plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | aristofanio/freemat-master | test_sparse64.m | .m | freemat-master/FreeMat/tests/array/test_sparse64.m | 1,500 | utf_8 | d1fe613bb4fc6cd22d4263b4290325a3 | % Test sparse matrix array ndim-subset extraction
function x = test_sparse64
[yi1,zi1] = sparse_test_mat('int32',300,400);
[yf1,zf1] = sparse_test_mat('float',300,400);
[yd1,zd1] = sparse_test_mat('double',300,400);
[yc1,zc1] = sparse_test_mat('complex',300,400);
[yz1,zz1] = sparse_test_mat('dcomplex',300,400);
row_ndx... |
github | aristofanio/freemat-master | test_assign15.m | .m | freemat-master/FreeMat/tests/array/test_assign15.m | 195 | utf_8 | 318d1a636d92b7a2bccc700e8c019060 | % Test for error on illegal (incomplete) assign to empty variables
function test_val = test_assign15
a = [2,3;4,5];
test_val = 0;
try
c(2,:) = a;
catch
test_val = 1;
end
|
github | aristofanio/freemat-master | test_assign13.m | .m | freemat-master/FreeMat/tests/array/test_assign13.m | 131 | utf_8 | d03c63cb697eec755acc509190cca021 | % Test for assignment of empties to an empty
function test_val = test_assign13
A = [];
A([],[],[]) = [];
test_val = 1;
|
github | aristofanio/freemat-master | test_sparse69.m | .m | freemat-master/FreeMat/tests/array/test_sparse69.m | 433 | utf_8 | b4ce15112b9a18d0126cb0afeb2a0fdd | % Test the zeros function
function x = test_sparse69
xi = int32(sparse(100,200));
yi = int32(zeros(100,200));
xf = float(sparse(100,200));
yf = float(zeros(100,200));
xd = double(sparse(100,200));
yd = double(zeros(100,200));
xc = complex(sparse(100,200));
yc = complex(zeros(100,200));
xz = dcomplex(sparse(100,200));
y... |
github | aristofanio/freemat-master | test_diag2.m | .m | freemat-master/FreeMat/tests/array/test_diag2.m | 177 | utf_8 | 729c659b6f91940f98b4b0dcdc564c0c | % Test the diagonal extraction function with a non-zero diagonal
function test_val = test_diag2
a = [1,2,3,4;5,6,7,8;9,10,11,12];
b = diag(a,1);
test_val = test(b == [2;7;12]);
|
github | aristofanio/freemat-master | test_assign16.m | .m | freemat-master/FreeMat/tests/array/test_assign16.m | 405 | utf_8 | 2a62134e40d8e2a3e3377c0e852322ff | % Test for bug 1808557 - incorrect subset assignment with complex arrays
function test_val = test_assign16
x = rand(4,2)+i*rand(4,2);
y = rand(2,2)+i*rand(2,2);
x(2:2:4,:)=y;
z = x;
for i=1:2;
for j=1:2;
z(2+(i-1)*2,j) = y(i,j);
end
end
q = x;
q(:,1) = y(:);
p = [x,x];
p(1,:) ... |
github | aristofanio/freemat-master | test_diag3.m | .m | freemat-master/FreeMat/tests/array/test_diag3.m | 126 | utf_8 | a279479a9e99d3c948ef57ab203eca66 | % Test the diagonal creation function
function test_val = test_diag3
a = [2,3];
b = diag(a);
test_val = test(b == [2,0;0,3]);
|
github | aristofanio/freemat-master | test_det1.m | .m | freemat-master/FreeMat/tests/array/test_det1.m | 127 | utf_8 | c642060d6dfee6f8769f62ab53002c9d | % Test the determinant calculation (bug 1584651)
function test_val = test_det1
A = [1 2
3 4];
test_val = (det(A) == -2)
|
github | aristofanio/freemat-master | test_sparse56.m | .m | freemat-master/FreeMat/tests/array/test_sparse56.m | 361 | utf_8 | 23598d7930bdc3b5e08445004baa02da | % Test DeleteSparseMatrix function
function x = test_sparse56
xi = sparse_test_mat('int32',100);
xf = sparse_test_mat('float',100);
xd = sparse_test_mat('double',100);
xc = sparse_test_mat('complex',100);
xz = sparse_test_mat('dcomplex',100);
xi = [];
xf = [];
xd = [];
xc = [];
xz = [];
x = isempty(xi) & isempty(xf) & ... |
github | aristofanio/freemat-master | test_sparse63.m | .m | freemat-master/FreeMat/tests/array/test_sparse63.m | 843 | utf_8 | 27ff259ab36229288350e3c35b8fc78e | % Test sparse matrix array vector-subset extraction
function x = test_sparse63
[yi1,zi1] = sparse_test_mat('int32',300,400);
[yf1,zf1] = sparse_test_mat('float',300,400);
[yd1,zd1] = sparse_test_mat('double',300,400);
[yc1,zc1] = sparse_test_mat('complex',300,400);
[yz1,zz1] = sparse_test_mat('dcomplex',300,400);
ndx =... |
github | aristofanio/freemat-master | test_sparse73.m | .m | freemat-master/FreeMat/tests/array/test_sparse73.m | 606 | utf_8 | 738bec8f512641306606a2e602b8b98a | % Test sparse matrix array vector deletion
function x = test_sparse73
[yi1,zi1] = sparse_test_mat('int32',300,400);
[yf1,zf1] = sparse_test_mat('float',300,400);
[yd1,zd1] = sparse_test_mat('double',300,400);
[yc1,zc1] = sparse_test_mat('complex',300,400);
[yz1,zz1] = sparse_test_mat('dcomplex',300,400);
ndxr = randi(o... |
github | aristofanio/freemat-master | test_assign14.m | .m | freemat-master/FreeMat/tests/array/test_assign14.m | 227 | utf_8 | 174f6d7f2bde87175e7aa4a10eb9e5ff | % Test for auto sizing of assignment to undefined variables
function test_val = test_assign14
r = [2,3;3,4];
a(2,:,:) = r;
b = zeros(2,2,2);
b(2,:,:) = r;
test_val = all(a(:) == b(:)) && all(size(a) == size(b));
|
github | aristofanio/freemat-master | test_sparse66.m | .m | freemat-master/FreeMat/tests/array/test_sparse66.m | 878 | utf_8 | 2248a1092753a19750a8b2977afabd82 | % Test sparse matrix array ndim-subset assignment
function x = test_sparse66
[yi1,zi1] = sparse_test_mat('int32',300,400);
[yf1,zf1] = sparse_test_mat('float',300,400);
[yd1,zd1] = sparse_test_mat('double',300,400);
[yc1,zc1] = sparse_test_mat('complex',300,400);
[yz1,zz1] = sparse_test_mat('dcomplex',300,400);
ndxr = ... |
github | aristofanio/freemat-master | test_sparse72.m | .m | freemat-master/FreeMat/tests/array/test_sparse72.m | 619 | utf_8 | acde0b477934bd18ab7610119044a187 | % Test sparse matrix array row deletion
function x = test_sparse72
[yi1,zi1] = sparse_test_mat('int32',300,400);
[yf1,zf1] = sparse_test_mat('float',300,400);
[yd1,zd1] = sparse_test_mat('double',300,400);
[yc1,zc1] = sparse_test_mat('complex',300,400);
[yz1,zz1] = sparse_test_mat('dcomplex',300,400);
ndxr = randi(ones... |
github | aristofanio/freemat-master | test_sparse74.m | .m | freemat-master/FreeMat/tests/array/test_sparse74.m | 493 | utf_8 | e508b38c1e8bdf43768560481cec9021 | % Test sparse matrix array diagonal extraction
function x = test_sparse74
[yi1,zi1] = sparse_test_mat('int32',300,400);
[yf1,zf1] = sparse_test_mat('float',300,400);
[yd1,zd1] = sparse_test_mat('double',300,400);
[yc1,zc1] = sparse_test_mat('complex',300,400);
[yz1,zz1] = sparse_test_mat('dcomplex',300,400);
x = testeq... |
github | aristofanio/freemat-master | test_image1.m | .m | freemat-master/FreeMat/tests/handle/test_image1.m | 181 | utf_8 | 9446caca41bea90fe5f63c079eb862d1 | % test image of an empty argument
function test_val = test_image1
a = [];
try
% If this causes a segfault, it won't be caught.
image(a);
catch
end
close all;
test_val = 1;
|
github | aristofanio/freemat-master | test_plot1.m | .m | freemat-master/FreeMat/tests/handle/test_plot1.m | 175 | utf_8 | 45d6961d6824b09440f3b5cc22c4e27e | % test plot of an empty argument
function test_val = test_plot1
a = [];
try
% If this causes a segfault, it won't be caught.
plot(a,a);
catch
end
close all;
test_val = 1;
|
github | aristofanio/freemat-master | ode45.m | .m | freemat-master/PyFM/ode45.m | 9,430 | utf_8 | 5c8323aeb3542e75ee68970d98ab8d28 | % DOCBLOCK num_ode45
function varargout = ode45(f,tspan,y0,options,varargin)
if(nargin<4) options={}; end
abstol=generic_get(options,'AbsTol',1e-6);
reltol=generic_get(options,'RelTol',1e-3);
maxstep=generic_get(options,'MaxStep',(tspan(2)-tspan(1))/10);
h=generic_get(options,'InitialStep',maxstep/100);
stepper=generic... |
github | kilho/NIA-master | drlse_edge.m | .m | NIA-master/lib/drlse_edge.m | 3,517 | utf_8 | 4a8c971268c07e1a13a673e1871e7668 | function phi = drlse_edge(phi_0, g, lambda,mu, alfa, epsilon, timestep, iter, potentialFunction)
% This Matlab code implements an edge-based active contour model as an
% application of the Distance Regularized Level Set Evolution (DRLSE) formulation in Li et al's paper:
%
% C. Li, C. Xu, C. Gui, M. D. Fox, "Dist... |
github | guorongwu/DynamicBC-master | DynamicBC_Spectrum.m | .m | DynamicBC-master/DynamicBC_Spectrum.m | 19,281 | utf_8 | 2c62ba6765d865a59664fc271d21723c | function DynamicBC_Spectrum()
D.fig = figure('Name','Amplitude Spectrum',...
'units','normalized',...
'menubar','none',...
'numbertitle','off',...
'unit','normalized',...
'color',[0.95 0.95 0.95],...
'position',[0.25 0.2 0.4 0.4]);
movegui(D.fig,'center'); ... |
github | guorongwu/DynamicBC-master | DynamicBC_SpectrumGUI.m | .m | DynamicBC-master/DynamicBC_SpectrumGUI.m | 28,726 | utf_8 | 2489d845262625054771cb8bc323775a | function DynamicBC_SpectrumGUI()
%version 2.0 2018.10.20
D.fig = figure('Name','Amplitude Spectrum (v2.0)',...
'units','normalized',...
'menubar','none',...
'numbertitle','off',...
'unit','normalized',...
'color',[0.95 0.95 0.95],...
'position',[0.25 0.2 0.6 0.2... |
github | guorongwu/DynamicBC-master | DynamicBC.m | .m | DynamicBC-master/DynamicBC.m | 45,049 | utf_8 | 03ac8e87d7c7c1fd5c040d818b39481c | function DynamicBC
S.fig = figure('Visible','on',...
'numbertitle','off',...
'menubar','none',... 'units','normalized',...
'color','w',...
'position',[563 98 480 360],...[0.4 0.4 0.309 0.5],...
'name',['DynamicBC Version 2.2(',getenv('USERNAME'),')'],...
'resiz... |
github | guorongwu/DynamicBC-master | DynamicBC_run.m | .m | DynamicBC-master/DynamicBC_run.m | 18,734 | utf_8 | d1f679b0b7dc49a76adaeeb9fc9a0b6e | function DynamicBC_run(F)
%% batch of running DynamicBC
E = F.E;
R = cell2mat(get(E.rd_tvmodel,'val')); %sld,fls
R1 = cell2mat(get(E.rd_rvw,'val')); %voxels ROI FCD
R2 = cell2mat(get(E.rd_mr,'val')); %Default mask, User-Defined Mask,Nifti Label,TXT,Mat
flag_seed_ROI = cell2mat(get(E.rd_seed_ROI,'val'));
Data... |
github | guorongwu/DynamicBC-master | DynamicBC_fls_FC.m | .m | DynamicBC-master/DynamicBC_fls_FC.m | 7,241 | utf_8 | d060bfb4f30012b919b82f892f451bea | function [] = DynamicBC_fls_FC(ROI_sig,mu,save_info)
[nobs,nvar] = size(ROI_sig);
ROI_sig = zscore(ROI_sig);
num0 = ceil(log10(nobs))+2;
% mu=100;
% tic
nii_name = cell(nobs,1);
if save_info.flag_nii % save nii: seed FC,FCD.
data_save = zeros(save_info.v.dim);
v = save_info.v;
v.fname = strcat(sa... |
github | guorongwu/DynamicBC-master | DynamicBC_Cluster.m | .m | DynamicBC-master/DynamicBC_Cluster.m | 17,137 | utf_8 | 2c3771e2cb8774df4579803d39d1acb8 | function DynamicBC_Cluster()
%version 3.0 2018.08.02
D.fig = figure('Name','K-means Clustering (v3.0)',...
'units','normalized',...
'menubar','none',...
'numbertitle','off',...
'unit','normalized',...
'color',[0.95 0.95 0.95],...
'position',[0.25 0.2 0.5 0.2]);
... |
github | guorongwu/DynamicBC-master | DynamicBC_sliding_window_FC.m | .m | DynamicBC-master/DynamicBC_sliding_window_FC.m | 12,512 | utf_8 | 4d1eda3fe128dd60b7b2ed3d4ffdef59 | function [varargout] = DynamicBC_sliding_window_FC(data,window,overlap,pvalue,save_info)
%% calculate sliding window functional connectivity (bivariate)
% overlap = 0.1; %e.g. time bin: [1:50],[46:95], [91:140],...
% window = 50;
[nobs, nvar] = size(data);
step=ceil(window-overlap*window); % 10% overlap
% step=ce... |
github | guorongwu/DynamicBC-master | DynamicBC_dALFF_main.m | .m | DynamicBC-master/DynamicBC_dALFF_main.m | 12,286 | utf_8 | 077a323a606ee21a4fd5470123de07b0 | function [] = DynamicBC_dALFF_main
D.fig = figure('Name','dynamic ALFF analysis',...
'units','normalized',...
'menubar','none',...
'numbertitle','off',...
'unit','normalized',...
'color',[0.95 0.95 0.95],...
'position',[0.25 0.2 0.5 0.4]);
movegui(D.fig,'center'... |
github | guorongwu/DynamicBC-master | DynamicBC_CONGRAMGUI.m | .m | DynamicBC-master/DynamicBC_CONGRAMGUI.m | 19,309 | utf_8 | c006c48e09af9c53df765f8dd4024d38 | function DynamicBC_CONGRAMGUI
% Hsize = get(0,'screensize');
[pat,nam,ext] = fileparts(which('DynamicBC_CONGRAMGUI.m'));
load(fullfile(pat,'COLORMODULE.mat'));
if ~isempty(dir(fullfile(pat,'COLTEMP.mat')))
delete(fullfile(pat,'COLTEMP.mat'));
end
DBCCGG.fig = figure('units','norm','pos',[0.1,0.1,0.6,0.6],'na... |
github | guorongwu/DynamicBC-master | DynamicBC_dALFF.m | .m | DynamicBC-master/DynamicBC_dALFF.m | 5,692 | utf_8 | f55365d26bdfc9cca1132e3f8ec66260 | function [dALFF] = DynamicBC_dALFF(data,window,overlap,save_info)
%%
% save_info.slw_alignment = 1 :
% save_info.TR = ;
% save_info.highcut = ;
% save_info.lowcut = ;
%%
[nobs, nvar] = size(data);
step=ceil(window-overlap*window); % 10% overlap
% step=ceil((1-overlap)*window); % 10% overlap
if ~step||step<0
... |
github | OSHPark/DRCBotV2-master | intersect.m | .m | DRCBotV2-master/model/intersect.m | 693 | utf_8 | 3ab39367ca8463cb1b268a7476708cd7 | function ans = perp(m)
ans = m;
ans(1) = -m(2);
ans(2) = m(1);
endfunction
function ans = mmat(A,B,C,D)
ans = dot((A - B), perp(C - D));
endfunction
function ans = calc_WEC(A,B,C,D)
ans = (A(2) - B(2)) * (C(1) - D(1)) - (A(1) - B(1)) * (C(2) - D(2));
endfunction
function intersect(P1, P2, Q1, Q2)
WEC_P1 = mmat(P... |
github | SteerSuite/steersuite-rutgers-master | OptimizeAlgorithm.m | .m | steersuite-rutgers-master/steerstats/OptimizeAlgorithm.m | 18,321 | utf_8 | 7fb59d771036bda9ceac6cb8dd4ca3f4 |
% matlab -nodesktop -nosplash -r OptimizeAlgorithm
% matlab -nodesktop -nosplash -r "options.opts='steak'; options.ai='ppr'; options.cmaLogFilenamePrefix='CMA_PPR'; OptimizeAlgorithm(options)"
% matlab -nodesktop -nosplash -r "options.opts='steak'; options.ai='ppr'; options.cmaLogFilenamePrefix='data/optimization/CMA... |
github | andrewpaulreeves/soapy-master | readMeta.m | .m | soapy-master/soapy/pyqtgraph/metaarray/readMeta.m | 1,752 | utf_8 | 274fb9beeede592c8b60dc697d518dcd | function f = readMeta(file)
info = hdf5info(file);
f = readMetaRecursive(info.GroupHierarchy.Groups(1));
end
function f = readMetaRecursive(root)
typ = 0;
for i = 1:length(root.Attributes)
if strcmp(root.Attributes(i).Shortname, '_metaType_')
typ = root.Attributes(i).Value.Data;
break
... |
github | karenamckinnon/summer-temperature-distributions-master | pcolorPH.m | .m | summer-temperature-distributions-master/pcolorPH.m | 811 | utf_8 | 91b74ad8bef208959a29f794a6b23c43 | %Front end to pcolor to get it to plot everything
%
% x: x-axis coordinate (vector,constant spacing);
% y: y-axis coordinate (vector, constant spacing);
% z: z-axis coardinate (lengh(x) by length(y) matrix);
%
% x and y should referance the middle of each grid-point.
function pcolorPH(x,y,z);
if min(size... |
github | karenamckinnon/summer-temperature-distributions-master | rq.m | .m | summer-temperature-distributions-master/rq.m | 2,999 | utf_8 | cd9e5384a353d318514f48b2cc816d10 | function b = rq(X, y, p)
% Construct the dual problem of quantile regression
% Solve it with lp_fnm
%
% Function rq_fnm of Daniel Morillo & Roger Koenker
% Found at: http://www.econ.uiuc.edu/~roger/rqn/rq.ox
% Translated from Ox to Matlab by Paul Eilers 1999
%
[m n] = size(X);
u = ones(m, 1);
a = (1 - p) .... |
github | karenamckinnon/summer-temperature-distributions-master | DataHash.m | .m | summer-temperature-distributions-master/DataHash.m | 15,429 | utf_8 | e725a80cb9180de1eb03e47b850a95dc | function Hash = DataHash(Data, Opt)
% DATAHASH - Checksum for Matlab array of any type
% This function creates a hash value for an input of any type. The type and
% dimensions of the input are considered as default, such that UINT8([0,0]) and
% UINT16(0) have different hash values. Nested STRUCTs and CELLs are parsed
%... |
github | karenamckinnon/summer-temperature-distributions-master | xcPH.m | .m | summer-temperature-distributions-master/xcPH.m | 1,077 | utf_8 | 942125d63c74e4d35f8c6ae8fba66e62 | %This function computes the cross correlation (r^2) at zero lag for
%two input records. Records should be sampled at the same
%unifrom intervals.
%
%records y1 and y2, default is r^2, but if type==1 then returns r.
%
%function [XC]=xcPH(y1,y2,type,demean);
%
%y1: 1st record
%y2: 2nd record
%type: ... |
github | jwildey/ec601GPSproject-master | cacode_original.m | .m | ec601GPSproject-master/docs/matlab_gps/hw2/cacode_original.m | 2,271 | utf_8 | efaa9fa72d7a94eb70d6b3cdc45eede2 | % Rizwan Qureshi. HW 2
function g=cacode_original(sv,fs)
% function G=CACODE_ORIGINAL(SV,FS)
% Generates 1023 length C/A Codes for GPS PRNs 1-37
%
%
% g: nx1023 matrix- with each PRN in each row with symbols 1 and 0
% sv: a row or column vector of the SV's to be generated
% valid entries are 1 to 37
%... |
github | gardner-lab/syllable-detector-learn-master | replot_accuracies_concatanated.m | .m | syllable-detector-learn-master/replot_accuracies_concatanated.m | 9,324 | utf_8 | 1967cd49f80482a4ff197c8b8119bf2e | %% Plot the figure of errors for all networks over all trials...
% The input file is created in show_confusion.m. No effort is made to
% ensure that it doesn't contain values for different configurations,
% or even different-sized columns! So if you want to use it, best make
% sure you start by deleting the previous c... |
github | gardner-lab/syllable-detector-learn-master | get_target_offsets.m | .m | syllable-detector-learn-master/get_target_offsets.m | 1,844 | utf_8 | 18b84537265063c4c2367b7d834d788c | % Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free software: you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundatio... |
github | gardner-lab/syllable-detector-learn-master | trigger_max.m | .m | syllable-detector-learn-master/trigger_max.m | 1,236 | utf_8 | 17f10c1b19b673fea01f9faf5caa6d2e | % Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free software: you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundatio... |
github | gardner-lab/syllable-detector-learn-master | suggest_moments_of_interest.m | .m | syllable-detector-learn-master/suggest_moments_of_interest.m | 3,334 | utf_8 | 15d4283c348bba9aa4cf846bffea46e0 | % Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free software: you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundatio... |
github | gardner-lab/syllable-detector-learn-master | load_params.m | .m | syllable-detector-learn-master/load_params.m | 1,569 | utf_8 | f974d701e89e786b66577ef950753c87 | % Place all params from the param file into an output struct. This lets us make sure they're all valid before importing them into
% the real workspace.
% Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free so... |
github | gardner-lab/syllable-detector-learn-master | create_training_set.m | .m | syllable-detector-learn-master/create_training_set.m | 4,149 | utf_8 | e7fa4781954a615fdf4736eba9096dc2 | % Create the training set. But that is actually a bit of a misnomer: it takes all songs and nonsongs and aligns the relevant bits
% of their spectrograms with the correct Y training value. Training and testing will then use disjoint subsets of nnset[XY].
% Copyright (C) 2017 Ben Pearre
%
% This file is part of the Ze... |
github | gardner-lab/syllable-detector-learn-master | load_roboaggregate_file.m | .m | syllable-detector-learn-master/load_roboaggregate_file.m | 7,691 | utf_8 | 01f55f506790f26165fc8c9be9e6e7c2 | % Load data from an aggregate file (song.mat). Loads all the song data, and then only as much nonsong as is needed for the
% specified nonsinging_fraction.
% Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is fre... |
github | gardner-lab/syllable-detector-learn-master | trigger_threshold_cost_continuous.m | .m | syllable-detector-learn-master/trigger_threshold_cost_continuous.m | 3,739 | utf_8 | 4be02c52eef5c2731aad024745d38917 | % Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free software: you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundatio... |
github | gardner-lab/syllable-detector-learn-master | show_confusion.m | .m | syllable-detector-learn-master/show_confusion.m | 3,550 | utf_8 | 9711539fc9fe44539ab3ba6621578c24 | % Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free software: you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundatio... |
github | gardner-lab/syllable-detector-learn-master | trigger_threshold_cost.m | .m | syllable-detector-learn-master/trigger_threshold_cost.m | 2,989 | utf_8 | 3e4b104ac07987a251c122dc9489d89e | % Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free software: you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundatio... |
github | gardner-lab/syllable-detector-learn-master | learn_detector.m | .m | syllable-detector-learn-master/learn_detector.m | 44,068 | utf_8 | e48f2633105ab3ffad9b907bacb7c212 | % learn_detector: train a neural network to detect zebra finch syllables.
%
% Requires data in (by default) 'song.mat', and training configuration
% in 'params.m' and/or parameters given as
% learn_detector('parameter', value) pairs. See README.md for
% instructions.
%
% Copyright (C) 2017 ... |
github | gardner-lab/syllable-detector-learn-master | optimise_network_output_unit_trigger_thresholds.m | .m | syllable-detector-learn-master/optimise_network_output_unit_trigger_thresholds.m | 5,742 | utf_8 | 2deb32fdf8c87479e6eb41e363214fa5 | % Search for optimal thresholds given false-positive vs
% false-negagive weights (the latter := 1).
% Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free software: you can redistribute it and/or
% modify it u... |
github | gardner-lab/syllable-detector-learn-master | trigger.m | .m | syllable-detector-learn-master/trigger.m | 1,546 | utf_8 | faad44f5f7aad569e89e3637ae3064a2 | % Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free software: you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundatio... |
github | gardner-lab/syllable-detector-learn-master | trigger_all.m | .m | syllable-detector-learn-master/trigger_all.m | 1,026 | utf_8 | 64a8102357cd6a755a4a1c56b6254051 | % Copyright (C) 2017 Ben Pearre
%
% This file is part of the Zebra Finch Syllable Detector, syllable-detector-learn.
%
% The Zebra Finch Syllable Detector is free software: you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public License as published by
% the Free Software Foundatio... |
github | gardner-lab/syllable-detector-learn-master | plot_one_spectrogram.m | .m | syllable-detector-learn-master/data_importers/plot_one_spectrogram.m | 2,034 | utf_8 | 68216313c330c8d31e391ea65a6f402f | % Plot a spectrogram from microphone data.
% mic_data: audio data
% fs: sampling frequency
% threshold: OPTIONAL detection threshold; if included, also add 'scores', 'starts' and 'ends'
% scores: Dynamic Timewarping match scores
% starts: points at which ostensible matches start (seconds)
% en... |
github | wertzaj/Machine-Learning-master | submit.m | .m | Machine-Learning-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 | wertzaj/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,787 | utf_8 | 2f09e754e72c1b501aabae667f96ff56 | 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 | wertzaj/Machine-Learning-master | savejson.m | .m | Machine-Learning-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 | wertzaj/Machine-Learning-master | loadjson.m | .m | Machine-Learning-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 | wertzaj/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-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 | wertzaj/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-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 | wertzaj/Machine-Learning-master | submit.m | .m | Machine-Learning-master/JC/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 | wertzaj/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/JC/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 | wertzaj/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/JC/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 | wertzaj/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/JC/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 | wertzaj/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/JC/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 | wertzaj/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/JC/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 | wertzaj/Machine-Learning-master | submit.m | .m | Machine-Learning-master/JC/machine-learning-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 | wertzaj/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/JC/machine-learning-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 | wertzaj/Machine-Learning-master | submit.m | .m | Machine-Learning-master/JC/machine-learning-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 | wertzaj/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/JC/machine-learning-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 | wertzaj/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/JC/machine-learning-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 | wertzaj/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/JC/machine-learning-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 | wertzaj/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/JC/machine-learning-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 | wertzaj/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/JC/machine-learning-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 | wertzaj/Machine-Learning-master | submit.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/submit.m | 1,567 | utf_8 | 1dba733a05282b2db9f2284548483b81 | function submit()
addpath('./lib');
conf.assignmentSlug = 'multi-class-classification-and-neural-networks';
conf.itemName = 'Multi-class Classification and Neural Networks';
conf.partArrays = { ...
{ ...
'1', ...
{ 'lrCostFunction.m' }, ...
'Regularized Logistic Regression', ...
}, ..... |
github | wertzaj/Machine-Learning-master | submitWithConfiguration.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/submitWithConfiguration.m | 3,845 | utf_8 | 1d5c995c41f688757a6f9636ed87a79d | 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 | wertzaj/Machine-Learning-master | savejson.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | wertzaj/Machine-Learning-master | loadjson.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | wertzaj/Machine-Learning-master | loadubjson.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | wertzaj/Machine-Learning-master | saveubjson.m | .m | Machine-Learning-master/machine-learning-ex3/ex3/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | johanga/ml-coursera-stanford-master | submit.m | .m | ml-coursera-stanford-master/machine-learning-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 | johanga/ml-coursera-stanford-master | submitWithConfiguration.m | .m | ml-coursera-stanford-master/machine-learning-ex2/ex2/lib/submitWithConfiguration.m | 3,995 | utf_8 | 74845afdc970e616c3a08a5d413de179 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | johanga/ml-coursera-stanford-master | savejson.m | .m | ml-coursera-stanford-master/machine-learning-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 | johanga/ml-coursera-stanford-master | loadjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | saveubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | submit.m | .m | ml-coursera-stanford-master/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 | johanga/ml-coursera-stanford-master | submitWithConfiguration.m | .m | ml-coursera-stanford-master/machine-learning-ex4/ex4/lib/submitWithConfiguration.m | 3,995 | utf_8 | 74845afdc970e616c3a08a5d413de179 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | johanga/ml-coursera-stanford-master | savejson.m | .m | ml-coursera-stanford-master/machine-learning-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 | johanga/ml-coursera-stanford-master | loadjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | loadubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | saveubjson.m | .m | ml-coursera-stanford-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 | johanga/ml-coursera-stanford-master | submit.m | .m | ml-coursera-stanford-master/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 | johanga/ml-coursera-stanford-master | porterStemmer.m | .m | ml-coursera-stanford-master/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 | johanga/ml-coursera-stanford-master | submitWithConfiguration.m | .m | ml-coursera-stanford-master/machine-learning-ex6/ex6/lib/submitWithConfiguration.m | 3,995 | utf_8 | 74845afdc970e616c3a08a5d413de179 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | johanga/ml-coursera-stanford-master | savejson.m | .m | ml-coursera-stanford-master/machine-learning-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... |
github | johanga/ml-coursera-stanford-master | loadjson.m | .m | ml-coursera-stanford-master/machine-learning-ex6/ex6/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | johanga/ml-coursera-stanford-master | loadubjson.m | .m | ml-coursera-stanford-master/machine-learning-ex6/ex6/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | johanga/ml-coursera-stanford-master | saveubjson.m | .m | ml-coursera-stanford-master/machine-learning-ex6/ex6/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | johanga/ml-coursera-stanford-master | submit.m | .m | ml-coursera-stanford-master/machine-learning-ex7/ex7/submit.m | 1,438 | utf_8 | 665ea5906aad3ccfd94e33a40c58e2ce | function submit()
addpath('./lib');
conf.assignmentSlug = 'k-means-clustering-and-pca';
conf.itemName = 'K-Means Clustering and PCA';
conf.partArrays = { ...
{ ...
'1', ...
{ 'findClosestCentroids.m' }, ...
'Find Closest Centroids (k-Means)', ...
}, ...
{ ...
'2', ...
... |
github | johanga/ml-coursera-stanford-master | submitWithConfiguration.m | .m | ml-coursera-stanford-master/machine-learning-ex7/ex7/lib/submitWithConfiguration.m | 3,995 | utf_8 | 74845afdc970e616c3a08a5d413de179 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | johanga/ml-coursera-stanford-master | savejson.m | .m | ml-coursera-stanford-master/machine-learning-ex7/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
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