Dunder Methods
Published:
What are special methods?
They are ‘predefined’ methods that allow you to customize the behavior of classes. Can also be referred to as “dunder” (double-underscore) methods.
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Books
More About me
Published:
This website will primarily focus on my professional and academic development, so you likely won’t find posts on topics like politics, philosophy, religion, music, or personal anecdotes. However, to add a more personal touch, here are a few of my favorite books on topics I do enjoy—though, these days, finding time to read them is a luxury:
Computer Vision
Harris Detector
Published:
Harris Detector
MinHashing
TMAP
Published:
The general process of TMAP looks like this:
Python
Dunder Methods
Published:
What are special methods?
They are ‘predefined’ methods that allow you to customize the behavior of classes. Can also be referred to as “dunder” (double-underscore) methods.
Random
More About me
Published:
This website will primarily focus on my professional and academic development, so you likely won’t find posts on topics like politics, philosophy, religion, music, or personal anecdotes. However, to add a more personal touch, here are a few of my favorite books on topics I do enjoy—though, these days, finding time to read them is a luxury:
TMAP
KNN-Graph
Published:
Let’s say we have a dataset of 10 molecules (A through J), and we want to construct a 3-NN graph (k=3) where k indicates the number of neighbours.
TMAP
Published:
The general process of TMAP looks like this:
LSH Forest
Published:
Locality-sensitive hashing or LSH, allows us to focus on pairs that are likely to be similar, instead of having to look at all pairs possible. It reduces therefore the amount of computational time required by a brute force approach.
MinHashing
Published:
TL;DR: MinHashing is a way to reduce the dimensionality of fingerprints. Instead of comparing 512-bit vectors directly -these are the vectors that we obtain after the fingerprint is applied to a SMILES format molecule-, MinHash reduces these vectors to smaller hashes that preserve similarity between molecules.
category1
MinHashing
Published:
TL;DR: MinHashing is a way to reduce the dimensionality of fingerprints. Instead of comparing 512-bit vectors directly -these are the vectors that we obtain after the fingerprint is applied to a SMILES format molecule-, MinHash reduces these vectors to smaller hashes that preserve similarity between molecules.
category2
MinHashing
Published:
TL;DR: MinHashing is a way to reduce the dimensionality of fingerprints. Instead of comparing 512-bit vectors directly -these are the vectors that we obtain after the fingerprint is applied to a SMILES format molecule-, MinHash reduces these vectors to smaller hashes that preserve similarity between molecules.