np.cross函数详解

2023-09-11 16:48| 来源: 网络整理| 查看: 265

numpy.cross

Reference: Official Document of Numpy

语法

numpy.cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None)

功能

Return the cross product of two (arrays of) vectors. The cross product of a and b in :math:R^3 is a vector perpendicular to both a and b. If a and b are arrays of vectors, the vectors are defined by the last axis of a and b by default, and these axes can have dimensions 2 or 3. Where the dimension of either a or b is 2, the third component of the input vector is assumed to be zero and the cross product calculated accordingly. In cases where both input vectors have dimension 2, the z-component of the cross product is returned.

计算两个向量（向量数组）的叉乘。叉乘返回的数组既垂直于a，又垂直于b。如果a,b是向量数组，则向量在最后一维定义。该维度可以为2，也可以为3. 为2的时候会自动将第三个分量视作0补充进去计算。

Parameters a : array_like Components of the first vector(s).b : array_like Components of the second vector(s).axisa : int, optional Axis of a that defines the vector(s). By default, the last axis.axisb : int, optional Axis of b that defines the vector(s). By default, the last axis.axisc : int, optional Axis of c containing the cross product vector(s). Ignored if both input vectors have dimension 2, as the return is scalar. By default, the last axis.axis : int, optional If defined, the axis of a, b and c that defines the vector(s) and cross product(s). Overrides axisa, axisb and axisc.

axisa, axisb, axisc 分别指定两个输入和输出c的向量所在的维度。而axis则可以覆盖前三个参数，为全局指定向量所在维度。

Returns c : ndarray Vector cross product(s). Raises ValueError： When the dimension of the vector(s) in a and/or b does not equal 2 or 3.

当向量所在axis的dimension不为2或者3时，raise ValueError.

See Also(相关函数) inner : Inner product 内积outer : Outer product 外积ix_ : Construct index arrays. Notes

… versionadded:: 1.9.0 Supports full broadcasting of the inputs. 支持广播。

Examples Vector cross-product. >>> x = [1, 2, 3] >>> y = [4, 5, 6] >>> np.cross(x, y) array([-3, 6, -3]) One vector with dimension 2. >>> x = [1, 2] >>> y = [4, 5, 6] >>> np.cross(x, y) array([12, -6, -3]) Equivalently: >>> x = [1, 2, 0] >>> y = [4, 5, 6] >>> np.cross(x, y) array([12, -6, -3]) Both vectors with dimension 2. >>> x = [1,2] >>> y = [4,5] >>> np.cross(x, y) array(-3) Multiple vector cross-products. Note that the direction of the cross product vector is defined by the `right-hand rule`. >>> x = np.array([[1,2,3], [4,5,6]]) >>> y = np.array([[4,5,6], [1,2,3]]) >>> np.cross(x, y) array([[-3, 6, -3], [ 3, -6, 3]]) The orientation of `c` can be changed using the `axisc` keyword. >>> np.cross(x, y, axisc=0) array([[-3, 3], [ 6, -6], [-3, 3]]) Change the vector definition of `x` and `y` using `axisa` and `axisb`. >>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]]) >>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]]) >>> np.cross(x, y) array([[ -6, 12, -6], [ 0, 0, 0], [ 6, -12, 6]]) >>> np.cross(x, y, axisa=0, axisb=0) array([[-24, 48, -24], [-30, 60, -30], [-36, 72, -36]])

【本文地址】

公司简介

联系我们