euclidean_distance#
- euclidean_distance(x: ndarray, y: ndarray, **kwargs: Any) float [source]#
Compute the Euclidean distance between two time series.
Euclidean distance is supported for 1d, 2d and 3d arrays.
The Euclidean distance between two time series of length m is the square root of the squared distance and is defined as:
\[ed(x, y) = \sqrt{\sum_{i=1}^{n} (x_i - y_i)^2}\]- Parameters:
- x: np.ndarray (1d or 2d array)
First time series.
- y: np.ndarray (1d or 2d array)
Second time series.
- kwargs: Any
Extra kwargs. For euclidean there are none however, this is kept for consistency.
- Returns:
- float
Euclidean distance between x and y.
- Raises:
- ValueError
If the value of x or y provided is not a numpy array. If the value of x or y has more than 2 dimensions. If a metric string provided, and is not a defined valid string. If a metric object (instance of class) is provided and doesn’t inherit from NumbaDistance. If a resolved metric is not no_python compiled. If the metric type cannot be determined.
Examples
>>> import numpy as np >>> from sktime.distances import euclidean_distance >>> x_1d = np.array([1, 2, 3, 4]) # 1d array >>> y_1d = np.array([5, 6, 7, 8]) # 1d array >>> euclidean_distance(x_1d, y_1d) 8.0
>>> x_2d = np.array([[1, 2, 3, 4], [5, 6, 7, 8]]) # 2d array >>> y_2d = np.array([[9, 10, 11, 12], [13, 14, 15, 16]]) # 2d array >>> euclidean_distance(x_2d, y_2d) 22.627416997969522