text stringlengths 8 6.12M |
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v=1:1:max_speed;
v=v./3.6;
trac=Power./(v)/(Mass*1000);
figure, plot(v*3.6,trac.*1000*Mass)
grid on
kec = 1:0.1:max_speed;
kec = kec';
a_trac = Power./kec./mass;
Power_brake = max_brake.*kec;
braking = Power_brake./kec;
Res = zeros(size(kec));
for i = 1:length(kec)
if a_trac(i,1) > max_accel
... |
%% Plane XY
q_xy=[phi_z; theta_z];
q_xy_dot=[phi_z_dot; theta_z_dot];
x_xy=[theta_z, phi_z_dot theta_z_dot];
u_xy=T_z;
T_f=-((r_s*r_w*I_b_xy*sin(alpha)*T_z)/(r_w^2*I_b_xy+r_s^2*I_w_xy*sin(alpha)^2));
f_xy = [theta_z_dot;
-(((r_w^2*I_b_xy+r_s^2*I_w_xy*sin(alpha)^2)*T_f+r_s*r_w*I_b_xy*sin(alpha)*T_z)/(r_w... |
function[] = permutation_test_review(which_strain, which_nucleus)
%e.g.: permutation_test_review('RC',{'dLGN','vLGN','OPN','pret'})
rng(1); %for reproducibility
Nshuffle = 100000;
filepath = 'Data\classification\';
filename = []; count_nuclei = 0; count = 0;
x_infra = []; x_gamma = []; x_both = []; x_mfr = [];... |
function [ ids ] = nearestRT( data, w )
% Solves tracking problem in the easiest way - just selecting the nearest
% rect from the next frame. Distance is measured using coordinates, scale,
% velocity and hog with some weights w.
%
% Inputs:
% data - struct that can be read from data.mat or generated with
% redu... |
function b = npend_alphas_DAE_b_1(I1,L1,d1,g,m1,omega1,theta1,in8)
%NPEND_ALPHAS_DAE_B_1
% B = NPEND_ALPHAS_DAE_B_1(I1,L1,D1,G,M1,OMEGA1,THETA1,IN8)
% This function was generated by the Symbolic Math Toolbox version 8.1.
% 04-Dec-2018 02:30:11
d_a1 = in8(:,1);
d_a2 = in8(:,2);
t2 = omega1.^2;
b = [0.0;-g.*m1... |
function script()
close all;
monte_carlo = load('monte_carlo.txt');
figure;
hold on
for i = 1 : length(monte_carlo)
plot(monte_carlo(i,1) , monte_carlo(i,2),'+');
end
hold off
mean(monte_carlo(:,1))
mean(monte_carlo(:,2))
|
%--------------------------------------------------------------------------
% Plot data consisting of 8-bit samples with 1 channel
%
% The input format is [ch1 sample 1 | ch1 sample 2 | ch1 sample 3 | ... ]
%--------------------------------------------------------------------------
clear;
% input file
% fn='/raid/ibo... |
function MPHI=mphi(results,NAR,N)
A=[];
for i=1:N
A=[A;results(i).beta(1:N*NAR)'];
end
MPHI=zeros(N,N*NAR);
for i=1:NAR
PHI=zeros(N,N);
for j=1:N
PHI(:,j)=A(:,(j-1)*NAR+i);
end
MPHI(:,(i-1)*N+1:i*N)=PHI;
end
|
function [FIRA, W, MTindex] = getML_RLSimPerceptualLearning9(Monk, NSEN, NTUNE, eln, DIRS, MTDIRS, SIMNUM, RECOMPUTE)
% Simulation learning of the motion discrimination task with a
% reinforcement learning rule (A-reward-penalty rule) using response
% statistics of real MT neurons
%
% % add constant pooling noise (not... |
solver = 'mosek';
basis = 'CG_red';
slack = 1;
% nout = 2; P_ABC = get_dist('W-type');
nout = 2; P_ABC = get_dist('complete correlation');
% nout = 2; P_ABC = get_dist('anti-correlation');
% nout = 4; P_ABC = get_dist('EJM');
% Constraints
[A_, b_, tmp, tmp] = get_spiral_constraints(nout, P_ABC, basis);
K = struct();... |
endTime = 0.005;
tau = 0.1;
tau1 = 0.1;
k = 1;
r = 2;
l = 20;
m = 100;
p = 10;
alpha = 1.0;
beta = 1.0;
main_event(endTime,l,k,r,tau,tau1,m,p,alpha,beta)
%Softmax function, takes in a tau parameter and a normalized list of values for actions (should be the length of 'actions'); here these are estimated success p... |
function [act_val, grid_pos, grid_coords] = eric_calc_winner_location(activations)
%% Function called by eric_choice.m
global NUM_ROWS NUM_COLS NUM_INPUT_DIMS
global ETA G
[act_val grid_pos] = max(activations(:)); %% Returns activation value and position in grid of winner
%fprintf('\nact_val = %f\n', act_val);
%fpr... |
function ret = refineU(u,periodic)
% refineU - Refine u using linear interpolation. Assuming u periodic.
% Syntax: ret = refineU(u)
%
% Input:
% u - A vector of size (1,M) containing the function u
% periodic - A boolean value which decides if u is interpreted as periodic
%
% Output:
% ret - A vector conta... |
function metric = behaviouralMetrics_n(data, func, nobs)
% function behaviouralMetrics_n(data, func, nobs)
% Runs behavioralMetrics(data, func) with nobs randomly sampled (without
% replacement) observations from data.
NOBS = size(data,1);
if nobs > NOBS; nobs = NOBS; end
i = randsample(NOBS, nobs);
... |
function FI=filt(I,F)
%dimensioni del filtro
[r,c] = size(F);
%dimensioni dell'immagine
[h,w] = size(I);
%padding dell'immagine
v_off = floor(r/2);
h_off = floor(c/2);
%crea un immagine nera di dimensioni adeguate
PAD = zeros(h+r-1, c+w-1);
%copia al centro l'immagine da filtrare
PAD(v_off+1:(h+v_off),h_off+1:(w+h... |
function gsr_resp = infer_gsr_resp(ID,can_corr,can_wrong)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Function computing the mean response w.r.t. some mode of our experiment.
%
% Parameters:
% -------------
% ID {int} ID of the subject
% CAN_CORR {double} canonical SC... |
function [mag, phase, freq, powerDb]=fftTwoSide(signal, fs, plotOpt)
% fftTwoSide: Two-sided FFT for real/complex signals
% Usage: [mag, phase, freq, powerDb]=fftTwoSide(signal, fs)
% Roger Jang, 20060411
if nargin<1, selfdemo; return; end
if nargin<2, fs=1; end
if nargin<3, plotOpt=0; end
N = length(signal); % 點數
... |
%% 0.4 C1 a
x = [10^-1, 10^-2, 10^-3, 10^-4, 10^-5, 10^-6, 10^-7, 10^-8, 10^-9, 10^-10, 10^-11, 10^-12, 10^-13, 10^-14];
a = @(x) (1-sec(x))./(tan(x)).^2;
xa = a(x);
%% 0.4 C1 b
%% 1.1 E5 a
x = linspace(0, 3);
y = @(x) x.^4 - x.^3 - 10;
a = y(x);
plot(x, a);
% P? ploten ser vi klart och tydligt att x = 0 i interval... |
function []=svsNucleiInstrumented(impath,filename,fileext, resultpath, validationpath)
%%, folder,tile)
if nargin==0
image = {'astroII.1/astroII.1.ndpi-0000008192-0000008192',...
'gbm2.1/gbm2.1.ndpi-0000004096-0000004096',...
'oligoIII.1/oligoIII.1.ndpi-0000012288-0000028672',...
... |
%Limpar
close all
clear all
clc
%Inicio
mdl_sawyer %Load do Modelo Sawyer
t = [0:.05:2]; % Tempo da transição: 1 a 2 com incrementação de 0.5
posicao1 = [0, -pi/2, 0, 0, 0, 0, 0] % Posição angular inicial (radianos) do Robot Sawyer
posicao2 = [0.4, 0, -pi/2, 0, pi/2-0.2, 0, -0.3] % Posição angular s (ra... |
function phase=BiChannels2Phase(encoder,varargin)
% CopyRight:vastera@163.com
% General introduction:% Convert the two channels of signals into a single monotonously increasing phase
%% analyze two channels of encoder signals by combining them together as
% complex nubmers
%% ====================== INPUT ==============... |
clear all; % clears plot
close all; % closes plot
xlim = [0 1]; % establish range for x
t = 0.25; % time for Hubbard
u = linspace(0,1,1001); % 1001 points between 0 and 1
y1k = hubbard(u,t); % y1k is hubbard using 1001 points
iplot = 0; ... |
function [profile,zero] = level_norm(profile)
% input: profile - gaussian distributed value vector
% output: profile - normalized profile value, so the distribution converges
% to zero, zero - subtracted amount
[hist_count,hist_binval] = histcounts(profile,round(abs(min(profile)-max(profile))));
[~, max_index] = m... |
fromPath = '../../sumMe/videos/';
toPath = '../../data/temporalAttention/';
videos = dir(fromPath);
nVid = length(videos);
for i = 4:nVid
[temporalAttention,~] = getTemporalAttention(strcat(fromPath,videos(i).name));
temp = strcat(toPath,videos(i).name);
newName = temp(1:size(temp,2)-4);
save(strc... |
%{
example.Session (manual) # an experiment recording session for the given animal
-> example.Animal
session :smallint # recording session number for this animal
----
user :varchar(16) # experimenter's name
session_date :date # the date on which the recording session began
session_fol... |
function y = fun2_lc
num=[1 -7 0];
den=[1 3 8];
[num2,den2]=cloop(num,den);
step(num2,den2);
grid on;
end
|
function target=returnsubstituted(value,target,index)
% target=returnsubstituted(value,target,index)
% return Target with all values indicated by Index substituted with Value
if length(value)>1
error('Multiple values suggested')
elseif length(value)<1
error('Attempted to fill nonexistent value')
else
target... |
function [Bx,By,Bz,x,y,z,zx,zy,zz]=phase2
clc
T = 290; %K
P = 101.3e3; %Pa
% C1, C2, C3, nC4, nC5, H2O, CO2, N2
index = [61,100,132,181,223,20,46,29];
n=[0.588235,0.058824,0.058824,0.058824,0.058824,0.058824,0.058824,0.058824];
% c2 c3 N-hexane water xylene
%[Bx,By,Bz,x,y,z,zx,zy,zz]=phase... |
function [ wp ] = wpDecision( peakx )
%UNTITLED6 Summary of this function goes here
% Detailed explanation goes here
wp = cell(1,2);
for i=1:size(peakx,1)
wl = [];
for j=2:size(peakx,2)
s = setdiff(peakx{i,j}.peaki,peakx{i,j-1}.peaki);
wl = [wl, selectNew(s,peakx{i,j}.peaki)];
end
wp{i} = wl;
... |
classdef (Abstract) IlluminationAbstractHandler < handle
%ILLUMINATIONHANDLER helps use illuminants
properties(Access = protected)
solution
space
Magnituds
scalingCoef
filename
end
methods
function obj = IlluminationAbstractHandler(solution, space)
% Construct an object with existing values
ob... |
function obj = geom_interval( obj,varargin )
p=inputParser;
my_addParameter(p,'geom','area');
my_addParameter(p,'dodge',[]);
my_addParameter(p,'width',[]);
parse(p,varargin{:});
obj.geom=vertcat(obj.geom,{@(dobj,dd)my_ci(dobj,dd,p.Results)});
obj.results.geom_interval={};
end
function hndl=my_ci(obj,draw_data,param... |
function tree = BuildGenericTdTreesViaClustering(data, params)
[vecs, vals] = CalcEigs(data, params.eigs_num);
embeddings = vecs*vals;
%initialize root (top-most level):
N = size(embeddings, 1);
rev_tree{1}.folder_count = 1;
rev_tree{1}.folder_sizes = N;
rev_tree{1}.clustering = ones(1, N);
rev_tree{1}.super_fo... |
%start the ROS node in MATLAB and connect to the existing ROS network
rosinit();
%determine the location of the Vel_pose_connect file
vel_pose_connect_folder = fullfile(autoware.getRootDirectory(), ...
'benchmark', 'computing', 'perception', 'localization', 'autoware_connector', 'vel_pose_conne... |
# -*- octave -*-
function [f,b,bcic,bp,bt]=cic_design(N, ... # CIC order
M, ... # diff. delay
R, ... # reduction factor
fc) # passband edge
L = 64; # fir filter order
Fo = R*fc;
p = 2e3;
s = 0.25/p;
fp = [0:s:Fo];
fs = (Fo+s):s:0.5;
f = [fp fs]*2;
Mf = [1 abs(M*R*sin... |
function ann = ProjectOutPose(obj,ann)
%ProjectOutPose Summary of this function goes here
% Detailed explanation goes here
shape = obj.Ann2Shape(ann);
shape = shape - obj.L2Shape(obj.Shape2L(shape)) + repmat(obj.pose_mu,[1,size(shape,2)]);
ann = obj.Shape2Ann(shape);
end
|
classdef M_2D
%M_2D Summary of this class goes here
% Detailed explanation goes here
properties
n_level = 0
sm
w
tm
end
methods
function obj = M_2D(sm,w,tm)
obj.n_level = length(sm);
obj.sm = sm;
obj.w = w;
obj.tm = tm;
end
[n... |
%This script allows to evaluate statistics of a given face detector.
%Assuming haarCascade is loaded in memory, representing the detector
%this code confirms that the 20 x 20 pictures are represented
%with rectangles numbered from 0 to 19.
Scale=1;
x=0;
y=0;
minW=Inf;
maxW=-Inf;
RectX=[];
RectY=[];
RectWidth=[];
RectH... |
function [S_period,fit_model,accel_axis,other_axis_fit,other_axis]=fit_man_accel(Data,si,f_name,poi,if_plot_figures)
[~,D_y] = size(Data);
[~,poi_y] = size(poi);
S_period = cell(1,poi_y);
fit_model = cell(1,poi_y);
accel_axis = cell(1,poi_y);
all_axis=2:D_y;
other_axis_fit=cell(2,poi_y);
... |
function string_without_number=Number2Word(string_with_number,library_set)
if nargin<2
library_set='ENGLISH';
end
%constructing language library
LIB.ENGLISH={'Zero','One','Two','Three','Four','Five','Six','Seven',...
'Eight','Nine'};
LIB.SPANISH={'Cero','Uno','Dos','Tres','Cuatro','Cinco','Seis','Siete',...
... |
clear
clc
close all
format bank
%%%%Wing Cube Loading - I get to decide this
WCL = 9;
%%%Weight Estimate (oz)
weight_servo = 3;
x_servo = 2; %%%in reference to the LE of the main wing
weight_battery = 5.0; %%
x_battery = -3;
weight_esc = 4; %%% Power supply
x_esc = -2;
weight_motor = 4; %% and propulsion
x_motor =... |
f = open('Fig149.fig');
%
f_children = get(f, 'Children');
f_grandchildren = get(f_children, 'Children');
f_lines = f_grandchildren{2};
time_scales = [25, 128, 250, 500, 1000] / 1000;
mfcc_none_inst = f_lines(12).YData;
mfcc_none_mode = f_lines(11).YData;
mfcc_lmnn_inst = f_lines(10).YData;
mfcc_lmnn_mode = f_lines... |
%% SVD & SVD Analysis
% Finding ranks
rank_X_crp = rank(X_crp);
fprintf('\nRank of X_crp is %d',rank_X_crp)
rank_X_unc = rank(X_unc);
fprintf('\nRank of X_unc is %d',rank_X_unc)
% Taking the SVDs
fprintf('\nTaking SVD of the cropped images...\n')
[U_crp,S_crp,V_crp] = svd(X_crp,'econ');
V_crp = conj(V_crp)';
fprin... |
function output = convInterp(pathInput, pathOutput, pathA, pathB, xFade)
% CONVOLUTION W/ INTERPOLATION - compute an interpolation b/t FIR filters using a phase-vocoder
% output = convInterp(pathInput, pathOutput, pathA, pathB, xFade)
% use this for filtering problems
% impulse responses should be ... |
clc
clear
addpath(genpath('F:\Matlab_Home_HonsLab_sync'));
%% CAPTURE LONG_AVERAGE_1 AND LONG_AVERAGE_2
frames_for_capture = 20;
crop_scale = 0.5;
sizeX = crop_scale*320; sizeY = crop_scale*240;
downsample_gridstep = 10;
[D1, D2, I1, I2] = Senz3D_capture_long2( frames_for_capture, crop_scale );
ND1 = cropD... |
clear,clc
SA='../../';
%add utilities to the path temorarily
addpath ../../utilities
%read inputs from LESinputs.txt
readinputs(SA)
z_u=linspace(dz/2,l_z+dz/2,Nz);
z_w=linspace(0,l_z,Nz);
awt=loadbin('../../output/temperature/awt.bin',Nz,'l')*u_star*scalarScales;
Mwt=mean(awt(size(awt,1)/2:end,:));
figure;
... |
function degree_data = pixel2degreexy(eyedata,col_t,col_x,col_y)
% this function can convert pixel data of x/y to velocity of saccade degree in x/y in every ms
% inputdata shoule be in at least three column. The first column is for time marker, the second is for x-axis position in pixel, the third is
% for y-axis po... |
% HAMZA HAFEEZ(2K18/ELE/37)
% MUHAMMAD WASEEM HAYAT(2K18/ELE/75)
clear all
clc
%Detect objects using Viola-Jones Algorithm
%To detect Face
FDetect = vision.CascadeObjectDetector;
%Read the input image
I = imread('Hamza37.jpg');
%Returns Bounding Box values based on number of objects
BB = step(FDetect,I);
figure,... |
% TN 11: American Options via Monte Carlo Simulations
% ===================================================
close all
clear all
%tic % starting the clock timer
% ==========
% Parameters
% ==========
% =====
% Stock
% =====
sigma=0.6656; % Volatility
S0=8.15;
% =============
% Interest rate and Dividend Yield
% ===... |
function [J, grad] = costFunction(x, A, L, y, lambda)
%COSTFUNCTION Compute cost and gradient for gradient descent
% J = COSTFUNCTION(x, A, y) computes the cost of using x as the
% parameter for ||Ax-y||^2 + lambda*||Lx||^2
c = y'*y;
Q = (A'*A)+lambda*(L'*L);
J = x'*Q*x - 2*y'*A*x + c;
grad = 2*A'*(A*x - y... |
% 3차 스플라인 보간법으로 보간
clear; clc; close all;
% 초기값 설정 %
h=pi/10;
t = 0:h:2*pi; n=size(t',1); % t는 z의 범위, n은 단면의 개수
the=t; % 나눌 평면의 개수
X=zeros(n); Y=X; Z=X; % 단면을 나타낼 X행렬과 Y행렬을 초기화
for ik=1:n
r=2+cos(t(ik)); % 반지름
X(ik,:)=r*cos(the); ... |
function all_tri = get_area_tri(face, key_idx)
%input:
% face should be 3*N
% key index corrosponding two eye, nose, two mouse
%
%output: a 22*1 cell containing area triangle
tri_idx{1} = [2 6 19];
tri_idx{2} = [2 6 7];
tri_idx{3} = [2 7 8];
tri_idx{4} = [2 8 9];
tri_idx{5} = [2 9 10];
tri_idx{6} = [2 10 11];
tri... |
function retval = build_tree (rows,labels)
bestsplit = find_best_split(rows,labels);
%disp(bestsplit{1});
if(bestsplit{1}(1)==0 )%best gain
retval = struct('predictions',rows);
return;
endif
true_branch = build_tree(bestsplit{2},labels);
false_branch = build_tree(bestsplit{3},labels);
ret... |
function FillKapaCylinderSym(nGx,nGy,epsa,epsb,L,R,Na,a1,a2,d1)
global Kapa;
invepsa1=inv(epsa);
invepsa=[invepsa1(1,1) invepsa1(2,2) invepsa1(3,3) imag(invepsa1(1,3))];
invepsb1=inv(epsb);
invepsb=[invepsb1(1,1) invepsb1(2,2) invepsb1(3,3) imag(invepsb1(1,3))];
nk=length(invepsa);
ff=Na*pi*R*R/(a1*L);
d11=d1/L... |
function sol=simulationEulerACablation_Competence_v9(solutionorfates)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %
% v1 Vertebrate... |
function [overlap] = polyoverlap(x1, y1, x2, y2)
[xi, yi] = polybool('intersection', x1, y1, x2, y2);
A1 = polyarea(x1, y1);
A2 = polyarea(x2, y2);
Ai = polyarea(xi, yi);
overlap = Ai / (A1 + A2 - Ai);
|
clear,clc
%Get access to the data files.
parpath = fileparts(pwd);
datapath = [parpath, filesep, 'ResData'];
%Access to all files.
dataFilesInfo = dir([datapath, filesep, 'EOG*']);
dataFilesName = {dataFilesInfo.name};
thisFile = [datapath, filesep, dataFilesName{9}];
cblink(thisFile)
|
function [dp,sp] = params()
%% Author: James Wilmott
% Function that defines and returns the relevant parameters needed for
% running the eyetracking hemifield relation experiment
global display_params
global stimulus_params
rng(now+rem(now,1)*1000+cputime*1000); %change the seed of the random number generator
%% D... |
function handle = initializeMagTrackingNetworkStatistics()
%initializeMagTrackingNetworkStatistics()
%
%This function will initialize the histogram part of mag tracking, which
%shows stats about the incoming packets
global MAG_NET_STATS
subplotRows=3;
subplotCols=1;
%create the figure that we'll be using
MAG_NET... |
function im_pol = im_cart2pol(im_cart, origin, interp_method)
%{
Converts an image from cartesian coordinates to polar coordinates.
The output image will expand to fit the entirety of the Cartesian coordinates. It does this
by computing the largest distance from origin to any pixel and setting that
value as the radial... |
function [y]=compute_fit(observed,model,varargin)
% Takes a vector of observed latencies and a vector of simulated latencies
% and computes some measure of fit: How well does the simulated
% distribution approximate the observed data? To call, type for example
% 'y=compute_fit([observed distribution], [simulated
%... |
% Question 1 Part 2
% Quang Anh Tran - 40075748
n = [0:9];
x=zeros(1:10);
x(1:3)=1;
y=zeros(1:10);
y(1)=0;
for (i=2:10);
y(i)=x(i)+((1/4).*y(i-1));
end
stem (n,y);
xlabel('n');
ylabel('y[n]');
title('Output Response y[n]');
|
%------------- BEGIN CODE --------------
% ekran ve bellek on temizleme
close all ; clear all ; clc ;
% cikti formatini ayarlama
format compact ; format short ;
disp("A * x = b")
% A = [4 -2 1 ; 2 3 -1 ; 8 -5 2]
A = [3 -0.1 -0.2 ; 0.1 7 -0.3 ; 0.3 -0.2 10]
b = [7.85 ; -19.3 ; 71.4]
% A = [-1 -2 1 1 ; 2 1 1 0 ; 4 ... |
function [m,p] = max_env(h)
%function [m,p] = max_env(h)
%
% MAX_ENV calculates the maximum value and its position of the envelop
% of an impusle response
env=etc(h);
[m,p]=max(env);
end;
|
function [updatedFeatureMat,updatedRealColInfo] = getHOGFeature(greyImg,binImg,avgWidth)
[nRw,nCol] = size(greyImg);
avgWidth = round(avgWidth);
% Now we try to divide the image using avgWidth, if the image width can be
% truly divided by the avgWidth, then it is very good but it is a bit hard
% to happen
% So, here ou... |
function FeatureName=ParseFeatureName(FeaturePath, FeatureCategory)
if exist([FeaturePath, '\', FeatureCategory, '_Feature.m'], 'file')
FileType='.m';
else
FileType='.p';
end
if ~isdeployed && ~isequal(FileType, '.p') %Mcode
FeatureName=[];
for i=1:2
switch i
cas... |
function [Tx,G,I,X]=HovorkaModelSimulation(T,x0,d,u,par)
% HOVORKAMODELSIMULATION Simulation using the Hovorka model
%
% Syntax: [Tx,G,I,X]=HovorkaModelSimulation(T,x0,U,D,par)
options = odeset('RelTol',1e-6,'AbsTol',1e-6);
nx = length(x0);
N = length(T);
Tx(1) = T(1);
X = x0';
for k=1:N-1
x = X(end,:)';
[... |
function varargout=sml1_imana_repsup(what,varargin)
% ------------------------- Directories -----------------------------------
baseDir ='/Users/eberlot/Documents/Data/SuperMotorLearning';
%baseDir ='/Volumes/MotorControl/data/SuperMotorLearning';
betaDir =[baseDir '/betas'];
behavDir =[... |
% Copied from https://gitlab.erlichlab.org/erlichlab/elutils.git/stats
function y = nanstderr(x,dim)
if nargin==1
dim=1;
end
gd=sum(~isnan(x),dim);
y=nanstd(x,0,dim)./sqrt(gd-1);
y(y==Inf)=nan;
y(gd==0)=nan;
|
classdef (Abstract) Filling
methods (Access = public, Static)
function fillingData = generate(polygon,section)
fillingData = [];
offset = section.hatchOffset;
distance = section.hatchDistance;
pattern = section.hatchPattern;
Xgenerate = ...
... |
function im_chscaledepth(img,num1,num2)
|
% function [out]=readShake(fold)
fold='B:\SmartSimResults\11_26\LWvary';
c=dir2(fold,'folders');
for i=1:length(c)
d=regexp(c(i).name,['\d*.\d*'],'match');
d=abs(str2double(d));
lw(i)=d(1);
n(i)=d(2)*d(3);
inFolds=dir2(fullfile(fold,c(i).name),'folders');
for j=1:length(inFolds)
data... |
function [true_heatmap] = cal_heatmap_groundtruth(person1_data,person2_data,calib_const)
% % % Input
% person1_data: the location of the first person over time
% person2_data: the location of the second person over time
% % % Output
% true_heatmap: the true heatmap
cc = calib_const;
nX = floor((cc.x_range(2)-cc.x_rang... |
function [constraintFcns,isterminal,direction] = guardFunctions(t,x,contactMode)
% Split up state vector into generalized coordinates and velocities
q = x(1:2);
dq = x(3:4);
% Compute constraint function (transition if goes negative)
a = compute_a(q);
a = a(setdiff([1:3], contactMode));
% Solve the equations of motio... |
[FileName,PathName] = uigetfile('*.nii','Select the registered template file','/home/sophie/Desktop/');
file=strcat(PathName,FileName)
D=MRIread(file);
Masks=D.vol;
Sm=size(Masks);
Masks2=zeros(Sm(1),Sm(2),Sm(3),87);
for j=1:87
Masks2(:,:,:,j)=(Masks==j);
end
load('87to75.mat')
R2stack=zeros(Sm(1),Sm(2),Sm(3));
Co... |
clc; clear all; close all;
%% 원신호 생성
% 원신호의 주파수 설정
fm = 2;
% 원신호의 진폭 설정
A=2;
% 원신호와 sinc 함수의 discrete time 설정
T0 = 0.04;
t=[0:T0:1];
f0=1/T0;
% 원신호
x=A*cos(2*pi*fm*t);
%% 샘플링 신호 생성
% 샘플링 주파수
fs=4.2;
Ts= round(1/fs,2);
sample_step = floor(Ts/T0);
n= 0:1/Ts;
t_s(1) = t(1);
x_s(1) = x(1);
for i1 ... |
%{
bs.TuningCondition (lookup) # conditions for tuning
tuning_cond : tinyint # brain condition index
-----
bs_min : float # brain state min value
bs_max : float # brain state max value
%}
classdef TuningCondition < dj.Relvar
properties(Constant)
table = dj.Table('bs.TuningCondition')
end
met... |
% Henry_Haustein_Lars_Ortscheidt_P1_Octave
clear all
fun = @(x) (1+cos(1.5*pi*x)).^(2/3);
fun_abl = @(x) -pi*sin(1.5*pi*x)*(1+cos(1.5*pi*x))^(-1/3);
%N = input('Anzahl der Stuetzstellen -1 :=N : ')
for l = 0:11
N = 4* 2^l
%Abstand Stuetzstellen h
h = 2/N;
%Stuetzstellen x
x = -1:h:1;
for... |
function [fs] = ExtractGridHistogram(im, ng, bins)
%EXTRACTGRIDHISTOGRAM Summary of this function goes here
% Detailed explanation goes here
xs = floor(linspace(1, size(im, 2) + 1, ng + 1));
ys = floor(linspace(1, size(im, 1) + 1, ng + 1));
fs = zeros(1, bins*ng*ng);
count = 0;
for i=1:ng
ii = xs(i):xs(i+1)-1;
... |
%--------------------------------------------------------------------------
% EXERCISE: BINARY CLASSIFIER
%--------------------------------------------------------------------------
close all;
clear variables;
%Training
N_images = 500;
N_images_types = 10;
path_train = 'train/imagen';
images=zero... |
clc
clear all
close all
x1=[1 2 3 4];
x2=[4 3 2 1];
x3=x1+x2;
x4=x1.*x2;
x5=x1-x2;
subplot(611);
stem(x1);
ylabel('amplitude');
xlabel('time');
title('first sequence');
subplot(612);
stem(x2);
ylabel('amplitude');
xlabel('time');
title('second sequence');
subplot(613);
stem(x3);
ylabel('amplitude');
xlabel('time');
tit... |
function POW = getcorpow(CSpect, ASpect)
[Lng, Width, Height] = size(ASpect);
%CCOR = real(ifft(CSpect));
ACOR = real(ifft(ASpect)); %inverse fft, correlation function, 0 delay is variance
VAR = squeeze(ACOR(1,:,:)); %estimate of total variance for each pixel
TVAR = VAR(2:Width-1,2:Height-1);
%surrounding 8 pixel... |
%% Iterative Shrinkage-Thresholding Algorithm (ISTA)
%----------------------- A Proximal-Gradient Algorithm Method-------------------------%
% CS_ISTA Algorithm (迭代收缩阈值算法 ISTA)
% 输入：y---测量信号 M X 1
% A---恢复矩阵 M X N
% lambda---正则化参数
% iter---最大迭代次数
% 输出 ：xhk---估计的稀疏向量 N X 1
%... |
classdef Orientation < handle
%ORIENTATION Summary of this class goes here
% Detailed explanation goes here
%----------------------------------------------------------------------------
properties(Constant)
HFS = 'HFS';
HFP = 'HFP';
FFS = 'FFS';
FFP = 'FFP';
end
methods
end
end
|
function temp_PAUSE_exec(PauseInBetween, PauseDuration)
% THIS FUNCTION CAUSES A TEMPORARY PAUSE IN EXECUTION FOR A TIME DURATION SPECIFIED BY "PauseDuration", ONLY IF "PauseInBetween == 1"
if PauseInBetween == 1
pause(PauseDuration)
end
end |
function score = scoreData( data )
%SCOREDATA Calculate a score for the data
% The score is a combined measure of how good the data is
% to be used when selecting S-parameters for classification
end
|
%script allOff
%turn off all channels
%Note this resets all frequencies and eta values
for n=0:511
configCryoChannel( rootPath, n, 0, 0, 0, 0, 1 );
end |
function [type, MtypeLIST, numIons]=GetPOP_MOL(numMols)
global ORG_STRUC
MtypeLIST = [];
for ind = 1: length(numMols)
MtypeLIST = cat(1, MtypeLIST, ind*ones(numMols(ind),1));
end
numIons = [];
type = [];
for num = 1:length(MtypeLIST)
if length(ORG_STRUC.atomType)==1
type = cat... |
% University of British Columbia, Vancouver, 2017
% Alex Kyriazis
% William Choi
%
% Separates a large clump of cells into its constituent cells
%
function [ flag, somas ] = resolve_clump( dpcell )
somas = 0;
flag = 0;
Iobrcbr = dpcell.oImage;
%
% figure;
% forshow = I... |
function vi = KinematickaViskoznost(ro,ni)
vi = ni/ro;
end
|
function VAF=getVAF(Mb,E2,S)
T=size(Mb,1)/S;
M=size(Mb,2);
% Get mean muscle pattern
Mmean=zeros(T,M);
for s=1:S
Mmean=Mmean+Mb(T*(s-1)+1:T*s,:)/S;
end
% Total Variance
SST=0;
for s=1:S
Mb0=Mb(T*(s-1)+1:T*s,:)-Mmean;
SST=SST+norm(Mb0,'fro')^2;
end
% Variance Accounted For
VAF=1-E2/SS... |
% This loop generates a wide array of simulation outcomes for every
% combination of parameter values.
% See notes on Numerical Simulation Parameters for details on where these
% parameter values come from
% Note that the parameter vector should take the following form:
% par = [a,b,c1,c2,x,k,t,Qm,s,entry,alpha,bet... |
function [J_history, Theta1, Theta2] = trainNN(X, y, hidden_layer_size, num_labels, lambda, alpha, iters, Theta1, Theta2)
input_layer_size = size(X, 2);
if isnan(Theta1)
Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size);
Theta2 = randInitializeWeights(hidden_layer_size, num_la... |
function [ flag] = ConflictTest(A,row_i,col_j)
flag=0
%% col test
p=256;
for ii=1:p
if ii~=row_i
if A(ii,col_j)==A(row_i,col_j)
return
end
end
end
%%row test
for jj=1:p
if jj~=col_j
if A(row_i,jj)==A(row_i,col_j)
return
end
... |
clear all
a = arduino('/dev/cu.usbmodem1421', 'Uno', 'Libraries', 'Adafruit\MotorShieldV2');
for i = 1:10
writeDigitalPin(a, 'D13', 1);
pause(0.5);
writeDigitalPin(a, 'D13', 0);
pause(0.5);
end
clear a |
function geneCopyFactor = childCopyGivenFreqsFactor(alleleFreqs, geneCopyVar)
% This function creates a factor whose values are the frequencies of each
% allele in the population.
%
% Input:
% alleleFreqs: A list of the frequencies of the alleles in the population
% genotypeVar: The variable number for the g... |
function [ S] = sigma(A )
S = A;
a = 1;
if S(1,1) == 0
if S(2,1) ~= 0
S = swap_rows(S);
S = givens_columns(S);
elseif S(1,2) ~= 0
S = swap_columns(S);
S = givens_rows(S)
elseif S(2,2) ~=0
S = swap_rows(S);
S = swap_columns(S);
end
else
if det(S) < 0
... |
function [TRI,U] = trimeshgrid(d)
% Opis:
% trimeshhgrid sestavi triangulacijo trikotnika z oglisci
% (0,0), (1,0), (0,1) in izracuna tocke triangulacije v
% obliki baricentricnih koordinat
%
% Definicija:
% [TRI,U] = trimeshgrid(d)
%
% Vhodni podatek:
% d stevilo intervalov, na katere je razdeljena s... |
function [ result ] = gauss( fx, x_ )
%gauss(fx, x) Summary of this function goes here
% fx is the input function and must be a string
% x is the limit of integral
f = sym(strcat('(', fx, ')* exp(-x^2)'));
result = double(int(f, -inf, x_));
format long
end |
% restoredefaultpath
startup
addpath([home '/ls_brain/methods/MASNET/Integrated Code'])
addpath([home '/ls_brain/methods/MASNET/SRL'])
|
function [ labels ] = sample_split( nElem, split )
%SAMPLE_SPLIT(nElem, split) assignes labels to a nElem length array with
% frequency given by split
% e.g. split = [0.8 0.1 0.1]
% Will give a labels vector with 80% 1, 10% 2, 10% 3
%
% Written by: Michael Hutchins
%% Check inputs
if numel(nElem) ~= 1
error('nElem... |
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