erp_pairwise_distance#

erp_pairwise_distance(X: ndarray, y: ndarray = None, window: float = None, g: float = 0.0, g_arr: ndarray = None) → ndarray[source]#

Compute the erp pairwise distance between a set of time series.

The optimal value of g is selected from the range [σ/5, σ], where σ is the standard deviation of the training data. When there is > 1 channel, g should be a np.ndarray where the nth value is the standard deviation of the nth channel.

Parameters:

X: np.ndarray, of shape (n_instances, n_channels, n_timepoints) or: (n_instances, n_timepoints)

A collection of time series instances.
y: np.ndarray, of shape (m_instances, m_channels, m_timepoints) or: (m_instances, m_timepoints) or (m_timepoints,), default=None

A collection of time series instances.
window: float, default=None: The window to use for the bounding matrix. If None, no bounding matrix is used.
g: float.: The reference value to penalise gaps. The default is 0.
g_arr: np.ndarray of shape (n_channels), defaults=None: Numpy array that must be the length of the number of channels in x and y.

Returns:

np.ndarray (n_instances, n_instances): erp pairwise matrix between the instances of X.

Raises:

ValueError: If X is not 2D or 3D array when only passing X. If X and y are not 1D, 2D or 3D arrays when passing both X and y.

Examples

>>> import numpy as np
>>> from aeon.distances import erp_pairwise_distance
>>> # Distance between each time series in a collection of time series
>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]])
>>> erp_pairwise_distance(X)
array([[ 0.,  9., 18.],
       [ 9.,  0.,  9.],
       [18.,  9.,  0.]])

>>> # Distance between two collections of time series
>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]])
>>> y = np.array([[[11, 12, 13]],[[14, 15, 16]], [[17, 18, 19]]])
>>> erp_pairwise_distance(X, y)
array([[30., 39., 48.],
       [21., 30., 39.],
       [12., 21., 30.]])

>>> X = np.array([[[1, 2, 3]],[[4, 5, 6]], [[7, 8, 9]]])
>>> y_univariate = np.array([[11, 12, 13],[14, 15, 16], [17, 18, 19]])
>>> erp_pairwise_distance(X, y_univariate)
array([[30.],
       [21.],
       [12.]])