clustering

type CentroidBasedClusterer interface {
	Clusterer

	// GetCentroids returns the cluster centroids
	GetCentroids() []Point

	// GetInertia returns the within-cluster sum of squares
	GetInertia() float64
}

type Cluster struct {
	Points   []int // Indices of points in the cluster
	Centroid Point // Centroid of the cluster (if applicable)
	Label    int   // Cluster label/ID
}

type ClusterResult struct {
	Clusters []Cluster
	Labels   []int                  // Label for each point (-1 for noise)
	Metadata map[string]interface{} // Algorithm-specific metadata
}

type Clusterer interface {
	// Fit trains the clustering algorithm on the provided dataset
	Fit(data Dataset) error

	// Predict returns cluster assignments for the provided data
	Predict(data Dataset) (*ClusterResult, error)

	// FitPredict is a convenience method that fits and predicts in one step
	FitPredict(data Dataset) (*ClusterResult, error)

	// GetParams returns the algorithm parameters
	GetParams() map[string]interface{}

	// SetParams sets the algorithm parameters
	SetParams(params map[string]interface{}) error
}

type ClusteringMetrics struct {
	SilhouetteScore  float64
	DaviesBouldin    float64
	CalinskiHarabasz float64
}

type Dataset []Point

type DensityBasedClusterer interface {
	Clusterer

	// GetOrdering returns the ordering of points (for algorithms like OPTICS)
	GetOrdering() []int

	// GetReachabilityDistances returns reachability distances (for OPTICS)
	GetReachabilityDistances() []float64

	// GetCoreDistances returns core distances (for OPTICS)
	GetCoreDistances() []float64
}

type Point []float64