Finding an inverse is one of the peculiar operations to be carried out in the field of Mathematics. In this article, we shall explore one such function from the numpy library to its length & breadth. This function is used to return the inverse of an N-dimensional array. For those who haven’t figured it out yet, […]
Numpy linalg.tensorinv( ): Computing Inverse of an N-Dimensional Array Read More »
Digital electronics always rely on processing signals for their routine functioning. The support from Python extends to this part of the spectrum too! The operation of combining signals is known as convolution and Python has an exclusive function to carry it out. This function lies within the numpy library. So, let us start by importing
Working with matrices is always fascinating. The numpy library has a ton of functions that helps with carrying out complicated calculations using matrices. In this article, we shall demonstrate one such function that decomposes a given matrix into a pair of entities. This process is called factorization of a matrix & the function of interest
How to find the QR Factorization of Matrix using Numpy? Read More »
This article will talk about the Numpy i0 function. The year was 1817. It was a fine evening when a German astronomer by the name of Friedrich Wilhelm Bessel was taking a closer look at the movement of planets. Well, what else could he have referred to rather than the most advanced source available around
In this article, we will understand and implement numpy.interp() which is a NumPy function. When given discrete data points (xp, fp), this function returns the one-dimensional piecewise linear interpolant to that function, which is evaluated at x. What is Numpy interp? numpy.interp() calculates linear interpolant to a function with given data points, the data points given (xp values)are
This article would be covering a rather strange function within the numpy library of Python. It is so strange that at prima facie one might even question its very existence. Enter the numpy.positive( ) function! It returns the element-wise numerical positive for the input array. Makes sense right? But, here is the catch! It does
There are many articles in AskPython detailing the various functions that the numpy library of Python has got to offer. In this article, we shall explore one which combines the logarithmic and exponential functions to the base of 2 – the logaddexp2( ) function. Let’s get things started by first importing the numpy library using the
The numpy or the numerical python library is like a gold mine of functions that can be used for computing difficult linear algebraic problems. One such function is the numpy linalg.eigh() which can be used to calculate the eigenvalues and the eigenvectors of a complex Hermitian or a real symmetric matrix. A Hermitian matrix is
Numpy linalg.eigh: Finding Eigenvalues and Vectors of Symmetric Matrices Read More »
Normalization of the matrix is to scale the elements of the matrix in such a way that their values remain between zero and one. Depending on the order of a matrix, the function linalg.norm() returns one of the seven/eight different matrix norms or in some cases one of the many infinite matrix norms depending on
Matrices in mathematics are rectangular arrangements of an array of homogeneous data types such as numbers, symbols, or expressions. They are arranged in the form of rows and columns which represents objects or some specific properties of that object. Eigenvalues are values that are scalar in type and are associated with linear matrix equations. They
Numpy linalg.eigvals – Compute the eigenvalues of a general matrix Read More »