Quentin Dauprat, PhD
65 lines
2.0 KiB
TOML
65 lines
2.0 KiB
TOML
name = "distance"
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description = "A formally verified library for calculating numeric, text distances"
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version = "0.1.0"
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authors = ["Heziode"]
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maintainers = ["Heziode <heziode@protonmail.com>"]
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maintainers-logins = ["heziode"]
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licenses = "Apache-2.0 WITH LLVM-exception"
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website = ""
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tags = ["ada", "spark", "distance", "metrics", "algorithms", "mathematics", "string", "similarity", "levenshtein", "geospatial", "high-integrity", "alire", "data-science"]
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long-description = """
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# Distance
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A formally verified Ada/SPARK library for computing distance and similarity metrics.
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## Features
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- **Numeric Distances**: Euclidean, Manhattan, Minkowski, Chebyshev, Canberra
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- **Statistical Measures**: Cosine Similarity
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- **Text Distances**: Levenshtein, Damerau-Levenshtein, Hamming, Jaro-Winkler, Sørensen-Dice
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## Highlights
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- Pure Ada 2012 with SPARK annotations
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- Generic implementations for flexible type support
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- No external dependencies (test suite uses AUnit)
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- Comprehensive preconditions and postconditions
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## Usage
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```ada
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with Ada.Numerics.Elementary_Functions;
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with Distance.Numeric.Signatures;
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with Distance.Numeric.Euclidean_Generic;
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procedure Example is
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type Float_Vector is array (Positive range <>) of Float;
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package Float_Math is new Ada.Numerics.Elementary_Functions (Float);
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package Float_Sig is new Distance.Numeric.Signatures
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(Element_Type => Float,
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Zero => 0.0,
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One => 1.0,
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Sqrt => Float_Math.Sqrt,
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"**" => Float_Math."**",
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Max_Element => Float'Last);
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function Euclidean is new Distance.Numeric.Euclidean_Generic
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(Numeric_Ops => Float_Sig, Index_Type => Positive, Vector => Float_Vector);
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D : Float := Euclidean ((0.0, 0.0), (3.0, 4.0)); -- D = 5.0
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begin
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null;
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end Example;
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```
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For fixed-point and integer types, you can use [Spark_Math](https://github.com/adarium-labs/spark_math) to provide the required operations.
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"""
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[origin]
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commit = "e265e32656db1284ef750cbd473db95424e5b215"
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url = "git+https://github.com/adarium-labs/distance.git"
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