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github | TUDelft-DataDrivenControl/Predictor-Based-Subspace-IDentification-toolbox-master | reggcv.m | .m | Predictor-Based-Subspace-IDentification-toolbox-master/extra/backwards/private/reggcv.m | 4,183 | utf_8 | 96e102b790fbfa5f01c26445d60a36fc | function reg_min=reggcv(Y,Vn,Sn,method,show)
%REGGCV Compute regularization using generalized cross validation.
% Determine the regularization parameter for ordkernel
% using Generalized Cross-Validation (GCV). It plots the
% GCV function as a function of the regularization
% ... |
github | TUDelft-DataDrivenControl/Predictor-Based-Subspace-IDentification-toolbox-master | exls_back.m | .m | Predictor-Based-Subspace-IDentification-toolbox-master/extra/backwards/private/exls_back.m | 6,802 | utf_8 | 17f72f64cc45a64fce83e4c308ef12ae | function [VARMAX,Z] = exls_back(Y,Z,p,r,method,tol,reg,opt,VARMAX0)
%EXLS Extended Least Squares
% [VARMAX,Z] = EXLS(Y,Z,P,R,METHOD,TOL,REG,OPT,VARMAX0) computes the
% extended least squares regression for the VARMAX estimation problem
% using recursive least squares. This function is intended for DORDVARMAX.
... |
github | TUDelft-DataDrivenControl/Predictor-Based-Subspace-IDentification-toolbox-master | reglcurve.m | .m | Predictor-Based-Subspace-IDentification-toolbox-master/extra/backwards/private/reglcurve.m | 9,669 | utf_8 | 4ba1982fb3c44a326be1ff4bec03edef | function reg_c=reglcurve(Y,Vn,Sn,method,show)
%REGLCURVE Compute regularization using L-curve criterion.
% Determine the regularization parameter for ordkernel
% using L-curve criterion. It plots the L-curve and
% find its corner. If the regularization method is
% 'tsvd' t... |
github | TUDelft-DataDrivenControl/Predictor-Based-Subspace-IDentification-toolbox-master | reggcv.m | .m | Predictor-Based-Subspace-IDentification-toolbox-master/extra/greybox/private/reggcv.m | 4,183 | utf_8 | 96e102b790fbfa5f01c26445d60a36fc | function reg_min=reggcv(Y,Vn,Sn,method,show)
%REGGCV Compute regularization using generalized cross validation.
% Determine the regularization parameter for ordkernel
% using Generalized Cross-Validation (GCV). It plots the
% GCV function as a function of the regularization
% ... |
github | TUDelft-DataDrivenControl/Predictor-Based-Subspace-IDentification-toolbox-master | reglcurve.m | .m | Predictor-Based-Subspace-IDentification-toolbox-master/extra/greybox/private/reglcurve.m | 9,669 | utf_8 | 4ba1982fb3c44a326be1ff4bec03edef | function reg_c=reglcurve(Y,Vn,Sn,method,show)
%REGLCURVE Compute regularization using L-curve criterion.
% Determine the regularization parameter for ordkernel
% using L-curve criterion. It plots the L-curve and
% find its corner. If the regularization method is
% 'tsvd' t... |
github | TUDelft-DataDrivenControl/Predictor-Based-Subspace-IDentification-toolbox-master | sfun_xygraph.m | .m | Predictor-Based-Subspace-IDentification-toolbox-master/simulink/sfun_xygraph.m | 13,103 | utf_8 | 5cf0dd62ab47b4f64b3e2f897ecaee5d | function [sys, x0, str, ts, simStateCompliance] = sfunxy_new(t,x,u,flag,ax,varargin)
%SFUNXY S-function that acts as an X-Y scope using MATLAB plotting functions.
% This M-file is designed to be used in a Simulink S-function block.
% It draws a line from the previous input point, which is stored using
% discrete ... |
github | misztal/GRIT-master | meshdemond.m | .m | GRIT-master/UTILITIES/meshing/distmesh/meshdemond.m | 1,036 | utf_8 | 1610b2dc0c78ee32ea73e9a715f3bb47 | function meshdemond
%MESHDEMOND distmeshnd examples.
% Copyright (C) 2004-2012 Per-Olof Persson. See COPYRIGHT.TXT for details.
rand('state',1); % Always the same results
set(gcf,'rend','opengl');
disp('(9) 3-D Unit ball')
fd=inline('sqrt(sum(p.^2,2))-1','p');
[p,t]=distmeshnd(fd,@huniform,0.2,[-1,-1,-1;... |
github | cwkx/IGAC-master | objwrite.m | .m | IGAC-master/wrappers/matlab/lib/objwrite.m | 7,931 | utf_8 | 75fc005aa03085efb4f12d8879670ec6 | function objwrite(OBJ,fullfilename)
% Write objects to a Wavefront OBJ file
%
% write_wobj(OBJ,filename);
%
% OBJ struct containing:
%
% OBJ.vertices : Vertices coordinates
% OBJ.vertices_texture: Texture coordinates
% OBJ.vertices_normal : Normal vectors
% OBJ.vertices_point : Vertice data used for points and lines ... |
github | cwkx/IGAC-master | stlwrite.m | .m | IGAC-master/wrappers/matlab/lib/stlwrite.m | 10,024 | utf_8 | 501ff36176fdfe30bfa6352a0991d7c3 | function stlwrite(filename, varargin)
%STLWRITE Write STL file from patch or surface data.
%
% STLWRITE(FILE, FV) writes a stereolithography (STL) file to FILE for a
% triangulated patch defined by FV (a structure with fields 'vertices'
% and 'faces').
%
% STLWRITE(FILE, FACES, VERTICES) takes faces and verti... |
github | cwkx/IGAC-master | myaa.m | .m | IGAC-master/wrappers/matlab/lib/myaa.m | 11,141 | utf_8 | a66dd7fc188c3f6a1a0a0c07623cf831 | function [varargout] = myaa(varargin)
%MYAA Render figure with anti-aliasing.
% MYAA
% Anti-aliased rendering of the current figure. This makes graphics look
% a lot better than in a standard matlab figure, which is useful for
% publishing results on the web or to better see the fine details in a
% complex... |
github | yangyangHu/deblur-master | create_greenspan_settings.m | .m | deblur-master/code/create_greenspan_settings.m | 2,527 | utf_8 | cfad68ce50fdb9d9dcc4b38e9b9c14fe | function S = create_greenspan_settings(varargin)
% Author: Bryan Russell
% Version: 1.0, distribution code.
% Project: Removing Camera Shake from a Single Image, SIGGRAPH 2006 paper
% Copyright 2006, Massachusetts Institute of Technology
% CREATE_GREENSPAN_SETTINGS - Creates a data structure containing the
% various ... |
github | dmarcosg/RotEqNet-master | cnn_mnist_rot_dag.m | .m | RotEqNet-master/cnn_mnist_rot_dag.m | 3,269 | utf_8 | 097dc6b8c124ce44eaff65b75b03bd81 | function [net, info] = cnn_mnist_rot_dag(varargin)
%CNN_MNIST Demonstrates MatConvNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
run(fullfile(fileparts(mfilename('fullpath')),...
'setup_mcnRotEqNet.m')) ;
opts.batchNormalization = true ;
[opts, varargin] =... |
github | dmarcosg/RotEqNet-master | vl_nnpoolangle.m | .m | RotEqNet-master/matlab/vl_nnpoolangle.m | 5,992 | utf_8 | 41d89f1d4728b2ec44b115fb1382e485 | function y = vl_nnpoolangle(x,varargin)
dzdy = [];
if nargin > 1
if ~ischar(varargin{1})
dzdy = varargin{1};
if numel(varargin) > 1
varargin = varargin(2:end);
end
end
end
opts.bins = 1;
opts.angle_n = 8;
opts.max_angle = 360;
opts.output_relative_angles = false;
opts.outpu... |
github | dmarcosg/RotEqNet-master | vl_nnconvsteer.m | .m | RotEqNet-master/matlab/vl_nnconvsteer.m | 3,965 | utf_8 | 0164847c23a50e4cd3af8614cb0f6978 | function [y,dzdf,dzdb] = vl_nnconvsteer(x,f,b,varargin)
% Forward: y = vl_nnconvsteer(x,f,b)
% Backward: [dzdx,dzdf,dzdb] = vl_nnconvsteer(x,f,b,dzdy)
% Options:
% 'angle_n': number of rotations to compute for each filter in f
if isa(x,'gpuArray')
f = gpuArray(f);
b = gpuArray(b);
end
cudnn = 'CuDNN';
dzdy =... |
github | dmarcosg/RotEqNet-master | vl_nnpool_ext.m | .m | RotEqNet-master/matlab/vl_nnpool_ext.m | 7,800 | utf_8 | c1face47f04b27495af521b42d58ec6e | function y = vl_nnpool_ext(x,ext,pool,varargin)
% Check whether forward or backward
dzdy = [];
if nargin > 3
if ~ischar(varargin{1})
dzdy = varargin{1};
if numel(varargin) > 1
varargin = varargin(2:end);
else
varargin = [];
end
end
end
opts.pad = 0 ;
%op... |
github | startcode/qp-oases-master | make.m | .m | qp-oases-master/interfaces/simulink/make.m | 8,453 | utf_8 | b56fed5a0b41150b8af75373612324c7 | function [] = make( varargin )
%MAKE Compiles the Simulink interface of qpOASES.
%
%Type make to compile all interfaces that
% have been modified,
%type make clean to delete all compiled interfaces,
%type make clean all to first delete and then compile
% ... |
github | startcode/qp-oases-master | qpOASES_options.m | .m | qp-oases-master/interfaces/octave/qpOASES_options.m | 10,357 | utf_8 | 719207ae527db13f4b22333e9550c579 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2017 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | startcode/qp-oases-master | make.m | .m | qp-oases-master/interfaces/octave/make.m | 8,296 | utf_8 | 2ab639ac67f632a68b41a91e0b666f74 | function [] = make( varargin )
%MAKE Compiles the octave interface of qpOASES.
%
%Type make to compile all interfaces that
% have been modified,
%type make clean to delete all compiled interfaces,
%type make clean all to first delete and then compile
% ... |
github | startcode/qp-oases-master | qpOASES_auxInput.m | .m | qp-oases-master/interfaces/octave/qpOASES_auxInput.m | 4,436 | utf_8 | fcc9652ca47c9fe89d24f7dc500fcb49 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2017 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | startcode/qp-oases-master | qpOASES_options.m | .m | qp-oases-master/interfaces/matlab/qpOASES_options.m | 10,357 | utf_8 | 719207ae527db13f4b22333e9550c579 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2017 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | startcode/qp-oases-master | make.m | .m | qp-oases-master/interfaces/matlab/make.m | 8,997 | utf_8 | 55ce9b55d80b98c042742e61a7372014 | function [] = make( varargin )
%MAKE Compiles the Matlab interface of qpOASES.
%
%Type make to compile all interfaces that
% have been modified,
%type make clean to delete all compiled interfaces,
%type make clean all to first delete and then compile
% ... |
github | startcode/qp-oases-master | qpOASES_auxInput.m | .m | qp-oases-master/interfaces/matlab/qpOASES_auxInput.m | 4,436 | utf_8 | fcc9652ca47c9fe89d24f7dc500fcb49 | %qpOASES -- An Implementation of the Online Active Set Strategy.
%Copyright (C) 2007-2017 by Hans Joachim Ferreau, Andreas Potschka,
%Christian Kirches et al. All rights reserved.
%
%qpOASES is distributed under the terms of the
%GNU Lesser General Public License 2.1 in the hope that it will be
%useful, but WITHOUT ANY... |
github | startcode/qp-oases-master | runAllTests.m | .m | qp-oases-master/testing/matlab/runAllTests.m | 5,548 | utf_8 | bdff35e75080becaee269cf4f487d115 | function [ successFlag ] = runAllTests( doPrint )
if ( nargin < 1 )
doPrint = 0;
end
successFlag = 1;
curWarnLevel = warning;
warning('off');
% add sub-folders to Matlab path
setupTestingPaths();
clc;
%% run interface tests
fprintf(... |
github | startcode/qp-oases-master | runInterfaceTest.m | .m | qp-oases-master/testing/matlab/tests/runInterfaceTest.m | 16,774 | utf_8 | 695fea461f4e440770b787dcafe361d2 | function [ successFlag ] = runInterfaceTest( nV,nC, doPrint,seed )
if ( nargin < 4 )
seed = 42;
if ( nargin < 3 )
doPrint = 1;
if ( nargin < 2 )
nC = 10;
if ( nargin < 1 )
nV = 5;
end
... |
github | startcode/qp-oases-master | runRandomZeroHessian.m | .m | qp-oases-master/testing/matlab/tests/runRandomZeroHessian.m | 14,399 | utf_8 | 62c1efb1adf03ca9009d830d211f3ef8 | function [ successFlag ] = runRandomZeroHessian( nV,nC, doPrint,seed )
if ( nargin < 4 )
seed = 42;
if ( nargin < 3 )
doPrint = 1;
if ( nargin < 2 )
nC = 10;
if ( nargin < 1 )
nV = 5;
end
... |
github | startcode/qp-oases-master | runInterfaceSeqTest.m | .m | qp-oases-master/testing/matlab/tests/runInterfaceSeqTest.m | 15,458 | utf_8 | 7fd12067642b559d3dd08130be565119 | function [ successFlag ] = runInterfaceSeqTest( nV,nC, doPrint,seed )
if ( nargin < 4 )
seed = 42;
if ( nargin < 3 )
doPrint = 1;
if ( nargin < 2 )
nC = 10;
if ( nargin < 1 )
nV = 5;
end
... |
github | startcode/qp-oases-master | runRandomIdHessian.m | .m | qp-oases-master/testing/matlab/tests/runRandomIdHessian.m | 14,687 | utf_8 | 1bf0849e7175e73709bbae55057eeff5 | function [ successFlag ] = runRandomIdHessian( nV,nC, doPrint,seed )
if ( nargin < 4 )
seed = 42;
if ( nargin < 3 )
doPrint = 1;
if ( nargin < 2 )
nC = 10;
if ( nargin < 1 )
nV = 5;
end
... |
github | startcode/qp-oases-master | isoctave.m | .m | qp-oases-master/testing/matlab/auxFiles/isoctave.m | 508 | utf_8 | c857dec2b164c5835c0d5235cd7ad8f0 |
% ISOCTAVE True if the operating environment is octave.
% Usage: t=isoctave();
%
% Returns 1 if the operating environment is octave, otherwise
% 0 (Matlab)
%
% ---------------------------------------------------------------
function t=isoctave()
%ISOCTAVE True if the operating environment is octave.
% U... |
github | c2jahnke/THB-Spline-FE-master | ImpointCurvePlot.m | .m | THB-Spline-FE-master/CurvePlot/ImpointCurvePlot.m | 798 | utf_8 | eaab8eb10e3e833c1d6d4e8da4f5a4b5 |
function ImpointCurvePlot(n,sP,p,U,plotVector,Points)
% function to plot a curve with control points
C = zeros( sP , 2 );
for l = 1:sP
C(l,:) = CurvePoint(n,p,U,Points,plotVector(l));
end
plt_curve = plot(C(:,1),C(:,2),'k','LineWidth',1.2);
hold on;
xlabel('x');
ylabel('y');
... |
github | hliangzhao/Mathematical-Model-Implementation-master | DeJong_f2.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/DeJong_f2.m | 705 | utf_8 | f474c060e61ab63d089b22de5a3cdcd9 | % DeJong_f2.m
% De Jong's f2 function, also called a Rosenbrock Variant
% This is a 2D only equation
%
% described by Clerc in ...
% http://clerc.maurice.free.fr/pso/Semi-continuous_challenge/Semi-continuous_challenge.htm
%
% used to test optimization/global minimization problems
% in Clerc's "Semi-continuous challeng... |
github | hliangzhao/Mathematical-Model-Implementation-master | ackley.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/ackley.m | 839 | utf_8 | f53febad1b1241ed2b82eb51990c494a | % ackley.m
% Ackley's function, from http://www.cs.vu.nl/~gusz/ecbook/slides/16
% and further shown at:
% http://clerc.maurice.free.fr/pso/Semi-continuous_challenge/Semi-continuous_challenge.htm
%
% commonly used to test optimization/global minimization problems
%
% f(x)= [ 20 + e ...
% -20*exp(-0.2*sqrt((1/n)*... |
github | hliangzhao/Mathematical-Model-Implementation-master | f6_spiral_dyn.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/f6_spiral_dyn.m | 842 | utf_8 | a68995830769a8c36f01a49a1b854227 | % f6_spiral_dyn.m
% Schaffer's F6 function
% commonly used to test optimization/global minimization problems
%
% This version moves the minimum about a Fermat Spiral
% according to the equation: r = a*(theta^2)
% theta is a function of time and is checked internally (not an input)
% x_center = r*cos(theta)
% y_ce... |
github | hliangzhao/Mathematical-Model-Implementation-master | f6_linear_dyn.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/f6_linear_dyn.m | 593 | utf_8 | ff08de37e8934d90aaf0da2cd9be2020 | % f6_linear_dyn.m
% Schaffer's F6 function
% commonly used to test optimization/global minimization problems
%
% This version moves the minimum linearly along a 45 deg angle in x,y space
% Brian Birge
% Rev 1.0
% 9/12/04
function [out]=f6_linear_dyn(in)
% parse input
x = in(:,1);
y = in(:,2);
% find current m... |
github | hliangzhao/Mathematical-Model-Implementation-master | NDparabola.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/NDparabola.m | 641 | utf_8 | 85e34a0950db121bff8caeefcc737538 | % NDparabola.m
% ND Parabola function (also called a Sphere function and DeJong's f1),
% described by Clerc...
% http://clerc.maurice.free.fr/pso/Semi-continuous_challenge/Semi-continuous_challenge.htm
%
% used to test optimization/global minimization problems
% in Clerc's "Semi-continuous challenge"
%
% f(x) = sum( x... |
github | hliangzhao/Mathematical-Model-Implementation-master | spiral_dyn.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/spiral_dyn.m | 805 | utf_8 | 9ed110a6f7dc7937e4f689a5a3b912ee | % spiral_dyn.m
% returns x,y position along an archimedean spiral of degree n
% based on cputime, first time it is called is start time
%
% based on: r = a*(theta^n)
%
% usage: [x_cnt,y_cnt] = spiral_dyn(n,a)
% i.e.,
% n = 2 (Fermat)
% = 1 (Archimedes)
% = -1 (Hyberbolic)
% = -2 (Lituus)
% Brian Birge... |
github | hliangzhao/Mathematical-Model-Implementation-master | alpine.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/alpine.m | 617 | utf_8 | 350e9ce69d84cc997f59ab6d77b6c4e5 | % alpine.m
% ND Alpine function, described by Clerc...
% http://clerc.maurice.free.fr/pso/Semi-continuous_challenge/Semi-continuous_challenge.htm
%
% used to test optimization/global minimization problems
% in Clerc's "Semi-continuous challenge"
%
% f(x) = sum( abs(x.*sin(x) + 0.1.*x) )
%
% x = N element row vector c... |
github | hliangzhao/Mathematical-Model-Implementation-master | Griewank.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/Griewank.m | 1,175 | utf_8 | 1922426be7c11651ad663dd929a16606 | % Griewank.m
% Griewank function
% described by Clerc in ...
% http://clerc.maurice.free.fr/pso/Semi-continuous_challenge/Semi-continuous_challenge.htm
%
% used to test optimization/global minimization problems
% in Clerc's "Semi-continuous challenge"
%
% f(x) = sum((x-100).^2,2)./4000 - ...
% prod(cos((x-100).... |
github | hliangzhao/Mathematical-Model-Implementation-master | f6mod.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/f6mod.m | 675 | utf_8 | 35ebfbe0fff22261e101217b1604fe22 | % f6mod.m
% Schaffer's F6 function
% commonly used to test optimization/global minimization problems
%
% This version is a modified form, just the sum of 5 f6 functions with
% different centers to look at local minimum issues
% normal f6=
% z = 0.5+ (sin^2(sqrt(x^2+y^2))-0.5)/((1+0.01*(x^2+y^2))^2)
function [out]=f6mo... |
github | hliangzhao/Mathematical-Model-Implementation-master | linear_dyn.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/linear_dyn.m | 724 | utf_8 | c630c42d5de136b31ca954328cde13d2 | % linear_dyn.m
% returns an offset that can be added to data that increases linearly with
% time, based on cputime, first time it is called is start time
%
% equation is: offset = (cputime - tnot)*scalefactor
% where tnot = cputime at the first call
% scalefactor = value that slows or speeds up linear movement
... |
github | hliangzhao/Mathematical-Model-Implementation-master | Rastrigin.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/Rastrigin.m | 500 | utf_8 | 49633129edf0685033f0b4fc126a822c | % Rastrigin.m
% Rastrigin function
%
% used to test optimization/global minimization problems
%
% f(x) = sum([x.^2-10*cos(2*pi*x) + 10], 2);
%
% x = N element row vector containing [x0, x1, ..., xN]
% each row is processed independently,
% you can feed in matrices of timeXN no prob
%
% example: cost = Rastrigin([1,2;... |
github | hliangzhao/Mathematical-Model-Implementation-master | f6.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/f6.m | 299 | utf_8 | 6e328b2ad0f76540e9c80d87b5ad06dc | % f6.m
% Schaffer's F6 function
% commonly used to test optimization/global minimization problems
%
% z = 0.5+ (sin^2(sqrt(x^2+y^2))-0.5)/((1+0.01*(x^2+y^2))^2)
function [out]=f6(in)
x=in(:,1);
y=in(:,2);
num=sin(sqrt(x.^2+y.^2)).^2 - 0.5;
den=(1.0+0.01*(x.^2+y.^2)).^2;
out=0.5 +num./den;
|
github | hliangzhao/Mathematical-Model-Implementation-master | f6_bubbles_dyn.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/f6_bubbles_dyn.m | 1,458 | utf_8 | 00614b7d2beaf3ea99b48c5ccdd00063 | % f6_bubbles_dyn.m
% 2 separate Schaffer's F6 functions, one with min at [-8,-8] and the
% other with min at [8,8]
% as time goes on, each bubbles magnitude cycles up and down,
% they are 180 deg out of phase with each other
%
% commonly used to test optimization/global minimization problems
function [out]=f6_bubbles... |
github | hliangzhao/Mathematical-Model-Implementation-master | Rosenbrock.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/Rosenbrock.m | 697 | utf_8 | 9b342f29fa2367ba43883cb0b50432cb | % Rosenbrock.m
% Rosenbrock function
%
% described by Clerc in ...
% http://clerc.maurice.free.fr/pso/Semi-continuous_challenge/Semi-continuous_challenge.htm
%
% used to test optimization/global minimization problems
% in Clerc's "Semi-continuous challenge"
%
% f(x) = sum([ 100*(x(i+1) - x(i)^2)^2 + (x(i) -1)^2])
%
%... |
github | hliangzhao/Mathematical-Model-Implementation-master | tripod.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/tripod.m | 860 | utf_8 | 9e3c6b8565897f712d6297c5e041ebef | % tripod.m
% 2D tripod function, described by Clerc...
% http://clerc.maurice.free.fr/pso/Semi-continuous_challenge/Semi-continuous_challenge.htm
%
% used to test optimization/global minimization problems
% in Clerc's "Semi-continuous challenge"
%
% f(x)= [ p(x2)*(1+p(x1)) ...
% + abs(x1 + 50*p(x2)*(1-2*p(x1)))... |
github | hliangzhao/Mathematical-Model-Implementation-master | Foxhole.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/Foxhole.m | 1,232 | utf_8 | 21a1c5dd5e44f43ef92759b2e174b59c | % Foxhole.m
% Foxhole function, 2D multi-minima function
%
% from: http://www.cs.rpi.edu/~hornda/pres/node10.html
%
% f(x) = 0.002 + sum([1/(j + sum( [x(i) - a(i,j)].^6 ) )])
%
% x = 2 element row vector containing [ x, y ]
% each row is processed independently,
% you can feed in matrices of timeX2 no prob
%
% exampl... |
github | hliangzhao/Mathematical-Model-Implementation-master | pso_Trelea_vectorized.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/pso_Trelea_vectorized.m | 22,526 | utf_8 | 83cd4e518a70437a5b760d6cb5ceaa82 | % pso_Trelea_vectorized.m
% a generic particle swarm optimizer
% to find the minimum or maximum of any
% MISO matlab function
%
% Implements Common, Trelea type 1 and 2, and Clerc's class 1". It will
% also automatically try to track to a changing environment (with varied
% success - BKB 3/18/05)
%
% This vectorized ve... |
github | hliangzhao/Mathematical-Model-Implementation-master | DeJong_f4.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/DeJong_f4.m | 971 | utf_8 | 9df4774e7545c69c3ded2336203917ac | % DeJong_f4.m
% De Jong's f4 function, ND, no noise
%
% described by Clerc in ...
% http://clerc.maurice.free.fr/pso/Semi-continuous_challenge/Semi-continuous_challenge.htm
%
% used to test optimization/global minimization problems
% in Clerc's "Semi-continuous challenge"
%
% f(x) = sum( [1:N].*(in.^4), 2)
%
% x = N e... |
github | hliangzhao/Mathematical-Model-Implementation-master | DeJong_f3.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/testfunctions/DeJong_f3.m | 488 | utf_8 | 60082b5c3e0231c66b32f383ab9aca68 | % DeJong_f3.m
% De Jong's f3 function, ND, also called STEP
% from: http://www.cs.rpi.edu/~hornda/pres/node4.html
%
% f(x) = sum( floor(x) )
%
% x = N element row vector containing [ x0, x1,..., xN ]
% each row is processed independently,
% you can feed in matrices of timeXN no prob
%
% example: cost = DeJong_f3([1... |
github | hliangzhao/Mathematical-Model-Implementation-master | spiral_dyn.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/PSOt/spiral_dyn.m | 805 | utf_8 | 9ed110a6f7dc7937e4f689a5a3b912ee | % spiral_dyn.m
% returns x,y position along an archimedean spiral of degree n
% based on cputime, first time it is called is start time
%
% based on: r = a*(theta^n)
%
% usage: [x_cnt,y_cnt] = spiral_dyn(n,a)
% i.e.,
% n = 2 (Fermat)
% = 1 (Archimedes)
% = -1 (Hyberbolic)
% = -2 (Lituus)
% Brian Birge... |
github | hliangzhao/Mathematical-Model-Implementation-master | linear_dyn.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/PSOt/linear_dyn.m | 724 | utf_8 | c630c42d5de136b31ca954328cde13d2 | % linear_dyn.m
% returns an offset that can be added to data that increases linearly with
% time, based on cputime, first time it is called is start time
%
% equation is: offset = (cputime - tnot)*scalefactor
% where tnot = cputime at the first call
% scalefactor = value that slows or speeds up linear movement
... |
github | hliangzhao/Mathematical-Model-Implementation-master | pso_Trelea_vectorized.m | .m | Mathematical-Model-Implementation-master/IntelligenceAlgorithm/chapter17 基于PSO工具箱的函数寻优算法/PSOt/pso_Trelea_vectorized.m | 22,526 | utf_8 | 83cd4e518a70437a5b760d6cb5ceaa82 | % pso_Trelea_vectorized.m
% a generic particle swarm optimizer
% to find the minimum or maximum of any
% MISO matlab function
%
% Implements Common, Trelea type 1 and 2, and Clerc's class 1". It will
% also automatically try to track to a changing environment (with varied
% success - BKB 3/18/05)
%
% This vectorized ve... |
github | hliangzhao/Mathematical-Model-Implementation-master | distancematrix.m | .m | Mathematical-Model-Implementation-master/HeuristicAlgorithm/遗传算法/TSP(GA)/distancematrix.m | 883 | utf_8 | 1e2d36405073bd86e4af83903a01299b | function dis = distancematrix(city)
% DISTANCEMATRIX
% dis = DISTANCEMATRIX(city) return the distance matrix, dis(i,j) is the
% distance between city_i and city_j
numberofcities = length(city);
R = 6378.137; % The radius of the Earth
for i = 1:numberofcities
for j = i+1:numberofcities
dis(i,j) = distance(... |
github | hliangzhao/Mathematical-Model-Implementation-master | distancematrix.m | .m | Mathematical-Model-Implementation-master/HeuristicAlgorithm/模拟退火算法/TSP(SA)/distancematrix.m | 883 | utf_8 | 1e2d36405073bd86e4af83903a01299b | function dis = distancematrix(city)
% DISTANCEMATRIX
% dis = DISTANCEMATRIX(city) return the distance matrix, dis(i,j) is the
% distance between city_i and city_j
numberofcities = length(city);
R = 6378.137; % The radius of the Earth
for i = 1:numberofcities
for j = i+1:numberofcities
dis(i,j) = distance(... |
github | hliangzhao/Mathematical-Model-Implementation-master | grMinSpanTree.m | .m | Mathematical-Model-Implementation-master/GraphTheory/basic/grMinSpanTree.m | 2,112 | utf_8 | 56d552b6d33551eda5bc8d5d1fe22a3d | function nMST=grMinSpanTree(E)
% Function nMST=grMinSpanTree(E) solve
% the minimal spanning tree problem for a connected graph.
% Input parameter:
% E(m,2) or (m,3) - the edges of graph and their weight;
% 1st and 2nd elements of each row is numbers of vertexes;
% 3rd elements of each row is weight of edge... |
github | hliangzhao/Mathematical-Model-Implementation-master | grPlot.m | .m | Mathematical-Model-Implementation-master/GraphTheory/basic/grPlot.m | 7,247 | utf_8 | f1fabec67aba7eda59ab08e1a77ffe2f | function h=grPlot(V,E,kind,vkind,ekind)
% Function h=grPlot(V,E,kind,vkind,ekind)
% draw the plot of the graph (digraph).
% Input parameters:
% V(n,2) or (n,3) - the coordinates of vertexes
% (1st column - x, 2nd - y) and, maybe, 3rd - the weights;
% n - number of vertexes.
% If V(n,2), we write labels:... |
github | avst34/nlp-master | loadData.m | .m | nlp-master/datasets/pp_attachement/boknilev/code/loadData.m | 3,566 | utf_8 | 4df1832c9e035e29642054c26367c290 | function data = loadData(model, params, pref, wordVectors, varargin)
%%%% default values %%%%
numvarargs = length(varargin);
if numvarargs > 2
error('loadData:TooManyInputs', ...
'requires at most 2 optional input');
end
if params.useExt && numvarargs ~= 2
error('loadData:TooFewInputs', ...
'if... |
github | avst34/nlp-master | saveParameters.m | .m | nlp-master/datasets/pp_attachement/boknilev/code/saveParameters.m | 1,511 | utf_8 | b8d196562c783d006af5c5637a90fed3 | function saveParameters(model, theta, params, saveFile)
if model == 6
saveParametersHeadDist(theta, params, saveFile);
else
error('Error', ['Unknown model ' num2str(model) ' in saveParameters()']);
end
end
function saveParametersHeadDist(theta, params, saveFile)
numDistances = params.numDistances;
inputS... |
github | avst34/nlp-master | functionCostGrad.m | .m | nlp-master/datasets/pp_attachement/boknilev/code/functionCostGrad.m | 2,380 | utf_8 | 42b121b38683ac9aa8f922d8886c5c81 | function [cost, grad] = functionCostGrad(theta, model, params, data)
if model == 6
[cost, grad] = SingleWordPPHeadDistCostDispatcher(theta, params, data);
else
error('Error', ['unknown model ' num2str(model) ' in functionCostGrad()']);
end
end
function [cost, grad] = SingleWordPPHeadDistCostDis... |
github | avst34/nlp-master | applyNonLinearity.m | .m | nlp-master/datasets/pp_attachement/boknilev/code/applyNonLinearity.m | 265 | utf_8 | a3ea8f80b368b381289a9c9009f02d57 | function result = applyNonLinearity(x)
% result = 1 ./ (1 + exp(-x)); % sigmoid
result = tanh(x); % tanh
end
function invResult = applyInverseNonLinearity(x)
% invResult = log(x) - log(1-x); % sigmoid case
invResult = atanh(x); % tanh case
end |
github | avst34/nlp-master | loadVerbnetWordnet.m | .m | nlp-master/datasets/pp_attachement/boknilev/code/loadVerbnetWordnet.m | 3,466 | utf_8 | dfdf794e13ef30fc292e9f1fb63a70b9 | function [vn, wn] = loadVerbnetWordnet(vnDir, wnDir, language)
% load Verbnet and Wordnet from disk (prepared by Python scripts)
if strcmpi(language, 'english')
[vn, wn] = loadEnglishVerbnetWordnet([vnDir '/' 'verb2prep.txt'], [vnDir '/' 'verbalnoun2prep.txt'], ...
[vnDir '/' 'verb2c... |
github | avst34/nlp-master | loadDataSingleWordPP.m | .m | nlp-master/datasets/pp_attachement/boknilev/code/loadDataSingleWordPP.m | 9,103 | utf_8 | f4d60f7b74808ed6aab5ce354b57df83 | function [heads, preps, ppChildren, labels, nheads, includeInd] = loadDataSingleWordPP(wordVectors, ...
inputSize, maxNumHeads, ...
headWordsFilename, prepWordsFilename, ...
ppChildWordsFilename, labelsFilename, ...
... |
github | avst34/nlp-master | filterWordVectors.m | .m | nlp-master/datasets/pp_attachement/boknilev/code/filterWordVectors.m | 3,221 | utf_8 | 9065ab66e8f71b6dcef413834b11a2a1 |
function filteredWordVectors = filterWordVectors(wordVectors, model, params, filenames)
disp('filtering word vectors based on train/test data');
disp(['wordVectors size before filtering: ' num2str(wordVectors.Count)]);
intrainWordVectors = filterWordVectorsFromData(wordVectors, model, params, filenames.trainFilePref)... |
github | avst34/nlp-master | initializeParameters.m | .m | nlp-master/datasets/pp_attachement/boknilev/code/initializeParameters.m | 1,892 | utf_8 | 74b70c503762678519fc03716d5fa542 | function theta = initializeParameters(params, model)
inputSize = double(params.inputSize+params.extDim); % add extended dimensions
scaleParam = params.scaleParam;
if model == 6
theta = initializeParametersHeadDist(inputSize, params.numDistances, scaleParam);
else
disp(['Error: unknown model ' num2str(model)]... |
github | avst34/nlp-master | trainModel.m | .m | nlp-master/datasets/pp_attachement/boknilev/code/trainModel.m | 5,365 | utf_8 | 1b6fa3a315a9881cf0367c07b870d658 | function opttheta = trainModel(theta, data, wordVectors, params, model, trainParams, filenames)
opttheta = theta;
datasize = size(data.heads, 3);
batchsize = trainParams.batchsize;
numBatches = floor(datasize/batchsize)+1;
iters = trainParams.iters; % iterations per batch
sumSquares = ones(size(theta)... |
github | phuselab/DANTE-master | MaximumLikelihoodEstimator.m | .m | DANTE-master/matlab/MaximumLikelihoodEstimator.m | 404 | utf_8 | 1eed500aaf9d695e0a4f7e77f7fe090a | % Finding an optimal estimate of the true emotion based on k evaluators and
% N speech samples tham minimizes the mean square error result in the
% Maximum Likelyhood Estimator (MLE)
function MLE = MaximumLikelihoodEstimator(matriceDati)
MLE = sum(transpose(matriceDati))/size(matriceDati, 2);
end
% Each of th... |
github | phuselab/DANTE-master | main.m | .m | DANTE-master/matlab/main.m | 1,510 | utf_8 | e69eadd07e2c434b046ab959016af967 | dati1 = dlmread('../annotation/1/vid1_ogg/arousal.csv',';',1,4);
dati2 = dlmread('../annotation/2/vid1_ogg/arousal.csv',';',1,4);
dati3 = dlmread('../annotation/3/vid1_ogg/arousal.csv',';',1,4);
dati4 = dlmread('../annotation/6/vid1_ogg/arousal.csv',';',1,4);
dati5 = dlmread('../annotation/8/vid1_ogg/arousal.csv',';',1... |
github | EvgeniDubov/FEAST-master | FCBF.m | .m | FEAST-master/matlab/FCBF.m | 1,521 | utf_8 | 3264683aaf05a2b37369f50d37fed22b | function [selectedFeatures] = FCBF(featureMatrix,classColumn,threshold)
%function [selectedFeatures] = FCBF(featureMatrix,classColumn,threshold)
%
%Performs feature selection using the FCBF measure by Yu and Liu 2004.
%
%Instead of selecting a fixed number of features it provides a relevancy threshold and selects all
%... |
github | kasimp93/Image-Classification-using-ML-and-Artificial-Neural-Networks-master | Train_ANN.m | .m | Image-Classification-using-ML-and-Artificial-Neural-Networks-master/Final_Code/Train_ANN.m | 2,652 | utf_8 | ee25a75d73d259bacea0b897899c1697 | %Author: Iman Abdalla, April 2017.
function [cost_vec,Weights,predicted_train,output]=Train_ANN_is(lambda,iterations,Data,OutputNodes,W,S,Sh,L,alpha,bias,labels,S_vec)
Der=ones(size(W));
counter=2;
m=size(Data,1);
input=zeros(L,S+1);
output=zeros(size(Data,1),OutputNodes);
for ind2=1:iterations
waitbar(ind2/iter... |
github | kasimp93/Image-Classification-using-ML-and-Artificial-Neural-Networks-master | Test_ANN.m | .m | Image-Classification-using-ML-and-Artificial-Neural-Networks-master/Final_Code/Test_ANN.m | 875 | utf_8 | 3786fe2f638ae1cb2a172cd522ce10b1 | %Author: Iman Abdalla, April 2017.
function [Predictions,output]=Test_ANN(TestData,OutputNodes,W,S,L,bias,S_vec,Sh)
input=zeros(L,S+1);
output=zeros(size(TestData,1),OutputNodes);
for n=1:size(TestData,1)
% for n=1:10
%---------------------Forward Propagation--------------------------
%----... |
github | kasimp93/Image-Classification-using-ML-and-Artificial-Neural-Networks-master | comp_combined_15class.m | .m | Image-Classification-using-ML-and-Artificial-Neural-Networks-master/Final_Code/comp_combined_15class.m | 1,058 | utf_8 | 0046c2449ffe6a860bb5546e4a5accb7 | % For a test image, get combined feature vector.
function featurevec = comp_combined_15class(img)
%% load means of 2 class data
% load('X_cent.mat')
% load('X_gist.mat');
%
% %% standardize features (subtract mean and div by variance)
% disp('standardizing test examples');
% meanXggist = mean(X_gist)
% stdXgist = std... |
github | kasimp93/Image-Classification-using-ML-and-Artificial-Neural-Networks-master | comp_combined_2class.m | .m | Image-Classification-using-ML-and-Artificial-Neural-Networks-master/Final_Code/comp_combined_2class.m | 1,068 | utf_8 | 8e17e094772b8e50ddf394d7d24c1cfd | % For a test image, get combined feature vector.
function featurevec = comp_combined_2class(img)
%% load means of 2 class data
% load('X_cent2.mat');
% load('X_gist2.mat');
%
% %% standardize features (subtract mean and div by variance)
% disp('standardizing test examples');
% meanXggist = mean(X_gist2);
% stdXgist =... |
github | kasimp93/Image-Classification-using-ML-and-Artificial-Neural-Networks-master | flatten.m | .m | Image-Classification-using-ML-and-Artificial-Neural-Networks-master/Final_Code/feature extraction/centrist/flatten.m | 441 | utf_8 | 2fbf5d34ed9644b3610e990b009d71e0 | % Flatten a nested cell array, taken from
% http://groups.google.com/group/comp.soft-sys.matlab/browse_thread/thread/83e6ad0772bf68b8
function flatCell = flatten(cellArray)
flatCell{1} = []; %#ok<*AGROW>
for i=1:numel(cellArray)
if iscell(cellArray{i})
currentCell = flatten(cellArray{i});
[flatCell{end+1:end+... |
github | kasimp93/Image-Classification-using-ML-and-Artificial-Neural-Networks-master | searchGUI.m | .m | Image-Classification-using-ML-and-Artificial-Neural-Networks-master/Final_Code/feature extraction/centrist/searchGUI.m | 3,547 | utf_8 | fa191382c94bd4fada86c548399b3381 | function varargout = searchGUI(varargin)
% SEARCHGUI MATLAB code for searchGUI.fig
% SEARCHGUI, by itself, creates a new SEARCHGUI or raises the existing
% singleton*.
%
% H = SEARCHGUI returns the handle to a new SEARCHGUI or the handle to
% the existing singleton*.
%
% SEARCHGUI('CALLBACK',hO... |
github | kasimp93/Image-Classification-using-ML-and-Artificial-Neural-Networks-master | LMgist.m | .m | Image-Classification-using-ML-and-Artificial-Neural-Networks-master/Final_Code/feature extraction/gist/LMgist.m | 8,240 | utf_8 | bfdf40d00f3439f3864ce453bfce69d6 | function [gist, param] = LMgist(D, HOMEIMAGES, param, HOMEGIST)
%
% [gist, param] = LMgist(D, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param);
% [gist, param] = LMgist(filename, HOMEIMAGES, param, HOMEGIST);
%
% For a set of images:
% gist = LMgist(img, [], param);
%
% When calling LMgist with... |
github | kasimp93/Image-Classification-using-ML-and-Artificial-Neural-Networks-master | showGist.m | .m | Image-Classification-using-ML-and-Artificial-Neural-Networks-master/Final_Code/feature extraction/gist/showGist.m | 1,954 | utf_8 | 926839f0ab3e7182c10a1b52d06e5e31 | function showGist(gist, param)
%
% Visualization of the gist descriptor
% showGist(gist, param)
%
% The plot is color coded, with one color per scale
%
% Example:
% img = zeros(256,256);
% img(64:128,64:128) = 255;
% gist = LMgist(img, '', param);
% showGist(gist, param)
[Nimages, Ndim] = size(gist);
nx = c... |
github | GatorSense/MICI-master | evalFitness_softmax.m | .m | MICI-master/util/evalFitness_softmax.m | 1,879 | utf_8 | 6dd18d55aa4d0dbfa74fdaa03e96c2c2 |
function [fitness] = evalFitness_softmax(Labels, measure, nPntsBags, oneV, bag_row_ids, diffM,p)
% Evaluate the fitness a measure, similar to evalFitness_minmax() for
% classification but uses generalized mean (sometimes also named "softmax") model
%
% INPUT
% Labels - 1xNumTrainBags double - Training labe... |
github | GatorSense/MICI-master | invcdf_TruncatedGaussian.m | .m | MICI-master/util/invcdf_TruncatedGaussian.m | 1,199 | utf_8 | 50a5c7959b4606c1f3d6d03e9a8d0f47 | function [val] = invcdf_TruncatedGaussian(cdf,x_bar,sigma_bar,lowerBound,upperBound)
%stats_TruncatedGaussian - stats for a truncated gaussian distribution
% INPUT
% - cdf: evaluated at the values at cdf
% - x_bar,sigma_bar,lowerBound,upperBound: suppose X~N(mu,sigma^2) has a normal distribution and lies within
% ... |
github | GatorSense/MICI-master | evalInterval.m | .m | MICI-master/util/evalInterval.m | 1,164 | utf_8 | 036195d98abaadc9bc9fb2a9cec22289 |
function [subsetInterval] = evalInterval(measure,nSources,lowerindex,upperindex)
% Evaluate the valid interval width of a measure, then sort in descending order.
%
% INPUT
% measure - measure to be evaluated after update
% nSources - number of sources
% lowerindex - the cell that stores all the corresponding su... |
github | GatorSense/MICI-master | quadLearnChoquetMeasure_3Source.m | .m | MICI-master/util/quadLearnChoquetMeasure_3Source.m | 5,070 | utf_8 | 5f6e52334705601a119498e6bf458adb | function g = quadLearnChoquetMeasure_3Source(H, Y)
% g = quadLearnChoquetMeasure(H, Y)
% This code only works with 3 sources
%
% Purpose: Learn the fuzzy measures of a choquet integral for fusing sources
% of information. Learning the measures is framed as the
% following quadratic ... |
github | GatorSense/MICI-master | quadLearnChoquetMeasure_5Source.m | .m | MICI-master/util/quadLearnChoquetMeasure_5Source.m | 18,196 | utf_8 | f359c41388a5f67c036650506d60fd61 | function g = quadLearnChoquetMeasure_5Source(H, Y)
% g = quadLearnChoquetMeasure(H, Y) for 5 sources
%
% Purpose: Learn the fuzzy measures of a choquet integral for fusing sources
% of information. Learning the measures is framed as the
% following quadratic programming problem:
%
%
%... |
github | GatorSense/MICI-master | quadLearnChoquetMeasure_4Source.m | .m | MICI-master/util/quadLearnChoquetMeasure_4Source.m | 8,043 | utf_8 | e8c8141ac0b625ed7f9ec38f54b3186d | function g = quadLearnChoquetMeasure_4Source(H, Y)
% g = quadLearnChoquetMeasure(H, Y) for 4 sources
%
% Purpose: Learn the fuzzy measures of a choquet integral for fusing sources
% of information. Learning the measures is framed as the
% following quadratic programming problem:
%
%... |
github | GatorSense/MICI-master | evalFitness_minmax.m | .m | MICI-master/util/evalFitness_minmax.m | 1,791 | utf_8 | 458a4f32ed7c7c5cd2103d2f52d35897 |
function [fitness] = evalFitness_minmax(Labels, measure, nPntsBags, oneV, bag_row_ids, diffM)
% Evaluate the fitness a measure, using min( sum(max((ci-0)^2)) + sum(min(ci-1)^2) ) for classification.
% min-max model
%
% INPUT
% Labels - 1xNumTrainBags double - Training labels for each bag
% measure ... |
github | GatorSense/MICI-master | evalFitness_reg.m | .m | MICI-master/util/evalFitness_reg.m | 1,700 | utf_8 | 1d2c493a725df54f1f82c58428fd5a6a |
function [fitness] = evalFitness_reg(Labels, measure, nPntsBags, oneV, bag_row_ids, diffM)
% Evaluate the fitness a measure, using min(sum(min((ci-d)^2))) for regression.
%
% INPUT
% Labels - 1xNumTrainBags double - Training labels for each bag
% measure - measure to be evaluated after update
% ... |
github | amoudgl/mosse-tracker-master | window2.m | .m | mosse-tracker-master/src/window2.m | 1,476 | utf_8 | 5d8ce11dc20f5afbafd1fb2bf2957add | % This function creates a 2 dimentional window for a sample image, it takes
% the dimension of the window and applies the 1D window function
% This is does NOT using a rotational symmetric method to generate a 2 window
%
% Disi A ---- May,16, 2013
% [N,M]=size(imgage);
% --------------------------------------------... |
github | Davonter/openairinterface5g-master | gen_7_5_kHz.m | .m | openairinterface5g-master/openair1/PHY/MODULATION/gen_7_5_kHz.m | 3,298 | utf_8 | a08e730b234a112cbf6aac5b44c3af8b |
function [] = gen_7_5_kHz()
[s6_n2, s6_e2] = gen_sig(6);
[s15_n2, s15_e2] = gen_sig(15);
[s25_n2, s25_e2] = gen_sig(25);
[s50_n2, s50_e2] = gen_sig(50);
[s75_n2, s75_e2] = gen_sig(75);
[s100_n2, s100_e2] = gen_sig(100);
fd=fopen("kHz_7_5.h","w");
fprintf(fd,"s16 s6n_kHz_7_5[%d]__attribute__((aligned(16))) = {",lengt... |
github | Davonter/openairinterface5g-master | f_tls_diag.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/f_tls_diag.m | 1,272 | utf_8 | 443132469284a3d4d0b38bd5fb7d0522 | %
% PURPOSE : TLS solution for AX = B based on SVD assuming X is diagonal
%
% ARGUMENTS :
%
% A : observation of A
% B : observation of B
%
% OUTPUTS :
%
% X : TLS solution for X (Diagonal)
%
%**********************************************************************************************
% ... |
github | Davonter/openairinterface5g-master | f_tls_ap.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/f_tls_ap.m | 1,368 | utf_8 | 223603e551ebede67ff13452e95097b1 | %
% PURPOSE : TLS solution for AX = B based on alternative projection
%
% ARGUMENTS :
%
% A : observation of A
% B : observation of B
%
% OUTPUTS :
%
% X : TLS solution for X
%
%**********************************************************************************************
% ... |
github | Davonter/openairinterface5g-master | f_ofdm_rx.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/f_ofdm_rx.m | 1,545 | utf_8 | ade01596524abbe660fc84064cbb4724 | %
% PURPOSE : OFDM Receiver
%
% ARGUMENTS :
%
% m_sig_R : received signal with dimension ((d_N_FFT+d_N_CP)*d_N_ofdm) x d_N
% d_N_FFT : total carrier number
% d_N_CP : extented cyclic prefix
% d_N_OFDM : OFDM symbol number per frame
% v_active_rf : active RF antenna indicator
%
% OUTPUTS :
%
% m_sym_R ... |
github | Davonter/openairinterface5g-master | f_ch_est.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/f_ch_est.m | 1,711 | utf_8 | f583032bb1c37167a7ff2a029a629de9 | %
% PURPOSE : channel estimation using least square method
%
% ARGUMENTS :
%
% m_sym_T : transmitted symbol, d_N_f x d_N_ofdm x d_N_ant_act x d_N_meas
% m_sym_R : received symbol, d_N_f x d_N_ofdm x d_N_ant_act x d_N_meas
% d_N_meas : number of measurements
%
% OUTPUTS :
%
% m_H_est : estimation o... |
github | Davonter/openairinterface5g-master | f_ofdm_tx.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/f_ofdm_tx.m | 1,997 | utf_8 | 042917cd72b493f1384cbc5fa2fa2de5 | %
% PURPOSE : OFDM Transmitter
%
% ARGUMENTS :
%
% d_M : modulation order
% d_N_f : carrier number carrying data
% d_N_FFT : total carrier number
% d_N_CP : extented cyclic prefix
% d_N_OFDM : OFDM symbol number per frame
% v_active_rf : active RF antenna indicator
% d_amp : amplitude
%
% O... |
github | Davonter/openairinterface5g-master | f_ofdm_rx.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/v4_CH_EST/f_ofdm_rx.m | 1,545 | utf_8 | 798a54f027b266189ab1fdc57569dac1 | %
% PURPOSE : OFDM Receiver
%
% ARGUMENTS :
%
% m_sig_R : received signal with dimension ((d_N_FFT+d_N_CP)*d_N_ofdm) x d_N
% d_N_FFT : total carrier number
% d_N_CP : extented cyclic prefix
% d_N_OFDM : OFDM symbol number per frame
% v_active_rf : active RF antenna indicator
%
% OUTPUTS :
%
% m_sym_R ... |
github | Davonter/openairinterface5g-master | f_ch_est.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/v4_CH_EST/f_ch_est.m | 1,708 | utf_8 | ad1741afb57ea0bbc0da1f1f0d410ce6 |
% PURPOSE : channel estimation using least square method
%% ARGUMENTS :
%
% m_sym_T : transmitted symbol, d_N_f x d_N_ofdm x d_N_ant_act x d_N_meas
% m_sym_R : received symbol, d_N_f x d_N_ofdm x d_N_ant_act x d_N_meas
% d_N_meas : number of measurements
%
% OUTPUTS :
%
% m_H_est : estimation of s... |
github | Davonter/openairinterface5g-master | f_ofdm_tx.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/v4_CH_EST/f_ofdm_tx.m | 1,997 | utf_8 | 52b00cfe39a99e4f6d079fcc3cdad1f8 | %
% PURPOSE : OFDM Transmitter
%
% ARGUMENTS :
%
% d_M : modulation order
% d_N_f : carrier number carrying data
% d_N_FFT : total carrier number
% d_N_CP : extented cyclic prefix
% d_N_OFDM : OFDM symbol number per frame
% v_active_rf : active RF antenna indicator
% d_amp : amplitude
%
% O... |
github | Davonter/openairinterface5g-master | genorthqpskseq.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/v0/genorthqpskseq.m | 1,143 | utf_8 | 330b0dc2a723aa7a373d8a0cd01fcabb | # % Author: Mirsad Cirkic
# % Organisation: Eurecom (and Linkoping University)
# % E-mail: mirsad.cirkic@liu.se
function [carrierdata, s]=genorthqpskseq(Ns,N,amp)
if(N!=512*150)
error('The sequence length must be 76800.');
endif
s = zeros(N,Ns);
H=1; for k=1:log2(128) H=[H H; H -H]; end; H=H(:,1:120);
i=1; while... |
github | Davonter/openairinterface5g-master | genrandpskseq.m | .m | openairinterface5g-master/targets/PROJECTS/TDDREC/v0/genrandpskseq.m | 776 | utf_8 | 5cb33d8e20311847ffaa652cf70a4865 | % Author: Mirsad Cirkic
% Organisation: Eurecom (and Linkoping University)
% E-mail: mirsad.cirkic@liu.se
function [carrierdata, s]=genrandpskseq(N,M,amp)
if(mod(N,640)~=0)
error('The sequence length must be divisible with 640.');
end
s = zeros(N,1);
MPSK=exp(sqrt(-1)*([1:M]*2*pi/M+pi/M));
% OFDM sequence with ... |
github | Davonter/openairinterface5g-master | rfldec.m | .m | openairinterface5g-master/targets/ARCH/EXMIMO/USERSPACE/OCTAVE/rfldec.m | 363 | utf_8 | 24448e69682b1f6a6ea652c2426b08f7 | ## Decodes rf_local values: [ txi, txq, rxi, rxq ] = rfldec(rflocal)
## Author: Matthias Ihmig <ihmig@solstice>
## Created: 2012-12-05
function [ txi, txq, rxi, rxq ] = rfldec(rflocal)
txi = mod(floor( rflocal /1 ), 64)
txq = mod(floor( rflocal /64), 64)
rxi = mod(floor( rflocal /4096), 64)
rxq = mo... |
github | Davonter/openairinterface5g-master | rfl.m | .m | openairinterface5g-master/targets/ARCH/EXMIMO/USERSPACE/OCTAVE/rfl.m | 225 | utf_8 | 9ad201a94d01db3e65ecedb02252ca19 | ## Composes rf_local values: rfl(txi, txq, rxi, rxq)
## Author: Matthias Ihmig <ihmig@solstice>
## Created: 2012-12-05
function [ ret ] = rfl(txi, txq, rxi, rxq)
ret = txi + txq*2^6 + rxi*2^12 + rxq*2^18;
endfunction
|
github | metocean/pysmc-master | create_grid_smcbase.m | .m | pysmc-master/SMCPy/matlab/create_grid_smcbase.m | 11,589 | utf_8 | aa1c3bf7514af29170217dbe658c50c0 | % THIS IS AN EXAMPLE SCRIPT FOR GENERATING A GRID AND CAN BE USED
% AS A TEMPLATE FOR DESIGNING GRIDS
function []=create_grid_smcbase(id,latmin,latmax,lonmin,lonmax,dlat,dlon,ref_grid,boundary,IS_GLOBAL,LAKE_TOL,out_dir,testmode,fname_poly, shift_userpolys)
% 0. Initialization
% 0.a Path to directories
bin_di... |
github | sophont01/fStackIID-master | smooth_d.m | .m | fStackIID-master/utils/smooth_d.m | 688 | utf_8 | f82b36cb85b140dae43358166f5681b9 | %smooth the depth map to eliminate outliers
function x=smooth_d(im,tmp,mask)
[m n d]=size(im);
depth=reshape(tmp,[],1);
feature=reshape(im,[],d)';
c=20;
lambda=0.02;
row=[1:m*(n-1);m+1:m*n];
[a b]=ndgrid(1:m-1,1:n);
tmp=sub2ind([m n],reshape(a,1,[]),reshape(b,1,[]));
row=[row [tmp;tmp+1]];
value=exp(-c*sum(ab... |
github | sophont01/fStackIID-master | getVectors.m | .m | fStackIID-master/utils/getVectors.m | 305 | utf_8 | 3c948da1c7970d6eaf018498e9d38436 | %compute the view vector at each pixel
function p=getVectors(m,n,fov)
if(~exist('fov','var'))
fov=60;
end
x=((1:n)-(n+1)/2)/(n/2)*tan(fov/2/180*pi);
y=-((1:m)-(m+1)/2)/(m/2)*tan(fov/2/180*pi)*(m/n);
p=zeros(m,n,3);
for i=1:m
p(i,:,2)=y(i);
end
for i=1:n
p(:,i,1)=x(i);
end
p(:,:,3)=-1;
end |
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