// 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])
}
}
