Average Error: 0.6 → 1.0
Time: 2.6s
Precision: 64
\[1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}\]
↓
\[1 - \frac{1}{\left(y - z\right) \cdot \frac{y - t}{x}}\]
1 - \frac{x}{\left(y - z\right) \cdot \left(y - t\right)}
↓
1 - \frac{1}{\left(y - z\right) \cdot \frac{y - t}{x}}
double code(double x, double y, double z, double t) {
	return (1.0 - (x / ((y - z) * (y - t))));
}
↓
double code(double x, double y, double z, double t) {
	return (1.0 - (1.0 / ((y - z) * ((y - t) / x))));
}

Error

Bits error versus x

Bits error versus y

Bits error versus z

Bits error versus t

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Your Program's Arguments

Results

Enter valid numbers for all inputs

Derivation

  1. Initial program 0.6

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

  3. Applied clear-num0.6

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

  5. Applied *-un-lft-identity0.6

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

  6. Applied times-frac1.0

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

  7. Simplified1.0

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

  8. Final simplification1.0

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

Reproduce

herbie shell --seed 2020103 
(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)))))