sklearn.metrics.pairwise欧式距离
时间: 2023-05-23 21:05:43 浏览: 54
和曼哈顿距离分别是什么?
A: sklearn.metrics.pairwise是一个用于计算数据点之间相似性的函数库。欧式距离和曼哈顿距离是其中最常用的两种距离度量方式。
欧式距离(也称作欧几里得距离)就是两点之间的距离,可以看作是一个直线距离,即在坐标系中两点之间的直线距离。对于n维空间的两个点(x1,y1,z1,…,xn,yn,zn),它们之间的欧式距离就是d=√((x2-x1)²+(y2-y1)²+(z2-z1)²+…+(xn-x1)²+(yn-y1)²+(zn-z1)²)。
曼哈顿距离(也称为城市街区距离)是坐标轴上两点之间的距离,相当于从一个点到另一个点水平和竖直方向上的距离之和。对于n维空间的两个点(x1,y1,z1,…,xn,yn,zn),它们之间的曼哈顿距离就是d=|x1-x2|+|y1-y2|+|z1-z2|+…+|xn-xn|+|yn-yn|+|zn-zn|。
在sklearn.metrics.pairwise中,可以使用pairwise_distances函数来计算欧式距离和曼哈顿距离。可以通过指定metric参数的值为"euclidean"或"manhattan"来计算相应的距离。
相关问题
sklearn.metrics.pairwise绝对值距离
A:sklearn.metrics.pairwise绝对值距离是一种计算两个向量之间距离的方法,也称为曼哈顿距离。它通常用于处理非常大的维度和稀疏数据。该距离定义为每个维度上向量差值的绝对值之和。在scikit-learn中,可以使用函数pairwise_distances来计算绝对值距离。
sklearn.metrics.pairwise.distance.minkowski
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.