Average Error: 0.6 → 0.7
Time: 2.5s
Precision: binary64
\[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]
↓
\[1 - x \cdot \frac{1}{\left(y - z\right) \cdot \left(y - t\right)}\]
1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}
↓
1 - x \cdot \frac{1}{\left(y - z\right) \cdot \left(y - t\right)}
double code(double x, double y, double z, double t) {
	return ((double) (1.0 - ((double) (x / ((double) (((double) (y - z)) * ((double) (y - t))))))));
}
↓
double code(double x, double y, double z, double t) {
	return ((double) (1.0 - ((double) (x * ((double) (1.0 / ((double) (((double) (y - z)) * ((double) (y - t))))))))));
}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.6

    \[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]
  2. Using strategy rm

  3. Applied div-inv0.7

    \[\leadsto 1 - \color{blue}{x \cdot \frac{1}{\left(y - z\right) \cdot \left(y - t\right)}}\]

  4. Final simplification0.7

    \[\leadsto 1 - x \cdot \frac{1}{\left(y - z\right) \cdot \left(y - t\right)}\]

Reproduce

herbie shell --seed 2020150 
(FPCore (x y z t)
  :name "Data.Random.Distribution.Triangular:triangularCDF from random-fu-0.2.6.2, A"
  :precision binary64
  (- 1.0 (/ x (* (- y z) (- y t)))))