Average Error: 0.7 → 0.7
Time: 8.8s
Precision: 64
\[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]
↓
\[1 - \frac{1}{\frac{\left(y - z\right) \cdot \left(y - t\right)}{x}}\]
1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}
↓
1 - \frac{1}{\frac{\left(y - z\right) \cdot \left(y - t\right)}{x}}
double f(double x, double y, double z, double t) {
        double r267418 = 1.0;
        double r267419 = x;
        double r267420 = y;
        double r267421 = z;
        double r267422 = r267420 - r267421;
        double r267423 = t;
        double r267424 = r267420 - r267423;
        double r267425 = r267422 * r267424;
        double r267426 = r267419 / r267425;
        double r267427 = r267418 - r267426;
        return r267427;
}
↓
double f(double x, double y, double z, double t) {
        double r267428 = 1.0;
        double r267429 = 1.0;
        double r267430 = y;
        double r267431 = z;
        double r267432 = r267430 - r267431;
        double r267433 = t;
        double r267434 = r267430 - r267433;
        double r267435 = r267432 * r267434;
        double r267436 = x;
        double r267437 = r267435 / r267436;
        double r267438 = r267429 / r267437;
        double r267439 = r267428 - r267438;
        return r267439;
}

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 clear-num0.7

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

  4. Final simplification0.7

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

Reproduce

herbie shell --seed 2020042 
(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)))))