Sequence Types
Published:
Built-in Sequences in Python
Python provides several built-in sequence types that are implemented in C, which are divided into two categories based on how they store their contents: Container sequences and Flat sequences.
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Books
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.
More About me
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Hardware
Computer Vision
Harris Detector
Published:
Widely used for corner detection in computer
Graph Theory
Laplacian Positional Encoding
Published:
Laplacian Positional Encoding
MinHashing
TMAP
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The general process of TMAP looks like this:
Python
Introduction to Unittesting
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Why use Unit Testing?
Unittesting for any serious project is mandatory, not optional. It’s a good negative indicator -i.e. it points out poor-quality code with relatively high accuracy-. If the code is hard to unit test, it’s a strong sing that the code needs improvement.
Sequence Types
Published:
Built-in Sequences in Python
Python provides several built-in sequence types that are implemented in C, which are divided into two categories based on how they store their contents: Container sequences and Flat sequences.
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:
Hardware
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
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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.