text stringlengths 8 6.12M |
|---|
% Repeat string n times into an array struct
function res = repeatstr(val, n)
res = {};
for i=1:n
res{i} = val;
end
|
function [bb_struct] = bb2struct(bb)
%--------------------------------------------------------------------------
%
% Copyright (c) 2014 Jeffrey Byrne
%
%--------------------------------------------------------------------------
xmin = bb(:,1);
ymin = bb(:,2);
xmax = bb(:,3);
ymax = bb(:,4);
bb_struct.xmin = xmin;
bb_... |
% Confidence Intervals
clear all;
close all;
clc;
ylim([45, 90]);
line([1 2],[88.11 88.11],'Color','r','LineWidth',2)
hold on
line([1 2],[84.71 84.71],'Color','r','LineWidth',2)
hold on
plot(1.5, 86.41, 'o', 'MarkerSize', 6, 'MarkerEdgeColor', 'r', 'LineWidth',2)
hold on
line([3 4],[77.15 77.15],'Color','r... |
rng(mean('hyperalignment'));
%% Plot left vs. right fields for both actual and predicted data
data = Q;
[~, ~, predicted_Q_mat] = predict_with_L_R([], data);
out_predicted_Q_mat = set_withsubj_nan([], predicted_Q_mat);
w_len = size(data{1}.left, 2);
figure;
set(gcf, 'Position', [395 524 1023 366]);
datas = {Q, out_pr... |
function no_red = no_reduction_f( )
%% Loading all given vectors
load grass_vector.mat;
load straw_vector.mat;
load unknown_vector.mat;
load unknown_file_names.mat;
[rows col]=size(grass_vector);
%% Output Structure
file1='File_Name';
file2='No_Reduction_Output';
no_red=struct(file1,[],file2,[]);
%% Mean and varia... |
function [photon_path,scattering_x] = adjust_one_photon_path(photon_path,one_photon_path,scattering_x,phot)
% ADJUST PHOTON_PATH
if length(one_photon_path) < size(photon_path,1)
one_photon_path = [one_photon_path; nan*ones(size(photon_path,1) - length(one_photon_path),1)];
elseif le... |
function [f,power]=cnm_power_spectra(cfg, data)
% compute power spectrum for data
% cfg = configuration structure containing parameter for power spectrum
% data = data structure from preprocessing
% cfg.channels 'channels', 'all', 'MZ' for MEG central
% 'ML' for MEG left
% 'MR' for MEG r... |
clear;
%% Variables para modficiar
%name= "Ejemplo1.csv";
name= "Prueba_04.csv";
[ALL]=table2array(readtable(name));
of=1000; %offset de toma de datos
fin=length(ALL);%length(ALL);
%% Programa
con=0;
[AA]=ALL(of:fin,1);%muestras
[A]=ALL(of:fin,2); %tiempo
[B]=ALL(of:fin,3); %Temperatura
[C]=ALL(of:fin,4); %Humedad
[D... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% NCK 보간전 가감속 / Version 1.0.1 / Yonsei %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 이찬영, 김성현, 이동열 %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc
clear all
% close all
profile off;
profile on;
%% NCK 파라미터
global X_axis Y_axis Z_axis A_axis B_a... |
warning('all', 'off');
close all;
fileName = 'group23.jpg';
% startPoint = [549,399];
% endPoint =[731,524];
meanValue= [0, 0, 0];
% imtool(fileName);
img = imread(fileName);
%
% for j = startPoint(1):endPoint(1)
% for i = startPoint(2):endPoint(2)
% meanValue(1) = meanValue(1) + img(i, j, 1);
% ... |
function [theta] = normalEqn(X, y)
% regression using the normal equations.
%theta = zeros(size(X, 2), 1);
theta = pinv(X'*X)*X'* y;
end
|
%model assumptions:
% notation: N1, S2, E3, W4 for node directions N is north for example
rpm = 466.67; %rotation per second
i=0;
cp = 1.996; %kJ/(kg*K)
vol = zeros(100,100,100); %# of control volumes
timerange = [0,100]; %seconds
Pi = 0; %Pa
Po = 101325*2; %Pa, assuming it is 2 atm at outlet....(guessed)
P = linspace(... |
%--- coordinate plot with respect to time
% you need to define model.C_posn in order to work properly
file= 'exa3_1';
load([ '2filter_',file,'.mat']);
[X_track,k_birth,k_death]= extract_tracks(X,track_list,total_tracks);
figure(1); clf; subplot(211);
figure(2); clf; subplot(211);
figure(3); clf;
%--- plot true tra... |
function [kernel_c1,kernel_f1] = init_kernel(layer_c1_num,layer_f1_num)
% initial the convolutional kernel
for n = 1:layer_c1_num
kernel_c1(:,:,n) = (2*rand(5,5)-ones(5,5))/12;
end
for n = 1:layer_f1_num
kernel_f1(:,:,n) = (2*rand(12,12)-ones(12,12));
end
end |
function Fpol = rec2pol(Frec)
Fpol = [abs(Frec) angle(Frec)*180/pi];
|
function out = createRescaledImageFromRtDoses(rtDoses, refImage)
%CREATERESCALEDIMAGEFROMRTDOSES creates rescaled image from an array of rtDose objects.
%
% image = createImageFromRtDose(rtDoses, refImage) creates a new image object for each RTDOSE
% in the RtDose array on the refence spacing refImage and sums the pi... |
%% generates samples from the true GP
function y = sampleGP(xstar, covfunc, loghyper, MIN_NOISE)
n = size(xstar,1);
Ktilde = feval(covfunc, loghyper, xstar) + MIN_NOISE*eye(n);
%z = randn(n,1);
%y = chol(Ktilde)'*z;
mu = zeros(n,1);
y = sampleGauss(mu, Ktilde, 1);
return;
|
close all;
clear all;
relative = 1;
FieldSize = [ 15 15 ]; % cm
MU = 400;
% Location of cross profiles
x_offset = 0;
y_offset = 0;
FilterRad = 10;
BG_Dose = 0;
%%
% Load Heterogeneous Electrons
% Middle of Heterogeneity
Film_MU = 400;
filename = 'W:\Private\Physics\21eX91 Validation - DJJ\Film Data for Validati... |
%% Script Helga_szakdolgozat_tproba
%
% File: Helga_szakdolgozat_tproba.m
% Directory:
% Author: Peter Polcz (ppolcz@gmail.com)
%
% Created on 2018. March 25.
%
%%
% Automatically generated stuff
global SCOPE_DEPTH VERBOSE LATEX_EQNR
SCOPE_DEPTH = 0;
VERBOSE = 1;
LATEX_EQNR = 0;
try c = evalin('caller','p... |
prob_offset{j} = cell2mat(importdata([fileSave{1} filesep 'prob_offset.mat']));
prob_onset_on_offset{j} = cell2mat(importdata([fileSave{1} filesep 'prob_OnsetOnOffset_online.mat']));
output_onset_on_offset{j} = importdata([fileSave{1} filesep 'output_OnsetOnOffset_online.mat']);
output_offset{j} = importdata([fileSave... |
%% 3-D Brain Tumor Segmentation Using Deep Learning
% This example shows how to train a 3-D U-Net neural network and perform semantic
% segmentation of brain tumors from 3-D medical images. The example shows how
% to train a 3-D U-Net network and also provides a pretrained network. Use of
% a CUDA-capable NVIDIA™ GP... |
%{
Filter 1 - Part 1 :
apply 1-D median filter on noisy gray scale image
using "medfilt1" bulit-in function
-------------------------------------------------------------------------
Authors :
Hadis Ahmadian - 9622613
Maede Shamirzaei - 9629743
Hamidreza Moalem - 9635593
%}
clc... |
function [m3,n3]=floatingpoint_add(m1,n1,m2,n2)
%make sure inputs are integers
m1=int32(m1);
n1=int32(n1);
m2=int32(m2);
n2=int32(n2);
while n1>n2
m2=idivide(m2,int32(10));
n2=n2+1;
end
while n2>n1
m1=idivide(m1,int32(10));
n1=n1+1;
end
m3=m1+m... |
function calibrationCoeffs = generateNaiveCalibrationCoeffs(rangingData, varargin)
%calibrationCoeffs = generateNaiveCalibrationCoeffs(rangingData, varargin)
%
%this function uses the data in rangingData to calculate calibration
%coefficients for each sounder/microphone. It does this by choosing
%parameters for ... |
clear; clc; close all;
% Aluno: Cesar Vinicius Zuge
% Prova 2 de PDS
%--------------------------------------------------------------------------
% A)
Fs = 8000;
F1 = 1000; F2 = 2000; F3 = 3000;
n1 = 0:(Fs*2-1);
n2 = 0:(Fs*3-1);
n3 = 0:(Fs*5-1);
n = 0:(Fs*10-1);
x1 = cos(2*pi*(F1/Fs).*n1);
x2 = cos(... |
function [fitProbC,fitThresh,fitParams] = PALweibullFit(levels,probC,probThresh,numTrials,fitLevels)
% PALWEIBULLFIT
%
% [fitProbC, fitThresh, fitParams] = PALweibullFit(levels,probC,probThresh,numTrials,fitLevels)
%
% Fits a cumulative Weibull to the (levels,probC) data. Returns the threshold at
% the probThresh va... |
function def = HData( )
% HData Default options for the HData class.
%
% Backend IRIS function.
% No help provided.
% -IRIS Macroeconomic Modeling Toolbox.
% -Copyright (c) 2007-2017 IRIS Solutions Team.
%--------------------------------------------------------------------------
def = struct( );
def.h... |
%% Gradient Calculator
% Initialize
cost = 0;
structure = variables;
change = zeros(1,len);
untrained_val = 0;
trained_val = 0;
% Calculate the chain rule derivative for the gradient calculation
gradient_chain_calc;
% Derive wrt alpha and beta
change_alpha = - log(((alpha*sum_trained*prop_vec(l) + sum_untrained*(pro... |
clc;clear;
% measurements
deflec=[0.020 0.0185 0.0181 0.0178 0.0175 0.0173];
dV_read= [0.56 0.53 0.51 0.50 0.49 0.48];
% constants
I=2.083e-12;
t = 1e-3;
l = 0.255;
Vs = 5;
gain = 560;
strain = 3.*deflec*t/(2*l^2);
dV = dV_read./gain;
r = 4.*dV./(Vs-2.*dV);
K = r./strain;
mean_K = mean(K)*ones(1,length(K));
plot(... |
%% Creating the dataset in Q1
X=[2.5 2.4; 0.5 0.7;2.2 2.9; 1.9 2.2; 3.1 3.0; 2.3 2.7; 2.0 1.6; 1.0 1.1; 1.5 1.6; 1.1 0.9];
%% Calculating the new space
meanX=mean(X);
meanX=repmat(meanX,[length(X) 1]);
D=(X-meanX)';
S=D*D';
[V,~]=eig(S);
e1=V(:,1)'; %Row vector
e2=V(:,2)'; %Row vector
E=[e1;e2]; %Each row is an eig... |
% ADVISOR data file: ESS_PB54_14V_saber.m
%
% Data source:
%
%
% Data confirmation:
%
% Notes:
% These parameters are used in the Saber lead acid battery
%
% Created on: 08-March-2002
% By: AB, NREL, aaron_brooker@nrel.gov
%
% Revision history at end of file.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
WP(1,1) = RES(1,1);
WP(1,2) = RES(1,2);
flag = 0;
for iter = 1 : 1000
for n = 1 : length(RES)
if ((RES(n,1) == WP(iter,1)) && (RES(n,2) == WP(iter,2)+range)) && (RES(n,3) == 0)
WP(iter+1,1) = RES(n,1);
WP(iter+1,2) = RES(n,2);
flag = 1;
RES(n,3) = 1... |
function buildSFun(ecalPath)
arguments
ecalPath {mustBeFolder} = {};
end
proj = currentProject;
projFolder = proj.RootFolder;
incPath = {};
libPath = {};
cmexsfcnList = {'s_ecal_subscriber.cpp','s_ecal_publisher.cpp'};
cmexcommonList = {'s_ecal_common.cpp'};
% Search eCAL path for Windows
if is... |
function [clarity, pfMat] = frame2clarity(frameMat, fs, pfType, pfMethod, plotOpt);
% frame2clarity: Frame (or frame matrix) to clarity
%
% Usage:
% [clarity, pf] = frame2clarity(frameMat, fs, pfType, pfMethod, plotOpt);
% frameMat: a column vector of a frame, or a matrix where each column is a frame
% fs: samplin... |
function JBodyL = ContactJacobianL(s1,phi1,theta,s2,phi2,x1,y1,g,k1,k2,L_sp0,L_mB,mB,IB,m2)
%CONTACTJACOBIANL
% JBODYL = CONTACTJACOBIANL(S1,PHI1,THETA,S2,PHI2,X1,Y1,G,K1,K2,L_SP0,L_MB,MB,IB,M2)
% This function was generated by the Symbolic Math Toolbox version 7.1.
% 14-Jan-2017 01:29:06
t2 = phi1-theta;
t3... |
function [mu,sigma] = statsAB(n,Nab)
% for use with Rand z-score calculation from Traud, Kelsic, Mucha, & Porter 2010.
% gives mean and standard deviation of Wab under null model used in paper
% (which preserves row&column sums, i.e. number and size of communities in
% each partition.
% INPUTS: n (number of nodes)
% ... |
function [chars] = SourceB(type)
alphabet = ['a' 'b' 'c' 'd' 'e' 'f' 'g' 'h' 'i' 'j' 'k' 'l' 'm' 'n' 'o' 'p' 'q' 'r' 's' 't' 'u' 'v' 'w' 'x' 'y' 'z'];
fileID = fopen('kwords.txt','r');
chars = fscanf(fileID,'%s');
if type == 2
new_alphabet = []
a =1;
for i=1:26
for j=1:26
... |
clc
clear all
close all
%% Filtering x
% We have the audio signal x, and
% we filter through H(z)=B(z)/A(z), obtaining y
[x, Fs]=audioread('Toms_diner_16.wav');
b=[1, -1.5173, -0.0121, 0.7863, 0.1440];
a=[1. 0, 0.5 0 0.24, 0, 0.12];
y=filter(b,a,x);
%% what kind of filter is H?
% make some plots and ... |
%plot each family
colors=['r-';'go';'b*';'kx';'m.']
figure;
names={'en_roc','roc_1','roc_2'};
titles={'ensemble centroid','biggest centroid','2nd biggest centroid'};
for i=1:3
name=names{i};
eval(['cd ',name]);
subplot(2,2,i)
axis([0.3 1 0.2 1])
for ii=2:2:10
hold on
a=load([num2str(ii) '.roc']);
plot([a(1:8,2);a(end,2... |
clear;clc;close all;
x_bar = [1.1, 1.35, 1.25, 1.05];
one = ones(1,4);
sigma = [0.2, -0.2, -.12, .02; -.2, 1.4, 0.02, 0; -.12, 0.02, 1, -.4;0.02,0,-.4, 0.2];
R_min = 1.00:0.05:1.35;
risk = zeros(length(R_min),1);
Portfolios = [];
for i=1:length(R_min)
r_min = R_min(i);
n =4;
cvx_begin quiet
variable p(n)
... |
classdef range < irisinp.generic
properties
ReportName = 'Date Range';
Value = NaN;
Omitted = @error;
ValidFn = @(x) isdatrange(x);
end
methods
function this = preprocess(this,~)
if ischar(this.Value)
this.Value = textinp2dat(this.Val... |
function createVideoFromSimulationFile( )
%ds filepath
strFilepath = '../bin/simulation.txt';
%ds open the file
fileID = fopen( strFilepath );
%ds get the first line
cCell = textscan( fileID, '%u %u', 1 );
%ds get number of particles N and timesteps T
uNumberOfParticles = cCell{1};
uNumberOfTimesteps = cCell{2};
%... |
function simulate_fall
alpha = pi/4;
r = 1;
m = 1;
x0 = [1 5 0 0]';
h = 0.01;
N = 150;
sys1 = ContactImplicitSystem(@()ball_trough(alpha, r, m), 'ball');
sys1 = sys1.addVisualizer(@(x) ball_trough_visual(x, alpha, r));
[t, x] = sys1.simulate(0.01, N, x0, true, 4);
%qx = x(1,:);
%qy = x(2,:);
%figure(1);
%plot(qx, qy... |
function [u, v, hitMap] = opticalFlow(I1, I2, windowSize, tau)
%I1= imread('data/corridor/bt.000.png');
%I1=(rgb2gray(I1));
img = im2double(I1);
%I2 = imread('data/corridor/bt.001.png');
%I2=(rgb2gray(I2));
im2 = im2double(I2);
%tau =1.38;
% Compute a gaussian kernel
sigma = 1;
%windowSize = 100;
gaussianWidth = 3*sigm... |
function matEvaluateARK232( obj )
[EXa, IMa, EXb, IMb, c] = GetRKParamter();
time = obj.startTime;
ftime = obj.finalTime;
fphys2d = obj.fphys2d;
fphys = obj.fphys;
%> allocate space for the rhs to be stored
ExplicitRHS2d = zeros(obj.mesh2d(1).cell.Np, obj.mesh2d(1).K,3);
ExplicitRHS3d = zeros(obj.meshUnion(1).cell.Np,... |
%[RMSE, maxPctDiff, maxError, RMSEsamples, trueMean, sampleMeans, waveNum] = sampleSpectra_decMult(swFile, lwFile, nSamples, nSimulations, useSW, useLW, lwHiRes, allSky, latRange, lonRange)
%Uses MCMC integration to estimate the spectral mean for a given set of
%MODTRAN data by randomly sampling spectra from latitude a... |
fid = fopen('notmoving3corners2.2.txt');
nument = 0;
tline = fgetl(fid);
while ischar(tline)
matches = strfind(tline, '<<<');
num = length(matches);
if num > 0
nument = nument + num;
%fprintf(1,'%d:%s\n',num,tline);
end
tline = fgetl(fid);
end
frewind(fid);
Planedata = cell([1,nument]);
f... |
%{
common.OpticalMovie (manual) # intrinsic imaging movie$
-> common.OpticalSession
opt_movie : smallint # optical movie id within the optical session
---
purpose=null : enum('structure','stimulus','bar') # purpose of movie
filename : varchar(255) #... |
clear
clc
% global alpha beta
global Mot_I;
%%Configuration Parameters
Vmax=1000;
AcelT=0.4;
DcelT=0.4;
%%Variables de entrada
alpha=[0 0 45 0 0 0 0 0]; %Direccion de maxima pendiente
beta=[0 0 0 0 0 0 0 0]; %Angulo de giro
delete(instrfindall);
Arduino = serial("COM16",'BaudRate',115200);
s... |
function CC_select_var(varargin)
H=varargin{1};
handles=guidata(H);
var=varargin{3};
value=get(H,'value');
eval(['handles.var' num2str(var) '=value;'])
%%% Rescale axis
P=handles.spikeMatrix(:,3+value);
maxval=max(abs(P));Range=[-maxval maxval]*1.5;
if var==1
set(handles.figure_handles(1),'Xlim',Range... |
% clear;clc;
%load align wave
[x_talk fs] = audioread('t1.wav');
[x_sing fs] = audioread('s1.wav');
% x_sing = x_sing(10000:14000);
len = min(length(x_sing),length(x_talk));
x_sing = x_sing(1:len);
x_talk = x_talk(1:len);
param.hop = 1;
param.sr = fs;
[feat,t] = yin_best(x_sing,param); %get freq_tbl
f0 = feat.f0;
% ... |
nT = 100;
ISI = 1;
DAQ = InitializeDAQ;
tstim_max_noise_exp = 3;
t_stim_noise_exp = 0:(1/DAQ.s3.Rate):tstim_max_noise_exp;
for iT=1:nT
if DAQ.s3.ScansQueued <=3
sigOut = zeros(length(t_stim_noise_exp),1);
ix = 1:length(sigOut);
switch ISI
case 1
sigOut(ix... |
aparam=2; % 设置参数
for i=1:fn
Sp = abs(fft(y(:,i))); % FFT变换取幅值
Sp = Sp(1:wlen/2+1); % 只取正频率部分
Esum(i) = log10(1+sum(Sp.*Sp)/aparam); % 计算对数能量值
prob = Sp/(sum(Sp)); % 计算概率
H(i) = -sum(prob.*log(prob+eps)); % 求... |
%See http://www.cse.msu.edu/prip/Files/DubuissonJain.pdf
%to see notations and d1 distance
%h is the hausdorff distance h
function d = Hf2(A, B, h)
d1 = h(A, B);
d2 = h(B, A);
d = max(d1, d2);
end |
% This script draws time-averaged profiles of zonal flow drives
% versus time1
% Copy this script to the folder of the data and then run it
clear; close all;
global den Te pe vi jz ve phi vEx vEy dt inv_nustar
load('parameters.mat');
addpath(code_path);
last_file = get_last_file('./');
last_diag = str2num(last_fil... |
function [ fphys ] = matEvaluateLimiter( obj, fphys )
[ fphys ] = obj.limiterSolver.apply( obj, fphys );
end
|
function [endPos, prevPathRows, prevPathCols] = BestPosition(startPos,grid)
% This function determines the best path that can be travelled with the
% minimum cost, from the starting position to anywhere on the eastern edge.
% Instead of using Greedy Pick method, this function evaluates all possible
% paths and dete... |
function [f,J]=quad_equal_const(V,B,d,z)
V=V(:);
n=length(d);
f=zeros(n,1);
for i=1:1:n
f(i)=V'*B{i}*V+V'*d{i}+z{i};
end
% computing the jacobian
J=zeros(n,length(V));
for i=1:1:n
J(i,:)=(B{i}+B{i}')*V+d{i};
end
|
function [f,P1] = T2F(X,Fs)
%输出f频率轴，P1DFT变换后序列 输入X为采集序列，Fs为采样频率
%[f,P1] = T2F(X,Fs) 输出的频谱为复数，如果画功率谱记得进一步运算
L = length(X);
P2 = fft(X);
P1 = fftshift(P2);
f = (-L/2:L/2-1)*Fs/L;
end
|
%% node2control.m
%
% Function that returns the coordinates of the control points of the curve
% (spline parametrization), given the node points (Bezier).
%
% Matthieu Guerquin-Kern, Biomedical Imaging Group / EPF Lausanne,
% 23-07-2009 (dd-mm-yyyy)
function control = node2control(node,shift)
if nargin<2
shift = ... |
function [ difference ] = GaussianAs1D(w,s)
% Genarates 2 1D gaussian and convolves with an image.
% Convolves 2D gaussian kernel with the same image and compares it with the
% above result.
img = imread('hw1_images\lena.bmp');
X = 1:w;
for i = 1:w
X(i) = exp(-((i-(w+1)/2)^2)/(2*s^2));
end
X = X/sum(X... |
function x = stop_dist_calc(v0, a0, s)
% calculate -s time
t2 = sqrt(v0 / s + 0.5 * a0^2 / s^2);
% calculate -s time
t1 = t2 + a0 / s;
if (t1 < 0 || t2 < 0)
s = -s;
t2 = sqrt(v0 / s + 0.5 * a0^2 / s^2); % calculate -s time
t1 = t2 + a0 / s; % calculate -s time
end
% calculate t1 end point condition
... |
function x = frobinnerproduct( A,B )
x = sum(sum(A.*B));
end
|
function varargout = configure_window(varargin)
% CONFIGURE_WINDOW MATLAB code for configure_window.fig
% CONFIGURE_WINDOW, by itself, creates a new CONFIGURE_WINDOW or raises the existing
% singleton*.
%
% H = CONFIGURE_WINDOW returns the handle to a new CONFIGURE_WINDOW or the handle to
% the exis... |
% This function takes as its inputs coefficient matrix ('coeffs'),
% a matrix of exponents ('exps'), and a 'facet_rule'. It returns a matrix of coefficients
%where all columns corresponding to elements of 'exps' where the only nonzero elements correspond to
%'facet_rule' are removed.
%
%Details:
%Inputs:
%Coef... |
function dGrf_heel_c3_q2 = dGrf_heel_c3_q2(in1,in2,s)
%DGRF_HEEL_C3_Q2
% DGRF_HEEL_C3_Q2 = DGRF_HEEL_C3_Q2(IN1,IN2,S)
% This function was generated by the Symbolic Math Toolbox version 8.4.
% 06-Jul-2020 22:18:20
q1 = in1(:,1);
q2 = in1(:,2);
q3 = in1(:,3);
q4 = in1(:,4);
q5 = in1(:,5);
q6 = in1(:,6);
t2 = c... |
function [ret]=Fun2(vector)
len = length(vector);
if len==1
ret = vector;
else
Part1 = vector(1:floor(len/2));
Part2 = vector(floor(len/2)+1:end);
%ret = Gun(Part1) + Hun(Part2);
%ret = Hun(Part1) + Hun(Part2);
%ret = Gun(Part1) + Gun(Part2);
ret = [Fun2(Part1) Fun2(Part2)];
ret = ... |
function [elbo grad] = regvarbayes_elbo(y, ptr_kl, ptr_elike, ptr_reg, ...
ptr_fwd, prior_param, like_param, ...
post_param, fwd_param, reg_param )
% Regularized Variational Bayes evidence lower bound
% ptr_kl: pointer to KL(q||p)
% ptr_lik... |
function merge_t = all_feature(mp3_dir, midi_dir)
mp3_list = py.os.listdir(mp3_dir);
midi_list = py.os.listdir(midi_dir);
N = length(mp3_list);
for i = 1:N
mp3_fname = mp3_list(i);
mp3_fname = mp3_fname{1};
mp3_fpath = py.os.path.join(mp3_dir, mp3_fname);
mp3 = char(mp3_f... |
% ECE 273 - Convex Optimization and Applications
% Final Project - On Convex Optimization and Support Vector Machines
% By: Arkin Gupta, Andrew Gates
%
% This program is designed to run SVM on a pseudo random generated set of
% data using CVX. It will use CVX to calculate the separating hyperplane as
% well as the ... |
clear
load('data\xi_LH.mat')
load('data\xi_SH.mat')
load('data\eta_LH.mat')
load('data\eta_SH.mat')
load('data\sgm0_LH.mat')
load('data\sgm0_SH.mat')
load('data\adhd.mat')
load('data\adhd_new.mat')
load('data\age.mat')
Mode = 'Spearman';
X = [xi_LH]; % xi_SH eta_LH eta_SH sgm0_LH sgm0_SH];
Y = a... |
function [imu, state, pose, frame] = readRFBag(bagpath)
%READBAG Extracts messages into matrices without using custom msg defn
% Data is in FRD
frame = 'FRD';
bag = rosbag(bagpath);
% Get IMU messages
bagsel = select(bag, 'Topic', '/imu/data'); %'interpolate_imu/imu'
msgs = readMessages(bagsel,'DataFormat','struct')... |
function splot(searchString,xvect,yvect,varargin)
% Plots struct.(xvect) vs struct.(yvect) for all structures containing
% searchString in their name. if xvect and yvect have length one, all
% values will be plotted as one.
%
% Varargin can specify a color map
% e.g.
% RGB fade:
% splot('PKDynamic', 'Field', 'Kerr', ... |
function d_new_350 = boxplot_normalize_A9_350(d_new)
d_new(1:350) = boxplot_normalize(d_new(1:350));
d_new(351:650) = boxplot_normalize(d_new(351:650));
d_new(651:950) = boxplot_normalize(d_new(651:950));
d_new(951:end) = boxplot_normalize(d_new(951:end));
d_new_350 = d_new(3:end); |
function Covariance(N)
% Discrete time Kalman filter for position estimation of a Newtonian system.
% This example illustrates the effectiveness of the Kalman filter for state
% estimation. It also shows how the variance of the estimation error
% propagates between time steps and decreases as each measurement is... |
c=Circuit();
c.AddResistor(241,1,3);
c.AddResistor(412,2,1);
c.AddResistor(912,3,2);
c.SetGround(3);
c.MakeEquations(); |
function [J, theta1_grad, theta2_grad] = costFunction(theta1, theta2, x, y, lambda)
% コストファンクションの実装
% コストと次のthetaの計算に使うgradをかえす
% X 9 * 500 => biasとして1を追加するので 10 * 500 またきちんと並べ替えて 10 * 500でやる
% y 9 * 500
% theta1 15 * 9 => biasの分 15 * 10
% theta2 9 * 15 => biasの分 9 * 16
J = 0;
theta1_grad = zeros(size(theta1));
theta2_... |
function RefreshGUI(obj)
%
% Copyright 2015 Yulin Wu, Institute of Physics, Chinese Academy of Sciences
% mail4ywu@gmail.com/mail4ywu@icloud.com
handles = obj.uihandles;
if isempty(handles)
return;
end
set(handles.xsliceax,'Visible','off');
set(handles.ysliceax,'Visible','off');
set(... |
% bal.m
% Uses the results developed by gui_post_process.m to compare energy input to
% losses. Displays imbalance in kJ.
% collect inputs
input_kj=0;
if ~isnan(fuel_in_kj)
input_kj=input_kj+fuel_in_kj;
end
if exist('ess_stored_kj') & ~isnan('ess_stored_kj')
input_kj=input_kj-ess_stored_kj;
end
% collect losse... |
classdef COPdataSet < matlab.System
% Raw data path for FileInput object
%This object holds the two x and y data sets and creates the transverse
%data set "Hypot"
%AP and ML are the X and Y coordinate sets respectively,Hypot is the
%data set calculated as the length of the hypotenuse formed fro... |
function [A,x] = hw4p5(k)
% generalized function for creating k length long trusses
% calculate dimensions of the matrix: dimension = 8 * (k-1) + 13
d = 8 * (k-1) + 13;
% a = sin(pi/4)
alpha = sqrt(2)/2;
% initialize Ax=b
A = sparse(d,d);
x = zeros(d,1);
b = zeros(d,1);
% create the first 4 force equations for the ... |
function [theta freq phase] = Synchronize_V2( Signal, training, Q)
%SYNCHRONIZE Use cyclic prefix to find timing and frequency offset
%
%Input
%SIGNAL - the entire modulated signal
%N - the nr of data symbols per frame
%L - the length of the cyclic prefix, in symbols
%Q - the length of the pulse
%Output
%THETA - eleme... |
function [images] = loadPedestrian()
foldername = './pedestrian/';
addpath(foldername);
Files=dir(foldername);
images = [];
for k=3:length(Files)
filename = Files(k).name;
% filename = foldername + fieldnames;
image = imread(filename);
if size(image,3)>1
image = rgb2gray(image);
end
vect... |
function [EEG] = VpixxEarlyTriggerFix(EEG)
%adds 6 ms to every trigger time in order to account for the delay between
%the trigger and the pixel onset in the top left corner (could always add
%more time to account for the delay until the stimulus pixel is
%illuminated)
for i_event = 1:lengt... |
function [W_significant network_load components] = prune_insignificant_links(W_observed,W_ALL,X_hist,alpha)
Wsize = size(W_observed);
days = Wsize(3);
N = Wsize(1);
simulations = length(W_ALL);
W_significant = zeros(Wsize);
network_load = zeros(days,1);
components = zeros(days,1);
for day=1:days
%% LINK-SPE... |
display('----------Ejercicio 3-----------');
%si se quiere correr solo, correr loadData y utils
corr_temp = [];
gcc_sin_ventaneo = [];
for k = 1:4 % recorremos los audios
%correlacion cruzada
tau = utils.tau_correlacion_cruzada(mics(:,k),mics(:,k+1),fs);
corr_temp = [corr_temp tau];
%gccphat
tau... |
function [ converted ] = util_escape_string( inputs )
%UTIL_ESCAPE_STRING Convert escape sequence into string
% We just replace '\' into '\\'
%
% Created on Aug/17/2010 By Pu Jiangbo
% Britton Chance Center for Biomedical Photonics
converted = strrep(inputs, '\', '\\');
end
|
% 距离＋角度测量，使用间接测量量进行相对导航
disp(strcat('Simulation Start:',datestr(clock)));
clear
close all
load leo20
%% filter
rou_error = 10/1000/3; % 距离测量误差
angle_error = 0.05*rad/3; % 角度测量误差
atti_error = 0.05*rad/3; % 姿态控制误差
atti_measure_error = 0.005*rad/3; % 姿态测量误差
realtime = 0;
rou = zeros(length(t),1);
alpha = zeros(leng... |
%% shstat_taxa
% plots statistics and/or parameters for a taxon as function of taxonomic distance
function [Hfig Hleg val entries missing] = shstat_taxa(vars, legend, label_title, Hfig)
% created 2017/04/22 by Bas Kooijman
%% Syntax
% [Hfig val entries] = <../shstat_taxa.m *shstat_taxa*>(vars, legend, label_title, H... |
function c = mfcc(s, fs)
% MFCC Calculate the mel frequencey cepstrum coefficients (MFCC) of a signal
% Inputs:
% s : speech signal
% fs : sample rate in Hz
% Outputs:
% c : MFCC output, each column contains the MFCC's for one speech frame
N = 256;
M=100;
win_frames = zeros... |
% Jiaxin Cindy Tu 20190423
% Strait et al. 2015 Fig 3B sliding window
function [significance,selectivity]=calc_tuning_slidwind(data)
if ~exist('spikeBinMs','var')
spikeBinMs = 10;
end
sigma = 20*spikeBinMs; % ms gaussian smoothing kernel
wind = -15:15; % 310 ms window
plot_t = 16:5:385;
x = [-100:299];
event_ts =... |
function iid = prueba_portmanteau(data)
aut=xcorr(data);
alfa=.05;
% stem(1:length(aut),aut);
% hold on
% plot(1:length(aut),1.96/sqrt(length(data))*ones(length(aut),1),'r-');
[maxaut idx]=max(aut);
Q=length(data)*sum((aut(idx+1:end)./maxaut).^2);
if Q>chi2inv(1-alfa,length(data))
iid=0;
else
i... |
function Pn = sympoly2polyn(sp)
% sympoly2polyn: converts a sympoly to something that polyvaln can evaluate efficiently
% usage: Pn = sympoly2polyn(sp)
%
% arguments: (input)
% sp - any sympoly object, as created by the sympoly toolbox
%
% arguments: (output)
% Pn - a struct that polyvaln can use
%
% Example:
% sym... |
classdef MatrixFunctions
% Also see the class MatrixTransformers.
methods(Static=true)
function [sumAlongRows, sumAlongColumns] = getRowColSums(A)
sumAlongRows = sum(A, 1);
sumAlongColumns = sum(A, 2);
end
function [sumGraphs] = plotRowColSums(A)
sumGraphs = figure;
[sumAlongRows, sumAlongColumns] = Ma... |
%--------------------------------------------------------------------------
% open appropriate files, do basic analysis and create arrays that
% are ready to be plotted.
%
% source strings for the data are defined in cf_tdtlw_4pan.m
%
% figures are made in cf_tdtlw_4pan.m
%
% levi silvers ... |
filename='C:\Users\15 ek00004nl\Desktop\Trapano ML\Acquisizioni con accelerometri\15_01\FORZA\5\V1.txt'
F15 = import_forces(filename);
save F15
filename='C:\Users\15 ek00004nl\Desktop\Trapano ML\Acquisizioni con accelerometri\15_01\FORZA\5\V2.txt'
F25 = import_forces(filename);
save F25
filename='C:\Users\15 ek00004nl\... |
% omitoption =1 % omit any set of data that has all zero
function [result] = stat_combine_mat(MatData,dimention,omitoption,savematdata)
totalnum=size(MatData);
if dimention==1
checkdimention=2;
elseif dimention==2
checkdimention=1;
else
error ('choose dimention between 1 or 2)')
end
% omit,... |
function glmb_out= prune(glmb_in,filter)
%prune components with weights lower than specified threshold
idxkeep= find(glmb_in.w > filter.hyp_threshold);
glmb_out.tt= glmb_in.tt;
glmb_out.w= glmb_in.w(idxkeep);
glmb_out.I= glmb_in.I(idxkeep);
glmb_out.n= glmb_in.n(idxkeep);
glmb_out.w= glmb_out.w/sum(glmb_out.w);
for ca... |
function [lee_h, lee_v] = lvp_lee(img, block_size)
if size(img,3) == 3 % % Check if the input image is grayscale
img = rgb2gray(img);
end
[m, n] = size(img);
l= floor(m/block_size);
l2= floor(n/block_size);
l = l * l;
l2 = l2 * l2;
sizeX = m/sqrt(l);
sizeY = n/sqrt(l2);
while( (sizeX - floor(size... |
function [varargout] = CanLoad(filepath,varargin)
% CanLoad -- Loads a CANalyzer Data file into a Matlab Structure
%
% CONFIDENTIAL FORD MOTOR COMPANY (see notice in .m source file)
%
% USAGE:
% [cancel_button,test_name,can] = CanLoad(full_name)
%
% DESCRIPTION:
% Uses the Common Open Dialog Box to load a CANAly... |
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