Average Error: 0.0 → 0.0
Time: 3.5s
Precision: binary64
\[x + \left(y - z\right) \cdot \left(t - x\right)\]
↓
\[x + \left(\left(y - z\right) \cdot t + x \cdot \left(z - y\right)\right)\]
x + \left(y - z\right) \cdot \left(t - x\right)
↓
x + \left(\left(y - z\right) \cdot t + x \cdot \left(z - y\right)\right)
(FPCore (x y z t) :precision binary64 (+ x (* (- y z) (- t x))))
↓
(FPCore (x y z t) :precision binary64 (+ x (+ (* (- y z) t) (* x (- z y)))))
double code(double x, double y, double z, double t) {
	return ((double) (x + ((double) (((double) (y - z)) * ((double) (t - x))))));
}
↓
double code(double x, double y, double z, double t) {
	return ((double) (x + ((double) (((double) (((double) (y - z)) * t)) + ((double) (x * ((double) (z - y))))))));
}

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 Error: 0.0 bits

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

  3. Applied sub-negError: 0.0 bits

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

  4. Applied distribute-lft-inError: 0.0 bits

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

  5. Final simplificationError: 0.0 bits

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

Reproduce

herbie shell --seed 2020204 
(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))))