NumPy 中一维数组（第一个参数）与二维数组（第二个参数）的矩阵乘积

要查找二维数组和一维数组的矩阵乘积，请在 Python NumPy 中使用 **numpy.matmul()** 方法。如果第一个参数是一维的，则通过在其维度前添加 1 来将其提升为矩阵。矩阵乘法完成后，将删除前置的 1。

返回输入的矩阵乘积。仅当 x1、x2 都是一维向量时，此结果才是标量。out 是一个存储结果的位置。如果提供，它必须具有与签名 (n,k),(k,m)->(n,m) 匹配的形状。如果未提供或为 None，则返回一个新分配的数组。

步骤

首先，导入所需的库 -

import numpy as np

创建一个一维数组和一个二维数组 -

arr1 = np.array([25, 35])
arr2 = np.array([[5, 7], [10, 15]])

显示数组 -

print("Array 1 (Two Dimensional)...
", arr1)
print("
Array 2 (One Dimensional)...
", arr2)

获取数组的类型 -

print("
Our Array 1 type...
", arr1.dtype)
print("
Our Array 2 type...
", arr2.dtype)

获取数组的维度 -

print("
Our Array 1 Dimensions...
",arr1.ndim)
print("
Our Array 2 Dimensions...
",arr2.ndim)

获取数组的形状 -

print("
Our Array 1 Shape...
",arr1.shape)
print("
Our Array 2 Shape...
",arr2.shape)

要查找二维数组和一维数组的矩阵乘积，请在 Python NumPy 中使用 numpy.matmul() 方法。如果第一个参数是一维的，则通过在其维度前添加 1 来将其提升为矩阵。矩阵乘法完成后，将删除前置的 1 -

print("
Result (matrix product)...
",np.matmul(arr1, arr2))

示例

import numpy as np

# Create a 1D and a 2D array
arr1 = np.array([25, 35])
arr2 = np.array([[5, 7], [10, 15]])

# Display the arrays
print("Array 1 (Two Dimensional)...
", arr1)
print("
Array 2 (One Dimensional)...
", arr2)

# Get the type of the arrays
print("
Our Array 1 type...
", arr1.dtype)
print("
Our Array 2 type...
", arr2.dtype)

# Get the dimensions of the Arrays
print("
Our Array 1 Dimensions...
",arr1.ndim)
print("
Our Array 2 Dimensions...
",arr2.ndim)

# Get the shape of the Arrays
print("
Our Array 1 Shape...
",arr1.shape)
print("
Our Array 2 Shape...
",arr2.shape)

# To find the matrix product of a 2D and a 1D array, use the numpy.matmul() method in Python Numpy
# If the first argument is 1-D, it is promoted to a matrix by prepending a 1 to its dimensions.
# After matrix multiplication the prepended 1 is removed.
print("
Result (matrix product)...
",np.matmul(arr1, arr2))

输出

Array 1 (Two Dimensional)...
[25 35]

Array 2 (One Dimensional)...
[[ 5 7]
[10 15]]

Our Array 1 type...
int64

Our Array 2 type...
int64

Our Array 1 Dimensions...
1

Our Array 2 Dimensions...
2

Our Array 1 Shape...
(2,)

Our Array 2 Shape...
(2, 2)

Result (matrix product)...
[475 700]

更新于: 2022-02-07

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