// ml.sy -- Machine Learning Primitives
// Model training, REINFORCE, deployment.
//
// Aislinn McCubbins, April 2026

use cascade::Cascade
use topos::ConceptBank
use schema::SchemaStore

// === TRAINER ===

struct ReinforceTrainer {
    learning_rate: Float,
    baseline: Float,
    epochs: Int,
}

impl ReinforceTrainer {
    fn new() -> ReinforceTrainer {
        ReinforceTrainer {
            learning_rate: 0.001,
            baseline: 0.0,
            epochs: 1,
        }
    }

    fn with_lr(mut self, lr: Float) -> ReinforceTrainer {
        self.learning_rate = lr
        self
    }

    fn with_epochs(mut self, n: Int) -> ReinforceTrainer {
        self.epochs = n
        self
    }
}

// === DATASET ===

struct Dataset {
    pairs: Vec<DataPair>,
}

struct DataPair {
    prompt: String,
    response: String,
}

impl Dataset {
    fn load(path: String) -> Dataset {
        Dataset { pairs: [] }
    }

    fn len(&self) -> Int {
        0
    }
}

// === AGENT ===
// The complete cognitive agent: cascade + concepts + schemas + neurons.

temporal struct Agent {
    cascade: Cascade,
    concepts: ConceptBank,
    schemas: SchemaStore,
}

impl Evolve for Agent {
    fn evolve(&mut self, dt: Duration) {
        // All temporal fields auto-evolve
    }
}

impl Agent {
    fn new(dim: Int) -> Agent {
        Agent {
            cascade: Cascade::standard_34(),
            concepts: ConceptBank::new(dim),
            schemas: SchemaStore::new(),
        }
    }

    fn process(&mut self, input: String) -> String {
        // 1. Convert input to neurochemical stimulus
        // 2. Let cascade evolve
        // 3. Match schemas
        // 4. Generate response
        ""
    }

    fn train(&mut self, data: Dataset, trainer: ReinforceTrainer) {
        // REINFORCE training loop
    }

    fn save(&self, path: String) {
        // Save checkpoint
    }

    fn load(path: String) -> Agent {
        Agent::new(34)
    }
}
