Linear Algebra Operations
NumPy offers a plethora of functions for executing linear algebra operations on arrays, including matrix multiplication, transposition, inversion, and decomposition. Key functions include:
dot(): Computes the dot product of two arrays;transpose(): Transposes an array;inv(): Computes the inverse of a matrix;linalg.svd(): Performs the singular value decomposition of a matrix;linalg.eig(): Determines the eigenvalues and eigenvectors of a matrix.
Taak
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- Compute the dot product of the arrays.
- Transpose the first array.
- Compute the inverse of the second array.
Oplossing
import numpy as np
# Create two NumPy arrays
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
# Calculate the dot product of the arrays
dot_product = np.dot(a, b)
# Transpose the first array
a_transposed = np.transpose(a)
# Calculate the inverse of the second array
b_inverse = np.linalg.inv(b)
display(dot_product, a_transposed, b_inverse)
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