PearsonDissimilarity¶
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class
gtda.time_series.PearsonDissimilarity(absolute_value=False, n_jobs=None)[source]¶ Pearson dissimilarities from collections of multivariate time series.
The sample Pearson correlation coefficients between pairs of components of an \(N\)-variate time series form an \(N \times N\) matrix \(R\) with entries
\[R_{ij} = \frac{ C_{ij} }{ \sqrt{ C_{ii} C_{jj} } },\]where \(C\) is the covariance matrix. Setting \(D_{ij} = (1 - R_{ij})/2\) or \(D_{ij} = 1 - |R_{ij}|\) we obtain a dissimilarity matrix with entries between 0 and 1.
This transformer computes one dissimilarity matrix per multivariate time series in a collection. Examples of such collections are the outputs of
SlidingWindow.- Parameters
absolute_value (bool, default:
False) – Whether absolute values of the Pearson correlation coefficients should be taken. Doing so makes pairs of strongly anti-correlated variables as similar as pairs of strongly correlated ones.n_jobs (int or None, optional, default:
None) – The number of jobs to use for the computation.Nonemeans 1 unless in ajoblib.parallel_backendcontext.-1means using all processors.
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__init__(absolute_value=False, n_jobs=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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fit(X, y=None)[source]¶ Do nothing and return the estimator unchanged.
This method is here to implement the usual scikit-learn API and hence work in pipelines.
- Parameters
X (ndarray of shape (n_samples, n_observations, n_features)) – Input data. Each entry along axis 0 is a sample of
n_featuresdifferent variables, of sizen_observations.y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
self
- Return type
object
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fit_transform(X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (ndarray of shape (n_samples, n_observations, n_features)) – Input data. Each entry along axis 0 is a sample of
n_featuresdifferent variables, of sizen_observations.y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
Xt – Array of Pearson dissimilarities.
- Return type
ndarray of shape (n_samples, n_features, n_features)
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get_params(deep=True)¶ Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
mapping of string to any
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set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
object
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transform(X, y=None)[source]¶ Compute Pearson dissimilarities.
- Parameters
X (ndarray of shape (n_samples, n_observations, n_features)) – Input data. Each entry along axis 0 is a sample of
n_featuresdifferent variables, of sizen_observations.y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
Xt – Array of Pearson dissimilarities.
- Return type
ndarray of shape (n_samples, n_features, n_features)