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 | ojwoodford/ojwul-master | vl_mser.m | .m | ojwul-master/features/vlfeat/vl_mser.m | 3,185 | utf_8 | 3f713cf370e5d15b71f6f7b90dd91390 | % VL_MSER Maximally Stable Extremal Regions
% R=VL_MSER(I) computes the Maximally Stable Extremal Regions (MSER)
% [1] of image I with stability threshold DELTA. I is any array of
% class UINT8. R is a vector of region seeds.
%
% A (maximally stable) extremal region is just a connected component
% of one of ... |
github | ojwoodford/ojwul-master | vl_sift.m | .m | ojwul-master/features/vlfeat/vl_sift.m | 3,096 | utf_8 | 27d8451c04817442563daeb990c87b58 | % VL_SIFT Scale-Invariant Feature Transform
% F = VL_SIFT(I) computes the SIFT frames [1] (keypoints) F of the
% image I. I is a gray-scale image in single precision. Each column
% of F is a feature frame and has the format [X;Y;S;TH], where X,Y
% is the (fractional) center of the frame, S is the scale and TH ... |
github | ojwoodford/ojwul-master | vl_siftdescriptor.m | .m | ojwul-master/features/vlfeat/vl_siftdescriptor.m | 2,682 | utf_8 | 0753efd3f2ffd45604b62f2aec10d0e5 | % VL_SIFTDESCRIPTOR Raw SIFT descriptor
% D = VL_SIFTDESCRIPTOR(GRAD, F) calculates the SIFT descriptors of
% the keypoints F on the pre-processed image GRAD. GRAD is a 2xMxN
% array. The first layer GRAD(1,:,:) contains the modulus of
% gradient of the original image modulus. The second layer
% GRAD(2,:,:) ... |
github | ojwoodford/ojwul-master | vl_hog.m | .m | ojwul-master/features/vlfeat/vl_hog.m | 2,429 | utf_8 | ad12f0d85adb05c48fae844aac4c4fd5 | % VL_HOG Compute HOG features
% HOG = VL_HOG(IM, CELLSIZE) computes the HOG features for image IM
% and the specified CELLSIZE. IM can be either grayscale or colour
% in SINGLE storage class. HOG is an array of cells: its number
% of columns is approximately the number of columns of IM divided
% by CELLSIZE a... |
github | monark12/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | monark12/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/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 | monark12/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | monark12/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | monark12/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | monark12/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | monark12/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-Coursera-master/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | monark12/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex4/ex4/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 | monark12/Machine-Learning-Coursera-master | savejson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | monark12/Machine-Learning-Coursera-master | loadjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | monark12/Machine-Learning-Coursera-master | loadubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | monark12/Machine-Learning-Coursera-master | saveubjson.m | .m | Machine-Learning-Coursera-master/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | monark12/Machine-Learning-Coursera-master | submit.m | .m | Machine-Learning-Coursera-master/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | monark12/Machine-Learning-Coursera-master | porterStemmer.m | .m | Machine-Learning-Coursera-master/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | monark12/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex6/ex6/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 | monark12/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 | monark12/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 | monark12/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 | monark12/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 | monark12/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 | monark12/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex7/ex7/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 | monark12/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 | monark12/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 | monark12/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 | monark12/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 | monark12/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 | monark12/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/machine-learning-ex5/ex5/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 | monark12/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 | monark12/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 | monark12/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 | monark12/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 | monark12/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 | monark12/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/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 | monark12/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 | monark12/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 | monark12/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 | monark12/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 | monark12/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 | monark12/Machine-Learning-Coursera-master | submitWithConfiguration.m | .m | Machine-Learning-Coursera-master/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 | monark12/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 | monark12/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 | monark12/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 | monark12/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 | ee368/EE368-Android-Samples-master | vl_compile.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/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 | ee368/EE368-Android-Samples-master | vl_noprefix.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/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 | ee368/EE368-Android-Samples-master | vl_override.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/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
... |
github | ee368/EE368-Android-Samples-master | vl_quickvis.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/quickshift/vl_quickvis.m | 3,696 | utf_8 | 27f199dad4c5b9c192a5dd3abc59f9da | function [Iedge dists map gaps] = vl_quickvis(I, ratio, kernelsize, maxdist, maxcuts)
% VL_QUICKVIS Create an edge image from a Quickshift segmentation.
% IEDGE = VL_QUICKVIS(I, RATIO, KERNELSIZE, MAXDIST, MAXCUTS) creates an edge
% stability image from a Quickshift segmentation. RATIO controls the tradeoff
% bet... |
github | ee368/EE368-Android-Samples-master | vl_demo_aib.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/demo/vl_demo_aib.m | 2,928 | utf_8 | 590c6db09451ea608d87bfd094662cac | function vl_demo_aib
% VL_DEMO_AIB Test Agglomerative Information Bottleneck (AIB)
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
figure(1) ; clf ; hold on ;
vl_plotframe(X... |
github | ee368/EE368-Android-Samples-master | vl_demo_alldist.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/demo/vl_demo_alldist.m | 5,460 | utf_8 | 6d008a64d93445b9d7199b55d58db7eb | function vl_demo_alldist
%
numRepetitions = 3 ;
numDimensions = 1000 ;
numSamplesRange = [300] ;
settingsRange = {{'alldist2', 'double', 'l2', }, ...
{'alldist', 'double', 'l2', 'nosimd'}, ...
{'alldist', 'double', 'l2' }, ...
{'alldist2', 's... |
github | ee368/EE368-Android-Samples-master | vl_demo_kdtree_sift.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/demo/vl_demo_kdtree_sift.m | 6,822 | utf_8 | 191589ff45e0f5cdb79b1eed1b1bb906 | function vl_demo_kdtree_sift
% VL_DEMO_KDTREE_SIFT
% Demonstrates the use of a kd-tree forest to match SIFT
% features. If FLANN is present, this function runs a comparison
% against it.
% AUTORIGHS
rand('state',0) ;
randn('state',0);
do_median = 0 ;
do_mean = 1 ;
% try to setup flann
if ~exist('flann_search'... |
github | ee368/EE368-Android-Samples-master | vl_tpsu.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/imop/vl_tpsu.m | 1,755 | utf_8 | 09f36e1a707c069b375eb2817d0e5f13 | function [U,dU,delta]=vl_tpsu(X,Y)
% VL_TPSU Compute the U matrix of a thin-plate spline transformation
% U=VL_TPSU(X,Y) returns the matrix
%
% [ U(|X(:,1) - Y(:,1)|) ... U(|X(:,1) - Y(:,N)|) ]
% [ ]
% [ U(|X(:,M) - Y(:,1)|) ... U(|X(:,M) - Y(:,N)|) ]
%
% where X... |
github | ee368/EE368-Android-Samples-master | vl_xyz2lab.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/imop/vl_xyz2lab.m | 1,570 | utf_8 | 09f95a6f9ae19c22486ec1157357f0e3 | function J=vl_xyz2lab(I,il)
% VL_XYZ2LAB Convert XYZ color space to LAB
% J = VL_XYZ2LAB(I) converts the image from XYZ format to LAB format.
%
% VL_XYZ2LAB(I,IL) uses one of the illuminants A, B, C, E, D50, D55,
% D65, D75, D93. The default illuminatn is E.
%
% See also: VL_XYZ2LUV(), VL_HELP().
% Copyright ... |
github | ee368/EE368-Android-Samples-master | vl_test_twister.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_twister.m | 1,162 | utf_8 | 1ae9040a416db503ad73600f081d096b | function results = vl_test_twister(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_illegal_args()
vl_assert_exception(@() vl_twister(-1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister(1, -1), 'vl:invalidArgument') ;
vl_assert_exception(@() vl_twister([1, -1]), 'vl:invalidArgument') ;
function te... |
github | ee368/EE368-Android-Samples-master | vl_test_kdtree.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_kdtree.m | 2,448 | utf_8 | 66f429ff8286089a34c193d7d3f9f016 | function results = vl_test_kdtree(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.X = single(randn(10, 1000)) ;
s.Q = single(randn(10, 10)) ;
function test_nearest(s)
for tmethod = {'median', 'mean'}
for type = {@single, @double}
conv = type{1} ;
tmethod = char(tmethod) ;... |
github | ee368/EE368-Android-Samples-master | vl_test_imwbackward.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_imwbackward.m | 514 | utf_8 | 33baa0784c8f6f785a2951d7f1b49199 | function results = vl_test_imwbackward(varargin)
% VL_TEST_IMWBACKWARD
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
function test_identity(s)
xr = 1:size(s.I,2) ;
yr = 1:size(s.I,1) ;
[x,y] = meshgrid(xr,yr) ;
vl_assert_almost_equal(s.I, vl_imwbackward(xr,yr,s.I,... |
github | ee368/EE368-Android-Samples-master | vl_test_pegasos.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_pegasos.m | 2,852 | utf_8 | 45a09a3bfefa3facd439fefbb7f1a903 | function results = vl_test_pegasos(varargin)
% VL_TEST_KDTREE
vl_test_init ;
function s = setup()
randn('state',0) ;
s.biasMultiplier = 10 ;
s.lambda = 0.01 ;
Np = 10 ;
Nn = 10 ;
Xp = diag([1 3])*randn(2, Np) ;
Xn = diag([1 3])*randn(2, Nn) ;
Xp(1,:) = Xp(1,:) + 2 + 1 ;
Xn(1,:) = Xn(1,:) - 2 + 1 ;
s.X = [Xp Xn] ;
s... |
github | ee368/EE368-Android-Samples-master | vl_test_alphanum.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_alphanum.m | 1,624 | utf_8 | 2da2b768c2d0f86d699b8f31614aa424 | function results = vl_test_alphanum(varargin)
% VL_TEST_ALPHANUM
vl_test_init ;
function s = setup()
s.strings = ...
{'1000X Radonius Maximus','10X Radonius','200X Radonius','20X Radonius','20X Radonius Prime','30X Radonius','40X Radonius','Allegia 50 Clasteron','Allegia 500 Clasteron','Allegia 50B Clasteron','Al... |
github | ee368/EE368-Android-Samples-master | vl_test_imintegral.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_imintegral.m | 1,429 | utf_8 | 4750f04ab0ac9fc4f55df2c8583e5498 | function results = vl_test_imintegral(varargin)
% VL_TEST_IMINTEGRAL
vl_test_init ;
function state = setup()
state.I = ones(5,6) ;
state.correct = [ 1 2 3 4 5 6 ;
2 4 6 8 10 12 ;
3 6 9 12 15 18 ;
4 8 12 ... |
github | ee368/EE368-Android-Samples-master | vl_test_sift.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_sift.m | 1,318 | utf_8 | 806c61f9db9f2ebb1d649c9bfcf3dc0a | function results = vl_test_sift(varargin)
% VL_TEST_SIFT
vl_test_init ;
function s = setup()
s.I = im2single(imread(fullfile(vl_root,'data','box.pgm'))) ;
[s.ubc.f, s.ubc.d] = ...
vl_ubcread(fullfile(vl_root,'data','box.sift')) ;
function test_ubc_descriptor(s)
err = [] ;
[f, d] = vl_sift(s.I,...
... |
github | ee368/EE368-Android-Samples-master | vl_test_binsum.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_binsum.m | 1,301 | utf_8 | 5bbd389cbc4d997e413d809fe4efda6d | function results = vl_test_binsum(varargin)
% VL_TEST_BINSUM
vl_test_init ;
function test_three_args()
vl_assert_almost_equal(...
vl_binsum([0 0], 1, 2), [0 1]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, 1), [0 7]) ;
vl_assert_almost_equal(...
vl_binsum([1 7], -1, [1 2 2 2 2 2 2 2]), [0 0]) ;
function te... |
github | ee368/EE368-Android-Samples-master | vl_test_lbp.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_lbp.m | 1,056 | utf_8 | 3b5cca50109af84014e56a4280a3352a | function results = vl_test_lbp(varargin)
% VL_TEST_TWISTER
vl_test_init ;
function test_one_on()
I = {} ;
I{1} = [0 0 0 ; 0 0 1 ; 0 0 0] ;
I{2} = [0 0 0 ; 0 0 0 ; 0 0 1] ;
I{3} = [0 0 0 ; 0 0 0 ; 0 1 0] ;
I{4} = [0 0 0 ; 0 0 0 ; 1 0 0] ;
I{5} = [0 0 0 ; 1 0 0 ; 0 0 0] ;
I{6} = [1 0 0 ; 0 0 0 ; 0 0 0] ;
I{7} = [0 1 0 ;... |
github | ee368/EE368-Android-Samples-master | vl_test_colsubset.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_colsubset.m | 828 | utf_8 | be0c080007445b36333b863326fb0f15 | function results = vl_test_colsubset(varargin)
% VL_TEST_COLSUBSET
vl_test_init ;
function s = setup()
s.x = [5 2 3 6 4 7 1 9 8 0] ;
function test_beginning(s)
vl_assert_equal(1:5, vl_colsubset(1:10, 5, 'beginning')) ;
vl_assert_equal(1:5, vl_colsubset(1:10, .5, 'beginning')) ;
function test_ending(s)
vl_assert_equa... |
github | ee368/EE368-Android-Samples-master | vl_test_alldist.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_alldist.m | 2,373 | utf_8 | 9ea1a36c97fe715dfa2b8693876808ff | function results = vl_test_alldist(varargin)
% VL_TEST_ALLDIST
vl_test_init ;
function s = setup()
vl_twister('state', 0) ;
s.X = 3.1 * vl_twister(10,10) ;
s.Y = 4.7 * vl_twister(10,7) ;
function test_null_args(s)
vl_assert_equal(...
vl_alldist(zeros(15,12), zeros(15,0), 'kl2'), ...
zeros(12,0)) ;
vl_assert_equa... |
github | ee368/EE368-Android-Samples-master | vl_test_grad.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_grad.m | 434 | utf_8 | 4d03eb33a6a4f68659f868da95930ffb | function results = vl_test_grad(varargin)
% VL_TEST_GRAD
vl_test_init ;
function s = setup()
s.I = rand(150,253) ;
s.I_small = rand(2,2) ;
function test_equiv(s)
vl_assert_equal(gradient(s.I), vl_grad(s.I)) ;
function test_equiv_small(s)
vl_assert_equal(gradient(s.I_small), vl_grad(s.I_small)) ;
function test_equiv... |
github | ee368/EE368-Android-Samples-master | vl_test_whistc.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_whistc.m | 1,384 | utf_8 | 81c446d35c82957659840ab2a579ec2c | function results = vl_test_whistc(varargin)
% VL_TEST_WHISTC
vl_test_init ;
function test_acc()
x = ones(1, 10) ;
e = 1 ;
o = 1:10 ;
vl_assert_equal(vl_whistc(x, o, e), 55) ;
function test_basic()
x = 1:10 ;
e = 1:10 ;
o = ones(1, 10) ;
vl_assert_equal(histc(x, e), vl_whistc(x, o, e)) ;
x = linspace(-1,11,100) ;
o =... |
github | ee368/EE368-Android-Samples-master | vl_test_dsift.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_dsift.m | 2,048 | utf_8 | fbbfb16d5a21936c1862d9551f657ccc | function results = vl_test_dsift(varargin)
% VL_TEST_DSIFT
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = rgb2gray(single(I)) ;
function test_fast_slow(s)
binSize = 4 ; % bin size in pixels
magnif = 3 ; % bin size / keypoint scale
scale = binSize... |
github | ee368/EE368-Android-Samples-master | vl_test_imsmooth.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_imsmooth.m | 1,837 | utf_8 | 718235242cad61c9804ba5e881c22f59 | function results = vl_test_imsmooth(varargin)
% VL_TEST_IMSMOOTH
vl_test_init ;
function s = setup()
I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
I = max(min(vl_imdown(I),1),0) ;
s.I = single(I) ;
function test_pad_by_continuity(s)
% Convolving a constant signal padded with continuity does not change... |
github | ee368/EE368-Android-Samples-master | vl_test_phow.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_phow.m | 549 | utf_8 | f761a3bb218af855986263c67b2da411 | function results = vl_test_phow(varargin)
% VL_TEST_PHOPW
vl_test_init ;
function s = setup()
s.I = im2double(imread(fullfile(vl_root,'data','spots.jpg'))) ;
s.I = single(s.I) ;
function test_gray(s)
[f,d] = vl_phow(s.I, 'color', 'gray') ;
assert(size(d,1) == 128) ;
function test_rgb(s)
[f,d] = vl_phow(s.I, 'color',... |
github | ee368/EE368-Android-Samples-master | vl_test_kmeans.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_kmeans.m | 2,788 | utf_8 | 14374b7dbae832fc3509e02caf00cdf5 | function results = vl_test_kmeans(varargin)
% VL_TEST_KMEANS
% Copyright (C) 2007-12 Andrea Vedaldi and Brian Fulkerson.
% 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).
vl_test_init ;
function s = setup()
randn('sta... |
github | ee368/EE368-Android-Samples-master | vl_test_imarray.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_imarray.m | 795 | utf_8 | c5e6a5aa8c2e63e248814f5bd89832a8 | function results = vl_test_imarray(varargin)
% VL_TEST_IMARRAY
vl_test_init ;
function test_movie_rgb(s)
A = rand(23,15,3,4) ;
B = vl_imarray(A,'movie',true) ;
function test_movie_indexed(s)
cmap = get(0,'DefaultFigureColormap') ;
A = uint8(size(cmap,1)*rand(23,15,4)) ;
A = min(A,size(cmap,1)-1) ;
B = vl_imarray(A,'m... |
github | ee368/EE368-Android-Samples-master | vl_test_homkermap.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_homkermap.m | 1,903 | utf_8 | c157052bf4213793a961bde1f73fb307 | function results = vl_test_homkermap(varargin)
% VL_TEST_HOMKERMAP
vl_test_init ;
function check_ker(ker, n, window, period)
args = {n, ker, 'window', window} ;
if nargin > 3
args = {args{:}, 'period', period} ;
end
x = [-1 -.5 0 .5 1] ;
y = linspace(0,2,100) ;
for conv = {@single, @double}
x = feval(conv{1}, x) ;... |
github | ee368/EE368-Android-Samples-master | vl_test_slic.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_slic.m | 229 | utf_8 | 42c827b383cca74cae2540e5da870bbf | function results = vl_test_slic(varargin)
% VL_TEST_SLIC
vl_test_init ;
function s = setup()
s.im = im2single(imread(fullfile(vl_root,'data','a.jpg'))) ;
function test_slic(s)
segmentation = vl_slic(s.im, 10, 0.1, 'verbose') ;
|
github | ee368/EE368-Android-Samples-master | vl_test_imdisttf.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_imdisttf.m | 1,885 | utf_8 | ae921197988abeb984cbcdf9eaf80e77 | function results = vl_test_imdisttf(varargin)
% VL_TEST_DISTTF
vl_test_init ;
function test_basic()
for conv = {@single, @double}
conv = conv{1} ;
I = conv([0 0 0 ; 0 -2 0 ; 0 0 0]) ;
D = vl_imdisttf(I);
assert(isequal(D, conv(- [0 1 0 ; 1 2 1 ; 0 1 0]))) ;
I(2,2) = -3 ;
[D,map] = vl_imdisttf(I) ;
asse... |
github | ee368/EE368-Android-Samples-master | vl_test_argparse.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_argparse.m | 795 | utf_8 | e72185b27206d0ee1dfdc19fe77a5be6 | function results = vl_test_argparse(varargin)
% VL_TEST_ARGPARSE
vl_test_init ;
function test_basic()
opts.field1 = 1 ;
opts.field2 = 2 ;
opts.field3 = 3 ;
opts_ = opts ;
opts_.field1 = 3 ;
opts_.field2 = 10 ;
opts = vl_argparse(opts, {'field2', 10, 'field1', 3}) ;
assert(isequal(opts, opts_)) ;
opts_.field1 = 9 ;
... |
github | ee368/EE368-Android-Samples-master | vl_test_binsearch.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/xtest/vl_test_binsearch.m | 1,339 | utf_8 | 85dc020adce3f228fe7dfb24cf3acc63 | function results = vl_test_binsearch(varargin)
% VL_TEST_BINSEARCH
vl_test_init ;
function test_inf_bins()
x = [-inf -1 0 1 +inf] ;
vl_assert_equal(vl_binsearch([], x), [0 0 0 0 0]) ;
vl_assert_equal(vl_binsearch([-inf 0], x), [1 1 2 2 2]) ;
vl_assert_equal(vl_binsearch([-inf], x), [1 1 1 1 1]) ;
vl_a... |
github | ee368/EE368-Android-Samples-master | vl_plotframe.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/plotop/vl_plotframe.m | 5,410 | utf_8 | 8c48bac1c5d80dba361b67cd135103d9 | function h=vl_plotframe(frames,varargin)
% VL_PLOTFRAME Plot feature frame
% VL_PLOTFRAME(FRAME) plots the frames FRAME. Frames are attributed
% image regions (as, for example, extracted by a feature detector). A
% frame is a vector of D=2,3,..,6 real numbers, depending on its
% class. VL_PLOTFRAME() supports the... |
github | ee368/EE368-Android-Samples-master | vl_roc.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/plotop/vl_roc.m | 6,848 | utf_8 | 3d7ed746da2d3f389ad56c8e36f006d7 | function [tpr,tnr,info] = vl_roc(labels, scores, varargin)
% VL_ROC Compute ROC curve
% [TP,TN] = VL_ROC(LABELS, SCORES) computes the receiver operating
% characteristic (ROC curve). LABELS are the ground thruth labels (+1
% or -1) and SCORE is the scores assigned to them by a classifier
% (higher scores correspond... |
github | ee368/EE368-Android-Samples-master | vl_click.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/plotop/vl_click.m | 2,661 | utf_8 | 6982e869cf80da57fdf68f5ebcd05a86 | function P = vl_click(N,varargin) ;
% VL_CLICK Click a point
% P=VL_CLICK() let the user click a point in the current figure and
% returns its coordinates in P. P is a two dimensiona vectors where
% P(1) is the point X-coordinate and P(2) the point Y-coordinate. The
% user can abort the operation by pressing any k... |
github | ee368/EE368-Android-Samples-master | vl_ubcread.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/sift/vl_ubcread.m | 3,015 | utf_8 | e8ddd3ecd87e76b6c738ba153fef050f | function [f,d] = vl_ubcread(file, varargin)
% SIFTREAD Read Lowe's SIFT implementation data files
% [F,D] = VL_UBCREAD(FILE) reads the frames F and the descriptors D
% from FILE in UBC (Lowe's original implementation of SIFT) format
% and returns F and D as defined by VL_SIFT().
%
% VL_UBCREAD(FILE, 'FORMAT', '... |
github | ee368/EE368-Android-Samples-master | vl_plotsiftdescriptor.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/sift/vl_plotsiftdescriptor.m | 4,348 | utf_8 | b9a98b0c298fa249fb5fcd1314762b88 | function h=vl_plotsiftdescriptor(d,f,varargin)
% VL_PLOTSIFTDESCRIPTOR Plot SIFT descriptor
% VL_PLOTSIFTDESCRIPTOR(D) plots the SIFT descriptors D, stored as
% columns of the matrix D. D has the same format used by VL_SIFT().
%
% VL_PLOTSIFTDESCRIPTOR(D,F) plots the SIFT descriptors warped to
% the SIFT fram... |
github | ee368/EE368-Android-Samples-master | vl_test_twister.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/test/vl_test_twister.m | 1,166 | utf_8 | 1e18a0b343ffe164ec9c941e18575c05 | function vl_test_twister
% VL_TEST_TWISTER
% test seed by scalar
rand('twister',1) ; a = rand ;
vl_twister('state',1) ; b = vl_twister ;
check(a,b,'twister: seed by scalar + VL_TWISTER()') ;
% read state
rand('twister') ; a = rand('twister') ;
vl_twister('state') ; b = vl_twister('state') ;
check(a,b,'twister: read s... |
github | ee368/EE368-Android-Samples-master | vl_test_imintegral.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/test/vl_test_imintegral.m | 1,257 | utf_8 | d5ad8d073e99ff451cc1b692da99ec6d | function vl_test_imintegral
I = ones(5,6);
correct = [1 2 3 4 5 6;
2 4 6 8 10 12;
3 6 9 12 15 18;
4 8 12 16 20 24;
5 10 15 20 25 30;];
if ~all(all(slow_imintegral(I) == correct))
fpri... |
github | ee368/EE368-Android-Samples-master | vl_test_sift.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/test/vl_test_sift.m | 1,849 | utf_8 | cfae71614a40aebf645eb42102ca53f3 | function vl_test_sift
% VL_TEST_SIFT Test VL_SIFT implementation(s)
I = vl_test_pattern(101);
% run various instances of the code
[a0,b0] = vl_sift(single(I),'verbose','peaktresh',0,'levels',4) ;
[a1,b1] = cmd_sift(I,'--first-octave=0 --peak-tresh=0 --levels=4') ;
[a2,b2] = cmd_sift(I,'--first-octave=0',1) ;
[a3,... |
github | ee368/EE368-Android-Samples-master | vl_test_binsum.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/test/vl_test_binsum.m | 1,030 | utf_8 | c69da861d697e8228e243a385f5ba545 | function vl_test_binsum
% VL_TEST_BINSUM Test VL_BINSUM function
testh({[0 0], 1, 2}, [0 1] ) ;
testh({[1 7], -1, 1}, [0 7] ) ;
testh({[1 7], -1, [1 2 2 2 2 2 2 2]}, [0 0] ) ;
testh({eye(3), [1 1 1], [1 2 3], 1 }, 2*eye(3)) ;
testh({eye(3), [1 1 1]', [1 2 3]', 2 }, 2*eye... |
github | ee368/EE368-Android-Samples-master | vl_test_imsmooth.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/test/vl_test_imsmooth.m | 1,566 | utf_8 | 27ae6791e4ca852539a031b78ae7a00b | function vl_test_imsmooth
I = im2double(imread('data/spots.jpg')) ;
I = max(min(imresize(I,2),1),0) ;
I = single(I) ;
global fign ;
fign = 1 ;
step = 1 ;
ker = 'gaussian' ;
testmany(I,'triangular',1) ;
testmany(I,'triangular',2) ;
testmany(I,'gaussian',1) ;
testmany(I,'gaussian',2) ;
function testmany(I,ker,step)... |
github | ee368/EE368-Android-Samples-master | vl_test_hikmeans.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/test/vl_test_hikmeans.m | 2,037 | utf_8 | f57532e5de667fbe2f6cb9c714f20457 | function vl_test_hikmeans
% VL_TEST_HIKMEANS Test VL_HIKMEANS function
K = 2;
nleaves = 2;
data = uint8(rand(2,100)*255);
[tree,A] = vl_hikmeans(data,K,nleaves,'verbose','verbose');
%keyboard;
K = 3 ;
nleaves = 100 ;
data = uint8(rand(2,1000) * 255) ;
datat = uint8(rand(2,10000)* 255) ;
[... |
github | ee368/EE368-Android-Samples-master | vl_test_homkmap.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/test/vl_test_homkmap.m | 1,493 | utf_8 | a78c933efd15a4279e2724ba4441ad76 | function vl_test_homkmap
x = 2.^(-12:.1:0) ;
L = .3 ;
n = 4 ;
V = vl_homkmap(x, n, L, 'kchi2') ;
V_ = featureMap('chi2', n, L, x, 1) ;
V
V_
figure(1) ; clf ;
subplot(1,2,1) ;
semilogx(x,V_','-') ; hold on ;
semilogy(x,V','--') ;
subplot(1,2,2);
plot(x,V_','-') ; hold on ;
plot(x,V','--') ;
function psi = feat... |
github | ee368/EE368-Android-Samples-master | vl_test_aibhist.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/test/vl_test_aibhist.m | 2,263 | utf_8 | d46c6fa557ab0d00e465eaedd060add9 | % VL_TEST_AIBHIST
function vl_test_aibhist
D = 4 ;
K = 20 ;
randn('state',0) ;
rand('state',0) ;
X1 = randn(2,300) ; X1(1,:) = X1(1,:) + 2 ;
X2 = randn(2,300) ; X2(1,:) = X2(1,:) - 2 ;
X3 = randn(2,300) ; X3(2,:) = X3(2,:) + 2 ;
C = 1:K*K ;
Pcx = zeros(3,K*K) ;
f1 = quantize(X1,D,K) ;
f2 = quantize(X2,D,K) ;... |
github | ee368/EE368-Android-Samples-master | vl_test_ikmeans.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/toolbox/test/vl_test_ikmeans.m | 1,552 | utf_8 | 1d5747a991a0d81ed4f7a2c90cd2a213 | function vl_test_ikmeans
% VL_TEST_IKMEANS Test VL_IKMEANS function
fprintf('test_ikmeans: Testing VL_IKMEANS and IKMEANSPUSH\n')
% -----------------------------------------------------------------------
fprintf('test_ikmeans: Testing Lloyd algorithm\n')
K = 3 ;
data = uint8(rand(2,1000) * 255) ;
datat = ... |
github | ee368/EE368-Android-Samples-master | phow_caltech101.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/apps/phow_caltech101.m | 11,269 | utf_8 | 91ef403a7a3865b32e7a5673350fec49 | function phow_caltech101
% PHOW_CALTECH101 Image classification in the Caltech-101 dataset
% This program demonstrates how to use VLFeat to construct an image
% classifier on the Caltech-101 data. The classifier uses PHOW
% features (dense SIFT), spatial histograms of visual words, and a
% Chi2 SVM. To speedup ... |
github | ee368/EE368-Android-Samples-master | sift_mosaic.m | .m | EE368-Android-Samples-master/Tutorial3/ServerCode/vlfeat-0.9.14/apps/sift_mosaic.m | 4,621 | utf_8 | 8fa3ad91b401b8f2400fb65944c79712 | function mosaic = sift_mosaic(im1, im2)
% SIFT_MOSAIC Demonstrates matching two images using SIFT and RANSAC
%
% SIFT_MOSAIC demonstrates matching two images based on SIFT
% features and RANSAC and computing their mosaic.
%
% SIFT_MOSAIC by itself runs the algorithm on two standard test
% images. Use SIFT_MOSAI... |
github | jacksky64/imageProcessing-master | metaImageInfo.m | .m | imageProcessing-master/Matlab Slicer/imStacks/metaImageInfo.m | 8,259 | utf_8 | 769e7b03c38d70ddec2d85cd9430d077 | function info = metaImageInfo(fileName, varargin)
%METAIMAGEINFO Read information header of meta image data
%
% INFO = metaImageInfo(FILENAME)
% Read and decodes the information stored in metaimage header file.
%
% Metaimage header files are text files containing parameters name/value
% pairs in each lin... |
github | jacksky64/imageProcessing-master | metaImageWrite.m | .m | imageProcessing-master/Matlab Slicer/imStacks/metaImageWrite.m | 7,057 | utf_8 | df8a6d61ef09a3a7575d62ff1765f76f | function metaImageWrite(img, fileName, varargin)
%METAIMAGEWRITE Write header and data files of an image in MetaImage format
%
% metaImageWrite(IMG, FILENAME);
% IMG is a matlab array, and FILENAME is the generic name (without
% extension) of the metaimage file.
% The functions tries to determine which... |
github | jacksky64/imageProcessing-master | orthoSlices.m | .m | imageProcessing-master/Matlab Slicer/imStacks/orthoSlices.m | 12,391 | utf_8 | a8b1b8223f887fe71954a2d76eb53635 | function varargout = orthoSlices(img, varargin)
%ORTHOSLICES Display three orthogonal slices in the same figure
%
% orthoSlices(IMG)
% Show three orthogonal slices of the 3D image IMG in the same figure.
% Each slice is displayed to occupy the maximum amount of space within
% the figure, keeping same prop... |
github | jacksky64/imageProcessing-master | metaImageRead.m | .m | imageProcessing-master/Matlab Slicer/imStacks/metaImageRead.m | 5,728 | utf_8 | 6acee507c8af1e296e6000ec8993817f | function [img info] = metaImageRead(info, varargin)
%METAIMAGEREAD Read an image in MetaImage format
%
% IMG = metaImageRead(INFO)
% Read the image IMG from data given in structure INFO. INFO is typically
% returned by the metaImageInfo function.
%
% IMG = metaImageRead(FILENAME)
% Read the image from... |
github | jacksky64/imageProcessing-master | TabPanel.m | .m | imageProcessing-master/Matlab Slicer/imStacks/+uiextras/TabPanel.m | 20,903 | utf_8 | cd35a9887b8563870e78f3635ef7341c | classdef TabPanel < uiextras.CardPanel & uiextras.DecoratedPanel
%TabPanel Show one element inside a tabbed panel
%
% obj = uiextras.TabPanel() creates a panel with tabs along one edge
% to allow selection between the different child objects contained.
%
% obj = uiextras.TabPanel(pa... |
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