Compute dissimiliarity matrix for samples in a dataset
This Measure returns the upper triangle of the n x n disimilarity matrix defined as the pairwise distances between samples in the dataset, and where n is the number of samples.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
Methods
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_space() | Query the processing space name of this node. |
reset() | |
set_postproc(node) | Assigns a post-processing node |
set_space(name) | Set the processing space name of this node. |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |
Parameters: | pairwise_metric : str, optional
center_data : bool, optional
square : bool, optional
enable_ca : None or list of str
disable_ca : None or list of str
null_dist : instance of distribution estimator
auto_train : bool
force_train : bool
space : str, optional
pass_attr : str, list of str|tuple, optional
postproc : Node instance, optional
descr : str
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Returns: | Dataset :
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Methods
generate(ds) | Yield processing results. |
get_postproc() | Returns the post-processing node or None. |
get_space() | Query the processing space name of this node. |
reset() | |
set_postproc(node) | Assigns a post-processing node |
set_space(name) | Set the processing space name of this node. |
train(ds) | The default implementation calls _pretrain(), _train(), and finally _posttrain(). |
untrain() | Reverts changes in the state of this node caused by previous training |