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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | ambarpal/3d-hough-master | ransac.m | .m | 3d-hough-master/code/vidal/HopkinsMultiviewMultibody/helper_functions/ransac.m | 7,904 | utf_8 | 7847ae59e99503f67c783cf8b6735f5c | % RANSAC - Robustly fits a model to data with the RANSAC algorithm
%
% Usage:
%
% [M, inliers] = ransac(x, fittingfn, distfn, degenfn s, t, feedback)
%
% Arguments:
% x - Data sets to which we are seeking to fit a model M
% It is assumed that x is of size [d x Npts]
% where d... |
github | ambarpal/3d-hough-master | spectralclusternormalcut_recursive.m | .m | 3d-hough-master/code/vidal/HopkinsMultiviewMultibody/helper_functions/spectralclusternormalcut_recursive.m | 1,002 | utf_8 | 0717854da603614e0d716528bf36f589 | %function group=spectralclusternormalcut_recursive(n,simMat)
%n final number of cluster
%simMat similarity matrix
function group=spectralclusternormalcut_recursive(n,simMat)
%trivial case with 1 cluster
if(n==1)
group=ones(size(simMat,1),1);
end
%initial bipartition with spectral clustering
group=spectralclust... |
github | ambarpal/3d-hough-master | normalise2dpts.m | .m | 3d-hough-master/code/vidal/HopkinsMultiviewMultibody/helper_functions/normalise2dpts.m | 2,166 | utf_8 | b0206a3a2a4b9aa7d280efae69701fdd | % NORMALISE2DPTS - normalises 2D homogeneous points
%
% Function translates and normalises a set of 2D homogeneous points
% so that their centroid is at the origin and their mean distance from
% the origin is sqrt(2). This process typically improves the
% conditioning of any equations used to solve homographies, fun... |
github | ambarpal/3d-hough-master | Misclassification.m | .m | 3d-hough-master/code/vidal/SSC_CVX/Misclassification.m | 734 | utf_8 | 3b553cd5fc50c024270bd4ba8de1c2fc | %--------------------------------------------------------------------------
% This function takes the groups resulted from spectral clutsering and the
% ground truth to compute the misclassification rate.
% groups: [grp1,grp2,grp3] for three different forms of Spectral Clustering
% s: ground truth vector
% Missrate: 3x... |
github | ambarpal/3d-hough-master | BuildAdjacency.m | .m | 3d-hough-master/code/vidal/SSC_CVX/BuildAdjacency.m | 1,036 | utf_8 | 1788e227e213caedb31998d4a254034c | %--------------------------------------------------------------------------
% This function takes a NxN coefficient matrix and returns a NxN adjacency
% matrix by choosing only the K strongest connections in the similarity
% graph
% CMat: NxN coefficient matrix
% K: number of strongest edges to keep; if K=0 use all the... |
github | ambarpal/3d-hough-master | missclassGroups.m | .m | 3d-hough-master/code/vidal/SSC_CVX/missclassGroups.m | 1,099 | utf_8 | 6ffcc03323c664987bd37aae59e91b29 | %--------------------------------------------------------------------------
% [miss,index] = missclass(Segmentation,RefSegmentation,ngroups)
% Computes the number of missclassified points in the vector Segmentation.
% Segmentation: 1 by sum(npoints) or sum(ngroups) by 1 vector containing
% the label for each group, r... |
github | ambarpal/3d-hough-master | SparseCoefRecovery.m | .m | 3d-hough-master/code/vidal/SSC_CVX/SparseCoefRecovery.m | 3,615 | utf_8 | cc831870c242b6a31b96a4e5f1475ff7 | %--------------------------------------------------------------------------
% This function takes the D x N matrix of N data points and write every
% point as a sparse linear combination of other points.
% Xp: D x N matrix of N data points
% cst: 1 if using the affine constraint sum(c)=1, else 0
% Opt: type of optimiza... |
github | ambarpal/3d-hough-master | DataProjection.m | .m | 3d-hough-master/code/vidal/SSC_CVX/DataProjection.m | 1,505 | utf_8 | cef106e68a393a7e5deed6b368101a9b | %--------------------------------------------------------------------------
% This function takes the D x N data matrix with columns indicating
% different data points and project the D dimensional data into the r
% dimensional space. Different types of projections are possible:
% (1) Projection using PCA
% (2) Project... |
github | ambarpal/3d-hough-master | SpectralClustering.m | .m | 3d-hough-master/code/vidal/SSC_CVX/SpectralClustering.m | 2,037 | utf_8 | fa07e79a2d0dd4c5b0919934bbd7c796 | %--------------------------------------------------------------------------
% This function takes a NxN matrix CMat as adjacency of a graph and
% computes the segmentation of data from spectral clustering.
% CMat: NxN adjacency matrix
% n: number of groups for segmentation
% K: number of largest coefficients to choose... |
github | ambarpal/3d-hough-master | OutlierDetection.m | .m | 3d-hough-master/code/vidal/SSC_CVX/OutlierDetection.m | 1,322 | utf_8 | b6f1a1dc98d866c5be38ea52873cbb09 | %--------------------------------------------------------------------------
% This function takes the coefficient matrix resulted from sparse
% representation using \ell_1 minimization. If a point cannot be written as
% a linear combination of other points, it should be an outlier. The
% function detects the indices of... |
github | ambarpal/3d-hough-master | ransacfitarbitraryplane.m | .m | 3d-hough-master/code/vidal/RANSAC/helper_functions/ransacfitarbitraryplane.m | 1,126 | utf_8 | b6dfe588e6abfe9c862f5bc7788b97bf | function [B,inliers,Borth]=ransacfitarbitraryplane(x,d,t)
[K,N]=size(x);
if(d>=K)
error('Dimension requested for the plane equal or greater than the dimension of the data')
end
if(N<d)
error('Number of points less than the dimension of the hyperplane')
end
s = 3; % Minimum No of points needed to fit a plan... |
github | ambarpal/3d-hough-master | gramsmithorth.m | .m | 3d-hough-master/code/vidal/RANSAC/helper_functions/gramsmithorth.m | 303 | utf_8 | bac0b1b0bd989cf218054231c4156017 | %function y=gramsmithorth(x)
% Returns Y the Gram-Smith orthogonalization of the colums of X
% Y and X have the same dimensions
function y=gramsmithorth(x)
[K,D]=size(x);
I=eye(K);
y=x(:,1)/norm(x(:,1));
for(i=2:D)
newcol=(I-y*y')*x(:,i);
newcol=newcol/norm(newcol);
y=[y newcol];
end
|
github | ambarpal/3d-hough-master | ransac.m | .m | 3d-hough-master/code/vidal/RANSAC/helper_functions/ransac.m | 7,904 | utf_8 | 7847ae59e99503f67c783cf8b6735f5c | % RANSAC - Robustly fits a model to data with the RANSAC algorithm
%
% Usage:
%
% [M, inliers] = ransac(x, fittingfn, distfn, degenfn s, t, feedback)
%
% Arguments:
% x - Data sets to which we are seeking to fit a model M
% It is assumed that x is of size [d x Npts]
% where d... |
github | ambarpal/3d-hough-master | cheegerpartition.m | .m | 3d-hough-master/code/vidal/LSA/helper_functions/cheegerpartition.m | 540 | utf_8 | 805fd3f3628a368e8e290d864080c6c7 | %evaluates the cheeger constant for a given partition
function h=cheegerpartition(group,simMat);
d=sum(simMat,2); %grade of each node (sum of distances on the row)
[IcutA,IcutB]=meshgrid(group-1,2-group); %bool that indicates if a group is connected to A and/or B
IcutAB=and(IcutA,IcutB); ... |
github | ambarpal/3d-hough-master | evaluatenormalcut.m | .m | 3d-hough-master/code/vidal/LSA/helper_functions/evaluatenormalcut.m | 875 | utf_8 | 632d9c3976c06872e169c6341f601334 | %evaluates the normal cut function
% group is a vector of zeros and ones that indicates the two partitions
% simMat is the similarity matrix
function cost=evaluatenormalcut(group,simMat);
d=sum(simMat,2); %grade of each node (sum of distances on the row)
assocA=sum(d(find(group==0))... |
github | ambarpal/3d-hough-master | spectralcluster.m | .m | 3d-hough-master/code/vidal/LSA/helper_functions/spectralcluster.m | 765 | utf_8 | bb76077d06ddfb666bba7353094f70c1 | % affmat is the affinity matrix A
% k is the number of largest eigenvectors in matrix L
% num_class is the number of classes
%diagmat is the diagonal matrix D^(-0.5)
% Lmat is the matrix L
%X and Y ar matrices formed from eigenvectors of L
% IDX is the clustering results
% errorsum is the distance from kmeans
functio... |
github | ambarpal/3d-hough-master | gramsmithorth.m | .m | 3d-hough-master/code/vidal/LSA/helper_functions/gramsmithorth.m | 303 | utf_8 | bac0b1b0bd989cf218054231c4156017 | %function y=gramsmithorth(x)
% Returns Y the Gram-Smith orthogonalization of the colums of X
% Y and X have the same dimensions
function y=gramsmithorth(x)
[K,D]=size(x);
I=eye(K);
y=x(:,1)/norm(x(:,1));
for(i=2:D)
newcol=(I-y*y')*x(:,i);
newcol=newcol/norm(newcol);
y=[y newcol];
end
|
github | ambarpal/3d-hough-master | spectralclusternormalcut_recursive.m | .m | 3d-hough-master/code/vidal/LSA/helper_functions/spectralclusternormalcut_recursive.m | 1,002 | utf_8 | 0717854da603614e0d716528bf36f589 | %function group=spectralclusternormalcut_recursive(n,simMat)
%n final number of cluster
%simMat similarity matrix
function group=spectralclusternormalcut_recursive(n,simMat)
%trivial case with 1 cluster
if(n==1)
group=ones(size(simMat,1),1);
end
%initial bipartition with spectral clustering
group=spectralclust... |
github | ambarpal/3d-hough-master | ndstest.m | .m | 3d-hough-master/code/resources/ndstest.m | 28,392 | utf_8 | 67f6679514ef3396ec0655aebabd626b | function varargout=ndstest(TOL)
%Performs numerous tests of ndSparse math operations,
%
% ndstest(TOL)
%
%TOL is a tolerance value on the percent error. Execution will pause in debug
%mode for inspection if any one of the tests exhibits an error greater than
%TOL.
if nargin<1
TOL=inf; %default tolerance value on di... |
github | ambarpal/3d-hough-master | ndSparse.m | .m | 3d-hough-master/code/resources/ndSparse.m | 83,713 | windows_1250 | 84fb8aad8ef9db370bdf9a8ee89a566a | classdef ndSparse
%ndSparse - A class of N-dimensional sparse arrays.
%
% by Matt Jacobson
%
% Copyright, Xoran Technologies, Inc. 2010
%
%
% USAGE:
%
% S=ndSparse(X) where X is an ordinary MATLAB sparse matrix converts X into
% an ndSparse object. S can be reshaped into an N-dimensional sparse array using
%... |
github | ambarpal/3d-hough-master | meshReduce.m | .m | 3d-hough-master/code/resources/geom3d/meshes3d/meshReduce.m | 9,863 | utf_8 | fe0668cc856377deaeeb38dfce52916d | function varargout = meshReduce(nodes, varargin)
%MESHREDUCE Merge coplanar faces of a polyhedral mesh
%
% Note: deprecated, should use "mergeCoplanarFaces" instead
%
% [NODES FACES] = meshReduce(NODES, FACES)
% [NODES EDGES FACES] = meshReduce(NODES, EDGES, FACES)
% NODES is a set of 3D points (as a Nn-by-3 ar... |
github | ambarpal/3d-hough-master | meshSurfaceArea.m | .m | 3d-hough-master/code/resources/geom3d/meshes3d/meshSurfaceArea.m | 1,881 | utf_8 | cb41ab6d25b207c9d6858e95e7cb65b6 | function area = meshSurfaceArea(vertices, edges, faces)
%MESHSURFACEAREA Surface area of a polyhedral mesh
%
% S = meshSurfaceArea(V, F)
% S = meshSurfaceArea(V, E, F)
% Computes the surface area of the mesh specified by vertex array V and
% face array F. Vertex array is a NV-by-3 array of coordinates.
% Fac... |
github | ambarpal/3d-hough-master | mergeCoplanarFaces.m | .m | 3d-hough-master/code/resources/geom3d/meshes3d/mergeCoplanarFaces.m | 10,111 | utf_8 | 28a4d5c9c038ed1d5250c1babe495a14 | function varargout = mergeCoplanarFaces(nodes, varargin)
%MERGECOPLANARFACES Merge coplanar faces of a polyhedral mesh
%
% [NODES FACES] = mergeCoplanarFaces(NODES, FACES)
% [NODES EDGES FACES] = mergeCoplanarFaces(NODES, EDGES, FACES)
% NODES is a set of 3D points (as a nNodes-by-3 array),
% and FACES is one ... |
github | ambarpal/3d-hough-master | loadCalibrationCamToCam.m | .m | 3d-hough-master/code/kitti/devkit/matlab/loadCalibrationCamToCam.m | 1,894 | utf_8 | 88db832a2338f205ea36b1a9f6231aed | function calib = loadCalibrationCamToCam(filename)
% open file
fid = fopen(filename,'r');
if fid<0
calib = [];
return;
end
% read corner distance
calib.cornerdist = readVariable(fid,'corner_dist',1,1);
% read all cameras (maximum: 100)
for cam=1:100
% read variables
S_ = readVariable(fid,['S_' num2s... |
github | ambarpal/3d-hough-master | loadCalibrationRigid.m | .m | 3d-hough-master/code/kitti/devkit/matlab/loadCalibrationRigid.m | 855 | utf_8 | 9148661cd7335b41dace4f57bd25b3a4 | function Tr = loadCalibrationRigid(filename)
% open file
fid = fopen(filename,'r');
if fid<0
error(['ERROR: Could not load: ' filename]);
end
% read calibration
R = readVariable(fid,'R',3,3);
T = readVariable(fid,'T',3,1);
Tr = [R T;0 0 0 1];
% close file
fclose(fid);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | klabhub/neurostim-master | neurostimEyelinkDispatchCallback.m | .m | neurostim-master/tools/neurostimEyelinkDispatchCallback.m | 15,643 | utf_8 | c7d9904414993935728029312255f618 | function rc = neurostimEyelinkDispatchCallback(callArgs, msg)
% Retrieve live eye-image from Eyelink, show it in onscreen window.
%
% This function is normally called from within the Eyelink() mex file.
% Normal user code only calls it once to supply the eyelink defaults struct.
% This is handled within the EyelinkInit... |
github | klabhub/neurostim-master | testingGetDelay.m | .m | neurostim-master/tools/testingGetDelay.m | 1,777 | utf_8 | 21b2c3c5f5492f58b8b1c9a4577e9a82 | function testingGetDelay
%Script shows 3 ways of creating a cic and adding a plugin. All seem like
%they SHOULD be equivalent, but show vastly different delays in reading basic properties.
import neurostim.*
commandwindow;
elapsed = zeros(200,1);
%Approach 1:
disp('*** Approach 1 (cic and plugin created locally):');... |
github | klabhub/neurostim-master | starstimClosedLoopDemo.m | .m | neurostim-master/demos/starstimClosedLoopDemo.m | 3,942 | utf_8 | 93ff80be0016f2bf9c50c370728d3ccb | function starstimClosedLoopDemo
% Shows how a closed loop paradigm with stimulation and eeg can be setup.
% See also starstimDemo for stimulation only examples
%
% BK - Jan 2019
import neurostim.*;
%% Setup CIC and the stimuli.
c = myRig('debug',true);
c.screen.colorMode = 'RGB'; % Allow specification of RGB luminanc... |
github | klabhub/neurostim-master | contrastDetection.m | .m | neurostim-master/demos/contrastDetection.m | 2,153 | utf_8 | ac9eb652fd1eac08b423d3cdfd354b40 | function contrastDetection
% Contrast detection experiment.
% Shows Gabor patches in random locations, user is required to click on
% them
%% Prerequisites.
import neurostim.*
%% Setup CIC and the stimuli.
c = myRig;
c.trialDuration = Inf; % A trial can only be ended by a mouse click
c.cursor = 'arrow';
c.scree... |
github | klabhub/neurostim-master | noiseGridDemo.m | .m | neurostim-master/demos/noiseGridDemo.m | 5,188 | utf_8 | fe420a84a6592c8b9c15729d3f6e10f2 | function noiseGridDemo(varargin)
% These demos show how to present a grid of luminance/color noise, for reverse
% correlation analysis and/or signal-in-noise detection tasks.
% Demonstrates:
% - how to make use of Matlab's built-in sampling distributions
% - Different types of grid (Cartesian, polar grid... |
github | klabhub/neurostim-master | scripting.m | .m | neurostim-master/demos/scripting.m | 3,314 | utf_8 | 97a1b2cdb992ee4dad281c93b26c33f1 | function c= scripting
%% This demo shows how to use control scripts.
import neurostim.*
%% Setup CIC and the stimuli.
c = myRig;
% We'll use experiment scripts to control this experiment. One is called before
% every frame and it is specified in a separate m-file that should be on
% the pat... |
github | klabhub/neurostim-master | textureDemo.m | .m | neurostim-master/demos/textureDemo.m | 5,504 | utf_8 | 5e4ffd5686b1d34df18fbb7fbc138e8b | function textureDemo(varargin)
% TEXTUREDEMO demo of the texture stimulus plugin.
% TEXTUREDEMO([NUM][,NAME1,VALUE1]) runs demo NUM with the supplied
% options (given as a list of name-value pairs).
%
% NUM defines the demo to run (Default: 1).
%
% Available options are:
% RSVP - TRUE or FALSE, enables an r... |
github | klabhub/neurostim-master | DataHash.m | .m | neurostim-master/+neurostim/+utils/DataHash.m | 20,971 | utf_8 | d95b10498af264538a911ca227ab3467 | function Hash = DataHash(Data, varargin)
% 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 par... |
github | darenlee/SimulinkARDroneTarget-master | createFit_Automate.m | .m | SimulinkARDroneTarget-master/AR_Drone_Models/Calibration/GyrometerTemp_Offset_Calib/createFit_Automate.m | 1,019 | utf_8 | 083216c20de042d99544edbeef35299c | % Copyright 2014 The MathWorks, Inc.
function [fitresult, gof] = createFit_Automate(TempMeasureRefined, GyroDrift_Roll,PlotName)
%CREATEFIT(TEMPMEASUREREFINED,GYRODRIFT_ROLL)
% Create a fit.
%
% Data for 'CurveFit_Bias' fit:
% X Input : TempMeasureRefined
% Y Output: GyroDrift_Roll
% Output:
% ... |
github | darenlee/SimulinkARDroneTarget-master | plot_and_calibrate_mag.m | .m | SimulinkARDroneTarget-master/AR_Drone_Models/Calibration/Magnetometer_calib/plot_and_calibrate_mag.m | 4,175 | utf_8 | 17fec1e72e61054c2d3395700d4e4ddf | % Copyright 2014 The MathWorks, Inc.
function MagCurveFit_Results_Struct = plot_and_calibrate_mag(MagData)
close all
Mag_3d = MagData.signals.values;
Mag_X = Mag_3d(:,1);
Mag_Y = Mag_3d(:,2);
Mag_Z = Mag_3d(:,3);
shiftx = 1;
shifty = 2;
shiftz = 3;
% scatter(Mag_X,Mag_Z)
plot3(Mag_X,Mag_Y,Mag_Z, '.r'... |
github | darenlee/SimulinkARDroneTarget-master | ellipsoid_fit.m | .m | SimulinkARDroneTarget-master/AR_Drone_Models/Calibration/Magnetometer_calib/ellipsoid_fit.m | 4,929 | utf_8 | f2290af354b9dc4e1d9146bd321f3d65 | % Copyright 2014 The MathWorks, Inc.
function [ center, radii, evecs, v ] = ellipsoid_fit( X, flag, equals )
%
% Fit an ellispoid/sphere to a set of xyz data points:
%
% [center, radii, evecs, pars ] = ellipsoid_fit( X )
% [center, radii, evecs, pars ] = ellipsoid_fit( [x y z] );
% [center, radii, evecs, p... |
github | darenlee/SimulinkARDroneTarget-master | Calibrating_The_AR_Drone_Sensors.m | .m | SimulinkARDroneTarget-master/AR_Drone_Models/Calibration_Models/GUI_Source/Calibrating_The_AR_Drone_Sensors.m | 21,463 | utf_8 | c12b3acbce449993c9a7c023f36ad9b3 | function varargout = Calibrating_The_AR_Drone_Sensors(varargin)
% calibrating_the_ar_drone_sensors MATLAB code for Calibrating_The_AR_Drone_Sensors.fig
% calibrating_the_ar_drone_sensors, by itself, creates a new calibrating_the_ar_drone_sensors or raises the existing
% singleton*.
%
% H = calibrating_th... |
github | darenlee/SimulinkARDroneTarget-master | sl_customization.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/sl_customization.m | 1,779 | utf_8 | a2a53f0e626acf4a7bab0f853e697875 | % Copyright 2014 The MathWorks, Inc.
function sl_customization(cm)
%SL_CUSTOMIZATION Register individual targets with Coder Target
% Copyright 2013 The MathWorks, Inc.
cm.registerTargetRegistry(@loc_registerThisTarget);
cm.registerTargetBoardRegistry(@loc_registerBoardsForThisTarget);
cm.registerTargetInfo(... |
github | darenlee/SimulinkARDroneTarget-master | gcc_codesourcery_arm_linux_gnueabihf.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/registry/gcc_codesourcery_arm_linux_gnueabihf.m | 6,348 | utf_8 | 7d5ff3a7b54eb103bb1268ecfc0cdd52 | % Copyright 2014 The MathWorks, Inc.
function [tc, results] = gcc_codesourcery_arm_linux_gnueabihf()
%gcc_codesourcery_arm_linux_gnueabihf
% Copyright 2013 The MathWorks, Inc.
toolchain.Platforms = {'win64', 'win32'};
toolchain.Versions = {'4.8'};
toolchain.Artifacts = {'gmake'};
toolchain.FuncHandle = s... |
github | darenlee/SimulinkARDroneTarget-master | rtwTargetInfo.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/registry/rtwTargetInfo.m | 1,256 | utf_8 | 948ec9b0e1a40e8008de8c0ea2eb3f7e | % Copyright 2014 The MathWorks, Inc.
function rtwTargetInfo(tr)
%RTWTARGETINFO Register toolchain
% Copyright 2013 The MathWorks, Inc.
tr.registerTargetInfo(@loc_createToolchain);
end
%--------------------------------------------------------------------------
function config = loc_createToolchain
rootDir =... |
github | darenlee/SimulinkARDroneTarget-master | slblocks.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/blocks/slblocks.m | 399 | utf_8 | 993da7a5a7cf39287a9b3cd0f82fac39 | % Copyright 2014 The MathWorks, Inc.
function blkStruct = slblocks
blkStruct.Name = 'AR Drone 2 Library Blocks'; %Display name
blkStruct.OpenFcn = 'AR_Drone_2_Library'; %Library name
blkStruct.MaskDisplay = '';
Browser(1).Library = 'AR_Drone_2_Library'; %Library name
% Copyright 2010 The MathWorks, Inc.
Br... |
github | darenlee/SimulinkARDroneTarget-master | check_init_block.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/blocks/check_init_block.m | 281 | utf_8 | 99533c92a69714d01b45af1f505f713c | % Copyright 2014 The MathWorks, Inc.
function check_init_block
persistent x;
if(isempty(x))
msgbox('Both the Actuator and the LED blocks need the Init_Actuator block to work properly. Add this block to the model. It is part of the AR Drone 2 Target library');
x=0;
end
end |
github | darenlee/SimulinkARDroneTarget-master | ARDroneVideoViewer.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/blocks/ARDroneVideoViewer.m | 2,825 | utf_8 | 0b4b6317f3947375f0b3ed44e918f30b | function ARDroneVideoViewer(block)
% Level-2 MATLAB file S-Function for unit delay demo.
% Copyright 1990-2009 The MathWorks, Inc.
setup(block);
%endfunction
function setup(block)
%% Register number of input and output ports
block.NumInputPorts = 1;
block.NumOutputPorts = 0;
block.Num... |
github | darenlee/SimulinkARDroneTarget-master | rtwmakecfg.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/blocks/rtwmakecfg.m | 10,187 | utf_8 | 95b7808279ac005162c40088f1c332bd | % Copyright 2014 The MathWorks, Inc.
function makeInfo = rtwmakecfg()
%RTWMAKECFG adds include and source directories to the make files.
% makeInfo=RTWMAKECFG returns a structured array containing build info.
% Please refer to the rtwmakecfg API section in the Simulink Coder
% documentation for details on th... |
github | darenlee/SimulinkARDroneTarget-master | RGBDroneVideoViewer.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/blocks/RGBDroneVideoViewer.m | 1,373 | utf_8 | 46904fb6114d94061cd8357355715fdc | function RGBDroneVideoViewer(block)
% Level-2 MATLAB file S-Function for unit delay demo.
% Copyright 1990-2009 The MathWorks, Inc.
setup(block);
%endfunction
function setup(block)
%% Register number of input and output ports
block.NumInputPorts = 3;
block.NumOutputPorts = 0;
block.Nu... |
github | darenlee/SimulinkARDroneTarget-master | onAfterCodeGen.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/+codertarget/+arm_cortex_a_drone/+internal/onAfterCodeGen.m | 2,467 | utf_8 | 6af59e625dd732487a70f7b9a8060938 | function onAfterCodeGen(hCS, buildInfo)
%ONAFTERCODEGEN Hook point for after code generation
% Copyright 2013-2104 The MathWorks, Inc.
if ~isequal(get_param(hCS, 'PositivePriorityOrder'), 'on')
error('You have probably recently updated your AR Drone 2.0 Coder Target with the new UDP blocks. A new change has been m... |
github | darenlee/SimulinkARDroneTarget-master | my_function.m | .m | SimulinkARDroneTarget-master/AR_Drone_Target/+codertarget/+arm_cortex_a_drone/+internal/my_function.m | 254 | utf_8 | 8a4c502a6f3e7d03b5b36aada9170bcf | % Copyright 2014 The MathWorks, Inc.
function my_function()
ab = getActiveConfigSet(bdroot); %was gcs but this is not the best method
IP_String = codertarget.data.getParameterValue(ab,'IP');
setenv('AR_DRONE_IP_ADDRESS',IP_String)
end
|
github | space-physics/glowaurora-master | glow.m | .m | glowaurora-master/glow.m | 1,233 | utf_8 | 1cab8da653572188740c9ab2c1dd442d | function glow()
% quick demo calling GLOW model from Matlab.
% https://www.scivision.co/matlab-python-user-module-import/
flux = 1; % [erg ...]
E0 = 1e3; % [eV]
glat = 65.1;
glon = -147.5;
t = '2015-12-13T10';
G = py.glowaurora.runglowaurora(flux, E0, t, glat, glon);
ver = xarray2mat(G(1));
z_km = xarrayind2vec... |
github | pins-ocs/TS-OCS-master | ArmijoSimpleSearch.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/scripts/ArmijoSimpleSearch.m | 1,890 | utf_8 | 95f86a8ca27f5241d988084ed0490974 | %
% Perform Armijo Line Search
%
% the input are
% fun = the function to minimize with the derivative of the function
% xk = starting point
% d = search direction
%
% optional extra arguments
%
% tau = 0.5 reduction parameter for Armijo algorithm
% c1 = 10-3 parameter for Armijo test
% f... |
github | pins-ocs/TS-OCS-master | TRESNEI.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/scripts/TRESNEI.m | 31,349 | utf_8 | 3e226638573a9390e8dc49cfc05e6360 | function [sol,ierr,output] = TRESNEI(x,e_i,fun,l,u,options,varargin)
% TRESNEI solves systems of nonlinear equalities and inequalities
%
% TRESNEI implements a trust-region Gauss-Newton method for
% bound-constrained least-squares problem:
%
% min || F(x... |
github | pins-ocs/TS-OCS-master | ArmijoLineSearch.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/scripts/ArmijoLineSearch.m | 3,728 | utf_8 | db79568e5de7cdf8ac158d2ee752b46c | %
% Perform Armijo Line Search
% the input are
% fun = the function to minimize with the derivative of the function
% xk = starting point
% d = search direction
%
% optional extra arguments
%
% tau = 0.5 reduction parameter for Armijo algorithm
% c1 = 10-3 parameter for Armijo test
% f0 ... |
github | pins-ocs/TS-OCS-master | parseArgs.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/scripts/parseArgs.m | 1,494 | utf_8 | 0abfa36a0c9f5a084c36b335558b4d48 | % Copyright (C) 2011 Enrico Bertolazzi
%
% This program 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 2
% of the License, or (at your option) any later version.
%
% This program ... |
github | pins-ocs/TS-OCS-master | WolfeLineSearch.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/scripts/WolfeLineSearch.m | 4,961 | utf_8 | 890d3c7385db59610cf324db2510b302 | %
% Perform Wolfe Line Search
%
% the input are
% fun = the function to minimize with the derivative of the function
% xk = starting point
% d = search direction
%
% optional extra arguments
%
% tau = 0.5 reduction parameter for Armijo algorithm
% c1 = 10E-3 parameter for Wolfe test
% c2 ... |
github | pins-ocs/TS-OCS-master | NewtonNonlinear.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/scripts/NewtonNonlinear.m | 1,989 | utf_8 | aea7b28f6f066f25e0274bca31b8cce7 | function [xk,fk,niter,ierr] = NewtonNonlinear( fun, X0, varargin )
global NewtonNonlinear_fun ;
ierr = 0 ;
NewtonNonlinear_fun = fun ;
opts = { 'tol', 'linesearch', 'maxiter', 'c1', 'c2', 'tau', 'lmin', 'lmax', 'plotstep' } ;
defs = { 1E-8, 1, 100, 1E-3, 0.5, 0.5, 1E-8, 1E+8, @plot... |
github | pins-ocs/TS-OCS-master | direct_method_plot_solution.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_plot_solution.m | 1,303 | utf_8 | 08658de370e34d8c4a04219f8b9731a8 | %% Compute NLP solution with IPOPT
%
% This function computes the numerical solution of the optimal control problem
% as a direct transcription problem (NLP) and unsing IPOPT Optimizer.
%
% See the paper for model details.
%
% Input parameters:
% N number of grid points
% p_data structure w... |
github | pins-ocs/TS-OCS-master | direct_method_constraints.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_constraints.m | 826 | utf_8 | 7ff4f18246f3806015fc3cb7d312dbd0 | %
% discretization of the constraints for the Direct Method Eqns. (27)
%
% (x[k+1]-x[k])/h-v[k+1/2] = 0
% (v[k+1]-v[k])/h-u[k+1/2]-k0-k1*v[k+1/2]-k2*v[k+1/2]^2 = 0
% x[0] = 0, v[0] = 0, v[N+1] = 0
% -1 <= u/(g+k3*v[k+1/2]^2) <= 1
%
function f = direct_method_constraints(z,auxdata)
% split vector z into z, v, an... |
github | pins-ocs/TS-OCS-master | indirect_method_JF_pattern.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_JF_pattern.m | 1,300 | utf_8 | db28a413812df79fc2ac520cecb6bded | %%
% map the indices with the corresponding index in the spase matrix
function jac = JfunPattern(auxdata)
N = auxdata.N ;
nvars = auxdata.nvars ;
% calcolo f(z)
sx = 0 ;
sv = sx+N+1 ;
sl = sv+N+1 ;
sm = sl+N+1 ;
nnz = 16*N+4 ;
I = zeros(nnz,1) ;
J = zeros(nnz,1) ;
eq = 0 ;
nz = 0 ;
... |
github | pins-ocs/TS-OCS-master | direct_method_gradient.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_gradient.m | 539 | utf_8 | b9cc241f8e2e1643d0ab2ab294c48c1c | %
% discretization of the constraints for the Direct Method Eqns. (27)
%
% sum (v[k+1]+v[k])/2
%
function grad = direct_method_gradient(z,auxdata)
% split vector z into z, v, and u part Eqns. (28)
[x,v,u] = direct_method_extract_xvu(z,auxdata) ;
N = auxdata.N ;
nvars = auxdata.nvars ;
% z = [ x, v, u... |
github | pins-ocs/TS-OCS-master | test_indirect_method_with_matlab.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/test_indirect_method_with_matlab.m | 1,137 | utf_8 | bf158d4865d2f4ce40d5f3144e93ab45 |
function [z,elapsed,ok] = test_indirect_method_with_matlab(auxdata_in)
global NF_eval NJF_eval auxdata ;
auxdata = auxdata_in ;
NF_eval = 0 ;
NJF_eval = 0 ;
% compute guess solution
z0 = indirect_method_guess_solution( auxdata ) ;
opt = optimoptions('lsqnonlin','Display','iter', ...
... |
github | pins-ocs/TS-OCS-master | indirect_method_guess_solution.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_guess_solution.m | 451 | utf_8 | dc3b23d0826a7eff1f561f81823f57cc | %%
% discretization of dynamical system
%
function z = indirect_method_guess_solution(auxdata)
% step
T_size = auxdata.T_size ;
N = auxdata.N ;
g = auxdata.g ;
h = T_size/N ;
k0 = auxdata.k0 ;
k1 = auxdata.k1 ;
k2 = auxdata.k2 ;
k3 = auxdata.k3 ;
x = zer... |
github | pins-ocs/TS-OCS-master | direct_method_constraints_jacobian.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_constraints_jacobian.m | 2,309 | utf_8 | ff95b0c886deeb5c9270204b552f6040 | %
% discretization of the constraints for the Direct Method Eqns. (29-34)
%
% (x[k+1]-x[k])/h-v[k+1/2] = 0
% (v[k+1]-v[k])/h-u[k+1/2]-k0-k1*v[k+1/2]-k2*v[k+1/2]^2 = 0
% x[0] = 0, v[0] = 0, v[N+1] = 0
% g+k3*v[k+1/2]^2+u >= 0
% g+k3*v[k+1/2]^2-u >= 0
% Jacobian of cosntraints functions
%
%
function jac = direct_m... |
github | pins-ocs/TS-OCS-master | test_direct_method_with_ipopt.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/test_direct_method_with_ipopt.m | 2,473 | utf_8 | eeb6a033f84ab16d6eafa94c4f2f821d | %% Compute NLP solution with IPOPT
%
% This function computes the numerical solution of the optimal control problem
% as a direct transcription problem (NLP) and unsing IPOPT Optimizer.
%
% See the paper for model details.
%
% Input parameters:
% N number of grid points
% p_data structure w... |
github | pins-ocs/TS-OCS-master | direct_method_constraints_jacobian_pattern.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_constraints_jacobian_pattern.m | 1,381 | utf_8 | 7cfaec5d7c3d2b5a06ffa5abe7d688ea | %%
% discretization of dynamical system
%
function jac = direct_method_constraints_jacobian_pattern(auxdata)
% step
T_size = auxdata.T_size ;
N = auxdata.N ;
nvars = auxdata.nvars ;
% non zeros elements of sparse jacobian
nnz = 10*N+3 ;
I = zeros(1,nnz) ;
J = zeros(1,nnz) ;
VAL = zeros(1,... |
github | pins-ocs/TS-OCS-master | indirect_method_u_Dmu.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_u_Dmu.m | 510 | utf_8 | 29c8aa050f07479adbb2e2e7a1b42d16 | %%
% map the indices with the corresponding index in the spase matrix
function DuDmu = indirect_method_u_Dmu(z,auxdata)
g = auxdata.g ;
k3 = auxdata.k3 ;
epsilon = auxdata.epsilon ;
N = auxdata.N ;
% calcolo f(z)
sx = 0 ;
sv = sx+N+1 ;
sl = sv+N+1 ;
sm = sl+N+1 ;
su = sm+N+1 ;
... |
github | pins-ocs/TS-OCS-master | direct_method_guess_solution.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_guess_solution.m | 391 | utf_8 | 216e4848d365f2e530c0415ef3634222 | %%
% discretization of dynamical system
%
function z = direct_method_guess_solution(auxdata)
% step
T_size = auxdata.T_size ;
N = auxdata.N ;
g = auxdata.g ;
h = T_size/N ;
k0 = auxdata.k0 ;
k1 = auxdata.k1 ;
k2 = auxdata.k2 ;
k3 = auxdata.k3 ;
x = zeros(N+1,... |
github | pins-ocs/TS-OCS-master | direct_method_save_solution.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_save_solution.m | 2,119 | utf_8 | 66c6208624391619abc5095c0cca8751 | %% Compute NLP solution with IPOPT
%
% This function computes the numerical solution of the optimal control problem
% as a direct transcription problem (NLP) and unsing IPOPT Optimizer.
%
% See the paper for model details.
%
% Input parameters:
% N number of grid points
% p_data structure w... |
github | pins-ocs/TS-OCS-master | indirect_method_auxdata.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_auxdata.m | 441 | utf_8 | c7bf8b3cffba0d24a9a2a0d6d989a06e | %%
% Setup data structure for computation
%
function auxdata = indirect_method_auxdata(N,p_data)
auxdata.N = N ;
auxdata.nvars = 4*N+4 ;
auxdata.g = p_data.g ;
auxdata.T_size = p_data.T_size ; % final time
auxdata.h = auxdata.T_size/auxdata.N ;
auxdata.k0 = p_data.k0 ;
auxda... |
github | pins-ocs/TS-OCS-master | direct_method_hessian_pattern.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_hessian_pattern.m | 1,254 | utf_8 | ebad160a30dd1dcfa1515fafee17b505 | %
% discretization of the constraints for the Direct Method Eqns. (29-34)
%
% hessian objective(z) + sum lambda(k) * hessian constraint[k](z)
%
% The hessian of objective is zero
%
% The hessian of constaints: (x[k+1]-x[k])/h-v[k+1/2] is 0
% The hessian of constaints: (v[k+1]-v[k])/h-u[k+1/2]-k0-k1*v[k+1/2]-k2*v[k... |
github | pins-ocs/TS-OCS-master | direct_method_objective.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_objective.m | 313 | utf_8 | d6311449d771d3ca216248ad345b5701 | %
% discretization of the constraints for the Direct Method Eqns. (27)
%
% sum (v[k+1]+v[k])/2
%
function f = direct_method_objective(z,auxdata)
% split vector z into z, v, and u part Eqns. (28)
[x,v,u] = direct_method_extract_xvu(z,auxdata) ;
vave = (v(2:end)+v(1:end-1))/2 ;
f = -sum(vave) ;
end
|
github | pins-ocs/TS-OCS-master | direct_method_hessian.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_hessian.m | 1,173 | utf_8 | 03a8d97ab909e8e8e30c6513b4280d5d | %
% discretization of the constraints for the Direct Method Eqns. (29-34)
%
% hessian objective(z) + sum lambda(k) * hessian constraint[k](z)
%
%
function jac = direct_method_hessian(z,sigma,lambda,auxdata)
% step
N = auxdata.N ;
nvars = auxdata.nvars ;
h = auxdata.h ;
k2 = auxdata.k2 ;
k3 ... |
github | pins-ocs/TS-OCS-master | direct_method_extract_xvu.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_extract_xvu.m | 901 | utf_8 | 7379dcdb84980ab822b28cc5b7ab9dc5 | %%
% discretization of dynamical system
%
function [x,v,u,varargout] = direct_method_extract_xvu(z,auxdata)
% map x, v, lambda, mu, u from vector z
N = auxdata.N ;
T_size = auxdata.T_size ;
h = T_size/N ;
start_v = N+1 ;
start_u = start_v+N+1 ;
x = z(1:N+1) ;
v = z(start_v+(1:N+1)) ;
u = z... |
github | pins-ocs/TS-OCS-master | test_direct_method_with_matlab.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/test_direct_method_with_matlab.m | 1,389 | utf_8 | 3b04f71f72226b0426d321f0bf8d3167 | function [z,elapsed,ok] = test_direct_method_with_matlab(auxdata_in)
global NF_eval NJF_eval auxdata ;
auxdata = auxdata_in ;
NF_eval = 0 ;
NJF_eval = 0 ;
[lb,ub,cl,cu] = direct_method_bound(auxdata) ;
% compute guess solution
z0 = direct_method_guess_solution( auxdata ) ;
opt = optimoptions('f... |
github | pins-ocs/TS-OCS-master | indirect_method_save_solution.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_save_solution.m | 2,135 | utf_8 | a75136f745645804ac1949f61e3bcdca | %% Compute NLP solution with IPOPT
%
% This function computes the numerical solution of the optimal control problem
% as a direct transcription problem (NLP) and unsing IPOPT Optimizer.
%
% See the paper for model details.
%
% Input parameters:
% N number of grid points
% p_data structure w... |
github | pins-ocs/TS-OCS-master | indirect_method_plot_solution.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_plot_solution.m | 1,315 | utf_8 | ff816e1d58f59e1fd8bd81de53acc7c9 | %% Compute NLP solution with IPOPT
%
% This function computes the numerical solution of the optimal control problem
% as a direct transcription problem (NLP) and unsing IPOPT Optimizer.
%
% See the paper for model details.
%
% Input parameters:
% N number of grid points
% p_data structure w... |
github | pins-ocs/TS-OCS-master | indirect_method_u_eval.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_u_eval.m | 507 | utf_8 | 134de39b44a3aa1670dc5cb8a1c7d3e3 | %%
% map the indices with the corresponding index in the spase matrix
function u = indirect_method_u_eval(z,auxdata)
g = auxdata.g ;
k3 = auxdata.k3 ;
epsilon = auxdata.epsilon ;
N = auxdata.N ;
% calcolo f(z)
sx = 0 ;
sv = sx+N+1 ;
sl = sv+N+1 ;
sm = sl+N+1 ;
v = z(sv+1:sl) ;
... |
github | pins-ocs/TS-OCS-master | direct_method_auxdata.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_auxdata.m | 517 | utf_8 | 024cc09109cc25e97ae448e0e747332b | %%
% Setup data structure for computation
%
function auxdata = direct_method_auxdata(N,p_data)
nvars = 3*N+2 ;
nconts = 4*N+3 ; % x, v, u min, u max, BC
auxdata.N = N ;
auxdata.nvars = nvars ;
auxdata.nconts = nconts ;
auxdata.g = p_data.g ;
auxdata.T_size = p_data.... |
github | pins-ocs/TS-OCS-master | direct_method_bound.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/direct_method_bound.m | 340 | utf_8 | 5e668060dedba9759c959360ce0f23ee | %%
% discretization of dynamical system
%
function [lb,ub,cl,cu] = direct_method_bound(auxdata)
N = auxdata.N ;
lb = [ -Inf*ones(2*(N+1),1) ; -Inf*ones(N,1) ] ;
ub = [ Inf*ones(2*(N+1),1) ; Inf*ones(N,1) ] ;
cl = [ zeros(2*N+3,1) ; zeros(2*N,1) ] ; % 2*N+3 equality constraints
cu = [ zeros(2*N+3,1) ; Inf*... |
github | pins-ocs/TS-OCS-master | test_indirect_method_with_strscne.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/test_indirect_method_with_strscne.m | 1,076 | utf_8 | 6683e7f1363481bf3434248c7a276ef2 | function [z,elapsed,ok] = test_indirect_method_with_strscne(auxdata_in)
global auxdata ;
addpath('../scripts') ;
auxdata = auxdata_in ;
% compute guess solution
z0 = indirect_method_guess_solution( auxdata ) ;
% compute guess solution
lb = -1000*ones(auxdata.nvars,1) ;
ub = 1000*ones(auxdata.nva... |
github | pins-ocs/TS-OCS-master | indirect_method_f_model.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_f_model.m | 263 | utf_8 | e77b33a93e59602367f493cbb82954ac | %%
% map the indices with the corresponding index in the spase matrix
function varargout = indirect_method_f_model(z)
global auxdata ;
varargout{1} = indirect_method_F(z,auxdata) ;
if nargout > 1
varargout{2} = indirect_method_JF(z,auxdata) ;
end
end |
github | pins-ocs/TS-OCS-master | indirect_method_JF.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_JF.m | 2,203 | utf_8 | 35a42127406704cdf0a79e1f90e6d679 | %%
% map the indices with the corresponding index in the spase matrix
function jac = Jfun(z,auxdata)
N = auxdata.N ;
h = auxdata.h ;
k1 = auxdata.k1 ;
k2 = auxdata.k2 ;
nvars = auxdata.nvars ;
% calcolo f(z)
sx = 0 ;
sv = sx+N+1 ;
sl = sv+N+1 ;
sm = sl+N+1 ;
%x = z(sx+1:sv) ... |
github | pins-ocs/TS-OCS-master | indirect_method_extract_xvu.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_extract_xvu.m | 973 | utf_8 | 6ac60c6d8c7e9fb418eb5e31a5b18635 | %%
% discretization of dynamical system
%
function [x,v,u,lambda,mu,varargout] = indirect_method_extract_xvu(z,auxdata)
% map x, v, lambda, mu, u from vector z
N = auxdata.N ;
T_size = auxdata.T_size ;
h = T_size/N ;
sx = 0 ;
sv = sx+N+1 ;
sl = sv+N+1 ;
sm = sl+N+1 ;
x = z(sx+1:sv) ... |
github | pins-ocs/TS-OCS-master | indirect_method_F.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_F.m | 762 | utf_8 | 796163b461b2a6c0e734925a48e60151 | %
%
%
function f = indirect_method_F(z,auxdata)
N = auxdata.N ;
h = auxdata.h ;
k0 = auxdata.k0 ;
k1 = auxdata.k1 ;
k2 = auxdata.k2 ;
sx = 0 ;
sv = sx+N+1 ;
sl = sv+N+1 ;
sm = sl+N+1 ;
x = z(sx+1:sv) ;
v = z(sv+1:sl) ;
lambda = z(sl+1:sm) ;
mu = z(sm+1:sm+N+1) ;
u =... |
github | pins-ocs/TS-OCS-master | indirect_method_u_Dv.m | .m | TS-OCS-master/Notes_on_Numerical_Methods/point-mass-test/indirect_method_u_Dv.m | 641 | utf_8 | ff6af8d393a370cf55fa94f13e43b71c | %%
% map the indices with the corresponding index in the spase matrix
function DuDv = indirect_method_u_Dv(z,auxdata)
g = auxdata.g ;
k3 = auxdata.k3 ;
epsilon = auxdata.epsilon ;
N = auxdata.N ;
% calcolo f(z)
sx = 0 ;
sv = sx+N+1 ;
sl = sv+N+1 ;
sm = sl+N+1 ;
su = sm+N+1 ;
... |
github | CJ-Davies/Crossing-Reality-master | FtspDataAnalyzer.m | .m | Crossing-Reality-master/demo/tinyos-2.1.0/apps/tests/TestFtsp/FtspDataAnalyzer.m | 1,814 | utf_8 | b5321ca4514924c4534deb7594a32166 | %load file written out by FtspDataLogger.java class
%arg0 - filename, e.g. '1205543689171.report'
function FTSPDataAnalyzer(file, varargin)
[c1 c2 c3 c4 c5]= textread(file, '%u %u %u %u %u', 'commentstyle', 'shell');
data = [c2 c3 c4 c5]; %skipping the first column (java time)
data1 = sortrows(sortrows(data,1),2);... |
github | audiofilter/nmflib-master | nmf_kl_con.m | .m | nmflib-master/nmf_kl_con.m | 6,532 | utf_8 | c9cc0b8f5d052f36809c49a2aa5e7d50 | function [W,H,errs,vout] = nmf_kl_con(V,r,varargin)
% function [W,H,errs,vout] = nmf_kl_con(V,r,varargin)
%
% Implements Convolutive NMF as described in [1]:
%
% min D(V||W*H) s.t. W>=0, H>=0
%
% where V = sum_t W(t) shift(H,t) and the shift function moves H's columns
% t positions to the right (introducing colum... |
github | unconditional/projektarbeitcuda-master | idr.m | .m | projektarbeitcuda-master/Doc/IDR/idr.m | 6,474 | utf_8 | c91af7264e9e3dad28e573d7788fda5c | function [x,resvec,iter,err,info]=idrs(A,b,s,tol,maxit,P,x0,Q,angle );
%IDRS Induced Dimension Reduction method
% X = IDRS(A,B) attempts to solve the system of linear equations A*X=B
% for X. The N-by-N coefficient matrix A must be square and the right hand
% side column vector B must have length N. A may... |
github | unconditional/projektarbeitcuda-master | Kondensator_Edge.m | .m | projektarbeitcuda-master/reference/src/xby/matlab/AW_ MatrixCreator-Solvertester/Kondensator_Edge.m | 2,112 | utf_8 | fd8d3da1aeb9617abc298a5e93e52057 | function [Star,q]=Kondensator_Edge(ngrid)
xmin = -0.25; xmax = 0.25;
ymin = -0.25; ymax = 0.25;
zmin = 0; zmax = 0.01;
xbox = 0;
ybox = 0;
D.nx = ngrid;
D.ny = ngrid;
D.nz =1;
Mx = 1;
My = D.nx;
Np = D.nx*D.ny;
D.xmesh = linspace(xmin, xmax, D.nx);
D.ymesh = linspace(ymin, ymax, D.ny);
D.zmesh = linspace(... |
github | unconditional/projektarbeitcuda-master | nvmex_helper.m | .m | projektarbeitcuda-master/reference/src/Matlab_CUDA_1.1/nvmex_helper.m | 8,678 | utf_8 | 95177d5818ea641df37a6453697b13b1 | function errorCode = nvmex_helper(varargin)
%MEX_HELPER is a helper function that contains the code that MEX.M (an
% autogenerated file) executes. It sets up the inputs to call mex.pl (on PC)
% and mex (on Unix).
%
% For information on how to use MEX see MEX help by typing "help mex" or
% "mex -h".
... |
github | salivian/volsegtree-master | RecursiveSeg.m | .m | volsegtree-master/matlab/RecursiveSeg.m | 1,428 | utf_8 | fba5b89d752a02572ef835433c5931ca | %%% Place this file in and run it from the ncut code directory
function SegLabel = RecursiveSeg(I,nbSegments);
levels = ceil(log(nbSegments)/log(2));
[W,imageEdges] = ICgraph(I);
%%Filter out the zero
mask=find(I > 0);
LocalW=W(mask,mask);
[NcutDiscrete,NcutEigenvectors,NcutEigenvalues] = ncutW(LocalW,2);
nsegs=2;
... |
github | chiahan/vfx-project1-hdr-master | alignment.m | .m | vfx-project1-hdr-master/program/alignment.m | 2,876 | utf_8 | 7878ac3db0a8d106450804e19ffa1f18 | %
% This function takes two exposure images, and determines how much to move
% the second exposure (img2) in x and y to align it with the first exposure
% (img1)
%
% input
% g_img: 3 dimensional matrices, represneting the whole gray_image set.
% [row, col, i] for i = 1:number of images.
% shift_bits: the maximum n... |
github | chiahan/vfx-project1-hdr-master | tmoReinhard02.m | .m | vfx-project1-hdr-master/program/tmoReinhard02.m | 2,306 | utf_8 | 2d2d87829473c0ee5fa6fa8f28534e51 | %
% Tone Mapping Operator, by Reinhard 02 paper.
%
% input:
% img: 3 channel HDR img
% type_: 'global'(default) or 'local'.
% alpha_: scalar constant to specify a high key or low key. (0.18)
% delta: scalar constant to prevent log(0). (1e-6)
% white_: scalar constant, the smallest luminance to be mapped to 1.... |
github | chiahan/vfx-project1-hdr-master | readImages.m | .m | vfx-project1-hdr-master/program/readImages.m | 1,439 | utf_8 | 2fad8cc24ed0a9e76c2fb0011275fdcf | %
% read in several images with different exposures.
%
% input
% folder: folder name containing images.
% extension: file extension. default to 'jpg'.
%
% output
% images: 4 dimensional matrices, representing the whole image set.
% [row, col, channel, i] for i = 1:number of images.
% exposureTimes: (number, 1) matr... |
github | chiahan/vfx-project1-hdr-master | main.m | .m | vfx-project1-hdr-master/program/main.m | 3,180 | utf_8 | caf14abb423822e31ca2a62e3a9b62a1 | %
% alignment images, convert an image set into HDR, then tone mapping it.
%
% input:
% folder: the (relative) path containing the image set.
% type_: 'global' or 'local' tone mapping
% phi: used by local tone mapping
% epsilon: used by local tone mapping (find the max gaussian scale)
% lambda: smoothness fac... |
github | chiahan/vfx-project1-hdr-master | gsolve.m | .m | vfx-project1-hdr-master/program/gsolve.m | 1,638 | utf_8 | 6bf42b84750a0d74d011f6e271db9643 | %
% This code is from the following paper:
%
% P. E. Debevec and J. Malik, “Recovering High Dynamic Range Radiance Maps from
% Photographs,” Proceedings of SIGGRAPH 1997, ACM Press / ACM SIGGRAPH, 369–
% 378, 1997.
%
%
% gsolve.m − Solve for imaging system response function
%
% Given a set of pixel values observed for ... |
github | chiahan/vfx-project1-hdr-master | hdrDebevec.m | .m | vfx-project1-hdr-master/program/hdrDebevec.m | 1,040 | utf_8 | 0b94a0f174dd0e24018079b5374be842 | % input
% images: 4 dimensional matrices, representing the whole image set.
% [row, col, channel, i] for i = 1:number of images.
% g: 2 dimensional matrices, [0~255, channel]
% ln_t: [ln_e, i]for i = 1:number of images, representing image's log exposure time in second.
% w: the weighting function value for pixel v... |
github | Alzathar/b-tk.googlecode.backup-master | testSample.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/architecture/testSample.m | 150 | utf_8 | 8b67b7df9d9f1b950b8071780e330763 | function test_suite = testSample
initTestSuite;
function testMyCode
assertEqual(1, 1);
assertElementsAlmostEqual(1, 1.1);
assertTrue(10 == 10); |
github | Alzathar/b-tk.googlecode.backup-master | testFliplr.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/doc/example_subfunction_tests/testFliplr.m | 210 | utf_8 | dba2423d0ec496209cb1ad0e9db20302 | function test_suite = testFliplr
initTestSuite;
function testFliplrMatrix
in = magic(3);
assertEqual(fliplr(in), in(:, [3 2 1]));
function testFliplrVector
assertEqual(fliplr([1 4 10]), [10 4 1]);
|
github | Alzathar/b-tk.googlecode.backup-master | test_that.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/doc/+abc/+tests/test_that.m | 161 | utf_8 | 97300fd8d3adec69102d836a63110ca5 | % Do-nothing test used in the examples for organizing tests inside packages.
%
% Steven L. Eddins
% Copyright 2010 The MathWorks, Inc.
function test_that
|
github | Alzathar/b-tk.googlecode.backup-master | test_this.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/doc/+abc/+tests/test_this.m | 161 | utf_8 | fa26021122fc1ebe7ff54a143d85c458 | % Do-nothing test used in the examples for organizing tests inside packages.
%
% Steven L. Eddins
% Copyright 2010 The MathWorks, Inc.
function test_this
|
github | Alzathar/b-tk.googlecode.backup-master | testWithSetupError.m | .m | b-tk.googlecode.backup-master/Utilities/matlab_xunit/doc/examples_general/testWithSetupError.m | 297 | utf_8 | 754e7acc098bd1d5b72421ff071bc6af | function test_suite = testWithSetupError
%Example of a test with an error. The setup function calls cos with
%too many input arguments.
initTestSuite;
function testData = setup
testData = cos(1, 2);
function testMyFeature(testData)
assertEqual(1, 1);
function teardown(testData)
|
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