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 | BashBlefari/New-Insights-into-Projective-Quadrangle-Geometry-master | tool_meets.m | .m | New-Insights-into-Projective-Quadrangle-Geometry-master/Matlab/Tools/tool_meets.m | 336 | utf_8 | 05886e9e04721fd666cabf4f0ac0a8b7 | % This is a meete tool that calls the meets tool which is essentially the
% cross product
% Which is the meet tool by for actual valued vectors not just symmbolic
% ones
function a=tool_meets(a1,a2)
s1=size(a1);s2=size(a2);
if s1(1)==s2(1)
a=cross(a1,a2).';
else
display('different dimensions')
a=zeros(s1(1)... |
github | bernardrhall/bicoherence-suite-master | tutorial_filters.m | .m | bicoherence-suite-master/src/tutorial_filters.m | 5,669 | utf_8 | 2851aa1347386c724ec5b56aa321f9a6 | function tutorial_filters
file = 'matterhorn_small.jpg';
fig = figure;
set(fig, 'DefaultAxesFontName', 'Arial')
set(fig, 'DefaultAxesFontSize', 6)
plotCounter = 1;
xPlots = 3;
yPlots = 4;
% a) Original plot with a line, patch, and text object
subplot(yPlots, xPlots, plotCounter)
plotCounter = plotCounter + 1;... |
github | bernardrhall/bicoherence-suite-master | stft.m | .m | bicoherence-suite-master/src/stft.m | 1,547 | utf_8 | d38b73eee539e2f08c415ad0108eefd9 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Short-Time Fourier Transform %
% with MATLAB Implementation %
% %
% Author: M.Sc. Eng. Hristo Zhivomirov 12/21/13 %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | bernardrhall/bicoherence-suite-master | plot2svg.m | .m | bicoherence-suite-master/src/plot2svg.m | 169,254 | utf_8 | 209988b40f12b22792876106c90308e8 | function varargout = plot2svg(param1,id,pixelfiletype)
% Matlab to SVG converter
% Prelinary version supporting 3D plots as well
%
% Usage: plot2svg(filename,graphic handle,pixelfiletype)
% optional optional optional
% or
%
% plot2svg(figuresize,graphic handle,pixelfiletype)
... |
github | luoqiaoen/matlab_pvcam64-master | roioverlap.m | .m | matlab_pvcam64-master/pvcam-lib/roioverlap.m | 5,563 | utf_8 | ee6a6295e9c6129efd93c0001ec02761 | function new_struct = roioverlap(old_struct, ser_size, par_size);
% ROIOVERLAP - separate overlapping ROIs
%
% NEW = ROIOVERLAP(OLD, SER, PAR) recreates a set of ROIs for camera
% readout if overlap between parallel registers is detected:
%
% ##### #####
% ##... |
github | chenxinfeng4/scalebar-master | scalebar.m | .m | scalebar-master/scalebar.m | 11,285 | utf_8 | 188cf4cbc8cf4b38bcf7f937d136b6b9 | % dragable & resizeable & unit-support %menucommand-support SCALEBAR
% @Chenxinfeng, 2016-9-10
% @JupeNupes, 2022-8-23-- added color properties
%
% ================================HOW TO USE==============================
% ----PREPARE---
% plot(sin(1:0.1:10));
% obj = scalebar; %default, recommanded
% obj = scalebar(_... |
github | eerdil/cvpr16-master | generateLevelSet.m | .m | cvpr16-master/generateLevelSet.m | 169 | utf_8 | 3159c35beb59b30acf4f07bc16e6493a | % Psi_ : inside -1, outside 1
function Psi = generateLevelSet(Psi_)
Psi = double((Psi_ > 0).*(bwdist(Psi_ < 0) - 0.5) - (Psi_ < 0).*(bwdist(Psi_ > 0) - 0.5));
end |
github | hpatches/hpatches-benchmark-master | hb.m | .m | hpatches-benchmark-master/matlab/hb.m | 3,256 | utf_8 | ed42808e72b5e9a276d923254a3bdbf8 | function res = hb(cmd, varargin)
%HB HPatches command line interface
% `HB help`
% Print this help string.
% `HB help COMMAND`
% Print a help string for a COMMAND.
%
% `HB dataset`
% Provision the HPatches dataset to `<hb_root>/data/hpatches_v1.1/`.
%
% `HB computedesc DESCNAME`
% Compute descriptor... |
github | hpatches/hpatches-benchmark-master | hb_deploy.m | .m | hpatches-benchmark-master/matlab/hb_deploy.m | 1,132 | utf_8 | 07d4dfa15cc8a29734e08ad95180e968 | function hb_deploy()
% HB_DEPLOY Deploy the binary command line interface of the HBenchmark
% Copyright (C) 2016 Karel Lenc
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
hb_setup();
target_dir = fullfile(hb_path, '... |
github | hpatches/hpatches-benchmark-master | memdesc.m | .m | hpatches-benchmark-master/matlab/+desc/memdesc.m | 4,752 | utf_8 | 588630a23e03f0007c97117fd870b3af | function [obj, varargin] = memdesc(descname, varargin)
%MEMDESC Returns object with loaded descriptor and simple access methods
% OBJ = MEMDESC(DESCNAME) Loads CSV descriptor from:
%
% `<HB_ROOT>/data/descritpors/DESCNAME`
%
% and returns an object with methods to access the descriptor data from
% memory.
%
%
% ... |
github | hpatches/hpatches-benchmark-master | normdesc.m | .m | hpatches-benchmark-master/matlab/+desc/normdesc.m | 5,233 | utf_8 | 796d890ede45bdc606b5be0e5c4ba286 | function [desc, varargin] = normdesc(desc, varargin)
%NORMDESC Descriptor normalisation
% NORMDESC is a helper wrapper used for descriptor normalisation. By
% default performs square root followed by L2 normalisation. NORMDESC
% renames the descriptor -> stores results in a new path.
%
% The order of the operations... |
github | hpatches/hpatches-benchmark-master | rgb.m | .m | hpatches-benchmark-master/matlab/+utls/rgb.m | 9,295 | UNKNOWN | 25b3a567fdfd1716654ee697c1437107 | % RGB Rgb triple for given CSS color name
%
% RGB = RGB('COLORNAME') returns the red-green-blue triple corresponding
% to the color named COLORNAME by the CSS3 proposed standard [1], which
% contains 139 different colors (an rgb triple is a 1x3 vector of
% numbers between 0 and 1). COLORNAME is case... |
github | hpatches/hpatches-benchmark-master | yael_pca.m | .m | hpatches-benchmark-master/matlab/+utls/yael_pca.m | 1,677 | utf_8 | 802f9f50bfcc4a91bf8d18d51e768dcb | % PCA with automatic selection of the method: covariance or gram matrix
% Usage: [X, eigvec, eigval, Xm] = pca (X, dout, center, verbose)
% X input vector set (1 vector per column)
% dout number of principal components to be computed
% center need to center data?
%
% Note: the eigenvalues are given in ... |
github | hpatches/hpatches-benchmark-master | yael_vecs_normalize.m | .m | hpatches-benchmark-master/matlab/+utls/yael_vecs_normalize.m | 718 | utf_8 | 113b79aad4badd1d53937499f8078616 | % This function normalize a set of vectors
% Parameters:
% v the set of vectors to be normalized (column stored)
% nr the norm for which the normalization is performed (Default: Euclidean)
% rval replace value in case the vector is 0-norm
%
% Output:
% vout the normalized vector
% vnr the norms of ... |
github | hpatches/hpatches-benchmark-master | textprogressbar.m | .m | hpatches-benchmark-master/matlab/+utls/textprogressbar.m | 8,926 | utf_8 | 519fcbd1206f2dbfb0edc68eaff27702 | function upd = textprogressbar(n, varargin)
% UPD = TEXTPROGRESSBAR(N) initializes a text progress bar for monitoring a
% task comprising N steps (e.g., the N rounds of an iteration) in the
% command line. It returns a function handle UPD that is used to update and
% render the progress bar. UPD takes a single argument... |
github | hpatches/hpatches-benchmark-master | provision.m | .m | hpatches-benchmark-master/matlab/+utls/provision.m | 1,777 | utf_8 | 7b5cb03fbd11e1a929237537b173d768 | function downloaded = provision( url_file, tgt_dir, override )
if nargin < 3, override = false; end;
downloaded = false;
if ~exist(url_file, 'file')
error('Unable to find the URL file %s.', url_file);
end;
[~, url_file_nm] = fileparts(url_file);
done_file = fullfile(tgt_dir, ['.', url_file_nm, '.done']);
if exist(tgt... |
github | hpatches/hpatches-benchmark-master | eval.m | .m | hpatches-benchmark-master/matlab/+bench/+retrieval/eval.m | 3,231 | utf_8 | 3fa0159d8ce97d3bf1fa2c3a2346e120 | function res = eval( descs, geom_noise, queryfile, distfile, varargin )
% Copyright (C) 2016-2017 Karel Lenc
% All rights reserved.
%
% This file is part of the VLFeat library and is made available under
% the terms of the BSD license (see the COPYING file).
opts.debug = false;
opts.queryims = 1;
opts.pos_ims = [2,3,... |
github | hpatches/hpatches-benchmark-master | eval_chance.m | .m | hpatches-benchmark-master/matlab/+bench/+retrieval/eval_chance.m | 1,078 | utf_8 | 445965c7ffbae69d90e4760969a8efb6 | function out = eval_chance( imdb, labelspath )
% RETRIEVAL_EVAL_CHANCE
% Read the files
labels = utls.readfile(labelspath);
poolSignatures = strsplit(labels{1}, ',');
% Compare the headers
numQueries = numel(labels) - 1;
imRetAps = zeros(1, numQueries); patchRetAps = zeros(1, numQueries);
numAllFeats = sum(cellfun(@(s... |
github | abhutani/FoodSense-master | Get_Functions_details.m | .m | FoodSense-master/Get_Functions_details.m | 7,723 | utf_8 | c53e9ec8a93c74484b45144fa3772453 | %______________________________________________________________________________________________
% Moth-Flame Optimization Algorithm (MFO)
% Source codes demo version 1.0
% ... |
github | abhutani/FoodSense-master | func_plot.m | .m | FoodSense-master/func_plot.m | 3,870 | utf_8 | 07af832de5fac2123f69ed67c87be9ec | %______________________________________________________________________________________________
% Moth-Flame Optimization Algorithm (MFO)
% Source codes demo version 1.0
% ... |
github | abhutani/FoodSense-master | Get_Functions_details.m | .m | FoodSense-master/DOCS/Final/Code/K-Means/Get_Functions_details.m | 5,694 | utf_8 | 5c5efc4a52cd7ccd292e179e970e452e |
function [lb,ub,dim,fobj] = Get_Functions_details(F)
switch F
case 'F1'
fobj = @F1;
lb=-100;
ub=100;
dim=10;
case 'F2'
fobj = @F2;
lb=-10;
ub=10;
dim=10;
case 'F3'
fobj = @F3;
lb=-100;
ub=100;
... |
github | abhutani/FoodSense-master | func_plot.m | .m | FoodSense-master/DOCS/Final/Code/K-Means/func_plot.m | 2,114 | utf_8 | aada54f5dc6daa5ac004795132ef3a2a |
function func_plot(func_name)
[lb,ub,dim,fobj]=Get_Functions_details(func_name);
switch func_name
case 'F1'
x=-100:2:100; y=x; %[-100,100]
case 'F2'
x=-100:2:100; y=x; %[-10,10]
case 'F3'
x=-100:2:100; y=x; %[-100,100]
case 'F4'
x=-100:2... |
github | abhutani/FoodSense-master | mt.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/mt.m | 347 | utf_8 | 6aa67898bc5b3eae73e5325c65d70200 |
function new_population = mt(population, p_mutation, new_population)
population_size = size(population, 1);
% Random permutation of genomes order
mutation_order = randperm(population_size);
for i = 1:round(p_mutation*population_size);
new_population = [new_population; mt_one(population(muta... |
github | abhutani/FoodSense-master | Get_Functions_details.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/Get_Functions_details.m | 5,694 | utf_8 | 5c5efc4a52cd7ccd292e179e970e452e |
function [lb,ub,dim,fobj] = Get_Functions_details(F)
switch F
case 'F1'
fobj = @F1;
lb=-100;
ub=100;
dim=10;
case 'F2'
fobj = @F2;
lb=-10;
ub=10;
dim=10;
case 'F3'
fobj = @F3;
lb=-100;
ub=100;
... |
github | abhutani/FoodSense-master | mt_one.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/mt_one.m | 312 | utf_8 | f952e3cd02a8dd22249e8c7fa911dfce |
function new_chromosome = mt_one(chromosome)
new_chromosome = chromosome;
chromosome_size = size(chromosome, 2);
gene = round(unifrnd(1, chromosome_size));
% Mutate one gene
if (chromosome(gene) == 1)
new_chromosome(gene) = 0;
else
new_chromosome(gene) = 1;
end
|
github | abhutani/FoodSense-master | cs.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/cs.m | 682 | utf_8 | b93bdde82599771a750354d2fd6c8ff8 |
function new_population = cs(population, p_crossover, new_population)
population_size = size(population, 1);
% Random permutation of genomes order
parent_first = randperm(population_size);
parent_second = randperm(population_size);
% Number of couples used for crossover
n_crossovers = round... |
github | abhutani/FoodSense-master | func_plot.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/func_plot.m | 2,114 | utf_8 | aada54f5dc6daa5ac004795132ef3a2a |
function func_plot(func_name)
[lb,ub,dim,fobj]=Get_Functions_details(func_name);
switch func_name
case 'F1'
x=-100:2:100; y=x; %[-100,100]
case 'F2'
x=-100:2:100; y=x; %[-10,10]
case 'F3'
x=-100:2:100; y=x; %[-100,100]
case 'F4'
x=-100:2... |
github | abhutani/FoodSense-master | convert_thresholds.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/convert_thresholds.m | 258 | utf_8 | 5630443b1cc8524f22a59877311dfa6d |
function thresholds = convert_thresholds(population, n_thresholds)
thresholds = [];
population_size = size(population, 1);
for i = 1:population_size
thresholds = [thresholds; threshold_bin2dec(population(i,:), n_thresholds)];
end
|
github | abhutani/FoodSense-master | first_best.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/first_best.m | 292 | utf_8 | 6317c5bb8b5d5bb5051680c9fcb0bf19 |
function new_population = first_best(ranking, population, p_selection, new_population)
population_size = size(population, 1);
[best, best_i] = sort(ranking);
for i = 1:round(p_selection*population_size)
new_population = [new_population; population(best_i(i), :)];
end
|
github | abhutani/FoodSense-master | cs_one.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/cs_one.m | 415 | utf_8 | e8106080055094daa6fe1d98652c0d81 |
function [desc_first desc_second] = cs_one(parent_first, parent_second)
parent_size = size(parent_first, 2);
% Randomly generated number between 1 and the length of parent's genome.
point = round(unifrnd(1, parent_size-1));
% Crossover
desc_first = [parent_first(1:point) parent_second(point+1... |
github | abhutani/FoodSense-master | bi2de.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/bi2de.m | 720 | utf_8 | 1a845eb5c97330de0c3f86de302f1000 |
function d = bi2de (b, p, f)
switch (nargin)
case 1,
p = 2;
f = 'right-msb';
case 2,
if (ischar(p))
f = p;
p = 2;
else
f = 'right-msb';
end
case 3,
if (ischar(p))
tmp = f;
f = p;
p = tmp;
end
otherwise
e... |
github | abhutani/FoodSense-master | fitness.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/fitness.m | 420 | utf_8 | 11ec6486271bd88b484642b8df559b95 |
function ranking = fitness(image, population, n_thresholds)
ranking = [];
% Convert thresholds to decimal representation
thresholds = convert_thresholds(population, n_thresholds);
% Vectorize image
image_vec = image(:);
% Computes fitness ranking for all thresholds in population
for i ... |
github | abhutani/FoodSense-master | initialization.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/initialization.m | 163 | utf_8 | f77e1ec7f0338bdb9c770e0db4109023 |
function population = initialization(n_population, n_bins, n_thresholds)
population = round(unifrnd(0, 1, [n_population ceil(log2(n_bins))*n_thresholds]));
|
github | abhutani/FoodSense-master | fitness_one.m | .m | FoodSense-master/DOCS/Final/Code/MultiLevel Thresholding/fitness_one.m | 1,138 | utf_8 | 837912ea4370eb0f8266801a66b49e95 |
function ranking = fitness_one(image_vec, thresholds_vec)
ranking = 1;
inter_var = 0;
intra_var = 0; % TODO implement
% Sort thresholds
thresholds_vec = sort(thresholds_vec);
end_i = size(thresholds_vec, 2) + 1;
for i = 1:end_i
if ((i == 1 && end_i == 2) || i == 1)
% One... |
github | mathieuboudreau/qmt-optimization-master | prep.m | .m | qmt-optimization-master/src/montecarlo/@SPGR_MonteCarlo/prep.m | 4,252 | utf_8 | b99afbb0e1cc4aa735f0a09196bcd179 | function data = prep(obj, prepErrorStruct)
%PREP Prepare/reorganize the data into the format needed for fitting.
% In qMRLab, SPGR qMT data requires a "data" structure with the fields
% MTdata, B0map, B1map, R1map, and Mask.
%
% The fourth dimension of MTdata is the MT dimension (the first three are
% the vol... |
github | mathieuboudreau/qmt-optimization-master | convert_T1f_T1meas.m | .m | qmt-optimization-master/src/t1/convert_T1f_T1meas.m | 1,498 | utf_8 | aa27c15506a1d5b15671b8229ded6040 | function returnVal = convert_T1f_T1meas(params, modeFlag)
%CONVERT_T1F_T1MEAS Converts between the T1 of the free pool T1f (of qMT
%two-pool model) and the measurable T1 value using conventional T1 mapping
%techniques.
%
% --args--
% params:1x4 array of parameters needed to do conversion. Flag-dependent.
% Fl... |
github | mathieuboudreau/qmt-optimization-master | compute.m | .m | qmt-optimization-master/src/jacobian/@SeqJacobian/compute.m | 5,273 | utf_8 | 90db54d75dd0a2b02ce04bfad5405690 | function computeOpts = compute(obj, computeOpts)
%COMPUTE Computes the Jacobian matrix for the objects acquisition protocol
% and tissue parameters.
%
% computeOpts: Struct containing required jacobian computation options.
% --Fields--
% mode: String to flag the mode of the computation. Valid flags:
% ... |
github | mathieuboudreau/qmt-optimization-master | genDeltaTissueParams.m | .m | qmt-optimization-master/src/jacobian/@SPGR_Jacobian/genDeltaTissueParams.m | 2,626 | utf_8 | 0d44a022dcef5326358d7194ab1da824 | function deltaTissueParams = genDeltaTissueParams(obj, tissueJacStruct, tissueParams, computeOpts, paramIndex)
%GENDELTATISSUEPARAMS Generate the tissue parameters for the partial
%derivative calculation relative to the tissue indexed.
switch obj.jacobianStruct.paramsKeys{paramIndex}
case 'B1_IR'
... |
github | mathieuboudreau/qmt-optimization-master | generateSPGRSimParam.m | .m | qmt-optimization-master/src/util/generateSPGRSimParam.m | 1,781 | utf_8 | 1c5001db5d718556a3f27e8d331a8227 | function Sim = generateSPGRSimParam(filename, qMT5Params, noiseFlag)
%generateSPGRSimParam Generates parameters required to save a SimParam file
% in the formate matching qMTLab's.
%
% qMT5Params: [F,kf,R1f,T2f,T2r]
% filename : string for output filename. Do not include .mat extention
% nois... |
github | xwasco/DominantSetLibrary-master | inImDynM.m | .m | DominantSetLibrary-master/dynamics/inImDynM.m | 2,043 | utf_8 | af2a986420c88d7d5302efcf4cf97ac4 | function [x,iters,NashError] = inImDynM(A,x,toll,maxIters)
%INIMDYNM Infection-Immunization dynamcs.
%
% Input:
% A A pairwise nxn similarity matrix (with zero diagonal)
%
% x An nx1 vector in the n-dimensional simplex (it should add up to 1).
%
% toll The precision required from the d... |
github | corenel/Notes-for-ML-master | submit.m | .m | Notes-for-ML-master/Week_4/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 | corenel/Notes-for-ML-master | submitWithConfiguration.m | .m | Notes-for-ML-master/Week_4/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | corenel/Notes-for-ML-master | savejson.m | .m | Notes-for-ML-master/Week_4/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 | corenel/Notes-for-ML-master | loadjson.m | .m | Notes-for-ML-master/Week_4/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 | corenel/Notes-for-ML-master | loadubjson.m | .m | Notes-for-ML-master/Week_4/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-... |
github | corenel/Notes-for-ML-master | saveubjson.m | .m | Notes-for-ML-master/Week_4/machine-learning-ex3/ex3/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 | corenel/Notes-for-ML-master | submit.m | .m | Notes-for-ML-master/Week_2/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | corenel/Notes-for-ML-master | submitWithConfiguration.m | .m | Notes-for-ML-master/Week_2/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | corenel/Notes-for-ML-master | savejson.m | .m | Notes-for-ML-master/Week_2/machine-learning-ex1/ex1/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 | corenel/Notes-for-ML-master | loadjson.m | .m | Notes-for-ML-master/Week_2/machine-learning-ex1/ex1/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 | corenel/Notes-for-ML-master | loadubjson.m | .m | Notes-for-ML-master/Week_2/machine-learning-ex1/ex1/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 | corenel/Notes-for-ML-master | saveubjson.m | .m | Notes-for-ML-master/Week_2/machine-learning-ex1/ex1/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 | corenel/Notes-for-ML-master | submit.m | .m | Notes-for-ML-master/Week_3/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 | corenel/Notes-for-ML-master | submitWithConfiguration.m | .m | Notes-for-ML-master/Week_3/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | 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 | corenel/Notes-for-ML-master | savejson.m | .m | Notes-for-ML-master/Week_3/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 | corenel/Notes-for-ML-master | loadjson.m | .m | Notes-for-ML-master/Week_3/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 | corenel/Notes-for-ML-master | loadubjson.m | .m | Notes-for-ML-master/Week_3/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 | corenel/Notes-for-ML-master | saveubjson.m | .m | Notes-for-ML-master/Week_3/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 | jfear/s2rnai-master | lyap.m | .m | s2rnai-master/NetREX/SourceCodes/lyap.m | 3,763 | utf_8 | 9b325b3cf91bfeadeb8778a30539b68d | function X = lyap(A, B, C, E)
%LYAP Solve continuous-time Lyapunov equations.
% LYAP() aims to mirror the functionality and syntax of the LYAP() function in
% the MATLAB Control Toolbox. It is written entirely in MATLAB and so is a
% little slower than the implementation in the Control Toobox (which is
% essen... |
github | jfear/s2rnai-master | DNCA_l0_xi.m | .m | s2rnai-master/NetREX/SourceCodes/DNCA_l0_xi.m | 8,226 | utf_8 | 1150f8f0288cd5ef0f6f507337e420ef | function [output] = DNCA_l0_xi(Input)
% optimization:
% min_{S,A}: 1/2||E-SA||_F^2 + (\eta - \lambda)||S_0oS||_0 +
% (\eta + \lambda)||\bar{S_0}oS||_0 + \kappa tr(S^TL_GS) + \mu
% ||A||_F^2
% s.t. ||A||_{\infty} \leq M
% ||S||_{\infty} \... |
github | jfear/s2rnai-master | bartelsStewart.m | .m | s2rnai-master/NetREX/SourceCodes/bartelsStewart.m | 6,213 | utf_8 | 24734339f328c11b48c924cfc5c1427c | function X = bartelsStewart(A, B, C, D, E, split)
%BARTELSSTEWART Solve generalized Sylvester matrix equation.
% BARTELSSTEWART(A, B, C, D, E) solves the generalized Sylvester equation
%
% AXB^T + CXD^T = E
%
% using the Bartels--Stewart algorithm [1,2].
%
% BARTELSSTEWART(A, [], [], D, E) assumes B = I... |
github | jfear/s2rnai-master | NetREX_EdgeControl.m | .m | s2rnai-master/NetREX/SourceCodes/NetREX_EdgeControl.m | 8,380 | utf_8 | fc61df7f7bdb8f2b77876d953db70a02 | function [output] = NetREX_EdgeControl(Input)
% optimization:
% min_{S,A}: 1/2||E-SA||_F^2 + \kappa tr(S^TL_GS) + \mu ||A||_F^2
% s.t. ||A||_{\infty} \leq M
% ||S||_{\infty} \leq C
% ||S_0oS||_0 \leq # kept edges
% ... |
github | ABRG-Models/GPR_Izhikevich-master | mean_firing_rate.m | .m | GPR_Izhikevich-master/analysis/mean_firing_rate.m | 1,567 | utf_8 | a5dc61257a01ce6bc260b5fb0e65a609 | %% A script to compute the mean firing rate of the three channels
%% in the bg1 model.
function [ch1t, fr] = mean_firing_rate (basepath, popcsv, fignum)
filepath = [basepath '/' popcsv];
%sl = csvread
%('/home/seb/src/SpineML_2_BRAHMS/temp/log/SNr_spike_log.csv');
sl = csvread (filepath);
% sl is... |
github | ABRG-Models/GPR_Izhikevich-master | phaseplane_izhi_paperform.m | .m | GPR_Izhikevich-master/analysis/phaseplane_izhi_paperform.m | 19,236 | utf_8 | b15711f548b97e05b9d023633c2ea89a | % A function to take u and v from an Izhykevich neuron simulation
% carried out in SpineCreator and show phase plane and quiver plots
% and analysis. Based on Kevin Gurney's demo matlab code for the MS
% course - specifically dynamicsIz.m with the simulation removed
% from the code.
%
% Adapted by Seb James
%
% This ve... |
github | ABRG-Models/GPR_Izhikevich-master | phaseplane_izhi_paperform_tscaled.m | .m | GPR_Izhikevich-master/analysis/phaseplane_izhi_paperform_tscaled.m | 20,496 | utf_8 | ffea40d5fc73fe5ebff6b86c192a22d4 | % A function to take u and v from an Izhykevich neuron simulation
% carried out in SpineCreator and show phase plane and quiver plots
% and analysis. Based on Kevin Gurney's demo matlab code for the MS
% course - specifically dynamicsIz.m with the simulation removed
% from the code.
%
% Adapted by Seb James
%
% This ve... |
github | ABRG-Models/GPR_Izhikevich-master | phaseplane_izhi.m | .m | GPR_Izhikevich-master/analysis/phaseplane_izhi.m | 16,574 | utf_8 | e8b8efa09c0917e1abf45c61e32a3f15 | % A function to take u and v from an Izhykevich neuron simulation
% carried out in SpineCreator and show phase plane and quiver plots
% and analysis. Based on Kevin Gurney's demo matlab code for the MS
% course - specifically dynamicsIz.m with the simulation removed
% from the code.
%
% Adapted by Seb James
% I'll nee... |
github | DuongNguyenHai/kaldi-master-master | Generate_mcTrainData_cut.m | .m | kaldi-master-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 | unsky/draw-style-master | test_examples.m | .m | draw-style-master/matconvnet-1.0-beta22/utils/test_examples.m | 1,591 | utf_8 | 16831be7382a9343beff5cc3fe301e51 | function test_examples()
%TEST_EXAMPLES Test some of the examples in the `examples/` directory
addpath examples/mnist ;
addpath examples/cifar ;
trainOpts.gpus = [] ;
trainOpts.continue = true ;
num = 1 ;
exps = {} ;
for networkType = {'dagnn', 'simplenn'}
for index = 1:4
clear ex ;
ex.trainOpts = trainOp... |
github | unsky/draw-style-master | cnn_train_dag.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/cnn_train_dag.m | 13,551 | utf_8 | 8fa8458fac3db171d77162692670a597 | function [net,stats] = cnn_train_dag(net, imdb, getBatch, varargin)
%CNN_TRAIN_DAG Demonstrates training a CNN using the DagNN wrapper
% CNN_TRAIN_DAG() is similar to CNN_TRAIN(), but works with
% the DagNN wrapper instead of the SimpleNN wrapper.
% Copyright (C) 2014-16 Andrea Vedaldi.
% All rights reserved.
%
... |
github | unsky/draw-style-master | cnn_train.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/cnn_train.m | 19,072 | utf_8 | b8c8039c817fb4fb690b1f124f88e4c9 | function [net, stats] = cnn_train(net, imdb, getBatch, varargin)
%CNN_TRAIN An example implementation of SGD for training CNNs
% CNN_TRAIN() is an example learner implementing stochastic
% gradient descent with momentum to train a CNN. It can be used
% with different datasets and tasks by providing a suitable... |
github | unsky/draw-style-master | cnn_stn_cluttered_mnist.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/spatial_transformer/cnn_stn_cluttered_mnist.m | 3,873 | utf_8 | af8b38215c3fcddc5ae1c86431c0a141 | function [net, info] = cnn_stn_cluttered_mnist(varargin)
%CNN_STN_CLUTTERED_MNIST Demonstrates training a spatial transformer
% The spatial transformer network (STN) is trained on the
% cluttered MNIST dataset.
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.data... |
github | unsky/draw-style-master | cnn_cifar.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/cifar/cnn_cifar.m | 5,337 | utf_8 | 5bcb6d0b2ab367aca747317814a10458 | function [net, info] = cnn_cifar(varargin)
% CNN_CIFAR Demonstrates MatConvNet on CIFAR-10
% The demo includes two standard model: LeNet and Network in
% Network (NIN). Use the 'modelType' option to choose one.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.... |
github | unsky/draw-style-master | cnn_cifar_init_nin.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/cifar/cnn_cifar_init_nin.m | 5,561 | utf_8 | aca711e04a8cd82821f658922218368c | function net = cnn_cifar_init_nin(varargin)
opts.networkType = 'simplenn' ;
opts = vl_argparse(opts, varargin) ;
% CIFAR-10 model from
% M. Lin, Q. Chen, and S. Yan. Network in network. CoRR,
% abs/1312.4400, 2013.
%
% It reproduces the NIN + Dropout result of Table 1 (<= 10.41% top1 error).
net.layers = {} ;
lr = [... |
github | unsky/draw-style-master | cnn_imagenet_init_resnet.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/imagenet/cnn_imagenet_init_resnet.m | 6,627 | utf_8 | 7c72c8d9c5df43fc1a9eeeed0438773e | function net = cnn_imagenet_init_resnet(varargin)
%CNN_IMAGENET_INIT_RESNET Initialize the ResNet-50 model for ImageNet classification
opts.classNames = {} ;
opts.classDescriptions = {} ;
opts.averageImage = zeros(3,1) ;
opts.colorDeviation = zeros(3) ;
opts.cudnnWorkspaceLimit = 1024*1024*1204 ; % 1GB
opts = vl_argp... |
github | unsky/draw-style-master | cnn_imagenet_init.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/imagenet/cnn_imagenet_init.m | 15,279 | utf_8 | 43bffc7ab4042d49c4f17c0e44c36bf9 | function net = cnn_imagenet_init(varargin)
% CNN_IMAGENET_INIT Initialize a standard CNN for ImageNet
opts.scale = 1 ;
opts.initBias = 0 ;
opts.weightDecay = 1 ;
%opts.weightInitMethod = 'xavierimproved' ;
opts.weightInitMethod = 'gaussian' ;
opts.model = 'alexnet' ;
opts.batchNormalization = false ;
opts.networkType... |
github | unsky/draw-style-master | cnn_imagenet.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/imagenet/cnn_imagenet.m | 6,211 | utf_8 | f11556c91bb9796f533c8f624ad8adbd | function [net, info] = cnn_imagenet(varargin)
%CNN_IMAGENET Demonstrates training a CNN on ImageNet
% This demo demonstrates training the AlexNet, VGG-F, VGG-S, VGG-M,
% VGG-VD-16, and VGG-VD-19 architectures on ImageNet data.
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m... |
github | unsky/draw-style-master | cnn_imagenet_deploy.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/imagenet/cnn_imagenet_deploy.m | 6,585 | utf_8 | 2f3e6d216fa697ff9adfce33e75d44d8 | function net = cnn_imagenet_deploy(net)
%CNN_IMAGENET_DEPLOY Deploy a CNN
isDag = isa(net, 'dagnn.DagNN') ;
if isDag
dagRemoveLayersOfType(net, 'dagnn.Loss') ;
dagRemoveLayersOfType(net, 'dagnn.DropOut') ;
else
net = simpleRemoveLayersOfType(net, 'softmaxloss') ;
net = simpleRemoveLayersOfType(net, 'dropout')... |
github | unsky/draw-style-master | cnn_imagenet_evaluate.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/imagenet/cnn_imagenet_evaluate.m | 5,089 | utf_8 | f22247bd3614223cad4301daa91f6bd7 | function info = cnn_imagenet_evaluate(varargin)
% CNN_IMAGENET_EVALUATE Evauate MatConvNet models on ImageNet
run(fullfile(fileparts(mfilename('fullpath')), ...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.dataDir = fullfile('data', 'ILSVRC2012') ;
opts.expDir = fullfile('data', 'imagenet12-eval-vgg-f') ;
opts.m... |
github | unsky/draw-style-master | cnn_mnist_init.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/mnist/cnn_mnist_init.m | 3,111 | utf_8 | 367b1185af58e108aec40b61818ec6e7 | function net = cnn_mnist_init(varargin)
% CNN_MNIST_LENET Initialize a CNN similar for MNIST
opts.batchNormalization = true ;
opts.networkType = 'simplenn' ;
opts = vl_argparse(opts, varargin) ;
rng('default');
rng(0) ;
f=1/100 ;
net.layers = {} ;
net.layers{end+1} = struct('type', 'conv', ...
... |
github | unsky/draw-style-master | cnn_mnist.m | .m | draw-style-master/matconvnet-1.0-beta22/examples/mnist/cnn_mnist.m | 4,613 | utf_8 | d23586e79502282a6f6d632c3cf8a47e | function [net, info] = cnn_mnist(varargin)
%CNN_MNIST Demonstrates MatConvNet on MNIST
run(fullfile(fileparts(mfilename('fullpath')),...
'..', '..', 'matlab', 'vl_setupnn.m')) ;
opts.batchNormalization = false ;
opts.network = [] ;
opts.networkType = 'simplenn' ;
[opts, varargin] = vl_argparse(opts, varargin) ;
s... |
github | unsky/draw-style-master | vl_nnloss.m | .m | draw-style-master/matconvnet-1.0-beta22/matlab/vl_nnloss.m | 11,212 | utf_8 | e4c325752a9cddab59f01afa0d561ea1 | function y = vl_nnloss(x,c,dzdy,varargin)
%VL_NNLOSS CNN categorical or attribute loss.
% Y = VL_NNLOSS(X, C) computes the loss incurred by the prediction
% scores X given the categorical labels C.
%
% The prediction scores X are organised as a field of prediction
% vectors, represented by a H x W x D x N array... |
github | unsky/draw-style-master | vl_compilenn.m | .m | draw-style-master/matconvnet-1.0-beta22/matlab/vl_compilenn.m | 29,728 | utf_8 | 178cd7ac3143c6616c6e21c0cb018b6b | function vl_compilenn(varargin)
%VL_COMPILENN Compile the MatConvNet toolbox.
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following options:
%... |
github | unsky/draw-style-master | getVarReceptiveFields.m | .m | draw-style-master/matconvnet-1.0-beta22/matlab/+dagnn/@DagNN/getVarReceptiveFields.m | 3,635 | utf_8 | 6d61896e475e64e9f05f10303eee7ade | function rfs = getVarReceptiveFields(obj, var)
%GETVARRECEPTIVEFIELDS Get the receptive field of a variable
% RFS = GETVARRECEPTIVEFIELDS(OBJ, VAR) gets the receptivie fields RFS of
% all the variables of the DagNN OBJ into variable VAR. VAR is a variable
% name or index.
%
% RFS has one entry for each variable... |
github | unsky/draw-style-master | rebuild.m | .m | draw-style-master/matconvnet-1.0-beta22/matlab/+dagnn/@DagNN/rebuild.m | 3,243 | utf_8 | e368536d9e70c805d8424cdd6b593960 | function rebuild(obj)
%REBUILD Rebuild the internal data structures of a DagNN object
% REBUILD(obj) rebuilds the internal data structures
% of the DagNN obj. It is an helper function used internally
% to update the network when layers are added or removed.
varFanIn = zeros(1, numel(obj.vars)) ;
varFanOut = zero... |
github | unsky/draw-style-master | print.m | .m | draw-style-master/matconvnet-1.0-beta22/matlab/+dagnn/@DagNN/print.m | 14,071 | utf_8 | 7c20afb2965627d0dc8fe7ec97182887 | function str = print(obj, inputSizes, varargin)
%PRINT Print information about the DagNN object
% PRINT(OBJ) displays a summary of the functions and parameters in the network.
% STR = PRINT(OBJ) returns the summary as a string instead of printing it.
%
% PRINT(OBJ, INPUTSIZES) where INPUTSIZES is a cell array of ... |
github | unsky/draw-style-master | fromSimpleNN.m | .m | draw-style-master/matconvnet-1.0-beta22/matlab/+dagnn/@DagNN/fromSimpleNN.m | 7,168 | utf_8 | d5cc2d8faeb926ca6972f64c4086cbb0 | function obj = fromSimpleNN(net, varargin)
% FROMSIMPLENN Initialize a DagNN object from a SimpleNN network
% FROMSIMPLENN(NET) initializes the DagNN object from the
% specified CNN using the SimpleNN format.
%
% SimpleNN objects are linear chains of computational layers. These
% layers exchange information th... |
github | unsky/draw-style-master | vl_simplenn_display.m | .m | draw-style-master/matconvnet-1.0-beta22/matlab/simplenn/vl_simplenn_display.m | 12,455 | utf_8 | 65bb29cd7c27b68c75fdd27acbd63e2b | function [info, str] = vl_simplenn_display(net, varargin)
%VL_SIMPLENN_DISPLAY Display the structure of a SimpleNN network.
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
%
% INFO = VL_SIMPLENN_DISPLAY(NET) returns instead a structure INFO
% with several statistics for each layer of the netw... |
github | unsky/draw-style-master | vl_test_economic_relu.m | .m | draw-style-master/matconvnet-1.0-beta22/matlab/xtest/vl_test_economic_relu.m | 790 | utf_8 | 35a3dbe98b9a2f080ee5f911630ab6f3 | % VL_TEST_ECONOMIC_RELU
function vl_test_economic_relu()
x = randn(11,12,8,'single');
w = randn(5,6,8,9,'single');
b = randn(1,9,'single') ;
net.layers{1} = struct('type', 'conv', ...
'filters', w, ...
'biases', b, ...
'stride', 1, ...
... |
github | jianxiongxiao/SFMedu-master | ransac5point.m | .m | SFMedu-master/ransac5point.m | 3,580 | utf_8 | c9a96ed07bd6336d2a11600269ab1318 | function [E, inliers] = ransac5point(x1, x2, t, K, feedback)
% written by Fisher Yu @ 2014
if ~all(size(x1)==size(x2))
error('Data sets x1 and x2 must have the same dimension');
end
if nargin == 4
feedback = 0;
end
[rows,npts] = size(x1);
if rows~=2 && rows~=3
... |
github | jianxiongxiao/SFMedu-master | peig5pt.m | .m | SFMedu-master/peig5pt.m | 144,129 | utf_8 | b5702cc64bf14871875dc11c3a4f70cc | % fast implementation of the 5pt relative pose problem
%
% by M. Bujnak, Z. Kukelova (c)sep2008
%
%
% Please refer to the following paper, when using this code :
%
% Kukelova, Z., Bujnak, M. and Pajdla, Polynomial eigenvalue solutions
% to the 5-pt and 6-pt relative pose problems, BMVC 2008, Leeds, ... |
github | jianxiongxiao/SFMedu-master | PoseEMat.m | .m | SFMedu-master/PoseEMat.m | 1,222 | utf_8 | 198d2b20a94e2387527a6c982c5a6167 | % PoseEMat - estimate the pose from essential matrix with SVD.
%
% Usage:
% [R1, R2, t1, t2] = PoseEMat(E)
%
% Input:
% E : essential matrix
%
% Output:
% R1 : 3x3 rotation matrix 1
% R2 : 3x3 rotation matrix 2
% t1 : 3x1 translation vector 1
% ... |
github | jianxiongxiao/SFMedu-master | vgg_X_from_xP_lin.m | .m | SFMedu-master/vgg_X_from_xP_lin.m | 1,095 | utf_8 | 81a7771dabb46e6074b13ade0ad2a66f | %vgg_X_from_xP_lin Estimation of 3D point from image matches and camera matrices, linear.
% X = vgg_X_from_xP_lin(x,P,imsize) computes projective 3D point X (column 4-vector)
% from its projections in K images x (2-by-K matrix) and camera matrices P (K-cell
% of 3-by-4 matrices). Image sizes imsize (2-by-K ma... |
github | jianxiongxiao/SFMedu-master | vgg_X_from_xP_nonlin.m | .m | SFMedu-master/vgg_X_from_xP_nonlin.m | 1,745 | utf_8 | de8473e8b82ea73502176fcd3eac4865 | %vgg_X_from_xP_nonlin Estimation of 3D point from image matches and camera matrices, nonlinear.
% X = vgg_X_from_xP_lin(x,P,imsize) computes max. likelihood estimate of projective
% 3D point X (column 4-vector) from its projections in K images x (2-by-K matrix)
% and camera matrices P (K-cell of 3-by-4 matrices)... |
github | jianxiongxiao/SFMedu-master | ransacfitfundmatrix.m | .m | SFMedu-master/matchSIFT/MatlabFns/ransacfitfundmatrix.m | 5,559 | utf_8 | d72411c71e86dc0e246ad9c5e65404da | % RANSACFITFUNDMATRIX - fits fundamental matrix using RANSAC
%
% Usage: [F, inliers] = ransacfitfundmatrix(x1, x2, t)
%
% Arguments:
% x1 - 2xN or 3xN set of homogeneous points. If the data is
% 2xN it is assumed the homogeneous scale factor is 1.
% x2 - 2xN or 3xN set of homogeneo... |
github | jianxiongxiao/SFMedu-master | fundmatrix.m | .m | SFMedu-master/matchSIFT/MatlabFns/fundmatrix.m | 3,961 | utf_8 | 250dfa8051640daab30229f35667f4d6 | % FUNDMATRIX - computes fundamental matrix from 8 or more points
%
% Function computes the fundamental matrix from 8 or more matching points in
% a stereo pair of images. The normalised 8 point algorithm given by
% Hartley and Zisserman p265 is used. To achieve accurate results it is
% recommended that 12 or more poi... |
github | jianxiongxiao/SFMedu-master | ransac.m | .m | SFMedu-master/matchSIFT/MatlabFns/ransac.m | 9,904 | utf_8 | 712357fa72b8ac686f489772c11832f3 | % RANSAC - Robustly fits a model to data with the RANSAC algorithm
%
% Usage:
%
% [M, inliers] = ransac(x, fittingfn, distfn, degenfn s, t, feedback, ...
% maxDataTrials, maxTrials)
%
% Arguments:
% x - Data sets to which we are seeking to fit a model M
% It is assumed ... |
github | jianxiongxiao/SFMedu-master | normalise2dpts.m | .m | SFMedu-master/matchSIFT/MatlabFns/normalise2dpts.m | 2,361 | utf_8 | 2b9d94a3681186006a3fd47a45faf939 | % NORMALISE2DPTS - normalises 2D homogeneous points
%
% Function translates and normalises a set of 2D homogeneous points
% so that their centroid is at the origin and their mean distance from
% the origin is sqrt(2). This process typically improves the
% conditioning of any equations used to solve homographies, fun... |
github | jianxiongxiao/SFMedu-master | vl_compile.m | .m | SFMedu-master/matchSIFT/vlfeat/toolbox/vl_compile.m | 5,060 | utf_8 | 978f5189bb9b2a16db3368891f79aaa6 | function vl_compile(compiler)
% VL_COMPILE Compile VLFeat MEX files
% VL_COMPILE() uses MEX() to compile VLFeat MEX files. This command
% works only under Windows and is used to re-build problematic
% binaries. The preferred method of compiling VLFeat on both UNIX
% and Windows is through the provided Makefile... |
github | jianxiongxiao/SFMedu-master | vl_noprefix.m | .m | SFMedu-master/matchSIFT/vlfeat/toolbox/vl_noprefix.m | 1,875 | utf_8 | 97d8755f0ba139ac1304bc423d3d86d3 | function vl_noprefix
% VL_NOPREFIX Create a prefix-less version of VLFeat commands
% VL_NOPREFIX() creats prefix-less stubs for VLFeat functions
% (e.g. SIFT for VL_SIFT). This function is seldom used as the stubs
% are included in the VLFeat binary distribution anyways. Moreover,
% on UNIX platforms, the stub... |
github | jianxiongxiao/SFMedu-master | vl_override.m | .m | SFMedu-master/matchSIFT/vlfeat/toolbox/misc/vl_override.m | 4,654 | utf_8 | e233d2ecaeb68f56034a976060c594c5 | function config = vl_override(config,update,varargin)
% VL_OVERRIDE Override structure subset
% CONFIG = VL_OVERRIDE(CONFIG, UPDATE) copies recursively the fileds
% of the structure UPDATE to the corresponding fields of the
% struture CONFIG.
%
% Usually CONFIG is interpreted as a list of paramters with their
... |
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