quantrs2-ml 0.1.3

//! Core quantum clustering functionality

use crate::dimensionality_reduction::QuantumDistanceMetric;
use crate::error::{MLError, Result};
use scirs2_core::ndarray::{Array1, Array2};

use super::config::*;

/// Clustering result containing labels and metadata
#[derive(Debug, Clone)]
pub struct ClusteringResult {
    /// Cluster labels for each data point
    pub labels: Array1<usize>,
    /// Number of clusters found
    pub n_clusters: usize,
    /// Cluster centers (if available)
    pub cluster_centers: Option<Array2<f64>>,
    /// Inertia/within-cluster sum of squares (if available)
    pub inertia: Option<f64>,
    /// Cluster probabilities (for soft clustering)
    pub probabilities: Option<Array2<f64>>,
}

/// Main quantum clusterer
#[derive(Debug)]
pub struct QuantumClusterer {
    config: QuantumClusteringConfig,
    cluster_centers: Option<Array2<f64>>,
    labels: Option<Array1<usize>>,
    // Algorithm-specific configurations
    pub kmeans_config: Option<QuantumKMeansConfig>,
    pub dbscan_config: Option<QuantumDBSCANConfig>,
    pub spectral_config: Option<QuantumSpectralConfig>,
    pub fuzzy_config: Option<QuantumFuzzyCMeansConfig>,
    pub gmm_config: Option<QuantumGMMConfig>,
}

impl QuantumClusterer {
    /// Create new quantum clusterer
    pub fn new(config: QuantumClusteringConfig) -> Self {
        Self {
            config,
            cluster_centers: None,
            labels: None,
            kmeans_config: None,
            dbscan_config: None,
            spectral_config: None,
            fuzzy_config: None,
            gmm_config: None,
        }
    }

    /// Create quantum K-means clusterer
    pub fn kmeans(config: QuantumKMeansConfig) -> Self {
        let mut clusterer = Self::new(QuantumClusteringConfig {
            algorithm: ClusteringAlgorithm::QuantumKMeans,
            n_clusters: config.n_clusters,
            max_iterations: config.max_iterations,
            tolerance: config.tolerance,
            num_qubits: 4,
            random_state: config.seed,
        });
        clusterer.kmeans_config = Some(config);
        clusterer
    }

    /// Create quantum DBSCAN clusterer
    pub fn dbscan(config: QuantumDBSCANConfig) -> Self {
        let mut clusterer = Self::new(QuantumClusteringConfig {
            algorithm: ClusteringAlgorithm::QuantumDBSCAN,
            n_clusters: 0, // DBSCAN determines clusters automatically
            max_iterations: 100,
            tolerance: 1e-4,
            num_qubits: 4,
            random_state: config.seed,
        });
        clusterer.dbscan_config = Some(config);
        clusterer
    }

    /// Create quantum spectral clusterer
    pub fn spectral(config: QuantumSpectralConfig) -> Self {
        let mut clusterer = Self::new(QuantumClusteringConfig {
            algorithm: ClusteringAlgorithm::QuantumSpectral,
            n_clusters: config.n_clusters,
            max_iterations: 100,
            tolerance: 1e-4,
            num_qubits: 4,
            random_state: config.seed,
        });
        clusterer.spectral_config = Some(config);
        clusterer
    }

    /// Fit the clustering model
    pub fn fit(&mut self, data: &Array2<f64>) -> Result<ClusteringResult> {
        // Placeholder implementation
        let n_clusters = if self.config.algorithm == ClusteringAlgorithm::QuantumDBSCAN {
            // DBSCAN determines clusters automatically
            2 // placeholder
        } else {
            self.config.n_clusters
        };
        let n_features = data.ncols();
        let n_samples = data.nrows();

        // Create placeholder cluster centers
        let cluster_centers = Array2::zeros((n_clusters, n_features));
        let labels = Array1::zeros(n_samples);

        // Store for later use
        self.cluster_centers = Some(cluster_centers.clone());
        self.labels = Some(labels.clone());

        // Create result
        let result = ClusteringResult {
            labels,
            n_clusters,
            cluster_centers: Some(cluster_centers),
            inertia: Some(0.0),  // placeholder
            probabilities: None, // Will be set for fuzzy clustering
        };

        Ok(result)
    }

    /// Predict cluster labels
    pub fn predict(&self, data: &Array2<f64>) -> Result<Array1<usize>> {
        if self.cluster_centers.is_none() {
            return Err(MLError::ModelNotTrained(
                "Clusterer must be fitted before predict".to_string(),
            ));
        }

        Ok(Array1::zeros(data.nrows()))
    }

    /// Predict cluster probabilities (for soft clustering)
    pub fn predict_proba(&self, data: &Array2<f64>) -> Result<Array2<f64>> {
        if self.cluster_centers.is_none() {
            return Err(MLError::ModelNotTrained(
                "Clusterer must be fitted before predict_proba".to_string(),
            ));
        }

        let n_samples = data.nrows();
        let n_clusters = self.config.n_clusters;

        // Return uniform probabilities as placeholder
        Ok(Array2::from_elem(
            (n_samples, n_clusters),
            1.0 / n_clusters as f64,
        ))
    }

    /// Compute quantum distance between two points
    pub fn compute_quantum_distance(
        &self,
        point1: &Array1<f64>,
        point2: &Array1<f64>,
        metric: QuantumDistanceMetric,
    ) -> Result<f64> {
        // Placeholder implementation for quantum distance computation
        match metric {
            QuantumDistanceMetric::QuantumEuclidean => {
                let diff = point1 - point2;
                Ok(diff.dot(&diff).sqrt())
            }
            QuantumDistanceMetric::QuantumManhattan => {
                Ok((point1 - point2).mapv(|x| x.abs()).sum())
            }
            QuantumDistanceMetric::QuantumCosine => {
                let dot_product = point1.dot(point2);
                let norm1 = point1.dot(point1).sqrt();
                let norm2 = point2.dot(point2).sqrt();
                Ok(1.0 - (dot_product / (norm1 * norm2)))
            }
            _ => {
                // For other quantum metrics, return Euclidean as fallback
                let diff = point1 - point2;
                Ok(diff.dot(&diff).sqrt())
            }
        }
    }

    /// Fit and predict in one step
    pub fn fit_predict(&mut self, data: &Array2<f64>) -> Result<Array1<usize>> {
        let result = self.fit(data)?;
        Ok(result.labels)
    }

    /// Get cluster centers
    pub fn cluster_centers(&self) -> Option<&Array2<f64>> {
        self.cluster_centers.as_ref()
    }

    /// Evaluate clustering performance
    pub fn evaluate(
        &self,
        data: &Array2<f64>,
        _true_labels: Option<&Array1<usize>>,
    ) -> Result<ClusteringMetrics> {
        if self.cluster_centers.is_none() {
            return Err(MLError::ModelNotTrained(
                "Clusterer must be fitted before evaluation".to_string(),
            ));
        }

        // Placeholder evaluation metrics
        Ok(ClusteringMetrics {
            silhouette_score: 0.5,
            davies_bouldin_index: 1.0,
            calinski_harabasz_index: 100.0,
            inertia: 0.0,
            adjusted_rand_index: None,
            normalized_mutual_info: None,
        })
    }
}

/// Clustering evaluation metrics
#[derive(Debug, Clone)]
pub struct ClusteringMetrics {
    /// Silhouette score
    pub silhouette_score: f64,
    /// Davies-Bouldin index
    pub davies_bouldin_index: f64,
    /// Calinski-Harabasz index
    pub calinski_harabasz_index: f64,
    /// Within-cluster sum of squares
    pub inertia: f64,
    /// Adjusted Rand Index (if true labels provided)
    pub adjusted_rand_index: Option<f64>,
    /// Normalized Mutual Information (if true labels provided)
    pub normalized_mutual_info: Option<f64>,
}

/// Helper function to create default quantum K-means clusterer
pub fn create_default_quantum_kmeans(n_clusters: usize) -> QuantumClusterer {
    let config = QuantumKMeansConfig {
        n_clusters,
        ..Default::default()
    };
    QuantumClusterer::kmeans(config)
}

/// Helper function to create default quantum DBSCAN clusterer
pub fn create_default_quantum_dbscan(eps: f64, min_samples: usize) -> QuantumClusterer {
    let config = QuantumDBSCANConfig {
        eps,
        min_samples,
        ..Default::default()
    };
    QuantumClusterer::dbscan(config)
}