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function [IRF,FVD,M,tot_weights] = pop_analysis(model,settings);
% Compute impulse responses and forecast variance decompositions
% implied by ABCD representation of model
%----------------------------------------------------------------
% Preparations
%----------------------------------------------------------------... |
function res = opdracht_7_versie_1_CHECK(apStudentSol)
res = 0;
%%========== PLACE SOLUTION IN COMMENTS HERE
%% Opdracht 7
% % % % % % % Gegeven onderstaande script met een bijzondere vector.
% % % % % % % Vraag m.b.v. een standaard Matlab functie de afmetingen op van de
% % % % % % % vector 'matx'.
% % % % % % % Deze... |
function variation_dim()
%UNTITLED4 Summary of this function goes here
% Detailed explanation goes here
[x,y] = ginput(2);
plot(x,y,'-*r');
end
|
v1 = [1 0 0];
v2 = [0 1 0];
u = [0.5 0.5 0];
% u = [1 0 0];
[V, c] = plane_to_subspace([1,0,0]',[0,0,0]');
% u = [-1,-1,0];
u = u./norm(u,2);
rad2deg(acos(g_cos_distance_1d_2d(u, V(:,1), V(:,2), 1.1))) |
width=4;
ssq=1;
[x,y]=meshgrid(-width:0.1:width,-width:0.1:width);
dgau2D=-x.*exp(-(x.*x+y.*y)/(2*ssq))/(pi*ssq);
surf(dgau2D); |
function [RR, bestParam] = RRCVTuning(X, y, trainSize, numFolds, reg)
% RRCVTuning exectues and tunes ridge regression for a range of possible
% regularisation parameters (reg) using cross validation
%
% Exepcts the additional mandatory inputs:
% X= matrix of predictors
% Y= corresponding vector of responses (... |
clear;
x=importdata('data.txt');
minerr=inf;hc1=0;hc2=0;
tic;ahcrou1=0;ahcrou2=0;ahcrou3=0;
for hcrou1=linspace(1.14e-6,1.18e-6,4)
for hcrou2=linspace(1.85e-6,1.88e-6,4)
for hcrou3=linspace(4.85e-7,4.95e-7,4)
y=model_function(hcrou1,hcrou2,hcrou3);
st=find(y(:,1)==19);
... |
%% Wrapper function to simulate the experiment for diff state spaces
%%
rng(2);
withinChunkUpdate = 0;
stateSpace = [3 6 9];
chunk = [3 2 1];
beta = [0.5 1 1.5 2 2.5];
numBetas = length(beta);
chunk_freq = 0.1;
agent.lrate_V = 0.2;
agent.lrate_p = 0.01;
agent.lrate_theta = 0.2;
agent.test = 1;
beta = [0.5 1 1.5 2 2.... |
function Process_multi_daq_Song(folder,song_range)
%function Process_multi_daq_Song(folder_containing_multiple_daqs,song_range)
%This function allows you to analyze mutiple daqs in a folder and returns
%output in separate folders.
if strcmp(folder(end),'/') == 0
folder = [folder '/'];
end
dir_list = dir(... |
data = audioread('go.au');
figure(1)
n = linspace(0,(length(data)-1)/8000, length(data));
plot(n,data)
title("'go' utterance")
xlabel('Time(s)')
ylabel("'go' utterance")
f = linspace(0, 8000, 512);
%f1 = (0:511)/512*8000;
X = DFTwin(data,405,2720,512);
%X = DFTsum(data,405,2720,512);
figure(2)
subplot(2,1,1... |
function dir_ls = filter_to_time_series(dir_ls)
%FILTER_TO_TIME_SERIES Takes in a dir struct, outputs the same dir
% struct with all the folder names that includes
% 'time_series'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%Main ... |
x=0:1:15;
x1=1*(x>=1&x<=10);
x2=1*(x>=2&x<=10);
N1=length(x1);
N2=length(x2);
x3=conv(x1,x2);
n1=0:1:N1+N2-2;
subplot(3,1,1);
stem(x,x1);
xlabel('X');
ylabel('x1(x)');
title('signal x1(n)');
subplot(3,1,2);
stem(x,x2);
xlabel('x');
ylabel('x2(x)');
title('signal x2(x)');
subplot(3,1,3);
stem(x,x3);
xlabel('x');
ylabel(... |
clear
[slices1, left1] = pizzaParty(15, 3);
% slices1 => 1
% left1 => 9
% Output variable(s) should be identical to that produced by the solution file
%
[slices2, left2] = pizzaParty(100, 25);
% slices2 => 2
% left2 => 0
% Output variable(s) should be identical to that produced by the solu... |
function X = GaussElimination(A,B)
% GaussElimination: Gauss elimination with pivoting (partial)
% input:
% A = coefficient matrix
% B = right hand side vector
% output:
% X = solution vector
[row,column] = size(A); % NOTICE
if row ~= column
error('Matrix A must be square');
end
newC... |
function img_dilate = remove_noise(img)
img_dilate = imopen(img, strel('disk',5));
|
function network = lcod_train( X, Wd, Zstar, alpha, T, num_of_classes, learning_rate, max_iter, conv_thres, conv_count_thres, error_check_iter )
%TRAIN Summary of this function goes here
% X: training input signal nxm (m input of size n)
% W: dictionary nxk (k basis vector size n)
% Zstar: kxm (m sparse code with coeff... |
%% set data folder
if ispc
dataroot = 'C:\Users\rhc307\data';
else
dataroot = '/data/raeed';
end
cdslib = fullfile(dataroot,'cds-library');
% files = dir(fullfile(cdslib,'*COactpas*'));
% names = vertcat({files.name})';
names = {...
'Han_20171206_CObumpcurl_basel... |
function f=getsltc(path,slno,tlocs,imlocs,imsize,detord,ext)
% Usage ... f=getsltc(path,slno,tlocs,imlocs,imsize,detord,ext)
if nargin<5,
imsize=[64 64];
end;
if nargin<7,
ext='';
end;
if nargin<6,
detord=0;
end;
if length(tlocs)==1,
tlocs(2)=tlocs(1);
tlocs(1)=1;
end;
for m=tlocs(1):tlocs(2),
a=getslim(... |
% Input: a structure with any subset of the following fields (a user will be prompted
% for whatever details that are not supplied)
% .animal - a string
% .series - a string or a number
% .selectedExperiments - a cell array of strings or a column vector... |
%根据主题产生文档
clear all;close all;clc;
imsize = 5;%图片尺寸
noiselevel = 0.01;%加入噪声
numbarpermix = [0 ones(1,3)]/3;%什么用途
% numbarpermix = [0 ones(1,2)]/2;
numcluster = 2;
numgroup = 4;%文档个数
numdata = 20;%每篇文档中单词数目
% load trainss
[trainss,thetas] = genbars2(imsize,noiselevel,numbarpermix,numgroup,numdata,numcluster); |
clc
clear
close all
labeltsize=35;
fw = 'normal'; %%是否加粗斜体之类
fn='Times New Roman';
linewide1=3;
mkft = 15;
load 1K.mat
figure(1);
hold on
plot(SNRout,Pd_PGLRT_mc,'k-o','linewidth',linewide1,'MarkerSize',mkft)
plot(SNRout,Pd_PRAO_mc,'r-x','linewidth',linewide1,'MarkerSize',mkft)
plot(SNRout,Pd_PWALD_mc,'b-x','linewidth... |
function threshs = getIntelligentThresholds( vals );
threshs = unique(vals) - 1e-16;
|
function [ new_pos ] = LOCAL_SEARCH( pos, Xmin, Xmax )
% Mutates the integer part of the particle
global proc
global ps
global deepso_par
new_pos = pos;
% switch proc.system
% case 41
% % Select which type of variables will be mutated
% prob = deepso_par.localSearchContinuousDiscrete;
% if r... |
%pStarSolver.m
%Purpose: uses Newton's method to iteratively compute the pressure in
% Regions 3 and 4 of the exact shock tube solution
%Parameters: startVal initial guess for p-star
% tol the tolerance value for Newton's method
% (iterations will stop when the abso... |
function Flag = isVAR(X)
% isVAR True if variable is VAR object.
%
% Syntax
% =======
%
% Flag = isVAR(X)
%
% Input arguments
% ================
%
% * `X` [ numeric ] - Variable that will be tested.
%
% Output arguments
%
% * `Flag` [ `true` | `false` ] - True if the input variable `X` is a VAR
% ... |
function f = curry(fn,varargin)
args = varargin;
f = @(varargin)fn(varargin{:},args{:});
end |
function [] = SimulateAdaptvCurrent(simfunction,PopParams_in,Irange,numI,brange,numb,ii,bb,varargin)
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
%
%INPUTS
% simfunction simulation function. function should be passed through
% as @SimFunctionName. And should ... |
function [pvals,tvals]=kjm_natbar(data,dt_cols,bar_off)
% this function makes modified vertical barplots that also show individual
% datapoints, with errorbars showing the standard error of the mean
% it expects:
% data{M} in cell array format, with each of M array elements containing a column vector
% dt_cols... |
classdef Processor < ether.process.Node
%PROCESSOR Summary of this class goes here
% Detailed explanation goes here
properties(Constant)
Unknown = 1;
ImageVolume = 2;
ProblemResult = 3;
end
properties(SetAccess=protected)
targetType = ether.process.Processor.Unknown;
end
properties(SetAccess=private... |
% Read image
s_dir = pwd;
cd Images;
X1 = imread('aivazovsky78g.tif', 'tif');
[N, M] = size(X1);
cd(s_dir);
% resample
N2 = N/2;
M2 = M/2;
X2 = X1(1:2:N, 1:2:M);
% display the image
subplot(2, 2, 1);
imshow(X2);
title('downsampled image');
% reconstruct image
X3 = X2(ceil(0.5:0.5:N2), cei... |
function [rate_all,peakrate,nofailrate,mean_holdingvoltage,mean_r_m,std_r_m,mean_time_constant,std_time_constant,...
mean_c_m,std_c_m,numberspikes,thresholds,imp,pulse_duration,pause_duration,delay,increments,currents,pf_all,std_noise]=...
fi_rate_old(module,recdate,cellnum,trials,points,sample_rate,shouldplotF... |
% use cylindrical coordination
function [imgOut, descriptor, alpha] = imgWarp(img, description, f)
[row, col, channel] = size(img);
col_warped = 2 * ceil(f * atan(col / 2 / f));
imgOut = zeros(row, col_warped, channel, 'uint8');
alpha = zeros(row, col_warped, 'uint8');
for r = 1:row
for c = 1:col
r_tmp = row ... |
function Prediction = CorrFilter_SVR_LOOCV(Subjects_Data, Subjects_Scores, CoefThreshold, Pre_Method, ResultantFolder)
%
% Subject_Data:
% m*n matrix
% m is the number of subjects
% n is the number of features
%
% Subject_Scores:
% the continuous variable to be predicted
%
% Pre_... |
classdef h5_wc_reader < handle
properties
sr
min_index
max_segments
raw_filename
sr_infile
t0_segments
max_index
segmentLength
spikes_file
end
methods
function obj = h5_wc_reader(par, raw_filename)
obj.max_segments = [];... |
%% Housekeeping
clear
%% Run Simulink Model
Simulation_Time = 300;
out=sim('Dynamic_Model',Simulation_Time);
%% Import Data from Simulink
% Data for Plot 1
x1 = out.tout;
y1 = out.simout.signals.values;
% Data for Plot 2
%L = length(y1(40000:end,1));
x2 = out.tout;
y2 = out.simout2.signals.values;
% Data for Pl... |
% Dylan Jackson
% ME 3165-308
% 2/12/2018
% Thomas Algorithm
clear;clc;close all
% Domain
h=.25;
n=100;
dy=h/n;
U=zeros(n+1,5);
rho=1000;
L=.1;
% Boundary conditions
U(1,:)=0;
U(n+1,:)=.01;
% Pressure Gradient
dpdx=[-1e-3,-.5e-3,0,.5e-3,1e-3];
vis=8.9e-4;
b=ones(n,5)*-2;
d=ones(n,length(dpdx)... |
% Generate n samples from a multivariate normal distribution using the
% provided mean (mu) and covariance matrix (sigma).
function samples = gen_samples(mu, sigma, n)
dim = numel(mu);
% Sample the unit normal distribution
samples = sample_unit_normal([dim, n]);
% Color noise using inverse whiteni... |
[curXs, curYs] ...
= meshgrid(coverageMapsEHataXLabels{idxH}, ...
coverageMapsEHataYLabels{idxH});
[curLats, curLons] ...
= utm2deg(curXs(:), curYs(:), ...
repmat(UTM_ZONE, length(curXs(:)), 1));
%%
hCurPLMap = figure;
hold on;
hTxs = plot3(cellAntsLatLon(:,2), cellAntsLatLon(:,1), ...
ones(length(... |
% small_parallel_STEP_in.m ADVISOR 2003-00-r0116 input file created: 22-Feb-2014 22:22:02
global vinf
vinf.name='small_parallel_STEP_in';
vinf.drivetrain.name='parallel';
vinf.fuel_converter.name='FC_SI41_emis';
vinf.fuel_converter.ver='ic';
vinf.fuel_converter.type='si';
vinf.torque_coupling.name='TC_DUMMY';
vinf.... |
classdef Constants
%CONSTANTS Temporal constants
properties(Constant)
DAYS_IN_LEAP_YEAR = 366;
DAYS_IN_YEAR = 365;
HOURS_IN_DAY = 24;
MINUTES_IN_HOUR = 60;
SECONDS_IN_MINUTE = 60;
end
methods(Static)
function dayFraction = stepToDay(s... |
function pca_on_feature(X)
% Scaling
X = bsxfun(@minus, X, mean(X));
X = bsxfun(@rdivide, X, max(abs(X)));
[coeff, score, latent] = princomp(X);
cumsum(latent)./sum(latent) |
% 2. Target constant, speed not constant
function [CFG, DATA] = EB2(CFG, DATA, test_idx)
[CFG, DATA] = EB_Core(CFG, DATA, test_idx); |
function fe = exact_pde1d(X,t,c,nu,L)
% Calculate exact solution of f profile
a = (X-c*t)/(2*nu*t)+L;
b = 1/(4*t)+1;
fe = 1/sqrt(4*pi*nu*t)*exp(-(X-c*t).^2/(4*nu*t)-L^2/4+a.^2./(4*b))*sqrt(pi)./sqrt(b);
end
|
function ret = fsvolume_crop(subjectDir)
% NAME
%
% function ret = fsvolume_crop(subjectDir)
%
% ARGUMENTS
%
% subjectDir in (string) the "root" directory to process
%
% ret out (int) Zero if no error has occurred
% One if some error has occurred.
%
% DESCRIPTION
%
% 'fsvolume_crop' in its simplest sense crops a >... |
%addpath(genpath(pwd));
run setup.m
|
function [SimulateResult, ExitFlag] = SIMULATE_TRANS(VfiTransResult, SimulateSsResult, Params, Rt, Wt, Trt, Tsst)
% Taxing Human Capital
% @Author: Wenlan Luo
% Simulate economy, return distribution
% unpack structure
v2struct(Params);
v2struct(VfiTransResult);
r = [];
w = [];
Tr = [];
Tss = [];
% write TauGs to empt... |
% Newman-Girvan community finding algorithm
% source: Newman, M.E.J., Girvan, M., "Finding and evaluating community structure in networks"
% Algorithm idea:
% 1. Calculate betweenness scores for all edges in the network.
% 2. Find the edge with the highest score and remove it from the network.
% 3. Recalculate betweenn... |
function [n_epitope, n_snap, t, traj_array] = load_TcellDat(filepath)
% This function reads in data from the file Tcell_traj.dat found in the
% directory 'filepath'. Tcell_traj.dat is producted by the Population
% Dynamics code. It is a binary file that contains the time step, number of
% naive, effector, and memory ce... |
for i = 1:10
e(i) = [10^(-2*i)];
end
for i=1:10
A = [e(i) 1
1 1];
b = [1+e(i);2];
z = LUFact(A,b);
firstrow(i)= z(1);
secondrow(i)=z(2);
end;
firstrow
secondrow
|
% MLE of the Mixed Matern model
clear
% HEALPix grid
[theta, phi, n] = HEALPix_sampling(2);
% convert coordinates
[x, y, z] = trans_coord(theta, phi);
% initial computation
[h_mat, r, P_cell, Q_cell, A_cell] = init_comp(x, y, z, n, theta, phi);
% generate samples
p = 2;
% set the same seed
rng('default')
samples =... |
echo off
clear
global k m M g l
g=9.81 ; k=4 ; m=1 ; M=8 ; l=10;
options = odeset('RelTol',1e-10);
[t,theta] = ode45(@simple_pendulum_deriv,[0:0.1:40],[6 0 pi/4 0], options);
[t,theta2] = ode45(@simple_pendulum_deriv,[0:0.1:40],[6.001 0 pi/4 0], options);
fig1=figure('Name','Representation temporelle du mouvemen... |
% judgment = IsInBounds(matrix,point)
%
% Tests whether N-dimensional vector of coordinates POINT is within the
% bounds of matrix MATRIX -- i.e., whether it is a valid index for the matrix.
%
% Returns 1 if POINT is a valid index for MATRIX; returns 0 if POINT is
% outside the bounds of MATRIX or if POINT is not... |
function[datos_x_filtrado, datos_y_filtrado, mediana, sigma] = filtro_pendiente_nula(datos_x, datos_y, cant_sigmas)
delta_x = diff(datos_x);
delta_y = diff(datos_y);
pendiente = delta_y./delta_x;
% mediana = median(pendiente);
% sigma = std(pendiente);
%
% umbral = mediana ... |
function gctool_pre()
% create *.dat file for gctool
% make vinf global
global vinf
advisor_path=strrep(which('advisor.m'),'\advisor.m','');
gctool_path=vinf.gctool.fullpath(1:end-1);
if ~vinf.gctool.user_defined
% open file for reading and writing
fid=fopen([advisor_path,'\gctool\gctool_model.dat'],'wt');... |
% mlp_classify_bayes_output
% for outputing var' estimate on toy 1d dataset
% Copyright (C) 2015 Hao Wang
% Adapted from code by KyungHyun Cho, Tapani Raiko, Alexander Ilin
%
%This program is free software; you can redistribute it and/or
%modify it under the terms of the GNU General Public License
%as published by the... |
% eks2005_opgavea
parfile='eksamen2005_a.cmd';
p=mgstat(parfile);
G=read_gstat_par(parfile);
[pred,x,y]=read_arcinfo_ascii(G.predictions{1}.file);
% randomly select np points
np=500;
nx=length(x);
ny=length(y);
rand('seed',2);
ix=round(rand(1,np)*(nx-1))+1;
iy=round(rand(1,np)*(ny-1))+1;
for i=1:np
xobs(i)=x(ix(i))... |
function [popX, popY, popZ] = populationProb_02(popX0, popY0, popZ0, decayX, decayY, deltaT)
%POPULATIONPROB Function recieves arrays with number of atoms in arrays
%such that if number n is in array popX0, the n-th atom is in the isotope
%form X. Other input include half-lives of X and Y and the time interval.
%The ou... |
function [ret1,ret2] = get_facts_values( facts, values )
% facts = ArrayList<Fact>
% values = {'value1','value2',...}
ret1 = cell( length(values), facts.size );
ret2 = cell( length(values), facts.size );
for i = 0:facts.size-1
for j = 1:length(values)
ret1{j,i+1} = jess_str_val... |
%%%Script for 3a and b homework set 1 Due 2/11/19
%%%by Dean Geisel
%%%for Dr. Mohamed Sulman
%%%in MTH 7260
%-------------------------------------------------------------------------
%%%The purpose of this script is to solve the matrix equation found in
%%%problem 3 for the Vector of U values that form the n... |
function h = include_model_sim_NEW(R,m,ind_item,varargin)
[clist,ind] = colormap_model_sim_NEW(R,m);
if ~isempty(varargin)
i_ax = m.plotinfo.ax(varargin{1});
else
i_ax = m.plotinfo.ax(ind_item);
end
switch m.plotinfo.color_coding
case 'error'
for i_it = ... |
for i=1:35,
run_plot_wind_at_h('wrfout_d01_2006-02-23_12:42:00',i,[1,6.1],[],1,10.0,10),
end |
function NormalizedPWR(base,trials)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% INPUTS:
% base - baseline recording (usually 1 min recorded before trials)
% trials - trials of experiemnt (dim samp X trials)
%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Define parameters (if already defined outside of function the comment
% ... |
clear all;
close all;
clc;
global eps SanityCheck mu var1 var2 tprThresh W H;
W = 19;
H = 19;
tprThresh = 0.95;
eps = 1e-6;
SanityCheck = 1;
mu = 0;
var1 = 0;
var2 = 0;
% %%
% %%%%%%%%%Simple Weak Classifier
%
% FTData = load('FeaturesToUse.mat');
% feature = FTData.fmat(:,12026);
%
% [theta,p,err] = TrainAndPlot(... |
clear all;
clc;
Image=imread('image1.tif');
[m,n]=size(Image); % size of the image
block_size=32;
hash=[]; % initialization of the hash to be extracted from each
% DCT-transformed block.
for i=1:block_size:m,
for j=1:block_size:n,
blk=dct2(Image(i:i+block_size-1,j:j+block_size-1)); % get the block
... |
function [lambdak] = mywblfit (Y)
m1 = mean(Y);
m2 = mean(Y.^2);
m = fzero(@(m) (m1/gamma(1+1/m))^2*gamma(1+2/m) - m2, 10);
alpha = (m1 / gamma(1+1/m))^m;
lambdak = [alpha^(1/m), m];
end |
q = [1,4,7,10; 2,5,8,11; 3,6,9,12]
t = [1,4,7,10,11;2,4,8,10,12;3,4,9,10,13]
q = norm_code(q)
t = norm_code(t)
dist = 1 - t' * q
dist_2 = zeros(5, 4);
for i = 1:5
for j = 1:4
X1 = t(:,i);
X2 = q(:,j);
dist_2(i, j) = bhattacharyya(X1, X2);
end
end
dist_2
function d = bhattacharyya(X1, X2)
%check... |
% calculate rate of CH3O + NO2 -> CH2O + HONO
% Updated 7/14/06 AEP
% Based on JPL Data Evaluation #15
% rate=kNO2CH3Oa(T,M)
function j=kNO2CH3Oa(T,M)
j=1.1e-11.*exp(-1200../T); |
% This code serves as a framework to optimize regional welfare among
% aggregated customers while tracking the reference point of p* and q*.
function hello(param)
fprintf('\n\tHello %s!\n\n',param);
|
function fh = plot_by_school(x_isM, y_isM, xLabelStr, yLabelStr, saveFigures, cS)
% Plot a bunch of data series for each school level
%{
Line graph
IN
x_isM
x values by [observation, school group]
can be a single vector, applied then to all school groups
%}
if min(size(x_isM)) == 1
% Vector; expand ... |
function env2matlab(ruta,envfile)
% Convierte archivos .ENV/.SRF a una estructura de Matlab, guardando esta y
% algunos parámetros más en el archivo data_struct.mat:
% numsurfaces: almacena el número de superficies que definen la geometría
% surfaces: se trata de una estructura de Matlab donde se guarda la información ... |
function opts = getDefaultOpts_IRLS
% =========================================================================
% Reference:
% [1] C. Kuemmerle, C. Mayrink Verdun, "A Scalable Second Order Method for
% Ill-Conditioned Matrix Completion from Few Samples", ICML 2021.
% ===================================================... |
function winning_full = comb_win2(SwPt,M_X,Grid,u1,Cont_list)
num_p = 0;
for i = 1:length(SwPt)
num_p = num_p + length(Cont_list{SwPt{i}(2)});
end
winning_full = zeros(2,num_p);
counter = 1;
for i = 1:length(SwPt)
if(~isempty(SwPt{i}))
origin = Grid.V{1}(SwP... |
ts=[0 0.05]
x0=[0.01 0 0 0]';
[t,x]=ode23('robotadapt123',ts,x0)
[qd,e]=robout(t,x)
plot(t,e) |
% Pulizia
clear; clc
% Rappresentazione di
% (s+3)
% k -----------
% (s-1)(s²+4)
z = [-3];
p = [1, 2i, -2i];
k = 1;
funzione = zpk(z, p, k);
% Zeri e poli della funzione
zero(funzione)
pole(funzione) |
%2016-05-12 OFDM transmitter. carrier-less OFDM over the entire 0-22KHz band
%2016-06-06 improved notations
%2016-06-07 realised OFDM receiver, added BER calculation
%2016-06-08 added white noise. Added jitter: algorithm is very sensitive to
%jitter
%2016-06-16 added OFDM signal BW = Sub-carrier BW*Nsc2_real, Number of... |
%% EXTRACTION FEATURES v2.0
% This script performs the extraction of the created dataset features
clear all
close all
clc
% Folders of images - each user has one folder
folders=dir('~/conAi/imgbasedtest/');
folders={folders.name};
folders=setdiff(folders,{'.','..','.DS_Store'})';
num=length(folders);
%%
% ... |
function [mr]=mregs(X,Y,outlier,getts,prettyprint)
% performs a multiple regression
% where X is a matrix of column vectors and Y is a column vector
% Useage: [mr]=mregs(X,Y[,outlier])
% where mr is a structure with relevant values
% including Rsq,B,B0,Ypred,dfr,dff,F,p
if nargin<3, outlier=zeros(size(Y)); en... |
function T = kine(robj, q, link)
% Syntax
%
% T = kine(robj, q);
% T = kine(robj, q, link);
%
% Description
%
% Return the orientation and position or the equivalent homogeneous
% transform for the last (if not supplied) or the ith link. For example:
%
% % forward kinematics up to ... |
function exportdataset(dataset,filename,delimiter)
% exportdataset(dataset,filename,delimiter)
% Save the struct 'dataset' as a delimited ASCII file. Variables saved as
% columns, with column/field names specified in dataset.elements
fp = fopen(filename,'w');
if length(delimiter)==2 && all(delimiter == '\t')
de... |
function [success] = downloadCnmfGithubRepositories(varargin)
% Biafra Ahanonu
% Downloads CNMF and CNMF-E repositories.
% started: 2019.01.14 [10:23:05]
[success] = ciapkg.download.downloadCnmfGithubRepositories('passArgs', varargin);
end |
% Code belonging to figure 3 of Ma et al. (2011), Behavior and neural basis
% of near-optimal visual search, Nature Neuroscience 14 (6), 783-90
% Generates distributions of decision variable and ROCs
% Change line 20 to switch between homogeneous and heterogeneous
% distractors
clear all; close all;
set(0,'De... |
%This function generates the headfile needed for archiving
function []=gen_archiving_headfile(headfile_name, repeat, key_hole, total_nproj, undersampling_factor, recon_type, runno, civmid, projectcode, specimenid, dimx, dimy, dimz, fovX, fovY, fovZ, im_plane, FA, TE, TR, BW, baseline_time)
%==========INPUTS======... |
% Run script for solving Darcy's system (fine scale) with finite volume
% method in the mixed form, two point flux approximation (TPFA)
% clear all;
% tic;
%% Set up the parameter
nx = 101;
ny = nx;
hx = 1/(nx-1);
hy = 1/(ny-1);
% load coef.mat
p = @(x)cos(pi*x(:,1)).*cos(pi*x(:,2));
v1 = @(x)-pi... |
function [l1,l4,refinedTop,refinedBottom] = testAscenderDescenderFunc(ImgRef)
% ImgRef = imread(strcat(PathName,FileName));
[nRw,nCol] = size(ImgRef);
if(nCol > (3*30)) % the image width has to be greater than (3*30)
nParts = ceil((nCol/(3*30))); % for dividing the imae into parts
eachPartWidth = ceil(nCol/nP... |
function [all_theta] = oneVsAll(X, y, num_labels, lambda, iterations)
% X ya ha sido extendida
initial_theta = zeros(columns(X), 1);
options = optimset('GradObj', 'on', 'MaxIter', iterations);
all_theta = zeros(columns(X), num_labels);
for i = 1:num_labels
all_theta(:, i) = fmincg(@(t) (lrCostFunction(t, X, (... |
function output = getObjectiveValue( input,Neval,mode )
if(mode==1)
output = mean(input) + std(input);
else
inp_mean = sum(input)/Neval;
output = inp_mean;
end
end
|
clear all
load('Ic.mat');
load('tempSatno.mat');
Tamb=tempSatno;
Tpv = Tamb + (25+35)/2 * Ic/1000;
niZ = 0.96;
niN = 0.97;
deltaPdc = 0.005;
niT = (1-deltaPdc*(Tpv-25)/100);
Psc = 265*30;
Pdc = Psc*niZ.*niT*niN.*Ic/1000;
niEuro = 0.98;
niavg = mean(niEuro*niZ*niN.*niT);
Pac = niEuro*Pdc;
Pac... |
%BME 3053C Final Project
%Cell Detection and Counting
%4/10/2020
%This code is used to identify the number of cells in an image that contain
%magnetic nanoparticles.
clc;clear;
pix=imread('Cell3.png');
[h,w,p]=size(pix);
binary=false(h,w);
new=pix; I=pix;
%Changes the image to binary
for ii= 1:h
fo... |
function fin()
disp('Bonjour Monsieur et bienvenue dans la fonction Pseudospectre qui correspond au programme de Gershgorin:')
%%% Variables %%%
n = 3; %Taille de la matrice qu'il sera possible de changer
A=ones(n,n);
eps=10;
%%% Fonctions %%%
% 1. Premiere fonction Random
% Fonction qui va donner un nombre aleat... |
%This script separates the blue and red channels for easy camera-projector
%calibration
%Go to images location
images_loc = '/Users/jacoverster/Desktop/Test_scan_data/2017_11_15/';
nr_of_images = 13;
image_basename = 'canon';
fileType = '.JPG';
disp('Writing images...')
for i = 1:nr_of_images
cd(images_loc)
... |
embed = imread('embed.png');
figure
imshow(embed);
%p = rgb2gray(lena);
%imshow(I);
[m,n] = size(embed);
Blocks = cell(m/8,n/8);
counti = 0;
for i = 1:8:m-7
counti = counti + 1;
countj = 0;
for j = 1:8:n-7
countj = countj + 1;
Blocks{counti,countj} = lena(i:i+7,j:j+7);
end
end
%Blocks... |
load filterBank.mat;
load imStack.mat; % i created this.
textons=createTextons2(imStack,F,5);
% imput the images.
gumballs=imread('gumballs.jpg');
gumballs_gray=rgb2gray(gumballs);
snake=imread('snake.jpg');
snake_gray=rgb2gray(snake);
twins=imread('twins.jpg');
twins_gray=rgb2gray(twins);
coins=imread('coins.jpg')... |
classdef DENNSplitGain < SplitGain
% NNSplitGain evaluates split functions used in decision trees using
% k-th nearest neighbor (in 1D).
%
% Kozachenko (1987): Sample estimate of the entropy of a random vector.
% (english description - Beirlant (2001): Nonparametric entropy estimation:
% An overview)
properties
... |
function template=fingerTemplateRead
%dialog for opening fingerprint files
[templatefile , pathname]= uigetfile('*.dat','Open An Fingerprint template file');
if templatefile ~= 0
cd(pathname);
template=load(char(templatefile));
end;
|
%% Staple diagrams
clc
clear all
figure(1)
clf
dataBoarding = dlmread('boardingData500.txt');
randomBoarding = dataBoarding(2:5:end,1:2)/60;
backToFrontBoarding = dataBoarding(3:5:end,1:2)/60;
outsideInBoarding = dataBoarding(4:5:end,1:2)/60;
flyingCarpetBoarding = dataBoarding(5:5:end,1:2)/60;
meanTime = [randomB... |
function OneWheelAccel()
%rho = 1.225;
%[CfdragT, CfdownT] = AeroMap(AP);
%Fdrag1 = 1/2*rho*CfdragT*Drag_Var; %Calculates Fdrag prime which is simply the collection of constants such that Drag Force = Fdrag1*Velocity^2
%Fdown1 = 1/2*rho*CfdownT*Down_Var;
CarMass = 270*0.25;
TireCf =... |
function coords = polar2cartesian(screen_properties,theta,magnitude_mm)
%convert magnitude in mm to x and y components
[x_mm,y_mm] = pol2cart(theta,magnitude_mm);
%convert mm to pix independently for x and y
x_pix = x_mm*screen_properties.width_res_pix/screen_properties.width_mm;
y_pix = y_mm*screen_properties... |
f=imread('cameraman.tif')
fd=double(f)
F=fftshift(fft2(fd))
[P,Q]=size(F)
D0=input('Enter D0:')
w=input('enter w:')
for u=0:P-1
for v=0:Q-1
D(u+1,v+1)=(((u-(P/2)).^2)+((v-(Q/2)).^2)).^0.5;
H(u+1,v+1)=exp(-((D(u+1,v+1).^2-D0.^2)/(D(u+1,v+1)*w)).^2);
Hr(u+1,v+1)=1-H(u+... |
function funv = nsm_funv(x, model)
% This function evaluates the function value of the nonsmooth regularizer
rg = model.regpara;
pn = model.penalty;
switch pn
case 'ell1'
funv = rg*norm(x,1);
case 'ell2'
funv = rg*norm(x,2);
otherwise
error('nsm_funv: nonsmooth regula... |
% freq = [3000,2500,2000,1500,1000,800,700,600,500];
% dist = [30,50,70,100,150,200,250,300];
% PDR = [1 0.995884 0.98 0 0 0 0 0;
% 1 0.971098 0.96 0 0 0 0 0;
% 0.980263 1 0.925925 0 0 0 0 0;
% 1 0.689655 0 0 0 0 0 0;
% 1 0 0 0 0 0 0 0];
% marks=['+','o','*','x','square'];
%
% figure('N... |
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