Change to time-based estimator functions with exponential parameter smoothing.
This commit is contained in:
parent
a20355bc17
commit
b39deb1986
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@ -216,7 +216,7 @@ func main() {
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os.Exit(1)
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}
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var weights, reps []float64
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var date []time.Time
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var dates []time.Time
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isbw := review.IsBodyweight(ps[0].Exercise, ps[0].Type)
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for _, p := range (ps) {
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@ -227,16 +227,14 @@ func main() {
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if p.RIR[i] == 0 {
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weights = append(weights, w)
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reps = append(reps, float64(p.Reps[i]))
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date = append(date, p.Times[i].UTC())
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dates = append(dates, p.Times[i].UTC())
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fmt.Printf("Set: weight %0.0f, reps %0.0f\n", w, float64(p.Reps[i]))
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}
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}
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}
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//a, b := review.FitPowerLaw(weights, reps, date, 14.0)
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est, err := review.Fit(weights, reps, date,
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review.WithAllModels(),
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review.WithHalfLife(14.0),
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)
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est := review.NewEstimator(review.WithHalfLife(14.0))
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err = est.Fit(weights, reps, dates)
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if err != nil {
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fmt.Printf("Error estimating performance: %v\n", err)
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os.Exit(1)
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@ -246,7 +244,7 @@ func main() {
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if isbw {
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adj = ps[len(ps)-1].Bodyweight
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}
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fmt.Printf("%d: %0.0f\n", i, est.EstimateMaxWeight(float64(i)) - adj)
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fmt.Printf("%d: %0.0f\n", i, est.EstimateMaxWeight(time.Now(), float64(i)) - adj)
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}
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case "predict":
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if flag.NArg() != 3 {
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@ -2,199 +2,170 @@ package review
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import (
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"errors"
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"fmt"
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"math"
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"sort"
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"time"
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"gonum.org/v1/gonum/optimize"
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)
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// Estimator encapsulates a fitted model and exposes estimation methods.
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// Estimator is the main interface for incremental, time-aware strength estimation.
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type Estimator interface {
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Estimate1RM() float64
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EstimateReps(targetWeight float64) float64
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EstimateMaxWeight(nReps float64) float64
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ModelType() string
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Params() []float64
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Fit(weight, reps []float64, dates []time.Time) error
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Estimate1RM(t time.Time) float64
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EstimateReps(t time.Time, targetWeight float64) float64
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EstimateMaxWeight(t time.Time, nReps float64) float64
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Params(t time.Time) []float64
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}
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// Supported model types
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const (
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ModelPowerLaw = "powerlaw"
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ModelLinear = "linear"
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ModelExponential = "exponential"
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)
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// --- Functional Options ---
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// FitOption is a functional option for configuring the Fit process.
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type FitOption func(*fitConfig)
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// fitConfig holds configuration for fitting.
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type fitConfig struct {
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modelTypes []string
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halfLifeDays float64
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type estimatorConfig struct {
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modelType string
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halfLife float64
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smoothAlpha float64 // Exponential smoothing factor (0 < alpha <= 1)
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}
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// WithModel specifies which model(s) to fit. If multiple, Fit selects the best.
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func WithModel(models ...string) FitOption {
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return func(cfg *fitConfig) {
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cfg.modelTypes = models
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type EstimatorOption func(*estimatorConfig)
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// WithModel sets the model type (currently only "powerlaw" is implemented).
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func WithModel(model string) EstimatorOption {
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return func(cfg *estimatorConfig) {
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cfg.modelType = model
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}
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}
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// WithAllModels configures Fit to try all built-in model types.
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func WithAllModels() FitOption {
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return func(cfg *fitConfig) {
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cfg.modelTypes = []string{ModelPowerLaw, ModelLinear, ModelExponential}
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// WithHalfLife sets the half-life for time weighting in curve fitting.
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func WithHalfLife(days float64) EstimatorOption {
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return func(cfg *estimatorConfig) {
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cfg.halfLife = days
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}
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}
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// WithHalfLife sets the half-life (in days) for time weighting.
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func WithHalfLife(days float64) FitOption {
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return func(cfg *fitConfig) {
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cfg.halfLifeDays = days
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// WithSmoothingAlpha sets the exponential smoothing factor for parameter smoothing.
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func WithSmoothingAlpha(alpha float64) EstimatorOption {
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return func(cfg *estimatorConfig) {
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cfg.smoothAlpha = alpha
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}
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}
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// WithModelSelection is an alias for WithModel, for clarity.
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func WithModelSelection(models []string) FitOption {
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return WithModel(models...)
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// --- Estimator Implementation ---
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type timePoint struct {
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date time.Time
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a float64
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b float64
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}
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// Default settings
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const defaultHalfLife = 30.0
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var defaultModelTypes = []string{ModelPowerLaw}
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type estimatorImpl struct {
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cfg estimatorConfig
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data []timePoint // sorted by date
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smoothedA []float64 // smoothed a for each timePoint
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smoothedB []float64 // smoothed b for each timePoint
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}
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// Fit fits the specified model(s) to the data and returns an Estimator.
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// If multiple models are specified, Fit selects the best based on residual sum of squares.
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func Fit(weight, reps []float64, dates []time.Time, opts ...FitOption) (Estimator, error) {
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if len(weight) != len(reps) || len(weight) != len(dates) {
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return nil, errors.New("weight, reps, and dates must have the same length")
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}
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if len(weight) < 2 {
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return nil, errors.New("at least two data points are required")
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}
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// Apply options
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cfg := &fitConfig{
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modelTypes: defaultModelTypes,
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halfLifeDays: defaultHalfLife,
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// NewEstimator creates a new Estimator with the given options.
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func NewEstimator(opts ...EstimatorOption) Estimator {
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cfg := estimatorConfig{
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modelType: "powerlaw",
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halfLife: 30.0,
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smoothAlpha: 0.3,
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}
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for _, opt := range opts {
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opt(cfg)
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opt(&cfg)
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}
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if len(cfg.modelTypes) == 0 {
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cfg.modelTypes = defaultModelTypes
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return &estimatorImpl{
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cfg: cfg,
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}
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// Fit each model and select the best (lowest residual)
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var best Estimator
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var bestResidual float64 = math.Inf(1)
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now := time.Now()
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for _, model := range cfg.modelTypes {
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var est Estimator
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var residual float64
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var err error
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switch model {
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case ModelPowerLaw:
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est, residual, err = fitPowerLaw(weight, reps, dates, now, cfg.halfLifeDays)
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case ModelLinear:
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est, residual, err = fitLinear(weight, reps, dates, now, cfg.halfLifeDays)
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case ModelExponential:
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est, residual, err = fitExponential(weight, reps, dates, now, cfg.halfLifeDays)
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default:
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return nil, fmt.Errorf("unknown model type: %s", model)
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}
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if err != nil {
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continue // Skip models that fail to fit
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}
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if residual < bestResidual {
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best = est
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bestResidual = residual
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}
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}
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if best == nil {
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return nil, errors.New("no model could be fitted to the data")
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}
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return best, nil
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}
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// --- Model Implementations ---
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// Fit adds new data and updates the parameter time series and smoothing.
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func (e *estimatorImpl) Fit(weight, reps []float64, dates []time.Time) error {
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if len(weight) != len(reps) || len(weight) != len(dates) {
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return errors.New("weight, reps, and dates must have the same length")
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}
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// Add new data points
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for i := range weight {
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e.data = append(e.data, timePoint{
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date: dates[i],
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a: math.NaN(), // to be filled in
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b: math.NaN(),
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})
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}
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// Sort all data points by date
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sort.Slice(e.data, func(i, j int) bool {
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return e.data[i].date.Before(e.data[j].date)
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})
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// PowerLawEstimator: w = a * reps^b
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type PowerLawEstimator struct {
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a, b float64
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halfLife float64
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modelType string
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residualSum float64
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// For each time point, fit the model to all data up to that point
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for i := range e.data {
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var w, r []float64
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var d []time.Time
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for j := 0; j <= i; j++ {
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w = append(w, weight[j])
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r = append(r, reps[j])
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d = append(d, dates[j])
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}
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a, b := fitPowerLaw(w, r, d, e.cfg.halfLife)
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e.data[i].a = a
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e.data[i].b = b
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}
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// Smooth the parameter time series
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e.smoothedA = exponentialSmoothing(extractA(e.data), e.cfg.smoothAlpha)
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e.smoothedB = exponentialSmoothing(extractB(e.data), e.cfg.smoothAlpha)
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return nil
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}
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func (e *PowerLawEstimator) Estimate1RM() float64 {
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return e.a * math.Pow(1, e.b)
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// Estimate1RM returns the smoothed 1RM estimate at time t.
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func (e *estimatorImpl) Estimate1RM(t time.Time) float64 {
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a, b := e.smoothedParamsAt(t)
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return a * math.Pow(1, b)
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}
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func (e *PowerLawEstimator) EstimateReps(targetWeight float64) float64 {
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if e.a == 0 || e.b == 0 {
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// EstimateReps returns the predicted number of reps at a given weight and time.
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func (e *estimatorImpl) EstimateReps(t time.Time, targetWeight float64) float64 {
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a, b := e.smoothedParamsAt(t)
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if a == 0 || b == 0 {
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return 0
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}
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return math.Pow(targetWeight/e.a, 1/e.b)
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}
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func (e *PowerLawEstimator) EstimateMaxWeight(nReps float64) float64 {
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return e.a * math.Pow(nReps, e.b)
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}
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func (e *PowerLawEstimator) ModelType() string { return e.modelType }
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func (e *PowerLawEstimator) Params() []float64 { return []float64{e.a, e.b} }
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// LinearEstimator: w = a + b*reps
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type LinearEstimator struct {
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a, b float64
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halfLife float64
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modelType string
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residualSum float64
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return math.Pow(targetWeight/a, 1/b)
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}
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func (e *LinearEstimator) Estimate1RM() float64 {
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return e.a + e.b*1
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// EstimateMaxWeight returns the predicted max weight for a given number of reps at time t.
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func (e *estimatorImpl) EstimateMaxWeight(t time.Time, nReps float64) float64 {
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a, b := e.smoothedParamsAt(t)
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return a * math.Pow(nReps, b)
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}
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func (e *LinearEstimator) EstimateReps(targetWeight float64) float64 {
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if e.b == 0 {
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return 0
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// Params returns the smoothed model parameters at time t.
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func (e *estimatorImpl) Params(t time.Time) []float64 {
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a, b := e.smoothedParamsAt(t)
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return []float64{a, b}
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}
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// --- Internal Helpers ---
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// smoothedParamsAt returns the smoothed parameters for the closest time point <= t.
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func (e *estimatorImpl) smoothedParamsAt(t time.Time) (float64, float64) {
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if len(e.data) == 0 {
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return 0, 0
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}
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return (targetWeight - e.a) / e.b
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}
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func (e *LinearEstimator) EstimateMaxWeight(nReps float64) float64 {
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return e.a + e.b*nReps
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}
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func (e *LinearEstimator) ModelType() string { return e.modelType }
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func (e *LinearEstimator) Params() []float64 { return []float64{e.a, e.b} }
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// ExponentialEstimator: w = a * exp(b * reps)
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type ExponentialEstimator struct {
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a, b float64
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halfLife float64
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modelType string
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residualSum float64
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}
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func (e *ExponentialEstimator) Estimate1RM() float64 {
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return e.a * math.Exp(e.b*1)
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}
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func (e *ExponentialEstimator) EstimateReps(targetWeight float64) float64 {
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if e.a == 0 || e.b == 0 {
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return 0
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idx := sort.Search(len(e.data), func(i int) bool {
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return !e.data[i].date.Before(t)
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})
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if idx == 0 {
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return e.smoothedA[0], e.smoothedB[0]
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}
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return math.Log(targetWeight/e.a) / e.b
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if idx >= len(e.data) {
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return e.smoothedA[len(e.data)-1], e.smoothedB[len(e.data)-1]
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}
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return e.smoothedA[idx-1], e.smoothedB[idx-1]
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}
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func (e *ExponentialEstimator) EstimateMaxWeight(nReps float64) float64 {
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return e.a * math.Exp(e.b*nReps)
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}
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func (e *ExponentialEstimator) ModelType() string { return e.modelType }
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func (e *ExponentialEstimator) Params() []float64 { return []float64{e.a, e.b} }
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// --- Fitting Functions ---
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// fitPowerLaw fits w = a * reps^b
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func fitPowerLaw(weight, reps []float64, dates []time.Time, now time.Time, halfLifeDays float64) (Estimator, float64, error) {
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// fitPowerLaw fits a power law model to the data.
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func fitPowerLaw(weight, reps []float64, dates []time.Time, halfLifeDays float64) (a, b float64) {
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now := dates[len(dates)-1]
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params := []float64{max(weight), -0.1}
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problem := optimize.Problem{
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Func: func(x []float64) float64 {
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@ -203,16 +174,9 @@ func fitPowerLaw(weight, reps []float64, dates []time.Time, now time.Time, halfL
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}
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result, err := optimize.Minimize(problem, params, nil, nil)
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if err != nil {
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return nil, 0, err
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return 0, 0
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}
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residual := weightedResidualsPowerLaw(result.X, weight, reps, dates, now, halfLifeDays)
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return &PowerLawEstimator{
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a: result.X[0],
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b: result.X[1],
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halfLife: halfLifeDays,
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modelType: ModelPowerLaw,
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residualSum: residual,
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}, residual, nil
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return result.X[0], result.X[1]
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}
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func weightedResidualsPowerLaw(params, weight, reps []float64, dates []time.Time, now time.Time, halfLifeDays float64) float64 {
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@ -228,79 +192,6 @@ func weightedResidualsPowerLaw(params, weight, reps []float64, dates []time.Time
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return sum
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}
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// fitLinear fits w = a + b*reps
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func fitLinear(weight, reps []float64, dates []time.Time, now time.Time, halfLifeDays float64) (Estimator, float64, error) {
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params := []float64{weight[0], 0.0}
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problem := optimize.Problem{
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Func: func(x []float64) float64 {
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return weightedResidualsLinear(x, weight, reps, dates, now, halfLifeDays)
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},
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}
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result, err := optimize.Minimize(problem, params, nil, nil)
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if err != nil {
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return nil, 0, err
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}
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residual := weightedResidualsLinear(result.X, weight, reps, dates, now, halfLifeDays)
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return &LinearEstimator{
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a: result.X[0],
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b: result.X[1],
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halfLife: halfLifeDays,
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modelType: ModelLinear,
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residualSum: residual,
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}, residual, nil
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}
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func weightedResidualsLinear(params, weight, reps []float64, dates []time.Time, now time.Time, halfLifeDays float64) float64 {
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a, b := params[0], params[1]
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var sum float64
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for i := range weight {
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daysAgo := now.Sub(dates[i]).Hours() / 24
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weightDecay := math.Exp(-math.Ln2 * daysAgo / halfLifeDays)
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predicted := a + b*reps[i]
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residual := weight[i] - predicted
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sum += weightDecay * residual * residual
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}
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return sum
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}
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// fitExponential fits w = a * exp(b*reps)
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func fitExponential(weight, reps []float64, dates []time.Time, now time.Time, halfLifeDays float64) (Estimator, float64, error) {
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params := []float64{max(weight), -0.01}
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problem := optimize.Problem{
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Func: func(x []float64) float64 {
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return weightedResidualsExponential(x, weight, reps, dates, now, halfLifeDays)
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},
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}
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result, err := optimize.Minimize(problem, params, nil, nil)
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if err != nil {
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return nil, 0, err
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}
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residual := weightedResidualsExponential(result.X, weight, reps, dates, now, halfLifeDays)
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return &ExponentialEstimator{
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a: result.X[0],
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b: result.X[1],
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halfLife: halfLifeDays,
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modelType: ModelExponential,
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residualSum: residual,
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}, residual, nil
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}
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func weightedResidualsExponential(params, weight, reps []float64, dates []time.Time, now time.Time, halfLifeDays float64) float64 {
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a, b := params[0], params[1]
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var sum float64
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for i := range weight {
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daysAgo := now.Sub(dates[i]).Hours() / 24
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weightDecay := math.Exp(-math.Ln2 * daysAgo / halfLifeDays)
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predicted := a * math.Exp(b*reps[i])
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residual := weight[i] - predicted
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sum += weightDecay * residual * residual
|
||||
}
|
||||
return sum
|
||||
}
|
||||
|
||||
// --- Utility Functions ---
|
||||
|
||||
// max returns the maximum value in a slice
|
||||
func max(slice []float64) float64 {
|
||||
m := slice[0]
|
||||
for _, v := range slice {
|
||||
|
|
@ -311,3 +202,32 @@ func max(slice []float64) float64 {
|
|||
return m
|
||||
}
|
||||
|
||||
func extractA(data []timePoint) []float64 {
|
||||
out := make([]float64, len(data))
|
||||
for i, d := range data {
|
||||
out[i] = d.a
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
func extractB(data []timePoint) []float64 {
|
||||
out := make([]float64, len(data))
|
||||
for i, d := range data {
|
||||
out[i] = d.b
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
// exponentialSmoothing applies exponential smoothing to a time series.
|
||||
func exponentialSmoothing(series []float64, alpha float64) []float64 {
|
||||
if len(series) == 0 {
|
||||
return nil
|
||||
}
|
||||
smoothed := make([]float64, len(series))
|
||||
smoothed[0] = series[0]
|
||||
for i := 1; i < len(series); i++ {
|
||||
smoothed[i] = alpha*series[i] + (1-alpha)*smoothed[i-1]
|
||||
}
|
||||
return smoothed
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user