To get the Inner product of two multi-dimensional arrays, use the numpy.inner() method in Python. Ordinary inner product of vectors for 1-D arrays, in higher dimensions a sum product over the last axes. The parameters are 1 and b, two vectors. If a and b are nonscalar, their last dimensions must match.StepsAt first, import the required libraries -import numpy as npCreating two numpy Two-Dimensional array using the array() method −arr1 = np.array([[5, 10], [15, 20]]) arr2 = np.array([[6, 12], [18, 24]])Display the arrays −print("Array1...", arr1) print("Array2...", arr2)Check the Dimensions of both the arrays −print("Dimensions of Array1...", arr1.ndim) print("Dimensions of Array2...", ... Read More

The arcsinh is a multivalued function: for each x there are infinitely many numbers z such that sinh(z) = x. The convention is to return the z whose imaginary part lies in [-pi/2, pi/2]. For real-valued input data types, arcsinh always returns real output. For each value that cannot be expressed as a real number or infinity, it returns nan and sets the invalid floating point error flag. For complex-valued input, arccos is a complex analytical function that has branch cuts [1j, infj] and [-1j, -infj] and is continuous from the right on the former and from the left on ... Read More

To compute the Hyperbolic cosine, use the numpy.cosh() method in Python Numpy. The method is equivalent to 1/2 * (np.exp(x) + np.exp(-x)) and np.cos(1j*x). Returns the corresponding hyperbolic cosine values. This is a scalar if x is a scalar. The 1st parameter, x is input array. The 2nd and 3rd parameters are optional.The 2nd parameter is an ndarray, A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. The 3rd parameter is the condition is broadcast over the input. At ... Read More

To compute the Hyperbolic sine of the array elements, use the numpy.sinh() method in Python Numpy. The method is equivalent to 1/2 * (np.exp(x) - np.exp(-x)) or -1j * np.sin(1j*x). Returns the corresponding hyperbolic sine values. This is a scalar if x is a scalar. The 1st parameter, x is input array. The 2nd and 3rd parameters are optional.The 2nd parameter is an ndarray, A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.The 3rd parameter is the condition is ... Read More

To convert a radian array to degrees, use the numpy.rad2deg() method in Python Numpy. The method returns the corresponding angle in degrees. This is a scalar if x is a scalar. The 1st parameter is an input angle in radians. The 2nd and 3rd parameters are optional.The 2nd parameter is an ndarray, A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.The 3rd parameter is the condition is broadcast over the input. At locations where the condition is True, the ... Read More

To compute log-determinants for a stack of matrices, use the numpy.linalg.slogdet() method in Python. The 1st parameter, s is an input array, has to be a square 2-D array. The method, with sign returns a number representing the sign of the determinant. For a real matrix, this is 1, 0, or -1. For a complex matrix, this is a complex number with absolute value 1, or else 0.The method, with logdet returns the natural log of the absolute value of the determinant. If the determinant is zero, then sign will be 0 and logdet will be -Inf. In all cases, ... Read More

To convert a radian array to degrees, use the numpy.degrees() method in Python Numpy. The 1st parameter is an input array in radians. The 2nd and 3rd parameters are optional. The 2nd parameter is an ndarray, A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.The 3rd parameter is the condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its ... Read More

To return matrix rank of array using Singular Value Decomposition method, use the numpy.linalg.matrix_rank() method in Python. Rank of the array is the number of singular values of the array that are greater than tol. The 1st parameter, A is the input vector or stack of matrices.The 2nd parameter, tol is the Threshold below which SVD values are considered zero. If tol is None, and S is an array with singular values for M, and eps is the epsilon value for datatype of S, then tol is set to S.max() * max(M, N) * eps. The 3rd parameter, hermitian, If ... Read More

The quadrant is chosen so that arctan2(x1, x2) is the signed angle in radians between the ray ending at the origin and passing through the point (1, 0), and the ray ending at the origin and passing through the point (x2, x1).The 1st parameter is the y-coordinates. The 2nd parameter is the x-coordinates. If x1.shape != x2.shape, they must be broadcastable to a common shape. The method returns array of angles in radians, in the range [-pi, pi]. This is a scalar if both x1 and x2 are scalars.StepsAt first, import the required library −import numpy as npCreating arrays using ... Read More

To compute the determinant for a stack of matrices in linear algebra, use the np.linalg.det() in Python Numpy. The 1st parameter, a is the input array to compute determinants for. The method returns the determinant of a.StepsAt first, import the required libraries -import numpy as npCreate an array −arr = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])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 determinant for a stack of ... Read More