(FPCore (x y z t) :precision binary64 (+ x (* (- y z) (- t x))))
(FPCore (x y z t) :precision binary64 (- (fma t y (fma z x x)) (fma z t (* y x))))
double code(double x, double y, double z, double t) {
return x + ((y - z) * (t - x));
}
double code(double x, double y, double z, double t) {
return fma(t, y, fma(z, x, x)) - fma(z, t, (y * x));
}
function code(x, y, z, t) return Float64(x + Float64(Float64(y - z) * Float64(t - x))) end
function code(x, y, z, t) return Float64(fma(t, y, fma(z, x, x)) - fma(z, t, Float64(y * x))) end
code[x_, y_, z_, t_] := N[(x + N[(N[(y - z), $MachinePrecision] * N[(t - x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
code[x_, y_, z_, t_] := N[(N[(t * y + N[(z * x + x), $MachinePrecision]), $MachinePrecision] - N[(z * t + N[(y * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]
x + \left(y - z\right) \cdot \left(t - x\right)
\mathsf{fma}\left(t, y, \mathsf{fma}\left(z, x, x\right)\right) - \mathsf{fma}\left(z, t, y \cdot x\right)




Bits error versus x




Bits error versus y




Bits error versus z




Bits error versus t
| Original | 0.0 |
|---|---|
| Target | 0.0 |
| Herbie | 0.0 |
Initial program 0.0
Simplified0.0
Applied add-sqr-sqrt_binary6432.1
Taylor expanded in y around 0 0.0
Simplified0.0
Final simplification0.0
herbie shell --seed 2022130
(FPCore (x y z t)
:name "Data.Metrics.Snapshot:quantile from metrics-0.3.0.2"
:precision binary64
:herbie-target
(+ x (+ (* t (- y z)) (* (- x) (- y z))))
(+ x (* (- y z) (- t x))))