skbio.core.distance.DissimilarityMatrix¶

class skbio.core.distance.DissimilarityMatrix(data, ids=None)[source]¶

Store dissimilarities between objects.

A DissimilarityMatrix instance stores a square, hollow, two-dimensional matrix of dissimilarities between objects. Objects could be, for example, samples or DNA sequences. A sequence of IDs accompanies the dissimilarities.

Methods are provided to load and save dissimilarity matrices from/to disk, as well as perform common operations such as extracting dissimilarities based on object ID.

Parameters:

Parameters:	data : array_like or DissimilarityMatrix Square, hollow, two-dimensional `numpy.ndarray` of dissimilarities (floats), or a structure that can be converted to a `numpy.ndarray` using `numpy.asarray`. Can instead be a DissimilarityMatrix (or subclass) instance, in which case the instance’s data will be used. Data will be converted to a float `dtype` if necessary. A copy will not be made if already a `numpy.ndarray` with a float `dtype`. ids : sequence of str, optional Sequence of strings to be used as object IDs. Must match the number of rows/cols in data. If `None` (the default), IDs will be monotonically-increasing integers cast as strings, with numbering starting from zero, e.g., `('0', '1', '2', '3', ...)`.

data : array_like or DissimilarityMatrix

Square, hollow, two-dimensional numpy.ndarray of dissimilarities (floats), or a structure that can be converted to a numpy.ndarray using numpy.asarray. Can instead be a DissimilarityMatrix (or subclass) instance, in which case the instance’s data will be used. Data will be converted to a float dtype if necessary. A copy will not be made if already a numpy.ndarray with a float dtype.

ids : sequence of str, optional

Sequence of strings to be used as object IDs. Must match the number of rows/cols in data. If None (the default), IDs will be monotonically-increasing integers cast as strings, with numbering starting from zero, e.g., ('0', '1', '2', '3', ...).

See also

DistanceMatrix

Notes

The dissimilarities are stored in redundant (square-form) format [R49].

The data are not checked for symmetry, nor guaranteed/assumed to be symmetric.

References

[R49]

(1, 2) http://docs.scipy.org/doc/scipy/reference/spatial.distance.html

Attributes

`data`	Array of dissimilarities.
`ids`	Tuple of object IDs.
`dtype`	Data type of the dissimilarities.
`shape`	Two-element tuple containing the dissimilarity matrix dimensions.
`size`	Total number of elements in the dissimilarity matrix.
`T`	Transpose of the dissimilarity matrix.

Methods

`__contains__`(lookup_id)	Check if the specified ID is in the dissimilarity matrix.
`__eq__`(other)	Compare this dissimilarity matrix to another for equality.
`__getitem__`(index)	Slice into dissimilarity data by object ID or numpy indexing.
`__ne__`(other)	Determine whether two dissimilarity matrices are not equal.
`__str__`()	Return a string representation of the dissimilarity matrix.
`copy`()	Return a deep copy of the dissimilarity matrix.
`filter`(ids)	Filter the dissimilarity matrix by IDs.
`from_file`(dm_f[, delimiter])	Load dissimilarity matrix from a delimited text file or file path.
`index`(lookup_id)	Return the index of the specified ID.
`redundant_form`()	Return an array of dissimilarities in redundant format.
`to_file`(out_f[, delimiter])	Save the dissimilarity matrix to file in delimited text format.
`transpose`()	Return the transpose of the dissimilarity matrix.