//! Real implementation of temporal neural solver with actual sublinear solver integration
//! No mocking, no artificial delays - just genuine computation

pub mod optimized;
pub mod solver_integration;

use ndarray::{Array1, Array2};
use nalgebra::{DMatrix, DVector};
use std::time::{Duration, Instant};
use thiserror::Error;

// Use our solver integration module
use solver_integration::{SparseMatrix, NeumannSolver};

#[derive(Debug, Error)]
pub enum TemporalSolverError {
    #[error("Dimension mismatch: expected {expected}, got {got}")]
    DimensionMismatch { expected: usize, got: usize },

    #[error("Solver error: {0}")]
    SolverError(String),

    #[error("Numerical error: {0}")]
    NumericalError(String),

    #[error("Certificate validation failed: error {error} exceeds threshold {threshold}")]
    CertificateError { error: f64, threshold: f64 },
}

type Result<T> = std::result::Result<T, TemporalSolverError>;

/// Mathematical certificate for prediction confidence
#[derive(Debug, Clone)]
pub struct Certificate {
    /// Estimated error bound from solver
    pub error_bound: f64,
    /// Confidence level (1 - error_bound/prediction_norm)
    pub confidence: f64,
    /// Whether the prediction passes the gate check
    pub gate_pass: bool,
    /// Number of solver iterations used
    pub iterations: usize,
    /// Computational work (operations performed)
    pub computational_work: usize,
}

/// Real Kalman filter implementation for temporal predictions
pub struct KalmanFilter {
    /// State vector (position, velocity for each dimension)
    state: DVector<f64>,
    /// State covariance matrix
    covariance: DMatrix<f64>,
    /// Process noise covariance
    process_noise: DMatrix<f64>,
    /// Measurement noise covariance
    measurement_noise: DMatrix<f64>,
    /// State transition matrix
    transition: DMatrix<f64>,
    /// Measurement matrix
    measurement: DMatrix<f64>,
}

impl KalmanFilter {
    pub fn new(state_dim: usize) -> Self {
        // Initialize for constant velocity model
        let full_dim = state_dim * 2; // position + velocity

        let mut transition = DMatrix::identity(full_dim, full_dim);
        // Update position based on velocity (assuming dt=0.001)
        for i in 0..state_dim {
            transition[(i, state_dim + i)] = 0.001;
        }

        let mut measurement = DMatrix::zeros(state_dim, full_dim);
        for i in 0..state_dim {
            measurement[(i, i)] = 1.0; // Measure only positions
        }

        Self {
            state: DVector::zeros(full_dim),
            covariance: DMatrix::identity(full_dim, full_dim) * 0.1,
            process_noise: DMatrix::identity(full_dim, full_dim) * 0.001,
            measurement_noise: DMatrix::identity(state_dim, state_dim) * 0.01,
            transition,
            measurement,
        }
    }

    /// Prediction step of Kalman filter
    pub fn predict(&mut self) -> DVector<f64> {
        // State prediction: x_k|k-1 = F * x_k-1|k-1
        self.state = &self.transition * &self.state;

        // Covariance prediction: P_k|k-1 = F * P_k-1|k-1 * F^T + Q
        self.covariance = &self.transition * &self.covariance * self.transition.transpose()
            + &self.process_noise;

        // Return predicted measurement
        &self.measurement * &self.state
    }

    /// Update step with measurement
    pub fn update(&mut self, measurement: &DVector<f64>) -> Result<()> {
        // Innovation: y = z - H * x_k|k-1
        let innovation = measurement - &self.measurement * &self.state;

        // Innovation covariance: S = H * P_k|k-1 * H^T + R
        let innovation_cov = &self.measurement * &self.covariance
            * self.measurement.transpose() + &self.measurement_noise;

        // Kalman gain: K = P_k|k-1 * H^T * S^-1
        let kalman_gain = &self.covariance * self.measurement.transpose()
            * innovation_cov.try_inverse()
                .ok_or(TemporalSolverError::NumericalError("Singular matrix".into()))?;

        // State update: x_k|k = x_k|k-1 + K * y
        self.state = &self.state + &kalman_gain * innovation;

        // Covariance update: P_k|k = (I - K * H) * P_k|k-1
        let identity = DMatrix::identity(self.state.len(), self.state.len());
        self.covariance = (identity - &kalman_gain * &self.measurement) * &self.covariance;

        Ok(())
    }
}

/// Neural network layer with real computation
pub struct NeuralLayer {
    weights: Array2<f32>,
    bias: Array1<f32>,
    activation: ActivationType,
}

#[derive(Clone, Copy)]
pub enum ActivationType {
    ReLU,
    Tanh,
    Linear,
}

impl NeuralLayer {
    pub fn new(input_size: usize, output_size: usize, activation: ActivationType) -> Self {
        use ndarray_rand::RandomExt;
        use rand_distr::Normal;

        // Xavier initialization
        let scale = (2.0 / input_size as f32).sqrt();
        let dist = Normal::new(0.0, scale).unwrap();

        Self {
            weights: Array2::random((output_size, input_size), dist),
            bias: Array1::zeros(output_size),
            activation,
        }
    }

    pub fn forward(&self, input: &Array1<f32>) -> Array1<f32> {
        let z = self.weights.dot(input) + &self.bias;

        match self.activation {
            ActivationType::ReLU => z.mapv(|x| x.max(0.0)),
            ActivationType::Tanh => z.mapv(|x| x.tanh()),
            ActivationType::Linear => z,
        }
    }
}

/// Real neural network implementation
pub struct TemporalNeuralNetwork {
    layers: Vec<NeuralLayer>,
}

impl TemporalNeuralNetwork {
    pub fn new(layer_sizes: &[usize], activations: &[ActivationType]) -> Self {
        assert_eq!(layer_sizes.len() - 1, activations.len());

        let mut layers = Vec::new();
        for i in 0..layer_sizes.len() - 1 {
            layers.push(NeuralLayer::new(
                layer_sizes[i],
                layer_sizes[i + 1],
                activations[i],
            ));
        }

        Self { layers }
    }

    pub fn forward(&self, input: &Array1<f32>) -> Array1<f32> {
        self.layers.iter().fold(input.clone(), |x, layer| layer.forward(&x))
    }

    /// Get Jacobian for solver verification (simplified)
    pub fn jacobian(&self, input: &Array1<f32>) -> Array2<f32> {
        // Approximate Jacobian using finite differences
        let output_dim = self.layers.last().unwrap().weights.shape()[0];
        let input_dim = input.len();
        let mut jacobian = Array2::zeros((output_dim, input_dim));

        let epsilon = 1e-4;
        let base_output = self.forward(input);

        for i in 0..input_dim {
            let mut perturbed_input = input.clone();
            perturbed_input[i] += epsilon;
            let perturbed_output = self.forward(&perturbed_input);

            for j in 0..output_dim {
                jacobian[[j, i]] = (perturbed_output[j] - base_output[j]) / epsilon;
            }
        }

        jacobian
    }
}

/// Solver gate for mathematical verification
pub struct SolverGate {
    epsilon: f64,
    max_iterations: usize,
    budget: usize,
}

impl SolverGate {
    pub fn new(epsilon: f64, max_iterations: usize, budget: usize) -> Self {
        Self {
            epsilon,
            max_iterations,
            budget,
        }
    }

    /// Verify prediction using sublinear solver
    pub fn verify(
        &self,
        prediction: &Array1<f32>,
        jacobian: &Array2<f32>,
    ) -> Result<Certificate> {
        // Convert to sparse matrix for solver
        let n = jacobian.shape()[0];
        let m = jacobian.shape()[1];

        // Create diagonally dominant system for stability
        // A = I + 0.1 * J^T * J (guaranteed positive definite)
        let mut triplets = Vec::new();

        // Add identity matrix
        for i in 0..n.min(m) {
            triplets.push((i, i, 1.0));
        }

        // Add contribution from Jacobian (making it diagonally dominant)
        for i in 0..n {
            for j in 0..m {
                if i < m && j < n {
                    let value = 0.1 * jacobian[[i, j]] * jacobian[[j, i]];
                    if value.abs() > 1e-10 {
                        triplets.push((i, j, value as f64));
                    }
                }
            }
        }

        let matrix = SparseMatrix::from_triplets(triplets, n.min(m), n.min(m));

        // Right-hand side is the prediction
        let b: Vec<f64> = prediction.iter()
            .take(n.min(m))
            .map(|&x| x as f64)
            .collect();

        // Solve using Neumann series
        let solver = NeumannSolver::new(self.max_iterations, self.epsilon);
        let result = solver.solve(&matrix, &b);

        // Calculate error bound
        let solution_norm: f64 = result.solution.iter().map(|x| x * x).sum::<f64>().sqrt();
        let residual_norm = result.residual_norm;
        let error_bound = residual_norm / solution_norm.max(1.0);

        // Create certificate
        Ok(Certificate {
            error_bound,
            confidence: 1.0 - error_bound.min(1.0),
            gate_pass: error_bound < self.epsilon,
            iterations: result.iterations,
            computational_work: result.iterations * n, // Approximate work
        })
    }
}

/// PageRank-based active sample selection
pub struct PageRankSelector {
    damping: f64,
    tolerance: f64,
    max_iterations: usize,
}

impl PageRankSelector {
    pub fn new() -> Self {
        Self {
            damping: 0.85,
            tolerance: 1e-6,
            max_iterations: 100,
        }
    }

    /// Select top K samples based on PageRank scores
    pub fn select_samples(
        &self,
        adjacency: &Array2<f32>,
        errors: &Array1<f32>,
        k: usize,
    ) -> Vec<usize> {
        let n = adjacency.shape()[0];
        let mut scores = Array1::from_elem(n, 1.0 / n as f32);
        let mut new_scores = Array1::zeros(n);

        // Power iteration for PageRank
        for _ in 0..self.max_iterations {
            // Compute new scores: (1-d)/n + d * A^T * scores
            new_scores.fill((1.0 - self.damping as f32) / n as f32);

            for i in 0..n {
                for j in 0..n {
                    if adjacency[[j, i]] > 0.0 {
                        let out_degree: f32 = (0..n).map(|k| adjacency[[j, k]]).sum();
                        if out_degree > 0.0 {
                            new_scores[i] += (self.damping as f32) * adjacency[[j, i]]
                                * scores[j] / out_degree;
                        }
                    }
                }
            }

            // Weight by errors for active learning
            for i in 0..n {
                new_scores[i] *= 1.0 + errors[i];
            }

            // Check convergence
            let diff: f32 = (&new_scores - &scores)
                .iter()
                .map(|x| x.abs())
                .sum();

            scores.assign(&new_scores);

            if diff < self.tolerance as f32 {
                break;
            }
        }

        // Select top k indices
        let mut indexed_scores: Vec<(usize, f32)> =
            scores.iter().enumerate().map(|(i, &s)| (i, s)).collect();
        indexed_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

        indexed_scores.into_iter().take(k).map(|(i, _)| i).collect()
    }
}

/// Complete temporal solver system
pub struct TemporalSolver {
    neural_net: TemporalNeuralNetwork,
    kalman_filter: KalmanFilter,
    solver_gate: SolverGate,
    pagerank: PageRankSelector,
}

impl TemporalSolver {
    pub fn new(input_size: usize, hidden_size: usize, output_size: usize) -> Self {
        let neural_net = TemporalNeuralNetwork::new(
            &[input_size, hidden_size, output_size],
            &[ActivationType::ReLU, ActivationType::Linear],
        );

        let kalman_filter = KalmanFilter::new(output_size);
        let solver_gate = SolverGate::new(0.02, 100, 200000);
        let pagerank = PageRankSelector::new();

        Self {
            neural_net,
            kalman_filter,
            solver_gate,
            pagerank,
        }
    }

    /// Complete prediction with all components
    pub fn predict(&mut self, input: &Array1<f32>) -> Result<(Array1<f32>, Certificate, Duration)> {
        let start = Instant::now();

        // 1. Kalman filter prediction (prior)
        let kalman_pred = self.kalman_filter.predict();
        let prior: Array1<f32> = Array1::from_vec(
            kalman_pred.iter().map(|&x| x as f32).collect()
        );

        // 2. Neural network residual prediction
        let residual = self.neural_net.forward(input);

        // 3. Combine: prediction = prior + residual
        let prediction = &prior + &residual;

        // 4. Get Jacobian for verification
        let jacobian = self.neural_net.jacobian(input);

        // 5. Mathematical verification with solver
        let certificate = self.solver_gate.verify(&prediction, &jacobian)?;

        // 6. Update Kalman filter if gate passes
        if certificate.gate_pass {
            let measurement = DVector::from_vec(
                prediction.iter().map(|&x| x as f64).collect()
            );
            self.kalman_filter.update(&measurement)?;
        }

        let duration = start.elapsed();

        Ok((prediction, certificate, duration))
    }

    /// Train with active selection (simplified)
    pub fn train_step(
        &mut self,
        samples: &[Array1<f32>],
        targets: &[Array1<f32>],
        adjacency: &Array2<f32>,
    ) -> Result<Vec<usize>> {
        // Calculate errors for all samples
        let mut errors = Array1::zeros(samples.len());
        for (i, (sample, target)) in samples.iter().zip(targets.iter()).enumerate() {
            let (pred, _, _) = self.predict(sample)?;
            let error: f32 = (pred - target).mapv(|x| x * x).sum().sqrt();
            errors[i] = error;
        }

        // Select best samples using PageRank
        let selected_indices = self.pagerank.select_samples(adjacency, &errors, 15);

        Ok(selected_indices)
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_real_kalman_filter() {
        let mut kf = KalmanFilter::new(2);
        let pred = kf.predict();
        assert_eq!(pred.len(), 2);
    }

    #[test]
    fn test_real_neural_network() {
        let nn = TemporalNeuralNetwork::new(&[10, 5, 2], &[ActivationType::ReLU, ActivationType::Linear]);
        let input = Array1::from_vec(vec![0.1; 10]);
        let output = nn.forward(&input);
        assert_eq!(output.len(), 2);
    }

    #[test]
    fn test_real_solver_gate() {
        let gate = SolverGate::new(0.02, 100, 10000);
        let prediction = Array1::from_vec(vec![1.0, 2.0, 3.0]);
        let jacobian = Array2::from_shape_vec((3, 3), vec![
            2.0, -1.0, 0.0,
            -1.0, 2.0, -1.0,
            0.0, -1.0, 2.0,
        ]).unwrap();

        let cert = gate.verify(&prediction, &jacobian).unwrap();
        println!("Certificate: {:?}", cert);
        assert!(cert.error_bound >= 0.0);
    }

    #[test]
    fn test_complete_system() {
        let mut solver = TemporalSolver::new(128, 32, 4);
        let input = Array1::from_vec(vec![0.1; 128]);

        let (prediction, certificate, duration) = solver.predict(&input).unwrap();

        println!("Prediction: {:?}", prediction);
        println!("Certificate: {:?}", certificate);
        println!("Duration: {:?}", duration);

        assert_eq!(prediction.len(), 4);
        assert!(duration.as_nanos() > 0);
    }
}