theory_core 0.1.0

//! Theory Generator: Neural Network → Constraint IR
//!
//! Converts raw inputs into typed constraint intermediate representation
//! using neural networks trained to understand problem structure.

use crate::ir::ConstraintIR;
use anyhow::Result;
// Placeholder for tensor type - would use eenn::tensor::Tensor in full implementation
pub struct Tensor {
    pub shape: Shape,
}

#[derive(Debug, Clone)]
pub struct Shape(pub Vec<Option<usize>>);
use serde::{Deserialize, Serialize};

/// Trait for generating constraint IR from neural network inputs
pub trait TheoryGenerator: Send + Sync {
    /// Generate constraints from input tensor
    fn generate_constraints(&self, input: &Tensor) -> Result<ConstraintIR>;
    
    /// Get confidence score for generated constraints (0.0 to 1.0)
    fn confidence(&self, input: &Tensor) -> Result<f32>;
    
    /// Update generator based on feedback from solver results
    fn update_from_feedback(&mut self, input: &Tensor, expected: &ConstraintIR) -> Result<()>;
}

/// Configuration for neural theory generator
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TheoryGeneratorConfig {
    /// Hidden layer dimensions
    pub hidden_dims: Vec<usize>,
    /// Dropout rate during training
    pub dropout_rate: f32,
    /// Learning rate
    pub learning_rate: f32,
    /// Maximum number of variables to generate
    pub max_variables: usize,
    /// Maximum number of constraints to generate
    pub max_constraints: usize,
}

impl Default for TheoryGeneratorConfig {
    fn default() -> Self {
        Self {
            hidden_dims: vec![512, 256, 128],
            dropout_rate: 0.1,
            learning_rate: 0.001,
            max_variables: 50,
            max_constraints: 100,
        }
    }
}

/// Simple neural theory generator implementation
pub struct NeuralTheoryGenerator {
    config: TheoryGeneratorConfig,
    // TODO: Add actual neural network implementation
}

impl NeuralTheoryGenerator {
    pub fn new(config: TheoryGeneratorConfig) -> Self {
        Self { config }
    }
}

impl TheoryGenerator for NeuralTheoryGenerator {
    fn generate_constraints(&self, input: &Tensor) -> Result<ConstraintIR> {
        // TODO: Implement actual neural network forward pass
        // For now, return a simple example constraint IR
        
        let mut ir = ConstraintIR::new();
        
        // Generate based on input tensor properties
        let shape = &input.shape;
        let num_vars = shape.0.iter()
            .filter_map(|x| *x)
            .product::<usize>()
            .min(self.config.max_variables);
        
        // Add variables
        for i in 0..num_vars {
            use crate::ir::{Variable, Domain, VariableMetadata};
            
            let var = Variable {
                name: format!("x_{}", i),
                domain: Domain::Real { min: Some(-10.0), max: Some(10.0) },
                metadata: VariableMetadata {
                    description: Some(format!("Generated variable {}", i)),
                    importance: 0.5,
                    computation_cost: 1.0,
                },
            };
            
            ir.add_variable(var);
        }
        
        // Add example constraints
        use crate::ir::{Constraint, Expr, VarId, ConstValue};
        
        if num_vars >= 2 {
            // x_0 + x_1 <= 5.0
            let constraint = Constraint::LessEqual {
                lhs: Expr::Binary {
                    op: crate::ir::BinaryOp::Add,
                    lhs: Box::new(Expr::Var(VarId(0))),
                    rhs: Box::new(Expr::Var(VarId(1))),
                },
                rhs: Expr::Const(ConstValue::Real(5.0)),
            };
            
            ir.add_constraint(constraint);
        }
        
        // Add theory tags based on constraints
        ir.add_theory_tag(crate::ir::TheoryTag::LRA);
        
        Ok(ir)
    }
    
    fn confidence(&self, _input: &Tensor) -> Result<f32> {
        // TODO: Implement actual confidence estimation
        Ok(0.8)
    }
    
    fn update_from_feedback(&mut self, _input: &Tensor, _expected: &ConstraintIR) -> Result<()> {
        // TODO: Implement learning from solver feedback
        Ok(())
    }
}