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function [C,f] = op_coherence_2continus_f(data1,data2,Fs,window,band,fpass)
% 利用coherence 工具包计算两个连续序列的coherence关系
TW = window*band; % 窗长度为 2 s，半频带宽度为 4；TW = 2*4 = 8；
K = 2*TW - 5; % K = 2*TW -1 = 15
tapers = [TW,K];
params.tapers = tapers;
params.Fs = Fs;
params.fpass = fpass ; % show results between 0 and 100 Hz
param... |
function [ dominanteFace ] = getMostDominanteFace( boxes )
%return the most dominante face in image
if(isempty(boxes))
dominanteFace=[];
return;
end
maxArea=0;
for i=1:size(boxes,2)
if(iscell(boxes)) %cell type handle
currArr = cell2mat(boxes(1,i)); %convert cell to matrix
currArea = ... |
% Set the wecSimPath variable to the location of the WEC-Sim source directory
% Copy the code in this file and place it in your'startup.m' file
wecSimPath = "C:\Users\user\Documents\FYP-2020";
addpath(genpath(wecSimPath));
|
function uff1 = ReviseMOL(uff, atomType)
uff1 = uff;
tmp = find(uff.bound==1);
if length(tmp) ~=2
disp('For polymers, there must exist 2 active atoms in MOL_1 ...')
disp('Please go back and recheck the MOL_1 file...')
quit;
else
format = uff.format;
coords = uff.molecule;
bound = u... |
function fde=fd(imagename)
img=imread(imagename);
if(size(img,3)==3)
img=rgb2gray(img);
end
level=graythresh(img);
bimg=imbinarize(img,level);
limg=bwareafilt(bimg,1);
bound=bwconncomp(limg);
ll=size(img,1);
mm=size(img,2);
[x,y]=ind2sub([ll mm],bound.PixelIdxList{1});
... |
function [ Miu, Sigma, P, U, S, V ] = MFGMulMF( Miu1, Sigma1, P1, U1, S1, V1, FM )
% Multiply a MFG density and a matrix Fisher density, and match the
% resulting density to a new MFG.
% See W Wang, T Lee, https://arxiv.org/abs/2003.02180, 2020
% Inputs: Miu1, Sigma1, P1, U1, S1, V1 - parameters for the MFG
% F... |
function auc = evaluation_AUC(decision_values, label)
L = length(label);
pos = sum(label == 1);
neg = sum(label == -1);
error = 0;
for t = 1:10
rp = randperm(L);
dv = decision_values(rp);
lb = label(rp);
[value,inx] = sort(dv,'descend');
flag = 0;
for i = 1:L
if lb(inx(i)) == -1
... |
function [L,U,P] = LUdispPiv(omega,lambda,R,S)
% function [L,U,P] = LUdispPiv(omega,lambda,R,S)
% Fast LU factorization of a Cauchy-like matrix with partial pivoting.
% omega and lambda are column n-vectors with no common entries.
% R and S are nxr matrices.
% L is nxn unit lower triangular.
% U is nxn upper t... |
clear variables;
x = linspace(0, 7, 10001);
x0 = 1.0;
m0 = 2.4;
f0 = (m0/x0).*((x./x0).^(m0-1)).*exp(-1.*((x./x0).^(m0)));
legendname0 = 'Southern HWK';
x1 = 2.5;
m1 = 2.0;
f1 = (m1/x1).*((x./x1).^(m1-1)).*exp(-1.*((x./x1).^(m1)));
legendname1 = 'Southern SWK';
x2 = 0.67;
m2 = 1.4;
f2 = (m2/x2).*((x./x2).^(... |
%% Bonus Problem
addpath('/home/derek/Tools/MATLAB/');
q =sym('q', [3 1], 'real');
p =sym('p', [3 1], 'real');
r =sym('r', [3 1], 'real');
A1 = cross(q,r)/(dot(r,r));
A2 = cross(r,p)/(dot(r,r));
A3 = cross(p,q)/(dot(r,r));
U1 = -skewsym(cross(A1,p))+(dot(A1,p))*eye(3);
U2 = -skewsym(cross(A2,q))+(dot(A2,q))*eye(3);
U3 ... |
%%%%%%%%%%%%%%
%% Vortex: BIC
%%%%%%%%%%%%%%
% Runs 4 possible models of the two main regression models presented in the
% Supplementary Material (Table S3 and S4), calculates the BIC for these
% models and produces the plots S1 and S2.
% Requires function from SPM toolbox (spm_BMS).
%% LOAD DATA
clc
clear
close al... |
w = 15;
x = 0:0.1:15;
y = exp(-0.7*x).*sin(w*x);
plot(x,y);
xlabel("x");
ylabel("y");
title("y(x) = e^-^0^.^7^x sin\omega x"); |
function [ ] = afficherKeypoints(image, listPoints )
% Affiche des cercles correspondants aux points clés avec leurs orientations.
%La taille et la couleur varient selon le sigma.
figure;
imshow(image);
% Affichage des points clés sur l'image
[t,u] = size(listPoints);
totalSizeListPoints = t*u;
... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Codes for estimating lattice constant of ABX using PLSR and PCR.
% Dataset can be seen in data_ABX.mat. Dataset contains experimental
% lattice constant [C1], ionic radii [C2-C4], atomic number [C5-C7],
% electronegativity [C8-C10], and dens... |
function url = gendemolink(name, text)
%GENDEMOLINK Generate a weblink for a demo
% Author(s): Nico Vervliet (Nico.Vervliet@esat.kuleuven.be)
%
% Version History:
% - 2015/07/10 NV Initial version
baseurl = 'http://www.tensorlab.net';
url = sprintf('<a href="matlab: web(''%s/demos/%s.html... |
% tent map example
% %%%% generate training and testing time series for ESN training
% The learning-and-testing script learn.m expects data to be contained in
% two matrices named sampleinput (of size inputdim x samplelength) and
% sampleout (of size outputdim x samplelength). Such two matrices must be
% the result of... |
% edited for amplitude Cara Stepp 08-15-2013
%edited by Liz Heller Murray 6/3/2014
%edited by Defne Abur 4/7/2015 to normalize sound, 65dB on MOTU line 179
%edited by Defne Abur 4/9/2015 to add in 'order' and 'ordersave' variables to
%randomize presentation order - see lines 93 - 99
% edited by Liz Heller Murray 5... |
function fdetect(filepath)
FDetect = vision.CascadeObjectDetector();
I = imread(filepath);
BB = step(FDetect,I);
figure,
imshow(I);hold on
for i= 1:size(BB,1)
rectangle('Position',BB(i,:),'LineWidth',5,'LineStyle','-','EdgeColor','r');
end
title('Face Detection');
... |
function [neighbors,D,loc] = nearestNeighbor(S,T,K)
if nargin < 3, K=size(S,1);end
D = zeros(size(S,1),1);
loc = zeros(size(S,1),1);
for k=1:size(S,1)
dk = sqrt(sum((bsxfun(@minus,S(k,:),T)).^2,2));
[D(k),loc(k)] = min(dk);
end
neighbors = T(loc(1:K),:);
end
|
function [flag, test] = isstationary(this, varargin)
% isstationary True if model or specified combination of variables is stationary.
%
%
% Syntax
% =======
%
% Flag = isstationary(M)
% Flag = isstationary(M,Name)
% Flag = isstationary(M,LinComb)
%
%
% Input arguments
% ================
%
%... |
function [xU,yU,zU] = UniformVelocity(x,y,z,Time)
xU = 3;
yU = 0;
zU = 0;
|
warning off
rmpath(genpath(fileparts(mfilename('fullpath'))));
warning on
addpath(genpath('helpercode'));
% % % % % % % % % % % % InitAll
%% Specific student assignments that need to be checked
for nw = 1:4
weekName = ['week' num2str(nw)];
cd(con.STUDENTSUBFOLDER)
apSubmitted = pwd;
postfxZipped = '_un... |
function [hmask] = make_hmask(mbb_par, bb, sz)
mbb_pnts = double(mbb_par - [bb(1:2)]'+1);
hmask = logical(poly2mask(mbb_pnts(1,:), mbb_pnts(2,:), sz(1), sz(2))); |
% Represents the kinematics and dynamics of a single cable segment
%
% Author : Darwin LAU
% Created : 2011
% Description :
% Cable segments are fundamental for MCDMs since cables can pass through
% multiple links and hence have multiple segments.
classdef CableSegmentModel < handle
properties ... |
function X = outlierlim(d,gname,varargin)
multiplier = 2;
vararginProcessor
T = grpstatsTable(d,gname,{'gname','mean','std'});
X = table;
X.gname = T.gnameu;
X.upperlim = T.mean + (T.sd.*multiplier);
X.lowerlim = T.mean - (T.sd.*multiplier); |
% This function executes a Min intra- and max inter-class distance dimension reduction.
function LocMat=GetMemdCorrIndex(Num_imf,NumSub_MCI,NumSub_NC)
CorrMeanImf_MCI=CorrGroupMeanImf('MCI',NumSub_MCI,Num_imf);
CorrMeanImf_NC=CorrGroupMeanImf('NC',NumSub_NC,Num_imf);
corrtmp=abs(CorrM... |
function dec = bytes2dec(bytes)
%dec = bytes2dec(bytes)
%
%this function will take a string of bytes and
%turn it into a decimal value.
%
%USE BIG_ENDIAN to determine the endianness
% "Copyright (c) 2000 and The Regents of the University of California. All rights reserved.
%
% Permission to use, copy, modify... |
function [iter,x,ea] = mjacobi
%Función para solucionar un sistema de tres ecuaciones lineales
syms x1 x2 x3
x0=input('Digite f(x0)=');
tol=input('Digite el porcentaje de tolerancia=');
ecu=leerecu; %subfunción de las ecuaciones a resolver
y(1)=solve(ecu(1),x1);
y(2)=solve(ecu(2),x2);
y(3)=solve(ecu(3),... |
function example_coch(data,fs,time_step)
fmin = 200;
fmax = fs/2;
total_no_freq = 70;
freq_step = 3;
dt = 5;
[X_ft, t, params] = cochleagram(data, fs, dt, 'log', fmin, fmax, total_no_freq);
figure('units','normalized','outerposition',[0 0 1 1]);
imagesc(flipud(X_ft));
time_s = t(1:time_step:numel(t));
xticks(1:time_s... |
function varargout = ceo_tool(varargin)
% CEO_TOOL MATLAB code for ceo_tool.fig
% CEO_TOOL, by itself, creates a new CEO_TOOL or raises the existing
% singleton*.
%
% H = CEO_TOOL returns the handle to a new CEO_TOOL or the handle to
% the existing singleton*.
%
% CEO_TOOL('CALLBACK',hObject,ev... |
addpath(genpath('feature-extraction'));
addpath(genpath('liblinear-1.94'));
%% STEP 1
% Compile the feature-extraction toolbox
% - cd to feature-extraction folder
% - type compile (there will be warnings printed - ignore them)
%% STEP 2
% Compile liblinear (only for non-Windows machines)
% - cd to liblinear-1.94/m... |
function Main(varargin)
%% BC-VARETA toolbox v1.0
%%%%%%%%%%%%%%%%%%%%
% Includes the routines of the Brain Connectivity Variable Resolution
% Tomographic Analysis (BC-VARETA), an example for real EEG analysis.
% BC-VARETA toolbox extracts the Source Activity and Connectivity given
% a single frequency componen... |
function [ ] = plotAngles( queue )
%PLOT1D Summary of this function goes here
% Detailed explanation goes here
a = zeros(8,200)
while(1)
a(1:end - 1) = a(2:end);
% blocks until new data is available
a(end) = queue.take;
queue.getQueue.clear;
plot(a);
getframe;
end
end
|
clear
clc
format compact
%% System Model
%need NB, Parent(i), jtype(i), X_T, Ii
[P, PARENT, KINE, INER, CNCTPTS] = PendModel();
[RobotLinks,RobotParam] = PendRobot(P, PARENT, KINE, INER, CNCTPTS);
NB = P.NB;
%% Dynamics
q = zeros(3,1);
dq = zeros(3,1);
ddq = zeros(3,1);
q = [0 -0.3 0.6]';
dq = [1 1 1]';
ddq = [1 ... |
function [outfile] = label_videobbox(indir)
%--------------------------------------------------------------------------
%
% Copyright (c) 2014 Jeffrey Byrne
%
%--------------------------------------------------------------------------
[bbox, is_occluded] = bobo.annotate.videobbox(indir, 1, fullfile(indir, 'detections.... |
function blo = prepa_num_cifrar(tama, bloque)
%prepa_num_cifrar - Description
%
% Syntax: blo = prepa_num_cifrar(tama, bloque)
%
% Función que convierte una cadena numerica en bloques
% de un tamaño dado, despues convierte dichos bloques en
% numeros y los almacena en un vector. Si es necesario para
% completar el u... |
function [classes_avg] = get_class_avg_falpha_measure(conf_matrix)
classes_avg = zeros(1,6);
for i = 1:6
recall_rate = get_recall_rate(conf_matrix);
prec_rate = get_precion_rate(conf_matrix);
classes_avg(i) = get_falpha_measure(recall_rate, prec_rate);
end
end
|
function [t,d]=propagate_init(tign,distance)
% [t,d]=propagate_init(tign,distance)
% create the initial state before the first call of propagate
[m,n]=size(tign);
t=zeros(m,n,3,3);
for i=1:m,
for j=1:n
t(i,j,:,:)=tign(i,j);
end
end
d=distance;
end |
function ShowStimulusMovie(input,settings)
%ShowStimulus
%
%version 0.1
%08/16/2007
%written by Gert Van den Bergh
%
%Shows a stimulus with the required parameters
if nargin<1
%ScreenData
settings.screendistance = 25; %distance from eyes to screen
settings.pixelsize = 47/1280; %pixelsize in centimeters: screen width... |
function [ targets, track_candidates, confirmed_tracks, deleted_tracks, target_id ] = target_tracking( targets, total_clusters, step,...
dimensions, precision, Re_width, radius_t, n_memory, n_confirm, n_max_confirm, n_delete, target_id, track_candidates, ...
confirmed_tracks, deleted_tracks)
%TARGET_TRACKING Su... |
present = true;
%I AM HERE I SWEAR |
function Solitarywave(obj, mesh)
syms x t;
H0 = 0.32;
d = H0;
A = H0 * obj.Ratio;
l = H0*sqrt((A+H0)/A);
C0 = l/d*sqrt(obj.gra * H0^3/(l^2-H0^2));
h = H0 + A * ( sech( (x-C0*t)/l ) )^2;
U = C0 * (1 - d/h);
W = -( A * C0 * d )/( l * h ) * sech( (x - C0 * t)/l )*( diff( sech( (x - C0 * t)/l ), x ) * l );
% P = A*C0^2... |
function img = extendVoi( img, k )
if length( size(img) ) == 2
return;
end
for i = 1:k
img(:,:,i) = ( img(:,:,i) + img(:,:,k+1) * ( k + 1 - i ) ) / ( k + 2 - i );
img(:,:,end-i+1) = ( img(:,:,end-i+1) + img(:,:,end-k) * ( k + 1 - i ) ) / ( k + 2 - i );
end
end |
classdef Merton
properties
T;
mu;
sigma;
riskyAsset;
risklessAsset;
initialWealth;
utility;
end
methods
function o=Merton(initialWealth,T,mu,sigma)
o.initialWealth=initialWealth;
o.T=T;
o.mu=mu;
... |
%--------------------------------------------------------------------------
% Gauss_weights.m
% determines Gaussian quadrature weights using Gauss nodes
%--------------------------------------------------------------------------
% w = Gauss_weights(tau)
% tau: Gauss nodes
% w: Gaussian quadrature weights
%----... |
clear;
sampleMount=40;%分类数
imageMount=10;%每个分类的样本数
n=112*92;%特征数
%读入数据
y=zeros(sampleMount*imageMount,1);
x=zeros(sampleMount*imageMount,n);
for i=1:sampleMount
for j=1:imageMount
x((i-1)*10+j,:)=double(reshape(imread(['orl_faces/s',num2str(i),'/',num2str(j),'.pgm']),1,n));
y((i-1)*10+j,1)... |
%COMPUTEINITIALPOTENTIALS Sets up the cliques in the clique tree that is
%passed in as a parameter.
%
% P = COMPUTEINITIALPOTENTIALS(C) Takes the clique tree skeleton C which is a
% struct with three fields:
% - nodes: cell array representing the cliques in the tree.
% - edges: represents the adjacency matrix o... |
function x = jacobifun(A,b,tol,n)
% Mètode iteratiu de Jacobi
if nargin == 2
n = 100;
tol = 0.5e-5;
end
if nargin == 3
n = 100;
end
D = diag(diag(A));
L = tril(A-D);
U = triu(A-D);
DI = inv(D);
BJ = -DI*(L+U);
cJ = DI*b;
rhoJ = abs(eigs(BJ,1));
if rhoJ < 1
k = 0;
x = zeros(size(b))... |
function [P,F]=updatesecondaryvariables(Fhat,GradNref,unmas1i,pgausselem,nnodes)
% la i que acompaña a unmas1 en las variables de entrada viene a
% recordar que los valores de entrada Piola1i y unmas1i, para esta función son los de la
% iteración i de N-R para el tiempo n+1..
global lambda mu
... |
function Score=GetScore(A)
% function Score=GetScore(A)
% Input A: A sequence need to sort
% Output Score: the sort index
% eg:
% if A=[1 2 4 1 5]
% then Score=[1 2 3 1 4]
SortA=sort(A);
N=length(A);
ScoreA=zeros(1,N);
for i=1:N
for j=1:N
if A(i)==SortA(j)
ScoreA(i)=j;
break;
... |
%\\zubjects\Subjects\FR141%
i =0;
i =0;
i = i+1;
db(i).mouse_name = 'FR141';
db(i).date = '2019-05-03';
db(i).expts = [2 3 5 7 8];
db(i).waveL = [1000 1000 1000 1000 760];
db(i).expID = 1:4;
db(i).stimType = {'spontaneous', 'spontaneous', 'spontaneous', 'oris', 'oris'}; % grey... |
%{
tp.TraceVon (computed) # VonMises tuning fits for traces
-> tp.TraceOri
-----
von_r2 : double # fraction of variance explained (after gaussinization)
von_fp : double # p-value of F-test (after gaussinization)
sharpness : float # tuning sharpness
pref_dir : float # (radians) preferred direction
peak_a... |
function [Melem] = ElemAcouMass(XYZ,mat)
% (1-ig)/b
if isfield(mat,'cs') && ~isempty(mat.cs)
Keff = mat.K_s;
rho0= 1;
else
rho0 = 1;%mat.rho;
Keff = mat.c^2*mat.rho;
end
NPE = size(XYZ,1);
if NPE == 8
IP.XI = [-1 -1 -1;...
1 -1 -1;...
1 1 -1;...
... |
function [SkipList,lftloc,rgtloc,lftamp,rgtamp,lftwid,rgtwid,avgwid] = ...
func_guesspeaks(data1D,TPH,frames,sensitivity,graph,trim)
global yBin
%Height of data
global yBotEnd
global xRgt
%% ---------------- Fitting parameters
trimStart = trim;
trimEnd = xRgt-trim;
clear rgtloc;
clear lftloc;
%Intialize/Reset ... |
function [output] = model_stats(lin_corrs, fft_corrs)
%% Function input
% lin_corrs: Matrix of linear correlations. Rows are test data, columns
% are database entries
% fft_corrs: Matrix of fft correlations. Rows are test data, columns
% are database entries
assert(size(lin_corrs, 1)==size(fft... |
% trainModel.m
% train model for metric learning
% all model data are saved in ./data/model/
clear all;
close all;
tic;
setInitial;
dataSetName = 'msramm';
featureName = 'EHD';
scale = true;
imgClassNo = get_dataSetInfo(dataSetName,'imgClassNo');
imgClass = get_dataSetInfo(dataSetName, 'imgClass');
labels = get_dataS... |
function [remtimes,remidx]= RemoveCrosstalk(g,ch1,ctchannels,h)
sortchannels=getappdata(h,'sortchannels');
handles = getappdata(h,'handles');
% nfiles=size(g.spikefiles,2);
nfiles = length(g.snipfiles);
chindices=find(ismember(g.channels,ctchannels));
if g.pwflag
global sptimes
else
%Load in times
for ch=1:size(ctch... |
function net_int = train_2_layers(s,r)
%function takes sample data an trains a neural network to aaproximate the
%integral used to compute the fuel fraction.
%inputs -
% s -- samples from gauss_samps(1000,0.8), etc
% r -- responses from fuel_quad(s,50), etc
%create CNN with two hidden layers of size 10
net_int = f... |
function [ fig ] = plot_test_train_same_plot( one_over_lambda,train_erros,test_errors )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
fig = figure;
plot(one_over_lambda,train_erros)
hold on
plot(one_over_lambda,test_errors)
legend('train','test')
% title('iterations vs train,test err... |
close all;
clear all;
tic;
subfolders = dir( 'data\zz\' );
kk = 1; %ignore the . and ..
for i = 1 : numel( subfolders )
if( isequal(subfolders(i).name, '.')||...
isequal(subfolders(i).name, '..')||...
~subfolders(i).isdir)
continue;
else
specimen_index(kk) = subfolders... |
%% read image 256 x 256
im=double(imread('D:\Users\Lenovo\Desktop\pic\lena_256.bmp'));
subplot(3,3,1);
image(im);
im2=double(imread('D:\Users\Lenovo\Desktop\pic\car.jpg'));
subplot(3,3,4);
image(im2);
colormap(gray(256));
%% lowpass mask
lp_mask = zeros(256,256);
lp_bound = round(256/10);
N = 0;
for ... |
function smap=saliencyimage_convolution(img,scale)
% function smap=saliencyimage(img,scale)
% Calculate saliency map for color image, at certain scale
% each filter is zero summed
% load ICA basis functions
load stats;
d=size(B1,1); % number of filters
D=size(B1,2); % color filter streched length
fsize=D/3; % length... |
dataFilePath = 'D:/RFID/groundtruth/a2';
fileNmae = '';
EPCData=[dataFilePath, '/EPC_1','.data'];
fid=fopen(EPCData, 'r');
epc=fscanf(fid,'%f %f',[2,inf]);
fclose(fid);
epc=abs(epc(1,:)+epc(2,:)*1i);
plot(epc) |
function [subj] = raicar_reformResult (subj)
%
% function [subj] = raicar_reformResult (subj)
%
% Author: Zhi Yang
% Version: 2.0
% Last change: June 28, 2007
%
% Purpose:
% Z-normalize the component maps and corresponding mixing matrix,
% and then reshape it to 4D matrix (x, y, z, numComp)
%
% Inp... |
function [ k ] = Cal_Curvature( x , p )
%syms x y;
y = zeros(length(x),1);
k = zeros(length(x),1);
for i = 1:length(x)
%Generating all y values
y(i) = p(1)*x(i)^4 + p(2)*x(i)^3 + p(3)*x(i)^2 + p(4)*x(i) + p(5);
%% Method one: Calc curvature based on polynomial equation
% k(i) = abs(12*p(1)*(x(i).^... |
function photons = hDetectorEnergyResponseFunction(photons, energy)
scintillatorThickness = 1.5; % cm
scintillatorMaterial = 'CWO'; %
detectorGeometryLoss = 0.90; %
mu = materialAttenuation( energy, scintillatorMaterial );
photons = detectorGeometryLoss * photons .* ( 1 - exp( - mu .* scintillatorThickness )) ;
e... |
function [pos,neg] = significant_cluster_time_freq_channel(stat,sig)
% find relevant clusters
pos=[];neg=[];
if (isfield(stat,'posclusters'))
if not(isempty(stat.posclusters))
ipos = 1:length(stat.posclusters);
% loop over all sig positive clusters
for i=1:length(stat.posclusters)
... |
function t = FEMLinElement2d()
%TELEMENT TElement class constructor.
% t = TElement() creates a element object;
t.kxxc=[];
t.kyyc=[];
t.masselec=[];
telement2d=TElement2d(2);
t = class(t,'FEMLinElement2d',telement2d);
|
function [answer]=getwordlist
i=1;
answer=[];
while 1
word = input('enter word:','s');
if isempty(word)
return
end
answer{i}=word;
i=i+1;
end
end |
%% For a set of surface wave ASCii files process the data to filter out bad data and
%% remove the first twenty seconds as well as any data that does not fit within
%% a certain standard deviation of the mean
%%
%% Created by Nick Talavera on October 26, 2015, modified from swmod by Anibal Sosa
%%
%% To run, type:
%% S... |
close all
waveFile='18.wav';
[y, fs, nbits]=wavread(waveFile);
y=y(:,1);
frameSize=256;
overlap=0;
framedY=buffer(y, frameSize, overlap);
volume=sum(abs(framedY));
plot(volume);
[segment, threshold]=splitVector(volume, 10*fs/frameSize, 0.5*fs/frameSize, 1);
for i=1:length(segment)
sampleIndex1=(segment(i).beginIndex... |
GT = readpfm(['MiddEval3/training',imgsize,'/',image_names{4},'/disp0GT.pfm']);%Modify
d = DisparityMap_sparse{1};
o = hole_filling(d,3);
df = Occ_fill(DisparityMap_sparse{1},40,0);
D_full = BGF(o);
% Error5(~mask) = 0;
% GT(~mask) = 0;
p = psnr(D_full, round(GT),1)
% Erroro = abs(o - GT) > bad;
Errordf = abs(D_full... |
% -------------------------------------------------------------------
% Generated by MATLAB on 13-Dec-2016 20:34:28
% MATLAB version: 9.0.0.341360 (R2016a)
% -------------------------------------------------------------------
saveVarsMat = load('PalmesBogdan_sp_t2.mat');
C_0 = 0;
D = 21;
Duty = 52.5;
... |
%Script to set up a neural network to solve the ILDAlone specint problem
%Specifically set up for Batch Training of a Static Network (see manual)
%Architecture:
% 2-Layer network
% Input(16x21 ILD/freq spec) --> Layer 1 (numfreqs X numILDs) --> Layer 2 (1 ICCls or ICx unit)
% \ / ... |
%% Main Simulator Script
clc;clcl;
% Initalizaiton
%----------------------------------------------------------------------
% Set current directory
compName=getComputerName;
setCD(compName);
params = setParams();
% Create fitting operator -> E
%----------------------------------------------------------------------
... |
%Author: Kartik S. Pandya, PhD (email: kartikpandya.ee@charusat.ac.in)
%Professor, Dept. of Electrical Engg., CSPIT, CHRUSAT, Gujarat, INDIA
%Co-Author: Dharmesh A. Dabhi, PhD(Pursuing) (email: dharmeshdabhi.ee@charusat.ac.in)
%Assistant Professor, Dept. of Electrical Engg., CSPIT, CHRUSAT, Gujarat, INDIA
% Enhanced V... |
function this = trim(this)
% trim Remove leading and trailing NaNs from time series data.
%
% Backend IRIS function.
% No help provided.
% -IRIS Macroeconomic Modeling Toolbox.
% -Copyright (c) 2007-2017 IRIS Solutions Team.
%--------------------------------------------------------------------------
this... |
function resourcesDir = getResourceDir
%GETRESOURCEDIR Summary of this function goes here
% Detailed explanation goes here
p = mfilename( 'fullpath' );
p = fileparts( p );
p = fileparts( p );
resourcesDir = fullfile( p, 'resources' );
end
|
% profile_cell.m (beta)
% copyright: Brian Drawert 2010,2011
function profile_cell(varargin)
% if(nargin==0)
% input_file_mask = {...
% 'Ste20Spa2.1m.1_C001T%.3d.tif',... %red channel
% 'Ste20Spa2.1m.1_C002T%.3d.tif'}; %green channel
% tspan=1:21;
% else
% %TODO... |
%% -----------------------------------------------------------------------
%
% Title : test.m
% Author : Alexander Kapitanov
% Company : Insys
% E-mail : sallador@bk.ru
% Version : 1.0
%
%-------------------------------------------------------------------------
%
% Description :
% Top le... |
function [ W ] = meanvalueWeights( mesh, vertices )
% W = meanvalueWeights(mesh, vertices)
% Compute mean-value weights
% - if vertices is undefined/empty, compute for entire mesh
%
% [Ryan Schmidt rms@dgp.toronto.edu 09/2009]
if ~ exist('vertices', 'var') || numel(vertices) == 0
vertices = mesh... |
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?
% your code and answer... |
% Copyright (c) 2017, Amos Egel (KIT), Lorenzo Pattelli (LENS)
% Giacomo Mazzamuto (LENS)
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modification, are permitted provided that the following conditions are met:
%
% * Redistributions of source ... |
% Finds the indices of the cell that holds the minimum cost
% costs : The matrix that contains the estimation costs for a block
% dx : the motion vector component in columns
% dy : the motion vector component in rows
function [dx, dy, min] = minCost(costs)
[row, col] = size(costs);
% we check whether the current
% v... |
#
# Find Quetelet index for females who spent 100$ each and express it per cent.
#
# my answer = 313 INCORRECT!
data = load("spendings.dat");
num_people = sum(sum(data))
gender_amount_probs = data ./ num_people
gender_probs = sum(data, 2) ./ num_people
amount_probs = sum(data) ./ num_people
# p(Gender|spent amount)... |
clear
clc
%5.5a)
n=4
Consumo=[ 5 5.7 6.2 6.7 7.0 ]
tempo =[ 85 89 93 95 96 ]
difdiv1=fdifdiv(n,Consumo,tempo)
Consumo1=7.5
tempo1=fgregoryn(n,Consumo,tempo,difdiv1,Consumo1)
xp=Consumo(1):0.01:7.5;
yp=fgregoryn(n,Consumo,tempo,difdiv1,xp);
plot(Consumo,tempo,'*k','markersize',20,xp,yp,'k','LineWidth',2,[... |
clc;
fprintf('===================================[ tsdrect.m ]===================================\n\n');
fprintf('This example calculates the transient pressure field of a rectangular piston using\n');
fprintf('the Fast Nearfield Method with Time-Space decomposition. It outputs an animation\n');
fprintf('of the pre... |
function [no_InTerms,InTerms,no_OutTerms,OutTerms,Rules,Rules_semantic] = RuleGen_FRIE(TrainData_IN,TrainData_OUT,Alpha,Beta,Forgetfactor,numSamples)
% Partitioning using CLIP
InTerms = CLIP(TrainData_IN(1:numSamples,1),Alpha,Beta); %%% % passes col 1 of TrainData_IN (values of 1st input variable)Interms stores the ... |
function result = javaFromMatlabStruct(value)
if ~strcmp(class(value), 'struct')
throw(MException('MDSplus:javaFromMatlabStruct', 'only struct allowed'));
end
if isscalar(value)
fields = fieldnames(value);
result = MDSplus.Dictionary();
for fieldIdx = 1:length(fields)
f... |
t = (0:0.1:10)';
x = sawtooth(t);
%Apply white Gaussian noise and plot the results.
y = awgn(x,10,'measured');
plot(t,[x y])
legend('Original Signal','Signal with AWGN') |
function pattern = aoctanary(h,w,d)
sz = [h,w,d];
b1 = [-1;1;-1i;1i];
b2 = [1;1;1;1;realsqrt(6)];
pattern = b1(randi(4,sz,'uint8')).*b2(randi(5,sz,'uint8'));
end
|
function output = quatroFVNresponseAnalysisQ(fvnSet, xRec, xRef, fs, nRepeat, nto)
% Acoustic analysis using four unit FVNs
% output = quatroFVNresponseAnalysisQ(fvnSet, xRec, xRef, fs, nRepeat, nto)
%
% Input argument
% fvnSet : matrix with four unit FVNs as its column
% xRec : response to the mixed FVN s... |
function [err phv_fwd] = disperr(periods,phv,phvstd,grv,grvstd,initmodel)
% Write model file
writemod_surf96(initmodel,'start.mod');
% Write dipersion file
outfp = fopen('disp_obs.dsp','w');
for ifreq=1:length(periods)
fprintf(outfp,'SURF96 R C X 0 %6.4f %6.4f %6.4f\n',...
periods(ifreq),phv... |
% Homework #7a
% Driver for jacobi.m
clc
clear;
close all;
%===================================
% (6a)
%-----------------------------------
tol=1e-4;
% Here go the three matrix definitions
A1=[1.01 0.99; 0.99 1.01];
b1=[2.0;2.0];
A2=[1.5 .5; .5 1.5];
b2=[2.0;2.0];
%making the a3 matrix and b3 rhs
x... |
function simdata = sim_noPenalty(nS, chunk, chunk_freq, agent)
rng(24);
nA = nS + 1;
theta = zeros(nS,nA); % policy parameters
V = zeros(nS,1); % state value weights
p = ones(1,nA)/nA; % marginal action probabilities
blockstruct.chunk = chunk;
blockstruct.len... |
%% Check that we can find the dataset
opto_data_file = fullfile(files_path, 'preprocessed_data', 'ofc_learning_choosing_dataset_opto.mat');
assert(exist(opto_data_file, 'file') == 2, ...
'Unable to find opto dataset. Please find "ofc_learning_choosing_dataset_opto.mat", and place it in the Matlab path.')
%% Check... |
function [N,D] = design_lowpass(fp, fs, Rp, Rs, Fs)
Wp=(fp/Fs)*2*pi;
Ws=(fs/Fs)*2*pi;
c=power(tan(Wp/2),-1);
Op=1;
Os=c*tan(Ws/2);
sc=sqrt(power(10,Rp/10)-1);
A=sqrt(power(10,Rs/10));
m1=log10((power(A,2)-1)/power(sc,2));
m2=2*log10(Os);
M=ceil(m1/m2);
for k=1:M
pp=exp(pi*i*(1/2+(2*k-1)/(2*M)));
P(k)=power(p... |
function writeTriFile(V,F,triFile,comment)
% writes 3 column tri-files
%
% writeTriFile(vertices,faces,filename,comment)
% vertices = vertex matrix [ +right, +anterior, +superior ] origin @ volume center
% faces = triangular face matrix [ inward normals ] ???zero-indexed???
[fid,msg] = fopen(triFile,'w');
if f... |
function data_filepath = save_variable_data(subject_id, variable_name, data)
% data_filepath = '';
if isempty(data)
error('Input data is empty. Cannot save empty data to csv files.');
% return;
end
if has_variable(subject_id, variable_name)
warning('Subject %d already save data file %s.csv. It will be re... |
function result=mydetrend( data)
% function result=mydetrend( data)
%
% disp('detrending with 3rd order polynomial')
%
disp('detrending with 3rd order polynomial')
t=[1:length(data)];
[coeffs, error] = polyfit(t,data,3)
result = data ...
-coeffs(4) ...
- coeffs(3)*t ...
- coeffs(2)*t.^2 ...
- coeffs(1)... |
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