Average Error: 0.0 → 0.0
Time: 16.2s
Precision: 64
\[x + \left(y - z\right) \cdot \left(t - x\right)\]
↓
\[x + \left(y \cdot t + \left(\left(y - z\right) \cdot \left(-x\right) - t \cdot z\right)\right)\]
x + \left(y - z\right) \cdot \left(t - x\right)
↓
x + \left(y \cdot t + \left(\left(y - z\right) \cdot \left(-x\right) - t \cdot z\right)\right)
double f(double x, double y, double z, double t) {
        double r38999855 = x;
        double r38999856 = y;
        double r38999857 = z;
        double r38999858 = r38999856 - r38999857;
        double r38999859 = t;
        double r38999860 = r38999859 - r38999855;
        double r38999861 = r38999858 * r38999860;
        double r38999862 = r38999855 + r38999861;
        return r38999862;
}
↓
double f(double x, double y, double z, double t) {
        double r38999863 = x;
        double r38999864 = y;
        double r38999865 = t;
        double r38999866 = r38999864 * r38999865;
        double r38999867 = z;
        double r38999868 = r38999864 - r38999867;
        double r38999869 = -r38999863;
        double r38999870 = r38999868 * r38999869;
        double r38999871 = r38999865 * r38999867;
        double r38999872 = r38999870 - r38999871;
        double r38999873 = r38999866 + r38999872;
        double r38999874 = r38999863 + r38999873;
        return r38999874;
}

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

Target

Original0.0
Target0.0
Herbie0.0
\[x + \left(t \cdot \left(y - z\right) + \left(-x\right) \cdot \left(y - z\right)\right)\]

Derivation

  1. Initial program 0.0

    \[x + \left(y - z\right) \cdot \left(t - x\right)\]
  2. Using strategy rm

  3. Applied sub-neg0.0

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

  4. Applied distribute-rgt-in0.0

    \[\leadsto x + \color{blue}{\left(t \cdot \left(y - z\right) + \left(-x\right) \cdot \left(y - z\right)\right)}\]
  5. Using strategy rm

  6. Applied sub-neg0.0

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

  7. Applied distribute-lft-in0.0

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

  8. Applied associate-+l+0.0

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

  9. Simplified0.0

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

  10. Final simplification0.0

    \[\leadsto x + \left(y \cdot t + \left(\left(y - z\right) \cdot \left(-x\right) - t \cdot z\right)\right)\]

Reproduce

herbie shell --seed 2019174 
(FPCore (x y z t)
  :name "Data.Metrics.Snapshot:quantile from metrics-0.3.0.2"

  :herbie-target
  (+ x (+ (* t (- y z)) (* (- x) (- y z))))

  (+ x (* (- y z) (- t x))))