text stringlengths 8 6.12M |
|---|
function [hits,results,seqhits,uniquehits] = databaseID_ALL_v1(database,ordseq,taxid,minlength,parent)
% load database
taxonomy = database.taxonomy;
refString = database.string;
protein = database.protein;
% **TEST** load trio frequency (ONLY 9913 for now) **TEST**
% load('singles_9913.mat','tabletters')
% patt1 = t... |
function [ tau ] = step ( d, z, delta );
a = d'*d;
b = 2*(z'*d);
c = z'*z - delta^2;
tau = (-b + sqrt(b^2 - 4*a*c))/(2*a);
|
function [metric, output_log, total_steps] = train_diagoasis(images, labels, parms, images_tst, labels_tst)
%
%
%
%% checks whether a file with training results for the given variables already exists
% if yes (and do_force is 0), then load the file and skip the training.
% otherwise, continue to the traini... |
function [r, theta] = cart_to_pol_coord(x,y)
r=sqrt(x.*x+y.*y);
theta=atan2(y,x);
|
function Psi=getRandomBasisVectors(numRandomBasisVectorsPerDimension,probabilityInfo)
% Hugo Esquivel, 2021
% -
% Remark:
% - For simplicity, a full tensor product is used below to get the required random basis over the probability space.
numRandomVariables=length(probabilityInfo.name);
numRandomBasisVectors=numRandom... |
function EEG = oe2eeglab(pathname,filebasename)
%
% EEG = oe2eeglab(pathname,basefilename)
%
% Loads open-ephys-format data into EEGLAB structure.
%
% Inputs:
% pathname: the path to the folder where the files are.
% filename: any filename up to '.continuous'
%
% (if no inputs are given, a dialog box wil... |
function Problem = ITERmodify_LB_X0_UB(Problem)
X0 = Problem.x0(:);
LB = Problem.lb(:);
UB = Problem.ub(:);
XLabels = Problem.XLabels;
repeat = true;
while repeat
disp(' ')
BOUNDS = {'Index', 'Parameter'};
for ii = 1:length(X0)
BOUNDS{ii+1,1} = ii;
BOUNDS{ii+1,2} = XLabels{ii};
... |
function [D,x0,alpha,e_sw,Icr,dIcr] = Ei400_uvw111_at110ver()
% simplified verification script, which allows one to check
% how do sqw cut works
%-------------------------------------------------
% Parameters :
%
data_source= fullfile(pwd,'Data','Fe_ei200.sqw');
bragg = [1,1,0]; % selected bragg
%
% Cut properties:
... |
% clear all
% nIND = 2;
% nDOF = 16;
% PriorID = 18;
% DS_rate = 4*1024;
% % DS_rate = 1;
%% Inference
% Truth
Kscale = 0.005;
K0 = 2;
% Sinusodial
K_fn = @(x) (sin(2*pi*x) + K0)*Kscale;
dK_fn = @(x) 2*pi*cos(2*pi*x)*Kscale;
d2K_fn = @(x) -2*pi*2*pi*sin(2*pi*x)*Kscale;
% Mixed Gaussian
K0 = 1;
dnormpdf = @(x,mu,sigm... |
function [] = spharmY(l, m, n)
% Plots the Yml spherical harmonic.
theta=linspace(0, pi, n);
phi=linspace(0, 2*pi, 2*n);
ct=cos(theta(:));
st=sin(theta(:));
% Evaluate Legendre associated polynomial
a=0; a(l+1)=1;
P=LegendreP(a,m,ct);
g=sqrt((2*l+1)*factorial(l-abs(m))/(4*pi*factorial(l+abs(m))));
rho=g*P*exp(1i*m*phi... |
function k = NAND(X1,X2);
% NAND gate
% 2 layers --> Input and output layer
% AND and NOT neural net
% ================================================================================================
answer1=[];
fprintf("\n\t----NAND gate----\n\nx1 x2 x1 nand x2\n\n");
for iter=1:4
x1=X1(iter);
x2=X... |
function [Iex,Iexmask] = esvm_get_seg_icon(models,index,flip,subind,VOCopts)
% get the segmentation icon, used to transfer segmentation
%
% Copyright (C) 2011-12 by Tomasz Malisiewicz
% All rights reserved.
%
% This file is part of the Exemplar-SVM library and is made
% available under the terms of the MIT license (s... |
% Make new eyeCatch library
% find ori_map on eyeCatch webiste, filename:
% 'eyeChannelWeightNormalizedpart1.mat' and 'eyeChannelWeightNormalizedpart1.mat'
function new_map = interpo_lib(ori_map, downsize)
[sampleNumber, oldsize] = size(ori_map);
if downsize > sqrt(oldsize)
error('Downsize map should be smaller ... |
clear all;close all;clc
load('x3.mat');
load('t3.mat');
train_i = cat( 1,X(1:40,:),X(51:90,:),X(101:140,:) );
train_t = cat( 1,T(1:40),T(51:90),T(101:140));
M = length(train_t); % # of training examples
test_i = cat( 1,X(41:50,:),X(91:100,:),X(141:150,:) );
test_t = cat( 1,T(41:50),T(91:100),T(141:150) );
N = leng... |
function lik = q2_loglik(Xtrain, Ytrain, theta)
% Computes the log likelihood value for training data (Xtrain, Ytrain) and parameter theta
% INPUT
% Xtrain : [m x n] matrix, where each row is a n-dimensional input example (assume it
% already contains the constant feature set to 1)
% Ytrain : [m x 1] v... |
function [dx_x1,dx_x2,dy_x1,dy_x2] =...
ellipse_coordinates_and_derivatives(ellipse_parameters,n_points,uni)
R1 = ellipse_parameters(1);
R2 = ellipse_parameters(2);
P1 = ellipse_parameters(3);
P2 = ellipse_parameters(4);
Q1 = ellipse_parameters(5);
Q2 = ellipse_parameters(6);
N_intervals_x = 3;
N_intervals_y = 3;... |
function MESH = updateMesh(SIM, MESH, xp, PART)
%% =======================================================================%
% update the mesh: account for periodic BC on particles, or growing the
% mesh if particle cross boundaries
% ========================================================================%
pad = S... |
function y = ShannonEntropy(P)
%--------------------------------------------------------------------------
% ShannonEntropy(P) computes the Shannon entropy of a probability distribution P.
%
% INPUT:
% P : array of discrete probabilities
% (P can be a row vector of probabilities if a single random
% ... |
init
%%
model.type_data = 'Loop_current_kriging';
% model.type_data = 'Gula_kriging';
param_estimated_by_MLE = true;
model.grid.dX = 500e3/1024 * [1 1];
model.grid.MX = [1536 512];
% model.grid.MX = [1538 512];
dt = 0.5 *24 *3600;
n_x_min = 1;
n_y_max = 512;
error_obs_h = true;
init_model
vectname_data = {'zeta'... |
classdef Solenoid < handle
% Solenoid class
%
% Properties:
% name
% length
% field
% taper
% aperture
%
% Methods:
% Track
% TrackSpin
% GetBField
properties
name = ''; % string
length = 0; % solenoid length in metres
... |
function varargout = UIPanel_Selector(varargin)
% UIPANEL_SELECTOR MATLAB code for UIPanel_Selector.fig
% UIPANEL_SELECTOR, by itself, creates a new UIPANEL_SELECTOR or raises the existing
% singleton*.
%
% H = UIPANEL_SELECTOR returns the handle to a new UIPANEL_SELECTOR or the handle to
% the exis... |
function [x y theta S slope L ] = mesh_circle(M,r)
%UNTITLED2 returns x and y vectors that plot a cirlce
% x = r*cos(theta)
% y = r*sin(theta)
% returns x,y,theta,T-vector,slope of each element,Length of each element
theta = 0:2*pi/M:2*pi; % discretization in theta direction
x = r*cos(theta); % x-distance
x(end) = ... |
function [bounds, axis, bins] = bin_generator(bins,varargin)
% binstyle='quantile' produces quantile bins and the quantile centers of those bins for the
% predicted distribution of stimuli in a Qamar-like experiment. For
% instance, bins=3 will produce a vector bounds with points at the
% following quantiles [.333 .66... |
% clear all;
% close all;
R = 2;
k = 0.1;
ke = 5;
mr = 5;
r = 0.5;
mw = 0.5;
L = 0.1;
%moment bezwladnosci ramienia
Jr = 1/3*mr*r*r;
%moment bezwładności bez wody
J1 = Jr;
%moment bezwładności z wodą
J2 = Jr + mw * r*r;
P1 = 0.9102;
I1 = 2.8992e-08;
P2 = 0.738385826082795;
I2 = 0.004789704258728;
W = [P1 I1 0
... |
function makePileBetaMaps(isPC,dirT,nameAna,nSm)
nFiles = 16;%64;%
rsa_tool_path = 'rsatoolbox';
if isPC==0
based = '/home/smark/fMRI_ana';
spm_path = '/data/smark/spm';
data_path = '/data/smark/fmri_sub_preproc_dir/';
addpath(genpath(fullfile(data_path,rsa_tool_path)))
else
based = 'C:\Users\smar... |
function penalty=atGetPenalty(ConstVal,Constraints)
%
% Evaluate the penalty function (distance from the target value of every constraint)
%
vals=cat(2,ConstVal{:});
low=cat(2,Constraints.Min);
high=cat(2,Constraints.Max);
weight=cat(2,Constraints.Weight);
penalty=zeros(size(vals));
toohigh=vals>high;
toolow=vals<low... |
function result = mInverseJacobiSC( X, M )
%MINVERSEJACOBISC Inverse of the Jacobi function SC.
% MINVERSEJACOBISC(X,M) is the inverse of Jacobi function SC for the
% elements of argument X and parameter M. X and M must all be real and
% the same size or any of them can be scalar.
%
% See also INVERSEJACOBIS... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% nome_q1d.m
%%% Matlab function to detect & sort spikes
%%% by Antonio Padilha L. Bo (antonio.plb@lara.unb.br)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [spikeTimesA, spikeTimesB] =... |
%------------------------------------------------------------------------
% Fixed parameter controller - Canonical realization +
%------------------------------------------------------------------------
global data % some data and time administration
data=[]; Je=0; Ju=0;
dets=0; % determinis... |
clc, clear, close all;
fig=openfig('ghoc_impulse.fig','new','invisible');
saveas(fig,'newghoc_impulse.jpg','jpg')
close(fig);
fig1=openfig('balloon.fig','new','invisible');
saveas(fig1,'newBalloon.jpg','jpg')
close(fig1);
fig2=openfig('mic_plot.fig','new','invisible');
saveas(fig2,'newMic_plot.jpg','jpg')... |
function [x_corr] = filteredspikes_xcorr(data,spikes,params)
params.flags.plot_online=0;
if params.flags.plot_online==1
plotYN=1;
else
plotYN=0;
end
%% choices for strongest channel
x_corr.x_corr_radius=[];
channel_index=1:64;
%%% 1 choose modal stongest channel
for sweep_id=data.sweep_sort.successful_swe... |
%1)
%Vmall = spm_vol( 'E:\FILER\Lars Jonasson\Belöningsstudie\PT_Sharp\PT_Sharp_A01\wrA01_PT_Sharp');
Vmall = spm_vol( 'E:\FILER\Lars Nyberg\COBRA\COBRA - validering Logan\rc_fPT_rPETsh.nii');
Ymall = spm_read_vols(Vmall(1));
Vmall = Vmall(1);
Pstart = Vmall.mat(1:3,1:4)*[1 1 1 1]';
Pend = Vmall.mat(1:3,1:4)*... |
pkg load image;
img = imread("pratica7.png");
figure, imshow(img);
sz = size(img);
img = im2double(img);
spectre = fft2(img, sz(1) * 2, sz(2) * 2);
fourier = fftshift(spectre); #Passo 5
spectre = abs(fourier); #Passo 6
newImg = uint8(spectre);
figure, imshow(newImg);
filter = zeros... |
beam = Beam(Positron);
beam.energy = 2.0 * PhysicalUnits.GeV;
ncells = 16;
drift1 = Drift;
drift1.length = 5.00; % metres
drift2 = Drift;
drift2.length = 6.00; % metres
drift3 = Drift;
drift3.length = 0.10; % metres
drift4 = Drift;
drift... |
% Get some scalar stuff!
function wall_results = quick_results_fpi(fname, state, parse_Ns, parse_Nt, blocksize, tmin, tmax, diag, fold, m_l, noerrors)
% Check if we're ignoring error analysis.
check_errs = 0;
if (exist('noerrors', 'var'))
check_errs = noerrors;
end
if (~strcmp(state, 'ps2'))
err... |
clear;
% 2.2.1 How do the transmitted signalu[k], the (unknown) channel IR-vector h,
% and the recorded signaly[k] relate to each other (assuming that noise can
% be neglected)? Give a matrix description of this relation (in
% the timedomain). This yields an overdetermined system of linear equations, ... |
1;
c=csvread ("results/out_consolidated.csv" );
csize=size(c(:,2:end))(2);
cmean=mean(c(:,2:end),2);
sdv=std(c(:,2:end),0,2);
confhi=cmean.+(1.960).*sdv./sqrt(csize);
conflo=cmean.-(1.960).*sdv./sqrt(csize);
csvwrite ("results/out-mean.csv",[c(:,1) cmean(:,1) conflo(:,1) confhi(:,1)]);
|
%% Create sound: harmonic tone
% Copyright (c) 2019, Sijia Zhao. All rights reserved.
% Contact: sijia.zhao.10@ucl.ac.uk
clear;
Fs = 44100;
stimDur = 0.5; % in [sec]
freqStep = 200;
nonComponent = 30;
Ns = floor(Fs*stimDur);
t = 1/Fs:1/Fs:stimDur;
nr = 150;
R = sin(linspace(0,pi/2,nr));
R =... |
function plotstatsandmetrics(filepath, subjRange)
for subjNum = subjRange
for mode = 1:4
switch mode
case 1
modeText = 'fsconn';
case 2
modeText = 'fscos';
case 3
modeText = 'fullconn';
case 4
... |
function [T, R, d_cell, x1, x2, P1] = rekonstruktion(T1, T2, R1, R2, Korrespondenzen, K)
%% Preparation
T_cell={T1,T2,T1,T2};
R_cell={R1,R1,R2,R2};
vones = ones(1,size(Korrespondenzen,2));
x1_ = [Korrespondenzen(1,:); Korrespondenzen(2,:);vones];
x2_ = [Korrespondenzen(3,:); Korrespondenzen(4,:)... |
function computeDogExtrema(obj)
% COMPUTEDOGEXTREMA Compute Difference-of-Gaussian extrema
%
% Other m-files required: Matcha.m
% Subfunctions: none
% MAT-files required: none
%
% Author: Gabriel Moreira
% email: gmoreira (at) isr.tecnico.ulisboa.pt
% Website: https://www.github.com/gabmoreira/maks... |
% produce a summary of the LHq variables by experiment
clear all; clc
data = readtable('all_variables_4groups_LSW.csv', 'ReadVariableNames', false);
groups = {'IE', 'ISS'};
% find the groups
for g = 1:length(groups)
p = 1;
for w = 1:width(data)
if strncmp(table2cell(data(1,... |
function [competition] = calc_competition(competition_model,B,plants,K,sum_R)
%CALC_COMPETITION Calculate the effect of competition on plant per-capita
%growth rate. Two models are implemented: 'community-wide', where all
%plant species' vegetative biomass (B) contribute to how close the plant
%community is to carryin... |
function [ shift_im ] = align_edge_ssd( im_ref, im )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
im_ref = double(im_ref);
im = double(im);
im_ref_edge = edge(im_ref, 'canny');
im_edge = edge(im, 'canny');
s_range = 30;
err = 1000000000;
[h, w, c] = s... |
% TNM034 - ADVANCED IMAGE PROCESSING
% Isabell Jansson isaja187
% Ronja Grosz rongr946
% Christoffer Engelbrektsson chren574
% Jens Jakobsson jenja698
% 2015-12-11
%------------------------------------
function div = DivVal( im )
%Function to calculate the diviation from 1
% If it... |
% mxTV.
% Version 1.0 28-april-09.
% Copyright (c) 2008 by J. Dahl, P.C. Hansen, S.H. Jensen, and T.L. Jensen
%
% Requires Matlab version 7.5 or later versions.
%
% These functions accompany the paper
% J. Dahl, P.C. Hansen, S.H. Jensen, and T.L Jensen, "Algorithms and
% Software for Total Variation Image Re... |
function[centers] = mean_shift(points,count,bandwidth,iterations)
centers=clusterTranslation(points,count,bandwidth,iterations);
centers=floor(centers);
end
function [ new_set]= clusterTranslation(points ,count,bandwidth,iterations)
%figure;
%scatter(points(:,1), points(:,2));... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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#... |
function [ JAMatrix ] = IKinBody( B, M, Tsd, theta0, epsw, epsv )
%UNTITLED Summary of this function goes here
% Detailed explanation goes here
if(size(theta0, 2) > 1)
theta0 = theta0';
end
epsilon = 10^-5;
if (numel(size(M)) ~= 2 || size(M,1) ~= size(M,2))
disp('M is not a valid transformation matrix either... |
%------------- level set for equi affine flow ------ %
%------------ initlization ------------------------ %
clear ;
close all;
deltat = 0.2;
dt = 0.01;
iter = 1000;
[x,y] = meshgrid(-3:deltat:3);
% t = 0 : deltat: 2 * pi;
% x = 3 * cos(t);
% y = 2*sin(t)./exp(3*cos(t)/10);
%------------ construct signed distance f... |
clear
clc
t=0.005; %initial guess
D=3; %fuselage diameter
L=0.5; %frame spacing
n=50; % number of stringers
As=5e-4;
b=pi*D/n; %stringer pitch
theta=linspace(0,2*pi,n);
y=D/2.*sin(theta);% stringer position in y direction
x=D/2.*cos(theta); % stringer position in x direction
figure (1);
plot(x,y,'o');
grid on
%%
... |
% ---------------------------------------------------------------------
% Book: MVA
% ---------------------------------------------------------------------
% Quantlet: MVAedfnormal
% ---------------------------------------------------------------------
% Description: MVAedfnormal draws n observations ... |
clear;
clc;
Pos = xlsread('Charging.xlsx');
load Charging.xlsx;
Pos(:,2) = 700 - Pos(:,2);
Pos(:,4) = 700 - Pos(:,4);
Charging_info.posx = Pos(:,1);
Charging_info.posy = Pos(:,2);
Charging_info.sumcar = 0;
Consum_info.posx = Pos(:,3);
Consum_info.posx = Pos(:,4);
Consum_info.car = Pos(:,5);
x = Charging_info.posx(i... |
function plotter(gcf, docked)
%PLOTTER This function edits figure aesthetics
% Set desired properties below
newcolors = [53, 79, 254;
255, 83, 0;
15, 200, 154;
146, 0, 239] /255;
colororder(newcolors);
grid on
grid minor
box on
% Set title, axes labels, and ... |
function runsim(sim_options)
% set constants used in simulation
set_sim_consts;
global sim_consts;
% Set Random number generators initial state
% reset random number generators based on current clock value
rand('state',sum(100*clock));
randn('state',sum(100*clock));
% Main simulation loop
% Initialize simulation ti... |
function R=polyMult(P, Q)
m=size(P,2);
n=size(Q,2);
R=zeros(max(size(P,1), size(Q,1)), m+n-1);
for i=1:m
for j=1:n
k=i+j-1;
R(:,k)=R(:,k)+P(:,i).*Q(:,j);
end
end
end |
function [U,A, density]=sparse_LSA(X, ReduceDim, lambda, init, g_opts)
if isfield(init, 'n_alter')
n_alter=init.n_alter;
else
n_alter=100;
end
if isfield(init, 'U_tol')
U_tol=init.U_tol;
else
U_tol=1e-2;
end
if isfield(init, 'A... |
function policy=pol_finder(stat)
global NO_REPLICATIONS ITERMAX NA NS SMALL TPM TRM
for state=1:NS
for action=1:NA
Q(state,action)=value_finder(state,action,stat.vector)
end
end
for state=1:NS
[maxQfactor,index]=max(Q(state,:));
policy(state)=index;
... |
function [ flux_vanleer ] = flux_vanleer( qL,qR )
%Function "flux_vanleer"
% MATLAB version of Joe Derlaga's "flux_vanleer.f90",
% created 070313.
% Takes left and right state primitive variables [rho,u,p] and
% returns Van Leer FVS flux.
global gm1 xg xgm1 xg2m1
% ----------------
%Calculate Left... |
% demonstrate matrix operations in matlab (pgs 341-342)
a=[3 1 6]
b=[4 5 2]
c=a-b
|
function [all_theta] = oneVsAll(X, y, num_labels, lambda)
m = size(X, 1);
n = size(X, 2);
all_theta = zeros(num_labels, n + 1);
X = [ones(m, 1) X];
for index = 1 : num_labels
theta = zeros(n+1, 1);
options = optimset('GradObj', 'on', 'MaxIter', 130);
[thetaVal] = fmincg(@(t)(lrCostFunction(t... |
function IODist = normIOSelectFrame(ptloc, frames)
% function of normalized inter-organelle distance
% input:
% (1) ptloc: 1 x n or n x 1 structure with a field xy containing point positions and n =
% frame numbers
% (2) frames: frame indices
% output:
% normIODist: normalized inter-organelle distances
if isempty(f... |
function T = dct_init(imsz,blsz)
% image size to block size ratio should be integer
nch = numel(imsz);
T = cell(1,nch);
%%create measurements
for c = 1:nch
D = dctmtx(blsz(c));
Tc = D;
for i = 1:(imsz(c)/blsz(c))-1
Tc = blkdiag(Tc,D);
end
T{c} = single(Tc);
end
end |
function BLOM_SaveDataFile(data, filename, formatstr, default_value)
% This function saves a Matlab variable to a data file, in the format
% specified by the formatstr argument
% Input arguments:
% data - the Matlab data to save
% filename - the file name to save to
% formatstr - a string indicating the data format (... |
function [confounds, stats, fd]= legacy_getconfounds(data,filepathM,filepathW,filepathC,TR,HPF,NPC,FDthresh,DetrendOrder,IncludeCensor,Trim,varargin)
IncludeAROMA = 0;
AROMA = [];
if (nargin > 11)
IncludeAROMA = varargin{1};
if (IncludeAROMA==1)
melodic = varargin{2};
... |
function phi = GF(varargin)
% Returns a polytope
assert(nargin == 1, 'GF takes one variable');
% Returns a polytope
if strcmp(varargin{1}.type, 'inner') || strcmp(varargin{1}.type, 'ap')
phi = struct('type', 'inner', 'Op', 'GF', 'args', {varargin}, ...
'formula', strcat('GF(', varargin{1}.formula, ')'));
el... |
% low pass and then downsampled (will not affect low freq)
function [out, fs] = myDownsample(in, n, fs)
in = myLPF(in);
out = downsample(in,n);
fs = fs/n; |
% function convert_T_LIDAR_from_txt_to_mat()
% converts the lidar time to a mat file. This function is only run once
% before running MAIN
% open and read the text file
%fileID= fopen('ouster_frames_timestamps.txt','r');
a=load("corrected_time.mat");
T_LIDAR = a.time (2:end,:);
%tmp_mat = table2array(file(:,1));
%T_L... |
% MIT License
%
% Copyright (c) 2017 JM Joseph
%
% Permission is hereby granted, free of charge, to any person obtaining a copy
% of this software and associated documentation files (the "Software"), to deal
% in the Software without restriction, including without limitation the rights
% to use, copy, modify, merge, ... |
% Example of winning neuron in a SOM (self organising map)
% Every neuron is represented as a vector, with synaptic weights from all
% neurons that connect to it, plus from the connection to itself (bias)
% 3 neurons connected to each other (2 connections each), plus one
% connection to itself (bias) making for a tot... |
function [celldata, cellpic, ee, err] = Func_Segmentation(MIP,res)
% input
% MIP = Max intensity image
% res = the resolution of MIP
% sample name
% MAX_12-5-2016 Position2.tif
% 'Sample.jpg'
I = MIP;
figure (1)
imshow(I);
title('Original Image')
% Ajust Brightness
I = I -25;
I = I*5;
bw = imbinarize(I);
for i = ... |
%path0 = './xp11/';
%path1 = './xp12/'
%path2 = './xp13/'
%path3 = './xp14/'
%path4 = './xp9/'
path1 = './xp4/'
path2 = './xp5/'
path3 = './xp6/'
path4 = './xp7/'
path5 = './xp8/'
path6 = './xp9/'
logFile = 'myLog.txt';
string = 'Finished epoch';
y1 = getStat(path1, logFile, string);
y2 = getStat(path2, logFile, st... |
clear all
close all
clc
x = [-5,10,20;5,-10,-10;5,-20,-20];
x
%x = input('Enter the matrix');
[m, n] = size(x);
flag = 1;
count_n=0;
for i = 1:n
for j = i+1:n
flag = 1;
for k = 1:m
if x(k,i)<x(k,j)
flag = 0;
break;
... |
%
% ITROB2 Robot
%
global verbose;
try
% ROB toolbox + Machine vision !
toolboxPath = '..\..\Toolbox\rvctools';
toolboxPath3 = '..\rvctools';
% Verbose
verbose = true;
% Add subfolders to path
addpath(genpath(pwd));
% Load robotics toolbox
%mydir = pwd;
%cd(toolbo... |
function [mu,vec] = powermethod(A,x,tol,N)
% power vec = powermethod(A,x,tol,N)
x=x./max(x)
k=1
error=2*tol
while k<=N & error>tol,
y=A*x
mu=max(y)
y=y./mu
error=max(abs(x-y))
x=y
end
vec=x
|
function [video_mat] = traces_to_mat(traces, szv, keep_ind)
%[I J] = ind2sub([szv(1,1) szv(1,2)], keep_ind);
%set_to_zero = setdiff(all_ind, keep_ind); % return values in all-ind that are not in to_keep
% setting all pixels not in traces to zero
video_mat = zeros(szv);
frame = zeros(szv(1,1), szv(1,2));
for i... |
s0 = [v0; r0];
% Part (a1)
alpha = 0.1;
[~, s1] = ode45(@(t,s) particleDeriv(s, alpha), [0 1000], s0);
r1 = s1(size(s1, 1), 4:6);
% Part (a2)
alpha = 0.2;
[~, s2] = ode45(@(t,s) particleDeriv(s, alpha), [0 1000], s0);
r2 = s2(size(s2, 1), 4:6);
% Part (a3)
alpha = 0.3;
[~, s3] = ode45(@(t,s) particleDeriv(s, alpha),... |
function T0_n=mat_hom(theta,alpha,r,d,k)
%matrice homogène
n=length(theta);
T0_n=eye(4);%= future matrice homogène
for i=1:k
T=[cos(theta(i)) , -sin(theta(i)) ,0 ,d(i);
cos(alpha(i))*sin(theta(i)), cos(alpha(i))*cos(theta(i)), -sin(alpha(i)), -r(i)*sin(... |
function [ y,f ] = fftAnalysis(y,fs)
n = length(y);
y = fft(y,n,2);
y = abs(y/n);
f = (0:n-1)*(fs/n);
end
|
%apply the formula proven by thr previous subquestion
%(b)
clear all;
L=2*10^-3;
R=8;
C=5*10^-6;
I(1)=0;
DI(1)=0;
dt=0.00001;
Vs(1)=0;
for t=1:1:1000
Vs1(t)=(cos(6000.*(t.*dt+dt))-cos(6000.*t.*dt))./dt;
DI(t+1)=DI(t)+((Vs1(t)-R.*DI(t)-I(t)./C)./L).*dt;
I(t+1)=I(t)+DI(t+1)*dt;
... |
%takes an array, indicesStart, and a traceLength, and returns a matrix
%containing the elements of indicesStart through indicesStart+traceLength.
%For Example a = [3 17 42], traceLength = 2 will return
% windows = [3, 4, 17, 18, 42, 43]
function windows = getWindows(indicesStart, traceLength)
tempOnes = ones(length... |
%% PLANT DISCRETIZATION AND AUGMENTATION
for i = 1:length(S)
hs = S(i);
delay = delays(i);
sysd = c2d(sysc, hs);
Ad{i} = sysd.a;
Bd{i} = sysd.b;
Cd{i} = sysd.c;
sysd_b0 = c2d(sysc, hs-delay);
sysd_b1 = c2d(sysc, hs);
B_0{i} = sysd_b0.b;
B_temp = sysd... |
function [] = plotAccuracyVsC(Cs, trainX, train_y, testX, test_y, alg, maxIter)
tr_accuracies = zeros(length(Cs), 1);
ts_accuracies = zeros(length(Cs), 1);
for i=1:length(Cs)
c = Cs(i);
[w, b, SupVec] = trainSVM_QP(trainX, train_y, c, alg, maxIter);
[e1_train_svm, e1_test_svm, e2_train_svm, ...
e2_test_svm... |
function y = decoder_constellation(x,con)
x = conj(x);
xr = zeros(size(x));
xi = zeros(size(x));
consts = [3; 1; -1; -3;];
difs = zeros(4,1);
difsj = zeros(4,1);
%decide which element of the constellation a received symbol is closest to
for k=1:length(x)
difs = real(x(k)) - consts;
... |
%cd '/Volumes/LJBIGBOY/prospectus_analysis/dissertation/conrols_LOO';
cd '/Volumes/LJBIGBOY/prospectus_analysis/dissertation/aim_2/originalsLOOZ';
warning ('off', 'MATLAB:unknownElementsNowStruc');
d = dir ('FINz*FIN.nii');
for i = 1:length (d)
pname = d(i).name;
hdr = spm_vol(pname);
img = spm_read_... |
data = load('ex1data1.txt' );
X = data(:, 1);
y = data(:, 2);
plot(X, y, 'rx', 'MarkerSize', 10);
ylabel('Profit in $10000');
xlabel('Population of City in 10000s');
m = length(y);
X = [ones(m, 1), X];
theta = zeros(2,1);
alpha = 0.01;
//theta = theta - alpha * (1 / m) * ((theta' * X - y) * X);
J = computeCost(X, y, th... |
function ret = visualize_featurecsv(inputfilename)
% visualize csvfile of feature-temporal 2D
% USAGE
% vonormalize( filename.csv )
% OUTPUT
% filename.png
[pathstr,name,ext] = fileparts(inputfilename);
A = load(inputfilename);
img = image_rowsc(A');
caxis([0 1.0]);
saveas(img, [pathstr, name, '.png']);
waitfor(im... |
clear all
hold on
% 1. Dla danych ze strony www narysuj wykres rozproszenia, traktujac
% pierwsza kolumne jak zmienna objasniajaca, a druga jako zmienna
% objasniana. Znajdz przyblizona zaleznosc funkcyjna pomiedzy danymi
% wykorzystujac toolbox cftool.
data_1 = load('dane_zad1.m');
x1 = data_1(:, 1);
y1... |
function h = imagescnan(varargin)
% IMAGESC with NaNs transparent
%
% MvdM 2014
switch (nargin)
case 1
hh = imagesc(varargin{1});
a = varargin{1};
case 3
hh = imagesc(varargin{:});
a = varargin{3};
end
set(hh,'alphadata',~isnan(a));
if nargout > 0
h = hh;
end |
% Function to calculate the relative error
function e = RelativeError(val1, val2, type)
for i = 1:length(val1)
e = max(max(abs(val1{i} - val2{i})./max(eps, abs(val1{i} + val2{i}))));
disp(['Relative error grad_' type num2str(i) ': ' num2str(e)]);
end
end
|
function baseRepresentation = ind2base(d, b, n)
%IND2BASE Convert decimal integer to base-B representation.
% IND2BASE(D,B) returns the representation
% of a non-negative integer D as a integer in base B. IND2BASE(D,B,N)
% produces a representation with at least N digits. If D is an array,
% IND2BASE return... |
clear;
clear figs;
%monkey = 'Pepe';
monkey = 'Satchel';
load(sprintf('%s/bcidistance.mat',monkey));
load(sprintf('%s/bciacqtime.mat',monkey));
load(sprintf('%s/blockshamacqtime.mat',monkey));
load(sprintf('%s/blockshamdistance.mat',monkey));
load(sprintf('%s/sessindindex.mat',monkey));
unique_sessidx = unique(sess... |
% Returns the covariance matrix of the Distance Sensor
% INPUT:
% Sensor Error Constants (S=[sigma_rho sigma_phi])
% sigma_rho~Distance error
% sigma_phi~Angle error
%
% OUTPUT:
% Qt
function [ Qt ] = getSensorCovariance(S)
sigma_rho=S(1);
sigma_phi=S(2);
Qt=[(sigma_rho)^2 0;... |
% INTRODUCTION
% Script to process image data of Atlantis phantom.
function [] = Proton(VarFile)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PREPARATION
% Prepare workspace:
clearvars -except... |
function [lambda, V, iter] = power(A,X,epsilon,maxI)
%Input - A is an N x N matrix
% - X is and N x 1 matrix: the initial guess
% - epsilon is the tolerance
% - maxI is the maximum number of iterations
%Output- lambda is the dominant eigenvalue
% - V is the dominant eigenvector
% - iter is the... |
% Author: Aditya Prawira
%% STALKER ROBOT SIMULATIONS
clear
clc
set(0,'DefaultFigureWindowStyle','docked') % Docked simulation.
%% KEY BINDING ASCII CODE CONSTANTS
FORWARD = 82;
BACKWARD = 81;
LEFT = 80;
RIGHT = 79;
ESC = 41;
ENTER = 13;
KEY_D = 7; % d key
KEY_A = 4; % a key
%% SIMULATION ENVIRONMENT SETUP INTERFAC... |
function [ output_args ] = printMV(label, f, motion_vectors )
%PRINTMV Summary of this function goes here
% Detailed explanation goes here
x=[1 17 33 49 65 81 97 113 129 145 161];
x_axis=[x x x x x x x x x];
y=[1 17 33 49 65 81 97 113 129];
y_axis=[... |
function [ds_quant,p] = quantize_prob(ds,edges)
%This functions gives the probability of each samples according to the
%quantization dictated by the bins described in the edges
%inputs
% ds: n*p matrix representing the n samples of p parameters
% edges: cell of p matrix containing the sequence of bi... |
%% Position et Vitesse depuis Gauss
function xC = Gauss2Cart(mu0,x)
P = x(1); ex = x(2); ey = x(3); hx = x(4); hy = x(5); L = x(6);
co = cos(L); si = sin(L);
W = 1 + ex*co + ey*si;
Z = hx*si - hy*co;
C = 1 + hx^2 + hy^2;
r = P/(C*W)*[(1+hx^2-hy^2)*co + 2*hx*hy*si;...
(1-hx^2+hy^2)*si + 2*hx*hy*co;...
2*Z];
v = s... |
%% Matlab Colony Analyzer Tutorial
% Gordon Bean, June 2013
% gbean@ucsd.edu
%
% I suggest making a copy of this tutorial (or sections thereof) for
% analyzing you own images.
%
% This toolkit depends on the MATLAB Image Processing Toolbox and the
% Statistics Toolbox.
% If you would like to use the toolkit and do not ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.