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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,285 @@ | ||
| function T = cluster_oct(Z, varargin) | ||
| %CLUSTER Construct clusters from a hierarchical cluster tree. [octave inclusion version] | ||
| % T = CLUSTER(Z,'Cutoff',C) constructs clusters from the hierarchical | ||
| % cluster tree represented by Z. Z is a (M-1)-by-3 matrix, generated by | ||
| % LINKAGE. C is a threshold for defining clusters. Starting from the | ||
| % root, CLUSTER steps down through the tree until it encounters a node | ||
| % whose inconsistent value (see INCONSISTENT) is less than C, and whose | ||
| % descendants' values are all less than C. All leaves below that node | ||
| % are grouped into a cluster (a singleton if the node itself is a leaf). | ||
| % CLUSTER follows every branch in the tree until all leaf nodes are in | ||
| % clusters. T is a vector M containing the cluster number for each of | ||
| % the M observations in the original data. | ||
| % | ||
| % T = CLUSTER(Z,'Cutoff',C,'Depth',D) evaluates inconsistent values by | ||
| % looking to a depth D below each node. The default depth is 2. | ||
| % | ||
| % T = CLUSTER(Z,'Cutoff',C,'Criterion','distance') uses distance as the | ||
| % criterion for forming clusters. Each node's height in the tree | ||
| % represents the distance between the two subnodes merged at that | ||
| % node. All leaves below any node whose height is less than C are | ||
| % grouped into a cluster (a singleton if the node itself is a leaf). | ||
| % | ||
| % T = CLUSTER(Z,'Cutoff',C,'Criterion','inconsistent') is equivalent to | ||
| % CLUSTER(Z,'Cutoff',C). | ||
| % | ||
| % T = CLUSTER(Z,'MaxClust',N) constructs a maximum of N clusters using | ||
| % distance as a criterion. Each node's height in the tree represents the | ||
| % distance between the two subnodes merged at that node. CLUSTER finds | ||
| % the smallest height at which a "horizontal cut" through the tree will | ||
| % leave N or fewer clusters. | ||
| % | ||
| % See also PDIST, LINKAGE, COPHENET, INCONSISTENT, CLUSTERDATA. | ||
|
|
||
| % Obsolete syntax: T = CLUSTER(Z, CUTOFF, DEPTH, FLAG). If FLAG is | ||
| % 'clusters', CUTOFF is interpreted as the maximum number of clusters | ||
| % to create. If FLAG is 'inconsistent', CLUSTER splits nodes whose | ||
| % inconsistent values are greater than CUTOFF. If FLAG is 'distance', | ||
| % CLUSTER splits nodes whose distance values are greater than CUTOFF. | ||
|
|
||
| % Copyright 1993-2006 The MathWorks, Inc. | ||
| % $Revision: 1.13.4.3 $ $Date: 2006/10/02 16:34:03 $ | ||
|
|
||
| if nargin < 2 | ||
| error('stats:cluster:TooFewInputs','Not enough input arguments.'); | ||
| end | ||
| depth = 2; | ||
| criterion = 'inconsistent'; | ||
| maxclust = Inf; | ||
| cutoff = NaN; | ||
| inconsistent_set=0; | ||
| % For backward compatibility, handle older syntax | ||
| if ~ischar(varargin{1}) | ||
| cutoff = varargin{1}; | ||
| if nargin >= 3 | ||
| depth = varargin{2};; | ||
| end | ||
| if nargin < 4 | ||
| if (cutoff >= 2) & (cutoff == fix(cutoff)) | ||
| maxclust = cutoff; | ||
| cutoff = NaN; | ||
| end | ||
| else | ||
| flag = varargin{3}; | ||
| j = strmatch(flag, strvcat('clusters','inconsistent','distance')); | ||
| if isempty(j), | ||
| error('stats:cluster:BadFlag',... | ||
| 'FLAG value must be ''clusters'' or ''inconsistent'' or ''distance'''); | ||
| end | ||
| if j==1 | ||
| maxclust = cutoff; | ||
| cutoff = NaN; | ||
| elseif j==3 | ||
| criterion='distance'; | ||
| end | ||
| end | ||
|
|
||
| % Otherwise, must have param/value pairs | ||
| else | ||
| if rem(nargin,2)==0 | ||
| error('stats:cluster:BadNumArgs',... | ||
| 'Incorrect number of arguments to CLUSTER.'); | ||
| end | ||
| okargs = strvcat('cutoff','maxclust','criterion','depth'); | ||
| for j=1:2:nargin-1 | ||
| pname = varargin{j}; | ||
| pval = varargin{j+1}; | ||
| k = strmatch(lower(pname), okargs); | ||
| if isempty(k) | ||
| error('stats:cluster:BadParameter',... | ||
| 'Unknown parameter name: %s.',pname); | ||
| elseif length(k)>1 | ||
| error('stats:cluster:BadParameter',... | ||
| 'Ambiguous parameter name: %s.',pname); | ||
| else | ||
| switch(k) | ||
| case 1 | ||
| cutoff = pval; | ||
| case 2 | ||
| maxclust = pval; | ||
| case 3 | ||
| okcrit = strvcat('inconsistent','distance'); | ||
| kk = strmatch(lower(pval),strvcat('inconsistent','distance')); | ||
| if length(kk)~=1 | ||
| error('stats:cluster:BadCriterion','Bad clustering criterion.'); | ||
| else | ||
| criterion = deblank(okcrit(kk,:)); | ||
| end | ||
| if isequal (criterion, 'inconsistent') | ||
| inconsistent_set=1; | ||
| end | ||
|
|
||
| case 4 | ||
| depth = pval; | ||
| end | ||
| end | ||
| end | ||
| end | ||
|
|
||
| % Check arguments | ||
| if length(maxclust)~=1 | ~isnumeric(maxclust) | (isfinite(maxclust) & maxclust<1) | ||
| error('stats:cluster:BadMaxclust',... | ||
| 'The MAXCLUST value must be a number 1 or larger.'); | ||
| end | ||
| if length(depth)~=1 | ~isnumeric(depth) | ~isfinite(depth) | depth<1 | ||
| error('stats:cluster:BadDepth','The DEPTH value must be at least 1.'); | ||
| end | ||
| if isnan(cutoff) | ||
| if ~isfinite(maxclust) | ||
| error('stats:cluster:MissingParameter',... | ||
| 'Must specify a MAXCLUST or CUTOFF value.'); | ||
| end | ||
| else | ||
| if ~isinf(maxclust) | ||
| error('stats:cluster:ConflictingParameters',... | ||
| 'Must specify a MAXCLUST or CUTOFF value, but not both.'); | ||
| elseif cutoff<=0 | ||
| error('stats:cluster:BadCutoff','CUTOFF must be positive.'); | ||
| end | ||
| end | ||
| if (~isinf(maxclust) && inconsistent_set==1) | ||
| warning('stats:cluster:ConflictingParameters',... | ||
| ['Cannot specify the MAXCLUST option with the INCONSISTENT criterion.\n'... | ||
| 'Using DISTANCE criterion instead.']); | ||
| end | ||
|
|
||
|
|
||
| % Start of algorithm | ||
| m = size(Z,1)+1; | ||
| T = zeros(m,1); | ||
|
|
||
| if isinf(maxclust) % inconsistency or distance cutoff criterion for forming clusters | ||
| if isequal(criterion,'inconsistent') | ||
| Y = inconsistent(Z,depth); | ||
| crit = Y(:,4); | ||
| else | ||
| crit = Z(:,3); % distance criterion | ||
| end | ||
| conn = checkcut(Z, cutoff, crit); | ||
| T = labeltree(Z, conn); | ||
| else % maximum number of clusters based on distance | ||
| if m <= maxclust | ||
| T = (1:m)'; | ||
| elseif maxclust==1 | ||
| T = ones(m,1); | ||
| else | ||
| clsnum = 1; | ||
| for k = (m-maxclust+1):(m-1) | ||
| i = Z(k,1); % left tree | ||
| if i <= m % original node, no leafs | ||
| T(i) = clsnum; | ||
| node(clsnum) = i; | ||
| clsnum = clsnum + 1; | ||
| elseif i < (2*m-maxclust+1) % created before cutoff, search down the tree | ||
| T = clusternum(Z, T, i-m, clsnum); | ||
| node(clsnum) = i; | ||
| clsnum = clsnum + 1; | ||
| end | ||
| i = Z(k,2); % right tree | ||
| if i <= m % original node, no leafs | ||
| T(i) = clsnum; | ||
| node(clsnum) = i; | ||
| clsnum = clsnum + 1; | ||
| elseif i < (2*m-maxclust+1) % created before cutoff, search down the tree | ||
| T = clusternum(Z, T, i-m, clsnum); | ||
| node(clsnum) = i; | ||
| clsnum = clsnum + 1; | ||
| end | ||
| end | ||
| end | ||
| end | ||
|
|
||
| % -------- assign leaves under cluster c to c. | ||
| function T = clusternum(X, T, k, c) | ||
|
|
||
| m = size(X,1)+1; | ||
| while(~isempty(k)) | ||
| % Get the children of nodes at this level | ||
| children = X(k,1:2); | ||
| children = children(:); | ||
|
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||
| % Assign this node number to leaf children | ||
| t = (children<=m); | ||
| T(children(t)) = c; | ||
|
|
||
| % Move to next level | ||
| k = children(~t) - m; | ||
| end | ||
|
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||
|
|
||
| % -------- assign cluster numbers | ||
| function T = labeltree(X,conn) | ||
|
|
||
| n = size(X,1); | ||
| nleaves = n+1; | ||
| T = ones(n+1,1); | ||
|
|
||
| % Each cut potentially yields an additional cluster | ||
| todo = true(n,1); | ||
|
|
||
| % Define cluster numbers for each side of each non-leaf node | ||
| clustlist = reshape(1:2*n,n,2); | ||
|
|
||
| % Propagate cluster numbers down the tree | ||
| while(any(todo)) | ||
| % Work on rows that are now split but not yet processed | ||
| rows = find(todo & ~conn); | ||
| if isempty(rows), break; end | ||
|
|
||
| for j=1:2 % 1=left, 2=right | ||
| children = X(rows,j); | ||
|
|
||
| % Assign cluster number to child leaf node | ||
| leaf = (children <= nleaves); | ||
| if any(leaf) | ||
| T(children(leaf)) = clustlist(rows(leaf),j); | ||
| end | ||
|
|
||
| % Also assign it to both children of any joined child non-leaf nodes | ||
| joint = ~leaf; | ||
| joint(joint) = conn(children(joint)-nleaves); | ||
| if any(joint) | ||
| clustnum = clustlist(rows(joint),j); | ||
| childnum = children(joint) - nleaves; | ||
| clustlist(childnum,1) = clustnum; | ||
| clustlist(childnum,2) = clustnum; | ||
| conn(childnum) = 0; | ||
| end | ||
| end | ||
|
|
||
| % Mark these rows as done | ||
| todo(rows) = 0; | ||
| end | ||
|
|
||
| % Renumber starting from 1 | ||
| [xx1,xx2,T] = unique(T); | ||
|
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||
|
|
||
| % -------- cut the tree at a specified point | ||
| function conn = checkcut(X, cutoff, crit) | ||
|
|
||
| % See which nodes are below the cutoff, disconnect those that aren't | ||
| n = size(X,1)+1; | ||
| conn = (crit <= cutoff); % these are still connected | ||
|
|
||
| % We may still disconnect a node unless all non-leaf children are | ||
| % below the cutoff, and grand-children, and so on | ||
| todo = conn & (X(:,1)>n | X(:,2)>n); | ||
| while(any(todo)) | ||
| rows = find(todo); | ||
|
|
||
| % See if each child is done, or if it requires disconnecting its parent | ||
| cdone = true(length(rows),2); | ||
| for j=1:2 % 1=left child, 2=right child | ||
| crows = X(rows,j); | ||
| t = (crows>n); | ||
| if any(t) | ||
| child = crows(t)-n; | ||
| cdone(t,j) = ~todo(child); | ||
| conn(rows(t)) = conn(rows(t)) & conn(child); | ||
| end | ||
| end | ||
|
|
||
| % Update todo list | ||
| todo(rows(cdone(:,1) & cdone(:,2))) = 0; | ||
| end |
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