Average Error: 0.6 → 1.0
Time: 15.8s
Precision: 64
\[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]
↓
\[1 - \frac{1}{y - z} \cdot \frac{x}{y - t}\]
1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}
↓
1 - \frac{1}{y - z} \cdot \frac{x}{y - t}
double f(double x, double y, double z, double t) {
        double r185527 = 1.0;
        double r185528 = x;
        double r185529 = y;
        double r185530 = z;
        double r185531 = r185529 - r185530;
        double r185532 = t;
        double r185533 = r185529 - r185532;
        double r185534 = r185531 * r185533;
        double r185535 = r185528 / r185534;
        double r185536 = r185527 - r185535;
        return r185536;
}
↓
double f(double x, double y, double z, double t) {
        double r185537 = 1.0;
        double r185538 = 1.0;
        double r185539 = y;
        double r185540 = z;
        double r185541 = r185539 - r185540;
        double r185542 = r185538 / r185541;
        double r185543 = x;
        double r185544 = t;
        double r185545 = r185539 - r185544;
        double r185546 = r185543 / r185545;
        double r185547 = r185542 * r185546;
        double r185548 = r185537 - r185547;
        return r185548;
}

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 *-un-lft-identity0.6

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

  4. Applied times-frac1.0

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

  5. Final simplification1.0

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

Reproduce

herbie shell --seed 2019323 
(FPCore (x y z t)
  :name "Data.Random.Distribution.Triangular:triangularCDF from random-fu-0.2.6.2, A"
  :precision binary64
  (- 1 (/ x (* (- y z) (- y t)))))