Eccentricity¶
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class
gtda.mapper.Eccentricity(exponent=2, metric='euclidean', metric_params=None)[source]¶ Eccentricities of points in a point cloud or abstract metric space.
Let D be a square matrix representing distances between points in a point cloud, or directly defining an abstract metric (or metric-like) space. The eccentricity of point i in the point cloud or abstract metric space is the p-norm (for some p) of row i in D.
- Parameters
exponent (int or float, optional, default:
2) – p-norm exponent used to calculate eccentricities from the distance matrix.metric (str or function, optional, default:
'euclidean') – Metric to use to compute the distance matrix if point cloud data is passed as input, or'precomputed'to specify that the input is already a distance matrix. If not'precomputed', it may be anything allowed byscipy.spatial.distance.pdist.metric_params (dict or None, optional, default:
None) – Additional keyword arguments for the metric function.
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__init__(exponent=2, metric='euclidean', metric_params=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 exists to implement the usual scikit-learn API and hence work in pipelines.
- Parameters
X (array-like of shape (n_samples, n_features) or (n_samples, n_samples)) – Input data.
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 (array-like of shape (n_samples, n_features) or (n_samples, n_samples)) – Input data.
y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
Xt – Column vector of eccentricities of points in X.
- Return type
ndarray of shape (n_samples, 1)
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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 the eccentricities of points (i.e. rows) in X.
- Parameters
X (array-like of shape (n_samples, n_features) or (n_samples, n_samples)) – Input data.
y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
Xt – Column vector of eccentricities of points in X.
- Return type
ndarray of shape (n_samples, 1)