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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | HaizhaoYang/SynLab-master | BregmanIter_FitL1Curl.m | .m | SynLab-master/Applications/SynCrystal/VarSSTmethod/src/srcOptPart/BregmanIter_FitL1Curl.m | 5,681 | utf_8 | f624dbe13e6a7b90a6d5dab8e4f4884c | function [G,curlG] = BregmanIter_FitL1Curl( waveVecs, masses, stencil, G, weights, lambda, nt, GPUflag )
% function [G,curlG] = l1CurlBregman( waveVecs, masses, stencil, G, weights, lambda, nt1, GPUflag )
% assumes grid spacing of 1
% assumes origin in top left corner
% x-coordinate points right, y-coordinate down
% as... |
github | HaizhaoYang/SynLab-master | elasticDenoising.m | .m | SynLab-master/Applications/SynCrystal/VarSSTmethod/src/srcOptPart/elasticDenoising.m | 2,631 | utf_8 | 191015bf509d2293a6a0f6fef6c74bef | function G = elasticDenoising( G0, mask, B, R, L, numIter )
% Minimizes the functional
% E[G]=\int_{\Omega\setminus D} (G-G_0)^2 + hyperelastic energy(G) dx
% under the constraint findCurl( G = \sum_{k=1}^K b_k \delta_{x_k}.
% Assumes periodic image pixel spacing 1.
% G0 - G_0 (m x n x 2 x 2)
% mask - characteristic ... |
github | HaizhaoYang/SynLab-master | SpectralClustering_est.m | .m | SynLab-master/Applications/GeneralModeDecom1D/src/SpectralClustering_est.m | 2,673 | utf_8 | fff973f52a059d9af98911b148d42de4 | %--------------------------------------------------------------------------
% This function takes a NxN matrix CMat as adjacency of a graph and
% computes the segmentation of data from spectral clustering. It estimates
% the number of subspaces using eigengap heuristic
% CMat: NxN adjacency matrix
% K: number of large... |
github | HaizhaoYang/SynLab-master | g.m | .m | SynLab-master/Source/SS_WP_3D/src/g.m | 847 | utf_8 | ca89e8bc97649ef1b68fa6ff1abe72d4 | function r = g(w)
% g.m - 'g' function Villemoes's construction
%
% Written by Lexing Ying and Laurent Demanet, 2006
r = zeros(size(w));
gd = w<5*pi/6 & w>-7*pi/6;
r(gd) = abs(sf(w(gd)-3*pi/2));
%----------------------------------------------------------------------
function r = sf(w)
r = zeros(size(w));
... |
github | HaizhaoYang/SynLab-master | g.m | .m | SynLab-master/Source/SS_CT_2D/src/g.m | 847 | utf_8 | ca89e8bc97649ef1b68fa6ff1abe72d4 | function r = g(w)
% g.m - 'g' function Villemoes's construction
%
% Written by Lexing Ying and Laurent Demanet, 2006
r = zeros(size(w));
gd = w<5*pi/6 & w>-7*pi/6;
r(gd) = abs(sf(w(gd)-3*pi/2));
%----------------------------------------------------------------------
function r = sf(w)
r = zeros(size(w));
... |
github | Behzadb/wsn-flow-Matlab-master | Dist_route.m | .m | wsn-flow-Matlab-master/Dist_route.m | 4,848 | utf_8 | d92437378b063e200b8ed96ffde12af0 | function Dist_route(numNodes,grid,receiver)
%optimal path flow in routing
% minimize q
% subject to sigma_(j member of N_i)(r_ij-r_jr)=S_i (for every i member of V)
% 0<= r_ij <= R_ij (for every i member of V , for every j member of N_i)
% T*sigma_(j member of N_i)*E_ij <= B... |
github | LCAV/RegularizedTauEstimator-master | FastTauRegFinal.m | .m | RegularizedTauEstimator-master/FastTauRegFinal.m | 10,343 | utf_8 | a73ddfe4b8e17b92af827313ff17d604 | function result = FastTauRegFinal(x, y,lambda, control)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Computes regularized tau estimate of regression
%
% tau-estimate is tuned to have 95% efficiency, and 50% bdp,
% using Optimal rho-function
%
% INPUT:
% x : mixing matrix
% y : vector of measurement... |
github | LCAV/RegularizedTauEstimator-master | FastTauFinal.m | .m | RegularizedTauEstimator-master/FastTauFinal.m | 9,641 | utf_8 | 439f23cce464a62951b6f7e3621c6f1c | function result = FastTauFinal(x, y, control)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Computes tau-estimate of regression
%
% tau-estimate is tuned to have 95% efficiency, and 50% bdp,
% using Optimal rho-function
%
% INPUT:
% x : matrix of covariates (should include column of 1s for inter... |
github | lostanlen/scattering.m-master | mlsp_synthesize_scattering.m | .m | scattering.m-master/research/mlsp15/mlsp_synthesize_scattering.m | 2,747 | utf_8 | 8712c97773c1e3836afb621a8951cdbf | % The integer method_index specifies the scattering architecture.
% 1: first-order scattering only
% 2: with plain, second-order coefficinets
% 3: with joint coefficients (up to the scale of 1 octave)
% 4: same, up to the scale of 2 octaves
% 5: same, up to the scale of 4 octaves
function mlsp_synthesize_scattering(y,... |
github | lostanlen/scattering.m-master | quantize.m | .m | scattering.m-master/research/tsp19/synthesis/quantize.m | 1,004 | utf_8 | 7915cfcb021a7065d01ca6833dd18dd8 | % QUANTIZE Quantize array into a number of bins
%
% Usage
% y = QUANTIZE(x, bins, scaling);
%
% Input
% x: The array to be quantized.
% bins: The number of bins to use (default 256).
% scaling: One of 'linear' (default) or 'logarithmic'.
%
% Output
% y: The array x, but with each value mapped into its corresp... |
github | lostanlen/scattering.m-master | arrow3.m | .m | scattering.m-master/research/jasmp20/arrow3.m | 34,268 | utf_8 | b8826271396017d5dadad34d9f822cdf | function hn=arrow3(p1,p2,s,w,h,ip,alpha,beta)
% ARROW3 (R13)
% ARROW3(P1,P2) draws lines from P1 to P2 with directional arrowheads.
% P1 and P2 are either nx2 or nx3 matrices. Each row of P1 is an
% initial point, and each row of P2 is a terminal point.
%
% ARROW3(P1,P2,S,W,H,IP,ALPHA,BETA) can be used to spec... |
github | priyank87/cs246w15-master | driver.m | .m | cs246w15-master/HW2/hw2_q2-Data/driver.m | 247 | utf_8 | 5292bd27df1e8c9ab04e37535d3b7f1b | %% driver: function description
function [op] = driver(X, y)
k = [1:20:601];
[U,S,V] = svd(X, 'econ');
n = size(S,1);
perf = [];
for idx = k,
newX = U(:, [1:idx]);
res = predictor(newX, y);
perf = [perf res];
end
op = perf;
end
|
github | priyank87/cs246w15-master | submission.m | .m | cs246w15-master/HW2/hw2_q2-Data/submission.m | 724 | utf_8 | bfcaf970b50e8c8ffee53429ec34e951 | %% predictor: get agreement for given X
function [outputs] = predictor(X, y)
modelX = X(41:640, :);
modelY = y(41:640, :);
model = sum(bsxfun(@times, modelX, modelY));
testX = X(1:40, :);
testY = y(1:40, :);
mult = repmat(model, size(testY), 1);
coeff = testX * mult';
coeff = coeff(:,1);
prodCoeff = coeff ... |
github | priyank87/cs246w15-master | predictor.m | .m | cs246w15-master/HW2/hw2_q2-Data/predictor.m | 358 | utf_8 | 6b93b91714df3574077ce68183c61067 | %% predictor: function description
function [outputs] = predictor(X, y)
modelX = X(41:640, :);
modelY = y(41:640, :);
model = sum(bsxfun(@times, modelX, modelY));
testX = X(1:40, :);
testY = y(1:40, :);
mult = repmat(model, size(testY), 1);
coeff = testX * mult';
coeff = coeff(:,1);
prodCoeff = coeff .* te... |
github | priyank87/cs246w15-master | driver.m | .m | cs246w15-master/HW1/lsh/driver.m | 542 | utf_8 | 3008aa991ecb928502668895532f4b80 | %% driver: function description
function [errors] = driver(data)
errors = [];
% for i=10:2:20,
% T1=lsh('lsh',i,24,size(data,1),data,'range',255);
% linn = findnn(data, 'linearsearch', T1, 3);
% lshnn = findnn(data, 'lsh', T1, 4);
% err = calcerror(linn, lshnn, data);
% errors = [errors err];
% end
for ... |
github | priyank87/cs246w15-master | calcerror.m | .m | cs246w15-master/HW1/lsh/calcerror.m | 600 | utf_8 | dc5ad89e596468e8d5c0a40e14f818ac | %% calcerror: function description
function [err] = calcerror(linn, lshnn, data)
err=0;
for i=1:10,
qcol = data(:, lshnn(i, 1));
lshids = lshnn(i, 2:4);
lshids = lshids(lshids ~= 0);
lshdcols = data(:, lshids);
linids = linn(i, 2:4);
linids = linids(linids ~= 0);
lindcols = data(:, linids);
lshdis... |
github | priyank87/cs246w15-master | combined.m | .m | cs246w15-master/HW1/lsh/combined.m | 2,987 | utf_8 | 7da9b16fca6d6725d372cd95ee618967 |
%% One time initialization
load patches;
% Perform linear search and return nearest neighbors
function nnlinind = linearsearch(query, data, num)
d = sum(abs(bsxfun(@minus, query, data)));
[ignore,ind]=sort(d);
cand = ind(1:num+1);
nnlinind = cand;
end
%% Calculate error ratio
function [err] = calcerror(lin... |
github | priyank87/cs246w15-master | lpnorm.m | .m | cs246w15-master/HW1/lsh/lpnorm.m | 3,884 | utf_8 | fe08995d6c12d63cc575ab959304f41f | function d = lpnorm(x1,x2,p,CHUNKSIZE)
% d = lpnorm(X1,X2,P)
%
% Computate distances between X1 and X2, using L_P norm (default P=1)
% Assumes that the data are in columns of x1 and x2, and
% d(i)=dist(x1(:,i),x2(:,i).
% If x1 or x2 is a vector, it is repmat'ed appropriately - i.e., if x1 is
% a single column, d(i)=di... |
github | priyank87/cs246w15-master | findnn.m | .m | cs246w15-master/HW1/lsh/findnn.m | 774 | utf_8 | a9687f75ecd20d64a3449349025f2c08 | %% One time initialization per model
% load patches;
% T1=lsh('lsh',10,24,size(patches,1),patches,'range',255);
function op = findnn(patches, searchtype, T1, num)
neigbours = [];
disp(searchtype);
tic;
for i=1:10,
colno = i*100;
query = patches(:, colno);
if strcmp(searchtype, 'linearsearch'),
nn=linears... |
github | priyank87/cs246w15-master | lshlookup.m | .m | cs246w15-master/HW1/lsh/lshlookup.m | 3,147 | utf_8 | ec252cdd3efc8d542a1c26bf1c615ce1 | function [iNN,cand] = lshlookup(x0,x,T,varargin)
% [iNN,cand] = lshlookup(x0,x,T)
%
% iNN contains indices of matches in T for a single query x0;
% x is the representation in the feature space; assumes to be a cell
% array with equal size cells (this is a hack around Matlab's problem
% with allocating large con... |
github | priyank87/cs246w15-master | plotnn.m | .m | cs246w15-master/HW1/lsh/plotnn.m | 480 | utf_8 | 19db5c4aaac95a815f1a206cd513835c | %% plotnn: function description
function plotnn(data, query, nn, numcand)
% plot the query point
figure(1); clf;
imagesc(reshape(query,20,20));
colormap gray;
axis image;
set(gca,'YTickLabel', sprintf('',[]));
set(gca,'XTickLabel', sprintf('',[]));
figure(2);clf;
for k=1:numcand,
subplot(2,5,k);
image... |
github | priyank87/cs246w15-master | bsxarg.m | .m | cs246w15-master/HW1/lsh/bsx/bsxarg.m | 2,978 | utf_8 | 5d1647ced6a1da3a32a4d310052b11a0 | % bsxarg does singleton expansion of two input arrays (related to bsxfun)
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: bsxarg
% Filename: bsxarg.c
% Programmer: James Tursa
% Version: 1.0
% ... |
github | priyank87/cs246w15-master | bsxfun.m | .m | cs246w15-master/HW1/lsh/bsx/bsxfun.m | 3,710 | utf_8 | 949852ae1252681ebe4b52f28a7a0ebf | % bsxfun does binary operation with singleton expansion (uses bsxarg)
%--------------------------------------------------------------------------
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: bsxfun
% Filename: bsxfun.m
% Programmer: James Tursa
% Version: 1.0
% Date: ... |
github | hmofrad/Adaptive-CLPSO-master | fit_func.m | .m | Adaptive-CLPSO-master/src/MaPSO/fit_func.m | 1,797 | utf_8 | f81748f0c9216d24883cffb07a1234d7 | % fitness function
function f = fit_func(c,x)
global orthm
% c as the fitness function number
func = c{1}; % benchmark number
[nop D]=size(x);
R = 9:14;
greal=[0 1 0 0 0 0 0 4.209687462275036e+002 0 0 0 0 0 0];
x=x-greal(func);
if ismember(func,R)
x=x*orthm;
end
x=x+grea... |
github | hmofrad/Adaptive-CLPSO-master | fit_func.m | .m | Adaptive-CLPSO-master/src/MiPSO/fit_func.m | 1,797 | utf_8 | f81748f0c9216d24883cffb07a1234d7 | % fitness function
function f = fit_func(c,x)
global orthm
% c as the fitness function number
func = c{1}; % benchmark number
[nop D]=size(x);
R = 9:14;
greal=[0 1 0 0 0 0 0 4.209687462275036e+002 0 0 0 0 0 0];
x=x-greal(func);
if ismember(func,R)
x=x*orthm;
end
x=x+grea... |
github | LundUniversityComputerVision/multipol-master | pepsolve.m | .m | multipol-master/pepsolve.m | 4,347 | utf_8 | 5c9c366f6415caff4b0ff5748d5b6568 | function [sol info] = pepsolve(eq,evar,num_eqs,varargin)
verb = 1;
method = 'none';
order = 'grevlex';
redmat = 1;
visualize = 0;
for i=1:2:length(varargin)
switch varargin{i}
case 'verb', verb = varargin{i+1};
case 'method', method = varargin{i+1};
case 'order', order = varargin{i+1};
case 'R', redmat = var... |
github | LundUniversityComputerVision/multipol-master | polynomials2matrix.m | .m | multipol-master/polynomials2matrix.m | 1,353 | utf_8 | 9350604a0bae269eb2d2a409a1755e72 | %POLYNOMIALS2MATRIX Decomposes a system of multipol equations into a
% coefficient matrix and a vector of monomials.
%
% [C MON] = POLYNOMIALS2MATRIX(P,[order]) returns a vector MON of the
% unique monomials in the polynomials P and a matrix C so that P = C*MON.
% Possible monomial orders are
% 'same' : don't change... |
github | LundUniversityComputerVision/multipol-master | char.m | .m | multipol-master/@multipol/char.m | 1,672 | utf_8 | cb406de34c13440af292ce387df42fed | function s = char(mp,xyzw,prec)
% MULTIPOL/CHAR
% Convert polynomial expression to readable string representation
if nargin < 2
xyzw = true;
end
if nargin < 3
prec = [];
end
s = cell(size(mp));
mp = eqsize(mp);
% If few variables and xyzw==true, use x, y, z, w instead of numbered x's.
n = nvars(mp);
n = max(n... |
github | fietew/ekfukf-master | ekf_predict2.m | .m | ekfukf-master/ekf_predict2.m | 3,343 | UNKNOWN | e361d1331b5970202696843b127cb7e9 | %EKF_PREDICT2 2nd order Extended Kalman Filter prediction step
%
% Syntax:
% [M,P] = EKF_PREDICT2(M,P,[A,F,Q,a,W,param])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% A - Derivative of a() with respect to state as
% matrix, inline function, function ... |
github | fietew/ekfukf-master | ut_transform.m | .m | ekfukf-master/ut_transform.m | 3,564 | UNKNOWN | f75fd5abda99dff018cbeec9e7061f9f | %UT_TRANSFORM Perform unscented transform
%
% Syntax:
% [mu,S,C,X,Y,w] = UT_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,param) as
% matrix, inline function, fu... |
github | fietew/ekfukf-master | quad_transform.m | .m | ekfukf-master/quad_transform.m | 2,486 | UNKNOWN | c1cc63e44335d77b4d42a0e5b2732cb5 | %UT_TRANSFORM Perform quadratic approximation based transform of a Gaussian rv
%
%
% Syntax:
% [mu,S,C,X,Y,w] = QUAD_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,para... |
github | fietew/ekfukf-master | uimm_predict.m | .m | ekfukf-master/uimm_predict.m | 3,833 | utf_8 | c3e69773e05f6724c702dc85eebd5e72 | %IMM_PREDICT UKF based Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = UIMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% ... |
github | fietew/ekfukf-master | imm_filter.m | .m | ekfukf-master/imm_filter.m | 4,152 | utf_8 | 71128ac12e721c163f0c582ff2af4e6d | %IMM_FILTER Interacting Multiple Model (IMM) Filter prediction and update steps
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_FILTER(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,Q,Y,H,R)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_... |
github | fietew/ekfukf-master | ukf_update1.m | .m | ekfukf-master/ukf_update1.m | 3,047 | UNKNOWN | c6fa396ccaa417dcc9a2e8bfebc304a5 | %UKF_UPDATE1 - Additive form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,S,LH] = UKF_UPDATE1(M,P,Y,h,R,param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement model functio... |
github | fietew/ekfukf-master | imm_smooth.m | .m | ekfukf-master/imm_smooth.m | 8,587 | utf_8 | 299bb594c62b9fe2b393c54e2dfd9625 | %IMM_SMOOTH Fixed-interval IMM smoother using two IMM-filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = IMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,Q,R,H,Y)
%
% In:
% MM - NxK matrix containing the means of forward-time
% IMM-filter on each time step
% PP - NxNxK matrix containing the c... |
github | fietew/ekfukf-master | ukf_predict3.m | .m | ekfukf-master/ukf_predict3.m | 2,671 | UNKNOWN | f53661137ceedfac68cf499295cec7a8 | %UKF_PREDICT3 Augmented (state, process and measurement noise) UKF prediction step
%
% Syntax:
% [M,P,X,w] = UKF_PREDICT3(M,P,f,Q,R,f_param,alpha,beta,kappa)
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as inline function,
% ... |
github | fietew/ekfukf-master | kf_loop.m | .m | ekfukf-master/kf_loop.m | 1,888 | utf_8 | 0db0b34d194f33879dab4a27c7fbf0da | %KF_LOOP Performs the prediction and update steps of the Kalman filter
% for a set of measurements.
%
% Syntax:
% [MM,PP] = KF_LOOP(X,P,H,R,Y,A,Q)
%
% In:
% X - Nx1 initial estimate for the state mean
% P - NxN initial estimate for the state covariance
% H - DxN measurement matrix
% R - DxD meas... |
github | fietew/ekfukf-master | imm_update.m | .m | ekfukf-master/imm_update.m | 2,533 | utf_8 | 860499b62caf6e4b660ec41b713d4e0e | %IMM_UPDATE Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,R)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j state covari... |
github | fietew/ekfukf-master | ukf_update3.m | .m | ekfukf-master/ukf_update3.m | 3,076 | UNKNOWN | d6a6f273f4495654caee03c01ff019e8 | %UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,IS,LH] = UKF_UPDATE3(M,P,Y,h,R,X,w,h_param,alpha,beta,kappa,mat,sigmas)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement... |
github | fietew/ekfukf-master | ut_weights.m | .m | ekfukf-master/ut_weights.m | 1,585 | UNKNOWN | 5ae13a5f605593674a60c12f8f55bbe8 | %UT_WEIGHTS - Generate unscented transformation weights
%
% Syntax:
% [WM,WC,c] = ut_weights(n,alpha,beta,kappa)
%
% In:
% n - Dimensionality of random variable
% alpha - Transformation parameter (optional, default 0.5)
% beta - Transformation parameter (optional, default 2)
% kappa - Transformation pa... |
github | fietew/ekfukf-master | ukf_predict2.m | .m | ekfukf-master/ukf_predict2.m | 2,307 | UNKNOWN | da4d237360c2ea17a88a15b18de0a65d | %UKF_PREDICT2 Augmented (state and process noise) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT2(M,P,a,Q,[param,alpha,beta,kappa])
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as inline function,
% function handle ... |
github | fietew/ekfukf-master | ukf_predict1.m | .m | ekfukf-master/ukf_predict1.m | 2,297 | UNKNOWN | e470b6641ff341b56241835f6b908dd3 | %UKF_PREDICT1 Nonaugmented (Additive) UKF prediction step
%
% Syntax:
% [M,P] = UKF_PREDICT1(M,P,f,Q,f_param,alpha,beta,kappa,mat)
%
% In:
% M - Nx1 mean state estimate of previous step
% P - NxN state covariance of previous step
% f - Dynamic model function as a matrix A defining
% linear function a(x) ... |
github | fietew/ekfukf-master | imm_predict.m | .m | ekfukf-master/imm_predict.m | 3,459 | utf_8 | a557c545bef965afe036ee2e59f5274b | %IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = IMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_ip - Cell arra... |
github | fietew/ekfukf-master | eimm_predict.m | .m | ekfukf-master/eimm_predict.m | 4,185 | utf_8 | 0e65c7ab1a1e1717c2e76e785f151d22 | %IMM_PREDICT Interacting Multiple Model (IMM) Filter prediction step
%
% Syntax:
% [X_p,P_p,c_j,X,P] = EIMM_PREDICT(X_ip,P_ip,MU_ip,p_ij,ind,dims,A,a,param,Q)
%
% In:
% X_ip - Cell array containing N^j x 1 mean state estimate vector for
% each model j after update step of previous time step
% P_ip - ... |
github | fietew/ekfukf-master | uimm_smooth.m | .m | ekfukf-master/uimm_smooth.m | 9,008 | utf_8 | f165a3da19e62f9a637fc4caac8ad45f | %UIMM_SMOOTH UKF based Fixed-interval IMM smoother using two IMM-UKF filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = UIMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,a,a_param,Q,R,H,h,h_param,Y)
%
% In:
% MM - Means of forward-time IMM-filter on each time step
% PP - Covariances of forward-time IMM... |
github | fietew/ekfukf-master | eimm_update.m | .m | ekfukf-master/eimm_update.m | 3,594 | utf_8 | 2be8defc9605899d693c67d6496abc39 | %IMM_UPDATE Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,h,R,param)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j stat... |
github | fietew/ekfukf-master | urts_smooth1.m | .m | ekfukf-master/urts_smooth1.m | 3,687 | UNKNOWN | 3e5bdb3c14f06c08d0f41cf62ad81e82 | %URTS_SMOOTH1 Additive form Unscented Rauch-Tung-Striebel smoother
%
% Syntax:
% [M,P,D] = URTS_SMOOTH1(M,P,f,Q,[f_param,alpha,beta,kappa,mat,same_p])
%
% In:
% M - NxK matrix of K mean estimates from Unscented Kalman filter
% P - NxNxK matrix of K state covariances from Unscented Kalman Filter
% f - Dynamic m... |
github | fietew/ekfukf-master | lin_transform.m | .m | ekfukf-master/lin_transform.m | 1,798 | UNKNOWN | a145a61ccd833f3d7100c7d651dfb779 | %UT_TRANSFORM Perform linearization based transform of a Gaussian rv
%
%
% Syntax:
% [mu,S,C,X,Y,w] = LIN_TRANSFORM(M,P,g,g_param,tr_param)
%
% In:
% M - Random variable mean (Nx1 column vector)
% P - Random variable covariance (NxN pos.def. matrix)
% g - Transformation function of the form g(x,param) as
% ... |
github | fietew/ekfukf-master | uimm_update.m | .m | ekfukf-master/uimm_update.m | 2,973 | utf_8 | e386b64264ff328d4c5e5fa35c945487 | %IMM_UPDATE UKF based Interacting Multiple Model (IMM) Filter update step
%
% Syntax:
% [X_i,P_i,MU,X,P] = IMM_UPDATE(X_p,P_p,c_j,ind,dims,Y,H,R)
%
% In:
% X_p - Cell array containing N^j x 1 mean state estimate vector for
% each model j after prediction step
% P_p - Cell array containing N^j x N^j st... |
github | fietew/ekfukf-master | ukf_update2.m | .m | ekfukf-master/ukf_update2.m | 3,187 | UNKNOWN | 0a303ec8bcb980daf834c0d75ff65fb8 | %UKF_UPDATE2 - Augmented form Unscented Kalman Filter update step
%
% Syntax:
% [M,P,K,MU,IS,LH] = UKF_UPDATE2(M,P,Y,h,R,h_param,alpha,beta,kappa,mat)
%
% In:
% M - Mean state estimate after prediction step
% P - State covariance after prediction step
% Y - Measurement vector.
% h - Measurement model func... |
github | fietew/ekfukf-master | urts_smooth2.m | .m | ekfukf-master/urts_smooth2.m | 3,098 | UNKNOWN | eafbc7975b06ca22a4d6002269610046 | %URTS_SMOOTH2 Augmented form Unscented Rauch-Tung-Striebel smoother
%
% Syntax:
% [M,P,S] = URTS_SMOOTH2(M,P,f,Q,[f_param,alpha,beta,kappa,mat,same_p])
%
% In:
% M - NxK matrix of K mean estimates from Unscented Kalman filter
% P - NxNxK matrix of K state covariances from Unscented Kalman Filter
% f - Dynamic ... |
github | fietew/ekfukf-master | eimm_smooth.m | .m | ekfukf-master/eimm_smooth.m | 10,081 | utf_8 | 14cc37296d6aa91b1c0ded61dd76c73f | %EIMM_SMOOTH EKF based fixed-interval IMM smoother using two IMM-EKF filters.
%
% Syntax:
% [X_S,P_S,X_IS,P_IS,MU_S] = EIMM_SMOOTH(MM,PP,MM_i,PP_i,MU,p_ij,mu_0j,ind,dims,A,a,a_param,Q,R,H,h,h_param,Y)
%
% In:
% MM - Means of forward-time IMM-filter on each time step
% PP - Covariances of forward-time IMM-f... |
github | fietew/ekfukf-master | f_turn.m | .m | ekfukf-master/demos/eimm_demo/f_turn.m | 924 | utf_8 | 7ef714a1030afbd53e53eab3ff996db5 | %
% A coordinated turn model for extended IMM filter demonstration
%
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function x_k = f_turn(x,param)
... |
github | fietew/ekfukf-master | bot_d2h_dx2.m | .m | ekfukf-master/demos/eimm_demo/bot_d2h_dx2.m | 991 | utf_8 | 121065207e815dd017245b07a9f807b6 | % Hessian of the measurement function in BOT-demo.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dY = bot_d2h_dx2(x,s)
% Space for Hessia... |
github | fietew/ekfukf-master | f_turn_inv.m | .m | ekfukf-master/demos/eimm_demo/f_turn_inv.m | 919 | utf_8 | 81845969367ed59c79f6f535a775dddc | %
% Inverse prediction for the coordinated turn model
% used in extended IMM filter demonstration
%
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
f... |
github | fietew/ekfukf-master | ekf_sine_d2h_dx2.m | .m | ekfukf-master/demos/ekf_sine_demo/ekf_sine_d2h_dx2.m | 473 | utf_8 | 6a5d46b28c8d2b088180bbe20bc81c1b | % Hessian of the measurement model function in the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function df = ekf_sine_d2h_... |
github | fietew/ekfukf-master | ekf_sine_h.m | .m | ekfukf-master/demos/ekf_sine_demo/ekf_sine_h.m | 411 | utf_8 | 4636d4ebcbeacdd8a0dd352e576511d2 | % Measurement model function for the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function Y = ekf_sine_h(x,param)
f = x... |
github | fietew/ekfukf-master | ekf_sine_dh_dx.m | .m | ekfukf-master/demos/ekf_sine_demo/ekf_sine_dh_dx.m | 420 | utf_8 | d75fee0ef60b44bc6f56b32735b4b1ea | % Jacobian of the measurement model function in the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dY = ekf_sine_dh_... |
github | fietew/ekfukf-master | ekf_sine_f.m | .m | ekfukf-master/demos/ekf_sine_demo/ekf_sine_f.m | 479 | utf_8 | 3e305873b26ce735e10f3cc3bf0cbee1 | % Dynamical model function for the random sine signal demo
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function x_n = ekf_sine_f(x,param)
dt =... |
github | fietew/ekfukf-master | ungm_d2h_dx2.m | .m | ekfukf-master/demos/ungm_demo/ungm_d2h_dx2.m | 354 | utf_8 | 7dfa2e46358bf758dafc4800e05ce95f | % Hessian of the measurement model function in UNGM-model.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function d = ungm_d2h_dx2(x,param)
d = ... |
github | fietew/ekfukf-master | ungm_d2f_dx2.m | .m | ekfukf-master/demos/ungm_demo/ungm_d2f_dx2.m | 356 | utf_8 | 9ffb3dfcf6d01d351901d55c4e263b84 | % Hessian of the state transition function in UNGM-model.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function d = ungm_d2f_dx2(x,param)
d = 25... |
github | fietew/ekfukf-master | ungm_h.m | .m | ekfukf-master/demos/ungm_demo/ungm_h.m | 382 | utf_8 | 0f3485c123b9aa66a21f68bce20aa18a | % Measurement model function for the UNGM-model.
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function y_n = ungm_h(x_n,param)
y_n = x_n(1,:).*x_n... |
github | fietew/ekfukf-master | ungm_dh_dx.m | .m | ekfukf-master/demos/ungm_demo/ungm_dh_dx.m | 328 | utf_8 | f502cd540115362bbb125287a8f4c55c | % Jacobian of the measurement model function for the UNGM-model.
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dh = ungm_dh_dx(x,param)
dh... |
github | fietew/ekfukf-master | ungm_f.m | .m | ekfukf-master/demos/ungm_demo/ungm_f.m | 440 | utf_8 | 6923e25b28640fe8a310079cafe82c97 | % State transition function for the UNGM-model.
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function x_n = ungm_f(x,param)
n = param(1);
x_n = 0.... |
github | fietew/ekfukf-master | ungm_df_dx.m | .m | ekfukf-master/demos/ungm_demo/ungm_df_dx.m | 358 | utf_8 | 2b593c3666a1c0e89fbf5e9ce173953f | % Jacobian of the state transition function for the UNGM-model.
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function df = ungm_df_dx(x,param)
df ... |
github | fietew/ekfukf-master | kf_cwpa_demo.m | .m | ekfukf-master/demos/kf_cwpa_demo/kf_cwpa_demo.m | 7,431 | utf_8 | 83f5b06ca7f301fb9a3c358fa88bba60 | % Demonstration for Kalman filter and smoother using a 2D CWPA model
%
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function kf_cwpa_demo
% Transit... |
github | fietew/ekfukf-master | bot_d2h_dx2.m | .m | ekfukf-master/demos/bot_demo/bot_d2h_dx2.m | 991 | utf_8 | 121065207e815dd017245b07a9f807b6 | % Hessian of the measurement function in BOT-demo.
% Copyright (C) 2007 Jouni Hartikainen
%
% This software is distributed under the GNU General Public
% Licence (version 2 or later); please refer to the file
% Licence.txt, included with the software, for details.
function dY = bot_d2h_dx2(x,s)
% Space for Hessia... |
github | trentool/TRENTOOL3_exampledata-master | plot_mooney_example.m | .m | TRENTOOL3_exampledata-master/Mooney/plot_mooney_example.m | 2,971 | utf_8 | f0209878c40d4ee6d9f3226e8228f9e2 | function plot_mooney_example(outputpath)
% FUNCTION PLOT_MOONEY_EXAMPLE(OUTPUTPATH) Plots results from
% 'analyze_mooney_example_CPU.m' and conducts a binomial test for the
% results.
%
% Note that you may need to adjust the file paths in this example script to
% run it on your computer.
%
% OUTPUTPATH is a string ... |
github | Jokeijk/us-master | BSGD.m | .m | us-master/BSGD/BSGD.m | 2,893 | utf_8 | 806975ffa8b9e811745134475c9b0e42 | function BSGD
rng(0);
test_par = 0; % whether test the parameters
% read the data
[userID1,movieID1,rate1] = read_data('data.txt');
userID = unique(userID1); M = numel(userID);
movieID= unique(movieID1); N = numel(movieID);
if test_par
[lambda_best, iter_best] = test_par(userID1,movieID1,rate1,M,N);
else
i... |
github | Jokeijk/us-master | sparse_matrix.m | .m | us-master/BSGD/sparse_matrix.m | 498 | utf_8 | 20353cf30434df0ecabaa75a020e5414 | % User-Movie matrix
function [M,N,Y_s] = sparse_matrix(userID_all, movieID_all, userID1, movieID1,rate1)
M = length(userID_all);
N = length(movieID_all);
% the indices of moveID1 in movieID_all
[a, sub_2] = ismember(movieID1,movieID_all);
if isempty(find(a,1))
error('Not all the movie found!');
end
% the indices ... |
github | Jokeijk/us-master | read_movie.m | .m | us-master/BSGD/read_movie.m | 530 | utf_8 | c5a1e869fd934d9f1c0aa220bf45103f | % Read movie.txt data file
function [movieID,movieName,movieGenre,Genres] = read_movie(filename)
if nargin < 1
filename = 'movies.txt';
end
fid = fopen(filename);
m = textscan(fid,['%d %s ',repmat('%d',1,19)],'delimiter','\t');
fclose(fid);
movieID = m{1};
movieName = m{2};
movieGenre= [m{3:end}];
Genres = {'... |
github | Jokeijk/us-master | read_data.m | .m | us-master/BSGD/read_data.m | 229 | utf_8 | cdd12ab185f9bb81ce5e02a02241d5e1 | % Read the data file
function [userID,movieID,rate] = read_data(filename)
if nargin < 1
filename = 'data.txt';
end
fid = fopen(filename);
m = textscan(fid,'%f %f %f');
fclose(fid);
userID = m{1};
movieID= m{2};
rate = m{3}; |
github | Jokeijk/us-master | BSGD.m | .m | us-master/BSGD/submit/src/BSGD/BSGD.m | 2,893 | utf_8 | 806975ffa8b9e811745134475c9b0e42 | function BSGD
rng(0);
test_par = 0; % whether test the parameters
% read the data
[userID1,movieID1,rate1] = read_data('data.txt');
userID = unique(userID1); M = numel(userID);
movieID= unique(movieID1); N = numel(movieID);
if test_par
[lambda_best, iter_best] = test_par(userID1,movieID1,rate1,M,N);
else
i... |
github | Jokeijk/us-master | read_data.m | .m | us-master/BSGD/submit/src/BSGD/read_data.m | 229 | utf_8 | cdd12ab185f9bb81ce5e02a02241d5e1 | % Read the data file
function [userID,movieID,rate] = read_data(filename)
if nargin < 1
filename = 'data.txt';
end
fid = fopen(filename);
m = textscan(fid,'%f %f %f');
fclose(fid);
userID = m{1};
movieID= m{2};
rate = m{3}; |
github | Jokeijk/us-master | yma_001.m | .m | us-master/src/yma_001.m | 1,086 | utf_8 | a341c3ece8275001f93215ea2fe5f8fe | % Decision tree
function DCT_001
Nf = 500;
data_dir = '../data';
% read the training data
M = csvread([data_dir,'/kaggle_train_tf_idf.csv'],1);
x_train = M(:,2:Nf+1);
y_train = M(:, end);
% read the test data
M = csvread([data_dir,'/kaggle_test_tf_idf.csv'],1);
x_test = M(:,2:Nf+1);
ID = M(:,1);
% % test the minima... |
github | Jokeijk/us-master | yma_003.m | .m | us-master/src/yma_003.m | 1,005 | utf_8 | 5e0fd0a0ab0a22ee3aebdfddb62e7441 | % Decision tree
% ensemble method
function ENS_002
Nf = 500;
data_dir = '../data';
% read the training data
M = csvread([data_dir,'/kaggle_train_tf_idf.csv'],1);
%M = csvread([data_dir,'/kaggle_train_wc.csv'],1);
x_train = M(:,2:Nf+1);
y_train = M(:, end);
% read the test data
M = csvread([data_dir,'/kaggle_test_tf_... |
github | Jokeijk/us-master | yma_004.m | .m | us-master/src/yma_004.m | 1,111 | utf_8 | cf3f8af6af766538dd16c1b1e389fa83 | % Decision tree
% ensemble method
function ENS_002
Nf = 500;
data_dir = '../data';
% read the training data
M = csvread([data_dir,'/kaggle_train_tf_idf.csv'],1);
%M = csvread([data_dir,'/kaggle_train_wc.csv'],1);
x_train = M(:,2:Nf+1);
y_train = M(:, end);
% read the test data
M = csvread([data_dir,'/kaggle_test_tf_... |
github | Jokeijk/us-master | yma_Ada.m | .m | us-master/src/yma_Ada.m | 1,398 | utf_8 | 155faa45fd6af35f8e8d51e753436ea5 | % Decision tree
% ensemble method
function yma_Ada
format long
Nf = 500;
data_dir = '../data';
% read the training data
M = csvread([data_dir,'/kaggle_train_tf_idf.csv'],1);
%M = csvread([data_dir,'/kaggle_train_wc.csv'],1);
x_train = M(:,2:Nf+1);
y_train = M(:, end);
% read the test data
M = csvread([data_dir,'/kag... |
github | Jokeijk/us-master | yma_002.m | .m | us-master/src/yma_002.m | 1,079 | utf_8 | b2ac10aa20f2b889ab4b9e4250c3ec56 | % Decision tree
% ensemble method
function ENS_001
Nf = 500;
data_dir = '../data';
% read the training data
M = csvread([data_dir,'/kaggle_train_tf_idf.csv'],1);
x_train = M(:,2:Nf+1);
y_train = M(:, end);
% read the test data
M = csvread([data_dir,'/kaggle_test_tf_idf.csv'],1);
x_test = M(:,2:Nf+1);
ID = M(:,1);
n... |
github | lvsn/outdoorPS-public-master | plotHemisphereMLVs.m | .m | outdoorPS-public-master/3DV15/plotHemisphereMLVs.m | 4,281 | utf_8 | 696ee56b075cc4d8e391f5809b0a9357 | function [] = plotHemisphereMLVs( varargin )
% Plot the mean light vectors for some normals
%
matA = [];
parseVarargin(varargin{:});
MLVs = matA.MLVs;
N = matA.normal;
% normals bo be drawn on the sphere
%a = (90:-1:-90)'; N0 = [ 0*a sind(a) -cosd(a) ]; % from Zenith to Nadir
DEG90 = 90 / 180 * pi; DEG45 = 45 / 18... |
github | lvsn/outdoorPS-public-master | main.m | .m | outdoorPS-public-master/3DV15/main.m | 3,680 | utf_8 | c14c019cb281876e38800c8188d6e44b | % Computes the figures 3-6 in holdgeoffroy_3dv_15
%
% To use this code, please specify the 'databasePath' and 'dateValue'
%
% For example, invoke with:
% main('databasePath', '/home/user/envmaps/', 'dateValue', '20141003');
%
% -----------
%
function [] = main(varargin)
setpath;
% locate the sky images
databasePath =... |
github | lvsn/outdoorPS-public-master | plotSunIntensity.m | .m | outdoorPS-public-master/3DV15/plotSunIntensity.m | 1,413 | utf_8 | 25a77b35dada9c54c9f552a4b5979fdd | function [] = plotSunIntensity(varargin)
% Plot a figure to show the sun intensity throughout the day.
%
% Inputs:
% matA : Matrix A containing the Mean Light Vectors dans time information
%
matA = [];
exportPlots = false;
parseVarargin(varargin{:});
%% Get the relevant information from matA
sunIntensity = matA.sunI... |
github | lvsn/outdoorPS-public-master | plotCorrelationBetweenRcondAndSunInts.m | .m | outdoorPS-public-master/3DV15/plotCorrelationBetweenRcondAndSunInts.m | 6,431 | utf_8 | c55bc8aa2561c65360dcd9ca1d848cff | function [] = plotCorrelationBetweenRcondAndSunInts(varargin)
% Plot a figure to show the correlation between rcond and the sky
% appearance
%
% 1. rcond of the MLV matrix
% 2. sun intensity in linear / log space
% 3. mean / variance in a hour
%
% Inputs:
% matA : Matrix A containing the Mean Light Vectors dans t... |
github | lvsn/outdoorPS-public-master | ParticleSampleSphere.m | .m | outdoorPS-public-master/3rd_party/Uniform_Sampling_of_S2/Uniform Sampling of S2/ParticleSampleSphere.m | 11,491 | utf_8 | 9f337f01a3dd9604711fb4dca0f956e2 | function [V,Tri,Ue_i,Ue]=ParticleSampleSphere(varargin)
% Create an approximately uniform triangular tessellation of the unit
% sphere by minimizing generalized electrostatic potential energy
% (aka Reisz s-energy) of the system of charged particles. Effectively,
% this function produces a locally optimal soluti... |
github | olmozavala/DCE_MRI_Preproc-master | oz_normalize.m | .m | DCE_MRI_Preproc-master/All_For_OneImage/oz_normalize.m | 1,537 | utf_8 | 00da5bdbcf2362a4c7adf768148266ae | % This file is used to create the animations that show the classification of different files
% and to modify the second DCE-MRI image to take into account the classification
function oz_normalize(imagesFolder,classifier)
if(classifier == 'NB')% NB classifier
secondNifti = strcat(imagesFolder,'2_enhancedNB.... |
github | olmozavala/DCE_MRI_Preproc-master | oz_registration.m | .m | DCE_MRI_Preproc-master/All_For_OneImage/oz_registration.m | 2,187 | utf_8 | 803f65ce83eaa881599362365af53a8e | %% This function makes the registration of the DCE-Images with 5 images
% Registration algorithm to use 'grad' for Gradient Descent and 'evol' for genetic algorithm
function oz_registration(imagesRootFolder, optimizer)
addpath(imagesRootFolder);
% Iterate over folders
folder = imagesRootFolder
% Sav... |
github | olmozavala/DCE_MRI_Preproc-master | Main.m | .m | DCE_MRI_Preproc-master/All_For_OneImage/Main.m | 5,958 | utf_8 | b77f4b388b8401770ec62780e7cc8a19 | function PreProcessingDCE_MRI()
close all;
clear all;
clc;
matlabpool close force 'local'
matlabpool open 4
folders = {'3107404_p7_ok' '8256301_p1_ok', '7585734_p14_ok_huge_tumor', '6107252_p2_ok', '5641445_p1_ok_non-mass_from_mass', '0847664_p6_ok'};
%folderId = 1;
for folderId = 1:l... |
github | olmozavala/DCE_MRI_Preproc-master | showNii.m | .m | DCE_MRI_Preproc-master/All_For_OneImage/showNii.m | 114 | utf_8 | e23efbf270d43b96c64693bb192217d0 |
function showNii(niiData, z)
maxVal = max(max(max(niiData)));
imshow(niiData(:,:,z)',[0 maxVal]);
end
|
github | olmozavala/DCE_MRI_Preproc-master | oz_imageenhance.m | .m | DCE_MRI_Preproc-master/All_For_OneImage/oz_imageenhance.m | 3,077 | utf_8 | bad9ab6386d94f867bdfe7cf72168496 | % Classifier can be RT (Regression trees) or NB (Naive Bayes)
% Optimizer can be grad (Gradient Descent) or evol (Evolutionary algorithm)
function enhanced = oz_imageenhance(classified, niftis, imagesFolder, classifier,z)
lessionWeight = 1.3;
nonLessionWeight= 0.7;
% Reading the classifers
load('NBCl... |
github | olmozavala/DCE_MRI_Preproc-master | oz_imageclassify.m | .m | DCE_MRI_Preproc-master/All_For_OneImage/oz_imageclassify.m | 2,580 | utf_8 | 1d28a8de18113dab01a7f9c4c4c00dce |
% Classifier can be RT (Regression trees) or NB (Naive Bayes)
% Optimizer can be grad (Gradient Descent) or evol (Evolutionary algorithm)
function classified = oz_imageclassify(niftis, imagesFolder, optimizer, classifier,z)
% Reading the classifers
load('NBClassifier.mat');
load('RTClassifier.mat');
... |
github | olmozavala/DCE_MRI_Preproc-master | Registration_ShowResults.m | .m | DCE_MRI_Preproc-master/Registration/Registration_ShowResults.m | 5,995 | utf_8 | b1ce83c4176683a44563a6accb8982c2 | % This function is used to create nice figures showing the results of the registration algorithm
function showRegistrationResults()
close all;
clear all;
clc;
% Create curve for specific point in the image
folderImage = '/home/olmozavala/Dropbox/TestImages/nifti/RealExample1/';
addpath('/hom... |
github | olmozavala/DCE_MRI_Preproc-master | Prepoc_oz_registration.m | .m | DCE_MRI_Preproc-master/Registration/Prepoc_oz_registration.m | 4,486 | utf_8 | 7c04626455abb890ed65a24cd2d0a71b | %% This function makes the registration of the DCE-Images with 5 images
function oz_registration()
close all;
clear all;
clc;
matlabpool close force 'local'
matlabpool open 4
imagesRootFolder = '/media/USBSimpleDrive/BigData_Images_and_Others/PhD_Thesis/DCE_MRI/';
addpath('/home/olmozavala/Dropbox/OzOpenCL/MatlabActi... |
github | olmozavala/DCE_MRI_Preproc-master | Register_From_TransformationMatrix.m | .m | DCE_MRI_Preproc-master/Registration/Register_From_TransformationMatrix.m | 3,951 | utf_8 | 25113d1688f65b215277d6d58278bce3 | %% This function makes the registration of the DCE-Images with 5 images
function reg_from_matrices()
close all;
clear all;
clc;
%matlabpool close force 'local'
%matlabpool open 4
imagesRootFolder = '/media/USBSimpleDrive/BigData_Images_and_Others/PhD_Thesis/DCE_MRI/';
addpath('/home/olmozavala/Dropbox/OzOpenCL/Matlab... |
github | olmozavala/DCE_MRI_Preproc-master | Make_All_Plots.m | .m | DCE_MRI_Preproc-master/Kinetics/Make_All_Plots.m | 3,120 | utf_8 | 7b8ad592fa1ff3d06605c84d56bc7161 | % This function is used to generate all the images related with the pre-processing
% of the images using kinetic information, used on my thesis
% Separating the data in the four classes 'background', 'softtissue', 'chest', 'lesion'
function drawAll()
close all;
clear all;
clc;
addpath('/home/olmozavala/Dropbox/OzOpen... |
github | olmozavala/DCE_MRI_Preproc-master | Fifth_Normalize_ImagesFor_OPENCL.m | .m | DCE_MRI_Preproc-master/Kinetics/Fifth_Normalize_ImagesFor_OPENCL.m | 2,210 | utf_8 | 1a2965e1645b2f11eb5fc1b65518ef6a | % This file is used to create the animations that show the classification of different files
% and to modify the second DCE-MRI image to take into account the classification
function makeAnimationsAndEnhanceImage()
close all;
clear all;
clc;
% Loads nifti library
addpath('/home/olmozavala/Dropbox/OzOpenCL/Matlab_Crea... |
github | olmozavala/DCE_MRI_Preproc-master | Second_Save_Kinetic_Curves_by_Class.m | .m | DCE_MRI_Preproc-master/Kinetics/Second_Save_Kinetic_Curves_by_Class.m | 5,443 | utf_8 | a6eef6c022b598da425245b513c2b8a4 | % Reads the positions of each file and obtains the kinetic curves of all of them,
% separating the data in the four classes 'background', 'softtissue', 'chest', 'lesion'
function dispAverageKinetics()
close all;
clear all;
clc;
% Info about view_nii.m
% The new positions get updated at 'set_image_value' line 2914
% Th... |
github | olmozavala/DCE_MRI_Preproc-master | Third_Compute_Kinetics_Attributes_and_make_Classiffier.m | .m | DCE_MRI_Preproc-master/Kinetics/Third_Compute_Kinetics_Attributes_and_make_Classiffier.m | 6,658 | utf_8 | 5a13fe85d640f53d614ee10cc9d7d218 | % This function reads the curves for each class, obtain a list of features from them, creates a classifier
function makeClassifier()
close all;
clear all;
clc;
addpath('/home/olmozavala/Dropbox/OzOpenCL/MatlabActiveContours/Load_NIfTI_Images/External_Tools');
addpath('/home/olmozavala/Dropbox/OzOpenCL/Matlab_ImagePre... |
github | olmozavala/DCE_MRI_Preproc-master | Forth_Make_Animations_Of_Classifications.m | .m | DCE_MRI_Preproc-master/Kinetics/Forth_Make_Animations_Of_Classifications.m | 6,005 | utf_8 | ff7b1dbcb9e1513f35d6220ee293e296 | % This file is used to create the animations that show the classification of different files
% and to modify the second DCE-MRI image to take into account the classification
function makeAnimationsAndEnhanceImage()
close all;
clear all;
clc;
applyClassifier(1);%This correspond to the NaiveBayes classifier
close all;
c... |
github | hussamaa/dsverifier-master | dsv_extraction.m | .m | dsverifier-master/matlab-validation/matlab-scripts/dsv_extraction.m | 973 | utf_8 | 903356a7699f792ca9b766764767279e | %% Script to extraction from counterexamples folder all parameters necessaries
%% for validation and reproduction in MATLAB
function dsv_extraction(directory)
sh = 'sh';
cp = 'cp';
%extraction of parameters
script1 = '../shell-scripts/dsverifier-directory-data-extractor-script.sh';
command = [sh ' ' script1 ' ' dire... |
github | deisseroth-lab/multifiber-master | calibrationgui.m | .m | multifiber-master/calibrationgui.m | 8,280 | utf_8 | 3d4930d611dc42b4254576afbda135a8 | function varargout = calibrationgui(varargin)
% CALIBRATIONGUI MATLAB code for calibrationgui.fig
% CALIBRATIONGUI, by itself, creates a new CALIBRATIONGUI or raises the existing
% singleton*.
%
% H = CALIBRATIONGUI returns the handle to a new CALIBRATIONGUI or the handle to
% the existing singleton... |
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