erp_cost_matrix¶

erp_cost_matrix(x: ndarray, y: ndarray, window: float | None = None, g: float = 0.0, g_arr: ndarray | None = None, itakura_max_slope: float | None = None) → ndarray[source]¶

Compute the ERP cost matrix between two time series.

Parameters:

xnp.ndarray: First time series, either univariate, shape (n_timepoints,), or multivariate, shape (n_channels, n_timepoints).
ynp.ndarray: Second time series, either univariate, shape (n_timepoints,), or multivariate, shape (n_channels, n_timepoints).
windowfloat, default=None: The window to use for the bounding matrix. If None, no bounding matrix is used.
gfloat, defualt=0.0: The reference value to penalise gaps. The default is 0.
g_arrnp.ndarray, of shape (n_channels), default=None: Numpy array that must be the length of the number of channels in x and y.
itakura_max_slopefloat, default=None: Maximum slope as a proportion of the number of time points used to create Itakura parallelogram on the bounding matrix. Must be between 0. and 1.

Returns:

np.ndarray (n_timepoints_x, n_timepoints_y): ERP cost matrix between x and y.

Raises:

ValueError: If x and y are not 1D or 2D arrays.

Examples

>>> import numpy as np
>>> from aeon.distances import erp_cost_matrix
>>> x = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
>>> y = np.array([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]])
>>> erp_cost_matrix(x, y)
array([[ 0.,  2.,  5.,  9., 14., 20., 27., 35., 44., 54.],
       [ 2.,  0.,  3.,  7., 12., 18., 25., 33., 42., 52.],
       [ 5.,  3.,  0.,  4.,  9., 15., 22., 30., 39., 49.],
       [ 9.,  7.,  4.,  0.,  5., 11., 18., 26., 35., 45.],
       [14., 12.,  9.,  5.,  0.,  6., 13., 21., 30., 40.],
       [20., 18., 15., 11.,  6.,  0.,  7., 15., 24., 34.],
       [27., 25., 22., 18., 13.,  7.,  0.,  8., 17., 27.],
       [35., 33., 30., 26., 21., 15.,  8.,  0.,  9., 19.],
       [44., 42., 39., 35., 30., 24., 17.,  9.,  0., 10.],
       [54., 52., 49., 45., 40., 34., 27., 19., 10.,  0.]])