text stringlengths 8 6.12M |
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function output = test_function(x)
output = sin(x);
end |
function xyz = rgb2xyz_cvip(varargin)
% RGB2XYZ_CVIP - Converts Red-Green-Blue Color value to XYZ Chromaticity Color value.
%
% Syntax:
% ------
% OutputImage = rgb2hsv_cvip(InputImage, type)
%
% Input parameters include :
% ------------------------
% 'InputImage' RGB Image
% 'type' o... |
fo = 4; %frequency of the sine wave
Fs = 100; %sampling rate
Ts = 1/Fs; %sampling time interval
t = 0:Ts:1-Ts; %sampling period
n = length(t); %number of samples
y = 2*sin(2*pi*fo*t); %the sine curve
sequence = amplitudeA(:,2);
sequence = medfilt1(sequence,40);%ÖÐÖµÂ˲¨
y = sequence;
Fs = 100; %sampling rate
Ts = 1/Fs... |
function BorderAndLuminance(varargin)
% Wrapper to call JitterBackTex_JitterObjTex
%
% Divide the screen in object and background.
% Object will be a given texture changing phases every so often.
% Back will be a grating of a giving contrast and spatial frequency
% that reverses periodically at backReverseFreq.
global ... |
function x=getRealizationsFromRandomVariables(numRealizations,probabilityInfo)
% Hugo Esquivel, 2021
% -
numRandomVariables=length(probabilityInfo.name);
x=zeros(numRealizations,numRandomVariables);
for i=1:numRandomVariables
if strcmpi(probabilityInfo.name{i},'Uniform') % uniform distribution (defined in [-1,1]... |
function CLIA(Global)
% <algorithm> <C>
% An Interactive Many Objective Evolutioinary Algorithm with Cascade Clustering & Reference Point Incremental Learning
% Hongwei Ge & Mingde Zhao et al., 2018
% stable_threshold --- --- BLAHBLAHBLAH
% delta --- --- maximum number of reference vectors participating the selection... |
function matunion=fununion(mat)
if ~isempty(mat)
[nrow,~]=size(mat);
[fcolsor,indsort]=sort(mat,1);
mat=mat(indsort(:,1),:);
itrow=1;
while itrow<nrow
if mat(itrow+1,1)<=mat(itrow,2)
if mat(itrow,2)<mat(itrow+1,2)
mat(itrow,2)=mat(itrow+1,2);
end
... |
function f = structdata_plotTemporalWaveform(S_structdata,S_fig)
if strcmp(S_structdata.type,'coeffs')
[~,CFvecs,~,x] = cavity_loadBasis(S_structdata.basis_loc,S_structdata.k_a);
field_t = userdata_calcTemporalField(S_structdata.Y,CFvecs,x,S_structdata.x0);
elseif strcmp(S_structdata.type,'fi... |
%%
%Plot ESD results for each Earth Station
figure(1)
phimax=20;
fontsize=14;
num=0;
close all
[trash, idx] = sort([EMISSION{:,4}], 'descend');
EMISSION = EMISSION(idx,:);
Uplink_Emission={}; %(Pwr_Max,PwrDens_Max,Pwr_Min,PwrDens_Min)
for es = 1:length(p.EarthStation{:,1})
PwrDensUpMax=[];PwrDensUpMin=... |
close all
clear all
clc
Obj1.mean = 1 * [0.3; 5];
Obj2.mean = 1 * [-3; 5];
Obj3.mean = 1 * [3; -5];
Obj4.mean = 1 * [-3; -5];
% K-NN
Inv = 7;
K = 2;
Im = zeros(300);
for iii = 1 : 300
for jjj = 1 : 300
xx = (iii - 150) / 150 * Inv;
yy = (jjj - 150) / 150 * Inv;
d(1) = norm(Obj1.mean - [x... |
function n_est = ess(y)
%ESS Calculate the Estimated Sample Size (ESS) of MCMC chain
% N_EST = ESS(Y) calculates the ESS of MCMC chain, Y.
% See: Kass, Carlin, Gelman, & Neal, 1998, p. 99.
%
% Kyle Honegger, Harvard University
% h------r@fas.harvard.edu
%
% Version: v1.0
% Last modified: Sept 22, 2... |
% Test program to do some RSA example
maple('p:= nextprime(1897345789)')
maple('q:= nextprime(278478934897)')
maple('n:=p*q');
maple('x:=101');
maple('e:=nextprime(12345678)')
maple('d:=e&^(-1) mod ((p-1)*(q-1))')
maple('y:=x&^(e) mod n')
maple('c:= y&^(d) mod n')
|
function orms = odd_rms(nn)
odd = 1:2:2*(nn-1)+1;
orms = sqrt(mean(odd.^2, 2));
end |
0load('C:\Users\WHY\Desktop\JK_Aux\workspace\json_all_data_16\squat_coords\Alessandro.mat');
x = squat_coor... |
function bob=booster_base(proto, boost_steps, param1, param2)
% bob=booster_base(proto, boost_steps)
%
% Constructor for booster_base class
%
% BASECLASS(ES) : learner
% Copyright (c) 1998 GMD Berlin - All rights reserved
% THIS IS UNPUBLISHED PROPRIETARY SOURCE CODE of GMD Berlin
% The copyright notice above does no... |
%% Reset
fclose('all');
close all
clear
clc
%% File paths
topDir = '\\root\programs\Light-and-Health\IAI_CircadianMonitoringAndRegulation\CMR2-Exp1-Data';
sub300 = '300-43DF\43DF_2017_01_11_10_06_04_archive'; % last
sub301 = '301-A4D7\A4D7_2017_01_04_09_13_14_archive'; % last
sub302 = '302-0780-F900\F900... |
function efficiency = box_efficiency(points,rect)
%BOX_EFFICIENCY Box efficiency of covering flagged cells.
% EFFICIENCY = BOX_EFFICIENCY(POINTS,RECT) returns the efficiency of the box RECT
% of covering the flagged cells POINTS. POINTS is a binary image, and RECT is described
% by the d-dimensional box conve... |
% load_captured_data.m
% Copyright 2003-2010 The MathWorks, Inc.
[fileName,pathName]=uigetfile('captured*.mat','Select file');
if length(fileName)>0
load([pathName fileName])
else
error('User aborted program')
end
if exist('fps','var')
if exist('t_stats','var')
disp(sprintf('fps=%.3g, Cv=%.2g%%'... |
classdef Motorcycle < Vehicle
methods
function obj = Motorcycle(varargin)
obj@Vehicle(varargin{:});
end
end
end |
%=========================================================================
%
% Simulation of nonlinear and linear exponential models.
%
%=========================================================================
clear all
clc
RandStream.setDefaultStream( RandStream('mt19937ar','seed',1) )
% Simulate data
t = 5... |
function ivim__rmalloutliers(iSubj,iPrePost)
disp('Saving voxelwise map data... ')
global vars init_vars
% difference between rmalloutliers_2 and rmalloutliers_1 is that the
% boundary exclusion criteria used for D* is actually the minimum bound,
% whereas in rmalloutliers_1 it is the theoretical boundary (0.00251).
% ... |
function disableStim(hObject, ~, hGui)
global stimEnable
if get(hObject, 'value')
stimEnable=0;
set(hGui.stimDisableButton,'Enable','off')
set(hGui.stimDisableButton,'Value',0);
cla((hGui.Axes6));
end
set(hGui.stimDisableButton,'Enable','on')
end
|
function [Y] = convolution_output_side(X,H)
N = size(X)(1,2);
M = size(H)(1,2);
H = flip(H)';
X = [zeros(1,M-1),X,zeros(1,M-1)];
Y = zeros(1,(M+N-1));
for i = 1:(M+N-1)
Y(1,i) = X(1,i:i+M-1)*H;
endfor
endfunction
|
function move_cb_distribution(obj,delta_hsl)
% Adjust for filament compliance
delta_x = delta_hsl * obj.parameters.compliance_factor;
% Shift populations by interpolation
switch (obj.kinetic_scheme)
case '3state_with_SRX'
% 3state SRX model
interp_positions = obj.myofilaments.x - delta_x;
... |
%We used a Quasi Newton method where the Hessian used in each update step
%is the Hessian evaluated at the initial point, x0. I.e. it is fixed
%throughout the procedure.
%Newton method converges in approx 30 iterations to the same
%result as acheived by BB method of HW3 (and in fewer iterations) for the same
%data @x ... |
function r = funbiseccion(x)
r = (x.*x) - 25;
endfunction
|
function compare_HRF_peak(session_dir,srcROI,trgROI,map_type,ROIs)
% Creates scatter plots of HRF peadata.
%
% Usage:
% compare_CF_pRF(session_dir,srcROI,trgROI,map_type,ROIs)
%
% Examples:
% compare_CF_pRF(session_dir,'volume','prf_V1','movie')
%
% Written by Andrew S Bock Jul 2015
%%
if ~exist('ROIs','var'... |
function [L_star]=poiProxOperator(Tr,Content,Z,lambda_5,tau)
if lambda_5~=0
L_star=zeros(size(Z));
n_clusters=length(unique(Content.poi_cidx));
[n_users,~]=size(Tr.R_bool);
for h=1:n_clusters
h_idx=find(Content.poi_cidx==h);
rho_h=sqrt(length(h_idx));
... |
function mean = localmean(f, nhood)
%LOCALMEAN Computes an array of local means
% MEAN = LOCALMEAN(F, NHOOD) computes the mean at the center of every
% neighborhood of F defined by NHOOD, an array of zeros and ones where the
% nonzero elements specify the neighbors used in computation of the local
% means. The siz... |
%% Introduction
% This Matlab script runs the complete NLO algorithm and uses the supporting Matlab functions located in this folder.
% The approach is based on
% "State observation in medium-voltage grids with incomplete measurement infrastructure through online correction of power forecasts"
% which was extended ... |
function [O_error, O_grad]=jacobiandet_cost_gradient(O_grid,sizes,O_goal,sizeI,Spacing)
% Convert Grid vector to grid matrix
O_grid=reshape(O_grid,sizes);
% Delta step used for error gradient
step=0.001;
if(nargout>1)
if(length(Spacing)==2)
[O_error1,O_grad1]=jacobiandet_error_2d_double(O_grid(:,... |
function result = PSNR(f, g)
assert(min(min(f)) >= 0);
max_intensity = max(max(f));
% assumptions about the max intensity..
assert(max_intensity <= 255);
if (max_intensity <= 1)
max_intensity = 1;
else
max_intensity = 255;
endif
result = 10*log10( (max_intensity^2) / MSE(f,g) );
end
function result = MSE... |
% remove files
% mixed= dir('./images/mixed/*.png');
% text= dir('./images/text/*.png');
% pictorial= dir('./images/pictorial/*.png');
%
% % sample 17 images from each category
% ind = randsample(length(mixed), 17);
% sample_mixed = mixed(ind);
% ind = randsample(length(text), 17);
% sample_text = text(ind);
% ind = r... |
function [FDPD,MDPD,Thrust] = defQuadPropulsionModel6DoF(...
w1, w2, w3, w4,...
T2W_max, RPM_max, cM, m, g,...
r1, r2, r3, r4)
%
% Inputs:
% - Motor commands:
% - w1 [rpm]: rotational speed motor 1
% - w2 [rpm]: rotational speed motor 1
% - w3 [rpm]: r... |
function [g, y, z] = mllcalcf(mllparam)
% Calculates filter and pulse
% 1. Determine whether nop even or odd (even = 1 or 0) ******
% starts at 0 for odd, at +1/2 for even ***
% Initialize ***
global mllmu mllcu mpgdtmu mmggamma;
% Set local values
mu = mllmu ;
n = mllcu ;
cycls = mllparam(5) ;
nop = mllparam(4) ... |
%% Test that JtreeInfEng Returns the Same Results as VarElimInfEng
% We compute prior marginals of each single node
%#testPMTK
dgmVE = mkAlarmNetworkDgm;
dgmVE.infMethod = VarElimInfEng('verbose',true);
dgmJT = dgmVE;
dgmJT.infMethod = JtreeInfEng('verbose',true);
queries = num2cell(1:37);
tic; [mVE, logZVE] = ... |
function varargout = graymat2avi(data, scale, frameRate, fileName, map)
% graymat2avi converts grayscale data matrix to avi video and matlab mov
% file
% graymat2avi(data, scale, frameRate, fileName, map)
% mv = graymat2avi(data, scale, frameRate, fileName, map)
% scale is used by image resize, like [320 400] or 0.9
%... |
% pop_readedf() - load a EDF EEG file (pop out window if no arguments).
%
% Usage:
% >> [dat] = pop_readedf( filename);
%
% Inputs:
% filename - EDF file name
%
% Outputs:
% dat - EEGLAB data structure
%
% Author: Arnaud Delorme, CNL / Salk Institute, 13 March 2002
%
% See also: eeglab(), readed... |
function [ projy ] = proj_box( y ,a,b)
%See "Fast Projection onto the Simplex and the ?1 Ball", Laurent Condat, 2015
for i =1 : size(y,2)
low_ind = y(:,i)<a;
up_ind = y(:,i)>b;
y(low_ind,i)=a;
y(up_ind,i)=b;
projy = y;
end
end
|
clear
clc
lambda_avg = 1.11;
mu_avg = 1.67;
CV = 0.1;
Lc = 5;
L = 100;
H = 100;
Nx = 50;
Ny = 50;
%solving shape and scale parameters
A = 1/CV^2;
B_lambda = lambda_avg*CV^2;
B_mu = mu_avg*CV^2;
%mesh
Np = Nx*Ny;
Sx = linspace(0,L,Nx);
Sy = linspace(0,H,Ny);
[Xmesh,Ymesh] = meshgrid(Sx,Sy);
%KL expansion
tol = 0.01;... |
function f_x = f_marginal_benifit_desc_16(x, base_point, target_point, p)
k = -log(1 - p) ./ (target_point - base_point);
f_x = 1 - exp(-k * (x - base_point));
% f_x = (1 ./ (1 + exp( -a * (x - b))));
end |
function [a_up, a_diag, a_down] = tridiagonal_LU(a_up, a_diag, a_down, n)
% a_down: a vector with (n-1) elements containig lower diag elements
% a_up : a vector with (n-1) elements containig upper diag elements
% a_diag: a vector with n elements containig main diag elements
for k = 1:n-1
a_down(k) = a_d... |
clc;
clear all;
close all;
%Iniciando vetor D com valores de 1 a 10
D = [0:0.1:10];
D = D';
%Gerando valores entre -0,5 e 0,5
xmin= -0.5;
xmax = 0.5;
[nPadroes, nEntradas] = size(D);
a=xmin+rand(nPadroes,1)*(xmax-xmin);
%Adicionando o ruído na reta
aleatorio = D + a;
%Gerando as entradas através dos pont... |
% This is the test script for the image_sort function.
% The pixels of an image are sorted.
% Author: Atakan Varol
I = imread('exp_curve4.bmp'); % Read the image
I_not = uint8(not(I)); % WB conversion
I_edge = edge(I_not,'sobel'); % Edge detection
[y x] = find(I_edge); ... |
function net = somunpak(net, w)
%SOMUNPAK Replaces node weights in SOM.
errstring = consist(net, 'som');
if ~isempty(errstring)
error(errstring);
end
% Put weights back into network data structure
net.map = reshape(w', [net.nin net.map_size]);
|
function f_bubble(x,y,z,mx,sq,srt)
% - create B&W bubble plots
%
% USAGE: f_bubble(x,y,z,mx,sq,srt)
%
% x = x-coordinates
% y = y-coordinates
% z = symbol size at each x,y coordinate (pos values produce black symbols, neg
% vaues produce white symbols)
% mx = maximum symbol size
% sq = scale symbols ac... |
%
% Path -> /Users/djones/work/mWork/Fathom/RF
%
% f_RFaic - AIC-based variable selection for Random Forest Classification
% f_RFclass - train a Random Forest classifier
% f_RFclassPlot - create diagnostic plots of a Random Forest classification
% f_RFclassPredict - predict class membersh... |
%% Redes Neuronales Competitivas MultiCapa 2 (Back Propagation)
clear all %limpiar Workplace
close all %limpia ventanas
clc %limpia command window
%% Cargar Datos
load RNCDatos4.mat;
%% Datos del Modelo
data = IPCfinal(:,5);
nsal = 8; % Numero de Salidas
nrez = 3; % Numero de Rezagos (debe ser mayor )
temp = []; %Vecto... |
function a = ann_forward(X, theta, L, s)
a = zeros(s, 1, L);
a(:,:,1) = X';
for l=(2:L)
z = theta(:,:,l-1) * a(:,:,l-1);
a(:, :, l) = ann_sigmoid(z);
end
end
|
% Noms des fichiers d'output a analyser
filename1 = '200_steps.out';
filename2 = '400_steps.out';
filename3 = '800_steps.out';
filename4 = '1600_steps.out';
filename5 = '3200_steps.out';
filename6 = '6400_steps.out';
% Chargement des donnees
data1 = load(filename1);
data2 = load(filename2);
data3 = load(filename3);
d... |
%% Header
% Project Name: OPT Projekat: Prepoznavanje Cifara
%
% File Name: AnalyseGroup.m
%
% Author: Nemanja Jankovic, 2015/3303
%
% Date: /9/2016
function ErrorArray = AnalyseGroup(InputImage, FileName)
NUMBER_OF_SAMPLES = 1000;
H = 28;
W = 28;
Xcenter = W / 2;
Ycenter = H / 2;
File =... |
Tstart = 0;
Tend=1; % length of time
Tspan = Tend - Tstart;
Ts=1/1000; % time interval between samples
mean = 0;
variance = 3;
f=10; phi = 0;
t = Tstart:Ts:Tend;
sp = subplot(2,2,1)
x = cos(2*pi*f*t+phi)
[phase_cos, ssf] = phase_calc_max(x,Ts) % call plotspec ... |
function [angle_est,nn_idx] = nearest_neighbor(Y,euc_dist_vect)
[~,nn_idx] = min(euc_dist_vect);
angle_est = Y(nn_idx);
end
|
%% gettfparams: Gets paramaters for a transfer function
function [K, Tau, Theta] = gettfparams(t, u, y)
%% Initialize variables
deltay = max(y) - min(y);
deltau = max(u) - min(u);
y2 = 0.02*deltay + y(1);
y10 = 0.10*deltay + y(1);
y90 = 0.90*deltay + y(1);
%% Find K
K = deltay/deltau;
%% Find Tau
i = 1;
w... |
clc;clear all;close all;
m=.1;Fricc=0.1; long=0.6;g=9.8;M=.5;
h=0.0001;tiempo=(20/h);dpp=0;fipp=0; t=0:h:tiempo*h;
omega=0:h:tiempo*h; alfa=0:h:tiempo*h; d=0:h:tiempo*h;
dp=0:h:tiempo*h; u=linspace(0,0,tiempo+1);
%Condiciones iniciales
alfa(1)=pi-0.8; color='b';
d(1)=0; dp(1)=0; u(1)=0; d(1)=0; i=1;
%Versión linealizad... |
%%
% We design a LQR controller for stabilyzing the fractional reaction diffusion equation
%%
% $$
% \begin{cases}
% y_t + (-d_x^2)^s y-\delta y = u\chi_\omega, & (x,t) \in(-L,L)\times(0,T)
% \\ y = 0, & (x,t)\in [\mathbb{R}\setminus(-L,L)]\times(0,T)
% \\ y(x,0) = y_0(x), & x\in(-L,L),
% \end{cases}
% $$
%%
% where... |
%% Pilot_In_Loop_Bank_Angle_Plot
%% Bank Angle-Time Diagram
figure('Name','Bank Angle-Time Diagram')
plot(Phi)
title('Bank Angle Variation due to Time')
xlabel('Time (sec)')
ylabel('Bank Angle Phi (rad)') |
function [results, success] = ...
scopf(mpc, cont, mpopt, tol)
%SCOPF Solves an optimal power flow with security constraints.
% [RESULTS, SUCCESS] = SCOPF(MPC, CONT, MPOPT)
%
% Returns either a RESULTS struct and an optional SUCCESS flag, or individual
% data matrices, the objective function value and a SUCC... |
classdef (Abstract) CustomGuiInterface < handle
properties (Access = protected)
uiInstance
text
backgroundColor
fontColor
fontName
fontWeight
fontAngle
fontSize
end
methods (Abstract)
setText(obj, text);
s... |
% Clustering data in groupsNum groups
% Returns groupsNum cells with three columns, one for
% each outcome variable (TE,PE,SL3)
function WCSTclusters_stats = createClusters(indivStats, groupsNum)
% indivStats_matrix format: CC, WTE, PE, SL3
indivStats_matrix = [[indivStats.TE]', [indivStats.PE]', [indivStats.SL3]', [... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright 2013 Analog Devices, Inc.
%
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
%
% http:/... |
%% Receiver Operating Characteristic (ROC) curve
% This script plots the ROC for the Gibbs concentration factor
% and a 5-point detector.
close all; clear all; clc
%% Initialization
% Define the Fourier modes
nModes = 64;
fourModes = (-nModes:nModes).';
% Generate a (periodic) physical space grid
nGridPts = 256;
x... |
% group bounding boxes using non-maximum surpression
% Written by Xikang Zhang, 06/21/2013
function [bb, weights] = groupBBox_nonMaxSup(bb,weights,winSiz)
if nargin<3
winSiz = 30;
end
assert(size(bb,1)==length(weights));
valid = true(size(weights));
xc = bb(:,1)+bb(:,3)/2;
yc = bb(:,2)+bb(:,4)/2;
for i=1:length(v... |
function [] = train()
symbolVecs = readSymProps('spade');
symbolVecs = [symbolVecs;readSymProps('heart')];
symbolVecs = [symbolVecs;readSymProps('club')];
symbolVecs = [symbolVecs;readSymProps('diamond')];
% numberVecs = [];
% for i = 2:9
% numberVecs = [numberVecs;readNumProps(i)]... |
function s=ks(); c=clock; s=sum(c(4:6).*[60^2 60 1])/1000; end
|
F = 50;
vector_1 = 0:0.001:0.2;
sinusoidal_1 = 2*sin(2*pi*F*vector_1);
figure(1)
subplot(3,1,1) , plot(vector_1,sinusoidal_1,'.-'),xlabel('Timp [s]'),grid
F = 50;
vector_2 = 0:0.01:0.2;
sinusoidal_2 = 2*sin(2*pi*F*vector_2);
subplot(3,1,2) , plot(vector_2,sinusoidal_2,'.-'),xlabel('Timp [s]'),grid
... |
function test_svm_struct_learn
% TEST_SVM_STRUCT_LEARN
% A demo function for SVM_STRUCT_LEARN(). It shows how to use
% SVM-struct to learn a standard linear SVM.
randn('state',0) ;
rand('state',0) ;
% ------------------------------------------------------------------
% ... |
function [ fis, extended_training_set, extended_actions, new_state ] = q_learning( fis, sys_tf, training_set, actions, state, iteration, sample_time, epsilon, gamma )
% fis - нечеткая система
% training_set - обучающее множество
% extended_training_set - обучающее множество, расширенное новым
... |
function [ydot] = fofy(t,y,u)
ydot=u(1)*y;
%ydot=-u(1)*(y-(t*t+1))+2*t;
end
|
function I = filtro_general(A, text)
% A en 8 bits
% I en 8 bits
if text == 'promedio'
B = (1/9) * ones(3,3);
elseif text == 'gaussiano'
B = (1/16) * [1 2 1; 2 4 2; 1 2 1];
elseif text == 'gaussiano5'
B = (1/256) * [1 2 64 1; 4 16 24 16 4; 6 24 36 24 6; 4 16 24 16 4; 1 4 6 4 ... |
% Mean Function Abstract Class
% All mean function classes must inherent from this class.
classdef MeanFunc < tacopig.taco
methods(Abstract)
% Returns the number of parameters required by the mean function.
% n = npar(D);
% Input : D (dimensionality of the input da... |
%CGpipeline
%close all; clear; clc; CGpipeline
%Load neural data
load bint_fishmovie32_100
data=reshape(bint(1,:,:),[160,953]);
data=data*2-1;
mean(sum(data>0,2)/953);
mean(mean(data,2));
%Pre-allocate space for saving each GC step
CGevents=70; %Number of iterations
C{1,CGevents+1}=[];
C{1}=data;
Rec=d... |
function dfdxval = SEIR_var_dfdx(t,y,p,more)
p = more.p.fun(t,more.pdef);
beta = more.beta.fun(t,p,more.betadef);
r = zeros(length(t),size(y,2),size(y,2),size(y,2));
% dimnames(r) = list(NULL,colnames(y),colnames(y),colnames(y))
r(:,1,1,1) = beta.*(p(2)+y(:,3)) + p(3);
r(:,1,1,3) = beta.*y(:,1);
r(:,2,2,1) = beta.*(p(... |
function save_lda_model(model, model_root)
filename = sprintf("%s.beta", model_root);
file = fopen(filename, "w");
disp(filename)
for i=1:model.num_topics
for j=1:model.num_terms
fprintf(file, '%5.10f ', model.log_beta(i, j));
end
end
fprintf(file, '\n');
fclose(file);
filename = sprintf("%s.ohter", m... |
function [out1,out2,out3] = Localize_Fix(closestPiMat,nVals,BCNstruct,theWPs,RSSI_0,noise)
% Method to localize a patient carrying a mobile beacon
% out1: a structure containing the (x,y) coordinates
% out2: a vector of known location errors for testing
global locPiOne locPiTwo locPiThree...
locPiFour l... |
function adaboost_model = ADABOOST_tr(tr_func_handle, te_func_handle, train_set, labels, no_of_hypothesis)
%
% ADABOOST TRAINING: A META-LEARNING ALGORITHM
% adaboost_model = ADABOOST_tr(tr_func_handle,te_func_handle,
% train_set,labels,no_of_hypothesis)
%
% 'tr_func_handle' and '... |
%q1
[y1, y] = fourierseries(1, 1, 200, .25, .75);
[y2, ya] = fourierseries(1, 2, 200, .25, .75);
[y3, yb] = fourierseries(1, 3, 200, .25, .75);
x = linspace(0,1,200);
%plot of first 3
figure
hold on
plot(x, y)
plot(x, y1, '--')
plot(x, y2, ':')
plot(x, y3, '.')
title('Square Wave with First 3 Nonzero Fourier Modes')
x... |
function [ BoWvec, Label ] = encodingImage( ImageSets, Vocabulary, Norm )
% SIFT Feature extraction: via hard voting (i.e, assignment to the nearest(K=1) codeword)
%
% Inputs->
%
% ImageSets : imageSet class
% Vocubulary: visual words or centroids or cluster centers (each column is a word)
% Norm : Normal... |
figure(3)
% close all
clear phi_val theta_val pot_s
pos_r1=interp1(radial_r_value_flux,1:Nradial,r1)
pos_r2=interp1(radial_r_value_flux,1:Nradial,r2)
% pos_r3=interp1(radial_r_value_flux,1:Nradial,r3)
for offset=1:2:size_r_TAE-1
posTAE_value=pTAE_inf+offset
qcheck=q_initial_profile(posTAE_value)
Phi_thet... |
function weights=fitnessfun(x)
% a=weight_to_optimise;
weights=x;
|
% File: P2_92.m
clear;
M = 6;
N = 2^M;
T1 = 10;
T = 1;
dt = T1/N;
n = 0:1:N-1;
tk = n*dt;
tk = tk(:);
% Generating waveform
% Note in the FFT time domain, points for negative time are the same as those
% measured from the end of the data span-lenght T1 for positive time.
w = zeros(length(n),1);
for (i = 1:1:length(w... |
function [x, y, z] = voxelRange(neuron, voxelSize)
% VOXELRANGE
%
% Description:
% Split the area within a bounding box into voxels of a specific size
%
% Syntax:
% [x, y, z] = voxelRange(neuron, voxelSize)
%
% History:
% 9Feb2021 - SSP
% ------------------------------... |
function [ y ] = findVal( x1,x2,y1,y2,x)
%findVal Finds linearly interpolated value
slope=(y2-y1)/(x2-x1);
y=slope*(x-x1)+y1;
end
|
function [expVals] = expectedVal(obsVectors)
%Calculates expected values for chi2
%Each row points to one observation
rowSums=sum(obsVectors,2);
colSums=sum(obsVectors,1);
totalSum=sum(obsVectors,'all');
expVals=rowSums.*colSums;
expVals=expVals./totalSum;
end
|
%optimization script
N=3000;
f=@(x) Par_est_obj2(x(1:3),x(4:7),N);
OPS=optimset('Display','iter','MaxIter',120);
[a,b,c,d,e]=fminsearch3(f,[1.7,5,10,0.01,16,2.5,10],OPS)
%resposta é a=[3,5,8,0,16,2.47,1.4]
f2=@(x) Par_est_obj_LS(x(1:3),x(4:7));
opt = optimoptions(@lsqnonlin,'Display','iter-detailed','MaxIter',20... |
function dydt = f(t, y, p)
%
eval(p);
force = 2/(1+sqrt(1+8*keq*y(2)));
dydt = [
vm/(1+(y(2)*(1-force)/(2*pc))^2)-km*y(1);
vp*y(1)-(kp1*y(2)*force+kp2*y(2))/(jp+y(2))-kp3*y(2);
];
% =======================================================================
% the information below is written into the file name... |
% must run liat's script first!
% file_name = '11-Mar-2019 09_12_39.mat';
% file_name = '11-Mar-2019 09_11_33.mat';
file_name = '11-Mar-2019 10_28_43.mat';
record.TX.info_msg_bits = info_msg_bits;
record.TX.info_msg_with_CRC = info_msg_with_CRC;
record.TX.crc = [1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1];
reco... |
clear;
%Low-pass Gaussian Filter (LPGF);
f = imread('monarchg.png');
[N M] = size(f);
f = double(f);
% Compute padded size to prepare for FFT-based filtering.
NM = [N M];
PQ = 2*NM;
% Set up range of variables.
u = single(0:(PQ(1)-1));
v = single(0:(PQ(2)-1));
% Compute the indices for use in meshgrid.
idx = find(u > P... |
function [mND, Index] = ave_NearNeighborStats(rrt,Nt,nbins, Maxx, Scale, Fign,figname)
%% average NN stats for a group
% Created by Wan-Qing Yu
No = length(rrt);
NeighData = [];
for i = 1:No
[ND{i},Index(i)] = NearNeighborsStats(rrt{i},Nt(i),nbins,Scale(i),Maxx,i);
mND(i) = mean(ND{i});
NeighData = cat(2,N... |
function vectors3d(varargin)
%VECTORS3D Description of functions operating on 3D vectors.
%
% Vectors are represented by their 3 Cartesian coordinates:
% V = [VX VY VZ];
%
% List of vectors are represented by N-by-3 arrays, with the coordinates
% of each vector on a row.
%
%
% See also
% vectorN... |
function imVolWrite(data, imDir, fileList)
% writes image stack variable into grayscale tiff images.
% SYNTAX:
% imVolWrite(data, imDir, fileList)
%
% INPUT:
% "data" is the returned 3d matrix contains the image stack. Elements of
% data are assumed to have real value within [0, 1].
% "fileList" is the... |
data = dlmread('full_params.csv',',');
data = data(1:66404,:);
data(:,1) = data(:,1)./60;
n = size(data,1);
nTest = 10000;
sets = [500, 1000, 2000, 4000, 8000, 15000, 30000, n-nTest];
ind = randperm(n);
% dlmwrite('bayesNetData.csv',data(ind(1:10000),:));
%%
dlmwrite('testSet.csv',data(ind(1:nTest),:));
data=data(in... |
function layersE = makeLayersE(layers_our,E,num_points,all_layers)
% initializing fields for layersE struct
layersE = layers_our;
interpnames = cellstr(['N/A']);
[layersE(:).interp_name] = deal(interpnames{:});
efieldnames = cellstr(['M1_PA_MCB70']);
[layersE(:).Efield_solution] = deal(efie... |
h1 = subplot(3, 1, [1 2]);
iP = 1;
n_c = find(abs(diff(ux)) > D/2);
n_c = [n_c'; n_c'+1];
n_c = [1; n_c(:); length(t)];
uxc = ux(n_c);
lxc = lx(n_c);
t_c = t(n_c);
%fill([t fliplr(t)], [lx; flipud(ux)]', 0.8*[1 1 1], 'EdgeColor','none');
fill([t_c fliplr(t_c)], [lxc; flipud(uxc)]', 0.8*[1 1 1], 'EdgeColor','... |
function [] = pupilUncertaintyTimecourse(plotAll)
% This code reproduces the analyses in the paper
% Urai AE, Braun A, Donner THD (2016) Pupil-linked arousal is driven
% by decision uncertainty and alters serial choice bias.
%
% Permission is hereby granted, free of charge, to any person obtaining a
% copy of this soft... |
function [result] = simpson033(f, atas, bawah, segmen, h)
totalGenap = 0;
totalGanjil = 0;
for i = 1:segmen-1
xi = bawah + i*h;
if(mod(i,2) == 0)
totalGenap = totalGenap + f(xi);
else
totalGanjil = totalGanjil + f(xi);
end
end
result = (... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function doPrecomputeHistoObjBg_testData
% Pre-computes the histograms of the entire image, each of its objects, and the background of each
% of these objects.
%
% Input parameters:
%
%
% Output parameters:
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%... |
function [energy_per_kg_mango, air_per_kg_mango] = ...
idealDrying(UIaxes, m_hum_in, m_hum_out, s_air_t, s_air_hum, ...
t_in, t_out)
%IDEALDRYING calculates an ideal convective dryer (e.g. ideal ATESTA)
%
% inputs:
% m_hum_in: humidity of raw-mango (in percent)
% m_hum_out: humidity of dry-mango (in... |
%Case 1:
dimx=256;
indata = single(rand(dimx,1));
outdata = tom_bandpass(indata,5,126,10);
fid = fopen('Input_Bandpass_1.bin','W');
fwrite(fid,indata,'single');
fclose(fid);
fid = fopen('Output_Bandpass_1.bin','W');
fwrite(fid,outdata,'single');
fclose(fid);
%Case 2:
dimx=256;
dimy=255;
indata = single(rand(dimx,dimy)... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 该函数用于获得color数据的各个特征数据，如直方图、纹理等信息 2017.03.18
% image_rgb：输入数据为归一化的double数据
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function imdata = get_imgData2( infolder, frameName, ScaleH, ScaleW )
image = imread(fullfile(info... |
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