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 | wmacnair/TreeTop-master | plot_fig.m | .m | TreeTop-master/TreeTop/private/plot_fig.m | 858 | utf_8 | c0d30c22fa37f091bceb6dc215bb3867 | %% plot_fig:
function plot_fig(fig, name_stem, file_ext, fig_size)
plot_file = sprintf('%s.%s', name_stem, file_ext);
% set up figure
units = 'inches';
set_up_figure_size(fig, units, fig_size)
% do plot
switch file_ext
case 'png'
r = 300; % pixels per inch
print(fig, '-dpng', sprintf('-r%d', r)... |
github | wmacnair/TreeTop-master | plot_contingency_table.m | .m | TreeTop-master/TreeTop/private/plot_contingency_table.m | 2,989 | utf_8 | 4476340d0f46aebd6b9dd05bb505a3a5 | %% plot_contingency_table:
function plot_contingency_table(branches, celltypes, row_order, col_order)
% do crosstab of branches against gates
[branch_xtab, ~, ~, labels] = crosstab(branches, celltypes);
% calculate NMI between them
[~, ~, branch_int] = unique(branches);
[~, ~, cell_int] = unique(celltypes);
... |
github | wmacnair/TreeTop-master | fca_readfcs_3_1.m | .m | TreeTop-master/TreeTop/private/fca_readfcs_3_1.m | 19,308 | utf_8 | 5806366bf7585fcde160efc7aeb30af3 | function [fcsdat, fcshdr, fcsdatscaled, fcsdatcomp] = fca_readfcs(filename)
% [fcsdat, fcshdr, fcsdatscaled, fcsdat_comp] = fca_readfcs(filename);
%
%
% Read FCS 2.0 and FCS 3.0 type flow cytometry data file and put the list mode
% parameters to the fcsdat array with the size of [NumOfPar TotalEvents].
% Some import... |
github | wmacnair/TreeTop-master | get_treetop_outputs.m | .m | TreeTop-master/TreeTop/private/get_treetop_outputs.m | 3,876 | utf_8 | 1586e5cc275b9c45526eb6eb5d33c6d4 | %% get_treetop_outputs: open up saved variables from trees section
function [treetop_struct] = get_treetop_outputs(input_struct)
fprintf('getting treetop outputs\n');
% unpack
output_dir = input_struct.output_dir;
save_stem = input_struct.save_stem;
% get all data (= node_idx_array, tree_array, tree_dist_a... |
github | wmacnair/TreeTop-master | fca_readfcs.m | .m | TreeTop-master/TreeTop/private/fca_readfcs.m | 17,790 | utf_8 | e8db0d0a0d4cd9f97addebbf00a99de9 | function [fcsdat, fcshdr, fcsdatscaled, fcsdatcomp] = fca_readfcs(filename)
% [fcsdat, fcshdr, fcsdatscaled, fcsdat_comp] = fca_readfcs(filename);
%
%
% Read FCS 2.0 and FCS 3.0 type flow cytometry data file and put the list mode
% parameters to the fcsdat array with the size of [NumOfPar TotalEvents].
% Some import... |
github | wmacnair/TreeTop-master | all_distance_fn_par.m | .m | TreeTop-master/TreeTop/private/all_distance_fn_par.m | 2,177 | utf_8 | 0111e9215c526a0edbdd98b7ef9feb27 | %% all_distance_fn_par: flexible and memory-safe distance calculation function
% expects X, Y to be formatted as n observations by d features for both
function dist = all_distance_fn_par(X, Y, metric)
switch metric
case 'L1'
dist = comp_dist_L1(X, Y);
case 'L2'
dist = comp_dist_euclidean(X, Y);
case 'a... |
github | wmacnair/TreeTop-master | better_gscatter.m | .m | TreeTop-master/TreeTop/private/better_gscatter.m | 1,701 | utf_8 | 7b9b02b3e7528d59cc93bd3ce63b64c9 | %% better_gscatter: gscatter looks rubbish. this is a bit better
% consider adding ishold stuff
function [h_legend] = better_gscatter(x, y, g, options)
% sort out holding
hold_status = ishold;
if ~hold_status
hold on
end
% get palette
ctype = 'qual';
palette = 'Set1';
point_size = 10;
legend_flag = fal... |
github | wmacnair/TreeTop-master | get_all_files.m | .m | TreeTop-master/TreeTop/private/get_all_files.m | 5,693 | utf_8 | 4289b6b50fc67a156bf66aa6288d9d22 | %% get_all_files: loads all files
function all_struct = get_all_files(input_struct)
% either get from mat file
if isfield(input_struct, 'mat_file')
all_struct = get_from_mat_file(input_struct);
return
end
% or from fcs
% unpack
filenames = input_struct.filenames;
file_annot = input_struct.file_ann... |
github | wmacnair/TreeTop-master | all_distance_fn.m | .m | TreeTop-master/TreeTop/private/all_distance_fn.m | 1,928 | utf_8 | 48c71406add1e518651b4f91e3ab523a | %% all_distance_fn: flexible and memory-safe distance calculation function
% expects X, Y to be formatted as n observations by d features for both
function dist = all_distance_fn(X, Y, metric)
switch metric
case 'L1'
dist = comp_dist_L1(X, Y);
case 'L2'
dist = comp_dist_euclidean(X, Y);
case 'angle'
... |
github | sibirbil/VBYO-master | ex0.m | .m | VBYO-master/kodlar/YuksekBasarimliHesaplama/Matlab/ex0.m | 307 | utf_8 | a46204430cb95f7303b507c1d434a901 | function total = ex0()
clear A;
for i = 1:10
A(i) = i*1000000;
end
B = zeros(10,1);
tic
for i = 1:length(A)
B(i) = f(A(i));
end
total = sum(B);
toc
end
function total = f(n)
total = 0;
for i = 1:n
total = total + 1/i;
end
end
|
github | djangraw/FelixScripts-master | pop_logisticregression_fast.m | .m | FelixScripts-master/MATLAB/LogReg/pop_logisticregression_fast.m | 12,958 | utf_8 | e956b7f27c65b85e6cd62272eff529df | % pop_logisticregression() - Determine linear discriminating vector between two datasets.
% using logistic regression.
%
% Usage:
% >> pop_logisticregression_fast( ALLEEG, datasetlist, chansubset, chansubset2, trainingwindowlength, trainingwindowoffset, regularize, lambda, lambdasearch, eig... |
github | djangraw/FelixScripts-master | rocarea.m | .m | FelixScripts-master/MATLAB/LogReg/rocarea.m | 2,126 | utf_8 | 2be03622d12ae6f9025efde8ba6014d6 | % rocarea() - computes the area under the ROC curve
% If no output arguments are specified
% it will display an ROC curve with the
% Az and approximate fraction correct.
%
% Usage:
% >> [Az,tp,fp,fc]=rocarea(p,label);
%
% Inputs:
% p - classification output
% label - truth labels {0,1}
%
% Outputs... |
github | djangraw/FelixScripts-master | GetLooResults.m | .m | FelixScripts-master/MATLAB/LogReg/GetLooResults.m | 16,151 | utf_8 | 9cb3acac53fa63ba05b692f2cbb1aff8 | function varargout = GetLooResults(varargin)
% GETLOORESULTS M-file for GetLooResults.fig
% GETLOORESULTS, by itself, creates a new GETLOORESULTS or raises the existing
% singleton*.
%
% H = GETLOORESULTS returns the handle to a new GETLOORESULTS or the handle to
% the existing singleton*.
%
% ... |
github | djangraw/FelixScripts-master | pop_logisticregression.m | .m | FelixScripts-master/MATLAB/LogReg/pop_logisticregression.m | 13,157 | utf_8 | 63e70f3159414b889bbb30aae887ba6d | % pop_logisticregression() - Determine linear discriminating vector between two datasets.
% using logistic regression.
%
% Usage:
% >> pop_logisticregression( ALLEEG, datasetlist, chansubset, chansubset2, trainingwindowlength, trainingwindowoffset, regularize, lambda, lambdasearch, eigv... |
github | djangraw/FelixScripts-master | logist.m | .m | FelixScripts-master/MATLAB/LogReg/logist.m | 9,656 | utf_8 | 32dc6838e6797558e29e95e0497192ea | % logist() - Iterative recursive least squares algorithm for linear
% logistic model
%
% Usage:
% >> [v] = logist(x,y,vinit,show,regularize,lambda,lambdasearch,eigvalratio);
%
% Inputs:
% x - N input samples [N,D]
% y - N binary labels [N,1] {0,1}
%
% Optional parameters:
% vinit - initi... |
github | djangraw/FelixScripts-master | bernoull.m | .m | FelixScripts-master/MATLAB/LogReg/bernoull.m | 1,630 | utf_8 | 858cf1a152c7a6041d64ad220a523d34 | % bernoull() - Computes Bernoulli distribution of x for
% "natural parameter" eta. The mean m of a
% Bernoulli distributions relates to eta as,
% m = exp(eta)/(1+exp(eta));
%
% Usage:
% >> [p]=bernoull(x,eta);
%
% Inputs:
% x - data
% eta - distribution parameter
%
% Outputs:
% p - probab... |
github | djangraw/FelixScripts-master | JitteredLR_GUI.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/JitteredLR_GUI.m | 26,769 | utf_8 | 316a0fdded05e8b94a43677296d7978c | function varargout = JitteredLR_GUI(varargin)
% JITTEREDLR_GUI M-file for JitteredLR_GUI.fig
% JITTEREDLR_GUI, by itself, creates a new JITTEREDLR_GUI or raises the existing
% singleton*.
%
% H = JITTEREDLR_GUI returns the handle to a new JITTEREDLR_GUI or the handle to
% the existing singleton*.
%
... |
github | djangraw/FelixScripts-master | GetFinalPosteriors.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/GetFinalPosteriors.m | 8,381 | utf_8 | df09867dec471f5f87f29d98426a6bb4 | function pt = GetFinalPosteriors(subject, weightornoweight, startorend, crossval, suffix)
% Put the posterior distributions of jittered logistic regression results
% into a cell array.
%
% pt = GetFinalPosteriors(subject, resultsfile, startorend)
%
% INPUTS:
% -subject is a string indicating the name of the folder con... |
github | djangraw/FelixScripts-master | setGroupedCrossValidationStruct.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/setGroupedCrossValidationStruct.m | 3,714 | utf_8 | 1415746f28b1603cc15af6f600d5464f | % Automatically separates two eeglab datasets into multi-fold trials.
%
% cv = setGroupedCrossValidationStruct(cvmode,ALLEEG1,ALLEEG2)
%
% INPUTS:
% -cvmode is a string indicating the type of cross-validation to be
% performed. The options are 'nocrossval' (1 fold),'loo' (leave one out),
% or '<x>fold', where x is a w... |
github | djangraw/FelixScripts-master | run_logisticregression_jittered_EM_saccades.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/SaccadeBased/run_logisticregression_jittered_EM_saccades.m | 12,766 | utf_8 | 7b99b4b1e502dada507aeab1ae59be57 | function run_logisticregression_jittered_EM_saccades(outDirName,ALLEEG,setlist, chansubset, saccadeTimes1, saccadeTimes2, scope_settings, pop_settings, logist_settings)
% Perform logistic regression with trial jitter on a dataset.
%
%
% [ALLEEG,v,Azloo,time] = run_logisticregression_jittered_EM_saccades(outDirName,ALLE... |
github | djangraw/FelixScripts-master | eegplugin_lr.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/LogisticRegression/eegplugin_lr.m | 1,962 | utf_8 | 603ec4783c4b9ad94543edcd2d4c2f76 | % eegplugin_lr() - Logistic Regression plugin
%
% Usage:
% >> eegplugin_lr(fig, trystrs, catchstrs);
%
% Inputs:
% fig - [integer] eeglab figure.
% trystrs - [struct] "try" strings for menu callbacks.
% catchstrs - [struct] "catch" strings for menu callbacks.
%
% Authors: Adam Gerson (adg71... |
github | djangraw/FelixScripts-master | pop_logisticregression_fast.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/LogisticRegression/pop_logisticregression_fast.m | 12,958 | utf_8 | e956b7f27c65b85e6cd62272eff529df | % pop_logisticregression() - Determine linear discriminating vector between two datasets.
% using logistic regression.
%
% Usage:
% >> pop_logisticregression_fast( ALLEEG, datasetlist, chansubset, chansubset2, trainingwindowlength, trainingwindowoffset, regularize, lambda, lambdasearch, eig... |
github | djangraw/FelixScripts-master | rocarea.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/LogisticRegression/rocarea.m | 2,126 | utf_8 | 2be03622d12ae6f9025efde8ba6014d6 | % rocarea() - computes the area under the ROC curve
% If no output arguments are specified
% it will display an ROC curve with the
% Az and approximate fraction correct.
%
% Usage:
% >> [Az,tp,fp,fc]=rocarea(p,label);
%
% Inputs:
% p - classification output
% label - truth labels {0,1}
%
% Outputs... |
github | djangraw/FelixScripts-master | logist_fast.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/LogisticRegression/logist_fast.m | 23,146 | utf_8 | d816cebd3b3559f21409e7617ef050f7 | function [ws_loo,ys_loo,Az_loo,Az_perm_dist] = logist_fast(X,y,l2_lambda,mode,varargin)
% X is the feature data (matrix size DxN -- D = # features, N = # trials)
% y are the binary truth labels (vector size Nx1)
% l2_lambda is the l2 regularization value (e.g., 1e-6)
% mode can be either 'loo' or 'permutation'
X = dou... |
github | djangraw/FelixScripts-master | eventnum.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/LogisticRegression/eventnum.m | 2,095 | utf_8 | 19af52530257a702de592f0cbe1bb036 | % eventnum() - converts voltage levels v into integer event number based
% on predefined voltage levels. It also makes sure that discretized value
% is consistent for at least two samples. However, this can not be tested
% for first and last value and so those may not be correct. To fix that
% append the previous... |
github | djangraw/FelixScripts-master | pop_logisticregression.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/LogisticRegression/pop_logisticregression.m | 13,162 | utf_8 | ec54474281f8a17f711f7e32803e0c2e | % pop_logisticregression() - Determine linear discriminating vector between two datasets.
% using logistic regression.
%
% Usage:
% >> pop_logisticregression( ALLEEG, datasetlist, chansubset, chansubset2, trainingwindowlength, trainingwindowoffset, regularize, lambda, lambdasearch, eigv... |
github | djangraw/FelixScripts-master | logist.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/LogisticRegression/logist.m | 9,356 | utf_8 | 9856c6f21dcfcf687525cb7ac3391941 | % logist() - Iterative recursive least squares algorithm for linear
% logistic model
%
% Usage:
% >> [v] = logist(x,y,vinit,show,regularize,lambda,lambdasearch,eigvalratio);
%
% Inputs:
% x - N input samples [N,D]
% y - N binary labels [N,1] {0,1}
%
% Optional parameters:
% vinit - initi... |
github | djangraw/FelixScripts-master | bernoull.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/LogisticRegression/bernoull.m | 1,630 | utf_8 | 858cf1a152c7a6041d64ad220a523d34 | % bernoull() - Computes Bernoulli distribution of x for
% "natural parameter" eta. The mean m of a
% Bernoulli distributions relates to eta as,
% m = exp(eta)/(1+exp(eta));
%
% Usage:
% >> [p]=bernoull(x,eta);
%
% Inputs:
% x - data
% eta - distribution parameter
%
% Outputs:
% p - probab... |
github | djangraw/FelixScripts-master | SetUpMultiWindowJlr_v1p2.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/MultiWindow/SetUpMultiWindowJlr_v1p2.m | 7,578 | utf_8 | f33cb93b3ae998a5ec7afa1f150dd77e | function [vOut,fmOut] = SetUpMultiWindowJlr_v1p2(ALLEEG, trainingwindowlength, trainingwindowoffset, vinit, jitterPrior, pop_settings, logist_settings)
% [vOut] = SetUpMultiWindowJlr_v1p2(ALLEEG, trainingwindowlength, trainingwindowoffset, vinit, pop_settings, logist_settings)
%
% INPUTS:
% -ALLEEG is 1x2, each with D... |
github | djangraw/FelixScripts-master | SetUpMultiWindowJlr_v1p3.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/MultiWindow/Unfinished_v1p3/SetUpMultiWindowJlr_v1p3.m | 7,464 | utf_8 | 1f09490f9076a27e92c77fb9f4423a80 | function [vOut,fmOut] = SetUpMultiWindowJlr_v1p3(ALLEEG, trainingwindowlength, trainingwindowoffset, vinit, jitterPrior, pop_settings, logist_settings)
% [vOut] = SetUpMultiWindowJlr_v1p3(ALLEEG, trainingwindowlength, trainingwindowoffset, vinit, pop_settings, logist_settings)
%
% INPUTS:
% -ALLEEG is 1x2, each with D... |
github | djangraw/FelixScripts-master | run_logisticregression_jittered_EM_gaussian.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/ReactionTimeRecovery/run_logisticregression_jittered_EM_gaussian.m | 13,213 | utf_8 | 99f7596b5bc1c59e5e580eca6862e26e | function run_logisticregression_jittered_EM_gaussian(outDirName,ALLEEG,setlist, chansubset, scope_settings, pop_settings, logist_settings)
% Perform logistic regression with trial jitter on a dataset.
%
%
% [ALLEEG,v,Azloo,time] = run_logisticregression_jittered_EM_gaussian(
% outDirName,ALLEEG,setlist,chansubset,scop... |
github | djangraw/FelixScripts-master | run_logisticregression_jittered_EM_oddball.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/ReactionTimeRecovery/run_logisticregression_jittered_EM_oddball.m | 11,802 | utf_8 | 225039a4fe8e7f7b10dadf5d467f5b89 | function run_logisticregression_jittered_EM_oddball(outDirName,ALLEEG,setlist, chansubset, scope_settings, pop_settings, logist_settings)
% Perform logistic regression with trial jitter on a dataset.
%
%
% [ALLEEG,v,Azloo,time] = run_logisticregression_jittered_EM_oddball(
% outDirName,ALLEEG,setlist,chansubset,scope_... |
github | djangraw/FelixScripts-master | GetFinalPosteriors_gaussian.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/ReactionTimeRecovery/GetFinalPosteriors_gaussian.m | 6,840 | utf_8 | ff8fae23f29e568b7ae2fbdb5f5b5811 | function [pt, truth_trials, times, jitter] = GetFinalPosteriors_gaussian(foldername,cvmode,subject)
% Put the posterior distributions of jittered logistic regression results
% into a cell array.
%
% pt = GetFinalPosteriors_gaussian(foldername,cvmode,subject)
%
% INPUTS:
% -foldername is a string indicating the folder ... |
github | djangraw/FelixScripts-master | GetFinalPosteriors_oddball.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/ReactionTimeRecovery/GetFinalPosteriors_oddball.m | 6,384 | utf_8 | 425a4a16fa34115cd466209f11313a2f | function [pt, truth, times, jitter] = GetFinalPosteriors_oddball(foldername,cvmode,subject)
% Put the posterior distributions of jittered logistic regression results
% into a cell array.
%
% pt = GetFinalPosteriors_oddball(subject, resultsfile, startorend)
%
% INPUTS:
% -foldername is a string indicating the folder in... |
github | djangraw/FelixScripts-master | RerunJlrTesting.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/ReactionTimeRecovery/PlotResults/GetResults/RerunJlrTesting.m | 3,631 | utf_8 | 9cf1fecbead8027aba0ecfe3b1762c9f | function [Azloo,posterior,posterior2] = RerunJlrTesting(JLR,JLP,pop_settings)
% Reruns just the test part of Jittered LR.
%
% function RerunTest_JLR(JLR,JLP)
%
% INPUTS:
% -JLR and JLP are the outputs of LoadJlrResults
%
% Created 10/1/12 by DJ.
if nargin<3
pop_settings = JLR.pop_settings_out;
elseif numel(pop_se... |
github | djangraw/FelixScripts-master | RecalculateForwardModel_v3p1.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/ReactionTimeRecovery/PlotResults/GetResults/RecalculateForwardModel_v3p1.m | 6,589 | utf_8 | 49c9c1e9b9624ba333f536f701115ce9 | function [fwdmodels,fwdmodels_v2,fwdmodels_old] = RecalculateForwardModel_v3p1(JLR,JLP)
% Created 9/28/12 by DJ.
nWin = numel(JLR.trainingwindowoffset);
nFolds = JLP.cv.numFolds;
trainingwindowlength = JLR.trainingwindowlength;
trainingwindowoffset = JLR.trainingwindowoffset;
UnpackStruct(JLP.pop_settings); % jitterr... |
github | djangraw/FelixScripts-master | RecalculateForwardModel.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/ReactionTimeRecovery/PlotResults/GetResults/RecalculateForwardModel.m | 6,255 | utf_8 | fafbff3bee99d4fa90b9ae2267d1e59b | function [fwdmodels,fwdmodels_old] = RecalculateForwardModel(JLR,JLP)
% Created 9/28/12 by DJ.
nWin = numel(JLR.trainingwindowoffset);
nFolds = JLP.cv.numFolds;
trainingwindowlength = JLR.trainingwindowlength;
trainingwindowoffset = JLR.trainingwindowoffset;
UnpackStruct(JLP.pop_settings); % jitterrange,weightprior,f... |
github | djangraw/FelixScripts-master | TEMP_TestWithTrueWeights.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/ReactionTimeRecovery/OneTimeScripts/TEMP_TestWithTrueWeights.m | 5,319 | utf_8 | 9ad545efb03adcb6d7737719dc96ef8c | % TEMP_TestWithTrueWeights.m
function TEMP_TestWithTrueWeights(x,ptprior,truth,pop_settings,ALLEEG,nully)
plotbinolike = 1;
convergencethreshold = 0.0100;
jitterrange = [-500 500];
weightprior = 0;
forceOneWinner = 0;
conditionPrior = 0;
null_sigmamultiplier = 1;
... |
github | djangraw/FelixScripts-master | logist_weighted_betatest_v3p0.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/New_v3/logist_weighted_betatest_v3p0.m | 15,269 | utf_8 | 874df08121f7efb64f87b0fcfa0912a9 | % logist_weighted_betatest_v3p0() - Iterative recursive least squares
% algorithm for linear logistic model
%
% Usage:
% >> [v] =
% logist_weighted_betatest_v3p0(x,y,vinit,d,show,regularize,lambda,lambdasearch,eigvalratio,posteriorOpts,trialnum,ptprior,useOffset)
%
% Inputs:
% x - N input samples ... |
github | djangraw/FelixScripts-master | logist_weighted_betatest_v3p1.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/New_v3/logist_weighted_betatest_v3p1.m | 8,564 | utf_8 | 16ae31a125f7a66bfa78ab3de26db595 | % logist_weighted_betatest_v3p0() - Iterative recursive least squares
% algorithm for linear logistic model
%
% Usage:
% >> [v] =
% logist_weighted_betatest_v3p1(x,y,vinit,d,show,regularize,lambda,lambdasearch,eigvalratio,useOffset)
%
% Inputs:
% x - N input samples [N,D]
% y - N binary labels ... |
github | djangraw/FelixScripts-master | pop_logisticregression_jittered_EM_v3p0.m | .m | FelixScripts-master/MATLAB/JitteredLR/2013_02_06-DropboxCode/New_v3/pop_logisticregression_jittered_EM_v3p0.m | 26,764 | utf_8 | 78c407f8b0a751842b43752694beb57b | % pop_logisticregression_jittered_EM_v3p0() - Determine linear discriminating vector between two datasets.
% using logistic regression on data where jitter of
% each trial is uncertain with Expectation
% Maximization
%
% Usage:
% >> pop_logisticregress... |
github | djangraw/FelixScripts-master | distinguishable_colors.m | .m | FelixScripts-master/MATLAB/FormatFigures/distinguishable_colors.m | 5,753 | utf_8 | 57960cf5d13cead2f1e291d1288bccb2 | function colors = distinguishable_colors(n_colors,bg,func)
% DISTINGUISHABLE_COLORS: pick colors that are maximally perceptually distinct
%
% When plotting a set of lines, you may want to distinguish them by color.
% By default, Matlab chooses a small set of colors and cycles among them,
% and so if you have more than ... |
github | SoheilFeizi/Tensor-Biclustering-master | th_ind_fibers.m | .m | Tensor-Biclustering-master/th_ind_fibers.m | 837 | utf_8 | eab514d9abaedf43e26bc3382b81f1f1 | %function [J1,J2]=th_ind_fibers(T,k1,k2)
% T is the input tensor of size n1 x n2 x m
% |J_1|=k1 and |J_2|=k2
% J1: subspace row index set
% J2: subspace column index set
% Ref: Tensor Biclustering
% By Soheil Feizi, Hamid Javadi and David Tse
% NIPS 2017
%**************************************
function [J1,J2]=th_in... |
github | SoheilFeizi/Tensor-Biclustering-master | th_sum_fibers.m | .m | Tensor-Biclustering-master/th_sum_fibers.m | 614 | utf_8 | 1319df50e3f3281535fe04455ee20020 | %function [J1,J2]=th_sum_fibers(T,k1,k2)
% T is the input tensor of size n1 x n2 x m
% |J_1|=k1 and |J_2|=k2
% J1: subspace row index set
% J2: subspace column index set
% Ref: Tensor Biclustering
% By Soheil Feizi, Hamid Javadi and David Tse
% NIPS 2017
%**************************************
function [J1,J2]=th_su... |
github | SoheilFeizi/Tensor-Biclustering-master | tensor_unfolding_spectral.m | .m | Tensor-Biclustering-master/tensor_unfolding_spectral.m | 983 | utf_8 | ae4e4ae7168b61447feb4f446f0930e4 | %function [J1,J2,T_uf,tt]=tensor_unfolding_spectral(T,k1,k2)
% T is the input tensor of size n1 x n2 x m
% |J_1|=k1 and |J_2|=k2
% T_uf: unfolded tensor
% J1: subspace row index set
% J2: subspace column index set
% Ref: Tensor Biclustering
% By Soheil Feizi, Hamid Javadi and David Tse
% NIPS 2017
%****************... |
github | SoheilFeizi/Tensor-Biclustering-master | tensor_folding_spectral.m | .m | Tensor-Biclustering-master/tensor_folding_spectral.m | 865 | utf_8 | 56b0207f0febeb85ebb2a68876cba8eb | %function [J1,J2,T_f1,T_f2]=tensor_folding_spectral(T,k1,k2)
% T is the input tensor of size n1 x n2 x m
% |J_1|=k1 and |J_2|=k2
% T_f1: folded tensor on rows
% T_f2: folded tensor on columns
% J1: subspace row index set
% J2: subspace column index set
% Ref: Tensor Biclustering
% By Soheil Feizi, Hamid Javadi and D... |
github | mjlaine/dlm-master | dlmfit.m | .m | dlm-master/dlmfit.m | 9,456 | utf_8 | 16a0acd5157835eac146e360c0434b1c | function out = dlmfit(y,s,wdiag,x0,C0, X, options)
%DLMFIT Fit DLM time series model
% Fits dlm time series model with local level, trend, seasonal, and proxies
% out = dlmfit(y,s,wdiag,x0,C0, X, options)
% Input:
% y time series, n*p
% s obs uncertainty, n*p or 1*1
% w sqrt of first diagonal entries of the model erro... |
github | mjlaine/dlm-master | dlmtsfit.m | .m | dlm-master/dlmtsfit.m | 4,598 | utf_8 | 7e6455cae73adee4ca81690c60f6759a | function out=dlmtsfit(t,y,s,X,options)
% Fit a time series model
% t time in matlab format
% y n*p data matrix
% s n*p std uncertainty
% X n*nx proxy variables
if nargin < 4
X = [];
end
if nargin < 5
options = [];
end
% remove NaN's from the begining and end
i0 = isnan(s) | s<=0;
y(i0) = NaN;
if size(y,2)>1
i1... |
github | mjlaine/dlm-master | dlmdisp.m | .m | dlm-master/dlmdisp.m | 1,219 | utf_8 | c95234faa3b98756663e9c521151e59d | function out = dlmdisp(dlm)
%DLMDISP print information about the DLM fit
% Marko Laine <marko.laine@fmi.fi>
% $Revision: 0.0 $ $Date: 2015/06/03 12:00:00 $
if not(isstruct(dlm)) || not(strcmp(dlm.class,'dlmfit')||strcmp(dlm.class,'dlmsmo'))
error('works only for dlm output structure');
end
fprintf('DLM model ou... |
github | bodlaranjithkumar/MachineLearning-master | submit.m | .m | MachineLearning-master/Week8 - K-Means Clustering, PCA/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 | bodlaranjithkumar/MachineLearning-master | submitWithConfiguration.m | .m | MachineLearning-master/Week8 - K-Means Clustering, PCA/ex7/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | bodlaranjithkumar/MachineLearning-master | savejson.m | .m | MachineLearning-master/Week8 - K-Means Clustering, PCA/ex7/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | bodlaranjithkumar/MachineLearning-master | loadjson.m | .m | MachineLearning-master/Week8 - K-Means Clustering, PCA/ex7/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | bodlaranjithkumar/MachineLearning-master | loadubjson.m | .m | MachineLearning-master/Week8 - K-Means Clustering, PCA/ex7/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | bodlaranjithkumar/MachineLearning-master | saveubjson.m | .m | MachineLearning-master/Week8 - K-Means Clustering, PCA/ex7/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | bodlaranjithkumar/MachineLearning-master | submit.m | .m | MachineLearning-master/Week3 - Logistic Regression/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 | bodlaranjithkumar/MachineLearning-master | submitWithConfiguration.m | .m | MachineLearning-master/Week3 - Logistic Regression/ex2/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | bodlaranjithkumar/MachineLearning-master | savejson.m | .m | MachineLearning-master/Week3 - Logistic Regression/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 | bodlaranjithkumar/MachineLearning-master | loadjson.m | .m | MachineLearning-master/Week3 - Logistic Regression/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 | bodlaranjithkumar/MachineLearning-master | loadubjson.m | .m | MachineLearning-master/Week3 - Logistic Regression/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 | bodlaranjithkumar/MachineLearning-master | saveubjson.m | .m | MachineLearning-master/Week3 - Logistic Regression/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 | bodlaranjithkumar/MachineLearning-master | submit.m | .m | MachineLearning-master/Week6 - Regularized Linear Regression and Bias or Variance/ex5/submit.m | 1,765 | utf_8 | b1804fe5854d9744dca981d250eda251 | function submit()
addpath('./lib');
conf.assignmentSlug = 'regularized-linear-regression-and-bias-variance';
conf.itemName = 'Regularized Linear Regression and Bias/Variance';
conf.partArrays = { ...
{ ...
'1', ...
{ 'linearRegCostFunction.m' }, ...
'Regularized Linear Regression Cost Fun... |
github | bodlaranjithkumar/MachineLearning-master | submitWithConfiguration.m | .m | MachineLearning-master/Week6 - Regularized Linear Regression and Bias or Variance/ex5/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | bodlaranjithkumar/MachineLearning-master | savejson.m | .m | MachineLearning-master/Week6 - Regularized Linear Regression and Bias or Variance/ex5/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | bodlaranjithkumar/MachineLearning-master | loadjson.m | .m | MachineLearning-master/Week6 - Regularized Linear Regression and Bias or Variance/ex5/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | bodlaranjithkumar/MachineLearning-master | loadubjson.m | .m | MachineLearning-master/Week6 - Regularized Linear Regression and Bias or Variance/ex5/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | bodlaranjithkumar/MachineLearning-master | saveubjson.m | .m | MachineLearning-master/Week6 - Regularized Linear Regression and Bias or Variance/ex5/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | bodlaranjithkumar/MachineLearning-master | submit.m | .m | MachineLearning-master/Week9 - Anomaly Detection and Recommender Systems/ex8/submit.m | 2,135 | utf_8 | eebb8c0a1db5a4df20b4c858603efad6 | function submit()
addpath('./lib');
conf.assignmentSlug = 'anomaly-detection-and-recommender-systems';
conf.itemName = 'Anomaly Detection and Recommender Systems';
conf.partArrays = { ...
{ ...
'1', ...
{ 'estimateGaussian.m' }, ...
'Estimate Gaussian Parameters', ...
}, ...
{ ...... |
github | bodlaranjithkumar/MachineLearning-master | submitWithConfiguration.m | .m | MachineLearning-master/Week9 - Anomaly Detection and Recommender Systems/ex8/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | bodlaranjithkumar/MachineLearning-master | savejson.m | .m | MachineLearning-master/Week9 - Anomaly Detection and Recommender Systems/ex8/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 | bodlaranjithkumar/MachineLearning-master | loadjson.m | .m | MachineLearning-master/Week9 - Anomaly Detection and Recommender Systems/ex8/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 | bodlaranjithkumar/MachineLearning-master | loadubjson.m | .m | MachineLearning-master/Week9 - Anomaly Detection and Recommender Systems/ex8/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 | bodlaranjithkumar/MachineLearning-master | saveubjson.m | .m | MachineLearning-master/Week9 - Anomaly Detection and Recommender Systems/ex8/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 | bodlaranjithkumar/MachineLearning-master | submit.m | .m | MachineLearning-master/Week7 - Support Vector Machines and Kernels/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 | bodlaranjithkumar/MachineLearning-master | porterStemmer.m | .m | MachineLearning-master/Week7 - Support Vector Machines and Kernels/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 | bodlaranjithkumar/MachineLearning-master | submitWithConfiguration.m | .m | MachineLearning-master/Week7 - Support Vector Machines and Kernels/ex6/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | bodlaranjithkumar/MachineLearning-master | savejson.m | .m | MachineLearning-master/Week7 - Support Vector Machines and Kernels/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 | bodlaranjithkumar/MachineLearning-master | loadjson.m | .m | MachineLearning-master/Week7 - Support Vector Machines and Kernels/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 | bodlaranjithkumar/MachineLearning-master | loadubjson.m | .m | MachineLearning-master/Week7 - Support Vector Machines and Kernels/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 | bodlaranjithkumar/MachineLearning-master | saveubjson.m | .m | MachineLearning-master/Week7 - Support Vector Machines and Kernels/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 | bodlaranjithkumar/MachineLearning-master | submit.m | .m | MachineLearning-master/Week4 - Neural Networks Representation/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 | bodlaranjithkumar/MachineLearning-master | submitWithConfiguration.m | .m | MachineLearning-master/Week4 - Neural Networks Representation/ex3/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | bodlaranjithkumar/MachineLearning-master | savejson.m | .m | MachineLearning-master/Week4 - Neural Networks Representation/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 | bodlaranjithkumar/MachineLearning-master | loadjson.m | .m | MachineLearning-master/Week4 - Neural Networks Representation/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 | bodlaranjithkumar/MachineLearning-master | loadubjson.m | .m | MachineLearning-master/Week4 - Neural Networks Representation/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 | bodlaranjithkumar/MachineLearning-master | saveubjson.m | .m | MachineLearning-master/Week4 - Neural Networks Representation/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 | bodlaranjithkumar/MachineLearning-master | submit.m | .m | MachineLearning-master/Week5 - Neural Networks 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 | bodlaranjithkumar/MachineLearning-master | submitWithConfiguration.m | .m | MachineLearning-master/Week5 - Neural Networks Learning/ex4/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | bodlaranjithkumar/MachineLearning-master | savejson.m | .m | MachineLearning-master/Week5 - Neural Networks Learning/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 | bodlaranjithkumar/MachineLearning-master | loadjson.m | .m | MachineLearning-master/Week5 - Neural Networks Learning/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 | bodlaranjithkumar/MachineLearning-master | loadubjson.m | .m | MachineLearning-master/Week5 - Neural Networks Learning/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 | bodlaranjithkumar/MachineLearning-master | saveubjson.m | .m | MachineLearning-master/Week5 - Neural Networks Learning/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 | bodlaranjithkumar/MachineLearning-master | submit.m | .m | MachineLearning-master/Week2 - Linear Regression/ex1/submit.m | 1,876 | utf_8 | 8d1c467b830a89c187c05b121cb8fbfd | function submit()
addpath('./lib');
conf.assignmentSlug = 'linear-regression';
conf.itemName = 'Linear Regression with Multiple Variables';
conf.partArrays = { ...
{ ...
'1', ...
{ 'warmUpExercise.m' }, ...
'Warm-up Exercise', ...
}, ...
{ ...
'2', ...
{ 'computeCost.m... |
github | bodlaranjithkumar/MachineLearning-master | submitWithConfiguration.m | .m | MachineLearning-master/Week2 - Linear Regression/ex1/lib/submitWithConfiguration.m | 5,562 | utf_8 | 4ac719ea6570ac228ea6c7a9c919e3f5 | function submitWithConfiguration(conf)
addpath('./lib/jsonlab');
parts = parts(conf);
fprintf('== Submitting solutions | %s...\n', conf.itemName);
tokenFile = 'token.mat';
if exist(tokenFile, 'file')
load(tokenFile);
[email token] = promptToken(email, token, tokenFile);
else
[email token] = p... |
github | bodlaranjithkumar/MachineLearning-master | savejson.m | .m | MachineLearning-master/Week2 - Linear Regression/ex1/lib/jsonlab/savejson.m | 17,462 | utf_8 | 861b534fc35ffe982b53ca3ca83143bf | function json=savejson(rootname,obj,varargin)
%
% json=savejson(rootname,obj,filename)
% or
% json=savejson(rootname,obj,opt)
% json=savejson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a JSON (JavaScript
% Object Notation) string
%
% author: Qianqian Fa... |
github | bodlaranjithkumar/MachineLearning-master | loadjson.m | .m | MachineLearning-master/Week2 - Linear Regression/ex1/lib/jsonlab/loadjson.m | 18,732 | ibm852 | ab98cf173af2d50bbe8da4d6db252a20 | function data = loadjson(fname,varargin)
%
% data=loadjson(fname,opt)
% or
% data=loadjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2011/09/09, including previous works from
%
% ... |
github | bodlaranjithkumar/MachineLearning-master | loadubjson.m | .m | MachineLearning-master/Week2 - Linear Regression/ex1/lib/jsonlab/loadubjson.m | 15,574 | utf_8 | 5974e78e71b81b1e0f76123784b951a4 | function data = loadubjson(fname,varargin)
%
% data=loadubjson(fname,opt)
% or
% data=loadubjson(fname,'param1',value1,'param2',value2,...)
%
% parse a JSON (JavaScript Object Notation) file or string
%
% authors:Qianqian Fang (fangq<at> nmr.mgh.harvard.edu)
% created on 2013/08/01
%
% $Id: loadubjson.m 460 2015-01-... |
github | bodlaranjithkumar/MachineLearning-master | saveubjson.m | .m | MachineLearning-master/Week2 - Linear Regression/ex1/lib/jsonlab/saveubjson.m | 16,123 | utf_8 | 61d4f51010aedbf97753396f5d2d9ec0 | function json=saveubjson(rootname,obj,varargin)
%
% json=saveubjson(rootname,obj,filename)
% or
% json=saveubjson(rootname,obj,opt)
% json=saveubjson(rootname,obj,'param1',value1,'param2',value2,...)
%
% convert a MATLAB object (cell, struct or array) into a Universal
% Binary JSON (UBJSON) binary string
%
% author... |
github | atcemgil/VBYO-master | ex0.m | .m | VBYO-master/kodlar/YuksekBasarimliHesaplama/Matlab/ex0.m | 307 | utf_8 | a46204430cb95f7303b507c1d434a901 | function total = ex0()
clear A;
for i = 1:10
A(i) = i*1000000;
end
B = zeros(10,1);
tic
for i = 1:length(A)
B(i) = f(A(i));
end
total = sum(B);
toc
end
function total = f(n)
total = 0;
for i = 1:n
total = total + 1/i;
end
end
|
github | sania-irfan/Document-Layout-Analysis-MATLAB-master | DAP.m | .m | Document-Layout-Analysis-MATLAB-master/Document_layout-code/DAP.m | 6,431 | utf_8 | 539fb9c917f14560546d477433272782 | function varargout = DAP(varargin)
% DAP MATLAB code for DAP.fig
% DAP, by itself, creates a new DAP or raises the existing
% singleton*.
%
% H = DAP returns the handle to a new DAP or the handle to
% the existing singleton*.
%
% DAP('CALLBACK',hObject,eventData,handles,...) calls the local
% ... |
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