どれが最も効率的なソリューションかを調べるために、パフォーマンスを評価するテスト スクリプトを提供します。最初のプロットは vector の長さの増加に対するランタイムを示してrunLengthsいます。エントリは最大長 200 で均一に分散されています。gnovice のソリューションの修正が最も速く、Divakarのソリューションが2位です。
この 2 番目のプロットは、 length の最初の実行が含まれていることを除いて、ほぼ同じテスト データを使用します2000。これは主に両方のソリューションに影響を与えbsxfunますが、他のソリューションはまったく同じように機能します。
テストは、gnoviceの元の回答の変更が最もパフォーマンスが高いことを示唆しています。
自分で速度を比較したい場合は、上記のプロットを生成するために使用したコードを次に示します。
function theLastRunLengthDecodingComputationComparisonYoullEverNeed()
Funcs = {@knedlsepp0, ...
@LuisMendo1bsxfun, ...
@LuisMendo2cumsum, ...
@gnovice3cumsum, ...
@Divakar4replicate_bsxfunmask, ...
@knedlsepp5cumsumaccumarray
};
%% Growing number of runs, low maximum sizes in runLengths
ns = 2.^(1:25);
paramGenerators{1} = arrayfun(@(n) @(){randi(200,n,1)}, ns,'uni',0);
paramGenerators{2} = arrayfun(@(n) @(){[2000;randi(200,n,1)]}, ns,'uni',0);
for i = 1:2
times = compareFunctions(Funcs, paramGenerators{i}, 0.5);
finishedComputations = any(~isnan(times),2);
h = figure('Visible', 'off');
loglog(ns(finishedComputations), times(finishedComputations,:));
legend(cellfun(@func2str,Funcs,'uni',0),'Location','NorthWest','Interpreter','none');
title('Runtime comparison for run length decoding - Growing number of runs');
xlabel('length(runLengths)'); ylabel('seconds');
print(['-f',num2str(h)],'-dpng','-r100',['RunLengthComparsion',num2str(i)]);
end
end
function times = compareFunctions(Funcs, paramGenerators, timeLimitInSeconds)
if nargin<3
timeLimitInSeconds = Inf;
end
times = zeros(numel(paramGenerators),numel(Funcs));
for i = 1:numel(paramGenerators)
Params = feval(paramGenerators{i});
for j = 1:numel(Funcs)
if max(times(:,j))<timeLimitInSeconds
times(i,j) = timeit(@()feval(Funcs{j},Params{:}));
else
times(i,j) = NaN;
end
end
end
end
%% // #################################
%% // HERE COME ALL THE FANCY FUNCTIONS
%% // #################################
function V = knedlsepp0(runLengths, values)
[~,V] = histc(1:sum(runLengths), cumsum([1,runLengths(:).']));%'
if nargin>1
V = reshape(values(V), 1, []);
end
V = shiftdim(V, ~isrow(runLengths));
end
%% // #################################
function V = LuisMendo1bsxfun(runLengths, values)
nn = 1:numel(runLengths);
if nargin==1 %// handle one-input case
values = nn;
end
V = values(nonzeros(bsxfun(@times, nn,...
bsxfun(@le, (1:max(runLengths)).', runLengths(:).'))));
if size(runLengths,1)~=size(values,1) %// adjust orientation of output vector
V = V.'; %'
end
end
%% // #################################
function V = LuisMendo2cumsum(runLengths, values)
if nargin==1 %// handle one-input case
values = 1:numel(runLengths);
end
[ii, ~, jj] = find(runLengths(:));
V(cumsum(jj(end:-1:1))) = 1;
V = values(ii(cumsum(V(end:-1:1))));
if size(runLengths,1)~=size(values,1) %// adjust orientation of output vector
V = V.'; %'
end
end
%% // #################################
function V = gnovice3cumsum(runLengths, values)
isColumnVector = size(runLengths,1)>1;
if nargin==1 %// handle one-input case
values = 1:numel(runLengths);
end
values = reshape(values(runLengths~=0),1,[]);
if isempty(values) %// If there are no runs
V = []; return;
end
runLengths = nonzeros(runLengths(:));
index = zeros(1,sum(runLengths));
index(cumsum([1;runLengths(1:end-1)])) = 1;
V = values(cumsum(index));
if isColumnVector %// adjust orientation of output vector
V = V.'; %'
end
end
%% // #################################
function V = Divakar4replicate_bsxfunmask(runLengths, values)
if nargin==1 %// Handle one-input case
values = 1:numel(runLengths);
end
%// Do size checking to make sure that both values and runlengths are row vectors.
if size(values,1) > 1
values = values.'; %//'
end
if size(runLengths,1) > 1
yes_transpose_output = false;
runLengths = runLengths.'; %//'
else
yes_transpose_output = true;
end
maxlen = max(runLengths);
all_values = repmat(values,maxlen,1);
%// OR all_values = values(ones(1,maxlen),:);
V = all_values(bsxfun(@le,(1:maxlen)',runLengths)); %//'
%// Bring the shape of V back to the shape of runlengths
if yes_transpose_output
V = V.'; %//'
end
end
%% // #################################
function V = knedlsepp5cumsumaccumarray(runLengths, values)
isRowVector = size(runLengths,2)>1;
%// Actual computation using column vectors
V = cumsum(accumarray(cumsum([1; runLengths(:)]), 1));
V = V(1:end-1);
%// In case of second argument
if nargin>1
V = reshape(values(V),[],1);
end
%// If original was a row vector, transpose
if isRowVector
V = V.'; %'
end
end