NumPy 中一維陣列（第一個引數）與二維陣列（第二個引數）的矩陣乘積

要查詢二維陣列和一維陣列的矩陣乘積，請在 Python NumPy 中使用 **numpy.matmul()** 方法。如果第一個引數是一維的，則透過在其維度前新增 1 來將其提升為矩陣。矩陣乘法完成後，將刪除前置的 1。

返回輸入的矩陣乘積。僅當 x1、x2 都是一維向量時，此結果才是標量。out 是一個儲存結果的位置。如果提供，它必須具有與簽名 (n,k),(k,m)->(n,m) 匹配的形狀。如果未提供或為 None，則返回一個新分配的陣列。

步驟

首先，匯入所需的庫 -

import numpy as np

建立一個一維陣列和一個二維陣列 -

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

顯示陣列 -

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

獲取陣列的型別 -

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

獲取陣列的維度 -

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

獲取陣列的形狀 -

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

要查詢二維陣列和一維陣列的矩陣乘積，請在 Python NumPy 中使用 numpy.matmul() 方法。如果第一個引數是一維的，則透過在其維度前新增 1 來將其提升為矩陣。矩陣乘法完成後，將刪除前置的 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))

輸出

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]

AmitDiwan

更新於: 2022-02-07

347 次檢視

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