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 |
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github | weibeld/Understanding-LTE-With-Matlab-master | lteModulate.m | .m | Understanding-LTE-With-Matlab-master/Chap4_ModAndCode/processes/lteModulate.m | 2,454 | utf_8 | a023ae7d534bad3b3bbefa55c9551fe8 | % lteModulate - Modulate a bit string with QPSK, 16QAM, or 64QAM
%
% Usage:
% symb = modulate(bits, scheme)
%
% Input:
% bits: column vector of bits
% scheme: modulation scheme, 'QPSK'|'16QAM'|'64QAM'
%
% Output:
% symb: complex vector representing sequence of modulation symbols
%
% Understandig LTE With M... |
github | weibeld/Understanding-LTE-With-Matlab-master | lteDemodulate.m | .m | Understanding-LTE-With-Matlab-master/Chap4_ModAndCode/processes/lteDemodulate.m | 3,881 | utf_8 | 01cd3670352089d33452ab292702d75a | % lteDemodulate - Demodulate signal with QPSK, 16QAM, or 64QAM.
%
% Usage:
% out = lteDemodulate(signal, scheme, method, noiseVar)
%
% Input:
% signal: received signal as a complex vector (in-phase vs. quadrature)
% scheme: demodulation scheme, 'QPSK'|'16QAM'|'64QAM'
% method: demodulation decision method... |
github | weibeld/Understanding-LTE-With-Matlab-master | lteTurboDecode.m | .m | Understanding-LTE-With-Matlab-master/Chap4_ModAndCode/processes/lteTurboDecode.m | 1,892 | utf_8 | ac840764f70b7bdcb0e1c26a3b48c05d | % lteTurboDecode - Decode a coded block with the LTE turbo decoder.
%
% Usage:
% outBits = lteTurboDecode(llr, blockLength, maxIter)
%
% Input:
% llr: column vector of log-likelihood ratios for the received bits,
% as calculated by the soft-decision demodulator
% blockLength: length of the ... |
github | NISOx-BDI/Software_Comparison_Analyses-master | copy_gunzip.m | .m | Software_Comparison_Analyses-master/scripts/lib/copy_gunzip.m | 2,810 | utf_8 | c893880dc5407374a5d6060488ca2275 | % _________________________________________________________________________
% Copy to 'preproc_dir' and gunzip anatomical and fmri files found in
% 'study_dir' (and organised according to BIDS)
% _________________________________________________________________________
function copy_gunzip(study_dir, preproc_dir, vara... |
github | jianxiongxiao/SUN3Dtoolbox-master | visualizePointCloud.m | .m | SUN3Dtoolbox-master/SUN3Dloader/visualizePointCloud.m | 319 | utf_8 | cbee0bf4ab880bdd9fb16dc4e8ed626d |
function visualizePointCloud(XYZ,RGB, subsampleGap)
if ~exist('subsampleGap','var')
subsampleGap = 50;
end
XYZ = XYZ(:,1:subsampleGap:end);
RGB = RGB(:,1:subsampleGap:end);
scatter3(XYZ(1,:),XYZ(2,:),XYZ(3,:),ones(1,size(XYZ,2)),double(RGB)'/255,'filled');
axis equal
axis tight
end |
github | jianxiongxiao/SUN3Dtoolbox-master | loadjson.m | .m | SUN3Dtoolbox-master/SUN3Dloader/loadjson.m | 18,481 | ibm852 | 2e20e6284334f74abbd734c4ab630d2f | %% the rest of this file is all from JSON lab for reading a JSON file.
% http://www.mathworks.com/matlabcentral/fileexchange/33381
% version: JSONLAB v1.0 alpha (Optimus) is updated on 08/23/2013.
function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'par... |
github | jianxiongxiao/SUN3Dtoolbox-master | transformPointCloud.m | .m | SUN3Dtoolbox-master/SUN3Dloader/transformPointCloud.m | 128 | utf_8 | beb3c1e47fe0449a4e2694e0981ef459 |
function XYZtransform = transformPointCloud(XYZ,Rt)
XYZtransform = Rt(1:3,1:3) * XYZ + repmat(Rt(1:3,4),1,size(XYZ,2));
end |
github | jianxiongxiao/SUN3Dtoolbox-master | loadSUN3D.m | .m | SUN3Dtoolbox-master/SUN3Dloader/loadSUN3D.m | 4,761 | utf_8 | 38bf4e4b5facd72b616ab12217125c86 | function data = loadSUN3D(sequenceName, frameIDs)
if ~exist('sequenceName','var')
% load demo sequence
%sequenceName = 'hotel_mr/scan1';
sequenceName = 'hotel_umd/maryland_hotel3';
%sequenceName = 'brown_bm_1/brown_bm_1';
end
% the root path of SUN3D
% change it... |
github | JulBenistant/SMS-master | model_sabotage_last.m | .m | SMS-master/model_sabotage_last.m | 3,796 | utf_8 | 6444fe6aec91b4de2bfb717f41bbb001 | function model_sabotage
clear all;
close all;
%%DATA%%
reward_all(1,1) = 16;reward_all(1,2) = 12;reward_all(1,3) = 8;reward_all(1,4) = 4;
rank(1,1) = 2;rank(1,2) =3 ;rank(1,3) = 4;rank(1,4) = 5;
%%%% n = 10 per conditions
%Creation parameters, alpha first line and beta second line
% First column Beta... |
github | warehouse-picking-automation-challenges/rutgers_arm-master | calcPosRot.m | .m | rutgers_arm-master/prx_sensing/estimate_kinect_pos_rot_motoman/calcPosRot.m | 6,261 | utf_8 | 6bed56d6e81485286aeb1108e45729b3 | %{
@brief This function estimates the position and orientation of kinects on Motoman in world frame.
camPos estimated position
camRot estimated orientation
avgErr average error in case of evaluation
maxErr maximum error in case of evaluation
@param kinectName nam... |
github | warehouse-picking-automation-challenges/rutgers_arm-master | rot2quat.m | .m | rutgers_arm-master/prx_sensing/estimate_kinect_pos_rot_motoman/rot2quat.m | 277 | utf_8 | 71fa09326d8f1ee0f5613e23f1d70d37 | %[x y z w]
function [quat] = rot2quat(r)
aa = vrrotmat2vec(r);
if ( abs(aa(4)) < 1e-6 )
quat= NaN;
return;
end
aa(4) = aa(4) * 0.5;
sinAng = sin(aa(4));
quat = [ aa(1)*sinAng aa(2)*sinAng aa(3)*sinAng cos(aa(4))];
end |
github | rickyHong/caffe-rcnn-action-recognition-master | AveChannelToN.m | .m | caffe-rcnn-action-recognition-master/examples/action_recognition/matlab/AveChannelToN.m | 3,563 | utf_8 | ebf26d17d5dcc713702986b45f4778d6 | %%% Example
%%% model_def_file and model_def_file only the first filter size is different
%%%
%{
model_def_file = './Developy/ResNet-50-rgb-Test.prototxt';
model_file = '../../../models/action_recognition/ResNet-50-rgb-model.caffemodel';
Final_def_file = './Developy/ResNet-50-flow-Test-without-conv1.prototxt';
saved... |
github | rickyHong/caffe-rcnn-action-recognition-master | Make_VOC_Special_Data.m | .m | caffe-rcnn-action-recognition-master/matlab/FRCNN/Make_VOC_Special_Data.m | 1,566 | utf_8 | 2ffcb26a88c7d6850436fd2e796594a2 | % this code is inspired by VOCevaldet in the PASVAL VOC devkit
% Note: this function has been significantly optimized since ILSVRC2013
% Make_Faster_RCNN_Train_Data('../../VOCdevkit/VOC2007/ImageSets/Main/test.txt', '../../VOCdevkit/VOCcode', '../../VOCdevkit/VOC2007/Annotations', '../../examples/FRCNN/dataset/voc2007_... |
github | rickyHong/caffe-rcnn-action-recognition-master | Make_Faster_RCNN_Train_Data.m | .m | caffe-rcnn-action-recognition-master/matlab/FRCNN/Make_Faster_RCNN_Train_Data.m | 2,612 | utf_8 | f322b5a94577571815dccddf8a13c024 |
% this code is inspired by VOCevaldet in the PASVAL VOC devkit
% Note: this function has been significantly optimized since ILSVRC2013
% Make_Faster_RCNN_Train_Data('../../VOCdevkit/VOC2007/ImageSets/Main/train.txt', '../../VOCdevkit/VOCcode', '../../VOCdevkit/VOC2007/Annotations', '../../examples/FRCNN/dataset/voc200... |
github | rickyHong/caffe-rcnn-action-recognition-master | classification_demo.m | .m | caffe-rcnn-action-recognition-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | tvajtay/Click-master | sitrep.m | .m | Click-master/sitrep.m | 5,794 | utf_8 | 873c91bcc9b2839fac51703aad7e4a23 | function [] = sitrep( B )
%SITREP Creates Summary graphs from 2016 summer duration and intensity experiment
% Takes a string for an argument which should be the path to the starting
% directory of files to analyze. Function will look for all the *.mat
% files in the directory/s and create a figure with subplo... |
github | tvajtay/Click-master | seqer.m | .m | Click-master/seqer.m | 1,898 | utf_8 | 5209539f6078a5f7e80123139774f348 | function [] = seqer( start_directory )
%SEQER Identifies seq files and deletes them from the current and all
%sub-directories
% Usage: seqer('PATH TO DIRECTORY')
tstart = tic;
working_directory = cd;
function [fold_detect,file_detect] = detector(path)
cd(path)
b = dir();
... |
github | tvajtay/Click-master | tiffcrop.m | .m | Click-master/tiffcrop.m | 2,811 | utf_8 | 1f43face049f90c35b8b67322a48210b | function [] = tiffcrop( start_dir, coordinates )
%TIFFCROP Crops all tiff files
% Detailed explanation goes here
tstart = tic;
working_directory = cd;
addpath(cd)
addpath matlab
addpath(start_dir);
function [fold_detect,file_detect] = detector(path)
cd(path)
b = dir();
files = di... |
github | tvajtay/Click-master | rdir.m | .m | Click-master/rdir.m | 12,435 | utf_8 | 04112133f25d66e254ca35af639d4281 | function [varargout] = rdir(rootdir,varargin)
% RDIR - Recursive directory listing
%
% D = rdir(ROOT)
% D = rdir(ROOT, TEST)
% D = rdir(ROOT, TEST, RMPATH)
% D = rdir(ROOT, TEST, 1)
% D = rdir(ROOT, '', ...)
% [D, P] = rdir(...)
% rdir(...)
%
%
% *Inputs*
%
% * ROOT
%
% rdir(ROOT) lists the spec... |
github | tvajtay/Click-master | datastruct2.m | .m | Click-master/datastruct2.m | 4,992 | utf_8 | 17f4a05af32c560adee82eda48ea1e8d | function [] = datastruct2( start_directory, whiskers )
%DATASTRUCT Function to organize optogenetic whisker data
% The testing paradigm of AB 2017 optogenetic testing
% uses a unique sequence of durations. This function aims to organize all
% the individual data into a 3D struct with averages and SEM included. The
% ... |
github | tvajtay/Click-master | redoall.m | .m | Click-master/redoall.m | 4,873 | utf_8 | 476e74cbed512c3111ac74f1e842c50a | function [] = redoall( start_directory )
%REDOALL Does the tail end of Click using only the existing/corrected
%measurements files for all files in the directory
% Redo and Redoall are designed to be used while correcting whisker
% tracking data. When you save your corrections in the Whiski GUI it
% modifies... |
github | tvajtay/Click-master | datastruct.m | .m | Click-master/datastruct.m | 4,586 | utf_8 | eb42ce22a1185fc6868d8705f8bb22c9 | function [] = datastruct( start_directory, whiskers )
%DATASTRUCT Function to organize optogenetic whisker data
% The testing paradigm of the summer 2016 optogenetic testing and beyond
% uses a unique sequence of durations. This function aims to organize all
% the individual data into a 3D struct with averages and SE... |
github | SylviaHerbert/SitToStand-master | dynamics.m | .m | SitToStand-master/dynSys/@STS4D/dynamics.m | 2,021 | utf_8 | df6bc85faf338c8196c756e2f09281bb | function dx = dynamics(obj, ~, x, u, ~)
dx = cell(obj.nx,1);
dims = obj.dims;
returnVector = false;
if ~iscell(x)
returnVector = true;
x = num2cell(x);
u = num2cell(u);
end
for i = 1:length(dims)
dx{i} = dynamics_cell_helper(obj, x, u, obj.dims, obj.dims(i));
end
if returnVector
dx = cell2mat(dx);
end
end... |
github | SylviaHerbert/SitToStand-master | dynamics.m | .m | SitToStand-master/dynSys/@DubinsCar/dynamics.m | 820 | utf_8 | 5303b09b04854f3301eb067e846412ca | function dx = dynamics(obj, ~, x, u, d)
% Dynamics of the Dubins Car
% \dot{x}_1 = v * cos(x_3)
% \dot{x}_2 = v * sin(x_3)
% \dot{x}_3 = w
% Control: u = w;
%
% Mo Chen, 2016-06-08
if nargin < 5
d = [0; 0; 0];
end
dx = cell(obj.nx,1);
dims = obj.dims;
returnVector = false;
if ~iscell(x)
returnVector = ... |
github | SylviaHerbert/SitToStand-master | plotInitialStates.m | .m | SitToStand-master/testCode_sylvia/plotInitialStates.m | 1,306 | utf_8 | 06b2ce85427d6523e9385f1362e1eaaa | function plotInitialStates(g,data,schemeData)
max_v = (pi/8); % allowing for some sway
standing_min = [-pi/15, -max_v, -pi/15, -max_v];
standing_max = [pi/15, max_v, 0.15, max_v];
data0 = shapeRectangleByCorners(g, standing_min, standing_max);
[gPos,dataPos]=proj(g,data,[0 1 0 1],'min');
[~,dataPos0]=proj(g,dat... |
github | SylviaHerbert/SitToStand-master | simulateTrajectories.m | .m | SitToStand-master/testCode_sylvia/simulateTrajectories.m | 6,363 | utf_8 | c1fff9974d4430a65b67196cca8c12fe | function body=simulateTrajectories(g,data,tau,z0,schemeData)
uMode = 'min';
dt = .01;
if nargin < 5
height = 1.72;
mass = 62;
schemeData.M1 = 2*(0.1416*mass); % mass of thighs
schemeData.M2 = (.0694 +.4346)*mass; % mass of head-arms-trunk
schemeData.L0 = .25*height; % length of segment (shank)
sc... |
github | SylviaHerbert/SitToStand-master | lipsol_quiet.m | .m | SitToStand-master/OldCode/lipsol_quiet.m | 56,317 | utf_8 | 5a61914ad83d16cde2c60214937018ad | function [xsol,fval,lambda,exitflag,output] = lipsol_quiet(f,Aineq,bineq,Aeq,beq,lb,ub,options,defaultopt,computeLambda)
%LIPSOL Linear programming Interior-Point SOLver.
% X = LIPSOL(f,A,b) solves the linear programming problem
%
% min f'*x subject to: A*x <= b
% x
%
% X = LIPSOL(f,A,b,Aeq... |
github | joebling/graduate_essay-master | Sample2D.m | .m | graduate_essay-master/2D/Sample2D.m | 717 | utf_8 | be53a9f67b172f80cb774efda6b67d4b |
function [sampleweights,sampletri] = Sample2D(xout, yout)
% function [sampleweights,sampletri] = Sample2D(xout, yout)
% purpose: input = coordinates of output data point
% output = number of containing tri and interpolation weights
% [ only works for straight sided triangles ]
Globals2D;
% find... |
github | siguoyi/AIC_code-master | yall1_ext.m | .m | AIC_code-master/yall1_ext.m | 8,754 | utf_8 | 76c886a54959d5839d6d19d14211d8f2 | function [x Out] = yall1_ext(A, b, opts)
%
% A solver for L1-minimization models:
%
% min ||Wx||_{w,1}, st Ax = b
% min ||Wx||_{w,1} + (1/nu)||Ax - b||_1
% min ||Wx||_{w,1} + (1/2*rho)||Ax - b||_2^2
% min ||x||_{w,1}, st Ax = b and x > = 0
% min ||x||_{w,1} + (1/nu)||Ax - b||_1, st x > = 0
% min ||... |
github | siguoyi/AIC_code-master | GoogleOMP.m | .m | AIC_code-master/GoogleOMP.m | 8,546 | utf_8 | de9f2f56013b2dadfb66a12fd58a7a60 | function [x,r,normR,residHist, errHist] = GoogleOMP( A, b, k, errFcn, opts )
% x = OMP( A, b, k )
% uses the Orthogonal Matching Pursuit algorithm (OMP)
% to estimate the solution to the equation
% b = A*x (or b = A*x + noise )
% where there is prior information that x is sparse.
%
% "A" may be a matr... |
github | siguoyi/AIC_code-master | rd_sampling.m | .m | AIC_code-master/rd_sampling.m | 1,330 | utf_8 | 8ac7659eb0f60d9ff2d12dce917784f8 | function [y,S]=rd_sampling(x,L,h,N,M)
% This script randomly demodulates the input signal x and uniformly samples the result.
% Usage: [y,S]=rd_sampling(x,L,h,N,M)
% x: input multitone signal(approximates continuous-time multitone signal)
% L: specifies period of random +/- 1 sequence wrt Nyquist rate (1/Tp=W/L)
% h: ... |
github | siguoyi/AIC_code-master | greed_omp_chol_SparseLabWrap.m | .m | AIC_code-master/sparsify_0_4/GreedLab/OMP_algos/greed_omp_chol_SparseLabWrap.m | 1,720 | utf_8 | 00e1d59fdc4589755a9f03d6d7d9d3f8 | function [s, err_norm, iter_time]=greed_omp_chol_SparseLabWrap(A,Pt,x,m,s_initial,STOPCRIT,STOPTOL,MAXITER,verbose,comp_err,comp_time)
% Wrapper function for SparseLab SolveOMP algorithm
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Make P and Pt functions
%%... |
github | charlesq34/3dmodel_feature-master | polygon2voxel_double.m | .m | 3dmodel_feature-master/polygon2voxel/polygon2voxel_double.m | 5,872 | utf_8 | d0e1eae2993db447be74e337e5bcab4b | function Volume=polygon2voxel_double(FacesA,FacesB,FacesC,VerticesX,VerticesY,VerticesZ,VolumeSize,Wrap)
Vertices=[VerticesX(:) VerticesY(:) VerticesZ(:)]-1;
% List with all vertices coordinates of a face
FaceVertices=[Vertices(FacesA,:) Vertices(FacesB,:) Vertices(FacesC,:)];
Volume=false(VolumeSize);
Volume=Dr... |
github | harmankumar/AutoNav-master | findinvpoly.m | .m | AutoNav-master/test/undistort/undistortFunctions/findinvpoly.m | 1,757 | utf_8 | 2f701c21b6e7a02305a3e67ead605792 | %FINDINVPOLY finds the inverse polynomial specified in the argument.
% [POL, ERR, N] = FINDINVPOLY(SS, RADIUS, N) finds an approximation of the inverse polynomial specified in OCAM_MODEL.SS.
% The returned polynomial POL is used in WORLD2CAM_FAST to compute the reprojected point very efficiently.
%
% SS is... |
github | waps101/depth-from-polarisation-master | TRSfit.m | .m | depth-from-polarisation-master/utils/TRSfit.m | 797 | utf_8 | 7fb1249a64e6e065eede04ab1122584a | function [ Iun,rho,phi ] = TRSfit( angles,I )
%TRSFIT Nonlinear least squares optimisation to fit sinusoid
% Inputs:
% angles - vector of polarising filter angles
% I - vector of measured intensities
% Outputs:
% Iun, rho, phi - scalar values containing polarisation image params
%
% William Smit... |
github | vijaykoju/3D_ScatteringMatrix_RCWA-master | sqrte.m | .m | 3D_ScatteringMatrix_RCWA-master/sqrte.m | 1,456 | utf_8 | 1a23539c5a8ed61805f7a44c1f83e7a4 | % sqrte.m - evanescent SQRT for waves problems
%
% Usage: y = sqrte(z)
%
% z = array of complex numbers
% y = square root of z
%
% Notes: for z = a-j*b, y is defined as follows:
%
% [ sqrt(a-j*b), if b~=0
% y = [ sqrt(a), if b==0 and a>=0 ... |
github | JulienDufour/velodyne_tracking-master | analyze_leafsize_stats.m | .m | velodyne_tracking-master/include/nanoflann/perf-tests/analyze_leafsize_stats.m | 1,502 | utf_8 | 5bcc7168b358ccc2a7e4345373d29a85 | function [] = analyze_leafsize_stats()
% Compute the stats from the result files of performance tests wrt
% the max. leaf size
close all;
%D=load('LEAF_STATS.txt');
%D=load('LEAF_STATS_DOUBLE.txt');
D=load('LEAF_STATS_DATASET.txt');
MAXs = unique(D(:,2));
COLs = {'k','b','r','g'}; % C... |
github | JulienDufour/velodyne_tracking-master | analyze_stats.m | .m | velodyne_tracking-master/include/nanoflann/perf-tests/analyze_stats.m | 2,369 | utf_8 | b6d766f7cf55dee127cf6bb3085f2776 | function [] = analyze_stats()
% Compute the stats from the result files of flann & nanoflann performance tests
%
close all;
[Nsf, Tf_M, Tf_STD] = analyze_file('stats_flann.txt');
[Nsnf, Tnf_M, Tnf_STD]= analyze_file('stats_nanoflann.txt');
titles={'Convert into Matrix<>', 'Build index', 'One 3D... |
github | iiscleap/FeatureExtractionUsingFDLP-master | do_lpc_wiener.m | .m | FeatureExtractionUsingFDLP-master/do_lpc_wiener.m | 5,316 | utf_8 | 461db6771e4592975ba5858ca6d6e482 | function a=do_lpc_wiener(signal,fs,flen,fp,NIS)
% ***************************************************************
% USAGE
% output=do_lpc_wiener(signal,fs,flen,fp,NIS)
% Wiener filtering using noise estimates obtained from the ETSI VAD
% Implementation adapted from Rainer Martin IEEE SP 2007
% Feb 2011
% *******... |
github | iiscleap/FeatureExtractionUsingFDLP-master | check_VAD.m | .m | FeatureExtractionUsingFDLP-master/check_VAD.m | 2,732 | utf_8 | 922f37f1e88f5b76300cec2f14cdb8f1 | function flag_VAD = check_VAD(x,sr)
% FUnction to perform VAD similar to ETSI feature extraction
% Samples should be read from raw format file
% Details in ETSI ES 202 050 Document
% CONSTANTS
if sr ~= 8000
x = resample(x,8000,sr); % Resample the test data to 8kHz for determining VAD information
end
sr = 8000;
... |
github | iiscleap/FeatureExtractionUsingFDLP-master | do_lpc_wiener2.m | .m | FeatureExtractionUsingFDLP-master/do_lpc_wiener2.m | 4,687 | utf_8 | ef3b0ecc7b75a113b0d3be30a910cd7c | function a=do_lpc_wiener2(signal,fs,flen,fp)
% output=hlpc_Wiener(signal,fs,flen,fp)
% Implements FDLP with temporal envelope subtraction.
% Created: MAY-09
% Modification to work on spectral autocorrelation function
IS=0.18; %Initial Silence or Noise Only part in secon8s
W=fix(.025*fs); ... |
github | mcv-m1-project/Team4-master | TrafficSignDetection.m | .m | Team4-master/TrafficSignDetection.m | 6,423 | utf_8 | fb3ab5108eed95507bf253c519d06025 | %
% Template example for using on the validation set.
%
function TrafficSignDetection(directory, pixel_method, window_method, decision_method)
% TrafficSignDetection
% Perform detection of Traffic signs on images. Detection is performed first at the pixel level
% using a color segmentation. Then, using t... |
github | mcv-m1-project/Team4-master | TrafficSignDetection_test.m | .m | Team4-master/TrafficSignDetection_test.m | 3,833 | utf_8 | 3226776d5783a04983ae4ac076decf92 | %
% Template example for using on the test set (no annotations).
%
function TrafficSignDetection_validation(input_dir, output_dir, pixel_method, window_method, decision_method)
% TrafficSignDetection
% Perform detection of Traffic signs on images. Detection is performed first at the pixel level
% using a... |
github | mcv-m1-project/Team4-master | HoughSquareTriangle.m | .m | Team4-master/week5/HoughSquareTriangle.m | 740 | utf_8 | 148e7c803508a80f487e7120dfc8cdb0 |
function SquareTriangle = HoughSquareTriangle(mask, windowCandidate)
SquareTriangle = 0;
% Compute standard hough transform
[H, ~, ~] = hough(mask);
%[H, THETA, RHO] = hough(mask);
% imshow(H, [], 'XData', THETA, 'YData', RHO, 'InitialMagnification', 'fit');
% xlabel('\theta'), ylabel('\rho');
% axis on, a... |
github | mcv-m1-project/Team4-master | colorspace_demo.m | .m | Team4-master/colorspace/colorspace_demo.m | 6,856 | utf_8 | f7d66bc3e0e1bf1611fbd525c617323c | function colorspace_demo(Cmd)
% Demo for colorspace.m - 3D visualizations of various color spaces
% Pascal Getreuer 2006
if nargin == 0
% Create a figure with a drop-down menu
figure('Color',[1,1,1]);
h = uicontrol('Style','popup','Position',[15,10,90,21],...
'BackgroundColor',[1,1,1],'Value',2,...
... |
github | mcv-m1-project/Team4-master | colorspace.m | .m | Team4-master/colorspace/colorspace.m | 16,178 | utf_8 | 2ca0aee9ae4d0f5c12a7028c45ef2b8d | function varargout = colorspace(Conversion,varargin)
%COLORSPACE Transform a color image between color representations.
% B = COLORSPACE(S,A) transforms the color representation of image A
% where S is a string specifying the conversion. The input array A
% should be a real full double array of size Mx3 or MxN... |
github | mcv-m1-project/Team4-master | CreateCircles.m | .m | Team4-master/week4/CreateCircles.m | 275 | utf_8 | e72d5b436744e88c4cc4f015c01ad53d |
function [circleedge] = CreateCircles(rad)
width = rad*2;
height = rad*2;
radius = rad;
centerW = width/2;
centerH = height/2;
[W,H] = meshgrid(1:width,1:height);
kl = ((W-centerW).^2 + (H-centerH).^2) < radius^2;
circleedge = edge(kl,'Canny');
%imshow(Circ);
end |
github | openWSNet/CoCMA-master | fitness.m | .m | CoCMA-master/fitness.m | 1,345 | utf_8 | 47a744da7831fbb62bfebb289d6afb74 | % generation_index : which generation is picked up to evaluate the fitness of each chromosome
function [f,coveraged_target_count,active_node_num]=fitness(generation_index)
global pop_size sense_node target_x target_y sensor_selected target_coveraged target_covered_for_each_node
for pop=1:pop_size
for... |
github | openWSNet/CoCMA-master | LEACH_original_m.m | .m | CoCMA-master/comapred LEACH-based algorithms/LEACH_original_m.m | 7,434 | utf_8 | 5897893bcf84d471552ee8039cfadc36 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [DEAD,avg_packets_to_bs,avg_packets_to_ch,last_round,avg_ch]=LEACH_original_m(rmax,rs,p)
% load data1; %load the same node_x node_y
sense_node=400;
packet_bit=2000;
grid_range_x=200;
grid_range_y=200;
span=0.04;
si... |
github | openWSNet/CoCMA-master | LEACH_coverage_u.m | .m | CoCMA-master/comapred LEACH-based algorithms/LEACH_coverage_u.m | 8,210 | utf_8 | fc345995a0b2e001a0388aab44cc172e | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [DEAD,x,coverage_rec,avg_packets_to_bs,avg_packets_to_ch,last_round,avg_ch]=LEACH_coverage_u(rmax,a,b,rs,p)
% load data1; %load the same node_x node_y
sense_node=400;
packet_bit=2000;
grid_range_x=200;
grid_range_y=200;
... |
github | openWSNet/CoCMA-master | LEACH_original.m | .m | CoCMA-master/comapred LEACH-based algorithms/LEACH_original.m | 7,579 | utf_8 | 387489da9f9cfa7e3f822a9105e75241 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% PARAMETERS %%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [avg_packets_to_bs,avg_packets_to_ch,last_round,avg_ch]=LEACH_original(rmax,a,b,rs,p)
load data1; %load the same node_x node_y
sense_node=100;
packet_bit=2000;
grid_range_x=100;
grid_range_y=100;
rand_range_x=10;
rand_range... |
github | nzhao/OpticalPumping-master | dispMat.m | .m | OpticalPumping-master/misc/dispMat.m | 2,668 | utf_8 | 168255f89acd029d14bb0b4a15920206 | function fig = dispMat( mat, xlabel, ylabel )
[dim1, dim2]=size(mat);
if nargin == 1
xlabel=cell(1, dim1);
ylabel=cell(1, dim2);
for nx=1:dim2
xlabel{nx}=['x', num2str(nx)];
end
for ny=1:dim1
ylabel{ny}=['y', num2str(ny)];
end
end
f... |
github | kxcontrib/weaves-master | doxytest.m | .m | weaves-master/tools/doxygen0/doxytest.m | 522 | utf_8 | 63b5f62d66f2a26dbc0938e7ac7d286d | %% @file
% test file
%%
% test function returns one
% @param car input variable
% @return one
function m = doxytest(car)
n=car;
% normal comment
m=n;
%%
% test function2 returns nothing
% @param philbert input variable
%
function subfunct(philbert)
n=philbert/2; % end of line comment
m=n;
%%
% last ... |
github | kxcontrib/weaves-master | matlab-sample.m | .m | weaves-master/tools/doxygen0/matlab-sample.m | 522 | utf_8 | 63b5f62d66f2a26dbc0938e7ac7d286d | %% @file
% test file
%%
% test function returns one
% @param car input variable
% @return one
function m = doxytest(car)
n=car;
% normal comment
m=n;
%%
% test function2 returns nothing
% @param philbert input variable
%
function subfunct(philbert)
n=philbert/2; % end of line comment
m=n;
%%
% last ... |
github | shreyas253/variational_NP_BMM-master | freeEnergyCalc.m | .m | variational_NP_BMM-master/freeEnergyCalc.m | 5,290 | utf_8 | 2e228dd0f5f65e995386622eb1bdf77d | % (C) 2016 Shreyas Seshadri, Ulpu Remes and Okko Rasaen
% MIT license
% For license terms and references, see README.txt
function [ freeEnergy,term1 , term2 , term3 , term4,term4_1,term4_2 ] = freeEnergyCalc( prior,post,r,op,extra )
[N,K] = size(r);
D = size(extra.xkBar,1);%length(prior.m0);
%% term1 = E_q[ln(p(X|Z,m... |
github | shreyas253/variational_NP_BMM-master | logNormalize.m | .m | variational_NP_BMM-master/logNormalize.m | 415 | utf_8 | 35e0df2aa047d33cadca5b87b8d74b49 | % (C) 2016 Shreyas Seshadri, Ulpu Remes and Okko Rasaen
% MIT license
% For license terms and references, see README.txt
function y = logNormalize( x )
%LOGNORMALIZE
% x is a 2 D matrix to be normalized along dim 2
% y(:,i) = exp(x(:,i)) / sum(exp(x(:,i)))
[d,k] = size(x);
x_max = max(x, [], 2);
x_max(x_max==-inf) =... |
github | shreyas253/variational_NP_BMM-master | postUpdate.m | .m | variational_NP_BMM-master/postUpdate.m | 4,246 | utf_8 | 8b73a4faeb1e6a7bdb200c8eae5658a7 | % (C) 2016 Shreyas Seshadri, Ulpu Remes and Okko Rasaen
% MIT license
% For license terms and references, see README.txt
function [ post,extra,extra_V ] = postUpdate(x,r,prior,op,extra_V)
K = size(r, 2);
[N,D] = size(x);
threshold_for_Nk = 1.0e-200; % to avoid the problem of infinity
Nk = sum(r,1); % 1*K
I = find(Nk... |
github | shreyas253/variational_NP_BMM-master | updateR.m | .m | variational_NP_BMM-master/updateR.m | 2,345 | utf_8 | d96c19598c9d4dc08e1ca311fb7c2275 | % (C) 2016 Shreyas Seshadri, Ulpu Remes and Okko Rasaen
% MIT license
% For license terms and references, see README.txt
function [ r,extra ] = updateR( x,post,op,extra_V )
[N,D] = size(x);
K = op.K;
rho = zeros(N,K);
%rho2= rho;
E_lnx = zeros(N,K);
for k=1:K
%% weight part
if strcmp(op.Pi_Type,'DP') || st... |
github | shreyas253/variational_NP_BMM-master | logdet.m | .m | variational_NP_BMM-master/logdet.m | 281 | utf_8 | 40b8b1dd68ce32c49253a6f461024099 | % (C) 2016 Shreyas Seshadri, Ulpu Remes and Okko Rasaen
% MIT license
% For license terms and references, see README.txt
function [y] = logdet( x )
% y = logdet(x)
% calculates the log determinant of x
[t error] = chol(x);
if error
error('error');
end
y = sum(log(diag(t))) *2;
|
github | shreyas253/variational_NP_BMM-master | reorderFE.m | .m | variational_NP_BMM-master/reorderFE.m | 671 | utf_8 | 460db17c0e40efcaa2f8cd6bed336cc7 | % (C) 2016 Shreyas Seshadri, Ulpu Remes and Okko Rasaen
% MIT license
% For license terms and references, see README.txt
function [ extra,r ] = reorderFE( extra,r,op,extra_V )
%REORDERFE
% reorder all variables according to the descending Nk
[~,newI] = sort(extra.Nk,'descend');
r = r(:,newI);
extra.E_lnPik = extra... |
github | shreyas253/variational_NP_BMM-master | wishartEntropy.m | .m | variational_NP_BMM-master/wishartEntropy.m | 611 | utf_8 | f4f12762a1de3ce42252bbbc0260e23c | % (C) 2016 Shreyas Seshadri, Ulpu Remes and Okko Rasaen
% MIT license
% For license terms and references, see README.txt
function [ entropyWish ] = wishartEntropy( W,v )
%WISHARTENTROPY
% inputs - K Wishart distribution parameters W(:,:,k) and v(k), for k=1:K
% output - K array with entropy of each Wishart distributio... |
github | sysbiolux/FALCON-master | distinguishable_colors.m | .m | FALCON-master/FALCON/ThirdParty/distinguishable_colors.m | 5,753 | utf_8 | 57960cf5d13cead2f1e291d1288bccb2 | function colors = distinguishable_colors(n_colors,bg,func)
% DISTINGUISHABLE_COLORS: pick colors that are maximally perceptually distinct
%
% When plotting a set of lines, you may want to distinguish them by color.
% By default, Matlab chooses a small set of colors and cycles among them,
% and so if you have more than ... |
github | sysbiolux/FALCON-master | FalconGUI.m | .m | FALCON-master/FALCON/Source/FalconGUI.m | 26,749 | utf_8 | 3c79a6f9cc1f7e5f0549f3527df0303d | function varargout = FalconGUI(varargin)
% Run FALCON from the Graphical User Interface
%
% :: Contact ::
% Prof. Thomas Sauter, University of Luxembourg, thomas.sauter@uni.lu
% Sebastien De Landtsheer, University of Luxembourg, sebastien.delandtsheer@uni.lu
%FALCONGUI MATLAB code file for FalconGUI.fig
% FALCONG... |
github | TaihuaLi/DMC-Hackathon-master | LSPF_case.m | .m | DMC-Hackathon-master/LSPF_case.m | 3,160 | utf_8 | 41a34bba92344bb793a1dfe752e4bbba |
function [x_prediction,X1part,Error]=LSPF_case(N,wear,Time,m_setting)
Pp1=[0.01 -10 -10 -1 0.03];
Pp2=[1 10 10 1 0.03];
f=m_setting(1);
d=m_setting(2);
y=wear;
n_data=length(y);
for i=1:n_data
if i==1
train_time(i)=Time(i);
else
train_time(i)=Time(i)-Time(i-1);
end
end
ti... |
github | sckangz/CIKM16-master | dualgraph.m | .m | CIKM16-master/dualgraph.m | 576 | utf_8 | c19053a2bf9c0c68c0af0cba1eb38989 |
function [hr,arhr]=dualgraph(Trainn,test,test_zhong,L2,L,alpha,beta)
X=Trainn;
U=lyap(beta*L2+eye(size(L2)),alpha*L,-X);
zhong = zeros(1,5);
po = 0;
REC=U;
hr = zeros(1,5);
for i = 1:size(Trainn,1)
value = REC(i,test{i});
value1 = REC(i,test_zhong(i));
position = length(find(value > value1)) + 1;
for n... |
github | AndyWood91/automatic_attention-master | awareInstructions.m | .m | automatic_attention-master/Reward vs predictiveness/functions/awareInstructions.m | 2,030 | utf_8 | ca351ff50f5583656fa96a8857b648d6 |
function awareInstructions()
global awareInstrPause
instructStr1 = 'The eye tracking task is now finished - it''s fine to take your chin out of the chin rest.\n\nDuring this task, the amount that you could win on each trial was determined by the colour of the coloured circle that appeared on that trial. \n\nIn ... |
github | AndyWood91/automatic_attention-master | runTrials.m | .m | automatic_attention-master/Reward vs predictiveness/functions/runTrials.m | 26,982 | utf_8 | 5182421dddd5cffd0f3a508c0b4575c9 |
function sessionPay = runTrials(exptPhase)
global MainWindow
global scr_centre DATA datafilename p_number
global distract_col
global white gray yellow
global bigMultiplier smallMultiplier medMultiplier
global stim_size stimLocs
global stimCentre aoiRadius
global fix_aoi_radius
global instrCondition
global... |
github | AndyWood91/automatic_attention-master | initialInstructions.m | .m | automatic_attention-master/Reward vs predictiveness/functions/initialInstructions.m | 2,260 | utf_8 | 9bf667c3725402d691376092d5371c96 |
function initialInstructions()
global MainWindow white
instructStr1 = 'On each trial a cross will appear inside a circle, and a yellow spot will show you where the computer thinks your eyes are looking. You should fix your eyes on the cross. After a short time the cross will turn yellow and the spot will disapp... |
github | AndyWood91/automatic_attention-master | gaze_contingent_fixation.m | .m | automatic_attention-master/Reward vs predictiveness/functions/gaze_contingent_fixation.m | 21,183 | utf_8 | eff5ad8d36ff540f8cfb5d580414e5d5 | function [] = gaze_contingent_fixation(main_window, screen_dimensions)
% GAZE_CONTINGENT_FIXATION:
global scr_centre DATA p_number
global stimLocs
global fix_aoi_radius
global softTimeoutDuration
scr_centre
exptPhase = 0;
gamma = 0.2; % Controls smoothing of displayed gaze location. Lower values gi... |
github | AndyWood91/automatic_attention-master | exptInstructions.m | .m | automatic_attention-master/Reward vs predictiveness/functions/exptInstructions.m | 4,289 | utf_8 | 40c2aa4fee425ff3905b8551f342b135 |
function exptInstructions
global MainWindow white
global bigMultiplier smallMultiplier medMultiplier
global centOrCents
global instrCondition
global softTimeoutDuration
instructStr1 = 'The rest of this experiment is similar to the trials you have just completed. On each trial, you should move your eyes to t... |
github | AndyWood91/automatic_attention-master | get_details.m | .m | automatic_attention-master/program/functions/get_details.m | 11,841 | utf_8 | 154b6a2ffb0eafaf3f68b4e0ac334cc5 | %% get_details
% identifying information (age, gender, hand) are stored separately from
% experiment information for anonymity.
% TODO: turn inputs into a class and make validation a method.
%% code
function [DATA] = get_details(title, conditions, sessions, bonus)
% variable declarations
star... |
github | ZenDevelopmentSystems/Coursera-Robotics-Perception-master | Nonlinear_Triangulation.m | .m | Coursera-Robotics-Perception-master/RoboticsPerceptionWeek4AssignmentCode/Nonlinear_Triangulation.m | 1,785 | utf_8 | 32a047773130a4ab071bbf38cec9e3e3 | function X = Nonlinear_Triangulation(K, C1, R1, C2, R2, C3, R3, x1, x2, x3, X0)
%% Nonlinear_Triangulation
% Refining the poses of the cameras to get a better estimate of the points
% 3D position
% Inputs:
% K - size (3 x 3) camera calibration (intrinsics) matrix for both
% cameras
% x
% Outputs:
% X ... |
github | ZenDevelopmentSystems/Coursera-Robotics-Perception-master | project_objects.m | .m | Coursera-Robotics-Perception-master/RoboticsPerceptionWeek1AssignmentCode/project_objects.m | 1,746 | utf_8 | 8afaa0a5652ddc9c5258451f76584c1f | function project_objects( f, pos, points, fid )
% render synthetic image using given camera focal length and camera
% position
%
% Input:
% - f: double camera focal length
% - pos: double represent camera center position in z axis.
% - points: 3D coordinates for vetice on polygons (use "load points.mat" to get)
... |
github | NSGeophysics/GPR-O-master | detrendData.m | .m | GPR-O-master/tools/detrendData.m | 1,077 | utf_8 | f2c7085420ed1231311fed7d363b695c | function data=detrendData(data,lines)
% dataout=detrendData(data,lines)
%
% Removes a linear trend in the data
%
% INPUT:
%
% data The data you want to smooth
% lines If you only want to do gain for one single line or a goup of
% lines, then you can enter them: [0] or [0 3 4]. If you want to
% ... |
github | noxtoby/mrtrix3_14-master | read_mrtrix.m | .m | mrtrix3_14-master/matlab/read_mrtrix.m | 3,249 | utf_8 | 0bcbcfe37284821d3767456cfd6e5724 | function image = read_mrtrix (filename)
% function: image = read_mrtrix (filename)
%
% returns a structure containing the header information and data for the MRtrix
% format image 'filename' (i.e. files with the extension '.mif' or '.mih').
image.comments = {};
f = fopen (filename, 'r');
assert(f ~= -1, 'error open... |
github | xuehuazhao/network-master | VBS_syn_main.m | .m | network-master/VBS_syn_main.m | 7,752 | utf_8 | 3a8ff94ccddc890c5431b7cba63dc253 | function VBS_syn_main()
close all;clc;clear all;
nmiset=zeros(11,1);
times=1;
for p=0:0 %10
for i=1:times
%% Data input
[A,Q] = GenerateSignAYang(4,32,32,0.5,0.5,p*0.05);
% A=load('war.mat');
% A=A.g_A;
imagesc(A)
k_min=4; k_max=4;
evidenceset=[];s_tao=[];s_rho=[];s_mu=[];s_evi... |
github | xuehuazhao/network-master | VBS_fast_main.m | .m | network-master/VBS_fast_main.m | 7,583 | utf_8 | affeceb8b1ca0d53977f8e82f28a6812 | function VBS_fast_main()
close all;clc;clear all;
nmiset=zeros(11,1);
times=1;
for p=0:0 %10
for i=1:times
%% Data input
% [A,Q] = GenerateSignAYang(4,32,32,0.5,p*0.05,0.5);
A=load('4_10000.dat');
A=sparse(A(:,1),A(:,2),A(:,3),10000,10000);
% [A]=generate_RN_signed_large()
% A=load('war_max.ma... |
github | lskk/AnalisisHRVFrekuensiDomain-master | grafik.m | .m | AnalisisHRVFrekuensiDomain-master/grafik.m | 11,850 | utf_8 | 5411d8ddf821de39710f5ea95ff001a0 | function varargout = grafik(varargin)
% grafik MATLAB code for grafik.fig
% grafik, by itself, creates a new grafik or raises the existing
% singleton*.
%
% H = grafik returns the handle to a new grafik or the handle to
% the existing singleton*.
%
% grafik('CALLBACK',hObject,eventData,handles,... |
github | lskk/AnalisisHRVFrekuensiDomain-master | progressbar.m | .m | AnalisisHRVFrekuensiDomain-master/progressbar.m | 11,767 | utf_8 | 06705e480618e134da62478338e8251c | function progressbar(varargin)
% Description:
% progressbar() provides an indication of the progress of some task using
% graphics and text. Calling progressbar repeatedly will update the figure and
% automatically estimate the amount of time remaining.
% This implementation of progressbar is intended to be extreme... |
github | lskk/AnalisisHRVFrekuensiDomain-master | gui.m | .m | AnalisisHRVFrekuensiDomain-master/gui.m | 13,137 | utf_8 | b228ebc0bbbab5219de6edcb3f6cc5f4 | function varargout = gui(varargin)
% GUI M-file for gui.fig
% GUI, by itself, creates a new GUI or raises the existing
% singleton*.
%
% H = GUI returns the handle to a new GUI or the handle to
% the existing singleton*.
%
% GUI('CALLBACK',hObject,eventData,handles,...) calls the local
% f... |
github | lskk/AnalisisHRVFrekuensiDomain-master | tabel.m | .m | AnalisisHRVFrekuensiDomain-master/tabel.m | 3,508 | utf_8 | cd0b40fa091b822759b089d7c3c7c6c6 | function varargout = tabel(varargin)
% TABEL MATLAB code for tabel.fig
% TABEL, by itself, creates a new TABEL or raises the existing
% singleton*.
%
% H = TABEL returns the handle to a new TABEL or the handle to
% the existing singleton*.
%
% TABEL('CALLBACK',hObject,eventData,handles,...) cal... |
github | lskk/AnalisisHRVFrekuensiDomain-master | svmtrain.m | .m | AnalisisHRVFrekuensiDomain-master/svm/svmtrain.m | 21,061 | utf_8 | a33d60eaa540522a02fddac6c14a0918 | function net = svmtrain(net, X, Y, alpha0, dodisplay)
% SVMTRAIN - Train a Support Vector Machine classifier
%
% NET = SVMTRAIN(NET, X, Y)
% Train the SVM given by NET using the training data X with target values
% Y. X is a matrix of size (N,NET.nin) with N training examples (one per
% row). Y is a column vect... |
github | cvjena/analyzing-chimpanzees-master | progressbar.m | .m | analyzing-chimpanzees-master/misc/progressbar/progressbar.m | 12,448 | utf_8 | a729a93aef1e25fd2049e323bd66394a | function remain_time_string = progressbar(varargin)
% Description:
% progressbar() provides an indication of the progress of some task using
% graphics and text. Calling progressbar repeatedly will update the figure and
% automatically estimate the amount of time remaining.
% This implementation of progressbar... |
github | cvjena/analyzing-chimpanzees-master | pipeline_all_about_apes.m | .m | analyzing-chimpanzees-master/pipeline/pipeline_all_about_apes.m | 11,784 | utf_8 | 75ac9a9450913b6acd133c5654583cc4 | function str_results = pipeline_all_about_apes ( img, str_settings )
% function str_results = pipeline_all_about_apes ( img, str_settings )
% BRIEF
%
%
% INPUT
%
% str_settings -- struct, optional, the following fields are supported
%
% OUTPUT
%
% author: Alexander Freytag
str_results = [];
%... |
github | cvjena/analyzing-chimpanzees-master | getDatasetNamesAllChimpansees.m | .m | analyzing-chimpanzees-master/preprocess/getDatasetNamesAllChimpansees.m | 3,435 | utf_8 | 86e31e444f18291efafc3c84087216ae | function s_dataset_names = getDatasetNamesAllChimpansees ( s_filelist )
% fileId value - open the file
fid = fopen( s_filelist );
% reads data from open test file into cell array (%s -> read string)
s_images = textscan(fid, '%s', 'Delimiter','\n');
% get all images
s_images = s_images... |
github | cvjena/analyzing-chimpanzees-master | xml2struct.m | .m | analyzing-chimpanzees-master/preprocess/xml2struct/xml2struct.m | 6,955 | utf_8 | 58f0b998cc71b30b4a6a12b330cfe950 | function [ s ] = xml2struct( file )
%Convert xml file into a MATLAB structure
% [ s ] = xml2struct( file )
%
% A file containing:
% <XMLname attrib1="Some value">
% <Element>Some text</Element>
% <DifferentElement attrib2="2">Some more text</Element>
% <DifferentElement attrib3="2" attrib4="1">Even more t... |
github | qijiezhao/MachineLearning-master | submit.m | .m | MachineLearning-master/Exercise 8/ex8/submit.m | 17,509 | utf_8 | 11676b36395cc2443b2fdf3ad55b562e | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | qijiezhao/MachineLearning-master | submitWeb.m | .m | MachineLearning-master/Exercise 8/ex8/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | qijiezhao/MachineLearning-master | submit.m | .m | MachineLearning-master/Exercise 7/ex7/submit.m | 16,952 | utf_8 | bc03673b87f8ab399ff79b67b7f30f73 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | qijiezhao/MachineLearning-master | submitWeb.m | .m | MachineLearning-master/Exercise 7/ex7/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | qijiezhao/MachineLearning-master | submit.m | .m | MachineLearning-master/Exercise 1/ex1/submit.m | 15,593 | utf_8 | d718bd2b3f48972e91120193823816c5 | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | qijiezhao/MachineLearning-master | submit.m | .m | MachineLearning-master/Exercise 2/ex2/submit.m | 17,080 | utf_8 | 64dff98a31cec6eb759506bbed3364fe | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | qijiezhao/MachineLearning-master | submitWeb.m | .m | MachineLearning-master/Exercise 2/ex2/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | qijiezhao/MachineLearning-master | submit.m | .m | MachineLearning-master/Exercise 4/ex4/submit.m | 17,123 | utf_8 | b9d8a27e8fe9b6f74252918dc255947d | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | qijiezhao/MachineLearning-master | submitWeb.m | .m | MachineLearning-master/Exercise 4/ex4/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | qijiezhao/MachineLearning-master | submit.m | .m | MachineLearning-master/Exercise 3/ex3/submit.m | 15,315 | utf_8 | b48d02d5c50869b6409f5e6d1b99ad06 | function submit(partId)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isempty(partId)... |
github | qijiezhao/MachineLearning-master | submit.m | .m | MachineLearning-master/Exercise 5/ex5/submit.m | 17,205 | utf_8 | 3ec3e311dc8ee1f8ee36bc04f8e89804 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | qijiezhao/MachineLearning-master | submitWeb.m | .m | MachineLearning-master/Exercise 5/ex5/submitWeb.m | 827 | utf_8 | bfb2fa08cac9d8d797e3071d3fdd7ca1 | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on ... |
github | qijiezhao/MachineLearning-master | submit.m | .m | MachineLearning-master/Exercise 6/ex6/submit.m | 16,830 | utf_8 | 6844114ab6410b81b00d62f25f193d97 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | qijiezhao/MachineLearning-master | porterStemmer.m | .m | MachineLearning-master/Exercise 6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
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