ghostflow-ml 1.0.0

//! Neural ODE Architectures - Continuous-depth models, Augmented Neural ODEs, etc.

use ghostflow_core::Tensor;
use crate::deep::layers::Dense;
use crate::deep::activations::{ReLU, Tanh};

/// ODE Function (defines the dynamics)
pub struct ODEFunc {
    layers: Vec<Dense>,
}

impl ODEFunc {
    pub fn new(hidden_dim: usize, num_layers: usize) -> Self {
        let mut layers = Vec::new();
        
        for _ in 0..num_layers {
            layers.push(Dense::new(hidden_dim, hidden_dim));
        }
        
        ODEFunc { layers }
    }

    pub fn forward(&mut self, t: f32, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for (i, layer) in self.layers.iter_mut().enumerate() {
            out = layer.forward(&out, training);
            if i < self.layers.len() - 1 {
                out = Tanh::new().forward(&out);
            }
        }
        
        out
    }
}

/// Euler ODE Solver
pub struct EulerSolver {
    dt: f32,
}

impl EulerSolver {
    pub fn new(dt: f32) -> Self {
        EulerSolver { dt }
    }

    pub fn solve(&self, ode_func: &mut ODEFunc, x0: &Tensor, t0: f32, t1: f32, training: bool) -> Tensor {
        let num_steps = ((t1 - t0) / self.dt).ceil() as usize;
        let mut x = x0.clone();
        let mut t = t0;
        
        for _ in 0..num_steps {
            let dx = ode_func.forward(t, &x, training);
            x = self.add_scaled(&x, &dx, self.dt);
            t += self.dt;
        }
        
        x
    }

    fn add_scaled(&self, x: &Tensor, dx: &Tensor, scale: f32) -> Tensor {
        let x_data = x.data_f32();
        let dx_data = dx.data_f32();
        
        let result: Vec<f32> = x_data.iter()
            .zip(dx_data.iter())
            .map(|(&x_val, &dx_val)| x_val + scale * dx_val)
            .collect();
        
        Tensor::from_slice(&result, x.dims()).unwrap()
    }
}

/// RK4 (Runge-Kutta 4th order) ODE Solver
pub struct RK4Solver {
    dt: f32,
}

impl RK4Solver {
    pub fn new(dt: f32) -> Self {
        RK4Solver { dt }
    }

    pub fn solve(&self, ode_func: &mut ODEFunc, x0: &Tensor, t0: f32, t1: f32, training: bool) -> Tensor {
        let num_steps = ((t1 - t0) / self.dt).ceil() as usize;
        let mut x = x0.clone();
        let mut t = t0;
        
        for _ in 0..num_steps {
            let k1 = ode_func.forward(t, &x, training);
            
            let x_k2 = self.add_scaled(&x, &k1, self.dt / 2.0);
            let k2 = ode_func.forward(t + self.dt / 2.0, &x_k2, training);
            
            let x_k3 = self.add_scaled(&x, &k2, self.dt / 2.0);
            let k3 = ode_func.forward(t + self.dt / 2.0, &x_k3, training);
            
            let x_k4 = self.add_scaled(&x, &k3, self.dt);
            let k4 = ode_func.forward(t + self.dt, &x_k4, training);
            
            // Combine: x = x + dt/6 * (k1 + 2*k2 + 2*k3 + k4)
            x = self.rk4_step(&x, &k1, &k2, &k3, &k4);
            t += self.dt;
        }
        
        x
    }

    fn add_scaled(&self, x: &Tensor, dx: &Tensor, scale: f32) -> Tensor {
        let x_data = x.data_f32();
        let dx_data = dx.data_f32();
        
        let result: Vec<f32> = x_data.iter()
            .zip(dx_data.iter())
            .map(|(&x_val, &dx_val)| x_val + scale * dx_val)
            .collect();
        
        Tensor::from_slice(&result, x.dims()).unwrap()
    }

    fn rk4_step(&self, x: &Tensor, k1: &Tensor, k2: &Tensor, k3: &Tensor, k4: &Tensor) -> Tensor {
        let x_data = x.data_f32();
        let k1_data = k1.data_f32();
        let k2_data = k2.data_f32();
        let k3_data = k3.data_f32();
        let k4_data = k4.data_f32();
        
        let result: Vec<f32> = (0..x_data.len())
            .map(|i| {
                x_data[i] + self.dt / 6.0 * (k1_data[i] + 2.0 * k2_data[i] + 2.0 * k3_data[i] + k4_data[i])
            })
            .collect();
        
        Tensor::from_slice(&result, x.dims()).unwrap()
    }
}

/// Neural ODE Block
pub struct NeuralODEBlock {
    ode_func: ODEFunc,
    solver: RK4Solver,
    t0: f32,
    t1: f32,
}

impl NeuralODEBlock {
    pub fn new(hidden_dim: usize, num_layers: usize, t0: f32, t1: f32, dt: f32) -> Self {
        NeuralODEBlock {
            ode_func: ODEFunc::new(hidden_dim, num_layers),
            solver: RK4Solver::new(dt),
            t0,
            t1,
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        self.solver.solve(&mut self.ode_func, x, self.t0, self.t1, training)
    }
}

/// Neural ODE Network
pub struct NeuralODE {
    input_layer: Dense,
    ode_block: NeuralODEBlock,
    output_layer: Dense,
}

impl NeuralODE {
    pub fn new(input_dim: usize, hidden_dim: usize, output_dim: usize, num_ode_layers: usize) -> Self {
        NeuralODE {
            input_layer: Dense::new(input_dim, hidden_dim),
            ode_block: NeuralODEBlock::new(hidden_dim, num_ode_layers, 0.0, 1.0, 0.1),
            output_layer: Dense::new(hidden_dim, output_dim),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = self.input_layer.forward(x, training);
        out = ReLU::new().forward(&out);
        
        out = self.ode_block.forward(&out, training);
        
        self.output_layer.forward(&out, training)
    }
}

/// Augmented Neural ODE
pub struct AugmentedNeuralODE {
    input_layer: Dense,
    augment_layer: Dense,
    ode_block: NeuralODEBlock,
    output_layer: Dense,
    augment_dim: usize,
}

impl AugmentedNeuralODE {
    pub fn new(input_dim: usize, hidden_dim: usize, output_dim: usize, augment_dim: usize, num_ode_layers: usize) -> Self {
        AugmentedNeuralODE {
            input_layer: Dense::new(input_dim, hidden_dim),
            augment_layer: Dense::new(hidden_dim, augment_dim),
            ode_block: NeuralODEBlock::new(hidden_dim + augment_dim, num_ode_layers, 0.0, 1.0, 0.1),
            output_layer: Dense::new(hidden_dim + augment_dim, output_dim),
            augment_dim,
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut h = self.input_layer.forward(x, training);
        h = ReLU::new().forward(&h);
        
        // Augment with additional dimensions
        let aug = self.augment_layer.forward(&h, training);
        let augmented = self.concatenate(&h, &aug);
        
        let out = self.ode_block.forward(&augmented, training);
        
        self.output_layer.forward(&out, training)
    }

    fn concatenate(&self, a: &Tensor, b: &Tensor) -> Tensor {
        let a_data = a.data_f32();
        let b_data = b.data_f32();
        let batch_size = a.dims()[0];
        let dim_a = a.dims()[1];
        let dim_b = b.dims()[1];
        
        let mut result = Vec::new();
        for i in 0..batch_size {
            for j in 0..dim_a {
                result.push(a_data[i * dim_a + j]);
            }
            for j in 0..dim_b {
                result.push(b_data[i * dim_b + j]);
            }
        }
        
        Tensor::from_slice(&result, &[batch_size, dim_a + dim_b]).unwrap()
    }
}

/// Continuous Normalizing Flow (CNF)
pub struct ContinuousNormalizingFlow {
    ode_func: ODEFunc,
    solver: RK4Solver,
}

impl ContinuousNormalizingFlow {
    pub fn new(dim: usize, num_layers: usize) -> Self {
        ContinuousNormalizingFlow {
            ode_func: ODEFunc::new(dim, num_layers),
            solver: RK4Solver::new(0.1),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> (Tensor, f32) {
        let z = self.solver.solve(&mut self.ode_func, x, 0.0, 1.0, training);
        
        // Compute log determinant (simplified)
        let log_det = self.compute_log_det(x, &z);
        
        (z, log_det)
    }

    pub fn inverse(&mut self, z: &Tensor, training: bool) -> Tensor {
        // Integrate backwards in time
        self.solver.solve(&mut self.ode_func, z, 1.0, 0.0, training)
    }

    fn compute_log_det(&self, x: &Tensor, z: &Tensor) -> f32 {
        // Simplified log determinant computation
        let x_data = x.data_f32();
        let z_data = z.data_f32();
        
        let mut log_det = 0.0f32;
        for (x_val, z_val) in x_data.iter().zip(z_data.iter()) {
            if *x_val != 0.0 {
                log_det += (z_val / x_val).abs().ln();
            }
        }
        
        log_det
    }
}

/// Second Order Neural ODE
pub struct SecondOrderNeuralODE {
    ode_func: ODEFunc,
    velocity_func: ODEFunc,
    solver: RK4Solver,
}

impl SecondOrderNeuralODE {
    pub fn new(dim: usize, num_layers: usize) -> Self {
        SecondOrderNeuralODE {
            ode_func: ODEFunc::new(dim * 2, num_layers),
            velocity_func: ODEFunc::new(dim, num_layers),
            solver: RK4Solver::new(0.1),
        }
    }

    pub fn forward(&mut self, x: &Tensor, v: &Tensor, training: bool) -> (Tensor, Tensor) {
        // Concatenate position and velocity
        let state = self.concatenate(x, v);
        
        // Solve ODE
        let final_state = self.solver.solve(&mut self.ode_func, &state, 0.0, 1.0, training);
        
        // Split back into position and velocity
        self.split(&final_state)
    }

    fn concatenate(&self, a: &Tensor, b: &Tensor) -> Tensor {
        let a_data = a.data_f32();
        let b_data = b.data_f32();
        
        let mut result = Vec::new();
        result.extend_from_slice(a_data);
        result.extend_from_slice(b_data);
        
        let batch_size = a.dims()[0];
        let total_dim = a.dims()[1] + b.dims()[1];
        
        Tensor::from_slice(&result, &[batch_size, total_dim]).unwrap()
    }

    fn split(&self, state: &Tensor) -> (Tensor, Tensor) {
        let data = state.data_f32();
        let batch_size = state.dims()[0];
        let total_dim = state.dims()[1];
        let half_dim = total_dim / 2;
        
        let mut x_data = Vec::new();
        let mut v_data = Vec::new();
        
        for b in 0..batch_size {
            for d in 0..half_dim {
                x_data.push(data[b * total_dim + d]);
            }
            for d in half_dim..total_dim {
                v_data.push(data[b * total_dim + d]);
            }
        }
        
        let x = Tensor::from_slice(&x_data, &[batch_size, half_dim]).unwrap();
        let v = Tensor::from_slice(&v_data, &[batch_size, half_dim]).unwrap();
        
        (x, v)
    }
}

/// Hamiltonian Neural Network
pub struct HamiltonianNeuralNetwork {
    hamiltonian: Dense,
    layers: Vec<Dense>,
}

impl HamiltonianNeuralNetwork {
    pub fn new(dim: usize, hidden_dim: usize, num_layers: usize) -> Self {
        let mut layers = Vec::new();
        
        layers.push(Dense::new(dim * 2, hidden_dim));
        for _ in 1..num_layers {
            layers.push(Dense::new(hidden_dim, hidden_dim));
        }
        layers.push(Dense::new(hidden_dim, 1));
        
        HamiltonianNeuralNetwork {
            hamiltonian: Dense::new(dim * 2, 1),
            layers,
        }
    }

    pub fn forward(&mut self, q: &Tensor, p: &Tensor, training: bool) -> Tensor {
        let state = self.concatenate(q, p);
        
        let mut out = state;
        for (i, layer) in self.layers.iter_mut().enumerate() {
            out = layer.forward(&out, training);
            if i < self.layers.len() - 1 {
                out = Tanh::new().forward(&out);
            }
        }
        
        out
    }

    fn concatenate(&self, a: &Tensor, b: &Tensor) -> Tensor {
        let a_data = a.data_f32();
        let b_data = b.data_f32();
        
        let mut result = Vec::new();
        result.extend_from_slice(a_data);
        result.extend_from_slice(b_data);
        
        let batch_size = a.dims()[0];
        let total_dim = a.dims()[1] + b.dims()[1];
        
        Tensor::from_slice(&result, &[batch_size, total_dim]).unwrap()
    }
}

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

    #[test]
    fn test_neural_ode() {
        let mut node = NeuralODE::new(10, 64, 10, 3);
        let input = Tensor::from_slice(&vec![0.5f32; 2 * 10], &[2, 10]).unwrap();
        let output = node.forward(&input, false);
        assert_eq!(output.dims()[1], 10);
    }

    #[test]
    fn test_augmented_neural_ode() {
        let mut anode = AugmentedNeuralODE::new(10, 64, 10, 16, 3);
        let input = Tensor::from_slice(&vec![0.5f32; 2 * 10], &[2, 10]).unwrap();
        let output = anode.forward(&input, false);
        assert_eq!(output.dims()[1], 10);
    }

    #[test]
    fn test_continuous_normalizing_flow() {
        let mut cnf = ContinuousNormalizingFlow::new(10, 3);
        let input = Tensor::from_slice(&vec![0.5f32; 2 * 10], &[2, 10]).unwrap();
        let (z, log_det) = cnf.forward(&input, false);
        assert_eq!(z.dims()[1], 10);
    }
}