use std::fs::File;
use std::io::{self, BufRead, BufReader};

#[derive(Clone, Debug)]
struct Point {
    value: u32,
}

impl Point {
    fn new(value: u32) -> Self {
        Point { value }
    }
}

#[derive(Debug, Clone)]
struct ClusterGapInfo {
    span_length: f32, // Full span length
    num_elements: usize, // Number of elements, 0 for gaps
    centroid: f32, // Centroid value
    z_score: Option<f32>, // Z-score, to be calculated later
}


fn load_dataset(filename: &str) -> io::Result<Vec<Point>> {
    let file = File::open(filename)?;
    let reader = BufReader::new(file);
    let mut dataset = Vec::new();

    for line in reader.lines() {
        let value: u32 = line?.trim().parse().unwrap();
        dataset.push(Point::new(value));
    }

    dataset.sort_by_key(|p| p.value);
    Ok(dataset)
}

// Define the ClusterGapInfo struct as described above

fn calculate_densities_and_gaps(dataset: &[Point], factor: f32, min_cluster_size: usize) -> Vec<ClusterGapInfo> {
    let mut results: Vec<ClusterGapInfo> = Vec::new();
    if dataset.len() < 2 {
        return results;
    }

    let mean_distance = dataset.windows(2)
                               .map(|w| distance(&w[0], &w[1]) as f32)
                               .sum::<f32>() / (dataset.len() - 1) as f32;

    let cluster_threshold = 1.0 / factor * mean_distance;
    let gap_threshold = factor * mean_distance * 2.0;

    let mut current_cluster = Vec::new();
    for window in dataset.windows(2) {
        let gap_distance = distance(&window[0], &window[1]) as f32;
        if gap_distance <= cluster_threshold {
            current_cluster.push(window[1].clone());
        } else {
            // Before clearing the current_cluster, check if it meets the size requirement
            if !current_cluster.is_empty() && current_cluster.len() >= min_cluster_size {
                let cluster_info = create_cluster_info(&current_cluster);
                results.push(cluster_info);
            }
            current_cluster.clear();

            // Add a gap if the distance exceeds the gap threshold
            if gap_distance > gap_threshold {
                results.push(ClusterGapInfo {
                    span_length: gap_distance,
                    num_elements: 0,
                    centroid: (window[0].value as f32 + window[1].value as f32) / 2.0,
                    z_score: None,
                });
            }
        }
    }

    // Handle the last cluster if it meets the size requirement
    if !current_cluster.is_empty() && current_cluster.len() >= min_cluster_size {
        let cluster_info = create_cluster_info(&current_cluster);
        results.push(cluster_info);
    }

    results
}

// Additional helper function to create cluster information
fn create_cluster_info(cluster: &[Point]) -> ClusterGapInfo {
    let num_elements = cluster.len();
    let span_length = (cluster.last().unwrap().value as f32) - (cluster.first().unwrap().value as f32);
    let centroid = cluster.iter().map(|p| p.value as f32).sum::<f32>() / num_elements as f32;

    ClusterGapInfo {
        span_length,
        num_elements,
        centroid,
        z_score: None, // Placeholder, to be calculated later
    }
}

// Adjust the main function and subsequent calculations to work with the new structure and calculate Z-scores accordingly


fn calculate_cluster_density(cluster: &[u32]) -> f32 {
    if cluster.len() < 2 { return 0.0; } // Adjust based on how you define density for single-element clusters

    let &max_value = cluster.iter().max().unwrap();
    let &min_value = cluster.iter().min().unwrap();
    let span = (max_value - min_value) as f32;
    if span == 0.0 {
        return cluster.len() as f32; // Handle clusters where all points have the same value
    }
    cluster.len() as f32 / span
}

// Assume distance function is defined as before


fn distance(p1: &Point, p2: &Point) -> u32 {
    if p1.value > p2.value { p1.value - p2.value } else { p2.value - p1.value }
}


fn calculate_mean_and_std_dev(densities: &[f32]) -> (f32, f32) {
    let mean = densities.iter().sum::<f32>() / densities.len() as f32;
    let variance = densities.iter().map(|&v| (v - mean).powi(2)).sum::<f32>() / densities.len() as f32;
    (mean, variance.sqrt())
}

fn calculate_z_scores(densities: &[f32], mean: f32, std_dev: f32) -> Vec<f32> {
    densities.iter().map(|&density| (density - mean) / std_dev).collect()
}

fn main() -> io::Result<()> {
    let filename = "random_values.txt";
    let dataset = load_dataset(filename)?;

    let factor = 3.8;
    let min_cluster_size = 7;

    let mut cluster_gap_infos = calculate_densities_and_gaps(&dataset, factor, min_cluster_size);

    // Calculate mean distance for Z-score computation
    let total_distances: f32 = dataset.windows(2)
                                      .map(|w| (w[1].value as f32 - w[0].value as f32))
                                      .sum();
    let mean_distance = total_distances / (dataset.len() as f32 - 1.0);

    // Calculate Z-scores for clusters and gaps
    for info in cluster_gap_infos.iter_mut() {
        if info.num_elements == 0 {
            // Z-score for gaps
            info.z_score = Some((info.span_length - mean_distance) / mean_distance); // Simplified deviation measure
        } else {
            // Z-score for clusters, based on density deviation
            let density = info.num_elements as f32 / info.span_length;
            let expected_density = 1.0 / mean_distance; // Expected: one element per mean distance
            info.z_score = Some((density - expected_density) / expected_density); // Simplified deviation measure
        }
    }

    // Output results with distinction between clusters and gaps
    for (index, info) in cluster_gap_infos.iter().enumerate() {
        let element_type = if info.num_elements > 0 { "Cluster" } else { "Gap" };
        println!("Num Elements: {}, Centroid: {:.2}, Z-Score: {:.2}, Span: {:.2}, ", 
                info.num_elements, info.centroid, info.z_score.unwrap_or(0.0), info.span_length);
    }

    Ok(())
}