如何将两个向量相乘并得到一个矩阵?

问题描述

在 numpy 操作中，我有两个向量，假设向量 A 是 4X1，向量 B 是 1X5，如果我做 AXB，它应该产生一个大小为 4X5 的矩阵.

但是我尝试了很多次，做了很多类型的reshape和transpose，它们要么抛出错误，提示未对齐要么返回一个值.

我应该如何得到我想要的矩阵的输出产品?

解决方案

只要向量具有正确的形状，普通矩阵乘法就可以工作.请记住，Numpy 中的 * 是元素乘法，矩阵乘法可通过 numpy.dot()(或@ 运算符，在 Python 3.5 中)

>>>numpy.dot(numpy.array([[1], [2]]), numpy.array([[3, 4]]))数组([[3, 4],[6, 8]])

这称为外积".您可以使用 numpy.outer() 使用普通向量获得它:

>>>numpy.outer(numpy.array([1, 2]), numpy.array([3, 4]))数组([[3, 4],[6, 8]])

In numpy operation, I have two vectors, let's say vector A is 4X1, vector B is 1X5, if I do AXB, it should result a matrix of size 4X5.

But I tried lot of times, doing many kinds of reshape and transpose, they all either raise error saying not aligned or return a single value.

How should I get the output product of matrix I want?

解决方案

Normal matrix multiplication works as long as the vectors have the right shape. Remember that * in Numpy is elementwise multiplication, and matrix multiplication is available with numpy.dot() (or with the @ operator, in Python 3.5)

>>> numpy.dot(numpy.array([[1], [2]]), numpy.array([[3, 4]]))
array([[3, 4],
       [6, 8]])

This is called an "outer product." You can get it using plain vectors using numpy.outer():

>>> numpy.outer(numpy.array([1, 2]), numpy.array([3, 4]))
array([[3, 4],
       [6, 8]])

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