Average Error: 0.6 → 0.6
Time: 10.4s
Precision: 64
\[\log \left(1 + e^{x}\right) - x \cdot y\]
\[\log \left(1 + e^{x}\right) - x \cdot y\]
\log \left(1 + e^{x}\right) - x \cdot y
\log \left(1 + e^{x}\right) - x \cdot y
double f(double x, double y) {
        double r123087 = 1.0;
        double r123088 = x;
        double r123089 = exp(r123088);
        double r123090 = r123087 + r123089;
        double r123091 = log(r123090);
        double r123092 = y;
        double r123093 = r123088 * r123092;
        double r123094 = r123091 - r123093;
        return r123094;
}

double f(double x, double y) {
        double r123095 = 1.0;
        double r123096 = x;
        double r123097 = exp(r123096);
        double r123098 = r123095 + r123097;
        double r123099 = log(r123098);
        double r123100 = y;
        double r123101 = r123096 * r123100;
        double r123102 = r123099 - r123101;
        return r123102;
}

Error

Bits error versus x

Bits error versus y

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Target

Original0.6
Target0.1
Herbie0.6
\[\begin{array}{l} \mathbf{if}\;x \le 0.0:\\ \;\;\;\;\log \left(1 + e^{x}\right) - x \cdot y\\ \mathbf{else}:\\ \;\;\;\;\log \left(1 + e^{-x}\right) - \left(-x\right) \cdot \left(1 - y\right)\\ \end{array}\]

Derivation

  1. Initial program 0.6

    \[\log \left(1 + e^{x}\right) - x \cdot y\]
  2. Final simplification0.6

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

Reproduce

herbie shell --seed 2019350 
(FPCore (x y)
  :name "Logistic regression 2"
  :precision binary64

  :herbie-target
  (if (<= x 0.0) (- (log (+ 1 (exp x))) (* x y)) (- (log (+ 1 (exp (- x)))) (* (- x) (- 1 y))))

  (- (log (+ 1 (exp x))) (* x y)))