use serde::{Deserialize, Serialize};

/// Universal Slate (per §13.1) — Stage 2 layer 1 only: a dense embedding
/// vector. The deeper LLM-comparison layer arrives in Stage 4.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Slate(pub Vec<f32>);

impl Slate {
    pub fn dim(&self) -> usize {
        self.0.len()
    }

    pub fn norm(&self) -> f32 {
        self.0.iter().map(|x| x * x).sum::<f32>().sqrt()
    }

    /// Cosine similarity in `[-1, 1]`. Returns 0 if either vector is zero or
    /// dimensions disagree (the latter can happen across embedding model
    /// changes — see §13 risk #3).
    pub fn cosine(&self, other: &Slate) -> f32 {
        if self.0.len() != other.0.len() {
            return 0.0;
        }
        let mut dot = 0.0_f32;
        let mut a2 = 0.0_f32;
        let mut b2 = 0.0_f32;
        for (a, b) in self.0.iter().zip(other.0.iter()) {
            dot += a * b;
            a2 += a * a;
            b2 += b * b;
        }
        let denom = (a2.sqrt() * b2.sqrt()).max(1e-9);
        dot / denom
    }
}