python-igraph API reference

List of all classes, functions and methods in python-igraph

class documentation

class Clustering:

Known subclasses: igraph.VertexClustering

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Class representing a clustering of an arbitrary ordered set.

This is now used as a base for VertexClustering, but it might be useful for other purposes as well.

Members of an individual cluster can be accessed by the [] operator:

>>> cl = Clustering([0,0,0,0,1,1,1,2,2,2,2])
>>> cl[0]
[0, 1, 2, 3]

The membership vector can be accessed by the membership property:

>>> cl.membership
[0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2]

The number of clusters can be retrieved by the len function:

>>> len(cl)
3

You can iterate over the clustering object as if it were a regular list of clusters:

>>> for cluster in cl:
...     print(" ".join(str(idx) for idx in cluster))
...
0 1 2 3
4 5 6
7 8 9 10

If you need all the clusters at once as lists, you can simply convert the clustering object to a list:

>>> cluster_list = list(cl)
>>> print(cluster_list)
[[0, 1, 2, 3], [4, 5, 6], [7, 8, 9, 10]]
Method __getitem__ Returns the members of the specified cluster.
Method __init__ Constructor.
Method __iter__ Iterates over the clusters in this clustering.
Method __len__ Returns the number of clusters.
Method __str__ Undocumented
Method as_cover Returns a Cover that contains the same clusters as this clustering.
Method compare_to Compares this clustering to another one using some similarity or distance metric.
Method size Returns the size of a given cluster.
Method size_histogram Returns the histogram of cluster sizes.
Method sizes Returns the size of given clusters.
Method summary Returns the summary of the clustering.
Property membership Returns the membership vector.
Property n Returns the number of elements covered by this clustering.
Method _formatted_cluster_iterator Iterates over the clusters and formats them into a string to be presented in the summary.
Instance Variable _len Undocumented
Instance Variable _membership Undocumented
def __getitem__(self, idx):

Returns the members of the specified cluster.

Parameters
idxthe index of the cluster
Returns
the members of the specified cluster as a list
Raises
IndexErrorif the index is out of bounds
def __init__(self, membership, params=None):

Constructor.

Parameters
membershipthe membership list -- that is, the cluster index in which each element of the set belongs to.
paramsadditional parameters to be stored in this object's dictionary.
def __iter__(self):

Iterates over the clusters in this clustering.

This method will return a generator that generates the clusters one by one.

def __len__(self):

Returns the number of clusters.

Returns
the number of clusters
def __str__(self):
overridden in igraph.Cut

Undocumented

def as_cover(self):

Returns a Cover that contains the same clusters as this clustering.

def compare_to(self, other, *args, **kwds):

Compares this clustering to another one using some similarity or distance metric.

This is a convenience method that simply calls compare_communities with the two clusterings as arguments. Any extra positional or keyword argument is also forwarded to compare_communities.

def size(self, idx):

Returns the size of a given cluster.

Parameters
idxthe cluster in which we are interested.
def size_histogram(self, bin_width=1):

Returns the histogram of cluster sizes.

Parameters
bin_widththe bin width of the histogram
Returns
a Histogram object
def sizes(self, *args):

Returns the size of given clusters.

The indices are given as positional arguments. If there are no positional arguments, the function will return the sizes of all clusters.

def summary(self, verbosity=0, width=None):

Returns the summary of the clustering.

The summary includes the number of items and clusters, and also the list of members for each of the clusters if the verbosity is nonzero.

Parameters
verbositydetermines whether the cluster members should be printed. Zero verbosity prints the number of items and clusters only.
widthUndocumented
Returns
the summary of the clustering as a string.
@property
membership =

Returns the membership vector.

@property
n =

Returns the number of elements covered by this clustering.

def _formatted_cluster_iterator(self):

Iterates over the clusters and formats them into a string to be presented in the summary.

_len =

Undocumented

_membership =

Undocumented