Numpy or numerical python is a free software tool that makes scientific computations easy in python. It is very powerful python library that provides a huge range of functions for operating on arrays. It contains logical, linear algebraic and also…
In this post, we will learn about the Cholesky decomposition, definitions of the terms related to this decomposition, the syntax for the function used to implement Cholesky decomposition, and a few examples. What is Matrix Decomposition? In linear algebra, matrix…
The dot product of the arrays can be calculated by the function called linalg.multi_dot of the NumPy library, which is easier to compute and takes less computational time than its predecessor, np.dot. We are also going to see the comparison…
nan_to_num is a function of numpy library that converts NaN (not a number) to a numeric value. The NaN is generally replaced with zero whenever we call upon nan_to_num function. We might come across some datasets with records or values…
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…
When you want to try your luck with solving a system of linear equations, your odds are better if you have the information on the rank of the matrices beforehand. The possibility of deducing a solution is concrete when the…
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…
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…
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 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.…