Faster alternative to containers.Map

12 visualizzazioni (ultimi 30 giorni)
Paolo Binetti
Paolo Binetti il 31 Ago 2017
Commentato: JS2018 il 16 Ott 2022
Profiling a script (attached, along with a sample input data file), I have found that looking up a Map generated with containers.Map is the bottleneck. Namely the table is:
s = containers.Map(nodes, num2cell([1:numel(nodes)]'));
and the script looks it up within a while-loop a few thousands times:
idx = s(temp1); % same as above if s is a Map object
I have tried replacing the Map object with a data structure, but it did not seem to work, due to field name limitations. Are there other faster methods?
  3 Commenti
Paolo Binetti
Paolo Binetti il 1 Set 2017
Thank you. I have just tried it. Referring to my original code (attached to the question), you can create the hash table like this:
t = java.util.Hashtable;
for k = 1:numel(nodes)
t.put(nodes{k}, k);
This takes much longer than creating a Map object, but maybe it can be sped up by vectorizing, if possible.
Then you can simply look the table up as follows:
idx = t.get(temp1);
Unfortunately this takes much longer than looking up the equivalent Matlab object.
Perhaps it would be faster using Python dictionaries in Matlab, but still have not figured out how.
Nikolaus Koopmann
Nikolaus Koopmann il 16 Mar 2022
have you found a solution??
i'm faced with a similar problem. Was going with containers.Map first but it didnt scale. java.uitl.HashTable is just as slow :(

Accedi per commentare.

Risposte (2)

Walter Roberson
Walter Roberson il 1 Set 2017
fid = fopen('dataset_203_2.txt', 'rt');
approx_num_nodes = 3000;
used_nodes = 0;
known_nodes = nan(1, approx_num_nodes);
node_connections = cell(1, approx_num_nodes);
while true
thisline = fgetl(fid);
if ~ischar(thisline); break; end %end of file
toks = regexp(thisline, '^(?<src>\d+)\s*->\s*(?<dst>(\d+,\s*)*\d+)', 'names');
src = str2double(toks.src);
dst = str2double( regexp(toks.dst, ',\s*', 'split') );
mentioned = [src, dst];
[known, idx] = ismember(mentioned, known_nodes);
unknown = ~known;
num_new_nodes = nnz(unknown);
newnodes = used_nodes+(1:num_new_nodes);
known_nodes(newnodes) = mentioned(unknown);
used_nodes = used_nodes + num_new_nodes;
idx(unknown) = newnodes;
node_connections{idx(1)} = idx(2:end);
known_nodes = known_nodes(1:used_nodes);
At the end of this code, known_nodes will be a numeric list of node numbers from the file, in the order encountered, and node_connections will be a cell array of numeric vectors listing all of the connections. The connections listed will be in terms of the indices into the known_nodes list, not in terms of the original node numbers.
Another way of phrasing this is that the known_nodes is something would something you would use for the node labels, but the information in the node_connections list uses internal node numbers. It would be each to reconfigure this for text labels instead of numeric labels.
  3 Commenti
Walter Roberson
Walter Roberson il 1 Set 2017
I did not do any timing tests on this. On my system it executed quickly on the test file.
Taking out the str2double() and fixing up the indexing to suit should speed it a little.
This code does have the disadvantage of calling ismember over and over again. I have it ismember against the complete nan-padded list, so it should take pretty much constant time per pass. In theory comparing against only the part of the array that has been used would make it faster, but in practice the extracting of the used subset would probably negate the gains. You would not want to build upwards with the list getting longer and longer, as then you end up doing reallocations every pass.
Paolo Binetti
Paolo Binetti il 1 Set 2017
Modificato: Paolo Binetti il 1 Set 2017
Thank you. I also had tried a method based on repeated calls of ismember, but it was very slow. I was wondering if it could be improved by using undocumented ismembc2, but I am not sure it's a good approach.

Accedi per commentare.

Mike Croucher
Mike Croucher il 15 Set 2022
MATLAB R2022b has a new dictionary datatype that's much faster than A tutorial-like introduction at An introduction to dictionaries (associative arrays) in MATLAB » The MATLAB Blog - MATLAB & Simulink (
  1 Commento
JS2018 il 16 Ott 2022
Looking forward to your blog post about the differences between and dictionaries!

Accedi per commentare.


Scopri di più su Matrices and Arrays in Help Center e File Exchange


Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by