dtw_cost_matrix¶
- dtw_cost_matrix(x: ndarray, y: ndarray, window: float | None = None, itakura_max_slope: float | None = None) ndarray[source]¶
Compute the DTW cost matrix between two time series.
The cost matrix is the pairwise Euclidean distance between all points \(M_{i,j}=(x_i-x_j)^2\). It is used in the DTW path calculations.
- 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. window is a percentage deviation, so if
window = 0.1, 10% of the series length is the max warping allowed. is used.- 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, m_timepoints)
dtw 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 dtw_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]]) >>> dtw_cost_matrix(x, y) array([[ 0., 1., 5., 14., 30., 55., 91., 140., 204., 285.], [ 1., 0., 1., 5., 14., 30., 55., 91., 140., 204.], [ 5., 1., 0., 1., 5., 14., 30., 55., 91., 140.], [ 14., 5., 1., 0., 1., 5., 14., 30., 55., 91.], [ 30., 14., 5., 1., 0., 1., 5., 14., 30., 55.], [ 55., 30., 14., 5., 1., 0., 1., 5., 14., 30.], [ 91., 55., 30., 14., 5., 1., 0., 1., 5., 14.], [140., 91., 55., 30., 14., 5., 1., 0., 1., 5.], [204., 140., 91., 55., 30., 14., 5., 1., 0., 1.], [285., 204., 140., 91., 55., 30., 14., 5., 1., 0.]])