Icon for MATLAB .m files in blob content viewer
What does this MR do and why?
- tries to set a file icon based on the language of the file first
- if not provided, the file name is used
- file icon on the blob viewer provides a file language to better match the icon
Note: At this moment only BlobRepository query has the language field. We'll need to add it to Blob query as well, to have the right icon in the tree table.
References
Please include cross links to any resources that are relevant to this MR. This will give reviewers and future readers helpful context to give an efficient review of the changes introduced.
- Icon for MATLAB .m files in repository tree is not appropriate
- Include matlab icon in used file icons
MR acceptance checklist
Please evaluate this MR against the MR acceptance checklist. It helps you analyze changes to reduce risks in quality, performance, reliability, security, and maintainability.
Screenshots or screen recordings
Screenshots are required for UI changes, and strongly recommended for all other merge requests.
| Before | After |
|---|---|
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How to set up and validate locally
Numbered steps to set up and validate the change are strongly suggested.
- Go to a Project/Repository
- Upload an example file below
- Verify the icon on the content viewer.
Example matlab file
function [P,Uinit,output] = cp_arls_lev(X,R,varargin)
%CP_ARLS_LEV CP decomposition of tensor via randomized least squares.
%
% M = CP_ARLS_LEV(X,R) computes an estimate of the best rank-R
% CP model of a sparse or dense tensor X using a randomized alternating
% least-squares algorithm based on levarage-score sampling. The input X
% must be a dense or sparse tensor. The result M is a ktensor.
%
% CP_ARLS_LEV is similar to CP_ARLS except that it works for both dense
% and sparse tensors and requires much less preprocessing.
%
% *Important Note:* The fit computed by CP_ARLS_LEV may be an approximate
% fit, so the stopping conditions are necessarily more conservative. The
% approximation is based on sampling entries from the full tensor and
% estimating the overall fit based on their individual errors.
%
% M = CP_ARLS_LEV(X,R,'param',value,...) specifies optional parameters
% and values. Valid parameters and their default values are:
% o 'thresh' - Hybrid sampling include threshold, empty for none {[]}
% o 'epoch' - Number of iterations between convergence checks {5}
% o 'maxepochs' - Maximum number of epochs {50}
% o 'newitol' - Quit after this many epochs with no improvement {3}
% o 'tol' - Improvement tolerance , i.e., fit - maxfit > tol {1e-4}
% o 'printitn' - Print fit every n epochs; 0 for no printing {10}
% o 'init' - Initial guess [{'random'}|'RRF'|cell array]
% o 'nsamplsq' - Number of least-squares row samples {2^17}
% o 'nsampfit' - Number of samples for estimated fit {[]}
% o 'truefit' - Calculate true fit [{'never'}|'final'|'iter']
% o 'dimorder' - Order to loop through dimensions {1:ndims(A)}
%
% [M,U0] = CP_ARLS_LEV(...) also returns the initial guess.
%
% [M,U0,INFO] = CP_ARLS_LEV(...) also returns additional output that
% contains the input parameters and other information.
%
% Examples:
% info = create_problem('Size',[100 100 100],'Num_Factors',2);
% M = cp_arls_lev(info.Data,2);
%
% REFERENCE: B. W. Larsen and T. G. Kolda.
% Practical Leverage-Based Sampling for Low-Rank Tensor Decompositions.
% SIAM Journal on Matrix Analysis and Applications 43(3):1488-1517, 2022.
% https://doi.org/10.1137/21M1441754
%
% <a href="matlab:web(strcat('file://',...
% fullfile(getfield(what('tensor_toolbox'),'path'),'doc','html',...
% 'cp_arls_lev_doc.html')))">Documentation page for CP-ARLS-LEV</a>
%
% See also CP_ARLS, CP_ALS, KTENSOR, TENSOR.
%
%Tensor Toolbox for MATLAB: <a href="https://www.tensortoolbox.org">www.tensortoolbox.org</a>
% There is an undocumented 'fsampler' option that allows the user to
% pass in a specific sampler method. This is particularly useful for using
% the same set of samples across multiple runs.
% This is the MATLAB Tensor Toolbox by T. Kolda, B. Bader, and others.
% http://www.sandia.gov/~tgkolda/TensorToolbox.
% Copyright (2015) Sandia Corporation. Under the terms of Contract
% DE-AC04-94AL85000, there is a non-exclusive license for use of this
% work by or on behalf of the U.S. Government. Export of this data may
% require a license from the United States Government.
% The full license terms can be found in the file LICENSE.txt
%% Start Timer
main_start = tic;
%% Extract some sizes, etc.
d = ndims(X);
sz = size(X);
normX = norm(X);
tsz = prod(sz);
if isa(X,'sptensor')
nnonzeros = nnz(X);
nzeros = tsz - nnonzeros;
end
preproc_time(1) = toc(main_start);
%% Parse parameters
params = inputParser;
params.addParameter('init', 'random', @(x) (iscell(x) || ismember(x,{'random', 'RRF'})));
params.addParameter('dimorder', 1:d, @(x) isequal(sort(x),1:d));
params.addParameter('printitn', 1, @isscalar);
params.addParameter('nsamplsq', 2^17);
params.addParameter('thresh', []);
params.addParameter('maxepochs', 50);
params.addParameter('tol', 1e-4, @isscalar);
params.addParameter('epoch', 5)
params.addParameter('newitol', 3);
params.addParameter('truefit', 'never', @(x) ismember(x,{'never', 'final', 'iter'}));
params.addParameter('nsampfit', []);
params.addParameter('fsampler', []);
params.parse(varargin{:});
% Copy from params object
fitchangetol = params.Results.tol;
maxepochs = params.Results.maxepochs;
dimorder = params.Results.dimorder;
init = params.Results.init;
printitn = params.Results.printitn;
newitol = params.Results.newitol;
nsamplsq = params.Results.nsamplsq;
thresh = params.Results.thresh;
epochsize = params.Results.epoch;
truefit = params.Results.truefit;
fsamp = params.Results.nsampfit;
fsampler = params.Results.fsampler;
preproc_time(2) = toc(main_start);
%% Preprocessing for sparse: Get fiber indices for each nonzero and each mode
Xfidxs = cell(d,1);
for kk = 1:d
if isa(X,'sptensor')
Xfidxs{kk} = findices(X,kk);
else
Xfidxs{kk} = [];
end
end
preproc_time(3) = toc(main_start);
%% Set up initial guess for U (factor matrices)
if iscell(init)
Uinit = init;
if numel(Uinit) ~= d
error('init does not have %d cells',d);
end
for kk = dimorder(2:end)
if ~isequal(size(Uinit{kk}),[size(X,kk) R])
error('init{%d} is the wrong size',kk);
end
end
init_str = 'user-provided';
else
% Observe that we don't need to calculate an initial guess for the
% first index in dimorder because that will be solved for in the first
% inner iteration.
if strcmp(init,'random')
Uinit = cell(d,1);
for kk = dimorder(2:end)
Uinit{kk} = rand(sz(kk),R);
end
init_str = 'random (uniform)';
elseif strcmp(init,'RRF')
Uinit = cell(d,1);
for kk = dimorder(2:end)
Uinit{kk} = rrf(X, kk, Xfidxs{kk}, R, 100000);
end
init_str = 'randomized range finder';
else
error('The selected initialization method is not supported');
end
end
U = Uinit;
preproc_time(4) = toc(main_start);
%% Calculate initial leverage scores of the factors:
Ulev = cell(d,1);
for kk = 1:d
Ulev{kk} = tt_leverage_scores(U{kk});
end
preproc_time(5) = toc(main_start);
%% Set up residual calculation
if strcmp(truefit, 'iter')
% Calculate true fit
normresfunc = @(P) sqrt( normX^2 + norm(P)^2 - 2 * innerprod(X,P) );
resid_str = 'True residual';
f_str = 'f';
else
% Use estimated fit
[fh, gh] = tt_gcp_fg_setup('Gaussian', X);
% Obtain fsampler
[fsampler_internal, resid_str] = tt_fsampler_setup(X, 'fsampler_type', fsampler, 'fsamp', fsamp);
[fsubs,fvals,fwgts] = fsampler_internal();
normresfunc = @(P) sqrt(tt_gcp_fg_est(normalize(P, 1),fh,gh,...
fsubs,fvals,fwgts,true,false,false,false));
f_str = 'f~';
end
%% Print Welcome Message
if printitn > 0
fprintf('\n');
fprintf('CP-ARLS with Leverage Score Sampling:\n');
fprintf('\n');
fprintf('Tensor size: %s (%d total entries)\n', tt_size2str(size(X)), tsz);
if isa(X,'sptensor')
fprintf('Sparse tensor: %d (%.2g%%) Nonzeros and %d (%.2f%%) Zeros\n', ...
nnonzeros, 100*nnonzeros/tsz, nzeros, 100*nzeros/tsz);
end
fprintf('Finding CP decomposition with R=%d\n', R);
fprintf('Initialization: %s\n', init_str);
fprintf('Fit change tolerance: %.2e\n', fitchangetol);
fprintf('Epoch size: %d\n', epochsize);
fprintf('Max epochs without improvement: %d\n', newitol);
fprintf('Max epochs overall: %d\n', maxepochs);
fprintf('Row samples per solve: %d\n', nsamplsq);
if (isempty(thresh))
fprintf('No deterministic inclusion \n');
else
fprintf('Threshold for deterministic sampling: %d\n', thresh);
end
fprintf('Fit based on: %s\n', resid_str);
fprintf('When to calculate true fit? %s\n', truefit);
fprintf('\n');
end
%% Main Loop: Iterate until convergence
fit = 0; % Set initial fit to zero
maxfit = 0; % best fit seen so far
newi = 0; % number of epochs without improvement
% Initalize trace
fit_trace = zeros(maxepochs+1,1);
res_trace = zeros(maxepochs+1,1);
time_trace = zeros(maxepochs+1,1);
fit_trace(1) = 0;
res_trace(1) = 0;
time_trace(1) = toc(main_start);
%% ALS Loop
for epoch = 1:maxepochs
% Do a bunch of iterations within each epoch
for eiters = 1:epochsize
% Iterate over all d modes of the tensor
for k = dimorder(1:end)
% Sketched linear solve
[Unew, ~] = tt_sampled_solve(X, U, Ulev, k, nsamplsq, [], ...
thresh, Xfidxs{k},0,true);
if issparse(Unew)
Unew = full(Unew); % for the case R=1
end
% Normalize each vector to prevent singularities in coefmatrix
if epoch == 1
lambda = sqrt(sum(abs(Unew).^2,1))'; %2-norm
else
lambda = max( max(abs(Unew),[],1), 1 )'; %max-norm
end
Unew = bsxfun(@rdivide, Unew, lambda');
% Update leverage scores of updated factor
U{k} = Unew;
Ulev{k} = tt_leverage_scores(Unew);
end
end
P = ktensor(lambda, U);
% After each epoch, check convergence conditions
fit_start = tic;
normresidual = normresfunc(P);
fit = 1 - (normresidual / normX);
fit_time = toc(fit_start);
% Record the traces
fit_trace(epoch+1) = fit;
res_trace(epoch+1) = normresidual;
time_trace(epoch+1) = toc(main_start);
% Check convergence
fitchange = fit - maxfit;
if fitchange > fitchangetol
newi = 0;
maxfit = fit;
Psave = P; % Keep the best one seen so far!
else
newi = newi + 1;
end
if (epoch > 1) && (newi >= newitol)
flag = 0;
else
flag = 1;
end
if (mod(epoch,printitn)==0) || ((printitn>0) && (flag==0))
fprintf('Iter %2dx%d: %s = %e f-delta = %6.0e time = %.1fs (fit time = %.1fs) newi = %i\n', ...
epoch, epochsize, f_str, fit, fitchange, time_trace(epoch+1) - time_trace(epoch), fit_time, newi);
end
% Check for convergence
if (flag == 0)
break;
end
end
%% Clean up final result
% Arrange the final tensor so that the columns are normalized.
P = Psave;
P = arrange(P);
P = fixsigns(P); % Fix the signs
% Calculate and output final fit
if printitn > 0
fprintf('\n');
fprintf('Final %s = %e\n', f_str, maxfit);
end
if strcmp(truefit, 'iter')
finalfit = maxfit;
elseif strcmp(truefit, 'final')
normresidual = sqrt( normX^2 + norm(P)^2 - 2 * innerprod(X,P) );
finalfit = 1 - (normresidual / normX);%fraction explained by model
if printitn > 0
fprintf('Final f = %e \n', finalfit);
end
else
finalfit = NaN;
end
total_time = toc(main_start);
if printitn
fprintf('Total time: %.2s\n\n', total_time);
end
%% Save out results
output.params = params.Results;
output.truefit = truefit;
output.preproc_time = preproc_time;
output.iters = epoch;
output.finalfit = finalfit;
output.time_trace = time_trace(1:epoch+1);
output.fit_trace = fit_trace(1:epoch+1);
output.normresidual_trace = res_trace(1:epoch+1);
output.total_time = total_time;
endRelated to #226921 (closed)
Edited by Paulina Sedlak-Jakubowska