text stringlengths 8 6.12M |
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clear;
data=normrnd(600,196.6292,1,10000);
out=find((data>1200)|(data<=0));
data(out)=[];
minfee=inf;
for n=20
for m=315
f=mean(test2fun(data,n,m));
if f<minfee
minfee=f;
n0=n;m0=m;
end
end
end
n0,m0,minfee
|
% KCCTest01
N = 3000; k = 4; p=k/(N-1); Graph = mexGraphGeneratePoissonRandomGraph(N,p);
% load('D:\Network.mat');
tic
KCC = mexGraphLinkDirectionality(Graph);
toc
CC=mexGraphClusteringCoefficient(Graph);
for i = 1 : numel(KCC.KCCData)
if (KCC.KCCData{i}(2).LinksAt~=CC.NodeNeighboursLinks(i))
error... |
function var_list = plotBgrid(phi, u, v, it)
% write a netcdf files of the data
nc = netcdf('Bgridu.nc', 'c');
nc('x') = size(u,2);
nc('y') = size(u,1);
nc{'u'} = ncdouble('x', 'y');
nc{'u'}(:) = u';
close(nc);
nc = netcdf('Bgridv.nc', 'c');
nc('x') = size(v,2);
nc('y') = size(v,1);
nc{'v'} = ncdouble('x', 'y');
nc{... |
function [Wt] = bci_gradient_descent(FeaturesBundle,targetBundle,statsBundle,W,alpha)
%BCI_GRADIENT_DESCENT Summary of this function goes here
% Detailed explanation goes here
[Nt] = size(FeaturesBundle,1)
NFr = size(FeaturesBundle{1},2);
g = 15;
Wt = zeros(NFr,1);
Wt_1 = W;
NIter = 1000;
for j=1:NIter
range = ra... |
function [delta_nu, eta] = LLSE_tracking(data_FFT,pilot_pattern,nr_car, length_cpre,L)
M=length(pilot_pattern);
delta_phase=angle(data_FFT);
weight=[0:1:L-1]*(nr_car+length_cpre)+length_cpre;
weightM=ones(M,1)*weight;
num=sum(sum(weightM.*delta_phase));
den=2*pi*M*sum(weight.^2);
delta_nu=num/den;
k=pilot_pattern-1... |
function [SynthData] = gen_synth_data_cube(nViews)
dim = 0.25;
cube_points = [ -dim -dim 1
dim -dim 1
dim dim 1
-dim dim 1
-dim -dim 2
dim -dim 2
dim dim 2
-dim dim 2
]';
lineIndices = [
1 2
2 3
3 4
4 1
5 6
6 7
7 8
8 5
1 5
6 2
7 3
4 8
... |
close all;
clear all;
allEventTypes = [-1 11 12 41 46 42 43 0 21 22 23 31 32 44 45 ]';
allTypeNames = {'Other',...
'Mem IO',...
'GPU mem IO', ...
'ADIOS init', ...
'ADIOS finalize', ...
'ADIOS open', ...
'ADIOS alloc', ...
'Compute',...
'Network IO', ...
'Network wait', ...
'Ne... |
function y = unit_amp(x,amp,point)
if size(x,1)<size(x,2)
x = x';
end
N = size(x,1);
[env_up,env_down] = envelope(x,point,'peak');
y = zeros(N,1);
for i = 1:N
if(env_up(i)>10e-3 )
if(x(i)>0)
y(i) = x(i)*(amp/env_up(i));
else
... |
classdef AbstractAnimator < handle
properties(Access = private)
tmr timer
end
properties (GetAccess = protected, SetAccess = immutable)
fig
axs
end
properties (Access = public)
currentTime double
speed double
end
properties (Access = pub... |
function [unc] = est_scovint(x,x0)
[nix,sql,sqr] = scovint(x,0.95,x0);
if isempty(sql)
[nix,sql,sqr] = scovint(x,0.95);
end
unc = max(abs(sqr - x0),abs(sql - x0));
end |
clc; clear all; close all;
A = [4 -2 1; 2 3 -1; 8 -5 2]
% i - satir, j - sutun
n=size(A,1);
U=A;
L=eye(n);
for i=1:n-1
for j=i+1:n
L(j,i) = U(j,i)/U(i,i);
U(j,:) = U(j,:)-(U(i,:)*U(j,i))/U(i,i);
end
end
L
U
|
function Hd = BPFilter
%TEST Returns a discrete-time filter object.
% MATLAB Code
% Generated by MATLAB(R) 9.1 and the Signal Processing Toolbox 7.3.
% Generated on: 23-Dec-2016 09:53:22
% FIR Window Bandpass filter designed using the FIR1 function.
% All frequency values are in Hz.
Fs = 62500000; % Sampling Freque... |
1
[x, Fs] = audioread('xkolcu00.wav');
Fs; % vzorkovacia frekvencia
N = length(x); % dlzka signalu
t = length(x)/Fs; % dlzka v sekundach
2
% zobrazenie modulu spektra signalu
X = abs(fft(x)); % fourir. trans. povodneho signalu
om_f = ((0 : (N / 2) - 1) / N * Fs); % získame frekvencnu osu
X = X(1 : N / 2); ... |
function [respuesta] = definidaPositiva(matriz)
%DEFINIDAPOSITIVA Summary of this function goes here
% Detailed explanation goes here
[n,m] = size(matriz);
respuesta = false;
if n == m
otraMatriz = matriz;
respuesta = true;
for i=1:n
[n2,m2] = size(otraMatriz);
%esDeterminante = determinan... |
%Copyright (c) 2020, Denielle Ricciardi
%All rights reserved.
function [full_cov,e_trans,cov_1,cov_2] = gen_cov(sigma_1,sigma_2,w_1,w_2,nug_1,nug_2,N,...
strain_inc,el_pl_trans,show_figs,analyze)
u = strain_inc;
[~,e_trans] = min(abs(u - el_pl_trans)); %find strain index corresonding to elastic-pl... |
close all
clc
clear
run('C:\Users\ashle\Desktop\Masters\MATLAB\ElastomerDrops\Alignmentcheck.m')
fprintf('done running Alignment Check \n Now running Angle DIC Complex \n')
% frame=30;
jet2 = jet(2049);
jet2(1025,:)=[1 1 1];
%put stephane's stretch code here
for frame=1:10
load('E:\Elastomer Drop Redo\MorphDOWNWARD\nc... |
%Some data
%x = 1:5 ;
y = rand(size(x)) ;
% The plot
bar(y) ;
% add the values
for i=1:length(x),
TH(i) = text(x(i),y(i),num2str(y(i))) ;
end
% Use the handles TH to modify some properties
set(TH,'Horizontalalignment','center',...
'verticalalignment','bottom') ; |
pa = 1/5;
ps = 1/3;
P = [1 - pa pa 0 0 0;
(1-pa)*ps (1-pa)*(1-ps)+pa*ps pa*(1-ps) 0 0;
0 (1-pa)*ps (1-pa)*(1-ps)+pa*ps pa*(1-ps) 0;
0 0 (1-pa)*ps (1-pa)*(1-ps)+pa*ps pa*(1-ps);
0 0 0 (1-pa)*ps (1-pa)*(1-ps)+pa*ps];
P0 = [1 0 0 0 0];
res=P0*P^2;
res(3)
% ooor
P(1,:)*P(:,3) |
function [opt,combinedpop,frontpop]=infeasort(opt,combinedpop)
%1. Split the combined pop into feasible and infeasible subsets
[feaspop,infeaspop]=split(combinedpop);
%2. sort the feasible subsets using non-dominated sorting;
if ~isempty(feaspop)
[opt,feaspop,frontpopfeas]=ndsort(opt,feaspop);
end
%3. sort the inf... |
function run_ex5()
% load image
img = imread('cow.jpg');
% img = imread('zebra_b.jpg');
% for faster debugging you might want to decrease the size of your image
% (use imresize)
% (especially for the mean-shift part!)
% img = imresize(img, 0.3); % scale for zebra
% figure, imshow(img), title('original image')
% smoo... |
% Measure approximation error and compression ratio for several images.
%
% NOTE Images must be have .png ending and reside in the same folder.
global U;
file_list = dir();
k = 1;
Errors = []; % mean squared errors for each image would be stored here
% UPDATE:
% If available load the dictionary.mat f... |
function WigCreateAmbiJS(dec,az,elev,norm,HV,pname)
% WigCreateAmbiJS(dec,az,elev,norm,HV,pname)
%
% Create a JS effect that implements an Ambisonic Decoder. Dec is the 2D
% decoding matrix. Az and Elev are the speaker positions (used for
% labelling the ins outs and 'norm' is the normalisation scheme/channel
% orde... |
% @Input :
% image - RGB color space input image
% metric - only support 'gabor' or 'Law'
%
% @Output :
% feature - texture filter response
% -------------------------------------------------------------------- %
% extract feature vector from Gabor texture
function gImg = ex... |
function [y,xGTNO,xNOTR,errFGTNO,errXGTNO,no]=synthesizeY(xGT,TGT,TFov,S,A,B,M,kGrid,rkGrid,NS,sigma,no)
%SYNTHESIZEY Synthesizes motion corrupted data
% [Y,XGTNO,XGTNOTR,ERRFGTNO,ERRXGTNO,NO]=SYNTHESIZEY(XGT,TGT,TFOV,S,A,B,M,KGRID,RKGRID,NS,{SIGMA},{NO})
% synthesizes motion corrupted data according to the forw... |
function [d] = zadatak10(A)
[mn, ind_min] = min(A);
[mx, ind_max] = max(A);
min_pt_x = [mn(1), A(ind_min(1), 2)]; %tacka koja ima najmanju x koordinatu
min_pt_y = [A(ind_min(2),1), mn(2)]; %tacka koja ima najmanju y koordinatu
max_pt_x = [mx(1), A(ind_max(1), 2)]; %tacka koja ima najvecu x koordinatu
max_pt_y = [A(in... |
classdef TestSizeof < matlab.unittest.TestCase
% Properties List
properties
dataTypeIn_Int
dataTypeIn_Uint
dataTypeIn_Bits
dataTypeIn_Float
dataTypeIn_Bad
dataTypeIn_Unrec
dataTypeOut_Int
dataTypeOut_Uint
dataTypeOut_Bits
dataT... |
% band=4:7;
Cpli=[];Cx=[];O=[];SNR=[];Cxp=[];
for i=1:length(FT)
fprintf(num2str(i))
out=tensor_connectivity3(FT{1,i}{4},FT{1,i}{2},FT{1,i}{3},band);
% out=tensor_connectivity2(FT{1,i}{4},FT{1,i}{2},band);
% out=mean(out,3);
out=mean(out(:,:,band),3);
O(i)=max(max(out));
Cx(:,:,i)=topoconn_... |
function h3p3
function [r,it] = newton(fcn,fpr,p0,TOL,Mmax)
x(1) = p0;
%x(2) = p1;
for n=1:Mmax-1
num = fcn(x(n));
den = fpr(x(n));
x(n+1) = x(n) - (num/den);
abserr = abs(x(n+1)-x(n));
relerr = abserr/(abs(x(n+1))+eps);
... |
function [Q,eigvalue]=DivideAndConquer(A)
%%
% Using the idea of divide and conquer to solve all eigenvalues of A.
% Return: All eigenvalues of A; eigvector matrix Q
% precondition: A is m by m (m>=2), symmetric, tridiagonal.
% 1. If A is not irreducable, reduce A
% 2. Divide: find eigenvalues/e... |
function [J, grad] = linearRegCostFunction(X, y, theta, lambda)
%LINEARREGCOSTFUNCTION Compute cost and gradient for regularized linear
%regression with multiple variables
% [J, grad] = LINEARREGCOSTFUNCTION(X, y, theta, lambda) computes the
% cost of using theta as the parameter for linear regression to fit the ... |
classdef Visual1d < AbstractVisual
%VISUAL1D 此处显示有关此类的摘要
% 此处显示详细说明
properties
end
methods
function obj = Visual1d( mesh )
obj = obj@AbstractVisual( mesh );
end
drawResult( obj, field );
drawOutputResult( obj, outputObj, step, fieldId ... |
function [h, r1_1, r1_2, r2_1, r2_2] = plotFigCompare2(h, rmse_fct, feature_optsim1, feature_optsim2, feature_optsim3, ...
param_docsim1, param_docsim2, param_docsim3, secondWindowOffset, thirdWindowOffset)
len1 = length(feature_optsim1.q);
len2 = length(feature_optsim2.q);
len3 = length(feature_optsim3.q);
indSe... |
function y=F_Retrieval(Interferogram,matrix,lambda,lambda_in,sigm)
PinRet=abs(Interferogram*(pinv(matrix)));
gaus=exp(-0.5*((lambda-lambda_in)/sigm).^2)./(sqrt(2*pi)*sigm);
c=conv(gaus,PinRet,'same');
y=c;
end
|
% Tao Du
% taodu@stanford.edu
% Feb 12, 2015
% Test renderObj. Incidentally, it also tests model2Image.
% Clear.
clear all; clc;
% Read V and F from .obj files.
[V, F] = readObj('data/cube.obj');
% The camera coordinate is:
% x: left to right
% y: down to up
% z: outside to inside.
% Note that this coordinate is lef... |
function y = normcols(x)
%NORMCOLS Normalize matrix columns.
% Y = NORMCOLS(X) normalizes the columns of X to unit length, returning
% the result as Y.
%
% See also ADDTOCOLS.
% Ron Rubinstein
% Computer Science Department
% Technion, Haifa 32000 Israel
% ronrubin@cs
%
% April 2009
y = x*spdiag(1./sqrt(sum(... |
function data_file_read(option)
% function data_file_read
% requires a text file with one header line containing names(no spaces) for each column of data
% allows user to choose a data file with header(s) and assigns each column of data to
% variables named by the header names. The user can then save the information... |
% Ex 2
% a)
N = 20;
P = 10000;
alfa = 0.1;
lambda = 1800;
C = 10;
f = 1000000;
b = 10^-6;
PL_lst = zeros(1,N);
APD_lst = zeros(1,N);
MPD_lst = zeros(1,N);
TT_lst = zeros(1,N);
for i=1:N
[PL_lst(i),APD_lst(i),MPD_lst(i),TT_lst(i)] = simulator2(lambda,C,f,P,b);
end
% Calculate Packet Loss
PL = mean(PL_lst);
PL_con... |
clear;
close all;
%%
validationSet = [1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67];
Rvalues = [];
Gvalues = [];
Bvalues = [];
RGvalues = [];
GBvalues = [];
BRvalues = [];
for N = 1:length(validationSet)
% Current sign id
nSign = validationSet(N);
%nSign ... |
% % contencates new imageJ code to the matlab code
%
% % N.B. im.m cuts the upper intensities by imadjust!
%
%clear sequence newseq f nam novseq anoseq
% finding factor for normalizing: analyzing the first time step only
tcon = datacon(:,1); % time
intd = find(tcon == 1); % finds indices
nc = max(intd);
con = data... |
function fixtures = SharedFixtures
%SHAREDFIXTURES Shared fixtures for Silver Lab Analysis Pipeline tests.
%
% Sets up paths so source files are found.
% Also defines a shared temporary folder.
import matlab.unittest.fixtures.PathFixture;
tests_folder = fileparts(mfilename('fullpath'));
so... |
%% About
% This script run ICA on one dataset
% TODO: CHECK IF BAD CHANS CORRESPOND TO FILE IN CURRENT LOOP
%% 0.0 Prepare enviroment
eeglab; % open eeglab to lighten paths to the eeglab ways
close all; % close everything
clc; % clear command line
clear all; % clear enviroment
%% 0.1 Directory and file names
my_di... |
function a = str2numcell(b)
a= cell2mat(cellfun(@str2num,b,'UniformOutput',0));
|
%function theta= gradientDescent(X, y, theta)
function[theta, J_history] = gradientDescentMy(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
% theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by
% taking num_iters gradient steps with learning rate alpha
... |
%LU dengan pivoting
function x = lu_pivoting(A,b)
n = length(A);
AA = A; bb = b;
m = [];
for i=1:n-1
t = find(abs(A(i:end,i))==max(abs(A(i:end,i))));
p = t(1)+i-1;
A1 = A; b1 = b;
A1(i,:) = A(p,:);A1(p,:)=A(i,:);
b1(i)=b(p);b1(p)=b(i);
m = [m;p];
for j=i+1:n
lambda = A1(j,i)/A1(i,i);... |
%%spherical surface
% r=2;
% surface=@(x,y) sqrt((r^2)-(x^2)-(y^2));
% surface_normal=@(x,y,z) [2*x,2*y,2*z];
% incident_direction=[0,0,-1];
%
% x1=-1; x2=1; y1=-1; y2=1;
% h=0.2;
% x=x1:h:x2; y=y1:h:y2;
% [X,Y]=meshgrid(x,y);
% n=length(x);m=length(y);
% Zexact=zeros(n,m);
% for i=1:n
% for j=1:m
% ... |
function integ=calka(c_in, i, dt, tt, lambda,Pe, NN)
sum=0;
t=i*dt;
for j=1:i-1
it=j;
tau=j*dt;
switch(NN)
%tlok
case 1
f=c_in(it)*(dirac((t-tau)-tt)==inf)*exp(-lambda*(t-tau));
%expotencjalny
case 2
f=c_in(it)*1... |
function [] = coefficients_to_package_file_vhdl( coefficients,B,FileName)
h = coefficients/max(abs(coefficients));
%el maximo del valor absoluto era positivo o negativo?
abs_max_positive=0;%false
for i=1:1:length(h)
if h(i)==1
abs_max_positive=1;%true
end
end
if abs_max_positive==1 %caso positivo(o a... |
function [im_SR] = TRNRSR(im_l,XH,XL,upscale,patch_size,overlap,K,lambda)
im_l = im_l./255;
[imrow imcol ~] = size(XH);
Img_SUM = zeros(imrow,imcol);
overlap_FLAG = zeros(imrow,imcol);
U = ceil((imrow-overlap)/(patch_size-overlap));
V = ceil((imcol-overlap)/(patch_size-overlap));
% hallucinate the HR patch ... |
%
% ISEL - Instituto Superior de Engenharia de Lisboa.
%
% LEIC - Licenciatura em Engenharia Informatica e de Computadores.
%
% Com - Comunicações.
%
%
% time2.m
% Função de teste.
%
function time2()
% Limpar a consola.
clc;
% Mensagem de início
fprintf(' Função time2 \n');
% Dimensão do vetor.
vetor_size = 15000... |
function setup_fastRPCA
% SETUP_FASTRPCA Adds the fastRPCA toolbox to the path
baseDirectory = fileparts(mfilename('fullpath'));
addpath(genpath(baseDirectory));
fprintf('Loaded fastRPCA');
|
function linSys = ss(sys)
%SS Convert a linear POLYSYS model to an SS object.
%
% LINSYS = SS(SYS) converts the POLYSYS model SYS to an SS object LINSYS.
% The input SYS must be linear.
%
% See also LINEARIZE, ISLINEAR, SS.
% 7.22.2007: TJW - Initial coding
if islinear(sys)
[nOutputs,nInputs] = size(sys)... |
classdef PF_Fc_DwS < PF_Fc
%PF_Fc_DwS Summary of this class goes here
% Detailed explanation goes here
methods
function obj = PF_Fc_DwS(f)
obj = obj@PF_Fc(f);
end
end
end
|
function [A,B,C,D]= bcdLrMrNr_init(X,L_vec,M_vec,N_vec)
%BCDLMN_INIT Random initialization for the BCD-(Lr,Mr,Nr)
[I,J,K]=size(X);
R=length(L_vec);
A=cell(1,R);
B=cell(1,R);
C=cell(1,R);
D=cell(1,R);
% generate random matrices
if isreal(X)
for r=1:R
A{r}=randn(I,L_vec(r));
B{r}=randn(J,M_vec(r));
C{r}... |
function varargout = fPAFOIL7(varargin)
func = varargin{1};
switch func
case 'readline'
varargout{1} = readline(varargin{2},varargin{3},varargin{4});
case 'minRows'
varargout{1} = 1;
case 'maxRows'
varargout{1} = 1;
otherwise
error('invalid input optio... |
function [a,c] = test_struct( s )
%UNTITLED4 此处显示有关此函数的摘要
% 此处显示详细说明
s = struct('a',1,'b',2);
a = s.a/s.b;
c = s.b;
end
|
clear all
% close all
clc
addpath data
addpath toolbox
set(groot,'defaultLineLineWidth',1.5);
%% load and scale data
load('australian_label.mat');
load('australian_sample.mat');
h = full(h);
% rescale the data
for j=1:size(h,2)
h(:,j) = rescale(h(:,j), -1, 1);
end
%% parameters
[m, n] = size(h);
para.m = m;
para... |
function [returnx]=capacity_STAT_IFN_parallelfun(i,stim_spanMesh,par,name,N,freq,init_T,y0,obsv,sigma,type,stim_ind,t_discont,path)
disp(num2str(i))
%Fisher Information estimation
tic;
par(stim_ind)=stim_spanMesh(i,:);
[Tptemp M_term V_term TptempN M_termN V_termN stemp vartemp varinfo conc_traj_... |
% A simple code to test convergence of codes
%
% problem parameters
m = 2048; Tf = 0.30; n = 32; nref = 25600*4;
% set reference solution
if ~exist('uref', 'var')
[xref, uref, tfref] = RK4_ammc(m, nref, Tf);
end
% check final time
if abs(Tf-tfref)>1e-14, disp('Issue with final time'); end
% select... |
% 3.6 from 3 .csv file of trimming data, analysis and compare each other
table_trim_no_redundant = readtable('trim_result_no_redundant.csv');
table_trim_redundant_prop = readtable('trim_result_redundant_prop_simple.csv');
table_trim_redundant_full = readtable('trim_result_redundant_full_jde_fix_03_08_17_49.csv');
arr... |
% This function takes in a HED remap file and stores the old HED to new HED
% tag mappings in a Map object.
%
% Usage:
% >> remapMap = remap2map(remapFile);
%
% Input:
% 'remap' The name or the path of the HED remap file containing
% the mappings of old HED tags to new HED tags. This
% ... |
function Plot_sorted(Data,idx,C)
[m,n]=size(C);
figure;
Colors=hsv(m);
for i=1:m %plot data points
plot3(Data(idx == i,1),Data(idx == i,2),Data(idx == i,3),'s','MarkerEdgeColor','k','MarkerFaceColor',Colors(i,:));
hold on;
plot3(C(i,1),C(i,2),C(i,3),'s','Color',Colors(i,:));
... |
function ret = fibmat(n)
A = [1,1; 1,0];
B = A^n;
ret = B(1,2);
endfunction
%!assert(fibmat(0) == 0);
%!assert(fibmat(1) == 1);
%!assert(fibmat(2) == 1);
%!assert(fibmat(3) == 2);
%!assert(fibmat(4) == 3);
%!assert(fibmat(5) == 5);
%!assert(fibmat(6) == 8);
%!assert(fibmat(25) == 75025); |
function [good_labels,all_labels,all_thdist,all_gmask] = CC_extract_good_labels(meth)
% function [good_labels,all_labels,all_thdist,all_gmask] = CC_extract_good_labels(meth)
%
% This function loads the mean correlation and mean RMS amplitude values
% computed by CC_transcort2_auto and applies a 2-dimensional linear
% t... |
function [ label, proba,negative, depth ] = classifyProba( tree, x )
% CLASSIFYPROBA classify input to a label using a decision tree.
% label = classify(tree, x)
% use of an accumulator function to multiply the probability of each node at each iteration
[ label, proba, negative, depth ] = classifyProbaAcc(tree... |
% update dataFile name
if XStimParams.testnum < 10
FN.data = [num2str(XStimParams.bird_number)...
XStimParams.recording_site...
'00'...
num2str(XStimParams.testnum)];
elseif XStimParams.testnum < 100
FN.data = [num2str(XStimParams.bird_number)...
XStimPa... |
function y = pad_divisible(v, M)
% pad zeros at the end of v to make its length divisible by M
s=size(v);
padlen = mod(M- mod(s(2),M),M);
if padlen > 0
y = zeros(s(1),s(2)+padlen);
y(:,1:s(2))=v;
else
y=v;
end
|
clear;
normal = [1,2];
interictal = [3,4];
ictal = 5;
snr_ssim_index = 1; %% Naming = metric_optimizedForMetric
ssim_ssim_index = 2;
snr_snr_index = 3;
ssim_snr_index = 4;
snr_smooth_index = 5;
ssim_smooth_index = 6;
avgSNRNormal = zeros(1,100);
avgSNRInterictal = zeros(1,100);
avgSNRIctal = zeros(1,100);
avgSSIMNo... |
clc;
% Масса
params.m = 3000.0;
% Матрица тензора инерции
params.J = [5000.0, 0, 0;
0 , 10000, 0;
0 , 0 , 10000];
% Матрциа обратная матрице тензора инерции
params.invJ = inv(params.J);
% Функция силы от времени
% Текущее значение силы интерполируется по таблице значений... |
function [ stopSampling, p, whichCaudal ] = determineMisMatch( judging, p, trial)
%% determineMisMatch: determines if stimuli are misMatching
stopSampling = 0;
whichCaudal = 0;
% compares the difference in the maximumly selective layer of the network
% to the current and previous stim to a funning familiarity differenc... |
%Determining and removing drawbacks of exponential and running mean
%Eugenii Israelit,Dmitry Shadrin,Skoltech, 2016
clc
clear all
close all
n=200;
t=32;
X(n)=zeros();
A(n)=zeros();
Z(n)=zeros();
W=normrnd(0,0.08,1,n);
N=normrnd(0,0.05,1,n);
w=2*pi()/t;
A(1)=1;
%generating an oscilations
for i=2:n
A(i)=A(i-1)+W... |
close all;
clear all;
clc;
Time = 0.0;
dt = 0.01;
figure
while Time < 15
theta = sin(Time);
ang = theta*pi/180;
c = cos(ang);
s = sin(ang);
a = [1;0;0];
R = [c -s 0; s c 0; 0 0 1];
b = R*a;
axb = cross(a,b);
k = axb/norm(axb);
Q1 = [0.1;0.1;0.1]*10;
Q2 = [-0.1;-0... |
% Generates data (and plots) for mantle density structure and figure S1.
addpath('figures/')
% Sulfide EOS.
EOS_params_MgS.molar_mass = molar_mass(["Mg" 1; "S" 1]);
EOS_params_MgS.T_0 = 298;
EOS_params_MgS.rho_0 = 2661; % 4 MgS/cell molar_mass / 140.68 A^3/cell
EOS_params_MgS.K = 78.9e9;
EOS_params_MgS.K_d = 3.71;
EO... |
function obs = gen_signals_140618_kwv2(parms, timing_parms, dofigs,doSub)
% function obs = gen_signals_140328(parms, timing_parms, dofigs,doSub)
% set up parms for pulse sequence and known constants
% ... time units are in seconds
doRK = 1; % do Runge-kutta method
dt = 0.01;
r1a = 1/1.67;
% get parms from t... |
clc
clearvars
dataSet = load('pima-indians-diabetes.csv');
format long;
n = size(dataSet, 2) - 1;
%Labels
training_label = dataSet(1:615,9);
test_label = dataSet(615:768,9);
%Feature Scaling
temp_data = dataSet;
min_temp_data = min(temp_data,[],1);
max_temp_data = max(temp_data,[],1);
diff = max_temp_da... |
function f_mem = force_membrane_2D(S,P)
% S is the vector containing the location of the vertices
% instead of for loops vector and matrices are used , the notation is related
% with the Supplementary Material S_x = s_i^x, S_x_p = s_{i+1}^x
% pomega_ps = d\Omega / ds
S_x = S(:,1);
S_x_p = [S(2:end,1); S(1,1)];... |
function [ ineqc, eqc] = constraints(w)
%UNTITLED3 Summary of this function goes here
% Detailed explanation goes here
% currently, no constraints for weights
ineqc = [];
eqc = [];
end
|
function [B,A,C,M,M1,N,N1,NumFrames,NumCols,NumRows,FrameInt,AcquisitionRatio,ActMap] = daRead3(daFile,XCoord,YCoord)
%this function plots the time series for a single pixel of the redshirt
%camera, both filtered and unfiltered. Pass it the file name and
%coordinates. (1,1) is top left. It returns three matrices:M, N,... |
function [ fullfills_kraft, summation ] = test_kraft( cl )
summation = sum(2 .^ -cl);
fullfills_kraft = summation <= 1;
end
|
function segments = HelperSegmentSpeech(audio,Fs)
%HELPERGETSPEECHSEGMENTS Extract speech segments from audio signal.
% Copyright (c) Theodoros Giannakopoulos All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following condition... |
% the true value of hand-eye calibration
% [Rc Tc; 0 1]
Tc = [0.0325;0.27;0.0438];
Rc = [1 0 0; 0 -1 0; 0 0 -1];
% you can also use the following Rc for a test
%Rc = [0.9942 0.0537 -0.0931;
% 0.0398 -0.9886 -0.1450;
% -0.0998 0.1404 -0.9850];
invRc = inv(Rc);
% the true value of TCP c... |
set(0, 'defaultAxesFontSize', 12);
set(0, 'defaultTextFontSize', 20);
set(0, 'DefaultAxesLineWidth', 1.0, 'DefaultLineLineWidth', 1.5);
a_arr = 1:-1:-1;
% a_arr = 0;
for a0 = a_arr
%% parameter definition
% jerk constraint
s = 1.5;
% initial condition
kmph2mps = 1 / 3.6;
v0 = 10.0 * kmph2mps; % [m/s]
% a0 = -2.0; % ... |
function fig1_regulation(Fig1ax,linewidth,fontsize,seed,cutoff)
%This script plots the timecourses of figure 1
%Good regulation cell
rng(seed);
axes(Fig1ax(5))
par.N = 1;
par.gamma_tot = 50;
par.P = 1;
par.c = 100;
par.max_t = 2500;
par.beta = 2;
par.delta = 1;
par.feedback = 1;
par.epsi = 1e-5;
max_growth = 0.5*par... |
% This script generates the roots of Bessel functions.
% The (m+1)-th line for the Bessel function of order m
% The l-th colomn for the l-th positive zero
% Up to the order of m_max
m_max = 10;
% First l_max positive zeros
l_max = 15;
% The accuracy of the roots
accuracy = 1e-5;
% The maximum root should be less th... |
%% TEST GAUSSIAN 1D FILTER
clc
clear all
close all
t=linspace(0,20,2^11); %time vector in seconds
w=1; %in Hz (is the frequency of the created signal)
signal=cos(2*pi*w*t) + cos(40*2*pi*w*t); % Signal in seconds
% dt=t(2)-t(1) ; %Is the inverse of the samplig rate of th... |
%*********************************************************************
% Theory of the Hilbert Spectrum: Code Examples for Reproducible Research
%*********************************************************************
%
% Description: This Demo file shows illustrates Hilbert Spectral Analysis
% on a synthiti... |
function organizedata = organizedata(colposition)
folder = 'C:\repos\ann-arrhythmia\diag\standard\abnormal\';
files = dir([folder '*.csv']);
for i=1:length(files)
load([folder files(i).name]);
end
all_vectors = [X107m;X108m;X109m;X112m;X113m;X114m;
X115m;X116m;X119m;X2... |
function proj=loadprojindexed(projfile,chindices,numchannels,numfiles,indexes)
fid=fopen(projfile,'r');
projfp=fread(fid,[numchannels,numfiles],'int32');
projfp=projfp(chindices,:);
for ch=1:size(chindices,2)
for fnum=1:numfiles
fseek(fid,projfp(ch,fnum),'bof');
[proj{ch,fnum},count]=fread(fid,[3,max(indexes{ch,fn... |
function [xJ] = NewtonRaphsonStep(xO, fdotValue,fddotValue)
xJ = xO -(fdotValue/fddotValue);
end |
function [J, sigma1]=simplewnrsigma(I,psf,sigma,regparam)
if nargin<4
regparam = 0.001;
end
[ny,nx]=size(I);
H = psf2otf(psf, [ny,nx]);
RegInvfilter = (conj(H)) ./ ((abs(H).^2 ) + regparam);
J= real(ifft2(RegInvfilter .* fft2(I)));
if nargout>1
Invfilt = real(ifft2(RegInvfilter));
cx=floor(nx/2)+1;
cy=... |
function [chosen] = roulette_selection(candidate, mu)
% linear (roulette wheel) selection
fitsum = sum(candidate.fit); % sum up all of the fitnesses from every candidate
candidate.p = candidate.fit./fitsum; % compute the 'probability' of picking each candidate
candidate.ps ... |
format long
A = [];
b = [];
Aeq = [];
beq = [];
nVars = 7;
%Decision vars
% nrot L b, C, theta0, thetaMin, Airfoil
%int : nrot b airfoil
lb = [4; 1; 2; 0.05; deg2rad(0); deg2rad(0); 0];
ub = [8; 1.5; 6 ; 0.3; deg2rad(14); deg2rad(14); 1];
x0 = [6; 1; 0.15; deg2rad(10); deg2rad(10); 0];
options = optimoptions('g... |
function [xymatrix_ring, s_cloud, colvect_cloud] = al_staticConfettiCloud()
% AL_STATICCONFETTICLOUD This function loads the data for a static
% confetti cloud that "covers" the confetti cannon
load('x_cloud.mat', 'x_cloud')
load('y_cloud.mat', 'y_cloud')
load('s_cloud.mat', 's_cloud')
load('col... |
%**********************************************************************************************
%**************************** CHAPTER 3: RIGID-BODY MOTIONS *********************************
%**********************************************************************************************
function V = se3ToVec(se3ma... |
function [DOS, e]=kvspec(E,d,g,T,r,B)
% builds the d-wave DOS for given delta and gamma and Temperature
% Input: E = range of energies
% d = delta
% g = gamma (optional)
% T = temperature (optional)
% r = number of data points on plot. (optional)
% B = how much weight on the second ha... |
function [out,inRange,outRange] = imScale(varargin)
%IMSCALE Rescale image pixel values as desired
% OUT = IMSCALE(IMG) Rescales image IMG with default parameters.
% OUT = IMSCALE(IMG, INRANGE) Specifies input range INRANGE.
% Default is [min(img(:)),max(img(:)]
% OUT = IMSCALE(IMG, INRANGE, OUTRANGE) Specifies ... |
function comp_hist(X, bin)
figure;
histfit(X, bin, 'Normal');
% Normalize histogram
Y = get(gca, 'YTick');
set(gca, 'YTick', Y, 'YTickLabel', Y/numel(X));
end |
function [options,Gamma] = checkoptions_spectra (options,ndim,T)
if nargin==3
if iscell(T)
T = cell2mat(T); T = T(:);
end
end
% MT and common
if ~isfield(options,'p'), options.p = 0; end
if ~isfield(options,'removezeros'), options.removezeros = 0; end
if ~isfield(options,'completelags'), options.compl... |
function [] = squeeze_axes(ax, fx, fy)
%% function [] = squeeze_axes(ax , fx, fy)
% this function squeezes a given set of axes ax
% by factor fx in the horizintal and fy in the vertical
ox = 1;
oy = 1;
ex = 1;
ey = 1;
for i = 1:prod(size(ax))
tmp = get(ax(i), 'Position');
ox = min(ox, tmp(1... |
function z = applyWT(est,w,N);
% z = applyWT(est,w,N);
% transpose operator of applyW.
% est is a data vector of length N,
% w is an array of weights to convolve
K = size(w,2);
n = size(w,1);
% apply w < in backward (i.e. transpose) direction
gtg = floor(n/2) - 1 + (1:N);
for k = 1:K
intz = conv(est, w(n:-1:1,k)... |
function [f] = dobjfunc(X, str_labelFileName, varargin)
% NAME
%
% function [f] = dobjfunc(X, str_labelFileName, varargin)
%
% ARGUMENTS
%
% X in (vector) a vector of weights
% str_labelFileName in (string) FreeSurfer label file
% describing refernce
% (optimal) trajectory
% verbosity in (integer) an op... |
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