Average Error: 0.7 → 0.7
Time: 14.0s
Precision: 64
\[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]
↓
\[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]
1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}
↓
1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}
double f(double x, double y, double z, double t) {
        double r148006 = 1.0;
        double r148007 = x;
        double r148008 = y;
        double r148009 = z;
        double r148010 = r148008 - r148009;
        double r148011 = t;
        double r148012 = r148008 - r148011;
        double r148013 = r148010 * r148012;
        double r148014 = r148007 / r148013;
        double r148015 = r148006 - r148014;
        return r148015;
}
↓
double f(double x, double y, double z, double t) {
        double r148016 = 1.0;
        double r148017 = x;
        double r148018 = y;
        double r148019 = z;
        double r148020 = r148018 - r148019;
        double r148021 = t;
        double r148022 = r148018 - r148021;
        double r148023 = r148020 * r148022;
        double r148024 = r148017 / r148023;
        double r148025 = r148016 - r148024;
        return r148025;
}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

Try it out

Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.7

    \[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]
  2. Using strategy rm

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

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

  4. Final simplification0.7

    \[\leadsto 1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]

Reproduce

herbie shell --seed 2019306 +o rules:numerics
(FPCore (x y z t)
  :name "Data.Random.Distribution.Triangular:triangularCDF from random-fu-0.2.6.2, A"
  :precision binary64
  (- 1 (/ x (* (- y z) (- y t)))))