ForgetDimension¶
-
class
gtda.diagrams.ForgetDimension[source]¶ Replaces all homology dimensions in persistence diagrams with
numpy.inf.Useful when downstream tasks require the use of topological features all at once – and not separated between different homology dimensions.
See also
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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_features, 3)) – Input data. Array of persistence diagrams, each a collection of triples [b, d, q] representing persistent topological features through their birth (b), death (d) and homology dimension (q).
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_features, 3)) – Input data. Array of persistence diagrams, each a collection of triples [b, d, q] representing persistent topological features through their birth (b), death (d) and homology dimension (q).
y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
Xt – Output persistence diagram.
- Return type
ndarray of shape (n_samples, n_features, 3)
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fit_transform_plot(X, y=None, sample=0, **plot_params)¶ Fit to data, then apply
transform_plot.- Parameters
X (ndarray of shape (n_samples, ..)) – Input data.
y (ndarray of shape (n_samples,) or None) – Target values for supervised problems.
sample (int) – Sample to be plotted.
**plot_params – Optional plotting parameters.
- Returns
Xt – Transformed one-sample slice from the input.
- Return type
ndarray of shape (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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static
plot(Xt, sample=0)[source]¶ Plot a sample from a collection of persistence diagrams.
- Parameters
Xt (ndarray of shape (n_samples, n_points, 3)) – Collection of persistence diagrams, such as returned by
transform.sample (int, optional, default:
0) – Index of the sample in Xt to be plotted.
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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]¶ Replace all homology dimensions in X with
numpy.inf.- Parameters
X (ndarray of shape (n_samples, n_features, 3)) – Input data. Array of persistence diagrams, each a collection of triples [b, d, q] representing persistent topological features through their birth (b), death (d) and homology dimension (q).
y (None) – There is no need for a target in a transformer, yet the pipeline API requires this parameter.
- Returns
Xt – Output persistence diagram.
- Return type
ndarray of shape (n_samples, n_features, 3)
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transform_plot(X, sample=0, **plot_params)¶ Take a one-sample slice from the input collection and transform it. Before returning the transformed object, plot the transformed sample.
- Parameters
X (ndarray of shape (n_samples, ..)) – Input data.
sample (int) – Sample to be plotted.
plot_params (dict) – Optional plotting parameters.
- Returns
Xt – Transformed one-sample slice from the input.
- Return type
ndarray of shape (1, ..)
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