ConsecutiveRescaling¶

class gtda.point_clouds.ConsecutiveRescaling(metric='euclidean', metric_params=None, factor=0.0, n_jobs=None)[source]¶

Rescaling of distances between consecutive pairs of points by a fixed factor.

The computation during transform depends on the nature of the array X. If each entry in X along axis 0 represents a distance matrix \(D\), then the corresponding entry in the transformed array is the distance matrix \(D'_{i,i+1} = \alpha D_{i,i+1}\) where \(\alpha\) is a positive factor. If the entries in X represent point clouds, their distance matrices are first computed, and then rescaled according to the same formula.

Parameters

metric (string or callable, optional, default: 'euclidean') – If set to 'precomputed', each entry in X along axis 0 is interpreted to be a distance matrix. Otherwise, entries are interpreted as feature arrays, and metric determines a rule with which to calculate distances between pairs of instances (i.e. rows) in these arrays. If metric is a string, it must be one of the options allowed by scipy.spatial.distance.pdist for its metric parameter, or a metric listed in sklearn.pairwise.PAIRWISE_DISTANCE_FUNCTIONS, including “euclidean”, “manhattan” or “cosine”. If metric is a callable function, it is called on each pair of instances and the resulting value recorded. The callable should take two arrays from the entry in X as input, and return a value indicating the distance between them.
metric_params (dict or None, optional, default: None) – Additional keyword arguments for the metric function.
factor (float, optional, default: 0.) – Factor by which to multiply the distance between consecutive points.
n_jobs (int 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.

effective_metric_params_¶

Dictionary containing all information present in metric_params. If metric_params is None, it is set to the empty dictionary.

Type: dict

Examples

>>> import numpy as np
>>> from gtda.point_clouds import ConsecutiveRescaling
>>> X = np.array([[[0, 0], [1, 2], [5, 6]]])
>>> cr = ConsecutiveRescaling()
>>> X_rescaled = cr.fit_transform(X)
>>> print(X_rescaled.shape)
(1, 3, 3)