Skip to main content

calculate condition number and determinant in R and python


Introduction:


If you are working on machine learning, more than often, you will need to perform different types of matrix manipulations in r/python. I will mention some of such manipulations and regarding functions to use in r and python.

condition number finding:

condition number is generally used to find the stability as well non-singularity of a matrix. It is defined to be the ratio of absolute value of highest singular value and smallest singular value ( in terms of mod value). If a matrix has condition number more than 1000, then it is generally considered to be a unstable matrix.

For finding condition number of a matrix in r, we have to use the kappa() function. For normally using kappa, you need to use two parameters. First input to kappa has to be the matrix. The second one is exact parameter. This exact is set to be FALSE in general. In this setting, a cheap (computationally) approximation of condition number is obtained and provided. If you set kappa to be TRUE, then the method uses SVD and provides the exact value.

so the usecase is condition_number = kappa( matrix, exact = FALSE)

In case of python, you can import cond from numpy.linalg, i.e. the linear algebra library. cond takes the matrix as parameter and calculates the condition number.

determinant:

For determinant, it is amazing that both numpy.linalg and R has same function, called det(). det(), in both cases takes the matrix as parameter and provides the determinant value.
Labels:

Comments

Post a Comment

Popular posts from this blog

Mastering SQL for Data Science: Top SQL Interview Questions by Experience Level

Introduction: SQL (Structured Query Language) is a cornerstone of data manipulation and querying in data science. SQL technical rounds are designed to assess a candidate’s ability to work with databases, retrieve, and manipulate data efficiently. This guide provides a comprehensive list of SQL interview questions segmented by experience level—beginner, intermediate, and experienced. For each level, you'll find key questions designed to evaluate the candidate’s proficiency in SQL and their ability to solve data-related problems. The difficulty increases as the experience level rises, and the final section will guide you on how to prepare effectively for these rounds. Beginner (0-2 Years of Experience) At this stage, candidates are expected to know the basics of SQL, common commands, and elementary data manipulation. What is SQL? Explain its importance in data science. Hint: Think about querying, relational databases, and data manipulation. What is the difference between WHERE ...
Post a Comment
Read more

Spacy errors and their solutions

 Introduction: There are a bunch of errors in spacy, which never makes sense until you get to the depth of it. In this post, we will analyze the attribute error E046 and why it occurs. (1) AttributeError: [E046] Can't retrieve unregistered extension attribute 'tag_name'. Did you forget to call the set_extension method? Let's first understand what the error means on superficial level. There is a tag_name extension in your code. i.e. from a doc object, probably you are calling doc._.tag_name. But spacy suggests to you that probably you forgot to call the set_extension method. So what to do from here? The problem in hand is that your extension is not created where it should have been created. Now in general this means that your pipeline is incorrect at some level.  So how should you solve it? Look into the pipeline of your spacy language object. Chances are that the pipeline component which creates the extension is not included in the pipeline. To check the pipe eleme...
Post a Comment
Read more

20 Must-Know Math Puzzles for Data Science Interviews: Test Your Problem-Solving Skills

Introduction:   When preparing for a data science interview, brushing up on your coding and statistical knowledge is crucial—but math puzzles also play a significant role. Many interviewers use puzzles to assess how candidates approach complex problems, test their logical reasoning, and gauge their problem-solving efficiency. These puzzles are often designed to test not only your knowledge of math but also your ability to think critically and creatively. Here, we've compiled 20 challenging yet exciting math puzzles to help you prepare for data science interviews. We’ll walk you through each puzzle, followed by an explanation of the solution. 1. The Missing Dollar Puzzle Puzzle: Three friends check into a hotel room that costs $30. They each contribute $10. Later, the hotel realizes there was an error and the room actually costs $25. The hotel gives $5 back to the bellboy to return to the friends, but the bellboy, being dishonest, pockets $2 and gives $1 back to each friend. No...
Post a Comment
Read more