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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | ncohn/Windsurf-master | Windsurf_RunXBeach.m | .m | Windsurf-master/Windsurf_RunXBeach.m | 14,078 | utf_8 | 003698e6550bf677a8770bbc1144421f | %Windsurf_RunXBeach.m - This code serves to run individual simulations of the XBeach to be used with the Windsurf set of codes for coupled model simulations
%Created By: N. Cohn, Oregon State University
if project.flag.XB == 1 %only run XBeach if need to
%Enter appropriate model directory
... |
github | ncohn/Windsurf-master | setproperty.m | .m | Windsurf-master/XBToolbox/setproperty.m | 18,079 | utf_8 | 1eb47d3ab29ecfd5684c11034b3265a4 | function [OPT Set Default] = setproperty(OPT, inputCell, varargin)
% SETPROPERTY generic routine to set values in PropertyName-PropertyValue pairs
%
% Routine to set properties based on PropertyName-PropertyValue
% pairs (aka <keyword,value> pairs). Can be used in any function
% where PropertyName-PropertyValue pair... |
github | ncohn/Windsurf-master | cdm_params.m | .m | Windsurf-master/SubRoutines/cdm_params.m | 4,220 | utf_8 | ae97a11957e28f0ecc308fa05be70f86 | %cdm_params.m - Code to set up Coastal Dune Model (CDM) parameter file for Windsurf coupler
%Created By: N. Cohn, Oregon State University
function cdm_params(project, grid, veg, sed, idx, winds)
%open file
fid = fopen([project.Directory, filesep, 'cdm', filesep, 'cdm.par'], 'w');
%write out param... |
github | ncohn/Windsurf-master | progressbar.m | .m | Windsurf-master/SubRoutines/progressbar.m | 11,767 | utf_8 | 06705e480618e134da62478338e8251c | function progressbar(varargin)
% Description:
% progressbar() provides an indication of the progress of some task using
% graphics and text. Calling progressbar repeatedly will update the figure and
% automatically estimate the amount of time remaining.
% This implementation of progressbar is intended to be extreme... |
github | ncohn/Windsurf-master | xb_params.m | .m | Windsurf-master/SubRoutines/xb_params.m | 17,952 | utf_8 | 85769a1f7fe0b8001213ba7691c57bf3 | %xb_params.m - This code ssets up XBeach params file for Windsurf
%Created By: N. Cohn, Oregon State University
function xb_params(project, grid, waves, flow, tides, winds, sed, veg, run_number)
%open filename
fid = fopen(['params.txt'], 'w');
fprintf(fid, '%s\n', 'left = 0');
fprintf(fid, '%s\n',... |
github | ncohn/Windsurf-master | cdm_veg_routine_1d.m | .m | Windsurf-master/SubRoutines/cdm_veg_routine_1d.m | 1,548 | utf_8 | 4273ab78df05e8eb850f62b862d6004a | %cdm_veg_routine.m - Re-implementation of Coastal Dune Model (CDM) vegetation growth rate within Matlab
%Created By: N. Cohn, Oregon State University and E. Goldstein, University of North Carolina Chapel Hill
function [veggie_matrix] = cdm_veg_routine_1d(project, veg, grids, veggie_matrix, dhdt, xmin)
%INPUT A... |
github | ncohn/Windsurf-master | cdm_run.m | .m | Windsurf-master/SubRoutines/cdm_run.m | 3,386 | utf_8 | 978dfd4a0e7bdb5a6d1eaa65abe42c08 | %cdm_params.m - Code to run Coastal Dune Model (CDM) parameter file for Windsurf coupler
%Created By: N. Cohn, Oregon State University
function output = cdm_run(project, idx)
%Run the simulation
if numel(project.CDM.CDMExecutable)>3
if isunix == 1
try %try up to 3 times becaus... |
github | brianhhu/FG_RNN-master | normalizeImage.m | .m | FG_RNN-master/mfiles/normalizeImage.m | 1,043 | utf_8 | 49b43f9266877e51fbc39a8c78e6ca59 | % normalizeImage - linearly normalize an array.
%
% res = normalizeImage(data);
% Linearly normalize data between 0 and 1.
%
% res = normalizeImage(img,range);
% Lineary normalize data between range(1) and range(2)
% instead of [0,1]. The special value range = [0 0]
% means that no normalization is performe... |
github | brianhhu/FG_RNN-master | safeDivide.m | .m | FG_RNN-master/mfiles/safeDivide.m | 561 | utf_8 | 7ac0a35b93ad9811c932d0fbacf398ce | % safeDivide - divides two arrays, checking for 0/0.
%
% result = safeDivide(arg1,arg2)
% returns arg1./arg2, where 0/0 is assumed to be 0 instead of NaN.
% This file is part of the SaliencyToolbox - Copyright (C) 2006-2008
% by Dirk B. Walther and the California Institute of Technology.
% See the enclosed LICENSE.... |
github | brianhhu/FG_RNN-master | configureSimpleCell.m | .m | FG_RNN-master/other/CORFPushPull/configureSimpleCell.m | 3,124 | utf_8 | f9cc38ffdf37e6625e5e696ee5add0c9 | function filterModel = configureSimpleCell(sigma,sigmaRatio,t2)
% create a synthetic stimulus of a vertical edge
stimulus = zeros(200);
stimulus(:,1:100) = 1;
params.eta = 1;
params.radius = ceil(sigma*2.5)*2+1;
% Apply DoG filter
DoG(:,:,1) = getDoG(stimulus, sigma, 0, sigmaRatio, 1, 0);
DoG(:,:,2) = -Do... |
github | brianhhu/FG_RNN-master | getSimpleCellResponse.m | .m | FG_RNN-master/other/CORFPushPull/getSimpleCellResponse.m | 4,839 | utf_8 | 75601e8e2b2491a784044c4e348dec9a | function [response, responseParams] = getSimpleCellResponse(img,model,orientlist,inhibitionFactor,responseParams)
% Compute all blurred responses and store the results in a hash table for
% fast retrieval. This can be implemented in a parallel mode.
if nargin == 4
[SimpleCellHashTable, weightsigma] = computeBlurre... |
github | brianhhu/FG_RNN-master | CORFContourDetection.m | .m | FG_RNN-master/other/CORFPushPull/CORFContourDetection.m | 3,797 | UNKNOWN | cb2e6616a8479085cd4db36c756309e6 | % CORFContourDetection Compute contour map
%
% Input:
% img - a coloured or grayscale image
% sigma - The standard deviation of the DoG functions used
% beta - The increase in the distance between the sets of center-on
% and ceter-off DoG receptive fields.
% inhibitionFactor - The factor ... |
github | brianhhu/FG_RNN-master | hysthresh.m | .m | FG_RNN-master/other/CORFPushPull/Utilities/hysthresh.m | 3,438 | utf_8 | 564ed52aa191fbb399daf6f3b0accddb | % HYSTHRESH - Hysteresis thresholding
%
% Usage: bw = hysthresh(im, T1, T2)
%
% Arguments:
% im - image to be thresholded (assumed to be non-negative)
% T1 - upper threshold value
% T2 - lower threshold value
%
% Returns:
% bw - the thresholded image (containing value... |
github | HidekiKawahara/SparkNG-master | recordingGUIV7.m | .m | SparkNG-master/GUI/recordingGUIV7.m | 41,797 | utf_8 | f302ffad8218e87eea938f7f8fa92b5d | function varargout = recordingGUIV7(varargin)
% Reconding GUI with realtime FFT analyzer and other viewers. Type:
% recordingGUIV7
% to start.
% Designed and coded by Hideki Kawahara (kawahara AT sys.wakayama-u.ac.jp)
% 28/Nov./2013
% 29/Nov./2013 minor bug fix
% 13/Dec./2013 spectrogram and playback fun... |
github | HidekiKawahara/SparkNG-master | eventScopeR4.m | .m | SparkNG-master/GUI/eventScopeR4.m | 30,279 | utf_8 | 460bbfa47cf7a91999ccc691d5a81f61 | function varargout = eventScopeR4(varargin)
% EVENTSCOPER4 MATLAB code for eventScopeR4.fig
% EVENTSCOPER4, by itself, creates a new EVENTSCOPER4 or raises the existing
% singleton*.
%
% H = EVENTSCOPER4 returns the handle to a new EVENTSCOPER4 or the handle to
% the existing singleton*.
%
% EV... |
github | HidekiKawahara/SparkNG-master | vtlDisplay.m | .m | SparkNG-master/GUI/vtlDisplay.m | 11,245 | utf_8 | 07cc1bbf3f4ed0cd5105cd13546798c0 | function varargout = vtlDisplay(varargin)
% VTLDISPLAY MATLAB code for vtlDisplay.fig
% VTLDISPLAY, by itself, creates a new VTLDISPLAY or raises the existing
% singleton*.
%
% H = VTLDISPLAY returns the handle to a new VTLDISPLAY or the handle to
% the existing singleton*.
%
% VTLDISPLAY('CALL... |
github | HidekiKawahara/SparkNG-master | lfModelDesignerX.m | .m | SparkNG-master/GUI/lfModelDesignerX.m | 40,031 | utf_8 | 1e06bc59b6ccf1ee3fbee8cafead9b73 | function varargout = lfModelDesignerX(varargin)
% LFMODELDESIGNERX MATLAB code for lfModelDesignerX.fig
% LFMODELDESIGNERX, by itself, creates a new LFMODELDESIGNERX or raises the existing
% singleton*.
%
% H = LFMODELDESIGNERX returns the handle to a new LFMODELDESIGNERX or the handle to
% the exis... |
github | HidekiKawahara/SparkNG-master | waveletVisualizer.m | .m | SparkNG-master/GUI/waveletVisualizer.m | 32,693 | utf_8 | 7cc7ab0245964392e37e6c3d1cff7bc3 | function varargout = waveletVisualizer(varargin)
% WAVELETVISUALIZER MATLAB code for waveletVisualizer.fig
% WAVELETVISUALIZER, by itself, creates a new WAVELETVISUALIZER or raises the existing
% singleton*.
%
% H = WAVELETVISUALIZER returns the handle to a new WAVELETVISUALIZER or the handle to
% t... |
github | HidekiKawahara/SparkNG-master | realtimeSpectrogramV3.m | .m | SparkNG-master/GUI/realtimeSpectrogramV3.m | 23,645 | utf_8 | dd384dea13ba9edbf74507b6050125a3 | function varargout = realtimeSpectrogramV3(varargin)
% Running spectrogram in realtime. Type:
% realtimeSpectrogramV3
% to start.
% Designed and coded by Hideki Kawahara (kawahara AT sys.wakayama-u.ac.jp)
% 19/Dec./2013
% 20/Dec./2013 added dynamic range control and zooming and pan tools
% 21/Dec./2013 b... |
github | HidekiKawahara/SparkNG-master | vtShapeToSoundTestV28.m | .m | SparkNG-master/GUI/vtShapeToSoundTestV28.m | 83,654 | utf_8 | 1544b6fc71765702b944121ae08923e2 | function varargout = vtShapeToSoundTestV28(varargin)
% VTSHAPETOSOUNDTESTV28 MATLAB code for vtShapeToSoundTestV28.fig
% VTSHAPETOSOUNDTESTV28, by itself, creates a new VTSHAPETOSOUNDTESTV28 or raises the existing
% singleton*.
%
% H = VTSHAPETOSOUNDTESTV28 returns the handle to a new VTSHAPETOSOUNDTESTV... |
github | HidekiKawahara/SparkNG-master | desa2.m | .m | SparkNG-master/src/desa2.m | 352 | utf_8 | 645b8fdf5631bf95c13092c833d1a575 | function output = desa2(x, fs)
phx = tkeo(x);
phy = tkeo(x([2:end, end]) - x([1, 1:end-1]));
omg = asin(real(sqrt(phy ./ phx / 4)));
amp = 2 * phx ./ real(sqrt(phy));
output.omg = omg([3, 3, 3:end-2, end-2, end-2]) * fs;
output.amp = amp([3, 3, 3:end-2, end-2, end-2]);
end
function phi = tkeo(x)
phi = x .^ 2 - x([1, 1... |
github | HidekiKawahara/SparkNG-master | closeI2k0xi0.m | .m | SparkNG-master/src/closeI2k0xi0.m | 129 | utf_8 | 106882da17683d26bcbe66801e17795a | function out = closeI2k0xi0(t,Tw,td)
out = -r(t-Tw)+r(t+Tw)+r(t-td-Tw)-r(t-td+Tw);
function rout = r(x)
rout = double(x.*(x>0)); |
github | HidekiKawahara/SparkNG-master | signal2logArea.m | .m | SparkNG-master/src/signal2logArea.m | 959 | utf_8 | 82344b850fa30bbc3d4b20845e0f129e | function logArea = signal2logArea(x)
% by Hideki Kawahara
% This is only a quick and dirty hack. Please refer to proper reference
% for estimating vocal tract area function.
% 18/Jan./2014
% This is valid only for signals sampled at 8000Hz.
n = length(x);
w = blackman(n);
ww = w/sqrt(sum(w.^2));
fftl = 2^ce... |
github | HidekiKawahara/SparkNG-master | simulatedFilterBank.m | .m | SparkNG-master/src/simulatedFilterBank.m | 4,335 | utf_8 | a6ae00763c4ae00e1f23d759d6b2a59d | function filterBankStr = simulatedFilterBank(sgram,fx,bankType)
% Filter bank simulator based on FFT power spectrum
% 22/Dec./2013 by Hideki Kawahara
% This simulation is rough approximation. Please refer to the original
% papers for serious scientific applications. This routine is provided "as
% is" and no ... |
github | Lilin2015/Author---Dehazing-via-Graph-Cut-master | Lee_Dehaze.m | .m | Author---Dehazing-via-Graph-Cut-master/Lee_Dehaze.m | 266 | utf_8 | 4b067c548346575b612c62b29b3795b6 | %% dehaze
% I, input image, balanced
% T, transmission map
% J, haze removal result
function [ J ] = Lee_Dehaze( I, T, A)
J = ( I - repmat(reshape(A,[1,1,3]),size(I,1),size(I,2)))./repmat(T,[1,1,size(I,3)])+repmat(reshape(A,[1,1,3]),size(I,1),size(I,2));
end
|
github | Lilin2015/Author---Dehazing-via-Graph-Cut-master | Lee_Get_A.m | .m | Author---Dehazing-via-Graph-Cut-master/Lee_Get_A.m | 178 | utf_8 | 26dfea8bfdf18ebcd99a254d8c6e2ee6 | %% estimate A based on Retinex Theory
% I, input image
% A, atmospheric color
function [ A ] = Lee_Get_A( I )
A = get_atmosphere(im2double(I), get_dark_channel(I, 25));
end
|
github | Lilin2015/Author---Dehazing-via-Graph-Cut-master | Get_Dx.m | .m | Author---Dehazing-via-Graph-Cut-master/Get_Dx.m | 877 | utf_8 | 51085ba282ca9bd4bc0cb00757ff9262 | %% a tool
% I, input image
% step, 1 - D is the minimal map of each channel.
% 2 - D is eroded.
% 3 - D is dark channel.
% r, mask radius
function [ D ] = Get_Dx( I, step, r )
%% prepare
[m,n,c] = size(I);
if nargin <= 2
r = ceil(0.05*min(m,n));
end
if nargin <= 1
step = ... |
github | Lilin2015/Author---Dehazing-via-Graph-Cut-master | Lee_Get_WhiteI.m | .m | Author---Dehazing-via-Graph-Cut-master/Lee_Get_WhiteI.m | 302 | utf_8 | d3d2d3cb4266414c933c8b7648b7630e | %% white balance based on Retinex theory
% I, input image
% I, output image, balanced
function [ Iw ] = Lee_Get_WhiteI( I )
[~,~,c] = size(I);
A = Lee_Get_A(I);
if c==3
Iw(:,:,1) = I(:,:,1) ./ A(1);
Iw(:,:,2) = I(:,:,2) ./ A(2);
Iw(:,:,3) = I(:,:,3) ./ A(3);
else
Iw = I./A;
end
end
|
github | Lilin2015/Author---Dehazing-via-Graph-Cut-master | Lee_CompressT.m | .m | Author---Dehazing-via-Graph-Cut-master/Lee_CompressT.m | 958 | utf_8 | 9e44c77a5e30caea42e52df5be57c39e | %% compress T
% T, input trans map, [0,1]
% num, finial range of T
% type, mapping method
% inverse, bool, inverse_mapping
% T, output trans map, [1,num]
function [ T ] = Lee_CompressT( T, num, type, inverse )
% prepare
if nargin <= 3
inverse = 0;
end
if nargin <= 2
type = 'linear';
... |
github | Lilin2015/Author---Dehazing-via-Graph-Cut-master | Lee_EnergyMinimization_Dehazing.m | .m | Author---Dehazing-via-Graph-Cut-master/Lee_EnergyMinimization_Dehazing.m | 1,267 | utf_8 | b36e8db76aa57382bc2a7bc5f6e5e0c0 | %% energy minimization dehazing
% I, input image
% J, dehazing result
% T, transmission map
% A, atmospheric color
% Cache, Intermediate data
function [ J, T, A, Cache] = Lee_EnergyMinimization_Dehazing( I )
Cache = cell(3,1);
%% atmospheric color estimate
fprintf('est. atmospheric color...\n');
... |
github | Lilin2015/Author---Dehazing-via-Graph-Cut-master | Lee_Get_InitialTrans.m | .m | Author---Dehazing-via-Graph-Cut-master/Lee_Get_InitialTrans.m | 831 | utf_8 | e3c52673984def91292bda4b75d902bf | %% initial transmission map estimated by alpha-expansion
% Iw, input image, white balanced
% T, output map, initial transmission map
function [ LS ] = Lee_Get_InitialTrans( I, mask )
%% prepare
[m,n,~] = size(I);
pixel_num = m*n;
if nargin <= 1
mask = ceil(0.05*min(m,n));
end
%% Label se... |
github | Lilin2015/Author---Dehazing-via-Graph-Cut-master | Lee_Regularization.m | .m | Author---Dehazing-via-Graph-Cut-master/Lee_Regularization.m | 478 | utf_8 | 56aba4f6f1eb448ab6e24df861aff744 | %% regularization
% I, input image
% T, input transmission map
% lambda, weight of data term
% r, neighbor range, laplacian matrix
function [ T ] = Lee_Regularization( I, T_initial, lambda, r )
L = Lee_Get_Laplacian(I,r);
U = speye(size(L));
A = L + lambda * U;
b = lambda * T_initial(:);
T = A \ b;... |
github | Lilin2015/Author---Dehazing-via-Graph-Cut-master | Lee_AlphaExpansion.m | .m | Author---Dehazing-via-Graph-Cut-master/Lee_AlphaExpansion.m | 348 | utf_8 | d61e28f7b3ef8b4f016f56d0b15563d7 | %% initial transmission map estimated by alpha-expansion
% I, input image
% label_num, range of label
% label, output map, initial transmission map
function [ T ] = Lee_AlphaExpansion( I, label_num )
%% prepare
I = im2double(I);
%% form label set of each pixel
B = round((label_num-1)*Get_Dx(I,1))+... |
github | ecntrk/BuildHDR-master | fish2Cube.m | .m | BuildHDR-master/source/fish2Cube.m | 722 | utf_8 | 1b49758d50d007ae991eebf855b0dbb4 | function [op] = fish2Cube(img)
% takes a fisheye HDR image and coverts it into latitude longitude format
[h w z] = size(img);
op = zeros(h, 2*h, 3);
r = floor(h/2);
for j = 1:h
for i = 1:2*h
theta = (pi/(2*h))*(j-1);% Pi /2*h
phi = (pi/h)*(i-1); % 2*Pi / w
a = r*cos(theta);
x = round(a*cos(phi)+ r +0.5);
y = round(a... |
github | MBradbury/Packages-master | syntax_test_matlab.m | .m | Packages-master/Matlab/syntax_test_matlab.m | 4,860 | utf_8 | a003a307f9054a4ae45ebe5ce22f2829 | % SYNTAX TEST "Packages/Matlab/Matlab.sublime-syntax"
%---------------------------------------------
% Matlab OOP test
classdef (Sealed = false) classname < baseclass
% <- keyword.other
% ^ variable.parameter
% ^ keyword.operator.symbols
% ^ constant.language
% ... |
github | trebledawson/Machine-Learning-Examples-master | gradient_descent_linear.m | .m | Machine-Learning-Examples-master/MATLAB/gradient_descent_linear.m | 1,717 | utf_8 | d17f41d66df43bcb55c1abde34728561 | % ***************************************** %
% Linear Regression using Gradient Descent %
% Glenn Dawson %
% 2017-09-15 %
% ***************************************** %
theta = [0;0];
alpha = 0.0001;
gradient_descent(theta,alpha);
% Arguments: 1x2 vector the... |
github | trebledawson/Machine-Learning-Examples-master | simple_perceptron_.m | .m | Machine-Learning-Examples-master/MATLAB/simple_perceptron_.m | 5,850 | utf_8 | 4ac884bce86bd763fe3d8efad73c8713 | % ********************************************* %
% Perceptron Discriminant with Gradient Descent %
% Glenn Dawson %
% 2017-10-03 %
% ********************************************* %
clear all; clc
% ####################################################... |
github | trebledawson/Machine-Learning-Examples-master | bayes_classifier_1d.m | .m | Machine-Learning-Examples-master/MATLAB/bayes_classifier_1d.m | 1,537 | utf_8 | 2b8ad7bbdc53785fbf4b1ff745c7e778 | % ***************************** %
% Bayes Classifier in 1-D Data %
% Glenn Dawson %
% 2017-09-21 %
% ***************************** %
% Argument(s):
% S is the nth term of Z, where 1 < n < 1000
function P=bayes(S);
% Generate two 1-D data sets
MU1 = 0.5; % Mean of clas... |
github | trebledawson/Machine-Learning-Examples-master | naive_bayes.m | .m | Machine-Learning-Examples-master/MATLAB/naive_bayes.m | 2,004 | utf_8 | 7904c91fe8ec3c46367a7b39f5b1efca | % ********************************* %
% Naive Bayes Classifier %
% Glenn Dawson %
% 2017-09-25 %
% ********************************* %
% Input arguments:
% X is an MxN matrix of training data whose rows correspond to instances
% and whose columns correspond to fe... |
github | trebledawson/Machine-Learning-Examples-master | cumulative_distgen.m | .m | Machine-Learning-Examples-master/MATLAB/cumulative_distgen.m | 1,095 | utf_8 | ef76ed24c7a725f8fa678b5a5093daee |
% ******************************************************************* %
% Cumulative Distribution Function of Arbitrary Discrete Distribution %
% Glenn Dawson %
% 2017-09-19 %
% *****************************... |
github | trebledawson/Machine-Learning-Examples-master | knn.m | .m | Machine-Learning-Examples-master/MATLAB/knn.m | 1,359 | utf_8 | 9b717edc42e5085b87beb356a887653b | % ********************* %
% K-Nearest Neighbor %
% Glenn Dawson %
% 2017-09-27 %
% ********************* %
function p=knn(X,Y,Z,K)
% Input arguments:
% X is an MxN matrix of training data whose rows correspond to instances
% and whose columns correspond to features
% Y is an Mx1 vector contain... |
github | smart-media-lab/Visual-saliency-detection-in-image-using-ant-colony-optimisation-and-local-phase-coherence-master | saliency_EL_2010_main.m | .m | Visual-saliency-detection-in-image-using-ant-colony-optimisation-and-local-phase-coherence-master/saliency_EL_2010_main.m | 17,931 | utf_8 | 858cf5d707b0ff0dbb43b03c9a8d2c01 | function saliency_EL_2010_main
%
% This is a demo program of the paper L. Ma, J. Tian, and W. Yu,
% "Visual saliency detection in image using ant colony optimisation and local phase coherence,"
% Electronics Letters, Vol. 46, Jul. 2010, pp. 1066-1068.
%
clear all; close all; clc;
% Load test image
img_in =... |
github | Akki369/Fetal-Heart-Beat_ECG-master | miso.m | .m | Fetal-Heart-Beat_ECG-master/miso.m | 6,192 | utf_8 | bde7f1d4a03b4774df86d2cbcfb5d3ab | % MATLAB code for MISO system
clc;
clear all;
close all;
load('foetal_ecg.dat');
x=foetal_ecg;
% time signal;
timesig=x(:,1);
% abdnomial signals
abdomin1=x(:,2);
abdomin2=x(:,3);
abdomin3=x(:,4);
abdomin4=x(:,5);
abdomin5=x(:,6);
%thoriad signals
thoirad1=x(:,7);
thoirad2=x(:,8);
thoirad3=x(:,9);
fig... |
github | Akki369/Fetal-Heart-Beat_ECG-master | lms.m | .m | Fetal-Heart-Beat_ECG-master/lms.m | 471 | utf_8 | 6463d6fe97da6c97d5d3c82887fe12aa | %%LMS CODE ALGORITHM FOR THE SOURSE CODE
%%
function [A,E,Y] = lms(x,d,mu,nord)
%lms function
X=convm(x,nord);
[M,N]=size(X);
if nargin < 5, a0 = zeros(1,N); end
a0=a0(:).';
Y(1)=a0*X(1,:).';
E(1)=d(1) - Y(1);
A(1,:) = a0 + mu*E(1)*conj(X(1,:));
if M>1
for k=2:M-nord+1;
Y(k,:)=A(k-1,:)*X(k,... |
github | Akki369/Fetal-Heart-Beat_ECG-master | nlms.m | .m | Fetal-Heart-Beat_ECG-master/nlms.m | 605 | utf_8 | ca8d2bd60f55008f0eafdacc616dc731 | %%NLMS CALGORITHM FOR THE SOURSE CODE
%%
function [A,E,Y] = nlms(x,d,beta,nord,a0)
X=convm(x,nord);
[M,N]=size(X);
if nargin < 5, a0 = zeros(1,N); end
%initialization
a0=a0(:).';
Y(1)=a0*X(1,:).';
E(1)=d(1) - a0*X(1,:).';
DEN=X(1,:)*X(1,:)'+0.0001;
A(1,:) = a0 + beta/DEN*E(1)*conj(X(1,:));
if M>1
... |
github | Akki369/Fetal-Heart-Beat_ECG-master | llms.m | .m | Fetal-Heart-Beat_ECG-master/llms.m | 477 | utf_8 | 2525bb025827cc4f752992a4655b27e8 | %%LLMS FUNCTION OF THE SOURSE CODE
%%
function [A,E,Y]= llms(x,d,mu,gama,nord,a0)
X=convm(x,nord);
[M,N]=size(X);
if nargin < 6, a0 = zeros(1,N); end
a0=a0(:).';
Y(1)=a0*X(1,:).';
E(1)=d(1) - Y(1);
A(1,:)=(1-mu*gama)*a0+mu*E(1)*conj(X(1,:));
if M>1
for k=2:M-nord+1;
Y(k,:)=A(k-1,:)*X(k,:).... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | laplacianfun.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/JacobiSmooth/laplacianfun.m | 1,910 | utf_8 | 229c2016c307956cb856011c9c0b33d7 | % function [MAT,NODES,DN]=laplacianfun(NPTS,[BCS])
%
% Generates a discretized Laplacian operator in any arbitrarily
% large number of dimensions.
% Input:
% NPTS - Vector containing the number of points per dimension of
% the discretization
% [BCS] - Boundary conditions for each variable. 0 [default]
%... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | getDiag.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/JacobiSmooth/getDiag.m | 186 | utf_8 | e2f27271754871c6760c2c1c494d8291 | %Use getDiag() as "setup" function for MatlabSmoother
function D = getDiag(A)
D = sparse(diag(diag(A))); %first call generates vector of diagonal, second call puts that in a matrix
end
|
github | rsln-s/algebraic-distance-on-hypergraphs-master | jacobi.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/JacobiSmooth/jacobi.m | 991 | utf_8 | 01fa09d69d259ca0c60e2bda75e6ce4a | % function [sol,resnrm]=jacobi(A,x0,b,omega,nits,[varargin])
%
% Runs SOR-Jacobi iteration on the matrix A to solve
% the equation Ax=b. Assumes A is sparse. Uses
% initial guess x0.
% -------------- Parameters ---------------
% A = Matrix of the system.
% x0 = Initial guess
% b = RHS of system
% D = M... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | laplacianfun.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/CustomP/laplacianfun.m | 1,910 | utf_8 | 229c2016c307956cb856011c9c0b33d7 | % function [MAT,NODES,DN]=laplacianfun(NPTS,[BCS])
%
% Generates a discretized Laplacian operator in any arbitrarily
% large number of dimensions.
% Input:
% NPTS - Vector containing the number of points per dimension of
% the discretization
% [BCS] - Boundary conditions for each variable. 0 [default]
%... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | customP.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/CustomP/customP.m | 697 | utf_8 | abfd3776eeb8f084a395e9b9ce8e65cb | %Prototype function to generate unsmoothed P from aggregates
function [P, Nullspace, KeepNodes] = createP(A, KeepNodesFine)
N = size(A, 1);
Nkeep = length(KeepNodesFine);
OK_IDX = setdiff(1:N, KeepNodesFine);
A_OK = A(OK_IDX,OK_IDX);
h = muelu('setup', A_OK, 'max levels', 2, 'coarse: max size', int32(size(A, 1) / ... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | laplacianfun.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/Brick/laplacianfun.m | 1,910 | utf_8 | 229c2016c307956cb856011c9c0b33d7 | % function [MAT,NODES,DN]=laplacianfun(NPTS,[BCS])
%
% Generates a discretized Laplacian operator in any arbitrarily
% large number of dimensions.
% Input:
% NPTS - Vector containing the number of points per dimension of
% the discretization
% [BCS] - Boundary conditions for each variable. 0 [default]
%... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | simpleAggregation.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/Brick/simpleAggregation.m | 665 | utf_8 | ba56479c76377500d24e55d361220920 | %A function implementing a very simple aggregation scheme.
%Triplets of nodes with consecutive IDs are grouped together.
%Should simulate brick with some set of parameters?
function agg = simpleAggregation(A)
[m, n] = size(A);
nVerts = m; %number of rows -> number of nodes
nAggs = nVerts / 3;
vertToAgg = int... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | laplacianfun.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/ReentrantDemo/laplacianfun.m | 1,910 | utf_8 | 229c2016c307956cb856011c9c0b33d7 | % function [MAT,NODES,DN]=laplacianfun(NPTS,[BCS])
%
% Generates a discretized Laplacian operator in any arbitrarily
% large number of dimensions.
% Input:
% NPTS - Vector containing the number of points per dimension of
% the discretization
% [BCS] - Boundary conditions for each variable. 0 [default]
%... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | modifyP.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/ReentrantDemo/modifyP.m | 964 | utf_8 | 6b61d2a8676b33a7f01f86ad361b4d98 | %Function to demonstrate reentrant capability in muemex
%Take a P from a default MueLu hierarchy and modify it
%so that all nonzero values only have one significant figure
function P = createP(A)
%Create a separate hierarchy to get a fresh, default P
disp('Generating inner hierarchy...');
newHier = muelu('setup',... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | laplacianfun.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/UnsmoothedP/laplacianfun.m | 1,910 | utf_8 | 229c2016c307956cb856011c9c0b33d7 | % function [MAT,NODES,DN]=laplacianfun(NPTS,[BCS])
%
% Generates a discretized Laplacian operator in any arbitrarily
% large number of dimensions.
% Input:
% NPTS - Vector containing the number of points per dimension of
% the discretization
% [BCS] - Boundary conditions for each variable. 0 [default]
%... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | createP.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/UnsmoothedP/createP.m | 289 | utf_8 | 3bb25e08b22327ef6cc67556b84c5df3 | %Prototype function to generate unsmoothed P from aggregates
function [Ptent, Nullspace] = createP(agg)
Ptent = sparse(double(agg.nVertices), double(agg.nAggregates));
for i = 1:agg.nVertices
Ptent(i, 1 + agg.vertexToAggID(i)) = 1;
end
Nullspace = ones(1, agg.nVertices);
end
|
github | rsln-s/algebraic-distance-on-hypergraphs-master | laplacianfun.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/Evolution/laplacianfun.m | 1,910 | utf_8 | 229c2016c307956cb856011c9c0b33d7 | % function [MAT,NODES,DN]=laplacianfun(NPTS,[BCS])
%
% Generates a discretized Laplacian operator in any arbitrarily
% large number of dimensions.
% Input:
% NPTS - Vector containing the number of points per dimension of
% the discretization
% [BCS] - Boundary conditions for each variable. 0 [default]
%... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | evolutionSoCFactory.m | .m | algebraic-distance-on-hypergraphs-master/packages/muelu/matlab/tests/Evolution/evolutionSoCFactory.m | 3,244 | utf_8 | de6304ee1b7cdd865c0eea5c906940e0 | %Prototype function to test evolution strength-of-connection
%
% reference: Algorithm 1, "Evolution Measure", p. 724 of
% "A new perspective on strength measures in algebraic multigrid"
% Luke Olson, Jacob Schroder, and Ray Tuminaro
% Numerical Linear Algebra with Applications, Vol. 17,... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | plotresults.m | .m | algebraic-distance-on-hypergraphs-master/packages/rol/example/PDE-OPT/0ld/adv-diff-react/plotresults.m | 3,456 | utf_8 | f75d75d87da9229726780340d3183367 | function plotresults
adj = load('cell_to_node_quad.txt') + 1; %% load node adjacency table, increment by 1 for 1-based indexing
nodes = load('nodes.txt'); %% load node coordinates
publish_results = 0;
axsize = 400;
figure('Position', [100 100 3.3*axsize 1.6*axsize])
localplot('state.txt', 'control.txt', 'weights.t... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | VanderPol.m | .m | algebraic-distance-on-hypergraphs-master/packages/rythmos/test/VanderPol/VanderPol.m | 895 | utf_8 | b4985ee0d3b112de000a0c5353665db0 | %
% Exact discrete solution to Van der Pol equation with Backward Euler
%
function [x0n1,x1n1] = VanderPol(N)
format long e;
% IC:
x0n = 2.0;
x1n = 0.0;
x0n1 = [x0n]; % IC
x1n1 = [x1n]; % IC
epsilon = 0.5;
h = 0.1;
for i=[1:N]
t = 0.0 + i*h;
fn = forcing_term(t,epsilon);
[x0n1_temp,x1n1_temp] = solveCubic(x0n,x1... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | FE.m | .m | algebraic-distance-on-hypergraphs-master/packages/rythmos/test/complicatedExample/FE.m | 295 | utf_8 | 97e90972599f7096259e6d12d735e1b7 | %
% This is the exact output of Forward Euler applied to
% \dot{x} - \Lambda x = 0
% x(0) = x0
% t = 0 .. 1
% with fixed step-sizes equal to 1/n.
%
% Example:
% n = 4;
% lambda = [-2:3/(n-1):1]';
% x0 = 10*ones(n,1);
% FE(x0,lambda,n);
%
function xn = FE(x0,lambda,n)
xn = x0.*(1+lambda/n).^n;
|
github | rsln-s/algebraic-distance-on-hypergraphs-master | BE.m | .m | algebraic-distance-on-hypergraphs-master/packages/rythmos/test/complicatedExample/BE.m | 301 | utf_8 | 5e7652c74c4ee4fb8b581bad9c6010af | %
% This is the exact output of Backward Euler applied to
% \dot{x} - \Lambda x = 0
% x(0) = x0
% t = 0 .. 1
% with fixed step-sizes equal to 1/n.
%
% Example:
% n = 4;
% lambda = [-2:3/(n-1):1]';
% x0 = 10*ones(n,1);
% BE(x0,lambda,n);
%
function xn = BE(x0,lambda,n)
xn = x0.*(1./(1-lambda/n)).^n;
|
github | rsln-s/algebraic-distance-on-hypergraphs-master | triangle_mesh_reader.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/mesh/triangle_mesh_reader.m | 4,167 | utf_8 | 0ab6701aa066ef2697a0796c4845daec | %
% function [mesh] = triangle_mesh_reader( filename )
%
% PURPOSE: Read mesh information from filename.node, filename.ele
% and filename.edge files generated by the 2D mesh generator
% TRIANGLE
%
% Input:
% filename string containing the base of the filenames
% generated by TRIANG... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | RectGridDD.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/mesh/RectGridDD.m | 1,433 | utf_8 | 92dfc8d69c5602c395b9164b5226d91e |
function [mesh] = RectGridDD(nsdx, nsdy, t, p, e)
%
% AUTHOR: Matthias Heinkenschloss and Denis Ridzal
% Department of Computational and Applied Mathematics
% Rice University
% November 23, 2005
xmin = min(p(:,1));
xmax = max(p(:,1));
ymin = min(p(:,2));
ymax = max(p(:,2));
elempart =... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | vel_pres_vtk2d.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/mesh/vel_pres_vtk2d.m | 4,549 | utf_8 | ad3b298d72ef7e3456f5a158dbe893a1 | %
% vel_pres_vtk2d(vtkfile, xy, t, vel, pressure)
% vel_pres_vtk2d(vtkfile, xy, t, vel, pressure, vort, stream)
%
%
% INPUT:
% vtkfile base name of file
% vel_pres_vtk2d generates vtkfile_vel.vtu
% and vel_pres_vtk2d_pres.vtu
%
% xy ... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | RectGridQuad.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/mesh/RectGridQuad.m | 4,792 | utf_8 | c3becc3dffb4abbbd715c48ac19c1e76 |
function [mesh] = RectGridQuad(xmin, xmax, ymin, ymax, nx, ny)
%
% [mesh] = RectGridQuad(xmin, xmax, ymin, ymax, nx, ny)
%
%RECTGRID sets up the grid for piecewise linear elements
% in a rectangular domain on quadrilateral cells.
%
% The grid is constructed by subdividing the x-interval into
% nx subintervals... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | RectGridTri.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/mesh/RectGridTri.m | 5,059 | utf_8 | c0bad327e12ce727266b74a28a5cfd88 |
function [mesh] = RectGrid(xmin, xmax, ymin, ymax, nx, ny)
%
% [mesh] = RectGrid(xmin, xmax, ymin, ymax, nx, ny)
%
%RECTGRID sets up the grid for piecewise linear elements
% in a rectangular domain.
%
% The grid is constructed by subdividing the x-interval into
% nx subintervals and the y-interval into ny su... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | reg_mesh_refine2_mid.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/mesh/reg_mesh_refine2_mid.m | 9,000 | utf_8 | 3103a9464b4bf6af260b31619fc9799e | function [mesh1, I1] = reg_mesh_refine2_mid(mesh)
%FIX THE COMMENTS LATER
% [mesh1,I1]=reg_mesh_refine2(mesh)
%
% Compute a regular refinement of a mesh given by p, e, t.
% Each triangle of the original mesh is refined into four triangles
% by dividing each edge into two.
%
% Input
% mesh struc... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | reg_mesh_refine2.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/mesh/reg_mesh_refine2.m | 5,027 | utf_8 | 69c7e3f6eb32f842d2afe5cc3b6cf3cf | function [mesh1,I1]=reg_mesh_refine2(mesh)
%
% [mesh1,I1]=reg_mesh_refine2(mesh)
%
% Compute a regular refinement of a mesh given by p, e, t.
% Each triangle of the original mesh is refined into four triangles
% by dividing each edge into two.
%
% Input
% mesh structure with the fields mesh.p, ... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | build_elastic_rbm.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/src/build_elastic_rbm.m | 1,224 | utf_8 | 0c478367726c0666fea83faeb72d1718 | %function [RBM, NDOF]=build_elastic_rbm(NODES)
% Given a set of nodes in 1, 2 or 3 dimensions construct the
% rigid-body modes for the (elastic) structure
% by: Chris Siefert
% Modified by Denis Ridzal on 10/24/2012:
% - fixed performance issue with sparse vs. zeros
% - returned dimension and number of rigid body m... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | basicTest.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/test/basic/basicTest.m | 372 | utf_8 | f667342dacdfdc2a756f854c8483aff0 | function tests = basicTest
tests = functiontests(localfunctions);
end
function testIntrepid(testCase)
act_fdiff = m2i_test;
exp_fdiff = 0;
verifyEqual(testCase,act_fdiff,exp_fdiff,'AbsTol',1e-8);
end
function testIntrepidAndML(testCase)
act_solnorm = m2ml_test;
exp_solnorm = 2.5888736e+03;
verifyEqual(t... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | dofmaps.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/test/poisson_fem/dofmaps.m | 2,528 | utf_8 | 81a4cce4a72b169ea56b53ef1caba4f7 | % function [cellNodes, iIdx, jIdx, iVecIdx] = dofmaps(mesh, nVert, numCells)
%
% PURPOSE: Given a mesh in point/triangle/edge format, generate
% degree-of-freedom maps compatible with Intrepid.
%
% INPUT: mesh structure array with the following fields
%
% - mesh.p ... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | poissonTest.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/test/poisson_fem/poissonTest.m | 212 | utf_8 | bfb7ce34e64fc9f1a545f1f45f4a8027 | function tests = poissonTest
tests = functiontests(localfunctions);
end
function testConvergence(testCase)
act_rate = conv_test;
exp_rate = 1.9;
verifyGreaterThanOrEqual(testCase,act_rate,exp_rate);
end
|
github | rsln-s/algebraic-distance-on-hypergraphs-master | conv_test.m | .m | algebraic-distance-on-hypergraphs-master/packages/intrepid/matlab/intrelab/test/poisson_fem/conv_test.m | 4,513 | utf_8 | 593863c7fb1ef86b1390cf94f23909b3 | % Convergence test for Poisson problem.
function rate = conv_test
clear all;
set(0, 'defaultaxesfontsize',12,'defaultaxeslinewidth',0.7,...
'defaultlinelinewidth',0.8,'defaultpatchlinewidth',0.7,...
'defaulttextfontsize',12);
% mesh utilities directory
addpath ../../mesh/
% choose solution plotting
iplot =... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | zoltPartSpy.m | .m | algebraic-distance-on-hypergraphs-master/packages/zoltan/src/matlab/zoltPartSpy.m | 5,538 | utf_8 | 25b420e3bee7272a09ff3bdb9ae16c74 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This matlab function produces spy-like plots in which the nonzeros are
% colored based on the partition that owns them.
%
% The function takes 3 arguments: the filename of a matrix in matrix market
% format, the name of the zoltan output ... |
github | rsln-s/algebraic-distance-on-hypergraphs-master | plotcolors.m | .m | algebraic-distance-on-hypergraphs-master/packages/zoltan/src/matlab/plotcolors.m | 1,481 | utf_8 | dc7cd5753308a48fd7b6e77b2c9b0c03 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% This matlab function returns a unique color/symbol pair for plotting.
% The total number of color/symbol pairs needed and the index of this
% color/symbol are taken as input arguments.
%
% Written by Michael Wolf
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%... |
github | soichih/app-conn-preprocessing-master | preprocess.m | .m | app-conn-preprocessing-master/preprocess.m | 6,024 | utf_8 | b9b5bfd25d2d73636e34ef1c81fea5ae | % batch processing script derived from the CONN toolbox script (conn_batch_workshop_nyudataset.m; https://sites.google.com/view/conn/resources/source) for the NYU_CSC_TestRetest dataset (published in Shehzad et al., 2009, The Resting Brain: Unconstrained yet Reliable. Cerebral Cortex. doi:10.1093/cercor/bhn256)
%
% Lo... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submit.m | .m | Coursera_MachineLearning_Course-master/Week 2/machine-learning-ex1/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submitWithConfiguration.m | .m | Coursera_MachineLearning_Course-master/Week 2/machine-learning-ex1/ex1/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | savejson.m | .m | Coursera_MachineLearning_Course-master/Week 2/machine-learning-ex1/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadjson.m | .m | Coursera_MachineLearning_Course-master/Week 2/machine-learning-ex1/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadubjson.m | .m | Coursera_MachineLearning_Course-master/Week 2/machine-learning-ex1/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | saveubjson.m | .m | Coursera_MachineLearning_Course-master/Week 2/machine-learning-ex1/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submit.m | .m | Coursera_MachineLearning_Course-master/Week 6/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 | durgeshsamariya/Coursera_MachineLearning_Course-master | submitWithConfiguration.m | .m | Coursera_MachineLearning_Course-master/Week 6/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 5,569 | utf_8 | cc10d7a55178eb991c495a2b638947fd | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
partss = 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] = ... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | savejson.m | .m | Coursera_MachineLearning_Course-master/Week 6/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 | durgeshsamariya/Coursera_MachineLearning_Course-master | loadjson.m | .m | Coursera_MachineLearning_Course-master/Week 6/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 | durgeshsamariya/Coursera_MachineLearning_Course-master | loadubjson.m | .m | Coursera_MachineLearning_Course-master/Week 6/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 | durgeshsamariya/Coursera_MachineLearning_Course-master | saveubjson.m | .m | Coursera_MachineLearning_Course-master/Week 6/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 | durgeshsamariya/Coursera_MachineLearning_Course-master | submit.m | .m | Coursera_MachineLearning_Course-master/Week 8/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submitWithConfiguration.m | .m | Coursera_MachineLearning_Course-master/Week 8/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 5,569 | utf_8 | cc10d7a55178eb991c495a2b638947fd | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
partss = 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] = ... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | savejson.m | .m | Coursera_MachineLearning_Course-master/Week 8/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadjson.m | .m | Coursera_MachineLearning_Course-master/Week 8/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 | durgeshsamariya/Coursera_MachineLearning_Course-master | loadubjson.m | .m | Coursera_MachineLearning_Course-master/Week 8/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 | durgeshsamariya/Coursera_MachineLearning_Course-master | saveubjson.m | .m | Coursera_MachineLearning_Course-master/Week 8/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 | durgeshsamariya/Coursera_MachineLearning_Course-master | submit.m | .m | Coursera_MachineLearning_Course-master/Week 5/machine-learning-ex4/ex4/submit.m | 1,635 | utf_8 | ae9c236c78f9b5b09db8fbc2052990fc | function submit()
addpath('./lib');
conf.assignmentSlug = 'neural-network-learning';
conf.itemName = 'Neural Networks Learning';
conf.partArrays = { ...
{ ...
'1', ...
{ 'nnCostFunction.m' }, ...
'Feedforward and Cost Function', ...
}, ...
{ ...
'2', ...
{ 'nnCostFunct... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | submitWithConfiguration.m | .m | Coursera_MachineLearning_Course-master/Week 5/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | savejson.m | .m | Coursera_MachineLearning_Course-master/Week 5/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | durgeshsamariya/Coursera_MachineLearning_Course-master | loadjson.m | .m | Coursera_MachineLearning_Course-master/Week 5/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
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