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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | jagmoreira/machine-learning-coursera-master | submitWithConfiguration.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | savejson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | loadjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | loadubjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | saveubjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | submit.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | submitWithConfiguration.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | savejson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | loadjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | loadubjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | saveubjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | submit.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | submitWithConfiguration.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | savejson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | loadjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | loadubjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | saveubjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | submit.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | submitWithConfiguration.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | savejson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | loadjson.m | .m | machine-learning-coursera-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 | jagmoreira/machine-learning-coursera-master | loadubjson.m | .m | machine-learning-coursera-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-... |
github | jagmoreira/machine-learning-coursera-master | saveubjson.m | .m | machine-learning-coursera-master/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 | jagmoreira/machine-learning-coursera-master | submit.m | .m | machine-learning-coursera-master/machine-learning-ex8/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | jagmoreira/machine-learning-coursera-master | submitWithConfiguration.m | .m | machine-learning-coursera-master/machine-learning-ex8/ex8/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 | jagmoreira/machine-learning-coursera-master | savejson.m | .m | machine-learning-coursera-master/machine-learning-ex8/ex8/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 | jagmoreira/machine-learning-coursera-master | loadjson.m | .m | machine-learning-coursera-master/machine-learning-ex8/ex8/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 | jagmoreira/machine-learning-coursera-master | loadubjson.m | .m | machine-learning-coursera-master/machine-learning-ex8/ex8/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 | jagmoreira/machine-learning-coursera-master | saveubjson.m | .m | machine-learning-coursera-master/machine-learning-ex8/ex8/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 | jagmoreira/machine-learning-coursera-master | submit.m | .m | machine-learning-coursera-master/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 | jagmoreira/machine-learning-coursera-master | submitWithConfiguration.m | .m | machine-learning-coursera-master/machine-learning-ex1/ex1/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 | jagmoreira/machine-learning-coursera-master | savejson.m | .m | machine-learning-coursera-master/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 | jagmoreira/machine-learning-coursera-master | loadjson.m | .m | machine-learning-coursera-master/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 | jagmoreira/machine-learning-coursera-master | loadubjson.m | .m | machine-learning-coursera-master/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 | jagmoreira/machine-learning-coursera-master | saveubjson.m | .m | machine-learning-coursera-master/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 | NitinJSanket/CMSC828THW1-master | gtsamExamples.m | .m | CMSC828THW1-master/gtsam_toolbox/gtsam_examples/gtsamExamples.m | 5,664 | utf_8 | f2621b78fabdb370c4f63d5e0309b7e9 | function varargout = gtsamExamples(varargin)
% GTSAMEXAMPLES MATLAB code for gtsamExamples.fig
% GTSAMEXAMPLES, by itself, creates a new GTSAMEXAMPLES or raises the existing
% singleton*.
%
% H = GTSAMEXAMPLES returns the handle to a new GTSAMEXAMPLES or the handle to
% the existing singleton*.
%
% ... |
github | NitinJSanket/CMSC828THW1-master | VisualISAM_gui.m | .m | CMSC828THW1-master/gtsam_toolbox/gtsam_examples/VisualISAM_gui.m | 10,009 | utf_8 | ed501f5a7d855d179385d3bb29e65500 | function varargout = VisualISAM_gui(varargin)
% VisualISAM_gui: runs VisualSLAM iSAM demo in GUI
% Interface is defined by VisualISAM_gui.fig
% You can run this file directly, but won't have access to globals
% By running ViusalISAMDemo, you see all variables in command prompt
% Authors: Duy Nguyen Ta
% Last Mod... |
github | wanwanbeen/toy_code-master | GMM_MCMC.m | .m | toy_code-master/GMM_MCMC.m | 4,935 | utf_8 | 0eba8c5c4c31e3ab80702f37c99e6982 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Gibbs Sampling for Gaussian Mixture Model
% Jie Yang 2015
%
% Note: All parameters named by My_** are
% parameters to be iterated/optimized
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function GMM_MCMC()
load('data.mat')
n_obs=size(X,2);
n_dim=... |
github | wanwanbeen/toy_code-master | GMM_EM.m | .m | toy_code-master/GMM_EM.m | 3,362 | utf_8 | ed8c29426673f51b7824924dc2c86a51 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Expectation Maximization for Gaussian Mixture Model
% Jie Yang 2015
%
% Note: All parameters named by My_** are
% parameters to be iterated/optimized
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function GMM_EM()
load('data.mat')
n_obs=size(X,2)... |
github | wanwanbeen/toy_code-master | GMM_VI.m | .m | toy_code-master/GMM_VI.m | 8,108 | utf_8 | cc496089b2b0c182f36db24702794124 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Variational Inference for Gaussian Mixture Model
% Jie Yang 2015
%
% Note: All parameters named by My_** are
% parameters to be iterated/optimized
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function GMM_VI()
load('data.mat')
n_obs=size(X,2);
... |
github | akhilesh-k/Robotics-Specialization-master | QuadPlot.m | .m | Robotics-Specialization-master/AerialRobotics/GainTuningExercise/QuadPlot.m | 5,212 | utf_8 | 9b869b07631faa5214a3a9c6cfae2cac | classdef QuadPlot < handle
%QUADPLOT Visualization class for quad
properties (SetAccess = public)
k = 0;
qn; % quad number
time = 0; % time
state; % state
rot; % rotation matrix body to world
color; % color of quad
... |
github | akhilesh-k/Robotics-Specialization-master | QuadPlot.m | .m | Robotics-Specialization-master/AerialRobotics/GainTuningQuiz/QuadPlot.m | 5,220 | utf_8 | 1f4b7d11af220cf6f1d5e395f4a180f9 | classdef QuadPlot < handle
%QUADPLOT Visualization class for quad
properties (SetAccess = public)
k = 0;
qn; % quad number
time = 0; % time
state; % state
rot; % rotation matrix body to world
color; % color of quad
... |
github | xkunwu/Conformal-master | subaxis.m | .m | Conformal-master/+Utility/subaxis.m | 7,846 | utf_8 | 7bede64f6313fa3bb699728d149cda90 | function h=subaxis(varargin)
%SUBAXIS Create axes in tiled positions. (just like subplot)
% Usage:
% h=subaxis(rows,cols,cellno[,settings])
% h=subaxis(rows,cols,cellx,celly[,settings])
% h=subaxis(rows,cols,cellx,celly,spanx,spany[,settings])
%
% SETTINGS: Spacing,SpacingHoriz,SpacingVert
% ... |
github | xkunwu/Conformal-master | layout_vertices_from_la.m | .m | Conformal-master/+ConvexAngleSum/layout_vertices_from_la.m | 4,508 | utf_8 | 04dbb7176ee11ef2b6919f55bbc5c409 | function [positions, seedi] = layout_vertices_from_la(obj)
num_vert = obj.vertData.num_entry;
i_v_star = obj.meshTri.i_v_star;
ledge = obj.edgeData.length;
alpha = obj.halfData.alpha;
positions = zeros(3, num_vert);
markv = false(1, num_vert);
seedi = seed_vertex(obj);
s0vec = zeros(3, 1);
markv(seedi) = t... |
github | xkunwu/Conformal-master | layout_voronoi.m | .m | Conformal-master/+ConvexAngleSum/layout_voronoi.m | 2,938 | utf_8 | 7b1727a2c8b8baeb4e935dbefa37a835 | function [positions] = layout_voronoi(obj)
i_v_star = obj.meshTri.i_v_star;
i_e2h = obj.meshTri.i_e2h;
ledge = obj.edgeData.length;
alpha = obj.halfData.alpha;
faces = obj.meshTri.faces;
num_vert = obj.vertData.num_entry;
face_vert = obj.meshTri.face_vert;
face_link = obj.meshTri.face_link;
num_face = size(fac... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | vl_nnloss_regression.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/demo5_analysis_MShift_gradient/vl_nnloss_regression.m | 12,261 | utf_8 | 06b83e6c343531803ed5696beb89388b | function y = vl_nnloss_regression(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 ... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | linspecer.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/demo5_analysis_MShift_gradient/linspecer.m | 8,087 | utf_8 | b7cd4dab49656ba92d0e006cc5a912e9 | % function lineStyles = linspecer(N)
% This function creates an Nx3 array of N [R B G] colors
% These can be used to plot lots of lines with distinguishable and nice
% looking colors.
%
% lineStyles = linspecer(N); makes N colors for you to use: lineStyles(ii,:)
%
% colormap(linspecer); set your colormap to have easil... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | pdftops.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/pdftops.m | 3,687 | utf_8 | 43c139e49fce63cb78060895bd13137a | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for futur... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | crop_borders.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/crop_borders.m | 3,791 | utf_8 | 2c8fc83f142f1d5b28b99080556c791e | function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how much paddi... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | isolate_axes.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/isolate_axes.m | 4,851 | utf_8 | 611d9727e84ad6ba76dcb3543434d0ce | function fh = isolate_axes(ah, vis)
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The o... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | im2gif.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/im2gif.m | 6,234 | utf_8 | 8ee74d7d94e524410788276aa41dd5f1 | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function c... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | read_write_entire_textfile.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/read_write_entire_textfile.m | 961 | utf_8 | 775aa1f538c76516c7fb406a4f129320 | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fn... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | pdf2eps.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/pdf2eps.m | 1,522 | utf_8 | 4c8f0603619234278ed413670d24bdb6 | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.c... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | print2array.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/print2array.m | 9,613 | utf_8 | e398a6296734121e6e1983a45298549a | function [A, bcol] = print2array(fig, res, renderer, gs_options)
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% A = print2array(figur... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | append_pdfs.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/append_pdfs.m | 2,759 | utf_8 | 9b52be41aff48bea6f27992396900640 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the o... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | using_hg2.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/using_hg2.m | 1,037 | utf_8 | 3303caab5694b040103ccb6b689387bf | %USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for i... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | eps2pdf.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/eps2pdf.m | 8,543 | utf_8 | a63a364925b89dac21030d36b0dd29a3 | function eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, de... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | ghostscript.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/ghostscript.m | 7,902 | utf_8 | ff62a40d651197dbea5d3c39998b3bad | function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | fix_lines.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/exportFig/fix_lines.m | 6,441 | utf_8 | ffda929ebad8144b1e72d528fa5d9460 | %FIX_LINES Improves the line style of eps files generated by print
%
% Examples:
% fix_lines fname
% fix_lines fname fname2
% fstrm_out = fixlines(fstrm_in)
%
% This function improves the style of lines in eps files generated by
% MATLAB's print function, making them more similar to those seen on
% scre... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | AddDilationErosionObjectives.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/layerExt/AddDilationErosionObjectives.m | 2,955 | utf_8 | 475beab90ff4584fde1b8972ff685f73 | function net = AddDilationErosionObjectives(net, upsample_fac, rec_upsample, var_to_upsample, bases_size, num_basis, neigh_size, learningrate, opts)
up_name = [num2str(upsample_fac) 'x'];
net = AddSegObjective(net, var_to_upsample, up_name, upsample_fac, upsample_fac/rec_upsample, rec_upsample, neigh_size, num_basis, b... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | test_examples.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | simplenn_caffe_compare.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/utils/simplenn_caffe_compare.m | 5,638 | utf_8 | 8e9862ffbf247836e6ff7579d1e6dc85 | function diffStats = simplenn_caffe_compare( net, caffeModelBaseName, testData, varargin)
% SIMPLENN_CAFFE_COMPARE compare the simplenn network and caffe models
% SIMPLENN_CAFFE_COMPARE(NET, CAFFE_BASE_MODELNAME) Evaluates a forward
% pass of a simplenn network NET and caffe models stored in
% CAFFE_BASE_MODELNAM... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_train_dag.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/examples/cnn_train_dag.m | 16,099 | utf_8 | 326a535b1d18f74d19e5526a8a5c195b | 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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_train.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/examples/cnn_train.m | 22,309 | utf_8 | 7cd588eb330fec6caf497e384b4a2734 | 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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_stn_cluttered_mnist.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/examples/spatial_transformer/cnn_stn_cluttered_mnist.m | 3,872 | utf_8 | 3235801f70028cc27d54d15ec2964808 | 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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | fast_rcnn_train.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/examples/fast_rcnn/fast_rcnn_train.m | 6,399 | utf_8 | 54b0bc7fa26d672ed6673d3f1832944e | function [net, info] = fast_rcnn_train(varargin)
%FAST_RCNN_TRAIN Demonstrates training a Fast-RCNN detector
% Copyright (C) 2016 Hakan Bilen.
% 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).
run(fullfile(fileparts(m... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | fast_rcnn_evaluate.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/examples/fast_rcnn/fast_rcnn_evaluate.m | 6,941 | utf_8 | a54a3f8c3c8e5a8ff7ebe4e2b12ede30 | function [aps, speed] = fast_rcnn_evaluate(varargin)
%FAST_RCNN_EVALUATE Evaluate a trained Fast-RCNN model on PASCAL VOC 2007
% Copyright (C) 2016 Hakan Bilen.
% 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).
run(fu... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_cifar.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/examples/cifar/cnn_cifar.m | 5,334 | utf_8 | eb9aa887d804ee635c4295a7a397206f | 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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_cifar_init_nin.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_imagenet_init_resnet.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/examples/imagenet/cnn_imagenet_init_resnet.m | 6,717 | utf_8 | aa905a97830e90dc7d33f75ad078301e | 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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_imagenet_init.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_imagenet.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_imagenet_deploy.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_imagenet_evaluate.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_mnist_init.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | cnn_mnist.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/examples/mnist/cnn_mnist.m | 4,662 | utf_8 | 39844185155240d4f0ebfcf8db493148 | 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' ; % dagnn, simplenn
opts.networkType = 'dagnn' ;
[o... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | vl_nnloss.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/matlab/vl_nnloss.m | 12,021 | utf_8 | 1f4bacf5f0df0f547019f23730c5f742 | 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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | vl_compilenn.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/matlab/vl_compilenn.m | 30,050 | utf_8 | 6339b625106e6c7b479e57c2b9aa578e | 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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | getVarReceptiveFields.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | rebuild.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | print.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/matlab/+dagnn/@DagNN/print.m | 15,032 | utf_8 | 7da4e68e624f559f815ee3076d9dd966 | 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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | fromSimpleNN.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/matlab/+dagnn/@DagNN/fromSimpleNN.m | 7,258 | utf_8 | 83f914aec610125592263d74249f54a7 | 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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | vl_simplenn_display.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | vl_test_economic_relu.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/matconvnet-1.0-beta23_modifiedDagnn/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 | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | switchFigure.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/myFunctions/switchFigure.m | 142 | utf_8 | eeda5d7fca9d055f8636347f3cc56aa8 |
function switchFigure(n)
if get(0,'CurrentFigure') ~= n
try
set(0,'CurrentFigure',n) ;
catch
figure(n) ;
end
end
|
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | myfindLastCheckpoint.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/libs/myFunctions/myfindLastCheckpoint.m | 432 | utf_8 | 673b6d6d681d7b5f31b4982a68ac07af | % -------------------------------------------------------------------------
function epoch = myfindLastCheckpoint(modelDir, prefixStr)
% -------------------------------------------------------------------------
list = dir(fullfile(modelDir, sprintf('%snet-epoch-*.mat', prefixStr))) ;
tokens = regexp({list.name}, 'net-e... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | vl_nnloss_modified.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/demo1_tutorial_instance_segmentation/fun4MeanShift/vl_nnloss_modified.m | 16,226 | utf_8 | 928ff76f02b6600caa8becbfc152dbc1 | function y = vl_nnloss_modified(x, c, 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... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | getBatchWrapper4toyDigitV2.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/demo1_tutorial_instance_segmentation/fun4MeanShift/getBatchWrapper4toyDigitV2.m | 795 | utf_8 | d4dd7f6389ff6f9829ff67027a84bc94 | % return a get batch function
% -------------------------------------------------------------------------
function fn = getBatchWrapper4toyDigitV2(opts)
% -------------------------------------------------------------------------
fn = @(images, mode) getBatch_dict4toyDigitV2(images, mode, opts) ;
end
% ------------... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | vl_nnloss_modified.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/demo4_InstSegTraining_VOC2012/vl_nnloss_modified.m | 16,226 | utf_8 | 928ff76f02b6600caa8becbfc152dbc1 | function y = vl_nnloss_modified(x, c, 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... |
github | aimerykong/Recurrent-Pixel-Embedding-for-Instance-Grouping-master | getBatchWrapper_augVOC2012.m | .m | Recurrent-Pixel-Embedding-for-Instance-Grouping-master/demo4_InstSegTraining_VOC2012/getBatchWrapper_augVOC2012.m | 460 | utf_8 | 08f8928d4415a1bfaecc30c16de254f7 | % return a get batch function
% -------------------------------------------------------------------------
function fn = getBatchWrapper_augVOC2012(opts)
fn = @(images, mode) getBatch_dict(images, mode, opts) ;
end
function [imBatch, semanticMaskBatch, instanceMaskBatch, weightBatch] = getBatch_dict(images, mode, opts... |
github | thejihuijin/VideoDilation-master | sc.m | .m | VideoDilation-master/saliency/sc.m | 38,646 | utf_8 | 26cc0ce889b98168991324c9e25bf156 | function I = sc(I, varargin)
%SC Display/output truecolor images with a range of colormaps
%
% Examples:
% sc(image)
% sc(image, limits)
% sc(image, map)
% sc(image, limits, map)
% sc(image, map, limits)
% sc(..., col1, mask1, col2, mask2,...)
% out = sc(...)
% sc
%
% Generates a truecolor... |
github | thejihuijin/VideoDilation-master | compute_OF.m | .m | VideoDilation-master/opticalFlow/compute_OF.m | 739 | utf_8 | 94ea800e5b3a4b7ef848c0cf94fe2ad2 | % COMPUTE_OF returns the optical flow magnitudes for a given video
% Currently uses Horn Schunck
% Assumes input video is in grey scale
% Dimensions = (rows, cols, frames)
%
% vid : 3D video matrix
%
% flow_mags : 3D Optical Flow magnitudes
function [flow_mags] = compute_OF(vid)
% ECE6258: Digital image processing
%... |
github | thejihuijin/VideoDilation-master | vidToMat.m | .m | VideoDilation-master/videoDilation/vidToMat.m | 854 | utf_8 | 8f4d19cf089e628cb93ed6b3f7b25844 |
% VIDTOMAT Convert a RGB video file to a 4D matrix of image frames
%
% INPUT
% filename : String filename of video
%
% OUTPUT
% vidMatrix : 4D matrix of rgb frames
% frame_rate : Framerate at which the video file was encoded
function [vidMatrix, frame_rate] = vidToMat( filename )
% ECE6258: Digital imag... |
github | thejihuijin/VideoDilation-master | playDilatedFrames.m | .m | VideoDilation-master/videoDilation/playDilatedFrames.m | 1,958 | utf_8 | a2c1f2cad1274451be6b2d79a8233abf | % PLAYDILATEDFRAMES Plays the frames as designated by the vector of
% indices, frameIndices, at a constant framerate.
%
% INPUTS
% vidMat : 3D or 4D video matrix
% frameIndices : Vector of indices into vidMat to be played sequentially
% fr : Constant framerate at which to play frames
% dilated_fr : Variable fra... |
github | thejihuijin/VideoDilation-master | compute_energy.m | .m | VideoDilation-master/videoDilation/compute_energy.m | 2,922 | utf_8 | 35abf09cb0299c8c80c0dfce81728436 | % COMPUTE_ENERGY Converts heat maps to energy of frame.
% Frames with higher energy will be slowed down and frames with lower
% energy will be sped up.
% Heat Maps are assumed to be 3D matrices with the same number of "frames".
% Size of individual frames may differ between different heat maps
%
% OF_mags : Optical Flo... |
github | thejihuijin/VideoDilation-master | playVidMat.m | .m | VideoDilation-master/videoDilation/playVidMat.m | 1,804 | utf_8 | 5cfd657016548630c075c86521cac133 | % PLAYVIDMAT Plays a sequence of RGB or Grayscale frames with framerate
% determined by input framerate vector or scalar.
%
% INPUTS
% vidMat : 3D or 4D video matrix
% frameRates : Vector of variable framerates at which to play each frame of
% vidMat, or a constant at which to play the entire video.
%
% OUTP... |
github | thejihuijin/VideoDilation-master | resize_vid.m | .m | VideoDilation-master/videoDilation/resize_vid.m | 967 | utf_8 | 15405abf35c91807c072c1e51842aaa5 | % RESIZE_VID Saves a video with new dimensions [newrows newcols]
% Input videos can be RGB or Greyscale
%
% inputName : Path to input video file
% outputName : Path and name for output video file
% newrows : Vertical size of resized video
% newcols : Horizontal size of resized video
function resize_vid(inputName,output... |
github | thejihuijin/VideoDilation-master | adjustFR.m | .m | VideoDilation-master/videoDilation/adjustFR.m | 1,452 | utf_8 | cbe748a493be302e2de00094db2b4c6c | % ADJUSTFR Extends the slowmo sections of a framerate vector to
% preemptively slow before 'exciting' segements and smoothly speed back up
% afterward.
%
% INPUTS
% frVect : vector w/ time-varying framerate
% timeShift : Time to shift slow/speedups by, in seconds
% fr : Framerate video was taken at
%
% OUTPUT
... |
github | thejihuijin/VideoDilation-master | rgbToGrayVid.m | .m | VideoDilation-master/videoDilation/rgbToGrayVid.m | 696 | utf_8 | 531aa72f922bf66a47ac0528f47a2a43 | % RGBTOGRAYVID Convert a 4D RGB matrix to a 3D grayscale matrix
%
% INPUT
% rgbVidMatrix : matrix (rows x cols x 3 x frames)
%
% OUTPUT
% 3D matrix of a grayscale video
function grayVidMatrix = rgbToGrayVid( rgbVidMatrix )
% ECE6258: Digital image processing
% School of Electrical and Computer Engineering
... |
github | thejihuijin/VideoDilation-master | energy2fr.m | .m | VideoDilation-master/videoDilation/energy2fr.m | 1,311 | utf_8 | e00baa8902e1f4f6046cdf77ced22078 | % ENERGY2FR Converts energy to time padded frame rate
% Inverts an energy function, then scales it between -scale to scale and
% passes it through an exponential to determine speed up factor.
% Frame rate is then padded to begin slow down prior to "interesting"
% events
%
% energy : 1D energy function. High values will... |
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