Calculate the correlations of PDist measures across chunks
This measures the consistency in similarity structure across runs within individuals, or across individuals if the target dataset is made from several subjects in some common space and where the sample attribute specified as the chunks_attr codes for subject identity.
@author: ACC Aug 2013
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: | chunks_attr : str, optional
pairwise_metric : str, optional
consistency_metric : {pearson, spearman}, 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 :
|
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 |
Indicate that this measure is always trained.