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removing unused example
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5 files changed

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crates/radiate-engines/src/steps/speciate.rs

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@@ -133,6 +133,7 @@ where
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return Some(species_idx);
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}
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}
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None
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})
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.flatten();

docs/source/fitness.md

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@@ -311,24 +311,20 @@ You can implement your own behavioral descriptors by implementing the `Novelty`
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}
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// Create novelty search fitness function
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let novelty_fitness = NoveltySearch::new(
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MyModelBehaviorDescriptor,
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10, // k: number of nearest neighbors
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0.1 // threshold: novelty threshold for archive addition
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)
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.with_archive_size(1000) // Optional: set archive size - default is 1000
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.cosine_distance(); // Optional set the distance parameter used
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// .euclidean_distance() // euclidean_distance is the default
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// .hamming_distance()
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let novelty_fitness = NoveltySearch::new(MyModelBehaviorDescriptor)
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.k(10)
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.threshold(0.1)
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.with_archive_size(1000) // Optional: set archive size - default is 1000
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.cosine_distance(); // Optional set the distance parameter used
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// .euclidean_distance() // euclidean_distance is the default
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// .hamming_distance()
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// Novelty is also implemented for any F where F: Fn(&T) -> Vec<f32>. Meaning, you can
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// just as easily feed a function to NoveltySearch as long as it takes a borrowed T (&T)
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// and returns a Vec<f32>
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let function_novelty_fitness = NoveltySearch::new(
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|individual: &MyModel| // ... return a Vec<f32> that describes MyModel ...
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10,
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0.1
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)
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|individual: &MyModel| // ... return a Vec<f32> that describes MyModel ...
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);
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let engine = GeneticEngine::builder()
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// The decoded genotype from your codec (my_model_codec in this case) will be fed
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py-radiate/examples/image.py

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