optimizers

type BootstrapFewShot struct {
	Metric          func(example map[string]interface{}, prediction map[string]interface{}, ctx context.Context) bool
	MaxBootstrapped int
}

type CandidateResult struct {
	Program     core.Program `json:"-"`
	Score       float64      `json:"score"`
	Step        int          `json:"step"`
	Temperature float64      `json:"temperature"`
	CreatedAt   time.Time    `json:"created_at"`
}

type Copro struct {
	Metric          func(example, prediction map[string]interface{}, ctx context.Context) bool
	MaxBootstrapped int
	SubOptimizer    core.Optimizer
}

type InstructionGenerator struct {
	PromptModel   core.LLM
	MaxCandidates int
	Temperature   float64
}

type IntrospectionResult struct {
	Analysis             string   `json:"analysis"`
	Recommendations      []string `json:"recommendations"`
	Confidence           float64  `json:"confidence"`
	IdentifiedPatterns   []string `json:"identified_patterns"`
	SuggestedAdjustments []string `json:"suggested_adjustments"`
}

type MIPRO struct {
	// contains filtered or unexported fields
}

type MIPROConfig struct {
	Mode           RunMode
	NumTrials      int
	ValSize        int
	MiniBatchSize  int
	AdaptiveParams bool
	ScalingFactors struct {
		TrialsPerVariable float64
		BatchSizeScaling  float64
	}
	TeacherSettings map[string]interface{}

	// TPE specific configuration
	TPEGamma        float64
	TPEGenerations  int
	Seed            int64
	NumModules      int // Number of modules to optimize (can be inferred from program)
	MaxLabeledDemos int // Maximum number of labeled demonstrations to use
}

type MIPROMetrics struct {
	TeacherPerformance  float64
	StudentPerformance  float64
	PromptEffectiveness map[string]float64
	OptimizationHistory []OptimizationStep
	TokenUsage          *core.TokenInfo
}

type MIPROOption func(*MIPRO)

type OptimizationState struct {
	SuccessfulPatterns []string
	PromptEvolution    []PromptVersion
	TeacherScores      map[string]float64
	CurrentIteration   int
	BestScore          float64
	Convergence        float64
}

type OptimizationStep struct {
	Trial         int
	Performance   float64
	Improvements  []string
	FailurePoints []string
}

type PromptComponent struct {
	Type    string
	Content string
	Score   float64
}

type PromptVersion struct {
	Template    string
	Performance float64
	Components  []PromptComponent
}

type RunMode string

type SIMBA struct {
	// contains filtered or unexported fields
}

type SIMBAConfig struct {
	// Mini-batch configuration
	BatchSize     int `json:"batch_size"`     // Default: 32
	MaxSteps      int `json:"max_steps"`      // Default: 8
	NumCandidates int `json:"num_candidates"` // Default: 6

	// Temperature controls
	SamplingTemperature float64 `json:"sampling_temperature"` // Default: 0.2

	// Introspective learning
	IntrospectionFrequency int `json:"introspection_frequency"` // Default: 2

	// Performance thresholds
	ConvergenceThreshold float64 `json:"convergence_threshold"` // Default: 0.001
	MinImprovementRatio  float64 `json:"min_improvement_ratio"` // Default: 0.05

	// Concurrency and resources
	MaxGoroutines int `json:"max_goroutines"` // Default: 10
}

type SIMBAOption func(*SIMBA)

type SIMBAState struct {
	CurrentStep      int
	BestScore        float64
	BestProgram      core.Program
	CandidateHistory []CandidateResult
	PerformanceLog   []StepResult
	IntrospectionLog []string
	StartTime        time.Time
}

type SearchConfig struct {
	ParamSpace  map[string][]interface{}
	MaxTrials   int
	Seed        int64
	Constraints map[string]interface{}
}

type SearchStrategy interface {
	SuggestParams(ctx context.Context) (map[string]interface{}, error)
	UpdateResults(params map[string]interface{}, score float64) error
	GetBestParams() (map[string]interface{}, float64)
	Initialize(config SearchConfig) error
}

type StepResult struct {
	Step            int           `json:"step"`
	BestScore       float64       `json:"best_score"`
	CandidateScores []float64     `json:"candidate_scores"`
	Temperature     float64       `json:"temperature"`
	BatchSize       int           `json:"batch_size"`
	Introspection   string        `json:"introspection,omitempty"`
	Duration        time.Duration `json:"duration"`
	Improvement     float64       `json:"improvement"`
}

type TPEConfig struct {
	// Gamma is the percentile split between good and bad observations (default: 0.25)
	Gamma float64
	// Seed is used for random number generation
	Seed int64
	// NumEIGenerations is the number of random points to evaluate EI on
	NumEIGenerations int
	// Prior distributions for each parameter (optional)
	PriorWeight float64
	// Kernel bandwidth factor
	BandwidthFactor float64
}

type TPEOptimizer struct {
	// contains filtered or unexported fields
}

type TeacherStudentOptimizer struct {
	Teacher         core.LLM
	Student         core.LLM
	TeacherSettings map[string]interface{}
	MaxExamples     int
	// contains filtered or unexported fields
}