Average Error: 0.0 → 0.0
Time: 12.9s
Precision: 64
\[e^{\left(x + y \cdot \log y\right) - z}\]
↓
\[e^{\left(x + y \cdot \log y\right) - z}\]
e^{\left(x + y \cdot \log y\right) - z}
↓
e^{\left(x + y \cdot \log y\right) - z}
double f(double x, double y, double z) {
        double r257571 = x;
        double r257572 = y;
        double r257573 = log(r257572);
        double r257574 = r257572 * r257573;
        double r257575 = r257571 + r257574;
        double r257576 = z;
        double r257577 = r257575 - r257576;
        double r257578 = exp(r257577);
        return r257578;
}
↓
double f(double x, double y, double z) {
        double r257579 = x;
        double r257580 = y;
        double r257581 = log(r257580);
        double r257582 = r257580 * r257581;
        double r257583 = r257579 + r257582;
        double r257584 = z;
        double r257585 = r257583 - r257584;
        double r257586 = exp(r257585);
        return r257586;
}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Try it out

Your Program's Arguments

Results

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Target

Original0.0
Target0.0
Herbie0.0
\[e^{\left(x - z\right) + \log y \cdot y}\]

Derivation

  1. Initial program 0.0

    \[e^{\left(x + y \cdot \log y\right) - z}\]
  2. Using strategy rm

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

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

  4. Final simplification0.0

    \[\leadsto e^{\left(x + y \cdot \log y\right) - z}\]

Reproduce

herbie shell --seed 2020047 +o rules:numerics
(FPCore (x y z)
  :name "Statistics.Distribution.Poisson.Internal:probability from math-functions-0.1.5.2"
  :precision binary64

  :herbie-target
  (exp (+ (- x z) (* (log y) y)))

  (exp (- (+ x (* y (log y))) z)))