Inheritance diagram for nipy.algorithms.clustering.hierarchical_clustering:
These routines perform some hierrachical agglomerative clustering of some input data. The following alternatives are proposed: - Distance based average-link - Similarity-based average-link - Distance based maximum-link - Ward’s algorithm under graph constraints - Ward’s algorithm without graph constraints
In this latest version, the results are returned in a ‘WeightedForest’ structure, which gives access to the clustering hierarchy, facilitates the plot of the result etc.
For back-compatibility, *_segment versions of the algorithms have been appended, with the old API (except the qmax parameter, which now represents the number of wanted clusters)
Author : Bertrand Thirion,Pamela Guevara, 2006-2009
Bases: nipy.algorithms.graph.forest.Forest
This is a weighted Forest structure, i.e. a tree - each node has one parent and children (hierarchical structure) - some of the nodes can be viewed as leaves, other as roots - the edges within a tree are associated with a weight: +1 from child to parent -1 from parent to child - additionally, the nodes have a value, which is called ‘height’, especially useful from dendrograms
Methods
Parameters : | V: the number of edges of the graph : parents=None: array of shape (V) :
height=None: array of shape(V) :
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returns the adjacency matrix of the graph as a sparse coo matrix
Returns : | adj: scipy.sparse matrix instance, :
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returns all the distances of the graph as a tree
Parameters : | seed=None array of shape(nbseed) with valuesin [0..self.V-1] :
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Returns : | dg: array of shape(nseed, self.V), the resulting distances : |
Notes
By convention infinite distances are given the distance np.inf
anti-symmeterize self, i.e. produces the graph whose adjacency matrix would be the antisymmetric part of its current adjacency matrix
Compte the different connected components of the graph.
Returns : | label: array of shape(self.V), labelling of the vertices : |
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Check that self is indeed a forest, i.e. contains no loop
Returns : | a boolean b=0 iff there are loops, 1 otherwise : |
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Notes
Slow implementation, might be rewritten in C or cython
Check that height[parents[i]]>=height[i] for all nodes
Extraction of the graphe cliques these are defined using replicator dynamics equations
Returns : | cliques: array of shape (self.V), type (np.int) :
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returns a compact representation of self
Returns : | idx: array of of shape(self.V + 1): :
neighb: array of shape(self.E), concatenated list of neighbors : weights: array of shape(self.E), concatenated list of weights : |
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Define the children of each node (stored in self.children)
returns a copy of self
Returns a graph with redundant edges removed: ecah edge (ab) is present ony once in the edge matrix: the correspondng weights are added.
Returns : | the resulting WeightedGraph : |
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define the edge and weights array
Returns the degree of the graph vertices.
Returns : | rdegree: (array, type=int, shape=(self.V,)), the right degrees : ldegree: (array, type=int, shape=(self.V,)), the left degrees : |
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compute an index for each node: 0 for the leaves, 1 for their parents etc. and maximal for the roots.
Returns : | depth: array of shape (self.V): the depth values of the vertices : |
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Returns all the [graph] geodesic distances starting from seed x
- seed (int, >-1, <self.V) or array of shape(p)
- edge(s) from which the distances are computed
Returns : | dg: array of shape (self.V), :
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Notes
It is mandatory that the graph weights are non-negative
Compute all the geodesic distances starting from seeds
Parameters : | seed= None: array of shape (nbseed), type np.int :
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Returns : | dg array of shape (nbseed, self.V) :
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Notes
It is mandatory that the graph weights are non-negative. The algorithm proceeds by repeating Dijkstra’s algo for each seed. Floyd’s algo is not used (O(self.V)^3 complexity...)
Sets the graph to be the topological neighbours graph of the three-dimensional coordinates set xyz, in the k-connectivity scheme
Parameters : | xyz: array of shape (self.V, 3) and type np.int, : k = 18: the number of neighbours considered. (6, 18 or 26) : |
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Returns : | E(int): the number of edges of self : |
To get the number of edges in the graph
To get the number of vertices in the graph
Get the children of a node/each node
Parameters : | v: int, optional :
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Returns : | children: list of int the list of children of node v (if v is provided) :
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returns the nodes that are children of v as a list
Parameters : | v: int, a node index : |
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Returns : | desc: list of int, the list of all descendant of the input node : |
To get the graph’s edges
Get the height array
To get the graph’s vertices (as id)
States whether self is connected or not
Identification of the leaves of the forest
Returns : | leaves: bool array of shape(self.V), indicator of the forest’s leaves : |
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Returns an indicator of nodes being roots
Returns : | roots, array of shape(self.V, bool), indicator of the forest’s roots : |
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Creates the Minimum Spanning Tree of self using Kruskal’s algo. efficient is self is sparse
Returns : | K, WeightedGraph instance: the resulting MST : |
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Notes
If self contains several connected components, will have the same number k of connected components
tests whether the given nodes are the leaves of a certain subtree
Parameters : | ids: array of shape (n) that takes values in [0..self.V-1] : custom == False, boolean :
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Return left incidence matrix
Returns : | left_incid: list :
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returns the set of neighbors of self as a list of arrays
returns the list of all non-trivial subtrees in the graph Caveat: theis function assumes that the vertices are sorted in a way such that parent[i]>i forall i Only the leaves are listeed, not the subtrees themselves
Returns the indexes of the vertices within the main cc
Returns : | idx: array of shape (sizeof main cc) : |
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Return a subforest, where chained branches are collapsed
Returns : | sf, Forest instance, same as self, without any chain : |
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Normalize the graph according to the index c Normalization means that the sum of the edges values that go into or out each vertex must sum to 1
Parameters : | c=0 in {0, 1, 2}, optional: index that designates the way :
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Notes
Note that when sum_{edge[e, .] == a } D[e] = 0, nothing is performed
Partition the tree according to a cut criterion
Plot the dendrogram associated with self the rank of the data in the dendogram is returned
Parameters : | ax: axis handle, optional : |
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Returns : | ax, the axis handle : |
Plot the height of the non-leaves nodes
Propagation of a certain labelling from leves to roots Assuming that label is a certain positive integer field this propagates these labels to the parents whenever the children nodes have coherent properties otherwise the parent value is unchanged
Parameters : | label: array of shape(self.V) : |
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Returns : | label: array of shape(self.V) : |
propagates from leaves to roots some binary property of the nodes so that prop[parents] = logical_and(prop[children])
Parameters : | prop, array of shape(self.V), the input property : |
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Returns : | prop, array of shape(self.V), the output property field : |
Removes all the edges for which valid==0
Parameters : | valid : (self.E,) array |
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Removes trivial edges, i.e. edges that are (vv)-like self.weights and self.E are corrected accordingly
Returns : | self.E (int): The number of edges : |
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reorder the tree so that the leaves come first then their parents and so on, and the roots are last.
Returns : | order: array of shape(self.V) :
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Return right incidence matrix
Returns : | right_incid: list :
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Sets the graph’s edges
Compute the weights of the graph as the distances between the corresponding rows of X, which represents an embdedding of self
Parameters : | X array of shape (self.V, edim), :
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Compute the weights of the graph as a gaussian function of the distance between the corresponding rows of X, which represents an embdedding of self
Parameters : | X array of shape (self.V, dim) :
sigma=0, float: the parameter of the gaussian function : |
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Notes
When sigma == 0, the following value is used: sigma = sqrt(mean(||X[self.edges[:, 0], :]-X[self.edges[:, 1], :]||^2))
Set the height array
Set edge weights
Parameters : | weights: array :
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Plots the current graph in 2D
Parameters : | X : None or array of shape (self.V, 2)
ax: None or int, optional :
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Returns : | ax: axis handle : |
Notes
This should be used only for small graphs.
idem as partition, but a number of components are supplied instead
Creates a subforest with the vertices for which valid > 0
Parameters : | valid: array of shape (self.V): idicator of the selected nodes : |
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Returns : | subforest: a new forest instance, with a reduced set of nodes : |
Notes
The children of deleted vertices become their own parent
Creates a subgraph with the vertices for which valid>0 and with the correponding set of edges
Parameters : | valid, array of shape (self.V): nonzero for vertices to be retained : |
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Returns : | G, WeightedGraph instance, the desired subgraph of self : |
Notes
The vertices are renumbered as [1..p] where p = sum(valid>0) when sum(valid==0) then None is returned
Symmeterize self, modify edges and weights so that self.adjacency becomes the symmetric part of the current self.adjacency.
Return adjacency matrix as coo sparse
Returns : | sp: scipy.sparse matrix instance :
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Returns the number of hierarchical levels in the tree
Defines the graph as the Voronoi diagram (VD) that links the seeds. The VD is defined using the sample points.
Parameters : | seeds: array of shape (self.V, dim) : samples: array of shape (nsamples, dim) : |
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Notes
By default, the weights are a Gaussian function of the distance The implementation is not optimal
Performs a voronoi labelling of the graph
Parameters : | seed: array of shape (nseeds), type (np.int), :
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Returns : | labels: array of shape (self.V) the labelling of the vertices : |
Agglomerative function based on a (hopefully sparse) similarity graph
Parameters : | G the input graph : |
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Returns : | t a weightForest structure that represents the dendrogram of the data : |
Agglomerative function based on a (hopefully sparse) similarity graph
Parameters : | G the input graph : stop: float :
qmax: int, optional :
verbose : bool, optional
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Returns : | u: array of shape (G.V) :
cost: array of shape (G.V (?)) :
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Modifies the graph K to merge nodes i and j into nodes k
The similarity values are weighted averaged, where pop[i] and pop[j] yield the relative weights. this is used in average_link_slow (deprecated)
Agglomerative function based on a topology-defining graph and a feature matrix.
Parameters : | G : graph
feature : array of shape (G.V,dim_feature)
verbose : bool, optional
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Returns : | t : WeightedForest instance
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Notes
When G has more than 1 connected component, t is no longer a tree. This case is handled cleanly now
Agglomerative function based on a field structure
Parameters : | F the input field (graph+feature) : stop: float, optional :
qmax: int, optional :
verbose : bool, optional
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Returns : | u: array of shape (F.V) :
cost array of shape (F.V - 1) :
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Notes
See ward_quick_segment for more information
Caveat : only approximate
Agglomerative function based on a topology-defining graph and a feature matrix.
Parameters : | G : graph instance
feature: array of shape (G.V,dim_feature) :
verbose : bool, optional
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Returns : | t: weightForest instance, :
Notes : —- : Hopefully a quicker version : A euclidean distance is used in the feature space : Caveat : only approximate |
Agglomerative function based on a topology-defining graph and a feature matrix.
Parameters : | G: labs.graph.WeightedGraph instance :
feature array of shape (G.V,dim_feature) :
stop1 : int or float, optional
qmax : int, optional
verbose : bool, optional
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Returns : | u: array of shape (G.V) :
cost: array of shape (G.V - 1) :
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Notes
Hopefully a quicker version
A euclidean distance is used in the feature space
Caveat : only approximate
Agglomerative function based on a topology-defining graph and a feature matrix.
Parameters : | G : graph object
feature : array of shape (G.V,dim_feature)
stop : int or float, optional
qmax : int, optional
verbose : bool, optional
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Returns : | u: array of shape (G.V): :
cost: array of shape (G.V - 1) :
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Notes
A euclidean distance is used in the feature space
Caveat : when the number of cc in G (nbcc) is greter than qmax, u contains nbcc values, not qmax !