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%script to create voltage vs soc curves from battery models
LoadCurrent_Max = 40;
LoadCurrent_Step = .5;
SimulationTime = 3600;
LogSimData_FileName = "model_sim_12_v_vs_soc_";
hiddenLayerSize = 10;
Load_Simulation = 'Load_Simulation_07';
% Array of Neural Net Structures containing performance data
NNs = {};
% Run ba... |
% CH2 Solving the real business cycle model
%
% ECON 6140, Karel Mertens, Cornell University
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear all;
close all;
% Utility function: u(C,L)=log(C)+thetal*log (L)
% Production function Y = AK^(1-alpha) (XN)^alpha
% The Euler conditions are:
% 1/C(t) ... |
function y = mvgaussian_pdf(x, mu, sigma)
% MVGAUSSIAN_PDF Computes multivariate normal pdf
% Inputs
% @x Samples in a NxM matrix where N is the dimensions and M is the
% number of samples
% @mu The mean of the distribution
% @sigma The covariance matrix for the normal ... |
function [out,re_out,im_out,info1,info2,info3,ind]=filterraw(pfilename, bad_range ,fastrecflag, showData)
% Usage ... [out,re_out,im_out,info1,info2,info3,ind]=filterraw(pfilename, bad_range ,fastrecflag, showData)
%
% this file zeroes the frequencies indicated in the range (a vector of
% numbers)
% in all the frames o... |
function [optLambda] = getOptLambda(X, Y, setPara)
% Get the optimal lamda
%
% INPUTS:
% X(MxN) : trData(i,j) is the i-th feature from the j-th trial
% Y(Nx1): trData(j) is the label of the j-th trial (1 or -1)
% setPara : Initialized parameters
% setPara.t
% setPara.zeta
%... |
function [e] = R2(x, y, mask)
%R2 [e] = R2(x, y, mask)
narginchk(2, 3);
if nargin < 3
x = vec(x);
y = vec(y);
else
x = x(mask);
y = y(mask);
end
e = 1 - sum((x - y).^2) / sum((y - mean(y)).^2);
end
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Author : Muhammad Nadzeri Munawar
%
%
%This code is to create absolute_power variable of subject no. 20 such as :
%delta, theta, alpha, beta, gamma with shape 32x5x40. 32 mean total of
%channel, 5 mean total absolute power, 40 mean 40 trial
%
% project_path -> 'D:\TUGAS AKH... |
%%
ucb = ucboard('COM5');
%%
delete(ucb);
clear('ucb');
%%
ucb.sendCmd('?VER');
%%
N = 100;
for k = 1:N
ucb.sendRaw('?VER');
end % for k
|
function Nodes = GetChebyshevNodes(Number,Start,End)
% Function Name:
% GetChebyshevNodes
%
% Toolbox:
% Integration
%
% Purpose:
% Produce a vector of Chebyshev nodes for use in numerical integration.
%
% Uses:
%
% Nodes = GetChebyshevNodes(Number);
% % Produces a vector %Number% long spanni... |
%% example script AOADMM for CMTF with Frobenius loss
% In this example, we create a synthetic dataset consisting of one CP tensor
% of order 3 (modes 1,2,3) and size [50,30,40] with 4 components and a matrix (modes 4,5) of size [50,70] with 3 components. They are
% coupled in the first mode, i.e. mode 1 and 4 have 3 ... |
function result = micrographMRA_heterogeneous(y, sigma, L, K, list2, list3, ...
X0, gamma0, gamma_fixed, n_init_optim)
% Collect the moments from the micrograph.
T = tic();
[M1, M2, M3] = moments_from_data_no_debias_1D_batch(y, list2, list3, 1e8);
%! We normali... |
%% Inclass assignment 2
% 1. a. fill in this loop with a conditional statement so that it displays
% only the odd numbers
for ii = 1:20
if mod(ii, 2) ~= 0
disp(ii)
end
end
% b. Write a new loop to display the same odd numbers which doesn't use a conditional statement
for ii = 1:2:20;
disp(int2s... |
[filename, pathname] = uigetfile({'*.txt'});
fname = [pathname filename];
filetext = fileread(fname);
expr = '[^\n]*Image:[^\n]*';
matches = regexp(filetext,expr,'match');
sz = size(matches);
coordinates = zeros(sz(1,2),3); % variable for holding coordinates
for i = 1:sz(1,2)
Str = char(matches(1,i)); ... |
function [resp_obj_z,resp_obj_z_3D] = fun_ResZscoreRe(resp_reshape,face_idx,numCLS,numIMG)
resp_obj = resp_reshape(face_idx,1:numCLS*numIMG);
resp_obj_3D = zeros(length(face_idx),numCLS,numIMG);
for ii = 1:numCLS
resp_obj_ii = resp_obj(:,(ii-1)*numIMG+1:numIMG*ii);
resp_obj_3D(:,ii,:) = resp_obj_ii;
end
... |
function lap=EffectiveLAP(deltavis,rg,mu0,Z,varargin)
%solve for effective lap concentration of dust (ppmw) given deltavis
%only scalar input/output
%you must run this in a loop for images, cubes, etc
%inputs, all scalars
%deltavis, 0 to 0.40, expressed as a fraction, eg 0.05
%rg - grain radius, um, 30-1800 um, e.g. 50... |
function [ExpOut,fjac] = ExpR(r,rf,W0,Wmin,alpha,gamma,w)
%This function computes the expectation given parameters.
% INPUT:
% r: Txn matrix of returns
% rf: scalar of risk-free rate
% W0: scalar of initial wealth
% Wmin: scalar of minimum wealth
% alpha: nx1 vector of portfolio weights
% gamma... |
function DataMat=singleblock(window,windowRect,fresh_interval,instr_index,target_ctg_No,tgt_num,T,para_id)
%Single Block Presentation
%N:number of images in a single block
%target_num:number of target images in a block
%tgt_loc:location of target images in a block(1-2).1.not in first/last 5
%images;2.interval of at lea... |
function varargout = band(varargin)
% band Line-and-band graph for tseries objects.
%
% Syntax
% =======
%
% [Ln,Bd,Range] = band(X,Low,High...)
% [Ln,Bd,Range] = band(Range,X,Low,High,...)
% [Ln,Bd,Range] = band(Ax,Range,X,Low,High,...)
%
% Input arguments
% ================
%
% * `Ax` [ nume... |
function deriv_wrt_Gd_conc = compute_deriv_enhancement_wrt_Gd_conc(Gd_conc, T10, flip_angle, TR, TE)
deriv_wrt_Gd_conc = [];
% Check that Gd conc is a row vector
if size(Gd_conc,1) ~= 1
disp(sprintf('Gd conc should be a row vector', mfilename));
return;
end
T = length(Gd_conc);
% Check that only a single T10 v... |
function plot_car(x, y, theta, phi, l, w, color)
if nargin < 7
color = 'y';
end
scatter(x, y, ['+', color]);
wheel_len = 0.25 * l;
offset_w = 0.8 * w;
offset_l_rear = 0.3 * l;
offset_l_front = 1.3 * l;
rot = [cos(theta), -sin(theta);
sin(theta), cos(theta)];
lf = [x; y] + rot * [ offset_l_front; offse... |
%求逆矩阵
%用法 B=inv(A) 其中A为数值或符号方阵,B返回A的逆
%例如
% inv([1 2;3 4]) %数值
% syms a b c d;inv([[a,b;c,d]) %符号
%
%INV Matrix inverse.
% INV(X) is the inverse of the square matrix X.
% A warning message is printed if X is badly scaled or
% nearly singular.
%
% See also SLASH, PINV,... |
%generates a random impedance progress for the system and a random energy consumption.
%nt: number of transmitting devices
%nr: number of receiving devices
%nSlots: number of time slots
%maxId: vector where maxId(i) is the maximum discharge current for device i
%sparsity: a scalar that varies between 0 (very sparce ind... |
function E = EssentialMatrixFromFundamentalMatrix( F,K )
%ESSENTIALMATRIXFROMFUNDAMENTALMATRIX Summary of this function goes here
% Detailed explanation goes here
[U,~,V] = svd(K.'*F*K);
% Enforcing essential matrix singular values
E = U*diag([1 1 0])*V.';
end
|
% % 最大持续亏损次数，输入参数R是交易盈亏情况
function maxConLossTimeRet = maxConLossTime(R)
R(R<0) = -1;
if R(end) == -1
R = [R;1];
end
indexMax = 0;
maxTemp = 0;
for i = 1:length(R)
if R(i) == -1
maxTemp = maxTemp + 1;
else... |
function [network] = removeUnusedMetabolites(network)
%% Author: J. Goldford
%
S = ~~network.S;
to_remove = sum(S,2) == 0;
network.S(to_remove,:) = [];
if isfield(network,'compounds')
network.compounds(to_remove) = [];
else
network.mets(to_remove) = [];
network.b(to_remove) = [];
end
end
|
conf = dsp2.config.load();
epoch = 'targacq';
load_path = fullfile( conf.PATHS.analyses, 'onslow_pac', epoch );
mats = dsp2.util.general.load_mats( load_path );
pac = dsp2.util.general.concat( mats );
pac = pac.add_field( 'method', 'cfc' );
pac = pac.append( mi_pac );
mi_day = unique( pac('days', pac.where('mi')) );
... |
% CLASSIFYRF Classify data with random forest classifier.
% OUT = CLASSIFYRF(MODEL) classifies out-of-bag data by using a random
% forest classifier, where MODEL is a structure with the RF parameters.
% OUT is a structure with both the votes and the class labels assigned to
% each sample in the original datas... |
%Syntax
% [N, err] = PS_GetNStim()
% Description
% Gets number of available Plexon electrostimulators. The maximum number of stimulators you can work with is four
% Returns:
% 0 - OK
% N - updated with the number of avilable Plexon Stimulators
|
function [ FSMC_L2 ] = FSMC_L2( control, SNR )
%This function creates an object containing all the information required
%to describe a Markov chain per month. The Markov chain captures the
%behaviour of the passed in SNR values.
% INPUT: m x 1 SNR: vector containing the SNR values
% OUTPUT: 1 x... |
%> Khan AA, Lai W. Modeling shallow water flows using the discontinuous
%> Galerkin method. CRC Press; 2014.
%>
classdef ParabolicBowl1d < SWEConventional1d
properties(Constant)
hmin = 1e-3
gra = 9.81
a = 3000
h0 = 10
B = 5
end
methods
function obj ... |
function [ expanded ] = matrix_expand3( matrix , integer)
%MATRIX_EXPAND Expands a matrix in size by a given integer in the 3rd
%dimension
% Given an N x M x P matrix, matrix_expand will convert it to
% an N x M x integer * P matrix with entries duplicated
% to fill the new space.
% Michael Hutchins - 6/13/2012... |
function [BvF, BvT] = cp_2dbv(Block_length, N_startf, N_finalf, NumDCS, NumDCTC, F_Warp_Fact, T_Warp_S, T_Warp_E, ...
BVF_flag, BVT_flag, EnNorm_flag);
% Advanced version of comp_feat.m by adding DCS_length changing paras
% adding energy increasing
% This comp_feat has a function to compute a [i, j, k] fea... |
% author: Tyler Thompson
% date: 2/6/2019
% description: ECE4550 Matlab Assignment #1 Problem 2
clear;
close all;
n = 0:60;
f = 0.1;
f1 = sin((pi*n/3)*f);
f2 = n>=0; % unit step function
figure(1)
% part A plot
subplot(1,2,1)
stem(n,f1)
grid on
title('Part A: x(n)=sin((pi*n/3)*f)');
xlabel('n');
ylabel('x(n)');
%... |
% 6. 已知y1=x*x,y2=cos(2*x),y3=y1*y3;,完成下列操作：
% （1）在同一坐标系下用不同的颜色和线性绘制三条曲线。
% （2）以子图形式绘制三条曲线。
% （3）分别用条形图(bar)、阶梯图(stairs)、杆图(stem)和填充图(fill)绘制三条曲线。
% (1)
x=-10:0.1:10;
y1=x.*x;
y2=cos(2*x);
y3=y1.*y2;
% figure
% hold on
% plot(x,y1,'r--')
% plot(x,y2,'g:')
% plot(x,y3,'b')
%(2)
% subplot(2,2,1)
% plot(x,y1,'r--')
% subpl... |
function [x] = AffSub_solveNormal(w, Ais, Cis)
numSubspace = length(Ais);
A = zeros(size(Ais{1}));
b = zeros(size(Cis{1}));
I = eye(size(A));
for i=1:numSubspace
Mi = (I-Ais{i});
A = A + w(i)*Mi;
b = b + w(i)*Mi*Cis{i};
end
x = A\b;
end |
%//////////////////////////////////////////////////////////////////////////
% Computes CIF (clustering improvement factor) across syndrome_labels given an arbitrary struct of entries of interest
%
% adapted from ./PROJECT/PACS1_CASE/TEST_CODE_PROOF/MASTERSCRIPT.m
% adapted 140319 to use SSRF_compute_factor_multiple.m w... |
% adapt1_plotstim
randStimFlag = get(H.Adapt1_randStimFlag,'value');
nEpochmsecs = XStimParams.epoch_duration; % durations for epochs1 and 2
SPLs_1 = 10 + XStimParams.SPL1; % SPLs for epoch1 and epoch2
SPLs_2 = 10 + XStimParams.SPL2;
silence_lead_pts = (XStimParams.silence_lead * round(TD... |
function nlp = Time(nlp, varargin)
plant = nlp.Plant;
T = SymVariable('t',[2,1]);
tf = SymFunction(['time_' plant.Name],T(2),{T});
addNodeCost(nlp,tf,{'T'},'first');
end |
function [ output_args ] = Helper_GetSpecificValue( tdb, value, output )
%Helper_GetSpecificValue get a specific .mat database, a searchvalue and
%and output, to extract certain values out of the complete database
% Helper_GetSpecificValue('textures_alle.mat', 'Zoom',
% 'textures_Zoom.mat')
% Changelog: - [07.0... |
function[CD_LandP]= LandP(CD_Parasitic)
%This function evaluates the Leakage and Protuberance Drag on the a/c
CD_LandP=1.035*CD_Parasitic;
end
|
% "an example where ugpdc3 is 6times greater than GC"
A2d = -[0 0 0.1; 0 0 -5; 0 0 0.95];
D = diag([1 1 1]);
ARroots(A2d);
od = 200;
fftlen = 8192;
S = A2S(A2d, D, fftlen);
R = S2cov(S, od);
GC = RGrangerT(R)
ugpdc = uGPDC_A(A2d, D, fftlen);
ugpdc3 = real(mean(log(1+ugpdc.*conj(ugpdc)),3));ugpdc3 = ugpdc3 - diag(... |
function plotYscale( maxFR, x, y )
if maxFR<30
halfRate = floor(maxFR/2);
elseif maxFR > 140
halfRate = 60;
elseif maxFR > 100
halfRate = 40;
else
halfRate = 20;
end
scaleHeight = (halfRate/maxFR);
plot([x x],[y scaleHeight+y],'LineWidth',3,'Color','k')
xRange = range(get(gca,'XLim'));
text(x-.02*xRange, y... |
function out=concatenatedatasets(in1,in2)
% Append two struct-based datasets variable-by-variable
% If the input data is not a struct, return error
if ~isstruct(in1) || ~isstruct(in2)
error('Error: requires struct input.')
end
if isempty(fieldnames(in1))
out = in2;
elseif isempty(fieldnames(in2))
out = in... |
function [result,remainder]=integer_div_mod_negs(m,n)
remainder=m;
result=0;
if m>0 && n>0
while 1
if remainder-n > 0
result=result+1;
remainder=remainder-n;
else
break;
end
end
elseif m<0 && n>0
... |
function HabituationPhase_MC
% This protocol presents the mouse with two stimuli, a valve click (from odor valve module) and a water click.
% Written by Marco Colnaghi and Riccardo Tambone, 05.27.2021.
%
% SETUP
% You will need:
% - A Bpod.
% - An Olfactometer.
% - A Lickport.
% To be presented on DAY 0.
global Bp... |
function [ ch ] = centroidDist( cenOne,cenTwo )
%UNTITLED2 Summary of this function goes here
% Detailed explanation goes here
ch=sqrt((cenOne(1)-cenTwo(1))^2 +(cenOne(2)-cenTwo(2))^2);
end
|
%
% f: tone/sine frequency.
% amp: amplitude of sine wave.
% fs: sampling frequnecy of sine wave.
%
function [t,y] = create_sine_wave(f,amp,fs,T)
ts=1/fs;
t=0:ts:T;
y=amp*sin(2*pi*f*t);
sound(y,fs) |
load('lsdata.mat','-ascii')
x = lsdata(1,:)';
y = lsdata(2,:)';
ynoisy = y +.1*randn(size(x));
A = lsdata;
plot(x,y,'.');
hold on
D = A'*A;
e = A'* ynoisy';
c = D\e;
yfit = c(1).*x + c(2);
plot(x,yfit,'X')
|
% stat plots script file
load dump_stats_all_t
dc=d.e.counts-d.s.counts;
dur=d.e.time-d.s.time;
dur=dur';
st=[d.s.v1t(:,1), d.s.v2t(:,1), d.s.v3t(:,1), d.s.v4t(:,1)];
et=[d.e.v1t(:,1), d.e.v2t(:,1), d.e.v3t(:,1), d.e.v4t(:,1)];
dt=et-st;
% duration plot
plot(d.s.time,dur)
title('Dump Durations')
xlabel('Date')
ylab... |
function Candidate_P = Find_Cadidate_P_in2GW(P1,P2,GW_Pathloss_perPixel,CoverRange_perPixel,GW_Serve_Limit,TxP_Thres,User_Served)
% GW_Serve_Limit = (6-1)*3/0.25;
[Sort_P1,~] = sort(GW_Pathloss_perPixel(P1,:),'descend');
ServedRange_Thres = max(TxP_Thres,Sort_P1(GW_Serve_Limit));
Served_P1 = find(GW_Pathloss_perP... |
context = Z_C1(:,9);
dC = diff(context);
SwitchTrial = find(dC~=0); % switch occurs after this trial
if length(SwitchTrial) == 1
RangeC1 = 1:SwitchTrial;
RangeC2 = SwitchTrial+1:size(Z_C1,1);
elseif length(SwitchTrial) == 2
RangeC1 = 1:SwitchTrial(1);
RangeC2 = SwitchTrial(1)+1 : Sw... |
function [b rsq p_sq beta_distMatrix] = eigenDMA(vox_pattern,modelRDM)
%decompose voxel pattern into eigenvectors and run a regression on their
%RDMs
%Output
%b = beta values for each eigen rdm
%rsq = overall r-sq val for model
%s = 'sparseness' val
%Andreas Marouchos 2014
[U S V] = svd(vox_pattern);
for n=1:leng... |
function download(obj, varargin)
% DOWNLOAD Method to download CloudAppendBlob(s)
% Use this method to download a CloudAppendBlob or an array of CloudAppendBlobs if
% a vector input is provided. If no argument is provided the current working
% directory will be used as the download destination and the blob name will be... |
function xout = lorenz96(xin)
% xin should be a vector
global F
N=size(xin,1);
xout=zeros(N,1);
%first the 3 edge cases: i=1,2,N
xout(1)=xin(N)*(xin(2)-xin(N-1))-xin(1);
xout(2)=xin(1)*(xin(3)-xin(N))-xin(2);
xout(N)=xin(N-1)*(xin(1)-xin(N-2))-xin(N);
%then the general case
for i=3:N-1
xout(i)=xin(i-1... |
function [classCube, classLabels] = UpdateClassSampleCubes(className, dataCubeSample, dataLabels, existOnWs)
% Combine training data cubes to prepare final traing data sets for
% each class.
dataCubeName = strcat(className, '_cube_Ref');
labelCubeName = strcat(className, '_labels');
... |
function [hmm,fe] = dropstate(hmm,k,X,T)
K = length(hmm.state);
no_k = setdiff(1:K,k);
K = K - 1;
hmm.state(k) = [];
hmm.K = K;
hmm.Dir2d_alpha = hmm.Dir2d_alpha(no_k,no_k);
hmm.Dir_alpha = hmm.Dir_alpha(no_k);
hmm.prior.Dir2d_alpha = hmm.prior.Dir2d_alpha(no_k,no_k);
hmm.prior.Dir_alpha = hmm.prior.Dir_alpha(no_k);
... |
load H2ext
rout=int16(zeros(2,1024^2));
L=512*512;
rout(:,1:L)=routxy;
tem=[routxy(2,:)+512; routxy(1,:)]; %[tem(:,1) tem(:,262144)]
rout(:,L+1:2*L)=tem;
tem=[routxy(2,:)+512; routxy(1,:)+512]; %[tem(:,1) tem(:,262144)]
rout(:,2*L+1:3*L)=tem;
tem=[513-routxy(1,:); (513-routxy(2,:))+512]; %[tem(:,1) tem(:,262144)]
ro... |
function [mov5d, dimLabels] = parseScanimageTiff(mov, siStruct)
% Nomenclature: frames and slices refer to the concepts used in
% ScanImage. For clarity, the z-dimenstion in the tiff is therefore
% called "tiffPage" here.
[height, width, nTiffPages] = size(mov);
fZ = siStruct.fastZEnable;
nChannels... |
function A = jac3DoF(x, u, trajData)
prm.m = 4.5;
prm.S = 0.473;
prm.CD0 = 0.0173;
prm.CD1 = -0.0337;
prm.CD2 = 0.0517;
prm.p_exp = trajData.p;
% evaluate jacobian
U = [u, trajData.VR];
Z = x;
A = JacEval(Z, U, prm);
if(trajData.p == 0.25)
A = [A(1:3,1:3), A(1:3,6); A... |
ts_conn = importdata('data/rsfmri-dataset2/sub015.txt');
test = standard_sliding(ts_conn, 50);
figure
imagesc(squeeze(test(1,:,:))) |
%% %%######################################################################
%% %%################ Define Sizes Here ##########################
%% %%######################################################################
dimx=2; %% number of states
dimu=1; %% number of inputs
dimec=0; ... |
function [d,netcdfInfo] = loadByDate(startDate,endDate,varargin)
%LOADBYDATE Loads the Amdar files for the date range of interest.
%
%INPUTS:
% startDate - Integer in Matlab date format.
% endDate - Integer in Matlab date format.
% varargin - Optional arguments, passed to Amdar.load()
%
%OUTPUTS:
% d - Structure c... |
function main(sourcePath, destinationPath, visualize, smoothing, quantization)
%MAIN Transcribe drums into a MIDI file given an audio file.
% Create a MIDI file transcription of the drums contained in the input
% audio file. sourcePath should be the path to an audio file,
% destinationPath is the path to the crea... |
% Inicio
v = F(x0)
M = inv(DF(x0))
% Inversa Jacobiano
s = - M*v;
x = x0+s;
% Paso de Newton
incr = norm(s);
while incr > tol
w = v; % F(x(k-1))
v = F(x);
y = v-w; % F(x(k)) - F(x(k-1))
z = - M*y; % -inv(A(k-1))*y(k)
p = - s' *z;
q = s' *M; % s(k)'*inv(A(k-1)
R = (s+z)*q/p... |
% testing accessing a variable several levels outside of the scope
var x:int;
fun outer(a:int):int
{
fun middle(a:int):int
{
fun inner(a:int):int
{
begin
return x * (1 / 2) - a;
end
};
var y:int;
begin
y := inner(a);
if (x =< y)
then y := x * x
else y := x - 2;
return y;
end... |
run('identification_init');
% Subspace model
close all; disp('Testing SUBSPACE model ...');
nx = 3:20;
[model, fit, aic_value, mag, wout ] = fun_subspace_model(nx,...
preprocessed_prbs_est, preprocessed_prbs_val, true)
figureNumber=5;
fun_bode_plot(wout,mag,'subspace model',figureNumber); |
% Timothy Smith
% Averaging FFT analyzer
figure
subplot(2,1,1)
plot(NFreq,1E3*DbV,'m')
xlabel('Frequency')
ylabel('Voltage Spectral Density (mVrms/{\surd}Hz)')
t=title( 'Before Averaging');
set(gca,'XTickLabel',{'0','10kHz','20kHz','30kHz','40kHz','50kHz','60kHz','70kHz','80kHz','90kHz','100kHz'})
subplot(2,1,2)
plot... |
function stat = reconstructBurstStats(feat,support,featInd)
stat = nan(2,numel(feat));
for i = 1:numel(feat)
if ~isnan(feat{i}{1})
[ampMeanSTD(1),ampMeanSTD(2)] = pdfExpVal(feat{i}{featInd},support{featInd}');
stat(:,i) = ampMeanSTD;
else
stat(:,i) = nan;
end
end
stat = nanmean(stat... |
basedir = '/home/sheng/workspace/DeepCNNData/cifar/out/';
trainCostFilename = [basedir, '/train_totalCost.txt'];
testCostFilename = [basedir, '/test_totalCost.txt'];
costOutfilename = [basedir, '/totalCost.png'];
trainCostFile = fopen(trainCostFilename, 'r');
if(trainCostFile < 0)
disp(['Cost file ', trainCo... |
% Ogrenci No: 185112007
% Ad Soyad : Gafur HAYYTBAYEV
close all ; clear all ; clc ;
% verilmis fonksyon
%fx = (x^3) + (x^2) - (12 * x);
% fonksyon turevi
%fxd = (3 * x^2) + (2 * x) - 12;
% aralik
a = 2;
b = 4;
% durma kosulu
h = 0.0001;
iter = 0;
while abs(a - b) > h
fa = (a^3) + (a^2) - (12 * a);
fb =... |
function oddball_analyse(EEG, scaling, title_str, savefig, savestr)
width = 4.25;
height=3.75;
% Select only relevent channels of data
EEG = pop_select( EEG,'channel',{'Channel 1'});
EEG = pop_eegfiltnew(EEG, [],0.5,[],true); %HP filtering
EEG = pop_eegfiltnew(EEG, [],20); %LP... |
close all;
clear all;
fid = fopen('data_mgd2DKSE','r');
output = textscan(fid,'%*s %f %*s %f %*s %f');
fclose(fid);
col1 = output{1}; col2 = output{2}; col3 = output{3};
M = col1(1); N = col2(1); dt = col3(1);
Tfinal = col1(2); p = col2(2); q = col3(2);
alpha = col1(3); beta = col2(3); gamma = col3(3)... |
clear, close all;
clc;
addpath( 'kernel' );
addpath( 'numerics' );
addpath( 'helper' );
addpath( genpath('scripts') );
%######################################################
% This code is part of the Matlab-based toolbox
% OPTIK --- Optimization Toolkit
% For details see https://github.com/andreasmang/optik
%###... |
function pLabels = predictKNN(KNNmodel, testingF)
%PREDICTKNN Function takes a K-nearest neighbour model as argument
% and returns the predicted label(s) for input data.
%
% P = predictKNN(MODEL, TESTDATA) returns vector P containing 'n'
% number of labels where 'n' is the number of examples inputed
%
% MOD... |
function SortedInsDataArray = getSortedInsData(thisInsData)
%getSortedInsData 获取数据文件不同排序后的更新数据
% 输入
% thisInsData 待排序的instance数据
% 输出
% SortedInsDataArray 排序后的instance数据
%% function getSortedInsData(thisInsData)
idxCus = {}; idxVeh = {};
% NOTE 核心 修改customer和vehicle顺序
% idx : ... |
function lower_layer = buildLowerLayer(gen_rules)
num_rule = 2;
num_category= size(gen_rules.W2,1);
nlatent = num_rule*num_category;
lower_layer = nnSetup([size(gen_rules.W1,3),nlatent]);
lower_layer.W{1}(:) = 0;
lower_layer.b{1}(:) = 0;
%Set weights
for i=1:num_category
[~,idx]... |
%The following script is for Math 6230: Differential Games and Optimal
%Control. The script solves the first numerical problem. The problem is
%implement the Marker/Particle Method in a programming enviorment of your
%choice. Use it to advance (A) the curve y=sin(x) moving upwards with F=1 ;
%(B) a unit circle expandin... |
function [] = assert_mission_struct(mission)
r = global_jess_engine();
if iscell(mission.instrument_list)
instr_list_str = StringArraytoStringWithSpaces(mission.instrument_list);
else
instr_list_str = mission.instrument_list;
end
call = ['(assert (MANIFEST::Mission (Name ' char(mission.name) ')' ...
' ... |
for k=4:length(STANDARD)
cellContents=STANDARD{k};
RF_X{k-3}=cellContents(1:20);
RF_Y{k-3}=cellContents(21:33);
RF_Z{k-3}=cellContents(34:46);
end |
1;
function [y, dy] = exactResult1(f, T, x)
y = (f / (2*T)) .* x .* (1 - x);
dy = (f / (2*T)) * (1 - 2 .*x);
endfunction
function [y, dy] = exactResult2(k, x)
num = exp((x - 1) ./ k);
denom = (exp(-1/k) - 1);
% y = (num - 1) ./ denom;
t1 = exp(x./k);
t2 = exp(1/k);
y = (t1 - t2) / (1 - t2);
dy = (... |
clear all;
% close all;
% cam_t = [-4,5,5]'
% cam_r = [2.7,0.15,-1.7]
cam_t = [-3,0,10]'
cam_r = [3.14,0.35,1.57]
rot = rotmat3d(cam_r(1),cam_r(2),cam_r(3))
t = cam_t
K = camProjMat(609,609,305,225)
points3d = [1,0,0;
0,-1,0;
-1,0,0;
0,2,0;
0,5,0;];
points3d = [-0.33,0.1,0;
0.33,0.1,0;
... |
function segments = getSegments(idx)
% GETSEGMENTS
% Returns the continous lines on an idx image
%
% Part of a *FAILED* approch to segmentation optimization by genetic
% optimization.
%
% Writen by Markus Mayer, Pattern Recognition Lab, University of
% Erlangen-Nuremberg, markus.mayer@informatik.uni-erlangen.de
% Ini... |
%Form_Directory_List
%Required for Browser usage
%Changes 'string' property to the current directory listing
dlist = dir;
set(hbrowser,'String',str2mat(dlist(1:length(dlist)).name)); |
clear
close all
clc
x = load ('DataN.txt');
t = 1:2000;
x = x(t);
figure
plot(t, x)
title('ECG signal before filtering')
ylabel('Amplitude') %plotting the raw ECG signal data
print('Before_Filter.jpg','-djpeg')
w = 50/(256/2);
bw = w;
[num, den] = iirnotch(w,bw);
x_notch = filter(num, den, x);
figu... |
function y = iseven(n)
if floor(n/2)*2 == n
y = 1;
else
y = 0;
end
end
|
function move_dummys
go_back = pwd;
subs = 47:48;
base_dir = '/projects/crunchie/hipp/wavepain/';
run_dir = {'run001/mrt/dummy/', 'run002/mrt/dummy/'};
for sub = subs
name = sprintf('sub%03d',sub');
for run = run_dir
path = fullfile(base_dir, name, run);
cd(path{1});
... |
close all;
clear all;
clc;
load('spikes.mat');
% Plot all spikes on same plot
figure('Color','w');
for i=1:6000
plot(spikes(i,:));
hold on;
end
%verify axis labels
set(gca,'XLim',[0 100],'XTick',linspace(0,100,9));
xticklabels({'0' '0.25' '0.5' '0.75' '1' '1.25' '1.5' '1.75' '2'});
xlabel('Features as Time');... |
x = linspace(0,65,4000);
load('speed_anna');
plot(distance_km, speed_kmph);
hold on
plot(x,velocity(x,'speed_anna'),'--');
xlabel('km');
ylabel('km/h');
hold off
|
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %manipulate the mutual information by injecting malicious data points
addpath /home/hengl/matlab/bin/scripts/ITAFS/source/
index = 4;
limit = 0.2; %maximal percent of points we want to inject
fid = fopen('/home/hengl/matlab/bin/scripts/ITAFS... |
% Visualises the HDM priors for comparison against Friston et al. 2000
% Get priors
% -------------------------------------------------------------------------
n_inputs = 1;
M.B0 = 3;
M.age = 18;
[pE,pC,D,is_logscale] = spm_hdm_priors_hdm2(n_inputs,M);
is_logscale = logical(spm_vec(is_logscale));
% Unpack
% -------... |
function R = Fie0_packet_drop(beta,epsilon,N,k,kernel,RHS1,AbsTol,RelTol)
%N is the truncation, some large number
%k is the threshold, k << N
rescaled_RHS1=@(x) RHS1(x)/beta;
scalar=1/beta;
%define discontinuous kernel due to packet drop
kernel_packet=@(e,x) epsilon*kernel(e,x).*(abs(e)>=k)+kernel(e,x).*(abs(... |
function varargout = ltr( glob )
if (nargin < 1)
glob = '*';
end
directoryName = pwd();
s = dir( fullfile(directoryName, glob) );
tf_isCWD = strcmp('.', {s.name});
tf_isParentDir = strcmp('..', {s.name});
s(tf_isCWD | tf_isParentDir) = [];
if isempty(s)
if nargo... |
function [EVs, EC] = EvalObjGA( D,Px,Pl,Cd )
%{
Evaluate solutions
INPUT:
D: decision rules to be evaluated - Nsol x Nx matrix
Px: probability matrix for comp's (members)
Pl: probability vector for Load
Cd: cost matrix for comp's
OUTPUT:
EVs: E[Vs|d] - Nsol x 1 vector
EC: E[C|d] - Nsol x 1 vecto... |
function [fin, x_steps, counter] = modPRalg(maxsteps, x0, eps, beta, sigma, delta1, delta2, f, g)
% Test, ob alle Parameter im passanden Bereich sind
if (eps<0 || beta<=0 || beta>=1 || sigma<=0 || sigma>=1 || delta1<=0 || delta1>=1 || delta2 <=1)
error("one of your chosen parameters does not fit the re... |
% Exercise4
% ソローモデルのダイナミクスをプロット
%変数
g = 0.05;
n = 0.1;
alpha = 0.33;
rho = 0.05;
delta = 0.4;
%s = 0.3;
%k=0:0.01:0.1;
%kplus = s*k.^alpha - (n + g + delta).*k;
%for i=1:3
%s = 0.3;
s=0.3
k=0:0.001:1;
kplus = s*k.^alpha - (n + g + delta).*k;
plot(k,kplus,k,k)
hold on
title('transition')
xlabel('kt')
ylabel('... |
% Problem 2.1 - question 2
% x2(n) = sum_{k=-5}^{5} e^{-|k|}*dirac(n-2k), -10 <= n <= 10
n2 = [-10:10]; x2 = zeros(1, length(n2));%zeros create an array
for k = -5:5
x2 = x2 + exp(-abs(k)) * impseq(2*k, -10, 10)
end
Hf_1 = figure; set(Hf_1, 'NumberTitle', 'off', 'name', 'problem 2 - question 2');
Hs = stem(n2, x... |
function population = createInitPopulation(sizePop, length, values, mode, option)
% CREATEINITPOPULATION
% Implements various ways to create a initial population for a genetic
% algorithm, as well as the possibility to create all possible combinations
% (for a brute force method)
%
% Part of a *FAILED* approch to segm... |
function u=Adams4i(dufun,h,a,b,u0)
format long;
N=(b-a)/h;
u=zeros(1,N+1);
t=a:h:b;
u(1)=u0;
u(2)=1.108960991057469;
u(3)=1.160335224588050;
u(4)=
for i=3:N
u(i+1)=u(i)+(h/24)*(9*feval(dufun,t(i+1),u(i+1))+19*feval(dufun,t(i),u(i))-5*feval(dufun,t(i-1),u(i-1))+feval(dufun,t(i-2),u(i-2)));
end
plot(t,u,'-rs');
hold ... |
addpath('..');
u = @(x,y) sin(2*pi*x).*sin(2*pi*y);
LapU = @(x,y) -8*pi*pi*u(x,y);
NN = [64 128 256 512 1024];
L1 = 0*NN; L2 = 0*NN; max_e = 0*NN;
i = 1;
for N = NN
x_side = linspace(0,1,N+1); y_side = linspace(0,1,N+1);
x_cent = 0.5*(x_side(2:end)+x_side(1:end-1));
y_cent = 0.5*(y_side(2:end)+y_side(1:end-1));
[xx,yy]... |
%% Funziona su Octave. Non provato su Matlab.
%
% analisi sull'ammissibilità del parametro k.
%
clc
clear all
close all
ssave = 1;
% parametri
b0 = 25;
gamma = 10;
k = 1e-3;
Nass = 100; % size of population costante
I0 = 1; S0 = Nass-I0;
x0 = [S0;I0]/Nass; %... |
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