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 | layumi/2015_Face_Detection-master | encodeImage.m | .m | 2015_Face_Detection-master/vlfeat/apps/recognition/encodeImage.m | 5,278 | utf_8 | 5d9dc6161995b8e10366b5649bf4fda4 | function descrs = encodeImage(encoder, im, varargin)
% ENCODEIMAGE Apply an encoder to an image
% DESCRS = ENCODEIMAGE(ENCODER, IM) applies the ENCODER
% to image IM, returning a corresponding code vector PSI.
%
% IM can be an image, the path to an image, or a cell array of
% the same, to operate on multiple ... |
github | layumi/2015_Face_Detection-master | experiments.m | .m | 2015_Face_Detection-master/vlfeat/apps/recognition/experiments.m | 6,905 | utf_8 | 1e4a4911eed4a451b9488b9e6cc9b39c | function experiments()
% EXPERIMENTS Run image classification experiments
% The experimens download a number of benchmark datasets in the
% 'data/' subfolder. Make sure that there are several GBs of
% space available.
%
% By default, experiments run with a lite option turned on. This
% quickly runs all... |
github | layumi/2015_Face_Detection-master | getDenseSIFT.m | .m | 2015_Face_Detection-master/vlfeat/apps/recognition/getDenseSIFT.m | 1,679 | utf_8 | 2059c0a2a4e762226d89121408c6e51c | function features = getDenseSIFT(im, varargin)
% GETDENSESIFT Extract dense SIFT features
% FEATURES = GETDENSESIFT(IM) extract dense SIFT features from
% image IM.
% Author: Andrea Vedaldi
% Copyright (C) 2013 Andrea Vedaldi
% All rights reserved.
%
% This file is part of the VLFeat library and is made availab... |
github | layumi/2015_Face_Detection-master | vl_argparse.m | .m | 2015_Face_Detection-master/matlab/vl_argparse.m | 3,148 | utf_8 | 74459d2b851e027208bd7ca9ea999037 | function [opts, args] = vl_argparse(opts, args)
% VL_ARGPARSE Parse list of parameter-value pairs
% OPTS = VL_ARGPARSE(OPTS, ARGS) updates the structure OPTS based on
% the specified parameter-value pairs ARGS={PAR1, VAL1, ... PARN,
% VALN}. The function produces an error if an unknown parameter name
% is pass... |
github | layumi/2015_Face_Detection-master | vl_compilenn.m | .m | 2015_Face_Detection-master/matlab/vl_compilenn.m | 24,645 | utf_8 | 8ffdf03179d5108a10b46a7fee6410e9 | function vl_compilenn( varargin )
% VL_COMPILENN Compile the MatConvNet toolbox
% The `vl_compilenn()` function compiles the MEX files in the
% MatConvNet toolbox. See below for the requirements for compiling
% CPU and GPU code, respectively.
%
% `vl_compilenn('OPTION', ARG, ...)` accepts the following opt... |
github | layumi/2015_Face_Detection-master | vl_simplenn_display.m | .m | 2015_Face_Detection-master/matlab/simplenn/vl_simplenn_display.m | 10,932 | utf_8 | c7ed88fccca92a96ffe9c36dcb72d278 | function info = vl_simplenn_display(net, varargin)
% VL_SIMPLENN_DISPLAY Simple CNN statistics
% VL_SIMPLENN_DISPLAY(NET) prints statistics about the network NET.
%
% INFO=VL_SIMPLENN_DISPLAY(NET) returns instead a structure INFO
% with several statistics for each layer of the network NET.
%
% The function... |
github | layumi/2015_Face_Detection-master | vl_test_economic_relu.m | .m | 2015_Face_Detection-master/matlab/xtest/vl_test_economic_relu.m | 790 | utf_8 | 35a3dbe98b9a2f080ee5f911630ab6f3 | % VL_TEST_ECONOMIC_RELU
function vl_test_economic_relu()
x = randn(11,12,8,'single');
w = randn(5,6,8,9,'single');
b = randn(1,9,'single') ;
net.layers{1} = struct('type', 'conv', ...
'filters', w, ...
'biases', b, ...
'stride', 1, ...
... |
github | bearpaw/clothing-co-parsing-master | show_image_anno.m | .m | clothing-co-parsing-master/show_image_anno.m | 1,095 | utf_8 | d7da2a40c62162641428e789a2d08ad2 | function show_image_anno
% SHOW_IMAGE_ANNO visualize image tags
% Wei YANG 2014
% platero.yang (at) gmail.com
fprintf('Press any key to continue. Press Ctrl+Z to quit.')
load('label_list', 'label_list'); % load label list
imlist = dir('annotations/image-level/*.mat'); % browsing annotations
for i = 1:length(imlist)
... |
github | cNikolaou/UFLDL-Tutorial-master | checkNumericalGradient.m | .m | UFLDL-Tutorial-master/sparseae_exercise/starter/checkNumericalGradient.m | 1,982 | utf_8 | 689a352eb2927b0838af5dc508f6374d | function [] = checkNumericalGradient()
% This code can be used to check your numerical gradient implementation
% in computeNumericalGradient.m
% It analytically evaluates the gradient of a very simple function called
% simpleQuadraticFunction (see below) and compares the result with your numerical
% solution. Your num... |
github | cNikolaou/UFLDL-Tutorial-master | sparseAutoencoderCost.m | .m | UFLDL-Tutorial-master/sparseae_exercise/starter/sparseAutoencoderCost.m | 5,099 | utf_8 | f61c92aab9f9297824b73e8fd5cb04aa | function [cost,grad] = sparseAutoencoderCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay parameter
% sparsityPar... |
github | cNikolaou/UFLDL-Tutorial-master | sampleIMAGES.m | .m | UFLDL-Tutorial-master/sparseae_exercise/starter/sampleIMAGES.m | 2,361 | utf_8 | 5bc148213efc91f20fe2ca18f84b7469 | function patches = sampleIMAGES()
% sampleIMAGES
% Returns 10000 patches for training
load IMAGES; % load images from disk
patchsize = 8; % we'll use 8x8 patches
numpatches = 10000;
% Initialize patches with zeros. Your code will fill in this matrix--one
% column per patch, 10000 columns.
patches = zeros(pat... |
github | cNikolaou/UFLDL-Tutorial-master | WolfeLineSearch.m | .m | UFLDL-Tutorial-master/sparseae_exercise/starter/minFunc/WolfeLineSearch.m | 11,478 | utf_8 | d10187f2fedfa4143ebd6300537b6be4 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | cNikolaou/UFLDL-Tutorial-master | minFunc_processInputOptions.m | .m | UFLDL-Tutorial-master/sparseae_exercise/starter/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | dc74c67d849970de7f16c873fcf155bc |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | cNikolaou/UFLDL-Tutorial-master | WolfeLineSearch.m | .m | UFLDL-Tutorial-master/softmax_exercise/minFunc/WolfeLineSearch.m | 11,478 | utf_8 | d10187f2fedfa4143ebd6300537b6be4 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | cNikolaou/UFLDL-Tutorial-master | minFunc_processInputOptions.m | .m | UFLDL-Tutorial-master/softmax_exercise/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | dc74c67d849970de7f16c873fcf155bc |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | cNikolaou/UFLDL-Tutorial-master | sparseAutoencoderCost.m | .m | UFLDL-Tutorial-master/stl_exercise/sparseAutoencoderCost.m | 4,803 | utf_8 | 08ee2296b7f8fc2b5f0a0f87be847df9 | function [cost,grad] = sparseAutoencoderCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay parameter
% sparsityPar... |
github | cNikolaou/UFLDL-Tutorial-master | feedForwardAutoencoder.m | .m | UFLDL-Tutorial-master/stl_exercise/feedForwardAutoencoder.m | 1,321 | utf_8 | 3b9592dd9c982beab9320d5588d238cd | function [activation] = feedForwardAutoencoder(theta, hiddenSize, visibleSize, data)
% theta: trained weights from the autoencoder
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% data: Our matrix containing the training data as columns. So, data(:,i) i... |
github | cNikolaou/UFLDL-Tutorial-master | WolfeLineSearch.m | .m | UFLDL-Tutorial-master/stl_exercise/minFunc/WolfeLineSearch.m | 11,478 | utf_8 | d10187f2fedfa4143ebd6300537b6be4 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | cNikolaou/UFLDL-Tutorial-master | minFunc_processInputOptions.m | .m | UFLDL-Tutorial-master/stl_exercise/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | dc74c67d849970de7f16c873fcf155bc |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | cNikolaou/UFLDL-Tutorial-master | checkNumericalGradient.m | .m | UFLDL-Tutorial-master/sparseae_exercise_vectorized/starter/checkNumericalGradient.m | 1,982 | utf_8 | 689a352eb2927b0838af5dc508f6374d | function [] = checkNumericalGradient()
% This code can be used to check your numerical gradient implementation
% in computeNumericalGradient.m
% It analytically evaluates the gradient of a very simple function called
% simpleQuadraticFunction (see below) and compares the result with your numerical
% solution. Your num... |
github | cNikolaou/UFLDL-Tutorial-master | sparseAutoencoderCost.m | .m | UFLDL-Tutorial-master/sparseae_exercise_vectorized/starter/sparseAutoencoderCost.m | 4,803 | utf_8 | 08ee2296b7f8fc2b5f0a0f87be847df9 | function [cost,grad] = sparseAutoencoderCost(theta, visibleSize, hiddenSize, ...
lambda, sparsityParam, beta, data)
% visibleSize: the number of input units (probably 64)
% hiddenSize: the number of hidden units (probably 25)
% lambda: weight decay parameter
% sparsityPar... |
github | cNikolaou/UFLDL-Tutorial-master | sampleIMAGES.m | .m | UFLDL-Tutorial-master/sparseae_exercise_vectorized/starter/sampleIMAGES.m | 2,361 | utf_8 | 5bc148213efc91f20fe2ca18f84b7469 | function patches = sampleIMAGES()
% sampleIMAGES
% Returns 10000 patches for training
load IMAGES; % load images from disk
patchsize = 8; % we'll use 8x8 patches
numpatches = 10000;
% Initialize patches with zeros. Your code will fill in this matrix--one
% column per patch, 10000 columns.
patches = zeros(pat... |
github | cNikolaou/UFLDL-Tutorial-master | WolfeLineSearch.m | .m | UFLDL-Tutorial-master/sparseae_exercise_vectorized/starter/minFunc/WolfeLineSearch.m | 11,478 | utf_8 | d10187f2fedfa4143ebd6300537b6be4 | function [t,f_new,g_new,funEvals,H] = WolfeLineSearch(...
x,t,d,f,g,gtd,c1,c2,LS,maxLS,tolX,debug,doPlot,saveHessianComp,funObj,varargin)
%
% Bracketing Line Search to Satisfy Wolfe Conditions
%
% Inputs:
% x: starting location
% t: initial step size
% d: descent direction
% f: function value at st... |
github | cNikolaou/UFLDL-Tutorial-master | minFunc_processInputOptions.m | .m | UFLDL-Tutorial-master/sparseae_exercise_vectorized/starter/minFunc/minFunc_processInputOptions.m | 3,704 | utf_8 | dc74c67d849970de7f16c873fcf155bc |
function [verbose,verboseI,debug,doPlot,maxFunEvals,maxIter,tolFun,tolX,method,...
corrections,c1,c2,LS_init,LS,cgSolve,qnUpdate,cgUpdate,initialHessType,...
HessianModify,Fref,useComplex,numDiff,LS_saveHessianComp,...
DerivativeCheck,Damped,HvFunc,bbType,cycle,...
HessianIter,outputFcn,useMex,use... |
github | plartoo/product_image_classifier-main | plotKeypoints.m | .m | product_image_classifier-main/plotKeypoints.m | 956 | utf_8 | c46418fa72acd11d4f6c441794743989 | % plotKeypoints.m
% Plot selected keypoints.
% Run as
% plotKeypoints('../images/la_pointy_21',1,50,'_nKeypoints=10000_keypointDetector=canny_sigmaPdf=oneOverXto0.5')
function plotKeypoints(stemFileName,minSigma,minSiftNorm,howChoseKeypoints)
[f d] = loadDescriptors(stemFileName,minSigma,minSiftNorm,howChoseKeypoi... |
github | plartoo/product_image_classifier-main | chi2Dist.m | .m | product_image_classifier-main/chi2Dist.m | 922 | utf_8 | 497a97a70aa16722e6cd954bc40df13b | % chi2Dist.m
function dist = chi2Dist(vector1,vector2,varargin)
% Get input.
parser = inputParser;
parser.FunctionName = 'chi2Dist';
parser.addRequired('vector1');
parser.addRequired('vector2');
parser.parse(vector1,vector2,varargin{:});
vector1 = parser.Results.vector1;
vector2 = parser.Results.vector2;
% Check tha... |
github | plartoo/product_image_classifier-main | signatureDistances.m | .m | product_image_classifier-main/signatureDistances.m | 1,788 | utf_8 | dfff891d216d986fd0b37a859813f929 | % signatureDistances.m
% curSignature: column vector (already converted to double)
% otherSignatures: matrix with # cols == # other signatures (already
% converted to double)
function distanceVector = ...
signatureDistances(curSignature,otherSignatures,varargin)
% Set constants.
DEFAULT = 2*pi+3.2342; % some... |
github | plartoo/product_image_classifier-main | writeDescriptionOfRun.m | .m | product_image_classifier-main/writeDescriptionOfRun.m | 4,578 | utf_8 | 751065e87788cdaeb78c92348afa7060 | % Write a text file that describes the parameters, images, etc. used in
% this run.
function writeDescriptionOfRun(fullFolderName,paramStruct,view,...
imageNamesByNumber,results,varargin)
% Get input.
parser = inputParser;
parser.FunctionName = 'writeDescriptionOfRun';
parser.addRequired('fullFolderName');
parser... |
github | plartoo/product_image_classifier-main | sigmasRandom.m | .m | product_image_classifier-main/sigmasRandom.m | 927 | utf_8 | 0b8e7d4c8969e941a8d30b0eb90bc5fe | % sigmasRandom.m
% Produce a nSigmas x 1 vector of random sigma values such that their inverses
% are uniformly distributed.
function randSigmas = sigmasRandom(nSigmas,min,max,sigmaPdf)
% Get input.
parser = inputParser;
parser.FunctionName = 'sigmasRandom';
parser.addRequired('nSigmas', @(x)x>0);
parser.addRequired... |
github | plartoo/product_image_classifier-main | paramSweep.m | .m | product_image_classifier-main/paramSweep.m | 2,368 | utf_8 | 25147ca7cbfe311d555bd13794832078 | % paramSweep.m
% Run a parameter sweep with getResults.m. Output the results to a .csv
% file.
function paramSweep(paramName,functionHandle,paramValues,catNumsToRun)
% Set constant.
USE_TRY = 1;
% Define categories.
listOfCategories = {{'velcro','laces'},... %1
{'pointy','nonpointy'},... %2
{'shortslv','lo... |
github | plartoo/product_image_classifier-main | phogDistances.m | .m | product_image_classifier-main/phogDistances.m | 774 | utf_8 | a404b873d55f4d49bbb15388f8a57a7f | % phogDistances.m
% curPhog: column vector
% otherPhogs: matrix with # cols == # other phogs
function distanceVector = ...
phogDistances(curPhog,otherPhogs,expchi2Avg,varargin)
% Get input.
parser = inputParser;
parser.FunctionName = 'phogDistances';
parser.addRequired('curPhog');
parser.addRequired('otherPhogs... |
github | plartoo/product_image_classifier-main | chi2TestConfusionMatrices.m | .m | product_image_classifier-main/chi2TestConfusionMatrices.m | 917 | utf_8 | 7978a90b3fa0cd70ecf237c929b2a12e | % chi2TestConfusionMatrices.m
% Compute p-value of chi2 test for independence. See, e.g.,
% http://homepage.mac.com/samchops/B733177502/C1517039664/E20060507073109/index.html
% for the formula.
function pVal = chi2TestConfusionMatrices(confusionMatrix,varargin)
% Get input.
parser = inputParser;
parser.FunctionName =... |
github | plartoo/product_image_classifier-main | gauss.m | .m | product_image_classifier-main/canny_edge_test/gauss.m | 91 | utf_8 | b17cd93c67fcf48ce6d4bde690b0dc80 | % Function "gauss.m":
function y = gauss(x,std)
y = exp(-x^2/(2*std^2)) / (std*sqrt(2*pi)); |
github | plartoo/product_image_classifier-main | dgauss.m | .m | product_image_classifier-main/canny_edge_test/dgauss.m | 122 | utf_8 | 591d5fbf3ebeaa21e61b145e3ebac82d | % Function "dgauss.m"(first order derivative of gauss function):
function y = dgauss(x,std)
y = -x * gauss(x,std) / std^2; |
github | plartoo/product_image_classifier-main | canny.m | .m | product_image_classifier-main/canny_edge_test/canny.m | 2,818 | utf_8 | cab1abbd5a250eb5c62df3245c656a14 | % canny.m
function [x y] = canny(jpgFile,varargin)
% Get input.
parser = inputParser;
parser.FunctionName = 'canny';
parser.addRequired('jpgFile', @(x)exist(x,'file')==2);
parser.addParamValue('showFigs', 0, @(x)or(x==0,x==1));
parser.parse(jpgFile,varargin{:});
jpgFile = parser.Results.jpgFile;
showFigs = parser.Res... |
github | plartoo/product_image_classifier-main | d2dgauss.m | .m | product_image_classifier-main/canny_edge_test/d2dgauss.m | 623 | utf_8 | 166c4ddaefe733dbb7a5f153db9ca5da | %%%%%%% The functions used in the main.m file %%%%%%%
% Function "d2dgauss.m":
% This function returns a 2D edge detector (first order derivative
% of 2D Gaussian function) with size n1*n2; theta is the angle that
% the detector rotated counter clockwise; and sigma1 and sigma2 are the
% standard deviation of the gaussi... |
github | CourseAce/Stanford-MachineLearning-master | submit.m | .m | Stanford-MachineLearning-master/mlclass-ex3-008/mlclass-ex3/submit.m | 17,041 | utf_8 | 07a62d95df0814b4ffbc6c2f4b433e22 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | CourseAce/Stanford-MachineLearning-master | submitWeb.m | .m | Stanford-MachineLearning-master/mlclass-ex3-008/mlclass-ex3/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | CourseAce/Stanford-MachineLearning-master | submit.m | .m | Stanford-MachineLearning-master/mlclass-ex2-008/mlclass-ex2/submit.m | 17,086 | utf_8 | 7b02ce6b9daa919a9a66ef0adb401b07 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | CourseAce/Stanford-MachineLearning-master | submitWeb.m | .m | Stanford-MachineLearning-master/mlclass-ex2-008/mlclass-ex2/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | CourseAce/Stanford-MachineLearning-master | submit.m | .m | Stanford-MachineLearning-master/mlclass-ex7-008/mlclass-ex7/submit.m | 16,958 | utf_8 | cd11307f72915c0d3b58176b66081197 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | CourseAce/Stanford-MachineLearning-master | submitWeb.m | .m | Stanford-MachineLearning-master/mlclass-ex7-008/mlclass-ex7/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | CourseAce/Stanford-MachineLearning-master | submit.m | .m | Stanford-MachineLearning-master/mlclass-ex1-008/mlclass-ex1/submit.m | 17,317 | utf_8 | 14dfeccc6eb749406cb5d77fabb6bf47 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | CourseAce/Stanford-MachineLearning-master | submitWeb.m | .m | Stanford-MachineLearning-master/mlclass-ex1-008/mlclass-ex1/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | CourseAce/Stanford-MachineLearning-master | submit.m | .m | Stanford-MachineLearning-master/mlclass-ex8-008/mlclass-ex8/submit.m | 17,515 | utf_8 | 2949fbde41e47f99c42171e2e0a39efc | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | CourseAce/Stanford-MachineLearning-master | submitWeb.m | .m | Stanford-MachineLearning-master/mlclass-ex8-008/mlclass-ex8/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | CourseAce/Stanford-MachineLearning-master | submit.m | .m | Stanford-MachineLearning-master/mlclass-ex6-008/mlclass-ex6/submit.m | 16,836 | utf_8 | d4c87e5dbf32a81bdaf04fd017fe4cb3 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | CourseAce/Stanford-MachineLearning-master | porterStemmer.m | .m | Stanford-MachineLearning-master/mlclass-ex6-008/mlclass-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 | CourseAce/Stanford-MachineLearning-master | submitWeb.m | .m | Stanford-MachineLearning-master/mlclass-ex6-008/mlclass-ex6/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | CourseAce/Stanford-MachineLearning-master | submit.m | .m | Stanford-MachineLearning-master/mlclass-ex5-008/mlclass-ex5/submit.m | 17,211 | utf_8 | 057662350ffa8db95583373185a26a6b | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | CourseAce/Stanford-MachineLearning-master | submitWeb.m | .m | Stanford-MachineLearning-master/mlclass-ex5-008/mlclass-ex5/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | CourseAce/Stanford-MachineLearning-master | submit.m | .m | Stanford-MachineLearning-master/mlclass-ex4-008/mlclass-ex4/submit.m | 17,129 | utf_8 | 917c487f37cf14037c77e3c57ad78ce1 | function submit(partId, webSubmit)
%SUBMIT Submit your code and output to the ml-class servers
% SUBMIT() will connect to the ml-class server and submit your solution
fprintf('==\n== [ml-class] Submitting Solutions | Programming Exercise %s\n==\n', ...
homework_id());
if ~exist('partId', 'var') || isem... |
github | CourseAce/Stanford-MachineLearning-master | submitWeb.m | .m | Stanford-MachineLearning-master/mlclass-ex4-008/mlclass-ex4/submitWeb.m | 807 | utf_8 | a53188558a96eae6cd8b0e6cda4d478d | % submitWeb Creates files from your code and output for web submission.
%
% If the submit function does not work for you, use the web-submission mechanism.
% Call this function to produce a file for the part you wish to submit. Then,
% submit the file to the class servers using the "Web Submission" button on the ... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | calibrated_photometric_stereo.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/calibrated_photometric_stereo.m | 1,486 | utf_8 | bcf4095cd0ff945b798f2c857cd61627 | % Computes the facet vector (surface normal times albedo) at each pixel given a set of images
% illuminated by a varying point light source and the light source directions / intensities.
%
% function [B,S] = calibrated_photometric_stereo(I,S,mask)
%
% I : NxM Image data; N is the number of pixels per image, M is t... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | uncalibrated_photometric_stereo.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/uncalibrated_photometric_stereo.m | 4,850 | utf_8 | b6f9ffe4ccc0b886abf879ca1f8bacf5 | % function [B,S] = uncalibrated_photometric_stereo(Icell,mask,gbrinit)
%
% An implementation of the uncalibrated photometric stereo technique described in
% "Shape and Albedo from Multple Images using Integrability" by Yuille and Snow.
%
% I : NxM Image data; N is the number of pixels per image, M is the number of... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | B2normals.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/B2normals.m | 391 | utf_8 | 820d73ac4c83dfe48122f6e6715d2ef4 | % Converts an Nx3 facet matrix into unit normal and albedo images
%
% function [nx,ny,nz,a] = B2normals(B,sizeI)
%
% ============
% Neil Alldrin
%
function [nx,ny,nz,a] = B2normals(B,sizeI)
dim=size(B,1);
B = reshape(B,dim,3);
a = reshape(sqrt(sum(B.^2,2)),sizeI);
nx = reshape(B(:,1),sizeI)./(a+(a==0));
ny = resh... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | Poisson.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/Poisson.m | 8,235 | utf_8 | 27f5aedcd5010abdb7248c26018e8169 | classdef Poisson < handle
methods(Static)
function [I_y I_x] = get_gradients(I)
% Get the vertical (derivative-row) and horizontal (derivative-column) gradients
% of an image. (or multiple images)
I_y = diff(I,1,1);
I_y = padarray(I_y, [1 0],... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | Auxiliary.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/Auxiliary.m | 3,562 | utf_8 | 1f65382aece9af0e1ade52c913060d68 | classdef Auxiliary < handle
methods(Static)
function showR(r, mask, ims, ti, padding)
if nargin < 4
ti = 'reflectance';
end
if nargin < 5
padding = 0;
end
show(rMap(r, mask, ims, padding), ti);
... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | Metric.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/Metric.m | 6,437 | utf_8 | 8e384e3f3ea49bb6b0fa9b2969d54295 | classdef Metric < handle
methods(Static)
function [mean_angle, angle] = angularN(gt_n, est_n, mask)
est_n = normr(est_n(mask,:));
gt_n = normr(gt_n(mask,:));
inner = abs(sum(est_n.*gt_n,2));
inner(inner > 1) = 1;
angle = acosd(inner);... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | solve_gbr.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/MinimizeEntropy/solve_gbr.m | 943 | utf_8 | 6f06e3da54e04515628f4dc508b6aee7 | % Given an uncalibrated photometric stereo output, uB and uS, solve for the GBR transformation
% that transforms uB and uS into the true B and S. The transformation has the form,
% B = uB*G
% S = inv(G)*uS
%
% ============
% Neil Alldrin
%
function [mu,nu,lambda,e] = solve_gbr(uB,uS,lb,ub,stepSize)
% Specify an init... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | solve_gbr_coarse_to_fine.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/MinimizeEntropy/solve_gbr_coarse_to_fine.m | 1,034 | utf_8 | 48c1d43849bb62de41892f9f435d23e3 | % Performs a coarse-to-fine search over a 3D grid of (mu,nu,lambda) values.
%
% ============
% Neil Alldrin
%
function [mu,nu,lambda] = solve_gbr_coarse_to_fine(cost_function,lb,ub,stepSize,tol)
if ~exist('stepSize') stepSize = (ub-lb)/20; end;
if ~exist('tol') tol = stepSize/5; end;
lb_orig = lb; ub_orig = ub;
step... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | solve_gbr_bruteforce.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/MinimizeEntropy/solve_gbr_bruteforce.m | 903 | utf_8 | ff436c35ec5318d28d862e1e2fad9530 | % Performs a brute-force discrete search over a 3D grid of (mu,nu,lambda) values.
%
% ============
% Neil Alldrin
%
function [mu,nu,lambda] = solve_gbr_bruteforce(cost_function,lb,ub,stepSize)
if ~exist('stepSize') stepSize = (ub-lb)/20; end;
mus = lb(1):stepSize(1):ub(1);
nus = lb(2):stepSize(2):ub(2);
lambd... |
github | shenjianbing/Accurate-normal-and-reflectance-recovery-using-energy-optimization-master | cost_entropy.m | .m | Accurate-normal-and-reflectance-recovery-using-energy-optimization-master/MinimizeEntropy/cost_entropy.m | 607 | utf_8 | f3f1971c83992dfb1f9316ac4f949ff9 | % Takes in a vector x = [mu, nu, lambda] and returns the entropy.
%
% ============
% Neil Alldrin
%
function e = cost_entropy(x,uB,binWidth,showHist)
G = [1 0 0; 0 1 0; x(1) x(2) x(3)];
if ~exist('binWidth') binWidth = range(sqrt(sum(uB.^2,2)))/128; end;
if ~exist('showHist') showHist = false; end;
rhoG = sqrt(sum((... |
github | wschwanghart/topotoolbox-master | flowpathapp.m | .m | topotoolbox-master/flowpathapp.m | 34,896 | utf_8 | 734cc80dd560b49bdaa6670114a8d62f | function flowpathapp(FD,DEM,S)
%FLOWPATHAPP Map, visualize and export flowpaths that start at manually set channelheads
%
% Syntax
%
% flowpathapp
% flowpathapp(FD,DEM)
% flowpathapp(FD,DEM,S)
%
% Description
%
% flowpathapp provides an interactive tool to visualize and generate
% flow paths o... |
github | wschwanghart/topotoolbox-master | mappingapp.m | .m | topotoolbox-master/mappingapp.m | 14,821 | utf_8 | ffe073ffbfeb9ca660c30b6d55805d19 | function mappingapp(DEM,S,varargin)
% map knickpoints combining planform and profile view
%
% Syntax
%
% mappingapp(DEM,S)
%
% Description
%
% This light-weight tool enables mapping points in planform and profile
% view simultaneously based on a digital elevation model (DEM) and a
% stream network (S).... |
github | wschwanghart/topotoolbox-master | STREAMobj2cell.m | .m | topotoolbox-master/@STREAMobj/STREAMobj2cell.m | 11,145 | utf_8 | 2c3e536a80b6350646f0a5c4f1e78576 | function [CS,locS,order] = STREAMobj2cell(S,ref,n)
%STREAMOBJ2CELL convert instance of STREAMobj to cell array of stream objects
%
% Syntax
%
% CS = STREAMobj2cell(S)
% CS = STREAMobj2cell(S,'outlets')
% CS = STREAMobj2cell(S,'tributaries')
% CS = STREAMobj2cell(S,'channelheads')
% CS = STREAMobj2c... |
github | wschwanghart/topotoolbox-master | crsapp.m | .m | topotoolbox-master/@STREAMobj/crsapp.m | 9,021 | utf_8 | 2e3923689f7f7f669f2e50fc615f2557 | function crsapp(S,DEM)
%CRSAPP interactive smoothing of river long profiles
%
% Syntax
%
% crsapp(S,DEM)
%
% Description
%
% CRSAPP is an interactive tool to visually assess the results of the
% function STREAMobj/crs. You can export the results and the parameters
% to the workspace.
%
% The graphi... |
github | wschwanghart/topotoolbox-master | knickpointfinder.m | .m | topotoolbox-master/@STREAMobj/knickpointfinder.m | 9,830 | utf_8 | 0874c8247d46fd1a27df15a28216ed92 | function [zs,kp] = knickpointfinder(S,DEM,varargin)
%KNICKPOINTFINDER find knickpoints in river profiles
%
% Syntax
%
% [zk,kp] = knickpointfinder(S,DEM)
% [zk,kp] = knickpointfinder(S,z)
% [zk,kp] = knickpointfinder(...,pn,pv,...)
%
% Description
%
% Rivers that adjust to changing base levels or have ... |
github | wschwanghart/topotoolbox-master | modify.m | .m | topotoolbox-master/@STREAMobj/modify.m | 20,160 | utf_8 | 5c159852f8d743dab916ca0ed4cc47f7 | function [Sout,nalix] = modify(S,varargin)
%MODIFY modify instance of STREAMobj to meet user-defined criteria
%
% Syntax
%
% S2 = modify(S,pn,pv)
% [S2,nalix] = ...
%
% Description
%
% The function modify changes the geometry of a stream network to meet
% different user-defined criteria. See
%
% de... |
github | wschwanghart/topotoolbox-master | plot.m | .m | topotoolbox-master/@STREAMobj/plot.m | 3,874 | utf_8 | 100b5f9ed345a98e7bd8bd286eba6fc4 | function h = plot(S,varargin)
%PLOT plot instance of STREAMobj
%
% Syntax
%
% plot(S)
% plot(S,...)
% h = ...;
%
% Description
%
% plot overloads the builtin plot command and graphically outputs the
% stream network in S. Additional plot options can be set in the same
% way as with the builtin ... |
github | wschwanghart/topotoolbox-master | labelreach.m | .m | topotoolbox-master/@STREAMobj/labelreach.m | 4,399 | utf_8 | e9ed777e70d697c79be738a328f49925 | function [label,varargout] = labelreach(S,varargin)
%LABELREACH create node-attribute list with labelled reaches
%
% Syntax
%
% label = labelreach(S)
% label = labelreach(S,pn,pv,...)
% [label,ix] = ...
% [label,x,y] = ...
%
% Description
%
% labelreach creates a node attribute list where each elem... |
github | wschwanghart/topotoolbox-master | binarize.m | .m | topotoolbox-master/@STREAMobj/binarize.m | 2,128 | utf_8 | 5624f214e918f9062c23dcc030fb7918 | function S = binarize(S,a)
%BINARIZE Make a stream network a strictly binary tree
%
% Syntax
%
% S2 = binarize(S)
% S2 = binarize(S,a)
%
% Description
%
% Stream networks are commonly binary trees. This means that, when
% moving upstream, a stream branches into a two streams. However, there
% are ca... |
github | wschwanghart/topotoolbox-master | getlocation.m | .m | topotoolbox-master/@STREAMobj/getlocation.m | 6,678 | utf_8 | a21f2338872b1a7706f3c06f49b42e97 | function varargout = getlocation(S,d0,varargin)
%GETLOCATION Get locations along a stream network
%
% Syntax
%
% [x,y,value] = getlocation(S,val)
% [x,y,value] = getlocation(S,val,'value',S.distance)
% [P,value] = getlocation(...,'output','PPS','z',DEM)
% [xc,yc,value] = getlocation(...,'o... |
github | wschwanghart/topotoolbox-master | crslin.m | .m | topotoolbox-master/@STREAMobj/crslin.m | 12,568 | utf_8 | 2fd3709790684c8985bf919a1f0899da | function [zs,exitflag,output] = crslin(S,DEM,varargin)
%CRSLIN constrained regularized smoothing of the channel length profile
%
% Syntax
%
% zs = crslin(S,DEM)
% zs = crslin(S,DEM,pn,pv,...)
%
% Description
%
% Elevation values along stream networks are frequently affected by
% large scatter, often as... |
github | wschwanghart/topotoolbox-master | STREAMobj2mapstruct.m | .m | topotoolbox-master/@STREAMobj/STREAMobj2mapstruct.m | 18,292 | utf_8 | 6f517eed0d2304432b7f2490e393475c | function [GS,x,y] = STREAMobj2mapstruct(S,varargin)
%STREAMOBJ2MAPSTRUCT convert instance of STREAMobj to mapstruct
%
% Syntax
%
% MS = STREAMobj2mapstruct(S)
% MS = STREAMobj2mapstruct(S,type)
% MS = STREAMobj2mapstruct(S,'seglength',length,...
% 'attributes',{'fieldname1' var1 aggf... |
github | wschwanghart/topotoolbox-master | union.m | .m | topotoolbox-master/@STREAMobj/union.m | 3,666 | utf_8 | 37cb7faf4b6966748f4eaee363a5f96b | function S = union(varargin)
%UNION merge different instances of STREAMobj into a new instance
%
% Syntax
%
% S = union(S1,S2,...)
% S = union(S1,S2,...,FD)
%
% Description
%
% union combines different instances of STREAMobj into a new STREAMobj.
%
% union(S1,S2,...) combines all instances into a new S... |
github | wschwanghart/topotoolbox-master | netdist.m | .m | topotoolbox-master/@STREAMobj/netdist.m | 3,816 | utf_8 | fa9cee81d7efe936d657212e04678a0e | function d = netdist(S,a,varargin)
%NETDIST distance transform on a stream network
%
% Syntax
%
% d = netdist(S,a)
% d = netdist(S,ix)
% d = netdist(S,I)
% d = netdist(S,c)
% d = netdist(...,pn,pv,...)
%
% Description
%
% netdist computes the distance along a stream network S. It calculates
% ... |
github | wschwanghart/topotoolbox-master | loessksn.m | .m | topotoolbox-master/@STREAMobj/loessksn.m | 3,693 | utf_8 | 70fdb9e5b80407ffc3b3f64ce980280b | function [k,zhat] = loessksn(S,DEM,A,varargin)
%LOESSKSN Loess-smoothed river steepness
%
% Syntax
%
% k = loessksn(S,DEM,A)
% k = loessksn(S,DEM,A,'pn',pv,...)
%
% Description
%
% loessksn calculates the chitransformation of the horizontal river
% coordinate. Based on the transformed coordinate, it th... |
github | wschwanghart/topotoolbox-master | intersect.m | .m | topotoolbox-master/@STREAMobj/intersect.m | 2,384 | utf_8 | bfb31c873cc2737ec5302fb066bd8081 | function S = intersect(varargin)
%INTERSECT intersect different instances of STREAMobj
%
% Syntax
%
% S = intersect(S1,S2,...)
%
% Description
%
% intersect combines different instances of STREAMobj into a new STREAMobj.
% All STREAMobjs must have been derived
% from the same FLOWobj (e.g., all STREAM... |
github | wschwanghart/topotoolbox-master | densify.m | .m | topotoolbox-master/@STREAMobj/densify.m | 2,449 | utf_8 | 4b475448dfd27966658ecbfe4ca50e58 | function varargout = densify(S,spacing)
%DENSIFY Increase number of vertices in stream network using splines
%
% Syntax
%
% [x,y] = densify(S,spacing)
% MS = densify(S,spacing)
%
% Description
%
% This function increases the number of vertices in a stream network
% (STREAMobj) using spline interpola... |
github | wschwanghart/topotoolbox-master | mnoptim.m | .m | topotoolbox-master/@STREAMobj/mnoptim.m | 7,461 | utf_8 | 367778c000e27de046274a6a31e5aca0 | function [mn,results] = mnoptim(S,DEM,A,varargin)
%MNOPTIM Bayesian optimization of the mn ratio
%
% Syntax
%
% [mn,results] = mnoptim(S,DEM,A)
% [mn,results] = mnoptim(S,z,a)
% [mn,results] = mnoptim(...,pn,pv,...)
%
% Description
%
% mnoptim uses Bayesian optimization to find an optimal mn-ratio b... |
github | wschwanghart/topotoolbox-master | sinuosity.m | .m | topotoolbox-master/@STREAMobj/sinuosity.m | 1,689 | utf_8 | c99b8b777c1a1ad9d0192eb994281fa0 | function s = sinuosity(S,seglength)
%SINUOSITY sinuosity coefficient
%
% Syntax
%
% s = sinuosity(S,seglength)
%
% Description
%
% The sinuosity is the actual river length divided by the shortest path
% length. This function calculates the sinuosity for a streamnetwork S
% along segments of the networ... |
github | wschwanghart/topotoolbox-master | chiplot.m | .m | topotoolbox-master/@STREAMobj/chiplot.m | 11,938 | utf_8 | da692b7baad4c58c35a3f12d7501079f | function OUT = chiplot(S,DEM,A,varargin)
%CHIPLOT CHI analysis for bedrock river analysis
%
% Syntax
%
% C = chiplot(S,DEM,A)
% C = chiplot(S,z,a)
% C = chiplot(S,DEM,A,pn,pv,...)
%
% Description
%
% CHI plots are an alternative to slope-area plots for bedrock river
% analysis. CHI plots are based ... |
github | wschwanghart/topotoolbox-master | mnoptimvar.m | .m | topotoolbox-master/@STREAMobj/mnoptimvar.m | 5,058 | utf_8 | 5b0db0503d47a7470bccd2d10d0abf32 | function [mn,cm,zm,zsd] = mnoptimvar(S,DEM,A,varargin)
%MNOPTIMVAR optimize the mn ratio using minimum variance method
%
% Syntax
%
% mn = mnoptimvar(S,DEM,A)
% mn = mnoptimvar(S,z,a)
% mn = mnoptimvar(...,pn,pv,...)
% [mn,cm,zm,zsd] = ...
%
% Description
%
% mnoptimvar finds an optimal value of th... |
github | wschwanghart/topotoolbox-master | extractconncomps.m | .m | topotoolbox-master/@STREAMobj/extractconncomps.m | 3,753 | utf_8 | 97cbe0629081a02fb8b9da18f269a4d0 | function extractconncomps(S)
%EXTRACTCONNCOMPS interactive stream network selection
%
% Syntax
%
% extractconncomps(S)
%
% Description
%
% extractconncomps displays a figure and plots the stream network S.
% Here you can mouse-select individual connected components and export
% them to the workspace.
%... |
github | wschwanghart/topotoolbox-master | funerosion_impnlin_par.m | .m | topotoolbox-master/ttlem/private/funerosion_impnlin_par.m | 2,828 | utf_8 | 75783ec5ffdbd652177ac64e859f9fc5 | function Z = funerosion_impnlin_par(p,Z,dt, A, S,upl)
% Implicit solution for nonlinear river incision (parallel)
%
% Syntax
%
% Z = funerosion_impnlin_par(n,Z,dt, A, i,k,dx_ik)
%
%
% Description
%
% Implicit solution for nonlinear river incision, calculated by
% solving the Stream Power Law. This sch... |
github | wschwanghart/topotoolbox-master | linearDiffusion.m | .m | topotoolbox-master/ttlem/private/linearDiffusion.m | 1,066 | utf_8 | a46308e5bb228e8e7802b4197c983afd | function DEM=linearDiffusion(DEM,EYE,p,dx2,C,nrc,L,dt)
% Calcualte linear diffusion using a Crank-Nicolson method
%
% Syntax
%
% DEM_D=linearDiffusion(DEM,EYE,p,dx2,C,nrc,L,dt)
%
% Description
%
% Calcualte linear diffusion using a Crank-Nicholson method
%
% Input
%
% DEM DEM (digital elevation ... |
github | wschwanghart/topotoolbox-master | get1up1down.m | .m | topotoolbox-master/ttlem/private/get1up1down.m | 547 | utf_8 | 7f144c307f5574263fd607040a1b3888 | function [ii,kk] = get1up1down(i,k,A)
% get receiver of receiver and giver of giver...
% ii and kk will have nans where neighbors do not exist, % either because, there is none % or because the upstream node has a smaller % contribution area.
i = double(i);
k = double(k);
nrc = numel(A);
% Downstream neighbor
kk = na... |
github | wschwanghart/topotoolbox-master | preparegui.m | .m | topotoolbox-master/ttlem/private/preparegui.m | 2,542 | utf_8 | 10288e915c28ef730d714cbaddb6ddef | function hGUI = preparegui(H1)
% prepare GUI for TTLEM
%
% Syntax
%
% hGui = preparegui(H1)
%
%
% create figure with panels
hGUI.fig = figure('Name','TTLEM',...
'NumberTitle','off',...
'Toolbar','none',...
'MenuBar','none',...
'V... |
github | wschwanghart/topotoolbox-master | BC_Disturbance.m | .m | topotoolbox-master/ttlem/LateralDisplacement/BC_Disturbance.m | 847 | utf_8 | 09c6b42e1aae00bfdf74fae8b2672974 | %TODO define seperate BCs for edges (eg. combination of Neuman and Dirichlet etc. )
function u_g= BC_Disturbance(u_g,distSites,BC)
if any(strcmp(distSites,{'l','lr','tl','bl','tblr'}))
u_g(BC.BC_indices.leftRow)=u_g(BC.BC_indices.leftRow).*rand(size(u_g(BC.BC_indices.leftRow)))*BC.BC_dir_Dist_Value;
end
if any(st... |
github | wschwanghart/topotoolbox-master | dpsimplify.m | .m | topotoolbox-master/utilities/dpsimplify.m | 6,439 | utf_8 | 520039e696aaacc7377a4ba3f7f12ebe | function [ps,ix] = dpsimplify(p,tol)
% Recursive Douglas-Peucker Polyline Simplification, Simplify
%
% [ps,ix] = dpsimplify(p,tol)
%
% dpsimplify uses the recursive Douglas-Peucker line simplification
% algorithm to reduce the number of vertices in a piecewise linear curve
% according to a specified tolerance. The a... |
github | wschwanghart/topotoolbox-master | ylinerel.m | .m | topotoolbox-master/utilities/ylinerel.m | 2,150 | utf_8 | 1ae474bcc6273835c08434daf19f2ece | function ht = ylinerel(values,rel,varargin)
%YLINEREL Plot vertical lines with constant y values and relative length
%
% Syntax
%
% ylinerel(y)
% ylinerel(y, fraction, pn, pv)
%
% Description
%
% ylinerel plots vertical lines at the values y with length (or
% positions) relative to the limits of the x-... |
github | wschwanghart/topotoolbox-master | subplotlabel.m | .m | topotoolbox-master/utilities/subplotlabel.m | 12,326 | utf_8 | 077a645d7d8efbb60675636ae7c7066e | classdef subplotlabel < handle
%SUBPLOTLABEL Label subplots (works only for 2D plots so far)
%
% Syntax
%
% h = subplotlabel(ax,letter)
% h = subplotlabel(fig,letter)
% h = subplotlabel(...,pn,pv,...)
%
% Description
%
% subplotlabel adds labels to the corner of each panel of a composite
% figu... |
github | wschwanghart/topotoolbox-master | ginputc.m | .m | topotoolbox-master/utilities/ginputc.m | 14,143 | utf_8 | a450a3853eafb56c18eae28bedda2956 |
function [x, y, button, ax] = ginputc(varargin)
%GINPUTC Graphical input from mouse.
% GINPUTC behaves similarly to GINPUT, except you can customize the
% cursor color, line width, and line style.
%
% [X,Y] = GINPUTC(N) gets N points from the current axes and returns
% the X- and Y-coordinates in length N vect... |
github | wschwanghart/topotoolbox-master | label2poly.m | .m | topotoolbox-master/utilities/label2poly.m | 3,316 | utf_8 | b33b9f4de3cb8d7c5dab5a3e1535c89d | function [xy,Adj] = label2poly(L,X,Y,c)
% plot region outlines with polyline
%
% Syntax
%
% label2poly(L)
% label2poly(L,X,Y)
% [xy,Adj] = label2poly(...)
%
% Description
%
% label2poly extracts the outlines of regions in a label matrix and
% plots them. The outlines are drawn along the pixel edge... |
github | wschwanghart/topotoolbox-master | niceticks.m | .m | topotoolbox-master/utilities/niceticks.m | 2,608 | utf_8 | 34d0f23a2cc44119e26ffca9e835359e | function niceticks(ax,varargin)
%NICETICKS Makes nice ticks in a 2D plot
%
% Syntax
%
% niceticks
% niceticks(ax)
% niceticks(ax,pn,pv,...)
%
% Description
%
% Coordinate values are often quite large numbers which are displayed
% with an exponent along the graphics axes. This function identifi... |
github | wschwanghart/topotoolbox-master | xlinerel.m | .m | topotoolbox-master/utilities/xlinerel.m | 2,174 | utf_8 | 37a9fd9de56f5f3d40109d94423c8a13 | function ht = xlinerel(values,rel,varargin)
%XLINEREL Plot vertical lines with constant x values and relative length
%
% Syntax
%
% xlinerel(x)
% xlinerel(x, fraction, pn, pv)
%
% Description
%
% xlinerel plots vertical lines at the values x with length (or
% positions) relative to the limits of the y-... |
github | wschwanghart/topotoolbox-master | ScaleBar.m | .m | topotoolbox-master/utilities/ScaleBar.m | 14,766 | utf_8 | f427b2f29039b6551befa3179d811c51 | classdef ScaleBar < handle
%ScaleBar A dynamic scalebar
%
% Syntax
%
% SB = ScaleBar
% SB = ScaleBar(pn,pv,...)
%
% Description
%
% ScaleBar plots a dynamic scale bar in the 2D-axis. The function is
% currently beta and only supports axis with projected coordinate
% systems (no geographic coord... |
github | wschwanghart/topotoolbox-master | ttcmap.m | .m | topotoolbox-master/colormaps/ttcmap.m | 7,409 | utf_8 | f69ae3172e6256ac1df5fa16898e307e | function [cmap,zlimits] = ttcmap(zlimits,varargin)
%TTCMAP create elevation color map optimized for elevation range
%
% Syntax
%
% [cmap,zlimits] = ttcmap(zlimits)
% [cmap,zlimits] = ttcmap(DEM)
% [cmap,zlimits] = ttcmap(...,pn,pv,...)
% ttcmap
% t = ttcmap
%
% Description
%
% TTCMAP has a... |
github | wschwanghart/topotoolbox-master | demprofile.m | .m | topotoolbox-master/@GRIDobj/demprofile.m | 2,234 | utf_8 | ad3eb1effcb69999451ce63954a9ac00 | function [dn,z,x,y] = demprofile(DEM,n,x,y)
%DEMPROFILE get profile along path
%
% Syntax
%
% [d,z] = demprofile(DEM)
% [d,z] = demprofile(DEM,n)
% [d,z,x,y] = demprofile(DEM,n,x,y)
% p = demprofile(DEM,...)
% p = demprofile(DEM,p)
%
% Description
%
% demprofile enables to interactively... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.