sklearn.metrics.pairwise.distance.minkowski
时间: 2024-01-23 21:03:31 浏览: 31
The `sklearn.metrics.pairwise.distance.minkowski` function is a distance metric that computes the Minkowski distance between two vectors. It is a generalization of the Euclidean distance and the Manhattan distance.
The Minkowski distance between two points x and y is defined as:
d(x, y) = (sum(|x_i - y_i|^p))^(1/p)
where p is a positive integer. When p=1, the Minkowski distance is equivalent to the Manhattan distance, and when p=2, it is equivalent to the Euclidean distance.
The `sklearn.metrics.pairwise.distance.minkowski` function takes the following parameters:
- X: An array-like object representing the first set of vectors.
- Y: An array-like object representing the second set of vectors. If Y is not provided, the function computes the distance between each pair of vectors in X.
- p: The order of the Minkowski distance. Default is p=2, which corresponds to the Euclidean distance.
- w: An array of weights to apply to the dimensions of the vectors. Default is None, which corresponds to equal weights.
The output of the function is a distance matrix, where the (i,j) entry represents the distance between the i-th vector in X and the j-th vector in Y.