Numpy linalg tensorsolve() function is used to calculate the equation of ax=b for x. It is assumed that all x indices are summarized above the product and the right indices of a, as is done.
Numpy linalg tensorsolve() function is used to calculate the equation of ax=b for x. It is assumed that all x indices are summarized above the product and the right indices of a, as is done. For example, tensordot (a, x, axes = b.ndim).
numpy.linalg.tensorsolve(A, B, axes=None )
The linalg tensorsolve() function returns a ndarray of shape the same as Q.
The linalg tensorsolve() function throws LinAlgError if A is singular or not a square matrix.
## Program to show working of solve() import numpy as np ## creating the array "a" A = np.array([[3, 4, 5], [1, 2, 3], [2, 4, 5]]) B = np.array([9, 8, 7]) print("Array A is: \n", A) print("Array B is : \n", B) ## Calculating the equation ans = np.linalg.tensorsolve(A, B) ## Printing the answer print("Answer of the equation is :\n", ans) ## Checking if the answer if correct print(np.allclose(np.dot(A, ans), B))
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