plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | rossimattia/light-field-super-resolution-master | lc1c2.m | .m | light-field-super-resolution-master/lc1c2.m | 2,809 | utf_8 | 2cb0985cfde92e5980cbb393c8b8f752 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | psnrfr.m | .m | light-field-super-resolution-master/psnrfr.m | 1,095 | utf_8 | 63c482a4498723630c581c282420c089 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | lf2col.m | .m | light-field-super-resolution-master/lf2col.m | 1,007 | utf_8 | 4a7d4b7e99d8a0246e5ceb3d3cc1fca6 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | blurmat.m | .m | light-field-super-resolution-master/blurmat.m | 1,939 | utf_8 | c2a87fb10acdb46bebdd93ec0aeb8e13 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | psnrlf.m | .m | light-field-super-resolution-master/psnrlf.m | 1,370 | utf_8 | 4c46dc0d7cbf0d5675f32ad1da2dabc7 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | crop.m | .m | light-field-super-resolution-master/crop.m | 2,003 | utf_8 | 5a3dcceb9cf126f648639bfb7e1f3e9b |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | split.m | .m | light-field-super-resolution-master/split.m | 2,303 | utf_8 | 600c57c8231202bf27c695576ba7b8e1 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | decimat.m | .m | light-field-super-resolution-master/decimat.m | 1,756 | utf_8 | 26507b373b8ab7de4aa7de056b990ae0 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | ppm.m | .m | light-field-super-resolution-master/ppm.m | 2,856 | utf_8 | bf9675b230471590d35d0289073e95cb |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | super.m | .m | light-field-super-resolution-master/super.m | 8,367 | utf_8 | 06b04661004034a0a1796a4c4826521f |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | lf2grid.m | .m | light-field-super-resolution-master/lf2grid.m | 1,186 | utf_8 | 8df882ce23e800d6d040f671ccfde7ad |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | col2lf.m | .m | light-field-super-resolution-master/col2lf.m | 1,406 | utf_8 | 2effc6971bcd2d6e35de61e43fe313a0 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | gbsuper.m | .m | light-field-super-resolution-master/gbsuper.m | 5,376 | utf_8 | d3dd7a1f2be7dc9c83473e293bb4b220 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | upsample.m | .m | light-field-super-resolution-master/upsample.m | 1,929 | utf_8 | be135aad06ea8c8c8203a8c063d9879a |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | merge.m | .m | light-field-super-resolution-master/merge.m | 3,178 | utf_8 | 0992c1b1b7d2fd7580d346eea694798d |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | plotlf.m | .m | light-field-super-resolution-master/plotlf.m | 4,831 | utf_8 | 3bc68461c55592b205c0193ab55ff25f |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | stdlap.m | .m | light-field-super-resolution-master/stdlap.m | 999 | utf_8 | ddea561bef99c4f03a31a1c0b01c8f47 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | intergraph.m | .m | light-field-super-resolution-master/intergraph.m | 12,278 | utf_8 | f784f75d4f1004b7a92cb4517939f219 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | high2low.m | .m | light-field-super-resolution-master/high2low.m | 3,181 | utf_8 | 490ef4877ae6c00fe162f4635a9ebf22 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | readstf.m | .m | light-field-super-resolution-master/readstf.m | 1,255 | utf_8 | 424072ab3ada3cf18c345c9ac8d3ebbe |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | rossimattia/light-field-super-resolution-master | warpmask.m | .m | light-field-super-resolution-master/warpmask.m | 1,956 | utf_8 | d39b7a20045286a76b0866fc01e260b1 |
% =========================================================================
% =========================================================================
%
% Author:
% Mattia Rossi (rossi.mattia@gmail.com)
% Signal Processing Laboratory 4 (LTS4)
% Ecole Polytechnique Federale de Lausanne (Switzerland)
%
% ==============... |
github | Stanford-STAGES/sleep-staging-master | load_signal.m | .m | sleep-staging-master/matlab_training_data_scripts/load_signal.m | 6,256 | utf_8 | 640c59c8395b2817463097637c5ad833 | function c_sig = load_signal(filepath,fs)
filepath
hdr = loadHDR(filepath);
ind = zeros(13,1);
for i=1:13
try
ind(i) = find(get_alternative_name(i,hdr.label));
end
end
test1 = (sum(ind([1 3]))==0 & (sum(ind([2 4]))==0 | sum(ind(9:10))==0));
test2 = ... |
github | iamsakil/BoundaryTrackingMATLAB-master | boundarytrack.m | .m | BoundaryTrackingMATLAB-master/boundarytrack.m | 1,577 | utf_8 | c9d676fc728fd87e3a9580a25b5d30f5 |
% r,c = positions of boundary
% H,W = image height and width
% figN = figure number for result display (0 for no display)
% [tr,tc] = consecutive set of tracked boundary points
function [tr,tc] = boundarytrack(r,c,H,W,figN)
% next direction offsets
% 1 2 3
% 8 x 4
% 7 6 5
mr = [-1,-1,-1,0,1,1,1,0];
mc =... |
github | teenagerold/FPGA_SDR-mars-board-master | mnco_model.m | .m | FPGA_SDR-mars-board-master/SDRrceeiver/mnco_model.m | 1,345 | utf_8 | 837483ceb2c31f737ddeeaae252ed98c | % Altera NCO version 13.1
% function [s,c] = mnco_model(phi_inc_i,phase_mod_i,freq_mod_i)
% input : phi_inc_i : phase increment input (required)
% phase_mod_i : phase modulation input(optional)
% freq_mod_i : frequency modulation input(optional)
% output : s : sine wav... |
github | teenagerold/FPGA_SDR-mars-board-master | mcic_fir_comp_coeff.m | .m | FPGA_SDR-mars-board-master/SDRrceeiver/mcic_fir_comp_coeff.m | 8,343 | utf_8 | b1c5d49a50e23e4a249456c06af998c4 | %% ================================================================================
%% Legal Notice: Copyright (C) 1991-2008 Altera Corporation
%% Any megafunction design, and related net list (encrypted or decrypted),
%% support information, device programming or simulation file, and any other
%% associated documentat... |
github | matthewberger/tfgan-master | fast_tsne.m | .m | tfgan-master/renderer/bh_tsne/fast_tsne.m | 4,820 | utf_8 | ea635b52c1f372c46c31b2b389a87b27 | function mappedX = fast_tsne(X, no_dims, initial_dims, perplexity, theta)
%FAST_TSNE Runs the C++ implementation of Barnes-Hut t-SNE
%
% mappedX = fast_tsne(X, no_dims, initial_dims, perplexity, theta)
%
% Runs the C++ implementation of Barnes-Hut-SNE. The high-dimensional
% datapoints are specified in the NxD... |
github | frederikgeth/PowerModelsReliability.jl-master | case118_scopf.m | .m | PowerModelsReliability.jl-master/test/data/case118_scopf.m | 58,773 | utf_8 | 61213536e0c5fd662c0fc60e57414d54 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%% %%%%%
%%%% NICTA Energy System Test Case Archive (NESTA) - v0.6.1 %%%%%
%%%% Optimal Power Flow - Typical Operation %%%%%
%%%% ... |
github | frederikgeth/PowerModelsReliability.jl-master | case5_tf.m | .m | PowerModelsReliability.jl-master/test/data/case5_tf.m | 3,500 | utf_8 | 12aaf471416cd06ee299b8ae433511cb | % NESTA v0.6.0
function mpc = nesta_case5_pjm
mpc.version = '2';
mpc.baseMVA = 100.0;
%% area data
% area refbus
mpc.areas = [
1 4;
];
%% bus data
% bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin
mpc.bus = [
1 2 0.0 0.0 0.0 0.0 1 1.07762 2.80377 230.0 1 1.10000 0.90000;
2 1 300.... |
github | frederikgeth/PowerModelsReliability.jl-master | case5_scopf.m | .m | PowerModelsReliability.jl-master/test/data/case5_scopf.m | 3,745 | utf_8 | d440a3c168c5c7b48b400da449938110 | % NESTA v0.6.0
function mpc = nesta_case5_pjm
mpc.version = '2';
mpc.baseMVA = 100.0;
%% area data
% area refbus
mpc.areas = [
1 4;
];
%% bus data
% bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin
mpc.bus = [
1 2 0.0 0.0 0.0 0.0 1 1.07762 2.80377 230.0 1 1.10000 0.90000;
2 1 300.... |
github | frederikgeth/PowerModelsReliability.jl-master | case5_scopf_load.m | .m | PowerModelsReliability.jl-master/test/data/case5_scopf_load.m | 3,816 | utf_8 | b0fe0f418987daec60be5f36b88bb4ab | % NESTA v0.6.0
function mpc = nesta_case5_pjm
mpc.version = '2';
mpc.baseMVA = 100.0;
%% area data
% area refbus
mpc.areas = [
1 4;
];
%% bus data
% bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin
mpc.bus = [
1 2 0.0 0.0 0.0 0.0 1 1.07762 2.80377 230.0 1 1.10000 0.90000;
2 1 300.... |
github | zhichaowang/kaldi-master | Generate_mcTrainData_cut.m | .m | kaldi-master/egs/reverb/s5/local/Generate_mcTrainData_cut.m | 7,311 | utf_8 | f59dd892f0f8da04a515a2c58ff50a69 | function Generate_mcTrainData_cut(WSJ_dir_name, save_dir)
%
% Input variables:
% WSJ_dir_name: string name of user's clean wsjcam0 corpus directory
% (*Directory structure for wsjcam0 corpushas to be kept as it is after obtaining it from LDC.
% Otherwise this script does not wor... |
github | yihui-he/reconstructing-pascal-voc-master | montage_new.m | .m | reconstructing-pascal-voc-master/external_src/montage_new.m | 12,139 | utf_8 | 0bbddfdad558a03de80a4e5889f64ca9 | function handles = montage_new(I, titles, varargin)
%MONTAGE Display multiple images as a montage of subplots
% Changed by Joao Carreira to be able to include titles (cell array of
% strings)
%
% Examples:
% montage
% montage(I)
% montage(I, map)
% montage(..., param1, value1, param2, value2, ...)
%
% This fun... |
github | yihui-he/reconstructing-pascal-voc-master | parseXML.m | .m | reconstructing-pascal-voc-master/external_src/parseXML.m | 2,107 | utf_8 | ecc51819782827aa19e181f13cc7a1cc | function theStruct = parseXML(filename)
% PARSEXML Convert XML file to a MATLAB structure.
try
tree = xmlread(filename);
catch
error('Failed to read XML file %s.',filename);
end
% Recurse over child nodes. This could run into problems
% with very deeply nested trees.
try
theStruct = parseChildNodes(tree);
ca... |
github | yihui-he/reconstructing-pascal-voc-master | SvmSegm_show_best_segments.m | .m | reconstructing-pascal-voc-master/external_src/SvmSegm_show_best_segments.m | 825 | utf_8 | e486320500066ff0bb4543e0fef15585 | %function SvmSegm_show_best_segments(I, Q, masks)
% I is the image
% Q is the qualities of each segment (find the file in SegmentEval folder)
% masks are the computed segments
function [best, max_score, best_seg_id] = SvmSegm_show_best_segments(I, Q, masks, n)
DefaultVal('*n', '1');
if(iscell(Q))
Q = Q{1};
... |
github | yihui-he/reconstructing-pascal-voc-master | myCalcCandScoreFigureGroundAll.m | .m | reconstructing-pascal-voc-master/external_src/myCalcCandScoreFigureGroundAll.m | 4,625 | utf_8 | 7c7df819fd640b0633d87d97561e43e1 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Calculate the F-score of the evaluated method %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% best_s is the filename of the best figure-ground segmentation (needs to
% be binary!!!) Only consid... |
github | yihui-he/reconstructing-pascal-voc-master | DefaultVal.m | .m | reconstructing-pascal-voc-master/external_src/DefaultVal.m | 1,258 | utf_8 | 518947d797d61092d4e16bd1b3ba1e27 | % Function written by Dr. Adrian Ion
%
% This code is part of the extended implementation of the paper:
%
% J. Carreira, C. Sminchisescu, Constrained Parametric Min-Cuts for Automatic Object Segmentation, IEEE CVPR 2010
%
function DefaultVal(varargin)
% assign default values to variables if they do not exist or are... |
github | yihui-he/reconstructing-pascal-voc-master | VOCevalseg.m | .m | reconstructing-pascal-voc-master/external_src/VOCcode/VOCevalseg.m | 3,238 | utf_8 | 454757a787993ac892eb5dc277abc04d | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEVALSEG(... |
github | yihui-he/reconstructing-pascal-voc-master | VOClabelcolormap.m | .m | reconstructing-pascal-voc-master/external_src/VOCcode/VOClabelcolormap.m | 669 | utf_8 | 565f5eb4134900aa85056d6becc880f5 | % VOCLABELCOLORMAP Creates a label color map such that adjacent indices have different
% colors. Useful for reading and writing index images which contain large indices,
% by encoding them as RGB images.
%
% CMAP = VOCLABELCOLORMAP(N) creates a label color map with N entries.
function cmap = labelcolormap(N)
if nargi... |
github | yihui-he/reconstructing-pascal-voc-master | VOCwritexml.m | .m | reconstructing-pascal-voc-master/external_src/VOCcode/VOCwritexml.m | 1,126 | utf_8 | d7749b6f4def796021e352fcf987fae6 | function VOCwritexml(rec, path)
fid=fopen(path,'w');
writexml(fid,rec,0);
fclose(fid);
function xml = writexml(fid,rec,depth)
fn=fieldnames(rec);
for i=1:length(fn)
f=rec.(fn{i});
if ~isempty(f)
if isstruct(f)
for j=1:length(f)
fprintf(fid,'%s',repmat(char(9),1... |
github | yihui-he/reconstructing-pascal-voc-master | VOCreadrecxml.m | .m | reconstructing-pascal-voc-master/external_src/VOCcode/VOCreadrecxml.m | 2,158 | utf_8 | e48003524ee25fff35c9a76c8e9044fa | function rec = VOCreadrecxml(path)
x=VOCreadxml(path);
x=x.annotation;
rec.folder=x.folder;
rec.filename=x.filename;
rec.source.database=x.source.database;
rec.source.annotation=x.source.annotation;
rec.source.image=x.source.image;
rec.size.width=str2double(x.size.width);
rec.size.height=str2double(x.size.height);
r... |
github | yihui-he/reconstructing-pascal-voc-master | VOCxml2struct.m | .m | reconstructing-pascal-voc-master/external_src/VOCcode/VOCxml2struct.m | 1,830 | utf_8 | 86b8ceeb5ce8c143aa78e2e805fef5d6 | function res = VOCxml2struct(xml)
xml(xml==9|xml==10|xml==13)=[];
[res,xml]=parse(xml,1,[]);
function [res,ind]=parse(xml,ind,parent)
res=[];
if ~isempty(parent)&&xml(ind)~='<'
i=findchar(xml,ind,'<');
res=trim(xml(ind:i-1));
ind=i;
[tag,ind]=gettag(xml,i);
if ~strcmp(tag,['/' parent])
e... |
github | yihui-he/reconstructing-pascal-voc-master | PASreadrectxt.m | .m | reconstructing-pascal-voc-master/external_src/VOCcode/PASreadrectxt.m | 3,179 | utf_8 | 3b0bdbeb488c8292a1744dace066bb73 | function record=PASreadrectxt(filename)
[fd,syserrmsg]=fopen(filename,'rt');
if (fd==-1),
PASmsg=sprintf('Could not open %s for reading',filename);
PASerrmsg(PASmsg,syserrmsg);
end;
matchstrs=initstrings;
record=PASemptyrecord;
notEOF=1;
while (notEOF),
line=fgetl(fd);
notEOF=ischar(li... |
github | yihui-he/reconstructing-pascal-voc-master | icp.m | .m | reconstructing-pascal-voc-master/external_src/icp/icp.m | 18,342 | utf_8 | 283054154b9888ad4a7e24be82f8851c | function [TR, TT, ER, t] = icp(q,p,varargin)
% Perform the Iterative Closest Point algorithm on three dimensional point
% clouds.
%
% [TR, TT] = icp(q,p) returns the rotation matrix TR and translation
% vector TT that minimizes the distances from (TR * p + TT) to q.
% p is a 3xm matrix and q is a 3xn matrix.
%
% [TR,... |
github | yihui-he/reconstructing-pascal-voc-master | factorization.m | .m | reconstructing-pascal-voc-master/external_src/marques_costeira/factorization.m | 3,358 | utf_8 | 70c3edcecdca748814f20a5eba52ed7a | % [Motion, Shape, T] = factorization(Wo, iterMax1, iterMax2)
%
% This function computes the 3D object shape from missing and degenerate data.
%
%
% Input arguments:
%
% Wo - The data matrix is defined as:
% Wo = [ u_1^1 ... u_P^1
% v_1^1 ... v_P^1
% ... |
github | yihui-he/reconstructing-pascal-voc-master | factorization_plane.m | .m | reconstructing-pascal-voc-master/external_src/marques_costeira/factorization_plane.m | 5,238 | utf_8 | 56df592f3605e585151493ee02875a01 | % [Motion, Shape, T] = factorization(Wo, iterMax1, iterMax2)
%
% This function computes the 3D object shape from missing and degenerate data.
%
%
% Input arguments:
%
% Wo - The data matrix is defined as:
% Wo = [ u_1^1 ... u_P^1
% v_1^1 ... v_P^1
% ... |
github | yihui-he/reconstructing-pascal-voc-master | factorization_custom.m | .m | reconstructing-pascal-voc-master/external_src/marques_costeira/factorization_custom.m | 5,143 | utf_8 | e0d3459a70acb2997412cb6f0b92272f | % [Motion, Shape, T] = factorization(Wo, iterMax1, iterMax2)
%
% This function computes the 3D object shape from missing and degenerate data.
%
%
% Input arguments:
%
% Wo - The data matrix is defined as:
% Wo = [ u_1^1 ... u_P^1
% v_1^1 ... v_P^1
% ... |
github | yihui-he/reconstructing-pascal-voc-master | collect_imgset_keypoints_mirror.m | .m | reconstructing-pascal-voc-master/src/collect_imgset_keypoints_mirror.m | 4,414 | utf_8 | fb223ff8fd94d952935a3918e7973298 | function collect_imgset_keypoints_mirror(exp_dir, imgset, imgset_mirror)
dest_dir = [exp_dir 'merged_Correspondences_GT_BRKL/'];
load('./voc_kp_metadata.mat', 'metadata');
if(~exist(dest_dir, 'dir'))
mkdir(dest_dir);
end
total_n_objects = 0 ;
assert(iscell(imgset));
... |
github | yihui-he/reconstructing-pascal-voc-master | regionprops_BB_mine.m | .m | reconstructing-pascal-voc-master/src/regionprops_BB_mine.m | 791 | utf_8 | 920c4ed1dd131ce13583e1e7ab4c931a | % returns bounding box even is mask is composed of multiple connected
% components
function rp_bbox = regionprops_BB_mine(mask, slack)
DefaultVal('*slack', '0');
[c_x, c_y] = find(mask);
if(isempty(c_x)) % robust to masks with zero pixels.
rp_bbox = [1 1 1 1];
else
rp_bbox(1) = min(c_y);
rp_bbox(... |
github | yihui-he/reconstructing-pascal-voc-master | gen_all_gt_segm_kp_imgset.m | .m | reconstructing-pascal-voc-master/src/gen_all_gt_segm_kp_imgset.m | 3,912 | utf_8 | 9a92930b19d9c7f7819f21f949741570 | % generate imgset containing only those images where all objects have
% keypoints and segmentations and where there's no obvious problem
% with the keypoints (eg. keypoints missing on one object, repeated for another)
function filename = gen_all_gt_segm_kp_imgset()
exp_dir = add_all_paths();
mask_type = 'g... |
github | yihui-he/reconstructing-pascal-voc-master | reconstruct_GT_puff_baseline_pascal.m | .m | reconstructing-pascal-voc-master/src/reconstruct_GT_puff_baseline_pascal.m | 4,817 | utf_8 | 323f0943ea99fc1f3ae34ec276a39de7 | function reconstruct_GT_puff_baseline_pascal(sel_class)
OFFICE = true;
exp_dir = add_all_paths(OFFICE);
DefaultVal('*sel_class', '1');
mask_type = 'ground_truth';
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%% Get data of images having ground trut... |
github | yihui-he/reconstructing-pascal-voc-master | evaluate_reconstruction_ranking.m | .m | reconstructing-pascal-voc-master/src/evaluate_reconstruction_ranking.m | 7,355 | utf_8 | 5f45c481f720bdbb739c988b2063f828 | % sometimes this crashes, (something related to parfor and writing and reading to files)
% just remove any .off files and rerun and it should be fine.
function evaluate_reconstruction_ranking(exp_dir,reconstr_name,withICP)
if(nargin<3)
withICP = 0;
end
for i=1:20
c... |
github | yihui-he/reconstructing-pascal-voc-master | cam_refinement.m | .m | reconstructing-pascal-voc-master/src/cam_refinement.m | 4,387 | utf_8 | 08acc8f4e9ed942cc7b81d9b99f095ec | function [R,T,best_en] = cam_refinement(Rot,Tr,Shape,mask,projections,use_dt)
% Refines the camera position based on the mask, so that all points
% reproject inside the mask
% minimizes:
%weigths.proj*(keypoints - (rot*shape+tr))^2 + weights.dt*dist_mask(rot*shape +tr);
if(use_dt)
weights.p... |
github | yihui-he/reconstructing-pascal-voc-master | normalize.m | .m | reconstructing-pascal-voc-master/src/normalize.m | 1,601 | utf_8 | 688deea9c3947acdbcc4ae85e6551565 | % Copyright (C) 2010 Joao Carreira
%
% This code is part of the extended implementation of the paper:
%
% J. Carreira, C. Sminchisescu, Constrained Parametric Min-Cuts for Automatic Object Segmentation, IEEE CVPR 2010
%
function [Feats] = normalize(Feats, scaling)
if(iscell(Feats))
scaling.to_subtract = ... |
github | yihui-he/reconstructing-pascal-voc-master | reconstruct_pascal_getall.m | .m | reconstructing-pascal-voc-master/src/reconstruct_pascal_getall.m | 10,752 | utf_8 | 034311e75946795443f89c7cd9ec4006 | function reconstruct_pascal_getall(sel_class, n_iter_1, n_iter_2, N_SAMPLES_PER_OBJ, MAX_DEV, name, refinement, imprinting)
DefaultVal('*refinement', 'true');
DefaultVal('*imprinting', 'true');
exp_dir = add_all_paths();
mask_type = 'ground_truth';
imgset_pascal = 'all_gt_segm_k... |
github | yihui-he/reconstructing-pascal-voc-master | puffball.m | .m | reconstructing-pascal-voc-master/src/puffball.m | 2,705 | utf_8 | 0a191a968a1f9911e80ae2417db3f7c1 | function [tri, coord] = puffball(mask)
%Weighted skeleton, where each non-zero value corresponds to the maximum
%size of a sphere that can be centered there.
wSkel = get_SkelRadius(mask);
heightFunction = puffbalIInflation(mask,wSkel);
[tri,coord] = mesh_from_height(mask, heightFunction);
end
function [tri, coo... |
github | yihui-he/reconstructing-pascal-voc-master | sel_best_reconstruction_getall.m | .m | reconstructing-pascal-voc-master/src/sel_best_reconstruction_getall.m | 9,550 | utf_8 | d5c6f71127bf609e4861602155ba9ac1 | function [fv2,statistics] = sel_best_reconstruction_getall(all_triples,R,T,kp,really_all_masks, N_VOXELS,axis_masks,flags)
%flags is a structure with the following fields:
%
%flags.is_articulated
%flags.rot_symmetry
%flags.rot_axis (only important if rot_symmetry == 1)
%flags.angle_step (only important if rot_symmetry... |
github | yihui-he/reconstructing-pascal-voc-master | subplot_auto_transparent.m | .m | reconstructing-pascal-voc-master/src/SegmBrowser/subplot_auto_transparent.m | 2,761 | utf_8 | 188f00f27307f9049757d2bfe1176afa | % Copyright (C) 2010 Joao Carreira
%
% This code is part of the extended implementation of the paper:
%
% J. Carreira, C. Sminchisescu, Constrained Parametric Min-Cuts for Automatic Object Segmentation, IEEE CVPR 2010
%
%function h = subplot_auto_transparent(segments, I, titles)
function [Imgs] = subplot_auto_transp... |
github | yihui-he/reconstructing-pascal-voc-master | create_transparent_multiple_colors.m | .m | reconstructing-pascal-voc-master/src/SegmBrowser/create_transparent_multiple_colors.m | 3,650 | utf_8 | 684d20c449e8916275aac6ee636b84e1 | %function I = create_transparent_multiple_colors(segments, I, titles)
function merged_Img = create_transparent_multiple_colors(segments, I, use_voc_colors, labels, transparency, intensities)
DefaultVal('*use_voc_colors', 'false');
DefaultVal('*transparency', '0.8');
DefaultVal('*intensities', '[]');
if(isem... |
github | yihui-he/reconstructing-pascal-voc-master | subplot_auto_transparent_parts.m | .m | reconstructing-pascal-voc-master/src/SegmBrowser/subplot_auto_transparent_parts.m | 2,461 | utf_8 | 91078f7661ffb5b11fda99cc06c46ce6 | %function h = subplot_auto_transparent(segments, I, titles)
function [Imgs] = subplot_auto_transparent_parts(whole_segment, part_segments, I, titles, grid_type)
if(isempty(part_segments))
Imgs = [];
return;
end
part_segments(part_segments==inf) = 10000;
border_side = 0.05;
border_top = 0.05;... |
github | yihui-he/reconstructing-pascal-voc-master | SvmSegm_study_segment_quality.m | .m | reconstructing-pascal-voc-master/src/SegmBrowser/SvmSegm_study_segment_quality.m | 9,068 | utf_8 | 21b9677e95348739b7b27093457cfe01 | %function SvmSegm_study_segment_quality(exp_dir, segm_name, the_imgset, segm_quality_type)
function SvmSegm_study_segment_quality(exp_dir, segm_name, the_imgset, segm_quality_type, class_label)
DefaultVal('class_label', '[]');
if(~iscell(segm_name))
segm_name = {segm_name};
end
% can wr... |
github | yihui-he/reconstructing-pascal-voc-master | subplot_auto_transparent_multiple_imgs.m | .m | reconstructing-pascal-voc-master/src/SegmBrowser/subplot_auto_transparent_multiple_imgs.m | 2,014 | utf_8 | d81c1bc1a30614034d8e3c52877170bd | %function h = subplot_auto_transparent(segments, I, titles)
function [Imgs, handles] = subplot_auto_transparent_multiple_imgs(segments, I, titles, voc_cmap_ids, grid_type)
border_side = 0.05;
border_top = 0.05;
for i=1:numel(I)
if(length(size(I{i}))==2)
I{i} = repmat(I{i}, [1 1 3]);
end
... |
github | yihui-he/reconstructing-pascal-voc-master | test_SegmBrowser.m | .m | reconstructing-pascal-voc-master/src/SegmBrowser/test_SegmBrowser.m | 1,945 | utf_8 | 6f119003d6ae9e17e0586114c290e539 | % Joao Carreira September 2012
% Run this in debug mode and do step by step for checking the outputs it gives
function test_SegmBrowser()
% run this after setting up the VOC dataset (see in VOC_experiment
% folder)
exp_dir = '../../VOC_experiment/VOC/';
mask_type = 'CPMC_segms_150_sp_approx';
imgset = 'train... |
github | yihui-he/reconstructing-pascal-voc-master | subplot_auto_transparent_parts_multiple_imgs.m | .m | reconstructing-pascal-voc-master/src/SegmBrowser/subplot_auto_transparent_parts_multiple_imgs.m | 1,518 | utf_8 | b998d26146d60227cdfe498683e72ebe | %function h = subplot_auto_transparent(segments, I, titles)
function [Imgs] = subplot_auto_transparent_parts_multiple_imgs(whole_segments, part_segments, I, titles)
border_side = 0.05;
border_top = 0.05;
Imgs = I;
for i=1:numel(I)
if(length(size(I{i}))==2)
I{i} = repmat(I{i}, [1 1 3]);
... |
github | yihui-he/reconstructing-pascal-voc-master | subplot_auto_transparent_parts_noov.m | .m | reconstructing-pascal-voc-master/src/SegmBrowser/subplot_auto_transparent_parts_noov.m | 2,811 | utf_8 | a7b44e92af8526f4c46cc3a01df16eb6 | %function h = subplot_auto_transparent(segments, I, titles)
function [Imgs] = subplot_auto_transparent_parts_noov(whole_segment, part_segments, I, voc_color_ids, titles, grid_type)
DefaultVal('*voc_color_ids', '[]');
if(isempty(part_segments))
Imgs = [];
return;
end
if(~iscell(whole_segment))
... |
github | zzlyw/machine-learning-exercises-master | submit.m | .m | machine-learning-exercises-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | zzlyw/machine-learning-exercises-master | submitWithConfiguration.m | .m | machine-learning-exercises-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | zzlyw/machine-learning-exercises-master | savejson.m | .m | machine-learning-exercises-master/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | zzlyw/machine-learning-exercises-master | loadjson.m | .m | machine-learning-exercises-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | zzlyw/machine-learning-exercises-master | loadubjson.m | .m | machine-learning-exercises-master/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | zzlyw/machine-learning-exercises-master | saveubjson.m | .m | machine-learning-exercises-master/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | zzlyw/machine-learning-exercises-master | submit.m | .m | machine-learning-exercises-master/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | zzlyw/machine-learning-exercises-master | submitWithConfiguration.m | .m | machine-learning-exercises-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | zzlyw/machine-learning-exercises-master | savejson.m | .m | machine-learning-exercises-master/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | zzlyw/machine-learning-exercises-master | loadjson.m | .m | machine-learning-exercises-master/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | zzlyw/machine-learning-exercises-master | loadubjson.m | .m | machine-learning-exercises-master/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | zzlyw/machine-learning-exercises-master | saveubjson.m | .m | machine-learning-exercises-master/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | zzlyw/machine-learning-exercises-master | submit.m | .m | machine-learning-exercises-master/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | zzlyw/machine-learning-exercises-master | porterStemmer.m | .m | machine-learning-exercises-master/machine-learning-ex6/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... |
github | zzlyw/machine-learning-exercises-master | submitWithConfiguration.m | .m | machine-learning-exercises-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | zzlyw/machine-learning-exercises-master | savejson.m | .m | machine-learning-exercises-master/machine-learning-ex6/ex6/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | zzlyw/machine-learning-exercises-master | loadjson.m | .m | machine-learning-exercises-master/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | zzlyw/machine-learning-exercises-master | loadubjson.m | .m | machine-learning-exercises-master/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | zzlyw/machine-learning-exercises-master | saveubjson.m | .m | machine-learning-exercises-master/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | zzlyw/machine-learning-exercises-master | submit.m | .m | machine-learning-exercises-master/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | zzlyw/machine-learning-exercises-master | submitWithConfiguration.m | .m | machine-learning-exercises-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | zzlyw/machine-learning-exercises-master | savejson.m | .m | machine-learning-exercises-master/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | zzlyw/machine-learning-exercises-master | loadjson.m | .m | machine-learning-exercises-master/machine-learning-ex7/ex7/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | zzlyw/machine-learning-exercises-master | loadubjson.m | .m | machine-learning-exercises-master/machine-learning-ex7/ex7/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | zzlyw/machine-learning-exercises-master | saveubjson.m | .m | machine-learning-exercises-master/machine-learning-ex7/ex7/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | zzlyw/machine-learning-exercises-master | submit.m | .m | machine-learning-exercises-master/machine-learning-ex5/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | zzlyw/machine-learning-exercises-master | submitWithConfiguration.m | .m | machine-learning-exercises-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | zzlyw/machine-learning-exercises-master | savejson.m | .m | machine-learning-exercises-master/machine-learning-ex5/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | zzlyw/machine-learning-exercises-master | loadjson.m | .m | machine-learning-exercises-master/machine-learning-ex5/ex5/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | zzlyw/machine-learning-exercises-master | loadubjson.m | .m | machine-learning-exercises-master/machine-learning-ex5/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | zzlyw/machine-learning-exercises-master | saveubjson.m | .m | machine-learning-exercises-master/machine-learning-ex5/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | zzlyw/machine-learning-exercises-master | submit.m | .m | machine-learning-exercises-master/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | zzlyw/machine-learning-exercises-master | submitWithConfiguration.m | .m | machine-learning-exercises-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | zzlyw/machine-learning-exercises-master | savejson.m | .m | machine-learning-exercises-master/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | zzlyw/machine-learning-exercises-master | loadjson.m | .m | machine-learning-exercises-master/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | zzlyw/machine-learning-exercises-master | loadubjson.m | .m | machine-learning-exercises-master/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
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