Average Error: 0.0 → 0.0
Time: 2.6s
Precision: 64
\[x + \left(y - z\right) \cdot \left(t - x\right)\]
↓
\[\left(x + \left(t \cdot y + t \cdot \left(-z\right)\right)\right) + \left(y - z\right) \cdot \left(-x\right)\]
x + \left(y - z\right) \cdot \left(t - x\right)
↓
\left(x + \left(t \cdot y + t \cdot \left(-z\right)\right)\right) + \left(y - z\right) \cdot \left(-x\right)
double f(double x, double y, double z, double t) {
        double r1005777 = x;
        double r1005778 = y;
        double r1005779 = z;
        double r1005780 = r1005778 - r1005779;
        double r1005781 = t;
        double r1005782 = r1005781 - r1005777;
        double r1005783 = r1005780 * r1005782;
        double r1005784 = r1005777 + r1005783;
        return r1005784;
}
↓
double f(double x, double y, double z, double t) {
        double r1005785 = x;
        double r1005786 = t;
        double r1005787 = y;
        double r1005788 = r1005786 * r1005787;
        double r1005789 = z;
        double r1005790 = -r1005789;
        double r1005791 = r1005786 * r1005790;
        double r1005792 = r1005788 + r1005791;
        double r1005793 = r1005785 + r1005792;
        double r1005794 = r1005787 - r1005789;
        double r1005795 = -r1005785;
        double r1005796 = r1005794 * r1005795;
        double r1005797 = r1005793 + r1005796;
        return r1005797;
}

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-lft-in0.0

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

  5. Applied associate-+r+0.0

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

  6. Simplified0.0

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

  8. Applied sub-neg0.0

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

  9. Applied distribute-lft-in0.0

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

  10. Final simplification0.0

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

Reproduce

herbie shell --seed 2019353 
(FPCore (x y z t)
  :name "Data.Metrics.Snapshot:quantile from metrics-0.3.0.2"
  :precision binary64

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

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