The process of turning a colored image into black and white is known as gray scaling. A colorful image can be converted to monochrome in python using more than one method. In this article, we will look at one of the many ways for doing the same. The four main methods in which gray scaling […]
Convert an RGB image into grayscale using Matplotlib Read More »
In python, the Base exception is the class from which all instances of exceptions are derived. Exceptions may be raised by built-in functions or the interpreter or both. User-defined programs can also raise exceptions . One such example of a built-in exception is the LinAlgError. The LinAlgError is raised by the linalg or the linear
Numpy LinAlgError – Handling Matrix-related Errors Read More »
Numpy contains a multitude of functions for easier mathematical calculations. One such function is the numpy linalg.slogdet() which is used to compute the sine and natural logarithm of the determinant of an array. In multivariate statistics, the log determinant of a matrix is useful in a high-dimensional space. It is used to determine the convexity
Numpy linalg.slogdet – Compute logarithmic determinant of arrays Read More »
The NumPy library in Python provides a powerful set of tools for numerical and scientific computing. One of the important functions in NumPy is the linalg.lstsq function, which solves the linear matrix equation using the least-squares method. This function is commonly used in a variety of applications such as regression analysis, curve fitting, and other
Numpy linalg.lstsq – Return the least-squares solution to a linear matrix equation Read More »
The Numpy library provides a function linalg.eigvalsh that calculates the eigenvalues of a complex Hermitian or real symmetric matrix. This function is efficient and robust, making it a valuable tool for linear algebraic computations. In this article, we will explore the functionality of linalg.eigvalsh and how it can be used to obtain the eigenvalues of
Numpy linalg.eigvalsh: A Guide to Eigenvalue Computation Read More »
The condition number defines the sensitivity of the output with respect to minute changes in the input data. It is an important feature of a matrix as it helps in identifying rounding off errors while solving systems of linear equations. It predicts the worst-case change in output due to some relative change in the input
Numpy linalg.cond() – Compute the condition number of a matrix Read More »
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 »
Scientific calculations can sometimes be extremely tedious when done manually especially when they involve matrices of humongous sizes and complicated values. The Numerical Python or simply the Numpy library already contains built-in functions involving matrices and linear algebra that make computing faster and more accurate. In this article, we will dissect and look at the
Numpy linalg.inv – Compute the (multiplicative) inverse of a matrix Read More »