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value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
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|---|---|---|---|---|---|---|---|---|
github | mribri999/MRSignalsSeqs-master | exrecsignal.m | .m | MRSignalsSeqs-master/Matlab/exrecsignal.m | 525 | utf_8 | 739a3c2f8ff99ce98b383dbb8818b20f | %
%[sig] = exrecsignal(T1,T2,TE,TR,flip)
%
% Calculates the steady-state signal of a
% simple excitation recovery sequence (or SPGR).
%
% T1,T2,TE,TR are tissue/sequence parameters.
% flip is the flip angle in degrees.
% The signal as a fraction of Mo is returned.
%
function [sig,M] = exrecsignal(T1,T2,TE,TR,flip);... |
github | mribri999/MRSignalsSeqs-master | displogim.m | .m | MRSignalsSeqs-master/Matlab/displogim.m | 394 | utf_8 | 15e0d58b6b15e9ee17ceac106aca806d | % displogim(im)
% displays log-magnitude version of image im (complex array)
%
% im = 2D image array (real or complex, magnitude displayed)
%
% ===========================================================
function displogim(im)
im = squeeze((im));
lowexp = 0; % Make negative if max(im) is small.
im = log(abs(im));
... |
github | mribri999/MRSignalsSeqs-master | setprops.m | .m | MRSignalsSeqs-master/Matlab/setprops.m | 486 | utf_8 | d102d65180a29160f152b96e8bf8184c | %
% function setprops(handle, propertylist,debug)
%
% Function sets the properties of the handle and children
% according to propertylist.
%
% propertylist is a list object of the form
% { type1, prop1, val1, type2, prop2, val2, ... }
%
% For each child of handle, if the type matches typeN, and
% there is a property m... |
github | mribri999/MRSignalsSeqs-master | showspins.m | .m | MRSignalsSeqs-master/Matlab/showspins.m | 3,202 | utf_8 | ca809fe420d5a8550bf28da3a7bdf70c | %function showspins(M,scale,spinorig,myc)
%
% Show vector plot on one axis, that can then be rotated for 2D or 3D
% viewing.
%
% M = 3xN spins to show
% scale = axis scaling [-scale scale] defaults to 1.
% spinorig = 3xN origin of spins
% myc = colors to show spins (will default to something reasonable.)
%
% Get arrow3... |
github | mribri999/MRSignalsSeqs-master | diamond.m | .m | MRSignalsSeqs-master/Matlab/diamond.m | 411 | utf_8 | 96a7ec2f0bdf60d8d0a03262f0380a15 | % function im = diamond(size,w)
%
% Make a diamond of given width in a given image size
%
% INPUT:
% size = matrix size (2D)
% w = width (widest point)
%
% OUTPUT:
% image that is 1 inside diamond, 0 elsewhere.
%
function im = diamond(size,w)
[x,y] = meshgrid([1:size]-size/2,[1:size]-size/2);
im = ones(size,size);... |
github | mribri999/MRSignalsSeqs-master | gresignal.m | .m | MRSignalsSeqs-master/Matlab/gresignal.m | 660 | utf_8 | 0998ede31b51b02565f67c4bed822512 | % [sig] = gresignal(T1,T2,TE,TR,flip)
%
% Plot the theoretical signal for a gradient-spoiled sequence.
% Note that this is NOT RF-spoiled GRE, which is the same
% (roughly) as excitation-recovery.
%
% T1,T2,TE,TR are parameters for tissue and sequence.
% flip is the flip angle in degrees.
% The signal as a fraction of... |
github | mribri999/MRSignalsSeqs-master | lec7kimhist.m | .m | MRSignalsSeqs-master/Matlab/lectures/lec7kimhist.m | 558 | utf_8 | 5f9a53916c654698521d1556d44e09d1 | %
% Function plots k-space, image and histogram, and returns mean and sd.
function [mn,sd] = lec7kimhist(ksp,ftitle,histpts,refsnr)
[N,M] = size(ksp);
im = (1/N)*ft(ksp);
subplot(2,2,1); dispim(log(1+abs(ksp)));
tt = sprintf('kspace - %s',ftitle);
title(tt); axis off;
subplot(2,2,2); dispim(im);
tt = sprintf('imag... |
github | yikouniao/MTMCT-master | distHSV1.m | .m | MTMCT-master/distHSV1.m | 389 | utf_8 | 1a407c98a3a355ba29c35a7be65d0854 | %function [dist] = distHSV1(dAppr1, dAppr2, w_f)
function [dist] = distHSV1(flag1, appr1, flag2, appr2, w_f)
k1 = find(flag1'); k2 = find(flag2');
%n1 = size(k1,1); n2 = size(k2,1);
w = zeros(8,8);
dist = 0;
for i = k1
for j = k2
w(i,j) = w_f(abs(dirDiff(i, j))+1);
dist = dist + w(i,j) * distHSV(... |
github | yikouniao/MTMCT-master | knnsearch2.m | .m | MTMCT-master/utils/misc/knnsearch2.m | 3,977 | utf_8 | 6121bae49c29fe897da0d67a9d77ecfa | function [idx,D]=knnsearch2(varargin)
% KNNSEARCH Linear k-nearest neighbor (KNN) search
% IDX = knnsearch(Q,R,K) searches the reference data set R (n x d array
% representing n points in a d-dimensional space) to find the k-nearest
% neighbors of each query point represented by eahc row of Q (m x d array).
% The res... |
github | niravshah241/master_thesis-master | mygridnirav3.m | .m | master_thesis-master/misc/mygridnirav3.m | 1,388 | utf_8 | 9a6faaff1ebcca1720cc1ed2c6a0531b | % This script is written and read by pdetool and should NOT be edited.
% There are two recommended alternatives:
% 1) Export the required variables from pdetool and create a MATLAB script
% to perform operations on these.
% 2) Define the problem completely using a MATLAB script. See
% http://www.mathworks.com... |
github | niravshah241/master_thesis-master | mygridnirav2.m | .m | master_thesis-master/misc/mygridnirav2.m | 1,485 | utf_8 | 0577163a9727f7bcfe0ae7bd844775ea | % This script is written and read by pdetool and should NOT be edited.
% There are two recommended alternatives:
% 1) Export the required variables from pdetool and create a MATLAB script
% to perform operations on these.
% 2) Define the problem completely using a MATLAB script. See
% http://www.mathworks.com... |
github | niravshah241/master_thesis-master | ch3_fig_1.m | .m | master_thesis-master/thesis_latex/ch3_fig_1.m | 1,418 | utf_8 | 474509356b96297c891b0905e6c85308 | % This script is written and read by pdetool and should NOT be edited.
% There are two recommended alternatives:
% 1) Export the required variables from pdetool and create a MATLAB script
% to perform operations on these.
% 2) Define the problem completely using a MATLAB script. See
% http://www.mathworks.com... |
github | niravshah241/master_thesis-master | figsame.m | .m | master_thesis-master/figsame/figsame.m | 895 | utf_8 | 83086746f829eac6e6af849a8d84e69f | % FIGSAME Makes figures the same size
%
% FIGSAME Resizes all figures to be the size of gcf
% FIGSAME(f1) Resizes all figures to be the size as figure f1
% FIGSAME(f1,f2) Resizes figure(s) specified by vector f2 to be the same
% size as f1
function figsame(f1,f2)
if ~exist('f1','v... |
github | niravshah241/master_thesis-master | mygridnirav3.m | .m | master_thesis-master/my_grids/mygridnirav3.m | 1,388 | utf_8 | 299b51eaa73f8d5968f74152aeeabae8 | % This script is written and read by pdetool and should NOT be edited.
% There are two recommended alternatives:
% 1) Export the required variables from pdetool and create a MATLAB script
% to perform operations on these.
% 2) Define the problem completely using a MATLAB script. See
% http://www.mathworks.com... |
github | niravshah241/master_thesis-master | mygridnirav2.m | .m | master_thesis-master/my_grids/mygridnirav2.m | 1,485 | utf_8 | 0577163a9727f7bcfe0ae7bd844775ea | % This script is written and read by pdetool and should NOT be edited.
% There are two recommended alternatives:
% 1) Export the required variables from pdetool and create a MATLAB script
% to perform operations on these.
% 2) Define the problem completely using a MATLAB script. See
% http://www.mathworks.com... |
github | niravshah241/master_thesis-master | pdegrid_2.m | .m | master_thesis-master/my_grids/pdegrid_2.m | 1,343 | utf_8 | edaa38fe40d2dcff1967c1e8831b8419 | % This script is written and read by pdetool and should NOT be edited.
% There are two recommended alternatives:
% 1) Export the required variables from pdetool and create a MATLAB script
% to perform operations on these.
% 2) Define the problem completely using a MATLAB script. See
% http://www.mathworks.com... |
github | niravshah241/master_thesis-master | pdegrid_1.m | .m | master_thesis-master/my_grids/pdegrid_1.m | 2,011 | utf_8 | 410bda61cc054bf1b3dd7d184c98af49 | % This script is written and read by pdetool and should NOT be edited.
% There are two recommended alternatives:
% 1) Export the required variables from pdetool and create a MATLAB script
% to perform operations on these.
% 2) Define the problem completely using a MATLAB script. See
% http://www.mathworks.com... |
github | niravshah241/master_thesis-master | pdegrid_convergenece_tests.m | .m | master_thesis-master/my_grids/testgrids/pdegrid_convergenece_tests.m | 1,788 | utf_8 | de0a42a66cffa00f56ba36b4b84a3810 | % This script is written and read by pdetool and should NOT be edited.
% There are two recommended alternatives:
% 1) Export the required variables from pdetool and create a MATLAB script
% to perform operations on these.
% 2) Define the problem completely using a MATLAB script. See
% http://www.mathworks.com... |
github | IrvingShu/XQDA-master | LOMO.m | .m | XQDA-master/code/LOMO.m | 10,916 | utf_8 | b6e9a0ae258aa1945212a176248cde9f | function descriptors = LOMO(images, options)
%% function Descriptors = LOMO(images, options)
% Function for the Local Maximal Occurrence (LOMO) feature extraction
%
% Input:
% <images>: a set of n RGB color images. Size: [h, w, 3, n]
% [optioins]: optional parameters. A structure containing any of the
% following... |
github | JamesLinus/webrtc-master | readDetection.m | .m | webrtc-master/modules/audio_processing/transient/test/readDetection.m | 927 | utf_8 | f6af5020971d028a50a4d19a31b33bcb | %
% Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | JamesLinus/webrtc-master | readPCM.m | .m | webrtc-master/modules/audio_processing/transient/test/readPCM.m | 821 | utf_8 | 76b2955e65258ada1c1e549a4fc9bf79 | %
% Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | JamesLinus/webrtc-master | plotDetection.m | .m | webrtc-master/modules/audio_processing/transient/test/plotDetection.m | 923 | utf_8 | e8113bdaf5dcfe4f50200a3ca29c3846 | %
% Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | JamesLinus/webrtc-master | apmtest.m | .m | webrtc-master/modules/audio_processing/test/apmtest.m | 9,874 | utf_8 | 17ad6af59f6daa758d983dd419e46ff0 | %
% Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | JamesLinus/webrtc-master | parse_delay_file.m | .m | webrtc-master/modules/audio_coding/neteq/test/delay_tool/parse_delay_file.m | 6,405 | utf_8 | 4cc70d6f90e1ca5901104f77a7e7c0b3 | %
% Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | JamesLinus/webrtc-master | plot_neteq_delay.m | .m | webrtc-master/modules/audio_coding/neteq/test/delay_tool/plot_neteq_delay.m | 5,967 | utf_8 | cce342fed6406ef0f12d567fe3ab6eef | %
% Copyright (c) 2011 The WebRTC project authors. All Rights Reserved.
%
% Use of this source code is governed by a BSD-style license
% that can be found in the LICENSE file in the root of the source
% tree. An additional intellectual property rights grant can be found
% in the file PATENTS. All contributing pro... |
github | JamesLinus/webrtc-master | rtpAnalyze.m | .m | webrtc-master/tools_webrtc/matlab/rtpAnalyze.m | 7,892 | utf_8 | 46e63db0fa96270c14a0c205bbab42e4 | function rtpAnalyze( input_file )
%RTP_ANALYZE Analyze RTP stream(s) from a txt file
% The function takes the output from the command line tool rtp_analyze
% and analyzes the stream(s) therein. First, process your rtpdump file
% through rtp_analyze (from command line):
% $ out/Debug/rtp_analyze my_file.rtp my_f... |
github | Jiankai-Sun/Digital-Image-Processing-master | myMarrHildreth.m | .m | Digital-Image-Processing-master/Problem9/SourceCode/myMarrHildreth.m | 2,895 | utf_8 | b16702e08457d60ca13cf2daf7537f0e | function edges = myMarrHildreth(Image, sigma)
% This is a simple implementation of the LoG edge detector.
% Image: Gray-level input image
% sigma: try values like 1, 2, 4, 8, etc.
% edges: Output edge map
% Form the LoG filter
nLoG = filter_LoG(sigma);
% convResult = conv2(double(Image), nLoG, 'same');
... |
github | Jiankai-Sun/Digital-Image-Processing-master | myMarrHildreth5.m | .m | Digital-Image-Processing-master/Problem9/SourceCode/myMarrHildreth5.m | 959 | utf_8 | 251c861c08cd746dcd5ca377334d7840 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% my_edgeMarrHildreth.m
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function edgeMagnitude = myMarrHildreth(image, sigma)
% Determine filter size N = [sigma x 3] x 2 + 1 and the
% size of the image.
N ... |
github | Jiankai-Sun/Digital-Image-Processing-master | myCanny3.m | .m | Digital-Image-Processing-master/Problem9/SourceCode/myCanny3.m | 2,757 | utf_8 | bced8264db9a7776e98d429eab5e6fa3 | function I_temp = myCanny3( w )
% The algorithm parameters:
% 1. Parameters of edge detecting filters:
% X-axis direction filter:
Nx1=10;Sigmax1=1;Nx2=10;Sigmax2=1;Theta1=pi/2;
% Y-axis direction filter:
Ny1=10;Sigmay1=1;Ny2=10;Sigmay2=1;Theta2=0;
% 2. The thresholding parameter alfa:
alfa=... |
github | Jiankai-Sun/Digital-Image-Processing-master | myCanny.m | .m | Digital-Image-Processing-master/Problem9/SourceCode/myCanny.m | 2,196 | utf_8 | c8ead2e036dc8028e672d7cdd42b1532 | function outputImg = myCanny(inputImg)
[h,w] = size(inputImg);
% gaussian blur
sigma = round(min(h,w) * 0.005);
ksize = ceil(sigma*6);
if mod(ksize, 2) == 0
ksize = ksize + 1;
end
inputImgGaussian = convolutionDouble(inputImg,GaussianFilter(ksize,ksize,sigma));
% sobel
SobelKernelX = [-1,0,1; -2,0,2; -1,0... |
github | Jiankai-Sun/Digital-Image-Processing-master | otsu.m | .m | Digital-Image-Processing-master/Problem9/SourceCode/otsu.m | 5,859 | utf_8 | 51c9aaa2aaf3a9a9554b8abe4dc30089 | function [IDX,sep] = otsu(I,n)
%OTSU Global image thresholding/segmentation using Otsu's method.
% IDX = OTSU(I,N) segments the image I into N classes by means of Otsu's
% N-thresholding method. OTSU returns an array IDX containing the cluster
% indices (from 1 to N) of each point. Zero values are assigned ... |
github | Jiankai-Sun/Digital-Image-Processing-master | myReconstruction.m | .m | Digital-Image-Processing-master/Problem8/SourceCode/myReconstruction.m | 1,330 | utf_8 | f5f1a39b90159153219ddbd6249dff36 | %% Self defined function for opening by reconstruction
% function recon1 = myReconstruction( marker, mask )
% % se = strel('square', 3);
% se = ones(3, 3);
% recon1 = marker;
% recon1_old = zeros(size(recon1));
% while (sum(sum(recon1 - recon1_old)) ~= 0)
% % Retain output of previous... |
github | Jiankai-Sun/Digital-Image-Processing-master | myHoleFill3.m | .m | Digital-Image-Processing-master/Problem8/SourceCode/myHoleFill3.m | 11,431 | utf_8 | df8610f8bc88bb1dad620088a4293cd6 | %% Self defined function for hole filling
function [ g ] = myHoleFill( f)
narginchk(1,1);
if ~islogical(f)
f = imbinarize(f);
end
fc = logical(1 - f);
[r, s] = size(f);
fm = false(r, s);
for i = 1:r
for j= 1:s
if i==1 || i==r || j==1 || j==s
... |
github | Jiankai-Sun/Digital-Image-Processing-master | chainCode.m | .m | Digital-Image-Processing-master/Problem10/SourceCode/chainCode.m | 8,576 | utf_8 | 3ed31c4b670992c84d111a6949064fee | function c = chainCode(b, conn, dir)
% CHAINCODE Computes the Freeman chain code of a boundary.
% C = FCHCODE(B) computes the 8-connected Freeman chain code of a
% set of 2-D coordinate pairs contained in B, an np-by-2 array. C
% is a structure with the following fields:
%
% c.fcc = Freeman chain... |
github | Jiankai-Sun/Digital-Image-Processing-master | wavefast.m | .m | Digital-Image-Processing-master/Problem7/SourceCode/wavefast.m | 5,094 | utf_8 | d143429208f8cb9eec34c5878f14a892 | function [ c, s ] = wavefast( x, n, varargin )
%WAVEFAST Computes the FWT of a '3-D extended' 2-D array
% [C, L] = WAVEFAST(X, N, LP, HP) computes 'PAGES' 2D N-level
% FWTs of a 'ROWS x COLUMNS x PAGES' matrix X with respect to
% decomposition filters LP and HP.
%
% [C, L] = WAVEFAST(X, N, WNAME) performs the ... |
github | Jiankai-Sun/Digital-Image-Processing-master | waveback.m | .m | Digital-Image-Processing-master/Problem7/SourceCode/waveback.m | 4,489 | utf_8 | 92a54874b3ac290b1071b5c812768816 | function [ varargout ] = waveback( c, s, varargin )
%WAVEBACK Computes inverse FWTs for multi-level decomposition [C, S].
% [VARARGOUT] = WAVEBACK(C, S, VARARGOUT) performs a 2D N-level
% partial or complete wavelet reconstruction of decomposition
% structure [C, S];
%
% SYNTAX:
% Y = WAVEBACK(C, S, 'WNAME'); ... |
github | nrodrig4/Matlab-Codes-Vapor-Droplet-Evaporation-master | optimizedPolyFit.m | .m | Matlab-Codes-Vapor-Droplet-Evaporation-master/optimizedPolyFit.m | 12,255 | utf_8 | 8ef8e3cf54c6f074bdf74b45d20da862 | function [resultsOutput,new_results_Size] = optimizedPolyFit(app,z_length,z_loc,mu,compound,M,C_v,R,x_spacing,noise_IA,rootdir,SpacingOption,powers,Fitting,error_rel_rms,error_rel2_rms,RMSresults)
for i = 1:z_length
for j = 1:length(powers)
r_max = 50;
z_o = z_loc(i... |
github | nrodrig4/Matlab-Codes-Vapor-Droplet-Evaporation-master | normalIA_and_CT.m | .m | Matlab-Codes-Vapor-Droplet-Evaporation-master/normalIA_and_CT.m | 6,876 | utf_8 | a6784cf14d20c8ab8f728198ebc3c66d | function [resultsOutput,new_results_Size] = normalIA_and_CT(app,z_length,z_loc,mu,compound,M,C_v,R,x_spacing,noise_IA,rootdir,SpacingOption,power,Fitting,error_rel_rms,error_rel2_rms,RMSresults)
for i = 1:z_length
r_max = 50;
z_o = z_loc(i);
SavesFolder = [rootdir,'/z_'... |
github | nrodrig4/Matlab-Codes-Vapor-Droplet-Evaporation-master | gridfit.m | .m | Matlab-Codes-Vapor-Droplet-Evaporation-master/gridfit.m | 35,038 | utf_8 | be54090de8b2dea9c071c9308ca41c67 | function [zgrid,xgrid,ygrid] = gridfit(x,y,z,xnodes,ynodes,varargin)
% gridfit: estimates a surface on a 2d grid, based on scattered data
% Replicates are allowed. All methods extrapolate to the grid
% boundaries. Gridfit uses a modified ridge estimator to
% generate the surface, where the bi... |
github | nrodrig4/Matlab-Codes-Vapor-Droplet-Evaporation-master | IA_minus_Concentration_mod.m | .m | Matlab-Codes-Vapor-Droplet-Evaporation-master/IA_minus_Concentration_mod.m | 1,673 | utf_8 | 34c06378b7fcb0eb5f68893321662da7 | %IA function
function mat = Integrated_Absorbance_minus_Concentration_mod(noise_IA,z_o,mu,M,C_v,R,SavesFolder,x_spacing)
%Setting up General Parameters and pre-allocating memory for speed
r = 50;
x_loc = x_spacing';
mat = zeros(length(x_loc),2);
%creating file name ... |
github | carsonburr/code_world_godot_ext-master | echo_diagnostic.m | .m | code_world_godot_ext-master/thirdparty/speex/echo_diagnostic.m | 2,076 | utf_8 | 8d5e7563976fbd9bd2eda26711f7d8dc | % Attempts to diagnose AEC problems from recorded samples
%
% out = echo_diagnostic(rec_file, play_file, out_file, tail_length)
%
% Computes the full matrix inversion to cancel echo from the
% recording 'rec_file' using the far end signal 'play_file' using
% a filter length of 'tail_length'. The output is saved to 'o... |
github | uygarsumbul/spines-master | readDataset_spines_mod_shuffleNodesWithinBranchTypes.m | .m | spines-master/repo/readDataset_spines_mod_shuffleNodesWithinBranchTypes.m | 22,314 | utf_8 | 26728e15e7edf60ada823c3c08a9a601 | function [allTrees, allPAs] = readDataset_spines_mod_shuffleNodesWithinBranchTypes(code,directory,options)
if ~isfield(options,'normalize') || isempty(options.normalize); normalize = false; else; normalize = options.normalize; end;
if ~isfield(options,'absoluteLengths') || isempty(options.absoluteLengths); absoluteLen... |
github | uygarsumbul/spines-master | save1000WithinDomainShuffledTrees.m | .m | spines-master/repo/save1000WithinDomainShuffledTrees.m | 1,553 | utf_8 | 42eed829143646f2c9485add1896afdf | function save1000ShuffledTrees
code{1}{1} = 'aibs_8_spinerecon_stitched_connected_labeled.eswc';
code{end+1}{1} = 'aibs_9_stitched_connected_labeled.eswc';
code{end+1}{1} = 'aibs_13_mouse2_spinerecon_stitched_connected_labeled.eswc';
cod... |
github | uygarsumbul/spines-master | readDataset_spines_mod_shuffleNodes.m | .m | spines-master/repo/readDataset_spines_mod_shuffleNodes.m | 21,724 | utf_8 | 37c69dc1c0170986da79f8214b1428bc | function [allTrees, allPAs] = readDataset_spines_mod_shuffleNodes(code,directory,options)
if ~isfield(options,'normalize') || isempty(options.normalize); normalize = false; else; normalize = options.normalize; end;
if ~isfield(options,'absoluteLengths') || isempty(options.absoluteLengths); absoluteLengths = true; else... |
github | uygarsumbul/spines-master | readDataset_spines_mod.m | .m | spines-master/repo/readDataset_spines_mod.m | 21,610 | utf_8 | a3561864846b71a1cbd86029910f5e8c | function [allTrees, allPAs] = readDataset_spines(code,directory,options)
if ~isfield(options,'normalize') || isempty(options.normalize); normalize = false; else; normalize = options.normalize; end;
if ~isfield(options,'absoluteLengths') || isempty(options.absoluteLengths); absoluteLengths = true; else; absoluteLengths... |
github | uygarsumbul/spines-master | save1000ShuffledTrees.m | .m | spines-master/repo/save1000ShuffledTrees.m | 1,830 | utf_8 | f5430488baa773ac073930204d1cfc86 | function save1000ShuffledTrees
code{1}{1} = 'aibs_8_spinerecon_stitched_connected_labeled.eswc';
code{end+1}{1} = 'aibs_9_stitched_connected_labeled.eswc';
code{end+1}{1} = 'aibs_13_mouse2_spinerecon_stitched_connected_labeled.eswc';
cod... |
github | JinleiMa/Image-fusion-with-VSM-and-WLS-master | RollingGuidanceFilter_Guided.m | .m | Image-fusion-with-VSM-and-WLS-master/RollingGuidanceFilter_Guided.m | 2,141 | utf_8 | ebf8d74ce32feb253b6ff563aea8ded5 | %
% Rolling Guidance Filter
%
% res = RollingGuidanceFilter(I,sigma_s,sigma_r,iteration) filters image
% "I" by removing its small structures. The borderline between "small"
% and "large" is determined by the parameter sigma_s. The sigma_r is
% fixed to 0.1. The filter is an iteration process. "iteration" is... |
github | linhan94/robotics-slam-playground-master | visual_odometry_skeleton.m | .m | robotics-slam-playground-master/src/visual_odometry_skeleton.m | 15,174 | utf_8 | b726de421122268eef9e9911a1177c9c | clear
import gtsam.*
%% Data Options
NUM_FRAMES = 0; % 0 for all
NUM_INITIALISE = 10; %TODO find a nice values
WINDOW_SIZE = 6; %TODO find a nice value
MAX_ITERATIONS = 30;
LOOP_THRESHOLD = 1000;
ADD_NOISE = true; % Try false for debugging (only)
SAVE_FIGURES = true; % Save plots to hard-drive?
PLOT_LANDMARKS = true... |
github | hsuisme/TensorCompletion-master | lrr.m | .m | TensorCompletion-master/lib/algorithms/lrr.m | 3,529 | utf_8 | f415a5263180f31dc35bdec719b7bdf4 | function [X,E,obj,err,iter] = lrr(A,B,lambda,opts)
% Solve the Low-Rank Representation minimization problem by M-ADMM
%
% min_{X,E} ||X||_*+lambda*loss(E), s.t. A=BX+E
% loss(E) = ||E||_1 or 0.5*||E||_F^2 or ||E||_{2,1}
%
% ---------------------------------------------
% Input:
% A - d*na matrix
% ... |
github | hsuisme/TensorCompletion-master | groupl1.m | .m | TensorCompletion-master/lib/algorithms/groupl1.m | 2,730 | utf_8 | 71035c51c2852449c2ddbc3091fe41ed | function [X,obj,err,iter] = groupl1(A,B,G,opts)
% Solve the group l1-minimization problem by ADMM
%
% min_X \sum_{i=1}^n\sum_{g in G} ||(x_i)_g||_2, s.t. AX=B
%
% x_i is the i-th column of X
% ---------------------------------------------
% Input:
% A - d*na matrix
% B - d*nb matrix
% ... |
github | hsuisme/TensorCompletion-master | rpca.m | .m | TensorCompletion-master/lib/algorithms/rpca.m | 2,944 | utf_8 | 1930326cf4bf01c764909897658853ca | function [L,S,obj,err,iter] = rpca(X,lambda,opts)
% Solve the Robust Principal Component Analysis minimization problem by M-ADMM
%
% min_{L,S} ||L||_*+lambda*loss(S), s.t. X=L+S
% loss(S) = ||S||_1 or ||S||_{2,1}
%
% ---------------------------------------------
% Input:
% X - d*n matrix
% lambda ... |
github | hsuisme/TensorCompletion-master | tracelasso.m | .m | TensorCompletion-master/lib/algorithms/tracelasso.m | 2,583 | utf_8 | 536f5ce74c82d5f183c3c967e14d6cf6 | function [x,obj,err,iter] = tracelasso(A,b,opts)
% Solve the trace Lasso minimization problem by ADMM
%
% min_x ||A*Diag(x)||_*, s.t. Ax=b
%
% ---------------------------------------------
% Input:
% A - d*n matrix
% b - d*1 vector
% opts - Structure value in Matlab. The field... |
github | hsuisme/TensorCompletion-master | groupl1R.m | .m | TensorCompletion-master/lib/algorithms/groupl1R.m | 3,417 | utf_8 | daad367680f297bfd5a82197bec8b72d | function [X,E,obj,err,iter] = groupl1R(A,B,G,lambda,opts)
% Solve the group l1 norm regularized minimization problem by M-ADMM
%
% min_{X,E} loss(E)+lambda*\sum_{i=1}^n\sum_{g in G} ||(x_i)_g||_2, s.t. AX+E=B
% x_i is the i-th column of X
% loss(E) = ||E||_1 or 0.5*||E||_F^2
% -----------------------------------------... |
github | hsuisme/TensorCompletion-master | mlap.m | .m | TensorCompletion-master/lib/algorithms/mlap.m | 4,761 | utf_8 | ad408cb013b2ffa24973702254b4d4e0 | function [Z,E,obj,err,iter] = mlap(X,lambda,alpha,opts)
% Solve the Multi-task Low-rank Affinity Pursuit (MLAP) minimization problem by M-ADMM
%
% Reference: Cheng, Bin, Guangcan Liu, Jingdong Wang, Zhongyang Huang, and Shuicheng Yan.
% Multi-task low-rank affinity pursuit for image segmentation. ICCV, 2011.
%
% min_{... |
github | hsuisme/TensorCompletion-master | lrsr.m | .m | TensorCompletion-master/lib/algorithms/lrsr.m | 3,838 | utf_8 | 8bd2f6bd0800a5a346a5a4bfbb011702 | function [X,E,obj,err,iter] = lrsr(A,B,lambda1,lambda2,opts)
% Solve the Low-Rank and Sparse Representation (LRSR) minimization problem by M-ADMM
%
% min_{X,E} ||X||_*+lambda1*||X||_1+lambda2*loss(E), s.t. A=BX+E
% loss(E) = ||E||_1 or 0.5*||E||_F^2 or ||E||_{2,1}
% ---------------------------------------------
% Inpu... |
github | hsuisme/TensorCompletion-master | fusedl1R.m | .m | TensorCompletion-master/lib/algorithms/fusedl1R.m | 3,714 | utf_8 | 145be29163c05b2175bd848ba37d18d1 | function [x,e,obj,err,iter] = fusedl1R(A,b,lambda1,lambda2,opts)
% Solve the fused Lasso regularized minimization problem by ADMM
%
% min_{x,e} loss(e) + lambda1*||x||_1 + lambda2*\sum_{i=2}^p |x_i-x_{i-1}|,
% loss(e) = ||e||_1 or 0.5*||e||_2^2
%
% ---------------------------------------------
% Input:
% A ... |
github | hsuisme/TensorCompletion-master | fusedl1.m | .m | TensorCompletion-master/lib/algorithms/fusedl1.m | 3,027 | utf_8 | e180c20b97ac834bfde5d505c23bdb1e | function [x,obj,err,iter] = fusedl1(A,b,lambda,opts)
% Solve the fused Lasso (Fused L1) minimization problem by ADMM
%
% min_x ||x||_1 + lambda*\sum_{i=2}^p |x_i-x_{i-1}|,
% s.t. Ax=b
%
% ---------------------------------------------
% Input:
% A - d*n matrix
% b - d*1 vector
% la... |
github | hsuisme/TensorCompletion-master | tracelassoR.m | .m | TensorCompletion-master/lib/algorithms/tracelassoR.m | 3,247 | utf_8 | 8bc2e00ce23aaa6478590829303525b6 | function [x,e,obj,err,iter] = tracelassoR(A,b,lambda,opts)
% Solve the trace Lasso regularized minimization problem by M-ADMM
%
% min_{x,e} loss(e)+lambda*||A*Diag(x)||_*, s.t. Ax+e=b
% loss(e) = ||e||_1 or 0.5*||e||_2^2
% ---------------------------------------------
% Input:
% A - d*n matrix
% b... |
github | hsuisme/TensorCompletion-master | latlrr.m | .m | TensorCompletion-master/lib/algorithms/latlrr.m | 3,636 | utf_8 | 51a08d8f2880a125c6d1dda689ba9f7f | function [Z,L,obj,err,iter] = latlrr(X,lambda,opts)
% Solve the Latent Low-Rank Representation by M-ADMM
%
% min_{Z,L,E} ||Z||_*+||L||_*+lambda*loss(E),
% s.t., XZ+LX-X=E.
% loss(E) = ||E||_1 or 0.5*||E||_F^2 or ||E||_{2,1}
% ---------------------------------------------
% Input:
% X - d*n matrix
% ... |
github | gingsmith/fmtl-master | split_data.m | .m | fmtl-master/util/split_data.m | 1,207 | utf_8 | 667b79186ed24019b82fc2ae7e0e5e84 | %% FUNCTION split_data
% Splitting multi-task data into training / testing by percentage.
%
%% INPUT
% X: {n * d} * t - input matrix
% Y: {n * 1} * t - output matrix
% percent: percentage of the splitting range (0, 1)
%
%% OUTPUT
% X_train: the split of X that has the specified percent of samples
% Y_t... |
github | tyger2020/HAWC-master | submit.m | .m | HAWC-master/machine-learning-ex2/ex2/submit.m | 1,605 | utf_8 | 9b63d386e9bd7bcca66b1a3d2fa37579 | function submit()
addpath('./lib');
conf.assignmentSlug = 'logistic-regression';
conf.itemName = 'Logistic Regression';
conf.partArrays = { ...
{ ...
'1', ...
{ 'sigmoid.m' }, ...
'Sigmoid Function', ...
}, ...
{ ...
'2', ...
{ 'costFunction.m' }, ...
'Logistic R... |
github | tyger2020/HAWC-master | submitWithConfiguration.m | .m | HAWC-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | tyger2020/HAWC-master | savejson.m | .m | HAWC-master/machine-learning-ex2/ex2/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | tyger2020/HAWC-master | loadjson.m | .m | HAWC-master/machine-learning-ex2/ex2/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | tyger2020/HAWC-master | loadubjson.m | .m | HAWC-master/machine-learning-ex2/ex2/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | tyger2020/HAWC-master | saveubjson.m | .m | HAWC-master/machine-learning-ex2/ex2/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | tyger2020/HAWC-master | submit.m | .m | HAWC-master/machine-learning-ex1 - Copy/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 | tyger2020/HAWC-master | submitWithConfiguration.m | .m | HAWC-master/machine-learning-ex1 - Copy/ex1/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | tyger2020/HAWC-master | savejson.m | .m | HAWC-master/machine-learning-ex1 - Copy/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 | tyger2020/HAWC-master | loadjson.m | .m | HAWC-master/machine-learning-ex1 - Copy/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 | tyger2020/HAWC-master | loadubjson.m | .m | HAWC-master/machine-learning-ex1 - Copy/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 | tyger2020/HAWC-master | saveubjson.m | .m | HAWC-master/machine-learning-ex1 - Copy/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 | tyger2020/HAWC-master | savejson.m | .m | HAWC-master/machine-learning-ex4/ex4/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | tyger2020/HAWC-master | loadjson.m | .m | HAWC-master/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | tyger2020/HAWC-master | loadubjson.m | .m | HAWC-master/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | tyger2020/HAWC-master | saveubjson.m | .m | HAWC-master/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | tyger2020/HAWC-master | submitWithConfiguration.m | .m | HAWC-master/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | tyger2020/HAWC-master | savejson.m | .m | HAWC-master/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | tyger2020/HAWC-master | loadjson.m | .m | HAWC-master/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | tyger2020/HAWC-master | loadubjson.m | .m | HAWC-master/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | tyger2020/HAWC-master | saveubjson.m | .m | HAWC-master/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | shawnngtq/machine-learning-master | submit.m | .m | machine-learning-master/andrew-ng-machine-learning/week04/Programming Assignment/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | shawnngtq/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/andrew-ng-machine-learning/week04/Programming Assignment/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | shawnngtq/machine-learning-master | savejson.m | .m | machine-learning-master/andrew-ng-machine-learning/week04/Programming Assignment/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | shawnngtq/machine-learning-master | loadjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week04/Programming Assignment/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | shawnngtq/machine-learning-master | loadubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week04/Programming Assignment/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | shawnngtq/machine-learning-master | saveubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week04/Programming Assignment/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | shawnngtq/machine-learning-master | submit.m | .m | machine-learning-master/andrew-ng-machine-learning/week05/Programming Assignment/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 | shawnngtq/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/andrew-ng-machine-learning/week05/Programming Assignment/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | shawnngtq/machine-learning-master | savejson.m | .m | machine-learning-master/andrew-ng-machine-learning/week05/Programming Assignment/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 | shawnngtq/machine-learning-master | loadjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week05/Programming Assignment/machine-learning-ex4/ex4/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | shawnngtq/machine-learning-master | loadubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week05/Programming Assignment/machine-learning-ex4/ex4/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | shawnngtq/machine-learning-master | saveubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week05/Programming Assignment/machine-learning-ex4/ex4/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | shawnngtq/machine-learning-master | submit.m | .m | machine-learning-master/andrew-ng-machine-learning/week06/Programming Assignment/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 | shawnngtq/machine-learning-master | submitWithConfiguration.m | .m | machine-learning-master/andrew-ng-machine-learning/week06/Programming Assignment/machine-learning-ex5/ex5/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | shawnngtq/machine-learning-master | savejson.m | .m | machine-learning-master/andrew-ng-machine-learning/week06/Programming Assignment/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 | shawnngtq/machine-learning-master | loadjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week06/Programming Assignment/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 | shawnngtq/machine-learning-master | loadubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week06/Programming Assignment/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 | shawnngtq/machine-learning-master | saveubjson.m | .m | machine-learning-master/andrew-ng-machine-learning/week06/Programming Assignment/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 | shawnngtq/machine-learning-master | submit.m | .m | machine-learning-master/andrew-ng-machine-learning/week02/Programming Assignment/machine-learning-ex1/ex1/submit.m | 1,882 | utf_8 | a6e3fa010429b5a6b4ee97447dcf50b9 | 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... |
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