Diagrams.TwoD.Segment:bezierClip from diagrams-lib-1.3.0.3

Average Accuracy: 97.9% → 100.0%

Time: 4.2s

Precision: binary64

Cost: 6720

• 20.6% of points produce a very large (infinite) output. You may want to add a precondition.

Math

\[x \cdot y + z \cdot \left(1 - y\right) \]

↓

\[\mathsf{fma}\left(y, x - z, z\right) \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* x y) (* z (- 1.0 y))))

↓

(FPCore (x y z) :precision binary64 (fma y (- x z) z))

C

double code(double x, double y, double z) {
	return (x * y) + (z * (1.0 - y));
}

↓

double code(double x, double y, double z) {
	return fma(y, (x - z), z);
}

Julia

function code(x, y, z)
	return Float64(Float64(x * y) + Float64(z * Float64(1.0 - y)))
end

↓

function code(x, y, z)
	return fma(y, Float64(x - z), z)
end

Wolfram

code[x_, y_, z_] := N[(N[(x * y), $MachinePrecision] + N[(z * N[(1.0 - y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_, z_] := N[(y * N[(x - z), $MachinePrecision] + z), $MachinePrecision]

Target

Original	97.9%
Target	100.0%
Herbie	100.0%

\[z - \left(z - x\right) \cdot y \]

Derivation

1. Initial program 97.6%

   \[x \cdot y + z \cdot \left(1 - y\right) \]

2. Simplified 100.0%

   \[\leadsto \color{blue}{\mathsf{fma}\left(y, x - z, z\right)} \]
   Proof

   [Start] 97.6	\[ x \cdot y + z \cdot \left(1 - y\right) \]
   +-commutative [=>] 97.6	\[ \color{blue}{z \cdot \left(1 - y\right) + x \cdot y} \]
   sub-neg [=>] 97.6	\[ z \cdot \color{blue}{\left(1 + \left(-y\right)\right)} + x \cdot y \]
   distribute-rgt-in [=>] 97.6	\[ \color{blue}{\left(1 \cdot z + \left(-y\right) \cdot z\right)} + x \cdot y \]
   *-lft-identity [=>] 97.6	\[ \left(\color{blue}{z} + \left(-y\right) \cdot z\right) + x \cdot y \]
   associate-+l+ [=>] 97.6	\[ \color{blue}{z + \left(\left(-y\right) \cdot z + x \cdot y\right)} \]
   +-commutative [=>] 97.6	\[ \color{blue}{\left(\left(-y\right) \cdot z + x \cdot y\right) + z} \]
   *-commutative [=>] 97.6	\[ \left(\color{blue}{z \cdot \left(-y\right)} + x \cdot y\right) + z \]
   neg-mul-1 [=>] 97.6	\[ \left(z \cdot \color{blue}{\left(-1 \cdot y\right)} + x \cdot y\right) + z \]
   associate-*r* [=>] 97.6	\[ \left(\color{blue}{\left(z \cdot -1\right) \cdot y} + x \cdot y\right) + z \]
   distribute-rgt-out [=>] 100.0	\[ \color{blue}{y \cdot \left(z \cdot -1 + x\right)} + z \]
   fma-def [=>] 100.0	\[ \color{blue}{\mathsf{fma}\left(y, z \cdot -1 + x, z\right)} \]
   +-commutative [=>] 100.0	\[ \mathsf{fma}\left(y, \color{blue}{x + z \cdot -1}, z\right) \]
   *-commutative [=>] 100.0	\[ \mathsf{fma}\left(y, x + \color{blue}{-1 \cdot z}, z\right) \]
   neg-mul-1 [<=] 100.0	\[ \mathsf{fma}\left(y, x + \color{blue}{\left(-z\right)}, z\right) \]
   unsub-neg [=>] 100.0	\[ \mathsf{fma}\left(y, \color{blue}{x - z}, z\right) \]

3. Final simplification 100.0%

   \[\leadsto \mathsf{fma}\left(y, x - z, z\right) \]

Alternatives

Alternative 1
Accuracy	59.0%
Cost	1444

\[\begin{array}{l} t_0 := y \cdot \left(-z\right)\\ \mathbf{if}\;y \leq -2.4 \cdot 10^{+274}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -3.6 \cdot 10^{+235}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -2.9 \cdot 10^{+146}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -1.5 \cdot 10^{+73}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq -7.8 \cdot 10^{+43}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -1.4 \cdot 10^{-52}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 7.8 \cdot 10^{-140}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 1.02 \cdot 10^{-83}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 1.3 \cdot 10^{-17}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 2
Accuracy	82.9%
Cost	848

\[\begin{array}{l} t_0 := y \cdot \left(x - z\right)\\ \mathbf{if}\;y \leq -4 \cdot 10^{-65}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 2 \cdot 10^{-139}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 4.9 \cdot 10^{-85}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 3 \cdot 10^{-18}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 3
Accuracy	59.8%
Cost	720

\[\begin{array}{l} \mathbf{if}\;y \leq -1.6 \cdot 10^{-55}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 2.35 \cdot 10^{-139}:\\ \;\;\;\;z\\ \mathbf{elif}\;y \leq 8.5 \cdot 10^{-86}:\\ \;\;\;\;y \cdot x\\ \mathbf{elif}\;y \leq 1.8 \cdot 10^{-18}:\\ \;\;\;\;z\\ \mathbf{else}:\\ \;\;\;\;y \cdot x\\ \end{array} \]

Alternative 4
Accuracy	98.3%
Cost	585

\[\begin{array}{l} \mathbf{if}\;y \leq -440000000 \lor \neg \left(y \leq 1.3 \cdot 10^{-17}\right):\\ \;\;\;\;y \cdot \left(x - z\right)\\ \mathbf{else}:\\ \;\;\;\;z + y \cdot x\\ \end{array} \]

Alternative 5
Accuracy	100.0%
Cost	448

\[z + y \cdot \left(x - z\right) \]

Alternative 6
Accuracy	36.6%
Cost	64

\[z \]

Reproduce

herbie shell --seed 2023160 
(FPCore (x y z)
  :name "Diagrams.TwoD.Segment:bezierClip from diagrams-lib-1.3.0.3"
  :precision binary64

  :herbie-target
  (- z (* (- z x) y))

  (+ (* x y) (* z (- 1.0 y))))