SelfOrganizingMap

saorsa_core::adaptive::som

Struct SelfOrganizingMap 

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pub struct SelfOrganizingMap { /* private fields */ }
Expand description

Self-Organizing Map for node clustering

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impl SelfOrganizingMap

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pub fn new(config: SomConfig) -> Self

Create a new Self-Organizing Map

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pub fn find_best_matching_unit(&self, features: &NodeFeatures) -> (usize, usize)

Find the Best Matching Unit (BMU) for given features

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pub fn gaussian_neighborhood(distance: f64, radius: f64) -> f64

Calculate Gaussian neighborhood function

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pub fn get_neighborhood_radius(&self, iteration: usize) -> f64

Get current neighborhood radius based on iteration

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pub fn get_learning_rate(&self, iteration: usize) -> f64

Get current learning rate based on iteration

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pub fn train_single(&mut self, features: &NodeFeatures, iteration: usize)

Train the SOM with a single sample

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pub fn train_batch(&mut self, features_batch: &[NodeFeatures])

Train the SOM with a batch of samples

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pub fn assign_node(&mut self, node_id: NodeId, features: NodeFeatures)

Assign a node to its BMU

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pub fn get_assigned_nodes(&self, x: usize, y: usize) -> HashSet<NodeId>

Get nodes assigned to a specific neuron

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pub fn get_all_assigned_nodes(&self) -> HashSet<NodeId>

Get all assigned nodes

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pub fn find_similar_nodes( &self, features: &NodeFeatures, radius: usize, ) -> Vec<NodeId> ⓘ

Find nodes similar to given features

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pub fn get_grid_dimensions(&self) -> (usize, usize)

Get current grid dimensions

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pub fn set_neuron_weights(&mut self, x: usize, y: usize, weights: Vec<f64>)

Set neuron weights (for testing)

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pub fn get_neuron_weights(&self, x: usize, y: usize) -> Option<Vec<f64>>

Get neuron weights (for testing)

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pub fn get_visualization_data(&self) -> VisualizationData

Get visualization data for the SOM

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pub fn generate_u_matrix(&self) -> Vec<Vec<f64>>

Generate U-Matrix (unified distance matrix) for visualization

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