Average Error: 2.0 → 2.0
Time: 35.9s
Precision: 64
\[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y}\]
↓
\[\frac{x \cdot e^{\left(\left(t - 1\right) \cdot \log a + y \cdot \log z\right) - b}}{y}\]
\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y}
↓
\frac{x \cdot e^{\left(\left(t - 1\right) \cdot \log a + y \cdot \log z\right) - b}}{y}
double f(double x, double y, double z, double t, double a, double b) {
        double r65459 = x;
        double r65460 = y;
        double r65461 = z;
        double r65462 = log(r65461);
        double r65463 = r65460 * r65462;
        double r65464 = t;
        double r65465 = 1.0;
        double r65466 = r65464 - r65465;
        double r65467 = a;
        double r65468 = log(r65467);
        double r65469 = r65466 * r65468;
        double r65470 = r65463 + r65469;
        double r65471 = b;
        double r65472 = r65470 - r65471;
        double r65473 = exp(r65472);
        double r65474 = r65459 * r65473;
        double r65475 = r65474 / r65460;
        return r65475;
}
↓
double f(double x, double y, double z, double t, double a, double b) {
        double r65476 = x;
        double r65477 = t;
        double r65478 = 1.0;
        double r65479 = r65477 - r65478;
        double r65480 = a;
        double r65481 = log(r65480);
        double r65482 = r65479 * r65481;
        double r65483 = y;
        double r65484 = z;
        double r65485 = log(r65484);
        double r65486 = r65483 * r65485;
        double r65487 = r65482 + r65486;
        double r65488 = b;
        double r65489 = r65487 - r65488;
        double r65490 = exp(r65489);
        double r65491 = r65476 * r65490;
        double r65492 = r65491 / r65483;
        return r65492;
}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Bits error versus a

Bits error versus b

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 2.0

    \[\frac{x \cdot e^{\left(y \cdot \log z + \left(t - 1\right) \cdot \log a\right) - b}}{y}\]
  2. Using strategy rm

  3. Applied pow12.0

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

  4. Final simplification2.0

    \[\leadsto \frac{x \cdot e^{\left(\left(t - 1\right) \cdot \log a + y \cdot \log z\right) - b}}{y}\]

Reproduce

herbie shell --seed 2019303 +o rules:numerics
(FPCore (x y z t a b)
  :name "Numeric.SpecFunctions:incompleteBetaWorker from math-functions-0.1.5.2"
  :precision binary64
  (/ (* x (exp (- (+ (* y (log z)) (* (- t 1) (log a))) b))) y))