Average Error: 0.6 → 1.0
Time: 15.7s
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 r143777 = 1.0;
        double r143778 = x;
        double r143779 = y;
        double r143780 = z;
        double r143781 = r143779 - r143780;
        double r143782 = t;
        double r143783 = r143779 - r143782;
        double r143784 = r143781 * r143783;
        double r143785 = r143778 / r143784;
        double r143786 = r143777 - r143785;
        return r143786;
}
↓
double f(double x, double y, double z, double t) {
        double r143787 = 1.0;
        double r143788 = 1.0;
        double r143789 = y;
        double r143790 = z;
        double r143791 = r143789 - r143790;
        double r143792 = r143788 / r143791;
        double r143793 = x;
        double r143794 = t;
        double r143795 = r143789 - r143794;
        double r143796 = r143793 / r143795;
        double r143797 = r143792 * r143796;
        double r143798 = r143787 - r143797;
        return r143798;
}

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)))))