plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | Sinan81/PSAT-master | fm_threed.m | .m | PSAT-master/psat-oct/psat/fm_threed.m | 11,096 | utf_8 | a9f48ea419066334caf6e7e619ec2e0b | function varargout = fm_threed(varargin)
% FM_THREED create GUI for network visualisations
%
% HDL = FM_THREED()
%
%Author: Federico Milano
%Date: 21-Aug-2007
%Version: 1.0.0
%
%E-mail: federico.milano@ucd.ie
%Web-site: faraday1.ucd.ie/psat.html
%
% Copyright (C) 2002-2019 Federico Milano
global Settings... |
github | Sinan81/PSAT-master | fm_dirset.m | .m | PSAT-master/psat-oct/psat/fm_dirset.m | 30,919 | utf_8 | 5fd21968fdac71735d7ea592d1e51a8e | function varargout = fm_dirset(type)
% FM_DIRSET define settings and actions for the data format
% conversion GUI
%
% FM_DIRSET(TYPE)
% TYPE action indentifier
%
%see also FM_DIR
%
%Author: Federico Milano
%Date: 11-Nov-2002
%Update: 05-Jul-2003
%Update: 31-Jul-2003
%Update: 07-Oct-2003
... |
github | Sinan81/PSAT-master | fm_input.m | .m | PSAT-master/psat-oct/psat/fm_input.m | 13,124 | utf_8 | a4a7b367ecb64f3f86fd6e1ea1f62c27 | function Answer = fm_input(Prompt, Title, NumLines, DefAns,Resize)
%INPUTDLG Input dialog box.
% Answer = INPUTDLG(Prompt) creates a modal dialog box that returns
% user input for multiple prompts in the cell array Answer. Prompt
% is a cell array containing the Prompt strings.
%
% INPUTDLG uses WAITFOR to suspend... |
github | Sinan81/PSAT-master | zbuildpi.m | .m | PSAT-master/psat-oct/psat/zbuildpi.m | 3,207 | utf_8 | 77e32ffc8651729d7ba2a3fb4c88cb6c | % This program forms the complex bus impedance matrix by the method
% of building algorithm. Bus zero is taken as reference.
% This program is compatible with power flow data.
% Copyright (C) 1998 by H. Saadat.
function [Zbus, linedata] = zbuildpi(linedata, gendata, yload)
% gendata generator data syn.con
ng = len... |
github | Sinan81/PSAT-master | fm_gams.m | .m | PSAT-master/psat-oct/psat/fm_gams.m | 45,016 | utf_8 | 37a2129685b6794bad0fe98843d008ce | function fm_gams
% FM_GAMS initialize and call GAMS to solve
% several kind of Market Clearing Mechanisms
%
% FM_GAMS
%
%GAMS settings are stored in the structure GAMS, with
%the following fields:
%
% METHOD 1 -> simple auction
% 2 -> linear OPF (DC power flow)
% 3 -> nonlinea... |
github | Sinan81/PSAT-master | fm_build.m | .m | PSAT-master/psat-oct/psat/fm_build.m | 25,468 | utf_8 | 688cdaed0380b34975aab856abfbaf9e | function fm_build
%FM_BUILD build new component functions (Symbolic Toolbox is needed)
%
%FM_BUILD
%
%see also FM_MAKE FM_COMPONENT
%
%Author: Federico Milano
%Date: 11-Nov-2002
%Update: 19-Dec-2003
%Version: 1.0.1
%
%E-mail: federico.milano@ucd.ie
%Web-site: faraday1.ucd.ie/psat.html
%
% Copyright (C)... |
github | Sinan81/PSAT-master | symfault.m | .m | PSAT-master/psat-oct/psat/symfault.m | 4,578 | utf_8 | 7a0f660fadb898109463adb029ac24ae | % The program symfault is designed for the balanced three-phase
% fault analysis of a power system network. The program requires
% the bus impedance matrix Zbus. Zbus may be defined by the
% user, obtained by the inversion of Ybus or it may be
% determined either from the function Zbus = zbuild(zdata)
% or the function... |
github | Sinan81/PSAT-master | fm_plot.m | .m | PSAT-master/psat-oct/psat/fm_plot.m | 30,010 | utf_8 | 916052824f8021af416d6d8bd704c7d7 | function fm_plot(flag)
% FM_PLOT plot results of Continuation Power Flow,
% Optimal Power Flow and Time Domain
% Simulations.
%
% FM_PLOT(FLAG)
% FLAG 0 -> create variable list
% 1 -> plot selected variables
% 2 -> save graph
% 3 -> set layout
%
%Author: Federico Milano... |
github | Sinan81/PSAT-master | fm_uwfig.m | .m | PSAT-master/psat-oct/psat/fm_uwfig.m | 36,174 | utf_8 | e21245f8662dfac8f223c8e9980016a2 | function fig = fm_uwfig(varargin)
% FM_UWFIG create GUI for PSAT/UWPFLOW interface.
%
% FIG = FM_UWFIG
%
%see UWPFLOW structure for settings
%
%Author: Federico Milano
%Date: 31-Mar-2003
%Version: 1.0.0
%
%E-mail: federico.milano@ucd.ie
%Web-site: faraday1.ucd.ie/psat.html
%
% Copyright (C) 2002-2019 Fede... |
github | Sinan81/PSAT-master | sim2psat.m | .m | PSAT-master/psat-oct/psat/filters/sim2psat.m | 29,948 | utf_8 | eaf79c62c1825a9a71b6f65fe0cb7b15 | function check_model = sim2psat(varargin)
% SIM2PSAT convert Simulink models into PSAT data files
%
% CHECK = SIM2PSAT
% CHECK = 0 conversion failed
% CHECK = 1 conversion completed
%
%see also FM_LIB, FM_SIMREP, FM_SIMSET
%
%Author: Federico Milano
%Date: 01-Jan-2006
%
%E-mail: federico.milano@... |
github | Sinan81/PSAT-master | psat2epri.m | .m | PSAT-master/psat-oct/psat/filters/psat2epri.m | 10,049 | utf_8 | fd2ab45d2c3ab7faff9a51b6be3ac9d2 | function check = psat2epri(filename, pathname)
% PSAT2EPRI converts PSAT data file into EPRI Data Format
%
% CHECK = PSAT2EPRI(FILENAME,PATHNAME)
% FILENAME name of the file to be converted
% PATHNAME path of the file to be converted
%
% CHECK = 1 conversion completed
% CHECK = 0 problem encount... |
github | haller-group/Closed-Null-Geodesics-2D-master | add_path.m | .m | Closed-Null-Geodesics-2D-master/add_path.m | 644 | utf_8 | b693c741666d3c95b8f19581f1d83c7c | %------------------------------------Set path
function add_path
fp = mfilename('fullpath');
rootdir = fileparts(fp);
p{1} = fullfile(rootdir,'data');
p{2} = fullfile(rootdir,'doc');
p{3} = fullfile(rootdir,'Main');
p{4} = fullfile(rootdir,'Subfunctions');
for i = 1:4
addpath(rootdi... |
github | haller-group/Closed-Null-Geodesics-2D-master | PlotOutmost.m | .m | Closed-Null-Geodesics-2D-master/Subfunctions/PlotOutmost.m | 2,786 | utf_8 | c4ea33d3292589c47e67d3f2ecedd59a | % function PlotOutmost(xLcOutM,yLcOutM,LamLcOutM,lamV,x_g,y_g,lam2)
% Input arguments:
% xLcOutM : x-component of the outermost closed null-geodesics
% yLcOutM : x-component of the outermost closed null-geodesics
% LamLcOutM : \lambda values of the o... |
github | haller-group/Closed-Null-Geodesics-2D-master | Phi_prime.m | .m | Closed-Null-Geodesics-2D-master/Subfunctions/Phi_prime.m | 2,243 | utf_8 | 3c5f187b58b0314eee6b9906f4ae3a7e | %% References:
%[1] Mattia Serra and George Haller, "Efficient Computation of Null-Geodesic with
% Applications to Coherent Vortex Detection", sumbitted, (2016).
%%
% [phiPrGr,C22mC11Gr,C12Gr]=Phi_prime(C11,C11x1,C11x2,C12,C12x1,C12x2,C22,C22x1,C22x2,x1_g,x2_g)
% Input arguments:
% Cij : ij entries of the C... |
github | haller-group/Closed-Null-Geodesics-2D-master | FindOutermost.m | .m | Closed-Null-Geodesics-2D-master/Subfunctions/FindOutermost.m | 2,834 | utf_8 | 803304c13610eb9655ad4a5d39d10c44 | %% References:
%[1] Mattia Serra and George Haller, "Efficient Computation of Null-Geodesic with
% Applications to Coherent Vortex Detection", sumbitted, (2016).
%%
% function [xLcOutM,yLcOutM,LamLcOutM]=FindOutermost(xPsol,yPsol,lamV,sVec);
% Input arguments:
% x1Psol : x1-component of cl... |
github | haller-group/Closed-Null-Geodesics-2D-master | FindClosedNullGeod.m | .m | Closed-Null-Geodesics-2D-master/Subfunctions/FindClosedNullGeod.m | 3,053 | utf_8 | e648b671dcb8d9222859dbc53f0cb579 | %% References:
%[1] Mattia Serra and George Haller, "Efficient Computation of Null-Geodesic with
% Applications to Coherent Vortex Detection", sumbitted, (2016).
%%
% function [x1Psol,x2Psol]=FindClosedNullGeod(C22mC11Gr,C12Gr,phiPrGr,x1_g,x2_g,lamV,sVec,options);
% Input arguments:
% C22mC11Gr, C12Gr, phi... |
github | haller-group/Closed-Null-Geodesics-2D-master | Advect_r.m | .m | Closed-Null-Geodesics-2D-master/Subfunctions/Advect_r.m | 2,455 | utf_8 | 978d0c7efae18777e437ca0a4f95e6a9 | %% References:
%[1] Mattia Serra and George Haller, "Efficient Computation of Null-Geodesic with
% Applications to Coherent Vortex Detection", sumbitted, (2016).
%%
% function [~,xxx,yyy,zzz]=Advect_r(phiPrGr,C22mC11Gr,C12Gr,x_glim,y_glim,sVec,Z0,options)
% Input arguments:
% C22mC11Gr, C12Gr, phiPrGr : see... |
github | haller-group/Closed-Null-Geodesics-2D-master | r0_lam.m | .m | Closed-Null-Geodesics-2D-master/Subfunctions/r0_lam.m | 2,234 | utf_8 | 1be846cd61314ab3dd3afb6f7a3c7d25 | %% References:
%[1] Mattia Serra and George Haller, "Efficient Computation of Null-Geodesic with
% Applications to Coherent Vortex Detection", sumbitted, (2016).
%%
% function [x0lam,y0lam,phi0lam]=r0_lam(lamV,C11,C12,C22,x_g,y_g)
% Input arguments:
% lamV : Desired set of \lambda values
% Cij : ij entri... |
github | haller-group/Closed-Null-Geodesics-2D-master | PeriodicSolutions.m | .m | Closed-Null-Geodesics-2D-master/Subfunctions/PeriodicSolutions.m | 5,724 | utf_8 | 89699760a218aea7448fc5c4c93964b2 | %% References:
%[1] Mattia Serra and George Haller, "Efficient Computation of Null-Geodesic with
% Applications to Coherent Vortex Detection", sumbitted, (2016).
%%
% function [X1lco,X2lco,philco]=PeriodicSolutions(X_Vf,Y_Vf,Z_Vf)
% Input arguments:
% X_Vf : x1-component of the trajectories of the ODE (38) i... |
github | haller-group/Closed-Null-Geodesics-2D-master | PlotAllClosedNullGeodesics.m | .m | Closed-Null-Geodesics-2D-master/Subfunctions/PlotAllClosedNullGeodesics.m | 2,577 | utf_8 | 265d9d1e1e7298b68083091ccc61ebb6 | % function PlotAllClosedNullGeodesics(x1Psol,x2Psol,x1_g,x2_g,lamV,lam2)
% Input arguments:
% lamV : Desired set of \lambda values
% phi0 : initial \phi value (cf. Fig. 2 of [1])
% CGij : ij entries of the CG strain tensor
% x1_g : x1 component of the spatial grid
% x2_g : x2 component of the s... |
github | PrincetonUniversity/3D3A-SABRE-Toolkit-master | SABRE_SphericalHarmonic.m | .m | 3D3A-SABRE-Toolkit-master/SABRE_SphericalHarmonic.m | 3,326 | utf_8 | 8364057f4d5325bc0be08b1baff9b0ca | function Y = SABRE_SphericalHarmonic(L, R)
%SABRE_SphericalHarmonic Real-valued spherical harmonic function for ambiX.
% Y = SABRE_SphericalHarmonic(L,R) computes the real-valued, SN3D
% normalized spherical harmonics, up to order L and for positions R,
% used in the ambiX plugins. The ambiX spherical har... |
github | PrincetonUniversity/3D3A-SABRE-Toolkit-master | SABRE_InterpolateHRTFs.m | .m | 3D3A-SABRE-Toolkit-master/SABRE_InterpolateHRTFs.m | 7,851 | utf_8 | 21888ca5a9f7b7c50fb72a87ae244f1e | function [hrirL, hrirR, desiredGrid] = SABRE_InterpolateHRTFs(hrirDataL, hrirDataR, measuredGrid, varargin)
%SABRE_InterpolateHRTFs Interpolate measured HRTFs to a desired grid.
% [XL, XR, RD] = SABRE_InterpolateHRTFs(HL, HR, RM, RD) returns HRIRs XL
% and XR for the desired positions RD, given input HRIRs HL a... |
github | kohpangwei/data-poisoning-release-master | upperBoundTrue.m | .m | data-poisoning-release-master/matlab/upperBoundTrue.m | 6,944 | utf_8 | 212d5d2bb6860fc19366e570c83dd283 | % G, Constraint are yalmip data for debugging
function [G, Constraint, val, X_eps, probs_eps] = upperBoundTrue(X_train, y_train, theta, bias, probs, mus, epsilon, r_slab, r_sphere, randomize, solver)
% we don't have a good way of splitting u pthe probabilities, so let's
% just do it randomly
if randomize
... |
github | kohpangwei/data-poisoning-release-master | extractVecs.m | .m | data-poisoning-release-master/matlab/extractVecs.m | 1,607 | utf_8 | 6ed8c565810c85c34eda832eb00d180a | function V_full = extractVecs(G_full, G_partial, V_partial)
% G_full is Graham matrix of inner products
% G_partial is lower-right corner of G
% V_partial is collection of vectors realizing G_partial
n_full = size(G_full, 1);
assert(n_full == size(G_full, 2));
n_partial = size(G_partial, 1);
... |
github | aharley/segaware-master | classification_demo.m | .m | segaware-master/caffe/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 | aharley/segaware-master | MyVOCevalseg.m | .m | segaware-master/caffe/matlab/my_script/MyVOCevalseg.m | 4,625 | utf_8 | 128c24319d520c2576168d1cf17e068f | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | aharley/segaware-master | MyVOCevalsegBoundary.m | .m | segaware-master/caffe/matlab/my_script/MyVOCevalsegBoundary.m | 4,415 | utf_8 | 1b648714e61bafba7c08a8ce5824b105 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | aharley/segaware-master | MyVOCevalseg.m | .m | segaware-master/scripts/segaware/matlab/eval/MyVOCevalseg.m | 4,821 | utf_8 | e2ba8ed0ce8588906a6c63e3a76eb9b2 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | Unisens/unisensMatlabTools-master | unisensBin2Csv.m | .m | unisensMatlabTools-master/unisensBin2Csv.m | 5,391 | utf_8 | 02ce00d793b19e479aaa3464084aa4f4 | function unisensBin2Csv(path, keepSensorScaling, new_path)
%UNISENSBIN2CSV convert unisens dataset with bin entries to dataset with csv entries
% Converts all unisens signal entries from binary format (*.bin) to csv format (*.csv)
% Copyright 2017 movisens GmbH
addUnisensJar();
if nargin==0 || nargin>3
error('u... |
github | Unisens/unisensMatlabTools-master | unisensAddZerosEnd.m | .m | unisensMatlabTools-master/unisensAddZerosEnd.m | 7,664 | utf_8 | c567890472a5e0394fdfe8d1e7a8fa5d | function unisensAddZerosEnd(path, new_path, addZeros_samplerate, end_samplestamp)
%UNISENSADDZEROSEND adds zeros to the end of a unisens dataset
% Copyright 2020 movisens GmbH, Germany
addZeros_end_time = end_samplestamp / addZeros_samplerate;
%open unisens dataset
j_unisensFactory = org.unisens.Unisens... |
github | Unisens/unisensMatlabTools-master | unisensCsv2Bin.m | .m | unisensMatlabTools-master/unisensCsv2Bin.m | 4,937 | utf_8 | 93aaf329972417ca112b688c76105878 | function unisensCsv2Bin(path, new_path)
%UNISENSCSV2BIN convert unisens dataset with csv entries to dataset with bin entries
% Converts all unisens signal entries from csv format (*.csv) to bin format (*.bin).
% Event entries and values entries are not affected
% Copyright 2017 movisens GmbH
addUnisensJar();
if ... |
github | Unisens/unisensMatlabTools-master | unisensCrop.m | .m | unisensMatlabTools-master/unisensCrop.m | 8,048 | utf_8 | f40ca68854d3743c34c1e1cdd289865a | function unisensCrop(path, new_path, crop_samplerate, start_samplestamp, end_samplestamp)
%UNISENSCROP crop a unisens dataset to a specified region
% Copyright 2017 movisens GmbH, Germany
crop_start_time = start_samplestamp / crop_samplerate;
crop_end_time = end_samplestamp / crop_samplerate;
%check if c... |
github | lightyears1998/a-gzhu-coder-master | figure.m | .m | a-gzhu-coder-master/period/freshman/物理实验/变温粘滞系数的测定/figure.m | 473 | utf_8 | 66121fba75c6072fcbef078143902f7f | % 变温粘滞系数 Figure %
nw = [.548, .469, .343, .222, .182];
t = [ 30, 35, 40, 45, 50];
function rslt = fun(var, data)
rslt = var(1) * exp(-var(2) * data);
endfunction
var0 = [0 0];
P = lsqcurvefit(@fun, var0, t, nw);
graphics_toolkit("gnuplot");
figure();
hold on;
grid off;
axis([25, 55, 0,... |
github | vitoruapt/lartkv5-master | matlab2opencv.m | .m | lartkv5-master/src/utils/human_leader/matlab/matlab2opencv.m | 1,002 | utf_8 | faa2274109d4211d124825eeac37a400 | %creates yaml file from matlab var, so it can be loaded by opencv
function matlab2opencv( variable, fileName, flag)
[rows cols] = size(variable);
% Beware of Matlab's linear indexing
variable = variable';
% Write mode as default
if ( ~exist('flag','var') )
flag = 'w';
end
if ( ~exist(fileName,'file') || flag ... |
github | vitoruapt/lartkv5-master | show_labels.m | .m | lartkv5-master/src/utils/human_leader/matlab/show_labels.m | 1,338 | utf_8 | ab280e637c4213c23d1ea1b9786aec23 | %compare the labels created by the three evaluators with, also plotting the
%final tag, for illustrative purposes only.
%input must be xx files
function [] = plot_labels(test)
features = test(:,5:end);
low_plot = min(min(features));
up_plot = max(max(features));
time = test(:,1);
% Create figure
F = figure('position... |
github | vitoruapt/lartkv5-master | relative_velocities.m | .m | lartkv5-master/src/utils/human_leader/matlab/relative_velocities.m | 901 | utf_8 | f9ecea158bfff8175dccdbc6bb95383f | %compute relative velocities, decomposing original feature that was scalar
%only, requires target_velocity, relative_velocity and relative_heading
function [rel_vx rel_vy] = relative_velocities(dataset)
tgt_v = dataset(:,3);
rel_v = dataset(:,4);
rel_h = dataset(:,5);
%recompute robot velocity
robot_v = rel_v + tgt_... |
github | vitoruapt/lartkv5-master | plot_class_error.m | .m | lartkv5-master/src/utils/human_leader/matlab/plot_class_error.m | 942 | utf_8 | 293c87fc993554746f6e4632eebf2aec | %show the error in classification as a graph, for all the test set, shown
%in the x axis, input is a matrix copied from excel, containing the value
%of the errors, but could come directly from evaluate_model function. it
%has been done this way because excel already had all the errors in tables.
function [] = plot_cla... |
github | vitoruapt/lartkv5-master | show_thresholds.m | .m | lartkv5-master/src/utils/human_leader/matlab/show_thresholds.m | 943 | utf_8 | 7bdaa72b918d819190140d9908221b5d | %create linear variation of features to find what are the thresholds
%used by the classifier on each feature, receive as argument the classifier
%and the dimension to evaluate
function []=show_thresholds(model,dim)
clear new_model;
clear j; j=1; clear test_thresh;
% test_thresh = zeros(401,9);
test_thresh(:,1) = 1... |
github | vitoruapt/lartkv5-master | compare_classifiers.m | .m | lartkv5-master/src/utils/human_leader/matlab/compare_classifiers.m | 3,391 | utf_8 | 49da591a25df76b44b60c94c25ddbf32 | % i think this was created to comparte an adaboost classificer with
% a neural network classifier
%test neural network
function [] = compare_classifiers(model,net,test)
%prepare the data
features = test(:,3:7);
input_val = features';
target_val = test(:,2)';
target_val(2,find(target_val == 0)) = 1; %for ann
test(tes... |
github | vitoruapt/lartkv5-master | train_net.m | .m | lartkv5-master/src/utils/human_leader/matlab/train_net.m | 907 | utf_8 | 0d219bb6a1a146ccf798b6ec9b25b86f | %train neural network
% function [net] = train_net(inputs,targets)
function [net] = train_net(train_set,n_neurons)
inputs = train_set(:,3:end);
targets = train_set(:,2);
targets(targets==0,2)=1;
% Solve a Pattern Recognition Problem with a Neural Network
inputs = inputs';
targets = targets';
% Create a Pattern Reco... |
github | vitoruapt/lartkv5-master | test_net.m | .m | lartkv5-master/src/utils/human_leader/matlab/test_net.m | 3,230 | utf_8 | 8dc4a43efed1f2b19867805d622306f8 | %test neural network
function [] = test_net(net,test_set)
for number = 1:length(test_set)
test = test_set(number).set;
name = test_set(number).name;
%prepare the data
features = test(:,3:end);
input_val = features';
target_val = test(:,2)';
target_val(2,target_val == 0) = 1;
... |
github | vitoruapt/lartkv5-master | evaluate_model_single.m | .m | lartkv5-master/src/utils/human_leader/matlab/evaluate_model_single.m | 3,609 | utf_8 | b86fe2360a8aafd9d15d87f1c08468b1 | %evaluates adaboost classifier, comparing groundtruth
%with class output, may receive a single dataset or a structure of them,
%create plots comparing ground truth and classification, also prints error
%of false good, false bad and false total
function [] = evaluate_model(model,test_set)
if size(test_set,1)~=1
te... |
github | vitoruapt/lartkv5-master | adaboost_vs_ann.m | .m | lartkv5-master/src/utils/human_leader/matlab/adaboost_vs_ann.m | 1,794 | utf_8 | 2b9a6ca896a568cbfad96012f03beb47 | %compare the labels created by the three evaluators with, also plotting the
%final tag, for illustrative purposes only.
function [] = adaboost_vs_ann()
% Create figure
% F = figure('position',[360 260 750 600]);
F = figure('position',[360 260 750 500]);
set(F,'defaultlinelinewidth',3);
set(F,'defaultaxeslinewidth',1.... |
github | vitoruapt/lartkv5-master | train_adaboost.m | .m | lartkv5-master/src/utils/human_leader/matlab/train_adaboost.m | 5,522 | utf_8 | 2362578a48eadb03855f3d3a151234ea | %trains an adaboost classifier, input is training set, and iterations are
%the max number of weak classifiers allowed
function [classestimate,model,feat_of_wc] = train_adaboost(data,iterations)
%train adaboost classifier downloaded from internet
%features:
% 3: target velocity
% 4: lateral displacement (former relat... |
github | vitoruapt/lartkv5-master | leader_features.m | .m | lartkv5-master/src/utils/human_leader/matlab/leader_features.m | 3,099 | utf_8 | 55c282a169e38696d0583e4ede61126d | %extract features from chosen target and
%stores it in a matlab variable (proc_target)
%must pass as arguments the name of the file, generated by ROS
%log file from process_target, and the id of the desired subject
function [proc_target]=leader_features(file,target_id)
% input file format:
% 1: id
% 2: good/bad tag... |
github | vitoruapt/lartkv5-master | plot_labels.m | .m | lartkv5-master/src/utils/human_leader/matlab/plot_labels.m | 4,314 | utf_8 | ebee9d9693bb54f2e92624c850c4223d | %compare the labels created by the three evaluators with, also plotting the
%final tag, for illustrative purposes only.
function [] = plot_labels(test,final_tag)
features = test(:,5:end);
low_plot = min(min(features));
up_plot = max(max(features));
time = test(:,1);
% Create figure
F = figure('position',[360 260 900... |
github | vitoruapt/lartkv5-master | plot_test2.m | .m | lartkv5-master/src/utils/human_leader/matlab/plot_test2.m | 2,313 | utf_8 | 968d8618023c1cc38d47190720487c42 | %used in do_tag to show comparison between labels
function [] = plot_test2(test_a)
features = test_a(:,5:end);
%test(test(:,2)==0,2)=-1; %transform from 0 to -1
gd_class = test_a(:,2); %second column has leader tag
low_plot = min(min(features));
up_plot = max(max(features));
time = test_a(:,1);
bad_tag = test_a(g... |
github | vitoruapt/lartkv5-master | enhance_features.m | .m | lartkv5-master/src/utils/human_leader/matlab/enhance_features.m | 4,253 | utf_8 | 891a77b7a8827b581b59f2bf33af6c4e | %compute new features based on existing ones
%
% derivatives from position 10 to 16
% standard deviation, based on winsize, position 17 to 23
% mean, based on winsize, position 24 to 30
%
% in final part, must uncomment the desired set, using only portions of
% the features computed, eliminating features with reduced ... |
github | vitoruapt/lartkv5-master | new_features.m | .m | lartkv5-master/src/utils/human_leader/matlab/new_features.m | 263 | utf_8 | 64fbb408c530fb0bd97e9027cb513024 | %decompose relative velocity in x and y, compute lateral displacement
function [out]=new_features(in)
out = in;
[rel_vel_x rel_vel_y] = relative_velocities(in);
out(:,8) = rel_vel_x;
out(:,9) = rel_vel_y;
out(:,4) = sin(in(:,6)).*in(:,7); %put ld in place of rv |
github | vitoruapt/lartkv5-master | crop_features.m | .m | lartkv5-master/src/utils/human_leader/matlab/crop_features.m | 1,981 | utf_8 | a3fc318bef5c147ab0e5fb5c62756f4f | %crop variable containing leader features
%so transitory measurments from beginning and
%end can be removed
%inputs:
%in_data : input data
%x1: inferior crop limit
%x2: superior crop limit
function [out_data] = crop_features(in_data,x1,x2)
% Show the data
H = figure;
set(H,'defaultlinelinewidth',3);
set(H,'defaultax... |
github | vitoruapt/lartkv5-master | do_tag.m | .m | lartkv5-master/src/utils/human_leader/matlab/do_tag.m | 1,227 | utf_8 | 19d31561f6a92e6f273b5b9fa70294a9 | %new tag and shift time
%as tests have different initial times, this function put all of them
%in the same reference frame. the offsets are computed based on the
%recorded images, because they have the correct time
%first the tags are obatined using rxbag, then the first image of each
%bag is compared wrt their clock ... |
github | vitoruapt/lartkv5-master | evaluate_model.m | .m | lartkv5-master/src/utils/human_leader/matlab/evaluate_model.m | 5,501 | utf_8 | ecf0d85ca58f00874687aa0be33c94df | %evaluates adaboost classifier, comparing groundtruth
%with class output, may receive a single dataset or a structure of them,
%create plots comparing ground truth and classification, also prints error
%of false good, false bad and false total
function [] = evaluate_model(model,test_set)
if size(test_set,1)~=1
te... |
github | vitoruapt/lartkv5-master | show_labels.m | .m | lartkv5-master/src/utils/process_target/matlab/show_labels.m | 1,338 | utf_8 | ab280e637c4213c23d1ea1b9786aec23 | %compare the labels created by the three evaluators with, also plotting the
%final tag, for illustrative purposes only.
%input must be xx files
function [] = plot_labels(test)
features = test(:,5:end);
low_plot = min(min(features));
up_plot = max(max(features));
time = test(:,1);
% Create figure
F = figure('position... |
github | vitoruapt/lartkv5-master | show_thresholds.m | .m | lartkv5-master/src/utils/process_target/matlab/show_thresholds.m | 943 | utf_8 | 7bdaa72b918d819190140d9908221b5d | %create linear variation of features to find what are the thresholds
%used by the classifier on each feature, receive as argument the classifier
%and the dimension to evaluate
function []=show_thresholds(model,dim)
clear new_model;
clear j; j=1; clear test_thresh;
% test_thresh = zeros(401,9);
test_thresh(:,1) = 1... |
github | vitoruapt/lartkv5-master | compare_classifiers.m | .m | lartkv5-master/src/utils/process_target/matlab/compare_classifiers.m | 3,391 | utf_8 | 49da591a25df76b44b60c94c25ddbf32 | % i think this was created to comparte an adaboost classificer with
% a neural network classifier
%test neural network
function [] = compare_classifiers(model,net,test)
%prepare the data
features = test(:,3:7);
input_val = features';
target_val = test(:,2)';
target_val(2,find(target_val == 0)) = 1; %for ann
test(tes... |
github | vitoruapt/lartkv5-master | train_net.m | .m | lartkv5-master/src/utils/process_target/matlab/train_net.m | 907 | utf_8 | 0d219bb6a1a146ccf798b6ec9b25b86f | %train neural network
% function [net] = train_net(inputs,targets)
function [net] = train_net(train_set,n_neurons)
inputs = train_set(:,3:end);
targets = train_set(:,2);
targets(targets==0,2)=1;
% Solve a Pattern Recognition Problem with a Neural Network
inputs = inputs';
targets = targets';
% Create a Pattern Reco... |
github | vitoruapt/lartkv5-master | test_net.m | .m | lartkv5-master/src/utils/process_target/matlab/test_net.m | 3,230 | utf_8 | 8dc4a43efed1f2b19867805d622306f8 | %test neural network
function [] = test_net(net,test_set)
for number = 1:length(test_set)
test = test_set(number).set;
name = test_set(number).name;
%prepare the data
features = test(:,3:end);
input_val = features';
target_val = test(:,2)';
target_val(2,target_val == 0) = 1;
... |
github | vitoruapt/lartkv5-master | evaluate_model_single.m | .m | lartkv5-master/src/utils/process_target/matlab/evaluate_model_single.m | 3,609 | utf_8 | b86fe2360a8aafd9d15d87f1c08468b1 | %evaluates adaboost classifier, comparing groundtruth
%with class output, may receive a single dataset or a structure of them,
%create plots comparing ground truth and classification, also prints error
%of false good, false bad and false total
function [] = evaluate_model(model,test_set)
if size(test_set,1)~=1
te... |
github | vitoruapt/lartkv5-master | train_adaboost.m | .m | lartkv5-master/src/utils/process_target/matlab/train_adaboost.m | 5,522 | utf_8 | 2362578a48eadb03855f3d3a151234ea | %trains an adaboost classifier, input is training set, and iterations are
%the max number of weak classifiers allowed
function [classestimate,model,feat_of_wc] = train_adaboost(data,iterations)
%train adaboost classifier downloaded from internet
%features:
% 3: target velocity
% 4: lateral displacement (former relat... |
github | vitoruapt/lartkv5-master | leader_features.m | .m | lartkv5-master/src/utils/process_target/matlab/leader_features.m | 4,394 | utf_8 | c2c7a6b0afe6ec5c38886b61eda5b343 | %extract features from chosen target and
%stores it in a matlab variable (proc_target)
%must pass as arguments the name of the file, generated by ROS
%log file from process_target, and the id of the desired subject
%function [robot,proc_target]=leader_features(file,target_id)
function [proc_target]=leader_features(fi... |
github | vitoruapt/lartkv5-master | plot_labels.m | .m | lartkv5-master/src/utils/process_target/matlab/plot_labels.m | 4,314 | utf_8 | ebee9d9693bb54f2e92624c850c4223d | %compare the labels created by the three evaluators with, also plotting the
%final tag, for illustrative purposes only.
function [] = plot_labels(test,final_tag)
features = test(:,5:end);
low_plot = min(min(features));
up_plot = max(max(features));
time = test(:,1);
% Create figure
F = figure('position',[360 260 900... |
github | vitoruapt/lartkv5-master | plot_test2.m | .m | lartkv5-master/src/utils/process_target/matlab/plot_test2.m | 2,313 | utf_8 | 968d8618023c1cc38d47190720487c42 | %used in do_tag to show comparison between labels
function [] = plot_test2(test_a)
features = test_a(:,5:end);
%test(test(:,2)==0,2)=-1; %transform from 0 to -1
gd_class = test_a(:,2); %second column has leader tag
low_plot = min(min(features));
up_plot = max(max(features));
time = test_a(:,1);
bad_tag = test_a(g... |
github | vitoruapt/lartkv5-master | enhance_features.m | .m | lartkv5-master/src/utils/process_target/matlab/enhance_features.m | 4,253 | utf_8 | 891a77b7a8827b581b59f2bf33af6c4e | %compute new features based on existing ones
%
% derivatives from position 10 to 16
% standard deviation, based on winsize, position 17 to 23
% mean, based on winsize, position 24 to 30
%
% in final part, must uncomment the desired set, using only portions of
% the features computed, eliminating features with reduced ... |
github | vitoruapt/lartkv5-master | new_features.m | .m | lartkv5-master/src/utils/process_target/matlab/new_features.m | 263 | utf_8 | 64fbb408c530fb0bd97e9027cb513024 | %decompose relative velocity in x and y, compute lateral displacement
function [out]=new_features(in)
out = in;
[rel_vel_x rel_vel_y] = relative_velocities(in);
out(:,8) = rel_vel_x;
out(:,9) = rel_vel_y;
out(:,4) = sin(in(:,6)).*in(:,7); %put ld in place of rv |
github | vitoruapt/lartkv5-master | crop_features.m | .m | lartkv5-master/src/utils/process_target/matlab/crop_features.m | 1,981 | utf_8 | a3fc318bef5c147ab0e5fb5c62756f4f | %crop variable containing leader features
%so transitory measurments from beginning and
%end can be removed
%inputs:
%in_data : input data
%x1: inferior crop limit
%x2: superior crop limit
function [out_data] = crop_features(in_data,x1,x2)
% Show the data
H = figure;
set(H,'defaultlinelinewidth',3);
set(H,'defaultax... |
github | vitoruapt/lartkv5-master | evaluate_model.m | .m | lartkv5-master/src/utils/process_target/matlab/evaluate_model.m | 5,501 | utf_8 | ecf0d85ca58f00874687aa0be33c94df | %evaluates adaboost classifier, comparing groundtruth
%with class output, may receive a single dataset or a structure of them,
%create plots comparing ground truth and classification, also prints error
%of false good, false bad and false total
function [] = evaluate_model(model,test_set)
if size(test_set,1)~=1
te... |
github | vitoruapt/lartkv5-master | ros_interface.m | .m | lartkv5-master/src/utils/process_target/matlab/old/ros_interface.m | 1,701 | utf_8 | 84fa1bd62878c2e5189bebed40138987 | %interface with ros, receives a feature message and
%apply classifier, then outputs a new message with
%the classification
function []=ros_subscriber(model)
% create a publisher for a geometry_msgs/Pose message
pub_process = geometry_msgs_Pose(...
'connect','publisher','subscribe_to_matlab','pose');
% create a s... |
github | vitoruapt/lartkv5-master | plot_test.m | .m | lartkv5-master/src/utils/process_target/matlab/old/plot_test.m | 2,514 | utf_8 | 4802dbf48aec6bab59fcfc490193758f | %compares pro tag before and after shift,
function [] = plot_test(test_set)
features = test_set(:,3:end);
good_tag = test_set(:,2); %second column has leader tag
bad_tag = test_set(good_tag == 1,1);
low_plot = min(min(features));
up_plot = max(max(features));
time = test_set(:,1);
% Show the data
H = figure;
set... |
github | vitoruapt/lartkv5-master | test_ensemble.m | .m | lartkv5-master/src/utils/process_target/matlab/old/test_ensemble.m | 219 | utf_8 | 2057f682ef3cecaa6ab5d76aa489ef52 | %test different 4 situations with classifier
function [] = test_ensemble(model,stopped,good,aside,far)
evaluate_model(model,stopped);
evaluate_model(model,good);
evaluate_model(model,aside);
evaluate_model(model,far); |
github | vitoruapt/lartkv5-master | ros_ann.m | .m | lartkv5-master/src/utils/process_target/matlab/old/ros_ann.m | 1,651 | utf_8 | 87dfddc5f632bdf0e31bf7bf52a2a6bc | %interface with ros, receives a feature message and
%apply classifier, then outputs a new message with
%the classification
function []=ros_subscriber(model)
% create a publisher for a geometry_msgs/Pose message
pub_process = geometry_msgs_Pose(...
'connect','publisher','subscribe_to_matlab','pose');
% create a s... |
github | vitoruapt/lartkv5-master | do_transition.m | .m | lartkv5-master/src/utils/process_target/matlab/old/do_transition.m | 245 | utf_8 | 9c9e7c379455091aff623a8171bc64f3 | %back tagging, never used
function out = do_transition(in)
out = in;
tag_i = find(diff(in(:,2))==1);
timestep = mean(diff(in(:,1)));
window = 1;
steps = round(window/timestep);
trans_i = tag_i - steps;
out(:,2)=0;
out(trans_i:tag_i,2)=1;
end
|
github | vitoruapt/lartkv5-master | pdftops.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/pdftops.m | 3,077 | utf_8 | 8dff856e4b450072050d8aa571d1a08e | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for future refere... |
github | vitoruapt/lartkv5-master | crop_borders.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/crop_borders.m | 1,669 | utf_8 | 725f526e7270a9b417300035d8748a9c | %CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, v] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how many pixels padding to have. Default: 0.
%
%OUT:
% B - JxKxCxN cropped stack of images.
% ... |
github | vitoruapt/lartkv5-master | isolate_axes.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/isolate_axes.m | 3,668 | utf_8 | e2dce471e433886fcb87f9dcb284a2cb | %ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
% specified must all be in the same f... |
github | vitoruapt/lartkv5-master | im2gif.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/im2gif.m | 6,048 | utf_8 | 5a7437140f8d013158a195de1e372737 | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function converts a mu... |
github | vitoruapt/lartkv5-master | read_write_entire_textfile.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/read_write_entire_textfile.m | 924 | utf_8 | 779e56972f5d9778c40dee98ddbd677e | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fname - Pathn... |
github | vitoruapt/lartkv5-master | pdf2eps.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/pdf2eps.m | 1,471 | utf_8 | a1f41f0c7713c73886a2323e53ed982b | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.com/xpdf ... |
github | vitoruapt/lartkv5-master | print2array.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/print2array.m | 6,273 | utf_8 | c2feb752d8836426a74edd9357f1ff17 | %PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% [A bcol] = print2array(...)
%
% This function outputs a bitmap image of the given figure, at t... |
github | vitoruapt/lartkv5-master | append_pdfs.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/append_pdfs.m | 2,010 | utf_8 | 1034abde9642693c404671ff1c693a22 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the output fi... |
github | vitoruapt/lartkv5-master | using_hg2.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/using_hg2.m | 365 | utf_8 | 6a7f56042fda1873d8225eb3ec1cc197 | %USING_HG2 Determine if the HG2 graphics pipeline is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics pipeline is being used
% (true) or not (false).
function tf = using_hg2(fig)
try
tf = ~graphicsversion(fig, 'han... |
github | vitoruapt/lartkv5-master | eps2pdf.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/eps2pdf.m | 5,009 | utf_8 | 5658b3d96232e138be7fd49693d88453 | %EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, dest, crop, append, gray, quality)
%
% This function converts an eps file to pdf f... |
github | vitoruapt/lartkv5-master | copyfig.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/copyfig.m | 812 | utf_8 | b6b1fa9a9351df33ae0d42056c3df40a | %COPYFIG Create a copy of a figure, without changing the figure
%
% Examples:
% fh_new = copyfig(fh_old)
%
% This function will create a copy of a figure, but not change the figure,
% as copyobj sometimes does, e.g. by changing legends.
%
% IN:
% fh_old - The handle of the figure to be copied. Default: gcf.
%
% OU... |
github | vitoruapt/lartkv5-master | user_string.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/user_string.m | 2,460 | utf_8 | e8aa836a5140410546fceccb4cca47aa | %USER_STRING Get/set a user specific string
%
% Examples:
% string = user_string(string_name)
% saved = user_string(string_name, new_string)
%
% Function to get and set a string in a system or user specific file. This
% enables, for example, system specific paths to binaries to be saved.
%
% IN:
% string_name - ... |
github | vitoruapt/lartkv5-master | export_fig.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/export_fig.m | 29,720 | utf_8 | 923dcc1ad89f1381ee70abbf422b20a5 | %EXPORT_FIG Exports figures suitable for publication
%
% Examples:
% im = export_fig
% [im alpha] = export_fig
% export_fig filename
% export_fig filename -format1 -format2
% export_fig ... -nocrop
% export_fig ... -transparent
% export_fig ... -native
% export_fig ... -m<val>
% export_fig ... -r<val... |
github | vitoruapt/lartkv5-master | ghostscript.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/ghostscript.m | 5,009 | utf_8 | e93de4034ac6e4ac154729dc2c12f725 | %GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path
% is stored for future reference.
%
% Once ... |
github | vitoruapt/lartkv5-master | fix_lines.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/export_fig/fix_lines.m | 5,759 | utf_8 | 3338572f35c4669b79cc3265892d35de | %FIX_LINES Improves the line style of eps files generated by print
%
% Examples:
% fix_lines fname
% fix_lines fname fname2
% fstrm_out = fixlines(fstrm_in)
%
% This function improves the style of lines in eps files generated by
% MATLAB's print function, making them more similar to those seen on
% screen. Grid ... |
github | vitoruapt/lartkv5-master | unbreakxaxis.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/breakxaxis/unbreakxaxis.m | 299 | utf_8 | 27245623b049d3a3f78c2c622c82aeae |
function unbreakxaxis(breakInfo)
delete(breakInfo.leftAxes);
delete(breakInfo.rightAxes);
delete(breakInfo.breakAxes);
delete(breakInfo.annotationAxes);
for i = 1:numel(breakInfo.invisibleObjects)
set(breakInfo.invisibleObjects(i),'Visible','on');
end
end |
github | vitoruapt/lartkv5-master | breakxaxis.m | .m | lartkv5-master/src/perception/pedestrians/multimodal_pedestrian_detection/matlab/breakxaxis/breakxaxis.m | 11,663 | utf_8 | 86d70f4a907a88c29fa10ebfec1b904b |
% breakxaxes splits data in an axes so that data is in a left and right pane.
%
% breakXAxes(splitXLim) splitXLim is a 2 element vector containing a range
% of x values from splitXLim(1) to splitXLim(2) to remove from the axes.
% They must be within the current xLimis of the axes.
%
% breakXAxes(splitXLim,spli... |
github | vitoruapt/lartkv5-master | ccvGetLaneDetectionStats.m | .m | lartkv5-master/src/perception/road/caltech_lanes/matlab/ccvGetLaneDetectionStats.m | 5,767 | utf_8 | 3b1a1bbdb2c02cde9f338e584d4523e4 | function ccvGetLaneDetectionStats(detectionFiles, truthFiles)
% CCVGETLANEDETECTIONSTATS computes stats for the results compared to the
% ground truth
%
% INPUTS
% ------
% detectionFiles - a cell array of the detection files
% truthFiles - a cell array of the corresponding ground truth files
%
% OUTPUTS
% -------
%
... |
github | vitoruapt/lartkv5-master | ccvLabel.m | .m | lartkv5-master/src/perception/road/caltech_lanes/matlab/ccvLabel.m | 9,282 | utf_8 | a4cfee3bd06cea44bcb2ba59e53582b8 | function varargout = ccvLabel(f, varargin)
% CCVLABEL performs different tasks on the label structure, like creating
% new structure, adding frames, labels, ...etc.
%
% INPUTS
% ------
% f - the input function to perform
% varargin - the rest of the inputs (potentially zero)
%
% OUTPUTS
% -------
% varargout... |
github | VIP-Group/DBP-master | DBP_detector_sim.m | .m | DBP-master/uplink/DBP_detector_sim.m | 13,321 | utf_8 | 15048a8d5848729bdbfbb43738f311f9 | % =========================================================================
% Decentralized UPLINK simulator for the paper
% "Decentralized Baseband Processing for Massive MU-MIMO Systems"
% -------------------------------------------------------------------------
% Revision history:
%
% - aug-13-2017 v0.1 cs: s... |
github | VIP-Group/DBP-master | DBP_precoder_sim.m | .m | DBP-master/downlink/DBP_precoder_sim.m | 10,550 | utf_8 | 1ee2ead02cd6045fb55ef5a1f4b75d1b | % =========================================================================
% Decentralized DOWNLINK simulator for the paper
% "Decentralized Baseband Processing for Massive MU-MIMO Systems"
% -------------------------------------------------------------------------
% Revision history:
%
% - aug-13-2017 v0.1 cs: s... |
github | athakapo/Continuously-Informed-Heuristic-A---Optimal-path-retrieval-inside-an-unknown-environment-master | Continuously_Informed_Astar.m | .m | Continuously-Informed-Heuristic-A---Optimal-path-retrieval-inside-an-unknown-environment-master/Continuously_Informed_Astar.m | 26,747 | utf_8 | 5d783088f424b821356b527dd8560e48 | function varargout = Continuously_Informed_Astar(varargin)
% CONTINUOUSLY_INFORMED_ASTAR MATLAB code for Continuously_Informed_Astar.fig
% CONTINUOUSLY_INFORMED_ASTAR, by itself, creates a new CONTINUOUSLY_INFORMED_ASTAR or raises the existing
% singleton*.
%
% H = CONTINUOUSLY_INFORMED_ASTAR returns the... |
github | athakapo/Continuously-Informed-Heuristic-A---Optimal-path-retrieval-inside-an-unknown-environment-master | v2struct.m | .m | Continuously-Informed-Heuristic-A---Optimal-path-retrieval-inside-an-unknown-environment-master/matlabFunctions/v2struct.m | 15,949 | utf_8 | 20912a1f0ff4635fa430ce427d925be3 | %% v2struct
% v2struct Pack/Unpack Variables to/from a scalar structure.
function varargout = v2struct(varargin)
%% Description
% v2struct has dual functionality in packing & unpacking variables into structures and
% vice versa, according to the syntax and inputs.
%
% Function features:
% * Pack variable... |
github | robical/BlindSourceSeparation-master | wavexread.m | .m | BlindSourceSeparation-master/wavexread.m | 20,102 | utf_8 | f35f68e29ec4b1545c1597f333b27469 | function [y,Fs,nbits,speakers] = wavexread(file,ext)
%WAVEXREAD Read Microsoft WAVE-FORMAT-EXTENSIBLE (".wav") sound file.
% Y=WAVEXREAD(FILE) reads a WAVE file specified by the string FILE,
% returning the sampled data in Y. The ".wav" extension is appended
% if no extension is given. Amplitude values are in th... |
github | robical/BlindSourceSeparation-master | wavexwrite.m | .m | BlindSourceSeparation-master/wavexwrite.m | 8,855 | utf_8 | 5e65dd381efc54d3f9865c9c79100c35 | function wavexwrite(y,Fs,nbits,wavefile,speakers)
%WAVEXWRITE Write WAVE_FORMAT_EXTENSIBLE sound file.
% WAVEXWRITE(Y,FS,NBITS,WAVEFILE, SPEAKERS) writes data Y to a WAVEX
% file specified by the file name WAVEFILE, with a sample rate
% of FS Hz and with NBITS number of bits. NBITS must be 8, 16,
% 24, or 32. ... |
github | robical/BlindSourceSeparation-master | OLAfft.m | .m | BlindSourceSeparation-master/OLAfft.m | 1,047 | utf_8 | 86f0ef14501a48d7746ebec4b9055b70 | %&Implementazione STFT
%
% av=percentuale di avanzamento espressa in forma decimale es. 50%=0.5
% win=tipo di finestra
function [fourier1]=OLAfft(signal1,win,av)
durata=length(signal1); %durata segnale
pas=length(win); %durata finestra
splice1=zeros(pas,(fix(durata/(pas*av)))+1);
part1=zero... |
github | robical/BlindSourceSeparation-master | wavexread.m | .m | BlindSourceSeparation-master/img/Progetto/wavexread.m | 20,102 | utf_8 | f35f68e29ec4b1545c1597f333b27469 | function [y,Fs,nbits,speakers] = wavexread(file,ext)
%WAVEXREAD Read Microsoft WAVE-FORMAT-EXTENSIBLE (".wav") sound file.
% Y=WAVEXREAD(FILE) reads a WAVE file specified by the string FILE,
% returning the sampled data in Y. The ".wav" extension is appended
% if no extension is given. Amplitude values are in th... |
github | robical/BlindSourceSeparation-master | STFT.m | .m | BlindSourceSeparation-master/img/Progetto/STFT.m | 560 | utf_8 | c29454de99da3167b1a34b11c3ac81a9 | %STFT
% CALCOLO DEL NUMERO DI SPLICE:
% numero_splice=1+((length(signal)-length(win))/(length(win)/2))
%
%
function [trasf]=STFT(signal,win)
M=length(win);
R=M/2; %hop size del 50% (M/2)
part(1:M,1)=signal(1:M,1);
splice(1:M,1)=part(1:M,1)'.*win';
cicli=fix((length(signal)-length(win))/(length(win)/2)); %2 o 4
... |
github | robical/BlindSourceSeparation-master | wavexwrite.m | .m | BlindSourceSeparation-master/img/Progetto/wavexwrite.m | 8,855 | utf_8 | 5e65dd381efc54d3f9865c9c79100c35 | function wavexwrite(y,Fs,nbits,wavefile,speakers)
%WAVEXWRITE Write WAVE_FORMAT_EXTENSIBLE sound file.
% WAVEXWRITE(Y,FS,NBITS,WAVEFILE, SPEAKERS) writes data Y to a WAVEX
% file specified by the file name WAVEFILE, with a sample rate
% of FS Hz and with NBITS number of bits. NBITS must be 8, 16,
% 24, or 32. ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.