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function getTrialtimes(obj)
%GETTRIALTIMES Gets trial starts / ends from DAQ AI Ch2 or 3
%
% Detection of trials is divided into two functions:
%
% getTrialtimes - parses inputs and outputs
% - checks trialtimes before assigning to object
% - can be customized or overloaded in subclass
%
... |
function [MMMSE] = mmmse(X,Y)
muY = mean(Y);
varY = var(Y);
[N, N_obs] = size(X);
varR = zeros(N, N_obs);
for i = 1:N_obs
varR(:, i) = X(:, i) - Y;
end
avgVarR = var(reshape(varR, [], 1));
MMMSE = (1 / (N_obs * varY + avgVarR)) * (avgVarR * muY + varY * sum(X, 2));
end |
global MPI_COMM_WORLD;
load 'MatMPI/MPI_COMM_WORLD.mat';
MPI_COMM_WORLD.rank = 64;
Alluxio_Row_mv_version3;
|
function save_esn(esn, file_name, varargin)
% SAVE_ESN save an Esn structure to disk. It's saved under the name
% file_name.
%
% inputs:
% esn - our Esn structure we want to save
% file_name - a string containing the file we want
% option - string containing further options that will be forwarded to
% the matlab sa... |
%--- coordinate plot with respect to time
% you need to define model.C_posn in order to work properly
[X_track,k_birth,k_death]= extract_tracks(X,track_list,total_tracks);
figure(1); clf; subplot(211);
figure(2); clf; subplot(211);
%--- plot true tracks
for i=1:total_tracks
P= model.C_xy_posn*X_track(:,k_birth(i)... |
clear all; clc; close all;
load Data\S8_midPoints;% midpoint
load Data\S5_glottis_struct;% a_lout A B c_point newx newy origin alpha; %%change for rotate
%load data\glottis_lout;% a_lout u_lout A B c_point;
%% random selection point
l=size(midpoint,1);
midshu=[];
midshu=[midshu;midpoint(1,:)];
for i=2:l-140 % change... |
function V_q = PotentialEnergyVariation(q)
% Calculates the Variation in Potential Energy term of Lagrange's Equation
% Which is equal/opposite to the spring force, amiright?
global Mooring
lines = Mooring.lines;
% Initialize Spring Force vector F_spring
F_spring = zeros(size(q,1),1);
for i = 1:Mooring.NumLine % Do... |
%Eli Gonzalez
%Dr. Qiao
%Lab 02
%Note: I will use the built-in PCA to check my answer
%Read the data from a CSV file
x = csvread('F:\CSE 516\Eli_Gonzalez_Lab02\Task02\Bigger Data\source.csv');
%Find the mean and centralized matrix
m = mean(x);
y = bsxfun(@minus, x, m);
% find covariance matrix
c=cov... |
function [ W ] = update_W( X, Y, C )
V = length(X);
[k, c] = size(C{1});
W = cell(V, 1);
for v=1:V
YTXv = zeros(k, size(X{v},2));
for j=1:k
YTXv(j,:) = sum(X{v}(find(Y==j),:), 1)+eps;
end
[U,~,V] = svds(C{v}'*YTXv, c);
W{v} = U*V';
end
end
|
function [meta] = load_meta(raw_dir,save_dir)
%This function extracts useful meta info from many recordings so this can
%be easily read and fed to suite2p for processing later
%>>INPUT>>
%raw_dir - Folder containing the data from a given animal.
%The assumed folder tree structure is Animal--->Areaxx--->Stimx
%save_dir... |
function lfs = projectionontodipolefields(tfs,mask,vox,weight,z_prjs)
if ~ exist('z_prjs','var') || isempty(z_prjs)
z_prjs = [0, 0, 1]; % PURE axial slices
end
% add zero slices
tfs = padarray(tfs,[0 0 20]);
mask = padarray(mask,[0 0 20]);
weight = padarray(weight,[0 0 20]);
[Nx,Ny,Nz] = size(tfs);
% creat... |
function p=dam_hist_par();
% dam_hist_par
% parameters (default values) for dam_hist & fly_hist:
% hour (24): number of hours per day
% skipDays (0): number of days to remove at the beginning
% lightsOn (600): time (military) when lights go on
% lightsOff (2400): time (military) when lights go off
% firstHour (800): ho... |
clear
f0 = 80;
dur = 0.4;
sequence = [f0, f0* (2^(1/6)), f0, f0*(2^(5/12)), f0 * (2^(1/3))];
fs = 44100;
[y,d] = lowpass(randn(1,fs*dur),5000,fs);
stim_os = [];
for s = 1:length(sequence)
stim_s = OSCOR(dur,fs,sequence(s),d,1);
stim_os = [stim_os stim_s];
end
stim_am = [];
t =0:1/fs:dur-1/fs;
for s =1:len... |
format rational; % assign format as rational in answer
% type given vectors
v1 = [1 -1 3 2 5];
v2 = [2 0 -1 -1 3];
v3 = [3 2 -4 -6 -4];
v4 = [-5 -4 12 12 8];
A= [v1;v2;v3;v4]; %making a matrix whose rows are v1,v2,v3,v4
B = rref(A) %compute RREF directly
|
copyB = B;
air_time = copyB(:,7);
air_time = str2double(air_time);
for i = 2:size(air_time,1)
if air_time(i) >= 300
air_time_group = 7;
elseif air_time(i) >= 240
air_time_group = 6;
elseif air_time(i) >= 180
air_time_group = 5;
elseif air_time(i) >= 120
air_time_group... |
# Red Hebbiana para simulacion de lenguajes artificiales.
#function [Wf] = hebb_lenguaje(A)# Matriz de activaciones umbral es el umbral
A = input('Introduce tu matriz >> '); ##Eliminar cuando este lista la función##
b = input('Introduce un valor para beta >> '); # b es la tasa de aprendizaje
umbral = input('Intro... |
function figure_3
%% Display parameters for the plots
font_size = 18;
linewidth = 2;
%% Scanning parameters
M = 256; % samples per scan
d = 10; % spin density, in arbitrary units
sigma_N = 1; % noise standard deviation, in arbitrary units
%% Simulation parameters
simulations = 20; % how many simulations... |
clc
% This scripts need the x_i of p=1/2 , 1/10 and 1/100
N = length(x1);
x1sum = zeros(N,1);
x10sum = zeros(N,1);
x50sum = zeros(N,1);
x1sum(1) = 1;
x10sum(1) = 1;
x50sum(1) = 1;
for t = 2:N
x1sum(t) = x1sum(t-1) + x1(t);
x10sum(t) = x10sum(t-1) + x10(t);
x50sum(t) = x50sum(t-1) + x50(t);
end
plot(... |
colors={'-ok','-^k','-or','-^g','--or','--^g','-sb','md','ob'}
figure;
hold on
for i=1:9
plot(1,1,colors{i})
end
legend('Gibbs Sampling (ensemble)','Gibbs Sampling (1st biggest cluster)','CentroidAlifold(new; al-mc)','CentroidAlifold (new; al-ct)','CentroidAlifold (old; mc)','CentroidAlifold (old; ct)','RNAalipfold−Cen... |
Nc=max(max(L));
f=figure;
imagesc(L);
hold on;
Features=[];
for i=1:Nc;
[r,c]=find(L==i);
maxr=max(r);
minr=min(r);
maxc=max(c);
minc=min(c);
rectangle('Position',[minc,minr,maxc-minc+1,maxr-minr+1],'EdgeColor','w');
height = maxr - minr;
width = maxc - minc;
threshold=7;
if( h... |
clear all
close all
load 'H:\ReconSim\LPW_REMUS_Run.txt'
lpw = LPW_REMUS_Run; clear LPW_REMUS_Run
easting = lpw(:,1);
northing = lpw(:,2);
plot(easting,northing)
hold on
plot(easting,northing,'o')
axis equal
hold on;
grid on;
%This sets the labels for the axes and title - these may not be relevant
xlabel('Meters');
y... |
function [c1,vc1] = C1Instant(im);
%function [c1,vc1] = C1Instant(im);
%
%optionless version of c1 using the most common parameter values
% returns both the cell array and vector versions of c1
%
% for refererence the regions of the vector corresponding to the
% different bands are:
%
%
% 1 : 4096
% 4097 : ... |
function [c,ceq,gradc,gradceq] = five_link_nonlcon(x,p)
% x = gpuArray(x);
[c1,gradc1]=yposCon(x,p);
[c2,gradc2]=headPosCon(x,p);
% [c3,gradc3]=ankPosCon(x,p);
[c3,gradc3]=toe_heel(x,p);
c = [c1;c2;c3];
gradc=[gradc1,gradc2,gradc3];
[ceq1,gradceq1]=dynConst(x,p);
%[ceq2,gradceq2]=gaitLenCon(x,p);
[ceq3,gradceq3]=in... |
function ctime=ctimeForFile(createFileDateDir)
if nargin < 1
createFileDateDir=true;
end
now=datetime('now')
if createFileDateDir
% keep data organized by date
%datapath=getenv('SMURF_DATA');
datapath = '/home/common/data/cpu-b000-hp01/cryo_data/d... |
% Rechnerunterstützte Mechanik I
% Wintersemester 2017/18
%
% Übung 4:
% Differentialoperatoren
%
clc; % Konsole löschen
close all; % Alle Figures löschen & schließen
clear; % Speicher leeren
disp('4. Übung im Fach Rechnerunterstützte Mechanik I (WS 2017/18)');
% %%%%%%%%%%%%%%%%%
% %%% AUFGABE 1 %%%
% %%%... |
% Copyright (c) 2018 Gopalakrishnan, Krishnakumar <krishnak@vt.edu>
% Author: Gopalakrishnan, Krishnakumar <krishnak@vt.edu>
% clear;clc; format short g; format compact; close all;
golden_ratio = 1.618;
fig_width_factor = 1;
ax_width_factor = 0.725;
fig_ht_factor = 0.75;
figW_cm = 15.74776*fig_width_factor; % te... |
function plsig1
% close all
% fig10 = figure;
% set(fig10,'Position',[182 150 1131 813]);
hold on;
% load seq_MTS_adi_2000_298K.dat;
% load eps_MTS_adi_2000_298K.dat;
% seq1 = seq_MTS_adi_2000_298K;
% eps1 = eps_MTS_adi_2000_298K;
% e331 = eps1(:,4);
% seqv1 = seq1(:,4)/1.0e6;
% seqv1 = seqv1;
% % plot(e33, seqv, 'k-',... |
fid = fopen('out-bwareaopen-test--111.32SC1.raw', 'r');
d2 = fread(fid, [4096,4096], '*int32');
d2 = uint32(d2');
fclose(fid);
fid = fopen('out-bwareaopen-test--151.32SC1.raw', 'r');
d3 = fread(fid, [4096,4096], '*int32');
d3 = uint32(d3');
fclose(fid);
fid = fopen('out-bwareaopen-test--101.32SC1.raw', 'r');
d1 = fread... |
function [i_knee, varargout] = segmented_linear(roc, knee_range, doplot)
%
% by Jeff Weiler, Paul Gribble, Andrew Pruzynski
%
% This function takes the area under an ROC curve as a function of time (roc), and a time
% range to search for the knee point (knee_range = [start, stop]). The function
% finds the knee poin... |
function [Y,X] = Objfunction(x,lenchrom)
%%目标函数
% 输入:
% x 二进制编码
% lenchrom 各变量的二进制位数
% 输出:
% Y 目标值
% X 十进制数
% 函数自变量的范围，每一行对应一个自变量
bound =[-3.0 12.1;4.1 5.8];
% 将binary数组转换成十进制数组
X = bin2decFun(x,lenchrom,bound);
% 计算适应度-函数值
Y= sin(4*pi* X(1))* X(1) + sin(20 * pi * X(2)) * X(2);
|
classdef ClusterBasedPredictors
methods(Static=true)
function [precisionClRand, completenessClRand ] = getClusterRandomPrediction(A, partition, numPredictions, numTargetEdges)
% How helpful is a clustering in link prediction? One way to find out is to randomly predict links by picking endpoints randomly, while ensurin... |
function [str] = azWASBRoot(varargin)
% AZWASBROOT Helper function to locate the Azure WASB interface
%
% Locate the installation of the Azure interface package to allow easier construction
% of absolute paths to the required dependencies.
% Copyright 2016 The MathWorks, Inc.
str = fileparts(fileparts(fileparts(mfile... |
function topoplotNode(nch, ijw, w_scal,brainNum)
if (brainNum == 2)
w_atribut = ijw(:,2); %taking the weights' attributes
w_scaling = w_scal; %taking the scaling of weigths
n_atribut = ijw(:,2); %fixing nodes' attributes
n_scaling = 'n_nn2nx'; %fixing ... |
function [] = plot_particles_vs_threshold()
% reads 'data.txt' and plots first two columns.
% in this case, we expect these columns to correspond to
% number of particles found vs threshold value
x = []; % threshold value
y1 = []; % number of particles found
y2 = []; % mean particle diameter
% read file an... |
clc;
clear all;
w=[0,1414,0,1414,0,-1414,0,-1414,0,1414,0,1414,0,-1414,0,-1414,0,1414,0,1414,0,-1414,0,-1414,0,1414,0,1414,0,-1414,0,-1414];
figure(1)
hold on;
grid on;
xlabel('sample')
ylabel('w[n]')
stem(1:32,w);
%
fs=8000;
ts=1/fs;
figure(2)
dft=fft(w);
dft_abs=abs(dft);
theta=(2*pi)/32;
omega_badid=0:theta:(2*pi)-(... |
classdef MixModelEmFitEng < EmFitEng
methods(Access = 'protected')
function model = initEm(eng,model,data) %#ok
% override to do something more intelligent, i.e. kmeans for a GMM etc.
% Here we just fit each mixture component on a random partition of
% the data.
... |
%% Exploit PPG signal to get heart rate and SpO2 level
% Friendly to embedded
% Thinh Nguyen
% mecheng.hn@gmail.com
% THIS ONE IS NOT OPTIMIZED. DO NOT USE FOR COMMERCIAL
%% Start
clear all
%% Load sample data
clear all
[X1,X2]=textread('PPG.dat','%s %s');
data_st = 150; % Data start
data_length =... |
function [regMovie, pixelShifts] = motionCorrectCaImagingFile(tifName, regImage, framesToUse, trimBorders)
% [regMovie, pixelShifts] = ...
% motionCorrectCaImagingFile(tifName, regImage [, framesToUse] [, trimBorders])
%
% Use the efficient subpixel registration algorithm to motion correct a
% calcium imagin... |
% %clc;
% %clear all;
%
% %Serial = serial('COM4');
% Serial = serial('/dev/ttyUSB0');
% set(Serial,'Terminator','CR/LF');
% set(Serial,'BaudRate',115200);
% set(Serial,'DataBits',8);
% % set(Serial,'Parity','none');
% set(Serial,'StopBits',1);
% % set(Serial,'FlowControl','none');
% fopen(Serial);
% blocking = 0;
%%
... |
function [xsim, ysim] = getML_simLIPslopeMvsV(Monk, N, recompute)
savepath = ['/work/mirror_jeff/code/matlab/tmp-mat/getML_simLIPslopeMvsV_' Monk '.mat'];
if recompute
[R m v] = getML_LIPSlopeDist(Monk, 0);
% get simulated m/v for the mixture model
coh = [0 3.2 6.4 12.8 25.6 51.2 99.9];
xsim = nans(... |
function distance = Mah( x, y ,SIGMA )
% calulate two point's Mahalanobis distances.
% formula: Dist(x, y, SIGMA) = (x - y)T*SIGMA*(x - y)
% x, y: two n-dimensioanl points (column vector).
% SIGMA: n * n matrix.
% distance: Mahalanobis distances.
distance = (x - y)' * SIGMA * (x - y);
end
|
function [flash_map_response,time_window, y, cp, cp_time_windows] = calculate_flash_map_response(session, get_single_flash_responses, calculate_choice_probabilities, green_or_cyan,time_window)
sorted_window = get_sorted_window(session.x);
response_window = [100 400];
if ~exist('time_window','var') || isempty(time... |
function [ blob_ellipse_manhattan ] = Bsxfun( pixels,ellipse_centres )
for i=1:1:size(ellipse_centres,1)
BLOB(:,:,i) = pixels;
ELLIPSE(:,:,i) = ellipse_centres(i,:);
end
blob_ellipse_manhattan = bsxfun(@minus,BLOB,ELLIPSE);
%squeeze command then transpose; |
function [v0,v1]=like1(dw,t,stretch)
% [v0,v1]=like1(dw,t,stretch)
% dw > 0 if fire detected, <0 if not, 0 if cloud
% t = time since fire arrival (days)
Tmin=stretch(1);Tmax=stretch(2);Tneg=stretch(3);Tpos=stretch(4);
tneg = (t-Tmin)./Tneg;
tpos = (t-Tmax)./Tpos;
v0y = -tneg.*tneg.*(t<Tmin)-tpos.*tpos.*(t>Tmax);
g0 ... |
function X=regular_mesh(n,h,v)
% X=regular_mesh(n,h)
% Creates a regular mesh that is stretched in the vertical direction but
% uniform horizationally
% in:
% n number of cells in each direction
% h step size in each direction
% v vertical stretch factor
% out:
% Mmat ... |
function [E,gradE,normgrad] = Energy_KarcherMean(betamean,Data,mu,basis,register)
% Compute the objective function to be minimized, and its gradient, in the
% gradient descent Karcher mean algorithm implemented in findKarcherMean.m.
% The objective function is E(q) = \sum_{i=1}^N d(q,q_i)^2, where q_i is
% the... |
function [p,w] = int3d_centroid1()
% function [p,w] = int3d_centroid1()
%
% This is the simplest triangle integration method.
p = [1/4, 1/4, 1/4];
w = 1/6;
|
%% Robotic Vision
%% Lecture 2 - Getting an image into MATLAB – MATLAB transcript
%%
clear all; close all; clc;
%%
% Read in the 'street.png' image using imread() and display using idisp().
im = imread('street.png');
idisp(im);
%%
|
function [ Torque_dash, Torque, correction] = fuzzy_controller( B_q, C_q_qdot, F_ext, err, err_dot, thetaD_ddot, theta_dot)
% fis = readfis('Triangular.fis');
fis = readfis('Gaussian.fis');
for j= 1:1:4
correction(j)= evalfis(fis,[err(j) err_dot(j)]);
% correction(j) = 0.0;
end
Torque_dash = thetaD_ddot + ... |
function error_code=vdoc_cs_opt_post(name)
% scan the optimization output files for new values and update the workspace
% make vinf global and define drivetrain switch
global vinf
drivetrain=vinf.drivetrain.name;
% print parameter for debugging
debug=0; % 1==> writes additional output to command window, 0==> no debug... |
%% Stelling 10
%
% Matlab kan headers van databestanden altijd probleemloos
% inlezen.
%
Antwoord = NaN; % vul hier het juiste antwoord in 1 (WAAR) of 0 (ONWAAR)
|
clear;
micro = 1e-6;
voltage_points = -0.9:0.1:0.9;
T_PULSE_x1_theoretical = zeros(size(voltage_points));
for i = 1:1:19
if(voltage_points(i) < 0)
T_PULSE_x1_theoretical(i) = 0;
else
T_PULSE_x1_theoretical(i) = 1*micro*(voltage_points(i));
end
end
t_pulse_x1_experimental = extract_td_da... |
clear all
% Load TS
[FileName,PathName] = uigetfile('*.mat','Select the region time series');
file2=strcat(PathName,FileName)
load(file2)
load('87to75.mat')
% Open Data
%[FileName,PathName] = uigetfile('*.nii','Select the Nifti file');
%file=strcat(PathName,FileName)
file='/media/test15/ControlCombo/100682series/100682... |
function plot_sensor_data(ftdate);
close all;
clc
loadpath = '/data/sba_data/ft';
eval(['load ',loadpath,ftdate,'99/sensor_data.mat']);
%load sensor_data.mat
figure(1)
plot(DMU_Out(:,1),DMU_Out(:,2),'r-');
hold on;
plot(INS_Omega_Acc(:,1),INS_Omega_Acc(:,2),'b-');
grid;
legend('X-Bow DMU','INS');
xlabel('GPS Time... |
%% test_oct1.m
parents{1} = PACK_fix(randi(5,[1 10]));parents{1} = [1 2 3 4 2 2 4 3 1 1];
parents{2} = PACK_fix(randi(5,[1 10]));parents{2} = [1 1 1 1 1 1 2 2 2 3];
options.LinearConstr.type = 'unconstrained';
% xoverKids = PACK_crossover2(parents,options,10,[],[],[]);
mutationChildren = PACK_mutation2(2,options,10,[... |
% EJERCICIOS RESUELTOS DE VISIÓN POR COMPUTADOR
% Autores: Gonzalo Pajares y Jesús Manuel de la Cruz
% Copyright RA-MA, 2007
% Ejercicio 9.10: Clasificación: redes neuronales y máquinas de vectores
% soporte
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 9.9.4 Cuantización vectori... |
% MACIERZ = zmienna przechowująca informację o wybranej do dalszych działań macierzy
% 1 - macierz 2x2 z punktu a)
% 2 - macierz Vandermonde'a o rozmiarze 20x20
% 3 - macierz trójprzekątniowa z przekątną dominującą
% 4 - macierz trójprzekątniowa bez przekątnej dominującej
% 5 - dowolna wprowadzona macierz
cl... |
function [X1,u1,v1,w1]=wc2rc(X,Xref,azi_surf,tilt_surf)
% Use to transform world coordinate to relative coordinate
% Date create: 8/16/2013 by Saran Salakij
% See Kenneth Joy UC Davis COORDINATE CONVERSION BETWEEN THE CARTESIAN FRAME AND AN ARBITRARY FRAME (Cartesian-Frame-to-Frame.pdf)
% Input: X == vector ... |
%% This script contains all T2 q2 WGT2 calculation
clear all
close all
clc
addpath('./MatlabFunctions')
addpath('./MatlabFunctions/TRM')
addpath('./MatlabFunctions/HW')
data_path = './Generate Data/esm2mb-urban-continue-sheffield-8xdaily-20years/';
land_type_all = {'urbn','rural'};
%% load variables
disp('load...'... |
% mlepAwsInit
% This initializes MLE+/AWS environment
global MLEPAWSSETTINGS
% Credentials Path
credPath = 'YOUR CREDENTIALS PATH';
credPath = 'TaoAwsCredentials.properties'
% Private Key Path
keyPath = 'YOUR PRIVATE KEY PATH';
keyPath = 'initial.pem'
noOfInstance = 2;
%% DO NOT MODIFY
% AWS Home Path
fullname = mf... |
dataset_name = 'feature_dataset_6.mat';
dataset = load(dataset_name);
svmStruct = svmtrain(dataset.train_features,dataset.train_labels,'showplot',true);
|
function plotBars(data,texts)
M = mean(data);
S = std(data);
figure;
hold on;
bar(1:3,M);
errorbar(1:3,M,S,'.');
smartStuff = title(texts);
smartStuff.FontSize = 20;
xticks([1 2 3]);
xticklabels({'Novice', 'Intermediate', 'Master'});
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%solution to SS RDE for GDL with area source
%c needs to be integrated over all space for xi eta and zeta
%const mu, non const s
const1=1
const2=2
u=@(phi,theta,phip,thetap,rp) Const1*exp(const2*rp*sin(theta)*sin(thetap)*cos(phi-phip))*exp(const2*cos(theta)*cos(thetap))
integral(@(u,)
|
mT = 590 + 70 + 58;
%%
Fx2Vel = tf([1/mT], [1 0])
figure(1)
optsFx2Vel = bodeoptions('cstprefs');
optsFx2Vel.FreqUnits = 'Hz';
optsFx2Vel.Title.String = 'Fx to Vel transfer function - Bode diagram';
optsFx2Vel.Grid = 'on';
bode(Fx2Vel, optsFx2Vel)
%%
Reg = tf([6500], [1 0]) * tf([1 0.8 0.16], [1 0.2])
OpenLoopTF = Reg ... |
function [ProjectMatrix Mean_Vector]= DiscriminantAnalysis(X,PCAN,IN,BN)
% X is the data matrix for training ;
% N is the number of samples for each individual;
% PCAN is the dimensionality of the first reduced space ;
% IN is the number of the second reduced space;
% BN is the number of the final... |
function [H, F, Sx, Su, Sdu, T_du, Qlinha, Rulinha, Rdulinha, Clinha] = setpoint_deltaU_matrices_cost_function(A, B, C, Q, Ru, Rdu, Nstate, Ncontrol, Nout, Ny, Nu)
%[H, F, Sx, Su, Sdu, T_du, Qlinha, Rulinha, Rdulinha, Clinha] = setpoint_deltaU_matrices_cost_function(A, B, C, Q, Ru, Rdu, Nstate, Ncontrol, Nout, Ny, Nu)... |
function postCx(varargin)
%% INPUT
% Handle different cases
[~, ~, ~, ~, opts] = util.pre('', varargin{:});
% Analysis variables
name = util.constructname(extractAfter(mfilename, 'post'), {opts.case, opts.analysis});
lim = opts.maxevery;
NT = opts.steps;
p = opts.polynomialorder;
% Load data
files = util.l... |
pos = find (y == 1);
neg = find (y == 0);
plot(x(pos,2),x(pos,3),'+');
hold on;
plot(x(neg,2),x(neg,3),'o');
xlabel('Exam 1 score');
ylabel('Exam 2 score'); |
function multiplexedReadout_4()
import qes.*
import sqc.*
import sqc.op.physical.*
qubits = {'q6','q7','q8','q9'};
legends = {'|0000>','|0001>','|0010>','|0011>','|0100>','|0101>','|0110>','|0111>',...
'|1000>','|1001>','|1010>','|1011>','|1100>','|1101>','|1110>','|1111>'};
numSamples... |
%{
example.Ephys (manual) # electrophysiology recordings
-> example.Sessions
ephys_start : time # time when recording was started
---
sampling_rate : double # sampling rate of recording
gain : double # multiplier to convert to muV
%}
classdef Ephys < dj.Relvar
properties(Constant)... |
% given a generic planar graph, computes the
% ray-graph intersection. Graph is a curvegraph data structure
%
% WRITTEN 5 March 2010
function ray_t = ray_graph_intersection2( ray_p, ray_n, CG )
DEBUG_MODE = 0;
if DEBUG_MODE
warning('debug mode enabled'); %#ok<UNRCH>
clc, close all;
load ray_graph_intersect... |
givenFun = @(x) x*x*x - 2*x - 5;
a = input('enter a: ');
b = input('enter b: ');
x = 0;
for i = 1:1:100
x = a - (givenFun(a)*(b - a))/((givenFun(b) - givenFun(a)));
if givenFun(x) > 0.00001
b = x;
else
a = x;
end
if abs(givenFun(x)) <= 0.00001
break;
end
end... |
function [vecpow]=ps_rip(p,w)
[ran]=rip_select(p);
p=p(ran);
for j=1:length(p)
F = fft(p{j}(w,:));
pow = F.*conj(F);
vecpow(1,j)=sum(pow);
end
end |
function [ClassificationEin, ClassificationEout, likehoodEout, likehoodEin] = CalError(G, Xtrain, Ytrain, Xtest, Ytest)
%Likelihood error out of the sample
AlikelihoodEout = 0 ;
for j = 1:1000
tempA = Ytest(j)*(dot(G,Xtest(j,:)));
AlikelihoodEout = AlikelihoodEout + log(1+exp(-tempA));
end
likehoo... |
%% zero the world
clear,clc
%% Exp 01
%% parameter
ramp = [30 30]; % in ms
% key parameter
fs = 44100; % in hz/s
amp = 1;
% parameters of linear up/down-sweep
freq1 = 200;
freq2 = 1000;
% length of a sweep in one direction
endTime = 0.5;
%% gen original sin tone
outputFile{1} = [pwd filesep 'ramp_sin_increase.wav'];
ou... |
function SIG = setSignalParams(varargin)
% Last Update: 08/01/2019
%% Input Parameters
if nargin <= 3
SIG = varargin{1};
symRate = SIG.symRate;
if isfield(SIG,'nBpS')
nBpS = SIG.nBpS;
end
if isfield(SIG,'nPol')
nPol = SIG.nPol;
end
if isfield(SIG,'rollOff')
rollOff... |
function [cells,stim,files,chandef] = ImportCellsStim(shapefilename,stimfilename)
% ImportCellsStim: read in results of shape-based spike sorting & stimulus information
% [cells,stim,files,chandef] = ImportCellsStim(shapefilename,stimfilename)
% Outputs:
% cells{filenumber,cellnumber} is the time (in secs) of the spike... |
function rho = rho_snow(D, model)
switch model
case 'Brandes'
% Brandes et al. 2007 "A Statistical and Physical Description of
% Hydrometeor Distributions in Colorado Snowstorms Using a Video
% Disdrometer"
D = D*1e3;
rho = 0.178 * D.^(-0.922);
rho = rho*1e3;
otherwise
err... |
classdef Camera < matlab.mixin.Heterogeneous & handle
% Protected C++ pointer
properties (SetAccess = protected)
% This is the actual integer pointer to C++ object in mem
c_ptr = 0;
sensor;
observation = [];
projection = [];
... |
function weights = retrieve_weights(net)
% Funktion wird benötigt im Training von FFN mit aktivierten Bais Knoten.
% Funktion kürzt die Gewichtsmatrizen um die jeweilige Spalte, welche die
% Biasinformationen enthält, da die Bias keine Fehler in das Netz
% rückpropagieren.
if strcmp(net.Bias,'inactive') == 1
... |
clear all;
setpaths
disp('Warning, the autotuning demonstrated in this demo is still experimental. Updates will follow soon...');
%% load data
try
load bal.mat
catch % if it fails, download it from the web
disp('Downloading data ...');
urlwrite('https://dl.dropboxusercontent.com/u/4284723/DATA/bal.mat'... |
function mfcc = mymfcc(speech, fs, frame_start, frame_end, nceps)
%MFCC
%%この関数は、サンプリング周波数fsの音声サンプルデータspeechの
%%frame_startからframe_endまでの区間の次数ncepsのMFCCを求める
%メルフィルタバンクの設計を行う
m=40; %バンド数
n=512; %fftの点数
maxmel=hz2mel(fs/2);
mstep=maxmel/(m+1);
h=round(mel2hz(0:mstep:maxmel)/(fs/2)*(n/2))+1;
H=zeros(m,n/2+1);... |
% After commit, create a report and the benchmark inside the output folder.
DESTINATION = fullfile(pwd, 'outputs');
% Retrieving git hash
[status, commitHash] = system('git rev-parse HEAD');
commitHash = strtrim(commitHash);
if status ~= 0
error('Failed to retrieve git hash: %d', status)
end
cprintf('blue', 'Cur... |
[samples, nmb ]= mcmc(1000,data,ctrl,(@(p) uniformprior(p',bounds))) |
function [assignedStores assignedEmployee assignedMixedEmployee daysWorkedEmployee] = assign_employee(index,allele,assignedEmployee,assignedMixedEmployee,assignedStores,genderEmployee,daysWorkedEmployee)
%% ASSIGN_EMPLOYEE
%
% Function responsible for mapping genotype to phenotype
%
% Copyright (C) 2020
%
% This progr... |
function X=poisson_1d_eig(n,h);
% X=poisson_1d_eig(n,h)
% eigenvalues of d^2/dx^2 with zero b.c. mesh 1:n stepsize h
X=4*(sin([1:n]*pi/(2*(n+1)))/h).^2;
end |
function rho = get_rho_sat(a, T)
%rho = exp(a(1) - a(2) ./ (T + a(3))) ./ T;
rho = exp(a(1) - a(2) ./ T) ./ T.^a(3);
end
|
function [Mtrl,Field] = GetECouplFFT(Mtrl, Field)
%%
F(1:2:2*(Mtrl.nHarms),:) = Field.Sin;
F(2:2:2*(Mtrl.nHarms),:) = Field.Cos;
nlFFT = fft(Mtrl.func(abs(Field.E).'*F))/Mtrl.N;
couplFFT = fft( ones(2*Mtrl.nHarms,1)*((nlFFT)*(Mtrl.Cos - 1i*Mtrl.Sin)) .* ...
(F/Mtrl.N),[],2);
Mtrl.Dsin = 1i*(couplFFT(:,Mtrl.... |
clc;
close all;
clear all;
%this code is to generate h for product code of different message bits
%eg(4,9),(9,16),(16,25);
m=9;%message bits
ib=[1 0 1 1 1 0 1 0 0];%place the info bits here which yoy want to send to reciver.
r=sqrt(m)+1;
h=zeros(r*r-(m),r*r);
r=r-1;
for i=1:r
for j=1:r+1
k=i-1;
... |
function dist = DTW(t,r)
%Dynamic Time Warping 动态时间规整DTW是一个典型的优化问题
%它的本质是一种衡量两个长度不同的时间序列的相似度的方法
n = size(t,1);
m = size(r,1);
% 帧匹配距离矩阵
d = zeros(n,m);
for i = 1:n
for j = 1:m
d(i,j) = sum((t(i,:)-r(j,:)).^2);
end
end
% 累积距离矩阵
D = ones(n,m) * realmax;
D(1,1) = d(1,1);
% 动态规划
for i = 2:n
for j = 1:m
... |
% reads the file polyfile line by line and constructs a cell array of
% strings representing each polynomial
function polyarray = readPolysFromFile(filename)
file = fopen( filename );
polyarray = textscan(file, '%s');
fclose(file);
end
|
function [Q_val_opt,action] = Q_greedy_act_test(theta,s,params,gpr)
N_act = params.N_act;
act_val = zeros(1,N_act);
act_val_var = zeros(1,N_act);
for l=1:N_act
a = l;
[act_val(l),act_val_var(l)] = Q_value(theta,s,a,params,gpr);
end
if sum(act_val==max(act_val))>1
pos = find(... |
function course_ch6(n)
% 第六章 图像压缩
if(n == 1)
% 压缩比 Cr = imratio(f1, f2);
% 相对数据冗余度 R = 1 - 1/Cr;
% 均方根误差 rmse = compare(f1, f2, scale); [scale: 伸缩比例]
end
end |
%% Wavelet Coherence
% The continuous wavelet transform (CWT) allows you to analyze the temporal
% evolution of the frequency content of a given signal or time series. The
% application of the CWT to two time series and the cross examination of
% the two decompositions can reveal localized similarities in time and
% sc... |
sessionList = {'Ellie_2017-06-27_15-30-22', ...
'Ellie_2017-07-19_15-49-43_Shnk2', ...
'Ellie_2017-07-20_09-41-04_Shank2D2', ...
'Ellie_2017-07-24_15-59-49_shank2D3', ...
'Ellie_2017-07-25_13-29-24_Shank2D4', ...
'Ellie_2017-07-26_13-51-39_Shank2D5', ...
'Ellie_2017-07-27_14-17-50_Shan... |
function data = fft_clean(imec, data, varargin)
% from Will Allen
% data is passed in as nChannels x nSamples, and is processed internally as
% this transposed and then transposed on the way out
p = inputParser();
p.addParameter('thresh', 10, @isscalar);
p.addParameter('nbins', 20, @isscalar);
p.addParameter('showFigu... |
function fig = plot_pos(base_avg,eff_hip_avg,eff_kne_avg,eff_ank_avg,eff_hk_avg,eff_ha_avg,eff_ka_avg,time,batchName,savefig)
fig=figure('visible','off');
set(gcf,'Position',[233 499 1303 200]);
subplot(1,3,3);
hold on;
plot(time,base_avg(1,:)*180/pi);
plot(time,eff_hip_avg(1,:)*180/pi);
plot(time,eff_kne_avg(1,:)*180/... |
%% GLUECK: Growth pattern Learning for Unsupervised Extraction of Cancer Kinetics
%% PREPARE ENVIRONMENT
clear all; clc; close all; format long; pause(2);
%% INIT SIMULATION
% enables dynamic visualization on network runtime
DYN_VISUAL = 1;
% number of populations in the network
N_SOM = 2;
% number of neurons in e... |
%--------------------------------------------------------------------------
%--------------------------------------------------------------------------
%This function computes the distance based upon the acqusition number in
%the .mat files
% Function: main_METADATA_postprocessing
% Author: Jeanne Quimby - 03/21/2016
%... |
close all;
global pets
pets = {'Coptodon_rendalli'};
check_my_pet(pets);
estim_options('default');
estim_options('max_step_number',2e3);
estim_options('max_fun_evals',5e3);
estim_options('pars_init_method', 1);
estim_options('results_output', 5);
estim_options('method', 'nm');
for ijk = 1:1
... |
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