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17 lines
1.2 KiB
17 lines
1.2 KiB
2 years ago
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# city-transformers
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Generates dataset of cities (US only for now) and their geodesic distances.
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Uses that dataset to fine-tune a neural-net to understand that cities closer to one another are more similar.
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Distances become `labels` through the formula `1 - distance/MAX_DISTANCE`, where `MAX_DISTANCE=20_037.5 # km` represents half of the Earth's circumfrence.
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There are other factors that can make cities that are "close together" on the globe "far apart" in reality, due to political borders.
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Factors like this are not considered in this model, it is only considering geography.
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However, for use-cases that involve different measures of distances (perhaps just time-zones, or something that considers the reality of travel), the general principals proven here should be applicable (pick a metric, generate data, train).
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A particularly useful addition to the dataset here:
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- airports: they (more/less) have unique codes, and this semantic understanding would be helpful for search engines.
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- aliases for cities: the dataset used for city data (lat/lon) contains a pretty exhaustive list of aliases for the cities. It would be good to generate examples of these with a distance of 0 and train the model on this knowledge.
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see `Makefile` for instructions.
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