Average Error: 0.4 → 0.1
Time: 15.0s
Precision: 64
\[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120\]
↓
\[\frac{60}{z - t} \cdot \left(x - y\right) + a \cdot 120\]
\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120
↓
\frac{60}{z - t} \cdot \left(x - y\right) + a \cdot 120
double f(double x, double y, double z, double t, double a) {
        double r600097 = 60.0;
        double r600098 = x;
        double r600099 = y;
        double r600100 = r600098 - r600099;
        double r600101 = r600097 * r600100;
        double r600102 = z;
        double r600103 = t;
        double r600104 = r600102 - r600103;
        double r600105 = r600101 / r600104;
        double r600106 = a;
        double r600107 = 120.0;
        double r600108 = r600106 * r600107;
        double r600109 = r600105 + r600108;
        return r600109;
}
↓
double f(double x, double y, double z, double t, double a) {
        double r600110 = 60.0;
        double r600111 = z;
        double r600112 = t;
        double r600113 = r600111 - r600112;
        double r600114 = r600110 / r600113;
        double r600115 = x;
        double r600116 = y;
        double r600117 = r600115 - r600116;
        double r600118 = r600114 * r600117;
        double r600119 = a;
        double r600120 = 120.0;
        double r600121 = r600119 * r600120;
        double r600122 = r600118 + r600121;
        return r600122;
}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Bits error versus a

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Target

Original0.4
Target0.2
Herbie0.1
\[\frac{60}{\frac{z - t}{x - y}} + a \cdot 120\]

Derivation

  1. Initial program 0.4

    \[\frac{60 \cdot \left(x - y\right)}{z - t} + a \cdot 120\]
  2. Using strategy rm

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

    \[\leadsto \frac{60 \cdot \left(x - y\right)}{\color{blue}{1 \cdot \left(z - t\right)}} + a \cdot 120\]

  4. Applied times-frac0.1

    \[\leadsto \color{blue}{\frac{60}{1} \cdot \frac{x - y}{z - t}} + a \cdot 120\]

  5. Simplified0.1

    \[\leadsto \color{blue}{60} \cdot \frac{x - y}{z - t} + a \cdot 120\]
  6. Using strategy rm

  7. Applied *-un-lft-identity0.1

    \[\leadsto \color{blue}{\left(1 \cdot 60\right)} \cdot \frac{x - y}{z - t} + a \cdot 120\]

  8. Applied associate-*l*0.1

    \[\leadsto \color{blue}{1 \cdot \left(60 \cdot \frac{x - y}{z - t}\right)} + a \cdot 120\]

  9. Simplified0.1

    \[\leadsto 1 \cdot \color{blue}{\left(\left(x - y\right) \cdot \frac{60}{z - t}\right)} + a \cdot 120\]

  10. Final simplification0.1

    \[\leadsto \frac{60}{z - t} \cdot \left(x - y\right) + a \cdot 120\]

Reproduce

herbie shell --seed 2019323 
(FPCore (x y z t a)
  :name "Data.Colour.RGB:hslsv from colour-2.3.3, B"
  :precision binary64

  :herbie-target
  (+ (/ 60 (/ (- z t) (- x y))) (* a 120))

  (+ (/ (* 60 (- x y)) (- z t)) (* a 120)))