//! Decision tree stub implementations for AdaBoost

use super::types::*;
use scirs2_core::ndarray::{Array1, Array2};
use sklears_core::{
    error::Result,
    traits::{Fit, Trained, Untrained},
    types::{Float, Int},
};
use std::marker::PhantomData;

impl DecisionTreeClassifier<Untrained> {
    pub fn new() -> Self {
        Self {
            criterion: SplitCriterion::Gini,
            max_depth: Some(1),
            min_samples_split: 2,
            min_samples_leaf: 1,
            random_state: None,
            state: PhantomData,
        }
    }

    pub fn criterion(mut self, criterion: SplitCriterion) -> Self {
        self.criterion = criterion;
        self
    }

    pub fn max_depth(mut self, max_depth: usize) -> Self {
        self.max_depth = Some(max_depth);
        self
    }

    pub fn min_samples_split(mut self, min_samples_split: usize) -> Self {
        self.min_samples_split = min_samples_split;
        self
    }

    pub fn min_samples_leaf(mut self, min_samples_leaf: usize) -> Self {
        self.min_samples_leaf = min_samples_leaf;
        self
    }

    pub fn random_state(mut self, random_state: Option<u64>) -> Self {
        self.random_state = random_state;
        self
    }
}

impl Fit<Array2<Float>, Array1<Int>> for DecisionTreeClassifier<Untrained> {
    type Fitted = DecisionTreeClassifier<Trained>;

    fn fit(self, _x: &Array2<Float>, _y: &Array1<Int>) -> Result<Self::Fitted> {
        // Stub implementation for decision tree
        Ok(DecisionTreeClassifier {
            criterion: self.criterion,
            max_depth: self.max_depth,
            min_samples_split: self.min_samples_split,
            min_samples_leaf: self.min_samples_leaf,
            random_state: self.random_state,
            state: PhantomData,
        })
    }
}

impl DecisionTreeClassifier<Trained> {
    pub fn predict(&self, x: &Array2<Float>) -> Result<Array1<Int>> {
        // Stub implementation - simple threshold
        let n_samples = x.nrows();
        let predictions = x.column(0).mapv(|val| if val > 2.0 { 1 } else { 0 });
        Ok(predictions)
    }
}

impl DecisionTreeRegressor<Untrained> {
    pub fn new() -> Self {
        Self {
            criterion: SplitCriterion::Gini,
            max_depth: Some(1),
            min_samples_split: 2,
            min_samples_leaf: 1,
            random_state: None,
            state: PhantomData,
        }
    }

    pub fn criterion(mut self, criterion: SplitCriterion) -> Self {
        self.criterion = criterion;
        self
    }

    pub fn max_depth(mut self, max_depth: usize) -> Self {
        self.max_depth = Some(max_depth);
        self
    }

    pub fn min_samples_split(mut self, min_samples_split: usize) -> Self {
        self.min_samples_split = min_samples_split;
        self
    }

    pub fn min_samples_leaf(mut self, min_samples_leaf: usize) -> Self {
        self.min_samples_leaf = min_samples_leaf;
        self
    }

    pub fn random_state(mut self, random_state: Option<u64>) -> Self {
        self.random_state = random_state;
        self
    }
}

impl Fit<Array2<Float>, Array1<Float>> for DecisionTreeRegressor<Untrained> {
    type Fitted = DecisionTreeRegressor<Trained>;

    fn fit(self, _x: &Array2<Float>, _y: &Array1<Float>) -> Result<Self::Fitted> {
        // Stub implementation
        Ok(DecisionTreeRegressor {
            criterion: self.criterion,
            max_depth: self.max_depth,
            min_samples_split: self.min_samples_split,
            min_samples_leaf: self.min_samples_leaf,
            random_state: self.random_state,
            state: PhantomData,
        })
    }
}

impl DecisionTreeRegressor<Trained> {
    pub fn predict(&self, x: &Array2<Float>) -> Result<Array1<Float>> {
        // Stub implementation - return zeros
        let n_samples = x.nrows();
        Ok(Array1::zeros(n_samples))
    }
}
