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github | mathematical-tours/mathematical-tours.github.io-master | lfshfn2.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/hfun-util/lfshfn2.m | 3,892 | utf_8 | 8db5e79d06701560b4bd9a106f60b80f | function [vert,tria,hlfs] = lfshfn2(varargin)
%LFSHFN2 calc. a discrete "local-feature-size" estimate for
%a polygonal domain embedded in R^2.
% [VERT,TRIA,HFUN] = LFSHFN2(NODE,EDGE) returns the trian-
% gulated "feature-size" estimate for the polygonal region
% {NODE,EDGE}. NODE is an N-by-2 array of polygonal ... |
github | mathematical-tours/mathematical-tours.github.io-master | fixgeo2.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/geom-util/fixgeo2.m | 10,544 | utf_8 | c097a18ad1af5411340e8cb30c7fc9e4 | function [node,PSLG,part] = fixgeo2(varargin)
%FIXGEO2 attempts to "fix" issues with geometry definitions.
% [NNEW,ENEW,PNEW] = FIXGEO2(NODE,EDGE,PART) returns a new
% "repaired" geometry definition. Currently, the following
% operations are performed:
%
% (1) redundant nodes are "zipped" together.
% (2) redu... |
github | mathematical-tours/mathematical-tours.github.io-master | cdtbal2.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/mesh-ball/cdtbal2.m | 2,758 | utf_8 | ea151d04cd4d88d44d28a99cf4e15fe8 | function [cc] = cdtbal2(pp,ee,tt)
%CDTBAL2 compute the modified circumballs associated with a
%constrained 2-simplex Delaunay triangulation in R^2.
% [CC] = CDTBAL2(PP,EE,TT) returns the smallest enclosing
% balls associated with the triangles in [PP,TT], such th-
% at CC = [XC,YC,RC.^2]. Such balls never lie o... |
github | mathematical-tours/mathematical-tours.github.io-master | inv_3x3.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/mesh-ball/inv_3x3.m | 2,424 | utf_8 | b76b9942e6e86ea0e3a1daaab678a128 | function [II,DA] = inv_3x3(AA)
%INV_3X3 calc. the inverses for a block of 3-by-3 matrices.
% [IA,DA] = INV_3X3(AA) returns a set of 'inverses' IA and
% an array of determinants DA for the set of 3-by-3 linear
% systems in AA. SIZE(AA), SIZE(IA) = [3,3,N], where N is
% the number of linear systems. DA is an N-... |
github | mathematical-tours/mathematical-tours.github.io-master | inv_2x2.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/mesh-ball/inv_2x2.m | 1,589 | utf_8 | f7e512ded796279979a3a7f51b100682 | function [II,DA] = inv_2x2(AA)
%INV_2X2 calc. the inverses for a block of 2-by-2 matrices.
% [IA,DA] = INV_2X2(AA) returns a set of 'inverses' IA and
% an array of determinants DA for the set of 2-by-2 linear
% systems in AA. SIZE(AA), SIZE(IA) = [2,2,N], where N is
% the number of linear systems. DA is an N-... |
github | mathematical-tours/mathematical-tours.github.io-master | cfmtri2.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/mesh-util/cfmtri2.m | 4,180 | utf_8 | 1baaabc57525f7d9a1f5a63ba3b0c71e | function [vert,econ,tria] = cfmtri2(vert,econ)
%CFMTRI2 compute a conforming 2-simplex Delaunay triangulat-
%ion in the two-dimensional plane.
% [VERT,CONN,TRIA]=CFMTRI2(VERT,CONN) computes the confor-
% ming Delaunay trianguation, given the points VERT, and
% edge constraints CONN. New points are inserted to bis... |
github | mathematical-tours/mathematical-tours.github.io-master | findtria.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/aabb-tree/findtria.m | 9,689 | utf_8 | ad0b56aa465a27794b31d72778bdbc37 | function [tp,tj,tr] = findtria(pp,tt,pj,varargin)
%FINDTRIA spatial queries for collections of d-simplexes.
% [TP,TI] = FINDTRIA(PP,TT,PJ) finds the set of simple-
% xes that intersect with a given spatial query. Simplexes
% are specified via the vertex array PP = [X1,X2,...,XN]
% and the indexing array TT = ... |
github | mathematical-tours/mathematical-tours.github.io-master | findball.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/aabb-tree/findball.m | 4,273 | utf_8 | 80c125cbd7c2a3c289815aa85ca5abe4 | function [bp,bj,tr] = findball(bb,pp,varargin)
%FINDBALL spatial queries for collections of d-balls.
% [BP,BI] = FINDBALL(BB,PI) finds the set of d-dim. balls
% that intersect with a given spatial query. Balls are sp-
% ecified as a set of centres BB(:,1:ND) and (squared)
% radii BB(:,ND+1), where ND is the nu... |
github | mathematical-tours/mathematical-tours.github.io-master | maprect.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/aabb-tree/maprect.m | 1,778 | utf_8 | 45dd0babfa5c52a02357a4113393a8f2 | function [tm,im] = maprect(tr,pr)
%MAPRECT find the tree-to-rectangle mappings.
% [TM,IM] = MAPRECT(TR,PR) returns the tree-to-rectangle
% and rectangle-to-tree mappings for a given aabb-tree TR
% and a collection of query vertices PI.
%
% The tree-to-item mapping TM is a structure representing
% the inters... |
github | mathematical-tours/mathematical-tours.github.io-master | lineline.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/aabb-tree/lineline.m | 5,298 | utf_8 | 5f1a805f76b9916f61e0829b369d0e83 | function [lp,lj,tr] = lineline(pa,pb,pc,pd,varargin)
%LINELINE intersection between lines in d-dimensional space.
% [LP,LI] = LINELINE(PA,PB,PC,PD) finds intersections bet-
% ween line segments in d-dimensions. Lines are specified
% as a set of endpoints [PA,PB] and [PC,PD] where PA, PB,
% PC and PD are NL-by... |
github | mathematical-tours/mathematical-tours.github.io-master | mapvert.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/aabb-tree/mapvert.m | 1,672 | utf_8 | ff2204c6f5b7bcd9236192aa1fff2452 | function [tm,im] = mapvert(tr,pi)
%MAPVERT find the tree-to-vertex mappings.
% [TM,IM] = MAPVERT(TR,PI) returns the tree-to-vertex and
% vertex-to-tree mappings for a given aabb-tree TR and a
% collection of query vertices PI.
%
% The tree-to-item mapping TM is a structure representing
% the intersection of... |
github | mathematical-tours/mathematical-tours.github.io-master | findline.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/mesh-2d/aabb-tree/findline.m | 5,207 | utf_8 | 71cd7b97c04f52cbf1bbbdfe82889989 | function [lp,lj,tr] = findline(pa,pb,pp,varargin)
%FINDLINE "point-on-line" queries in d-dimensional space.
% [LP,LI] = FINDLINE(PA,PB,PI) finds the set of d-dimensi-
% onal line-segments that intersect with a given spatial
% query. Lines are specified as a set of endpoints [PA,PB]
% where both PA and PB are N... |
github | mathematical-tours/mathematical-tours.github.io-master | dubins_curve.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/dubins-curve/dubins_curve.m | 10,121 | UNKNOWN | c6cc17156ca3c3853320aefe0e75abd8 | %DUBINS_CURVE Find the Dubins path (shortest curve) between two points.
% PATH = DUBINS_CURVE(P1, P2, r, stepsize) finds the shortest curve that
% connects two points in the Euclidean plane with a constraint of the
% curvature of the path. The start and finish orientations P1 and P2 are
% defined as [x, y... |
github | mathematical-tours/mathematical-tours.github.io-master | ReedsShepp.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/dubins-curve/ReedsShepp.m | 9,885 | utf_8 | aedc5bab7ee62e19a72c72aad82955c3 | % Reeds Shepp path planner sample code
%
% based on python code from Python Robotics by Atsushi Sakai(@Atsushi_twi)
%
% Peter 3/18
%
% Finds the shortest path between 2 configurations:
% - robot can move forward or backward
% - the robot turns at zero or maximum curvature
% - there are discontinuities in velocity and s... |
github | mathematical-tours/mathematical-tours.github.io-master | FindRSPath.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/dubins-curve/FindRSPath.m | 17,089 | utf_8 | 9537726df6a8dc6c5abc5ce04a976530 | function path = FindRSPath(x,y,phi,veh)
rmin = veh.MIN_CIRCLE; %minimum turning radius
x = x/rmin;
y = y/rmin;
% traverse 5 methods to reach the target point, and then select the shortest path
[isok1,path1] = CSC(x,y,phi);
[isok2,path2] = CCC(x,y,phi);
[isok3,path3] = CCCC(x,y,phi);
[isok4,p... |
github | mathematical-tours/mathematical-tours.github.io-master | RSPath.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/dubins-curve/RSPath.m | 1,589 | utf_8 | 76c4a0499224b2d8301b5b107c649b13 | classdef RSPath
properties (Constant)
Types = [
'L', 'R', 'L', 'N', 'N' ; %1
'R', 'L', 'R', 'N', 'N' ; %2
'L', 'R', 'L', 'R', 'N' ; %3
'R', 'L', 'R', 'L', 'N' ; %4
'L', 'R', 'S', 'L', 'N' ; %5
... |
github | mathematical-tours/mathematical-tours.github.io-master | dubins_core.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/dubins-curve/dubins_core.m | 7,249 | ibm852 | 88b7685863b811633ce617ac6e09745c | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% This function will find the shortest dubins curve between two points
% Input:
% p1/p2: Initial and ending 2-D pose
% In row vectors, e.g. [x, y, theta]
% r: turning radius of the curve
% Output:
% param: a struct that ... |
github | mathematical-tours/mathematical-tours.github.io-master | nbECGM.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/color-displacement/toolbox-lsap/nbECGM.m | 737 | utf_8 | 12c013e9e8fa1ded80b1fdb944a77e4f | % -----------------------------------------------------------
% file: nbECGM.m
% -----------------------------------------------------------
% authors: Sebastien Bougleux (UNICAEN) and Luc Brun (ENSICAEN)
% institution: Normandie Univ, CNRS - ENSICAEN - UNICAEN, GREYC UMR 6072
% ---------------------------------------... |
github | mathematical-tours/mathematical-tours.github.io-master | nelder_mead.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/nelder-mead/nelder_mead.m | 8,272 | utf_8 | ce9915d194f1aeb83bdaefc7043fbe7b | function [x_opt, n_feval, Xlist] = nelder_mead ( x, function_handle, flag )
%*****************************************************************************80
%
%% NELDER_MEAD performs the Nelder-Mead optimization search.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 1... |
github | mathematical-tours/mathematical-tours.github.io-master | perform_wavortho_transf.m | .m | mathematical-tours.github.io-master/tweets-sources/codes/daubechies/perform_wavortho_transf.m | 2,736 | utf_8 | 362bed43d951f6bdefb520003047e2ea | function f = perform_wavortho_transf(f,Jmin,dir,options)
% perform_wavortho_transf - compute orthogonal wavelet transform
%
% fw = perform_wavortho_transf(f,Jmin,dir,options);
%
% You can give the filter in options.h.
%
% Works in arbitrary dimension.
%
% Copyright (c) 2009 Gabriel Peyre
options.n... |
github | mathematical-tours/mathematical-tours.github.io-master | mult_convol.m | .m | mathematical-tours.github.io-master/daft-sources/exercices/exo-algo-karatsuba/mult_convol.m | 170 | utf_8 | 6f6e58c75c22525070f3ec0c8f215bde | % multiplication par convolution acyclique
function r = mult_convol(p,q)
n = length(p);
p = [p;zeros(n-1,1)];
q = [q;zeros(n-1,1)];
r = real( ifft(fft(p).*fft(q)) ); |
github | mathematical-tours/mathematical-tours.github.io-master | u_x1.m | .m | mathematical-tours.github.io-master/daft-sources/listings/poisson/u_x1.m | 76 | utf_8 | 06778cff7e3dd387726804aa22a782ed | % valeur de la solution au bord y=1
function res = f_x1(x)
res = sol(x,1); |
github | mathematical-tours/mathematical-tours.github.io-master | u_x0.m | .m | mathematical-tours.github.io-master/daft-sources/listings/poisson/u_x0.m | 76 | utf_8 | 74a3a4907665875dd0637b608a34899d | % valeur de la solution au bord y=0
function res = f_x0(x)
res = sol(x,0); |
github | mathematical-tours/mathematical-tours.github.io-master | u_0y.m | .m | mathematical-tours.github.io-master/daft-sources/listings/poisson/u_0y.m | 76 | utf_8 | 2b5238a6d2093ec84ccfaa4f4a8ac4db | % valeur de la solution au bord x=0
function res = f_0y(y)
res = sol(0,y); |
github | mathematical-tours/mathematical-tours.github.io-master | u_1y.m | .m | mathematical-tours.github.io-master/daft-sources/listings/poisson/u_1y.m | 76 | utf_8 | 0018ed65d993b0b44bc698ee3d22aa21 | % valeur de la solution au bord x=1
function res = f_1y(y)
res = sol(1,y); |
github | mathematical-tours/mathematical-tours.github.io-master | number2vector.m | .m | mathematical-tours.github.io-master/daft-sources/listings/mult-grands-entiers-fft/number2vector.m | 182 | utf_8 | 506bbacc3451f9bd81fccc150928b3af | % Transforme un nombre en vecteur.
function res = number2vector(x,b)
N = floor( log(x)/log(b) )+1;
res = zeros(N,1);
for i=1:N
q = floor(x/b);
res(i) = x - q*b;
x = q;
end |
github | mathematical-tours/mathematical-tours.github.io-master | vector2number.m | .m | mathematical-tours.github.io-master/daft-sources/listings/mult-grands-entiers-fft/vector2number.m | 119 | utf_8 | eab6957b78ba2c74fbb154bf9b8c8f00 | % Transforme un vecteur en nombre.
function res = vector2number(v,b)
N = length(v);
res = sum( v.*( b.^(0:N-1)' ) ); |
github | mathematical-tours/mathematical-tours.github.io-master | rev_index.m | .m | mathematical-tours.github.io-master/daft-sources/listings/fft/rev_index.m | 192 | utf_8 | 734cfa214dd6b8fa88d93ab82adb88ce | % Calcule l'inversion des bits d'un entier.
function res = rev_index(t,index)
res = 0;
tmp = index;
for i=0:t-1
bit = mod(tmp,2);
tmp = floor(tmp/2);
res = res*2 + bit;
end |
github | mathematical-tours/mathematical-tours.github.io-master | rev_index.m | .m | mathematical-tours.github.io-master/daft-sources/images/rev-bit-matrix/rev_index.m | 192 | utf_8 | 734cfa214dd6b8fa88d93ab82adb88ce | % Calcule l'inversion des bits d'un entier.
function res = rev_index(t,index)
res = 0;
tmp = index;
for i=0:t-1
bit = mod(tmp,2);
tmp = floor(tmp/2);
res = res*2 + bit;
end |
github | mathematical-tours/mathematical-tours.github.io-master | load_image.m | .m | mathematical-tours.github.io-master/codes/coding/toolbox/load_image.m | 20,275 | utf_8 | c700b54853577ab37402e27e4ca061b8 | function M = load_image(type, n, options)
% load_image - load benchmark images.
%
% M = load_image(name, n, options);
%
% name can be:
% Synthetic images:
% 'chessboard1', 'chessboard', 'square', 'squareregular', 'disk', 'diskregular', 'quaterdisk', '3contours', 'line',
% 'line_vertical', 'l... |
github | mathematical-tours/mathematical-tours.github.io-master | plot_hufftree.m | .m | mathematical-tours.github.io-master/codes/coding/toolbox/plot_hufftree.m | 984 | utf_8 | b681c8a8cd90d93bc37045b68361bc86 | function plot_hufftree(T,offs,S)
% plot_hufftree - plot a huffman tree
%
% plot_hufftree(T);
%
% Copyright (c) 2008 Gabriel Peyre
if nargin<2
offs=0;
end
if nargin<3
S = [];
end
hold on;
plot_tree(T{1},[0,0],1, offs, S);
hold off;
axis tight;
axis off;
end
%%
function plot_tree... |
github | mathematical-tours/mathematical-tours.github.io-master | perform_huffcoding.m | .m | mathematical-tours.github.io-master/codes/coding/toolbox/perform_huffcoding.m | 1,491 | utf_8 | 41a9144e1a2da192d37cf10a40add3e2 | function y = perform_huffcoding(x,T,dir)
% perform_huffcoding - perform huffman coding
%
% y = perform_huffcoding(x,T,dir);
%
% dir=+1 for coding
% dir=-1 for decoding
%
% T is a Huffman tree, computed with compute_hufftree
%
% Copyright (c) 2008 Gabriel Peyre
if dir==1
%%% CODING %%%
... |
github | mathematical-tours/mathematical-tours.github.io-master | cauchy_residue.m | .m | mathematical-tours.github.io-master/codes/certificates/toolbox_certif/cauchy_residue.m | 449 | utf_8 | e62ef7eaa3b655e01b0494648deb7aba | %computes the contour integral of F, given the poles p inside the contour
function res = cauchy_residue(F,p)
syms z;
res = 0;
for k=1:length(p)
f = (z-p(k))*F;
i=0;
while true
try
subs(f, z, p(k));
break;
catch
f = (z-p(k))*f;
... |
github | mathematical-tours/mathematical-tours.github.io-master | TaylorMtx.m | .m | mathematical-tours.github.io-master/codes/certificates/toolbox_certif/TaylorMtx.m | 4,164 | utf_8 | 455ecf21c52e4a2038783f21db7eecf7 | % P: number of nodes to generate randomly
% N: number of derivatives to expand up to
%
% M= M_fac{1}*M_fac{2}*M_fac{3} is a matrix describing the Taylor expansion, up to order 'N'
% of a kernel at each node point in 'Nodes'.
%
% C = C_sep{1}+C_sep{2} says which partial derivative each column of M corresponds to.
% For ... |
github | auralius/matlab-ode-solvers-master | solvers.m | .m | matlab-ode-solvers-master/solvers.m | 11,768 | utf_8 | d33bcbf91cfa1b3815864614d56eb898 | %% Collection of Solvers
% Author: Auralius Manurung, ME, Universitas Pertamina,
% auralius.manurung@ieee.org
%
% For an ODE: $\dot{y}(t,y) = f(t,y)$ The solution takes a form of
% $y(t)=\dots$
%
%% Test 1 : A very stiff system
disp('Running Test 1 ...')
[ta, ya] = feuler(@myode1, 1, 0, 0.02, 0.001);
[tb, yb] = rk4... |
github | ranjeethks/Least-Squares-master | TransDOPtoENU.m | .m | Least-Squares-master/TransDOPtoENU.m | 502 | utf_8 | 0672684cb77635cee8bc70b23cb18be7 | % Ranjeeth KS, University of Calgary, Canada
function Qp_ENU = TransDOPtoENU(r_lat,r_long,Qp_ECEF)
%Transformation matrix from ECEF frame to ENU frame
T= [-sin(r_long) cos(r_long) 0 0;
-sin(r_lat)*cos(r_long) -sin(r_lat)*sin(r_long) cos(r_lat) 0;
... |
github | ranjeethks/Least-Squares-master | ReadData.m | .m | Least-Squares-master/ReadData.m | 525 | utf_8 | 21eced161954cb51a437b8971dea64f7 | % Ranjeeth KS, University of Calgary, Canada
function [InpData k]= ReadData ()
fid = fopen('D:\Study\ENGO 620\Lab\data\Data.bin' ,'rb');
k = 0; % k stands for k'th epoch
while(k<4295)
k=k+1;
InpData.gpstime(k) = fread(fid,1,'double');
InpData.numsats(k) = fread(fid,1,'char');
for m = 1:InpDat... |
github | ranjeethks/Least-Squares-master | singlepoint.m | .m | Least-Squares-master/singlepoint.m | 12,184 | utf_8 | 3954216a9317c30d9f8833ea81362421 | % Ranjeeth KS, University of Calgary, Canada
function [run]=singlepoint(InpData,settings)
%%
%Initializing task related variables
epochs=settings.epochs; %All the epochs are considered
no_iterationsinLS= settings.no_iterationsinLS;
task4check = settings.task4check;
method = settings.method;
h_constrain... |
github | ranjeethks/Least-Squares-master | update_state.m | .m | Least-Squares-master/update_state.m | 957 | utf_8 | 44e9a4e12568de674da80f286efae993 |
% Ranjeeth KS, University of Calgary, Canada
function [pos_r_plh r_bias]= update_state(method,h_constraint,dx,pos_r,x0,r_bias,pos_r_plh)
if(method == 'carti')
correction = [pos_r r_bias];
pos_r(1) = correction(1) + dx(1);
pos_r(2) = correction(2) + dx(2);
pos_r(3) = correc... |
github | ranjeethks/Least-Squares-master | globaltest.m | .m | Least-Squares-master/globaltest.m | 332 | utf_8 | 39d6e3062d08c690b23880d3bfa2ad51 | % Ranjeeth KS, University of Calgary, Canada
%%%%%global test%%%%%
function global_test = globaltest(r,store_R,X2_1_minus_alpha_by_2,X2_alpha_by_2)
%set test statistics
zeta = r'*inv(store_R)*r;
if(X2_alpha_by_2> zeta & zeta > X2_1_minus_alpha_by_2)
global_test = 'pass';
else
global_test = '... |
github | Johnnymei/Non_Linear_NCA_DeepLearning-master | CG_MNIST_NCA(optimized by Zheng Le at Tsinghua Univ).m | .m | Non_Linear_NCA_DeepLearning-master/CG_MNIST_NCA(optimized by Zheng Le at Tsinghua Univ).m | 2,465 | utf_8 | b9ca2cc0588cdd2913e5c2b17cd55ec8 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | Johnnymei/Non_Linear_NCA_DeepLearning-master | CG_MNIST.m | .m | Non_Linear_NCA_DeepLearning-master/CG_MNIST.m | 2,727 | utf_8 | 4679a67a7470f0d5835c1cad7f2d0896 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | Johnnymei/Non_Linear_NCA_DeepLearning-master | CG_CLASSIFY.m | .m | Non_Linear_NCA_DeepLearning-master/CG_CLASSIFY.m | 1,853 | utf_8 | 6ed770942ea0c0f3a0f53cfe675bb5ff | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | Johnnymei/Non_Linear_NCA_DeepLearning-master | mnistdisp.m | .m | Non_Linear_NCA_DeepLearning-master/mnistdisp.m | 1,084 | utf_8 | fe0cdd3b44b770d51322d5c6e9f4fd91 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | Johnnymei/Non_Linear_NCA_DeepLearning-master | CG_MNIST_NCA.m | .m | Non_Linear_NCA_DeepLearning-master/CG_MNIST_NCA.m | 3,900 | utf_8 | d45d6dc16428d8f6fa1836cde4269cdf | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | Johnnymei/Non_Linear_NCA_DeepLearning-master | CG_CLASSIFY_INIT.m | .m | Non_Linear_NCA_DeepLearning-master/CG_CLASSIFY_INIT.m | 1,136 | utf_8 | 22b98fdbaa2f63132f19a95e73c35d22 | % Version 1.000
%
% Code provided by Ruslan Salakhutdinov and Geoff Hinton
%
% Permission is granted for anyone to copy, use, modify, or distribute this
% program and accompanying programs and documents for any purpose, provided
% this copyright notice is retained and prominently displayed, along with
% a note saying t... |
github | gihanjayatilaka/foreground-estimation-in-dynamic-background-conditions-master | lanbpro.m | .m | foreground-estimation-in-dynamic-background-conditions-master/exact_alm_rpca/PROPACK/lanbpro.m | 19,514 | utf_8 | 897b157335c2a5c269845380328709c4 | function [U,B_k,V,p,ierr,work] = lanbpro(varargin)
%LANBPRO Lanczos bidiagonalization with partial reorthogonalization.
% LANBPRO computes the Lanczos bidiagonalization of a real
% matrix using the with partial reorthogonalization.
%
% [U_k,B_k,V_k,R,ierr,work] = LANBPRO(A,K,R0,OPTIONS,U_old,B_old,V_old)
% ... |
github | gihanjayatilaka/foreground-estimation-in-dynamic-background-conditions-master | lanpro.m | .m | foreground-estimation-in-dynamic-background-conditions-master/exact_alm_rpca/PROPACK/lanpro.m | 14,762 | utf_8 | ff3aa513289e3776117575af43b5ed1b | function [Q_k,T_k,r,anorm,ierr,work] = lanpro(A,nin,kmax,r,options,...
Q_k,T_k,anorm)
%LANPRO Lanczos tridiagonalization with partial reorthogonalization
% LANPRO computes the Lanczos tridiagonalization of a real symmetric
% matrix using the symmetric Lanczos algorithm with partial
% reorthogonalization... |
github | tmlishuai2/GPS-BDS_pseudorange-master | read_sp3.m | .m | GPS-BDS_pseudorange-master/read_sp3.m | 4,288 | utf_8 | 65865f932de5f6d59a861ecdc1db2220 | function sp3 = read_sp3(files, folder, satsys, start_time, end_time)
% READ_SP3 reads SP3 files.
%
% SYNTAX:
% [sp3, hdr] = read_sp3(sp3_file);
%
% INPUT:
% files - SP3 files
% folder -
% satsys =
%
% OUTPUT:
% sp3 - matrix of the satellite position, velocity and clock data
% hdr - Struct of the sp3 file header
%... |
github | tmlishuai2/GPS-BDS_pseudorange-master | read_rinex_obs.m | .m | GPS-BDS_pseudorange-master/read_rinex_obs.m | 8,044 | utf_8 | 8a4e7129efb3eb3263bd5bf6d2d040a6 | function [hdr, obs] = read_rinex_obs(obs_file)
% READ_RINEX_OBS read a RINEX observation file and save it in a *.mat file
% with the input file name as prefix, e.g. data read from alic0520.03o will
% be saved in alic0520.03o.mat, which will be loaded next time to save time.
%
% SYNTAX:
% [hdr, obs] = read_rinex_obs(... |
github | tmlishuai2/GPS-BDS_pseudorange-master | read_rinex_nav.m | .m | GPS-BDS_pseudorange-master/read_rinex_nav.m | 7,564 | utf_8 | 1321dfcaf4623eda7940f459348cbc27 | function [hdr, nav] = read_rinex_nav( nav_file )
% READ_RINEX_NAV: read a RINEX navigation file and save it to a *.mat file
% with the input file name as prefix, e.g. data read from alic0520.03n will
% be saved in alic0520.03n.mat.
%
% SYNTAX:
% [hdr, nav] = read_rinex_nav(nav_file);
%
% INPUT:
% nav_file - Rinex navi... |
github | RushingCorgi/MNIST_Classification-master | myTrain.m | .m | MNIST_Classification-master/myTrain.m | 1,515 | utf_8 | 57f3bab02be21e1294df0da2913f67dc | %train a CNN model for image category classification
function [classifier,testSet]=myTrain(net,featureLayer)
%-------------------load image------------------------------------
categories = {'0', '1', '2','3','4','5','6','7','8','9'};
imds = imageDatastore(fullfile(categories), 'LabelSource', 'foldernames');
tbl = coun... |
github | christeefy/ECOstudioMPC-master | Successive_Linearizer_Building.m | .m | ECOstudioMPC-master/Successive_Linearizer_Building.m | 6,003 | utf_8 | ee082bdf4eac53260731606dcf37f1ca | function [A, B, C, D, U, Y, X, DX, poles] = Successive_Linearizer_Building(x, u, d)
%#codegen
% Define constant outputs
Ts = 0.5;
C = eye(2);
D = zeros(2,2);
% Nominal U are obtained from measurements
U = [u1; u2];
% Nominal X and Y are obtained from estimated MPC states
Y = x;
X = y;
% Analytical linearization of mec... |
github | BodoBookhagen/ChanGeom-master | chanextract.m | .m | ChanGeom-master/chanextract.m | 12,722 | utf_8 | 65a0f793f23a5e595d573037222756ec | function chanextract(inputtif, exporttif, start_pt)
% function chanextract(inputtif, exporttif, start_pt)
%
% Input parameters
% <inputtif>: logical or binary TIF file.
% <exporttif>: name of the exported channel width data geotiff. This file will have width values
% <cellsize>: taken from <inputtif> TIF file
... |
github | StephenLasky/ECE5554_HW1-master | boundaryBenchGraphs.m | .m | ECE5554_HW1-master/prob_edge/util/boundaryBenchGraphs.m | 2,248 | utf_8 | c3a3b145b9fd12ba99efcb9df6ffac38 | function boundaryBenchGraphs(pbDir, iids)
% function boundaryBenchGraphs(pbDir)
%
% Create graphs, after boundaryBench(pbDir) has been run.
%
% See also boundaryBench.
%
% David Martin <dmartin@eecs.berkeley.edu>
% May 2003
fname = fullfile(pbDir,'scores.txt');
scores = dlmread(fname); % iid,thresh,r,p,f
f... |
github | StephenLasky/ECE5554_HW1-master | boundaryBench.m | .m | ECE5554_HW1-master/prob_edge/util/boundaryBench.m | 3,743 | utf_8 | 00ac15375b4669323749195739e19485 | function boundaryBench(pbDir,iids,pres,nthresh,fast)
% function boundaryBench(pbDir,pres,nthresh,fast)
%
% Run the boundary detector benchmark on the Pb files found in
% pbDir for the BSDS test images.
%
% See also imgList, bsdsRoot.
%
% David Martin <dmartin@eecs.berkeley.edu>
% March 2003
if nargin<3, nth... |
github | StephenLasky/ECE5554_HW1-master | boundaryBenchGraphsMulti.m | .m | ECE5554_HW1-master/prob_edge/util/boundaryBenchGraphsMulti.m | 3,482 | utf_8 | 0de26c4b8ac8f977b74078d8bf55f10e | function boundaryBenchGraphsMulti(baseDir, iidsTest)
% function boundaryBenchGraphsMulti(baseDir)
%
% See also boundaryBenchGraphs.
%
% David Martin <dmartin@eecs.berkeley.edu>
% July 2003
presentations = {''};
presNames = {''};
%presentations = {'gray','color'};
%presNames = {'Grayscale','Color'};
%iidsTe... |
github | StephenLasky/ECE5554_HW1-master | boundaryBenchHuman.m | .m | ECE5554_HW1-master/prob_edge/util/boundaryBenchHuman.m | 2,673 | utf_8 | 581d9142391e4de622846c1cf478fc28 | function boundaryBenchHuman(pbRoot,pres, iids)
% function boundaryBenchHuman(pbRoot,pres)
%
% Compute the human precision/recall data for the BSDS test images.
%
% See also imgList, bsdsRoot.
%
% David Martin <dmartin@eecs.berkeley.edu>
% March 2003
%iids = imgList('test');
cR_total = 0;
sR_total = 0;
... |
github | StephenLasky/ECE5554_HW1-master | runThis.m | .m | ECE5554_HW1-master/prob_pyramids/runThis.m | 595 | utf_8 | fe655fd7ed270a89eebe61fce0e5c723 |
%SEE FUNCTION IMPLEMNTTION IN OWN FILE%
function [G, L] = pyramidsGL(im, N)
% [G, L] = pyramidsGL(im, N)
% Creates Gaussian (G) and Laplacian (L) pyramids of level N from image im.
% G and L are cell where G{i}, L{i} stores the i-th level of Gaussian and Laplacian pyramid, respectively.
end
%SEE FUNCTION... |
github | hzy033212/Compressed-Sensing-master | BCS_SPL_DCT_Decoder.m | .m | Compressed-Sensing-master/BCS_SPL_DCT_Decoder.m | 2,856 | utf_8 | c6ac325c7ad58bf3dd9b9e1ca22ef45e | %
% function reconstructed_image = BCS_SPL_DCT_Decoder(y, Phi, num_rows, num_cols)
%
% This function performs SPL reconstruction of y using a DCT
% sparsity basis. Phi gives the projection matrix. The reconstructed
% image, of size num_rows x num_cols, is returned as
% reconstructed_image.
%
% See:
% S.... |
github | hzy033212/Compressed-Sensing-master | PSNR.m | .m | Compressed-Sensing-master/PSNR.m | 1,290 | utf_8 | 3d644a06c220a8d910d2d3da5178045c | %
% function r = PSNR(x1, x2)
%
% This function returns the PSNR between images x1 and x2.
%
% See:
% S. Mun and J. E. Fowler, "Block Compressed Sensing of Images
% Using Directional Transforms," submitted to the IEEE
% International Conference on Image Processing, 2009
%
% Originally written by Sung... |
github | hzy033212/Compressed-Sensing-master | BCS_SPL_DCT_Decoder.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/BCS_SPL_DCT_Decoder.m | 2,852 | utf_8 | 49784d5bffbaf4f100b080db1a2c2e36 | %
% function reconstructed_image = BCS_SPL_DCT_Decoder(y, Phi, num_rows, num_cols)
%
% This function performs SPL reconstruction of y using a DCT
% sparsity basis. Phi gives the projection matrix. The reconstructed
% image, of size num_rows x num_cols, is returned as
% reconstructed_image.
%
% See:
% S.... |
github | hzy033212/Compressed-Sensing-master | run_experiment_dct.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/run_experiment_dct.m | 2,009 | utf_8 | da1689e67082deb1a42f377e4ee478e3 | %
% function psnr = run_experiment_dct()
%
% This function runs the experiments for BCS-SPL-DCT in Table 1 of
% S. Mun and J. E. Fowler, "Block Compressed Sensing of Images
% Using Directional Transforms," submitted to the IEEE
% International Conference on Image Processing, 2009
%
% Originally writte... |
github | hzy033212/Compressed-Sensing-master | PSNR.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/PSNR.m | 1,290 | utf_8 | 3d644a06c220a8d910d2d3da5178045c | %
% function r = PSNR(x1, x2)
%
% This function returns the PSNR between images x1 and x2.
%
% See:
% S. Mun and J. E. Fowler, "Block Compressed Sensing of Images
% Using Directional Transforms," submitted to the IEEE
% International Conference on Image Processing, 2009
%
% Originally written by Sung... |
github | hzy033212/Compressed-Sensing-master | run_experiment_ct.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/run_experiment_ct.m | 2,169 | utf_8 | f910ae2e3d344d49801a1fc27cafb32c | %
% function psnr = run_experiment_ct()
%
% This function runs the experiments for BCS-SPL-CT in Table 1 of
% S. Mun and J. E. Fowler, "Block Compressed Sensing of Images
% Using Directional Transforms," submitted to the IEEE
% International Conference on Image Processing, 2009
%
% Originally written ... |
github | hzy033212/Compressed-Sensing-master | BCS_SPL_CT_Decoder.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/BCS_SPL_CT_Decoder.m | 5,885 | utf_8 | c608a34d9d5e17d7d0721f6ac6dd6590 | %
% function reconstructed_image = ...
% BCS_SPL_CT_Decoder(y, Phi, num_rows, num_cols, contourlet)
%
% This function performs SPL reconstruction of y using a contourlet
% sparsity basis. Phi gives the projection matrix. The reconstructed
% image, of size num_rows x num_cols, is returned as
% reconstructe... |
github | hzy033212/Compressed-Sensing-master | run_experiment_ddwt.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/run_experiment_ddwt.m | 2,073 | utf_8 | 9103b9afe26af23075d089149a7f277e | %
% function psnr = run_experiment_ddwt()
%
% This function runs the experiments for BCS-SPL-DDWT in Table 1 of
% S. Mun and J. E. Fowler, "Block Compressed Sensing of Images
% Using Directional Transforms," submitted to the IEEE
% International Conference on Image Processing, 2009
%
% Originally writ... |
github | hzy033212/Compressed-Sensing-master | BCS_SPL_GenerateProjection.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/BCS_SPL_GenerateProjection.m | 2,187 | utf_8 | ad3426aae0be43388fdfe8329fbfbeb1 | %
% function Phi = BCS_SPL_GenerateProjection(block_size, subrate, filename)
%
% This function generates the random projection matrix
% Phi for the given block size and subrate.
%
% Phi is returned as a M x N matrix, where N = block_size *
% block_size, and M = round(subrate * N).
%
% If filename is not spec... |
github | hzy033212/Compressed-Sensing-master | DCT2D_Matrix.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/DCT2D_Matrix.m | 1,455 | utf_8 | 0a7a5b0061bafe6eaddc9d93cec729b9 | %
% function Psi = DCT2D_Matrix(N)
%
% This function returns the N^2 x N^2 orthonormal transform matrix
% associated with the N^2-point DCT.
%
% See:
% S. Mun and J. E. Fowler, "Block Compressed Sensing of Images
% Using Directional Transforms," submitted to the IEEE
% International Conference on Ima... |
github | hzy033212/Compressed-Sensing-master | BCS_SPL_Encoder.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/BCS_SPL_Encoder.m | 1,645 | utf_8 | 4369f7fa978027e979349bc525c19d56 | %
% function y = BCS_SPL_Encoder(current_image, Phi)
%
% This function performs BCS projections of each block of
% current_image. The number of columns of the projection matrix,
% Phi, determines the size of the blocks into which current_image
% is partitioned. The projections are returned as the columns of y... |
github | hzy033212/Compressed-Sensing-master | BCS_SPL_DWT_Decoder.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/BCS_SPL_DWT_Decoder.m | 4,406 | utf_8 | c5e5eb413d0dff11e2e1b824be20dffe | %
% function reconstructed_image = ...
% BCS_SPL_DWT_Decoder(y, Phi, num_rows, num_cols, num_levels)
%
% This function performs SPL reconstruction of y using a DWT
% sparsity basis. Phi gives the projection matrix. The reconstructed
% image, of size num_rows x num_cols, is returned as
% reconstructed_imag... |
github | hzy033212/Compressed-Sensing-master | BCS_SPL_DDWT_Decoder.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/BCS_SPL_DDWT_Decoder.m | 5,370 | utf_8 | c37ce8a67572d60fdbf9cae70b2b41cb | %
% function reconstructed_image = ...
% BCS_SPL_DDWT_Decoder(y, Phi, num_rows, num_cols, num_levels, ...
% max_iterations)
%
% This function performs SPL reconstruction of y using a DDWT
% sparsity basis. Phi gives the projection matrix. The reconstructed
% image, of size num_rows x num_cols, is return... |
github | hzy033212/Compressed-Sensing-master | RMS.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/RMS.m | 1,273 | utf_8 | 5457f39feae3340caec9e4d12472e156 | %
% function r = RMS(x1, x2)
%
% This function returns the RMS between images x1 and x2.
%
% See:
% S. Mun and J. E. Fowler, "Block Compressed Sensing of Images
% Using Directional Transforms," submitted to the IEEE
% International Conference on Image Processing, 2009
%
% Originally written by SungKw... |
github | hzy033212/Compressed-Sensing-master | run_experiment_dwt.m | .m | Compressed-Sensing-master/BCS-SPL-1.5-1/run_experiment_dwt.m | 2,067 | utf_8 | 968edc479fb6a3dd139db040ba942b16 | %
% function psnr = run_experiment_dwt()
%
% This function runs the experiments for BCS-SPL-DWT in Table 1 of
% S. Mun and J. E. Fowler, "Block Compressed Sensing of Images
% Using Directional Transforms," submitted to the IEEE
% International Conference on Image Processing, 2009
%
% Originally writte... |
github | chintak/fast-hair-segmentation-master | readtext.m | .m | fast-hair-segmentation-master/readtext.m | 20,055 | utf_8 | 7b3346f716ff63e968f5dd63a12d5554 | function [data, result]= readtext(text, delimiter, comment, quotes, options)
% Usage: [data, result]= readtext(source, delimiter, comment, quotes, options)
%
% Whatever text (file) you give it, readtext returns an array of the contents (or send me a
% bug report). Matlab can't read variable length l... |
github | kevinjoseph1995/Depth-from-Defocus-NN-master | myNeuralNetworkFunction.m | .m | Depth-from-Defocus-NN-master/myNeuralNetworkFunction.m | 53,767 | utf_8 | 5242e3643a6539f2d178b416f232884f | function [y1] = myNeuralNetworkFunction(x1)
%MYNEURALNETWORKFUNCTION neural network simulation function.
%
% Generated by Neural Network Toolbox function genFunction, 24-Jun-2016 20:57:52.
%
% [y1] = myNeuralNetworkFunction(x1) takes these arguments:
% x = 121xQ matrix, input #1
% and returns:
% y = 1xQ matrix, out... |
github | kevinjoseph1995/Depth-from-Defocus-NN-master | myNeuralNetworkFunction.m | .m | Depth-from-Defocus-NN-master/Neural Network Training/myNeuralNetworkFunction.m | 23,222 | utf_8 | 6dc171e2ead863092988d9125fc7f924 | function [y1] = myNeuralNetworkFunction(x1)
%MYNEURALNETWORKFUNCTION neural network simulation function.
%
% Generated by Neural Network Toolbox function genFunction, 25-Jun-2016 17:23:05.
%
% [y1] = myNeuralNetworkFunction(x1) takes these arguments:
% x = 49xQ matrix, input #1
% and returns:
% y = 1xQ matrix, outp... |
github | songyouwei/coursera-machine-learning-assignments-master | submit.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | songyouwei/coursera-machine-learning-assignments-master | submitWithConfiguration.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | songyouwei/coursera-machine-learning-assignments-master | savejson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | songyouwei/coursera-machine-learning-assignments-master | loadjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | songyouwei/coursera-machine-learning-assignments-master | loadubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | songyouwei/coursera-machine-learning-assignments-master | saveubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | songyouwei/coursera-machine-learning-assignments-master | submit.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | songyouwei/coursera-machine-learning-assignments-master | submitWithConfiguration.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | songyouwei/coursera-machine-learning-assignments-master | savejson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | songyouwei/coursera-machine-learning-assignments-master | loadjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | songyouwei/coursera-machine-learning-assignments-master | loadubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | songyouwei/coursera-machine-learning-assignments-master | saveubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | songyouwei/coursera-machine-learning-assignments-master | submit.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | songyouwei/coursera-machine-learning-assignments-master | porterStemmer.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | songyouwei/coursera-machine-learning-assignments-master | submitWithConfiguration.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | songyouwei/coursera-machine-learning-assignments-master | savejson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex6/ex6/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | songyouwei/coursera-machine-learning-assignments-master | loadjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | songyouwei/coursera-machine-learning-assignments-master | loadubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | songyouwei/coursera-machine-learning-assignments-master | saveubjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | songyouwei/coursera-machine-learning-assignments-master | submit.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | songyouwei/coursera-machine-learning-assignments-master | submitWithConfiguration.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | songyouwei/coursera-machine-learning-assignments-master | savejson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | songyouwei/coursera-machine-learning-assignments-master | loadjson.m | .m | coursera-machine-learning-assignments-master/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
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