// NewFloatHoltWintersReducer creates a new FloatHoltWintersReducer. func NewFloatHoltWintersReducer(h, m int, includeFitData bool, interval time.Duration) *FloatHoltWintersReducer { seasonal := true if m < 2 { seasonal = false } return &FloatHoltWintersReducer{ h: h, m: m, seasonal: seasonal, includeFitData: includeFitData, interval: int64(interval), halfInterval: int64(interval) / 2, optim: neldermead.New(), epsilon: hwDefaultEpsilon, } }
// NewFloatHoltWintersReducer creates a new FloatHoltWintersReducer. func NewFloatHoltWintersReducer(h, m int, includeFitData bool, interval time.Duration) *FloatHoltWintersReducer { seasonal := true if m < 2 { seasonal = false } return &FloatHoltWintersReducer{ alpha: defaultAlpha, beta: defaultBeta, gamma: defaultGamma, phi: defaultPhi, h: h, m: m, seasonal: seasonal, includeFitData: includeFitData, interval: int64(interval), halfInterval: int64(interval) / 2, optim: neldermead.New(), epsilon: defaultEpsilon, } }
func Test_Optimize(t *testing.T) { constraints := func(x []float64) { for i := range x { x[i] = round(x[i], 5) } } // 100*(b-a^2)^2 + (1-a)^2 // // Obvious global minimum at (a,b) = (1,1) // // Useful visualization: // https://www.wolframalpha.com/input/?i=minimize(100*(b-a%5E2)%5E2+%2B+(1-a)%5E2) f := func(x []float64) float64 { constraints(x) // a = x[0] // b = x[1] return 100*(x[1]-x[0]*x[0])*(x[1]-x[0]*x[0]) + (1.0-x[0])*(1.0-x[0]) } start := []float64{-1.2, 1.0} opt := neldermead.New() epsilon := 1e-5 min, parameters := opt.Optimize(f, start, epsilon, 1) if !almostEqual(min, 0, epsilon) { t.Errorf("unexpected min: got %f exp 0", min) } if !almostEqual(parameters[0], 1, 1e-2) { t.Errorf("unexpected parameters[0]: got %f exp 1", parameters[0]) } if !almostEqual(parameters[1], 1, 1e-2) { t.Errorf("unexpected parameters[1]: got %f exp 1", parameters[1]) } }