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How to use remove_cc method in Kiwi

Best Python code snippet using Kiwi_python

affect.py

Source:affect.py

1function [clu,W_bar,alpha_est,eigenvectors,eigenvalues] ...2	= batch_affect_spectral(ids,W,num_clust,varargin)3% batch_affect_spectral(ids,W,num_clust) performs AFFECT evolutionary4% spectral clustering with adaptively estimated forgetting factor.5% 6% ids is a length T cell array corresponding to the object IDs of the rows7% and columns of the similarity matrices specified by the length T cell8% array W. The object IDs at each time step must be stored in a cell array9% of strings. The similarity matrices can be either full or sparse matrices.10% 11% num_clust specifies the number of clusters and can either be a scalar, 12% vector of length T, or a string specifying the name of a cluster selection13% heuristic to apply. The choices for heuristic are14%	- 'modularity': calculate the first m eigenvectors, perform15%	  spectral clustering with 2 to m clusters, and select the number16%	  of clusters with maximum modularity.17%	- 'silhouette': similar to modularity except that the number of18%	  clusters with the maximum average silhouette index is chosen.19%	- 'eigengap': plot the first m eigenvalues of the Laplacian matrix20%	  obtained from the mean similarity matrix over all time steps and ask21%	  the user to select the number of clusters. This method results in a22%	  fixed number of clusters over all time steps.23%	- 'eigengap_var': plot the first m eigenvalues of the Laplacian matrix24%	  obtained from each similarity matrix. This method allows the number25%	  of clusters to vary over time, but requires the user to select the26%	  number of clusters at each iteration during each time step.27% m is specified through the optional parameter max_clust, described below.28% 29% Additional parameters are specified in ('name',value) pairs, e.g.30% batch_affect_spectral(ids,W,num_clust,'name1',value1,'name2',value2) and31% are as follows:32%	- max_clust (default: 10): Maximum number of clusters for cluster33%	  selection heuristics. Has no effect when number of clusters is34%	  specified by the user.35%	- alpha (default: 'estimate'): The choice of forgetting factor alpha36%	  can be a scalar between 0 and 1 (for constant forgetting factor) or 37%	  the string 'estimate' to adaptively estimate the forgetting factor at38%	  each time step.39%	- num_iter (default: 3): Number of iterations to use when estimating40%	  forgetting factor. Has no effect for constant forgetting factor.41%	- initialize (default: 'previous'): How to initialize iterative42%	  estimation of alpha. Choices are to initialize with the previous43%	  clusters ('previous') or to first perform clustering with alpha = 044%	  ('ordinary'). Has no effect for constant forgetting factor.45%	- objective (default: 'NC'): The spectral clustering objective function46%	  to optimize. Choices are average association ('AA'), ratio cut47%	  ('RC'), and normalized cut ('NC').48%	- eig_type (default: 'lanczos'): The method of eigendecomposition to49%	  use. Choices are full eigendecomposition ('full') or Lanczos50%	  iteration ('lanczos'), which is recommended for sparse matrices.51%	- disc_type (default: 'kmeans'): The method of discretization to use.52%	  Choices are k-means ('kmeans') and Yu and Shi's (2003) method of53%	  orthogonal transformations ('ortho'). 'kmeans' requires the54%	  Statistics Toolbox, and 'ortho' requires Yu and Shi's (2003) NCut55%	  clustering toolbox.56%	- num_reps (default: 1): The number of times the k-means algorithm57%	  should be run to determine the final clustering. Has no effect when58%	  discretizing by orthogonal transformations.59%	- remove_cc (default: false): Set to true to remove all connected60%	  components aside from the giant connected component before performing61%	  eigendecomposition. The clustering result would then contain k + c62%	  clusters, where c is the number of connected components. It is63%	  recommended to set remove_cc to true if there are a lot of such64%	  components, because they will be identified much more quickly than by65%	  eigendecomposition. Requires the Bioinformatics Toolbox.66%	- output (default: 0): Set to 0 to suppress most output messages and to67%	  higher numbers to display progressively more output.68% 69% Additional outputs can be obtained by specifying them as follows: 70% [clu,W_bar,alpha_est,eigenvectors,eigenvalues] = batch_affect_spectral(...)71%	- W_bar: Length T cell array of smoothed similarity matrices.72%	- alpha_est: M-by-T matrix of estimated forgetting factor at each73%	  iteration. M is specified in the second cell of input parameter alpha.74%	- eigenvectors: Length T cell array of leading eigenvectors.75%	- eigenvalues: Length T cell array of leading eigenvalues.76% 77% Author: Kevin Xu7879ip = inputParser;80ip.addRequired('ids',@iscell);81ip.addRequired('W',@iscell);82ip.addRequired('num_clust');83ip.addParamValue('max_clust',10,@(x)floor(x)==x);84ip.addParamValue('alpha','estimate');85ip.addParamValue('num_iter',3,@(x)floor(x)==x);86ip.addParamValue('initialize','previous');87ip.addParamValue('objective','NC');88ip.addParamValue('eig_type','lanczos');89ip.addParamValue('disc_type','kmeans');90ip.addParamValue('num_reps',1);91ip.addParamValue('remove_cc',false,@(x)islogical(x));92ip.addParamValue('output',0,@(x)(floor(x)==x) && (x>=0));93ip.parse(ids,W,num_clust,varargin{:});94max_clust = ip.Results.max_clust;95alpha = ip.Results.alpha;96num_iter = ip.Results.num_iter;97initialize = ip.Results.initialize;98objective = ip.Results.objective;99eig_type = ip.Results.eig_type;100disc_type = ip.Results.disc_type;101num_reps = ip.Results.num_reps;102remove_cc = ip.Results.remove_cc;103output = ip.Results.output;104105t_max = length(W);106% Validate number of clusters107num_ev_plot = 10;	% Number of eigenvalues to plot, if necessary108if isnumeric(num_clust)109	% Scalar or vector specifying number of clusters at each time step110	if isscalar(num_clust)111		num_clust = num_clust*ones(1,t_max);112	end113	assert(length(num_clust) == t_max, ...114		'Length of num_clust must equal the number of time steps');115else116	% Use specified heuristic for choosing number of clusters117	if strcmp(num_clust,'eigengap_var')118		m = 0;119		num_ev_plot = max_clust;120	elseif strcmp(num_clust,'eigengap')121		num_ev_plot = max_clust;122	elseif strcmp(num_clust,'modularity') || strcmp(num_clust, ...123			'silhouette')124		m = 2:max_clust;125	else126		error(['num_clust must be one of ' ...127			'''eigengap'', ''modularity'', or ''silhouette'''])128	end129end130131% Validate alpha and other parameters related to forgetting factor132if isnumeric(alpha)133	num_iter = 0;134	assert((alpha>=0) && (alpha<=1),'alpha must be between 0 and 1');135else136	if ~strcmp(alpha,'estimate')137		error('alpha must either be a number or ''estimate''');138	end139	if ~(strcmp(initialize,'previous') || strcmp(initialize,'ordinary'))140		error('initialize must either be ''previous'' or ''ordinary''')141	end142end143144% Normalize eigenvectors before running k-means for normalized cut spectral145% clustering146if strcmp(objective,'NC')147	norm_ev = true;148else149	norm_ev = false;150end151152% Initialize variable sizes153alpha_est = zeros(num_iter,t_max);154clu = cell(1,t_max);155W_bar = cell(1,t_max);156eigenvectors = cell(1,t_max);157eigenvalues = cell(1,t_max);158159% For the eigengap heuristic compute the mean similarity matrix over all160% time steps and plot the leading eigenvalues161if strcmp(num_clust,'eigengap')162	num_clust = num_clusters_eigengap(ids,W,objective,num_ev_plot) ...163		* ones(1,t_max);164end165166for t = 1:t_max167	if output > 0168		disp(['Processing time step ' int2str(t)])169	end170	171	if isnumeric(num_clust)172		m = num_clust(t);173	end174	175	n = length(ids{t});	176	% Only do temporal smoothing if not the first time step177	if t > 1178		% Identify rows and columns of new objects in current similarity179		% matrix180		[both_tf,both_loc] = ismember(ids{t},ids{t-1});181		W_prev = W_bar{t-1}(both_loc(both_tf),both_loc(both_tf));182		clu_prev = clu{t-1}(both_loc(both_tf));183		new_tf = ~both_tf;184		185		% Initialize smoothed similarity matrix186		if issparse(W{t})187			W_bar{t} = sparse(n,n);188		else189			W_bar{t} = zeros(n,n);190		end191		192		% No smoothing for new objects193		W_bar{t}(new_tf,:) = W{t}(new_tf,:);194		W_bar{t}(:,new_tf) = W{t}(:,new_tf);195		196		% Smoothing for objects present at both time steps197		if strcmp(alpha,'estimate')198			% Initialize current clustering result to be previous clustering199			% result or the result of one run of ordinary clustering200			clu{t} = zeros(n,1);201			if strcmp(initialize,'previous')202				clu{t}(both_tf) = clu_prev;203			else204				[clu{t},eigenvectors{t},eigenvalues{t}] = spectral_cluster ...205					(W{t},m,'objective',objective,'eig_type',eig_type, ...206					'disc_type',disc_type,'num_reps',num_reps,'norm_ev', ...207					norm_ev,'num_ev_plot',num_ev_plot,'remove_cc',remove_cc);208			end209			210			% Estimate alpha iteratively211			for iter = 1:num_iter212				alpha_est(iter,t) = estimate_alpha(W{t}(both_tf,both_tf), ...213					W_prev,clu{t}(both_tf));214				W_bar{t}(both_tf,both_tf) = alpha_est(iter,t)*W_prev ...215					+ (1-alpha_est(iter,t))*W{t}(both_tf,both_tf);216				[clu{t},eigenvectors{t},eigenvalues{t}] = spectral_cluster ...217					(W_bar{t},m,'objective',objective,'eig_type',eig_type, ...218					'disc_type',disc_type,'num_reps',num_reps,'norm_ev', ...219					norm_ev,'num_ev_plot',num_ev_plot,'remove_cc',remove_cc);220			end221		else222			W_bar{t}(both_tf,both_tf) = alpha*W_prev + (1-alpha) ...223				*W{t}(both_tf,both_tf);224			225			% Perform ordinary spectral clustering on W_bar226			[clu{t},eigenvectors{t},eigenvalues{t}] = spectral_cluster ...227				(W_bar{t},m,'objective',objective,'eig_type',eig_type, ...228				'disc_type',disc_type,'num_reps',num_reps,'norm_ev', ...229				norm_ev,'num_ev_plot',num_ev_plot,'remove_cc',remove_cc);			230		end231	else232		W_bar{t} = W{t};233		234		% Perform ordinary spectral clustering235		[clu{t},eigenvectors{t},eigenvalues{t}] = spectral_cluster ...236			(W_bar{t},m,'objective',objective,'eig_type',eig_type, ...237			'disc_type',disc_type,'num_reps',num_reps,'norm_ev', ...238			norm_ev,'num_ev_plot',num_ev_plot,'remove_cc',remove_cc);239	end240	241	% Select optimal number of clusters242	if ~isnumeric(num_clust)243		if strcmp(num_clust,'silhouette')244			[clu{t},avg_width] = select_clu_silhouette(W_bar{t}, ...245				clu{t},'similarity');246		elseif strcmp(num_clust,'modularity')247			[clu{t},Q] = select_clu_modularity(W_bar{t},clu{t},0);248		end249	end250	251	if t > 1	252		% Match clusters (using greedy method if more than 4 clusters)253		k = length(unique(clu{t}));254		if k > 4255			clu{t} = permute_clusters_greedy(ids{t},clu{t},ids{t-1}, ...256				clu{t-1},unmatched);257		else258			clu{t} = permute_clusters_opt(ids{t},clu{t},ids{t-1}, ...259				clu{t-1},unmatched);260		end261		unmatched = max(unmatched,max(clu{t})+1);262	else263		unmatched = max(clu{t})+1;264	end265end266267function k = num_clusters_eigengap(ids,W,objective,num_ev_plot)268269% Create mean similarity matrix over all time steps270W_seq = cell2matseq(ids,[],W,'union');271% W_seq will be a cell array if the similarity matrices are sparse and a272% 3-D matrix if the similarity matrices are full273if iscell(W_seq)274	t_max = length(W_seq);275	n = size(W_seq{1},1);276	W = sparse(n,n);277	for t = 1:t_max278		W = W + W_seq{t}/t_max;279	end280	281	if strcmp(objective,'AA')282		eigenvalues = eigs(W,num_ev_plot,'LA');283	else284		% Add eps to prevent zero entries, which cannot be inverted285		d = sum(abs(W),2) + eps;286		if strcmp(objective,'NC')287			D_inv_sqrt = spdiags(1./sqrt(d),0,n,n);288			L = speye(n) - D_inv_sqrt*W*D_inv_sqrt;289		else290			D = spdiags(d,0,n,n);291			L = D - W;292		end293		L = (L+L')/2;294		eigenvalues = eigs(L,num_ev_plot,'SA');295	end296else297	n = size(W_seq,1);298	W = mean(W_seq,3);299	300	if strcmp(objective,'AA')301		eigenvalues = sort(eig(W),'descend');302	else303		% Add eps to prevent zero entries, which cannot be inverted304		d = sum(abs(W),2) + eps;305		D = diag(d);306		if strcmp(objective,'NC')307			L = eye(n) - D^-0.5*W*D^-0.5;308		else309			L = D - W;310		end311		L = (L+L')/2;312		eigenvalues = sort(eig(L));313	end314end315plot(real(eigenvalues(1:min(n,num_ev_plot))),'x')
...

formats.py

Source:formats.py

...54                # Loop for each member of format catalog55                for j in i.dictvals:56                    # start57                    if i.fmt_type==Format_type.N:58                        f.write(f"\t\t{remove_cc(j[0])} = ")59                    else:60                        f.write(f"\t\t\"{remove_cc(j[0])}\" = ")61                    # label62                    if i.fmt_type == Format_val.I:63                        f.write(f"{remove_cc(j[1])}\n")64                    else:65                        f.write(f'"{remove_cc(j[1])}"\n')66                f.write("\t\t;\n")67            f.write("run;")68    @staticmethod69    def write_comment(fmt):70        # Break text into max segments71        fit_txt=[fmt.desc[i:i+65] for i in range(0, len(fmt.desc), 65)]72        com_txt=[]73        com_txt.append(f"\t*{'-' * 68}*")74        com_txt.append(f"\t|Format: {fmt.name}\n\t|Extensible:{fmt.is_extensible}")75        com_txt.append(f"\t|Description:")76        [com_txt.append(f"\t| {i}") for i in fit_txt]77        com_txt.append(f"\t*{'-' * 68}*;\n")78        return "\n".join(com_txt)79def remove_cc(s):...

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