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function archive = update_archive(archive, P, Z, picks, Global)
global disable_flag;
if disable_flag
return;
end
if ~isempty(archive)
P = [archive, P];
end
[O, ia, ~] = unique([P.objs], 'rows');
P = P(ia);
[index_frontiers, ~] = find_frontiers(O, [P.cons]);
archive = P(index_frontiers);
O = [archive.objs];
if n... |
function [] = plot_BNV(cmat,outputFilename)
% plotting the networks using BrainNet Viewer (http://www.nitrc.org/projects/bnv/)
% Xia M, Wang J, He Y (2013) BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics. PLoS ONE 8: e68910.
% This code was originally developped by Sahar Allouch.
% contact... |
function tests = offsetTicksTest
tests = functiontests(localfunctions);
end
function setupOnce(testCase)
testCase.TestData.orig_path = path();
% On my install, the perl script called in restoredefaultpath.m finds the
% stateflow folder twice. I don't know why but it throws an annoying
% warning.
state = warni... |
function [output_file_name] = shortest_paths( filename )
% This function computes shortest path from a weighted network and saves the corresponding distance matrix.
% Input: text file with each line storing a triple 'i j w_{i,j}'
% where i and j are positive integers representing two nodes of the network
% and w_{i,j}... |
function [pq] = priorityPrepare()
%PRIORITYPREPARE initializes an empty priority queue with fields key and cost
pq = repmat(struct('key', [], 'cost', []), 0);
end
|
% Copyright Claudio Menghi, University of Luxembourg, 2018-2019, claudio.menghi@uni.lu
function [data, abstractedmodel]=refine(data, model, abstractedmodel, aristeo_options, idOptions)
global absreftime;
absreftimetic=tic;
data = misdata(data);
try
if (isempty(idOptions))
abstract... |
function [ sprt_data_original, sprt_data_balanced, sprt_train, sprt_test ] = prepare_dataset()
%PREPAREDATASET Prepares the dataset for testing
if exist('preprocessedData.mat', 'file') == 2
load('preprocessedData.mat');
return ;
end
target_number_of_features_feature_extraction ... |
function uv = sceneFlow2Flow(uv3, z1, params)
%%
% convert the 3D scene flow UV3 and scene depth Z1 (current frame) to 2D
% optical flow UV
[H,W,~] = size(z1);
[x,y] = meshgrid(1:W, 1:H);
X = (x-params.cx).*z1/params.fx;
Y = (y-params.cy).*z1/params.fy;
X2 = X + uv3(:,:,1);
Y2 = Y + uv3(:,:,2);
z2 =... |
% Floyd-Warshall algorithm to complete a partial Euclidean distance matrix
% assume input is square matrix
function [D] = floydwarshall(P)
[m,n] = size(P);
D = symmfromupper(P);
for i = 1 : n
for j = 1 : n
if (D(i,j) < 0 || D(i,j) >= Inf)
D(i,j) = Inf;
end
end
end
for i = 1 : n
... |
LUCY = 1;
WIENER = 1;
noises = [0.005, 0.01, 0.05, 0.1];
iters = [1, 2, 5, 10, 20, 50];
for sigma = noises
for iter = iters
% Read data
aperture = imread('apertures/circular.bmp');
image = imread('images/penguins.jpg');
image = image(:,:,1);
% Noise level (gaussian ... |
%% get precision, recall, f-score from 'ellipse_fitting.m'
% 19.4.23-24
% imshow(gt_path);
clear
Indicator = [];
for pic = 74:118
gt_path = strcat('real-bin\',num2str(pic),'.png');
img_path = strcat('result423\',num2str(pic),'_mask.png');
gt = imread(gt_path);
img = imread(img_path);
% figure... |
SetupEnv;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% You do not need to chage values below
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if is_server
if strcmp(dataset, 'voc12')
VOC_root_folder = '/rmt/data/pascal/VOCdevkit';
elseif strcmp(dataset, 'coco')
VOC_root_folder = '/rmt/data/coco';
else
error... |
function [] = RecipEntropyConsolidate(Ningred_used,orders,Nsubsamps)
% Consolidates subsamples for order-order maxent model of Ningred_used most
% common ingredients
%
% Written by: DJ Strouse
% Last updated: Aug 19, 2013 by DJ Strouse
%
% INPUTS
% Ningred_used [=] positive integer = number of ingredients to use
% orde... |
%@(#) bransleflytt.m 1.1 05/12/08 14:34:05
%
%function f = bransleflytt(eocfil, bocfil, v, utfil)
%
%Inparametrar:
%EOCFIL: tex eoc23.dat
%BOCFIL: tex boc24.dat
%V: Om v väljs till 1 skrivs även asyid ut, annars väljs v till 0.
%UTFIL: Utfilen som resultatet skrivs ut i.
%
%Funktionen skriver ut kedjorna som b... |
function [dataz] = zscore(data)
[~, ncol] = size(data);
dataz = zeros(size(data));
for ii = 1:ncol
dataz(:, ii) = (data(:,ii) - mean(data(:,ii)))./std(data(:,ii));
end
end
|
function result=movingmean_glen(data,window,dim,option)
%Calculates the centered moving average of an n-dimensional matrix in any direction.
% result=movingmean(data,window,dim,option)
% Inputs:
% 1) data = The matrix to be averaged.
% 2) window = The window size. This works best as an odd number. If an... |
function px_settick(axis,ticks)
%axis为'x'或'y'，分别表示更改x或y刻度
%ticks是字符cell
% example 1. x=0:0.1:4*pi;plot(x,sin(x));ticks={'G_1' 'G_2' 'G_3' 'G_4' 'G_5'};settick('x',ticks)
%example 2. x=0:0.1:4*pi;plot(x,sin(x));ticks={'G_1' 'G_2' 'G_3' 'G_4' 'G_5'};settick('y',ticks)
n=length(ticks);
tkx=get(gca,'XTick')... |
function [out1,out2]=submean(dat)
x=dat(:,1); y=dat(:,2); d=dat(:,3); vx=dat(:,4); vy=dat(:,5);
dat(:,5)=dat(:,5)+1.5.*dat(:,1);
xlist=unique(x); ylist=unique(y);
Nx=length(xlist); Ny=length(ylist); Ly=max(ylist)-min(ylist);
datbar=zeros(size(xlist)+[0 3]);
datprime = zeros(size(dat(:,3:5)));
disp(size(datbar));
for... |
function result = masconv(a, b)
%Perform convolution of two arbitrary length
%sequences say x = [1 2 3] & y = [4 5 6 7].
%This is the most easily understood program
%of the convolution as it uses the idea of
%simple polynomial multiplication of the two
%vectors to obtain the convolution sum.
% Initializing a t... |
clc
clear variables
close all
addpath('Data')
addpath('Functions')
%Fait par : Francois
%Date : 2019-11-11
%% Data
load('Data/capteur.mat');
% 1 Original Data
% 2 Y = mx + b
% 3 Y = a + Bx^2
% 4 Y = a cos(x)
% 5 Y = ae^(bx)+c
% 6
% 7
% 8
% 9
%Figure 1 2 3 4 5 6 7 8 9
plots... |
function MACafter = ChangeMacAdress( MACtest )
%CHANGEMACADRESS modifies the current MAC adress.
% - "MACafter": the MAC adress (a string) that will be used to replace
% the current one. If not specified a random one is generated.
% Get the initial MAC adress
[~, MACbefore] = system('ifconfig en0 |grep ether');
... |
function subgrad = calc_subgrad_scp_hinge_loss(features_list,C,list_weights,lambda,beta)
% features_list is cell of size [1,B]. features_list{i} is [L,d]
% C is [B,L]
% list_weights is [1,L]
% beta is [d,1]
[B,L] = size(C);
d = length(beta);
subgrad = zeros(d,1);
% prediction error terms
for k = 1:B
features = featu... |
function [result,b]=c_4_grad(r1,r2,r3,r4,b1,b2,b3,b4,option)
%C_4_GRAD calculate gradient of physical field using 4 spacecraft technique
% [grad_b,[b]]=c_4_grad(r1,r2,r3,r4,b1,b2,b3,b4)
% [grad_b,[b]]=c_4_grad('r?','b?','grad')
% [curl_b,[b]]=c_4_grad('r?','b?','curl')
% [div_b,[b]]=c_4_grad('r?','b?','div')
% [cu... |
function G=cpd_G(x,y,beta)
% alternative to the original
if nargin<3, error('cpd_G.m error! Not enough input parameters.'); end;
k=-2*beta^2;
m=size(x,1); n=size(y,1);
G = zeros(m,n);
if isequal(x,y), % using the symmetry property
for i=1:m,
for j=1:i,
G(i,j) = exp(sum((x(i,:)-y(j,:)).^... |
function [ output_frame ] = get_audio_frame( W_in, W_out, input, beta )
X_in = [input 1]';
h = W_in*X_in;
out_first_layer = tanh(h*beta);
X_out = [ out_first_layer ;1];
h_s = W_out*X_out;
output_frame = ta... |
function [g1,g2] = constraints(X)
initialisation;
g2 = 0;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Discretize each line segment into a no.of points specified by evaliter
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
line1x = linspace(A(1),X(... |
function offscreenHist(data,fname,varargin)
%OFFSCREENHIST Plot a histogram off screen.
%
% offscreenHist(data, fname)
% offscreenHist(data, fname, ...)
% offscreenHist(data, fname, nbins)
% offscreenHist(data, fname, nbins, ...)
% offscreenHist(data, fname, edges)
% offscreenHist(data, fname, edges, ...)
%... |
% funcion que genera la busqueda de grilla mas chica
function [svmFinal Exac Cfinal Sfinal] = iterSVM(C,Sigma,DATAFIN)
i=0;
svmFinal = [];
Exac( round(C(1,2)-C(1,1) + 1) , round( Sigma(1,2)-Sigma(1,1) + 1 ) ) = 0;
for c = C(1,1):0.2:C(1,2)
i = i + 1;
j = 0;
for s = Sigma(1,1):0.2:Sigma(1,2)
... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% plot_figures_from_ultimate_optimization.m
% This script shows some plots based off the results of
% ultimate_optimization. ultimate_optimization should be run first.
% ME271E, Fall 2018
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
function to_sharpen_T(I_file,s)
%generally s = 0.1-0.3
tiff_info = imfinfo(I_file);
[pathstr,name, ~] = fileparts(I_file);
I_file_F = [name,'_RM_T.tif'];
%hist_averaging(master);
%I_file = 'Fabs05_488TIRF_wt_netrin_1_WOO_100_mod_final_hist_average.tif';
tiff_info2 = imfinfo(I_file_F);
out_file_coral3 = fullfi... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Created: 19-May-2014 16:59:11
% Computer: GLNXA64
% Matlab: 8.1
% Author: NK
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [xx,yy] = meshgridQuick(xrow,ycol)
% xrow = full(x(:)).'; % Make sure x is a full row vector.
% ycol = full(y(:)); % Make sure y is... |
function mit(senf)
if(strcmp(senf, 'senf'))
fprintf("doener mit senf");
end
end
|
function wsEva = movSalEva(src, wsSal, parEva, varargin)
% Evaluate the salience result.
%
% Input
% src - source
% wsSal - saliency
% parEva - parameter
% nL - #level, {255}
% varargin
% save option
%
% Output
% wsEva - evaluation
% Pre - precision, nL x nF
% Rec ... |
function [p1,stat1,p,stat] = indtestimpl_nloop_pstat(i,j,k,ds,alpha,S,N)
%This function implements the bootstrap method in combination with HSIC
%implementation. It cannot be used in combination with bnt learning
%bayesian model. It's a test function to get all the pvalues and statistics
%of the sub tests
%Usage [... |
function [] = draw_graphs(simu_desc)
%**************************************************************************
% UCLOUVAIN/MIT CONFIDENTIAL
% ALL RIGHTS RESERVED
%**************************************************************************
%******************************... |
clc;
clear;
im_0=imread('1.tif');
[m,n]=size(im_0);
im_reverse=~im_0;
im_temp=logical(ones(m,n));
im_temp(2:m-1,2:n-1)=logical(zeros(m-2,n-2));
im_label=im_reverse & im_temp;
%se=logical(ones(3000,3000));
%im_dilate=imdilate(im_label,se);
%im_next= im_dilate & im_reverse;
im_next=myreconstruct(im_label... |
function recall = compute_recall_combined(query_B, database_B, attr_pred_query, attr_pred_database, query_label, database_label, query_Attribute, database_Attribute, leave_one_out)
% This function computes recall.
% query_B ---- size: Nq * k, value: {1, -1}
% database_B ---- size: Nd * k, value: {1, -1}
% attr_pre... |
function lfpnew = remove_spk(lfp, lfptime, spktime, wnd)
% remove spike from lfp by a linear interpolation
if isempty(spktime)
lfpnew = lfp;
return
end
lfpnew = lfp;
nspk = length(spktime);
for n = 1:nspk
[~, idx1] = min(abs(lfptime - spktime(n) - wnd));
[~, idx2] = min(abs(lfptime - spktime(n) + wnd));... |
function [action,state] = player2(G,TR,S2,S1,N,M,plnum)
%action = round(rand*3)*ones(size(S2,1),1);
action = 4*ones(size(S2,1),1);
state = 0;
return; |
function y=ramsize
% Returns the size of machines physical RAM in bytes
y=1250*2^20; % for 1.2GB use 1250*2^20 |
function [ imageFiles, imageFileCount] = listFilesInDirectory( targetFolder, imageFileType )
%LISTFILESINDIRECTORY
listing = dir(fullfile(pwd, targetFolder, ['*', imageFileType]));
imageFileCount = length(listing);
[imageFiles{1:imageFileCount}] = listing.name;
[~, lengthIndex] = sort(cellfun('length', imageFil... |
function displayGutVideo(gutMesh, gutMeshVels, gutMeshVelsPCoords, thetas, imPath, filetype)
% Load data and images from directory
matData=dir(strcat(imPath,filesep,'*.mat')); % Assumes filesep at end of imPath
load(strcat(imPath,filesep,matData(1).name));
ims=dir(strcat(imPath,filesep,filetype));
nT=size(ims,1)-1; % ... |
%% load_nova reads data from two column data files exported by NOVA
%%
%% [y x] = load_nova(file, st)
%%
%% OUTPUT:
%% y 2nd column data
%% x 1st column data
%%
%% INPUT:
%% file Name of file to be read
%% st Row to start reading from (optional)
%%
function [y x] = load_nova(file, st = 6, op = 10)
... |
function [ax,profil] = mplltpro(expan,pcde)
% Profile plot for excitation pulse
% Called by mplcalcc
% Note that splotn etc removed for now ****
% Declare globals *************************
global mpgbb mpgaa mpgnofp mpgdtmu mpgang ;
global mmgfincr mmgfxyoff mmgfxypos mmgplotn ;
% Declare local variables ***
h = mpg... |
function [board,cntFences] = findFences(board,bluewoCircles,demo)
%findFences Hledani ohrad
% board - vstup/vystup(s podbarvenim pastvin) hraci desky
% cntFences - pocet pastvin
% bluewoCircles - modry rastr bez kruhu
% smazani sumu
bluewoCircles=imopen(bluewoCircles,ones(3));
% spojeni ohrad
dil=roun... |
%
% VOLYMUNDER
%
% Beräknar volymen av båten under ett visst z-värde.
% Returnerar dessutom 'spunkter' som är en vektor av skärningspunkter
% med planet zguess. Volymen integreras i x-led över snittareorna i
% båten. Dessa räknas ut genom att hitta snittets bézierkurvas t-värde
% för där fz(t)=z (Löses med Newton-Raphs... |
%this calculates the AIC of the various models for the purpose of
%comparing and selecting the best model
numParam1 = length(info1.X);
numParam2 = length(info2.X);
numParam3 = length(info3.X);
numParam4 = length(info4.X);
aic1 = aicbic(LogL1, numParam1);
aic2 = aicbic(LogL2, numParam2);
aic3 = aicbic(LogL3, numParam3)... |
% EJERCICIOS DE PROGRAMACIÓN - SCRIPTS EN MATLAB PARA RESOLVER E.D
% Autores: Camila Cueva, Jonnathan Espinoza y Edgar Sánchez
% Fecha: 03 de junio de 2021
% EJERCICIO 01
% Representar un campo direccional de la ecuación diferencial dy/dx = 2x
% Sobre la misma gráfica superponer:
% > La gráfica de la solución de l... |
function [q_seed,qd_seed,qdd_seed,t_seed] = GenManualSeed(obj, rbm)
arguments
obj (1,1) NLP
rbm (1,1) DynamicalSystem
end
% Guess initial pose
[qGuess, tGuess] = Opt.ManualInit();
validateattributes(tGuess, {'double'}, {'size', [1,1]})
mustBePositive(tGuess)
validateattributes(qGuess, {'double'}, {'s... |
function [ rmse ] = rmse_calc( R_pred, R_test, z_test )
%RMSE Calculation
% Calculates RMSE based on test set
sample_no = sum(z_test(:));
tmp = sum(sum(z_test.*(R_test - R_pred).^2));
rmse = sqrt(tmp/sample_no);
end
|
clear
format long
%global n;
%global I5;
%global betaO;
%global X;
%global logY;
%global t;
%global h;
%global h1;
n=100;
I5= eye(5);
betaO= [0.25 -0.25 -0.25 0.5 0.75]';
matlabpool open
parfor m=1:12
innerPro=0;
while innerPro < 0.9995
X = mvnrnd(0*ones(1,5),I5,n);
Y = (X*betaO).^3;
e = normrnd(0,1,n,1);
... |
function drawGrid(ax,grid_size,width,height)
hold on;
for ii = 1:grid_size
x = 1:width;
y = ii*(width/grid_size)*ones(width,1);
plot(ax,x,y,'k:');
xx = ii*(height/grid_size)*ones(width,1);
yy = 1:width;
plot(ax,xx,yy,'k:');
end
|
clear
clc
data = load("spikes.mat");
spikes = data.spikes;
test_number = 5;
% normalize
spikes_mean = mean(spikes);
spikes_normalized = spikes - spikes_mean;
s = std(spikes);
spikes_normalized = spikes_normalized ./s;
% pca
[CO, SCORE, LATENT] = pca(spikes_normalized);
co_2 = CO(:,1:2);
score_2 = SCORE(:,1:2);
k = ... |
% This is a demo script for conducting two-sampled t-tests in matlab
% Clear the previous variables in the workspace
clearvars;
% state the full name of the input data file
filename = 'tea_time_data.xlsx';
% read file content and save them into a variable
input_file = importdata(filename);
% save the data portion o... |
cd('C:\Users\Lukas Litzellachner\OneDrive\PhD\Values as Goals\Program')
addpath('C:\Users\Lukas Litzellachner\OneDrive\PhD\Values as Goals\Program\fun');
addpath('C:\Users\Lukas Litzellachner\OneDrive\PhD\Values as Goals\Program\Pics');
addpath('C:\Users\Lukas Litzellachner\OneDrive\PhD\Values as Goals\Program\Data');
... |
clear all; close all; clc
%Plastic Properties Given
Mod_El=230000; %Modulus of Elasticity E = 230000psi
Sig_y=2500; %Yeild Strength Sig_y = 2500psi
Sig_uts=3000; %Ultamate Tensile Strength Sig_uts = 3000psi
Density=0.0376; %Density = 0.0376lb/in^3
%Extra Definitions
w=50; %Omega = 50rpm
theta=(0:1:360).*(pi/180); ... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FEM for - div . (u_x,u_y) + u = f in \Omega %
% (u_x, u_y). n = u_N on \partial\Omega_N %
% u = g on \partial\Omega_D %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
function init_maze(id)
global maze
if(id==1)
maze1();
elseif(id==2)
maze2();
elseif(id==3)
maze3();
end
save('maze.mat','maze');
disp('maze saved');
end
function maze1()
global maze
maze=zeros(103);
maze(1,1)=1;
% obs(1,:)=[.3,.5,.5,.7];
maze(30:50,50:70)=1;
maze(50:80,60:68)=1;
maze(40:48,30:60)=1;
%borde... |
% plot results from might.m
close all;
%clear all;
load('mat/might_knn_rsa=6_us=0_r=6.6667_znone.mat')
%load('mat/might_lda_shrinkage_rsa=5_us=0_r=2.6667_znone.mat');
%load('mat/might_knn_rsa=5_us=0_r=6.6667_zrun.mat');
%load('mat/might_knn_rsa=5_us=0_r=6.6667_znone.mat');
%load('mat/might_knn_rsa=5_us=1_r=6.6667_zno... |
clear all
close all
% Vector array length
N=1024;
%% Matlab Task 2, Section 1
e = randn(N,1,'double');
%% Matlab Task 2, Section 2
e_p = fft(e);
for i=1:N
per(i) = 1/N*e_p(i)*conj(e_p(i));
end
%% Matlab Task 2, Section 3
num=[1];
den=[1 -0.9 0.5];
P = tf([1], [1 -0.9 0.5], 1);
sample = [0:1:N-1];
w = lsi... |
% Plotea la curva de Kinetic-over-Noise (opcionalmente muestra los errores muestrales):
function [] = konPlot(errors)
clc;
grey = [0.7, 0.7, 0.7];
konN40 = table2array(readtable('kon-N40.data', 'ReadVariableNames', false, 'FileType', 'text', 'Delimiter', ' '));
konN100 = table2array(readtable('kon-N100.dat... |
function varargout = DPABI_SelectROIIndices(varargin)
% DPABI_SELECTROIINDICES MATLAB code for DPABI_SelectROIIndices.fig
% DPABI_SELECTROIINDICES, by itself, creates a new DPABI_SELECTROIINDICES or raises the existing
% singleton*.
%
% H = DPABI_SELECTROIINDICES returns the handle to a new DPABI_SELECTR... |
function play2(varargin)
% MATLAB Tetris
% To launch, simply type 'play2' in your command window.
% Alternatively, you can choose run from this editor window, or press 'F5'.
%
% To start the game, place the cursor in the 'Start' edit box, and press Enter
% on your keyboard. Controls are as follows:
%
% [Q/q]:... |
function weight = measurement_model(z, x, l)
% Computes the observation likelihood of all particles.
%
% The employed sensor model is range only.
%
% z: set of landmark observations. Each observation contains the id of the landmark observed in z(i).id and the measured range in z(i).range.
% x: s... |
function logp = logpolyapdf_counts_base_eqiv(nn, base, alphas, unWtBase)
% logp = logpolyapdf_counts(nn,K,alphas)
%
% Compute the log-polya probability of data nn (a histogram) given Dirichlet
% prior with parameter alpha.
% Make alphas a column vector, zcts and ncts row vectors
alphas = alphas(:);
nn = nn... |
% Gütefunktion für Maßsynthese von Robotern (allgemein); vektorisierte Form
%
% Eingabe:
% R
% Matlab-Klasse für Roboter (SerRob/ParRob)
% Set
% Einstellungen des Optimierungsalgorithmus
% Traj_W
% Trajektorie (bezogen auf Welt-KS)
% Structure
% Eigenschaften der Roboterstruktur
% P [Nf x Np]
% Matrix mit Ve... |
function MPCs = simulation_MPCs(p,simvals,income,basemodel,grids)
% This function is called by simulate.m to compute MPCs. Outputs are
% cell arrays, each cell associated with one mpcamount.
Tmax = p.freq * 4;
ynetsim_mpc = simvals.ynetsim;
diesim_mpc = simvals.diesim;
csim_baseline = simv... |
function [ distMat ] = calcDistMat(x_center, w, h)
% distMat is of size (h, w)
h_list = 1 : h;
w_list = 1 : w;
distMat_h = (ones(w, 1) * h_list)' - ones([h, w]) * floor(h/2);
distMat_w = ones(h, 1) * w_list - ones([h, w]) * floor(w/2);
distMat = (distMat_h .^ 2 + distMat_w .^ 2) / (w ^ 2 + h ^ 2);
distMat = ones(h... |
%
% Example to use igtlreceive()
%
%%% read image data
sd = igtlopen('localhost', 18944);
if sd == -1
error('Could not connect to the server.');
end
for n=1:10
DATA = igtlreceive(sd);
end
igtlclose(sd);
|
Dalpha_23_t = abs(alpha_2_t-alpha_3_t);
Dalpha_23_m = abs(alpha_2_m-alpha_3_m);
Dalpha_23_h = abs(alpha_2_h-alpha_3_h);
% Incidence: i_opt_stat = Ksh*Kth*i0_10+n_LIE*teta
Ksh_i = 0.7; % for DCA
q = 0.28/(0.1+(th_c)^0.3);
Kth = (10*th_c)^q;
p_tip = 0.914+sigma_S_t^3/160;
p_mid = 0.914+sigma_S_m^3/160;
p_hub = 0.914+... |
function [hand L Elast TendS] = readfile
%UNTITLED7 Summary of this function goes here
% Detailed explanation goes here
finger = parseXML('finger.xml');
tendons=parseXML('tendons.xml');
hand=[];
for i=1:3
for j=1:(size(finger.Children,2)-1)/2
data=str2double(finger.Children(1,j*2).Children(1,2+i*2).Child... |
function peaks = peak_finder(x,fs,thresh,blank,doplot)
% peaks = peak_finder(X,thresh,blank,doplot) % X is a sensor structure
% or
% peaks = peak_finder(X,fs,thresh,blank,doplot) % x is a vector
% This function detects peaks in a signal that exceed a specified
% threshold and returns each peak's start tim... |
function [A] = sourceContributionNormal(panels,N)
%UNTITLED6 Summary of this function goes here
% Detailed explanation goes here
A = zeros(N);
n = 0.5*ones(N,1);
A(1:(N_panels+1):end) = n;
for i=1:max(N_panels)
for j=1:max(N_panels)
if (i ~= j)
A(i,j) = (0.5/pi) * integrate_radial(panels(... |
%% MATLAB xUnit Test Framework: Architectural Notes
% This document summarizes the key classes and design choices for MATLAB xUnit,
% a MATLAB unit testing framework based on xUnit patterns.
%
% Note: Testing pattern and smell terminology in this document is drawn from
% _xUnit Test Patterns: Refactoring Test Code... |
function xyr = Xr(s)
x = 1 ;
y = s ;
xyr = [x ; y] ; |
clear;
clc;
close all;
f0 = 76500;
f1 = 85400;
fS = 4000000;
sps = 100; %Samples Per Symbol
n = 0:(sps-1);
t = n*1/fS;
SymbolZero = 1 + sin(... |
% Returns the index of the screen on which most of the figure window lies.
% Copyright (c) 2012 Howard Hughes Medical Institute.
% All rights reserved.
% Use is subject to Janelia Farm Research Campus Software Copyright 1.1 license terms.
% http://license.janelia.org/license/jfrc_copyright_1_1.html
functio... |
function [ V ] = create_input_EFIE(k,theta,Ezo,r_s)
%create_input Summary of this function goes here
% Detailed explanation goes here
% K = [k*cos(theta) k*sin(theta)];
m_s = [(.5*((r_s(:,1)-r_s(:,2)))+r_s(:,2)) (.5*((r_s(:,3)-r_s(:,1)))+r_s(:,1))];
P_in = m_s(:,2)-m_s(:,1);
P_in = [P_in(1) P_in(2) 0];
% Ezo = [ 0 1 ... |
rng(3);
%{
%% Housekeeping
clc; close all; clear all;
%% Add paths
addpath('classes');
%% Read data
[accel, grav, gyro, accel_lin, mag, euler,...
gps, displacement, speed, heading] = parseRawData('data/rawdata');
%}
%% Estimated displacement vector
[estimate] = getEstimatedDisplacement(accel_lin, gyro);
%% Anim... |
function Ei200_uvw110_at0m11TPmod2Peaks(varargin)
root = fileparts(pwd);
data_source= fullfile(root ,'sqw','Data','Fe_ei200.sqw');
bragg = [0,-1,1];
dE = 5;
dK = 0.05;
repPoints1= [-0.3,-0.09,0,0.1,0.25;
85,10,0,10,60];
repPoints2 = [-0.25,-0.09,0,0.1,0.28;
60,10,0,10,85];
repPoints3 = [-0.28,-0.09,0,0.09,0... |
function AAtensorP = computeTensorProductAsub_Asub(dimi, AiTensor, dimj, AjTensor)
dim = [] ;
for k = 1 : 3
dim = [dim, dimi(k), dimj(k)] ;
end
AAtensorP = zeros(dim) ;
for i1 = 1 : dimi(1)
for i2 = 1 : dimj(1)
for j1 = 1 : dimi(2)
for j2 = 1 : dimj(2)
for k1 = 1 ... |
%% PARAMETER SPACE EXPLORATION SCRIPT
% this script's purpose is to explore the parameter space for the ocular
% artefacts extraction procedure, in order to determine the parameter
% values that produce a best trade off of number of saccades detected and
% and accuracy of gaze prediction
%% RESET + LOAD LIBS
... |
function mask = computeMask(img_cell)
% Initialization
num_img = size(img_cell, 1);
mask = zeros(size(img_cell{1}));
% Overlay all masks from other images
for i = 1:num_img
mask = mask | im2bw(img_cell{i}, 0);
end
end |
clc
clear
close all
% Matric A0138993L
% Classes not chosen: 9 and 3
load('characters10.mat');
%set seed for reproducibility
rng(234);
train_idx = find(train_label ~= 3 & train_label ~= 9);
TrLabel = train_label(train_idx);
train_x = train_data(train_idx, :);
% normalizing train data
train_x = mat2gray(train_x(:,:))';... |
% Takes a chromosome and a mutatino probability and returns a mutated chromosome
function newChromosome = Mutate(chromosome, mutationProbability)
newChromosome = chromosome;
chromLength = length(chromosome);
for i = 1:chromLength
r = rand;
if r < mutationProbability
newChromosome(i) = 1 - chrom... |
reset_data_analysis_environment
% P_initial_profile=PTOT_profile_interp_ini;
% load('NBI_Phot_data.mat')
load('NBIco_Phot_data.mat', 'deltaW_fast_hat')
deltaW_fast_hat
% rescaling to use only the passing contribution
% deltaW_fast_hat=(1359049/1992454)*deltaW_fast_hat
% save('NBI_Phot_data.mat','-append','deltaW_fast_h... |
function feat= StabilitySelection(X,Y,M,thr,options)
% options in glmnet;
% options.lambda: Predetermined lambda sequence
% options.alpha: Elastic-net mixing parameter
% thr: Threshold value for feature selection
% M: Number of iterations
% X: n x m data matrix, where n is the number of datapoints and m is the
% dim... |
function [Ytest] = new_classifier(Xtest, mu1, mu2)
m1Minusm2 = mu1 - mu2;
b = 0.5*(mu1 + mu2);
Ytest = sign((m1Minusm2*(Xtest - b)')/(norm(m1Minusm2)));
|
function D = polyD(P, n)
% Returns the nth derivate of a set of polynomials.
if(nargin==1)
n=1;
end
if n==0
D=P;
return;
end
D=zeros(size(P,1), size(P,2)-n);
for i=1:size(P,2)-n
k=1;
for j=i:i+n-1
k=k*j;
end
D(:,i)=k*P(:,i+n);
end
end
|
function varargout = untitled122(varargin)
% UNTITLED122 MATLAB code for untitled122.fig
% UNTITLED122, by itself, creates a new UNTITLED122 or raises the existing
% singleton*.
%
% H = UNTITLED122 returns the handle to a new UNTITLED122 or the handle to
% the existing singleton*.
%
% UNTITLED1... |
classdef TrainBrain < handle
%GENOME, this class contains the entire gene pool for the neural net
% AI that controls the computer mallet, genome can be shared across
% various, simultaneous simulations of mallet
properties (SetAccess = private)
% NETWORK PROPERTIES
height; % h... |
function Y = FastLegendreTransform(lp,T)
%p == the degree of the Legendre polynomial
%T == the domain, a=T(1)<...<T(end)=b
%Returns Y, which satisfies Y(i)=Lp(ksi(T(i))), where Lp is the
%legendre polynomial of degree p,and ksi is the transformation
%ksi(t)=(2*t-(a+b))/(b-a), -1<=ksi<=1.
... |
%Use two different points in different views to triangulate a match-point
%and the match-error of this point. Unit: mm.
function [P,error] = my_triangulate_new(point1, point2, m, n)
% a=[298.42,416.69];
% b=[996.32,398.53];
% a=undistortPoints(a, calib(1).cameraParams);
% b=undistortPoints(b, calib(4).cameraParams);
%... |
function [x, y, r] = three_point_circle(P)
% solve circle equation for all points
% x^2 + y^2 + A*x + B*y + C = 0
% A*x + B*y + C*1 = -1*x^2 + -1*y^2
% [x1, y1, 1] [A] [(-x1*x1 -y1*y1)]
% [x2, y2, 1] [B] = [(-x2*x2 -y2*y2)]
% [x3, y3, 1] [C] [(-x3*x3 -y3*y3)]
% Ts = Q
% s = T\Q
%... |
function [d,U] = HermitianEigen(H,tol)
%HERMITIANEIGEN Returns the eigenvalues and eigenvectors of a hermitian matrix H.
% Returns the eigenvalues and eigenvectors of a hermitian matrix H.
% Args:
% H (ndarray): n x n hermitian matrix
% tolerance (float): the relative precison
% Returns:
% d (ndarray):... |
import gtsam.*
fsize = 16; % font size
set(groot, 'defaultAxesTickLabelInterpreter','latex');
set(groot, 'defaultLegendInterpreter','latex');
%% Parse the data from the front end
global measurement_path
global steps
measurement_path = 'observations';
steps = 100;
[gt, Measurements, Transform] = Data_Parsing(measure... |
clear all;
clc;
x = (0:0.01:10)';
BdataFull = 0.4*(1-exp(-2.2*x))+0.55*exp(-3.6*x);
bEstimate = fittype(@(A,B,C,D,x) A*(1-exp(-1*B*x))+C*exp(-1*D*x));
[Bfit, gof] = fit(x, BdataFull, bEstimate, 'StartPoint', [0.5, 2.5, 0.5, 3.0]);
figure;
plot(x,BdataFull);
hold on;
plot(x,Bfit(x));
hold off; |
function [beta, sdd, sid, offcenter_dis, delta_theta] = Xu2DPmatrixGetSDDSIDetcFromPmatrix(pmatrix,det_elt_counts)
[paras, delta_u] = Xu2DPmatrixGetParasFromPmatrix(pmatrix);
theta = paras(1);
x_do = paras(2);
x_s = paras(3);
y_do = paras(4);
y_s = paras(5);
beta = angle(x_s+1i*y_s);
delta_theta = theta-beta+pi/2;... |
function net = residualDenseBlock(net,d,nch,f,k,C,inname,outname)
leak = 0; % Relu
nch0 = nch;
inname0 = inname;
prevout = {inname};
for c=1:C
% Convolution
block = convBlock(k,k,nch,f);
name = ['conv_' num2str(c) '_rdb_' num2str(d)];
coutname = ['c' num2str(c) '_rdb_' num2str(d)];
weights = {['w... |
% add Add object(s) to the group
%
% This method adds one or more BoundaryCurve objects to the group.
% >> add(group,bound);
% >> add(group,bound1,bound2,...);
% An empty boundary is added when no existing bounary is specified.
% >> add(group);
%
% See also BoundingCurveGroup, remove
%
%
% created December... |
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