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 | zhuhan1236/dhn-caffe-master | matcaffe_demo_vgg_mean_pix.m | .m | dhn-caffe-master/matlab/caffe/matcaffe_demo_vgg_mean_pix.m | 3,069 | utf_8 | 04b831d0f205ef0932c4f3cfa930d6f9 | function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
%
% Demo of the matlab wrapper based on the networks used for the "VGG" entry
% in the ILSVRC-2014 competition and described in the tech. report
% "Very Deep Convo... |
github | willamowius/openmcu-master | echo_diagnostic.m | .m | openmcu-master/libs/speex/libspeex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | themattinthehatt/rlvm-master | myProcessOptions.m | .m | rlvm-master/lib/minConf/myProcessOptions.m | 674 | utf_8 | b94d252a960faa95a3074129247619e6 | function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
... |
github | themattinthehatt/rlvm-master | minConf_PQN.m | .m | rlvm-master/lib/minConf/minConf_PQN.m | 8,246 | utf_8 | 982955326f59fecc4cf6993c3b7428aa | function [x,f,funEvals] = minConf_PQN(funObj,x,funProj,options)
% function [x,f] = minConf_PQN(funObj,funProj,x,options)
%
% Function for using a limited-memory projected quasi-Newton to solve problems of the form
% min funObj(x) s.t. x in C
%
% The projected quasi-Newton sub-problems are solved the spectral pr... |
github | themattinthehatt/rlvm-master | minConf_QNST.m | .m | rlvm-master/lib/minConf/minConf_QNST.m | 5,460 | utf_8 | d3af055fa412ac52b199c8238fc83783 | function [x,f,funEvals] = minConf_QNST(funObj1,funObj2,x,funProj,options)
nVars = length(x);
if nargin < 5
options = [];
end
[verbose,numDiff,optTol,progTol,maxIter,maxProject,suffDec,corrections,adjustStep,bbInit,...
BBSToptTol,BBSTprogTol,BBSTiters,BBSTtestOpt] = ...
myProcessOptions(...
... |
github | themattinthehatt/rlvm-master | WolfeLineSearch.m | .m | rlvm-master/lib/minFunc_2012/minFunc/WolfeLineSearch.m | 10,590 | utf_8 | f962bc5ae0a1e9f80202a9aaab106dab | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS_interp,LS_multi,maxLS,progTol,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function v... |
github | themattinthehatt/rlvm-master | minFunc_processInputOptions.m | .m | rlvm-master/lib/minFunc_2012/minFunc/minFunc_processInputOptions.m | 4,103 | utf_8 | 8822581c3541eabe5ce7c7927a57c9ab |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,optTol,progTol,method,...
corrections,c1,c2,LS_init,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,useNegCurv,precFunc... |
github | levyfan/bow-master | evalData.m | .m | bow-master/src/main/matlab/KISSME/toolbox/evalData.m | 4,143 | utf_8 | fa4260fdbaa73509795057250201aece | function [ds,rocPlot] = evalData(pairs, ds, params)
% EVALDATA Evaluate results and plot figures
%
% Input:
% pairs - [1xN] struct. N is the number of pairs. Fields: pairs.fold
% pairs.match, pairs.img1, pairs.img2.
% ds - [1xF] data struct. F is the number of folds.
% ds.method.dist is required to comp... |
github | levyfan/bow-master | LearnAlgoLMNN.m | .m | bow-master/src/main/matlab/KISSME/toolbox/learnAlgos/LearnAlgoLMNN.m | 2,829 | utf_8 | f833d30dfe0476ecab72fc14f7cacc8a | %LEARNALGOLMNN Wrapper class to the actual LMNN code
classdef LearnAlgoLMNN < LearnAlgo
properties
p %parameters
s %struct
available
fhanlde
end
properties (Constant)
type = 'lmnn'
end
methods
function obj = LearnAlgoLMNN(p)
if... |
github | levyfan/bow-master | icg_roc.m | .m | bow-master/src/main/matlab/KISSME/toolbox/helper/icg_roc.m | 1,425 | utf_8 | 11d04e9c4c3db15aa1c3b9b771eff30e | function [tpr,fpr,thresh] = icg_roc(tp,confs)
% ICG_ROC computes ROC measures (tpr,fpr)
%
% Input:
% tp - [m x n] matrix of zero-one labels. one row per class.
% confs - [m x n] matrix of classifier scores. one row per class.
%
% Output:
% tpr - true positive rate in interval [0,1], [m x n+1] matrix
% ... |
github | levyfan/bow-master | lmnn.m | .m | bow-master/src/main/matlab/KISSME/toolbox/lib/LMNN/lmnn.m | 15,400 | utf_8 | cb91112611f161bfe0a081b291878dea | function [L,Det]=lmnn(x,y,varargin);
%
% function [L,Det]=lmnn(maxiter,L,x,y,Kg,'Parameter1',Value1,'Parameter2',Value2,...);
%
% Input:
%
% x = input matrix (each column is an input vector)
% y = labels
% (*optional*) L = initial transformation matrix (e.g eye(size(x,1)))
% (*optional*) Kg = attract Kg nearest simi... |
github | levyfan/bow-master | knnclassify.m | .m | bow-master/src/main/matlab/KISSME/toolbox/lib/LMNN/knnclassify.m | 2,559 | utf_8 | 02d7cf7e68cc0dc6f86bc8765e02e66b | function [Eval,Details]=LSevaluate(L,xTr,lTr,xTe,lTe,KK);
% function [Eval,Details]=LSevaluate(L,xTr,yTr,xTe,yTe,Kg);
%
% INPUT:
% L : transformation matrix (learned by LMNN)
% xTr : training vectors (each column is an instance)
% yTr : training labels (row vector!!)
% xTe : test vectors
% yTe : test ... |
github | levyfan/bow-master | energyclassify.m | .m | bow-master/src/main/matlab/KISSME/toolbox/lib/LMNN/energyclassify.m | 2,926 | utf_8 | 9155befdcfcd16052c23bbab1cf7b530 | function [err,yy,Value]=energyclassify(L,x,y,xTest,yTest,Kg,varargin);
% function [err,yy,Value]=energyclassify(L,xTr,yTr,xTe,yTe,Kg,varargin);
%
% INPUT:
% L : transformation matrix (learned by LMNN)
% xTr : training vectors (each column is an instance)
% yTr : training labels (row vector!!)
% xTe : test... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | GRACE_Matlab_Toolbox.m | .m | GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox.m | 5,933 | utf_8 | 852fbc11927286fc4ac8b0d61dcc2624 | function varargout = GRACE_Matlab_Toolbox(varargin)
% GRACE_MATLAB_TOOLBOX MATLAB code for GRACE_Matlab_Toolbox.fig
% GRACE_MATLAB_TOOLBOX, by itself, creates a new GRACE_MATLAB_TOOLBOX or raises the existing
% singleton*.
%
% H = GRACE_MATLAB_TOOLBOX returns the handle to a new GRACE_MATLAB_TOOLBOX or t... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | GRACE_Matlab_Toolbox_preprocessing.m | .m | GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_preprocessing.m | 29,614 | utf_8 | 7ba0deb4a2a5efbfd17e13c6705230a4 | function varargout = GRACE_Matlab_Toolbox_preprocessing(varargin)
% GRACE_MATLAB_TOOLBOX_PREPROCESSING MATLAB code for GRACE_Matlab_Toolbox_preprocessing.fig
% GRACE_MATLAB_TOOLBOX_PREPROCESSING, by itself, creates a new GRACE_MATLAB_TOOLBOX_PREPROCESSING or raises the existing
% singleton*.
%
% H = GRAC... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | GRACE_Matlab_Toolbox_HarmonicAnalysis.m | .m | GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_HarmonicAnalysis.m | 22,263 | utf_8 | 160d792627938e5f0f3ba354c01c48a6 | function varargout = GRACE_Matlab_Toolbox_HarmonicAnalysis(varargin)
% GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS MATLAB code for GRACE_Matlab_Toolbox_HarmonicAnalysis.fig
% GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS, by itself, creates a new GRACE_MATLAB_TOOLBOX_HARMONICANALYSIS or raises the existing
% singleton*.
%
... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | GRACE_Matlab_Toolbox_SHGrid.m | .m | GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_SHGrid.m | 10,513 | utf_8 | 034ca5e2ececb7735df50033a84e4717 | function varargout = GRACE_Matlab_Toolbox_SHGrid(varargin)
% GRACE_MATLAB_TOOLBOX_SHGRID MATLAB code for GRACE_Matlab_Toolbox_SHGrid.fig
% GRACE_MATLAB_TOOLBOX_SHGRID, by itself, creates a new GRACE_MATLAB_TOOLBOX_SHGRID or raises the existing
% singleton*.
%
% H = GRACE_MATLAB_TOOLBOX_SHGRID returns the... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | GRACE_Matlab_Toolbox_LeakageReductionSpatial.m | .m | GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_LeakageReductionSpatial.m | 12,935 | utf_8 | 1347aa0cb7a59cc3b4ee3bce77b0377e | function varargout = GRACE_Matlab_Toolbox_LeakageReductionSpatial(varargin)
% GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL MATLAB code for GRACE_Matlab_Toolbox_LeakageReductionSpatial.fig
% GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL, by itself, creates a new GRACE_MATLAB_TOOLBOX_LEAKAGEREDUCTIONSPATIAL or raises... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | GRACE_Matlab_Toolbox_Grid2Series.m | .m | GRACE_Matlab_Toolbox-master/GRACE_Matlab_Toolbox_Grid2Series.m | 10,462 | utf_8 | 2af44185348673e29b28f1c5effed356 | function varargout = GRACE_Matlab_Toolbox_Grid2Series(varargin)
% GRACE_MATLAB_TOOLBOX_GRID2SERIES MATLAB code for GRACE_Matlab_Toolbox_Grid2Series.fig
% GRACE_MATLAB_TOOLBOX_GRID2SERIES, by itself, creates a new GRACE_MATLAB_TOOLBOX_GRID2SERIES or raises the existing
% singleton*.
%
% H = GRACE_MATLAB_T... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | gmt_destriping_ddk.m | .m | GRACE_Matlab_Toolbox-master/GRACE_functions/gmt_destriping_ddk.m | 3,472 | utf_8 | 631d74e74801849ae929bbec74c6b556 |
function dataDDK=gmt_destriping_ddk(number,data)
% DDK filtering
%
% INPUT:
% number the type of DDK filter
% data spherical harmonic coefficients before filtering
%
% OUTPUT:
% grid_filter equi-angular grid N*2N, N=180 or 720
%
% DDK1d11: filtered with inverse signal... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | xyz2plm.m | .m | GRACE_Matlab_Toolbox-master/GRACE_functions/simons/xyz2plm.m | 9,366 | utf_8 | 04d6208d0c886556d07c21d6c75a52f5 | function [lmcosi,dw]=xyz2plm(fthph,L,method,lat,lon,cnd)
% [lmcosi,dw]=XYZ2PLM(fthph,L,method,lat,lon,cnd)
%
% Forward real spherical harmonic transform in the 4pi normalized basis.
%
% Converts a spatially gridded field into spherical harmonics.
% For complete and regular spatial samplings [0 360 -90 90].
% If regular... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | testddk.m | .m | GRACE_Matlab_Toolbox-master/GRACE_functions/ddk_filtercoef/testddk.m | 2,100 | utf_8 | b83bf6d30660ea515968e1e59b24a607 |
function nouv=ddk(number,data);
% ddk filtering
switch number
case 1 %strongest
file='Wbd_2-120.a_1d14p_4';
case 2
file='Wbd_2-120.a_1d13p_4';
case 3
file='Wbd_2-120.a_1d12p_4';
case 4
file='Wbd_2-120.a_5d11p_4';
case 5
file='Wbd_2-120.a_1d11p_... |
github | fengweiigg/GRACE_Matlab_Toolbox-master | read_BIN.m | .m | GRACE_Matlab_Toolbox-master/GRACE_functions/ddk_filtercoef/read_BIN.m | 4,694 | utf_8 | 099496edd812b75154803c4cb6023ab2 | %function which reads in a binary file containing symmetric/full or block
%diagonal matrices and associated vectors and parameters
%Roelof Rietbroek, 7-1-2008
%updated: 29-07-2008
%
%usage: dat=read_BIN(file)
%returns a structure array 'dat' with the file content
% the matrix remains in packed form (dat.pack1 field)
%o... |
github | zhimingluo/MovingObjectSegmentation-master | training.m | .m | MovingObjectSegmentation-master/SBMI/Cascade/training.m | 3,369 | utf_8 | 13d359faf4fd100e37021dd78cacb7d2 | function training(video)
previousMethod = 'MSCNN'; % BasicCNN or MSCNN
opts.expDir = [previousMethod 'net/', video];
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = true ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir;
% ---------... |
github | zhimingluo/MovingObjectSegmentation-master | cnn_train_adagrad.m | .m | MovingObjectSegmentation-master/SBMI/Cascade/cnn_train_adagrad.m | 11,673 | utf_8 | 313227873e4c91a0c6db387ba794ec99 | function [net, info] = cnn_train_adagrad(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a sui... |
github | zhimingluo/MovingObjectSegmentation-master | cnn_train_adagrad_ms.m | .m | MovingObjectSegmentation-master/SBMI/MSCNN/cnn_train_adagrad_ms.m | 13,645 | utf_8 | f04bf6fcc2468adcfb032edb55192a75 | function [net, info] = cnn_train_adagrad_ms(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suit... |
github | zhimingluo/MovingObjectSegmentation-master | regular_training_ms.m | .m | MovingObjectSegmentation-master/SBMI/MSCNN/regular_training_ms.m | 2,734 | utf_8 | ac270ae6d004596e4f3555eef3262c42 | function regular_training_ms(video)
opts.dataDir = 'net/traffic_Simple' ;
opts.expDir = ['net/' video] ;
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = false ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir ;
scales = [1, 0.75, 0.5];
%opt... |
github | zhimingluo/MovingObjectSegmentation-master | regular_training.m | .m | MovingObjectSegmentation-master/SBMI/BasicCNN/regular_training.m | 2,730 | utf_8 | a9196ae9e46f45d17b86ba05cff18112 | function regular_training(video)
opts.expDir = ['net/' video] ;
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = false ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir ;
%opts = vl_argparse(opts, varargin) ;
% --------------------------------... |
github | zhimingluo/MovingObjectSegmentation-master | cnn_train_adagrad.m | .m | MovingObjectSegmentation-master/SBMI/BasicCNN/cnn_train_adagrad.m | 11,359 | utf_8 | b97433eec24865c7f9bc705af9dcbb58 | function [net, info] = cnn_train_adagrad(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitabl... |
github | zhimingluo/MovingObjectSegmentation-master | training.m | .m | MovingObjectSegmentation-master/CDNet/Cascade/training.m | 3,369 | utf_8 | 55bd6c6fc17bd619f84337f5ab575009 | function training(video, method, frames)
opts.expDir = ['net/' method '/' num2str(frames) '/' video];
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = true ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir;
% -------------------------... |
github | zhimingluo/MovingObjectSegmentation-master | cnn_train_adagrad.m | .m | MovingObjectSegmentation-master/CDNet/Cascade/cnn_train_adagrad.m | 11,673 | utf_8 | 313227873e4c91a0c6db387ba794ec99 | function [net, info] = cnn_train_adagrad(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a sui... |
github | zhimingluo/MovingObjectSegmentation-master | ms_regular_training.m | .m | MovingObjectSegmentation-master/CDNet/MSCNN/ms_regular_training.m | 2,806 | utf_8 | 84fba4417e546a61491110751487c70a | function ms_regular_training(video, method, frames)
opts.expDir = ['net/' method '/' num2str(frames) '/' video] ;
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = false ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir ;
scales = [1, 0.75, 0.5... |
github | zhimingluo/MovingObjectSegmentation-master | cnn_train_adagrad_ms.m | .m | MovingObjectSegmentation-master/CDNet/MSCNN/cnn_train_adagrad_ms.m | 13,645 | utf_8 | f04bf6fcc2468adcfb032edb55192a75 | function [net, info] = cnn_train_adagrad_ms(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suit... |
github | zhimingluo/MovingObjectSegmentation-master | regular_training.m | .m | MovingObjectSegmentation-master/CDNet/BasicCNN/regular_training.m | 2,883 | utf_8 | 1cd69f4eb86fc4053c598bf51bbc5147 | function regular_training(video, method, frames)
opts.expDir = ['net/' method '/' num2str(frames) '/' video] ;
opts.train.batchSize = 5 ;
opts.train.numEpochs = 20;
opts.train.continue = false ;
opts.train.useGpu = true ;
opts.train.learningRate = 1e-3;
opts.train.expDir = opts.expDir ;
% -----------------------... |
github | zhimingluo/MovingObjectSegmentation-master | cnn_train_adagrad.m | .m | MovingObjectSegmentation-master/CDNet/BasicCNN/cnn_train_adagrad.m | 11,359 | utf_8 | b97433eec24865c7f9bc705af9dcbb58 | function [net, info] = cnn_train_adagrad(net, imdb, getBatch, varargin)
% CNN_TRAIN Demonstrates training a CNN
% CNN_TRAIN() is an example learner implementing stochastic gradient
% descent with momentum to train a CNN for image classification.
% It can be used with different datasets by providing a suitabl... |
github | gramuah/pose-errors-master | writeNumObjClass.m | .m | pose-errors-master/src/writeNumObjClass.m | 303 | utf_8 | a8dcea39cdb9809fe77f44ecbf61b417 | function writeNumObjClass(outdir, objects)
if ~exist(outdir, 'file'), mkdir(outdir); end;
global fid
fid = fopen(fullfile(outdir, ['classes.tex']), 'w');
for obj=1:length(objects)
pr('\\input{%s}\n', objects{obj});
end
fclose(fid);
function pr(varargin)
global fid;
fprintf(fid, varargin{:}); |
github | gramuah/pose-errors-master | writeTexObject.m | .m | pose-errors-master/src/writeTexObject.m | 17,543 | utf_8 | 03d088c85af1a2b7ee209ac56eaf02e6 | function writeTexObject(name, outdir, gt, metric_type, dataset, detector)
% writeTexObject(name, outdir, gt)
%
% Adds latex code to an existing file for one object:
switch metric_type
case 1
metric = 'AOS';
case 2
metric = 'AVP';
case 3
metric = 'PEAP';
case 4
metric = 'M... |
github | gramuah/pose-errors-master | matchDetectionsWithGroundTruth.m | .m | pose-errors-master/src/matchDetectionsWithGroundTruth.m | 5,491 | utf_8 | cd0e182e842c1e3357082c6701324245 | function [det, gt] = matchDetectionsWithGroundTruth(dataset, dataset_params, objname, ann, det, localization)
% [det, gt] = matchDetectionsWithGroundTruth(dataset, dataset_params, objname, ann, det, localization)
%
% Determines which detections are correct based on dataset and localization
% criteria. See matchDetecti... |
github | gramuah/pose-errors-master | analyzeDetections.m | .m | pose-errors-master/src/analyzeDetections.m | 19,712 | utf_8 | 659b017995dcdb25acaf67df4197f3aa | function result = analyzeDetections(dataset, dataset_params, objname, det, ann, localization)
% result = analyzeDetections(dataset, dataset_params, objname, det, ann, localization)
%
% Input:
% dataset: name of the dataset (e.g., PASCAL3D+)
% dataset_params: parameters of the dataset
% objname: name of the object... |
github | gramuah/pose-errors-master | analyzePoseError.m | .m | pose-errors-master/src/analyzePoseError.m | 7,939 | utf_8 | ff08935c01bb85fd65958948f3efd616 | function [result, resulclass] = analyzePoseError(dataset, dataset_params, ann, objind, det, localization)
% result = analyzePoseError(dataset, dataset_params, ann, objind, similar_ind, det)
% Pose Error Analysis.
switch dataset
case {'PASCAL3D+'}
[result, resulclass] = analyzePoseError_PASCAL3D(dataset, ..... |
github | gramuah/pose-errors-master | writeTexHeader.m | .m | pose-errors-master/src/writeTexHeader.m | 2,580 | utf_8 | dcb5ce28f51374b9f67f7518477d7740 | function writeTexHeader(outdir, detname)
ch = sprintf('%c', '%');
if ~exist(outdir, 'file'), mkdir(outdir); end;
global fid
fid = fopen(fullfile(outdir, ['header.tex']), 'w');
pr('\\section{Information}\n');
pr('\\label{info}')
pr('The \\textbf{%s} detector is analyzed. This is an automatically generated report.\n\n'... |
github | gramuah/pose-errors-master | writeTableResults.m | .m | pose-errors-master/src/writeTableResults.m | 15,577 | utf_8 | 0e5f03d0af7f21ac22d7f5a429be3e10 | function writeTableResults(outdir, detector, res, avp_matrix, peap_matrix, dataset, objects, metric_type)
switch metric_type
case 1
metric = 'AOS';
case 2
metric = 'AVP';
case 3
metric = 'PEAP';
case 4
metric = 'MAE';
case 5
metric = 'MedError';
end
if ~exis... |
github | gramuah/pose-errors-master | displayPerCharacteristicPosePlots.m | .m | pose-errors-master/src/displayPerCharacteristicPosePlots.m | 35,305 | utf_8 | 6f6e849114b00ea0f0b75027b316bc43 | function [resultclass, f] = displayPerCharacteristicPosePlots(resultfp, result, detector, error_type)
%function [resutclass,f] = displayPerCharacteristicDetPlots(results_all, error_type)
%
% Object characteristic effect on pose estimation: save and display plots
%
% Inputs:
% result: detection results
% resultfp: pose... |
github | gramuah/pose-errors-master | averagePoseDetectionPrecision.m | .m | pose-errors-master/src/averagePoseDetectionPrecision.m | 11,758 | utf_8 | c860088de2a9ab843d34b5c7d4ea649d | function [result, resultclass] = averagePoseDetectionPrecision(det, gt, npos, diff_flag)
% result = averagePoseDetectionPrecision(det, gt, npos, diff_flag)
%
% Computes full interpolated average precision
% Normally, p = tp ./ (fp + tp)
%
% Input:
% det(ndet, 1): detections
% gt: ground truth annotations
% npos: ... |
github | gramuah/pose-errors-master | displayPerCharacteristicDetPlots.m | .m | pose-errors-master/src/displayPerCharacteristicDetPlots.m | 15,123 | utf_8 | 8b2b7edcad03a9fefdfea69282b0546b | function [resutclass,f] = displayPerCharacteristicDetPlots(results_all, error_type)
%function [resutclass,f] = displayPerCharacteristicDetPlots(results_all, error_type)
%
% Object characteristic effect on detection: save and display plots
%
% Inputs:
% results_all: detection results
% error_type: metric to analysis
... |
github | gramuah/pose-errors-master | VOCevalseg.m | .m | pose-errors-master/src/VOCcode/VOCevalseg.m | 3,330 | utf_8 | df4ea45a026fbf0caff5d9fb135af1a2 | %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 | gramuah/pose-errors-master | VOClabelcolormap.m | .m | pose-errors-master/src/VOCcode/VOClabelcolormap.m | 691 | utf_8 | 0bfcd3122e62038f83e2d64f456d556b | % VOCLABELCOLORMAP Creates a label color map such that adjacent indices have different
% colors. Useful for reading and writing index images which contain large indices,
% by encoding them as RGB images.
%
% CMAP = VOCLABELCOLORMAP(N) creates a label color map with N entries.
function cmap = labelcolormap(N)
i... |
github | gramuah/pose-errors-master | VOCwritexml.m | .m | pose-errors-master/src/VOCcode/VOCwritexml.m | 1,166 | utf_8 | 5eee01a8259554f83bf00cf9cf2992a2 | function VOCwritexml(rec, path)
fid=fopen(path,'w');
writexml(fid,rec,0);
fclose(fid);
function xml = writexml(fid,rec,depth)
fn=fieldnames(rec);
for i=1:length(fn)
f=rec.(fn{i});
if ~isempty(f)
if isstruct(f)
for j=1:length(f)
fprintf(fid,'%s',re... |
github | gramuah/pose-errors-master | VOCreadrecxml.m | .m | pose-errors-master/src/VOCcode/VOCreadrecxml.m | 1,914 | utf_8 | 174191a85122cb6b823846389450728e | function rec = VOCreadrecxml(path)
x=VOCreadxml(path);
x=x.annotation;
rec=rmfield(x,'object');
rec.size.width=str2double(rec.size.width);
rec.size.height=str2double(rec.size.height);
rec.size.depth=str2double(rec.size.depth);
rec.segmented=strcmp(rec.segmented,'1');
rec.imgname=[x.folder '/JPEGImages... |
github | gramuah/pose-errors-master | VOCxml2struct.m | .m | pose-errors-master/src/VOCcode/VOCxml2struct.m | 1,920 | utf_8 | 6a873dba4b24c57e9f86a15ee12ea366 | function res = VOCxml2struct(xml)
xml(xml==9|xml==10|xml==13)=[];
[res,xml]=parse(xml,1,[]);
function [res,ind]=parse(xml,ind,parent)
res=[];
if ~isempty(parent)&&xml(ind)~='<'
i=findchar(xml,ind,'<');
res=trim(xml(ind:i-1));
ind=i;
[tag,ind]=gettag(xml,i);
if ~strcmp(tag,['/' pare... |
github | gramuah/pose-errors-master | PASreadrectxt.m | .m | pose-errors-master/src/VOCcode/PASreadrectxt.m | 3,179 | utf_8 | 3b0bdbeb488c8292a1744dace066bb73 | function record=PASreadrectxt(filename)
[fd,syserrmsg]=fopen(filename,'rt');
if (fd==-1),
PASmsg=sprintf('Could not open %s for reading',filename);
PASerrmsg(PASmsg,syserrmsg);
end;
matchstrs=initstrings;
record=PASemptyrecord;
notEOF=1;
while (notEOF),
line=fgetl(fd);
notEOF=ischar(li... |
github | gramuah/pose-errors-master | plotFigure10.m | .m | pose-errors-master/src/utils/plot_figures/plotFigure10.m | 6,834 | utf_8 | 029d97648c31c4be2ed6446dc55aebdc | function plotFigure10()
%% plot Figure 10 from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = { 'vdpm','vpskps', '3ddpm','bhf'};
objnames = {'aeroplane', 'bicycle', 'boat', 'bus', 'car', ...
'chair', 'diningtable', 'motorbike', 'sofa', 'train', 't... |
github | gramuah/pose-errors-master | plotFigure9c.m | .m | pose-errors-master/src/utils/plot_figures/plotFigure9c.m | 4,647 | utf_8 | 60ea095bf103f6922a56f6c8bb3956b2 | function plotFigure9c()
%% plot Figure 9(c) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm', 'vpskps','3ddpm', 'bhf'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}... |
github | gramuah/pose-errors-master | plotFigure4a.m | .m | pose-errors-master/src/utils/plot_figures/plotFigure4a.m | 4,099 | utf_8 | b50b1944f9dc8db0abb6e694983fce6f | function plotFigure4a()
%% plot Figure 4(a) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm-gt','vpskps-gt', 'bhf-gt'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}... |
github | gramuah/pose-errors-master | plotFigure9b.m | .m | pose-errors-master/src/utils/plot_figures/plotFigure9b.m | 4,716 | utf_8 | 83ef7d0ecc26b31b2a73badb85ef0142 | function plotFigure9b()
%% plot Figure 9(b) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm','vpskps', '3ddpm', 'bhf'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}... |
github | gramuah/pose-errors-master | plotFigure5.m | .m | pose-errors-master/src/utils/plot_figures/plotFigure5.m | 6,499 | utf_8 | ed30bc380ffcbf3d1de53920c67a86f5 | function plotFigure5()
%% plot Figure 5 from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = { 'vdpm-gt','vpskps-gt','bhf-gt'};
objnames = {'aeroplane', 'bicycle', 'boat', 'bus', 'car', ...
'chair', 'diningtable', 'motorbike', 'sofa', 'train', '... |
github | gramuah/pose-errors-master | plotFigure4c.m | .m | pose-errors-master/src/utils/plot_figures/plotFigure4c.m | 4,593 | utf_8 | 078fde124eb8a63a7dcbe0a1577947bf | function plotFigure4c()
%% plot Figure 4(c) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm-gt', 'vpskps-gt', 'bhf-gt'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj... |
github | gramuah/pose-errors-master | plotFigure4b.m | .m | pose-errors-master/src/utils/plot_figures/plotFigure4b.m | 4,566 | utf_8 | d189abcb9012e29ad8400c07fe4a1493 | function plotFigure4b()
%% plot Figure 4(b) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm-gt','vpskps-gt', 'bhf-gt'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj}... |
github | gramuah/pose-errors-master | plotFigure9a.m | .m | pose-errors-master/src/utils/plot_figures/plotFigure9a.m | 4,149 | utf_8 | c29f1498294dfcc7870ca22632824264 | function plotFigure9a()
%% plot Figure 9(a) from paper
f=0;
fs = 18;
resultDir = '/home/carolina/projects/pose-estimation/eccv2016/eval_code/results';
detectors = {'vdpm','vpskps','3ddpm', 'bhf'};
% Visible parts vs pose estimation
for obj = 1: length(detectors)
tmp(obj) = load ([resultDir, '/', detectors{obj},... |
github | kstrotz/URToolbox-master | installURToolbox.m | .m | URToolbox-master/installURToolbox.m | 10,069 | utf_8 | 7fad6ecb89fa082153fe4d8db039c4b5 | function installURToolbox(replaceExisting)
% INSTALLURTOOLBOX installs UR Toolbox for MATLAB.
% INSTALLURTOOLBOX installs UR Toolbox into the following
% locations:
% Source: Destination
% URToolboxFunctions: matlabroot\toolbox\optitrack
% URToolboxSupport: matlabroot\toolbox\optit... |
github | kstrotz/URToolbox-master | URToolboxUpdate.m | .m | URToolbox-master/URToolboxFunctions/URToolboxUpdate.m | 1,675 | utf_8 | f9676ebb610a475bb41b827d331033ad | function URToolboxUpdate
% URTOOLBOXUPDATE download and update the UR Toolbox.
%
% M. Kutzer 27Feb2016, USNA
% TODO - Find a location for "URToolbox Example SCRIPTS"
% TODO - update function for general operation
% Install UR Toolbox
ToolboxUpdate('UR');
end
function ToolboxUpdate(toolboxName)
%% Setup function... |
github | Mark-Kramer/Spike-Ripple-Detector-Method-master | spike_ripple_detector.m | .m | Spike-Ripple-Detector-Method-master/spike_ripple_detector.m | 12,942 | utf_8 | 8552c4cead1053e7c0b1f5b34f70d65e | % Spike-ripple detector.
% Developed by Catherine Chu, Arthur Chan, and Mark Kramer.
%
% INPUTS:
% data = the time series data, in this case EEG from one electrode.
% time = the time axis for the data, in units of seconds.
% ADVANCED INPUTS:
% varargin = 'PercentileEnvelope', value
% set 'value' between 0 and 1 to c... |
github | Mark-Kramer/Spike-Ripple-Detector-Method-master | spike_ripple_visualizer.m | .m | Spike-Ripple-Detector-Method-master/spike_ripple_visualizer.m | 5,754 | utf_8 | 9fddc4b8bfa03634727d213351a8345c | % Function to visualize and classify the candidate spike ripple events
% detected in spike_ripple_detector.m
%
% This function produces a figure showing the (1) the original data, (2)
% the filtered data, and (3) the spectrogram surrounding each candidate spike ripple
% event.
%
% For each candidate spike ripple event,... |
github | Mark-Kramer/Spike-Ripple-Detector-Method-master | findseq.m | .m | Spike-Ripple-Detector-Method-master/findseq.m | 5,921 | utf_8 | 21500cb2129c3e3e6c06539f219a9c9a | function varargout = findseq(A,dim)
% FINDSEQ Find sequences of repeated (adjacent/consecutive) numeric values
%
% FINDSEQ(A) Find sequences of repeated numeric values in A along the
% first non-singleton dimension. A should be numeric.
%
% FINDSEQ(...,DIM) Look for sequences along the dimensio... |
github | dschick/udkm1DsimML-master | bool2str.m | .m | udkm1DsimML-master/helpers/functions/bool2str.m | 185 | utf_8 | 7a576a35879a9481b26e8782d0918d2b | %% bool2str
% Returns the according string for a boolean input.
function str = bool2str(bool)
if bool
str = 'true';
else
str = 'false';
end%if
end%function
|
github | dschick/udkm1DsimML-master | dataHash.m | .m | udkm1DsimML-master/helpers/functions/dataHash.m | 15,297 | utf_8 | 8592e240500ffb55915cf0df7bffbd33 | function Hash = dataHash(Data, Opt)
%% DATAHASH - Checksum for Matlab array of any type
% This function creates a hash value for an input of any type. The type and
% dimensions of the input are considered as default, such that UINT8([0,0]) and
% UINT16(0) have different hash values. Nested STRUCTs and CELLs are parsed
... |
github | dschick/udkm1DsimML-master | mtimesx_test_ssspeed.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_ssspeed.m | 415,311 | utf_8 | c663b5bc66edbfec752f88862a1805d1 | % Test routine for mtimesx, op(single) * op(single) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ssspeed
% Filename: mtimesx_test_ssspeed.m
% Programmer: James Tursa
% ... |
github | dschick/udkm1DsimML-master | mtimesx_build.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_build.m | 16,405 | utf_8 | 838ce3d9c7bc33beb0d2f75546ead978 | % mtimesx_build compiles mtimesx.c with BLAS libraries
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_build
% Filename: mtimesx_build.m
% Programmer: James Tursa
% Version: 1.40
% Dat... |
github | dschick/udkm1DsimML-master | mtimesx_test_nd.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_nd.m | 14,364 | utf_8 | 0d3b436cea001bccb9c6cccdaa21b34d | % Test routine for mtimesx, multi-dimensional speed and equality to MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_nd
% Filename: mtimesx_test_nd.m
% Programmer: James Tursa
% ... |
github | dschick/udkm1DsimML-master | mtimesx_test_sdequal.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_sdequal.m | 350,821 | utf_8 | 7e6a367b3ad6154ce1e4da70a91ba4cf | % Test routine for mtimesx, op(single) * op(double) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_sdequal
% Filename: mtimesx_test_sdequal.m
% Programmer: James Tursa... |
github | dschick/udkm1DsimML-master | mtimesx_test_ddequal.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_ddequal.m | 94,229 | utf_8 | 219fa3623cf14a54da7d267a29e61151 | % Test routine for mtimesx, op(double) * op(double) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ddequal
% Filename: mtimesx_test_ddequal.m
% Programmer: James Tur... |
github | dschick/udkm1DsimML-master | mtimesx_test_dsequal.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_dsequal.m | 350,693 | utf_8 | 325490ae690791eb9f0e7d03408cc540 | % Test routine for mtimesx, op(double) * op(single) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_dsequal
% Filename: mtimesx_test_dsequal.m
% Programmer: James Tursa... |
github | dschick/udkm1DsimML-master | mtimesx_test_sdspeed.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_sdspeed.m | 388,309 | utf_8 | 1ed55a613d5cbfe9a11579562f600c9a | % Test routine for mtimesx, op(single) * op(double) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_sdspeed
% Filename: mtimesx_test_sdspeed.m
% Programmer: James Tursa
% ... |
github | dschick/udkm1DsimML-master | mtimesx_test_ddspeed.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_ddspeed.m | 121,611 | utf_8 | 32613fb321b2de56bd52cb4b4567187d | % Test routine for mtimesx, op(double) * op(double) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ddspeed
% Filename: mtimesx_test_ddspeed.m
% Programmer: James Tursa
... |
github | dschick/udkm1DsimML-master | mtimesx_sparse.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_sparse.m | 3,015 | utf_8 | eeb3eb2df4d70c69695b45188807e91c | % mtimesx_sparse does sparse matrix multiply of two inputs
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_sparse
% Filename: mtimesx_sparse.m
% Programmer: James Tursa
% Version: 1.00
... |
github | dschick/udkm1DsimML-master | mtimesx_test_dsspeed.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_dsspeed.m | 388,140 | utf_8 | 53e3e8d0e86784747c58c68664ae0d85 | % Test routine for mtimesx, op(double) * op(single) speed vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_dsspeed
% Filename: mtimesx_test_dsspeed.m
% Programmer: James Tursa
% ... |
github | dschick/udkm1DsimML-master | mtimesx_test_ssequal.m | .m | udkm1DsimML-master/helpers/functions/mtimesx/mtimesx_test_ssequal.m | 355,156 | utf_8 | 4c01cb508f7cf6adb1b848f98ee9ca41 | % Test routine for mtimesx, op(single) * op(single) equality vs MATLAB
%******************************************************************************
%
% MATLAB (R) is a trademark of The Mathworks (R) Corporation
%
% Function: mtimesx_test_ssequal
% Filename: mtimesx_test_ssequal.m
% Programmer: James Tursa... |
github | dschick/udkm1DsimML-master | xml_write.m | .m | udkm1DsimML-master/helpers/functions/xml_io_tools/xml_write.m | 18,325 | utf_8 | 24bd3dc683e5a0a0ad4080deaa6a93a5 | function DOMnode = xml_write(filename, tree, RootName, Pref)
%XML_WRITE Writes Matlab data structures to XML file
%
% DESCRIPTION
% xml_write( filename, tree) Converts Matlab data structure 'tree' containing
% cells, structs, numbers and strings to Document Object Model (DOM) node
% tree, then saves it to XML file 'fi... |
github | dschick/udkm1DsimML-master | xml_read.m | .m | udkm1DsimML-master/helpers/functions/xml_io_tools/xml_read.m | 23,864 | utf_8 | 5bba7e1f07a293d773aed5616ad3d5c9 | function [tree, RootName, DOMnode] = xml_read(xmlfile, Pref)
%XML_READ reads xml files and converts them into Matlab's struct tree.
%
% DESCRIPTION
% tree = xml_read(xmlfile) reads 'xmlfile' into data structure 'tree'
%
% tree = xml_read(xmlfile, Pref) reads 'xmlfile' into data structure 'tree'
% according to your pref... |
github | happynear/MTCNN_face_detection_alignment-master | test.m | .m | MTCNN_face_detection_alignment-master/code/codes/camera_demo/test.m | 8,824 | utf_8 | 484a3e8719f2102fd5aa6c841209b5ed | function varargout = test(varargin)
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @test_OpeningFcn, ...
'gui_OutputFcn', @test_OutputFcn, ...
'gui_LayoutFcn', [] ... |
github | UCL-SML/pilco-matlab-master | print_pdf.m | .m | pilco-matlab-master/doc/plots/print_pdf.m | 7,570 | utf_8 | 97a41030f45e2c5d10cc569c07e12a19 | %PRINT_PDF Prints cropped figures to pdf with fonts embedded
%
% Examples:
% print_pdf filename
% print_pdf(filename, fig_handle)
%
% This function saves a figure as a pdf nicely, without the need to specify
% multiple options. It improves on MATLAB's print command (using default
% options) in several ways:
% - ... |
github | UCL-SML/pilco-matlab-master | draw_pendubot.m | .m | pilco-matlab-master/scenarios/pendubot/draw_pendubot.m | 2,880 | utf_8 | d1b44901843520801be32a25b7efd3b2 | %% draw_pendubot.m
% *Summary:* Draw the Pendubot system with reward, applied torque,
% and predictive uncertainty of the tips of the pendulums
%
% function draw_pendubot(theta1, theta2, force, cost, text1, text2, M, S)
%
%
% *Input arguments:*
%
% theta1 angle of inner pendulum
% theta2 angle of outer ... |
github | UCL-SML/pilco-matlab-master | loss_pendubot.m | .m | pilco-matlab-master/scenarios/pendubot/loss_pendubot.m | 4,307 | utf_8 | acf6555f4c0bb7d9c47e31649d69239e | %% loss_pendubot.m
% *Summary:* Pendubot loss function; the loss is
% $1-\exp(-0.5*d^2*a)$, where $a>0$ and $d^2$ is the squared difference
% between the actual and desired position of the tip of the outer pendulum.
% The mean and the variance of the loss are computed by averaging over the
% Gaussian distribution of ... |
github | UCL-SML/pilco-matlab-master | dynamics_pendubot.m | .m | pilco-matlab-master/scenarios/pendubot/dynamics_pendubot.m | 2,465 | utf_8 | d04c0a8c0506c14bafcb992ee945135a | %% dynamics_pendubot.m
% *Summary:* Implements ths ODE for simulating the Pendubot
% dynamics, where an input torque f can be applied to the inner link
%
% function dz = dynamics_pendubot(t,z,f)
%
%
% *Input arguments:*
%
% t current time step (called from ODE solver)
% z state ... |
github | UCL-SML/pilco-matlab-master | getPlotDistr_pendubot.m | .m | pilco-matlab-master/scenarios/pendubot/getPlotDistr_pendubot.m | 2,792 | utf_8 | 90cd4837258b5d69ecce949b34088e6f | %% getPlotDistr_pendubot.m
% *Summary:* Compute means and covariances of the Cartesian coordinates of
% the tips both the inner and outer pendulum assuming that the joint state
% $x$ of the cart-double-pendulum system is Gaussian, i.e., $x\sim N(m, s)$
%
%
% function [M1, S1, M2, S2] = getPlotDistr_pendubot(m, s, e... |
github | UCL-SML/pilco-matlab-master | loss_dp.m | .m | pilco-matlab-master/scenarios/doublePendulum/loss_dp.m | 4,296 | utf_8 | 5aa6f225ab08ea5144106b58baf9624c | %% loss_dp.m
% *Summary:* Double-Pendulum loss function; the loss is
% $1-\exp(-0.5*d^2*a)$, where $a>0$ and $d^2$ is the squared difference
% between the actual and desired position of the tip of the outer pendulum.
% The mean and the variance of the loss are computed by averaging over the
% Gaussian distribution of... |
github | UCL-SML/pilco-matlab-master | dynamics_dp.m | .m | pilco-matlab-master/scenarios/doublePendulum/dynamics_dp.m | 2,575 | utf_8 | 67e3af4bfc5975be48d8c58ec68c0c33 | %% dynamics_dp.m
% *Summary:* Implements ths ODE for simulating the double pendulum
% dynamics, where an input torque can be applied to both links,
% f1:torque at inner joint, f2:torque at outer joint
%
% function dz = dynamics_dp(t, z, f1, f2)
%
%
% *Input arguments:*
%
% t current time step (called from ODE... |
github | UCL-SML/pilco-matlab-master | draw_dp.m | .m | pilco-matlab-master/scenarios/doublePendulum/draw_dp.m | 2,955 | utf_8 | a367561c45b347a90f121a366b70c407 | %% draw_dp.m
% *Summary:* Draw the double-pendulum system with reward, applied torques,
% and predictive uncertainty of the tips of the pendulums
%
% function draw_dp(theta1, theta2, f1, f2, cost, text1, text2, M, S)
%
% *Input arguments:*
%
% theta1 angle of inner pendulum
% theta2 angle of outer pendu... |
github | UCL-SML/pilco-matlab-master | getPlotDistr_dp.m | .m | pilco-matlab-master/scenarios/doublePendulum/getPlotDistr_dp.m | 2,772 | utf_8 | 9be4d2bdc8b4beb731e6776c5dd791a1 | %% getPlotDistr_dp.m
% *Summary:* Compute means and covariances of the Cartesian coordinates of
% the tips both the inner and outer pendulum assuming that the joint state
% $x$ of the cart-double-pendulum system is Gaussian, i.e., $x\sim N(m, s)$
%
%
% function [M1, S1, M2, S2] = getPlotDistr_dp(m, s, ell1, ell2)
%... |
github | UCL-SML/pilco-matlab-master | draw_cp.m | .m | pilco-matlab-master/scenarios/cartPole/draw_cp.m | 2,120 | utf_8 | 7d6605b0e25af85fb84cf253639ba531 | %% draw_cp.m
% *Summary:* Draw the cart-pole system with reward, applied force, and
% predictive uncertainty of the tip of the pendulum
%
% function draw_cp(x, theta, force, cost, text1, text2, M, S)
%
%
% *Input arguments:*
%
% x position of the cart
% theta angle of pendulum
% force force ... |
github | UCL-SML/pilco-matlab-master | loss_cp.m | .m | pilco-matlab-master/scenarios/cartPole/loss_cp.m | 4,079 | utf_8 | 0dfef958e239873ab8f870cbcc59b496 | %% loss_cp.m
% *Summary:* Cart-Pole loss function; the loss is
% $1-\exp(-0.5*d^2*a)$, where $a>0$ and $d^2$ is the squared difference
% between the actual and desired position of tip of the pendulum.
% The mean and the variance of the loss are computed by averaging over the
% Gaussian state distribution $p(x) = \m... |
github | UCL-SML/pilco-matlab-master | getPlotDistr_cp.m | .m | pilco-matlab-master/scenarios/cartPole/getPlotDistr_cp.m | 2,037 | utf_8 | bd4a0c9d5da54b0aa0d1a011ee47db5b | %% getPlotDistr_cp.m
% *Summary:* Compute means and covariances of the Cartesian coordinates of
% the tips both the inner and outer pendulum assuming that the joint state
% $x$ of the cart-double-pendulum system is Gaussian, i.e., $x\sim N(m, s)$
%
%
% function [M, S] = getPlotDistr_cp(m, s, ell)
%
%
%
% *Input arg... |
github | UCL-SML/pilco-matlab-master | dynamics_cp.m | .m | pilco-matlab-master/scenarios/cartPole/dynamics_cp.m | 1,720 | utf_8 | 3782addcb8146afff9473fcc8b22a948 | %% dynamics_cp.m
% *Summary:* Implements ths ODE for simulating the cart-pole dynamics.
%
% function dz = dynamics_cp(t, z, f)
%
%
% *Input arguments:*
%
% t current time step (called from ODE solver)
% z state [4 x 1]
% f (optional): force f(t)
%
%... |
github | UCL-SML/pilco-matlab-master | augment_unicycle.m | .m | pilco-matlab-master/scenarios/unicycle/augment_unicycle.m | 2,372 | utf_8 | 3d526959966588683c66fa49b3cf8b0b | %% augment_unicycle.m
% *Summary:* The function computes the $(x,y)$ velocities of the contact point
% in both absolute and unicycle coordinates as well as the the unicycle
% coordinates of the contact point themselves.
%
% function r = augment(s)
%
% *Input arguments:*
%
% s state of the unicycle (includi... |
github | UCL-SML/pilco-matlab-master | loss_unicycle.m | .m | pilco-matlab-master/scenarios/unicycle/loss_unicycle.m | 5,277 | utf_8 | b20d304ef5667ff9e596087b42191a58 | %% loss_unicycle.m
% Robotic unicycle loss function. The loss is $1-\exp(-0.5*a*d^2)$, where
% $a$ is a (positive) constant and $d^2$ is the squared difference between
% the current configuration of the unicycle and a target set point.
%
% The mean and the variance of the loss are computed by averaging over the
% Gauss... |
github | UCL-SML/pilco-matlab-master | draw_unicycle.m | .m | pilco-matlab-master/scenarios/unicycle/draw_unicycle.m | 5,222 | utf_8 | 4e5a8f9508c83cf1118de1536b5b45b0 | %% draw_unicycle.m
% *Summary:* Draw the unicycle with cost and applied torques
%
% function draw_unicycle(latent, plant,t2,cost,text1, text2)
%
%
% *Input arguments:*
%
% latent state of the unicycle (including the torques)
% plant plant structure
% .dt sampling time
% .dyno state indice... |
github | UCL-SML/pilco-matlab-master | dynamics_unicycle.m | .m | pilco-matlab-master/scenarios/unicycle/dynamics_unicycle.m | 10,284 | utf_8 | 60b3423e2e04ed55c08fbc8bb164bf6c | %% dynamics_unicycle.m
% *Summary:* Implements ths ODE for simulating the cart-pole dynamics.
%
% function dz = dz = dynamics_unicycle(t, z, V, U)
%
%
% *Input arguments:*
%
% t current time step (called from ODE solver)
% z state [12 x 1]
% V torq... |
github | UCL-SML/pilco-matlab-master | draw_cdp.m | .m | pilco-matlab-master/scenarios/cartDoublePendulum/draw_cdp.m | 3,088 | utf_8 | 04065885f673565eaffc1971cde2a74e | %% draw_cdp.m
% *Summary:* Draw the cart-double-pendulum system with reward, applied force,
% and predictive uncertainty of the tips of the pendulums
%
% function draw_cdp(x, theta2, theta3, force, cost, M, S, text1, text2)
%
%
% *Input arguments:*
%
% x position of the cart
% theta2 angle of inner ... |
github | UCL-SML/pilco-matlab-master | getPlotDistr_cdp.m | .m | pilco-matlab-master/scenarios/cartDoublePendulum/getPlotDistr_cdp.m | 2,860 | utf_8 | bd0fa24486bfa30d58e3ee07349684d0 | %% getPlotDistr_cdp.m
% *Summary:* Compute means and covariances of the Cartesian coordinates of
% the tips both the inner and outer pendulum assuming that the joint state
% $x$ of the cart-double-pendulum system is Gaussian, i.e., $x\sim N(m, s)$
%
%
% function [M1, S1, M2, S2] = getPlotDistr_cdp(m, s, ell1, ell2)... |
github | UCL-SML/pilco-matlab-master | loss_cdp.m | .m | pilco-matlab-master/scenarios/cartDoublePendulum/loss_cdp.m | 4,261 | utf_8 | 50a62e605756c9036c520f7886803a73 | %% loss_cdp.m
% *Summary:* Cart-Double-Pendulum loss function; the loss is
% $1-\exp(-0.5*d^2*a)$, where $a>0$ and $d^2$ is the squared difference
% between the actual and desired position of the end of the outer pendulum.
% The mean and the variance of the loss are computed by averaging over the
% Gaussian distribut... |
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