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 | johanga/ml-coursera-stanford-master | loadjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | saveubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | submit.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | submitWithConfiguration.m | .m | ml-coursera-stanford-master/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,995 | utf_8 | 74845afdc970e616c3a08a5d413de179 | 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 | johanga/ml-coursera-stanford-master | savejson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | saveubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | submit.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | submitWithConfiguration.m | .m | ml-coursera-stanford-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,995 | utf_8 | 74845afdc970e616c3a08a5d413de179 | 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 | johanga/ml-coursera-stanford-master | savejson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | saveubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | submit.m | .m | ml-coursera-stanford-master/machine-learning-ex8/ex8/submit.m | 2,064 | utf_8 | 7c4fcf60df3a7e09d05a74f7772fed3b | 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 | johanga/ml-coursera-stanford-master | submitWithConfiguration.m | .m | ml-coursera-stanford-master/machine-learning-ex8/ex8/lib/submitWithConfiguration.m | 3,995 | utf_8 | 74845afdc970e616c3a08a5d413de179 | 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 | johanga/ml-coursera-stanford-master | savejson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | saveubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | submit.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | submitWithConfiguration.m | .m | ml-coursera-stanford-master/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 3,995 | utf_8 | 74845afdc970e616c3a08a5d413de179 | 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 | johanga/ml-coursera-stanford-master | savejson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | saveubjson.m | .m | ml-coursera-stanford-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 | echoorchid/Super-Resolution-Gather-master | immaxproduct.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/immaxproduct.m | 4,991 | utf_8 | 63134b108b43613ee53f95414d8be926 | % max-product belief propagation on image lattice (2D matrix)
% This implementation contains no product. The compatibility function
% should be changed to exp{-E} where E is energy or error. Here E is the
% input. Output is the MAP estimation of the graph
%
% This implementation is based on W.T. Freeman et al's IJCV p... |
github | echoorchid/Super-Resolution-Gather-master | imband.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/imband.m | 793 | utf_8 | c68ad2fdb078b2cd60291292cf276e73 | % function to generate the high, low and band-pass channels of an image
% im = im_lowlow + im_bandpass + im_laplacian
function [im_laplacian,im_bandpass,im_lowlow]=imband(im)
[height,width,nchannels]=size(im);
if isfloat(im)
im = im2double(im);
end
% downsample
im_Low = imresize(imfilter(im,fspecial('gaussian',7... |
github | echoorchid/Super-Resolution-Gather-master | imgraphen.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/imgraphen.m | 505 | utf_8 | fcf09b9d17018fce6c5fefc7e27f85ce | % function to compute energy of the graph defiend on image lattice
function E=imgraphen(IDX,CO,CM_h,CM_v)
if length(size(IDX))==2
[nh,nw]=size(IDX);
IDX=reshape(IDX,[1 nh nw]);
end
E=CO(IDX);
E=sum(E(:));
IDX=squeeze(IDX);
[nh,nw]=size(IDX);
for i=1:nh
for j=1:nw
% horizontal energy
p=IDX(i,... |
github | echoorchid/Super-Resolution-Gather-master | imcontrastnormalize.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/imcontrastnormalize.m | 399 | utf_8 | b9c827dc329c2f795371bcf8d11e7af9 | % function to contrast normalize an image
function Im = imcontrastnormalize(im,wsize)
[height,width] = size(im);
H = zeros(height,width);
for i = 1:height
for j = 1:width
x1 = max(j-wsize,1);
y1 = max(i-wsize,1);
x2 = min(j+wsize,width);
y2 = min(i+wsize,height);
patch = i... |
github | echoorchid/Super-Resolution-Gather-master | decomposeImage.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/decomposeImage.m | 437 | utf_8 | 396e236e3e68700f460870153d6b775b | % function to decompose an image into luminance and chrominance part
function [Im_luminance,Im_chrominance] = decomposeImage(im)
s = [0.2989,0.5870,0.1140];
A = null(repmat(s,[3,1]));
[height,width,nchannels] = size(im);
if nchannels ~= 3
error('The input images must be a rgb image!');
end
im = reshape(im,[heigh... |
github | echoorchid/Super-Resolution-Gather-master | mergePatches.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/mergePatches.m | 1,075 | utf_8 | 8d3cffb2b6e9bb2b635f4cf5851a5680 | % function to merge all the patches onto one image
function im = mergePatches(Patches,overlapSize,width,height)
[nDim,h,w]=size(Patches);
patchDim = sqrt(nDim);
patchSize = (patchDim-1)/2;
intervalSize = patchSize*2-overlapSize;
% set the dimension for the output image
if exist('width','var')~=1
width = patchDim... |
github | echoorchid/Super-Resolution-Gather-master | composeImage.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/composeImage.m | 455 | utf_8 | 3f3410f60775c8991dc177a901217022 | % function to compose a luminance and chrominance image into one color image
function im = composeImage(Im_luminance,Im_chrominance)
s = [0.2989,0.5870,0.1140];
A = null(repmat(s,[3,1]));
[height,width] = size(Im_luminance);
im = zeros(height,width,3);
Im_luminance = reshape(Im_luminance,[height*width,1]);
Im_chrom... |
github | echoorchid/Super-Resolution-Gather-master | extractPatches.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/extractPatches.m | 1,229 | utf_8 | 5ed50f1297935a7d0ad35d8253490e84 | % function to extract pathces
function [Patches,Mask] = extractPatches(im,patchSize,overlapSize,gridPatchSize)
if exist('gridPatchSize','var')~=1
gridPatchSize = patchSize;
end
% first assume that the image is gray scale
[height,width] = size(im);
intervalSize = gridPatchSize*2 - overlapSize;
[grid_xx, grid_yy] =... |
github | echoorchid/Super-Resolution-Gather-master | im2patches.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/im2patches.m | 1,140 | utf_8 | 71d8d43b39a99fa21b58c08611596b38 | % function to convert an image into patches according to a possible mask
% note that for now im has to be a grayscale image
function patches = im2patches(im,patchSize,intervalSize,mask,boundarySize)
if exist('boundarySize','var')~=1
boundarySize = patchSize;
end
if boundarySize < patchSize
error('The boundary ... |
github | echoorchid/Super-Resolution-Gather-master | imaddbd.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/library/imaddbd.m | 364 | utf_8 | 2b7ac3d6a3dff1385688e08a1bd1ff53 | % function to add boundary to an image
function Im = imaddbd(im,color,margin)
if exist('margin','var')~=1
margin = 2;
end
[h,w,nchannels] = size(im);
if nchannels == 1
im = repmat(im,[1 1 3]);
end
if isfloat(im)~=1
im = im2double(im);
end
Im = repmat(reshape(color,[1 1 3]),[h+margin*2,w+margin*2,1]);
Im(... |
github | echoorchid/Super-Resolution-Gather-master | getSubfolders.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/library/getSubfolders.m | 399 | utf_8 | 6c7d8aaf0d0406731b16fb80c2b06b08 | % function to get subfolders of a given folder
function folderlist = getSubfolders(srcpath)
foo = dir(srcpath);
folderlist = [];
for i=1:length(foo)
% if the file is not a dir
if foo(i).isdir == 0
continue;
end
% if the file is . or ..
if strcmpi(foo(i).name,'.') || strcmpi(foo(i).name,'..... |
github | echoorchid/Super-Resolution-Gather-master | progressbar.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/library/progressbar.m | 11,009 | utf_8 | 3f6a267112a9ed41cdfe1713daf28998 | %this m-file modified by Quan Quach on 12/12/07
%email: quan.quach@gmail.com
%Original Author: Steve Hoelzer
function [stopBar] = progressbar(fractiondone, position)
if(~exist('fractiondone'))
return
end
% Description:
% progressbar(fractiondone,position) provides an indication of the progress of
% some task us... |
github | echoorchid/Super-Resolution-Gather-master | structMkdir.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/library/structMkdir.m | 264 | utf_8 | 95d1205d8802a594dfa65b2e028cd4bb | % function to make dir for all the fiends in a structure
function structMkdir(s)
names = fieldnames(s);
for i=1:length(names)
f = getfield(s,cell2mat(names(i)));
if isa(f,'char')
if exist(f,'dir')~=7
mkdir(f);
end
end
end
|
github | echoorchid/Super-Resolution-Gather-master | samplediscrete.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/library/samplediscrete.m | 186 | utf_8 | 3cdfe760f4890b26e5946ae3782fdc3d | % function to sample from discrete density distribution
function y=samplediscrete(f)
f=f/sum(f);
Pr2=cumsum(f);
Pr1=Pr2;Pr1(2:end)=Pr1(1:end-1);Pr1(1)=0;
x=rand;
y=find(Pr2>=x & x>Pr1);
|
github | echoorchid/Super-Resolution-Gather-master | readImages.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/library/readImages.m | 428 | utf_8 | 79cddccb1b1dce218cc7b6b4b14d2b6b | % function to read the info of images into a list
function filelist = readImages(srcpath)
if exist('srcpath','var')~=1
srcpath = pwd;
end
formats = imformats;
filelist = [];
for i = 1:length(formats)
for j=1:length(formats(i).ext)
extname = ['*.' cell2mat(formats(i).ext(j))];
foo = dir(fullfile... |
github | echoorchid/Super-Resolution-Gather-master | imaddboundary.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/library/imaddboundary.m | 457 | utf_8 | 16d65db64ce9c7443bb5aaff0cfaf83a | % function to add boundary to an image
% written by Ce Liu
% Oct 22, 2008
function Im=imaddboundary(im,margin,color)
if exist('margin','var')~=1
margin=2;
end
[height,width,nchannels]=size(im);
if exist('color','var')~=1
color=zeros(1,nchannels);
end
if size(color,1)>size(color,2)
color=color';
end
h=height... |
github | echoorchid/Super-Resolution-Gather-master | writeImage.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/library/writeImage.m | 687 | utf_8 | 430b9b5c2fa0498ece4bcf3b2b1662c2 | % function to write image data to a format that can be recognized by Image
% class developed by Ce Liu
function writeImage(im,filename,isderivative)
if ~isfloat(im)
im=im2double(im);
else if ~isa(im,'double')
im=double(im);
end
end
if exist('isderivative','var')~=1
isderivative=false;
end
[height,w... |
github | echoorchid/Super-Resolution-Gather-master | num2digits.m | .m | Super-Resolution-Gather-master/SRmatlab/Matlab/library/num2digits.m | 226 | utf_8 | 9da3bfede090243fbad3385277698555 | % function to convert a number to a digit
function str=num2digits(x,ndigits)
if exist('ndigits')~=1
ndigits=3;
end
str=num2str(x);
if length(str)<ndigits
for i=1:ndigits-length(str)
str=['0' str];
end
end |
github | kreimanlab/occlusion-models-master | computeHopTimeFeatures.m | .m | occlusion-models-master/computeHopTimeFeatures.m | 2,776 | utf_8 | 7801f3c9a53085c66864508ca653a856 | function computeHopTimeFeatures(varargin)
%% Setup
argParser = inputParser();
argParser.KeepUnmatched = true;
argParser.addParameter('objectForRow', [], @(x) ~isempty(x) && isnumeric(x));
argParser.addParameter('savesteps', [1:100, 110:10:300], @isnumeric);
argParser.addParameter('trainDirectory', [], @(p) exist(p, 'd... |
github | kreimanlab/occlusion-models-master | computeFeatures.m | .m | occlusion-models-master/computeFeatures.m | 7,169 | utf_8 | b044aa7af362e836b8a4f199c5aaff3b | function computeFeatures(varargin)
%% Setup
argParser = inputParser();
argParser.KeepUnmatched = true;
argParser.addParameter('dataSelection', [], @isnumeric);
argParser.addParameter('splitSize', 2000, @(x) isnumeric(x) && x > 0);
argParser.addParameter('images', [], @(i) iscell(i) && ~isempty(i));
argParser.addParamet... |
github | kreimanlab/occlusion-models-master | C1.m | .m | occlusion-models-master/feature_extractors/hmax/C1.m | 4,936 | utf_8 | 199e05664eb0c4e3d49739adcf3383fa | function [c1,s1] = C1(stim, filters, fSiz, c1SpaceSS, c1ScaleSS, c1OL,INCLUDEBORDERS)
%function [c1,s1] = C1(stim, filters, fSiz, c1SpaceSS, c1ScaleSS, c1OL,INCLUDEBORDERS)
%
% A matlab implementation of the C1 code originally by Max Riesenhuber
% and Thomas Serre.
% Adapted by Stanley Bileschi
%
% Returns the C1 a... |
github | kreimanlab/occlusion-models-master | prepareGrayscaleImage.m | .m | occlusion-models-master/feature_extractors/alexnet/prepareGrayscaleImage.m | 1,097 | utf_8 | 8c9b218db5b5b4c80fd44f7829fdab6a | %Return an image that serves as input to AlexNet.
%imageData is the KLAB grayscale image data
%preprocessed_image is a 3d matrix with dimensions 227x227x3.
%preprocessed_image is WxHxC major in BGR.
function [preprocessedImage] = prepareGrayscaleImage(imageData, imagesMean)
IMAGE_DIM = 256;
CROPPED_DIM = 227;
... |
github | kreimanlab/occlusion-models-master | lrn.m | .m | occlusion-models-master/feature_extractors/alexnet/lrn.m | 823 | utf_8 | 95bd499932d8e2f83341700dc30dd29d | %Local Response Normalization accross nearby channels.
%bottom is a 3d matrix: W x H x N.
%top is a 3d matrix: W x H x N.
%localSize, alpha, beta and k are integers.
%The output pixels depend only on pixels of nearby feature maps at the same position
%(same w/h coordinates).
%Formula:
%top_xy_i=bottom_xy_i/(k+alpha/loc... |
github | kreimanlab/occlusion-models-master | createRandomWeights.m | .m | occlusion-models-master/feature_extractors/alexnet/createRandomWeights.m | 1,290 | utf_8 | dcd76fef5492855271de083c8b2dbbba | function randomWeights = createRandomWeights(originalWeights)
dir = fileparts(mfilename('fullpath'));
if ~exist('originalWeights', 'var')
originalWeights = load([dir '/ressources/alexnetParams.mat']);
end
randomWeights = originalWeights.weights;
rng(0, 'twister');
for i = 1:8
fprintf('Layer %d\n', i);
kern... |
github | kreimanlab/occlusion-models-master | alexNetForward.m | .m | occlusion-models-master/feature_extractors/alexnet/alexNetForward.m | 2,548 | utf_8 | b5e34e778036d18101b39e8b7020985b | function alexNetForward()
% Forward path implementation of AlexNet
%% Preparation
% Load network parameters.
dir = fileparts(mfilename('fullpath'));
netParams = load([dir '/ressources/netParams.mat']); % obtained from https://drive.google.com/file/d/0B-VdpVMYRh-pQWV1RWt5NHNQNnc/view
conv1Kernels=netParams.weights(1).w... |
github | kreimanlab/occlusion-models-master | alexNetAttractors.m | .m | occlusion-models-master/feature_extractors/alexnet/alexNetAttractors.m | 1,171 | utf_8 | 93c653471e0a6c4cdddb96364ec4ebf4 | function alexNetAttractors()
addpath('../data');
netParams=load('./ressources/alexnetParams.mat');
imagesMeanData = load('./ressources/ilsvrc_2012_mean.mat');
imagesMean = imagesMeanData.mean_data;
%% train
[trainImages, trainLabels] = getWholeImages([5:6 65:66]);
p5Outputs = cell(1, length(trainImages));
for i = 1:l... |
github | kreimanlab/occlusion-models-master | maxpool.m | .m | occlusion-models-master/feature_extractors/alexnet/maxpool.m | 712 | utf_8 | 816b614e308e0ddf745672a1155c8f09 | %Maxpool over a window of K*K.
%bottom is a 3d matrix: Win x Hin x N.
%top is a 3d matrix: Wout x Hout x N.
%The kernel size K and stride S are integers.
%Pool the input (bottom) with windows of size K and with the specified stride.
%No padding needed.
function [ top ] = maxpool( bottom, K, S )
[Win,Hin,N]=size(bot... |
github | kreimanlab/occlusion-models-master | relu.m | .m | occlusion-models-master/feature_extractors/alexnet/relu.m | 134 | utf_8 | c65842fb4b7db9752f5d5f8e2ec33ef1 | %ReLU Nonlinearity: Rectified Linear Units.
%Formula: top=max(0,bottom).
function [ top ] = relu( bottom )
top=max(0,bottom);
end
|
github | kreimanlab/occlusion-models-master | dropout.m | .m | occlusion-models-master/feature_extractors/alexnet/dropout.m | 104 | utf_8 | 277b5e8ae73c6b155e4a4def619220f8 | %Dropout does not do anything in test phase.
function [ top ] = dropout( bottom )
top = bottom;
end
|
github | kreimanlab/occlusion-models-master | softmax.m | .m | occlusion-models-master/feature_extractors/alexnet/softmax.m | 342 | utf_8 | 41d44f23ac79b0e9fa4601f26b914f5b | %Softmax Function.
%bottom is a 2d matrix: N x 1.
%top is a 2d matrix: M x 1.
%For formula, see https://en.wikipedia.org/wiki/Softmax_function.
%Formula: top_i=(exp(bottom_i-bottom_max)/(sum_i(exp(bottom_i-bottom_max))).
function [ top ] = softmax( bottom )
bottomExp=exp(bottom-max(bottom(:)));
top=bottomExp./s... |
github | kreimanlab/occlusion-models-master | prepareImage.m | .m | occlusion-models-master/feature_extractors/alexnet/prepareImage.m | 1,110 | utf_8 | 87741ee641d4fd032585fa1f652277a8 | %Return an image that serves as input to AlexNet.
%fileName is the path to the image file.
%preprocessed_image is a 3d matrix with dimensions 227x227x3.
%preprocessed_image is WxHxC major in BGR.
function [ preprocessed_image ] = prepareImage( fileName )
im = imread(fileName);
d = load('ressources/ilsvrc_2012_m... |
github | kreimanlab/occlusion-models-master | fc.m | .m | occlusion-models-master/feature_extractors/alexnet/fc.m | 406 | utf_8 | b7f3358f12df7cce232525d41dc6bb36 | %Fully Connected Layer.
%bottom is a 2d matrix: N x 1.
%top is a 2d matrix: M x 1.
%weight is a 4d matrix: 1 x 1 x N x M.
%bias is a 4d matrix: 1 x 1 x 1 x M.
%Formula: top=weights'*bottom+bias.
function [ top ] = fc( bottom, weight, bias )
[~,~,N,M]=size(weight);
weightFlattened=reshape(weight, [N, M]);
bi... |
github | kreimanlab/occlusion-models-master | conv.m | .m | occlusion-models-master/feature_extractors/alexnet/conv.m | 3,016 | utf_8 | 91a85e9ddc249fd94b52dbf86e5cf06b | %Convolution Layer.
%bottom is a 3d matrix: Win x Hin x N.
%top is a 3d matrix: Wout x Hout x M.
%weight is a 4d matrix: K x K x N x M (or K x K x N/2 x M in case of group==2).
%bias is a 4d matrix: 1 x 1 x 1 x M.
%Kernel size K and stride S are integers.
%Padding 'pad' specifies the number of pixels to (implicitly) ad... |
github | kreimanlab/occlusion-models-master | displayPrediction.m | .m | occlusion-models-master/feature_extractors/alexnet/displayPrediction.m | 506 | utf_8 | 6661e9955fa8a36a571290b2cf4e9247 | %Display the predicted image category.
%prob is a 1d vector (numOfPredictions x 1) with the probabilities for each
%image category.
function [ ] = displayPrediction( prob )
one_top_prediction=find(prob==max(prob));
fid=fopen('ressources/synset_words.txt');
C = textscan(fid, '%s','delimiter', '\n');
textLin... |
github | kreimanlab/occlusion-models-master | retrainAlexnet.m | .m | occlusion-models-master/feature_extractors/alexnet/retrainAlexnet.m | 4,793 | utf_8 | 9ae491ffc433795e90faa9588d6a539a | function retrainAlexnet(predictOnly, predictSubdirectory)
if ~exist('predictOnly', 'var') || ~predictOnly
%% Data
fprintf('Loading data\n');
images = imageDatastore('data/images',...
'IncludeSubfolders', true, ...
'LabelSource', 'foldernames');
%% Network
fprintf('Creating netwo... |
github | kreimanlab/occlusion-models-master | prepareDataAcrossObjects.m | .m | occlusion-models-master/feature_extractors/alexnet/finetune_alexnet_with_tensorflow/prepareDataAcrossObjects.m | 7,083 | utf_8 | c1725d01824f13876b88e88b47f09bf5 | function prepareDataAcrossObjects(occludedWholeRatio, writeFeatures)
%PREPAREDATAACROSSOBJECTS prepares the data for fine-tuning (mixed training)
% occludedWholeRatio: number of occluded images to train on divided by
% number of whole images to train on
%% settings
if ~exist('occludedWholeRatio', 'var')
occlu... |
github | kreimanlab/occlusion-models-master | prepareDataAcrossVisibilities.m | .m | occlusion-models-master/feature_extractors/alexnet/finetune_alexnet_with_tensorflow/prepareDataAcrossVisibilities.m | 7,205 | utf_8 | 243fc44cdee91dbbdbed2cc034d50025 | function prepareDataAcrossVisibilities(occludedWholeRatio, visibilityRange)
%% settings
if ~exist('occludedWholeRatio', 'var')
occludedWholeRatio = 1/1;
end
assert(numel(visibilityRange) == 2); % [min, max]
if max(visibilityRange) <= 30
trainLessOcclusion = false;
else
trainLessOcclusion = true;
end
imageSi... |
github | kreimanlab/occlusion-models-master | prepareDataAcrossCategories.m | .m | occlusion-models-master/feature_extractors/alexnet/finetune_alexnet_with_tensorflow/prepareDataAcrossCategories.m | 6,823 | utf_8 | c6aef92b721da04905fe9b19b4d12721 | function prepareDataAcrossCategories(occludedWholeRatio)
%PREPAREDATAACROSSCATEGORIES prepares the data for fine-tuning (mixed training)
% occludedWholeRatio: number of occluded images to train on divided by
% number of whole images to train on
%% settings
if ~exist('occludedWholeRatio', 'var')
occludedWholeR... |
github | kreimanlab/occlusion-models-master | make.m | .m | occlusion-models-master/lib/libsvm-3.21/matlab/make.m | 900 | utf_8 | 7335a07387dfd4a294b8e0ab32d3ce63 | % This make.m is for MATLAB and OCTAVE under Windows, Mac, and Unix
function make()
try
% This part is for OCTAVE
if (exist ('OCTAVE_VERSION', 'builtin'))
mex libsvmread.c
mex libsvmwrite.c
mex -I.. libsvmtrain.c ../svm.cpp svm_model_matlab.c
mex -I.. libsvmpredict.c ../svm.cpp svm_model_matlab.c
% This part... |
github | kreimanlab/occlusion-models-master | curry.m | .m | occlusion-models-master/lib/functional/curry.m | 2,662 | utf_8 | 135ebd799adb0f14d98aac5ff48fe562 |
function func = curry(varargin)
% FUNC = CURRY(N, F, ARGS)
% FUNC = CURRY(F, ARGS)
% Return a curried function of F. If N is given, then N arguments are
% expected for F (otherwise, N is taken to be NARGIN(F)). If N is
% negative, then |N| or more arguments are accepted (but F is invoked as
% soon as there a... |
github | kreimanlab/occlusion-models-master | islambda.m | .m | occlusion-models-master/lib/functional/islambda.m | 213 | utf_8 | 15d3383b797df3e14a5083e436e2f1f7 |
function b = islambda(a)
% BOOL = ISLAMBDA(VALUE)
% Returns true when VALUE is a function handle, either an explicit lambda
% expression or a named function.
%
b = strcmp(class(a), 'function_handle');
end
|
github | interactiveaudiolab/MCFT-master | mfct_visualizer_wide.m | .m | MCFT-master/mcft_matlab/examples/mfct_visualizer_wide.m | 22,074 | utf_8 | f50f2af1e637344c2a6d6b6eecc74a52 | function varargout = mfct_visualizer_wide(varargin)
% MFCT_VISUALIZER_WIDE MATLAB code for mfct_visualizer_wide.fig
% MFCT_VISUALIZER_WIDE, by itself, creates a new MFCT_VISUALIZER_WIDE or raises the existing
% singleton*.
%
% H = MFCT_VISUALIZER_WIDE returns the handle to a new MFCT_VISUALIZER_WIDE or t... |
github | interactiveaudiolab/MCFT-master | filt_default_centers.m | .m | MCFT-master/mcft_matlab/mcft/filt_default_centers.m | 4,763 | utf_8 | c7f3471c188de3f4d5970e3002f93117 | function filt_ctrs = filt_default_centers(filt_params)
% This function computes the default set of filter centers along scale
% or rate axes. Two cases are considered:
% 1. Inputs only include the resolution, number of fft points, and
% sample rate. In this case, default values will be used for all
% filters.
% ... |
github | aurotripathy/ssd-spacenet-master | classification_demo.m | .m | ssd-spacenet-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | jcbyts/GLMtutorial-master | neglogli_poissGLM.m | .m | GLMtutorial-master/code/neglogli_poissGLM.m | 320 | utf_8 | 263a50004a906a3bef1f3c0afdbade79 | function L = neglogli_poissGLM(w, X, y, g, dt)
% poisson negative log likelihood
% L = neglogpost_poissonGLM(w, X, y, g, dt)
L=0; % initialize at 0
for k=1:size(y,2) % loop over neurons
L = L - poiss_logli(y(:,k), g(X*w(:,k)), dt);
end
function L = poiss_logli(r, lambda, dt)
L=r'*log(lambda*dt) - sum(lambda); |
github | zhenglab/2016DL-master | classification_demo.m | .m | 2016DL-master/ChaoWang/caffe-Chao/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | thomaspingel/smrf-matlab-master | hillshade2.m | .m | smrf-matlab-master/hillshade2.m | 3,448 | utf_8 | 912613184e7f5f8bf831354665c9985e | function h = hillshade2(dem,R,varargin)
% PUPROSE: Calculate hillshade for a digital elevation model (DEM)
% -------------------------------------------------------------------
% USAGE: h = hillshade(dem,X,Y,varagin)
% where: dem is the DEM to calculate hillshade for
% X and Y are the DEM coordinate vectors
% ... |
github | thomaspingel/smrf-matlab-master | createNet.m | .m | smrf-matlab-master/createNet.m | 3,037 | utf_8 | 259e8199e73a76e4a89ceeab987d6b49 | % createNet
% Simple utility to cut a "net" of background values into a digital surface model
%
% Syntax
% [ZInet isNetCell] = createNet(ZI,cellSize,netWidth)
%
%
% Description
% createNet removes columns and rows from ZI according to the spacing
% specified in gridSpacing, which is a spacing specified according ... |
github | thomaspingel/smrf-matlab-master | createDSM.m | .m | smrf-matlab-master/createDSM.m | 7,630 | utf_8 | afaef9ddd118307cb2d50de31eb31a15 | % createDSM
% Simple utility to construct a Digital Surface Model from LIDAR data.
%
%
% Syntax
% [DSM R isEmptyCell xi yi] = createDSM(x,y,z,varargin);
%
%
% Description
% createDSM takes as input a three dimensional point cloud (usually LIDAR
% data) and creates an initial ground surface, useful for further
% ... |
github | thomaspingel/smrf-matlab-master | progressiveFilter.m | .m | smrf-matlab-master/progressiveFilter.m | 6,043 | utf_8 | e2e9da7a1a9cb981988a41f296cf750e | % progressiveFilter
% Removes high points from a Digital Surface Model by progressive
% morphological filtering.
%
% Syntax
% function [isObjectCell] = progressiveFilter(ZI,varargin)
%
%
% Description
% The progressive morphological filter is the heart of the SMRF lidar
% ground filtering package. Given an i... |
github | thomaspingel/smrf-matlab-master | smrf.m | .m | smrf-matlab-master/smrf.m | 13,066 | utf_8 | 32b4114e39a18a7f99e0ef6c34cc29bb | % smrf
% A Simple Morphological Filter for Ground Identification of LIDAR point
% clouds.
%
%
% Syntax
% [ZIfin R isObject ZIpro ZImin isObjectCell] = smrf(x,y,z,'c',c,'s',s,'w',w);
%
%
% Description
% SMRF is designed to apply a series of opening operations against a
% digital surface model derived from a LIDAR ... |
github | nplot/nplot-master | decorateQplot.m | .m | nplot-master/decorateQplot.m | 3,783 | utf_8 | 10abb31e20f4bd9a2252dd1400ba5df6 | function decorateQplot(ax,bx,lattice,qv,type,axhandle,opt)
% Plots :
% a) the orienting vectors, as given in the scan (more precisely: their projection onto the scattering plane)
% b) a grid consisting of these vectors
% c) constant-psi lines
% into the coordinate system (for qx,qy-axes only!)
%
% type: "arrows" for (... |
github | nplot/nplot-master | integratepatch.m | .m | nplot-master/integratepatch.m | 4,693 | utf_8 | cbdaddc13bd8a62be9fc0a78b82a772e | function valsum = integratepatch(faces, vertices, values, errors, corner, sides, steps, avgopt)
% Performs integration along different directions (projection) of a defined region as a weighted average.
% The intersection of the patch cells with the slices to be integrated is calculated in each step.
% - faces, vertic... |
github | nplot/nplot-master | tasreadpanda.m | .m | nplot-master/tasreadpanda.m | 7,635 | utf_8 | ac63a45e6fee35f58611661a53dc4afe | function [scans, nscans, nodata]=tasreadpanda(filenames, varargin)
% [scans, nscans, nodata] = tasread(filenames, varargin)
% Flexible load routine for ILL TAS data file.
% varargin may contain 'cells' (gives cell array output),
% 'download' (tries to download file from server if not found)
%
% Can load multiple fi... |
github | nplot/nplot-master | nfitgui.m | .m | nplot-master/nfitgui.m | 9,797 | utf_8 | 7a679db836b4cc4f32611d52b94c727b | function varargout = nfitgui(varargin)
% NFITGUI MATLAB code for nfitgui.fig
% NFITGUI, by itself, creates a new NFITGUI or raises the existing
% singleton*.
%
% H = NFITGUI returns the handle to a new NFITGUI or the handle to
% the existing singleton*.
%
% NFITGUI('CALLBACK',hObject,eventData,... |
github | nplot/nplot-master | calcpowderline.m | .m | nplot-master/calcpowderline.m | 2,966 | utf_8 | 85db68b2c6228ad33b9870a3607e4e0a | function [vertices, connection] = calcpowderline(data, dval, type)
% Calculate the cut of a powder line through a 2D data slice in n-dim coordinate space
% dval: d-spacing of the powder reflection
% type: 1 incoherent on Ana (kf'=ki)
% 2 incoherent on Mono (ki'=kf)
% P. Steffens, 04/2009 - 08/2014
% helpe... |
github | nplot/nplot-master | powxbu.m | .m | nplot-master/calibration/powxbu.m | 22,820 | utf_8 | 7c56f274473d9d27dd0ba0c6e2a7c699 | function varargout = powxbu(varargin)
% Type "powxbu" to open an interactive dialog window.
% *****************************
% Script version of "powxbu":
% (1) powxbu create [powder] [ki] ([a4min]) ([a4max])
% (2) powxbu set [reflections] ['ti'|'da4'|'np'] [value]
% (3) powxbu writexbu ([filename]) ([reflections])... |
github | nplot/nplot-master | powcal.m | .m | nplot-master/calibration/powcal.m | 16,349 | utf_8 | 83243e9f40a19b52be196bd5aaec09dd | function varargout = powcal(varargin)
% Type "powcal" to open an interactive dialog window.
% *****************************
% Script version of "powcal":
% (1) powcal readfiles [filename]
% (2) powcal fitzeros
% (3) powcal savereport
% *****************************
% "powcal readfiles [filename]" loads the data f... |
github | HydroComplexity/MLCan2.0-master | main_MLCan.m | .m | MLCan2.0-master/main_MLCan.m | 59,235 | utf_8 | 661f606f088026226fdef533aea37dc3 | function varargout = main_MLCan(varargin)
%:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::%
%% MAIN INTERFACE PROGRAM %%
%% Canopy-Root-Soil-Atmosphere Exchange Model %%
%:::::::::::::::::::::::::::::::::::::::::::::::::... |
github | HydroComplexity/MLCan2.0-master | model_help.m | .m | MLCan2.0-master/users/model_help.m | 25,502 | utf_8 | f97053755fbffa94500fd45e9c99c7bb | function varargout = model_help(varargin)
% MODEL_HELP MATLAB code for model_help.fig
% MODEL_HELP, by itself, creates a new MODEL_HELP or raises the existing
% singleton*.
%
% H = MODEL_HELP returns the handle to a new MODEL_HELP or the handle to
% the existing singleton*.
%
% MODEL_HELP('CALL... |
github | HydroComplexity/MLCan2.0-master | setup_root_profile.m | .m | MLCan2.0-master/users/setup_root_profile.m | 20,180 | utf_8 | 0f4d53ccd481444d7ca14d1183e6f493 | function varargout = setup_root_profile(varargin)
% SETUP_ROOT_PROFILE M-file for setup_root_profile.fig
% SETUP_ROOT_PROFILE, by itself, creates a new SETUP_ROOT_PROFILE or raises the existing
% singleton*.
%
% H = SETUP_ROOT_PROFILE returns the handle to a new SETUP_ROOT_PROFILE or the handle to
% ... |
github | HydroComplexity/MLCan2.0-master | Forcing_view.m | .m | MLCan2.0-master/users/Forcing_view.m | 28,439 | utf_8 | 9be8cef84516867852a5ea41b97c4689 | function varargout = Forcing_view(varargin)
% FORCING_VIEW MATLAB code for Forcing_view.fig
% FORCING_VIEW, by itself, creates a new FORCING_VIEW or raises the existing
% singleton*.
%
% H = FORCING_VIEW returns the handle to a new FORCING_VIEW or the handle to
% the existing singleton*.
%
% FO... |
github | HydroComplexity/MLCan2.0-master | model_parameters.m | .m | MLCan2.0-master/users/model_parameters.m | 43,772 | utf_8 | e2a373568d74ce8dfdfff54e16383b87 | function varargout = model_parameters(varargin)
% MODEL_PARAMETERS M-file for model_parameters.fig
% MODEL_PARAMETERS, by itself, creates a new MODEL_PARAMETERS or raises the existing
% singleton*.
%
% H = MODEL_PARAMETERS returns the handle to a new MODEL_PARAMETERS or the handle to
% the existing ... |
github | HydroComplexity/MLCan2.0-master | uigetdate.m | .m | MLCan2.0-master/users/uigetdate.m | 8,081 | utf_8 | 5deb68c97fca76a9bbd15e3b6e64d041 | function out = uigetdate(varargin)
% UIGETDATE date selection dialog box
% T = UIGETDATE(D) displays a dialog box in form of a calendar
%
% UIGETDATE expects serial date number or standard MATLAB Date
% format (see DATESTR) as input data und returns serial date number
% for the selected date and tim... |
github | HydroComplexity/MLCan2.0-master | model_results.m | .m | MLCan2.0-master/users/model_results.m | 126,008 | utf_8 | db1eb4623487777b41ecb97080d18f1f | function varargout = model_results(varargin)
% MODEL_RESULTS M-file for model_results.fig
% MODEL_RESULTS, by itself, creates a new MODEL_RESULTS or raises the existing
% singleton*.
%
% H = MODEL_RESULTS returns the handle to a new MODEL_RESULTS or the handle to
% the existing singleton*.
%
% ... |
github | HydroComplexity/MLCan2.0-master | model_parameters_decom.m | .m | MLCan2.0-master/users/model_parameters_decom.m | 8,082 | utf_8 | b1d8586514e39dd9e1503b65525cf4d4 | function varargout = model_parameters_decom(varargin)
% MODEL_PARAMETERS_DECOM M-file for model_parameters_decom.fig
% MODEL_PARAMETERS_DECOM, by itself, creates a new MODEL_PARAMETERS_DECOM or raises the existing
% singleton*.
%
% H = MODEL_PARAMETERS_DECOM returns the handle to a new MODEL_PARAMETERS_D... |
github | HydroComplexity/MLCan2.0-master | model_option.m | .m | MLCan2.0-master/users/model_option.m | 45,893 | utf_8 | 73767e40ac44d0c2bad75eb295f2ed9c | function varargout = model_option(varargin)
% MODEL_OPTION M-file for model_option.fig
% MODEL_OPTION, by itself, creates a new MODEL_OPTION or raises the existing
% singleton*.
%
% H = MODEL_OPTION returns the handle to a new MODEL_OPTION or the handle to
% the existing singleton*.
%
% MODEL_O... |
github | HydroComplexity/MLCan2.0-master | setup_root_profile2.m | .m | MLCan2.0-master/users/setup_root_profile2.m | 20,265 | utf_8 | 0beb1d2bc81acbf81444174a885934c5 | function varargout = setup_root_profile2(varargin)
% SETUP_ROOT_PROFILE2 M-file for setup_root_profile2.fig
% SETUP_ROOT_PROFILE2, by itself, creates a new SETUP_ROOT_PROFILE2 or raises the existing
% singleton*.
%
% H = SETUP_ROOT_PROFILE2 returns the handle to a new SETUP_ROOT_PROFILE2 or the handle to... |
github | HydroComplexity/MLCan2.0-master | setup_LAD_profile.m | .m | MLCan2.0-master/users/setup_LAD_profile.m | 6,434 | utf_8 | ddfdbe579d9ebe4aed10168337324867 | function varargout = setup_LAD_profile(varargin)
% SETUP_LAD_PROFILE M-file for setup_LAD_profile.fig
% SETUP_LAD_PROFILE, by itself, creates a new SETUP_LAD_PROFILE or raises the existing
% singleton*.
%
% H = SETUP_LAD_PROFILE returns the handle to a new SETUP_LAD_PROFILE or the handle to
% the ex... |
github | HydroComplexity/MLCan2.0-master | model_setup.m | .m | MLCan2.0-master/users/model_setup.m | 52,862 | utf_8 | 2863401caa2f29860b23c9009bd543ef | function varargout = model_setup(varargin)
%:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::%
%% FUNCTION CODE INTERFACE %%
%% MODEL SETUP %%
%::::::::::::::::::::::::::::::::::::::::::::::::... |
github | HydroComplexity/MLCan2.0-master | setup_LAD_profile2.m | .m | MLCan2.0-master/users/setup_LAD_profile2.m | 6,942 | utf_8 | 39e7c11f8dd6c7d145357ee406d8c67f | function varargout = setup_LAD_profile2(varargin)
% SETUP_LAD_PROFILE2 M-file for setup_LAD_profile2.fig
% SETUP_LAD_PROFILE2, by itself, creates a new SETUP_LAD_PROFILE2 or raises the existing
% singleton*.
%
% H = SETUP_LAD_PROFILE2 returns the handle to a new SETUP_LAD_PROFILE2 or the handle to
% ... |
github | HydroComplexity/MLCan2.0-master | setup_root_profile3.m | .m | MLCan2.0-master/users/setup_root_profile3.m | 20,197 | utf_8 | 9374e3586c31fcf1bd150fa05bff7168 | function varargout = setup_root_profile3(varargin)
% SETUP_ROOT_PROFILE3 M-file for setup_root_profile3.fig
% SETUP_ROOT_PROFILE3, by itself, creates a new SETUP_ROOT_PROFILE3 or raises the existing
% singleton*.
%
% H = SETUP_ROOT_PROFILE3 returns the handle to a new SETUP_ROOT_PROFILE3 or the handle to... |
github | HydroComplexity/MLCan2.0-master | model_about.m | .m | MLCan2.0-master/users/model_about.m | 6,380 | utf_8 | b358eb4f41ddb7ae2c2719fdd09fdb5a | function varargout = model_about(varargin)
% MODEL_ABOUT MATLAB code for model_about.fig
% MODEL_ABOUT, by itself, creates a new MODEL_ABOUT or raises the existing
% singleton*.
%
% H = MODEL_ABOUT returns the handle to a new MODEL_ABOUT or the handle to
% the existing singleton*.
%
% MODEL_ABO... |
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