# giotto.time_series.PearsonDissimilarity¶

class giotto.time_series.PearsonDissimilarity(absolute_value=False, n_jobs=None)

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_valuebool, 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_jobsint or None, optional, default: None

The number of jobs to use for the computation. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.

Methods

 fit(self, X[, y]) Do nothing and return the estimator unchanged. fit_transform(self, X[, y]) Fit to data, then transform it. get_params(self[, deep]) Get parameters for this estimator. set_params(self, \*\*params) Set the parameters of this estimator. transform(self, X[, y]) Compute Pearson dissimilarities.
__init__(self, absolute_value=False, n_jobs=None)

Initialize self. See help(type(self)) for accurate signature.

fit(self, X, y=None)

Do nothing and return the estimator unchanged.

This method is there to implement the usual scikit-learn API and hence work in pipelines.

Parameters
Xndarray, shape (n_samples, n_observations, n_features)

Input data. Each entry along axis 0 is a sample of n_features different variables, of size n_observations.

yNone

There is no need for a target in a transformer, yet the pipeline API requires this parameter.

Returns
selfobject
fit_transform(self, 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
Xnumpy array of shape [n_samples, n_features]

Training set.

ynumpy array of shape [n_samples]

Target values.

Returns
X_newnumpy array of shape [n_samples, n_features_new]

Transformed array.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters
deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

set_params(self, **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.

Returns
self
transform(self, X, y=None)

Compute Pearson dissimilarities.

Parameters
Xndarray, shape (n_samples, n_observations, n_features)

Input data. Each entry along axis 0 is a sample of n_features different variables, of size n_observations.

yNone

There is no need for a target in a transformer, yet the pipeline API requires this parameter.

Returns
Xtndarray, shape (n_samples, n_features, n_features)

Array of Pearson dissimilarities.