//! Dummy regressor for baseline comparisons
//!
//! This module provides comprehensive baseline regression strategies for comparing
//! against other regressors. This is a basic implementation that will be expanded
//! with modular architecture as described in TODO.md.

use scirs2_core::ndarray::Array1;
use scirs2_core::random::{rngs::StdRng, thread_rng, RngExt, SeedableRng};
use sklears_core::error::{Result, SklearsError};
use sklears_core::traits::{Estimator, Fit, Predict, Trained, Untrained};
use sklears_core::types::{Features, Float};
use std::marker::PhantomData;

/// Regression strategy for dummy regressor
#[derive(Debug, Clone, PartialEq, Copy)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub enum Strategy {
    /// Predict the mean of the training targets
    Mean,
    /// Predict a constant value
    Constant(f64),
    /// Predict the median of the training targets
    Median,
    /// Predict a random value from the training target distribution
    Uniform,
    /// Predict using normal distribution sampling
    Normal { mean: Option<f64>, std: Option<f64> },
    /// Predict a quantile of the training targets
    Quantile(f64),
    /// Automatic strategy selection based on data characteristics
    Auto,
    /// Seasonal naive forecasting (repeat value from same season)
    SeasonalNaive(usize),
}

/// Basic dummy regressor implementation
#[derive(Debug, Clone)]
pub struct DummyRegressor<State = Untrained> {
    /// strategy
    pub strategy: Strategy,
    /// random_state
    pub random_state: Option<u64>,
    // Fitted data (only used in Trained state)
    pub(crate) fitted_value_: Option<f64>,
    pub(crate) fitted_std_: Option<f64>,
    pub(crate) n_samples_: Option<usize>,
    _state: PhantomData<State>,
}

/// Fitted dummy regressor
pub type DummyRegressorTrained = DummyRegressor<Trained>;

#[derive(Debug, Clone)]
pub struct DummyRegressorFitted {
    strategy: Strategy,
    fitted_value: f64,
    fitted_std: Option<f64>,
    n_samples: usize,
    random_state: Option<u64>,
}

impl DummyRegressor<Untrained> {
    /// Create new dummy regressor
    pub fn new(strategy: Strategy) -> Self {
        Self {
            strategy,
            random_state: None,
            fitted_value_: None,
            fitted_std_: None,
            n_samples_: None,
            _state: PhantomData,
        }
    }

    /// Set the random state for reproducible output
    pub fn with_random_state(mut self, random_state: u64) -> Self {
        self.random_state = Some(random_state);
        self
    }

    /// Set the constant value to predict (replaces current strategy with Constant)
    pub fn with_constant(mut self, constant: f64) -> Self {
        self.strategy = Strategy::Constant(constant);
        self
    }
}

impl Estimator for DummyRegressor<Untrained> {
    type Config = ();
    type Error = SklearsError;
    type Float = Float;

    fn config(&self) -> &Self::Config {
        &()
    }
}

impl Fit<Features, Array1<Float>> for DummyRegressor<Untrained> {
    type Fitted = DummyRegressor<Trained>;

    fn fit(self, _x: &Features, y: &Array1<Float>) -> Result<DummyRegressor<Trained>> {
        if y.is_empty() {
            return Err(SklearsError::InvalidInput("Empty target array".to_string()));
        }

        let n_samples = y.len();
        let fitted_value = match &self.strategy {
            Strategy::Mean => y.iter().sum::<f64>() / n_samples as f64,
            Strategy::Constant(value) => *value,
            Strategy::Median => {
                let mut sorted = y.to_vec();
                sorted.sort_by(|a, b| a.partial_cmp(b).expect("operation should succeed"));
                if sorted.len() % 2 == 0 {
                    (sorted[sorted.len() / 2 - 1] + sorted[sorted.len() / 2]) / 2.0
                } else {
                    sorted[sorted.len() / 2]
                }
            }
            Strategy::Uniform => {
                let min_val = y.iter().fold(f64::INFINITY, |a, &b| a.min(b));
                let max_val = y.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
                (min_val + max_val) / 2.0 // Use midpoint as fitted value
            }
            Strategy::Normal { mean, std: _ } => {
                mean.unwrap_or_else(|| y.iter().sum::<f64>() / n_samples as f64)
            }
            Strategy::Quantile(q) => {
                let mut sorted = y.to_vec();
                sorted.sort_by(|a, b| a.partial_cmp(b).expect("operation should succeed"));
                let index =
                    ((*q * (sorted.len() - 1) as f64).round() as usize).min(sorted.len() - 1);
                sorted[index]
            }
            Strategy::Auto => {
                // Simple auto strategy: use median for now
                let mut sorted = y.to_vec();
                sorted.sort_by(|a, b| a.partial_cmp(b).expect("operation should succeed"));
                if sorted.len() % 2 == 0 {
                    (sorted[sorted.len() / 2 - 1] + sorted[sorted.len() / 2]) / 2.0
                } else {
                    sorted[sorted.len() / 2]
                }
            }
            Strategy::SeasonalNaive(_period) => {
                // For seasonal naive, just use the mean for now (simplified)
                y.iter().sum::<f64>() / n_samples as f64
            }
        };

        let fitted_std = if n_samples > 1 {
            let mean = fitted_value;
            let variance =
                y.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / (n_samples - 1) as f64;
            Some(variance.sqrt())
        } else {
            None
        };

        Ok(DummyRegressor {
            strategy: self.strategy,
            random_state: self.random_state,
            fitted_value_: Some(fitted_value),
            fitted_std_: fitted_std,
            n_samples_: Some(n_samples),
            _state: PhantomData::<Trained>,
        })
    }
}

impl Predict<Features, Array1<Float>> for DummyRegressor<Trained> {
    fn predict(&self, x: &Features) -> Result<Array1<Float>> {
        let n_samples = x.nrows();
        let fitted_value = self.fitted_value_.expect("operation should succeed");
        let fitted_std = self.fitted_std_;

        let predictions = match &self.strategy {
            Strategy::Mean
            | Strategy::Constant(_)
            | Strategy::Median
            | Strategy::Quantile(_)
            | Strategy::Auto
            | Strategy::SeasonalNaive(_) => Array1::from_elem(n_samples, fitted_value),
            Strategy::Uniform => {
                // Sample uniformly from observed range (simplified)
                let predictions: Vec<f64> = if let Some(seed) = self.random_state {
                    let mut rng = StdRng::seed_from_u64(seed);
                    (0..n_samples)
                        .map(|_| {
                            fitted_value + rng.random_range(-1.0..1.0) * fitted_std.unwrap_or(1.0)
                        })
                        .collect()
                } else {
                    let mut rng = thread_rng();
                    (0..n_samples)
                        .map(|_| {
                            fitted_value + rng.random_range(-1.0..1.0) * fitted_std.unwrap_or(1.0)
                        })
                        .collect()
                };
                Array1::from_vec(predictions)
            }
            Strategy::Normal { mean: _, std } => {
                let std_val = std.unwrap_or_else(|| fitted_std.unwrap_or(1.0));
                let predictions: Vec<f64> = if let Some(seed) = self.random_state {
                    let mut rng = StdRng::seed_from_u64(seed);
                    (0..n_samples)
                        .map(|_| fitted_value + rng.random_range(-2.0..2.0) * std_val) // Simplified normal sampling
                        .collect()
                } else {
                    let mut rng = thread_rng();
                    (0..n_samples)
                        .map(|_| fitted_value + rng.random_range(-2.0..2.0) * std_val) // Simplified normal sampling
                        .collect()
                };
                Array1::from_vec(predictions)
            }
        };

        Ok(predictions)
    }
}

// Placeholder types and traits that are referenced in the exports
pub trait PredictConfidenceInterval<T> {
    fn predict_interval(&self, x: &T, confidence: f64) -> Result<Vec<(f64, f64)>>;
}

pub trait ProbabilisticRegression<T> {
    fn predict_distribution(&self, x: &T) -> Result<Vec<(f64, f64)>>;
}

impl PredictConfidenceInterval<Features> for DummyRegressorFitted {
    fn predict_interval(&self, x: &Features, confidence: f64) -> Result<Vec<(f64, f64)>> {
        if !(0.0..=1.0).contains(&confidence) {
            return Err(SklearsError::InvalidInput(
                "Confidence must be between 0 and 1".to_string(),
            ));
        }

        let n_samples = x.nrows();
        let z_score = if confidence >= 0.99 {
            2.576
        } else if confidence >= 0.95 {
            1.96
        } else {
            1.0
        };
        let margin = z_score * self.fitted_std.unwrap_or(1.0);
        let lower = self.fitted_value - margin;
        let upper = self.fitted_value + margin;

        Ok(vec![(lower, upper); n_samples])
    }
}

impl ProbabilisticRegression<Features> for DummyRegressorFitted {
    fn predict_distribution(&self, x: &Features) -> Result<Vec<(f64, f64)>> {
        let n_samples = x.nrows();
        let mean = self.fitted_value;
        let variance = self.fitted_std.unwrap_or(1.0).powi(2);

        Ok(vec![(mean, variance); n_samples])
    }
}

// Placeholder enums and types
#[derive(Debug, Clone, PartialEq)]
pub enum SeasonalAdjustmentMethod {
    /// Additive
    Additive,
    /// Multiplicative
    Multiplicative,
}

#[derive(Debug, Clone, PartialEq)]
pub enum SeasonalType {
    /// Additive
    Additive,
    /// Multiplicative
    Multiplicative,
}

#[derive(Debug, Clone, PartialEq)]
pub enum DecompositionMethod {
    /// Classical
    Classical,
    /// STL
    STL,
    /// X11
    X11,
}

#[derive(Debug, Clone, PartialEq)]
pub enum CyclicalMethod {
    /// Fourier
    Fourier,
    /// Wavelet
    Wavelet,
    /// Spectral
    Spectral,
}
