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Solve the tensor equation in Python
To solve the tensor equation, use the numpy.linalg.tensorsolve() method in Python. It is assumed that all indices of x are summed over in the product, together with the rightmost indices of a, as is done in, for example, tensordot(a, x, axes=b.ndim).
The 1st parameter, a is a coefficient tensor, of shape b.shape + Q. Q, a tuple, equals the shape of that sub-tensor of a consisting of the appropriate number of its rightmost indices, and must be such that prod(Q) == prod(b.shape). The 2nd parameter, b is a right-hand tensor, which can be of any shape. The 3rd parameter, axis is an axes in a to reorder to the right, before inversion. If None (default), no reordering is done.
Steps
At first, import the required libraries-
import numpy as np
Creating two numpy arrays using the array() method
arr1 = np.eye(2*3*4) arr1.shape = (2*3, 4, 2, 3, 4) arr2 = np.random.randn(2*3, 4)
Display the arrays −
print("Array1...\n",arr1)
print("\nArray2...\n",arr2)Check the Dimensions of both the arrays −
print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)Check the Shape of both the arrays −
print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)To solve the tensor equation, use the numpy.linalg.tensorsolve() method in Python. It is assumed that all indices of x are summed over in the product, together with the rightmost indices of a, as is done in, for example, tensordot(a, x, axes=b.ndim) −
print("\nResult...\n",np.linalg.tensorsolve(arr1, arr2))Example
import numpy as np
# Creating two numpy arrays using the array() method
arr1 = np.eye(2*3*4)
arr1.shape = (2*3, 4, 2, 3, 4)
arr2 = np.random.randn(2*3, 4)
# Display the arrays
print("Array1...\n",arr1)
print("\nArray2...\n",arr2)
# Check the Dimensions of both the arrays
print("\nDimensions of Array1...\n",arr1.ndim)
print("\nDimensions of Array2...\n",arr2.ndim)
# Check the Shape of both the arrays
print("\nShape of Array1...\n",arr1.shape)
print("\nShape of Array2...\n",arr2.shape)
# To solve the tensor equation, use the numpy.linalg.tensorsolve() method in Python.
print("\nResult...\n",np.linalg.tensorsolve(arr1, arr2))Output
Array1... [[[[[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.]]]]] Array2... [[ 0.31376716 0.63443741 0.58628101 0.62313096] [ 1.12528958 -1.18403238 -0.64663325 -0.24241201] [ 0.55598965 -2.00059925 -1.97946414 -1.72478953] [ 0.18976226 0.60572953 1.50157692 -2.4491463 ] [ 0.42461806 -2.17872016 0.49677904 -1.11634625] [-1.09074462 0.35475618 0.42474987 -1.34391368]] Dimensions of Array1... 5 Dimensions of Array2... 2 Shape of Array1... (6, 4, 2, 3, 4) Shape of Array2... (6, 4) Result... [[[ 0.31376716 0.63443741 0.58628101 0.62313096] [ 1.12528958 -1.18403238 -0.64663325 -0.24241201] [ 0.55598965 -2.00059925 -1.97946414 -1.72478953]] [[ 0.18976226 0.60572953 1.50157692 -2.4491463 ] [ 0.42461806 -2.17872016 0.49677904 -1.11634625] [-1.09074462 0.35475618 0.42474987 -1.34391368]]]
Updated on 25-Feb-2022 06:55:20
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