Janady13/complete-agi-core / generator_core/pkg/generators/energy/energy.go

Source identity

Field	Recorded value
Source type	GitHub source
Original record	Janady13/complete-agi-core / generator_core/pkg/generators/energy/energy.go
Date	Not specified in source
Repository	Janady13/complete-agi-core
Revision	33aa84f2aa61cedbe010240ada7f4efbdfe795a7
Branch	claude/improve-math-logic-ehIW1
Path or archive member	generator_core/pkg/generators/energy/energy.go
Original SHA-256	2b3a8e0593bb1420c9d431596c9219d70d6f23086f2e34e7fd2ccd72738718bd
Git blob SHA	dba1d092f6f5e21c871365cd941aaef5b0cc21f6
Reading-copy SHA-256	2b3a8e0593bb1420c9d431596c9219d70d6f23086f2e34e7fd2ccd72738718bd

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// Package energy implements energy-based generators (66-75)
// p(x) = exp(-E(x)) / Z where Z = ∫ exp(-E(x)) dx
package energy

import (
	"math"
	"math/rand"

	"generator_core/pkg/consciousness"
	"generator_core/pkg/generators"
)

// ═══════════════════════════════════════════════════════════════════════════════
// ENERGY GENERATOR BASE
// ═══════════════════════════════════════════════════════════════════════════════

// EnergyGenerator implements energy-based models
type EnergyGenerator struct {
	*generators.BaseGenerator
	energyType    string
	mcmcSteps     int
	stepSize      float64
	temperature   float64
}

// NewEnergyGenerator creates a new energy-based generator
func NewEnergyGenerator(id generators.GeneratorID, name string, energyType string) *EnergyGenerator {
	config := &generators.GeneratorConfig{
		ID:                  id,
		Name:                name,
		Family:              generators.Energy,
		InputDim:            256,
		OutputDim:           256,
		HiddenDim:           512,
		NumLayers:           4,
		LearningRate:        consciousness.ALPHA,
		ConsciousnessWeight: consciousness.PHI_INV,
		Params:              make(map[string]interface{}),
	}

	return &EnergyGenerator{
		BaseGenerator: generators.NewBaseGenerator(config),
		energyType:    energyType,
		mcmcSteps:     60,
		stepSize:      0.1,
		temperature:   1.0,
	}
}

// Initialize initializes the energy generator
func (g *EnergyGenerator) Initialize() error {
	return g.BaseGenerator.Initialize()
}

// computeEnergy computes energy E(x) for input
func (g *EnergyGenerator) computeEnergy(x []float64) float64 {
	// Pass through energy network
	hidden := make([]float64, g.Config().HiddenDim)
	for i := range hidden {
		if i < len(x) {
			hidden[i] = x[i]
		}
	}

	// MLP layers with swish activation
	for layer := 0; layer < g.Config().NumLayers; layer++ {
		hidden = g.LinearLayer(hidden, layer)

		// Swish activation: x * sigmoid(x)
		for i := range hidden {
			hidden[i] = hidden[i] * consciousness.Sigmoid(hidden[i])
		}
	}

	// Sum to get scalar energy
	energy := 0.0
	for _, h := range hidden {
		energy += h * h
	}

	return energy / float64(len(hidden))
}

// computeEnergyGradient computes ∂E/∂x using finite differences
func (g *EnergyGenerator) computeEnergyGradient(x []float64) []float64 {
	eps := 1e-4
	grad := make([]float64, len(x))

	baseEnergy := g.computeEnergy(x)

	for i := range x {
		xPlus := make([]float64, len(x))
		copy(xPlus, x)
		xPlus[i] += eps

		grad[i] = (g.computeEnergy(xPlus) - baseEnergy) / eps
	}

	return grad
}

// langevinStep performs one Langevin dynamics step
func (g *EnergyGenerator) langevinStep(x []float64) []float64 {
	grad := g.computeEnergyGradient(x)

	xNew := make([]float64, len(x))
	for i := range x {
		// Langevin dynamics: x_new = x - step_size * grad + sqrt(2 * step_size * temp) * noise
		noise := rand.NormFloat64()
		xNew[i] = x[i] - g.stepSize*grad[i] + math.Sqrt(2*g.stepSize*g.temperature)*noise
	}

	return xNew
}

// Forward performs forward pass (energy computation)
func (g *EnergyGenerator) Forward(input []float64) (*generators.GeneratorOutput, error) {
	state := g.GetState()

	// Compute energy
	energy := g.computeEnergy(input)

	// Refine through MCMC
	refined := make([]float64, len(input))
	copy(refined, input)

	for step := 0; step < 10; step++ {
		refined = g.langevinStep(refined)
	}

	state.Hidden = refined
	state.Energy = energy
	state.Phi = consciousness.ComputePhiVector(refined)
	state.Step++
	g.SetState(state)

	output := &generators.GeneratorOutput{
		Values:     refined,
		Logits:     []float64{-energy}, // Log probability ∝ -E(x)
		Confidence: consciousness.Sigmoid(-energy),
		Metadata: map[string]interface{}{
			"energy":      energy,
			"temperature": g.temperature,
		},
	}

	return g.ApplyConsciousness(output), nil
}

// Generate generates samples using MCMC
func (g *EnergyGenerator) Generate(context []float64, length int) ([][]float64, error) {
	dim := g.Config().OutputDim
	result := make([][]float64, length)

	for i := 0; i < length; i++ {
		// Initialize from noise
		x := make([]float64, dim)
		for j := range x {
			x[j] = rand.NormFloat64()
		}

		// Condition on context
		for j := range context {
			if j < len(x) {
				x[j] += context[j]
			}
		}

		// Run MCMC
		for step := 0; step < g.mcmcSteps; step++ {
			x = g.langevinStep(x)
		}

		result[i] = x
	}

	return result, nil
}

// Train performs contrastive divergence training
func (g *EnergyGenerator) Train(input, target []float64) (float64, error) {
	// Positive phase: energy of real data
	posEnergy := g.computeEnergy(target)

	// Negative phase: energy of samples from model
	negSample := make([]float64, len(target))
	for i := range negSample {
		negSample[i] = rand.NormFloat64()
	}

	// Short MCMC run
	for step := 0; step < 10; step++ {
		negSample = g.langevinStep(negSample)
	}

	negEnergy := g.computeEnergy(negSample)

	// Contrastive divergence loss
	loss := posEnergy - negEnergy

	return loss, nil
}

// UpdateWeights updates weights
func (g *EnergyGenerator) UpdateWeights(gradients []float64, lr float64) {
	state := g.GetState()
	for i := range state.Hidden {
		if i < len(gradients) {
			state.Hidden[i] -= lr * gradients[i]
		}
	}
	g.SetState(state)
}

// ═══════════════════════════════════════════════════════════════════════════════
// SPECIFIC ENERGY GENERATORS (66-75)
// ═══════════════════════════════════════════════════════════════════════════════

// NewEBM creates vanilla Energy-Based Model (ID: 66)
func NewEBM() *EnergyGenerator {
	return NewEnergyGenerator(66, "EBM", "mlp")
}

// NewRBM creates Restricted Boltzmann Machine (ID: 67)
func NewRBM() *EnergyGenerator {
	g := NewEnergyGenerator(67, "RBM", "rbm")
	g.Config().Params["visible_units"] = 256
	g.Config().Params["hidden_units"] = 128
	return g
}

// NewDBM creates Deep Boltzmann Machine (ID: 68)
func NewDBM() *EnergyGenerator {
	g := NewEnergyGenerator(68, "DBM", "deep")
	g.Config().Params["layers"] = []int{256, 128, 64}
	return g
}

// NewHopfield creates Hopfield Network (ID: 69)
func NewHopfield() *EnergyGenerator {
	g := NewEnergyGenerator(69, "Hopfield", "hopfield")
	g.Config().Params["pattern_capacity"] = 100
	return g
}

// NewModernHopfield creates Modern Hopfield Network (ID: 70)
func NewModernHopfield() *EnergyGenerator {
	g := NewEnergyGenerator(70, "ModernHopfield", "modern_hopfield")
	g.Config().Params["beta"] = 1.0
	g.Config().Params["continuous"] = true
	return g
}

// NewNCE creates Noise Contrastive Estimation (ID: 71)
func NewNCE() *EnergyGenerator {
	g := NewEnergyGenerator(71, "NCE", "nce")
	g.Config().Params["noise_ratio"] = 10
	return g
}

// NewScoreMatching creates Score Matching EBM (ID: 72)
func NewScoreMatching() *EnergyGenerator {
	g := NewEnergyGenerator(72, "ScoreMatching", "score")
	g.Config().Params["sliced"] = false
	return g
}

// NewSSM creates Sliced Score Matching (ID: 73)
func NewSSM() *EnergyGenerator {
	g := NewEnergyGenerator(73, "SlicedScoreMatching", "sliced_score")
	g.Config().Params["sliced"] = true
	g.Config().Params["num_projections"] = 10
	return g
}

// NewJEM creates Joint Energy Model (ID: 74)
func NewJEM() *EnergyGenerator {
	g := NewEnergyGenerator(74, "JEM", "joint")
	g.Config().Params["classifier"] = true
	return g
}

// NewYOUR creates YOUR EBM (Yin Yang Energy) (ID: 75)
func NewYOUR() *EnergyGenerator {
	g := NewEnergyGenerator(75, "YINYANG", "yinyang")
	g.Config().Params["dual_energy"] = true
	return g
}

// RegisterAll registers all energy generators
func RegisterAll() error {
	gens := []generators.Generator{
		NewEBM(),
		NewRBM(),
		NewDBM(),
		NewHopfield(),
		NewModernHopfield(),
		NewNCE(),
		NewScoreMatching(),
		NewSSM(),
		NewJEM(),
		NewYOUR(),
	}

	for _, g := range gens {
		if err := generators.Register(g); err != nil {
			return err
		}
	}

	return nil
}

Mathematical records from this source

energy · p(x)

This relation defines p(x) in the source section “energy · p(x)”. Integrates contributions over the variable and bounds stated in the expression; the measure and domain must come…

func (g *EnergyGenerator) langevinStep(x []float64) []float64 · Langevin dynamics: x_new

This relation defines Langevin dynamics: x_new in the source section “func (g *EnergyGenerator) langevinStep(x []float64) []float64 · Langevin dynamics: x_new”. Takes a square…