- numpy. matrix — NumPy v2. 3 Manual
A matrix is a specialized 2-D array that retains its 2-D nature through operations It has certain special operators, such as * (matrix multiplication) and ** (matrix power)
- numpy. matrix. all — NumPy v2. 3 Manual
numpy matrix all # method matrix all(axis=None, out=None) [source] # Test whether all matrix elements along a given axis evaluate to True Parameters: See `numpy all` for complete descriptions See also numpy all
- numpy. linalg. inv — NumPy v2. 3 Manual
To detect ill-conditioned matrices, you can use numpy linalg cond to compute its condition number [1] The larger the condition number, the more ill-conditioned the matrix is
- numpy. linalg. eig — NumPy v2. 3 Manual
numpy linalg eig # linalg eig(a) [source] # Compute the eigenvalues and right eigenvectors of a square array Parameters: a(…, M, M) array Matrices for which the eigenvalues and right eigenvectors will be computed Returns: A namedtuple with the following attributes: eigenvalues(…, M) array The eigenvalues, each repeated according to its
- Array manipulation routines — NumPy v2. 3 Manual
NumPy reference Routines and objects by topic Array manipulation routines
- numpy. matmul — NumPy v2. 3 Manual
numpy matmul(x1, x2, , out=None, *, casting='same_kind', order='K', dtype=None, subok=True[, signature, axes, axis]) = <ufunc 'matmul'> # Matrix product of two arrays
- numpy. linalg. solve — NumPy v2. 3 Manual
Solve a linear matrix equation, or system of linear scalar equations Computes the “exact” solution, x, of the well-determined, i e , full rank, linear matrix equation ax = b
|