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 | anantsrivastava30/SUVR-PET-ADNI-master | linsysolvefun.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/linsysolvefun.m | 1,276 | utf_8 | 7ca4a2896ad33a210a95d53138f0676f | %%*************************************************************************
%% linsysolvefun: Solve H*x = b
%%
%% x = linsysolvefun(L,b)
%% where L contains the triangular factors of H.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%***************... |
github | anantsrivastava30/SUVR-PET-ADNI-master | sqlpdemo.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/sqlpdemo.m | 6,011 | utf_8 | 793deabad8688259781993ce346cea24 | %%*****************************************************************
%% Examples of SQLP.
%%
%% this is an illustration on how to use our SQLP solvers
%% coded in sqlp.m
%%
%% feas = 1 if want feasible initial iterate
%% = 0 otherwise
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tu... |
github | anantsrivastava30/SUVR-PET-ADNI-master | gpcomp.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/gpcomp.m | 5,095 | utf_8 | 7e23e98588f834156a9f13545f77cb3b | %%*********************************************************************
%% gpcomp: Compute tp=1/gp in Proposition 2 of the paper:
%%
%% R.M. Freund, F. Ordonez, and K.C. Toh,
%% Behavioral measures and their correlation with IPM iteration counts
%% on semi-definite programming problems,
%% Mathematical Programming, 109... |
github | anantsrivastava30/SUVR-PET-ADNI-master | sqlparameters.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/sqlparameters.m | 5,137 | utf_8 | 76bbd09e284250f7770836baabcc95e5 | %%*************************************************************************
%% parameters.m: set OPTIONS structure to specify default
%% parameters for sqlp.m
%%
%% OPTIONS.vers : version of direction to use.
%% 1 for HKM direction
%% 2 for NT direction
%... |
github | anantsrivastava30/SUVR-PET-ADNI-master | convertcmpsdp.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/convertcmpsdp.m | 3,465 | utf_8 | 6bd9a62cfb0249da5d75665cfb26063b | %%*********************************************************
%% convertcmpsdp: convert SDP with complex data into one
%% with real data by converting
%%
%% C - sum_{k=1}^m yk*Ak psd
%% to
%% [CR,-CI] - sum ykR*[AkR,-AkI] psd
%% [CI, CR] [AkI, AkR]
%%
%% ykI = 0 for k = 1:m
%%
%% [bblk,AAt,C... |
github | anantsrivastava30/SUVR-PET-ADNI-master | sqlpsummary.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/sqlpsummary.m | 4,047 | utf_8 | 78361a4e3dfeaeb73a7102b08ad30733 | %%*****************************************************************************
%% sqlpsummary: print summary
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*****************************************************************************
function sq... |
github | anantsrivastava30/SUVR-PET-ADNI-master | checkdepconstr.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/checkdepconstr.m | 6,926 | utf_8 | 186362786fef9d7d3328ab9e5a9e117c | %%*****************************************************************************
%% checkdepconst: compute AAt to determine if the
%% constraint matrices Ak are linearly independent.
%%
%% [At,b,y,idxB,neardepconstr,feasible,AAt] = checkdepconstr(blk,At,b,y,rmdepconstr);
%%
%% rmdepconstr = 1, if want to rem... |
github | anantsrivastava30/SUVR-PET-ADNI-master | convertRcone.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/convertRcone.m | 948 | utf_8 | afa3b210699dbf796a257cb53e92ef0d | %%***************************************************************
%% convertRcone: convert rotated cone to socp cone
%%
%% [blk,At,C,b,T] = convertRcone(blk,At,C,b);
%%
%%***************************************************************
function [blk,At,C,b,T] = convertRcone(blk,At,C,b)
T = cell(size(blk,1),1);
for p =... |
github | anantsrivastava30/SUVR-PET-ADNI-master | qops.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/qops.m | 1,421 | utf_8 | bddd4e93643ef0eb5b6868ef41d2bec7 | %%********************************************************
%% qops: Fu = qops(pblk,w,f,options,u);
%%
%% options = 1, Fu(i) = <wi,fi>
%% = 2, Fu(i) = 2*wi(1)*fi(1)-<wi,fi>
%% = 3, Fui = w(i)*fi
%% = 4, Fui = w(i)*fi, Fui(1) = -Fui(1).
%% options = 5, Fu = w [ f'*u ; ub + fb*alp ], where
%% ... |
github | anantsrivastava30/SUVR-PET-ADNI-master | HKMdirfun.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/HKMdirfun.m | 1,703 | utf_8 | 2e90b8de8daed6f5a9a6894232dbff6a | %%*******************************************************************
%% HKMdirfun: compute (dX,dZ), given dy, for the HKM direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*******************************************************************... |
github | anantsrivastava30/SUVR-PET-ADNI-master | symqmr.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/symqmr.m | 3,957 | utf_8 | bf28cb72305cb0378a7b572d42f39ff6 | %%*************************************************************************
%% symqmr: symmetric QMR with left (symmetric) preconditioner.
%% The preconditioner used is based on the analytical
%% expression of inv(A).
%%
%% [x,resnrm,solve_ok] = symqmr(A,b,L,tol,maxit)
%%
%% child function: linsysolvefu... |
github | anantsrivastava30/SUVR-PET-ADNI-master | validate_startpoint.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/validate_startpoint.m | 3,067 | utf_8 | 572146bf8639c3d5066b6a790bca3ba8 | %%***********************************************************************
%% validate_startpoint: validate_startpoint starting point X0,y0,Z0
%%
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%********************************************************... |
github | anantsrivastava30/SUVR-PET-ADNI-master | Atyfun.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/Atyfun.m | 1,471 | utf_8 | 5af173811d5528ab49ca2a48bd4748ce | %%*********************************************************
%% Atyfun: compute sum_{k=1}^m yk*Ak.
%%
%% Q = Atyfun(blk,At,permA,isspAy,y);
%%
%% Note: permA and isspAy may be set to [].
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%**************... |
github | anantsrivastava30/SUVR-PET-ADNI-master | blkcholfun.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/blkcholfun.m | 1,247 | utf_8 | 6d78842d748fad818d79a003e1238916 | %%******************************************************************
%% blkcholfun: compute Cholesky factorization of X.
%%
%% [Xchol,indef] = blkcholfun(blk,X,permX);
%%
%% X = Xchol'*Xchol;
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*******... |
github | anantsrivastava30/SUVR-PET-ADNI-master | smat.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/smat.m | 880 | utf_8 | 184f4081013cebeae6150a22b224ebf9 | %%*********************************************************
%% smat: compute the matrix smat(x).
%%
%% M = smat(blk,x,isspM);
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%**********************************************************
function M = ... |
github | anantsrivastava30/SUVR-PET-ADNI-master | sqlpcheckconvg.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/sqlpcheckconvg.m | 8,298 | utf_8 | 6acb41aefb05d5a53dbd501dabab2b33 | %%*****************************************************************************
%% sqlpcheckconvg: check convergence.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*****************************************************************************
func... |
github | anantsrivastava30/SUVR-PET-ADNI-master | SDPT3soln_SEDUMIsoln.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/SDPT3soln_SEDUMIsoln.m | 2,743 | utf_8 | 1f7236a38116d782e7084afc40e90a10 | %%**********************************************************
%% SDPT3soln_SEDUMIsoln: convert SQLP solution in SDPT3 format to
%% SeDuMi format
%%
%% [xx,yy,zz] = SDPT3soln_SEDUMIsoln(blk,X,y,Z,perm);
%%
%% usage: load SEDUMI_data_file (containing say, A,b,c,K)
%% [blk,At,C,b,perm] = read_s... |
github | anantsrivastava30/SUVR-PET-ADNI-master | detect_ublk.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/detect_ublk.m | 2,815 | utf_8 | 819f087d73a97e1717b952ca2f3a407d | %%*******************************************************************
%% detect_ublk: search for implied free variables in linear
%% block.
%% [blk2,At2,C2,ublkinfo] = detect_ublk(blk,At,C);
%%
%% i1,i2: indices corresponding to splitting of unrestricted varaibles
%% i3 : remaining indices in the linear ... |
github | anantsrivastava30/SUVR-PET-ADNI-master | combine_blk.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/combine_blk.m | 2,358 | utf_8 | 205279faec8f7b11c04bc75707a6eb77 | %%*******************************************************************
%% combine_blk: combine small SDP blocks together,
%% combine all SOCP blocks together, etc
%%
%% [blk2,At2,C2,blkinfo] = combine_blk(blk,At,C);
%%
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%%... |
github | anantsrivastava30/SUVR-PET-ADNI-master | Prod2.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/Prod2.m | 1,882 | utf_8 | b291dcf07608872ed75bd82e48ff0b54 | %%*******************************************************************
%% Prod2: compute the block diagonal matrix A*B
%%
%% C = Prod2(blk,A,B,options);
%%
%% INPUT: blk = a cell array describing the block structure of A and B
%% A,B = square matrices or column vectors.
%%
%% options = 0 if no special str... |
github | anantsrivastava30/SUVR-PET-ADNI-master | NTdirfun.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sdpt3/Solver/NTdirfun.m | 1,614 | utf_8 | a236e4d45e59db8824375b14ed6090a1 | %%*******************************************************************
%% NTdirfun: compute (dX,dZ), given dy, for the NT direction.
%%
%% SDPT3: version 3.1
%% Copyright (c) 1997 by
%% K.C. Toh, M.J. Todd, R.H. Tutuncu
%% Last Modified: 16 Sep 2004
%%*******************************************************************
... |
github | anantsrivastava30/SUVR-PET-ADNI-master | make.m | .m | SUVR-PET-ADNI-master/scripts/cvx/examples/make.m | 23,079 | utf_8 | 8b04233b872cb2ab50851644ae0e486a | function make( varargin )
%
% Determine the base path
%
odir = pwd;
base = mfilename('fullpath');
base = fileparts( base );
%
% Check the force and runonly flags
%
args = varargin;
is_octave = exist( 'OCTAVE_VERSION', 'builtin' );
if is_octave,
force = true;
runonly = true;
indexonly = false;
page_o... |
github | anantsrivastava30/SUVR-PET-ADNI-master | cantilever_beam_plot.m | .m | SUVR-PET-ADNI-master/scripts/cvx/examples/cvxbook/Ch04_cvx_opt_probs/cantilever_beam_plot.m | 1,050 | utf_8 | e8c8c9e1b601e4102f96e0436649d132 | % Plots a cantilever beam as a 3D figure.
% This is a helper function for the optimal cantilever beam example.
%
% Inputs:
% values: an array of heights and widths of each segment
% [h1 h2 ... hN w1 w2 ... wN]
%
% Almir Mutapcic 01/25/06
function cantilever_beam_plot(values)
N = length(values)/2;
for k ... |
github | anantsrivastava30/SUVR-PET-ADNI-master | simple_step.m | .m | SUVR-PET-ADNI-master/scripts/cvx/examples/circuit_design/simple_step.m | 235 | utf_8 | b8043326fe5966f9432b69b584891e0f | % Computes the step response of a linear system
function X = simple_step(A,B,DT,N)
n = size(A,1);
Ad = expm( full( A * DT ) );
Bd = ( Ad - eye(n) ) * B;
Bd = A \ Bd;
X = zeros(n,N);
for k = 2 : N,
X(:,k) = Ad*X(:,k-1)+Bd;
end
|
github | anantsrivastava30/SUVR-PET-ADNI-master | spectral_fact.m | .m | SUVR-PET-ADNI-master/scripts/cvx/examples/filter_design/spectral_fact.m | 1,292 | utf_8 | 014eebfa2dfbbd038c1383ff2ef97b0e | % Spectral factorization using Kolmogorov 1939 approach.
% (code follows pp. 232-233, Signal Analysis, by A. Papoulis)
%
% Computes the minimum-phase impulse response which satisfies
% given auto-correlation.
%
% Input:
% r: top-half of the auto-correlation coefficients
% starts from 0th element to end of the au... |
github | anantsrivastava30/SUVR-PET-ADNI-master | polar_plot_ant.m | .m | SUVR-PET-ADNI-master/scripts/cvx/examples/antenna_array_design/polar_plot_ant.m | 1,149 | utf_8 | 34a08a3bc75c474d61e01ea58b16e54e | % Plot a polar plot of an antenna array sensitivity
% with lines denoting the target direction and beamwidth.
% This is a helper function used in the broadband antenna examples.
%
% Inputs:
% X: an array of abs(y(theta)) where y is the antenna array pattern
% theta0: target direction
% bw: total beamw... |
github | anantsrivastava30/SUVR-PET-ADNI-master | spectral_fact.m | .m | SUVR-PET-ADNI-master/scripts/cvx/examples/antenna_array_design/spectral_fact.m | 1,385 | utf_8 | 570e7ae2165d19abd477494c52e609f8 | % Spectral factorization using Kolmogorov 1939 approach
% (code follows pp. 232-233, Signal Analysis, by A. Papoulis)
%
% Computes the minimum-phase impulse response which satisfies
% given auto-correlation.
%
% Input:
% r: top-half of the auto-correlation coefficients
% starts from 0th element to end of the aut... |
github | anantsrivastava30/SUVR-PET-ADNI-master | plotgraph.m | .m | SUVR-PET-ADNI-master/scripts/cvx/examples/graph_laplacian/plotgraph.m | 3,172 | utf_8 | a46b1d761798c492e96a5b9504aea9aa | function plotgraph(A,xy,weights)
% Plots a graph with each edge width proportional to its weight.
%
% Edges with positive weights are drawn in blue; negative weights in red.
%
% Input parameters:
% A --- incidence matrix of the graph (size is n x m)
% (n is the number of nodes and m is the number of e... |
github | anantsrivastava30/SUVR-PET-ADNI-master | disp.m | .m | SUVR-PET-ADNI-master/scripts/cvx/lib/@cvxprob/disp.m | 5,405 | utf_8 | c8f4506efcff564b81f1f3cc1dbb8a9c | function disp( prob, prefix )
if nargin < 2, prefix = ''; end
global cvx___
p = cvx___.problems( prob.index_ );
if isempty( p.variables ),
nvars = 0;
else
nvars = length( fieldnames( p.variables ) );
end
if isempty( p.duals ),
nduls = 0;
else
nduls = length( fieldnames( p.duals ) );
end
neqns = ( len... |
github | anantsrivastava30/SUVR-PET-ADNI-master | apply.m | .m | SUVR-PET-ADNI-master/scripts/cvx/lib/@cvxtuple/apply.m | 505 | utf_8 | f59f25021974462a358e5189ea5415e8 | function y = apply( func, x )
y = do_apply( func, x.value_ );
function y = do_apply( func, x )
switch class( x ),
case 'struct',
y = cell2struct( do_apply( func, struct2cell( x ) ), fieldnames( x ), 1 );
case 'cell',
y = cellfun( func, x, 'UniformOutput', false );
otherwise,
y = fev... |
github | anantsrivastava30/SUVR-PET-ADNI-master | cvx_setdual.m | .m | SUVR-PET-ADNI-master/scripts/cvx/lib/@cvxtuple/cvx_setdual.m | 954 | utf_8 | b6deb370985cc299023b091bbba5dfd6 | function x = setdual( x, y )
x.dual_ = y;
x.value_ = do_setdual( x.value_, y );
function x = do_setdual( x, y )
switch class( x ),
case 'struct',
nx = numel( x );
if nx > 1,
error( 'Dual variables may not be attached to struct arrays.' );
end
f = fieldnames(x);
y... |
github | anantsrivastava30/SUVR-PET-ADNI-master | testall.m | .m | SUVR-PET-ADNI-master/scripts/cvx/lib/@cvxtuple/testall.m | 452 | utf_8 | be9d0553e0e560f2c73fdb02a37b08b6 | function y = testall( func, x )
y = do_test( func, x.value_ );
function y = do_test( func, x )
switch class( x ),
case 'struct',
y = do_test( func, struct2cell( x ) );
case 'cell',
y = all( cellfun( func, x ) );
otherwise,
y = feval( func, x );
end
% Copyright 2005-2016 CVX Researc... |
github | anantsrivastava30/SUVR-PET-ADNI-master | disp.m | .m | SUVR-PET-ADNI-master/scripts/cvx/lib/@cvxtuple/disp.m | 1,366 | utf_8 | ed9c53cbe23c1f5ab4440b5d9a10cbfd | function disp( x, prefix )
if nargin < 2,
prefix = '';
end
disp( [ prefix, 'cvx tuple object: ' ] );
prefix = [ prefix, ' ' ];
do_disp( x.value_, {}, prefix, prefix, '' );
if ~isempty( x.dual_ ),
dn = cvx_subs2str( x.dual_ );
disp( [ prefix, 'dual variable: ', dn(2:end) ] );
end
function do_disp( x, f, f... |
github | anantsrivastava30/SUVR-PET-ADNI-master | sparsify.m | .m | SUVR-PET-ADNI-master/scripts/cvx/lib/@cvx/sparsify.m | 4,013 | utf_8 | 2eda31274fafa84387d3aad5be748107 | function x = sparsify( x, mode )
global cvx___
narginchk(2,2);
persistent remap
%
% Check mode argument
%
if ~ischar( mode ) || size( mode, 1 ) ~= 1,
error( 'Second arugment must be a string.' );
end
isobj = strcmp( mode, 'objective' );
pr = cvx___.problems( end );
touch( pr.self, x );
bz = x.basis_ ~= 0;
bs = ... |
github | anantsrivastava30/SUVR-PET-ADNI-master | prelp.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sedumi/conversion/prelp.m | 3,887 | utf_8 | 4d1e23d094e3f1b35ec666df11a34003 | % PRELP Loads and preprocesses LP from an MPS file.
%
% > [A,b,c,lenx,lbounds] = PRELP('problemname')
% The above command results in an LP in standard form,
% - Instead of specifying the problemname, you can also use PRELP([]), to
% get the problem from the file /tmp/default.mat.
% - Also, you may type PRE... |
github | anantsrivastava30/SUVR-PET-ADNI-master | feasreal.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sedumi/conversion/feasreal.m | 4,144 | utf_8 | 454dcb6c42c0642ed5c5a524e84bec58 | % FEASREAL Generates a random sparse optimization problem with
% linear, quadratic and semi-definite constraints. Output
% can be used by SEDUMI. All data will be real-valued.
%
% The following two lines are typical:
% > [AT,B,C,K] = FEASREAL;
% > [X,Y,INFO] = SEDUMI(AT,B,C,K);
%
% An extended version is:
% > [... |
github | anantsrivastava30/SUVR-PET-ADNI-master | sdpa2vec.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sedumi/conversion/sdpa2vec.m | 2,352 | utf_8 | f055b4df357f6cf3b426ce27869bc738 | % x = sdpavec(E,K)
% Takes an SDPA type sparse data description E, i.e.
% E(1,:) = block, E(2,:) = row, E(3,:) = column, E(4,:) = entry,
% and transforms it into a "long" vector, with vectorized matrices for
% each block stacked under each other. The size of each matrix block
% is given in the field K.s.
% **********... |
github | anantsrivastava30/SUVR-PET-ADNI-master | blk2vec.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sedumi/conversion/blk2vec.m | 1,653 | utf_8 | 0d48b96f66e746fce480e7a3e9c271a5 | % x = blk2vec(X,nL)
%
% Converts a block diagonal matrix into a vector.
%
% ********** INTERNAL FUNCTION OF FROMPACK **********
function x = blk2vec(X,nL)
%
% This file is part of SeDuMi 1.1 by Imre Polik and Oleksandr Romanko
% Copyright (C) 2005 McMaster University, Hamilton, CANADA (since 1.1)
%
% Copyright (C)... |
github | anantsrivastava30/SUVR-PET-ADNI-master | writesdp.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sedumi/conversion/writesdp.m | 4,708 | utf_8 | 31f196bca4c11b9610c98de8e4d7b107 | % This function takes a problem in SeDuMi MATLAB format and writes it out
% in SDPpack format.
%
% Usage:
%
% writesdp(fname,A,b,c,K)
%
% fname Name of SDPpack file, in quotes
% A,b,c,K Problem in SeDuMi form
%
% Notes:
%
% Problems with complex data are not allowed.
%
% ... |
github | anantsrivastava30/SUVR-PET-ADNI-master | frompack.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sedumi/conversion/frompack.m | 2,509 | utf_8 | a4730dcb4ec069944953dce753da973d | % FROMPACK Converts a cone problem in SDPPACK format to SEDUMI format.
%
% [At,c] = frompack(A,b,C,blk) Given a problem (A,b,C,blk) in the
% SDPPACK-0.9-beta format, this produces At and c for use with
% SeDuMi. This lets you execute
%
% [x,y,info] = SEDUMI(At,b,c,blk);
%
% IMPORTANT: this function assumes that th... |
github | anantsrivastava30/SUVR-PET-ADNI-master | feascpx.m | .m | SUVR-PET-ADNI-master/scripts/cvx/sedumi/conversion/feascpx.m | 4,385 | utf_8 | c48c6cf336cdb94ad12b04e1efd2417b | % FEASCPX Generates a random sparse optimization problem with
% linear, quadratic and semi-definite constraints. Output
% can be used by SEDUMI. Includes complex-valued data.
%
% The following two lines are typical:
% > [AT,B,C,K] = FEASCPX;
% > [X,Y,INFO] = SEDUMI(AT,B,C,K);
%
% An extended version is:
% > [AT... |
github | anantsrivastava30/SUVR-PET-ADNI-master | cvx_glpk.m | .m | SUVR-PET-ADNI-master/scripts/cvx/shims/cvx_glpk.m | 4,344 | utf_8 | fc695a24b6757c72ebcc0c9422e98dca | function shim = cvx_glpk( shim )
% CVX_SOLVER_SHIM GLPK interface for CVX.
% This procedure returns a 'shim': a structure containing the necessary
% information CVX needs to use this solver in its modeling framework.
if ~isempty( shim.solve ),
return
end
if isempty( shim.name ),
fname = 'glpk.m';
ps =... |
github | anantsrivastava30/SUVR-PET-ADNI-master | cvx_sedumi.m | .m | SUVR-PET-ADNI-master/scripts/cvx/shims/cvx_sedumi.m | 10,740 | utf_8 | 42324556d877c6a43224d410f1a12290 | function shim = cvx_sedumi( shim )
% CVX_SOLVER_SHIM SeDuMi interface for CVX.
% This procedure returns a 'shim': a structure containing the necessary
% information CVX needs to use this solver in its modeling framework.
global cvx___
if ~isempty( shim.solve ),
return
end
if isempty( shim.name ),
fname = ... |
github | anantsrivastava30/SUVR-PET-ADNI-master | cvx_sdpt3.m | .m | SUVR-PET-ADNI-master/scripts/cvx/shims/cvx_sdpt3.m | 12,657 | utf_8 | b42871a1a82cd274121bc52919caa778 | function shim = cvx_sdpt3( shim )
% CVX_SOLVER_SHIM SDPT3 interface for CVX.
% This procedure returns a 'shim': a structure containing the necessary
% information CVX needs to use this solver in its modeling framework.
global cvx___
if ~isempty( shim.solve ),
return
end
if isempty( shim.name ),
fname = 's... |
github | dipankarsk/Feature-Selection-Hybrid-master | LoadData.m | .m | Feature-Selection-Hybrid-master/LoadData.m | 226 | utf_8 | 78032a391706c39e86c1fa384a530fe1 |
function data=LoadData()
dataset=load('bodyfat_data3');
data.x=dataset.input;
data.t=dataset.target;
data.nx=size(data.x,1);
data.nt=size(data.t,1);
data.nSample=size(data.x,2);
end |
github | dipankarsk/Feature-Selection-Hybrid-master | SinglePointCrossover.m | .m | Feature-Selection-Hybrid-master/SinglePointCrossover.m | 179 | utf_8 | bd27bb61610d49f21b6958bf5537fc87 |
function [y1, y2]=SinglePointCrossover(x1,x2)
nVar=numel(x1);
c=randi([1 nVar-1]);
y1=[x1(1:c) x2(c+1:end)];
y2=[x2(1:c) x1(c+1:end)];
end |
github | dipankarsk/Feature-Selection-Hybrid-master | UniformCrossover.m | .m | Feature-Selection-Hybrid-master/UniformCrossover.m | 164 | utf_8 | 5bb55be5ce6a3ead7481905bc412d7b5 |
function [y1, y2]=UniformCrossover(x1,x2)
alpha=randi([0 1],size(x1));
y1=alpha.*x1+(1-alpha).*x2;
y2=alpha.*x2+(1-alpha).*x1;
end |
github | dipankarsk/Feature-Selection-Hybrid-master | Crossover.m | .m | Feature-Selection-Hybrid-master/Crossover.m | 491 | utf_8 | b77ebcbddbbe391a41c9e2712e2b38c5 |
function [y1, y2]=Crossover(x1,x2)
pSinglePoint=0.1;
pDoublePoint=0.2;
pUniform=1-pSinglePoint-pDoublePoint;
METHOD=RouletteWheelSelection([pSinglePoint pDoublePoint pUniform]);
switch METHOD
case 1
[y1, y2]=SinglePointCrossover(x1,x2);
... |
github | dipankarsk/Feature-Selection-Hybrid-master | FeatureSelectionCost.m | .m | Feature-Selection-Hybrid-master/FeatureSelectionCost.m | 1,054 | utf_8 | db056076415b1d99276adc81424f91fb |
function [z, out]=FeatureSelectionCost(s,data)
% Read Data Elements
x=data.x;
t=data.t;
% Selected Features
S=find(s~=0);
% Number of Selected Features
nf=numel(S);
% Ratio of Selected Features
rf=nf/numel(s);
% Selecting Features
xs=x(S,:... |
github | dipankarsk/Feature-Selection-Hybrid-master | RouletteWheelSelection.m | .m | Feature-Selection-Hybrid-master/RouletteWheelSelection.m | 121 | utf_8 | 57f6bdead1494c070c43bc21d455f517 |
function i=RouletteWheelSelection(P)
r=rand;
c=cumsum(P);
i=find(r<=c,1,'first');
end |
github | dipankarsk/Feature-Selection-Hybrid-master | DoublePointCrossover.m | .m | Feature-Selection-Hybrid-master/DoublePointCrossover.m | 245 | utf_8 | db8e17565fbfda7bc414bf2a0f3f8382 |
function [y1, y2]=DoublePointCrossover(x1,x2)
nVar=numel(x1);
cc=randsample(nVar-1,2);
c1=min(cc);
c2=max(cc);
y1=[x1(1:c1) x2(c1+1:c2) x1(c2+1:end)];
y2=[x2(1:c1) x1(c1+1:c2) x2(c2+1:end)];
end |
github | dipankarsk/Feature-Selection-Hybrid-master | Mutate.m | .m | Feature-Selection-Hybrid-master/Mutate.m | 155 | utf_8 | c1f0095051ac33990eaad85e0ba0be8c |
function y=Mutate(x,mu)
nVar=numel(x);
nmu=ceil(mu*nVar);
j=randsample(nVar,nmu);
y=x;
y(j)=1-x(j);
end |
github | dipankarsk/Feature-Selection-Hybrid-master | CreateAndTrainANN.m | .m | Feature-Selection-Hybrid-master/CreateAndTrainANN.m | 2,118 | utf_8 | 068f56c52d912b04ba16da76e575b2bd |
function results=CreateAndTrainANN(x,t)
if ~isempty(x)
hiddenLayerSize = 5;
net = fitnet(hiddenLayerSize,trainFcn);
net.input.processFcns = {'removeconstantrows','mapminmax'};
net.output.processFcns = {'removeconstantrows','mapminmax'};
ne... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | uniquepots.m | .m | bayesian-reasoning-machine-learning-master/src/uniquepots.m | 1,370 | utf_8 | 01144806ae1fd2ada3aaeeaa4a6c90ad | function [newpot A] = uniquepots(pot,varargin)
%UNIQUEPOTS Eliminate redundant potentials (those contained wholly within another) by multiplying redundant potentials
% [newpot A]= uniquepots(pot,<tables>)
% if tables=0 then just merge the variables
% The matrix has A(j,i)=1 if pot(i) is contained within pot(j)
tab... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | subsetsum.m | .m | bayesian-reasoning-machine-learning-master/src/subsetsum.m | 2,689 | utf_8 | f8d7d15173c2e31dec9a155c9cc80873 | function [q st]=subsetsum(x)
%SUBSETSUM Solve the zero subset sum problem.
% Input: x - a vector of integers
% Outputs: q=1 if there is a binary 0/1 vector st such that
% sum_i x(i).*st(i) = =0
% The method used is either dynamic programming or explicit enumeration,
% depending on the number of variables and the... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | cliquedecomp.m | .m | bayesian-reasoning-machine-learning-master/src/cliquedecomp.m | 4,181 | utf_8 | eb0a06cc65d47f1b7a21215da7603bc7 | function[z zbest]=cliquedecomp(A,C,varargin)
%CLIQUEDECOMP Clique matrix decomposition
% A: adjacency matrix
% C: maximum number of clusters required
% opts.zloops: innerloop for a fixed number of cliques
% opts.aloops: outerloop to find optimal number of clusters
% opts.beta: inverse temperature
% opts.burnin: ... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | hinton.m | .m | bayesian-reasoning-machine-learning-master/src/hinton.m | 1,287 | utf_8 | c2796065ccb0df313df10b48a18ce866 | function hinton(M,varargin)
%HINTON Plot a Hinton diagram
% hinton(M,opts)
% Plot a Hinton diagram for matrix M. Positive is Green, and negative Red
% use hinton(M,1) to turn on the grid
% opts.grid = 1/0
% opts.coloursRGB: 2 x 4 matrix of RGB colours for the patches. First row is the RGB for
% the positive and ... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | mix2mix.m | .m | bayesian-reasoning-machine-learning-master/src/mix2mix.m | 1,590 | utf_8 | 366a4c119d7a19c2b93a6e136e5503c3 | function [newcoeff, newmean, newcov] = mix2mix(coeff, mean, cov, I)
%MIX2MIX Fit a mixture of Gaussians with another mixture of Gaussians
% (but with a smaller number of components I) by retaining the
% I-1 most probable coeffs, and merging the rest.
%
% Inputs:
% coeff(:) : coefficients of the mixtures
% mean(:,coeff)... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | mygamrnd.m | .m | bayesian-reasoning-machine-learning-master/src/mygamrnd.m | 1,780 | utf_8 | a2f65264ce7e91b85cc3d4f99a8af08e | %
%
% Gamma random variate generator
% Simon Rogers, 30/01/2007
% --------------------------------------------
% function g = mygamrnd(k,theta,N,varargin)
% generates N random variates from a Gamma(k,theta) pdf
% defined as
% p(g|k,theta) = g^{k-1} \frac{e^{-g/theta}}{\theta^k \Gamma(k)}
%
% Uses an acceptanc... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | singleparenttree.m | .m | bayesian-reasoning-machine-learning-master/src/singleparenttree.m | 1,274 | utf_8 | 9880861b5dbb90b5d6b0d7017ad8724b | function [spTree elimseq]=singleparenttree(Atree,varargin)
%SINGLEPARENTTREE From an undirected tree, form a directed tree with at most one parent
%[spTree elimseq]=singleparenttree(Atree,<orient away from this node>)
% Get an elimination elimseq such that each eliminated node has at most 1 parent:
% By default con... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | SVMtrain.m | .m | bayesian-reasoning-machine-learning-master/src/SVMtrain.m | 2,022 | utf_8 | a7a5dadc2f12e2d3b37c2775d6a74631 | function [A,G] = SVMtrain(Q,y,C)
%SVMTRAIN train a Support vector Machine
% compute the SMO decomposition algorithm from Fan et al JMLR 2005
%
% Inputs:
% Q_ij - y_i y_j K_ij (Where K is the kernel)
% y - labels
% C - C parameter
%
% Outputs:
% A - alpha v... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | uniquepots.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/uniquepots.m | 1,370 | utf_8 | 01144806ae1fd2ada3aaeeaa4a6c90ad | function [newpot A] = uniquepots(pot,varargin)
%UNIQUEPOTS Eliminate redundant potentials (those contained wholly within another) by multiplying redundant potentials
% [newpot A]= uniquepots(pot,<tables>)
% if tables=0 then just merge the variables
% The matrix has A(j,i)=1 if pot(i) is contained within pot(j)
tab... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | subsetsum.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/subsetsum.m | 2,689 | utf_8 | f8d7d15173c2e31dec9a155c9cc80873 | function [q st]=subsetsum(x)
%SUBSETSUM Solve the zero subset sum problem.
% Input: x - a vector of integers
% Outputs: q=1 if there is a binary 0/1 vector st such that
% sum_i x(i).*st(i) = =0
% The method used is either dynamic programming or explicit enumeration,
% depending on the number of variables and the... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | cliquedecomp.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/cliquedecomp.m | 4,181 | utf_8 | eb0a06cc65d47f1b7a21215da7603bc7 | function[z zbest]=cliquedecomp(A,C,varargin)
%CLIQUEDECOMP Clique matrix decomposition
% A: adjacency matrix
% C: maximum number of clusters required
% opts.zloops: innerloop for a fixed number of cliques
% opts.aloops: outerloop to find optimal number of clusters
% opts.beta: inverse temperature
% opts.burnin: ... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | hinton.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/hinton.m | 1,287 | utf_8 | c2796065ccb0df313df10b48a18ce866 | function hinton(M,varargin)
%HINTON Plot a Hinton diagram
% hinton(M,opts)
% Plot a Hinton diagram for matrix M. Positive is Green, and negative Red
% use hinton(M,1) to turn on the grid
% opts.grid = 1/0
% opts.coloursRGB: 2 x 4 matrix of RGB colours for the patches. First row is the RGB for
% the positive and ... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | mix2mix.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/mix2mix.m | 1,590 | utf_8 | 366a4c119d7a19c2b93a6e136e5503c3 | function [newcoeff, newmean, newcov] = mix2mix(coeff, mean, cov, I)
%MIX2MIX Fit a mixture of Gaussians with another mixture of Gaussians
% (but with a smaller number of components I) by retaining the
% I-1 most probable coeffs, and merging the rest.
%
% Inputs:
% coeff(:) : coefficients of the mixtures
% mean(:,coeff)... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | mygamrnd.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/mygamrnd.m | 1,780 | utf_8 | a2f65264ce7e91b85cc3d4f99a8af08e | %
%
% Gamma random variate generator
% Simon Rogers, 30/01/2007
% --------------------------------------------
% function g = mygamrnd(k,theta,N,varargin)
% generates N random variates from a Gamma(k,theta) pdf
% defined as
% p(g|k,theta) = g^{k-1} \frac{e^{-g/theta}}{\theta^k \Gamma(k)}
%
% Uses an acceptanc... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | singleparenttree.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/singleparenttree.m | 1,274 | utf_8 | 9880861b5dbb90b5d6b0d7017ad8724b | function [spTree elimseq]=singleparenttree(Atree,varargin)
%SINGLEPARENTTREE From an undirected tree, form a directed tree with at most one parent
%[spTree elimseq]=singleparenttree(Atree,<orient away from this node>)
% Get an elimination elimseq such that each eliminated node has at most 1 parent:
% By default con... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | SVMtrain.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/SVMtrain.m | 2,022 | utf_8 | a7a5dadc2f12e2d3b37c2775d6a74631 | function [A,G] = SVMtrain(Q,y,C)
%SVMTRAIN train a Support vector Machine
% compute the SMO decomposition algorithm from Fan et al JMLR 2005
%
% Inputs:
% Q_ij - y_i y_j K_ij (Where K is the kernel)
% y - labels
% C - C parameter
%
% Outputs:
% A - alpha v... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | textoval.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/graphlayout/textoval.m | 2,151 | utf_8 | 654d6f5117ff68685aac2921423cb76d | function [t, wd] = textoval(x, y, str)
% TEXTOVAL Draws an oval around text objects
%
% [T, WIDTH] = TEXTOVAL(X, Y, STR)
% [..] = TEXTOVAL(STR) % Interactive
%
% Inputs :
% X, Y : Coordinates
% TXT : Strings
%
% Outputs :
% T : Object Handles
% WIDTH : x and y Width of ovals
%
% Usage Example : [t] = t... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | textbox.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/graphlayout/textbox.m | 2,118 | utf_8 | f7750958b5bdcd1839e4800abdeb9c21 | function [t, wd] = textbox(x,y,str)
% TEXTBOX Draws A Box around the text
%
% [T, WIDTH] = TEXTBOX(X, Y, STR)
% [..] = TEXTBOX(STR)
%
% Inputs :
% X, Y : Coordinates
% TXT : Strings
%
% Outputs :
% T : Object Handles
% WIDTH : x and y Width of boxes
%%
% Usage Example : t = textbox({'Ali','Veli','49','50... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | arrow.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/graphlayout/private/arrow.m | 56,774 | utf_8 | 2dc514962f607a6855ee3f8bfbee74c4 | function [h,yy,zz] = arrow(varargin)
% ARROW Draw a line with an arrowhead.
%
% ARROW(Start,Stop) draws a line with an arrow from Start to Stop (points
% should be vectors of length 2 or 3, or matrices with 2 or 3
% columns), and returns the graphics handle of the arrow(s).
%
% ARROW uses the mouse (cl... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | demoBayesErrorAnalysis.m | .m | bayesian-reasoning-machine-learning-master/src/BRMLtoolkit/DemosExercises/demoBayesErrorAnalysis.m | 1,860 | utf_8 | 2cd17f883c53fbb75be770a1da1a00a3 | function demoBayesErrorAnalysis
%DEMOBAYESERRORANALYSIS demo of Bayesian error analysis
N = 20; % number of datapoints
Q = 3; % number of error types
% make some errors for the two classifers :
errtype={'IndepSameErrorGenerator','IndepDiffErrorGenerator','DepErrorGenerator'};
GenerateError=errtype{randgen(1:3)}... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | textoval.m | .m | bayesian-reasoning-machine-learning-master/src/graphlayout/textoval.m | 2,151 | utf_8 | 654d6f5117ff68685aac2921423cb76d | function [t, wd] = textoval(x, y, str)
% TEXTOVAL Draws an oval around text objects
%
% [T, WIDTH] = TEXTOVAL(X, Y, STR)
% [..] = TEXTOVAL(STR) % Interactive
%
% Inputs :
% X, Y : Coordinates
% TXT : Strings
%
% Outputs :
% T : Object Handles
% WIDTH : x and y Width of ovals
%
% Usage Example : [t] = t... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | textbox.m | .m | bayesian-reasoning-machine-learning-master/src/graphlayout/textbox.m | 2,118 | utf_8 | f7750958b5bdcd1839e4800abdeb9c21 | function [t, wd] = textbox(x,y,str)
% TEXTBOX Draws A Box around the text
%
% [T, WIDTH] = TEXTBOX(X, Y, STR)
% [..] = TEXTBOX(STR)
%
% Inputs :
% X, Y : Coordinates
% TXT : Strings
%
% Outputs :
% T : Object Handles
% WIDTH : x and y Width of boxes
%%
% Usage Example : t = textbox({'Ali','Veli','49','50... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | arrow.m | .m | bayesian-reasoning-machine-learning-master/src/graphlayout/private/arrow.m | 56,774 | utf_8 | 2dc514962f607a6855ee3f8bfbee74c4 | function [h,yy,zz] = arrow(varargin)
% ARROW Draw a line with an arrowhead.
%
% ARROW(Start,Stop) draws a line with an arrow from Start to Stop (points
% should be vectors of length 2 or 3, or matrices with 2 or 3
% columns), and returns the graphics handle of the arrow(s).
%
% ARROW uses the mouse (cl... |
github | cosmicBboy/bayesian-reasoning-machine-learning-master | demoBayesErrorAnalysis.m | .m | bayesian-reasoning-machine-learning-master/src/DemosExercises/demoBayesErrorAnalysis.m | 1,860 | utf_8 | 2cd17f883c53fbb75be770a1da1a00a3 | function demoBayesErrorAnalysis
%DEMOBAYESERRORANALYSIS demo of Bayesian error analysis
N = 20; % number of datapoints
Q = 3; % number of error types
% make some errors for the two classifers :
errtype={'IndepSameErrorGenerator','IndepDiffErrorGenerator','DepErrorGenerator'};
GenerateError=errtype{randgen(1:3)}... |
github | smilingmaria/speech2vec-master | generalized_MLPG_ver2.m | .m | speech2vec-master/fbank_matlab/generalized_MLPG_ver2.m | 4,240 | utf_8 | adbbb93fe0898f8cf875a705feac00c3 | % 2015.2.26 constructed by Hwang, Hsin-Te
% 2016.1.26 modified by Wu, Ted
% 1.This function performs Maximum likelihood parameter generation (MLPG) algorithm to
% smooth ANN-mapping's output
function [converted_seq_mlpg]=generalized_MLPG_ver2(Input_seq,Cov,dynamic_flag,featureDIM)
converted_seq = zeros(featureD... |
github | smilingmaria/speech2vec-master | dynamic_feature_ver1.m | .m | speech2vec-master/fbank_matlab/dynamic_feature_ver1.m | 1,358 | utf_8 | 7610f02fbfe4fe6461f77397b724202e | % 2013.11.25 constructed by Hwang, Hsin-Te:
% 2013.11.25 modified by Hwang, Hsin-Te
% Computing static and dynamic features
function output_sequence=dynamic_feature_ver1(static_feature_seq,dynamic_flag)
% static_feature_seq: static feature sequence
% dynamic_flag: 1=>delta, 2=> delta^2
% output_sequence: joint... |
github | smilingmaria/speech2vec-master | fft2melmx.m | .m | speech2vec-master/fbank_matlab/fft2melmx.m | 5,099 | utf_8 | bc5ec77cf66dbf11ed4f40f366c81a85 | function [wts,binfrqs] = fft2melmx(nfft, sr, nfilts, width, minfrq, maxfrq, htkmel, constamp)
% wts = fft2melmx(nfft, sr, nfilts, width, minfrq, maxfrq, htkmel, constamp)
% Generate a matrix of weights to combine FFT bins into Mel
% bins. nfft defines the source FFT size at sampling rate sr.
% Optional ... |
github | Ewenwan/MVision-master | resampindex.m | .m | MVision-master/3D_Object_Detection/Object_Tracking/pf_socker/resampindex.m | 616 | utf_8 | 17caee9b7bd62253da8e82b86d55eead |
function indices = resampindex(weights)
weights = max(0,weights);
weights = weights/sum(weights);
N = length(weights);
cumprob=[0 cumsum(weights)];
indices = zeros(1,N);
if (0)
%usual version where each sample drawn randomly
uni=rand(1,N);
for j=1:N
ind=find((uni>cumprob(j)) & (uni<=cumprob(j+1)));
... |
github | Ewenwan/MVision-master | genfilename.m | .m | MVision-master/3D_Object_Detection/Object_Tracking/pf_socker/genfilename.m | 301 | utf_8 | ce642ba63a549af477c6165624c061ec | %function fname = genfilename(sequencestruct, framenumber)
function fname = genfilename(sequencestruct, framenumber)
digstr = sprintf('%%0%dd',sequencestruct.digits);
filstr = sprintf('%%s%s%%s',digstr);
fname = sprintf(filstr,sequencestruct.prefix,framenumber,sequencestruct.postfix);
return
|
github | OliverKohlDSc/x11-novnc-docker-master | lorenz.m | .m | x11-novnc-docker-master/octave_scr/lorenz.m | 464 | utf_8 | b641d130b724a86bacead286119a74bf | % "Visualizing the Lorenz Strange Attractor with Octave"
% http://www.ibm.com/developerworks/library/l-datavistools/ (Listing 4)
%
% Big Thanks to Douglas (http://lists.gnu.org/archive/html/help-octave/2015-07/msg00159.html)
% Usage:
% x = lsode("lorenz", [3;15;1], (0:0.01:25)');
% plot3(x(:,1),x(:,2),x(:,3))
% plo... |
github | NeuBtracker/acquisition-master | fmeasure.m | .m | acquisition-master/03_Util/external/fmeasure.m | 9,062 | utf_8 | ddd88bb978206f149aff0960807c38e2 | function FM = fmeasure(Image, Measure, ROI)
%This function measures the relative degree of focus of
%an image. It may be invoked as:
%
% FM = fmeasure(IMAGE, METHOD, ROI)
%
%Where
% IMAGE, is a grayscale image and FM is the computed
% focus value.
% METHOD, is the focus measure algorithm as a string... |
github | NeuBtracker/acquisition-master | progressbar.m | .m | acquisition-master/03_Util/external/progressbar.m | 11,820 | utf_8 | 08d8ff8b281b8fa1ee4a4a8e6a8d21b5 | % Description:
% progressbar() provides an indication of the progress of some task using
% graphics and text. Calling progressbar repeatedly will update the figure and
% automatically estimate the amount of time remaining.
% This implementation of progressbar is intended to be extremely simple to use
% while provid... |
github | NeuBtracker/acquisition-master | dftregistration.m | .m | acquisition-master/03_Util/external/dftregistration.m | 9,281 | utf_8 | 8ba8c554cc9ad00df6d4276b74c64b36 |
function [output, Greg] = dftregistration(buf1ft,buf2ft,usfac)
% function [output Greg] = dftregistration(buf1ft,buf2ft,usfac);
% Efficient subpixel image registration by crosscorrelation. This code
% gives the same precision as the FFT upsampled cross correlation in a
% small fraction of the computation time and wit... |
github | NeuBtracker/acquisition-master | mouseinput_timeout.m | .m | acquisition-master/01_Acquisition/Functions/mouseinput_timeout.m | 3,655 | utf_8 | 480bb97f4f606096de22a342aed79ef2 | % MOUSEINPUT_TIMEOUT returns continuous mouse locations with timeout
% OUT = MOUSEINPUT_TIMEOUT returns the sequence of mouse locations between
% a button press and a button release in the current axes. It does
% not timeout. OUT is an Nx2 matrix, where OUT(1,:) is the location
% at button press and OUT(END,:)... |
github | NeuBtracker/acquisition-master | fmeasure.m | .m | acquisition-master/01_Acquisition/Functions/Autofocus/fmeasure.m | 9,062 | utf_8 | ddd88bb978206f149aff0960807c38e2 | function FM = fmeasure(Image, Measure, ROI)
%This function measures the relative degree of focus of
%an image. It may be invoked as:
%
% FM = fmeasure(IMAGE, METHOD, ROI)
%
%Where
% IMAGE, is a grayscale image and FM is the computed
% focus value.
% METHOD, is the focus measure algorithm as a string... |
github | NeuBtracker/acquisition-master | fmeasure.m | .m | acquisition-master/01_Acquisition/Functions/Autofocus/fmeasure/fmeasure.m | 9,062 | utf_8 | ddd88bb978206f149aff0960807c38e2 | function FM = fmeasure(Image, Measure, ROI)
%This function measures the relative degree of focus of
%an image. It may be invoked as:
%
% FM = fmeasure(IMAGE, METHOD, ROI)
%
%Where
% IMAGE, is a grayscale image and FM is the computed
% focus value.
% METHOD, is the focus measure algorithm as a string... |
github | NeuBtracker/acquisition-master | F_IMdif2Coord_131.m | .m | acquisition-master/01_Acquisition/Functions/OriginalTrackingObsolete/F_IMdif2Coord_131.m | 3,598 | utf_8 | 4ba37d580fe7c5d3367af2e6c07e8efd | function [status, Coord, IMdif] = F_IMdif2Coord_131(IM, IM_o,ill_type, pl, blur_fact, nsig_bin, npxl_min, npxl_max)
% This function computes the difference between the coordinates of the
% fish in the two input images.
% INPUT: IM first input image
% IM_o second input image (presuma... |
github | NeuBtracker/acquisition-master | F_IMdif2Coord_132.m | .m | acquisition-master/01_Acquisition/Functions/OriginalTrackingObsolete/F_IMdif2Coord_132.m | 3,625 | utf_8 | ed66f02f1c3f1a080a230a504fcad6dc | function [status, Coord, IMdif] = F_IMdif2Coord_131(IM, IM_o,ill_type, pl, blur_fact, nsig_bin, npxl_min, npxl_max)
% This function computes the difference between the coordinates of the
% fish in the two input images.
% INPUT: IM first input image
% IM_o second input image (presuma... |
github | NeuBtracker/acquisition-master | Acq_Controll.m | .m | acquisition-master/01_Acquisition/GUI_version/Acq_Controll.m | 45,165 | utf_8 | eb7104ec7b6f60c56a4426acc59d9afc | function varargout = Acq_Controll(varargin)
% ACQ_CONTROLL MATLAB code for Acq_Controll.fig
% ACQ_CONTROLL, by itself, creates a new ACQ_CONTROLL or raises the existing
% singleton*.
%
% H = ACQ_CONTROLL returns the handle to a new ACQ_CONTROLL or the handle to
% the existing singleton*.
%
% AC... |
github | NeuBtracker/acquisition-master | Image_Previewing_GUI.m | .m | acquisition-master/01_Acquisition/GUI_version/Image_Previewing_GUI.m | 16,485 | utf_8 | 937bf2a71a48d2e5b6a58fe2b4da4b86 | function varargout = Image_Previewing_GUI(varargin)
% IMAGE_PREVIEWING_GUI MATLAB code for Image_Previewing_GUI.fig
% IMAGE_PREVIEWING_GUI, by itself, creates a new IMAGE_PREVIEWING_GUI or raises the existing
% singleton*.
%
% H = IMAGE_PREVIEWING_GUI returns the handle to a new IMAGE_PREVIEWING_GUI or t... |
github | xiashang0624/electrochemical_simulation-master | CDI_2D_Demo.m | .m | electrochemical_simulation-master/CDI_2D_Demo.m | 29,273 | utf_8 | ca5de5c008d8a778fe031ee65181fc9a | %% Capacitive deionization 2D simulation
%
% Developed by: Xia Shang
% Advisors: Kyle Smith, Roland Cusick
% Copyright: Xia Shang, Roland Cusick, Kyle Smith
% University of Illinois at Urbana-Champaign
% All rights reserved.
%
% Funded by: US National Science Foundation Award No. 1605290 entitiled
% "SusChEM: ... |
github | pablogmendez/gerris-master | panial.m | .m | gerris-master/Gerris/Gerris-ControllerModule/MLC/panial.m | 1,728 | utf_8 | 4967b798a0104c1e6cfe6d432e7b9934 | function panial
timelimit=0.007;
fprintf('Panial is on\n')
while 1
try
fprintf('Panial is')
if ~exist('cylinder_control/log.txt','file')
pause(1)
fprintf(' waiting for log to appear\n');
pause(1)
continue
end
system('tail -n 20 cylinder_control/log.txt > last.txt');
... |
github | pablogmendez/gerris-master | MLC_Gerris_cylinder_evaluator.m | .m | gerris-master/Gerris/Gerris-ControllerModule/MLC/MLC_Gerris_cylinder_evaluator.m | 2,926 | utf_8 | 438365e79e29569e66cc7ca051fc0d11 | function J=MLC_Gerris_cylinder_evaluator(idv,parameters,i,fig)
%% Variable grocery
curdir=pwd;
if nargin<3
i=[];
end
%% Quick drop of innapropriate control laws
if ~MLC_Gerris_cylinder_pre_evaluator(idv,parameters)
J=parameters.badvalue;
return
end
%% Se... |
github | pablogmendez/gerris-master | panial.m | .m | gerris-master/Gerris/Gerris-ControllerModule/MLC/MLC_cyl.old/panial.m | 1,296 | utf_8 | 22b6149da0d41efbae44deaf503a0350 | function [ok]=panial(MLC_parameters,varargin)
% PANIAL function that checks that the simulation did not shit itself and
% does what is needed if it did.
check = 1; % the programm waits until either the sim is finished, either it is
% frozen
old_time=-1;
while check
new_time=get_time(MLC_paramet... |
github | mehta-lab/Instantaneous-PolScope-master | interactivePolHist.m | .m | Instantaneous-PolScope-master/MehtaetalPNAS2016/interactivePolHist.m | 5,399 | utf_8 | 5ed4134946be75ed9f2d1f06e7f1f4e0 | function hROI=interactivePolHist(I0,I45,I90,I135,aniso,orient,avg,varargin)
% hROI=interactivePolHist(I0,I45,I90,I135,aniso,orient,avg)
% Allows interactive exploration of orientation histogram.
% hROI is the handle of ROI used to display the orientation histogram if
% 'analyzeROI' option is set true. Otherwise hROI is... |
github | mehta-lab/Instantaneous-PolScope-master | tiffread.m | .m | Instantaneous-PolScope-master/MehtaetalPNAS2016/tiffread.m | 23,985 | utf_8 | ad13854ba30cf671ab07bfc42e12bd32 | function stack = tiffread(filename, indices)
% tiffread, version 2.7 January 28, 2009
%
% stack = tiffread;
% stack = tiffread(filename);
% stack = tiffread(filename, indices);
%
% Reads 8,16,32 bits uncompressed grayscale and (some) color tiff files,
% as well as stacks or multiple tiff images, for example those prod... |
github | mehta-lab/Instantaneous-PolScope-master | parsepropval.m | .m | Instantaneous-PolScope-master/MehtaetalPNAS2016/parsepropval.m | 2,622 | utf_8 | 71cde36da325e23d6e64232c3598e0f4 | function prop = parsepropval(prop,varargin)
%parsepropval: Parse property/value pairs and return a structure.
% Manages property/value pairs like MathWorks Handle Graphics functions.
% This means that in addition to passing in Property name strings and
% Values, you can also include structures with appropriately nam... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.