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clc;clear;
xi = 1994:2003;
xi = xi';
yi = [67.052, 68.008, 69.803, 72.024, 73.400, 72.063, 74.669, 74.487, 74.065, 76.777];
x = sym('x');
p = newtonpoly(x, xi, yi);
% 绘制9次多项式图像
a = 1994:1:2003;
p = subs(p, x, a);
plot(a, p);
% 计算2010年石油产量
res = newtonpoly(2010, xi, yi);
sprintf('%.3f%', res)
|
function varargout = rect2patch(C,R)
% [rect] = rect2patch(C,R)
% C = centre
% R = 'radius'
if numel(C)<2
error
end
if numel(R)==1
R = [R R];
end
if nargout==1
%% rectangle matrix
rect = [C(1) C(2) R(1) R(2)];
varargout(1) = {rect};
else
%% patch matrix
X = [C(1) C(1)] + ... |
function P = getProbabilitiesForPoints(X, directions, thresholds, leafProbabilities)
% For each point in X, calculate the probability of belonging to the
% implicitly given label described by leafProbabilities.
%
% Based on the learned splits the input data point traverses the tree until
% reaching a leaf node. The lea... |
function call_copy()
% clicking on axes within a figure, opens a new figure with only the
% clicked axes in it.
% Usage:
% ff = findobj('type','axes');
% set(ff,'ButtonDownFcn','call_copy');
% (c) Tonio
copyobj(gca,figure);
set(gca,'Position',[0.13 0.11 0.775 0.815]);
set(gca,'ButtonDownFcn','');
h_child=get(gca,'Chi... |
function [x, y, z] = shifting(xq, yq, zq, size_pic, fr)
widht_pix = 2999/36;
x = xq + ceil(size_pic(2)/2);
y = yq + ceil(size_pic(1)/2);
z = zq + widht_pix*fr;
|
function [X, Y, Z] = mregularize(xx, yy, idepths)
x = idepths;
x_bar = x;
x_old = x;
sigma = 25;
tau = 0.01;
theta = 0.5;
yLeft = zeros(length(yy), length(xx));
yUp = yLeft;
niters = 50;
for it = 1:niters
for r = 2:length(yy)
for c = 2:length(xx)
de_xi_xj = x_bar(r,c) - x_bar(r-1, c);
... |
function [ fitness ] = karma(soul, input_data)
sz = size(input_data,1);
total_dist = 0;
for(i = 1:sz-1)
total_dist = total_dist + input_data(soul(i),soul(i+1));
end
total_dist = total_dist + input_data(soul(sz),soul(1));
fitness = total_dist;
end
|
clear all;
close all;
% Gaussian mean and covariance
d = 10; % number of dimensions
mu1 = rand(1,d);
sigma = rand(d,d); sigma = sigma*sigma';
% generate 100 samples from above distribution
num = 500 ;
sampleData = mvnrnd(mu1, sigma, num);
mu2 = [(mu1(:,1:5)+5),mu1(:,6:end)];
sampleData =... |
function [ costMat, minCutNodes] = PRMAnalysisSparse( A,startNodes,goalNodes,total_config)
%UNTITLED4 Summary of this function goes here
% Detailed explanation goes here
% close all;
% A = [];
% As = [];
% Ae = [];
% convert into sparse matrix and then graph
% As = spconvert(Ds);
% A = full(As);
% G = graph(As);
G ... |
function g_cb(x)
% G_CB(x)
%
% Copy data from vector x to clipboard as a string
%
% Gunnar Voet, APL - UW - Seattle
% voet@apl.washington.edu
%
% Created: 01/28/2014
% Create strings
str = ['[', sprintf('%1.3f ',x),']'];
% s = sprintf('%s\n%s');
% Copy output string to clibpoard
clipboard('copy',str) |
function b = log_mvtpdf(y,mu,Sigma,nu)
%for multivariate t with arguments y,mu,Sigma,nu
%evaluate the t density
k=length(y);
c = k*.5*log(pi)+gammaln(.5*nu)-.5*nu*log(nu)-gammaln(.5*(nu+k));
b = -c-.5*log(det(Sigma))-.5*(nu+k)*log(nu+(y-mu)'*inv(Sigma)*(y-mu));
|
function [mImage] = updateMultBoLongImageSoftMem(mAdj, mImage, mMembership, fDistanceFunc)
%
% Updates mImage according to KKT condition derived multiplicative update
% rule.
%
% Bo Long formulation for Soft cluster membership
%
%
% @author: Jeffrey Chan, 2014
%
mMemSq = mMembership' * mMembership;
% mDenom =... |
clear('pman');
pman.name = 'Laplace';
pman.size = 100;
pman.pars{1}=[0];
pman.pars{2}=[1.2];
% Comment out check at the end:
irp = ept.potman_intrepres(pman);
% Now run check
s = eptools_potmanager_isvalid(irp.potids,irp.numpot,irp.parvec, ...
irp.parshrd);
clear('pman2');
pman2{1} = pman;
pman2{2}.name = 'G... |
function X = huberMean(Y, numberOfStds, iters)
% Perform a Huber mean using the Huber loss function.
% x = huber_mean(Y,rho,iters)
%
% Input:
% Y : MxN matrix over which to average (columnwise)
% numberOfStds : Number of robust standard deviation in which the function acts like regular mean (default: 1)
% iters :... |
% specnoise.m plot the spectrum of a noise signal
% The randn function returns a sample of random numbers from a normal distribution with mean 0 and variance 1.
% The general theory of random variables states that if x is a random variable whose mean is μx and variance is σ2x,
% then the random variable, y, defined ... |
%%%%%%%%%% OPEN FEATURES %%%%%%%%%%%%
if 0
elseif strcmp(typeObj,'goose')
load('goose_kernel.mat');
param.imFileList = param_dummy.imFileList;
param.featFileList = param_dummy.featFileList ;
param.lW_px = param_dummy.lW_px;
clear param_dummy
elseif strcmp(typeObj,'statue')
load('stat... |
options.face_vertex_color = [];
vertex1 = perform_normal_displacement(vertex_u',faces_u', .03);
clf;
subplot(1,2,1);
plot_mesh(vertex_u,faces_u,options); shading interp; axis tight;
subplot(1,2,2);
plot_mesh(vertex1,faces_u,options); shading interp; axis tight; |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright 2010 - 2015 Moon Express, Inc.
% All Rights Reserved.
%
% PROPRIETARY DATA NOTICE:
% The data herein include Proprietary Data and are restricted under the
% Data Rights provisions of Lunar CATALYST Space Act Agreement
% No. SAAM ID#... |
% This script produces figure 3 of the article
clear all
dt = .0002;
T = 0.5;
x = load('signals_figure3.mat');
signal = x.signal3;
rand('state',1)
% generate base pattern
[spikes, cov] = genUncorrelated(1500, T, dt, 20, [1 0 0]);
nt = 32;
ne = 2;
% find optimal weights under noise
tic
[weights, err, t, corrupt, e... |
function output = solveFuncNew(X,x,g,amp,sigma,h)
eta = X(1:end-1); c = X(end);N = (length(eta)-1)/2;
[cc cV] = getC(eta,h,g);
firstEqn = hat(c^2 - cV.^2);
firstEqn(N+1)=0;
finalEqn = abs(amp - .5*(max(eta) - min(eta)));
output = [firstEqn; finalEqn];
|
clear all;
close all;
clc
I = imread('E:\Semester VII\Final Year Project\iCub_jpg\00001323.jpg');
subplot(3,3,1)
imshow(I);
cform = makecform('srgb2lab');
J = applycform(I,cform);
%subplot(3,3,2)
%imshow(J);
K=J(:,:,2);
%subplot(3,3,3)
%imshow(K);
L=graythresh(J(:,:,2));
BW1=im2bw(J(:,:,2),L);
subplot(3,... |
function sampled_af = minor_allele_frq(af, smooth_interval)
% convert to minor and major allele frequency
[nmarkers, c] = size(af);
if( c ~= 3 )
disp('error in allele frequency');
return;
end
major(1:nmarkers) = 0;
minor(1:nmarkers) = 0;
for i = 1:nmarkers
major(i) = max(af(i,1:2));
minor(i) = min(a... |
function [u_coord, sutm]=unanimity_games(clv)
% UNANIMITY_GAMES computes the unanimity coordinates or Harsanyi dividends.
% For n>14 this function needs some time to complete.
%
% Usage: [u_coord utmat]=unanimity_games(clv)
%
% Define variables:
% output:
% u_coord -- Unanimity coordinates or Harsanyi dividends.
% ... |
% MPLMPLTC.M
% For plot of magnetization profile
% Called by MPLPM
mplmplt = get(mpluimplt,'Value') ;
if mplmplt == 1 ;
set(mpluimplt, 'BackGroundColor', 'white') ;
if mplslplt == 1 ;
set(mpluislplt, 'BackGroundColor', 'white') ;
end ;
elseif mplmplt == 0 ;
set(mpluimplt,'BackGroundColor',[0.501961 0.501961 ... |
function computeConeResponsesToDriftingGratings(runParams, ...
stimColor, stimTemporalParams, stimSpatialParams, ...
theConeMosaic, theOptics, ...
recomputeNullResponses, ...
instancesNum, ...
opticsPostFix, PolansSubjectID, ...
saveDir, varargin)
% Parse input
p = inputParser;
p.a... |
function fitSimpleHLN(handles, side)
% Perform fit of the data on the 'side' (positive or negative) of the field
set(handles.tlStatus, 'String', 'Fitting Truncated HLN...');
set(handles.tlStatus, 'String', 'Estimating Parameters...');
[alpha, bPhi] = estimateAlphaBphi(handles, side);
set(handles.tlStatus, 'String', '... |
%% 2016.12.02
% Numerical Analysis Programming
% Chapter 10 & 11. LU Factorization & Matrix Inverse and Condition
clc; clear;
%% Problem 1.
A1=[7 2 -3; 2 5 -3; 1 -1 -6]; b1=[-12;-20;-26];
[m,n]=size(A1); L1=eye(n);
f21=A1(2,1)/A1(1,1); f31=A1(3,1)/A1(1,1);
Ui1=[A1(1,:); A1(2,:)-A1(1,:)*f21; A1(3,:)-A1(1,:)*f31];
f32... |
function rbm_lab()
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Exprimental code for Restricted Boltzmann Machines %
% The code is developed for Neural Computing tutorial %
% MSc on Data Science, CITY UNIVERSITY LONDON %
% Authors: Son Tran, Artur Garcez %
%%%%%%%%%%%... |
%This MATLAB code is associated with the following manuscript: Barrick,
%S.K., S.R. Clippinger, L. Greenberg, M.J. Greenberg. 2019. Computational
%tool to study perturbations in muscle regulation and its application to
%heart disease.
%This script tests whether a sufficient number of bootstraps have been
%perfo... |
function plotHopDiffs(timesteps, ...
totalAbsDiffs, absDiffsPerFeature, absDiffsPerObject, ...
absDiffsPerImage, signChangesPerFeature)
figure();
subplot(1, 4, 1);
plotHopConvergence(timesteps, totalAbsDiffs, signChangesPerFeature);
xlabel('Time');
ylabel('Total absolute difference');
subplot(1, 4, 2);
images... |
clear
clear java
clear classes;
vid = hex2dec('3742');
pid = hex2dec('0007');
disp (vid);
disp (pid);
javaaddpath ../lib/SimplePacketComsJavaFat-0.6.4.jar;
import edu.wpi.SimplePacketComs.*;
import edu.wpi.SimplePacketComs.device.*;
import edu.wpi.SimplePacketComs.phy.*;
import java.util.*;
import org.hid4java.*;
ve... |
%% a RL based model for counting
function output = touch(epochs, seed, doPlotting, showSteps, showProgress)
if nargin == 0
epochs = 100; seed = randi(99);
doPlotting = true; showSteps = false; showProgress = false;
end
rng(seed);
%% initialization
% modeing parameters
p = setupParameters(epochs);
% preallocat... |
function [distOut,timeOut,xOut,yOut] = feasibleTrajectory(start,goal)
diffs = goal - start;
N = size(diffs,2);
slopes = [-1E-6,-2;
-1,-2;
-2,-2;
-2,-1;
-2,0;
-2,1;
-2,2;
-1,2;
0,2;
1,2;
2,2;
2,1;
2,0;
2,-1;
2,-2;
1,-2;
0,-2];
speed = vecnorm(slopes,2,2);
speed(1) = 2;
thetas = ze... |
function [y,Ak] = interpSinc(tk,yk,t,T0,B)
Ak = B * yk; % Coefficients
y=zeros(size(t)); % Reconstruction
for i=1:length(Ak)
y = y + Ak(i)*(sinc((t-tk(i))/T0)/T0);
end |
function [ y ] = rotNaVecF( agol1, n, org )%codegen
agol=deg2rad(agol1);
nNorm=norm(n);
pom=n./nNorm;
n=pom;
M=[(n(1)^2)*(1-cos(agol))+cos(agol) n(1)*n(2)*(1-cos(agol))+n(3)*sin(agol) n(1)*n(3)*(1-cos(agol))-n(2)*sin(agol);
n(1)*n(2)*(1-cos(agol))-n(3)*sin(agol) (n(2)^2)*(1-cos(agol))+cos... |
% Blair, Gupta, and Zhang 2008's ring attractor oscillatory interference model
% eric zilli - 20110908 - v1.0
%
% Here we carry out the extension to the model that the authors suggest
% so that it can handle 2D trajectories.
%
% See BlairEtAl2008.m for more.
%
% This code is released into the public domain. Not for us... |
clc; clear;
MTOW = 24266;
MTOWcg = 24.75;
plot(MTOWcg,MTOW,'x')
hold on
MTOWcgret = 24.71;
plot(MTOWcgret,MTOW,'s')
hold on
NoPayload = 21266;
NPcg = 24.6;
plot(NPcg,NoPayload,'o')
hold on
NoPayNoFuel = 16103;
NPNFcg = 24.02;
plot(NPNFcg,NoPayNoFuel,'d')
y = linspace(0,50000,100);
x1(1:100) = 24;
x2(1:1... |
function ress = gpuNUFFT_forw(a,bb)
% ress = gpuNUFFT_forw(a,bb)
% Performs forward gpuNUFFT
% from image to k-space
%
% supports multi-channel data
%
% a ... GpuNUFFT Operator
% bb ... image data
% W x H x D x (nChn) for 3d
% W x H x (nChn) for 2d
%
%check if imgDims are 2d or 3d
if (a.params.... |
% Javier Salazar 1001144647
% HW6 Neural Network (Multi-layer Perceptron)
clc
%--------input arguments---------------------------
trainName = 'USPS_train.txt'; % training and test files
testName = 'USPS_test.txt';
hiddenLayers = 1; % excludingnput and output layers
perceptronCount = 15; % hidden layers perceptrons per ... |
%
% Copyright (c) 2015, Yarpiz (www.yarpiz.com)
% All rights reserved. Please read the "license.txt" for license terms.
%
% Project Code: YPAP111
% Project Title: Inventory Control using PSO and Firefly Algorithm
% Publisher: Yarpiz (www.yarpiz.com)
%
% Developer: S. Mostapha Kalami Heris (Member of Yarpiz Tea... |
% this is used to generate random targets among the intervals in adaptive
% measurements
demo = 0; % CHANGE AS NEEDED
if demo == 1
nameTag = '_demo';
nrep = 1; % CHNAGE AS NEEDED
else
nameTag = '';
nrep = 8; % CHANGE AS NEEDED
end
NmaxTrials = 1000;
NminTrials = 20;
rightOrLeft = cell(1, nrep);
dirTmp... |
function [TS_f,f_vec,pings,r_tot]=TS_f_from_region(trans_obj,reg_obj,varargin)
p = inputParser;
addRequired(p,'trans_obj',@(x) isa(x,'transceiver_cl'));
addRequired(p,'reg_obj',@(x) isa(x,'region_cl')||isstruct(x));
addParameter(p,'envdata',env_data_cl,@(x) isa(x,'env_data_cl'));
addParameter(p,'cal',[],@(x) isempty(x... |
% limpando a memória
clear all; close all; clc
% Input values for debug
%%
path = strcat(pwd, '\Dataset_Placas');
im = iread(path,'grey','double');
ths = 0.2;
w = ones(2);
str = placa2str(im,ths,w,'display');
%%
path = strcat(pwd, '\Dataset_Placas');
im = iread(path,'grey','double');
ths = 0.2;
w = ones(2,3);
str... |
function dat2= energyinfo1(data2,winsize,wininc,datawin,dispstatus)
if nargin < 6
if nargin < 5
if nargin < 4
if nargin < 3
winsize = size(data2{1},1);
end
wininc = winsize;
end
datawin = ones(winsize,1);
end
dispstatus... |
function [hypo_list, score_list, bbox_list, scale_list, mask_heights_list] = ...
collect_hypo_across_scale(recog_result, ratio_list)
nb_scale = length(recog_result);
hypo_list = [];
score_list = [];
bbox_list = [];
scale_list = [];
mask_heights_list = [];
for scale_no=1:nb_scale
hypo_list... |
A=zeros(5);
for i=1:5
for j=1:5
A(i,j)=(i-1)*5+j;
end
end
sum(sum(A))
sum(diag(A))
sum(diag(A(1:end,end:-1:1)))
|
%无自衡对象辨识
clc;
clear all;
close all;
load wtankjy;
TA=clock;
Y1=Y;
[lp,m]=size(Y1);
if m>lp
lp=m;
end
sum=0;
for i=lp:-1:fix(lp*0.95)
sum=sum+(Y1(i)-Y1(i-1))/dt;
end
k=sum/(lp-fix(lp*0.95));
oh=Y1(lp)-k*dt*lp;
ota=-oh/k;
T=ota;
ta=fix(ota/dt);
ob=Y1(ta+1);
a=exp(-dt/T);
b=1-a;
... |
clear;
clc;
close all;
% a = [1:3]
% b = a'
% c = rand(2,3)
% d = eye(4)
% e = zeros(3,2)
% f = ones(2,3)
A = [1,2,3;4,5,6]
B = ['abcde','fffee']
C = {'123',123;'abc','edf'} %元胞数组
whos A B
D = num2str(A)
whos A D
E = str2num(D)
F = num2cell(A)
whos E F
G = {'123','','dfg'}
H = cell2mat(G) %元胞转字符串，空的位置会被忽略
I = {'abc'... |
function [timeStamps]=visual_stimulation(nFrames,stimul,repetitions)
close all
PsychDefaultSetup(2);
%choosing the screen n2 where stim should be presented in fullscreen mode
screenNumbers=Screen('Screens');
screenNumber=max(screenNumbers);
white = WhiteIndex(screenNumber);
waitframes=1;%by default we want to refresh e... |
% Returns the number of 0, + - 1+i0 elements of matrix A and the number of other elements.
% count_A(1) = the number of other elements of A.
% count_A(2) = the number of -1+i0 of A.
% count_A(3) = the number of 0+i0 of A.
% count_A(4) = the number of 1+i0 of A.
function [count_A] = COUNT(A)
count_A = zeros(4,1);
count... |
function X = ranNor(X0, ran)
% Normalize each dimension spearately with respect to the variance of distance.
%
% Input
% X0 - original image, h x w
% ran - range, 2 x 1
%
% Output
% X - new image, h x w
%
% History
% create - Feng Zhou (zhfe99@gmail.com), 02-13-2009
% modify - Feng Zhou ... |
%% Codes to create figure 3 in document
clear all
% Parameter values
nsim = 200;
p=2;
nVal = [50 100 200];
noise = {'Low', 'High'};
% create the vector for MSE values
eta_sim = zeros(200, length(nVal)*length(noise));
for nlev = 1:length(noise)
for nInd = 1:length(nVal)
cur = (nlev - 1)*(1+length(noise)... |
function [delta, loss] = GFC_L2_loss(active, gt, mode)
[r,c,cha,bz] = size(active);
if size(gt,1)~= r
gt = imresize(gt,[r,c]);
end
dt = active - gt;
loss = 0.5 * sum(dt(:).^2)/bz;
if strcmp(mode, 'train')
delta = single(dt/bz);
else
delta = 0;
end
end
|
% TESTFIN
%
% Function to demonstrate use of fingerprint code
%
% Usage: [newim, binim, mask, reliability] = testfin(im);
%
% Argument: im - Fingerprint image to be enhanced.
%
% Returns: newim - Ridge enhanced image.
% binim - Binary version of enhanced image.
% mask - Ridge-like regio... |
function expmu = nlpart(vx, rc_sigma)
% Computes the mu or nonlinear part of utility
[n,m,K] = size(vx);
mu = zeros(n, m);
for k = 1:K
mu = mu + vx(:,:,k) * rc_sigma(k);
end
expmu = exp(mu);
end |
function varargout = SPAMS_Calibration_GUI(varargin)
% UNTITLED MATLAB code for untitled.fig
% UNTITLED, by itself, creates a new UNTITLED or raises the existing
% singleton*.
%
% H = UNTITLED returns the handle to a new UNTITLED or the handle to
% the existing singleton*.
%
% UNTITLED('CALLBAC... |
% Plot time series of system near feasible coexistence state (if exists) with
%(a) phage adsorption rate \phi=10^-9.5 (b) \phi=10^-10.5 and (c) \phi=10^-11.5
close all;
clear all;
%% Initialize parameters
TT=100; % Length of time series (h)
tstep=0.1; % Time step (h)
ntime=int64(1+TT/tstep); % Total number ... |
function job_sigmas_grid(varargin)
ratname = varargin{1};
ntrials = str2double(varargin{2});
A_dim = str2double(varargin{3});
B_dim = str2double(varargin{4});
Amin = str2double(varargin{5});
Astep = str2double(varargin{6});
Amax = str2double(varargin{7});
Bmin = str2double(varargin{8});
Bstep = str2double(varargin{... |
function results = ClassicalGradient(A,theta0,tspan,u0,varargin)
%CLASSICALGRADIENT Summary of this function goes here
% Detailed explanation goes here
p = inputParser;
addRequired(p,'A')
addRequired(p,'tspan')
addRequired(p,'u0')
addOptional(p,'maxiter',100)
addOptional(p,'beta',0.0... |
%
%
% unit3_phase_learning_part2.m
% S8 PROJECT
%
% Created by Mathias de Cacqueray on 14/02/14.
% Copyright (c) 2014 Mathias de Cacqueray. All rights reserved.
clear all;
close all;
clc;
%UNIT 0 : CREATION OF TRAME
%BLOC 0 : Creation de Trame.
%[ trame,Fc,Fech ] = bloc0_sensor_gen( );
load('../../../data/data_... |
function T = lnfft(xk,l)
M = log2(l);
xk( 1, l)=0; %序列补0
Wn = exp(-1i * 2 * pi / l); %旋转因子
T = bitrevorder(xk); %二进制码倒序
for K1 = 1 :1 :M %蝶形的个数
K2 = 2 ^ (K1 - 1); %每一级内节点间的距离
for K3 = 0 : K2-1 %m每一级内的旋转因子个数
Wni = Wn^( K3 * 2^( M - K1 )... |
function SecondLevelFuncCAndBehaviour(nameF,conStart,conEnd,Hrat,outF,maxS,addN,addN2)
% Second-level analysis of Distances Regressors Basis set
% experiment
goodP =1;
based = '/home/smark/fMRI_ana';
spm_path = '/data/smark/spm';
data_path = '/data/smark/fmri_sub_preproc_dir/';
cleaned_data_path = '/data/smark/fmri_su... |
function B = weights(C)
R = response()
B = R^(-1) * C'
end
|
function [x, err] = qmr(A, b, max_iters)
m_dot = @(x, y) dot(conj(x), y);
n = length(b);
x = zeros(n, 1);
x_Q = x;
% xhat = zeros(n, 1);
r = b - A * x;
rhat = r;
p = r;
phat = rhat;
rho = zeros(max_iters, 1);
rho(1) = m_dot(rhat, r);
err = zeros(max_iters, 1);
% Ex... |
function z = calculate_vt(set_vt,set_dp)
%
% calculates the delivered tidal volume in milliliters to the rat in HUPC using the small
% animal ventilator made by Harvard instruments.
%
% synatax:
% desired_vt = calculated_vt(set_vt,driving_pressure)
% written by MP on 9/1/2016
plot_linear_fits = 0;
% driving pressure ... |
function [weights] = map_correlation(map, idx)
% compute correlation weight between map and idx
range_map = map(idx);
hits = sum(range_map >= 0.5) * 10;
misses = sum(range_map < -0.2) * 10;
weights = hits - misses;
end
|
% Kronecker tensor product.
%
% See also: kron
% Copyright 2008-2009 Levente Hunyadi
function K = kronecker(X,Y)
validateattributes(X, {'numeric'}, {'2d'});
validateattributes(Y, {'numeric'}, {'2d'});
if ~issparse(X) && ~issparse(Y) && (isreal(X) && isreal(Y) || ~isreal(X) && ~isreal(Y))
K = kron2(X,Y);... |
close all;
%% Current vs Time Plot ---------------------------------------------------
f = figure;
%subplot(3,1,1);
plot(t_vec(9761:25820), i(9761:25820),'LineWidth', 2, 'Color', 'b');
grid on
title('$Current\,vs\,\,Time$ ',...
'fontsize',18, 'fontweight','b', 'interpreter', 'latex')
xlabel('$Time\,[s]$','font... |
a25_Symplectic;
global THERING refOptic;
dk=0.01;
elementlist;
nmax=length(qlist)
for j=1:nmax;
refTwiss=gettwiss();
K=THERING{qlist(j)}.PolynomB(2);
THERING{qlist(j)}.PolynomB(2)=K+dk;
theTwiss=gettwiss();
rowx=(theTwiss.betax(qlist)-refTwiss.betax(qlist))/dk;
rowy=(theTwiss.betay(qlist)-refTw... |
function [flow_x, flow_y, matchScore] = computeSpecularPointMatches(xGrid, yGrid, f_depth, frot_depth, R, X_fl, Y_fl)
xRes = xGrid(1,2)-xGrid(1,1);
yRes = yGrid(2,1)-yGrid(1,1);
[f_grad_x, f_grad_y] = gradient(f_depth, xRes, yRes);
[fnew_grad_x, fnew_grad_y] = gradient(frot_depth, xRes, yRes);
%X_fl =... |
function [ evalU ] = spEval( space, mesh, geo, u_h, opt )
%SPEVAL computes the value or the derivatives, of a function given by its
%degrees of freedom, at the quadrature nodes defined in the mesh. We recall
%that in space_2d and space_3d the basis functions are defined as:
%
% 2d:
% u(x) = p1(x1)*p2(x2)
%
% 3d:
% ... |
function [ t ] = bhaskara(a, b, c )
t1 = ( -b + sqrt(b*b - 4*a*c) )/(2*a)
t2 = ( -b - sqrt(b*b - 4*a*c) )/(2*a)
t = max(t1,t2);
end
|
% Define a data structure to describe the state of the physical system
% for a given position and energy. This is done by describing the
% Riccati parametrized Green's function 'g', it's tilde conjugate 'gt',
% and their first derivatives 'dg' and 'dgt' for that configuration.
%
% This class is mainly intended for use... |
function f_export_PT(fname)
% - export otolith profile data to a comma-separated-values (CSV) text file
%
% USAGE: f_export_PT('fname')
%
% fname = cell array of file(s) to process
%
% SEE ALSO: f_cps2ppm_PT, f_plot_PT, f_export_cps
% -----Author:-----
% by David L. Jones, Oct-2011
%
% This file is part of the FATHOM ... |
% Example 9.2.
% Computes the weighted residual for the model described by p
% from Parameter Estimation and Inverse Problems, 3rd edition, 2018
% by R. Aster, B. Borchers, C. Thurber
%
% f=fun(p)
%
function f=fun(p)
% global variables, the x and y points and their sigmas
global x;
global y;
global sigma;
f=zeros(len... |
function [E,F_gt_val] = flow_error_map (F_gt,F_est)
F_gt_du = shiftdim(F_gt(:,:,1));
F_gt_dv = shiftdim(F_gt(:,:,2));
F_gt_val = shiftdim(F_gt(:,:,3));
F_est_du = shiftdim(F_est(:,:,1));
F_est_dv = shiftdim(F_est(:,:,2));
E_du = F_gt_du-F_est_du;
E_dv = F_gt_dv-F_est_dv;
E = sqrt(E_du.*E_du+E_dv.*E_dv);
E(F_gt_... |
% fevaled in aamod_pilab_importrsapredictors
%
% [rdms,ss] = facedist_rsapredictors(subname)
function [rdms,ss] = facedist_rsapredictors(subname,varargin)
oldpath = path;
% function to add psychtoolbox functions to the path - you may not need this,
% but on the CBU cluster Matlab figure rendering breaks when this is ... |
p=10;
M = 1; %number of simulations
N1 = 10; %dimension of g_s
N2 = 15;
Sum_X_power_p = zeros(N1+N2,N1+N2);
Mean_X_power_p = zeros(N1+N2,N1+N2);
for i=1:M
g1 = randn(N1,1);
g2 = randn(N2,1);
X = [zeros(N1,N1) g1*g2'; g2*g1' zeros(N2,N2)];
Sum_X_power_p = Sum_X_power_p + (X)^p;
end
Mean_X_power_p = Sum_X... |
function OUT = cross(A, B)
% DESCRIPTION:
% Returns the cross product of A and B along the first dimension of
% length three.
%
% PARAMETERS:
% A - first input matrix
% B - second input matrix
%
% AUTHOR:
% Daniel Jennings
% dsj@ll.mit.edu
%
% DATE CREATED:
% June 1, 2005
global pMATLA... |
function [coRefI, coHolBg, coRefBg] = AtomHologramOptimization(holI, refI, holBg, refBg, refZ, atomZ, params, FilePath)
gpuDevice(1);
wavelength = params.wavelength;
pixelsize = params.pixelsize;
NAs = params.NAs;
resize = 6;
k = 2 * pi / wavelength; %Wave Vector
resPath = [FilePath 'Result\'];
if(exist([resPath '... |
function [Y,S,res] = f_rosner(X,ave,s,p,crit)
% - Rosner's test for spike elimination (many outlier removal)
%
% USAGE: [Y,S,res] = f_rosner(X,ave,s,p,crit);
%
% X = input data matrix (rows = observations, cols = variables)
% ave = replace spikes with smoothed data (= 1) or NaN (= 0) (default = 1)
% s ... |
function varargout = getsamples(filename, varargin)
% Read samples from acq file
% filename - acq file
% varargin - channel names
% Returns column vectors of all channel data samples of requested channels.
% Example:
% [SourceR, EMG_L, EMG_R] = getsamples('myfile.acq', 'SourceR', 'EMG_L', 'EMG_R');
% Note:... |
function [est_all ] = estimatorVI(rhist,qhist,tau_body,dt)
%{
An estimator function that uses the measurements and applied torques
to estimate the parameters using the discrete dual quaternion rigid
body equations.
%}
R = [];
z = [];
est_all = [];
err_est_all = [];
[phi_k,psi_k] = PhikPsiK_calc(rh... |
function y = graph_sine_khan(theta_max,n)
% function to plot sine function from 0 to max_angles
% Syeduzzaman Khan
% input: theta= angle in degrees
% n=number of temrs
% output: 1. y=sine value (dimensionless) from Taylor series
%2. y=sin value from Matlab's sine function
% sample: graph_sine_khan(1... |
% ZCA operation
function output = zca(features)
output = zeros(size(features));
if size(features,3)>1
for i = 1:size(features,3)
temp = features(:,:,i);
if mean2(temp)~=0
matrix_co = (temp-mean2(temp))*(temp-mean2(temp))';
[U1,S1,V1] = svd(matrix_c... |
%% Heatmaps and frames directories (test videos)
heatmaps_root = '/home/mahdyar/Documents/MATLAB/gan_anomaly/data/UCSD/ped2/fused_heatmaps/';
videos_root = '/home/mahdyar/Documents/MATLAB/gan_anomaly/data/UCSD_original/ped2/test/';
save_root = '/home/mahdyar/Documents/MATLAB/gan_anomaly/data/UCSD/visualizations/ped2/f... |
function [varargout] = ttcvode(events, odefun, tspan, y0, options, LMM, NLS, last)
%TTCVODE Solve ODE with time-based events using CVODE.
% Solve a system of differential equations with time-based events.
% Time-based events (time-triggered) are events that happen at pre-defined
% time instants, for example periodic ev... |
function histMontage( HS, mm, nn )
% Used to display multiple 1D histograms.
%
% USAGE
% histMontage( HS, mm, nn )
%
% INPUTS
% HS - HS(i,j) is the jth bin in the ith histogram
% mm - [] #images/row (if [] then calculated based on nn)
% nn - [] #images/col (if [] then calculated based on mm)
%
% OUTPUTS
%
% EXAM... |
function [a,ZDVMX,ZDVAG,CTDZZ] = POLYFITTING(x,y,n,INTNB)
%[a,ZDVMX,ZDVAG,CTDZZ] = POLYFITTING(x,y,n,INTNB) performs polynomial
%interpolation
% x = sample points
% y = measurements
% n = polynomial order for interpolation
% INTNB = kind of interpolation (=1 monomial, =2 Newton, =3 Lagrange-> relevant for BEM)
% OUTPUT... |
% Zhorack [xhollyo]
% 22.3.2020
classdef SegmentaionByColor < handle
properties
m_tresholds = HsvTresholds;
end
methods (Access = public)
function obj = SegmentaionByColor(tresholds)
obj.m_tresholds = tresholds;
end
function mask = binMask(~,... |
%% Loop through connected neurons
gammas = [];
for i = 1:length(connections)
if nonzero(i) == 24
spiketrains = spiketrain_generate([connections(i,1), connections(i,2)], binedges, Spike_timeline, dt);
beta = betas(i,:);
test = tests(i,:);
gamma = gammafit(spiketrains, beta, test);
... |
function [result, H] = gaussian_low_pass_filter(img, sigma)
% function to implement gaussian low-pass filter
[h, w] = size(img);
% pad_img=cat(2,zeros([dim,dim/2]),img, zeros([dim,dim/2]));
% pad_img=cat(1,zeros([dim/2,2*dim]),pad_img,zeros([dim/2,2*dim]));
pad_img = img;
imgfft = fft2(pad_img);
... |
% ps1
%% 1-a
img = imread(fullfile('input', 'ps1-input0.png')); % already grayscale
%% TODO: Compute edge image img_edges
imwrite(img_edges, fullfile('output', 'ps1-1-a-1.png')); % save as output/ps1-1-a-1.png
%% 2-a
[H, theta, rho] = hough_lines_acc(img_edges); % defined in hough_lines_acc.m
%% TODO: Plot/show ac... |
%*******************************************************************************
%* Program: struc_pos.m
%* Description: Calculates the postions of the structure (wave/shock).
%* Author: Andrew Kercher
%* References:
%* [1] Toro, E. F., "Riemann Solvers and Numerical Methods for
%* Fluid Dynamics... |
% Remove NN diretory paths if they still exist
CleanUp2D;
close all
clear all
clc
%Model
model = 'Euler';
gas_const = 1.0;
gas_gamma = 1.4;
test_name = '1DSod';
InitialCond = @IC;
BC_cond = {101,'D'; 102,'D'; 103,'P'; 104,'P'};
FinalTime = 1;
CFL = 0.5;
%fixed_dt ... |
function [pass,maxerr] = test(opt)
% Check that multi-pathway kernels with higher harmonics are properly generated
r = linspace(2,6,50);
% Example timings for five-pulse DEER (all in us)
t1 = linspace(0,10,300);
t2 = 0.3;
tau1 = 4.24;
tau2 = 4.92;
t = (tau1 + tau2) - (t1 + t2);
% Pathway amplitudes and... |
function [sim1,regimes,retcode]=simul_occbin(y0,T,ss,state_vars_location,...
options,shocks)
% H1 line
%
% Syntax
% -------
% ::
%
% Inputs
% -------
%
% Outputs
% --------
%
% More About
% ------------
%
% Examples
% ---------
%
% See also:
use_pinv=true;
% find the first-order approximation of the system
%----... |
function [E_x,x] = get_P_x_micro(num,D0_act,t0,inds)
load('TD_init_parameters.mat','S_dparameters');
x = S_dparameters(num).x;
nCF = S_dparameters(num).nCF;
dirname = ['micro/' num2str(num)];
filename = ['p_' num2str(D0_act) '.mat'];
load([dirname '/' filename]);
t = T;
Y = Y_p;
... |
clc, clear all
fig('unit','inches','width',7,'height',1,'fontsize',8)
hold on
plot(0,0,'k-','linewidth',2)
plot(0,0,'y-','linewidth',2)
h=legend(gca,'Cell SD','Cell support radius', ...
'Orientation','horizontal');
set(allchild(gca),'visible','off');
set(gca,'visible','off')
set(h,'OuterPosition',[0 0 1 1],'color... |
clear all;
close all;
[x,fs]=audioread('doremi.wav');
x=x(:,1);
[x1,fs]=audioread('do.wav');
x1=x1(:,1);
[x2,fs]=audioread('re.wav');
x2=x2(:,1);
[x3,fs]=audioread('mi.wav');
x3=x3(:,1);
[x4,fs]=audioread('fa.wav');
x4=x4(:,1);
[x5,fs]=audioread('so.wav');
x5=x5(:,1);
[x6,fs]=audioread('lya.wav');
x6=x6(:,1);
[x7,fs]... |
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