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% function [tdata,udata] =
% SpectralChebyshevFFT(Nx,Ny,a,b,nu,x0,y0,u0,tmax,jacobian)
%
%
%
% Author: Diako Darian
% Date: 11.07.2015
%
%
% This function advances RHS of the 2D Burger's equation in time
% by means of fourth order Runge-Kutta method,
% in the case of Chebyshev spectral collocation method... |
% TODO: Add comments
%% Init
clear;
clc;
close all;
addpath(genpath('../../Solvers'));
addpath(genpath('../../Utilities'));
% Parameters
sigma = .001; % Standard deviation of noise
seed = 18; % Seed
bSilent = false; % Silent/verbose mode
bSave = 0; % Flag for saving all results
RandStream.setDefaultStream(RandStre... |
function [s] = create_s(triggers, t)
[rows,columns] = size(triggers);
rows_triggers = rows;
[rows,columns] = size(t);
rows_t = rows;
w = [];
m_index = 1;
t_index = 1;
markers_added = 0; % track number of markers added (for managing inconsistent sample rate)
time_frame = 1.0;
%scan only for the ran... |
clc; close all; clear all;
%% parameters
lambda = 0.00053; %mm
ff = 100; %mm
Dslm = 0.008; %slm pixel size
NN = 2054;
MM = 2452;
dx = 0.00345; %mm % Object pixel sizes
dy = 0.00345; %mm
du = lambda * ff / (dx * NN);
dv = lambda * ff / (dy * MM);
u_b = double(loadFPImage('USAF_z=0.fpimg')... |
function [segmented, centroids] = kmeans_segment(image, Kclusters)
[a, b, c] = size(image);
tmp = im2double(image);
tmp = reshape(tmp, [a*b, 3]);
[idx, centroids] = kmeans(tmp, Kclusters);
segmented = zeros(a*b,3);
for i=1 : a*b
segmented(i, :) = centroids(idx(i), :);
end
s... |
function [yi, ypi] = said(x, y, xi, chi, eta, c)
% SAID 1-D piecewise Said interpolation
% SAID(X,Y,XI,CHI,ETA,C) interpolates to find YI, the values of the
% underlying function Y at the points in the array XI, using
% piecewise Said interpolation. X and Y must be vectors
% of length N.
%
% C... |
function [ output ] = crowdCPush( echoQueryNum, edgeNum, deviceVector, queryVector, cacheSize, contentDataRaw )
edgeCacheContent = zeros(edgeNum, 1e5);
edgeCacheSize = zeros(edgeNum, 1);
edgeCacheSize(:, :) = cacheSize;
deviceVectorCum = zeros( edgeNum, 1 );
for i = 1 : edgeNum
deviceVectorCum(i) = sum(deviceVect... |
function W = constructW2(feat1,feat2,options)
k = options.k;
len1 = size(feat1,1);
len2 = size(feat2,1);
W = zeros(len1+len2);
dist21 = EuDist2(feat2,feat1);
%dist22 = EuDist2(feat2,feat2);
[~,sortIndex] = sort(dist21,2);
for i = 1:len2
W(len1+i,sortIndex(i,1:k)) = 1;
W(sortIndex(i,1:k),len1+i) = 1;... |
%此程序比较单期和多期单一价格货物收益情况
clc
clear
close all
load JL_Single2_p0_1
[R1,id1] = max(revenue_m);
h1= plot(c(id1),R1,'k*','MarkerSize',5);
hold on
h2=plot(c,revenue_m,'k-','LineWidth',2);
tran_T2=tran_T(id1);
fprintf('多期，最大收益为：%.2f，发生在当仓容为：%d\n', R1,c(id1))
load JL_Single3
h3=plot(c,revenue_m,'r-','LineWidth',2);
[R2,id2] ... |
function [p]=Update_Pressure(w,Constant)
gamma=Constant.gamma;
rho=w{1};
rhou=w{2};
rhov=w{3};
rhoE=w{4};
u=rhou./rho;
v=rhov./rho;
p=(gamma-1)*(rhoE-(1/2).*rho.*(u.^2+v.^2));
end |
%% REminer 결과 그래프에서 패턴을 분리하여 여러 기능을 수행하는 프로그램
clear;
Pixels = zeros(49,3);
WeightedPixels = zeros(49,3);
Arrays = zeros(49,1);
WeightedArrays = zeros(49,1);
load('name.mat');
for ecoli = 1:49
clearvars -except ecoli Pixels Arrays name WeightedPixels WeightedArrays;
str = sprintf('Screenshot_%d.png',e... |
function fnval = Lorenz_fun(t,x,p,more)
% Right side function for Lorenz differential equation
% x(1) = x, x(2) = y, x(3) = z
% p(1) = sigma, p(2) = r, p(3) = b
p = exp(p);
n = size(x,2);
if n == 1, x = x'; end
r = x;
r(:,1) = -p(1).*x(:,1) + p(1).*x(:,2);
r(:,2) = p(2).*x(:,1) - x(:,2) + x(:,1).*x(:,3);
r(:... |
clear;
syms l0 l1 l2 l3 x1 x2 x3 x1d x2d x3d;
l0=4;l1=4;l2=1;l3=1;x1d=0;x2d=0;x3d=1;
tt(6,100)=0;
for k=1:50
x1=0;x3=0;x2=2*pi*k/100;
t3=twist_wqh([0,0,1]',[0,l1+l2,0]',0);
t2=twist_wqh([0,0,1]',[0,l1,0]',0);
t1=twist_wqh([0,0,1]',[0,0,0]',0);
g3=twist2gab(t3,x3);
g2=twist2gab(t2,x2);
g1=twi... |
clear;
joints_3D = load('/home/chenf/Documents/pose_estimation/data/human3.6M_ori/joints_3D.mat');
joint = joints_3D.joints_3D{1,1}(:,:,1);
joints_2D = load('/home/chenf/Documents/pose_estimation/data/human3.6M_ori/S1/joints.mat');
joints1 = joints_2D.joints{1}(:,:,1);
joints2 = joints_2D.joints{2}(:,:,1);
joints3 = j... |
% P3.10
colordef white; clear; clc;
M = 250; k = -M:M; w = (pi/M)*k; % [0, pi] axis divided into 501 points.
w0 = pi/2; figure(1);
n=[-25,25];
h=(0.9).^(abs(n));
H=dtft(h,n,w);
subplot(2,1,1);
plot(w/pi,abs(H)); grid;
xlabel('frequency in pi units');
ylabel('|H|');
title('Impulse Response M... |
function outstruct = buildStructWrtTime(obj, timestr, label2find, tfind, deltaT)
% BUILDSTRUCTWRTTIME(obj, timestr, label) Find static/dynamic embryos
% Give the times, folders, and time uncertainties of all
% dynamic samples or fixed samples matching the supplied channel 'label',
% depending on whether they ma... |
function [bin] = img_segment(img)
imghsv = hsv_segment(img); %Call user defined function for HSV segmentation
imgcbcr = ycbcr_segment(img); %Call user defined function for YCbCr segmentation
[r,c,v] = size(imgcbcr);... |
function y=vecrev(x)
%Usage: y=vecrev(x)
%
% Reverse a vector, x.
[n,m]=size(x);
if n>m
y=fliplr(x);
else
y=flipup(x);
end;
|
function new_image = harmonic_mean_cvip( imageP, mask_size, varargin)
% HARMONIC_MEAN_CVIP - performs a harmonic mean filter
%
% Syntax :
% -------
% new_image = harmonic_mean_cvip( imageP, mask_size)
% new_image = harmonic_mean_cvip( imageP, mask_size, ignore_zeros)
%
% Input Parameters include :... |
%%%%% INTRODUCTION %%%%%
% RRT, the Rapidly-Exploring Random Trees is a ramdomized method of
% exploring within dimensions. This method can effectively generate a path
% to reach any point within certain limited steps due to its random
% characteristics.
% This method is proprosed by LaValle, Steven M. in
% Oc... |
function unixTime = datenum2unix(matlabDatenum)
%DATENUM2UNIX Converts MATLAB datenums to UNIX time
% Rounds down to whole seconds
unixPivotDatenum = datenum(1970,01,01);
unixTime = floor(86400*(matlabDatenum - unixPivotDatenum));
end
|
clear all ; close all ;
cd('C:\shared\mkt') ;
fid = fopen('ES.txt') ;
data = textscan(fid,'%s %s %f %f %f %f %f','delimiter',',','Headerlines',1) ;
fclose(fid) ;
d5 = data{6} ; d5 = d5(1:5:end) ;
d5 = d5(length(d5)/2:end) ;
%d5 = d5(end-50000:end) ;
% make a moving average:
clear mvg p mean
for p = ... |
function [ kurt ] = Kurtosis( X )
%[ kurt ] = Kurtosis( X )
% Calculation of Kurtosis based on
% H. Yu and T. Fingscheidt, "A Figure of Merit for Instrumental
% Optimization of Noise Reduction Algorithms", in Proc. 5th Biennial
% Workshop on DSP for In-Vehicle Systems, Kiel, Germany, Sep. 2011, pp.
% 140-147.... |
function [tau] = mjd20002tau(mjd2000)
%
% - tau: represents te value of the time at the Modified Julian Date since 2000 Jan
% 12:00:00 (mjd2000) following the criteria from Matlab to measure time.
Date = mjd20002date(mjd2000);
tau = datenum(datetime(Date));
|
<<<<<<< HEAD
clear; close all;
file_name = './data/test_2semg_12345.txt';
semg_channel = 1:2;
semg_channel_count = 2;
force_channel = 3;
semg_max_value = 2048;
semg_min_value = -2048;
force_max_value = 5;
semg_sample_rate = 5100; % Approximate
target_sample_rate = 300;
test_output_filename = './data/output/exp_ard_D... |
global config;
config.setatten = @(a)atten_santec_1550.attenuation(a);
attens = [15:-1:4];
gain_piezo = 2000;
atten_piezo = 28;
wlmin = 1530;
wlmax = 1540;
% attens = [attens flip(attens)];
data_all = [];
Ps_all = [];
fname = sprintf('TD67_powerSweep_%s', datestr(now, 30));
%%
for atten = attens
% at... |
function [detectedPlane, isObjectDetected, supportEdges, vanishingPoint_uv,inclinationAngle, gpCorrected]=detectStair(figHandle, grayImg, uvdPoints, xyzPoints, gp, selectMethod, predictedPlane, inclinationDetected, stepsPlanes )
% selectMethod can be 'RansacFit' or 'PlanePredicted'
% for 'RansacFit', the stair plane i... |
answ = randi(3, 100000, 1);
prize = randi(3, 100000, 1);
sum(answ == prize)/100000 #nie zmieniamy wyboru
answ = randi(3, 100000, 1);
prize = randi(3, 100000, 1);
sum(answ != prize)/100000 |
function [ dy ] = ffun1( t,y )
v1 = 1;
v2 = 2;
dy = zeros(2,1);
dy(1) = v2/(sqrt(1+((y(2)-v1*t)/y(1)).^2));
dy(2) = v2/(sqrt(1+((y(2)-v1*t)/y(1)).^2)) * ((y(2)-v1*t)/y(1)).^2;
end
|
function SaveDataPts( cX, cFileName)
%
% function SaveDataPts( cX, cFileName)
%
% cX : N by D matrix of N points in D ddimensions
% cFileName
%
lHeader = [size(cX,1) size(cX,2)];
fid = fopen(cFileName,'wb');
fwrite(fid,lHeader,'integer*8');
fwrite(fid,cX','real*8');
fclose(fid);
return; |
%read binary data given cmm_type t and cmm_dim s from fid
%for lists read nr entries and skip ns entries
function data = cmm_read_data(fid, t, s, nr, ns)
if nargin <4
nr=-1;
ns=0;
elseif nargin<5
ns = nr;
nr = 1;
end
c = from_cmm_type(t);
if isempty(s) % scalar
if str... |
% function: make_compressed_paired_plot
% ###################################
% removes nodes that aren't in large flows
% David Choi
% 10/01/2017
function [small_flow, small_cluster, flow_Z, original_K, new_param] = make_compressed_paired_plots(Z, ...
flow_rec, A_rec, param, common_ordering)
if ~isempty(common... |
function P_in = ncav2Pin(n_c, eta, CAL, f_mech)
% convert n_cav wanted to input power Pin, taken into account the various
% efficiencies
% n_c: intracavity photon number wanted
% eta: efficiency between P_in and the reflector, i.e., eta = eta_coupler * eta_switch ...
% CAL: from piezo_scan_fit
% f_mech: mech freq... |
%randomly generate industry indeces
%NFIRM = 3000;
%NOBS = 100; means time
%NIND = 30;
%industry matrix likes:
% ind1 ind2 ind3 ind1 ind2 ind3 ind1 ind2 ind3
%firm1 1 0 0 1 0 0 1 0 0
%firm2 1 0 0 ... |
function [ ] = eddiesTable( fileName )
eddies = load(fileName);
names = fieldnames(eddies);
eddies = eddies.(names{1});
eddies = filterBU(eddies);
areas = zeros(1, length(eddies));
amps = zeros(1, length(eddies));
majTomin = zeros(1, length(eddies));
extremas = zeros(1, length(eddies));
for i = 1:length(eddies)
a... |
% With this script, we seek to push further the analysis of trajectories at
% the time of the detection of deterministic regularities. In particular,
% we show that the extent to which subjects transiently increase their
% beliefs in the probabilistic hypothesis correlates with the extent to
% which the ideal observer ... |
function ReportML
recordMAT = 'C:\Matlab\MyMATLABRecord\recordML.mat';
if exist(recordMAT)
load(recordMAT);
writetable(recordML,'Report.xlsx');
disp('数据写入：Report.xlsx');
else
disp(['找不到数据文件：',recordMAT]);
end
end |
function [ax,h]=plott(varargin)
% [ax,h]=plott(X) % X is a sensor structure
% or
% [ax,h]=plott(X,r) % X is a sensor structure
% or
% [ax,h]=plott(X,fsx) % X is a vector or matrix of sensor data
% or
% [ax,h]=plott(X,fsx,r) % X is a vector or matrix of sensor data
% or
% [ax,h]=... |
%@(#) utgles.m 1.3 98/09/10 10:24:37
%
%function utgles(compfile,resfil,resfil2,compwork)
function utgles(compfile,resfil,resfil2,compwork)
%
if nargin<2,
resfil='utgles.results';
disp('results will be printed on utgles.results and utgles2.results');
end
if nargin<3,
resfil2='utgles2.results';
if nargin>... |
%{
Análisis de Señales y Sistemas - TP Laboratorio Nº 3
Función FSERIED - Grupo 6
Argumentos:
- x: Señal de entrada
- n: Variable independiente
- N: Cantidad de coeficientes a calcular
- k: Cantidad de coeficientes a calcular
Retorna:
- ak: Coeficientes de Fourier
Repositorio disponible en: http... |
%This programs does a brute fit to look for initial guesses of A and B.
% If hf constants are unknown for upper or lower or both levels, make an
% array of Au values to test
close all
addpath(genpath('C:\Users\Felix\Desktop\Thesis full program 16 jan 2017\Base programs'))
addpath('C:\Users\Felix\OneDrive\Thesis fu... |
close all
set1 = [.098 .091 .086 .078 .071 .065 .061 .058 .054 .05 .047];
set2 = [.094 .087 .079 .072 .067 .064 .061 .057 .054 .051 .048];
set3 = [.096 .09 .083 .076 .071 .067 .061 .056 .054 .051 .049];
set4 = [.22 .2 .187 .175 .167 .158 .15 .146 .139 .133 .129];
set5 = [.145 .125 .114 .103 .093 .084 .079 .072 .... |
function result = splain(xi,yi)
% sprawdzenie danych wejciowych (dlugosci wektorow)
if length(xi) ~= length(yi)
error('Wektor xi oraz yi nie sa tej samej długoci');
end
% wyznaczanie dlugosc wektora
n = length(xi);
di=yi;
% wektor h - odleglosc przesuniec miedzy poszczegolnymi punktami
h = zeros(n... |
function [evals, settings, results] = catEvalSet(folders, funcSet, maxFE)
% Finds unique settings and prepares its data for further processing.
% [evals, settings] = catEvalSet(folders, funcSet) returns cell array
% 'data' of size functions x dimensions x unique settings and appropriate
% 'settings'.
%
% Input:
% f... |
function [tp, fp, fn, n] = calculate_accuracies(plotdata, labels)
blink_length = 10;
lookahead = 1;
tp = 0;
detections = plotdata.pts > plotdata.ucl | plotdata.pts < plotdata.lcl;
blinks = [0; diff(labels)];
n = sum(blinks==1);
detections = [0; diff(detections)];
blinks = [0; diff(labels)];
sequences = [fi... |
function Simulation_mapper()
%Simulation_mapper: Simulationsmethode der <a href= "matlab:help('mapper')">mapper</a> Funktion
%Dabei werden 1000000 Bits mit der Methode <a href=
%"matlab:help('generateBits')">generateBits</a> generiert
%Anschließend werden die Bits in Symbole mithilfe der Konstellationspunkte
%umgewande... |
function imgOut = add_tint_img(imgIn, RED, GREEN, BLUE)
% tints an image by adding RED, GREEN, AND BLUE directly to the pixel
% values of an image. imgIn can be an RGB or GRAYSCALE
% written by Efron Licht in 2016. You can use, copy, or edit this code for
% any reason whatsoever. Go nuts.
%% INPUT CHECKING
assert(nar... |
classdef MicroClusterList < handle % A `handle` is a reference to an object.
% List of micro-clusters
properties
MC_list;
end
properties (Constant)
INF = 1e15;
end
methods
function obj = MicroClusterList()
obj.MC_list = cell(1,0);
... |
function text = indentText(text,n)
% indentText Indent text
% Copyright 2012-2015 The MathWorks, Inc.
if nargin < 2
n = 1;
end
spaces = repmat(' ',1,n*4);
for i=1:numel(text)
text{i} = [spaces text{i}];
end
|
% findIdentifiable Find all k-identifiable nodes from a test matrix.
% V = findIdentifiable(T, k) returns all k-identifiable nodes of test
% matrix T.
%
% V = findIdentifiable(T, k, ID) uses identifiability matrix ID instead
% of computing it from scratch. Test matrix T is ignored.
%
% [V, ID] = fin... |
function checkRes = isCombineDataStructHaveField( combineDataStruct,baseField,checkField )
%判断combine结构体是否存在字段
% combineDataStruct 联合结构体
% baseField 基本字段，‘rawData’
% checkField 要检查的字段
checkRes = isfield(combineDataStruct,baseField);
if ~checkField
return;
end
st = getfield(combineDataStruc... |
function mRes = idAuxMergeCorTableWithArg2D(vArg1, vArg2, mCor)
sizeArg1 = size(mCor, 1);
sizeArg2 = size(mCor, 2);
lenArg1 = length(vArg1);
lenArg2 = length(vArg2);
if((sizeArg1 ~= lenArg1) || (sizeArg2 ~= lenArg2))
sprintf('Inconsistent lengths of the matrix and the argument vectors');
end
mRes = zeros(lenArg... |
% The following is your RPI's IP Address. Make sure it's correct
PI_IP = '172.24.1.1';
pi = tcpclient(PI_IP, 3000);
pi.write(uint8('Hello from MATLAB'))
pause(0.1)
char(pi.read()) |
clear all
close all
filez=dir('outputs*.mat');
numsubs=42;
for i =1:length(filez)
load(filez(i).name);
end
xvals=linspace(0,1,25);
for i=1:length(rois)
roival=rois(i); % the value of the roi being inspected
dist=openfig(sprintf('distmtx%d.fig', roival),'reuse');
distax=gca;
hell=openfig(sprin... |
function [BCSpectrum] = stripOffset(xScaleLocal, rawSpectrumLocal)
searchMin = 1400;
searchMax = 3700;
BCSpectrum = rawSpectrumLocal - min(rawSpectrumLocal(searchX(xScaleLocal, searchMin):searchX(xScaleLocal, searchMax)));
end |
function [xx,yy]=a_pdata(x,y,flag)
% assumes y is piece-wise const between x values
% length(y)=length(x)-1;
nx=length(x);
ny=size(y,2);
if ny ~= nx-1
error('a_pdata: length(y) must equal length(x)-1')
end
nn=2*nx-2;
if flag == 1
xx=zeros(1,nn);
xx(1)=x(1);
xx(2:2:nn-2)=x(2:nx-1);
xx(3:2:nn... |
function p = get_frontcon(m, C)
% To draw the efficient portfolio frontier
% m - mean (expected return)
% C - Covariances (risk)
% NumPorts - number of random portfolio
p = Portfolio;
p = setAssetMoments(p, m, C);
p = setDefaultConstraints(p);
end |
function MNBP=minNoBlockPayoff(clv,tol);
% EXACT_GAME computes the minimum no blocking payoff from game v using
% the Matlab's Optimization toolbox. Uses Dual-Simplex (Matlab R2015a).
% A core is non-empty iff mnbp<=v(N)
%
% Source: J. Zhao (2001), The relative interior of base polyhedron and the core, Economic Theo... |
%This code is for extension of the models, implementing hoicks in the boid
%world
%LEGEND FOR STUFF
% CHECK
% TEMPORARY
% DELETE
% OPTIMIZE
function [polarisation]=hoick_world(p)
close all;
%-------- CONTROL VARIABLES----------%
phase_mode = 1;
hoick_mode = 0;
hoick_type_mode=1;
make_movie = 0;
type=1;
hoick_advan... |
function[NRMSE,Xest]=NRMSE(hest,V,N,X)
Xest=conv(hest,V);
figure
plot(X,'blue');
hold on
plot(Xest,'red');
legend('Original','Estimated')
rmse=0;
Xest=Xest(1:N);
for k=1:N
rmse=(Xest(k)-X(k))^2+rmse;
end
rmse=sqrt(rmse/N);
NRMSE=rmse/(max(X)-min(X));
end |
function [sol] = ProxMappingCVX(domain, Bundle, data, BarX, f, g, x_lbt, c, LS)
x = zeros(domain.n,1);
delta = 1.0e-16 / domain.n;
domega_c = 1 + log(c + delta);
if Bundle.size == 0,
cvx_begin
cvx_quiet(true);
variable x(domain.n);
minimize -sum(entr(x+delta) -domega_c.*x);
... |
clear all ;close all ;
cd c:/shared/MRE ; ls
t1 = load_untouch_nii('RF_Test_MRE_2017_05_25_2_WIP_3D_T1_0.8mm_SENSE_2_1.nii');
vc = load_untouch_nii('rf_vc4_in_t1.nii.gz');
vc.img(1,1,:) = -11; vc.img(1,2,:) = 38;
mre = load_untouch_nii('rf_procpval_ImagBulk_in_t1.nii.gz');
mkdir imgs
cd imgs;
for i=70:100
... |
function [w, k0, z, deps, cs, rhos, alphas] = Initialization(freq, ...
c0, dz, depth, Layers, N, Coll, dep, c, rho, alpha)
w = 2 * pi * freq;
k0 = w / c0;
z = 0 : dz : depth(end);
deps = cell(Layers, N);
cs = cell(Layers, N);
rhos = cell(Layers, N);
alphas = cell(Layer... |
filename=strcat(DATA_FOLDER,'q_profile.mat');
load(filename,'q_initial_profile');
%filename=strcat(DATA_FOLDER,'flux_geometry.mat');
%load(filename,'X_PR_map');
%load(filename,'Z_PR_map');
%for p=1:NB_THETA
% q_PR_map(p,:)=q_initial_profile;
%end
%flc_inc=sqrt((X_PR_map.^2+Z_PR_map.^2)+((R0+X_PR_map).*q_PR_map).^2)... |
function K = SamplePrecisionMatrix(A,b,D)
% SAMPLEPRECISIONMATRIX(A,b,D) Sample precision matrix K ~ G-Wishart_A(b,D)
%
% Sample a precision matrix K which is distributed according to a
% G-Wishart distribution with graph structure A, degrees of freedom b,
% and scale matrix D
%
% Inputs:
% (1) A is the adjacency matr... |
%% Main with Lab difference
load flagDatabase.mat; load meanLabDatabase.mat;
% Load file and adjust if neccessary
im = im2double(imread('test_images\test8.jpg'));
im = adjustInputSmall(im);
% Calculate mean L, a, b values for each cell (16x8) in inputImage
meanLabCellColors = extractLabCellColorsSmall(im);
% Reprodu... |
function features = analyze_sound(soundfile,parameters,handles)
% ANALYZE_SOUND - Extract sound features from sound target.
%
% Usage: features = analyze_sound(soundfile,parameters,handles)
%
% Check if in server mode
if nargin < 3
handles = [];
end
% Get pm2 path
pm2path = find_pm2_pathes;
if isempty(pm2path)
... |
% This is material illustrating the methods from the book
% Financial Modelling - Theory, Implementation and Practice with Matlab
% source
% Wiley Finance Series
% ISBN 978-0-470-74489-5
%
% Date: 02.05.2012
%
% Authors: Joerg Kienitz
% Daniel Wetterau
%
% Please send comments, suggestions, bugs,... |
% split - splits a string (or a cell array of string) into a cell array
%
% Syntax
% B = split(A, sep)
%
% Reference
% "Estimation of low-rank tensors via convex optimization"
% Ryota Tomioka, Kohei Hayashi, and Hisashi Kashima
% arXiv:1010.0789
% http://arxiv.org/abs/1010.0789
%
% "Statistical Performance of Convex T... |
function subgroup_list = getSubGroup(s,group_list)
subgroup_list = [];
for i = 1:length(group_list)
if (group_list(i,1) == s)
subgroup_list = cat(1,subgroup_list,group_list(i,:));
end
end
|
function ppl=Scaled_Rank(y,n,ppl,index) % fitness assignment based on rank
if index==0, % not index==1 since it is 1 over something....
[y_sort, perm]=sort(y,'descend');
else,
[y_sort, perm]=sort(y);
end
raw_rank=[1:n];
scaled_rank=1./((raw_rank).^(1/2)); % it is one over somet... |
function [X,S] = calculator2(D)
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
global K_S S_0 mu_max Y_XS
X = Y_XS ./ 3 .* (S_0 - D .* K_S ./ (mu_max - D));
S = D .* K_S ./ (mu_max - D);
end |
% this function return the transformer's (input argument) loss
function FValue=SP_tlosscost1(InValue)
global KWTransformersTypes ShortCircuitLosses
Tag = find(KWTransformersTypes == InValue);
FValue = ShortCircuitLosses(1,Tag);
|
function proj = cuboid_proj(cg, cubs, varargin)
%function proj = cuboid_proj(cg, cubs, varargin)
%
% Compute a set of 2d line-integral projection views of one or more cuboids.
% Works for both parallel-beam and cone-beam geometry.
%
% in
% cg ct_geom()
% ells [ne,9] cuboid parameters:
% [x_center y_center z_center... |
function d=humanInterp(drad,thetas)
bothlegs=[7 8 9 16 10 11 12 17];
d=fitradbas(drad(bothlegs,:),drad,10,thetas);
d(bothlegs)=thetas;
d(1:3)=0;
|
clc; clear all; close all;
global t_prev q_prev
%m1=6; m2=6; m3=6; l1=4; l2=4; l3=4; a=4; g=9.81;
x1=pi;
x2=pi/2;
x3=pi/2;
q_prev = [x1 x2 x3]';
xdot1=0;
xdot2=0;
xdot3=0;
x0=[x1,xdot1,x2,xdot2,x3,xdot3]';
tstart=0; tfinal=10; t_prev = 0;
[TT,XX]=ode45('dynderiv',[tstart tfinal],x0);
figure(1... |
%CONTENTS MATGEOM Geometric Computing Toolbox.
% Version 1.0 26-07-2017.
%
% MatGeom Provides low-level functions for geometric computing. It is
% possible to create, display, compute intersections... of various
% geometrical primitives, in 2D and 3D.
%
% The library is organized into several modules:
%... |
function [closing, opening, blinks] = extract_eye_movements(s, d1, thr_opening, thr_closing)
%EXTRACT_EYE_MOVEMENTS Summary of this function goes here
% Detailed explanation goes here
closing = zeros(size(s));
opening = zeros(size(s));
p_closing = (d1 <= thr_closing);
p_opening = (d1 >= thr_opening);
% Find event... |
%Main_code % Latest_Heuristic_NE
%% 3 Individuals, Performance and corresponding Index Individual for each Gen, Connection & Input Connections are added
% Present Code: Gen and Speciation is different.
% Speciation could be same, Gen might be different
% Speciation depends on Connection matrix and input Array(In)... |
function [x_UKF, Ps] = UKF_RTS_smooth(x_UKF, Ps, Q, dt, weights)
for k = length(x_UKF)-1:-1:1
sigmaPoints = MerweScaledSigmaPoints(x_UKF(k,:),Ps(k).M,weights);
sigmas_F = UKF_f(sigmaPoints,dt);
[xb,Pb] = UnscentedTransform(sigmas_F,weights,Q);
Pxb = UKF_cross_variance(xb,x_UKF(k,:),weights,sigmas_F,sig... |
function plot_mse(features_struct, labels, settings)
% Load custom Google colors
custom_colors = load_google_colors();
% Convert MSE values within struct to matrix of singles
mse_ptsd = single([features_struct(logical(labels)).mse])';
mse_ctrl = single([features_struct(logical(~labels)).mse])';
% Count number of sub... |
function [signalVideo] = readData(path)
data = load(path);
signal = data.dane_wynikowe.EEG_signal;
%podzielic dane dla kazdego etapu
events = data.dane_wynikowe.Events{:,[1 4]}; %nazwy eventow z czasem ich rozpoczecia
time = data.dane_wynikowe.EEG_time;
eventSignal = {};
... |
function dJ=fuzzy_ga_temperature(x)
pro_mem_eff;
N=50;
fis=readfis('fuzzy_con_ga_3');
para_in1=x(1:14);
para_in2=x(15:28);
para_out=x(29:42);
fis_ga=set_fuzzy(para_in1,para_in2,para_out);
con_e_max=[0.0697*ones(1,25) .0886*ones(1,25)];% .1003*ones(1,25) .1052*ones(1,25) .1003*ones(1,25) .0886*ones(1,25) .0697*ones... |
function Q = wskaJak2(params)
R = 2;
k = 0.1;
ke = 5;
mr = 5;
r = 0.5;
mw = 0.5;
L = 0.1;
%moment bezwladnosci ramienia
Jr = 1/3*mr*r*r;
%moment bezwładności bez wody
J1 = Jr;
%moment bezwładności z wodą
J2 = Jr + mw * r*r;
P = params(1);
I = params(2);
... |
%WB_Patlak_process.m
%
% PROCESS
%
% TACT curves
handles1=guidata(handle1); % Populate from handle, so that new ROI is added
handles2=guidata(handle2); % Populate from handle, so that new ROI is added
[activity1, NPixels1, stdev1]=generateTACT(handles1, handles1.image.ROI);
... |
function rip_fitcdf1
%% Clean
close all
clear all
clc
Par(1) = .1; % decision level
Par(2) = .1; % standard deviation of drift (units/sec)
Par(3) = 1; % drift rate (units/sec)
Par(4) = 0.100; % indecision time
p.dt= 0.001; %step size for simulations (seconds)
nSteps = 600; %number of time steps
[Y... |
function [Y, SigmaArr] = Im2Patch( imgEst,imgNoisy, par )
b = par.patsize;
NumPatches = (size(imgEst,1)-b+1)*(size(imgEst,2)-b+1);
Y = zeros(b*b, NumPatches, par.Chas, 'single');
NY = zeros(b*b, NumPatches, par.Chas, 'single');
k = 0;
for i = 1:b
for j = 1:b
k = k+1;
Epatch = imgEst(i:... |
%*********************************************************************
% Aposteriori error estimation
%
% This code is based on:
% Bahriawati, C., & Carstensen, C. (2005).
% Three MATLAB implementations of the lowest-order Raviart-Thomas
% MFEM with a posteriori error control.
% Computational Methods in Applied Math... |
#############################################################################################################
# Part of the Bayesian Spectrum Analysis (BSA) package in Octave, by Emma
# Granqvist & Richard J. Morris, June 2011
# This code is only proof-of-principle, it is released freely and without any warranty
# For ... |
%%
clear all
% add analysis pathway
project_dir = '';
log_dir = fullfile(project_dir, ['raw_data/logfiles']);
subj_ID = {'01', '02', '03', '04', '09', '10', '11', '12', '13', '14',...
'15', '16', '17', '18', '19', '20', '21', '22', '23', '24',...
'25', '26', '27', '28'};
valid_runs = rep... |
clc
clear
load \\10.106.67.26\matlab-data\EUMETSAT\T10\T10_20120226_P1;
%Overlaying latitude and longitude over image case 2
figure('Color','white')
Z=flipud(T10_P1(:,:,1));
R = georasterref('RasterSize', size(Z), ...
'Latlim', [10.0065163418805 39.9949317428787], 'Lonlim', [30.0055330666120 59.9944665088319])... |
%% Practice_Part1_4
% exponentially damped sinusoidal signals
A = 60;
w0 = 20*pi;
phi = 0;
a = 6;
t = 0:.001:1;
expsin = A*sin(w0*t + phi).*exp(-a*t);
plot(t, expsin) |
% A table that is like a chess
%0 for white cells and 1 for black ones
n=7; % n*n table (up to you)
table=zeros(n); %first we fill it with zeros
% We scan every cell of the array and if the sum of
% i (number of row) and j (number of column) of a cell
% is... |
function results = FitTCMData(dataFilePath, filmName, filmThickness, varargin)
% Syntax: results = FitTCMData(<inputs>)
%
% Description: This function is adapted from the sequence of files used to
% fit data generated by the TCM to extract thermal properties
% of the sample mater... |
function Reducer(key, intermValIter, outKV)
count = 0;
% The reducer function counts the occurances of all the keywords
% from intermKVStore object
while hasnext(intermValIter)
data = getnext(intermValIter);
count = count + data;
end
% add the frequency for each of the keywords
add(outKV, key, count)
end |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Author: Hamza Bourbouh <hamza.bourbouh@nasa.gov>
% Notices:
%
% Copyright @ 2020 United States Government as represented by the
% Administrator of the National Aeronautics and Space Administration. All
% Rights Reserved.
%
% Disclaimers
%
% No Warranty:... |
function r = fhnfunode(t,y,p)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% fhnfunode
%
% The FitzHugh-Nagumo equations in scalar form
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
r = y;
r(1) = p(3)*(y(1) - y(1).^3/3 + y(2));
r(2) = -(y(1) - p(1) + p(2)*y(2))/p(3... |
%Find the transformation that registers model with the
% scene, S <== T M.
% Output:
% param =[dx dy theta]
function [tmodel,template_point,source_point,model_correspondence,transdist] = ICP(templates,source,thr,display_it)
if nargin < 3
thr = 20;
display_it = 0;
end;
if nargin < 4
display... |
function test_suite = test_triangleArea
%TEST_TRIANGLEAREA Test case for the file triangleArea
%
% Test case for the file triangleArea
% Example
% test_triangleArea
%
% See also
%
%
% ------
% Author: David Legland
% e-mail: david.legland@grignon.inra.fr
% Created: 2011-08-23, using Matlab 7.... |
function [ interpolating_GPS,GridID_Set_for_interpolating_GPS ] = Get_GridID_Set_for_interpolating_GPS( gps_matrix)
%% Get_GridID_Set_at_GPS_Singal_Blockage function description:
% Imput:
% Output:
% GridID_LineID_near_GPS: Grid_ID, line num, line ID 12345....n
%
% Example
% [ GridID... |
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