plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | uncledickHe/FastICA-1-master | pcamat.m | .m | FastICA-1-master/pcamat.m | 12,075 | utf_8 | bcb1117d4132558d0d54d8b7b616a902 | function [E, D] = pcamat(vectors, firstEig, lastEig, s_interactive, ...
s_verbose);
%PCAMAT - Calculates the pca for data
%
% [E, D] = pcamat(vectors, firstEig, lastEig, ...
% interactive, verbose);
%
% Calculates the PCA matrices for given data (row) vectors. Returns
% the eigenvector (E) and diag... |
github | uncledickHe/FastICA-1-master | icaplot.m | .m | FastICA-1-master/icaplot.m | 13,259 | utf_8 | dde3e6d852f657a3c1eaacbd03f5dcc7 | function icaplot(mode, varargin);
%ICAPLOT - plot signals in various ways
%
% ICAPLOT is mainly for plottinf and comparing the mixed signals and
% separated ica-signals.
%
% ICAPLOT has many different modes. The first parameter of the function
% defines the mode. Other parameters and their order depends on the
% mode. ... |
github | ECNURoboLab/nimbro_picking-master | getColorFromID.m | .m | nimbro_picking-master/apc_object_perception/apc_segmentation/matlab/getColorFromID.m | 2,097 | utf_8 | dcfebcc07c95fb5a9cd79bfd1f991086 | function col=getColorFromID(id)
% get rgb [0 1] values from id
%
if id==0, col=zeros(1, 3); return; end
colors=getIDColors;
col=colors((mod(id, size(colors, 1)))+1, :);
end
function colors=getIDColors()
colors=[
128 255 255; %
255 0 0; % red 1
0 255 0; ... |
github | ECNURoboLab/nimbro_picking-master | pclviewer.m | .m | nimbro_picking-master/apc_object_perception/apc_segmentation/matlab/external/pcd/matpcl/pclviewer.m | 1,291 | utf_8 | f58f1e96b93686f38351e26b2c2db93f | %PCLVIEWER View a point cloud using PCL
%
% PCLVIEWER(P) writes the point cloud P (MxN) to a temporary file and invokes
% the PCL point cloud viewer for fast display and visualization. The columns of P
% represent the 3D points.
%
% If M=3 then the rows are x, y, z.
% If M=6 then the rows are x, y, z, R, G, B where R,... |
github | ECNURoboLab/nimbro_picking-master | loadpcd.m | .m | nimbro_picking-master/apc_object_perception/apc_segmentation/matlab/external/pcd/matpcl/loadpcd.m | 8,507 | utf_8 | bc2b81bf77de6a881efb122b16176a1f | %LOADPCD Load a point cloud from a PCD format file
%
% P = LOADPCD(FNAME) is a set of points loaded from the PCD format
% file FNAME.
%
% For an unorganized point cloud the columns of P represent the 3D points,
% and the rows are: x, y, z, r, g, b, a depending on the FIELDS in the file.
%
% For an organized point clo... |
github | ECNURoboLab/nimbro_picking-master | lzfd.m | .m | nimbro_picking-master/apc_object_perception/apc_segmentation/matlab/external/pcd/matpcl/lzfd.m | 2,169 | utf_8 | 1280ce0a291d2c9d98b06a4673a98535 | %LZFD LZF decompression
%
% OUT = LZFD(IN) is the decompressed version of the uint8 array IN.
%
% OUT = LZFD(IN, LEN) as above but sets the internal working buffer to length
% LEN which should exceed the expected uncompressed data size.
%
% Notes::
% - LZF is an algorithm that is efficient and gives reasonable compres... |
github | ECNURoboLab/nimbro_picking-master | lspcd.m | .m | nimbro_picking-master/apc_object_perception/apc_segmentation/matlab/external/pcd/matpcl/lspcd.m | 2,071 | utf_8 | e67de9778584a673b9569720ea4c72f9 | %LSPCD List attributes of PCD format files
%
% LSPCD() list the attributes of all .PCD files in the current folder.
%
% LSPCD(FILESPEC) as above but list only files that match FILESPEC which
% might contain a directory name and/or a wildcard.
%
%
% See also pclviewer, loadpcd.
%
% Copyright (C) 2013, by Peter I. Corke... |
github | ECNURoboLab/nimbro_picking-master | savepcd.m | .m | nimbro_picking-master/apc_object_perception/apc_segmentation/matlab/external/pcd/matpcl/savepcd.m | 4,547 | utf_8 | b6fc9de72f9c31f773ed98eb76072db1 | %SAVEPCD Write a point cloud to file in PCD format
%
% SAVEPCD(FNAME, P) writes the point cloud P to the file FNAME as an
% as a PCD format file.
%
% SAVEPCD(FNAME, P, 'binary') as above but save in binary format. Default
% is ascii format.
%
% If P is a 2-dimensional matrix (MxN) then the columns of P represent the
%... |
github | nevaehRen/Yeastbow_Cluster-master | Step3_1_Training_Process_Movie.m | .m | Yeastbow_Cluster-master/Step3_1_Training_Process_Movie.m | 1,290 | utf_8 | adc1e429b4c409f3bee79aa0fd4038c1 |
function Step3_1_Training_Process_Movie()
clear;clc;
File=dir('process*.mat');
for m=1:length(File)
load([File(m).name]);
Image = reshape(Image,PatchSize,PatchSize,[]);
Prediction = double(reshape(Prediction,PatchSize,PatchSize,[]));
close all;
figure(1);set(1,'Position',[100,100,800,400],'Color','w')... |
github | mengchuangji/AmazingTransferLearning-master | MyTJM.m | .m | AmazingTransferLearning-master/code/MyTJM.m | 3,517 | utf_8 | ce3d34bcb6ed86fc570f1f4f818ff2aa | function [acc,acc_list,A] = MyTJM(X_src,Y_src,X_tar,Y_tar,options)
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
%%% options:option struct
% Outputs:
%%% acc :f... |
github | mengchuangji/AmazingTransferLearning-master | MyJGSA.m | .m | AmazingTransferLearning-master/code/MyJGSA.m | 6,642 | utf_8 | 09a8f009556a3e0b09d10483558976ec | function [acc,acc_list,A,B] = MyJGSA(X_src,Y_src,X_tar,Y_tar,options)
%% Joint Geometrical and Statistic Adaptation
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
%%% options:option struct
% Ou... |
github | mengchuangji/AmazingTransferLearning-master | MyJDA.m | .m | AmazingTransferLearning-master/code/MyJDA.m | 4,118 | utf_8 | 54f4173e19b0dbf7b2572a964a6a3277 | function [acc,acc_ite,A] = MyJDA(X_src,Y_src,X_tar,Y_tar,options)
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
%%% options:option struct
% Outputs:
%%% acc ... |
github | mengchuangji/AmazingTransferLearning-master | MyGFK.m | .m | AmazingTransferLearning-master/code/MyGFK.m | 2,152 | utf_8 | a01af2b801cc7b96695684ce8e803547 | function [acc,G] = MyGFK(X_src,Y_src,X_tar,Y_tar,dim)
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
% Outputs:
%%% acc :accuracy after GFK and 1NN
%%% G ... |
github | mengchuangji/AmazingTransferLearning-master | MyTCA.m | .m | AmazingTransferLearning-master/code/MyTCA.m | 2,818 | utf_8 | 7aee1d32ebfb97f5974be024ce450ce1 | function [X_src_new,X_tar_new,A] = MyTCA(X_src,X_tar,options)
% Inputs: [dim is the dimension of features]
%%% X_src:source feature matrix, ns * dim
%%% X_tar:target feature matrix, nt * dim
%%% options:option struct
% Outputs:
%%% X_src_new:transformed source feature matrix, ns * dim_new
%%... |
github | mengchuangji/AmazingTransferLearning-master | lapgraph.m | .m | AmazingTransferLearning-master/code/MyARTL/lapgraph.m | 20,244 | utf_8 | cfed436191fe6a863089f6da80644260 | function [W, elapse] = lapgraph(fea,options)
% Usage:
% W = graph(fea,options)
%
% fea: Rows of vectors of data points. Each row is x_i
% options: Struct value in Matlab. The fields in options that can be set:
% Metric - Choices are:
% 'Euclidean' - Will use the Euclidean distance of two data... |
github | mengchuangji/AmazingTransferLearning-master | MyARTL.m | .m | AmazingTransferLearning-master/code/MyARTL/MyARTL.m | 3,503 | utf_8 | 91802921f23d322f2ffca0e311f9372a | function [acc,acc_ite,Alpha] = MyARTL(X_src,Y_src,X_tar,Y_tar,options)
% Inputs:
%%% X_src :source feature matrix, ns * m
%%% Y_src :source label vector, ns * 1
%%% X_tar :target feature matrix, nt * m
%%% Y_tar :target label vector, nt * 1
%%% options:option struct
% Outputs:
%%% ac... |
github | xhwang/joint_image_restoration-master | cross_field_re.m | .m | joint_image_restoration-master/cross_field_re.m | 4,606 | utf_8 | d7f02c729db2a411acad86d5f23b7ce0 |
function I = cross_field_re(I0, G, lambda, beta, eps, eta_sqr, phi_alpha, phi_eps, iternum)
show = 0;
S = ones(size(I0));
I = I0;
[m, n] = size(I);
Cx = get_Cx(m, n); Cy = get_Cy(m, n);
Cxt = get_Cxt(m, n); Cyt = get_Cyt(m, n);
Gx = cal(Cx, G); Gy = cal(Cy, G);
Px = 1 ./ (msign(Gx) .* max(abs(Gx), eps));
Py = 1... |
github | f-fathurrahman/ffr-MetodeNumerik-master | newton_v2.m | .m | ffr-MetodeNumerik-master/AkarPersamaan/octave/newton_v2.m | 1,424 | utf_8 | 0f526d7022f022cf086ae470328d0273 | % newtons.m to solve a set of nonlinear eqs f1(x)=0, f2(x)=0,..
function [x,fx,xx]= newton_v2(f, x0, TolX, MaxIter, varargin)
%input: f = a 1st-order vector ftn equivalent to a set of equations
% x0 = the initial guess of the solution
% TolX = the upper limit of |x(k)-x(k-1)|
% MaxIter= ... |
github | f-fathurrahman/ffr-MetodeNumerik-master | lagranp.m | .m | ffr-MetodeNumerik-master/InterpolasiFitting/octave/lagranp.m | 434 | utf_8 | 51f9232e1569f01f4b4d6d44bb88a0ae | %Program 3.1
function [l,L]=lagranp(x,y)
%Input : x=[x0 x1 ... xN], y=[y0 y1 ... yN]
%Output: l=Lagrange polynomial coefficients of order N
% L=Lagrange coefficient polynomial
N= length(x)-1; %the order of polynomial
l=0;
for m=1:N+1
P=1;
for k=1:N+1
if k~=m
P=conv(P,poly(x(k)))/(x(m... |
github | f-fathurrahman/ffr-MetodeNumerik-master | stdDev.m | .m | ffr-MetodeNumerik-master/InterpolasiFitting/octave/stdDev.m | 661 | utf_8 | 498d95cf79725fd2973f08e380188268 | function sigma = stdDev(coeff,xData,yData)
% Returns the standard deviation between data points and the polynomial
% a(1)*x^(m-1) + a(2)*x^(m-2) + ... + a(m)
% USAGE: sigma = stdDev(coeff,xData,yData)
% coeff = coefficients of the polynomial.
% xData = x-coordinates of data points.
% yData = y-coordinates of data point... |
github | f-fathurrahman/ffr-MetodeNumerik-master | splineEval.m | .m | ffr-MetodeNumerik-master/InterpolasiFitting/octave/splineEval.m | 831 | utf_8 | ae95c918e043b4c17f6e25114a27f407 | function y = splineEval(xData,yData,k,x)
% Returns value of cubic spline interpolant at x.
% USAGE: y = splineEval(xData,yData,k,x)
% xData = x-coordinates of data points.
% yData = y-coordinates of data points.
% k = curvatures of spline at the knots;
% returned by the function splineCurv.
i = findSeg(xData,x);
... |
github | f-fathurrahman/ffr-MetodeNumerik-master | shoot2.m | .m | ffr-MetodeNumerik-master/BVP/octave/shoot2.m | 845 | utf_8 | 31b78e74f29b92c8d8c1c1181218dead |
addpath('../../AkarPersamaan/octave')
addpath('../../IVP/octave')
function F = dEqs(x,y)
% First-order differential
F = [y(2), -3*y(1)*y(2)]; % equations.
endfunction
function y = inCond(u) % Initial conditions (u is
y = [0 u]; % the unknown condition).
endfunction
%function shoot2
% Shooting method for 2nd... |
github | f-fathurrahman/ffr-MetodeNumerik-master | powell.m | .m | ffr-MetodeNumerik-master/Optimisasi/octave/powell.m | 1,713 | utf_8 | a11ec775a54d5dd44174a17ab2300ff0 | function [xMin,fMin,nCyc] = powell(func,x,h,tol)
% Powell’s method for minimizing f(x1,x2,...,xn).
% USAGE: [xMin,fMin,nCyc] = powell(h,tol)
% INPUT:
% func = handle of function that returns f.
% x = starting point
% h = initial search increment (default = 0.1).
% tol = error tolerance (default = 1.0e-6).
% OUTPUT:
% x... |
github | taodeng/Top-down-based-traffic-driving-saliency-model-master | convolve_gLoG.m | .m | Top-down-based-traffic-driving-saliency-model-master/vanishing point detection/convolve_gLoG.m | 1,193 | utf_8 | 229fb14b4020504281e6a1e6f1458577 | %%%%%%%% convoluation of an image with gLoG filters of the same
%%%%%%%% orientation
function [responseMap] = convolve_gLoG(img, smallestSigma, largestSigma, theta)
imgH = size(img,1);
imgW = size(img,2);
responseMap = zeros(imgH,imgW);
sigmaStep = -1;
newKerSize = 3*largestSigma;
hsize2 = newKerSize/2;
... |
github | mehryaragha/NoseBiometrics-master | compute_mid_points.m | .m | NoseBiometrics-master/compute_mid_points.m | 1,086 | utf_8 | 022157866ed49903cde7725ca6881ee6 | % Written by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 25 June 2017
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
function f... |
github | mehryaragha/NoseBiometrics-master | curve_cropper.m | .m | NoseBiometrics-master/curve_cropper.m | 878 | utf_8 | ffa95181dbba13a6e79e968f5b086978 | % Written by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 31 December 2018
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
... |
github | mehryaragha/NoseBiometrics-master | Gabor_wavelet_computer.m | .m | NoseBiometrics-master/Gabor_wavelet_computer.m | 1,789 | utf_8 | 9eb1dd59591ecdab09b928bca2af477f | % Written by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 25 June 2017
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
function a... |
github | mehryaragha/NoseBiometrics-master | feature_extraction_spheres.m | .m | NoseBiometrics-master/feature_extraction_spheres.m | 2,504 | utf_8 | 6d16837470ca9308bfb3af956400be7d | % Written by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 25 June 2017
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
function a... |
github | mehryaragha/NoseBiometrics-master | gabor_by_meshgrid.m | .m | NoseBiometrics-master/gabor_by_meshgrid.m | 2,357 | utf_8 | 42f358abce39be02aebaff491ab62072 | % Modified by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 25 June 2017
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
% This is... |
github | mehryaragha/NoseBiometrics-master | get_the_line3.m | .m | NoseBiometrics-master/get_the_line3.m | 582 | utf_8 | d54cc850b64476213a09b001b8a9bc37 | % Written by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 31 December 2018
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
... |
github | mehryaragha/NoseBiometrics-master | create_landmarks.m | .m | NoseBiometrics-master/create_landmarks.m | 7,941 | utf_8 | ea1aaacee536f04bccc309b824111b54 | % Written by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 25 June 2017
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
function m... |
github | mehryaragha/NoseBiometrics-master | Demo_nasal_curves_patched.m | .m | NoseBiometrics-master/Demo_nasal_curves_patched.m | 5,350 | utf_8 | 8aa65f1b7eef94b7dd34d4f3310b9197 | % Written by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 31 December 2018
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
functi... |
github | mehryaragha/NoseBiometrics-master | Demo_spherical_patched.m | .m | NoseBiometrics-master/Demo_spherical_patched.m | 3,416 | utf_8 | 291efe76e06a8168af93e4d49740eeec | % Written by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 25 June 2017
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
function D... |
github | mehryaragha/NoseBiometrics-master | Normal_vector_computer.m | .m | NoseBiometrics-master/Normal_vector_computer.m | 1,013 | utf_8 | c4c6e10e8455301d70f5b71cec0fffa9 | % Written by: Mehryar Emambakhsh
% Email: mehryar_emam@yahoo.com
% Date: 25 June 2017
% Paper:
% M. Emambakhsh and A. Evans, “Nasal patches and curves for an expression-robust 3D face recognition,”
% IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 39, no. 5, pp. 995-1007, 2017.
function a... |
github | yqueau/shape_from_shading-master | export_obj2.m | .m | shape_from_shading-master/Toolbox/export_obj2.m | 11,644 | iso_8859_1 | 9242380a00fbba473f3004c6ea2ff5d4 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Nom ............ : export_obj.m
% Version ........ : 1
%
% Description..... : Save the reconstruction OBJ format, in
% mesh.obj, mesh.mtl and mesh.png
% INPUT : XYZ, N, RHO -- nrows x ncols x 3
%
% Auteur ......... : Yvain ... |
github | yqueau/shape_from_shading-master | make_gradient.m | .m | shape_from_shading-master/Toolbox/make_gradient.m | 3,744 | utf_8 | 81093565d669618752ea1c3a8765a8ef | % Functions for computing the gradient operator on non-rectangular domains
function [M,imask] = make_gradient(mask)
% Compute forward (Dxp and Dyp) and backward (Dxm and Dym) operators
[Dyp,Dym,Dxp,Dxm,Sup,Sum,Svp,Svm,Omega,index_matrix,imask] = gradient_operators(mask);
[nrows,ncols] = size(mask);
% When there i... |
github | yqueau/shape_from_shading-master | WolfeLineSearch.m | .m | shape_from_shading-master/Toolbox/minFunc/minFunc/WolfeLineSearch.m | 10,590 | utf_8 | f962bc5ae0a1e9f80202a9aaab106dab | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS_interp,LS_multi,maxLS,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function v... |
github | yqueau/shape_from_shading-master | minFunc_processInputOptions.m | .m | shape_from_shading-master/Toolbox/minFunc/minFunc/minFunc_processInputOptions.m | 4,103 | utf_8 | 8822581c3541eabe5ce7c7927a57c9ab |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,optTol,progTol,method,...
corrections,c1,c2,LS_init,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCurv,precFunc... |
github | beckja/sgp4-matlab-master | dspace.m | .m | sgp4-matlab-master/dspace.m | 7,880 | utf_8 | 347493d5de5dfaf0d63365d060fe842f | % -----------------------------------------------------------------------------
%
% procedure dspace
%
% this procedure provides deep space contributions to mean elements for
% perturbing third body. these effects have been averaged over one
% revolution of the sun and moon. ... |
github | beckja/sgp4-matlab-master | twoline2rv.m | .m | sgp4-matlab-master/twoline2rv.m | 7,690 | utf_8 | 847f10aaec770fb70b0cf6860cad87c7 | % -----------------------------------------------------------------------------
%
% procedure twoline2rv
%
% this procedure converts the two line element set character string data to
% variables and initializes the sgp4 variables. several intermediate varaibles
% and quanti... |
github | beckja/sgp4-matlab-master | dscom.m | .m | sgp4-matlab-master/dscom.m | 8,536 | utf_8 | 9d59d451aa72b865cf7d6f6f4d1a3901 | % -----------------------------------------------------------------------------
%
% procedure dscom
%
% this procedure provides deep space common items used by both the secular
% and periodics subroutines. input is provided as shown. this routine
% used to be called dpper, bu... |
github | beckja/sgp4-matlab-master | initl.m | .m | sgp4-matlab-master/initl.m | 3,468 | utf_8 | bc9bed1f2a7eec0c0bebf4d4a2ff7b5e | % -----------------------------------------------------------------------------
%
% procedure initl
%
% this procedure initializes the spg4 propagator. all the initialization is
% consolidated here instead of having multiple loops inside other routines.
%
% Author:
% Jeff Bec... |
github | beckja/sgp4-matlab-master | sgp4.m | .m | sgp4-matlab-master/sgp4.m | 11,728 | utf_8 | 4a678ba2460bab657b90e30b4475d8fa | % -----------------------------------------------------------------------------
%
% procedure sgp4
%
% this procedure is the sgp4 prediction model from space command. this is an
% updated and combined version of sgp4 and sdp4, which were originally
% published separately in ... |
github | beckja/sgp4-matlab-master | sgp4init.m | .m | sgp4-matlab-master/sgp4init.m | 14,082 | utf_8 | e81058548e3f659b302e532f39a9d5eb | % -----------------------------------------------------------------------------
%
% procedure sgp4init
%
% this procedure initializes variables for sgp4.
%
% Author:
% Jeff Beck
% beckja@alumni.lehigh.edu
% 1.0 (aug 7, 2006) - update for paper dav
% original comments fro... |
github | beckja/sgp4-matlab-master | dsinit.m | .m | sgp4-matlab-master/dsinit.m | 11,670 | utf_8 | da55f2ee28e1972f38a6439b8ffb42cb | % -----------------------------------------------------------------------------
%
% procedure dsinit
%
% this procedure provides deep space contributions to mean motion dot due
% to geopotential resonance with half day and one day orbits.
%
% Author:
% Jeff Beck
% beckja@... |
github | beckja/sgp4-matlab-master | getgravc.m | .m | sgp4-matlab-master/getgravc.m | 2,528 | utf_8 | ae2503a6d6bc8364024c498f6aa53c1d | % -----------------------------------------------------------------------------
%
% function getgravc
%
% this function gets constants for the propagator. note that mu is identified to
% facilitiate comparisons with newer models.
%
% author : david vallado 7... |
github | beckja/sgp4-matlab-master | dpper.m | .m | sgp4-matlab-master/dpper.m | 6,425 | utf_8 | 68de1aa803b8f81da5d8c912ea768925 | % -----------------------------------------------------------------------------
%
% procedure dpper
%
% this procedure provides deep space long period periodic contributions
% to the mean elements. by design, these periodics are zero at epoch.
% this used to be dscom which i... |
github | WTCN-computational-anatomy-group/Shape-Appearance-Model-master | RunSegment.m | .m | Shape-Appearance-Model-master/RunSegment.m | 1,836 | utf_8 | 46624029608ef5ccac13d7c99650b8fc | function RunSegment
% Segment a bunch of images to generate "imported" versions.
% This is a very simple function for doing this.
images = spm_select(Inf,'nifti');
parfor i=1:size(images,1)
image = deblank(images(i,:));
fprintf('Segmenting %s... ', image);
tic
do_seg(image)
fprintf('...%g s\n', to... |
github | WTCN-computational-anatomy-group/Shape-Appearance-Model-master | PG.m | .m | Shape-Appearance-Model-master/PG.m | 5,032 | utf_8 | 2a7dc384d9b8af45b4366ac123830288 |
function PG(dat,s)
% Combined principal geodesic analysis and generalized(ish) PCA
% FORMAT PG(dat,s)
% dat - a data structure containing filenames etc
% s - various settings
%__________________________________________________________________________
% Copyright (C) 2017 Wellcome Trust Centre for Neuroimaging
% Joh... |
github | WTCN-computational-anatomy-group/Shape-Appearance-Model-master | UpdateZpar.m | .m | Shape-Appearance-Model-master/UpdateZpar.m | 10,887 | utf_8 | 9aa54220e670de9748ac5e9db0e85705 | function [z,S,L,omisc] = UpdateZpar(z,f,mu,Wa,Wv,A,s,noise)
% Update latent variables for all images
% FORMAT [z,S,L,omisc] = UpdateZpar(z,f,mu,Wa,Wv,A,s,noise)
%
% z - Cell array of latent variables
% f - Cell array of observations
% mu - Mean
% Wa - Appearance basis functions
% Wv - Shape basis funct... |
github | WTCN-computational-anatomy-group/Shape-Appearance-Model-master | ShapeDerivatives.m | .m | Shape-Appearance-Model-master/ShapeDerivatives.m | 5,129 | utf_8 | 59874b2be246eebdea37d2415bbbeba4 | function [gv,Hv,nll] = ShapeDerivatives(dat,mu,Wa,Wv,noise,s)
% Return derivatives w.r.t. shape basis functions
% FORMAT [gv,Hv,nll] = ShapeDerivatives(dat,mu,Wa,Wv,noise,s)
%
% dat - Structure containing various information about each image.
% Fields for each image n are:
% dat(n).f - Image data.
% ... |
github | WTCN-computational-anatomy-group/Shape-Appearance-Model-master | runPGfit.m | .m | Shape-Appearance-Model-master/runPGfit.m | 2,121 | utf_8 | 9a1ff3483c2c998e3811edd46c72e5e9 | function runPGfit(jsn_settings)
% Run The shape-appearance model on "imported" scans
% For help, type:
% runPG --help
%__________________________________________________________________________
% Copyright (C) 2017 Wellcome Trust Centre for Neuroimaging
% John Ashburner
% $Id$
if nargin<1 || (ischar(jsn_settings) && ... |
github | WTCN-computational-anatomy-group/Shape-Appearance-Model-master | OrthogonalisationMat.m | .m | Shape-Appearance-Model-master/OrthogonalisationMat.m | 4,481 | utf_8 | 804e54eaf9b5550ccec1cc2ea8ec6236 | function [T,iT,A] = OrthogonalisationMat(ZZ,S,WW,N,s)
% Orthogonalisation matrix
% FORMAT [T,iT,A] = OrthogonalisationMat(ZZ,S,WW,N,s)
%
% ZZ - Z*Z', where Z are the latent variables
% S - E[Z*Z'] = Z*Z' + S
% WW - Wa'*La*Wa + Wv'*Lv*Wv
% N - size(Z,2)
% s - Settings.
%
% T - Transform.
% iT - ... |
github | WTCN-computational-anatomy-group/Shape-Appearance-Model-master | runPG.m | .m | Shape-Appearance-Model-master/runPG.m | 4,027 | utf_8 | 2655cc1399f4ae964792a6e35f3394ec | function runPG(jsn_filenames, jsn_settings)
% Run The shape-appearance model on "imported" scans
% For help, type:
% runPG --help
%__________________________________________________________________________
% Copyright (C) 2017 Wellcome Trust Centre for Neuroimaging
% John Ashburner
% $Id$
if nargin>=2
settings = ... |
github | oshaban/MusicRecognition-master | orionGUI.m | .m | MusicRecognition-master/orionGUI.m | 5,156 | utf_8 | f8e18abecc150c003678e78f42b0b9f9 | function varargout = orionGUI(varargin)
% ORIONGUI MATLAB code for orionGUI.fig
% ORIONGUI, by itself, creates a new ORIONGUI or raises the existing
% singleton*.
%
% H = ORIONGUI returns the handle to a new ORIONGUI or the handle to
% the existing singleton*.
%
% ORIONGUI('CALLBACK',hObject,ev... |
github | emilymacq/Project-Clear-Lungs-master | mfcc.m | .m | Project-Clear-Lungs-master/ARCHIVE/Matlab Files/mfcc.m | 4,866 | utf_8 | 953a29880fb3e712bee932d0f8ca9e4a | [x Fs] = audioread('Crackles - Early Inspiratory (Rales).mp3');
left_channel = x(:,1);
coefficients = melcepst(left_channel, Fs)
function [c,tc]=melcepst(s,fs,w,nc,p,n,inc,fl,fh)
%MELCEPST Calculate the mel cepstrum of a signal C=(S,FS,W,NC,P,N,INC,FL,FH)
%
%
% Simple use: (1) c=melcepst(s,fs) % cal... |
github | zelanolab/breathmetrics-master | getSecondaryRespiratoryFeatures.m | .m | breathmetrics-master/breathmetrics_functions/getSecondaryRespiratoryFeatures.m | 8,041 | utf_8 | 8977404fbda7d4cd229c1b0202d4d726 | function respirationStatistics = getSecondaryRespiratoryFeatures( Bm, verbose )
%calculates features of respiratory data. Running this method assumes that
% you have already derived all possible features
if nargin < 2
verbose = 0;
end
if verbose == 1
disp('Calculating secondary respiratory features')
end
... |
github | zelanolab/breathmetrics-master | bmGui.m | .m | breathmetrics-master/breathmetrics_functions/bmGui.m | 31,014 | utf_8 | 4e14dbe96f2d5f05f74db27414b25bb4 | function newBM = bmGui(bmObj)
% This is the GUI for breathmetrics that allows users to edit respiratory
% features, annotate breaths, and reject breaths from analysis.
%
% input: bmObj is a breathmetrics class object that has feature
% estimations complete
%
% output: newBM is the modified breathmetrics object. If t... |
github | lbrandt/ez-shocks-master | jln_compute_uy.m | .m | ez-shocks-master/JLN2015/jln_compute_uy.m | 1,919 | utf_8 | b8836eddf77388356060468d2369e765 | function [U, evy, phi, lambda, phiy] = jln_compute_uy(xy,thy,yb,py,evf,phif)
% -------------------------------------------------------------------------
% Compute expected volatility of predictors up to horizon h
% -------------------------------------------------------------------------
% Initialize parameters
h = l... |
github | lbrandt/ez-shocks-master | jln_compute_uf.m | .m | ez-shocks-master/JLN2015/jln_compute_uf.m | 1,211 | utf_8 | 1a05c90e2da7e0b8622fbdf4cd008662 | function [evf,phif] = jln_compute_uf(xf,thf,fb,h)
% -------------------------------------------------------------------------
% Compute expected volatility of predictors up to horizon h,and construct
% coefficient matrix phiF
% -------------------------------------------------------------------------
% Initialize para... |
github | lbrandt/ez-shocks-master | compute_uy.m | .m | ez-shocks-master/MATLAB/compute_uy.m | 2,279 | utf_8 | fb890a41e1a15dc300343172f9686568 | function [U, evy] = compute_uy(xy,thy,yb,py,evf,phif)
% -------------------------------------------------------------------------
% Compute expected volatility of predictors up to horizon h
%
%
% Input
% xy Latent?
% thy Parameter vector of logVar process [3 x 1]
% yb ybeta... |
github | lbrandt/ez-shocks-master | kde.m | .m | ez-shocks-master/MATLAB/kde.m | 5,478 | utf_8 | 83553f2f78c03c6231ba48e24eb20343 | function [bandwidth,density,xmesh,cdf]=kde(data,n,MIN,MAX)
% Reliable and extremely fast kernel density estimator for one-dimensional data;
% Gaussian kernel is assumed and the bandwidth is chosen automatically;
% Unlike many other implementations, this one is immune to problems
% caused by multimo... |
github | MatthewPeterKelly/DirCol5i-master | getPpState.m | .m | DirCol5i-master/DirCol5i/getPpState.m | 2,418 | utf_8 | 7e0733032826758d23a4c10b4ecdebde | function [PPx, PPdx, PPddx, PPdddx, PPddddx] = getPpState(t,x,dx,ddx)
% [PPx, PPdx, PPddx, PPdddx, PPddddx] = getPpState(t,x,dx,ddx)
%
% This function computes pp-form splines for the state and derivatives, to
% be evalauted by Matlab's ppval() command.
%
% INPUTS:
% t = [1,nt] = time at the knot points
% x = [nx,n... |
github | MatthewPeterKelly/DirCol5i-master | Derive_Splines.m | .m | DirCol5i-master/DirCol5i/Derive_Splines.m | 2,334 | utf_8 | daf7621dfaf229534a357716fc7cbb59 | function Derive_Splines()
%
% Derive the equations that are usedto construct the splines to interpolate
% the solution.
%
% Here we assume that the function is defined over the domain [tA, tB]
%
% The state and control at the upper boundary is given by xB and uB, while
% the state and control ad the lower boundary is g... |
github | MatthewPeterKelly/DirCol5i-master | meshAnalysis.m | .m | DirCol5i-master/DirCol5i/meshAnalysis.m | 2,386 | utf_8 | 380d2c8d28aadcda6056613a31f81ccc | function mesh = meshAnalysis(soln,F,Opt)
%
% This function computes the error estimates for a given solution to the
% trajectory optimization problem.
%
t = soln.knotPts.t;
ns = length(t)-1;
nx = size(soln.knotPts.x,1);
Eta = zeros(nx,ns); % Integral of absolute error in each segment
dynErr = @(time)( abs(getDynErr... |
github | MatthewPeterKelly/DirCol5i-master | dirCol5i.m | .m | DirCol5i-master/DirCol5i/dirCol5i.m | 20,012 | utf_8 | eb1247a06c75c294e407759448a9dfe9 | function output = dirCol5i(problem)
% output = dirCol5i(problem)
%
% Solves a continuous-time, single-phase trajectory optimization problem,
% with implicit second order dynamics.
%
% NOTATION:
%
% continuous (path) input, listed in order:
% t = [1, nTime] = time vector (col points)
% ... |
github | MatthewPeterKelly/DirCol5i-master | Derive_Coeffs.m | .m | DirCol5i-master/DirCol5i/Derive_Coeffs.m | 3,023 | utf_8 | a117095c256a3938299f213fff8977c9 | function Derive_Coeffs()
%
% Derive the equations that are used to compute the collocation points
%
% The state and control at the upper boundary is given by xB and uB, while
% the state and control ad the lower boundary is given by xA and uA;
%
deriveCollocationState();
deriveCollocationControl();
end
%%%%%%%%%%%%... |
github | MatthewPeterKelly/DirCol5i-master | getPpControl.m | .m | DirCol5i-master/DirCol5i/getPpControl.m | 1,475 | utf_8 | 1a1cecad8544790a88c775fe0200eb24 | function [PPu, PPdu] = getPpControl(t,u,du)
% [PPu, PPdu] = getPpControl(t,u,du)
%
% This function computes pp-form splines for the control and control rate,
% to be evalauted by Matlab's ppval() command.
%
% INPUTS:
% t = [1,nt] = time at the knot points
% u = [nu,nt] = position at the knot points
% du = [nu,nt]... |
github | MatthewPeterKelly/DirCol5i-master | drawCartPoleAnim.m | .m | DirCol5i-master/DirCol5i/demo/cartPole/drawCartPoleAnim.m | 2,133 | utf_8 | 2334402558a3114d7f969148319c70cd | function drawCartPoleAnim(~,p,xLow, xUpp, yLow, yUpp)
% drawCartPoleTraj(t,p,xLow, xUpp, yLow, yUpp)
%
% INPUTS:
% t = [1,n] = time stamp for the data in p1 and p2
% p = [4,n] = [p1;p2];
%
clf; hold on;
Cart_Width = 0.15;
Cart_Height = 0.05;
p1 = p(1:2,:);
p2 = p(3:4,:);
Pole_Width = 4; %pixels
%%%% Figure... |
github | MatthewPeterKelly/DirCol5i-master | drawCartPoleTraj.m | .m | DirCol5i-master/DirCol5i/demo/cartPole/drawCartPoleTraj.m | 2,398 | utf_8 | 20f52ba1988786981dace289cd54ae77 | function drawCartPoleTraj(t,p1,p2,nFrame,scale)
% drawCartPoleTraj(t,p1,p2,nFrame,scale)
%
% INPUTS:
% t = [1,n] = time stamp for the data in p1 and p2
% p1 = [2,n] = [x;y] = position of center of the cart
% p2 = [2,n] = [x;y] = position of tip of the pendulum
% nFrame = scalar integer = number of "freeze" fra... |
github | MatthewPeterKelly/DirCol5i-master | Derive_Equations.m | .m | DirCol5i-master/DirCol5i/demo/fiveLinkBiped/Derive_Equations.m | 22,988 | utf_8 | 5a6b34d66510b3b791e404e07d4dc5ec | function Derive_Equations()
%%%% Derive Equations - Five Link Biped Model %%%%
%
% This function derives the equations of motion, as well as some other useful
% equations (kinematics, contact forces, ...) for the five-link biped
% model.
%
%
% Nomenclature:
%
% - There are five links, which will be numbered starting wi... |
github | MaralKay/Scale-invariant-Heat-Kernel-Signature-Descriptor-Evaluation-master | hks.m | .m | Scale-invariant-Heat-Kernel-Signature-Descriptor-Evaluation-master/hks.m | 774 | utf_8 | 04ef538fa99a2f2ff4c904bd1f45fbd3 | % Computes heat kernel signature H_t(x,x), where H_t(x,y) is the heat kernel
%
% Usage: desc = hks(evecs,evals,T)
%
% Input: evecs - (n x k) Laplace-Beltrami eigenvectors arranged as columns
% evals - (k x 1) corresponding Laplace-Beltrami eigenvalues
% T - (1 x t) time values
%
% Output: ... |
github | MaralKay/Scale-invariant-Heat-Kernel-Signature-Descriptor-Evaluation-master | sihks.m | .m | Scale-invariant-Heat-Kernel-Signature-Descriptor-Evaluation-master/sihks.m | 1,242 | utf_8 | 4965726d4d73b1af91710031977ab428 | % Computes scale-covariant and scale-invariant heat kernel signature (SI-HKS)
%
% Usage: [sc,si] = sihks(evecs,evals,T,Omega)
%
% Input: evecs - (n x k) Laplace-Beltrami eigenvectors arranged as columns
% evals - (k x 1) corresponding Laplace-Beltrami eigenvalues
% alpha - log scalespace basis... |
github | MaralKay/Scale-invariant-Heat-Kernel-Signature-Descriptor-Evaluation-master | mshlp_matrix.m | .m | Scale-invariant-Heat-Kernel-Signature-Descriptor-Evaluation-master/mshlp_matrix.m | 1,679 | utf_8 | 769d89f1fad49c165703383fd490805c | function [W A] = mshlp_matrix(shape, opt)
%
% Compute the Laplace-Beltrami matrix from mesh
%
% INPUTS
% filename: off file of triangle mesh.
% opt.htype: the way to compute the parameter h. h = hs * neighborhoodsize
% if htype = 'ddr' (data driven); h = hs if hytpe = 'psp' (pre-specify)
% De... |
github | albanie/mcnDeepLab-master | setup_mcnDeepLab.m | .m | mcnDeepLab-master/setup_mcnDeepLab.m | 1,531 | utf_8 | 5dc2dfa0f371dc30c1138a3a658907f2 | function setup_mcnDeepLab()
%SETUP_MCNDEEPLAB Sets up mcnDeepLab, by adding its folders
% to the Matlab path
%
% Copyright (C) 2017 Samuel Albanie
% Licensed under The MIT License [see LICENSE.md for details]
% add dependencies
check_dependency('autonn') ;
check_dependency('mcnExtraLayers') ;
check_dependency... |
github | albanie/mcnDeepLab-master | deeplab_demo.m | .m | mcnDeepLab-master/deeplab_demo.m | 3,026 | utf_8 | 9e0427727fae3e9a179bbd095c3ae49d | function deeplab_demo(varargin)
%DEEPLAB_DEMO Minimalistic demonstration of a pretrained deeplab model
% DEEPLAB_DEMO a semantic segmentation demo with a deeplab model
%
% DEEPLAB_DEMO(..., 'option', value, ...) accepts the following
% options:
%
% `modelPath`:: ''
% Path to a valid deeplab matconvnet model.... |
github | albanie/mcnDeepLab-master | deeplab_evaluation.m | .m | mcnDeepLab-master/core/deeplab_evaluation.m | 6,225 | utf_8 | fc1dc52c89ad6ed118bf665a3e5e6e74 | function info = deeplab_evaluation(expDir, net, opts)
% Setup data
if exist(opts.dataOpts.imdbPath, 'file')
imdb = load(opts.dataOpts.imdbPath) ;
else
imdb = opts.dataOpts.getImdb(opts) ;
imdbDir = fileparts(opts.dataOpts.imdbPath) ;
if ~exist(imdbDir, 'dir'), mkdir(imdbDir) ; end
save(opts.d... |
github | albanie/mcnDeepLab-master | deeplab_zoo.m | .m | mcnDeepLab-master/core/deeplab_zoo.m | 1,653 | utf_8 | 310b5820ee1d14f7689d844df7c474d4 | function net = deeplab_zoo(modelName)
%DEEPLAB_ZOO - load segmentation network by name
% DEEPLAB_ZOO(MODELNAME) - loads a segmenter by its given name.
% If it cannot be found on disk, it will be downloaded via the world
% wide web.
%
% Copyright (C) 2017 Samuel Albanie
% Licensed under The MIT License [see LICENSE... |
github | albanie/mcnDeepLab-master | deeplab_pascal_evaluation.m | .m | mcnDeepLab-master/pascal/deeplab_pascal_evaluation.m | 5,141 | utf_8 | c0b0c72bdc03a36ad8dc210abfa818fc | function deeplab_pascal_evaluation(varargin)
%DEEPLAB_PASCAL_EVALUATION evaluate FCN on pascal VOC 2012
%
% Copyright (C) 2017 Samuel Albanie
% Licensed under The MIT License [see LICENSE.md for details]
opts.net = [] ;
opts.gpus = 1 ;
opts.modelName = 'deeplab-vggvd-v2' ;
opts.dataDir = fullfile(vl_rootnn, '... |
github | gregjeffrey/Power-System-Fault-Analysis-master | get_Ybus.m | .m | Power-System-Fault-Analysis-master/get_Ybus.m | 1,087 | utf_8 | 9dfa600ce65ddbdda2e4bb5615c7144a | %%
%Function get_Ybus
%Creates admittance matrix given dataset of resistances, reactances, and
%susceptances.
%Inputs:
%data: matrix containing the given data
%R: Number of the column in 'data' containing Resistance
%X: Number of the column in 'data' containing Reactance
%B: Number of the column in 'data' containing S... |
github | dbyun425/micscanning-master | load_mic.m | .m | micscanning-master/load_mic.m | 898 | utf_8 | b664719a4bce1a20a54bcafed037c6b7 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% load mic file - just like XDM Toolkit
%
%
% sidewidth is the width of one side of the triangle
%
%
% File Format:
% Col 1-3 x, y, z
% Col 4 1 = triangle pointing up, 2 = triangle pointing down
% Col 5 - Generation number
% Col 7-9 orientation
% Col 10 Confidence
%
%
f... |
github | dbyun425/micscanning-master | plot_mic_V2017.m | .m | micscanning-master/plot_mic_V2017.m | 24,584 | utf_8 | 91e26a25a250747f9a587a1c4e5bf52f | % The code contained in this file originated in the CMU Suter Research
% Group with contributions from Frankie Li, Jonathan Lind, David Menasche,
% Rulin Chen, RM Suter, and others.
% A set of utility functions (rotation representation conversions) from the
% Cornell University group of Paul Dawson et al is used and... |
github | EvanZhuang/MRI-Reconstruction-with-Sparse-Optimization-master | project.m | .m | MRI-Reconstruction-with-Sparse-Optimization-master/project.m | 1,132 | utf_8 | c5d93efc36b5bf7d07eb13d0de914d34 | % Take an image and project in into a matrix
% suitable for backprojection
%
% Calling: ct_data = project (image_data, K)
%
% Input: image_data - The gray scale image
% K - Number of projections
%
% Output: ct_data - The projected data
%
% version 1.2, 6/11-97 Joergen Arendt Jensen... |
github | wenchaodudu/SNLDA-master | LapPLSI.m | .m | SNLDA-master/LapPLSI/LapPLSI.m | 8,559 | utf_8 | 6da6b433a5a9f0bc241b5c792b1c5d4b | function [Pz_d_final, Pw_z_final, Obj_final, nIter_final,Pz_d_init,Pw_z_init] = LapPLSI(X, K, W, options, Pz_d, Pw_z)
% Laplacian Probabilistic Latent Semantic Indexing/Alnalysis (LapPLSI) using generalized EM
%
% where
% X
% Notation:
% X ... (mFea x nSmp) term-document matrix (observed data)
% X(i,j) s... |
github | fennialesmana/sleep-stage-identification-master | PSOforSVM.m | .m | sleep-stage-identification-master/PSOforSVM.m | 8,792 | utf_8 | 49a47260c88138452e9cff72a3245d90 | function [result, startTime, endTime] = PSOforSVM(nFeatures, trainingData, ...
testingData, PSOSettings)
%Running PSO with SVM for feature selection
% Syntax:
% [result, startTime, endTime] = PSOforSVM(nFeatures, trainingData, ...
% testingData, PSOSettings)
%
% Input:
% *) nFeatures - total number... |
github | fennialesmana/sleep-stage-identification-master | PSOforELM.m | .m | sleep-stage-identification-master/PSOforELM.m | 10,611 | utf_8 | 204d568999568aafa1e6b26495535184 | function [result, startTime, endTime] = PSOforELM(nFeatures, trainingData, ...
testingData, PSOSettings)
%Running PSO with ELM for feature selection and number of hidden nodes
%optimization
% Syntax:
% [result, startTime, endTime] = PSOforELM(nFeatures, trainingData, ...
% testingData, PSOSettings)
%
% ... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | kfold_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/TWSVM/kfold_eval.m | 4,734 | utf_8 | c1ec58774ff997676fa8879f8fcaf986 |
function [mean_acc] = seperate_eval(name)
addpath([pwd '\twsvm']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
tot_data = load([datapath name '\' name '_R.dat']);
index_tune = importdata ([d... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | seperate_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/TWSVM/seperate_eval.m | 4,663 | utf_8 | e28eab0313e4b288c9b4fdafd338b3e9 |
function [test_acc] = seperate_eval(name)
addpath([pwd '\twsvm']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
train = load ([datapath name '\' name '_train_R.dat']);% for datasets where trai... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | kfold_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/TBSVM/kfold_eval.m | 5,080 | utf_8 | aa320b87a76fbacccd0746456baeb945 |
function [mean_acc] = seperate_eval(name)
addpath([pwd '\TWSVM']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
tot_data = load([datapath name '\' name '_R.dat']);
index_tune = importdata ([d... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | seperate_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/TBSVM/seperate_eval.m | 5,095 | utf_8 | f619d250ebb42830f697d2dcfa3a232e |
function [test_acc] = seperate_eval(name)
addpath([pwd '\TWSVM']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
train = load ([datapath name '\' name '_train_R.dat']);% for datasets where trai... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | kfold_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/WLTSVM/kfold_eval.m | 4,880 | utf_8 | c32281432da1d1f5d5e5753ed9ce2b78 |
function [mean_acc] = seperate_eval(name)
addpath([pwd '\wltsvm']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
tot_data = load([datapath name '\' name '_R.dat']);
index_tune = importdata ([... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | seperate_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/WLTSVM/seperate_eval.m | 4,895 | utf_8 | 9ef1248fb63e1bc397f500ec87441620 |
function [test_acc] = seperate_eval(name)
addpath([pwd '\wltsvm']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
train = load ([datapath name '\' name '_train_R.dat']);% for datasets where tra... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | kfold_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/PinTSVM/kfold_eval.m | 5,592 | utf_8 | 9b8af63cdf93adf1d233c2530c9b31f5 |
function [mean_acc] = seperate_eval(name)
addpath([pwd '\pintsvm']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
tot_data = load([datapath name '\' name '_R.dat']);
index_tune = importdata (... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | seperate_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/PinTSVM/seperate_eval.m | 5,545 | utf_8 | 85a4067f1bff841c4aa5569a64d701cb |
function [test_acc] = seperate_eval(name)
addpath([pwd '\pintsvm']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
train = load ([datapath name '\' name '_train_R.dat']);% for datasets where tr... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | scale_range_rbf.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/PinTSVM/scale_range_rbf.m | 1,009 | utf_8 | 80c8fa1d98467244e1fe73d77c0bcf00 | %SCALE_RANGE Give a vector of scales
%
% SIG = SCALE_RANGE(X,NR,NMAX)
%
% INPUT
% X Data matrix or dataset
% NR Number of scales (default = 20)
% NMAX Number of (random) points to consider (default = 500)
%
% OUTPUT
% SIG Vector of scale values
%
% DESCRIPTION
% Give a reasonable ... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | kfold_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/LSTWSVM/kfold_eval.m | 4,742 | utf_8 | 24f7c3407eb23e045e11c43c07bd31ee |
function [mean_acc] = seperate_eval(name)
addpath([pwd '\lstwsvm']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
tot_data = load([datapath name '\' name '_R.dat']);
index_tune = importdata (... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | seperate_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/LSTWSVM/seperate_eval.m | 4,671 | utf_8 | 7ee0c2b90c2bb1083404d2a41a220ac1 |
function [test_acc] = seperate_eval(name)
addpath([pwd '\lstwsvm']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
train = load ([datapath name '\' name '_train_R.dat']);% for datasets where tr... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | kfold_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/RELS_TWSVM/kfold_eval.m | 5,896 | utf_8 | b0c426cfc81535deb2d09cacc1343c1c |
function [mean_acc] = seperate_eval(name)
addpath([pwd '\rels_TWSVM']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
tot_data = load([datapath name '\' name '_R.dat']);
index_tune = importdat... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | seperate_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/RELS_TWSVM/seperate_eval.m | 5,818 | utf_8 | 087b68883003601402d424fc77ed16f9 |
function [test_acc] = seperate_eval(name)
addpath([pwd '\rels_TWSVM']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
train = load ([datapath name '\' name '_train_R.dat']);% for datasets where... |
github | Chandan-IITI/Analysis-of-Twin-SVM-on-44-binary-datasets-master | kfold_eval.m | .m | Analysis-of-Twin-SVM-on-44-binary-datasets-master/LPP_TSVM/kfold_eval.m | 4,767 | utf_8 | 8516e828d53b5cf7372b23a490c1c0e5 |
function [mean_acc] = seperate_eval(name)
addpath([pwd '\lppTSVM']);
%%% datasets can be downloaded from "http://persoal.citius.usc.es/manuel.fernandez.delgado/papers/jmlr/data.tar.gz"
datapath = 'provide the path of your dataset';
tot_data = load([datapath name '\' name '_R.dat']);
index_tune = importdata (... |
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