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 | platte/ssparse-master | SB_SPA_MD.m | .m | ssparse-master/SB_SPA_MD.m | 2,077 | utf_8 | b8618fb4cb1123b127627ac6967928f2 |
function u = SB_SPA_MD(R,f, mu, lambda, gamma, nInner, nBreg,m_spa)
[rows,cols] = size(f);
% Reserve memory for the auxillary variables
N = rows;
C_spa = ones(m_spa+1,1);
for j = 1:m_spa+1
for js =1:m_spa+1
if js ~= j
C_spa(j) = C_spa(j)/(j-js)... |
github | platte/ssparse-master | SB_SPA.m | .m | ssparse-master/SB_SPA.m | 2,114 | utf_8 | f2764ff3383026e203e1f106aacccc38 |
function [u l2_err] = SB_SPA(R,f, mu, lambda, gamma, nInner, nBreg,m_spa,UT)
[rows,cols] = size(f);
% Reserve memory for the auxillary variables
N = rows;
C_spa = ones(m_spa+1,1);
for j = 1:m_spa+1
for js =1:m_spa+1
if js ~= j
C_spa(j) = C_spa(... |
github | kirthevasank/if-estimators-master | kernel.m | .m | if-estimators-master/kde/kernel.m | 1,272 | utf_8 | 146602cb647fe8a85eabc585d278c900 | function K = kernel(X, C, h, order)
% Returns the value of the kernel evaluated at the points X centred at C and
% with bandwidth h.
% Inputs
% X : nxd data matrix
% C : mxd centre matrix. If empty is initialized to zero(1, d)
% h : the bandwidth of the kernel
% order : order of the kernel
% Ouputs
% K : The nxm kernel... |
github | kirthevasank/if-estimators-master | kdePickBW.m | .m | if-estimators-master/kde/kdePickBW.m | 3,366 | utf_8 | 34c2c3f58393913063c5808838a599c1 | function [optBW, kdeFuncH] = kdePickBW(X, smoothness, params, bwLogBounds)
% This picks a bandwidth for the KDE. We use k-fold cross validation in the
% range specified by bwLogBounds.
% If params.getKdeFuncH is True, then it also returns a function handle for the
% kde with the optimal bandiwidth.
% prelims
numDa... |
github | kirthevasank/if-estimators-master | kdeLegendreKernel.m | .m | if-estimators-master/kde/kdeLegendreKernel.m | 1,283 | utf_8 | 5a4c2ff6b16ab3a41970ed6e72a09aeb | function K = kdeLegendreKernel(X, C, h, order)
% Returns the value of the kernel evaluated at the points X centred at C and
% with bandwidth h.
% Inputs
% X : nxd data matrix
% C : mxd centre matrix. If empty is initialized to zero(1, d)
% h : the bandwidth of the kernel
% order : order of the kernel
% Ouputs
% K : The... |
github | kirthevasank/if-estimators-master | kdeGivenBW.m | .m | if-estimators-master/kde/kdeGivenBW.m | 3,586 | utf_8 | f9df94183d2183fdfe392e58ce70ad96 | function kde = kdeGivenBW(X, h, smoothness, params)
% Implements Kernel Density Estimator with kernels of order floor(smoothness)
% for the given bandwidth. You should cross validate h externally.
% Inputs
% X: the nxd data matrix
% h: bandwidth
% smoothness: If using a Gaussian Kernel this should be 'gaussian'. ... |
github | kirthevasank/if-estimators-master | demo4.m | .m | if-estimators-master/demos/demo4.m | 3,878 | utf_8 | 142d1bd9fc433e712ea2b47d36fd966e | function demo4
% Unit tests for functionals on X, Y where they come from a joint distribution
% X, Y
close all;
clear all;
fprintf('\nSome demos on conditional functionals of two distribution.\n');
functionals = {'condShannonMI', 'condKLDiv', 'condTsallisDiv'};
tests = {'1D-UnifUnifUnif', 'Indep-Gaussians'... |
github | kirthevasank/if-estimators-master | demo2.m | .m | if-estimators-master/demos/demo2.m | 3,672 | utf_8 | d83b47289d2a626114221320185b180a | function demo2
% Unit tests for all one distro functionals
close all;
clear all;
clc;
fprintf('\nSome demos on estimating functionals of two distribution.\n');
functionals = {'hellingerDiv', 'tsallisDiv', 'chiSqDiv', 'renyiDiv', 'klDiv'};
tests = {'1D-UnifUnif', '1D-UnifConv', 'Gaussian'};
% This is f... |
github | kirthevasank/if-estimators-master | demo3.m | .m | if-estimators-master/demos/demo3.m | 3,023 | utf_8 | a04f8458c27d5dc24e12ad8d51666b47 | function demo3
% Unit tests for functionals on X, Y where they come from a joint distribution
% X, Y
close all;
clear all;
fprintf('\nSome demos on conditional functionals of one distribution.\n');
functionals = {'shannonMI', 'condShannonEntropy'};
tests = {'1D-UnifUnif', 'Indep-Gaussians', 'Gaussian'};
... |
github | kirthevasank/if-estimators-master | demo1.m | .m | if-estimators-master/demos/demo1.m | 2,498 | utf_8 | 4b96dcf8d3e11832fb9d9f5c725ae646 | function demo1
% Unit tests for all one distro functionals
close all;
clear all;
clc;
fprintf('\nSome demos on estimating functionals of a single distribution.\n');
functionals = {'shannonEntropy'};
tests = {'1D-Uniform', '1D-Conv', '2D-Gaussian'};
% This is for storing parameters specific to the func... |
github | kirthevasank/if-estimators-master | klDivergence.m | .m | if-estimators-master/estimators/klDivergence.m | 1,285 | utf_8 | 9014a792eea5eac1bd21d21cebce4a8b | function [estim, asympAnalysis, bwX, bwY] = klDivergence(X, Y, ...
functionalParams, params)
% Estimates the KL Divergence between f and g where X comes from f and Y comes
% from g.
params = parseTwoDistroParams(params, X, Y);
[estim, asympAnalysis, bwX, bwY] = ...
getTwoDistroInfFunAvgs(X, Y, @klInfFunX, @k... |
github | kirthevasank/if-estimators-master | shannonEntropy.m | .m | if-estimators-master/estimators/shannonEntropy.m | 572 | utf_8 | 9b57ad6b1e7301efb646b877e15eb959 | function [estim, asympAnalysis, bw] = shannonEntropy(X, functionalParams, params)
% This estimates the shannon entropy -\int plog(p)
params = parseOneDistroParams(params, X);
[estim, asympAnalysis, bw] = getInfFunAvgs(X, @entropyInfFun, ...
@entropyAsympVar, params);
end
function infFunVals = entropyInfFun(den... |
github | kirthevasank/if-estimators-master | ut_oneDistro.m | .m | if-estimators-master/estimators/ut_oneDistro.m | 2,667 | utf_8 | dd2ceda932e6b8585a133ef55711896b | function ut_oneDistro
% Unit tests for all one distro functionals
addpath ../kde
close all;
clear all;
rng('default');
functionals = {'shannonEntropy'};
tests = {'1D-Uniform', '1D-Conv', '2D-Conv'};
functionalParams = struct;
params = struct;
params.alpha = 0.05;
params.doAsympAnalysis = true;
p... |
github | kirthevasank/if-estimators-master | ut_twoDistro.m | .m | if-estimators-master/estimators/ut_twoDistro.m | 1,895 | utf_8 | a2680e5cdae9574d5ea4ae58fab0e3d8 | function ut_oneDistro
% Unit tests for all one distro functionals
addpath ../kde
close all;
clear all;
functionals = {'hellingerDiv'};
tests = {'1D-UnifUnif', '1D-UnifConv'};
functionalParams = struct;
params = struct;
params.alpha = 0.05;
params.doAsympAnalysis = true;
% Test 1
%%%%%%%%%%%%%%%... |
github | kirthevasank/if-estimators-master | fAlphaGBeta.m | .m | if-estimators-master/estimators/fAlphaGBeta.m | 1,662 | utf_8 | bae2bee469820fa89770ee39ac94302a | function [estim, asympAnalysis, bwX, bwY] = ...
fAlphaGBeta(X, Y, functionalParams, params)
% Estimates the integral \int f^alpha g^beta where beta = 1-alpha. X comes
% from f and Y comes from g. functionalParams should contain the field alpha.
params = parseTwoDistroParams(params, X, Y);
infFunX = @(u,v) fA... |
github | Christiaanvandertol/SCOPE-master | lut_search.m | .m | SCOPE-master/src/+lut/lut_search.m | 1,658 | utf_8 | 4c9813edf5966c6cdfc5f38cf0133a55 | function [res, res_std] = lut_search(params, lut_params, response)
%% check validity: all lut params in params
p_names = params.Properties.VariableNames;
lut_names = lut_params.Properties.VariableNames;
% absent = setdiff(lut_names, p_names);
% assert(isempty(absent), '%s parameter must be in inpu... |
github | Christiaanvandertol/SCOPE-master | bin_to_csv.m | .m | SCOPE-master/src/IO/bin_to_csv.m | 5,756 | utf_8 | ea668c6b5838d7f867287651868cb0d7 | function bin_to_csv(fnames, V, vmax, n_col, ns)
%% pars
if sum(vmax>1)
write_output(['n_pars', {V(vmax>1).Name}], {''}, fnames.pars_file, n_col.pars, ns)
end
%% aPAR
apar_names = {'simulation_number', 'year', 'DoY', 'iPAR', 'iPARE', 'LAIsunlit', 'LAIshaded'...
'aPARtot', 'aPARsun', 'aPARsha',...
'aPARCabtot... |
github | Christiaanvandertol/SCOPE-master | fluspect_B_CX.m | .m | SCOPE-master/src/RTMs/fluspect_B_CX.m | 9,787 | utf_8 | 5290f36318db731d7d3c2ecc677e5b35 | function leafopt = fluspect_B_CX(spectral,leafbio,optipar)
%
% function [leafopt] = fluspect(spectral,leafbio,optipar)
% calculates reflectance and transmittance spectra of a leaf using FLUSPECT,
% plus four excitation-fluorescence matrices
%
% Authors: Wout Verhoef, Christiaan van der Tol (c.vandertol@utwente.nl),
%... |
github | Christiaanvandertol/SCOPE-master | RTMo.m | .m | SCOPE-master/src/RTMs/RTMo.m | 36,593 | utf_8 | bee76f8b9360cfc63a65dd9ee097f430 | function [rad,gap,canopy,profiles] = RTMo(spectral,atmo,soil,leafopt,canopy,angles,constants,meteo,options)
% calculates the spectra of hemisperical and directional observed visible
% and thermal radiation (fluxes E and radiances L), as well as the single
% and bi-directional gap probabilities
%
% the function does n... |
github | Christiaanvandertol/SCOPE-master | RTMz.m | .m | SCOPE-master/src/RTMs/RTMz.m | 10,359 | utf_8 | 37bdc512c7b4853b0294e2ca2a0f9074 | function [rad] = RTMz(constants,spectral,rad,soil,leafopt,canopy,gap,angles,Knu,Knh)
% function 'RTMz' calculates the small modification of TOC outgoing
% radiance due to the conversion of Violaxanthin into Zeaxanthin in leaves
%
% Author: Christiaan van der Tol (c.vandertol@utwente.nl)
% Date: 08 Dec 2016
% ... |
github | Christiaanvandertol/SCOPE-master | RTMt_sb.m | .m | SCOPE-master/src/RTMs/RTMt_sb.m | 7,495 | utf_8 | ebda63214b86ac5a6bdfe5beabce834c | function [rad] = RTMt_sb(constants,rad,soil,leafbio,canopy,gap,Tcu,Tch,Tsu,Tsh,obsdir,spectral)
% function 'RTMt_sb' calculates total outgoing radiation in hemispherical
% direction and total absorbed radiation per leaf and soil component.
% Radiation is integrated over the whole thermal spectrum with
% Stefan-Boltzma... |
github | Christiaanvandertol/SCOPE-master | BSM.m | .m | SCOPE-master/src/RTMs/BSM.m | 6,258 | utf_8 | 0e39ebac12bc2036638eabdcf6260e71 | function rwet = BSM(soilpar,spec,emp)
% Spectral parameters
%wl = spec.wl; % wavelengths
GSV = spec.GSV; % Global Soil Vectors spectra (nwl * 3)
kw = spec.Kw; % water absorption spectrum
nw = spec.nw; % water refraction index spectrum
% Soil p... |
github | Christiaanvandertol/SCOPE-master | ebal_bigleaf.m | .m | SCOPE-master/src/fluxes/ebal_bigleaf.m | 12,877 | utf_8 | 86237f68713a8db84719fab75f4d7ff7 | function [iter,rad,thermal,soil,bcu,bch,fluxes] ...
= ebal_bigleaf(constants,options,rad,gap, ...
meteo,soil,canopy,leafbio)
% function ebal.m calculates the energy balance of a vegetated surface
%
% authors: Christiaan van der Tol (c.vandertol@utwente.nl)
% Joris Timmermans
% d... |
github | Christiaanvandertol/SCOPE-master | resistances.m | .m | SCOPE-master/src/fluxes/resistances.m | 6,700 | utf_8 | f0968d6b41183d556a58256965afec89 | function [resist_out] = resistances(constants,soil,canopy,meteo)
%
% function resistances calculates aerodynamic and boundary resistances
% for soil and vegetation
%
% Date: 01 Feb 2008
% Authors: Anne Verhoef (a.verhoef@reading.ac.uk)
% Christiaan van der Tol (tol@itc.nl)
% ... |
github | Christiaanvandertol/SCOPE-master | ebal_sunshade.m | .m | SCOPE-master/src/fluxes/ebal_sunshade.m | 12,567 | utf_8 | bb65697bc5cc76fded6dd518634864f2 | function [iter,rad,thermal,soil,bcu,bch,fluxes] ...
= ebal_sunshade(constants,options,rad,gap, ...
meteo,soil,canopy,leafbio)
% function ebal.m calculates the energy balance of a vegetated surface
%
% authors: Christiaan van der Tol (c.vandertol@utwente.nl)
% Joris Timmermans
% ... |
github | Christiaanvandertol/SCOPE-master | biochemical.m | .m | SCOPE-master/src/fluxes/biochemical.m | 24,506 | utf_8 | 34cc1093974dc219492706ec6414a8be | function biochem_out = biochemical(leafbio,meteo,options,constants,fV)
%
% Date: 21 Sep 2012
% Update: 20 Feb 2013
% Update: Aug 2013: correction of L171: Ci = Ci*1e6 ./ p .* 1E3;
% Update: 2016-10 - (JAK) major rewrite to accomodate an iterative solution to the Ball-Berry equation
% - also ... |
github | Christiaanvandertol/SCOPE-master | biochemical_MD12.m | .m | SCOPE-master/src/fluxes/biochemical_MD12.m | 27,278 | utf_8 | 2cdfb981cc02f660f46da17cba02bc0c | function biochem_out = biochemical_MD12(leafbio,meteo,~,constants,fV,Q)
%[A,Ci,eta] = biochemical_VCM(Cs,Q,T,eb,O,p,Vcmo,m,Type,Rdparam,stress,Tyear,beta,qLs,NPQs)
% Date: 21 Sep 2012
% Update: 28 Jun 2013 Adaptation for use of Farquhar model of C3 photosynthesis (Farquhar et al 1980)
% 18 Jul 2013 Inc... |
github | Christiaanvandertol/SCOPE-master | ebal.m | .m | SCOPE-master/src/fluxes/ebal.m | 13,615 | utf_8 | 4a6297a306fe321d532040bbe2254337 | function [iter,rad,thermal,soil,bcu,bch,fluxes,resist_out,meteo] ...
= ebal(constants,options,rad,gap, ...
meteo,soil,canopy,leafbio,k,xyt,integr)
% function ebal.m calculates the energy balance of a vegetated surface
%
% authors: Christiaan van der Tol (c.vandertol@utwente.nl)
% ... |
github | Christiaanvandertol/SCOPE-master | leafangles.m | .m | SCOPE-master/src/supporting/leafangles.m | 2,171 | utf_8 | f75c20fd775190446a1b01537666fe9b | function [lidf]= leafangles(a,b)
% Subroutine FluorSail_dladgen
% Version 2.3
% For more information look to page 128 of "theory of radiative transfer models applied in optical remote sensing of
% vegetation canopies"
%
% FluorSail for Matlab
% FluorSail is created by Wout Verhoef... |
github | leelening/Contourlet-transform-based-image-compression-code-master | Huffman.m | .m | Contourlet-transform-based-image-compression-code-master/Huffman.m | 6,389 | utf_8 | a0e0a443ff878d9bae16249f1e0266b8 |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Dictionary Function
% function dict= Dictionary(image_name)
%
% This function is used to create a huffman dictionary
% (generating huffman code) by using tree search method.
% Read the image file and get the information
disp('Enter the na... |
github | leelening/Contourlet-transform-based-image-compression-code-master | fhtree1.m | .m | Contourlet-transform-based-image-compression-code-master/08297407wenben/Huffman3/fhtree1.m | 725 | utf_8 | 9856889f9cfb5abda79d3b4174de9a7c | %Hufman tree drawing phase
function HT=fhtree1(lst,img)
[p,q]=size(lst);
[tt,mx]=size(lst);
sz1=q;
xx=1;
k1=0;
prt=0;
while (k1<1)
k1=lst(2)+lst(4);
prt=prt-1;
lstn(xx)=lst(1);
lstn(xx+1)=0;
lstn(xx+2)=prt;
xx=xx+3;
lstn(xx)=lst(3);
lstn(xx+1)=1;
lstn(xx+2)=prt;
xx=xx+3;
lst(... |
github | leelening/Contourlet-transform-based-image-compression-code-master | fhcode.m | .m | Contourlet-transform-based-image-compression-code-master/08297407wenben/Huffman3/fhcode.m | 1,841 | utf_8 | d2557a7f20d75a5ff9176426ba6932e7 | %Hufman code generating phase
function HC=fhcode(lstn,img)
disp('Code generating phase entered...');
[lm,ln]=size(lstn);
ntt=ln-1;
[im,in]=size(img);
t=0;
idd=input('Enter destination huffman code file name : ','s');
tab=input('Enter the Huff Table name (for decoding purpose): ','s');
tb = fopen(tab,'w+');
idd=fopen(id... |
github | leelening/Contourlet-transform-based-image-compression-code-master | trible_dec.m | .m | Contourlet-transform-based-image-compression-code-master/3des_matlab/trible_dec.m | 976 | utf_8 | 707a189a5a080f34fb731771675e0539 | %one round for des encryption
function Data=trible_des(temp,kf)
r=temp;
%initial permutation
m=[r(58) r(50) r(42) r(34) r(26) r(18) r(10) r(2) r(60) r(52) r(44) r(36) r(28) r(20) r(12) r(4) r(62) r(54) r(46) r(38) r(30) r(22) r(14) r(6) r(64) r(56) r(48) r(40) r(32) r(24) r(16) r(8) r(57) r(49) r(41) r(33) r(25) r(17)... |
github | leelening/Contourlet-transform-based-image-compression-code-master | SBOX.m | .m | Contourlet-transform-based-image-compression-code-master/3des_matlab/SBOX.m | 2,020 | utf_8 | 33f96eefde674ca01e35b952e864f2b0 | %this program to get sbox value
function SValue=des_sbox(ZIn6BitFormat,IndexOfZ)
SBox=zeros(4,16,8);
SBox(:,:,1)=[14 4 13 1 2 15 11 8 3 10 6 12 5 9 0 7;
0 15 7 4 14 2 13 1 10 6 12 11 9 5 3 8;
4 1 14 8 13 6 2 11 15 12 9 7 3 10 5 0;
15 12 8 2 4 9 1 7 5 11 3 14 10 0 6 13];
SBox(:,:,... |
github | leelening/Contourlet-transform-based-image-compression-code-master | KG.m | .m | Contourlet-transform-based-image-compression-code-master/3des_matlab/KG.m | 1,030 | utf_8 | a7548035366a2e46fc8389d92edd2934 | %generate the subkey
function h=deskg(xl,xr)
h=zeros(48*16,1);
for i=1:16
switch i
case 1
case 2
case 9
case 16
xl=[xl(2:28) xl(1)];
xr=[xr(2:28) xr(1)];
w=[xl xr];
h((i-1)*48+1:i*48)=[w(14) w(17) w(11) w(24) w(1) w(5) ... |
github | leelening/Contourlet-transform-based-image-compression-code-master | trible_enc.m | .m | Contourlet-transform-based-image-compression-code-master/3des_matlab/trible_enc.m | 972 | utf_8 | 74f51b57ddac56e9cf9f5da27da7022d | %one round for des encryption
function Data=trible_enc(temp,kf)
r=temp;
m=[r(58) r(50) r(42) r(34) r(26) r(18) r(10) r(2) r(60) r(52) r(44) r(36) r(28) r(20) r(12) r(4) r(62) r(54) r(46) r(38) r(30) r(22) r(14) r(6) r(64) r(56) r(48) r(40) r(32) r(24) r(16) r(8) r(57) r(49) r(41) r(33) r(25) r(17) r(9) r(1) r(59) r(51... |
github | leelening/Contourlet-transform-based-image-compression-code-master | encryption.m | .m | Contourlet-transform-based-image-compression-code-master/3des_matlab/encryption.m | 804 | utf_8 | 77c44c59536acc04c7b4945fbc9c9b80 | %one round for trible des encryption
function sw=desencryption(m,r,kf)
yl=m(1:32);
yr=m(33:64);
%expansion and permutation
x=[yr(32) yr(1) yr(2) yr(3) yr(4) yr(5) yr(4) yr(5) yr(6) yr(7) yr(8) yr(9) yr(8) yr(9) yr(10) yr(11) yr(12) yr(13) yr(12) yr(13) yr(14) yr(15) yr(16) yr(17) yr(16) yr(17) yr(18) yr(19) yr(20) yr... |
github | leelening/Contourlet-transform-based-image-compression-code-master | extend2.m | .m | Contourlet-transform-based-image-compression-code-master/00278228contourlet-CP-RLC/contourlet-CP-RLC/extend2.m | 1,792 | utf_8 | 607c7de17e89483c3983b26b6987cb80 | function y = extend2(x, ru, rd, cl, cr, extmod)
% EXTEND2 2D extension
%
% y = extend2(x, ru, rd, cl, cr, extmod)
%
% Input:
% x: input image
% ru, rd: amount of extension, up and down, for rows
% cl, cr: amount of extension, left and rigth, for column
% extmod: extension mode. The valid modes are:
% 'per': period... |
github | leelening/Contourlet-transform-based-image-compression-code-master | extend2.m | .m | Contourlet-transform-based-image-compression-code-master/contourlet - 混沌 - 副本/extend2.m | 1,792 | utf_8 | 607c7de17e89483c3983b26b6987cb80 | function y = extend2(x, ru, rd, cl, cr, extmod)
% EXTEND2 2D extension
%
% y = extend2(x, ru, rd, cl, cr, extmod)
%
% Input:
% x: input image
% ru, rd: amount of extension, up and down, for rows
% cl, cr: amount of extension, left and rigth, for column
% extmod: extension mode. The valid modes are:
% 'per': period... |
github | leelening/Contourlet-transform-based-image-compression-code-master | huffmanencode.m | .m | Contourlet-transform-based-image-compression-code-master/contourlet-CBC反馈加密 - 副本/huffmanencode.m | 2,044 | utf_8 | 5cc2d8fb65e43a935a48a16b6e8c436a | %vector=imread('BABOOEYE.BMP');
%vector=[0.4 0.175 0.15 0.15 0.125];
%vector=uint8(vector);
function [zipped,info]=huffmanencode(vector);
%vector=imread('BABOO.BMP');
if ~isa(vector,'uint8')
error('input must be uint8 vector')
end
[m,n]=size(vector);
vector=vector(:)';
f=frequency(vector);
simbols=find(f~=0);
f=f(si... |
github | leelening/Contourlet-transform-based-image-compression-code-master | extend2.m | .m | Contourlet-transform-based-image-compression-code-master/contourlet-CBC反馈加密 - 副本/extend2.m | 1,792 | utf_8 | 607c7de17e89483c3983b26b6987cb80 | function y = extend2(x, ru, rd, cl, cr, extmod)
% EXTEND2 2D extension
%
% y = extend2(x, ru, rd, cl, cr, extmod)
%
% Input:
% x: input image
% ru, rd: amount of extension, up and down, for rows
% cl, cr: amount of extension, left and rigth, for column
% extmod: extension mode. The valid modes are:
% 'per': period... |
github | leelening/Contourlet-transform-based-image-compression-code-master | huffdecode.m | .m | Contourlet-transform-based-image-compression-code-master/contourlet-CBC反馈加密 - 副本/huffdecode.m | 794 | utf_8 | 9f6822d58d93024e8333db8e786fda1a | function vector=huffdecode(zipped,info,vector)
if ~isa(zipped,'uint8')
error('input mudt be a uint8 vector');
end
len=length(zipped);
string=repmat(uint8(0),1,len*8);
bitindex=1:8;
for index=1:len
string(bitindex+8.*(index-1))=uint8(bitget(zipped(index),bitindex));
end
string=logical(string(:)');
len=length(str... |
github | leelening/Contourlet-transform-based-image-compression-code-master | Decoding.m | .m | Contourlet-transform-based-image-compression-code-master/contourlet-CBC反馈加密/Decoding.m | 2,798 | utf_8 | 73237ff8cad1277573e1a1159a984a4f |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Decoding Function
function image1=Decoding(dict,y)
%
% This function decodes the matrix y into original huffman codes and
% compose the codes into orginal image.
symbols= (0:255)';
N=length(y);
% Elements of the dictionary
% symbols=dict... |
github | leelening/Contourlet-transform-based-image-compression-code-master | huffmanencode.m | .m | Contourlet-transform-based-image-compression-code-master/contourlet-CBC反馈加密/huffmanencode.m | 2,044 | utf_8 | 5cc2d8fb65e43a935a48a16b6e8c436a | %vector=imread('BABOOEYE.BMP');
%vector=[0.4 0.175 0.15 0.15 0.125];
%vector=uint8(vector);
function [zipped,info]=huffmanencode(vector);
%vector=imread('BABOO.BMP');
if ~isa(vector,'uint8')
error('input must be uint8 vector')
end
[m,n]=size(vector);
vector=vector(:)';
f=frequency(vector);
simbols=find(f~=0);
f=f(si... |
github | leelening/Contourlet-transform-based-image-compression-code-master | extend2.m | .m | Contourlet-transform-based-image-compression-code-master/contourlet-CBC反馈加密/extend2.m | 1,792 | utf_8 | 607c7de17e89483c3983b26b6987cb80 | function y = extend2(x, ru, rd, cl, cr, extmod)
% EXTEND2 2D extension
%
% y = extend2(x, ru, rd, cl, cr, extmod)
%
% Input:
% x: input image
% ru, rd: amount of extension, up and down, for rows
% cl, cr: amount of extension, left and rigth, for column
% extmod: extension mode. The valid modes are:
% 'per': period... |
github | leelening/Contourlet-transform-based-image-compression-code-master | huffdecode.m | .m | Contourlet-transform-based-image-compression-code-master/contourlet-CBC反馈加密/huffdecode.m | 794 | utf_8 | 9f6822d58d93024e8333db8e786fda1a | function vector=huffdecode(zipped,info,vector)
if ~isa(zipped,'uint8')
error('input mudt be a uint8 vector');
end
len=length(zipped);
string=repmat(uint8(0),1,len*8);
bitindex=1:8;
for index=1:len
string(bitindex+8.*(index-1))=uint8(bitget(zipped(index),bitindex));
end
string=logical(string(:)');
len=length(str... |
github | leelening/Contourlet-transform-based-image-compression-code-master | extend2.m | .m | Contourlet-transform-based-image-compression-code-master/contourlet分解与重构/extend2.m | 1,792 | utf_8 | 607c7de17e89483c3983b26b6987cb80 | function y = extend2(x, ru, rd, cl, cr, extmod)
% EXTEND2 2D extension
%
% y = extend2(x, ru, rd, cl, cr, extmod)
%
% Input:
% x: input image
% ru, rd: amount of extension, up and down, for rows
% cl, cr: amount of extension, left and rigth, for column
% extmod: extension mode. The valid modes are:
% 'per': period... |
github | tomazas/icist2015-master | run_experiment.m | .m | icist2015-master/run_experiment.m | 3,067 | utf_8 | a1521b51cc79b61fa2d33fe740676d86 | % runs all tests for passed feature function and classifier function
function [xfold_kappas] = run(feat_func, class_func, p)
addpath('features/');
addpath('classifiers/')
addpath('utils/');
addpath('other/');
addpath('libsvm/matlab/');
% structure of the signal
% S = struct:
% sig1:... |
github | tomazas/icist2015-master | test_all.m | .m | icist2015-master/test_all.m | 916 | utf_8 | 03073fd7761d686224482f90db128310 | % tests passed feature function with all existing classifiers defined in their folder
function test_all(feat_func, params, outname, restrict)
% find and test all classifiers
listing = dir('classifiers/*.m');
% output results to CSV file
fp = fopen(outname, 'wt');
fprintf(fp, 'sep=;\n'); % ensure delimiter ... |
github | tomazas/icist2015-master | classify_qda.m | .m | icist2015-master/classifiers/classify_qda.m | 239 | utf_8 | 39fbd12ee95c45c664474713d60c2189 | % Matlab quadratic discriminant analysis classification
function [c, training_err] = classify_qda(p, test_data, train_data, train_labels)
[c, training_err, post, logl, str] = classify(test_data, train_data, train_labels, 'quadratic');
end |
github | tomazas/icist2015-master | classify_knn.m | .m | icist2015-master/classifiers/classify_knn.m | 759 | utf_8 | c3af3e0f68b1d077425dbb64f463e6c5 | % function doing kNN classification of data
function [c, training_err] = classify_knn(p, test_data, train_data, train_labels)
k = 15;
% create and train kNN model
mdl = ClassificationKNN.fit(train_data, train_labels, 'NumNeighbors', k, 'Distance', 'euclidean');
%train_accuracy = 1-resubLoss(mdl); %... |
github | tomazas/icist2015-master | classify_lda.m | .m | icist2015-master/classifiers/classify_lda.m | 229 | utf_8 | f4c89b966d1ab668530f58b45f7efa62 | % Matlab linear discriminant analysis classifier
function [c, training_err] = classify_lda(p, test_data, train_data, train_labels)
[c, training_err, post, logl, str] = classify(test_data, train_data, train_labels, 'linear');
end |
github | tomazas/icist2015-master | classify_svm.m | .m | icist2015-master/classifiers/classify_svm.m | 556 | utf_8 | eac20738ec66cdfa1c977a5f3c710df5 | % libSVM SVM classifier
function [predict_label, training_err] = classify_svm(p, test_data, train_data, train_labels)
model = svmtrain(train_labels, train_data, '-c 10 -g 0.07');
fake_labels = zeros(size(test_data,1), 1); % since we don't know the true labels, pass fake ones
[predict_label, accuracy, d... |
github | tomazas/icist2015-master | get_trials.m | .m | icist2015-master/utils/get_trials.m | 4,232 | utf_8 | 2031e732d811c8739618bf2a3f9d40b6 | % extract all trials from the EEG signal
function [s, trials, csp_matrix] = get_features(s,h,p,bare)
fprintf('Removing signal artifacts...\n');
if ~bare % apply some post processing
s = strip_artifacts(s,h.SampleRate);
end
num_samples = size(s,1);
num_channels = size(s,2);
signal_time ... |
github | tomazas/icist2015-master | eval_feats.m | .m | icist2015-master/utils/eval_feats.m | 1,311 | utf_8 | c991cac4b11c85bcff20ac7e1f68be3d | % extract features, classify them and verify correctness using 10xfold crossvalidation
function [training_err, testing_err, tenfold_train_err, tenfold_test_err] = eval_feats(p, feat_func, class_func, train_trials, test_trials, train_labels, test_labels)
[train_data, test_data] = feat_func(p, train_trials, test_tri... |
github | tomazas/icist2015-master | normalize.m | .m | icist2015-master/utils/normalize.m | 418 | utf_8 | 2f0fb3740db686cffce612a503475860 | % normalize signal
function ret = normalize(s,mode)
if nargin < 2
mode = 0;
end
if mode == 0
% 0 mean and unit variance
mu = mean(s(:));
sigma = sqrt(var(s(:)));
ret = (s - ones(size(s))*mu) ./ sigma;
else
% 0 mean, norm by max amplitude
mu =... |
github | tomazas/icist2015-master | channel_diff_feats.m | .m | icist2015-master/features/channel_diff_feats.m | 1,657 | utf_8 | 244e628440eba1e513f6ef284dface87 | % implementation of channel difference filtering for feature generation
function [train_data, test_data] = channel_diff_feats(p, train_trials, test_trials, train_labels, test_labels)
[channels, samples, trials] = size(train_trials);
ch = [8 10 12 20];
ky = [
2 3 9 15 14 7 0 0; ...
... |
github | clzirbel/Random_Processes-master | transition_matrix_powers.m | .m | Random_Processes-master/Matlab/transition_matrix_powers.m | 1,854 | utf_8 | 3770b2d90d4ed31dca7dee5cb1de4504 | % transition_matrix_powers(P,n,minimumstate) uses grayscale heatmaps to display powers n of the matrix P
% the variable n is a vector of up to 6 numbers
% the default value for P is gambler_transition_matrix(10,20,0.5)
% the default value for n is n = [1 2 3 20 225 2000]
% the default value for minimumstate is 0; state... |
github | clzirbel/Random_Processes-master | pcolor_fixed.m | .m | Random_Processes-master/Matlab/pcolor_fixed.m | 375 | utf_8 | 2b191f6f780c59f2e26dc4770cf1755d | % pcolor_fixed(x,y,M) adds a last row and column to M and to x and y so that the last row and column are not cut off
function [void] = pcolor_fixed(x,y,M)
[A,B] = size(M);
x(B+1) = max(x) + 1; % add one element
y(A+1) = max(y) + 1; % add one element
M(A+1,B+1) = 0; % add one row and one column to M... |
github | clzirbel/Random_Processes-master | print_matrix.m | .m | Random_Processes-master/Matlab/print_matrix.m | 332 | utf_8 | a0b5663716524439e9d757980e80325f | % print_matrix(P) prints a matrix to the screen in a reasonable way for probability transition matrices
% It is set to print four places after the decimal, but you can adjust that if you want.
function [void] = print_matrix(P)
[a,b] = size(P);
for i = 1:a,
for j = 1:b,
fprintf('%0.4f ',P(i,j));
end
fprintf... |
github | clzirbel/Random_Processes-master | gambler_transition_matrix.m | .m | Random_Processes-master/Matlab/gambler_transition_matrix.m | 1,131 | utf_8 | 9dddc1124deacd64ba38288f67b4a5b9 | % gambler_transition_matrix(m,n,p) is a function which sets up the transition matrix for a gambler's wealth after successive iid bets of 1 dollar.
% Input parameters are m, the amount of money the gambler has, n, the amount the opponent has, and p, the probability that the gambler wins each bet.
function [P] = gambler... |
github | moonlightlane/pitch-detection-master | plotMarker.m | .m | pitch-detection-master/plotMarker.m | 995 | utf_8 | dd91bb4935ce565296f265d887cb745a | %% ------------------------------------------------------------------------
%% the timer callback function definition
function plotMarker(...
obj, ... % refers to the object that called this function (necessary parameter for all callback functions)
eventdata, ... % this parameter is not used but... |
github | moonlightlane/pitch-detection-master | MPM_pitch_detection.m | .m | pitch-detection-master/MPM_pitch_detection.m | 5,940 | utf_8 | 98c415aade048c651886e027eaebf56c | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Matlab Implementation of MPM pitch detection algorithm %%
% Version V1.0
% Date: Feb 15, 2015
% Author: Zichao Wang
%% Documentation
% This Function implements the MPM pitch detection algorithm
% by Dr. Pilip McLeod. You can read more about this algori... |
github | moonlightlane/pitch-detection-master | MPM.m | .m | pitch-detection-master/MPM.m | 6,113 | utf_8 | 4af452d5e40aa627aa2cf053f2b98ed4 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Matlab Implementation of MPM pitch detection algorithm %%
% Version V2.0
% Date: March 16, 2015
% Author: Zichao Wang
%% Documentation
% This Function implements the MPM pitch detection algorithm
% by Dr. Pilip McLeod. You can read more about this algo... |
github | moonlightlane/pitch-detection-master | AudioDisplay.m | .m | pitch-detection-master/AudioDisplay.m | 5,016 | utf_8 | e8de62c2d879c6dcbd6204046ee00ab2 | function varargout = AudioDisplay(varargin)
% AUDIODISPLAY M-file for AudioDisplay.fig
% AUDIODISPLAY, by itself, creates a new AUDIODISPLAY or raises the existing
% singleton*.
%
% H = AUDIODISPLAY returns the handle to a new AUDIODISPLAY or the handle to
% the existing singleton*.
%
% AUDIODI... |
github | moonlightlane/pitch-detection-master | AudioDisplay.m | .m | pitch-detection-master/AudioDisplay/AudioDisplay.m | 5,167 | utf_8 | 24b245e0832673cb8b1bcc4aa9a25a19 | function varargout = AudioDisplay(varargin)
% AUDIODISPLAY M-file for AudioDisplay.fig
% AUDIODISPLAY, by itself, creates a new AUDIODISPLAY or raises the existing
% singleton*.
%
% H = AUDIODISPLAY returns the handle to a new AUDIODISPLAY or the handle to
% the existing singleton*.
%
% ... |
github | krzyzanowskim/Google1Password-master | OnePasswordExtension.m | .m | Google1Password-master/Pods/1PasswordExtension/OnePasswordExtension.m | 32,137 | utf_8 | d788cadccae39642009f7fdca943c92e | //
// 1Password Extension
//
// Lovingly handcrafted by Dave Teare, Michael Fey, Rad Azzouz, and Roustem Karimov.
// Copyright (c) 2014 AgileBits. All rights reserved.
//
#import "OnePasswordExtension.h"
// Version
#define VERSION_NUMBER @(112)
static NSString *const AppExtensionVersionNumberKey = @"version_number... |
github | jn2clark/2DPhaseRetrieval-master | align_arrays.m | .m | 2DPhaseRetrieval-master/Algorithm/align_arrays.m | 8,919 | utf_8 | c69c3de3f92d0f7299b16f9eb0654c45 | function [array2] = align_arrays(array1,array2)
%jclark
%aligns array2 with array1 based on cross corr (upsampled)
%get the hkl required for shifting using upsampled cross-corr
[h k l]=register_3d_reconstruction(array1,array2);
%since we are dealing with intensities for phasing, want only integer
%shifting to avoid a... |
github | jn2clark/2DPhaseRetrieval-master | bin_data_lite.m | .m | 2DPhaseRetrieval-master/Algorithm/bin_data_lite.m | 1,994 | utf_8 | 6139b4a47df937bf44755b30a7867f01 | function [data] = bin_data_lite(data,params)
bin = params.binning;
nx=size(data);
%pad the array so that it bins exactly
x0=nx(2);
y0=nx(1);
if max(size(nx)) == 2,nx=[nx,1];end
disp(' ')
disp('Resizing data....')
disp(['Current data size [x,y,z] - [',num2str([x0,y0,nx(3)]),']'])
while mod(x0,bin(1)) ~=0,x0=x0+1;en... |
github | jn2clark/2DPhaseRetrieval-master | align_iterates_lite.m | .m | 2DPhaseRetrieval-master/Algorithm/align_iterates_lite.m | 12,293 | utf_8 | 47dbea2088c813a37676fae01d88835e | function [aligned_its] = align_iterates_lite( iterates,ind,val,sub_pix)
%Jclark
% aligns a whole series of iterates
% ind is the one to align to. default == 1
% use ind = -1 to align sequenialy, ie. 2 -1,3-2,4-3 etc
% set val to any number to do phase offset removal
try
ind;
catch
ind = 1;
end
try
val;
c... |
github | jn2clark/2DPhaseRetrieval-master | breed_iterates_lite.m | .m | 2DPhaseRetrieval-master/Algorithm/breed_iterates_lite.m | 3,265 | utf_8 | 4e9a67e8d04e9cbbcdace6815756cbc1 | function [params] = breed_iterates_lite(params)
%jclark
%combine iterates for guided algorithm
params=set_breed_defaults(params);
%assume that each time is aligned (time here is pop)
ntimes=size(params.pnm,ndims(params.pnm)); %get the ntimes
%get the best
disp(' ')
disp(['Using ',params.GA_metric,' as the metric f... |
github | jn2clark/2DPhaseRetrieval-master | modulus_projector_lite.m | .m | 2DPhaseRetrieval-master/Algorithm/modulus_projector_lite.m | 1,220 | utf_8 | d9f340a96b9b3b761e313b60f5f91745 | function [pnm error params] = modulus_projector_lite(pn,data,params)
% jclark
% modulus constraint projector
% get estimate for scattered wave
psi = fftxy(pn,1);
% calculate the current error
error=calc_chi(abs(psi(data ~= 0)) ,sqrt(data(data ~= 0)));
% replace modulus
psi=replace_modulus(psi,sqrt(data),params);
% ... |
github | jn2clark/2DPhaseRetrieval-master | save_matlabphasing_lite.m | .m | 2DPhaseRetrieval-master/Algorithm/save_matlabphasing_lite.m | 4,072 | utf_8 | a173ddb64cc6aca26f9e866f69aaef52 | function save_matlabphasing_lite(params)
% jclark
% saves the output from the phasing
% saves the params and images as well as
% copying the original script
disp(' ')
disp('Saving reconstruction....')
disp(' ')
% create save name from params
[ name ] = create_save_name_lite(params);
disp(name)
disp(' ')
% make the ... |
github | jn2clark/2DPhaseRetrieval-master | bin_crop_center_lite.m | .m | 2DPhaseRetrieval-master/Algorithm/bin_crop_center_lite.m | 15,124 | utf_8 | 26d10e090c2df9faed0e50703a6b7dd4 | function [ params ] = bin_crop_center_lite(params)
%jclark
%loads data, aligns data, centers data, crops data, bins data
%thresholds data, removes aliens (spurious data)
%returns the intensity
%set defualts
params = set_params_defaults(params);
%background subtract flag, 1 =yes. will turn off if no file found
do_bg ... |
github | jn2clark/2DPhaseRetrieval-master | center_array_lite.m | .m | 2DPhaseRetrieval-master/Algorithm/center_array_lite.m | 855 | utf_8 | 81c70c566cd3f17ba326b7a0520dcd22 | function [ array xyz] = center_array_lite(array)
%jclark
%returns the ceom, for even arrays want n/2+1 as the center
xyz = center_of_mass_v2(array);
%remeber order is different to xyz
nn = size(array);
switch ndims(array)
case 2
cent_xyz = [nn(2)/2+1,nn(1)/2+1];
shift_xyz =
cas... |
github | jn2clark/2DPhaseRetrieval-master | create_annulus.m | .m | 2DPhaseRetrieval-master/Algorithm/create_annulus.m | 3,445 | utf_8 | 5821c6b0c3ae81a58632eb07bd71087b | function [ annulus ] = create_annulus(n1,n2,rad1,rad2)
%jclark
vals = sort([rad1,rad2]);
nn=min([n1,n2]);
[ annulus ] = generate_circle_nd(nn,nn,vals(2))-generate_circle_nd(nn,nn,vals(1));
annulus=zero_pad_ver3(annulus,n2,n1);
end
function [ circ ] = generate_circle_nd(n1,n2,rad)
%jclark
[x y]=meshgrid(-n2... |
github | jn2clark/2DPhaseRetrieval-master | init_phasing_lite.m | .m | 2DPhaseRetrieval-master/Algorithm/init_phasing_lite.m | 4,587 | utf_8 | 50d7ff24dfa0a5e97ede7f6218775a55 | function params = init_phasing_lite(params)
%jclark
%init the arrays and support. need to add support for loading a support
%need to add for 3D
%create support
if numel(size(params.data)) == 3
support=zero_pad_ver3(ones(round([params.sy,params.sx,params.sz])),params.nn(2),params.nn(1),params.nn(3) );
else su... |
github | jn2clark/2DPhaseRetrieval-master | zero_pad_ver3.m | .m | 2DPhaseRetrieval-master/Algorithm/zero_pad_ver3.m | 3,049 | utf_8 | 159934675b723e0ecbf918b775a0096a | function [ new_array ] = zero_pad_ver3( input,newx,newy,newz )
%jclark
nd=ndims(input);
nn=size(input);
x=newx-nn(2);
y=newy-nn(1);
if ndims(input) == 3,z=newz-nn(3);else z=0;end
nnc=[floor(x/2),ceil(x/2),floor(y/2),ceil(y/2),floor(z/2),ceil(z/2)];
new_array = init_pad(input,nnc);
new_array = init_crop(new_array,... |
github | jn2clark/2DPhaseRetrieval-master | iterative_phasing_lite.m | .m | 2DPhaseRetrieval-master/Algorithm/iterative_phasing_lite.m | 14,464 | utf_8 | 582253a6032e526d0655c5944aab4257 | function [params] = iterative_phasing_lite(params)
% jclark
% performs phasing from diffraction. This version
% specifially designed for 2D XFEL data with or without missing
% data. params is a structure created using Matlab_phasing_ver1_1.m file
% set defaults if they don't exist
params = set_defaults(params);
% ... |
github | jn2clark/2DPhaseRetrieval-master | register_3d_reconstruction.m | .m | 2DPhaseRetrieval-master/Algorithm/register_3d_reconstruction.m | 8,531 | utf_8 | 63f2f703bb63fdbb0162e28f4c82843f |
function [h k l] = register_3d_reconstruction(a,b)
%jclark
%returns the hkl required to shift b to a
%e.g a=circshift(b,[h,k,l])
hk=dftregistration(fft2(squeeze(sum(a,3))),fft2(squeeze(sum(b,3))),100);
hl=dftregistration(fft2(squeeze(sum(a,2))),fft2(squeeze(sum(b,2))),100);
kl=dftregistration(fft2(squeeze(sum(a,1))),... |
github | chuhang/GPS_Refinement-master | vpdetection.m | .m | GPS_Refinement-master/street_image_proc/vpdetection.m | 1,876 | utf_8 | c73ef502a723db8acea3a426ff09870f | %%
% Copyright (c) 2011 Chen Feng (cforrest[at]umich[dot]edu)
% and the University of Michigan
%
% This program is free software; you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation; either version 2 of the License, or
% ... |
github | cnettel/jackdaw-master | diffpoisson.m | .m | jackdaw-master/diffpoisson.m | 3,089 | utf_8 | 51495bc84269f07bbbfad64b4a071916 | function [f] = diffpoisson(scale,y,basey,minval,absrefpoint,filter,qbarrier)
mask = ~(y<0 | isnan(y));
rscale = 1./scale;
filterrsq = 1./filter.^2;
baseyscaled = basey .* rscale;
absrefpointscaled = absrefpoint .* rscale;
%y(mask) = y(mask) + qbarrier * 0.5 .* rscale(mask) .* filterrsq(mask);
f = @(varargin)diff_func... |
github | cnettel/jackdaw-master | createwindows.m | .m | jackdaw-master/createwindows.m | 2,410 | utf_8 | c3e3c37f4ef4de7c43631d83ec4b07e1 | function [factor, basepenalty] = createwindows(pattern, mask, qbarrier)
[dims, side2, fullsize, pshape, cshape] = getdims(pattern);
function [factor] = createfilter(filter, pshape, side2, fullsize)
shape1 = pshape;
shape1(1) = 1
filter1 = repmat(filter, shape1);
shapeb = pshape;
shapeb(:) = 1;
shapeb(2) ... |
github | cnettel/jackdaw-master | jackdawlinop.m | .m | jackdaw-master/jackdawlinop.m | 1,787 | utf_8 | 22e16c079481273b276029da30627540 | function linop = jackdawlinop(pattern, filter)
[dims, side2, fullsize, pshape, cshape] = getdims(pattern);
if dims == 3
% 3D mode also implies half-pixel shift in centering, for now...
%range = linspace(0, -pi + (pi / side), side);
range = fftshift(pi / 2 + ((0.25:(side2 - 0.75)) * pi/side2));
%range = fftshift(ran... |
github | vsubhashini/caffe-master | classification_demo.m | .m | caffe-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | PrincetonUniversity/msddm-master | chisq.m | .m | msddm-master/chisq.m | 727 | utf_8 | 8fdfa15d3b9563bdd44956ce74dec924 | % Compute Chi sq for one set of (correct or incorrect) RTs
% QUANTILE WEIGHTS HERE
function [val, df,q] = chisq(rtData,tArray,ddmcdf,nTotalTrials)
nTrials = length(rtData);
% qBins = [.1 .2 .2 .2 .2 .1];
% qBins = [.1 .1 .1 .1 .1 .1];
qBins = [.05 .1*ones(1,9) .05];
cpv = cumsum(qBins);
q = quantile(rtData,cpv);
q(end... |
github | PrincetonUniversity/msddm-master | test_msfit_2stage.m | .m | msddm-master/test_msfit_2stage.m | 933 | utf_8 | 8168f9df0e3a3de83c4e729122194ff0 | function test_msfit_2stage
close all
a = [.04 .08];
z = [.215 .215];
realX = [a(1) a(2) z(1)];
s = [.3 .3];
x0 = 0;
dl = [0 1];
x0dist = 1;
dt = .005;
nSims = 5e3;
[rt,er,rtP,rtM,tFinal] = sim_msddm(nSims,a,s,dt,z,x0,x0dist,dl)
rtResp = -1*(er-1); % responses (>0 for top boundary)
%Let's go with fminsearch first.... |
github | PrincetonUniversity/msddm-master | obj2a1d2z1xT0.m | .m | msddm-master/obj2a1d2z1xT0.m | 288 | utf_8 | cb425a3d7e3d7a5ed42a35f6206a1cd3 | % 6 params: 2 drifts, 1 deadline, 2 thresholds, x0, T0
function val = obj2a1d2z1xT0(x,rt,rtResp)
a = [x(1) x(2)];
dl = [0 x(3)];
z = [x(4) x(5)];
x0 = x(6);
T0 = x(7);
% Fixed (experimentally set) values
s = [1 1];
tFinal = max(rt) + 3;
val = rt2002(rt,rtResp,a,s,z,x0,1,dl,tFinal,T0);
|
github | PrincetonUniversity/msddm-master | test_msfit.m | .m | msddm-master/test_msfit.m | 829 | utf_8 | a85d9a7a838ccc10fe92f6452df52970 | function test_msfit
close all
a = .09;
z = .215;
realX = [a z];
s = .3;
x0 = 0;
x0dist = 1;
dt = .005;
dl = [0];
nSims = 5e3;
[rt,er,rtP,rtM,tFinal] = sim_msddm(nSims,a,s,dt,z,x0,x0dist,dl)
rtResp = -1*(er-1); % responses (>0 for top boundary)
%Let's go with fminsearch first...
opts = optimset('fminsearch');
opts ... |
github | PrincetonUniversity/msddm-master | multi_stage_ddm_metrics.m | .m | msddm-master/multi_stage_ddm_metrics.m | 9,512 | utf_8 | 2619367bce9b32639e6396f685afdb90 | function [mean_RT, mean_ER, mean_RT_plus, mean_RT_minus]=multi_stage_ddm_metrics(a ,s, deadlines, thresholds, x0, x0dist)
% Input:
% a = vector of drift rates at each stage
% s = vector of diffusion rates at each stage
% deadlines = vector of times when stages start. First entry should be 0.
% thresholds = vector of t... |
github | PrincetonUniversity/msddm-master | rt2002.m | .m | msddm-master/rt2002.m | 833 | utf_8 | f65898219ff8ab92ab79916c0de4ef78 | % rtResp: >0 for correct, <=0 for incorrect
function [val,df] = rt2002(rtData, rtResp, a,s,th,x0,x0dist, ...
dl,tFinal,T0)
if nargin < 10
T0 = 0;
end
[tArray,~,yPlus,yMinus] = multistage_ddm_fpt_dist(...
a,s,th,x0,x0dist,dl,tFinal);
dt = tArray(3)-tArray(2);
nShift = round(T0/... |
github | PrincetonUniversity/msddm-master | multistage_ddm_fpt_dist.m | .m | msddm-master/multistage_ddm_fpt_dist.m | 5,604 | utf_8 | 1677edefa224b4c8179ceed31e9fbf18 | function [T,Y, Yplus, Yminus]=multistage_ddm_fpt_dist(a,s,threshold,x0,x0dist,deadlines,tfinal)
%Input:
% a = vector of drift rates
% s = vector of diffusion rates
% z = threshold
% x0= discretized initial condition support set
% x0dist= discretized pdf of the initial condition (equal to 1 if x0 is deterministic)
% d... |
github | PrincetonUniversity/msddm-master | chisq_rt2002.m | .m | msddm-master/chisq_rt2002.m | 471 | utf_8 | 69eb02d73dda5187ce2fac8be934f52d | % rtResp: >0 for correct, <=0 for incorrect
function val = chisq_rt2002(rtData, rtResp, a,s,th,x0,x0dist, ...
dl,tFinal)
[tArray,~,yPlus,yMinus] = multistage_ddm_fpt_dist(...
a,s,th,x0,x0dist,dl,tFinal);
% From vanila Chi-sq from Ratcliffe Tuerlinckx 2002.
nTotalTrials = lengt... |
github | StewartNash/particle_diffusion_4-master | updatepositionborder.m | .m | particle_diffusion_4-master/updatepositionborder.m | 3,058 | utf_8 | 03d4659b562160fb57bb2c809a815cd5 | %PLEASE SUBMIT MODIFICATIONS AND IMPROVEMENTS!
%File: updatepositionborder.m (MATLAB)
%Version: 0.0
%Author: Stewart Nash
%Date: February 27, 2015
%Description: Function returns position of indexed particles given indexed velocity, current position, time increment, index size, and grid size. It also updates the angle ... |
github | StewartNash/particle_diffusion_4-master | enlarge.m | .m | particle_diffusion_4-master/enlarge.m | 2,780 | utf_8 | 402b9e157cf017ef1c5651a93d3c0182 | %PLEASE SUBMIT MODIFICATIONS AND IMPROVEMENTS!
%File: enlarge.m (MATLAB)
%Version: 0.0
%Author: Stewart Nash
%Date: February 26, 2015
%Description: Increases visual size of particle on grid by turning on immediately adjacent (non-diagonal) pixels
%Note: Particle index means a consecutive list of integers starting wit... |
github | StewartNash/particle_diffusion_4-master | updatedomain.m | .m | particle_diffusion_4-master/updatedomain.m | 1,079 | utf_8 | 2b96568fec513123cf95d5a1be9219d9 | %PLEASE SUBMIT MODIFICATIONS AND IMPROVEMENTS!
%File: updatedomain.m (MATLAB)
%Version: 0.1
%Author: Stewart Nash
%Date: February 27, 2015
%Description: Function indicates position in square grid where particles are present with a value of 1
%Note: Particle index means a consecutive list of integers starting with 1.
... |
github | StewartNash/particle_diffusion_4-master | updatevelocity.m | .m | particle_diffusion_4-master/updatevelocity.m | 797 | utf_8 | 7859b8f9d4648c6ebba8b4a3b2adb471 | %PLEASE SUBMIT MODIFICATIONS AND IMPROVEMENTS!
%File: updatevelocity.m (MATLAB)
%Version: 0.0
%Author: Stewart Nash
%Date: February 25, 2015
%Description: Update velocity vector given speed and angle of particles
%Note: Particle index means a consecutive list of integers starting with 1.
%>>>>Input Parameters<<<<%
%... |
github | rodrigo-garcia-leon/thesis-matlab-master | matrix2latex.m | .m | thesis-matlab-master/Text/matrix2latex.m | 18,532 | utf_8 | ec33fe311ffa88b2054d61286564a88f | function varargout = matrix2latex(varargin)
% MATRIX2LATEX M-file for matrix2latex.fig
% MATRIX2LATEX, by itself, creates a new MATRIX2LATEX or raises the existing
% singleton*.
%
% H = MATRIX2LATEX returns the handle to a new MATRIX2LATEX or the handle to
% the existing singleton*.
%
% MATRIX2... |
github | rodrigo-garcia-leon/thesis-matlab-master | plotlangendijk2002.m | .m | thesis-matlab-master/tb_AMT/monaural/plotlangendijk2002.m | 14,332 | utf_8 | f7fdc99f6cc242e006ab476d31d63d98 | function out = plotlangendijk2002( p,rang,tang,varargin)
%PLOTLANGENDIJK2002 plots pdf-matrixes with gray colormap according to Langendijk et al. (2002)
% Usage: plotlangendijk2002(p,rang,tang);
%
% Input parameters:
% p : pdf-matrix for all target and response positions
% rang : response angles
%... |
github | rodrigo-garcia-leon/thesis-matlab-master | baumgartner2014.m | .m | thesis-matlab-master/tb_AMT/monaural/baumgartner2014.m | 12,856 | utf_8 | 44a8ace62c76c6065b5a8002f507ad0f | function varargout = baumgartner2014( target,template,varargin )
%BAUMGARTNER2014 Model for localization in saggital planes
% Usage: [p,respang] = baumgartner2014( target,template )
% [p,respang,tang] = baumgartner2014( target,template )
% [p,respang,tang] = baumgartner2014( target,template... |
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