مشاهده کدهای توابع متلب
- ر متلب، توابع بسیاری وجود دارد که هر کدام، در واقع یک برنامه می باشند که یک یا چند ورودی را دریافت می کنند و یک یا چند خروجی را بر می گردانند. ممکن است نیاز داشته باشیم که کدهای نوشته شده برای یک تابع در متلب را مشاهده کنیم. امکان مشاهده کدهای برخی از توابع داخلی متلب وجود ندارد، اما چنانچه این امکان برای یک تابع متلب فعال باشد، باید از دستور type استفاده کنیم. به مثال زیر توجه کنید : مثال : به عنوان مثال، تابع graphminspantree در متلب، برای حل مسئله minimal spanning tree در یک گراف به کار می رود. چنانچه فردی بخواهد کدهای نوشته شده برای این تابع را مشاهده کند، باید دستور زیر را اجرا کند
- type graphminspantree نتیجه : function [T,pred] = graphminspantree(G,varargin) %GRAPHMINSPANTREE finds the minimal spanning tree in graph. % % [T, PRED] = GRAPHMINSPANTREE(G) finds an acyclic subset of edges that % connects all the nodes in the undirected graph G and for which the total % weight is minimized. Weights of the edges are all nonzero entries in the % lower triangle of the n-by-n sparse matrix G. T is a spanning tree % represented by a sparse matrix. The output PRED contains the predecessor % nodes of the minimal spanning tree with the root node indicated by a % zero. The root defaults to the first node in the largest connected % component, which requires an extra call to the graphconncomp function. % % [T, PRED] = GRAPHMINSPANTREE(G,R) sets the root of the minimal spanning % tree to node R. % % GRAPHMINSPANTREE(…,’METHOD’,METHOD) selects the algorithm to use, % options are: % [‘Prim’] – Prim’s algorithm grows the MST one edge at a time by % adding a minimal edge that connects a node in the % growing MST with any other node. Time complexity is % O(elog(n)). % ‘Kruskal’ – Kruskal’s algorithm grows the MST one edge at a time by % finding an edge that connects two trees in a spreading % forest of growing MSTs. Time complexity is % O(e+xlog(n)) where x is the number of edges no longer % than the longest edge in the MST. % % Note: n and e are number of nodes and edges respectively. % % GRAPHMINSPANTREE(…,’WEIGHTS’,W) provides custom weights for the edges, % useful to indicate zero valued weights. W is a column vector with one % entry for every edge in G, traversed column-wise. % % Remarks: When the graph is unconnected, Prim’s algorithm only returns the % tree that contains R, while Kruskal’s algorithm returns an MST for every % component. % % Example: % % Create an undirected graph with 6 nodes % W = [.41 .29 .51 .32 .50 .45 .38 .32 .36 .29 .21]; % DG = sparse([1 1 2 2 3 4 4 5 5 6 6],[2 6 3 5 4 1 6 3 4 2 5],W) % UG = tril(DG + DG’) % view(biograph(UG,[],’ShowArrows’,’off’,’ShowWeights’,’on’)) % % Find the minimum spanning tree of UG % [ST,pred] = graphminspantree(UG) % view(biograph(ST,[],’ShowArrows’,’off’,’ShowWeights’,’on’)) % % See also: GRAPHALLSHORTESTPATHS, GRAPHCONNCOMP, GRAPHISDAG, % GRAPHISOMORPHISM, GRAPHISSPANTREE, GRAPHMAXFLOW, GRAPHPRED2PATH, % GRAPHSHORTESTPATH, GRAPHTHEORYDEMO, GRAPHTOPOORDER, GRAPHTRAVERSE. % % References: % [1] J. B. Kruskal. “On the shortest spanning subtree of a graph and the % traveling salesman problem” In Proceedings of the American % Mathematical Society, 7:48-50, 1956. % [2] R. Prim. “Shortest connection networks and some generalizations” % Bell System Technical Journal, 36:1389-1401, 1957. % Copyright 2006-2008 The MathWorks, Inc. % Revision:1.1.6.7 Date:2010/09/0213:28:55 algorithms = {‘prim’,’kruskal’}; algorithmkeys = {‘pri’,’kru’}; debug_level = 0; % set defaults of optional input arguments W = []; % no custom weights R = []; % no root given algorithm = 1; % defaults to prim % find out signature of input arguments if nargin>1 && isnumeric(varargin{1}) R = varargin{1}; varargin(1) = []; end % read in optional PV input arguments nvarargin = numel(varargin); if nvarargin if rem(nvarargin,2) == 1 error(‘Bioinfo:graphminspantree:IncorrectNumberOfArguments’,… ‘Incorrect number of arguments to %s.’,mfilename); end okargs = {‘method’,’weights’}; for j=1:2:nvarargin-1 pname = varargin{j}; pval = varargin{j+1}; k = find(strncmpi(pname,okargs,numel(pname))); if isempty(k) error(‘Bioinfo:graphminspantree:UnknownParameterName’,… ‘Unknown parameter name: %s.’,pname); elseif length(k)>1 error(‘Bioinfo:graphminspantree:AmbiguousParameterName’,… ‘Ambiguous parameter name: %s.’,pname); else switch(k) case 1 % ‘method’ algorithm = find(strncmpi(pval,algorithms,numel(pval))); if isempty(algorithm) error(‘Bioinfo:graphminspantree:NotValidMethod’,… ‘String “%s” is not a valid algorithm.’,pval) elseif numel(algorithm)>1 error(‘Bioinfo:graphminspantree:AmbiguousMethod’,… ‘String “%s” is ambiguous.’,pval) end case 2 % ‘weights’ W = pval(:); end end end end % find manually the best root (if it was not given) if isempty(R) [num_comp,classes] = graphconncomp(G,’directed’,false); if num_comp==1 R = 1; else R = find(classes==mode(classes),1,’first’); end end % call the mex implementation of the graph algorithms if nargout>1 if isempty(W) [T,pred] = graphalgs(algorithmkeys{algorithm},debug_level,false,G,R); else [T,pred] = graphalgs(algorithmkeys{algorithm},debug_level,false,G,R,W); end else if isempty(W) T = graphalgs(algorithmkeys{algorithm},debug_level,false,G,R); else T = graphalgs(algorithmkeys{algorithm},debug_level,false,G,R,W); end end مشاهده می کنید که در ابتدای برنامه نیز توضیحاتی نوشته شده است تا به راحتی بتوانید به نحوه عملکرد تابع و کدهای نوشته شده برای آن پی ببرید.
