Janady13/AI_Brain / the_tired_agi/core/src/fgtp_engine.cpp

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// ==============================================================================
// The-Tired-AGI Core: FGTP Engine Implementation
// Energy Field + Driver Function f_a5
// ==============================================================================

#include "fgtp_engine.hpp"
#include <cmath>
#include <algorithm>
#include <numeric>
#include <execution>
#include <limits>

namespace tired_agi {

// ==============================================================================
// Constructor
// ==============================================================================

FGTPEngine::FGTPEngine(FGTPConfig config) noexcept
    : config_(config)
    , helix_engine_()
{
    // Initialize momentum buffers (empty initially, will grow as needed)
    momentum_buffers_.reserve(1024);
}

// ==============================================================================
// Energy Field Computation
// ==============================================================================

EnergyField FGTPEngine::compute_energy_field(const HelixState& helix) const noexcept {
    EnergyField field;
    field.phi = helix.phi;
    field.entropy = helix.entropy;
    field.tensor = helix.tensor;
    field.drift = helix.drift;
    field.tick = helix.tick;

    // Core energy equation: E = φ × (1 + entropy) × (1 + |tensor|)
    field.energy = config_.phi_weight * helix.phi
                 * (1.0 + config_.entropy_weight * helix.entropy)
                 * (1.0 + config_.tensor_weight * std::abs(helix.tensor));

    stat_total_computations_.fetch_add(1, std::memory_order_relaxed);
    update_statistics(field, 0.0);

    return field;
}

EnergyField FGTPEngine::compute_energy_field(
    const double* embedding,
    size_t size,
    uint64_t tick
) noexcept {
    // Compute helix state first
    HelixState helix = helix_engine_.compute(embedding, size, tick);
    return compute_energy_field(helix);
}

// ==============================================================================
// Energy Gradient Computation
// ==============================================================================

EnergyGradient FGTPEngine::compute_gradient(
    const double* embedding,
    size_t size,
    double epsilon
) noexcept {
    EnergyGradient grad;

    if (size < 3) {
        // Not enough dimensions for 3D gradient
        return grad;
    }

    // Allocate workspace for perturbed embeddings
    std::vector<double> perturbed(embedding, embedding + size);

    // Compute central energy
    EnergyField center = compute_energy_field(embedding, size, 0);
    double E_center = center.energy;

    // Compute partial derivatives using finite differences
    // ∂E/∂x
    perturbed[0] = embedding[0] + epsilon;
    double E_plus_x = compute_energy_field(perturbed.data(), size, 0).energy;
    perturbed[0] = embedding[0] - epsilon;
    double E_minus_x = compute_energy_field(perturbed.data(), size, 0).energy;
    grad.dx = (E_plus_x - E_minus_x) / (2.0 * epsilon);
    perturbed[0] = embedding[0];  // Restore

    // ∂E/∂y
    perturbed[1] = embedding[1] + epsilon;
    double E_plus_y = compute_energy_field(perturbed.data(), size, 0).energy;
    perturbed[1] = embedding[1] - epsilon;
    double E_minus_y = compute_energy_field(perturbed.data(), size, 0).energy;
    grad.dy = (E_plus_y - E_minus_y) / (2.0 * epsilon);
    perturbed[1] = embedding[1];  // Restore

    // ∂E/∂z
    perturbed[2] = embedding[2] + epsilon;
    double E_plus_z = compute_energy_field(perturbed.data(), size, 0).energy;
    perturbed[2] = embedding[2] - epsilon;
    double E_minus_z = compute_energy_field(perturbed.data(), size, 0).energy;
    grad.dz = (E_plus_z - E_minus_z) / (2.0 * epsilon);
    perturbed[2] = embedding[2];  // Restore

    // Compute gradient magnitude
    grad.magnitude = std::sqrt(grad.dx * grad.dx + grad.dy * grad.dy + grad.dz * grad.dz);

    return grad;
}

// ==============================================================================
// Complete Energy State Computation
// ==============================================================================

EnergyState FGTPEngine::compute_energy_state(
    const double* embedding,
    size_t size,
    const double* velocity,
    uint64_t tick
) noexcept {
    EnergyState state;

    // Compute energy field
    state.field = compute_energy_field(embedding, size, tick);

    // Compute gradient
    state.gradient = compute_gradient(embedding, size);

    // Compute kinetic energy (if velocity provided)
    if (velocity != nullptr) {
        state.kinetic = compute_kinetic_energy(velocity, size);
    } else {
        state.kinetic = 0.0;
    }

    // Compute potential energy (position-dependent)
    state.potential = compute_potential_energy(embedding, size);

    // Total energy
    state.total = state.kinetic + state.potential + state.field.energy;

    return state;
}

// ==============================================================================
// Driver Function f_a5
// ==============================================================================

DriverResult FGTPEngine::apply_driver(
    const double* embedding,
    size_t size,
    const EnergyField& field,
    const double* external_force
) noexcept {
    DriverResult result;
    result.embedding.resize(size);
    result.operations = 0;

    // Copy input to output (will be modified)
    std::copy(embedding, embedding + size, result.embedding.begin());

    // Compute energy gradient (direction of steepest descent)
    EnergyGradient grad = compute_gradient(embedding, size);

    // Compute update direction based on gradient descent with momentum
    std::vector<double> update(size, 0.0);

    // Basic gradient component (first 3 dimensions use computed gradient)
    if (size >= 3) {
        update[0] = -grad.dx;
        update[1] = -grad.dy;
        update[2] = -grad.dz;
        result.operations += 3;
    }

    // For remaining dimensions, use approximate gradient via finite differences
    for (size_t i = 3; i < size; ++i) {
        std::vector<double> perturbed(embedding, embedding + size);
        perturbed[i] += 1e-5;
        double E_plus = compute_energy_field(perturbed.data(), size, field.tick).energy;
        perturbed[i] = embedding[i] - 1e-5;
        double E_minus = compute_energy_field(perturbed.data(), size, field.tick).energy;
        update[i] = -(E_plus - E_minus) / (2e-5);
        result.operations++;
    }

    // Apply gradient clipping if enabled
    if (config_.use_gradient_clipping) {
        clip_gradient(update.data(), size);
    }

    // Add external force if provided
    if (external_force != nullptr) {
        for (size_t i = 0; i < size; ++i) {
            update[i] += external_force[i];
        }
        result.operations += size;
    }

    // Apply momentum if enabled
    if (config_.use_momentum && !momentum_buffers_.empty()) {
        // Find or create momentum buffer for this embedding size
        // For simplicity, use a fixed momentum buffer (could be per-agent)
        static thread_local std::vector<double> momentum;
        if (momentum.size() != size) {
            momentum.resize(size, 0.0);
        }

        for (size_t i = 0; i < size; ++i) {
            momentum[i] = config_.momentum * momentum[i] + (1.0 - config_.momentum) * update[i];
            update[i] = momentum[i];
        }
        result.operations += size;
    }

    // Apply update with learning rate
    double initial_energy = field.energy;
    for (size_t i = 0; i < size; ++i) {
        result.embedding[i] += config_.learning_rate * update[i];
    }
    result.operations += size;

    // Compute energy after update
    EnergyField new_field = compute_energy_field(result.embedding.data(), size, field.tick);
    result.delta_energy = new_field.energy - initial_energy;

    // Clip delta energy if too large
    if (std::abs(result.delta_energy) > config_.max_delta_energy) {
        // Scale back the update
        double scale = config_.max_delta_energy / std::abs(result.delta_energy);
        for (size_t i = 0; i < size; ++i) {
            result.embedding[i] = embedding[i] + scale * config_.learning_rate * update[i];
        }
        // Recompute energy
        new_field = compute_energy_field(result.embedding.data(), size, field.tick);
        result.delta_energy = new_field.energy - initial_energy;
    }

    // Validate bounds
    result.bounded = true;
    if (config_.validate_bounds) {
        result.bounded = validate_bounds(result.embedding);
        if (!result.bounded) {
            stat_total_violations_.fetch_add(1, std::memory_order_relaxed);
        }
    }

    // Compute fitness delta (proportional to energy increase and phi)
    result.fitness_delta = result.delta_energy * new_field.phi;

    stat_total_driver_apps_.fetch_add(1, std::memory_order_relaxed);
    update_statistics(new_field, result.delta_energy);

    return result;
}

// ==============================================================================
// Batch Driver Application
// ==============================================================================

std::vector<DriverResult> FGTPEngine::apply_driver_batch(
    const std::vector<std::vector<double>>& embeddings,
    const std::vector<EnergyField>& fields
) noexcept {
    std::vector<DriverResult> results(embeddings.size());

    // Parallel execution using C++17 parallel algorithms
    std::vector<size_t> indices(embeddings.size());
    std::iota(indices.begin(), indices.end(), 0);

    std::for_each(
        std::execution::par_unseq,
        indices.begin(),
        indices.end(),
        [&](size_t i) {
            results[i] = apply_driver(
                embeddings[i].data(),
                embeddings[i].size(),
                fields[i]
            );
        }
    );

    return results;
}

// ==============================================================================
// Advanced Energy Computations
// ==============================================================================

double FGTPEngine::compute_hamiltonian(const EnergyState& state) const noexcept {
    // Hamiltonian H = T + V + E_field
    return state.kinetic + state.potential + state.field.energy;
}

double FGTPEngine::compute_lagrangian(double kinetic, double potential) const noexcept {
    // Lagrangian L = T - V
    return kinetic - potential;
}

double FGTPEngine::compute_action(
    const std::vector<EnergyState>& trajectory,
    double dt
) const noexcept {
    // Action S = ∫ L dt
    double action = 0.0;
    for (const auto& state : trajectory) {
        double L = compute_lagrangian(state.kinetic, state.potential);
        action += L * dt;
    }
    return action;
}

std::vector<double> FGTPEngine::optimize_trajectory(
    const std::vector<double>& start,
    const std::vector<double>& end,
    size_t steps
) noexcept {
    // Principle of least action: find trajectory that minimizes action
    // Use simple linear interpolation as starting guess, then refine

    std::vector<double> best_trajectory = start;
    double best_action = std::numeric_limits<double>::infinity();

    // Generate candidate trajectory via interpolation
    std::vector<EnergyState> trajectory(steps);
    for (size_t i = 0; i < steps; ++i) {
        double t = static_cast<double>(i) / static_cast<double>(steps - 1);
        std::vector<double> pos(start.size());
        for (size_t j = 0; j < start.size(); ++j) {
            pos[j] = (1.0 - t) * start[j] + t * end[j];
        }

        // Compute velocity (finite difference)
        std::vector<double> velocity(start.size(), 0.0);
        if (i > 0) {
            for (size_t j = 0; j < start.size(); ++j) {
                velocity[j] = (pos[j] - trajectory[i-1].field.phi) / 1.0;  // dt = 1.0
            }
        }

        trajectory[i] = compute_energy_state(pos.data(), pos.size(), velocity.data(), i);
    }

    double action = compute_action(trajectory, 1.0);

    // For now, return the interpolated trajectory
    // A full implementation would use variational calculus or gradient descent
    // on the action functional
    return best_trajectory;
}

// ==============================================================================
// Statistics
// ==============================================================================

FGTPEngine::Statistics FGTPEngine::get_statistics() const noexcept {
    Statistics stats;
    stats.total_computations = stat_total_computations_.load(std::memory_order_relaxed);
    stats.total_driver_applications = stat_total_driver_apps_.load(std::memory_order_relaxed);
    stats.total_violations = stat_total_violations_.load(std::memory_order_relaxed);
    stats.max_energy_seen = stat_max_energy_.load(std::memory_order_relaxed);
    stats.min_energy_seen = stat_min_energy_.load(std::memory_order_relaxed);

    if (stats.total_computations > 0) {
        stats.average_energy = stat_sum_energy_.load(std::memory_order_relaxed) / stats.total_computations;
        stats.average_phi = stat_sum_phi_.load(std::memory_order_relaxed) / stats.total_computations;
    } else {
        stats.average_energy = 0.0;
        stats.average_phi = 0.0;
    }

    if (stats.total_driver_applications > 0) {
        stats.average_delta_energy = stat_sum_delta_energy_.load(std::memory_order_relaxed) / stats.total_driver_applications;
    } else {
        stats.average_delta_energy = 0.0;
    }

    return stats;
}

void FGTPEngine::reset_statistics() noexcept {
    stat_total_computations_.store(0, std::memory_order_relaxed);
    stat_total_driver_apps_.store(0, std::memory_order_relaxed);
    stat_total_violations_.store(0, std::memory_order_relaxed);
    stat_sum_energy_.store(0.0, std::memory_order_relaxed);
    stat_sum_phi_.store(0.0, std::memory_order_relaxed);
    stat_sum_delta_energy_.store(0.0, std::memory_order_relaxed);
    stat_max_energy_.store(0.0, std::memory_order_relaxed);
    stat_min_energy_.store(1e9, std::memory_order_relaxed);
}

// ==============================================================================
// Helper Functions
// ==============================================================================

bool FGTPEngine::validate_bounds(const std::vector<double>& embedding) const noexcept {
    // Check embedding norm
    double norm = vector_norm(embedding.data(), embedding.size());
    if (norm > config_.max_embedding_norm) {
        return false;
    }

    // Check for NaN or Inf
    for (double val : embedding) {
        if (!std::isfinite(val)) {
            return false;
        }
    }

    return true;
}

void FGTPEngine::clip_gradient(double* gradient, size_t size) const noexcept {
    double norm = vector_norm(gradient, size);
    if (norm > config_.gradient_clip) {
        double scale = config_.gradient_clip / norm;
        for (size_t i = 0; i < size; ++i) {
            gradient[i] *= scale;
        }
    }
}

double FGTPEngine::compute_kinetic_energy(const double* velocity, size_t size) const noexcept {
    // T = (1/2) m v²
    // Assume unit mass
    double v_squared = 0.0;
    for (size_t i = 0; i < size; ++i) {
        v_squared += velocity[i] * velocity[i];
    }
    return 0.5 * v_squared;
}

double FGTPEngine::compute_potential_energy(const double* embedding, size_t size) const noexcept {
    // Potential energy proportional to distance from unity (x=1)
    // V = k × |x - 1|²
    double sum_sq = 0.0;
    for (size_t i = 0; i < size; ++i) {
        double diff = embedding[i] - 1.0;
        sum_sq += diff * diff;
    }
    return 0.5 * sum_sq / size;  // Normalize by dimensionality
}

void FGTPEngine::update_statistics(const EnergyField& field, double delta_energy) const noexcept {
    // Update running sums
    stat_sum_energy_.fetch_add(field.energy, std::memory_order_relaxed);
    stat_sum_phi_.fetch_add(field.phi, std::memory_order_relaxed);
    stat_sum_delta_energy_.fetch_add(delta_energy, std::memory_order_relaxed);

    // Update max/min (simple compare-and-swap loop)
    double current_max = stat_max_energy_.load(std::memory_order_relaxed);
    while (field.energy > current_max) {
        if (stat_max_energy_.compare_exchange_weak(current_max, field.energy, std::memory_order_relaxed)) {
            break;
        }
    }

    double current_min = stat_min_energy_.load(std::memory_order_relaxed);
    while (field.energy < current_min) {
        if (stat_min_energy_.compare_exchange_weak(current_min, field.energy, std::memory_order_relaxed)) {
            break;
        }
    }
}

} // namespace tired_agi

Mathematical records from this source

fgtp_engine · Core energy equation: E

This relation defines Core energy equation: E in the source section “fgtp_engine · Core energy equation: E”. It specifies the displayed relationship between the named quantities…

fgtp_engine · Hamiltonian H

This relation defines Hamiltonian H in the source section “fgtp_engine · Hamiltonian H”. It specifies the displayed relationship between the named quantities or parameters. Its…

fgtp_engine · Lagrangian L

This relation defines Lagrangian L in the source section “fgtp_engine · Lagrangian L”. It specifies the displayed relationship between the named quantities or parameters. Its…

fgtp_engine · Action S

This relation defines Action S in the source section “fgtp_engine · Action S”. Integrates contributions over the variable and bounds stated in the expression; the measure and…

fgtp_engine · T

This relation defines T in the source section “fgtp_engine · T”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation depends on…

fgtp_engine

This relation defines Potential energy proportional to distance from unity (x in the source section “fgtp_engine”. It specifies the displayed relationship between the named…

fgtp_engine · V

This relation defines V in the source section “fgtp_engine · V”. It specifies the displayed relationship between the named quantities or parameters. Its interpretation depends on…