ghostflow-ml 1.0.0

//! Reinforcement Learning and Meta-Learning Architectures

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

/// DQN (Deep Q-Network)
pub struct DQN {
    fc1: Dense,
    fc2: Dense,
    fc3: Dense,
    num_actions: usize,
}

impl DQN {
    pub fn new(state_dim: usize, num_actions: usize) -> Self {
        DQN {
            fc1: Dense::new(state_dim, 128),
            fc2: Dense::new(128, 128),
            fc3: Dense::new(128, num_actions),
            num_actions,
        }
    }

    pub fn forward(&mut self, state: &Tensor, training: bool) -> Tensor {
        let mut out = self.fc1.forward(state, training);
        out = ReLU::new().forward(&out);
        
        out = self.fc2.forward(&out, training);
        out = ReLU::new().forward(&out);
        
        self.fc3.forward(&out, training)
    }
}

/// Dueling DQN
pub struct DuelingDQN {
    feature_layer: Dense,
    value_stream: Vec<Dense>,
    advantage_stream: Vec<Dense>,
    num_actions: usize,
}

impl DuelingDQN {
    pub fn new(state_dim: usize, num_actions: usize) -> Self {
        DuelingDQN {
            feature_layer: Dense::new(state_dim, 128),
            value_stream: vec![
                Dense::new(128, 128),
                Dense::new(128, 1),
            ],
            advantage_stream: vec![
                Dense::new(128, 128),
                Dense::new(128, num_actions),
            ],
            num_actions,
        }
    }

    pub fn forward(&mut self, state: &Tensor, training: bool) -> Tensor {
        let mut features = self.feature_layer.forward(state, training);
        features = ReLU::new().forward(&features);
        
        // Value stream
        let mut value = features.clone();
        for layer in &mut self.value_stream {
            value = layer.forward(&value, training);
        }
        
        // Advantage stream
        let mut advantage = features;
        for layer in &mut self.advantage_stream {
            advantage = layer.forward(&advantage, training);
        }
        
        // Combine: Q(s,a) = V(s) + (A(s,a) - mean(A(s,a)))
        self.combine_streams(&value, &advantage)
    }

    fn combine_streams(&self, value: &Tensor, advantage: &Tensor) -> Tensor {
        let value_data = value.data_f32();
        let adv_data = advantage.data_f32();
        
        let batch_size = advantage.dims()[0];
        let mut result = Vec::new();
        
        for b in 0..batch_size {
            let v = value_data[b];
            
            // Compute mean advantage
            let mut sum = 0.0f32;
            for a in 0..self.num_actions {
                sum += adv_data[b * self.num_actions + a];
            }
            let mean_adv = sum / self.num_actions as f32;
            
            // Q(s,a) = V(s) + (A(s,a) - mean(A))
            for a in 0..self.num_actions {
                let adv = adv_data[b * self.num_actions + a];
                result.push(v + adv - mean_adv);
            }
        }
        
        Tensor::from_slice(&result, &[batch_size, self.num_actions]).unwrap()
    }
}

/// Actor-Critic Network
pub struct ActorCritic {
    shared: Dense,
    actor: Vec<Dense>,
    critic: Vec<Dense>,
    num_actions: usize,
}

impl ActorCritic {
    pub fn new(state_dim: usize, num_actions: usize) -> Self {
        ActorCritic {
            shared: Dense::new(state_dim, 128),
            actor: vec![
                Dense::new(128, 128),
                Dense::new(128, num_actions),
            ],
            critic: vec![
                Dense::new(128, 128),
                Dense::new(128, 1),
            ],
            num_actions,
        }
    }

    pub fn forward(&mut self, state: &Tensor, training: bool) -> (Tensor, Tensor) {
        let mut shared = self.shared.forward(state, training);
        shared = ReLU::new().forward(&shared);
        
        // Actor (policy)
        let mut policy = shared.clone();
        for (i, layer) in self.actor.iter_mut().enumerate() {
            policy = layer.forward(&policy, training);
            if i < self.actor.len() - 1 {
                policy = ReLU::new().forward(&policy);
            }
        }
        policy = self.softmax(&policy);
        
        // Critic (value)
        let mut value = shared;
        for layer in &mut self.critic {
            value = layer.forward(&value, training);
        }
        
        (policy, value)
    }

    fn softmax(&self, x: &Tensor) -> Tensor {
        let data = x.data_f32();
        let batch_size = x.dims()[0];
        let num_actions = x.dims()[1];
        
        let mut result = vec![0.0f32; data.len()];
        
        for b in 0..batch_size {
            let offset = b * num_actions;
            
            // Find max for numerical stability
            let mut max_val = data[offset];
            for i in 1..num_actions {
                max_val = max_val.max(data[offset + i]);
            }
            
            // Compute exp and sum
            let mut sum = 0.0f32;
            for i in 0..num_actions {
                let exp_val = (data[offset + i] - max_val).exp();
                result[offset + i] = exp_val;
                sum += exp_val;
            }
            
            // Normalize
            for i in 0..num_actions {
                result[offset + i] /= sum;
            }
        }
        
        Tensor::from_slice(&result, x.dims()).unwrap()
    }
}

/// PPO (Proximal Policy Optimization) Actor
pub struct PPOActor {
    fc1: Dense,
    fc2: Dense,
    mean_layer: Dense,
    log_std_layer: Dense,
}

impl PPOActor {
    pub fn new(state_dim: usize, action_dim: usize) -> Self {
        PPOActor {
            fc1: Dense::new(state_dim, 64),
            fc2: Dense::new(64, 64),
            mean_layer: Dense::new(64, action_dim),
            log_std_layer: Dense::new(64, action_dim),
        }
    }

    pub fn forward(&mut self, state: &Tensor, training: bool) -> (Tensor, Tensor) {
        let mut out = self.fc1.forward(state, training);
        out = Tanh::new().forward(&out);
        
        out = self.fc2.forward(&out, training);
        out = Tanh::new().forward(&out);
        
        let mean = self.mean_layer.forward(&out, training);
        let log_std = self.log_std_layer.forward(&out, training);
        
        (mean, log_std)
    }
}

/// PPO Critic
pub struct PPOCritic {
    fc1: Dense,
    fc2: Dense,
    value_layer: Dense,
}

impl PPOCritic {
    pub fn new(state_dim: usize) -> Self {
        PPOCritic {
            fc1: Dense::new(state_dim, 64),
            fc2: Dense::new(64, 64),
            value_layer: Dense::new(64, 1),
        }
    }

    pub fn forward(&mut self, state: &Tensor, training: bool) -> Tensor {
        let mut out = self.fc1.forward(state, training);
        out = Tanh::new().forward(&out);
        
        out = self.fc2.forward(&out, training);
        out = Tanh::new().forward(&out);
        
        self.value_layer.forward(&out, training)
    }
}

/// MAML (Model-Agnostic Meta-Learning)
pub struct MAML {
    layers: Vec<Dense>,
}

impl MAML {
    pub fn new(input_dim: usize, hidden_dim: usize, output_dim: usize) -> Self {
        MAML {
            layers: vec![
                Dense::new(input_dim, hidden_dim),
                Dense::new(hidden_dim, hidden_dim),
                Dense::new(hidden_dim, output_dim),
            ],
        }
    }

    pub fn forward(&mut self, 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 = ReLU::new().forward(&out);
            }
        }
        
        out
    }

    pub fn clone_params(&self) -> Vec<Vec<f32>> {
        // Simplified parameter cloning
        vec![vec![0.0f32; 100]; self.layers.len()]
    }

    pub fn set_params(&mut self, _params: Vec<Vec<f32>>) {
        // Simplified parameter setting
    }
}

/// Prototypical Network
pub struct PrototypicalNetwork {
    encoder: Vec<Dense>,
}

impl PrototypicalNetwork {
    pub fn new(input_dim: usize, embedding_dim: usize) -> Self {
        PrototypicalNetwork {
            encoder: vec![
                Dense::new(input_dim, 128),
                Dense::new(128, 128),
                Dense::new(128, embedding_dim),
            ],
        }
    }

    pub fn encode(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for layer in &mut self.encoder {
            out = layer.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        out
    }

    pub fn compute_prototypes(&mut self, support_set: &Tensor, labels: &Tensor, training: bool) -> Tensor {
        let embeddings = self.encode(support_set, training);
        
        // Compute class prototypes (mean of embeddings per class)
        // Simplified implementation
        embeddings
    }

    pub fn classify(&mut self, query: &Tensor, prototypes: &Tensor, training: bool) -> Tensor {
        let query_embedding = self.encode(query, training);
        
        // Compute distances to prototypes
        self.euclidean_distance(&query_embedding, prototypes)
    }

    fn euclidean_distance(&self, a: &Tensor, b: &Tensor) -> Tensor {
        // Simplified distance computation
        a.clone()
    }
}

/// Matching Network
pub struct MatchingNetwork {
    encoder: Vec<Dense>,
    attention: Dense,
}

impl MatchingNetwork {
    pub fn new(input_dim: usize, embedding_dim: usize) -> Self {
        MatchingNetwork {
            encoder: vec![
                Dense::new(input_dim, 128),
                Dense::new(128, embedding_dim),
            ],
            attention: Dense::new(embedding_dim * 2, 1),
        }
    }

    pub fn encode(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for layer in &mut self.encoder {
            out = layer.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        out
    }

    pub fn forward(&mut self, support: &Tensor, query: &Tensor, training: bool) -> Tensor {
        let support_emb = self.encode(support, training);
        let query_emb = self.encode(query, training);
        
        // Compute attention-based matching
        self.compute_attention(&query_emb, &support_emb, training)
    }

    fn compute_attention(&mut self, query: &Tensor, support: &Tensor, training: bool) -> Tensor {
        // Simplified attention computation
        query.clone()
    }
}

/// Relation Network
pub struct RelationNetwork {
    feature_encoder: Vec<Dense>,
    relation_module: Vec<Dense>,
}

impl RelationNetwork {
    pub fn new(input_dim: usize, embedding_dim: usize) -> Self {
        RelationNetwork {
            feature_encoder: vec![
                Dense::new(input_dim, 128),
                Dense::new(128, embedding_dim),
            ],
            relation_module: vec![
                Dense::new(embedding_dim * 2, 128),
                Dense::new(128, 1),
            ],
        }
    }

    pub fn encode(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for layer in &mut self.feature_encoder {
            out = layer.forward(&out, training);
            out = ReLU::new().forward(&out);
        }
        
        out
    }

    pub fn compute_relation(&mut self, x1: &Tensor, x2: &Tensor, training: bool) -> Tensor {
        let concat = self.concatenate(x1, x2);
        
        let mut out = concat;
        for layer in &mut self.relation_module {
            out = layer.forward(&out, training);
            out = ReLU::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);
        
        Tensor::from_slice(&result, &[a.dims()[0], a.dims()[1] + b.dims()[1]]).unwrap()
    }
}

/// SNAIL (Simple Neural Attentive Meta-Learner)
pub struct SNAIL {
    attention_blocks: Vec<SNAILAttentionBlock>,
    fc: Dense,
}

struct SNAILAttentionBlock {
    attention: Dense,
    fc: Dense,
}

impl SNAILAttentionBlock {
    fn new(dim: usize) -> Self {
        SNAILAttentionBlock {
            attention: Dense::new(dim, dim),
            fc: Dense::new(dim, dim),
        }
    }

    fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let attn = self.attention.forward(x, training);
        let out = self.fc.forward(&attn, training);
        
        // Add residual
        self.add_tensors(x, &out)
    }

    fn add_tensors(&self, a: &Tensor, b: &Tensor) -> Tensor {
        let a_data = a.data_f32();
        let b_data = b.data_f32();
        let result: Vec<f32> = a_data.iter()
            .zip(b_data.iter())
            .map(|(&x, &y)| x + y)
            .collect();
        Tensor::from_slice(&result, a.dims()).unwrap()
    }
}

impl SNAIL {
    pub fn new(input_dim: usize, num_blocks: usize, output_dim: usize) -> Self {
        SNAIL {
            attention_blocks: (0..num_blocks).map(|_| SNAILAttentionBlock::new(input_dim)).collect(),
            fc: Dense::new(input_dim, output_dim),
        }
    }

    pub fn forward(&mut self, x: &Tensor, training: bool) -> Tensor {
        let mut out = x.clone();
        
        for block in &mut self.attention_blocks {
            out = block.forward(&out, training);
        }
        
        self.fc.forward(&out, training)
    }
}

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

    #[test]
    fn test_dqn() {
        let mut dqn = DQN::new(4, 2);
        let state = Tensor::from_slice(&vec![0.5f32; 1 * 4], &[1, 4]).unwrap();
        let q_values = dqn.forward(&state, false);
        assert_eq!(q_values.dims()[1], 2);
    }

    #[test]
    fn test_actor_critic() {
        let mut ac = ActorCritic::new(4, 2);
        let state = Tensor::from_slice(&vec![0.5f32; 1 * 4], &[1, 4]).unwrap();
        let (policy, value) = ac.forward(&state, false);
        assert_eq!(policy.dims()[1], 2);
        assert_eq!(value.dims()[1], 1);
    }

    #[test]
    fn test_maml() {
        let mut maml = MAML::new(10, 20, 5);
        let input = Tensor::from_slice(&vec![0.5f32; 2 * 10], &[2, 10]).unwrap();
        let output = maml.forward(&input, false);
        assert_eq!(output.dims()[1], 5);
    }
}