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values | md5 stringlengths 32 32 | text stringlengths 23 843k |
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github | Fusion-Goettingen/ExtendedObjectTracking-master | get_ground_truth.m | .m | ExtendedObjectTracking-master/MEM-EKFstar/get_ground_truth.m | 1,310 | utf_8 | 87368bb3da7d314d3f92cc52f82fd807 | % Implementation of the MEM-EKF* algorithm based on the article
%
% "Tracking the Orientation and Axes Lengths of an Elliptical Extended Object"
% Shishan Yang and Marcus Baum
% arXiv preprint, 2018,
% https://arxiv.org/abs/1805.03276
%
% Further information:
% http://www.fusion.informatik.uni-goettingen.de
% https://... |
github | Fusion-Goettingen/ExtendedObjectTracking-master | MEOT_JPDA.m | .m | ExtendedObjectTracking-master/MEOT/linearJPDA/MEOT_JPDA.m | 2,758 | utf_8 | 51dee81933a8739f6db40d67a63fca1c | % Implementation of the multiple extended object tracking algorithm based on the article
%
% "Linear-Time Joint Probabilistic Data Association for Multiple Extended Object Tracking (to appear)"
% S. Yang, K. Thormann, and M. Baum
% 2018 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2018), Sheffield... |
github | Fusion-Goettingen/ExtendedObjectTracking-master | getMeasGt.m | .m | ExtendedObjectTracking-master/MEOT/linearJPDA/getMeasGt.m | 3,550 | utf_8 | 3440bf9c1cf6394c592840e85aeb4793 | % Implementation of the multiple extended object tracking algorithm based on the article
%
% "Linear-Time Joint Probabilistic Data Association for Multiple Extended Object Tracking (to appear)"
% S. Yang, K. Thormann, and M. Baum
% 2018 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2018), Sheffiel... |
github | Fusion-Goettingen/ExtendedObjectTracking-master | plot_extent.m | .m | ExtendedObjectTracking-master/MEOT/linearJPDA/plot_extent.m | 1,454 | utf_8 | dc87b6af94da88106af2c7fb9c94c433 | % Implementation of the multiple extended object tracking algorithm based on the article
%
% "Linear-Time Joint Probabilistic Data Association for Multiple Extended Object Tracking (to appear)"
% S. Yang, K. Thormann, and M. Baum
% 2018 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2018), Sheffiel... |
github | Fusion-Goettingen/ExtendedObjectTracking-master | prob_measurement_update.m | .m | ExtendedObjectTracking-master/MEOT/linearJPDA/prob_measurement_update.m | 2,445 | utf_8 | 99153aec160862491087cef8d377f1ab | % Implementation of the multiple extended object tracking algorithm based on the article
%
% "Linear-Time Joint Probabilistic Data Association for Multiple Extended Object Tracking (to appear)"
% S. Yang, K. Thormann, and M. Baum
% 2018 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM 2018), Sheffiel... |
github | GBJim/py-faster-rcnn-master | voc_eval.m | .m | py-faster-rcnn-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | mmdagent/distantspeechrecognition-mirror-master | mynull.m | .m | distantspeechrecognition-mirror-master/btk/tools/filterbank/mynull.m | 734 | utf_8 | fc97bb26b63387a500bbe38e5e4018c8 |
function [y,val] = mynull( A, num, tol )
[U,W,V] = svd(A);
[rowN,colN] = size(A);
if (nargin == 1)
num = 0;
end
if ( num > 0 )
sIdx = colN-num+1;
val = zeros(num,1);
else
if rowN > 1, s = diag(W);
elseif rowN == 1, s = W(1);
else s = 0;
end
told = max(rowN,colN) * max(s) * eps( 'double' );
if (... |
github | mmdagent/distantspeechrecognition-mirror-master | DesignNyquistFilterBank.m | .m | distantspeechrecognition-mirror-master/btk/tools/filterbank/DesignNyquistFilterBank.m | 7,914 | utf_8 | 13294f41ffbb57c719b48ee2e6351efa | % create prototypes
% Filter Bank Design for Subband Adaptive Beamforming
function [h,g] = NyquistFB( fftLen, m, r, wpW, disp )
%
% last changed: $Date$
% revision: $Rev$
%
%
if( m < 2 )
fprintf(1, 'm = %d < 2?', m);
end
x_m = m; % factor of a filter length
D = ( fftLen ) / 2 ^ r; %... |
github | kmcmonigal/Optimal-interpolation-master | rotate_hydro.m | .m | Optimal-interpolation-master/rotate_hydro.m | 257 | utf_8 | e0f5d85f90054573c14931bdf018ce2c | % This will rotate u and v components to the angle of the hydrographic line
function [across,along,dist,angle] = rotate_hydro(lat,lon,u,v)
[dist,angle] = sw_dist(lat,lon,'km');
along = u.*cos(angle)+v.*sin(angle);
across = -u.*sin(angle)+v.*cos(angle);
|
github | qbzhao/BRTF-master | calRRSE.m | .m | BRTF-master/BRTF/calRRSE.m | 124 | utf_8 | cb2a5780a7dea199ce7a48b18de8ea09 |
function rrse = calRRSE(Xhat, X)
err = Xhat(:) - X(:);
rmse = sqrt(mean(err.^2));
rrse = sqrt(sum(err.^2)/sum(X(:).^2)); |
github | qbzhao/BRTF-master | BayesRCP_TC.m | .m | BRTF-master/BRTF/BayesRCP_TC.m | 17,603 | utf_8 | 337285eb6b92d1968b26909dafca1079 | function [model] = BayesRCP_TC(Y, varargin)
% [model] = BayesRCP_TC(Y, 'PARAM1', val1, 'PARAM2', val2, ...)
%
% INPUTS
% Y - input tensor
% 'obs' - binary (0-1) indicator tensor of same size as Y
% (0: missing, 1: observed)
% (default: ones(siz... |
github | qbzhao/BRTF-master | BayesRCP.m | .m | BRTF-master/BRTF/BayesRCP.m | 16,110 | utf_8 | 9505292e4eeda02edd5ec27af423a93c | function [model] = BayesRCP(Y, varargin)
% [model] = BayesRCP(Y, 'PARAM1', val1, 'PARAM2', val2, ...)
%
% INPUTS
% Y - input tensor
% 'init' - Initialization method.
% - 'ml' : Apply SVD to Y and initialize factor matrices (default)
% - 'rand': ini... |
github | qbzhao/BRTF-master | showbgfg.m | .m | BRTF-master/BRTF/tensor_plot/showbgfg.m | 717 | utf_8 | bc1e042009fbbcf2d3a9cebc235d9af4 |
function showbgfg(V, X, S)
figure('position', [200, 300, 600, 200]);
if ndims(X)==4
nFrames = size(X,4);
S = (S - min(S(:)))/(max(S(:))-min(S(:)));
for i=1:nFrames
subplot(1,3,1); imshow(uint8(V(:,:,:,i)));
subplot(1,3,2); imshow(uint8(X(:,:,:,i))); title(['Frame: ', num2str(i)],'C... |
github | qbzhao/BRTF-master | slice3.m | .m | BRTF-master/BRTF/tensor_plot/slice3.m | 3,526 | utf_8 | 1db90141806bdfeb3592d94c22ef96d7 | function slice3(T,varargin)
%SLICE3 Visualize a third-order tensor with slices.
% slice3(T) visualizes the third-order tensor T by drawing its mode-1,
% -2, and -3 slices using sliders to define their respective indices.
% Press 'h' to show/hide the figure's controls.
%
% slice3(T,varargin) passes the parame... |
github | qbzhao/BRTF-master | plotGamma.m | .m | BRTF-master/BRTF/tensor_plot/plotGamma.m | 1,954 | utf_8 | 14d8cc6c031eb0c440a1c4cdd09b14d1 | function plotGamma(as,bs)
% as = [1 1.5 2];
% b = 1;
% bs = b*ones(1,length(as));
% figure;
[styles, colors, symbols] = plotColors;
legendStr = cell(1, length(as));
%styles = {'k:', 'b--', 'r-'};
% textstr={'Iter.=20', 'Iter.=22', 'Iter.=25', 'Iter.=33'};
for i=1:length(as)
a = as(i); b = bs(i);
xs = linspace(... |
github | qbzhao/BRTF-master | surf3.m | .m | BRTF-master/BRTF/tensor_plot/surf3.m | 3,873 | utf_8 | 56cb09bfb502065c0f177bd052f23f88 | function surf3(T,varargin)
%SURF3 Visualize a third-order tensor with surfaces.
% surf3(T) visualizes the third-order tensor T by drawing its mode-1, -2,
% and -3 slices using sliders to define their respective indices. Press
% 'h' to show/hide the figure's controls.
%
% surf3(T,varargin) passes the parameters ... |
github | qbzhao/BRTF-master | voxel3.m | .m | BRTF-master/BRTF/tensor_plot/voxel3.m | 6,439 | utf_8 | 6abc5ea0d29070388950b48d37bb1707 | function voxel3(T,varargin)
%VOXEL3 Visualize a third-order tensor with voxels.
% voxel3(T) visualizes the third-order tensor T by plotting its elements
% as voxels whose color and opacity are proportional to their value. The
% figure contains two sliders for setting the parameters thresh and
% degree (press 'h... |
github | qbzhao/BRTF-master | cp_apr.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/cp_apr.m | 7,920 | utf_8 | 5e7833d58cab0d616e25f7d89a374e1d | function [M,Minit,output] = cp_apr(X, R, varargin)
%CP_APR Compute nonnegative CP with alternating Poisson regression.
%
% M = CP_APR(X, R) computes an estimate of the best rank-R
% CP model of a tensor X using an alternating Poisson regression.
% The input X can be a tensor, sptensor, ktensor, or ttensor. The
% ... |
github | qbzhao/BRTF-master | export_data.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/export_data.m | 2,285 | utf_8 | 538198494bec6a8dff8f7556fb6f8255 | function export_data(A, fname)
%EXPORT_DATA Export tensor-related data to a file.
%
% EXPORT(A,FNAME) exports object A to the file named FNAME in plain ASCII
% text. Export currently supports exporting the following data types:
%
% - tensor
% - matrix
%
% In the case of a tensor, the first three line... |
github | qbzhao/BRTF-master | cp_nmu.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/cp_nmu.m | 4,976 | utf_8 | 633d7166c5a07d5533e57ba3ccb25162 | function [P,Uinit] = cp_nmu(X,R,opts)
%CP_NMU Compute nonnegative CP with multiplicative updates.
%
% P = CP_NMU(X,R) computes an estimate of the best rank-R PARAFAC
% model of a tensor X with nonnegative constraints on the factors.
% This version uses the Lee & Seung multiplicative updates from
% their NMF alg... |
github | qbzhao/BRTF-master | tt_combinator.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/tt_combinator.m | 12,324 | utf_8 | 9a8339d3a109b275537a2e5a3e9e3c9e | function [A] = tt_combinator(N,K,s1,s2)
%TT_COMBINATOR Perform basic permutation and combination samplings.
% COMBINATOR will return one of 4 different samplings on the set 1:N,
% taken K at a time. These samplings are given as follows:
%
% PERMUTATIONS WITH REPETITION/REPLACEMENT
% COMBINATOR(N,K,'p','r') -... |
github | qbzhao/BRTF-master | import_data.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/import_data.m | 1,738 | utf_8 | a654bb87c41d4fa404de6c65787299ab | function A = import_data(fname)
%IMPORT_DATA Import tensor-related data to a file.
%
% A = IMPORT_DATA(FNAME) imports an object A from the file named FNAME.
% The supported data types and formatting of the file are explained in
% EXPORT_DATA.
%
% See also TENSOR, EXPORT_DATA
%
%MATLAB Tensor Toolbox.
%Copyrigh... |
github | qbzhao/BRTF-master | create_problem.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/create_problem.m | 12,523 | utf_8 | 2ce0a9e79d83fb24168da4552f52c44a | function [info,params] = create_problem(varargin)
%CREATE_PROBLEM Create test problems for tensor factorizations.
%
% INFO = CREATE_PROBLEM('Param',value,...) creates a tensor factorization
% test problem. It generates a solution corresponding to a ktensor or a
% ttensor, and then it generates an example data ten... |
github | qbzhao/BRTF-master | renumber.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/@sptensor/private/renumber.m | 1,606 | utf_8 | b0e7ca64b6641a9f0ac50bef3e9ae5ef | function [newsubs, newsz] = renumber(subs, sz, range)
%RENUMBER indices for sptensor subsref
%
% [NEWSUBS,NEWSZ] = RENUMBER(SUBS,SZ,RANGE) takes a set of
% original subscripts SUBS with entries from a tensor of size
% SZ. All the entries in SUBS are assumed to be within the
% specified RANGE. These subscripts are t... |
github | qbzhao/BRTF-master | tucker_me.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/met/tucker_me.m | 4,560 | utf_8 | 3216bec3b59aecd3b4a11c8e4c559039 | function [T, max_mem, Uinit] = tucker_me(X, R, esz, opts)
%TUCKER_ME Memory-efficient Tucker higher-order orthogonal iteration.
%
% T = TUCKER_ME(X,R,ESZ) computes the best rank(R1,R2,..,Rn)
% approximation of tensor X, according to the specified dimensions
% in vector R. ESZ specifies the number of dimensions th... |
github | qbzhao/BRTF-master | tucker_me_test.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/met/tucker_me_test.m | 3,079 | utf_8 | 7cd5d5bf56ea1d8a82ef189d3c71d564 | function tucker_me_test
%TUCKER_ME_TEST Very simple tests of tucker_me.
% Code by Tamara Kolda and Jimeng Sun, 2008.
%
% Based on the paper:
% T. G. Kolda and J. Sun. Scalable Tensor Decompositions for Multi-aspect
% Data Mining. In: ICDM 2008: Proceedings of the 8th IEEE International
% Conference on Data M... |
github | qbzhao/BRTF-master | ttm_me.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/met/ttm_me.m | 4,085 | utf_8 | 241d426fee6a1d228fd2ca6c01db66b1 | function Y = ttm_me(X, U, edims, sdims, tflag)
%TTM_ME Memory-efficient sptensor times matrix.
%
% Y = TTM_ME(X, U, EDIMS, SDIMS, TFLAG) handles some dimensions
% elementwise and others in the standard way. Here, X is a sparse tensor
% (sptensor), U is a cell array of matrices of length ndims(X),
% EDIMS speci... |
github | qbzhao/BRTF-master | fixsigns.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/@ktensor/fixsigns.m | 3,162 | utf_8 | 8a24ae047737b6b7c2b6e62347dcd2d0 | function K = fixsigns(K,K0)
%FIXSIGNS Fix sign ambiguity of a ktensor.
%
% K = FIXSIGNS(K) makes it so that the largest magnitude entries for
% each vector in each factor of K are positive, provided that the
% sign on *pairs* of vectors in a rank-1 component can be flipped.
%
% K = FIXSIGNS(K,K0) returns a vers... |
github | qbzhao/BRTF-master | datadisp.m | .m | BRTF-master/BRTF/tensor_toolbox_2.5/@ktensor/datadisp.m | 3,162 | utf_8 | 0b9d29cbfc884a9dbb6eec3a8729b7b1 | function datadisp(T, dimlabels, opts)
%DATADISP Special display of a ktensor.
%
% DATADISP(T,LABELS) displays the largest positive entries of each rank-1
% factor of T using the corresponding labels. LABELS is a cell array of
% size ndims(T) such that LABELS{n} is a string cell array of length
% size(T,n).
%
%... |
github | bastibe/MatlabCodeAnalyzer-master | check.m | .m | MatlabCodeAnalyzer-master/check.m | 40,429 | utf_8 | a8d2c3d19f5485812b39a943ec2e69e1 | function check(filename)
%CHECK a source file FILENAME for problems
%
% CHECK does a deep analysis of the code in FILENAME, and reports on
% problems with the code.
%
% Each function defined in the file is reported separately, with
% separate statistics and warnings. Minor warnings are written in
% black, whi... |
github | bastibe/MatlabCodeAnalyzer-master | analyze_file.m | .m | MatlabCodeAnalyzer-master/analyze_file.m | 11,498 | utf_8 | ce20c5692ac08889dd48d2981cb2bb1d | function blocks = analyze_file(filename, tokenlist)
%ANALYZE_FILE analyzes TOKENLIST and extracts information about BLOCKS
% in FILENAME. TOKENLIST is assumed to be the content of FILENAME.
%
% Returns a struct array with fields:
% - name: the function name
% - body: the tokens that make up the body of the func... |
github | benoitberanger/StimTemplate-master | Eyelink_GUI.m | .m | StimTemplate-master/Eyelink_GUI.m | 16,011 | utf_8 | bae8e49d82ce940849b108f8e41820b7 | function varargout = Eyelink_GUI
% Eyelink_GUI is the function that creates (or bring to focus) Eyelink_GUI.
% Then, Eyelink_main is always called to start each task. It is the
% "main" program.
% debug=1 closes previous figure and reopens it, and send the gui handles
% to base workspace.
debug = 0;
%% Open a single... |
github | benoitberanger/StimTemplate-master | ShowCellTree.m | .m | StimTemplate-master/ShowCellTree.m | 2,623 | utf_8 | 5b187372c76dbf4d163889dd7198b411 | function ShowCellTree(C)
% SHOWCELLTREE display the content of cells recursively, such as celldisp
% (form matlab) but with a "tree" look.
%
% See also celldisp (official MATLAB function) ShowStructTree (created by a
% user and published on Mathworks)
%% Paramters
if nargin<1
error('Not enough input arguments.')... |
github | benoitberanger/StimTemplate-master | scrollplot.m | .m | StimTemplate-master/scrollplot.m | 81,896 | utf_8 | bbc7de7a9a319cec4415454d3a13ebc9 | function scrollHandles = scrollplot(varargin)
%SCROLLPLOT add scroll sub-window to the supplied plot handle
%
% scrollplot adds a scroll sub-window to any supplied plot handle(s).
% The user may specify initial view window parameters or use defaults.
% Dragging the side-bars or central patch modifies the respecti... |
github | benoitberanger/StimTemplate-master | cprintf.m | .m | StimTemplate-master/cprintf.m | 24,446 | utf_8 | 702c2b5fda885039a06c006d79c29548 | function count = cprintf(style,format,varargin)
% CPRINTF displays styled formatted text in the Command Window
%
% Syntax:
% count = cprintf(style,format,...)
%
% Description:
% CPRINTF processes the specified text using the exact same FORMAT
% arguments accepted by the built-in SPRINTF and FPRINTF functions.
... |
github | ori-drs/loam_continuous-master | kalmanfilter.m | .m | loam_continuous-master/parsers/kalmanfilter.m | 4,617 | utf_8 | 30bd25e816fbd599b0f1a2fea676470d | % Kalman Filter Implementation
%
% USAGE:
%
% s = kalmanfilter(s)
%
% "s" is a "system" struct containing various fields used as input
% and output. The state estimate "x" and its covariance "P" are
% updated by the function. The other fields describe the mechanics
% of the system and are left unchanged. A cal... |
github | ori-drs/loam_continuous-master | plot_google_map.m | .m | loam_continuous-master/parsers/plot_google_map.m | 22,077 | utf_8 | 28b4abe162c9d5290066f08ee2d460fe | function varargout = plot_google_map(varargin)
% function h = plot_google_map(varargin)
% Plots a google map on the current axes using the Google Static Maps API
%
% USAGE:
% h = plot_google_map(Property, Value,...)
% Plots the map on the given axes. Used also if no output is specified
%
% Or:
% [lonVec latVec imag] = ... |
github | ori-drs/loam_continuous-master | slerp.m | .m | loam_continuous-master/parsers/slerp.m | 759 | utf_8 | ce0fa8e1ab27a7879549ce3984bbf55c | function [ q3 ] = slerp( q1, q2, t )
%SLERP quaternion slerp
% computes the slerp of value t between quaternions q1 and q2
q1 = q1 ./ norm(q1);
q2 = q2 ./ norm(q2);
one = 1.0 - eps;
d = q1'*q2;
absD = abs(d);
if(absD >= one)
scale0 = 1 - t;
scale1 = t;
else
% theta is the angle between the 2 quaternion... |
github | smguo/SVM-for-FX-rate-master | plot_data.m | .m | SVM-for-FX-rate-master/ps2code/plot_data.m | 311 | utf_8 | 533788fb7d1b8a7190fdbfc939f7b37d |
function [] = plot_data(data)
set(0,'DefaultLineLineWidth',2)
set(0,'DefaultAxesFontSize',14)
neg = find(data.y < 0);
pos = find(data.y > 0);
h=plot(data.X(neg,1),data.X(neg,2),'ro'); hold on;
set(h,'MarkerSize',5);
h=plot(data.X(pos,1),data.X(pos,2),'b+'); hold off;
set(h,'MarkerSize',5);
|
github | smguo/SVM-for-FX-rate-master | svm_discrim_func.m | .m | SVM-for-FX-rate-master/ps2code/svm_discrim_func.m | 90 | utf_8 | 81e2b74ae67be5cbd85f42528db3a97e |
function f = svm_discrim_func(X,svm)
f = svm.w0 + feval(svm.kernel,X,svm.XS)*svm.beta;
|
github | smguo/SVM-for-FX-rate-master | svm_plot.m | .m | SVM-for-FX-rate-master/ps2code/svm_plot.m | 631 | utf_8 | b23992d4399966d5337816d292122db6 |
function [] = svm_plot(data,svm)
plot_data(data); hold on;
% plot support vectors
h=plot(svm.XS(:,1),svm.XS(:,2),'ko');
set(h,'MarkerSize',10);
myax = axis;
m = 50; % grid points for contour
tx = myax(1) + (myax(2)-myax(1))*(0:m)'/m;
ty = myax(3) + (myax(4)-myax(3))*(0:m)'/m;
[n,d] = size(data.X);
Z = zeros(m+1... |
github | smguo/SVM-for-FX-rate-master | svm_build.m | .m | SVM-for-FX-rate-master/ps2code/svm_build.m | 1,024 | utf_8 | 6db20be1810d4404a9f1e0ba286013eb |
function [svm gamma]= svm_build(data,kernel,C)
y = data.Y;
X = data.X;
n = length(y); % y is n x 1
% initialize A matrix
A = feval(kernel,X,X);
A = diag(y)*A*diag(y);
% solve dual problem...
options_org = optimset(@quadprog);
options = optimset(options_org, 'MaxIter',int32(10000));
alpha = quadprog(A,-ones(n,1),... |
github | abhi2610/ohem-master | voc_eval.m | .m | ohem-master/lib/datasets/VOCdevkit-matlab-wrapper/voc_eval.m | 1,332 | utf_8 | 3ee1d5373b091ae4ab79d26ab657c962 | function res = voc_eval(path, comp_id, test_set, output_dir)
VOCopts = get_voc_opts(path);
VOCopts.testset = test_set;
for i = 1:length(VOCopts.classes)
cls = VOCopts.classes{i};
res(i) = voc_eval_cls(cls, VOCopts, comp_id, output_dir);
end
fprintf('\n~~~~~~~~~~~~~~~~~~~~\n');
fprintf('Results:\n');
aps = [res(:... |
github | giangzuzana/Metrics-master | quadraticWeightedKappa.m | .m | Metrics-master/MATLAB/metrics/quadraticWeightedKappa.m | 1,841 | utf_8 | 1f008d25efe57b152118d3985102f6ac | function score = quadraticWeightedKappa(actual, predicted, minRating, maxRating)
%QUADRATICWEIGHTEDKAPPA Calculates the quadratic weighted kappa
% scoreQuadraticWeightedKappa calculates the quadratic weighted kappa
% value, which is a measure of inter-rater agreement between two raters
% that provide discr... |
github | giangzuzana/Metrics-master | auc.m | .m | Metrics-master/MATLAB/metrics/auc.m | 1,139 | utf_8 | 4d20c857e7b3755f9d0c3cbfd41f2f87 | function auc = auc(category,posterior)
% auc = scoreAUC(category,posterior)
%
% Calculates the area under the ROC for a given set
% of posterior predictions and labels. Currently limited to two classes.
%
% posterior: n*1 matrix of posterior probabilities for class 1
% category: n*1 matrix of categories {0,1}
% auc: A... |
github | giangzuzana/Metrics-master | testRMSLE.m | .m | Metrics-master/MATLAB/metrics/test/testRMSLE.m | 439 | utf_8 | e0b4e675710831cc277e5702221b1396 | function testRMSLE()
%TESTRMSLE Test cases for mean squared log error
%
% Author: Ben Hamner (ben@benhamner.com)
fprintf('Testing RMSLE ...');
test_case(exp(2)-1,exp(1)-1,1);
test_case([0 .5 1 1.5 2],[0 .5 1 1.5 2], 0);
test_case([1 2;3 exp(1)-1], [1 2;3 exp(2)-1], 0.5);
fprintf('tests passed\n');
function tes... |
github | giangzuzana/Metrics-master | testMSLE.m | .m | Metrics-master/MATLAB/metrics/test/testMSLE.m | 436 | utf_8 | 02b54a49a8de0b8602a20259c5235838 | function testMSLE()
%TESTMSLE Test cases for mean squared log error
%
% Author: Ben Hamner (ben@benhamner.com)
fprintf('Testing MSLE ...');
test_case(exp(2)-1,exp(1)-1,1);
test_case([0 .5 1 1.5 2],[0 .5 1 1.5 2], 0);
test_case([1 2;3 exp(1)-1], [1 2;3 exp(2)-1], 0.25);
fprintf('tests passed\n');
function test_... |
github | giangzuzana/Metrics-master | testGini.m | .m | Metrics-master/MATLAB/metrics/test/testGini.m | 735 | utf_8 | 1cc0a13705cddc1e7944b4300c978afb | function testGini()
%TESTGINI Test cases for Gini
%
% Author: Ben Hamner (ben@benhamner.com)
fprintf('Testing gini ...');
test_case(1:3, [10 20 30], 1/9);
test_case(1:3, [30 20 10], -1/9);
test_case([2,1,4,3], [0,0,2,1], 0.125);
test_case([0,20,40,0,10], [40,40,10,5,5], 0);
test_case([40,0,20,0,10], [1000000 40 ... |
github | giangzuzana/Metrics-master | testNormalizedGini.m | .m | Metrics-master/MATLAB/metrics/test/testNormalizedGini.m | 747 | utf_8 | 15cb844c475bdb87cf1ebfd97c4c5748 | function testNormalizedGini()
%TESTNORMALIZEDGINI Test cases for Normalized Gini
%
% Author: Ben Hamner (ben@benhamner.com)
fprintf('Testing gini ...');
test_case(1:3, [10 20 30], 1);
test_case(1:3, [30 20 10], -1);
test_case([2,1,4,3], [0,0,2,1], 1);
test_case([0,20,40,0,10], [40,40,10,5,5], 0);
test_case([40,0... |
github | giangzuzana/Metrics-master | testAveragePrecisionAtK.m | .m | Metrics-master/MATLAB/metrics/test/testAveragePrecisionAtK.m | 686 | utf_8 | e58b7d28b8ce1d44d7904468ec0b3008 | function testAveragePrecisionAtK()
%TESTAVERAGEPRECISIONATK Test cases for AP@K
%
% Author: Ben Hamner (ben@benhamner.com)
fprintf('Testing averagePrecisionAtK ...');
actual = 1:5;
prediction = 1:10;
score = averagePrecisionAtK(actual, prediction);
assert(abs(1-score) < eps);
test_case(1:5, [6 4 7 1 2], 2, 0.25)... |
github | giangzuzana/Metrics-master | testMeanAveragePrecisionAtK.m | .m | Metrics-master/MATLAB/metrics/test/testMeanAveragePrecisionAtK.m | 742 | utf_8 | e5166acb03fe3dbe8e0b561cd9a98bbe | function testMeanAveragePrecisionAtK()
%TESTMEANAVERAGEPRECISIONATK Test cases for MAP@K
%
% Author: Ben Hamner (ben@benhamner.com)
fprintf('Testing meanAveragePrecisionAtK ...');
actual = {(1:5) [1 2 3]};
prediction = {(1:10) [1 2 4:11 3]};
score = meanAveragePrecisionAtK(actual, prediction);
assert(abs(5/6-scor... |
github | giangzuzana/Metrics-master | testLogLoss.m | .m | Metrics-master/MATLAB/metrics/test/testLogLoss.m | 479 | utf_8 | 9dac22b7fa7cb76b1bc75193533f31d5 | function testLogLoss()
%TESTLOGLOSS Test cases for logLoss
%
% Author: Ben Hamner (ben@benhamner.com)
fprintf('Testing logLoss ...');
test_case([1 1 1 0 0 0], [.5 .1 .01 .9 .75 .001], 1.881797068998267);
test_case([1 1 1 0 0 0], [1 1 1 0 0 0], 0);
score = logLoss([1 1 0 0], [1 0 0 0]);
assert(score == Inf);
fpr... |
github | giangzuzana/Metrics-master | testClassificationError.m | .m | Metrics-master/MATLAB/metrics/test/testClassificationError.m | 610 | utf_8 | 56bb56a872843828c64ef8bdf58e2395 | function testClassificationError()
%TESTCLASSIFICATIONERROR Test cases for classificationError
%
% Author: Ben Hamner (ben@benhamner.com)
fprintf('Testing classificationError ...');
test_case([1 1 1 0 0 0], [1 1 1 0 0 0], 0);
test_case([1 1 1 0 0 0], [1 1 1 1 0 0], 1/6);
test_case([1 2;3 4], [1 2;3 3], 1/4);
test... |
github | SergeDmi/ConfocalGN-master | gauss3filter.m | .m | ConfocalGN-master/cgn/gauss3filter.m | 6,029 | UNKNOWN | 73ff9803c2093395bce8604cfe8c0a17 | % Perform three-dimensional Gaussian smoothing in the frequency domain.
% This is achieved by replacing the spatial domain convolution with
% Fourier coefficient multiplication.
% R = gauss3filter(I);
% R = gauss3filter(I, sigma);
% R = gauss3filter(I, sigma, pixelspacing);
% In a spatial domain representation, ... |
github | SergeDmi/ConfocalGN-master | readtext.m | .m | ConfocalGN-master/readfiles/readtext.m | 21,444 | utf_8 | 28323aca57e67264e88ef07945985c30 | function [data, result]= readtext(text, delimiter, comment, quotes, options)
% Usage: [data, result]= readtext(source, delimiter, comment, quotes, options)
%
% Copyright (c) 2009, Peder Axensten
% All rights reserved.
%
% Redistribution and use in source and binary forms, with or without
% modificatio... |
github | wme7/fig2u3d-master | view2vws.m | .m | fig2u3d-master/view2vws.m | 4,447 | utf_8 | 02c77ca5b99a633f296665b5065f28b6 | function [] = view2vws(ax, filename, part_renderers, fix_daspect)
%VIE2VWS Saves current view in a views file for LaTeX media9 package.
%
% See also FIG2U3D.
%
% File: view2vws.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.06.10 - 2012.07.16
% Language: MATLAB R2012a
% Pur... |
github | wme7/fig2u3d-master | fig2u3d.m | .m | fig2u3d-master/fig2u3d.m | 10,497 | utf_8 | 5d1a92cc309ca11768eac710ba32f7d8 | function [] = fig2u3d(ax, fname, imgtype, addaxes, varargin)
%FIG2U3D Convert figure to U3D file.
% FIG2U3D saves the current axes as a U3D file for inclusion as an
% interactive 3-dimensional figure within a PDF. Either LaTeX or Adobe
% Acrobat can be used to embed the U3D file in the PDF.
%
% A VWS fi... |
github | wme7/fig2u3d-master | fig2pdf3d.m | .m | fig2u3d-master/fig2pdf/fig2pdf3d.m | 2,047 | utf_8 | 790140ec129ff314cf6c74d87f82bcfc | function [] = fig2pdf3d(ax, filename, media9_or_movie15, pdforxelatex)
%FIG2PDF3D Convert axes to PDF with embedded interactive 3D image.
%
% usage
% FIG2PDF3D(ax, filename, media9_or_movie15, pdforxelatex)
%
% input
% ax = axes object handle
% filename = file name string (default = 'surface')
% media9_or_movi... |
github | wme7/fig2u3d-master | fig2latex.m | .m | fig2u3d-master/fig2pdf/fig2latex.m | 2,949 | utf_8 | 4bcced1dcd8bae1a8a470d262ba52d84 | function [] = fig2latex(ax, fname, media9_or_movie15, pdforxelatex)
%FIG2LATEX Convert axes to U3D file, generating LaTeX code including it.
%
% usage
% FIG2LATEX(ax, fname, media9_or_movie15, pdforxelatex)
%
% input
% ax = axes object handle
% fname = file name
% media9_or_movie15 = select LaTeX package for i... |
github | wme7/fig2u3d-master | u3d_in_latex.m | .m | fig2u3d-master/fig2pdf/u3d_in_latex.m | 2,120 | utf_8 | e17cb7ed66ff96b036ee906f780218b7 | function [] = u3d_in_latex(fname, media9_or_movie15)
%U3D_IN_LATEX LaTeX code which includes a U3D file.
%
% usage
% U3D_IN_LATEX(fname, media9_or_movie15)
%
% input
% fname = LaTeX file name (a '_small.tex' will be appended)
% media9_or_movie15 = LaTeX package to use for including the 3D file
%
% out... |
github | wme7/fig2u3d-master | doc_examples.m | .m | fig2u3d-master/examples/doc_examples.m | 1,317 | utf_8 | 9eb49ab3a7d606380ade99ae4874bc55 | function [] = doc_examples
% File: doc_examples.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.06.14 - 2012.06.16
% Language: MATLAB R2012a
% Purpose: documentation examples of fig2u3d
% Copyright: Ioannis Filippidis, 2012-
%fname = two_variable_func;
%fname = three_variable_level_s... |
github | wme7/fig2u3d-master | examples.m | .m | fig2u3d-master/examples/examples.m | 1,746 | utf_8 | 492ca62ae66856ad5051ef35fd79a61d | function [] = examples
% File: examples.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.06.10 - 2012.07.24
% Language: MATLAB R2012a
% Purpose: examples for using fig2u3d
% Copyright: Ioannis Filippidis, 2012-
% depends
% plotmd, quivermd
ax = gca;
%% plot
% uncomme... |
github | wme7/fig2u3d-master | idtf2u3d.m | .m | fig2u3d-master/idtf2u3d/idtf2u3d.m | 3,672 | utf_8 | 02e932fa439ff8a0475cb992222440e1 | function [] = idtf2u3d(idtffile, u3dfile)
%IDTF2U3D Convert IDTF to U3D file.
%
% usage
% IDTF2U3D
% IDTF2U3D(IDTF_filename)
% IDTF2U3D(IDTF_filename, U3D_filename)
%
% optional input
% idtffile = filename string for IDTF file (default = 'matfig.idtf')
% u3dfile = filename string for U3D file (def... |
github | wme7/fig2u3d-master | fig2idtf.m | .m | fig2u3d-master/fig2idtf/fig2idtf.m | 3,498 | utf_8 | 1b772df2b3a2f08f734ac2d658790fa9 | function [] = fig2idtf(filename,...
surface_vertices, faces, face_vertex_data,...
line_vertices, line_edges, line_colors,...
pointset_points, pointset_colors)
%FIG2IDTF Save figure in IDTF format.
%
% usage
% count = FIG2IDTF(fid, surface_verti... |
github | wme7/fig2u3d-master | u3d_pre_surface.m | .m | fig2u3d-master/fig2idtf/preprocess/u3d_pre_surface.m | 5,920 | utf_8 | dfbec25edff089550504bd1dfd058fb7 | function [vertices, faces, facevertexcdata, renderer] = u3d_pre_surface(ax)
%U3D_PRE_SURFACE Preprocess surface output to u3d.
% U3D_PRE generates the input for the MESH_TO_LATEX function from
% Alexandre Gramfort from your surface-graphs. The surface graphs 3d-model can be
% displayed in u3d - format i... |
github | wme7/fig2u3d-master | u3d_pre_contourgroup.m | .m | fig2u3d-master/fig2idtf/preprocess/u3d_pre_contourgroup.m | 5,009 | utf_8 | ded278fcdb9cc8f05df99e6382370f68 | function [vertices, edges, colors] = u3d_pre_contourgroup(ax)
%U3D_PRE_CONTOURGROUP Preprocess contour output to u3d.
%
% usage
% [vertices, edges, colors] = U3D_PRE_CONTOURGROUP
% [vertices, edges, colors] = U3D_PRE_CONTOURGROUP(ax)
%
% input
% ax = axes object handle
%
% output
% vertices = posit... |
github | wme7/fig2u3d-master | u3d_pre_line.m | .m | fig2u3d-master/fig2idtf/preprocess/u3d_pre_line.m | 6,065 | utf_8 | e34fdc635fb0f665ab3c7568b962b968 | function [vertices, edges, colors, points, point_colors] = u3d_pre_line(ax)
%U3D_PRE_LINE Preprocess line output to u3d.
%
% usage
% points = U3D_PRE_LINE
% points = U3D_PRE_LINE(ax)
%
% optional input
% ax = axes object handle
%
% output
% points = position vectors as columns of matrix, as row cel... |
github | wme7/fig2u3d-master | u3d_pre_patch.m | .m | fig2u3d-master/fig2idtf/preprocess/u3d_pre_patch.m | 5,777 | utf_8 | f6861ffa482a4ee987f880be3268b9f1 | function [vertices, faces, facevertexcdata, renderer] = u3d_pre_patch(ax)
%U3D_PRE_PATCH Preprocess surface output to u3d.
%
% usage
% [vertices, faces, facevertexcdata] = U3D_PRE_PATCH
% [vertices, faces, facevertexcdata] = U3D_PRE_PATCH(h)
%
% optional input
% ax = axes object handle
%
% output
%... |
github | wme7/fig2u3d-master | u3d_pre_quivergroup.m | .m | fig2u3d-master/fig2idtf/preprocess/u3d_pre_quivergroup.m | 5,589 | utf_8 | bc76935f3748740bfc5f70180c7e0a82 | function [vertices, edges, colors] = u3d_pre_quivergroup(ax)
%U3D_PRE_QUIVERGROUP Preprocess quiver output to u3d.
%
% usage
% [vertices, edges, colors] = U3D_PRE_QUIVERGROUP
% [vertices, edges, colors] = U3D_PRE_QUIVERGROUP(ax)
%
% optional input
% ax = axes object handle
%
% output
% vertices = ... |
github | wme7/fig2u3d-master | create_marker_lines.m | .m | fig2u3d-master/fig2idtf/auxiliary/create_marker_lines.m | 4,626 | utf_8 | 06a93938e6ed762cc642a5481cefdcde | function [vertices, edges, line_colors] = create_marker_lines(h, type)
% File: create_marker_lines.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.06.14
% Language: MATLAB R2012a
% Purpose: create linesets for markers which are not points
% Copyright: Ioannis Filippidis, 2012-
... |
github | wme7/fig2u3d-master | shaders_materials_modifiers.m | .m | fig2u3d-master/fig2idtf/output/shaders_materials_modifiers.m | 4,436 | utf_8 | bf8f5bf281eb632061cfe3561912c190 | function [shaders, materials, modifiers] = shaders_materials_modifiers(v, f, c)
% File: shaders_materials_modifiers.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.06.24
% Language: MATLAB R2012a
% Purpose: mesh shaders, materials and modifiers
% Copyright: Ioannis Filippidis, 2... |
github | wme7/fig2u3d-master | mesh_diffuse_colors.m | .m | fig2u3d-master/fig2idtf/output/mesh_diffuse_colors.m | 3,130 | utf_8 | 5877c81aa0a445e6d49bbb15ed036337 | function [strmodelshading, strfaces_colors, strfaceshading, strcolors,...
nface_vertex_data_unique] = mesh_diffuse_colors(faces, points, face_vertex_data)
% File: mesh_diffuse_colors.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.06.10- 2012.06.24
% Language: MATLAB R201... |
github | wme7/fig2u3d-master | idtf_model_nodes.m | .m | fig2u3d-master/fig2idtf/output/idtf_model_nodes.m | 2,883 | utf_8 | 3e2dc0af5e92c2bdd0b5eac6335c8e44 | function [nodes] = idtf_model_nodes(n_meshes, n_lines, n_pointsets)
% File: idtf_model_nodes.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.06.10 - 2012.06.24
% Language: MATLAB R2012a
% Purpose: create IDTF model nodes
% Copyright: Ioannis Filippidis, 2012-
% depends
% v... |
github | wme7/fig2u3d-master | populate_line_resource_str.m | .m | fig2u3d-master/fig2idtf/output/populate_line_resource_str.m | 3,591 | utf_8 | bf6cbf9c449ea8ab62c57cd073097b10 | function [line_resources] = populate_line_resource_str(line_vertices,...
line_lines, line_colors, n_resources)
%
% See also FACE_VERTEX_DATA_EQUALS_NPOINTS, VERBATIM.
%
% File: populate_line_resource_str.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 20... |
github | wme7/fig2u3d-master | populate_point_resource_str.m | .m | fig2u3d-master/fig2idtf/output/populate_point_resource_str.m | 3,285 | utf_8 | cdec8f9d5e21f9485712e9aa96990b47 | function [pointset_resources] = populate_point_resource_str(points, colors, n_resources)
%
% See also VERBATIM.
%
% File: populate_point_resource_str.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.06.14 -
% Language: MATLAB R2012a
% Purpose: point resource strings
% Copyrig... |
github | wme7/fig2u3d-master | single_mesh_resource_str.m | .m | fig2u3d-master/fig2idtf/output/single_mesh_resource_str.m | 3,828 | utf_8 | 899f8df9c3ca5f8c4669bf9db377d02f | function [mesh_resource] = single_mesh_resource_str(faces, points, face_vertex_data, i)
%
% See also u3d_pre_patch.
%
% File: single_mesh_resource_str.m
% Author: Ioannis Filippidis, jfilippidis@gmail.com
% Date: 2012.06.10 - 2012.06.24
% Language: MATLAB R2012a
% Purpose: export single mesh to ... |
github | ma-compbio/LDGM-master | differential_graph.m | .m | LDGM-master/LDGM/LDGM/differential_graph.m | 505 | utf_8 | 297ce2895eb7e9ae1ad638037893e653 | function [Theta] = differential_graph(Sigma1,Sigma2,lambda)
Q = kron(Sigma1,Sigma2);
d = size(Sigma1,1);
b = Sigma1 - Sigma2;
b = b(:);
nVars = d*d;
w_init = zeros(nVars,1);
funObj = @(w)DGLoss(w,Q,b);
params = [];
params.verbose = 0;
[w] = L1GeneralCompositeGradientAccelerated(funObj,w_init,lambda,params);
Th... |
github | ma-compbio/LDGM-master | myProcessOptions.m | .m | LDGM-master/LDGM/LDGM/myProcessOptions.m | 674 | utf_8 | b94d252a960faa95a3074129247619e6 | function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
... |
github | ma-compbio/LDGM-master | L1GeneralCompositeGradientAccelerated.m | .m | LDGM-master/LDGM/LDGM/L1GeneralCompositeGradientAccelerated.m | 1,960 | utf_8 | 48119aa501194db6f3858f241c5676b8 | function [w,fEvals] = L1GeneralProjectedSubGradient(gradFunc,w,lambda,params,varargin)
% Process input options
[verbose,maxIter,optTol,L] = ...
myProcessOptions(params,'verbose',1,'maxIter',500,...
'optTol',1e-6,'L',[]);
% Start log
if verbose
fprintf('%10s %10s %15s %15s %15s %8s\n','Iteration','FunEvals... |
github | ma-compbio/LDGM-master | differential_graph.m | .m | LDGM-master/Stand_alone_example_by_LDGM/LDGM/differential_graph.m | 711 | utf_8 | 325c5eebfee32614acee887621c6f727 | function [Theta] = differential_graph(Sigma1,Sigma2,lambda)
% Q = kron(Sigma1,Sigma2);
d = size(Sigma1,1);
b = Sigma1 - Sigma2;
b = b(:);
nVars = d*d;
w_init = zeros(nVars,1);
funObj = @(w)DGLoss(w,Sigma1,Sigma2,b);
% funObj = @(w)DGLoss(w,Q,b);
params = [];
params.verbose = 0;
[w] = L1GeneralCompositeGradientA... |
github | ma-compbio/LDGM-master | myProcessOptions.m | .m | LDGM-master/Stand_alone_example_by_LDGM/LDGM/myProcessOptions.m | 674 | utf_8 | b94d252a960faa95a3074129247619e6 | function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
... |
github | ma-compbio/LDGM-master | L1GeneralCompositeGradientAccelerated.m | .m | LDGM-master/Stand_alone_example_by_LDGM/LDGM/L1GeneralCompositeGradientAccelerated.m | 2,020 | utf_8 | bdbdbf551d9808ff23ef285c935bae06 | function [w,fEvals] = L1GeneralProjectedSubGradient(gradFunc,w,lambda,params,varargin)
% Process input options
[verbose,maxIter,optTol,L] = ...
myProcessOptions(params,'verbose',1,'maxIter',500,...
'optTol',1e-6,'L',[]);
% Start log
if verbose
fprintf('%10s %10s %15s %15s %15s %8s\n','Iteration','FunEvals... |
github | ma-compbio/LDGM-master | Estimate_delta.m | .m | LDGM-master/Stand_alone_example_by_LDGM/LDGM/Estimate_delta.m | 603 | utf_8 | 238d61cde722c31d4e078353975737b1 | function result = Estimate_delta1(hsigma1, hsigma2, lambda_seq, delta, delta_union)
result = [];
for lambda = lambda_seq
hdelta = differential_graph(hsigma1, hsigma2, lambda);
[x, y, z] = Compare_delta_hdelta(hdelta, delta, delta_union);
result = [result; [x, y, x, z]];
end
end
function [x, y, z] = Compare_de... |
github | rvdg/CS378_PfCandP-master | MatVec_var1.m | .m | CS378_PfCandP-master/Assignments/Week1/matlab/MatVec_var1.m | 392 | utf_8 | caf1958e0f208b9b1d5151a86a819245 |
function [ y_out ] = MatVec_var1( A, x, y )
% Compute y := A x + y
% Extract the row and column size of A
[ m, n ] = size( A );
% (Strictly speaking you should check that x is a vector size n and y is a
% vector of size m...)
% Copy y into y_out
y_out = y;
% Compute y_out = A * x + y_out
for i = 1:m
for j=1:n... |
github | zakk0610/ML_study-master | submit.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | submitWithConfiguration.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | savejson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | loadjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | loadubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | saveubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | submit.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | submitWithConfiguration.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | savejson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | loadjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | loadubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | saveubjson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/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 | zakk0610/ML_study-master | submit.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex6/ex6/submit.m | 1,318 | utf_8 | bfa0b4ffb8a7854d8e84276e91818107 | function submit()
addpath('./lib');
conf.assignmentSlug = 'support-vector-machines';
conf.itemName = 'Support Vector Machines';
conf.partArrays = { ...
{ ...
'1', ...
{ 'gaussianKernel.m' }, ...
'Gaussian Kernel', ...
}, ...
{ ...
'2', ...
{ 'dataset3Params.m' }, ...
... |
github | zakk0610/ML_study-master | porterStemmer.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex6/ex6/porterStemmer.m | 9,902 | utf_8 | 7ed5acd925808fde342fc72bd62ebc4d | function stem = porterStemmer(inString)
% Applies the Porter Stemming algorithm as presented in the following
% paper:
% Porter, 1980, An algorithm for suffix stripping, Program, Vol. 14,
% no. 3, pp 130-137
% Original code modeled after the C version provided at:
% http://www.tartarus.org/~martin/PorterStemmer/c.tx... |
github | zakk0610/ML_study-master | submitWithConfiguration.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,734 | utf_8 | 84d9a81848f6d00a7aff4f79bdbb6049 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | zakk0610/ML_study-master | savejson.m | .m | ML_study-master/ML_course_by_Andrew_Ng/machine-learning-ex6/ex6/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
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