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values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | mtudaya/ProjectP-master | fista.m | .m | ProjectP-master/simulation_old/Ivan_SP/matlab (2)/matlab/misc/fista.m | 1,368 | utf_8 | d088459c7e4667bc3008f4556e220723 | function [x,J] = fista(y,H,Ht,lambda,alpha,Nit,x_init)
% [x, J] = fista(y,H,Ht,lambda,alpha,Nit)
%
% l1 regularization with 'fast iterated soft-thresholding algorithm'
% by Beck and Teboulle,
% Minimizes J(x) = ||y-H*x||_2^2 + lambda*||x||_1
% INPUT
% y - observed signal
% H - function handle for operator
% Ht -... |
github | mtudaya/ProjectP-master | LoadImage.m | .m | ProjectP-master/MCALabWithUtilities/MCALabWithUtilities/MCALab110/Two-D/DemoGUI/LoadImage.m | 5,337 | utf_8 | 9e82a7cb291f212faf71285bea462467 | function varargout = LoadImage(varargin)
% LOADIMAGE Application M-file for LoadImage.fig
% LOADIMAGE, by itself, creates a new LOADIMAGE or raises the existing
% singleton*.
%
% H = LOADIMAGE returns the handle to a new LOADIMAGE or the handle to
% the existing singleton*.
%
% LOADIMAGE('CALLBACK',hObject,ev... |
github | mtudaya/ProjectP-master | LoadMask.m | .m | ProjectP-master/MCALabWithUtilities/MCALabWithUtilities/MCALab110/Two-D/DemoGUI/LoadMask.m | 5,432 | utf_8 | 6c5d18518623eaec4f7fa38bcd578c03 | function varargout = LoadMask(varargin)
% LOADMASK Application M-file for LoadMask.fig
% LOADMASK, by itself, creates a new LOADMASK or raises the existing
% singleton*.
%
% H = LOADMASK returns the handle to a new LOADMASK or the handle to
% the existing singleton*.
%
% LOADMASK('CALLBACK',hObject,eventData,... |
github | mtudaya/ProjectP-master | MCA2DGUI.m | .m | ProjectP-master/MCALabWithUtilities/MCALabWithUtilities/MCALab110/Two-D/DemoGUI/MCA2DGUI.m | 19,069 | utf_8 | 2c89f6df4a9711d70b10178baeebb928 | function varargout = MCA2DGUI(varargin)
% MCA2DGUI M-file for MCA2DGUI.fig
% MCA2DGUI, by itself, creates a new MCA2DGUI or raises the existing
% singleton*.
%
% H = MCA2DGUI returns the handle to a new MCA2DGUI or the handle to
% the existing singleton*.
%
% MCA2DGUI('Property','Value',...) cr... |
github | mtudaya/ProjectP-master | EM_Inpaint.m | .m | ProjectP-master/MCALabWithUtilities/MCALabWithUtilities/MCALab110/Two-D/Decomposition/EM_Inpaint.m | 12,210 | utf_8 | 40a204195aa81a727546a2a955731653 | function [imginp,options]=EM_Inpaint(img,dict,pars1,pars2,pars3,itermax,epsilon,lambda,sigma,thdtype,ecmtype,mask,display)
% EM_Inpaint: Bayesian Inpainting of 2D images (a matrix) using redundant dictionaries.
% The optimization pb is solved using the EM algorithm.
% EM_Inpaint solves the following (MAP) optimiz... |
github | mtudaya/ProjectP-master | MCA2_Bcr.m | .m | ProjectP-master/MCALabWithUtilities/MCALabWithUtilities/MCALab110/Two-D/Decomposition/MCA2_Bcr.m | 11,252 | utf_8 | d2e4fa8411eaf3408ef2886a0d8548eb | function [part,options]=MCA2_Bcr(img,dict,pars1,pars2,pars3,itermax,gamma,comptv,expdecrease,stop,mask,sigma,display)
% MCA2_Bcr: Morphological Component Analysis of a 2D images (a matrix) using highly redundant dictionaries and sparstity promoting penalties.
% The optimization pb is solved using a modified version ... |
github | mtudaya/ProjectP-master | MCAGUI.m | .m | ProjectP-master/MCALabWithUtilities/MCALabWithUtilities/MCALab110/One-D/DemoGUI/MCAGUI.m | 17,348 | utf_8 | 8a5a2fc1f62e84c77a6677606f2865d9 | function varargout = MCAGUI(varargin)
% MCAGUI M-file for MCAGUI.fig
% MCAGUI, by itself, creates a new MCAGUI or raises the existing
% singleton*.
%
% H = MCAGUI returns the handle to a new MCAGUI or the handle to
% the existing singleton*.
%
% MCAGUI('CALLBACK',hObject,eventData,handles,...) ... |
github | mtudaya/ProjectP-master | LoadSignal.m | .m | ProjectP-master/MCALabWithUtilities/MCALabWithUtilities/MCALab110/One-D/DemoGUI/LoadSignal.m | 5,307 | utf_8 | 40610b0375892c16c8b9821128b017ba | function varargout = LoadSignal(varargin)
% LOADSIGNAL Application M-file for LoadSignal.fig
% LOADSIGNAL, by itself, creates a new LOADSIGNAL or raises the existing
% singleton*.
%
% H = LOADSIGNAL returns the handle to a new LOADSIGNAL or the handle to
% the existing singleton*.
%
% LOADSIGNAL('CALLBACK',hO... |
github | mtudaya/ProjectP-master | MCA_Bcr.m | .m | ProjectP-master/MCALabWithUtilities/MCALabWithUtilities/MCALab110/One-D/Decomposition/MCA_Bcr.m | 8,677 | utf_8 | 98ebfc3044dc34be8d672d34dd2fad7b | function [part,options]=MCA_Bcr(signal,dict,pars1,pars2,pars3,itermax,gamma,comptv,expdecrease,stop,mask,sigma,display)
% MCA_Bcr: Morphological Component Analysis of a 1D signal (a vector) using highly redundant dictionaries.
% The optimization pb is solved using a modified version of the BCR algorithm.
% MCA_bc... |
github | mtudaya/ProjectP-master | fdct_wrapping_dispcoef.m | .m | ProjectP-master/CurveLab-2.1.3.tar/CurveLab-2.1.3/fdct_wrapping_matlab/fdct_wrapping_dispcoef.m | 1,919 | utf_8 | 2af5a55f76ce583e6879244514db1b37 | function img = fdct_wrapping_dispcoef(C)
% fdct_wrapping_dispcoef - returns an image containing all the curvelet coefficients
%
% Inputs
% C Curvelet coefficients
%
% Outputs
% img Image containing all the curvelet coefficients. The coefficents are rescaled so that
% the largest coefficent... |
github | mtudaya/ProjectP-master | SV_channel_all.m | .m | ProjectP-master/SV2/SV_channel_all.m | 7,856 | utf_8 | 28b635061e16606f3a75a57690d4a8b6 |
%[h,h_ct,t,t_ct,ts,actual_cluster_times,Pcluster_actual,actual_ray_times,num_channels]=SV_channel_all(num_clusters_fc);
function [h,h_ct,t,t_ct,ts,actual_cluster_times,Pcluster_actual,actual_ray_times,num_channels]=SV_channel_all(num_clusters_fc);
Lam = 0.8; lambda = 10; Lmean = 2;
lambda_mode = 2; %0 -> Poisson... |
github | mtudaya/ProjectP-master | uwb_sv_eval_ct_15_4a.m | .m | ProjectP-master/SV2/uwb_sv_eval_ct_15_4a.m | 8,555 | utf_8 | 40acc01a18be93630dcf85b87739d8df | % modified S-V channel model evaluation
% Written by Sun Xu, Kim Chee Wee, B. Kannan & Francois Chin on 14/09/2004
% EDITED BY RF WIRELESS WORLD for useful plots only
% RF Wireless world do not have any copyright for this code
% Respective designers and coders reserve the rights for the same including
% any patents i... |
github | nguyenthanhphuong/SBP-master | centralmoment.m | .m | SBP-master/centralmoment.m | 3,143 | utf_8 | c4518caf6d4c52950ccd9fe68b3c42bf | %%
% Central moment
% Author: Thanh Phuong NGUYEN, U2IS-ENSTA Paristech
%
%
function [M1, M2, M3, M4] = centralmoment(varargin)
% Version: 0.1.0
% Check number of input arguments.
%error(nargchk(1,5,nargin));
image=varargin{1};
d_image=double(image);
if nargin==1
spoints=[-1 -1; -1 0; -1 1; 0 -1; -0 1; 1 -1; ... |
github | nguyenthanhphuong/SBP-master | ClassificationkNN.m | .m | SBP-master/ClassificationkNN.m | 627 | utf_8 | b8723a70ffe81ad7dbd82fcf02b94abf | % Author: Thanh Phuong NGUYEN, U2IS-ENSTA Paristech
%
function [CP fdetection]=ClassificationkNN(LBPH,trainIDs,trainClassIDs,testIDs,testClassIDs,fid,label)
trains = LBPH(trainIDs,:);
tests = LBPH(testIDs,:);
trainNum = size(trains,1);
testNum = size(tests,1);
DM = zeros(testNum,trainNum);
parfor i=1:testNum;
te... |
github | nguyenthanhphuong/SBP-master | clbp.m | .m | SBP-master/thirparty/clbp.m | 6,720 | utf_8 | 2186dd824032168966ee5cab2404f97e | %CLBP returns the complete local binary pattern image or LBP histogram of an image.
% [CLBP_S,CLBP_M,CLBP_C] = CLBP(I,R,N,MAPPING,MODE) returns either a local binary pattern
% coded image or the local binary pattern histogram of an intensity
% image I. The CLBP codes are computed using N sampling points on a
% ... |
github | nguyenthanhphuong/SBP-master | ReadOutexTxt.m | .m | SBP-master/thirparty/ReadOutexTxt.m | 812 | utf_8 | 6ec585d92385ef37625737377bc4b458 | % ReadOutexTxt gets picture IDs and class IDs from txt for Outex Database
% [filenames, classIDs] = ReadOutexTxt(txtfile) gets picture IDs and class
% IDs from TXT file for Outex Database
function [filenames, classIDs] = ReadOutexTxt(txtfile)
% Version 1.0
% Authors: Zhenhua Guo, Lei Zhang and David Zhang
... |
github | nguyenthanhphuong/SBP-master | dist_chi2.m | .m | SBP-master/thirparty/dist_chi2.m | 382 | utf_8 | f6ba87461574ce994b778272a2e6ba2b | % Chi^2 histogram distance. A,B are matrices of example data
% vectors, one per column. The distance is sum_i
% (u_i-v_i)^2/(u_i+v_i+epsilon). The output distance matrix is
% (#examples in A)x(#examples in B)
function D = dist_chi2(A,B,epsilon)
if nargin<3, epsilon=1e-100; end
%fprintf('\n *** calculating CHI^2 histog... |
github | nguyenthanhphuong/SBP-master | distMATChiSquare.m | .m | SBP-master/thirparty/distMATChiSquare.m | 1,220 | utf_8 | 039d21412330d18ab5518d4d0c53b035 | % distMATChiSquare computes the dissimilarity between training samples and a test sample
% DV = distMATChiSquare(train, test) returns the distance vector between training samples and a test sample.
% The input "train" is a n*d matrix, and each row of it represent one
% training sample. The "test" is a 1*d vecto... |
github | nguyenthanhphuong/SBP-master | ClassifyOnNN.m | .m | SBP-master/thirparty/ClassifyOnNN.m | 1,684 | utf_8 | a3816862854b998797412b4d662da269 | % ClassifyOnNN computes the classification accuracy
% CP=ClassifyOnNN(DM,trainClassIDs,testClassIDs) returns the classification accuracy
% The input "DM" is a m*n distance matrix, m is the number of test samples, n is the number of training samples
% 'trainClassIDs' and 'testClassIDs' stores the class ID of tra... |
github | Macbull/ELM-Caffe-master | classification_demo.m | .m | ELM-Caffe-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | ygrek/ocaml-master | ocamlc.m | .m | ocaml-master/man/ocamlc.m | 25,303 | utf_8 | 4c65a8ef95e2faf1fa4ea61698b76e65 | .\"***********************************************************************
.\"* *
.\"* OCaml *
.\"* *
.\"* Xavi... |
github | ygrek/ocaml-master | ocamlcp.m | .m | ocaml-master/man/ocamlcp.m | 3,189 | utf_8 | c0a1b30dfc95039ddf29e97a735ff4f6 | .\"***********************************************************************
.\"* *
.\"* OCaml *
.\"* *
.\"* Xavi... |
github | rlafoy/matlab_3d_piv_code-master | piv_3d_10.m | .m | matlab_3d_piv_code-master/piv_3d_10.m | 166,961 | utf_8 | d8e401a5714e2dce5299d99a182a5c50 | function piv_3d_10(piv_parameters);
% This function is designed to process two or three dimensional PIV data using
% the parameters specified by the 'piv_parameters' data structure.
%
% This code is based upon the code 'basic_3d_rpc_processing_05.m'.
%
% Updates on previous versions:
%
% Version 08
%
% Saves th... |
github | rlafoy/matlab_3d_piv_code-master | piv_3d_parallel_runner_02.m | .m | matlab_3d_piv_code-master/piv_3d_parallel_runner_02.m | 60,660 | utf_8 | a893258a69619611bed0495d08760a7f | function piv_3d_parallel_runner_02;
% This function is designed to create a parameters structure to pass into
% the piv_3d code and to then call the piv_3d code in parallel across
% multiple computers.
%
% This code is based upon the code 'basic_3d_rpc_processing_05.m'.
% This is the temporary directory to save the li... |
github | jcamata/HexMesh-master | read_swbd.m | .m | HexMesh-master/matlab/read_swbd.m | 68,537 | utf_8 | fb38eb5f96ef29e1db4466ff6a5d60b7 | function [d,fname] = read_swbd(file,outdir)
% READ_SWBD Read SWBD-ShapeFiles (SRTM Water Body Data)
%
% [Struct,File] = READ_SWBD( File )
%
% [Struct,File] = READ_SWBD( [ Lon Lat ] )
%
% The FileName is build like "*LON#LA$.ext",
% where "*" is "e" or "w",
% "#" is "n" or "s",
% "$" is the Continen... |
github | jcamata/HexMesh-master | distanceCoastline.m | .m | HexMesh-master/matlab/distanceCoastline.m | 3,165 | utf_8 | b3bb11c6c5d6d87ecfa15f0385005fb2 | function [dist,distc] = distanceCoastline(dt,Cl)
% DISTANCECOASTLINE to compute the distance from the elements in a
% triangulation to a coastline
%
% syntax: [dist,distc] = distanceCoastline(dt)
%
% dt: delaunay triangulation of the mesh
% Cl structured array of information on Coastlines
%
% dist: distance of each no... |
github | jcamata/HexMesh-master | altimetryCoastline.m | .m | HexMesh-master/matlab/altimetryCoastline.m | 3,981 | utf_8 | 9d5026c9fb9ac4017ca0ddb2ea520d5c | function dt = altimetryCoastline( Xb, Cl, lp )
% BATHYMETRYCOASTLINE to integrate the information from bathymetry and
% coastlines
%
% syntax: [X,z0] = bathyTopoCoastline( Xb, Cl )
%
% Xb structured array of information on bathymetry
% Cl structured array of information on Coastlines
% lp handle to a figure for plott... |
github | jcamata/HexMesh-master | smoothCurve.m | .m | HexMesh-master/matlab/smoothCurve.m | 1,497 | utf_8 | b5cb933f76a538414e1f0d9f8c06daed | function c = smoothCurve(c,h)
% SMOOTHCURVE to smoothen a curve on a caracteristic length h
%
% syntax: c = smoothCurve(c,h)
%
% c: 2*N closed curve of (x,y) pairs of points
% h: caracteristic size of details that should be removed (larger h means
% more smoothing)
% lout: logical indicating that the interior
% loo... |
github | guo2004131/Automated-Lesion-Detection-Public-master | Final_CleanUp.m | .m | Automated-Lesion-Detection-Public-master/Final_CleanUp.m | 3,816 | utf_8 | 2c6be47dfe70109a791e923270bc149c | function CleanMRIs = Final_CleanUp(Merged_MRIs, NormV)
Merge_MRIs = char(Merged_MRIs);
NormV = char(NormV);
BrainMask_MRI_filename = fullfile(spm('Dir'),'toolbox','AutoLesionDetection','Templates','BrainMask.nii');
BrainMask = spm_vol(BrainMask_MRI_filename);
BrainMask_V = spm_read_vols(BrainMask);
CleanMRIs = cell(s... |
github | guo2004131/Automated-Lesion-Detection-Public-master | tbx_cfg_AutoLesionDetectioin_Tool.m | .m | Automated-Lesion-Detection-Public-master/tbx_cfg_AutoLesionDetectioin_Tool.m | 7,411 | utf_8 | aa42c3f3b5f43785552d4d41a3c1a0d6 | function ALD_Tools = tbx_cfg_AutoLesionDetectioin_Tool
% Configuration file for toolbox 'AutoLesionDetection'
% Dazhou Guo
% $Id: tbx_cfg_AutoLesionDetection.m
if ~isdeployed
[p,nam] = fileparts(fileparts(mfilename('fullpath')));
nam = fullfile(spm('Dir'),'toolbox',nam);
if ~exist(nam, 'file')
fp... |
github | ENSTABretagneRobotics/Hardware-MATLAB-master | GetValueFromThreadPololu.m | .m | Hardware-MATLAB-master/GetValueFromThreadPololu.m | 282 | utf_8 | d9a511d7a5e8aca0086b8a9feea960cb | % Only channel 11 can be used for now...
function [result, value] = GetValueFromThreadPololu(pPololu, channel)
value = 0;
pValue = libpointer('int32Ptr', value);
result = calllib('hardwarex', 'GetValueFromThreadPololux', pPololu, channel, pValue);
value = pValue.value;
|
github | RuinaLab/Ranger-master | PLOT_Results.m | .m | Ranger-master/Ranger/Control_Pranav/Ranger_Simulator/PLOT_Results.m | 5,127 | utf_8 | 5cb585c14d8b40d33123ccd53a6ea711 | function PLOT_Results(Results,N)
PlotResults = FormatResults(Results)
%N is the figure to start plotting on
if nargin == 1
N = 1;
end
%% Plot the actuator command currents:
figure(N+1); clf;
subplot(2,2,1)
%BLANK%
axis off
subplot(2,2,3)
title('Total Mechanical Energy')
PlotNames={};... |
github | RuinaLab/Ranger-master | Animate_Ranger.m | .m | Ranger-master/Ranger/Control_Pranav/Ranger_Simulator/Animate_Ranger.m | 4,916 | utf_8 | bde374d762a08efe69d5e45b00635efb | function Animate_Ranger(Results,N)
%FUNCTION:
% This function takes in the results of a simulation and a parameter
% struct and uses this to run a simulation.
%
%INPUTS:
% (Nss = number of integration steps in single stance, Nds = double stance)
% OUT_Animate is a struct with 5 fields: X_SS: [17xNss double... |
github | RuinaLab/Ranger-master | Smooth_Saturation.m | .m | Ranger-master/Ranger/Control_Pranav/Ranger_Simulator/Smooth_Saturation.m | 3,127 | utf_8 | 6046e193db69ee9c3f92e1e6be116451 | function g = Smooth_Saturation(x,Bounds,alpha)
%FUNCTION:
% This function produces a smooth saturation of an input x.
%
%INPUTS:
% x = input vector of states to be saturated
% Bounds = a (1x2) row vector of the saturation bounds
% alpha = the smoothing parameter. small alpha corresponds to minimal
% smoo... |
github | RuinaLab/Ranger-master | GenerateInterpolantData.m | .m | Ranger-master/templates/RangerMath/GenerateInterpolantData.m | 3,161 | utf_8 | 697d60440aa44c1d48ec55cc61c76bbb | function GenerateInterpolantData()
% This function generates the data that is used by the quadratic
% interpolation function approximator that is used in RangerMath.m
%
fid = fopen('QuadInterpData.txt','w');
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~%
% T... |
github | diegonegretto/ML-Algorithms-Matlab-master | labelprop.m | .m | ML-Algorithms-Matlab-master/Semi-supervised learning algorithms/labelprop.m | 1,541 | utf_8 | 9ef322f4eb021dea6279a8553c776d65 | % Algorithm "Label Propagation", by Zhu and Ghahramani (2002)
%
% Usage: owner = labelprop(X,slabel,sigma,disttype,nclass,iter)
%
% X = attributes vector (line = elements, columns = attributes)
% slabel = vector with numerical labels (>0) of pre-labeled elements (0 for
% unlabeled elements)
% n... |
github | diegonegretto/ML-Algorithms-Matlab-master | lnp.m | .m | ML-Algorithms-Matlab-master/Semi-supervised learning algorithms/lnp.m | 2,218 | utf_8 | 0beaaa106d03b15d1a8731b7f5b97318 | % Algorithm Linear Neighborhood Propagation by Fei Wang et. al.
%
% Usage: owner = lnp(X,slabel,k,alpha,nclass,iter)
%
% X = attributes vector (line = elements, columns = attributes)
% slabel = vector with numerical labels (>0) of pre-labeled elements (0 for
% unlabeled elements)
% nclass = number of... |
github | paxorus/facebook-rotovap-master | score_ranking.m | .m | facebook-rotovap-master/matlab/score_ranking.m | 731 | utf_8 | e280d1a4083cca0bc0a627693cee7065 | % Prakhar Sahay 04/14/2015
% This takes two rankings, an attempt and the base, and scores the attempt
% ranking by summing the deviations. Each ranking is a struct with the
% fields 'names' and 'ranks'.
function [score] = score_ranking(attempt,base)
len=length(attempt.names);
% Hash the attempt, name to rank
... |
github | paxorus/facebook-rotovap-master | delineate.m | .m | facebook-rotovap-master/matlab/delineate.m | 442 | utf_8 | abe548b5c2e8dbc6bf8f3f2d67003557 | % Prakhar Sahay 04/15/2015
% This is a quick-and-dirty function that corrects a range of numbers
% obtained from a logarithmic function to the 1-10 scale to compare with the
% survey rankings.
function [attempt] = delineate(attempt)
len=length(attempt.ranks);
x0=min(attempt.ranks);
xf=max(attempt.ranks);
... |
github | paxorus/facebook-rotovap-master | algorithm.m | .m | facebook-rotovap-master/matlab/algorithm.m | 1,074 | utf_8 | 1b6d70925b22ce77eb992fa9f7df0d1f | % Prakhar Sahay 04/15/2015
% This function optimizes two coefficients given some relational
% data (2 factors). It tries all different values and returns the
% coefficients with the minimum penalty score.
function [c1,c2,score_vec] = algorithm(base,d1,d2,names)
base=delineate(base); % deviations are additive, so ... |
github | paxorus/facebook-rotovap-master | json2data.m | .m | facebook-rotovap-master/matlab/json2data.m | 943 | utf_8 | 1e2968009799f86e3ec9222a0e12b84d | % Prakhar Sahay 04/15/2015
% This function converts JSON strings to extract all relational data.
function [data] = json2data(file_name)
raw_data=importdata(file_name,'\t');
len=length(raw_data);
% preallocate
data=cell(1,len);%char(zeros(1,len));
for i=1:len
temp=parse_json(raw_data{i});
... |
github | paxorus/facebook-rotovap-master | create_base.m | .m | facebook-rotovap-master/matlab/create_base.m | 297 | utf_8 | 21f417f9dff6790e049781cecb5ada7b | % Prakhar Sahay 04/14/2015
% This uses 'prakhar_survey.tsv' to create a base ranking based on how people in my
% friend group rank themselves on a scale of 1-10.
function [base] = create_base(file_name)
mat=importdata(file_name,'\t');
base.names=mat.textdata;
base.ranks=mat.data;
end |
github | paxorus/facebook-rotovap-master | strip_column.m | .m | facebook-rotovap-master/matlab/strip_column.m | 325 | utf_8 | 5acf78a477c98f9a3f8bd430f8b4ccca | % Prakhar Sahay 04/15/2015
% This is a quick-and-dirty function to access some field for a cell array
% of structs. The usual functions fail because some values are [].
function [d] = strip_column(data,field_name)
len=length(data);
d=zeros(1,12);
for i=1:len
d(i)=log10(data{i}.(field_name));
en... |
github | paxorus/facebook-rotovap-master | parse_json.m | .m | facebook-rotovap-master/matlab/parse_json.m | 5,591 | utf_8 | 511272119247075d3fa284c56d18e945 | function [data json] = parse_json(json)
% [DATA JSON] = PARSE_JSON(json)
% This function parses a JSON string and returns a cell array with the
% parsed data. JSON objects are converted to structures and JSON arrays are
% converted to cell arrays.
%
% Example:
% google_search = 'http://ajax.googleapis.com/ajax/services... |
github | BrainDynamicsUSYD/spikegrid-master | showdynamics.m | .m | spikegrid-master/Analysis/showdynamics.m | 4,835 | utf_8 | 97188da4a2d67c4d75df778775e7cf57 | function showdynamics(varGrid,varargin)
% Displays voltage and conductance data. When the job these two figures will pop up:
% Figure 1: shows values across the grid at one time step.
% Figure 2: shows values across time at one grid point, with dashed horizontal line indicating the current time
% There will also be a ... |
github | BrainDynamicsUSYD/spikegrid-master | txt2mat.m | .m | spikegrid-master/Analysis/txt2mat.m | 7,947 | utf_8 | a80531717d8839d9ec5a5022f0195b7c | % Converts the text file output of the C model into data that matlab can work with and saves as a .mat file. I have tried to add as much validation as possible but if you accidentally try to apply this to exc+inh inputs with two different formats it will probably give very weird seeming errors.
function txt2mat(p)
% ... |
github | TeachingReps/Stochastic-Processes-master | distafun.m | .m | Stochastic-Processes-master/2017/Projects/Coding vs Replication/Project Code/distafun.m | 272 | utf_8 | ea8ddcabc0472b3981304575785afe74 | %n = 4; k = 2
function result = distafun(x)
global n;
global k;
% n = 6;
% k = 3;
%result = x^n;
%result = n*(x^(n-1)) - (n-2)*(x^n);
result = 0;
for i = 1:k
temp = nchoosek(n,n-k+i)*nchoosek(n-k+i-2,i-1)*((-1)^(i-1))*x^(n-k+i);
result = result + temp;
end
end |
github | TeachingReps/Stochastic-Processes-master | sample.m | .m | Stochastic-Processes-master/2017/Projects/Coding vs Replication/Project Code/sample.m | 233 | utf_8 | 61fd7622ed6fe0f3ba98dd8f39d17ad0 |
function length = sample()
lamda = 0.99;
n=2;
s(1) = 1;
for i =2:50
s(i) = lamda^((n^(i-1) - 1)/(n-1));
end
for i = 1:49
p(i) = s(i) - s(i+1);
end
mat2 = cumsum(p) ;
x = rand();
length = find(mat2 > x , 1);
end
|
github | enoonIT/nbnn-nbnl-master | adaptation.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/adaptation.m | 4,132 | utf_8 | f9f3fcec4513e649d41a2c48bea79d90 |
% This code is part of the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as publ... |
github | enoonIT/nbnn-nbnl-master | run_DANBNN.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/run_DANBNN.m | 2,043 | utf_8 | e36ce93792cea48279ff8f829ccec1c6 |
% This code is part of the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as publ... |
github | enoonIT/nbnn-nbnl-master | select.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/select.m | 1,689 | utf_8 | 780bb5225b49419397ee7da79c1ad3c7 |
% This code is part of the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as publ... |
github | enoonIT/nbnn-nbnl-master | run_UnsupervisedDANBNN.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/run_UnsupervisedDANBNN.m | 2,015 | utf_8 | c0ac737b1adefb126e0c5bc8878a4085 |
% This code is adapted from the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as... |
github | enoonIT/nbnn-nbnl-master | add.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/add.m | 1,474 | utf_8 | cbe7f4d9af77b63add441a57a9a338f0 |
% This code is part of the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as publ... |
github | enoonIT/nbnn-nbnl-master | fn_create_dist.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/fn_create_dist.m | 2,884 | utf_8 | b5d5f8b1dd4664fa2ef3c3f43c62e467 |
% This code is part of the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as publ... |
github | enoonIT/nbnn-nbnl-master | run_NN.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/run_NN.m | 2,420 | utf_8 | 9bf7013a532455fd186853bf80e851ec |
% This code is part of the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as publ... |
github | enoonIT/nbnn-nbnl-master | adaptation_nomem.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/adaptation_nomem.m | 3,923 | utf_8 | ef59eb1e085fa9a396299a0ee260bbd7 |
% This code is part of the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as publ... |
github | enoonIT/nbnn-nbnl-master | fn_create_metric_on_the_fly.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/fn_create_metric_on_the_fly.m | 5,149 | utf_8 | e14b1adc5187fe3e7732ceefa1a6ec25 |
% This code was adapted from the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License a... |
github | enoonIT/nbnn-nbnl-master | run_NBNN.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/run_NBNN.m | 2,999 | utf_8 | c231efbd538c6094151daa5bf19aa1b0 |
% This code is part of the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as publ... |
github | enoonIT/nbnn-nbnl-master | run_UnsupervisedNBNN.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/run_UnsupervisedNBNN.m | 2,363 | utf_8 | d414c43c594336623c4b08121b364b98 |
% This code is adapted from the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as... |
github | enoonIT/nbnn-nbnl-master | fn_create_metric.m | .m | nbnn-nbnl-master/DANBNN_demo/functions/fn_create_metric.m | 4,968 | utf_8 | e3bde07f69eb343f321fbabd4d5b8993 |
% This code is part of the supplementary material to the ICCV 2013 paper
% "Frustratingly Easy NBNN Domain Adaptation", T. Tommasi, B. Caputo.
%
% Copyright (C) 2013, Tatiana Tommasi
%
% This program is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as publ... |
github | enoonIT/nbnn-nbnl-master | flann_search.m | .m | nbnn-nbnl-master/DANBNN_demo/flann/flann_search.m | 3,564 | utf_8 | a5a9b7cb6bc8b49d8f0f6c2737c17608 | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | enoonIT/nbnn-nbnl-master | flann_load_index.m | .m | nbnn-nbnl-master/DANBNN_demo/flann/flann_load_index.m | 1,578 | utf_8 | f9bcc41fd5972c5c987d6a4d41bdc796 | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | enoonIT/nbnn-nbnl-master | test_flann.m | .m | nbnn-nbnl-master/DANBNN_demo/flann/test_flann.m | 10,328 | utf_8 | 151c22994b0192f8a071649ad26fbc6b | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | enoonIT/nbnn-nbnl-master | flann_free_index.m | .m | nbnn-nbnl-master/DANBNN_demo/flann/flann_free_index.m | 1,614 | utf_8 | 5d719d8d60539b6c90bee08d01e458b5 | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | enoonIT/nbnn-nbnl-master | flann_save_index.m | .m | nbnn-nbnl-master/DANBNN_demo/flann/flann_save_index.m | 1,563 | utf_8 | 5a44d911827fba5422041529b3c01cf6 | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | enoonIT/nbnn-nbnl-master | flann_set_distance_type.m | .m | nbnn-nbnl-master/DANBNN_demo/flann/flann_set_distance_type.m | 1,926 | utf_8 | 8ba72989a4ac1bd6b30bec841b9def25 | %Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
%Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
%
%THE BSD LICENSE
%
%Redistribution and use in source and binary forms, with or without
%modification, are permitted provided that the following conditions
%are met:
%
... |
github | enoonIT/nbnn-nbnl-master | load_patches.m | .m | nbnn-nbnl-master/matlab/load_patches.m | 754 | utf_8 | d2c3747ec05dcd2131dcc4da604fe8cb |
function data = load_patches(input_folder, patch_level)
fprintf('Loading files from %s\n',input_folder);
disp(input_folder)
hdf5_files = dir(strcat(input_folder,'*.hdf5'));
k=1;
data = cell(numel(hdf5_files),1);
for class_file = hdf5_files'
class_file_path = strcat(input_folder, class_f... |
github | enoonIT/nbnn-nbnl-master | matcaffe_demo.m | .m | nbnn-nbnl-master/matlab/MOPCNN_code/feature extraction/matcaffe_demo.m | 2,440 | utf_8 | 48e4407662174dc51255c9f3ef39ea86 | function scores = matcaffe_demo(im, use_gpu)
% scores = matcaffe_demo(im, use_gpu)
%
% Demo of the matlab wrapper using the ILSVRC network.
%
% input
% im color image as uint8 HxWx3
% use_gpu 1 to use the GPU, 0 to use the CPU
%
% output
% scores 1000-dimensional ILSVRC score vector
%
% You may need to ... |
github | enoonIT/nbnn-nbnl-master | doMOP.m | .m | nbnn-nbnl-master/matlab/MOPCNN_code/pooling/doMOP.m | 1,846 | utf_8 | 6305fb1457a71052c9f28e45c205b24f | function doMOP(filelistpath, nLevels, outpath)
FSIZE=4096
filelist = dataread('file', filelistpath, '%s', 'delimiter', '\n');
[savedFilenames, datasetNames] = savePooledFeatures(filelist, nLevels, FSIZE);
saveConcatenatedFeatures(savedFilenames, outpath, ...
datasetNames);
... |
github | enoonIT/nbnn-nbnl-master | fkmeans.m | .m | nbnn-nbnl-master/matlab/MOPCNN_code/pooling/fkmeans.m | 7,398 | utf_8 | 30fdd8798175f532a25c045926fde858 | function [label, centroid, dis] = fkmeans(X, k, options)
% FKMEANS Fast K-means with optional weighting and careful initialization.
% [L, C, D] = FKMEANS(X, k) partitions the vectors in the n-by-p matrix X
% into k (or, rarely, fewer) clusters by applying the well known batch
% K-means algorithm. Rows of X correspo... |
github | rfgiusti/timebox-master | weighbydist.m | .m | timebox-master/+runs/+dme/weighbydist.m | 3,258 | utf_8 | f545e724f99b917b7da36af09cda1aba | function [votes, weights, rankings] = weighbydist(dsname, trainclasses, testclasses, ~, distm, basecc, ~)
%RUNS.DME.WEIGHTBYDIST Run a partitioned train/test evaluation of the
%weighted ensemble on a data set, using distance from the test sample to
%the nearest neighbor as a measure of classification confidence
% T... |
github | rfgiusti/timebox-master | simplerank.m | .m | timebox-master/+runs/+dme/simplerank.m | 7,868 | utf_8 | af6c6aacd56fc7af218cec45269273fd | function [votes, weights, rankings] = simplerank(dsname, trainclasses, testclasses, labels, distm, basecc, options)
%RUNS.DME.SIMPLERANK Run a partitioned train/test evaluation of the
%weighted ensemble on a data set, using a simple rank analysis to estimate
%the importance of each nearest-neighbor to weight base cla... |
github | rfgiusti/timebox-master | merge.m | .m | timebox-master/+runs/+dme/merge.m | 4,298 | utf_8 | acd377a64524bd77cb07857e9ff5431b | function [acc, classes] = merge(votes, weights, rankings, testclasses, labels)
%RUNS.DME.MERGE Evalutes the ensemble output and determines the class
%assigned by the ensemble to each test instance
% This function is part of the ensemble evaluation set.
%
% Each ensemble implementation in the RUNS.DME package... |
github | rfgiusti/timebox-master | simplerank.m | .m | timebox-master/+runs/+dme/+aux/simplerank.m | 8,042 | utf_8 | b71a30500a383eb9b1b3df6cb0636655 | function [ranking, points] = simplerank(dsname, repname, distname, trainclasses, numclasses, ranking_k, matchvalues, ...
cachepath, norun)
%RUNS.DME.AUX.SIMPLERANK Rank training instances according to the Simple
%Rank anytime algorithm, without tiebreak strategy.
% This function is part of the ensemble evaluatio... |
github | rfgiusti/timebox-master | shift_euclidean.m | .m | timebox-master/+dists/shift_euclidean.m | 618 | utf_8 | ca4d8fb26114f88f145cfa1f234595ec | function d = shift_euclidean(P, Q)
%DISTS.SHIFT_EUCLIDEAN Rotation-invariant Euclidean distance between two
%time series
% SHIFT_EUCLIDEAN(S,Z) returns the rotation-invariant Euclidean distance
% between time series S and Z. This is the smallest distance between S
% and Z for all rotations of Z
% This file i... |
github | rfgiusti/timebox-master | calcmatrix.m | .m | timebox-master/+dists/calcmatrix.m | 5,040 | utf_8 | 7b0975dead26e0df8c436963aaf6204b | function [traintrain, testtrain] = calcmatrix(train, test, distfun, options)
%DISTS.CALCMATRIX Calculate distance matrix for a data set.
% CALCMATRIX(DS) returns an n-by-n matrix containing the Euclidean
% distance between all pairs of time series in the data set DS, where n
% is the number of time series.
%
% ... |
github | rfgiusti/timebox-master | dtw.m | .m | timebox-master/+dists/dtw.m | 492 | utf_8 | e41885f66cb35ec943d24b31df8c8c29 | % DTW function. W is half-window size in number of observations.
function dist = dtw(t, r, W)
%This function was deprecated in TimeBox 0.11.9
N = length(t);
M = length(r);
if ~exist('W', 'var')
W = max(N, M);
end
D = ones(N + 1, M + 1) * inf;
D(1, 1) = 0;
for i = 2 : N+1
for j = max(2, i - W) : min(M + 1, i + ... |
github | rfgiusti/timebox-master | mdlookup.m | .m | timebox-master/+dists/mdlookup.m | 1,889 | utf_8 | b73ea8fd8b24ecfbba8c7f239144b115 | function L = mdlookup(a)
%MDLOOKUP Return a look-up table for MINDIST of SAX words produced with
%alphabet size a
% L=MDLOOKUP(a) gives an a-by-a look-up matrix where each L(i,j) is the
% distance between the i-th and the j-th SAX letters in a SAX alphabet of
% size a. This matrix may be used with DISTS.MINDIST... |
github | rfgiusti/timebox-master | abs_dtw.m | .m | timebox-master/+dists/abs_dtw.m | 515 | utf_8 | 69c2e6fd3028836631d005f4d08cf832 | % DTW function. W is half-window size in number of observations.
function dist = abs_dtw(t, r, W)
% Deprecated since TimeBox 0.11.9
warnobsolete('dists:absdtw');
N = length(t);
M = length(r);
if ~exist('W', 'var')
W = max(N, M);
end
D = ones(N + 1, M + 1) * inf;
D(1, 1) = 0;
for i = 2 : N+1
for j = max(2, i - ... |
github | rfgiusti/timebox-master | norm1.m | .m | timebox-master/+ts/norm1.m | 1,054 | utf_8 | 6aafb686a7899863b3547bfeb9115377 | function [train, test] = norm1(train, test)
%TS.NORM1 Normalize time series to the interval [0,1]
% NORM1(X) normalizes the time series in the data set such that all
% observations fall into the interval [0,1].
%
% [X,Y]=NORM1(X,Y) behaves the same as two repeated calls.
%
% In either case, X and Y must be co... |
github | rfgiusti/timebox-master | znorm.m | .m | timebox-master/+ts/znorm.m | 1,235 | utf_8 | 9208a5b669b499a463746ddaf0ce94d8 | function [outtrain, outtest] = znorm(intrain, intest)
%TS.ZNORM Normalize time series dataset.
% N = ZNORM(D) normalize the dataset of time series D and return to N.
%
% [NTRAIN,NTEST] = ZNORM(TRAIN,TEST) normalize both the training and test
% data sets.
% This file is part of TimeBox. Copyright 2015-16 Rafael G... |
github | rfgiusti/timebox-master | normh.m | .m | timebox-master/+ts/normh.m | 1,214 | utf_8 | 9cb1372e0c1afcb21b6b4c4305a27e81 | function [train, test] = normh(train, test)
%TS.NORMH Normalize time series as if they were histograms
% NORMH(X) normalizes the time series in the data set such that all
% observations fall into the interval [0,1] and the sum of the
% observations equals one.
%
% This kind of normalization is required for so... |
github | rfgiusti/timebox-master | checkversion.m | .m | timebox-master/+tb/checkversion.m | 3,048 | utf_8 | 0b63fff3056c2272e16e987fb5b42c7c | function checkversion(version)
%TB.CHECKVERSION Check if TimeBox is compatible with the specified
%version. Raises an exception if not.
% CHECKVERSION('1.0.0') will throw an exception if the current TimeBox
% version is older than '1.0.0'.
%
% CHECKVERSION(struct('major', 1, 'minor', 0, 'patch', 0)) is the same... |
github | rfgiusti/timebox-master | mergecells.m | .m | timebox-master/+tb/mergecells.m | 1,981 | utf_8 | a7576fe85b2941d0a7e0812f1321d583 | function [merged, removed] = mergecells(varargin)
%TB.MERGECELLS Takes several columns cells and concatenate them into a
%single cell, excluding rows where any cell contains empty data
% MERGECELLS(cell1,cell2) returns the cell [cell1(:) cell2(:)]
%
% Example:
%
% cell1 = { 'a1' 'b1'; ...
% ... |
github | rfgiusti/timebox-master | loadfiles.m | .m | timebox-master/+tb/loadfiles.m | 1,671 | utf_8 | bb6e0c98cf216fd6ea0f7b3050707b22 | function data = loadfiles(files, mask)
%TB.LOADFILES Load several files at once and returns the data in a
%cell array
% data = LOADFILES(files), where `files' is a cell array of file
% names, returns in data{i} the contents of the i-th file. If files{i}
% is not found, then data{i} will contain the empty matrix... |
github | rfgiusti/timebox-master | acf.m | .m | timebox-master/+transform/acf.m | 905 | utf_8 | df3cb7cbcf189100306bb2d4b2dd1544 | function [trainacf, testacf] = acf(train, test, ~)
%TRANSFORM.ACF Time series transform to sample autocorrelation
%coefficients.
% DSA = ACF(DS) transforms the time series data set DS into sample
% correlation coefficients.
%
% [TRAINA,TESTA] = ACF(TRAIN,TEST) converts both a training and a test
% data sets i... |
github | rfgiusti/timebox-master | dct.m | .m | timebox-master/+transform/dct.m | 630 | utf_8 | 79e4d79751949ccad5165fce2892c0ec | function [traind, testd] = dct(train, test, ~)
%TRANSFORM.DCT Discrete Cosine Transform of data sets.
% DCT(DS) returns the Discrete Cosine Transform of the data set DS. The
% transform is calculated from the Matlab function DCT.
%
% [TRAIND,TESTD] = DCT(TRAIN,TEST) returns the DCT transform of both the
% train... |
github | rfgiusti/timebox-master | pca.m | .m | timebox-master/+transform/pca.m | 3,631 | utf_8 | 11fc28eb774a77f834cca030bacb09ab | function [trainpca, testpca] = pca(train, test, options)
%TRANSFORM.PCA Performs Principal Component Analysis on the training data
%set and returns observations for the training and test data sets in the
%transformed space.
% DSPC = PCA(DS) performs principal component analysis on the data set
% DS, and returns coe... |
github | rfgiusti/timebox-master | bmp.m | .m | timebox-master/+transform/bmp.m | 9,249 | utf_8 | 5dc4fb2d123eaebce5eba0184f264516 | function [trainbmp, testbmp] = bmp(train, test, options)
%TRANSFORM.BITMAP Transform a data set into time series bitmap
%representation.
% B=BMP(D) transforms the time series in D into time series bitmaps. Each
% time series in D is a row vector with the instance class in the first
% column.
%
% B=BMP(D,O) w... |
github | rfgiusti/timebox-master | paa.m | .m | timebox-master/+transform/paa.m | 5,030 | utf_8 | 6135b303f17f0072923ada42652de6b0 | function [trainpaa, testpaa] = paa(train, test, options)
%TRANSFORM.PAA Piecewise Aggregate Approximation of time series data set.
% DSP = PAA(DS) returns the Piecewise Aggregate Approximation of the time
% series in DS.
%
% The PAA transform of a time series is the averages of subsequences of
% the original ... |
github | rfgiusti/timebox-master | dwt.m | .m | timebox-master/+transform/dwt.m | 1,942 | utf_8 | f6fe670e39176e21de64cb4075c882d0 | function [train, test] = dwt(train, test, options)
%TRANSFORM.DWT Discrete Wavelet Transform of time series data set.
% DWT(DS) transforms the time series in DS using the Haar wavelet (db1)
% at the maximum decomposition level.
%
% DWT(DS,o), where "o" is an OPTS object, makes it possible to pass
% options to... |
github | rfgiusti/timebox-master | sax.m | .m | timebox-master/+transform/sax.m | 2,836 | utf_8 | 79d7665f38355f214235c84d811249bf | function [trainsax, testsax] = sax(train, test, options)
%TRANSFORM.SAX Get the SAX representation for time series data sets.
% DSX = SAX(DS) returns the SAX representation for the time series in the
% data set DS, using integers 1, 2, 3, ... to index the symbols 'a', 'b',
% 'c', ....
%
% DSX = SAX(DS,OPTS) ... |
github | rfgiusti/timebox-master | psd.m | .m | timebox-master/+transform/psd.m | 3,283 | utf_8 | 41d059b0e2234059ef7c629058649640 | function [trainp, testp] = psd(train, test, options)
%TRANSFORM.PSD Estimate the power spectral density of time series.
% Dp=PSD(D,...) treats the time series in D as discretized signals and
% returns in Dp a data set that represents the original time series
% transformed into power spectral density representat... |
github | rfgiusti/timebox-master | haar.m | .m | timebox-master/+transform/haar.m | 2,992 | utf_8 | 4dd8b5c787304263ce12f99430198db2 | function [trainwavelets, testwavelets] = haar(train, test, options)
%TRANSFORM.HAAR Get the Haar wavelets for time series data sets.
% DSH = HAAR(DS) returns the Haar wavelets for the time series of the
% data set DS. Haar wavelets are the same as Db1 wavelets. The time
% series are expected to have length 2^n. I... |
github | boonjiashen/HDR-master | Reinhard.m | .m | HDR-master/Implementation_1/Reinhard.m | 1,133 | utf_8 | ac7b8fd164f37cb761a1b637cecac0b1 | %
% Tone Mapping Operator, by Reinhard 02 paper.
% "Photographic Tone Reproduction for Digital Images"
%
% input:
% img: 3 channel HDR img
% alpha_: scalar constant to specify a high key or low key. (0.18)
% delta: scalar constant to prevent log(0). (1e-6)
% white_: scalar constant, the smallest luminance to be... |
github | ws15code/prob-trans-master | trainEnv_pilot2.m | .m | prob-trans-master/EEG/code/trainEnv_pilot2.m | 6,419 | utf_8 | 5be38e0c4028f04ddf39dcdea3a1c52a | % Author: Giovanni Di Liberto
% Date: 03/03/2015
% Project: WS15
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function [trainData, modelParams] = trainEnv_pilot2(mode... |
github | ws15code/prob-trans-master | epochsAvg.m | .m | prob-trans-master/EEG/code/epochsAvg.m | 2,850 | utf_8 | ff6a819a0e4a2237726d72d1c419eaa0 | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function epocsResult = epochsAvg(modelParams... |
github | ws15code/prob-trans-master | simpleClassification_3UW_old.m | .m | prob-trans-master/EEG/code/simpleClassification_3UW_old.m | 11,903 | utf_8 | 848cf529ca2d0637e922f8cec3248b79 | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function classificationResult = simpleClassi... |
github | ws15code/prob-trans-master | exportEEG4ICA_3.m | .m | prob-trans-master/EEG/code/exportEEG4ICA_3.m | 3,370 | utf_8 | 265d5fac5e391c74ee33664fa4791c02 | %% ICA export - eye blink and muscolar activity noise removal: this
% noise should be condition independent, therefore we concatenate all the
% trials and all the conditions
% This function downsamples the EEG data before the exportation
function exportEEG4ICA_3(modelParams, subjectsIdx)
conditionLabel = {'part1';'... |
github | ws15code/prob-trans-master | epochsAvgPre_pilot2.m | .m | prob-trans-master/EEG/code/epochsAvgPre_pilot2.m | 3,110 | utf_8 | dc64c8bb738068c9b2ec9b83c955bf97 | % Author: Giovanni Di Liberto
% Date: 19/01/2015
% Project: Vocoding Project
%
% For each subject (vector of integers), this method fits a linear
% regression model which maps the amplitude envelope representation of the
% speech signal to the correspondent recorded EEG
function epocsResult = epochsAvgPre_pilot2(m... |
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