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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 ... Read More
To replace NaN with zero and infinity with large finite numbers, use the numpy.nan_to_num() method in Python. The method returns, x, with the non-finite values replaced. If copy is False, this may be x itself. The 1st parameter is the input data. The 2nd parameter is copy, whether to create a copy of x (True) or to replace values in-place (False). The in-place operation only occurs if casting to an array does not require a copy. Default is True.The 3rd parameter is nan, the value to be used to fill NaN values. If no value is passed then NaN values ... Read More
To replace NaN with zero and infinity with large finite numbers, use the numpy.nan_to_num() method in Python. The method returns, x, with the non-finite values replaced. If copy is False, this may be x itself. The 1st parameter is the input data. The 2nd parameter is copy, whether to create a copy of x (True) or to replace values in-place (False). The in-place operation only occurs if casting to an array does not require a copy. Default is True.The 3rd parameter is nan, the value to be used to fill NaN values. If no value is passed then NaN values ... Read More
To return the element-wise square of the array input, use the numpy.square() method in Python. The method returns the element-wise x*x, of the same shape and dtype as x. This is a scalar if x is a scalar. The 1st parameter, x is the input data. The 2nd parameter, out is 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. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.The 3rd parameter, ... Read More
Return the lowest index in the string where substring sub is found using the numpy.char.index() method in Python Numpy. The method returns the output array of ints. Raises ValueError if sub is not found. The first parameter is the input array. The second parameter is the substring to be searched. The third and fourth parameter are optional arguments, wherein start and end are interpreted as in slice notation.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of strings −arr = np.array(['KATIE', 'KATE', 'CRATE']) Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype) Get the dimensions ... Read More
Return the lowest index in the string where substring sub is found using the numpy.char.index() method in Python Numpy. The method returns the output array of ints. Raises ValueError if sub is not found. The first parameter is the input array. The second parameter is the substring to be searched.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of strings −arr = np.array(['KATIE', 'KATE']) Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype) Get the dimensions of the Array −print("Array Dimensions...", arr.ndim)Get the shape of the Array −print("Our Array Shape...", arr.shape) Get the number of ... Read More
To solve a linear matrix equation, use the numpy.linalg.solve() method in Python. The method computes the “exact” solution, x, of the well-determined, i.e., full rank, linear matrix equation ax = b. Returns a solution to the system a x = b. Returned shape is identical to b. The 1st parameter a is the Coefficient matrix. The 2nd parameter b is the Ordinate or “dependent variable” values.StepsAt first, import the required libraries -import numpy as npCreating two 2D numpy arrays using the array() method. Consider the system of equations x0 + 2 * x1 = 1 and 3 * x0 + ... 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 arccosh() is a multivalued function: for each x there are infinitely many numbers z such that cosh(z) = x. The convention is to return the z whose imaginary part lies in [-pi, pi] and the real part in [0, inf]. For real-valued input data types, arccosh always returns real output. For each value that cannot be expressed as a real number or infinity, it yields nan and sets the invalid floating point error flag. For complex-valued input, arccosh is a complex analytical function that has a branch cut [-inf, 1] and is continuous from above on it.To compute the ... Read More
To get the Inner product of two 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 One-Dimensional array using the array() method −arr1 = np.array([5, 10, 15]) arr2 = np.array([20, 25, 30])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...", arr2.ndim)Check the Shape ... Read More