func fitnessRMSE(ind, targ *imgut.Image) float64 {
// Images to vector
dataInd := imgut.ToSlice(ind)
dataTarg := imgut.ToSlice(targ)
// (root mean square) error
floats.Sub(dataInd, dataTarg)
// (root mean) square error
floats.Mul(dataInd, dataInd)
// (root) mean square error
totErr := floats.Sum(dataInd)
return math.Sqrt(totErr / float64(len(dataInd)))
}
func MakeFitLinScale(targetImage *imgut.Image) func(*imgut.Image) float64 {
// Pre-compute image to slice of floats
dataTarg := imgut.ToSlice(targetImage)
// Pre-compute average
avgt := floats.Sum(dataTarg) / float64(len(dataTarg))
return func(indImage *imgut.Image) float64 {
// Images to vector
dataInd := imgut.ToSlice(indImage)
// Compute average pixels
avgy := floats.Sum(dataInd) / float64(len(dataInd))
// Difference y - avgy
y_avgy := make([]float64, len(dataInd))
copy(y_avgy, dataInd)
floats.AddConst(-avgy, y_avgy)
// Difference t - avgt
t_avgt := make([]float64, len(dataTarg))
copy(t_avgt, dataTarg)
floats.AddConst(-avgt, t_avgt)
// Multuplication (t - avgt)(y - avgy)
floats.Mul(t_avgt, y_avgy)
// Summation
numerator := floats.Sum(t_avgt)
// Square (y - avgy)^2
floats.Mul(y_avgy, y_avgy)
denomin := floats.Sum(y_avgy)
// Compute b-value
b := numerator / denomin
// Compute a-value
a := avgt - b*avgy
// Compute now the scaled RMSE, using y' = a + b*y
floats.Scale(b, dataInd) // b*y
floats.AddConst(a, dataInd) // a + b*y
floats.Sub(dataInd, dataTarg) // (a + b * y - t)
floats.Mul(dataInd, dataInd) // (a + b * y - t)^2
total := floats.Sum(dataInd) // Sum(...)
return math.Sqrt(total / float64(len(dataInd)))
}
}