplices, simplices] = construct_clique_complex(G)
nnodes = length(G(1, :));
% Exhaust all cliques.
nsimplices = 0; simplices = {};
% Nodes.
for n = 1:nnodes
simplices{n} = [n];
end
... |
github | numpde/as-master | atof2.m | .m | as-master/p/network/20171002-RatPathways-EPFL/B-subgraphs/atof2.m | 911 | utf_8 | a0aa9721ee4c6eda6667d14146264604 | % Convert a string into a real number.
% Also works for exponential representation.
function val = atof2(s)
% If s=NA then return NaN.
if (strcmp(s,'NA')==1)
val=NaN;
else
val=0; sgn=1; cnt=1; afterpoint=0; afterexp=0;
expval=0; expsign=1; power=1.0;
while (cnt<=length(s))
ch=s(cnt);
if ((cnt==1)&(ch=='-'))
sgn=... |
github | numpde/as-master | find_conn_comps2.m | .m | as-master/p/network/20171002-RatPathways-EPFL/B-subgraphs/find_conn_comps2.m | 1,813 | utf_8 | 389f33c6086c7446c4d2627197f3666b | % Find all connected components of a graph.
% Difference from find_conn_comps.m: do not incur recursive functions.
function [nconncomps, conncomps] = find_conn_comps2(nnodes, G)
% Label nodes in the graph until all nodes are labeled.
labeled=zeros(1,nnodes); flag=1;
% Label all the singletons.
tmpG=G; tmpG=tmpG-diag... |
github | numpde/as-master | getitemval4.m | .m | as-master/p/network/20171002-RatPathways-EPFL/B-subgraphs/getitemval4.m | 703 | utf_8 | f54f8a064512756737e0a6eda7b4ae6d | % Get the selected item in a string.
% Difference from getitemval3: fix the bug that the last entry contains \n.
function item = getitemval4(s, ind, sepch)
tabcnt=0; curind=1;
while ((tabcnt<ind)&(curind<=length(s)))
ch=s(curind);
if (ch==sepch)
tabcnt=tabcnt+1;
elseif ((ch=='\n')&(tabcnt<ind))
tabcnt=ind+1;
end
curi... |
github | numpde/as-master | evaluate_complex_homology.m | .m | as-master/p/network/20171002-RatPathways-EPFL/B-subgraphs/evaluate_complex_homology.m | 1,821 | utf_8 | a603270bb0a52f38cb60830f5c7e75af | % Evaluate the Betti numbers of a simplicial complex.
function bs = evaluate_complex_homology(nsimplices, simplices)
nnodes=0;
for n=1:nsimplices
nnodes=max(nnodes,max(simplices{n}));
end
% Construct the boundary maps.
% rho_{k}.
maxdim=length(simplices{nsimplices})-1;
clear bds rs;
% Debug
%for k=1:min(3,maxdim)
... |
github | numpde/as-master | sample_discrete_rv.m | .m | as-master/p/network/20171002-RatPathways-EPFL/B-subgraphs/sample_discrete_rv.m | 764 | utf_8 | a589464d25a383adb18d965b86a7c5b4 | % Sample a value from a discrete distribution.
% RA: discretize(rand(1, n) * sum(p), [0; cumsum(p)])
% https://www.mathworks.com/matlabcentral/fileexchange/21912-sampling-from-a-discrete-distribution
function randval = sample_discrete_rv(vals, ps)
nstates=length(vals);
[Y,I]=sort(ps,'descend');
bds=zeros(1,nstates);... |
github | numpde/as-master | find_conn_comps.m | .m | as-master/p/network/20171002-RatPathways-EPFL/B-subgraphs/find_conn_comps.m | 1,685 | utf_8 | 22ce1726dfe96d45a20696926c0702fd | % Find all connected components of a graph.
function [nconncomps, conncomps] = find_conn_comps(nnodes, G)
% Recursively label nodes in the graph until all nodes are labeled.
labeled=zeros(1,nnodes); flag=1;
% Label all the singletons.
%k=0;
%for i=1:nnodes
%tmp=find(G(i,:)>0);
%if (length(tmp)==0)
%k=k+1; labeled(i)... |
github | numpde/as-master | betti.m | .m | as-master/p/network/20171002-RatPathways-EPFL/D-topology/correctness/betti.m | 5,084 | utf_8 | 29d444ea4384c7afc49469c62236f239 | function b = betti(file)
G = getfield(load(file), 'G');
[nsimplices, simplices] = construct_clique_complex(G);
b = evaluate_complex_homology(nsimplices,simplices);
save(file, 'G', 'b');
end
%%% CODE BY C-H YEANG %%%
% Construct the clique complex from an undirected graph.
% Apply only to small graph... |
github | Lancelot899/ICRA2018-master | rukfUpdate.m | .m | ICRA2018-master/filters/rukfUpdate.m | 1,788 | utf_8 | 24325b68cd25a2d02a2b35e7dd34f88d | function [chi,omega_b,a_b,S] = rukfUpdate(chi,omega_b,a_b,...
S,y,param,R,ParamFilter)
param.Pi = ParamFilter.Pi;
param.chiC = ParamFilter.chiC;
k = length(y);
q = length(S);
N_aug = q+k;
Rc = chol(kron(eye(k/2),R));
S_aug = blkdiag(S,Rc);
% scaled unsented transform
W0 = 1-N_aug/3;
Wj = (1-W0)/(2*N_aug);
gamma =... |
github | Lancelot899/ICRA2018-master | EsimatePosAmers.m | .m | ICRA2018-master/filters/EsimatePosAmers.m | 2,093 | utf_8 | 98a1882400a96d76ab9789928887664e | function [points3d, errors] = EsimatePosAmers(pointTracks, ...
camPoses, cameraParams)
numTracks = numel(pointTracks);
points3d = zeros(numTracks, 3);
numCameras = size(camPoses, 2);
cameraMatrices = containers.Map('KeyType', 'uint32', 'ValueType', 'any');
for i = 1:numCameras
id = camPoses(i).ViewId;
R =... |
github | Lancelot899/ICRA2018-master | manageAmers.m | .m | ICRA2018-master/filters/manageAmers.m | 5,456 | utf_8 | bf85aa3a6e9c58d710fe3434db202d1f | function [S,PosAmers,ParamFilter,trackerBis,myTracks,PosAmersNew,...
IdxAmersNew,trackCov,pointsMain,validityMain] = manageAmers(S,...
PosAmers,ParamFilter,ParamGlobal,trackerBis,trajFilter,I,...
pointsMain,validityMain,IdxImage,myTracks,pointsBis)
PosAmersNew = [];
IdxAmersNew = [];
trackCov = [];
MaxAmer... |
github | Lancelot899/ICRA2018-master | ukfRefUpdate.m | .m | ICRA2018-master/filters/ukfRefUpdate.m | 2,508 | utf_8 | 0ffab5e57239a98e26302370a16c3f8c | function [chi,v,PosAmers,omega_b,a_b,S,xidot] = ukfRefUpdate(chi,v,omega_b,a_b,...
S,y,param,R,ParamFilter,PosAmers,xidot)
param.Pi = ParamFilter.Pi;
param.chiC = ParamFilter.chiC;
k = length(y);
q = length(S);
N_aug = q+k;
Rc = chol(kron(eye(k/2),R));
S_aug = blkdiag(S,Rc);
% scaled unsented transform
W0 = 1-N_a... |
github | Lancelot899/ICRA2018-master | ukfUpdate.m | .m | ICRA2018-master/filters/ukfUpdate.m | 1,933 | utf_8 | 0c034d87cb979ce37c640d5fdf9a74b4 | function [Rot,v,x,PosAmers,omega_b,a_b,S] = ukfUpdate(Rot,v,x,omega_b,a_b,...
S,y,param,R,ParamFilter,PosAmers)
param.Pi = ParamFilter.Pi;
param.chiC = ParamFilter.chiC;
k = length(y);
q = length(S);
N_aug = q+k;
Rc = chol(kron(eye(k/2),R));
S_aug = blkdiag(S,Rc);
% scaled unsented transform
W0 = 1-N_aug/3;
Wj = ... |
github | Lancelot899/ICRA2018-master | lukfUpdate.m | .m | ICRA2018-master/filters/lukfUpdate.m | 1,788 | utf_8 | 9b390dc82185a8f43fa24167dcce1816 | function [chi,omega_b,a_b,S] = lukfUpdate(chi,omega_b,a_b,...
S,y,param,R,ParamFilter)
param.Pi = ParamFilter.Pi;
param.chiC = ParamFilter.chiC;
k = length(y);
q = length(S);
N_aug = q+k;
Rc = chol(kron(eye(k/2),R));
S_aug = blkdiag(S,Rc);
% scaled unsented transform
W0 = 1-N_aug/3;
Wj = (1-W0)/(2*N_aug);
gamma =... |
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