Example #1
0
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)))
}
Example #2
0
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)))
	}
}