Average Error: 0.0 → 0.0
Time: 854.0ms
Precision: binary64
\[e^{-\left(1 - x \cdot x\right)}\]
↓
\[{e}^{\left(x \cdot x - 1\right)}\]
e^{-\left(1 - x \cdot x\right)}
↓
{e}^{\left(x \cdot x - 1\right)}
double code(double x) {
	return ((double) exp(((double) -(((double) (1.0 - ((double) (x * x))))))));
}
↓
double code(double x) {
	return ((double) pow(((double) M_E), ((double) (((double) (x * x)) - 1.0))));
}

Error

Bits error versus x

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Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.0

    \[e^{-\left(1 - x \cdot x\right)}\]

  2. Simplified0.0

    \[\leadsto \color{blue}{e^{x \cdot x - 1}}\]
  3. Using strategy rm

  4. Applied *-un-lft-identity0.0

    \[\leadsto e^{\color{blue}{1 \cdot \left(x \cdot x - 1\right)}}\]

  5. Applied exp-prod0.0

    \[\leadsto \color{blue}{{\left(e^{1}\right)}^{\left(x \cdot x - 1\right)}}\]

  6. Simplified0.0

    \[\leadsto {\color{blue}{e}}^{\left(x \cdot x - 1\right)}\]

  7. Final simplification0.0

    \[\leadsto {e}^{\left(x \cdot x - 1\right)}\]

Reproduce

herbie shell --seed 2020199 
(FPCore (x)
  :name "exp neg sub"
  :precision binary64
  (exp (- (- 1.0 (* x x)))))