Diagrams.ThreeD.Transform:aboutY from diagrams-lib-1.3.0.3

Average Accuracy: 99.8% → 99.8%

Time: 8.8s

Precision: binary64

Cost: 19520

Math

\[x \cdot \cos y + z \cdot \sin y \]

↓

\[\mathsf{fma}\left(\sin y, z, x \cdot \cos y\right) \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* x (cos y)) (* z (sin y))))

↓

(FPCore (x y z) :precision binary64 (fma (sin y) z (* x (cos y))))

C

double code(double x, double y, double z) {
	return (x * cos(y)) + (z * sin(y));
}

↓

double code(double x, double y, double z) {
	return fma(sin(y), z, (x * cos(y)));
}

Julia

function code(x, y, z)
	return Float64(Float64(x * cos(y)) + Float64(z * sin(y)))
end

↓

function code(x, y, z)
	return fma(sin(y), z, Float64(x * cos(y)))
end

Wolfram

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

↓

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

Derivation

1. Initial program 99.8%

   \[x \cdot \cos y + z \cdot \sin y \]

2. Applied egg-rr 99.8%

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

   [Start] 99.8	\[ x \cdot \cos y + z \cdot \sin y \]
   +-commutative [=>] 99.8	\[ \color{blue}{z \cdot \sin y + x \cdot \cos y} \]
   *-commutative [=>] 99.8	\[ \color{blue}{\sin y \cdot z} + x \cdot \cos y \]
   fma-def [=>] 99.8	\[ \color{blue}{\mathsf{fma}\left(\sin y, z, x \cdot \cos y\right)} \]

3. Final simplification 99.8%

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

Alternatives

Alternative 1
Accuracy	99.8%
Cost	19520

\[\mathsf{fma}\left(x, \cos y, \sin y \cdot z\right) \]

Alternative 2
Accuracy	99.8%
Cost	13248

\[\sin y \cdot z + x \cdot \cos y \]

Alternative 3
Accuracy	74.9%
Cost	7252

\[\begin{array}{l} t_0 := \sin y \cdot z\\ t_1 := x \cdot \cos y\\ \mathbf{if}\;y \leq -1.65 \cdot 10^{+102}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -4.5 \cdot 10^{+72}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -3.95 \cdot 10^{-7}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1.75:\\ \;\;\;\;x + y \cdot z\\ \mathbf{elif}\;y \leq 1.15 \cdot 10^{+152}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 4
Accuracy	74.9%
Cost	7252

\[\begin{array}{l} t_0 := \sin y \cdot z\\ t_1 := x \cdot \cos y\\ \mathbf{if}\;y \leq -5.8 \cdot 10^{+103}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq -1.8 \cdot 10^{+62}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;y \leq -3.95 \cdot 10^{-7}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 1.75:\\ \;\;\;\;\mathsf{fma}\left(z, y, x\right)\\ \mathbf{elif}\;y \leq 3.3 \cdot 10^{+152}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 5
Accuracy	86.1%
Cost	6985

\[\begin{array}{l} \mathbf{if}\;z \leq -1.5 \cdot 10^{-75} \lor \neg \left(z \leq 1.95 \cdot 10^{-53}\right):\\ \;\;\;\;x + \sin y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x \cdot \cos y\\ \end{array} \]

Alternative 6
Accuracy	74.9%
Cost	6857

\[\begin{array}{l} \mathbf{if}\;y \leq -3.95 \cdot 10^{-7} \lor \neg \left(y \leq 0.0025\right):\\ \;\;\;\;\sin y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]

Alternative 7
Accuracy	41.4%
Cost	456

\[\begin{array}{l} \mathbf{if}\;z \leq -1.6 \cdot 10^{+124}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq 5.8 \cdot 10^{+134}:\\ \;\;\;\;x\\ \mathbf{else}:\\ \;\;\;\;y \cdot z\\ \end{array} \]

Alternative 8
Accuracy	52.3%
Cost	320

\[x + y \cdot z \]

Alternative 9
Accuracy	39.1%
Cost	64

\[x \]

Reproduce

herbie shell --seed 2023146 
(FPCore (x y z)
  :name "Diagrams.ThreeD.Transform:aboutY from diagrams-lib-1.3.0.3"
  :precision binary64
  (+ (* x (cos y)) (* z (sin y))))