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 | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-MinimumEnergyProblem/ocp-interfaces/Matlab/CompileOctave.m | 3,061 | utf_8 | 76b4e3540cbcb385b8b8aa2fa59d2bc4 | %-----------------------------------------------------------------------%
% file: MinimumEnergyProblem_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-MinimumEnergyProblem/ocp-interfaces/Matlab/CompileMex.m | 2,355 | utf_8 | 5fd0daa298f4d5cd09f2c99ed0de1ece | %-----------------------------------------------------------------------%
% file: MinimumEnergyProblem_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-BangBangFtminP/ocp-interfaces/Matlab/CompileOctave.m | 3,055 | utf_8 | a3ff0a271450e4213217474c1a9f96d9 | %-----------------------------------------------------------------------%
% file: BangBangFtminP_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-BangBangFtminP/ocp-interfaces/Matlab/CompileMex.m | 2,349 | utf_8 | ed14538e0c0f24e0cf5ff5a4a6b3f000 | %-----------------------------------------------------------------------%
% file: BangBangFtminP_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-EconomicGrowthModel/ocp-interfaces/Matlab/CompileOctave.m | 3,060 | utf_8 | 9bde4e223e4a803338883c87b0b03f0b | %-----------------------------------------------------------------------%
% file: EconomicGrowthModel_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-EconomicGrowthModel/ocp-interfaces/Matlab/CompileMex.m | 2,354 | utf_8 | 33710ba93cc23b568ee24068482ff7d6 | %-----------------------------------------------------------------------%
% file: EconomicGrowthModel_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | SingularDae.m | .m | OCP-tests-master/test-SingularCalogeroModified/Altri solutori/gpops/SingularDae.m | 463 | utf_8 | c34f82d04e12c3f5f2a7a71fdef46785 | %-------------------------------------
% BEGIN: function SingularDae.m
%-------------------------------------
function dae = SingularDae(sol);
global CONSTANTS
t = sol.time;
x = sol.state(:,1);
% y = sol.state(:,2);
% v = sol.state(:,3);
u = sol.control;
xdot = u;
% ydot = -v.*cos(u);
% vdot = CONSTANTS.g*cos(u);
% d... |
github | pins-ocs/OCP-tests-master | SingularCost.m | .m | OCP-tests-master/test-SingularCalogeroModified/Altri solutori/gpops/SingularCost.m | 373 | utf_8 | d731397e0094b347dfb6f7bd1bc4cea5 | %--------------------------------------
% BEGIN: function SingularCost.m
%--------------------------------------
% function [Mayer,Lagrange]=brachistochroneCost(sol);
function [Mayer, Lagrange]=SingularCost(sol);
tf = sol.terminal.time;
t = sol.time;
x = sol.state(:,1);
u = sol.control;
Mayer = 0; %zeros(size(t));
... |
github | pins-ocs/OCP-tests-master | SingularControlContinuous.m | .m | OCP-tests-master/test-SingularCalogeroModified/Altri solutori/gpops2/SingularControlContinuous.m | 1,897 | utf_8 | a03c27e14c3f40ec0b3267a05d685de8 | %--------------------------------------------%
% BEGIN: function dynamicSoaringContinuous.m %
%--------------------------------------------%
function phaseout = SingularControl(input)
t = input.phase(1).time;
x = input.phase(1).state;
u = input.phase(1).control;
%p = inp... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-SingularCalogeroModified/ocp-interfaces/Matlab/CompileOctave.m | 3,065 | utf_8 | 2c6905cdfb5b4544fd5373cb94b2144d | %-----------------------------------------------------------------------%
% file: SingularCalogeroModified_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-SingularCalogeroModified/ocp-interfaces/Matlab/CompileMex.m | 2,359 | utf_8 | 167a4a7601754776608ee618fb24e837 | %-----------------------------------------------------------------------%
% file: SingularCalogeroModified_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-AliChan/ocp-interfaces/Matlab/CompileOctave.m | 3,048 | utf_8 | 257195e8c0f2038edcf5fcd41e7527e7 | %-----------------------------------------------------------------------%
% file: AliChan_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-AliChan/ocp-interfaces/Matlab/CompileMex.m | 2,342 | utf_8 | 94ee4cdafb92e6dc2c628eb894f29be9 | %-----------------------------------------------------------------------%
% file: AliChan_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-AlpRider/ocp-interfaces/Matlab/CompileOctave.m | 3,049 | utf_8 | 60a216255fdaa371c8a3e59439d02ac2 | %-----------------------------------------------------------------------%
% file: AlpRider_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-AlpRider/ocp-interfaces/Matlab/CompileMex.m | 2,343 | utf_8 | f799b092311a8f2ede4fef7e746f7090 | %-----------------------------------------------------------------------%
% file: AlpRider_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-BangBangFredundant/ocp-interfaces/Matlab/CompileOctave.m | 3,059 | utf_8 | d6c26226cd4f2354cafabd7ab232f1bf | %-----------------------------------------------------------------------%
% file: BangBangFredundant_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-BangBangFredundant/ocp-interfaces/Matlab/CompileMex.m | 2,353 | utf_8 | 72251bf75df009d03f393a07cdfa4b77 | %-----------------------------------------------------------------------%
% file: BangBangFredundant_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-SecondOrderSingularRegulator/ocp-interfaces/Matlab/CompileOctave.m | 3,069 | utf_8 | 524d15c35c70f5d447e4c5ea1e16382f | %-----------------------------------------------------------------------%
% file: SecondOrderSingularRegulator_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-SecondOrderSingularRegulator/ocp-interfaces/Matlab/CompileMex.m | 2,363 | utf_8 | 3065f7bae9b0874e28d33befa1f24103 | %-----------------------------------------------------------------------%
% file: SecondOrderSingularRegulator_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-Train/ocp-interfaces/Matlab/CompileOctave.m | 3,046 | utf_8 | e202c81ab7f7161ade7daaf35f009ca1 | %-----------------------------------------------------------------------%
% file: Train_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-Train/ocp-interfaces/Matlab/CompileMex.m | 2,340 | utf_8 | 173ef76a81b66b50eb3a70fbca96509d | %-----------------------------------------------------------------------%
% file: Train_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-GoddardRocket/ocp-interfaces/Matlab/CompileOctave.m | 3,054 | utf_8 | d3dfc7daf03e7aaaf7a99686866b1ad4 | %-----------------------------------------------------------------------%
% file: GoddardRocket_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-GoddardRocket/ocp-interfaces/Matlab/CompileMex.m | 2,348 | utf_8 | d80eda1c71008bd9f9b0ffbbb2690e58 | %-----------------------------------------------------------------------%
% file: GoddardRocket_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-SingularLuus04_FreeTime/ocp-interfaces/Matlab/CompileOctave.m | 3,064 | utf_8 | d72c75bf6ad75197b7c0a4f0f9a04cc8 | %-----------------------------------------------------------------------%
% file: SingularLuus04_FreeTime_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-SingularLuus04_FreeTime/ocp-interfaces/Matlab/CompileMex.m | 2,358 | utf_8 | ee52f123eddfc4efd4bd4ffc6623c29d | %-----------------------------------------------------------------------%
% file: SingularLuus04_FreeTime_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-Bike1D/ocp-interfaces/Matlab/CompileOctave.m | 3,047 | utf_8 | 175709185d9721050313b4e94969205c | %-----------------------------------------------------------------------%
% file: Bike1D_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-Bike1D/ocp-interfaces/Matlab/CompileMex.m | 2,341 | utf_8 | a0176651c42e659ab837049955aa3e61 | %-----------------------------------------------------------------------%
% file: Bike1D_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileOctave.m | .m | OCP-tests-master/test-HangingChain/ocp-interfaces/Matlab/CompileOctave.m | 3,053 | utf_8 | adb7a9cd290b1b396624e44a29b0dcb7 | %-----------------------------------------------------------------------%
% file: HangingChain_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | pins-ocs/OCP-tests-master | CompileMex.m | .m | OCP-tests-master/test-HangingChain/ocp-interfaces/Matlab/CompileMex.m | 2,347 | utf_8 | ff5faf43aeb5c70735e5d62abbfb21de | %-----------------------------------------------------------------------%
% file: HangingChain_Data.rb %
% %
% version: 1.0 date 28/3/2020 %
% ... |
github | mobeets/gaborMotionPulses-master | fitAllSTRFs.m | .m | gaborMotionPulses-master/fitAllSTRFs.m | 3,874 | utf_8 | 5e6fb46d762c7bc720f1425005db87e9 | function fitAllSTRFs(runName, ~, fitType, dts, brainArea)
% fitAllSTRFs(runName, ~, fitType, dts)
%
% n.b. make sure to add path to mASD and cbrewer
%
if nargin < 4
dts = {};
end
if nargin < 5
brainArea = '';
end
if isempty(dts)
stimdir = '/Users/mobeets/code/gaborMotionReb... |
github | mobeets/gaborMotionPulses-master | summaryByCell.m | .m | gaborMotionPulses-master/+plot/summaryByCell.m | 2,550 | utf_8 | e1dc2ee17314ff256c19f13f9c0b8e54 | function figs = summaryByCell(dt, cellind, isNancy, fitdir, outdir, figext)
if nargin < 6
figext = 'png';
end
if nargin < 5
outdir = '';
end
if nargin < 4
fitdir = 'fits';
end
data = io.loadDataByDate(dt, isNancy);
vs = io.makeFitSummaries(fitdir, isNancy, 'ASD', ... |
github | mobeets/gaborMotionPulses-master | quickPmfByDate.m | .m | gaborMotionPulses-master/+plot/quickPmfByDate.m | 1,241 | utf_8 | fb9bf3d7e897555cd3f5cc17254121ea | function fig = quickPmfByDate(dt, isNancy, nbins, byPulse)
if nargin < 4
byPulse = false;
end
if nargin < 3
nbins = 10;
end
data = io.loadDataByDate(dt, isNancy);
Y = data.R;
fig = figure; hold on; set(gcf,'color','w');
xlabel('marginal stimulus strength');
y... |
github | mobeets/gaborMotionPulses-master | visualizePairwiseCorr.m | .m | gaborMotionPulses-master/+plot/visualizePairwiseCorr.m | 4,191 | utf_8 | 6110a16112dfbc29a68ac48d105cc659 | function S = visualizePairwiseCorr(r, c, showHists)
% visualize joint responses for neuron pairs
% S = vizualizePairwiseCorr(spikeCount, Condition)
% INPUT:
% spikeCount = [nTrials x 2]
% Condition = [nTrials x 1] logical condition
if nargin < 3
showHists = true;
end
cmap = lines(2);
cmap = flipud(cmap);
ix0... |
github | mobeets/gaborMotionPulses-master | colorScheme.m | .m | gaborMotionPulses-master/+plot/colorScheme.m | 496 | utf_8 | 45810bb9fe2ff62feb03e126b252d249 | function f = colorScheme(clrNeg, clrMid, clrPos)
if nargin < 1
clrPos = [0.3, 0.3, 0.9];
clrNeg = [0.9, 0.3, 0.3];
clrMid = [0.95, 0.95, 0.95];
end
f = @(x) getColor(x, clrNeg, clrMid, clrPos);
end
function v = getColor(x, clrNeg, clrMid, clrPos)
if x >= 0
v = getColor2(... |
github | mobeets/gaborMotionPulses-master | plotKernel.m | .m | gaborMotionPulses-master/+plot/plotKernel.m | 2,348 | utf_8 | d4044f642e87679dacab8d7b5de47d1a | function [fig, ha] = plotKernel(xy, wf, vmax, figLbl, sz, figSz, clrFcn, xLblFcn, yLblFcn)
% plots an nw-by-nt spatiotemporal kernel
% creates nt subplots each with nw weights
%
% xy - spatial coords or wf
% wf - weights to plot
% vmax - normalizer for wf (default is maximum value in wf)
% sz - size of markers
% fig... |
github | mobeets/gaborMotionPulses-master | plotSaccadeKernelOverlay.m | .m | gaborMotionPulses-master/+plot/plotSaccadeKernelOverlay.m | 2,786 | utf_8 | 3e549527930908f1ea75bdcfb84dbef6 | function plotSaccadeKernelOverlay(stim, n, f, showTargs, showHyperflow, ...
contourNoQuiver, lbl)
if nargin < 7
if ~isstruct(n)
lbl = [f.label ' = ' sprintf('%0.2f', f.score)];
else
lbl = [n.exname ' - ' f.label ' = ' sprintf('%0.2f', f.score)];
end
end
if... |
github | mobeets/gaborMotionPulses-master | loadDataByDate2.m | .m | gaborMotionPulses-master/+io/loadDataByDate2.m | 4,317 | utf_8 | 0e8c7d89a9e0a6888070908c0012c3bd | function data = loadDataByDate2(dt, isNancy, basedir, stimdir, spikesdir, ignoreFrozen)
if nargin < 6
ignoreFrozen = true;
end
if nargin < 2
isNancy = false;
end
if isNancy
mnkNm = 'nancy';
else
mnkNm = 'pat';
end
if nargin < 5 || isempty(spikesdir... |
github | mobeets/gaborMotionPulses-master | loadDataByDate.m | .m | gaborMotionPulses-master/+io/loadDataByDate.m | 5,929 | utf_8 | 795583f82453baf3c325c378d0eb7a99 | function data = loadDataByDate(dt, isNancy, basedir, stimdir, ...
spikesdir, ignoreFrozen, ignoreEarlyRepeats)
%
if nargin < 7
ignoreEarlyRepeats = true;
end
if nargin < 6
ignoreFrozen = false;
end
if nargin < 2
isNancy = str2num(dt(4)) > 4;
end
% if isNancy
... |
github | mobeets/gaborMotionPulses-master | decodeWithCellsAndShuffle.m | .m | gaborMotionPulses-master/+tools/decodeWithCellsAndShuffle.m | 3,563 | utf_8 | 825a19bee94240551906b8c8bcef7fec | function [scs0, scsSh] = decodeWithCellsAndShuffle(X, Y, nshuffles, doPlot)
% for each entry in vs,
% decode X using Y
% and decode X using Y shuffled conditional on X
%
if nargin < 4
doPlot = false;
end
scoreFcn = @(Y, Yh) mean(Y == Yh);
% X = {vs.stim};
% Y = {vs.Ys};
scs0 = ... |
github | mobeets/gaborMotionPulses-master | decodeAndShuffle.m | .m | gaborMotionPulses-master/+tools/decodeAndShuffle.m | 3,019 | utf_8 | e769c2b537bf2e6ed8a35faab7f3ed50 | function scs = decodeAndShuffle(X, Y, nshuffles, G)
% for each entry in vs,
% decode X using Y
% and decode X using Y shuffled conditional on X
% if C is provided, shuffle only within each group of C
%
if nargin < 4
G = cell(numel(X), 1);
end
nreps = nshuffles; % reps applied only to scsRaw
... |
github | mobeets/gaborMotionPulses-master | makeFitSummaries0.m | .m | gaborMotionPulses-master/+tools/makeFitSummaries0.m | 16,451 | utf_8 | 4dd70cc53304ddceda428f85a2251ff6 | function vals = makeFitSummaries(fitdir, isNancy, fitstr, dts)
if nargin < 1
fitdir = 'fits';
end
if nargin < 2
isNancy = false;
end
if nargin < 3
fitstr = 'ASD';
end
if nargin < 4 || isempty(dts)
dts = io.getDates(fitdir);
end
isSpaceOnly = false;
... |
github | mobeets/gaborMotionPulses-master | AUC.m | .m | gaborMotionPulses-master/+tools/AUC.m | 1,551 | utf_8 | c7914f824ccf822a873ad590116e4b77 | function cp = AUC(A, B, dim)
% cp = AUC(A, B, DIM)
%
% returns the area under the roc curve for discriminating the two groups
% using any criterion
%
% A, B - target and null distributions
% If A, B are matrices:
% AUC(A, B, DIM) computes AUC along the dimension DIM
%
% Jay Hennig (2/1/2011, 3/17/2015)
%
if... |
github | mobeets/gaborMotionPulses-master | makeFitSummaries.m | .m | gaborMotionPulses-master/+tools/makeFitSummaries.m | 3,754 | utf_8 | 6e654f194a7c139ade467bc267b615ee | function cells = makeFitSummaries(fitdir, dts, fitstr)
if nargin < 2|| isempty(dts)
dts = io.getDates(fitdir);
end
if nargin < 3
fitstr = 'ASD';
end
flipTargPrefNames = {'20150304a-MT_5', '20150518-MT_5'};
badCells = {'20150407a_25', '20150407a_26', '20150407a_28', ...
'2... |
github | mobeets/gaborMotionPulses-master | decodeWithCells.m | .m | gaborMotionPulses-master/+tools/decodeWithCells.m | 7,268 | utf_8 | 3ce37b0d61dd193568c97c85cc16fcd2 | function [scs, scsP, scsA] = decodeWithCells(vs, useAllCells, ...
predictChoice, scoreFcn, nfolds, nshuffles)
% vs - struct array of all data, from tools.makeFitSummaries()
% scoreFcn - function handle @(Y, Yh) ...;
%
% 1. score for each monkey
% 2. score for each cell
% 3. score for each cell pair
%
% dt cell1 c... |
github | mobeets/gaborMotionPulses-master | datahigh_main.m | .m | gaborMotionPulses-master/+tools/datahigh_main.m | 1,486 | utf_8 | 4f468ce1b6e11033233f7f884222e54c | function D = datahigh_main(dt)
data = io.loadDataByDate(dt);
D = spikesByTrial(data.stim, data.neurons, 3);
end
function D = spikesByTrial(stim, neurons, nsigfigs)
stimEventLength = 2.0;
stimPreEvent = 0.2;
inds = stim.goodtrial; % trials without broken fixation
motionStartTimes = [stim.timing.... |
github | mobeets/gaborMotionPulses-master | autoRegressModelSpikes.m | .m | gaborMotionPulses-master/+tools/autoRegressModelSpikes.m | 1,376 | utf_8 | ce1231f7e4cef256f10ff7b37c315378 | function v = autoRegressModelSpikes(v, nlags, nfolds, nshuffles)
if nargin < 3
nfolds = 10;
end
if nargin < 4
nshuffles = 10;
end
scoreFcn = @tools.rsq;
Y = v.Y;
ix = ~isnan(Y);
Y0 = Y(ix);
Yh = v.Yh(ix);
sc0 = scoreFcn(Yh, Y0);
[X, Y] = makeLagMats(Yh, ... |
github | mobeets/gaborMotionPulses-master | psthByEvent.m | .m | gaborMotionPulses-master/+tools/psthByEvent.m | 1,005 | utf_8 | eafcb62e8d7624c52c8260b51d2b9c05 | function [Z, lbins, rbins, categs] = psthByEvent(sps, splitEvent, ...
alignEvent, tL, tR, tWidth, tShift)
%
inds = ~isnan(alignEvent) & ~isnan(splitEvent);
alignEvent = alignEvent(inds);
splitEvent = splitEvent(inds);
t0 = alignEvent - tL;
t1 = alignEvent + tR;
[Y, nY, categs] = splitSp... |
github | mobeets/gaborMotionPulses-master | countSpikesWithinWindow.m | .m | gaborMotionPulses-master/+tools/countSpikesWithinWindow.m | 907 | utf_8 | 91b9a3908f9c07e1f991f18fe86c2b3c | function [Y, lbins, rbins] = countSpikesWithinWindow(sps, t0s, t1s, ...
binwidth, binshift)
%
if nargin < 5 || isnan(binshift)
binshift = t1s(1) - t0s(1);
end
if nargin < 4 || isnan(binwidth)
binwidth = t1s(1) - t0s(1);
end
[lbins, rbins] = binEdges(0, max(t1s - t0s), binwidth, ... |
github | mobeets/gaborMotionPulses-master | updateStruct.m | .m | gaborMotionPulses-master/+tools/updateStruct.m | 2,089 | utf_8 | 4ef0177eba9573569193115d7c4c128d | function newObj = updateStruct(filename, obj)
%
% updates data stored in filename, if any
% by adding obj to its collection
%
% EXAMPLE:
% filename contains two previous objs:
% ML: {2x1 cell}
% ASD: {2x1 cell}
% ...
% test: {2x1 cell}
%
% while obj looks like:
% ML: [anything]
% ASD: [anything]
... |
github | luma/webrtc-master | apmtest.m | .m | webrtc-master/src/webrtc/modules/audio_processing/test/apmtest.m | 9,470 | utf_8 | ad72111888b4bb4b7c4605d0bf79d572 | function apmtest(task, testname, filepath, casenumber, legacy)
%APMTEST is a tool to process APM file sets and easily display the output.
% APMTEST(TASK, TESTNAME, CASENUMBER) performs one of several TASKs:
% 'test' Processes the files to produce test output.
% 'list' Prints a list of cases in the test set,... |
github | luma/webrtc-master | plot_neteq_delay.m | .m | webrtc-master/src/webrtc/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,563 | utf_8 | 8b6a66813477863da513b1e6971dbc97 | function [delay_struct, delayvalues] = plot_neteq_delay(delayfile, varargin)
% InfoStruct = plot_neteq_delay(delayfile)
% InfoStruct = plot_neteq_delay(delayfile, 'skipdelay', skip_seconds)
%
% Henrik Lundin, 2006-11-17
% Henrik Lundin, 2011-05-17
%
try
s = parse_delay_file(delayfile);
catch
error(lasterr);
e... |
github | pgadosey/Matlab-Realtime-multiple-face-detection-and-tracking-master | multiple_tracking.m | .m | Matlab-Realtime-multiple-face-detection-and-tracking-master/multiple_tracking.m | 10,697 | utf_8 | 8a174b7c9e433fc1c0538ecc5a86793d | function multiObjectTracking()
% Create system objects used for reading video, detecting moving objects,
% and displaying the results.
obj = setupSystemObjects();
% frame = readFrame();
% bbox = step(obj.detector, frame);
% tracks = initializeTracks(); % Create an empty array of tracks.
%Get a bounding box around t... |
github | Korogodin/jammer-seeker-master | fig_main.m | .m | jammer-seeker-master/fig_main.m | 3,457 | utf_8 | 6138c166e1c5166015c25fb5fde3b78c | function varargout = fig_main(varargin)
% FIG_MAIN M-file for fig_main.fig
% FIG_MAIN, by itself, creates a new FIG_MAIN or raises the existing
% singleton*.
%
% H = FIG_MAIN returns the handle to a new FIG_MAIN or the handle to
% the existing singleton*.
%
% FIG_MAIN('CALLBACK',hObject,eventDa... |
github | Korogodin/jammer-seeker-master | MapClick.m | .m | jammer-seeker-master/MapClick.m | 4,455 | utf_8 | 25ac97f9cc4fe40a555e41d0af648222 | function MapClick(hObject,~)
globals;
pos=get(hObject,'CurrentPoint');
pos_a = get(h_fig_main.axes_Map,'Position');
X = (pos(1) - pos_a(1))*x_masht - Image_x_0_m;
Y = (pos(2) - pos_a(2))*y_masht - Image_y_0_m;
% disp(['You clicked X:',num2str(X),', Y:',num2str(Y)]);
if (MapBounds(1) > X)||(MapBounds(2) < X)||(MapBoun... |
github | aleslab/psychtoolboxProjects-master | exampleNoiseTrial.m | .m | psychtoolboxProjects-master/ptbCorgi/trialFiles/exampleNoiseTrial.m | 6,172 | utf_8 | 29b08eb8492d272bc80076f0d208a1ad | function [trialData] = exampleNoiseTrial(screenInfo, conditionInfo)
totalDuration = conditionInfo.preStimDuration+conditionInfo.stimDuration+conditionInfo.postStimDuration;
nFrames = round(totalDuration / screenInfo.ifi);
trialData.actualDuration = nFrames*screenInfo.ifi;
trialData.validTrial = false;
trialData.abortN... |
github | aleslab/psychtoolboxProjects-master | validateTrialData.m | .m | psychtoolboxProjects-master/ptbCorgi/functionLibrary/validateTrialData.m | 1,479 | utf_8 | f2251c0d755cf57e14688ebdd97ab035 | function [ trialData ] = validateTrialData( trialData )
%validateTrialData Ensures that the trial structure has required fields
%[ trialData ] = validateConditions( trialData )
%
% This function checks to see if all required fields are set in the
% trialData structure. If not it sets things to a default value.
%l... |
github | aleslab/psychtoolboxProjects-master | writeFilesFromBackup.m | .m | psychtoolboxProjects-master/ptbCorgi/functionLibrary/writeFilesFromBackup.m | 1,893 | utf_8 | 7a9ffc7f76659bf4c284eaf45255c331 | function [ ] = writeFilesFromBackup( mfileBackup, outputDirectory )
%writeFilesFromBackup write out all the files from a backup.
%[ ] = writeFilesFromBackup( mfileBackup, [outputDirectory] )
%
% This function will write out the files backed up in a ptbCorgi
% mfileBackup structure to the chosen directory. Used for ... |
github | aleslab/psychtoolboxProjects-master | validateConditions.m | .m | psychtoolboxProjects-master/ptbCorgi/functionLibrary/validateConditions.m | 3,372 | utf_8 | f2a6f3569cc001d4cf0064f134c8f896 | function [ conditionInfo ] = validateConditions( expInfo, conditionInfo )
%validateConditions Sets missing fields of conditionInfo to default values
% [ conditionInfo ] = validateConditions( expInfo, conditionInfo )
%
% This function checks to see if all required fields are set in each
% condition. If not it sets ... |
github | aleslab/psychtoolboxProjects-master | propertiesGUI.m | .m | psychtoolboxProjects-master/ptbCorgi/functionLibrary/GUI/propertiesGUI.m | 74,227 | utf_8 | be5b1fdfa249ff13c7df4b3997d4dd29 | function [hPropsPane,parameters] = propertiesGUI(hParent, parameters, filename, selectedBranch)
% propertiesGUI displays formatted editable list of properties
%
% Syntax:
%
% Initialization:
% [hPropsPane,parameters] = propertiesGUI(hParent, parameters)
%
% Run-time interaction:
% propertiesGUI(hProp... |
github | aleslab/psychtoolboxProjects-master | pmGui.m | .m | psychtoolboxProjects-master/ptbCorgi/functionLibrary/GUI/pmGui.m | 31,055 | utf_8 | 31d7c4fda85b6e01fade5fea38283830 | function varargout = pmGui(varargin)
% PMGUI MATLAB code for pmGui.fig
% PMGUI, by itself, creates a new PMGUI or raises the existing
% singleton*.
%
% H = PMGUI returns the handle to a new PMGUI or the handle to
% the existing singleton*.
%
% PMGUI('CALLBACK',hObject,eventData,handles,...) cal... |
github | aleslab/psychtoolboxProjects-master | BitsPlusDIO2Matrix.m | .m | psychtoolboxProjects-master/ptbCorgi/functionLibrary/bitsSharp/BitsPlusDIO2Matrix.m | 6,858 | utf_8 | 7825b86189d18b591a2c91df6bfbe811 | function encodedDIOdata = BitsPlusDIO2Matrix(mask, data, command, goggle, DAC)
% encodedDIOdata = BitsPlusDIO2Matrix(mask, data, command [,goggle ,DAC]);
%
% Generates a Matlab matrix containing the magic code and data
% required to set the DIO port of CRS Bits++ box in Bits++ mode.
%
% 'mask', 'data', and 'command' ha... |
github | aleslab/psychtoolboxProjects-master | drawFixation.m | .m | psychtoolboxProjects-master/ptbCorgi/functionLibrary/stimulusPresentation/drawFixation.m | 7,797 | utf_8 | c83ad2f791cf36d712df37631c7fe30b | function [expInfo] = drawFixation(expInfo, fixationInfo)
%function [expInfo] = drawFixation(expInfo, [fixationInfo])
%This function is used to draw fixation markers.
% Since it is called throughout the experiment it can also be used to draw
% other things that should be on screen in the intertrial interval. For
% examp... |
github | aleslab/psychtoolboxProjects-master | overloadOpenPtbCorgiData.m | .m | psychtoolboxProjects-master/ptbCorgi/functionLibrary/dataWrangling/overloadOpenPtbCorgiData.m | 6,462 | utf_8 | 4c3647adc9f48e5ad222798a8854b30f | function [ ptbCorgiData ] = overloadOpenPtbCorgiData( varargin )
%overloadOpenPtbCorgiData Implements input overloading for ptbCorgiData
%
%[ ptbCorgiData ] = overloadOpenPtbCorgiData( varargin )
% This is an important function that abstracts loading datafiles into a
% single place and implements multiple ways to loa... |
github | aleslab/psychtoolboxProjects-master | ptbCorgiDataBrowser.m | .m | psychtoolboxProjects-master/ptbCorgi/functionLibrary/dataWrangling/ptbCorgiDataBrowser.m | 28,833 | utf_8 | 5a441526311ce7f4fbcdfd22fe4d6ec0 | function varargout = ptbCorgiDataBrowser(varargin)
% PTBCORGIDATABROWSER GUI to use to browse and load ptbCorgi projects
%
% ptbCorgiDataBrowser()
%
% This function creates a GUI that is used to browse multiple data
% created by ptbCorgi. It allows for easily loading multiple
% particpant datasets,... |
github | jordandcarter/RTIMULib-master | mag_fit_display.m | .m | RTIMULib-master/RTEllipsoidFit/mag_fit_display.m | 1,220 | utf_8 | 12d8d12ef7f37e898204f4486f64cb9f | %//
%// Copyright (c) 2014, richards-tech
%//
%// This file is part of RTEllipsoidFit
%//
%// RTEllipsoidFit is free software: you can redistribute it and/or modify
%// it under the terms of the GNU General Public License as published by
%// the Free Software Foundation, either version 3 of the License, or
%// (... |
github | ovcharenkoo/matlab_seismic_cpml_iso_2d_curvil-master | svdinv.m | .m | matlab_seismic_cpml_iso_2d_curvil-master/svdinv.m | 200 | utf_8 | a536e4a2a96381f83ccf196fc470e2cf | %SVD-like matrix inversion
function B=svdinv(A)
[U, S, V]= svd(A);
s= diag(S);
k= sum(s> 1e-9); % simple thresholding based decision
B= V(:, 1: k)* diag(1./ s(1: k))* U(:, 1: k)';
end |
github | ovcharenkoo/matlab_seismic_cpml_iso_2d_curvil-master | func_curv_jacob.m | .m | matlab_seismic_cpml_iso_2d_curvil-master/func_curv_jacob.m | 4,152 | utf_8 | 67751f62bf21371627743d96d645e2ce | %Constructs curvilinear mesh and its Cartesian analogus. Calculates
%Jacobian
% J=[dksi_dx dksi_dy;
% deta_dx deta_dy];
% Ji=[dx_dksi dx_deta;
% dy_dksi dy_deta];
% Input arguments:
% nx - number of nx grid points
% ny - number of ny grid points
% xmin, xmax - min and max values over OX
% ymin, ymax - min and ... |
github | ovcharenkoo/matlab_seismic_cpml_iso_2d_curvil-master | func_curv_jacob_pml.m | .m | matlab_seismic_cpml_iso_2d_curvil-master/func_curv_jacob_pml.m | 4,727 | utf_8 | 966ed20aae16a1f0ae3aaf228a896206 | %Constructs curvilinear mesh with regular regions for pmls and its Cartesian analogus. Calculates
%Jacobian
% J=[dksi_dx dksi_dy;
% deta_dx deta_dy];
% Ji=[dx_dksi dx_deta;
% dy_dksi dy_deta];
% Input arguments:
% nx - number of nx grid points
% ny - number of ny grid points
% xmin, xmax - min and max values o... |
github | ovcharenkoo/matlab_seismic_cpml_iso_2d_curvil-master | func_find_closest_grid_nodes.m | .m | matlab_seismic_cpml_iso_2d_curvil-master/func_find_closest_grid_nodes.m | 3,331 | utf_8 | d6839238a04eee0a3aae2c02bae976cd | %Function as output gives arrays of markers of nearby grid points that then
%can beb visualized by:
% for i=1:nx+1
% for j=1:ny+1
% if markers(i,j)==1
% scatter(gr_x(i,j),gr_y(i,j),'r','filled'); drawnow; hold on;
% end
% end
% end
% + discretized curve and it's normals
% To plot no... |
github | shaibagon/ann_wrapper-master | test_ann_class.m | .m | ann_wrapper-master/test_ann_class.m | 3,328 | utf_8 | 35e851aad5c31c4237b3b8f04d638f68 | function test_ann_class
fprintf(1,'start test...\n');
dbstop if error
for dim = 10:25:60
for n = 2:3
[anno pts Y] = make_ann(dim,10^n);
test_ksearch(anno, pts, Y,'ksearch');
test_ksearch(anno, pts, Y, 'prisearch');
test_frsearch(anno, pts, Y);
close(anno);
end
end
% loa... |
github | samarth-robo/edges-master | edgeBoxesCanny.m | .m | edges-master/edgeBoxesCanny.m | 4,864 | UNKNOWN | 0802fff77b2d6733be9bf7c723faa705 | function bbs = edgeBoxesCanny( I, model, varargin )
% Generate Edge Boxes object proposals in given image(s).
%
% Compute Edge Boxes object proposals as described in:
% C. Lawrence Zitnick and Piotr Doll�r
% "Edge Boxes: Locating Object Proposals from Edges", ECCV 2014.
% The proposal boxes are fast to compute and gi... |
github | samarth-robo/edges-master | boxesEval.m | .m | edges-master/boxesEval.m | 5,118 | utf_8 | 92042e7eff2def2fcafd0202645b23c0 | function recall = boxesEval( varargin )
% Perform object proposal bounding box evaluation and plot results.
%
% boxesEval evaluates a set bounding box object proposals on the dataset
% specified by the 'data' parameter (which is generated by boxesData.m).
% The methods are specified by the vector 'names'. For each meth... |
github | samarth-robo/edges-master | edgesEvalDir.m | .m | edges-master/edgesEvalDir.m | 5,852 | utf_8 | b708b92045eaa75fa68d09e169447bb6 | function varargout = edgesEvalDir( varargin )
% Calculate edge precision/recall results for directory of edge images.
%
% Enhanced replacement for boundaryBench() from BSDS500 code:
% http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/
% Uses same format for results and is fully compatible with boundary... |
github | samarth-robo/edges-master | edgeBoxesSweeps.m | .m | edges-master/edgeBoxesSweeps.m | 3,411 | utf_8 | e5a9cecaa2b2e071c5d8729811f751dc | function edgeBoxesSweeps()
% Parameter sweeps for Edges Boxes object proposals.
%
% Running the parameter sweeps requires altering internal flags.
% The sweeps are not well documented, use at your own discretion.
%
% Structured Edge Detection Toolbox Version 3.01
% Code written by Piotr Dollar and Larry Zitnick, 2... |
github | samarth-robo/edges-master | edgeBoxes.m | .m | edges-master/edgeBoxes.m | 4,829 | UNKNOWN | 23181490ad59c3fa253c01706a4a8e22 | function bbs = edgeBoxes( I, model, varargin )
% Generate Edge Boxes object proposals in given image(s).
%
% Compute Edge Boxes object proposals as described in:
% C. Lawrence Zitnick and Piotr Doll�r
% "Edge Boxes: Locating Object Proposals from Edges", ECCV 2014.
% The proposal boxes are fast to compute and give st... |
github | samarth-robo/edges-master | edgesTrain.m | .m | edges-master/edgesTrain.m | 13,669 | utf_8 | c29662f392dd5074db27a50767e39cef | function model = edgesTrain( varargin )
% Train structured edge detector.
%
% For an introductory tutorial please see edgesDemo.m.
%
% USAGE
% opts = edgesTrain()
% model = edgesTrain( opts )
%
% INPUTS
% opts - parameters (struct or name/value pairs)
% (1) model parameters:
% .imWidth - [32] width of i... |
github | samarth-robo/edges-master | spAffinities.m | .m | edges-master/spAffinities.m | 4,227 | utf_8 | c8d1c1cc618a7266fee4b2d10651c8c2 | function [A,E,U] = spAffinities( S, E, segs, nThreads )
% Compute superpixel affinities and optionally corresponding edge map.
%
% Computes an m x m affinity matrix A where A(i,j) is the affinity between
% superpixels i and j. A has values in [0,1]. Only affinities between
% spatially nearby superpixels are computed; t... |
github | samarth-robo/edges-master | edgesSweeps.m | .m | edges-master/edgesSweeps.m | 8,831 | utf_8 | c36ed011e7daa4ea08d83453e0cf8125 | function edgesSweeps()
% Parameter sweeps for structured edge detector.
%
% Running the parameter sweeps requires altering internal flags.
% The sweeps are not well documented, use at your own discretion.
%
% Structured Edge Detection Toolbox Version 3.01
% Code written by Piotr Dollar, 2014.
% Licensed under the ... |
github | samarth-robo/edges-master | compile.m | .m | edges-master/cpp/external/gop_1.3/matlab/compile.m | 3,183 | utf_8 | 7b5b53e931775afdf031ae3d8038086a | %{
Copyright (c) 2014, Philipp Krähenbühl
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this li... |
github | NynkeDekkerLab/qtrk-master | plot_results.m | .m | qtrk-master/cudatrack-test/plot_results.m | 1,784 | utf_8 | affe00aa414f98ad15b4f871e0aa428d | function plot_results()
figure(1);
plot_roi_sizes();
figure(2);
plot_qirad();
end
function plot_roi_sizes()
r=dlmread('roi-sizes.txt');
r=r(3:end,:);
PixelSize = 146; % nm
StepSize = 50; % nm
xacc = r(:,2) * PixelSize;
xbias = r(:,5) * PixelSize;
zacc = r(:,4) * StepSiz... |
github | NynkeDekkerLab/qtrk-master | mlegaussfit.m | .m | qtrk-master/old_projects/matlab/mlegaussfit.m | 4,341 | utf_8 | 3638d09fcc13a9eddaa77333fb0cb781 | % MATLAB implementation test
function mlegaussfit()
% Parameters format: X, Y, Sigma, I_0, I_bg
W = 32; H =32;
Pcenter = [ W/2 H/2 4 3000 5 ]; %
[img, imgcv] = makesample ([H W], Pcenter);
% Localize
N = 20;
iterations = 8;
for k = 1 : N
P = Pcenter+(rand(1,5)-.5).*[5 5 ... |
github | NynkeDekkerLab/qtrk-master | gensimtraces.m | .m | qtrk-master/old_projects/matlab/gensimtraces.m | 2,098 | utf_8 | 209c001569cd04e2a78648403ade3e9d | function gensimtraces()
mag = 50;
fixluterr = simulate(mag, 1, 1);
bmluterr = simulate(mag, 0, 1);
plot ( [20:10:200], beadcount ( [20:10:200]));
end
function numbeads = beadcount(mag)
magfactor = 0.5.^(mag / 50 - 1);
% good yield per 2000x2000 view in falcon2
yield = 200;
n... |
github | NynkeDekkerLab/qtrk-master | autobeadfind.m | .m | qtrk-master/cputrack-test/autobeadfind.m | 1,069 | utf_8 | 0deedc498952b0f19a679e3220f89798 | function autobeadfind(image, smp)
if nargin==0
image=normalize(imread('00008153.jpg'));
%smp = imread('00008153-s.jpg');
smp = imread('00008153-nc.jpg'); % badly centered
smp=normalize(smp(:,:,1));
end
image = image-mean(image(:));
image = makepowerof2(image);
s = si... |
github | NynkeDekkerLab/qtrk-master | fisher_graphs.m | .m | qtrk-master/cputrack-test/fisher_graphs.m | 333 | utf_8 | 2c2d01aa6dc351b87633c973cf95db39 |
function fisher_graphs()
figure(1);
showcsvimg('u');
figure(2);
showcsvimg('dudr');
figure(3);
showcsvimg('dudz');
figure(5);
stdxz=dlmread('stdev-xz.txt');
plot(stdxz(:,2));
end
function d=showcsvimg(fn)
d=dlmread([fn '.txt']); imshow(normalize(d));
title(fn);
fprintf('%s: min=%f, max=%f\n', fn, min(d(:)), ma... |
github | STOR-i/GaussianProcesses.jl-master | benchmark_gpml.m | .m | GaussianProcesses.jl-master/perf/benchmarks/benchmark_gpml.m | 2,631 | utf_8 | f610eb726df68901dc60e0e7b8f1816a | % run('gpml-matlab-v4.1-2017-10-19/startup.m')
run('gpml-matlab-v4.2-2018-06-11/startup.m')
rng(1);
kernels = containers.Map;
kernels('se') = @covSEiso;
kernels('mat12') = {@covMaterniso, 1};
kernels('rq') = @covRQiso;
kernels('se+rq') = { 'covSum', { 'covSEiso', 'covRQiso' } };
kernels('se*rq') = { 'covProd', { 'co... |
github | gijzelaerr/sonic-gesture-master | svmcPK.m | .m | sonic-gesture-master/evaluate/part1/chi^2/svmcPK.m | 4,739 | utf_8 | 5ca5d5b2a8a567674f1e02c88d7a4436 | function w = svmcPK(D, C, varargin)
% w = svmcPK(D, C, varargin)
%
% Trains a two class svm-classifier with a precomputed kernel.
%
% INPUT:
%
% D: PR-tools dataset containing a NxM distance matrix, where N
% is the number of samples.
% Target labels of this dataset should be 1.
%... |
github | gijzelaerr/sonic-gesture-master | mog_threshold.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/mog_threshold.m | 1,467 | utf_8 | 2b7c0faa66de5074d54983a3780fa2b9 | %MOG_THRESHOLD Set threshold of a MoG
%
% W = MOG_THRESHOLD(W,X,FRACREJ)
%
% Set the threshold of the Mixture of Gaussians mapping W. The threshold
% is set such that a pre-specified fraction FRACREJ of the target data X
% is rejected.
%
% I still have problems to be sure when the obtained decision boundary
% is c... |
github | gijzelaerr/sonic-gesture-master | dknndd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/dknndd.m | 2,862 | utf_8 | 4d315e92131cb902bf4e67c07755b098 | %DKNNDD Distance K-Nearest neighbour data description method.
%
% W = DKNNDD(D,FRACREJ,K,METHOD)
%
% Calculates the K-Nearest neighbour data description on distance
% dataset D. Two methods are defined to compute a distance to the
% dataset using the k-nearest neighbours:
%
% METHOD does:
% 'kappa' us... |
github | gijzelaerr/sonic-gesture-master | knndd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/knndd.m | 3,296 | utf_8 | c3a2be3e58fc316a1613ba08928794d5 | %KNNDD K-Nearest neighbour data description method.
%
% W = KNNDD(A,FRACREJ,K,METHOD)
%
% Calculates the K-Nearest neighbour data description on dataset A.
% Three methods are defined to compute a distance to the dataset using
% the k-nearest neighbours:
%
% METHOD uses the
% 'kappa' distance to the k-... |
github | gijzelaerr/sonic-gesture-master | svdd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/svdd.m | 3,879 | utf_8 | 624a149f7dab3c07e29627776dacb9d3 | %SVDD Support Vector Data Description
%
% W = SVDD(A,FRACREJ,SIGMA)
%
% Optimizes a support vector data description for the dataset A by
% quadratic programming. The data description uses the Gaussian kernel
% by default. FRACREJ gives the fraction of the target set which will
% be rejected, when suppli... |
github | gijzelaerr/sonic-gesture-master | rankboostc.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/rankboostc.m | 4,187 | utf_8 | 70e0475ed24cb143633e9a9bf7b93db1 | function W = rankboostc(a,fracrej,T)
%RANKBOOSTB Binary rankboost
%
% W = RANKBOOSTC(A,FRACREJ,T)
%
% Train a simple binary version of rankboost containing T weak
% classifiers. The base (weak) classifiers only threshold a single
% feature.
%
% See also dd_auc, auclpm
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org... |
github | gijzelaerr/sonic-gesture-master | nndist_range.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/nndist_range.m | 741 | utf_8 | bec5713622f69cc016863f60e886cf4b | %NNDIST_RANGE Give a vector of scales
%
% D = NNDIST_RANGE(X)
% D = NNDIST_RANGE(X,NR)
%
% Give the average nearest neighbor distance in dataset X. When NR is
% specified, the first NR nearest distances are returned.
%
% Default: NR = 1
%
% See also: svdd
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Facult... |
github | gijzelaerr/sonic-gesture-master | lpball_dist.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/lpball_dist.m | 839 | utf_8 | e6cf07857d5c4126cf051a50e750ff52 | %LPBALL_DIST Compute Lp distance to a mean
%
% [F,G,H] = LPBALL_DIST(M,X,P,FRAC)
%
% Compute the maximum distance of objects X to the mean M, using Lp
% distances with P. To make the distance a bit more robust, just a
% fraction FRAC of the data is taken into account. The distance is
% returned in F, the derivative... |
github | gijzelaerr/sonic-gesture-master | isocset.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/isocset.m | 711 | utf_8 | f7b443f72fbeb1438f1baf11ee8185f1 | %ISOCSET True for one-class datasets
%
% isocset(a) returns true if the dataset a is a one-class dataset,
% containing only classes 'target' and/or 'outlier'.
%
% See also: is_occ, gendatoc
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA De... |
github | gijzelaerr/sonic-gesture-master | mog_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/mog_dd.m | 4,180 | utf_8 | a33de7bb2c0b6139ebf846e374d206ad | %MOG_DD Mixture of Gaussians data description
%
% W = MOG_DD(A,FRACREJ,[N1 N2],CTYPE,REG,NUMITERS)
%
% Train a Mixture of Gaussians model on data A, using N1 clusters to
% model the target class, and N2 clusters for the outlier data. The
% position, size and priors of each of the clusters is optimized using
% the EM ... |
github | gijzelaerr/sonic-gesture-master | autoenc_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/autoenc_dd.m | 2,125 | utf_8 | e1ef7f4176e2cddf28cdc2c233e42e86 | %AUTOENC_DD Auto-Encoder data description.
%
% W = AUTOENC_DD(A,FRACREJ,N)
%
% Train an Auto-Encoder network with N hidden units. The network should
% recover the original data A at its output. The difference between the
% network output and the original pattern (in MSE sense) is used as a
% charaterization of ... |
github | gijzelaerr/sonic-gesture-master | rob_gauss_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/rob_gauss_dd.m | 2,714 | utf_8 | 657fc445300b78f74d5035a27d63845d | %ROB_GAUSS_DD Robust Gaussian data description.
%
% W = ROB_GAUSS_DD(A,FRACREJ)
%
% Fit a robust Gaussian density on dataset A. The algorithm is taken
% from
% Huber, P.J. "Robust Statistics", John Wiley&Sons, 1981, pg 238
%
% To be perfectly honest, there are some personal choices for some weighting
% factor... |
github | gijzelaerr/sonic-gesture-master | nparzen_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/nparzen_dd.m | 2,629 | utf_8 | 28bd974ae3d5a72cc417756255b8d2a8 | %NPARZEN_DD Naive Parzen data description.
%
% W = nparzen_dd(A,fracrej)
%
% Fit a Parzen density on each individual feature in dataset A and
% multiply the results for the final density estimate. This is similar
% to the Naive Bayes approach used for classification.
% The threshold is put such that fracrej of ... |
github | gijzelaerr/sonic-gesture-master | kcenter_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/kcenter_dd.m | 1,402 | utf_8 | 6d2415c8f493eaeb2753af878efe7bd3 | %KCENTER_DD k-center data description.
%
% W = kcenter_dd(A,fracrej,K)
%
% Train a k-center method with K prototypes on dataset A.
%
% See also kmeans_dd, som_dd, dd_roc
% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands... |
github | gijzelaerr/sonic-gesture-master | gauss_dd.m | .m | sonic-gesture-master/evaluate/part1/dd_tools/gauss_dd.m | 2,070 | utf_8 | 930d7ff9db8b347b5aaf0b1464c2012d | %GAUSS_DD Gaussian data description.
%
% W = gauss_dd(A,fracrej,r)
%
% Fit a Gaussian density on dataset A. If requested, the r can be
% given to add some regularization to the estimated covariance matrix:
% sig_new = (1-r)*sig + r*eye(dim). Default r = 0.01!!! (might be
% dangerous!)
%
% This version acutally ... |
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