Common Distance Metrics Implemented in Ruby

2023/07/22
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The article introduces various distance metrics commonly used in machine learning tasks and demonstrates how they can be implemented in Ruby. The author starts by explaining the importance of proximity in clustering algorithms, nearest-neighbor algorithms, and recommendation systems. They then delve into different distance measures, beginning with the Euclidean distance, which is a generalized version of the familiar 2D and 3D distance calculations. The article provides a straightforward Ruby implementation for calculating the Euclidean distance between two vectors.

Next, the author introduces the Manhattan distance, also known as the City Block distance. This metric restricts movement along the axes of a grid system, similar to navigating city streets. The article includes a Ruby implementation for the Manhattan distance as well.

Other distance metrics discussed in the article include the Cosine similarity and Jaccard distance, which have slightly different conditions for calculation. The author emphasizes the importance of ensuring vectors are of the same length and not comparing vectors in different dimensional spaces.

Overall, this article serves as a valuable resource for developers working on machine learning tasks or seeking to understand and implement distance metrics in Ruby. The provided code examples and explanations make it easier for developers to incorporate these metrics into their projects.