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 |
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github | EnricoGiordano1992/LMI-Matlab-master | mtimes.m | .m | LMI-Matlab-master/yalmip/@sdpvar/mtimes.m | 30,871 | utf_8 | a02bcbc2cab008ade86fac60c7baef00 | function Z = mtimes(X,Y)
%MTIMES (overloaded)
% Cannot use isa here since blkvar is marked as sdpvar
X_class = class(X);
Y_class = class(Y);
X_is_spdvar = strcmp(X_class,'sdpvar');
Y_is_spdvar = strcmp(Y_class,'sdpvar');
% Convert block objects
if ~X_is_spdvar
if isa(X,'blkvar')
X = sdpvar(X);
X_i... |
github | EnricoGiordano1992/LMI-Matlab-master | cosh.m | .m | LMI-Matlab-master/yalmip/@sdpvar/cosh.m | 867 | utf_8 | c13a2d8c9f1f455224d9d5fc5a232af4 | function varargout = cosh(varargin)
%COSH (overloaded)
switch class(varargin{1})
case 'double'
error('Overloaded SDPVAR/COSH CALLED WITH DOUBLE. Report error')
case 'sdpvar'
varargout{1} = InstantiateElementWise(mfilename,varargin{:});
case 'char'
operator = struct('convexity','... |
github | EnricoGiordano1992/LMI-Matlab-master | det.m | .m | LMI-Matlab-master/yalmip/@sdpvar/det.m | 2,020 | utf_8 | c62ed564f88ca694ab3f483f7c7664ef | function varargout = det(varargin)
%DET (overloaded)
%
% t = DET(X)
switch class(varargin{1})
case 'sdpvar' % Overloaded operator for SDPVAR objects. Pass on args and save them.
X = varargin{1};
[n,m] = size(X);
if n~=m
error('Matrix must be square.')
en... |
github | EnricoGiordano1992/LMI-Matlab-master | value.m | .m | LMI-Matlab-master/yalmip/@sdpvar/value.m | 11,462 | utf_8 | ec8d9598bf4fccc447aff873eb963275 | function [sys,values] = value(X,allextended,allevaluators,allStruct,mt,variabletype,solution,values)
%VALUE Returns current numerical value of an SDPVAR object
%
% After solving an optimization problem, we can extract the current
% solution by applying VALUE on a variable of interest
%
% xvalue = value(x)
%
% If you h... |
github | EnricoGiordano1992/LMI-Matlab-master | cos.m | .m | LMI-Matlab-master/yalmip/@sdpvar/cos.m | 1,708 | utf_8 | 8f6b7d263e781c2959afd94381b1a131 | function varargout = cos(varargin)
%COS (overloaded)
switch class(varargin{1})
case 'double'
error('Overloaded SDPVAR/COS CALLED WITH DOUBLE. Report error')
case 'sdpvar'
varargout{1} = InstantiateElementWiseUnitary(mfilename,varargin{:});
case 'char'
operator = ... |
github | EnricoGiordano1992/LMI-Matlab-master | erfcx.m | .m | LMI-Matlab-master/yalmip/@sdpvar/erfcx.m | 674 | utf_8 | 1f0ede69a805c09aa20815d4ee2aa46b | function varargout = erfcx(varargin)
%ERFCX (overloaded)
switch class(varargin{1})
case 'double'
error('Overloaded SDPVAR/ERFCX CALLED WITH DOUBLE. Report error')
case 'sdpvar'
varargout{1} = InstantiateElementWise(mfilename,varargin{:});
case 'char'
operator = struct('convexity... |
github | EnricoGiordano1992/LMI-Matlab-master | sin.m | .m | LMI-Matlab-master/yalmip/@sdpvar/sin.m | 1,835 | utf_8 | 70d381366a829f9cd6b32d6075ea3ce2 | function varargout = sin(varargin)
%SIN (overloaded)
switch class(varargin{1})
case 'double'
error('Overloaded SDPVAR/SIN CALLED WITH DOUBLE. Report error')
case 'sdpvar'
varargout{1} = InstantiateElementWiseUnitary(mfilename,varargin{:});
%varargout{1} = InstantiateElementWise(mfilen... |
github | EnricoGiordano1992/LMI-Matlab-master | min.m | .m | LMI-Matlab-master/yalmip/@sdpvar/min.m | 4,676 | utf_8 | 3e49b9ee5fa32b5c1891a289f51da69d | function y=min(varargin)
%MIN (overloaded)
%
% t = MIN(X)
% t = MIN(X,Y)
% t = MIN(X,[],DIM)
%
% Creates an internal structure relating the variable t with concave
% operator MIN(X).
%
% The variable t is primarily meant to be used in convexity preserving
% operations such as t>=0, maximize t etc.
%
% If the variable i... |
github | EnricoGiordano1992/LMI-Matlab-master | pow2.m | .m | LMI-Matlab-master/yalmip/@sdpvar/pow2.m | 975 | utf_8 | 39f0baec231cb6e3e01ff432c5ec3237 | function varargout = pow2(varargin)
%POW2 (overloaded)
switch class(varargin{1})
case 'double'
error('Overloaded SDPVAR/POW2 CALLED WITH DOUBLE. Report error')
case 'sdpvar'
varargout{1} = InstantiateElementWise(mfilename,varargin{:});
case 'char'
operator = struct('conv... |
github | EnricoGiordano1992/LMI-Matlab-master | acosh.m | .m | LMI-Matlab-master/yalmip/@sdpvar/acosh.m | 716 | utf_8 | b0d93d0f408c0c67ffbc9791f4e52e1b | function varargout = acosh(varargin)
%ACOSH (overloaded)
switch class(varargin{1})
case 'sdpvar'
varargout{1} = InstantiateElementWise(mfilename,varargin{:});
case 'char'
operator = struct('convexity','none','monotonicity','none','definiteness','positive','model','callback');
operato... |
github | EnricoGiordano1992/LMI-Matlab-master | diff.m | .m | LMI-Matlab-master/yalmip/@sdpvar/diff.m | 2,025 | utf_8 | 2a39574fd1ff1b4b733230eeead686ba | function Y=diff(varargin)
%DIFF (overloaded)
X = varargin{1};
n = X.dim(1);
m = X.dim(2);
switch nargin
case 1
if n == 1
% Default is diff along first dimension, unless we only have
% one row. This happens to be the case in which the core function operates
Y... |
github | EnricoGiordano1992/LMI-Matlab-master | erfinv.m | .m | LMI-Matlab-master/yalmip/@sdpvar/erfinv.m | 803 | utf_8 | 810f64595d183f3910c960d722dcc702 | function varargout = erfinv(varargin)
%ERFINV (overloaded)
switch class(varargin{1})
case 'double'
error('Overloaded SDPVAR/ERFINV CALLED WITH DOUBLE. Report error')
case 'sdpvar'
varargout{1} = InstantiateElementWise(mfilename,varargin{:});
case 'char'
X = varargin{3};
... |
github | EnricoGiordano1992/LMI-Matlab-master | callkypd.m | .m | LMI-Matlab-master/yalmip/solvers/callkypd.m | 4,451 | utf_8 | 62267054f26da5f4af3119e87fecc8f8 | function diagnostic = callkypd(F,h,options)
%F = constraint2kyp(F);
kyps = is(F,'kyp');
if all(kyps)
kypConstraints = F;
otherConstraints = lmi;
else
kypConstraints = F(find(kyps));
otherConstraints = F(find(~kyps));
end
% Trivial case
if length(kypConstraints) == 0
if ~isempty(options)
... |
github | EnricoGiordano1992/LMI-Matlab-master | callscs.m | .m | LMI-Matlab-master/yalmip/solvers/callscs.m | 5,222 | utf_8 | f7780f5b754144a484627c8bbc36f191 | function output = callscs(model)
% Retrieve needed data
options = model.options;
F_struc = model.F_struc;
c = model.c;
K = model.K;
ub = model.ub;
lb = model.lb;
% *********************************************
% Bounded variables converted to constraints
% N.B. Only happens when caller is BNB
% ... |
github | EnricoGiordano1992/LMI-Matlab-master | callsdpnal.m | .m | LMI-Matlab-master/yalmip/solvers/callsdpnal.m | 3,766 | utf_8 | 301376600dc2200cedc573f6f81ce14e | function output = callsdpnal(interfacedata)
% Retrieve needed data
options = interfacedata.options;
F_struc = interfacedata.F_struc;
c = interfacedata.c;
K = interfacedata.K;
x0 = interfacedata.x0;
ub = interfacedata.ub;
lb = interfacedata.lb;
% Bounded variables converted to constraints
if... |
github | EnricoGiordano1992/LMI-Matlab-master | callquadprogbb.m | .m | LMI-Matlab-master/yalmip/solvers/callquadprogbb.m | 2,112 | utf_8 | 9c075a2736463b4b85a7c58e6fa44997 | function output = callquadprogbb(interfacedata)
options = interfacedata.options;
model = yalmip2quadprog(interfacedata);
if options.savedebug
save debugfile model
end
if options.showprogress;showprogress(['Calling ' interfacedata.solver.tag],options.showprogress);end
solvertime = tic;
solveroutput = callsolver(m... |
github | EnricoGiordano1992/LMI-Matlab-master | callmpt3.m | .m | LMI-Matlab-master/yalmip/solvers/callmpt3.m | 3,688 | utf_8 | ad7eb3b682d81797392f46cd553fad13 | function output = callmpt3(interfacedata)
% Speeds up solving LPs in mpmilp
global MPTOPTIONS
if ~isstruct(MPTOPTIONS)
mpt_error
end
% Convert
Matrices = yalmip2mpt(interfacedata);
% Get some MPT options
options = interfacedata.options;
options.mpt.lpsolver = MPTOPTIONS.lpsolver;
options.mpt.milpsolver = MPTOPTI... |
github | EnricoGiordano1992/LMI-Matlab-master | callsedumi.m | .m | LMI-Matlab-master/yalmip/solvers/callsedumi.m | 4,208 | utf_8 | 06ea0482ba007c5e4e0f579308a9def3 | function output = callsedumi(model)
% Retrieve needed data
options = model.options;
F_struc = model.F_struc;
c = model.c;
K = model.K;
ub = model.ub;
lb = model.lb;
% Create the parameter structure
pars = options.sedumi;
pars.fid = double(options.verbose);
% ************************************... |
github | EnricoGiordano1992/LMI-Matlab-master | callscipnl.m | .m | LMI-Matlab-master/yalmip/solvers/callscipnl.m | 6,643 | utf_8 | eb1a63a10553a041f4b536ce146a8c68 | function output = callscipnl(model)
% This sets up everything and more. Can be simplified significantly since
% baron handles its own computational tree etc
model = yalmip2nonlinearsolver(model);
% [Anonlinear*f(x) <= b;Anonlinear*f(x) == b]
cu = full([model.bnonlinineq;model.bnonlineq]);
cl = full([repmat(-inf,lengt... |
github | EnricoGiordano1992/LMI-Matlab-master | callsdplr.m | .m | LMI-Matlab-master/yalmip/solvers/callsdplr.m | 5,257 | utf_8 | e6972916ac1a58d754eb41383f04cc74 | function output = callsdplr(interfacedata)
% Retrieve needed data
options = interfacedata.options;
F_struc = interfacedata.F_struc;
c = interfacedata.c;
K = interfacedata.K;
ub = interfacedata.ub;
lb = interfacedata.lb;
lowrankdetails = interfacedata.lowrankdetails;
% Create the parameter struc... |
github | EnricoGiordano1992/LMI-Matlab-master | callmpt.m | .m | LMI-Matlab-master/yalmip/solvers/callmpt.m | 4,404 | utf_8 | d55f6f404d70471bb0592d9456465ffd | function output = callmpt(interfacedata)
% This file is kept for MPT2 compatability
% Speeds up solving LPs in mpmilp
global mptOptions
if ~isstruct(mptOptions)
mpt_error
end
% Convert
% interfacedata = pwa_linearize(interfacedata);
Matrices = yalmip2mpt(interfacedata);
% Get some MPT options
options = interfac... |
github | EnricoGiordano1992/LMI-Matlab-master | callpenbmim.m | .m | LMI-Matlab-master/yalmip/solvers/callpenbmim.m | 8,291 | utf_8 | 55f1ccca944ad4f85054c6f277ec359f | function output = callpenbmi(interfacedata);
if any(interfacedata.variabletype > 2)
% Polynomial problem, YALMIP has to bilienarize
interfacedata.high_monom_model=[];
output = callpenbmi_with_bilinearization(interfacedata);
else
% Old standard code
output = callpenbmi_without_bilinearization(interf... |
github | EnricoGiordano1992/LMI-Matlab-master | yalmip2xpress.m | .m | LMI-Matlab-master/yalmip/solvers/yalmip2xpress.m | 3,380 | utf_8 | 939c179fa34ef53a916b36c325cff8ab | function model = yalmip2xpress(interfacedata)
options = interfacedata.options;
F_struc = interfacedata.F_struc;
H = interfacedata.Q;
c = interfacedata.c;
K = interfacedata.K;
x0 = interfacedata.x0;
integer_variables = interfacedata.integer_variables;
binary_variables = interfacedata.binary_varia... |
github | EnricoGiordano1992/LMI-Matlab-master | calllogdetppa.m | .m | LMI-Matlab-master/yalmip/solvers/calllogdetppa.m | 4,738 | utf_8 | 5e811439e286c2bc2cfa708872c26cb3 | function output = calllogdetppa(interfacedata)
% Retrieve needed data
options = interfacedata.options;
F_struc = interfacedata.F_struc;
c = interfacedata.c;
K = interfacedata.K;
x0 = interfacedata.x0;
ub = interfacedata.ub;
lb = interfacedata.lb;
% Bounded variables converted to constraints... |
github | EnricoGiordano1992/LMI-Matlab-master | callbaron.m | .m | LMI-Matlab-master/yalmip/solvers/callbaron.m | 5,132 | utf_8 | f37bb6f75fdde9f4e8b566d0e92d6c76 | function output = callbaron(model)
% This sets up everything and more. Can be simplified significantly since
% baron handles its own computational tree etc
model = yalmip2nonlinearsolver(model);
% [Anonlinear*f(x) <= b;Anonlinear*f(x) == b]
cu = full([model.bnonlinineq;model.bnonlineq]);
cl = full([repmat(-inf,length... |
github | EnricoGiordano1992/LMI-Matlab-master | mpcvx.m | .m | LMI-Matlab-master/yalmip/solvers/mpcvx.m | 11,816 | utf_8 | 9c49d6f2332bc7a0b4d072b435a732a1 | function output = mpcvx(p)
%MPCVX Approximate multi-parametric programming
%
% MPCVX is never called by the user directly, but is called by
% YALMIP from SOLVESDP, by choosing the solver tag 'mpcvx' in sdpsettings
%
% The behaviour of MPCVX can be altered using the fields
% in the field 'mpcvx' in SDPSETTINGS
... |
github | EnricoGiordano1992/LMI-Matlab-master | callvsdp.m | .m | LMI-Matlab-master/yalmip/solvers/callvsdp.m | 4,295 | utf_8 | 3db429710eaea8f4494a100384c6f9e9 | function output = callvsdp(interfacedata)
% Retrieve needed data
options = interfacedata.options;
F_struc = interfacedata.F_struc;
c = interfacedata.c;
K = interfacedata.K;
x0 = interfacedata.x0;
ub = interfacedata.ub;
lb = interfacedata.lb;
% Bounded variables converted to constraints
if ~... |
github | EnricoGiordano1992/LMI-Matlab-master | callsparsecolo.m | .m | LMI-Matlab-master/yalmip/solvers/callsparsecolo.m | 4,501 | utf_8 | 376b38f73357ddec826c7ac3db7e6704 | function output = callsparsecolo(interfacedata)
% Retrieve needed data
options = interfacedata.options;
F_struc = interfacedata.F_struc;
c = interfacedata.c;
K = interfacedata.K;
x0 = interfacedata.x0;
ub = interfacedata.ub;
lb = interfacedata.lb;
% Bounded variables converted to constraint... |
github | EnricoGiordano1992/LMI-Matlab-master | yalmip2mosek.m | .m | LMI-Matlab-master/yalmip/solvers/yalmip2mosek.m | 2,991 | utf_8 | b793cd2a38c2e96dcf47933b6a79a456 | function prob = yalmip2mosek(interfacedata);
% Retrieve needed data
options = interfacedata.options;
F_struc = interfacedata.F_struc;
c = interfacedata.c;
Q = interfacedata.Q;
K = interfacedata.K;
x0 = interfacedata.x0;
integer_variables = interfacedata.integer_variables;
binary_variables = inte... |
github | EnricoGiordano1992/LMI-Matlab-master | callsdpt34.m | .m | LMI-Matlab-master/yalmip/solvers/callsdpt34.m | 8,427 | utf_8 | b646fbbeaba6c0cdbbbbf847b0314fdd | function output = callsdpt34(interfacedata)
% Retrieve needed data
options = interfacedata.options;
F_struc = interfacedata.F_struc;
c = interfacedata.c;
K = interfacedata.K;
x0 = interfacedata.x0;
ub = interfacedata.ub;
lb = interfacedata.lb;
% Bounded variables converted to constraints
if... |
github | EnricoGiordano1992/LMI-Matlab-master | callquadprog.m | .m | LMI-Matlab-master/yalmip/solvers/callquadprog.m | 3,531 | utf_8 | 0dfff0ae70dc7afa1992f59380fc39c4 | function output = callquadprog(interfacedata)
options = interfacedata.options;
model = yalmip2quadprog(interfacedata);
if options.savedebug
save debugfile model
end
if options.showprogress;showprogress(['Calling ' interfacedata.solver.tag],options.showprogress);end
solvertime = tic;
solveroutput = callsolver(mod... |
github | EnricoGiordano1992/LMI-Matlab-master | callmosek.m | .m | LMI-Matlab-master/yalmip/solvers/callmosek.m | 14,228 | utf_8 | 9584d63092bb2893e014683ac5a5eddb | function output = callmosek(model)
% Retrieve needed data
options = model.options;
F_struc = model.F_struc;
c = model.c;
Q = model.Q;
K = model.K;
x0 = model.x0;
integer_variables = model.integer_variables;
binary_variables = model.binary_variables;
extended_variables = model.extended_variables;... |
github | EnricoGiordano1992/LMI-Matlab-master | calllsqlin.m | .m | LMI-Matlab-master/yalmip/solvers/calllsqlin.m | 2,847 | utf_8 | 170763f45e1ac6f29996fc8e82535665 | function output = calllsqlin(interfacedata)
K = interfacedata.K;
c = interfacedata.c;
CA = interfacedata.F_struc;
options = interfacedata.options;
% To begin with, with try to figure out if this is a simple non-negative
% least squares in disguise
if length(K.q)~=1
output = error_output;
return
end
% total n... |
github | EnricoGiordano1992/LMI-Matlab-master | callbonmin.m | .m | LMI-Matlab-master/yalmip/solvers/callbonmin.m | 4,532 | utf_8 | 3e4061b2327fce758da882c69816d9fe | function output = callbonmin(model)
model = yalmip2nonlinearsolver(model);
options = [];
try
options.bonmin = optiRemoveDefaults(model.options.bonmin,bonminset());
catch
options.bonmin = model.options.bonmin;
end
options.ipopt = model.options.ipopt;
options.display = model.options.verbose;
if ~model.derivat... |
github | EnricoGiordano1992/LMI-Matlab-master | calllsqnonneg.m | .m | LMI-Matlab-master/yalmip/solvers/calllsqnonneg.m | 2,939 | utf_8 | f42645b72b16302e04543c90729d2f65 | function output = calllsqnonneg(interfacedata)
K = interfacedata.K;
c = interfacedata.c;
CA = interfacedata.F_struc;
options = interfacedata.options;
% To begin with, with try to figure out if this is a simple non-negative
% least squares in disguise
if length(K.q)~=1
output = error_output;
return
end
% tota... |
github | EnricoGiordano1992/LMI-Matlab-master | callqpoases.m | .m | LMI-Matlab-master/yalmip/solvers/callqpoases.m | 1,710 | utf_8 | 42faef3cd8048ea6e498b94d4de8bb35 | function output = callqpoases(interfacedata)
options = interfacedata.options;
model = yalmip2quadprog(interfacedata);
if options.savedebug
save debugfile model
end
if options.showprogress;showprogress(['Calling ' interfacedata.solver.tag],options.showprogress);end
solvertime = tic;
solution = callsolver(model,op... |
github | EnricoGiordano1992/LMI-Matlab-master | sdpfun.m | .m | LMI-Matlab-master/yalmip/operators/sdpfun.m | 5,014 | utf_8 | 0c13f9a11b9b9d4d5504b55e0d04cc52 | function varargout = sdpfun(varargin)
%SDPFUN Gateway to general (elementwise) functions on SDPVAR variables (overloaded)
if ~isa(varargin{1},'char') && any(strcmp(cellfun(@class,varargin,'UniformOutput',0),'sdpvar'))
fun_handles = zeros(nargin,1);
for i = 1:nargin
if isstr(varargin{end}) && isequa... |
github | EnricoGiordano1992/LMI-Matlab-master | absexp.m | .m | LMI-Matlab-master/yalmip/operators/absexp.m | 972 | utf_8 | 1c9eb8d6258431984094357b18a76a4a | function varargout = absexp(varargin)
switch class(varargin{1})
case 'double'
varargout{1} = abs(exp(varargin{1}) - 1);
case 'sdpvar'
varargout{1} = InstantiateBuiltInScalar(mfilename,varargin{:});
case 'char'
t = varargin{2};
X = varargin{3};
F = SetupEvaluation... |
github | EnricoGiordano1992/LMI-Matlab-master | entropy.m | .m | LMI-Matlab-master/yalmip/operators/entropy.m | 2,257 | utf_8 | 311340b986854921e33a7272e402651d | function varargout = entropy(varargin)
%ENTROPY
%
% y = ENTROPY(x)
%
% Computes/declares concave entropy -sum(x.*log(x))
%
% Implemented as evalutation based nonlinear operator. Hence, the concavity
% of this function is exploited to perform convexity analysis and rigorous
% modelling.
%
% See also CROSSENTROPY, KULLBA... |
github | EnricoGiordano1992/LMI-Matlab-master | xexpintinv.m | .m | LMI-Matlab-master/yalmip/operators/xexpintinv.m | 816 | utf_8 | bd8857363df0521d4ca537237872b1f5 | function varargout = xexpintinv(varargin)
%XEXPINTINV EXPINT(1/Z)/Z
switch class(varargin{1})
case 'double'
z = varargin{1};
varargout{1} = (1./z).*expint(1./z);
case 'sdpvar'
varargout{1} = InstantiateElementWise(mfilename,varargin{:});
case 'char'
varar... |
github | EnricoGiordano1992/LMI-Matlab-master | kullbackleibler.m | .m | LMI-Matlab-master/yalmip/operators/kullbackleibler.m | 1,913 | utf_8 | b60195db713567cbb6515e32703c04e8 | function varargout = kullbackleibler(varargin)
% KULLBACKLEIBLER
%
% y = KULLBACKLEIBLER(x,y)
%
% Computes/declares the convex Kullback-Leibler divergence sum(x.*log(x./y))
% Alternatively -sum(x.*log(y/x)), i.e., negated perspectives of log(y)
%
% See also ENTROPY, CROSSENTROPY
switch class(varargin{1})
case 'd... |
github | EnricoGiordano1992/LMI-Matlab-master | plog.m | .m | LMI-Matlab-master/yalmip/operators/plog.m | 3,011 | utf_8 | c2d6cca279a1861bf8dfe62cf593dc28 | function varargout = plog(varargin)
%PLOG
%
% y = PLOG(x)
%
% Computes concave perspective log, x(1)*log(x(2)/x(1)) on x>0
%
% Implemented as evalutation based nonlinear operator. Hence, the concavity
% of this function is exploited to perform convexity analysis and rigorous
% modelling.
switch class(varargin{1})
... |
github | EnricoGiordano1992/LMI-Matlab-master | crossentropy.m | .m | LMI-Matlab-master/yalmip/operators/crossentropy.m | 1,815 | utf_8 | dd20a786dd2a42dbd13576cf33b307b0 | function varargout = crossentropy(varargin)
% CROSSENTROPY
%
% y = CROSSENTROPY(x,y)
%
% Computes/declares cross entropy -sum(x.*log(y))
%
% See also ENTROPY, KULLBACKLEIBLER
switch class(varargin{1})
case 'double'
if nargin == 1
% YALMIP flattens internally to [x(:);y(:)]
z = ... |
github | EnricoGiordano1992/LMI-Matlab-master | max_internal.m | .m | LMI-Matlab-master/yalmip/operators/max_internal.m | 2,193 | utf_8 | 1e843d6b9fb476c9c4d77aa38e2f6171 | function varargout = max_internal(varargin)
switch class(varargin{1})
case 'double'
varargout{1} = max(varargin{:});
case 'char'
extstruct.var = varargin{2};
extstruct.arg = {varargin{3:end}};
[F,properties,arguments]=max_model([],varargin{1},[],extstruct);
varargout{1} ... |
github | EnricoGiordano1992/LMI-Matlab-master | expexpintinv.m | .m | LMI-Matlab-master/yalmip/operators/expexpintinv.m | 842 | utf_8 | 2befcb9c4f10aff4f339806f9a4828a5 | function varargout = expexpintinv(varargin)
%EXPINT (overloaded)
switch class(varargin{1})
case 'double'
z = varargin{1};
varargout{1} = exp(1./z).*expint(1./z);
case 'sdpvar'
varargout{1} = InstantiateElementWise(mfilename,varargin{:});
case 'char'
varar... |
github | EnricoGiordano1992/LMI-Matlab-master | logistic.m | .m | LMI-Matlab-master/yalmip/operators/logistic.m | 1,787 | utf_8 | 7db6dafadc94775db52ca52054d9f148 | function varargout = logistic(varargin)
% LOGISTIC Returns logistic function 1./(1+exp(-x))
%
% y = LOGISTIC(x)
%
% For a real vector x, LOGISTIC returns (1+exp(-x)).^-1
switch class(varargin{1})
case 'double'
x = varargin{1};
varargout{1} = 1./(1+exp(-x));
case 'sdpvar'
varargout{1}... |
github | EnricoGiordano1992/LMI-Matlab-master | slog.m | .m | LMI-Matlab-master/yalmip/operators/slog.m | 2,540 | utf_8 | 337f77232e578d581ec03cc60aaec8a1 | function varargout = slog(varargin)
%ENTROPY
%
% y = SLOG(x)
%
% Computes/declares concave shifted logarithm log(1+x)
%
% Implemented as evalutation based nonlinear operator. Hence, the concavity
% of this function is exploited to perform convexity analysis and rigorous
% modelling. Implemented in order to avoid singul... |
github | EnricoGiordano1992/LMI-Matlab-master | pexp.m | .m | LMI-Matlab-master/yalmip/operators/pexp.m | 1,269 | utf_8 | f334c5023924d04359e729add5b1df1d | function varargout = pexp(varargin)
%PEXP
%
% y = PEXP(x)
%
% Computes perspective exp, x(1)*exp(x(2)/x(1)) on x>0
%
% Implemented as evalutation based nonlinear operator. Hence, the convexity
% of this function is exploited to perform convexity analysis and rigorous
% modelling.
switch class(varargin{1})
cas... |
github | EnricoGiordano1992/LMI-Matlab-master | acos_internal.m | .m | LMI-Matlab-master/yalmip/operators/acos_internal.m | 879 | utf_8 | 1e3261fde0507f4701cb739fbb4be919 | function varargout = acos_internal(varargin)
%ACOS (overloaded)
switch class(varargin{1})
case 'double'
x = varargin{1};
y = acos(x);
y(x<-1) = pi;
y(x>1) = 0;
varargout{1} = y;
case 'char'
operator = struct('convexity','none','monotonicity','decreasi... |
github | EnricoGiordano1992/LMI-Matlab-master | implies_internal.m | .m | LMI-Matlab-master/yalmip/operators/implies_internal.m | 5,075 | utf_8 | e53fb93c351693a8fa46d1e56ec7162a | function varargout = implies_internal(varargin)
X = varargin{1};
Y = varargin{2};
if nargin == 2
zero_tolerance = 1e-5;
else
zero_tolerance = varargin{3};
end
% Normalize
if isa(X,'constraint')
X = lmi(X,[],[],1);
end
if isa(Y,'constraint')
Y = lmi(Y,[],[],1);
end
% % Special case something implies ... |
github | EnricoGiordano1992/LMI-Matlab-master | cpower.m | .m | LMI-Matlab-master/yalmip/operators/cpower.m | 2,821 | utf_8 | 7da9a3176a88314e9825801fed666649 | function varargout = cpower(varargin)
%CPOWER Power of SDPVAR variable with convexity knowledge
%
% CPOWER is recommended if your goal is to obtain
% a convex model, since the function CPOWER is implemented
% as a so called nonlinear operator. (For p/q ==2 you can
% however just as well use the overloaded power)
%
% t ... |
github | EnricoGiordano1992/LMI-Matlab-master | min_internal.m | .m | LMI-Matlab-master/yalmip/operators/min_internal.m | 1,846 | utf_8 | 000eee732402247d0ba89563345fdef3 | function varargout = min_internal(varargin)
switch class(varargin{1})
case 'double'
varargout{1} = min(varargin{:});
case 'char'
extstruct.var = varargin{2};
extstruct.arg = {varargin{3:end}};
[F,properties,arguments]=min_model([],varargin{1},[],extstruct);
varargout{1} ... |
github | EnricoGiordano1992/LMI-Matlab-master | power_internal1.m | .m | LMI-Matlab-master/yalmip/operators/power_internal1.m | 2,439 | utf_8 | 8fdb9032081e36221ef83491e13f0ea4 | function varargout = power_internal1(varargin)
%power_internal1
% Used for cases such as 2^x, and is treated as evaluation-based operators
switch class(varargin{1})
case 'double'
varargout{1} = varargin{2}.^varargin{1};
case 'sdpvar'
if isa(varargin{2},'sdpvar')
x = varargin{2};
... |
github | EnricoGiordano1992/LMI-Matlab-master | pnorm.m | .m | LMI-Matlab-master/yalmip/operators/pnorm.m | 3,905 | utf_8 | 6ea7721daa042f84ee690bffafed5bdf | function varargout = pnorm(varargin)
%PNORM P-Norm of SDPVAR variable with convexity knowledge
%
% PNORM is recommended if your goal is to obtain
% a convex model, since the function PNORM is implemented
% as a so called nonlinear operator. (For p/q ==1,2,inf you should use the
% overloaded norm)
%
% t = pnorm(x,p/q), ... |
github | EnricoGiordano1992/LMI-Matlab-master | logsumexp.m | .m | LMI-Matlab-master/yalmip/operators/logsumexp.m | 1,345 | utf_8 | 6b83cc0ac3e1ba57ad80dba22db81d41 | function varargout = logsumexp(varargin)
%LOGSUMEXP
%
% y = LOGSUMEXP(x)
%
% Computes/declares log of sum of exponentials log(sum(exp(x)))
%
% Implemented as evalutation based nonlinear operator. Hence, the convexity
% of this function is exploited to perform convexity analysis and rigorous
% modelling.
switch class(v... |
github | EnricoGiordano1992/LMI-Matlab-master | iff_internal.m | .m | LMI-Matlab-master/yalmip/operators/iff_internal.m | 3,921 | utf_8 | ae2278fd236008b34aa5db44cc6c4c72 | function varargout = iff_internal(varargin)
X = varargin{1};
Y = varargin{2};
if nargin == 2
zero_tolerance = 1e-5;
else
zero_tolerance = abs(varargin{3});
end
% Normalize data
if isa(Y,'constraint')
Y=lmi(Y,[],[],1);
end
if isa(X,'constraint')
X=lmi(X,[],[],1);
end
if isa(X,'lmi') & isa(Y,'sdpvar')
... |
github | EnricoGiordano1992/LMI-Matlab-master | veccomplex.m | .m | LMI-Matlab-master/sedumi/veccomplex.m | 2,989 | utf_8 | 591fbdddd59de32e4e5fd57b33ebc1d7 |
function z = veccomplex(x,cpx,K)
% z = veccomplex(x,cpx,K)
%
% ********** INTERNAL FUNCTION OF SEDUMI **********
% 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) 2001 Jos F. Sturm (up to 1.05R5)
% Dept. E... |
github | EnricoGiordano1992/LMI-Matlab-master | prelp.m | .m | LMI-Matlab-master/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 | EnricoGiordano1992/LMI-Matlab-master | feasreal.m | .m | LMI-Matlab-master/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 | EnricoGiordano1992/LMI-Matlab-master | sdpa2vec.m | .m | LMI-Matlab-master/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 | EnricoGiordano1992/LMI-Matlab-master | blk2vec.m | .m | LMI-Matlab-master/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 | EnricoGiordano1992/LMI-Matlab-master | writesdp.m | .m | LMI-Matlab-master/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 | EnricoGiordano1992/LMI-Matlab-master | frompack.m | .m | LMI-Matlab-master/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 | EnricoGiordano1992/LMI-Matlab-master | feascpx.m | .m | LMI-Matlab-master/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 | EnricoGiordano1992/LMI-Matlab-master | check_A.m | .m | LMI-Matlab-master/old/kypd/check_A.m | 2,103 | utf_8 | 5cc8ebd00480a27a665e76443dc0b1eb | function [check,matrix_info,T,c]=check_A(matrix_info,i)
% [check,matrix_info,T,c]=check_A(matrix_info,i)
%
% Checks if the system is controllable or stabilizable. If check=0
% the system is not stabilizable, if check=1 the system is stabilizable
% and if check=2 the system is controllable. If the system is only
% sta... |
github | EnricoGiordano1992/LMI-Matlab-master | derankcross_sort.m | .m | LMI-Matlab-master/old/kypd/derankcross_sort.m | 2,700 | utf_8 | 01855791da5b13f65d47985a4168c416 | function schurpar = derankcross_sort(Basis_matrices,len,block_end,n,nm)
rcum = ones(len+1,1);
alphas = [];
V = [];
%Make 'derankcross' for double-crosses
for i = 1:(nm-n)*block_end %number of dubble-crosses
Fi = Basis_matrices{i};
block_number = ceil(i/(2*(nm-n)));
non_zero = [2*block_number-1 2*block_... |
github | EnricoGiordano1992/LMI-Matlab-master | check_M.m | .m | LMI-Matlab-master/old/kypd/check_M.m | 5,029 | utf_8 | ec13945d05cc7097b4b9d8847b95e3d8 | function [check,matrix_info,Pbar,V]=check_M(matrix_info,solver,lowrank,tol)
% [check,matrix_info,Pbar,V]=check_M(matrix_info,tol)
%
% Eliminates the (1,1)-block of the M-matrices and checks if they
% are linearly independent with tolerance tol. If not the number of
% M-matrices are reduced if possible. The values of ... |
github | EnricoGiordano1992/LMI-Matlab-master | kypd.m | .m | LMI-Matlab-master/old/kypd/kypd.m | 1,440 | utf_8 | a3f4c76e79be3a54e3ac79de6ebc7e9b | function [u,P,x,Z,soltime,errorflag]=kypd(matrix_info,options)
if nargin<2
options = sdpsettings;
end
for i=1:matrix_info.N
matrix_info.M0{i}=matrix_info.M0{i}-options.kypd.tol*...
eye(size(matrix_info.M0{i}));
end;
[F,n_basis,ntot_basis,F0]=basis_matrices(matrix_info,...
... |
github | M-MohammadPour/EEGClassification-master | gradient_boosting_predict.m | .m | EEGClassification-master/Ensemble Learning/Boosting/gradient_boosting_predict.m | 1,914 | utf_8 | b9930d13df8f681614d35fdf971acae0 | % Practicum, Task #3, 'Compositions of algorithms'.
%
% FUNCTION:
% [prediction, err] = gradient_boosting_predict (model, X, y)
%
% DESCRIPTION:
% This function use the composition of algorithms, trained with gradient
% boosting method, for prediction.
%
% INPUT:
% X --- matrix of objects, N x K double matrix, N --- n... |
github | M-MohammadPour/EEGClassification-master | gradient_boosting_train.m | .m | EEGClassification-master/Ensemble Learning/Boosting/gradient_boosting_train.m | 4,308 | utf_8 | dfa172848dafd66a5e29d53c7f5ee8b2 | % Practicum, Task #3, 'Compositions of algorithms'.
%
% FUNCTION:
% [model] = gradient_boosting_train (X, y, num_iterations, base_algorithm, loss, ...
% param_name1, param_value1, param_name2, param_value2, ...
% param_name3, param_value3, param_name3, param_v... |
github | M-MohammadPour/EEGClassification-master | bagging_train.m | .m | EEGClassification-master/Ensemble Learning/Bagging/bagging_train.m | 2,754 | utf_8 | 7c6dc91c1019d451c23e46502a63ca75 | % Practicum, Task #3, 'Compositions of algorithms'.
%
% FUNCTION:
% [model] = bagging_train (X, y, num_iterations, base_algorithm, ...
% param_name1, param_value1, param_name2, param_value2)
%
% DESCRIPTION:
% This function train the composition of algorithms using bagging method.
%
% INPUT:
% X --- m... |
github | M-MohammadPour/EEGClassification-master | bagging_predict.m | .m | EEGClassification-master/Ensemble Learning/Bagging/bagging_predict.m | 1,913 | utf_8 | 3e79de35c9fe4d67be86750ef18c1aeb | % Practicum, Task #3, 'Compositions of algorithms'.
%
% FUNCTION:
% [prediction, err] = bagging_predict (model, X, y)
%
% DESCRIPTION:
% This function use the composition of algorithms, trained with bagging
% method, for prediction.
%
% INPUT:
% X --- matrix of objects, N x K double matrix, N --- number of objects,
%... |
github | M-MohammadPour/EEGClassification-master | predStump.m | .m | EEGClassification-master/Ensemble Learning/AdaBoost/predStump.m | 240 | utf_8 | 33aef76407d65dfa83f957307334842c | % Make prediction based on a decision stump
function label = predStump(X, stump)
N = size(X, 1);
x = X(:, stump.dim);
idx = logical(x >= stump.threshold); % N x 1
label = zeros(N, 1);
label(idx) = stump.more;
label(~idx) = stump.less;
end
|
github | M-MohammadPour/EEGClassification-master | buildOneDStump.m | .m | EEGClassification-master/Ensemble Learning/AdaBoost/buildOneDStump.m | 959 | utf_8 | 1b1d03178b568de1fb78b4f663a935fb | function stump = buildOneDStump(x, y, d, w)
[err_1, t_1] = searchThreshold(x, y, w, '>'); % > t_1 -> +1
[err_2, t_2] = searchThreshold(x, y, w, '<'); % < t_2 -> +1
stump = initStump(d);
if err_1 <= err_2
stump.threshold = t_1;
stump.error = err_1;
stump.less = -1;
stump.more = 1;
else
stump.threshol... |
github | claudia-lat/MAPest-master | save2pdf.m | .m | MAPest-master/external/save2pdf.m | 2,184 | utf_8 | 28056d1c6584d93469211eb0e05bdd6a | %SAVE2PDF Saves a figure as a properly cropped pdf
%
% save2pdf(pdfFileName,handle,dpi)
%
% - pdfFileName: Destination to write the pdf to.
% - handle: (optional) Handle of the figure to write to a pdf. If
% omitted, the current figure is used. Note that handles
% are typically... |
github | claudia-lat/MAPest-master | tightfig.m | .m | MAPest-master/external/tightfig.m | 5,419 | utf_8 | 1f6f3f5059ca866348b7edf7add7db02 |
% Copyright (c) 2011, Richard Crozier
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are
% met:
%
% * Redistributions of source code must retain the above copyright
% notice, this li... |
github | claudia-lat/MAPest-master | xml_write.m | .m | MAPest-master/external/xml_io_tools/xml_write.m | 18,772 | utf_8 | 4f952d9ca0351040dbffeb946e877bb0 | function DOMnode = xml_write(filename, tree, RootName, Pref)
%XML_WRITE Writes Matlab data structures to XML file
%
% DESCRIPTION
% xml_write( filename, tree) Converts Matlab data structure 'tree' containing
% cells, structs, numbers and strings to Document Object Model (DOM) node
% tree, then saves it to XML fi... |
github | claudia-lat/MAPest-master | xml_read.m | .m | MAPest-master/external/xml_io_tools/xml_read.m | 24,408 | utf_8 | 4931c3d512db336d744ec43f7fa0b368 | function [tree, RootName, DOMnode] = xml_read(xmlfile, Pref)
%XML_READ reads xml files and converts them into Matlab's struct tree.
%
% DESCRIPTION
% tree = xml_read(xmlfile) reads 'xmlfile' into data structure 'tree'
%
% tree = xml_read(xmlfile, Pref) reads 'xmlfile' into data structure 'tree'
% according to yo... |
github | claudia-lat/MAPest-master | valueFromName.m | .m | MAPest-master/src/valueFromName.m | 178 | utf_8 | 1a145d50bb61efd5b8dec292d6ed8dbf |
function [indx] = valueFromName(VectorOfString, stringName)
for i = 1: length(VectorOfString)
if strcmp(VectorOfString(i,:), stringName)
indx = i;
break;
end
end |
github | claudia-lat/MAPest-master | MAPcomputation.m | .m | MAPest-master/src/MAPcomputation.m | 7,376 | utf_8 | 6d2ded5dfa4746710cb6be336151d93f | function [mu_dgiveny, Sigma_dgiveny] = MAPcomputation(berdy, state, y, priors, varargin)
% MAPCOMPUTATION solves the inverse dynamics problem with a
% maximum-a-posteriori estimation by using the Newton-Euler algorithm and
% redundant sensor measurements as originally described in the paper
% [Whole-Body Human Inver... |
github | claudia-lat/MAPest-master | MAPcomputation_floating.m | .m | MAPest-master/src/MAPcomputation_floating.m | 7,460 | utf_8 | f999fc5c938e82433f426e49b548f996 | function [mu_dgiveny, Sigma_dgiveny] = MAPcomputation_floating(berdy, traversal, state, y, priors, baseAngVel, varargin)
% MAPCOMPUTATION_FLOATING solves the inverse dynamics problem with a
% maximum-a-posteriori estimation by using the Newton-Euler algorithm and
% redundant sensor measurements as originally describe... |
github | claudia-lat/MAPest-master | matchOrderToTraversal.m | .m | MAPest-master/src/matchOrderToTraversal.m | 647 | utf_8 | 404c4d927481166123de53813847d5b3 | function [ orderedData ] = matchOrderToTraversal( originalOrder, originalData, finalOrder )
%UNTITLED5 Summary of this function goes here
% Detailed explanation goes here
orderedData = zeros(size(originalData));
for i = 1 : length(finalOrder)
index = indexOfString (originalOrder, finalOrder{i});
orderedData... |
github | claudia-lat/MAPest-master | computeSuitSensorPosition.m | .m | MAPest-master/Experiments/23links_human/src/computeSuitSensorPosition.m | 1,530 | utf_8 | 2ac3f049adfb5cd57aa88a128a0911a9 | function [suit] = computeSuitSensorPosition(suit, len)
% COMPUTESUITSENSORPOSITION computes the position of the sensors in the
% suit wrt the link frame. It returns its value in a new field of the same
% suit stucture. Notation: G = global, S = sensor; L = link.
% NOTE: Check/modify manually len value. You can decide ... |
github | gkaguirrelab/WheresWaldo_EyeTracking-master | preprocessEyetracking_general.m | .m | WheresWaldo_EyeTracking-master/preprocessEyetracking_general.m | 4,810 | utf_8 | 2b71cc5847e1848c4fbf6c63bcb7d900 | function preprocessEyetracking_general(subFolder,saveME)
%%function for preprocessing LiveTrack eye tracking data.
%expects data to be in the form output from HID2struct
%subFolder is a string of the directory name containing t he data to be
%analyzed
%saveME is either 1 or 0, depending if you want to save outputs, inc... |
github | gkaguirrelab/WheresWaldo_EyeTracking-master | preprocessEyetracking.m | .m | WheresWaldo_EyeTracking-master/preprocessEyetracking.m | 5,710 | utf_8 | 263e05e962fbfa001b851da6499cf84f | function preprocessEyetracking(subFolder)
% Get Calibration Matrices
calibrationMatrices = dir([subFolder '/*cal*.mat']);
calNames = cat(2,{calibrationMatrices(:).name});
% Get targets
targets = dir([subFolder '/*dat*.mat']);
targetNames = cat(2,{targets(:).name});
%... |
github | gkaguirrelab/WheresWaldo_EyeTracking-master | help_fit_match.m | .m | WheresWaldo_EyeTracking-master/for_videos/Functions/help_fit_match.m | 1,085 | utf_8 | c6bde26f487492b8e93ecb3be73dfbe6 | function [distance path transform matched] = help_fit_match(oldX,oldY,pupil_X,pupil_Y,frameInds,isBlink,shift)
shift = round(shift);
badInds = find(frameInds==1,1,'first')-1;
frameInds = frameInds+shift;
oldX(isBlink) = nan;
oldY(isBlink) = nan;
oldX(1:badInds) = nan;
oldY(1:badI... |
github | zhouli2025/some-gadgets-scripts-master | detectMSERFeatures_zx.m | .m | some-gadgets-scripts-master/matlab_tools/mser/detectMSERFeatures_zx.m | 11,886 | utf_8 | 6f3850f8d1f0fd28e22c677002bc4bdc | function Regions=detectMSERFeatures_zx(I, varargin)
%detectMSERFeatures Finds MSER features.
% regions = detectMSERFeatures(I) returns an MSERRegions object, regions,
% containing region pixel lists and other information about MSER features
% detected in a 2-D grayscale image I. detectMSERFeatures uses Maximally
... |
github | clatfd/ThresSeg3d-master | sliderimg.m | .m | ThresSeg3d-master/sliderimg.m | 3,314 | utf_8 | 46e46c45854a6912b4e961e205cdc6da | function varargout=sliderimg(img)
S.SystemFrameHandle=figure;
clf reset
set(gcf,'name','img','numbertitle','off',...
'unit','normalized','position',[0.2,0.1,0.5,0.5]);
% menu_file=uimenu(gcf,'Label','File(&F)');
% menu_open_image=uimenu(menu_file,'Label','Open Images(&O)')... |
github | mitschabaude/nanopores-master | CloneFig.m | .m | nanopores-master/scripts/finfet/collocation_methods/CloneFig.m | 667 | utf_8 | cca00ad847ab499d1396a59a594a07df | function CloneFig(inFigNum,OutFigNum)
% this program copies a figure to another figure
% example: CloneFig(1,4) would copy Fig. 1 to Fig. 4
% Matt Fetterman, 2009
% pretty much taken from Matlab Technical solutions:
% http://www.mathworks.com/support/solutions/en/data/1-1UTBOL/?solution=1-1UTBOL
hf1=figure(inFigN... |
github | matheusmlopess-zz/Fuzzy-Logic-power-transmission-analysis-master | ktropf_solver.m | .m | Fuzzy-Logic-power-transmission-analysis-master/matpower5.1/ktropf_solver.m | 11,710 | utf_8 | 5cd97b9ba5b0916c199be22be6a70a8f | function [results, success, raw] = ktropf_solver(om, mpopt)
%KTROPF_SOLVER Solves AC optimal power flow using KNITRO.
%
% [RESULTS, SUCCESS, RAW] = KTROPF_SOLVER(OM, MPOPT)
%
% Inputs are an OPF model object and a MATPOWER options struct.
%
% Outputs are a RESULTS struct, SUCCESS flag and RAW output struct.
%
% ... |
github | matheusmlopess-zz/Fuzzy-Logic-power-transmission-analysis-master | modcost.m | .m | Fuzzy-Logic-power-transmission-analysis-master/matpower5.1/modcost.m | 4,148 | utf_8 | 7ad37d349aef8613d44ca72096179496 | function gencost = modcost(gencost, alpha, modtype)
%MODCOST Modifies generator costs by shifting or scaling (F or X).
% NEWGENCOST = MODCOST(GENCOST, ALPHA)
% NEWGENCOST = MODCOST(GENCOST, ALPHA, MODTYPE)
%
% For each generator cost F(X) (for real or reactive power) in
% GENCOST, this function modifies the co... |
github | matheusmlopess-zz/Fuzzy-Logic-power-transmission-analysis-master | ipoptopf_solver.m | .m | Fuzzy-Logic-power-transmission-analysis-master/matpower5.1/ipoptopf_solver.m | 11,041 | utf_8 | 591f13b41b7cc6d1cc333c404ad42ffd | function [results, success, raw] = ipoptopf_solver(om, mpopt)
%IPOPTOPF_SOLVER Solves AC optimal power flow using MIPS.
%
% [RESULTS, SUCCESS, RAW] = IPOPTOPF_SOLVER(OM, MPOPT)
%
% Inputs are an OPF model object and a MATPOWER options struct.
%
% Outputs are a RESULTS struct, SUCCESS flag and RAW output struct.
... |
github | matheusmlopess-zz/Fuzzy-Logic-power-transmission-analysis-master | loadcase.m | .m | Fuzzy-Logic-power-transmission-analysis-master/matpower5.1/loadcase.m | 9,744 | utf_8 | 3b9f2a0546e217b2be54a408712e93bc | function [baseMVA, bus, gen, branch, areas, gencost, info] = loadcase(casefile)
%LOADCASE Load .m or .mat case files or data struct in MATPOWER format.
%
% [BASEMVA, BUS, GEN, BRANCH, AREAS, GENCOST] = LOADCASE(CASEFILE)
% [BASEMVA, BUS, GEN, BRANCH, GENCOST] = LOADCASE(CASEFILE)
% [BASEMVA, BUS, GEN, BRANCH] =... |
github | matheusmlopess-zz/Fuzzy-Logic-power-transmission-analysis-master | have_fcn.m | .m | Fuzzy-Logic-power-transmission-analysis-master/matpower5.1/have_fcn.m | 23,477 | utf_8 | b233d7dc93c96513131e66f4ee959fbc | function rv = have_fcn(tag, rtype)
%HAVE_FCN Test for optional functionality / version info.
% TORF = HAVE_FCN(TAG)
% TORF = HAVE_FCN(TAG, TOGGLE)
% VER_STR = HAVE_FCN(TAG, 'vstr')
% VER_NUM = HAVE_FCN(TAG, 'vnum')
% DATE = HAVE_FCN(TAG, 'date')
% INFO = HAVE_FCN(TAG, 'all')
%
% Returns availabilit... |
github | matheusmlopess-zz/Fuzzy-Logic-power-transmission-analysis-master | mpoption.m | .m | Fuzzy-Logic-power-transmission-analysis-master/matpower5.1/mpoption.m | 65,137 | utf_8 | da412795e04899d174df7909834e891c | function opt = mpoption(varargin)
%MPOPTION Used to set and retrieve a MATPOWER options struct.
%
% OPT = MPOPTION
% Returns the default options struct.
%
% OPT = MPOPTION(OVERRIDES)
% Returns the default options struct, with some fields overridden
% by values from OVERRIDES, which can be a struc... |
github | jay-mahadeokar/deeplab-public-ver2-master | classification_demo.m | .m | deeplab-public-ver2-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | jay-mahadeokar/deeplab-public-ver2-master | MyVOCevalseg.m | .m | deeplab-public-ver2-master/matlab/my_script/MyVOCevalseg.m | 4,625 | utf_8 | 128c24319d520c2576168d1cf17e068f | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | jay-mahadeokar/deeplab-public-ver2-master | MyVOCevalsegBoundary.m | .m | deeplab-public-ver2-master/matlab/my_script/MyVOCevalsegBoundary.m | 4,415 | utf_8 | 1b648714e61bafba7c08a8ce5824b105 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | happyharrycn/unsupervised_edges-master | computeColor.m | .m | unsupervised_edges-master/flow_utils/computeColor.m | 3,139 | utf_8 | 4344a6f1decdd6631805bd81be0b4442 | function img = computeColor(u,v)
% computeColor color codes flow field U, V
% According to the c++ source code of Daniel Scharstein
% Contact: schar@middlebury.edu
% Author: Deqing Sun, Department of Computer Science, Brown University
% Contact: dqsun@cs.brown.edu
% $Date: 2007-10-31 21:20:30 (Wed, 31 O... |
github | happyharrycn/unsupervised_edges-master | distinguishable_colors.m | .m | unsupervised_edges-master/flow_utils/distinguishable_colors.m | 5,753 | utf_8 | 57960cf5d13cead2f1e291d1288bccb2 | function colors = distinguishable_colors(n_colors,bg,func)
% DISTINGUISHABLE_COLORS: pick colors that are maximally perceptually distinct
%
% When plotting a set of lines, you may want to distinguish them by color.
% By default, Matlab chooses a small set of colors and cycles among them,
% and so if you have more than ... |
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