將 ufunc outer() 函式應用於 Numpy 中的所有對

將ufunc outer() 函式應用於所有對。numpy.ufunc 具有按元素對整個陣列進行操作的函式。ufunc 用 C 語言編寫（為了獲得速度），並透過 NumPy 的 ufunc 設施連結到 Python 中。通用函式（簡稱 ufunc）是按元素的方式對 ndarray 進行操作的函式，支援陣列廣播、型別轉換以及其他一些標準功能。也就是說，ufunc 是一個“向量化”的包裝器，用於處理具有固定數量特定輸入併產生固定數量特定輸出的函式。

步驟

首先，匯入所需的庫 –

import numpy as np

建立兩個陣列 –

arr1 = np.array([[5, 10, 15, 20], [25, 30, 35, 40]])
arr2 = np.array([[7, 14, 21, 28, 35]])

顯示陣列 –

print("Array 1...
", arr1)
print("
Array 2...
", 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)

將 ufunc outer() 函式應用於所有對 –

res = np.multiply.outer(arr1, arr2)
print("
Result...
",res)
print("
Shape...
",res.shape)

舉例

import numpy as np

# The numpy.ufunc has functions that operate element by element on whole arrays.
# ufuncs are written in C (for speed) and linked into Python with NumPy's ufunc facility

# Create two arrays
arr1 = np.array([[5, 10, 15, 20], [25, 30, 35, 40]])
arr2 = np.array([[7, 14, 21, 28, 35]])

# Display the arrays
print("Array 1...
", arr1)
print("
Array 2...
", 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)

# Apply the ufunc outer() function to all pairs
res = np.multiply.outer(arr1, arr2)
print("
Result...
",res)
print("
Shape...
",res.shape)

輸出

Array 1...
[[ 5 10 15 20]
[25 30 35 40]]

Array 2...
[[ 7 14 21 28 35]]

Our Array 1 type...
int64

Our Array 2 type...
int64

Our Array 1 Dimensions...
2

Our Array 2 Dimensions...
2

Our Array 1 Shape...
(2, 4)

Our Array 2 Shape...
(1, 5)

Result...
[[[[ 35 70 105 140 175]]

[[ 70 140 210 280 350]]

[[ 105 210 315 420 525]]

[[ 140 280 420 560 700]]]


[[[ 175 350 525 700 875]]

[[ 210 420 630 840 1050]]

[[ 245 490 735 980 1225]]

[[ 280 560 840 1120 1400]]]]

Shape...
(2, 4, 1, 5)

AmitDiwan

更新於：07-02-2022

273 次瀏覽

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