![]() ![]() The existence of this separate matrix class should be a red flag–why would we need a separate matrix class if matrices are just 2D ndarrays? In fact, did you know that NumPy actually has a separate class named numpy.matrix? Probably not–it’s not what the Python community typically uses. But they’re not! They have some fundamental differences, and these differences are eventually going to trip you up if you’re not made aware of them. This is great, and it makes the transition to Python a lot easier.īased on these similarities, you’ll be tempted to think of the ndarray as generally equivalent to a Matlab matrix–I certainly did. NumPy arrays behave very similarly to variables in Matlab–for instance, they both support very similar syntax for making selections within a matrix. Once you have the basics of Python down, you’ll find that, in the machine learning field, we use NumPy ndarray to store our matrix and vector data. Side Note: The NumPy documentation has a very nice “quick reference” type guide on migrating from Matlab to NumPy here. Instead, I wanted to highlight some false assumptions that you may have brought with you from Matlab about how vector and matrix operations should work. Coding in Python obviously means learning a whole new programming language, with many important differences, but those aren’t the subject of this post. It’s also likely that you have since switched from Octave to Python. Octave is great for expressing linear algebra operations cleanly, and (as I hear it) for being easier for non-programmers to get going with. If your first foray into Machine Learning was with Andrew Ng’s popular Coursera course (which is where I started back in 2012!), then you learned the fundamentals of Machine Learning using example code in “Octave” (the open-source version of Matlab). However, best practice is to insert the explicit multiplication operator into your expressions.Chris McCormick About Membership Blog Archive Become an NLP expert with videos & code for BERT and beyond → Join NLP Basecamp now! Matrix Operations in NumPy vs. Note also that implicit multiplication is interpreted based on the operands, and when it can, Maple parses these as follows: For Vector/Matrix operands this will be interpreted as the `.` (dot) non-commutative multiplication operator, while for Array operands this will be interpreted as the elementwise operator: Note that when multiplying Arrays together (not with Vectors or Matrices), the standard multiplication operator will result in the elementwise product, so the `~` is not necessary: ![]() To multiply Vectors and/or Matrices and/or Arrays together using elementwise multiplication, use the standard multiplication operator, `*` followed by the "elementwise" operator, `~`: Implicit multiplication (using a space to mean multiplication) can also be ambiguous. ![]() To multiply Matrices and/or Vectors together using the standard Linear Algebra multiplication operation, use the non-commutative multiplication operator, `.` (dot): This error results if Matrices, or a Matrix and a Vector, are multiplied using a commutative multiplication operator, `*`: If instead you want to perform elementwise multiplication, use *~. This display can be modified through the interactive Typesetting Rule Assistant. Note that in 2-D math `*` displays as a center dot: `⋅`, and typing a dot (using the period key) displays as ` (dot) for Vector/Matrix multiplicationĪn expression involving the multiplication of Vectors and/or Matrices (possibly and/or Arrays) has been constructed using the standard multiplication operator, `*`, which is ambiguous. Error, (in rtable/Product) use *~ for elementwise multiplication of Vectors or Matrices use. ![]()
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