# skbio.stats.distance.DistanceMatrix¶

class skbio.stats.distance.DistanceMatrix(data, ids=None)[source]

Store distances between objects.

A DistanceMatrix is a DissimilarityMatrix with the additional requirement that the matrix data is symmetric. There are additional methods made available that take advantage of this symmetry.

Notes

The distances are stored in redundant (square-form) format 1. To facilitate use with other scientific Python routines (e.g., scipy), the distances can be retrieved in condensed (vector-form) format using condensed_form.

DistanceMatrix only requires that the distances it stores are symmetric. Checks are not performed to ensure the other three metric properties hold (non-negativity, identity of indiscernibles, and triangle inequality) 2. Thus, a DistanceMatrix instance can store distances that are not metric.

References

1

http://docs.scipy.org/doc/scipy/reference/spatial.distance.html

2

http://planetmath.org/metricspace

Attributes

 T Transpose of the dissimilarity matrix. data Array of dissimilarities. default_write_format dtype Data type of the dissimilarities. ids Tuple of object IDs. png Display heatmap in IPython Notebook as PNG. shape Two-element tuple containing the dissimilarity matrix dimensions. size Total number of elements in the dissimilarity matrix. svg Display heatmap in IPython Notebook as SVG.

Built-ins

 x in dm Check if the specified ID is in the dissimilarity matrix. dm1 == dm2 Compare this dissimilarity matrix to another for equality. dm[x] Slice into dissimilarity data by object ID or numpy indexing. dm1 != dm2 Determine whether two dissimilarity matrices are not equal. str(dm) Return a string representation of the dissimilarity matrix.

Methods

 between(from_, to_[, allow_overlap]) Obtain the distances between the two groups of IDs Return an array of distances in condensed format. Return a deep copy of the dissimilarity matrix. filter(ids[, strict]) Filter the dissimilarity matrix by IDs. from_iterable(iterable, metric[, key, keys, …]) Create DistanceMatrix from all pairs in an iterable given a metric. index(lookup_id) Return the index of the specified ID. permute([condensed]) Randomly permute both rows and columns in the matrix. plot([cmap, title]) Creates a heatmap of the dissimilarity matrix read(file[, format]) Create a new DistanceMatrix instance from a file. Return an array of dissimilarities in redundant format. Create a pandas.DataFrame from this DissimilarityMatrix. Create a pandas.Series from this DistanceMatrix. Return the transpose of the dissimilarity matrix. within(ids) Obtain all the distances among the set of IDs write(file[, format]) Write an instance of DistanceMatrix to a file.