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法律研究在 Python 中計算 N 維陣列的逆矩陣
要計算 N 維陣列的逆矩陣，可以使用 Python 中的 numpy.linalg.tensorinv() 方法。結果是相對於 tensordot 操作 tensordot(a, b, ind) 的 a 的逆矩陣，即，在浮點精度範圍內，tensordot(tensorinv(a), a, ind) 是 tensordot 操作的“單位”張量。
該方法返回 a 的 tensordot 逆矩陣，形狀為 a.shape[ind:] + a.shape[:ind]。第一個引數是 a，要“反轉”的張量。它的形狀必須是“方形”，即 prod(a.shape[:ind]) == prod(a.shape[ind:])。第二個引數是 ind，參與逆和運算的前幾個索引的數量。必須是正整數，預設為 2。
步驟
首先，匯入所需的庫 -
import numpy as np from numpy.linalg import inv
建立一個數組。numpy.eye() 返回一個二維陣列，對角線上的元素為 1，其他位置的元素為 0 -
arr = np.eye(4*6)
更改上面建立的陣列的形狀 -
arr.shape = (4, 6, 8, 3)
顯示陣列 -
print("Our Array...
",arr)
檢查維度 -
print("
Dimensions of our Array...
",arr.ndim)獲取資料型別 -
print("
Datatype of our Array object...
",arr.dtype)獲取形狀 -
print("
Shape of our Array object...
",arr.shape)要計算 N 維陣列的逆矩陣，可以使用 Python 中的 numpy.linalg.tensorinv() 方法 -
print("
Result...
",np.linalg.tensorinv(arr))
示例
import numpy as np
from numpy.linalg import inv
# Create an array
# The numpy.eye() returns a 2-D array with ones on the diagonal and zeros elsewhere
arr = np.eye(4*6)
# Changing the shape of the array created above
arr.shape = (4, 6, 8, 3)
# Display the array
print("Our Array...
",arr)
# Check the Dimensions
print("
Dimensions of our Array...
",arr.ndim)
# Get the Datatype
print("
Datatype of our Array object...
",arr.dtype)
# Get the Shape
print("
Shape of our Array object...
",arr.shape)
# To compute the inverse of an N-dimensional array, use the numpy.linalg.tensorinv() method in Python.
print("
Result...
",np.linalg.tensorinv(arr))輸出
Our Array... [[[[1. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 1. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 1.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [1. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 1. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 1.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]]] [[[0. 0. 0.] [0. 0. 0.] [1. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 1. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 1.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [1. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 1. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 1.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]]] [[[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [1. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 1. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 1.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [1. 0. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 1. 0.] [0. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 1.] [0. 0. 0.] [0. 0. 0.]]] [[[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [1. 0. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 1. 0.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 1.] [0. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [1. 0. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 1. 0.]] [[0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 0.] [0. 0. 1.]]]] Dimensions of our Array... 4 Datatype of our Array object... float64 Shape of our Array object... (4, 6, 8, 3) Result... [[[[1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]]] [[[0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]]] [[[0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]]] [[[0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]]] [[[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0.] [0. 0. 0. 0. 0. 0.]]] [[[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0.] [0. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0.]]] [[[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [1. 0. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 1. 0. 0. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 1. 0. 0. 0.]]] [[[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 1. 0. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 1. 0.]] [[0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 0.] [0. 0. 0. 0. 0. 1.]]]]
更新於: 2022-02-25
182 次瀏覽
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