metric : str, callable
The pairwise distance function to apply. See the scipy pdist docs
and the scikit-bio functions linked under See Also for available
metrics. Passing metrics as a strings is preferable as this often
results in an optimized version of the metric being used.
counts : 2D array_like of ints or floats
Matrix containing count/abundance data where each row contains counts
of OTUs in a given sample.
ids : iterable of strs, optional
Identifiers for each sample in counts . By default, samples will be
assigned integer identifiers in the order that they were provided
(where the type of the identifiers will be str ).
validate : bool, optional
If False, validation of the input won’t be performed. This step can
be slow, so if validation is run elsewhere it can be disabled here.
However, invalid input data can lead to invalid results or error
messages that are hard to interpret, so this step should not be
bypassed if you’re not certain that your input data are valid. See
skbio.diversity for the description of what validation entails
so you can determine if you can safely disable validation.
pairwise_func : callable, optional
The function to use for computing pairwise distances. This function
must take counts and metric and return a square, hollow, 2-D
numpy.ndarray of dissimilarities (floats). Examples of functions
that can be provided are scipy.spatial.distance.pdist and
sklearn.metrics.pairwise_distances . By default,
scipy.spatial.distance.pdist will be used.
kwargs : kwargs, optional
Metric-specific parameters.
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