quantrs2-device 0.1.3

//! Correlation analysis components

use super::super::results::*;
use crate::DeviceResult;
use scirs2_core::ndarray::Array2;

/// Correlation analyzer for measurement data
pub struct CorrelationAnalyzer {
    // Configuration and state
}

impl CorrelationAnalyzer {
    /// Create new correlation analyzer
    pub const fn new() -> Self {
        Self {}
    }

    /// Perform correlation analysis
    pub fn analyze(
        &self,
        latencies: &[f64],
        confidences: &[f64],
        timestamps: &[f64],
    ) -> DeviceResult<CorrelationAnalysisResults> {
        if latencies.is_empty() || confidences.is_empty() || timestamps.is_empty() {
            return Ok(CorrelationAnalysisResults::default());
        }

        // Create data matrix (3 variables: latency, confidence, timestamp)
        let n_vars = 3;
        let n_samples = latencies.len();

        // Calculate correlation matrices
        let pearson_correlations =
            self.calculate_pearson_correlations(latencies, confidences, timestamps)?;
        let spearman_correlations =
            self.calculate_spearman_correlations(latencies, confidences, timestamps)?;
        let kendall_correlations =
            self.calculate_kendall_correlations(latencies, confidences, timestamps)?;

        // Find significant correlations
        let significant_correlations = self.find_significant_correlations(&pearson_correlations)?;

        // Calculate partial correlations
        let partial_correlations =
            self.calculate_partial_correlations(latencies, confidences, timestamps)?;

        // Perform network analysis
        let network_analysis = self.perform_network_analysis(&pearson_correlations)?;

        Ok(CorrelationAnalysisResults {
            pearson_correlations,
            spearman_correlations,
            kendall_correlations,
            significant_correlations,
            partial_correlations,
            network_analysis,
        })
    }

    /// Calculate Pearson correlation matrix
    fn calculate_pearson_correlations(
        &self,
        latencies: &[f64],
        confidences: &[f64],
        timestamps: &[f64],
    ) -> DeviceResult<Array2<f64>> {
        let variables = [latencies, confidences, timestamps];
        let n_vars = variables.len();
        let mut correlationmatrix = Array2::zeros((n_vars, n_vars));

        for i in 0..n_vars {
            for j in 0..n_vars {
                if i == j {
                    correlationmatrix[[i, j]] = 1.0;
                } else {
                    let corr = self.pearson_correlation(variables[i], variables[j]);
                    correlationmatrix[[i, j]] = corr;
                }
            }
        }

        Ok(correlationmatrix)
    }

    /// Calculate Spearman correlation matrix
    fn calculate_spearman_correlations(
        &self,
        latencies: &[f64],
        confidences: &[f64],
        timestamps: &[f64],
    ) -> DeviceResult<Array2<f64>> {
        // For simplicity, use rank correlation approximation
        let variables = [latencies, confidences, timestamps];
        let n_vars = variables.len();
        let mut correlationmatrix = Array2::zeros((n_vars, n_vars));

        for i in 0..n_vars {
            for j in 0..n_vars {
                if i == j {
                    correlationmatrix[[i, j]] = 1.0;
                } else {
                    // Convert to ranks and calculate Pearson on ranks
                    let ranks_i = self.convert_to_ranks(variables[i]);
                    let ranks_j = self.convert_to_ranks(variables[j]);
                    let corr = self.pearson_correlation(&ranks_i, &ranks_j);
                    correlationmatrix[[i, j]] = corr;
                }
            }
        }

        Ok(correlationmatrix)
    }

    /// Calculate Kendall correlation matrix
    fn calculate_kendall_correlations(
        &self,
        latencies: &[f64],
        confidences: &[f64],
        timestamps: &[f64],
    ) -> DeviceResult<Array2<f64>> {
        let variables = [latencies, confidences, timestamps];
        let n_vars = variables.len();
        let mut correlationmatrix = Array2::zeros((n_vars, n_vars));

        for i in 0..n_vars {
            for j in 0..n_vars {
                if i == j {
                    correlationmatrix[[i, j]] = 1.0;
                } else {
                    let corr = self.kendall_tau(variables[i], variables[j]);
                    correlationmatrix[[i, j]] = corr;
                }
            }
        }

        Ok(correlationmatrix)
    }

    /// Calculate Pearson correlation between two variables
    fn pearson_correlation(&self, x: &[f64], y: &[f64]) -> f64 {
        if x.len() != y.len() || x.len() < 2 {
            return 0.0;
        }

        let n = x.len() as f64;
        let mean_x = x.iter().sum::<f64>() / n;
        let mean_y = y.iter().sum::<f64>() / n;

        let numerator: f64 = x
            .iter()
            .zip(y.iter())
            .map(|(&xi, &yi)| (xi - mean_x) * (yi - mean_y))
            .sum();

        let sum_sq_x: f64 = x.iter().map(|&xi| (xi - mean_x).powi(2)).sum();
        let sum_sq_y: f64 = y.iter().map(|&yi| (yi - mean_y).powi(2)).sum();

        let denominator = (sum_sq_x * sum_sq_y).sqrt();

        if denominator > 1e-10 {
            numerator / denominator
        } else {
            0.0
        }
    }

    /// Convert values to ranks
    fn convert_to_ranks(&self, values: &[f64]) -> Vec<f64> {
        let mut indexed_values: Vec<(usize, f64)> =
            values.iter().enumerate().map(|(i, &v)| (i, v)).collect();
        indexed_values.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap_or(std::cmp::Ordering::Equal));

        let mut ranks = vec![0.0; values.len()];
        for (rank, &(original_index, _)) in indexed_values.iter().enumerate() {
            ranks[original_index] = (rank + 1) as f64;
        }

        ranks
    }

    /// Calculate Kendall's tau
    fn kendall_tau(&self, x: &[f64], y: &[f64]) -> f64 {
        if x.len() != y.len() || x.len() < 2 {
            return 0.0;
        }

        let n = x.len();
        let mut concordant = 0;
        let mut discordant = 0;

        for i in 0..n {
            for j in (i + 1)..n {
                let x_diff = x[i] - x[j];
                let y_diff = y[i] - y[j];

                if x_diff * y_diff > 0.0 {
                    concordant += 1;
                } else if x_diff * y_diff < 0.0 {
                    discordant += 1;
                }
            }
        }

        let total_pairs = n * (n - 1) / 2;
        if total_pairs > 0 {
            (concordant as f64 - discordant as f64) / total_pairs as f64
        } else {
            0.0
        }
    }

    /// Find significant correlations
    fn find_significant_correlations(
        &self,
        correlationmatrix: &Array2<f64>,
    ) -> DeviceResult<Vec<CorrelationPair>> {
        let mut significant_correlations = Vec::new();
        let variable_names = ["latency", "confidence", "timestamp"];

        let threshold = 0.3; // Significance threshold

        for i in 0..correlationmatrix.nrows() {
            for j in (i + 1)..correlationmatrix.ncols() {
                let correlation = correlationmatrix[[i, j]];
                if correlation.abs() > threshold {
                    significant_correlations.push(CorrelationPair {
                        variable1: variable_names[i].to_string(),
                        variable2: variable_names[j].to_string(),
                        correlation,
                        p_value: self.estimate_p_value(correlation, correlationmatrix.nrows()),
                        correlation_type: CorrelationType::Pearson,
                    });
                }
            }
        }

        Ok(significant_correlations)
    }

    /// Estimate p-value for correlation (simplified)
    fn estimate_p_value(&self, correlation: f64, n: usize) -> f64 {
        if n < 3 {
            return 0.5;
        }

        // Simplified t-test approximation
        let t_stat = correlation * ((n - 2) as f64 / correlation.mul_add(-correlation, 1.0)).sqrt();

        // Very rough p-value approximation
        if t_stat.abs() > 2.0 {
            0.01
        } else if t_stat.abs() > 1.5 {
            0.05
        } else {
            0.10
        }
    }

    /// Calculate partial correlations
    fn calculate_partial_correlations(
        &self,
        latencies: &[f64],
        confidences: &[f64],
        timestamps: &[f64],
    ) -> DeviceResult<Array2<f64>> {
        // For simplicity, return the same as Pearson for now
        // In a full implementation, this would calculate partial correlations
        self.calculate_pearson_correlations(latencies, confidences, timestamps)
    }

    /// Perform network analysis
    fn perform_network_analysis(
        &self,
        correlationmatrix: &Array2<f64>,
    ) -> DeviceResult<CorrelationNetworkAnalysis> {
        let threshold = 0.3;
        let n = correlationmatrix.nrows();

        // Create adjacency matrix
        let mut adjacency_matrix = Array2::zeros((n, n));
        for i in 0..n {
            for j in 0..n {
                if i != j && correlationmatrix[[i, j]].abs() > threshold {
                    adjacency_matrix[[i, j]] = correlationmatrix[[i, j]].abs();
                }
            }
        }

        // Calculate centrality measures
        let centrality_measures = self.calculate_centrality_measures(&adjacency_matrix)?;

        // Simple community detection (single community for now)
        let communities = vec![(0..n).collect()];

        // Calculate network density
        let total_possible_edges = n * (n - 1);
        let actual_edges = adjacency_matrix.iter().filter(|&&x| x > 0.0).count();
        let network_density = if total_possible_edges > 0 {
            actual_edges as f64 / total_possible_edges as f64
        } else {
            0.0
        };

        // Calculate clustering coefficient (simplified)
        let clustering_coefficient = 0.5; // Placeholder

        Ok(CorrelationNetworkAnalysis {
            adjacency_matrix,
            centrality_measures,
            communities,
            network_density,
            clustering_coefficient,
        })
    }

    /// Calculate centrality measures
    fn calculate_centrality_measures(
        &self,
        adjacency_matrix: &Array2<f64>,
    ) -> DeviceResult<NodeCentralityMeasures> {
        let n = adjacency_matrix.nrows();

        // Degree centrality
        let mut degree = vec![0.0; n];
        for i in 0..n {
            for j in 0..n {
                if adjacency_matrix[[i, j]] > 0.0 {
                    degree[i] += 1.0;
                }
            }
        }

        // Normalize degree centrality
        let max_degree = (n - 1) as f64;
        for d in &mut degree {
            if max_degree > 0.0 {
                *d /= max_degree;
            }
        }

        // Placeholder values for other centrality measures
        let betweenness = vec![0.5; n];
        let closeness = vec![0.5; n];
        let eigenvector = vec![0.5; n];

        Ok(NodeCentralityMeasures {
            betweenness,
            closeness,
            eigenvector,
            degree,
        })
    }
}

impl Default for CorrelationAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}