Average Error: 0.7 → 1.1
Time: 3.1s
Precision: binary64
\[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]
↓
\[1 + \frac{x}{y - t} \cdot \frac{-1}{y - z}\]
1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}
↓
1 + \frac{x}{y - t} \cdot \frac{-1}{y - z}
(FPCore (x y z t) :precision binary64 (- 1.0 (/ x (* (- y z) (- y t)))))
↓
(FPCore (x y z t)
 :precision binary64
 (+ 1.0 (* (/ x (- y t)) (/ -1.0 (- y z)))))
double code(double x, double y, double z, double t) {
	return ((double) (1.0 - (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) (y - t))) * (-1.0 / ((double) (y - z)))))));
}

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 0.7

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

  3. Applied *-un-lft-identity0.7

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

  4. Applied times-frac1.1

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

  5. Final simplification1.1

    \[\leadsto 1 + \frac{x}{y - t} \cdot \frac{-1}{y - z}\]

Reproduce

herbie shell --seed 2020198 
(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)))))