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# skbio.stats.distance.DissimilarityMatrix

class `skbio.stats.distance.``DissimilarityMatrix`(data, ids=None)

  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:

  - 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` or a one-dimensional vector of dissimilarities (floats), as defined by scipy.spatial.distance.squareform. 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`, `scipy.spatial.distance.squareform`

  Notes

  The dissimilarities are stored in redundant (square-form) format [1].

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

  References

  [1] | http://docs.scipy.org/doc/scipy/reference/spatial.distance.html

  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
  `copy`() | Return a deep copy of the dissimilarity matrix.
  `filter`(ids[, strict]) | Filter the dissimilarity matrix by IDs.
  `from_iterable`(iterable, metric[, key, keys]) | Create DissimilarityMatrix from an iterable given a metric.
  `index`(lookup_id) | Return the index of the specified ID.
  `plot`([cmap, title]) | Creates a heatmap of the dissimilarity matrix
  `read`(file[, format]) | Create a new `DissimilarityMatrix` instance from a file.
  `redundant_form`() | Return an array of dissimilarities in redundant format.
  `to_data_frame`() | Create a `pandas.DataFrame` from this `DissimilarityMatrix`.
  `transpose`() | 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 `DissimilarityMatrix` to a file.